From fd4618b20dd8391e65a249bb5dba4e55b64b6e9b Mon Sep 17 00:00:00 2001
From: Ayzen <ayzen>
Date: Thu, 5 Mar 2026 14:42:33 +0300
Subject: [PATCH] init commit

---
 .gitignore                                    |   221 +
 Makefile                                      |    83 +
 .../common_cpp/config/include/run_config.hpp  |    97 +
 .../common_cpp/config/src/run_config.cpp      |   398 +
 .../common_cpp/ipc/include/shared_types.hpp   |    91 +
 .../common_cpp/ipc/include/shm_ring.hpp       |    61 +
 .../common_cpp/ipc/src/shared_types.cpp       |   336 +
 .../common_cpp/ipc/src/shm_ring.cpp           |   380 +
 .../include/calibration_master.hpp            |    32 +
 .../include/calibrator_interface.hpp          |    21 +
 .../src/calibration_master.cpp                |   101 +
 .../src/through_calibrator.cpp                |    76 +
 .../include/data_preprocessor.hpp             |    47 +
 .../src/data_preprocessor.cpp                 |    85 +
 .../data_preprocessor/src/main.cpp            |    86 +
 .../include/reference_master.hpp              |    30 +
 .../reference_master/src/reference_master.cpp |   141 +
 .../data_processor/include/data_processor.hpp |    48 +
 .../include/processing_live_config.hpp        |    49 +
 .../data_processor/src/data_processor.cpp     |   156 +
 .../processing/data_processor/src/main.cpp    |    69 +
 .../src/processing_live_config.cpp            |   189 +
 .../processors/include/bscan_processor.hpp    |    16 +
 .../include/passthrough_processor.hpp         |    16 +
 .../include/processor_interface.hpp           |    21 +
 .../processors/src/bscan_processor.cpp        |   237 +
 .../processors/src/passthrough_processor.cpp  |    39 +
 .../interfaces/radar_driver.hpp               |    37 +
 .../interfaces/switch_driver.hpp              |    24 +
 .../librevna_minimal_driver_lifecycle.cpp     |   212 +
 .../librevna_minimal_driver_protocol.cpp      |   130 +
 .../librevna_minimal_driver_transport.cpp     |   359 +
 .../librevna/librevna_protocol_common.hpp     |   112 +
 .../radar/librevna_minimal_driver.hpp         |    97 +
 .../switches/h7992_minimal_driver.cpp         |   160 +
 .../switches/h7992_minimal_driver.hpp         |    51 +
 .../switches/hmc349a_minimal_driver.cpp       |   147 +
 .../switches/hmc349a_minimal_driver.hpp       |    52 +
 .../include/sweep_orchestrator.hpp            |    62 +
 .../sweep_orchestrator/include/sweep_plan.hpp |    22 +
 .../sweep_orchestrator/src/main.cpp           |   135 +
 .../src/sweep_orchestrator.cpp                |   178 +
 .../sweep_orchestrator/src/sweep_plan.cpp     |    19 +
 .../third_party/eigen/COPYING.APACHE          |   203 +
 .../third_party/eigen/COPYING.BSD             |    26 +
 .../third_party/eigen/COPYING.MINPACK         |    51 +
 .../third_party/eigen/COPYING.MPL2            |   373 +
 .../third_party/eigen/COPYING.README          |    11 +
 .../third_party/eigen/Eigen/AccelerateSupport |    52 +
 .../third_party/eigen/Eigen/Cholesky          |    43 +
 .../third_party/eigen/Eigen/CholmodSupport    |    48 +
 .../third_party/eigen/Eigen/Core              |   482 +
 .../third_party/eigen/Eigen/Dense             |     7 +
 .../third_party/eigen/Eigen/Eigen             |     2 +
 .../third_party/eigen/Eigen/Eigenvalues       |    63 +
 .../third_party/eigen/Eigen/Geometry          |    62 +
 .../third_party/eigen/Eigen/Householder       |    31 +
 .../eigen/Eigen/IterativeLinearSolvers        |    52 +
 .../third_party/eigen/Eigen/Jacobi            |    33 +
 .../third_party/eigen/Eigen/KLUSupport        |    43 +
 .../third_party/eigen/Eigen/LU                |    49 +
 .../third_party/eigen/Eigen/MetisSupport      |    35 +
 .../third_party/eigen/Eigen/OrderingMethods   |    73 +
 .../third_party/eigen/Eigen/PaStiXSupport     |    51 +
 .../third_party/eigen/Eigen/PardisoSupport    |    38 +
 .../third_party/eigen/Eigen/QR                |    48 +
 .../third_party/eigen/Eigen/QtAlignedMalloc   |    32 +
 .../third_party/eigen/Eigen/SPQRSupport       |    41 +
 .../third_party/eigen/Eigen/SVD               |    55 +
 .../third_party/eigen/Eigen/Sparse            |    33 +
 .../third_party/eigen/Eigen/SparseCholesky    |    40 +
 .../third_party/eigen/Eigen/SparseCore        |    70 +
 .../third_party/eigen/Eigen/SparseLU          |    50 +
 .../third_party/eigen/Eigen/SparseQR          |    38 +
 .../third_party/eigen/Eigen/StdDeque          |    30 +
 .../third_party/eigen/Eigen/StdList           |    29 +
 .../third_party/eigen/Eigen/StdVector         |    30 +
 .../third_party/eigen/Eigen/SuperLUSupport    |    70 +
 .../third_party/eigen/Eigen/ThreadPool        |    80 +
 .../third_party/eigen/Eigen/UmfPackSupport    |    42 +
 .../third_party/eigen/Eigen/Version           |    14 +
 .../src/AccelerateSupport/AccelerateSupport.h |   423 +
 .../AccelerateSupport/InternalHeaderCheck.h   |     3 +
 .../Eigen/src/Cholesky/InternalHeaderCheck.h  |     3 +
 .../eigen/Eigen/src/Cholesky/LDLT.h           |   661 +
 .../eigen/Eigen/src/Cholesky/LLT.h            |   516 +
 .../eigen/Eigen/src/Cholesky/LLT_LAPACKE.h    |   124 +
 .../Eigen/src/CholmodSupport/CholmodSupport.h |   738 +
 .../src/CholmodSupport/InternalHeaderCheck.h  |     3 +
 .../eigen/Eigen/src/Core/ArithmeticSequence.h |   239 +
 .../third_party/eigen/Eigen/src/Core/Array.h  |   374 +
 .../eigen/Eigen/src/Core/ArrayBase.h          |   210 +
 .../eigen/Eigen/src/Core/ArrayWrapper.h       |   166 +
 .../third_party/eigen/Eigen/src/Core/Assign.h |    84 +
 .../eigen/Eigen/src/Core/AssignEvaluator.h    |  1066 +
 .../eigen/Eigen/src/Core/Assign_AOCL.h        |   301 +
 .../eigen/Eigen/src/Core/Assign_MKL.h         |   183 +
 .../eigen/Eigen/src/Core/BandMatrix.h         |   338 +
 .../third_party/eigen/Eigen/src/Core/Block.h  |   427 +
 .../eigen/Eigen/src/Core/CommaInitializer.h   |   148 +
 .../eigen/Eigen/src/Core/ConditionEstimator.h |   173 +
 .../eigen/Eigen/src/Core/CoreEvaluators.h     |  1972 ++
 .../eigen/Eigen/src/Core/CoreIterators.h      |   141 +
 .../eigen/Eigen/src/Core/CwiseBinaryOp.h      |   166 +
 .../eigen/Eigen/src/Core/CwiseNullaryOp.h     |   975 +
 .../eigen/Eigen/src/Core/CwiseTernaryOp.h     |   171 +
 .../eigen/Eigen/src/Core/CwiseUnaryOp.h       |    95 +
 .../eigen/Eigen/src/Core/CwiseUnaryView.h     |   169 +
 .../eigen/Eigen/src/Core/DenseBase.h          |   672 +
 .../eigen/Eigen/src/Core/DenseCoeffsBase.h    |   583 +
 .../eigen/Eigen/src/Core/DenseStorage.h       |   546 +
 .../eigen/Eigen/src/Core/DeviceWrapper.h      |   153 +
 .../eigen/Eigen/src/Core/Diagonal.h           |   221 +
 .../eigen/Eigen/src/Core/DiagonalMatrix.h     |   473 +
 .../eigen/Eigen/src/Core/DiagonalProduct.h    |    30 +
 .../third_party/eigen/Eigen/src/Core/Dot.h    |   267 +
 .../eigen/Eigen/src/Core/EigenBase.h          |   149 +
 .../third_party/eigen/Eigen/src/Core/Fill.h   |   140 +
 .../eigen/Eigen/src/Core/FindCoeff.h          |   464 +
 .../eigen/Eigen/src/Core/ForceAlignedAccess.h |   108 +
 .../third_party/eigen/Eigen/src/Core/Fuzzy.h  |   132 +
 .../eigen/Eigen/src/Core/GeneralProduct.h     |   518 +
 .../eigen/Eigen/src/Core/GenericPacketMath.h  |  1706 ++
 .../eigen/Eigen/src/Core/GlobalFunctions.h    |   230 +
 .../third_party/eigen/Eigen/src/Core/IO.h     |   233 +
 .../eigen/Eigen/src/Core/IndexedView.h        |   322 +
 .../eigen/Eigen/src/Core/InnerProduct.h       |   260 +
 .../Eigen/src/Core/InternalHeaderCheck.h      |     3 +
 .../eigen/Eigen/src/Core/Inverse.h            |   108 +
 .../third_party/eigen/Eigen/src/Core/Map.h    |   154 +
 .../eigen/Eigen/src/Core/MapBase.h            |   283 +
 .../eigen/Eigen/src/Core/MathFunctions.h      |  2160 ++
 .../eigen/Eigen/src/Core/MathFunctionsImpl.h  |   265 +
 .../third_party/eigen/Eigen/src/Core/Matrix.h |   536 +
 .../eigen/Eigen/src/Core/MatrixBase.h         |   557 +
 .../eigen/Eigen/src/Core/NestByValue.h        |    91 +
 .../eigen/Eigen/src/Core/NoAlias.h            |   102 +
 .../eigen/Eigen/src/Core/NumTraits.h          |   335 +
 .../Eigen/src/Core/PartialReduxEvaluator.h    |   253 +
 .../eigen/Eigen/src/Core/PermutationMatrix.h  |   555 +
 .../eigen/Eigen/src/Core/PlainObjectBase.h    |  1008 +
 .../eigen/Eigen/src/Core/Product.h            |   307 +
 .../eigen/Eigen/src/Core/ProductEvaluators.h  |  1275 +
 .../third_party/eigen/Eigen/src/Core/Random.h |   207 +
 .../eigen/Eigen/src/Core/RandomImpl.h         |   265 +
 .../eigen/Eigen/src/Core/RealView.h           |   292 +
 .../third_party/eigen/Eigen/src/Core/Redux.h  |   535 +
 .../third_party/eigen/Eigen/src/Core/Ref.h    |   384 +
 .../eigen/Eigen/src/Core/Replicate.h          |   130 +
 .../eigen/Eigen/src/Core/Reshaped.h           |   363 +
 .../eigen/Eigen/src/Core/ReturnByValue.h      |   111 +
 .../eigen/Eigen/src/Core/Reverse.h            |   202 +
 .../third_party/eigen/Eigen/src/Core/Select.h |    92 +
 .../eigen/Eigen/src/Core/SelfAdjointView.h    |   329 +
 .../eigen/Eigen/src/Core/SelfCwiseBinaryOp.h  |    50 +
 .../Eigen/src/Core/SkewSymmetricMatrix3.h     |   382 +
 .../third_party/eigen/Eigen/src/Core/Solve.h  |   174 +
 .../eigen/Eigen/src/Core/SolveTriangular.h    |   237 +
 .../eigen/Eigen/src/Core/SolverBase.h         |   167 +
 .../eigen/Eigen/src/Core/StableNorm.h         |   216 +
 .../eigen/Eigen/src/Core/StlIterators.h       |   618 +
 .../third_party/eigen/Eigen/src/Core/Stride.h |   115 +
 .../third_party/eigen/Eigen/src/Core/Swap.h   |   100 +
 .../eigen/Eigen/src/Core/Transpose.h          |   428 +
 .../eigen/Eigen/src/Core/Transpositions.h     |   323 +
 .../eigen/Eigen/src/Core/TriangularMatrix.h   |   928 +
 .../eigen/Eigen/src/Core/VectorBlock.h        |    83 +
 .../eigen/Eigen/src/Core/VectorwiseOp.h       |   735 +
 .../eigen/Eigen/src/Core/Visitor.h            |   537 +
 .../eigen/Eigen/src/Core/arch/AVX/Complex.h   |   580 +
 .../Eigen/src/Core/arch/AVX/MathFunctions.h   |   129 +
 .../Eigen/src/Core/arch/AVX/PacketMath.h      |  3045 ++
 .../Eigen/src/Core/arch/AVX/Reductions.h      |   353 +
 .../Eigen/src/Core/arch/AVX/TypeCasting.h     |   342 +
 .../Eigen/src/Core/arch/AVX512/Complex.h      |   479 +
 .../Eigen/src/Core/arch/AVX512/GemmKernel.h   |  1249 +
 .../src/Core/arch/AVX512/MathFunctions.h      |   119 +
 .../src/Core/arch/AVX512/MathFunctionsFP16.h  |    75 +
 .../Eigen/src/Core/arch/AVX512/PacketMath.h   |  3172 ++
 .../src/Core/arch/AVX512/PacketMathFP16.h     |  1411 +
 .../Eigen/src/Core/arch/AVX512/Reductions.h   |   297 +
 .../Eigen/src/Core/arch/AVX512/TrsmKernel.h   |  1171 +
 .../src/Core/arch/AVX512/TrsmUnrolls.inc      |  1219 +
 .../Eigen/src/Core/arch/AVX512/TypeCasting.h  |   285 +
 .../src/Core/arch/AVX512/TypeCastingFP16.h    |   130 +
 .../Eigen/src/Core/arch/AltiVec/Complex.h     |   639 +
 .../src/Core/arch/AltiVec/MathFunctions.h     |    81 +
 .../src/Core/arch/AltiVec/MatrixProduct.h     |  3686 +++
 .../Core/arch/AltiVec/MatrixProductCommon.h   |   205 +
 .../src/Core/arch/AltiVec/MatrixProductMMA.h  |   900 +
 .../arch/AltiVec/MatrixProductMMAbfloat16.h   |   742 +
 .../Core/arch/AltiVec/MatrixVectorProduct.inc |  2818 ++
 .../Eigen/src/Core/arch/AltiVec/PacketMath.h  |  3651 +++
 .../Eigen/src/Core/arch/AltiVec/TypeCasting.h |   153 +
 .../Eigen/src/Core/arch/Default/BFloat16.h    |   903 +
 .../Eigen/src/Core/arch/Default/ConjHelper.h  |   146 +
 .../arch/Default/GenericPacketMathComplex.h   |   282 +
 .../Default/GenericPacketMathDoubleWord.h     |   208 +
 .../Default/GenericPacketMathFrexpLdexp.h     |   162 +
 .../arch/Default/GenericPacketMathFunctions.h |   608 +
 .../Default/GenericPacketMathFunctionsFwd.h   |   243 +
 .../Default/GenericPacketMathPolynomials.h    |   151 +
 .../Core/arch/Default/GenericPacketMathPow.h  |   710 +
 .../Core/arch/Default/GenericPacketMathTrig.h |   833 +
 .../eigen/Eigen/src/Core/arch/Default/Half.h  |  1126 +
 .../Eigen/src/Core/arch/Default/Settings.h    |    47 +
 .../eigen/Eigen/src/Core/arch/GPU/Complex.h   |   196 +
 .../Eigen/src/Core/arch/GPU/MathFunctions.h   |   104 +
 .../Eigen/src/Core/arch/GPU/PacketMath.h      |  1710 ++
 .../eigen/Eigen/src/Core/arch/GPU/Tuple.h     |   268 +
 .../Eigen/src/Core/arch/GPU/TypeCasting.h     |    77 +
 .../src/Core/arch/HIP/hcc/math_constants.h    |    23 +
 .../Eigen/src/Core/arch/HVX/PacketMath.h      |  1092 +
 .../eigen/Eigen/src/Core/arch/LSX/Complex.h   |   520 +
 .../Core/arch/LSX/GeneralBlockPanelKernel.h   |    23 +
 .../Eigen/src/Core/arch/LSX/MathFunctions.h   |    43 +
 .../Eigen/src/Core/arch/LSX/PacketMath.h      |  2846 ++
 .../Eigen/src/Core/arch/LSX/TypeCasting.h     |   526 +
 .../eigen/Eigen/src/Core/arch/MSA/Complex.h   |   613 +
 .../Eigen/src/Core/arch/MSA/MathFunctions.h   |   379 +
 .../Eigen/src/Core/arch/MSA/PacketMath.h      |  1199 +
 .../eigen/Eigen/src/Core/arch/NEON/Complex.h  |   738 +
 .../Core/arch/NEON/GeneralBlockPanelKernel.h  |   243 +
 .../Eigen/src/Core/arch/NEON/MathFunctions.h  |    63 +
 .../Eigen/src/Core/arch/NEON/PacketMath.h     |  6100 ++++
 .../Eigen/src/Core/arch/NEON/TypeCasting.h    |  1642 +
 .../Eigen/src/Core/arch/NEON/UnaryFunctors.h  |    57 +
 .../Core/arch/RVV10/GeneralBlockPanelKernel.h |   236 +
 .../Eigen/src/Core/arch/RVV10/MathFunctions.h |    30 +
 .../Eigen/src/Core/arch/RVV10/PacketMath.h    |  2442 ++
 .../Eigen/src/Core/arch/RVV10/PacketMath2.h   |  1537 +
 .../Eigen/src/Core/arch/RVV10/PacketMath4.h   |  1462 +
 .../src/Core/arch/RVV10/PacketMathBF16.h      |   839 +
 .../src/Core/arch/RVV10/PacketMathFP16.h      |   999 +
 .../Eigen/src/Core/arch/RVV10/TypeCasting.h   |   284 +
 .../eigen/Eigen/src/Core/arch/SSE/Complex.h   |   501 +
 .../Eigen/src/Core/arch/SSE/MathFunctions.h   |    88 +
 .../Eigen/src/Core/arch/SSE/PacketMath.h      |  2144 ++
 .../Eigen/src/Core/arch/SSE/Reductions.h      |   324 +
 .../Eigen/src/Core/arch/SSE/TypeCasting.h     |   180 +
 .../Eigen/src/Core/arch/SVE/MathFunctions.h   |    24 +
 .../Eigen/src/Core/arch/SVE/PacketMath.h      |   678 +
 .../Eigen/src/Core/arch/SVE/TypeCasting.h     |    52 +
 .../Eigen/src/Core/arch/SYCL/InteropHeaders.h |   226 +
 .../Eigen/src/Core/arch/SYCL/MathFunctions.h  |   113 +
 .../Eigen/src/Core/arch/SYCL/PacketMath.h     |   551 +
 .../Eigen/src/Core/arch/SYCL/TypeCasting.h    |    83 +
 .../Eigen/src/Core/arch/ZVector/Complex.h     |   553 +
 .../src/Core/arch/ZVector/MathFunctions.h     |   244 +
 .../Eigen/src/Core/arch/ZVector/PacketMath.h  |  1353 +
 .../eigen/Eigen/src/Core/arch/clang/Complex.h |   406 +
 .../Eigen/src/Core/arch/clang/MathFunctions.h |    47 +
 .../Eigen/src/Core/arch/clang/PacketMath.h    |   875 +
 .../Eigen/src/Core/arch/clang/Reductions.h    |   162 +
 .../Eigen/src/Core/arch/clang/TypeCasting.h   |   100 +
 .../src/Core/functors/AssignmentFunctors.h    |   174 +
 .../Eigen/src/Core/functors/BinaryFunctors.h  |   748 +
 .../Eigen/src/Core/functors/NullaryFunctors.h |   274 +
 .../Eigen/src/Core/functors/StlFunctors.h     |   149 +
 .../Eigen/src/Core/functors/TernaryFunctors.h |    53 +
 .../Eigen/src/Core/functors/UnaryFunctors.h   |  1421 +
 .../Core/products/GeneralBlockPanelKernel.h   |  2355 ++
 .../src/Core/products/GeneralMatrixMatrix.h   |   459 +
 .../products/GeneralMatrixMatrixTriangular.h  |   330 +
 .../GeneralMatrixMatrixTriangular_BLAS.h      |   150 +
 .../Core/products/GeneralMatrixMatrix_BLAS.h  |   206 +
 .../src/Core/products/GeneralMatrixVector.h   |   674 +
 .../Core/products/GeneralMatrixVector_BLAS.h  |   141 +
 .../Eigen/src/Core/products/Parallelizer.h    |   282 +
 .../Core/products/SelfadjointMatrixMatrix.h   |   483 +
 .../products/SelfadjointMatrixMatrix_BLAS.h   |   281 +
 .../Core/products/SelfadjointMatrixVector.h   |   251 +
 .../products/SelfadjointMatrixVector_BLAS.h   |   117 +
 .../src/Core/products/SelfadjointProduct.h    |   133 +
 .../Core/products/SelfadjointRank2Update.h    |    95 +
 .../Core/products/TriangularMatrixMatrix.h    |   373 +
 .../products/TriangularMatrixMatrix_BLAS.h    |   329 +
 .../Core/products/TriangularMatrixVector.h    |   347 +
 .../products/TriangularMatrixVector_BLAS.h    |   278 +
 .../Core/products/TriangularSolverMatrix.h    |   448 +
 .../products/TriangularSolverMatrix_BLAS.h    |   168 +
 .../Core/products/TriangularSolverVector.h    |   122 +
 .../eigen/Eigen/src/Core/util/AOCL_Support.h  |   175 +
 .../eigen/Eigen/src/Core/util/Assert.h        |   158 +
 .../eigen/Eigen/src/Core/util/BlasUtil.h      |   626 +
 .../src/Core/util/ConfigureVectorization.h    |   609 +
 .../eigen/Eigen/src/Core/util/Constants.h     |   601 +
 .../src/Core/util/DisableStupidWarnings.h     |   146 +
 .../eigen/Eigen/src/Core/util/EmulateArray.h  |   270 +
 .../Eigen/src/Core/util/ForwardDeclarations.h |   527 +
 .../Eigen/src/Core/util/GpuHipCudaDefines.inc |   101 +
 .../src/Core/util/GpuHipCudaUndefines.inc     |    45 +
 .../Eigen/src/Core/util/IndexedViewHelper.h   |   487 +
 .../Eigen/src/Core/util/IntegralConstant.h    |   279 +
 .../eigen/Eigen/src/Core/util/MKL_support.h   |   139 +
 .../eigen/Eigen/src/Core/util/Macros.h        |  1392 +
 .../eigen/Eigen/src/Core/util/MaxSizeVector.h |   138 +
 .../eigen/Eigen/src/Core/util/Memory.h        |  1383 +
 .../eigen/Eigen/src/Core/util/Meta.h          |   680 +
 .../eigen/Eigen/src/Core/util/MoreMeta.h      |   634 +
 .../src/Core/util/ReenableStupidWarnings.h    |    44 +
 .../Eigen/src/Core/util/ReshapedHelper.h      |    51 +
 .../eigen/Eigen/src/Core/util/Serializer.h    |   209 +
 .../eigen/Eigen/src/Core/util/StaticAssert.h  |   105 +
 .../eigen/Eigen/src/Core/util/SymbolicIndex.h |   445 +
 .../eigen/Eigen/src/Core/util/XprHelper.h     |  1091 +
 .../src/Eigenvalues/ComplexEigenSolver.h      |   314 +
 .../eigen/Eigen/src/Eigenvalues/ComplexQZ.h   |   651 +
 .../Eigen/src/Eigenvalues/ComplexSchur.h      |   443 +
 .../src/Eigenvalues/ComplexSchur_LAPACKE.h    |    95 +
 .../eigen/Eigen/src/Eigenvalues/EigenSolver.h |   579 +
 .../src/Eigenvalues/GeneralizedEigenSolver.h  |   402 +
 .../GeneralizedSelfAdjointEigenSolver.h       |   212 +
 .../src/Eigenvalues/HessenbergDecomposition.h |   356 +
 .../src/Eigenvalues/InternalHeaderCheck.h     |     3 +
 .../src/Eigenvalues/MatrixBaseEigenvalues.h   |   141 +
 .../eigen/Eigen/src/Eigenvalues/RealQZ.h      |   587 +
 .../eigen/Eigen/src/Eigenvalues/RealSchur.h   |   519 +
 .../Eigen/src/Eigenvalues/RealSchur_LAPACKE.h |    83 +
 .../src/Eigenvalues/SelfAdjointEigenSolver.h  |   865 +
 .../SelfAdjointEigenSolver_LAPACKE.h          |    90 +
 .../src/Eigenvalues/Tridiagonalization.h      |   529 +
 .../eigen/Eigen/src/Geometry/AlignedBox.h     |   485 +
 .../eigen/Eigen/src/Geometry/AngleAxis.h      |   245 +
 .../eigen/Eigen/src/Geometry/EulerAngles.h    |   203 +
 .../eigen/Eigen/src/Geometry/Homogeneous.h    |   452 +
 .../eigen/Eigen/src/Geometry/Hyperplane.h     |   273 +
 .../Eigen/src/Geometry/InternalHeaderCheck.h  |     3 +
 .../eigen/Eigen/src/Geometry/OrthoMethods.h   |   257 +
 .../Eigen/src/Geometry/ParametrizedLine.h     |   232 +
 .../eigen/Eigen/src/Geometry/Quaternion.h     |   923 +
 .../eigen/Eigen/src/Geometry/Rotation2D.h     |   201 +
 .../eigen/Eigen/src/Geometry/RotationBase.h   |   209 +
 .../eigen/Eigen/src/Geometry/Scaling.h        |   195 +
 .../eigen/Eigen/src/Geometry/Transform.h      |  1494 +
 .../eigen/Eigen/src/Geometry/Translation.h    |   204 +
 .../eigen/Eigen/src/Geometry/Umeyama.h        |   161 +
 .../Eigen/src/Geometry/arch/Geometry_SIMD.h   |   154 +
 .../Eigen/src/Householder/BlockHouseholder.h  |    86 +
 .../eigen/Eigen/src/Householder/Householder.h |   162 +
 .../src/Householder/HouseholderSequence.h     |   504 +
 .../src/Householder/InternalHeaderCheck.h     |     3 +
 .../BasicPreconditioners.h                    |   213 +
 .../src/IterativeLinearSolvers/BiCGSTAB.h     |   219 +
 .../ConjugateGradient.h                       |   217 +
 .../IncompleteCholesky.h                      |   402 +
 .../IterativeLinearSolvers/IncompleteLUT.h    |   449 +
 .../InternalHeaderCheck.h                     |     3 +
 .../IterativeSolverBase.h                     |   395 +
 .../LeastSquareConjugateGradient.h            |   193 +
 .../IterativeLinearSolvers/SolveWithGuess.h   |   111 +
 .../Eigen/src/Jacobi/InternalHeaderCheck.h    |     3 +
 .../eigen/Eigen/src/Jacobi/Jacobi.h           |   468 +
 .../src/KLUSupport/InternalHeaderCheck.h      |     3 +
 .../eigen/Eigen/src/KLUSupport/KLUSupport.h   |   261 +
 .../eigen/Eigen/src/LU/Determinant.h          |    98 +
 .../eigen/Eigen/src/LU/FullPivLU.h            |   825 +
 .../eigen/Eigen/src/LU/InternalHeaderCheck.h  |     3 +
 .../eigen/Eigen/src/LU/InverseImpl.h          |   353 +
 .../eigen/Eigen/src/LU/PartialPivLU.h         |   583 +
 .../eigen/Eigen/src/LU/PartialPivLU_LAPACKE.h |    97 +
 .../eigen/Eigen/src/LU/arch/InverseSize4.h    |   353 +
 .../src/MetisSupport/InternalHeaderCheck.h    |     3 +
 .../Eigen/src/MetisSupport/MetisSupport.h     |   125 +
 .../eigen/Eigen/src/OrderingMethods/Amd.h     |   413 +
 .../Eigen/src/OrderingMethods/Eigen_Colamd.h  |  1690 ++
 .../src/OrderingMethods/InternalHeaderCheck.h |     3 +
 .../Eigen/src/OrderingMethods/Ordering.h      |   146 +
 .../src/PaStiXSupport/InternalHeaderCheck.h   |     3 +
 .../Eigen/src/PaStiXSupport/PaStiXSupport.h   |   632 +
 .../src/PardisoSupport/InternalHeaderCheck.h  |     3 +
 .../Eigen/src/PardisoSupport/PardisoSupport.h |   500 +
 .../eigen/Eigen/src/QR/ColPivHouseholderQR.h  |   674 +
 .../src/QR/ColPivHouseholderQR_LAPACKE.h      |   161 +
 .../src/QR/CompleteOrthogonalDecomposition.h  |   648 +
 .../eigen/Eigen/src/QR/FullPivHouseholderQR.h |   727 +
 .../eigen/Eigen/src/QR/HouseholderQR.h        |   543 +
 .../Eigen/src/QR/HouseholderQR_LAPACKE.h      |    77 +
 .../eigen/Eigen/src/QR/InternalHeaderCheck.h  |     3 +
 .../src/SPQRSupport/InternalHeaderCheck.h     |     3 +
 .../src/SPQRSupport/SuiteSparseQRSupport.h    |   315 +
 .../third_party/eigen/Eigen/src/SVD/BDCSVD.h  |  1470 +
 .../eigen/Eigen/src/SVD/BDCSVD_LAPACKE.h      |   174 +
 .../eigen/Eigen/src/SVD/InternalHeaderCheck.h |     3 +
 .../eigen/Eigen/src/SVD/JacobiSVD.h           |   851 +
 .../eigen/Eigen/src/SVD/JacobiSVD_LAPACKE.h   |   127 +
 .../third_party/eigen/Eigen/src/SVD/SVDBase.h |   436 +
 .../Eigen/src/SVD/UpperBidiagonalization.h    |   366 +
 .../src/SparseCholesky/InternalHeaderCheck.h  |     3 +
 .../src/SparseCholesky/SimplicialCholesky.h   |   868 +
 .../SparseCholesky/SimplicialCholesky_impl.h  |   388 +
 .../eigen/Eigen/src/SparseCore/AmbiVector.h   |   329 +
 .../Eigen/src/SparseCore/CompressedStorage.h  |   206 +
 .../ConservativeSparseSparseProduct.h         |   308 +
 .../src/SparseCore/InternalHeaderCheck.h      |     3 +
 .../eigen/Eigen/src/SparseCore/SparseAssign.h |   279 +
 .../eigen/Eigen/src/SparseCore/SparseBlock.h  |   560 +
 .../Eigen/src/SparseCore/SparseColEtree.h     |   194 +
 .../src/SparseCore/SparseCompressedBase.h     |   592 +
 .../src/SparseCore/SparseCwiseBinaryOp.h      |   938 +
 .../Eigen/src/SparseCore/SparseCwiseUnaryOp.h |   142 +
 .../Eigen/src/SparseCore/SparseDenseProduct.h |   489 +
 .../src/SparseCore/SparseDiagonalProduct.h    |   146 +
 .../eigen/Eigen/src/SparseCore/SparseDot.h    |   100 +
 .../eigen/Eigen/src/SparseCore/SparseFuzzy.h  |    31 +
 .../eigen/Eigen/src/SparseCore/SparseMap.h    |   295 +
 .../eigen/Eigen/src/SparseCore/SparseMatrix.h |  1876 ++
 .../Eigen/src/SparseCore/SparseMatrixBase.h   |   451 +
 .../Eigen/src/SparseCore/SparsePermutation.h  |   249 +
 .../Eigen/src/SparseCore/SparseProduct.h      |   178 +
 .../eigen/Eigen/src/SparseCore/SparseRedux.h  |    47 +
 .../eigen/Eigen/src/SparseCore/SparseRef.h    |   371 +
 .../src/SparseCore/SparseSelfAdjointView.h    |   613 +
 .../Eigen/src/SparseCore/SparseSolverBase.h   |   115 +
 .../SparseSparseProductWithPruning.h          |   181 +
 .../Eigen/src/SparseCore/SparseTranspose.h    |    83 +
 .../src/SparseCore/SparseTriangularView.h     |   177 +
 .../eigen/Eigen/src/SparseCore/SparseUtil.h   |   209 +
 .../eigen/Eigen/src/SparseCore/SparseVector.h |   532 +
 .../eigen/Eigen/src/SparseCore/SparseView.h   |   225 +
 .../Eigen/src/SparseCore/TriangularSolver.h   |   270 +
 .../Eigen/src/SparseLU/InternalHeaderCheck.h  |     3 +
 .../eigen/Eigen/src/SparseLU/SparseLU.h       |   968 +
 .../eigen/Eigen/src/SparseLU/SparseLUImpl.h   |    79 +
 .../Eigen/src/SparseLU/SparseLU_Memory.h      |   210 +
 .../Eigen/src/SparseLU/SparseLU_Structs.h     |   113 +
 .../src/SparseLU/SparseLU_SupernodalMatrix.h  |   315 +
 .../eigen/Eigen/src/SparseLU/SparseLU_Utils.h |    75 +
 .../Eigen/src/SparseLU/SparseLU_column_bmod.h |   177 +
 .../Eigen/src/SparseLU/SparseLU_column_dfs.h  |   168 +
 .../src/SparseLU/SparseLU_copy_to_ucol.h      |   106 +
 .../src/SparseLU/SparseLU_heap_relax_snode.h  |   114 +
 .../Eigen/src/SparseLU/SparseLU_kernel_bmod.h |   133 +
 .../Eigen/src/SparseLU/SparseLU_panel_bmod.h  |   215 +
 .../Eigen/src/SparseLU/SparseLU_panel_dfs.h   |   232 +
 .../Eigen/src/SparseLU/SparseLU_pivotL.h      |   136 +
 .../Eigen/src/SparseLU/SparseLU_pruneL.h      |   130 +
 .../Eigen/src/SparseLU/SparseLU_relax_snode.h |    81 +
 .../Eigen/src/SparseQR/InternalHeaderCheck.h  |     3 +
 .../eigen/Eigen/src/SparseQR/SparseQR.h       |   705 +
 .../eigen/Eigen/src/StlSupport/StdDeque.h     |    51 +
 .../eigen/Eigen/src/StlSupport/StdList.h      |    50 +
 .../eigen/Eigen/src/StlSupport/StdVector.h    |    51 +
 .../eigen/Eigen/src/StlSupport/details.h      |    82 +
 .../src/SuperLUSupport/InternalHeaderCheck.h  |     3 +
 .../Eigen/src/SuperLUSupport/SuperLUSupport.h |   929 +
 .../eigen/Eigen/src/ThreadPool/Barrier.h      |    70 +
 .../src/ThreadPool/CoreThreadPoolDevice.h     |   336 +
 .../eigen/Eigen/src/ThreadPool/EventCount.h   |   241 +
 .../eigen/Eigen/src/ThreadPool/ForkJoin.h     |   140 +
 .../src/ThreadPool/InternalHeaderCheck.h      |     4 +
 .../src/ThreadPool/NonBlockingThreadPool.h    |   587 +
 .../eigen/Eigen/src/ThreadPool/RunQueue.h     |   230 +
 .../eigen/Eigen/src/ThreadPool/ThreadCancel.h |    21 +
 .../Eigen/src/ThreadPool/ThreadEnvironment.h  |    43 +
 .../eigen/Eigen/src/ThreadPool/ThreadLocal.h  |   289 +
 .../src/ThreadPool/ThreadPoolInterface.h      |    50 +
 .../eigen/Eigen/src/ThreadPool/ThreadYield.h  |    16 +
 .../src/UmfPackSupport/InternalHeaderCheck.h  |     3 +
 .../Eigen/src/UmfPackSupport/UmfPackSupport.h |   606 +
 .../third_party/eigen/Eigen/src/misc/Image.h  |    80 +
 .../Eigen/src/misc/InternalHeaderCheck.h      |     3 +
 .../third_party/eigen/Eigen/src/misc/Kernel.h |    77 +
 .../third_party/eigen/Eigen/src/misc/blas.h   |    97 +
 .../eigen/Eigen/src/misc/lapacke.h            | 10085 +++++++
 .../eigen/Eigen/src/misc/lapacke_helpers.h    |   163 +
 .../eigen/Eigen/src/misc/lapacke_mangling.h   |    16 +
 .../Eigen/src/plugins/ArrayCwiseBinaryOps.inc |   324 +
 .../Eigen/src/plugins/ArrayCwiseUnaryOps.inc  |   548 +
 .../eigen/Eigen/src/plugins/BlockMethods.inc  |  1380 +
 .../src/plugins/CommonCwiseBinaryOps.inc      |   122 +
 .../Eigen/src/plugins/CommonCwiseUnaryOps.inc |   167 +
 .../Eigen/src/plugins/IndexedViewMethods.inc  |   192 +
 .../Eigen/src/plugins/InternalHeaderCheck.inc |     3 +
 .../src/plugins/MatrixCwiseBinaryOps.inc      |   335 +
 .../Eigen/src/plugins/MatrixCwiseUnaryOps.inc |   129 +
 .../Eigen/src/plugins/ReshapedMethods.inc     |   133 +
 .../eigen/unsupported/CMakeLists.txt          |    11 +
 .../eigen/unsupported/Eigen/AdolcForward      |   153 +
 .../eigen/unsupported/Eigen/AlignedVector3    |   211 +
 .../eigen/unsupported/Eigen/ArpackSupport     |    33 +
 .../eigen/unsupported/Eigen/AutoDiff          |    48 +
 .../third_party/eigen/unsupported/Eigen/BVH   |   108 +
 .../eigen/unsupported/Eigen/CMakeLists.txt    |    31 +
 .../unsupported/Eigen/CXX11/CMakeLists.txt    |     8 +
 .../eigen/unsupported/Eigen/CXX11/Tensor      |   142 +
 .../unsupported/Eigen/CXX11/TensorSymmetry    |    40 +
 .../eigen/unsupported/Eigen/CXX11/ThreadPool  |     1 +
 .../CXX11/src/Tensor/InternalHeaderCheck.h    |     3 +
 .../Eigen/CXX11/src/Tensor/README.md          |  2352 ++
 .../Eigen/CXX11/src/Tensor/Tensor.h           |   382 +
 .../Eigen/CXX11/src/Tensor/TensorArgMax.h     |   282 +
 .../Eigen/CXX11/src/Tensor/TensorAssign.h     |   211 +
 .../Eigen/CXX11/src/Tensor/TensorBase.h       |  1244 +
 .../Eigen/CXX11/src/Tensor/TensorBlock.h      |  1474 +
 .../CXX11/src/Tensor/TensorBroadcasting.h     |  1001 +
 .../Eigen/CXX11/src/Tensor/TensorChipping.h   |   469 +
 .../CXX11/src/Tensor/TensorConcatenation.h    |   351 +
 .../CXX11/src/Tensor/TensorContraction.h      |   958 +
 .../src/Tensor/TensorContractionBlocking.h    |    69 +
 .../CXX11/src/Tensor/TensorContractionCuda.h  |     7 +
 .../CXX11/src/Tensor/TensorContractionGpu.h   |  1387 +
 .../src/Tensor/TensorContractionMapper.h      |   537 +
 .../CXX11/src/Tensor/TensorContractionSycl.h  |  1653 ++
 .../src/Tensor/TensorContractionThreadPool.h  |  1544 +
 .../Eigen/CXX11/src/Tensor/TensorConversion.h |   416 +
 .../CXX11/src/Tensor/TensorConvolution.h      |  1106 +
 .../CXX11/src/Tensor/TensorConvolutionSycl.h  |   538 +
 .../Eigen/CXX11/src/Tensor/TensorCostModel.h  |   189 +
 .../Eigen/CXX11/src/Tensor/TensorCustomOp.h   |   307 +
 .../Eigen/CXX11/src/Tensor/TensorDevice.h     |   138 +
 .../Eigen/CXX11/src/Tensor/TensorDeviceCuda.h |     7 +
 .../CXX11/src/Tensor/TensorDeviceDefault.h    |   113 +
 .../Eigen/CXX11/src/Tensor/TensorDeviceGpu.h  |   364 +
 .../Eigen/CXX11/src/Tensor/TensorDeviceSycl.h |   567 +
 .../CXX11/src/Tensor/TensorDeviceThreadPool.h |   346 +
 .../CXX11/src/Tensor/TensorDimensionList.h    |   117 +
 .../Eigen/CXX11/src/Tensor/TensorDimensions.h |   334 +
 .../Eigen/CXX11/src/Tensor/TensorEvalTo.h     |   188 +
 .../Eigen/CXX11/src/Tensor/TensorEvaluator.h  |   859 +
 .../Eigen/CXX11/src/Tensor/TensorExecutor.h   |   670 +
 .../Eigen/CXX11/src/Tensor/TensorExpr.h       |   338 +
 .../Eigen/CXX11/src/Tensor/TensorFFT.h        |   650 +
 .../Eigen/CXX11/src/Tensor/TensorFixedSize.h  |   221 +
 .../Eigen/CXX11/src/Tensor/TensorForcedEval.h |   230 +
 .../src/Tensor/TensorForwardDeclarations.h    |   215 +
 .../Eigen/CXX11/src/Tensor/TensorFunctors.h   |   422 +
 .../Eigen/CXX11/src/Tensor/TensorGenerator.h  |   269 +
 .../CXX11/src/Tensor/TensorGlobalFunctions.h  |    36 +
 .../Eigen/CXX11/src/Tensor/TensorIO.h         |   413 +
 .../Eigen/CXX11/src/Tensor/TensorImagePatch.h |   590 +
 .../Eigen/CXX11/src/Tensor/TensorIndexList.h  |   617 +
 .../Eigen/CXX11/src/Tensor/TensorInflation.h  |   224 +
 .../CXX11/src/Tensor/TensorInitializer.h      |    78 +
 .../Eigen/CXX11/src/Tensor/TensorIntDiv.h     |   258 +
 .../Eigen/CXX11/src/Tensor/TensorLayoutSwap.h |   185 +
 .../Eigen/CXX11/src/Tensor/TensorMacros.h     |    85 +
 .../Eigen/CXX11/src/Tensor/TensorMap.h        |   189 +
 .../Eigen/CXX11/src/Tensor/TensorMeta.h       |   291 +
 .../Eigen/CXX11/src/Tensor/TensorMorphing.h   |   980 +
 .../Eigen/CXX11/src/Tensor/TensorPadding.h    |   620 +
 .../Eigen/CXX11/src/Tensor/TensorPatch.h      |   258 +
 .../Eigen/CXX11/src/Tensor/TensorRandom.h     |   297 +
 .../Eigen/CXX11/src/Tensor/TensorReduction.h  |  1023 +
 .../CXX11/src/Tensor/TensorReductionGpu.h     |   958 +
 .../CXX11/src/Tensor/TensorReductionSycl.h    |   584 +
 .../Eigen/CXX11/src/Tensor/TensorRef.h        |   390 +
 .../Eigen/CXX11/src/Tensor/TensorReverse.h    |   406 +
 .../Eigen/CXX11/src/Tensor/TensorRoll.h       |   361 +
 .../Eigen/CXX11/src/Tensor/TensorScan.h       |   474 +
 .../Eigen/CXX11/src/Tensor/TensorScanSycl.h   |   506 +
 .../Eigen/CXX11/src/Tensor/TensorShuffling.h  |   413 +
 .../Eigen/CXX11/src/Tensor/TensorStorage.h    |   143 +
 .../Eigen/CXX11/src/Tensor/TensorStriding.h   |   312 +
 .../Eigen/CXX11/src/Tensor/TensorTrace.h      |   281 +
 .../Eigen/CXX11/src/Tensor/TensorTraits.h     |   232 +
 .../Eigen/CXX11/src/Tensor/TensorUInt128.h    |   229 +
 .../CXX11/src/Tensor/TensorVolumePatch.h      |   621 +
 .../src/TensorSymmetry/DynamicSymmetry.h      |   296 +
 .../src/TensorSymmetry/InternalHeaderCheck.h  |     4 +
 .../CXX11/src/TensorSymmetry/StaticSymmetry.h |   223 +
 .../Eigen/CXX11/src/TensorSymmetry/Symmetry.h |   335 +
 .../TensorSymmetry/util/TemplateGroupTheory.h |   492 +
 .../Eigen/CXX11/src/util/CXX11Meta.h          |    18 +
 .../Eigen/CXX11/src/util/CXX11Workarounds.h   |    85 +
 .../eigen/unsupported/Eigen/EulerAngles       |    44 +
 .../third_party/eigen/unsupported/Eigen/FFT   |   430 +
 .../eigen/unsupported/Eigen/IterativeSolvers  |    92 +
 .../eigen/unsupported/Eigen/KroneckerProduct  |    35 +
 .../unsupported/Eigen/LevenbergMarquardt      |    49 +
 .../eigen/unsupported/Eigen/MPRealSupport     |   214 +
 .../eigen/unsupported/Eigen/MatrixFunctions   |   504 +
 .../third_party/eigen/unsupported/Eigen/NNLS  |   388 +
 .../unsupported/Eigen/NonLinearOptimization   |   141 +
 .../eigen/unsupported/Eigen/NumericalDiff     |    57 +
 .../eigen/unsupported/Eigen/OpenGLSupport     |   350 +
 .../eigen/unsupported/Eigen/Polynomials       |   140 +
 .../eigen/unsupported/Eigen/SparseExtra       |    60 +
 .../eigen/unsupported/Eigen/SpecialFunctions  |   107 +
 .../eigen/unsupported/Eigen/Splines           |    36 +
 .../Eigen/src/AutoDiff/AutoDiffJacobian.h     |    76 +
 .../Eigen/src/AutoDiff/AutoDiffScalar.h       |   571 +
 .../Eigen/src/AutoDiff/AutoDiffVector.h       |   169 +
 .../Eigen/src/AutoDiff/CoherentPadOp.h        |   152 +
 .../Eigen/src/AutoDiff/InternalHeaderCheck.h  |     3 +
 .../unsupported/Eigen/src/BVH/BVAlgorithms.h  |   291 +
 .../Eigen/src/BVH/InternalHeaderCheck.h       |     3 +
 .../eigen/unsupported/Eigen/src/BVH/KdBVH.h   |   223 +
 .../ArpackSelfAdjointEigenSolver.h            |   699 +
 .../src/Eigenvalues/InternalHeaderCheck.h     |     3 +
 .../Eigen/src/EulerAngles/CMakeLists.txt      |     6 +
 .../Eigen/src/EulerAngles/EulerAngles.h       |   350 +
 .../Eigen/src/EulerAngles/EulerSystem.h       |   283 +
 .../src/EulerAngles/InternalHeaderCheck.h     |     3 +
 .../Eigen/src/FFT/InternalHeaderCheck.h       |     3 +
 .../unsupported/Eigen/src/FFT/duccfft_impl.h  |    71 +
 .../unsupported/Eigen/src/FFT/fftw_impl.h     |   216 +
 .../unsupported/Eigen/src/FFT/imklfft_impl.h  |   248 +
 .../unsupported/Eigen/src/FFT/kissfft_impl.h  |   416 +
 .../Eigen/src/FFT/pocketfft_impl.h            |    67 +
 .../Eigen/src/IterativeSolvers/BiCGSTABL.h    |   339 +
 .../Eigen/src/IterativeSolvers/DGMRES.h       |   490 +
 .../Eigen/src/IterativeSolvers/GMRES.h        |   315 +
 .../Eigen/src/IterativeSolvers/IDRS.h         |   395 +
 .../Eigen/src/IterativeSolvers/IDRSTABL.h     |   476 +
 .../Eigen/src/IterativeSolvers/IncompleteLU.h |    88 +
 .../IterativeSolvers/InternalHeaderCheck.h    |     4 +
 .../Eigen/src/IterativeSolvers/MINRES.h       |   254 +
 .../Eigen/src/IterativeSolvers/Scaling.h      |   172 +
 .../KroneckerProduct/InternalHeaderCheck.h    |     4 +
 .../KroneckerProduct/KroneckerTensorProduct.h |   284 +
 .../LevenbergMarquardt/CopyrightMINPACK.txt   |    52 +
 .../LevenbergMarquardt/InternalHeaderCheck.h  |     4 +
 .../Eigen/src/LevenbergMarquardt/LMcovar.h    |    80 +
 .../Eigen/src/LevenbergMarquardt/LMonestep.h  |   187 +
 .../Eigen/src/LevenbergMarquardt/LMpar.h      |   146 +
 .../Eigen/src/LevenbergMarquardt/LMqrsolv.h   |   178 +
 .../LevenbergMarquardt/LevenbergMarquardt.h   |   360 +
 .../src/MatrixFunctions/InternalHeaderCheck.h |     4 +
 .../src/MatrixFunctions/MatrixExponential.h   |   457 +
 .../src/MatrixFunctions/MatrixFunction.h      |   543 +
 .../src/MatrixFunctions/MatrixLogarithm.h     |   366 +
 .../Eigen/src/MatrixFunctions/MatrixPower.h   |   694 +
 .../src/MatrixFunctions/MatrixSquareRoot.h    |   345 +
 .../Eigen/src/MatrixFunctions/StemFunction.h  |   115 +
 .../HybridNonLinearSolver.h                   |   545 +
 .../InternalHeaderCheck.h                     |     4 +
 .../LevenbergMarquardt.h                      |   587 +
 .../Eigen/src/NonLinearOptimization/chkder.h  |    57 +
 .../Eigen/src/NonLinearOptimization/covar.h   |    66 +
 .../Eigen/src/NonLinearOptimization/dogleg.h  |   103 +
 .../Eigen/src/NonLinearOptimization/fdjac1.h  |    73 +
 .../Eigen/src/NonLinearOptimization/lmpar.h   |   263 +
 .../Eigen/src/NonLinearOptimization/qrsolv.h  |    89 +
 .../Eigen/src/NonLinearOptimization/r1mpyq.h  |    33 +
 .../Eigen/src/NonLinearOptimization/r1updt.h  |    96 +
 .../Eigen/src/NonLinearOptimization/rwupdt.h  |    47 +
 .../src/NumericalDiff/InternalHeaderCheck.h   |     3 +
 .../Eigen/src/NumericalDiff/NumericalDiff.h   |   127 +
 .../Eigen/src/Polynomials/Companion.h         |   255 +
 .../src/Polynomials/InternalHeaderCheck.h     |     3 +
 .../Eigen/src/Polynomials/PolynomialSolver.h  |   387 +
 .../Eigen/src/Polynomials/PolynomialUtils.h   |   144 +
 .../Eigen/src/SparseExtra/BlockSparseMatrix.h |   931 +
 .../src/SparseExtra/InternalHeaderCheck.h     |     3 +
 .../Eigen/src/SparseExtra/MarketIO.h          |   366 +
 .../src/SparseExtra/MatrixMarketIterator.h    |   217 +
 .../Eigen/src/SparseExtra/RandomSetter.h      |   308 +
 .../Eigen/src/SparseExtra/SparseInverse.h     |   232 +
 .../BesselFunctionsArrayAPI.h                 |   276 +
 .../BesselFunctionsBFloat16.h                 |    71 +
 .../BesselFunctionsFunctors.h                 |   323 +
 .../SpecialFunctions/BesselFunctionsHalf.h    |    69 +
 .../SpecialFunctions/BesselFunctionsImpl.h    |  1638 +
 .../BesselFunctionsPacketMath.h               |   108 +
 .../SpecialFunctions/HipVectorCompatibility.h |    71 +
 .../SpecialFunctions/InternalHeaderCheck.h    |     4 +
 .../SpecialFunctionsArrayAPI.h                |   159 +
 .../SpecialFunctionsBFloat16.h                |    73 +
 .../SpecialFunctionsFunctors.h                |   326 +
 .../SpecialFunctions/SpecialFunctionsHalf.h   |    73 +
 .../SpecialFunctions/SpecialFunctionsImpl.h   |  2071 ++
 .../SpecialFunctionsPacketMath.h              |   112 +
 .../arch/AVX/BesselFunctions.h                |    13 +
 .../arch/AVX/SpecialFunctions.h               |    13 +
 .../arch/AVX512/BesselFunctions.h             |    13 +
 .../arch/AVX512/SpecialFunctions.h            |    13 +
 .../arch/GPU/SpecialFunctions.h               |   317 +
 .../arch/NEON/BesselFunctions.h               |    43 +
 .../arch/NEON/SpecialFunctions.h              |    33 +
 .../Eigen/src/Splines/InternalHeaderCheck.h   |     3 +
 .../unsupported/Eigen/src/Splines/Spline.h    |   479 +
 .../Eigen/src/Splines/SplineFitting.h         |   393 +
 .../unsupported/Eigen/src/Splines/SplineFwd.h |   109 +
 .../third_party/eigen/unsupported/README.txt  |    50 +
 .../eigen/unsupported/bench/bench_svd.cpp     |   111 +
 .../benchmarks/AutoDiff/CMakeLists.txt        |     1 +
 .../benchmarks/AutoDiff/bench_autodiff.cpp    |   177 +
 .../unsupported/benchmarks/CMakeLists.txt     |    35 +
 .../IterativeSolvers/CMakeLists.txt           |     1 +
 .../bench_iterative_solvers.cpp               |   209 +
 .../KroneckerProduct/CMakeLists.txt           |     1 +
 .../KroneckerProduct/bench_kronecker.cpp      |    83 +
 .../benchmarks/MatrixFunctions/CMakeLists.txt |     3 +
 .../bench_matrix_exponential.cpp              |    52 +
 .../bench_matrix_logarithm.cpp                |    51 +
 .../MatrixFunctions/bench_matrix_power.cpp    |    99 +
 .../SpecialFunctions/CMakeLists.txt           |     1 +
 .../bench_special_functions.cpp               |   127 +
 .../benchmarks/Splines/CMakeLists.txt         |     1 +
 .../benchmarks/Splines/bench_splines.cpp      |    98 +
 .../benchmarks/Tensor/CMakeLists.txt          |     8 +
 .../benchmarks/Tensor/bench_broadcasting.cpp  |   111 +
 .../Tensor/bench_coefficient_wise.cpp         |   131 +
 .../benchmarks/Tensor/bench_contraction.cpp   |   148 +
 .../benchmarks/Tensor/bench_convolution.cpp   |   151 +
 .../benchmarks/Tensor/bench_morphing.cpp      |   142 +
 .../benchmarks/Tensor/bench_reduction.cpp     |   158 +
 .../benchmarks/Tensor/bench_shuffling.cpp     |   115 +
 .../benchmarks/Tensor/bench_tensor_fft.cpp    |    80 +
 .../eigen/unsupported/doc/CMakeLists.txt      |     4 +
 .../eigen/unsupported/doc/Overview.dox        |    31 +
 .../eigen/unsupported/doc/SYCL.dox            |     9 +
 .../unsupported/doc/eigendoxy_layout.xml.in   |   269 +
 .../unsupported/doc/examples/BVH_Example.cpp  |    62 +
 .../unsupported/doc/examples/CMakeLists.txt   |    23 +
 .../unsupported/doc/examples/EulerAngles.cpp  |    44 +
 .../eigen/unsupported/doc/examples/FFT.cpp    |   103 +
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diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..9c7e674
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,221 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+build*
+# C extensions
+*.so
+*.npy
+snapshots/
+test_results*
+meta.json
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#   Usually these files are written by a python script from a template
+#   before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+.hypothesis/
+.pytest_cache/
+cover/
+tests/artifacts/
+tests/tests/artifacts/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+# Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+# uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+# poetry.lock
+# poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+# pdm.lock
+# pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+# pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# Redis
+*.rdb
+*.aof
+*.pid
+
+# RabbitMQ
+mnesia/
+rabbitmq/
+rabbitmq-data/
+
+# ActiveMQ
+activemq-data/
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#   JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#   be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#   and can be added to the global gitignore or merged into this file.  For a more nuclear
+#   option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# .idea/
+
+# Abstra
+#   Abstra is an AI-powered process automation framework.
+#   Ignore directories containing user credentials, local state, and settings.
+#   Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#   and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#   you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/
+
+# Streamlit
+.streamlit/secrets.toml
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..75d2816
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,83 @@
+CXX := g++
+CXXFLAGS := -std=c++20 -O2 -Wall -Wextra -Wpedantic -pthread
+LDFLAGS := -pthread -lrt
+ORCH_LDFLAGS := $(LDFLAGS) -lusb-1.0
+
+BUILD_DIR := build
+BIN_DIR := $(BUILD_DIR)/bin
+
+INCLUDES := \
+	-Idata_acq_and_processing/common_cpp/ipc/include \
+	-Idata_acq_and_processing/common_cpp/config/include \
+	-Idata_acq_and_processing/third_party \
+	-Idata_acq_and_processing/third_party/eigen \
+	-Idata_acq_and_processing/sweep_orchestrator/include \
+	-Idata_acq_and_processing/sweep_orchestrator/device_drivers/interfaces \
+	-Idata_acq_and_processing/sweep_orchestrator/device_drivers/radar \
+	-Idata_acq_and_processing/sweep_orchestrator/device_drivers/switches \
+	-Idata_acq_and_processing/preprocessing/calibration_master/include \
+	-Idata_acq_and_processing/preprocessing/reference_master/include \
+	-Idata_acq_and_processing/preprocessing/data_preprocessor/include \
+	-Idata_acq_and_processing/processing/processors/include \
+	-Idata_acq_and_processing/processing/data_processor/include
+
+COMMON_SOURCES := \
+	data_acq_and_processing/common_cpp/ipc/src/shm_ring.cpp \
+	data_acq_and_processing/common_cpp/ipc/src/shared_types.cpp \
+	data_acq_and_processing/common_cpp/config/src/run_config.cpp
+
+ORCH_SOURCES := \
+	data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_lifecycle.cpp \
+	data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_transport.cpp \
+	data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_protocol.cpp \
+	data_acq_and_processing/sweep_orchestrator/device_drivers/switches/h7992_minimal_driver.cpp \
+	data_acq_and_processing/sweep_orchestrator/device_drivers/switches/hmc349a_minimal_driver.cpp \
+	data_acq_and_processing/sweep_orchestrator/src/sweep_plan.cpp \
+	data_acq_and_processing/sweep_orchestrator/src/sweep_orchestrator.cpp \
+	data_acq_and_processing/sweep_orchestrator/src/main.cpp
+
+PREPROC_SOURCES := \
+	data_acq_and_processing/preprocessing/calibration_master/src/calibration_master.cpp \
+	data_acq_and_processing/preprocessing/calibration_master/src/through_calibrator.cpp \
+	data_acq_and_processing/preprocessing/reference_master/src/reference_master.cpp \
+	data_acq_and_processing/preprocessing/data_preprocessor/src/data_preprocessor.cpp \
+	data_acq_and_processing/preprocessing/data_preprocessor/src/main.cpp
+
+PROCESSOR_SOURCES := \
+	data_acq_and_processing/processing/processors/src/bscan_processor.cpp \
+	data_acq_and_processing/processing/processors/src/passthrough_processor.cpp \
+	data_acq_and_processing/processing/data_processor/src/processing_live_config.cpp \
+	data_acq_and_processing/processing/data_processor/src/data_processor.cpp \
+	data_acq_and_processing/processing/data_processor/src/main.cpp
+
+SWEEP_ORCH_OBJS := $(addprefix $(BUILD_DIR)/,$(COMMON_SOURCES:.cpp=.o) $(ORCH_SOURCES:.cpp=.o))
+PREPROCESSOR_OBJS := $(addprefix $(BUILD_DIR)/,$(COMMON_SOURCES:.cpp=.o) $(PREPROC_SOURCES:.cpp=.o))
+DATA_PROCESSOR_OBJS := $(addprefix $(BUILD_DIR)/,$(COMMON_SOURCES:.cpp=.o) $(PROCESSOR_SOURCES:.cpp=.o))
+
+TARGETS := \
+	$(BIN_DIR)/sweep_orchestrator \
+	$(BIN_DIR)/data_preprocessor \
+	$(BIN_DIR)/data_processor
+
+.PHONY: all clean
+
+all: $(TARGETS)
+
+$(BIN_DIR)/sweep_orchestrator: $(SWEEP_ORCH_OBJS)
+	@mkdir -p $(BIN_DIR)
+	$(CXX) $(SWEEP_ORCH_OBJS) -o $@ $(ORCH_LDFLAGS)
+
+$(BIN_DIR)/data_preprocessor: $(PREPROCESSOR_OBJS)
+	@mkdir -p $(BIN_DIR)
+	$(CXX) $(PREPROCESSOR_OBJS) -o $@ $(LDFLAGS)
+
+$(BIN_DIR)/data_processor: $(DATA_PROCESSOR_OBJS)
+	@mkdir -p $(BIN_DIR)
+	$(CXX) $(DATA_PROCESSOR_OBJS) -o $@ $(LDFLAGS)
+
+$(BUILD_DIR)/%.o: %.cpp
+	@mkdir -p $(dir $@)
+	$(CXX) $(CXXFLAGS) $(INCLUDES) -c $< -o $@
+
+clean:
+	rm -rf $(BUILD_DIR)
diff --git a/data_acq_and_processing/common_cpp/config/include/run_config.hpp b/data_acq_and_processing/common_cpp/config/include/run_config.hpp
new file mode 100644
index 0000000..3728db3
--- /dev/null
+++ b/data_acq_and_processing/common_cpp/config/include/run_config.hpp
@@ -0,0 +1,97 @@
+#pragma once
+
+#include <cstdint>
+#include <string>
+#include <vector>
+
+#include "shared_types.hpp"
+
+namespace radar::config {
+
+enum class DriverMode {
+    Mock,
+    Native,
+};
+
+enum class SwitchDriverKind {
+    H7992,
+    HMC349A,
+};
+
+struct RadarSweepSettings {
+    // Frequency sweep bounds and acquisition settings.
+    float start_hz = 1'000'000.0F;
+    float stop_hz = 6'000'000'000.0F;
+    std::uint32_t points = 501;
+    float if_bandwidth_hz = 1'000.0F;
+    float power_dbm = -10.0F;
+};
+
+struct RadarConfig {
+    // Radar device identity and runtime mode.
+    std::string model = "librevna";
+    std::string serial{};
+    DriverMode driver_mode = DriverMode::Mock;
+    float mock_signal_hz = 5'000'000.0F;
+    RadarSweepSettings sweep{};
+};
+
+struct SwitchConfig {
+    // One RF switch attached to a specific radar port.
+    std::string name{};
+    DriverMode driver_mode = DriverMode::Mock;
+    SwitchDriverKind driver_kind = SwitchDriverKind::H7992;
+    std::uint32_t radar_port = 0;
+    std::uint32_t positions = 4;
+    std::uint32_t default_position = 0;
+    std::string gpio_chip = "/dev/gpiochip0";
+    std::int32_t pin_a = 17;
+    std::int32_t pin_b = 27;
+    bool invert_logic = false;
+};
+
+struct RingEndpointConfig {
+    // Shared-memory endpoint geometry.
+    std::string name{};
+    std::uint32_t capacity = 32;
+    std::uint32_t slot_size_bytes = 2U * 1024U * 1024U;
+};
+
+struct RingsConfig {
+    RingEndpointConfig raw{.name = "/radar_raw"};
+    RingEndpointConfig raw_tap{.name = "/radar_raw_tap"};
+    RingEndpointConfig preprocessed{.name = "/radar_preprocessed"};
+    RingEndpointConfig preprocessed_tap{.name = "/radar_preprocessed_tap"};
+    RingEndpointConfig results{.name = "/radar_results"};
+};
+
+struct RuntimeConfig {
+    // Runtime pacing and behavior flags for C++ workers.
+    std::uint32_t settling_ms = 0;
+    std::uint32_t idle_sleep_ms = 2;
+    bool continuous = true;
+    std::string processing_live_config_path = "python_app/runtime/processing_live.json";
+};
+
+struct PreprocessConfig {
+    // Names and bundle paths selected by Python GUI layer.
+    std::string calibration_set{};
+    std::string reference_set{};
+    std::string calibration_bundle_path{};
+    std::string reference_bundle_path{};
+};
+
+struct RunConfig {
+    // Single configuration object shared by all C++ processes.
+    RadarConfig radar{};
+    SwitchConfig input_switch{};
+    SwitchConfig output_switch{};
+    RingsConfig rings{};
+    RuntimeConfig runtime{};
+    PreprocessConfig preprocess{};
+    std::vector<radar::ipc::ComboKey> run_combos{};
+};
+
+[[nodiscard]] auto load_run_config(const std::string& path) -> RunConfig;
+
+}  // namespace radar::config
diff --git a/data_acq_and_processing/common_cpp/config/src/run_config.cpp b/data_acq_and_processing/common_cpp/config/src/run_config.cpp
new file mode 100644
index 0000000..9d8cae9
--- /dev/null
+++ b/data_acq_and_processing/common_cpp/config/src/run_config.cpp
@@ -0,0 +1,398 @@
+#include "run_config.hpp"
+
+#include <cmath>
+#include <cstdint>
+#include <fstream>
+#include <iterator>
+#include <limits>
+#include <stdexcept>
+#include <string>
+
+#include <nlohmann/json.hpp>
+
+namespace radar::config {
+namespace {
+
+using Json = nlohmann::json;
+
+[[nodiscard]] auto required_field(const Json& object, const std::string& key) -> const Json& {
+    const auto found = object.find(key);
+    if (found == object.end()) {
+        throw std::runtime_error("Missing required config field: " + key);
+    }
+    return *found;
+}
+
+[[nodiscard]] auto optional_field(const Json& object, const std::string& key) -> const Json* {
+    const auto found = object.find(key);
+    if (found == object.end()) {
+        return nullptr;
+    }
+    return &(*found);
+}
+
+[[nodiscard]] auto as_object(const Json& value, const std::string& context) -> const Json* {
+    if (!value.is_object()) {
+        throw std::runtime_error("Expected JSON object at " + context);
+    }
+    return &value;
+}
+
+[[nodiscard]] auto as_array(const Json& value, const std::string& context) -> const Json* {
+    if (!value.is_array()) {
+        throw std::runtime_error("Expected JSON array at " + context);
+    }
+    return &value;
+}
+
+[[nodiscard]] auto as_string(const Json& value, const std::string& context) -> const std::string& {
+    if (!value.is_string()) {
+        throw std::runtime_error("Expected JSON string at " + context);
+    }
+    return value.get_ref<const std::string&>();
+}
+
+[[nodiscard]] auto as_bool(const Json& value, const std::string& context) -> bool {
+    if (!value.is_boolean()) {
+        throw std::runtime_error("Expected JSON bool at " + context);
+    }
+    return value.get<bool>();
+}
+
+[[nodiscard]] auto as_number(const Json& value, const std::string& context) -> double {
+    if (!value.is_number()) {
+        throw std::runtime_error("Expected JSON number at " + context);
+    }
+    return value.get<double>();
+}
+
+[[nodiscard]] auto number_to_u32(double value, const std::string& context) -> std::uint32_t {
+    if (value < 0.0 || value > static_cast<double>(std::numeric_limits<std::uint32_t>::max())) {
+        throw std::runtime_error("Value out of uint32 range at " + context);
+    }
+
+    const auto rounded = std::round(value);
+    if (std::fabs(value - rounded) > 0.000001) {
+        throw std::runtime_error("Expected integer value at " + context);
+    }
+    return static_cast<std::uint32_t>(rounded);
+}
+
+[[nodiscard]] auto number_to_i32(double value, const std::string& context) -> std::int32_t {
+    const auto rounded = std::round(value);
+    if (std::fabs(value - rounded) > 0.000001) {
+        throw std::runtime_error("Expected integer value at " + context);
+    }
+
+    if (rounded < static_cast<double>(std::numeric_limits<std::int32_t>::min()) ||
+        rounded > static_cast<double>(std::numeric_limits<std::int32_t>::max())) {
+        throw std::runtime_error("Value out of int32 range at " + context);
+    }
+    return static_cast<std::int32_t>(rounded);
+}
+
+[[nodiscard]] auto optional_string(
+    const Json& object,
+    const std::string& key,
+    const std::string& fallback
+) -> std::string {
+    if (const auto* value = optional_field(object, key); value != nullptr) {
+        return as_string(*value, key);
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto optional_bool(const Json& object, const std::string& key, bool fallback) -> bool {
+    if (const auto* value = optional_field(object, key); value != nullptr) {
+        return as_bool(*value, key);
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto optional_u32(
+    const Json& object,
+    const std::string& key,
+    std::uint32_t fallback
+) -> std::uint32_t {
+    if (const auto* value = optional_field(object, key); value != nullptr) {
+        return number_to_u32(as_number(*value, key), key);
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto optional_i32(
+    const Json& object,
+    const std::string& key,
+    std::int32_t fallback
+) -> std::int32_t {
+    if (const auto* value = optional_field(object, key); value != nullptr) {
+        return number_to_i32(as_number(*value, key), key);
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto optional_f32(const Json& object, const std::string& key, float fallback) -> float {
+    if (const auto* value = optional_field(object, key); value != nullptr) {
+        return static_cast<float>(as_number(*value, key));
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto parse_driver_mode(const std::string& value) -> DriverMode {
+    if (value == "mock") {
+        return DriverMode::Mock;
+    }
+    if (value == "native") {
+        return DriverMode::Native;
+    }
+    throw std::runtime_error("Unsupported driver_mode value: " + value);
+}
+
+[[nodiscard]] auto parse_switch_driver_kind(const std::string& value) -> SwitchDriverKind {
+    if (value == "h7992") {
+        return SwitchDriverKind::H7992;
+    }
+    if (value == "hmc349a") {
+        return SwitchDriverKind::HMC349A;
+    }
+    throw std::runtime_error("Unsupported switch driver value: " + value);
+}
+
+void validate_radar_sweep(const RadarSweepSettings& sweep) {
+    if (sweep.points == 0U) {
+        throw std::runtime_error("radar.sweep.points must be > 0");
+    }
+    if (sweep.stop_hz < sweep.start_hz) {
+        throw std::runtime_error("radar.sweep.stop_hz must be >= start_hz");
+    }
+}
+
+void validate_switch_config(const SwitchConfig& config) {
+    if (config.radar_port != 1U && config.radar_port != 2U) {
+        throw std::runtime_error("Switch radar_port must be 1 or 2 for " + config.name);
+    }
+
+    if (config.positions == 0U) {
+        throw std::runtime_error("Switch positions must be > 0 for " + config.name);
+    }
+    if (config.default_position >= config.positions) {
+        throw std::runtime_error("Switch default_position is out of range for " + config.name);
+    }
+
+    if (config.driver_mode != DriverMode::Native) {
+        return;
+    }
+
+    if (config.gpio_chip.empty()) {
+        throw std::runtime_error("gpio_chip is required for native switch mode: " + config.name);
+    }
+
+    if (config.driver_kind == SwitchDriverKind::H7992) {
+        if (config.positions > 4U) {
+            throw std::runtime_error("Native H7992 switch driver supports positions <= 4 for " + config.name);
+        }
+        if (config.pin_a < 0 || config.pin_b < 0 || config.pin_a == config.pin_b) {
+            throw std::runtime_error("pin_a/pin_b are invalid for native H7992 mode: " + config.name);
+        }
+        return;
+    }
+
+    if (config.positions > 2U) {
+        throw std::runtime_error("Native HMC349A switch driver supports positions <= 2 for " + config.name);
+    }
+    if (config.pin_a < 0) {
+        throw std::runtime_error("pin_a (control) is required for native HMC349A mode: " + config.name);
+    }
+}
+
+void validate_combo_key(const ipc::ComboKey& key, const RunConfig& config) {
+    if (key.input_pos >= config.input_switch.positions) {
+        throw std::runtime_error("run.combos input index out of range");
+    }
+    if (key.output_pos >= config.output_switch.positions) {
+        throw std::runtime_error("run.combos output index out of range");
+    }
+}
+
+[[nodiscard]] auto parse_switch_config(
+    const Json& object,
+    const std::string& default_name,
+    std::uint32_t default_radar_port
+) -> SwitchConfig {
+    const auto* switch_obj = as_object(object, default_name);
+
+    SwitchConfig config{};
+    config.name = optional_string(*switch_obj, "name", default_name);
+    config.driver_mode = parse_driver_mode(optional_string(*switch_obj, "driver_mode", "mock"));
+    config.driver_kind = parse_switch_driver_kind(optional_string(*switch_obj, "driver", "h7992"));
+    config.radar_port = optional_u32(*switch_obj, "radar_port", default_radar_port);
+    config.positions = optional_u32(*switch_obj, "positions", 4);
+    config.default_position = optional_u32(*switch_obj, "default_position", 0);
+    config.gpio_chip = optional_string(*switch_obj, "gpio_chip", "/dev/gpiochip0");
+    config.pin_a = optional_i32(*switch_obj, "pin_a", 17);
+    config.pin_b = optional_i32(*switch_obj, "pin_b", 27);
+    config.invert_logic = optional_bool(*switch_obj, "invert_logic", false);
+
+    validate_switch_config(config);
+
+    return config;
+}
+
+[[nodiscard]] auto parse_ring_endpoint(
+    const Json& object,
+    const std::string& key,
+    const RingEndpointConfig& fallback
+) -> RingEndpointConfig {
+    RingEndpointConfig endpoint = fallback;
+    const auto* value = optional_field(object, key);
+    if (value == nullptr) {
+        return endpoint;
+    }
+
+    const auto* endpoint_obj = as_object(*value, key);
+    endpoint.name = optional_string(*endpoint_obj, "name", endpoint.name);
+    endpoint.capacity = optional_u32(*endpoint_obj, "capacity", endpoint.capacity);
+    endpoint.slot_size_bytes = optional_u32(*endpoint_obj, "slot_size_bytes", endpoint.slot_size_bytes);
+
+    if (endpoint.capacity == 0U) {
+        throw std::runtime_error("Ring capacity must be > 0 for " + key);
+    }
+    if (endpoint.slot_size_bytes == 0U) {
+        throw std::runtime_error("Ring slot_size_bytes must be > 0 for " + key);
+    }
+    if (endpoint.name.empty() || endpoint.name.front() != '/') {
+        throw std::runtime_error("Ring name must start with '/' for " + key);
+    }
+
+    return endpoint;
+}
+
+}  // namespace
+
+auto load_run_config(const std::string& path) -> RunConfig {
+    std::ifstream stream(path);
+    if (!stream.is_open()) {
+        throw std::runtime_error("Failed to open config file: " + path);
+    }
+
+    const std::string json_text((std::istreambuf_iterator<char>(stream)), std::istreambuf_iterator<char>());
+    if (json_text.empty()) {
+        throw std::runtime_error("Config file is empty: " + path);
+    }
+
+    Json root{};
+    try {
+        root = Json::parse(json_text);
+    } catch (const Json::parse_error& error) {
+        throw std::runtime_error("Failed to parse config file: " + path + ": " + std::string(error.what()));
+    }
+
+    const auto* root_obj = as_object(root, "root");
+    RunConfig config{};
+
+    {
+        const auto* radar_obj = as_object(required_field(*root_obj, "radar"), "radar");
+        config.radar.model = optional_string(*radar_obj, "model", "librevna");
+        config.radar.serial = optional_string(*radar_obj, "serial", "");
+        config.radar.driver_mode = parse_driver_mode(optional_string(*radar_obj, "driver_mode", "mock"));
+        config.radar.mock_signal_hz = optional_f32(*radar_obj, "mock_signal_hz", 5'000'000.0F);
+
+        const auto* sweep_obj = as_object(required_field(*radar_obj, "sweep"), "radar.sweep");
+        config.radar.sweep.start_hz =
+            static_cast<float>(as_number(required_field(*sweep_obj, "start_hz"), "radar.sweep.start_hz"));
+        config.radar.sweep.stop_hz =
+            static_cast<float>(as_number(required_field(*sweep_obj, "stop_hz"), "radar.sweep.stop_hz"));
+        config.radar.sweep.points =
+            number_to_u32(as_number(required_field(*sweep_obj, "points"), "radar.sweep.points"), "radar.sweep.points");
+        config.radar.sweep.if_bandwidth_hz = static_cast<float>(
+            as_number(required_field(*sweep_obj, "if_bandwidth_hz"), "radar.sweep.if_bandwidth_hz")
+        );
+        config.radar.sweep.power_dbm = optional_f32(*sweep_obj, "stimulus_power_dbm", -10.0F);
+        validate_radar_sweep(config.radar.sweep);
+    }
+
+    {
+        const auto* switches_obj = as_object(required_field(*root_obj, "switches"), "switches");
+
+        if (const auto* port1_value = optional_field(*switches_obj, "port1"); port1_value != nullptr) {
+            const auto* port1_obj = as_object(*port1_value, "switches.port1");
+            const auto* port2_obj = as_object(required_field(*switches_obj, "port2"), "switches.port2");
+            config.output_switch = parse_switch_config(*port1_obj, "port1", 1);
+            config.input_switch = parse_switch_config(*port2_obj, "port2", 2);
+        } else {
+            config.input_switch = parse_switch_config(
+                *as_object(required_field(*switches_obj, "input"), "switches.input"),
+                "input",
+                2
+            );
+            config.output_switch = parse_switch_config(
+                *as_object(required_field(*switches_obj, "output"), "switches.output"),
+                "output",
+                1
+            );
+        }
+
+        if (config.input_switch.radar_port == config.output_switch.radar_port) {
+            throw std::runtime_error("Switch radar_port mapping must be unique for two switches");
+        }
+    }
+
+    {
+        const auto* run_obj = as_object(required_field(*root_obj, "run"), "run");
+        config.runtime.settling_ms = optional_u32(*run_obj, "settling_ms", 0);
+        config.runtime.idle_sleep_ms = optional_u32(*run_obj, "idle_sleep_ms", 2);
+        config.runtime.continuous = optional_bool(*run_obj, "continuous", true);
+        config.runtime.processing_live_config_path = optional_string(
+            *run_obj,
+            "processing_live_config_path",
+            "python_app/runtime/processing_live.json"
+        );
+
+        const auto* combos = as_array(required_field(*run_obj, "combos"), "run.combos");
+        if (combos->empty()) {
+            throw std::runtime_error("run.combos must not be empty");
+        }
+
+        config.run_combos.reserve(combos->size());
+        for (const auto& combo_value : *combos) {
+            const auto* combo_obj = as_object(combo_value, "run.combos[]");
+
+            ipc::ComboKey key{};
+            key.input_pos = number_to_u32(
+                as_number(required_field(*combo_obj, "input"), "run.combos.input"),
+                "run.combos.input"
+            );
+            key.output_pos = number_to_u32(
+                as_number(required_field(*combo_obj, "output"), "run.combos.output"),
+                "run.combos.output"
+            );
+            validate_combo_key(key, config);
+            config.run_combos.push_back(key);
+        }
+    }
+
+    {
+        const auto* preprocess_obj = as_object(required_field(*root_obj, "preprocess"), "preprocess");
+        config.preprocess.calibration_set = optional_string(*preprocess_obj, "calibration_set", "");
+        config.preprocess.reference_set = optional_string(*preprocess_obj, "reference_set", "");
+        config.preprocess.calibration_bundle_path = optional_string(*preprocess_obj, "calibration_bundle_path", "");
+        config.preprocess.reference_bundle_path = optional_string(*preprocess_obj, "reference_bundle_path", "");
+    }
+
+    {
+        const auto* rings_obj = as_object(required_field(*root_obj, "rings"), "rings");
+        config.rings.raw = parse_ring_endpoint(*rings_obj, "raw", config.rings.raw);
+        config.rings.raw_tap = parse_ring_endpoint(*rings_obj, "raw_tap", config.rings.raw_tap);
+        config.rings.preprocessed = parse_ring_endpoint(*rings_obj, "preprocessed", config.rings.preprocessed);
+        config.rings.preprocessed_tap = parse_ring_endpoint(
+            *rings_obj,
+            "preprocessed_tap",
+            config.rings.preprocessed_tap
+        );
+        config.rings.results = parse_ring_endpoint(*rings_obj, "results", config.rings.results);
+    }
+
+    return config;
+}
+
+}  // namespace radar::config
diff --git a/data_acq_and_processing/common_cpp/ipc/include/shared_types.hpp b/data_acq_and_processing/common_cpp/ipc/include/shared_types.hpp
new file mode 100644
index 0000000..1118951
--- /dev/null
+++ b/data_acq_and_processing/common_cpp/ipc/include/shared_types.hpp
@@ -0,0 +1,91 @@
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+#include <span>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+namespace radar::ipc {
+
+struct Complex32 {
+    float re = 0.0F;
+    float im = 0.0F;
+};
+
+struct ComboKey {
+    // Zero-based switch positions.
+    std::uint32_t input_pos = 0;
+    std::uint32_t output_pos = 0;
+
+    [[nodiscard]] auto operator==(const ComboKey& other) const -> bool {
+        return input_pos == other.input_pos && output_pos == other.output_pos;
+    }
+};
+
+struct ComboKeyHash {
+    [[nodiscard]] auto operator()(const ComboKey& key) const noexcept -> std::size_t {
+        return (static_cast<std::size_t>(key.input_pos) << 32U) ^ static_cast<std::size_t>(key.output_pos);
+    }
+};
+
+struct SweepTraceBlock {
+    ComboKey combo{};
+    // Frequency axis in Hz. Must have the same size as `s21`.
+    std::vector<float> frequency_hz{};
+    // Complex S21 samples for matching frequency points.
+    std::vector<Complex32> s21{};
+};
+
+struct RawSweepCollection {
+    std::uint64_t collection_id = 0;
+    std::uint64_t monotonic_ns = 0;
+    std::vector<SweepTraceBlock> traces{};
+};
+
+using PreprocessedCollection = RawSweepCollection;
+
+enum class ResultKind : std::uint8_t {
+    TraceComplex = 1,
+    ScalarF32 = 2,
+};
+
+struct ResultPayload {
+    std::string processing_name{};
+    ResultKind kind = ResultKind::TraceComplex;
+    // Valid for TraceComplex payloads.
+    std::vector<float> frequency_hz{};
+    // Valid for TraceComplex payloads.
+    std::vector<Complex32> trace{};
+    // Valid for ScalarF32 payloads.
+    float scalar_value = 0.0F;
+};
+
+struct ResultBlock {
+    ComboKey combo{};
+    std::vector<ResultPayload> payloads{};
+};
+
+struct ResultCollection {
+    std::uint64_t collection_id = 0;
+    std::uint64_t monotonic_ns = 0;
+    std::vector<ResultBlock> blocks{};
+};
+
+// Wire-format serialization helpers for IPC rings.
+[[nodiscard]] auto serialize_raw_collection(const RawSweepCollection& collection) -> std::vector<std::uint8_t>;
+[[nodiscard]] auto deserialize_raw_collection(std::span<const std::uint8_t> bytes) -> RawSweepCollection;
+
+[[nodiscard]] auto serialize_preprocessed_collection(const PreprocessedCollection& collection)
+    -> std::vector<std::uint8_t>;
+[[nodiscard]] auto deserialize_preprocessed_collection(std::span<const std::uint8_t> bytes)
+    -> PreprocessedCollection;
+
+[[nodiscard]] auto serialize_result_collection(const ResultCollection& collection) -> std::vector<std::uint8_t>;
+[[nodiscard]] auto deserialize_result_collection(std::span<const std::uint8_t> bytes) -> ResultCollection;
+
+// Monotonic timestamp helper used for produced collections.
+[[nodiscard]] auto current_monotonic_ns() -> std::uint64_t;
+
+}  // namespace radar::ipc
diff --git a/data_acq_and_processing/common_cpp/ipc/include/shm_ring.hpp b/data_acq_and_processing/common_cpp/ipc/include/shm_ring.hpp
new file mode 100644
index 0000000..648147e
--- /dev/null
+++ b/data_acq_and_processing/common_cpp/ipc/include/shm_ring.hpp
@@ -0,0 +1,61 @@
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+#include <span>
+#include <string>
+#include <vector>
+
+namespace radar::ipc {
+
+class ShmRing {
+  public:
+    ShmRing() = default;
+    ~ShmRing();
+
+    ShmRing(const ShmRing&) = delete;
+    auto operator=(const ShmRing&) -> ShmRing& = delete;
+
+    ShmRing(ShmRing&& other) noexcept;
+    auto operator=(ShmRing&& other) noexcept -> ShmRing&;
+
+    [[nodiscard]] static auto open_or_create(
+        const std::string& name,
+        std::uint32_t capacity,
+        std::uint32_t slot_size_bytes
+    ) -> ShmRing;
+
+    // Opens ring by name and validates wire-format header fields.
+    [[nodiscard]] static auto open_existing(const std::string& name) -> ShmRing;
+    // Removes shared memory object by name (safe if it does not exist).
+    static void unlink_ring(const std::string& name);
+
+    [[nodiscard]] auto is_open() const -> bool;
+    [[nodiscard]] auto capacity() const -> std::uint32_t;
+    [[nodiscard]] auto slot_size_bytes() const -> std::uint32_t;
+    [[nodiscard]] auto dropped_count() const -> std::uint64_t;
+
+    // Pushes payload with overwrite-oldest policy on overflow.
+    [[nodiscard]] auto push(std::span<const std::uint8_t> payload) -> bool;
+    // Pops the next payload if available.
+    [[nodiscard]] auto pop(std::vector<std::uint8_t>& payload) -> bool;
+
+  private:
+    struct Header;
+    struct SlotHeader;
+
+    int fd_ = -1;
+    std::size_t mapped_size_ = 0;
+    void* mapped_ = nullptr;
+    Header* header_ = nullptr;
+
+    // Slot geometry helpers.
+    [[nodiscard]] auto slot_stride_bytes() const -> std::size_t;
+    [[nodiscard]] auto slot_header(std::uint64_t sequence) const -> SlotHeader*;
+    [[nodiscard]] auto slot_payload(SlotHeader* slot) const -> std::uint8_t*;
+
+    void close();
+    static void validate_name(const std::string& name);
+};
+
+}  // namespace radar::ipc
diff --git a/data_acq_and_processing/common_cpp/ipc/src/shared_types.cpp b/data_acq_and_processing/common_cpp/ipc/src/shared_types.cpp
new file mode 100644
index 0000000..45eb1c7
--- /dev/null
+++ b/data_acq_and_processing/common_cpp/ipc/src/shared_types.cpp
@@ -0,0 +1,336 @@
+#include "shared_types.hpp"
+
+#include <chrono>
+#include <cstring>
+#include <limits>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+namespace radar::ipc {
+namespace {
+
+constexpr std::uint32_t kRawCollectionMagic = 0x31574152U;      // RAW1
+constexpr std::uint32_t kPreprocessedCollectionMagic = 0x31525050U;  // PRP1
+constexpr std::uint32_t kResultCollectionMagic = 0x314C5352U;   // RSL1
+
+template <typename T>
+concept TriviallySerializable = std::is_trivially_copyable_v<T>;
+
+[[nodiscard]] auto checked_count_to_u32(std::size_t count, const std::string& label) -> std::uint32_t {
+    if (count > std::numeric_limits<std::uint32_t>::max()) {
+        throw std::runtime_error(label + " exceeds uint32 wire-format limit");
+    }
+    return static_cast<std::uint32_t>(count);
+}
+
+void ensure_equal_sizes(std::size_t left, std::size_t right, const std::string& label) {
+    if (left != right) {
+        throw std::runtime_error(label + " has inconsistent vector sizes");
+    }
+}
+
+class BinaryWriter {
+  public:
+    template <TriviallySerializable T>
+    void write(const T& value) {
+        const auto old_size = bytes_.size();
+        bytes_.resize(old_size + sizeof(T));
+        std::memcpy(bytes_.data() + old_size, &value, sizeof(T));
+    }
+
+    void write_string(const std::string& value) {
+        if (value.size() > std::numeric_limits<std::uint16_t>::max()) {
+            throw std::runtime_error("String is too large for wire format");
+        }
+
+        write(static_cast<std::uint16_t>(value.size()));
+
+        const auto old_size = bytes_.size();
+        bytes_.resize(old_size + value.size());
+        std::memcpy(bytes_.data() + old_size, value.data(), value.size());
+    }
+
+    [[nodiscard]] auto finish() && -> std::vector<std::uint8_t> {
+        return std::move(bytes_);
+    }
+
+  private:
+    std::vector<std::uint8_t> bytes_{};
+};
+
+class BinaryReader {
+  public:
+    explicit BinaryReader(std::span<const std::uint8_t> bytes) : bytes_(bytes) {}
+
+    template <TriviallySerializable T>
+    [[nodiscard]] auto read() -> T {
+        ensure_available(sizeof(T));
+
+        T value{};
+        std::memcpy(&value, bytes_.data() + offset_, sizeof(T));
+        offset_ += sizeof(T);
+        return value;
+    }
+
+    [[nodiscard]] auto read_string() -> std::string {
+        const auto size = read<std::uint16_t>();
+        ensure_available(size);
+
+        const auto* begin = reinterpret_cast<const char*>(bytes_.data() + offset_);
+        std::string value(begin, begin + size);
+        offset_ += size;
+        return value;
+    }
+
+    [[nodiscard]] auto is_consumed() const -> bool {
+        return offset_ == bytes_.size();
+    }
+
+  private:
+    void ensure_available(std::size_t size) const {
+        if (offset_ + size > bytes_.size()) {
+            throw std::runtime_error("Unexpected end of serialized payload");
+        }
+    }
+
+    std::span<const std::uint8_t> bytes_{};
+    std::size_t offset_ = 0;
+};
+
+void write_trace_block(BinaryWriter& writer, const SweepTraceBlock& trace) {
+    ensure_equal_sizes(trace.frequency_hz.size(), trace.s21.size(), "Sweep trace");
+
+    writer.write(trace.combo.input_pos);
+    writer.write(trace.combo.output_pos);
+    writer.write(checked_count_to_u32(trace.frequency_hz.size(), "Trace point count"));
+
+    for (const auto frequency_hz : trace.frequency_hz) {
+        writer.write(frequency_hz);
+    }
+
+    for (const auto& point : trace.s21) {
+        writer.write(point.re);
+        writer.write(point.im);
+    }
+}
+
+[[nodiscard]] auto read_trace_block(BinaryReader& reader) -> SweepTraceBlock {
+    SweepTraceBlock trace{};
+    trace.combo.input_pos = reader.read<std::uint32_t>();
+    trace.combo.output_pos = reader.read<std::uint32_t>();
+
+    const auto point_count = reader.read<std::uint32_t>();
+    trace.frequency_hz.reserve(point_count);
+    trace.s21.reserve(point_count);
+
+    for (std::uint32_t index = 0; index < point_count; ++index) {
+        trace.frequency_hz.push_back(reader.read<float>());
+    }
+
+    for (std::uint32_t index = 0; index < point_count; ++index) {
+        trace.s21.push_back(Complex32{
+            .re = reader.read<float>(),
+            .im = reader.read<float>(),
+        });
+    }
+
+    return trace;
+}
+
+void write_trace_collection(BinaryWriter& writer, std::uint32_t magic, const RawSweepCollection& collection) {
+    writer.write(magic);
+    writer.write(collection.collection_id);
+    writer.write(collection.monotonic_ns);
+    writer.write(checked_count_to_u32(collection.traces.size(), "Trace count"));
+
+    for (const auto& trace : collection.traces) {
+        write_trace_block(writer, trace);
+    }
+}
+
+[[nodiscard]] auto read_trace_collection(BinaryReader& reader, std::uint32_t expected_magic) -> RawSweepCollection {
+    const auto magic = reader.read<std::uint32_t>();
+    if (magic != expected_magic) {
+        throw std::runtime_error("Unexpected trace collection magic");
+    }
+
+    RawSweepCollection collection{};
+    collection.collection_id = reader.read<std::uint64_t>();
+    collection.monotonic_ns = reader.read<std::uint64_t>();
+
+    const auto trace_count = reader.read<std::uint32_t>();
+    collection.traces.reserve(trace_count);
+    for (std::uint32_t index = 0; index < trace_count; ++index) {
+        collection.traces.push_back(read_trace_block(reader));
+    }
+
+    return collection;
+}
+
+void write_trace_result_payload(BinaryWriter& writer, const ResultPayload& payload) {
+    ensure_equal_sizes(payload.frequency_hz.size(), payload.trace.size(), "Result trace payload");
+
+    writer.write(checked_count_to_u32(payload.trace.size(), "Result trace point count"));
+    for (const auto frequency_hz : payload.frequency_hz) {
+        writer.write(frequency_hz);
+    }
+    for (const auto& sample : payload.trace) {
+        writer.write(sample.re);
+        writer.write(sample.im);
+    }
+}
+
+auto read_trace_result_payload(BinaryReader& reader, ResultPayload* payload) -> void {
+    const auto point_count = reader.read<std::uint32_t>();
+
+    payload->frequency_hz.reserve(point_count);
+    payload->trace.reserve(point_count);
+
+    for (std::uint32_t index = 0; index < point_count; ++index) {
+        payload->frequency_hz.push_back(reader.read<float>());
+    }
+    for (std::uint32_t index = 0; index < point_count; ++index) {
+        payload->trace.push_back(Complex32{
+            .re = reader.read<float>(),
+            .im = reader.read<float>(),
+        });
+    }
+}
+
+void write_result_payload(BinaryWriter& writer, const ResultPayload& payload) {
+    writer.write(static_cast<std::uint8_t>(payload.kind));
+    writer.write_string(payload.processing_name);
+
+    switch (payload.kind) {
+        case ResultKind::TraceComplex:
+            write_trace_result_payload(writer, payload);
+            return;
+        case ResultKind::ScalarF32:
+            writer.write(payload.scalar_value);
+            return;
+        default:
+            throw std::runtime_error("Unsupported result payload kind");
+    }
+}
+
+[[nodiscard]] auto read_result_payload(BinaryReader& reader) -> ResultPayload {
+    ResultPayload payload{};
+    payload.kind = static_cast<ResultKind>(reader.read<std::uint8_t>());
+    payload.processing_name = reader.read_string();
+
+    switch (payload.kind) {
+        case ResultKind::TraceComplex:
+            read_trace_result_payload(reader, &payload);
+            return payload;
+        case ResultKind::ScalarF32:
+            payload.scalar_value = reader.read<float>();
+            return payload;
+        default:
+            throw std::runtime_error("Unsupported result payload kind in stream");
+    }
+}
+
+void write_result_block(BinaryWriter& writer, const ResultBlock& block) {
+    writer.write(block.combo.input_pos);
+    writer.write(block.combo.output_pos);
+    writer.write(checked_count_to_u32(block.payloads.size(), "Result payload count"));
+
+    for (const auto& payload : block.payloads) {
+        write_result_payload(writer, payload);
+    }
+}
+
+[[nodiscard]] auto read_result_block(BinaryReader& reader) -> ResultBlock {
+    ResultBlock block{};
+    block.combo.input_pos = reader.read<std::uint32_t>();
+    block.combo.output_pos = reader.read<std::uint32_t>();
+
+    const auto payload_count = reader.read<std::uint32_t>();
+    block.payloads.reserve(payload_count);
+    for (std::uint32_t index = 0; index < payload_count; ++index) {
+        block.payloads.push_back(read_result_payload(reader));
+    }
+
+    return block;
+}
+
+void ensure_reader_consumed(const BinaryReader& reader, const std::string& payload_label) {
+    if (!reader.is_consumed()) {
+        throw std::runtime_error("Unexpected trailing bytes in " + payload_label);
+    }
+}
+
+}  // namespace
+
+auto serialize_raw_collection(const RawSweepCollection& collection) -> std::vector<std::uint8_t> {
+    BinaryWriter writer{};
+    write_trace_collection(writer, kRawCollectionMagic, collection);
+    return std::move(writer).finish();
+}
+
+auto deserialize_raw_collection(std::span<const std::uint8_t> bytes) -> RawSweepCollection {
+    BinaryReader reader(bytes);
+    auto collection = read_trace_collection(reader, kRawCollectionMagic);
+    ensure_reader_consumed(reader, "raw collection");
+    return collection;
+}
+
+auto serialize_preprocessed_collection(const PreprocessedCollection& collection) -> std::vector<std::uint8_t> {
+    BinaryWriter writer{};
+    write_trace_collection(writer, kPreprocessedCollectionMagic, collection);
+    return std::move(writer).finish();
+}
+
+auto deserialize_preprocessed_collection(std::span<const std::uint8_t> bytes) -> PreprocessedCollection {
+    BinaryReader reader(bytes);
+    auto collection = read_trace_collection(reader, kPreprocessedCollectionMagic);
+    ensure_reader_consumed(reader, "preprocessed collection");
+    return collection;
+}
+
+auto serialize_result_collection(const ResultCollection& collection) -> std::vector<std::uint8_t> {
+    BinaryWriter writer{};
+    writer.write(kResultCollectionMagic);
+    writer.write(collection.collection_id);
+    writer.write(collection.monotonic_ns);
+    writer.write(checked_count_to_u32(collection.blocks.size(), "Result block count"));
+
+    for (const auto& block : collection.blocks) {
+        write_result_block(writer, block);
+    }
+
+    return std::move(writer).finish();
+}
+
+auto deserialize_result_collection(std::span<const std::uint8_t> bytes) -> ResultCollection {
+    BinaryReader reader(bytes);
+
+    const auto magic = reader.read<std::uint32_t>();
+    if (magic != kResultCollectionMagic) {
+        throw std::runtime_error("Unexpected result collection magic");
+    }
+
+    ResultCollection collection{};
+    collection.collection_id = reader.read<std::uint64_t>();
+    collection.monotonic_ns = reader.read<std::uint64_t>();
+
+    const auto block_count = reader.read<std::uint32_t>();
+    collection.blocks.reserve(block_count);
+    for (std::uint32_t index = 0; index < block_count; ++index) {
+        collection.blocks.push_back(read_result_block(reader));
+    }
+
+    ensure_reader_consumed(reader, "result collection");
+    return collection;
+}
+
+auto current_monotonic_ns() -> std::uint64_t {
+    const auto now = std::chrono::steady_clock::now().time_since_epoch();
+    const auto now_ns = std::chrono::duration_cast<std::chrono::nanoseconds>(now).count();
+    return static_cast<std::uint64_t>(now_ns);
+}
+
+}  // namespace radar::ipc
diff --git a/data_acq_and_processing/common_cpp/ipc/src/shm_ring.cpp b/data_acq_and_processing/common_cpp/ipc/src/shm_ring.cpp
new file mode 100644
index 0000000..d683c0a
--- /dev/null
+++ b/data_acq_and_processing/common_cpp/ipc/src/shm_ring.cpp
@@ -0,0 +1,380 @@
+#include "shm_ring.hpp"
+
+#include <atomic>
+#include <chrono>
+#include <cerrno>
+#include <cstring>
+#include <fcntl.h>
+#include <new>
+#include <stdexcept>
+#include <string>
+#include <string_view>
+#include <sys/mman.h>
+#include <sys/stat.h>
+#include <thread>
+#include <unistd.h>
+#include <utility>
+
+namespace radar::ipc {
+
+struct alignas(64) ShmRing::Header {
+    char magic[8]{};
+    std::uint32_t version = 0;
+    std::uint32_t capacity = 0;
+    std::uint32_t slot_size_bytes = 0;
+    std::uint32_t reserved = 0;
+    std::atomic<std::uint64_t> write_seq{};
+    std::atomic<std::uint64_t> read_seq{};
+    std::atomic<std::uint64_t> dropped{};
+};
+
+struct alignas(16) ShmRing::SlotHeader {
+    std::uint32_t payload_size = 0;
+    std::uint32_t reserved = 0;
+    std::uint64_t sequence = 0;
+};
+
+namespace {
+
+constexpr std::string_view kRingMagic = "RDRRING2";
+constexpr std::uint32_t kRingVersion = 1;
+constexpr auto kHeaderInitWaitTimeout = std::chrono::milliseconds(1000);
+constexpr auto kHeaderInitPollInterval = std::chrono::milliseconds(2);
+
+[[nodiscard]] auto checked_u64_diff(std::uint64_t left, std::uint64_t right) -> std::uint64_t {
+    return left >= right ? left - right : 0;
+}
+
+[[nodiscard]] auto errno_message(const std::string& action, const std::string& name) -> std::runtime_error {
+    return std::runtime_error(action + " " + name + ": " + std::strerror(errno));
+}
+
+class ScopedFd {
+  public:
+    explicit ScopedFd(int fd) : fd_(fd) {}
+
+    ~ScopedFd() {
+        if (fd_ >= 0) {
+            ::close(fd_);
+        }
+    }
+
+    ScopedFd(const ScopedFd&) = delete;
+    auto operator=(const ScopedFd&) -> ScopedFd& = delete;
+
+    ScopedFd(ScopedFd&& other) noexcept : fd_(std::exchange(other.fd_, -1)) {}
+
+    auto release() -> int {
+        return std::exchange(fd_, -1);
+    }
+
+  private:
+    int fd_ = -1;
+};
+
+class ScopedMmap {
+  public:
+    ScopedMmap(void* address, std::size_t size) : address_(address), size_(size) {}
+
+    ~ScopedMmap() {
+        if (address_ != nullptr && address_ != MAP_FAILED) {
+            munmap(address_, size_);
+        }
+    }
+
+    ScopedMmap(const ScopedMmap&) = delete;
+    auto operator=(const ScopedMmap&) -> ScopedMmap& = delete;
+
+    ScopedMmap(ScopedMmap&& other) noexcept
+        : address_(std::exchange(other.address_, nullptr)),
+          size_(std::exchange(other.size_, 0U)) {}
+
+    auto release() -> std::pair<void*, std::size_t> {
+        return {std::exchange(address_, nullptr), std::exchange(size_, 0U)};
+    }
+
+  private:
+    void* address_ = nullptr;
+    std::size_t size_ = 0;
+};
+
+}  // namespace
+
+ShmRing::~ShmRing() {
+    close();
+}
+
+ShmRing::ShmRing(ShmRing&& other) noexcept {
+    *this = std::move(other);
+}
+
+auto ShmRing::operator=(ShmRing&& other) noexcept -> ShmRing& {
+    if (this != &other) {
+        close();
+
+        fd_ = std::exchange(other.fd_, -1);
+        mapped_size_ = std::exchange(other.mapped_size_, 0U);
+        mapped_ = std::exchange(other.mapped_, nullptr);
+        header_ = std::exchange(other.header_, nullptr);
+    }
+    return *this;
+}
+
+auto ShmRing::open_or_create(
+    const std::string& name,
+    std::uint32_t capacity,
+    std::uint32_t slot_size_bytes
+) -> ShmRing {
+    validate_name(name);
+    if (capacity == 0U) {
+        throw std::runtime_error("Ring capacity must be > 0");
+    }
+    if (slot_size_bytes == 0U) {
+        throw std::runtime_error("Ring slot size must be > 0");
+    }
+
+    bool created = false;
+    int fd = shm_open(name.c_str(), O_RDWR | O_CREAT | O_EXCL, 0660);
+    if (fd >= 0) {
+        created = true;
+    } else if (errno == EEXIST) {
+        fd = shm_open(name.c_str(), O_RDWR, 0660);
+    }
+
+    if (fd < 0) {
+        throw errno_message("Failed to open shared memory ring", name);
+    }
+
+    ScopedFd scoped_fd(fd);
+
+    const auto slot_stride = sizeof(SlotHeader) + static_cast<std::size_t>(slot_size_bytes);
+    const auto mapped_size = sizeof(Header) + slot_stride * capacity;
+
+    if (created) {
+        if (ftruncate(fd, static_cast<off_t>(mapped_size)) != 0) {
+            throw errno_message("Failed to resize shared memory ring", name);
+        }
+    }
+
+    void* mapped = mmap(nullptr, mapped_size, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
+    if (mapped == MAP_FAILED) {
+        throw errno_message("Failed to mmap shared memory ring", name);
+    }
+
+    ScopedMmap scoped_mmap(mapped, mapped_size);
+    auto* header = static_cast<Header*>(mapped);
+
+    if (created) {
+        std::memset(mapped, 0, mapped_size);
+        std::memcpy(header->magic, kRingMagic.data(), kRingMagic.size());
+        header->version = kRingVersion;
+        header->capacity = capacity;
+        header->slot_size_bytes = slot_size_bytes;
+
+        new (&header->write_seq) std::atomic<std::uint64_t>(0);
+        new (&header->read_seq) std::atomic<std::uint64_t>(0);
+        new (&header->dropped) std::atomic<std::uint64_t>(0);
+        std::atomic_thread_fence(std::memory_order_release);
+    } else {
+        const auto deadline = std::chrono::steady_clock::now() + kHeaderInitWaitTimeout;
+
+        while (true) {
+            std::atomic_thread_fence(std::memory_order_acquire);
+
+            const bool magic_ok = std::memcmp(header->magic, kRingMagic.data(), kRingMagic.size()) == 0;
+            const bool version_ok = header->version == kRingVersion;
+            const bool geometry_ok = header->capacity == capacity && header->slot_size_bytes == slot_size_bytes;
+
+            if (magic_ok && version_ok && geometry_ok) {
+                break;
+            }
+
+            if (std::chrono::steady_clock::now() >= deadline) {
+                if (!magic_ok) {
+                    throw std::runtime_error("Shared memory ring magic mismatch for " + name);
+                }
+                if (!version_ok) {
+                    throw std::runtime_error("Shared memory ring version mismatch for " + name);
+                }
+                throw std::runtime_error("Shared memory ring geometry mismatch for " + name);
+            }
+
+            std::this_thread::sleep_for(kHeaderInitPollInterval);
+        }
+    }
+
+    ShmRing ring{};
+    ring.fd_ = scoped_fd.release();
+
+    const auto [released_mapped, released_size] = scoped_mmap.release();
+    ring.mapped_ = released_mapped;
+    ring.mapped_size_ = released_size;
+    ring.header_ = static_cast<Header*>(released_mapped);
+    return ring;
+}
+
+auto ShmRing::open_existing(const std::string& name) -> ShmRing {
+    validate_name(name);
+
+    const int fd = shm_open(name.c_str(), O_RDWR, 0660);
+    if (fd < 0) {
+        throw errno_message("Failed to open shared memory ring", name);
+    }
+
+    ScopedFd scoped_fd(fd);
+
+    struct stat info {};
+    if (fstat(fd, &info) != 0) {
+        throw errno_message("Failed to stat shared memory ring", name);
+    }
+
+    if (info.st_size < static_cast<off_t>(sizeof(Header))) {
+        throw std::runtime_error("Shared memory ring size is too small for " + name);
+    }
+
+    const auto mapped_size = static_cast<std::size_t>(info.st_size);
+    void* mapped = mmap(nullptr, mapped_size, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
+    if (mapped == MAP_FAILED) {
+        throw errno_message("Failed to mmap shared memory ring", name);
+    }
+
+    ScopedMmap scoped_mmap(mapped, mapped_size);
+    auto* header = static_cast<Header*>(mapped);
+
+    if (std::memcmp(header->magic, kRingMagic.data(), kRingMagic.size()) != 0) {
+        throw std::runtime_error("Shared memory ring magic mismatch for " + name);
+    }
+    if (header->version != kRingVersion) {
+        throw std::runtime_error("Shared memory ring version mismatch for " + name);
+    }
+
+    ShmRing ring{};
+    ring.fd_ = scoped_fd.release();
+
+    const auto [released_mapped, released_size] = scoped_mmap.release();
+    ring.mapped_ = released_mapped;
+    ring.mapped_size_ = released_size;
+    ring.header_ = static_cast<Header*>(released_mapped);
+    return ring;
+}
+
+void ShmRing::unlink_ring(const std::string& name) {
+    validate_name(name);
+    if (shm_unlink(name.c_str()) != 0 && errno != ENOENT) {
+        throw errno_message("Failed to unlink shared memory ring", name);
+    }
+}
+
+auto ShmRing::is_open() const -> bool {
+    return header_ != nullptr;
+}
+
+auto ShmRing::capacity() const -> std::uint32_t {
+    return header_ != nullptr ? header_->capacity : 0;
+}
+
+auto ShmRing::slot_size_bytes() const -> std::uint32_t {
+    return header_ != nullptr ? header_->slot_size_bytes : 0;
+}
+
+auto ShmRing::dropped_count() const -> std::uint64_t {
+    if (header_ == nullptr) {
+        return 0;
+    }
+    return header_->dropped.load(std::memory_order_acquire);
+}
+
+auto ShmRing::push(std::span<const std::uint8_t> payload) -> bool {
+    if (header_ == nullptr) {
+        throw std::runtime_error("Ring is not open");
+    }
+    if (payload.size() > header_->slot_size_bytes) {
+        return false;
+    }
+
+    const auto write_seq = header_->write_seq.load(std::memory_order_relaxed);
+    const auto read_seq = header_->read_seq.load(std::memory_order_acquire);
+
+    if (checked_u64_diff(write_seq, read_seq) >= header_->capacity) {
+        header_->read_seq.store(read_seq + 1U, std::memory_order_release);
+        header_->dropped.fetch_add(1U, std::memory_order_relaxed);
+    }
+
+    auto* slot = slot_header(write_seq);
+    slot->payload_size = static_cast<std::uint32_t>(payload.size());
+    slot->sequence = write_seq + 1U;
+    std::memcpy(slot_payload(slot), payload.data(), payload.size());
+
+    std::atomic_thread_fence(std::memory_order_release);
+    header_->write_seq.store(write_seq + 1U, std::memory_order_release);
+    return true;
+}
+
+auto ShmRing::pop(std::vector<std::uint8_t>& payload) -> bool {
+    if (header_ == nullptr) {
+        throw std::runtime_error("Ring is not open");
+    }
+
+    const auto read_seq = header_->read_seq.load(std::memory_order_relaxed);
+    const auto write_seq = header_->write_seq.load(std::memory_order_acquire);
+    if (read_seq >= write_seq) {
+        return false;
+    }
+
+    auto* slot = slot_header(read_seq);
+    if (slot->sequence != read_seq + 1U) {
+        // Producer overwrote this slot before consumer read it. Resync to latest.
+        header_->read_seq.store(write_seq, std::memory_order_release);
+        return false;
+    }
+
+    const auto payload_size = slot->payload_size;
+    if (payload_size > header_->slot_size_bytes) {
+        header_->read_seq.store(write_seq, std::memory_order_release);
+        throw std::runtime_error("Invalid payload size in shared memory slot");
+    }
+
+    payload.resize(payload_size);
+    std::memcpy(payload.data(), slot_payload(slot), payload_size);
+
+    std::atomic_thread_fence(std::memory_order_acquire);
+    header_->read_seq.store(read_seq + 1U, std::memory_order_release);
+    return true;
+}
+
+auto ShmRing::slot_stride_bytes() const -> std::size_t {
+    return sizeof(SlotHeader) + header_->slot_size_bytes;
+}
+
+auto ShmRing::slot_header(std::uint64_t sequence) const -> SlotHeader* {
+    const auto index = sequence % header_->capacity;
+    auto* slots_begin = static_cast<std::uint8_t*>(mapped_) + sizeof(Header);
+    return reinterpret_cast<SlotHeader*>(slots_begin + index * slot_stride_bytes());
+}
+
+auto ShmRing::slot_payload(SlotHeader* slot) const -> std::uint8_t* {
+    return reinterpret_cast<std::uint8_t*>(slot) + sizeof(SlotHeader);
+}
+
+void ShmRing::close() {
+    if (mapped_ != nullptr) {
+        munmap(mapped_, mapped_size_);
+        mapped_ = nullptr;
+    }
+
+    if (fd_ >= 0) {
+        ::close(fd_);
+        fd_ = -1;
+    }
+
+    mapped_size_ = 0;
+    header_ = nullptr;
+}
+
+void ShmRing::validate_name(const std::string& name) {
+    if (name.empty() || name.front() != '/') {
+        throw std::runtime_error("POSIX shm name must start with '/'");
+    }
+}
+
+}  // namespace radar::ipc
diff --git a/data_acq_and_processing/preprocessing/calibration_master/include/calibration_master.hpp b/data_acq_and_processing/preprocessing/calibration_master/include/calibration_master.hpp
new file mode 100644
index 0000000..a0489fc
--- /dev/null
+++ b/data_acq_and_processing/preprocessing/calibration_master/include/calibration_master.hpp
@@ -0,0 +1,32 @@
+#pragma once
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "calibrator_interface.hpp"
+#include "shared_types.hpp"
+
+namespace radar::preprocessing {
+
+class CalibrationMaster {
+  public:
+    // `calibrator` encapsulates the actual calibration algorithm (v1: through).
+    explicit CalibrationMaster(std::unique_ptr<CalibratorInterface> calibrator);
+
+    // Loads a serialized raw sweep bundle with one calibration standard per combo.
+    void load_bundle(const std::string& path);
+    // Ensures all runtime combos are present in loaded standards.
+    void validate_combos(const std::vector<ipc::ComboKey>& combos) const;
+    // Applies calibration standard corresponding to measured trace combo.
+    [[nodiscard]] auto apply(const ipc::SweepTraceBlock& measured_trace) const -> ipc::SweepTraceBlock;
+
+  private:
+    std::unique_ptr<CalibratorInterface> calibrator_impl_;
+    std::unordered_map<ipc::ComboKey, ipc::SweepTraceBlock, ipc::ComboKeyHash> standards_by_combo_{};
+};
+
+[[nodiscard]] auto make_through_calibrator() -> std::unique_ptr<CalibratorInterface>;
+
+}  // namespace radar::preprocessing
diff --git a/data_acq_and_processing/preprocessing/calibration_master/include/calibrator_interface.hpp b/data_acq_and_processing/preprocessing/calibration_master/include/calibrator_interface.hpp
new file mode 100644
index 0000000..159f9a5
--- /dev/null
+++ b/data_acq_and_processing/preprocessing/calibration_master/include/calibrator_interface.hpp
@@ -0,0 +1,21 @@
+#pragma once
+
+#include <string>
+#include <vector>
+
+#include "shared_types.hpp"
+
+namespace radar::preprocessing {
+
+class CalibratorInterface {
+  public:
+    virtual ~CalibratorInterface() = default;
+
+    [[nodiscard]] virtual auto name() const -> std::string = 0;
+    [[nodiscard]] virtual auto apply(
+        const std::vector<ipc::Complex32>& measured,
+        const std::vector<ipc::Complex32>& calibration
+    ) const -> std::vector<ipc::Complex32> = 0;
+};
+
+}  // namespace radar::preprocessing
diff --git a/data_acq_and_processing/preprocessing/calibration_master/src/calibration_master.cpp b/data_acq_and_processing/preprocessing/calibration_master/src/calibration_master.cpp
new file mode 100644
index 0000000..c20a8d1
--- /dev/null
+++ b/data_acq_and_processing/preprocessing/calibration_master/src/calibration_master.cpp
@@ -0,0 +1,101 @@
+#include "calibration_master.hpp"
+
+#include <cstdint>
+#include <fstream>
+#include <iterator>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+namespace radar::preprocessing {
+namespace {
+
+[[nodiscard]] auto combo_to_string(const ipc::ComboKey& combo) -> std::string {
+    return "input=" + std::to_string(combo.input_pos) + " output=" + std::to_string(combo.output_pos);
+}
+
+[[nodiscard]] auto read_binary_file(const std::string& path, const std::string& bundle_label)
+    -> std::vector<std::uint8_t> {
+    std::ifstream stream(path, std::ios::binary);
+    if (!stream.is_open()) {
+        throw std::runtime_error("Failed to open " + bundle_label + " bundle: " + path);
+    }
+
+    return std::vector<std::uint8_t>(std::istreambuf_iterator<char>(stream), std::istreambuf_iterator<char>());
+}
+
+void validate_trace_layout(const ipc::SweepTraceBlock& trace, const std::string& trace_label) {
+    if (trace.frequency_hz.size() != trace.s21.size()) {
+        throw std::runtime_error(
+            trace_label + " frequency/complex vector size mismatch for combo " + combo_to_string(trace.combo)
+        );
+    }
+}
+
+}  // namespace
+
+CalibrationMaster::CalibrationMaster(std::unique_ptr<CalibratorInterface> calibrator)
+    : calibrator_impl_(std::move(calibrator)) {
+    if (!calibrator_impl_) {
+        throw std::runtime_error("CalibrationMaster requires a non-null calibrator");
+    }
+}
+
+void CalibrationMaster::load_bundle(const std::string& path) {
+    if (path.empty()) {
+        throw std::runtime_error("Calibration bundle path must not be empty");
+    }
+
+    const auto bytes = read_binary_file(path, "calibration");
+    if (bytes.empty()) {
+        throw std::runtime_error("Calibration bundle is empty: " + path);
+    }
+
+    const auto collection = ipc::deserialize_raw_collection(bytes);
+    standards_by_combo_.clear();
+    standards_by_combo_.reserve(collection.traces.size());
+    for (const auto& trace : collection.traces) {
+        validate_trace_layout(trace, "Calibration standard");
+        standards_by_combo_.insert_or_assign(trace.combo, trace);
+    }
+
+    if (standards_by_combo_.empty()) {
+        throw std::runtime_error("Calibration bundle does not contain traces: " + path);
+    }
+}
+
+void CalibrationMaster::validate_combos(const std::vector<ipc::ComboKey>& combos) const {
+    for (const auto& combo : combos) {
+        if (!standards_by_combo_.contains(combo)) {
+            throw std::runtime_error("Calibration data is missing for combo " + combo_to_string(combo));
+        }
+    }
+}
+
+auto CalibrationMaster::apply(const ipc::SweepTraceBlock& measured_trace) const -> ipc::SweepTraceBlock {
+    validate_trace_layout(measured_trace, "Measured trace");
+
+    const auto found = standards_by_combo_.find(measured_trace.combo);
+    if (found == standards_by_combo_.end()) {
+        throw std::runtime_error(
+            "Calibration standard is missing for measured combo " + combo_to_string(measured_trace.combo)
+        );
+    }
+
+    const auto& standard = found->second;
+    validate_trace_layout(standard, "Calibration standard");
+    if (measured_trace.s21.size() != standard.s21.size()) {
+        throw std::runtime_error(
+            "Calibration standard point count mismatch for combo " + combo_to_string(measured_trace.combo)
+        );
+    }
+
+    ipc::SweepTraceBlock output{};
+    output.combo = measured_trace.combo;
+    output.frequency_hz = measured_trace.frequency_hz;
+    output.s21 = calibrator_impl_->apply(measured_trace.s21, standard.s21);
+
+    return output;
+}
+
+}  // namespace radar::preprocessing
diff --git a/data_acq_and_processing/preprocessing/calibration_master/src/through_calibrator.cpp b/data_acq_and_processing/preprocessing/calibration_master/src/through_calibrator.cpp
new file mode 100644
index 0000000..5aff8cd
--- /dev/null
+++ b/data_acq_and_processing/preprocessing/calibration_master/src/through_calibrator.cpp
@@ -0,0 +1,76 @@
+#include "calibration_master.hpp"
+
+#include <cstddef>
+#include <memory>
+#include <stdexcept>
+#include <vector>
+
+#include <Eigen/Core>
+
+namespace radar::preprocessing {
+namespace {
+
+class ThroughCalibrator final : public CalibratorInterface {
+  public:
+    [[nodiscard]] auto name() const -> std::string override {
+        return "through";
+    }
+
+    [[nodiscard]] auto apply(
+        const std::vector<ipc::Complex32>& measured,
+        const std::vector<ipc::Complex32>& calibration
+    ) const -> std::vector<ipc::Complex32> override {
+        if (measured.size() != calibration.size()) {
+            throw std::runtime_error("Through calibration vector size mismatch");
+        }
+
+        static_assert(sizeof(ipc::Complex32) == sizeof(float) * 2U, "Complex32 layout must be two contiguous floats");
+
+        constexpr float epsilon = 1e-12F;
+        using InterleavedComplexView = Eigen::Matrix<float, Eigen::Dynamic, 2, Eigen::RowMajor>;
+
+        const auto point_count = static_cast<Eigen::Index>(measured.size());
+        Eigen::Map<const InterleavedComplexView> measured_view(
+            reinterpret_cast<const float*>(measured.data()),
+            point_count,
+            2
+        );
+        Eigen::Map<const InterleavedComplexView> calibration_view(
+            reinterpret_cast<const float*>(calibration.data()),
+            point_count,
+            2
+        );
+
+        const auto measured_re = measured_view.col(0).array();
+        const auto measured_im = measured_view.col(1).array();
+        const auto calibration_re = calibration_view.col(0).array();
+        const auto calibration_im = calibration_view.col(1).array();
+
+        const Eigen::ArrayXf magnitude_squared = calibration_re.square() + calibration_im.square();
+        const auto stable_division_mask = (magnitude_squared > epsilon);
+
+        const Eigen::ArrayXf corrected_re = stable_division_mask.select(
+            ((measured_re * calibration_re) + (measured_im * calibration_im)) / magnitude_squared,
+            measured_re
+        );
+        const Eigen::ArrayXf corrected_im = stable_division_mask.select(
+            ((measured_im * calibration_re) - (measured_re * calibration_im)) / magnitude_squared,
+            measured_im
+        );
+
+        std::vector<ipc::Complex32> corrected(measured.size());
+        Eigen::Map<InterleavedComplexView> corrected_view(reinterpret_cast<float*>(corrected.data()), point_count, 2);
+        corrected_view.col(0) = corrected_re.matrix();
+        corrected_view.col(1) = corrected_im.matrix();
+
+        return corrected;
+    }
+};
+
+}  // namespace
+
+auto make_through_calibrator() -> std::unique_ptr<CalibratorInterface> {
+    return std::make_unique<ThroughCalibrator>();
+}
+
+}  // namespace radar::preprocessing
diff --git a/data_acq_and_processing/preprocessing/data_preprocessor/include/data_preprocessor.hpp b/data_acq_and_processing/preprocessing/data_preprocessor/include/data_preprocessor.hpp
new file mode 100644
index 0000000..8ee571f
--- /dev/null
+++ b/data_acq_and_processing/preprocessing/data_preprocessor/include/data_preprocessor.hpp
@@ -0,0 +1,47 @@
+#pragma once
+
+#include <atomic>
+#include <cstdint>
+#include <vector>
+
+#include "calibration_master.hpp"
+#include "reference_master.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+
+namespace radar::preprocessing {
+
+class DataPreprocessor {
+  public:
+    DataPreprocessor(
+        const config::RunConfig& config,
+        CalibrationMaster& calibration_master,
+        ReferenceMaster& reference_master,
+        ipc::ShmRing& raw_ring,
+        ipc::ShmRing& preprocessed_ring,
+        ipc::ShmRing* preprocessed_tap_ring = nullptr
+    );
+
+    // Main processing loop. Runs until stop flag is set.
+    void run(const std::atomic<bool>& stop_requested);
+
+  private:
+    void validate_startup_prerequisites() const;
+    [[nodiscard]] auto try_pop_raw_collection(
+        std::vector<std::uint8_t>& serialized_raw,
+        ipc::RawSweepCollection* out_collection
+    ) -> bool;
+    void publish_preprocessed_collection(const ipc::PreprocessedCollection& collection);
+
+    [[nodiscard]] auto preprocess_collection(const ipc::RawSweepCollection& raw_collection) const
+        -> ipc::PreprocessedCollection;
+
+    const config::RunConfig& config_;
+    CalibrationMaster& calibration_master_;
+    ReferenceMaster& reference_master_;
+    ipc::ShmRing& raw_ring_;
+    ipc::ShmRing& preprocessed_ring_;
+    ipc::ShmRing* preprocessed_tap_ring_ = nullptr;
+};
+
+}  // namespace radar::preprocessing
diff --git a/data_acq_and_processing/preprocessing/data_preprocessor/src/data_preprocessor.cpp b/data_acq_and_processing/preprocessing/data_preprocessor/src/data_preprocessor.cpp
new file mode 100644
index 0000000..3729de4
--- /dev/null
+++ b/data_acq_and_processing/preprocessing/data_preprocessor/src/data_preprocessor.cpp
@@ -0,0 +1,85 @@
+#include "data_preprocessor.hpp"
+
+#include <chrono>
+#include <stdexcept>
+#include <thread>
+#include <vector>
+
+namespace radar::preprocessing {
+
+DataPreprocessor::DataPreprocessor(
+    const config::RunConfig& config,
+    CalibrationMaster& calibration_master,
+    ReferenceMaster& reference_master,
+    ipc::ShmRing& raw_ring,
+    ipc::ShmRing& preprocessed_ring,
+    ipc::ShmRing* preprocessed_tap_ring
+)
+    : config_(config),
+      calibration_master_(calibration_master),
+      reference_master_(reference_master),
+      raw_ring_(raw_ring),
+      preprocessed_ring_(preprocessed_ring),
+      preprocessed_tap_ring_(preprocessed_tap_ring) {}
+
+void DataPreprocessor::validate_startup_prerequisites() const {
+    calibration_master_.validate_combos(config_.run_combos);
+    reference_master_.validate_combos(config_.run_combos);
+}
+
+auto DataPreprocessor::try_pop_raw_collection(
+    std::vector<std::uint8_t>& serialized_raw,
+    ipc::RawSweepCollection* out_collection
+) -> bool {
+    if (!raw_ring_.pop(serialized_raw)) {
+        std::this_thread::sleep_for(std::chrono::milliseconds(config_.runtime.idle_sleep_ms));
+        return false;
+    }
+
+    *out_collection = ipc::deserialize_raw_collection(serialized_raw);
+    return true;
+}
+
+void DataPreprocessor::publish_preprocessed_collection(const ipc::PreprocessedCollection& collection) {
+    const auto serialized_preprocessed = ipc::serialize_preprocessed_collection(collection);
+    if (!preprocessed_ring_.push(serialized_preprocessed)) {
+        throw std::runtime_error("Preprocessed ring slot is too small for serialized collection");
+    }
+    if (preprocessed_tap_ring_ != nullptr && !preprocessed_tap_ring_->push(serialized_preprocessed)) {
+        throw std::runtime_error("Preprocessed tap ring slot is too small for serialized collection");
+    }
+}
+
+void DataPreprocessor::run(const std::atomic<bool>& stop_requested) {
+    validate_startup_prerequisites();
+
+    std::vector<std::uint8_t> serialized_raw{};
+    ipc::RawSweepCollection raw_collection{};
+    while (!stop_requested.load(std::memory_order_relaxed)) {
+        if (!try_pop_raw_collection(serialized_raw, &raw_collection)) {
+            continue;
+        }
+
+        const auto preprocessed_collection = preprocess_collection(raw_collection);
+        publish_preprocessed_collection(preprocessed_collection);
+    }
+}
+
+auto DataPreprocessor::preprocess_collection(const ipc::RawSweepCollection& raw_collection) const
+    -> ipc::PreprocessedCollection {
+    ipc::PreprocessedCollection preprocessed{};
+    preprocessed.collection_id = raw_collection.collection_id;
+    preprocessed.monotonic_ns = ipc::current_monotonic_ns();
+    preprocessed.traces.reserve(raw_collection.traces.size());
+
+    for (const auto& raw_trace : raw_collection.traces) {
+        // Pipeline order is fixed: calibration first, then reference subtraction.
+        const auto calibrated = calibration_master_.apply(raw_trace);
+        const auto referenced = reference_master_.apply(calibrated);
+        preprocessed.traces.push_back(referenced);
+    }
+
+    return preprocessed;
+}
+
+}  // namespace radar::preprocessing
diff --git a/data_acq_and_processing/preprocessing/data_preprocessor/src/main.cpp b/data_acq_and_processing/preprocessing/data_preprocessor/src/main.cpp
new file mode 100644
index 0000000..48f671a
--- /dev/null
+++ b/data_acq_and_processing/preprocessing/data_preprocessor/src/main.cpp
@@ -0,0 +1,86 @@
+#include <atomic>
+#include <csignal>
+#include <exception>
+#include <iostream>
+#include <string>
+
+#include "calibration_master.hpp"
+#include "data_preprocessor.hpp"
+#include "reference_master.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+
+namespace {
+
+constexpr const char* kDefaultConfigPath = "run_config.json";
+
+std::atomic<bool> g_stop_requested{false};
+
+void signal_handler(int /*signal*/) {
+    g_stop_requested.store(true, std::memory_order_relaxed);
+}
+
+void install_signal_handlers() {
+    std::signal(SIGINT, signal_handler);
+    std::signal(SIGTERM, signal_handler);
+}
+
+[[nodiscard]] auto read_config_path(int argc, char** argv) -> std::string {
+    std::string config_path = kDefaultConfigPath;
+    for (int index = 1; index < argc; ++index) {
+        const std::string arg = argv[index];
+        if (arg == "--config" && (index + 1) < argc) {
+            config_path = argv[++index];
+        }
+    }
+    return config_path;
+}
+
+}  // namespace
+
+int main(int argc, char** argv) {
+    install_signal_handlers();
+
+    try {
+        const auto config_path = read_config_path(argc, argv);
+        const auto config = radar::config::load_run_config(config_path);
+
+        auto raw_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.raw.name,
+            config.rings.raw.capacity,
+            config.rings.raw.slot_size_bytes
+        );
+        auto preprocessed_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.preprocessed.name,
+            config.rings.preprocessed.capacity,
+            config.rings.preprocessed.slot_size_bytes
+        );
+        auto preprocessed_tap_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.preprocessed_tap.name,
+            config.rings.preprocessed_tap.capacity,
+            config.rings.preprocessed_tap.slot_size_bytes
+        );
+
+        // Load preprocessing assets once before entering run loop.
+        radar::preprocessing::CalibrationMaster calibration_master(radar::preprocessing::make_through_calibrator());
+        calibration_master.load_bundle(config.preprocess.calibration_bundle_path);
+
+        radar::preprocessing::ReferenceMaster reference_master;
+        reference_master.load_bundle(config.preprocess.reference_bundle_path);
+        reference_master.prepare_calibrated(calibration_master);
+
+        radar::preprocessing::DataPreprocessor preprocessor(
+            config,
+            calibration_master,
+            reference_master,
+            raw_ring,
+            preprocessed_ring,
+            &preprocessed_tap_ring
+        );
+        preprocessor.run(g_stop_requested);
+        return 0;
+    } catch (const std::exception& exception) {
+        std::cerr << "data_preprocessor error: " << exception.what() << '\n';
+        return 1;
+    }
+}
diff --git a/data_acq_and_processing/preprocessing/reference_master/include/reference_master.hpp b/data_acq_and_processing/preprocessing/reference_master/include/reference_master.hpp
new file mode 100644
index 0000000..cb23ca1
--- /dev/null
+++ b/data_acq_and_processing/preprocessing/reference_master/include/reference_master.hpp
@@ -0,0 +1,30 @@
+#pragma once
+
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "shared_types.hpp"
+
+namespace radar::preprocessing {
+
+class CalibrationMaster;
+
+class ReferenceMaster {
+  public:
+    // Loads a serialized raw sweep bundle with one reference trace per combo.
+    void load_bundle(const std::string& path);
+    // Builds calibrated references in memory using currently loaded raw references.
+    // Must be called after load_bundle() and after calibration standards are loaded.
+    void prepare_calibrated(const CalibrationMaster& calibration_master);
+    // Ensures all runtime combos are present in loaded references.
+    void validate_combos(const std::vector<ipc::ComboKey>& combos) const;
+    // Subtracts per-combo reference trace from calibrated trace.
+    [[nodiscard]] auto apply(const ipc::SweepTraceBlock& calibrated_trace) const -> ipc::SweepTraceBlock;
+
+  private:
+    std::unordered_map<ipc::ComboKey, ipc::SweepTraceBlock, ipc::ComboKeyHash> raw_references_by_combo_{};
+    std::unordered_map<ipc::ComboKey, ipc::SweepTraceBlock, ipc::ComboKeyHash> calibrated_references_by_combo_{};
+};
+
+}  // namespace radar::preprocessing
diff --git a/data_acq_and_processing/preprocessing/reference_master/src/reference_master.cpp b/data_acq_and_processing/preprocessing/reference_master/src/reference_master.cpp
new file mode 100644
index 0000000..2ef3695
--- /dev/null
+++ b/data_acq_and_processing/preprocessing/reference_master/src/reference_master.cpp
@@ -0,0 +1,141 @@
+#include "reference_master.hpp"
+
+#include <cstdint>
+#include <fstream>
+#include <iterator>
+#include <stdexcept>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include <Eigen/Core>
+
+#include "calibration_master.hpp"
+
+namespace radar::preprocessing {
+namespace {
+
+[[nodiscard]] auto combo_to_string(const ipc::ComboKey& combo) -> std::string {
+    return "input=" + std::to_string(combo.input_pos) + " output=" + std::to_string(combo.output_pos);
+}
+
+[[nodiscard]] auto read_binary_file(const std::string& path, const std::string& bundle_label)
+    -> std::vector<std::uint8_t> {
+    std::ifstream stream(path, std::ios::binary);
+    if (!stream.is_open()) {
+        throw std::runtime_error("Failed to open " + bundle_label + " bundle: " + path);
+    }
+
+    return std::vector<std::uint8_t>(std::istreambuf_iterator<char>(stream), std::istreambuf_iterator<char>());
+}
+
+void validate_trace_layout(const ipc::SweepTraceBlock& trace, const std::string& trace_label) {
+    if (trace.frequency_hz.size() != trace.s21.size()) {
+        throw std::runtime_error(
+            trace_label + " frequency/complex vector size mismatch for combo " + combo_to_string(trace.combo)
+        );
+    }
+}
+
+}  // namespace
+
+void ReferenceMaster::load_bundle(const std::string& path) {
+    if (path.empty()) {
+        throw std::runtime_error("Reference bundle path must not be empty");
+    }
+
+    const auto bytes = read_binary_file(path, "reference");
+    if (bytes.empty()) {
+        throw std::runtime_error("Reference bundle is empty: " + path);
+    }
+
+    const auto collection = ipc::deserialize_raw_collection(bytes);
+    raw_references_by_combo_.clear();
+    raw_references_by_combo_.reserve(collection.traces.size());
+    calibrated_references_by_combo_.clear();
+    for (const auto& trace : collection.traces) {
+        validate_trace_layout(trace, "Reference trace");
+        raw_references_by_combo_.insert_or_assign(trace.combo, trace);
+    }
+
+    if (raw_references_by_combo_.empty()) {
+        throw std::runtime_error("Reference bundle does not contain traces: " + path);
+    }
+}
+
+void ReferenceMaster::prepare_calibrated(const CalibrationMaster& calibration_master) {
+    if (raw_references_by_combo_.empty()) {
+        throw std::runtime_error("Reference bundle must be loaded before prepare_calibrated()");
+    }
+
+    calibrated_references_by_combo_.clear();
+    calibrated_references_by_combo_.reserve(raw_references_by_combo_.size());
+    for (const auto& [combo, raw_reference] : raw_references_by_combo_) {
+        auto calibrated = calibration_master.apply(raw_reference);
+        calibrated.combo = combo;
+        calibrated_references_by_combo_.insert_or_assign(combo, std::move(calibrated));
+    }
+}
+
+void ReferenceMaster::validate_combos(const std::vector<ipc::ComboKey>& combos) const {
+    if (calibrated_references_by_combo_.empty()) {
+        throw std::runtime_error(
+            "Calibrated references are not prepared. Call ReferenceMaster::prepare_calibrated() at startup."
+        );
+    }
+
+    for (const auto& combo : combos) {
+        if (!raw_references_by_combo_.contains(combo)) {
+            throw std::runtime_error("Raw reference data is missing for combo " + combo_to_string(combo));
+        }
+        if (!calibrated_references_by_combo_.contains(combo)) {
+            throw std::runtime_error("Calibrated reference data is missing for combo " + combo_to_string(combo));
+        }
+    }
+}
+
+auto ReferenceMaster::apply(const ipc::SweepTraceBlock& calibrated_trace) const -> ipc::SweepTraceBlock {
+    validate_trace_layout(calibrated_trace, "Calibrated trace");
+
+    const auto found = calibrated_references_by_combo_.find(calibrated_trace.combo);
+    if (found == calibrated_references_by_combo_.end()) {
+        throw std::runtime_error(
+            "Calibrated reference trace is missing for combo " + combo_to_string(calibrated_trace.combo)
+        );
+    }
+
+    const auto& reference = found->second;
+    validate_trace_layout(reference, "Calibrated reference trace");
+    if (calibrated_trace.s21.size() != reference.s21.size()) {
+        throw std::runtime_error(
+            "Reference point count mismatch for combo " + combo_to_string(calibrated_trace.combo)
+        );
+    }
+
+    ipc::SweepTraceBlock output{};
+    output.combo = calibrated_trace.combo;
+    output.frequency_hz = calibrated_trace.frequency_hz;
+    output.s21.resize(calibrated_trace.s21.size());
+
+    static_assert(sizeof(ipc::Complex32) == sizeof(float) * 2U, "Complex32 layout must be two contiguous floats");
+    using InterleavedComplexView = Eigen::Matrix<float, Eigen::Dynamic, 2, Eigen::RowMajor>;
+    const auto point_count = static_cast<Eigen::Index>(calibrated_trace.s21.size());
+
+    Eigen::Map<const InterleavedComplexView> calibrated_view(
+        reinterpret_cast<const float*>(calibrated_trace.s21.data()),
+        point_count,
+        2
+    );
+    Eigen::Map<const InterleavedComplexView> reference_view(
+        reinterpret_cast<const float*>(reference.s21.data()),
+        point_count,
+        2
+    );
+    Eigen::Map<InterleavedComplexView> output_view(reinterpret_cast<float*>(output.s21.data()), point_count, 2);
+
+    output_view = calibrated_view - reference_view;
+
+    return output;
+}
+
+}  // namespace radar::preprocessing
diff --git a/data_acq_and_processing/processing/data_processor/include/data_processor.hpp b/data_acq_and_processing/processing/data_processor/include/data_processor.hpp
new file mode 100644
index 0000000..23562e4
--- /dev/null
+++ b/data_acq_and_processing/processing/data_processor/include/data_processor.hpp
@@ -0,0 +1,48 @@
+#pragma once
+
+#include <atomic>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "processor_interface.hpp"
+#include "processing_live_config.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+
+namespace radar::processing {
+
+using ProcessorRegistry = std::unordered_map<std::string, std::unique_ptr<ProcessorInterface>>;
+
+class DataProcessor {
+  public:
+    DataProcessor(
+        const config::RunConfig& config,
+        ipc::ShmRing& preprocessed_ring,
+        ipc::ShmRing& results_ring,
+        ProcessorRegistry processors
+    );
+
+    void run(const std::atomic<bool>& stop_requested);
+
+  private:
+    [[nodiscard]] auto process_collection(
+        const ipc::PreprocessedCollection& preprocessed,
+        ProcessorInterface& processor,
+        const ProcessingLiveConfig& live_config
+    ) -> ipc::ResultCollection;
+
+    [[nodiscard]] auto resolve_processor(const ProcessingLiveConfig& live_config) -> ProcessorInterface&;
+
+    const config::RunConfig& config_;
+    ipc::ShmRing& preprocessed_ring_;
+    ipc::ShmRing& results_ring_;
+    ProcessorRegistry processors_{};
+    std::string default_processor_mode_{};
+    ProcessingLiveConfigLoader live_config_loader_;
+};
+
+[[nodiscard]] auto create_default_processors() -> ProcessorRegistry;
+
+}  // namespace radar::processing
diff --git a/data_acq_and_processing/processing/data_processor/include/processing_live_config.hpp b/data_acq_and_processing/processing/data_processor/include/processing_live_config.hpp
new file mode 100644
index 0000000..a15f32c
--- /dev/null
+++ b/data_acq_and_processing/processing/data_processor/include/processing_live_config.hpp
@@ -0,0 +1,49 @@
+#pragma once
+
+#include <chrono>
+#include <cstdint>
+#include <filesystem>
+#include <string>
+
+namespace radar::processing {
+
+enum class HistoryCommand {
+    None,
+    RemoveLast,
+    ClearAll,
+};
+
+struct ProcessingLiveConfig {
+    std::string processor_mode = "pass_through";
+    float gain_db = 0.0F;
+    float phase_deg = 0.0F;
+    std::string bscan_axis = "abs";
+    float bscan_cut_m = 0.824F;
+    float bscan_max_depth_m = 1.0F;
+    float bscan_gain = 1.0F;
+    float bscan_start_freq_mhz = 100.0F;
+    float bscan_stop_freq_mhz = 8800.0F;
+    std::uint64_t history_command_seq = 0;
+    HistoryCommand history_command = HistoryCommand::None;
+};
+
+class ProcessingLiveConfigLoader {
+  public:
+    explicit ProcessingLiveConfigLoader(std::string path);
+
+    [[nodiscard]] auto current() const -> ProcessingLiveConfig;
+    [[nodiscard]] auto refresh_if_needed() -> ProcessingLiveConfig;
+    [[nodiscard]] auto revision() const -> std::uint64_t;
+
+  private:
+    [[nodiscard]] auto read_from_file() const -> ProcessingLiveConfig;
+
+    std::string path_{};
+    ProcessingLiveConfig current_{};
+    std::filesystem::file_time_type last_write_time_{};
+    bool has_last_write_time_ = false;
+    std::uint64_t revision_ = 0;
+    std::chrono::steady_clock::time_point next_check_at_{};
+};
+
+}  // namespace radar::processing
diff --git a/data_acq_and_processing/processing/data_processor/src/data_processor.cpp b/data_acq_and_processing/processing/data_processor/src/data_processor.cpp
new file mode 100644
index 0000000..d39b5a0
--- /dev/null
+++ b/data_acq_and_processing/processing/data_processor/src/data_processor.cpp
@@ -0,0 +1,156 @@
+#include "data_processor.hpp"
+
+#include <algorithm>
+#include <chrono>
+#include <cstdint>
+#include <deque>
+#include <stdexcept>
+#include <thread>
+#include <utility>
+
+#include "bscan_processor.hpp"
+#include "passthrough_processor.hpp"
+
+namespace radar::processing {
+namespace {
+
+constexpr const char* kDefaultProcessorMode = "pass_through";
+constexpr std::size_t kBscanReplayWindow = 50U;
+
+[[nodiscard]] auto replay_history_limit(const config::RunConfig& config) -> std::size_t {
+    const auto preprocessed_capacity = static_cast<std::size_t>(std::max<std::uint32_t>(1U, config.rings.preprocessed.capacity));
+    const auto results_capacity = static_cast<std::size_t>(std::max<std::uint32_t>(1U, config.rings.results.capacity));
+    return std::min({preprocessed_capacity, results_capacity, kBscanReplayWindow});
+}
+
+void publish_result_collection(const ipc::ResultCollection& collection, ipc::ShmRing& results_ring) {
+    const auto out_bytes = ipc::serialize_result_collection(collection);
+    if (!results_ring.push(out_bytes)) {
+        throw std::runtime_error("Results ring slot is too small for serialized collection");
+    }
+}
+
+}  // namespace
+
+DataProcessor::DataProcessor(
+    const config::RunConfig& config,
+    ipc::ShmRing& preprocessed_ring,
+    ipc::ShmRing& results_ring,
+    ProcessorRegistry processors
+)
+    : config_(config),
+      preprocessed_ring_(preprocessed_ring),
+      results_ring_(results_ring),
+      processors_(std::move(processors)),
+      default_processor_mode_(kDefaultProcessorMode),
+      live_config_loader_(config.runtime.processing_live_config_path) {
+    if (processors_.empty()) {
+        throw std::runtime_error("DataProcessor requires at least one processor");
+    }
+    if (const auto found = processors_.find(default_processor_mode_); found == processors_.end()) {
+        default_processor_mode_ = processors_.begin()->first;
+    }
+}
+
+void DataProcessor::run(const std::atomic<bool>& stop_requested) {
+    std::vector<std::uint8_t> bytes{};
+    std::deque<ipc::PreprocessedCollection> preprocessed_history{};
+    const std::size_t history_limit = replay_history_limit(config_);
+    std::uint64_t last_replayed_revision = live_config_loader_.revision();
+    std::uint64_t last_applied_history_command_seq = 0;
+
+    while (!stop_requested.load(std::memory_order_relaxed)) {
+        const auto live_config = live_config_loader_.refresh_if_needed();
+        const auto live_revision = live_config_loader_.revision();
+        auto& processor = resolve_processor(live_config);
+
+        if (live_revision != last_replayed_revision) {
+            if (live_config.history_command_seq > last_applied_history_command_seq) {
+                if (live_config.history_command == HistoryCommand::RemoveLast) {
+                    if (!preprocessed_history.empty()) {
+                        preprocessed_history.pop_back();
+                    }
+                } else if (live_config.history_command == HistoryCommand::ClearAll) {
+                    preprocessed_history.clear();
+                }
+                last_applied_history_command_seq = live_config.history_command_seq;
+            }
+
+            if (live_config.processor_mode == "bscan") {
+                for (const auto& cached : preprocessed_history) {
+                    const auto replay_result = process_collection(cached, processor, live_config);
+                    publish_result_collection(replay_result, results_ring_);
+                }
+            } else if (!preprocessed_history.empty()) {
+                const auto replay_result = process_collection(preprocessed_history.back(), processor, live_config);
+                publish_result_collection(replay_result, results_ring_);
+            }
+            last_replayed_revision = live_revision;
+        }
+
+        if (preprocessed_ring_.pop(bytes)) {
+            auto preprocessed = ipc::deserialize_preprocessed_collection(bytes);
+            preprocessed_history.push_back(std::move(preprocessed));
+            while (preprocessed_history.size() > history_limit) {
+                preprocessed_history.pop_front();
+            }
+
+            const auto result_collection = process_collection(preprocessed_history.back(), processor, live_config);
+            publish_result_collection(result_collection, results_ring_);
+            continue;
+        }
+
+        std::this_thread::sleep_for(std::chrono::milliseconds(config_.runtime.idle_sleep_ms));
+    }
+}
+
+auto DataProcessor::process_collection(
+    const ipc::PreprocessedCollection& preprocessed,
+    ProcessorInterface& processor,
+    const ProcessingLiveConfig& live_config
+) -> ipc::ResultCollection {
+    ipc::ResultCollection results{};
+    results.collection_id = preprocessed.collection_id;
+    // Keep source monotonic timestamp stable across live-config replays.
+    results.monotonic_ns = preprocessed.monotonic_ns;
+    results.blocks.reserve(preprocessed.traces.size());
+
+    for (const auto& trace : preprocessed.traces) {
+        ipc::ResultBlock block{};
+        block.combo = trace.combo;
+        block.payloads.reserve(1U);
+        block.payloads.push_back(processor.process(trace, live_config));
+
+        results.blocks.push_back(std::move(block));
+    }
+
+    return results;
+}
+
+auto DataProcessor::resolve_processor(const ProcessingLiveConfig& live_config) -> ProcessorInterface& {
+    const std::string requested_mode =
+        live_config.processor_mode.empty() ? default_processor_mode_ : live_config.processor_mode;
+
+    if (auto found = processors_.find(requested_mode); found != processors_.end()) {
+        return *(found->second);
+    }
+    if (auto fallback = processors_.find(default_processor_mode_); fallback != processors_.end()) {
+        return *(fallback->second);
+    }
+    return *(processors_.begin()->second);
+}
+
+auto create_default_processors() -> ProcessorRegistry {
+    ProcessorRegistry processors{};
+    {
+        auto processor = std::make_unique<PassThroughProcessor>();
+        processors.emplace(processor->name(), std::move(processor));
+    }
+    {
+        auto processor = std::make_unique<BScanProcessor>();
+        processors.emplace(processor->name(), std::move(processor));
+    }
+    return processors;
+}
+
+}  // namespace radar::processing
diff --git a/data_acq_and_processing/processing/data_processor/src/main.cpp b/data_acq_and_processing/processing/data_processor/src/main.cpp
new file mode 100644
index 0000000..903c257
--- /dev/null
+++ b/data_acq_and_processing/processing/data_processor/src/main.cpp
@@ -0,0 +1,69 @@
+#include <atomic>
+#include <csignal>
+#include <exception>
+#include <iostream>
+#include <string>
+
+#include "data_processor.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+
+namespace {
+
+constexpr const char* kDefaultConfigPath = "run_config.json";
+
+std::atomic<bool> g_stop_requested{false};
+
+void signal_handler(int /*signal*/) {
+    g_stop_requested.store(true, std::memory_order_relaxed);
+}
+
+void install_signal_handlers() {
+    std::signal(SIGINT, signal_handler);
+    std::signal(SIGTERM, signal_handler);
+}
+
+[[nodiscard]] auto read_config_path(int argc, char** argv) -> std::string {
+    std::string config_path = kDefaultConfigPath;
+    for (int index = 1; index < argc; ++index) {
+        const std::string arg = argv[index];
+        if (arg == "--config" && (index + 1) < argc) {
+            config_path = argv[++index];
+        }
+    }
+    return config_path;
+}
+
+}  // namespace
+
+int main(int argc, char** argv) {
+    install_signal_handlers();
+
+    try {
+        const auto config_path = read_config_path(argc, argv);
+        const auto config = radar::config::load_run_config(config_path);
+
+        auto preprocessed_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.preprocessed.name,
+            config.rings.preprocessed.capacity,
+            config.rings.preprocessed.slot_size_bytes
+        );
+        auto results_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.results.name,
+            config.rings.results.capacity,
+            config.rings.results.slot_size_bytes
+        );
+
+        radar::processing::DataProcessor processor(
+            config,
+            preprocessed_ring,
+            results_ring,
+            radar::processing::create_default_processors()
+        );
+        processor.run(g_stop_requested);
+        return 0;
+    } catch (const std::exception& exception) {
+        std::cerr << "data_processor error: " << exception.what() << '\n';
+        return 1;
+    }
+}
diff --git a/data_acq_and_processing/processing/data_processor/src/processing_live_config.cpp b/data_acq_and_processing/processing/data_processor/src/processing_live_config.cpp
new file mode 100644
index 0000000..fc0124a
--- /dev/null
+++ b/data_acq_and_processing/processing/data_processor/src/processing_live_config.cpp
@@ -0,0 +1,189 @@
+#include "processing_live_config.hpp"
+
+#include <chrono>
+#include <cmath>
+#include <fstream>
+#include <iostream>
+#include <limits>
+#include <sstream>
+#include <stdexcept>
+#include <string>
+#include <utility>
+
+#include <nlohmann/json.hpp>
+
+namespace radar::processing {
+namespace {
+
+using Json = nlohmann::json;
+
+[[nodiscard]] auto parse_history_command(const std::string& value) -> HistoryCommand {
+    if (value == "none") {
+        return HistoryCommand::None;
+    }
+    if (value == "remove_last") {
+        return HistoryCommand::RemoveLast;
+    }
+    if (value == "clear_all") {
+        return HistoryCommand::ClearAll;
+    }
+    throw std::runtime_error("processing.history_command must be one of: none, remove_last, clear_all");
+}
+
+[[nodiscard]] auto parse_u64_number(const Json& value, const std::string& field_name) -> std::uint64_t {
+    if (!value.is_number()) {
+        throw std::runtime_error(field_name + " must be number");
+    }
+    const double numeric = value.get<double>();
+    if (numeric < 0.0 || numeric > static_cast<double>(std::numeric_limits<std::uint64_t>::max())) {
+        throw std::runtime_error(field_name + " is out of uint64 range");
+    }
+    const double rounded = std::round(numeric);
+    if (std::fabs(numeric - rounded) > 1e-6) {
+        throw std::runtime_error(field_name + " must be integer");
+    }
+    return static_cast<std::uint64_t>(rounded);
+}
+
+[[nodiscard]] auto parse_live_config(const std::string& json_text, const std::string& path) -> ProcessingLiveConfig {
+    if (json_text.empty()) {
+        throw std::runtime_error("Processing live config is empty: " + path);
+    }
+
+    Json root{};
+    try {
+        root = Json::parse(json_text);
+    } catch (const Json::parse_error& error) {
+        throw std::runtime_error("Failed to parse processing live config: " + std::string(error.what()));
+    }
+
+    if (!root.is_object()) {
+        throw std::runtime_error("Processing live config root must be JSON object");
+    }
+
+    ProcessingLiveConfig config{};
+    if (const auto found = root.find("processor_mode"); found != root.end()) {
+        if (!found->is_string()) {
+            throw std::runtime_error("processing.processor_mode must be string");
+        }
+        config.processor_mode = found->get<std::string>();
+    }
+    if (const auto found = root.find("gain_db"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.gain_db must be number");
+        }
+        config.gain_db = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("phase_deg"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.phase_deg must be number");
+        }
+        config.phase_deg = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("bscan_axis"); found != root.end()) {
+        if (!found->is_string()) {
+            throw std::runtime_error("processing.bscan_axis must be string");
+        }
+        config.bscan_axis = found->get<std::string>();
+    }
+    if (const auto found = root.find("bscan_cut_m"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.bscan_cut_m must be number");
+        }
+        config.bscan_cut_m = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("bscan_max_depth_m"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.bscan_max_depth_m must be number");
+        }
+        config.bscan_max_depth_m = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("bscan_gain"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.bscan_gain must be number");
+        }
+        config.bscan_gain = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("bscan_start_freq_mhz"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.bscan_start_freq_mhz must be number");
+        }
+        config.bscan_start_freq_mhz = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("bscan_stop_freq_mhz"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.bscan_stop_freq_mhz must be number");
+        }
+        config.bscan_stop_freq_mhz = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("history_command_seq"); found != root.end()) {
+        config.history_command_seq = parse_u64_number(*found, "processing.history_command_seq");
+    }
+    if (const auto found = root.find("history_command"); found != root.end()) {
+        if (!found->is_string()) {
+            throw std::runtime_error("processing.history_command must be string");
+        }
+        config.history_command = parse_history_command(found->get<std::string>());
+    }
+    return config;
+}
+
+}  // namespace
+
+ProcessingLiveConfigLoader::ProcessingLiveConfigLoader(std::string path)
+    : path_(std::move(path)),
+      next_check_at_(std::chrono::steady_clock::now()) {
+    if (path_.empty()) {
+        throw std::runtime_error("Processing live config path must not be empty");
+    }
+}
+
+auto ProcessingLiveConfigLoader::current() const -> ProcessingLiveConfig {
+    return current_;
+}
+
+auto ProcessingLiveConfigLoader::revision() const -> std::uint64_t {
+    return revision_;
+}
+
+auto ProcessingLiveConfigLoader::refresh_if_needed() -> ProcessingLiveConfig {
+    const auto now = std::chrono::steady_clock::now();
+    if (now < next_check_at_) {
+        return current_;
+    }
+    next_check_at_ = now + std::chrono::milliseconds(100);
+
+    std::error_code time_error{};
+    const auto file_time = std::filesystem::last_write_time(path_, time_error);
+    if (time_error) {
+        return current_;
+    }
+
+    if (has_last_write_time_ && file_time == last_write_time_) {
+        return current_;
+    }
+
+    try {
+        current_ = read_from_file();
+        last_write_time_ = file_time;
+        has_last_write_time_ = true;
+        ++revision_;
+    } catch (const std::exception& error) {
+        std::cerr << "data_processor warning: failed to refresh live processing config: " << error.what() << '\n';
+    }
+
+    return current_;
+}
+
+auto ProcessingLiveConfigLoader::read_from_file() const -> ProcessingLiveConfig {
+    std::ifstream stream(path_);
+    if (!stream.is_open()) {
+        throw std::runtime_error("Failed to open live processing config: " + path_);
+    }
+
+    std::stringstream buffer{};
+    buffer << stream.rdbuf();
+    return parse_live_config(buffer.str(), path_);
+}
+
+}  // namespace radar::processing
diff --git a/data_acq_and_processing/processing/processors/include/bscan_processor.hpp b/data_acq_and_processing/processing/processors/include/bscan_processor.hpp
new file mode 100644
index 0000000..fc9b931
--- /dev/null
+++ b/data_acq_and_processing/processing/processors/include/bscan_processor.hpp
@@ -0,0 +1,16 @@
+#pragma once
+
+#include "processor_interface.hpp"
+
+namespace radar::processing {
+
+class BScanProcessor final : public ProcessorInterface {
+  public:
+    [[nodiscard]] auto name() const -> std::string override;
+    [[nodiscard]] auto process(
+        const ipc::SweepTraceBlock& trace,
+        const ProcessingLiveConfig& live_config
+    ) -> ipc::ResultPayload override;
+};
+
+}  // namespace radar::processing
diff --git a/data_acq_and_processing/processing/processors/include/passthrough_processor.hpp b/data_acq_and_processing/processing/processors/include/passthrough_processor.hpp
new file mode 100644
index 0000000..c849913
--- /dev/null
+++ b/data_acq_and_processing/processing/processors/include/passthrough_processor.hpp
@@ -0,0 +1,16 @@
+#pragma once
+
+#include "processor_interface.hpp"
+
+namespace radar::processing {
+
+class PassThroughProcessor final : public ProcessorInterface {
+  public:
+    [[nodiscard]] auto name() const -> std::string override;
+    [[nodiscard]] auto process(
+        const ipc::SweepTraceBlock& trace,
+        const ProcessingLiveConfig& live_config
+    ) -> ipc::ResultPayload override;
+};
+
+}  // namespace radar::processing
diff --git a/data_acq_and_processing/processing/processors/include/processor_interface.hpp b/data_acq_and_processing/processing/processors/include/processor_interface.hpp
new file mode 100644
index 0000000..78990f4
--- /dev/null
+++ b/data_acq_and_processing/processing/processors/include/processor_interface.hpp
@@ -0,0 +1,21 @@
+#pragma once
+
+#include <string>
+
+#include "processing_live_config.hpp"
+#include "shared_types.hpp"
+
+namespace radar::processing {
+
+class ProcessorInterface {
+  public:
+    virtual ~ProcessorInterface() = default;
+
+    [[nodiscard]] virtual auto name() const -> std::string = 0;
+    [[nodiscard]] virtual auto process(
+        const ipc::SweepTraceBlock& trace,
+        const ProcessingLiveConfig& live_config
+    ) -> ipc::ResultPayload = 0;
+};
+
+}  // namespace radar::processing
diff --git a/data_acq_and_processing/processing/processors/src/bscan_processor.cpp b/data_acq_and_processing/processing/processors/src/bscan_processor.cpp
new file mode 100644
index 0000000..a20b01d
--- /dev/null
+++ b/data_acq_and_processing/processing/processors/src/bscan_processor.cpp
@@ -0,0 +1,237 @@
+#include "bscan_processor.hpp"
+
+#include <algorithm>
+#include <cmath>
+#include <complex>
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+#include <string_view>
+#include <utility>
+#include <vector>
+
+namespace radar::processing {
+namespace {
+
+constexpr double kPi = 3.14159265358979323846;
+constexpr double kSpeedOfLightMetersPerSec = 299'792'458.0;
+
+struct BScanProfile {
+    std::vector<float> depth_m{};
+    std::vector<float> response{};
+};
+
+[[nodiscard]] auto next_power_of_two(std::size_t value) -> std::size_t {
+    if (value <= 1U) {
+        return 1U;
+    }
+
+    std::size_t power = 1U;
+    while (power < value) {
+        if (power > (std::numeric_limits<std::size_t>::max() >> 1U)) {
+            return value;
+        }
+        power <<= 1U;
+    }
+    return power;
+}
+
+void fft_inplace(std::vector<std::complex<double>>& values, bool inverse) {
+    const std::size_t size = values.size();
+    if (size <= 1U) {
+        return;
+    }
+
+    for (std::size_t index = 1U, bit_reversed = 0U; index < size; ++index) {
+        std::size_t bit = size >> 1U;
+        while (bit_reversed & bit) {
+            bit_reversed ^= bit;
+            bit >>= 1U;
+        }
+        bit_reversed ^= bit;
+        if (index < bit_reversed) {
+            std::swap(values[index], values[bit_reversed]);
+        }
+    }
+
+    for (std::size_t len = 2U; len <= size; len <<= 1U) {
+        const double angle = 2.0 * kPi * (inverse ? 1.0 : -1.0) / static_cast<double>(len);
+        const std::complex<double> twiddle_step(std::cos(angle), std::sin(angle));
+        const std::size_t half_len = len >> 1U;
+
+        for (std::size_t offset = 0U; offset < size; offset += len) {
+            std::complex<double> twiddle(1.0, 0.0);
+            for (std::size_t i = 0U; i < half_len; ++i) {
+                const auto even = values[offset + i];
+                const auto odd = values[offset + i + half_len] * twiddle;
+
+                values[offset + i] = even + odd;
+                values[offset + i + half_len] = even - odd;
+                twiddle *= twiddle_step;
+            }
+        }
+    }
+
+    if (!inverse) {
+        return;
+    }
+
+    const double scale = 1.0 / static_cast<double>(size);
+    for (auto& value : values) {
+        value *= scale;
+    }
+}
+
+[[nodiscard]] auto select_axis(std::complex<double> sample, std::string_view axis) -> double {
+    if (axis == "real") {
+        return std::real(sample);
+    }
+    if (axis == "phase") {
+        return std::arg(sample);
+    }
+    return std::abs(sample);
+}
+
+[[nodiscard]] auto fallback_profile(const ipc::SweepTraceBlock& trace) -> BScanProfile {
+    const std::size_t point_count = std::min(trace.frequency_hz.size(), trace.s21.size());
+    BScanProfile fallback{};
+    fallback.depth_m.reserve(point_count);
+    fallback.response.reserve(point_count);
+
+    const float denominator = point_count > 1U ? static_cast<float>(point_count - 1U) : 1.0F;
+    for (std::size_t index = 0U; index < point_count; ++index) {
+        const auto& sample = trace.s21[index];
+        fallback.depth_m.push_back(static_cast<float>(static_cast<float>(index) / denominator));
+        fallback.response.push_back(std::sqrt(sample.re * sample.re + sample.im * sample.im));
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto compute_bscan_profile(
+    const ipc::SweepTraceBlock& trace,
+    const ProcessingLiveConfig& live_config
+) -> BScanProfile {
+    const std::size_t point_count = std::min(trace.frequency_hz.size(), trace.s21.size());
+    if (point_count < 2U) {
+        return fallback_profile(trace);
+    }
+
+    const double configured_start_hz = static_cast<double>(live_config.bscan_start_freq_mhz) * 1'000'000.0;
+    const double configured_stop_hz = static_cast<double>(live_config.bscan_stop_freq_mhz) * 1'000'000.0;
+    const double start_hz = std::min(configured_start_hz, configured_stop_hz);
+    const double stop_hz = std::max(configured_start_hz, configured_stop_hz);
+
+    std::vector<double> filtered_freq_hz{};
+    std::vector<std::complex<double>> filtered_s21{};
+    filtered_freq_hz.reserve(point_count);
+    filtered_s21.reserve(point_count);
+
+    for (std::size_t index = 0U; index < point_count; ++index) {
+        const double frequency_hz = static_cast<double>(trace.frequency_hz[index]);
+        if (frequency_hz < start_hz || frequency_hz > stop_hz) {
+            continue;
+        }
+        const auto& sample = trace.s21[index];
+        filtered_freq_hz.push_back(frequency_hz);
+        filtered_s21.emplace_back(static_cast<double>(sample.re), static_cast<double>(sample.im));
+    }
+
+    if (filtered_freq_hz.size() < 2U) {
+        return fallback_profile(trace);
+    }
+
+    const std::size_t filtered_count = filtered_freq_hz.size();
+    const double df = (filtered_freq_hz.back() - filtered_freq_hz.front()) / static_cast<double>(filtered_count - 1U);
+    if (df <= 0.0) {
+        return fallback_profile(trace);
+    }
+
+    const auto start_bin = static_cast<std::int64_t>(std::llround(filtered_freq_hz.front() / df));
+    if (start_bin < 0) {
+        return fallback_profile(trace);
+    }
+
+    const auto start_index = static_cast<std::size_t>(start_bin);
+    if (start_index > (std::numeric_limits<std::size_t>::max() / 2U)) {
+        return fallback_profile(trace);
+    }
+    if (start_index > (std::numeric_limits<std::size_t>::max() - filtered_count + 1U)) {
+        return fallback_profile(trace);
+    }
+
+    const std::size_t min_fft_len = 2U * (start_index + filtered_count - 1U);
+    const std::size_t fft_len = next_power_of_two(min_fft_len);
+    if (fft_len < min_fft_len || (fft_len & (fft_len - 1U)) != 0U) {
+        return fallback_profile(trace);
+    }
+    if (start_index > fft_len || filtered_count > (fft_len - start_index)) {
+        return fallback_profile(trace);
+    }
+
+    std::vector<std::complex<double>> spectrum(fft_len, std::complex<double>(0.0, 0.0));
+    for (std::size_t index = 0U; index < filtered_count; ++index) {
+        spectrum[start_index + index] = filtered_s21[index];
+    }
+
+    fft_inplace(spectrum, true);
+
+    const double dt = 1.0 / (static_cast<double>(fft_len) * df);
+    const double cut_m = std::max(0.0, static_cast<double>(live_config.bscan_cut_m));
+    const double max_depth_m = std::max(0.0, static_cast<double>(live_config.bscan_max_depth_m));
+    const double gain = static_cast<double>(live_config.bscan_gain);
+    const double window_start = 2.0 * cut_m;
+    const double window_stop = window_start + (2.0 * max_depth_m);
+
+    BScanProfile profile{};
+    profile.depth_m.reserve(spectrum.size());
+    profile.response.reserve(spectrum.size());
+
+    const std::string_view axis = live_config.bscan_axis;
+    for (std::size_t index = 0U; index < spectrum.size(); ++index) {
+        const double depth_raw = static_cast<double>(index) * dt * kSpeedOfLightMetersPerSec;
+        if (depth_raw < window_start || depth_raw > window_stop) {
+            continue;
+        }
+
+        const double one_way_depth = (depth_raw - window_start) / 2.0;
+        double gain_shape = std::pow(one_way_depth, gain);
+        if (!std::isfinite(gain_shape)) {
+            gain_shape = 0.0;
+        }
+
+        const double axis_value = select_axis(spectrum[index], axis);
+        const double output_value = axis_value * gain_shape;
+
+        profile.depth_m.push_back(static_cast<float>(one_way_depth));
+        profile.response.push_back(static_cast<float>(output_value));
+    }
+
+    return profile;
+}
+
+}  // namespace
+
+auto BScanProcessor::name() const -> std::string {
+    return "bscan";
+}
+
+auto BScanProcessor::process(const ipc::SweepTraceBlock& trace, const ProcessingLiveConfig& live_config)
+    -> ipc::ResultPayload {
+    ipc::ResultPayload payload{};
+    payload.processing_name = name();
+    payload.kind = ipc::ResultKind::TraceComplex;
+
+    auto profile = compute_bscan_profile(trace, live_config);
+    payload.frequency_hz = std::move(profile.depth_m);
+    payload.trace.reserve(profile.response.size());
+    for (const auto value : profile.response) {
+        payload.trace.push_back(ipc::Complex32{
+            .re = value,
+            .im = 0.0F,
+        });
+    }
+
+    return payload;
+}
+
+}  // namespace radar::processing
diff --git a/data_acq_and_processing/processing/processors/src/passthrough_processor.cpp b/data_acq_and_processing/processing/processors/src/passthrough_processor.cpp
new file mode 100644
index 0000000..cba60ed
--- /dev/null
+++ b/data_acq_and_processing/processing/processors/src/passthrough_processor.cpp
@@ -0,0 +1,39 @@
+#include "passthrough_processor.hpp"
+
+#include <cmath>
+
+namespace radar::processing {
+namespace {
+
+constexpr float kPi = 3.14159265358979323846F;
+
+}  // namespace
+
+auto PassThroughProcessor::name() const -> std::string {
+    return "pass_through";
+}
+
+auto PassThroughProcessor::process(const ipc::SweepTraceBlock& trace, const ProcessingLiveConfig& live_config)
+    -> ipc::ResultPayload {
+    ipc::ResultPayload payload{};
+    payload.processing_name = name();
+    payload.kind = ipc::ResultKind::TraceComplex;
+    payload.frequency_hz = trace.frequency_hz;
+    payload.trace = trace.s21;
+
+    const float linear_gain = std::pow(10.0F, live_config.gain_db / 20.0F);
+    const float phase_rad = live_config.phase_deg * (kPi / 180.0F);
+    const float cos_phase = std::cos(phase_rad);
+    const float sin_phase = std::sin(phase_rad);
+
+    for (auto& sample : payload.trace) {
+        const float re = sample.re;
+        const float im = sample.im;
+        sample.re = linear_gain * ((re * cos_phase) - (im * sin_phase));
+        sample.im = linear_gain * ((re * sin_phase) + (im * cos_phase));
+    }
+
+    return payload;
+}
+
+}  // namespace radar::processing
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/interfaces/radar_driver.hpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/interfaces/radar_driver.hpp
new file mode 100644
index 0000000..78c06a4
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/interfaces/radar_driver.hpp
@@ -0,0 +1,37 @@
+#pragma once
+
+#include <vector>
+
+#include "shared_types.hpp"
+
+namespace radar::drivers {
+
+/**
+ * @brief One S21 sweep acquired from the radar.
+ *
+ * Both vectors must have equal size and aligned indices.
+ */
+struct SweepTrace {
+    std::vector<float> frequency_hz{};
+    std::vector<ipc::Complex32> s21{};
+};
+
+/**
+ * @brief Minimal radar interface used by the sweep orchestrator.
+ *
+ * Implementations are expected to be lightweight: configuration is handled by
+ * the Python layer, while this interface only opens/closes and acquires S21.
+ */
+class RadarDriver {
+  public:
+    virtual ~RadarDriver() = default;
+
+    /** @brief Open underlying transport and prepare acquisition. */
+    virtual void open() = 0;
+    /** @brief Release all allocated resources. */
+    virtual void close() = 0;
+    /** @brief Acquire one S21 sweep. */
+    [[nodiscard]] virtual auto acquire_s21_sweep() -> SweepTrace = 0;
+};
+
+}  // namespace radar::drivers
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/interfaces/switch_driver.hpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/interfaces/switch_driver.hpp
new file mode 100644
index 0000000..e99ce8e
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/interfaces/switch_driver.hpp
@@ -0,0 +1,24 @@
+#pragma once
+
+#include <cstdint>
+
+namespace radar::drivers {
+
+/**
+ * @brief Unified RF switch interface consumed by the orchestrator.
+ */
+class SwitchDriver {
+  public:
+    virtual ~SwitchDriver() = default;
+
+    /** @brief Open underlying transport and set initial switch state. */
+    virtual void open() = 0;
+    /** @brief Release driver resources. */
+    virtual void close() = 0;
+    /** @brief Number of selectable positions exposed by this switch. */
+    [[nodiscard]] virtual auto position_count() const -> std::uint32_t = 0;
+    /** @brief Switch to a 0-based position. */
+    virtual void switch_to(std::uint32_t position) = 0;
+};
+
+}  // namespace radar::drivers
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_lifecycle.cpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_lifecycle.cpp
new file mode 100644
index 0000000..7e0cabe
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_lifecycle.cpp
@@ -0,0 +1,212 @@
+#include "../librevna_minimal_driver.hpp"
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <exception>
+#include <string>
+#include <string_view>
+#include <stdexcept>
+#include <utility>
+#include <vector>
+
+#include "librevna_protocol_common.hpp"
+
+namespace radar::drivers {
+namespace detail = radar::drivers::librevna::detail;
+namespace {
+
+constexpr std::uint32_t kNativeAcquireMaxAttempts = 3U;
+
+[[nodiscard]] auto is_retryable_native_acquire_error(std::string_view message) -> bool {
+    constexpr std::array<std::string_view, 5> kRetryableSubstrings = {
+        "Timeout waiting for expected LibreVNA packet type",
+        "Timeout waiting for LibreVNA ACK",
+        "LibreVNA returned NACK",
+        "Failed to read LibreVNA USB bulk packet",
+        "Failed to write LibreVNA USB bulk packet",
+    };
+
+    return std::any_of(
+        kRetryableSubstrings.begin(),
+        kRetryableSubstrings.end(),
+        [message](std::string_view needle) {
+            return message.find(needle) != std::string_view::npos;
+        }
+    );
+}
+
+}  // namespace
+
+LibreVnaMinimalDriver::LibreVnaMinimalDriver(LibreVnaMinimalDriverSettings settings) : settings_(std::move(settings)) {}
+
+void LibreVnaMinimalDriver::open() {
+    if (is_open_) {
+        return;
+    }
+    if (settings_.sweep.points == 0U) {
+        throw std::runtime_error("Radar sweep points must be > 0");
+    }
+
+    if (settings_.mode == config::DriverMode::Native) {
+        open_native();
+    }
+
+    is_open_ = true;
+}
+
+void LibreVnaMinimalDriver::close() {
+    if (!is_open_) {
+        return;
+    }
+
+    if (settings_.mode == config::DriverMode::Native) {
+        close_native();
+    }
+
+    is_open_ = false;
+}
+
+auto LibreVnaMinimalDriver::acquire_s21_sweep() -> SweepTrace {
+    if (!is_open_) {
+        throw std::runtime_error("Radar driver is not open");
+    }
+
+    ++sweep_index_;
+    switch (settings_.mode) {
+        case config::DriverMode::Mock:
+            return acquire_mock();
+        case config::DriverMode::Native: {
+            std::exception_ptr last_exception{};
+            std::string last_message{};
+
+            for (std::uint32_t attempt = 1; attempt <= kNativeAcquireMaxAttempts; ++attempt) {
+                try {
+                    return acquire_native();
+                } catch (const std::exception& exception) {
+                    last_exception = std::current_exception();
+                    last_message = exception.what();
+
+                    if (!is_retryable_native_acquire_error(last_message) || attempt == kNativeAcquireMaxAttempts) {
+                        break;
+                    }
+
+                    // Recover from transient USB/protocol stalls by reconnecting the device.
+                    close_native();
+                    open_native();
+                }
+            }
+
+            if (last_exception != nullptr) {
+                std::rethrow_exception(last_exception);
+            }
+            throw std::runtime_error("Native acquisition failed without exception detail");
+        }
+        default:
+            throw std::runtime_error("Unsupported radar driver mode");
+    }
+}
+
+auto LibreVnaMinimalDriver::acquire_mock() -> SweepTrace {
+    SweepTrace trace{};
+    trace.frequency_hz.reserve(settings_.sweep.points);
+    trace.s21.reserve(settings_.sweep.points);
+
+    const auto span_hz = settings_.sweep.stop_hz - settings_.sweep.start_hz;
+    const auto denominator = settings_.sweep.points > 1U ? static_cast<float>(settings_.sweep.points - 1U) : 1.0F;
+
+    for (std::uint32_t point = 0; point < settings_.sweep.points; ++point) {
+        const auto ratio = static_cast<float>(point) / denominator;
+        const auto frequency_hz = settings_.sweep.start_hz + span_hz * ratio;
+        const auto phase = 2.0F * detail::kPi * (frequency_hz / std::max(settings_.mock_signal_hz, 1.0F)) +
+                           static_cast<float>(sweep_index_) * 0.05F;
+        const auto envelope = 0.6F + 0.4F * std::sin(0.5F * phase);
+
+        ipc::Complex32 sample{};
+        sample.re = envelope * std::cos(phase);
+        sample.im = envelope * std::sin(phase);
+
+        trace.frequency_hz.push_back(frequency_hz);
+        trace.s21.push_back(sample);
+    }
+
+    return trace;
+}
+
+auto LibreVnaMinimalDriver::acquire_native() -> SweepTrace {
+    if (usb_handle_ == nullptr) {
+        throw std::runtime_error("LibreVNA native handle is not open");
+    }
+
+    // Drop stale datapoints left in queue by previous operations.
+    while (pop_packet(detail::kPacketVnaDatapoint).has_value()) {
+    }
+
+    send_packet_no_payload(detail::kPacketInitiateSweep, true);
+
+    SweepTrace trace{};
+    trace.frequency_hz.assign(settings_.sweep.points, 0.0F);
+    trace.s21.assign(settings_.sweep.points, ipc::Complex32{});
+
+    std::vector<std::uint8_t> received(settings_.sweep.points, 0U);
+    std::uint32_t received_count = 0;
+
+    const auto ifbw_hz = std::max(settings_.sweep.if_bandwidth_hz, 1.0F);
+    const auto estimated_sweep_ms = static_cast<std::uint64_t>(
+        std::ceil((1'000.0 * static_cast<double>(settings_.sweep.points)) / static_cast<double>(ifbw_hz))
+    );
+    // Keep generous timeout margin on slower hosts.
+    const auto timeout_ms = std::max<std::uint64_t>(20'000ULL, estimated_sweep_ms * 8ULL + 1'000ULL);
+    const auto deadline = std::chrono::steady_clock::now() + std::chrono::milliseconds(timeout_ms);
+
+    while (received_count < settings_.sweep.points) {
+        NativePacket packet{};
+        try {
+            packet = wait_for_packet(detail::kPacketVnaDatapoint, deadline);
+        } catch (const std::exception& exception) {
+            throw std::runtime_error(
+                "Timeout while collecting VNADatapoints (" + std::to_string(received_count) + "/" +
+                std::to_string(settings_.sweep.points) + " points received): " + exception.what()
+            );
+        }
+
+        std::uint32_t point_number = 0;
+        float frequency_hz = 0.0F;
+        ipc::Complex32 s21{};
+
+        if (!decode_vna_datapoint_s21(packet.payload, point_number, frequency_hz, s21)) {
+            throw std::runtime_error("Failed to decode S21 from VNADatapoint packet");
+        }
+        if (point_number >= settings_.sweep.points) {
+            throw std::runtime_error("Received out-of-range VNADatapoint index");
+        }
+
+        if (received[point_number] == 0U) {
+            received[point_number] = 1U;
+            ++received_count;
+        }
+
+        trace.frequency_hz[point_number] = frequency_hz;
+        trace.s21[point_number] = s21;
+    }
+
+    return trace;
+}
+
+void LibreVnaMinimalDriver::validate_device_info_payload(std::span<const std::uint8_t> payload) const {
+    if (payload.size() != detail::kDeviceInfoPayloadSize) {
+        throw std::runtime_error("Unexpected DeviceInfo payload size");
+    }
+
+    const auto protocol = detail::read_u16_le(payload, detail::kDeviceInfoProtocolOffset);
+    if (protocol != detail::kProtocolV14) {
+        throw std::runtime_error("Unsupported LibreVNA protocol version (expected v14)");
+    }
+
+    const auto ports = payload[detail::kDeviceInfoNumPortsOffset];
+    if (ports < 2U) {
+        throw std::runtime_error("LibreVNA reported invalid number of ports");
+    }
+}
+
+}  // namespace radar::drivers
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_protocol.cpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_protocol.cpp
new file mode 100644
index 0000000..793729b
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_protocol.cpp
@@ -0,0 +1,130 @@
+#include "../librevna_minimal_driver.hpp"
+
+#include <array>
+#include <complex>
+#include <cstddef>
+#include <stdexcept>
+#include <vector>
+
+#include "librevna_protocol_common.hpp"
+
+namespace radar::drivers {
+namespace detail = radar::drivers::librevna::detail;
+
+auto LibreVnaMinimalDriver::encode_frame(
+    std::uint8_t packet_type,
+    std::span<const std::uint8_t> payload
+) -> std::vector<std::uint8_t> {
+    const auto length = payload.size() + detail::kFrameOverheadBytes;
+    if (length > 0xFFFFU) {
+        throw std::runtime_error("Protocol frame is too large");
+    }
+
+    std::vector<std::uint8_t> frame(length, 0);
+    frame[0] = detail::kFrameHeader;
+    detail::write_u16_le(frame, 1, static_cast<std::uint16_t>(length));
+    frame[3] = packet_type;
+    std::copy(payload.begin(), payload.end(), frame.begin() + 4);
+
+    std::uint32_t crc = 0;
+    if (packet_type != detail::kPacketVnaDatapoint) {
+        crc = crc32(std::span<const std::uint8_t>(frame.data(), frame.size() - 4U));
+    }
+    detail::write_u32_le(frame, frame.size() - 4U, crc);
+
+    return frame;
+}
+
+auto LibreVnaMinimalDriver::decode_vna_datapoint_s21(
+    std::span<const std::uint8_t> payload,
+    std::uint32_t& point_number_out,
+    float& frequency_out,
+    ipc::Complex32& s21_out
+) -> bool {
+    // VNADatapoint payload layout:
+    // [0..7]=freq_or_time, [8..9]=cdbm, [10..11]=point_number,
+    // followed by N tuples of {real:f32, imag:f32, flags:u8}.
+    if (payload.size() < 12U) {
+        return false;
+    }
+
+    const auto values_block = payload.size() - 12U;
+    if ((values_block % 9U) != 0U) {
+        return false;
+    }
+
+    const auto num_values = values_block / 9U;
+    if (num_values == 0U) {
+        return false;
+    }
+
+    point_number_out = detail::read_u16_le(payload, 10);
+    frequency_out = static_cast<float>(detail::read_u64_le(payload, 0));
+
+    const auto real_offset = 12U;
+    const auto imag_offset = real_offset + (4U * num_values);
+    const auto flags_offset = imag_offset + (4U * num_values);
+
+    std::array<std::complex<float>, 8> ref_by_stage{};
+    std::array<std::complex<float>, 8> measured_by_stage{};
+    std::array<bool, 8> has_ref{};
+    std::array<bool, 8> has_measured{};
+
+    for (std::size_t index = 0; index < num_values; ++index) {
+        const auto flags = payload[flags_offset + index];
+        const auto stage = static_cast<std::size_t>(flags >> 5U);
+        if (stage >= ref_by_stage.size()) {
+            continue;
+        }
+
+        const auto value = std::complex<float>(
+            detail::read_f32_le(payload, real_offset + (4U * index)),
+            detail::read_f32_le(payload, imag_offset + (4U * index))
+        );
+
+        const bool is_reference = (flags & detail::kReferenceFlagMask) != 0U;
+        if ((flags & detail::kPort1Mask) != 0U && is_reference) {
+            ref_by_stage[stage] = value;
+            has_ref[stage] = true;
+        }
+        if ((flags & detail::kPort2Mask) != 0U && !is_reference) {
+            measured_by_stage[stage] = value;
+            has_measured[stage] = true;
+        }
+    }
+
+    // We need one reference sample from port1 and one measured sample from port2.
+    for (std::size_t stage = 0; stage < ref_by_stage.size(); ++stage) {
+        if (!has_ref[stage] || !has_measured[stage]) {
+            continue;
+        }
+        if (std::norm(ref_by_stage[stage]) <= 0.0F) {
+            continue;
+        }
+
+        const auto ratio = measured_by_stage[stage] / ref_by_stage[stage];
+        s21_out.re = ratio.real();
+        s21_out.im = ratio.imag();
+        return true;
+    }
+
+    return false;
+}
+
+auto LibreVnaMinimalDriver::crc32(std::span<const std::uint8_t> data) -> std::uint32_t {
+    std::uint32_t crc = 0xFFFFFFFFU;
+    for (const auto byte : data) {
+        crc ^= static_cast<std::uint32_t>(byte);
+        for (int bit = 0; bit < 8; ++bit) {
+            const auto lsb = crc & 1U;
+            crc >>= 1U;
+            if (lsb != 0U) {
+                crc ^= 0xEDB88320U;
+            }
+        }
+    }
+    return ~crc;
+}
+
+}  // namespace radar::drivers
+
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_transport.cpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_transport.cpp
new file mode 100644
index 0000000..bbfeb98
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_transport.cpp
@@ -0,0 +1,359 @@
+#include "../librevna_minimal_driver.hpp"
+
+#include <algorithm>
+#include <array>
+#include <cerrno>
+#include <cstring>
+#include <fcntl.h>
+#include <stdexcept>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "librevna_protocol_common.hpp"
+
+namespace radar::drivers {
+namespace detail = radar::drivers::librevna::detail;
+namespace {
+
+[[nodiscard]] auto libusb_error_message(const std::string& prefix, int status_code) -> std::string {
+    return prefix + ": " + libusb_error_name(status_code);
+}
+
+[[nodiscard]] auto is_supported_vid_pid(std::uint16_t vendor_id, std::uint16_t product_id) -> bool {
+    return std::any_of(
+        detail::kSupportedUsbIds.begin(),
+        detail::kSupportedUsbIds.end(),
+        [vendor_id, product_id](const auto& pair) {
+            return pair.first == vendor_id && pair.second == product_id;
+        }
+    );
+}
+
+[[nodiscard]] auto parse_serial_string(libusb_device_handle* handle, std::uint8_t serial_index) -> std::string {
+    if (handle == nullptr || serial_index == 0U) {
+        return {};
+    }
+
+    std::array<unsigned char, 256> serial_bytes{};
+    const auto length = libusb_get_string_descriptor_ascii(
+        handle,
+        serial_index,
+        serial_bytes.data(),
+        static_cast<int>(serial_bytes.size())
+    );
+    if (length <= 0) {
+        return {};
+    }
+
+    return std::string(
+        reinterpret_cast<const char*>(serial_bytes.data()),
+        reinterpret_cast<const char*>(serial_bytes.data() + length)
+    );
+}
+
+[[nodiscard]] auto find_matching_device_handle(
+    libusb_context* usb_context,
+    const std::string& expected_serial
+) -> libusb_device_handle* {
+    libusb_device** devices = nullptr;
+    const auto device_count = libusb_get_device_list(usb_context, &devices);
+    if (device_count < 0) {
+        throw std::runtime_error(
+            libusb_error_message("Failed to enumerate USB devices", static_cast<int>(device_count))
+        );
+    }
+
+    libusb_device_handle* selected_handle = nullptr;
+    for (ssize_t index = 0; index < device_count; ++index) {
+        auto* device = devices[index];
+        if (device == nullptr) {
+            continue;
+        }
+
+        libusb_device_descriptor descriptor{};
+        const auto descriptor_status = libusb_get_device_descriptor(device, &descriptor);
+        if (descriptor_status != LIBUSB_SUCCESS) {
+            continue;
+        }
+        if (!is_supported_vid_pid(descriptor.idVendor, descriptor.idProduct)) {
+            continue;
+        }
+
+        libusb_device_handle* candidate_handle = nullptr;
+        if (libusb_open(device, &candidate_handle) != LIBUSB_SUCCESS || candidate_handle == nullptr) {
+            continue;
+        }
+
+        if (!expected_serial.empty()) {
+            const auto serial = parse_serial_string(candidate_handle, descriptor.iSerialNumber);
+            if (serial != expected_serial) {
+                libusb_close(candidate_handle);
+                continue;
+            }
+        }
+
+        selected_handle = candidate_handle;
+        break;
+    }
+
+    libusb_free_device_list(devices, 1);
+    return selected_handle;
+}
+
+}  // namespace
+
+void LibreVnaMinimalDriver::open_native() {
+    if (usb_context_ != nullptr || usb_handle_ != nullptr) {
+        throw std::runtime_error("LibreVNA native state is already initialized");
+    }
+
+    const auto init_status = libusb_init(&usb_context_);
+    if (init_status != LIBUSB_SUCCESS) {
+        usb_context_ = nullptr;
+        throw std::runtime_error(libusb_error_message("Failed to initialize libusb", init_status));
+    }
+
+    try {
+        auto* selected_handle = find_matching_device_handle(usb_context_, settings_.serial);
+
+        if (selected_handle == nullptr) {
+            const auto serial_hint = settings_.serial.empty() ? std::string() : " for serial '" + settings_.serial + "'";
+            throw std::runtime_error("No compatible LibreVNA USB device found" + serial_hint);
+        }
+
+        usb_handle_ = selected_handle;
+
+        const auto auto_detach_status = libusb_set_auto_detach_kernel_driver(usb_handle_, 1);
+        if (auto_detach_status != LIBUSB_SUCCESS && auto_detach_status != LIBUSB_ERROR_NOT_SUPPORTED) {
+            throw std::runtime_error(
+                libusb_error_message("Failed to configure USB auto-detach kernel driver", auto_detach_status)
+            );
+        }
+
+        const auto claim_status = libusb_claim_interface(usb_handle_, detail::kUsbInterface);
+        if (claim_status != LIBUSB_SUCCESS) {
+            throw std::runtime_error(libusb_error_message("Failed to claim LibreVNA USB interface", claim_status));
+        }
+        interface_claimed_ = true;
+
+        rx_buffer_.clear();
+        packet_queue_.clear();
+
+        send_packet_no_payload(detail::kPacketRequestDeviceInfo, true);
+        const auto info_packet = wait_for_packet(
+            detail::kPacketDeviceInfo,
+            std::chrono::steady_clock::now() + std::chrono::seconds(detail::kDeviceInfoTimeoutSeconds)
+        );
+        validate_device_info_payload(info_packet.payload);
+        protocol_version_ = detail::read_u16_le(info_packet.payload, detail::kDeviceInfoProtocolOffset);
+        device_num_ports_ = info_packet.payload[detail::kDeviceInfoNumPortsOffset];
+    } catch (...) {
+        close_native();
+        throw;
+    }
+}
+
+void LibreVnaMinimalDriver::close_native() {
+    rx_buffer_.clear();
+    packet_queue_.clear();
+
+    if (usb_handle_ != nullptr && interface_claimed_) {
+        libusb_release_interface(usb_handle_, detail::kUsbInterface);
+        interface_claimed_ = false;
+    }
+
+    if (usb_handle_ != nullptr) {
+        libusb_close(usb_handle_);
+        usb_handle_ = nullptr;
+    }
+
+    if (usb_context_ != nullptr) {
+        libusb_exit(usb_context_);
+        usb_context_ = nullptr;
+    }
+
+    protocol_version_ = 0;
+    device_num_ports_ = 0;
+}
+
+void LibreVnaMinimalDriver::send_packet(
+    std::uint8_t packet_type,
+    std::span<const std::uint8_t> payload,
+    bool require_ack
+) {
+    if (usb_handle_ == nullptr) {
+        throw std::runtime_error("Cannot send packet: LibreVNA USB handle is not open");
+    }
+
+    auto frame = encode_frame(packet_type, payload);
+
+    int transferred = 0;
+    const auto transfer_status = libusb_bulk_transfer(
+        usb_handle_,
+        detail::kEndpointOut,
+        reinterpret_cast<unsigned char*>(frame.data()),
+        static_cast<int>(frame.size()),
+        &transferred,
+        detail::kUsbWriteTimeoutMs
+    );
+    if (transfer_status != LIBUSB_SUCCESS) {
+        throw std::runtime_error(libusb_error_message("Failed to write LibreVNA USB bulk packet", transfer_status));
+    }
+    if (transferred != static_cast<int>(frame.size())) {
+        throw std::runtime_error("Incomplete LibreVNA USB bulk write");
+    }
+
+    if (require_ack) {
+        wait_for_ack(std::chrono::steady_clock::now() + std::chrono::milliseconds(detail::kAckTimeoutMs));
+    }
+}
+
+void LibreVnaMinimalDriver::send_packet_no_payload(std::uint8_t packet_type, bool require_ack) {
+    send_packet(packet_type, std::span<const std::uint8_t>{}, require_ack);
+}
+
+void LibreVnaMinimalDriver::wait_for_ack(std::chrono::steady_clock::time_point deadline) {
+    while (std::chrono::steady_clock::now() < deadline) {
+        std::optional<NativePacket> ack_or_nack{};
+        for (auto iter = packet_queue_.begin(); iter != packet_queue_.end(); ++iter) {
+            if (iter->packet_type != detail::kPacketAck && iter->packet_type != detail::kPacketNack) {
+                continue;
+            }
+            ack_or_nack = std::move(*iter);
+            packet_queue_.erase(iter);
+            break;
+        }
+
+        if (ack_or_nack.has_value()) {
+            if (ack_or_nack->packet_type == detail::kPacketAck) {
+                return;
+            }
+            throw std::runtime_error("LibreVNA returned NACK");
+        }
+
+        pump_usb(deadline);
+    }
+
+    throw std::runtime_error("Timeout waiting for LibreVNA ACK");
+}
+
+auto LibreVnaMinimalDriver::wait_for_packet(
+    std::uint8_t expected_type,
+    std::chrono::steady_clock::time_point deadline
+) -> NativePacket {
+    while (std::chrono::steady_clock::now() < deadline) {
+        if (auto packet = pop_packet(expected_type); packet.has_value()) {
+            return *packet;
+        }
+        pump_usb(deadline);
+    }
+
+    throw std::runtime_error("Timeout waiting for expected LibreVNA packet type");
+}
+
+void LibreVnaMinimalDriver::pump_usb(std::chrono::steady_clock::time_point deadline) {
+    if (usb_handle_ == nullptr) {
+        throw std::runtime_error("Cannot read packets: LibreVNA USB handle is not open");
+    }
+
+    const auto now = std::chrono::steady_clock::now();
+    if (now >= deadline) {
+        return;
+    }
+
+    const auto remaining_ms = std::chrono::duration_cast<std::chrono::milliseconds>(deadline - now).count();
+    const auto timeout_ms = static_cast<unsigned int>(
+        std::clamp<std::int64_t>(remaining_ms, detail::kUsbReadPollMinTimeoutMs, detail::kUsbReadPollMaxTimeoutMs)
+    );
+
+    std::array<unsigned char, detail::kUsbReadChunkBytes> buffer{};
+    int transferred = 0;
+    const auto transfer_status = libusb_bulk_transfer(
+        usb_handle_,
+        detail::kEndpointIn,
+        buffer.data(),
+        static_cast<int>(buffer.size()),
+        &transferred,
+        timeout_ms
+    );
+
+    if (transfer_status == LIBUSB_ERROR_TIMEOUT) {
+        return;
+    }
+    if (transfer_status != LIBUSB_SUCCESS) {
+        throw std::runtime_error(libusb_error_message("Failed to read LibreVNA USB bulk packet", transfer_status));
+    }
+    if (transferred <= 0) {
+        return;
+    }
+
+    rx_buffer_.insert(rx_buffer_.end(), buffer.begin(), buffer.begin() + transferred);
+    decode_frames_from_buffer();
+}
+
+void LibreVnaMinimalDriver::decode_frames_from_buffer() {
+    while (true) {
+        const auto header_iter = std::find(rx_buffer_.begin(), rx_buffer_.end(), detail::kFrameHeader);
+        if (header_iter == rx_buffer_.end()) {
+            rx_buffer_.clear();
+            return;
+        }
+
+        if (header_iter != rx_buffer_.begin()) {
+            rx_buffer_.erase(rx_buffer_.begin(), header_iter);
+        }
+
+        if (rx_buffer_.size() < 4U) {
+            return;
+        }
+
+        const auto declared_length = detail::read_u16_le(rx_buffer_, 1);
+        if (declared_length < detail::kFrameOverheadBytes || declared_length > detail::kMaxFrameLengthBytes) {
+            rx_buffer_.erase(rx_buffer_.begin());
+            continue;
+        }
+
+        if (rx_buffer_.size() < declared_length) {
+            return;
+        }
+
+        const std::vector<std::uint8_t> frame(rx_buffer_.begin(), rx_buffer_.begin() + declared_length);
+        rx_buffer_.erase(rx_buffer_.begin(), rx_buffer_.begin() + declared_length);
+
+        const auto packet_type = frame[3];
+        const auto received_crc = detail::read_u32_le(frame, frame.size() - 4U);
+
+        if (packet_type == detail::kPacketVnaDatapoint) {
+            if (received_crc != 0U) {
+                throw std::runtime_error("Invalid VNADatapoint CRC (expected zero)");
+            }
+        } else {
+            const auto expected_crc = crc32(std::span<const std::uint8_t>(frame.data(), frame.size() - 4U));
+            if (received_crc != expected_crc) {
+                throw std::runtime_error("Invalid LibreVNA packet CRC");
+            }
+        }
+
+        NativePacket packet{};
+        packet.packet_type = packet_type;
+        packet.payload.assign(frame.begin() + 4, frame.end() - 4);
+        packet_queue_.push_back(std::move(packet));
+    }
+}
+
+auto LibreVnaMinimalDriver::pop_packet(std::uint8_t packet_type) -> std::optional<NativePacket> {
+    for (auto iter = packet_queue_.begin(); iter != packet_queue_.end(); ++iter) {
+        if (iter->packet_type != packet_type) {
+            continue;
+        }
+
+        auto packet = std::move(*iter);
+        packet_queue_.erase(iter);
+        return packet;
+    }
+
+    return std::nullopt;
+}
+
+}  // namespace radar::drivers
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_protocol_common.hpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_protocol_common.hpp
new file mode 100644
index 0000000..477976f
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_protocol_common.hpp
@@ -0,0 +1,112 @@
+#pragma once
+
+#include <array>
+#include <cstddef>
+#include <cstdint>
+#include <cstring>
+#include <span>
+#include <stdexcept>
+#include <utility>
+
+namespace radar::drivers::librevna::detail {
+
+constexpr float kPi = 3.14159265358979323846F;
+
+// Frame format:
+// [0]=header, [1..2]=length_le, [3]=packet_type, [...payload...], [crc32_le]
+constexpr std::uint8_t kFrameHeader = 0x5A;
+constexpr std::size_t kFrameOverheadBytes = 8;
+constexpr std::size_t kMaxFrameLengthBytes = 4096;
+
+constexpr unsigned char kEndpointOut = 0x01;
+constexpr unsigned char kEndpointIn = 0x81;
+constexpr int kUsbInterface = 0;
+
+constexpr std::uint8_t kPacketDeviceInfo = 5;
+constexpr std::uint8_t kPacketAck = 7;
+constexpr std::uint8_t kPacketNack = 10;
+constexpr std::uint8_t kPacketRequestDeviceInfo = 15;
+constexpr std::uint8_t kPacketVnaDatapoint = 27;
+constexpr std::uint8_t kPacketInitiateSweep = 32;
+
+constexpr std::size_t kDeviceInfoPayloadSize = 57;
+constexpr std::size_t kDeviceInfoProtocolOffset = 0;
+constexpr std::size_t kDeviceInfoNumPortsOffset = 54;
+constexpr std::uint16_t kProtocolV14 = 14;
+
+constexpr std::uint8_t kReferenceFlagMask = 0x10;
+constexpr std::uint8_t kPort1Mask = 0x01;
+constexpr std::uint8_t kPort2Mask = 0x02;
+
+constexpr std::array<std::pair<std::uint16_t, std::uint16_t>, 3> kSupportedUsbIds = {
+    std::pair<std::uint16_t, std::uint16_t>{0x0483, 0x564E},
+    std::pair<std::uint16_t, std::uint16_t>{0x0483, 0x4121},
+    std::pair<std::uint16_t, std::uint16_t>{0x1209, 0x4121},
+};
+
+constexpr int kUsbWriteTimeoutMs = 500;
+constexpr int kAckTimeoutMs = 800;
+constexpr int kDeviceInfoTimeoutSeconds = 2;
+constexpr std::size_t kUsbReadChunkBytes = 16 * 1024;
+constexpr int kUsbReadPollMinTimeoutMs = 1;
+constexpr int kUsbReadPollMaxTimeoutMs = 50;
+
+[[nodiscard]] inline auto read_u16_le(std::span<const std::uint8_t> data, std::size_t offset) -> std::uint16_t {
+    if ((offset + sizeof(std::uint16_t)) > data.size()) {
+        throw std::runtime_error("Failed to decode uint16 from payload");
+    }
+    return static_cast<std::uint16_t>(data[offset]) |
+           (static_cast<std::uint16_t>(data[offset + 1]) << 8U);
+}
+
+[[nodiscard]] inline auto read_u32_le(std::span<const std::uint8_t> data, std::size_t offset) -> std::uint32_t {
+    if ((offset + sizeof(std::uint32_t)) > data.size()) {
+        throw std::runtime_error("Failed to decode uint32 from payload");
+    }
+    return static_cast<std::uint32_t>(data[offset]) |
+           (static_cast<std::uint32_t>(data[offset + 1]) << 8U) |
+           (static_cast<std::uint32_t>(data[offset + 2]) << 16U) |
+           (static_cast<std::uint32_t>(data[offset + 3]) << 24U);
+}
+
+[[nodiscard]] inline auto read_u64_le(std::span<const std::uint8_t> data, std::size_t offset) -> std::uint64_t {
+    if ((offset + sizeof(std::uint64_t)) > data.size()) {
+        throw std::runtime_error("Failed to decode uint64 from payload");
+    }
+
+    std::uint64_t value = 0;
+    for (std::size_t index = 0; index < sizeof(std::uint64_t); ++index) {
+        value |= static_cast<std::uint64_t>(data[offset + index]) << (index * 8U);
+    }
+    return value;
+}
+
+[[nodiscard]] inline auto read_f32_le(std::span<const std::uint8_t> data, std::size_t offset) -> float {
+    const auto raw = read_u32_le(data, offset);
+    float out = 0.0F;
+    std::memcpy(&out, &raw, sizeof(float));
+    return out;
+}
+
+inline void write_u16_le(std::span<std::uint8_t> data, std::size_t offset, std::uint16_t value) {
+    if ((offset + sizeof(std::uint16_t)) > data.size()) {
+        throw std::runtime_error("Failed to encode uint16 into frame");
+    }
+
+    data[offset] = static_cast<std::uint8_t>(value & 0xFFU);
+    data[offset + 1] = static_cast<std::uint8_t>((value >> 8U) & 0xFFU);
+}
+
+inline void write_u32_le(std::span<std::uint8_t> data, std::size_t offset, std::uint32_t value) {
+    if ((offset + sizeof(std::uint32_t)) > data.size()) {
+        throw std::runtime_error("Failed to encode uint32 into frame");
+    }
+
+    data[offset] = static_cast<std::uint8_t>(value & 0xFFU);
+    data[offset + 1] = static_cast<std::uint8_t>((value >> 8U) & 0xFFU);
+    data[offset + 2] = static_cast<std::uint8_t>((value >> 16U) & 0xFFU);
+    data[offset + 3] = static_cast<std::uint8_t>((value >> 24U) & 0xFFU);
+}
+
+}  // namespace radar::drivers::librevna::detail
+
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna_minimal_driver.hpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna_minimal_driver.hpp
new file mode 100644
index 0000000..2340f9b
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna_minimal_driver.hpp
@@ -0,0 +1,97 @@
+#pragma once
+
+#include <chrono>
+#include <cstdint>
+#include <deque>
+#include <optional>
+#include <span>
+#include <string>
+#include <vector>
+
+#if __has_include(<libusb-1.0/libusb.h>)
+#include <libusb-1.0/libusb.h>
+#else
+#include <libusb.h>
+#endif
+
+#include "radar_driver.hpp"
+#include "run_config.hpp"
+
+namespace radar::drivers {
+
+/**
+ * @brief Runtime settings for minimal LibreVNA acquisition.
+ */
+struct LibreVnaMinimalDriverSettings {
+    config::DriverMode mode = config::DriverMode::Mock;
+    std::string serial{};
+    config::RadarSweepSettings sweep{};
+    float mock_signal_hz = 5'000'000.0F;
+};
+
+/**
+ * @brief Minimal radar driver that acquires S21 sweeps from LibreVNA.
+ *
+ * This class intentionally keeps scope narrow: open/close transport and
+ * acquire one sweep. Full device configuration is expected to be done by the
+ * Python layer before this process starts.
+ */
+class LibreVnaMinimalDriver final : public RadarDriver {
+  public:
+    explicit LibreVnaMinimalDriver(LibreVnaMinimalDriverSettings settings);
+
+    void open() override;
+    void close() override;
+    [[nodiscard]] auto acquire_s21_sweep() -> SweepTrace override;
+
+  private:
+    /**
+     * @brief Raw protocol packet decoded from framed USB stream.
+     */
+    struct NativePacket {
+        std::uint8_t packet_type = 0;
+        std::vector<std::uint8_t> payload{};
+    };
+
+    [[nodiscard]] auto acquire_mock() -> SweepTrace;
+    [[nodiscard]] auto acquire_native() -> SweepTrace;
+
+    void open_native();
+    void close_native();
+
+    void send_packet(std::uint8_t packet_type, std::span<const std::uint8_t> payload, bool require_ack);
+    void send_packet_no_payload(std::uint8_t packet_type, bool require_ack);
+    void wait_for_ack(std::chrono::steady_clock::time_point deadline);
+    [[nodiscard]] auto wait_for_packet(std::uint8_t expected_type, std::chrono::steady_clock::time_point deadline)
+        -> NativePacket;
+    void pump_usb(std::chrono::steady_clock::time_point deadline);
+    void decode_frames_from_buffer();
+
+    [[nodiscard]] auto pop_packet(std::uint8_t packet_type) -> std::optional<NativePacket>;
+    [[nodiscard]] static auto encode_frame(std::uint8_t packet_type, std::span<const std::uint8_t> payload)
+        -> std::vector<std::uint8_t>;
+    [[nodiscard]] static auto decode_vna_datapoint_s21(
+        std::span<const std::uint8_t> payload,
+        std::uint32_t& point_number_out,
+        float& frequency_out,
+        ipc::Complex32& s21_out
+    ) -> bool;
+    [[nodiscard]] static auto crc32(std::span<const std::uint8_t> data) -> std::uint32_t;
+    void validate_device_info_payload(std::span<const std::uint8_t> payload) const;
+
+    LibreVnaMinimalDriverSettings settings_{};
+    bool is_open_ = false;
+    std::uint64_t sweep_index_ = 0;
+
+    libusb_context* usb_context_ = nullptr;
+    libusb_device_handle* usb_handle_ = nullptr;
+    bool interface_claimed_ = false;
+
+    std::vector<std::uint8_t> rx_buffer_{};
+    std::deque<NativePacket> packet_queue_{};
+
+    std::uint16_t protocol_version_ = 0;
+    std::uint8_t device_num_ports_ = 0;
+};
+
+}  // namespace radar::drivers
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/h7992_minimal_driver.cpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/h7992_minimal_driver.cpp
new file mode 100644
index 0000000..74fe792
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/h7992_minimal_driver.cpp
@@ -0,0 +1,160 @@
+#include "h7992_minimal_driver.hpp"
+
+#include <array>
+#include <cerrno>
+#include <cstdio>
+#include <cstring>
+#include <fcntl.h>
+#include <linux/gpio.h>
+#include <stdexcept>
+#include <sys/ioctl.h>
+#include <unistd.h>
+#include <utility>
+
+namespace radar::drivers {
+namespace {
+
+// Position mapping for H7992 control pins:
+// position -> (A, B)
+constexpr std::array<std::array<std::uint8_t, 2>, 4> kPositionToAB = {
+    std::array<std::uint8_t, 2>{0, 0},
+    std::array<std::uint8_t, 2>{0, 1},
+    std::array<std::uint8_t, 2>{1, 0},
+    std::array<std::uint8_t, 2>{1, 1},
+};
+
+void validate_open_settings(const H7992MinimalDriverSettings& settings) {
+    if (settings.positions == 0U || settings.positions > 4U) {
+        throw std::runtime_error("H7992 switch positions must be in range [1, 4] for " + settings.name);
+    }
+    if (settings.default_position >= settings.positions) {
+        throw std::runtime_error("Switch default_position out of range for " + settings.name);
+    }
+}
+
+[[nodiscard]] auto make_line_values(std::uint8_t pin_a_state, std::uint8_t pin_b_state) -> gpio_v2_line_values {
+    gpio_v2_line_values values{};
+    values.mask = (1ULL << 0U) | (1ULL << 1U);
+    values.bits =
+        (static_cast<std::uint64_t>(pin_a_state) << 0U) | (static_cast<std::uint64_t>(pin_b_state) << 1U);
+    return values;
+}
+
+}  // namespace
+
+H7992MinimalDriver::H7992MinimalDriver(H7992MinimalDriverSettings settings) : settings_(std::move(settings)) {}
+
+void H7992MinimalDriver::open() {
+    if (is_open_) {
+        return;
+    }
+    validate_open_settings(settings_);
+
+    if (settings_.mode == config::DriverMode::Native) {
+        open_native();
+    }
+
+    is_open_ = true;
+    switch_to(settings_.default_position);
+}
+
+void H7992MinimalDriver::close() {
+    if (!is_open_) {
+        return;
+    }
+
+    if (settings_.mode == config::DriverMode::Native) {
+        close_native();
+    }
+
+    is_open_ = false;
+}
+
+auto H7992MinimalDriver::position_count() const -> std::uint32_t {
+    return settings_.positions;
+}
+
+void H7992MinimalDriver::switch_to(std::uint32_t position) {
+    if (!is_open_) {
+        throw std::runtime_error("Switch driver is not open for " + settings_.name);
+    }
+    if (position >= settings_.positions) {
+        throw std::runtime_error("Switch position out of range for " + settings_.name);
+    }
+
+    switch (settings_.mode) {
+        case config::DriverMode::Mock:
+            break;
+        case config::DriverMode::Native:
+            switch_native(position);
+            break;
+        default:
+            throw std::runtime_error("Unsupported switch driver mode for " + settings_.name);
+    }
+
+    current_position_ = position;
+}
+
+void H7992MinimalDriver::open_native() {
+    if (settings_.gpio_chip.empty()) {
+        throw std::runtime_error("gpio_chip is empty for native switch " + settings_.name);
+    }
+    if (settings_.pin_a < 0 || settings_.pin_b < 0 || settings_.pin_a == settings_.pin_b) {
+        throw std::runtime_error("Invalid pin_a/pin_b for native switch " + settings_.name);
+    }
+
+    chip_fd_ = ::open(settings_.gpio_chip.c_str(), O_RDONLY | O_CLOEXEC);
+    if (chip_fd_ < 0) {
+        throw std::runtime_error(
+            "Failed to open gpio chip '" + settings_.gpio_chip + "' for " + settings_.name + ": " + std::strerror(errno)
+        );
+    }
+
+    gpio_v2_line_request request{};
+    // The first requested line maps to logical bit 0, the second to bit 1.
+    request.offsets[0] = static_cast<std::uint32_t>(settings_.pin_a);
+    request.offsets[1] = static_cast<std::uint32_t>(settings_.pin_b);
+    request.num_lines = 2;
+    request.config.flags = GPIO_V2_LINE_FLAG_OUTPUT;
+    std::snprintf(request.consumer, sizeof(request.consumer), "radar_%s", settings_.name.c_str());
+
+    if (::ioctl(chip_fd_, GPIO_V2_GET_LINE_IOCTL, &request) != 0) {
+        const auto error = std::strerror(errno);
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+        throw std::runtime_error("Failed to request GPIO lines for " + settings_.name + ": " + error);
+    }
+
+    line_fd_ = request.fd;
+    if (line_fd_ < 0) {
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+        throw std::runtime_error("GPIO line request returned invalid fd for " + settings_.name);
+    }
+}
+
+void H7992MinimalDriver::close_native() {
+    if (line_fd_ >= 0) {
+        ::close(line_fd_);
+        line_fd_ = -1;
+    }
+    if (chip_fd_ >= 0) {
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+    }
+}
+
+void H7992MinimalDriver::switch_native(std::uint32_t position) {
+    if (line_fd_ < 0) {
+        throw std::runtime_error("Native GPIO line fd is not open for " + settings_.name);
+    }
+
+    const auto [pin_a_state, pin_b_state] = kPositionToAB[position];
+    auto values = make_line_values(pin_a_state, pin_b_state);
+
+    if (::ioctl(line_fd_, GPIO_V2_LINE_SET_VALUES_IOCTL, &values) != 0) {
+        throw std::runtime_error("Failed to switch GPIO state for " + settings_.name + ": " + std::strerror(errno));
+    }
+}
+
+}  // namespace radar::drivers
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/h7992_minimal_driver.hpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/h7992_minimal_driver.hpp
new file mode 100644
index 0000000..d927ebb
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/h7992_minimal_driver.hpp
@@ -0,0 +1,51 @@
+#pragma once
+
+#include <cstdint>
+#include <string>
+
+#include "run_config.hpp"
+#include "switch_driver.hpp"
+
+namespace radar::drivers {
+
+/**
+ * @brief Runtime settings for the H7992 GPIO switch driver.
+ */
+struct H7992MinimalDriverSettings {
+    std::string name{};
+    config::DriverMode mode = config::DriverMode::Native;
+    std::uint32_t positions = 4;
+    std::uint32_t default_position = 0;
+    std::string gpio_chip = "/dev/gpiochip0";
+    std::int32_t pin_a = 17;
+    std::int32_t pin_b = 27;
+};
+
+/**
+ * @brief Minimal H7992 implementation for fast position switching.
+ *
+ * Native mode uses Linux GPIO character-device API (uAPI v2).
+ * Mock mode keeps state in memory without touching hardware.
+ */
+class H7992MinimalDriver final : public SwitchDriver {
+  public:
+    explicit H7992MinimalDriver(H7992MinimalDriverSettings settings);
+
+    void open() override;
+    void close() override;
+    [[nodiscard]] auto position_count() const -> std::uint32_t override;
+    void switch_to(std::uint32_t position) override;
+
+  private:
+    void open_native();
+    void close_native();
+    void switch_native(std::uint32_t position);
+
+    H7992MinimalDriverSettings settings_{};
+    bool is_open_ = false;
+    std::uint32_t current_position_ = 0;
+    int chip_fd_ = -1;
+    int line_fd_ = -1;
+};
+
+}  // namespace radar::drivers
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/hmc349a_minimal_driver.cpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/hmc349a_minimal_driver.cpp
new file mode 100644
index 0000000..774ac82
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/hmc349a_minimal_driver.cpp
@@ -0,0 +1,147 @@
+#include "hmc349a_minimal_driver.hpp"
+
+#include <cerrno>
+#include <cstdio>
+#include <cstring>
+#include <fcntl.h>
+#include <linux/gpio.h>
+#include <stdexcept>
+#include <sys/ioctl.h>
+#include <unistd.h>
+#include <utility>
+
+namespace radar::drivers {
+namespace {
+
+void validate_open_settings(const HMC349AMinimalDriverSettings& settings) {
+    if (settings.positions == 0U || settings.positions > 2U) {
+        throw std::runtime_error("HMC349A switch positions must be in range [1, 2] for " + settings.name);
+    }
+    if (settings.default_position >= settings.positions) {
+        throw std::runtime_error("Switch default_position out of range for " + settings.name);
+    }
+}
+
+}  // namespace
+
+HMC349AMinimalDriver::HMC349AMinimalDriver(HMC349AMinimalDriverSettings settings)
+    : settings_(std::move(settings)) {}
+
+void HMC349AMinimalDriver::open() {
+    if (is_open_) {
+        return;
+    }
+
+    validate_open_settings(settings_);
+
+    if (settings_.mode == config::DriverMode::Native) {
+        open_native();
+    }
+
+    is_open_ = true;
+    switch_to(settings_.default_position);
+}
+
+void HMC349AMinimalDriver::close() {
+    if (!is_open_) {
+        return;
+    }
+
+    if (settings_.mode == config::DriverMode::Native) {
+        close_native();
+    }
+
+    is_open_ = false;
+}
+
+auto HMC349AMinimalDriver::position_count() const -> std::uint32_t {
+    return settings_.positions;
+}
+
+void HMC349AMinimalDriver::switch_to(std::uint32_t position) {
+    if (!is_open_) {
+        throw std::runtime_error("Switch driver is not open for " + settings_.name);
+    }
+    if (position >= settings_.positions) {
+        throw std::runtime_error("Switch position out of range for " + settings_.name);
+    }
+
+    switch (settings_.mode) {
+        case config::DriverMode::Mock:
+            break;
+        case config::DriverMode::Native:
+            switch_native(position);
+            break;
+        default:
+            throw std::runtime_error("Unsupported switch driver mode for " + settings_.name);
+    }
+
+    current_position_ = position;
+}
+
+void HMC349AMinimalDriver::open_native() {
+    if (settings_.gpio_chip.empty()) {
+        throw std::runtime_error("gpio_chip is empty for native switch " + settings_.name);
+    }
+    if (settings_.pin_a < 0) {
+        throw std::runtime_error("pin_a (control) is invalid for native switch " + settings_.name);
+    }
+
+    chip_fd_ = ::open(settings_.gpio_chip.c_str(), O_RDONLY | O_CLOEXEC);
+    if (chip_fd_ < 0) {
+        throw std::runtime_error(
+            "Failed to open gpio chip '" + settings_.gpio_chip + "' for " + settings_.name + ": " + std::strerror(errno)
+        );
+    }
+
+    gpio_v2_line_request request{};
+    // Bit 0 controls pin_a.
+    request.offsets[0] = static_cast<std::uint32_t>(settings_.pin_a);
+    request.num_lines = 1;
+    request.config.flags = GPIO_V2_LINE_FLAG_OUTPUT;
+    std::snprintf(request.consumer, sizeof(request.consumer), "radar_%s", settings_.name.c_str());
+
+    if (::ioctl(chip_fd_, GPIO_V2_GET_LINE_IOCTL, &request) != 0) {
+        const auto error = std::strerror(errno);
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+        throw std::runtime_error("Failed to request GPIO lines for " + settings_.name + ": " + error);
+    }
+
+    line_fd_ = request.fd;
+    if (line_fd_ < 0) {
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+        throw std::runtime_error("GPIO line request returned invalid fd for " + settings_.name);
+    }
+}
+
+void HMC349AMinimalDriver::close_native() {
+    if (line_fd_ >= 0) {
+        ::close(line_fd_);
+        line_fd_ = -1;
+    }
+    if (chip_fd_ >= 0) {
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+    }
+}
+
+void HMC349AMinimalDriver::switch_native(std::uint32_t position) {
+    if (line_fd_ < 0) {
+        throw std::runtime_error("Native GPIO line fd is not open for " + settings_.name);
+    }
+
+    const std::uint8_t requested_state = static_cast<std::uint8_t>(position & 0x01U);
+    const std::uint8_t control_state = settings_.invert_logic ? (requested_state ^ 0x01U) : requested_state;
+
+    gpio_v2_line_values values{};
+    values.mask = (1ULL << 0U);
+    values.bits = static_cast<std::uint64_t>(control_state);
+
+    if (::ioctl(line_fd_, GPIO_V2_LINE_SET_VALUES_IOCTL, &values) != 0) {
+        throw std::runtime_error("Failed to switch GPIO state for " + settings_.name + ": " + std::strerror(errno));
+    }
+}
+
+}  // namespace radar::drivers
diff --git a/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/hmc349a_minimal_driver.hpp b/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/hmc349a_minimal_driver.hpp
new file mode 100644
index 0000000..c70c0c6
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/device_drivers/switches/hmc349a_minimal_driver.hpp
@@ -0,0 +1,52 @@
+#pragma once
+
+#include <cstdint>
+#include <string>
+
+#include "run_config.hpp"
+#include "switch_driver.hpp"
+
+namespace radar::drivers {
+
+/**
+ * @brief Runtime settings for the HMC349A GPIO switch driver.
+ */
+struct HMC349AMinimalDriverSettings {
+    std::string name{};
+    config::DriverMode mode = config::DriverMode::Native;
+    std::uint32_t positions = 2;
+    std::uint32_t default_position = 0;
+    std::string gpio_chip = "/dev/gpiochip0";
+    std::int32_t pin_a = 17;   ///< Control pin.
+    bool invert_logic = false; ///< Invert control logic for position mapping.
+};
+
+/**
+ * @brief Minimal HMC349A implementation for deterministic GPIO control.
+ *
+ * Native mode uses Linux GPIO character-device API (uAPI v2).
+ * Mock mode keeps state in memory without touching hardware.
+ */
+class HMC349AMinimalDriver final : public SwitchDriver {
+  public:
+    explicit HMC349AMinimalDriver(HMC349AMinimalDriverSettings settings);
+
+    void open() override;
+    void close() override;
+    [[nodiscard]] auto position_count() const -> std::uint32_t override;
+    void switch_to(std::uint32_t position) override;
+
+  private:
+    void open_native();
+    void close_native();
+    void switch_native(std::uint32_t position);
+
+    HMC349AMinimalDriverSettings settings_{};
+    bool is_open_ = false;
+    std::uint32_t current_position_ = 0;
+
+    int chip_fd_ = -1;
+    int line_fd_ = -1;
+};
+
+}  // namespace radar::drivers
diff --git a/data_acq_and_processing/sweep_orchestrator/include/sweep_orchestrator.hpp b/data_acq_and_processing/sweep_orchestrator/include/sweep_orchestrator.hpp
new file mode 100644
index 0000000..2f2055d
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/include/sweep_orchestrator.hpp
@@ -0,0 +1,62 @@
+#pragma once
+
+#include <atomic>
+#include <cstdint>
+
+#include "radar_driver.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+#include "sweep_plan.hpp"
+#include "switch_driver.hpp"
+
+namespace radar::acq {
+
+/**
+ * @brief Produces raw sweep collections for configured switch combinations.
+ *
+ * Lifecycle:
+ * 1. Open radar and switch drivers.
+ * 2. Iterate configured combinations, acquire one sweep per combination.
+ * 3. Publish serialized collections into the raw shared-memory ring.
+ */
+class SweepOrchestrator {
+  public:
+    /**
+     * @param config Runtime config loaded from JSON.
+     * @param radar_driver Radar device implementation.
+     * @param input_switch_driver Switch connected to radar input path.
+     * @param output_switch_driver Switch connected to radar output path.
+     * @param raw_ring Shared-memory ring for raw collections.
+     * @param raw_tap_ring Optional tap ring for GUI/debug readers.
+     */
+    SweepOrchestrator(
+        const config::RunConfig& config,
+        drivers::RadarDriver& radar_driver,
+        drivers::SwitchDriver& input_switch_driver,
+        drivers::SwitchDriver& output_switch_driver,
+        ipc::ShmRing& raw_ring,
+        ipc::ShmRing* raw_tap_ring = nullptr
+    );
+
+    /**
+     * @brief Run acquisition loop until stop is requested or single run completes.
+     */
+    void run(const std::atomic<bool>& stop_requested);
+
+  private:
+    /**
+     * @brief Acquire one collection for all combinations in `plan_`.
+     */
+    [[nodiscard]] auto acquire_one_collection(std::uint64_t collection_id, const std::atomic<bool>& stop_requested)
+        -> ipc::RawSweepCollection;
+
+    const config::RunConfig& config_;
+    drivers::RadarDriver& radar_driver_;
+    drivers::SwitchDriver& input_switch_driver_;
+    drivers::SwitchDriver& output_switch_driver_;
+    ipc::ShmRing& raw_ring_;
+    ipc::ShmRing* raw_tap_ring_ = nullptr;
+    SweepPlan plan_{};
+};
+
+}  // namespace radar::acq
diff --git a/data_acq_and_processing/sweep_orchestrator/include/sweep_plan.hpp b/data_acq_and_processing/sweep_orchestrator/include/sweep_plan.hpp
new file mode 100644
index 0000000..246705a
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/include/sweep_plan.hpp
@@ -0,0 +1,22 @@
+#pragma once
+
+#include <vector>
+
+#include "run_config.hpp"
+#include "shared_types.hpp"
+
+namespace radar::acq {
+
+/**
+ * @brief Pre-validated execution order of switch combinations for one collection.
+ */
+struct SweepPlan {
+    std::vector<ipc::ComboKey> ordered_combos{};
+};
+
+/**
+ * @brief Build and validate sweep execution plan from runtime config.
+ */
+[[nodiscard]] auto build_sweep_plan(const config::RunConfig& config) -> SweepPlan;
+
+}  // namespace radar::acq
diff --git a/data_acq_and_processing/sweep_orchestrator/src/main.cpp b/data_acq_and_processing/sweep_orchestrator/src/main.cpp
new file mode 100644
index 0000000..c6d3fcc
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/src/main.cpp
@@ -0,0 +1,135 @@
+#include <atomic>
+#include <csignal>
+#include <exception>
+#include <iostream>
+#include <memory>
+#include <stdexcept>
+#include <string>
+
+#include "h7992_minimal_driver.hpp"
+#include "hmc349a_minimal_driver.hpp"
+#include "librevna_minimal_driver.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+#include "sweep_orchestrator.hpp"
+
+namespace {
+
+constexpr const char* kDefaultConfigPath = "run_config.json";
+std::atomic<bool> g_stop_requested{false};
+
+void signal_handler(int /*signal*/) {
+    g_stop_requested.store(true, std::memory_order_relaxed);
+}
+
+void install_signal_handlers() {
+    std::signal(SIGINT, signal_handler);
+    std::signal(SIGTERM, signal_handler);
+}
+
+[[nodiscard]] auto read_config_path(int argc, char** argv) -> std::string {
+    std::string config_path = kDefaultConfigPath;
+
+    for (int index = 1; index < argc; ++index) {
+        const std::string arg = argv[index];
+        if (arg == "--config" && (index + 1) < argc) {
+            config_path = argv[++index];
+        }
+    }
+
+    return config_path;
+}
+
+[[nodiscard]] auto make_radar_driver(const radar::config::RadarConfig& config) -> radar::drivers::LibreVnaMinimalDriver {
+    return radar::drivers::LibreVnaMinimalDriver({
+        .mode = config.driver_mode,
+        .serial = config.serial,
+        .sweep = config.sweep,
+        .mock_signal_hz = config.mock_signal_hz,
+    });
+}
+
+[[nodiscard]] auto make_h7992_driver(const radar::config::SwitchConfig& config)
+    -> std::unique_ptr<radar::drivers::SwitchDriver> {
+    return std::make_unique<radar::drivers::H7992MinimalDriver>(
+        radar::drivers::H7992MinimalDriverSettings{
+            .name = config.name,
+            .mode = config.driver_mode,
+            .positions = config.positions,
+            .default_position = config.default_position,
+            .gpio_chip = config.gpio_chip,
+            .pin_a = config.pin_a,
+            .pin_b = config.pin_b,
+        }
+    );
+}
+
+[[nodiscard]] auto make_hmc349a_driver(const radar::config::SwitchConfig& config)
+    -> std::unique_ptr<radar::drivers::SwitchDriver> {
+    return std::make_unique<radar::drivers::HMC349AMinimalDriver>(
+        radar::drivers::HMC349AMinimalDriverSettings{
+            .name = config.name,
+            .mode = config.driver_mode,
+            .positions = config.positions,
+            .default_position = config.default_position,
+            .gpio_chip = config.gpio_chip,
+            .pin_a = config.pin_a,
+            .invert_logic = config.invert_logic,
+        }
+    );
+}
+
+[[nodiscard]] auto make_switch_driver(const radar::config::SwitchConfig& config)
+    -> std::unique_ptr<radar::drivers::SwitchDriver> {
+    using radar::config::SwitchDriverKind;
+
+    switch (config.driver_kind) {
+        case SwitchDriverKind::H7992:
+            return make_h7992_driver(config);
+        case SwitchDriverKind::HMC349A:
+            return make_hmc349a_driver(config);
+        default:
+            throw std::runtime_error("Unsupported switch driver kind");
+    }
+}
+
+}  // namespace
+
+int main(int argc, char** argv) {
+    install_signal_handlers();
+
+    try {
+        const auto config_path = read_config_path(argc, argv);
+        const auto config = radar::config::load_run_config(config_path);
+
+        auto raw_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.raw.name,
+            config.rings.raw.capacity,
+            config.rings.raw.slot_size_bytes
+        );
+        auto raw_tap_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.raw_tap.name,
+            config.rings.raw_tap.capacity,
+            config.rings.raw_tap.slot_size_bytes
+        );
+
+        auto radar_driver = make_radar_driver(config.radar);
+
+        auto input_switch = make_switch_driver(config.input_switch);
+        auto output_switch = make_switch_driver(config.output_switch);
+
+        radar::acq::SweepOrchestrator orchestrator(
+            config,
+            radar_driver,
+            *input_switch,
+            *output_switch,
+            raw_ring,
+            &raw_tap_ring
+        );
+        orchestrator.run(g_stop_requested);
+        return 0;
+    } catch (const std::exception& exception) {
+        std::cerr << "sweep_orchestrator error: " << exception.what() << '\n';
+        return 1;
+    }
+}
diff --git a/data_acq_and_processing/sweep_orchestrator/src/sweep_orchestrator.cpp b/data_acq_and_processing/sweep_orchestrator/src/sweep_orchestrator.cpp
new file mode 100644
index 0000000..ac909d0
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/src/sweep_orchestrator.cpp
@@ -0,0 +1,178 @@
+#include "sweep_orchestrator.hpp"
+
+#include <chrono>
+#include <exception>
+#include <stdexcept>
+#include <thread>
+#include <vector>
+
+namespace radar::acq {
+namespace {
+
+[[nodiscard]] inline auto should_stop(const std::atomic<bool>& stop_requested) -> bool {
+    return stop_requested.load(std::memory_order_relaxed);
+}
+
+void sleep_if_needed_ms(std::uint32_t delay_ms) {
+    if (delay_ms == 0U) {
+        return;
+    }
+    std::this_thread::sleep_for(std::chrono::milliseconds(delay_ms));
+}
+
+void validate_sweep(const drivers::SweepTrace& sweep) {
+    if (sweep.frequency_hz.size() != sweep.s21.size()) {
+        throw std::runtime_error("Radar driver returned inconsistent sweep vectors");
+    }
+}
+
+class DriverLifecycleGuard {
+  public:
+    DriverLifecycleGuard(
+        drivers::RadarDriver& radar_driver,
+        drivers::SwitchDriver& input_switch_driver,
+        drivers::SwitchDriver& output_switch_driver
+    )
+        : radar_driver_(radar_driver),
+          input_switch_driver_(input_switch_driver),
+          output_switch_driver_(output_switch_driver) {}
+
+    ~DriverLifecycleGuard() noexcept {
+        if (!active_) {
+            return;
+        }
+
+        // Shutdown should never hide the primary error path.
+        try {
+            output_switch_driver_.close();
+        } catch (...) {
+        }
+        try {
+            input_switch_driver_.close();
+        } catch (...) {
+        }
+        try {
+            radar_driver_.close();
+        } catch (...) {
+        }
+    }
+
+    void open_all() {
+        radar_driver_.open();
+        input_switch_driver_.open();
+        output_switch_driver_.open();
+        active_ = true;
+    }
+
+    void close_all() {
+        if (!active_) {
+            return;
+        }
+        output_switch_driver_.close();
+        input_switch_driver_.close();
+        radar_driver_.close();
+        active_ = false;
+    }
+
+  private:
+    drivers::RadarDriver& radar_driver_;
+    drivers::SwitchDriver& input_switch_driver_;
+    drivers::SwitchDriver& output_switch_driver_;
+    bool active_ = false;
+};
+
+void publish_collection(ipc::ShmRing& raw_ring, ipc::ShmRing* raw_tap_ring, const ipc::RawSweepCollection& collection) {
+    const auto serialized_collection = ipc::serialize_raw_collection(collection);
+    if (!raw_ring.push(serialized_collection)) {
+        throw std::runtime_error("Raw ring slot is too small for serialized collection");
+    }
+    if (raw_tap_ring != nullptr && !raw_tap_ring->push(serialized_collection)) {
+        throw std::runtime_error("Raw tap ring slot is too small for serialized collection");
+    }
+}
+
+}  // namespace
+
+SweepOrchestrator::SweepOrchestrator(
+    const config::RunConfig& config,
+    drivers::RadarDriver& radar_driver,
+    drivers::SwitchDriver& input_switch_driver,
+    drivers::SwitchDriver& output_switch_driver,
+    ipc::ShmRing& raw_ring,
+    ipc::ShmRing* raw_tap_ring
+)
+    : config_(config),
+      radar_driver_(radar_driver),
+      input_switch_driver_(input_switch_driver),
+      output_switch_driver_(output_switch_driver),
+      raw_ring_(raw_ring),
+      raw_tap_ring_(raw_tap_ring),
+      plan_(build_sweep_plan(config)) {}
+
+void SweepOrchestrator::run(const std::atomic<bool>& stop_requested) {
+    DriverLifecycleGuard lifecycle_guard(radar_driver_, input_switch_driver_, output_switch_driver_);
+    // Open devices once and keep them active for the whole acquisition loop.
+    lifecycle_guard.open_all();
+
+    std::uint64_t collection_id = 0;
+    while (!should_stop(stop_requested)) {
+        auto raw_collection = acquire_one_collection(++collection_id, stop_requested);
+        if (raw_collection.traces.empty()) {
+            if (!config_.runtime.continuous) {
+                break;
+            }
+            sleep_if_needed_ms(config_.runtime.idle_sleep_ms);
+            continue;
+        }
+
+        publish_collection(raw_ring_, raw_tap_ring_, raw_collection);
+
+        if (!config_.runtime.continuous) {
+            break;
+        }
+    }
+
+    lifecycle_guard.close_all();
+}
+
+auto SweepOrchestrator::acquire_one_collection(
+    std::uint64_t collection_id,
+    const std::atomic<bool>& stop_requested
+) -> ipc::RawSweepCollection {
+    ipc::RawSweepCollection collection{};
+    collection.collection_id = collection_id;
+    collection.monotonic_ns = ipc::current_monotonic_ns();
+    collection.traces.reserve(plan_.ordered_combos.size());
+    bool interrupted = false;
+
+    for (const auto& combo : plan_.ordered_combos) {
+        if (should_stop(stop_requested)) {
+            interrupted = true;
+            break;
+        }
+
+        // Hardware flow for each combo:
+        // 1) switch output path, 2) switch input path, 3) optional settle.
+        output_switch_driver_.switch_to(combo.output_pos);
+        input_switch_driver_.switch_to(combo.input_pos);
+        sleep_if_needed_ms(config_.runtime.settling_ms);
+
+        auto sweep = radar_driver_.acquire_s21_sweep();
+        validate_sweep(sweep);
+
+        ipc::SweepTraceBlock trace{};
+        trace.combo = combo;
+        trace.frequency_hz = std::move(sweep.frequency_hz);
+        trace.s21 = std::move(sweep.s21);
+        collection.traces.push_back(std::move(trace));
+    }
+
+    if (interrupted) {
+        // Do not emit partial collections when stop was requested mid-cycle.
+        collection.traces.clear();
+    }
+
+    return collection;
+}
+
+}  // namespace radar::acq
diff --git a/data_acq_and_processing/sweep_orchestrator/src/sweep_plan.cpp b/data_acq_and_processing/sweep_orchestrator/src/sweep_plan.cpp
new file mode 100644
index 0000000..1c8fd85
--- /dev/null
+++ b/data_acq_and_processing/sweep_orchestrator/src/sweep_plan.cpp
@@ -0,0 +1,19 @@
+#include "sweep_plan.hpp"
+
+#include <stdexcept>
+
+namespace radar::acq {
+
+auto build_sweep_plan(const config::RunConfig& config) -> SweepPlan {
+    // RunConfig is already validated in common_cpp/config. Keep this function
+    // focused on plan construction only.
+    if (config.run_combos.empty()) {
+        throw std::runtime_error("run.combos must not be empty");
+    }
+
+    SweepPlan plan{};
+    plan.ordered_combos = config.run_combos;
+    return plan;
+}
+
+}  // namespace radar::acq
diff --git a/data_acq_and_processing/third_party/eigen/COPYING.APACHE b/data_acq_and_processing/third_party/eigen/COPYING.APACHE
new file mode 100644
index 0000000..61e948d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/COPYING.APACHE
@@ -0,0 +1,203 @@
+/*
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+*/
\ No newline at end of file
diff --git a/data_acq_and_processing/third_party/eigen/COPYING.BSD b/data_acq_and_processing/third_party/eigen/COPYING.BSD
new file mode 100644
index 0000000..8964ddf
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/COPYING.BSD
@@ -0,0 +1,26 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+*/
diff --git a/data_acq_and_processing/third_party/eigen/COPYING.MINPACK b/data_acq_and_processing/third_party/eigen/COPYING.MINPACK
new file mode 100644
index 0000000..132cc3f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/COPYING.MINPACK
@@ -0,0 +1,51 @@
+Minpack Copyright Notice (1999) University of Chicago.  All rights reserved
+
+Redistribution and use in source and binary forms, with or
+without modification, are permitted provided that the
+following conditions are met:
+
+1. Redistributions of source code must retain the above
+copyright notice, this list of conditions and the following
+disclaimer.
+
+2. Redistributions in binary form must reproduce the above
+copyright notice, this list of conditions and the following
+disclaimer in the documentation and/or other materials
+provided with the distribution.
+
+3. The end-user documentation included with the
+redistribution, if any, must include the following
+acknowledgment:
+
+   "This product includes software developed by the
+   University of Chicago, as Operator of Argonne National
+   Laboratory.
+
+Alternately, this acknowledgment may appear in the software
+itself, if and wherever such third-party acknowledgments
+normally appear.
+
+4. WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
+WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
+UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
+THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
+OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
+OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
+OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
+USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
+THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
+DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
+UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
+BE CORRECTED.
+
+5. LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
+HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
+ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
+INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
+ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
+PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
+SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
+(INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
+EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
+POSSIBILITY OF SUCH LOSS OR DAMAGES.
diff --git a/data_acq_and_processing/third_party/eigen/COPYING.MPL2 b/data_acq_and_processing/third_party/eigen/COPYING.MPL2
new file mode 100644
index 0000000..ee6256c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/COPYING.MPL2
@@ -0,0 +1,373 @@
+Mozilla Public License Version 2.0
+==================================
+
+1. Definitions
+--------------
+
+1.1. "Contributor"
+    means each individual or legal entity that creates, contributes to
+    the creation of, or owns Covered Software.
+
+1.2. "Contributor Version"
+    means the combination of the Contributions of others (if any) used
+    by a Contributor and that particular Contributor's Contribution.
+
+1.3. "Contribution"
+    means Covered Software of a particular Contributor.
+
+1.4. "Covered Software"
+    means Source Code Form to which the initial Contributor has attached
+    the notice in Exhibit A, the Executable Form of such Source Code
+    Form, and Modifications of such Source Code Form, in each case
+    including portions thereof.
+
+1.5. "Incompatible With Secondary Licenses"
+    means
+
+    (a) that the initial Contributor has attached the notice described
+        in Exhibit B to the Covered Software; or
+
+    (b) that the Covered Software was made available under the terms of
+        version 1.1 or earlier of the License, but not also under the
+        terms of a Secondary License.
+
+1.6. "Executable Form"
+    means any form of the work other than Source Code Form.
+
+1.7. "Larger Work"
+    means a work that combines Covered Software with other material, in 
+    a separate file or files, that is not Covered Software.
+
+1.8. "License"
+    means this document.
+
+1.9. "Licensable"
+    means having the right to grant, to the maximum extent possible,
+    whether at the time of the initial grant or subsequently, any and
+    all of the rights conveyed by this License.
+
+1.10. "Modifications"
+    means any of the following:
+
+    (a) any file in Source Code Form that results from an addition to,
+        deletion from, or modification of the contents of Covered
+        Software; or
+
+    (b) any new file in Source Code Form that contains any Covered
+        Software.
+
+1.11. "Patent Claims" of a Contributor
+    means any patent claim(s), including without limitation, method,
+    process, and apparatus claims, in any patent Licensable by such
+    Contributor that would be infringed, but for the grant of the
+    License, by the making, using, selling, offering for sale, having
+    made, import, or transfer of either its Contributions or its
+    Contributor Version.
+
+1.12. "Secondary License"
+    means either the GNU General Public License, Version 2.0, the GNU
+    Lesser General Public License, Version 2.1, the GNU Affero General
+    Public License, Version 3.0, or any later versions of those
+    licenses.
+
+1.13. "Source Code Form"
+    means the form of the work preferred for making modifications.
+
+1.14. "You" (or "Your")
+    means an individual or a legal entity exercising rights under this
+    License. For legal entities, "You" includes any entity that
+    controls, is controlled by, or is under common control with You. For
+    purposes of this definition, "control" means (a) the power, direct
+    or indirect, to cause the direction or management of such entity,
+    whether by contract or otherwise, or (b) ownership of more than
+    fifty percent (50%) of the outstanding shares or beneficial
+    ownership of such entity.
+
+2. License Grants and Conditions
+--------------------------------
+
+2.1. Grants
+
+Each Contributor hereby grants You a world-wide, royalty-free,
+non-exclusive license:
+
+(a) under intellectual property rights (other than patent or trademark)
+    Licensable by such Contributor to use, reproduce, make available,
+    modify, display, perform, distribute, and otherwise exploit its
+    Contributions, either on an unmodified basis, with Modifications, or
+    as part of a Larger Work; and
+
+(b) under Patent Claims of such Contributor to make, use, sell, offer
+    for sale, have made, import, and otherwise transfer either its
+    Contributions or its Contributor Version.
+
+2.2. Effective Date
+
+The licenses granted in Section 2.1 with respect to any Contribution
+become effective for each Contribution on the date the Contributor first
+distributes such Contribution.
+
+2.3. Limitations on Grant Scope
+
+The licenses granted in this Section 2 are the only rights granted under
+this License. No additional rights or licenses will be implied from the
+distribution or licensing of Covered Software under this License.
+Notwithstanding Section 2.1(b) above, no patent license is granted by a
+Contributor:
+
+(a) for any code that a Contributor has removed from Covered Software;
+    or
+
+(b) for infringements caused by: (i) Your and any other third party's
+    modifications of Covered Software, or (ii) the combination of its
+    Contributions with other software (except as part of its Contributor
+    Version); or
+
+(c) under Patent Claims infringed by Covered Software in the absence of
+    its Contributions.
+
+This License does not grant any rights in the trademarks, service marks,
+or logos of any Contributor (except as may be necessary to comply with
+the notice requirements in Section 3.4).
+
+2.4. Subsequent Licenses
+
+No Contributor makes additional grants as a result of Your choice to
+distribute the Covered Software under a subsequent version of this
+License (see Section 10.2) or under the terms of a Secondary License (if
+permitted under the terms of Section 3.3).
+
+2.5. Representation
+
+Each Contributor represents that the Contributor believes its
+Contributions are its original creation(s) or it has sufficient rights
+to grant the rights to its Contributions conveyed by this License.
+
+2.6. Fair Use
+
+This License is not intended to limit any rights You have under
+applicable copyright doctrines of fair use, fair dealing, or other
+equivalents.
+
+2.7. Conditions
+
+Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted
+in Section 2.1.
+
+3. Responsibilities
+-------------------
+
+3.1. Distribution of Source Form
+
+All distribution of Covered Software in Source Code Form, including any
+Modifications that You create or to which You contribute, must be under
+the terms of this License. You must inform recipients that the Source
+Code Form of the Covered Software is governed by the terms of this
+License, and how they can obtain a copy of this License. You may not
+attempt to alter or restrict the recipients' rights in the Source Code
+Form.
+
+3.2. Distribution of Executable Form
+
+If You distribute Covered Software in Executable Form then:
+
+(a) such Covered Software must also be made available in Source Code
+    Form, as described in Section 3.1, and You must inform recipients of
+    the Executable Form how they can obtain a copy of such Source Code
+    Form by reasonable means in a timely manner, at a charge no more
+    than the cost of distribution to the recipient; and
+
+(b) You may distribute such Executable Form under the terms of this
+    License, or sublicense it under different terms, provided that the
+    license for the Executable Form does not attempt to limit or alter
+    the recipients' rights in the Source Code Form under this License.
+
+3.3. Distribution of a Larger Work
+
+You may create and distribute a Larger Work under terms of Your choice,
+provided that You also comply with the requirements of this License for
+the Covered Software. If the Larger Work is a combination of Covered
+Software with a work governed by one or more Secondary Licenses, and the
+Covered Software is not Incompatible With Secondary Licenses, this
+License permits You to additionally distribute such Covered Software
+under the terms of such Secondary License(s), so that the recipient of
+the Larger Work may, at their option, further distribute the Covered
+Software under the terms of either this License or such Secondary
+License(s).
+
+3.4. Notices
+
+You may not remove or alter the substance of any license notices
+(including copyright notices, patent notices, disclaimers of warranty,
+or limitations of liability) contained within the Source Code Form of
+the Covered Software, except that You may alter any license notices to
+the extent required to remedy known factual inaccuracies.
+
+3.5. Application of Additional Terms
+
+You may choose to offer, and to charge a fee for, warranty, support,
+indemnity or liability obligations to one or more recipients of Covered
+Software. However, You may do so only on Your own behalf, and not on
+behalf of any Contributor. You must make it absolutely clear that any
+such warranty, support, indemnity, or liability obligation is offered by
+You alone, and You hereby agree to indemnify every Contributor for any
+liability incurred by such Contributor as a result of warranty, support,
+indemnity or liability terms You offer. You may include additional
+disclaimers of warranty and limitations of liability specific to any
+jurisdiction.
+
+4. Inability to Comply Due to Statute or Regulation
+---------------------------------------------------
+
+If it is impossible for You to comply with any of the terms of this
+License with respect to some or all of the Covered Software due to
+statute, judicial order, or regulation then You must: (a) comply with
+the terms of this License to the maximum extent possible; and (b)
+describe the limitations and the code they affect. Such description must
+be placed in a text file included with all distributions of the Covered
+Software under this License. Except to the extent prohibited by statute
+or regulation, such description must be sufficiently detailed for a
+recipient of ordinary skill to be able to understand it.
+
+5. Termination
+--------------
+
+5.1. The rights granted under this License will terminate automatically
+if You fail to comply with any of its terms. However, if You become
+compliant, then the rights granted under this License from a particular
+Contributor are reinstated (a) provisionally, unless and until such
+Contributor explicitly and finally terminates Your grants, and (b) on an
+ongoing basis, if such Contributor fails to notify You of the
+non-compliance by some reasonable means prior to 60 days after You have
+come back into compliance. Moreover, Your grants from a particular
+Contributor are reinstated on an ongoing basis if such Contributor
+notifies You of the non-compliance by some reasonable means, this is the
+first time You have received notice of non-compliance with this License
+from such Contributor, and You become compliant prior to 30 days after
+Your receipt of the notice.
+
+5.2. If You initiate litigation against any entity by asserting a patent
+infringement claim (excluding declaratory judgment actions,
+counter-claims, and cross-claims) alleging that a Contributor Version
+directly or indirectly infringes any patent, then the rights granted to
+You by any and all Contributors for the Covered Software under Section
+2.1 of this License shall terminate.
+
+5.3. In the event of termination under Sections 5.1 or 5.2 above, all
+end user license agreements (excluding distributors and resellers) which
+have been validly granted by You or Your distributors under this License
+prior to termination shall survive termination.
+
+************************************************************************
+*                                                                      *
+*  6. Disclaimer of Warranty                                           *
+*  -------------------------                                           *
+*                                                                      *
+*  Covered Software is provided under this License on an "as is"       *
+*  basis, without warranty of any kind, either expressed, implied, or  *
+*  statutory, including, without limitation, warranties that the       *
+*  Covered Software is free of defects, merchantable, fit for a        *
+*  particular purpose or non-infringing. The entire risk as to the     *
+*  quality and performance of the Covered Software is with You.        *
+*  Should any Covered Software prove defective in any respect, You     *
+*  (not any Contributor) assume the cost of any necessary servicing,   *
+*  repair, or correction. This disclaimer of warranty constitutes an   *
+*  essential part of this License. No use of any Covered Software is   *
+*  authorized under this License except under this disclaimer.         *
+*                                                                      *
+************************************************************************
+
+************************************************************************
+*                                                                      *
+*  7. Limitation of Liability                                          *
+*  --------------------------                                          *
+*                                                                      *
+*  Under no circumstances and under no legal theory, whether tort      *
+*  (including negligence), contract, or otherwise, shall any           *
+*  Contributor, or anyone who distributes Covered Software as          *
+*  permitted above, be liable to You for any direct, indirect,         *
+*  special, incidental, or consequential damages of any character      *
+*  including, without limitation, damages for lost profits, loss of    *
+*  goodwill, work stoppage, computer failure or malfunction, or any    *
+*  and all other commercial damages or losses, even if such party      *
+*  shall have been informed of the possibility of such damages. This   *
+*  limitation of liability shall not apply to liability for death or   *
+*  personal injury resulting from such party's negligence to the       *
+*  extent applicable law prohibits such limitation. Some               *
+*  jurisdictions do not allow the exclusion or limitation of           *
+*  incidental or consequential damages, so this exclusion and          *
+*  limitation may not apply to You.                                    *
+*                                                                      *
+************************************************************************
+
+8. Litigation
+-------------
+
+Any litigation relating to this License may be brought only in the
+courts of a jurisdiction where the defendant maintains its principal
+place of business and such litigation shall be governed by laws of that
+jurisdiction, without reference to its conflict-of-law provisions.
+Nothing in this Section shall prevent a party's ability to bring
+cross-claims or counter-claims.
+
+9. Miscellaneous
+----------------
+
+This License represents the complete agreement concerning the subject
+matter hereof. If any provision of this License is held to be
+unenforceable, such provision shall be reformed only to the extent
+necessary to make it enforceable. Any law or regulation which provides
+that the language of a contract shall be construed against the drafter
+shall not be used to construe this License against a Contributor.
+
+10. Versions of the License
+---------------------------
+
+10.1. New Versions
+
+Mozilla Foundation is the license steward. Except as provided in Section
+10.3, no one other than the license steward has the right to modify or
+publish new versions of this License. Each version will be given a
+distinguishing version number.
+
+10.2. Effect of New Versions
+
+You may distribute the Covered Software under the terms of the version
+of the License under which You originally received the Covered Software,
+or under the terms of any subsequent version published by the license
+steward.
+
+10.3. Modified Versions
+
+If you create software not governed by this License, and you want to
+create a new license for such software, you may create and use a
+modified version of this License if you rename the license and remove
+any references to the name of the license steward (except to note that
+such modified license differs from this License).
+
+10.4. Distributing Source Code Form that is Incompatible With Secondary
+Licenses
+
+If You choose to distribute Source Code Form that is Incompatible With
+Secondary Licenses under the terms of this version of the License, the
+notice described in Exhibit B of this License must be attached.
+
+Exhibit A - Source Code Form License Notice
+-------------------------------------------
+
+  This Source Code Form is subject to the terms of the Mozilla Public
+  License, v. 2.0. If a copy of the MPL was not distributed with this
+  file, You can obtain one at https://mozilla.org/MPL/2.0/.
+
+If it is not possible or desirable to put the notice in a particular
+file, then You may include the notice in a location (such as a LICENSE
+file in a relevant directory) where a recipient would be likely to look
+for such a notice.
+
+You may add additional accurate notices of copyright ownership.
+
+Exhibit B - "Incompatible With Secondary Licenses" Notice
+---------------------------------------------------------
+
+  This Source Code Form is "Incompatible With Secondary Licenses", as
+  defined by the Mozilla Public License, v. 2.0.
diff --git a/data_acq_and_processing/third_party/eigen/COPYING.README b/data_acq_and_processing/third_party/eigen/COPYING.README
new file mode 100644
index 0000000..52c077f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/COPYING.README
@@ -0,0 +1,11 @@
+Eigen is primarily MPL2 licensed. See COPYING.MPL2 and these links:
+  http://www.mozilla.org/MPL/2.0/
+  http://www.mozilla.org/MPL/2.0/FAQ.html
+
+Some files contain third-party code under BSD, LGPL, Apache, or other
+MPL2-compatible licenses, hence the other COPYING.* files here.
+
+Note that some optional external dependencies (e.g. FFTW, MPFR C++)
+and some bundled benchmark code (bench/btl/) are distributed under
+different licenses, including the GPL. Refer to the individual source
+files and their respective COPYING files for details.
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/AccelerateSupport b/data_acq_and_processing/third_party/eigen/Eigen/AccelerateSupport
new file mode 100644
index 0000000..533be68
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/AccelerateSupport
@@ -0,0 +1,52 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
+#define EIGEN_ACCELERATESUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \ingroup Support_modules
+ * \defgroup AccelerateSupport_Module AccelerateSupport module
+ *
+ * This module provides an interface to the Apple Accelerate library.
+ * It provides the seven following main factorization classes:
+ * - class AccelerateLLT: a Cholesky (LL^T) factorization.
+ * - class AccelerateLDLT: the default LDL^T factorization.
+ * - class AccelerateLDLTUnpivoted: a Cholesky-like LDL^T factorization with only 1x1 pivots and no pivoting
+ * - class AccelerateLDLTSBK: an LDL^T factorization with Supernode Bunch-Kaufman and static pivoting
+ * - class AccelerateLDLTTPP: an LDL^T factorization with full threshold partial pivoting
+ * - class AccelerateQR: a QR factorization
+ * - class AccelerateCholeskyAtA: a QR factorization without storing Q (equivalent to A^TA = R^T R)
+ *
+ * \code
+ * #include <Eigen/AccelerateSupport>
+ * \endcode
+ *
+ * In order to use this module, the Accelerate headers must be accessible from
+ * the include paths, and your binary must be linked to the Accelerate framework.
+ * The Accelerate library is only available on Apple hardware.
+ *
+ * Note that many of the algorithms can be influenced by the UpLo template
+ * argument. All matrices are assumed to be symmetric. For example, the following
+ * creates an LDLT factorization where your matrix is symmetric (implicit) and
+ * uses the lower triangle:
+ *
+ * \code
+ * AccelerateLDLT<SparseMatrix<float>, Lower> ldlt;
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/AccelerateSupport/AccelerateSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_ACCELERATESUPPORT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/Cholesky b/data_acq_and_processing/third_party/eigen/Eigen/Cholesky
new file mode 100644
index 0000000..b05ed82
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/Cholesky
@@ -0,0 +1,43 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CHOLESKY_MODULE_H
+#define EIGEN_CHOLESKY_MODULE_H
+
+#include "Core"
+#include "Jacobi"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Cholesky_Module Cholesky module
+ *
+ *
+ *
+ * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
+ * Those decompositions are also accessible via the following methods:
+ *  - MatrixBase::llt()
+ *  - MatrixBase::ldlt()
+ *  - SelfAdjointView::llt()
+ *  - SelfAdjointView::ldlt()
+ *
+ * \code
+ * #include <Eigen/Cholesky>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Cholesky/LLT.h"
+#include "src/Cholesky/LDLT.h"
+#ifdef EIGEN_USE_LAPACKE
+#include "src/misc/lapacke_helpers.h"
+#include "src/Cholesky/LLT_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_CHOLESKY_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/CholmodSupport b/data_acq_and_processing/third_party/eigen/Eigen/CholmodSupport
new file mode 100644
index 0000000..adc5f8d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/CholmodSupport
@@ -0,0 +1,48 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
+#define EIGEN_CHOLMODSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <cholmod.h>
+
+/** \ingroup Support_modules
+ * \defgroup CholmodSupport_Module CholmodSupport module
+ *
+ * This module provides an interface to the Cholmod library which is part of the <a
+ * href="http://www.suitesparse.com">suitesparse</a> package. It provides the two following main factorization classes:
+ * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
+ * - class CholmodDecomposition: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of
+ * the underlying factorization method (supernodal or simplicial).
+ *
+ * For the sake of completeness, this module also propose the two following classes:
+ * - class CholmodSimplicialLLT
+ * - class CholmodSimplicialLDLT
+ * Note that these classes does not bring any particular advantage compared to the built-in
+ * SimplicialLLT and SimplicialLDLT factorization classes.
+ *
+ * \code
+ * #include <Eigen/CholmodSupport>
+ * \endcode
+ *
+ * In order to use this module, the cholmod headers must be accessible from the include paths, and your binary must be
+ * linked to the cholmod library and its dependencies. The dependencies depend on how cholmod has been compiled. For a
+ * cmake based project, you can use our FindCholmod.cmake module to help you in this task.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/CholmodSupport/CholmodSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_CHOLMODSUPPORT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/Core b/data_acq_and_processing/third_party/eigen/Eigen/Core
new file mode 100644
index 0000000..616b184
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/Core
@@ -0,0 +1,482 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2011 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_MODULE_H
+#define EIGEN_CORE_MODULE_H
+
+// Eigen version information.
+#include "Version"
+
+// first thing Eigen does: stop the compiler from reporting useless warnings.
+#include "src/Core/util/DisableStupidWarnings.h"
+
+// then include this file where all our macros are defined. It's really important to do it first because
+// it's where we do all the compiler/OS/arch detections and define most defaults.
+#include "src/Core/util/Macros.h"
+
+// This detects SSE/AVX/NEON/etc. and configure alignment settings
+#include "src/Core/util/ConfigureVectorization.h"
+
+// We need cuda_runtime.h/hip_runtime.h to ensure that
+// the EIGEN_USING_STD macro works properly on the device side
+#if defined(EIGEN_CUDACC)
+#include <cuda_runtime.h>
+#elif defined(EIGEN_HIPCC)
+#include <hip/hip_runtime.h>
+#endif
+
+#ifdef EIGEN_EXCEPTIONS
+#include <new>
+#endif
+
+// Disable the ipa-cp-clone optimization flag with MinGW 6.x or older (enabled by default with -O3)
+// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
+#if EIGEN_COMP_MINGW && EIGEN_GNUC_STRICT_LESS_THAN(6, 0, 0)
+#pragma GCC optimize("-fno-ipa-cp-clone")
+#endif
+
+// Prevent ICC from specializing std::complex operators that silently fail
+// on device. This allows us to use our own device-compatible specializations
+// instead.
+#if EIGEN_COMP_ICC && defined(EIGEN_GPU_COMPILE_PHASE) && !defined(_OVERRIDE_COMPLEX_SPECIALIZATION_)
+#define _OVERRIDE_COMPLEX_SPECIALIZATION_ 1
+#endif
+#include <complex>
+
+// this include file manages BLAS and MKL related macros
+// and inclusion of their respective header files
+#include "src/Core/util/MKL_support.h"
+#include "src/Core/util/AOCL_Support.h"    // ← ADD THIS
+
+
+#if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
+#define EIGEN_HAS_GPU_FP16
+#endif
+
+#if defined(EIGEN_HAS_CUDA_BF16) || defined(EIGEN_HAS_HIP_BF16)
+#define EIGEN_HAS_GPU_BF16
+#endif
+
+#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
+#define EIGEN_HAS_OPENMP
+#endif
+
+#ifdef EIGEN_HAS_OPENMP
+#include <atomic>
+#include <omp.h>
+#endif
+
+// MSVC for windows mobile does not have the errno.h file
+#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM
+#define EIGEN_HAS_ERRNO
+#endif
+
+#ifdef EIGEN_HAS_ERRNO
+#include <cerrno>
+#endif
+#include <cstddef>
+#include <cstdlib>
+#include <cmath>
+#include <functional>
+#ifndef EIGEN_NO_IO
+#include <sstream>
+#include <iosfwd>
+#endif
+#include <cstring>
+#include <string>
+#include <limits>
+#include <climits>  // for CHAR_BIT
+// for min/max:
+#include <algorithm>
+
+#include <array>
+#include <memory>
+#include <vector>
+
+// for std::is_nothrow_move_assignable
+#include <type_traits>
+
+// for std::this_thread::yield().
+#if !defined(EIGEN_USE_BLAS) && (defined(EIGEN_HAS_OPENMP) || defined(EIGEN_GEMM_THREADPOOL))
+#include <thread>
+#endif
+
+// for __cpp_lib feature test macros
+#if defined(__has_include) && __has_include(<version>)
+#include <version>
+#endif
+
+// for std::bit_cast()
+#if defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L
+#include <bit>
+#endif
+
+// for outputting debug info
+#ifdef EIGEN_DEBUG_ASSIGN
+#include <iostream>
+#endif
+
+// required for __cpuid, needs to be included after cmath
+// also required for _BitScanReverse on Windows on ARM
+#if EIGEN_COMP_MSVC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM64) && !EIGEN_OS_WINCE
+#include <intrin.h>
+#endif
+
+#if defined(EIGEN_USE_SYCL)
+#undef min
+#undef max
+#undef isnan
+#undef isinf
+#undef isfinite
+#include <CL/sycl.hpp>
+#include <map>
+#include <thread>
+#include <utility>
+#ifndef EIGEN_SYCL_LOCAL_THREAD_DIM0
+#define EIGEN_SYCL_LOCAL_THREAD_DIM0 16
+#endif
+#ifndef EIGEN_SYCL_LOCAL_THREAD_DIM1
+#define EIGEN_SYCL_LOCAL_THREAD_DIM1 16
+#endif
+#endif
+
+#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || \
+    defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API ||  \
+    defined EIGEN2_SUPPORT
+// This will generate an error message:
+#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information
+#endif
+
+namespace Eigen {
+
+// we use size_t frequently and we'll never remember to prepend it with std:: every time just to
+// ensure QNX/QCC support
+using std::size_t;
+// gcc 4.6.0 wants std:: for ptrdiff_t
+using std::ptrdiff_t;
+
+}  // namespace Eigen
+
+/** \defgroup Core_Module Core module
+ * This is the main module of Eigen providing dense matrix and vector support
+ * (both fixed and dynamic size) with all the features corresponding to a BLAS library
+ * and much more...
+ *
+ * \code
+ * #include <Eigen/Core>
+ * \endcode
+ */
+
+#ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
+#include "src/misc/lapacke.h"
+#endif
+#endif
+
+// IWYU pragma: begin_exports
+#include "src/Core/util/Constants.h"
+#include "src/Core/util/Meta.h"
+#include "src/Core/util/Assert.h"
+#include "src/Core/util/ForwardDeclarations.h"
+#include "src/Core/util/StaticAssert.h"
+#include "src/Core/util/XprHelper.h"
+#include "src/Core/util/Memory.h"
+#include "src/Core/util/IntegralConstant.h"
+#include "src/Core/util/Serializer.h"
+#include "src/Core/util/SymbolicIndex.h"
+#include "src/Core/util/EmulateArray.h"
+#include "src/Core/util/MoreMeta.h"
+
+#include "src/Core/NumTraits.h"
+#include "src/Core/MathFunctions.h"
+#include "src/Core/RandomImpl.h"
+#include "src/Core/GenericPacketMath.h"
+#include "src/Core/MathFunctionsImpl.h"
+#include "src/Core/arch/Default/ConjHelper.h"
+// Generic half float support
+#include "src/Core/arch/Default/Half.h"
+#include "src/Core/arch/Default/BFloat16.h"
+#include "src/Core/arch/Default/GenericPacketMathFunctionsFwd.h"
+
+#if defined(EIGEN_VECTORIZE_GENERIC) && !defined(EIGEN_DONT_VECTORIZE)
+#include "src/Core/arch/clang/PacketMath.h"
+#include "src/Core/arch/clang/TypeCasting.h"
+#include "src/Core/arch/clang/Complex.h"
+#include "src/Core/arch/clang/Reductions.h"
+#include "src/Core/arch/clang/MathFunctions.h"
+#else
+#if defined EIGEN_VECTORIZE_AVX512
+#include "src/Core/arch/SSE/PacketMath.h"
+#include "src/Core/arch/SSE/Reductions.h"
+#include "src/Core/arch/AVX/PacketMath.h"
+#include "src/Core/arch/AVX/Reductions.h"
+#include "src/Core/arch/AVX512/PacketMath.h"
+#include "src/Core/arch/AVX512/Reductions.h"
+#if defined EIGEN_VECTORIZE_AVX512FP16
+#include "src/Core/arch/AVX512/PacketMathFP16.h"
+#endif
+#include "src/Core/arch/SSE/TypeCasting.h"
+#include "src/Core/arch/AVX/TypeCasting.h"
+#include "src/Core/arch/AVX512/TypeCasting.h"
+#if defined EIGEN_VECTORIZE_AVX512FP16
+#include "src/Core/arch/AVX512/TypeCastingFP16.h"
+#endif
+#include "src/Core/arch/SSE/Complex.h"
+#include "src/Core/arch/AVX/Complex.h"
+#include "src/Core/arch/AVX512/Complex.h"
+#include "src/Core/arch/SSE/MathFunctions.h"
+#include "src/Core/arch/AVX/MathFunctions.h"
+#include "src/Core/arch/AVX512/MathFunctions.h"
+#if defined EIGEN_VECTORIZE_AVX512FP16
+#include "src/Core/arch/AVX512/MathFunctionsFP16.h"
+#endif
+#include "src/Core/arch/AVX512/TrsmKernel.h"
+#elif defined EIGEN_VECTORIZE_AVX
+// Use AVX for floats and doubles, SSE for integers
+#include "src/Core/arch/SSE/PacketMath.h"
+#include "src/Core/arch/SSE/Reductions.h"
+#include "src/Core/arch/SSE/TypeCasting.h"
+#include "src/Core/arch/SSE/Complex.h"
+#include "src/Core/arch/AVX/PacketMath.h"
+#include "src/Core/arch/AVX/Reductions.h"
+#include "src/Core/arch/AVX/TypeCasting.h"
+#include "src/Core/arch/AVX/Complex.h"
+#include "src/Core/arch/SSE/MathFunctions.h"
+#include "src/Core/arch/AVX/MathFunctions.h"
+#elif defined EIGEN_VECTORIZE_SSE
+#include "src/Core/arch/SSE/PacketMath.h"
+#include "src/Core/arch/SSE/Reductions.h"
+#include "src/Core/arch/SSE/TypeCasting.h"
+#include "src/Core/arch/SSE/MathFunctions.h"
+#include "src/Core/arch/SSE/Complex.h"
+#endif
+
+#if defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
+#include "src/Core/arch/AltiVec/PacketMath.h"
+#include "src/Core/arch/AltiVec/TypeCasting.h"
+#include "src/Core/arch/AltiVec/MathFunctions.h"
+#include "src/Core/arch/AltiVec/Complex.h"
+#elif defined EIGEN_VECTORIZE_NEON
+#include "src/Core/arch/NEON/PacketMath.h"
+#include "src/Core/arch/NEON/TypeCasting.h"
+#include "src/Core/arch/NEON/MathFunctions.h"
+#include "src/Core/arch/NEON/Complex.h"
+#elif defined EIGEN_VECTORIZE_LSX
+#include "src/Core/arch/LSX/PacketMath.h"
+#include "src/Core/arch/LSX/TypeCasting.h"
+#include "src/Core/arch/LSX/MathFunctions.h"
+#include "src/Core/arch/LSX/Complex.h"
+#elif defined EIGEN_VECTORIZE_SVE
+#include "src/Core/arch/SVE/PacketMath.h"
+#include "src/Core/arch/SVE/TypeCasting.h"
+#include "src/Core/arch/SVE/MathFunctions.h"
+#elif defined EIGEN_VECTORIZE_RVV10
+#include "src/Core/arch/RVV10/PacketMath.h"
+#include "src/Core/arch/RVV10/PacketMath4.h"
+#include "src/Core/arch/RVV10/PacketMath2.h"
+#include "src/Core/arch/RVV10/TypeCasting.h"
+#include "src/Core/arch/RVV10/MathFunctions.h"
+#if defined EIGEN_VECTORIZE_RVV10FP16
+#include "src/Core/arch/RVV10/PacketMathFP16.h"
+#endif
+#if defined EIGEN_VECTORIZE_RVV10BF16
+#include "src/Core/arch/RVV10/PacketMathBF16.h"
+#endif
+#elif defined EIGEN_VECTORIZE_ZVECTOR
+#include "src/Core/arch/ZVector/PacketMath.h"
+#include "src/Core/arch/ZVector/MathFunctions.h"
+#include "src/Core/arch/ZVector/Complex.h"
+#elif defined EIGEN_VECTORIZE_MSA
+#include "src/Core/arch/MSA/PacketMath.h"
+#include "src/Core/arch/MSA/MathFunctions.h"
+#include "src/Core/arch/MSA/Complex.h"
+#elif defined EIGEN_VECTORIZE_HVX
+#include "src/Core/arch/HVX/PacketMath.h"
+#endif
+
+#if defined EIGEN_VECTORIZE_GPU
+#include "src/Core/arch/GPU/PacketMath.h"
+#include "src/Core/arch/GPU/MathFunctions.h"
+#include "src/Core/arch/GPU/TypeCasting.h"
+#endif
+
+#if defined(EIGEN_USE_SYCL)
+#include "src/Core/arch/SYCL/InteropHeaders.h"
+#if !defined(EIGEN_DONT_VECTORIZE_SYCL)
+#include "src/Core/arch/SYCL/PacketMath.h"
+#include "src/Core/arch/SYCL/MathFunctions.h"
+#include "src/Core/arch/SYCL/TypeCasting.h"
+#endif
+#endif
+
+#endif  // #ifndef EIGEN_VECTORIZE_GENERIC
+
+#include "src/Core/arch/Default/Settings.h"
+// This file provides generic implementations valid for scalar as well
+#include "src/Core/arch/Default/GenericPacketMathFunctions.h"
+
+#include "src/Core/functors/TernaryFunctors.h"
+#include "src/Core/functors/BinaryFunctors.h"
+#include "src/Core/functors/UnaryFunctors.h"
+#include "src/Core/functors/NullaryFunctors.h"
+#include "src/Core/functors/StlFunctors.h"
+#include "src/Core/functors/AssignmentFunctors.h"
+
+// Specialized functors for GPU.
+#ifdef EIGEN_GPUCC
+#include "src/Core/arch/GPU/Complex.h"
+#endif
+
+// Specializations of vectorized activation functions for NEON.
+#ifdef EIGEN_VECTORIZE_NEON
+#include "src/Core/arch/NEON/UnaryFunctors.h"
+#endif
+
+#include "src/Core/util/IndexedViewHelper.h"
+#include "src/Core/util/ReshapedHelper.h"
+#include "src/Core/ArithmeticSequence.h"
+#ifndef EIGEN_NO_IO
+#include "src/Core/IO.h"
+#endif
+#include "src/Core/DenseCoeffsBase.h"
+#include "src/Core/DenseBase.h"
+#include "src/Core/MatrixBase.h"
+#include "src/Core/EigenBase.h"
+
+#include "src/Core/Product.h"
+#include "src/Core/CoreEvaluators.h"
+#include "src/Core/AssignEvaluator.h"
+#include "src/Core/RealView.h"
+#include "src/Core/Assign.h"
+
+#include "src/Core/ArrayBase.h"
+#include "src/Core/util/BlasUtil.h"
+#include "src/Core/DenseStorage.h"
+#include "src/Core/NestByValue.h"
+
+// #include "src/Core/ForceAlignedAccess.h"
+
+#include "src/Core/ReturnByValue.h"
+#include "src/Core/NoAlias.h"
+#include "src/Core/PlainObjectBase.h"
+#include "src/Core/Matrix.h"
+#include "src/Core/Array.h"
+#include "src/Core/Fill.h"
+#include "src/Core/CwiseTernaryOp.h"
+#include "src/Core/CwiseBinaryOp.h"
+#include "src/Core/CwiseUnaryOp.h"
+#include "src/Core/CwiseNullaryOp.h"
+#include "src/Core/CwiseUnaryView.h"
+#include "src/Core/SelfCwiseBinaryOp.h"
+#include "src/Core/InnerProduct.h"
+#include "src/Core/Dot.h"
+#include "src/Core/StableNorm.h"
+#include "src/Core/Stride.h"
+#include "src/Core/MapBase.h"
+#include "src/Core/Map.h"
+#include "src/Core/Ref.h"
+#include "src/Core/Block.h"
+#include "src/Core/VectorBlock.h"
+#include "src/Core/IndexedView.h"
+#include "src/Core/Reshaped.h"
+#include "src/Core/Transpose.h"
+#include "src/Core/DiagonalMatrix.h"
+#include "src/Core/Diagonal.h"
+#include "src/Core/DiagonalProduct.h"
+#include "src/Core/SkewSymmetricMatrix3.h"
+#include "src/Core/Redux.h"
+#include "src/Core/Visitor.h"
+#include "src/Core/FindCoeff.h"
+#include "src/Core/Fuzzy.h"
+#include "src/Core/Swap.h"
+#include "src/Core/CommaInitializer.h"
+#include "src/Core/GeneralProduct.h"
+#include "src/Core/Solve.h"
+#include "src/Core/Inverse.h"
+#include "src/Core/SolverBase.h"
+#include "src/Core/PermutationMatrix.h"
+#include "src/Core/Transpositions.h"
+#include "src/Core/TriangularMatrix.h"
+#include "src/Core/SelfAdjointView.h"
+#include "src/Core/products/GeneralBlockPanelKernel.h"
+#include "src/Core/DeviceWrapper.h"
+#ifdef EIGEN_GEMM_THREADPOOL
+#include "ThreadPool"
+#endif
+#include "src/Core/products/Parallelizer.h"
+#include "src/Core/ProductEvaluators.h"
+#include "src/Core/products/GeneralMatrixVector.h"
+#include "src/Core/products/GeneralMatrixMatrix.h"
+#include "src/Core/SolveTriangular.h"
+#include "src/Core/products/GeneralMatrixMatrixTriangular.h"
+#include "src/Core/products/SelfadjointMatrixVector.h"
+#include "src/Core/products/SelfadjointMatrixMatrix.h"
+#include "src/Core/products/SelfadjointProduct.h"
+#include "src/Core/products/SelfadjointRank2Update.h"
+#include "src/Core/products/TriangularMatrixVector.h"
+#include "src/Core/products/TriangularMatrixMatrix.h"
+#include "src/Core/products/TriangularSolverMatrix.h"
+#include "src/Core/products/TriangularSolverVector.h"
+#include "src/Core/BandMatrix.h"
+#include "src/Core/CoreIterators.h"
+#include "src/Core/ConditionEstimator.h"
+
+#if !defined(EIGEN_VECTORIZE_GENERIC)
+#if defined(EIGEN_VECTORIZE_VSX)
+#include "src/Core/arch/AltiVec/MatrixProduct.h"
+#elif defined EIGEN_VECTORIZE_NEON
+#include "src/Core/arch/NEON/GeneralBlockPanelKernel.h"
+#elif defined EIGEN_VECTORIZE_LSX
+#include "src/Core/arch/LSX/GeneralBlockPanelKernel.h"
+#elif defined EIGEN_VECTORIZE_RVV10
+#include "src/Core/arch/RVV10/GeneralBlockPanelKernel.h"
+#endif
+
+#if defined(EIGEN_VECTORIZE_AVX512)
+#include "src/Core/arch/AVX512/GemmKernel.h"
+#endif
+#endif
+
+#include "src/Core/Select.h"
+#include "src/Core/VectorwiseOp.h"
+#include "src/Core/PartialReduxEvaluator.h"
+#include "src/Core/Random.h"
+#include "src/Core/Replicate.h"
+#include "src/Core/Reverse.h"
+#include "src/Core/ArrayWrapper.h"
+#include "src/Core/StlIterators.h"
+
+#ifdef EIGEN_USE_BLAS
+#include "src/Core/products/GeneralMatrixMatrix_BLAS.h"
+#include "src/Core/products/GeneralMatrixVector_BLAS.h"
+#include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h"
+#include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h"
+#include "src/Core/products/SelfadjointMatrixVector_BLAS.h"
+#include "src/Core/products/TriangularMatrixMatrix_BLAS.h"
+#include "src/Core/products/TriangularMatrixVector_BLAS.h"
+#include "src/Core/products/TriangularSolverMatrix_BLAS.h"
+#endif  // EIGEN_USE_BLAS
+
+#ifdef EIGEN_USE_MKL_VML
+#include "src/Core/Assign_MKL.h"
+#endif
+
+#ifdef EIGEN_USE_AOCL_VML
+#include "src/Core/Assign_AOCL.h"
+#endif
+
+#include "src/Core/GlobalFunctions.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_CORE_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/Dense b/data_acq_and_processing/third_party/eigen/Eigen/Dense
new file mode 100644
index 0000000..5768910
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/Dense
@@ -0,0 +1,7 @@
+#include "Core"
+#include "LU"
+#include "Cholesky"
+#include "QR"
+#include "SVD"
+#include "Geometry"
+#include "Eigenvalues"
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/Eigen b/data_acq_and_processing/third_party/eigen/Eigen/Eigen
new file mode 100644
index 0000000..654c8dc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/Eigen
@@ -0,0 +1,2 @@
+#include "Dense"
+#include "Sparse"
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/Eigenvalues b/data_acq_and_processing/third_party/eigen/Eigen/Eigenvalues
new file mode 100644
index 0000000..8039000
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/Eigenvalues
@@ -0,0 +1,63 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EIGENVALUES_MODULE_H
+#define EIGEN_EIGENVALUES_MODULE_H
+
+#include "Core"
+
+#include "Cholesky"
+#include "Jacobi"
+#include "Householder"
+#include "LU"
+#include "Geometry"
+#include "Sparse"  // Needed by ComplexQZ.
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Eigenvalues_Module Eigenvalues module
+ *
+ *
+ *
+ * This module mainly provides various eigenvalue solvers.
+ * This module also provides some MatrixBase methods, including:
+ *  - MatrixBase::eigenvalues(),
+ *  - MatrixBase::operatorNorm()
+ *
+ * \code
+ * #include <Eigen/Eigenvalues>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Eigenvalues/Tridiagonalization.h"
+#include "src/Eigenvalues/RealSchur.h"
+#include "src/Eigenvalues/EigenSolver.h"
+#include "src/Eigenvalues/SelfAdjointEigenSolver.h"
+#include "src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h"
+#include "src/Eigenvalues/HessenbergDecomposition.h"
+#include "src/Eigenvalues/ComplexSchur.h"
+#include "src/Eigenvalues/ComplexEigenSolver.h"
+#include "src/Eigenvalues/RealQZ.h"
+#include "src/Eigenvalues/ComplexQZ.h"
+#include "src/Eigenvalues/GeneralizedEigenSolver.h"
+#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
+#ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
+#include "src/misc/lapacke.h"
+#endif
+#include "src/Eigenvalues/RealSchur_LAPACKE.h"
+#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
+#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_EIGENVALUES_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/Geometry b/data_acq_and_processing/third_party/eigen/Eigen/Geometry
new file mode 100644
index 0000000..fd81ae2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/Geometry
@@ -0,0 +1,62 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GEOMETRY_MODULE_H
+#define EIGEN_GEOMETRY_MODULE_H
+
+#include "Core"
+
+#include "SVD"
+#include "LU"
+#include <limits>
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Geometry_Module Geometry module
+ *
+ * This module provides support for:
+ *  - fixed-size homogeneous transformations
+ *  - translation, scaling, 2D and 3D rotations
+ *  - \link Quaternion quaternions \endlink
+ *  - cross products (\ref MatrixBase::cross(), \ref MatrixBase::cross3())
+ *  - orthogonal vector generation (MatrixBase::unitOrthogonal)
+ *  - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes \endlink
+ *  - \link AlignedBox axis aligned bounding boxes \endlink
+ *  - \link umeyama() least-square transformation fitting \endlink
+ * \code
+ * #include <Eigen/Geometry>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Geometry/OrthoMethods.h"
+#include "src/Geometry/EulerAngles.h"
+#include "src/Geometry/Homogeneous.h"
+#include "src/Geometry/RotationBase.h"
+#include "src/Geometry/Rotation2D.h"
+#include "src/Geometry/Quaternion.h"
+#include "src/Geometry/AngleAxis.h"
+#include "src/Geometry/Transform.h"
+#include "src/Geometry/Translation.h"
+#include "src/Geometry/Scaling.h"
+#include "src/Geometry/Hyperplane.h"
+#include "src/Geometry/ParametrizedLine.h"
+#include "src/Geometry/AlignedBox.h"
+#include "src/Geometry/Umeyama.h"
+
+#ifndef EIGEN_VECTORIZE_GENERIC
+// TODO(rmlarsen): Make these work with generic vectorization if possible.
+// Use the SSE optimized version whenever possible.
+#if (defined EIGEN_VECTORIZE_SSE) || (defined EIGEN_VECTORIZE_NEON)
+#include "src/Geometry/arch/Geometry_SIMD.h"
+#endif
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_GEOMETRY_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/Householder b/data_acq_and_processing/third_party/eigen/Eigen/Householder
new file mode 100644
index 0000000..5070e07
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/Householder
@@ -0,0 +1,31 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HOUSEHOLDER_MODULE_H
+#define EIGEN_HOUSEHOLDER_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Householder_Module Householder module
+ * This module provides Householder transformations.
+ *
+ * \code
+ * #include <Eigen/Householder>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Householder/Householder.h"
+#include "src/Householder/HouseholderSequence.h"
+#include "src/Householder/BlockHouseholder.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_HOUSEHOLDER_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/IterativeLinearSolvers b/data_acq_and_processing/third_party/eigen/Eigen/IterativeLinearSolvers
new file mode 100644
index 0000000..fe5159e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/IterativeLinearSolvers
@@ -0,0 +1,52 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
+#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
+
+#include "SparseCore"
+#include "OrderingMethods"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/**
+  * \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
+  *
+  * This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a
+  squared matrix, usually very large and sparse.
+  * Those solvers are accessible via the following classes:
+  *  - ConjugateGradient for selfadjoint (hermitian) matrices,
+  *  - LeastSquaresConjugateGradient for rectangular least-square problems,
+  *  - BiCGSTAB for general square matrices.
+  *
+  * These iterative solvers are associated with some preconditioners:
+  *  - IdentityPreconditioner - not really useful
+  *  - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
+  *  - IncompleteLUT - incomplete LU factorization with dual thresholding
+  *
+  * Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport,
+  UmfPackSupport, SuperLUSupport, AccelerateSupport.
+  *
+    \code
+    #include <Eigen/IterativeLinearSolvers>
+    \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/IterativeLinearSolvers/SolveWithGuess.h"
+#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
+#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
+#include "src/IterativeLinearSolvers/ConjugateGradient.h"
+#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
+#include "src/IterativeLinearSolvers/BiCGSTAB.h"
+#include "src/IterativeLinearSolvers/IncompleteLUT.h"
+#include "src/IterativeLinearSolvers/IncompleteCholesky.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/Jacobi b/data_acq_and_processing/third_party/eigen/Eigen/Jacobi
new file mode 100644
index 0000000..31eb36a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/Jacobi
@@ -0,0 +1,33 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_JACOBI_MODULE_H
+#define EIGEN_JACOBI_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Jacobi_Module Jacobi module
+ * This module provides Jacobi and Givens rotations.
+ *
+ * \code
+ * #include <Eigen/Jacobi>
+ * \endcode
+ *
+ * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation:
+ *  - MatrixBase::applyOnTheLeft()
+ *  - MatrixBase::applyOnTheRight().
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Jacobi/Jacobi.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_JACOBI_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/KLUSupport b/data_acq_and_processing/third_party/eigen/Eigen/KLUSupport
new file mode 100644
index 0000000..13959a3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/KLUSupport
@@ -0,0 +1,43 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_KLUSUPPORT_MODULE_H
+#define EIGEN_KLUSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <btf.h>
+#include <klu.h>
+}
+
+/** \ingroup Support_modules
+ * \defgroup KLUSupport_Module KLUSupport module
+ *
+ * This module provides an interface to the KLU library which is part of the <a
+ * href="http://www.suitesparse.com">suitesparse</a> package. It provides the following factorization class:
+ * - class KLU: a sparse LU factorization, well-suited for circuit simulation.
+ *
+ * \code
+ * #include <Eigen/KLUSupport>
+ * \endcode
+ *
+ * In order to use this module, the klu and btf headers must be accessible from the include paths, and your binary must
+ * be linked to the klu library and its dependencies. The dependencies depend on how umfpack has been compiled. For a
+ * cmake based project, you can use our FindKLU.cmake module to help you in this task.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/KLUSupport/KLUSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_KLUSUPPORT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/LU b/data_acq_and_processing/third_party/eigen/Eigen/LU
new file mode 100644
index 0000000..64dcdee
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/LU
@@ -0,0 +1,49 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LU_MODULE_H
+#define EIGEN_LU_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup LU_Module LU module
+ * This module includes %LU decomposition and related notions such as matrix inversion and determinant.
+ * This module defines the following MatrixBase methods:
+ *  - MatrixBase::inverse()
+ *  - MatrixBase::determinant()
+ *
+ * \code
+ * #include <Eigen/LU>
+ * \endcode
+ */
+
+#include "src/misc/Kernel.h"
+#include "src/misc/Image.h"
+
+// IWYU pragma: begin_exports
+#include "src/LU/FullPivLU.h"
+#include "src/LU/PartialPivLU.h"
+#ifdef EIGEN_USE_LAPACKE
+#include "src/misc/lapacke_helpers.h"
+#include "src/LU/PartialPivLU_LAPACKE.h"
+#endif
+#include "src/LU/Determinant.h"
+#include "src/LU/InverseImpl.h"
+
+#ifndef EIGEN_VECTORIZE_GENERIC
+// TODO(rmlarsen): Make these work with generic vectorization if possible.
+#if defined EIGEN_VECTORIZE_SSE || defined EIGEN_VECTORIZE_NEON
+#include "src/LU/arch/InverseSize4.h"
+#endif
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_LU_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/MetisSupport b/data_acq_and_processing/third_party/eigen/Eigen/MetisSupport
new file mode 100644
index 0000000..3636d3a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/MetisSupport
@@ -0,0 +1,35 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_METISSUPPORT_MODULE_H
+#define EIGEN_METISSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <metis.h>
+}
+
+/** \ingroup Support_modules
+ * \defgroup MetisSupport_Module MetisSupport module
+ *
+ * \code
+ * #include <Eigen/MetisSupport>
+ * \endcode
+ * This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis).
+ * It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
+ */
+
+// IWYU pragma: begin_exports
+#include "src/MetisSupport/MetisSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_METISSUPPORT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/OrderingMethods b/data_acq_and_processing/third_party/eigen/Eigen/OrderingMethods
new file mode 100644
index 0000000..0167419
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/OrderingMethods
@@ -0,0 +1,73 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
+#define EIGEN_ORDERINGMETHODS_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/**
+ * \defgroup OrderingMethods_Module OrderingMethods module
+ *
+ * This module is currently for internal use only
+ *
+ * It defines various built-in and external ordering methods for sparse matrices.
+ * They are typically used to reduce the number of elements during
+ * the sparse matrix decomposition (LLT, LU, QR).
+ * Precisely, in a preprocessing step, a permutation matrix P is computed using
+ * those ordering methods and applied to the columns of the matrix.
+ * Using for instance the sparse Cholesky decomposition, it is expected that
+ * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
+ *
+ *
+ * Usage :
+ * \code
+ * #include <Eigen/OrderingMethods>
+ * \endcode
+ *
+ * A simple usage is as a template parameter in the sparse decomposition classes :
+ *
+ * \code
+ * SparseLU<MatrixType, COLAMDOrdering<int> > solver;
+ * \endcode
+ *
+ * \code
+ * SparseQR<MatrixType, COLAMDOrdering<int> > solver;
+ * \endcode
+ *
+ * It is possible as well to call directly a particular ordering method for your own purpose,
+ * \code
+ * AMDOrdering<int> ordering;
+ * PermutationMatrix<Dynamic, Dynamic, int> perm;
+ * SparseMatrix<double> A;
+ * //Fill the matrix ...
+ *
+ * ordering(A, perm); // Call AMD
+ * \endcode
+ *
+ * \note Some of these methods (like AMD or METIS), need the sparsity pattern
+ * of the input matrix to be symmetric. When the matrix is structurally unsymmetric,
+ * Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
+ * If your matrix is already symmetric (at least in structure), you can avoid that
+ * by calling the method with a SelfAdjointView type.
+ *
+ * \code
+ *  // Call the ordering on the pattern of the lower triangular matrix A
+ * ordering(A.selfadjointView<Lower>(), perm);
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/OrderingMethods/Amd.h"
+#include "src/OrderingMethods/Ordering.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_ORDERINGMETHODS_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/PaStiXSupport b/data_acq_and_processing/third_party/eigen/Eigen/PaStiXSupport
new file mode 100644
index 0000000..dd1cfcb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/PaStiXSupport
@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
+#define EIGEN_PASTIXSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <pastix_nompi.h>
+#include <pastix.h>
+}
+
+#ifdef complex
+#undef complex
+#endif
+
+/** \ingroup Support_modules
+ * \defgroup PaStiXSupport_Module PaStiXSupport module
+ *
+ * This module provides an interface to the <a href="http://pastix.gforge.inria.fr/">PaSTiX</a> library.
+ * PaSTiX is a general \b supernodal, \b parallel and \b opensource sparse solver.
+ * It provides the two following main factorization classes:
+ * - class PastixLLT : a supernodal, parallel LLt Cholesky factorization.
+ * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.
+ * - class PastixLU : a supernodal, parallel LU factorization (optimized for a symmetric pattern).
+ *
+ * \code
+ * #include <Eigen/PaStiXSupport>
+ * \endcode
+ *
+ * In order to use this module, the PaSTiX headers must be accessible from the include paths, and your binary must be
+ * linked to the PaSTiX library and its dependencies. This wrapper resuires PaStiX version 5.x compiled without MPI
+ * support. The dependencies depend on how PaSTiX has been compiled. For a cmake based project, you can use our
+ * FindPaSTiX.cmake module to help you in this task.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/PaStiXSupport/PaStiXSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_PASTIXSUPPORT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/PardisoSupport b/data_acq_and_processing/third_party/eigen/Eigen/PardisoSupport
new file mode 100644
index 0000000..4aef5fb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/PardisoSupport
@@ -0,0 +1,38 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
+#define EIGEN_PARDISOSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <mkl_pardiso.h>
+
+/** \ingroup Support_modules
+ * \defgroup PardisoSupport_Module PardisoSupport module
+ *
+ * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers.
+ *
+ * \code
+ * #include <Eigen/PardisoSupport>
+ * \endcode
+ *
+ * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be
+ * linked to the MKL library and its dependencies. See this \ref TopicUsingIntelMKL "page" for more information on
+ * MKL-Eigen integration.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/PardisoSupport/PardisoSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_PARDISOSUPPORT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/QR b/data_acq_and_processing/third_party/eigen/Eigen/QR
new file mode 100644
index 0000000..c38b453
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/QR
@@ -0,0 +1,48 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_QR_MODULE_H
+#define EIGEN_QR_MODULE_H
+
+#include "Core"
+
+#include "Cholesky"
+#include "Jacobi"
+#include "Householder"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup QR_Module QR module
+ *
+ *
+ *
+ * This module provides various QR decompositions
+ * This module also provides some MatrixBase methods, including:
+ *  - MatrixBase::householderQr()
+ *  - MatrixBase::colPivHouseholderQr()
+ *  - MatrixBase::fullPivHouseholderQr()
+ *
+ * \code
+ * #include <Eigen/QR>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/QR/HouseholderQR.h"
+#include "src/QR/FullPivHouseholderQR.h"
+#include "src/QR/ColPivHouseholderQR.h"
+#include "src/QR/CompleteOrthogonalDecomposition.h"
+#ifdef EIGEN_USE_LAPACKE
+#include "src/misc/lapacke_helpers.h"
+#include "src/QR/HouseholderQR_LAPACKE.h"
+#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_QR_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/QtAlignedMalloc b/data_acq_and_processing/third_party/eigen/Eigen/QtAlignedMalloc
new file mode 100644
index 0000000..585f8e8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/QtAlignedMalloc
@@ -0,0 +1,32 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_QTMALLOC_MODULE_H
+#define EIGEN_QTMALLOC_MODULE_H
+
+#include "Core"
+
+#if (!EIGEN_MALLOC_ALREADY_ALIGNED)
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+void *qMalloc(std::size_t size) { return Eigen::internal::aligned_malloc(size); }
+
+void qFree(void *ptr) { Eigen::internal::aligned_free(ptr); }
+
+void *qRealloc(void *ptr, std::size_t size) {
+  void *newPtr = Eigen::internal::aligned_malloc(size);
+  std::memcpy(newPtr, ptr, size);
+  Eigen::internal::aligned_free(ptr);
+  return newPtr;
+}
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif
+
+#endif  // EIGEN_QTMALLOC_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/SPQRSupport b/data_acq_and_processing/third_party/eigen/Eigen/SPQRSupport
new file mode 100644
index 0000000..c01dbe0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/SPQRSupport
@@ -0,0 +1,41 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPQRSUPPORT_MODULE_H
+#define EIGEN_SPQRSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include "SuiteSparseQR.hpp"
+
+/** \ingroup Support_modules
+ * \defgroup SPQRSupport_Module SuiteSparseQR module
+ *
+ * This module provides an interface to the SPQR library, which is part of the <a
+ * href="http://www.suitesparse.com">suitesparse</a> package.
+ *
+ * \code
+ * #include <Eigen/SPQRSupport>
+ * \endcode
+ *
+ * In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be
+ * linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...). For a cmake based project, you can use
+ * our FindSPQR.cmake and FindCholmod.Cmake modules
+ *
+ */
+
+#include "CholmodSupport"
+
+// IWYU pragma: begin_exports
+#include "src/SPQRSupport/SuiteSparseQRSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/SVD b/data_acq_and_processing/third_party/eigen/Eigen/SVD
new file mode 100644
index 0000000..b616a6a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/SVD
@@ -0,0 +1,55 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SVD_MODULE_H
+#define EIGEN_SVD_MODULE_H
+
+#include "QR"
+#include "Householder"
+#include "Jacobi"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup SVD_Module SVD module
+ *
+ *
+ *
+ * This module provides SVD decomposition for matrices (both real and complex).
+ * Two decomposition algorithms are provided:
+ *  - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very
+ * slow for larger ones.
+ *  - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast
+ * for large problems. These decompositions are accessible via the respective classes and following MatrixBase methods:
+ *  - MatrixBase::jacobiSvd()
+ *  - MatrixBase::bdcSvd()
+ *
+ * \code
+ * #include <Eigen/SVD>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SVD/UpperBidiagonalization.h"
+#include "src/SVD/SVDBase.h"
+#include "src/SVD/JacobiSVD.h"
+#include "src/SVD/BDCSVD.h"
+#ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
+#include "src/misc/lapacke.h"
+#endif
+#ifndef EIGEN_USE_LAPACKE_STRICT
+#include "src/SVD/JacobiSVD_LAPACKE.h"
+#endif
+#include "src/SVD/BDCSVD_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SVD_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/Sparse b/data_acq_and_processing/third_party/eigen/Eigen/Sparse
new file mode 100644
index 0000000..4d0ee8b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/Sparse
@@ -0,0 +1,33 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_MODULE_H
+#define EIGEN_SPARSE_MODULE_H
+
+/** \defgroup Sparse_Module Sparse meta-module
+  *
+  * Meta-module including all related modules:
+  * - \ref SparseCore_Module
+  * - \ref OrderingMethods_Module
+  * - \ref SparseCholesky_Module
+  * - \ref SparseLU_Module
+  * - \ref SparseQR_Module
+  * - \ref IterativeLinearSolvers_Module
+  *
+    \code
+    #include <Eigen/Sparse>
+    \endcode
+  */
+
+#include "SparseCore"
+#include "OrderingMethods"
+#include "SparseCholesky"
+#include "SparseLU"
+#include "SparseQR"
+#include "IterativeLinearSolvers"
+
+#endif  // EIGEN_SPARSE_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/SparseCholesky b/data_acq_and_processing/third_party/eigen/Eigen/SparseCholesky
new file mode 100644
index 0000000..6abdcd6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/SparseCholesky
@@ -0,0 +1,40 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
+#define EIGEN_SPARSECHOLESKY_MODULE_H
+
+#include "SparseCore"
+#include "OrderingMethods"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/**
+ * \defgroup SparseCholesky_Module SparseCholesky module
+ *
+ * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian)
+ * matrices. Those decompositions are accessible via the following classes:
+ *  - SimplicialLLt,
+ *  - SimplicialLDLt
+ *
+ * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module.
+ *
+ * \code
+ * #include <Eigen/SparseCholesky>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SparseCholesky/SimplicialCholesky.h"
+#include "src/SparseCholesky/SimplicialCholesky_impl.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SPARSECHOLESKY_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/SparseCore b/data_acq_and_processing/third_party/eigen/Eigen/SparseCore
new file mode 100644
index 0000000..56a9401
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/SparseCore
@@ -0,0 +1,70 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSECORE_MODULE_H
+#define EIGEN_SPARSECORE_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <vector>
+#include <map>
+#include <cstdlib>
+#include <cstring>
+#include <algorithm>
+#include <numeric>
+
+/**
+ * \defgroup SparseCore_Module SparseCore module
+ *
+ * This module provides a sparse matrix representation, and basic associated matrix manipulations
+ * and operations.
+ *
+ * See the \ref TutorialSparse "Sparse tutorial"
+ *
+ * \code
+ * #include <Eigen/SparseCore>
+ * \endcode
+ *
+ * This module depends on: Core.
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SparseCore/SparseUtil.h"
+#include "src/SparseCore/SparseMatrixBase.h"
+#include "src/SparseCore/SparseAssign.h"
+#include "src/SparseCore/CompressedStorage.h"
+#include "src/SparseCore/AmbiVector.h"
+#include "src/SparseCore/SparseCompressedBase.h"
+#include "src/SparseCore/SparseMatrix.h"
+#include "src/SparseCore/SparseMap.h"
+#include "src/SparseCore/SparseVector.h"
+#include "src/SparseCore/SparseRef.h"
+#include "src/SparseCore/SparseCwiseUnaryOp.h"
+#include "src/SparseCore/SparseCwiseBinaryOp.h"
+#include "src/SparseCore/SparseTranspose.h"
+#include "src/SparseCore/SparseBlock.h"
+#include "src/SparseCore/SparseDot.h"
+#include "src/SparseCore/SparseRedux.h"
+#include "src/SparseCore/SparseView.h"
+#include "src/SparseCore/SparseDiagonalProduct.h"
+#include "src/SparseCore/ConservativeSparseSparseProduct.h"
+#include "src/SparseCore/SparseSparseProductWithPruning.h"
+#include "src/SparseCore/SparseProduct.h"
+#include "src/SparseCore/SparseDenseProduct.h"
+#include "src/SparseCore/SparseSelfAdjointView.h"
+#include "src/SparseCore/SparseTriangularView.h"
+#include "src/SparseCore/TriangularSolver.h"
+#include "src/SparseCore/SparsePermutation.h"
+#include "src/SparseCore/SparseFuzzy.h"
+#include "src/SparseCore/SparseSolverBase.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SPARSECORE_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/SparseLU b/data_acq_and_processing/third_party/eigen/Eigen/SparseLU
new file mode 100644
index 0000000..6faf130
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/SparseLU
@@ -0,0 +1,50 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSELU_MODULE_H
+#define EIGEN_SPARSELU_MODULE_H
+
+#include "SparseCore"
+
+/**
+ * \defgroup SparseLU_Module SparseLU module
+ * This module defines a supernodal factorization of general sparse matrices.
+ * The code is fully optimized for supernode-panel updates with specialized kernels.
+ * Please, see the documentation of the SparseLU class for more details.
+ */
+
+// Ordering interface
+#include "OrderingMethods"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+#include "src/SparseLU/SparseLU_Structs.h"
+#include "src/SparseLU/SparseLU_SupernodalMatrix.h"
+#include "src/SparseLU/SparseLUImpl.h"
+#include "src/SparseCore/SparseColEtree.h"
+#include "src/SparseLU/SparseLU_Memory.h"
+#include "src/SparseLU/SparseLU_heap_relax_snode.h"
+#include "src/SparseLU/SparseLU_relax_snode.h"
+#include "src/SparseLU/SparseLU_pivotL.h"
+#include "src/SparseLU/SparseLU_panel_dfs.h"
+#include "src/SparseLU/SparseLU_kernel_bmod.h"
+#include "src/SparseLU/SparseLU_panel_bmod.h"
+#include "src/SparseLU/SparseLU_column_dfs.h"
+#include "src/SparseLU/SparseLU_column_bmod.h"
+#include "src/SparseLU/SparseLU_copy_to_ucol.h"
+#include "src/SparseLU/SparseLU_pruneL.h"
+#include "src/SparseLU/SparseLU_Utils.h"
+#include "src/SparseLU/SparseLU.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SPARSELU_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/SparseQR b/data_acq_and_processing/third_party/eigen/Eigen/SparseQR
new file mode 100644
index 0000000..b4f1cad
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/SparseQR
@@ -0,0 +1,38 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEQR_MODULE_H
+#define EIGEN_SPARSEQR_MODULE_H
+
+#include "SparseCore"
+#include "OrderingMethods"
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup SparseQR_Module SparseQR module
+ * \brief Provides QR decomposition for sparse matrices
+ *
+ * This module provides a simplicial version of the left-looking Sparse QR decomposition.
+ * The columns of the input matrix should be reordered to limit the fill-in during the
+ * decomposition. Built-in methods (COLAMD, AMD) or external  methods (METIS) can be used to this end.
+ * See the \link OrderingMethods_Module OrderingMethods\endlink module for the list
+ * of built-in and external ordering methods.
+ *
+ * \code
+ * #include <Eigen/SparseQR>
+ * \endcode
+ *
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SparseCore/SparseColEtree.h"
+#include "src/SparseQR/SparseQR.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/StdDeque b/data_acq_and_processing/third_party/eigen/Eigen/StdDeque
new file mode 100644
index 0000000..01e1d76
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/StdDeque
@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDDEQUE_MODULE_H
+#define EIGEN_STDDEQUE_MODULE_H
+
+#include "Core"
+#include <deque>
+
+#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
+    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+
+#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)
+
+#else
+
+// IWYU pragma: begin_exports
+#include "src/StlSupport/StdDeque.h"
+// IWYU pragma: end_exports
+
+#endif
+
+#endif  // EIGEN_STDDEQUE_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/StdList b/data_acq_and_processing/third_party/eigen/Eigen/StdList
new file mode 100644
index 0000000..1453c9f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/StdList
@@ -0,0 +1,29 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDLIST_MODULE_H
+#define EIGEN_STDLIST_MODULE_H
+
+#include "Core"
+#include <list>
+
+#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
+    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+
+#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)
+
+#else
+
+// IWYU pragma: begin_exports
+#include "src/StlSupport/StdList.h"
+// IWYU pragma: end_exports
+
+#endif
+
+#endif  // EIGEN_STDLIST_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/StdVector b/data_acq_and_processing/third_party/eigen/Eigen/StdVector
new file mode 100644
index 0000000..711a654
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/StdVector
@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDVECTOR_MODULE_H
+#define EIGEN_STDVECTOR_MODULE_H
+
+#include "Core"
+#include <vector>
+
+#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
+    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+
+#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)
+
+#else
+
+// IWYU pragma: begin_exports
+#include "src/StlSupport/StdVector.h"
+// IWYU pragma: end_exports
+
+#endif
+
+#endif  // EIGEN_STDVECTOR_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/SuperLUSupport b/data_acq_and_processing/third_party/eigen/Eigen/SuperLUSupport
new file mode 100644
index 0000000..79e2222
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/SuperLUSupport
@@ -0,0 +1,70 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
+#define EIGEN_SUPERLUSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#ifdef EMPTY
+#define EIGEN_EMPTY_WAS_ALREADY_DEFINED
+#endif
+
+typedef int int_t;
+#include <slu_Cnames.h>
+#include <supermatrix.h>
+#include <slu_util.h>
+
+// slu_util.h defines a preprocessor token named EMPTY which is really polluting,
+// so we remove it in favor of a SUPERLU_EMPTY token.
+// If EMPTY was already defined then we don't undef it.
+
+#if defined(EIGEN_EMPTY_WAS_ALREADY_DEFINED)
+#undef EIGEN_EMPTY_WAS_ALREADY_DEFINED
+#elif defined(EMPTY)
+#undef EMPTY
+#endif
+
+#define SUPERLU_EMPTY (-1)
+
+namespace Eigen {
+struct SluMatrix;
+}
+
+/** \ingroup Support_modules
+ * \defgroup SuperLUSupport_Module SuperLUSupport module
+ *
+ * This module provides an interface to the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library.
+ * It provides the following factorization class:
+ * - class SuperLU: a supernodal sequential LU factorization.
+ * - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative
+ * methods).
+ *
+ * \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
+ *
+ * \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined
+ * because it is too polluting.
+ *
+ * \code
+ * #include <Eigen/SuperLUSupport>
+ * \endcode
+ *
+ * In order to use this module, the superlu headers must be accessible from the include paths, and your binary must be
+ * linked to the superlu library and its dependencies. The dependencies depend on how superlu has been compiled. For a
+ * cmake based project, you can use our FindSuperLU.cmake module to help you in this task.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SuperLUSupport/SuperLUSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SUPERLUSUPPORT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/ThreadPool b/data_acq_and_processing/third_party/eigen/Eigen/ThreadPool
new file mode 100644
index 0000000..39e5d1e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/ThreadPool
@@ -0,0 +1,80 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_THREADPOOL_MODULE_H
+#define EIGEN_THREADPOOL_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup ThreadPool_Module ThreadPool Module
+ *
+ * This module provides 2 threadpool implementations
+ *  - a simple reference implementation
+ *  - a faster non blocking implementation
+ *
+ * \code
+ * #include <Eigen/ThreadPool>
+ * \endcode
+ */
+
+#include <cstddef>
+#include <cstring>
+#include <time.h>
+
+#include <vector>
+#include <atomic>
+#include <condition_variable>
+#include <deque>
+#include <mutex>
+#include <thread>
+#include <functional>
+#include <memory>
+#include <utility>
+
+// There are non-parenthesized calls to "max" in the  <unordered_map> header,
+// which trigger a check in test/main.h causing compilation to fail.
+// We work around the check here by removing the check for max in
+// the case where we have to emulate thread_local.
+#ifdef max
+#undef max
+#endif
+#include <unordered_map>
+
+#include "src/Core/util/Meta.h"
+#include "src/Core/util/MaxSizeVector.h"
+
+#ifndef EIGEN_MUTEX
+#define EIGEN_MUTEX std::mutex
+#endif
+#ifndef EIGEN_MUTEX_LOCK
+#define EIGEN_MUTEX_LOCK std::unique_lock<std::mutex>
+#endif
+#ifndef EIGEN_CONDVAR
+#define EIGEN_CONDVAR std::condition_variable
+#endif
+
+// IWYU pragma: begin_exports
+#include "src/ThreadPool/ThreadLocal.h"
+#include "src/ThreadPool/ThreadYield.h"
+#include "src/ThreadPool/ThreadCancel.h"
+#include "src/ThreadPool/EventCount.h"
+#include "src/ThreadPool/RunQueue.h"
+#include "src/ThreadPool/ThreadPoolInterface.h"
+#include "src/ThreadPool/ThreadEnvironment.h"
+#include "src/ThreadPool/Barrier.h"
+#include "src/ThreadPool/NonBlockingThreadPool.h"
+#include "src/ThreadPool/CoreThreadPoolDevice.h"
+#include "src/ThreadPool/ForkJoin.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_CXX11_THREADPOOL_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/UmfPackSupport b/data_acq_and_processing/third_party/eigen/Eigen/UmfPackSupport
new file mode 100644
index 0000000..126344c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/UmfPackSupport
@@ -0,0 +1,42 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
+#define EIGEN_UMFPACKSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <umfpack.h>
+}
+
+/** \ingroup Support_modules
+ * \defgroup UmfPackSupport_Module UmfPackSupport module
+ *
+ * This module provides an interface to the UmfPack library which is part of the <a
+ * href="http://www.suitesparse.com">suitesparse</a> package. It provides the following factorization class:
+ * - class UmfPackLU: a multifrontal sequential LU factorization.
+ *
+ * \code
+ * #include <Eigen/UmfPackSupport>
+ * \endcode
+ *
+ * In order to use this module, the umfpack headers must be accessible from the include paths, and your binary must be
+ * linked to the umfpack library and its dependencies. The dependencies depend on how umfpack has been compiled. For a
+ * cmake based project, you can use our FindUmfPack.cmake module to help you in this task.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/UmfPackSupport/UmfPackSupport.h"
+// IWYU pragma: endexports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_UMFPACKSUPPORT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/Version b/data_acq_and_processing/third_party/eigen/Eigen/Version
new file mode 100644
index 0000000..164c87f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/Version
@@ -0,0 +1,14 @@
+#ifndef EIGEN_VERSION_H
+#define EIGEN_VERSION_H
+
+// The "WORLD" version will forever remain "3" for the "Eigen3" library.
+#define EIGEN_WORLD_VERSION 3
+// As of Eigen3 5.0.0, we have moved to Semantic Versioning (semver.org).
+#define EIGEN_MAJOR_VERSION 5
+#define EIGEN_MINOR_VERSION 0
+#define EIGEN_PATCH_VERSION 1
+#define EIGEN_PRERELEASE_VERSION "dev"
+#define EIGEN_BUILD_VERSION "master"
+#define EIGEN_VERSION_STRING "5.0.1-dev+master"
+
+#endif  // EIGEN_VERSION_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/AccelerateSupport/AccelerateSupport.h b/data_acq_and_processing/third_party/eigen/Eigen/src/AccelerateSupport/AccelerateSupport.h
new file mode 100644
index 0000000..13a26df
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/AccelerateSupport/AccelerateSupport.h
@@ -0,0 +1,423 @@
+#ifndef EIGEN_ACCELERATESUPPORT_H
+#define EIGEN_ACCELERATESUPPORT_H
+
+#include <Accelerate/Accelerate.h>
+
+#include <Eigen/Sparse>
+
+namespace Eigen {
+
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+class AccelerateImpl;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateLLT
+ * \brief A direct Cholesky (LLT) factorization and solver based on Accelerate
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateLLT
+ */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationCholesky, true>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateLDLT
+ * \brief The default Cholesky (LDLT) factorization and solver based on Accelerate
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLT
+ */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLT, true>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateLDLTUnpivoted
+ * \brief A direct Cholesky-like LDL^T factorization and solver based on Accelerate with only 1x1 pivots and no pivoting
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTUnpivoted
+ */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLTUnpivoted = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTUnpivoted, true>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateLDLTSBK
+ * \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with Supernode Bunch-Kaufman and static
+ * pivoting
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTSBK
+ */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLTSBK = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTSBK, true>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateLDLTTPP
+ * \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with full threshold partial pivoting
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTTPP
+ */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLTTPP = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTTPP, true>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateQR
+ * \brief A QR factorization and solver based on Accelerate
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateQR
+ */
+template <typename MatrixType>
+using AccelerateQR = AccelerateImpl<MatrixType, 0, SparseFactorizationQR, false>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateCholeskyAtA
+ * \brief A QR factorization and solver based on Accelerate without storing Q (equivalent to A^TA = R^T R)
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateCholeskyAtA
+ */
+template <typename MatrixType>
+using AccelerateCholeskyAtA = AccelerateImpl<MatrixType, 0, SparseFactorizationCholeskyAtA, false>;
+
+namespace internal {
+template <typename T>
+struct AccelFactorizationDeleter {
+  void operator()(T* sym) {
+    if (sym) {
+      SparseCleanup(*sym);
+      delete sym;
+      sym = nullptr;
+    }
+  }
+};
+
+template <typename DenseVecT, typename DenseMatT, typename SparseMatT, typename NumFactT>
+struct SparseTypesTraitBase {
+  typedef DenseVecT AccelDenseVector;
+  typedef DenseMatT AccelDenseMatrix;
+  typedef SparseMatT AccelSparseMatrix;
+
+  typedef SparseOpaqueSymbolicFactorization SymbolicFactorization;
+  typedef NumFactT NumericFactorization;
+
+  typedef AccelFactorizationDeleter<SymbolicFactorization> SymbolicFactorizationDeleter;
+  typedef AccelFactorizationDeleter<NumericFactorization> NumericFactorizationDeleter;
+};
+
+template <typename Scalar>
+struct SparseTypesTrait {};
+
+template <>
+struct SparseTypesTrait<double> : SparseTypesTraitBase<DenseVector_Double, DenseMatrix_Double, SparseMatrix_Double,
+                                                       SparseOpaqueFactorization_Double> {};
+
+template <>
+struct SparseTypesTrait<float>
+    : SparseTypesTraitBase<DenseVector_Float, DenseMatrix_Float, SparseMatrix_Float, SparseOpaqueFactorization_Float> {
+};
+
+}  // end namespace internal
+
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+class AccelerateImpl : public SparseSolverBase<AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_> > {
+ protected:
+  using Base = SparseSolverBase<AccelerateImpl>;
+  using Base::derived;
+  using Base::m_isInitialized;
+
+ public:
+  using Base::_solve_impl;
+
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
+  enum { UpLo = UpLo_ };
+
+  using AccelDenseVector = typename internal::SparseTypesTrait<Scalar>::AccelDenseVector;
+  using AccelDenseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelDenseMatrix;
+  using AccelSparseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelSparseMatrix;
+  using SymbolicFactorization = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorization;
+  using NumericFactorization = typename internal::SparseTypesTrait<Scalar>::NumericFactorization;
+  using SymbolicFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorizationDeleter;
+  using NumericFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::NumericFactorizationDeleter;
+
+  AccelerateImpl() {
+    m_isInitialized = false;
+
+    auto check_flag_set = [](int value, int flag) { return ((value & flag) == flag); };
+
+    if (check_flag_set(UpLo_, Symmetric)) {
+      m_sparseKind = SparseSymmetric;
+      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
+    } else if (check_flag_set(UpLo_, UnitLower)) {
+      m_sparseKind = SparseUnitTriangular;
+      m_triType = SparseLowerTriangle;
+    } else if (check_flag_set(UpLo_, UnitUpper)) {
+      m_sparseKind = SparseUnitTriangular;
+      m_triType = SparseUpperTriangle;
+    } else if (check_flag_set(UpLo_, StrictlyLower)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseLowerTriangle;
+    } else if (check_flag_set(UpLo_, StrictlyUpper)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseUpperTriangle;
+    } else if (check_flag_set(UpLo_, Lower)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseLowerTriangle;
+    } else if (check_flag_set(UpLo_, Upper)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseUpperTriangle;
+    } else {
+      m_sparseKind = SparseOrdinary;
+      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
+    }
+
+    m_order = SparseOrderDefault;
+  }
+
+  explicit AccelerateImpl(const MatrixType& matrix) : AccelerateImpl() { compute(matrix); }
+
+  ~AccelerateImpl() {}
+
+  inline Index cols() const { return m_nCols; }
+  inline Index rows() const { return m_nRows; }
+
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  void analyzePattern(const MatrixType& matrix);
+
+  void factorize(const MatrixType& matrix);
+
+  void compute(const MatrixType& matrix);
+
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const;
+
+  /** Sets the ordering algorithm to use. */
+  void setOrder(SparseOrder_t order) { m_order = order; }
+
+ private:
+  template <typename T>
+  void buildAccelSparseMatrix(const SparseMatrix<T>& a, AccelSparseMatrix& A, std::vector<long>& columnStarts) {
+    const Index nColumnsStarts = a.cols() + 1;
+
+    columnStarts.resize(nColumnsStarts);
+
+    for (Index i = 0; i < nColumnsStarts; i++) columnStarts[i] = a.outerIndexPtr()[i];
+
+    SparseAttributes_t attributes{};
+    attributes.transpose = false;
+    attributes.triangle = m_triType;
+    attributes.kind = m_sparseKind;
+
+    SparseMatrixStructure structure{};
+    structure.attributes = attributes;
+    structure.rowCount = static_cast<int>(a.rows());
+    structure.columnCount = static_cast<int>(a.cols());
+    structure.blockSize = 1;
+    structure.columnStarts = columnStarts.data();
+    structure.rowIndices = const_cast<int*>(a.innerIndexPtr());
+
+    A.structure = structure;
+    A.data = const_cast<T*>(a.valuePtr());
+  }
+
+  void doAnalysis(AccelSparseMatrix& A) {
+    m_numericFactorization.reset(nullptr);
+
+    SparseSymbolicFactorOptions opts{};
+    opts.control = SparseDefaultControl;
+    opts.orderMethod = m_order;
+    opts.order = nullptr;
+    opts.ignoreRowsAndColumns = nullptr;
+    opts.malloc = malloc;
+    opts.free = free;
+    opts.reportError = nullptr;
+
+    m_symbolicFactorization.reset(new SymbolicFactorization(SparseFactor(Solver_, A.structure, opts)));
+
+    SparseStatus_t status = m_symbolicFactorization->status;
+
+    updateInfoStatus(status);
+
+    if (status != SparseStatusOK) m_symbolicFactorization.reset(nullptr);
+  }
+
+  void doFactorization(AccelSparseMatrix& A) {
+    SparseStatus_t status = SparseStatusReleased;
+
+    if (m_symbolicFactorization) {
+      m_numericFactorization.reset(new NumericFactorization(SparseFactor(*m_symbolicFactorization, A)));
+
+      status = m_numericFactorization->status;
+
+      if (status != SparseStatusOK) m_numericFactorization.reset(nullptr);
+    }
+
+    updateInfoStatus(status);
+  }
+
+ protected:
+  void updateInfoStatus(SparseStatus_t status) const {
+    switch (status) {
+      case SparseStatusOK:
+        m_info = Success;
+        break;
+      case SparseFactorizationFailed:
+      case SparseMatrixIsSingular:
+        m_info = NumericalIssue;
+        break;
+      case SparseInternalError:
+      case SparseParameterError:
+      case SparseStatusReleased:
+      default:
+        m_info = InvalidInput;
+        break;
+    }
+  }
+
+  mutable ComputationInfo m_info;
+  Index m_nRows, m_nCols;
+  std::unique_ptr<SymbolicFactorization, SymbolicFactorizationDeleter> m_symbolicFactorization;
+  std::unique_ptr<NumericFactorization, NumericFactorizationDeleter> m_numericFactorization;
+  SparseKind_t m_sparseKind;
+  SparseTriangle_t m_triType;
+  SparseOrder_t m_order;
+};
+
+/** Computes the symbolic and numeric decomposition of matrix \a a */
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::compute(const MatrixType& a) {
+  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
+
+  m_nRows = a.rows();
+  m_nCols = a.cols();
+
+  AccelSparseMatrix A{};
+  std::vector<long> columnStarts;
+
+  buildAccelSparseMatrix(a, A, columnStarts);
+
+  doAnalysis(A);
+
+  if (m_symbolicFactorization) doFactorization(A);
+
+  m_isInitialized = true;
+}
+
+/** Performs a symbolic decomposition on the sparsity pattern of matrix \a a.
+ *
+ * This function is particularly useful when solving for several problems having the same structure.
+ *
+ * \sa factorize()
+ */
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::analyzePattern(const MatrixType& a) {
+  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
+
+  m_nRows = a.rows();
+  m_nCols = a.cols();
+
+  AccelSparseMatrix A{};
+  std::vector<long> columnStarts;
+
+  buildAccelSparseMatrix(a, A, columnStarts);
+
+  doAnalysis(A);
+
+  m_isInitialized = true;
+}
+
+/** Performs a numeric decomposition of matrix \a a.
+ *
+ * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been
+ * performed.
+ *
+ * \sa analyzePattern()
+ */
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::factorize(const MatrixType& a) {
+  eigen_assert(m_symbolicFactorization && "You must first call analyzePattern()");
+  eigen_assert(m_nRows == a.rows() && m_nCols == a.cols());
+
+  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
+
+  AccelSparseMatrix A{};
+  std::vector<long> columnStarts;
+
+  buildAccelSparseMatrix(a, A, columnStarts);
+
+  doFactorization(A);
+}
+
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+template <typename Rhs, typename Dest>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::_solve_impl(const MatrixBase<Rhs>& b,
+                                                                              MatrixBase<Dest>& x) const {
+  if (!m_numericFactorization) {
+    m_info = InvalidInput;
+    return;
+  }
+
+  eigen_assert(m_nRows == b.rows());
+  eigen_assert(((b.cols() == 1) || b.outerStride() == b.rows()));
+
+  SparseStatus_t status = SparseStatusOK;
+
+  Scalar* b_ptr = const_cast<Scalar*>(b.derived().data());
+  Scalar* x_ptr = const_cast<Scalar*>(x.derived().data());
+
+  AccelDenseMatrix xmat{};
+  xmat.attributes = SparseAttributes_t();
+  xmat.columnCount = static_cast<int>(x.cols());
+  xmat.rowCount = static_cast<int>(x.rows());
+  xmat.columnStride = xmat.rowCount;
+  xmat.data = x_ptr;
+
+  AccelDenseMatrix bmat{};
+  bmat.attributes = SparseAttributes_t();
+  bmat.columnCount = static_cast<int>(b.cols());
+  bmat.rowCount = static_cast<int>(b.rows());
+  bmat.columnStride = bmat.rowCount;
+  bmat.data = b_ptr;
+
+  SparseSolve(*m_numericFactorization, bmat, xmat);
+
+  updateInfoStatus(status);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ACCELERATESUPPORT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/AccelerateSupport/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/AccelerateSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000..69bcff5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/AccelerateSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
+#error "Please include Eigen/AccelerateSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/InternalHeaderCheck.h
new file mode 100644
index 0000000..5de2b21
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CHOLESKY_MODULE_H
+#error "Please include Eigen/Cholesky instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LDLT.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LDLT.h
new file mode 100644
index 0000000..45912a2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LDLT.h
@@ -0,0 +1,661 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2011 Timothy E. Holy <tim.holy@gmail.com >
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LDLT_H
+#define EIGEN_LDLT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType_, int UpLo_>
+struct traits<LDLT<MatrixType_, UpLo_> > : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
+template <typename MatrixType, int UpLo>
+struct LDLT_Traits;
+
+// PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
+enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
+}  // namespace internal
+
+/** \ingroup Cholesky_Module
+ *
+ * \class LDLT
+ *
+ * \brief Robust Cholesky decomposition of a matrix with pivoting
+ *
+ * \tparam MatrixType_ the type of the matrix of which to compute the LDL^T Cholesky decomposition
+ * \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
+ *             The other triangular part won't be read.
+ *
+ * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
+ * matrix \f$ A \f$ such that \f$ A =  P^TLDL^*P \f$, where P is a permutation matrix, L
+ * is lower triangular with a unit diagonal and D is a diagonal matrix.
+ *
+ * The decomposition uses pivoting to ensure stability, so that D will have
+ * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
+ * on D also stabilizes the computation.
+ *
+ * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
+ * decomposition to determine whether a system of equations has a solution.
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
+ */
+template <typename MatrixType_, int UpLo_>
+class LDLT : public SolverBase<LDLT<MatrixType_, UpLo_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<LDLT> Base;
+  friend class SolverBase<LDLT>;
+
+  EIGEN_GENERIC_PUBLIC_INTERFACE(LDLT)
+  enum {
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    UpLo = UpLo_
+  };
+  typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
+
+  typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
+  typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
+
+  typedef internal::LDLT_Traits<MatrixType, UpLo> Traits;
+
+  /** \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via LDLT::compute(const MatrixType&).
+   */
+  LDLT()
+      : m_matrix(),
+        m_l1_norm(0),
+        m_transpositions(),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false),
+        m_info(InvalidInput) {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa LDLT()
+   */
+  explicit LDLT(Index size)
+      : m_matrix(size, size),
+        m_l1_norm(0),
+        m_transpositions(size),
+        m_temporary(size),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false),
+        m_info(InvalidInput) {}
+
+  /** \brief Constructor with decomposition
+   *
+   * This calculates the decomposition for the input \a matrix.
+   *
+   * \sa LDLT(Index size)
+   */
+  template <typename InputType>
+  explicit LDLT(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.rows(), matrix.cols()),
+        m_l1_norm(0),
+        m_transpositions(matrix.rows()),
+        m_temporary(matrix.rows()),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false),
+        m_info(InvalidInput) {
+    compute(matrix.derived());
+  }
+
+  /** \brief Constructs a LDLT factorization from a given matrix
+   *
+   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c
+   * MatrixType is a Eigen::Ref.
+   *
+   * \sa LDLT(const EigenBase&)
+   */
+  template <typename InputType>
+  explicit LDLT(EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()),
+        m_l1_norm(0),
+        m_transpositions(matrix.rows()),
+        m_temporary(matrix.rows()),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false),
+        m_info(InvalidInput) {
+    compute(matrix.derived());
+  }
+
+  /** Clear any existing decomposition
+   * \sa rankUpdate(w,sigma)
+   */
+  void setZero() { m_isInitialized = false; }
+
+  /** \returns a view of the upper triangular matrix U */
+  inline typename Traits::MatrixU matrixU() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return Traits::getU(m_matrix);
+  }
+
+  /** \returns a view of the lower triangular matrix L */
+  inline typename Traits::MatrixL matrixL() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return Traits::getL(m_matrix);
+  }
+
+  /** \returns the permutation matrix P as a transposition sequence.
+   */
+  inline const TranspositionType& transpositionsP() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_transpositions;
+  }
+
+  /** \returns the coefficients of the diagonal matrix D */
+  inline Diagonal<const MatrixType> vectorD() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_matrix.diagonal();
+  }
+
+  /** \returns true if the matrix is positive (semidefinite) */
+  inline bool isPositive() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
+  }
+
+  /** \returns true if the matrix is negative (semidefinite) */
+  inline bool isNegative(void) const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
+   *
+   * This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
+   *
+   * \note_about_checking_solutions
+   *
+   * More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
+   * by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
+   * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
+   * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
+   * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
+   * computes the least-square solution of \f$ A x = b \f$ if \f$ A \f$ is singular.
+   *
+   * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
+   */
+  template <typename Rhs>
+  inline Solve<LDLT, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  template <typename Derived>
+  bool solveInPlace(MatrixBase<Derived>& bAndX) const;
+
+  template <typename InputType>
+  LDLT& compute(const EigenBase<InputType>& matrix);
+
+  /** \returns an estimate of the reciprocal condition number of the matrix of
+   *  which \c *this is the LDLT decomposition.
+   */
+  RealScalar rcond() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return internal::rcond_estimate_helper(m_l1_norm, *this);
+  }
+
+  template <typename Derived>
+  LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha = 1);
+
+  /** \returns the internal LDLT decomposition matrix
+   *
+   * TODO: document the storage layout.
+   */
+  inline const MatrixType& matrixLDLT() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_matrix;
+  }
+
+  MatrixType reconstructedMatrix() const;
+
+  /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix
+   * is self-adjoint.
+   *
+   * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
+   * \code x = decomposition.adjoint().solve(b) \endcode
+   */
+  const LDLT& adjoint() const { return *this; }
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_matrix.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_matrix.cols(); }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the factorization failed because of a zero pivot.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_info;
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  /** \internal
+   * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
+   * The strict upper part is used during the decomposition, the strict lower
+   * part correspond to the coefficients of L (its diagonal is equal to 1 and
+   * is not stored), and the diagonal entries correspond to D.
+   */
+  MatrixType m_matrix;
+  RealScalar m_l1_norm;
+  TranspositionType m_transpositions;
+  TmpMatrixType m_temporary;
+  internal::SignMatrix m_sign;
+  bool m_isInitialized;
+  ComputationInfo m_info;
+};
+
+namespace internal {
+
+template <int UpLo>
+struct ldlt_inplace;
+
+template <>
+struct ldlt_inplace<Lower> {
+  template <typename MatrixType, typename TranspositionType, typename Workspace>
+  static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign) {
+    using std::abs;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename TranspositionType::StorageIndex IndexType;
+    eigen_assert(mat.rows() == mat.cols());
+    const Index size = mat.rows();
+    bool found_zero_pivot = false;
+    bool ret = true;
+
+    if (size <= 1) {
+      transpositions.setIdentity();
+      if (size == 0)
+        sign = ZeroSign;
+      else if (numext::real(mat.coeff(0, 0)) > static_cast<RealScalar>(0))
+        sign = PositiveSemiDef;
+      else if (numext::real(mat.coeff(0, 0)) < static_cast<RealScalar>(0))
+        sign = NegativeSemiDef;
+      else
+        sign = ZeroSign;
+      return true;
+    }
+
+    for (Index k = 0; k < size; ++k) {
+      // Find largest diagonal element
+      Index index_of_biggest_in_corner;
+      mat.diagonal().tail(size - k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
+      index_of_biggest_in_corner += k;
+
+      transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
+      if (k != index_of_biggest_in_corner) {
+        // apply the transposition while taking care to consider only
+        // the lower triangular part
+        Index s = size - index_of_biggest_in_corner - 1;  // trailing size after the biggest element
+        mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
+        mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
+        std::swap(mat.coeffRef(k, k), mat.coeffRef(index_of_biggest_in_corner, index_of_biggest_in_corner));
+        for (Index i = k + 1; i < index_of_biggest_in_corner; ++i) {
+          Scalar tmp = mat.coeffRef(i, k);
+          mat.coeffRef(i, k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner, i));
+          mat.coeffRef(index_of_biggest_in_corner, i) = numext::conj(tmp);
+        }
+        if (NumTraits<Scalar>::IsComplex)
+          mat.coeffRef(index_of_biggest_in_corner, k) = numext::conj(mat.coeff(index_of_biggest_in_corner, k));
+      }
+
+      // partition the matrix:
+      //       A00 |  -  |  -
+      // lu  = A10 | A11 |  -
+      //       A20 | A21 | A22
+      Index rs = size - k - 1;
+      Block<MatrixType, Dynamic, 1> A21(mat, k + 1, k, rs, 1);
+      Block<MatrixType, 1, Dynamic> A10(mat, k, 0, 1, k);
+      Block<MatrixType, Dynamic, Dynamic> A20(mat, k + 1, 0, rs, k);
+
+      if (k > 0) {
+        temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint();
+        mat.coeffRef(k, k) -= (A10 * temp.head(k)).value();
+        if (rs > 0) A21.noalias() -= A20 * temp.head(k);
+      }
+
+      // In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
+      // was smaller than the cutoff value. However, since LDLT is not rank-revealing
+      // we should only make sure that we do not introduce INF or NaN values.
+      // Remark that LAPACK also uses 0 as the cutoff value.
+      RealScalar realAkk = numext::real(mat.coeffRef(k, k));
+      bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
+
+      if (k == 0 && !pivot_is_valid) {
+        // The entire diagonal is zero, there is nothing more to do
+        // except filling the transpositions, and checking whether the matrix is zero.
+        sign = ZeroSign;
+        for (Index j = 0; j < size; ++j) {
+          transpositions.coeffRef(j) = IndexType(j);
+          ret = ret && (mat.col(j).tail(size - j - 1).array() == Scalar(0)).all();
+        }
+        return ret;
+      }
+
+      if ((rs > 0) && pivot_is_valid)
+        A21 /= realAkk;
+      else if (rs > 0)
+        ret = ret && (A21.array() == Scalar(0)).all();
+
+      if (found_zero_pivot && pivot_is_valid)
+        ret = false;  // factorization failed
+      else if (!pivot_is_valid)
+        found_zero_pivot = true;
+
+      if (sign == PositiveSemiDef) {
+        if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
+      } else if (sign == NegativeSemiDef) {
+        if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
+      } else if (sign == ZeroSign) {
+        if (realAkk > static_cast<RealScalar>(0))
+          sign = PositiveSemiDef;
+        else if (realAkk < static_cast<RealScalar>(0))
+          sign = NegativeSemiDef;
+      }
+    }
+
+    return ret;
+  }
+
+  // Reference for the algorithm: Davis and Hager, "Multiple Rank
+  // Modifications of a Sparse Cholesky Factorization" (Algorithm 1)
+  // Trivial rearrangements of their computations (Timothy E. Holy)
+  // allow their algorithm to work for rank-1 updates even if the
+  // original matrix is not of full rank.
+  // Here only rank-1 updates are implemented, to reduce the
+  // requirement for intermediate storage and improve accuracy
+  template <typename MatrixType, typename WDerived>
+  static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w,
+                            const typename MatrixType::RealScalar& sigma = 1) {
+    using numext::isfinite;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+
+    const Index size = mat.rows();
+    eigen_assert(mat.cols() == size && w.size() == size);
+
+    RealScalar alpha = 1;
+
+    // Apply the update
+    for (Index j = 0; j < size; j++) {
+      // Check for termination due to an original decomposition of low-rank
+      if (!(isfinite)(alpha)) break;
+
+      // Update the diagonal terms
+      RealScalar dj = numext::real(mat.coeff(j, j));
+      Scalar wj = w.coeff(j);
+      RealScalar swj2 = sigma * numext::abs2(wj);
+      RealScalar gamma = dj * alpha + swj2;
+
+      mat.coeffRef(j, j) += swj2 / alpha;
+      alpha += swj2 / dj;
+
+      // Update the terms of L
+      Index rs = size - j - 1;
+      w.tail(rs) -= wj * mat.col(j).tail(rs);
+      if (!numext::is_exactly_zero(gamma)) mat.col(j).tail(rs) += (sigma * numext::conj(wj) / gamma) * w.tail(rs);
+    }
+    return true;
+  }
+
+  template <typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
+  static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w,
+                     const typename MatrixType::RealScalar& sigma = 1) {
+    // Apply the permutation to the input w
+    tmp = transpositions * w;
+
+    return ldlt_inplace<Lower>::updateInPlace(mat, tmp, sigma);
+  }
+};
+
+template <>
+struct ldlt_inplace<Upper> {
+  template <typename MatrixType, typename TranspositionType, typename Workspace>
+  static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp,
+                                            SignMatrix& sign) {
+    Transpose<MatrixType> matt(mat);
+    return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
+  }
+
+  template <typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
+  static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w,
+                                         const typename MatrixType::RealScalar& sigma = 1) {
+    Transpose<MatrixType> matt(mat);
+    return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
+  }
+};
+
+template <typename MatrixType>
+struct LDLT_Traits<MatrixType, Lower> {
+  typedef const TriangularView<const MatrixType, UnitLower> MatrixL;
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
+};
+
+template <typename MatrixType>
+struct LDLT_Traits<MatrixType, Upper> {
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
+  typedef const TriangularView<const MatrixType, UnitUpper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
+};
+
+}  // end namespace internal
+
+/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
+ */
+template <typename MatrixType, int UpLo_>
+template <typename InputType>
+LDLT<MatrixType, UpLo_>& LDLT<MatrixType, UpLo_>::compute(const EigenBase<InputType>& a) {
+  eigen_assert(a.rows() == a.cols());
+  const Index size = a.rows();
+
+  m_matrix = a.derived();
+
+  // Compute matrix L1 norm = max abs column sum.
+  m_l1_norm = RealScalar(0);
+  // TODO: move this code to SelfAdjointView
+  for (Index col = 0; col < size; ++col) {
+    RealScalar abs_col_sum;
+    if (UpLo_ == Lower)
+      abs_col_sum =
+          m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
+    else
+      abs_col_sum =
+          m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
+    if (abs_col_sum > m_l1_norm) m_l1_norm = abs_col_sum;
+  }
+
+  m_transpositions.resize(size);
+  m_isInitialized = false;
+  m_temporary.resize(size);
+  m_sign = internal::ZeroSign;
+
+  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success
+                                                                                                    : NumericalIssue;
+
+  m_isInitialized = true;
+  return *this;
+}
+
+/** Update the LDLT decomposition:  given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T.
+ * \param w a vector to be incorporated into the decomposition.
+ * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column
+ * vectors. Optional; default value is +1. \sa setZero()
+ */
+template <typename MatrixType, int UpLo_>
+template <typename Derived>
+LDLT<MatrixType, UpLo_>& LDLT<MatrixType, UpLo_>::rankUpdate(
+    const MatrixBase<Derived>& w, const typename LDLT<MatrixType, UpLo_>::RealScalar& sigma) {
+  typedef typename TranspositionType::StorageIndex IndexType;
+  const Index size = w.rows();
+  if (m_isInitialized) {
+    eigen_assert(m_matrix.rows() == size);
+  } else {
+    m_matrix.resize(size, size);
+    m_matrix.setZero();
+    m_transpositions.resize(size);
+    for (Index i = 0; i < size; i++) m_transpositions.coeffRef(i) = IndexType(i);
+    m_temporary.resize(size);
+    m_sign = sigma >= 0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
+    m_isInitialized = true;
+  }
+
+  internal::ldlt_inplace<UpLo>::update(m_matrix, m_transpositions, m_temporary, w, sigma);
+
+  return *this;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType_, int UpLo_>
+template <typename RhsType, typename DstType>
+void LDLT<MatrixType_, UpLo_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
+  _solve_impl_transposed<true>(rhs, dst);
+}
+
+template <typename MatrixType_, int UpLo_>
+template <bool Conjugate, typename RhsType, typename DstType>
+void LDLT<MatrixType_, UpLo_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
+  // dst = P b
+  dst = m_transpositions * rhs;
+
+  // dst = L^-1 (P b)
+  // dst = L^-*T (P b)
+  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
+
+  // dst = D^-* (L^-1 P b)
+  // dst = D^-1 (L^-*T P b)
+  // more precisely, use pseudo-inverse of D (see bug 241)
+  using std::abs;
+  const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
+  // In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
+  // and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
+  // RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1)
+  // / NumTraits<RealScalar>::highest()); However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the
+  // highest diagonal element is not well justified and leads to numerical issues in some cases. Moreover, Lapack's
+  // xSYTRS routines use 0 for the tolerance. Using numeric_limits::min() gives us more robustness to denormals.
+  RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
+  for (Index i = 0; i < vecD.size(); ++i) {
+    if (abs(vecD(i)) > tolerance)
+      dst.row(i) /= vecD(i);
+    else
+      dst.row(i).setZero();
+  }
+
+  // dst = L^-* (D^-* L^-1 P b)
+  // dst = L^-T (D^-1 L^-*T P b)
+  matrixL().transpose().template conjugateIf<Conjugate>().solveInPlace(dst);
+
+  // dst = P^T (L^-* D^-* L^-1 P b) = A^-1 b
+  // dst = P^-T (L^-T D^-1 L^-*T P b) = A^-1 b
+  dst = m_transpositions.transpose() * dst;
+}
+#endif
+
+/** \internal use x = ldlt_object.solve(x);
+ *
+ * This is the \em in-place version of solve().
+ *
+ * \param bAndX represents both the right-hand side matrix b and result x.
+ *
+ * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
+ *
+ * This version avoids a copy when the right hand side matrix b is not
+ * needed anymore.
+ *
+ * \sa LDLT::solve(), MatrixBase::ldlt()
+ */
+template <typename MatrixType, int UpLo_>
+template <typename Derived>
+bool LDLT<MatrixType, UpLo_>::solveInPlace(MatrixBase<Derived>& bAndX) const {
+  eigen_assert(m_isInitialized && "LDLT is not initialized.");
+  eigen_assert(m_matrix.rows() == bAndX.rows());
+
+  bAndX = this->solve(bAndX);
+
+  return true;
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: P^T L D L^* P.
+ * This function is provided for debug purpose. */
+template <typename MatrixType, int UpLo_>
+MatrixType LDLT<MatrixType, UpLo_>::reconstructedMatrix() const {
+  eigen_assert(m_isInitialized && "LDLT is not initialized.");
+  const Index size = m_matrix.rows();
+  MatrixType res(size, size);
+
+  // P
+  res.setIdentity();
+  res = transpositionsP() * res;
+  // L^* P
+  res = matrixU() * res;
+  // D(L^*P)
+  res = vectorD().real().asDiagonal() * res;
+  // L(DL^*P)
+  res = matrixL() * res;
+  // P^T (LDL^*P)
+  res = transpositionsP().transpose() * res;
+
+  return res;
+}
+
+/** \cholesky_module
+ * \returns the Cholesky decomposition with full pivoting without square root of \c *this
+ * \sa MatrixBase::ldlt()
+ */
+template <typename MatrixType, unsigned int UpLo>
+inline LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo> SelfAdjointView<MatrixType, UpLo>::ldlt()
+    const {
+  return LDLT<PlainObject, UpLo>(m_matrix);
+}
+
+/** \cholesky_module
+ * \returns the Cholesky decomposition with full pivoting without square root of \c *this
+ * \sa SelfAdjointView::ldlt()
+ */
+template <typename Derived>
+inline LDLT<typename MatrixBase<Derived>::PlainObject> MatrixBase<Derived>::ldlt() const {
+  return LDLT<PlainObject>(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LDLT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LLT.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LLT.h
new file mode 100644
index 0000000..7066cd0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LLT.h
@@ -0,0 +1,516 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LLT_H
+#define EIGEN_LLT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename MatrixType_, int UpLo_>
+struct traits<LLT<MatrixType_, UpLo_> > : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
+template <typename MatrixType, int UpLo>
+struct LLT_Traits;
+}  // namespace internal
+
+/** \ingroup Cholesky_Module
+ *
+ * \class LLT
+ *
+ * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the LL^T Cholesky decomposition
+ * \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
+ *               The other triangular part won't be read.
+ *
+ * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
+ * matrix A such that A = LL^* = U^*U, where L is lower triangular.
+ *
+ * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like  D^*D x = b,
+ * for that purpose, we recommend the Cholesky decomposition without square root which is more stable
+ * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
+ * situations like generalised eigen problems with hermitian matrices.
+ *
+ * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive
+ * definite matrices, use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine
+ * whether a system of equations has a solution.
+ *
+ * Example: \include LLT_example.cpp
+ * Output: \verbinclude LLT_example.out
+ *
+ * \b Performance: for best performance, it is recommended to use a column-major storage format
+ * with the Lower triangular part (the default), or, equivalently, a row-major storage format
+ * with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
+ * step, and rank-updates can be up to 3 times slower.
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * Note that during the decomposition, only the lower (or upper, as defined by UpLo_) triangular part of A is
+ * considered. Therefore, the strict lower part does not have to store correct values.
+ *
+ * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
+ */
+template <typename MatrixType_, int UpLo_>
+class LLT : public SolverBase<LLT<MatrixType_, UpLo_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<LLT> Base;
+  friend class SolverBase<LLT>;
+
+  EIGEN_GENERIC_PUBLIC_INTERFACE(LLT)
+  enum { MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+  enum { PacketSize = internal::packet_traits<Scalar>::size, AlignmentMask = int(PacketSize) - 1, UpLo = UpLo_ };
+
+  typedef internal::LLT_Traits<MatrixType, UpLo> Traits;
+
+  /**
+   * \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via LLT::compute(const MatrixType&).
+   */
+  LLT() : m_matrix(), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa LLT()
+   */
+  explicit LLT(Index size) : m_matrix(size, size), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {}
+
+  template <typename InputType>
+  explicit LLT(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.rows(), matrix.cols()), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {
+    compute(matrix.derived());
+  }
+
+  /** \brief Constructs a LLT factorization from a given matrix
+   *
+   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
+   * \c MatrixType is a Eigen::Ref.
+   *
+   * \sa LLT(const EigenBase&)
+   */
+  template <typename InputType>
+  explicit LLT(EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {
+    compute(matrix.derived());
+  }
+
+  /** \returns a view of the upper triangular matrix U */
+  inline typename Traits::MatrixU matrixU() const {
+    eigen_assert(m_isInitialized && "LLT is not initialized.");
+    return Traits::getU(m_matrix);
+  }
+
+  /** \returns a view of the lower triangular matrix L */
+  inline typename Traits::MatrixL matrixL() const {
+    eigen_assert(m_isInitialized && "LLT is not initialized.");
+    return Traits::getL(m_matrix);
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+   *
+   * Since this LLT class assumes anyway that the matrix A is invertible, the solution
+   * theoretically exists and is unique regardless of b.
+   *
+   * Example: \include LLT_solve.cpp
+   * Output: \verbinclude LLT_solve.out
+   *
+   * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
+   */
+  template <typename Rhs>
+  inline Solve<LLT, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  template <typename Derived>
+  void solveInPlace(const MatrixBase<Derived>& bAndX) const;
+
+  template <typename InputType>
+  LLT& compute(const EigenBase<InputType>& matrix);
+
+  /** \returns an estimate of the reciprocal condition number of the matrix of
+   *  which \c *this is the Cholesky decomposition.
+   */
+  RealScalar rcond() const {
+    eigen_assert(m_isInitialized && "LLT is not initialized.");
+    eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
+    return internal::rcond_estimate_helper(m_l1_norm, *this);
+  }
+
+  /** \returns the LLT decomposition matrix
+   *
+   * TODO: document the storage layout
+   */
+  inline const MatrixType& matrixLLT() const {
+    eigen_assert(m_isInitialized && "LLT is not initialized.");
+    return m_matrix;
+  }
+
+  MatrixType reconstructedMatrix() const;
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix.appears not to be positive definite.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "LLT is not initialized.");
+    return m_info;
+  }
+
+  /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix
+   * is self-adjoint.
+   *
+   * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
+   * \code x = decomposition.adjoint().solve(b) \endcode
+   */
+  const LLT& adjoint() const noexcept { return *this; }
+
+  constexpr Index rows() const noexcept { return m_matrix.rows(); }
+  constexpr Index cols() const noexcept { return m_matrix.cols(); }
+
+  template <typename VectorType>
+  LLT& rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  /** \internal
+   * Used to compute and store L
+   * The strict upper part is not used and even not initialized.
+   */
+  MatrixType m_matrix;
+  RealScalar m_l1_norm;
+  bool m_isInitialized;
+  ComputationInfo m_info;
+};
+
+namespace internal {
+
+template <typename Scalar, int UpLo>
+struct llt_inplace;
+
+template <typename MatrixType, typename VectorType>
+static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec,
+                                   const typename MatrixType::RealScalar& sigma) {
+  using std::sqrt;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::ColXpr ColXpr;
+  typedef internal::remove_all_t<ColXpr> ColXprCleaned;
+  typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
+  typedef Matrix<Scalar, Dynamic, 1> TempVectorType;
+  typedef typename TempVectorType::SegmentReturnType TempVecSegment;
+
+  Index n = mat.cols();
+  eigen_assert(mat.rows() == n && vec.size() == n);
+
+  TempVectorType temp;
+
+  if (sigma > 0) {
+    // This version is based on Givens rotations.
+    // It is faster than the other one below, but only works for updates,
+    // i.e., for sigma > 0
+    temp = sqrt(sigma) * vec;
+
+    for (Index i = 0; i < n; ++i) {
+      JacobiRotation<Scalar> g;
+      g.makeGivens(mat(i, i), -temp(i), &mat(i, i));
+
+      Index rs = n - i - 1;
+      if (rs > 0) {
+        ColXprSegment x(mat.col(i).tail(rs));
+        TempVecSegment y(temp.tail(rs));
+        apply_rotation_in_the_plane(x, y, g);
+      }
+    }
+  } else {
+    temp = vec;
+    RealScalar beta = 1;
+    for (Index j = 0; j < n; ++j) {
+      RealScalar Ljj = numext::real(mat.coeff(j, j));
+      RealScalar dj = numext::abs2(Ljj);
+      Scalar wj = temp.coeff(j);
+      RealScalar swj2 = sigma * numext::abs2(wj);
+      RealScalar gamma = dj * beta + swj2;
+
+      RealScalar x = dj + swj2 / beta;
+      if (x <= RealScalar(0)) return j;
+      RealScalar nLjj = sqrt(x);
+      mat.coeffRef(j, j) = nLjj;
+      beta += swj2 / dj;
+
+      // Update the terms of L
+      Index rs = n - j - 1;
+      if (rs) {
+        temp.tail(rs) -= (wj / Ljj) * mat.col(j).tail(rs);
+        if (!numext::is_exactly_zero(gamma))
+          mat.col(j).tail(rs) =
+              (nLjj / Ljj) * mat.col(j).tail(rs) + (nLjj * sigma * numext::conj(wj) / gamma) * temp.tail(rs);
+      }
+    }
+  }
+  return -1;
+}
+
+template <typename Scalar>
+struct llt_inplace<Scalar, Lower> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  template <typename MatrixType>
+  static Index unblocked(MatrixType& mat) {
+    using std::sqrt;
+
+    eigen_assert(mat.rows() == mat.cols());
+    const Index size = mat.rows();
+    for (Index k = 0; k < size; ++k) {
+      Index rs = size - k - 1;  // remaining size
+
+      Block<MatrixType, Dynamic, 1> A21(mat, k + 1, k, rs, 1);
+      Block<MatrixType, 1, Dynamic> A10(mat, k, 0, 1, k);
+      Block<MatrixType, Dynamic, Dynamic> A20(mat, k + 1, 0, rs, k);
+
+      RealScalar x = numext::real(mat.coeff(k, k));
+      if (k > 0) x -= A10.squaredNorm();
+      if (x <= RealScalar(0)) return k;
+      mat.coeffRef(k, k) = x = sqrt(x);
+      if (k > 0 && rs > 0) A21.noalias() -= A20 * A10.adjoint();
+      if (rs > 0) A21 /= x;
+    }
+    return -1;
+  }
+
+  template <typename MatrixType>
+  static Index blocked(MatrixType& m) {
+    eigen_assert(m.rows() == m.cols());
+    Index size = m.rows();
+    if (size < 32) return unblocked(m);
+
+    Index blockSize = size / 8;
+    blockSize = (blockSize / 16) * 16;
+    blockSize = (std::min)((std::max)(blockSize, Index(8)), Index(128));
+
+    for (Index k = 0; k < size; k += blockSize) {
+      // partition the matrix:
+      //       A00 |  -  |  -
+      // lu  = A10 | A11 |  -
+      //       A20 | A21 | A22
+      Index bs = (std::min)(blockSize, size - k);
+      Index rs = size - k - bs;
+      Block<MatrixType, Dynamic, Dynamic> A11(m, k, k, bs, bs);
+      Block<MatrixType, Dynamic, Dynamic> A21(m, k + bs, k, rs, bs);
+      Block<MatrixType, Dynamic, Dynamic> A22(m, k + bs, k + bs, rs, rs);
+
+      Index ret;
+      if ((ret = unblocked(A11)) >= 0) return k + ret;
+      if (rs > 0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
+      if (rs > 0)
+        A22.template selfadjointView<Lower>().rankUpdate(A21,
+                                                         typename NumTraits<RealScalar>::Literal(-1));  // bottleneck
+    }
+    return -1;
+  }
+
+  template <typename MatrixType, typename VectorType>
+  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) {
+    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
+  }
+};
+
+template <typename Scalar>
+struct llt_inplace<Scalar, Upper> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  template <typename MatrixType>
+  static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat) {
+    Transpose<MatrixType> matt(mat);
+    return llt_inplace<Scalar, Lower>::unblocked(matt);
+  }
+  template <typename MatrixType>
+  static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat) {
+    Transpose<MatrixType> matt(mat);
+    return llt_inplace<Scalar, Lower>::blocked(matt);
+  }
+  template <typename MatrixType, typename VectorType>
+  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) {
+    Transpose<MatrixType> matt(mat);
+    return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
+  }
+};
+
+template <typename MatrixType>
+struct LLT_Traits<MatrixType, Lower> {
+  typedef const TriangularView<const MatrixType, Lower> MatrixL;
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
+  static bool inplace_decomposition(MatrixType& m) {
+    return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m) == -1;
+  }
+};
+
+template <typename MatrixType>
+struct LLT_Traits<MatrixType, Upper> {
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
+  typedef const TriangularView<const MatrixType, Upper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
+  static bool inplace_decomposition(MatrixType& m) {
+    return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m) == -1;
+  }
+};
+
+}  // end namespace internal
+
+/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
+ *
+ * \returns a reference to *this
+ *
+ * Example: \include TutorialLinAlgComputeTwice.cpp
+ * Output: \verbinclude TutorialLinAlgComputeTwice.out
+ */
+template <typename MatrixType, int UpLo_>
+template <typename InputType>
+LLT<MatrixType, UpLo_>& LLT<MatrixType, UpLo_>::compute(const EigenBase<InputType>& a) {
+  eigen_assert(a.rows() == a.cols());
+  const Index size = a.rows();
+  m_matrix.resize(size, size);
+  if (!internal::is_same_dense(m_matrix, a.derived())) m_matrix = a.derived();
+
+  // Compute matrix L1 norm = max abs column sum.
+  m_l1_norm = RealScalar(0);
+  // TODO: move this code to SelfAdjointView
+  for (Index col = 0; col < size; ++col) {
+    RealScalar abs_col_sum;
+    if (UpLo_ == Lower)
+      abs_col_sum =
+          m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
+    else
+      abs_col_sum =
+          m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
+    if (abs_col_sum > m_l1_norm) m_l1_norm = abs_col_sum;
+  }
+
+  m_isInitialized = true;
+  bool ok = Traits::inplace_decomposition(m_matrix);
+  m_info = ok ? Success : NumericalIssue;
+
+  return *this;
+}
+
+/** Performs a rank one update (or dowdate) of the current decomposition.
+ * If A = LL^* before the rank one update,
+ * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector
+ * of same dimension.
+ */
+template <typename MatrixType_, int UpLo_>
+template <typename VectorType>
+LLT<MatrixType_, UpLo_>& LLT<MatrixType_, UpLo_>::rankUpdate(const VectorType& v, const RealScalar& sigma) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType);
+  eigen_assert(v.size() == m_matrix.cols());
+  eigen_assert(m_isInitialized);
+  if (internal::llt_inplace<typename MatrixType::Scalar, UpLo>::rankUpdate(m_matrix, v, sigma) >= 0)
+    m_info = NumericalIssue;
+  else
+    m_info = Success;
+
+  return *this;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType_, int UpLo_>
+template <typename RhsType, typename DstType>
+void LLT<MatrixType_, UpLo_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
+  _solve_impl_transposed<true>(rhs, dst);
+}
+
+template <typename MatrixType_, int UpLo_>
+template <bool Conjugate, typename RhsType, typename DstType>
+void LLT<MatrixType_, UpLo_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
+  dst = rhs;
+
+  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
+  matrixU().template conjugateIf<!Conjugate>().solveInPlace(dst);
+}
+#endif
+
+/** \internal use x = llt_object.solve(x);
+ *
+ * This is the \em in-place version of solve().
+ *
+ * \param bAndX represents both the right-hand side matrix b and result x.
+ *
+ * This version avoids a copy when the right hand side matrix b is not needed anymore.
+ *
+ * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
+ * This function will const_cast it, so constness isn't honored here.
+ *
+ * \sa LLT::solve(), MatrixBase::llt()
+ */
+template <typename MatrixType, int UpLo_>
+template <typename Derived>
+void LLT<MatrixType, UpLo_>::solveInPlace(const MatrixBase<Derived>& bAndX) const {
+  eigen_assert(m_isInitialized && "LLT is not initialized.");
+  eigen_assert(m_matrix.rows() == bAndX.rows());
+  matrixL().solveInPlace(bAndX);
+  matrixU().solveInPlace(bAndX);
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: L L^*.
+ * This function is provided for debug purpose. */
+template <typename MatrixType, int UpLo_>
+MatrixType LLT<MatrixType, UpLo_>::reconstructedMatrix() const {
+  eigen_assert(m_isInitialized && "LLT is not initialized.");
+  return matrixL() * matrixL().adjoint().toDenseMatrix();
+}
+
+/** \cholesky_module
+ * \returns the LLT decomposition of \c *this
+ * \sa SelfAdjointView::llt()
+ */
+template <typename Derived>
+inline LLT<typename MatrixBase<Derived>::PlainObject> MatrixBase<Derived>::llt() const {
+  return LLT<PlainObject>(derived());
+}
+
+/** \cholesky_module
+ * \returns the LLT decomposition of \c *this
+ * \sa SelfAdjointView::llt()
+ */
+template <typename MatrixType, unsigned int UpLo>
+inline LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo> SelfAdjointView<MatrixType, UpLo>::llt()
+    const {
+  return LLT<PlainObject, UpLo>(m_matrix);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LLT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LLT_LAPACKE.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LLT_LAPACKE.h
new file mode 100644
index 0000000..cb55b15
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LLT_LAPACKE.h
@@ -0,0 +1,124 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *     LLt decomposition based on LAPACKE_?potrf function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_LLT_LAPACKE_H
+#define EIGEN_LLT_LAPACKE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+namespace lapacke_helpers {
+// -------------------------------------------------------------------------------------------------------------------
+//        Dispatch for rank update handling upper and lower parts
+// -------------------------------------------------------------------------------------------------------------------
+
+template <UpLoType Mode>
+struct rank_update {};
+
+template <>
+struct rank_update<Lower> {
+  template <typename MatrixType, typename VectorType>
+  static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
+    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
+  }
+};
+
+template <>
+struct rank_update<Upper> {
+  template <typename MatrixType, typename VectorType>
+  static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
+    Transpose<MatrixType> matt(mat);
+    return Eigen::internal::llt_rank_update_lower(matt, vec.conjugate(), sigma);
+  }
+};
+
+// -------------------------------------------------------------------------------------------------------------------
+//        Generic lapacke llt implementation that hands of to the dispatches
+// -------------------------------------------------------------------------------------------------------------------
+
+template <typename Scalar, UpLoType Mode>
+struct lapacke_llt {
+  EIGEN_STATIC_ASSERT(((Mode == Lower) || (Mode == Upper)), MODE_MUST_BE_UPPER_OR_LOWER)
+  template <typename MatrixType>
+  static Index blocked(MatrixType &m) {
+    eigen_assert(m.rows() == m.cols());
+    if (m.rows() == 0) {
+      return -1;
+    }
+    /* Set up parameters for ?potrf */
+    lapack_int size = to_lapack(m.rows());
+    lapack_int matrix_order = lapack_storage_of(m);
+    constexpr char uplo = Mode == Upper ? 'U' : 'L';
+    Scalar *a = &(m.coeffRef(0, 0));
+    lapack_int lda = to_lapack(m.outerStride());
+
+    lapack_int info = potrf(matrix_order, uplo, size, to_lapack(a), lda);
+    info = (info == 0) ? -1 : info > 0 ? info - 1 : size;
+    return info;
+  }
+
+  template <typename MatrixType, typename VectorType>
+  static Index rankUpdate(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
+    return rank_update<Mode>::run(mat, vec, sigma);
+  }
+};
+}  // namespace lapacke_helpers
+// end namespace lapacke_helpers
+
+/*
+ * Here, we just put the generic implementation from lapacke_llt into a full specialization of the llt_inplace
+ * type. By being a full specialization, the versions defined here thus get precedence over the generic implementation
+ * in LLT.h for double, float and complex double, complex float types.
+ */
+
+#define EIGEN_LAPACKE_LLT(EIGTYPE)                                                             \
+  template <>                                                                                  \
+  struct llt_inplace<EIGTYPE, Lower> : public lapacke_helpers::lapacke_llt<EIGTYPE, Lower> {}; \
+  template <>                                                                                  \
+  struct llt_inplace<EIGTYPE, Upper> : public lapacke_helpers::lapacke_llt<EIGTYPE, Upper> {};
+
+EIGEN_LAPACKE_LLT(double)
+EIGEN_LAPACKE_LLT(float)
+EIGEN_LAPACKE_LLT(std::complex<double>)
+EIGEN_LAPACKE_LLT(std::complex<float>)
+
+#undef EIGEN_LAPACKE_LLT
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LLT_LAPACKE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/CholmodSupport/CholmodSupport.h b/data_acq_and_processing/third_party/eigen/Eigen/src/CholmodSupport/CholmodSupport.h
new file mode 100644
index 0000000..dc3d6a3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/CholmodSupport/CholmodSupport.h
@@ -0,0 +1,738 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CHOLMODSUPPORT_H
+#define EIGEN_CHOLMODSUPPORT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+struct cholmod_configure_matrix;
+
+template <>
+struct cholmod_configure_matrix<double> {
+  template <typename CholmodType>
+  static void run(CholmodType& mat) {
+    mat.xtype = CHOLMOD_REAL;
+    mat.dtype = CHOLMOD_DOUBLE;
+  }
+};
+
+template <>
+struct cholmod_configure_matrix<std::complex<double> > {
+  template <typename CholmodType>
+  static void run(CholmodType& mat) {
+    mat.xtype = CHOLMOD_COMPLEX;
+    mat.dtype = CHOLMOD_DOUBLE;
+  }
+};
+
+// Other scalar types are not yet supported by Cholmod
+// template<> struct cholmod_configure_matrix<float> {
+//   template<typename CholmodType>
+//   static void run(CholmodType& mat) {
+//     mat.xtype = CHOLMOD_REAL;
+//     mat.dtype = CHOLMOD_SINGLE;
+//   }
+// };
+//
+// template<> struct cholmod_configure_matrix<std::complex<float> > {
+//   template<typename CholmodType>
+//   static void run(CholmodType& mat) {
+//     mat.xtype = CHOLMOD_COMPLEX;
+//     mat.dtype = CHOLMOD_SINGLE;
+//   }
+// };
+
+}  // namespace internal
+
+/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
+ * Note that the data are shared.
+ */
+template <typename Scalar_, int Options_, typename StorageIndex_>
+cholmod_sparse viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, StorageIndex_> > mat) {
+  cholmod_sparse res;
+  res.nzmax = mat.nonZeros();
+  res.nrow = mat.rows();
+  res.ncol = mat.cols();
+  res.p = mat.outerIndexPtr();
+  res.i = mat.innerIndexPtr();
+  res.x = mat.valuePtr();
+  res.z = 0;
+  res.sorted = 1;
+  if (mat.isCompressed()) {
+    res.packed = 1;
+    res.nz = 0;
+  } else {
+    res.packed = 0;
+    res.nz = mat.innerNonZeroPtr();
+  }
+
+  res.dtype = 0;
+  res.stype = -1;
+
+  if (internal::is_same<StorageIndex_, int>::value) {
+    res.itype = CHOLMOD_INT;
+  } else if (internal::is_same<StorageIndex_, SuiteSparse_long>::value) {
+    res.itype = CHOLMOD_LONG;
+  } else {
+    eigen_assert(false && "Index type not supported yet");
+  }
+
+  // setup res.xtype
+  internal::cholmod_configure_matrix<Scalar_>::run(res);
+
+  res.stype = 0;
+
+  return res;
+}
+
+template <typename Scalar_, int Options_, typename Index_>
+const cholmod_sparse viewAsCholmod(const SparseMatrix<Scalar_, Options_, Index_>& mat) {
+  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.const_cast_derived()));
+  return res;
+}
+
+template <typename Scalar_, int Options_, typename Index_>
+const cholmod_sparse viewAsCholmod(const SparseVector<Scalar_, Options_, Index_>& mat) {
+  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.const_cast_derived()));
+  return res;
+}
+
+/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
+ * The data are not copied but shared. */
+template <typename Scalar_, int Options_, typename Index_, unsigned int UpLo>
+cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<Scalar_, Options_, Index_>, UpLo>& mat) {
+  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.matrix().const_cast_derived()));
+
+  if (UpLo == Upper) res.stype = 1;
+  if (UpLo == Lower) res.stype = -1;
+  // swap stype for rowmajor matrices (only works for real matrices)
+  EIGEN_STATIC_ASSERT((Options_ & RowMajorBit) == 0 || NumTraits<Scalar_>::IsComplex == 0,
+                      THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  if (Options_ & RowMajorBit) res.stype *= -1;
+
+  return res;
+}
+
+/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix.
+ * The data are not copied but shared. */
+template <typename Derived>
+cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat) {
+  EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags & RowMajorBit) == 0,
+                      THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  typedef typename Derived::Scalar Scalar;
+
+  cholmod_dense res;
+  res.nrow = mat.rows();
+  res.ncol = mat.cols();
+  res.nzmax = res.nrow * res.ncol;
+  res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
+  res.x = (void*)(mat.derived().data());
+  res.z = 0;
+
+  internal::cholmod_configure_matrix<Scalar>::run(res);
+
+  return res;
+}
+
+/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
+ * The data are not copied but shared. */
+template <typename Scalar, typename StorageIndex>
+Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> > viewAsEigen(cholmod_sparse& cm) {
+  return Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> >(
+      cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol], static_cast<StorageIndex*>(cm.p),
+      static_cast<StorageIndex*>(cm.i), static_cast<Scalar*>(cm.x));
+}
+
+/** Returns a view of the Cholmod sparse matrix factor \a cm as an Eigen sparse matrix.
+ * The data are not copied but shared. */
+template <typename Scalar, typename StorageIndex>
+Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> > viewAsEigen(cholmod_factor& cm) {
+  return Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> >(
+      cm.n, cm.n, static_cast<StorageIndex*>(cm.p)[cm.n], static_cast<StorageIndex*>(cm.p),
+      static_cast<StorageIndex*>(cm.i), static_cast<Scalar*>(cm.x));
+}
+
+namespace internal {
+
+// template specializations for int and long that call the correct cholmod method
+
+#define EIGEN_CHOLMOD_SPECIALIZE0(ret, name)                        \
+  template <typename StorageIndex_>                                 \
+  inline ret cm_##name(cholmod_common& Common) {                    \
+    return cholmod_##name(&Common);                                 \
+  }                                                                 \
+  template <>                                                       \
+  inline ret cm_##name<SuiteSparse_long>(cholmod_common & Common) { \
+    return cholmod_l_##name(&Common);                               \
+  }
+
+#define EIGEN_CHOLMOD_SPECIALIZE1(ret, name, t1, a1)                         \
+  template <typename StorageIndex_>                                          \
+  inline ret cm_##name(t1& a1, cholmod_common& Common) {                     \
+    return cholmod_##name(&a1, &Common);                                     \
+  }                                                                          \
+  template <>                                                                \
+  inline ret cm_##name<SuiteSparse_long>(t1 & a1, cholmod_common & Common) { \
+    return cholmod_l_##name(&a1, &Common);                                   \
+  }
+
+EIGEN_CHOLMOD_SPECIALIZE0(int, start)
+EIGEN_CHOLMOD_SPECIALIZE0(int, finish)
+
+EIGEN_CHOLMOD_SPECIALIZE1(int, free_factor, cholmod_factor*, L)
+EIGEN_CHOLMOD_SPECIALIZE1(int, free_dense, cholmod_dense*, X)
+EIGEN_CHOLMOD_SPECIALIZE1(int, free_sparse, cholmod_sparse*, A)
+
+EIGEN_CHOLMOD_SPECIALIZE1(cholmod_factor*, analyze, cholmod_sparse, A)
+EIGEN_CHOLMOD_SPECIALIZE1(cholmod_sparse*, factor_to_sparse, cholmod_factor, L)
+
+template <typename StorageIndex_>
+inline cholmod_dense* cm_solve(int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common& Common) {
+  return cholmod_solve(sys, &L, &B, &Common);
+}
+template <>
+inline cholmod_dense* cm_solve<SuiteSparse_long>(int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common& Common) {
+  return cholmod_l_solve(sys, &L, &B, &Common);
+}
+
+template <typename StorageIndex_>
+inline cholmod_sparse* cm_spsolve(int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common& Common) {
+  return cholmod_spsolve(sys, &L, &B, &Common);
+}
+template <>
+inline cholmod_sparse* cm_spsolve<SuiteSparse_long>(int sys, cholmod_factor& L, cholmod_sparse& B,
+                                                    cholmod_common& Common) {
+  return cholmod_l_spsolve(sys, &L, &B, &Common);
+}
+
+template <typename StorageIndex_>
+inline int cm_factorize_p(cholmod_sparse* A, double beta[2], StorageIndex_* fset, std::size_t fsize, cholmod_factor* L,
+                          cholmod_common& Common) {
+  return cholmod_factorize_p(A, beta, fset, fsize, L, &Common);
+}
+template <>
+inline int cm_factorize_p<SuiteSparse_long>(cholmod_sparse* A, double beta[2], SuiteSparse_long* fset,
+                                            std::size_t fsize, cholmod_factor* L, cholmod_common& Common) {
+  return cholmod_l_factorize_p(A, beta, fset, fsize, L, &Common);
+}
+
+#undef EIGEN_CHOLMOD_SPECIALIZE0
+#undef EIGEN_CHOLMOD_SPECIALIZE1
+
+}  // namespace internal
+
+enum CholmodMode { CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt };
+
+/** \ingroup CholmodSupport_Module
+ * \class CholmodBase
+ * \brief The base class for the direct Cholesky factorization of Cholmod
+ * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
+ */
+template <typename MatrixType_, int UpLo_, typename Derived>
+class CholmodBase : public SparseSolverBase<Derived> {
+ protected:
+  typedef SparseSolverBase<Derived> Base;
+  using Base::derived;
+  using Base::m_isInitialized;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  enum { UpLo = UpLo_ };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef MatrixType CholMatrixType;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+ public:
+  CholmodBase() : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false) {
+    EIGEN_STATIC_ASSERT((internal::is_same<double, RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
+    m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
+    internal::cm_start<StorageIndex>(m_cholmod);
+  }
+
+  explicit CholmodBase(const MatrixType& matrix)
+      : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false) {
+    EIGEN_STATIC_ASSERT((internal::is_same<double, RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
+    m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
+    internal::cm_start<StorageIndex>(m_cholmod);
+    compute(matrix);
+  }
+
+  ~CholmodBase() {
+    if (m_cholmodFactor) internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
+    internal::cm_finish<StorageIndex>(m_cholmod);
+  }
+
+  inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
+  inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix.appears to be negative.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  /** Computes the sparse Cholesky decomposition of \a matrix */
+  Derived& compute(const MatrixType& matrix) {
+    analyzePattern(matrix);
+    factorize(matrix);
+    return derived();
+  }
+
+  /** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType& matrix) {
+    if (m_cholmodFactor) {
+      internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
+      m_cholmodFactor = 0;
+    }
+    cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
+    m_cholmodFactor = internal::cm_analyze<StorageIndex>(A, m_cholmod);
+
+    this->m_isInitialized = true;
+    this->m_info = Success;
+    m_analysisIsOk = true;
+    m_factorizationIsOk = false;
+  }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been
+   * performed.
+   *
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType& matrix) {
+    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+    cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
+    internal::cm_factorize_p<StorageIndex>(&A, m_shiftOffset, 0, 0, m_cholmodFactor, m_cholmod);
+
+    // If the factorization failed, either the input matrix was zero (so m_cholmodFactor == nullptr), or minor is the
+    // column at which it failed. On success minor == n.
+    this->m_info =
+        (m_cholmodFactor != nullptr && m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
+    m_factorizationIsOk = true;
+  }
+
+  /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations.
+   *  See the Cholmod user guide for details. */
+  cholmod_common& cholmod() { return m_cholmod; }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const {
+    eigen_assert(m_factorizationIsOk &&
+                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
+                 "symbolic()/numeric()");
+    const Index size = m_cholmodFactor->n;
+    EIGEN_UNUSED_VARIABLE(size);
+    eigen_assert(size == b.rows());
+
+    // Cholmod needs column-major storage without inner-stride, which corresponds to the default behavior of Ref.
+    Ref<const Matrix<typename Rhs::Scalar, Dynamic, Dynamic, ColMajor> > b_ref(b.derived());
+
+    cholmod_dense b_cd = viewAsCholmod(b_ref);
+    cholmod_dense* x_cd = internal::cm_solve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cd, m_cholmod);
+    if (!x_cd) {
+      this->m_info = NumericalIssue;
+      return;
+    }
+    // TODO: optimize this copy by swapping when possible (be careful with alignment, etc.)
+    // NOTE Actually, the copy can be avoided by calling cholmod_solve2 instead of cholmod_solve
+    dest = Matrix<Scalar, Dest::RowsAtCompileTime, Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),
+                                                                                 b.rows(), b.cols());
+    internal::cm_free_dense<StorageIndex>(x_cd, m_cholmod);
+  }
+
+  /** \internal */
+  template <typename RhsDerived, typename DestDerived>
+  void _solve_impl(const SparseMatrixBase<RhsDerived>& b, SparseMatrixBase<DestDerived>& dest) const {
+    eigen_assert(m_factorizationIsOk &&
+                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
+                 "symbolic()/numeric()");
+    const Index size = m_cholmodFactor->n;
+    EIGEN_UNUSED_VARIABLE(size);
+    eigen_assert(size == b.rows());
+
+    // note: cs stands for Cholmod Sparse
+    Ref<SparseMatrix<typename RhsDerived::Scalar, ColMajor, typename RhsDerived::StorageIndex> > b_ref(
+        b.const_cast_derived());
+    cholmod_sparse b_cs = viewAsCholmod(b_ref);
+    cholmod_sparse* x_cs = internal::cm_spsolve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cs, m_cholmod);
+    if (!x_cs) {
+      this->m_info = NumericalIssue;
+      return;
+    }
+    // TODO: optimize this copy by swapping when possible (be careful with alignment, etc.)
+    // NOTE cholmod_spsolve in fact just calls the dense solver for blocks of 4 columns at a time (similar to Eigen's
+    // sparse solver)
+    dest.derived() = viewAsEigen<typename DestDerived::Scalar, typename DestDerived::StorageIndex>(*x_cs);
+    internal::cm_free_sparse<StorageIndex>(x_cs, m_cholmod);
+  }
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+
+  /** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
+   *
+   * During the numerical factorization, an offset term is added to the diagonal coefficients:\n
+   * \c d_ii = \a offset + \c d_ii
+   *
+   * The default is \a offset=0.
+   *
+   * \returns a reference to \c *this.
+   */
+  Derived& setShift(const RealScalar& offset) {
+    m_shiftOffset[0] = double(offset);
+    return derived();
+  }
+
+  /** \returns the determinant of the underlying matrix from the current factorization */
+  Scalar determinant() const {
+    using std::exp;
+    return exp(logDeterminant());
+  }
+
+  /** \returns the log determinant of the underlying matrix from the current factorization */
+  Scalar logDeterminant() const {
+    using numext::real;
+    using std::log;
+    eigen_assert(m_factorizationIsOk &&
+                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
+                 "symbolic()/numeric()");
+
+    RealScalar logDet = 0;
+    Scalar* x = static_cast<Scalar*>(m_cholmodFactor->x);
+    if (m_cholmodFactor->is_super) {
+      // Supernodal factorization stored as a packed list of dense column-major blocks,
+      // as described by the following structure:
+
+      // super[k] == index of the first column of the j-th super node
+      StorageIndex* super = static_cast<StorageIndex*>(m_cholmodFactor->super);
+      // pi[k] == offset to the description of row indices
+      StorageIndex* pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
+      // px[k] == offset to the respective dense block
+      StorageIndex* px = static_cast<StorageIndex*>(m_cholmodFactor->px);
+
+      Index nb_super_nodes = m_cholmodFactor->nsuper;
+      for (Index k = 0; k < nb_super_nodes; ++k) {
+        StorageIndex ncols = super[k + 1] - super[k];
+        StorageIndex nrows = pi[k + 1] - pi[k];
+
+        Map<const Array<Scalar, 1, Dynamic>, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows + 1));
+        logDet += sk.real().log().sum();
+      }
+    } else {
+      // Simplicial factorization stored as standard CSC matrix.
+      StorageIndex* p = static_cast<StorageIndex*>(m_cholmodFactor->p);
+      Index size = m_cholmodFactor->n;
+      for (Index k = 0; k < size; ++k) logDet += log(real(x[p[k]]));
+    }
+    if (m_cholmodFactor->is_ll) logDet *= 2.0;
+    return logDet;
+  }
+
+  template <typename Stream>
+  void dumpMemory(Stream& /*s*/) {}
+
+ protected:
+  mutable cholmod_common m_cholmod;
+  cholmod_factor* m_cholmodFactor;
+  double m_shiftOffset[2];
+  mutable ComputationInfo m_info;
+  int m_factorizationIsOk;
+  int m_analysisIsOk;
+};
+
+/** \ingroup CholmodSupport_Module
+ * \class CholmodSimplicialLLT
+ * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
+ * using the Cholmod library.
+ * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical
+ * interest. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices X and B can be
+ * either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ *
+ * \implsparsesolverconcept
+ *
+ * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
+ * compressed.
+ *
+ * \warning Only double precision real and complex scalar types are supported by Cholmod.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
+ */
+template <typename MatrixType_, int UpLo_ = Lower>
+class CholmodSimplicialLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT<MatrixType_, UpLo_> > {
+  typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT> Base;
+  using Base::m_cholmod;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef TriangularView<const MatrixType, Eigen::Lower> MatrixL;
+  typedef TriangularView<const typename MatrixType::AdjointReturnType, Eigen::Upper> MatrixU;
+
+  CholmodSimplicialLLT() : Base() { init(); }
+
+  CholmodSimplicialLLT(const MatrixType& matrix) : Base() {
+    init();
+    this->compute(matrix);
+  }
+
+  ~CholmodSimplicialLLT() {}
+
+  /** \returns an expression of the factor L */
+  inline MatrixL matrixL() const { return viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor); }
+
+  /** \returns an expression of the factor U (= L^*) */
+  inline MatrixU matrixU() const { return matrixL().adjoint(); }
+
+ protected:
+  void init() {
+    m_cholmod.final_asis = 0;
+    m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+    m_cholmod.final_ll = 1;
+  }
+};
+
+/** \ingroup CholmodSupport_Module
+ * \class CholmodSimplicialLDLT
+ * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
+ * using the Cholmod library.
+ * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical
+ * interest. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices X and B can be
+ * either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ *
+ * \implsparsesolverconcept
+ *
+ * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
+ * compressed.
+ *
+ * \warning Only double precision real and complex scalar types are supported by Cholmod.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
+ */
+template <typename MatrixType_, int UpLo_ = Lower>
+class CholmodSimplicialLDLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT<MatrixType_, UpLo_> > {
+  typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT> Base;
+  using Base::m_cholmod;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef TriangularView<const MatrixType, Eigen::UnitLower> MatrixL;
+  typedef TriangularView<const typename MatrixType::AdjointReturnType, Eigen::UnitUpper> MatrixU;
+
+  CholmodSimplicialLDLT() : Base() { init(); }
+
+  CholmodSimplicialLDLT(const MatrixType& matrix) : Base() {
+    init();
+    this->compute(matrix);
+  }
+
+  ~CholmodSimplicialLDLT() {}
+
+  /** \returns a vector expression of the diagonal D */
+  inline VectorType vectorD() const {
+    auto cholmodL = viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor);
+
+    VectorType D{cholmodL.rows()};
+
+    for (Index k = 0; k < cholmodL.outerSize(); ++k) {
+      typename decltype(cholmodL)::InnerIterator it{cholmodL, k};
+      D(k) = it.value();
+    }
+
+    return D;
+  }
+
+  /** \returns an expression of the factor L */
+  inline MatrixL matrixL() const { return viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor); }
+
+  /** \returns an expression of the factor U (= L^*) */
+  inline MatrixU matrixU() const { return matrixL().adjoint(); }
+
+ protected:
+  void init() {
+    m_cholmod.final_asis = 1;
+    m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+  }
+};
+
+/** \ingroup CholmodSupport_Module
+ * \class CholmodSupernodalLLT
+ * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
+ * using the Cholmod library.
+ * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
+ * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
+ * X and B can be either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ *
+ * \implsparsesolverconcept
+ *
+ * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
+ * compressed.
+ *
+ * \warning Only double precision real and complex scalar types are supported by Cholmod.
+ *
+ * \sa \ref TutorialSparseSolverConcept
+ */
+template <typename MatrixType_, int UpLo_ = Lower>
+class CholmodSupernodalLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT<MatrixType_, UpLo_> > {
+  typedef CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT> Base;
+  using Base::m_cholmod;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+
+  CholmodSupernodalLLT() : Base() { init(); }
+
+  CholmodSupernodalLLT(const MatrixType& matrix) : Base() {
+    init();
+    this->compute(matrix);
+  }
+
+  ~CholmodSupernodalLLT() {}
+
+  /** \returns an expression of the factor L */
+  inline MatrixType matrixL() const {
+    // Convert Cholmod factor's supernodal storage format to Eigen's CSC storage format
+    cholmod_sparse* cholmodL = internal::cm_factor_to_sparse(*Base::m_cholmodFactor, m_cholmod);
+    MatrixType L = viewAsEigen<Scalar, StorageIndex>(*cholmodL);
+    internal::cm_free_sparse<StorageIndex>(cholmodL, m_cholmod);
+
+    return L;
+  }
+
+  /** \returns an expression of the factor U (= L^*) */
+  inline MatrixType matrixU() const { return matrixL().adjoint(); }
+
+ protected:
+  void init() {
+    m_cholmod.final_asis = 1;
+    m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
+  }
+};
+
+/** \ingroup CholmodSupport_Module
+ * \class CholmodDecomposition
+ * \brief A general Cholesky factorization and solver based on Cholmod
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
+ * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
+ * X and B can be either dense or sparse.
+ *
+ * This variant permits to change the underlying Cholesky method at runtime.
+ * On the other hand, it does not provide access to the result of the factorization.
+ * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ *
+ * \implsparsesolverconcept
+ *
+ * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
+ * compressed.
+ *
+ * \warning Only double precision real and complex scalar types are supported by Cholmod.
+ *
+ * \sa \ref TutorialSparseSolverConcept
+ */
+template <typename MatrixType_, int UpLo_ = Lower>
+class CholmodDecomposition : public CholmodBase<MatrixType_, UpLo_, CholmodDecomposition<MatrixType_, UpLo_> > {
+  typedef CholmodBase<MatrixType_, UpLo_, CholmodDecomposition> Base;
+  using Base::m_cholmod;
+
+ public:
+  typedef MatrixType_ MatrixType;
+
+  CholmodDecomposition() : Base() { init(); }
+
+  CholmodDecomposition(const MatrixType& matrix) : Base() {
+    init();
+    this->compute(matrix);
+  }
+
+  ~CholmodDecomposition() {}
+
+  void setMode(CholmodMode mode) {
+    switch (mode) {
+      case CholmodAuto:
+        m_cholmod.final_asis = 1;
+        m_cholmod.supernodal = CHOLMOD_AUTO;
+        break;
+      case CholmodSimplicialLLt:
+        m_cholmod.final_asis = 0;
+        m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+        m_cholmod.final_ll = 1;
+        break;
+      case CholmodSupernodalLLt:
+        m_cholmod.final_asis = 1;
+        m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
+        break;
+      case CholmodLDLt:
+        m_cholmod.final_asis = 1;
+        m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+        break;
+      default:
+        break;
+    }
+  }
+
+ protected:
+  void init() {
+    m_cholmod.final_asis = 1;
+    m_cholmod.supernodal = CHOLMOD_AUTO;
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CHOLMODSUPPORT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/CholmodSupport/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/CholmodSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000..0fb3abc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/CholmodSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
+#error "Please include Eigen/CholmodSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArithmeticSequence.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArithmeticSequence.h
new file mode 100644
index 0000000..4161771
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArithmeticSequence.h
@@ -0,0 +1,239 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARITHMETIC_SEQUENCE_H
+#define EIGEN_ARITHMETIC_SEQUENCE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Helper to cleanup the type of the increment:
+template <typename T>
+struct cleanup_seq_incr {
+  typedef typename cleanup_index_type<T, DynamicIndex>::type type;
+};
+
+}  // namespace internal
+
+//--------------------------------------------------------------------------------
+// seq(first,last,incr) and seqN(first,size,incr)
+//--------------------------------------------------------------------------------
+
+template <typename FirstType = Index, typename SizeType = Index, typename IncrType = internal::FixedInt<1> >
+class ArithmeticSequence;
+
+template <typename FirstType, typename SizeType, typename IncrType>
+ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                   typename internal::cleanup_index_type<SizeType>::type,
+                   typename internal::cleanup_seq_incr<IncrType>::type>
+seqN(FirstType first, SizeType size, IncrType incr);
+
+/** \class ArithmeticSequence
+ * \ingroup Core_Module
+ *
+ * This class represents an arithmetic progression \f$ a_0, a_1, a_2, ..., a_{n-1}\f$ defined by
+ * its \em first value \f$ a_0 \f$, its \em size (aka length) \em n, and the \em increment (aka stride)
+ * that is equal to \f$ a_{i+1}-a_{i}\f$ for any \em i.
+ *
+ * It is internally used as the return type of the Eigen::seq and Eigen::seqN functions, and as the input arguments
+ * of DenseBase::operator()(const RowIndices&, const ColIndices&), and most of the time this is the
+ * only way it is used.
+ *
+ * \tparam FirstType type of the first element, usually an Index,
+ *                   but internally it can be a symbolic expression
+ * \tparam SizeType type representing the size of the sequence, usually an Index
+ *                  or a compile time integral constant. Internally, it can also be a symbolic expression
+ * \tparam IncrType type of the increment, can be a runtime Index, or a compile time integral constant (default is
+ * compile-time 1)
+ *
+ * \sa Eigen::seq, Eigen::seqN, DenseBase::operator()(const RowIndices&, const ColIndices&), class IndexedView
+ */
+template <typename FirstType, typename SizeType, typename IncrType>
+class ArithmeticSequence {
+ public:
+  constexpr ArithmeticSequence() = default;
+  constexpr ArithmeticSequence(FirstType first, SizeType size) : m_first(first), m_size(size) {}
+  constexpr ArithmeticSequence(FirstType first, SizeType size, IncrType incr)
+      : m_first(first), m_size(size), m_incr(incr) {}
+
+  enum {
+    // SizeAtCompileTime = internal::get_fixed_value<SizeType>::value,
+    IncrAtCompileTime = internal::get_fixed_value<IncrType, DynamicIndex>::value
+  };
+
+  /** \returns the size, i.e., number of elements, of the sequence */
+  constexpr Index size() const { return m_size; }
+
+  /** \returns the first element \f$ a_0 \f$ in the sequence */
+  constexpr Index first() const { return m_first; }
+
+  /** \returns the value \f$ a_i \f$ at index \a i in the sequence. */
+  constexpr Index operator[](Index i) const { return m_first + i * m_incr; }
+
+  constexpr const FirstType& firstObject() const { return m_first; }
+  constexpr const SizeType& sizeObject() const { return m_size; }
+  constexpr const IncrType& incrObject() const { return m_incr; }
+
+ protected:
+  FirstType m_first;
+  SizeType m_size;
+  IncrType m_incr;
+
+ public:
+  constexpr auto reverse() const -> decltype(Eigen::seqN(m_first + (m_size + fix<-1>()) * m_incr, m_size, -m_incr)) {
+    return seqN(m_first + (m_size + fix<-1>()) * m_incr, m_size, -m_incr);
+  }
+};
+
+/** \returns an ArithmeticSequence starting at \a first, of length \a size, and increment \a incr
+ *
+ * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
+template <typename FirstType, typename SizeType, typename IncrType>
+ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                   typename internal::cleanup_index_type<SizeType>::type,
+                   typename internal::cleanup_seq_incr<IncrType>::type>
+seqN(FirstType first, SizeType size, IncrType incr) {
+  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                            typename internal::cleanup_index_type<SizeType>::type,
+                            typename internal::cleanup_seq_incr<IncrType>::type>(first, size, incr);
+}
+
+/** \returns an ArithmeticSequence starting at \a first, of length \a size, and unit increment
+ *
+ * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType) */
+template <typename FirstType, typename SizeType>
+ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                   typename internal::cleanup_index_type<SizeType>::type>
+seqN(FirstType first, SizeType size) {
+  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                            typename internal::cleanup_index_type<SizeType>::type>(first, size);
+}
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+
+/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and with positive (or negative) increment \a
+ * incr
+ *
+ * It is essentially an alias to:
+ * \code
+ * seqN(f, (l-f+incr)/incr, incr);
+ * \endcode
+ *
+ * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType)
+ */
+template <typename FirstType, typename LastType, typename IncrType>
+auto seq(FirstType f, LastType l, IncrType incr);
+
+/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and unit increment
+ *
+ * It is essentially an alias to:
+ * \code
+ * seqN(f,l-f+1);
+ * \endcode
+ *
+ * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType)
+ */
+template <typename FirstType, typename LastType>
+auto seq(FirstType f, LastType l);
+
+#else  // EIGEN_PARSED_BY_DOXYGEN
+
+template <typename FirstType, typename LastType>
+auto seq(FirstType f, LastType l)
+    -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+                     (typename internal::cleanup_index_type<LastType>::type(l) -
+                      typename internal::cleanup_index_type<FirstType>::type(f) + fix<1>()))) {
+  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+              (typename internal::cleanup_index_type<LastType>::type(l) -
+               typename internal::cleanup_index_type<FirstType>::type(f) + fix<1>()));
+}
+
+template <typename FirstType, typename LastType, typename IncrType>
+auto seq(FirstType f, LastType l, IncrType incr)
+    -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+                     (typename internal::cleanup_index_type<LastType>::type(l) -
+                      typename internal::cleanup_index_type<FirstType>::type(f) +
+                      typename internal::cleanup_seq_incr<IncrType>::type(incr)) /
+                         typename internal::cleanup_seq_incr<IncrType>::type(incr),
+                     typename internal::cleanup_seq_incr<IncrType>::type(incr))) {
+  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
+  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+              (typename internal::cleanup_index_type<LastType>::type(l) -
+               typename internal::cleanup_index_type<FirstType>::type(f) + CleanedIncrType(incr)) /
+                  CleanedIncrType(incr),
+              CleanedIncrType(incr));
+}
+
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+
+namespace placeholders {
+
+/** \cpp11
+ * \returns a symbolic ArithmeticSequence representing the last \a size elements with increment \a incr.
+ *
+ * It is a shortcut for: \code seqN(last-(size-fix<1>)*incr, size, incr) \endcode
+ * \anchor Eigen_placeholders_lastN
+ * \sa lastN(SizeType), seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
+template <typename SizeType, typename IncrType>
+auto lastN(SizeType size, IncrType incr)
+    -> decltype(seqN(Eigen::placeholders::last - (size - fix<1>()) * incr, size, incr)) {
+  return seqN(Eigen::placeholders::last - (size - fix<1>()) * incr, size, incr);
+}
+
+/** \cpp11
+ * \returns a symbolic ArithmeticSequence representing the last \a size elements with a unit increment.
+ *
+ *  It is a shortcut for: \code seq(last+fix<1>-size, last) \endcode
+ *
+ * \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */
+template <typename SizeType>
+auto lastN(SizeType size) -> decltype(seqN(Eigen::placeholders::last + fix<1>() - size, size)) {
+  return seqN(Eigen::placeholders::last + fix<1>() - size, size);
+}
+
+}  // namespace placeholders
+
+/** \namespace Eigen::indexing
+  * \ingroup Core_Module
+  *
+  * The sole purpose of this namespace is to be able to import all functions
+  * and symbols that are expected to be used within operator() for indexing
+  * and slicing. If you already imported the whole Eigen namespace:
+  * \code using namespace Eigen; \endcode
+  * then you are already all set. Otherwise, if you don't want/cannot import
+  * the whole Eigen namespace, the following line:
+  * \code using namespace Eigen::indexing; \endcode
+  * is equivalent to:
+  * \code
+  using Eigen::fix;
+  using Eigen::seq;
+  using Eigen::seqN;
+  using Eigen::placeholders::all;
+  using Eigen::placeholders::last;
+  using Eigen::placeholders::lastN;  // c++11 only
+  using Eigen::placeholders::lastp1;
+  \endcode
+  */
+namespace indexing {
+using Eigen::fix;
+using Eigen::seq;
+using Eigen::seqN;
+using Eigen::placeholders::all;
+using Eigen::placeholders::last;
+using Eigen::placeholders::lastN;
+using Eigen::placeholders::lastp1;
+}  // namespace indexing
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARITHMETIC_SEQUENCE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Array.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Array.h
new file mode 100644
index 0000000..b6b4637
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Array.h
@@ -0,0 +1,374 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARRAY_H
+#define EIGEN_ARRAY_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct traits<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>>
+    : traits<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> {
+  typedef ArrayXpr XprKind;
+  typedef ArrayBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> XprBase;
+};
+}  // namespace internal
+
+/** \class Array
+ * \ingroup Core_Module
+ *
+ * \brief General-purpose arrays with easy API for coefficient-wise operations
+ *
+ * The %Array class is very similar to the Matrix class. It provides
+ * general-purpose one- and two-dimensional arrays. The difference between the
+ * %Array and the %Matrix class is primarily in the API: the API for the
+ * %Array class provides easy access to coefficient-wise operations, while the
+ * API for the %Matrix class provides easy access to linear-algebra
+ * operations.
+ *
+ * See documentation of class Matrix for detailed information on the template parameters
+ * storage layout.
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
+ *
+ * \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
+ */
+template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+class Array : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> {
+ public:
+  typedef PlainObjectBase<Array> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Array)
+
+  enum { Options = Options_ };
+  typedef typename Base::PlainObject PlainObject;
+
+ protected:
+  template <typename Derived, typename OtherDerived, bool IsVector>
+  friend struct internal::conservative_resize_like_impl;
+
+  using Base::m_storage;
+
+ public:
+  using Base::base;
+  using Base::coeff;
+  using Base::coeffRef;
+
+  /**
+   * The usage of
+   *   using Base::operator=;
+   * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
+   * the usage of 'using'. This should be done only for operator=.
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived>& other) {
+    return Base::operator=(other);
+  }
+
+  /** Set all the entries to \a value.
+   * \sa DenseBase::setConstant(), DenseBase::fill()
+   */
+  /* This overload is needed because the usage of
+   *   using Base::operator=;
+   * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
+   * the usage of 'using'. This should be done only for operator=.
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const Scalar& value) {
+    Base::setConstant(value);
+    return *this;
+  }
+
+  /** Copies the value of the expression \a other into \c *this with automatic resizing.
+   *
+   * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
+   * it will be initialized.
+   *
+   * Note that copying a row-vector into a vector (and conversely) is allowed.
+   * The resizing, if any, is then done in the appropriate way so that row-vectors
+   * remain row-vectors and vectors remain vectors.
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other) {
+    return Base::_set(other);
+  }
+
+  /**
+   * \brief Assigns arrays to each other.
+   *
+   * \note This is a special case of the templated operator=. Its purpose is
+   * to prevent a default operator= from hiding the templated operator=.
+   *
+   * \callgraph
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const Array& other) { return Base::_set(other); }
+
+  /** Default constructor.
+   *
+   * For fixed-size matrices, does nothing.
+   *
+   * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
+   * is called a null matrix. This constructor is the unique way to create null matrices: resizing
+   * a matrix to 0 is not supported.
+   *
+   * \sa resize(Index,Index)
+   */
+#ifdef EIGEN_INITIALIZE_COEFFS
+  EIGEN_DEVICE_FUNC constexpr Array() : Base() { EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
+#else
+  EIGEN_DEVICE_FUNC constexpr Array() = default;
+#endif
+  /** \brief Move constructor */
+  EIGEN_DEVICE_FUNC constexpr Array(Array&&) = default;
+  EIGEN_DEVICE_FUNC Array& operator=(Array&& other) noexcept(std::is_nothrow_move_assignable<Scalar>::value) {
+    Base::operator=(std::move(other));
+    return *this;
+  }
+
+  /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients.
+   *
+   * \only_for_vectors
+   *
+   * This constructor is for 1D array or vectors with more than 4 coefficients.
+   *
+   * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
+   * constructor must match the fixed number of rows (resp. columns) of \c *this.
+   *
+   *
+   * Example: \include Array_variadic_ctor_cxx11.cpp
+   * Output: \verbinclude Array_variadic_ctor_cxx11.out
+   *
+   * \sa Array(const std::initializer_list<std::initializer_list<Scalar>>&)
+   * \sa Array(const Scalar&), Array(const Scalar&,const Scalar&)
+   */
+  template <typename... ArgTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3,
+                                              const ArgTypes&... args)
+      : Base(a0, a1, a2, a3, args...) {}
+
+  /** \brief Constructs an array and initializes it from the coefficients given as initializer-lists grouped by row.
+   * \cpp11
+   *
+   * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
+   *
+   * Example: \include Array_initializer_list_23_cxx11.cpp
+   * Output: \verbinclude Array_initializer_list_23_cxx11.out
+   *
+   * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is
+   * triggered.
+   *
+   * In the case of a compile-time column 1D array, implicit transposition from a single row is allowed.
+   * Therefore <code> Array<int,Dynamic,1>{{1,2,3,4,5}}</code> is legal and the more verbose syntax
+   * <code>Array<int,Dynamic,1>{{1},{2},{3},{4},{5}}</code> can be avoided:
+   *
+   * Example: \include Array_initializer_list_vector_cxx11.cpp
+   * Output: \verbinclude Array_initializer_list_vector_cxx11.out
+   *
+   * In the case of fixed-sized arrays, the initializer list sizes must exactly match the array sizes,
+   * and implicit transposition is allowed for compile-time 1D arrays only.
+   *
+   * \sa  Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+   */
+  EIGEN_DEVICE_FUNC constexpr Array(const std::initializer_list<std::initializer_list<Scalar>>& list) : Base(list) {}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Array(const T& x) {
+    Base::template _init1<T>(x);
+  }
+
+  template <typename T0, typename T1>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1) {
+    this->template _init2<T0, T1>(val0, val1);
+  }
+
+#else
+  /** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
+  EIGEN_DEVICE_FUNC explicit Array(const Scalar* data);
+  /** Constructs a vector or row-vector with given dimension. \only_for_vectors
+   *
+   * Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
+   * it is redundant to pass the dimension here, so it makes more sense to use the default
+   * constructor Array() instead.
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Array(Index dim);
+  /** constructs an initialized 1x1 Array with the given coefficient
+   * \sa const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args */
+  Array(const Scalar& value);
+  /** constructs an uninitialized array with \a rows rows and \a cols columns.
+   *
+   * This is useful for dynamic-size arrays. For fixed-size arrays,
+   * it is redundant to pass these parameters, so one should use the default constructor
+   * Array() instead. */
+  Array(Index rows, Index cols);
+  /** constructs an initialized 2D vector with given coefficients
+   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) */
+  Array(const Scalar& val0, const Scalar& val1);
+#endif  // end EIGEN_PARSED_BY_DOXYGEN
+
+  /** constructs an initialized 3D vector with given coefficients
+   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
+    m_storage.data()[0] = val0;
+    m_storage.data()[1] = val1;
+    m_storage.data()[2] = val2;
+  }
+  /** constructs an initialized 4D vector with given coefficients
+   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2,
+                                              const Scalar& val3) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
+    m_storage.data()[0] = val0;
+    m_storage.data()[1] = val1;
+    m_storage.data()[2] = val2;
+    m_storage.data()[3] = val3;
+  }
+
+  /** Copy constructor */
+  EIGEN_DEVICE_FUNC constexpr Array(const Array&) = default;
+
+ private:
+  struct PrivateType {};
+
+ public:
+  /** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Array(
+      const EigenBase<OtherDerived>& other,
+      std::enable_if_t<internal::is_convertible<typename OtherDerived::Scalar, Scalar>::value, PrivateType> =
+          PrivateType())
+      : Base(other.derived()) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return 1; }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return this->innerSize(); }
+
+#ifdef EIGEN_ARRAY_PLUGIN
+#include EIGEN_ARRAY_PLUGIN
+#endif
+
+ private:
+  template <typename MatrixType, typename OtherDerived, bool SwapPointers>
+  friend struct internal::matrix_swap_impl;
+};
+
+/** \defgroup arraytypedefs Global array typedefs
+ * \ingroup Core_Module
+ *
+ * %Eigen defines several typedef shortcuts for most common 1D and 2D array types.
+ *
+ * The general patterns are the following:
+ *
+ * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for
+ * dynamic size, and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c
+ * cd for complex double.
+ *
+ * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of
+ * floats.
+ *
+ * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is
+ * a fixed-size 1D array of 4 complex floats.
+ *
+ * With \cpp11, template alias are also defined for common sizes.
+ * They follow the same pattern as above except that the scalar type suffix is replaced by a
+ * template parameter, i.e.:
+ *   - `ArrayRowsCols<Type>` where `Rows` and `Cols` can be \c 2,\c 3,\c 4, or \c X for fixed or dynamic size.
+ *   - `ArraySize<Type>` where `Size` can be \c 2,\c 3,\c 4 or \c X for fixed or dynamic size 1D arrays.
+ *
+ * \sa class Array
+ */
+
+#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)        \
+  /** \ingroup arraytypedefs */                                              \
+  typedef Array<Type, Size, Size> Array##SizeSuffix##SizeSuffix##TypeSuffix; \
+  /** \ingroup arraytypedefs */                                              \
+  typedef Array<Type, Size, 1> Array##SizeSuffix##TypeSuffix;
+
+#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size)  \
+  /** \ingroup arraytypedefs */                                  \
+  typedef Array<Type, Size, Dynamic> Array##Size##X##TypeSuffix; \
+  /** \ingroup arraytypedefs */                                  \
+  typedef Array<Type, Dynamic, Size> Array##X##Size##TypeSuffix;
+
+#define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
+  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2)           \
+  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3)           \
+  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4)           \
+  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X)     \
+  EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2)        \
+  EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3)        \
+  EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
+
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int, i)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float, f)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double, d)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<float>, cf)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
+
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS
+#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
+
+#define EIGEN_MAKE_ARRAY_TYPEDEFS(Size, SizeSuffix)              \
+  /** \ingroup arraytypedefs */                                  \
+  /** \brief \cpp11 */                                           \
+  template <typename Type>                                       \
+  using Array##SizeSuffix##SizeSuffix = Array<Type, Size, Size>; \
+  /** \ingroup arraytypedefs */                                  \
+  /** \brief \cpp11 */                                           \
+  template <typename Type>                                       \
+  using Array##SizeSuffix = Array<Type, Size, 1>;
+
+#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Size)        \
+  /** \ingroup arraytypedefs */                      \
+  /** \brief \cpp11 */                               \
+  template <typename Type>                           \
+  using Array##Size##X = Array<Type, Size, Dynamic>; \
+  /** \ingroup arraytypedefs */                      \
+  /** \brief \cpp11 */                               \
+  template <typename Type>                           \
+  using Array##X##Size = Array<Type, Dynamic, Size>;
+
+EIGEN_MAKE_ARRAY_TYPEDEFS(2, 2)
+EIGEN_MAKE_ARRAY_TYPEDEFS(3, 3)
+EIGEN_MAKE_ARRAY_TYPEDEFS(4, 4)
+EIGEN_MAKE_ARRAY_TYPEDEFS(Dynamic, X)
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(2)
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(3)
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4)
+
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS
+#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
+
+#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
+  using Eigen::Matrix##SizeSuffix##TypeSuffix;                               \
+  using Eigen::Vector##SizeSuffix##TypeSuffix;                               \
+  using Eigen::RowVector##SizeSuffix##TypeSuffix;
+
+#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix)       \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X)
+
+#define EIGEN_USING_ARRAY_TYPEDEFS        \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i)  \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f)  \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d)  \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd)
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARRAY_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArrayBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArrayBase.h
new file mode 100644
index 0000000..dacc239
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArrayBase.h
@@ -0,0 +1,210 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARRAYBASE_H
+#define EIGEN_ARRAYBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename ExpressionType>
+class MatrixWrapper;
+
+/** \class ArrayBase
+ * \ingroup Core_Module
+ *
+ * \brief Base class for all 1D and 2D array, and related expressions
+ *
+ * An array is similar to a dense vector or matrix. While matrices are mathematical
+ * objects with well defined linear algebra operators, an array is just a collection
+ * of scalar values arranged in a one or two dimensional fashion. As the main consequence,
+ * all operations applied to an array are performed coefficient wise. Furthermore,
+ * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
+ * constructors allowing to easily write generic code working for both scalar values
+ * and arrays.
+ *
+ * This class is the base that is inherited by all array expression types.
+ *
+ * \tparam Derived is the derived type, e.g., an array or an expression type.
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
+ *
+ * \sa class MatrixBase, \ref TopicClassHierarchy
+ */
+template <typename Derived>
+class ArrayBase : public DenseBase<Derived> {
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** The base class for a given storage type. */
+  typedef ArrayBase StorageBaseType;
+
+  typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
+
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  typedef DenseBase<Derived> Base;
+  using Base::ColsAtCompileTime;
+  using Base::Flags;
+  using Base::IsVectorAtCompileTime;
+  using Base::MaxColsAtCompileTime;
+  using Base::MaxRowsAtCompileTime;
+  using Base::MaxSizeAtCompileTime;
+  using Base::RowsAtCompileTime;
+  using Base::SizeAtCompileTime;
+
+  using Base::coeff;
+  using Base::coeffRef;
+  using Base::cols;
+  using Base::const_cast_derived;
+  using Base::derived;
+  using Base::lazyAssign;
+  using Base::rows;
+  using Base::size;
+  using Base::operator-;
+  using Base::operator=;
+  using Base::operator+=;
+  using Base::operator-=;
+  using Base::operator*=;
+  using Base::operator/=;
+
+  typedef typename Base::CoeffReturnType CoeffReturnType;
+
+  typedef typename Base::PlainObject PlainObject;
+
+  /** \internal Represents a matrix with all coefficients equal to one another*/
+  typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> ConstantReturnType;
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
+#define EIGEN_DOC_UNARY_ADDONS(X, Y)
+#include "../plugins/MatrixCwiseUnaryOps.inc"
+#include "../plugins/ArrayCwiseUnaryOps.inc"
+#include "../plugins/CommonCwiseBinaryOps.inc"
+#include "../plugins/MatrixCwiseBinaryOps.inc"
+#include "../plugins/ArrayCwiseBinaryOps.inc"
+#ifdef EIGEN_ARRAYBASE_PLUGIN
+#include EIGEN_ARRAYBASE_PLUGIN
+#endif
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_DOC_UNARY_ADDONS
+
+  /** Special case of the template operator=, in order to prevent the compiler
+   * from generating a default operator= (issue hit with g++ 4.1)
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const ArrayBase& other) {
+    internal::call_assignment(derived(), other.derived());
+    return derived();
+  }
+
+  /** Set all the entries to \a value.
+   * \sa DenseBase::setConstant(), DenseBase::fill() */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Scalar& value) {
+    Base::setConstant(value);
+    return derived();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const Scalar& other) {
+    internal::call_assignment(this->derived(), PlainObject::Constant(rows(), cols(), other),
+                              internal::add_assign_op<Scalar, Scalar>());
+    return derived();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const Scalar& other) {
+    internal::call_assignment(this->derived(), PlainObject::Constant(rows(), cols(), other),
+                              internal::sub_assign_op<Scalar, Scalar>());
+    return derived();
+  }
+
+  /** replaces \c *this by \c *this + \a other.
+   *
+   * \returns a reference to \c *this
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const ArrayBase<OtherDerived>& other) {
+    call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+    return derived();
+  }
+
+  /** replaces \c *this by \c *this - \a other.
+   *
+   * \returns a reference to \c *this
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const ArrayBase<OtherDerived>& other) {
+    call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+    return derived();
+  }
+
+  /** replaces \c *this by \c *this * \a other coefficient wise.
+   *
+   * \returns a reference to \c *this
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator*=(const ArrayBase<OtherDerived>& other) {
+    call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar, typename OtherDerived::Scalar>());
+    return derived();
+  }
+
+  /** replaces \c *this by \c *this / \a other coefficient wise.
+   *
+   * \returns a reference to \c *this
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator/=(const ArrayBase<OtherDerived>& other) {
+    call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar, typename OtherDerived::Scalar>());
+    return derived();
+  }
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr ArrayBase<Derived>& array() { return *this; }
+  EIGEN_DEVICE_FUNC constexpr const ArrayBase<Derived>& array() const { return *this; }
+
+  /** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
+   * \sa MatrixBase::array() */
+  EIGEN_DEVICE_FUNC constexpr MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
+  EIGEN_DEVICE_FUNC constexpr const MatrixWrapper<const Derived> matrix() const {
+    return MatrixWrapper<const Derived>(derived());
+  }
+
+ protected:
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(ArrayBase)
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(ArrayBase)
+
+ private:
+  explicit ArrayBase(Index);
+  ArrayBase(Index, Index);
+  template <typename OtherDerived>
+  explicit ArrayBase(const ArrayBase<OtherDerived>&);
+
+ protected:
+  // mixing arrays and matrices is not legal
+  template <typename OtherDerived>
+  Derived& operator+=(const MatrixBase<OtherDerived>&) {
+    EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar)) == -1,
+                        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);
+    return *this;
+  }
+  // mixing arrays and matrices is not legal
+  template <typename OtherDerived>
+  Derived& operator-=(const MatrixBase<OtherDerived>&) {
+    EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar)) == -1,
+                        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);
+    return *this;
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARRAYBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArrayWrapper.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArrayWrapper.h
new file mode 100644
index 0000000..fb05ab5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArrayWrapper.h
@@ -0,0 +1,166 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARRAYWRAPPER_H
+#define EIGEN_ARRAYWRAPPER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class ArrayWrapper
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a mathematical vector or matrix as an array object
+ *
+ * This class is the return type of MatrixBase::array(), and most of the time
+ * this is the only way it is used.
+ *
+ * \sa MatrixBase::array(), class MatrixWrapper
+ */
+
+namespace internal {
+template <typename ExpressionType>
+struct traits<ArrayWrapper<ExpressionType> > : public traits<remove_all_t<typename ExpressionType::Nested> > {
+  typedef ArrayXpr XprKind;
+  // Let's remove NestByRefBit
+  enum {
+    Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
+    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
+    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
+  };
+};
+}  // namespace internal
+
+template <typename ExpressionType>
+class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> > {
+ public:
+  typedef ArrayBase<ArrayWrapper> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
+  typedef internal::remove_all_t<ExpressionType> NestedExpression;
+
+  typedef std::conditional_t<internal::is_lvalue<ExpressionType>::value, Scalar, const Scalar>
+      ScalarWithConstIfNotLvalue;
+
+  typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
+
+  using Base::coeffRef;
+
+  EIGEN_DEVICE_FUNC constexpr explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix)
+      : m_expression(matrix) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_expression.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_expression.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return m_expression.outerStride(); }
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_expression.innerStride(); }
+
+  EIGEN_DEVICE_FUNC constexpr ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_expression.data(); }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
+    return m_expression.coeffRef(rowId, colId);
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const { return m_expression.coeffRef(index); }
+
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC inline void evalTo(Dest& dst) const {
+    dst = m_expression;
+  }
+
+  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<NestedExpressionType>& nestedExpression() const {
+    return m_expression;
+  }
+
+  /** Forwards the resizing request to the nested expression
+   * \sa DenseBase::resize(Index)  */
+  EIGEN_DEVICE_FUNC void resize(Index newSize) { m_expression.resize(newSize); }
+  /** Forwards the resizing request to the nested expression
+   * \sa DenseBase::resize(Index,Index)*/
+  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) { m_expression.resize(rows, cols); }
+
+ protected:
+  NestedExpressionType m_expression;
+};
+
+/** \class MatrixWrapper
+ * \ingroup Core_Module
+ *
+ * \brief Expression of an array as a mathematical vector or matrix
+ *
+ * This class is the return type of ArrayBase::matrix(), and most of the time
+ * this is the only way it is used.
+ *
+ * \sa MatrixBase::matrix(), class ArrayWrapper
+ */
+
+namespace internal {
+template <typename ExpressionType>
+struct traits<MatrixWrapper<ExpressionType> > : public traits<remove_all_t<typename ExpressionType::Nested> > {
+  typedef MatrixXpr XprKind;
+  // Let's remove NestByRefBit
+  enum {
+    Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
+    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
+    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
+  };
+};
+}  // namespace internal
+
+template <typename ExpressionType>
+class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> > {
+ public:
+  typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
+  typedef internal::remove_all_t<ExpressionType> NestedExpression;
+
+  typedef std::conditional_t<internal::is_lvalue<ExpressionType>::value, Scalar, const Scalar>
+      ScalarWithConstIfNotLvalue;
+
+  typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
+
+  using Base::coeffRef;
+
+  EIGEN_DEVICE_FUNC constexpr explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_expression.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_expression.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return m_expression.outerStride(); }
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_expression.innerStride(); }
+
+  EIGEN_DEVICE_FUNC constexpr ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_expression.data(); }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
+    return m_expression.derived().coeffRef(rowId, colId);
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const { return m_expression.coeffRef(index); }
+
+  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<NestedExpressionType>& nestedExpression() const {
+    return m_expression;
+  }
+
+  /** Forwards the resizing request to the nested expression
+   * \sa DenseBase::resize(Index)  */
+  EIGEN_DEVICE_FUNC void resize(Index newSize) { m_expression.resize(newSize); }
+  /** Forwards the resizing request to the nested expression
+   * \sa DenseBase::resize(Index,Index)*/
+  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) { m_expression.resize(rows, cols); }
+
+ protected:
+  NestedExpressionType m_expression;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARRAYWRAPPER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign.h
new file mode 100644
index 0000000..3d30d86
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign.h
@@ -0,0 +1,84 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Michael Olbrich <michael.olbrich@gmx.net>
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ASSIGN_H
+#define EIGEN_ASSIGN_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::lazyAssign(
+    const DenseBase<OtherDerived>& other) {
+  enum { SameType = internal::is_same<typename Derived::Scalar, typename OtherDerived::Scalar>::value };
+
+  EIGEN_STATIC_ASSERT_LVALUE(Derived)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived, OtherDerived)
+  EIGEN_STATIC_ASSERT(
+      SameType,
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(rows() == other.rows() && cols() == other.cols());
+  internal::call_assignment_no_alias(derived(), other.derived());
+
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(
+    const DenseBase<OtherDerived>& other) {
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other) {
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other) {
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(
+    const DenseBase<OtherDerived>& other) {
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(
+    const EigenBase<OtherDerived>& other) {
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(
+    const ReturnByValue<OtherDerived>& other) {
+  other.derived().evalTo(derived());
+  return derived();
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ASSIGN_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/AssignEvaluator.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/AssignEvaluator.h
new file mode 100644
index 0000000..3afdd3e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/AssignEvaluator.h
@@ -0,0 +1,1066 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ASSIGN_EVALUATOR_H
+#define EIGEN_ASSIGN_EVALUATOR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// This implementation is based on Assign.h
+
+namespace internal {
+
+/***************************************************************************
+ * Part 1 : the logic deciding a strategy for traversal and unrolling       *
+ ***************************************************************************/
+
+// copy_using_evaluator_traits is based on assign_traits
+
+template <typename DstEvaluator, typename SrcEvaluator, typename AssignFunc, int MaxPacketSize = Dynamic>
+struct copy_using_evaluator_traits {
+  using Src = typename SrcEvaluator::XprType;
+  using Dst = typename DstEvaluator::XprType;
+  using DstScalar = typename Dst::Scalar;
+
+  static constexpr int DstFlags = DstEvaluator::Flags;
+  static constexpr int SrcFlags = SrcEvaluator::Flags;
+
+ public:
+  static constexpr int DstAlignment = DstEvaluator::Alignment;
+  static constexpr int SrcAlignment = SrcEvaluator::Alignment;
+  static constexpr int JointAlignment = plain_enum_min(DstAlignment, SrcAlignment);
+  static constexpr bool DstHasDirectAccess = bool(DstFlags & DirectAccessBit);
+  static constexpr bool SrcIsRowMajor = bool(SrcFlags & RowMajorBit);
+  static constexpr bool DstIsRowMajor = bool(DstFlags & RowMajorBit);
+  static constexpr bool IsVectorAtCompileTime = Dst::IsVectorAtCompileTime;
+  static constexpr int RowsAtCompileTime = size_prefer_fixed(Src::RowsAtCompileTime, Dst::RowsAtCompileTime);
+  static constexpr int ColsAtCompileTime = size_prefer_fixed(Src::ColsAtCompileTime, Dst::ColsAtCompileTime);
+  static constexpr int SizeAtCompileTime = size_at_compile_time(RowsAtCompileTime, ColsAtCompileTime);
+  static constexpr int MaxRowsAtCompileTime =
+      min_size_prefer_fixed(Src::MaxRowsAtCompileTime, Dst::MaxRowsAtCompileTime);
+  static constexpr int MaxColsAtCompileTime =
+      min_size_prefer_fixed(Src::MaxColsAtCompileTime, Dst::MaxColsAtCompileTime);
+  static constexpr int MaxSizeAtCompileTime =
+      min_size_prefer_fixed(Src::MaxSizeAtCompileTime, Dst::MaxSizeAtCompileTime);
+  static constexpr int InnerSizeAtCompileTime = IsVectorAtCompileTime ? SizeAtCompileTime
+                                                : DstIsRowMajor       ? ColsAtCompileTime
+                                                                      : RowsAtCompileTime;
+  static constexpr int MaxInnerSizeAtCompileTime = IsVectorAtCompileTime ? MaxSizeAtCompileTime
+                                                   : DstIsRowMajor       ? MaxColsAtCompileTime
+                                                                         : MaxRowsAtCompileTime;
+  static constexpr int RestrictedInnerSize = min_size_prefer_fixed(MaxInnerSizeAtCompileTime, MaxPacketSize);
+  static constexpr int RestrictedLinearSize = min_size_prefer_fixed(MaxSizeAtCompileTime, MaxPacketSize);
+  static constexpr int OuterStride = outer_stride_at_compile_time<Dst>::ret;
+
+  // TODO: distinguish between linear traversal and inner-traversal packet types.
+  using LinearPacketType = typename find_best_packet<DstScalar, RestrictedLinearSize>::type;
+  using InnerPacketType = typename find_best_packet<DstScalar, RestrictedInnerSize>::type;
+
+  static constexpr int LinearPacketSize = unpacket_traits<LinearPacketType>::size;
+  static constexpr int InnerPacketSize = unpacket_traits<InnerPacketType>::size;
+
+ public:
+  static constexpr int LinearRequiredAlignment = unpacket_traits<LinearPacketType>::alignment;
+  static constexpr int InnerRequiredAlignment = unpacket_traits<InnerPacketType>::alignment;
+
+ private:
+  static constexpr bool StorageOrdersAgree = DstIsRowMajor == SrcIsRowMajor;
+  static constexpr bool MightVectorize = StorageOrdersAgree && bool(DstFlags & SrcFlags & ActualPacketAccessBit) &&
+                                         bool(functor_traits<AssignFunc>::PacketAccess);
+  static constexpr bool MayInnerVectorize = MightVectorize && (InnerSizeAtCompileTime != Dynamic) &&
+                                            (InnerSizeAtCompileTime % InnerPacketSize == 0) &&
+                                            (OuterStride != Dynamic) && (OuterStride % InnerPacketSize == 0) &&
+                                            (EIGEN_UNALIGNED_VECTORIZE || JointAlignment >= InnerRequiredAlignment);
+  static constexpr bool MayLinearize = StorageOrdersAgree && (DstFlags & SrcFlags & LinearAccessBit);
+  static constexpr bool MayLinearVectorize =
+      MightVectorize && MayLinearize && DstHasDirectAccess &&
+      (EIGEN_UNALIGNED_VECTORIZE || (DstAlignment >= LinearRequiredAlignment) || MaxSizeAtCompileTime == Dynamic) &&
+      (MaxSizeAtCompileTime == Dynamic || MaxSizeAtCompileTime >= LinearPacketSize);
+  /* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
+     so it's only good for large enough sizes. */
+  static constexpr int InnerSizeThreshold = (EIGEN_UNALIGNED_VECTORIZE ? 1 : 3) * InnerPacketSize;
+  static constexpr bool MaySliceVectorize =
+      MightVectorize && DstHasDirectAccess &&
+      (MaxInnerSizeAtCompileTime == Dynamic || MaxInnerSizeAtCompileTime >= InnerSizeThreshold);
+  /* slice vectorization can be slow, so we only want it if the slices are big, which is
+     indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
+     in a fixed-size matrix
+     However, with EIGEN_UNALIGNED_VECTORIZE and unrolling, slice vectorization is still worth it */
+
+ public:
+  static constexpr int Traversal = SizeAtCompileTime == 0 ? AllAtOnceTraversal
+                                   : (MayLinearVectorize && (LinearPacketSize > InnerPacketSize))
+                                       ? LinearVectorizedTraversal
+                                   : MayInnerVectorize  ? InnerVectorizedTraversal
+                                   : MayLinearVectorize ? LinearVectorizedTraversal
+                                   : MaySliceVectorize  ? SliceVectorizedTraversal
+                                   : MayLinearize       ? LinearTraversal
+                                                        : DefaultTraversal;
+  static constexpr bool Vectorized = Traversal == InnerVectorizedTraversal || Traversal == LinearVectorizedTraversal ||
+                                     Traversal == SliceVectorizedTraversal;
+
+  using PacketType = std::conditional_t<Traversal == LinearVectorizedTraversal, LinearPacketType, InnerPacketType>;
+
+ private:
+  static constexpr int ActualPacketSize = Vectorized ? unpacket_traits<PacketType>::size : 1;
+  static constexpr int UnrollingLimit = EIGEN_UNROLLING_LIMIT * ActualPacketSize;
+  static constexpr int CoeffReadCost = int(DstEvaluator::CoeffReadCost) + int(SrcEvaluator::CoeffReadCost);
+  static constexpr bool MayUnrollCompletely =
+      (SizeAtCompileTime != Dynamic) && (SizeAtCompileTime * CoeffReadCost <= UnrollingLimit);
+  static constexpr bool MayUnrollInner =
+      (InnerSizeAtCompileTime != Dynamic) && (InnerSizeAtCompileTime * CoeffReadCost <= UnrollingLimit);
+
+ public:
+  static constexpr int Unrolling =
+      (Traversal == InnerVectorizedTraversal || Traversal == DefaultTraversal)
+          ? (MayUnrollCompletely ? CompleteUnrolling
+             : MayUnrollInner    ? InnerUnrolling
+                                 : NoUnrolling)
+      : Traversal == LinearVectorizedTraversal
+          ? (MayUnrollCompletely && (EIGEN_UNALIGNED_VECTORIZE || (DstAlignment >= LinearRequiredAlignment))
+                 ? CompleteUnrolling
+                 : NoUnrolling)
+      : Traversal == LinearTraversal ? (MayUnrollCompletely ? CompleteUnrolling : NoUnrolling)
+#if EIGEN_UNALIGNED_VECTORIZE
+      : Traversal == SliceVectorizedTraversal ? (MayUnrollInner ? InnerUnrolling : NoUnrolling)
+#endif
+                                              : NoUnrolling;
+  static constexpr bool UsePacketSegment = has_packet_segment<PacketType>::value;
+
+#ifdef EIGEN_DEBUG_ASSIGN
+  static void debug() {
+    std::cerr << "DstXpr: " << typeid(typename DstEvaluator::XprType).name() << std::endl;
+    std::cerr << "SrcXpr: " << typeid(typename SrcEvaluator::XprType).name() << std::endl;
+    std::cerr.setf(std::ios::hex, std::ios::basefield);
+    std::cerr << "DstFlags"
+              << " = " << DstFlags << " (" << demangle_flags(DstFlags) << " )" << std::endl;
+    std::cerr << "SrcFlags"
+              << " = " << SrcFlags << " (" << demangle_flags(SrcFlags) << " )" << std::endl;
+    std::cerr.unsetf(std::ios::hex);
+    EIGEN_DEBUG_VAR(DstAlignment)
+    EIGEN_DEBUG_VAR(SrcAlignment)
+    EIGEN_DEBUG_VAR(LinearRequiredAlignment)
+    EIGEN_DEBUG_VAR(InnerRequiredAlignment)
+    EIGEN_DEBUG_VAR(JointAlignment)
+    EIGEN_DEBUG_VAR(InnerSizeAtCompileTime)
+    EIGEN_DEBUG_VAR(MaxInnerSizeAtCompileTime)
+    EIGEN_DEBUG_VAR(LinearPacketSize)
+    EIGEN_DEBUG_VAR(InnerPacketSize)
+    EIGEN_DEBUG_VAR(ActualPacketSize)
+    EIGEN_DEBUG_VAR(StorageOrdersAgree)
+    EIGEN_DEBUG_VAR(MightVectorize)
+    EIGEN_DEBUG_VAR(MayLinearize)
+    EIGEN_DEBUG_VAR(MayInnerVectorize)
+    EIGEN_DEBUG_VAR(MayLinearVectorize)
+    EIGEN_DEBUG_VAR(MaySliceVectorize)
+    std::cerr << "Traversal"
+              << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
+    EIGEN_DEBUG_VAR(SrcEvaluator::CoeffReadCost)
+    EIGEN_DEBUG_VAR(DstEvaluator::CoeffReadCost)
+    EIGEN_DEBUG_VAR(Dst::SizeAtCompileTime)
+    EIGEN_DEBUG_VAR(UnrollingLimit)
+    EIGEN_DEBUG_VAR(MayUnrollCompletely)
+    EIGEN_DEBUG_VAR(MayUnrollInner)
+    std::cerr << "Unrolling"
+              << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
+    std::cerr << std::endl;
+  }
+#endif
+};
+
+/***************************************************************************
+ * Part 2 : meta-unrollers
+ ***************************************************************************/
+
+/************************
+*** Default traversal ***
+************************/
+
+template <typename Kernel, int Index_, int Stop>
+struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling {
+  static constexpr int Outer = Index_ / Kernel::AssignmentTraits::InnerSizeAtCompileTime;
+  static constexpr int Inner = Index_ % Kernel::AssignmentTraits::InnerSizeAtCompileTime;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    kernel.assignCoeffByOuterInner(Outer, Inner);
+    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Index_ + 1, Stop>::run(kernel);
+  }
+};
+
+template <typename Kernel, int Stop>
+struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel&) {}
+};
+
+template <typename Kernel, int Index_, int Stop>
+struct copy_using_evaluator_DefaultTraversal_InnerUnrolling {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel, Index outer) {
+    kernel.assignCoeffByOuterInner(outer, Index_);
+    copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Index_ + 1, Stop>::run(kernel, outer);
+  }
+};
+
+template <typename Kernel, int Stop>
+struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel&, Index) {}
+};
+
+/***********************
+*** Linear traversal ***
+***********************/
+
+template <typename Kernel, int Index_, int Stop>
+struct copy_using_evaluator_LinearTraversal_CompleteUnrolling {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    kernel.assignCoeff(Index_);
+    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Index_ + 1, Stop>::run(kernel);
+  }
+};
+
+template <typename Kernel, int Stop>
+struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel&) {}
+};
+
+/**************************
+*** Inner vectorization ***
+**************************/
+
+template <typename Kernel, int Index_, int Stop>
+struct copy_using_evaluator_innervec_CompleteUnrolling {
+  using PacketType = typename Kernel::PacketType;
+  static constexpr int Outer = Index_ / Kernel::AssignmentTraits::InnerSizeAtCompileTime;
+  static constexpr int Inner = Index_ % Kernel::AssignmentTraits::InnerSizeAtCompileTime;
+  static constexpr int NextIndex = Index_ + unpacket_traits<PacketType>::size;
+  static constexpr int SrcAlignment = Kernel::AssignmentTraits::SrcAlignment;
+  static constexpr int DstAlignment = Kernel::AssignmentTraits::DstAlignment;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
+    kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(Outer, Inner);
+    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
+  }
+};
+
+template <typename Kernel, int Stop>
+struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel&) {}
+};
+
+template <typename Kernel, int Index_, int Stop, int SrcAlignment, int DstAlignment>
+struct copy_using_evaluator_innervec_InnerUnrolling {
+  using PacketType = typename Kernel::PacketType;
+  static constexpr int NextIndex = Index_ + unpacket_traits<PacketType>::size;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel, Index outer) {
+    kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, Index_);
+    copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop, SrcAlignment, DstAlignment>::run(kernel,
+                                                                                                           outer);
+  }
+};
+
+template <typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
+struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop, SrcAlignment, DstAlignment> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel&, Index) {}
+};
+
+template <typename Kernel, int Start, int Stop, int SrcAlignment, int DstAlignment, bool UsePacketSegment>
+struct copy_using_evaluator_innervec_segment {
+  using PacketType = typename Kernel::PacketType;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel, Index outer) {
+    kernel.template assignPacketSegmentByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, Start, 0,
+                                                                                            Stop - Start);
+  }
+};
+
+template <typename Kernel, int Start, int Stop, int SrcAlignment, int DstAlignment>
+struct copy_using_evaluator_innervec_segment<Kernel, Start, Stop, SrcAlignment, DstAlignment,
+                                             /*UsePacketSegment*/ false>
+    : copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Start, Stop> {};
+
+template <typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
+struct copy_using_evaluator_innervec_segment<Kernel, Stop, Stop, SrcAlignment, DstAlignment,
+                                             /*UsePacketSegment*/ true> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel&, Index) {}
+};
+
+template <typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
+struct copy_using_evaluator_innervec_segment<Kernel, Stop, Stop, SrcAlignment, DstAlignment,
+                                             /*UsePacketSegment*/ false> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel&, Index) {}
+};
+
+/***************************************************************************
+ * Part 3 : implementation of all cases
+ ***************************************************************************/
+
+// dense_assignment_loop is based on assign_impl
+
+template <typename Kernel, int Traversal = Kernel::AssignmentTraits::Traversal,
+          int Unrolling = Kernel::AssignmentTraits::Unrolling>
+struct dense_assignment_loop_impl;
+
+template <typename Kernel, int Traversal = Kernel::AssignmentTraits::Traversal,
+          int Unrolling = Kernel::AssignmentTraits::Unrolling>
+struct dense_assignment_loop {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+#ifdef __cpp_lib_is_constant_evaluated
+    if (internal::is_constant_evaluated())
+      dense_assignment_loop_impl<Kernel, Traversal == AllAtOnceTraversal ? AllAtOnceTraversal : DefaultTraversal,
+                                 NoUnrolling>::run(kernel);
+    else
+#endif
+      dense_assignment_loop_impl<Kernel, Traversal, Unrolling>::run(kernel);
+  }
+};
+
+/************************
+***** Special Cases *****
+************************/
+
+// Zero-sized assignment is a no-op.
+template <typename Kernel, int Unrolling>
+struct dense_assignment_loop_impl<Kernel, AllAtOnceTraversal, Unrolling> {
+  static constexpr int SizeAtCompileTime = Kernel::AssignmentTraits::SizeAtCompileTime;
+
+  EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE constexpr run(Kernel& /*kernel*/) {
+    EIGEN_STATIC_ASSERT(SizeAtCompileTime == 0, EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT)
+  }
+};
+
+/************************
+*** Default traversal ***
+************************/
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, DefaultTraversal, NoUnrolling> {
+  EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE constexpr run(Kernel& kernel) {
+    for (Index outer = 0; outer < kernel.outerSize(); ++outer) {
+      for (Index inner = 0; inner < kernel.innerSize(); ++inner) {
+        kernel.assignCoeffByOuterInner(outer, inner);
+      }
+    }
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, DefaultTraversal, CompleteUnrolling> {
+  static constexpr int SizeAtCompileTime = Kernel::AssignmentTraits::SizeAtCompileTime;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, 0, SizeAtCompileTime>::run(kernel);
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, DefaultTraversal, InnerUnrolling> {
+  static constexpr int InnerSizeAtCompileTime = Kernel::AssignmentTraits::InnerSizeAtCompileTime;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    const Index outerSize = kernel.outerSize();
+    for (Index outer = 0; outer < outerSize; ++outer)
+      copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, 0, InnerSizeAtCompileTime>::run(kernel, outer);
+  }
+};
+
+/***************************
+*** Linear vectorization ***
+***************************/
+
+// The goal of unaligned_dense_assignment_loop is simply to factorize the handling
+// of the non vectorizable beginning and ending parts
+
+template <typename PacketType, int DstAlignment, int SrcAlignment, bool UsePacketSegment, bool Skip>
+struct unaligned_dense_assignment_loop {
+  // if Skip == true, then do nothing
+  template <typename Kernel>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& /*kernel*/, Index /*start*/, Index /*end*/) {}
+  template <typename Kernel>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& /*kernel*/, Index /*outer*/,
+                                                                  Index /*innerStart*/, Index /*innerEnd*/) {}
+};
+
+template <typename PacketType, int DstAlignment, int SrcAlignment>
+struct unaligned_dense_assignment_loop<PacketType, DstAlignment, SrcAlignment, /*UsePacketSegment*/ true,
+                                       /*Skip*/ false> {
+  template <typename Kernel>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel, Index start, Index end) {
+    Index count = end - start;
+    eigen_assert(count <= unpacket_traits<PacketType>::size);
+    if (count > 0) kernel.template assignPacketSegment<DstAlignment, SrcAlignment, PacketType>(start, 0, count);
+  }
+  template <typename Kernel>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel, Index outer, Index start, Index end) {
+    Index count = end - start;
+    eigen_assert(count <= unpacket_traits<PacketType>::size);
+    if (count > 0)
+      kernel.template assignPacketSegmentByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, start, 0, count);
+  }
+};
+
+template <typename PacketType, int DstAlignment, int SrcAlignment>
+struct unaligned_dense_assignment_loop<PacketType, DstAlignment, SrcAlignment, /*UsePacketSegment*/ false,
+                                       /*Skip*/ false> {
+  template <typename Kernel>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel, Index start, Index end) {
+    for (Index index = start; index < end; ++index) kernel.assignCoeff(index);
+  }
+  template <typename Kernel>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel, Index outer, Index innerStart,
+                                                                  Index innerEnd) {
+    for (Index inner = innerStart; inner < innerEnd; ++inner) kernel.assignCoeffByOuterInner(outer, inner);
+  }
+};
+
+template <typename Kernel, int Index_, int Stop>
+struct copy_using_evaluator_linearvec_CompleteUnrolling {
+  using PacketType = typename Kernel::PacketType;
+  static constexpr int SrcAlignment = Kernel::AssignmentTraits::SrcAlignment;
+  static constexpr int DstAlignment = Kernel::AssignmentTraits::DstAlignment;
+  static constexpr int NextIndex = Index_ + unpacket_traits<PacketType>::size;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
+    kernel.template assignPacket<DstAlignment, SrcAlignment, PacketType>(Index_);
+    copy_using_evaluator_linearvec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
+  }
+};
+
+template <typename Kernel, int Stop>
+struct copy_using_evaluator_linearvec_CompleteUnrolling<Kernel, Stop, Stop> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel&) {}
+};
+
+template <typename Kernel, int Index_, int Stop, bool UsePacketSegment>
+struct copy_using_evaluator_linearvec_segment {
+  using PacketType = typename Kernel::PacketType;
+  static constexpr int SrcAlignment = Kernel::AssignmentTraits::SrcAlignment;
+  static constexpr int DstAlignment = Kernel::AssignmentTraits::DstAlignment;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
+    kernel.template assignPacketSegment<DstAlignment, SrcAlignment, PacketType>(Index_, 0, Stop - Index_);
+  }
+};
+
+template <typename Kernel, int Index_, int Stop>
+struct copy_using_evaluator_linearvec_segment<Kernel, Index_, Stop, /*UsePacketSegment*/ false>
+    : copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Index_, Stop> {};
+
+template <typename Kernel, int Stop>
+struct copy_using_evaluator_linearvec_segment<Kernel, Stop, Stop, /*UsePacketSegment*/ true> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel&) {}
+};
+
+template <typename Kernel, int Stop>
+struct copy_using_evaluator_linearvec_segment<Kernel, Stop, Stop, /*UsePacketSegment*/ false> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel&) {}
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, LinearVectorizedTraversal, NoUnrolling> {
+  using Scalar = typename Kernel::Scalar;
+  using PacketType = typename Kernel::PacketType;
+  static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+  static constexpr int SrcAlignment = Kernel::AssignmentTraits::JointAlignment;
+  static constexpr int DstAlignment = plain_enum_max(Kernel::AssignmentTraits::DstAlignment, alignof(Scalar));
+  static constexpr int RequestedAlignment = unpacket_traits<PacketType>::alignment;
+  static constexpr bool Alignable =
+      (DstAlignment >= RequestedAlignment) || ((RequestedAlignment - DstAlignment) % sizeof(Scalar) == 0);
+  static constexpr int Alignment = Alignable ? RequestedAlignment : DstAlignment;
+  static constexpr bool DstIsAligned = DstAlignment >= Alignment;
+  static constexpr bool UsePacketSegment = Kernel::AssignmentTraits::UsePacketSegment;
+
+  using head_loop =
+      unaligned_dense_assignment_loop<PacketType, DstAlignment, SrcAlignment, UsePacketSegment, DstIsAligned>;
+  using tail_loop = unaligned_dense_assignment_loop<PacketType, Alignment, SrcAlignment, UsePacketSegment, false>;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    const Index size = kernel.size();
+    const Index alignedStart = DstIsAligned ? 0 : first_aligned<Alignment>(kernel.dstDataPtr(), size);
+    const Index alignedEnd = alignedStart + numext::round_down(size - alignedStart, PacketSize);
+
+    head_loop::run(kernel, 0, alignedStart);
+
+    for (Index index = alignedStart; index < alignedEnd; index += PacketSize)
+      kernel.template assignPacket<Alignment, SrcAlignment, PacketType>(index);
+
+    tail_loop::run(kernel, alignedEnd, size);
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, LinearVectorizedTraversal, CompleteUnrolling> {
+  using PacketType = typename Kernel::PacketType;
+  static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+  static constexpr int Size = Kernel::AssignmentTraits::SizeAtCompileTime;
+  static constexpr int AlignedSize = numext::round_down(Size, PacketSize);
+  static constexpr bool UsePacketSegment = Kernel::AssignmentTraits::UsePacketSegment;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    copy_using_evaluator_linearvec_CompleteUnrolling<Kernel, 0, AlignedSize>::run(kernel);
+    copy_using_evaluator_linearvec_segment<Kernel, AlignedSize, Size, UsePacketSegment>::run(kernel);
+  }
+};
+
+/**************************
+*** Inner vectorization ***
+**************************/
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, InnerVectorizedTraversal, NoUnrolling> {
+  using PacketType = typename Kernel::PacketType;
+  static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+  static constexpr int SrcAlignment = Kernel::AssignmentTraits::JointAlignment;
+  static constexpr int DstAlignment = Kernel::AssignmentTraits::DstAlignment;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    const Index innerSize = kernel.innerSize();
+    const Index outerSize = kernel.outerSize();
+    for (Index outer = 0; outer < outerSize; ++outer)
+      for (Index inner = 0; inner < innerSize; inner += PacketSize)
+        kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, InnerVectorizedTraversal, CompleteUnrolling> {
+  static constexpr int SizeAtCompileTime = Kernel::AssignmentTraits::SizeAtCompileTime;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
+    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, SizeAtCompileTime>::run(kernel);
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, InnerVectorizedTraversal, InnerUnrolling> {
+  static constexpr int InnerSize = Kernel::AssignmentTraits::InnerSizeAtCompileTime;
+  static constexpr int SrcAlignment = Kernel::AssignmentTraits::SrcAlignment;
+  static constexpr int DstAlignment = Kernel::AssignmentTraits::DstAlignment;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
+    const Index outerSize = kernel.outerSize();
+    for (Index outer = 0; outer < outerSize; ++outer)
+      copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, InnerSize, SrcAlignment, DstAlignment>::run(kernel,
+                                                                                                          outer);
+  }
+};
+
+/***********************
+*** Linear traversal ***
+***********************/
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, LinearTraversal, NoUnrolling> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    const Index size = kernel.size();
+    for (Index i = 0; i < size; ++i) kernel.assignCoeff(i);
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, LinearTraversal, CompleteUnrolling> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, 0, Kernel::AssignmentTraits::SizeAtCompileTime>::run(
+        kernel);
+  }
+};
+
+/**************************
+*** Slice vectorization ***
+***************************/
+
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, SliceVectorizedTraversal, NoUnrolling> {
+  using Scalar = typename Kernel::Scalar;
+  using PacketType = typename Kernel::PacketType;
+  static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+  static constexpr int SrcAlignment = Kernel::AssignmentTraits::JointAlignment;
+  static constexpr int DstAlignment = plain_enum_max(Kernel::AssignmentTraits::DstAlignment, alignof(Scalar));
+  static constexpr int RequestedAlignment = unpacket_traits<PacketType>::alignment;
+  static constexpr bool Alignable =
+      (DstAlignment >= RequestedAlignment) || ((RequestedAlignment - DstAlignment) % sizeof(Scalar) == 0);
+  static constexpr int Alignment = Alignable ? RequestedAlignment : DstAlignment;
+  static constexpr bool DstIsAligned = DstAlignment >= Alignment;
+  static constexpr bool UsePacketSegment = Kernel::AssignmentTraits::UsePacketSegment;
+
+  using head_loop = unaligned_dense_assignment_loop<PacketType, DstAlignment, Unaligned, UsePacketSegment, !Alignable>;
+  using tail_loop = unaligned_dense_assignment_loop<PacketType, Alignment, Unaligned, UsePacketSegment, false>;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    const Scalar* dst_ptr = kernel.dstDataPtr();
+    const Index innerSize = kernel.innerSize();
+    const Index outerSize = kernel.outerSize();
+    const Index alignedStep = Alignable ? (PacketSize - kernel.outerStride() % PacketSize) % PacketSize : 0;
+    Index alignedStart = ((!Alignable) || DstIsAligned) ? 0 : internal::first_aligned<Alignment>(dst_ptr, innerSize);
+
+    for (Index outer = 0; outer < outerSize; ++outer) {
+      const Index alignedEnd = alignedStart + numext::round_down(innerSize - alignedStart, PacketSize);
+
+      head_loop::run(kernel, outer, 0, alignedStart);
+
+      // do the vectorizable part of the assignment
+      for (Index inner = alignedStart; inner < alignedEnd; inner += PacketSize)
+        kernel.template assignPacketByOuterInner<Alignment, Unaligned, PacketType>(outer, inner);
+
+      tail_loop::run(kernel, outer, alignedEnd, innerSize);
+
+      alignedStart = numext::mini((alignedStart + alignedStep) % PacketSize, innerSize);
+    }
+  }
+};
+
+#if EIGEN_UNALIGNED_VECTORIZE
+template <typename Kernel>
+struct dense_assignment_loop_impl<Kernel, SliceVectorizedTraversal, InnerUnrolling> {
+  using PacketType = typename Kernel::PacketType;
+  static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+  static constexpr int InnerSize = Kernel::AssignmentTraits::InnerSizeAtCompileTime;
+  static constexpr int VectorizableSize = numext::round_down(InnerSize, PacketSize);
+  static constexpr bool UsePacketSegment = Kernel::AssignmentTraits::UsePacketSegment;
+
+  using packet_loop = copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, VectorizableSize, Unaligned, Unaligned>;
+  using packet_segment_loop = copy_using_evaluator_innervec_segment<Kernel, VectorizableSize, InnerSize, Unaligned,
+                                                                    Unaligned, UsePacketSegment>;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(Kernel& kernel) {
+    for (Index outer = 0; outer < kernel.outerSize(); ++outer) {
+      packet_loop::run(kernel, outer);
+      packet_segment_loop::run(kernel, outer);
+    }
+  }
+};
+#endif
+
+/***************************************************************************
+ * Part 4 : Generic dense assignment kernel
+ ***************************************************************************/
+
+// This class generalize the assignment of a coefficient (or packet) from one dense evaluator
+// to another dense writable evaluator.
+// It is parametrized by the two evaluators, and the actual assignment functor.
+// This abstraction level permits to keep the evaluation loops as simple and as generic as possible.
+// One can customize the assignment using this generic dense_assignment_kernel with different
+// functors, or by completely overloading it, by-passing a functor.
+template <typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
+class generic_dense_assignment_kernel {
+ protected:
+  typedef typename DstEvaluatorTypeT::XprType DstXprType;
+  typedef typename SrcEvaluatorTypeT::XprType SrcXprType;
+
+ public:
+  typedef DstEvaluatorTypeT DstEvaluatorType;
+  typedef SrcEvaluatorTypeT SrcEvaluatorType;
+  typedef typename DstEvaluatorType::Scalar Scalar;
+  typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor> AssignmentTraits;
+  typedef typename AssignmentTraits::PacketType PacketType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr generic_dense_assignment_kernel(DstEvaluatorType& dst,
+                                                                                  const SrcEvaluatorType& src,
+                                                                                  const Functor& func,
+                                                                                  DstXprType& dstExpr)
+      : m_dst(dst), m_src(src), m_functor(func), m_dstExpr(dstExpr) {
+#ifdef EIGEN_DEBUG_ASSIGN
+    AssignmentTraits::debug();
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index size() const noexcept { return m_dstExpr.size(); }
+  EIGEN_DEVICE_FUNC constexpr Index innerSize() const noexcept { return m_dstExpr.innerSize(); }
+  EIGEN_DEVICE_FUNC constexpr Index outerSize() const noexcept { return m_dstExpr.outerSize(); }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_dstExpr.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_dstExpr.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return m_dstExpr.outerStride(); }
+
+  EIGEN_DEVICE_FUNC constexpr DstEvaluatorType& dstEvaluator() noexcept { return m_dst; }
+  EIGEN_DEVICE_FUNC constexpr const SrcEvaluatorType& srcEvaluator() const noexcept { return m_src; }
+
+  /// Assign src(row,col) to dst(row,col) through the assignment functor.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void assignCoeff(Index row, Index col) {
+    m_functor.assignCoeff(m_dst.coeffRef(row, col), m_src.coeff(row, col));
+  }
+
+  /// \sa assignCoeff(Index,Index)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void assignCoeff(Index index) {
+    m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
+  }
+
+  /// \sa assignCoeff(Index,Index)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void assignCoeffByOuterInner(Index outer, Index inner) {
+    Index row = rowIndexByOuterInner(outer, inner);
+    Index col = colIndexByOuterInner(outer, inner);
+    assignCoeff(row, col);
+  }
+
+  template <int StoreMode, int LoadMode, typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index row, Index col) {
+    m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row, col),
+                                               m_src.template packet<LoadMode, Packet>(row, col));
+  }
+
+  template <int StoreMode, int LoadMode, typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index index) {
+    m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode, Packet>(index));
+  }
+
+  template <int StoreMode, int LoadMode, typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner) {
+    Index row = rowIndexByOuterInner(outer, inner);
+    Index col = colIndexByOuterInner(outer, inner);
+    assignPacket<StoreMode, LoadMode, Packet>(row, col);
+  }
+
+  template <int StoreMode, int LoadMode, typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketSegment(Index row, Index col, Index begin, Index count) {
+    m_functor.template assignPacketSegment<StoreMode>(
+        &m_dst.coeffRef(row, col), m_src.template packetSegment<LoadMode, Packet>(row, col, begin, count), begin,
+        count);
+  }
+
+  template <int StoreMode, int LoadMode, typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketSegment(Index index, Index begin, Index count) {
+    m_functor.template assignPacketSegment<StoreMode>(
+        &m_dst.coeffRef(index), m_src.template packetSegment<LoadMode, Packet>(index, begin, count), begin, count);
+  }
+
+  template <int StoreMode, int LoadMode, typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketSegmentByOuterInner(Index outer, Index inner, Index begin,
+                                                                             Index count) {
+    Index row = rowIndexByOuterInner(outer, inner);
+    Index col = colIndexByOuterInner(outer, inner);
+    assignPacketSegment<StoreMode, LoadMode, Packet>(row, col, begin, count);
+  }
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr Index rowIndexByOuterInner(Index outer, Index inner) {
+    typedef typename DstEvaluatorType::ExpressionTraits Traits;
+    return int(Traits::RowsAtCompileTime) == 1          ? 0
+           : int(Traits::ColsAtCompileTime) == 1        ? inner
+           : int(DstEvaluatorType::Flags) & RowMajorBit ? outer
+                                                        : inner;
+  }
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr Index colIndexByOuterInner(Index outer, Index inner) {
+    typedef typename DstEvaluatorType::ExpressionTraits Traits;
+    return int(Traits::ColsAtCompileTime) == 1          ? 0
+           : int(Traits::RowsAtCompileTime) == 1        ? inner
+           : int(DstEvaluatorType::Flags) & RowMajorBit ? inner
+                                                        : outer;
+  }
+
+  EIGEN_DEVICE_FUNC const Scalar* dstDataPtr() const { return m_dstExpr.data(); }
+
+ protected:
+  DstEvaluatorType& m_dst;
+  const SrcEvaluatorType& m_src;
+  const Functor& m_functor;
+  // TODO: find a way to avoid the needs of the original expression
+  DstXprType& m_dstExpr;
+};
+
+// Special kernel used when computing small products whose operands have dynamic dimensions.  It ensures that the
+// PacketSize used is no larger than 4, thereby increasing the chance that vectorized instructions will be used
+// when computing the product.
+
+template <typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor>
+class restricted_packet_dense_assignment_kernel
+    : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, BuiltIn> {
+ protected:
+  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, BuiltIn> Base;
+
+ public:
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::DstXprType DstXprType;
+  typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, 4> AssignmentTraits;
+  typedef typename AssignmentTraits::PacketType PacketType;
+
+  EIGEN_DEVICE_FUNC restricted_packet_dense_assignment_kernel(DstEvaluatorTypeT& dst, const SrcEvaluatorTypeT& src,
+                                                              const Functor& func, DstXprType& dstExpr)
+      : Base(dst, src, func, dstExpr) {}
+};
+
+/***************************************************************************
+ * Part 5 : Entry point for dense rectangular assignment
+ ***************************************************************************/
+
+template <typename DstXprType, typename SrcXprType, typename Functor>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize_if_allowed(DstXprType& dst, const SrcXprType& src,
+                                                                       const Functor& /*func*/) {
+  EIGEN_ONLY_USED_FOR_DEBUG(dst);
+  EIGEN_ONLY_USED_FOR_DEBUG(src);
+  eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+}
+
+template <typename DstXprType, typename SrcXprType, typename T1, typename T2>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize_if_allowed(DstXprType& dst, const SrcXprType& src,
+                                                                       const internal::assign_op<T1, T2>& /*func*/) {
+  Index dstRows = src.rows();
+  Index dstCols = src.cols();
+  if (((dst.rows() != dstRows) || (dst.cols() != dstCols))) {
+#ifdef EIGEN_NO_AUTOMATIC_RESIZING
+    eigen_assert(
+        (dst.size() == 0 || (DstXprType::IsVectorAtCompileTime ? (dst.size() == src.size())
+                                                               : (dst.rows() == dstRows && dst.cols() == dstCols))) &&
+        "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
+#else
+    dst.resize(dstRows, dstCols);
+    eigen_assert(dst.rows() == dstRows && dst.cols() == dstCols);
+#endif
+  }
+}
+
+template <typename DstXprType, typename SrcXprType, typename Functor>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE constexpr void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src,
+                                                                                const Functor& func) {
+  typedef evaluator<DstXprType> DstEvaluatorType;
+  typedef evaluator<SrcXprType> SrcEvaluatorType;
+
+  SrcEvaluatorType srcEvaluator(src);
+
+  // NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
+  // we need to resize the destination after the source evaluator has been created.
+  resize_if_allowed(dst, src, func);
+
+  DstEvaluatorType dstEvaluator(dst);
+
+  typedef generic_dense_assignment_kernel<DstEvaluatorType, SrcEvaluatorType, Functor> Kernel;
+  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
+
+  dense_assignment_loop<Kernel>::run(kernel);
+}
+
+template <typename DstXprType, typename SrcXprType>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src) {
+  call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>());
+}
+
+/***************************************************************************
+ * Part 6 : Generic assignment
+ ***************************************************************************/
+
+// Based on the respective shapes of the destination and source,
+// the class AssignmentKind determine the kind of assignment mechanism.
+// AssignmentKind must define a Kind typedef.
+template <typename DstShape, typename SrcShape>
+struct AssignmentKind;
+
+// Assignment kind defined in this file:
+struct Dense2Dense {};
+struct EigenBase2EigenBase {};
+
+template <typename, typename>
+struct AssignmentKind {
+  typedef EigenBase2EigenBase Kind;
+};
+template <>
+struct AssignmentKind<DenseShape, DenseShape> {
+  typedef Dense2Dense Kind;
+};
+
+// This is the main assignment class
+template <typename DstXprType, typename SrcXprType, typename Functor,
+          typename Kind = typename AssignmentKind<typename evaluator_traits<DstXprType>::Shape,
+                                                  typename evaluator_traits<SrcXprType>::Shape>::Kind,
+          typename EnableIf = void>
+struct Assignment;
+
+// The only purpose of this call_assignment() function is to deal with noalias() / "assume-aliasing" and automatic
+// transposition. Indeed, I (Gael) think that this concept of "assume-aliasing" was a mistake, and it makes thing quite
+// complicated. So this intermediate function removes everything related to "assume-aliasing" such that Assignment does
+// not has to bother about these annoying details.
+
+template <typename Dst, typename Src>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_assignment(Dst& dst, const Src& src) {
+  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar, typename Src::Scalar>());
+}
+template <typename Dst, typename Src>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_assignment(const Dst& dst, const Src& src) {
+  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar, typename Src::Scalar>());
+}
+
+// Deal with "assume-aliasing"
+template <typename Dst, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_assignment(
+    Dst& dst, const Src& src, const Func& func, std::enable_if_t<evaluator_assume_aliasing<Src>::value, void*> = 0) {
+  typename plain_matrix_type<Src>::type tmp(src);
+  call_assignment_no_alias(dst, tmp, func);
+}
+
+template <typename Dst, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_assignment(
+    Dst& dst, const Src& src, const Func& func, std::enable_if_t<!evaluator_assume_aliasing<Src>::value, void*> = 0) {
+  call_assignment_no_alias(dst, src, func);
+}
+
+// by-pass "assume-aliasing"
+// When there is no aliasing, we require that 'dst' has been properly resized
+template <typename Dst, template <typename> class StorageBase, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_assignment(NoAlias<Dst, StorageBase>& dst, const Src& src,
+                                                                     const Func& func) {
+  call_assignment_no_alias(dst.expression(), src, func);
+}
+
+template <typename Dst, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_assignment_no_alias(Dst& dst, const Src& src,
+                                                                              const Func& func) {
+  enum {
+    NeedToTranspose = ((int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1) ||
+                       (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)) &&
+                      int(Dst::SizeAtCompileTime) != 1
+  };
+
+  typedef std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst> ActualDstTypeCleaned;
+  typedef std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst&> ActualDstType;
+  ActualDstType actualDst(dst);
+
+  // TODO: check whether this is the right place to perform these checks:
+  EIGEN_STATIC_ASSERT_LVALUE(Dst)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned, Src)
+  EIGEN_CHECK_BINARY_COMPATIBILIY(Func, typename ActualDstTypeCleaned::Scalar, typename Src::Scalar);
+
+  Assignment<ActualDstTypeCleaned, Src, Func>::run(actualDst, src, func);
+}
+
+template <typename Dst, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_restricted_packet_assignment_no_alias(Dst& dst, const Src& src,
+                                                                                      const Func& func) {
+  typedef evaluator<Dst> DstEvaluatorType;
+  typedef evaluator<Src> SrcEvaluatorType;
+  typedef restricted_packet_dense_assignment_kernel<DstEvaluatorType, SrcEvaluatorType, Func> Kernel;
+
+  EIGEN_STATIC_ASSERT_LVALUE(Dst)
+  EIGEN_CHECK_BINARY_COMPATIBILIY(Func, typename Dst::Scalar, typename Src::Scalar);
+
+  SrcEvaluatorType srcEvaluator(src);
+  resize_if_allowed(dst, src, func);
+
+  DstEvaluatorType dstEvaluator(dst);
+  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
+
+  dense_assignment_loop<Kernel>::run(kernel);
+}
+
+template <typename Dst, typename Src>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_assignment_no_alias(Dst& dst, const Src& src) {
+  call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar, typename Src::Scalar>());
+}
+
+template <typename Dst, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src,
+                                                                                           const Func& func) {
+  // TODO: check whether this is the right place to perform these checks:
+  EIGEN_STATIC_ASSERT_LVALUE(Dst)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Dst, Src)
+  EIGEN_CHECK_BINARY_COMPATIBILIY(Func, typename Dst::Scalar, typename Src::Scalar);
+
+  Assignment<Dst, Src, Func>::run(dst, src, func);
+}
+template <typename Dst, typename Src>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src) {
+  call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar, typename Src::Scalar>());
+}
+
+// forward declaration
+template <typename Dst, typename Src>
+EIGEN_DEVICE_FUNC void check_for_aliasing(const Dst& dst, const Src& src);
+
+// Generic Dense to Dense assignment
+// Note that the last template argument "Weak" is needed to make it possible to perform
+// both partial specialization+SFINAE without ambiguous specialization
+template <typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
+struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Weak> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE constexpr void run(DstXprType& dst, const SrcXprType& src,
+                                                                  const Functor& func) {
+#ifndef EIGEN_NO_DEBUG
+    if (!internal::is_constant_evaluated()) {
+      internal::check_for_aliasing(dst, src);
+    }
+#endif
+
+    call_dense_assignment_loop(dst, src, func);
+  }
+};
+
+template <typename DstXprType, typename SrcPlainObject, typename Weak>
+struct Assignment<DstXprType, CwiseNullaryOp<scalar_constant_op<typename DstXprType::Scalar>, SrcPlainObject>,
+                  assign_op<typename DstXprType::Scalar, typename DstXprType::Scalar>, Dense2Dense, Weak> {
+  using Scalar = typename DstXprType::Scalar;
+  using NullaryOp = scalar_constant_op<Scalar>;
+  using SrcXprType = CwiseNullaryOp<NullaryOp, SrcPlainObject>;
+  using Functor = assign_op<Scalar, Scalar>;
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src,
+                                                        const Functor& /*func*/) {
+    eigen_fill_impl<DstXprType>::run(dst, src);
+  }
+};
+
+template <typename DstXprType, typename SrcPlainObject, typename Weak>
+struct Assignment<DstXprType, CwiseNullaryOp<scalar_zero_op<typename DstXprType::Scalar>, SrcPlainObject>,
+                  assign_op<typename DstXprType::Scalar, typename DstXprType::Scalar>, Dense2Dense, Weak> {
+  using Scalar = typename DstXprType::Scalar;
+  using NullaryOp = scalar_zero_op<Scalar>;
+  using SrcXprType = CwiseNullaryOp<NullaryOp, SrcPlainObject>;
+  using Functor = assign_op<Scalar, Scalar>;
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src,
+                                                        const Functor& /*func*/) {
+    eigen_zero_impl<DstXprType>::run(dst, src);
+  }
+};
+
+// Generic assignment through evalTo.
+// TODO: evaluate whether this generic evalTo-based assignment path is still needed.
+// Note that the last template argument "Weak" is needed to make it possible to perform
+// both partial specialization+SFINAE without ambiguous specialization
+template <typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
+struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Weak> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(
+      DstXprType& dst, const SrcXprType& src,
+      const internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+    src.evalTo(dst);
+  }
+
+  // NOTE The following two functions are templated to avoid their instantiation if not needed
+  //      This is needed because some expressions supports evalTo only and/or have 'void' as scalar type.
+  template <typename SrcScalarType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(
+      DstXprType& dst, const SrcXprType& src,
+      const internal::add_assign_op<typename DstXprType::Scalar, SrcScalarType>& /*func*/) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+    src.addTo(dst);
+  }
+
+  template <typename SrcScalarType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(
+      DstXprType& dst, const SrcXprType& src,
+      const internal::sub_assign_op<typename DstXprType::Scalar, SrcScalarType>& /*func*/) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+    src.subTo(dst);
+  }
+};
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ASSIGN_EVALUATOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign_AOCL.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign_AOCL.h
new file mode 100644
index 0000000..da3ef7c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign_AOCL.h
@@ -0,0 +1,301 @@
+/*
+ * This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at https://mozilla.org/MPL/2.0/.
+ *
+ * Assign_AOCL.h - AOCL Vectorized Math Dispatch Layer for Eigen
+ *
+ * Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Description:
+ * ------------
+ * This file implements a high-performance dispatch layer that automatically
+ * routes Eigen's element-wise mathematical operations to AMD Optimizing CPU
+ * Libraries (AOCL) Vector Math Library (VML) functions when beneficial for
+ * performance.
+ *
+ * The dispatch system uses C++ template specialization to intercept Eigen's
+ * assignment operations and redirect them to AOCL's VRDA functions, which
+ * provide optimized implementations for AMD Zen architectures.
+ *
+ * Key Features:
+ * -------------
+ * 1. Automatic Dispatch: Seamlessly routes supported operations to AOCL without
+ *    requiring code changes in user applications
+ *
+ * 2. Performance Optimization: Uses AOCL VRDA functions optimized for Zen
+ * family processors with automatic SIMD instruction selection (AVX2, AVX-512)
+ *
+ * 3. Threshold-Based Activation: Only activates for vectors larger than
+ *    EIGEN_AOCL_VML_THRESHOLD (default: 128 elements) to avoid overhead on
+ * small vectors
+ *
+ * 4. Precision-Specific Handling:
+ *    - Double precision: AOCL VRDA vectorized functions
+ *    - Single precision: Scalar fallback (preserves correctness)
+ *
+ * 5. Memory Layout Compatibility: Ensures direct memory access and compatible
+ *    storage orders between source and destination for optimal performance
+ *
+ * Supported Operations:
+ * ---------------------
+ * UNARY OPERATIONS (vector → vector):
+ * - Transcendental: exp(), sin(), cos(), sqrt(), log(), log10(), log2()
+ *
+ * BINARY OPERATIONS (vector op vector → vector):
+ * - Arithmetic: +, *, pow()
+ *
+ * Template Specialization Mechanism:
+ * -----------------------------------
+ * The system works by specializing Eigen's Assignment template for:
+ * 1. CwiseUnaryOp with scalar_*_op functors (unary operations)
+ * 2. CwiseBinaryOp with scalar_*_op functors (binary operations)
+ * 3. Dense2Dense assignment context with AOCL-compatible traits
+ *
+ * Dispatch conditions (all must be true):
+ * - Source and destination have DirectAccessBit (contiguous memory)
+ * - Compatible storage orders (both row-major or both column-major)
+ * - Vector size ≥ EIGEN_AOCL_VML_THRESHOLD or Dynamic size
+ * - Supported data type (currently double precision for VRDA)
+ *
+ * Integration Example:
+ * --------------------
+ * // Standard Eigen code - no changes required
+ * VectorXd x = VectorXd::Random(10000);
+ * VectorXd y = VectorXd::Random(10000);
+ * VectorXd result;
+ *
+ * // These operations are automatically dispatched to AOCL:
+ * result = x.array().exp();              // → amd_vrda_exp()
+ * result = x.array().sin();              // → amd_vrda_sin()
+ * result = x.array() + y.array();        // → amd_vrda_add()
+ * result = x.array().pow(y.array());     // → amd_vrda_pow()
+ *
+ * Configuration:
+ * --------------
+ * Required preprocessor definitions:
+ * - EIGEN_USE_AOCL_ALL or EIGEN_USE_AOCL_MT: Enable AOCL integration
+ * - EIGEN_USE_AOCL_VML: Enable Vector Math Library dispatch
+ *
+ * Compilation Requirements:
+ * -------------------------
+ * Include paths:
+ * - AOCL headers: -I${AOCL_ROOT}/include
+ * - Eigen headers: -I/path/to/eigen
+ *
+ * Link libraries:
+ * - AOCL MathLib: -lamdlibm
+ * - Standard math: -lm
+ *
+ * Compiler flags:
+ * - Optimization: -O3 (required for inlining)
+ * - Architecture: -march=znver5 or -march=native
+ * - Vectorization: -mfma -mavx512f (if supported)
+ *
+ * Platform Support:
+ * ------------------
+ * - Primary: Linux x86_64 with AMD Zen family processors
+ * - Compilers: GCC 8+, Clang 10+, AOCC (recommended)
+ * - AOCL Version: 4.0+ (with VRDA support)
+ *
+ * Error Handling:
+ * ---------------
+ * - Graceful fallback to scalar operations for unsupported configurations
+ * - Compile-time detection of AOCL availability
+ * - Runtime size and alignment validation with eigen_assert()
+ *
+ * Developer:
+ * ----------
+ * Name: Sharad Saurabh Bhaskar
+ * Email: shbhaska@amd.com
+ * Organization: Advanced Micro Devices, Inc.
+ */
+
+
+#ifndef EIGEN_ASSIGN_AOCL_H
+#define EIGEN_ASSIGN_AOCL_H
+
+namespace Eigen {
+namespace internal {
+
+// Traits for unary operations.
+template <typename Dst, typename Src> class aocl_assign_traits {
+private:
+  enum {
+    DstHasDirectAccess = !!(Dst::Flags & DirectAccessBit),
+    SrcHasDirectAccess = !!(Src::Flags & DirectAccessBit),
+    StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
+    InnerSize = Dst::IsVectorAtCompileTime   ? int(Dst::SizeAtCompileTime)
+                : (Dst::Flags & RowMajorBit) ? int(Dst::ColsAtCompileTime)
+                                             : int(Dst::RowsAtCompileTime),
+    LargeEnough =
+        (InnerSize == Dynamic) || (InnerSize >= EIGEN_AOCL_VML_THRESHOLD)
+  };
+
+public:
+  enum {
+    EnableAoclVML = DstHasDirectAccess && SrcHasDirectAccess &&
+                    StorageOrdersAgree && LargeEnough,
+    Traversal = LinearTraversal
+  };
+};
+
+// Traits for binary operations (e.g., add, pow).
+template <typename Dst, typename Lhs, typename Rhs>
+class aocl_assign_binary_traits {
+private:
+  enum {
+    DstHasDirectAccess = !!(Dst::Flags & DirectAccessBit),
+    LhsHasDirectAccess = !!(Lhs::Flags & DirectAccessBit),
+    RhsHasDirectAccess = !!(Rhs::Flags & DirectAccessBit),
+    StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Lhs::IsRowMajor)) &&
+                         (int(Dst::IsRowMajor) == int(Rhs::IsRowMajor)),
+    InnerSize = Dst::IsVectorAtCompileTime   ? int(Dst::SizeAtCompileTime)
+                : (Dst::Flags & RowMajorBit) ? int(Dst::ColsAtCompileTime)
+                                             : int(Dst::RowsAtCompileTime),
+    LargeEnough =
+        (InnerSize == Dynamic) || (InnerSize >= EIGEN_AOCL_VML_THRESHOLD)
+  };
+
+public:
+  enum {
+    EnableAoclVML = DstHasDirectAccess && LhsHasDirectAccess &&
+                    RhsHasDirectAccess && StorageOrdersAgree && LargeEnough
+  };
+};
+
+// Unary operation dispatch for float (scalar fallback).
+#define EIGEN_AOCL_VML_UNARY_CALL_FLOAT(EIGENOP)                               \
+  template <typename DstXprType, typename SrcXprNested>                        \
+  struct Assignment<                                                           \
+      DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<float>, SrcXprNested>,    \
+      assign_op<float, float>, Dense2Dense,                                    \
+      std::enable_if_t<                                                        \
+          aocl_assign_traits<DstXprType, SrcXprNested>::EnableAoclVML>> {      \
+    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<float>, SrcXprNested>           \
+        SrcXprType;                                                            \
+    static void run(DstXprType &dst, const SrcXprType &src,                    \
+                    const assign_op<float, float> &) {                         \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
+      Eigen::Index n = dst.size();                                             \
+      if (n <= 0)                                                              \
+        return;                                                                \
+      const float *input =                                                     \
+          reinterpret_cast<const float *>(src.nestedExpression().data());      \
+      float *output = reinterpret_cast<float *>(dst.data());                   \
+      for (Eigen::Index i = 0; i < n; ++i) {                                   \
+        output[i] = std::EIGENOP(input[i]);                                    \
+      }                                                                        \
+    }                                                                          \
+  };
+
+// Unary operation dispatch for double (AOCL vectorized).
+#define EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(EIGENOP, AOCLOP)                      \
+  template <typename DstXprType, typename SrcXprNested>                        \
+  struct Assignment<                                                           \
+      DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<double>, SrcXprNested>,   \
+      assign_op<double, double>, Dense2Dense,                                  \
+      std::enable_if_t<                                                        \
+          aocl_assign_traits<DstXprType, SrcXprNested>::EnableAoclVML>> {      \
+    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<double>, SrcXprNested>          \
+        SrcXprType;                                                            \
+    static void run(DstXprType &dst, const SrcXprType &src,                    \
+                    const assign_op<double, double> &) {                       \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
+      Eigen::Index n = dst.size();                                             \
+      eigen_assert(n <= INT_MAX && "AOCL does not support arrays larger than INT_MAX"); \
+      if (n <= 0)                                                              \
+        return;                                                                \
+      const double *input =                                                    \
+          reinterpret_cast<const double *>(src.nestedExpression().data());     \
+      double *output = reinterpret_cast<double *>(dst.data());                 \
+      int aocl_n = internal::convert_index<int>(n);                            \
+      AOCLOP(aocl_n, const_cast<double *>(input), output);                     \
+    }                                                                          \
+  };
+
+// Instantiate unary calls for float (scalar).
+// EIGEN_AOCL_VML_UNARY_CALL_FLOAT(exp)
+
+// Instantiate unary calls for double (AOCL vectorized).
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(exp2, amd_vrda_exp2)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(exp, amd_vrda_exp)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(sin, amd_vrda_sin)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(cos, amd_vrda_cos)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(sqrt, amd_vrda_sqrt)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(cbrt, amd_vrda_cbrt)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(abs, amd_vrda_fabs)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(log, amd_vrda_log)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(log10, amd_vrda_log10)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(log2, amd_vrda_log2)
+
+// Binary operation dispatch for float (scalar fallback).
+#define EIGEN_AOCL_VML_BINARY_CALL_FLOAT(EIGENOP, STDFUNC)                     \
+  template <typename DstXprType, typename LhsXprNested, typename RhsXprNested> \
+  struct Assignment<                                                           \
+      DstXprType,                                                              \
+      CwiseBinaryOp<scalar_##EIGENOP##_op<float, float>, LhsXprNested,         \
+                    RhsXprNested>,                                             \
+      assign_op<float, float>, Dense2Dense,                                    \
+      std::enable_if_t<aocl_assign_binary_traits<                              \
+          DstXprType, LhsXprNested, RhsXprNested>::EnableAoclVML>> {           \
+    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<float, float>, LhsXprNested,   \
+                          RhsXprNested>                                        \
+        SrcXprType;                                                            \
+    static void run(DstXprType &dst, const SrcXprType &src,                    \
+                    const assign_op<float, float> &) {                         \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
+      Eigen::Index n = dst.size();                                             \
+      if (n <= 0)                                                              \
+        return;                                                                \
+      const float *lhs = reinterpret_cast<const float *>(src.lhs().data());    \
+      const float *rhs = reinterpret_cast<const float *>(src.rhs().data());    \
+      float *output = reinterpret_cast<float *>(dst.data());                   \
+      for (Eigen::Index i = 0; i < n; ++i) {                                   \
+        output[i] = STDFUNC(lhs[i], rhs[i]);                                   \
+      }                                                                        \
+    }                                                                          \
+  };
+
+// Binary operation dispatch for double (AOCL vectorized).
+#define EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(EIGENOP, AOCLOP)                     \
+  template <typename DstXprType, typename LhsXprNested, typename RhsXprNested> \
+  struct Assignment<                                                           \
+      DstXprType,                                                              \
+      CwiseBinaryOp<scalar_##EIGENOP##_op<double, double>, LhsXprNested,       \
+                    RhsXprNested>,                                             \
+      assign_op<double, double>, Dense2Dense,                                  \
+      std::enable_if_t<aocl_assign_binary_traits<                              \
+          DstXprType, LhsXprNested, RhsXprNested>::EnableAoclVML>> {           \
+    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<double, double>, LhsXprNested, \
+                          RhsXprNested>                                        \
+        SrcXprType;                                                            \
+    static void run(DstXprType &dst, const SrcXprType &src,                    \
+                    const assign_op<double, double> &) {                       \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
+      Eigen::Index n = dst.size();                                             \
+      eigen_assert(n <= INT_MAX && "AOCL does not support arrays larger than INT_MAX"); \
+      if (n <= 0)                                                              \
+        return;                                                                \
+      const double *lhs = reinterpret_cast<const double *>(src.lhs().data());  \
+      const double *rhs = reinterpret_cast<const double *>(src.rhs().data());  \
+      double *output = reinterpret_cast<double *>(dst.data());                 \
+      int aocl_n = internal::convert_index<int>(n);                            \
+      AOCLOP(aocl_n, const_cast<double *>(lhs), const_cast<double *>(rhs), output); \
+    }                                                                          \
+  };
+
+// Instantiate binary calls for float (scalar).
+// EIGEN_AOCL_VML_BINARY_CALL_FLOAT(sum, std::plus<float>)  // Using
+// scalar_sum_op for addition EIGEN_AOCL_VML_BINARY_CALL_FLOAT(pow, std::pow)
+
+// Instantiate binary calls for double (AOCL vectorized).
+EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(sum, amd_vrda_add) // Using scalar_sum_op for addition
+EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(pow, amd_vrda_pow)
+EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(max, amd_vrda_fmax)
+EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(min, amd_vrda_fmin)
+
+} // namespace internal
+} // namespace Eigen
+
+#endif // EIGEN_ASSIGN_AOCL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign_MKL.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign_MKL.h
new file mode 100644
index 0000000..7636445
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign_MKL.h
@@ -0,0 +1,183 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+ Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin()
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_ASSIGN_VML_H
+#define EIGEN_ASSIGN_VML_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Dst, typename Src>
+class vml_assign_traits {
+ private:
+  enum {
+    DstHasDirectAccess = Dst::Flags & DirectAccessBit,
+    SrcHasDirectAccess = Src::Flags & DirectAccessBit,
+    StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
+    InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
+                : int(Dst::Flags) & RowMajorBit ? int(Dst::ColsAtCompileTime)
+                                                : int(Dst::RowsAtCompileTime),
+    InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
+                   : int(Dst::Flags) & RowMajorBit ? int(Dst::MaxColsAtCompileTime)
+                                                   : int(Dst::MaxRowsAtCompileTime),
+    MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
+
+    MightEnableVml = bool(StorageOrdersAgree) && bool(DstHasDirectAccess) && bool(SrcHasDirectAccess) &&
+                     Src::InnerStrideAtCompileTime == 1 && Dst::InnerStrideAtCompileTime == 1,
+    MightLinearize = bool(MightEnableVml) && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
+    VmlSize = bool(MightLinearize) ? MaxSizeAtCompileTime : InnerMaxSize,
+    LargeEnough = (VmlSize == Dynamic) || VmlSize >= EIGEN_MKL_VML_THRESHOLD
+  };
+
+ public:
+  enum { EnableVml = MightEnableVml && LargeEnough, Traversal = MightLinearize ? LinearTraversal : DefaultTraversal };
+};
+
+#define EIGEN_PP_EXPAND(ARG) ARG
+#if !defined(EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
+#define EIGEN_VMLMODE_EXPAND_xLA , VML_HA
+#else
+#define EIGEN_VMLMODE_EXPAND_xLA , VML_LA
+#endif
+
+#define EIGEN_VMLMODE_EXPAND_x_
+
+#define EIGEN_VMLMODE_PREFIX_xLA vm
+#define EIGEN_VMLMODE_PREFIX_x_ v
+#define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_x, VMLMODE)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                      \
+  template <typename DstXprType, typename SrcXprNested>                                                    \
+  struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>,              \
+                    assign_op<EIGENTYPE, EIGENTYPE>, Dense2Dense,                                          \
+                    std::enable_if_t<vml_assign_traits<DstXprType, SrcXprNested>::EnableVml>> {            \
+    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType;                       \
+    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE, EIGENTYPE> &func) { \
+      resize_if_allowed(dst, src, func);                                                                   \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                  \
+      if (vml_assign_traits<DstXprType, SrcXprNested>::Traversal == (int)LinearTraversal) {                \
+        VMLOP(dst.size(), (const VMLTYPE *)src.nestedExpression().data(),                                  \
+              (VMLTYPE *)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                     \
+      } else {                                                                                             \
+        const Index outerSize = dst.outerSize();                                                           \
+        for (Index outer = 0; outer < outerSize; ++outer) {                                                \
+          const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer, 0))         \
+                                                    : &(src.nestedExpression().coeffRef(0, outer));        \
+          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer, 0)) : &(dst.coeffRef(0, outer));     \
+          VMLOP(dst.innerSize(), (const VMLTYPE *)src_ptr,                                                 \
+                (VMLTYPE *)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                      \
+        }                                                                                                  \
+      }                                                                                                    \
+    }                                                                                                      \
+  };
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)                                                \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), s##VMLOP), float, float, VMLMODE) \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), d##VMLOP), double, double, VMLMODE)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)                                   \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), c##VMLOP), scomplex, \
+                                   MKL_Complex8, VMLMODE)                                                 \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), z##VMLOP), dcomplex, \
+                                   MKL_Complex16, VMLMODE)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE) \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)  \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)
+
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin, Sin, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin, Asin, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh, Sinh, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos, Cos, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos, Acos, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh, Cosh, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan, Tan, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan, Atan, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh, Tanh, LA)
+// EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs,   Abs,    _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp, Exp, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log, Ln, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt, Sqrt, _)
+
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr, _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg, _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round, _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor, _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(cbrt, Cbrt, _)
+
+#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                        \
+  template <typename DstXprType, typename SrcXprNested, typename Plain>                                    \
+  struct Assignment<DstXprType,                                                                            \
+                    CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE, EIGENTYPE>, SrcXprNested,               \
+                                  const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>, Plain>>,   \
+                    assign_op<EIGENTYPE, EIGENTYPE>, Dense2Dense,                                          \
+                    std::enable_if_t<vml_assign_traits<DstXprType, SrcXprNested>::EnableVml>> {            \
+    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE, EIGENTYPE>, SrcXprNested,                       \
+                          const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>, Plain>>            \
+        SrcXprType;                                                                                        \
+    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE, EIGENTYPE> &func) { \
+      resize_if_allowed(dst, src, func);                                                                   \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                  \
+      VMLTYPE exponent = reinterpret_cast<const VMLTYPE &>(src.rhs().functor().m_other);                   \
+      if (vml_assign_traits<DstXprType, SrcXprNested>::Traversal == LinearTraversal) {                     \
+        VMLOP(dst.size(), (const VMLTYPE *)src.lhs().data(), exponent,                                     \
+              (VMLTYPE *)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                     \
+      } else {                                                                                             \
+        const Index outerSize = dst.outerSize();                                                           \
+        for (Index outer = 0; outer < outerSize; ++outer) {                                                \
+          const EIGENTYPE *src_ptr =                                                                       \
+              src.IsRowMajor ? &(src.lhs().coeffRef(outer, 0)) : &(src.lhs().coeffRef(0, outer));          \
+          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer, 0)) : &(dst.coeffRef(0, outer));     \
+          VMLOP(dst.innerSize(), (const VMLTYPE *)src_ptr, exponent,                                       \
+                (VMLTYPE *)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                      \
+        }                                                                                                  \
+      }                                                                                                    \
+    }                                                                                                      \
+  };
+
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float, float, LA)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double, double, LA)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8, LA)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ASSIGN_VML_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/BandMatrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/BandMatrix.h
new file mode 100644
index 0000000..57b0322
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/BandMatrix.h
@@ -0,0 +1,338 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BANDMATRIX_H
+#define EIGEN_BANDMATRIX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Derived>
+class BandMatrixBase : public EigenBase<Derived> {
+ public:
+  enum {
+    Flags = internal::traits<Derived>::Flags,
+    CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
+    RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+    ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+    MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
+    Supers = internal::traits<Derived>::Supers,
+    Subs = internal::traits<Derived>::Subs,
+    Options = internal::traits<Derived>::Options
+  };
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime> DenseMatrixType;
+  typedef typename DenseMatrixType::StorageIndex StorageIndex;
+  typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
+  typedef EigenBase<Derived> Base;
+
+ protected:
+  enum {
+    DataRowsAtCompileTime = ((Supers != Dynamic) && (Subs != Dynamic)) ? 1 + Supers + Subs : Dynamic,
+    SizeAtCompileTime = min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)
+  };
+
+ public:
+  using Base::cols;
+  using Base::derived;
+  using Base::rows;
+
+  /** \returns the number of super diagonals */
+  inline Index supers() const { return derived().supers(); }
+
+  /** \returns the number of sub diagonals */
+  inline Index subs() const { return derived().subs(); }
+
+  /** \returns an expression of the underlying coefficient matrix */
+  inline const CoefficientsType& coeffs() const { return derived().coeffs(); }
+
+  /** \returns an expression of the underlying coefficient matrix */
+  inline CoefficientsType& coeffs() { return derived().coeffs(); }
+
+  /** \returns a vector expression of the \a i -th column,
+   * only the meaningful part is returned.
+   * \warning the internal storage must be column major. */
+  inline Block<CoefficientsType, Dynamic, 1> col(Index i) {
+    EIGEN_STATIC_ASSERT((int(Options) & int(RowMajor)) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+    Index start = 0;
+    Index len = coeffs().rows();
+    if (i <= supers()) {
+      start = supers() - i;
+      len = (std::min)(rows(), std::max<Index>(0, coeffs().rows() - (supers() - i)));
+    } else if (i >= rows() - subs())
+      len = std::max<Index>(0, coeffs().rows() - (i + 1 - rows() + subs()));
+    return Block<CoefficientsType, Dynamic, 1>(coeffs(), start, i, len, 1);
+  }
+
+  /** \returns a vector expression of the main diagonal */
+  inline Block<CoefficientsType, 1, SizeAtCompileTime> diagonal() {
+    return Block<CoefficientsType, 1, SizeAtCompileTime>(coeffs(), supers(), 0, 1, (std::min)(rows(), cols()));
+  }
+
+  /** \returns a vector expression of the main diagonal (const version) */
+  inline const Block<const CoefficientsType, 1, SizeAtCompileTime> diagonal() const {
+    return Block<const CoefficientsType, 1, SizeAtCompileTime>(coeffs(), supers(), 0, 1, (std::min)(rows(), cols()));
+  }
+
+  template <int Index>
+  struct DiagonalIntReturnType {
+    enum {
+      ReturnOpposite =
+          (int(Options) & int(SelfAdjoint)) && (((Index) > 0 && Supers == 0) || ((Index) < 0 && Subs == 0)),
+      Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex,
+      ActualIndex = ReturnOpposite ? -Index : Index,
+      DiagonalSize =
+          (RowsAtCompileTime == Dynamic || ColsAtCompileTime == Dynamic)
+              ? Dynamic
+              : (ActualIndex < 0 ? min_size_prefer_dynamic(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
+                                 : min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
+    };
+    typedef Block<CoefficientsType, 1, DiagonalSize> BuildType;
+    typedef std::conditional_t<Conjugate, CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, BuildType>, BuildType>
+        Type;
+  };
+
+  /** \returns a vector expression of the \a N -th sub or super diagonal */
+  template <int N>
+  inline typename DiagonalIntReturnType<N>::Type diagonal() {
+    return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers() - N, (std::max)(0, N), 1, diagonalLength(N));
+  }
+
+  /** \returns a vector expression of the \a N -th sub or super diagonal */
+  template <int N>
+  inline const typename DiagonalIntReturnType<N>::Type diagonal() const {
+    return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers() - N, (std::max)(0, N), 1, diagonalLength(N));
+  }
+
+  /** \returns a vector expression of the \a i -th sub or super diagonal */
+  inline Block<CoefficientsType, 1, Dynamic> diagonal(Index i) {
+    eigen_assert((i < 0 && -i <= subs()) || (i >= 0 && i <= supers()));
+    return Block<CoefficientsType, 1, Dynamic>(coeffs(), supers() - i, std::max<Index>(0, i), 1, diagonalLength(i));
+  }
+
+  /** \returns a vector expression of the \a i -th sub or super diagonal */
+  inline const Block<const CoefficientsType, 1, Dynamic> diagonal(Index i) const {
+    eigen_assert((i < 0 && -i <= subs()) || (i >= 0 && i <= supers()));
+    return Block<const CoefficientsType, 1, Dynamic>(coeffs(), supers() - i, std::max<Index>(0, i), 1,
+                                                     diagonalLength(i));
+  }
+
+  template <typename Dest>
+  inline void evalTo(Dest& dst) const {
+    dst.resize(rows(), cols());
+    dst.setZero();
+    dst.diagonal() = diagonal();
+    for (Index i = 1; i <= supers(); ++i) dst.diagonal(i) = diagonal(i);
+    for (Index i = 1; i <= subs(); ++i) dst.diagonal(-i) = diagonal(-i);
+  }
+
+  DenseMatrixType toDenseMatrix() const {
+    DenseMatrixType res(rows(), cols());
+    evalTo(res);
+    return res;
+  }
+
+ protected:
+  inline Index diagonalLength(Index i) const {
+    return i < 0 ? (std::min)(cols(), rows() + i) : (std::min)(rows(), cols() - i);
+  }
+};
+
+/**
+ * \class BandMatrix
+ * \ingroup Core_Module
+ *
+ * \brief Represents a rectangular matrix with a banded storage
+ *
+ * \tparam Scalar_ Numeric type, i.e. float, double, int
+ * \tparam Rows_ Number of rows, or \b Dynamic
+ * \tparam Cols_ Number of columns, or \b Dynamic
+ * \tparam Supers_ Number of super diagonal
+ * \tparam Subs_ Number of sub diagonal
+ * \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
+ *                  The former controls \ref TopicStorageOrders "storage order", and defaults to
+ *                  column-major. The latter controls whether the matrix represents a selfadjoint
+ *                  matrix in which case either Supers of Subs have to be null.
+ *
+ * \sa class TridiagonalMatrix
+ */
+
+template <typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+struct traits<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef Eigen::Index StorageIndex;
+  enum {
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    RowsAtCompileTime = Rows_,
+    ColsAtCompileTime = Cols_,
+    MaxRowsAtCompileTime = Rows_,
+    MaxColsAtCompileTime = Cols_,
+    Flags = LvalueBit,
+    Supers = Supers_,
+    Subs = Subs_,
+    Options = Options_,
+    DataRowsAtCompileTime = ((Supers != Dynamic) && (Subs != Dynamic)) ? 1 + Supers + Subs : Dynamic
+  };
+  typedef Matrix<Scalar, DataRowsAtCompileTime, ColsAtCompileTime, int(Options) & int(RowMajor) ? RowMajor : ColMajor>
+      CoefficientsType;
+};
+
+template <typename Scalar_, int Rows, int Cols, int Supers, int Subs, int Options>
+class BandMatrix : public BandMatrixBase<BandMatrix<Scalar_, Rows, Cols, Supers, Subs, Options> > {
+ public:
+  typedef typename internal::traits<BandMatrix>::Scalar Scalar;
+  typedef typename internal::traits<BandMatrix>::StorageIndex StorageIndex;
+  typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
+
+  explicit inline BandMatrix(Index rows = Rows, Index cols = Cols, Index supers = Supers, Index subs = Subs)
+      : m_coeffs(1 + supers + subs, cols), m_rows(rows), m_supers(supers), m_subs(subs) {}
+
+  /** \returns the number of columns */
+  constexpr Index rows() const { return m_rows.value(); }
+
+  /** \returns the number of rows */
+  constexpr Index cols() const { return m_coeffs.cols(); }
+
+  /** \returns the number of super diagonals */
+  constexpr Index supers() const { return m_supers.value(); }
+
+  /** \returns the number of sub diagonals */
+  constexpr Index subs() const { return m_subs.value(); }
+
+  inline const CoefficientsType& coeffs() const { return m_coeffs; }
+  inline CoefficientsType& coeffs() { return m_coeffs; }
+
+ protected:
+  CoefficientsType m_coeffs;
+  internal::variable_if_dynamic<Index, Rows> m_rows;
+  internal::variable_if_dynamic<Index, Supers> m_supers;
+  internal::variable_if_dynamic<Index, Subs> m_subs;
+};
+
+template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+class BandMatrixWrapper;
+
+template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+struct traits<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+  typedef typename CoefficientsType_::Scalar Scalar;
+  typedef typename CoefficientsType_::StorageKind StorageKind;
+  typedef typename CoefficientsType_::StorageIndex StorageIndex;
+  enum {
+    CoeffReadCost = internal::traits<CoefficientsType_>::CoeffReadCost,
+    RowsAtCompileTime = Rows_,
+    ColsAtCompileTime = Cols_,
+    MaxRowsAtCompileTime = Rows_,
+    MaxColsAtCompileTime = Cols_,
+    Flags = LvalueBit,
+    Supers = Supers_,
+    Subs = Subs_,
+    Options = Options_,
+    DataRowsAtCompileTime = ((Supers != Dynamic) && (Subs != Dynamic)) ? 1 + Supers + Subs : Dynamic
+  };
+  typedef CoefficientsType_ CoefficientsType;
+};
+
+template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+class BandMatrixWrapper
+    : public BandMatrixBase<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+ public:
+  typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
+  typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
+  typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
+
+  explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows = Rows_, Index cols = Cols_,
+                                    Index supers = Supers_, Index subs = Subs_)
+      : m_coeffs(coeffs), m_rows(rows), m_supers(supers), m_subs(subs) {
+    EIGEN_UNUSED_VARIABLE(cols);
+    // eigen_assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
+  }
+
+  /** \returns the number of columns */
+  constexpr Index rows() const { return m_rows.value(); }
+
+  /** \returns the number of rows */
+  constexpr Index cols() const { return m_coeffs.cols(); }
+
+  /** \returns the number of super diagonals */
+  constexpr Index supers() const { return m_supers.value(); }
+
+  /** \returns the number of sub diagonals */
+  constexpr Index subs() const { return m_subs.value(); }
+
+  inline const CoefficientsType& coeffs() const { return m_coeffs; }
+
+ protected:
+  const CoefficientsType& m_coeffs;
+  internal::variable_if_dynamic<Index, Rows_> m_rows;
+  internal::variable_if_dynamic<Index, Supers_> m_supers;
+  internal::variable_if_dynamic<Index, Subs_> m_subs;
+};
+
+/**
+ * \class TridiagonalMatrix
+ * \ingroup Core_Module
+ *
+ * \brief Represents a tridiagonal matrix with a compact banded storage
+ *
+ * \tparam Scalar Numeric type, i.e. float, double, int
+ * \tparam Size Number of rows and cols, or \b Dynamic
+ * \tparam Options Can be 0 or \b SelfAdjoint
+ *
+ * \sa class BandMatrix
+ */
+template <typename Scalar, int Size, int Options>
+class TridiagonalMatrix : public BandMatrix<Scalar, Size, Size, Options & SelfAdjoint ? 0 : 1, 1, Options | RowMajor> {
+  typedef BandMatrix<Scalar, Size, Size, Options & SelfAdjoint ? 0 : 1, 1, Options | RowMajor> Base;
+  typedef typename Base::StorageIndex StorageIndex;
+
+ public:
+  explicit TridiagonalMatrix(Index size = Size) : Base(size, size, Options & SelfAdjoint ? 0 : 1, 1) {}
+
+  inline typename Base::template DiagonalIntReturnType<1>::Type super() { return Base::template diagonal<1>(); }
+  inline const typename Base::template DiagonalIntReturnType<1>::Type super() const {
+    return Base::template diagonal<1>();
+  }
+  inline typename Base::template DiagonalIntReturnType<-1>::Type sub() { return Base::template diagonal<-1>(); }
+  inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const {
+    return Base::template diagonal<-1>();
+  }
+
+ protected:
+};
+
+struct BandShape {};
+
+template <typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+struct evaluator_traits<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> >
+    : public evaluator_traits_base<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+  typedef BandShape Shape;
+};
+
+template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+struct evaluator_traits<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> >
+    : public evaluator_traits_base<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+  typedef BandShape Shape;
+};
+
+template <>
+struct AssignmentKind<DenseShape, BandShape> {
+  typedef EigenBase2EigenBase Kind;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BANDMATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Block.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Block.h
new file mode 100644
index 0000000..c2d8bea
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Block.h
@@ -0,0 +1,427 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BLOCK_H
+#define EIGEN_BLOCK_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename XprType_, int BlockRows, int BlockCols, bool InnerPanel_>
+struct traits<Block<XprType_, BlockRows, BlockCols, InnerPanel_>> : traits<XprType_> {
+  typedef typename traits<XprType_>::Scalar Scalar;
+  typedef typename traits<XprType_>::StorageKind StorageKind;
+  typedef typename traits<XprType_>::XprKind XprKind;
+  typedef typename ref_selector<XprType_>::type XprTypeNested;
+  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
+  enum {
+    MatrixRows = traits<XprType_>::RowsAtCompileTime,
+    MatrixCols = traits<XprType_>::ColsAtCompileTime,
+    RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
+    ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
+    MaxRowsAtCompileTime = BlockRows == 0                 ? 0
+                           : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
+                                                          : int(traits<XprType_>::MaxRowsAtCompileTime),
+    MaxColsAtCompileTime = BlockCols == 0                 ? 0
+                           : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
+                                                          : int(traits<XprType_>::MaxColsAtCompileTime),
+
+    XprTypeIsRowMajor = (int(traits<XprType_>::Flags) & RowMajorBit) != 0,
+    IsRowMajor = (MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1)   ? 1
+                 : (MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1) ? 0
+                                                                            : XprTypeIsRowMajor,
+    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
+    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
+    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time<XprType_>::ret)
+                                                            : int(outer_stride_at_compile_time<XprType_>::ret),
+    OuterStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(outer_stride_at_compile_time<XprType_>::ret)
+                                                            : int(inner_stride_at_compile_time<XprType_>::ret),
+
+    // FIXME, this traits is rather specialized for dense object and it needs to be cleaned further
+    FlagsLvalueBit = is_lvalue<XprType_>::value ? LvalueBit : 0,
+    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
+    Flags = (traits<XprType_>::Flags & (DirectAccessBit | (InnerPanel_ ? CompressedAccessBit : 0))) | FlagsLvalueBit |
+            FlagsRowMajorBit,
+    // FIXME DirectAccessBit should not be handled by expressions
+    //
+    // Alignment is needed by MapBase's assertions
+    // We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the
+    // respective evaluator
+    Alignment = 0,
+    InnerPanel = InnerPanel_ ? 1 : 0
+  };
+};
+
+template <typename XprType, int BlockRows = Dynamic, int BlockCols = Dynamic, bool InnerPanel = false,
+          bool HasDirectAccess = internal::has_direct_access<XprType>::ret>
+class BlockImpl_dense;
+
+}  // end namespace internal
+
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind>
+class BlockImpl;
+
+/** \class Block
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a fixed-size or dynamic-size block
+ *
+ * \tparam XprType the type of the expression in which we are taking a block
+ * \tparam BlockRows the number of rows of the block we are taking at compile time (optional)
+ * \tparam BlockCols the number of columns of the block we are taking at compile time (optional)
+ * \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or
+ *         to set of columns of a column major matrix (optional). The parameter allows to determine
+ *         at compile time whether aligned access is possible on the block expression.
+ *
+ * This class represents an expression of either a fixed-size or dynamic-size block. It is the return
+ * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
+ * most of the time this is the only way it is used.
+ *
+ * However, if you want to directly manipulate block expressions,
+ * for instance if you want to write a function returning such an expression, you
+ * will need to use this class.
+ *
+ * Here is an example illustrating the dynamic case:
+ * \include class_Block.cpp
+ * Output: \verbinclude class_Block.out
+ *
+ * \note Even though this expression has dynamic size, in the case where \a XprType
+ * has fixed size, this expression inherits a fixed maximal size which means that evaluating
+ * it does not cause a dynamic memory allocation.
+ *
+ * Here is an example illustrating the fixed-size case:
+ * \include class_FixedBlock.cpp
+ * Output: \verbinclude class_FixedBlock.out
+ *
+ * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
+ */
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class Block
+    : public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> {
+  typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
+  using BlockHelper = internal::block_xpr_helper<Block>;
+
+ public:
+  // typedef typename Impl::Base Base;
+  typedef Impl Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
+
+  typedef internal::remove_all_t<XprType> NestedExpression;
+
+  /** Column or Row constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Block(XprType& xpr, Index i) : Impl(xpr, i) {
+    eigen_assert((i >= 0) && (((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) && i < xpr.rows()) ||
+                              ((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) && i < xpr.cols())));
+  }
+
+  /** Fixed-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Block(XprType& xpr, Index startRow, Index startCol)
+      : Impl(xpr, startRow, startCol) {
+    EIGEN_STATIC_ASSERT(RowsAtCompileTime != Dynamic && ColsAtCompileTime != Dynamic,
+                        THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
+    eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows() && startCol >= 0 &&
+                 BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
+  }
+
+  /** Dynamic-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Block(XprType& xpr, Index startRow, Index startCol, Index blockRows,
+                                                        Index blockCols)
+      : Impl(xpr, startRow, startCol, blockRows, blockCols) {
+    eigen_assert((RowsAtCompileTime == Dynamic || RowsAtCompileTime == blockRows) &&
+                 (ColsAtCompileTime == Dynamic || ColsAtCompileTime == blockCols));
+    eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows && startCol >= 0 &&
+                 blockCols >= 0 && startCol <= xpr.cols() - blockCols);
+  }
+
+  // convert nested blocks (e.g. Block<Block<MatrixType>>) to a simple block expression (Block<MatrixType>)
+
+  using ConstUnwindReturnType = Block<const typename BlockHelper::BaseType, BlockRows, BlockCols, InnerPanel>;
+  using UnwindReturnType = Block<typename BlockHelper::BaseType, BlockRows, BlockCols, InnerPanel>;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ConstUnwindReturnType unwind() const {
+    return ConstUnwindReturnType(BlockHelper::base(*this), BlockHelper::row(*this, 0), BlockHelper::col(*this, 0),
+                                 this->rows(), this->cols());
+  }
+
+  template <typename T = Block, typename EnableIf = std::enable_if_t<!std::is_const<T>::value>>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UnwindReturnType unwind() {
+    return UnwindReturnType(BlockHelper::base(*this), BlockHelper::row(*this, 0), BlockHelper::col(*this, 0),
+                            this->rows(), this->cols());
+  }
+};
+
+// The generic default implementation for dense block simply forward to the internal::BlockImpl_dense
+// that must be specialized for direct and non-direct access...
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
+    : public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> {
+  typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
+  typedef typename XprType::StorageIndex StorageIndex;
+
+ public:
+  typedef Impl Base;
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index i) : Impl(xpr, i) {}
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol)
+      : Impl(xpr, startRow, startCol) {}
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol,
+                                                            Index blockRows, Index blockCols)
+      : Impl(xpr, startRow, startCol, blockRows, blockCols) {}
+};
+
+namespace internal {
+
+/** \internal Internal implementation of dense Blocks in the general case. */
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess>
+class BlockImpl_dense : public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel>>::type {
+  typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
+  typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
+
+ public:
+  typedef typename internal::dense_xpr_base<BlockType>::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
+
+  /** Column or Row constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr BlockImpl_dense(XprType& xpr, Index i)
+      : m_xpr(xpr),
+        // It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
+        // and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
+        // all other cases are invalid.
+        // The case a 1x1 matrix seems ambiguous, but the result is the same anyway.
+        m_startRow((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) ? i : 0),
+        m_startCol((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) ? i : 0),
+        m_blockRows(BlockRows == 1 ? 1 : xpr.rows()),
+        m_blockCols(BlockCols == 1 ? 1 : xpr.cols()) {}
+
+  /** Fixed-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
+      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), m_blockRows(BlockRows), m_blockCols(BlockCols) {}
+
+  /** Dynamic-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr BlockImpl_dense(XprType& xpr, Index startRow, Index startCol, Index blockRows,
+                                              Index blockCols)
+      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), m_blockRows(blockRows), m_blockCols(blockCols) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_blockRows.value(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_blockCols.value(); }
+
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index rowId, Index colId) {
+    EIGEN_STATIC_ASSERT_LVALUE(XprType)
+    return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
+    return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const {
+    return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
+  }
+
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index) {
+    EIGEN_STATIC_ASSERT_LVALUE(XprType)
+    return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const {
+    return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+  }
+
+  EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const {
+    return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                       m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC inline PacketScalar packet(Index rowId, Index colId) const {
+    return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC inline void writePacket(Index rowId, Index colId, const PacketScalar& val) {
+    m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC inline PacketScalar packet(Index index) const {
+    return m_xpr.template packet<Unaligned>(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC inline void writePacket(Index index, const PacketScalar& val) {
+    m_xpr.template writePacket<Unaligned>(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \sa MapBase::data() */
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const;
+  EIGEN_DEVICE_FUNC inline Index innerStride() const;
+  EIGEN_DEVICE_FUNC inline Index outerStride() const;
+#endif
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression() const {
+    return m_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE XprType& nestedExpression() { return m_xpr; }
+
+  EIGEN_DEVICE_FUNC constexpr StorageIndex startRow() const noexcept { return m_startRow.value(); }
+
+  EIGEN_DEVICE_FUNC constexpr StorageIndex startCol() const noexcept { return m_startCol.value(); }
+
+ protected:
+  XprTypeNested m_xpr;
+  const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows == 1) ? 0 : Dynamic>
+      m_startRow;
+  const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols == 1) ? 0 : Dynamic>
+      m_startCol;
+  const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
+  const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
+};
+
+/** \internal Internal implementation of dense Blocks in the direct access case.*/
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel, true>
+    : public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel>> {
+  typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
+  typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
+  enum { XprTypeIsRowMajor = (int(traits<XprType>::Flags) & RowMajorBit) != 0 };
+
+  /** \internal Returns base+offset (unless base is null, in which case returns null).
+   * Adding an offset to nullptr is undefined behavior, so we must avoid it.
+   */
+  template <typename Scalar>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE static Scalar* add_to_nullable_pointer(Scalar* base, Index offset) {
+    return base != nullptr ? base + offset : nullptr;
+  }
+
+ public:
+  typedef MapBase<BlockType> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
+
+  /** Column or Row constructor
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index i)
+      : Base((BlockRows == 0 || BlockCols == 0)
+                 ? nullptr
+                 : add_to_nullable_pointer(
+                       xpr.data(),
+                       i * (((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor)) ||
+                                    ((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) &&
+                                     (XprTypeIsRowMajor))
+                                ? xpr.innerStride()
+                                : xpr.outerStride())),
+             BlockRows == 1 ? 1 : xpr.rows(), BlockCols == 1 ? 1 : xpr.cols()),
+        m_xpr(xpr),
+        m_startRow((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) ? i : 0),
+        m_startCol((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) ? i : 0) {
+    init();
+  }
+
+  /** Fixed-size constructor
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
+      : Base((BlockRows == 0 || BlockCols == 0)
+                 ? nullptr
+                 : add_to_nullable_pointer(xpr.data(),
+                                           xpr.innerStride() * (XprTypeIsRowMajor ? startCol : startRow) +
+                                               xpr.outerStride() * (XprTypeIsRowMajor ? startRow : startCol))),
+        m_xpr(xpr),
+        m_startRow(startRow),
+        m_startCol(startCol) {
+    init();
+  }
+
+  /** Dynamic-size constructor
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index startRow, Index startCol, Index blockRows,
+                                                        Index blockCols)
+      : Base((blockRows == 0 || blockCols == 0)
+                 ? nullptr
+                 : add_to_nullable_pointer(xpr.data(),
+                                           xpr.innerStride() * (XprTypeIsRowMajor ? startCol : startRow) +
+                                               xpr.outerStride() * (XprTypeIsRowMajor ? startRow : startCol)),
+             blockRows, blockCols),
+        m_xpr(xpr),
+        m_startRow(startRow),
+        m_startCol(startCol) {
+    init();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression() const noexcept {
+    return m_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE XprType& nestedExpression() { return m_xpr; }
+
+  /** \sa MapBase::innerStride() */
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept {
+    return internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.innerStride() : m_xpr.outerStride();
+  }
+
+  /** \sa MapBase::outerStride() */
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept {
+    return internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.outerStride() : m_xpr.innerStride();
+  }
+
+  EIGEN_DEVICE_FUNC constexpr StorageIndex startRow() const noexcept { return m_startRow.value(); }
+
+  EIGEN_DEVICE_FUNC constexpr StorageIndex startCol() const noexcept { return m_startCol.value(); }
+
+#ifndef __SUNPRO_CC
+  // FIXME sunstudio is not friendly with the above friend...
+  // META-FIXME there is no 'friend' keyword around here. Is this obsolete?
+ protected:
+#endif
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal used by allowAligned() */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows,
+                                                        Index blockCols)
+      : Base(data, blockRows, blockCols), m_xpr(xpr) {
+    init();
+  }
+#endif
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void init() {
+    m_outerStride =
+        internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.outerStride() : m_xpr.innerStride();
+  }
+
+  XprTypeNested m_xpr;
+  const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows == 1) ? 0 : Dynamic>
+      m_startRow;
+  const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols == 1) ? 0 : Dynamic>
+      m_startCol;
+  Index m_outerStride;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BLOCK_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CommaInitializer.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CommaInitializer.h
new file mode 100644
index 0000000..4541f47
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CommaInitializer.h
@@ -0,0 +1,148 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMMAINITIALIZER_H
+#define EIGEN_COMMAINITIALIZER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class CommaInitializer
+ * \ingroup Core_Module
+ *
+ * \brief Helper class used by the comma initializer operator
+ *
+ * This class is internally used to implement the comma initializer feature. It is
+ * the return type of MatrixBase::operator<<, and most of the time this is the only
+ * way it is used.
+ *
+ * \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
+ */
+template <typename XprType>
+struct CommaInitializer {
+  typedef typename XprType::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC constexpr CommaInitializer(XprType& xpr, const Scalar& s)
+      : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1) {
+    eigen_assert(m_xpr.rows() > 0 && m_xpr.cols() > 0 && "Cannot comma-initialize a 0x0 matrix (operator<<)");
+    m_xpr.coeffRef(0, 0) = s;
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
+      : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows()) {
+    eigen_assert(m_xpr.rows() >= other.rows() && m_xpr.cols() >= other.cols() &&
+                 "Cannot comma-initialize a 0x0 matrix (operator<<)");
+    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>(0, 0, other.rows(),
+                                                                                           other.cols()) = other;
+  }
+
+  /* Copy/Move constructor which transfers ownership. This is crucial in
+   * absence of return value optimization to avoid assertions during destruction. */
+  EIGEN_DEVICE_FUNC inline CommaInitializer(const CommaInitializer& o)
+      : m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
+    // Mark original object as finished. In absence of R-value references we need to const_cast:
+    const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
+    const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
+    const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
+  }
+
+  /* inserts a scalar value in the target matrix */
+  EIGEN_DEVICE_FUNC CommaInitializer &operator,(const Scalar& s) {
+    if (m_col == m_xpr.cols()) {
+      m_row += m_currentBlockRows;
+      m_col = 0;
+      m_currentBlockRows = 1;
+      eigen_assert(m_row < m_xpr.rows() && "Too many rows passed to comma initializer (operator<<)");
+    }
+    eigen_assert(m_col < m_xpr.cols() && "Too many coefficients passed to comma initializer (operator<<)");
+    eigen_assert(m_currentBlockRows == 1);
+    m_xpr.coeffRef(m_row, m_col++) = s;
+    return *this;
+  }
+
+  /* inserts a matrix expression in the target matrix */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC CommaInitializer &operator,(const DenseBase<OtherDerived>& other) {
+    if (m_col == m_xpr.cols() && (other.cols() != 0 || other.rows() != m_currentBlockRows)) {
+      m_row += m_currentBlockRows;
+      m_col = 0;
+      m_currentBlockRows = other.rows();
+      eigen_assert(m_row + m_currentBlockRows <= m_xpr.rows() &&
+                   "Too many rows passed to comma initializer (operator<<)");
+    }
+    eigen_assert((m_col + other.cols() <= m_xpr.cols()) &&
+                 "Too many coefficients passed to comma initializer (operator<<)");
+    eigen_assert(m_currentBlockRows == other.rows());
+    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>(m_row, m_col, other.rows(),
+                                                                                           other.cols()) = other;
+    m_col += other.cols();
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC inline ~CommaInitializer()
+#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
+      noexcept(false)  // Eigen::eigen_assert_exception
+#endif
+  {
+    finished();
+  }
+
+  /** \returns the built matrix once all its coefficients have been set.
+   * Calling finished is 100% optional. Its purpose is to write expressions
+   * like this:
+   * \code
+   * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
+   * \endcode
+   */
+  EIGEN_DEVICE_FUNC inline XprType& finished() {
+    eigen_assert(((m_row + m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0) && m_col == m_xpr.cols() &&
+                 "Too few coefficients passed to comma initializer (operator<<)");
+    return m_xpr;
+  }
+
+  XprType& m_xpr;            // target expression
+  Index m_row;               // current row id
+  Index m_col;               // current col id
+  Index m_currentBlockRows;  // current block height
+};
+
+/** \anchor MatrixBaseCommaInitRef
+ * Convenient operator to set the coefficients of a matrix.
+ *
+ * The coefficients must be provided in a row major order and exactly match
+ * the size of the matrix. Otherwise an assertion is raised.
+ *
+ * Example: \include MatrixBase_set.cpp
+ * Output: \verbinclude MatrixBase_set.out
+ *
+ * \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary
+ * order.
+ *
+ * \sa CommaInitializer::finished(), class CommaInitializer
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<<(const Scalar& s) {
+  return CommaInitializer<Derived>(*static_cast<Derived*>(this), s);
+}
+
+/** \sa operator<<(const Scalar&) */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<<(
+    const DenseBase<OtherDerived>& other) {
+  return CommaInitializer<Derived>(*static_cast<Derived*>(this), other);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMMAINITIALIZER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ConditionEstimator.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ConditionEstimator.h
new file mode 100644
index 0000000..dd1770b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ConditionEstimator.h
@@ -0,0 +1,173 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@google.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CONDITIONESTIMATOR_H
+#define EIGEN_CONDITIONESTIMATOR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Vector, typename RealVector, bool IsComplex>
+struct rcond_compute_sign {
+  static inline Vector run(const Vector& v) {
+    const RealVector v_abs = v.cwiseAbs();
+    return (v_abs.array() == static_cast<typename Vector::RealScalar>(0))
+        .select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs));
+  }
+};
+
+// Partial specialization to avoid elementwise division for real vectors.
+template <typename Vector>
+struct rcond_compute_sign<Vector, Vector, false> {
+  static inline Vector run(const Vector& v) {
+    return (v.array() < static_cast<typename Vector::RealScalar>(0))
+        .select(-Vector::Ones(v.size()), Vector::Ones(v.size()));
+  }
+};
+
+/**
+ * \returns an estimate of ||inv(matrix)||_1 given a decomposition of
+ * \a matrix that implements .solve() and .adjoint().solve() methods.
+ *
+ * This function implements Algorithms 4.1 and 5.1 from
+ *   http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf
+ * which also forms the basis for the condition number estimators in
+ * LAPACK. Since at most 10 calls to the solve method of dec are
+ * performed, the total cost is O(dims^2), as opposed to O(dims^3)
+ * needed to compute the inverse matrix explicitly.
+ *
+ * The most common usage is in estimating the condition number
+ * ||matrix||_1 * ||inv(matrix)||_1. The first term ||matrix||_1 can be
+ * computed directly in O(n^2) operations.
+ *
+ * Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, and
+ * LLT.
+ *
+ * \sa FullPivLU, PartialPivLU, LDLT, LLT.
+ */
+template <typename Decomposition>
+typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec) {
+  typedef typename Decomposition::MatrixType MatrixType;
+  typedef typename Decomposition::Scalar Scalar;
+  typedef typename Decomposition::RealScalar RealScalar;
+  typedef typename internal::plain_col_type<MatrixType>::type Vector;
+  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVector;
+  const bool is_complex = (NumTraits<Scalar>::IsComplex != 0);
+
+  eigen_assert(dec.rows() == dec.cols());
+  const Index n = dec.rows();
+  if (n == 0) return 0;
+
+    // Disable Index to float conversion warning
+#ifdef __INTEL_COMPILER
+#pragma warning push
+#pragma warning(disable : 2259)
+#endif
+  Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
+#ifdef __INTEL_COMPILER
+#pragma warning pop
+#endif
+
+  // lower_bound is a lower bound on
+  //   ||inv(matrix)||_1  = sup_v ||inv(matrix) v||_1 / ||v||_1
+  // and is the objective maximized by the ("super-") gradient ascent
+  // algorithm below.
+  RealScalar lower_bound = v.template lpNorm<1>();
+  if (n == 1) return lower_bound;
+
+  // Gradient ascent algorithm follows: We know that the optimum is achieved at
+  // one of the simplices v = e_i, so in each iteration we follow a
+  // super-gradient to move towards the optimal one.
+  RealScalar old_lower_bound = lower_bound;
+  Vector sign_vector(n);
+  Vector old_sign_vector;
+  Index v_max_abs_index = -1;
+  Index old_v_max_abs_index = v_max_abs_index;
+  for (int k = 0; k < 4; ++k) {
+    sign_vector = internal::rcond_compute_sign<Vector, RealVector, is_complex>::run(v);
+    if (k > 0 && !is_complex && sign_vector == old_sign_vector) {
+      // Break if the solution stagnated.
+      break;
+    }
+    // v_max_abs_index = argmax |real( inv(matrix)^T * sign_vector )|
+    v = dec.adjoint().solve(sign_vector);
+    v.real().cwiseAbs().maxCoeff(&v_max_abs_index);
+    if (v_max_abs_index == old_v_max_abs_index) {
+      // Break if the solution stagnated.
+      break;
+    }
+    // Move to the new simplex e_j, where j = v_max_abs_index.
+    v = dec.solve(Vector::Unit(n, v_max_abs_index));  // v = inv(matrix) * e_j.
+    lower_bound = v.template lpNorm<1>();
+    if (lower_bound <= old_lower_bound) {
+      // Break if the gradient step did not increase the lower_bound.
+      break;
+    }
+    if (!is_complex) {
+      old_sign_vector = sign_vector;
+    }
+    old_v_max_abs_index = v_max_abs_index;
+    old_lower_bound = lower_bound;
+  }
+  // The following calculates an independent estimate of ||matrix||_1 by
+  // multiplying matrix by a vector with entries of slowly increasing
+  // magnitude and alternating sign:
+  //   v_i = (-1)^{i} (1 + (i / (dim-1))), i = 0,...,dim-1.
+  // This improvement to Hager's algorithm above is due to Higham. It was
+  // added to make the algorithm more robust in certain corner cases where
+  // large elements in the matrix might otherwise escape detection due to
+  // exact cancellation (especially when op and op_adjoint correspond to a
+  // sequence of backsubstitutions and permutations), which could cause
+  // Hager's algorithm to vastly underestimate ||matrix||_1.
+  Scalar alternating_sign(RealScalar(1));
+  for (Index i = 0; i < n; ++i) {
+    // The static_cast is needed when Scalar is a complex and RealScalar implements expression templates
+    v[i] = alternating_sign * static_cast<RealScalar>(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
+    alternating_sign = -alternating_sign;
+  }
+  v = dec.solve(v);
+  const RealScalar alternate_lower_bound = (2 * v.template lpNorm<1>()) / (3 * RealScalar(n));
+  return numext::maxi(lower_bound, alternate_lower_bound);
+}
+
+/** \brief Reciprocal condition number estimator.
+ *
+ * Computing a decomposition of a dense matrix takes O(n^3) operations, while
+ * this method estimates the condition number quickly and reliably in O(n^2)
+ * operations.
+ *
+ * \returns an estimate of the reciprocal condition number
+ * (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
+ * its decomposition. Supports the following decompositions: FullPivLU,
+ * PartialPivLU, LDLT, and LLT.
+ *
+ * \sa FullPivLU, PartialPivLU, LDLT, LLT.
+ */
+template <typename Decomposition>
+typename Decomposition::RealScalar rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm,
+                                                         const Decomposition& dec) {
+  typedef typename Decomposition::RealScalar RealScalar;
+  eigen_assert(dec.rows() == dec.cols());
+  if (dec.rows() == 0) return NumTraits<RealScalar>::infinity();
+  if (numext::is_exactly_zero(matrix_norm)) return RealScalar(0);
+  if (dec.rows() == 1) return RealScalar(1);
+  const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
+  return (numext::is_exactly_zero(inverse_matrix_norm) ? RealScalar(0)
+                                                       : (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
+}
+
+}  // namespace internal
+
+}  // namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CoreEvaluators.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CoreEvaluators.h
new file mode 100644
index 0000000..9df0c96
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CoreEvaluators.h
@@ -0,0 +1,1972 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COREEVALUATORS_H
+#define EIGEN_COREEVALUATORS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// This class returns the evaluator kind from the expression storage kind.
+// Default assumes index based accessors
+template <typename StorageKind>
+struct storage_kind_to_evaluator_kind {
+  typedef IndexBased Kind;
+};
+
+// This class returns the evaluator shape from the expression storage kind.
+// It can be Dense, Sparse, Triangular, Diagonal, SelfAdjoint, Band, etc.
+template <typename StorageKind>
+struct storage_kind_to_shape;
+
+template <>
+struct storage_kind_to_shape<Dense> {
+  typedef DenseShape Shape;
+};
+template <>
+struct storage_kind_to_shape<SolverStorage> {
+  typedef SolverShape Shape;
+};
+template <>
+struct storage_kind_to_shape<PermutationStorage> {
+  typedef PermutationShape Shape;
+};
+template <>
+struct storage_kind_to_shape<TranspositionsStorage> {
+  typedef TranspositionsShape Shape;
+};
+
+// Evaluators have to be specialized with respect to various criteria such as:
+//  - storage/structure/shape
+//  - scalar type
+//  - etc.
+// Therefore, we need specialization of evaluator providing additional template arguments for each kind of evaluators.
+// We currently distinguish the following kind of evaluators:
+// - unary_evaluator    for expressions taking only one arguments (CwiseUnaryOp, CwiseUnaryView, Transpose,
+// MatrixWrapper, ArrayWrapper, Reverse, Replicate)
+// - binary_evaluator   for expression taking two arguments (CwiseBinaryOp)
+// - ternary_evaluator   for expression taking three arguments (CwiseTernaryOp)
+// - product_evaluator  for linear algebra products (Product); special case of binary_evaluator because it requires
+// additional tags for dispatching.
+// - mapbase_evaluator  for Map, Block, Ref
+// - block_evaluator    for Block (special dispatching to a mapbase_evaluator or unary_evaluator)
+
+template <typename T, typename Arg1Kind = typename evaluator_traits<typename T::Arg1>::Kind,
+          typename Arg2Kind = typename evaluator_traits<typename T::Arg2>::Kind,
+          typename Arg3Kind = typename evaluator_traits<typename T::Arg3>::Kind,
+          typename Arg1Scalar = typename traits<typename T::Arg1>::Scalar,
+          typename Arg2Scalar = typename traits<typename T::Arg2>::Scalar,
+          typename Arg3Scalar = typename traits<typename T::Arg3>::Scalar>
+struct ternary_evaluator;
+
+template <typename T, typename LhsKind = typename evaluator_traits<typename T::Lhs>::Kind,
+          typename RhsKind = typename evaluator_traits<typename T::Rhs>::Kind,
+          typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
+          typename RhsScalar = typename traits<typename T::Rhs>::Scalar>
+struct binary_evaluator;
+
+template <typename T, typename Kind = typename evaluator_traits<typename T::NestedExpression>::Kind,
+          typename Scalar = typename T::Scalar>
+struct unary_evaluator;
+
+// evaluator_traits<T> contains traits for evaluator<T>
+
+template <typename T>
+struct evaluator_traits_base {
+  // by default, get evaluator kind and shape from storage
+  typedef typename storage_kind_to_evaluator_kind<typename traits<T>::StorageKind>::Kind Kind;
+  typedef typename storage_kind_to_shape<typename traits<T>::StorageKind>::Shape Shape;
+};
+
+// Default evaluator traits
+template <typename T>
+struct evaluator_traits : public evaluator_traits_base<T> {};
+
+template <typename T, typename Shape = typename evaluator_traits<T>::Shape>
+struct evaluator_assume_aliasing {
+  static const bool value = false;
+};
+
+// By default, we assume a unary expression:
+template <typename T>
+struct evaluator : public unary_evaluator<T> {
+  typedef unary_evaluator<T> Base;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit evaluator(const T& xpr) : Base(xpr) {}
+};
+
+// TODO: Think about const-correctness
+template <typename T>
+struct evaluator<const T> : evaluator<T> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit evaluator(const T& xpr) : evaluator<T>(xpr) {}
+};
+
+// ---------- base class for all evaluators ----------
+
+template <typename ExpressionType>
+struct evaluator_base {
+  // TODO: find a way to avoid propagating all these traits. They are currently only needed to handle
+  // outer,inner indices.
+  typedef traits<ExpressionType> ExpressionTraits;
+
+  enum { Alignment = 0 };
+  // noncopyable:
+  // Don't make this class inherit noncopyable as this kills EBO (Empty Base Optimization)
+  // and make complex evaluator much larger than then should do.
+  EIGEN_DEVICE_FUNC constexpr evaluator_base() = default;
+
+ private:
+  EIGEN_DEVICE_FUNC evaluator_base(const evaluator_base&);
+  EIGEN_DEVICE_FUNC const evaluator_base& operator=(const evaluator_base&);
+};
+
+// -------------------- Matrix and Array --------------------
+//
+// evaluator<PlainObjectBase> is a common base class for the
+// Matrix and Array evaluators.
+// Here we directly specialize evaluator. This is not really a unary expression, and it is, by definition, dense,
+// so no need for more sophisticated dispatching.
+
+// this helper permits to completely eliminate m_outerStride if it is known at compiletime.
+template <typename Scalar, int OuterStride>
+class plainobjectbase_evaluator_data {
+ public:
+  EIGEN_DEVICE_FUNC constexpr plainobjectbase_evaluator_data(const Scalar* ptr, Index outerStride) : data(ptr) {
+#ifndef EIGEN_INTERNAL_DEBUGGING
+    EIGEN_UNUSED_VARIABLE(outerStride);
+#endif
+    eigen_internal_assert(outerStride == OuterStride);
+  }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return OuterStride; }
+  const Scalar* data;
+};
+
+template <typename Scalar>
+class plainobjectbase_evaluator_data<Scalar, Dynamic> {
+ public:
+  EIGEN_DEVICE_FUNC constexpr plainobjectbase_evaluator_data(const Scalar* ptr, Index outerStride)
+      : data(ptr), m_outerStride(outerStride) {}
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const { return m_outerStride; }
+  const Scalar* data;
+
+ protected:
+  Index m_outerStride;
+};
+
+template <typename Derived>
+struct evaluator<PlainObjectBase<Derived>> : evaluator_base<Derived> {
+  typedef PlainObjectBase<Derived> PlainObjectType;
+  typedef typename PlainObjectType::Scalar Scalar;
+  typedef typename PlainObjectType::CoeffReturnType CoeffReturnType;
+
+  enum {
+    IsRowMajor = PlainObjectType::IsRowMajor,
+    IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime,
+    RowsAtCompileTime = PlainObjectType::RowsAtCompileTime,
+    ColsAtCompileTime = PlainObjectType::ColsAtCompileTime,
+
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    Flags = traits<Derived>::EvaluatorFlags,
+    Alignment = traits<Derived>::Alignment
+  };
+  enum {
+    // We do not need to know the outer stride for vectors
+    OuterStrideAtCompileTime = IsVectorAtCompileTime ? 0
+                               : int(IsRowMajor)     ? ColsAtCompileTime
+                                                     : RowsAtCompileTime
+  };
+
+  EIGEN_DEVICE_FUNC constexpr evaluator() : m_d(0, OuterStrideAtCompileTime) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr explicit evaluator(const PlainObjectType& m)
+      : m_d(m.data(), IsVectorAtCompileTime ? 0 : m.outerStride()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr CoeffReturnType coeff(Index row, Index col) const {
+    return coeff(getIndex(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr CoeffReturnType coeff(Index index) const { return m_d.data[index]; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Scalar& coeffRef(Index row, Index col) {
+    return coeffRef(getIndex(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Scalar& coeffRef(Index index) {
+    return const_cast<Scalar*>(m_d.data)[index];
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    return packet<LoadMode, PacketType>(getIndex(row, col));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    return ploadt<PacketType, LoadMode>(m_d.data + index);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketType& x) {
+    writePacket<StoreMode, PacketType>(getIndex(row, col), x);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketType& x) {
+    pstoret<Scalar, PacketType, StoreMode>(const_cast<Scalar*>(m_d.data) + index, x);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    return packetSegment<LoadMode, PacketType>(getIndex(row, col), begin, count);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    return ploadtSegment<PacketType, LoadMode>(m_d.data + index, begin, count);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index row, Index col, const PacketType& x, Index begin,
+                                                                Index count) {
+    writePacketSegment<StoreMode, PacketType>(getIndex(row, col), x, begin, count);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index index, const PacketType& x, Index begin,
+                                                                Index count) {
+    pstoretSegment<Scalar, PacketType, StoreMode>(const_cast<Scalar*>(m_d.data) + index, x, begin, count);
+  }
+
+ protected:
+  plainobjectbase_evaluator_data<Scalar, OuterStrideAtCompileTime> m_d;
+
+ private:
+  EIGEN_DEVICE_FUNC constexpr Index getIndex(Index row, Index col) const {
+    return IsRowMajor ? row * m_d.outerStride() + col : row + col * m_d.outerStride();
+  }
+};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct evaluator<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols>>
+    : evaluator<PlainObjectBase<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols>>> {
+  typedef Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> XprType;
+
+  EIGEN_DEVICE_FUNC constexpr evaluator() = default;
+
+  EIGEN_DEVICE_FUNC constexpr explicit evaluator(const XprType& m) : evaluator<PlainObjectBase<XprType>>(m) {}
+};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct evaluator<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols>>
+    : evaluator<PlainObjectBase<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols>>> {
+  typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> XprType;
+
+  EIGEN_DEVICE_FUNC constexpr evaluator() = default;
+
+  EIGEN_DEVICE_FUNC constexpr explicit evaluator(const XprType& m) : evaluator<PlainObjectBase<XprType>>(m) {}
+};
+
+// -------------------- Transpose --------------------
+
+template <typename ArgType>
+struct unary_evaluator<Transpose<ArgType>, IndexBased> : evaluator_base<Transpose<ArgType>> {
+  typedef Transpose<ArgType> XprType;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+    Flags = evaluator<ArgType>::Flags ^ RowMajorBit,
+    Alignment = evaluator<ArgType>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit unary_evaluator(const XprType& t)
+      : m_argImpl(t.nestedExpression()) {}
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    return m_argImpl.coeff(col, row);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_argImpl.coeff(index);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
+    return m_argImpl.coeffRef(col, row);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename XprType::Scalar& coeffRef(Index index) {
+    return m_argImpl.coeffRef(index);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    return m_argImpl.template packet<LoadMode, PacketType>(col, row);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    return m_argImpl.template packet<LoadMode, PacketType>(index);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketType& x) {
+    m_argImpl.template writePacket<StoreMode, PacketType>(col, row, x);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketType& x) {
+    m_argImpl.template writePacket<StoreMode, PacketType>(index, x);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    return m_argImpl.template packetSegment<LoadMode, PacketType>(col, row, begin, count);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    return m_argImpl.template packetSegment<LoadMode, PacketType>(index, begin, count);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index row, Index col, const PacketType& x, Index begin,
+                                                                Index count) {
+    m_argImpl.template writePacketSegment<StoreMode, PacketType>(col, row, x, begin, count);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index index, const PacketType& x, Index begin,
+                                                                Index count) {
+    m_argImpl.template writePacketSegment<StoreMode, PacketType>(index, x, begin, count);
+  }
+
+ protected:
+  evaluator<ArgType> m_argImpl;
+};
+
+// -------------------- CwiseNullaryOp --------------------
+// Like Matrix and Array, this is not really a unary expression, so we directly specialize evaluator.
+// Likewise, there is not need to more sophisticated dispatching here.
+
+template <typename Scalar, typename NullaryOp, bool has_nullary = has_nullary_operator<NullaryOp>::value,
+          bool has_unary = has_unary_operator<NullaryOp>::value,
+          bool has_binary = has_binary_operator<NullaryOp>::value>
+struct nullary_wrapper {
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i,
+                                                                    IndexType j) const {
+    return op(i, j);
+  }
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const {
+    return op(i);
+  }
+
+  template <typename T, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const {
+    return op.template packetOp<T>(i, j);
+  }
+  template <typename T, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const {
+    return op.template packetOp<T>(i);
+  }
+};
+
+template <typename Scalar, typename NullaryOp>
+struct nullary_wrapper<Scalar, NullaryOp, true, false, false> {
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType = 0,
+                                                                    IndexType = 0) const {
+    return op();
+  }
+  template <typename T, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType = 0, IndexType = 0) const {
+    return op.template packetOp<T>();
+  }
+};
+
+template <typename Scalar, typename NullaryOp>
+struct nullary_wrapper<Scalar, NullaryOp, false, false, true> {
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i,
+                                                                    IndexType j = 0) const {
+    return op(i, j);
+  }
+  template <typename T, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j = 0) const {
+    return op.template packetOp<T>(i, j);
+  }
+};
+
+// We need the following specialization for vector-only functors assigned to a runtime vector,
+// for instance, using linspace and assigning a RowVectorXd to a MatrixXd or even a row of a MatrixXd.
+// In this case, i==0 and j is used for the actual iteration.
+template <typename Scalar, typename NullaryOp>
+struct nullary_wrapper<Scalar, NullaryOp, false, true, false> {
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i,
+                                                                    IndexType j) const {
+    eigen_assert(i == 0 || j == 0);
+    return op(i + j);
+  }
+  template <typename T, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const {
+    eigen_assert(i == 0 || j == 0);
+    return op.template packetOp<T>(i + j);
+  }
+
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const {
+    return op(i);
+  }
+  template <typename T, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const {
+    return op.template packetOp<T>(i);
+  }
+};
+
+template <typename Scalar, typename NullaryOp>
+struct nullary_wrapper<Scalar, NullaryOp, false, false, false> {};
+
+template <typename NullaryOp, typename PlainObjectType>
+struct evaluator<CwiseNullaryOp<NullaryOp, PlainObjectType>>
+    : evaluator_base<CwiseNullaryOp<NullaryOp, PlainObjectType>> {
+  typedef CwiseNullaryOp<NullaryOp, PlainObjectType> XprType;
+  typedef remove_all_t<PlainObjectType> PlainObjectTypeCleaned;
+
+  enum {
+    CoeffReadCost = functor_traits<NullaryOp>::Cost,
+
+    Flags = (evaluator<PlainObjectTypeCleaned>::Flags &
+             (HereditaryBits | (functor_has_linear_access<NullaryOp>::ret ? LinearAccessBit : 0) |
+              (functor_traits<NullaryOp>::PacketAccess ? PacketAccessBit : 0))) |
+            (functor_traits<NullaryOp>::IsRepeatable ? 0 : EvalBeforeNestingBit),
+    Alignment = AlignedMax
+  };
+
+  EIGEN_DEVICE_FUNC constexpr explicit evaluator(const XprType& n) : m_functor(n.functor()), m_wrapper() {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(IndexType row, IndexType col) const {
+    return m_wrapper(m_functor, row, col);
+  }
+
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(IndexType index) const {
+    return m_wrapper(m_functor, index);
+  }
+
+  template <int LoadMode, typename PacketType, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(IndexType row, IndexType col) const {
+    return m_wrapper.template packetOp<PacketType>(m_functor, row, col);
+  }
+
+  template <int LoadMode, typename PacketType, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(IndexType index) const {
+    return m_wrapper.template packetOp<PacketType>(m_functor, index);
+  }
+
+  template <int LoadMode, typename PacketType, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(IndexType row, IndexType col, Index /*begin*/,
+                                                                 Index /*count*/) const {
+    return packet<LoadMode, PacketType, IndexType>(row, col);
+  }
+
+  template <int LoadMode, typename PacketType, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(IndexType index, Index /*begin*/,
+                                                                 Index /*count*/) const {
+    return packet<LoadMode, PacketType, IndexType>(index);
+  }
+
+ protected:
+  const NullaryOp m_functor;
+  const nullary_wrapper<CoeffReturnType, NullaryOp> m_wrapper;
+};
+
+// -------------------- CwiseUnaryOp --------------------
+
+template <typename UnaryOp, typename ArgType>
+struct unary_evaluator<CwiseUnaryOp<UnaryOp, ArgType>, IndexBased> : evaluator_base<CwiseUnaryOp<UnaryOp, ArgType>> {
+  typedef CwiseUnaryOp<UnaryOp, ArgType> XprType;
+
+  enum {
+    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
+
+    Flags = evaluator<ArgType>::Flags &
+            (HereditaryBits | LinearAccessBit | (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)),
+    Alignment = evaluator<ArgType>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit unary_evaluator(const XprType& op) : m_d(op) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    return m_d.func()(m_d.argImpl.coeff(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_d.func()(m_d.argImpl.coeff(index));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    return m_d.func().packetOp(m_d.argImpl.template packet<LoadMode, PacketType>(row, col));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    return m_d.func().packetOp(m_d.argImpl.template packet<LoadMode, PacketType>(index));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    return m_d.func().packetOp(m_d.argImpl.template packetSegment<LoadMode, PacketType>(row, col, begin, count));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    return m_d.func().packetOp(m_d.argImpl.template packetSegment<LoadMode, PacketType>(index, begin, count));
+  }
+
+ protected:
+  // this helper permits to completely eliminate the functor if it is empty
+  struct Data {
+    EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Data(const XprType& xpr)
+        : op(xpr.functor()), argImpl(xpr.nestedExpression()) {}
+    EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const UnaryOp& func() const { return op; }
+    UnaryOp op;
+    evaluator<ArgType> argImpl;
+  };
+
+  Data m_d;
+};
+
+// ----------------------- Casting ---------------------
+
+template <typename SrcType, typename DstType, typename ArgType>
+struct unary_evaluator<CwiseUnaryOp<core_cast_op<SrcType, DstType>, ArgType>, IndexBased> {
+  using CastOp = core_cast_op<SrcType, DstType>;
+  using XprType = CwiseUnaryOp<CastOp, ArgType>;
+
+  // Use the largest packet type by default
+  using SrcPacketType = typename packet_traits<SrcType>::type;
+  static constexpr int SrcPacketSize = unpacket_traits<SrcPacketType>::size;
+  static constexpr int SrcPacketBytes = SrcPacketSize * sizeof(SrcType);
+
+  enum {
+    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<CastOp>::Cost),
+    PacketAccess = functor_traits<CastOp>::PacketAccess,
+    ActualPacketAccessBit = PacketAccess ? PacketAccessBit : 0,
+    Flags = evaluator<ArgType>::Flags & (HereditaryBits | LinearAccessBit | ActualPacketAccessBit),
+    IsRowMajor = (evaluator<ArgType>::Flags & RowMajorBit),
+    Alignment = evaluator<ArgType>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit unary_evaluator(const XprType& xpr)
+      : m_argImpl(xpr.nestedExpression()), m_rows(xpr.rows()), m_cols(xpr.cols()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<CastOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  template <typename DstPacketType>
+  using AltSrcScalarOp = std::enable_if_t<(unpacket_traits<DstPacketType>::size < SrcPacketSize &&
+                                           !find_packet_by_size<SrcType, unpacket_traits<DstPacketType>::size>::value),
+                                          bool>;
+  template <typename DstPacketType>
+  using SrcPacketArgs1 =
+      std::enable_if_t<(find_packet_by_size<SrcType, unpacket_traits<DstPacketType>::size>::value), bool>;
+  template <typename DstPacketType>
+  using SrcPacketArgs2 = std::enable_if_t<(unpacket_traits<DstPacketType>::size) == (2 * SrcPacketSize), bool>;
+  template <typename DstPacketType>
+  using SrcPacketArgs4 = std::enable_if_t<(unpacket_traits<DstPacketType>::size) == (4 * SrcPacketSize), bool>;
+  template <typename DstPacketType>
+  using SrcPacketArgs8 = std::enable_if_t<(unpacket_traits<DstPacketType>::size) == (8 * SrcPacketSize), bool>;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_array_bounds(Index row, Index col, Index begin, Index count) const {
+    return IsRowMajor ? (col + count + begin <= cols()) : (row + count + begin <= rows());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_array_bounds(Index index, Index begin, Index count) const {
+    return index + count + begin <= size();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE SrcType srcCoeff(Index row, Index col, Index offset) const {
+    Index actualRow = IsRowMajor ? row : row + offset;
+    Index actualCol = IsRowMajor ? col + offset : col;
+    return m_argImpl.coeff(actualRow, actualCol);
+  }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE SrcType srcCoeff(Index index, Index offset) const {
+    Index actualIndex = index + offset;
+    return m_argImpl.coeff(actualIndex);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE DstType coeff(Index row, Index col) const {
+    return cast<SrcType, DstType>(srcCoeff(row, col, 0));
+  }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE DstType coeff(Index index) const {
+    return cast<SrcType, DstType>(srcCoeff(index, 0));
+  }
+
+  template <int LoadMode, typename PacketType = SrcPacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType srcPacket(Index row, Index col, Index offset) const {
+    constexpr int PacketSize = unpacket_traits<PacketType>::size;
+    Index packetOffset = offset * PacketSize;
+    Index actualRow = IsRowMajor ? row : row + packetOffset;
+    Index actualCol = IsRowMajor ? col + packetOffset : col;
+    eigen_assert(check_array_bounds(actualRow, actualCol, 0, PacketSize) && "Array index out of bounds");
+    return m_argImpl.template packet<LoadMode, PacketType>(actualRow, actualCol);
+  }
+  template <int LoadMode, typename PacketType = SrcPacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType srcPacket(Index index, Index offset) const {
+    constexpr int PacketSize = unpacket_traits<PacketType>::size;
+    Index packetOffset = offset * PacketSize;
+    Index actualIndex = index + packetOffset;
+    eigen_assert(check_array_bounds(actualIndex, 0, PacketSize) && "Array index out of bounds");
+    return m_argImpl.template packet<LoadMode, PacketType>(actualIndex);
+  }
+  template <int LoadMode, typename PacketType = SrcPacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType srcPacketSegment(Index row, Index col, Index begin, Index count,
+                                                                    Index offset) const {
+    constexpr int PacketSize = unpacket_traits<PacketType>::size;
+    Index packetOffset = offset * PacketSize;
+    Index actualRow = IsRowMajor ? row : row + packetOffset;
+    Index actualCol = IsRowMajor ? col + packetOffset : col;
+    eigen_assert(check_array_bounds(actualRow, actualCol, begin, count) && "Array index out of bounds");
+    return m_argImpl.template packetSegment<LoadMode, PacketType>(actualRow, actualCol, begin, count);
+  }
+  template <int LoadMode, typename PacketType = SrcPacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType srcPacketSegment(Index index, Index begin, Index count,
+                                                                    Index offset) const {
+    constexpr int PacketSize = unpacket_traits<PacketType>::size;
+    Index packetOffset = offset * PacketSize;
+    Index actualIndex = index + packetOffset;
+    eigen_assert(check_array_bounds(actualIndex, begin, count) && "Array index out of bounds");
+    return m_argImpl.template packetSegment<LoadMode, PacketType>(actualIndex, begin, count);
+  }
+
+  template <int NumPackets, int LoadMode, typename PacketType = SrcPacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketBlock<PacketType, NumPackets> srcPacketSegmentHelper(Index row, Index col,
+                                                                                                   Index begin,
+                                                                                                   Index count) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    PacketBlock<PacketType, NumPackets> packets;
+    for (Index i = 0; i < NumPackets; i++) packets.packet[i] = pzero(PacketType());
+    Index offset = begin / SrcPacketSize;
+    Index actualBegin = begin % SrcPacketSize;
+    for (; offset < NumPackets; offset++) {
+      Index actualCount = numext::mini(SrcPacketSize - actualBegin, count);
+      packets.packet[offset] = srcPacketSegment<SrcLoadMode>(row, col, actualBegin, actualCount, offset);
+      if (count == actualCount) break;
+      actualBegin = 0;
+      count -= actualCount;
+    }
+    return packets;
+  }
+  template <int NumPackets, int LoadMode, typename PacketType = SrcPacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketBlock<PacketType, NumPackets> srcPacketSegmentHelper(Index index,
+                                                                                                   Index begin,
+                                                                                                   Index count) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    PacketBlock<PacketType, NumPackets> packets;
+    for (Index i = 0; i < NumPackets; i++) packets.packet[i] = pzero(PacketType());
+    Index offset = begin / SrcPacketSize;
+    Index actualBegin = begin % SrcPacketSize;
+    for (; offset < NumPackets; offset++) {
+      Index actualCount = numext::mini(SrcPacketSize - actualBegin, count);
+      packets.packet[offset] = srcPacketSegment<SrcLoadMode>(index, actualBegin, actualCount, offset);
+      if (count == actualCount) break;
+      actualBegin = 0;
+      count -= actualCount;
+    }
+    return packets;
+  }
+
+  // There is no source packet type with equal or fewer elements than DstPacketType.
+  // This is problematic as the evaluation loop may attempt to access data outside the bounds of the array.
+  // For example, consider the cast utilizing pcast<Packet4f,Packet2d> with an array of size 4: {0.0f,1.0f,2.0f,3.0f}.
+  // The first iteration of the evaluation loop will load 16 bytes: {0.0f,1.0f,2.0f,3.0f} and cast to {0.0,1.0}, which
+  // is acceptable. The second iteration will load 16 bytes: {2.0f,3.0f,?,?}, which is outside the bounds of the array.
+  template <int LoadMode, typename DstPacketType, AltSrcScalarOp<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packet(Index row, Index col) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(srcPacketSegment<SrcLoadMode>(row, col, 0, DstPacketSize, 0));
+  }
+  // Use the source packet type with the same size as DstPacketType, if it exists
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs1<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packet(Index row, Index col) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    using SizedSrcPacketType = typename find_packet_by_size<SrcType, DstPacketSize>::type;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    return pcast<SizedSrcPacketType, DstPacketType>(srcPacket<SrcLoadMode, SizedSrcPacketType>(row, col, 0));
+  }
+  // unpacket_traits<DstPacketType>::size == 2 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs2<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packet(Index row, Index col) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(srcPacket<SrcLoadMode>(row, col, 0),
+                                               srcPacket<SrcLoadMode>(row, col, 1));
+  }
+  // unpacket_traits<DstPacketType>::size == 4 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs4<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packet(Index row, Index col) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(srcPacket<SrcLoadMode>(row, col, 0), srcPacket<SrcLoadMode>(row, col, 1),
+                                               srcPacket<SrcLoadMode>(row, col, 2),
+                                               srcPacket<SrcLoadMode>(row, col, 3));
+  }
+  // unpacket_traits<DstPacketType>::size == 8 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs8<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packet(Index row, Index col) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(
+        srcPacket<SrcLoadMode>(row, col, 0), srcPacket<SrcLoadMode>(row, col, 1), srcPacket<SrcLoadMode>(row, col, 2),
+        srcPacket<SrcLoadMode>(row, col, 3), srcPacket<SrcLoadMode>(row, col, 4), srcPacket<SrcLoadMode>(row, col, 5),
+        srcPacket<SrcLoadMode>(row, col, 6), srcPacket<SrcLoadMode>(row, col, 7));
+  }
+
+  // packetSegment variants
+  template <int LoadMode, typename DstPacketType, AltSrcScalarOp<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packetSegment(Index row, Index col, Index begin,
+                                                                    Index count) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(srcPacketSegment<SrcLoadMode>(row, col, begin, count, 0));
+  }
+  // Use the source packet type with the same size as DstPacketType, if it exists
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs1<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packetSegment(Index row, Index col, Index begin,
+                                                                    Index count) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    using SizedSrcPacketType = typename find_packet_by_size<SrcType, DstPacketSize>::type;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    return pcast<SizedSrcPacketType, DstPacketType>(
+        srcPacketSegment<SrcLoadMode, SizedSrcPacketType>(row, col, begin, count, 0));
+  }
+  // unpacket_traits<DstPacketType>::size == 2 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs2<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packetSegment(Index row, Index col, Index begin,
+                                                                    Index count) const {
+    constexpr int NumPackets = 2;
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    PacketBlock<SrcPacketType, NumPackets> packets =
+        srcPacketSegmentHelper<NumPackets, SrcLoadMode>(row, col, begin, count);
+    return pcast<SrcPacketType, DstPacketType>(packets.packet[0], packets.packet[1]);
+  }
+  // unpacket_traits<DstPacketType>::size == 4 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs4<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packetSegment(Index row, Index col, Index begin,
+                                                                    Index count) const {
+    constexpr int NumPackets = 4;
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    PacketBlock<SrcPacketType, NumPackets> packets =
+        srcPacketSegmentHelper<NumPackets, SrcLoadMode>(row, col, begin, count);
+    return pcast<SrcPacketType, DstPacketType>(packets.packet[0], packets.packet[1], packets.packet[2],
+                                               packets.packet[3]);
+  }
+  // unpacket_traits<DstPacketType>::size == 8 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs8<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packetSegment(Index row, Index col, Index begin,
+                                                                    Index count) const {
+    constexpr int NumPackets = 8;
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    PacketBlock<SrcPacketType, NumPackets> packets =
+        srcPacketSegmentHelper<NumPackets, SrcLoadMode>(row, col, begin, count);
+    return pcast<SrcPacketType, DstPacketType>(packets.packet[0], packets.packet[1], packets.packet[2],
+                                               packets.packet[3], packets.packet[4], packets.packet[5],
+                                               packets.packet[6], packets.packet[7]);
+  }
+
+  // Analogous routines for linear access.
+  template <int LoadMode, typename DstPacketType, AltSrcScalarOp<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packet(Index index) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(srcPacketSegment<SrcLoadMode>(index, 0, DstPacketSize, 0));
+  }
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs1<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packet(Index index) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    using SizedSrcPacketType = typename find_packet_by_size<SrcType, DstPacketSize>::type;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    return pcast<SizedSrcPacketType, DstPacketType>(srcPacket<SrcLoadMode, SizedSrcPacketType>(index, 0));
+  }
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs2<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packet(Index index) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(srcPacket<SrcLoadMode>(index, 0), srcPacket<SrcLoadMode>(index, 1));
+  }
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs4<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packet(Index index) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(srcPacket<SrcLoadMode>(index, 0), srcPacket<SrcLoadMode>(index, 1),
+                                               srcPacket<SrcLoadMode>(index, 2), srcPacket<SrcLoadMode>(index, 3));
+  }
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs8<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packet(Index index) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(srcPacket<SrcLoadMode>(index, 0), srcPacket<SrcLoadMode>(index, 1),
+                                               srcPacket<SrcLoadMode>(index, 2), srcPacket<SrcLoadMode>(index, 3),
+                                               srcPacket<SrcLoadMode>(index, 4), srcPacket<SrcLoadMode>(index, 5),
+                                               srcPacket<SrcLoadMode>(index, 6), srcPacket<SrcLoadMode>(index, 7));
+  }
+
+  // packetSegment variants
+  template <int LoadMode, typename DstPacketType, AltSrcScalarOp<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packetSegment(Index index, Index begin, Index count) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(srcPacketSegment<SrcLoadMode>(index, begin, count, 0));
+  }
+  // Use the source packet type with the same size as DstPacketType, if it exists
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs1<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packetSegment(Index index, Index begin, Index count) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    using SizedSrcPacketType = typename find_packet_by_size<SrcType, DstPacketSize>::type;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    return pcast<SizedSrcPacketType, DstPacketType>(
+        srcPacketSegment<SrcLoadMode, SizedSrcPacketType>(index, begin, count, 0));
+  }
+  // unpacket_traits<DstPacketType>::size == 2 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs2<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packetSegment(Index index, Index begin, Index count) const {
+    constexpr int NumPackets = 2;
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    PacketBlock<SrcPacketType, NumPackets> packets =
+        srcPacketSegmentHelper<NumPackets, SrcLoadMode>(index, begin, count);
+    return pcast<SrcPacketType, DstPacketType>(packets.packet[0], packets.packet[1]);
+  }
+  // unpacket_traits<DstPacketType>::size == 4 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs4<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packetSegment(Index index, Index begin, Index count) const {
+    constexpr int NumPackets = 4;
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    PacketBlock<SrcPacketType, NumPackets> packets =
+        srcPacketSegmentHelper<NumPackets, SrcLoadMode>(index, begin, count);
+    return pcast<SrcPacketType, DstPacketType>(packets.packet[0], packets.packet[1], packets.packet[2],
+                                               packets.packet[3]);
+  }
+  // unpacket_traits<DstPacketType>::size == 8 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs8<DstPacketType> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstPacketType packetSegment(Index index, Index begin, Index count) const {
+    constexpr int NumPackets = 8;
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    PacketBlock<SrcPacketType, NumPackets> packets =
+        srcPacketSegmentHelper<NumPackets, SrcLoadMode>(index, begin, count);
+    return pcast<SrcPacketType, DstPacketType>(packets.packet[0], packets.packet[1], packets.packet[2],
+                                               packets.packet[3], packets.packet[4], packets.packet[5],
+                                               packets.packet[6], packets.packet[7]);
+  }
+
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rows() const { return m_rows; }
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index cols() const { return m_cols; }
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_rows * m_cols; }
+
+ protected:
+  const evaluator<ArgType> m_argImpl;
+  const variable_if_dynamic<Index, XprType::RowsAtCompileTime> m_rows;
+  const variable_if_dynamic<Index, XprType::ColsAtCompileTime> m_cols;
+};
+
+// -------------------- CwiseTernaryOp --------------------
+
+// this is a ternary expression
+template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
+struct evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>>
+    : public ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>> {
+  typedef CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> XprType;
+  typedef ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>> Base;
+
+  EIGEN_DEVICE_FUNC constexpr explicit evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
+struct ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>, IndexBased, IndexBased>
+    : evaluator_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>> {
+  typedef CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> XprType;
+
+  enum {
+    CoeffReadCost = int(evaluator<Arg1>::CoeffReadCost) + int(evaluator<Arg2>::CoeffReadCost) +
+                    int(evaluator<Arg3>::CoeffReadCost) + int(functor_traits<TernaryOp>::Cost),
+
+    Arg1Flags = evaluator<Arg1>::Flags,
+    Arg2Flags = evaluator<Arg2>::Flags,
+    Arg3Flags = evaluator<Arg3>::Flags,
+    SameType = is_same<typename Arg1::Scalar, typename Arg2::Scalar>::value &&
+               is_same<typename Arg1::Scalar, typename Arg3::Scalar>::value,
+    StorageOrdersAgree = (int(Arg1Flags) & RowMajorBit) == (int(Arg2Flags) & RowMajorBit) &&
+                         (int(Arg1Flags) & RowMajorBit) == (int(Arg3Flags) & RowMajorBit),
+    Flags0 = (int(Arg1Flags) | int(Arg2Flags) | int(Arg3Flags)) &
+             (HereditaryBits |
+              (int(Arg1Flags) & int(Arg2Flags) & int(Arg3Flags) &
+               ((StorageOrdersAgree ? LinearAccessBit : 0) |
+                (functor_traits<TernaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)))),
+    Flags = (Flags0 & ~RowMajorBit) | (Arg1Flags & RowMajorBit),
+    Alignment = plain_enum_min(plain_enum_min(evaluator<Arg1>::Alignment, evaluator<Arg2>::Alignment),
+                               evaluator<Arg3>::Alignment)
+  };
+
+  EIGEN_DEVICE_FUNC constexpr explicit ternary_evaluator(const XprType& xpr) : m_d(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<TernaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    return m_d.func()(m_d.arg1Impl.coeff(row, col), m_d.arg2Impl.coeff(row, col), m_d.arg3Impl.coeff(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_d.func()(m_d.arg1Impl.coeff(index), m_d.arg2Impl.coeff(index), m_d.arg3Impl.coeff(index));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    return m_d.func().packetOp(m_d.arg1Impl.template packet<LoadMode, PacketType>(row, col),
+                               m_d.arg2Impl.template packet<LoadMode, PacketType>(row, col),
+                               m_d.arg3Impl.template packet<LoadMode, PacketType>(row, col));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    return m_d.func().packetOp(m_d.arg1Impl.template packet<LoadMode, PacketType>(index),
+                               m_d.arg2Impl.template packet<LoadMode, PacketType>(index),
+                               m_d.arg3Impl.template packet<LoadMode, PacketType>(index));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    return m_d.func().packetOp(m_d.arg1Impl.template packetSegment<LoadMode, PacketType>(row, col, begin, count),
+                               m_d.arg2Impl.template packetSegment<LoadMode, PacketType>(row, col, begin, count),
+                               m_d.arg3Impl.template packetSegment<LoadMode, PacketType>(row, col, begin, count));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    return m_d.func().packetOp(m_d.arg1Impl.template packetSegment<LoadMode, PacketType>(index, begin, count),
+                               m_d.arg2Impl.template packetSegment<LoadMode, PacketType>(index, begin, count),
+                               m_d.arg3Impl.template packetSegment<LoadMode, PacketType>(index, begin, count));
+  }
+
+ protected:
+  // this helper permits to completely eliminate the functor if it is empty
+  struct Data {
+    EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Data(const XprType& xpr)
+        : op(xpr.functor()), arg1Impl(xpr.arg1()), arg2Impl(xpr.arg2()), arg3Impl(xpr.arg3()) {}
+    EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const TernaryOp& func() const { return op; }
+    TernaryOp op;
+    evaluator<Arg1> arg1Impl;
+    evaluator<Arg2> arg2Impl;
+    evaluator<Arg3> arg3Impl;
+  };
+
+  Data m_d;
+};
+
+template <typename Arg1, typename Arg2, typename Scalar, typename CmpLhsType, typename CmpRhsType, ComparisonName cmp>
+struct scalar_boolean_select_spec {
+  using DummyTernaryOp = scalar_boolean_select_op<Scalar, Scalar, bool>;
+  using DummyArg3 = CwiseBinaryOp<scalar_cmp_op<Scalar, Scalar, cmp, false>, CmpLhsType, CmpRhsType>;
+  using DummyXprType = CwiseTernaryOp<DummyTernaryOp, Arg1, Arg2, DummyArg3>;
+
+  // only use the typed comparison if it is vectorized
+  static constexpr bool UseTyped = functor_traits<scalar_cmp_op<Scalar, Scalar, cmp, true>>::PacketAccess;
+  using CondScalar = std::conditional_t<UseTyped, Scalar, bool>;
+
+  using TernaryOp = scalar_boolean_select_op<Scalar, Scalar, CondScalar>;
+  using Arg3 = CwiseBinaryOp<scalar_cmp_op<Scalar, Scalar, cmp, UseTyped>, CmpLhsType, CmpRhsType>;
+  using XprType = CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>;
+
+  using Base = ternary_evaluator<XprType>;
+};
+
+// specialization for expressions like (a < b).select(c, d) to enable full vectorization
+template <typename Arg1, typename Arg2, typename Scalar, typename CmpLhsType, typename CmpRhsType, ComparisonName cmp>
+struct evaluator<CwiseTernaryOp<scalar_boolean_select_op<Scalar, Scalar, bool>, Arg1, Arg2,
+                                CwiseBinaryOp<scalar_cmp_op<Scalar, Scalar, cmp, false>, CmpLhsType, CmpRhsType>>>
+    : public scalar_boolean_select_spec<Arg1, Arg2, Scalar, CmpLhsType, CmpRhsType, cmp>::Base {
+  using Helper = scalar_boolean_select_spec<Arg1, Arg2, Scalar, CmpLhsType, CmpRhsType, cmp>;
+  using Base = typename Helper::Base;
+  using DummyXprType = typename Helper::DummyXprType;
+  using Arg3 = typename Helper::Arg3;
+  using XprType = typename Helper::XprType;
+
+  EIGEN_DEVICE_FUNC constexpr explicit evaluator(const DummyXprType& xpr)
+      : Base(XprType(xpr.arg1(), xpr.arg2(), Arg3(xpr.arg3().lhs(), xpr.arg3().rhs()))) {}
+};
+
+// -------------------- CwiseBinaryOp --------------------
+
+// this is a binary expression
+template <typename BinaryOp, typename Lhs, typename Rhs>
+struct evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>> : public binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>> {
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
+  typedef binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>> Base;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+template <typename BinaryOp, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IndexBased>
+    : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs>> {
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
+
+  enum {
+    CoeffReadCost =
+        int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+
+    LhsFlags = evaluator<Lhs>::Flags,
+    RhsFlags = evaluator<Rhs>::Flags,
+    SameType = is_same<typename Lhs::Scalar, typename Rhs::Scalar>::value,
+    StorageOrdersAgree = (int(LhsFlags) & RowMajorBit) == (int(RhsFlags) & RowMajorBit),
+    Flags0 = (int(LhsFlags) | int(RhsFlags)) &
+             (HereditaryBits |
+              (int(LhsFlags) & int(RhsFlags) &
+               ((StorageOrdersAgree ? LinearAccessBit : 0) |
+                (functor_traits<BinaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)))),
+    Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit),
+    Alignment = plain_enum_min(evaluator<Lhs>::Alignment, evaluator<Rhs>::Alignment)
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit binary_evaluator(const XprType& xpr) : m_d(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    return m_d.func()(m_d.lhsImpl.coeff(row, col), m_d.rhsImpl.coeff(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_d.func()(m_d.lhsImpl.coeff(index), m_d.rhsImpl.coeff(index));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    return m_d.func().packetOp(m_d.lhsImpl.template packet<LoadMode, PacketType>(row, col),
+                               m_d.rhsImpl.template packet<LoadMode, PacketType>(row, col));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    return m_d.func().packetOp(m_d.lhsImpl.template packet<LoadMode, PacketType>(index),
+                               m_d.rhsImpl.template packet<LoadMode, PacketType>(index));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    return m_d.func().packetOp(m_d.lhsImpl.template packetSegment<LoadMode, PacketType>(row, col, begin, count),
+                               m_d.rhsImpl.template packetSegment<LoadMode, PacketType>(row, col, begin, count));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    return m_d.func().packetOp(m_d.lhsImpl.template packetSegment<LoadMode, PacketType>(index, begin, count),
+                               m_d.rhsImpl.template packetSegment<LoadMode, PacketType>(index, begin, count));
+  }
+
+ protected:
+  // this helper permits to completely eliminate the functor if it is empty
+  struct Data {
+    EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Data(const XprType& xpr)
+        : op(xpr.functor()), lhsImpl(xpr.lhs()), rhsImpl(xpr.rhs()) {}
+    EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const BinaryOp& func() const { return op; }
+    BinaryOp op;
+    evaluator<Lhs> lhsImpl;
+    evaluator<Rhs> rhsImpl;
+  };
+
+  Data m_d;
+};
+
+// -------------------- CwiseUnaryView --------------------
+
+template <typename UnaryOp, typename ArgType, typename StrideType>
+struct unary_evaluator<CwiseUnaryView<UnaryOp, ArgType, StrideType>, IndexBased>
+    : evaluator_base<CwiseUnaryView<UnaryOp, ArgType, StrideType>> {
+  typedef CwiseUnaryView<UnaryOp, ArgType, StrideType> XprType;
+
+  enum {
+    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
+
+    Flags = (evaluator<ArgType>::Flags & (HereditaryBits | LinearAccessBit | DirectAccessBit)),
+
+    Alignment = 0  // FIXME: clarify why alignment is lost for CwiseUnaryView.
+  };
+
+  EIGEN_DEVICE_FUNC constexpr explicit unary_evaluator(const XprType& op) : m_d(op) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    return m_d.func()(m_d.argImpl.coeff(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_d.func()(m_d.argImpl.coeff(index));
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
+    return m_d.func()(m_d.argImpl.coeffRef(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    return m_d.func()(m_d.argImpl.coeffRef(index));
+  }
+
+ protected:
+  // this helper permits to completely eliminate the functor if it is empty
+  struct Data {
+    EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Data(const XprType& xpr)
+        : op(xpr.functor()), argImpl(xpr.nestedExpression()) {}
+    EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const UnaryOp& func() const { return op; }
+    UnaryOp op;
+    evaluator<ArgType> argImpl;
+  };
+
+  Data m_d;
+};
+
+// -------------------- Map --------------------
+
+// FIXME: consider using Derived::PlainObject for PlainObjectType.
+// but that might complicate template specialization
+template <typename Derived, typename PlainObjectType>
+struct mapbase_evaluator;
+
+template <typename Derived, typename PlainObjectType>
+struct mapbase_evaluator : evaluator_base<Derived> {
+  typedef Derived XprType;
+  typedef typename XprType::PointerType PointerType;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  enum {
+    IsRowMajor = XprType::RowsAtCompileTime,
+    ColsAtCompileTime = XprType::ColsAtCompileTime,
+    CoeffReadCost = NumTraits<Scalar>::ReadCost
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit mapbase_evaluator(const XprType& map)
+      : m_data(const_cast<PointerType>(map.data())),
+        m_innerStride(map.innerStride()),
+        m_outerStride(map.outerStride()) {
+    EIGEN_STATIC_ASSERT(check_implication((evaluator<Derived>::Flags & PacketAccessBit) != 0,
+                                          inner_stride_at_compile_time<Derived>::ret == 1),
+                        PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    return m_data[col * colStride() + row * rowStride()];
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_data[index * m_innerStride.value()];
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
+    return m_data[col * colStride() + row * rowStride()];
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    return m_data[index * m_innerStride.value()];
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    PointerType ptr = m_data + row * rowStride() + col * colStride();
+    return ploadt<PacketType, LoadMode>(ptr);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    return ploadt<PacketType, LoadMode>(m_data + index * m_innerStride.value());
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketType& x) {
+    PointerType ptr = m_data + row * rowStride() + col * colStride();
+    pstoret<Scalar, PacketType, StoreMode>(ptr, x);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketType& x) {
+    pstoret<Scalar, PacketType, StoreMode>(m_data + index * m_innerStride.value(), x);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    PointerType ptr = m_data + row * rowStride() + col * colStride();
+    return ploadtSegment<PacketType, LoadMode>(ptr, begin, count);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    return ploadtSegment<PacketType, LoadMode>(m_data + index * m_innerStride.value(), begin, count);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index row, Index col, const PacketType& x, Index begin,
+                                                                Index count) {
+    PointerType ptr = m_data + row * rowStride() + col * colStride();
+    pstoretSegment<Scalar, PacketType, StoreMode>(ptr, x, begin, count);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index index, const PacketType& x, Index begin,
+                                                                Index count) {
+    pstoretSegment<Scalar, PacketType, StoreMode>(m_data + index * m_innerStride.value(), x, begin, count);
+  }
+
+ protected:
+  EIGEN_DEVICE_FUNC constexpr Index rowStride() const noexcept {
+    return XprType::IsRowMajor ? m_outerStride.value() : m_innerStride.value();
+  }
+  EIGEN_DEVICE_FUNC constexpr Index colStride() const noexcept {
+    return XprType::IsRowMajor ? m_innerStride.value() : m_outerStride.value();
+  }
+
+  PointerType m_data;
+  const variable_if_dynamic<Index, XprType::InnerStrideAtCompileTime> m_innerStride;
+  const variable_if_dynamic<Index, XprType::OuterStrideAtCompileTime> m_outerStride;
+};
+
+template <typename PlainObjectType, int MapOptions, typename StrideType>
+struct evaluator<Map<PlainObjectType, MapOptions, StrideType>>
+    : public mapbase_evaluator<Map<PlainObjectType, MapOptions, StrideType>, PlainObjectType> {
+  typedef Map<PlainObjectType, MapOptions, StrideType> XprType;
+  typedef typename XprType::Scalar Scalar;
+  // TODO: should check for smaller packet types once we can handle multi-sized packet types
+  typedef typename packet_traits<Scalar>::type PacketScalar;
+
+  enum {
+    InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
+                                   ? int(PlainObjectType::InnerStrideAtCompileTime)
+                                   : int(StrideType::InnerStrideAtCompileTime),
+    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
+                                   ? int(PlainObjectType::OuterStrideAtCompileTime)
+                                   : int(StrideType::OuterStrideAtCompileTime),
+    HasNoInnerStride = InnerStrideAtCompileTime == 1,
+    HasNoOuterStride = StrideType::OuterStrideAtCompileTime == 0,
+    HasNoStride = HasNoInnerStride && HasNoOuterStride,
+    IsDynamicSize = PlainObjectType::SizeAtCompileTime == Dynamic,
+
+    PacketAccessMask = bool(HasNoInnerStride) ? ~int(0) : ~int(PacketAccessBit),
+    LinearAccessMask =
+        bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime) ? ~int(0) : ~int(LinearAccessBit),
+    Flags = int(evaluator<PlainObjectType>::Flags) & (LinearAccessMask & PacketAccessMask),
+
+    Alignment = int(MapOptions) & int(AlignedMask)
+  };
+
+  EIGEN_DEVICE_FUNC constexpr explicit evaluator(const XprType& map)
+      : mapbase_evaluator<XprType, PlainObjectType>(map) {}
+};
+
+// -------------------- Ref --------------------
+
+template <typename PlainObjectType, int RefOptions, typename StrideType>
+struct evaluator<Ref<PlainObjectType, RefOptions, StrideType>>
+    : public mapbase_evaluator<Ref<PlainObjectType, RefOptions, StrideType>, PlainObjectType> {
+  typedef Ref<PlainObjectType, RefOptions, StrideType> XprType;
+
+  enum {
+    Flags = evaluator<Map<PlainObjectType, RefOptions, StrideType>>::Flags,
+    Alignment = evaluator<Map<PlainObjectType, RefOptions, StrideType>>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit evaluator(const XprType& ref)
+      : mapbase_evaluator<XprType, PlainObjectType>(ref) {}
+};
+
+// -------------------- Block --------------------
+
+template <typename ArgType, int BlockRows, int BlockCols, bool InnerPanel,
+          bool HasDirectAccess = has_direct_access<ArgType>::ret>
+struct block_evaluator;
+
+template <typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+struct evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>>
+    : block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel> {
+  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
+  typedef typename XprType::Scalar Scalar;
+  // TODO: should check for smaller packet types once we can handle multi-sized packet types
+  typedef typename packet_traits<Scalar>::type PacketScalar;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+
+    RowsAtCompileTime = traits<XprType>::RowsAtCompileTime,
+    ColsAtCompileTime = traits<XprType>::ColsAtCompileTime,
+    MaxRowsAtCompileTime = traits<XprType>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = traits<XprType>::MaxColsAtCompileTime,
+
+    ArgTypeIsRowMajor = (int(evaluator<ArgType>::Flags) & RowMajorBit) != 0,
+    IsRowMajor = (MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1)   ? 1
+                 : (MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1) ? 0
+                                                                            : ArgTypeIsRowMajor,
+    HasSameStorageOrderAsArgType = (IsRowMajor == ArgTypeIsRowMajor),
+    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
+    InnerStrideAtCompileTime = HasSameStorageOrderAsArgType ? int(inner_stride_at_compile_time<ArgType>::ret)
+                                                            : int(outer_stride_at_compile_time<ArgType>::ret),
+    OuterStrideAtCompileTime = HasSameStorageOrderAsArgType ? int(outer_stride_at_compile_time<ArgType>::ret)
+                                                            : int(inner_stride_at_compile_time<ArgType>::ret),
+    MaskPacketAccessBit = (InnerStrideAtCompileTime == 1 || HasSameStorageOrderAsArgType) ? PacketAccessBit : 0,
+
+    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 ||
+                            (InnerPanel && (evaluator<ArgType>::Flags & LinearAccessBit)))
+                               ? LinearAccessBit
+                               : 0,
+    FlagsRowMajorBit = XprType::Flags & RowMajorBit,
+    Flags0 = evaluator<ArgType>::Flags & ((HereditaryBits & ~RowMajorBit) | DirectAccessBit | MaskPacketAccessBit),
+    Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit,
+
+    PacketAlignment = unpacket_traits<PacketScalar>::alignment,
+    Alignment0 = (InnerPanel && (OuterStrideAtCompileTime != Dynamic) && (OuterStrideAtCompileTime != 0) &&
+                  (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % int(PacketAlignment)) == 0))
+                     ? int(PacketAlignment)
+                     : 0,
+    Alignment = plain_enum_min(evaluator<ArgType>::Alignment, Alignment0)
+  };
+  typedef block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel> block_evaluator_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit evaluator(const XprType& block)
+      : block_evaluator_type(block) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+};
+
+// no direct-access => dispatch to a unary evaluator
+template <typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /*HasDirectAccess*/ false>
+    : unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>> {
+  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit block_evaluator(const XprType& block)
+      : unary_evaluator<XprType>(block) {}
+};
+
+template <typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IndexBased>
+    : evaluator_base<Block<ArgType, BlockRows, BlockCols, InnerPanel>> {
+  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit unary_evaluator(const XprType& block)
+      : m_argImpl(block.nestedExpression()),
+        m_startRow(block.startRow()),
+        m_startCol(block.startCol()),
+        m_linear_offset(ForwardLinearAccess
+                            ? (ArgType::IsRowMajor
+                                   ? block.startRow() * block.nestedExpression().cols() + block.startCol()
+                                   : block.startCol() * block.nestedExpression().rows() + block.startRow())
+                            : 0) {}
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  enum {
+    RowsAtCompileTime = XprType::RowsAtCompileTime,
+    ForwardLinearAccess = (InnerPanel || int(XprType::IsRowMajor) == int(ArgType::IsRowMajor)) &&
+                          bool(evaluator<ArgType>::Flags & LinearAccessBit)
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    return m_argImpl.coeff(m_startRow.value() + row, m_startCol.value() + col);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return linear_coeff_impl(index, bool_constant<ForwardLinearAccess>());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
+    return m_argImpl.coeffRef(m_startRow.value() + row, m_startCol.value() + col);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    return linear_coeffRef_impl(index, bool_constant<ForwardLinearAccess>());
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    return m_argImpl.template packet<LoadMode, PacketType>(m_startRow.value() + row, m_startCol.value() + col);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    if (ForwardLinearAccess)
+      return m_argImpl.template packet<LoadMode, PacketType>(m_linear_offset.value() + index);
+    else
+      return packet<LoadMode, PacketType>(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketType& x) {
+    return m_argImpl.template writePacket<StoreMode, PacketType>(m_startRow.value() + row, m_startCol.value() + col, x);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketType& x) {
+    if (ForwardLinearAccess)
+      return m_argImpl.template writePacket<StoreMode, PacketType>(m_linear_offset.value() + index, x);
+    else
+      return writePacket<StoreMode, PacketType>(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0,
+                                                x);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    return m_argImpl.template packetSegment<LoadMode, PacketType>(m_startRow.value() + row, m_startCol.value() + col,
+                                                                  begin, count);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    if (ForwardLinearAccess)
+      return m_argImpl.template packetSegment<LoadMode, PacketType>(m_linear_offset.value() + index, begin, count);
+    else
+      return packetSegment<LoadMode, PacketType>(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0,
+                                                 begin, count);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index row, Index col, const PacketType& x, Index begin,
+                                                                Index count) {
+    return m_argImpl.template writePacketSegment<StoreMode, PacketType>(m_startRow.value() + row,
+                                                                        m_startCol.value() + col, x, begin, count);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index index, const PacketType& x, Index begin,
+                                                                Index count) {
+    if (ForwardLinearAccess)
+      return m_argImpl.template writePacketSegment<StoreMode, PacketType>(m_linear_offset.value() + index, x, begin,
+                                                                          count);
+    else
+      return writePacketSegment<StoreMode, PacketType>(RowsAtCompileTime == 1 ? 0 : index,
+                                                       RowsAtCompileTime == 1 ? index : 0, x, begin, count);
+  }
+
+ protected:
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType
+  linear_coeff_impl(Index index, internal::true_type /* ForwardLinearAccess */) const {
+    return m_argImpl.coeff(m_linear_offset.value() + index);
+  }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType
+  linear_coeff_impl(Index index, internal::false_type /* not ForwardLinearAccess */) const {
+    return coeff(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& linear_coeffRef_impl(
+      Index index, internal::true_type /* ForwardLinearAccess */) {
+    return m_argImpl.coeffRef(m_linear_offset.value() + index);
+  }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& linear_coeffRef_impl(
+      Index index, internal::false_type /* not ForwardLinearAccess */) {
+    return coeffRef(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
+  }
+
+  evaluator<ArgType> m_argImpl;
+  const variable_if_dynamic<Index, (ArgType::RowsAtCompileTime == 1 && BlockRows == 1) ? 0 : Dynamic> m_startRow;
+  const variable_if_dynamic<Index, (ArgType::ColsAtCompileTime == 1 && BlockCols == 1) ? 0 : Dynamic> m_startCol;
+  const variable_if_dynamic<Index, ForwardLinearAccess ? Dynamic : 0> m_linear_offset;
+};
+
+// TODO: This evaluator does not actually use the child evaluator;
+// all action is via the data() as returned by the Block expression.
+
+template <typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /* HasDirectAccess */ true>
+    : mapbase_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>,
+                        typename Block<ArgType, BlockRows, BlockCols, InnerPanel>::PlainObject> {
+  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
+  typedef typename XprType::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit block_evaluator(const XprType& block)
+      : mapbase_evaluator<XprType, typename XprType::PlainObject>(block) {
+    eigen_internal_assert((internal::is_constant_evaluated() ||
+                           (std::uintptr_t(block.data()) % plain_enum_max(1, evaluator<XprType>::Alignment)) == 0) &&
+                          "data is not aligned");
+  }
+};
+
+// -------------------- Replicate --------------------
+
+template <typename ArgType, int RowFactor, int ColFactor>
+struct unary_evaluator<Replicate<ArgType, RowFactor, ColFactor>>
+    : evaluator_base<Replicate<ArgType, RowFactor, ColFactor>> {
+  typedef Replicate<ArgType, RowFactor, ColFactor> XprType;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  enum { Factor = (RowFactor == Dynamic || ColFactor == Dynamic) ? Dynamic : RowFactor * ColFactor };
+  typedef typename nested_eval<ArgType, Factor>::type ArgTypeNested;
+  typedef remove_all_t<ArgTypeNested> ArgTypeNestedCleaned;
+
+  enum {
+    CoeffReadCost = evaluator<ArgTypeNestedCleaned>::CoeffReadCost,
+    LinearAccessMask = XprType::IsVectorAtCompileTime ? LinearAccessBit : 0,
+    Flags = (evaluator<ArgTypeNestedCleaned>::Flags & (HereditaryBits | LinearAccessMask) & ~RowMajorBit) |
+            (traits<XprType>::Flags & RowMajorBit),
+
+    Alignment = evaluator<ArgTypeNestedCleaned>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit unary_evaluator(const XprType& replicate)
+      : m_arg(replicate.nestedExpression()),
+        m_argImpl(m_arg),
+        m_rows(replicate.nestedExpression().rows()),
+        m_cols(replicate.nestedExpression().cols()) {}
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    // try to avoid using modulo; this is a pure optimization strategy
+    const Index actual_row = traits<XprType>::RowsAtCompileTime == 1 ? 0 : RowFactor == 1 ? row : row % m_rows.value();
+    const Index actual_col = traits<XprType>::ColsAtCompileTime == 1 ? 0 : ColFactor == 1 ? col : col % m_cols.value();
+
+    return m_argImpl.coeff(actual_row, actual_col);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    // try to avoid using modulo; this is a pure optimization strategy
+    const Index actual_index = traits<XprType>::RowsAtCompileTime == 1
+                                   ? (ColFactor == 1 ? index : index % m_cols.value())
+                                   : (RowFactor == 1 ? index : index % m_rows.value());
+
+    return m_argImpl.coeff(actual_index);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    const Index actual_row = traits<XprType>::RowsAtCompileTime == 1 ? 0 : RowFactor == 1 ? row : row % m_rows.value();
+    const Index actual_col = traits<XprType>::ColsAtCompileTime == 1 ? 0 : ColFactor == 1 ? col : col % m_cols.value();
+
+    return m_argImpl.template packet<LoadMode, PacketType>(actual_row, actual_col);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    const Index actual_index = traits<XprType>::RowsAtCompileTime == 1
+                                   ? (ColFactor == 1 ? index : index % m_cols.value())
+                                   : (RowFactor == 1 ? index : index % m_rows.value());
+
+    return m_argImpl.template packet<LoadMode, PacketType>(actual_index);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    const Index actual_row = traits<XprType>::RowsAtCompileTime == 1 ? 0 : RowFactor == 1 ? row : row % m_rows.value();
+    const Index actual_col = traits<XprType>::ColsAtCompileTime == 1 ? 0 : ColFactor == 1 ? col : col % m_cols.value();
+
+    return m_argImpl.template packetSegment<LoadMode, PacketType>(actual_row, actual_col, begin, count);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    const Index actual_index = traits<XprType>::RowsAtCompileTime == 1
+                                   ? (ColFactor == 1 ? index : index % m_cols.value())
+                                   : (RowFactor == 1 ? index : index % m_rows.value());
+
+    return m_argImpl.template packetSegment<LoadMode, PacketType>(actual_index, begin, count);
+  }
+
+ protected:
+  const ArgTypeNested m_arg;
+  evaluator<ArgTypeNestedCleaned> m_argImpl;
+  const variable_if_dynamic<Index, ArgType::RowsAtCompileTime> m_rows;
+  const variable_if_dynamic<Index, ArgType::ColsAtCompileTime> m_cols;
+};
+
+// -------------------- MatrixWrapper and ArrayWrapper --------------------
+//
+// evaluator_wrapper_base<T> is a common base class for the
+// MatrixWrapper and ArrayWrapper evaluators.
+
+template <typename XprType>
+struct evaluator_wrapper_base : evaluator_base<XprType> {
+  typedef remove_all_t<typename XprType::NestedExpressionType> ArgType;
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+    Flags = evaluator<ArgType>::Flags,
+    Alignment = evaluator<ArgType>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator_wrapper_base(const ArgType& arg) : m_argImpl(arg) {}
+
+  typedef typename ArgType::Scalar Scalar;
+  typedef typename ArgType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    return m_argImpl.coeff(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_argImpl.coeff(index);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
+    return m_argImpl.coeffRef(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { return m_argImpl.coeffRef(index); }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    return m_argImpl.template packet<LoadMode, PacketType>(row, col);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    return m_argImpl.template packet<LoadMode, PacketType>(index);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketType& x) {
+    m_argImpl.template writePacket<StoreMode>(row, col, x);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketType& x) {
+    m_argImpl.template writePacket<StoreMode>(index, x);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    return m_argImpl.template packetSegment<LoadMode, PacketType>(row, col, begin, count);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    return m_argImpl.template packetSegment<LoadMode, PacketType>(index, begin, count);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index row, Index col, const PacketType& x, Index begin,
+                                                                Index count) {
+    m_argImpl.template writePacketSegment<StoreMode>(row, col, x, begin, count);
+  }
+
+  template <int StoreMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index index, const PacketType& x, Index begin,
+                                                                Index count) {
+    m_argImpl.template writePacketSegment<StoreMode>(index, x, begin, count);
+  }
+
+ protected:
+  evaluator<ArgType> m_argImpl;
+};
+
+template <typename TArgType>
+struct unary_evaluator<MatrixWrapper<TArgType>> : evaluator_wrapper_base<MatrixWrapper<TArgType>> {
+  typedef MatrixWrapper<TArgType> XprType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit unary_evaluator(const XprType& wrapper)
+      : evaluator_wrapper_base<MatrixWrapper<TArgType>>(wrapper.nestedExpression()) {}
+};
+
+template <typename TArgType>
+struct unary_evaluator<ArrayWrapper<TArgType>> : evaluator_wrapper_base<ArrayWrapper<TArgType>> {
+  typedef ArrayWrapper<TArgType> XprType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit unary_evaluator(const XprType& wrapper)
+      : evaluator_wrapper_base<ArrayWrapper<TArgType>>(wrapper.nestedExpression()) {}
+};
+
+// -------------------- Reverse --------------------
+
+// defined in Reverse.h:
+template <typename PacketType, bool ReversePacket>
+struct reverse_packet_cond;
+
+template <typename ArgType, int Direction>
+struct unary_evaluator<Reverse<ArgType, Direction>> : evaluator_base<Reverse<ArgType, Direction>> {
+  typedef Reverse<ArgType, Direction> XprType;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  enum {
+    IsRowMajor = XprType::IsRowMajor,
+    IsColMajor = !IsRowMajor,
+    ReverseRow = (Direction == Vertical) || (Direction == BothDirections),
+    ReverseCol = (Direction == Horizontal) || (Direction == BothDirections),
+    ReversePacket = (Direction == BothDirections) || ((Direction == Vertical) && IsColMajor) ||
+                    ((Direction == Horizontal) && IsRowMajor),
+
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+
+    // let's enable LinearAccess only with vectorization because of the product overhead
+    // FIXME: consider enabling DirectAccess with negative strides.
+    Flags0 = evaluator<ArgType>::Flags,
+    LinearAccess =
+        ((Direction == BothDirections) && (int(Flags0) & PacketAccessBit)) ||
+                ((ReverseRow && XprType::ColsAtCompileTime == 1) || (ReverseCol && XprType::RowsAtCompileTime == 1))
+            ? LinearAccessBit
+            : 0,
+
+    Flags = int(Flags0) & (HereditaryBits | PacketAccessBit | LinearAccess),
+
+    Alignment = 0  // FIXME: in some rare cases, Alignment could be preserved.
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit unary_evaluator(const XprType& reverse)
+      : m_argImpl(reverse.nestedExpression()),
+        m_rows(ReverseRow ? reverse.nestedExpression().rows() : 1),
+        m_cols(ReverseCol ? reverse.nestedExpression().cols() : 1) {}
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    return m_argImpl.coeff(ReverseRow ? m_rows.value() - row - 1 : row, ReverseCol ? m_cols.value() - col - 1 : col);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_argImpl.coeff(m_rows.value() * m_cols.value() - index - 1);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
+    return m_argImpl.coeffRef(ReverseRow ? m_rows.value() - row - 1 : row, ReverseCol ? m_cols.value() - col - 1 : col);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    return m_argImpl.coeffRef(m_rows.value() * m_cols.value() - index - 1);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+    static constexpr int OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1;
+    static constexpr int OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1;
+    using reverse_packet = reverse_packet_cond<PacketType, ReversePacket>;
+
+    Index actualRow = ReverseRow ? m_rows.value() - row - OffsetRow : row;
+    Index actualCol = ReverseCol ? m_cols.value() - col - OffsetCol : col;
+
+    return reverse_packet::run(m_argImpl.template packet<LoadMode, PacketType>(actualRow, actualCol));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+
+    Index actualIndex = m_rows.value() * m_cols.value() - index - PacketSize;
+
+    return preverse(m_argImpl.template packet<LoadMode, PacketType>(actualIndex));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketType& x) {
+    static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+    static constexpr int OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1;
+    static constexpr int OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1;
+    using reverse_packet = reverse_packet_cond<PacketType, ReversePacket>;
+
+    Index actualRow = ReverseRow ? m_rows.value() - row - OffsetRow : row;
+    Index actualCol = ReverseCol ? m_cols.value() - col - OffsetCol : col;
+
+    m_argImpl.template writePacket<LoadMode>(actualRow, actualCol, reverse_packet::run(x));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketType& x) {
+    static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+
+    Index actualIndex = m_rows.value() * m_cols.value() - index - PacketSize;
+
+    m_argImpl.template writePacket<LoadMode>(actualIndex, preverse(x));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+    static constexpr int OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1;
+    static constexpr int OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1;
+    using reverse_packet = reverse_packet_cond<PacketType, ReversePacket>;
+
+    Index actualRow = ReverseRow ? m_rows.value() - row - OffsetRow : row;
+    Index actualCol = ReverseCol ? m_cols.value() - col - OffsetCol : col;
+    Index actualBegin = ReversePacket ? (PacketSize - count - begin) : begin;
+
+    return reverse_packet::run(
+        m_argImpl.template packetSegment<LoadMode, PacketType>(actualRow, actualCol, actualBegin, count));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+
+    Index actualIndex = m_rows.value() * m_cols.value() - index - PacketSize;
+    Index actualBegin = PacketSize - count - begin;
+
+    return preverse(m_argImpl.template packetSegment<LoadMode, PacketType>(actualIndex, actualBegin, count));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index row, Index col, const PacketType& x, Index begin,
+                                                                Index count) {
+    static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+    static constexpr int OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1;
+    static constexpr int OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1;
+    using reverse_packet = reverse_packet_cond<PacketType, ReversePacket>;
+
+    Index actualRow = ReverseRow ? m_rows.value() - row - OffsetRow : row;
+    Index actualCol = ReverseCol ? m_cols.value() - col - OffsetCol : col;
+    Index actualBegin = ReversePacket ? (PacketSize - count - begin) : begin;
+
+    m_argImpl.template writePacketSegment<LoadMode>(actualRow, actualCol, reverse_packet::run(x), actualBegin, count);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacketSegment(Index index, const PacketType& x, Index begin,
+                                                                Index count) {
+    static constexpr int PacketSize = unpacket_traits<PacketType>::size;
+
+    Index actualIndex = m_rows.value() * m_cols.value() - index - PacketSize;
+    Index actualBegin = PacketSize - count - begin;
+
+    m_argImpl.template writePacketSegment<LoadMode>(actualIndex, preverse(x), actualBegin, count);
+  }
+
+ protected:
+  evaluator<ArgType> m_argImpl;
+
+  // If we do not reverse rows, then we do not need to know the number of rows; same for columns
+  // Nonetheless, in this case it is important to set to 1 such that the coeff(index) method works fine for vectors.
+  const variable_if_dynamic<Index, ReverseRow ? ArgType::RowsAtCompileTime : 1> m_rows;
+  const variable_if_dynamic<Index, ReverseCol ? ArgType::ColsAtCompileTime : 1> m_cols;
+};
+
+// -------------------- Diagonal --------------------
+
+template <typename ArgType, int DiagIndex>
+struct evaluator<Diagonal<ArgType, DiagIndex>> : evaluator_base<Diagonal<ArgType, DiagIndex>> {
+  typedef Diagonal<ArgType, DiagIndex> XprType;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+
+    Flags =
+        (unsigned int)(evaluator<ArgType>::Flags & (HereditaryBits | DirectAccessBit) & ~RowMajorBit) | LinearAccessBit,
+
+    Alignment = 0
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit evaluator(const XprType& diagonal)
+      : m_argImpl(diagonal.nestedExpression()), m_index(diagonal.index()) {}
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index) const {
+    return m_argImpl.coeff(row + rowOffset(), row + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_argImpl.coeff(index + rowOffset(), index + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index) {
+    return m_argImpl.coeffRef(row + rowOffset(), row + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    return m_argImpl.coeffRef(index + rowOffset(), index + colOffset());
+  }
+
+ protected:
+  evaluator<ArgType> m_argImpl;
+  const variable_if_dynamicindex<Index, XprType::DiagIndex> m_index;
+
+ private:
+  EIGEN_DEVICE_FUNC constexpr Index rowOffset() const { return m_index.value() > 0 ? 0 : -m_index.value(); }
+  EIGEN_DEVICE_FUNC constexpr Index colOffset() const { return m_index.value() > 0 ? m_index.value() : 0; }
+};
+
+//----------------------------------------------------------------------
+// deprecated code
+//----------------------------------------------------------------------
+
+// -------------------- EvalToTemp --------------------
+
+// expression class for evaluating nested expression to a temporary
+
+template <typename ArgType>
+class EvalToTemp;
+
+template <typename ArgType>
+struct traits<EvalToTemp<ArgType>> : public traits<ArgType> {};
+
+template <typename ArgType>
+class EvalToTemp : public dense_xpr_base<EvalToTemp<ArgType>>::type {
+ public:
+  typedef typename dense_xpr_base<EvalToTemp>::type Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(EvalToTemp)
+
+  explicit EvalToTemp(const ArgType& arg) : m_arg(arg) {}
+
+  const ArgType& arg() const { return m_arg; }
+
+  constexpr Index rows() const noexcept { return m_arg.rows(); }
+
+  constexpr Index cols() const noexcept { return m_arg.cols(); }
+
+ private:
+  const ArgType& m_arg;
+};
+
+template <typename ArgType>
+struct evaluator<EvalToTemp<ArgType>> : public evaluator<typename ArgType::PlainObject> {
+  typedef EvalToTemp<ArgType> XprType;
+  typedef typename ArgType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  EIGEN_DEVICE_FUNC constexpr explicit evaluator(const XprType& xpr) : m_result(xpr.arg()) {
+    internal::construct_at<Base>(this, m_result);
+  }
+
+  // This constructor is used when nesting an EvalTo evaluator in another evaluator
+  EIGEN_DEVICE_FUNC constexpr evaluator(const ArgType& arg) : m_result(arg) {
+    internal::construct_at<Base>(this, m_result);
+  }
+
+ protected:
+  PlainObject m_result;
+};
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COREEVALUATORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CoreIterators.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CoreIterators.h
new file mode 100644
index 0000000..f62cf23
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CoreIterators.h
@@ -0,0 +1,141 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COREITERATORS_H
+#define EIGEN_COREITERATORS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
+ */
+
+namespace internal {
+
+template <typename XprType, typename EvaluatorKind>
+class inner_iterator_selector;
+
+}
+
+/** \class InnerIterator
+ * \brief An InnerIterator allows to loop over the element of any matrix expression.
+ *
+ * \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is
+ * constructed.
+ *
+ * TODO: add a usage example
+ */
+template <typename XprType>
+class InnerIterator {
+ protected:
+  typedef internal::inner_iterator_selector<XprType, typename internal::evaluator_traits<XprType>::Kind> IteratorType;
+  typedef internal::evaluator<XprType> EvaluatorType;
+  typedef typename internal::traits<XprType>::Scalar Scalar;
+
+ public:
+  /** Construct an iterator over the \a outerId -th row or column of \a xpr */
+  InnerIterator(const XprType &xpr, const Index &outerId) : m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize()) {}
+
+  /// \returns the value of the current coefficient.
+  EIGEN_STRONG_INLINE Scalar value() const { return m_iter.value(); }
+  /** Increment the iterator \c *this to the next non-zero coefficient.
+   * Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView
+   */
+  EIGEN_STRONG_INLINE InnerIterator &operator++() {
+    m_iter.operator++();
+    return *this;
+  }
+  EIGEN_STRONG_INLINE InnerIterator &operator+=(Index i) {
+    m_iter.operator+=(i);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE InnerIterator operator+(Index i) {
+    InnerIterator result(*this);
+    result += i;
+    return result;
+  }
+
+  /// \returns the column or row index of the current coefficient.
+  EIGEN_STRONG_INLINE Index index() const { return m_iter.index(); }
+  /// \returns the row index of the current coefficient.
+  EIGEN_STRONG_INLINE Index row() const { return m_iter.row(); }
+  /// \returns the column index of the current coefficient.
+  EIGEN_STRONG_INLINE Index col() const { return m_iter.col(); }
+  /// \returns \c true if the iterator \c *this still references a valid coefficient.
+  EIGEN_STRONG_INLINE operator bool() const { return m_iter; }
+
+ protected:
+  EvaluatorType m_eval;
+  IteratorType m_iter;
+
+ private:
+  // If you get here, then you're not using the right InnerIterator type, e.g.:
+  //   SparseMatrix<double,RowMajor> A;
+  //   SparseMatrix<double>::InnerIterator it(A,0);
+  template <typename T>
+  InnerIterator(const EigenBase<T> &, Index outer);
+};
+
+namespace internal {
+
+// Generic inner iterator implementation for dense objects
+template <typename XprType>
+class inner_iterator_selector<XprType, IndexBased> {
+ protected:
+  typedef evaluator<XprType> EvaluatorType;
+  typedef typename traits<XprType>::Scalar Scalar;
+  enum { IsRowMajor = (XprType::Flags & RowMajorBit) == RowMajorBit };
+
+ public:
+  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize)
+      : m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize) {}
+
+  EIGEN_STRONG_INLINE Scalar value() const {
+    return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner) : m_eval.coeff(m_inner, m_outer);
+  }
+
+  EIGEN_STRONG_INLINE inner_iterator_selector &operator++() {
+    m_inner++;
+    return *this;
+  }
+
+  EIGEN_STRONG_INLINE Index index() const { return m_inner; }
+  inline Index row() const { return IsRowMajor ? m_outer : index(); }
+  inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+  EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner >= 0; }
+
+ protected:
+  const EvaluatorType &m_eval;
+  Index m_inner;
+  const Index m_outer;
+  const Index m_end;
+};
+
+// For iterator-based evaluator, inner-iterator is already implemented as
+// evaluator<>::InnerIterator
+template <typename XprType>
+class inner_iterator_selector<XprType, IteratorBased> : public evaluator<XprType>::InnerIterator {
+ protected:
+  typedef typename evaluator<XprType>::InnerIterator Base;
+  typedef evaluator<XprType> EvaluatorType;
+
+ public:
+  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId,
+                                              const Index & /*innerSize*/)
+      : Base(eval, outerId) {}
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COREITERATORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseBinaryOp.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseBinaryOp.h
new file mode 100644
index 0000000..27fd434
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseBinaryOp.h
@@ -0,0 +1,166 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CWISE_BINARY_OP_H
+#define EIGEN_CWISE_BINARY_OP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename BinaryOp, typename Lhs, typename Rhs>
+struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs>> {
+  // we must not inherit from traits<Lhs> since it has
+  // the potential to cause problems with MSVC
+  typedef remove_all_t<Lhs> Ancestor;
+  typedef typename traits<Ancestor>::XprKind XprKind;
+  enum {
+    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
+    ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
+    MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
+  };
+
+  // even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor),
+  // we still want to handle the case when the result type is different.
+  typedef typename result_of<BinaryOp(const typename Lhs::Scalar&, const typename Rhs::Scalar&)>::type Scalar;
+  typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind, typename traits<Rhs>::StorageKind,
+                                              BinaryOp>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<Lhs>::StorageIndex, typename traits<Rhs>::StorageIndex>::type
+      StorageIndex;
+  typedef typename Lhs::Nested LhsNested;
+  typedef typename Rhs::Nested RhsNested;
+  typedef std::remove_reference_t<LhsNested> LhsNested_;
+  typedef std::remove_reference_t<RhsNested> RhsNested_;
+  enum {
+    Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind, typename traits<Rhs>::StorageKind,
+                                        LhsNested_::Flags & RowMajorBit, RhsNested_::Flags & RowMajorBit>::value
+  };
+};
+}  // end namespace internal
+
+template <typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
+class CwiseBinaryOpImpl;
+
+/** \class CwiseBinaryOp
+ * \ingroup Core_Module
+ *
+ * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
+ *
+ * \tparam BinaryOp template functor implementing the operator
+ * \tparam LhsType the type of the left-hand side
+ * \tparam RhsType the type of the right-hand side
+ *
+ * This class represents an expression  where a coefficient-wise binary operator is applied to two expressions.
+ * It is the return type of binary operators, by which we mean only those binary operators where
+ * both the left-hand side and the right-hand side are Eigen expressions.
+ * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
+ *
+ * Most of the time, this is the only way that it is used, so you typically don't have to name
+ * CwiseBinaryOp types explicitly.
+ *
+ * \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class
+ * CwiseNullaryOp
+ */
+template <typename BinaryOp, typename LhsType, typename RhsType>
+class CwiseBinaryOp : public CwiseBinaryOpImpl<BinaryOp, LhsType, RhsType,
+                                               typename internal::cwise_promote_storage_type<
+                                                   typename internal::traits<LhsType>::StorageKind,
+                                                   typename internal::traits<RhsType>::StorageKind, BinaryOp>::ret>,
+                      internal::no_assignment_operator {
+ public:
+  typedef internal::remove_all_t<BinaryOp> Functor;
+  typedef internal::remove_all_t<LhsType> Lhs;
+  typedef internal::remove_all_t<RhsType> Rhs;
+
+  typedef typename CwiseBinaryOpImpl<
+      BinaryOp, LhsType, RhsType,
+      typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
+                                                    typename internal::traits<Rhs>::StorageKind, BinaryOp>::ret>::Base
+      Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
+
+  EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp, typename Lhs::Scalar, typename Rhs::Scalar)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
+
+  typedef typename internal::ref_selector<LhsType>::type LhsNested;
+  typedef typename internal::ref_selector<RhsType>::type RhsNested;
+  typedef std::remove_reference_t<LhsNested> LhsNested_;
+  typedef std::remove_reference_t<RhsNested> RhsNested_;
+
+#if EIGEN_COMP_MSVC
+  // Required for Visual Studio, which may fail to inline the copy constructor otherwise.
+  EIGEN_STRONG_INLINE CwiseBinaryOp(const CwiseBinaryOp<BinaryOp, LhsType, RhsType>&) = default;
+#endif
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs,
+                                                                const BinaryOp& func = BinaryOp())
+      : m_lhs(aLhs), m_rhs(aRhs), m_functor(func) {
+    eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept {
+    // return the fixed size type if available to enable compile time optimizations
+    return internal::traits<internal::remove_all_t<LhsNested>>::RowsAtCompileTime == Dynamic ? m_rhs.rows()
+                                                                                             : m_lhs.rows();
+  }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept {
+    // return the fixed size type if available to enable compile time optimizations
+    return internal::traits<internal::remove_all_t<LhsNested>>::ColsAtCompileTime == Dynamic ? m_rhs.cols()
+                                                                                             : m_lhs.cols();
+  }
+
+  /** \returns the left hand side nested expression */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const LhsNested_& lhs() const { return m_lhs; }
+  /** \returns the right hand side nested expression */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const RhsNested_& rhs() const { return m_rhs; }
+  /** \returns the functor representing the binary operation */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const BinaryOp& functor() const { return m_functor; }
+
+ protected:
+  LhsNested m_lhs;
+  RhsNested m_rhs;
+  const BinaryOp m_functor;
+};
+
+// Generic API dispatcher
+template <typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
+class CwiseBinaryOpImpl : public internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs>>::type {
+ public:
+  typedef typename internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs>>::type Base;
+};
+
+/** replaces \c *this by \c *this - \a other.
+ *
+ * \returns a reference to \c *this
+ */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Derived& MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived>& other) {
+  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+  return derived();
+}
+
+/** replaces \c *this by \c *this + \a other.
+ *
+ * \returns a reference to \c *this
+ */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Derived& MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other) {
+  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+  return derived();
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CWISE_BINARY_OP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseNullaryOp.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseNullaryOp.h
new file mode 100644
index 0000000..7e117b6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseNullaryOp.h
@@ -0,0 +1,975 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CWISE_NULLARY_OP_H
+#define EIGEN_CWISE_NULLARY_OP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename NullaryOp, typename PlainObjectType>
+struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType> {
+  enum { Flags = traits<PlainObjectType>::Flags & RowMajorBit };
+};
+
+}  // namespace internal
+
+/** \class CwiseNullaryOp
+  * \ingroup Core_Module
+  *
+  * \brief Generic expression of a matrix where all coefficients are defined by a functor
+  *
+  * \tparam NullaryOp template functor implementing the operator
+  * \tparam PlainObjectType the underlying plain matrix/array type
+  *
+  * This class represents an expression of a generic nullary operator.
+  * It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods,
+  * and most of the time this is the only way it is used.
+  *
+  * However, if you want to write a function returning such an expression, you
+  * will need to use this class.
+  *
+  * The functor NullaryOp must expose one of the following method:
+    <table class="manual">
+    <tr            ><td>\c operator()() </td><td>if the procedural generation does not depend on the coefficient entries
+  (e.g., random numbers)</td></tr> <tr class="alt"><td>\c operator()(Index i)</td><td>if the procedural generation makes
+  sense for vectors only and that it depends on the coefficient index \c i (e.g., linspace) </td></tr> <tr ><td>\c
+  operator()(Index i,Index j)</td><td>if the procedural generation depends on the matrix coordinates \c i, \c j (e.g.,
+  to generate a checkerboard with 0 and 1)</td></tr>
+    </table>
+  * It is also possible to expose the last two operators if the generation makes sense for matrices but can be optimized
+  for vectors.
+  *
+  * See DenseBase::NullaryExpr(Index,const CustomNullaryOp&) for an example binding
+  * C++11 random number generators.
+  *
+  * A nullary expression can also be used to implement custom sophisticated matrix manipulations
+  * that cannot be covered by the existing set of natively supported matrix manipulations.
+  * See this \ref TopicCustomizing_NullaryExpr "page" for some examples and additional explanations
+  * on the behavior of CwiseNullaryOp.
+  *
+  * \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr
+  */
+template <typename NullaryOp, typename PlainObjectType>
+class CwiseNullaryOp : public internal::dense_xpr_base<CwiseNullaryOp<NullaryOp, PlainObjectType> >::type,
+                       internal::no_assignment_operator {
+ public:
+  typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
+
+  EIGEN_DEVICE_FUNC constexpr CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
+      : m_rows(rows), m_cols(cols), m_functor(func) {
+    eigen_assert(rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 &&
+                 (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
+  }
+  EIGEN_DEVICE_FUNC constexpr CwiseNullaryOp(Index size, const NullaryOp& func = NullaryOp())
+      : CwiseNullaryOp(RowsAtCompileTime == 1 ? 1 : size, RowsAtCompileTime == 1 ? size : 1, func) {
+    EIGEN_STATIC_ASSERT(CwiseNullaryOp::IsVectorAtCompileTime, YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows.value(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols.value(); }
+
+  /** \returns the functor representing the nullary operation */
+  EIGEN_DEVICE_FUNC constexpr const NullaryOp& functor() const { return m_functor; }
+
+ protected:
+  const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
+  const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
+  const NullaryOp m_functor;
+};
+
+/** \returns an expression of a matrix defined by a custom functor \a func
+ *
+ * The parameters \a rows and \a cols are the number of rows and of columns of
+ * the returned matrix. Must be compatible with this MatrixBase type.
+ *
+ * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+ * it is redundant to pass \a rows and \a cols as arguments, so NullaryExpr(const CustomNullaryOp&) should be used
+ * instead.
+ *
+ * The template parameter \a CustomNullaryOp is the type of the functor.
+ *
+ * \sa class CwiseNullaryOp
+ */
+template <typename Derived>
+template <typename CustomNullaryOp>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
+#else
+    const CwiseNullaryOp<CustomNullaryOp, PlainObject>
+#endif
+    DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func) {
+  return CwiseNullaryOp<CustomNullaryOp, PlainObject>(rows, cols, func);
+}
+
+/** \returns an expression of a matrix defined by a custom functor \a func
+ *
+ * The parameter \a size is the size of the returned vector.
+ * Must be compatible with this MatrixBase type.
+ *
+ * \only_for_vectors
+ *
+ * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
+ * it is redundant to pass \a size as argument, so NullaryExpr(const CustomNullaryOp&) should be used
+ * instead.
+ *
+ * The template parameter \a CustomNullaryOp is the type of the functor.
+ *
+ * Here is an example with C++11 random generators: \include random_cpp11.cpp
+ * Output: \verbinclude random_cpp11.out
+ *
+ * \sa class CwiseNullaryOp
+ */
+template <typename Derived>
+template <typename CustomNullaryOp>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
+#else
+    const CwiseNullaryOp<CustomNullaryOp, PlainObject>
+#endif
+    DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  if (RowsAtCompileTime == 1)
+    return CwiseNullaryOp<CustomNullaryOp, PlainObject>(1, size, func);
+  else
+    return CwiseNullaryOp<CustomNullaryOp, PlainObject>(size, 1, func);
+}
+
+/** \returns an expression of a matrix defined by a custom functor \a func
+ *
+ * This variant is only for fixed-size DenseBase types. For dynamic-size types, you
+ * need to use the variants taking size arguments.
+ *
+ * The template parameter \a CustomNullaryOp is the type of the functor.
+ *
+ * \sa class CwiseNullaryOp
+ */
+template <typename Derived>
+template <typename CustomNullaryOp>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
+#else
+    const CwiseNullaryOp<CustomNullaryOp, PlainObject>
+#endif
+    DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func) {
+  return CwiseNullaryOp<CustomNullaryOp, PlainObject>(RowsAtCompileTime, ColsAtCompileTime, func);
+}
+
+/** \returns an expression of a constant matrix of value \a value
+ *
+ * The parameters \a rows and \a cols are the number of rows and of columns of
+ * the returned matrix. Must be compatible with this DenseBase type.
+ *
+ * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+ * it is redundant to pass \a rows and \a cols as arguments, so Constant(const Scalar&) should be used
+ * instead.
+ *
+ * The template parameter \a CustomNullaryOp is the type of the functor.
+ *
+ * \sa class CwiseNullaryOp
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value) {
+  return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
+}
+
+/** \returns an expression of a constant matrix of value \a value
+ *
+ * The parameter \a size is the size of the returned vector.
+ * Must be compatible with this DenseBase type.
+ *
+ * \only_for_vectors
+ *
+ * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
+ * it is redundant to pass \a size as argument, so Constant(const Scalar&) should be used
+ * instead.
+ *
+ * The template parameter \a CustomNullaryOp is the type of the functor.
+ *
+ * \sa class CwiseNullaryOp
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Constant(Index size, const Scalar& value) {
+  return DenseBase<Derived>::NullaryExpr(size, internal::scalar_constant_op<Scalar>(value));
+}
+
+/** \returns an expression of a constant matrix of value \a value
+ *
+ * This variant is only for fixed-size DenseBase types. For dynamic-size types, you
+ * need to use the variants taking size arguments.
+ *
+ * The template parameter \a CustomNullaryOp is the type of the functor.
+ *
+ * \sa class CwiseNullaryOp
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Constant(const Scalar& value) {
+  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
+  return DenseBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime,
+                                         internal::scalar_constant_op<Scalar>(value));
+}
+
+/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(Index,const Scalar&,const Scalar&)
+ *
+ * \only_for_vectors
+ *
+ * Example: \include DenseBase_LinSpaced_seq_deprecated.cpp
+ * Output: \verbinclude DenseBase_LinSpaced_seq_deprecated.out
+ *
+ * \sa LinSpaced(Index,const Scalar&, const Scalar&), setLinSpaced(Index,const Scalar&,const Scalar&)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
+DenseBase<Derived>::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar>(low, high, size));
+}
+
+/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(const Scalar&,const Scalar&)
+ *
+ * \sa LinSpaced(const Scalar&, const Scalar&)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
+DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
+  return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime,
+                                         internal::linspaced_op<Scalar>(low, high, Derived::SizeAtCompileTime));
+}
+
+/**
+ * \brief Sets a linearly spaced vector.
+ *
+ * The function generates 'size' equally spaced values in the closed interval [low,high].
+ * When size is set to 1, a vector of length 1 containing 'high' is returned.
+ *
+ * \only_for_vectors
+ *
+ * Example: \include DenseBase_LinSpaced.cpp
+ * Output: \verbinclude DenseBase_LinSpaced.out
+ *
+ * For integer scalar types, an even spacing is possible if and only if the length of the range,
+ * i.e., \c high-low is a scalar multiple of \c size-1, or if \c size is a scalar multiple of the
+ * number of values \c high-low+1 (meaning each value can be repeated the same number of time).
+ * If one of these two considions is not satisfied, then \c high is lowered to the largest value
+ * satisfying one of this constraint.
+ * Here are some examples:
+ *
+ * Example: \include DenseBase_LinSpacedInt.cpp
+ * Output: \verbinclude DenseBase_LinSpacedInt.out
+ *
+ * \sa setLinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
+DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar>(low, high, size));
+}
+
+/**
+ * \copydoc DenseBase::LinSpaced(Index, const DenseBase::Scalar&, const DenseBase::Scalar&)
+ * Special version for fixed size types which does not require the size parameter.
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
+DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
+  return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime,
+                                         internal::linspaced_op<Scalar>(low, high, Derived::SizeAtCompileTime));
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessEqualSpacedReturnType
+DenseBase<Derived>::EqualSpaced(Index size, const Scalar& low, const Scalar& step) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return DenseBase<Derived>::NullaryExpr(size, internal::equalspaced_op<Scalar>(low, step));
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessEqualSpacedReturnType
+DenseBase<Derived>::EqualSpaced(const Scalar& low, const Scalar& step) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::equalspaced_op<Scalar>(low, step));
+}
+
+/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
+template <typename Derived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApproxToConstant(const Scalar& val, const RealScalar& prec) const {
+  typename internal::nested_eval<Derived, 1>::type self(derived());
+  for (Index j = 0; j < cols(); ++j)
+    for (Index i = 0; i < rows(); ++i)
+      if (!internal::isApprox(self.coeff(i, j), val, prec)) return false;
+  return true;
+}
+
+/** This is just an alias for isApproxToConstant().
+ *
+ * \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
+template <typename Derived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isConstant(const Scalar& val, const RealScalar& prec) const {
+  return isApproxToConstant(val, prec);
+}
+
+/** Alias for setConstant(): sets all coefficients in this expression to \a val.
+ *
+ * \sa setConstant(), Constant(), class CwiseNullaryOp
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val) {
+  setConstant(val);
+}
+
+/** Sets all coefficients in this expression to value \a val.
+ *
+ * \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(),
+ * Constant(), class CwiseNullaryOp, setZero(), setOnes()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val) {
+  internal::eigen_fill_impl<Derived>::run(derived(), val);
+  return derived();
+}
+
+/** Resizes to the given \a size, and sets all coefficients in this expression to the given value \a val.
+ *
+ * \only_for_vectors
+ *
+ * Example: \include Matrix_setConstant_int.cpp
+ * Output: \verbinclude Matrix_setConstant_int.out
+ *
+ * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp,
+ * MatrixBase::Constant(const Scalar&)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val) {
+  resize(size);
+  return setConstant(val);
+}
+
+/** Resizes to the given size, and sets all coefficients in this expression to the given value \a val.
+ *
+ * \param rows the new number of rows
+ * \param cols the new number of columns
+ * \param val the value to which all coefficients are set
+ *
+ * Example: \include Matrix_setConstant_int_int.cpp
+ * Output: \verbinclude Matrix_setConstant_int_int.out
+ *
+ * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp,
+ * MatrixBase::Constant(const Scalar&)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setConstant(Index rows, Index cols,
+                                                                                     const Scalar& val) {
+  resize(rows, cols);
+  return setConstant(val);
+}
+
+/** Resizes to the given size, changing only the number of columns, and sets all
+ * coefficients in this expression to the given value \a val. For the parameter
+ * of type NoChange_t, just pass the special value \c NoChange.
+ *
+ * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp,
+ * MatrixBase::Constant(const Scalar&)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setConstant(NoChange_t, Index cols,
+                                                                                     const Scalar& val) {
+  return setConstant(rows(), cols, val);
+}
+
+/** Resizes to the given size, changing only the number of rows, and sets all
+ * coefficients in this expression to the given value \a val. For the parameter
+ * of type NoChange_t, just pass the special value \c NoChange.
+ *
+ * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp,
+ * MatrixBase::Constant(const Scalar&)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setConstant(Index rows, NoChange_t,
+                                                                                     const Scalar& val) {
+  return setConstant(rows, cols(), val);
+}
+
+/**
+ * \brief Sets a linearly spaced vector.
+ *
+ * The function generates 'size' equally spaced values in the closed interval [low,high].
+ * When size is set to 1, a vector of length 1 containing 'high' is returned.
+ *
+ * \only_for_vectors
+ *
+ * Example: \include DenseBase_setLinSpaced.cpp
+ * Output: \verbinclude DenseBase_setLinSpaced.out
+ *
+ * For integer scalar types, do not miss the explanations on the definition
+ * of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
+ *
+ * \sa LinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low,
+                                                                                const Scalar& high) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar>(low, high, newSize));
+}
+
+/**
+ * \brief Sets a linearly spaced vector.
+ *
+ * The function fills \c *this with equally spaced values in the closed interval [low,high].
+ * When size is set to 1, a vector of length 1 containing 'high' is returned.
+ *
+ * \only_for_vectors
+ *
+ * For integer scalar types, do not miss the explanations on the definition
+ * of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
+ *
+ * \sa LinSpaced(Index,const Scalar&,const Scalar&), setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return setLinSpaced(size(), low, high);
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setEqualSpaced(Index newSize, const Scalar& low,
+                                                                                  const Scalar& step) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return derived() = Derived::NullaryExpr(newSize, internal::equalspaced_op<Scalar>(low, step));
+}
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setEqualSpaced(const Scalar& low,
+                                                                                  const Scalar& step) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return setEqualSpaced(size(), low, step);
+}
+
+// zero:
+
+/** \returns an expression of a zero matrix.
+ *
+ * The parameters \a rows and \a cols are the number of rows and of columns of
+ * the returned matrix. Must be compatible with this MatrixBase type.
+ *
+ * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+ * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
+ * instead.
+ *
+ * Example: \include MatrixBase_zero_int_int.cpp
+ * Output: \verbinclude MatrixBase_zero_int_int.out
+ *
+ * \sa Zero(), Zero(Index)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ZeroReturnType DenseBase<Derived>::Zero(
+    Index rows, Index cols) {
+  return ZeroReturnType(rows, cols);
+}
+
+/** \returns an expression of a zero vector.
+ *
+ * The parameter \a size is the size of the returned vector.
+ * Must be compatible with this MatrixBase type.
+ *
+ * \only_for_vectors
+ *
+ * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
+ * it is redundant to pass \a size as argument, so Zero() should be used
+ * instead.
+ *
+ * Example: \include MatrixBase_zero_int.cpp
+ * Output: \verbinclude MatrixBase_zero_int.out
+ *
+ * \sa Zero(), Zero(Index,Index)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ZeroReturnType DenseBase<Derived>::Zero(
+    Index size) {
+  return ZeroReturnType(size);
+}
+
+/** \returns an expression of a fixed-size zero matrix or vector.
+ *
+ * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
+ * need to use the variants taking size arguments.
+ *
+ * Example: \include MatrixBase_zero.cpp
+ * Output: \verbinclude MatrixBase_zero.out
+ *
+ * \sa Zero(Index), Zero(Index,Index)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ZeroReturnType DenseBase<Derived>::Zero() {
+  return ZeroReturnType(RowsAtCompileTime, ColsAtCompileTime);
+}
+
+/** \returns true if *this is approximately equal to the zero matrix,
+ *          within the precision given by \a prec.
+ *
+ * Example: \include MatrixBase_isZero.cpp
+ * Output: \verbinclude MatrixBase_isZero.out
+ *
+ * \sa class CwiseNullaryOp, Zero()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isZero(const RealScalar& prec) const {
+  typename internal::nested_eval<Derived, 1>::type self(derived());
+  for (Index j = 0; j < cols(); ++j)
+    for (Index i = 0; i < rows(); ++i)
+      if (!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<Scalar>(1), prec)) return false;
+  return true;
+}
+
+/** Sets all coefficients in this expression to zero.
+ *
+ * Example: \include MatrixBase_setZero.cpp
+ * Output: \verbinclude MatrixBase_setZero.out
+ *
+ * \sa class CwiseNullaryOp, Zero()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero() {
+  internal::eigen_zero_impl<Derived>::run(derived());
+  return derived();
+}
+
+/** Resizes to the given \a size, and sets all coefficients in this expression to zero.
+ *
+ * \only_for_vectors
+ *
+ * Example: \include Matrix_setZero_int.cpp
+ * Output: \verbinclude Matrix_setZero_int.out
+ *
+ * \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setZero(Index newSize) {
+  resize(newSize);
+  return setZero();
+}
+
+/** Resizes to the given size, and sets all coefficients in this expression to zero.
+ *
+ * \param rows the new number of rows
+ * \param cols the new number of columns
+ *
+ * Example: \include Matrix_setZero_int_int.cpp
+ * Output: \verbinclude Matrix_setZero_int_int.out
+ *
+ * \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setZero(Index rows, Index cols) {
+  resize(rows, cols);
+  return setZero();
+}
+
+/** Resizes to the given size, changing only the number of columns, and sets all
+ * coefficients in this expression to zero. For the parameter of type NoChange_t,
+ * just pass the special value \c NoChange.
+ *
+ * \sa DenseBase::setZero(), setZero(Index), setZero(Index, Index), setZero(Index, NoChange_t), class CwiseNullaryOp,
+ * DenseBase::Zero()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setZero(NoChange_t, Index cols) {
+  return setZero(rows(), cols);
+}
+
+/** Resizes to the given size, changing only the number of rows, and sets all
+ * coefficients in this expression to zero. For the parameter of type NoChange_t,
+ * just pass the special value \c NoChange.
+ *
+ * \sa DenseBase::setZero(), setZero(Index), setZero(Index, Index), setZero(NoChange_t, Index), class CwiseNullaryOp,
+ * DenseBase::Zero()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setZero(Index rows, NoChange_t) {
+  return setZero(rows, cols());
+}
+
+// ones:
+
+/** \returns an expression of a matrix where all coefficients equal one.
+ *
+ * The parameters \a rows and \a cols are the number of rows and of columns of
+ * the returned matrix. Must be compatible with this MatrixBase type.
+ *
+ * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+ * it is redundant to pass \a rows and \a cols as arguments, so Ones() should be used
+ * instead.
+ *
+ * Example: \include MatrixBase_ones_int_int.cpp
+ * Output: \verbinclude MatrixBase_ones_int_int.out
+ *
+ * \sa Ones(), Ones(Index), isOnes(), class Ones
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType DenseBase<Derived>::Ones(
+    Index rows, Index cols) {
+  return Constant(rows, cols, Scalar(1));
+}
+
+/** \returns an expression of a vector where all coefficients equal one.
+ *
+ * The parameter \a newSize is the size of the returned vector.
+ * Must be compatible with this MatrixBase type.
+ *
+ * \only_for_vectors
+ *
+ * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
+ * it is redundant to pass \a size as argument, so Ones() should be used
+ * instead.
+ *
+ * Example: \include MatrixBase_ones_int.cpp
+ * Output: \verbinclude MatrixBase_ones_int.out
+ *
+ * \sa Ones(), Ones(Index,Index), isOnes(), class Ones
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType DenseBase<Derived>::Ones(
+    Index newSize) {
+  return Constant(newSize, Scalar(1));
+}
+
+/** \returns an expression of a fixed-size matrix or vector where all coefficients equal one.
+ *
+ * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
+ * need to use the variants taking size arguments.
+ *
+ * Example: \include MatrixBase_ones.cpp
+ * Output: \verbinclude MatrixBase_ones.out
+ *
+ * \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType DenseBase<Derived>::Ones() {
+  return Constant(Scalar(1));
+}
+
+/** \returns true if *this is approximately equal to the matrix where all coefficients
+ *          are equal to 1, within the precision given by \a prec.
+ *
+ * Example: \include MatrixBase_isOnes.cpp
+ * Output: \verbinclude MatrixBase_isOnes.out
+ *
+ * \sa class CwiseNullaryOp, Ones()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isOnes(const RealScalar& prec) const {
+  return isApproxToConstant(Scalar(1), prec);
+}
+
+/** Sets all coefficients in this expression to one.
+ *
+ * Example: \include MatrixBase_setOnes.cpp
+ * Output: \verbinclude MatrixBase_setOnes.out
+ *
+ * \sa class CwiseNullaryOp, Ones()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes() {
+  return setConstant(Scalar(1));
+}
+
+/** Resizes to the given \a newSize, and sets all coefficients in this expression to one.
+ *
+ * \only_for_vectors
+ *
+ * Example: \include Matrix_setOnes_int.cpp
+ * Output: \verbinclude Matrix_setOnes_int.out
+ *
+ * \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setOnes(Index newSize) {
+  resize(newSize);
+  return setConstant(Scalar(1));
+}
+
+/** Resizes to the given size, and sets all coefficients in this expression to one.
+ *
+ * \param rows the new number of rows
+ * \param cols the new number of columns
+ *
+ * Example: \include Matrix_setOnes_int_int.cpp
+ * Output: \verbinclude Matrix_setOnes_int_int.out
+ *
+ * \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setOnes(Index rows, Index cols) {
+  resize(rows, cols);
+  return setConstant(Scalar(1));
+}
+
+/** Resizes to the given size, changing only the number of rows, and sets all
+ * coefficients in this expression to one. For the parameter of type NoChange_t,
+ * just pass the special value \c NoChange.
+ *
+ * \sa MatrixBase::setOnes(), setOnes(Index), setOnes(Index, Index), setOnes(NoChange_t, Index), class CwiseNullaryOp,
+ * MatrixBase::Ones()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setOnes(Index rows, NoChange_t) {
+  return setOnes(rows, cols());
+}
+
+/** Resizes to the given size, changing only the number of columns, and sets all
+ * coefficients in this expression to one. For the parameter of type NoChange_t,
+ * just pass the special value \c NoChange.
+ *
+ * \sa MatrixBase::setOnes(), setOnes(Index), setOnes(Index, Index), setOnes(Index, NoChange_t) class CwiseNullaryOp,
+ * MatrixBase::Ones()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setOnes(NoChange_t, Index cols) {
+  return setOnes(rows(), cols);
+}
+
+// Identity:
+
+/** \returns an expression of the identity matrix (not necessarily square).
+ *
+ * The parameters \a rows and \a cols are the number of rows and of columns of
+ * the returned matrix. Must be compatible with this MatrixBase type.
+ *
+ * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+ * it is redundant to pass \a rows and \a cols as arguments, so Identity() should be used
+ * instead.
+ *
+ * Example: \include MatrixBase_identity_int_int.cpp
+ * Output: \verbinclude MatrixBase_identity_int_int.out
+ *
+ * \sa Identity(), setIdentity(), isIdentity()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
+MatrixBase<Derived>::Identity(Index rows, Index cols) {
+  return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
+}
+
+/** \returns an expression of the identity matrix (not necessarily square).
+ *
+ * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
+ * need to use the variant taking size arguments.
+ *
+ * Example: \include MatrixBase_identity.cpp
+ * Output: \verbinclude MatrixBase_identity.out
+ *
+ * \sa Identity(Index,Index), setIdentity(), isIdentity()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
+MatrixBase<Derived>::Identity() {
+  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
+  return MatrixBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_identity_op<Scalar>());
+}
+
+/** \returns true if *this is approximately equal to the identity matrix
+ *          (not necessarily square),
+ *          within the precision given by \a prec.
+ *
+ * Example: \include MatrixBase_isIdentity.cpp
+ * Output: \verbinclude MatrixBase_isIdentity.out
+ *
+ * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), setIdentity()
+ */
+template <typename Derived>
+bool MatrixBase<Derived>::isIdentity(const RealScalar& prec) const {
+  typename internal::nested_eval<Derived, 1>::type self(derived());
+  for (Index j = 0; j < cols(); ++j) {
+    for (Index i = 0; i < rows(); ++i) {
+      if (i == j) {
+        if (!internal::isApprox(self.coeff(i, j), static_cast<Scalar>(1), prec)) return false;
+      } else {
+        if (!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<RealScalar>(1), prec)) return false;
+      }
+    }
+  }
+  return true;
+}
+
+namespace internal {
+
+template <typename Derived, bool Big = (Derived::SizeAtCompileTime >= 16)>
+struct setIdentity_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Derived& run(Derived& m) {
+    return m = Derived::Identity(m.rows(), m.cols());
+  }
+};
+
+template <typename Derived>
+struct setIdentity_impl<Derived, true> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Derived& run(Derived& m) {
+    m.setZero();
+    const Index size = numext::mini(m.rows(), m.cols());
+    for (Index i = 0; i < size; ++i) m.coeffRef(i, i) = typename Derived::Scalar(1);
+    return m;
+  }
+};
+
+}  // end namespace internal
+
+/** Writes the identity expression (not necessarily square) into *this.
+ *
+ * Example: \include MatrixBase_setIdentity.cpp
+ * Output: \verbinclude MatrixBase_setIdentity.out
+ *
+ * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity() {
+  return internal::setIdentity_impl<Derived>::run(derived());
+}
+
+/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
+ *
+ * \param rows the new number of rows
+ * \param cols the new number of columns
+ *
+ * Example: \include Matrix_setIdentity_int_int.cpp
+ * Output: \verbinclude Matrix_setIdentity_int_int.out
+ *
+ * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols) {
+  derived().resize(rows, cols);
+  return setIdentity();
+}
+
+/** \returns an expression of the i-th unit (basis) vector.
+ *
+ * \only_for_vectors
+ *
+ * \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(
+    Index newSize, Index i) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return BasisReturnType(SquareMatrixType::Identity(newSize, newSize), i);
+}
+
+/** \returns an expression of the i-th unit (basis) vector.
+ *
+ * \only_for_vectors
+ *
+ * This variant is for fixed-size vector only.
+ *
+ * \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(
+    Index i) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return BasisReturnType(SquareMatrixType::Identity(), i);
+}
+
+/** \returns an expression of the X axis unit vector (1{,0}^*)
+ *
+ * \only_for_vectors
+ *
+ * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(),
+ * MatrixBase::UnitW()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX() {
+  return Derived::Unit(0);
+}
+
+/** \returns an expression of the Y axis unit vector (0,1{,0}^*)
+ *
+ * \only_for_vectors
+ *
+ * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(),
+ * MatrixBase::UnitW()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY() {
+  return Derived::Unit(1);
+}
+
+/** \returns an expression of the Z axis unit vector (0,0,1{,0}^*)
+ *
+ * \only_for_vectors
+ *
+ * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(),
+ * MatrixBase::UnitW()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ() {
+  return Derived::Unit(2);
+}
+
+/** \returns an expression of the W axis unit vector (0,0,0,1)
+ *
+ * \only_for_vectors
+ *
+ * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(),
+ * MatrixBase::UnitW()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW() {
+  return Derived::Unit(3);
+}
+
+/** \brief Set the coefficients of \c *this to the i-th unit (basis) vector
+ *
+ * \param i index of the unique coefficient to be set to 1
+ *
+ * \only_for_vectors
+ *
+ * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Unit(Index,Index)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setUnit(Index i) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  eigen_assert(i < size());
+  derived().setZero();
+  derived().coeffRef(i) = Scalar(1);
+  return derived();
+}
+
+/** \brief Resizes to the given \a newSize, and writes the i-th unit (basis) vector into *this.
+ *
+ * \param newSize the new size of the vector
+ * \param i index of the unique coefficient to be set to 1
+ *
+ * \only_for_vectors
+ *
+ * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Unit(Index,Index)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setUnit(Index newSize, Index i) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  eigen_assert(i < newSize);
+  derived().resize(newSize);
+  return setUnit(i);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CWISE_NULLARY_OP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseTernaryOp.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseTernaryOp.h
new file mode 100644
index 0000000..8737791
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseTernaryOp.h
@@ -0,0 +1,171 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CWISE_TERNARY_OP_H
+#define EIGEN_CWISE_TERNARY_OP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
+struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>> {
+  // we must not inherit from traits<Arg1> since it has
+  // the potential to cause problems with MSVC
+  typedef remove_all_t<Arg1> Ancestor;
+  typedef typename traits<Ancestor>::XprKind XprKind;
+  enum {
+    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
+    ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
+    MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
+  };
+
+  // even though we require Arg1, Arg2, and Arg3 to have the same scalar type
+  // (see CwiseTernaryOp constructor),
+  // we still want to handle the case when the result type is different.
+  typedef typename result_of<TernaryOp(const typename Arg1::Scalar&, const typename Arg2::Scalar&,
+                                       const typename Arg3::Scalar&)>::type Scalar;
+
+  typedef typename internal::traits<Arg1>::StorageKind StorageKind;
+  typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
+
+  typedef typename Arg1::Nested Arg1Nested;
+  typedef typename Arg2::Nested Arg2Nested;
+  typedef typename Arg3::Nested Arg3Nested;
+  typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
+  typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
+  typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
+  enum { Flags = Arg1Nested_::Flags & RowMajorBit };
+};
+}  // end namespace internal
+
+template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3, typename StorageKind>
+class CwiseTernaryOpImpl;
+
+/** \class CwiseTernaryOp
+ * \ingroup Core_Module
+ *
+ * \brief Generic expression where a coefficient-wise ternary operator is
+ * applied to two expressions
+ *
+ * \tparam TernaryOp template functor implementing the operator
+ * \tparam Arg1Type the type of the first argument
+ * \tparam Arg2Type the type of the second argument
+ * \tparam Arg3Type the type of the third argument
+ *
+ * This class represents an expression where a coefficient-wise ternary
+ * operator is applied to three expressions.
+ * It is the return type of ternary operators, by which we mean only those
+ * ternary operators where
+ * all three arguments are Eigen expressions.
+ * For example, the return type of betainc(matrix1, matrix2, matrix3) is a
+ * CwiseTernaryOp.
+ *
+ * Most of the time, this is the only way that it is used, so you typically
+ * don't have to name
+ * CwiseTernaryOp types explicitly.
+ *
+ * \sa MatrixBase::ternaryExpr(const MatrixBase<Argument2> &, const
+ * MatrixBase<Argument3> &, const CustomTernaryOp &) const, class CwiseBinaryOp,
+ * class CwiseUnaryOp, class CwiseNullaryOp
+ */
+template <typename TernaryOp, typename Arg1Type, typename Arg2Type, typename Arg3Type>
+class CwiseTernaryOp : public CwiseTernaryOpImpl<TernaryOp, Arg1Type, Arg2Type, Arg3Type,
+                                                 typename internal::traits<Arg1Type>::StorageKind>,
+                       internal::no_assignment_operator {
+ public:
+  typedef internal::remove_all_t<Arg1Type> Arg1;
+  typedef internal::remove_all_t<Arg2Type> Arg2;
+  typedef internal::remove_all_t<Arg3Type> Arg3;
+
+  // require the sizes to match
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
+
+  // The index types should match
+  EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
+                                         typename internal::traits<Arg2Type>::StorageKind>::value),
+                      STORAGE_KIND_MUST_MATCH)
+  EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
+                                         typename internal::traits<Arg3Type>::StorageKind>::value),
+                      STORAGE_KIND_MUST_MATCH)
+
+  typedef typename CwiseTernaryOpImpl<TernaryOp, Arg1Type, Arg2Type, Arg3Type,
+                                      typename internal::traits<Arg1Type>::StorageKind>::Base Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp)
+
+  typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
+  typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
+  typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
+  typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
+  typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
+  typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2, const Arg3& a3,
+                                                       const TernaryOp& func = TernaryOp())
+      : m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
+    eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() && a1.rows() == a3.rows() && a1.cols() == a3.cols());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Index rows() const {
+    // return the fixed size type if available to enable compile time
+    // optimizations
+    if (internal::traits<internal::remove_all_t<Arg1Nested>>::RowsAtCompileTime == Dynamic &&
+        internal::traits<internal::remove_all_t<Arg2Nested>>::RowsAtCompileTime == Dynamic)
+      return m_arg3.rows();
+    else if (internal::traits<internal::remove_all_t<Arg1Nested>>::RowsAtCompileTime == Dynamic &&
+             internal::traits<internal::remove_all_t<Arg3Nested>>::RowsAtCompileTime == Dynamic)
+      return m_arg2.rows();
+    else
+      return m_arg1.rows();
+  }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Index cols() const {
+    // return the fixed size type if available to enable compile time
+    // optimizations
+    if (internal::traits<internal::remove_all_t<Arg1Nested>>::ColsAtCompileTime == Dynamic &&
+        internal::traits<internal::remove_all_t<Arg2Nested>>::ColsAtCompileTime == Dynamic)
+      return m_arg3.cols();
+    else if (internal::traits<internal::remove_all_t<Arg1Nested>>::ColsAtCompileTime == Dynamic &&
+             internal::traits<internal::remove_all_t<Arg3Nested>>::ColsAtCompileTime == Dynamic)
+      return m_arg2.cols();
+    else
+      return m_arg1.cols();
+  }
+
+  /** \returns the first argument nested expression */
+  EIGEN_DEVICE_FUNC constexpr const Arg1Nested_& arg1() const { return m_arg1; }
+  /** \returns the first argument nested expression */
+  EIGEN_DEVICE_FUNC constexpr const Arg2Nested_& arg2() const { return m_arg2; }
+  /** \returns the third argument nested expression */
+  EIGEN_DEVICE_FUNC constexpr const Arg3Nested_& arg3() const { return m_arg3; }
+  /** \returns the functor representing the ternary operation */
+  EIGEN_DEVICE_FUNC constexpr const TernaryOp& functor() const { return m_functor; }
+
+ protected:
+  Arg1Nested m_arg1;
+  Arg2Nested m_arg2;
+  Arg3Nested m_arg3;
+  const TernaryOp m_functor;
+};
+
+// Generic API dispatcher
+template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3, typename StorageKind>
+class CwiseTernaryOpImpl : public internal::generic_xpr_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>>::type {
+ public:
+  typedef typename internal::generic_xpr_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>>::type Base;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CWISE_TERNARY_OP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseUnaryOp.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseUnaryOp.h
new file mode 100644
index 0000000..fabf4fc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseUnaryOp.h
@@ -0,0 +1,95 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CWISE_UNARY_OP_H
+#define EIGEN_CWISE_UNARY_OP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename UnaryOp, typename XprType>
+struct traits<CwiseUnaryOp<UnaryOp, XprType> > : traits<XprType> {
+  typedef typename result_of<UnaryOp(const typename XprType::Scalar&)>::type Scalar;
+  typedef typename XprType::Nested XprTypeNested;
+  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
+  enum { Flags = XprTypeNested_::Flags & RowMajorBit };
+};
+}  // namespace internal
+
+template <typename UnaryOp, typename XprType, typename StorageKind>
+class CwiseUnaryOpImpl;
+
+/** \class CwiseUnaryOp
+ * \ingroup Core_Module
+ *
+ * \brief Generic expression where a coefficient-wise unary operator is applied to an expression
+ *
+ * \tparam UnaryOp template functor implementing the operator
+ * \tparam XprType the type of the expression to which we are applying the unary operator
+ *
+ * This class represents an expression where a unary operator is applied to an expression.
+ * It is the return type of all operations taking exactly 1 input expression, regardless of the
+ * presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix
+ * is considered unary, because only the right-hand side is an expression, and its
+ * return type is a specialization of CwiseUnaryOp.
+ *
+ * Most of the time, this is the only way that it is used, so you typically don't have to name
+ * CwiseUnaryOp types explicitly.
+ *
+ * \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
+ */
+template <typename UnaryOp, typename XprType>
+class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>,
+                     internal::no_assignment_operator {
+ public:
+  typedef typename CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>::Base Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
+  typedef typename internal::ref_selector<XprType>::type XprTypeNested;
+  typedef internal::remove_all_t<XprType> NestedExpression;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit CwiseUnaryOp(const XprType& xpr,
+                                                                        const UnaryOp& func = UnaryOp())
+      : m_xpr(xpr), m_functor(func) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_xpr.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_xpr.cols(); }
+
+  /** \returns the functor representing the unary operation */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const UnaryOp& functor() const { return m_functor; }
+
+  /** \returns the nested expression */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression()
+      const {
+    return m_xpr;
+  }
+
+  /** \returns the nested expression */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE internal::remove_all_t<XprTypeNested>& nestedExpression() {
+    return m_xpr;
+  }
+
+ protected:
+  XprTypeNested m_xpr;
+  const UnaryOp m_functor;
+};
+
+// Generic API dispatcher
+template <typename UnaryOp, typename XprType, typename StorageKind>
+class CwiseUnaryOpImpl : public internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type {
+ public:
+  typedef typename internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CWISE_UNARY_OP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseUnaryView.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseUnaryView.h
new file mode 100644
index 0000000..34546e8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/CwiseUnaryView.h
@@ -0,0 +1,169 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CWISE_UNARY_VIEW_H
+#define EIGEN_CWISE_UNARY_VIEW_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename ViewOp, typename MatrixType, typename StrideType>
+struct traits<CwiseUnaryView<ViewOp, MatrixType, StrideType> > : traits<MatrixType> {
+  typedef typename result_of<ViewOp(typename traits<MatrixType>::Scalar&)>::type1 ScalarRef;
+  static_assert(std::is_reference<ScalarRef>::value, "Views must return a reference type.");
+  typedef remove_all_t<ScalarRef> Scalar;
+  typedef typename MatrixType::Nested MatrixTypeNested;
+  typedef remove_all_t<MatrixTypeNested> MatrixTypeNested_;
+  enum {
+    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
+    Flags =
+        traits<MatrixTypeNested_>::Flags &
+        (RowMajorBit | FlagsLvalueBit | DirectAccessBit),  // FIXME DirectAccessBit should not be handled by expressions
+    MatrixTypeInnerStride = inner_stride_at_compile_time<MatrixType>::ret,
+    // need to cast the sizeof's from size_t to int explicitly, otherwise:
+    // "error: no integral type can represent all of the enumerator values
+    InnerStrideAtCompileTime =
+        StrideType::InnerStrideAtCompileTime == 0
+            ? (MatrixTypeInnerStride == Dynamic
+                   ? int(Dynamic)
+                   : int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)))
+            : int(StrideType::InnerStrideAtCompileTime),
+
+    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
+                                   ? (outer_stride_at_compile_time<MatrixType>::ret == Dynamic
+                                          ? int(Dynamic)
+                                          : outer_stride_at_compile_time<MatrixType>::ret *
+                                                int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)))
+                                   : int(StrideType::OuterStrideAtCompileTime)
+  };
+};
+
+// Generic API dispatcher
+template <typename ViewOp, typename XprType, typename StrideType, typename StorageKind,
+          bool Mutable = !std::is_const<XprType>::value>
+class CwiseUnaryViewImpl : public generic_xpr_base<CwiseUnaryView<ViewOp, XprType, StrideType> >::type {
+ public:
+  typedef typename generic_xpr_base<CwiseUnaryView<ViewOp, XprType, StrideType> >::type Base;
+};
+
+template <typename ViewOp, typename MatrixType, typename StrideType>
+class CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, false>
+    : public dense_xpr_base<CwiseUnaryView<ViewOp, MatrixType, StrideType> >::type {
+ public:
+  typedef CwiseUnaryView<ViewOp, MatrixType, StrideType> Derived;
+  typedef typename dense_xpr_base<CwiseUnaryView<ViewOp, MatrixType, StrideType> >::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
+
+  EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeffRef(0)); }
+
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const {
+    return StrideType::InnerStrideAtCompileTime != 0 ? int(StrideType::InnerStrideAtCompileTime)
+                                                     : derived().nestedExpression().innerStride() *
+                                                           sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const {
+    return StrideType::OuterStrideAtCompileTime != 0 ? int(StrideType::OuterStrideAtCompileTime)
+                                                     : derived().nestedExpression().outerStride() *
+                                                           sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar);
+  }
+
+ protected:
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl)
+
+  // Allow const access to coeffRef for the case of direct access being enabled.
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const {
+    return internal::evaluator<Derived>(derived()).coeffRef(index);
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index row, Index col) const {
+    return internal::evaluator<Derived>(derived()).coeffRef(row, col);
+  }
+};
+
+template <typename ViewOp, typename MatrixType, typename StrideType>
+class CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, true>
+    : public CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, false> {
+ public:
+  typedef CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, false> Base;
+  typedef CwiseUnaryView<ViewOp, MatrixType, StrideType> Derived;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
+
+  using Base::data;
+  EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
+    return internal::evaluator<Derived>(derived()).coeffRef(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    return internal::evaluator<Derived>(derived()).coeffRef(index);
+  }
+
+ protected:
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl)
+};
+
+}  // namespace internal
+
+/** \class CwiseUnaryView
+ * \ingroup Core_Module
+ *
+ * \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
+ *
+ * \tparam ViewOp template functor implementing the view
+ * \tparam MatrixType the type of the matrix we are applying the unary operator
+ *
+ * This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
+ * It is the return type of real() and imag(), and most of the time this is the only way it is used.
+ *
+ * \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
+ */
+template <typename ViewOp, typename MatrixType, typename StrideType>
+class CwiseUnaryView : public internal::CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType,
+                                                           typename internal::traits<MatrixType>::StorageKind> {
+ public:
+  typedef typename internal::CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType,
+                                                typename internal::traits<MatrixType>::StorageKind>::Base Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
+  typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
+  typedef internal::remove_all_t<MatrixType> NestedExpression;
+
+  explicit EIGEN_DEVICE_FUNC constexpr inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
+      : m_matrix(mat), m_functor(func) {}
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_matrix.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_matrix.cols(); }
+
+  /** \returns the functor representing unary operation */
+  EIGEN_DEVICE_FUNC constexpr const ViewOp& functor() const { return m_functor; }
+
+  /** \returns the nested expression */
+  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<MatrixTypeNested>& nestedExpression() const {
+    return m_matrix;
+  }
+
+  /** \returns the nested expression */
+  EIGEN_DEVICE_FUNC constexpr std::remove_reference_t<MatrixTypeNested>& nestedExpression() { return m_matrix; }
+
+ protected:
+  MatrixTypeNested m_matrix;
+  ViewOp m_functor;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CWISE_UNARY_VIEW_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DenseBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DenseBase.h
new file mode 100644
index 0000000..4331a72
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DenseBase.h
@@ -0,0 +1,672 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DENSEBASE_H
+#define EIGEN_DENSEBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
+EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned, THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
+
+/** \class DenseBase
+ * \ingroup Core_Module
+ *
+ * \brief Base class for all dense matrices, vectors, and arrays
+ *
+ * This class is the base that is inherited by all dense objects (matrix, vector, arrays,
+ * and related expression types). The common Eigen API for dense objects is contained in this class.
+ *
+ * \tparam Derived is the derived type, e.g., a matrix type or an expression.
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
+ *
+ * \sa \blank \ref TopicClassHierarchy
+ */
+template <typename Derived>
+class DenseBase
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    : public DenseCoeffsBase<Derived, internal::accessors_level<Derived>::value>
+#else
+    : public DenseCoeffsBase<Derived, DirectWriteAccessors>
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+{
+ public:
+  /** Inner iterator type to iterate over the coefficients of a row or column.
+   * \sa class InnerIterator
+   */
+  typedef Eigen::InnerIterator<Derived> InnerIterator;
+
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+
+  /**
+   * \brief The type used to store indices
+   * \details This typedef is relevant for types that store multiple indices such as
+   *          PermutationMatrix or Transpositions, otherwise it defaults to Eigen::Index
+   * \sa \blank \ref TopicPreprocessorDirectives, Eigen::Index, SparseMatrixBase.
+   */
+  typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+
+  /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc. */
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+
+  /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
+   *
+   * It is an alias for the Scalar type */
+  typedef Scalar value_type;
+
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef DenseCoeffsBase<Derived, internal::accessors_level<Derived>::value> Base;
+
+  using Base::coeff;
+  using Base::coeffByOuterInner;
+  using Base::colIndexByOuterInner;
+  using Base::cols;
+  using Base::const_cast_derived;
+  using Base::derived;
+  using Base::rowIndexByOuterInner;
+  using Base::rows;
+  using Base::size;
+  using Base::operator();
+  using Base::operator[];
+  using Base::colStride;
+  using Base::innerStride;
+  using Base::outerStride;
+  using Base::rowStride;
+  using Base::stride;
+  using Base::w;
+  using Base::x;
+  using Base::y;
+  using Base::z;
+  typedef typename Base::CoeffReturnType CoeffReturnType;
+
+  enum {
+
+    RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+    /**< The number of rows at compile-time. This is just a copy of the value provided
+     * by the \a Derived type. If a value is not known at compile-time,
+     * it is set to the \a Dynamic constant.
+     * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
+
+    ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+    /**< The number of columns at compile-time. This is just a copy of the value provided
+     * by the \a Derived type. If a value is not known at compile-time,
+     * it is set to the \a Dynamic constant.
+     * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
+
+    SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
+    /**< This is equal to the number of coefficients, i.e. the number of
+     * rows times the number of columns, or to \a Dynamic if this is not
+     * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
+
+    MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
+    /**< This value is equal to the maximum possible number of rows that this expression
+     * might have. If this expression might have an arbitrarily high number of rows,
+     * this value is set to \a Dynamic.
+     *
+     * This value is useful to know when evaluating an expression, in order to determine
+     * whether it is possible to avoid doing a dynamic memory allocation.
+     *
+     * \sa RowsAtCompileTime, MaxColsAtCompileTime, MaxSizeAtCompileTime
+     */
+
+    MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
+    /**< This value is equal to the maximum possible number of columns that this expression
+     * might have. If this expression might have an arbitrarily high number of columns,
+     * this value is set to \a Dynamic.
+     *
+     * This value is useful to know when evaluating an expression, in order to determine
+     * whether it is possible to avoid doing a dynamic memory allocation.
+     *
+     * \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime
+     */
+
+    MaxSizeAtCompileTime = internal::size_at_compile_time(internal::traits<Derived>::MaxRowsAtCompileTime,
+                                                          internal::traits<Derived>::MaxColsAtCompileTime),
+    /**< This value is equal to the maximum possible number of coefficients that this expression
+     * might have. If this expression might have an arbitrarily high number of coefficients,
+     * this value is set to \a Dynamic.
+     *
+     * This value is useful to know when evaluating an expression, in order to determine
+     * whether it is possible to avoid doing a dynamic memory allocation.
+     *
+     * \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime
+     */
+
+    IsVectorAtCompileTime =
+        internal::traits<Derived>::RowsAtCompileTime == 1 || internal::traits<Derived>::ColsAtCompileTime == 1,
+    /**< This is set to true if either the number of rows or the number of
+     * columns is known at compile-time to be equal to 1. Indeed, in that case,
+     * we are dealing with a column-vector (if there is only one column) or with
+     * a row-vector (if there is only one row). */
+
+    NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0
+                    : bool(IsVectorAtCompileTime)  ? 1
+                                                   : 2,
+    /**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors,
+     * and 2 for matrices.
+     */
+
+    Flags = internal::traits<Derived>::Flags,
+    /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
+     * constructed from this one. See the \ref flags "list of flags".
+     */
+
+    IsRowMajor = int(Flags) & RowMajorBit, /**< True if this expression has row-major storage order. */
+
+    InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
+                             : int(IsRowMajor)          ? int(ColsAtCompileTime)
+                                                        : int(RowsAtCompileTime),
+
+    InnerStrideAtCompileTime = internal::inner_stride_at_compile_time<Derived>::ret,
+    OuterStrideAtCompileTime = internal::outer_stride_at_compile_time<Derived>::ret
+  };
+
+  typedef typename internal::find_best_packet<Scalar, SizeAtCompileTime>::type PacketScalar;
+
+  enum { IsPlainObjectBase = 0 };
+
+  /** The plain matrix type corresponding to this expression.
+   * \sa PlainObject */
+  typedef Matrix<typename internal::traits<Derived>::Scalar, internal::traits<Derived>::RowsAtCompileTime,
+                 internal::traits<Derived>::ColsAtCompileTime,
+                 AutoAlign | (internal::traits<Derived>::Flags & RowMajorBit ? RowMajor : ColMajor),
+                 internal::traits<Derived>::MaxRowsAtCompileTime, internal::traits<Derived>::MaxColsAtCompileTime>
+      PlainMatrix;
+
+  /** The plain array type corresponding to this expression.
+   * \sa PlainObject */
+  typedef Array<typename internal::traits<Derived>::Scalar, internal::traits<Derived>::RowsAtCompileTime,
+                internal::traits<Derived>::ColsAtCompileTime,
+                AutoAlign | (internal::traits<Derived>::Flags & RowMajorBit ? RowMajor : ColMajor),
+                internal::traits<Derived>::MaxRowsAtCompileTime, internal::traits<Derived>::MaxColsAtCompileTime>
+      PlainArray;
+
+  /** \brief The plain matrix or array type corresponding to this expression.
+   *
+   * This is not necessarily exactly the return type of eval(). In the case of plain matrices,
+   * the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
+   * that the return type of eval() is either PlainObject or const PlainObject&.
+   */
+  typedef std::conditional_t<internal::is_same<typename internal::traits<Derived>::XprKind, MatrixXpr>::value,
+                             PlainMatrix, PlainArray>
+      PlainObject;
+
+  /** \returns the outer size.
+   *
+   * \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension
+   * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a
+   * column-major matrix, and the number of rows for a row-major matrix. */
+  EIGEN_DEVICE_FUNC constexpr Index outerSize() const {
+    return IsVectorAtCompileTime ? 1 : int(IsRowMajor) ? this->rows() : this->cols();
+  }
+
+  /** \returns the inner size.
+   *
+   * \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension
+   * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a
+   * column-major matrix, and the number of columns for a row-major matrix. */
+  EIGEN_DEVICE_FUNC constexpr Index innerSize() const {
+    return IsVectorAtCompileTime ? this->size() : int(IsRowMajor) ? this->cols() : this->rows();
+  }
+
+  /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
+   * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and
+   * does nothing else.
+   */
+  EIGEN_DEVICE_FUNC void resize(Index newSize) {
+    EIGEN_ONLY_USED_FOR_DEBUG(newSize);
+    eigen_assert(newSize == this->size() && "DenseBase::resize() does not actually allow to resize.");
+  }
+  /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
+   * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and
+   * does nothing else.
+   */
+  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) {
+    EIGEN_ONLY_USED_FOR_DEBUG(rows);
+    EIGEN_ONLY_USED_FOR_DEBUG(cols);
+    eigen_assert(rows == this->rows() && cols == this->cols() &&
+                 "DenseBase::resize() does not actually allow to resize.");
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal Represents a matrix with all coefficients equal to one another*/
+  typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> ConstantReturnType;
+  /** \internal Represents a matrix with all coefficients equal to zero*/
+  typedef CwiseNullaryOp<internal::scalar_zero_op<Scalar>, PlainObject> ZeroReturnType;
+  /** \internal \deprecated Represents a vector with linearly spaced coefficients that allows sequential access only. */
+  EIGEN_DEPRECATED typedef CwiseNullaryOp<internal::linspaced_op<Scalar>, PlainObject> SequentialLinSpacedReturnType;
+  /** \internal Represents a vector with linearly spaced coefficients that allows random access. */
+  typedef CwiseNullaryOp<internal::linspaced_op<Scalar>, PlainObject> RandomAccessLinSpacedReturnType;
+  /** \internal Represents a vector with equally spaced coefficients that allows random access. */
+  typedef CwiseNullaryOp<internal::equalspaced_op<Scalar>, PlainObject> RandomAccessEqualSpacedReturnType;
+  /** \internal the return type of MatrixBase::eigenvalues() */
+  typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real,
+                 internal::traits<Derived>::ColsAtCompileTime, 1>
+      EigenvaluesReturnType;
+
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+  /** Copies \a other into *this. \returns a reference to *this. */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other);
+
+  /** Special case of the template operator=, in order to prevent the compiler
+   * from generating a default operator= (issue hit with g++ 4.1)
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator=(const DenseBase& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr Derived& operator=(const EigenBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr Derived& operator+=(const EigenBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr Derived& operator-=(const EigenBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC Derived& operator=(const ReturnByValue<OtherDerived>& func);
+
+  /** \internal
+   * Copies \a other into *this without evaluating other. \returns a reference to *this. */
+  template <typename OtherDerived>
+  /** \deprecated */
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC constexpr Derived& lazyAssign(const DenseBase<OtherDerived>& other);
+
+  EIGEN_DEVICE_FUNC CommaInitializer<Derived> operator<<(const Scalar& s);
+
+  template <unsigned int Added, unsigned int Removed>
+  /** \deprecated it now returns \c *this */
+  EIGEN_DEPRECATED const Derived& flagged() const {
+    return derived();
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC CommaInitializer<Derived> operator<<(const DenseBase<OtherDerived>& other);
+
+  typedef Transpose<Derived> TransposeReturnType;
+  EIGEN_DEVICE_FUNC TransposeReturnType transpose();
+  typedef Transpose<const Derived> ConstTransposeReturnType;
+  EIGEN_DEVICE_FUNC const ConstTransposeReturnType transpose() const;
+  EIGEN_DEVICE_FUNC void transposeInPlace();
+
+  EIGEN_DEVICE_FUNC static const ConstantReturnType Constant(Index rows, Index cols, const Scalar& value);
+  EIGEN_DEVICE_FUNC static const ConstantReturnType Constant(Index size, const Scalar& value);
+  EIGEN_DEVICE_FUNC static const ConstantReturnType Constant(const Scalar& value);
+
+  EIGEN_DEPRECATED_WITH_REASON("The method may result in accuracy loss. Use .EqualSpaced() instead.")
+  EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType LinSpaced(Sequential_t, Index size, const Scalar& low,
+                                                                           const Scalar& high);
+  EIGEN_DEPRECATED_WITH_REASON("The method may result in accuracy loss. Use .EqualSpaced() instead.")
+  EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType LinSpaced(Sequential_t, const Scalar& low,
+                                                                           const Scalar& high);
+
+  EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType LinSpaced(Index size, const Scalar& low,
+                                                                           const Scalar& high);
+  EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType LinSpaced(const Scalar& low, const Scalar& high);
+
+  EIGEN_DEVICE_FUNC static const RandomAccessEqualSpacedReturnType EqualSpaced(Index size, const Scalar& low,
+                                                                               const Scalar& step);
+  EIGEN_DEVICE_FUNC static const RandomAccessEqualSpacedReturnType EqualSpaced(const Scalar& low, const Scalar& step);
+
+  template <typename CustomNullaryOp>
+  EIGEN_DEVICE_FUNC static const CwiseNullaryOp<CustomNullaryOp, PlainObject> NullaryExpr(Index rows, Index cols,
+                                                                                          const CustomNullaryOp& func);
+  template <typename CustomNullaryOp>
+  EIGEN_DEVICE_FUNC static const CwiseNullaryOp<CustomNullaryOp, PlainObject> NullaryExpr(Index size,
+                                                                                          const CustomNullaryOp& func);
+  template <typename CustomNullaryOp>
+  EIGEN_DEVICE_FUNC static const CwiseNullaryOp<CustomNullaryOp, PlainObject> NullaryExpr(const CustomNullaryOp& func);
+
+  EIGEN_DEVICE_FUNC static const ZeroReturnType Zero(Index rows, Index cols);
+  EIGEN_DEVICE_FUNC static const ZeroReturnType Zero(Index size);
+  EIGEN_DEVICE_FUNC static const ZeroReturnType Zero();
+  EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols);
+  EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size);
+  EIGEN_DEVICE_FUNC static const ConstantReturnType Ones();
+
+  EIGEN_DEVICE_FUNC void fill(const Scalar& value);
+  EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value);
+  EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
+  EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high);
+  EIGEN_DEVICE_FUNC Derived& setEqualSpaced(Index size, const Scalar& low, const Scalar& step);
+  EIGEN_DEVICE_FUNC Derived& setEqualSpaced(const Scalar& low, const Scalar& step);
+  EIGEN_DEVICE_FUNC Derived& setZero();
+  EIGEN_DEVICE_FUNC Derived& setOnes();
+  EIGEN_DEVICE_FUNC Derived& setRandom();
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr bool isApprox(const DenseBase<OtherDerived>& other,
+                                            const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+  EIGEN_DEVICE_FUNC constexpr bool isMuchSmallerThan(
+      const RealScalar& other, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr bool isMuchSmallerThan(
+      const DenseBase<OtherDerived>& other, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+  EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value,
+                                            const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+  EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value,
+                                    const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+  EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+  EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+  EIGEN_DEVICE_FUNC inline bool hasNaN() const;
+  EIGEN_DEVICE_FUNC inline bool allFinite() const;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator*=(const Scalar& other);
+  template <bool Enable = internal::complex_array_access<Scalar>::value, typename = std::enable_if_t<Enable>>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator*=(const RealScalar& other);
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator/=(const Scalar& other);
+  template <bool Enable = internal::complex_array_access<Scalar>::value, typename = std::enable_if_t<Enable>>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator/=(const RealScalar& other);
+
+  typedef internal::add_const_on_value_type_t<typename internal::eval<Derived>::type> EvalReturnType;
+  /** \returns the matrix or vector obtained by evaluating this expression.
+   *
+   * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
+   * a const reference, in order to avoid a useless copy.
+   *
+   * \warning Be careful with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page
+   * \endlink.
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvalReturnType eval() const {
+    // Even though MSVC does not honor strong inlining when the return type
+    // is a dynamic matrix, we desperately need strong inlining for fixed
+    // size types on MSVC.
+    return typename internal::eval<Derived>::type(derived());
+  }
+
+  /** swaps *this with the expression \a other.
+   *
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void swap(const DenseBase<OtherDerived>& other) {
+    EIGEN_STATIC_ASSERT(!OtherDerived::IsPlainObjectBase, THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+    eigen_assert(rows() == other.rows() && cols() == other.cols());
+    call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
+  }
+
+  /** swaps *this with the matrix or array \a other.
+   *
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void swap(PlainObjectBase<OtherDerived>& other) {
+    eigen_assert(rows() == other.rows() && cols() == other.cols());
+    call_assignment(derived(), other.derived(), internal::swap_assign_op<Scalar>());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline const NestByValue<Derived> nestByValue() const;
+  EIGEN_DEVICE_FUNC inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
+  EIGEN_DEVICE_FUNC inline ForceAlignedAccess<Derived> forceAlignedAccess();
+  template <bool Enable>
+  EIGEN_DEVICE_FUNC inline const std::conditional_t<Enable, ForceAlignedAccess<Derived>, Derived&>
+  forceAlignedAccessIf() const;
+  template <bool Enable>
+  EIGEN_DEVICE_FUNC inline std::conditional_t<Enable, ForceAlignedAccess<Derived>, Derived&> forceAlignedAccessIf();
+
+  EIGEN_DEVICE_FUNC Scalar sum() const;
+  EIGEN_DEVICE_FUNC Scalar mean() const;
+  EIGEN_DEVICE_FUNC Scalar trace() const;
+
+  EIGEN_DEVICE_FUNC Scalar prod() const;
+
+  template <int NaNPropagation>
+  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff() const;
+  template <int NaNPropagation>
+  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff() const;
+
+  // By default, the fastest version with undefined NaN propagation semantics is
+  // used.
+  // TODO(rmlarsen): Replace with default template argument (C++14 is now the minimum standard).
+  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar minCoeff() const {
+    return minCoeff<PropagateFast>();
+  }
+  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar maxCoeff() const {
+    return maxCoeff<PropagateFast>();
+  }
+
+  template <int NaNPropagation, typename IndexType>
+  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const;
+  template <int NaNPropagation, typename IndexType>
+  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const;
+  template <int NaNPropagation, typename IndexType>
+  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const;
+  template <int NaNPropagation, typename IndexType>
+  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const;
+
+  // TODO(rmlarsen): Replace these methods with a default template argument (C++14 is now the minimum standard).
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const {
+    return minCoeff<PropagateFast>(row, col);
+  }
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const {
+    return maxCoeff<PropagateFast>(row, col);
+  }
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const {
+    return minCoeff<PropagateFast>(index);
+  }
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const {
+    return maxCoeff<PropagateFast>(index);
+  }
+
+  template <typename BinaryOp>
+  EIGEN_DEVICE_FUNC Scalar redux(const BinaryOp& func) const;
+
+  template <typename Visitor>
+  EIGEN_DEVICE_FUNC void visit(Visitor& func) const;
+
+  /** \returns a WithFormat proxy object allowing to print a matrix the with given
+   * format \a fmt.
+   *
+   * See class IOFormat for some examples.
+   *
+   * \sa class IOFormat, class WithFormat
+   */
+  inline const WithFormat<Derived> format(const IOFormat& fmt) const { return WithFormat<Derived>(derived(), fmt); }
+
+  /** \returns the unique coefficient of a 1x1 expression */
+  EIGEN_DEVICE_FUNC CoeffReturnType value() const {
+    EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) eigen_assert(this->rows() == 1 && this->cols() == 1);
+    return derived().coeff(0, 0);
+  }
+
+  EIGEN_DEVICE_FUNC bool all() const;
+  EIGEN_DEVICE_FUNC bool any() const;
+  EIGEN_DEVICE_FUNC Index count() const;
+
+  typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
+  typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType;
+  typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType;
+  typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType;
+
+  /** \returns a VectorwiseOp wrapper of *this for broadcasting and partial reductions
+   *
+   * Example: \include MatrixBase_rowwise.cpp
+   * Output: \verbinclude MatrixBase_rowwise.out
+   *
+   * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
+   */
+  // Code moved here due to a CUDA compiler bug
+  EIGEN_DEVICE_FUNC inline ConstRowwiseReturnType rowwise() const { return ConstRowwiseReturnType(derived()); }
+  EIGEN_DEVICE_FUNC RowwiseReturnType rowwise();
+
+  /** \returns a VectorwiseOp wrapper of *this broadcasting and partial reductions
+   *
+   * Example: \include MatrixBase_colwise.cpp
+   * Output: \verbinclude MatrixBase_colwise.out
+   *
+   * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
+   */
+  EIGEN_DEVICE_FUNC inline ConstColwiseReturnType colwise() const { return ConstColwiseReturnType(derived()); }
+  EIGEN_DEVICE_FUNC ColwiseReturnType colwise();
+
+  typedef CwiseNullaryOp<internal::scalar_random_op<Scalar>, PlainObject> RandomReturnType;
+  static const RandomReturnType Random(Index rows, Index cols);
+  static const RandomReturnType Random(Index size);
+  static const RandomReturnType Random();
+
+  template <typename ThenDerived, typename ElseDerived>
+  inline EIGEN_DEVICE_FUNC constexpr CwiseTernaryOp<
+      internal::scalar_boolean_select_op<typename DenseBase<ThenDerived>::Scalar,
+                                         typename DenseBase<ElseDerived>::Scalar, Scalar>,
+      ThenDerived, ElseDerived, Derived>
+  select(const DenseBase<ThenDerived>& thenMatrix, const DenseBase<ElseDerived>& elseMatrix) const;
+
+  template <typename ThenDerived>
+  inline EIGEN_DEVICE_FUNC constexpr CwiseTernaryOp<
+      internal::scalar_boolean_select_op<typename DenseBase<ThenDerived>::Scalar,
+                                         typename DenseBase<ThenDerived>::Scalar, Scalar>,
+      ThenDerived, typename DenseBase<ThenDerived>::ConstantReturnType, Derived>
+  select(const DenseBase<ThenDerived>& thenMatrix, const typename DenseBase<ThenDerived>::Scalar& elseScalar) const;
+
+  template <typename ElseDerived>
+  inline EIGEN_DEVICE_FUNC constexpr CwiseTernaryOp<
+      internal::scalar_boolean_select_op<typename DenseBase<ElseDerived>::Scalar,
+                                         typename DenseBase<ElseDerived>::Scalar, Scalar>,
+      typename DenseBase<ElseDerived>::ConstantReturnType, ElseDerived, Derived>
+  select(const typename DenseBase<ElseDerived>::Scalar& thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
+
+  template <int p>
+  RealScalar lpNorm() const;
+
+  template <int RowFactor, int ColFactor>
+  EIGEN_DEVICE_FUNC const Replicate<Derived, RowFactor, ColFactor> replicate() const;
+  /**
+   * \return an expression of the replication of \c *this
+   *
+   * Example: \include MatrixBase_replicate_int_int.cpp
+   * Output: \verbinclude MatrixBase_replicate_int_int.out
+   *
+   * \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
+   */
+  // Code moved here due to a CUDA compiler bug
+  EIGEN_DEVICE_FUNC const Replicate<Derived, Dynamic, Dynamic> replicate(Index rowFactor, Index colFactor) const {
+    return Replicate<Derived, Dynamic, Dynamic>(derived(), rowFactor, colFactor);
+  }
+
+  typedef Reverse<Derived, BothDirections> ReverseReturnType;
+  typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
+  EIGEN_DEVICE_FUNC ReverseReturnType reverse();
+  /** This is the const version of reverse(). */
+  // Code moved here due to a CUDA compiler bug
+  EIGEN_DEVICE_FUNC ConstReverseReturnType reverse() const { return ConstReverseReturnType(derived()); }
+  EIGEN_DEVICE_FUNC void reverseInPlace();
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a>
+   * iterator type as returned by the begin() and end() methods.
+   */
+  typedef random_access_iterator_type iterator;
+  /** This is the const version of iterator (aka read-only) */
+  typedef random_access_iterator_type const_iterator;
+#else
+  typedef std::conditional_t<(Flags & DirectAccessBit) == DirectAccessBit,
+                             internal::pointer_based_stl_iterator<Derived>,
+                             internal::generic_randaccess_stl_iterator<Derived>>
+      iterator_type;
+
+  typedef std::conditional_t<(Flags & DirectAccessBit) == DirectAccessBit,
+                             internal::pointer_based_stl_iterator<const Derived>,
+                             internal::generic_randaccess_stl_iterator<const Derived>>
+      const_iterator_type;
+
+  // Stl-style iterators are supported only for vectors.
+
+  typedef std::conditional_t<IsVectorAtCompileTime, iterator_type, void> iterator;
+
+  typedef std::conditional_t<IsVectorAtCompileTime, const_iterator_type, void> const_iterator;
+#endif
+
+  inline iterator begin();
+  inline const_iterator begin() const;
+  inline const_iterator cbegin() const;
+  inline iterator end();
+  inline const_iterator end() const;
+  inline const_iterator cend() const;
+
+  using RealViewReturnType = std::conditional_t<NumTraits<Scalar>::IsComplex, RealView<Derived>, Derived&>;
+  using ConstRealViewReturnType =
+      std::conditional_t<NumTraits<Scalar>::IsComplex, RealView<const Derived>, const Derived&>;
+
+  EIGEN_DEVICE_FUNC RealViewReturnType realView();
+  EIGEN_DEVICE_FUNC ConstRealViewReturnType realView() const;
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
+#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
+#define EIGEN_DOC_UNARY_ADDONS(X, Y)
+#include "../plugins/CommonCwiseUnaryOps.inc"
+#include "../plugins/BlockMethods.inc"
+#include "../plugins/IndexedViewMethods.inc"
+#include "../plugins/ReshapedMethods.inc"
+#ifdef EIGEN_DENSEBASE_PLUGIN
+#include EIGEN_DENSEBASE_PLUGIN
+#endif
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
+#undef EIGEN_DOC_UNARY_ADDONS
+
+  // disable the use of evalTo for dense objects with a nice compilation error
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC inline void evalTo(Dest&) const {
+    EIGEN_STATIC_ASSERT((internal::is_same<Dest, void>::value),
+                        THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS);
+  }
+
+ protected:
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(DenseBase)
+  /** Default constructor. Do nothing. */
+#ifdef EIGEN_INTERNAL_DEBUGGING
+  EIGEN_DEVICE_FUNC constexpr DenseBase() {
+    /* Just checks for self-consistency of the flags.
+     * Only do it when debugging Eigen, as this borders on paranoia and could slow compilation down
+     */
+    EIGEN_STATIC_ASSERT(
+        (internal::check_implication(MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1, int(IsRowMajor)) &&
+         internal::check_implication(MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1, int(!IsRowMajor))),
+        INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION)
+  }
+#else
+  EIGEN_DEVICE_FUNC constexpr DenseBase() = default;
+#endif
+
+ private:
+  EIGEN_DEVICE_FUNC explicit DenseBase(int);
+  EIGEN_DEVICE_FUNC DenseBase(int, int);
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC explicit DenseBase(const DenseBase<OtherDerived>&);
+};
+
+/** Free-function swap.
+ */
+template <typename DerivedA, typename DerivedB>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    // Use forwarding references to capture all combinations of cv-qualified l+r-value cases.
+    std::enable_if_t<std::is_base_of<DenseBase<std::decay_t<DerivedA>>, std::decay_t<DerivedA>>::value &&
+                         std::is_base_of<DenseBase<std::decay_t<DerivedB>>, std::decay_t<DerivedB>>::value,
+                     void>
+    swap(DerivedA&& a, DerivedB&& b) {
+  a.swap(b);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_DENSEBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DenseCoeffsBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DenseCoeffsBase.h
new file mode 100644
index 0000000..c528416
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DenseCoeffsBase.h
@@ -0,0 +1,583 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DENSECOEFFSBASE_H
+#define EIGEN_DENSECOEFFSBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename T>
+struct add_const_on_value_type_if_arithmetic {
+  typedef std::conditional_t<is_arithmetic<T>::value, T, add_const_on_value_type_t<T>> type;
+};
+}  // namespace internal
+
+/** \brief Base class providing read-only coefficient access to matrices and arrays.
+ * \ingroup Core_Module
+ * \tparam Derived Type of the derived class
+ *
+ * \note #ReadOnlyAccessors Constant indicating read-only access
+ *
+ * This class defines the \c operator() \c const function and friends, which can be used to read specific
+ * entries of a matrix or array.
+ *
+ * \sa DenseCoeffsBase<Derived, WriteAccessors>, DenseCoeffsBase<Derived, DirectAccessors>,
+ *     \ref TopicClassHierarchy
+ */
+template <typename Derived>
+class DenseCoeffsBase<Derived, ReadOnlyAccessors> : public EigenBase<Derived> {
+ public:
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+
+  // Explanation for this CoeffReturnType typedef.
+  // - This is the return type of the coeff() method.
+  // - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references
+  // to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value).
+  // - The DirectAccessBit means exactly that the underlying data of coefficients can be directly accessed as a plain
+  // strided array, which means exactly that the underlying data of coefficients does exist in memory, which means
+  // exactly that the coefficients is const-referencable, which means exactly that we can have coeff() return a const
+  // reference. For example, Map<const Matrix> have DirectAccessBit but not LvalueBit, so that Map<const Matrix>.coeff()
+  // does points to a const Scalar& which exists in memory, while does not allow coeffRef() as it would not provide a
+  // lvalue. Notice that DirectAccessBit and LvalueBit are mutually orthogonal.
+  // - The is_arithmetic check is required since "const int", "const double", etc. will cause warnings on some systems
+  // while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is
+  // not possible, since the underlying expressions might not offer a valid address the reference could be referring to.
+  typedef std::conditional_t<bool(internal::traits<Derived>::Flags&(LvalueBit | DirectAccessBit)), const Scalar&,
+                             std::conditional_t<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>>
+      CoeffReturnType;
+
+  typedef typename internal::add_const_on_value_type_if_arithmetic<typename internal::packet_traits<Scalar>::type>::type
+      PacketReturnType;
+
+  typedef EigenBase<Derived> Base;
+  using Base::cols;
+  using Base::derived;
+  using Base::rows;
+  using Base::size;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const {
+    return int(Derived::RowsAtCompileTime) == 1   ? 0
+           : int(Derived::ColsAtCompileTime) == 1 ? inner
+           : int(Derived::Flags) & RowMajorBit    ? outer
+                                                  : inner;
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const {
+    return int(Derived::ColsAtCompileTime) == 1   ? 0
+           : int(Derived::RowsAtCompileTime) == 1 ? inner
+           : int(Derived::Flags) & RowMajorBit    ? inner
+                                                  : outer;
+  }
+
+  /** Short version: don't use this function, use
+   * \link operator()(Index,Index) const \endlink instead.
+   *
+   * Long version: this function is similar to
+   * \link operator()(Index,Index) const \endlink, but without the assertion.
+   * Use this for limiting the performance cost of debugging code when doing
+   * repeated coefficient access. Only use this when it is guaranteed that the
+   * parameters \a row and \a col are in range.
+   *
+   * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
+   * function equivalent to \link operator()(Index,Index) const \endlink.
+   *
+   * \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const
+   */
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType coeff(Index row, Index col) const {
+    eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+    return internal::evaluator<Derived>(derived()).coeff(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType coeffByOuterInner(Index outer, Index inner) const {
+    return coeff(rowIndexByOuterInner(outer, inner), colIndexByOuterInner(outer, inner));
+  }
+
+  /** \returns the coefficient at given the given row and column.
+   *
+   * \sa operator()(Index,Index), operator[](Index)
+   */
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType operator()(Index row, Index col) const {
+    eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+    return coeff(row, col);
+  }
+
+#ifdef EIGEN_MULTIDIMENSIONAL_SUBSCRIPT
+  /** \returns the coefficient at given the given row and column.
+   *
+   * \sa operator[](Index,Index), operator[](Index)
+   */
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType operator[](Index row, Index col) const { return operator()(row, col); }
+#endif
+
+  /** Short version: don't use this function, use
+   * \link operator[](Index) const \endlink instead.
+   *
+   * Long version: this function is similar to
+   * \link operator[](Index) const \endlink, but without the assertion.
+   * Use this for limiting the performance cost of debugging code when doing
+   * repeated coefficient access. Only use this when it is guaranteed that the
+   * parameter \a index is in range.
+   *
+   * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
+   * function equivalent to \link operator[](Index) const \endlink.
+   *
+   * \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const
+   */
+
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType coeff(Index index) const {
+    EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
+                        THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
+    eigen_internal_assert(index >= 0 && index < size());
+    return internal::evaluator<Derived>(derived()).coeff(index);
+  }
+
+  /** \returns the coefficient at given index.
+   *
+   * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
+   *
+   * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
+   * z() const, w() const
+   */
+
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType operator[](Index index) const {
+    EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
+                        THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
+    eigen_assert(index >= 0 && index < size());
+    return coeff(index);
+  }
+
+  /** \returns the coefficient at given index.
+   *
+   * This is synonymous to operator[](Index) const.
+   *
+   * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
+   *
+   * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
+   * z() const, w() const
+   */
+
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType operator()(Index index) const {
+    eigen_assert(index >= 0 && index < size());
+    return coeff(index);
+  }
+
+  /** equivalent to operator[](0).  */
+
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType x() const { return (*this)[0]; }
+
+  /** equivalent to operator[](1).  */
+
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType y() const {
+    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 2, OUT_OF_RANGE_ACCESS);
+    return (*this)[1];
+  }
+
+  /** equivalent to operator[](2).  */
+
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType z() const {
+    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 3, OUT_OF_RANGE_ACCESS);
+    return (*this)[2];
+  }
+
+  /** equivalent to operator[](3).  */
+
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType w() const {
+    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 4, OUT_OF_RANGE_ACCESS);
+    return (*this)[3];
+  }
+
+  /** \internal
+   * \returns the packet of coefficients starting at the given row and column. It is your responsibility
+   * to ensure that a packet really starts there. This method is only available on expressions having the
+   * PacketAccessBit.
+   *
+   * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
+   * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
+   * starting at an address which is a multiple of the packet size.
+   */
+
+  template <int LoadMode>
+  EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const {
+    typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
+    eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+    return internal::evaluator<Derived>(derived()).template packet<LoadMode, DefaultPacketType>(row, col);
+  }
+
+  /** \internal */
+  template <int LoadMode>
+  EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const {
+    return packet<LoadMode>(rowIndexByOuterInner(outer, inner), colIndexByOuterInner(outer, inner));
+  }
+
+  /** \internal
+   * \returns the packet of coefficients starting at the given index. It is your responsibility
+   * to ensure that a packet really starts there. This method is only available on expressions having the
+   * PacketAccessBit and the LinearAccessBit.
+   *
+   * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
+   * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
+   * starting at an address which is a multiple of the packet size.
+   */
+
+  template <int LoadMode>
+  EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
+                        THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
+    typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
+    eigen_internal_assert(index >= 0 && index < size());
+    return internal::evaluator<Derived>(derived()).template packet<LoadMode, DefaultPacketType>(index);
+  }
+
+ protected:
+  // explanation: DenseBase is doing "using ..." on the methods from DenseCoeffsBase.
+  // But some methods are only available in the DirectAccess case.
+  // So we add dummy methods here with these names, so that "using... " doesn't fail.
+  // It's not private so that the child class DenseBase can access them, and it's not public
+  // either since it's an implementation detail, so has to be protected.
+  void coeffRef();
+  void coeffRefByOuterInner();
+  void writePacket();
+  void writePacketByOuterInner();
+  void copyCoeff();
+  void copyCoeffByOuterInner();
+  void copyPacket();
+  void copyPacketByOuterInner();
+  void stride();
+  void innerStride();
+  void outerStride();
+  void rowStride();
+  void colStride();
+};
+
+/** \brief Base class providing read/write coefficient access to matrices and arrays.
+ * \ingroup Core_Module
+ * \tparam Derived Type of the derived class
+ *
+ * \note #WriteAccessors Constant indicating read/write access
+ *
+ * This class defines the non-const \c operator() function and friends, which can be used to write specific
+ * entries of a matrix or array. This class inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which
+ * defines the const variant for reading specific entries.
+ *
+ * \sa DenseCoeffsBase<Derived, DirectAccessors>, \ref TopicClassHierarchy
+ */
+template <typename Derived>
+class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors> {
+ public:
+  typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
+
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  using Base::coeff;
+  using Base::colIndexByOuterInner;
+  using Base::cols;
+  using Base::derived;
+  using Base::rowIndexByOuterInner;
+  using Base::rows;
+  using Base::size;
+  using Base::operator[];
+  using Base::operator();
+  using Base::w;
+  using Base::x;
+  using Base::y;
+  using Base::z;
+
+  /** Short version: don't use this function, use
+   * \link operator()(Index,Index) \endlink instead.
+   *
+   * Long version: this function is similar to
+   * \link operator()(Index,Index) \endlink, but without the assertion.
+   * Use this for limiting the performance cost of debugging code when doing
+   * repeated coefficient access. Only use this when it is guaranteed that the
+   * parameters \a row and \a col are in range.
+   *
+   * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
+   * function equivalent to \link operator()(Index,Index) \endlink.
+   *
+   * \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index)
+   */
+  EIGEN_DEVICE_FUNC constexpr Scalar& coeffRef(Index row, Index col) {
+    eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+    return internal::evaluator<Derived>(derived()).coeffRef(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRefByOuterInner(Index outer, Index inner) {
+    return coeffRef(rowIndexByOuterInner(outer, inner), colIndexByOuterInner(outer, inner));
+  }
+
+  /** \returns a reference to the coefficient at given the given row and column.
+   *
+   * \sa operator[](Index)
+   */
+  EIGEN_DEVICE_FUNC constexpr Scalar& operator()(Index row, Index col) {
+    eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+    return coeffRef(row, col);
+  }
+
+#ifdef EIGEN_MULTIDIMENSIONAL_SUBSCRIPT
+  /** \returns a reference to the coefficient at given the given row and column.
+   *
+   * \sa operator[](Index)
+   */
+  EIGEN_DEVICE_FUNC constexpr Scalar& operator[](Index row, Index col) { return operator()(row, col); }
+#endif
+
+  /** Short version: don't use this function, use
+   * \link operator[](Index) \endlink instead.
+   *
+   * Long version: this function is similar to
+   * \link operator[](Index) \endlink, but without the assertion.
+   * Use this for limiting the performance cost of debugging code when doing
+   * repeated coefficient access. Only use this when it is guaranteed that the
+   * parameters \a row and \a col are in range.
+   *
+   * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
+   * function equivalent to \link operator[](Index) \endlink.
+   *
+   * \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index)
+   */
+
+  EIGEN_DEVICE_FUNC constexpr Scalar& coeffRef(Index index) {
+    EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
+                        THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
+    eigen_internal_assert(index >= 0 && index < size());
+    return internal::evaluator<Derived>(derived()).coeffRef(index);
+  }
+
+  /** \returns a reference to the coefficient at given index.
+   *
+   * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
+   *
+   * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
+   */
+
+  EIGEN_DEVICE_FUNC constexpr Scalar& operator[](Index index) {
+    EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
+                        THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
+    eigen_assert(index >= 0 && index < size());
+    return coeffRef(index);
+  }
+
+  /** \returns a reference to the coefficient at given index.
+   *
+   * This is synonymous to operator[](Index).
+   *
+   * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
+   *
+   * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
+   */
+
+  EIGEN_DEVICE_FUNC constexpr Scalar& operator()(Index index) {
+    eigen_assert(index >= 0 && index < size());
+    return coeffRef(index);
+  }
+
+  /** equivalent to operator[](0).  */
+
+  EIGEN_DEVICE_FUNC constexpr Scalar& x() { return (*this)[0]; }
+
+  /** equivalent to operator[](1).  */
+
+  EIGEN_DEVICE_FUNC constexpr Scalar& y() {
+    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 2, OUT_OF_RANGE_ACCESS);
+    return (*this)[1];
+  }
+
+  /** equivalent to operator[](2).  */
+
+  EIGEN_DEVICE_FUNC constexpr Scalar& z() {
+    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 3, OUT_OF_RANGE_ACCESS);
+    return (*this)[2];
+  }
+
+  /** equivalent to operator[](3).  */
+
+  EIGEN_DEVICE_FUNC constexpr Scalar& w() {
+    EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime == -1 || Derived::SizeAtCompileTime >= 4, OUT_OF_RANGE_ACCESS);
+    return (*this)[3];
+  }
+};
+
+/** \brief Base class providing direct read-only coefficient access to matrices and arrays.
+ * \ingroup Core_Module
+ * \tparam Derived Type of the derived class
+ *
+ * \note #DirectAccessors Constant indicating direct access
+ *
+ * This class defines functions to work with strides which can be used to access entries directly. This class
+ * inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which defines functions to access entries read-only using
+ * \c operator() .
+ *
+ * \sa \blank \ref TopicClassHierarchy
+ */
+template <typename Derived>
+class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors> {
+ public:
+  typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  using Base::cols;
+  using Base::derived;
+  using Base::rows;
+  using Base::size;
+
+  /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
+   *
+   * \sa outerStride(), rowStride(), colStride()
+   */
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const { return derived().innerStride(); }
+
+  /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
+   *          in a column-major matrix).
+   *
+   * \sa innerStride(), rowStride(), colStride()
+   */
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const { return derived().outerStride(); }
+
+  // FIXME shall we remove it ?
+  constexpr Index stride() const { return Derived::IsVectorAtCompileTime ? innerStride() : outerStride(); }
+
+  /** \returns the pointer increment between two consecutive rows.
+   *
+   * \sa innerStride(), outerStride(), colStride()
+   */
+  EIGEN_DEVICE_FUNC constexpr Index rowStride() const { return Derived::IsRowMajor ? outerStride() : innerStride(); }
+
+  /** \returns the pointer increment between two consecutive columns.
+   *
+   * \sa innerStride(), outerStride(), rowStride()
+   */
+  EIGEN_DEVICE_FUNC constexpr Index colStride() const { return Derived::IsRowMajor ? innerStride() : outerStride(); }
+};
+
+/** \brief Base class providing direct read/write coefficient access to matrices and arrays.
+ * \ingroup Core_Module
+ * \tparam Derived Type of the derived class
+ *
+ * \note #DirectWriteAccessors Constant indicating direct access
+ *
+ * This class defines functions to work with strides which can be used to access entries directly. This class
+ * inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using
+ * \c operator().
+ *
+ * \sa \blank \ref TopicClassHierarchy
+ */
+template <typename Derived>
+class DenseCoeffsBase<Derived, DirectWriteAccessors> : public DenseCoeffsBase<Derived, WriteAccessors> {
+ public:
+  typedef DenseCoeffsBase<Derived, WriteAccessors> Base;
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  using Base::cols;
+  using Base::derived;
+  using Base::rows;
+  using Base::size;
+
+  /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
+   *
+   * \sa outerStride(), rowStride(), colStride()
+   */
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return derived().innerStride(); }
+
+  /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
+   *          in a column-major matrix).
+   *
+   * \sa innerStride(), rowStride(), colStride()
+   */
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return derived().outerStride(); }
+
+  // FIXME shall we remove it ?
+  constexpr Index stride() const noexcept { return Derived::IsVectorAtCompileTime ? innerStride() : outerStride(); }
+
+  /** \returns the pointer increment between two consecutive rows.
+   *
+   * \sa innerStride(), outerStride(), colStride()
+   */
+  EIGEN_DEVICE_FUNC constexpr Index rowStride() const noexcept {
+    return Derived::IsRowMajor ? outerStride() : innerStride();
+  }
+
+  /** \returns the pointer increment between two consecutive columns.
+   *
+   * \sa innerStride(), outerStride(), rowStride()
+   */
+  EIGEN_DEVICE_FUNC constexpr Index colStride() const noexcept {
+    return Derived::IsRowMajor ? innerStride() : outerStride();
+  }
+};
+
+namespace internal {
+
+template <int Alignment, typename Derived, bool JustReturnZero>
+struct first_aligned_impl {
+  static constexpr Index run(const Derived&) noexcept { return 0; }
+};
+
+template <int Alignment, typename Derived>
+struct first_aligned_impl<Alignment, Derived, false> {
+  static inline Index run(const Derived& m) { return internal::first_aligned<Alignment>(m.data(), m.size()); }
+};
+
+/** \internal \returns the index of the first element of the array stored by \a m that is properly aligned with respect
+ * to \a Alignment for vectorization.
+ *
+ * \tparam Alignment requested alignment in Bytes.
+ *
+ * There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more
+ * documentation.
+ */
+template <int Alignment, typename Derived>
+static inline Index first_aligned(const DenseBase<Derived>& m) {
+  enum { ReturnZero = (int(evaluator<Derived>::Alignment) >= Alignment) || !(Derived::Flags & DirectAccessBit) };
+  return first_aligned_impl<Alignment, Derived, ReturnZero>::run(m.derived());
+}
+
+template <typename Derived>
+static inline Index first_default_aligned(const DenseBase<Derived>& m) {
+  typedef typename Derived::Scalar Scalar;
+  typedef typename packet_traits<Scalar>::type DefaultPacketType;
+  return internal::first_aligned<int(unpacket_traits<DefaultPacketType>::alignment), Derived>(m);
+}
+
+template <typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
+struct inner_stride_at_compile_time {
+  enum { ret = traits<Derived>::InnerStrideAtCompileTime };
+};
+
+template <typename Derived>
+struct inner_stride_at_compile_time<Derived, false> {
+  enum { ret = 0 };
+};
+
+template <typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
+struct outer_stride_at_compile_time {
+  enum { ret = traits<Derived>::OuterStrideAtCompileTime };
+};
+
+template <typename Derived>
+struct outer_stride_at_compile_time<Derived, false> {
+  enum { ret = 0 };
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_DENSECOEFFSBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DenseStorage.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DenseStorage.h
new file mode 100644
index 0000000..2892127
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DenseStorage.h
@@ -0,0 +1,546 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2010-2013 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIXSTORAGE_H
+#define EIGEN_MATRIXSTORAGE_H
+
+#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) \
+  X;                                                \
+  EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
+#else
+#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X)
+#endif
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
+#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(Alignment)
+#else
+#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(Alignment)                                        \
+  eigen_assert((is_constant_evaluated() || (std::uintptr_t(array) % Alignment == 0)) &&     \
+               "this assertion is explained here: "                                         \
+               "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
+               " **** READ THIS WEB PAGE !!! ****");
+#endif
+
+#if EIGEN_STACK_ALLOCATION_LIMIT
+#define EIGEN_MAKE_STACK_ALLOCATION_ASSERT(X) \
+  EIGEN_STATIC_ASSERT(X <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG)
+#else
+#define EIGEN_MAKE_STACK_ALLOCATION_ASSERT(X)
+#endif
+
+/** \internal
+ * Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
+ * to 16 bytes boundary if the total size is a multiple of 16 bytes.
+ */
+
+template <typename T, int Size, int MatrixOrArrayOptions,
+          int Alignment = (MatrixOrArrayOptions & DontAlign) ? 0 : compute_default_alignment<T, Size>::value>
+struct plain_array {
+  EIGEN_ALIGN_TO_BOUNDARY(Alignment) T array[Size];
+#if defined(EIGEN_NO_DEBUG) || defined(EIGEN_TESTING_PLAINOBJECT_CTOR)
+  EIGEN_DEVICE_FUNC constexpr plain_array() = default;
+#else
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr plain_array() {
+    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(Alignment)
+    EIGEN_MAKE_STACK_ALLOCATION_ASSERT(Size * sizeof(T))
+  }
+#endif
+};
+
+template <typename T, int Size, int MatrixOrArrayOptions>
+struct plain_array<T, Size, MatrixOrArrayOptions, 0> {
+  // on some 32-bit platforms, stack-allocated arrays are aligned to 4 bytes, not the preferred alignment of T
+  EIGEN_ALIGN_TO_BOUNDARY(alignof(T)) T array[Size];
+#if defined(EIGEN_NO_DEBUG) || defined(EIGEN_TESTING_PLAINOBJECT_CTOR)
+  EIGEN_DEVICE_FUNC constexpr plain_array() = default;
+#else
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr plain_array() { EIGEN_MAKE_STACK_ALLOCATION_ASSERT(Size * sizeof(T)) }
+#endif
+};
+
+template <typename T, int Size, int Options, int Alignment>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void swap_plain_array(plain_array<T, Size, Options, Alignment>& a,
+                                                                      plain_array<T, Size, Options, Alignment>& b,
+                                                                      Index a_size, Index b_size) {
+  Index common_size = numext::mini(a_size, b_size);
+  std::swap_ranges(a.array, a.array + common_size, b.array);
+  if (a_size > b_size)
+    smart_copy(a.array + common_size, a.array + a_size, b.array + common_size);
+  else if (b_size > a_size)
+    smart_copy(b.array + common_size, b.array + b_size, a.array + common_size);
+}
+
+template <typename T, int Size, int Rows, int Cols, int Options>
+class DenseStorage_impl {
+  plain_array<T, Size, Options> m_data;
+
+ public:
+#ifndef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(const DenseStorage_impl&) = default;
+#else
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl() {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
+    smart_copy(other.m_data.array, other.m_data.array + Size, m_data.array);
+  }
+#endif
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(const DenseStorage_impl&) = default;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void swap(DenseStorage_impl& other) {
+    numext::swap(m_data, other.m_data);
+  }
+  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
+  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return Rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return Cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return Rows * Cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
+};
+template <typename T, int Size, int Cols, int Options>
+class DenseStorage_impl<T, Size, Dynamic, Cols, Options> {
+  plain_array<T, Size, Options> m_data;
+  Index m_rows = 0;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
+      : m_rows(other.m_rows) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
+    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index rows, Index /*cols*/)
+      : m_rows(rows) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+    EIGEN_UNUSED_VARIABLE(size)
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
+    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
+    m_rows = other.m_rows;
+    return *this;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void swap(DenseStorage_impl& other) {
+    swap_plain_array(m_data, other.m_data, size(), other.size());
+    numext::swap(m_rows, other.m_rows);
+  }
+  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index rows, Index /*cols*/) { m_rows = rows; }
+  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index rows, Index /*cols*/) { m_rows = rows; }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return Cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * Cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
+};
+template <typename T, int Size, int Rows, int Options>
+class DenseStorage_impl<T, Size, Rows, Dynamic, Options> {
+  plain_array<T, Size, Options> m_data;
+  Index m_cols = 0;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
+      : m_cols(other.m_cols) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
+    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index /*rows*/, Index cols)
+      : m_cols(cols) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+    EIGEN_UNUSED_VARIABLE(size)
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
+    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
+    m_cols = other.m_cols;
+    return *this;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void swap(DenseStorage_impl& other) {
+    swap_plain_array(m_data, other.m_data, size(), other.size());
+    numext::swap(m_cols, other.m_cols);
+  }
+  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index /*rows*/, Index cols) { m_cols = cols; }
+  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index /*rows*/, Index cols) { m_cols = cols; }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return Rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return Rows * m_cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
+};
+template <typename T, int Size, int Options>
+class DenseStorage_impl<T, Size, Dynamic, Dynamic, Options> {
+  plain_array<T, Size, Options> m_data;
+  Index m_rows = 0;
+  Index m_cols = 0;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
+      : m_rows(other.m_rows), m_cols(other.m_cols) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
+    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index rows, Index cols)
+      : m_rows(rows), m_cols(cols) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+    EIGEN_UNUSED_VARIABLE(size)
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
+    smart_copy(other.m_data.array, other.m_data.array + other.size(), m_data.array);
+    m_rows = other.m_rows;
+    m_cols = other.m_cols;
+    return *this;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void swap(DenseStorage_impl& other) {
+    swap_plain_array(m_data, other.m_data, size(), other.size());
+    numext::swap(m_rows, other.m_rows);
+    numext::swap(m_cols, other.m_cols);
+  }
+  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index rows, Index cols) {
+    m_rows = rows;
+    m_cols = cols;
+  }
+  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index rows, Index cols) {
+    m_rows = rows;
+    m_cols = cols;
+  }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * m_cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
+};
+// null matrix variants
+template <typename T, int Rows, int Cols, int Options>
+class DenseStorage_impl<T, 0, Rows, Cols, Options> {
+ public:
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(const DenseStorage_impl&) = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(const DenseStorage_impl&) = default;
+  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl&) {}
+  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
+  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index /*rows*/, Index /*cols*/) {}
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return Rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return Cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return Rows * Cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
+};
+template <typename T, int Cols, int Options>
+class DenseStorage_impl<T, 0, Dynamic, Cols, Options> {
+  Index m_rows = 0;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(const DenseStorage_impl&) = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(Index /*size*/, Index rows, Index /*cols*/) : m_rows(rows) {}
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(const DenseStorage_impl&) = default;
+  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept { numext::swap(m_rows, other.m_rows); }
+  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index rows, Index /*cols*/) { m_rows = rows; }
+  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index rows, Index /*cols*/) { m_rows = rows; }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return Cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * Cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
+};
+template <typename T, int Rows, int Options>
+class DenseStorage_impl<T, 0, Rows, Dynamic, Options> {
+  Index m_cols = 0;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(const DenseStorage_impl&) = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(Index /*size*/, Index /*rows*/, Index cols) : m_cols(cols) {}
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(const DenseStorage_impl&) = default;
+  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept { numext::swap(m_cols, other.m_cols); }
+  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index /*rows*/, Index cols) { m_cols = cols; }
+  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index /*rows*/, Index cols) { m_cols = cols; }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return Rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return Rows * m_cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
+};
+template <typename T, int Options>
+class DenseStorage_impl<T, 0, Dynamic, Dynamic, Options> {
+  Index m_rows = 0;
+  Index m_cols = 0;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(const DenseStorage_impl&) = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(Index /*size*/, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(const DenseStorage_impl&) = default;
+  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept {
+    numext::swap(m_rows, other.m_rows);
+    numext::swap(m_cols, other.m_cols);
+  }
+  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index /*size*/, Index rows, Index cols) {
+    m_rows = rows;
+    m_cols = cols;
+  }
+  EIGEN_DEVICE_FUNC constexpr void resize(Index /*size*/, Index rows, Index cols) {
+    m_rows = rows;
+    m_cols = cols;
+  }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * m_cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
+};
+// fixed-size matrix with dynamic memory allocation not currently supported
+template <typename T, int Rows, int Cols, int Options>
+class DenseStorage_impl<T, Dynamic, Rows, Cols, Options> {};
+// dynamic-sized variants
+template <typename T, int Cols, int Options>
+class DenseStorage_impl<T, Dynamic, Dynamic, Cols, Options> {
+  static constexpr bool Align = (Options & DontAlign) == 0;
+  T* m_data = nullptr;
+  Index m_rows = 0;
+
+ public:
+  static constexpr int Size = Dynamic;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
+      : m_data(conditional_aligned_new_auto<T, Align>(other.size())), m_rows(other.m_rows) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
+    smart_copy(other.m_data, other.m_data + other.size(), m_data);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index rows, Index /*cols*/)
+      : m_data(conditional_aligned_new_auto<T, Align>(size)), m_rows(rows) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+  }
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(DenseStorage_impl&& other) noexcept
+      : m_data(other.m_data), m_rows(other.m_rows) {
+    other.m_data = nullptr;
+    other.m_rows = 0;
+  }
+  EIGEN_DEVICE_FUNC ~DenseStorage_impl() { conditional_aligned_delete_auto<T, Align>(m_data, size()); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
+    resize(other.size(), other.rows(), other.cols());
+    smart_copy(other.m_data, other.m_data + other.size(), m_data);
+    return *this;
+  }
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(DenseStorage_impl&& other) noexcept {
+    this->swap(other);
+    return *this;
+  }
+  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept {
+    numext::swap(m_data, other.m_data);
+    numext::swap(m_rows, other.m_rows);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void conservativeResize(Index size, Index rows, Index /*cols*/) {
+    m_data = conditional_aligned_realloc_new_auto<T, Align>(m_data, size, this->size());
+    m_rows = rows;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize(Index size, Index rows, Index /*cols*/) {
+    Index oldSize = this->size();
+    if (oldSize != size) {
+      conditional_aligned_delete_auto<T, Align>(m_data, oldSize);
+      m_data = conditional_aligned_new_auto<T, Align>(size);
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+    }
+    m_rows = rows;
+  }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return Cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * Cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data; }
+};
+template <typename T, int Rows, int Options>
+class DenseStorage_impl<T, Dynamic, Rows, Dynamic, Options> {
+  static constexpr bool Align = (Options & DontAlign) == 0;
+  T* m_data = nullptr;
+  Index m_cols = 0;
+
+ public:
+  static constexpr int Size = Dynamic;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
+      : m_data(conditional_aligned_new_auto<T, Align>(other.size())), m_cols(other.m_cols) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
+    smart_copy(other.m_data, other.m_data + other.size(), m_data);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index /*rows*/, Index cols)
+      : m_data(conditional_aligned_new_auto<T, Align>(size)), m_cols(cols) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+  }
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(DenseStorage_impl&& other) noexcept
+      : m_data(other.m_data), m_cols(other.m_cols) {
+    other.m_data = nullptr;
+    other.m_cols = 0;
+  }
+  EIGEN_DEVICE_FUNC ~DenseStorage_impl() { conditional_aligned_delete_auto<T, Align>(m_data, size()); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
+    resize(other.size(), other.rows(), other.cols());
+    smart_copy(other.m_data, other.m_data + other.size(), m_data);
+    return *this;
+  }
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(DenseStorage_impl&& other) noexcept {
+    this->swap(other);
+    return *this;
+  }
+  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept {
+    numext::swap(m_data, other.m_data);
+    numext::swap(m_cols, other.m_cols);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void conservativeResize(Index size, Index /*rows*/, Index cols) {
+    m_data = conditional_aligned_realloc_new_auto<T, Align>(m_data, size, this->size());
+    m_cols = cols;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize(Index size, Index /*rows*/, Index cols) {
+    Index oldSize = this->size();
+    if (oldSize != size) {
+      conditional_aligned_delete_auto<T, Align>(m_data, oldSize);
+      m_data = conditional_aligned_new_auto<T, Align>(size);
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+    }
+    m_cols = cols;
+  }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return Rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return Rows * m_cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data; }
+};
+template <typename T, int Options>
+class DenseStorage_impl<T, Dynamic, Dynamic, Dynamic, Options> {
+  static constexpr bool Align = (Options & DontAlign) == 0;
+  T* m_data = nullptr;
+  Index m_rows = 0;
+  Index m_cols = 0;
+
+ public:
+  static constexpr int Size = Dynamic;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl() = default;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(const DenseStorage_impl& other)
+      : m_data(conditional_aligned_new_auto<T, Align>(other.size())), m_rows(other.m_rows), m_cols(other.m_cols) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = other.size())
+    smart_copy(other.m_data, other.m_data + other.size(), m_data);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl(Index size, Index rows, Index cols)
+      : m_data(conditional_aligned_new_auto<T, Align>(size)), m_rows(rows), m_cols(cols) {
+    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+  }
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl(DenseStorage_impl&& other) noexcept
+      : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {
+    other.m_data = nullptr;
+    other.m_rows = 0;
+    other.m_cols = 0;
+  }
+  EIGEN_DEVICE_FUNC ~DenseStorage_impl() { conditional_aligned_delete_auto<T, Align>(m_data, size()); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr DenseStorage_impl& operator=(const DenseStorage_impl& other) {
+    resize(other.size(), other.rows(), other.cols());
+    smart_copy(other.m_data, other.m_data + other.size(), m_data);
+    return *this;
+  }
+  EIGEN_DEVICE_FUNC constexpr DenseStorage_impl& operator=(DenseStorage_impl&& other) noexcept {
+    this->swap(other);
+    return *this;
+  }
+  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage_impl& other) noexcept {
+    numext::swap(m_data, other.m_data);
+    numext::swap(m_rows, other.m_rows);
+    numext::swap(m_cols, other.m_cols);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void conservativeResize(Index size, Index rows, Index cols) {
+    m_data = conditional_aligned_realloc_new_auto<T, Align>(m_data, size, this->size());
+    m_rows = rows;
+    m_cols = cols;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize(Index size, Index rows, Index cols) {
+    Index oldSize = this->size();
+    if (oldSize != size) {
+      conditional_aligned_delete_auto<T, Align>(m_data, oldSize);
+      m_data = conditional_aligned_new_auto<T, Align>(size);
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+    }
+    m_rows = rows;
+    m_cols = cols;
+  }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
+  EIGEN_DEVICE_FUNC constexpr Index size() const { return m_rows * m_cols; }
+  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data; }
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data; }
+};
+template <typename T, int Size, int Rows, int Cols>
+struct use_default_move {
+  static constexpr bool DynamicObject = Size == Dynamic;
+  static constexpr bool TrivialObject =
+      (!NumTraits<T>::RequireInitialization) && (Rows >= 0) && (Cols >= 0) && (Size == Rows * Cols);
+  static constexpr bool value = DynamicObject || TrivialObject;
+};
+}  // end namespace internal
+
+/** \internal
+ *
+ * \class DenseStorage_impl
+ * \ingroup Core_Module
+ *
+ * \brief Stores the data of a matrix
+ *
+ * This class stores the data of fixed-size, dynamic-size or mixed matrices
+ * in a way as compact as possible.
+ *
+ * \sa Matrix
+ */
+template <typename T, int Size, int Rows, int Cols, int Options,
+          bool Trivial = internal::use_default_move<T, Size, Rows, Cols>::value>
+class DenseStorage : public internal::DenseStorage_impl<T, Size, Rows, Cols, Options> {
+  using Base = internal::DenseStorage_impl<T, Size, Rows, Cols, Options>;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr DenseStorage() = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage&) = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage(Index size, Index rows, Index cols) : Base(size, rows, cols) {}
+  EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(const DenseStorage&) = default;
+  // if DenseStorage meets the requirements of use_default_move, then use the move construction and move assignment
+  // operation defined in DenseStorage_impl, or the compiler-generated version if none is defined
+  EIGEN_DEVICE_FUNC constexpr DenseStorage(DenseStorage&&) = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(DenseStorage&&) = default;
+};
+template <typename T, int Size, int Rows, int Cols, int Options>
+class DenseStorage<T, Size, Rows, Cols, Options, false>
+    : public internal::DenseStorage_impl<T, Size, Rows, Cols, Options> {
+  using Base = internal::DenseStorage_impl<T, Size, Rows, Cols, Options>;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr DenseStorage() = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage&) = default;
+  EIGEN_DEVICE_FUNC constexpr DenseStorage(Index size, Index rows, Index cols) : Base(size, rows, cols) {}
+  EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(const DenseStorage&) = default;
+  // if DenseStorage does not meet the requirements of use_default_move, then defer to the copy construction and copy
+  // assignment behavior
+  EIGEN_DEVICE_FUNC constexpr DenseStorage(DenseStorage&& other)
+      : DenseStorage(static_cast<const DenseStorage&>(other)) {}
+  EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(DenseStorage&& other) {
+    *this = other;
+    return *this;
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DeviceWrapper.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DeviceWrapper.h
new file mode 100644
index 0000000..1a326ae
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DeviceWrapper.h
@@ -0,0 +1,153 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2023 Charlie Schlosser <cs.schlosser@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DEVICEWRAPPER_H
+#define EIGEN_DEVICEWRAPPER_H
+
+namespace Eigen {
+template <typename Derived, typename Device>
+struct DeviceWrapper {
+  using Base = EigenBase<internal::remove_all_t<Derived>>;
+  using Scalar = typename Derived::Scalar;
+
+  EIGEN_DEVICE_FUNC DeviceWrapper(Base& xpr, Device& device) : m_xpr(xpr.derived()), m_device(device) {}
+  EIGEN_DEVICE_FUNC DeviceWrapper(const Base& xpr, Device& device) : m_xpr(xpr.derived()), m_device(device) {}
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived>& other) {
+    using AssignOp = internal::assign_op<Scalar, typename OtherDerived::Scalar>;
+    internal::call_assignment(*this, other.derived(), AssignOp());
+    return m_xpr;
+  }
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const EigenBase<OtherDerived>& other) {
+    using AddAssignOp = internal::add_assign_op<Scalar, typename OtherDerived::Scalar>;
+    internal::call_assignment(*this, other.derived(), AddAssignOp());
+    return m_xpr;
+  }
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const EigenBase<OtherDerived>& other) {
+    using SubAssignOp = internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>;
+    internal::call_assignment(*this, other.derived(), SubAssignOp());
+    return m_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& derived() { return m_xpr; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Device& device() { return m_device; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NoAlias<DeviceWrapper, EigenBase> noalias() {
+    return NoAlias<DeviceWrapper, EigenBase>(*this);
+  }
+
+  Derived& m_xpr;
+  Device& m_device;
+};
+
+namespace internal {
+
+// this is where we differentiate between lazy assignment and specialized kernels (e.g. matrix products)
+template <typename DstXprType, typename SrcXprType, typename Functor, typename Device,
+          typename Kind = typename AssignmentKind<typename evaluator_traits<DstXprType>::Shape,
+                                                  typename evaluator_traits<SrcXprType>::Shape>::Kind,
+          typename EnableIf = void>
+struct AssignmentWithDevice;
+
+// unless otherwise specified, use the default product implementation
+template <typename DstXprType, typename Lhs, typename Rhs, int Options, typename Functor, typename Device,
+          typename Weak>
+struct AssignmentWithDevice<DstXprType, Product<Lhs, Rhs, Options>, Functor, Device, Dense2Dense, Weak> {
+  using SrcXprType = Product<Lhs, Rhs, Options>;
+  using Base = Assignment<DstXprType, SrcXprType, Functor>;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src, const Functor& func,
+                                                        Device&) {
+    Base::run(dst, src, func);
+  }
+};
+
+// specialization for coeffcient-wise assignment
+template <typename DstXprType, typename SrcXprType, typename Functor, typename Device, typename Weak>
+struct AssignmentWithDevice<DstXprType, SrcXprType, Functor, Device, Dense2Dense, Weak> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src, const Functor& func,
+                                                        Device& device) {
+#ifndef EIGEN_NO_DEBUG
+    internal::check_for_aliasing(dst, src);
+#endif
+
+    call_dense_assignment_loop(dst, src, func, device);
+  }
+};
+
+// this allows us to use the default evaluation scheme if it is not specialized for the device
+template <typename Kernel, typename Device, int Traversal = Kernel::AssignmentTraits::Traversal,
+          int Unrolling = Kernel::AssignmentTraits::Unrolling>
+struct dense_assignment_loop_with_device {
+  using Base = dense_assignment_loop<Kernel, Traversal, Unrolling>;
+  static EIGEN_DEVICE_FUNC constexpr void run(Kernel& kernel, Device&) { Base::run(kernel); }
+};
+
+// entry point for a generic expression with device
+template <typename Dst, typename Src, typename Func, typename Device>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_assignment_no_alias(DeviceWrapper<Dst, Device> dst,
+                                                                              const Src& src, const Func& func) {
+  enum {
+    NeedToTranspose = ((int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1) ||
+                       (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)) &&
+                      int(Dst::SizeAtCompileTime) != 1
+  };
+
+  using ActualDstTypeCleaned = std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst>;
+  using ActualDstType = std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst&>;
+  ActualDstType actualDst(dst.derived());
+
+  // TODO: check whether this is the right place to perform these checks:
+  EIGEN_STATIC_ASSERT_LVALUE(Dst)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned, Src)
+  EIGEN_CHECK_BINARY_COMPATIBILIY(Func, typename ActualDstTypeCleaned::Scalar, typename Src::Scalar);
+
+  // this provides a mechanism for specializing simple assignments, matrix products, etc
+  AssignmentWithDevice<ActualDstTypeCleaned, Src, Func, Device>::run(actualDst, src, func, dst.device());
+}
+
+// copy and pasted from AssignEvaluator except forward device to kernel
+template <typename DstXprType, typename SrcXprType, typename Functor, typename Device>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src,
+                                                                                const Functor& func, Device& device) {
+  using DstEvaluatorType = evaluator<DstXprType>;
+  using SrcEvaluatorType = evaluator<SrcXprType>;
+
+  SrcEvaluatorType srcEvaluator(src);
+
+  // NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
+  // we need to resize the destination after the source evaluator has been created.
+  resize_if_allowed(dst, src, func);
+
+  DstEvaluatorType dstEvaluator(dst);
+
+  using Kernel = generic_dense_assignment_kernel<DstEvaluatorType, SrcEvaluatorType, Functor>;
+
+  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
+
+  dense_assignment_loop_with_device<Kernel, Device>::run(kernel, device);
+}
+
+}  // namespace internal
+
+template <typename Derived>
+template <typename Device>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<Derived, Device> EigenBase<Derived>::device(Device& device) {
+  return DeviceWrapper<Derived, Device>(derived(), device);
+}
+
+template <typename Derived>
+template <typename Device>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<const Derived, Device> EigenBase<Derived>::device(
+    Device& device) const {
+  return DeviceWrapper<const Derived, Device>(derived(), device);
+}
+}  // namespace Eigen
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Diagonal.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Diagonal.h
new file mode 100644
index 0000000..61a4784
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Diagonal.h
@@ -0,0 +1,221 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DIAGONAL_H
+#define EIGEN_DIAGONAL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class Diagonal
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix
+ *
+ * \tparam MatrixType the type of the object in which we are taking a sub/main/super diagonal
+ * \tparam DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
+ *              A positive value means a superdiagonal, a negative value means a subdiagonal.
+ *              You can also use DynamicIndex so the index can be set at runtime.
+ *
+ * The matrix is not required to be square.
+ *
+ * This class represents an expression of the main diagonal, or any sub/super diagonal
+ * of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the
+ * time this is the only way it is used.
+ *
+ * \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index)
+ */
+
+namespace internal {
+template <typename MatrixType, int DiagIndex>
+struct traits<Diagonal<MatrixType, DiagIndex> > : traits<MatrixType> {
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+  typedef typename MatrixType::StorageKind StorageKind;
+  enum {
+    RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic)
+                            ? Dynamic
+                            : (plain_enum_min(MatrixType::RowsAtCompileTime - plain_enum_max(-DiagIndex, 0),
+                                              MatrixType::ColsAtCompileTime - plain_enum_max(DiagIndex, 0))),
+    ColsAtCompileTime = 1,
+    MaxRowsAtCompileTime =
+        int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
+        : DiagIndex == DynamicIndex
+            ? min_size_prefer_fixed(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime)
+            : (plain_enum_min(MatrixType::MaxRowsAtCompileTime - plain_enum_max(-DiagIndex, 0),
+                              MatrixType::MaxColsAtCompileTime - plain_enum_max(DiagIndex, 0))),
+    MaxColsAtCompileTime = 1,
+    MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
+    Flags = (unsigned int)MatrixTypeNested_::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) &
+            ~RowMajorBit,  // FIXME DirectAccessBit should not be handled by expressions
+    MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
+    InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride + 1,
+    OuterStrideAtCompileTime = 0
+  };
+};
+}  // namespace internal
+
+template <typename MatrixType, int DiagIndex_>
+class Diagonal : public internal::dense_xpr_base<Diagonal<MatrixType, DiagIndex_> >::type {
+ public:
+  enum { DiagIndex = DiagIndex_ };
+  typedef typename internal::dense_xpr_base<Diagonal>::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
+
+  EIGEN_DEVICE_FUNC constexpr explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex)
+      : m_matrix(matrix), m_index(a_index) {
+    eigen_assert(a_index <= m_matrix.cols() && -a_index <= m_matrix.rows());
+  }
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
+
+  EIGEN_DEVICE_FUNC constexpr inline Index rows() const {
+    return m_index.value() < 0 ? numext::mini<Index>(m_matrix.cols(), m_matrix.rows() + m_index.value())
+                               : numext::mini<Index>(m_matrix.rows(), m_matrix.cols() - m_index.value());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return 1; }
+
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_matrix.outerStride() + 1; }
+
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return 0; }
+
+  typedef std::conditional_t<internal::is_lvalue<MatrixType>::value, Scalar, const Scalar> ScalarWithConstIfNotLvalue;
+
+  EIGEN_DEVICE_FUNC inline ScalarWithConstIfNotLvalue* data() {
+    return rows() > 0 ? &(m_matrix.coeffRef(rowOffset(), colOffset())) : nullptr;
+  }
+  EIGEN_DEVICE_FUNC inline const Scalar* data() const {
+    return rows() > 0 ? &(m_matrix.coeffRef(rowOffset(), colOffset())) : nullptr;
+  }
+
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index) {
+    EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
+    return m_matrix.coeffRef(row + rowOffset(), row + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index row, Index) const {
+    return m_matrix.coeffRef(row + rowOffset(), row + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC inline CoeffReturnType coeff(Index row, Index) const {
+    return m_matrix.coeff(row + rowOffset(), row + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index idx) {
+    EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
+    return m_matrix.coeffRef(idx + rowOffset(), idx + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index idx) const {
+    return m_matrix.coeffRef(idx + rowOffset(), idx + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC inline CoeffReturnType coeff(Index idx) const {
+    return m_matrix.coeff(idx + rowOffset(), idx + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline const internal::remove_all_t<typename MatrixType::Nested>& nestedExpression()
+      const {
+    return m_matrix;
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline Index index() const { return m_index.value(); }
+
+ protected:
+  typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
+  const internal::variable_if_dynamicindex<Index, DiagIndex> m_index;
+
+ private:
+  // some compilers may fail to optimize std::max etc in case of compile-time constants...
+  EIGEN_DEVICE_FUNC constexpr Index absDiagIndex() const noexcept {
+    return m_index.value() > 0 ? m_index.value() : -m_index.value();
+  }
+  EIGEN_DEVICE_FUNC constexpr Index rowOffset() const noexcept { return m_index.value() > 0 ? 0 : -m_index.value(); }
+  EIGEN_DEVICE_FUNC constexpr Index colOffset() const noexcept { return m_index.value() > 0 ? m_index.value() : 0; }
+  // trigger a compile-time error if someone try to call packet
+  template <int LoadMode>
+  typename MatrixType::PacketReturnType packet(Index) const;
+  template <int LoadMode>
+  typename MatrixType::PacketReturnType packet(Index, Index) const;
+};
+
+/** \returns an expression of the main diagonal of the matrix \c *this
+ *
+ * \c *this is not required to be square.
+ *
+ * Example: \include MatrixBase_diagonal.cpp
+ * Output: \verbinclude MatrixBase_diagonal.out
+ *
+ * \sa class Diagonal */
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr typename MatrixBase<Derived>::DiagonalReturnType MatrixBase<Derived>::diagonal() {
+  return DiagonalReturnType(derived());
+}
+
+/** This is the const version of diagonal(). */
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr const typename MatrixBase<Derived>::ConstDiagonalReturnType MatrixBase<Derived>::diagonal()
+    const {
+  return ConstDiagonalReturnType(derived());
+}
+
+/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
+ *
+ * \c *this is not required to be square.
+ *
+ * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
+ * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
+ *
+ * Example: \include MatrixBase_diagonal_int.cpp
+ * Output: \verbinclude MatrixBase_diagonal_int.out
+ *
+ * \sa MatrixBase::diagonal(), class Diagonal */
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr Diagonal<Derived, DynamicIndex> MatrixBase<Derived>::diagonal(Index index) {
+  return Diagonal<Derived, DynamicIndex>(derived(), index);
+}
+
+/** This is the const version of diagonal(Index). */
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr const Diagonal<const Derived, DynamicIndex> MatrixBase<Derived>::diagonal(
+    Index index) const {
+  return Diagonal<const Derived, DynamicIndex>(derived(), index);
+}
+
+/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
+ *
+ * \c *this is not required to be square.
+ *
+ * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
+ * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
+ *
+ * Example: \include MatrixBase_diagonal_template_int.cpp
+ * Output: \verbinclude MatrixBase_diagonal_template_int.out
+ *
+ * \sa MatrixBase::diagonal(), class Diagonal */
+template <typename Derived>
+template <int Index_>
+EIGEN_DEVICE_FUNC constexpr Diagonal<Derived, Index_> MatrixBase<Derived>::diagonal() {
+  return Diagonal<Derived, Index_>(derived());
+}
+
+/** This is the const version of diagonal<int>(). */
+template <typename Derived>
+template <int Index_>
+EIGEN_DEVICE_FUNC constexpr const Diagonal<const Derived, Index_> MatrixBase<Derived>::diagonal() const {
+  return Diagonal<const Derived, Index_>(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_DIAGONAL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DiagonalMatrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DiagonalMatrix.h
new file mode 100644
index 0000000..c14d081
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DiagonalMatrix.h
@@ -0,0 +1,473 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DIAGONALMATRIX_H
+#define EIGEN_DIAGONALMATRIX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class DiagonalBase
+ * \ingroup Core_Module
+ *
+ * \brief Base class for diagonal matrices and expressions
+ *
+ * This is the base class that is inherited by diagonal matrix and related expression
+ * types, which internally use a vector for storing the diagonal entries. Diagonal
+ * types always represent square matrices.
+ *
+ * \tparam Derived is the derived type, a DiagonalMatrix or DiagonalWrapper.
+ *
+ * \sa class DiagonalMatrix, class DiagonalWrapper
+ */
+template <typename Derived>
+class DiagonalBase : public EigenBase<Derived> {
+ public:
+  typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
+  typedef typename DiagonalVectorType::Scalar Scalar;
+  typedef typename DiagonalVectorType::RealScalar RealScalar;
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+
+  enum {
+    RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+    ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+    MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+    MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+    IsVectorAtCompileTime = 0,
+    Flags = NoPreferredStorageOrderBit
+  };
+
+  typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime>
+      DenseMatrixType;
+  typedef DenseMatrixType DenseType;
+  typedef DiagonalMatrix<Scalar, DiagonalVectorType::SizeAtCompileTime, DiagonalVectorType::MaxSizeAtCompileTime>
+      PlainObject;
+
+  /** \returns a reference to the derived object. */
+  EIGEN_DEVICE_FUNC inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+  /** \returns a const reference to the derived object. */
+  EIGEN_DEVICE_FUNC inline Derived& derived() { return *static_cast<Derived*>(this); }
+
+  /**
+   * Constructs a dense matrix from \c *this. Note, this directly returns a dense matrix type,
+   * not an expression.
+   * \returns A dense matrix, with its diagonal entries set from the derived object. */
+  EIGEN_DEVICE_FUNC DenseMatrixType toDenseMatrix() const { return derived(); }
+
+  /** \returns a reference to the derived object's vector of diagonal coefficients. */
+  EIGEN_DEVICE_FUNC inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
+  /** \returns a const reference to the derived object's vector of diagonal coefficients. */
+  EIGEN_DEVICE_FUNC inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
+
+  /** \returns the value of the coefficient as if \c *this was a dense matrix. */
+  EIGEN_DEVICE_FUNC inline Scalar coeff(Index row, Index col) const {
+    eigen_assert(row >= 0 && col >= 0 && row < rows() && col <= cols());
+    return row == col ? diagonal().coeff(row) : Scalar(0);
+  }
+
+  /** \returns the number of rows. */
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return diagonal().size(); }
+  /** \returns the number of columns. */
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return diagonal().size(); }
+
+  /** \returns the diagonal matrix product of \c *this by the dense matrix, \a matrix */
+  template <typename MatrixDerived>
+  EIGEN_DEVICE_FUNC const Product<Derived, MatrixDerived, LazyProduct> operator*(
+      const MatrixBase<MatrixDerived>& matrix) const {
+    return Product<Derived, MatrixDerived, LazyProduct>(derived(), matrix.derived());
+  }
+
+  template <typename OtherDerived>
+  using DiagonalProductReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+      DiagonalVectorType, typename OtherDerived::DiagonalVectorType, product)>;
+
+  /** \returns the diagonal matrix product of \c *this by the diagonal matrix \a other */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC const DiagonalProductReturnType<OtherDerived> operator*(
+      const DiagonalBase<OtherDerived>& other) const {
+    return diagonal().cwiseProduct(other.diagonal()).asDiagonal();
+  }
+
+  using DiagonalInverseReturnType =
+      DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType>>;
+
+  /** \returns the inverse \c *this. Computed as the coefficient-wise inverse of the diagonal. */
+  EIGEN_DEVICE_FUNC inline const DiagonalInverseReturnType inverse() const {
+    return diagonal().cwiseInverse().asDiagonal();
+  }
+
+  using DiagonalScaleReturnType =
+      DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType, Scalar, product)>;
+
+  /** \returns the product of \c *this by the scalar \a scalar */
+  EIGEN_DEVICE_FUNC inline const DiagonalScaleReturnType operator*(const Scalar& scalar) const {
+    return (diagonal() * scalar).asDiagonal();
+  }
+
+  using ScaleDiagonalReturnType =
+      DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar, DiagonalVectorType, product)>;
+
+  /** \returns the product of a scalar and the diagonal matrix \a other */
+  EIGEN_DEVICE_FUNC friend inline const ScaleDiagonalReturnType operator*(const Scalar& scalar,
+                                                                          const DiagonalBase& other) {
+    return (scalar * other.diagonal()).asDiagonal();
+  }
+
+  template <typename OtherDerived>
+  using DiagonalSumReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+      DiagonalVectorType, typename OtherDerived::DiagonalVectorType, sum)>;
+
+  /** \returns the sum of \c *this and the diagonal matrix \a other */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline const DiagonalSumReturnType<OtherDerived> operator+(
+      const DiagonalBase<OtherDerived>& other) const {
+    return (diagonal() + other.diagonal()).asDiagonal();
+  }
+
+  template <typename OtherDerived>
+  using DiagonalDifferenceReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+      DiagonalVectorType, typename OtherDerived::DiagonalVectorType, difference)>;
+
+  /** \returns the difference of \c *this and the diagonal matrix \a other */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline const DiagonalDifferenceReturnType<OtherDerived> operator-(
+      const DiagonalBase<OtherDerived>& other) const {
+    return (diagonal() - other.diagonal()).asDiagonal();
+  }
+};
+
+/** \class DiagonalMatrix
+ * \ingroup Core_Module
+ *
+ * \brief Represents a diagonal matrix with its storage
+ *
+ * \tparam Scalar_ the type of coefficients
+ * \tparam SizeAtCompileTime the dimension of the matrix, or Dynamic
+ * \tparam MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
+ *        to SizeAtCompileTime. Most of the time, you do not need to specify it.
+ *
+ * \sa class DiagonalBase, class DiagonalWrapper
+ */
+
+namespace internal {
+template <typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime>
+struct traits<DiagonalMatrix<Scalar_, SizeAtCompileTime, MaxSizeAtCompileTime>>
+    : traits<Matrix<Scalar_, SizeAtCompileTime, SizeAtCompileTime, 0, MaxSizeAtCompileTime, MaxSizeAtCompileTime>> {
+  typedef Matrix<Scalar_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> DiagonalVectorType;
+  typedef DiagonalShape StorageKind;
+  enum { Flags = LvalueBit | NoPreferredStorageOrderBit | NestByRefBit };
+};
+}  // namespace internal
+template <typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime>
+class DiagonalMatrix : public DiagonalBase<DiagonalMatrix<Scalar_, SizeAtCompileTime, MaxSizeAtCompileTime>> {
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType;
+  typedef const DiagonalMatrix& Nested;
+  typedef Scalar_ Scalar;
+  typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
+  typedef typename internal::traits<DiagonalMatrix>::StorageIndex StorageIndex;
+#endif
+
+ protected:
+  DiagonalVectorType m_diagonal;
+
+ public:
+  /** const version of diagonal(). */
+  EIGEN_DEVICE_FUNC constexpr inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
+  /** \returns a reference to the stored vector of diagonal coefficients. */
+  EIGEN_DEVICE_FUNC constexpr inline DiagonalVectorType& diagonal() { return m_diagonal; }
+
+  /** Default constructor without initialization */
+  EIGEN_DEVICE_FUNC constexpr inline DiagonalMatrix() {}
+
+  /** Constructs a diagonal matrix with given dimension  */
+  EIGEN_DEVICE_FUNC constexpr explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
+
+  /** 2D constructor. */
+  EIGEN_DEVICE_FUNC constexpr inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x, y) {}
+
+  /** 3D constructor. */
+  EIGEN_DEVICE_FUNC constexpr inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z)
+      : m_diagonal(x, y, z) {}
+
+  /** \brief Construct a diagonal matrix with fixed size from an arbitrary number of coefficients.
+   *
+   * \warning To construct a diagonal matrix of fixed size, the number of values passed to this
+   * constructor must match the fixed dimension of \c *this.
+   *
+   * \sa DiagonalMatrix(const Scalar&, const Scalar&)
+   * \sa DiagonalMatrix(const Scalar&, const Scalar&, const Scalar&)
+   */
+  template <typename... ArgTypes>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE DiagonalMatrix(const Scalar& a0, const Scalar& a1, const Scalar& a2,
+                                                                 const ArgTypes&... args)
+      : m_diagonal(a0, a1, a2, args...) {}
+
+  /** \brief Constructs a DiagonalMatrix and initializes it by elements given by an initializer list of initializer
+   * lists \cpp11
+   */
+  EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE DiagonalMatrix(
+      const std::initializer_list<std::initializer_list<Scalar>>& list)
+      : m_diagonal(list) {}
+
+  /** \brief Constructs a DiagonalMatrix from an r-value diagonal vector type */
+  EIGEN_DEVICE_FUNC constexpr explicit inline DiagonalMatrix(DiagonalVectorType&& diag) : m_diagonal(std::move(diag)) {}
+
+  /** Copy constructor. */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other)
+      : m_diagonal(other.diagonal()) {}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
+  inline DiagonalMatrix(const DiagonalMatrix& other) : m_diagonal(other.diagonal()) {}
+#endif
+
+  /** generic constructor from expression of the diagonal coefficients */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other)
+      : m_diagonal(other) {}
+
+  /** Copy operator. */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other) {
+    m_diagonal = other.diagonal();
+    return *this;
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** This is a special case of the templated operator=. Its purpose is to
+   * prevent a default operator= from hiding the templated operator=.
+   */
+  EIGEN_DEVICE_FUNC DiagonalMatrix& operator=(const DiagonalMatrix& other) {
+    m_diagonal = other.diagonal();
+    return *this;
+  }
+#endif
+
+  typedef DiagonalWrapper<const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, DiagonalVectorType>>
+      InitializeReturnType;
+
+  typedef DiagonalWrapper<const CwiseNullaryOp<internal::scalar_zero_op<Scalar>, DiagonalVectorType>>
+      ZeroInitializeReturnType;
+
+  /** Initializes a diagonal matrix of size SizeAtCompileTime with coefficients set to zero */
+  EIGEN_DEVICE_FUNC static const ZeroInitializeReturnType Zero() { return DiagonalVectorType::Zero().asDiagonal(); }
+  /** Initializes a diagonal matrix of size dim with coefficients set to zero */
+  EIGEN_DEVICE_FUNC static const ZeroInitializeReturnType Zero(Index size) {
+    return DiagonalVectorType::Zero(size).asDiagonal();
+  }
+  /** Initializes a identity matrix of size SizeAtCompileTime */
+  EIGEN_DEVICE_FUNC static const InitializeReturnType Identity() { return DiagonalVectorType::Ones().asDiagonal(); }
+  /** Initializes a identity matrix of size dim */
+  EIGEN_DEVICE_FUNC static const InitializeReturnType Identity(Index size) {
+    return DiagonalVectorType::Ones(size).asDiagonal();
+  }
+
+  /** Resizes to given size. */
+  EIGEN_DEVICE_FUNC inline void resize(Index size) { m_diagonal.resize(size); }
+  /** Sets all coefficients to zero. */
+  EIGEN_DEVICE_FUNC inline void setZero() { m_diagonal.setZero(); }
+  /** Resizes and sets all coefficients to zero. */
+  EIGEN_DEVICE_FUNC inline void setZero(Index size) { m_diagonal.setZero(size); }
+  /** Sets this matrix to be the identity matrix of the current size. */
+  EIGEN_DEVICE_FUNC inline void setIdentity() { m_diagonal.setOnes(); }
+  /** Sets this matrix to be the identity matrix of the given size. */
+  EIGEN_DEVICE_FUNC inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
+};
+
+/** \class DiagonalWrapper
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a diagonal matrix
+ *
+ * \tparam DiagonalVectorType_ the type of the vector of diagonal coefficients
+ *
+ * This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients,
+ * instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal()
+ * and most of the time this is the only way that it is used.
+ *
+ * \sa class DiagonalMatrix, class DiagonalBase, MatrixBase::asDiagonal()
+ */
+
+namespace internal {
+template <typename DiagonalVectorType_>
+struct traits<DiagonalWrapper<DiagonalVectorType_>> {
+  typedef DiagonalVectorType_ DiagonalVectorType;
+  typedef typename DiagonalVectorType::Scalar Scalar;
+  typedef typename DiagonalVectorType::StorageIndex StorageIndex;
+  typedef DiagonalShape StorageKind;
+  typedef typename traits<DiagonalVectorType>::XprKind XprKind;
+  enum {
+    RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+    ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+    MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+    MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+    Flags = (traits<DiagonalVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
+  };
+};
+}  // namespace internal
+
+template <typename DiagonalVectorType_>
+class DiagonalWrapper : public DiagonalBase<DiagonalWrapper<DiagonalVectorType_>>, internal::no_assignment_operator {
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  typedef DiagonalVectorType_ DiagonalVectorType;
+  typedef DiagonalWrapper Nested;
+#endif
+
+  /** Constructor from expression of diagonal coefficients to wrap. */
+  EIGEN_DEVICE_FUNC constexpr explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal)
+      : m_diagonal(a_diagonal) {}
+
+  /** \returns a const reference to the wrapped expression of diagonal coefficients. */
+  EIGEN_DEVICE_FUNC constexpr const DiagonalVectorType& diagonal() const { return m_diagonal; }
+
+ protected:
+  typename DiagonalVectorType::Nested m_diagonal;
+};
+
+/** \returns a pseudo-expression of a diagonal matrix with *this as vector of diagonal coefficients
+ *
+ * \only_for_vectors
+ *
+ * Example: \include MatrixBase_asDiagonal.cpp
+ * Output: \verbinclude MatrixBase_asDiagonal.out
+ *
+ * \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal()
+ **/
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr const DiagonalWrapper<const Derived> MatrixBase<Derived>::asDiagonal() const {
+  return DiagonalWrapper<const Derived>(derived());
+}
+
+/** \returns true if *this is approximately equal to a diagonal matrix,
+ *          within the precision given by \a prec.
+ *
+ * Example: \include MatrixBase_isDiagonal.cpp
+ * Output: \verbinclude MatrixBase_isDiagonal.out
+ *
+ * \sa asDiagonal()
+ */
+template <typename Derived>
+bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const {
+  if (cols() != rows()) return false;
+  RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
+  for (Index j = 0; j < cols(); ++j) {
+    RealScalar absOnDiagonal = numext::abs(coeff(j, j));
+    if (absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
+  }
+  for (Index j = 0; j < cols(); ++j)
+    for (Index i = 0; i < j; ++i) {
+      if (!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
+      if (!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;
+    }
+  return true;
+}
+
+/** \returns DiagonalWrapper.
+ *
+ * Example: \include MatrixBase_diagonalView.cpp
+ * Output: \verbinclude MatrixBase_diagonalView.out
+ *
+ * \sa diagonalView()
+ */
+
+/** This is the non-const version of diagonalView() with DiagIndex_ . */
+template <typename Derived>
+template <int DiagIndex_>
+EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<Derived, DiagIndex_>> MatrixBase<Derived>::diagonalView() {
+  typedef Diagonal<Derived, DiagIndex_> DiagType;
+  typedef DiagonalWrapper<DiagType> ReturnType;
+  DiagType diag(this->derived());
+  return ReturnType(diag);
+}
+
+/** This is the const version of diagonalView() with DiagIndex_ . */
+template <typename Derived>
+template <int DiagIndex_>
+EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<const Derived, DiagIndex_>> MatrixBase<Derived>::diagonalView()
+    const {
+  typedef Diagonal<const Derived, DiagIndex_> DiagType;
+  typedef DiagonalWrapper<DiagType> ReturnType;
+  DiagType diag(this->derived());
+  return ReturnType(diag);
+}
+
+/** This is the non-const version of diagonalView() with dynamic index. */
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<Derived, DynamicIndex>> MatrixBase<Derived>::diagonalView(
+    Index index) {
+  typedef Diagonal<Derived, DynamicIndex> DiagType;
+  typedef DiagonalWrapper<DiagType> ReturnType;
+  DiagType diag(this->derived(), index);
+  return ReturnType(diag);
+}
+
+/** This is the const version of diagonalView() with dynamic index. */
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<const Derived, DynamicIndex>> MatrixBase<Derived>::diagonalView(
+    Index index) const {
+  typedef Diagonal<const Derived, DynamicIndex> DiagType;
+  typedef DiagonalWrapper<DiagType> ReturnType;
+  DiagType diag(this->derived(), index);
+  return ReturnType(diag);
+}
+
+namespace internal {
+
+template <>
+struct storage_kind_to_shape<DiagonalShape> {
+  typedef DiagonalShape Shape;
+};
+
+struct Diagonal2Dense {};
+
+template <>
+struct AssignmentKind<DenseShape, DiagonalShape> {
+  typedef Diagonal2Dense Kind;
+};
+
+// Diagonal matrix to Dense assignment
+template <typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense> {
+  static EIGEN_DEVICE_FUNC void run(
+      DstXprType& dst, const SrcXprType& src,
+      const internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    dst.setZero();
+    dst.diagonal() = src.diagonal();
+  }
+
+  static EIGEN_DEVICE_FUNC void run(
+      DstXprType& dst, const SrcXprType& src,
+      const internal::add_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
+    dst.diagonal() += src.diagonal();
+  }
+
+  static EIGEN_DEVICE_FUNC void run(
+      DstXprType& dst, const SrcXprType& src,
+      const internal::sub_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
+    dst.diagonal() -= src.diagonal();
+  }
+};
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_DIAGONALMATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DiagonalProduct.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DiagonalProduct.h
new file mode 100644
index 0000000..bd0feea
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/DiagonalProduct.h
@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DIAGONALPRODUCT_H
+#define EIGEN_DIAGONALPRODUCT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
+ */
+template <typename Derived>
+template <typename DiagonalDerived>
+EIGEN_DEVICE_FUNC inline const Product<Derived, DiagonalDerived, LazyProduct> MatrixBase<Derived>::operator*(
+    const DiagonalBase<DiagonalDerived> &a_diagonal) const {
+  return Product<Derived, DiagonalDerived, LazyProduct>(derived(), a_diagonal.derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_DIAGONALPRODUCT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Dot.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Dot.h
new file mode 100644
index 0000000..d0a30dd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Dot.h
@@ -0,0 +1,267 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008, 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DOT_H
+#define EIGEN_DOT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Derived, typename Scalar = typename traits<Derived>::Scalar>
+struct squared_norm_impl {
+  using Real = typename NumTraits<Scalar>::Real;
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Real run(const Derived& a) {
+    return a.realView().cwiseAbs2().sum();
+  }
+};
+
+template <typename Derived>
+struct squared_norm_impl<Derived, bool> {
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE bool run(const Derived& a) { return a.any(); }
+};
+
+}  // end namespace internal
+
+/** \fn MatrixBase::dot
+ * \returns the dot product of *this with other.
+ *
+ * \only_for_vectors
+ *
+ * \note If the scalar type is complex numbers, then this function returns the hermitian
+ * (sesquilinear) dot product, conjugate-linear in the first variable and linear in the
+ * second variable.
+ *
+ * \sa squaredNorm(), norm()
+ */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+    typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,
+                                  typename internal::traits<OtherDerived>::Scalar>::ReturnType
+    MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const {
+  return internal::dot_impl<Derived, OtherDerived>::run(derived(), other.derived());
+}
+
+//---------- implementation of L2 norm and related functions ----------
+
+/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the squared Frobenius norm.
+ * In both cases, it consists in the sum of the square of all the matrix entries.
+ * For vectors, this is also equal to the dot product of \c *this with itself.
+ *
+ * \sa dot(), norm(), lpNorm()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+MatrixBase<Derived>::squaredNorm() const {
+  return internal::squared_norm_impl<Derived>::run(derived());
+}
+
+/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
+ * In both cases, it consists in the square root of the sum of the square of all the matrix entries.
+ * For vectors, this is also equal to the square root of the dot product of \c *this with itself.
+ *
+ * \sa lpNorm(), dot(), squaredNorm()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+MatrixBase<Derived>::norm() const {
+  return numext::sqrt(squaredNorm());
+}
+
+/** \returns an expression of the quotient of \c *this by its own norm.
+ *
+ * \warning If the input vector is too small (i.e., this->norm()==0),
+ *          then this function returns a copy of the input.
+ *
+ * \only_for_vectors
+ *
+ * \sa norm(), normalize()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject MatrixBase<Derived>::normalized()
+    const {
+  typedef typename internal::nested_eval<Derived, 2>::type Nested_;
+  Nested_ n(derived());
+  RealScalar z = n.squaredNorm();
+  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
+  if (z > RealScalar(0))
+    return n / numext::sqrt(z);
+  else
+    return n;
+}
+
+/** Normalizes the vector, i.e. divides it by its own norm.
+ *
+ * \only_for_vectors
+ *
+ * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
+ *
+ * \sa norm(), normalized()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::normalize() {
+  RealScalar z = squaredNorm();
+  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
+  if (z > RealScalar(0)) derived() /= numext::sqrt(z);
+}
+
+/** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow.
+ *
+ * \only_for_vectors
+ *
+ * This method is analogue to the normalized() method, but it reduces the risk of
+ * underflow and overflow when computing the norm.
+ *
+ * \warning If the input vector is too small (i.e., this->norm()==0),
+ *          then this function returns a copy of the input.
+ *
+ * \sa stableNorm(), stableNormalize(), normalized()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
+MatrixBase<Derived>::stableNormalized() const {
+  typedef typename internal::nested_eval<Derived, 3>::type Nested_;
+  Nested_ n(derived());
+  RealScalar w = n.cwiseAbs().maxCoeff();
+  RealScalar z = (n / w).squaredNorm();
+  if (z > RealScalar(0))
+    return n / (numext::sqrt(z) * w);
+  else
+    return n;
+}
+
+/** Normalizes the vector while avoid underflow and overflow
+ *
+ * \only_for_vectors
+ *
+ * This method is analogue to the normalize() method, but it reduces the risk of
+ * underflow and overflow when computing the norm.
+ *
+ * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
+ *
+ * \sa stableNorm(), stableNormalized(), normalize()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize() {
+  RealScalar w = cwiseAbs().maxCoeff();
+  RealScalar z = (derived() / w).squaredNorm();
+  if (z > RealScalar(0)) derived() /= numext::sqrt(z) * w;
+}
+
+//---------- implementation of other norms ----------
+
+namespace internal {
+
+template <typename Derived, int p>
+struct lpNorm_selector {
+  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const MatrixBase<Derived>& m) {
+    EIGEN_USING_STD(pow)
+    return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1) / p);
+  }
+};
+
+template <typename Derived>
+struct lpNorm_selector<Derived, 1> {
+  EIGEN_DEVICE_FUNC static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(
+      const MatrixBase<Derived>& m) {
+    return m.cwiseAbs().sum();
+  }
+};
+
+template <typename Derived>
+struct lpNorm_selector<Derived, 2> {
+  EIGEN_DEVICE_FUNC static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(
+      const MatrixBase<Derived>& m) {
+    return m.norm();
+  }
+};
+
+template <typename Derived>
+struct lpNorm_selector<Derived, Infinity> {
+  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const MatrixBase<Derived>& m) {
+    if (Derived::SizeAtCompileTime == 0 || (Derived::SizeAtCompileTime == Dynamic && m.size() == 0))
+      return RealScalar(0);
+    return m.cwiseAbs().maxCoeff();
+  }
+};
+
+}  // end namespace internal
+
+/** \returns the \b coefficient-wise \f$ \ell^p \f$ norm of \c *this, that is, returns the p-th root of the sum of the
+ * p-th powers of the absolute values of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity,
+ * this function returns the \f$ \ell^\infty \f$ norm, that is the maximum of the absolute values of the coefficients of
+ * \c *this.
+ *
+ * In all cases, if \c *this is empty, then the value 0 is returned.
+ *
+ * \note For matrices, this function does not compute the <a
+ * href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its
+ * coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm
+ * matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
+ *
+ * \sa norm()
+ */
+template <typename Derived>
+template <int p>
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+EIGEN_DEVICE_FUNC inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+#else
+EIGEN_DEVICE_FUNC MatrixBase<Derived>::RealScalar
+#endif
+MatrixBase<Derived>::lpNorm() const {
+  return internal::lpNorm_selector<Derived, p>::run(*this);
+}
+
+//---------- implementation of isOrthogonal / isUnitary ----------
+
+/** \returns true if *this is approximately orthogonal to \a other,
+ *          within the precision given by \a prec.
+ *
+ * Example: \include MatrixBase_isOrthogonal.cpp
+ * Output: \verbinclude MatrixBase_isOrthogonal.out
+ */
+template <typename Derived>
+template <typename OtherDerived>
+bool MatrixBase<Derived>::isOrthogonal(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const {
+  typename internal::nested_eval<Derived, 2>::type nested(derived());
+  typename internal::nested_eval<OtherDerived, 2>::type otherNested(other.derived());
+  return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
+}
+
+/** \returns true if *this is approximately an unitary matrix,
+ *          within the precision given by \a prec. In the case where the \a Scalar
+ *          type is real numbers, a unitary matrix is an orthogonal matrix, whence the name.
+ *
+ * \note This can be used to check whether a family of vectors forms an orthonormal basis.
+ *       Indeed, \c m.isUnitary() returns true if and only if the columns (equivalently, the rows) of m form an
+ *       orthonormal basis.
+ *
+ * Example: \include MatrixBase_isUnitary.cpp
+ * Output: \verbinclude MatrixBase_isUnitary.out
+ */
+template <typename Derived>
+bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const {
+  typename internal::nested_eval<Derived, 1>::type self(derived());
+  for (Index i = 0; i < cols(); ++i) {
+    if (!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec)) return false;
+    for (Index j = 0; j < i; ++j)
+      if (!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec)) return false;
+  }
+  return true;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_DOT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/EigenBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/EigenBase.h
new file mode 100644
index 0000000..dfe4a64
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/EigenBase.h
@@ -0,0 +1,149 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EIGENBASE_H
+#define EIGEN_EIGENBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class EigenBase
+ * \ingroup Core_Module
+ *
+ * Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
+ *
+ * In other words, an EigenBase object is an object that can be copied into a MatrixBase.
+ *
+ * Besides MatrixBase-derived classes, this also includes special matrix classes such as diagonal matrices, etc.
+ *
+ * Notice that this class is trivial, it is only used to disambiguate overloaded functions.
+ *
+ * \sa \blank \ref TopicClassHierarchy
+ */
+template <typename Derived>
+struct EigenBase {
+  //   typedef typename internal::plain_matrix_type<Derived>::type PlainObject;
+
+  /** \brief The interface type of indices
+   * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
+   * \sa StorageIndex, \ref TopicPreprocessorDirectives.
+   * DEPRECATED: Since Eigen 3.3, its usage is deprecated. Use Eigen::Index instead.
+   * Deprecation is not marked with a doxygen comment because there are too many existing usages to add the deprecation
+   * attribute.
+   */
+  typedef Eigen::Index Index;
+
+  // FIXME is it needed?
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+
+  /** \returns a reference to the derived object */
+  EIGEN_DEVICE_FUNC constexpr Derived& derived() { return *static_cast<Derived*>(this); }
+  /** \returns a const reference to the derived object */
+  EIGEN_DEVICE_FUNC constexpr const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  EIGEN_DEVICE_FUNC inline constexpr Derived& const_cast_derived() const {
+    return *static_cast<Derived*>(const_cast<EigenBase*>(this));
+  }
+  EIGEN_DEVICE_FUNC constexpr inline const Derived& const_derived() const { return *static_cast<const Derived*>(this); }
+
+  /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return derived().rows(); }
+  /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return derived().cols(); }
+  /** \returns the number of coefficients, which is rows()*cols().
+   * \sa rows(), cols(), SizeAtCompileTime. */
+  EIGEN_DEVICE_FUNC constexpr Index size() const noexcept { return rows() * cols(); }
+
+  /** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC constexpr inline void evalTo(Dest& dst) const {
+    derived().evalTo(dst);
+  }
+
+  /** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC constexpr inline void addTo(Dest& dst) const {
+    // This is the default implementation,
+    // derived class can reimplement it in a more optimized way.
+    typename Dest::PlainObject res(rows(), cols());
+    evalTo(res);
+    dst += res;
+  }
+
+  /** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC constexpr inline void subTo(Dest& dst) const {
+    // This is the default implementation,
+    // derived class can reimplement it in a more optimized way.
+    typename Dest::PlainObject res(rows(), cols());
+    evalTo(res);
+    dst -= res;
+  }
+
+  /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC constexpr inline void applyThisOnTheRight(Dest& dst) const {
+    // This is the default implementation,
+    // derived class can reimplement it in a more optimized way.
+    dst = dst * this->derived();
+  }
+
+  /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC constexpr inline void applyThisOnTheLeft(Dest& dst) const {
+    // This is the default implementation,
+    // derived class can reimplement it in a more optimized way.
+    dst = this->derived() * dst;
+  }
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<Derived, Device> device(Device& device);
+  template <typename Device>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<const Derived, Device> device(Device& device) const;
+};
+
+/***************************************************************************
+ * Implementation of matrix base methods
+ ***************************************************************************/
+
+/** \brief Copies the generic expression \a other into *this.
+ *
+ * \details The expression must provide a (templated) evalTo(Derived& dst) const
+ * function which does the actual job. In practice, this allows any user to write
+ * its own special matrix without having to modify MatrixBase
+ *
+ * \returns a reference to *this.
+ */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived>& other) {
+  call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived>& other) {
+  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived>& other) {
+  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+  return derived();
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_EIGENBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Fill.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Fill.h
new file mode 100644
index 0000000..cacf921
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Fill.h
@@ -0,0 +1,140 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Charles Schlosser <cs.schlosser@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FILL_H
+#define EIGEN_FILL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Xpr>
+struct eigen_fill_helper : std::false_type {};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct eigen_fill_helper<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols>> : std::true_type {};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct eigen_fill_helper<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols>> : std::true_type {};
+
+template <typename Xpr, int BlockRows, int BlockCols>
+struct eigen_fill_helper<Block<Xpr, BlockRows, BlockCols, /*InnerPanel*/ true>> : eigen_fill_helper<Xpr> {};
+
+template <typename Xpr, int BlockRows, int BlockCols>
+struct eigen_fill_helper<Block<Xpr, BlockRows, BlockCols, /*InnerPanel*/ false>>
+    : std::integral_constant<bool, eigen_fill_helper<Xpr>::value &&
+                                       (Xpr::IsRowMajor ? (BlockRows == 1) : (BlockCols == 1))> {};
+
+template <typename Xpr, int Options>
+struct eigen_fill_helper<Map<Xpr, Options, Stride<0, 0>>> : eigen_fill_helper<Xpr> {};
+
+template <typename Xpr, int Options, int OuterStride_>
+struct eigen_fill_helper<Map<Xpr, Options, Stride<OuterStride_, 0>>>
+    : std::integral_constant<bool, eigen_fill_helper<Xpr>::value &&
+                                       enum_eq_not_dynamic(OuterStride_, Xpr::InnerSizeAtCompileTime)> {};
+
+template <typename Xpr, int Options, int OuterStride_>
+struct eigen_fill_helper<Map<Xpr, Options, Stride<OuterStride_, 1>>>
+    : eigen_fill_helper<Map<Xpr, Options, Stride<OuterStride_, 0>>> {};
+
+template <typename Xpr, int Options, int InnerStride_>
+struct eigen_fill_helper<Map<Xpr, Options, InnerStride<InnerStride_>>>
+    : eigen_fill_helper<Map<Xpr, Options, Stride<0, InnerStride_>>> {};
+
+template <typename Xpr, int Options, int OuterStride_>
+struct eigen_fill_helper<Map<Xpr, Options, OuterStride<OuterStride_>>>
+    : eigen_fill_helper<Map<Xpr, Options, Stride<OuterStride_, 0>>> {};
+
+template <typename Xpr>
+struct eigen_fill_impl<Xpr, /*use_fill*/ false> {
+  using Scalar = typename Xpr::Scalar;
+  using Func = scalar_constant_op<Scalar>;
+  using PlainObject = typename Xpr::PlainObject;
+  using Constant = typename PlainObject::ConstantReturnType;
+  static EIGEN_DEVICE_FUNC constexpr void run(Xpr& dst, const Scalar& val) {
+    const Constant src(dst.rows(), dst.cols(), val);
+    run(dst, src);
+  }
+  template <typename SrcXpr>
+  static EIGEN_DEVICE_FUNC constexpr void run(Xpr& dst, const SrcXpr& src) {
+    call_dense_assignment_loop(dst, src, assign_op<Scalar, Scalar>());
+  }
+};
+
+#if EIGEN_COMP_MSVC || defined(EIGEN_GPU_COMPILE_PHASE)
+template <typename Xpr>
+struct eigen_fill_impl<Xpr, /*use_fill*/ true> : eigen_fill_impl<Xpr, /*use_fill*/ false> {};
+#else
+template <typename Xpr>
+struct eigen_fill_impl<Xpr, /*use_fill*/ true> {
+  using Scalar = typename Xpr::Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Xpr& dst, const Scalar& val) {
+    const Scalar val_copy = val;
+    using std::fill_n;
+    fill_n(dst.data(), dst.size(), val_copy);
+  }
+  template <typename SrcXpr>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Xpr& dst, const SrcXpr& src) {
+    resize_if_allowed(dst, src, assign_op<Scalar, Scalar>());
+    const Scalar& val = src.functor()();
+    run(dst, val);
+  }
+};
+#endif
+
+template <typename Xpr>
+struct eigen_memset_helper {
+  using Scalar = typename Xpr::Scalar;
+  static constexpr bool value = std::is_trivially_copyable<Scalar>::value &&
+                                !static_cast<bool>(NumTraits<Scalar>::RequireInitialization) &&
+                                eigen_fill_helper<Xpr>::value;
+};
+
+template <typename Xpr>
+struct eigen_zero_impl<Xpr, /*use_memset*/ false> {
+  using Scalar = typename Xpr::Scalar;
+  using PlainObject = typename Xpr::PlainObject;
+  using Zero = typename PlainObject::ZeroReturnType;
+  static EIGEN_DEVICE_FUNC constexpr void run(Xpr& dst) {
+    const Zero src(dst.rows(), dst.cols());
+    run(dst, src);
+  }
+  template <typename SrcXpr>
+  static EIGEN_DEVICE_FUNC constexpr void run(Xpr& dst, const SrcXpr& src) {
+    call_dense_assignment_loop(dst, src, assign_op<Scalar, Scalar>());
+  }
+};
+
+template <typename Xpr>
+struct eigen_zero_impl<Xpr, /*use_memset*/ true> {
+  using Scalar = typename Xpr::Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Xpr& dst) {
+    const std::ptrdiff_t num_bytes = dst.size() * static_cast<std::ptrdiff_t>(sizeof(Scalar));
+    if (num_bytes <= 0) return;
+    void* dst_ptr = static_cast<void*>(dst.data());
+#ifndef EIGEN_NO_DEBUG
+    eigen_assert((dst_ptr != nullptr) && "null pointer dereference error!");
+#endif
+    EIGEN_USING_STD(memset);
+    memset(dst_ptr, 0, static_cast<std::size_t>(num_bytes));
+  }
+  template <typename SrcXpr>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Xpr& dst, const SrcXpr& src) {
+    resize_if_allowed(dst, src, assign_op<Scalar, Scalar>());
+    run(dst);
+  }
+};
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_FILL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/FindCoeff.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/FindCoeff.h
new file mode 100644
index 0000000..0102e8a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/FindCoeff.h
@@ -0,0 +1,464 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Charlie Schlosser <cs.schlosser@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FIND_COEFF_H
+#define EIGEN_FIND_COEFF_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar, int NaNPropagation, bool IsInteger = NumTraits<Scalar>::IsInteger>
+struct max_coeff_functor {
+  EIGEN_DEVICE_FUNC inline bool compareCoeff(const Scalar& incumbent, const Scalar& candidate) const {
+    return candidate > incumbent;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet comparePacket(const Packet& incumbent, const Packet& candidate) const {
+    return pcmp_lt(incumbent, candidate);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Scalar predux(const Packet& a) const {
+    return predux_max(a);
+  }
+};
+
+template <typename Scalar>
+struct max_coeff_functor<Scalar, PropagateNaN, false> {
+  EIGEN_DEVICE_FUNC inline Scalar compareCoeff(const Scalar& incumbent, const Scalar& candidate) {
+    return (candidate > incumbent) || ((candidate != candidate) && (incumbent == incumbent));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet comparePacket(const Packet& incumbent, const Packet& candidate) {
+    return pandnot(pcmp_lt_or_nan(incumbent, candidate), pisnan(incumbent));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Scalar predux(const Packet& a) const {
+    return predux_max<PropagateNaN>(a);
+  }
+};
+
+template <typename Scalar>
+struct max_coeff_functor<Scalar, PropagateNumbers, false> {
+  EIGEN_DEVICE_FUNC inline bool compareCoeff(const Scalar& incumbent, const Scalar& candidate) const {
+    return (candidate > incumbent) || ((candidate == candidate) && (incumbent != incumbent));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet comparePacket(const Packet& incumbent, const Packet& candidate) const {
+    return pandnot(pcmp_lt_or_nan(incumbent, candidate), pisnan(candidate));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Scalar predux(const Packet& a) const {
+    return predux_max<PropagateNumbers>(a);
+  }
+};
+
+template <typename Scalar, int NaNPropagation, bool IsInteger = NumTraits<Scalar>::IsInteger>
+struct min_coeff_functor {
+  EIGEN_DEVICE_FUNC inline bool compareCoeff(const Scalar& incumbent, const Scalar& candidate) const {
+    return candidate < incumbent;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet comparePacket(const Packet& incumbent, const Packet& candidate) const {
+    return pcmp_lt(candidate, incumbent);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Scalar predux(const Packet& a) const {
+    return predux_min(a);
+  }
+};
+
+template <typename Scalar>
+struct min_coeff_functor<Scalar, PropagateNaN, false> {
+  EIGEN_DEVICE_FUNC inline Scalar compareCoeff(const Scalar& incumbent, const Scalar& candidate) {
+    return (candidate < incumbent) || ((candidate != candidate) && (incumbent == incumbent));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet comparePacket(const Packet& incumbent, const Packet& candidate) {
+    return pandnot(pcmp_lt_or_nan(candidate, incumbent), pisnan(incumbent));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Scalar predux(const Packet& a) const {
+    return predux_min<PropagateNaN>(a);
+  }
+};
+
+template <typename Scalar>
+struct min_coeff_functor<Scalar, PropagateNumbers, false> {
+  EIGEN_DEVICE_FUNC inline bool compareCoeff(const Scalar& incumbent, const Scalar& candidate) const {
+    return (candidate < incumbent) || ((candidate == candidate) && (incumbent != incumbent));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet comparePacket(const Packet& incumbent, const Packet& candidate) const {
+    return pandnot(pcmp_lt_or_nan(candidate, incumbent), pisnan(candidate));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Scalar predux(const Packet& a) const {
+    return predux_min<PropagateNumbers>(a);
+  }
+};
+
+template <typename Scalar>
+struct min_max_traits {
+  static constexpr bool PacketAccess = packet_traits<Scalar>::Vectorizable;
+};
+template <typename Scalar, int NaNPropagation>
+struct functor_traits<max_coeff_functor<Scalar, NaNPropagation>> : min_max_traits<Scalar> {};
+template <typename Scalar, int NaNPropagation>
+struct functor_traits<min_coeff_functor<Scalar, NaNPropagation>> : min_max_traits<Scalar> {};
+
+template <typename Evaluator, typename Func, bool Linear, bool Vectorize>
+struct find_coeff_loop;
+template <typename Evaluator, typename Func>
+struct find_coeff_loop<Evaluator, Func, /*Linear*/ false, /*Vectorize*/ false> {
+  using Scalar = typename Evaluator::Scalar;
+  static EIGEN_DEVICE_FUNC inline void run(const Evaluator& eval, Func& func, Scalar& res, Index& outer, Index& inner) {
+    Index outerSize = eval.outerSize();
+    Index innerSize = eval.innerSize();
+
+    /* initialization performed in calling function */
+    /* result = eval.coeff(0, 0); */
+    /* outer = 0; */
+    /* inner = 0; */
+
+    for (Index j = 0; j < outerSize; j++) {
+      for (Index i = 0; i < innerSize; i++) {
+        Scalar xprCoeff = eval.coeffByOuterInner(j, i);
+        bool newRes = func.compareCoeff(res, xprCoeff);
+        if (newRes) {
+          outer = j;
+          inner = i;
+          res = xprCoeff;
+        }
+      }
+    }
+  }
+};
+template <typename Evaluator, typename Func>
+struct find_coeff_loop<Evaluator, Func, /*Linear*/ true, /*Vectorize*/ false> {
+  using Scalar = typename Evaluator::Scalar;
+  static EIGEN_DEVICE_FUNC inline void run(const Evaluator& eval, Func& func, Scalar& res, Index& index) {
+    Index size = eval.size();
+
+    /* initialization performed in calling function */
+    /* result = eval.coeff(0); */
+    /* index = 0; */
+
+    for (Index k = 0; k < size; k++) {
+      Scalar xprCoeff = eval.coeff(k);
+      bool newRes = func.compareCoeff(res, xprCoeff);
+      if (newRes) {
+        index = k;
+        res = xprCoeff;
+      }
+    }
+  }
+};
+template <typename Evaluator, typename Func>
+struct find_coeff_loop<Evaluator, Func, /*Linear*/ false, /*Vectorize*/ true> {
+  using ScalarImpl = find_coeff_loop<Evaluator, Func, false, false>;
+  using Scalar = typename Evaluator::Scalar;
+  using Packet = typename Evaluator::Packet;
+  static constexpr int PacketSize = unpacket_traits<Packet>::size;
+  static EIGEN_DEVICE_FUNC inline void run(const Evaluator& eval, Func& func, Scalar& result, Index& outer,
+                                           Index& inner) {
+    Index outerSize = eval.outerSize();
+    Index innerSize = eval.innerSize();
+    Index packetEnd = numext::round_down(innerSize, PacketSize);
+
+    /* initialization performed in calling function */
+    /* result = eval.coeff(0, 0); */
+    /* outer = 0; */
+    /* inner = 0; */
+
+    bool checkPacket = false;
+
+    for (Index j = 0; j < outerSize; j++) {
+      Packet resultPacket = pset1<Packet>(result);
+      for (Index i = 0; i < packetEnd; i += PacketSize) {
+        Packet xprPacket = eval.template packetByOuterInner<Unaligned, Packet>(j, i);
+        if (predux_any(func.comparePacket(resultPacket, xprPacket))) {
+          outer = j;
+          inner = i;
+          result = func.predux(xprPacket);
+          resultPacket = pset1<Packet>(result);
+          checkPacket = true;
+        }
+      }
+
+      for (Index i = packetEnd; i < innerSize; i++) {
+        Scalar xprCoeff = eval.coeffByOuterInner(j, i);
+        if (func.compareCoeff(result, xprCoeff)) {
+          outer = j;
+          inner = i;
+          result = xprCoeff;
+          checkPacket = false;
+        }
+      }
+    }
+
+    if (checkPacket) {
+      result = eval.coeffByOuterInner(outer, inner);
+      Index i_end = inner + PacketSize;
+      for (Index i = inner; i < i_end; i++) {
+        Scalar xprCoeff = eval.coeffByOuterInner(outer, i);
+        if (func.compareCoeff(result, xprCoeff)) {
+          inner = i;
+          result = xprCoeff;
+        }
+      }
+    }
+  }
+};
+template <typename Evaluator, typename Func>
+struct find_coeff_loop<Evaluator, Func, /*Linear*/ true, /*Vectorize*/ true> {
+  using ScalarImpl = find_coeff_loop<Evaluator, Func, true, false>;
+  using Scalar = typename Evaluator::Scalar;
+  using Packet = typename Evaluator::Packet;
+  static constexpr int PacketSize = unpacket_traits<Packet>::size;
+  static constexpr int Alignment = Evaluator::Alignment;
+
+  static EIGEN_DEVICE_FUNC inline void run(const Evaluator& eval, Func& func, Scalar& result, Index& index) {
+    Index size = eval.size();
+    Index packetEnd = numext::round_down(size, PacketSize);
+
+    /* initialization performed in calling function */
+    /* result = eval.coeff(0); */
+    /* index = 0; */
+
+    Packet resultPacket = pset1<Packet>(result);
+    bool checkPacket = false;
+
+    for (Index k = 0; k < packetEnd; k += PacketSize) {
+      Packet xprPacket = eval.template packet<Alignment, Packet>(k);
+      if (predux_any(func.comparePacket(resultPacket, xprPacket))) {
+        index = k;
+        result = func.predux(xprPacket);
+        resultPacket = pset1<Packet>(result);
+        checkPacket = true;
+      }
+    }
+
+    for (Index k = packetEnd; k < size; k++) {
+      Scalar xprCoeff = eval.coeff(k);
+      if (func.compareCoeff(result, xprCoeff)) {
+        index = k;
+        result = xprCoeff;
+        checkPacket = false;
+      }
+    }
+
+    if (checkPacket) {
+      result = eval.coeff(index);
+      Index k_end = index + PacketSize;
+      for (Index k = index; k < k_end; k++) {
+        Scalar xprCoeff = eval.coeff(k);
+        if (func.compareCoeff(result, xprCoeff)) {
+          index = k;
+          result = xprCoeff;
+        }
+      }
+    }
+  }
+};
+
+template <typename Derived>
+struct find_coeff_evaluator : public evaluator<Derived> {
+  using Base = evaluator<Derived>;
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  static constexpr int Flags = Base::Flags;
+  static constexpr bool IsRowMajor = bool(Flags & RowMajorBit);
+  EIGEN_DEVICE_FUNC inline find_coeff_evaluator(const Derived& xpr) : Base(xpr), m_xpr(xpr) {}
+
+  EIGEN_DEVICE_FUNC inline Scalar coeffByOuterInner(Index outer, Index inner) const {
+    Index row = IsRowMajor ? outer : inner;
+    Index col = IsRowMajor ? inner : outer;
+    return Base::coeff(row, col);
+  }
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC inline PacketType packetByOuterInner(Index outer, Index inner) const {
+    Index row = IsRowMajor ? outer : inner;
+    Index col = IsRowMajor ? inner : outer;
+    return Base::template packet<LoadMode, PacketType>(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC inline Index innerSize() const { return m_xpr.innerSize(); }
+  EIGEN_DEVICE_FUNC inline Index outerSize() const { return m_xpr.outerSize(); }
+  EIGEN_DEVICE_FUNC inline Index size() const { return m_xpr.size(); }
+
+  const Derived& m_xpr;
+};
+
+template <typename Derived, typename Func>
+struct find_coeff_impl {
+  using Evaluator = find_coeff_evaluator<Derived>;
+  static constexpr int Flags = Evaluator::Flags;
+  static constexpr int Alignment = Evaluator::Alignment;
+  static constexpr bool IsRowMajor = Derived::IsRowMajor;
+  static constexpr int MaxInnerSizeAtCompileTime =
+      IsRowMajor ? Derived::MaxColsAtCompileTime : Derived::MaxRowsAtCompileTime;
+  static constexpr int MaxSizeAtCompileTime = Derived::MaxSizeAtCompileTime;
+
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename Evaluator::Packet;
+
+  static constexpr int PacketSize = unpacket_traits<Packet>::size;
+  static constexpr bool Linearize = bool(Flags & LinearAccessBit);
+  static constexpr bool DontVectorize =
+      enum_lt_not_dynamic(Linearize ? MaxSizeAtCompileTime : MaxInnerSizeAtCompileTime, PacketSize);
+  static constexpr bool Vectorize =
+      !DontVectorize && bool(Flags & PacketAccessBit) && functor_traits<Func>::PacketAccess;
+
+  using Loop = find_coeff_loop<Evaluator, Func, Linearize, Vectorize>;
+
+  template <bool ForwardLinearAccess = Linearize, std::enable_if_t<!ForwardLinearAccess, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& xpr, Func& func, Scalar& res, Index& outer,
+                                                        Index& inner) {
+    Evaluator eval(xpr);
+    Loop::run(eval, func, res, outer, inner);
+  }
+  template <bool ForwardLinearAccess = Linearize, std::enable_if_t<ForwardLinearAccess, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& xpr, Func& func, Scalar& res, Index& outer,
+                                                        Index& inner) {
+    // where possible, use the linear loop and back-calculate the outer and inner indices
+    Index index = 0;
+    run(xpr, func, res, index);
+    outer = index / xpr.innerSize();
+    inner = index % xpr.innerSize();
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& xpr, Func& func, Scalar& res, Index& index) {
+    Evaluator eval(xpr);
+    Loop::run(eval, func, res, index);
+  }
+};
+
+template <typename Derived, typename IndexType, typename Func>
+EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar findCoeff(const DenseBase<Derived>& mat, Func& func,
+                                                                       IndexType* rowPtr, IndexType* colPtr) {
+  eigen_assert(mat.rows() > 0 && mat.cols() > 0 && "you are using an empty matrix");
+  using Scalar = typename DenseBase<Derived>::Scalar;
+  using FindCoeffImpl = internal::find_coeff_impl<Derived, Func>;
+  Index outer = 0;
+  Index inner = 0;
+  Scalar res = mat.coeff(0, 0);
+  FindCoeffImpl::run(mat.derived(), func, res, outer, inner);
+  *rowPtr = internal::convert_index<IndexType>(Derived::IsRowMajor ? outer : inner);
+  if (colPtr) *colPtr = internal::convert_index<IndexType>(Derived::IsRowMajor ? inner : outer);
+  return res;
+}
+
+template <typename Derived, typename IndexType, typename Func>
+EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar findCoeff(const DenseBase<Derived>& mat, Func& func,
+                                                                       IndexType* indexPtr) {
+  eigen_assert(mat.size() > 0 && "you are using an empty matrix");
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  using Scalar = typename DenseBase<Derived>::Scalar;
+  using FindCoeffImpl = internal::find_coeff_impl<Derived, Func>;
+  Index index = 0;
+  Scalar res = mat.coeff(0);
+  FindCoeffImpl::run(mat.derived(), func, res, index);
+  *indexPtr = internal::convert_index<IndexType>(index);
+  return res;
+}
+
+}  // namespace internal
+
+/** \fn DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
+ * \returns the minimum of all coefficients of *this and puts in *row and *col its location.
+ *
+ * If there are multiple coefficients with the same extreme value, the location of the first instance is returned.
+ *
+ * In case \c *this contains NaN, NaNPropagation determines the behavior:
+ *   NaNPropagation == PropagateFast : undefined
+ *   NaNPropagation == PropagateNaN : result is NaN
+ *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
+ * \warning the matrix must be not empty, otherwise an assertion is triggered.
+ *
+ * \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visit(), DenseBase::minCoeff()
+ */
+template <typename Derived>
+template <int NaNPropagation, typename IndexType>
+EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar DenseBase<Derived>::minCoeff(IndexType* rowPtr,
+                                                                                          IndexType* colPtr) const {
+  using Func = internal::min_coeff_functor<Scalar, NaNPropagation>;
+  Func func;
+  return internal::findCoeff(derived(), func, rowPtr, colPtr);
+}
+
+/** \returns the minimum of all coefficients of *this and puts in *index its location.
+ *
+ * If there are multiple coefficients with the same extreme value, the location of the first instance is returned.
+ *
+ * In case \c *this contains NaN, NaNPropagation determines the behavior:
+ *   NaNPropagation == PropagateFast : undefined
+ *   NaNPropagation == PropagateNaN : result is NaN
+ *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
+ * \warning the matrix must be not empty, otherwise an assertion is triggered.
+ *
+ * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::visit(),
+ * DenseBase::minCoeff()
+ */
+template <typename Derived>
+template <int NaNPropagation, typename IndexType>
+EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar DenseBase<Derived>::minCoeff(IndexType* indexPtr) const {
+  using Func = internal::min_coeff_functor<Scalar, NaNPropagation>;
+  Func func;
+  return internal::findCoeff(derived(), func, indexPtr);
+}
+
+/** \fn DenseBase<Derived>::maxCoeff(IndexType* rowId, IndexType* colId) const
+ * \returns the maximum of all coefficients of *this and puts in *row and *col its location.
+ *
+ * If there are multiple coefficients with the same extreme value, the location of the first instance is returned.
+ *
+ * In case \c *this contains NaN, NaNPropagation determines the behavior:
+ *   NaNPropagation == PropagateFast : undefined
+ *   NaNPropagation == PropagateNaN : result is NaN
+ *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
+ * \warning the matrix must be not empty, otherwise an assertion is triggered.
+ *
+ * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::maxCoeff()
+ */
+template <typename Derived>
+template <int NaNPropagation, typename IndexType>
+EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar DenseBase<Derived>::maxCoeff(IndexType* rowPtr,
+                                                                                          IndexType* colPtr) const {
+  using Func = internal::max_coeff_functor<Scalar, NaNPropagation>;
+  Func func;
+  return internal::findCoeff(derived(), func, rowPtr, colPtr);
+}
+
+/** \returns the maximum of all coefficients of *this and puts in *index its location.
+ *
+ * If there are multiple coefficients with the same extreme value, the location of the first instance is returned.
+ *
+ * In case \c *this contains NaN, NaNPropagation determines the behavior:
+ *   NaNPropagation == PropagateFast : undefined
+ *   NaNPropagation == PropagateNaN : result is NaN
+ *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
+ * \warning the matrix must be not empty, otherwise an assertion is triggered.
+ *
+ * \sa DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(),
+ * DenseBase::maxCoeff()
+ */
+template <typename Derived>
+template <int NaNPropagation, typename IndexType>
+EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar DenseBase<Derived>::maxCoeff(IndexType* indexPtr) const {
+  using Func = internal::max_coeff_functor<Scalar, NaNPropagation>;
+  Func func;
+  return internal::findCoeff(derived(), func, indexPtr);
+}
+
+}  // namespace Eigen
+
+#endif  // EIGEN_FIND_COEFF_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ForceAlignedAccess.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ForceAlignedAccess.h
new file mode 100644
index 0000000..4f69c20
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ForceAlignedAccess.h
@@ -0,0 +1,108 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FORCEALIGNEDACCESS_H
+#define EIGEN_FORCEALIGNEDACCESS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class ForceAlignedAccess
+ * \ingroup Core_Module
+ *
+ * \brief Enforce aligned packet loads and stores regardless of what is requested
+ *
+ * \param ExpressionType the type of the object of which we are forcing aligned packet access
+ *
+ * This class is the return type of MatrixBase::forceAlignedAccess()
+ * and most of the time this is the only way it is used.
+ *
+ * \sa MatrixBase::forceAlignedAccess()
+ */
+
+namespace internal {
+template <typename ExpressionType>
+struct traits<ForceAlignedAccess<ExpressionType>> : public traits<ExpressionType> {};
+}  // namespace internal
+
+template <typename ExpressionType>
+class ForceAlignedAccess : public internal::dense_xpr_base<ForceAlignedAccess<ExpressionType>>::type {
+ public:
+  typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess)
+
+  EIGEN_DEVICE_FUNC explicit constexpr ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_expression.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_expression.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return m_expression.outerStride(); }
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_expression.innerStride(); }
+
+  EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const {
+    return m_expression.coeff(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col) {
+    return m_expression.const_cast_derived().coeffRef(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const { return m_expression.coeff(index); }
+
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index) { return m_expression.const_cast_derived().coeffRef(index); }
+
+  template <int LoadMode>
+  inline const PacketScalar packet(Index row, Index col) const {
+    return m_expression.template packet<Aligned>(row, col);
+  }
+
+  template <int LoadMode>
+  inline void writePacket(Index row, Index col, const PacketScalar& x) {
+    m_expression.const_cast_derived().template writePacket<Aligned>(row, col, x);
+  }
+
+  template <int LoadMode>
+  inline const PacketScalar packet(Index index) const {
+    return m_expression.template packet<Aligned>(index);
+  }
+
+  template <int LoadMode>
+  inline void writePacket(Index index, const PacketScalar& x) {
+    m_expression.const_cast_derived().template writePacket<Aligned>(index, x);
+  }
+
+  EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
+
+ protected:
+  const ExpressionType& m_expression;
+
+ private:
+  ForceAlignedAccess& operator=(const ForceAlignedAccess&);
+};
+
+/** \returns an expression of *this with forced aligned access
+ * \sa forceAlignedAccessIf(),class ForceAlignedAccess
+ */
+template <typename Derived>
+inline const ForceAlignedAccess<Derived> MatrixBase<Derived>::forceAlignedAccess() const {
+  return ForceAlignedAccess<Derived>(derived());
+}
+
+/** \returns an expression of *this with forced aligned access
+ * \sa forceAlignedAccessIf(), class ForceAlignedAccess
+ */
+template <typename Derived>
+inline ForceAlignedAccess<Derived> MatrixBase<Derived>::forceAlignedAccess() {
+  return ForceAlignedAccess<Derived>(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_FORCEALIGNEDACCESS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Fuzzy.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Fuzzy.h
new file mode 100644
index 0000000..eaa553c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Fuzzy.h
@@ -0,0 +1,132 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FUZZY_H
+#define EIGEN_FUZZY_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
+struct isApprox_selector {
+  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec) {
+    typename internal::nested_eval<Derived, 2>::type nested(x);
+    typename internal::nested_eval<OtherDerived, 2>::type otherNested(y);
+    return (nested.matrix() - otherNested.matrix()).cwiseAbs2().sum() <=
+           prec * prec * numext::mini(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
+  }
+};
+
+template <typename Derived, typename OtherDerived>
+struct isApprox_selector<Derived, OtherDerived, true> {
+  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&) {
+    return x.matrix() == y.matrix();
+  }
+};
+
+template <typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
+struct isMuchSmallerThan_object_selector {
+  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec) {
+    return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
+  }
+};
+
+template <typename Derived, typename OtherDerived>
+struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true> {
+  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&) {
+    return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
+  }
+};
+
+template <typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
+struct isMuchSmallerThan_scalar_selector {
+  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const typename Derived::RealScalar& y,
+                                    const typename Derived::RealScalar& prec) {
+    return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
+  }
+};
+
+template <typename Derived>
+struct isMuchSmallerThan_scalar_selector<Derived, true> {
+  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const typename Derived::RealScalar&,
+                                    const typename Derived::RealScalar&) {
+    return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
+  }
+};
+
+}  // end namespace internal
+
+/** \returns \c true if \c *this is approximately equal to \a other, within the precision
+ * determined by \a prec.
+ *
+ * \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
+ * are considered to be approximately equal within precision \f$ p \f$ if
+ * \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
+ * For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm
+ * L2 norm).
+ *
+ * \note Because of the multiplicativeness of this comparison, one can't use this function
+ * to check whether \c *this is approximately equal to the zero matrix or vector.
+ * Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix
+ * or vector. If you want to test whether \c *this is zero, use internal::isMuchSmallerThan(const
+ * RealScalar&, RealScalar) instead.
+ *
+ * \sa internal::isMuchSmallerThan(const RealScalar&, RealScalar) const
+ */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr bool DenseBase<Derived>::isApprox(const DenseBase<OtherDerived>& other,
+                                                              const RealScalar& prec) const {
+  return internal::isApprox_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
+}
+
+/** \returns \c true if the norm of \c *this is much smaller than \a other,
+ * within the precision determined by \a prec.
+ *
+ * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
+ * considered to be much smaller than \f$ x \f$ within precision \f$ p \f$ if
+ * \f[ \Vert v \Vert \leqslant p\,\vert x\vert. \f]
+ *
+ * For matrices, the comparison is done using the Hilbert-Schmidt norm. For this reason,
+ * the value of the reference scalar \a other should come from the Hilbert-Schmidt norm
+ * of a reference matrix of same dimensions.
+ *
+ * \sa isApprox(), isMuchSmallerThan(const DenseBase<OtherDerived>&, RealScalar) const
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr bool DenseBase<Derived>::isMuchSmallerThan(const typename NumTraits<Scalar>::Real& other,
+                                                                       const RealScalar& prec) const {
+  return internal::isMuchSmallerThan_scalar_selector<Derived>::run(derived(), other, prec);
+}
+
+/** \returns \c true if the norm of \c *this is much smaller than the norm of \a other,
+ * within the precision determined by \a prec.
+ *
+ * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
+ * considered to be much smaller than a vector \f$ w \f$ within precision \f$ p \f$ if
+ * \f[ \Vert v \Vert \leqslant p\,\Vert w\Vert. \f]
+ * For matrices, the comparison is done using the Hilbert-Schmidt norm.
+ *
+ * \sa isApprox(), isMuchSmallerThan(const RealScalar&, RealScalar) const
+ */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr bool DenseBase<Derived>::isMuchSmallerThan(const DenseBase<OtherDerived>& other,
+                                                                       const RealScalar& prec) const {
+  return internal::isMuchSmallerThan_object_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_FUZZY_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/GeneralProduct.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/GeneralProduct.h
new file mode 100644
index 0000000..707611a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/GeneralProduct.h
@@ -0,0 +1,518 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERAL_PRODUCT_H
+#define EIGEN_GENERAL_PRODUCT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum { Large = 2, Small = 3 };
+
+// Define the threshold value to fallback from the generic matrix-matrix product
+// implementation (heavy) to the lightweight coeff-based product one.
+// See generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
+// in products/GeneralMatrixMatrix.h for more details.
+// TODO This threshold should also be used in the compile-time selector below.
+#ifndef EIGEN_GEMM_TO_COEFFBASED_THRESHOLD
+// This default value has been obtained on a Haswell architecture.
+#define EIGEN_GEMM_TO_COEFFBASED_THRESHOLD 20
+#endif
+
+namespace internal {
+
+template <int Rows, int Cols, int Depth>
+struct product_type_selector;
+
+template <int Size, int MaxSize>
+struct product_size_category {
+  enum {
+#ifndef EIGEN_GPU_COMPILE_PHASE
+    is_large = MaxSize == Dynamic || Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
+               (Size == Dynamic && MaxSize >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
+#else
+    is_large = 0,
+#endif
+    value = is_large    ? Large
+            : Size == 1 ? 1
+                        : Small
+  };
+};
+
+template <typename Lhs, typename Rhs>
+struct product_type {
+  typedef remove_all_t<Lhs> Lhs_;
+  typedef remove_all_t<Rhs> Rhs_;
+  enum {
+    MaxRows = traits<Lhs_>::MaxRowsAtCompileTime,
+    Rows = traits<Lhs_>::RowsAtCompileTime,
+    MaxCols = traits<Rhs_>::MaxColsAtCompileTime,
+    Cols = traits<Rhs_>::ColsAtCompileTime,
+    MaxDepth = min_size_prefer_fixed(traits<Lhs_>::MaxColsAtCompileTime, traits<Rhs_>::MaxRowsAtCompileTime),
+    Depth = min_size_prefer_fixed(traits<Lhs_>::ColsAtCompileTime, traits<Rhs_>::RowsAtCompileTime)
+  };
+
+  // the splitting into different lines of code here, introducing the _select enums and the typedef below,
+  // is to work around an internal compiler error with gcc 4.1 and 4.2.
+ private:
+  enum {
+    rows_select = product_size_category<Rows, MaxRows>::value,
+    cols_select = product_size_category<Cols, MaxCols>::value,
+    depth_select = product_size_category<Depth, MaxDepth>::value
+  };
+  typedef product_type_selector<rows_select, cols_select, depth_select> selector;
+
+ public:
+  enum { value = selector::ret, ret = selector::ret };
+#ifdef EIGEN_DEBUG_PRODUCT
+  static void debug() {
+    EIGEN_DEBUG_VAR(Rows);
+    EIGEN_DEBUG_VAR(Cols);
+    EIGEN_DEBUG_VAR(Depth);
+    EIGEN_DEBUG_VAR(rows_select);
+    EIGEN_DEBUG_VAR(cols_select);
+    EIGEN_DEBUG_VAR(depth_select);
+    EIGEN_DEBUG_VAR(value);
+  }
+#endif
+};
+
+/* The following allows to select the kind of product at compile time
+ * based on the three dimensions of the product.
+ * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
+// FIXME: the current compile-time product-type mapping may not be optimal.
+template <int M, int N>
+struct product_type_selector<M, N, 1> {
+  enum { ret = OuterProduct };
+};
+template <int M>
+struct product_type_selector<M, 1, 1> {
+  enum { ret = LazyCoeffBasedProductMode };
+};
+template <int N>
+struct product_type_selector<1, N, 1> {
+  enum { ret = LazyCoeffBasedProductMode };
+};
+template <int Depth>
+struct product_type_selector<1, 1, Depth> {
+  enum { ret = InnerProduct };
+};
+template <>
+struct product_type_selector<1, 1, 1> {
+  enum { ret = InnerProduct };
+};
+template <>
+struct product_type_selector<Small, 1, Small> {
+  enum { ret = CoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<1, Small, Small> {
+  enum { ret = CoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<Small, Small, Small> {
+  enum { ret = CoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<Small, Small, 1> {
+  enum { ret = LazyCoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<Small, Large, 1> {
+  enum { ret = LazyCoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<Large, Small, 1> {
+  enum { ret = LazyCoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<1, Large, Small> {
+  enum { ret = CoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<1, Large, Large> {
+  enum { ret = GemvProduct };
+};
+template <>
+struct product_type_selector<1, Small, Large> {
+  enum { ret = CoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<Large, 1, Small> {
+  enum { ret = CoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<Large, 1, Large> {
+  enum { ret = GemvProduct };
+};
+template <>
+struct product_type_selector<Small, 1, Large> {
+  enum { ret = CoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<Small, Small, Large> {
+  enum { ret = GemmProduct };
+};
+template <>
+struct product_type_selector<Large, Small, Large> {
+  enum { ret = GemmProduct };
+};
+template <>
+struct product_type_selector<Small, Large, Large> {
+  enum { ret = GemmProduct };
+};
+template <>
+struct product_type_selector<Large, Large, Large> {
+  enum { ret = GemmProduct };
+};
+template <>
+struct product_type_selector<Large, Small, Small> {
+  enum { ret = CoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<Small, Large, Small> {
+  enum { ret = CoeffBasedProductMode };
+};
+template <>
+struct product_type_selector<Large, Large, Small> {
+  enum { ret = GemmProduct };
+};
+
+}  // end namespace internal
+
+/***********************************************************************
+ *  Implementation of Inner Vector Vector Product
+ ***********************************************************************/
+
+// FIXME: consider returning a Scalar instead of a 1x1 matrix for inner products.
+// Pro: more natural for the user.
+// Con: in a meta-unrolled algorithm a matrix-matrix product may reduce to a
+// row-vector times column-vector product. To handle this, we could specialize
+// Block<MatrixType,1,1> with operator=(Scalar x).
+
+/***********************************************************************
+ *  Implementation of Outer Vector Vector Product
+ ***********************************************************************/
+
+/***********************************************************************
+ *  Implementation of General Matrix Vector Product
+ ***********************************************************************/
+
+/*  According to the shape/flags of the matrix we have to distinguish 3 different cases:
+ *   1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine
+ *   2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine
+ *   3 - all other cases are handled using a simple loop along the outer-storage direction.
+ *  Therefore we need a lower level meta selector.
+ *  Furthermore, if the matrix is the rhs, then the product has to be transposed.
+ */
+namespace internal {
+
+template <int Side, int StorageOrder, bool BlasCompatible>
+struct gemv_dense_selector;
+
+}  // end namespace internal
+
+namespace internal {
+
+template <typename Scalar, int Size, int MaxSize, bool Cond>
+struct gemv_static_vector_if;
+
+template <typename Scalar, int Size, int MaxSize>
+struct gemv_static_vector_if<Scalar, Size, MaxSize, false> {
+  EIGEN_DEVICE_FUNC constexpr Scalar* data() {
+    eigen_internal_assert(false && "should never be called");
+    return 0;
+  }
+};
+
+template <typename Scalar, int Size>
+struct gemv_static_vector_if<Scalar, Size, Dynamic, true> {
+  EIGEN_DEVICE_FUNC constexpr Scalar* data() { return 0; }
+};
+
+template <typename Scalar, int Size, int MaxSize>
+struct gemv_static_vector_if<Scalar, Size, MaxSize, true> {
+#if EIGEN_MAX_STATIC_ALIGN_BYTES != 0
+  internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize), 0, AlignedMax> m_data;
+  constexpr Scalar* data() { return m_data.array; }
+#else
+  // Some architectures cannot align on the stack,
+  // => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
+  internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize) + EIGEN_MAX_ALIGN_BYTES, 0> m_data;
+  constexpr Scalar* data() {
+    return reinterpret_cast<Scalar*>((std::uintptr_t(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES - 1))) +
+                                     EIGEN_MAX_ALIGN_BYTES);
+  }
+#endif
+};
+
+// The vector is on the left => transposition
+template <int StorageOrder, bool BlasCompatible>
+struct gemv_dense_selector<OnTheLeft, StorageOrder, BlasCompatible> {
+  template <typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
+    Transpose<Dest> destT(dest);
+    enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
+    gemv_dense_selector<OnTheRight, OtherStorageOrder, BlasCompatible>::run(rhs.transpose(), lhs.transpose(), destT,
+                                                                            alpha);
+  }
+};
+
+template <>
+struct gemv_dense_selector<OnTheRight, ColMajor, true> {
+  template <typename Lhs, typename Rhs, typename Dest>
+  static inline void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
+    typedef typename Lhs::Scalar LhsScalar;
+    typedef typename Rhs::Scalar RhsScalar;
+    typedef typename Dest::Scalar ResScalar;
+
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+
+    typedef Map<Matrix<ResScalar, Dynamic, 1>, plain_enum_min(AlignedMax, internal::packet_traits<ResScalar>::size)>
+        MappedDest;
+
+    ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
+    ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);
+
+    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
+
+    // make sure Dest is a compile-time vector type (bug 1166)
+    typedef std::conditional_t<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr> ActualDest;
+
+    enum {
+      // FIXME: find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
+      // on, the other hand it is good for the cache to pack the vector anyways...
+      EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime == 1),
+      ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
+      MightCannotUseDest = ((!EvalToDestAtCompileTime) || ComplexByReal) && (ActualDest::MaxSizeAtCompileTime != 0)
+    };
+
+    typedef const_blas_data_mapper<LhsScalar, Index, ColMajor> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar, Index, RowMajor> RhsMapper;
+    RhsScalar compatibleAlpha = get_factor<ResScalar, RhsScalar>::run(actualAlpha);
+
+    if (!MightCannotUseDest) {
+      // shortcut if we are sure to be able to use dest directly,
+      // this ease the compiler to generate cleaner and more optimzized code for most common cases
+      general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, LhsBlasTraits::NeedToConjugate, RhsScalar,
+                                    RhsMapper, RhsBlasTraits::NeedToConjugate>::run(actualLhs.rows(), actualLhs.cols(),
+                                                                                    LhsMapper(actualLhs.data(),
+                                                                                              actualLhs.outerStride()),
+                                                                                    RhsMapper(actualRhs.data(),
+                                                                                              actualRhs.innerStride()),
+                                                                                    dest.data(), 1, compatibleAlpha);
+    } else {
+      gemv_static_vector_if<ResScalar, ActualDest::SizeAtCompileTime, ActualDest::MaxSizeAtCompileTime,
+                            MightCannotUseDest>
+          static_dest;
+
+      const bool alphaIsCompatible = (!ComplexByReal) || (numext::is_exactly_zero(numext::imag(actualAlpha)));
+      const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
+
+      ei_declare_aligned_stack_constructed_variable(ResScalar, actualDestPtr, dest.size(),
+                                                    evalToDest ? dest.data() : static_dest.data());
+
+      if (!evalToDest) {
+#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+        constexpr int Size = Dest::SizeAtCompileTime;
+        Index size = dest.size();
+        EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+#endif
+        if (!alphaIsCompatible) {
+          MappedDest(actualDestPtr, dest.size()).setZero();
+          compatibleAlpha = RhsScalar(1);
+        } else
+          MappedDest(actualDestPtr, dest.size()) = dest;
+      }
+
+      general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, LhsBlasTraits::NeedToConjugate, RhsScalar,
+                                    RhsMapper, RhsBlasTraits::NeedToConjugate>::run(actualLhs.rows(), actualLhs.cols(),
+                                                                                    LhsMapper(actualLhs.data(),
+                                                                                              actualLhs.outerStride()),
+                                                                                    RhsMapper(actualRhs.data(),
+                                                                                              actualRhs.innerStride()),
+                                                                                    actualDestPtr, 1, compatibleAlpha);
+
+      if (!evalToDest) {
+        if (!alphaIsCompatible)
+          dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size());
+        else
+          dest = MappedDest(actualDestPtr, dest.size());
+      }
+    }
+  }
+};
+
+template <>
+struct gemv_dense_selector<OnTheRight, RowMajor, true> {
+  template <typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
+    typedef typename Lhs::Scalar LhsScalar;
+    typedef typename Rhs::Scalar RhsScalar;
+    typedef typename Dest::Scalar ResScalar;
+
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+    typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
+
+    std::add_const_t<ActualLhsType> actualLhs = LhsBlasTraits::extract(lhs);
+    std::add_const_t<ActualRhsType> actualRhs = RhsBlasTraits::extract(rhs);
+
+    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
+
+    enum {
+      // FIXME: find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
+      // on, the other hand it is good for the cache to pack the vector anyways...
+      DirectlyUseRhs =
+          ActualRhsTypeCleaned::InnerStrideAtCompileTime == 1 || ActualRhsTypeCleaned::MaxSizeAtCompileTime == 0
+    };
+
+    gemv_static_vector_if<RhsScalar, ActualRhsTypeCleaned::SizeAtCompileTime,
+                          ActualRhsTypeCleaned::MaxSizeAtCompileTime, !DirectlyUseRhs>
+        static_rhs;
+
+    ei_declare_aligned_stack_constructed_variable(
+        RhsScalar, actualRhsPtr, actualRhs.size(),
+        DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
+
+    if (!DirectlyUseRhs) {
+#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      constexpr int Size = ActualRhsTypeCleaned::SizeAtCompileTime;
+      Index size = actualRhs.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+#endif
+      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
+    }
+
+    typedef const_blas_data_mapper<LhsScalar, Index, RowMajor> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar, Index, ColMajor> RhsMapper;
+    general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, LhsBlasTraits::NeedToConjugate, RhsScalar,
+                                  RhsMapper, RhsBlasTraits::NeedToConjugate>::
+        run(actualLhs.rows(), actualLhs.cols(), LhsMapper(actualLhs.data(), actualLhs.outerStride()),
+            RhsMapper(actualRhsPtr, 1), dest.data(),
+            dest.col(0).innerStride(),  // NOTE  if dest is not a vector at compile-time, then dest.innerStride() might
+                                        // be wrong. (bug 1166)
+            actualAlpha);
+  }
+};
+
+template <>
+struct gemv_dense_selector<OnTheRight, ColMajor, false> {
+  template <typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
+    EIGEN_STATIC_ASSERT((!nested_eval<Lhs, 1>::Evaluate),
+                        EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
+    // TODO: if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory,
+    // otherwise use a temp
+    typename nested_eval<Rhs, 1>::type actual_rhs(rhs);
+    const Index size = rhs.rows();
+    for (Index k = 0; k < size; ++k) dest += (alpha * actual_rhs.coeff(k)) * lhs.col(k);
+  }
+};
+
+template <>
+struct gemv_dense_selector<OnTheRight, RowMajor, false> {
+  template <typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
+    EIGEN_STATIC_ASSERT((!nested_eval<Lhs, 1>::Evaluate),
+                        EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
+    typename nested_eval<Rhs, Lhs::RowsAtCompileTime>::type actual_rhs(rhs);
+    const Index rows = dest.rows();
+    for (Index i = 0; i < rows; ++i)
+      dest.coeffRef(i) += alpha * (lhs.row(i).cwiseProduct(actual_rhs.transpose())).sum();
+  }
+};
+
+}  // end namespace internal
+
+/***************************************************************************
+ * Implementation of matrix base methods
+ ***************************************************************************/
+
+/** \returns the matrix product of \c *this and \a other.
+ *
+ * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*().
+ *
+ * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*()
+ */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Product<Derived, OtherDerived> MatrixBase<Derived>::operator*(
+    const MatrixBase<OtherDerived>& other) const {
+  // A note regarding the function declaration: In MSVC, this function will sometimes
+  // not be inlined since DenseStorage is an unwindable object for dynamic
+  // matrices and product types are holding a member to store the result.
+  // Thus it does not help tagging this function with EIGEN_STRONG_INLINE.
+  enum {
+    ProductIsValid = Derived::ColsAtCompileTime == Dynamic || OtherDerived::RowsAtCompileTime == Dynamic ||
+                     int(Derived::ColsAtCompileTime) == int(OtherDerived::RowsAtCompileTime),
+    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
+    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived, OtherDerived)
+  };
+  // note to the lost user:
+  //    * for a dot product use: v1.dot(v2)
+  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
+  EIGEN_STATIC_ASSERT(
+      ProductIsValid || !(AreVectors && SameSizes),
+      INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
+                      INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
+  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
+#ifdef EIGEN_DEBUG_PRODUCT
+  internal::product_type<Derived, OtherDerived>::debug();
+#endif
+
+  return Product<Derived, OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
+ *
+ * The returned product will behave like any other expressions: the coefficients of the product will be
+ * computed once at a time as requested. This might be useful in some extremely rare cases when only
+ * a small and no coherent fraction of the result's coefficients have to be computed.
+ *
+ * \warning This version of the matrix product can be much much slower. So use it only if you know
+ * what you are doing and that you measured a true speed improvement.
+ *
+ * \sa operator*(const MatrixBase&)
+ */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Product<Derived, OtherDerived, LazyProduct>
+MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived>& other) const {
+  enum {
+    ProductIsValid = Derived::ColsAtCompileTime == Dynamic || OtherDerived::RowsAtCompileTime == Dynamic ||
+                     int(Derived::ColsAtCompileTime) == int(OtherDerived::RowsAtCompileTime),
+    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
+    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived, OtherDerived)
+  };
+  // note to the lost user:
+  //    * for a dot product use: v1.dot(v2)
+  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
+  EIGEN_STATIC_ASSERT(
+      ProductIsValid || !(AreVectors && SameSizes),
+      INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
+                      INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
+  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
+
+  return Product<Derived, OtherDerived, LazyProduct>(derived(), other.derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PRODUCT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/GenericPacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/GenericPacketMath.h
new file mode 100644
index 0000000..10c798a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/GenericPacketMath.h
@@ -0,0 +1,1706 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERIC_PACKET_MATH_H
+#define EIGEN_GENERIC_PACKET_MATH_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * \file GenericPacketMath.h
+ *
+ * Default implementation for types not supported by the vectorization.
+ * In practice these functions are provided to make easier the writing
+ * of generic vectorized code.
+ */
+
+#ifndef EIGEN_DEBUG_ALIGNED_LOAD
+#define EIGEN_DEBUG_ALIGNED_LOAD
+#endif
+
+#ifndef EIGEN_DEBUG_UNALIGNED_LOAD
+#define EIGEN_DEBUG_UNALIGNED_LOAD
+#endif
+
+#ifndef EIGEN_DEBUG_ALIGNED_STORE
+#define EIGEN_DEBUG_ALIGNED_STORE
+#endif
+
+#ifndef EIGEN_DEBUG_UNALIGNED_STORE
+#define EIGEN_DEBUG_UNALIGNED_STORE
+#endif
+
+struct default_packet_traits {
+  enum {
+    // Ops that are implemented for most types.
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+    HasSign = 1,
+    HasAbsDiff = 1,
+    // By default, the nearest integer functions (rint, round, floor, ceil, trunc) are enabled for all scalar and packet
+    // types
+    HasRound = 1,
+
+    HasArg = 0,
+    // This flag is used to indicate whether packet comparison is supported.
+    // pcmp_eq and pcmp_lt should be defined for it to be true.
+    HasCmp = 0,
+
+    HasDiv = 0,
+    HasReciprocal = 0,
+    HasSqrt = 0,
+    HasRsqrt = 0,
+    HasCbrt = 0,
+    HasExp = 0,
+    HasExpm1 = 0,
+    HasLog = 0,
+    HasLog1p = 0,
+    HasLog10 = 0,
+    HasPow = 0,
+    HasSin = 0,
+    HasCos = 0,
+    HasTan = 0,
+    HasASin = 0,
+    HasACos = 0,
+    HasATan = 0,
+    HasATanh = 0,
+    HasSinh = 0,
+    HasCosh = 0,
+    HasTanh = 0,
+    HasLGamma = 0,
+    HasDiGamma = 0,
+    HasZeta = 0,
+    HasPolygamma = 0,
+    HasErf = 0,
+    HasErfc = 0,
+    HasNdtri = 0,
+    HasBessel = 0,
+    HasIGamma = 0,
+    HasIGammaDerA = 0,
+    HasGammaSampleDerAlpha = 0,
+    HasIGammac = 0,
+    HasBetaInc = 0
+  };
+};
+
+template <typename T>
+struct packet_traits : default_packet_traits {
+  typedef T type;
+  typedef T half;
+  enum {
+    Vectorizable = 0,
+    size = 1,
+    AlignedOnScalar = 0,
+  };
+  enum {
+    HasAdd = 0,
+    HasSub = 0,
+    HasAbsDiff = 0,
+    HasMul = 0,
+    HasNegate = 0,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasConj = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <typename T>
+struct packet_traits<const T> : packet_traits<T> {};
+
+struct default_unpacket_traits {
+  enum { vectorizable = false, masked_load_available = false, masked_store_available = false };
+};
+
+template <typename T>
+struct unpacket_traits : default_unpacket_traits {
+  typedef T type;
+  typedef T half;
+  typedef typename numext::get_integer_by_size<sizeof(T)>::signed_type integer_packet;
+  enum {
+    size = 1,
+    alignment = alignof(T),
+  };
+};
+
+template <typename T>
+struct unpacket_traits<const T> : unpacket_traits<T> {};
+
+/** \internal A convenience utility for determining if the type is a scalar.
+ * This is used to enable some generic packet implementations.
+ */
+template <typename Packet>
+struct is_scalar {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  enum { value = internal::is_same<Packet, Scalar>::value };
+};
+
+// automatically and succinctly define combinations of pcast<SrcPacket,TgtPacket> when
+// 1) the packets are the same type, or
+// 2) the packets differ only in sign.
+// In both of these cases, preinterpret (bit_cast) is equivalent to pcast (static_cast)
+template <typename SrcPacket, typename TgtPacket,
+          bool Scalar = is_scalar<SrcPacket>::value && is_scalar<TgtPacket>::value>
+struct is_degenerate_helper : is_same<SrcPacket, TgtPacket> {};
+template <>
+struct is_degenerate_helper<int8_t, uint8_t, true> : std::true_type {};
+template <>
+struct is_degenerate_helper<int16_t, uint16_t, true> : std::true_type {};
+template <>
+struct is_degenerate_helper<int32_t, uint32_t, true> : std::true_type {};
+template <>
+struct is_degenerate_helper<int64_t, uint64_t, true> : std::true_type {};
+
+template <typename SrcPacket, typename TgtPacket>
+struct is_degenerate_helper<SrcPacket, TgtPacket, false> {
+  using SrcScalar = typename unpacket_traits<SrcPacket>::type;
+  static constexpr int SrcSize = unpacket_traits<SrcPacket>::size;
+  using TgtScalar = typename unpacket_traits<TgtPacket>::type;
+  static constexpr int TgtSize = unpacket_traits<TgtPacket>::size;
+  static constexpr bool value = is_degenerate_helper<SrcScalar, TgtScalar, true>::value && (SrcSize == TgtSize);
+};
+
+// is_degenerate<T1,T2>::value == is_degenerate<T2,T1>::value
+template <typename SrcPacket, typename TgtPacket>
+struct is_degenerate {
+  static constexpr bool value =
+      is_degenerate_helper<SrcPacket, TgtPacket>::value || is_degenerate_helper<TgtPacket, SrcPacket>::value;
+};
+
+template <typename Packet>
+struct is_half {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  static constexpr int Size = unpacket_traits<Packet>::size;
+  using DefaultPacket = typename packet_traits<Scalar>::type;
+  static constexpr int DefaultSize = unpacket_traits<DefaultPacket>::size;
+  static constexpr bool value = Size != 1 && Size < DefaultSize;
+};
+
+template <typename Src, typename Tgt>
+struct type_casting_traits {
+  enum {
+    VectorizedCast =
+        is_degenerate<Src, Tgt>::value && packet_traits<Src>::Vectorizable && packet_traits<Tgt>::Vectorizable,
+    SrcCoeffRatio = 1,
+    TgtCoeffRatio = 1
+  };
+};
+
+// provides a succinct template to define vectorized casting traits with respect to the largest accessible packet types
+template <typename Src, typename Tgt>
+struct vectorized_type_casting_traits {
+  enum : int {
+    DefaultSrcPacketSize = packet_traits<Src>::size,
+    DefaultTgtPacketSize = packet_traits<Tgt>::size,
+    VectorizedCast = 1,
+    SrcCoeffRatio = plain_enum_max(DefaultTgtPacketSize / DefaultSrcPacketSize, 1),
+    TgtCoeffRatio = plain_enum_max(DefaultSrcPacketSize / DefaultTgtPacketSize, 1)
+  };
+};
+
+/** \internal Wrapper to ensure that multiple packet types can map to the same
+    same underlying vector type. */
+template <typename T, int unique_id = 0>
+struct eigen_packet_wrapper {
+  EIGEN_ALWAYS_INLINE operator T&() { return m_val; }
+  EIGEN_ALWAYS_INLINE operator const T&() const { return m_val; }
+  EIGEN_ALWAYS_INLINE eigen_packet_wrapper() = default;
+  EIGEN_ALWAYS_INLINE eigen_packet_wrapper(const T& v) : m_val(v) {}
+  EIGEN_ALWAYS_INLINE eigen_packet_wrapper& operator=(const T& v) {
+    m_val = v;
+    return *this;
+  }
+
+  T m_val;
+};
+
+template <typename Target, typename Packet, bool IsSame = is_same<Target, Packet>::value>
+struct preinterpret_generic;
+
+template <typename Target, typename Packet>
+struct preinterpret_generic<Target, Packet, false> {
+  // the packets are not the same, attempt scalar bit_cast
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Target run(const Packet& a) {
+    return numext::bit_cast<Target, Packet>(a);
+  }
+};
+
+template <typename Packet>
+struct preinterpret_generic<Packet, Packet, true> {
+  // the packets are the same type: do nothing
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& a) { return a; }
+};
+
+template <typename ComplexPacket>
+struct preinterpret_generic<typename unpacket_traits<ComplexPacket>::as_real, ComplexPacket, false> {
+  using RealPacket = typename unpacket_traits<ComplexPacket>::as_real;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE RealPacket run(const ComplexPacket& a) { return a.v; }
+};
+
+/** \internal \returns reinterpret_cast<Target>(a) */
+template <typename Target, typename Packet>
+EIGEN_DEVICE_FUNC inline Target preinterpret(const Packet& a) {
+  return preinterpret_generic<Target, Packet>::run(a);
+}
+
+template <typename SrcPacket, typename TgtPacket, bool Degenerate = is_degenerate<SrcPacket, TgtPacket>::value,
+          bool TgtIsHalf = is_half<TgtPacket>::value>
+struct pcast_generic;
+
+template <typename SrcPacket, typename TgtPacket>
+struct pcast_generic<SrcPacket, TgtPacket, false, false> {
+  // the packets are not degenerate: attempt scalar static_cast
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket run(const SrcPacket& a) {
+    return cast_impl<SrcPacket, TgtPacket>::run(a);
+  }
+};
+
+template <typename Packet>
+struct pcast_generic<Packet, Packet, true, false> {
+  // the packets are the same: do nothing
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& a) { return a; }
+};
+
+template <typename SrcPacket, typename TgtPacket, bool TgtIsHalf>
+struct pcast_generic<SrcPacket, TgtPacket, true, TgtIsHalf> {
+  // the packets are degenerate: preinterpret is equivalent to pcast
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket run(const SrcPacket& a) { return preinterpret<TgtPacket>(a); }
+};
+
+/** \internal \returns static_cast<TgtType>(a) (coeff-wise) */
+template <typename SrcPacket, typename TgtPacket>
+EIGEN_DEVICE_FUNC inline TgtPacket pcast(const SrcPacket& a) {
+  return pcast_generic<SrcPacket, TgtPacket>::run(a);
+}
+template <typename SrcPacket, typename TgtPacket>
+EIGEN_DEVICE_FUNC inline TgtPacket pcast(const SrcPacket& a, const SrcPacket& b) {
+  return pcast_generic<SrcPacket, TgtPacket>::run(a, b);
+}
+template <typename SrcPacket, typename TgtPacket>
+EIGEN_DEVICE_FUNC inline TgtPacket pcast(const SrcPacket& a, const SrcPacket& b, const SrcPacket& c,
+                                         const SrcPacket& d) {
+  return pcast_generic<SrcPacket, TgtPacket>::run(a, b, c, d);
+}
+template <typename SrcPacket, typename TgtPacket>
+EIGEN_DEVICE_FUNC inline TgtPacket pcast(const SrcPacket& a, const SrcPacket& b, const SrcPacket& c, const SrcPacket& d,
+                                         const SrcPacket& e, const SrcPacket& f, const SrcPacket& g,
+                                         const SrcPacket& h) {
+  return pcast_generic<SrcPacket, TgtPacket>::run(a, b, c, d, e, f, g, h);
+}
+
+template <typename SrcPacket, typename TgtPacket>
+struct pcast_generic<SrcPacket, TgtPacket, false, true> {
+  // TgtPacket is a half packet of some other type
+  // perform cast and truncate result
+  using DefaultTgtPacket = typename is_half<TgtPacket>::DefaultPacket;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket run(const SrcPacket& a) {
+    return preinterpret<TgtPacket>(pcast<SrcPacket, DefaultTgtPacket>(a));
+  }
+};
+
+/** \internal \returns a + b (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet padd(const Packet& a, const Packet& b) {
+  return a + b;
+}
+// Avoid compiler warning for boolean algebra.
+template <>
+EIGEN_DEVICE_FUNC inline bool padd(const bool& a, const bool& b) {
+  return a || b;
+}
+
+/** \internal \returns a packet version of \a *from, (un-aligned masked add)
+ * There is no generic implementation. We only have implementations for specialized
+ * cases. Generic case should not be called.
+ */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline std::enable_if_t<unpacket_traits<Packet>::masked_fpops_available, Packet> padd(
+    const Packet& a, const Packet& b, typename unpacket_traits<Packet>::mask_t umask);
+
+/** \internal \returns a - b (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet psub(const Packet& a, const Packet& b) {
+  return a - b;
+}
+
+/** \internal \returns -a (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pnegate(const Packet& a) {
+  EIGEN_STATIC_ASSERT((!is_same<typename unpacket_traits<Packet>::type, bool>::value),
+                      NEGATE IS NOT DEFINED FOR BOOLEAN TYPES)
+  return numext::negate(a);
+}
+
+/** \internal \returns conj(a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pconj(const Packet& a) {
+  return numext::conj(a);
+}
+
+/** \internal \returns a * b (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmul(const Packet& a, const Packet& b) {
+  return a * b;
+}
+// Avoid compiler warning for boolean algebra.
+template <>
+EIGEN_DEVICE_FUNC inline bool pmul(const bool& a, const bool& b) {
+  return a && b;
+}
+
+/** \internal \returns a / b (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pdiv(const Packet& a, const Packet& b) {
+  return a / b;
+}
+// Avoid compiler warning for boolean algebra.
+template <>
+EIGEN_DEVICE_FUNC inline bool pdiv(const bool& a, const bool& b) {
+  return a && b;
+}
+
+// In the generic packet case, memset to all one bits.
+template <typename Packet, typename EnableIf = void>
+struct ptrue_impl {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& /*a*/) {
+    Packet b;
+    memset(static_cast<void*>(&b), 0xff, sizeof(Packet));
+    return b;
+  }
+};
+
+// Use a value of one for scalars.
+template <typename Scalar>
+struct ptrue_impl<Scalar, std::enable_if_t<is_scalar<Scalar>::value>> {
+  static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar&) { return Scalar(1); }
+};
+
+// For booleans, we can only directly set a valid `bool` value to avoid UB.
+template <>
+struct ptrue_impl<bool, void> {
+  static EIGEN_DEVICE_FUNC inline bool run(const bool&) { return true; }
+};
+
+/** \internal \returns one bits. */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet ptrue(const Packet& a) {
+  return ptrue_impl<Packet>::run(a);
+}
+
+// In the general packet case, memset to zero.
+template <typename Packet, typename EnableIf = void>
+struct pzero_impl {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& /*a*/) {
+    Packet b;
+    memset(static_cast<void*>(&b), 0x00, sizeof(Packet));
+    return b;
+  }
+};
+
+// For scalars, explicitly set to Scalar(0), since the underlying representation
+// for zero may not consist of all-zero bits.
+template <typename T>
+struct pzero_impl<T, std::enable_if_t<is_scalar<T>::value>> {
+  static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/) { return T(0); }
+};
+
+/** \internal \returns packet of zeros */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pzero(const Packet& a) {
+  return pzero_impl<Packet>::run(a);
+}
+
+template <typename T>
+struct bit_and {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const { return a & b; }
+};
+
+template <typename T>
+struct bit_or {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const { return a | b; }
+};
+
+template <typename T>
+struct bit_xor {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const { return a ^ b; }
+};
+
+template <typename T>
+struct bit_not {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE T operator()(const T& a) const { return ~a; }
+};
+
+template <>
+struct bit_and<bool> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE bool operator()(const bool& a, const bool& b) const { return a && b; }
+};
+
+template <>
+struct bit_or<bool> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE bool operator()(const bool& a, const bool& b) const { return a || b; }
+};
+
+template <>
+struct bit_xor<bool> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE bool operator()(const bool& a, const bool& b) const { return a != b; }
+};
+
+template <>
+struct bit_not<bool> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE bool operator()(const bool& a) const { return !a; }
+};
+
+// Use operators &, |, ^, ~.
+template <typename T>
+struct operator_bitwise_helper {
+  EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) { return bit_and<T>()(a, b); }
+  EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) { return bit_or<T>()(a, b); }
+  EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) { return bit_xor<T>()(a, b); }
+  EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) { return bit_not<T>()(a); }
+};
+
+// Apply binary operations byte-by-byte
+template <typename T>
+struct bytewise_bitwise_helper {
+  EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) {
+    return binary(a, b, bit_and<unsigned char>());
+  }
+  EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) { return binary(a, b, bit_or<unsigned char>()); }
+  EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) {
+    return binary(a, b, bit_xor<unsigned char>());
+  }
+  EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) { return unary(a, bit_not<unsigned char>()); }
+
+ private:
+  template <typename Op>
+  EIGEN_DEVICE_FUNC static inline T unary(const T& a, Op op) {
+    const unsigned char* a_ptr = reinterpret_cast<const unsigned char*>(&a);
+    T c;
+    unsigned char* c_ptr = reinterpret_cast<unsigned char*>(&c);
+    for (size_t i = 0; i < sizeof(T); ++i) {
+      *c_ptr++ = op(*a_ptr++);
+    }
+    return c;
+  }
+
+  template <typename Op>
+  EIGEN_DEVICE_FUNC static inline T binary(const T& a, const T& b, Op op) {
+    const unsigned char* a_ptr = reinterpret_cast<const unsigned char*>(&a);
+    const unsigned char* b_ptr = reinterpret_cast<const unsigned char*>(&b);
+    T c;
+    unsigned char* c_ptr = reinterpret_cast<unsigned char*>(&c);
+    for (size_t i = 0; i < sizeof(T); ++i) {
+      *c_ptr++ = op(*a_ptr++, *b_ptr++);
+    }
+    return c;
+  }
+};
+
+// In the general case, use byte-by-byte manipulation.
+template <typename T, typename EnableIf = void>
+struct bitwise_helper : public bytewise_bitwise_helper<T> {};
+
+// For integers or non-trivial scalars, use binary operators.
+template <typename T>
+struct bitwise_helper<T, typename std::enable_if_t<is_scalar<T>::value &&
+                                                   (NumTraits<T>::IsInteger || NumTraits<T>::RequireInitialization)>>
+    : public operator_bitwise_helper<T> {};
+
+/** \internal \returns the bitwise and of \a a and \a b */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pand(const Packet& a, const Packet& b) {
+  return bitwise_helper<Packet>::bitwise_and(a, b);
+}
+
+/** \internal \returns the bitwise or of \a a and \a b */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet por(const Packet& a, const Packet& b) {
+  return bitwise_helper<Packet>::bitwise_or(a, b);
+}
+
+/** \internal \returns the bitwise xor of \a a and \a b */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pxor(const Packet& a, const Packet& b) {
+  return bitwise_helper<Packet>::bitwise_xor(a, b);
+}
+
+/** \internal \returns the bitwise not of \a a */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pnot(const Packet& a) {
+  return bitwise_helper<Packet>::bitwise_not(a);
+}
+
+/** \internal \returns the bitwise and of \a a and not \a b */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pandnot(const Packet& a, const Packet& b) {
+  return pand(a, pnot(b));
+}
+
+/** \internal \returns a < b as a bit mask */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pcmp_lt(const Packet& a, const Packet& b) {
+  return a < b ? ptrue(a) : pzero(a);
+}
+
+/** \internal \returns a == b as a bit mask */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pcmp_eq(const Packet& a, const Packet& b) {
+  return a == b ? ptrue(a) : pzero(a);
+}
+
+/** \internal \returns a <= b as a bit mask */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pcmp_le(const Packet& a, const Packet& b) {
+  return por(pcmp_eq(a, b), pcmp_lt(a, b));
+}
+
+/** \internal \returns a < b or a==NaN or b==NaN as a bit mask */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pcmp_lt_or_nan(const Packet& a, const Packet& b) {
+  return a >= b ? pzero(a) : ptrue(a);
+}
+
+// In the general case, use bitwise select.
+template <typename Packet, bool is_scalar = is_scalar<Packet>::value>
+struct pselect_impl {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) {
+    return por(pand(a, mask), pandnot(b, mask));
+  }
+};
+
+// For scalars, use ternary select.
+template <typename Packet>
+struct pselect_impl<Packet, true> {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) {
+    return numext::select(mask, a, b);
+  }
+};
+
+/** \internal \returns \a or \b for each field in packet according to \mask */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pselect(const Packet& mask, const Packet& a, const Packet& b) {
+  return pselect_impl<Packet>::run(mask, a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline bool pselect<bool>(const bool& cond, const bool& a, const bool& b) {
+  return cond ? a : b;
+}
+
+/** \internal \returns the min or max of \a a and \a b (coeff-wise)
+    If either \a a or \a b are NaN, the result is implementation defined. */
+template <int NaNPropagation, bool IsInteger>
+struct pminmax_impl {
+  template <typename Packet, typename Op>
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
+    return op(a, b);
+  }
+};
+
+/** \internal \returns the min or max of \a a and \a b (coeff-wise)
+    If either \a a or \a b are NaN, NaN is returned. */
+template <>
+struct pminmax_impl<PropagateNaN, false> {
+  template <typename Packet, typename Op>
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
+    Packet not_nan_mask_a = pcmp_eq(a, a);
+    Packet not_nan_mask_b = pcmp_eq(b, b);
+    return pselect(not_nan_mask_a, pselect(not_nan_mask_b, op(a, b), b), a);
+  }
+};
+
+/** \internal \returns the min or max of \a a and \a b (coeff-wise)
+    If both \a a and \a b are NaN, NaN is returned.
+    Equivalent to std::fmin(a, b).  */
+template <>
+struct pminmax_impl<PropagateNumbers, false> {
+  template <typename Packet, typename Op>
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
+    Packet not_nan_mask_a = pcmp_eq(a, a);
+    Packet not_nan_mask_b = pcmp_eq(b, b);
+    return pselect(not_nan_mask_a, pselect(not_nan_mask_b, op(a, b), a), b);
+  }
+};
+
+#define EIGEN_BINARY_OP_NAN_PROPAGATION(Type, Func) [](const Type& aa, const Type& bb) { return Func(aa, bb); }
+
+/** \internal \returns the min of \a a and \a b  (coeff-wise).
+    If \a a or \a b is NaN, the return value is implementation defined. */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmin(const Packet& a, const Packet& b) {
+  return numext::mini(a, b);
+}
+
+/** \internal \returns the min of \a a and \a b  (coeff-wise).
+    NaNPropagation determines the NaN propagation semantics. */
+template <int NaNPropagation, typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmin(const Packet& a, const Packet& b) {
+  constexpr bool IsInteger = NumTraits<typename unpacket_traits<Packet>::type>::IsInteger;
+  return pminmax_impl<NaNPropagation, IsInteger>::run(a, b, EIGEN_BINARY_OP_NAN_PROPAGATION(Packet, (pmin<Packet>)));
+}
+
+/** \internal \returns the max of \a a and \a b  (coeff-wise)
+    If \a a or \a b is NaN, the return value is implementation defined. */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmax(const Packet& a, const Packet& b) {
+  return numext::maxi(a, b);
+}
+
+/** \internal \returns the max of \a a and \a b  (coeff-wise).
+    NaNPropagation determines the NaN propagation semantics. */
+template <int NaNPropagation, typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmax(const Packet& a, const Packet& b) {
+  constexpr bool IsInteger = NumTraits<typename unpacket_traits<Packet>::type>::IsInteger;
+  return pminmax_impl<NaNPropagation, IsInteger>::run(a, b, EIGEN_BINARY_OP_NAN_PROPAGATION(Packet, (pmax<Packet>)));
+}
+
+/** \internal \returns the absolute value of \a a */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pabs(const Packet& a) {
+  return numext::abs(a);
+}
+template <>
+EIGEN_DEVICE_FUNC inline unsigned int pabs(const unsigned int& a) {
+  return a;
+}
+template <>
+EIGEN_DEVICE_FUNC inline unsigned long pabs(const unsigned long& a) {
+  return a;
+}
+template <>
+EIGEN_DEVICE_FUNC inline unsigned long long pabs(const unsigned long long& a) {
+  return a;
+}
+
+/** \internal \returns the addsub value of \a a,b */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet paddsub(const Packet& a, const Packet& b) {
+  return pselect(peven_mask(a), padd(a, b), psub(a, b));
+}
+
+/** \internal \returns the phase angle of \a a */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet parg(const Packet& a) {
+  using numext::arg;
+  return arg(a);
+}
+
+/** \internal \returns \a a arithmetically shifted by N bits to the right */
+template <int N, typename T>
+EIGEN_DEVICE_FUNC inline T parithmetic_shift_right(const T& a) {
+  return numext::arithmetic_shift_right(a, N);
+}
+
+/** \internal \returns \a a logically shifted by N bits to the right */
+template <int N, typename T>
+EIGEN_DEVICE_FUNC inline T plogical_shift_right(const T& a) {
+  return numext::logical_shift_right(a, N);
+}
+
+/** \internal \returns \a a shifted by N bits to the left */
+template <int N, typename T>
+EIGEN_DEVICE_FUNC inline T plogical_shift_left(const T& a) {
+  return numext::logical_shift_left(a, N);
+}
+
+/** \internal \returns the significant and exponent of the underlying floating point numbers
+ * See https://en.cppreference.com/w/cpp/numeric/math/frexp
+ */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pfrexp(const Packet& a, Packet& exponent) {
+  int exp;
+  EIGEN_USING_STD(frexp);
+  Packet result = static_cast<Packet>(frexp(a, &exp));
+  exponent = static_cast<Packet>(exp);
+  return result;
+}
+
+/** \internal \returns a * 2^((int)exponent)
+ * See https://en.cppreference.com/w/cpp/numeric/math/ldexp
+ */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pldexp(const Packet& a, const Packet& exponent) {
+  EIGEN_USING_STD(ldexp)
+  return static_cast<Packet>(ldexp(a, static_cast<int>(exponent)));
+}
+
+/** \internal \returns the min of \a a and \a b  (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline std::enable_if_t<NumTraits<typename unpacket_traits<Packet>::type>::IsInteger, Packet>
+pabsdiff(const Packet& a, const Packet& b) {
+  return pselect(pcmp_lt(a, b), psub(b, a), psub(a, b));
+}
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline std::enable_if_t<!NumTraits<typename unpacket_traits<Packet>::type>::IsInteger, Packet>
+pabsdiff(const Packet& a, const Packet& b) {
+  return pabs(psub(a, b));
+}
+
+/** \internal \returns a packet version of \a *from, from must be properly aligned */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pload(const typename unpacket_traits<Packet>::type* from) {
+  return *from;
+}
+
+/** \internal \returns n elements of a packet version of \a *from, from must be properly aligned
+ * offset indicates the starting element in which to load and
+ * offset + n <= unpacket_traits::size
+ * All elements before offset and after the last element loaded will initialized with zero */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pload_partial(const typename unpacket_traits<Packet>::type* from, const Index n,
+                                              const Index offset = 0) {
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_assert(n + offset <= packet_size && "number of elements plus offset will read past end of packet");
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  EIGEN_ALIGN_MAX Scalar elements[packet_size] = {Scalar(0)};
+  for (Index i = offset; i < numext::mini(n + offset, packet_size); i++) {
+    elements[i] = from[i - offset];
+  }
+  return pload<Packet>(elements);
+}
+
+/** \internal \returns a packet version of \a *from, (un-aligned load) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet ploadu(const typename unpacket_traits<Packet>::type* from) {
+  return *from;
+}
+
+/** \internal \returns n elements of a packet version of \a *from, (un-aligned load)
+ * All elements after the last element loaded will initialized with zero */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet ploadu_partial(const typename unpacket_traits<Packet>::type* from, const Index n,
+                                               const Index offset = 0) {
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_assert(n + offset <= packet_size && "number of elements plus offset will read past end of packet");
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  EIGEN_ALIGN_MAX Scalar elements[packet_size] = {Scalar(0)};
+  for (Index i = offset; i < numext::mini(n + offset, packet_size); i++) {
+    elements[i] = from[i - offset];
+  }
+  return pload<Packet>(elements);
+}
+
+/** \internal \returns a packet version of \a *from, (un-aligned masked load)
+ * There is no generic implementation. We only have implementations for specialized
+ * cases. Generic case should not be called.
+ */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline std::enable_if_t<unpacket_traits<Packet>::masked_load_available, Packet> ploadu(
+    const typename unpacket_traits<Packet>::type* from, typename unpacket_traits<Packet>::mask_t umask);
+
+/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pset1(const typename unpacket_traits<Packet>::type& a) {
+  return a;
+}
+
+/** \internal \returns a packet with constant coefficients set from bits */
+template <typename Packet, typename BitsType>
+EIGEN_DEVICE_FUNC inline Packet pset1frombits(BitsType a);
+
+/** \internal \returns a packet with constant coefficients \a a[0], e.g.: (a[0],a[0],a[0],a[0]) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pload1(const typename unpacket_traits<Packet>::type* a) {
+  return pset1<Packet>(*a);
+}
+
+/** \internal \returns a packet with elements of \a *from duplicated.
+ * For instance, for a packet of 8 elements, 4 scalars will be read from \a *from and
+ * duplicated to form: {from[0],from[0],from[1],from[1],from[2],from[2],from[3],from[3]}
+ * Currently, this function is only used for scalar * complex products.
+ */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet ploaddup(const typename unpacket_traits<Packet>::type* from) {
+  return *from;
+}
+
+/** \internal \returns a packet with elements of \a *from quadrupled.
+ * For instance, for a packet of 8 elements, 2 scalars will be read from \a *from and
+ * replicated to form: {from[0],from[0],from[0],from[0],from[1],from[1],from[1],from[1]}
+ * Currently, this function is only used in matrix products.
+ * For packet-size smaller or equal to 4, this function is equivalent to pload1
+ */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet ploadquad(const typename unpacket_traits<Packet>::type* from) {
+  return pload1<Packet>(from);
+}
+
+/** \internal equivalent to
+ * \code
+ * a0 = pload1(a+0);
+ * a1 = pload1(a+1);
+ * a2 = pload1(a+2);
+ * a3 = pload1(a+3);
+ * \endcode
+ * \sa pset1, pload1, ploaddup, pbroadcast2
+ */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline void pbroadcast4(const typename unpacket_traits<Packet>::type* a, Packet& a0, Packet& a1,
+                                          Packet& a2, Packet& a3) {
+  a0 = pload1<Packet>(a + 0);
+  a1 = pload1<Packet>(a + 1);
+  a2 = pload1<Packet>(a + 2);
+  a3 = pload1<Packet>(a + 3);
+}
+
+/** \internal equivalent to
+ * \code
+ * a0 = pload1(a+0);
+ * a1 = pload1(a+1);
+ * \endcode
+ * \sa pset1, pload1, ploaddup, pbroadcast4
+ */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline void pbroadcast2(const typename unpacket_traits<Packet>::type* a, Packet& a0, Packet& a1) {
+  a0 = pload1<Packet>(a + 0);
+  a1 = pload1<Packet>(a + 1);
+}
+
+/** \internal \brief Returns a packet with coefficients (a,a+1,...,a+packet_size-1). */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet plset(const typename unpacket_traits<Packet>::type& a) {
+  return a;
+}
+
+template <typename Packet, typename EnableIf = void>
+struct peven_mask_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet&) {
+    typedef typename unpacket_traits<Packet>::type Scalar;
+    const size_t n = unpacket_traits<Packet>::size;
+    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Scalar elements[n];
+    for (size_t i = 0; i < n; ++i) {
+      memset(elements + i, ((i & 1) == 0 ? 0xff : 0), sizeof(Scalar));
+    }
+    return ploadu<Packet>(elements);
+  }
+};
+
+template <typename Scalar>
+struct peven_mask_impl<Scalar, std::enable_if_t<is_scalar<Scalar>::value>> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run(const Scalar&) { return Scalar(1); }
+};
+
+/** \internal \returns a packet with constant coefficients \a a, e.g.: (x, 0, x, 0),
+     where x is the value of all 1-bits. */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet peven_mask(const Packet& a) {
+  return peven_mask_impl<Packet>::run(a);
+}
+
+/** \internal copy the packet \a from to \a *to, \a to must be properly aligned */
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline void pstore(Scalar* to, const Packet& from) {
+  (*to) = from;
+}
+
+/** \internal copy n elements of the packet \a from to \a *to, \a to must be properly aligned
+ * offset indicates the starting element in which to store and
+ * offset + n <= unpacket_traits::size */
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline void pstore_partial(Scalar* to, const Packet& from, const Index n, const Index offset = 0) {
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_assert(n + offset <= packet_size && "number of elements plus offset will write past end of packet");
+  EIGEN_ALIGN_MAX Scalar elements[packet_size];
+  pstore<Scalar>(elements, from);
+  for (Index i = 0; i < numext::mini(n, packet_size - offset); i++) {
+    to[i] = elements[i + offset];
+  }
+}
+
+/** \internal copy the packet \a from to \a *to, (un-aligned store) */
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline void pstoreu(Scalar* to, const Packet& from) {
+  (*to) = from;
+}
+
+/** \internal copy n elements of the packet \a from to \a *to, (un-aligned store) */
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline void pstoreu_partial(Scalar* to, const Packet& from, const Index n, const Index offset = 0) {
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_assert(n + offset <= packet_size && "number of elements plus offset will write past end of packet");
+  EIGEN_ALIGN_MAX Scalar elements[packet_size];
+  pstore<Scalar>(elements, from);
+  for (Index i = 0; i < numext::mini(n, packet_size - offset); i++) {
+    to[i] = elements[i + offset];
+  }
+}
+
+/** \internal copy the packet \a from to \a *to, (un-aligned store with a mask)
+ * There is no generic implementation. We only have implementations for specialized
+ * cases. Generic case should not be called.
+ */
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline std::enable_if_t<unpacket_traits<Packet>::masked_store_available, void> pstoreu(
+    Scalar* to, const Packet& from, typename unpacket_traits<Packet>::mask_t umask);
+
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pgather(const Scalar* from, Index /*stride*/) {
+  return ploadu<Packet>(from);
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pgather_partial(const Scalar* from, Index stride, const Index n) {
+  const Index packet_size = unpacket_traits<Packet>::size;
+  EIGEN_ALIGN_MAX Scalar elements[packet_size] = {Scalar(0)};
+  for (Index i = 0; i < numext::mini(n, packet_size); i++) {
+    elements[i] = from[i * stride];
+  }
+  return pload<Packet>(elements);
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, Index /*stride*/) {
+  pstore(to, from);
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline void pscatter_partial(Scalar* to, const Packet& from, Index stride, const Index n) {
+  const Index packet_size = unpacket_traits<Packet>::size;
+  EIGEN_ALIGN_MAX Scalar elements[packet_size];
+  pstore<Scalar>(elements, from);
+  for (Index i = 0; i < numext::mini(n, packet_size); i++) {
+    to[i * stride] = elements[i];
+  }
+}
+
+/** \internal tries to do cache prefetching of \a addr */
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* addr) {
+#if defined(EIGEN_HIP_DEVICE_COMPILE)
+  // do nothing
+#elif defined(EIGEN_CUDA_ARCH)
+#if defined(__LP64__) || EIGEN_OS_WIN64
+  // 64-bit pointer operand constraint for inlined asm
+  asm(" prefetch.L1 [ %1 ];" : "=l"(addr) : "l"(addr));
+#else
+  // 32-bit pointer operand constraint for inlined asm
+  asm(" prefetch.L1 [ %1 ];" : "=r"(addr) : "r"(addr));
+#endif
+#elif (!EIGEN_COMP_MSVC) && (EIGEN_COMP_GNUC || EIGEN_COMP_CLANG || EIGEN_COMP_ICC)
+  __builtin_prefetch(addr);
+#endif
+}
+
+/** \internal \returns the reversed elements of \a a*/
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet preverse(const Packet& a) {
+  return a;
+}
+
+/** \internal \returns \a a with real and imaginary parts flipped (for complex types only) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a) {
+  return Packet(numext::imag(a), numext::real(a));
+}
+
+/** \internal \returns \a a with real part duplicated (for complex types only) */
+// TODO(rmlarsen): Define and use in all complex backends.
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pdupreal(const Packet& a) {
+  return Packet(numext::real(a), numext::real(a));
+}
+
+/** \internal \returns \a a with imaginary part duplicated (for complex types only) */
+// TODO(rmlarsen): Define and use in all complex backends.
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pdupimag(const Packet& a) {
+  return Packet(numext::imag(a), numext::imag(a));
+}
+
+/**************************
+ * Special math functions
+ ***************************/
+
+/** \internal \returns isnan(a) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pisnan(const Packet& a) {
+  return pandnot(ptrue(a), pcmp_eq(a, a));
+}
+
+/** \internal \returns isinf(a) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pisinf(const Packet& a) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  constexpr Scalar inf = NumTraits<Scalar>::infinity();
+  return pcmp_eq(pabs(a), pset1<Packet>(inf));
+}
+
+/** \internal \returns the sine of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psin(const Packet& a) {
+  EIGEN_USING_STD(sin);
+  return sin(a);
+}
+
+/** \internal \returns the cosine of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcos(const Packet& a) {
+  EIGEN_USING_STD(cos);
+  return cos(a);
+}
+
+/** \internal \returns the tan of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet ptan(const Packet& a) {
+  EIGEN_USING_STD(tan);
+  return tan(a);
+}
+
+/** \internal \returns the arc sine of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pasin(const Packet& a) {
+  EIGEN_USING_STD(asin);
+  return asin(a);
+}
+
+/** \internal \returns the arc cosine of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pacos(const Packet& a) {
+  EIGEN_USING_STD(acos);
+  return acos(a);
+}
+
+/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psinh(const Packet& a) {
+  EIGEN_USING_STD(sinh);
+  return sinh(a);
+}
+
+/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcosh(const Packet& a) {
+  EIGEN_USING_STD(cosh);
+  return cosh(a);
+}
+
+/** \internal \returns the arc tangent of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet patan(const Packet& a) {
+  EIGEN_USING_STD(atan);
+  return atan(a);
+}
+
+/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet ptanh(const Packet& a) {
+  EIGEN_USING_STD(tanh);
+  return tanh(a);
+}
+
+/** \internal \returns the arc tangent of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet patanh(const Packet& a) {
+  EIGEN_USING_STD(atanh);
+  return atanh(a);
+}
+
+/** \internal \returns the exp of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pexp(const Packet& a) {
+  return numext::exp(a);
+}
+
+/** \internal \returns the exp2 of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pexp2(const Packet& a) {
+  return numext::exp2(a);
+}
+
+/** \internal \returns the expm1 of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pexpm1(const Packet& a) {
+  return numext::expm1(a);
+}
+
+/** \internal \returns the log of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog(const Packet& a) {
+  EIGEN_USING_STD(log);
+  return log(a);
+}
+
+/** \internal \returns the log1p of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog1p(const Packet& a) {
+  return numext::log1p(a);
+}
+
+/** \internal \returns the log10 of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog10(const Packet& a) {
+  EIGEN_USING_STD(log10);
+  return log10(a);
+}
+
+/** \internal \returns the log2 of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog2(const Packet& a) {
+  using Scalar = typename internal::unpacket_traits<Packet>::type;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+  return pmul(pset1<Packet>(Scalar(RealScalar(EIGEN_LOG2E))), plog(a));
+}
+
+/** \internal \returns the square-root of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psqrt(const Packet& a) {
+  return numext::sqrt(a);
+}
+
+/** \internal \returns the cube-root of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcbrt(const Packet& a) {
+  return numext::cbrt(a);
+}
+
+template <typename Packet, bool IsScalar = is_scalar<Packet>::value,
+          bool IsInteger = NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>
+struct nearest_integer_packetop_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_floor(const Packet& x) { return numext::floor(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_ceil(const Packet& x) { return numext::ceil(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_rint(const Packet& x) { return numext::rint(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_round(const Packet& x) { return numext::round(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_trunc(const Packet& x) { return numext::trunc(x); }
+};
+
+/** \internal \returns the rounded value of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pround(const Packet& a) {
+  return nearest_integer_packetop_impl<Packet>::run_round(a);
+}
+
+/** \internal \returns the floor of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pfloor(const Packet& a) {
+  return nearest_integer_packetop_impl<Packet>::run_floor(a);
+}
+
+/** \internal \returns the rounded value of \a a (coeff-wise) with current
+ * rounding mode */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet print(const Packet& a) {
+  return nearest_integer_packetop_impl<Packet>::run_rint(a);
+}
+
+/** \internal \returns the ceil of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pceil(const Packet& a) {
+  return nearest_integer_packetop_impl<Packet>::run_ceil(a);
+}
+
+/** \internal \returns the truncation of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet ptrunc(const Packet& a) {
+  return nearest_integer_packetop_impl<Packet>::run_trunc(a);
+}
+
+template <typename Packet, typename EnableIf = void>
+struct psign_impl {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) { return numext::sign(a); }
+};
+
+/** \internal \returns the sign of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet psign(const Packet& a) {
+  return psign_impl<Packet>::run(a);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline bool psign(const bool& a) {
+  return a;
+}
+
+/** \internal \returns the first element of a packet */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type pfirst(const Packet& a) {
+  return a;
+}
+
+/** \internal \returns the sum of the elements of upper and lower half of \a a if \a a is larger than 4.
+ * For a packet {a0, a1, a2, a3, a4, a5, a6, a7}, it returns a half packet {a0+a4, a1+a5, a2+a6, a3+a7}
+ * For packet-size smaller or equal to 4, this boils down to a noop.
+ */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline std::conditional_t<(unpacket_traits<Packet>::size % 8) == 0,
+                                            typename unpacket_traits<Packet>::half, Packet>
+predux_half(const Packet& a) {
+  return a;
+}
+
+// Slow generic implementation of Packet reduction.
+template <typename Packet, typename Op>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_helper(const Packet& a, Op op) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  const size_t n = unpacket_traits<Packet>::size;
+  EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Scalar elements[n];
+  pstoreu<Scalar>(elements, a);
+  for (size_t k = n / 2; k > 0; k /= 2) {
+    for (size_t i = 0; i < k; ++i) {
+      elements[i] = op(elements[i], elements[i + k]);
+    }
+  }
+  return elements[0];
+}
+
+/** \internal \returns the sum of the elements of \a a*/
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux(const Packet& a) {
+  return a;
+}
+
+/** \internal \returns the product of the elements of \a a */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmul<Scalar>)));
+}
+
+/** \internal \returns the min of the elements of \a a */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<Scalar>)));
+}
+
+/** \internal \returns the max of the elements of \a a */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<Scalar>)));
+}
+
+template <int NaNPropagation, typename Packet>
+struct predux_min_max_helper_impl {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  static constexpr bool UsePredux_ = NaNPropagation == PropagateFast || NumTraits<Scalar>::IsInteger;
+  template <bool UsePredux = UsePredux_, std::enable_if_t<!UsePredux, bool> = true>
+  static EIGEN_DEVICE_FUNC inline Scalar run_min(const Packet& a) {
+    return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<NaNPropagation, Scalar>)));
+  }
+  template <bool UsePredux = UsePredux_, std::enable_if_t<!UsePredux, bool> = true>
+  static EIGEN_DEVICE_FUNC inline Scalar run_max(const Packet& a) {
+    return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<NaNPropagation, Scalar>)));
+  }
+  template <bool UsePredux = UsePredux_, std::enable_if_t<UsePredux, bool> = true>
+  static EIGEN_DEVICE_FUNC inline Scalar run_min(const Packet& a) {
+    return predux_min(a);
+  }
+  template <bool UsePredux = UsePredux_, std::enable_if_t<UsePredux, bool> = true>
+  static EIGEN_DEVICE_FUNC inline Scalar run_max(const Packet& a) {
+    return predux_max(a);
+  }
+};
+
+template <int NaNPropagation, typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(const Packet& a) {
+  return predux_min_max_helper_impl<NaNPropagation, Packet>::run_min(a);
+}
+
+template <int NaNPropagation, typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(const Packet& a) {
+  return predux_min_max_helper_impl<NaNPropagation, Packet>::run_max(a);
+}
+
+#undef EIGEN_BINARY_OP_NAN_PROPAGATION
+
+/** \internal \returns true if all coeffs of \a a means "true"
+ * It is supposed to be called on values returned by pcmp_*.
+ */
+// TODO: implement predux_all when needed.
+
+/** \internal \returns true if any coeffs of \a a means "true"
+ * It is supposed to be called on values returned by pcmp_*.
+ */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline bool predux_any(const Packet& a) {
+  // Dirty but generic implementation where "true" is assumed to be non 0 and all the sames.
+  // It is expected that "true" is either:
+  //  - Scalar(1)
+  //  - bits full of ones (NaN for floats),
+  //  - or first bit equals to 1 (1 for ints, smallest denormal for floats).
+  // For all these cases, taking the sum is just fine, and this boils down to a no-op for scalars.
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  return numext::not_equal_strict(predux(a), Scalar(0));
+}
+
+/***************************************************************************
+ * The following functions might not have to be overwritten for vectorized types
+ ***************************************************************************/
+
+template <typename Packet, typename EnableIf = void>
+struct pmadd_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pmadd(const Packet& a, const Packet& b, const Packet& c) {
+    return padd(pmul(a, b), c);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pmsub(const Packet& a, const Packet& b, const Packet& c) {
+    return psub(pmul(a, b), c);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pnmadd(const Packet& a, const Packet& b, const Packet& c) {
+    return psub(c, pmul(a, b));
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pnmsub(const Packet& a, const Packet& b, const Packet& c) {
+    return pnegate(padd(pmul(a, b), c));
+  }
+};
+
+template <typename Scalar>
+struct pmadd_impl<Scalar, std::enable_if_t<is_scalar<Scalar>::value && NumTraits<Scalar>::IsSigned>> {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar pmadd(const Scalar& a, const Scalar& b, const Scalar& c) {
+    return numext::madd<Scalar>(a, b, c);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar pmsub(const Scalar& a, const Scalar& b, const Scalar& c) {
+    return numext::madd<Scalar>(a, b, Scalar(-c));
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar pnmadd(const Scalar& a, const Scalar& b, const Scalar& c) {
+    return numext::madd<Scalar>(Scalar(-a), b, c);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar pnmsub(const Scalar& a, const Scalar& b, const Scalar& c) {
+    return -Scalar(numext::madd<Scalar>(a, b, c));
+  }
+};
+
+// Multiply-add instructions.
+/** \internal \returns a * b + c (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmadd(const Packet& a, const Packet& b, const Packet& c) {
+  return pmadd_impl<Packet>::pmadd(a, b, c);
+}
+
+/** \internal \returns a * b - c (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmsub(const Packet& a, const Packet& b, const Packet& c) {
+  return pmadd_impl<Packet>::pmsub(a, b, c);
+}
+
+/** \internal \returns -(a * b) + c (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pnmadd(const Packet& a, const Packet& b, const Packet& c) {
+  return pmadd_impl<Packet>::pnmadd(a, b, c);
+}
+
+/** \internal \returns -((a * b + c) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pnmsub(const Packet& a, const Packet& b, const Packet& c) {
+  return pmadd_impl<Packet>::pnmsub(a, b, c);
+}
+
+/** \internal copy a packet with constant coefficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned
+ */
+// NOTE: this function must really be templated on the packet type (think about different packet types for the same
+// scalar type)
+template <typename Packet>
+inline void pstore1(typename unpacket_traits<Packet>::type* to, const typename unpacket_traits<Packet>::type& a) {
+  pstore(to, pset1<Packet>(a));
+}
+
+/** \internal \returns a packet version of \a *from.
+ * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
+template <typename Packet, int Alignment>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt(const typename unpacket_traits<Packet>::type* from) {
+  if (Alignment >= unpacket_traits<Packet>::alignment)
+    return pload<Packet>(from);
+  else
+    return ploadu<Packet>(from);
+}
+
+/** \internal \returns n elements of a packet version of \a *from.
+ * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
+template <typename Packet, int Alignment>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_partial(const typename unpacket_traits<Packet>::type* from,
+                                                            const Index n, const Index offset = 0) {
+  if (Alignment >= unpacket_traits<Packet>::alignment)
+    return pload_partial<Packet>(from, n, offset);
+  else
+    return ploadu_partial<Packet>(from, n, offset);
+}
+
+/** \internal copy the packet \a from to \a *to.
+ * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
+template <typename Scalar, typename Packet, int Alignment>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(Scalar* to, const Packet& from) {
+  if (Alignment >= unpacket_traits<Packet>::alignment)
+    pstore(to, from);
+  else
+    pstoreu(to, from);
+}
+
+/** \internal copy n elements of the packet \a from to \a *to.
+ * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
+template <typename Scalar, typename Packet, int Alignment>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret_partial(Scalar* to, const Packet& from, const Index n,
+                                                           const Index offset = 0) {
+  if (Alignment >= unpacket_traits<Packet>::alignment)
+    pstore_partial(to, from, n, offset);
+  else
+    pstoreu_partial(to, from, n, offset);
+}
+
+/** \internal \returns a packet version of \a *from.
+ * Unlike ploadt, ploadt_ro takes advantage of the read-only memory path on the
+ * hardware if available to speedup the loading of data that won't be modified
+ * by the current computation.
+ */
+template <typename Packet, int LoadMode>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_ro(const typename unpacket_traits<Packet>::type* from) {
+  return ploadt<Packet, LoadMode>(from);
+}
+
+/***************************************************************************
+ * Fast complex products (GCC generates a function call which is very slow)
+ ***************************************************************************/
+
+// Eigen+CUDA does not support complexes.
+#if !defined(EIGEN_GPUCC)
+
+template <>
+inline std::complex<float> pmul(const std::complex<float>& a, const std::complex<float>& b) {
+  return std::complex<float>(a.real() * b.real() - a.imag() * b.imag(), a.imag() * b.real() + a.real() * b.imag());
+}
+
+template <>
+inline std::complex<double> pmul(const std::complex<double>& a, const std::complex<double>& b) {
+  return std::complex<double>(a.real() * b.real() - a.imag() * b.imag(), a.imag() * b.real() + a.real() * b.imag());
+}
+
+#endif
+
+/***************************************************************************
+ * PacketBlock, that is a collection of N packets where the number of words
+ * in the packet is a multiple of N.
+ ***************************************************************************/
+template <typename Packet, int N = unpacket_traits<Packet>::size>
+struct PacketBlock {
+  Packet packet[N];
+};
+
+template <typename Packet, int size = 1>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet, size>& /*kernel*/) {
+  // Nothing to do in the scalar case, i.e. a 1x1 matrix.
+}
+
+/** \internal \returns 1 / a (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet preciprocal(const Packet& a) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  return pdiv(pset1<Packet>(Scalar(1)), a);
+}
+
+/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet prsqrt(const Packet& a) {
+  return preciprocal<Packet>(psqrt(a));
+}
+
+template <typename Packet, bool IsScalar = is_scalar<Packet>::value,
+          bool IsInteger = NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>
+struct psignbit_impl;
+template <typename Packet, bool IsInteger>
+struct psignbit_impl<Packet, true, IsInteger> {
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Packet run(const Packet& a) { return numext::signbit(a); }
+};
+template <typename Packet>
+struct psignbit_impl<Packet, false, false> {
+  // generic implementation if not specialized in PacketMath.h
+  // slower than arithmetic shift
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static Packet run(const Packet& a) {
+    const Packet cst_pos_one = pset1<Packet>(Scalar(1));
+    const Packet cst_neg_one = pset1<Packet>(Scalar(-1));
+    return pcmp_eq(por(pand(a, cst_neg_one), cst_pos_one), cst_neg_one);
+  }
+};
+template <typename Packet>
+struct psignbit_impl<Packet, false, true> {
+  // generic implementation for integer packets
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Packet run(const Packet& a) { return pcmp_lt(a, pzero(a)); }
+};
+/** \internal \returns the sign bit of \a a as a bitmask*/
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE constexpr Packet psignbit(const Packet& a) {
+  return psignbit_impl<Packet>::run(a);
+}
+
+/** \internal \returns the 2-argument arc tangent of \a y and \a x (coeff-wise) */
+template <typename Packet, std::enable_if_t<is_scalar<Packet>::value, int> = 0>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet patan2(const Packet& y, const Packet& x) {
+  return numext::atan2(y, x);
+}
+
+/** \internal \returns the 2-argument arc tangent of \a y and \a x (coeff-wise) */
+template <typename Packet, std::enable_if_t<!is_scalar<Packet>::value, int> = 0>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet patan2(const Packet& y, const Packet& x) {
+  typedef typename internal::unpacket_traits<Packet>::type Scalar;
+
+  // See https://en.cppreference.com/w/cpp/numeric/math/atan2
+  // for how corner cases are supposed to be handled according to the
+  // IEEE floating-point standard (IEC 60559).
+  const Packet kSignMask = pset1<Packet>(-Scalar(0));
+  const Packet kZero = pzero(x);
+  const Packet kOne = pset1<Packet>(Scalar(1));
+  const Packet kPi = pset1<Packet>(Scalar(EIGEN_PI));
+
+  const Packet x_has_signbit = psignbit(x);
+  const Packet y_signmask = pand(y, kSignMask);
+  const Packet x_signmask = pand(x, kSignMask);
+  const Packet result_signmask = pxor(y_signmask, x_signmask);
+  const Packet shift = por(pand(x_has_signbit, kPi), y_signmask);
+
+  const Packet x_and_y_are_same = pcmp_eq(pabs(x), pabs(y));
+  const Packet x_and_y_are_zero = pcmp_eq(por(x, y), kZero);
+
+  Packet arg = pdiv(y, x);
+  arg = pselect(x_and_y_are_same, por(kOne, result_signmask), arg);
+  arg = pselect(x_and_y_are_zero, result_signmask, arg);
+
+  Packet result = patan(arg);
+  result = padd(result, shift);
+  return result;
+}
+
+/** \internal \returns the argument of \a a as a complex number */
+template <typename Packet, std::enable_if_t<is_scalar<Packet>::value, int> = 0>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pcarg(const Packet& a) {
+  return Packet(numext::arg(a));
+}
+
+/** \internal \returns the argument of \a a as a complex number */
+template <typename Packet, std::enable_if_t<!is_scalar<Packet>::value, int> = 0>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pcarg(const Packet& a) {
+  EIGEN_STATIC_ASSERT(NumTraits<typename unpacket_traits<Packet>::type>::IsComplex,
+                      THIS METHOD IS FOR COMPLEX TYPES ONLY)
+  using RealPacket = typename unpacket_traits<Packet>::as_real;
+  // a                                              // r     i    r     i    ...
+  RealPacket aflip = pcplxflip(a).v;                // i     r    i     r    ...
+  RealPacket result = patan2(aflip, a.v);           // atan2 crap atan2 crap ...
+  return (Packet)pand(result, peven_mask(result));  // atan2 0    atan2 0    ...
+}
+
+/** \internal \returns a packet populated with values in the range [begin, begin + count). Elements
+ * outside this range are not defined. \a *from does not need to be aligned, and can be null if \a count is zero.*/
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet ploaduSegment(const typename unpacket_traits<Packet>::type* from, Index begin,
+                                              Index count) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  constexpr Index PacketSize = unpacket_traits<Packet>::size;
+  eigen_assert((begin >= 0 && count >= 0 && begin + count <= PacketSize) && "invalid range");
+  Scalar aux[PacketSize] = {};
+  for (Index k = begin; k < begin + count; k++) {
+    aux[k] = from[k];
+  }
+  return ploadu<Packet>(aux);
+}
+
+/** \internal \returns a packet populated with values in the range [begin, begin + count). Elements
+ * outside this range are not defined. \a *from must be aligned, and cannot be null.*/
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet ploadSegment(const typename unpacket_traits<Packet>::type* from, Index begin,
+                                             Index count) {
+  return ploaduSegment<Packet>(from, begin, count);
+}
+
+/** \internal copy the packet \a from in the range [begin, begin + count) to \a *to.
+Elements outside of the range [begin, begin + count) are not defined. \a *to does not need to be aligned, and can be
+null if \a count is zero.*/
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline void pstoreuSegment(Scalar* to, const Packet& from, Index begin, Index count) {
+  constexpr Index PacketSize = unpacket_traits<Packet>::size;
+  eigen_assert((begin >= 0 && count >= 0 && begin + count <= PacketSize) && "invalid range");
+  Scalar aux[PacketSize];
+  pstoreu<Scalar, Packet>(aux, from);
+  for (Index k = begin; k < begin + count; k++) {
+    to[k] = aux[k];
+  }
+}
+
+/** \internal copy the packet \a from in the range [begin, begin + count) to \a *to.
+Elements outside of the range [begin, begin + count) are not defined. \a *to must be aligned, and cannot be
+null.*/
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline void pstoreSegment(Scalar* to, const Packet& from, Index begin, Index count) {
+  return pstoreuSegment(to, from, begin, count);
+}
+
+/** \internal \returns a packet populated with values in the range [begin, begin + count). Elements
+ * outside this range are not defined.*/
+template <typename Packet, int Alignment>
+EIGEN_DEVICE_FUNC inline Packet ploadtSegment(const typename unpacket_traits<Packet>::type* from, Index begin,
+                                              Index count) {
+  constexpr int RequiredAlignment = unpacket_traits<Packet>::alignment;
+  if (Alignment >= RequiredAlignment) {
+    return ploadSegment<Packet>(from, begin, count);
+  } else {
+    return ploaduSegment<Packet>(from, begin, count);
+  }
+}
+
+/** \internal copy the packet \a from in the range [begin, begin + count) to \a *to.
+Elements outside of the range [begin, begin + count) are not defined.*/
+template <typename Scalar, typename Packet, int Alignment>
+EIGEN_DEVICE_FUNC inline void pstoretSegment(Scalar* to, const Packet& from, Index begin, Index count) {
+  constexpr int RequiredAlignment = unpacket_traits<Packet>::alignment;
+  if (Alignment >= RequiredAlignment) {
+    pstoreSegment<Scalar, Packet>(to, from, begin, count);
+  } else {
+    pstoreuSegment<Scalar, Packet>(to, from, begin, count);
+  }
+}
+
+#ifndef EIGEN_NO_IO
+
+template <typename Packet>
+class StreamablePacket {
+ public:
+  using Scalar = typename unpacket_traits<Packet>::type;
+  StreamablePacket(const Packet& packet) { pstoreu(v_, packet); }
+
+  friend std::ostream& operator<<(std::ostream& os, const StreamablePacket& packet) {
+    os << "{" << packet.v_[0];
+    for (int i = 1; i < unpacket_traits<Packet>::size; ++i) {
+      os << "," << packet.v_[i];
+    }
+    os << "}";
+    return os;
+  }
+
+ private:
+  Scalar v_[unpacket_traits<Packet>::size];
+};
+
+/**
+ * \internal \returns an intermediary that can be used to ostream packets, e.g. for debugging.
+ */
+template <typename Packet>
+StreamablePacket<Packet> postream(const Packet& packet) {
+  return StreamablePacket<Packet>(packet);
+}
+
+#endif  // EIGEN_NO_IO
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GENERIC_PACKET_MATH_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/GlobalFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/GlobalFunctions.h
new file mode 100644
index 0000000..c32aac9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/GlobalFunctions.h
@@ -0,0 +1,230 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GLOBAL_FUNCTIONS_H
+#define EIGEN_GLOBAL_FUNCTIONS_H
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME, FUNCTOR, DOC_OP, DOC_DETAILS)                                    \
+  /** \returns an expression of the coefficient-wise DOC_OP of \a x                                             \
+                                                                                                              \ \
+    DOC_DETAILS                                                                                                 \
+                                                                                                              \ \
+    \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_##NAME">Math functions</a>, class CwiseUnaryOp   \
+    */                                                                                                          \
+  template <typename Derived>                                                                                   \
+  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> NAME(     \
+      const Eigen::ArrayBase<Derived>& x);
+
+#else
+
+#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME, FUNCTOR, DOC_OP, DOC_DETAILS)                                    \
+  template <typename Derived>                                                                                   \
+  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(NAME)(    \
+      const Eigen::ArrayBase<Derived>& x) {                                                                     \
+    return Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(x.derived()); \
+  }
+
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME, FUNCTOR)                                                  \
+                                                                                                               \
+  template <typename Derived>                                                                                  \
+  struct NAME##_retval<ArrayBase<Derived> > {                                                                  \
+    typedef const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> type; \
+  };                                                                                                           \
+  template <typename Derived>                                                                                  \
+  struct NAME##_impl<ArrayBase<Derived> > {                                                                    \
+    static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) {  \
+      return typename NAME##_retval<ArrayBase<Derived> >::type(x.derived());                                   \
+    }                                                                                                          \
+  };
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real, scalar_real_op, real part,\sa ArrayBase::real)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag, scalar_imag_op, imaginary part,\sa ArrayBase::imag)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj, scalar_conjugate_op, complex conjugate,\sa ArrayBase::conjugate)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(inverse, scalar_inverse_op, inverse,\sa ArrayBase::inverse)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin, scalar_sin_op, sine,\sa ArrayBase::sin)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos, scalar_cos_op, cosine,\sa ArrayBase::cos)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan, scalar_tan_op, tangent,\sa ArrayBase::tan)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan, scalar_atan_op, arc - tangent,\sa ArrayBase::atan)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin, scalar_asin_op, arc - sine,\sa ArrayBase::asin)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos, scalar_acos_op, arc - consine,\sa ArrayBase::acos)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh, scalar_sinh_op, hyperbolic sine,\sa ArrayBase::sinh)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh, scalar_cosh_op, hyperbolic cosine,\sa ArrayBase::cosh)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh, scalar_tanh_op, hyperbolic tangent,\sa ArrayBase::tanh)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asinh, scalar_asinh_op, inverse hyperbolic sine,\sa ArrayBase::asinh)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acosh, scalar_acosh_op, inverse hyperbolic cosine,\sa ArrayBase::acosh)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atanh, scalar_atanh_op, inverse hyperbolic tangent,\sa ArrayBase::atanh)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(logistic, scalar_logistic_op, logistic function,\sa ArrayBase::logistic)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma, scalar_lgamma_op,
+                                 natural logarithm of the gamma function,\sa ArrayBase::lgamma)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma, scalar_digamma_op, derivative of lgamma,\sa ArrayBase::digamma)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf, scalar_erf_op, error function,\sa ArrayBase::erf)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc, scalar_erfc_op, complement error function,\sa ArrayBase::erfc)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ndtri, scalar_ndtri_op, inverse normal distribution function,\sa ArrayBase::ndtri)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp, scalar_exp_op, exponential,\sa ArrayBase::exp)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp2, scalar_exp2_op, exponential,\sa ArrayBase::exp2)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(expm1, scalar_expm1_op, exponential of a value minus 1,\sa ArrayBase::expm1)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log, scalar_log_op, natural logarithm,\sa Eigen::log10 DOXCOMMA ArrayBase::log)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log1p, scalar_log1p_op, natural logarithm of 1 plus the value,\sa ArrayBase::log1p)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10, scalar_log10_op, base 10 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log10)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log2, scalar_log2_op, base 2 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log2)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs, scalar_abs_op, absolute value,\sa ArrayBase::abs DOXCOMMA MatrixBase::cwiseAbs)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2, scalar_abs2_op,
+                                 squared absolute value,\sa ArrayBase::abs2 DOXCOMMA MatrixBase::cwiseAbs2)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg, scalar_arg_op, complex argument,\sa ArrayBase::arg DOXCOMMA MatrixBase::cwiseArg)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(carg, scalar_carg_op,
+                                 complex argument, \sa ArrayBase::carg DOXCOMMA MatrixBase::cwiseCArg)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt, scalar_sqrt_op, square root,\sa ArrayBase::sqrt DOXCOMMA MatrixBase::cwiseSqrt)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cbrt, scalar_cbrt_op, cube root,\sa ArrayBase::cbrt DOXCOMMA MatrixBase::cwiseCbrt)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rsqrt, scalar_rsqrt_op, reciprocal square root,\sa ArrayBase::rsqrt)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square, scalar_square_op,
+                                 square(power 2),\sa Eigen::abs2 DOXCOMMA Eigen::pow DOXCOMMA ArrayBase::square)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube, scalar_cube_op, cube(power 3),\sa Eigen::pow DOXCOMMA ArrayBase::cube)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rint, scalar_rint_op,
+                                 nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round, scalar_round_op,
+                                 nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(
+    floor, scalar_floor_op, nearest integer not greater than the given value,\sa Eigen::ceil DOXCOMMA ArrayBase::floor)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(
+    ceil, scalar_ceil_op, nearest integer not less than the given value,\sa Eigen::floor DOXCOMMA ArrayBase::ceil)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(trunc, scalar_trunc_op,
+                                 nearest integer not greater in magnitude than the given value,\sa Eigen::trunc DOXCOMMA
+                                     ArrayBase::trunc)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(
+    isnan, scalar_isnan_op, not -a - number test,\sa Eigen::isinf DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isnan)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(
+    isinf, scalar_isinf_op, infinite value test,\sa Eigen::isnan DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isinf)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite, scalar_isfinite_op,
+                                 finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite)
+EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign, scalar_sign_op, sign(or 0),\sa ArrayBase::sign)
+
+template <typename Derived, typename ScalarExponent>
+using GlobalUnaryPowReturnType = std::enable_if_t<
+    !internal::is_arithmetic<typename NumTraits<Derived>::Real>::value &&
+        internal::is_arithmetic<typename NumTraits<ScalarExponent>::Real>::value,
+    CwiseUnaryOp<internal::scalar_unary_pow_op<typename Derived::Scalar, ScalarExponent>, const Derived> >;
+
+/** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent.
+ *
+ * \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given
+ * expression (\c Derived::Scalar).
+ *
+ * \sa ArrayBase::pow()
+ *
+ * \relates ArrayBase
+ */
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+template <typename Derived, typename ScalarExponent>
+EIGEN_DEVICE_FUNC constexpr inline const GlobalUnaryPowReturnType<Derived, ScalarExponent> pow(
+    const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent);
+#else
+template <typename Derived, typename ScalarExponent>
+EIGEN_DEVICE_FUNC constexpr inline const GlobalUnaryPowReturnType<Derived, ScalarExponent> pow(
+    const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent) {
+  return GlobalUnaryPowReturnType<Derived, ScalarExponent>(
+      x.derived(), internal::scalar_unary_pow_op<typename Derived::Scalar, ScalarExponent>(exponent));
+}
+#endif
+
+/** \returns an expression of the coefficient-wise power of \a x to the given array of \a exponents.
+ *
+ * This function computes the coefficient-wise power.
+ *
+ * Example: \include Cwise_array_power_array.cpp
+ * Output: \verbinclude Cwise_array_power_array.out
+ *
+ * \sa ArrayBase::pow()
+ *
+ * \relates ArrayBase
+ */
+template <typename Derived, typename ExponentDerived>
+inline const Eigen::CwiseBinaryOp<
+    Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived,
+    const ExponentDerived>
+pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents) {
+  return Eigen::CwiseBinaryOp<
+      Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived,
+      const ExponentDerived>(x.derived(), exponents.derived());
+}
+
+/** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents.
+ *
+ * This function computes the coefficient-wise power between a scalar and an array of exponents.
+ *
+ * \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression
+ * (\c Derived::Scalar).
+ *
+ * Example: \include Cwise_scalar_power_array.cpp
+ * Output: \verbinclude Cwise_scalar_power_array.out
+ *
+ * \sa ArrayBase::pow()
+ *
+ * \relates ArrayBase
+ */
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+template <typename Scalar, typename Derived>
+inline const CwiseBinaryOp<internal::scalar_pow_op<Scalar, Derived::Scalar>, Constant<Scalar>, Derived> pow(
+    const Scalar& x, const Eigen::ArrayBase<Derived>& x);
+#else
+template <typename Scalar, typename Derived>
+EIGEN_DEVICE_FUNC inline const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(
+    typename internal::promote_scalar_arg<typename Derived::Scalar EIGEN_COMMA Scalar EIGEN_COMMA
+                                              EIGEN_SCALAR_BINARY_SUPPORTED(pow, Scalar,
+                                                                            typename Derived::Scalar)>::type,
+    Derived, pow) pow(const Scalar& x, const Eigen::ArrayBase<Derived>& exponents) {
+  typedef
+      typename internal::promote_scalar_arg<typename Derived::Scalar, Scalar,
+                                            EIGEN_SCALAR_BINARY_SUPPORTED(pow, Scalar, typename Derived::Scalar)>::type
+          PromotedScalar;
+  return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedScalar, Derived, pow)(
+      typename internal::plain_constant_type<Derived, PromotedScalar>::type(
+          exponents.derived().rows(), exponents.derived().cols(), internal::scalar_constant_op<PromotedScalar>(x)),
+      exponents.derived());
+}
+#endif
+
+/** \returns an expression of the coefficient-wise atan2(\a x, \a y). \a x and \a y must be of the same type.
+ *
+ * This function computes the coefficient-wise atan2().
+ *
+ * \sa ArrayBase::atan2()
+ *
+ * \relates ArrayBase
+ */
+template <typename LhsDerived, typename RhsDerived>
+inline const std::enable_if_t<
+    std::is_same<typename LhsDerived::Scalar, typename RhsDerived::Scalar>::value,
+    Eigen::CwiseBinaryOp<Eigen::internal::scalar_atan2_op<typename LhsDerived::Scalar, typename RhsDerived::Scalar>,
+                         const LhsDerived, const RhsDerived> >
+atan2(const Eigen::ArrayBase<LhsDerived>& x, const Eigen::ArrayBase<RhsDerived>& exponents) {
+  return Eigen::CwiseBinaryOp<
+      Eigen::internal::scalar_atan2_op<typename LhsDerived::Scalar, typename RhsDerived::Scalar>, const LhsDerived,
+      const RhsDerived>(x.derived(), exponents.derived());
+}
+
+namespace internal {
+EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real, scalar_real_op)
+EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag, scalar_imag_op)
+EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2, scalar_abs2_op)
+}  // namespace internal
+}  // namespace Eigen
+
+// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random,
+// internal::isApprox...)
+
+#endif  // EIGEN_GLOBAL_FUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/IO.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/IO.h
new file mode 100644
index 0000000..e2362fc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/IO.h
@@ -0,0 +1,233 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_IO_H
+#define EIGEN_IO_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum { DontAlignCols = 1 };
+enum { StreamPrecision = -1, FullPrecision = -2 };
+
+namespace internal {
+template <typename Derived>
+std::ostream& print_matrix(std::ostream& s, const Derived& _m, const IOFormat& fmt);
+}
+
+/** \class IOFormat
+ * \ingroup Core_Module
+ *
+ * \brief Stores a set of parameters controlling the way matrices are printed
+ *
+ * List of available parameters:
+ *  - \b precision number of digits for floating point values, or one of the special constants \c StreamPrecision and \c
+ * FullPrecision. The default is the special value \c StreamPrecision which means to use the stream's own precision
+ * setting, as set for instance using \c cout.precision(3). The other special value \c FullPrecision means that the
+ * number of digits will be computed to match the full precision of each floating-point type.
+ *  - \b flags an OR-ed combination of flags, the default value is 0, the only currently available flag is \c
+ * DontAlignCols which allows to disable the alignment of columns, resulting in faster code.
+ *  - \b coeffSeparator string printed between two coefficients of the same row
+ *  - \b rowSeparator string printed between two rows
+ *  - \b rowPrefix string printed at the beginning of each row
+ *  - \b rowSuffix string printed at the end of each row
+ *  - \b matPrefix string printed at the beginning of the matrix
+ *  - \b matSuffix string printed at the end of the matrix
+ *  - \b fill character printed to fill the empty space in aligned columns
+ *
+ * Example: \include IOFormat.cpp
+ * Output: \verbinclude IOFormat.out
+ *
+ * \sa DenseBase::format(), class WithFormat
+ */
+struct IOFormat {
+  /** Default constructor, see class IOFormat for the meaning of the parameters */
+  IOFormat(int _precision = StreamPrecision, int _flags = 0, const std::string& _coeffSeparator = " ",
+           const std::string& _rowSeparator = "\n", const std::string& _rowPrefix = "",
+           const std::string& _rowSuffix = "", const std::string& _matPrefix = "", const std::string& _matSuffix = "",
+           const char _fill = ' ')
+      : matPrefix(_matPrefix),
+        matSuffix(_matSuffix),
+        rowPrefix(_rowPrefix),
+        rowSuffix(_rowSuffix),
+        rowSeparator(_rowSeparator),
+        rowSpacer(""),
+        coeffSeparator(_coeffSeparator),
+        fill(_fill),
+        precision(_precision),
+        flags(_flags) {
+    // TODO: check if rowPrefix, rowSuffix or rowSeparator contains a newline
+    // don't add rowSpacer if columns are not to be aligned
+    if ((flags & DontAlignCols)) return;
+    int i = int(matPrefix.length()) - 1;
+    while (i >= 0 && matPrefix[i] != '\n') {
+      rowSpacer += ' ';
+      i--;
+    }
+  }
+  std::string matPrefix, matSuffix;
+  std::string rowPrefix, rowSuffix, rowSeparator, rowSpacer;
+  std::string coeffSeparator;
+  char fill;
+  int precision;
+  int flags;
+};
+
+/** \class WithFormat
+ * \ingroup Core_Module
+ *
+ * \brief Pseudo expression providing matrix output with given format
+ *
+ * \tparam ExpressionType the type of the object on which IO stream operations are performed
+ *
+ * This class represents an expression with stream operators controlled by a given IOFormat.
+ * It is the return type of DenseBase::format()
+ * and most of the time this is the only way it is used.
+ *
+ * See class IOFormat for some examples.
+ *
+ * \sa DenseBase::format(), class IOFormat
+ */
+template <typename ExpressionType>
+class WithFormat {
+ public:
+  WithFormat(const ExpressionType& matrix, const IOFormat& format) : m_matrix(matrix), m_format(format) {}
+
+  friend std::ostream& operator<<(std::ostream& s, const WithFormat& wf) {
+    return internal::print_matrix(s, wf.m_matrix.eval(), wf.m_format);
+  }
+
+ protected:
+  typename ExpressionType::Nested m_matrix;
+  IOFormat m_format;
+};
+
+namespace internal {
+
+// NOTE: This helper is kept for backward compatibility with previous code specializing
+//       this internal::significant_decimals_impl structure. In the future we should directly
+//       call max_digits10().
+template <typename Scalar>
+struct significant_decimals_impl {
+  static inline int run() { return NumTraits<Scalar>::max_digits10(); }
+};
+
+/** \internal
+ * print the matrix \a _m to the output stream \a s using the output format \a fmt */
+template <typename Derived>
+std::ostream& print_matrix(std::ostream& s, const Derived& _m, const IOFormat& fmt) {
+  using internal::is_same;
+
+  if (_m.size() == 0) {
+    s << fmt.matPrefix << fmt.matSuffix;
+    return s;
+  }
+
+  typename Derived::Nested m = _m;
+  typedef typename Derived::Scalar Scalar;
+  typedef std::conditional_t<is_same<Scalar, char>::value || is_same<Scalar, unsigned char>::value ||
+                                 is_same<Scalar, numext::int8_t>::value || is_same<Scalar, numext::uint8_t>::value,
+                             int,
+                             std::conditional_t<is_same<Scalar, std::complex<char> >::value ||
+                                                    is_same<Scalar, std::complex<unsigned char> >::value ||
+                                                    is_same<Scalar, std::complex<numext::int8_t> >::value ||
+                                                    is_same<Scalar, std::complex<numext::uint8_t> >::value,
+                                                std::complex<int>, const Scalar&> >
+      PrintType;
+
+  Index width = 0;
+
+  std::streamsize explicit_precision;
+  if (fmt.precision == StreamPrecision) {
+    explicit_precision = 0;
+  } else if (fmt.precision == FullPrecision) {
+    if (NumTraits<Scalar>::IsInteger) {
+      explicit_precision = 0;
+    } else {
+      explicit_precision = significant_decimals_impl<Scalar>::run();
+    }
+  } else {
+    explicit_precision = fmt.precision;
+  }
+
+  std::streamsize old_precision = 0;
+  if (explicit_precision) old_precision = s.precision(explicit_precision);
+
+  bool align_cols = !(fmt.flags & DontAlignCols);
+  if (align_cols) {
+    // compute the largest width
+    for (Index j = 0; j < m.cols(); ++j)
+      for (Index i = 0; i < m.rows(); ++i) {
+        std::stringstream sstr;
+        sstr.copyfmt(s);
+        sstr << static_cast<PrintType>(m.coeff(i, j));
+        width = std::max<Index>(width, Index(sstr.str().length()));
+      }
+  }
+  std::streamsize old_width = s.width();
+  char old_fill_character = s.fill();
+  s << fmt.matPrefix;
+  for (Index i = 0; i < m.rows(); ++i) {
+    if (i) s << fmt.rowSpacer;
+    s << fmt.rowPrefix;
+    if (width) {
+      s.fill(fmt.fill);
+      s.width(width);
+    }
+    s << static_cast<PrintType>(m.coeff(i, 0));
+    for (Index j = 1; j < m.cols(); ++j) {
+      s << fmt.coeffSeparator;
+      if (width) {
+        s.fill(fmt.fill);
+        s.width(width);
+      }
+      s << static_cast<PrintType>(m.coeff(i, j));
+    }
+    s << fmt.rowSuffix;
+    if (i < m.rows() - 1) s << fmt.rowSeparator;
+  }
+  s << fmt.matSuffix;
+  if (explicit_precision) s.precision(old_precision);
+  if (width) {
+    s.fill(old_fill_character);
+    s.width(old_width);
+  }
+  return s;
+}
+
+}  // end namespace internal
+
+/** \relates DenseBase
+ *
+ * Outputs the matrix, to the given stream.
+ *
+ * If you wish to print the matrix with a format different than the default, use DenseBase::format().
+ *
+ * It is also possible to change the default format by defining EIGEN_DEFAULT_IO_FORMAT before including Eigen headers.
+ * If not defined, this will automatically be defined to Eigen::IOFormat(), that is the Eigen::IOFormat with default
+ * parameters.
+ *
+ * \sa DenseBase::format()
+ */
+template <typename Derived>
+std::ostream& operator<<(std::ostream& s, const DenseBase<Derived>& m) {
+  return internal::print_matrix(s, m.eval(), EIGEN_DEFAULT_IO_FORMAT);
+}
+
+template <typename Derived>
+std::ostream& operator<<(std::ostream& s, const DiagonalBase<Derived>& m) {
+  return internal::print_matrix(s, m.derived(), EIGEN_DEFAULT_IO_FORMAT);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_IO_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/IndexedView.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/IndexedView.h
new file mode 100644
index 0000000..44d5c02
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/IndexedView.h
@@ -0,0 +1,322 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INDEXED_VIEW_H
+#define EIGEN_INDEXED_VIEW_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename XprType, typename RowIndices, typename ColIndices>
+struct traits<IndexedView<XprType, RowIndices, ColIndices>> : traits<XprType> {
+  enum {
+    RowsAtCompileTime = int(IndexedViewHelper<RowIndices>::SizeAtCompileTime),
+    ColsAtCompileTime = int(IndexedViewHelper<ColIndices>::SizeAtCompileTime),
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+
+    XprTypeIsRowMajor = (int(traits<XprType>::Flags) & RowMajorBit) != 0,
+    IsRowMajor = (MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1)   ? 1
+                 : (MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1) ? 0
+                                                                            : XprTypeIsRowMajor,
+
+    RowIncr = int(IndexedViewHelper<RowIndices>::IncrAtCompileTime),
+    ColIncr = int(IndexedViewHelper<ColIndices>::IncrAtCompileTime),
+    InnerIncr = IsRowMajor ? ColIncr : RowIncr,
+    OuterIncr = IsRowMajor ? RowIncr : ColIncr,
+
+    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
+    XprInnerStride = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time<XprType>::ret)
+                                                  : int(outer_stride_at_compile_time<XprType>::ret),
+    XprOuterstride = HasSameStorageOrderAsXprType ? int(outer_stride_at_compile_time<XprType>::ret)
+                                                  : int(inner_stride_at_compile_time<XprType>::ret),
+
+    InnerSize = XprTypeIsRowMajor ? ColsAtCompileTime : RowsAtCompileTime,
+    IsBlockAlike = InnerIncr == 1 && OuterIncr == 1,
+    IsInnerPannel = HasSameStorageOrderAsXprType &&
+                    is_same<AllRange<InnerSize>, std::conditional_t<XprTypeIsRowMajor, ColIndices, RowIndices>>::value,
+
+    InnerStrideAtCompileTime =
+        InnerIncr < 0 || InnerIncr == DynamicIndex || XprInnerStride == Dynamic || InnerIncr == Undefined
+            ? Dynamic
+            : XprInnerStride * InnerIncr,
+    OuterStrideAtCompileTime =
+        OuterIncr < 0 || OuterIncr == DynamicIndex || XprOuterstride == Dynamic || OuterIncr == Undefined
+            ? Dynamic
+            : XprOuterstride * OuterIncr,
+
+    ReturnAsScalar = is_single_range<RowIndices>::value && is_single_range<ColIndices>::value,
+    ReturnAsBlock = (!ReturnAsScalar) && IsBlockAlike,
+    ReturnAsIndexedView = (!ReturnAsScalar) && (!ReturnAsBlock),
+
+    // FIXME: we deal with compile-time strides if and only if we have DirectAccessBit flag,
+    // but this is too strict regarding negative strides...
+    DirectAccessMask = (int(InnerIncr) != Undefined && int(OuterIncr) != Undefined && InnerIncr >= 0 && OuterIncr >= 0)
+                           ? DirectAccessBit
+                           : 0,
+    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
+    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
+    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
+    Flags = (traits<XprType>::Flags & (HereditaryBits | DirectAccessMask)) | FlagsLvalueBit | FlagsRowMajorBit |
+            FlagsLinearAccessBit
+  };
+
+  typedef Block<XprType, RowsAtCompileTime, ColsAtCompileTime, IsInnerPannel> BlockType;
+};
+
+template <typename XprType, typename RowIndices, typename ColIndices, typename StorageKind, bool DirectAccess>
+class IndexedViewImpl;
+
+}  // namespace internal
+
+/** \class IndexedView
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a non-sequential sub-matrix defined by arbitrary sequences of row and column indices
+ *
+ * \tparam XprType the type of the expression in which we are taking the intersections of sub-rows and sub-columns
+ * \tparam RowIndices the type of the object defining the sequence of row indices
+ * \tparam ColIndices the type of the object defining the sequence of column indices
+ *
+ * This class represents an expression of a sub-matrix (or sub-vector) defined as the intersection
+ * of sub-sets of rows and columns, that are themself defined by generic sequences of row indices \f$
+ * \{r_0,r_1,..r_{m-1}\} \f$ and column indices \f$ \{c_0,c_1,..c_{n-1} \}\f$. Let \f$ A \f$  be the nested matrix, then
+ * the resulting matrix \f$ B \f$ has \c m rows and \c n columns, and its entries are given by: \f$ B(i,j) = A(r_i,c_j)
+ * \f$.
+ *
+ * The \c RowIndices and \c ColIndices types must be compatible with the following API:
+ * \code
+ * <integral type> operator[](Index) const;
+ * Index size() const;
+ * \endcode
+ *
+ * Typical supported types thus include:
+ *  - std::vector<int>
+ *  - std::valarray<int>
+ *  - std::array<int>
+ *  - Eigen::ArrayXi
+ *  - decltype(ArrayXi::LinSpaced(...))
+ *  - Any view/expressions of the previous types
+ *  - Eigen::ArithmeticSequence
+ *  - Eigen::internal::AllRange     (helper for Eigen::placeholders::all)
+ *  - Eigen::internal::SingleRange  (helper for single index)
+ *  - etc.
+ *
+ * In typical usages of %Eigen, this class should never be used directly. It is the return type of
+ * DenseBase::operator()(const RowIndices&, const ColIndices&).
+ *
+ * \sa class Block
+ */
+template <typename XprType, typename RowIndices, typename ColIndices>
+class IndexedView
+    : public internal::IndexedViewImpl<XprType, RowIndices, ColIndices, typename internal::traits<XprType>::StorageKind,
+                                       (internal::traits<IndexedView<XprType, RowIndices, ColIndices>>::Flags &
+                                        DirectAccessBit) != 0> {
+ public:
+  typedef typename internal::IndexedViewImpl<
+      XprType, RowIndices, ColIndices, typename internal::traits<XprType>::StorageKind,
+      (internal::traits<IndexedView<XprType, RowIndices, ColIndices>>::Flags & DirectAccessBit) != 0>
+      Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(IndexedView)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(IndexedView)
+
+  template <typename T0, typename T1>
+  IndexedView(XprType& xpr, const T0& rowIndices, const T1& colIndices) : Base(xpr, rowIndices, colIndices) {}
+};
+
+namespace internal {
+
+// Generic API dispatcher
+template <typename XprType, typename RowIndices, typename ColIndices, typename StorageKind, bool DirectAccess>
+class IndexedViewImpl : public internal::generic_xpr_base<IndexedView<XprType, RowIndices, ColIndices>>::type {
+ public:
+  typedef typename internal::generic_xpr_base<IndexedView<XprType, RowIndices, ColIndices>>::type Base;
+  typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
+  typedef internal::remove_all_t<XprType> NestedExpression;
+  typedef typename XprType::Scalar Scalar;
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(IndexedViewImpl)
+
+  template <typename T0, typename T1>
+  IndexedViewImpl(XprType& xpr, const T0& rowIndices, const T1& colIndices)
+      : m_xpr(xpr), m_rowIndices(rowIndices), m_colIndices(colIndices) {}
+
+  /** \returns number of rows */
+  Index rows() const { return IndexedViewHelper<RowIndices>::size(m_rowIndices); }
+
+  /** \returns number of columns */
+  Index cols() const { return IndexedViewHelper<ColIndices>::size(m_colIndices); }
+
+  /** \returns the nested expression */
+  const internal::remove_all_t<XprType>& nestedExpression() const { return m_xpr; }
+
+  /** \returns the nested expression */
+  std::remove_reference_t<XprType>& nestedExpression() { return m_xpr; }
+
+  /** \returns a const reference to the object storing/generating the row indices */
+  const RowIndices& rowIndices() const { return m_rowIndices; }
+
+  /** \returns a const reference to the object storing/generating the column indices */
+  const ColIndices& colIndices() const { return m_colIndices; }
+
+  constexpr Scalar& coeffRef(Index rowId, Index colId) {
+    return nestedExpression().coeffRef(m_rowIndices[rowId], m_colIndices[colId]);
+  }
+
+  constexpr const Scalar& coeffRef(Index rowId, Index colId) const {
+    return nestedExpression().coeffRef(m_rowIndices[rowId], m_colIndices[colId]);
+  }
+
+ protected:
+  MatrixTypeNested m_xpr;
+  RowIndices m_rowIndices;
+  ColIndices m_colIndices;
+};
+
+template <typename XprType, typename RowIndices, typename ColIndices, typename StorageKind>
+class IndexedViewImpl<XprType, RowIndices, ColIndices, StorageKind, true>
+    : public IndexedViewImpl<XprType, RowIndices, ColIndices, StorageKind, false> {
+ public:
+  using Base = internal::IndexedViewImpl<XprType, RowIndices, ColIndices,
+                                         typename internal::traits<XprType>::StorageKind, false>;
+  using Derived = IndexedView<XprType, RowIndices, ColIndices>;
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(IndexedViewImpl)
+
+  template <typename T0, typename T1>
+  IndexedViewImpl(XprType& xpr, const T0& rowIndices, const T1& colIndices) : Base(xpr, rowIndices, colIndices) {}
+
+  Index rowIncrement() const {
+    if (traits<Derived>::RowIncr != DynamicIndex && traits<Derived>::RowIncr != Undefined) {
+      return traits<Derived>::RowIncr;
+    }
+    return IndexedViewHelper<RowIndices>::incr(this->rowIndices());
+  }
+  Index colIncrement() const {
+    if (traits<Derived>::ColIncr != DynamicIndex && traits<Derived>::ColIncr != Undefined) {
+      return traits<Derived>::ColIncr;
+    }
+    return IndexedViewHelper<ColIndices>::incr(this->colIndices());
+  }
+
+  Index innerIncrement() const { return traits<Derived>::IsRowMajor ? colIncrement() : rowIncrement(); }
+
+  Index outerIncrement() const { return traits<Derived>::IsRowMajor ? rowIncrement() : colIncrement(); }
+
+  std::decay_t<typename XprType::Scalar>* data() {
+    Index row_offset = this->rowIndices()[0] * this->nestedExpression().rowStride();
+    Index col_offset = this->colIndices()[0] * this->nestedExpression().colStride();
+    return this->nestedExpression().data() + row_offset + col_offset;
+  }
+
+  const std::decay_t<typename XprType::Scalar>* data() const {
+    Index row_offset = this->rowIndices()[0] * this->nestedExpression().rowStride();
+    Index col_offset = this->colIndices()[0] * this->nestedExpression().colStride();
+    return this->nestedExpression().data() + row_offset + col_offset;
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept {
+    if (traits<Derived>::InnerStrideAtCompileTime != Dynamic) {
+      return traits<Derived>::InnerStrideAtCompileTime;
+    }
+    return innerIncrement() * this->nestedExpression().innerStride();
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept {
+    if (traits<Derived>::OuterStrideAtCompileTime != Dynamic) {
+      return traits<Derived>::OuterStrideAtCompileTime;
+    }
+    return outerIncrement() * this->nestedExpression().outerStride();
+  }
+};
+
+template <typename ArgType, typename RowIndices, typename ColIndices>
+struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
+    : evaluator_base<IndexedView<ArgType, RowIndices, ColIndices>> {
+  typedef IndexedView<ArgType, RowIndices, ColIndices> XprType;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost /* TODO + cost of row/col index */,
+
+    FlagsLinearAccessBit =
+        (traits<XprType>::RowsAtCompileTime == 1 || traits<XprType>::ColsAtCompileTime == 1) ? LinearAccessBit : 0,
+
+    FlagsRowMajorBit = traits<XprType>::FlagsRowMajorBit,
+
+    Flags = (evaluator<ArgType>::Flags & (HereditaryBits & ~RowMajorBit /*| LinearAccessBit | DirectAccessBit*/)) |
+            FlagsLinearAccessBit | FlagsRowMajorBit,
+
+    Alignment = 0
+  };
+
+  EIGEN_DEVICE_FUNC constexpr explicit unary_evaluator(const XprType& xpr)
+      : m_argImpl(xpr.nestedExpression()), m_xpr(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows() &&
+                 m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
+    return m_argImpl.coeff(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
+    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows() &&
+                 m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
+    return m_argImpl.coeffRef(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    EIGEN_STATIC_ASSERT_LVALUE(XprType)
+    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
+    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
+    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows() &&
+                 m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
+    return m_argImpl.coeffRef(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const {
+    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
+    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
+    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows() &&
+                 m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
+    return m_argImpl.coeffRef(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index index) const {
+    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
+    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
+    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows() &&
+                 m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
+    return m_argImpl.coeff(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
+  }
+
+ protected:
+  evaluator<ArgType> m_argImpl;
+  const XprType& m_xpr;
+};
+
+// Catch assignments to an IndexedView.
+template <typename ArgType, typename RowIndices, typename ColIndices>
+struct evaluator_assume_aliasing<IndexedView<ArgType, RowIndices, ColIndices>> {
+  static const bool value = true;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_INDEXED_VIEW_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/InnerProduct.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/InnerProduct.h
new file mode 100644
index 0000000..ad9844d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/InnerProduct.h
@@ -0,0 +1,260 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Charlie Schlosser <cs.schlosser@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INNER_PRODUCT_EVAL_H
+#define EIGEN_INNER_PRODUCT_EVAL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// recursively searches for the largest simd type that does not exceed Size, or the smallest if no such type exists
+template <typename Scalar, int Size, typename Packet = typename packet_traits<Scalar>::type,
+          bool Stop =
+              (unpacket_traits<Packet>::size <= Size) || is_same<Packet, typename unpacket_traits<Packet>::half>::value>
+struct find_inner_product_packet_helper;
+
+template <typename Scalar, int Size, typename Packet>
+struct find_inner_product_packet_helper<Scalar, Size, Packet, false> {
+  using type = typename find_inner_product_packet_helper<Scalar, Size, typename unpacket_traits<Packet>::half>::type;
+};
+
+template <typename Scalar, int Size, typename Packet>
+struct find_inner_product_packet_helper<Scalar, Size, Packet, true> {
+  using type = Packet;
+};
+
+template <typename Scalar, int Size>
+struct find_inner_product_packet : find_inner_product_packet_helper<Scalar, Size> {};
+
+template <typename Scalar>
+struct find_inner_product_packet<Scalar, Dynamic> {
+  using type = typename packet_traits<Scalar>::type;
+};
+
+template <typename Lhs, typename Rhs>
+struct inner_product_assert {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Lhs)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Rhs)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Lhs, Rhs)
+#ifndef EIGEN_NO_DEBUG
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, const Rhs& rhs) {
+    eigen_assert((lhs.size() == rhs.size()) && "Inner product: lhs and rhs vectors must have same size");
+  }
+#else
+  static EIGEN_DEVICE_FUNC void run(const Lhs&, const Rhs&) {}
+#endif
+};
+
+template <typename Func, typename Lhs, typename Rhs>
+struct inner_product_evaluator {
+  static constexpr int LhsFlags = evaluator<Lhs>::Flags;
+  static constexpr int RhsFlags = evaluator<Rhs>::Flags;
+  static constexpr int SizeAtCompileTime = size_prefer_fixed(Lhs::SizeAtCompileTime, Rhs::SizeAtCompileTime);
+  static constexpr int MaxSizeAtCompileTime =
+      min_size_prefer_fixed(Lhs::MaxSizeAtCompileTime, Rhs::MaxSizeAtCompileTime);
+  static constexpr int LhsAlignment = evaluator<Lhs>::Alignment;
+  static constexpr int RhsAlignment = evaluator<Rhs>::Alignment;
+
+  using Scalar = typename Func::result_type;
+  using Packet = typename find_inner_product_packet<Scalar, SizeAtCompileTime>::type;
+
+  static constexpr bool Vectorize =
+      bool(LhsFlags & RhsFlags & PacketAccessBit) && Func::PacketAccess &&
+      ((MaxSizeAtCompileTime == Dynamic) || (unpacket_traits<Packet>::size <= MaxSizeAtCompileTime));
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit inner_product_evaluator(const Lhs& lhs, const Rhs& rhs,
+                                                                         Func func = Func())
+      : m_func(func), m_lhs(lhs), m_rhs(rhs), m_size(lhs.size()) {
+    inner_product_assert<Lhs, Rhs>::run(lhs, rhs);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_size.value(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index index) const {
+    return m_func.coeff(m_lhs.coeff(index), m_rhs.coeff(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(const Scalar& value, Index index) const {
+    return m_func.coeff(value, m_lhs.coeff(index), m_rhs.coeff(index));
+  }
+
+  template <typename PacketType, int LhsMode = LhsAlignment, int RhsMode = RhsAlignment>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    return m_func.packet(m_lhs.template packet<LhsMode, PacketType>(index),
+                         m_rhs.template packet<RhsMode, PacketType>(index));
+  }
+
+  template <typename PacketType, int LhsMode = LhsAlignment, int RhsMode = RhsAlignment>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(const PacketType& value, Index index) const {
+    return m_func.packet(value, m_lhs.template packet<LhsMode, PacketType>(index),
+                         m_rhs.template packet<RhsMode, PacketType>(index));
+  }
+
+  const Func m_func;
+  const evaluator<Lhs> m_lhs;
+  const evaluator<Rhs> m_rhs;
+  const variable_if_dynamic<Index, SizeAtCompileTime> m_size;
+};
+
+template <typename Evaluator, bool Vectorize = Evaluator::Vectorize>
+struct inner_product_impl;
+
+// scalar loop
+template <typename Evaluator>
+struct inner_product_impl<Evaluator, false> {
+  using Scalar = typename Evaluator::Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval) {
+    const Index size = eval.size();
+    if (size == 0) return Scalar(0);
+
+    Scalar result = eval.coeff(0);
+    for (Index k = 1; k < size; k++) {
+      result = eval.coeff(result, k);
+    }
+
+    return result;
+  }
+};
+
+// vector loop
+template <typename Evaluator>
+struct inner_product_impl<Evaluator, true> {
+  using UnsignedIndex = std::make_unsigned_t<Index>;
+  using Scalar = typename Evaluator::Scalar;
+  using Packet = typename Evaluator::Packet;
+  static constexpr int PacketSize = unpacket_traits<Packet>::size;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval) {
+    const UnsignedIndex size = static_cast<UnsignedIndex>(eval.size());
+    if (size < PacketSize) return inner_product_impl<Evaluator, false>::run(eval);
+
+    const UnsignedIndex packetEnd = numext::round_down(size, PacketSize);
+    const UnsignedIndex quadEnd = numext::round_down(size, 4 * PacketSize);
+    const UnsignedIndex numPackets = size / PacketSize;
+    const UnsignedIndex numRemPackets = (packetEnd - quadEnd) / PacketSize;
+
+    Packet presult0, presult1, presult2, presult3;
+
+    presult0 = eval.template packet<Packet>(0 * PacketSize);
+    if (numPackets >= 2) presult1 = eval.template packet<Packet>(1 * PacketSize);
+    if (numPackets >= 3) presult2 = eval.template packet<Packet>(2 * PacketSize);
+    if (numPackets >= 4) {
+      presult3 = eval.template packet<Packet>(3 * PacketSize);
+
+      for (UnsignedIndex k = 4 * PacketSize; k < quadEnd; k += 4 * PacketSize) {
+        presult0 = eval.packet(presult0, k + 0 * PacketSize);
+        presult1 = eval.packet(presult1, k + 1 * PacketSize);
+        presult2 = eval.packet(presult2, k + 2 * PacketSize);
+        presult3 = eval.packet(presult3, k + 3 * PacketSize);
+      }
+
+      if (numRemPackets >= 1) presult0 = eval.packet(presult0, quadEnd + 0 * PacketSize);
+      if (numRemPackets >= 2) presult1 = eval.packet(presult1, quadEnd + 1 * PacketSize);
+      if (numRemPackets == 3) presult2 = eval.packet(presult2, quadEnd + 2 * PacketSize);
+
+      presult2 = padd(presult2, presult3);
+    }
+
+    if (numPackets >= 3) presult1 = padd(presult1, presult2);
+    if (numPackets >= 2) presult0 = padd(presult0, presult1);
+
+    Scalar result = predux(presult0);
+    for (UnsignedIndex k = packetEnd; k < size; k++) {
+      result = eval.coeff(result, k);
+    }
+
+    return result;
+  }
+};
+
+template <typename Scalar, bool Conj>
+struct conditional_conj;
+
+template <typename Scalar>
+struct conditional_conj<Scalar, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(const Scalar& a) { return numext::conj(a); }
+  template <typename Packet>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packet(const Packet& a) {
+    return pconj(a);
+  }
+};
+
+template <typename Scalar>
+struct conditional_conj<Scalar, false> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(const Scalar& a) { return a; }
+  template <typename Packet>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packet(const Packet& a) {
+    return a;
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool Conj>
+struct scalar_inner_product_op {
+  using result_type = typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType;
+  using conj_helper = conditional_conj<LhsScalar, Conj>;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type coeff(const LhsScalar& a, const RhsScalar& b) const {
+    return (conj_helper::coeff(a) * b);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type coeff(const result_type& accum, const LhsScalar& a,
+                                                          const RhsScalar& b) const {
+    return (conj_helper::coeff(a) * b) + accum;
+  }
+  static constexpr bool PacketAccess = false;
+};
+
+// Partial specialization for packet access if and only if
+// LhsScalar == RhsScalar == ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType.
+template <typename Scalar, bool Conj>
+struct scalar_inner_product_op<
+    Scalar,
+    std::enable_if_t<internal::is_same<typename ScalarBinaryOpTraits<Scalar, Scalar>::ReturnType, Scalar>::value,
+                     Scalar>,
+    Conj> {
+  using result_type = Scalar;
+  using conj_helper = conditional_conj<Scalar, Conj>;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(const Scalar& a, const Scalar& b) const {
+    return pmul(conj_helper::coeff(a), b);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(const Scalar& accum, const Scalar& a, const Scalar& b) const {
+    return pmadd(conj_helper::coeff(a), b, accum);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packet(const Packet& a, const Packet& b) const {
+    return pmul(conj_helper::packet(a), b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packet(const Packet& accum, const Packet& a, const Packet& b) const {
+    return pmadd(conj_helper::packet(a), b, accum);
+  }
+  static constexpr bool PacketAccess = packet_traits<Scalar>::HasMul && packet_traits<Scalar>::HasAdd;
+};
+
+template <typename Lhs, typename Rhs, bool Conj>
+struct default_inner_product_impl {
+  using LhsScalar = typename traits<Lhs>::Scalar;
+  using RhsScalar = typename traits<Rhs>::Scalar;
+  using Op = scalar_inner_product_op<LhsScalar, RhsScalar, Conj>;
+  using Evaluator = inner_product_evaluator<Op, Lhs, Rhs>;
+  using result_type = typename Evaluator::Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type run(const MatrixBase<Lhs>& a, const MatrixBase<Rhs>& b) {
+    Evaluator eval(a.derived(), b.derived(), Op());
+    return inner_product_impl<Evaluator>::run(eval);
+  }
+};
+
+template <typename Lhs, typename Rhs>
+struct dot_impl : default_inner_product_impl<Lhs, Rhs, true> {};
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_INNER_PRODUCT_EVAL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/InternalHeaderCheck.h
new file mode 100644
index 0000000..1cea572
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CORE_MODULE_H
+#error "Please include Eigen/Core instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Inverse.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Inverse.h
new file mode 100644
index 0000000..855e3b3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Inverse.h
@@ -0,0 +1,108 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INVERSE_H
+#define EIGEN_INVERSE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename XprType, typename StorageKind>
+class InverseImpl;
+
+namespace internal {
+
+template <typename XprType>
+struct traits<Inverse<XprType> > : traits<typename XprType::PlainObject> {
+  typedef typename XprType::PlainObject PlainObject;
+  typedef traits<PlainObject> BaseTraits;
+  enum { Flags = BaseTraits::Flags & RowMajorBit };
+};
+
+}  // end namespace internal
+
+/** \class Inverse
+ *
+ * \brief Expression of the inverse of another expression
+ *
+ * \tparam XprType the type of the expression we are taking the inverse
+ *
+ * This class represents an abstract expression of A.inverse()
+ * and most of the time this is the only way it is used.
+ *
+ */
+template <typename XprType>
+class Inverse : public InverseImpl<XprType, typename internal::traits<XprType>::StorageKind> {
+ public:
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::ref_selector<XprType>::type XprTypeNested;
+  typedef internal::remove_all_t<XprTypeNested> XprTypeNestedCleaned;
+  typedef typename internal::ref_selector<Inverse>::type Nested;
+  typedef internal::remove_all_t<XprType> NestedExpression;
+
+  explicit EIGEN_DEVICE_FUNC constexpr Inverse(const XprType& xpr) : m_xpr(xpr) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_xpr.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_xpr.rows(); }
+
+  EIGEN_DEVICE_FUNC constexpr const XprTypeNestedCleaned& nestedExpression() const { return m_xpr; }
+
+ protected:
+  XprTypeNested m_xpr;
+};
+
+// Generic API dispatcher
+template <typename XprType, typename StorageKind>
+class InverseImpl : public internal::generic_xpr_base<Inverse<XprType> >::type {
+ public:
+  typedef typename internal::generic_xpr_base<Inverse<XprType> >::type Base;
+  typedef typename XprType::Scalar Scalar;
+
+ private:
+  Scalar coeff(Index row, Index col) const;
+  Scalar coeff(Index i) const;
+};
+
+namespace internal {
+
+/** \internal
+ * \brief Default evaluator for Inverse expression.
+ *
+ * This default evaluator for Inverse expression simply evaluate the inverse into a temporary
+ * by a call to internal::call_assignment_no_alias.
+ * Therefore, inverse implementers only have to specialize Assignment<Dst,Inverse<...>, ...> for
+ * there own nested expression.
+ *
+ * \sa class Inverse
+ */
+template <typename ArgType>
+struct unary_evaluator<Inverse<ArgType> > : public evaluator<typename Inverse<ArgType>::PlainObject> {
+  typedef Inverse<ArgType> InverseType;
+  typedef typename InverseType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  enum { Flags = Base::Flags | EvalBeforeNestingBit };
+
+  EIGEN_DEVICE_FUNC unary_evaluator(const InverseType& inv_xpr) : m_result(inv_xpr.rows(), inv_xpr.cols()) {
+    internal::construct_at<Base>(this, m_result);
+    internal::call_assignment_no_alias(m_result, inv_xpr);
+  }
+
+ protected:
+  PlainObject m_result;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_INVERSE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Map.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Map.h
new file mode 100644
index 0000000..52a8eca
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Map.h
@@ -0,0 +1,154 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MAP_H
+#define EIGEN_MAP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename PlainObjectType, int MapOptions, typename StrideType>
+struct traits<Map<PlainObjectType, MapOptions, StrideType> > : public traits<PlainObjectType> {
+  typedef traits<PlainObjectType> TraitsBase;
+  enum {
+    PlainObjectTypeInnerSize = ((traits<PlainObjectType>::Flags & RowMajorBit) == RowMajorBit)
+                                   ? PlainObjectType::ColsAtCompileTime
+                                   : PlainObjectType::RowsAtCompileTime,
+
+    InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
+                                   ? int(PlainObjectType::InnerStrideAtCompileTime)
+                                   : int(StrideType::InnerStrideAtCompileTime),
+    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
+                                   ? (InnerStrideAtCompileTime == Dynamic || PlainObjectTypeInnerSize == Dynamic
+                                          ? Dynamic
+                                          : int(InnerStrideAtCompileTime) * int(PlainObjectTypeInnerSize))
+                                   : int(StrideType::OuterStrideAtCompileTime),
+    Alignment = int(MapOptions) & int(AlignedMask),
+    Flags0 = TraitsBase::Flags & (~NestByRefBit),
+    Flags = is_lvalue<PlainObjectType>::value ? int(Flags0) : (int(Flags0) & ~LvalueBit)
+  };
+
+ private:
+  enum { Options };  // Expressions don't have Options
+};
+}  // namespace internal
+
+/** \class Map
+ * \ingroup Core_Module
+ *
+ * \brief A matrix or vector expression mapping an existing array of data.
+ *
+ * \tparam PlainObjectType the equivalent matrix type of the mapped data
+ * \tparam MapOptions specifies the pointer alignment in bytes. It can be: \c #Aligned128, \c #Aligned64, \c #Aligned32,
+ * \c #Aligned16, \c #Aligned8 or \c #Unaligned. The default is \c #Unaligned. \tparam StrideType optionally specifies
+ * strides. By default, Map assumes the memory layout of an ordinary, contiguous array. This can be overridden by
+ * specifying strides. The type passed here must be a specialization of the Stride template, see examples below.
+ *
+ * This class represents a matrix or vector expression mapping an existing array of data.
+ * It can be used to let Eigen interface without any overhead with non-Eigen data structures,
+ * such as plain C arrays or structures from other libraries. By default, it assumes that the
+ * data is laid out contiguously in memory. You can however override this by explicitly specifying
+ * inner and outer strides.
+ *
+ * Here's an example of simply mapping a contiguous array as a \ref TopicStorageOrders "column-major" matrix:
+ * \include Map_simple.cpp
+ * Output: \verbinclude Map_simple.out
+ *
+ * If you need to map non-contiguous arrays, you can do so by specifying strides:
+ *
+ * Here's an example of mapping an array as a vector, specifying an inner stride, that is, the pointer
+ * increment between two consecutive coefficients. Here, we're specifying the inner stride as a compile-time
+ * fixed value.
+ * \include Map_inner_stride.cpp
+ * Output: \verbinclude Map_inner_stride.out
+ *
+ * Here's an example of mapping an array while specifying an outer stride. Here, since we're mapping
+ * as a column-major matrix, 'outer stride' means the pointer increment between two consecutive columns.
+ * Here, we're specifying the outer stride as a runtime parameter. Note that here \c OuterStride<> is
+ * a short version of \c OuterStride<Dynamic> because the default template parameter of OuterStride
+ * is  \c Dynamic
+ * \include Map_outer_stride.cpp
+ * Output: \verbinclude Map_outer_stride.out
+ *
+ * For more details and for an example of specifying both an inner and an outer stride, see class Stride.
+ *
+ * \b Tip: to change the array of data mapped by a Map object, you can use the C++
+ * placement new syntax:
+ *
+ * Example: \include Map_placement_new.cpp
+ * Output: \verbinclude Map_placement_new.out
+ *
+ * This class is the return type of PlainObjectBase::Map() but can also be used directly.
+ *
+ * \sa PlainObjectBase::Map(), \ref TopicStorageOrders
+ */
+template <typename PlainObjectType, int MapOptions, typename StrideType>
+class Map : public MapBase<Map<PlainObjectType, MapOptions, StrideType> > {
+ public:
+  typedef MapBase<Map> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Map)
+
+  typedef typename Base::PointerType PointerType;
+  typedef PointerType PointerArgType;
+  EIGEN_DEVICE_FUNC constexpr inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; }
+
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const {
+    return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const {
+    return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
+           : internal::traits<Map>::OuterStrideAtCompileTime != Dynamic
+               ? Index(internal::traits<Map>::OuterStrideAtCompileTime)
+           : IsVectorAtCompileTime    ? (this->size() * innerStride())
+           : int(Flags) & RowMajorBit ? (this->cols() * innerStride())
+                                      : (this->rows() * innerStride());
+  }
+
+  /** Constructor in the fixed-size case.
+   *
+   * \param dataPtr pointer to the array to map
+   * \param stride optional Stride object, passing the strides.
+   */
+  EIGEN_DEVICE_FUNC constexpr explicit inline Map(PointerArgType dataPtr, const StrideType& stride = StrideType())
+      : Base(cast_to_pointer_type(dataPtr)), m_stride(stride) {}
+
+  /** Constructor in the dynamic-size vector case.
+   *
+   * \param dataPtr pointer to the array to map
+   * \param size the size of the vector expression
+   * \param stride optional Stride object, passing the strides.
+   */
+  EIGEN_DEVICE_FUNC constexpr inline Map(PointerArgType dataPtr, Index size, const StrideType& stride = StrideType())
+      : Base(cast_to_pointer_type(dataPtr), size), m_stride(stride) {}
+
+  /** Constructor in the dynamic-size matrix case.
+   *
+   * \param dataPtr pointer to the array to map
+   * \param rows the number of rows of the matrix expression
+   * \param cols the number of columns of the matrix expression
+   * \param stride optional Stride object, passing the strides.
+   */
+  EIGEN_DEVICE_FUNC constexpr inline Map(PointerArgType dataPtr, Index rows, Index cols,
+                                         const StrideType& stride = StrideType())
+      : Base(cast_to_pointer_type(dataPtr), rows, cols), m_stride(stride) {}
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
+
+ protected:
+  StrideType m_stride;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MAP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MapBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MapBase.h
new file mode 100644
index 0000000..3e11725
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MapBase.h
@@ -0,0 +1,283 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MAPBASE_H
+#define EIGEN_MAPBASE_H
+
+#define EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)                                                               \
+  EIGEN_STATIC_ASSERT((int(internal::evaluator<Derived>::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \
+                      YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT)
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup Core_Module
+ *
+ * \brief Base class for dense Map and Block expression with direct access
+ *
+ * This base class provides the const low-level accessors (e.g. coeff, coeffRef) of dense
+ * Map and Block objects with direct access.
+ * Typical users do not have to directly deal with this class.
+ *
+ * This class can be extended by through the macro plugin \c EIGEN_MAPBASE_PLUGIN.
+ * See \link TopicCustomizing_Plugins customizing Eigen \endlink for details.
+ *
+ * The \c Derived class has to provide the following two methods describing the memory layout:
+ *  \code Index innerStride() const; \endcode
+ *  \code Index outerStride() const; \endcode
+ *
+ * \sa class Map, class Block
+ */
+template <typename Derived>
+class MapBase<Derived, ReadOnlyAccessors> : public internal::dense_xpr_base<Derived>::type {
+ public:
+  typedef typename internal::dense_xpr_base<Derived>::type Base;
+  enum {
+    RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+    ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+    InnerStrideAtCompileTime = internal::traits<Derived>::InnerStrideAtCompileTime,
+    SizeAtCompileTime = Base::SizeAtCompileTime
+  };
+
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef std::conditional_t<bool(internal::is_lvalue<Derived>::value), Scalar*, const Scalar*> PointerType;
+
+  using Base::derived;
+  //    using Base::RowsAtCompileTime;
+  //    using Base::ColsAtCompileTime;
+  //    using Base::SizeAtCompileTime;
+  using Base::Flags;
+  using Base::IsRowMajor;
+  using Base::IsVectorAtCompileTime;
+  using Base::MaxColsAtCompileTime;
+  using Base::MaxRowsAtCompileTime;
+  using Base::MaxSizeAtCompileTime;
+
+  using Base::coeff;
+  using Base::coeffRef;
+  using Base::cols;
+  using Base::eval;
+  using Base::lazyAssign;
+  using Base::rows;
+  using Base::size;
+
+  using Base::colStride;
+  using Base::innerStride;
+  using Base::outerStride;
+  using Base::rowStride;
+
+  // bug 217 - compile error on ICC 11.1
+  using Base::operator=;
+
+  typedef typename Base::CoeffReturnType CoeffReturnType;
+
+  /** \copydoc DenseBase::rows() */
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_rows.value(); }
+  /** \copydoc DenseBase::cols() */
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_cols.value(); }
+
+  /** Returns a pointer to the first coefficient of the matrix or vector.
+   *
+   * \note When addressing this data, make sure to honor the strides returned by innerStride() and outerStride().
+   *
+   * \sa innerStride(), outerStride()
+   */
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_data; }
+
+  /** \copydoc PlainObjectBase::coeff(Index,Index) const */
+  EIGEN_DEVICE_FUNC constexpr inline const Scalar& coeff(Index rowId, Index colId) const {
+    return m_data[colId * colStride() + rowId * rowStride()];
+  }
+
+  /** \copydoc PlainObjectBase::coeff(Index) const */
+  EIGEN_DEVICE_FUNC constexpr inline const Scalar& coeff(Index index) const {
+    EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
+    return m_data[index * innerStride()];
+  }
+
+  /** \copydoc PlainObjectBase::coeffRef(Index,Index) const */
+  EIGEN_DEVICE_FUNC constexpr inline const Scalar& coeffRef(Index rowId, Index colId) const {
+    return this->m_data[colId * colStride() + rowId * rowStride()];
+  }
+
+  /** \copydoc PlainObjectBase::coeffRef(Index) const */
+  EIGEN_DEVICE_FUNC constexpr inline const Scalar& coeffRef(Index index) const {
+    EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
+    return this->m_data[index * innerStride()];
+  }
+
+  /** \internal */
+  template <int LoadMode>
+  inline PacketScalar packet(Index rowId, Index colId) const {
+    return internal::ploadt<PacketScalar, LoadMode>(m_data + (colId * colStride() + rowId * rowStride()));
+  }
+
+  /** \internal */
+  template <int LoadMode>
+  inline PacketScalar packet(Index index) const {
+    EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
+    return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
+  }
+
+  /** \internal Constructor for fixed size matrices or vectors */
+  EIGEN_DEVICE_FUNC constexpr explicit inline MapBase(PointerType dataPtr)
+      : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime) {
+    EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
+    checkSanity<Derived>();
+  }
+
+  /** \internal Constructor for dynamically sized vectors */
+  EIGEN_DEVICE_FUNC constexpr inline MapBase(PointerType dataPtr, Index vecSize)
+      : m_data(dataPtr),
+        m_rows(RowsAtCompileTime == Dynamic ? vecSize : Index(RowsAtCompileTime)),
+        m_cols(ColsAtCompileTime == Dynamic ? vecSize : Index(ColsAtCompileTime)) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+    eigen_assert(vecSize >= 0);
+    eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize);
+    checkSanity<Derived>();
+  }
+
+  /** \internal Constructor for dynamically sized matrices */
+  EIGEN_DEVICE_FUNC constexpr inline MapBase(PointerType dataPtr, Index rows, Index cols)
+      : m_data(dataPtr), m_rows(rows), m_cols(cols) {
+    eigen_assert((dataPtr == 0) || (rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) &&
+                                    cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
+    checkSanity<Derived>();
+  }
+
+#ifdef EIGEN_MAPBASE_PLUGIN
+#include EIGEN_MAPBASE_PLUGIN
+#endif
+
+ protected:
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(MapBase)
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MapBase)
+
+  template <typename T>
+  EIGEN_DEVICE_FUNC void checkSanity(std::enable_if_t<(internal::traits<T>::Alignment > 0), void*> = 0) const {
+// Temporary macro to allow scalars to not be properly aligned.  This is while we sort out failures
+// in TensorFlow Lite that are currently relying on this UB.
+#ifndef EIGEN_ALLOW_UNALIGNED_SCALARS
+    // Pointer must be aligned to the Scalar type, otherwise we get UB.
+    eigen_assert((std::uintptr_t(m_data) % alignof(Scalar) == 0) && "data is not scalar-aligned");
+#endif
+#if EIGEN_MAX_ALIGN_BYTES > 0
+    // innerStride() is not set yet when this function is called, so we optimistically assume the lowest plausible
+    // value:
+    const Index minInnerStride = InnerStrideAtCompileTime == Dynamic ? 1 : Index(InnerStrideAtCompileTime);
+    EIGEN_ONLY_USED_FOR_DEBUG(minInnerStride);
+    eigen_assert((((std::uintptr_t(m_data) % internal::traits<Derived>::Alignment) == 0) ||
+                  (cols() * rows() * minInnerStride * sizeof(Scalar)) < internal::traits<Derived>::Alignment) &&
+                 "data is not aligned");
+#endif
+  }
+
+  template <typename T>
+  EIGEN_DEVICE_FUNC void checkSanity(std::enable_if_t<internal::traits<T>::Alignment == 0, void*> = 0) const {
+#ifndef EIGEN_ALLOW_UNALIGNED_SCALARS
+    // Pointer must be aligned to the Scalar type, otherwise we get UB.
+    eigen_assert((std::uintptr_t(m_data) % alignof(Scalar) == 0) && "data is not scalar-aligned");
+#endif
+  }
+
+  PointerType m_data;
+  const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
+  const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
+};
+
+/** \ingroup Core_Module
+ *
+ * \brief Base class for non-const dense Map and Block expression with direct access
+ *
+ * This base class provides the non-const low-level accessors (e.g. coeff and coeffRef) of
+ * dense Map and Block objects with direct access.
+ * It inherits MapBase<Derived, ReadOnlyAccessors> which defines the const variant for reading specific entries.
+ *
+ * \sa class Map, class Block
+ */
+template <typename Derived>
+class MapBase<Derived, WriteAccessors> : public MapBase<Derived, ReadOnlyAccessors> {
+  typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
+
+ public:
+  typedef MapBase<Derived, ReadOnlyAccessors> Base;
+
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::PacketScalar PacketScalar;
+  typedef typename Base::StorageIndex StorageIndex;
+  typedef typename Base::PointerType PointerType;
+
+  using Base::coeff;
+  using Base::coeffRef;
+  using Base::cols;
+  using Base::derived;
+  using Base::rows;
+  using Base::size;
+
+  using Base::colStride;
+  using Base::innerStride;
+  using Base::outerStride;
+  using Base::rowStride;
+
+  typedef std::conditional_t<internal::is_lvalue<Derived>::value, Scalar, const Scalar> ScalarWithConstIfNotLvalue;
+
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return this->m_data; }
+  EIGEN_DEVICE_FUNC constexpr ScalarWithConstIfNotLvalue* data() {
+    return this->m_data;
+  }  // no const-cast here so non-const-correct code will give a compile error
+
+  EIGEN_DEVICE_FUNC constexpr inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col) {
+    return this->m_data[col * colStride() + row * rowStride()];
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline ScalarWithConstIfNotLvalue& coeffRef(Index index) {
+    EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
+    return this->m_data[index * innerStride()];
+  }
+
+  template <int StoreMode>
+  inline void writePacket(Index row, Index col, const PacketScalar& val) {
+    internal::pstoret<Scalar, PacketScalar, StoreMode>(this->m_data + (col * colStride() + row * rowStride()), val);
+  }
+
+  template <int StoreMode>
+  inline void writePacket(Index index, const PacketScalar& val) {
+    EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
+    internal::pstoret<Scalar, PacketScalar, StoreMode>(this->m_data + index * innerStride(), val);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {}
+  EIGEN_DEVICE_FUNC constexpr inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {}
+  EIGEN_DEVICE_FUNC constexpr inline MapBase(PointerType dataPtr, Index rows, Index cols) : Base(dataPtr, rows, cols) {}
+
+  EIGEN_DEVICE_FUNC Derived& operator=(const MapBase& other) {
+    ReadOnlyMapBase::Base::operator=(other);
+    return derived();
+  }
+
+  // In theory we could simply refer to Base:Base::operator=, but MSVC does not like Base::Base,
+  // see bugs 821 and 920.
+  using ReadOnlyMapBase::Base::operator=;
+
+ protected:
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(MapBase)
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MapBase)
+};
+
+#undef EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MAPBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MathFunctions.h
new file mode 100644
index 0000000..4e7597c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MathFunctions.h
@@ -0,0 +1,2160 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATHFUNCTIONS_H
+#define EIGEN_MATHFUNCTIONS_H
+
+// TODO: consider moving these constants to NumTraits.
+// Source: WolframAlpha
+#define EIGEN_PI 3.141592653589793238462643383279502884197169399375105820974944592307816406L
+#define EIGEN_LOG2E 1.442695040888963407359924681001892137426645954152985934135449406931109219L
+#define EIGEN_LN2 0.693147180559945309417232121458176568075500134360255254120680009493393621L
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal \class global_math_functions_filtering_base
+ *
+ * What it does:
+ * Defines a typedef 'type' as follows:
+ * - if type T has a member typedef Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl, then
+ *   global_math_functions_filtering_base<T>::type is a typedef for it.
+ * - otherwise, global_math_functions_filtering_base<T>::type is a typedef for T.
+ *
+ * How it's used:
+ * To allow to defined the global math functions (like sin...) in certain cases, like the Array expressions.
+ * When you do sin(array1+array2), the object array1+array2 has a complicated expression type, all what you want to know
+ * is that it inherits ArrayBase. So we implement a partial specialization of sin_impl for ArrayBase<Derived>.
+ * So we must make sure to use sin_impl<ArrayBase<Derived> > and not sin_impl<Derived>, otherwise our partial
+ * specialization won't be used. How does sin know that? That's exactly what global_math_functions_filtering_base tells
+ * it.
+ *
+ * How it's implemented:
+ * SFINAE in the style of enable_if. Highly susceptible of breaking compilers. With GCC, it sure does work, but if you
+ * replace the typename dummy by an integer template parameter, it doesn't work anymore!
+ */
+
+template <typename T, typename dummy = void>
+struct global_math_functions_filtering_base {
+  typedef T type;
+};
+
+template <typename T>
+struct always_void {
+  typedef void type;
+};
+
+template <typename T>
+struct global_math_functions_filtering_base<
+    T, typename always_void<typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl>::type> {
+  typedef typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl type;
+};
+
+#define EIGEN_MATHFUNC_IMPL(func, scalar) \
+  Eigen::internal::func##_impl<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>
+#define EIGEN_MATHFUNC_RETVAL(func, scalar) \
+  typename Eigen::internal::func##_retval<  \
+      typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>::type
+
+/****************************************************************************
+ * Implementation of real                                                 *
+ ****************************************************************************/
+
+template <typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct real_default_impl {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static constexpr RealScalar run(const Scalar& x) { return x; }
+};
+
+template <typename Scalar>
+struct real_default_impl<Scalar, true> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar& x) {
+    using std::real;
+    return real(x);
+  }
+};
+
+template <typename Scalar>
+struct real_impl : real_default_impl<Scalar> {};
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+template <typename T>
+struct real_impl<std::complex<T>> {
+  typedef T RealScalar;
+  EIGEN_DEVICE_FUNC static inline T run(const std::complex<T>& x) { return x.real(); }
+};
+#endif
+
+template <typename Scalar>
+struct real_retval {
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+ * Implementation of imag                                                 *
+ ****************************************************************************/
+
+template <typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct imag_default_impl {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar&) { return RealScalar(0); }
+};
+
+template <typename Scalar>
+struct imag_default_impl<Scalar, true> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar& x) {
+    using std::imag;
+    return imag(x);
+  }
+};
+
+template <typename Scalar>
+struct imag_impl : imag_default_impl<Scalar> {};
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+template <typename T>
+struct imag_impl<std::complex<T>> {
+  typedef T RealScalar;
+  EIGEN_DEVICE_FUNC static inline T run(const std::complex<T>& x) { return x.imag(); }
+};
+#endif
+
+template <typename Scalar>
+struct imag_retval {
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+ * Implementation of real_ref                                             *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct real_ref_impl {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar& run(Scalar& x) { return reinterpret_cast<RealScalar*>(&x)[0]; }
+  EIGEN_DEVICE_FUNC static inline const RealScalar& run(const Scalar& x) {
+    return reinterpret_cast<const RealScalar*>(&x)[0];
+  }
+};
+
+template <typename Scalar>
+struct real_ref_retval {
+  typedef typename NumTraits<Scalar>::Real& type;
+};
+
+/****************************************************************************
+ * Implementation of imag_ref                                             *
+ ****************************************************************************/
+
+template <typename Scalar, bool IsComplex>
+struct imag_ref_default_impl {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar& run(Scalar& x) { return reinterpret_cast<RealScalar*>(&x)[1]; }
+  EIGEN_DEVICE_FUNC static inline const RealScalar& run(const Scalar& x) {
+    return reinterpret_cast<const RealScalar*>(&x)[1];
+  }
+};
+
+template <typename Scalar>
+struct imag_ref_default_impl<Scalar, false> {
+  EIGEN_DEVICE_FUNC constexpr static Scalar run(Scalar&) { return Scalar(0); }
+  EIGEN_DEVICE_FUNC constexpr static const Scalar run(const Scalar&) { return Scalar(0); }
+};
+
+template <typename Scalar>
+struct imag_ref_impl : imag_ref_default_impl<Scalar, NumTraits<Scalar>::IsComplex> {};
+
+template <typename Scalar>
+struct imag_ref_retval {
+  typedef typename NumTraits<Scalar>::Real& type;
+};
+
+}  // namespace internal
+
+namespace numext {
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(real, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline internal::add_const_on_value_type_t<EIGEN_MATHFUNC_RETVAL(real_ref, Scalar)> real_ref(
+    const Scalar& x) {
+  return internal::real_ref_impl<Scalar>::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) real_ref(Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(real_ref, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar select(const Scalar& mask, const Scalar& a, const Scalar& b) {
+  return numext::is_exactly_zero(mask) ? b : a;
+}
+
+}  // namespace numext
+
+namespace internal {
+
+/****************************************************************************
+ * Implementation of conj                                                 *
+ ****************************************************************************/
+
+template <typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct conj_default_impl {
+  EIGEN_DEVICE_FUNC static constexpr Scalar run(const Scalar& x) { return x; }
+};
+
+template <typename Scalar>
+struct conj_default_impl<Scalar, true> {
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x) {
+    using std::conj;
+    return conj(x);
+  }
+};
+
+template <typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct conj_impl : conj_default_impl<Scalar, IsComplex> {};
+
+template <typename Scalar>
+struct conj_retval {
+  typedef Scalar type;
+};
+
+/****************************************************************************
+ * Implementation of abs2                                                 *
+ ****************************************************************************/
+
+template <typename Scalar, bool IsComplex>
+struct abs2_impl_default {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar& x) { return x * x; }
+};
+
+template <typename Scalar>
+struct abs2_impl_default<Scalar, true>  // IsComplex
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar& x) {
+    return numext::real(x) * numext::real(x) + numext::imag(x) * numext::imag(x);
+  }
+};
+
+template <typename Scalar>
+struct abs2_impl {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar& x) {
+    return abs2_impl_default<Scalar, NumTraits<Scalar>::IsComplex>::run(x);
+  }
+};
+
+template <typename Scalar>
+struct abs2_retval {
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+ * Implementation of sqrt/rsqrt                                             *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct sqrt_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE Scalar run(const Scalar& x) {
+    EIGEN_USING_STD(sqrt);
+    return sqrt(x);
+  }
+};
+
+// Complex sqrt defined in MathFunctionsImpl.h.
+template <typename ComplexT>
+EIGEN_DEVICE_FUNC constexpr ComplexT complex_sqrt(const ComplexT& a_x);
+
+// Custom implementation is faster than `std::sqrt`, works on
+// GPU, and correctly handles special cases (unlike MSVC).
+template <typename T>
+struct sqrt_impl<std::complex<T>> {
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE std::complex<T> run(const std::complex<T>& x) { return complex_sqrt(x); }
+};
+
+template <typename Scalar>
+struct sqrt_retval {
+  typedef Scalar type;
+};
+
+// Default implementation relies on numext::sqrt, at bottom of file.
+template <typename T>
+struct rsqrt_impl;
+
+// Complex rsqrt defined in MathFunctionsImpl.h.
+template <typename ComplexT>
+EIGEN_DEVICE_FUNC constexpr ComplexT complex_rsqrt(const ComplexT& a_x);
+
+template <typename T>
+struct rsqrt_impl<std::complex<T>> {
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE std::complex<T> run(const std::complex<T>& x) {
+    return complex_rsqrt(x);
+  }
+};
+
+template <typename Scalar>
+struct rsqrt_retval {
+  typedef Scalar type;
+};
+
+/****************************************************************************
+ * Implementation of norm1                                                *
+ ****************************************************************************/
+
+template <typename Scalar, bool IsComplex>
+struct norm1_default_impl;
+
+template <typename Scalar>
+struct norm1_default_impl<Scalar, true> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar& x) {
+    EIGEN_USING_STD(abs);
+    return abs(numext::real(x)) + abs(numext::imag(x));
+  }
+};
+
+template <typename Scalar>
+struct norm1_default_impl<Scalar, false> {
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x) {
+    EIGEN_USING_STD(abs);
+    return abs(x);
+  }
+};
+
+template <typename Scalar>
+struct norm1_impl : norm1_default_impl<Scalar, NumTraits<Scalar>::IsComplex> {};
+
+template <typename Scalar>
+struct norm1_retval {
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+ * Implementation of hypot                                                *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct hypot_impl;
+
+template <typename Scalar>
+struct hypot_retval {
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+ * Implementation of cast                                                 *
+ ****************************************************************************/
+
+template <typename OldType, typename NewType, typename EnableIf = void>
+struct cast_impl {
+  EIGEN_DEVICE_FUNC static inline NewType run(const OldType& x) { return static_cast<NewType>(x); }
+};
+
+template <typename OldType>
+struct cast_impl<OldType, bool> {
+  EIGEN_DEVICE_FUNC static inline bool run(const OldType& x) { return x != OldType(0); }
+};
+
+// Casting from S -> Complex<T> leads to an implicit conversion from S to T,
+// generating warnings on clang.  Here we explicitly cast the real component.
+template <typename OldType, typename NewType>
+struct cast_impl<OldType, NewType,
+                 typename std::enable_if_t<!NumTraits<OldType>::IsComplex && NumTraits<NewType>::IsComplex>> {
+  EIGEN_DEVICE_FUNC static inline NewType run(const OldType& x) {
+    typedef typename NumTraits<NewType>::Real NewReal;
+    return static_cast<NewType>(static_cast<NewReal>(x));
+  }
+};
+
+// Returns NewType directly to avoid unintended intermediate conversions.
+
+template <typename OldType, typename NewType>
+EIGEN_DEVICE_FUNC inline NewType cast(const OldType& x) {
+  return cast_impl<OldType, NewType>::run(x);
+}
+
+/****************************************************************************
+ * Implementation of arg                                                     *
+ ****************************************************************************/
+
+// Visual Studio 2017 has a bug where arg(float) returns 0 for negative inputs.
+// This seems to be fixed in VS 2019.
+#if (!EIGEN_COMP_MSVC || EIGEN_COMP_MSVC >= 1920)
+// std::arg is only defined for types of std::complex, or integer types or float/double/long double
+template <typename Scalar, bool HasStdImpl = NumTraits<Scalar>::IsComplex || is_integral<Scalar>::value ||
+                                             is_same<Scalar, float>::value || is_same<Scalar, double>::value ||
+                                             is_same<Scalar, long double>::value>
+struct arg_default_impl;
+
+template <typename Scalar>
+struct arg_default_impl<Scalar, true> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar& x) {
+    // There is no official ::arg on device in CUDA/HIP, so we always need to use std::arg.
+    using std::arg;
+    return static_cast<RealScalar>(arg(x));
+  }
+};
+
+// Must be non-complex floating-point type (e.g. half/bfloat16).
+template <typename Scalar>
+struct arg_default_impl<Scalar, false> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar& x) {
+    return (x < Scalar(0)) ? RealScalar(EIGEN_PI) : RealScalar(0);
+  }
+};
+#else
+template <typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct arg_default_impl {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar& x) {
+    return (x < RealScalar(0)) ? RealScalar(EIGEN_PI) : RealScalar(0);
+  }
+};
+
+template <typename Scalar>
+struct arg_default_impl<Scalar, true> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline RealScalar run(const Scalar& x) {
+    EIGEN_USING_STD(arg);
+    return arg(x);
+  }
+};
+#endif
+template <typename Scalar>
+struct arg_impl : arg_default_impl<Scalar> {};
+
+template <typename Scalar>
+struct arg_retval {
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+ * Implementation of expm1                                                   *
+ ****************************************************************************/
+
+// This implementation is based on GSL Math's expm1.
+namespace std_fallback {
+// fallback expm1 implementation in case there is no expm1(Scalar) function in namespace of Scalar,
+// or that there is no suitable std::expm1 function available. Implementation
+// attributed to Kahan. See: http://www.plunk.org/~hatch/rightway.php.
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline Scalar expm1(const Scalar& x) {
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  EIGEN_USING_STD(exp);
+  Scalar u = exp(x);
+  if (numext::equal_strict(u, Scalar(1))) {
+    return x;
+  }
+  Scalar um1 = u - RealScalar(1);
+  if (numext::equal_strict(um1, Scalar(-1))) {
+    return RealScalar(-1);
+  }
+
+  EIGEN_USING_STD(log);
+  Scalar logu = log(u);
+  return numext::equal_strict(u, logu) ? u : (u - RealScalar(1)) * x / logu;
+}
+}  // namespace std_fallback
+
+template <typename Scalar>
+struct expm1_impl {
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x) {
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+    EIGEN_USING_STD(expm1);
+    return expm1(x);
+  }
+};
+
+template <typename Scalar>
+struct expm1_retval {
+  typedef Scalar type;
+};
+
+/****************************************************************************
+ * Implementation of log                                                     *
+ ****************************************************************************/
+
+// Complex log defined in MathFunctionsImpl.h.
+template <typename ComplexT>
+EIGEN_DEVICE_FUNC constexpr ComplexT complex_log(const ComplexT& z);
+
+template <typename Scalar>
+struct log_impl {
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x) {
+    EIGEN_USING_STD(log);
+    return static_cast<Scalar>(log(x));
+  }
+};
+
+template <typename Scalar>
+struct log_impl<std::complex<Scalar>> {
+  EIGEN_DEVICE_FUNC static inline std::complex<Scalar> run(const std::complex<Scalar>& z) { return complex_log(z); }
+};
+
+/****************************************************************************
+ * Implementation of log1p                                                   *
+ ****************************************************************************/
+
+namespace std_fallback {
+// fallback log1p implementation in case there is no log1p(Scalar) function in namespace of Scalar,
+// or that there is no suitable std::log1p function available
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline Scalar log1p(const Scalar& x) {
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_USING_STD(log);
+  Scalar x1p = RealScalar(1) + x;
+  Scalar log_1p = log_impl<Scalar>::run(x1p);
+  const bool is_small = numext::equal_strict(x1p, Scalar(1));
+  const bool is_inf = numext::equal_strict(x1p, log_1p);
+  return (is_small || is_inf) ? x : x * (log_1p / (x1p - RealScalar(1)));
+}
+}  // namespace std_fallback
+
+template <typename Scalar>
+struct log1p_impl {
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x) {
+    EIGEN_USING_STD(log1p);
+    return log1p(x);
+  }
+};
+
+// Specialization for complex types that are not supported by std::log1p.
+template <typename RealScalar>
+struct log1p_impl<std::complex<RealScalar>> {
+  EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
+
+  EIGEN_DEVICE_FUNC static inline std::complex<RealScalar> run(const std::complex<RealScalar>& x) {
+    return std_fallback::log1p(x);
+  }
+};
+
+template <typename Scalar>
+struct log1p_retval {
+  typedef Scalar type;
+};
+
+/****************************************************************************
+ * Implementation of pow                                                  *
+ ****************************************************************************/
+
+template <typename ScalarX, typename ScalarY,
+          bool IsInteger = NumTraits<ScalarX>::IsInteger && NumTraits<ScalarY>::IsInteger>
+struct pow_impl {
+  // typedef Scalar retval;
+  typedef typename ScalarBinaryOpTraits<ScalarX, ScalarY, internal::scalar_pow_op<ScalarX, ScalarY>>::ReturnType
+      result_type;
+  static EIGEN_DEVICE_FUNC inline result_type run(const ScalarX& x, const ScalarY& y) {
+    EIGEN_USING_STD(pow);
+    return pow(x, y);
+  }
+};
+
+template <typename ScalarX, typename ScalarY>
+struct pow_impl<ScalarX, ScalarY, true> {
+  typedef ScalarX result_type;
+  static EIGEN_DEVICE_FUNC inline ScalarX run(ScalarX x, ScalarY y) {
+    ScalarX res(1);
+    eigen_assert(!NumTraits<ScalarY>::IsSigned || y >= 0);
+    if (y & 1) res *= x;
+    y >>= 1;
+    while (y) {
+      x *= x;
+      if (y & 1) res *= x;
+      y >>= 1;
+    }
+    return res;
+  }
+};
+
+enum { meta_floor_log2_terminate, meta_floor_log2_move_up, meta_floor_log2_move_down, meta_floor_log2_bogus };
+
+template <unsigned int n, int lower, int upper>
+struct meta_floor_log2_selector {
+  enum {
+    middle = (lower + upper) / 2,
+    value = (upper <= lower + 1)  ? int(meta_floor_log2_terminate)
+            : (n < (1 << middle)) ? int(meta_floor_log2_move_down)
+            : (n == 0)            ? int(meta_floor_log2_bogus)
+                                  : int(meta_floor_log2_move_up)
+  };
+};
+
+template <unsigned int n, int lower = 0, int upper = sizeof(unsigned int) * CHAR_BIT - 1,
+          int selector = meta_floor_log2_selector<n, lower, upper>::value>
+struct meta_floor_log2 {};
+
+template <unsigned int n, int lower, int upper>
+struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_down> {
+  enum { value = meta_floor_log2<n, lower, meta_floor_log2_selector<n, lower, upper>::middle>::value };
+};
+
+template <unsigned int n, int lower, int upper>
+struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_up> {
+  enum { value = meta_floor_log2<n, meta_floor_log2_selector<n, lower, upper>::middle, upper>::value };
+};
+
+template <unsigned int n, int lower, int upper>
+struct meta_floor_log2<n, lower, upper, meta_floor_log2_terminate> {
+  enum { value = (n >= ((unsigned int)(1) << (lower + 1))) ? lower + 1 : lower };
+};
+
+template <unsigned int n, int lower, int upper>
+struct meta_floor_log2<n, lower, upper, meta_floor_log2_bogus> {
+  // no value, error at compile time
+};
+
+template <typename BitsType, typename EnableIf = void>
+struct count_bits_impl {
+  static_assert(std::is_integral<BitsType>::value && std::is_unsigned<BitsType>::value,
+                "BitsType must be an unsigned integer");
+  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
+    int n = CHAR_BIT * sizeof(BitsType);
+    int shift = n / 2;
+    while (bits > 0 && shift > 0) {
+      BitsType y = bits >> shift;
+      if (y > 0) {
+        n -= shift;
+        bits = y;
+      }
+      shift /= 2;
+    }
+    if (shift == 0) {
+      --n;
+    }
+    return n;
+  }
+
+  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
+    int n = CHAR_BIT * sizeof(BitsType);
+    int shift = n / 2;
+    while (bits > 0 && shift > 0) {
+      BitsType y = bits << shift;
+      if (y > 0) {
+        n -= shift;
+        bits = y;
+      }
+      shift /= 2;
+    }
+    if (shift == 0) {
+      --n;
+    }
+    return n;
+  }
+};
+
+// Count leading zeros.
+template <typename BitsType>
+EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
+  return count_bits_impl<BitsType>::clz(bits);
+}
+
+// Count trailing zeros.
+template <typename BitsType>
+EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
+  return count_bits_impl<BitsType>::ctz(bits);
+}
+
+#if EIGEN_COMP_GNUC || EIGEN_COMP_CLANG
+
+template <typename BitsType>
+struct count_bits_impl<
+    BitsType, std::enable_if_t<std::is_integral<BitsType>::value && sizeof(BitsType) <= sizeof(unsigned int)>> {
+  static constexpr int kNumBits = static_cast<int>(sizeof(BitsType) * CHAR_BIT);
+  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
+    static constexpr int kLeadingBitsOffset = (sizeof(unsigned int) - sizeof(BitsType)) * CHAR_BIT;
+    return bits == 0 ? kNumBits : __builtin_clz(static_cast<unsigned int>(bits)) - kLeadingBitsOffset;
+  }
+
+  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
+    return bits == 0 ? kNumBits : __builtin_ctz(static_cast<unsigned int>(bits));
+  }
+};
+
+template <typename BitsType>
+struct count_bits_impl<BitsType,
+                       std::enable_if_t<std::is_integral<BitsType>::value && sizeof(unsigned int) < sizeof(BitsType) &&
+                                        sizeof(BitsType) <= sizeof(unsigned long)>> {
+  static constexpr int kNumBits = static_cast<int>(sizeof(BitsType) * CHAR_BIT);
+  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
+    static constexpr int kLeadingBitsOffset = (sizeof(unsigned long) - sizeof(BitsType)) * CHAR_BIT;
+    return bits == 0 ? kNumBits : __builtin_clzl(static_cast<unsigned long>(bits)) - kLeadingBitsOffset;
+  }
+
+  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
+    return bits == 0 ? kNumBits : __builtin_ctzl(static_cast<unsigned long>(bits));
+  }
+};
+
+template <typename BitsType>
+struct count_bits_impl<BitsType,
+                       std::enable_if_t<std::is_integral<BitsType>::value && sizeof(unsigned long) < sizeof(BitsType) &&
+                                        sizeof(BitsType) <= sizeof(unsigned long long)>> {
+  static constexpr int kNumBits = static_cast<int>(sizeof(BitsType) * CHAR_BIT);
+  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
+    static constexpr int kLeadingBitsOffset = (sizeof(unsigned long long) - sizeof(BitsType)) * CHAR_BIT;
+    return bits == 0 ? kNumBits : __builtin_clzll(static_cast<unsigned long long>(bits)) - kLeadingBitsOffset;
+  }
+
+  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
+    return bits == 0 ? kNumBits : __builtin_ctzll(static_cast<unsigned long long>(bits));
+  }
+};
+
+#elif EIGEN_COMP_MSVC
+
+template <typename BitsType>
+struct count_bits_impl<
+    BitsType, std::enable_if_t<std::is_integral<BitsType>::value && sizeof(BitsType) <= sizeof(unsigned long)>> {
+  static constexpr int kNumBits = static_cast<int>(sizeof(BitsType) * CHAR_BIT);
+  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
+    unsigned long out;
+    _BitScanReverse(&out, static_cast<unsigned long>(bits));
+    return bits == 0 ? kNumBits : (kNumBits - 1) - static_cast<int>(out);
+  }
+
+  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
+    unsigned long out;
+    _BitScanForward(&out, static_cast<unsigned long>(bits));
+    return bits == 0 ? kNumBits : static_cast<int>(out);
+  }
+};
+
+#ifdef _WIN64
+
+template <typename BitsType>
+struct count_bits_impl<BitsType,
+                       std::enable_if_t<std::is_integral<BitsType>::value && sizeof(unsigned long) < sizeof(BitsType) &&
+                                        sizeof(BitsType) <= sizeof(__int64)>> {
+  static constexpr int kNumBits = static_cast<int>(sizeof(BitsType) * CHAR_BIT);
+  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
+    unsigned long out;
+    _BitScanReverse64(&out, static_cast<unsigned __int64>(bits));
+    return bits == 0 ? kNumBits : (kNumBits - 1) - static_cast<int>(out);
+  }
+
+  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
+    unsigned long out;
+    _BitScanForward64(&out, static_cast<unsigned __int64>(bits));
+    return bits == 0 ? kNumBits : static_cast<int>(out);
+  }
+};
+
+#endif  // _WIN64
+
+#endif  // EIGEN_COMP_GNUC || EIGEN_COMP_CLANG
+
+template <typename BitsType>
+struct log_2_impl {
+  static constexpr int kTotalBits = sizeof(BitsType) * CHAR_BIT;
+  static EIGEN_DEVICE_FUNC inline int run_ceil(const BitsType& x) {
+    const int n = kTotalBits - clz(x);
+    bool power_of_two = (x & (x - 1)) == 0;
+    return x == 0 ? 0 : power_of_two ? (n - 1) : n;
+  }
+  static EIGEN_DEVICE_FUNC inline int run_floor(const BitsType& x) {
+    const int n = kTotalBits - clz(x);
+    return x == 0 ? 0 : n - 1;
+  }
+};
+
+template <typename BitsType>
+int log2_ceil(const BitsType& x) {
+  return log_2_impl<BitsType>::run_ceil(x);
+}
+
+template <typename BitsType>
+int log2_floor(const BitsType& x) {
+  return log_2_impl<BitsType>::run_floor(x);
+}
+
+// Implementation of is* functions
+
+template <typename T>
+EIGEN_DEVICE_FUNC std::enable_if_t<!(std::numeric_limits<T>::has_infinity || std::numeric_limits<T>::has_quiet_NaN ||
+                                     std::numeric_limits<T>::has_signaling_NaN),
+                                   bool>
+isfinite_impl(const T&) {
+  return true;
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC std::enable_if_t<(std::numeric_limits<T>::has_infinity || std::numeric_limits<T>::has_quiet_NaN ||
+                                    std::numeric_limits<T>::has_signaling_NaN) &&
+                                       (!NumTraits<T>::IsComplex),
+                                   bool>
+isfinite_impl(const T& x) {
+  EIGEN_USING_STD(isfinite);
+  return isfinite EIGEN_NOT_A_MACRO(x);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC std::enable_if_t<!std::numeric_limits<T>::has_infinity, bool> isinf_impl(const T&) {
+  return false;
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC std::enable_if_t<(std::numeric_limits<T>::has_infinity && !NumTraits<T>::IsComplex), bool> isinf_impl(
+    const T& x) {
+  EIGEN_USING_STD(isinf);
+  return isinf EIGEN_NOT_A_MACRO(x);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC
+    std::enable_if_t<!(std::numeric_limits<T>::has_quiet_NaN || std::numeric_limits<T>::has_signaling_NaN), bool>
+    isnan_impl(const T&) {
+  return false;
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC std::enable_if_t<
+    (std::numeric_limits<T>::has_quiet_NaN || std::numeric_limits<T>::has_signaling_NaN) && (!NumTraits<T>::IsComplex),
+    bool>
+isnan_impl(const T& x) {
+  EIGEN_USING_STD(isnan);
+  return isnan EIGEN_NOT_A_MACRO(x);
+}
+
+// The following overload are defined at the end of this file
+template <typename T>
+EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex<T>& x);
+template <typename T>
+EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex<T>& x);
+template <typename T>
+EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex<T>& x);
+template <typename T>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS T ptanh_float(const T& a_x);
+
+/****************************************************************************
+ * Implementation of sign                                                 *
+ ****************************************************************************/
+template <typename Scalar, bool IsComplex = (NumTraits<Scalar>::IsComplex != 0),
+          bool IsInteger = (NumTraits<Scalar>::IsInteger != 0)>
+struct sign_impl {
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& a) { return Scalar((a > Scalar(0)) - (a < Scalar(0))); }
+};
+
+template <typename Scalar>
+struct sign_impl<Scalar, false, false> {
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& a) {
+    return (isnan_impl<Scalar>)(a) ? a : Scalar((a > Scalar(0)) - (a < Scalar(0)));
+  }
+};
+
+template <typename Scalar, bool IsInteger>
+struct sign_impl<Scalar, true, IsInteger> {
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& a) {
+    using real_type = typename NumTraits<Scalar>::Real;
+    EIGEN_USING_STD(abs);
+    real_type aa = abs(a);
+    if (aa == real_type(0)) return Scalar(0);
+    aa = real_type(1) / aa;
+    return Scalar(numext::real(a) * aa, numext::imag(a) * aa);
+  }
+};
+
+// The sign function for bool is the identity.
+template <>
+struct sign_impl<bool, false, true> {
+  EIGEN_DEVICE_FUNC static inline bool run(const bool& a) { return a; }
+};
+
+template <typename Scalar>
+struct sign_retval {
+  typedef Scalar type;
+};
+
+// suppress "unary minus operator applied to unsigned type, result still unsigned" warnings on MSVC
+// note: `0 - a` is distinct from `-a` when Scalar is a floating point type and `a` is zero
+
+template <typename Scalar, bool IsInteger = NumTraits<Scalar>::IsInteger>
+struct negate_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar run(const Scalar& a) { return -a; }
+};
+
+template <typename Scalar>
+struct negate_impl<Scalar, true> {
+  EIGEN_STATIC_ASSERT((!is_same<Scalar, bool>::value), NEGATE IS NOT DEFINED FOR BOOLEAN TYPES)
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar run(const Scalar& a) { return Scalar(0) - a; }
+};
+
+template <typename Scalar>
+struct negate_retval {
+  typedef Scalar type;
+};
+
+template <typename Scalar, bool IsInteger = NumTraits<typename unpacket_traits<Scalar>::type>::IsInteger>
+struct nearest_integer_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_floor(const Scalar& x) {
+    EIGEN_USING_STD(floor) return floor(x);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_ceil(const Scalar& x) {
+    EIGEN_USING_STD(ceil) return ceil(x);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_rint(const Scalar& x) {
+    EIGEN_USING_STD(rint) return rint(x);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_round(const Scalar& x) {
+    EIGEN_USING_STD(round) return round(x);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_trunc(const Scalar& x) {
+    EIGEN_USING_STD(trunc) return trunc(x);
+  }
+};
+template <typename Scalar>
+struct nearest_integer_impl<Scalar, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_floor(const Scalar& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_ceil(const Scalar& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_rint(const Scalar& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_round(const Scalar& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_trunc(const Scalar& x) { return x; }
+};
+
+// Extra namespace to prevent leaking std::fma into Eigen::internal.
+namespace has_fma_detail {
+
+template <typename T, typename EnableIf = void>
+struct has_fma_impl : public std::false_type {};
+
+using std::fma;
+
+template <typename T>
+struct has_fma_impl<
+    T, std::enable_if_t<std::is_same<T, decltype(fma(std::declval<T>(), std::declval<T>(), std::declval<T>()))>::value>>
+    : public std::true_type {};
+
+}  // namespace has_fma_detail
+
+template <typename T>
+struct has_fma : public has_fma_detail::has_fma_impl<T> {};
+
+// Default implementation.
+template <typename T, typename Enable = void>
+struct fma_impl {
+  static_assert(has_fma<T>::value, "No function fma(...) for type.  Please provide an implementation.");
+};
+
+// STD or ADL version if it exists.
+template <typename T>
+struct fma_impl<T, std::enable_if_t<has_fma<T>::value>> {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T run(const T& a, const T& b, const T& c) {
+    using std::fma;
+    return fma(a, b, c);
+  }
+};
+
+#if defined(EIGEN_GPUCC)
+template <>
+struct has_fma<float> : public true_type {};
+
+template <>
+struct fma_impl<float, void> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float run(const float& a, const float& b, const float& c) {
+    return ::fmaf(a, b, c);
+  }
+};
+
+template <>
+struct has_fma<double> : public true_type {};
+
+template <>
+struct fma_impl<double, void> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double run(const double& a, const double& b, const double& c) {
+    return ::fma(a, b, c);
+  }
+};
+#endif
+
+// Basic multiply-add.
+template <typename Scalar, typename EnableIf = void>
+struct madd_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run(const Scalar& x, const Scalar& y, const Scalar& z) {
+    return x * y + z;
+  }
+};
+
+#if EIGEN_SCALAR_MADD_USE_FMA
+template <typename Scalar>
+struct madd_impl<Scalar, std::enable_if_t<has_fma<Scalar>::value>> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run(const Scalar& x, const Scalar& y, const Scalar& z) {
+    return fma_impl<Scalar>::run(x, y, z);
+  }
+};
+#endif
+
+}  // end namespace internal
+
+/****************************************************************************
+ * Generic math functions                                                    *
+ ****************************************************************************/
+
+namespace numext {
+
+#if (!defined(EIGEN_GPUCC) || defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
+template <typename T>
+EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y) {
+  EIGEN_USING_STD(min)
+  return min EIGEN_NOT_A_MACRO(x, y);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y) {
+  EIGEN_USING_STD(max)
+  return max EIGEN_NOT_A_MACRO(x, y);
+}
+#else
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y) {
+  return y < x ? y : x;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float mini(const float& x, const float& y) {
+  return fminf(x, y);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double mini(const double& x, const double& y) {
+  return fmin(x, y);
+}
+
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE long double mini(const long double& x, const long double& y) {
+#if defined(EIGEN_HIPCC)
+  // no "fminl" on HIP yet
+  return (x < y) ? x : y;
+#else
+  return fminl(x, y);
+#endif
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y) {
+  return x < y ? y : x;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float maxi(const float& x, const float& y) {
+  return fmaxf(x, y);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double maxi(const double& x, const double& y) {
+  return fmax(x, y);
+}
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE long double maxi(const long double& x, const long double& y) {
+#if defined(EIGEN_HIPCC)
+  // no "fmaxl" on HIP yet
+  return (x > y) ? x : y;
+#else
+  return fmaxl(x, y);
+#endif
+}
+#endif
+#endif
+
+#if defined(SYCL_DEVICE_ONLY)
+
+#define SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_char)    \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_short)   \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_int)     \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_long)
+#define SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_char)    \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_short)   \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_int)     \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_long)
+#define SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_uchar)     \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_ushort)    \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_uint)      \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_ulong)
+#define SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_uchar)     \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_ushort)    \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_uint)      \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_ulong)
+#define SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(NAME, FUNC)  \
+  SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY(NAME, FUNC)
+#define SYCL_SPECIALIZE_INTEGER_TYPES_UNARY(NAME, FUNC)  \
+  SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY(NAME, FUNC)
+#define SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(NAME, FUNC)     \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_float) \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_double)
+#define SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(NAME, FUNC)     \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_float) \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_double)
+#define SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(NAME, FUNC, RET_TYPE) \
+  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, cl::sycl::cl_float)       \
+  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, cl::sycl::cl_double)
+
+#define SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE)     \
+  template <>                                                              \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE RET_TYPE NAME(const ARG_TYPE& x) { \
+    return cl::sycl::FUNC(x);                                              \
+  }
+
+#define SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, TYPE) SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, TYPE, TYPE)
+
+#define SYCL_SPECIALIZE_GEN1_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE1, ARG_TYPE2)            \
+  template <>                                                                                   \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE RET_TYPE NAME(const ARG_TYPE1& x, const ARG_TYPE2& y) { \
+    return cl::sycl::FUNC(x, y);                                                                \
+  }
+
+#define SYCL_SPECIALIZE_GEN2_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE) \
+  SYCL_SPECIALIZE_GEN1_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE, ARG_TYPE)
+
+#define SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, TYPE) SYCL_SPECIALIZE_GEN2_BINARY_FUNC(NAME, FUNC, TYPE, TYPE)
+
+SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(mini, min)
+SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(mini, fmin)
+SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(maxi, max)
+SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(maxi, fmax)
+
+#endif
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(arg, Scalar) arg(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(arg, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline internal::add_const_on_value_type_t<EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar)> imag_ref(
+    const Scalar& x) {
+  return internal::imag_ref_impl<Scalar>::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) imag_ref(Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(imag_ref, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(sign, Scalar) sign(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(sign, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(negate, Scalar) negate(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(negate, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(abs2, Scalar)::run(x);
+}
+
+EIGEN_DEVICE_FUNC inline bool abs2(bool x) { return x; }
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T absdiff(const T& x, const T& y) {
+  return x > y ? x - y : y - x;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float absdiff(const float& x, const float& y) {
+  return fabsf(x - y);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double absdiff(const double& x, const double& y) {
+  return fabs(x - y);
+}
+
+// HIP and CUDA do not support long double.
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE long double absdiff(const long double& x, const long double& y) {
+  return fabsl(x - y);
+}
+#endif
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(norm1, Scalar) norm1(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(norm1, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar& y) {
+  return EIGEN_MATHFUNC_IMPL(hypot, Scalar)::run(x, y);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(hypot, hypot)
+#endif
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(log1p, log1p)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float log1p(const float& x) {
+  return ::log1pf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double log1p(const double& x) {
+  return ::log1p(x);
+}
+#endif
+
+template <typename ScalarX, typename ScalarY>
+EIGEN_DEVICE_FUNC inline typename internal::pow_impl<ScalarX, ScalarY>::result_type pow(const ScalarX& x,
+                                                                                        const ScalarY& y) {
+  return internal::pow_impl<ScalarX, ScalarY>::run(x, y);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(pow, pow)
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC bool(isnan)(const T& x) {
+  return internal::isnan_impl(x);
+}
+template <typename T>
+EIGEN_DEVICE_FUNC bool(isinf)(const T& x) {
+  return internal::isinf_impl(x);
+}
+template <typename T>
+EIGEN_DEVICE_FUNC bool(isfinite)(const T& x) {
+  return internal::isfinite_impl(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isnan, isnan, bool)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isinf, isinf, bool)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isfinite, isfinite, bool)
+#endif
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar rint(const Scalar& x) {
+  return internal::nearest_integer_impl<Scalar>::run_rint(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar round(const Scalar& x) {
+  return internal::nearest_integer_impl<Scalar>::run_round(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar(floor)(const Scalar& x) {
+  return internal::nearest_integer_impl<Scalar>::run_floor(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar(ceil)(const Scalar& x) {
+  return internal::nearest_integer_impl<Scalar>::run_ceil(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar(trunc)(const Scalar& x) {
+  return internal::nearest_integer_impl<Scalar>::run_trunc(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(round, round)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(floor, floor)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(ceil, ceil)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(trunc, trunc)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float floor(const float& x) {
+  return ::floorf(x);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double floor(const double& x) {
+  return ::floor(x);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float ceil(const float& x) {
+  return ::ceilf(x);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double ceil(const double& x) {
+  return ::ceil(x);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float trunc(const float& x) {
+  return ::truncf(x);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double trunc(const double& x) {
+  return ::trunc(x);
+}
+#endif
+
+// Integer division with rounding up.
+// T is assumed to be an integer type with a>=0, and b>0
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE constexpr T div_ceil(T a, T b) {
+  using UnsignedT = typename internal::make_unsigned<T>::type;
+  EIGEN_STATIC_ASSERT((NumTraits<T>::IsInteger), THIS FUNCTION IS FOR INTEGER TYPES)
+  // Note: explicitly declaring a and b as non-negative values allows the compiler to use better optimizations
+  const UnsignedT ua = UnsignedT(a);
+  const UnsignedT ub = UnsignedT(b);
+  // Note: This form is used because it cannot overflow.
+  return ua == 0 ? 0 : (ua - 1) / ub + 1;
+}
+
+// Integer round down to nearest power of b
+// T is assumed to be an integer type with a>=0, and b>0
+template <typename T, typename U>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE constexpr T round_down(T a, U b) {
+  using UnsignedT = typename internal::make_unsigned<T>::type;
+  using UnsignedU = typename internal::make_unsigned<U>::type;
+  EIGEN_STATIC_ASSERT((NumTraits<T>::IsInteger), THIS FUNCTION IS FOR INTEGER TYPES)
+  EIGEN_STATIC_ASSERT((NumTraits<U>::IsInteger), THIS FUNCTION IS FOR INTEGER TYPES)
+  // Note: explicitly declaring a and b as non-negative values allows the compiler to use better optimizations
+  const UnsignedT ua = UnsignedT(a);
+  const UnsignedU ub = UnsignedU(b);
+  return ub * (ua / ub);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T log2(T x) {
+  EIGEN_USING_STD(log2);
+  return log2(x);
+}
+
+/** Log base 2 for 32 bits positive integers.
+ * Conveniently returns 0 for x==0. */
+constexpr int log2(int x) {
+  unsigned int v(x);
+  constexpr int table[32] = {0, 9,  1,  10, 13, 21, 2,  29, 11, 14, 16, 18, 22, 25, 3, 30,
+                             8, 12, 20, 28, 15, 17, 24, 7,  19, 27, 23, 6,  26, 5,  4, 31};
+  v |= v >> 1;
+  v |= v >> 2;
+  v |= v >> 4;
+  v |= v >> 8;
+  v |= v >> 16;
+  return table[(v * 0x07C4ACDDU) >> 27];
+}
+
+/** \returns the square root of \a x.
+ *
+ * It is essentially equivalent to
+ * \code using std::sqrt; return sqrt(x); \endcode
+ * but slightly faster for float/double and some compilers (e.g., gcc), thanks to
+ * specializations when SSE is enabled.
+ *
+ * It's usage is justified in performance critical functions, like norm/normalize.
+ */
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE EIGEN_MATHFUNC_RETVAL(sqrt, Scalar) sqrt(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(sqrt, Scalar)::run(x);
+}
+
+// Boolean specialization, avoids implicit float to bool conversion (-Wimplicit-conversion-floating-point-to-bool).
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC bool sqrt<bool>(const bool& x) {
+  return x;
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sqrt, sqrt)
+#endif
+
+/** \returns the cube root of \a x. **/
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::enable_if_t<!NumTraits<T>::IsComplex, T> cbrt(const T& x) {
+  EIGEN_USING_STD(cbrt);
+  return static_cast<T>(cbrt(x));
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::enable_if_t<NumTraits<T>::IsComplex, T> cbrt(const T& x) {
+  EIGEN_USING_STD(pow);
+  return pow(x, typename NumTraits<T>::Real(1.0 / 3.0));
+}
+
+/** \returns the reciprocal square root of \a x. **/
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T rsqrt(const T& x) {
+  return internal::rsqrt_impl<T>::run(x);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T log(const T& x) {
+  return internal::log_impl<T>::run(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(log, log)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float log(const float& x) {
+  return ::logf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double log(const double& x) {
+  return ::log(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+    std::enable_if_t<NumTraits<T>::IsSigned || NumTraits<T>::IsComplex, typename NumTraits<T>::Real>
+    abs(const T& x) {
+  EIGEN_USING_STD(abs);
+  return abs(x);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+    std::enable_if_t<!(NumTraits<T>::IsSigned || NumTraits<T>::IsComplex), typename NumTraits<T>::Real>
+    abs(const T& x) {
+  return x;
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_INTEGER_TYPES_UNARY(abs, abs)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(abs, fabs)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float abs(const float& x) {
+  return ::fabsf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double abs(const double& x) {
+  return ::fabs(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float abs(const std::complex<float>& x) {
+  return ::hypotf(x.real(), x.imag());
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double abs(const std::complex<double>& x) {
+  return ::hypot(x.real(), x.imag());
+}
+#endif
+
+template <typename Scalar, bool IsInteger = NumTraits<Scalar>::IsInteger, bool IsSigned = NumTraits<Scalar>::IsSigned>
+struct signbit_impl;
+template <typename Scalar>
+struct signbit_impl<Scalar, false, true> {
+  static constexpr size_t Size = sizeof(Scalar);
+  static constexpr size_t Shift = (CHAR_BIT * Size) - 1;
+  using intSize_t = typename get_integer_by_size<Size>::signed_type;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static Scalar run(const Scalar& x) {
+    intSize_t a = bit_cast<intSize_t, Scalar>(x);
+    a = a >> Shift;
+    Scalar result = bit_cast<Scalar, intSize_t>(a);
+    return result;
+  }
+};
+template <typename Scalar>
+struct signbit_impl<Scalar, true, true> {
+  static constexpr size_t Size = sizeof(Scalar);
+  static constexpr size_t Shift = (CHAR_BIT * Size) - 1;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Scalar run(const Scalar& x) { return x >> Shift; }
+};
+template <typename Scalar>
+struct signbit_impl<Scalar, true, false> {
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Scalar run(const Scalar&) { return Scalar(0); }
+};
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Scalar signbit(const Scalar& x) {
+  return signbit_impl<Scalar>::run(x);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T exp(const T& x) {
+  EIGEN_USING_STD(exp);
+  return exp(x);
+}
+
+// MSVC screws up some edge-cases for std::exp(complex).
+#ifdef EIGEN_COMP_MSVC
+template <typename RealScalar>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::complex<RealScalar> exp(const std::complex<RealScalar>& x) {
+  EIGEN_USING_STD(exp);
+  // If z is (x,±∞) (for any finite x), the result is (NaN,NaN) and FE_INVALID is raised.
+  // If z is (x,NaN) (for any finite x), the result is (NaN,NaN) and FE_INVALID may be raised.
+  if ((isfinite)(real_ref(x)) && !(isfinite)(imag_ref(x))) {
+    return std::complex<RealScalar>(NumTraits<RealScalar>::quiet_NaN(), NumTraits<RealScalar>::quiet_NaN());
+  }
+  // If z is (+∞,±∞), the result is (±∞,NaN) and FE_INVALID is raised (the sign of the real part is unspecified)
+  // If z is (+∞,NaN), the result is (±∞,NaN) (the sign of the real part is unspecified)
+  if ((real_ref(x) == NumTraits<RealScalar>::infinity() && !(isfinite)(imag_ref(x)))) {
+    return std::complex<RealScalar>(NumTraits<RealScalar>::infinity(), NumTraits<RealScalar>::quiet_NaN());
+  }
+  return exp(x);
+}
+#endif
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(exp, exp)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float exp(const float& x) {
+  return ::expf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double exp(const double& x) {
+  return ::exp(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::complex<float> exp(const std::complex<float>& x) {
+  float com = ::expf(x.real());
+  float res_real = com * ::cosf(x.imag());
+  float res_imag = com * ::sinf(x.imag());
+  return std::complex<float>(res_real, res_imag);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::complex<double> exp(const std::complex<double>& x) {
+  double com = ::exp(x.real());
+  double res_real = com * ::cos(x.imag());
+  double res_imag = com * ::sin(x.imag());
+  return std::complex<double>(res_real, res_imag);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T exp2(const T& x) {
+  EIGEN_USING_STD(exp2);
+  return exp2(x);
+}
+
+// MSVC screws up some edge-cases for std::exp2(complex).
+#ifdef EIGEN_COMP_MSVC
+template <typename RealScalar>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::complex<RealScalar> exp2(const std::complex<RealScalar>& x) {
+  EIGEN_USING_STD(exp);
+  // If z is (x,±∞) (for any finite x), the result is (NaN,NaN) and FE_INVALID is raised.
+  // If z is (x,NaN) (for any finite x), the result is (NaN,NaN) and FE_INVALID may be raised.
+  if ((isfinite)(real_ref(x)) && !(isfinite)(imag_ref(x))) {
+    return std::complex<RealScalar>(NumTraits<RealScalar>::quiet_NaN(), NumTraits<RealScalar>::quiet_NaN());
+  }
+  // If z is (+∞,±∞), the result is (±∞,NaN) and FE_INVALID is raised (the sign of the real part is unspecified)
+  // If z is (+∞,NaN), the result is (±∞,NaN) (the sign of the real part is unspecified)
+  if ((real_ref(x) == NumTraits<RealScalar>::infinity() && !(isfinite)(imag_ref(x)))) {
+    return std::complex<RealScalar>(NumTraits<RealScalar>::infinity(), NumTraits<RealScalar>::quiet_NaN());
+  }
+  return exp2(x);
+}
+#endif
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(exp2, exp2)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float exp2(const float& x) {
+  return ::exp2f(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double exp2(const double& x) {
+  return ::exp2(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::complex<float> exp2(const std::complex<float>& x) {
+  float com = ::exp2f(x.real());
+  float res_real = com * ::cosf(static_cast<float>(EIGEN_LN2) * x.imag());
+  float res_imag = com * ::sinf(static_cast<float>(EIGEN_LN2) * x.imag());
+  return std::complex<float>(res_real, res_imag);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::complex<double> exp2(const std::complex<double>& x) {
+  double com = ::exp2(x.real());
+  double res_real = com * ::cos(static_cast<double>(EIGEN_LN2) * x.imag());
+  double res_imag = com * ::sin(static_cast<double>(EIGEN_LN2) * x.imag());
+  return std::complex<double>(res_real, res_imag);
+}
+#endif
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(expm1, Scalar) expm1(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(expm1, Scalar)::run(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(expm1, expm1)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float expm1(const float& x) {
+  return ::expm1f(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double expm1(const double& x) {
+  return ::expm1(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T cos(const T& x) {
+  EIGEN_USING_STD(cos);
+  return cos(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(cos, cos)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float cos(const float& x) {
+  return ::cosf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double cos(const double& x) {
+  return ::cos(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T sin(const T& x) {
+  EIGEN_USING_STD(sin);
+  return sin(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sin, sin)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float sin(const float& x) {
+  return ::sinf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double sin(const double& x) {
+  return ::sin(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T tan(const T& x) {
+  EIGEN_USING_STD(tan);
+  return tan(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(tan, tan)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float tan(const float& x) {
+  return ::tanf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double tan(const double& x) {
+  return ::tan(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T acos(const T& x) {
+  EIGEN_USING_STD(acos);
+  return acos(x);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T acosh(const T& x) {
+  EIGEN_USING_STD(acosh);
+  return static_cast<T>(acosh(x));
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acos, acos)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acosh, acosh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float acos(const float& x) {
+  return ::acosf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double acos(const double& x) {
+  return ::acos(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T asin(const T& x) {
+  EIGEN_USING_STD(asin);
+  return asin(x);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T asinh(const T& x) {
+  EIGEN_USING_STD(asinh);
+  return static_cast<T>(asinh(x));
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asin, asin)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asinh, asinh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float asin(const float& x) {
+  return ::asinf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double asin(const double& x) {
+  return ::asin(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T atan(const T& x) {
+  EIGEN_USING_STD(atan);
+  return static_cast<T>(atan(x));
+}
+
+template <typename T, std::enable_if_t<!NumTraits<T>::IsComplex, int> = 0>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T atan2(const T& y, const T& x) {
+  EIGEN_USING_STD(atan2);
+  return static_cast<T>(atan2(y, x));
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T atanh(const T& x) {
+  EIGEN_USING_STD(atanh);
+  return static_cast<T>(atanh(x));
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atan, atan)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atanh, atanh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float atan(const float& x) {
+  return ::atanf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double atan(const double& x) {
+  return ::atan(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T cosh(const T& x) {
+  EIGEN_USING_STD(cosh);
+  return static_cast<T>(cosh(x));
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(cosh, cosh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float cosh(const float& x) {
+  return ::coshf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double cosh(const double& x) {
+  return ::cosh(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T sinh(const T& x) {
+  EIGEN_USING_STD(sinh);
+  return static_cast<T>(sinh(x));
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sinh, sinh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float sinh(const float& x) {
+  return ::sinhf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double sinh(const double& x) {
+  return ::sinh(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T tanh(const T& x) {
+  EIGEN_USING_STD(tanh);
+  return tanh(x);
+}
+
+#if (!defined(EIGEN_GPUCC)) && EIGEN_FAST_MATH && !defined(SYCL_DEVICE_ONLY)
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float tanh(float x) { return internal::ptanh_float(x); }
+#endif
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(tanh, tanh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float tanh(const float& x) {
+  return ::tanhf(x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double tanh(const double& x) {
+  return ::tanh(x);
+}
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T fmod(const T& a, const T& b) {
+  EIGEN_USING_STD(fmod);
+  return fmod(a, b);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(fmod, fmod)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float fmod(const float& a, const float& b) {
+  return ::fmodf(a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double fmod(const double& a, const double& b) {
+  return ::fmod(a, b);
+}
+#endif
+
+#if defined(SYCL_DEVICE_ONLY)
+#undef SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY
+#undef SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY
+#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY
+#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY
+#undef SYCL_SPECIALIZE_INTEGER_TYPES_BINARY
+#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY
+#undef SYCL_SPECIALIZE_FLOATING_TYPES_BINARY
+#undef SYCL_SPECIALIZE_FLOATING_TYPES_UNARY
+#undef SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE
+#undef SYCL_SPECIALIZE_GEN_UNARY_FUNC
+#undef SYCL_SPECIALIZE_UNARY_FUNC
+#undef SYCL_SPECIALIZE_GEN1_BINARY_FUNC
+#undef SYCL_SPECIALIZE_GEN2_BINARY_FUNC
+#undef SYCL_SPECIALIZE_BINARY_FUNC
+#endif
+
+template <typename Scalar, typename Enable = std::enable_if_t<std::is_integral<Scalar>::value>>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar logical_shift_left(const Scalar& a, int n) {
+  return a << n;
+}
+
+template <typename Scalar, typename Enable = std::enable_if_t<std::is_integral<Scalar>::value>>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar logical_shift_right(const Scalar& a, int n) {
+  using UnsignedScalar = typename numext::get_integer_by_size<sizeof(Scalar)>::unsigned_type;
+  return bit_cast<Scalar, UnsignedScalar>(bit_cast<UnsignedScalar, Scalar>(a) >> n);
+}
+
+template <typename Scalar, typename Enable = std::enable_if_t<std::is_integral<Scalar>::value>>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar arithmetic_shift_right(const Scalar& a, int n) {
+  using SignedScalar = typename numext::get_integer_by_size<sizeof(Scalar)>::signed_type;
+  return bit_cast<Scalar, SignedScalar>(bit_cast<SignedScalar, Scalar>(a) >> n);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar fma(const Scalar& x, const Scalar& y, const Scalar& z) {
+  return internal::fma_impl<Scalar>::run(x, y, z);
+}
+
+// Multiply-add.
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar madd(const Scalar& x, const Scalar& y, const Scalar& z) {
+  return internal::madd_impl<Scalar>::run(x, y, z);
+}
+
+}  // end namespace numext
+
+namespace internal {
+
+template <typename T>
+EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex<T>& x) {
+  return (numext::isfinite)(numext::real(x)) && (numext::isfinite)(numext::imag(x));
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex<T>& x) {
+  return (numext::isnan)(numext::real(x)) || (numext::isnan)(numext::imag(x));
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex<T>& x) {
+  return ((numext::isinf)(numext::real(x)) || (numext::isinf)(numext::imag(x))) && (!(numext::isnan)(x));
+}
+
+/****************************************************************************
+ * Implementation of fuzzy comparisons                                       *
+ ****************************************************************************/
+
+template <typename Scalar, bool IsComplex, bool IsInteger>
+struct scalar_fuzzy_default_impl {};
+
+template <typename Scalar>
+struct scalar_fuzzy_default_impl<Scalar, false, false> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  template <typename OtherScalar>
+  EIGEN_DEVICE_FUNC static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y,
+                                                         const RealScalar& prec) {
+    return numext::abs(x) <= numext::abs(y) * prec;
+  }
+  EIGEN_DEVICE_FUNC static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec) {
+    return numext::abs(x - y) <= numext::mini(numext::abs(x), numext::abs(y)) * prec;
+  }
+  EIGEN_DEVICE_FUNC static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar& prec) {
+    return x <= y || isApprox(x, y, prec);
+  }
+};
+
+template <typename Scalar>
+struct scalar_fuzzy_default_impl<Scalar, false, true> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  template <typename OtherScalar>
+  EIGEN_DEVICE_FUNC static inline bool isMuchSmallerThan(const Scalar& x, const Scalar&, const RealScalar&) {
+    return x == Scalar(0);
+  }
+  EIGEN_DEVICE_FUNC static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar&) { return x == y; }
+  EIGEN_DEVICE_FUNC static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar&) {
+    return x <= y;
+  }
+};
+
+template <typename Scalar>
+struct scalar_fuzzy_default_impl<Scalar, true, false> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  template <typename OtherScalar>
+  EIGEN_DEVICE_FUNC static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y,
+                                                         const RealScalar& prec) {
+    return numext::abs2(x) <= numext::abs2(y) * prec * prec;
+  }
+  EIGEN_DEVICE_FUNC static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec) {
+    return numext::abs2(x - y) <= numext::mini(numext::abs2(x), numext::abs2(y)) * prec * prec;
+  }
+};
+
+template <typename Scalar>
+struct scalar_fuzzy_impl
+    : scalar_fuzzy_default_impl<Scalar, NumTraits<Scalar>::IsComplex, NumTraits<Scalar>::IsInteger> {};
+
+template <typename Scalar, typename OtherScalar>
+EIGEN_DEVICE_FUNC inline bool isMuchSmallerThan(
+    const Scalar& x, const OtherScalar& y,
+    const typename NumTraits<Scalar>::Real& precision = NumTraits<Scalar>::dummy_precision()) {
+  return scalar_fuzzy_impl<Scalar>::template isMuchSmallerThan<OtherScalar>(x, y, precision);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline bool isApprox(
+    const Scalar& x, const Scalar& y,
+    const typename NumTraits<Scalar>::Real& precision = NumTraits<Scalar>::dummy_precision()) {
+  return scalar_fuzzy_impl<Scalar>::isApprox(x, y, precision);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline bool isApproxOrLessThan(
+    const Scalar& x, const Scalar& y,
+    const typename NumTraits<Scalar>::Real& precision = NumTraits<Scalar>::dummy_precision()) {
+  return scalar_fuzzy_impl<Scalar>::isApproxOrLessThan(x, y, precision);
+}
+
+/******************************************
+***  The special case of the  bool type ***
+******************************************/
+
+template <>
+struct scalar_fuzzy_impl<bool> {
+  typedef bool RealScalar;
+
+  template <typename OtherScalar>
+  EIGEN_DEVICE_FUNC static inline bool isMuchSmallerThan(const bool& x, const bool&, const bool&) {
+    return !x;
+  }
+
+  EIGEN_DEVICE_FUNC static inline bool isApprox(bool x, bool y, bool) { return x == y; }
+
+  EIGEN_DEVICE_FUNC static inline bool isApproxOrLessThan(const bool& x, const bool& y, const bool&) {
+    return (!x) || y;
+  }
+};
+
+}  // end namespace internal
+
+// Default implementations that rely on other numext implementations
+namespace internal {
+
+// Specialization for complex types that are not supported by std::expm1.
+template <typename RealScalar>
+struct expm1_impl<std::complex<RealScalar>> {
+  EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
+
+  EIGEN_DEVICE_FUNC static inline std::complex<RealScalar> run(const std::complex<RealScalar>& x) {
+    RealScalar xr = x.real();
+    RealScalar xi = x.imag();
+    // expm1(z) = exp(z) - 1
+    //          = exp(x +  i * y) - 1
+    //          = exp(x) * (cos(y) + i * sin(y)) - 1
+    //          = exp(x) * cos(y) - 1 + i * exp(x) * sin(y)
+    // Imag(expm1(z)) = exp(x) * sin(y)
+    // Real(expm1(z)) = exp(x) * cos(y) - 1
+    //          = exp(x) * cos(y) - 1.
+    //          = expm1(x) + exp(x) * (cos(y) - 1)
+    //          = expm1(x) + exp(x) * (2 * sin(y / 2) ** 2)
+    RealScalar erm1 = numext::expm1<RealScalar>(xr);
+    RealScalar er = erm1 + RealScalar(1.);
+    RealScalar sin2 = numext::sin(xi / RealScalar(2.));
+    sin2 = sin2 * sin2;
+    RealScalar s = numext::sin(xi);
+    RealScalar real_part = erm1 - RealScalar(2.) * er * sin2;
+    return std::complex<RealScalar>(real_part, er * s);
+  }
+};
+
+template <typename T>
+struct rsqrt_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE T run(const T& x) { return T(1) / numext::sqrt(x); }
+};
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+template <typename T>
+struct conj_impl<std::complex<T>, true> {
+  EIGEN_DEVICE_FUNC static inline std::complex<T> run(const std::complex<T>& x) {
+    return std::complex<T>(numext::real(x), -numext::imag(x));
+  }
+};
+#endif
+
+// Complex multiply and division operators.
+// Note that these do not handle the case if inf+NaNi, which is considered an infinity.
+// This is for consistency with our standard pmul, pdiv implementations.
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> complex_multiply(const std::complex<T>& a,
+                                                                       const std::complex<T>& b) {
+  const T a_real = numext::real(a);
+  const T a_imag = numext::imag(a);
+  const T b_real = numext::real(b);
+  const T b_imag = numext::imag(b);
+  return std::complex<T>(a_real * b_real - a_imag * b_imag, a_imag * b_real + a_real * b_imag);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> complex_divide_fast(const std::complex<T>& a,
+                                                                          const std::complex<T>& b) {
+  const T a_real = numext::real(a);
+  const T a_imag = numext::imag(a);
+  const T b_real = numext::real(b);
+  const T b_imag = numext::imag(b);
+  const T norm = (b_real * b_real + b_imag * b_imag);
+  return std::complex<T>((a_real * b_real + a_imag * b_imag) / norm, (a_imag * b_real - a_real * b_imag) / norm);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> complex_divide_smith(const std::complex<T>& a,
+                                                                           const std::complex<T>& b) {
+  const T a_real = numext::real(a);
+  const T a_imag = numext::imag(a);
+  const T b_real = numext::real(b);
+  const T b_imag = numext::imag(b);
+  // Smith's complex division (https://arxiv.org/pdf/1210.4539.pdf),
+  // guards against over/under-flow.
+  const bool scale_imag = numext::abs(b_imag) <= numext::abs(b_real);
+  const T rscale = scale_imag ? T(1) : b_real / b_imag;
+  const T iscale = scale_imag ? b_imag / b_real : T(1);
+  const T denominator = b_real * rscale + b_imag * iscale;
+  return std::complex<T>((a_real * rscale + a_imag * iscale) / denominator,
+                         (a_imag * rscale - a_real * iscale) / denominator);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> complex_divide(const std::complex<T>& a,
+                                                                     const std::complex<T>& b) {
+#if EIGEN_FAST_MATH
+  return complex_divide_fast(a, b);
+#else
+  return complex_divide_smith(a, b);
+#endif
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATHFUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MathFunctionsImpl.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MathFunctionsImpl.h
new file mode 100644
index 0000000..b8760e0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MathFunctionsImpl.h
@@ -0,0 +1,265 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATHFUNCTIONSIMPL_H
+#define EIGEN_MATHFUNCTIONSIMPL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal Fast reciprocal using Newton-Raphson's method.
+
+ Preconditions:
+   1. The starting guess provided in approx_a_recip must have at least half
+      the leading mantissa bits in the correct result, such that a single
+      Newton-Raphson step is sufficient to get within 1-2 ulps of the correct
+      result.
+   2. If a is zero, approx_a_recip must be infinite with the same sign as a.
+   3. If a is infinite, approx_a_recip must be zero with the same sign as a.
+
+   If the preconditions are satisfied, which they are for the _*_rcp_ps
+   instructions on x86, the result has a maximum relative error of 2 ulps,
+   and correctly handles reciprocals of zero, infinity, and NaN.
+*/
+template <typename Packet, int Steps>
+struct generic_reciprocal_newton_step {
+  static_assert(Steps > 0, "Steps must be at least 1.");
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet run(const Packet& a, const Packet& approx_a_recip) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet one = pset1<Packet>(Scalar(1));
+    // Refine the approximation using one Newton-Raphson step:
+    //   x_{i} = x_{i-1} * (2 - a * x_{i-1})
+    const Packet x = generic_reciprocal_newton_step<Packet, Steps - 1>::run(a, approx_a_recip);
+    const Packet tmp = pnmadd(a, x, one);
+    // If tmp is NaN, it means that a is either +/-0 or +/-Inf.
+    // In this case return the approximation directly.
+    const Packet is_not_nan = pcmp_eq(tmp, tmp);
+    // Use two FMAs instead of FMA+FMUL to improve precision.
+    return pselect(is_not_nan, pmadd(x, tmp, x), x);
+  }
+};
+
+template <typename Packet>
+struct generic_reciprocal_newton_step<Packet, 0> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet run(const Packet& /*unused*/, const Packet& approx_rsqrt) {
+    return approx_rsqrt;
+  }
+};
+
+/** \internal Fast reciprocal sqrt using Newton-Raphson's method.
+
+ Preconditions:
+   1. The starting guess provided in approx_a_recip must have at least half
+      the leading mantissa bits in the correct result, such that a single
+      Newton-Raphson step is sufficient to get within 1-2 ulps of the correct
+      result.
+   2. If a is zero, approx_a_recip must be infinite with the same sign as a.
+   3. If a is infinite, approx_a_recip must be zero with the same sign as a.
+
+   If the preconditions are satisfied, which they are for the _*_rcp_ps
+   instructions on x86, the result has a maximum relative error of 2 ulps,
+   and correctly handles zero, infinity, and NaN. Positive denormals are
+   treated as zero.
+*/
+template <typename Packet, int Steps>
+struct generic_rsqrt_newton_step {
+  static_assert(Steps > 0, "Steps must be at least 1.");
+  using Scalar = typename unpacket_traits<Packet>::type;
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet run(const Packet& a, const Packet& approx_rsqrt) {
+    const Scalar kMinusHalf = Scalar(-1) / Scalar(2);
+    const Packet cst_minus_half = pset1<Packet>(kMinusHalf);
+    const Packet cst_minus_one = pset1<Packet>(Scalar(-1));
+
+    Packet inv_sqrt = approx_rsqrt;
+    for (int step = 0; step < Steps; ++step) {
+      // Refine the approximation using one Newton-Raphson step:
+      // h_n = (x * inv_sqrt) * inv_sqrt - 1 (so that h_n is nearly 0).
+      // inv_sqrt = inv_sqrt - 0.5 * inv_sqrt * h_n
+      Packet r2 = pmul(a, inv_sqrt);
+      Packet half_r = pmul(inv_sqrt, cst_minus_half);
+      Packet h_n = pmadd(r2, inv_sqrt, cst_minus_one);
+      inv_sqrt = pmadd(half_r, h_n, inv_sqrt);
+    }
+
+    // If x is NaN, then either:
+    // 1) the input is NaN
+    // 2) zero and infinity were multiplied
+    // In either of these cases, return approx_rsqrt
+    return pselect(pisnan(inv_sqrt), approx_rsqrt, inv_sqrt);
+  }
+};
+
+template <typename Packet>
+struct generic_rsqrt_newton_step<Packet, 0> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet run(const Packet& /*unused*/, const Packet& approx_rsqrt) {
+    return approx_rsqrt;
+  }
+};
+
+/** \internal Fast sqrt using Newton-Raphson's method.
+
+ Preconditions:
+   1. The starting guess for the reciprocal sqrt provided in approx_rsqrt must
+      have at least half the leading mantissa bits in the correct result, such
+      that a single Newton-Raphson step is sufficient to get within 1-2 ulps of
+      the correct result.
+   2. If a is zero, approx_rsqrt must be infinite.
+   3. If a is infinite, approx_rsqrt must be zero.
+
+   If the preconditions are satisfied, which they are for the _*_rsqrt_ps
+   instructions on x86, the result has a maximum relative error of 2 ulps,
+   and correctly handles zero and infinity, and NaN. Positive denormal inputs
+   are treated as zero.
+*/
+template <typename Packet, int Steps = 1>
+struct generic_sqrt_newton_step {
+  static_assert(Steps > 0, "Steps must be at least 1.");
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet run(const Packet& a, const Packet& approx_rsqrt) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet one_point_five = pset1<Packet>(Scalar(1.5));
+    const Packet minus_half = pset1<Packet>(Scalar(-0.5));
+    // If a is inf or zero, return a directly.
+    const Packet inf_mask = pcmp_eq(a, pset1<Packet>(NumTraits<Scalar>::infinity()));
+    const Packet return_a = por(pcmp_eq(a, pzero(a)), inf_mask);
+    // Do a single step of Newton's iteration for reciprocal square root:
+    //   x_{n+1} = x_n * (1.5 + (-0.5 * x_n) * (a * x_n))).
+    // The Newton's step is computed this way to avoid over/under-flows.
+    Packet rsqrt = pmul(approx_rsqrt, pmadd(pmul(minus_half, approx_rsqrt), pmul(a, approx_rsqrt), one_point_five));
+    for (int step = 1; step < Steps; ++step) {
+      rsqrt = pmul(rsqrt, pmadd(pmul(minus_half, rsqrt), pmul(a, rsqrt), one_point_five));
+    }
+
+    // Return sqrt(x) = x * rsqrt(x) for non-zero finite positive arguments.
+    // Return a itself for 0 or +inf, NaN for negative arguments.
+    return pselect(return_a, a, pmul(a, rsqrt));
+  }
+};
+
+template <typename RealScalar>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE RealScalar positive_real_hypot(const RealScalar& x,
+                                                                               const RealScalar& y) {
+  // IEEE IEC 6059 special cases.
+  if ((numext::isinf)(x) || (numext::isinf)(y)) return NumTraits<RealScalar>::infinity();
+  if ((numext::isnan)(x) || (numext::isnan)(y)) return NumTraits<RealScalar>::quiet_NaN();
+
+  EIGEN_USING_STD(sqrt);
+  RealScalar p, qp;
+  p = numext::maxi(x, y);
+  if (numext::is_exactly_zero(p)) return RealScalar(0);
+  qp = numext::mini(y, x) / p;
+  return p * sqrt(RealScalar(1) + qp * qp);
+}
+
+template <typename Scalar>
+struct hypot_impl {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  static EIGEN_DEVICE_FUNC inline RealScalar run(const Scalar& x, const Scalar& y) {
+    EIGEN_USING_STD(abs);
+    return positive_real_hypot<RealScalar>(abs(x), abs(y));
+  }
+};
+
+// Generic complex sqrt implementation that correctly handles corner cases
+// according to https://en.cppreference.com/w/cpp/numeric/complex/sqrt
+template <typename ComplexT>
+EIGEN_DEVICE_FUNC constexpr ComplexT complex_sqrt(const ComplexT& z) {
+  // Computes the principal sqrt of the input.
+  //
+  // For a complex square root of the number x + i*y. We want to find real
+  // numbers u and v such that
+  //    (u + i*v)^2 = x + i*y  <=>
+  //    u^2 - v^2 + i*2*u*v = x + i*v.
+  // By equating the real and imaginary parts we get:
+  //    u^2 - v^2 = x
+  //    2*u*v = y.
+  //
+  // For x >= 0, this has the numerically stable solution
+  //    u = sqrt(0.5 * (x + sqrt(x^2 + y^2)))
+  //    v = y / (2 * u)
+  // and for x < 0,
+  //    v = sign(y) * sqrt(0.5 * (-x + sqrt(x^2 + y^2)))
+  //    u = y / (2 * v)
+  //
+  // Letting w = sqrt(0.5 * (|x| + |z|)),
+  //   if x == 0: u = w, v = sign(y) * w
+  //   if x > 0:  u = w, v = y / (2 * w)
+  //   if x < 0:  u = |y| / (2 * w), v = sign(y) * w
+  using T = typename NumTraits<ComplexT>::Real;
+  const T x = numext::real(z);
+  const T y = numext::imag(z);
+  const T zero = T(0);
+  const T w = numext::sqrt(T(0.5) * (numext::abs(x) + numext::hypot(x, y)));
+
+  return (numext::isinf)(y)           ? ComplexT(NumTraits<T>::infinity(), y)
+         : numext::is_exactly_zero(x) ? ComplexT(w, y < zero ? -w : w)
+         : x > zero                   ? ComplexT(w, y / (2 * w))
+                                      : ComplexT(numext::abs(y) / (2 * w), y < zero ? -w : w);
+}
+
+// Generic complex rsqrt implementation.
+template <typename ComplexT>
+EIGEN_DEVICE_FUNC constexpr ComplexT complex_rsqrt(const ComplexT& z) {
+  // Computes the principal reciprocal sqrt of the input.
+  //
+  // For a complex reciprocal square root of the number z = x + i*y. We want to
+  // find real numbers u and v such that
+  //    (u + i*v)^2 = 1 / (x + i*y)  <=>
+  //    u^2 - v^2 + i*2*u*v = x/|z|^2 - i*v/|z|^2.
+  // By equating the real and imaginary parts we get:
+  //    u^2 - v^2 = x/|z|^2
+  //    2*u*v = y/|z|^2.
+  //
+  // For x >= 0, this has the numerically stable solution
+  //    u = sqrt(0.5 * (x + |z|)) / |z|
+  //    v = -y / (2 * u * |z|)
+  // and for x < 0,
+  //    v = -sign(y) * sqrt(0.5 * (-x + |z|)) / |z|
+  //    u = -y / (2 * v * |z|)
+  //
+  // Letting w = sqrt(0.5 * (|x| + |z|)),
+  //   if x == 0: u = w / |z|, v = -sign(y) * w / |z|
+  //   if x > 0:  u = w / |z|, v = -y / (2 * w * |z|)
+  //   if x < 0:  u = |y| / (2 * w * |z|), v = -sign(y) * w / |z|
+  using T = typename NumTraits<ComplexT>::Real;
+  const T x = numext::real(z);
+  const T y = numext::imag(z);
+  const T zero = T(0);
+
+  const T abs_z = numext::hypot(x, y);
+  const T w = numext::sqrt(T(0.5) * (numext::abs(x) + abs_z));
+  const T woz = w / abs_z;
+  // Corner cases consistent with 1/sqrt(z) on gcc/clang.
+  return numext::is_exactly_zero(abs_z)               ? ComplexT(NumTraits<T>::infinity(), NumTraits<T>::quiet_NaN())
+         : ((numext::isinf)(x) || (numext::isinf)(y)) ? ComplexT(zero, zero)
+         : numext::is_exactly_zero(x)                 ? ComplexT(woz, y < zero ? woz : -woz)
+         : x > zero                                   ? ComplexT(woz, -y / (2 * w * abs_z))
+                    : ComplexT(numext::abs(y) / (2 * w * abs_z), y < zero ? woz : -woz);
+}
+
+template <typename ComplexT>
+EIGEN_DEVICE_FUNC constexpr ComplexT complex_log(const ComplexT& z) {
+  // Computes complex log.
+  using T = typename NumTraits<ComplexT>::Real;
+  T a = numext::abs(z);
+  EIGEN_USING_STD(atan2);
+  T b = atan2(z.imag(), z.real());
+  return ComplexT(numext::log(a), b);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATHFUNCTIONSIMPL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Matrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Matrix.h
new file mode 100644
index 0000000..ca8f622
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Matrix.h
@@ -0,0 +1,536 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_H
+#define EIGEN_MATRIX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct traits<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> {
+ private:
+  constexpr static int size = internal::size_at_compile_time(Rows_, Cols_);
+  typedef typename find_best_packet<Scalar_, size>::type PacketScalar;
+  enum {
+    row_major_bit = Options_ & RowMajor ? RowMajorBit : 0,
+    is_dynamic_size_storage = MaxRows_ == Dynamic || MaxCols_ == Dynamic,
+    max_size = is_dynamic_size_storage ? Dynamic : MaxRows_ * MaxCols_,
+    default_alignment = compute_default_alignment<Scalar_, max_size>::value,
+    actual_alignment = ((Options_ & DontAlign) == 0) ? default_alignment : 0,
+    required_alignment = unpacket_traits<PacketScalar>::alignment,
+    packet_access_bit = (packet_traits<Scalar_>::Vectorizable &&
+                         (EIGEN_UNALIGNED_VECTORIZE || (int(actual_alignment) >= int(required_alignment))))
+                            ? PacketAccessBit
+                            : 0
+  };
+
+ public:
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef Eigen::Index StorageIndex;
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = Rows_,
+    ColsAtCompileTime = Cols_,
+    MaxRowsAtCompileTime = MaxRows_,
+    MaxColsAtCompileTime = MaxCols_,
+    Flags = compute_matrix_flags(Options_),
+    Options = Options_,
+    InnerStrideAtCompileTime = 1,
+    OuterStrideAtCompileTime = (int(Options) & int(RowMajor)) ? ColsAtCompileTime : RowsAtCompileTime,
+
+    // FIXME, the following flag in only used to define NeedsToAlign in PlainObjectBase
+    EvaluatorFlags = LinearAccessBit | DirectAccessBit | packet_access_bit | row_major_bit,
+    Alignment = actual_alignment
+  };
+};
+}  // namespace internal
+
+/** \class Matrix
+ * \ingroup Core_Module
+ *
+ * \brief The matrix class, also used for vectors and row-vectors
+ *
+ * The %Matrix class is the work-horse for all \em dense (\ref dense "note") matrices and vectors within Eigen.
+ * Vectors are matrices with one column, and row-vectors are matrices with one row.
+ *
+ * The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note").
+ *
+ * The first three template parameters are required:
+ * \tparam Scalar_ Numeric type, e.g. float, double, int or std::complex<float>.
+ *                 User defined scalar types are supported as well (see \ref user_defined_scalars "here").
+ * \tparam Rows_ Number of rows, or \b Dynamic
+ * \tparam Cols_ Number of columns, or \b Dynamic
+ *
+ * The remaining template parameters are optional -- in most cases you don't have to worry about them.
+ * \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of either
+ *                 \b #AutoAlign or \b #DontAlign.
+ *                 The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter
+ * controls alignment, which is required for vectorization. It defaults to aligning matrices except for fixed sizes that
+ * aren't a multiple of the packet size. \tparam MaxRows_ Maximum number of rows. Defaults to \a Rows_ (\ref maxrows
+ * "note"). \tparam MaxCols_ Maximum number of columns. Defaults to \a Cols_ (\ref maxrows "note").
+ *
+ * Eigen provides a number of typedefs covering the usual cases. Here are some examples:
+ *
+ * \li \c Matrix2d is a 2x2 square matrix of doubles (\c Matrix<double, 2, 2>)
+ * \li \c Vector4f is a vector of 4 floats (\c Matrix<float, 4, 1>)
+ * \li \c RowVector3i is a row-vector of 3 ints (\c Matrix<int, 1, 3>)
+ *
+ * \li \c MatrixXf is a dynamic-size matrix of floats (\c Matrix<float, Dynamic, Dynamic>)
+ * \li \c VectorXf is a dynamic-size vector of floats (\c Matrix<float, Dynamic, 1>)
+ *
+ * \li \c Matrix2Xf is a partially fixed-size (dynamic-size) matrix of floats (\c Matrix<float, 2, Dynamic>)
+ * \li \c MatrixX3d is a partially dynamic-size (fixed-size) matrix of double (\c Matrix<double, Dynamic, 3>)
+ *
+ * See \link matrixtypedefs this page \endlink for a complete list of predefined \em %Matrix and \em Vector typedefs.
+ *
+ * You can access elements of vectors and matrices using normal subscripting:
+ *
+ * \code
+ * Eigen::VectorXd v(10);
+ * v[0] = 0.1;
+ * v[1] = 0.2;
+ * v(0) = 0.3;
+ * v(1) = 0.4;
+ *
+ * Eigen::MatrixXi m(10, 10);
+ * m(0, 1) = 1;
+ * m(0, 2) = 2;
+ * m(0, 3) = 3;
+ * \endcode
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
+ *
+ * <i><b>Some notes:</b></i>
+ *
+ * <dl>
+ * <dt><b>\anchor dense Dense versus sparse:</b></dt>
+ * <dd>This %Matrix class handles dense, not sparse matrices and vectors. For sparse matrices and vectors, see the
+ * Sparse module.
+ *
+ * Dense matrices and vectors are plain usual arrays of coefficients. All the coefficients are stored, in an ordinary
+ * contiguous array. This is unlike Sparse matrices and vectors where the coefficients are stored as a list of nonzero
+ * coefficients.</dd>
+ *
+ * <dt><b>\anchor fixedsize Fixed-size versus dynamic-size:</b></dt>
+ * <dd>Fixed-size means that the numbers of rows and columns are known at compile-time. In this case, Eigen allocates
+ * the array of coefficients as a fixed-size array, as a class member. This makes sense for very small matrices,
+ * typically up to 4x4, sometimes up to 16x16. Larger matrices should be declared as dynamic-size even if one happens to
+ * know their size at compile-time.
+ *
+ * Dynamic-size means that the numbers of rows or columns are not necessarily known at compile-time. In this case they
+ * are runtime variables, and the array of coefficients is allocated dynamically on the heap.
+ *
+ * Note that \em dense matrices, be they Fixed-size or Dynamic-size, <em>do not</em> expand dynamically in the sense of
+ * a std::map. If you want this behavior, see the Sparse module.</dd>
+ *
+ * <dt><b>\anchor maxrows MaxRows_ and MaxCols_:</b></dt>
+ * <dd>In most cases, one just leaves these parameters to the default values.
+ * These parameters mean the maximum size of rows and columns that the matrix may have. They are useful in cases
+ * when the exact numbers of rows and columns are not known at compile-time, but it is known at compile-time that they
+ * cannot exceed a certain value. This happens when taking dynamic-size blocks inside fixed-size matrices: in this case
+ * MaxRows_ and MaxCols_ are the dimensions of the original matrix, while Rows_ and Cols_ are Dynamic.</dd>
+ * </dl>
+ *
+ * <i><b>ABI and storage layout</b></i>
+ *
+ * The table below summarizes the ABI of some possible Matrix instances which is fixed thorough the lifetime of Eigen 3.
+ * <table  class="manual">
+ * <tr><th>Matrix type</th><th>Equivalent C structure</th></tr>
+ * <tr><td>\code Matrix<T,Dynamic,Dynamic> \endcode</td><td>\code
+ * struct {
+ *   T *data;                  // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
+ *   Eigen::Index rows, cols;
+ *  };
+ * \endcode</td></tr>
+ * <tr class="alt"><td>\code
+ * Matrix<T,Dynamic,1>
+ * Matrix<T,1,Dynamic> \endcode</td><td>\code
+ * struct {
+ *   T *data;                  // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
+ *   Eigen::Index size;
+ *  };
+ * \endcode</td></tr>
+ * <tr><td>\code Matrix<T,Rows,Cols> \endcode</td><td>\code
+ * struct {
+ *   T data[Rows*Cols];        // with (size_t(data)%A(Rows*Cols*sizeof(T)))==0
+ *  };
+ * \endcode</td></tr>
+ * <tr class="alt"><td>\code Matrix<T,Dynamic,Dynamic,0,MaxRows,MaxCols> \endcode</td><td>\code
+ * struct {
+ *   T data[MaxRows*MaxCols];  // with (size_t(data)%A(MaxRows*MaxCols*sizeof(T)))==0
+ *   Eigen::Index rows, cols;
+ *  };
+ * \endcode</td></tr>
+ * </table>
+ * Note that in this table Rows, Cols, MaxRows and MaxCols are all positive integers. A(S) is defined to the largest
+ * possible power-of-two smaller to EIGEN_MAX_STATIC_ALIGN_BYTES.
+ *
+ * \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy,
+ * \ref TopicStorageOrders
+ */
+
+template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+class Matrix : public PlainObjectBase<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> {
+ public:
+  /** \brief Base class typedef.
+   * \sa PlainObjectBase
+   */
+  typedef PlainObjectBase<Matrix> Base;
+
+  enum { Options = Options_ };
+
+  EIGEN_DENSE_PUBLIC_INTERFACE(Matrix)
+
+  typedef typename Base::PlainObject PlainObject;
+
+  using Base::base;
+  using Base::coeffRef;
+
+  /**
+   * \brief Assigns matrices to each other.
+   *
+   * \note This is a special case of the templated operator=. Its purpose is
+   * to prevent a default operator= from hiding the templated operator=.
+   *
+   * \callgraph
+   */
+  EIGEN_DEVICE_FUNC constexpr Matrix& operator=(const Matrix& other) { return Base::_set(other); }
+
+  /** \internal
+   * \brief Copies the value of the expression \a other into \c *this with automatic resizing.
+   *
+   * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
+   * it will be initialized.
+   *
+   * Note that copying a row-vector into a vector (and conversely) is allowed.
+   * The resizing, if any, is then done in the appropriate way so that row-vectors
+   * remain row-vectors and vectors remain vectors.
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Matrix& operator=(const DenseBase<OtherDerived>& other) {
+    return Base::_set(other);
+  }
+
+  /**
+   * \brief Copies the generic expression \a other into *this.
+   * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Matrix& operator=(const EigenBase<OtherDerived>& other) {
+    return Base::operator=(other);
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Matrix& operator=(const ReturnByValue<OtherDerived>& func) {
+    return Base::operator=(func);
+  }
+
+  /** \brief Default constructor.
+   *
+   * For fixed-size matrices, does nothing.
+   *
+   * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
+   * is called a null matrix. This constructor is the unique way to create null matrices: resizing
+   * a matrix to 0 is not supported.
+   *
+   * \sa resize(Index,Index)
+   */
+#if defined(EIGEN_INITIALIZE_COEFFS)
+  EIGEN_DEVICE_FUNC constexpr Matrix() { EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
+#else
+  EIGEN_DEVICE_FUNC constexpr Matrix() = default;
+#endif
+  /** \brief Move constructor */
+  EIGEN_DEVICE_FUNC constexpr Matrix(Matrix&&) = default;
+  /** \brief Moves the matrix into the other one.
+   *
+   */
+  EIGEN_DEVICE_FUNC constexpr Matrix& operator=(Matrix&& other) noexcept(
+      std::is_nothrow_move_assignable<Scalar>::value) {
+    Base::operator=(std::move(other));
+    return *this;
+  }
+
+  /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients.
+   *
+   * \only_for_vectors
+   *
+   * This constructor is for 1D array or vectors with more than 4 coefficients.
+   *
+   * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
+   * constructor must match the fixed number of rows (resp. columns) of \c *this.
+   *
+   *
+   * Example: \include Matrix_variadic_ctor_cxx11.cpp
+   * Output: \verbinclude Matrix_variadic_ctor_cxx11.out
+   *
+   * \sa Matrix(const std::initializer_list<std::initializer_list<Scalar>>&)
+   */
+  template <typename... ArgTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3,
+                                               const ArgTypes&... args)
+      : Base(a0, a1, a2, a3, args...) {}
+
+  /** \brief Constructs a Matrix and initializes it from the coefficients given as initializer-lists grouped by row.
+   * \cpp11
+   * \anchor matrix_initializer_list
+   *
+   * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
+   *
+   * Example: \include Matrix_initializer_list_23_cxx11.cpp
+   * Output: \verbinclude Matrix_initializer_list_23_cxx11.out
+   *
+   * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is
+   * triggered.
+   *
+   * In the case of a compile-time column vector, implicit transposition from a single row is allowed.
+   * Therefore <code>VectorXd{{1,2,3,4,5}}</code> is legal and the more verbose syntax
+   * <code>RowVectorXd{{1},{2},{3},{4},{5}}</code> can be avoided:
+   *
+   * Example: \include Matrix_initializer_list_vector_cxx11.cpp
+   * Output: \verbinclude Matrix_initializer_list_vector_cxx11.out
+   *
+   * In the case of fixed-sized matrices, the initializer list sizes must exactly match the matrix sizes,
+   * and implicit transposition is allowed for compile-time vectors only.
+   *
+   * \sa Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2,  const Scalar& a3, const ArgTypes&... args)
+   */
+  EIGEN_DEVICE_FUNC explicit constexpr EIGEN_STRONG_INLINE Matrix(
+      const std::initializer_list<std::initializer_list<Scalar>>& list)
+      : Base(list) {}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+  // This constructor is for both 1x1 matrices and dynamic vectors
+  template <typename T>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit Matrix(const T& x) {
+    Base::template _init1<T>(x);
+  }
+
+  template <typename T0, typename T1>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Matrix(const T0& x, const T1& y) {
+    Base::template _init2<T0, T1>(x, y);
+  }
+
+#else
+  /** \brief Constructs a fixed-sized matrix initialized with coefficients starting at \a data */
+  EIGEN_DEVICE_FUNC explicit Matrix(const Scalar* data);
+
+  /** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
+   *
+   * This is useful for dynamic-size vectors. For fixed-size vectors,
+   * it is redundant to pass these parameters, so one should use the default constructor
+   * Matrix() instead.
+   *
+   * \warning This constructor is disabled for fixed-size \c 1x1 matrices. For instance,
+   * calling Matrix<double,1,1>(1) will call the initialization constructor: Matrix(const Scalar&).
+   * For fixed-size \c 1x1 matrices it is therefore recommended to use the default
+   * constructor Matrix() instead, especially when using one of the non standard
+   * \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
+   */
+  EIGEN_STRONG_INLINE explicit Matrix(Index dim);
+  /** \brief Constructs an initialized 1x1 matrix with the given coefficient
+   * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...) */
+  Matrix(const Scalar& x);
+  /** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns.
+   *
+   * This is useful for dynamic-size matrices. For fixed-size matrices,
+   * it is redundant to pass these parameters, so one should use the default constructor
+   * Matrix() instead.
+   *
+   * \warning This constructor is disabled for fixed-size \c 1x2 and \c 2x1 vectors. For instance,
+   * calling Matrix2f(2,1) will call the initialization constructor: Matrix(const Scalar& x, const Scalar& y).
+   * For fixed-size \c 1x2 or \c 2x1 vectors it is therefore recommended to use the default
+   * constructor Matrix() instead, especially when using one of the non standard
+   * \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
+   */
+  EIGEN_DEVICE_FUNC Matrix(Index rows, Index cols);
+
+  /** \brief Constructs an initialized 2D vector with given coefficients
+   * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...) */
+  Matrix(const Scalar& x, const Scalar& y);
+#endif  // end EIGEN_PARSED_BY_DOXYGEN
+
+  /** \brief Constructs an initialized 3D vector with given coefficients
+   * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...)
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3)
+    m_storage.data()[0] = x;
+    m_storage.data()[1] = y;
+    m_storage.data()[2] = z;
+  }
+  /** \brief Constructs an initialized 4D vector with given coefficients
+   * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...)
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z,
+                                                         const Scalar& w) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4)
+    m_storage.data()[0] = x;
+    m_storage.data()[1] = y;
+    m_storage.data()[2] = z;
+    m_storage.data()[3] = w;
+  }
+
+  /** \brief Copy constructor */
+  EIGEN_DEVICE_FUNC constexpr Matrix(const Matrix&) = default;
+
+  /** \brief Copy constructor for generic expressions.
+   * \sa MatrixBase::operator=(const EigenBase<OtherDerived>&)
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Matrix(const EigenBase<OtherDerived>& other)
+      : Base(other.derived()) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return 1; }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return this->innerSize(); }
+
+  /////////// Geometry module ///////////
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC explicit Matrix(const RotationBase<OtherDerived, ColsAtCompileTime>& r);
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC Matrix& operator=(const RotationBase<OtherDerived, ColsAtCompileTime>& r);
+
+// allow to extend Matrix outside Eigen
+#ifdef EIGEN_MATRIX_PLUGIN
+#include EIGEN_MATRIX_PLUGIN
+#endif
+
+ protected:
+  template <typename Derived, typename OtherDerived, bool IsVector>
+  friend struct internal::conservative_resize_like_impl;
+
+  using Base::m_storage;
+};
+
+/** \defgroup matrixtypedefs Global matrix typedefs
+ *
+ * \ingroup Core_Module
+ *
+ * %Eigen defines several typedef shortcuts for most common matrix and vector types.
+ *
+ * The general patterns are the following:
+ *
+ * \c MatrixSizeType where \c Size can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
+ * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
+ * for complex double.
+ *
+ * For example, \c Matrix3d is a fixed-size 3x3 matrix type of doubles, and \c MatrixXf is a dynamic-size matrix of
+ * floats.
+ *
+ * There are also \c VectorSizeType and \c RowVectorSizeType which are self-explanatory. For example, \c Vector4cf is
+ * a fixed-size vector of 4 complex floats.
+ *
+ * With \cpp11, template alias are also defined for common sizes.
+ * They follow the same pattern as above except that the scalar type suffix is replaced by a
+ * template parameter, i.e.:
+ *   - `MatrixSize<Type>` where `Size` can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size.
+ *   - `MatrixXSize<Type>` and `MatrixSizeX<Type>` where `Size` can be \c 2,\c 3,\c 4 for hybrid dynamic/fixed matrices.
+ *   - `VectorSize<Type>` and `RowVectorSize<Type>` for column and row vectors.
+ *
+ * With \cpp11, you can also use fully generic column and row vector types: `Vector<Type,Size>` and
+ * `RowVector<Type,Size>`.
+ *
+ * \sa class Matrix
+ */
+
+#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)    \
+  /** \ingroup matrixtypedefs */                                   \
+  /** \brief `Size`&times;`Size` matrix of type `Type`. */         \
+  typedef Matrix<Type, Size, Size> Matrix##SizeSuffix##TypeSuffix; \
+  /** \ingroup matrixtypedefs */                                   \
+  /** \brief `Size`&times;`1` vector of type `Type`. */            \
+  typedef Matrix<Type, Size, 1> Vector##SizeSuffix##TypeSuffix;    \
+  /** \ingroup matrixtypedefs */                                   \
+  /** \brief `1`&times;`Size` vector of type `Type`. */            \
+  typedef Matrix<Type, 1, Size> RowVector##SizeSuffix##TypeSuffix;
+
+#define EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, Size)          \
+  /** \ingroup matrixtypedefs */                                   \
+  /** \brief `Size`&times;`Dynamic` matrix of type `Type`. */      \
+  typedef Matrix<Type, Size, Dynamic> Matrix##Size##X##TypeSuffix; \
+  /** \ingroup matrixtypedefs */                                   \
+  /** \brief `Dynamic`&times;`Size` matrix of type `Type`. */      \
+  typedef Matrix<Type, Dynamic, Size> Matrix##X##Size##TypeSuffix;
+
+#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
+  EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2)           \
+  EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3)           \
+  EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4)           \
+  EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X)     \
+  EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 2)        \
+  EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 3)        \
+  EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
+
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int, i)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float, f)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double, d)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<float>, cf)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
+
+#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES
+#undef EIGEN_MAKE_TYPEDEFS
+#undef EIGEN_MAKE_FIXED_TYPEDEFS
+
+#define EIGEN_MAKE_TYPEDEFS(Size, SizeSuffix)                    \
+  /** \ingroup matrixtypedefs */                                 \
+  /** \brief \cpp11 `Size`&times;`Size` matrix of type `Type`.*/ \
+  template <typename Type>                                       \
+  using Matrix##SizeSuffix = Matrix<Type, Size, Size>;           \
+  /** \ingroup matrixtypedefs */                                 \
+  /** \brief \cpp11 `Size`&times;`1` vector of type `Type`.*/    \
+  template <typename Type>                                       \
+  using Vector##SizeSuffix = Matrix<Type, Size, 1>;              \
+  /** \ingroup matrixtypedefs */                                 \
+  /** \brief \cpp11 `1`&times;`Size` vector of type `Type`.*/    \
+  template <typename Type>                                       \
+  using RowVector##SizeSuffix = Matrix<Type, 1, Size>;
+
+#define EIGEN_MAKE_FIXED_TYPEDEFS(Size)                              \
+  /** \ingroup matrixtypedefs */                                     \
+  /** \brief \cpp11 `Size`&times;`Dynamic` matrix of type `Type` */  \
+  template <typename Type>                                           \
+  using Matrix##Size##X = Matrix<Type, Size, Dynamic>;               \
+  /** \ingroup matrixtypedefs */                                     \
+  /** \brief \cpp11 `Dynamic`&times;`Size` matrix of type `Type`. */ \
+  template <typename Type>                                           \
+  using Matrix##X##Size = Matrix<Type, Dynamic, Size>;
+
+EIGEN_MAKE_TYPEDEFS(2, 2)
+EIGEN_MAKE_TYPEDEFS(3, 3)
+EIGEN_MAKE_TYPEDEFS(4, 4)
+EIGEN_MAKE_TYPEDEFS(Dynamic, X)
+EIGEN_MAKE_FIXED_TYPEDEFS(2)
+EIGEN_MAKE_FIXED_TYPEDEFS(3)
+EIGEN_MAKE_FIXED_TYPEDEFS(4)
+
+/** \ingroup matrixtypedefs
+ * \brief \cpp11 `Size`&times;`1` vector of type `Type`. */
+template <typename Type, int Size>
+using Vector = Matrix<Type, Size, 1>;
+
+/** \ingroup matrixtypedefs
+ * \brief \cpp11 `1`&times;`Size` vector of type `Type`. */
+template <typename Type, int Size>
+using RowVector = Matrix<Type, 1, Size>;
+
+#undef EIGEN_MAKE_TYPEDEFS
+#undef EIGEN_MAKE_FIXED_TYPEDEFS
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MatrixBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MatrixBase.h
new file mode 100644
index 0000000..ff1a2c2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/MatrixBase.h
@@ -0,0 +1,557 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIXBASE_H
+#define EIGEN_MATRIXBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class MatrixBase
+  * \ingroup Core_Module
+  *
+  * \brief Base class for all dense matrices, vectors, and expressions
+  *
+  * This class is the base that is inherited by all matrix, vector, and related expression
+  * types. Most of the Eigen API is contained in this class, and its base classes. Other important
+  * classes for the Eigen API are Matrix, and VectorwiseOp.
+  *
+  * Note that some methods are defined in other modules such as the \ref LU_Module LU module
+  * for all functions related to matrix inversions.
+  *
+  * \tparam Derived is the derived type, e.g. a matrix type, or an expression, etc.
+  *
+  * When writing a function taking Eigen objects as argument, if you want your function
+  * to take as argument any matrix, vector, or expression, just let it take a
+  * MatrixBase argument. As an example, here is a function printFirstRow which, given
+  * a matrix, vector, or expression \a x, prints the first row of \a x.
+  *
+  * \code
+    template<typename Derived>
+    void printFirstRow(const Eigen::MatrixBase<Derived>& x)
+    {
+      cout << x.row(0) << endl;
+    }
+  * \endcode
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
+  *
+  * \sa \blank \ref TopicClassHierarchy
+  */
+template <typename Derived>
+class MatrixBase : public DenseBase<Derived> {
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  typedef MatrixBase StorageBaseType;
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  typedef DenseBase<Derived> Base;
+  using Base::ColsAtCompileTime;
+  using Base::Flags;
+  using Base::IsVectorAtCompileTime;
+  using Base::MaxColsAtCompileTime;
+  using Base::MaxRowsAtCompileTime;
+  using Base::MaxSizeAtCompileTime;
+  using Base::RowsAtCompileTime;
+  using Base::SizeAtCompileTime;
+
+  using Base::coeff;
+  using Base::coeffRef;
+  using Base::cols;
+  using Base::const_cast_derived;
+  using Base::derived;
+  using Base::eval;
+  using Base::lazyAssign;
+  using Base::rows;
+  using Base::size;
+  using Base::operator-;
+  using Base::operator+=;
+  using Base::operator-=;
+  using Base::operator*=;
+  using Base::operator/=;
+
+  typedef typename Base::CoeffReturnType CoeffReturnType;
+  typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType;
+  typedef typename Base::RowXpr RowXpr;
+  typedef typename Base::ColXpr ColXpr;
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** type of the equivalent square matrix */
+  typedef Matrix<Scalar, internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime),
+                 internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)>
+      SquareMatrixType;
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+  /** \returns the size of the main diagonal, which is min(rows(),cols()).
+   * \sa rows(), cols(), SizeAtCompileTime. */
+  EIGEN_DEVICE_FUNC constexpr Index diagonalSize() const { return (numext::mini)(rows(), cols()); }
+
+  typedef typename Base::PlainObject PlainObject;
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal Represents a matrix with all coefficients equal to one another*/
+  typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> ConstantReturnType;
+  /** \internal the return type of MatrixBase::adjoint() */
+  typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+                             CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
+                             ConstTransposeReturnType>
+      AdjointReturnType;
+  /** \internal Return type of eigenvalues() */
+  typedef Matrix<internal::make_complex_t<Scalar>, internal::traits<Derived>::ColsAtCompileTime, 1, ColMajor>
+      EigenvaluesReturnType;
+  /** \internal the return type of identity */
+  typedef CwiseNullaryOp<internal::scalar_identity_op<Scalar>, PlainObject> IdentityReturnType;
+  /** \internal the return type of unit vectors */
+  typedef Block<const CwiseNullaryOp<internal::scalar_identity_op<Scalar>, SquareMatrixType>,
+                internal::traits<Derived>::RowsAtCompileTime, internal::traits<Derived>::ColsAtCompileTime>
+      BasisReturnType;
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase
+#define EIGEN_DOC_UNARY_ADDONS(X, Y)
+#include "../plugins/CommonCwiseBinaryOps.inc"
+#include "../plugins/MatrixCwiseUnaryOps.inc"
+#include "../plugins/MatrixCwiseBinaryOps.inc"
+#ifdef EIGEN_MATRIXBASE_PLUGIN
+#include EIGEN_MATRIXBASE_PLUGIN
+#endif
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_DOC_UNARY_ADDONS
+
+  /** Special case of the template operator=, in order to prevent the compiler
+   * from generating a default operator= (issue hit with g++ 4.1)
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator=(const MatrixBase& other);
+
+  // We cannot inherit here via Base::operator= since it is causing
+  // trouble with MSVC.
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr Derived& operator=(const EigenBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr Derived& operator=(const ReturnByValue<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const MatrixBase<OtherDerived>& other);
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const MatrixBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC const Product<Derived, OtherDerived> operator*(const MatrixBase<OtherDerived>& other) const;
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC const Product<Derived, OtherDerived, LazyProduct> lazyProduct(
+      const MatrixBase<OtherDerived>& other) const;
+
+  template <typename OtherDerived>
+  Derived& operator*=(const EigenBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  void applyOnTheLeft(const EigenBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  void applyOnTheRight(const EigenBase<OtherDerived>& other);
+
+  template <typename DiagonalDerived>
+  EIGEN_DEVICE_FUNC const Product<Derived, DiagonalDerived, LazyProduct> operator*(
+      const DiagonalBase<DiagonalDerived>& diagonal) const;
+
+  template <typename SkewDerived>
+  EIGEN_DEVICE_FUNC const Product<Derived, SkewDerived, LazyProduct> operator*(
+      const SkewSymmetricBase<SkewDerived>& skew) const;
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,
+                                                            typename internal::traits<OtherDerived>::Scalar>::ReturnType
+  dot(const MatrixBase<OtherDerived>& other) const;
+
+  EIGEN_DEVICE_FUNC constexpr RealScalar squaredNorm() const;
+  EIGEN_DEVICE_FUNC RealScalar norm() const;
+  RealScalar stableNorm() const;
+  RealScalar blueNorm() const;
+  RealScalar hypotNorm() const;
+  EIGEN_DEVICE_FUNC const PlainObject normalized() const;
+  EIGEN_DEVICE_FUNC const PlainObject stableNormalized() const;
+  EIGEN_DEVICE_FUNC void normalize();
+  EIGEN_DEVICE_FUNC void stableNormalize();
+
+  EIGEN_DEVICE_FUNC constexpr const AdjointReturnType adjoint() const;
+  EIGEN_DEVICE_FUNC void adjointInPlace();
+
+  typedef Diagonal<Derived> DiagonalReturnType;
+  EIGEN_DEVICE_FUNC constexpr DiagonalReturnType diagonal();
+
+  typedef Diagonal<const Derived> ConstDiagonalReturnType;
+  EIGEN_DEVICE_FUNC constexpr const ConstDiagonalReturnType diagonal() const;
+
+  template <int Index>
+  EIGEN_DEVICE_FUNC constexpr Diagonal<Derived, Index> diagonal();
+
+  template <int Index>
+  EIGEN_DEVICE_FUNC constexpr const Diagonal<const Derived, Index> diagonal() const;
+
+  EIGEN_DEVICE_FUNC constexpr Diagonal<Derived, DynamicIndex> diagonal(Index index);
+  EIGEN_DEVICE_FUNC constexpr const Diagonal<const Derived, DynamicIndex> diagonal(Index index) const;
+
+  template <unsigned int Mode>
+  struct TriangularViewReturnType {
+    typedef TriangularView<Derived, Mode> Type;
+  };
+  template <unsigned int Mode>
+  struct ConstTriangularViewReturnType {
+    typedef const TriangularView<const Derived, Mode> Type;
+  };
+
+  template <unsigned int Mode>
+  EIGEN_DEVICE_FUNC constexpr typename TriangularViewReturnType<Mode>::Type triangularView();
+  template <unsigned int Mode>
+  EIGEN_DEVICE_FUNC constexpr typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
+
+  template <unsigned int UpLo>
+  struct SelfAdjointViewReturnType {
+    typedef SelfAdjointView<Derived, UpLo> Type;
+  };
+  template <unsigned int UpLo>
+  struct ConstSelfAdjointViewReturnType {
+    typedef const SelfAdjointView<const Derived, UpLo> Type;
+  };
+
+  template <unsigned int UpLo>
+  EIGEN_DEVICE_FUNC constexpr typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
+  template <unsigned int UpLo>
+  EIGEN_DEVICE_FUNC constexpr typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
+
+  const SparseView<Derived> sparseView(
+      const Scalar& m_reference = Scalar(0),
+      const typename NumTraits<Scalar>::Real& m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
+  EIGEN_DEVICE_FUNC static const IdentityReturnType Identity();
+  EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(Index rows, Index cols);
+  EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index size, Index i);
+  EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index i);
+  EIGEN_DEVICE_FUNC static const BasisReturnType UnitX();
+  EIGEN_DEVICE_FUNC static const BasisReturnType UnitY();
+  EIGEN_DEVICE_FUNC static const BasisReturnType UnitZ();
+  EIGEN_DEVICE_FUNC static const BasisReturnType UnitW();
+
+  EIGEN_DEVICE_FUNC constexpr const DiagonalWrapper<const Derived> asDiagonal() const;
+  const PermutationWrapper<const Derived> asPermutation() const;
+  EIGEN_DEVICE_FUNC constexpr const SkewSymmetricWrapper<const Derived> asSkewSymmetric() const;
+
+  EIGEN_DEVICE_FUNC Derived& setIdentity();
+  EIGEN_DEVICE_FUNC Derived& setIdentity(Index rows, Index cols);
+  EIGEN_DEVICE_FUNC Derived& setUnit(Index i);
+  EIGEN_DEVICE_FUNC Derived& setUnit(Index newSize, Index i);
+
+  bool isIdentity(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+  bool isDiagonal(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+  bool isUpperTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+  bool isLowerTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+  bool isSkewSymmetric(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+  template <typename OtherDerived>
+  bool isOrthogonal(const MatrixBase<OtherDerived>& other,
+                    const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+  bool isUnitary(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+  /* diagonalView */
+  template <int DiagIndex_ = 0>
+  EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<Derived, DiagIndex_>> diagonalView();
+
+  template <int DiagIndex_ = 0>
+  EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<const Derived, DiagIndex_>> diagonalView() const;
+
+  EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<Derived, DynamicIndex>> diagonalView(Index index);
+
+  EIGEN_DEVICE_FUNC constexpr DiagonalWrapper<Diagonal<const Derived, DynamicIndex>> diagonalView(Index index) const;
+
+  /** \returns true if each coefficients of \c *this and \a other are all exactly equal.
+   * \warning When using floating point scalar values you probably should rather use a
+   *          fuzzy comparison such as isApprox()
+   * \sa isApprox(), operator!= */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline bool operator==(const MatrixBase<OtherDerived>& other) const {
+    return (this->rows() == other.rows()) && (this->cols() == other.cols()) && cwiseEqual(other).all();
+  }
+
+  /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
+   * \warning When using floating point scalar values you probably should rather use a
+   *          fuzzy comparison such as isApprox()
+   * \sa isApprox(), operator== */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline bool operator!=(const MatrixBase<OtherDerived>& other) const {
+    return !(*this == other);
+  }
+
+  NoAlias<Derived, Eigen::MatrixBase> EIGEN_DEVICE_FUNC noalias();
+
+  // TODO forceAlignedAccess is temporarily disabled
+  // Need to find a nicer workaround.
+  constexpr const Derived& forceAlignedAccess() const { return derived(); }
+  constexpr Derived& forceAlignedAccess() { return derived(); }
+  template <bool Enable>
+  constexpr const Derived& forceAlignedAccessIf() const {
+    return derived();
+  }
+  template <bool Enable>
+  constexpr Derived& forceAlignedAccessIf() {
+    return derived();
+  }
+
+  EIGEN_DEVICE_FUNC Scalar trace() const;
+
+  template <int p>
+  EIGEN_DEVICE_FUNC RealScalar lpNorm() const;
+
+  EIGEN_DEVICE_FUNC constexpr MatrixBase<Derived>& matrix() { return *this; }
+  EIGEN_DEVICE_FUNC constexpr const MatrixBase<Derived>& matrix() const { return *this; }
+
+  /** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
+   * \sa ArrayBase::matrix() */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() {
+    return ArrayWrapper<Derived>(derived());
+  }
+  /** \returns a const \link Eigen::ArrayBase Array \endlink expression of this matrix
+   * \sa ArrayBase::matrix() */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const {
+    return ArrayWrapper<const Derived>(derived());
+  }
+
+  /////////// LU module ///////////
+
+  template <typename PermutationIndex = DefaultPermutationIndex>
+  inline FullPivLU<PlainObject, PermutationIndex> fullPivLu() const;
+  template <typename PermutationIndex = DefaultPermutationIndex>
+  inline PartialPivLU<PlainObject, PermutationIndex> partialPivLu() const;
+
+  template <typename PermutationIndex = DefaultPermutationIndex>
+  inline PartialPivLU<PlainObject, PermutationIndex> lu() const;
+
+  EIGEN_DEVICE_FUNC inline Inverse<Derived> inverse() const;
+
+  template <typename ResultType>
+  inline void computeInverseAndDetWithCheck(
+      ResultType& inverse, typename ResultType::Scalar& determinant, bool& invertible,
+      const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()) const;
+
+  template <typename ResultType>
+  inline void computeInverseWithCheck(
+      ResultType& inverse, bool& invertible,
+      const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()) const;
+
+  EIGEN_DEVICE_FUNC Scalar determinant() const;
+
+  /////////// Cholesky module ///////////
+
+  inline LLT<PlainObject> llt() const;
+  inline LDLT<PlainObject> ldlt() const;
+
+  /////////// QR module ///////////
+
+  inline HouseholderQR<PlainObject> householderQr() const;
+  template <typename PermutationIndex = DefaultPermutationIndex>
+  inline ColPivHouseholderQR<PlainObject, PermutationIndex> colPivHouseholderQr() const;
+  template <typename PermutationIndex = DefaultPermutationIndex>
+  inline FullPivHouseholderQR<PlainObject, PermutationIndex> fullPivHouseholderQr() const;
+  template <typename PermutationIndex = DefaultPermutationIndex>
+  inline CompleteOrthogonalDecomposition<PlainObject, PermutationIndex> completeOrthogonalDecomposition() const;
+
+  /////////// Eigenvalues module ///////////
+
+  inline EigenvaluesReturnType eigenvalues() const;
+  inline RealScalar operatorNorm() const;
+
+  /////////// SVD module ///////////
+
+  template <int Options = 0>
+  inline JacobiSVD<PlainObject, Options> jacobiSvd() const;
+  template <int Options = 0>
+  EIGEN_DEPRECATED_WITH_REASON("Options should be specified using method's template parameter.")
+  inline JacobiSVD<PlainObject, Options> jacobiSvd(unsigned int computationOptions) const;
+
+  template <int Options = 0>
+  inline BDCSVD<PlainObject, Options> bdcSvd() const;
+  template <int Options = 0>
+  EIGEN_DEPRECATED_WITH_REASON("Options should be specified using method's template parameter.")
+  inline BDCSVD<PlainObject, Options> bdcSvd(unsigned int computationOptions) const;
+
+  /////////// Geometry module ///////////
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline typename internal::cross_impl<Derived, OtherDerived>::return_type cross(
+      const MatrixBase<OtherDerived>& other) const;
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline PlainObject cross3(const MatrixBase<OtherDerived>& other) const;
+
+  EIGEN_DEVICE_FUNC inline PlainObject unitOrthogonal(void) const;
+
+  EIGEN_DEPRECATED_WITH_REASON("Use .canonicalEulerAngles() instead.")
+  EIGEN_DEVICE_FUNC inline Matrix<Scalar, 3, 1> eulerAngles(Index a0, Index a1, Index a2) const;
+
+  EIGEN_DEVICE_FUNC inline Matrix<Scalar, 3, 1> canonicalEulerAngles(Index a0, Index a1, Index a2) const;
+
+  enum {
+    HomogeneousReturnTypeDirection =
+        ColsAtCompileTime == 1 && RowsAtCompileTime == 1
+            ? ((internal::traits<Derived>::Flags & RowMajorBit) == RowMajorBit ? Horizontal : Vertical)
+        : ColsAtCompileTime == 1 ? Vertical
+                                 : Horizontal
+  };
+  typedef Homogeneous<Derived, HomogeneousReturnTypeDirection> HomogeneousReturnType;
+  EIGEN_DEVICE_FUNC inline HomogeneousReturnType homogeneous() const;
+
+  enum { SizeMinusOne = SizeAtCompileTime == Dynamic ? Dynamic : SizeAtCompileTime - 1 };
+  typedef Block<const Derived, internal::traits<Derived>::ColsAtCompileTime == 1 ? SizeMinusOne : 1,
+                internal::traits<Derived>::ColsAtCompileTime == 1 ? 1 : SizeMinusOne>
+      ConstStartMinusOne;
+  typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(ConstStartMinusOne, Scalar, quotient) HNormalizedReturnType;
+  EIGEN_DEVICE_FUNC inline const HNormalizedReturnType hnormalized() const;
+
+  ////////// Householder module ///////////
+
+  EIGEN_DEVICE_FUNC void makeHouseholderInPlace(Scalar& tau, RealScalar& beta);
+  template <typename EssentialPart>
+  EIGEN_DEVICE_FUNC void makeHouseholder(EssentialPart& essential, Scalar& tau, RealScalar& beta) const;
+  template <typename EssentialPart>
+  EIGEN_DEVICE_FUNC void applyHouseholderOnTheLeft(const EssentialPart& essential, const Scalar& tau,
+                                                   Scalar* workspace);
+  template <typename EssentialPart>
+  EIGEN_DEVICE_FUNC void applyHouseholderOnTheRight(const EssentialPart& essential, const Scalar& tau,
+                                                    Scalar* workspace);
+
+  ///////// Jacobi module /////////
+
+  template <typename OtherScalar>
+  EIGEN_DEVICE_FUNC void applyOnTheLeft(Index p, Index q, const JacobiRotation<OtherScalar>& j);
+  template <typename OtherScalar>
+  EIGEN_DEVICE_FUNC void applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j);
+
+  ///////// SparseCore module /////////
+
+  template <typename OtherDerived>
+  EIGEN_STRONG_INLINE const typename SparseMatrixBase<OtherDerived>::template CwiseProductDenseReturnType<Derived>::Type
+  cwiseProduct(const SparseMatrixBase<OtherDerived>& other) const {
+    return other.cwiseProduct(derived());
+  }
+
+  ///////// MatrixFunctions module /////////
+
+  typedef typename internal::stem_function<Scalar>::type StemFunction;
+#define EIGEN_MATRIX_FUNCTION(ReturnType, Name, Description)                                                        \
+  /** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a            \
+   * href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the \
+   * coefficient-wise Description use ArrayBase::##Name . */                                                        \
+  const ReturnType<Derived> Name() const;
+#define EIGEN_MATRIX_FUNCTION_1(ReturnType, Name, Description, Argument)                                            \
+  /** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a            \
+   * href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the \
+   * coefficient-wise Description use ArrayBase::##Name . */                                                        \
+  const ReturnType<Derived> Name(Argument) const;
+
+  EIGEN_MATRIX_FUNCTION(MatrixExponentialReturnValue, exp, exponential)
+  /** \brief Helper function for the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported
+   * MatrixFunctions module</a>.*/
+  const MatrixFunctionReturnValue<Derived> matrixFunction(StemFunction f) const;
+  EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cosh, hyperbolic cosine)
+  EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sinh, hyperbolic sine)
+  EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, atanh, inverse hyperbolic cosine)
+  EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, acosh, inverse hyperbolic cosine)
+  EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, asinh, inverse hyperbolic sine)
+  EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cos, cosine)
+  EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sin, sine)
+  EIGEN_MATRIX_FUNCTION(MatrixSquareRootReturnValue, sqrt, square root)
+  EIGEN_MATRIX_FUNCTION(MatrixLogarithmReturnValue, log, logarithm)
+  EIGEN_MATRIX_FUNCTION_1(MatrixPowerReturnValue, pow, power to \c p, const RealScalar& p)
+  EIGEN_MATRIX_FUNCTION_1(MatrixComplexPowerReturnValue, pow, power to \c p, const internal::make_complex_t<Scalar>& p)
+
+ protected:
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(MatrixBase)
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MatrixBase)
+
+ private:
+  EIGEN_DEVICE_FUNC explicit MatrixBase(int);
+  EIGEN_DEVICE_FUNC MatrixBase(int, int);
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC explicit MatrixBase(const MatrixBase<OtherDerived>&);
+
+ protected:
+  // mixing arrays and matrices is not legal
+  template <typename OtherDerived>
+  Derived& operator+=(const ArrayBase<OtherDerived>&) {
+    EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar)) == -1,
+                        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);
+    return *this;
+  }
+  // mixing arrays and matrices is not legal
+  template <typename OtherDerived>
+  Derived& operator-=(const ArrayBase<OtherDerived>&) {
+    EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar)) == -1,
+                        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);
+    return *this;
+  }
+};
+
+/***************************************************************************
+ * Implementation of matrix base methods
+ ***************************************************************************/
+
+/** replaces \c *this by \c *this * \a other.
+ *
+ * \returns a reference to \c *this
+ *
+ * Example: \include MatrixBase_applyOnTheRight.cpp
+ * Output: \verbinclude MatrixBase_applyOnTheRight.out
+ */
+template <typename Derived>
+template <typename OtherDerived>
+inline Derived& MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived>& other) {
+  other.derived().applyThisOnTheRight(derived());
+  return derived();
+}
+
+/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=().
+ *
+ * Example: \include MatrixBase_applyOnTheRight.cpp
+ * Output: \verbinclude MatrixBase_applyOnTheRight.out
+ */
+template <typename Derived>
+template <typename OtherDerived>
+inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived>& other) {
+  other.derived().applyThisOnTheRight(derived());
+}
+
+/** replaces \c *this by \a other * \c *this.
+ *
+ * Example: \include MatrixBase_applyOnTheLeft.cpp
+ * Output: \verbinclude MatrixBase_applyOnTheLeft.out
+ */
+template <typename Derived>
+template <typename OtherDerived>
+inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived>& other) {
+  other.derived().applyThisOnTheLeft(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATRIXBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/NestByValue.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/NestByValue.h
new file mode 100644
index 0000000..f18559b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/NestByValue.h
@@ -0,0 +1,91 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NESTBYVALUE_H
+#define EIGEN_NESTBYVALUE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename ExpressionType>
+struct traits<NestByValue<ExpressionType> > : public traits<ExpressionType> {
+  enum { Flags = traits<ExpressionType>::Flags & ~NestByRefBit };
+};
+}  // namespace internal
+
+/** \class NestByValue
+ * \ingroup Core_Module
+ *
+ * \brief Expression which must be nested by value
+ *
+ * \tparam ExpressionType the type of the object of which we are requiring nesting-by-value
+ *
+ * This class is the return type of MatrixBase::nestByValue()
+ * and most of the time this is the only way it is used.
+ *
+ * \sa MatrixBase::nestByValue()
+ */
+template <typename ExpressionType>
+class NestByValue : public internal::dense_xpr_base<NestByValue<ExpressionType> >::type {
+ public:
+  typedef typename internal::dense_xpr_base<NestByValue>::type Base;
+  static constexpr bool HasDirectAccess = internal::has_direct_access<ExpressionType>::ret;
+
+  EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue)
+
+  EIGEN_DEVICE_FUNC constexpr explicit inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_expression.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_expression.cols(); }
+
+  EIGEN_DEVICE_FUNC constexpr operator const ExpressionType&() const { return m_expression; }
+
+  EIGEN_DEVICE_FUNC constexpr const ExpressionType& nestedExpression() const { return m_expression; }
+
+  EIGEN_DEVICE_FUNC constexpr std::enable_if_t<HasDirectAccess, const Scalar*> data() const {
+    return m_expression.data();
+  }
+
+  EIGEN_DEVICE_FUNC constexpr std::enable_if_t<HasDirectAccess, Index> innerStride() const {
+    return m_expression.innerStride();
+  }
+
+  EIGEN_DEVICE_FUNC constexpr std::enable_if_t<HasDirectAccess, Index> outerStride() const {
+    return m_expression.outerStride();
+  }
+
+ protected:
+  const ExpressionType m_expression;
+};
+
+/** \returns an expression of the temporary version of *this.
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr inline const NestByValue<Derived> DenseBase<Derived>::nestByValue() const {
+  return NestByValue<Derived>(derived());
+}
+
+namespace internal {
+
+// Evaluator of Solve -> eval into a temporary
+template <typename ArgType>
+struct evaluator<NestByValue<ArgType> > : public evaluator<ArgType> {
+  typedef evaluator<ArgType> Base;
+
+  EIGEN_DEVICE_FUNC constexpr explicit evaluator(const NestByValue<ArgType>& xpr) : Base(xpr.nestedExpression()) {}
+};
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_NESTBYVALUE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/NoAlias.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/NoAlias.h
new file mode 100644
index 0000000..6a88201
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/NoAlias.h
@@ -0,0 +1,102 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NOALIAS_H
+#define EIGEN_NOALIAS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class NoAlias
+ * \ingroup Core_Module
+ *
+ * \brief Pseudo expression providing an operator = assuming no aliasing
+ *
+ * \tparam ExpressionType the type of the object on which to do the lazy assignment
+ *
+ * This class represents an expression with special assignment operators
+ * assuming no aliasing between the target expression and the source expression.
+ * More precisely it alloas to bypass the EvalBeforeAssignBit flag of the source expression.
+ * It is the return type of MatrixBase::noalias()
+ * and most of the time this is the only way it is used.
+ *
+ * \sa MatrixBase::noalias()
+ */
+template <typename ExpressionType, template <typename> class StorageBase>
+class NoAlias {
+ public:
+  typedef typename ExpressionType::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC constexpr explicit NoAlias(ExpressionType& expression) : m_expression(expression) {}
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other) {
+    call_assignment_no_alias(m_expression, other.derived(),
+                             internal::assign_op<Scalar, typename OtherDerived::Scalar>());
+    return m_expression;
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other) {
+    call_assignment_no_alias(m_expression, other.derived(),
+                             internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+    return m_expression;
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other) {
+    call_assignment_no_alias(m_expression, other.derived(),
+                             internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+    return m_expression;
+  }
+
+  EIGEN_DEVICE_FUNC constexpr ExpressionType& expression() const { return m_expression; }
+
+ protected:
+  ExpressionType& m_expression;
+};
+
+/** \returns a pseudo expression of \c *this with an operator= assuming
+ * no aliasing between \c *this and the source expression.
+ *
+ * More precisely, noalias() allows to bypass the EvalBeforeAssignBit flag.
+ * Currently, even though several expressions may alias, only product
+ * expressions have this flag. Therefore, noalias() is only useful when
+ * the source expression contains a matrix product.
+ *
+ * Here are some examples where noalias is useful:
+ * \code
+ * D.noalias()  = A * B;
+ * D.noalias() += A.transpose() * B;
+ * D.noalias() -= 2 * A * B.adjoint();
+ * \endcode
+ *
+ * On the other hand the following example will lead to a \b wrong result:
+ * \code
+ * A.noalias() = A * B;
+ * \endcode
+ * because the result matrix A is also an operand of the matrix product. Therefore,
+ * there is no alternative than evaluating A * B in a temporary, that is the default
+ * behavior when you write:
+ * \code
+ * A = A * B;
+ * \endcode
+ *
+ * \sa class NoAlias
+ */
+template <typename Derived>
+NoAlias<Derived, MatrixBase> EIGEN_DEVICE_FUNC MatrixBase<Derived>::noalias() {
+  return NoAlias<Derived, Eigen::MatrixBase>(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_NOALIAS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/NumTraits.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/NumTraits.h
new file mode 100644
index 0000000..2bb1eed
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/NumTraits.h
@@ -0,0 +1,335 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NUMTRAITS_H
+#define EIGEN_NUMTRAITS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// default implementation of digits(), based on numeric_limits if specialized,
+// 0 for integer types, and log2(epsilon()) otherwise.
+template <typename T, bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
+          bool is_integer = NumTraits<T>::IsInteger>
+struct default_digits_impl {
+  EIGEN_DEVICE_FUNC constexpr static int run() { return std::numeric_limits<T>::digits; }
+};
+
+template <typename T>
+struct default_digits_impl<T, false, false>  // Floating point
+{
+  EIGEN_DEVICE_FUNC constexpr static int run() {
+    using std::ceil;
+    using std::log2;
+    typedef typename NumTraits<T>::Real Real;
+    return int(ceil(-log2(NumTraits<Real>::epsilon())));
+  }
+};
+
+template <typename T>
+struct default_digits_impl<T, false, true>  // Integer
+{
+  EIGEN_DEVICE_FUNC constexpr static int run() { return 0; }
+};
+
+// default implementation of digits10(), based on numeric_limits if specialized,
+// 0 for integer types, and floor((digits()-1)*log10(2)) otherwise.
+template <typename T, bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
+          bool is_integer = NumTraits<T>::IsInteger>
+struct default_digits10_impl {
+  EIGEN_DEVICE_FUNC constexpr static int run() { return std::numeric_limits<T>::digits10; }
+};
+
+template <typename T>
+struct default_digits10_impl<T, false, false>  // Floating point
+{
+  EIGEN_DEVICE_FUNC constexpr static int run() {
+    using std::floor;
+    using std::log10;
+    typedef typename NumTraits<T>::Real Real;
+    return int(floor((internal::default_digits_impl<Real>::run() - 1) * log10(2)));
+  }
+};
+
+template <typename T>
+struct default_digits10_impl<T, false, true>  // Integer
+{
+  EIGEN_DEVICE_FUNC constexpr static int run() { return 0; }
+};
+
+// default implementation of max_digits10(), based on numeric_limits if specialized,
+// 0 for integer types, and log10(2) * digits() + 1 otherwise.
+template <typename T, bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
+          bool is_integer = NumTraits<T>::IsInteger>
+struct default_max_digits10_impl {
+  EIGEN_DEVICE_FUNC constexpr static int run() { return std::numeric_limits<T>::max_digits10; }
+};
+
+template <typename T>
+struct default_max_digits10_impl<T, false, false>  // Floating point
+{
+  EIGEN_DEVICE_FUNC constexpr static int run() {
+    using std::ceil;
+    using std::log10;
+    typedef typename NumTraits<T>::Real Real;
+    return int(ceil(internal::default_digits_impl<Real>::run() * log10(2) + 1));
+  }
+};
+
+template <typename T>
+struct default_max_digits10_impl<T, false, true>  // Integer
+{
+  EIGEN_DEVICE_FUNC constexpr static int run() { return 0; }
+};
+
+}  // end namespace internal
+
+namespace numext {
+
+/** \internal bit-wise cast without changing the underlying bit representation. */
+#if defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L
+template <typename Tgt, typename Src>
+EIGEN_DEVICE_FUNC constexpr Tgt bit_cast(const Src& src) {
+  return std::bit_cast<Tgt>(src);
+}
+#elif EIGEN_HAS_BUILTIN(__builtin_bit_cast)
+template <typename Tgt, typename Src>
+EIGEN_DEVICE_FUNC constexpr Tgt bit_cast(const Src& src) {
+  EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Src>::value, THIS_TYPE_IS_NOT_SUPPORTED)
+  EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Tgt>::value, THIS_TYPE_IS_NOT_SUPPORTED)
+  EIGEN_STATIC_ASSERT(sizeof(Src) == sizeof(Tgt), THIS_TYPE_IS_NOT_SUPPORTED)
+  return __builtin_bit_cast(Tgt, src);
+}
+#else
+template <typename Tgt, typename Src>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Tgt bit_cast(const Src& src) {
+  // The behaviour of memcpy is not specified for non-trivially copyable types
+  EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Src>::value, THIS_TYPE_IS_NOT_SUPPORTED)
+  EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Tgt>::value && std::is_default_constructible<Tgt>::value,
+                      THIS_TYPE_IS_NOT_SUPPORTED)
+  EIGEN_STATIC_ASSERT(sizeof(Src) == sizeof(Tgt), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  Tgt tgt;
+  // Load src into registers first. This allows the memcpy to be elided by CUDA.
+  const Src staged = src;
+  EIGEN_USING_STD(memcpy)
+  memcpy(static_cast<void*>(&tgt), static_cast<const void*>(&staged), sizeof(Tgt));
+  return tgt;
+}
+#endif
+}  // namespace numext
+
+// clang-format off
+/** \class NumTraits
+ * \ingroup Core_Module
+ *
+ * \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen.
+ *
+ * \tparam T the numeric type at hand
+ *
+ * This class stores enums, typedefs and static methods giving information about a numeric type.
+ *
+ * The provided data consists of:
+ * \li A typedef \c Real, giving the "real part" type of \a T. If \a T is already real,
+ *     then \c Real is just a typedef to \a T. If \a T is `std::complex<U>` then \c Real
+ *     is a typedef to \a U.
+ * \li A typedef \c NonInteger, giving the type that should be used for operations producing non-integral values,
+ *     such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
+ *     \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to
+ *     take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
+ *     only intended as a helper for code that needs to explicitly promote types.
+ * \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for
+ *     `std::complex<U>`, Literal is defined as \a U. Of course, this type must be fully compatible with \a T. In doubt,
+ *     just use \a T here.
+ * \li A typedef \c Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
+ *     this means, just use \a T here.
+ * \li An enum value \c IsComplex. It is equal to 1 if \a T is a \c std::complex type, and to 0 otherwise.
+ * \li An enum value \c IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int, and to \c 0 otherwise.
+ * \li Enum values \c ReadCost, \c AddCost and \c MulCost representing a rough estimate of the number of CPU cycles needed to by
+ *     move / add / mul instructions respectively, assuming the data is already stored in CPU registers. Stay vague here.
+ *     No need to do architecture-specific stuff. If you don't know what this means, just use \c Eigen::HugeCost.
+ * \li An enum value \c IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
+ * \li An enum value \c RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must be
+ *     called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise.
+ * \li An epsilon() function which, unlike <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/epsilon">
+ *     `std::numeric_limits::epsilon()`</a>, it returns a \c Real instead of a \a T.
+ * \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default value by the fuzzy
+ *     comparison operators.
+ * \li highest() and lowest() functions returning the highest and lowest possible values respectively.
+ * \li digits() function returning the number of radix digits (non-sign digits for integers, mantissa for floating-point).
+ *     This is the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits">
+ *     `std::numeric_limits<T>::digits`</a> which is used as the default implementation if specialized.
+ * \li digits10() function returning the number of decimal digits that can be represented without change. This is the
+ *     analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits10">
+ *     `std::numeric_limits<T>::digits10`</a> which is used as the default implementation if specialized.
+ * \li max_digits10() function returning the number of decimal digits required to uniquely represent all distinct values
+ *     of the type. This is the analogue of <a
+ *     href="http://en.cppreference.com/w/cpp/types/numeric_limits/max_digits10">`std::numeric_limits<T>::max_digits10`</a>
+ *     which is used as the default implementation if specialized.
+ * \li min_exponent() and max_exponent() functions returning the highest and lowest possible values, respectively,
+ *     such that the radix raised to the power exponent-1 is a normalized floating-point number.  These are equivalent
+ *     to <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/min_exponent">
+ *     `std::numeric_limits<T>::min_exponent`</a>/<a
+ *     href="http://en.cppreference.com/w/cpp/types/numeric_limits/max_exponent">`std::numeric_limits<T>::max_exponent`</a>.
+ * \li infinity() function returning a representation of positive infinity, if available.
+ * \li quiet_NaN() function returning a non-signaling "not-a-number", if available.
+ */
+// clang-format on
+template <typename T>
+struct GenericNumTraits {
+  enum {
+    IsInteger = std::numeric_limits<T>::is_integer,
+    IsSigned = std::numeric_limits<T>::is_signed,
+    IsComplex = 0,
+    RequireInitialization = internal::is_arithmetic<T>::value ? 0 : 1,
+    ReadCost = 1,
+    AddCost = 1,
+    MulCost = 1
+  };
+
+  typedef T Real;
+  typedef std::conditional_t<IsInteger, std::conditional_t<sizeof(T) <= 2, float, double>, T> NonInteger;
+  typedef T Nested;
+  typedef T Literal;
+
+  EIGEN_DEVICE_FUNC constexpr static Real epsilon() { return numext::numeric_limits<T>::epsilon(); }
+
+  EIGEN_DEVICE_FUNC constexpr static int digits10() { return internal::default_digits10_impl<T>::run(); }
+
+  EIGEN_DEVICE_FUNC constexpr static int max_digits10() { return internal::default_max_digits10_impl<T>::run(); }
+
+  EIGEN_DEVICE_FUNC constexpr static int digits() { return internal::default_digits_impl<T>::run(); }
+
+  EIGEN_DEVICE_FUNC constexpr static int min_exponent() { return numext::numeric_limits<T>::min_exponent; }
+
+  EIGEN_DEVICE_FUNC constexpr static int max_exponent() { return numext::numeric_limits<T>::max_exponent; }
+
+  EIGEN_DEVICE_FUNC constexpr static Real dummy_precision() {
+    // make sure to override this for floating-point types
+    return Real(0);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr static T highest() { return (numext::numeric_limits<T>::max)(); }
+
+  EIGEN_DEVICE_FUNC constexpr static T lowest() { return (numext::numeric_limits<T>::lowest)(); }
+
+  EIGEN_DEVICE_FUNC constexpr static T infinity() { return numext::numeric_limits<T>::infinity(); }
+
+  EIGEN_DEVICE_FUNC constexpr static T quiet_NaN() { return numext::numeric_limits<T>::quiet_NaN(); }
+};
+
+template <typename T>
+struct NumTraits : GenericNumTraits<T> {};
+
+template <>
+struct NumTraits<float> : GenericNumTraits<float> {
+  EIGEN_DEVICE_FUNC constexpr static float dummy_precision() { return 1e-5f; }
+};
+
+template <>
+struct NumTraits<double> : GenericNumTraits<double> {
+  EIGEN_DEVICE_FUNC constexpr static double dummy_precision() { return 1e-12; }
+};
+
+// GPU devices treat `long double` as `double`.
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template <>
+struct NumTraits<long double> : GenericNumTraits<long double> {
+  EIGEN_DEVICE_FUNC constexpr static long double dummy_precision() { return static_cast<long double>(1e-15l); }
+
+#if defined(EIGEN_ARCH_PPC) && (__LDBL_MANT_DIG__ == 106)
+  // PowerPC double double causes issues with some values
+  EIGEN_DEVICE_FUNC constexpr static long double epsilon() {
+    // 2^(-(__LDBL_MANT_DIG__)+1)
+    return static_cast<long double>(2.4651903288156618919116517665087e-32l);
+  }
+#endif
+};
+#endif
+
+template <typename Real_>
+struct NumTraits<std::complex<Real_> > : GenericNumTraits<std::complex<Real_> > {
+  typedef Real_ Real;
+  typedef typename NumTraits<Real_>::Literal Literal;
+  enum {
+    IsComplex = 1,
+    IsSigned = NumTraits<Real_>::IsSigned,
+    RequireInitialization = NumTraits<Real_>::RequireInitialization,
+    ReadCost = 2 * NumTraits<Real_>::ReadCost,
+    AddCost = 2 * NumTraits<Real>::AddCost,
+    MulCost = 4 * NumTraits<Real>::MulCost + 2 * NumTraits<Real>::AddCost
+  };
+
+  EIGEN_DEVICE_FUNC constexpr static Real epsilon() { return NumTraits<Real>::epsilon(); }
+  EIGEN_DEVICE_FUNC constexpr static Real dummy_precision() { return NumTraits<Real>::dummy_precision(); }
+  EIGEN_DEVICE_FUNC constexpr static int digits10() { return NumTraits<Real>::digits10(); }
+  EIGEN_DEVICE_FUNC constexpr static int max_digits10() { return NumTraits<Real>::max_digits10(); }
+};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > {
+  typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> ArrayType;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Array<RealScalar, Rows, Cols, Options, MaxRows, MaxCols> Real;
+  typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar;
+  typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger;
+  typedef ArrayType& Nested;
+  typedef typename NumTraits<Scalar>::Literal Literal;
+
+  enum {
+    IsComplex = NumTraits<Scalar>::IsComplex,
+    IsInteger = NumTraits<Scalar>::IsInteger,
+    IsSigned = NumTraits<Scalar>::IsSigned,
+    RequireInitialization = 1,
+    ReadCost = ArrayType::SizeAtCompileTime == Dynamic
+                   ? HugeCost
+                   : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::ReadCost),
+    AddCost = ArrayType::SizeAtCompileTime == Dynamic ? HugeCost
+                                                      : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::AddCost),
+    MulCost = ArrayType::SizeAtCompileTime == Dynamic ? HugeCost
+                                                      : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::MulCost)
+  };
+
+  EIGEN_DEVICE_FUNC constexpr static RealScalar epsilon() { return NumTraits<RealScalar>::epsilon(); }
+  EIGEN_DEVICE_FUNC constexpr static RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); }
+
+  constexpr static int digits10() { return NumTraits<Scalar>::digits10(); }
+  constexpr static int max_digits10() { return NumTraits<Scalar>::max_digits10(); }
+};
+
+template <>
+struct NumTraits<std::string> : GenericNumTraits<std::string> {
+  enum { RequireInitialization = 1, ReadCost = HugeCost, AddCost = HugeCost, MulCost = HugeCost };
+
+  constexpr static int digits10() { return 0; }
+  constexpr static int max_digits10() { return 0; }
+
+ private:
+  static inline std::string epsilon();
+  static inline std::string dummy_precision();
+  static inline std::string lowest();
+  static inline std::string highest();
+  static inline std::string infinity();
+  static inline std::string quiet_NaN();
+};
+
+// Empty specialization for void to allow template specialization based on NumTraits<T>::Real with T==void and SFINAE.
+template <>
+struct NumTraits<void> {};
+
+template <>
+struct NumTraits<bool> : GenericNumTraits<bool> {};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_NUMTRAITS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/PartialReduxEvaluator.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/PartialReduxEvaluator.h
new file mode 100644
index 0000000..1f638f9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/PartialReduxEvaluator.h
@@ -0,0 +1,253 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2018 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARTIALREDUX_H
+#define EIGEN_PARTIALREDUX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/***************************************************************************
+ *
+ * This file provides evaluators for partial reductions.
+ * There are two modes:
+ *
+ *  - scalar path: simply calls the respective function on the column or row.
+ *    -> nothing special here, all the tricky part is handled by the return
+ *       types of VectorwiseOp's members. They embed the functor calling the
+ *       respective DenseBase's member function.
+ *
+ *  - vectorized path: implements a packet-wise reductions followed by
+ *    some (optional) processing of the outcome, e.g., division by n for mean.
+ *
+ * For the vectorized path let's observe that the packet-size and outer-unrolling
+ * are both decided by the assignment logic. So all we have to do is to decide
+ * on the inner unrolling.
+ *
+ * For the unrolling, we can reuse "internal::redux_vec_unroller" from Redux.h,
+ * but be need to be careful to specify correct increment.
+ *
+ ***************************************************************************/
+
+/* logic deciding a strategy for unrolling of vectorized paths */
+template <typename Func, typename Evaluator>
+struct packetwise_redux_traits {
+  enum {
+    OuterSize = int(Evaluator::IsRowMajor) ? Evaluator::RowsAtCompileTime : Evaluator::ColsAtCompileTime,
+    Cost = OuterSize == Dynamic ? HugeCost
+                                : OuterSize * Evaluator::CoeffReadCost + (OuterSize - 1) * functor_traits<Func>::Cost,
+    Unrolling = Cost <= EIGEN_UNROLLING_LIMIT ? CompleteUnrolling : NoUnrolling
+  };
+};
+
+/* Value to be returned when size==0 , by default let's return 0 */
+template <typename PacketType, typename Func>
+EIGEN_DEVICE_FUNC PacketType packetwise_redux_empty_value(const Func&) {
+  const typename unpacket_traits<PacketType>::type zero(0);
+  return pset1<PacketType>(zero);
+}
+
+/* For products the default is 1 */
+template <typename PacketType, typename Scalar>
+EIGEN_DEVICE_FUNC PacketType packetwise_redux_empty_value(const scalar_product_op<Scalar, Scalar>&) {
+  return pset1<PacketType>(Scalar(1));
+}
+
+/* Perform the actual reduction */
+template <typename Func, typename Evaluator, int Unrolling = packetwise_redux_traits<Func, Evaluator>::Unrolling>
+struct packetwise_redux_impl;
+
+/* Perform the actual reduction with unrolling */
+template <typename Func, typename Evaluator>
+struct packetwise_redux_impl<Func, Evaluator, CompleteUnrolling> {
+  typedef redux_novec_unroller<Func, Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
+  typedef typename Evaluator::Scalar Scalar;
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE PacketType run(const Evaluator& eval, const Func& func, Index /*size*/) {
+    return redux_vec_unroller<Func, Evaluator, 0,
+                              packetwise_redux_traits<Func, Evaluator>::OuterSize>::template run<PacketType>(eval,
+                                                                                                             func);
+  }
+};
+
+/* Add a specialization of redux_vec_unroller for size==0 at compiletime.
+ * This specialization is not required for general reductions, which is
+ * why it is defined here.
+ */
+template <typename Func, typename Evaluator, Index Start>
+struct redux_vec_unroller<Func, Evaluator, Start, 0> {
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE PacketType run(const Evaluator&, const Func& f) {
+    return packetwise_redux_empty_value<PacketType>(f);
+  }
+};
+
+/* Perform the actual reduction for dynamic sizes */
+template <typename Func, typename Evaluator>
+struct packetwise_redux_impl<Func, Evaluator, NoUnrolling> {
+  typedef typename Evaluator::Scalar Scalar;
+  typedef typename redux_traits<Func, Evaluator>::PacketType PacketScalar;
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC static PacketType run(const Evaluator& eval, const Func& func, Index size) {
+    if (size == 0) return packetwise_redux_empty_value<PacketType>(func);
+
+    const Index size4 = 1 + numext::round_down(size - 1, 4);
+    PacketType p = eval.template packetByOuterInner<Unaligned, PacketType>(0, 0);
+    // This loop is optimized for instruction pipelining:
+    // - each iteration generates two independent instructions
+    // - thanks to branch prediction and out-of-order execution we have independent instructions across loops
+    for (Index i = 1; i < size4; i += 4)
+      p = func.packetOp(
+          p, func.packetOp(func.packetOp(eval.template packetByOuterInner<Unaligned, PacketType>(i + 0, 0),
+                                         eval.template packetByOuterInner<Unaligned, PacketType>(i + 1, 0)),
+                           func.packetOp(eval.template packetByOuterInner<Unaligned, PacketType>(i + 2, 0),
+                                         eval.template packetByOuterInner<Unaligned, PacketType>(i + 3, 0))));
+    for (Index i = size4; i < size; ++i)
+      p = func.packetOp(p, eval.template packetByOuterInner<Unaligned, PacketType>(i, 0));
+    return p;
+  }
+};
+
+template <typename Func, typename Evaluator>
+struct packetwise_segment_redux_impl {
+  typedef typename Evaluator::Scalar Scalar;
+  typedef typename redux_traits<Func, Evaluator>::PacketType PacketScalar;
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC static PacketType run(const Evaluator& eval, const Func& func, Index size, Index begin,
+                                          Index count) {
+    if (size == 0) return packetwise_redux_empty_value<PacketType>(func);
+
+    PacketType p = eval.template packetSegmentByOuterInner<Unaligned, PacketType>(0, 0, begin, count);
+    for (Index i = 1; i < size; ++i)
+      p = func.packetOp(p, eval.template packetSegmentByOuterInner<Unaligned, PacketType>(i, 0, begin, count));
+    return p;
+  }
+};
+
+template <typename ArgType, typename MemberOp, int Direction>
+struct evaluator<PartialReduxExpr<ArgType, MemberOp, Direction> >
+    : evaluator_base<PartialReduxExpr<ArgType, MemberOp, Direction> > {
+  typedef PartialReduxExpr<ArgType, MemberOp, Direction> XprType;
+  typedef typename internal::nested_eval<ArgType, 1>::type ArgTypeNested;
+  typedef add_const_on_value_type_t<ArgTypeNested> ConstArgTypeNested;
+  typedef internal::remove_all_t<ArgTypeNested> ArgTypeNestedCleaned;
+  typedef typename ArgType::Scalar InputScalar;
+  typedef typename XprType::Scalar Scalar;
+  enum {
+    TraversalSize = Direction == int(Vertical) ? int(ArgType::RowsAtCompileTime) : int(ArgType::ColsAtCompileTime)
+  };
+  typedef typename MemberOp::template Cost<int(TraversalSize)> CostOpType;
+  enum {
+    CoeffReadCost = TraversalSize == Dynamic ? HugeCost
+                    : TraversalSize == 0
+                        ? 1
+                        : int(TraversalSize) * int(evaluator<ArgType>::CoeffReadCost) + int(CostOpType::value),
+
+    ArgFlags_ = evaluator<ArgType>::Flags,
+
+    Vectorizable_ = bool(int(ArgFlags_) & PacketAccessBit) && bool(MemberOp::Vectorizable) &&
+                    (Direction == int(Vertical) ? bool(ArgFlags_ & RowMajorBit) : (ArgFlags_ & RowMajorBit) == 0) &&
+                    (TraversalSize != 0),
+
+    Flags = (traits<XprType>::Flags & RowMajorBit) | (evaluator<ArgType>::Flags & (HereditaryBits & (~RowMajorBit))) |
+            (Vectorizable_ ? PacketAccessBit : 0) | LinearAccessBit,
+
+    Alignment = 0  // FIXME this will need to be improved once PartialReduxExpr is vectorized
+  };
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const XprType xpr) : m_arg(xpr.nestedExpression()), m_functor(xpr.functor()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(TraversalSize == Dynamic ? HugeCost
+                                                             : (TraversalSize == 0 ? 1 : int(CostOpType::value)));
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index i, Index j) const {
+    return coeff(Direction == Vertical ? j : i);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index index) const {
+    return m_functor(m_arg.template subVector<DirectionType(Direction)>(index));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index i, Index j) const {
+    return packet<LoadMode, PacketType>(Direction == Vertical ? j : i);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC PacketType packet(Index idx) const {
+    static constexpr int PacketSize = internal::unpacket_traits<PacketType>::size;
+    static constexpr int PanelRows = Direction == Vertical ? ArgType::RowsAtCompileTime : PacketSize;
+    static constexpr int PanelCols = Direction == Vertical ? PacketSize : ArgType::ColsAtCompileTime;
+    using PanelType = Block<const ArgTypeNestedCleaned, PanelRows, PanelCols, true /* InnerPanel */>;
+    using PanelEvaluator = typename internal::redux_evaluator<PanelType>;
+    using BinaryOp = typename MemberOp::BinaryOp;
+    using Impl = internal::packetwise_redux_impl<BinaryOp, PanelEvaluator>;
+
+    // FIXME
+    // See bug 1612, currently if PacketSize==1 (i.e. complex<double> with 128bits registers) then the storage-order of
+    // panel get reversed and methods like packetByOuterInner do not make sense anymore in this context. So let's just
+    // by pass "vectorization" in this case:
+    if (PacketSize == 1) return internal::pset1<PacketType>(coeff(idx));
+
+    Index startRow = Direction == Vertical ? 0 : idx;
+    Index startCol = Direction == Vertical ? idx : 0;
+    Index numRows = Direction == Vertical ? m_arg.rows() : PacketSize;
+    Index numCols = Direction == Vertical ? PacketSize : m_arg.cols();
+
+    PanelType panel(m_arg, startRow, startCol, numRows, numCols);
+    PanelEvaluator panel_eval(panel);
+    PacketType p = Impl::template run<PacketType>(panel_eval, m_functor.binaryFunc(), m_arg.outerSize());
+    return p;
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index i, Index j, Index begin, Index count) const {
+    return packetSegment<LoadMode, PacketType>(Direction == Vertical ? j : i, begin, count);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC PacketType packetSegment(Index idx, Index begin, Index count) const {
+    static constexpr int PanelRows = Direction == Vertical ? ArgType::RowsAtCompileTime : Dynamic;
+    static constexpr int PanelCols = Direction == Vertical ? Dynamic : ArgType::ColsAtCompileTime;
+    using PanelType = Block<const ArgTypeNestedCleaned, PanelRows, PanelCols, true /* InnerPanel */>;
+    using PanelEvaluator = typename internal::redux_evaluator<PanelType>;
+    using BinaryOp = typename MemberOp::BinaryOp;
+    using Impl = internal::packetwise_segment_redux_impl<BinaryOp, PanelEvaluator>;
+
+    Index startRow = Direction == Vertical ? 0 : idx;
+    Index startCol = Direction == Vertical ? idx : 0;
+    Index numRows = Direction == Vertical ? m_arg.rows() : begin + count;
+    Index numCols = Direction == Vertical ? begin + count : m_arg.cols();
+
+    PanelType panel(m_arg, startRow, startCol, numRows, numCols);
+    PanelEvaluator panel_eval(panel);
+    PacketType p = Impl::template run<PacketType>(panel_eval, m_functor.binaryFunc(), m_arg.outerSize(), begin, count);
+    return p;
+  }
+
+ protected:
+  ConstArgTypeNested m_arg;
+  const MemberOp m_functor;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PARTIALREDUX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/PermutationMatrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/PermutationMatrix.h
new file mode 100644
index 0000000..7713354
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/PermutationMatrix.h
@@ -0,0 +1,555 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PERMUTATIONMATRIX_H
+#define EIGEN_PERMUTATIONMATRIX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+enum PermPermProduct_t { PermPermProduct };
+
+}  // end namespace internal
+
+/** \class PermutationBase
+ * \ingroup Core_Module
+ *
+ * \brief Base class for permutations
+ *
+ * \tparam Derived the derived class
+ *
+ * This class is the base class for all expressions representing a permutation matrix,
+ * internally stored as a vector of integers.
+ * The convention followed here is that if \f$ \sigma \f$ is a permutation, the corresponding permutation matrix
+ * \f$ P_\sigma \f$ is such that if \f$ (e_1,\ldots,e_p) \f$ is the canonical basis, we have:
+ *  \f[ P_\sigma(e_i) = e_{\sigma(i)}. \f]
+ * This convention ensures that for any two permutations \f$ \sigma, \tau \f$, we have:
+ *  \f[ P_{\sigma\circ\tau} = P_\sigma P_\tau. \f]
+ *
+ * Permutation matrices are square and invertible.
+ *
+ * Notice that in addition to the member functions and operators listed here, there also are non-member
+ * operator* to multiply any kind of permutation object with any kind of matrix expression (MatrixBase)
+ * on either side.
+ *
+ * \sa class PermutationMatrix, class PermutationWrapper
+ */
+template <typename Derived>
+class PermutationBase : public EigenBase<Derived> {
+  typedef internal::traits<Derived> Traits;
+  typedef EigenBase<Derived> Base;
+
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  typedef typename Traits::IndicesType IndicesType;
+  enum {
+    Flags = Traits::Flags,
+    RowsAtCompileTime = Traits::RowsAtCompileTime,
+    ColsAtCompileTime = Traits::ColsAtCompileTime,
+    MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = Traits::MaxColsAtCompileTime
+  };
+  typedef typename Traits::StorageIndex StorageIndex;
+  typedef Matrix<StorageIndex, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime>
+      DenseMatrixType;
+  typedef PermutationMatrix<IndicesType::SizeAtCompileTime, IndicesType::MaxSizeAtCompileTime, StorageIndex>
+      PlainPermutationType;
+  typedef PlainPermutationType PlainObject;
+  using Base::derived;
+  typedef Inverse<Derived> InverseReturnType;
+  typedef void Scalar;
+#endif
+
+  /** Copies the other permutation into *this */
+  template <typename OtherDerived>
+  Derived& operator=(const PermutationBase<OtherDerived>& other) {
+    indices() = other.indices();
+    return derived();
+  }
+
+  /** Assignment from the Transpositions \a tr */
+  template <typename OtherDerived>
+  Derived& operator=(const TranspositionsBase<OtherDerived>& tr) {
+    setIdentity(tr.size());
+    for (Index k = size() - 1; k >= 0; --k) applyTranspositionOnTheRight(k, tr.coeff(k));
+    return derived();
+  }
+
+  /** \returns the number of rows */
+  inline EIGEN_DEVICE_FUNC Index rows() const { return Index(indices().size()); }
+
+  /** \returns the number of columns */
+  inline EIGEN_DEVICE_FUNC Index cols() const { return Index(indices().size()); }
+
+  /** \returns the size of a side of the respective square matrix, i.e., the number of indices */
+  inline EIGEN_DEVICE_FUNC Index size() const { return Index(indices().size()); }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename DenseDerived>
+  void evalTo(MatrixBase<DenseDerived>& other) const {
+    other.setZero();
+    for (Index i = 0; i < rows(); ++i) other.coeffRef(indices().coeff(i), i) = typename DenseDerived::Scalar(1);
+  }
+#endif
+
+  /** \returns a Matrix object initialized from this permutation matrix. Notice that it
+   * is inefficient to return this Matrix object by value. For efficiency, favor using
+   * the Matrix constructor taking EigenBase objects.
+   */
+  DenseMatrixType toDenseMatrix() const { return derived(); }
+
+  /** \returns the plain matrix representation of the permutation. */
+  DenseMatrixType eval() const { return toDenseMatrix(); }
+
+  /** const version of indices(). */
+  const IndicesType& indices() const { return derived().indices(); }
+  /** \returns a reference to the stored array representing the permutation. */
+  IndicesType& indices() { return derived().indices(); }
+
+  /** Resizes to given size.
+   */
+  inline void resize(Index newSize) { indices().resize(newSize); }
+
+  /** Sets *this to be the identity permutation matrix */
+  void setIdentity() {
+    StorageIndex n = StorageIndex(size());
+    for (StorageIndex i = 0; i < n; ++i) indices().coeffRef(i) = i;
+  }
+
+  /** Sets *this to be the identity permutation matrix of given size.
+   */
+  void setIdentity(Index newSize) {
+    resize(newSize);
+    setIdentity();
+  }
+
+  /** Multiplies *this by the transposition \f$(ij)\f$ on the left.
+   *
+   * \returns a reference to *this.
+   *
+   * \warning This is much slower than applyTranspositionOnTheRight(Index,Index):
+   * this has linear complexity and requires a lot of branching.
+   *
+   * \sa applyTranspositionOnTheRight(Index,Index)
+   */
+  Derived& applyTranspositionOnTheLeft(Index i, Index j) {
+    eigen_assert(i >= 0 && j >= 0 && i < size() && j < size());
+    for (Index k = 0; k < size(); ++k) {
+      if (indices().coeff(k) == i)
+        indices().coeffRef(k) = StorageIndex(j);
+      else if (indices().coeff(k) == j)
+        indices().coeffRef(k) = StorageIndex(i);
+    }
+    return derived();
+  }
+
+  /** Multiplies *this by the transposition \f$(ij)\f$ on the right.
+   *
+   * \returns a reference to *this.
+   *
+   * This is a fast operation, it only consists in swapping two indices.
+   *
+   * \sa applyTranspositionOnTheLeft(Index,Index)
+   */
+  Derived& applyTranspositionOnTheRight(Index i, Index j) {
+    eigen_assert(i >= 0 && j >= 0 && i < size() && j < size());
+    std::swap(indices().coeffRef(i), indices().coeffRef(j));
+    return derived();
+  }
+
+  /** \returns the inverse permutation matrix.
+   *
+   * \note \blank \note_try_to_help_rvo
+   */
+  inline InverseReturnType inverse() const { return InverseReturnType(derived()); }
+  /** \returns the transpose permutation matrix.
+   *
+   * \note \blank \note_try_to_help_rvo
+   */
+  inline InverseReturnType transpose() const { return InverseReturnType(derived()); }
+
+  /**** multiplication helpers to hopefully get RVO ****/
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+ protected:
+  template <typename OtherDerived>
+  void assignTranspose(const PermutationBase<OtherDerived>& other) {
+    for (Index i = 0; i < rows(); ++i) indices().coeffRef(other.indices().coeff(i)) = i;
+  }
+  template <typename Lhs, typename Rhs>
+  void assignProduct(const Lhs& lhs, const Rhs& rhs) {
+    eigen_assert(lhs.cols() == rhs.rows());
+    for (Index i = 0; i < rows(); ++i) indices().coeffRef(i) = lhs.indices().coeff(rhs.indices().coeff(i));
+  }
+#endif
+
+ public:
+  /** \returns the product permutation matrix.
+   *
+   * \note \blank \note_try_to_help_rvo
+   */
+  template <typename Other>
+  inline PlainPermutationType operator*(const PermutationBase<Other>& other) const {
+    return PlainPermutationType(internal::PermPermProduct, derived(), other.derived());
+  }
+
+  /** \returns the product of a permutation with another inverse permutation.
+   *
+   * \note \blank \note_try_to_help_rvo
+   */
+  template <typename Other>
+  inline PlainPermutationType operator*(const InverseImpl<Other, PermutationStorage>& other) const {
+    return PlainPermutationType(internal::PermPermProduct, *this, other.eval());
+  }
+
+  /** \returns the product of an inverse permutation with another permutation.
+   *
+   * \note \blank \note_try_to_help_rvo
+   */
+  template <typename Other>
+  friend inline PlainPermutationType operator*(const InverseImpl<Other, PermutationStorage>& other,
+                                               const PermutationBase& perm) {
+    return PlainPermutationType(internal::PermPermProduct, other.eval(), perm);
+  }
+
+  /** \returns the determinant of the permutation matrix, which is either 1 or -1 depending on the parity of the
+   * permutation.
+   *
+   * This function is O(\c n) procedure allocating a buffer of \c n booleans.
+   */
+  Index determinant() const {
+    Index res = 1;
+    Index n = size();
+    Matrix<bool, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime> mask(n);
+    mask.fill(false);
+    Index r = 0;
+    while (r < n) {
+      // search for the next seed
+      while (r < n && mask[r]) r++;
+      if (r >= n) break;
+      // we got one, let's follow it until we are back to the seed
+      Index k0 = r++;
+      mask.coeffRef(k0) = true;
+      for (Index k = indices().coeff(k0); k != k0; k = indices().coeff(k)) {
+        mask.coeffRef(k) = true;
+        res = -res;
+      }
+    }
+    return res;
+  }
+
+ protected:
+};
+
+namespace internal {
+template <int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
+struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_> >
+    : traits<
+          Matrix<StorageIndex_, SizeAtCompileTime, SizeAtCompileTime, 0, MaxSizeAtCompileTime, MaxSizeAtCompileTime> > {
+  typedef PermutationStorage StorageKind;
+  typedef Matrix<StorageIndex_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
+  typedef StorageIndex_ StorageIndex;
+  typedef void Scalar;
+};
+}  // namespace internal
+
+/** \class PermutationMatrix
+ * \ingroup Core_Module
+ *
+ * \brief Permutation matrix
+ *
+ * \tparam SizeAtCompileTime the number of rows/cols, or Dynamic
+ * \tparam MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to
+ * SizeAtCompileTime. Most of the time, you should not have to specify it. \tparam StorageIndex_ the integer type of the
+ * indices
+ *
+ * This class represents a permutation matrix, internally stored as a vector of integers.
+ *
+ * \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix
+ */
+template <int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
+class PermutationMatrix
+    : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_> > {
+  typedef PermutationBase<PermutationMatrix> Base;
+  typedef internal::traits<PermutationMatrix> Traits;
+
+ public:
+  typedef const PermutationMatrix& Nested;
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  typedef typename Traits::IndicesType IndicesType;
+  typedef typename Traits::StorageIndex StorageIndex;
+#endif
+
+  inline PermutationMatrix() {}
+
+  /** Constructs an uninitialized permutation matrix of given size.
+   */
+  explicit inline PermutationMatrix(Index size) : m_indices(size) {
+    eigen_internal_assert(size <= NumTraits<StorageIndex>::highest());
+  }
+
+  /** Copy constructor. */
+  template <typename OtherDerived>
+  inline PermutationMatrix(const PermutationBase<OtherDerived>& other) : m_indices(other.indices()) {}
+
+  /** Generic constructor from expression of the indices. The indices
+   * array has the meaning that the permutations sends each integer i to indices[i].
+   *
+   * \warning It is your responsibility to check that the indices array that you passes actually
+   * describes a permutation, i.e., each value between 0 and n-1 occurs exactly once, where n is the
+   * array's size.
+   */
+  template <typename Other>
+  explicit inline PermutationMatrix(const MatrixBase<Other>& indices) : m_indices(indices) {}
+
+  /** Convert the Transpositions \a tr to a permutation matrix */
+  template <typename Other>
+  explicit PermutationMatrix(const TranspositionsBase<Other>& tr) : m_indices(tr.size()) {
+    *this = tr;
+  }
+
+  /** Copies the other permutation into *this */
+  template <typename Other>
+  PermutationMatrix& operator=(const PermutationBase<Other>& other) {
+    m_indices = other.indices();
+    return *this;
+  }
+
+  /** Assignment from the Transpositions \a tr */
+  template <typename Other>
+  PermutationMatrix& operator=(const TranspositionsBase<Other>& tr) {
+    return Base::operator=(tr.derived());
+  }
+
+  /** const version of indices(). */
+  const IndicesType& indices() const { return m_indices; }
+  /** \returns a reference to the stored array representing the permutation. */
+  IndicesType& indices() { return m_indices; }
+
+  /**** multiplication helpers to hopefully get RVO ****/
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename Other>
+  PermutationMatrix(const InverseImpl<Other, PermutationStorage>& other)
+      : m_indices(other.derived().nestedExpression().size()) {
+    eigen_internal_assert(m_indices.size() <= NumTraits<StorageIndex>::highest());
+    StorageIndex end = StorageIndex(m_indices.size());
+    for (StorageIndex i = 0; i < end; ++i)
+      m_indices.coeffRef(other.derived().nestedExpression().indices().coeff(i)) = i;
+  }
+  template <typename Lhs, typename Rhs>
+  PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs) : m_indices(lhs.indices().size()) {
+    Base::assignProduct(lhs, rhs);
+  }
+#endif
+
+ protected:
+  IndicesType m_indices;
+};
+
+namespace internal {
+template <int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess_>
+struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>, PacketAccess_> >
+    : traits<
+          Matrix<StorageIndex_, SizeAtCompileTime, SizeAtCompileTime, 0, MaxSizeAtCompileTime, MaxSizeAtCompileTime> > {
+  typedef PermutationStorage StorageKind;
+  typedef Map<const Matrix<StorageIndex_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, PacketAccess_> IndicesType;
+  typedef StorageIndex_ StorageIndex;
+  typedef void Scalar;
+};
+}  // namespace internal
+
+template <int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess_>
+class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>, PacketAccess_>
+    : public PermutationBase<
+          Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>, PacketAccess_> > {
+  typedef PermutationBase<Map> Base;
+  typedef internal::traits<Map> Traits;
+
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  typedef typename Traits::IndicesType IndicesType;
+  typedef typename IndicesType::Scalar StorageIndex;
+#endif
+
+  inline Map(const StorageIndex* indicesPtr) : m_indices(indicesPtr) {}
+
+  inline Map(const StorageIndex* indicesPtr, Index size) : m_indices(indicesPtr, size) {}
+
+  /** Copies the other permutation into *this */
+  template <typename Other>
+  Map& operator=(const PermutationBase<Other>& other) {
+    return Base::operator=(other.derived());
+  }
+
+  /** Assignment from the Transpositions \a tr */
+  template <typename Other>
+  Map& operator=(const TranspositionsBase<Other>& tr) {
+    return Base::operator=(tr.derived());
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** This is a special case of the templated operator=. Its purpose is to
+   * prevent a default operator= from hiding the templated operator=.
+   */
+  Map& operator=(const Map& other) {
+    m_indices = other.m_indices;
+    return *this;
+  }
+#endif
+
+  /** const version of indices(). */
+  const IndicesType& indices() const { return m_indices; }
+  /** \returns a reference to the stored array representing the permutation. */
+  IndicesType& indices() { return m_indices; }
+
+ protected:
+  IndicesType m_indices;
+};
+
+template <typename IndicesType_>
+class TranspositionsWrapper;
+namespace internal {
+template <typename IndicesType_>
+struct traits<PermutationWrapper<IndicesType_> > {
+  typedef PermutationStorage StorageKind;
+  typedef void Scalar;
+  typedef typename IndicesType_::Scalar StorageIndex;
+  typedef IndicesType_ IndicesType;
+  enum {
+    RowsAtCompileTime = IndicesType_::SizeAtCompileTime,
+    ColsAtCompileTime = IndicesType_::SizeAtCompileTime,
+    MaxRowsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
+    MaxColsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
+    Flags = 0
+  };
+};
+}  // namespace internal
+
+/** \class PermutationWrapper
+ * \ingroup Core_Module
+ *
+ * \brief Class to view a vector of integers as a permutation matrix
+ *
+ * \tparam IndicesType_ the type of the vector of integer (can be any compatible expression)
+ *
+ * This class allows to view any vector expression of integers as a permutation matrix.
+ *
+ * \sa class PermutationBase, class PermutationMatrix
+ */
+template <typename IndicesType_>
+class PermutationWrapper : public PermutationBase<PermutationWrapper<IndicesType_> > {
+  typedef PermutationBase<PermutationWrapper> Base;
+  typedef internal::traits<PermutationWrapper> Traits;
+
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  typedef typename Traits::IndicesType IndicesType;
+#endif
+
+  inline PermutationWrapper(const IndicesType& indices) : m_indices(indices) {}
+
+  /** const version of indices(). */
+  const internal::remove_all_t<typename IndicesType::Nested>& indices() const { return m_indices; }
+
+ protected:
+  typename IndicesType::Nested m_indices;
+};
+
+/** \returns the matrix with the permutation applied to the columns.
+ */
+template <typename MatrixDerived, typename PermutationDerived>
+EIGEN_DEVICE_FUNC const Product<MatrixDerived, PermutationDerived, DefaultProduct> operator*(
+    const MatrixBase<MatrixDerived>& matrix, const PermutationBase<PermutationDerived>& permutation) {
+  return Product<MatrixDerived, PermutationDerived, DefaultProduct>(matrix.derived(), permutation.derived());
+}
+
+/** \returns the matrix with the permutation applied to the rows.
+ */
+template <typename PermutationDerived, typename MatrixDerived>
+EIGEN_DEVICE_FUNC const Product<PermutationDerived, MatrixDerived, DefaultProduct> operator*(
+    const PermutationBase<PermutationDerived>& permutation, const MatrixBase<MatrixDerived>& matrix) {
+  return Product<PermutationDerived, MatrixDerived, DefaultProduct>(permutation.derived(), matrix.derived());
+}
+
+template <typename PermutationType>
+class InverseImpl<PermutationType, PermutationStorage> : public EigenBase<Inverse<PermutationType> > {
+  typedef typename PermutationType::PlainPermutationType PlainPermutationType;
+  typedef internal::traits<PermutationType> PermTraits;
+
+ protected:
+  InverseImpl() {}
+
+ public:
+  typedef Inverse<PermutationType> InverseType;
+  using EigenBase<Inverse<PermutationType> >::derived;
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  typedef typename PermutationType::DenseMatrixType DenseMatrixType;
+  enum {
+    RowsAtCompileTime = PermTraits::RowsAtCompileTime,
+    ColsAtCompileTime = PermTraits::ColsAtCompileTime,
+    MaxRowsAtCompileTime = PermTraits::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = PermTraits::MaxColsAtCompileTime
+  };
+#endif
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename DenseDerived>
+  void evalTo(MatrixBase<DenseDerived>& other) const {
+    other.setZero();
+    for (Index i = 0; i < derived().rows(); ++i)
+      other.coeffRef(i, derived().nestedExpression().indices().coeff(i)) = typename DenseDerived::Scalar(1);
+  }
+#endif
+
+  /** \return the equivalent permutation matrix */
+  PlainPermutationType eval() const { return derived(); }
+
+  DenseMatrixType toDenseMatrix() const { return derived(); }
+
+  /** \returns the matrix with the inverse permutation applied to the columns.
+   */
+  template <typename OtherDerived>
+  friend const Product<OtherDerived, InverseType, DefaultProduct> operator*(const MatrixBase<OtherDerived>& matrix,
+                                                                            const InverseType& trPerm) {
+    return Product<OtherDerived, InverseType, DefaultProduct>(matrix.derived(), trPerm.derived());
+  }
+
+  /** \returns the matrix with the inverse permutation applied to the rows.
+   */
+  template <typename OtherDerived>
+  const Product<InverseType, OtherDerived, DefaultProduct> operator*(const MatrixBase<OtherDerived>& matrix) const {
+    return Product<InverseType, OtherDerived, DefaultProduct>(derived(), matrix.derived());
+  }
+};
+
+template <typename Derived>
+const PermutationWrapper<const Derived> MatrixBase<Derived>::asPermutation() const {
+  return derived();
+}
+
+namespace internal {
+
+template <>
+struct AssignmentKind<DenseShape, PermutationShape> {
+  typedef EigenBase2EigenBase Kind;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PERMUTATIONMATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/PlainObjectBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/PlainObjectBase.h
new file mode 100644
index 0000000..1544a58
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/PlainObjectBase.h
@@ -0,0 +1,1008 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DENSESTORAGEBASE_H
+#define EIGEN_DENSESTORAGEBASE_H
+
+#if defined(EIGEN_INITIALIZE_MATRICES_BY_ZERO)
+#define EIGEN_INITIALIZE_COEFFS
+#define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED \
+  for (Index i = 0; i < base().size(); ++i) coeffRef(i) = Scalar(0);
+#elif defined(EIGEN_INITIALIZE_MATRICES_BY_NAN)
+#define EIGEN_INITIALIZE_COEFFS
+#define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED \
+  for (Index i = 0; i < base().size(); ++i) coeffRef(i) = std::numeric_limits<Scalar>::quiet_NaN();
+#else
+#undef EIGEN_INITIALIZE_COEFFS
+#define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+#endif
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_NO_DEBUG
+template <int MaxSizeAtCompileTime, int MaxRowsAtCompileTime, int MaxColsAtCompileTime>
+struct check_rows_cols_for_overflow {
+  EIGEN_STATIC_ASSERT(MaxRowsAtCompileTime* MaxColsAtCompileTime == MaxSizeAtCompileTime,
+                      YOU MADE A PROGRAMMING MISTAKE)
+  template <typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE constexpr void run(Index, Index) {}
+};
+
+template <int MaxRowsAtCompileTime>
+struct check_rows_cols_for_overflow<Dynamic, MaxRowsAtCompileTime, Dynamic> {
+  template <typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE constexpr void run(Index, Index cols) {
+    constexpr Index MaxIndex = NumTraits<Index>::highest();
+    bool error = cols > (MaxIndex / MaxRowsAtCompileTime);
+    if (error) throw_std_bad_alloc();
+  }
+};
+
+template <int MaxColsAtCompileTime>
+struct check_rows_cols_for_overflow<Dynamic, Dynamic, MaxColsAtCompileTime> {
+  template <typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE constexpr void run(Index rows, Index) {
+    constexpr Index MaxIndex = NumTraits<Index>::highest();
+    bool error = rows > (MaxIndex / MaxColsAtCompileTime);
+    if (error) throw_std_bad_alloc();
+  }
+};
+
+template <>
+struct check_rows_cols_for_overflow<Dynamic, Dynamic, Dynamic> {
+  template <typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE constexpr void run(Index rows, Index cols) {
+    constexpr Index MaxIndex = NumTraits<Index>::highest();
+    bool error = cols == 0 ? false : (rows > (MaxIndex / cols));
+    if (error) throw_std_bad_alloc();
+  }
+};
+#endif
+
+template <typename Derived, typename OtherDerived = Derived,
+          bool IsVector = bool(Derived::IsVectorAtCompileTime) && bool(OtherDerived::IsVectorAtCompileTime)>
+struct conservative_resize_like_impl;
+
+template <typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
+struct matrix_swap_impl;
+
+}  // end namespace internal
+
+/** \class PlainObjectBase
+ * \ingroup Core_Module
+ * \brief %Dense storage base class for matrices and arrays.
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
+ *
+ * \tparam Derived is the derived type, e.g., a Matrix or Array
+ *
+ * \sa \ref TopicClassHierarchy
+ */
+template <typename Derived>
+class PlainObjectBase : public internal::dense_xpr_base<Derived>::type {
+ public:
+  enum { Options = internal::traits<Derived>::Options };
+  typedef typename internal::dense_xpr_base<Derived>::type Base;
+
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+
+  typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Derived DenseType;
+
+  using Base::ColsAtCompileTime;
+  using Base::Flags;
+  using Base::IsVectorAtCompileTime;
+  using Base::MaxColsAtCompileTime;
+  using Base::MaxRowsAtCompileTime;
+  using Base::MaxSizeAtCompileTime;
+  using Base::RowsAtCompileTime;
+  using Base::SizeAtCompileTime;
+
+  typedef Eigen::Map<Derived, Unaligned> MapType;
+  typedef const Eigen::Map<const Derived, Unaligned> ConstMapType;
+  typedef Eigen::Map<Derived, AlignedMax> AlignedMapType;
+  typedef const Eigen::Map<const Derived, AlignedMax> ConstAlignedMapType;
+  template <typename StrideType>
+  struct StridedMapType {
+    typedef Eigen::Map<Derived, Unaligned, StrideType> type;
+  };
+  template <typename StrideType>
+  struct StridedConstMapType {
+    typedef Eigen::Map<const Derived, Unaligned, StrideType> type;
+  };
+  template <typename StrideType>
+  struct StridedAlignedMapType {
+    typedef Eigen::Map<Derived, AlignedMax, StrideType> type;
+  };
+  template <typename StrideType>
+  struct StridedConstAlignedMapType {
+    typedef Eigen::Map<const Derived, AlignedMax, StrideType> type;
+  };
+
+ protected:
+  DenseStorage<Scalar, Base::MaxSizeAtCompileTime, Base::RowsAtCompileTime, Base::ColsAtCompileTime, Options> m_storage;
+
+ public:
+  enum { NeedsToAlign = (SizeAtCompileTime != Dynamic) && (internal::traits<Derived>::Alignment > 0) };
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
+
+  EIGEN_STATIC_ASSERT(internal::check_implication(MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1,
+                                                  (int(Options) & RowMajor) == RowMajor),
+                      INVALID_MATRIX_TEMPLATE_PARAMETERS)
+  EIGEN_STATIC_ASSERT(internal::check_implication(MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1,
+                                                  (int(Options) & RowMajor) == 0),
+                      INVALID_MATRIX_TEMPLATE_PARAMETERS)
+  EIGEN_STATIC_ASSERT((RowsAtCompileTime == Dynamic) || (RowsAtCompileTime >= 0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+  EIGEN_STATIC_ASSERT((ColsAtCompileTime == Dynamic) || (ColsAtCompileTime >= 0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+  EIGEN_STATIC_ASSERT((MaxRowsAtCompileTime == Dynamic) || (MaxRowsAtCompileTime >= 0),
+                      INVALID_MATRIX_TEMPLATE_PARAMETERS)
+  EIGEN_STATIC_ASSERT((MaxColsAtCompileTime == Dynamic) || (MaxColsAtCompileTime >= 0),
+                      INVALID_MATRIX_TEMPLATE_PARAMETERS)
+  EIGEN_STATIC_ASSERT((MaxRowsAtCompileTime == RowsAtCompileTime || RowsAtCompileTime == Dynamic),
+                      INVALID_MATRIX_TEMPLATE_PARAMETERS)
+  EIGEN_STATIC_ASSERT((MaxColsAtCompileTime == ColsAtCompileTime || ColsAtCompileTime == Dynamic),
+                      INVALID_MATRIX_TEMPLATE_PARAMETERS)
+  EIGEN_STATIC_ASSERT(((Options & (DontAlign | RowMajor)) == Options), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+
+  EIGEN_DEVICE_FUNC constexpr Base& base() { return *static_cast<Base*>(this); }
+  EIGEN_DEVICE_FUNC constexpr const Base& base() const { return *static_cast<const Base*>(this); }
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_storage.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_storage.cols(); }
+
+  /** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index,Index) const
+   * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
+   *
+   * See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
+  EIGEN_DEVICE_FUNC constexpr const Scalar& coeff(Index rowId, Index colId) const {
+    if (Flags & RowMajorBit)
+      return m_storage.data()[colId + rowId * m_storage.cols()];
+    else  // column-major
+      return m_storage.data()[rowId + colId * m_storage.rows()];
+  }
+
+  /** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const
+   * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
+   *
+   * See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
+  EIGEN_DEVICE_FUNC constexpr const Scalar& coeff(Index index) const { return m_storage.data()[index]; }
+
+  /** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const
+   * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
+   *
+   * See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const for details. */
+  EIGEN_DEVICE_FUNC constexpr Scalar& coeffRef(Index rowId, Index colId) {
+    if (Flags & RowMajorBit)
+      return m_storage.data()[colId + rowId * m_storage.cols()];
+    else  // column-major
+      return m_storage.data()[rowId + colId * m_storage.rows()];
+  }
+
+  /** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const
+   * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
+   *
+   * See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const for details. */
+  EIGEN_DEVICE_FUNC constexpr Scalar& coeffRef(Index index) { return m_storage.data()[index]; }
+
+  /** This is the const version of coeffRef(Index,Index) which is thus synonym of coeff(Index,Index).
+   * It is provided for convenience. */
+  EIGEN_DEVICE_FUNC constexpr const Scalar& coeffRef(Index rowId, Index colId) const {
+    if (Flags & RowMajorBit)
+      return m_storage.data()[colId + rowId * m_storage.cols()];
+    else  // column-major
+      return m_storage.data()[rowId + colId * m_storage.rows()];
+  }
+
+  /** This is the const version of coeffRef(Index) which is thus synonym of coeff(Index).
+   * It is provided for convenience. */
+  EIGEN_DEVICE_FUNC constexpr const Scalar& coeffRef(Index index) const { return m_storage.data()[index]; }
+
+  /** \internal */
+  template <int LoadMode>
+  EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const {
+    return internal::ploadt<PacketScalar, LoadMode>(
+        m_storage.data() + (Flags & RowMajorBit ? colId + rowId * m_storage.cols() : rowId + colId * m_storage.rows()));
+  }
+
+  /** \internal */
+  template <int LoadMode>
+  EIGEN_STRONG_INLINE PacketScalar packet(Index index) const {
+    return internal::ploadt<PacketScalar, LoadMode>(m_storage.data() + index);
+  }
+
+  /** \internal */
+  template <int StoreMode>
+  EIGEN_STRONG_INLINE void writePacket(Index rowId, Index colId, const PacketScalar& val) {
+    internal::pstoret<Scalar, PacketScalar, StoreMode>(
+        m_storage.data() + (Flags & RowMajorBit ? colId + rowId * m_storage.cols() : rowId + colId * m_storage.rows()),
+        val);
+  }
+
+  /** \internal */
+  template <int StoreMode>
+  EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& val) {
+    internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, val);
+  }
+
+  /** \returns a const pointer to the data array of this matrix */
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_storage.data(); }
+
+  /** \returns a pointer to the data array of this matrix */
+  EIGEN_DEVICE_FUNC constexpr Scalar* data() { return m_storage.data(); }
+
+  /** Resizes \c *this to a \a rows x \a cols matrix.
+   *
+   * This method is intended for dynamic-size matrices, although it is legal to call it on any
+   * matrix as long as fixed dimensions are left unchanged. If you only want to change the number
+   * of rows and/or of columns, you can use resize(NoChange_t, Index), resize(Index, NoChange_t).
+   *
+   * If the current number of coefficients of \c *this exactly matches the
+   * product \a rows * \a cols, then no memory allocation is performed and
+   * the current values are left unchanged. In all other cases, including
+   * shrinking, the data is reallocated and all previous values are lost.
+   *
+   * Example: \include Matrix_resize_int_int.cpp
+   * Output: \verbinclude Matrix_resize_int_int.out
+   *
+   * \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t)
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize(Index rows, Index cols) {
+    eigen_assert(internal::check_implication(RowsAtCompileTime != Dynamic, rows == RowsAtCompileTime) &&
+                 internal::check_implication(ColsAtCompileTime != Dynamic, cols == ColsAtCompileTime) &&
+                 internal::check_implication(RowsAtCompileTime == Dynamic && MaxRowsAtCompileTime != Dynamic,
+                                             rows <= MaxRowsAtCompileTime) &&
+                 internal::check_implication(ColsAtCompileTime == Dynamic && MaxColsAtCompileTime != Dynamic,
+                                             cols <= MaxColsAtCompileTime) &&
+                 rows >= 0 && cols >= 0 && "Invalid sizes when resizing a matrix or array.");
+#ifndef EIGEN_NO_DEBUG
+    internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime>::run(rows,
+                                                                                                                  cols);
+#endif
+#ifdef EIGEN_INITIALIZE_COEFFS
+    Index size = rows * cols;
+    bool size_changed = size != this->size();
+    m_storage.resize(size, rows, cols);
+    if (size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+#else
+    m_storage.resize(rows * cols, rows, cols);
+#endif
+  }
+
+  /** Resizes \c *this to a vector of length \a size
+   *
+   * \only_for_vectors. This method does not work for
+   * partially dynamic matrices when the static dimension is anything other
+   * than 1. For example it will not work with Matrix<double, 2, Dynamic>.
+   *
+   * Example: \include Matrix_resize_int.cpp
+   * Output: \verbinclude Matrix_resize_int.out
+   *
+   * \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t)
+   */
+  EIGEN_DEVICE_FUNC constexpr void resize(Index size) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
+    eigen_assert(((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime == Dynamic || size <= MaxSizeAtCompileTime)) ||
+                  SizeAtCompileTime == size) &&
+                 size >= 0);
+#ifdef EIGEN_INITIALIZE_COEFFS
+    bool size_changed = size != this->size();
+#endif
+    if (RowsAtCompileTime == 1)
+      m_storage.resize(size, 1, size);
+    else
+      m_storage.resize(size, size, 1);
+#ifdef EIGEN_INITIALIZE_COEFFS
+    if (size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+#endif
+  }
+
+  /** Resizes the matrix, changing only the number of columns. For the parameter of type NoChange_t, just pass the
+   * special value \c NoChange as in the example below.
+   *
+   * Example: \include Matrix_resize_NoChange_int.cpp
+   * Output: \verbinclude Matrix_resize_NoChange_int.out
+   *
+   * \sa resize(Index,Index)
+   */
+  EIGEN_DEVICE_FUNC constexpr void resize(NoChange_t, Index cols) { resize(rows(), cols); }
+
+  /** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special
+   * value \c NoChange as in the example below.
+   *
+   * Example: \include Matrix_resize_int_NoChange.cpp
+   * Output: \verbinclude Matrix_resize_int_NoChange.out
+   *
+   * \sa resize(Index,Index)
+   */
+  EIGEN_DEVICE_FUNC constexpr void resize(Index rows, NoChange_t) { resize(rows, cols()); }
+
+  /** Resizes \c *this to have the same dimensions as \a other.
+   * Takes care of doing all the checking that's needed.
+   *
+   * Note that copying a row-vector into a vector (and conversely) is allowed.
+   * The resizing, if any, is then done in the appropriate way so that row-vectors
+   * remain row-vectors and vectors remain vectors.
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other) {
+    const OtherDerived& other = _other.derived();
+#ifndef EIGEN_NO_DEBUG
+    internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime>::run(
+        other.rows(), other.cols());
+#endif
+    const Index othersize = other.rows() * other.cols();
+    if (RowsAtCompileTime == 1) {
+      eigen_assert(other.rows() == 1 || other.cols() == 1);
+      resize(1, othersize);
+    } else if (ColsAtCompileTime == 1) {
+      eigen_assert(other.rows() == 1 || other.cols() == 1);
+      resize(othersize, 1);
+    } else
+      resize(other.rows(), other.cols());
+  }
+
+  /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
+   *
+   * The method is intended for matrices of dynamic size. If you only want to change the number
+   * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
+   * conservativeResize(Index, NoChange_t).
+   *
+   * Matrices are resized relative to the top-left element. In case values need to be
+   * appended to the matrix they will be uninitialized.
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols) {
+    internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
+  }
+
+  /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
+   *
+   * As opposed to conservativeResize(Index rows, Index cols), this version leaves
+   * the number of columns unchanged.
+   *
+   * In case the matrix is growing, new rows will be uninitialized.
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t) {
+    // Note: see the comment in conservativeResize(Index,Index)
+    conservativeResize(rows, cols());
+  }
+
+  /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
+   *
+   * As opposed to conservativeResize(Index rows, Index cols), this version leaves
+   * the number of rows unchanged.
+   *
+   * In case the matrix is growing, new columns will be uninitialized.
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols) {
+    // Note: see the comment in conservativeResize(Index,Index)
+    conservativeResize(rows(), cols);
+  }
+
+  /** Resizes the vector to \a size while retaining old values.
+   *
+   * \only_for_vectors. This method does not work for
+   * partially dynamic matrices when the static dimension is anything other
+   * than 1. For example it will not work with Matrix<double, 2, Dynamic>.
+   *
+   * When values are appended, they will be uninitialized.
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void conservativeResize(Index size) {
+    internal::conservative_resize_like_impl<Derived>::run(*this, size);
+  }
+
+  /** Resizes the matrix to \a rows x \a cols of \c other, while leaving old values untouched.
+   *
+   * The method is intended for matrices of dynamic size. If you only want to change the number
+   * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
+   * conservativeResize(Index, NoChange_t).
+   *
+   * Matrices are resized relative to the top-left element. In case values need to be
+   * appended to the matrix they will copied from \c other.
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void conservativeResizeLike(const DenseBase<OtherDerived>& other) {
+    internal::conservative_resize_like_impl<Derived, OtherDerived>::run(*this, other);
+  }
+
+  /** This is a special case of the templated operator=. Its purpose is to
+   * prevent a default operator= from hiding the templated operator=.
+   */
+  EIGEN_DEVICE_FUNC constexpr Derived& operator=(const PlainObjectBase& other) { return _set(other); }
+
+  /** \sa MatrixBase::lazyAssign() */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& lazyAssign(const DenseBase<OtherDerived>& other) {
+    _resize_to_match(other);
+    return Base::lazyAssign(other.derived());
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue<OtherDerived>& func) {
+    resize(func.rows(), func.cols());
+    return Base::operator=(func);
+  }
+
+  // Prevent user from trying to instantiate PlainObjectBase objects
+  // by making all its constructor protected. See bug 1074.
+ protected:
+  EIGEN_DEVICE_FUNC constexpr PlainObjectBase() = default;
+  /** \brief Move constructor */
+  EIGEN_DEVICE_FUNC constexpr PlainObjectBase(PlainObjectBase&&) = default;
+  /** \brief Move assignment operator */
+  EIGEN_DEVICE_FUNC constexpr PlainObjectBase& operator=(PlainObjectBase&& other) noexcept {
+    m_storage = std::move(other.m_storage);
+    return *this;
+  }
+
+  /** Copy constructor */
+  EIGEN_DEVICE_FUNC constexpr PlainObjectBase(const PlainObjectBase&) = default;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols)
+      : m_storage(size, rows, cols) {}
+
+  /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients.
+   *
+   * \only_for_vectors
+   *
+   * This constructor is for 1D array or vectors with more than 4 coefficients.
+   *
+   * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
+   * constructor must match the fixed number of rows (resp. columns) of \c *this.
+   */
+  template <typename... ArgTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2,
+                                                        const Scalar& a3, const ArgTypes&... args)
+      : m_storage() {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, sizeof...(args) + 4);
+    m_storage.data()[0] = a0;
+    m_storage.data()[1] = a1;
+    m_storage.data()[2] = a2;
+    m_storage.data()[3] = a3;
+    Index i = 4;
+    auto x = {(m_storage.data()[i++] = args, 0)...};
+    static_cast<void>(x);
+  }
+
+  /** \brief Constructs a Matrix or Array and initializes it by elements given by an initializer list of initializer
+   * lists
+   */
+  EIGEN_DEVICE_FUNC explicit constexpr EIGEN_STRONG_INLINE PlainObjectBase(
+      const std::initializer_list<std::initializer_list<Scalar>>& list)
+      : m_storage() {
+    size_t list_size = 0;
+    if (list.begin() != list.end()) {
+      list_size = list.begin()->size();
+    }
+
+    // This is to allow syntax like VectorXi {{1, 2, 3, 4}}
+    if (ColsAtCompileTime == 1 && list.size() == 1) {
+      eigen_assert(list_size == static_cast<size_t>(RowsAtCompileTime) || RowsAtCompileTime == Dynamic);
+      resize(list_size, ColsAtCompileTime);
+      if (list.begin()->begin() != nullptr) {
+        Index index = 0;
+        for (const Scalar& e : *list.begin()) {
+          coeffRef(index++) = e;
+        }
+      }
+    } else {
+      eigen_assert(list.size() == static_cast<size_t>(RowsAtCompileTime) || RowsAtCompileTime == Dynamic);
+      eigen_assert(list_size == static_cast<size_t>(ColsAtCompileTime) || ColsAtCompileTime == Dynamic);
+      resize(list.size(), list_size);
+
+      Index row_index = 0;
+      for (const std::initializer_list<Scalar>& row : list) {
+        eigen_assert(list_size == row.size());
+        Index col_index = 0;
+        for (const Scalar& e : row) {
+          coeffRef(row_index, col_index) = e;
+          ++col_index;
+        }
+        ++row_index;
+      }
+    }
+  }
+
+  /** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived>& other) : m_storage() {
+    resizeLike(other);
+    _set_noalias(other);
+  }
+
+  /** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived>& other) : m_storage() {
+    resizeLike(other);
+    *this = other.derived();
+  }
+  /** \brief Copy constructor with in-place evaluation */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PlainObjectBase(const ReturnByValue<OtherDerived>& other) {
+    // FIXME this does not automatically transpose vectors if necessary
+    resize(other.rows(), other.cols());
+    other.evalTo(this->derived());
+  }
+
+ public:
+  /** \brief Copies the generic expression \a other into *this.
+   * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived>& other) {
+    _resize_to_match(other);
+    Base::operator=(other.derived());
+    return this->derived();
+  }
+
+  /** \name Map
+   * These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects,
+   * while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
+   * \a data pointers.
+   *
+   * Here is an example using strides:
+   * \include Matrix_Map_stride.cpp
+   * Output: \verbinclude Matrix_Map_stride.out
+   *
+   * \see class Map
+   */
+  ///@{
+  static inline ConstMapType Map(const Scalar* data) { return ConstMapType(data); }
+  static inline MapType Map(Scalar* data) { return MapType(data); }
+  static inline ConstMapType Map(const Scalar* data, Index size) { return ConstMapType(data, size); }
+  static inline MapType Map(Scalar* data, Index size) { return MapType(data, size); }
+  static inline ConstMapType Map(const Scalar* data, Index rows, Index cols) { return ConstMapType(data, rows, cols); }
+  static inline MapType Map(Scalar* data, Index rows, Index cols) { return MapType(data, rows, cols); }
+
+  static inline ConstAlignedMapType MapAligned(const Scalar* data) { return ConstAlignedMapType(data); }
+  static inline AlignedMapType MapAligned(Scalar* data) { return AlignedMapType(data); }
+  static inline ConstAlignedMapType MapAligned(const Scalar* data, Index size) {
+    return ConstAlignedMapType(data, size);
+  }
+  static inline AlignedMapType MapAligned(Scalar* data, Index size) { return AlignedMapType(data, size); }
+  static inline ConstAlignedMapType MapAligned(const Scalar* data, Index rows, Index cols) {
+    return ConstAlignedMapType(data, rows, cols);
+  }
+  static inline AlignedMapType MapAligned(Scalar* data, Index rows, Index cols) {
+    return AlignedMapType(data, rows, cols);
+  }
+
+  template <int Outer, int Inner>
+  static inline typename StridedConstMapType<Stride<Outer, Inner>>::type Map(const Scalar* data,
+                                                                             const Stride<Outer, Inner>& stride) {
+    return typename StridedConstMapType<Stride<Outer, Inner>>::type(data, stride);
+  }
+  template <int Outer, int Inner>
+  static inline typename StridedMapType<Stride<Outer, Inner>>::type Map(Scalar* data,
+                                                                        const Stride<Outer, Inner>& stride) {
+    return typename StridedMapType<Stride<Outer, Inner>>::type(data, stride);
+  }
+  template <int Outer, int Inner>
+  static inline typename StridedConstMapType<Stride<Outer, Inner>>::type Map(const Scalar* data, Index size,
+                                                                             const Stride<Outer, Inner>& stride) {
+    return typename StridedConstMapType<Stride<Outer, Inner>>::type(data, size, stride);
+  }
+  template <int Outer, int Inner>
+  static inline typename StridedMapType<Stride<Outer, Inner>>::type Map(Scalar* data, Index size,
+                                                                        const Stride<Outer, Inner>& stride) {
+    return typename StridedMapType<Stride<Outer, Inner>>::type(data, size, stride);
+  }
+  template <int Outer, int Inner>
+  static inline typename StridedConstMapType<Stride<Outer, Inner>>::type Map(const Scalar* data, Index rows, Index cols,
+                                                                             const Stride<Outer, Inner>& stride) {
+    return typename StridedConstMapType<Stride<Outer, Inner>>::type(data, rows, cols, stride);
+  }
+  template <int Outer, int Inner>
+  static inline typename StridedMapType<Stride<Outer, Inner>>::type Map(Scalar* data, Index rows, Index cols,
+                                                                        const Stride<Outer, Inner>& stride) {
+    return typename StridedMapType<Stride<Outer, Inner>>::type(data, rows, cols, stride);
+  }
+
+  template <int Outer, int Inner>
+  static inline typename StridedConstAlignedMapType<Stride<Outer, Inner>>::type MapAligned(
+      const Scalar* data, const Stride<Outer, Inner>& stride) {
+    return typename StridedConstAlignedMapType<Stride<Outer, Inner>>::type(data, stride);
+  }
+  template <int Outer, int Inner>
+  static inline typename StridedAlignedMapType<Stride<Outer, Inner>>::type MapAligned(
+      Scalar* data, const Stride<Outer, Inner>& stride) {
+    return typename StridedAlignedMapType<Stride<Outer, Inner>>::type(data, stride);
+  }
+  template <int Outer, int Inner>
+  static inline typename StridedConstAlignedMapType<Stride<Outer, Inner>>::type MapAligned(
+      const Scalar* data, Index size, const Stride<Outer, Inner>& stride) {
+    return typename StridedConstAlignedMapType<Stride<Outer, Inner>>::type(data, size, stride);
+  }
+  template <int Outer, int Inner>
+  static inline typename StridedAlignedMapType<Stride<Outer, Inner>>::type MapAligned(
+      Scalar* data, Index size, const Stride<Outer, Inner>& stride) {
+    return typename StridedAlignedMapType<Stride<Outer, Inner>>::type(data, size, stride);
+  }
+  template <int Outer, int Inner>
+  static inline typename StridedConstAlignedMapType<Stride<Outer, Inner>>::type MapAligned(
+      const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride) {
+    return typename StridedConstAlignedMapType<Stride<Outer, Inner>>::type(data, rows, cols, stride);
+  }
+  template <int Outer, int Inner>
+  static inline typename StridedAlignedMapType<Stride<Outer, Inner>>::type MapAligned(
+      Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride) {
+    return typename StridedAlignedMapType<Stride<Outer, Inner>>::type(data, rows, cols, stride);
+  }
+  ///@}
+
+  using Base::setConstant;
+  EIGEN_DEVICE_FUNC Derived& setConstant(Index size, const Scalar& val);
+  EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, Index cols, const Scalar& val);
+  EIGEN_DEVICE_FUNC Derived& setConstant(NoChange_t, Index cols, const Scalar& val);
+  EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, NoChange_t, const Scalar& val);
+
+  using Base::setZero;
+  EIGEN_DEVICE_FUNC Derived& setZero(Index size);
+  EIGEN_DEVICE_FUNC Derived& setZero(Index rows, Index cols);
+  EIGEN_DEVICE_FUNC Derived& setZero(NoChange_t, Index cols);
+  EIGEN_DEVICE_FUNC Derived& setZero(Index rows, NoChange_t);
+
+  using Base::setOnes;
+  EIGEN_DEVICE_FUNC Derived& setOnes(Index size);
+  EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, Index cols);
+  EIGEN_DEVICE_FUNC Derived& setOnes(NoChange_t, Index cols);
+  EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, NoChange_t);
+
+  using Base::setRandom;
+  Derived& setRandom(Index size);
+  Derived& setRandom(Index rows, Index cols);
+  Derived& setRandom(NoChange_t, Index cols);
+  Derived& setRandom(Index rows, NoChange_t);
+
+#ifdef EIGEN_PLAINOBJECTBASE_PLUGIN
+#include EIGEN_PLAINOBJECTBASE_PLUGIN
+#endif
+
+ protected:
+  /** \internal Resizes *this in preparation for assigning \a other to it.
+   * Takes care of doing all the checking that's needed.
+   *
+   * Note that copying a row-vector into a vector (and conversely) is allowed.
+   * The resizing, if any, is then done in the appropriate way so that row-vectors
+   * remain row-vectors and vectors remain vectors.
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase<OtherDerived>& other) {
+#ifdef EIGEN_NO_AUTOMATIC_RESIZING
+    eigen_assert((this->size() == 0 || (IsVectorAtCompileTime ? (this->size() == other.size())
+                                                              : (rows() == other.rows() && cols() == other.cols()))) &&
+                 "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
+    EIGEN_ONLY_USED_FOR_DEBUG(other);
+#else
+    resizeLike(other);
+#endif
+  }
+
+  /**
+   * \brief Copies the value of the expression \a other into \c *this with automatic resizing.
+   *
+   * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
+   * it will be initialized.
+   *
+   * Note that copying a row-vector into a vector (and conversely) is allowed.
+   * The resizing, if any, is then done in the appropriate way so that row-vectors
+   * remain row-vectors and vectors remain vectors.
+   *
+   * \sa operator=(const MatrixBase<OtherDerived>&), _set_noalias()
+   *
+   * \internal
+   */
+  // aliasing is dealt once in internal::call_assignment
+  // so at this stage we have to assume aliasing... and resising has to be done later.
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr Derived& _set(const DenseBase<OtherDerived>& other) {
+    internal::call_assignment(this->derived(), other.derived());
+    return this->derived();
+  }
+
+  /** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which
+   * is the case when creating a new matrix) so one can enforce lazy evaluation.
+   *
+   * \sa operator=(const MatrixBase<OtherDerived>&), _set()
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr Derived& _set_noalias(const DenseBase<OtherDerived>& other) {
+    // I don't think we need this resize call since the lazyAssign will anyways resize
+    // and lazyAssign will be called by the assign selector.
+    //_resize_to_match(other);
+    // the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because
+    // it wouldn't allow to copy a row-vector into a column-vector.
+    internal::call_assignment_no_alias(this->derived(), other.derived(),
+                                       internal::assign_op<Scalar, typename OtherDerived::Scalar>());
+    return this->derived();
+  }
+
+  template <typename T0, typename T1>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init2(Index rows, Index cols,
+                                                              std::enable_if_t<Base::SizeAtCompileTime != 2, T0>* = 0) {
+    EIGEN_STATIC_ASSERT(internal::is_valid_index_type<T0>::value && internal::is_valid_index_type<T1>::value,
+                        T0 AND T1 MUST BE INTEGER TYPES)
+    resize(rows, cols);
+  }
+
+  template <typename T0, typename T1>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1,
+                                                              std::enable_if_t<Base::SizeAtCompileTime == 2, T0>* = 0) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
+    m_storage.data()[0] = Scalar(val0);
+    m_storage.data()[1] = Scalar(val1);
+  }
+
+  template <typename T0, typename T1>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init2(
+      const Index& val0, const Index& val1,
+      std::enable_if_t<(!internal::is_same<Index, Scalar>::value) && (internal::is_same<T0, Index>::value) &&
+                           (internal::is_same<T1, Index>::value) && Base::SizeAtCompileTime == 2,
+                       T1>* = 0) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
+    m_storage.data()[0] = Scalar(val0);
+    m_storage.data()[1] = Scalar(val1);
+  }
+
+  // The argument is convertible to the Index type and we either have a non 1x1 Matrix, or a dynamic-sized Array,
+  // then the argument is meant to be the size of the object.
+  template <typename T>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(
+      Index size,
+      std::enable_if_t<(Base::SizeAtCompileTime != 1 || !internal::is_convertible<T, Scalar>::value) &&
+                           ((!internal::is_same<typename internal::traits<Derived>::XprKind, ArrayXpr>::value ||
+                             Base::SizeAtCompileTime == Dynamic)),
+                       T>* = 0) {
+    // NOTE MSVC 2008 complains if we directly put bool(NumTraits<T>::IsInteger) as the EIGEN_STATIC_ASSERT argument.
+    const bool is_integer_alike = internal::is_valid_index_type<T>::value;
+    EIGEN_UNUSED_VARIABLE(is_integer_alike);
+    EIGEN_STATIC_ASSERT(is_integer_alike, FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
+    resize(size);
+  }
+
+  // We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar
+  // type can be implicitly converted)
+  template <typename T>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(
+      const Scalar& val0,
+      std::enable_if_t<Base::SizeAtCompileTime == 1 && internal::is_convertible<T, Scalar>::value, T>* = 0) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
+    m_storage.data()[0] = val0;
+  }
+
+  // We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar
+  // type match the index type)
+  template <typename T>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(
+      const Index& val0,
+      std::enable_if_t<(!internal::is_same<Index, Scalar>::value) && (internal::is_same<Index, T>::value) &&
+                           Base::SizeAtCompileTime == 1 && internal::is_convertible<T, Scalar>::value,
+                       T*>* = 0) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
+    m_storage.data()[0] = Scalar(val0);
+  }
+
+  // Initialize a fixed size matrix from a pointer to raw data
+  template <typename T>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(const Scalar* data) {
+    this->_set_noalias(ConstMapType(data));
+  }
+
+  // Initialize an arbitrary matrix from a dense expression
+  template <typename T, typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(const DenseBase<OtherDerived>& other) {
+    this->_set_noalias(other);
+  }
+
+  // Initialize an arbitrary matrix from an object convertible to the Derived type.
+  template <typename T>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(const Derived& other) {
+    this->_set_noalias(other);
+  }
+
+  // Initialize an arbitrary matrix from a generic Eigen expression
+  template <typename T, typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(const EigenBase<OtherDerived>& other) {
+    this->derived() = other;
+  }
+
+  template <typename T, typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(const ReturnByValue<OtherDerived>& other) {
+    resize(other.rows(), other.cols());
+    other.evalTo(this->derived());
+  }
+
+  template <typename T, typename OtherDerived, int ColsAtCompileTime>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(const RotationBase<OtherDerived, ColsAtCompileTime>& r) {
+    this->derived() = r;
+  }
+
+  // For fixed-size Array<Scalar,...>
+  template <typename T>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(
+      const Scalar& val0,
+      std::enable_if_t<Base::SizeAtCompileTime != Dynamic && Base::SizeAtCompileTime != 1 &&
+                           internal::is_convertible<T, Scalar>::value &&
+                           internal::is_same<typename internal::traits<Derived>::XprKind, ArrayXpr>::value,
+                       T>* = 0) {
+    Base::setConstant(val0);
+  }
+
+  // For fixed-size Array<Index,...>
+  template <typename T>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void _init1(
+      const Index& val0,
+      std::enable_if_t<(!internal::is_same<Index, Scalar>::value) && (internal::is_same<Index, T>::value) &&
+                           Base::SizeAtCompileTime != Dynamic && Base::SizeAtCompileTime != 1 &&
+                           internal::is_convertible<T, Scalar>::value &&
+                           internal::is_same<typename internal::traits<Derived>::XprKind, ArrayXpr>::value,
+                       T*>* = 0) {
+    Base::setConstant(val0);
+  }
+
+  template <typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
+  friend struct internal::matrix_swap_impl;
+
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal
+   * \brief Override DenseBase::swap() since for dynamic-sized matrices
+   * of same type it is enough to swap the data pointers.
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void swap(DenseBase<OtherDerived>& other) {
+    enum {SwapPointers = internal::is_same<Derived, OtherDerived>::value && Base::SizeAtCompileTime == Dynamic};
+    internal::matrix_swap_impl<Derived, OtherDerived, bool(SwapPointers)>::run(this->derived(), other.derived());
+  }
+
+  /** \internal
+   * \brief const version forwarded to DenseBase::swap
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void swap(DenseBase<OtherDerived> const& other) {
+    Base::swap(other.derived());
+  }
+
+  enum {IsPlainObjectBase = 1};
+#endif
+ public:
+  // These apparently need to be down here for nvcc+icc to prevent duplicate
+  // Map symbol.
+  template <typename PlainObjectType, int MapOptions, typename StrideType>
+  friend class Eigen::Map;
+  friend class Eigen::Map<Derived, Unaligned>;
+  friend class Eigen::Map<const Derived, Unaligned>;
+#if EIGEN_MAX_ALIGN_BYTES > 0
+  // for EIGEN_MAX_ALIGN_BYTES==0, AlignedMax==Unaligned, and many compilers generate warnings for friend-ing a class
+  // twice.
+  friend class Eigen::Map<Derived, AlignedMax>;
+  friend class Eigen::Map<const Derived, AlignedMax>;
+#endif
+};
+
+namespace internal {
+
+template <typename Derived, typename OtherDerived, bool IsVector>
+struct conservative_resize_like_impl {
+  static constexpr bool IsRelocatable = std::is_trivially_copyable<typename Derived::Scalar>::value;
+  static void run(DenseBase<Derived>& _this, Index rows, Index cols) {
+    if (_this.rows() == rows && _this.cols() == cols) return;
+    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived)
+
+    if (IsRelocatable &&
+        ((Derived::IsRowMajor && _this.cols() == cols) ||  // row-major and we change only the number of rows
+         (!Derived::IsRowMajor && _this.rows() == rows)))  // column-major and we change only the number of columns
+    {
+#ifndef EIGEN_NO_DEBUG
+      internal::check_rows_cols_for_overflow<Derived::MaxSizeAtCompileTime, Derived::MaxRowsAtCompileTime,
+                                             Derived::MaxColsAtCompileTime>::run(rows, cols);
+#endif
+      _this.derived().m_storage.conservativeResize(rows * cols, rows, cols);
+    } else {
+      // The storage order does not allow us to use reallocation.
+      Derived tmp(rows, cols);
+      const Index common_rows = numext::mini(rows, _this.rows());
+      const Index common_cols = numext::mini(cols, _this.cols());
+      tmp.block(0, 0, common_rows, common_cols) = _this.block(0, 0, common_rows, common_cols);
+      _this.derived().swap(tmp);
+    }
+  }
+
+  static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other) {
+    if (_this.rows() == other.rows() && _this.cols() == other.cols()) return;
+
+    // Note: Here is space for improvement. Basically, for conservativeResize(Index,Index),
+    // neither RowsAtCompileTime or ColsAtCompileTime must be Dynamic. If only one of the
+    // dimensions is dynamic, one could use either conservativeResize(Index rows, NoChange_t) or
+    // conservativeResize(NoChange_t, Index cols). For these methods new static asserts like
+    // EIGEN_STATIC_ASSERT_DYNAMIC_ROWS and EIGEN_STATIC_ASSERT_DYNAMIC_COLS would be good.
+    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived)
+    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(OtherDerived)
+
+    if (IsRelocatable &&
+        ((Derived::IsRowMajor && _this.cols() == other.cols()) ||  // row-major and we change only the number of rows
+         (!Derived::IsRowMajor &&
+          _this.rows() == other.rows())))  // column-major and we change only the number of columns
+    {
+      const Index new_rows = other.rows() - _this.rows();
+      const Index new_cols = other.cols() - _this.cols();
+      _this.derived().m_storage.conservativeResize(other.size(), other.rows(), other.cols());
+      if (new_rows > 0)
+        _this.bottomRightCorner(new_rows, other.cols()) = other.bottomRows(new_rows);
+      else if (new_cols > 0)
+        _this.bottomRightCorner(other.rows(), new_cols) = other.rightCols(new_cols);
+    } else {
+      // The storage order does not allow us to use reallocation.
+      Derived tmp(other);
+      const Index common_rows = numext::mini(tmp.rows(), _this.rows());
+      const Index common_cols = numext::mini(tmp.cols(), _this.cols());
+      tmp.block(0, 0, common_rows, common_cols) = _this.block(0, 0, common_rows, common_cols);
+      _this.derived().swap(tmp);
+    }
+  }
+};
+
+// Here, the specialization for vectors inherits from the general matrix case
+// to allow calling .conservativeResize(rows,cols) on vectors.
+template <typename Derived, typename OtherDerived>
+struct conservative_resize_like_impl<Derived, OtherDerived, true>
+    : conservative_resize_like_impl<Derived, OtherDerived, false> {
+  typedef conservative_resize_like_impl<Derived, OtherDerived, false> Base;
+  using Base::IsRelocatable;
+  using Base::run;
+
+  static void run(DenseBase<Derived>& _this, Index size) {
+    const Index new_rows = Derived::RowsAtCompileTime == 1 ? 1 : size;
+    const Index new_cols = Derived::RowsAtCompileTime == 1 ? size : 1;
+    if (IsRelocatable)
+      _this.derived().m_storage.conservativeResize(size, new_rows, new_cols);
+    else
+      Base::run(_this.derived(), new_rows, new_cols);
+  }
+
+  static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other) {
+    if (_this.rows() == other.rows() && _this.cols() == other.cols()) return;
+
+    const Index num_new_elements = other.size() - _this.size();
+
+    const Index new_rows = Derived::RowsAtCompileTime == 1 ? 1 : other.rows();
+    const Index new_cols = Derived::RowsAtCompileTime == 1 ? other.cols() : 1;
+    if (IsRelocatable)
+      _this.derived().m_storage.conservativeResize(other.size(), new_rows, new_cols);
+    else
+      Base::run(_this.derived(), new_rows, new_cols);
+
+    if (num_new_elements > 0) _this.tail(num_new_elements) = other.tail(num_new_elements);
+  }
+};
+
+template <typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
+struct matrix_swap_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(MatrixTypeA& a, MatrixTypeB& b) { a.base().swap(b); }
+};
+
+template <typename MatrixTypeA, typename MatrixTypeB>
+struct matrix_swap_impl<MatrixTypeA, MatrixTypeB, true> {
+  EIGEN_DEVICE_FUNC static inline void run(MatrixTypeA& a, MatrixTypeB& b) {
+    static_cast<typename MatrixTypeA::Base&>(a).m_storage.swap(static_cast<typename MatrixTypeB::Base&>(b).m_storage);
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_DENSESTORAGEBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Product.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Product.h
new file mode 100644
index 0000000..bc8b9da
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Product.h
@@ -0,0 +1,307 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PRODUCT_H
+#define EIGEN_PRODUCT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Lhs, typename Rhs, int Option, typename StorageKind>
+class ProductImpl;
+
+namespace internal {
+
+template <typename Lhs, typename Rhs, int Option>
+struct traits<Product<Lhs, Rhs, Option>> {
+  typedef remove_all_t<Lhs> LhsCleaned;
+  typedef remove_all_t<Rhs> RhsCleaned;
+  typedef traits<LhsCleaned> LhsTraits;
+  typedef traits<RhsCleaned> RhsTraits;
+
+  typedef MatrixXpr XprKind;
+
+  typedef typename ScalarBinaryOpTraits<typename traits<LhsCleaned>::Scalar,
+                                        typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
+  typedef typename product_promote_storage_type<typename LhsTraits::StorageKind, typename RhsTraits::StorageKind,
+                                                internal::product_type<Lhs, Rhs>::ret>::ret StorageKind;
+  typedef typename promote_index_type<typename LhsTraits::StorageIndex, typename RhsTraits::StorageIndex>::type
+      StorageIndex;
+
+  enum {
+    RowsAtCompileTime = LhsTraits::RowsAtCompileTime,
+    ColsAtCompileTime = RhsTraits::ColsAtCompileTime,
+    MaxRowsAtCompileTime = LhsTraits::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = RhsTraits::MaxColsAtCompileTime,
+
+    // FIXME: only needed by GeneralMatrixMatrixTriangular
+    InnerSize = min_size_prefer_fixed(LhsTraits::ColsAtCompileTime, RhsTraits::RowsAtCompileTime),
+
+    // The storage order is somewhat arbitrary here. The correct one will be determined through the evaluator.
+    Flags = (MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1)   ? RowMajorBit
+            : (MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1) ? 0
+            : (((LhsTraits::Flags & NoPreferredStorageOrderBit) && (RhsTraits::Flags & RowMajorBit)) ||
+               ((RhsTraits::Flags & NoPreferredStorageOrderBit) && (LhsTraits::Flags & RowMajorBit)))
+                ? RowMajorBit
+                : NoPreferredStorageOrderBit
+  };
+};
+
+struct TransposeProductEnum {
+  // convenience enumerations to specialize transposed products
+  enum : int {
+    Default = 0x00,
+    Matrix = 0x01,
+    Permutation = 0x02,
+    MatrixMatrix = (Matrix << 8) | Matrix,
+    MatrixPermutation = (Matrix << 8) | Permutation,
+    PermutationMatrix = (Permutation << 8) | Matrix
+  };
+};
+template <typename Xpr>
+struct TransposeKind {
+  static constexpr int Kind = is_matrix_base_xpr<Xpr>::value        ? TransposeProductEnum::Matrix
+                              : is_permutation_base_xpr<Xpr>::value ? TransposeProductEnum::Permutation
+                                                                    : TransposeProductEnum::Default;
+};
+
+template <typename Lhs, typename Rhs>
+struct TransposeProductKind {
+  static constexpr int Kind = (TransposeKind<Lhs>::Kind << 8) | TransposeKind<Rhs>::Kind;
+};
+
+template <typename Lhs, typename Rhs, int Option, int Kind = TransposeProductKind<Lhs, Rhs>::Kind>
+struct product_transpose_helper {
+  // by default, don't optimize the transposed product
+  using Derived = Product<Lhs, Rhs, Option>;
+  using Scalar = typename Derived::Scalar;
+  using TransposeType = Transpose<const Derived>;
+  using ConjugateTransposeType = CwiseUnaryOp<scalar_conjugate_op<Scalar>, TransposeType>;
+  using AdjointType = std::conditional_t<NumTraits<Scalar>::IsComplex, ConjugateTransposeType, TransposeType>;
+
+  // return (lhs * rhs)^T
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TransposeType run_transpose(const Derived& derived) {
+    return TransposeType(derived);
+  }
+  // return (lhs * rhs)^H
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AdjointType run_adjoint(const Derived& derived) {
+    return AdjointType(TransposeType(derived));
+  }
+};
+
+template <typename Lhs, typename Rhs, int Option>
+struct product_transpose_helper<Lhs, Rhs, Option, TransposeProductEnum::MatrixMatrix> {
+  // expand the transposed matrix-matrix product
+  using Derived = Product<Lhs, Rhs, Option>;
+
+  using LhsScalar = typename traits<Lhs>::Scalar;
+  using LhsTransposeType = typename DenseBase<Lhs>::ConstTransposeReturnType;
+  using LhsConjugateTransposeType = CwiseUnaryOp<scalar_conjugate_op<LhsScalar>, LhsTransposeType>;
+  using LhsAdjointType =
+      std::conditional_t<NumTraits<LhsScalar>::IsComplex, LhsConjugateTransposeType, LhsTransposeType>;
+
+  using RhsScalar = typename traits<Rhs>::Scalar;
+  using RhsTransposeType = typename DenseBase<Rhs>::ConstTransposeReturnType;
+  using RhsConjugateTransposeType = CwiseUnaryOp<scalar_conjugate_op<RhsScalar>, RhsTransposeType>;
+  using RhsAdjointType =
+      std::conditional_t<NumTraits<RhsScalar>::IsComplex, RhsConjugateTransposeType, RhsTransposeType>;
+
+  using TransposeType = Product<RhsTransposeType, LhsTransposeType, Option>;
+  using AdjointType = Product<RhsAdjointType, LhsAdjointType, Option>;
+
+  // return rhs^T * lhs^T
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TransposeType run_transpose(const Derived& derived) {
+    return TransposeType(RhsTransposeType(derived.rhs()), LhsTransposeType(derived.lhs()));
+  }
+  // return rhs^H * lhs^H
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AdjointType run_adjoint(const Derived& derived) {
+    return AdjointType(RhsAdjointType(RhsTransposeType(derived.rhs())),
+                       LhsAdjointType(LhsTransposeType(derived.lhs())));
+  }
+};
+template <typename Lhs, typename Rhs, int Option>
+struct product_transpose_helper<Lhs, Rhs, Option, TransposeProductEnum::PermutationMatrix> {
+  // expand the transposed permutation-matrix product
+  using Derived = Product<Lhs, Rhs, Option>;
+
+  using LhsInverseType = typename PermutationBase<Lhs>::InverseReturnType;
+
+  using RhsScalar = typename traits<Rhs>::Scalar;
+  using RhsTransposeType = typename DenseBase<Rhs>::ConstTransposeReturnType;
+  using RhsConjugateTransposeType = CwiseUnaryOp<scalar_conjugate_op<RhsScalar>, RhsTransposeType>;
+  using RhsAdjointType =
+      std::conditional_t<NumTraits<RhsScalar>::IsComplex, RhsConjugateTransposeType, RhsTransposeType>;
+
+  using TransposeType = Product<RhsTransposeType, LhsInverseType, Option>;
+  using AdjointType = Product<RhsAdjointType, LhsInverseType, Option>;
+
+  // return rhs^T * lhs^-1
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TransposeType run_transpose(const Derived& derived) {
+    return TransposeType(RhsTransposeType(derived.rhs()), LhsInverseType(derived.lhs()));
+  }
+  // return rhs^H * lhs^-1
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AdjointType run_adjoint(const Derived& derived) {
+    return AdjointType(RhsAdjointType(RhsTransposeType(derived.rhs())), LhsInverseType(derived.lhs()));
+  }
+};
+template <typename Lhs, typename Rhs, int Option>
+struct product_transpose_helper<Lhs, Rhs, Option, TransposeProductEnum::MatrixPermutation> {
+  // expand the transposed matrix-permutation product
+  using Derived = Product<Lhs, Rhs, Option>;
+
+  using LhsScalar = typename traits<Lhs>::Scalar;
+  using LhsTransposeType = typename DenseBase<Lhs>::ConstTransposeReturnType;
+  using LhsConjugateTransposeType = CwiseUnaryOp<scalar_conjugate_op<LhsScalar>, LhsTransposeType>;
+  using LhsAdjointType =
+      std::conditional_t<NumTraits<LhsScalar>::IsComplex, LhsConjugateTransposeType, LhsTransposeType>;
+
+  using RhsInverseType = typename PermutationBase<Rhs>::InverseReturnType;
+
+  using TransposeType = Product<RhsInverseType, LhsTransposeType, Option>;
+  using AdjointType = Product<RhsInverseType, LhsAdjointType, Option>;
+
+  // return rhs^-1 * lhs^T
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TransposeType run_transpose(const Derived& derived) {
+    return TransposeType(RhsInverseType(derived.rhs()), LhsTransposeType(derived.lhs()));
+  }
+  // return rhs^-1 * lhs^H
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AdjointType run_adjoint(const Derived& derived) {
+    return AdjointType(RhsInverseType(derived.rhs()), LhsAdjointType(LhsTransposeType(derived.lhs())));
+  }
+};
+
+}  // end namespace internal
+
+/** \class Product
+ * \ingroup Core_Module
+ *
+ * \brief Expression of the product of two arbitrary matrices or vectors
+ *
+ * \tparam Lhs_ the type of the left-hand side expression
+ * \tparam Rhs_ the type of the right-hand side expression
+ *
+ * This class represents an expression of the product of two arbitrary matrices.
+ *
+ * The other template parameters are:
+ * \tparam Option     can be DefaultProduct, AliasFreeProduct, or LazyProduct
+ *
+ */
+template <typename Lhs_, typename Rhs_, int Option>
+class Product
+    : public ProductImpl<Lhs_, Rhs_, Option,
+                         typename internal::product_promote_storage_type<
+                             typename internal::traits<Lhs_>::StorageKind, typename internal::traits<Rhs_>::StorageKind,
+                             internal::product_type<Lhs_, Rhs_>::ret>::ret> {
+ public:
+  typedef Lhs_ Lhs;
+  typedef Rhs_ Rhs;
+
+  typedef
+      typename ProductImpl<Lhs, Rhs, Option,
+                           typename internal::product_promote_storage_type<
+                               typename internal::traits<Lhs>::StorageKind, typename internal::traits<Rhs>::StorageKind,
+                               internal::product_type<Lhs, Rhs>::ret>::ret>::Base Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(Product)
+
+  typedef typename internal::ref_selector<Lhs>::type LhsNested;
+  typedef typename internal::ref_selector<Rhs>::type RhsNested;
+  typedef internal::remove_all_t<LhsNested> LhsNestedCleaned;
+  typedef internal::remove_all_t<RhsNested> RhsNestedCleaned;
+
+  using TransposeReturnType = typename internal::product_transpose_helper<Lhs, Rhs, Option>::TransposeType;
+  using AdjointReturnType = typename internal::product_transpose_helper<Lhs, Rhs, Option>::AdjointType;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs) {
+    eigen_assert(lhs.cols() == rhs.rows() && "invalid matrix product" &&
+                 "if you wanted a coeff-wise or a dot product use the respective explicit functions");
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_lhs.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_rhs.cols(); }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const LhsNestedCleaned& lhs() const { return m_lhs; }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const RhsNestedCleaned& rhs() const { return m_rhs; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TransposeReturnType transpose() const {
+    return internal::product_transpose_helper<Lhs, Rhs, Option>::run_transpose(*this);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AdjointReturnType adjoint() const {
+    return internal::product_transpose_helper<Lhs, Rhs, Option>::run_adjoint(*this);
+  }
+
+ protected:
+  LhsNested m_lhs;
+  RhsNested m_rhs;
+};
+
+namespace internal {
+
+template <typename Lhs, typename Rhs, int Option, int ProductTag = internal::product_type<Lhs, Rhs>::ret>
+class dense_product_base : public internal::dense_xpr_base<Product<Lhs, Rhs, Option>>::type {};
+
+/** Conversion to scalar for inner-products */
+template <typename Lhs, typename Rhs, int Option>
+class dense_product_base<Lhs, Rhs, Option, InnerProduct>
+    : public internal::dense_xpr_base<Product<Lhs, Rhs, Option>>::type {
+  typedef Product<Lhs, Rhs, Option> ProductXpr;
+  typedef typename internal::dense_xpr_base<ProductXpr>::type Base;
+
+ public:
+  using Base::derived;
+  typedef typename Base::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE operator const Scalar() const {
+    return internal::evaluator<ProductXpr>(derived()).coeff(0, 0);
+  }
+};
+
+}  // namespace internal
+
+// Generic API dispatcher
+template <typename Lhs, typename Rhs, int Option, typename StorageKind>
+class ProductImpl : public internal::generic_xpr_base<Product<Lhs, Rhs, Option>, MatrixXpr, StorageKind>::type {
+ public:
+  typedef typename internal::generic_xpr_base<Product<Lhs, Rhs, Option>, MatrixXpr, StorageKind>::type Base;
+};
+
+template <typename Lhs, typename Rhs, int Option>
+class ProductImpl<Lhs, Rhs, Option, Dense> : public internal::dense_product_base<Lhs, Rhs, Option> {
+  typedef Product<Lhs, Rhs, Option> Derived;
+
+ public:
+  typedef typename internal::dense_product_base<Lhs, Rhs, Option> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
+ protected:
+  enum {
+    IsOneByOne = (RowsAtCompileTime == 1 || RowsAtCompileTime == Dynamic) &&
+                 (ColsAtCompileTime == 1 || ColsAtCompileTime == Dynamic),
+    EnableCoeff = IsOneByOne || Option == LazyProduct
+  };
+
+ public:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index row, Index col) const {
+    EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
+    eigen_assert((Option == LazyProduct) || (this->rows() == 1 && this->cols() == 1));
+
+    return internal::evaluator<Derived>(derived()).coeff(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index i) const {
+    EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
+    eigen_assert((Option == LazyProduct) || (this->rows() == 1 && this->cols() == 1));
+
+    return internal::evaluator<Derived>(derived()).coeff(i);
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PRODUCT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ProductEvaluators.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ProductEvaluators.h
new file mode 100644
index 0000000..ae860e2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ProductEvaluators.h
@@ -0,0 +1,1275 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PRODUCTEVALUATORS_H
+#define EIGEN_PRODUCTEVALUATORS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * Evaluator of a product expression.
+ * Since products require special treatments to handle all possible cases,
+ * we simply defer the evaluation logic to a product_evaluator class
+ * which offers more partial specialization possibilities.
+ *
+ * \sa class product_evaluator
+ */
+template <typename Lhs, typename Rhs, int Options>
+struct evaluator<Product<Lhs, Rhs, Options>> : public product_evaluator<Product<Lhs, Rhs, Options>> {
+  typedef Product<Lhs, Rhs, Options> XprType;
+  typedef product_evaluator<XprType> Base;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+// Catch "scalar * ( A * B )" and transform it to "(A*scalar) * B"
+// TODO: we should apply that rule only if that's really helpful
+template <typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
+struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_product_op<Scalar1, Scalar2>,
+                                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
+                                               const Product<Lhs, Rhs, DefaultProduct>>> {
+  static const bool value = true;
+};
+template <typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
+struct evaluator<CwiseBinaryOp<internal::scalar_product_op<Scalar1, Scalar2>,
+                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
+                               const Product<Lhs, Rhs, DefaultProduct>>>
+    : public evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1, Lhs, product), Rhs, DefaultProduct>> {
+  typedef CwiseBinaryOp<internal::scalar_product_op<Scalar1, Scalar2>,
+                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
+                        const Product<Lhs, Rhs, DefaultProduct>>
+      XprType;
+  typedef evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1, Lhs, product), Rhs, DefaultProduct>> Base;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
+      : Base(xpr.lhs().functor().m_other * xpr.rhs().lhs() * xpr.rhs().rhs()) {}
+};
+
+template <typename Lhs, typename Rhs, int DiagIndex>
+struct evaluator<Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex>>
+    : public evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex>> {
+  typedef Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> XprType;
+  typedef evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex>> Base;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
+      : Base(Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex>(
+            Product<Lhs, Rhs, LazyProduct>(xpr.nestedExpression().lhs(), xpr.nestedExpression().rhs()), xpr.index())) {}
+};
+
+// Helper class to perform a matrix product with the destination at hand.
+// Depending on the sizes of the factors, there are different evaluation strategies
+// as controlled by internal::product_type.
+template <typename Lhs, typename Rhs, typename LhsShape = typename evaluator_traits<Lhs>::Shape,
+          typename RhsShape = typename evaluator_traits<Rhs>::Shape,
+          int ProductType = internal::product_type<Lhs, Rhs>::value>
+struct generic_product_impl;
+
+template <typename Lhs, typename Rhs>
+struct evaluator_assume_aliasing<Product<Lhs, Rhs, DefaultProduct>> {
+  static const bool value = true;
+};
+
+// This is the default evaluator implementation for products:
+// It creates a temporary and call generic_product_impl
+template <typename Lhs, typename Rhs, int Options, int ProductTag, typename LhsShape, typename RhsShape>
+struct product_evaluator<Product<Lhs, Rhs, Options>, ProductTag, LhsShape, RhsShape>
+    : public evaluator<typename Product<Lhs, Rhs, Options>::PlainObject> {
+  typedef Product<Lhs, Rhs, Options> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+  enum { Flags = Base::Flags | EvalBeforeNestingBit };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit product_evaluator(const XprType& xpr)
+      : m_result(xpr.rows(), xpr.cols()) {
+    internal::construct_at<Base>(this, m_result);
+
+    // FIXME shall we handle nested_eval here?,
+    // if so, then we must take care at removing the call to nested_eval in the specializations (e.g., in
+    // permutation_matrix_product, transposition_matrix_product, etc.)
+    //     typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
+    //     typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
+    //     typedef internal::remove_all_t<LhsNested> LhsNestedCleaned;
+    //     typedef internal::remove_all_t<RhsNested> RhsNestedCleaned;
+    //
+    //     const LhsNested lhs(xpr.lhs());
+    //     const RhsNested rhs(xpr.rhs());
+    //
+    //     generic_product_impl<LhsNestedCleaned, RhsNestedCleaned>::evalTo(m_result, lhs, rhs);
+
+    generic_product_impl<Lhs, Rhs, LhsShape, RhsShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
+  }
+
+ protected:
+  PlainObject m_result;
+};
+
+// The following three shortcuts are enabled only if the scalar types match exactly.
+// TODO: we could enable them for different scalar types when the product is not vectorized.
+
+// Dense = Product
+template <typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs, Rhs, Options>, internal::assign_op<Scalar, Scalar>, Dense2Dense,
+                  std::enable_if_t<(Options == DefaultProduct || Options == AliasFreeProduct)>> {
+  typedef Product<Lhs, Rhs, Options> SrcXprType;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src,
+                                                        const internal::assign_op<Scalar, Scalar>&) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+    // FIXME shall we handle nested_eval here?
+    generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());
+  }
+};
+
+// Dense += Product
+template <typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs, Rhs, Options>, internal::add_assign_op<Scalar, Scalar>, Dense2Dense,
+                  std::enable_if_t<(Options == DefaultProduct || Options == AliasFreeProduct)>> {
+  typedef Product<Lhs, Rhs, Options> SrcXprType;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src,
+                                                        const internal::add_assign_op<Scalar, Scalar>&) {
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+    // FIXME shall we handle nested_eval here?
+    generic_product_impl<Lhs, Rhs>::addTo(dst, src.lhs(), src.rhs());
+  }
+};
+
+// Dense -= Product
+template <typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs, Rhs, Options>, internal::sub_assign_op<Scalar, Scalar>, Dense2Dense,
+                  std::enable_if_t<(Options == DefaultProduct || Options == AliasFreeProduct)>> {
+  typedef Product<Lhs, Rhs, Options> SrcXprType;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src,
+                                                        const internal::sub_assign_op<Scalar, Scalar>&) {
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+    // FIXME shall we handle nested_eval here?
+    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());
+  }
+};
+
+// Dense ?= scalar * Product
+// TODO: we should apply that rule if that's really helpful
+// for instance, this is not good for inner products
+template <typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis,
+          typename Plain>
+struct Assignment<DstXprType,
+                  CwiseBinaryOp<internal::scalar_product_op<ScalarBis, Scalar>,
+                                const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>, Plain>,
+                                const Product<Lhs, Rhs, DefaultProduct>>,
+                  AssignFunc, Dense2Dense> {
+  typedef CwiseBinaryOp<internal::scalar_product_op<ScalarBis, Scalar>,
+                        const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>, Plain>,
+                        const Product<Lhs, Rhs, DefaultProduct>>
+      SrcXprType;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src,
+                                                        const AssignFunc& func) {
+    call_assignment_no_alias(dst, (src.lhs().functor().m_other * src.rhs().lhs()) * src.rhs().rhs(), func);
+  }
+};
+
+//----------------------------------------
+// Catch "Dense ?= xpr + Product<>" expression to save one temporary
+// FIXME: consider enabling these rules for all product types, not only Dense and DefaultProduct.
+
+template <typename OtherXpr, typename Lhs, typename Rhs>
+struct evaluator_assume_aliasing<
+    CwiseBinaryOp<
+        internal::scalar_sum_op<typename OtherXpr::Scalar, typename Product<Lhs, Rhs, DefaultProduct>::Scalar>,
+        const OtherXpr, const Product<Lhs, Rhs, DefaultProduct>>,
+    DenseShape> {
+  static const bool value = true;
+};
+
+template <typename OtherXpr, typename Lhs, typename Rhs>
+struct evaluator_assume_aliasing<
+    CwiseBinaryOp<
+        internal::scalar_difference_op<typename OtherXpr::Scalar, typename Product<Lhs, Rhs, DefaultProduct>::Scalar>,
+        const OtherXpr, const Product<Lhs, Rhs, DefaultProduct>>,
+    DenseShape> {
+  static const bool value = true;
+};
+
+template <typename DstXprType, typename OtherXpr, typename ProductType, typename Func1, typename Func2>
+struct assignment_from_xpr_op_product {
+  template <typename SrcXprType, typename InitialFunc>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src,
+                                                        const InitialFunc& /*func*/) {
+    call_assignment_no_alias(dst, src.lhs(), Func1());
+    call_assignment_no_alias(dst, src.rhs(), Func2());
+  }
+};
+
+#define EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(ASSIGN_OP, BINOP, ASSIGN_OP2)                             \
+  template <typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, \
+            typename SrcScalar, typename OtherScalar, typename ProdScalar>                          \
+  struct Assignment<DstXprType,                                                                     \
+                    CwiseBinaryOp<internal::BINOP<OtherScalar, ProdScalar>, const OtherXpr,         \
+                                  const Product<Lhs, Rhs, DefaultProduct>>,                         \
+                    internal::ASSIGN_OP<DstScalar, SrcScalar>, Dense2Dense>                         \
+      : assignment_from_xpr_op_product<DstXprType, OtherXpr, Product<Lhs, Rhs, DefaultProduct>,     \
+                                       internal::ASSIGN_OP<DstScalar, OtherScalar>,                 \
+                                       internal::ASSIGN_OP2<DstScalar, ProdScalar>> {}
+
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op, scalar_sum_op, add_assign_op);
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op, scalar_sum_op, add_assign_op);
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op, scalar_sum_op, sub_assign_op);
+
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op, scalar_difference_op, sub_assign_op);
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op, scalar_difference_op, sub_assign_op);
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op, scalar_difference_op, add_assign_op);
+
+//----------------------------------------
+
+template <typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, InnerProduct> {
+  using impl = default_inner_product_impl<Lhs, Rhs, false>;
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    dst.coeffRef(0, 0) = impl::run(lhs, rhs);
+  }
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    dst.coeffRef(0, 0) += impl::run(lhs, rhs);
+  }
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    dst.coeffRef(0, 0) -= impl::run(lhs, rhs);
+  }
+};
+
+/***********************************************************************
+ *  Implementation of outer dense * dense vector product
+ ***********************************************************************/
+
+// Column major result
+template <typename Dst, typename Lhs, typename Rhs, typename Func>
+void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Func& func,
+                                                  const false_type&) {
+  evaluator<Rhs> rhsEval(rhs);
+  ei_declare_local_nested_eval(Lhs, lhs, Rhs::SizeAtCompileTime, actual_lhs);
+  // FIXME if cols is large enough, then it might be useful to make sure that lhs is sequentially stored
+  // FIXME not very good if rhs is real and lhs complex while alpha is real too
+  const Index cols = dst.cols();
+  for (Index j = 0; j < cols; ++j) func(dst.col(j), rhsEval.coeff(Index(0), j) * actual_lhs);
+}
+
+// Row major result
+template <typename Dst, typename Lhs, typename Rhs, typename Func>
+void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Func& func,
+                                                  const true_type&) {
+  evaluator<Lhs> lhsEval(lhs);
+  ei_declare_local_nested_eval(Rhs, rhs, Lhs::SizeAtCompileTime, actual_rhs);
+  // FIXME if rows is large enough, then it might be useful to make sure that rhs is sequentially stored
+  // FIXME not very good if lhs is real and rhs complex while alpha is real too
+  const Index rows = dst.rows();
+  for (Index i = 0; i < rows; ++i) func(dst.row(i), lhsEval.coeff(i, Index(0)) * actual_rhs);
+}
+
+template <typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, OuterProduct> {
+  template <typename T>
+  struct is_row_major : bool_constant<(int(T::Flags) & RowMajorBit)> {};
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  // TODO: it would be nice to be able to exploit our *_assign_op functors for that purpose
+  struct set {
+    template <typename Dst, typename Src>
+    EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const {
+      dst.const_cast_derived() = src;
+    }
+  };
+  struct add {
+    /** Add to dst. */
+    template <typename Dst, typename Src>
+    EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const {
+      dst.const_cast_derived() += src;
+    }
+  };
+  struct sub {
+    template <typename Dst, typename Src>
+    EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const {
+      dst.const_cast_derived() -= src;
+    }
+  };
+  /** Scaled add. */
+  struct adds {
+    Scalar m_scale;
+    /** Constructor */
+    explicit adds(const Scalar& s) : m_scale(s) {}
+    /** Scaled add to dst. */
+    template <typename Dst, typename Src>
+    void EIGEN_DEVICE_FUNC operator()(const Dst& dst, const Src& src) const {
+      dst.const_cast_derived() += m_scale * src;
+    }
+  };
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    internal::outer_product_selector_run(dst, lhs, rhs, set(), is_row_major<Dst>());
+  }
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    internal::outer_product_selector_run(dst, lhs, rhs, add(), is_row_major<Dst>());
+  }
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    internal::outer_product_selector_run(dst, lhs, rhs, sub(), is_row_major<Dst>());
+  }
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs,
+                                                                  const Scalar& alpha) {
+    internal::outer_product_selector_run(dst, lhs, rhs, adds(alpha), is_row_major<Dst>());
+  }
+};
+
+// This base class provides default implementations for evalTo, addTo, subTo, in terms of scaleAndAddTo
+template <typename Lhs, typename Rhs, typename Derived>
+struct generic_product_impl_base {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    dst.setZero();
+    scaleAndAddTo(dst, lhs, rhs, Scalar(1));
+  }
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    scaleAndAddTo(dst, lhs, rhs, Scalar(1));
+  }
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    scaleAndAddTo(dst, lhs, rhs, Scalar(-1));
+  }
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs,
+                                                                  const Scalar& alpha) {
+    Derived::scaleAndAddTo(dst, lhs, rhs, alpha);
+  }
+};
+
+template <typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, GemvProduct>
+    : generic_product_impl_base<Lhs, Rhs, generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, GemvProduct>> {
+  typedef typename nested_eval<Lhs, 1>::type LhsNested;
+  typedef typename nested_eval<Rhs, 1>::type RhsNested;
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+  enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
+  typedef internal::remove_all_t<std::conditional_t<int(Side) == OnTheRight, LhsNested, RhsNested>> MatrixType;
+
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs,
+                                                                  const Scalar& alpha) {
+    // Fallback to inner product if both the lhs and rhs is a runtime vector.
+    if (lhs.rows() == 1 && rhs.cols() == 1) {
+      dst.coeffRef(0, 0) += alpha * lhs.row(0).conjugate().dot(rhs.col(0));
+      return;
+    }
+    LhsNested actual_lhs(lhs);
+    RhsNested actual_rhs(rhs);
+    internal::gemv_dense_selector<Side, (int(MatrixType::Flags) & RowMajorBit) ? RowMajor : ColMajor,
+                                  bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)>::run(actual_lhs,
+                                                                                                       actual_rhs, dst,
+                                                                                                       alpha);
+  }
+};
+
+template <typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, CoeffBasedProductMode> {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    // Same as: dst.noalias() = lhs.lazyProduct(rhs);
+    // but easier on the compiler side
+    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar, Scalar>());
+  }
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    // dst.noalias() += lhs.lazyProduct(rhs);
+    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<typename Dst::Scalar, Scalar>());
+  }
+
+  template <typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    // dst.noalias() -= lhs.lazyProduct(rhs);
+    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar, Scalar>());
+  }
+
+  // This is a special evaluation path called from generic_product_impl<...,GemmProduct> in file GeneralMatrixMatrix.h
+  // This variant tries to extract scalar multiples from both the LHS and RHS and factor them out. For instance:
+  //   dst {,+,-}= (s1*A)*(B*s2)
+  // will be rewritten as:
+  //   dst {,+,-}= (s1*s2) * (A.lazyProduct(B))
+  // There are at least four benefits of doing so:
+  //  1 - huge performance gain for heap-allocated matrix types as it save costly allocations.
+  //  2 - it is faster than simply by-passing the heap allocation through stack allocation.
+  //  3 - it makes this fallback consistent with the heavy GEMM routine.
+  //  4 - it fully by-passes huge stack allocation attempts when multiplying huge fixed-size matrices.
+  //      (see https://stackoverflow.com/questions/54738495)
+  // For small fixed sizes matrices, however, the gains are less obvious, it is sometimes x2 faster, but sometimes x3
+  // slower, and the behavior depends also a lot on the compiler... This is why this re-writing strategy is currently
+  // enabled only when falling back from the main GEMM.
+  template <typename Dst, typename Func>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void eval_dynamic(Dst& dst, const Lhs& lhs, const Rhs& rhs,
+                                                                 const Func& func) {
+    enum {
+      HasScalarFactor = blas_traits<Lhs>::HasScalarFactor || blas_traits<Rhs>::HasScalarFactor,
+      ConjLhs = blas_traits<Lhs>::NeedToConjugate,
+      ConjRhs = blas_traits<Rhs>::NeedToConjugate
+    };
+    // FIXME: in c++11 this should be auto, and extractScalarFactor should also return auto
+    //        this is important for real*complex_mat
+    Scalar actualAlpha = combine_scalar_factors<Scalar>(lhs, rhs);
+
+    eval_dynamic_impl(dst, blas_traits<Lhs>::extract(lhs).template conjugateIf<ConjLhs>(),
+                      blas_traits<Rhs>::extract(rhs).template conjugateIf<ConjRhs>(), func, actualAlpha,
+                      bool_constant<HasScalarFactor>());
+  }
+
+ protected:
+  template <typename Dst, typename LhsT, typename RhsT, typename Func, typename Scalar>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs,
+                                                                      const Func& func, const Scalar& s /* == 1 */,
+                                                                      false_type) {
+    EIGEN_UNUSED_VARIABLE(s);
+    eigen_internal_assert(numext::is_exactly_one(s));
+    call_restricted_packet_assignment_no_alias(dst, lhs.lazyProduct(rhs), func);
+  }
+
+  template <typename Dst, typename LhsT, typename RhsT, typename Func, typename Scalar>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs,
+                                                                      const Func& func, const Scalar& s, true_type) {
+    call_restricted_packet_assignment_no_alias(dst, s * lhs.lazyProduct(rhs), func);
+  }
+};
+
+// This specialization enforces the use of a coefficient-based evaluation strategy
+template <typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, LazyCoeffBasedProductMode>
+    : generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, CoeffBasedProductMode> {};
+
+// Case 2: Evaluate coeff by coeff
+//
+// This is mostly taken from CoeffBasedProduct.h
+// The main difference is that we add an extra argument to the etor_product_*_impl::run() function
+// for the inner dimension of the product, because evaluator object do not know their size.
+
+template <int Traversal, int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar>
+struct etor_product_coeff_impl;
+
+template <int StorageOrder, int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl;
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape, DenseShape>
+    : evaluator_base<Product<Lhs, Rhs, LazyProduct>> {
+  typedef Product<Lhs, Rhs, LazyProduct> XprType;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit product_evaluator(const XprType& xpr)
+      : m_lhs(xpr.lhs()),
+        m_rhs(xpr.rhs()),
+        m_lhsImpl(m_lhs),  // FIXME the creation of the evaluator objects should result in a no-op, but check that!
+        m_rhsImpl(m_rhs),  //       Moreover, they are only useful for the packet path, so we could completely disable
+                           //       them when not needed, or perhaps declare them on the fly on the packet method... We
+                           //       have experiment to check what's best.
+        m_innerDim(xpr.lhs().cols()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::AddCost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  // Everything below here is taken from CoeffBasedProduct.h
+
+  typedef typename internal::nested_eval<Lhs, Rhs::ColsAtCompileTime>::type LhsNested;
+  typedef typename internal::nested_eval<Rhs, Lhs::RowsAtCompileTime>::type RhsNested;
+
+  typedef internal::remove_all_t<LhsNested> LhsNestedCleaned;
+  typedef internal::remove_all_t<RhsNested> RhsNestedCleaned;
+
+  typedef evaluator<LhsNestedCleaned> LhsEtorType;
+  typedef evaluator<RhsNestedCleaned> RhsEtorType;
+
+  enum {
+    RowsAtCompileTime = LhsNestedCleaned::RowsAtCompileTime,
+    ColsAtCompileTime = RhsNestedCleaned::ColsAtCompileTime,
+    InnerSize = min_size_prefer_fixed(LhsNestedCleaned::ColsAtCompileTime, RhsNestedCleaned::RowsAtCompileTime),
+    MaxRowsAtCompileTime = LhsNestedCleaned::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = RhsNestedCleaned::MaxColsAtCompileTime
+  };
+
+  typedef typename find_best_packet<Scalar, RowsAtCompileTime>::type LhsVecPacketType;
+  typedef typename find_best_packet<Scalar, ColsAtCompileTime>::type RhsVecPacketType;
+
+  enum {
+
+    LhsCoeffReadCost = LhsEtorType::CoeffReadCost,
+    RhsCoeffReadCost = RhsEtorType::CoeffReadCost,
+    CoeffReadCost = InnerSize == 0 ? NumTraits<Scalar>::ReadCost
+                    : InnerSize == Dynamic
+                        ? HugeCost
+                        : InnerSize * (NumTraits<Scalar>::MulCost + int(LhsCoeffReadCost) + int(RhsCoeffReadCost)) +
+                              (InnerSize - 1) * NumTraits<Scalar>::AddCost,
+
+    Unroll = CoeffReadCost <= EIGEN_UNROLLING_LIMIT,
+
+    LhsFlags = LhsEtorType::Flags,
+    RhsFlags = RhsEtorType::Flags,
+
+    LhsRowMajor = LhsFlags & RowMajorBit,
+    RhsRowMajor = RhsFlags & RowMajorBit,
+
+    LhsVecPacketSize = unpacket_traits<LhsVecPacketType>::size,
+    RhsVecPacketSize = unpacket_traits<RhsVecPacketType>::size,
+
+    // Here, we don't care about alignment larger than the usable packet size.
+    LhsAlignment =
+        plain_enum_min(LhsEtorType::Alignment, LhsVecPacketSize* int(sizeof(typename LhsNestedCleaned::Scalar))),
+    RhsAlignment =
+        plain_enum_min(RhsEtorType::Alignment, RhsVecPacketSize* int(sizeof(typename RhsNestedCleaned::Scalar))),
+
+    SameType = is_same<typename LhsNestedCleaned::Scalar, typename RhsNestedCleaned::Scalar>::value,
+
+    CanVectorizeRhs = bool(RhsRowMajor) && (RhsFlags & PacketAccessBit) && (ColsAtCompileTime != 1),
+    CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit) && (RowsAtCompileTime != 1),
+
+    EvalToRowMajor = (MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1) ? 1
+                     : (MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1)
+                         ? 0
+                         : (bool(RhsRowMajor) && !CanVectorizeLhs),
+
+    Flags = ((int(LhsFlags) | int(RhsFlags)) & HereditaryBits & ~RowMajorBit) |
+            (EvalToRowMajor ? RowMajorBit : 0)
+            // TODO: enable vectorization for mixed types
+            | (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0) |
+            (XprType::IsVectorAtCompileTime ? LinearAccessBit : 0),
+
+    LhsOuterStrideBytes =
+        int(LhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename LhsNestedCleaned::Scalar)),
+    RhsOuterStrideBytes =
+        int(RhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename RhsNestedCleaned::Scalar)),
+
+    Alignment = bool(CanVectorizeLhs)
+                    ? (LhsOuterStrideBytes <= 0 || (int(LhsOuterStrideBytes) % plain_enum_max(1, LhsAlignment)) != 0
+                           ? 0
+                           : LhsAlignment)
+                : bool(CanVectorizeRhs)
+                    ? (RhsOuterStrideBytes <= 0 || (int(RhsOuterStrideBytes) % plain_enum_max(1, RhsAlignment)) != 0
+                           ? 0
+                           : RhsAlignment)
+                    : 0,
+
+    /* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside
+     * of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner
+     * loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect
+     * the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI.
+     */
+    CanVectorizeInner = SameType && LhsRowMajor && (!RhsRowMajor) &&
+                        (int(LhsFlags) & int(RhsFlags) & ActualPacketAccessBit) &&
+                        (int(InnerSize) % packet_traits<Scalar>::size == 0)
+  };
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const {
+    return (m_lhs.row(row).transpose().cwiseProduct(m_rhs.col(col))).sum();
+  }
+
+  /* Allow index-based non-packet access. It is impossible though to allow index-based packed access,
+   * which is why we don't set the LinearAccessBit.
+   * TODO: this seems possible when the result is a vector
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index index) const {
+    const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime == 1) ? 0 : index;
+    const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime == 1) ? index : 0;
+    return (m_lhs.row(row).transpose().cwiseProduct(m_rhs.col(col))).sum();
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const PacketType packet(Index row, Index col) const {
+    PacketType res;
+    typedef etor_product_packet_impl<bool(int(Flags) & RowMajorBit) ? RowMajor : ColMajor,
+                                     Unroll ? int(InnerSize) : Dynamic, LhsEtorType, RhsEtorType, PacketType, LoadMode>
+        PacketImpl;
+    PacketImpl::run(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res);
+    return res;
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const PacketType packet(Index index) const {
+    const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime == 1) ? 0 : index;
+    const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime == 1) ? index : 0;
+    return packet<LoadMode, PacketType>(row, col);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const PacketType packetSegment(Index row, Index col, Index begin,
+                                                                       Index count) const {
+    PacketType res;
+    typedef etor_product_packet_impl<bool(int(Flags) & RowMajorBit) ? RowMajor : ColMajor,
+                                     Unroll ? int(InnerSize) : Dynamic, LhsEtorType, RhsEtorType, PacketType, LoadMode>
+        PacketImpl;
+    PacketImpl::run_segment(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res, begin, count);
+    return res;
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const PacketType packetSegment(Index index, Index begin, Index count) const {
+    const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime == 1) ? 0 : index;
+    const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime == 1) ? index : 0;
+    return packetSegment<LoadMode, PacketType>(row, col, begin, count);
+  }
+
+ protected:
+  add_const_on_value_type_t<LhsNested> m_lhs;
+  add_const_on_value_type_t<RhsNested> m_rhs;
+
+  LhsEtorType m_lhsImpl;
+  RhsEtorType m_rhsImpl;
+
+  // TODO: Get rid of m_innerDim if known at compile time
+  Index m_innerDim;
+};
+
+template <typename Lhs, typename Rhs>
+struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, LazyCoeffBasedProductMode, DenseShape, DenseShape>
+    : product_evaluator<Product<Lhs, Rhs, LazyProduct>, CoeffBasedProductMode, DenseShape, DenseShape> {
+  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
+  typedef Product<Lhs, Rhs, LazyProduct> BaseProduct;
+  typedef product_evaluator<BaseProduct, CoeffBasedProductMode, DenseShape, DenseShape> Base;
+  enum { Flags = Base::Flags | EvalBeforeNestingBit };
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit product_evaluator(const XprType& xpr)
+      : Base(BaseProduct(xpr.lhs(), xpr.rhs())) {}
+};
+
+/****************************************
+*** Coeff based product, Packet path  ***
+****************************************/
+
+template <int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<RowMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                        Index innerDim, Packet& res) {
+    etor_product_packet_impl<RowMajor, UnrollingIndex - 1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs,
+                                                                                            innerDim, res);
+    res = pmadd(pset1<Packet>(lhs.coeff(row, Index(UnrollingIndex - 1))),
+                rhs.template packet<LoadMode, Packet>(Index(UnrollingIndex - 1), col), res);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run_segment(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                                Index innerDim, Packet& res, Index begin, Index count) {
+    etor_product_packet_impl<RowMajor, UnrollingIndex - 1, Lhs, Rhs, Packet, LoadMode>::run_segment(
+        row, col, lhs, rhs, innerDim, res, begin, count);
+    res = pmadd(pset1<Packet>(lhs.coeff(row, Index(UnrollingIndex - 1))),
+                rhs.template packetSegment<LoadMode, Packet>(Index(UnrollingIndex - 1), col, begin, count), res);
+  }
+};
+
+template <int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<ColMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                        Index innerDim, Packet& res) {
+    etor_product_packet_impl<ColMajor, UnrollingIndex - 1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs,
+                                                                                            innerDim, res);
+    res = pmadd(lhs.template packet<LoadMode, Packet>(row, Index(UnrollingIndex - 1)),
+                pset1<Packet>(rhs.coeff(Index(UnrollingIndex - 1), col)), res);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run_segment(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                                Index innerDim, Packet& res, Index begin, Index count) {
+    etor_product_packet_impl<ColMajor, UnrollingIndex - 1, Lhs, Rhs, Packet, LoadMode>::run_segment(
+        row, col, lhs, rhs, innerDim, res, begin, count);
+    res = pmadd(lhs.template packetSegment<LoadMode, Packet>(row, Index(UnrollingIndex - 1), begin, count),
+                pset1<Packet>(rhs.coeff(Index(UnrollingIndex - 1), col)), res);
+  }
+};
+
+template <typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<RowMajor, 1, Lhs, Rhs, Packet, LoadMode> {
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                                  Index /*innerDim*/, Packet& res) {
+    res = pmul(pset1<Packet>(lhs.coeff(row, Index(0))), rhs.template packet<LoadMode, Packet>(Index(0), col));
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run_segment(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                                Index /*innerDim*/, Packet& res, Index begin,
+                                                                Index count) {
+    res = pmul(pset1<Packet>(lhs.coeff(row, Index(0))),
+               rhs.template packetSegment<LoadMode, Packet>(Index(0), col, begin, count));
+  }
+};
+
+template <typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<ColMajor, 1, Lhs, Rhs, Packet, LoadMode> {
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                                  Index /*innerDim*/, Packet& res) {
+    res = pmul(lhs.template packet<LoadMode, Packet>(row, Index(0)), pset1<Packet>(rhs.coeff(Index(0), col)));
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run_segment(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                                Index /*innerDim*/, Packet& res, Index begin,
+                                                                Index count) {
+    res = pmul(lhs.template packetSegment<LoadMode, Packet>(row, Index(0), begin, count),
+               pset1<Packet>(rhs.coeff(Index(0), col)));
+  }
+};
+
+template <typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<RowMajor, 0, Lhs, Rhs, Packet, LoadMode> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/,
+                                                        const Rhs& /*rhs*/, Index /*innerDim*/, Packet& res) {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run_segment(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/,
+                                                                const Rhs& /*rhs*/, Index /*innerDim*/, Packet& res,
+                                                                Index /*begin*/, Index /*count*/) {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+  }
+};
+
+template <typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<ColMajor, 0, Lhs, Rhs, Packet, LoadMode> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/,
+                                                        const Rhs& /*rhs*/, Index /*innerDim*/, Packet& res) {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run_segment(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/,
+                                                                const Rhs& /*rhs*/, Index /*innerDim*/, Packet& res,
+                                                                Index /*begin*/, Index /*count*/) {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+  }
+};
+
+template <typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, Packet, LoadMode> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                        Index innerDim, Packet& res) {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+    for (Index i = 0; i < innerDim; ++i)
+      res = pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packet<LoadMode, Packet>(i, col), res);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run_segment(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                                Index innerDim, Packet& res, Index begin, Index count) {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+    for (Index i = 0; i < innerDim; ++i)
+      res = pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packetSegment<LoadMode, Packet>(i, col, begin, count),
+                  res);
+  }
+};
+
+template <typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, Packet, LoadMode> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                        Index innerDim, Packet& res) {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+    for (Index i = 0; i < innerDim; ++i)
+      res = pmadd(lhs.template packet<LoadMode, Packet>(row, i), pset1<Packet>(rhs.coeff(i, col)), res);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run_segment(Index row, Index col, const Lhs& lhs, const Rhs& rhs,
+                                                                Index innerDim, Packet& res, Index begin, Index count) {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+    for (Index i = 0; i < innerDim; ++i)
+      res = pmadd(lhs.template packetSegment<LoadMode, Packet>(row, i, begin, count), pset1<Packet>(rhs.coeff(i, col)),
+                  res);
+  }
+};
+
+/***************************************************************************
+ * Triangular products
+ ***************************************************************************/
+template <int Mode, bool LhsIsTriangular, typename Lhs, bool LhsIsVector, typename Rhs, bool RhsIsVector>
+struct triangular_product_impl;
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs, Rhs, TriangularShape, DenseShape, ProductTag>
+    : generic_product_impl_base<Lhs, Rhs, generic_product_impl<Lhs, Rhs, TriangularShape, DenseShape, ProductTag>> {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  template <typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) {
+    triangular_product_impl<Lhs::Mode, true, typename Lhs::MatrixType, false, Rhs, Rhs::ColsAtCompileTime == 1>::run(
+        dst, lhs.nestedExpression(), rhs, alpha);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs, Rhs, DenseShape, TriangularShape, ProductTag>
+    : generic_product_impl_base<Lhs, Rhs, generic_product_impl<Lhs, Rhs, DenseShape, TriangularShape, ProductTag>> {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  template <typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) {
+    triangular_product_impl<Rhs::Mode, false, Lhs, Lhs::RowsAtCompileTime == 1, typename Rhs::MatrixType, false>::run(
+        dst, lhs, rhs.nestedExpression(), alpha);
+  }
+};
+
+/***************************************************************************
+ * SelfAdjoint products
+ ***************************************************************************/
+template <typename Lhs, int LhsMode, bool LhsIsVector, typename Rhs, int RhsMode, bool RhsIsVector>
+struct selfadjoint_product_impl;
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs, Rhs, SelfAdjointShape, DenseShape, ProductTag>
+    : generic_product_impl_base<Lhs, Rhs, generic_product_impl<Lhs, Rhs, SelfAdjointShape, DenseShape, ProductTag>> {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) {
+    selfadjoint_product_impl<typename Lhs::MatrixType, Lhs::Mode, false, Rhs, 0, Rhs::ColsAtCompileTime == 1>::run(
+        dst, lhs.nestedExpression(), rhs, alpha);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs, Rhs, DenseShape, SelfAdjointShape, ProductTag>
+    : generic_product_impl_base<Lhs, Rhs, generic_product_impl<Lhs, Rhs, DenseShape, SelfAdjointShape, ProductTag>> {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  template <typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) {
+    selfadjoint_product_impl<Lhs, 0, Lhs::RowsAtCompileTime == 1, typename Rhs::MatrixType, Rhs::Mode, false>::run(
+        dst, lhs, rhs.nestedExpression(), alpha);
+  }
+};
+
+/***************************************************************************
+ * Diagonal products
+ ***************************************************************************/
+
+template <typename MatrixType, typename DiagonalType, typename Derived, int ProductOrder>
+struct diagonal_product_evaluator_base : evaluator_base<Derived> {
+  typedef typename ScalarBinaryOpTraits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
+
+ public:
+  enum {
+    CoeffReadCost = int(NumTraits<Scalar>::MulCost) + int(evaluator<MatrixType>::CoeffReadCost) +
+                    int(evaluator<DiagonalType>::CoeffReadCost),
+
+    MatrixFlags = evaluator<MatrixType>::Flags,
+    DiagFlags = evaluator<DiagonalType>::Flags,
+
+    StorageOrder_ = (Derived::MaxRowsAtCompileTime == 1 && Derived::MaxColsAtCompileTime != 1)   ? RowMajor
+                    : (Derived::MaxColsAtCompileTime == 1 && Derived::MaxRowsAtCompileTime != 1) ? ColMajor
+                    : MatrixFlags & RowMajorBit                                                  ? RowMajor
+                                                                                                 : ColMajor,
+    SameStorageOrder_ = int(StorageOrder_) == ((MatrixFlags & RowMajorBit) ? RowMajor : ColMajor),
+
+    ScalarAccessOnDiag_ = !((int(StorageOrder_) == ColMajor && int(ProductOrder) == OnTheLeft) ||
+                            (int(StorageOrder_) == RowMajor && int(ProductOrder) == OnTheRight)),
+    SameTypes_ = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
+    // FIXME currently we need same types, but in the future the next rule should be the one
+    // Vectorizable_ = bool(int(MatrixFlags)&PacketAccessBit) && ((!_PacketOnDiag) || (SameTypes_ &&
+    // bool(int(DiagFlags)&PacketAccessBit))),
+    Vectorizable_ = bool(int(MatrixFlags) & PacketAccessBit) && SameTypes_ &&
+                    (SameStorageOrder_ || (MatrixFlags & LinearAccessBit) == LinearAccessBit) &&
+                    (ScalarAccessOnDiag_ || (bool(int(DiagFlags) & PacketAccessBit))),
+    LinearAccessMask_ =
+        (MatrixType::RowsAtCompileTime == 1 || MatrixType::ColsAtCompileTime == 1) ? LinearAccessBit : 0,
+    Flags =
+        ((HereditaryBits | LinearAccessMask_) & (unsigned int)(MatrixFlags)) | (Vectorizable_ ? PacketAccessBit : 0),
+    Alignment = evaluator<MatrixType>::Alignment,
+
+    AsScalarProduct =
+        (DiagonalType::SizeAtCompileTime == 1) ||
+        (DiagonalType::SizeAtCompileTime == Dynamic && MatrixType::RowsAtCompileTime == 1 &&
+         ProductOrder == OnTheLeft) ||
+        (DiagonalType::SizeAtCompileTime == Dynamic && MatrixType::ColsAtCompileTime == 1 && ProductOrder == OnTheRight)
+  };
+
+  EIGEN_DEVICE_FUNC diagonal_product_evaluator_base(const MatrixType& mat, const DiagonalType& diag)
+      : m_diagImpl(diag), m_matImpl(mat) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const {
+    if (AsScalarProduct)
+      return m_diagImpl.coeff(0) * m_matImpl.coeff(idx);
+    else
+      return m_diagImpl.coeff(idx) * m_matImpl.coeff(idx);
+  }
+
+ protected:
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::true_type) const {
+    return internal::pmul(m_matImpl.template packet<LoadMode, PacketType>(row, col),
+                          internal::pset1<PacketType>(m_diagImpl.coeff(id)));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::false_type) const {
+    enum {
+      InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
+      DiagonalPacketLoadMode = plain_enum_min(
+          LoadMode,
+          ((InnerSize % 16) == 0) ? int(Aligned16) : int(evaluator<DiagonalType>::Alignment))  // FIXME hardcoded 16!!
+    };
+    return internal::pmul(m_matImpl.template packet<LoadMode, PacketType>(row, col),
+                          m_diagImpl.template packet<DiagonalPacketLoadMode, PacketType>(id));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet_segment_impl(Index row, Index col, Index id, Index begin, Index count,
+                                                     internal::true_type) const {
+    return internal::pmul(m_matImpl.template packetSegment<LoadMode, PacketType>(row, col, begin, count),
+                          internal::pset1<PacketType>(m_diagImpl.coeff(id)));
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet_segment_impl(Index row, Index col, Index id, Index begin, Index count,
+                                                     internal::false_type) const {
+    enum {
+      InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
+      DiagonalPacketLoadMode = plain_enum_min(
+          LoadMode,
+          ((InnerSize % 16) == 0) ? int(Aligned16) : int(evaluator<DiagonalType>::Alignment))  // FIXME hardcoded 16!!
+    };
+    return internal::pmul(m_matImpl.template packetSegment<LoadMode, PacketType>(row, col, begin, count),
+                          m_diagImpl.template packetSegment<DiagonalPacketLoadMode, PacketType>(id, begin, count));
+  }
+
+  evaluator<DiagonalType> m_diagImpl;
+  evaluator<MatrixType> m_matImpl;
+};
+
+// diagonal * dense
+template <typename Lhs, typename Rhs, int ProductKind, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DiagonalShape, DenseShape>
+    : diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>,
+                                      OnTheLeft> {
+  typedef diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>,
+                                          OnTheLeft>
+      Base;
+  using Base::coeff;
+  using Base::m_diagImpl;
+  using Base::m_matImpl;
+  typedef typename Base::Scalar Scalar;
+
+  typedef Product<Lhs, Rhs, ProductKind> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef typename Lhs::DiagonalVectorType DiagonalType;
+
+  static constexpr int StorageOrder = Base::StorageOrder_;
+  using IsRowMajor_t = bool_constant<StorageOrder == RowMajor>;
+
+  EIGEN_DEVICE_FUNC constexpr explicit product_evaluator(const XprType& xpr) : Base(xpr.rhs(), xpr.lhs().diagonal()) {}
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const {
+    return m_diagImpl.coeff(row) * m_matImpl.coeff(row, col);
+  }
+
+#ifndef EIGEN_GPUCC
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    // FIXME: NVCC used to complain about the template keyword, but we have to check whether this is still the case.
+    // See also similar calls below.
+    return this->template packet_impl<LoadMode, PacketType>(row, col, row, IsRowMajor_t());
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet(Index idx) const {
+    return packet<LoadMode, PacketType>(int(StorageOrder) == ColMajor ? idx : 0,
+                                        int(StorageOrder) == ColMajor ? 0 : idx);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    // FIXME: NVCC used to complain about the template keyword, but we have to check whether this is still the case.
+    // See also similar calls below.
+    return this->template packet_segment_impl<LoadMode, PacketType>(row, col, row, begin, count, IsRowMajor_t());
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packetSegment(Index idx, Index begin, Index count) const {
+    return packetSegment<LoadMode, PacketType>(StorageOrder == ColMajor ? idx : 0, StorageOrder == ColMajor ? 0 : idx,
+                                               begin, count);
+  }
+#endif
+};
+
+// dense * diagonal
+template <typename Lhs, typename Rhs, int ProductKind, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DenseShape, DiagonalShape>
+    : diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>,
+                                      OnTheRight> {
+  typedef diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>,
+                                          OnTheRight>
+      Base;
+  using Base::coeff;
+  using Base::m_diagImpl;
+  using Base::m_matImpl;
+  typedef typename Base::Scalar Scalar;
+
+  typedef Product<Lhs, Rhs, ProductKind> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+
+  static constexpr int StorageOrder = Base::StorageOrder_;
+  using IsColMajor_t = bool_constant<StorageOrder == ColMajor>;
+
+  EIGEN_DEVICE_FUNC constexpr explicit product_evaluator(const XprType& xpr) : Base(xpr.lhs(), xpr.rhs().diagonal()) {}
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const {
+    return m_matImpl.coeff(row, col) * m_diagImpl.coeff(col);
+  }
+
+#ifndef EIGEN_GPUCC
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    return this->template packet_impl<LoadMode, PacketType>(row, col, col, IsColMajor_t());
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet(Index idx) const {
+    return packet<LoadMode, PacketType>(StorageOrder == ColMajor ? idx : 0, StorageOrder == ColMajor ? 0 : idx);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    return this->template packet_segment_impl<LoadMode, PacketType>(row, col, col, begin, count, IsColMajor_t());
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packetSegment(Index idx, Index begin, Index count) const {
+    return packetSegment<LoadMode, PacketType>(StorageOrder == ColMajor ? idx : 0, StorageOrder == ColMajor ? 0 : idx,
+                                               begin, count);
+  }
+#endif
+};
+
+/***************************************************************************
+ * Products with permutation matrices
+ ***************************************************************************/
+
+/** \internal
+ * \class permutation_matrix_product
+ * Internal helper class implementing the product between a permutation matrix and a matrix.
+ * This class is specialized for DenseShape below and for SparseShape in SparseCore/SparsePermutation.h
+ */
+template <typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
+struct permutation_matrix_product;
+
+template <typename ExpressionType, int Side, bool Transposed>
+struct permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape> {
+  typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
+  typedef remove_all_t<MatrixType> MatrixTypeCleaned;
+
+  template <typename Dest, typename PermutationType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const PermutationType& perm,
+                                                        const ExpressionType& xpr) {
+    MatrixType mat(xpr);
+    const Index n = Side == OnTheLeft ? mat.rows() : mat.cols();
+    // FIXME we need an is_same for expression that is not sensitive to constness. For instance
+    // is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true.
+    // if(is_same<MatrixTypeCleaned,Dest>::value && extract_data(dst) == extract_data(mat))
+    if (is_same_dense(dst, mat)) {
+      // apply the permutation inplace
+      Matrix<bool, PermutationType::RowsAtCompileTime, 1, 0, PermutationType::MaxRowsAtCompileTime> mask(perm.size());
+      mask.fill(false);
+      Index r = 0;
+      while (r < perm.size()) {
+        // search for the next seed
+        while (r < perm.size() && mask[r]) r++;
+        if (r >= perm.size()) break;
+        // we got one, let's follow it until we are back to the seed
+        Index k0 = r++;
+        Index kPrev = k0;
+        mask.coeffRef(k0) = true;
+        for (Index k = perm.indices().coeff(k0); k != k0; k = perm.indices().coeff(k)) {
+          Block<Dest, Side == OnTheLeft ? 1 : Dest::RowsAtCompileTime,
+                Side == OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
+              .swap(Block < Dest, Side == OnTheLeft ? 1 : Dest::RowsAtCompileTime,
+                    Side == OnTheRight
+                        ? 1
+                        : Dest::ColsAtCompileTime > (dst, ((Side == OnTheLeft) ^ Transposed) ? k0 : kPrev));
+
+          mask.coeffRef(k) = true;
+          kPrev = k;
+        }
+      }
+    } else {
+      for (Index i = 0; i < n; ++i) {
+        Block<Dest, Side == OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side == OnTheRight ? 1 : Dest::ColsAtCompileTime>(
+            dst, ((Side == OnTheLeft) ^ Transposed) ? perm.indices().coeff(i) : i)
+
+            =
+
+                Block < const MatrixTypeCleaned,
+            Side == OnTheLeft ? 1 : MatrixTypeCleaned::RowsAtCompileTime,
+            Side == OnTheRight ? 1
+                               : MatrixTypeCleaned::ColsAtCompileTime >
+                                     (mat, ((Side == OnTheRight) ^ Transposed) ? perm.indices().coeff(i) : i);
+      }
+    }
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, PermutationShape, MatrixShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) {
+    permutation_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, MatrixShape, PermutationShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) {
+    permutation_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Inverse<Lhs>, Rhs, PermutationShape, MatrixShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Inverse<Lhs>& lhs, const Rhs& rhs) {
+    permutation_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Inverse<Rhs>, MatrixShape, PermutationShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Inverse<Rhs>& rhs) {
+    permutation_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
+  }
+};
+
+/***************************************************************************
+ * Products with transpositions matrices
+ ***************************************************************************/
+
+// FIXME: consider unifying Transpositions and Permutation into a single shape.
+
+/** \internal
+ * \class transposition_matrix_product
+ * Internal helper class implementing the product between a permutation matrix and a matrix.
+ */
+template <typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
+struct transposition_matrix_product {
+  typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
+  typedef remove_all_t<MatrixType> MatrixTypeCleaned;
+
+  template <typename Dest, typename TranspositionType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const TranspositionType& tr,
+                                                        const ExpressionType& xpr) {
+    MatrixType mat(xpr);
+    typedef typename TranspositionType::StorageIndex StorageIndex;
+    const Index size = tr.size();
+    StorageIndex j = 0;
+
+    if (!is_same_dense(dst, mat)) dst = mat;
+
+    for (Index k = (Transposed ? size - 1 : 0); Transposed ? k >= 0 : k < size; Transposed ? --k : ++k)
+      if (Index(j = tr.coeff(k)) != k) {
+        if (Side == OnTheLeft)
+          dst.row(k).swap(dst.row(j));
+        else if (Side == OnTheRight)
+          dst.col(k).swap(dst.col(j));
+      }
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, TranspositionsShape, MatrixShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) {
+    transposition_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, MatrixShape, TranspositionsShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) {
+    transposition_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Transpose<Lhs>, Rhs, TranspositionsShape, MatrixShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Transpose<Lhs>& lhs, const Rhs& rhs) {
+    transposition_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Transpose<Rhs>, MatrixShape, TranspositionsShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Transpose<Rhs>& rhs) {
+    transposition_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
+  }
+};
+
+/***************************************************************************
+ * skew symmetric products
+ * for now we just call the generic implementation
+ ***************************************************************************/
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, SkewSymmetricShape, MatrixShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) {
+    generic_product_impl<typename Lhs::DenseMatrixType, Rhs, DenseShape, MatrixShape, ProductTag>::evalTo(dst, lhs,
+                                                                                                          rhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, MatrixShape, SkewSymmetricShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) {
+    generic_product_impl<Lhs, typename Rhs::DenseMatrixType, MatrixShape, DenseShape, ProductTag>::evalTo(dst, lhs,
+                                                                                                          rhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs, Rhs, SkewSymmetricShape, SkewSymmetricShape, ProductTag> {
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) {
+    generic_product_impl<typename Lhs::DenseMatrixType, typename Rhs::DenseMatrixType, DenseShape, DenseShape,
+                         ProductTag>::evalTo(dst, lhs, rhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, MatrixShape, HomogeneousShape, ProductTag>
+    : generic_product_impl<Lhs, typename Rhs::PlainObject, MatrixShape, DenseShape, ProductTag> {};
+
+template <typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, HomogeneousShape, MatrixShape, ProductTag>
+    : generic_product_impl<typename Lhs::PlainObject, Rhs, DenseShape, MatrixShape, ProductTag> {};
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs, Rhs, PermutationShape, HomogeneousShape, ProductTag>
+    : generic_product_impl<Lhs, Rhs, PermutationShape, DenseShape, ProductTag> {};
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs, Rhs, HomogeneousShape, PermutationShape, ProductTag>
+    : generic_product_impl<Lhs, Rhs, DenseShape, PermutationShape, ProductTag> {};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PRODUCT_EVALUATORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Random.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Random.h
new file mode 100644
index 0000000..532ca0e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Random.h
@@ -0,0 +1,207 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RANDOM_H
+#define EIGEN_RANDOM_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+struct scalar_random_op {
+  inline Scalar operator()() const { return random<Scalar>(); }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_random_op<Scalar> > {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false, IsRepeatable = false };
+};
+
+}  // end namespace internal
+
+/** \returns a random matrix expression
+ *
+ * Numbers are uniformly spread through their whole definition range for integer types,
+ * and in the [-1:1] range for floating point scalar types.
+ *
+ * The parameters \a rows and \a cols are the number of rows and of columns of
+ * the returned matrix. Must be compatible with this MatrixBase type.
+ *
+ * \not_reentrant
+ *
+ * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+ * it is redundant to pass \a rows and \a cols as arguments, so Random() should be used
+ * instead.
+ *
+ *
+ * Example: \include MatrixBase_random_int_int.cpp
+ * Output: \verbinclude MatrixBase_random_int_int.out
+ *
+ * This expression has the "evaluate before nesting" flag so that it will be evaluated into
+ * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
+ * behavior with expressions involving random matrices.
+ *
+ * See DenseBase::NullaryExpr(Index, const CustomNullaryOp&) for an example using C++11 random generators.
+ *
+ * \sa DenseBase::setRandom(), DenseBase::Random(Index), DenseBase::Random()
+ */
+template <typename Derived>
+inline const typename DenseBase<Derived>::RandomReturnType DenseBase<Derived>::Random(Index rows, Index cols) {
+  return NullaryExpr(rows, cols, internal::scalar_random_op<Scalar>());
+}
+
+/** \returns a random vector expression
+ *
+ * Numbers are uniformly spread through their whole definition range for integer types,
+ * and in the [-1:1] range for floating point scalar types.
+ *
+ * The parameter \a size is the size of the returned vector.
+ * Must be compatible with this MatrixBase type.
+ *
+ * \only_for_vectors
+ * \not_reentrant
+ *
+ * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
+ * it is redundant to pass \a size as argument, so Random() should be used
+ * instead.
+ *
+ * Example: \include MatrixBase_random_int.cpp
+ * Output: \verbinclude MatrixBase_random_int.out
+ *
+ * This expression has the "evaluate before nesting" flag so that it will be evaluated into
+ * a temporary vector whenever it is nested in a larger expression. This prevents unexpected
+ * behavior with expressions involving random matrices.
+ *
+ * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random()
+ */
+template <typename Derived>
+inline const typename DenseBase<Derived>::RandomReturnType DenseBase<Derived>::Random(Index size) {
+  return NullaryExpr(size, internal::scalar_random_op<Scalar>());
+}
+
+/** \returns a fixed-size random matrix or vector expression
+ *
+ * Numbers are uniformly spread through their whole definition range for integer types,
+ * and in the [-1:1] range for floating point scalar types.
+ *
+ * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
+ * need to use the variants taking size arguments.
+ *
+ * Example: \include MatrixBase_random.cpp
+ * Output: \verbinclude MatrixBase_random.out
+ *
+ * This expression has the "evaluate before nesting" flag so that it will be evaluated into
+ * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
+ * behavior with expressions involving random matrices.
+ *
+ * \not_reentrant
+ *
+ * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random(Index)
+ */
+template <typename Derived>
+inline const typename DenseBase<Derived>::RandomReturnType DenseBase<Derived>::Random() {
+  return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_random_op<Scalar>());
+}
+
+/** Sets all coefficients in this expression to random values.
+ *
+ * Numbers are uniformly spread through their whole definition range for integer types,
+ * and in the [-1:1] range for floating point scalar types.
+ *
+ * \not_reentrant
+ *
+ * Example: \include MatrixBase_setRandom.cpp
+ * Output: \verbinclude MatrixBase_setRandom.out
+ *
+ * \sa class CwiseNullaryOp, setRandom(Index), setRandom(Index,Index)
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline Derived& DenseBase<Derived>::setRandom() {
+  return *this = Random(rows(), cols());
+}
+
+/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values.
+ *
+ * Numbers are uniformly spread through their whole definition range for integer types,
+ * and in the [-1:1] range for floating point scalar types.
+ *
+ * \only_for_vectors
+ * \not_reentrant
+ *
+ * Example: \include Matrix_setRandom_int.cpp
+ * Output: \verbinclude Matrix_setRandom_int.out
+ *
+ * \sa DenseBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, DenseBase::Random()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setRandom(Index newSize) {
+  resize(newSize);
+  return setRandom();
+}
+
+/** Resizes to the given size, and sets all coefficients in this expression to random values.
+ *
+ * Numbers are uniformly spread through their whole definition range for integer types,
+ * and in the [-1:1] range for floating point scalar types.
+ *
+ * \not_reentrant
+ *
+ * \param rows the new number of rows
+ * \param cols the new number of columns
+ *
+ * Example: \include Matrix_setRandom_int_int.cpp
+ * Output: \verbinclude Matrix_setRandom_int_int.out
+ *
+ * \sa DenseBase::setRandom(), setRandom(Index), class CwiseNullaryOp, DenseBase::Random()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setRandom(Index rows, Index cols) {
+  resize(rows, cols);
+  return setRandom();
+}
+
+/** Resizes to the given size, changing only the number of columns, and sets all
+ * coefficients in this expression to random values. For the parameter of type
+ * NoChange_t, just pass the special value \c NoChange.
+ *
+ * Numbers are uniformly spread through their whole definition range for integer types,
+ * and in the [-1:1] range for floating point scalar types.
+ *
+ * \not_reentrant
+ *
+ * \sa DenseBase::setRandom(), setRandom(Index), setRandom(Index, NoChange_t), class CwiseNullaryOp, DenseBase::Random()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setRandom(NoChange_t, Index cols) {
+  return setRandom(rows(), cols);
+}
+
+/** Resizes to the given size, changing only the number of rows, and sets all
+ * coefficients in this expression to random values. For the parameter of type
+ * NoChange_t, just pass the special value \c NoChange.
+ *
+ * Numbers are uniformly spread through their whole definition range for integer types,
+ * and in the [-1:1] range for floating point scalar types.
+ *
+ * \not_reentrant
+ *
+ * \sa DenseBase::setRandom(), setRandom(Index), setRandom(NoChange_t, Index), class CwiseNullaryOp, DenseBase::Random()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE Derived& PlainObjectBase<Derived>::setRandom(Index rows, NoChange_t) {
+  return setRandom(rows, cols());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_RANDOM_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/RandomImpl.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/RandomImpl.h
new file mode 100644
index 0000000..4a622fc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/RandomImpl.h
@@ -0,0 +1,265 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Charles Schlosser <cs.schlosser@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RANDOM_IMPL_H
+#define EIGEN_RANDOM_IMPL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/****************************************************************************
+ * Implementation of random                                               *
+ ****************************************************************************/
+
+template <typename Scalar, bool IsComplex, bool IsInteger>
+struct random_default_impl {};
+
+template <typename Scalar>
+struct random_impl : random_default_impl<Scalar, NumTraits<Scalar>::IsComplex, NumTraits<Scalar>::IsInteger> {};
+
+template <typename Scalar>
+struct random_retval {
+  typedef Scalar type;
+};
+
+template <typename Scalar>
+inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y) {
+  return EIGEN_MATHFUNC_IMPL(random, Scalar)::run(x, y);
+}
+
+template <typename Scalar>
+inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random() {
+  return EIGEN_MATHFUNC_IMPL(random, Scalar)::run();
+}
+
+// TODO: replace or provide alternatives to this, e.g. std::random_device
+struct eigen_random_device {
+  using ReturnType = int;
+  static constexpr int Entropy = meta_floor_log2<(unsigned int)(RAND_MAX) + 1>::value;
+  static constexpr ReturnType Highest = RAND_MAX;
+  static EIGEN_DEVICE_FUNC inline ReturnType run() { return std::rand(); }
+};
+
+// Fill a built-in unsigned integer with numRandomBits beginning with the least significant bit
+template <typename Scalar>
+struct random_bits_impl {
+  EIGEN_STATIC_ASSERT(std::is_unsigned<Scalar>::value, SCALAR MUST BE A BUILT - IN UNSIGNED INTEGER)
+  using RandomDevice = eigen_random_device;
+  using RandomReturnType = typename RandomDevice::ReturnType;
+  static constexpr int kTotalBits = sizeof(Scalar) * CHAR_BIT;
+  static constexpr int kEntropy = plain_enum_min(kTotalBits, RandomDevice::Entropy);
+  // return a Scalar filled with numRandomBits beginning from the least significant bit
+  static EIGEN_DEVICE_FUNC inline Scalar run(int numRandomBits) {
+    eigen_assert((numRandomBits >= 0) && (numRandomBits <= kTotalBits));
+    Scalar randomBits = 0;
+    for (int filledBits = 0; filledBits < numRandomBits; filledBits += kEntropy) {
+      Scalar r = static_cast<Scalar>(RandomDevice::run());
+      int remainingBits = numRandomBits - filledBits;
+      if (remainingBits < kEntropy) {
+        // clear the excess bits to avoid UB and rounding bias
+        r >>= kEntropy - remainingBits;
+      }
+      randomBits |= r << filledBits;
+    }
+    return randomBits;
+  }
+};
+
+template <typename BitsType>
+EIGEN_DEVICE_FUNC inline BitsType getRandomBits(int numRandomBits) {
+  return random_bits_impl<BitsType>::run(numRandomBits);
+}
+
+// random implementation for a built-in floating point type
+template <typename Scalar, bool BuiltIn = std::is_floating_point<Scalar>::value>
+struct random_float_impl {
+  using BitsType = typename numext::get_integer_by_size<sizeof(Scalar)>::unsigned_type;
+  static constexpr EIGEN_DEVICE_FUNC inline int mantissaBits() {
+    const int digits = NumTraits<Scalar>::digits();
+    return digits - 1;
+  }
+  static EIGEN_DEVICE_FUNC inline Scalar run(int numRandomBits) {
+    eigen_assert(numRandomBits >= 0 && numRandomBits <= mantissaBits());
+    BitsType randomBits = getRandomBits<BitsType>(numRandomBits);
+    // if fewer than MantissaBits is requested, shift them to the left
+    randomBits <<= (mantissaBits() - numRandomBits);
+    // randomBits is in the half-open interval [2,4)
+    randomBits |= numext::bit_cast<BitsType>(Scalar(2));
+    // result is in the half-open interval [-1,1)
+    Scalar result = numext::bit_cast<Scalar>(randomBits) - Scalar(3);
+    return result;
+  }
+};
+// random implementation for a custom floating point type
+// uses double as the implementation with a mantissa with a size equal to either the target scalar's mantissa or that of
+// double, whichever is smaller
+template <typename Scalar>
+struct random_float_impl<Scalar, false> {
+  static EIGEN_DEVICE_FUNC inline int mantissaBits() {
+    const int digits = NumTraits<Scalar>::digits();
+    constexpr int kDoubleDigits = NumTraits<double>::digits();
+    return numext::mini(digits, kDoubleDigits) - 1;
+  }
+  static EIGEN_DEVICE_FUNC inline Scalar run(int numRandomBits) {
+    eigen_assert(numRandomBits >= 0 && numRandomBits <= mantissaBits());
+    Scalar result = static_cast<Scalar>(random_float_impl<double>::run(numRandomBits));
+    return result;
+  }
+};
+
+#if !EIGEN_COMP_NVCC
+// random implementation for long double
+// this specialization is not compatible with double-double scalars
+template <bool Specialize = (sizeof(long double) == 2 * sizeof(uint64_t)) &&
+                            ((std::numeric_limits<long double>::digits != (2 * std::numeric_limits<double>::digits)))>
+struct random_longdouble_impl {
+  static constexpr int Size = sizeof(long double);
+  static constexpr EIGEN_DEVICE_FUNC int mantissaBits() { return NumTraits<long double>::digits() - 1; }
+  static EIGEN_DEVICE_FUNC inline long double run(int numRandomBits) {
+    eigen_assert(numRandomBits >= 0 && numRandomBits <= mantissaBits());
+    EIGEN_USING_STD(memcpy);
+    int numLowBits = numext::mini(numRandomBits, 64);
+    int numHighBits = numext::maxi(numRandomBits - 64, 0);
+    uint64_t randomBits[2];
+    long double result = 2.0L;
+    memcpy(&randomBits, &result, Size);
+#if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__
+    randomBits[0] |= getRandomBits<uint64_t>(numLowBits);
+    randomBits[1] |= getRandomBits<uint64_t>(numHighBits);
+#elif __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
+    randomBits[0] |= getRandomBits<uint64_t>(numHighBits);
+    randomBits[1] |= getRandomBits<uint64_t>(numLowBits);
+#else
+#error Unexpected or undefined __BYTE_ORDER__
+#endif
+    memcpy(&result, &randomBits, Size);
+    result -= 3.0L;
+    return result;
+  }
+};
+template <>
+struct random_longdouble_impl<false> {
+  static constexpr EIGEN_DEVICE_FUNC int mantissaBits() { return NumTraits<double>::digits() - 1; }
+  static EIGEN_DEVICE_FUNC inline long double run(int numRandomBits) {
+    return static_cast<long double>(random_float_impl<double>::run(numRandomBits));
+  }
+};
+template <>
+struct random_float_impl<long double> : random_longdouble_impl<> {};
+#endif
+
+template <typename Scalar>
+struct random_default_impl<Scalar, false, false> {
+  using Impl = random_float_impl<Scalar>;
+  static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar& x, const Scalar& y, int numRandomBits) {
+    Scalar half_x = Scalar(0.5) * x;
+    Scalar half_y = Scalar(0.5) * y;
+    Scalar result = (half_x + half_y) + (half_y - half_x) * run(numRandomBits);
+    // result is in the half-open interval [x, y) -- provided that x < y
+    return result;
+  }
+  static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar& x, const Scalar& y) {
+    return run(x, y, Impl::mantissaBits());
+  }
+  static EIGEN_DEVICE_FUNC inline Scalar run(int numRandomBits) { return Impl::run(numRandomBits); }
+  static EIGEN_DEVICE_FUNC inline Scalar run() { return run(Impl::mantissaBits()); }
+};
+
+template <typename Scalar, bool IsSigned = NumTraits<Scalar>::IsSigned, bool BuiltIn = std::is_integral<Scalar>::value>
+struct random_int_impl;
+
+// random implementation for a built-in unsigned integer type
+template <typename Scalar>
+struct random_int_impl<Scalar, false, true> {
+  static constexpr int kTotalBits = sizeof(Scalar) * CHAR_BIT;
+  static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar& x, const Scalar& y) {
+    if (y <= x) return x;
+    Scalar range = y - x;
+    // handle edge case where [x,y] spans the entire range of Scalar
+    if (range == NumTraits<Scalar>::highest()) return run();
+    Scalar count = range + 1;
+    // calculate the number of random bits needed to fill range
+    int numRandomBits = log2_ceil(count);
+    Scalar randomBits;
+    do {
+      randomBits = getRandomBits<Scalar>(numRandomBits);
+      // if the random draw is outside [0, range), try again (rejection sampling)
+      // in the worst-case scenario, the probability of rejection is: 1/2 - 1/2^numRandomBits < 50%
+    } while (randomBits >= count);
+    Scalar result = x + randomBits;
+    return result;
+  }
+  static EIGEN_DEVICE_FUNC inline Scalar run() { return getRandomBits<Scalar>(kTotalBits); }
+};
+
+// random implementation for a built-in signed integer type
+template <typename Scalar>
+struct random_int_impl<Scalar, true, true> {
+  static constexpr int kTotalBits = sizeof(Scalar) * CHAR_BIT;
+  // avoid implicit integral promotion to `int`
+  using BitsType = std::conditional_t<(sizeof(Scalar) < sizeof(int)), unsigned int, std::make_unsigned_t<Scalar> >;
+  static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar& x, const Scalar& y) {
+    if (y <= x) return x;
+    // Avoid overflow by representing `range` as an unsigned type
+    BitsType range = static_cast<BitsType>(y) - static_cast<BitsType>(x);
+    BitsType randomBits = random_int_impl<BitsType>::run(0, range);
+    // Avoid overflow in the case where `x` is negative and there is a large range so
+    // `randomBits` would also be negative if cast to `Scalar` first.
+    Scalar result = static_cast<Scalar>(static_cast<BitsType>(x) + randomBits);
+    return result;
+  }
+  static EIGEN_DEVICE_FUNC inline Scalar run() { return static_cast<Scalar>(getRandomBits<BitsType>(kTotalBits)); }
+};
+
+// todo: custom integers
+template <typename Scalar, bool IsSigned>
+struct random_int_impl<Scalar, IsSigned, false> {
+  static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar&, const Scalar&) { return run(); }
+  static EIGEN_DEVICE_FUNC inline Scalar run() {
+    eigen_assert(std::false_type::value && "RANDOM FOR CUSTOM INTEGERS NOT YET SUPPORTED");
+    return Scalar(0);
+  }
+};
+
+template <typename Scalar>
+struct random_default_impl<Scalar, false, true> : random_int_impl<Scalar> {};
+
+template <>
+struct random_impl<bool> {
+  static EIGEN_DEVICE_FUNC inline bool run(const bool& x, const bool& y) {
+    if (y <= x) return x;
+    return run();
+  }
+  static EIGEN_DEVICE_FUNC inline bool run() { return getRandomBits<unsigned>(1) ? true : false; }
+};
+
+template <typename Scalar>
+struct random_default_impl<Scalar, true, false> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  using Impl = random_impl<RealScalar>;
+  static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar& x, const Scalar& y, int numRandomBits) {
+    return Scalar(Impl::run(x.real(), y.real(), numRandomBits), Impl::run(x.imag(), y.imag(), numRandomBits));
+  }
+  static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar& x, const Scalar& y) {
+    return Scalar(Impl::run(x.real(), y.real()), Impl::run(x.imag(), y.imag()));
+  }
+  static EIGEN_DEVICE_FUNC inline Scalar run(int numRandomBits) {
+    return Scalar(Impl::run(numRandomBits), Impl::run(numRandomBits));
+  }
+  static EIGEN_DEVICE_FUNC inline Scalar run() { return Scalar(Impl::run(), Impl::run()); }
+};
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_RANDOM_IMPL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/RealView.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/RealView.h
new file mode 100644
index 0000000..3be5556
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/RealView.h
@@ -0,0 +1,292 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Charlie Schlosser <cs.schlosser@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REALVIEW_H
+#define EIGEN_REALVIEW_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Write access and vectorization requires array-oriented access to the real and imaginary components.
+// From https://en.cppreference.com/w/cpp/numeric/complex.html:
+// For any pointer to an element of an array of std::complex<T> named p and any valid array index i,
+// reinterpret_cast<T*>(p)[2 * i] is the real part of the complex number p[i], and
+// reinterpret_cast<T*>(p)[2 * i + 1] is the imaginary part of the complex number p[i].
+
+template <typename T>
+struct complex_array_access : std::false_type {};
+template <typename T>
+struct complex_array_access<std::complex<T>> : std::true_type {};
+
+template <typename Xpr>
+struct traits<RealView<Xpr>> : public traits<Xpr> {
+  template <typename T>
+  static constexpr int double_size(T size, bool times_two) {
+    int size_as_int = int(size);
+    if (size_as_int == Dynamic) return Dynamic;
+    return times_two ? (2 * size_as_int) : size_as_int;
+  }
+
+  using Base = traits<Xpr>;
+  using ComplexScalar = typename Base::Scalar;
+  using Scalar = typename NumTraits<ComplexScalar>::Real;
+
+  static constexpr bool ArrayAccess = complex_array_access<ComplexScalar>::value;
+  static constexpr int ActualDirectAccessBit = ArrayAccess ? DirectAccessBit : 0;
+  static constexpr int ActualLvaluebit = !std::is_const<Xpr>::value && ArrayAccess ? LvalueBit : 0;
+  static constexpr int ActualPacketAccessBit = packet_traits<Scalar>::Vectorizable ? PacketAccessBit : 0;
+  static constexpr int FlagMask =
+      ActualDirectAccessBit | ActualLvaluebit | ActualPacketAccessBit | HereditaryBits | LinearAccessBit;
+  static constexpr int BaseFlags = int(evaluator<Xpr>::Flags) | int(Base::Flags);
+  static constexpr int Flags = BaseFlags & FlagMask;
+  static constexpr bool IsRowMajor = Flags & RowMajorBit;
+  static constexpr int RowsAtCompileTime = double_size(Base::RowsAtCompileTime, !IsRowMajor);
+  static constexpr int ColsAtCompileTime = double_size(Base::ColsAtCompileTime, IsRowMajor);
+  static constexpr int SizeAtCompileTime = size_at_compile_time(RowsAtCompileTime, ColsAtCompileTime);
+  static constexpr int MaxRowsAtCompileTime = double_size(Base::MaxRowsAtCompileTime, !IsRowMajor);
+  static constexpr int MaxColsAtCompileTime = double_size(Base::MaxColsAtCompileTime, IsRowMajor);
+  static constexpr int MaxSizeAtCompileTime = size_at_compile_time(MaxRowsAtCompileTime, MaxColsAtCompileTime);
+  static constexpr int OuterStrideAtCompileTime = double_size(outer_stride_at_compile_time<Xpr>::ret, true);
+  static constexpr int InnerStrideAtCompileTime = inner_stride_at_compile_time<Xpr>::ret;
+};
+
+template <typename Xpr>
+struct evaluator<RealView<Xpr>> : private evaluator<Xpr> {
+  using BaseEvaluator = evaluator<Xpr>;
+  using XprType = RealView<Xpr>;
+  using ExpressionTraits = traits<XprType>;
+  using ComplexScalar = typename ExpressionTraits::ComplexScalar;
+  using Scalar = typename ExpressionTraits::Scalar;
+
+  static constexpr int Flags = ExpressionTraits::Flags;
+  static constexpr int CoeffReadCost = BaseEvaluator::CoeffReadCost;
+  static constexpr int Alignment = BaseEvaluator::Alignment;
+  static constexpr bool IsRowMajor = ExpressionTraits::IsRowMajor;
+  static constexpr bool DirectAccess = Flags & DirectAccessBit;
+
+  using ComplexCoeffReturnType = std::conditional_t<DirectAccess, const ComplexScalar&, ComplexScalar>;
+  using CoeffReturnType = std::conditional_t<DirectAccess, const Scalar&, Scalar>;
+
+  EIGEN_DEVICE_FUNC explicit evaluator(XprType realView) : BaseEvaluator(realView.m_xpr) {}
+
+  template <bool Enable = DirectAccess, std::enable_if_t<!Enable, bool> = true>
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index row, Index col) const {
+    Index r = IsRowMajor ? row : row / 2;
+    Index c = IsRowMajor ? col / 2 : col;
+    bool p = (IsRowMajor ? col : row) & 1;
+    ComplexScalar ccoeff = BaseEvaluator::coeff(r, c);
+    return p ? numext::imag(ccoeff) : numext::real(ccoeff);
+  }
+  template <bool Enable = DirectAccess, std::enable_if_t<Enable, bool> = true>
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    Index r = IsRowMajor ? row : row / 2;
+    Index c = IsRowMajor ? col / 2 : col;
+    Index p = (IsRowMajor ? col : row) & 1;
+    ComplexCoeffReturnType ccoeff = BaseEvaluator::coeff(r, c);
+    return reinterpret_cast<const Scalar(&)[2]>(ccoeff)[p];
+  }
+  template <bool Enable = DirectAccess, std::enable_if_t<!Enable, bool> = true>
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index index) const {
+    ComplexScalar ccoeff = BaseEvaluator::coeff(index / 2);
+    bool p = index & 1;
+    return p ? numext::imag(ccoeff) : numext::real(ccoeff);
+  }
+  template <bool Enable = DirectAccess, std::enable_if_t<Enable, bool> = true>
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    ComplexCoeffReturnType ccoeff = BaseEvaluator::coeff(index / 2);
+    Index p = index & 1;
+    return reinterpret_cast<const Scalar(&)[2]>(ccoeff)[p];
+  }
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
+    Index r = IsRowMajor ? row : row / 2;
+    Index c = IsRowMajor ? col / 2 : col;
+    Index p = (IsRowMajor ? col : row) & 1;
+    ComplexScalar& ccoeffRef = BaseEvaluator::coeffRef(r, c);
+    return reinterpret_cast<Scalar(&)[2]>(ccoeffRef)[p];
+  }
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    ComplexScalar& ccoeffRef = BaseEvaluator::coeffRef(index / 2);
+    Index p = index & 1;
+    return reinterpret_cast<Scalar(&)[2]>(ccoeffRef)[p];
+  }
+
+  // If the first index is odd (imaginary), discard the first scalar
+  // in 'result' and assign the missing scalar.
+  // This operation is safe as the real component of the first scalar must exist.
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    constexpr int RealPacketSize = unpacket_traits<PacketType>::size;
+    using ComplexPacket = typename find_packet_by_size<ComplexScalar, RealPacketSize / 2>::type;
+    EIGEN_STATIC_ASSERT((find_packet_by_size<ComplexScalar, RealPacketSize / 2>::value),
+                        MISSING COMPATIBLE COMPLEX PACKET TYPE)
+    Index r = IsRowMajor ? row : row / 2;
+    Index c = IsRowMajor ? col / 2 : col;
+    bool p = (IsRowMajor ? col : row) & 1;
+    ComplexPacket cresult = BaseEvaluator::template packet<LoadMode, ComplexPacket>(r, c);
+    PacketType result = preinterpret<PacketType>(cresult);
+    if (p) {
+      Scalar aux[RealPacketSize + 1];
+      pstoreu(aux, result);
+      Index lastr = IsRowMajor ? row : row + RealPacketSize - 1;
+      Index lastc = IsRowMajor ? col + RealPacketSize - 1 : col;
+      aux[RealPacketSize] = coeff(lastr, lastc);
+      result = ploadu<PacketType>(aux + 1);
+    }
+    return result;
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    constexpr int RealPacketSize = unpacket_traits<PacketType>::size;
+    using ComplexPacket = typename find_packet_by_size<ComplexScalar, RealPacketSize / 2>::type;
+    EIGEN_STATIC_ASSERT((find_packet_by_size<ComplexScalar, RealPacketSize / 2>::value),
+                        MISSING COMPATIBLE COMPLEX PACKET TYPE)
+    ComplexPacket cresult = BaseEvaluator::template packet<LoadMode, ComplexPacket>(index / 2);
+    PacketType result = preinterpret<PacketType>(cresult);
+    bool p = index & 1;
+    if (p) {
+      Scalar aux[RealPacketSize + 1];
+      pstoreu(aux, result);
+      aux[RealPacketSize] = coeff(index + RealPacketSize - 1);
+      result = ploadu<PacketType>(aux + 1);
+    }
+    return result;
+  }
+
+  // The requested real packet segment forms the half-open interval [begin, end), where 'end' = 'begin' + 'count'.
+  // In order to access the underlying complex array, even indices must be aligned with the real components
+  // of the complex scalars. 'begin' and 'count' must be modified as follows:
+  // a) 'begin' must be rounded down to the nearest even number; and
+  // b) 'end' must be rounded up to the nearest even number.
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index row, Index col, Index begin, Index count) const {
+    constexpr int RealPacketSize = unpacket_traits<PacketType>::size;
+    using ComplexPacket = typename find_packet_by_size<ComplexScalar, RealPacketSize / 2>::type;
+    EIGEN_STATIC_ASSERT((find_packet_by_size<ComplexScalar, RealPacketSize / 2>::value),
+                        MISSING COMPATIBLE COMPLEX PACKET TYPE)
+    Index actualBegin = numext::round_down(begin, 2);
+    Index actualEnd = numext::round_down(begin + count + 1, 2);
+    Index actualCount = actualEnd - actualBegin;
+    Index r = IsRowMajor ? row : row / 2;
+    Index c = IsRowMajor ? col / 2 : col;
+    ComplexPacket cresult =
+        BaseEvaluator::template packetSegment<LoadMode, ComplexPacket>(r, c, actualBegin / 2, actualCount / 2);
+    PacketType result = preinterpret<PacketType>(cresult);
+    bool p = (IsRowMajor ? col : row) & 1;
+    if (p) {
+      Scalar aux[RealPacketSize + 1] = {};
+      pstoreu(aux, result);
+      Index lastr = IsRowMajor ? row : row + actualEnd - 1;
+      Index lastc = IsRowMajor ? col + actualEnd - 1 : col;
+      aux[actualEnd] = coeff(lastr, lastc);
+      result = ploadu<PacketType>(aux + 1);
+    }
+    return result;
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegment(Index index, Index begin, Index count) const {
+    constexpr int RealPacketSize = unpacket_traits<PacketType>::size;
+    using ComplexPacket = typename find_packet_by_size<ComplexScalar, RealPacketSize / 2>::type;
+    EIGEN_STATIC_ASSERT((find_packet_by_size<ComplexScalar, RealPacketSize / 2>::value),
+                        MISSING COMPATIBLE COMPLEX PACKET TYPE)
+    Index actualBegin = numext::round_down(begin, 2);
+    Index actualEnd = numext::round_down(begin + count + 1, 2);
+    Index actualCount = actualEnd - actualBegin;
+    ComplexPacket cresult =
+        BaseEvaluator::template packetSegment<LoadMode, ComplexPacket>(index / 2, actualBegin / 2, actualCount / 2);
+    PacketType result = preinterpret<PacketType>(cresult);
+    bool p = index & 1;
+    if (p) {
+      Scalar aux[RealPacketSize + 1] = {};
+      pstoreu(aux, result);
+      aux[actualEnd] = coeff(index + actualEnd - 1);
+      result = ploadu<PacketType>(aux + 1);
+    }
+    return result;
+  }
+};
+
+}  // namespace internal
+
+template <typename Xpr>
+class RealView : public internal::dense_xpr_base<RealView<Xpr>>::type {
+  using ExpressionTraits = internal::traits<RealView>;
+  EIGEN_STATIC_ASSERT(NumTraits<typename Xpr::Scalar>::IsComplex, SCALAR MUST BE COMPLEX)
+ public:
+  using Scalar = typename ExpressionTraits::Scalar;
+  using Nested = RealView;
+
+  EIGEN_DEVICE_FUNC explicit RealView(Xpr& xpr) : m_xpr(xpr) {}
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return Xpr::IsRowMajor ? m_xpr.rows() : 2 * m_xpr.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return Xpr::IsRowMajor ? 2 * m_xpr.cols() : m_xpr.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index size() const noexcept { return 2 * m_xpr.size(); }
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_xpr.innerStride(); }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return 2 * m_xpr.outerStride(); }
+  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) {
+    m_xpr.resize(Xpr::IsRowMajor ? rows : rows / 2, Xpr::IsRowMajor ? cols / 2 : cols);
+  }
+  EIGEN_DEVICE_FUNC void resize(Index size) { m_xpr.resize(size / 2); }
+  EIGEN_DEVICE_FUNC Scalar* data() { return reinterpret_cast<Scalar*>(m_xpr.data()); }
+  EIGEN_DEVICE_FUNC const Scalar* data() const { return reinterpret_cast<const Scalar*>(m_xpr.data()); }
+
+  EIGEN_DEVICE_FUNC RealView(const RealView&) = default;
+
+  EIGEN_DEVICE_FUNC RealView& operator=(const RealView& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC RealView& operator=(const RealView<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC RealView& operator=(const DenseBase<OtherDerived>& other);
+
+ protected:
+  friend struct internal::evaluator<RealView>;
+  Xpr& m_xpr;
+};
+
+template <typename Xpr>
+EIGEN_DEVICE_FUNC RealView<Xpr>& RealView<Xpr>::operator=(const RealView& other) {
+  internal::call_assignment(*this, other);
+  return *this;
+}
+
+template <typename Xpr>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC RealView<Xpr>& RealView<Xpr>::operator=(const RealView<OtherDerived>& other) {
+  internal::call_assignment(*this, other);
+  return *this;
+}
+
+template <typename Xpr>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC RealView<Xpr>& RealView<Xpr>::operator=(const DenseBase<OtherDerived>& other) {
+  internal::call_assignment(*this, other.derived());
+  return *this;
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC typename DenseBase<Derived>::RealViewReturnType DenseBase<Derived>::realView() {
+  return RealViewReturnType(derived());
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC typename DenseBase<Derived>::ConstRealViewReturnType DenseBase<Derived>::realView() const {
+  return ConstRealViewReturnType(derived());
+}
+
+}  // namespace Eigen
+
+#endif  // EIGEN_REALVIEW_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Redux.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Redux.h
new file mode 100644
index 0000000..fefb4c9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Redux.h
@@ -0,0 +1,535 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REDUX_H
+#define EIGEN_REDUX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// TODO
+//  * implement other kind of vectorization
+//  * factorize code
+
+/***************************************************************************
+ * Part 1 : the logic deciding a strategy for vectorization and unrolling
+ ***************************************************************************/
+
+template <typename Func, typename Evaluator>
+struct redux_traits {
+ public:
+  typedef typename find_best_packet<typename Evaluator::Scalar, Evaluator::SizeAtCompileTime>::type PacketType;
+  enum {
+    PacketSize = unpacket_traits<PacketType>::size,
+    InnerMaxSize = int(Evaluator::IsRowMajor) ? Evaluator::MaxColsAtCompileTime : Evaluator::MaxRowsAtCompileTime,
+    OuterMaxSize = int(Evaluator::IsRowMajor) ? Evaluator::MaxRowsAtCompileTime : Evaluator::MaxColsAtCompileTime,
+    SliceVectorizedWork = int(InnerMaxSize) == Dynamic   ? Dynamic
+                          : int(OuterMaxSize) == Dynamic ? (int(InnerMaxSize) >= int(PacketSize) ? Dynamic : 0)
+                                                         : (int(InnerMaxSize) / int(PacketSize)) * int(OuterMaxSize)
+  };
+
+  enum {
+    MayLinearize = (int(Evaluator::Flags) & LinearAccessBit),
+    MightVectorize = (int(Evaluator::Flags) & ActualPacketAccessBit) && (functor_traits<Func>::PacketAccess),
+    MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize),
+    MaySliceVectorize = bool(MightVectorize) && (int(SliceVectorizedWork) == Dynamic || int(SliceVectorizedWork) >= 3)
+  };
+
+ public:
+  enum {
+    Traversal = int(MayLinearVectorize)  ? int(LinearVectorizedTraversal)
+                : int(MaySliceVectorize) ? int(SliceVectorizedTraversal)
+                : int(MayLinearize)      ? int(LinearTraversal)
+                                         : int(DefaultTraversal)
+  };
+
+ public:
+  enum {
+    Cost = Evaluator::SizeAtCompileTime == Dynamic
+               ? HugeCost
+               : int(Evaluator::SizeAtCompileTime) * int(Evaluator::CoeffReadCost) +
+                     (Evaluator::SizeAtCompileTime - 1) * functor_traits<Func>::Cost,
+    UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize))
+  };
+
+ public:
+  enum { Unrolling = Cost <= UnrollingLimit ? CompleteUnrolling : NoUnrolling };
+
+#ifdef EIGEN_DEBUG_ASSIGN
+  static void debug() {
+    std::cerr << "Xpr: " << typeid(typename Evaluator::XprType).name() << std::endl;
+    std::cerr.setf(std::ios::hex, std::ios::basefield);
+    EIGEN_DEBUG_VAR(Evaluator::Flags)
+    std::cerr.unsetf(std::ios::hex);
+    EIGEN_DEBUG_VAR(InnerMaxSize)
+    EIGEN_DEBUG_VAR(OuterMaxSize)
+    EIGEN_DEBUG_VAR(SliceVectorizedWork)
+    EIGEN_DEBUG_VAR(PacketSize)
+    EIGEN_DEBUG_VAR(MightVectorize)
+    EIGEN_DEBUG_VAR(MayLinearVectorize)
+    EIGEN_DEBUG_VAR(MaySliceVectorize)
+    std::cerr << "Traversal"
+              << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
+    EIGEN_DEBUG_VAR(UnrollingLimit)
+    std::cerr << "Unrolling"
+              << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
+    std::cerr << std::endl;
+  }
+#endif
+};
+
+/***************************************************************************
+ * Part 2 : unrollers
+ ***************************************************************************/
+
+/*** no vectorization ***/
+
+template <typename Func, typename Evaluator, Index Start, Index Length>
+struct redux_novec_unroller {
+  static constexpr Index HalfLength = Length / 2;
+
+  typedef typename Evaluator::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC static constexpr EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval, const Func& func) {
+    return func(redux_novec_unroller<Func, Evaluator, Start, HalfLength>::run(eval, func),
+                redux_novec_unroller<Func, Evaluator, Start + HalfLength, Length - HalfLength>::run(eval, func));
+  }
+};
+
+template <typename Func, typename Evaluator, Index Start>
+struct redux_novec_unroller<Func, Evaluator, Start, 1> {
+  static constexpr Index outer = Start / Evaluator::InnerSizeAtCompileTime;
+  static constexpr Index inner = Start % Evaluator::InnerSizeAtCompileTime;
+
+  typedef typename Evaluator::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC static constexpr EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval, const Func&) {
+    return eval.coeffByOuterInner(outer, inner);
+  }
+};
+
+// This is actually dead code and will never be called. It is required
+// to prevent false warnings regarding failed inlining though
+// for 0 length run() will never be called at all.
+template <typename Func, typename Evaluator, Index Start>
+struct redux_novec_unroller<Func, Evaluator, Start, 0> {
+  typedef typename Evaluator::Scalar Scalar;
+  EIGEN_DEVICE_FUNC static constexpr EIGEN_STRONG_INLINE Scalar run(const Evaluator&, const Func&) { return Scalar(); }
+};
+
+template <typename Func, typename Evaluator, Index Start, Index Length>
+struct redux_novec_linear_unroller {
+  static constexpr Index HalfLength = Length / 2;
+
+  typedef typename Evaluator::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC static constexpr EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval, const Func& func) {
+    return func(redux_novec_linear_unroller<Func, Evaluator, Start, HalfLength>::run(eval, func),
+                redux_novec_linear_unroller<Func, Evaluator, Start + HalfLength, Length - HalfLength>::run(eval, func));
+  }
+};
+
+template <typename Func, typename Evaluator, Index Start>
+struct redux_novec_linear_unroller<Func, Evaluator, Start, 1> {
+  typedef typename Evaluator::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC static constexpr EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval, const Func&) {
+    return eval.coeff(Start);
+  }
+};
+
+// This is actually dead code and will never be called. It is required
+// to prevent false warnings regarding failed inlining though
+// for 0 length run() will never be called at all.
+template <typename Func, typename Evaluator, Index Start>
+struct redux_novec_linear_unroller<Func, Evaluator, Start, 0> {
+  typedef typename Evaluator::Scalar Scalar;
+  EIGEN_DEVICE_FUNC static constexpr EIGEN_STRONG_INLINE Scalar run(const Evaluator&, const Func&) { return Scalar(); }
+};
+
+/*** vectorization ***/
+
+template <typename Func, typename Evaluator, Index Start, Index Length>
+struct redux_vec_unroller {
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE PacketType run(const Evaluator& eval, const Func& func) {
+    constexpr Index HalfLength = Length / 2;
+
+    return func.packetOp(
+        redux_vec_unroller<Func, Evaluator, Start, HalfLength>::template run<PacketType>(eval, func),
+        redux_vec_unroller<Func, Evaluator, Start + HalfLength, Length - HalfLength>::template run<PacketType>(eval,
+                                                                                                               func));
+  }
+};
+
+template <typename Func, typename Evaluator, Index Start>
+struct redux_vec_unroller<Func, Evaluator, Start, 1> {
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE PacketType run(const Evaluator& eval, const Func&) {
+    constexpr Index PacketSize = unpacket_traits<PacketType>::size;
+    constexpr Index index = Start * PacketSize;
+    constexpr Index outer = index / int(Evaluator::InnerSizeAtCompileTime);
+    constexpr Index inner = index % int(Evaluator::InnerSizeAtCompileTime);
+    constexpr int alignment = Evaluator::Alignment;
+
+    return eval.template packetByOuterInner<alignment, PacketType>(outer, inner);
+  }
+};
+
+template <typename Func, typename Evaluator, Index Start, Index Length>
+struct redux_vec_linear_unroller {
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE PacketType run(const Evaluator& eval, const Func& func) {
+    constexpr Index HalfLength = Length / 2;
+
+    return func.packetOp(
+        redux_vec_linear_unroller<Func, Evaluator, Start, HalfLength>::template run<PacketType>(eval, func),
+        redux_vec_linear_unroller<Func, Evaluator, Start + HalfLength, Length - HalfLength>::template run<PacketType>(
+            eval, func));
+  }
+};
+
+template <typename Func, typename Evaluator, Index Start>
+struct redux_vec_linear_unroller<Func, Evaluator, Start, 1> {
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE PacketType run(const Evaluator& eval, const Func&) {
+    constexpr Index PacketSize = unpacket_traits<PacketType>::size;
+    constexpr Index index = (Start * PacketSize);
+    constexpr int alignment = Evaluator::Alignment;
+    return eval.template packet<alignment, PacketType>(index);
+  }
+};
+
+/***************************************************************************
+ * Part 3 : implementation of all cases
+ ***************************************************************************/
+
+template <typename Func, typename Evaluator, int Traversal = redux_traits<Func, Evaluator>::Traversal,
+          int Unrolling = redux_traits<Func, Evaluator>::Unrolling>
+struct redux_impl;
+
+template <typename Func, typename Evaluator>
+struct redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling> {
+  typedef typename Evaluator::Scalar Scalar;
+
+  template <typename XprType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval, const Func& func, const XprType& xpr) {
+    eigen_assert(xpr.rows() > 0 && xpr.cols() > 0 && "you are using an empty matrix");
+    Scalar res = eval.coeffByOuterInner(0, 0);
+    for (Index i = 1; i < xpr.innerSize(); ++i) res = func(res, eval.coeffByOuterInner(0, i));
+    for (Index i = 1; i < xpr.outerSize(); ++i)
+      for (Index j = 0; j < xpr.innerSize(); ++j) res = func(res, eval.coeffByOuterInner(i, j));
+    return res;
+  }
+};
+
+template <typename Func, typename Evaluator>
+struct redux_impl<Func, Evaluator, LinearTraversal, NoUnrolling> {
+  typedef typename Evaluator::Scalar Scalar;
+
+  template <typename XprType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval, const Func& func, const XprType& xpr) {
+    eigen_assert(xpr.size() > 0 && "you are using an empty matrix");
+    Scalar res = eval.coeff(0);
+    for (Index k = 1; k < xpr.size(); ++k) res = func(res, eval.coeff(k));
+    return res;
+  }
+};
+
+template <typename Func, typename Evaluator>
+struct redux_impl<Func, Evaluator, DefaultTraversal, CompleteUnrolling>
+    : redux_novec_unroller<Func, Evaluator, 0, Evaluator::SizeAtCompileTime> {
+  typedef redux_novec_unroller<Func, Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
+  typedef typename Evaluator::Scalar Scalar;
+  template <typename XprType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval, const Func& func,
+                                                          const XprType& /*xpr*/) {
+    return Base::run(eval, func);
+  }
+};
+
+template <typename Func, typename Evaluator>
+struct redux_impl<Func, Evaluator, LinearTraversal, CompleteUnrolling>
+    : redux_novec_linear_unroller<Func, Evaluator, 0, Evaluator::SizeAtCompileTime> {
+  typedef redux_novec_linear_unroller<Func, Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
+  typedef typename Evaluator::Scalar Scalar;
+  template <typename XprType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval, const Func& func,
+                                                          const XprType& /*xpr*/) {
+    return Base::run(eval, func);
+  }
+};
+
+template <typename Func, typename Evaluator>
+struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, NoUnrolling> {
+  typedef typename Evaluator::Scalar Scalar;
+  typedef typename redux_traits<Func, Evaluator>::PacketType PacketScalar;
+
+  template <typename XprType>
+  static Scalar run(const Evaluator& eval, const Func& func, const XprType& xpr) {
+    const Index size = xpr.size();
+
+    constexpr Index packetSize = redux_traits<Func, Evaluator>::PacketSize;
+    constexpr int packetAlignment = unpacket_traits<PacketScalar>::alignment;
+    constexpr int alignment0 =
+        (bool(Evaluator::Flags & DirectAccessBit) && bool(packet_traits<Scalar>::AlignedOnScalar))
+            ? int(packetAlignment)
+            : int(Unaligned);
+    constexpr int alignment = plain_enum_max(alignment0, Evaluator::Alignment);
+    const Index alignedStart = internal::first_default_aligned(xpr);
+    const Index alignedSize2 = ((size - alignedStart) / (2 * packetSize)) * (2 * packetSize);
+    const Index alignedSize = ((size - alignedStart) / (packetSize)) * (packetSize);
+    const Index alignedEnd2 = alignedStart + alignedSize2;
+    const Index alignedEnd = alignedStart + alignedSize;
+    Scalar res;
+    if (alignedSize) {
+      PacketScalar packet_res0 = eval.template packet<alignment, PacketScalar>(alignedStart);
+      if (alignedSize > packetSize)  // we have at least two packets to partly unroll the loop
+      {
+        PacketScalar packet_res1 = eval.template packet<alignment, PacketScalar>(alignedStart + packetSize);
+        for (Index index = alignedStart + 2 * packetSize; index < alignedEnd2; index += 2 * packetSize) {
+          packet_res0 = func.packetOp(packet_res0, eval.template packet<alignment, PacketScalar>(index));
+          packet_res1 = func.packetOp(packet_res1, eval.template packet<alignment, PacketScalar>(index + packetSize));
+        }
+
+        packet_res0 = func.packetOp(packet_res0, packet_res1);
+        if (alignedEnd > alignedEnd2)
+          packet_res0 = func.packetOp(packet_res0, eval.template packet<alignment, PacketScalar>(alignedEnd2));
+      }
+      res = func.predux(packet_res0);
+
+      for (Index index = 0; index < alignedStart; ++index) res = func(res, eval.coeff(index));
+
+      for (Index index = alignedEnd; index < size; ++index) res = func(res, eval.coeff(index));
+    } else  // too small to vectorize anything.
+            // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
+    {
+      res = eval.coeff(0);
+      for (Index index = 1; index < size; ++index) res = func(res, eval.coeff(index));
+    }
+
+    return res;
+  }
+};
+
+// NOTE: for SliceVectorizedTraversal we simply bypass unrolling
+template <typename Func, typename Evaluator, int Unrolling>
+struct redux_impl<Func, Evaluator, SliceVectorizedTraversal, Unrolling> {
+  typedef typename Evaluator::Scalar Scalar;
+  typedef typename redux_traits<Func, Evaluator>::PacketType PacketType;
+
+  template <typename XprType>
+  EIGEN_DEVICE_FUNC static Scalar run(const Evaluator& eval, const Func& func, const XprType& xpr) {
+    eigen_assert(xpr.rows() > 0 && xpr.cols() > 0 && "you are using an empty matrix");
+    constexpr Index packetSize = redux_traits<Func, Evaluator>::PacketSize;
+    const Index innerSize = xpr.innerSize();
+    const Index outerSize = xpr.outerSize();
+    const Index packetedInnerSize = ((innerSize) / packetSize) * packetSize;
+    Scalar res;
+    if (packetedInnerSize) {
+      PacketType packet_res = eval.template packet<Unaligned, PacketType>(0, 0);
+      for (Index j = 0; j < outerSize; ++j)
+        for (Index i = (j == 0 ? packetSize : 0); i < packetedInnerSize; i += Index(packetSize))
+          packet_res = func.packetOp(packet_res, eval.template packetByOuterInner<Unaligned, PacketType>(j, i));
+
+      res = func.predux(packet_res);
+      for (Index j = 0; j < outerSize; ++j)
+        for (Index i = packetedInnerSize; i < innerSize; ++i) res = func(res, eval.coeffByOuterInner(j, i));
+    } else  // too small to vectorize anything.
+            // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
+    {
+      res = redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>::run(eval, func, xpr);
+    }
+
+    return res;
+  }
+};
+
+template <typename Func, typename Evaluator>
+struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, CompleteUnrolling> {
+  typedef typename Evaluator::Scalar Scalar;
+
+  typedef typename redux_traits<Func, Evaluator>::PacketType PacketType;
+  static constexpr Index PacketSize = redux_traits<Func, Evaluator>::PacketSize;
+  static constexpr Index Size = Evaluator::SizeAtCompileTime;
+  static constexpr Index VectorizedSize = (int(Size) / int(PacketSize)) * int(PacketSize);
+
+  template <typename XprType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Evaluator& eval, const Func& func, const XprType& xpr) {
+    EIGEN_ONLY_USED_FOR_DEBUG(xpr)
+    eigen_assert(xpr.rows() > 0 && xpr.cols() > 0 && "you are using an empty matrix");
+    if (VectorizedSize > 0) {
+      Scalar res = func.predux(
+          redux_vec_linear_unroller<Func, Evaluator, 0, Size / PacketSize>::template run<PacketType>(eval, func));
+      if (VectorizedSize != Size)
+        res = func(
+            res, redux_novec_linear_unroller<Func, Evaluator, VectorizedSize, Size - VectorizedSize>::run(eval, func));
+      return res;
+    } else {
+      return redux_novec_linear_unroller<Func, Evaluator, 0, Size>::run(eval, func);
+    }
+  }
+};
+
+// evaluator adaptor
+template <typename XprType_>
+class redux_evaluator : public internal::evaluator<XprType_> {
+  typedef internal::evaluator<XprType_> Base;
+
+ public:
+  typedef XprType_ XprType;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit redux_evaluator(const XprType& xpr) : Base(xpr) {}
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketScalar PacketScalar;
+
+  enum {
+    MaxRowsAtCompileTime = XprType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = XprType::MaxColsAtCompileTime,
+    // TODO: we should not remove DirectAccessBit and rather find an elegant way to query the alignment offset at
+    // runtime from the evaluator
+    Flags = Base::Flags & ~DirectAccessBit,
+    IsRowMajor = XprType::IsRowMajor,
+    SizeAtCompileTime = XprType::SizeAtCompileTime,
+    InnerSizeAtCompileTime = XprType::InnerSizeAtCompileTime
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const {
+    return Base::coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetByOuterInner(Index outer, Index inner) const {
+    return Base::template packet<LoadMode, PacketType>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetSegmentByOuterInner(Index outer, Index inner, Index begin,
+                                                                             Index count) const {
+    return Base::template packetSegment<LoadMode, PacketType>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer,
+                                                              begin, count);
+  }
+};
+
+}  // end namespace internal
+
+/***************************************************************************
+ * Part 4 : public API
+ ***************************************************************************/
+
+/** \returns the result of a full redux operation on the whole matrix or vector using \a func
+ *
+ * The template parameter \a BinaryOp is the type of the functor \a func which must be
+ * an associative operator. Both current C++98 and C++11 functor styles are handled.
+ *
+ * \warning the matrix must be not empty, otherwise an assertion is triggered.
+ *
+ * \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise()
+ */
+template <typename Derived>
+template <typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar DenseBase<Derived>::redux(
+    const Func& func) const {
+  eigen_assert(this->rows() > 0 && this->cols() > 0 && "you are using an empty matrix");
+
+  typedef typename internal::redux_evaluator<Derived> ThisEvaluator;
+  ThisEvaluator thisEval(derived());
+
+  // The initial expression is passed to the reducer as an additional argument instead of
+  // passing it as a member of redux_evaluator to help
+  return internal::redux_impl<Func, ThisEvaluator>::run(thisEval, func, derived());
+}
+
+/** \returns the minimum of all coefficients of \c *this.
+ * In case \c *this contains NaN, NaNPropagation determines the behavior:
+ *   NaNPropagation == PropagateFast : undefined
+ *   NaNPropagation == PropagateNaN : result is NaN
+ *   NaNPropagation == PropagateNumbers : result is minimum of elements that are not NaN
+ * \warning the matrix must be not empty, otherwise an assertion is triggered.
+ */
+template <typename Derived>
+template <int NaNPropagation>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar DenseBase<Derived>::minCoeff() const {
+  return derived().redux(Eigen::internal::scalar_min_op<Scalar, Scalar, NaNPropagation>());
+}
+
+/** \returns the maximum of all coefficients of \c *this.
+ * In case \c *this contains NaN, NaNPropagation determines the behavior:
+ *   NaNPropagation == PropagateFast : undefined
+ *   NaNPropagation == PropagateNaN : result is NaN
+ *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
+ * \warning the matrix must be not empty, otherwise an assertion is triggered.
+ */
+template <typename Derived>
+template <int NaNPropagation>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar DenseBase<Derived>::maxCoeff() const {
+  return derived().redux(Eigen::internal::scalar_max_op<Scalar, Scalar, NaNPropagation>());
+}
+
+/** \returns the sum of all coefficients of \c *this
+ *
+ * If \c *this is empty, then the value 0 is returned.
+ *
+ * \sa trace(), prod(), mean()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar DenseBase<Derived>::sum() const {
+  if (SizeAtCompileTime == 0 || (SizeAtCompileTime == Dynamic && size() == 0)) return Scalar(0);
+  return derived().redux(Eigen::internal::scalar_sum_op<Scalar, Scalar>());
+}
+
+/** \returns the mean of all coefficients of *this
+ *
+ * \sa trace(), prod(), sum()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar DenseBase<Derived>::mean() const {
+#ifdef __INTEL_COMPILER
+#pragma warning push
+#pragma warning(disable : 2259)
+#endif
+  return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar, Scalar>())) / Scalar(this->size());
+#ifdef __INTEL_COMPILER
+#pragma warning pop
+#endif
+}
+
+/** \returns the product of all coefficients of *this
+ *
+ * Example: \include MatrixBase_prod.cpp
+ * Output: \verbinclude MatrixBase_prod.out
+ *
+ * \sa sum(), mean(), trace()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar DenseBase<Derived>::prod() const {
+  if (SizeAtCompileTime == 0 || (SizeAtCompileTime == Dynamic && size() == 0)) return Scalar(1);
+  return derived().redux(Eigen::internal::scalar_product_op<Scalar>());
+}
+
+/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal.
+ *
+ * \c *this can be any matrix, not necessarily square.
+ *
+ * \sa diagonal(), sum()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar MatrixBase<Derived>::trace() const {
+  return derived().diagonal().sum();
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_REDUX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Ref.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Ref.h
new file mode 100644
index 0000000..c845462
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Ref.h
@@ -0,0 +1,384 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REF_H
+#define EIGEN_REF_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename PlainObjectType_, int Options_, typename StrideType_>
+struct traits<Ref<PlainObjectType_, Options_, StrideType_> >
+    : public traits<Map<PlainObjectType_, Options_, StrideType_> > {
+  typedef PlainObjectType_ PlainObjectType;
+  typedef StrideType_ StrideType;
+  enum {
+    Options = Options_,
+    Flags = traits<Map<PlainObjectType_, Options_, StrideType_> >::Flags | NestByRefBit,
+    Alignment = traits<Map<PlainObjectType_, Options_, StrideType_> >::Alignment,
+    InnerStrideAtCompileTime = traits<Map<PlainObjectType_, Options_, StrideType_> >::InnerStrideAtCompileTime,
+    OuterStrideAtCompileTime = traits<Map<PlainObjectType_, Options_, StrideType_> >::OuterStrideAtCompileTime
+  };
+
+  template <typename Derived>
+  struct match {
+    enum {
+      IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime,
+      HasDirectAccess = internal::has_direct_access<Derived>::ret,
+      StorageOrderMatch =
+          IsVectorAtCompileTime || ((PlainObjectType::Flags & RowMajorBit) == (Derived::Flags & RowMajorBit)),
+      InnerStrideMatch = int(InnerStrideAtCompileTime) == int(Dynamic) ||
+                         int(InnerStrideAtCompileTime) == int(Derived::InnerStrideAtCompileTime) ||
+                         (int(InnerStrideAtCompileTime) == 0 && int(Derived::InnerStrideAtCompileTime) == 1),
+      OuterStrideMatch = IsVectorAtCompileTime || int(OuterStrideAtCompileTime) == int(Dynamic) ||
+                         int(OuterStrideAtCompileTime) == int(Derived::OuterStrideAtCompileTime),
+      // NOTE, this indirection of evaluator<Derived>::Alignment is needed
+      // to work around an MSVC bug related to the instantiation
+      // of has_*ary_operator in evaluator<CwiseNullaryOp>.
+      // This line is surprisingly very sensitive. For instance, simply adding parenthesis
+      // as "DerivedAlignment = (int(evaluator<Derived>::Alignment))," will make MSVC fail...
+      DerivedAlignment = int(evaluator<Derived>::Alignment),
+      AlignmentMatch = (int(traits<PlainObjectType>::Alignment) == int(Unaligned)) ||
+                       (DerivedAlignment >= int(Alignment)),  // FIXME the first condition is not very clear, it should
+                                                              // be replaced by the required alignment
+      ScalarTypeMatch = internal::is_same<typename PlainObjectType::Scalar, typename Derived::Scalar>::value,
+      MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch &&
+                           AlignmentMatch && ScalarTypeMatch
+    };
+    typedef std::conditional_t<MatchAtCompileTime, internal::true_type, internal::false_type> type;
+  };
+};
+
+template <typename Derived>
+struct traits<RefBase<Derived> > : public traits<Derived> {};
+
+}  // namespace internal
+
+template <typename Derived>
+class RefBase : public MapBase<Derived> {
+  typedef typename internal::traits<Derived>::PlainObjectType PlainObjectType;
+  typedef typename internal::traits<Derived>::StrideType StrideType;
+
+ public:
+  typedef MapBase<Derived> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(RefBase)
+
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const {
+    return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const {
+    return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
+           : IsVectorAtCompileTime                   ? this->size()
+           : int(Flags) & RowMajorBit                ? this->cols()
+                                                     : this->rows();
+  }
+
+  EIGEN_DEVICE_FUNC RefBase()
+      : Base(0, RowsAtCompileTime == Dynamic ? 0 : RowsAtCompileTime,
+             ColsAtCompileTime == Dynamic ? 0 : ColsAtCompileTime),
+        // Stride<> does not allow default ctor for Dynamic strides, so let' initialize it with dummy values:
+        m_stride(StrideType::OuterStrideAtCompileTime == Dynamic ? 0 : StrideType::OuterStrideAtCompileTime,
+                 StrideType::InnerStrideAtCompileTime == Dynamic ? 0 : StrideType::InnerStrideAtCompileTime) {}
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(RefBase)
+
+ protected:
+  typedef Stride<StrideType::OuterStrideAtCompileTime, StrideType::InnerStrideAtCompileTime> StrideBase;
+
+  // Resolves inner stride if default 0.
+  static EIGEN_DEVICE_FUNC constexpr Index resolveInnerStride(Index inner) { return inner == 0 ? 1 : inner; }
+
+  // Resolves outer stride if default 0.
+  static EIGEN_DEVICE_FUNC constexpr Index resolveOuterStride(Index inner, Index outer, Index rows, Index cols,
+                                                              bool isVectorAtCompileTime, bool isRowMajor) {
+    return outer == 0 ? isVectorAtCompileTime ? inner * rows * cols : isRowMajor ? inner * cols : inner * rows : outer;
+  }
+
+  // Returns true if construction is valid, false if there is a stride mismatch,
+  // and fails if there is a size mismatch.
+  template <typename Expression>
+  EIGEN_DEVICE_FUNC bool construct(Expression& expr) {
+    // Check matrix sizes.  If this is a compile-time vector, we do allow
+    // implicitly transposing.
+    EIGEN_STATIC_ASSERT(EIGEN_PREDICATE_SAME_MATRIX_SIZE(PlainObjectType, Expression)
+                            // If it is a vector, the transpose sizes might match.
+                            || (PlainObjectType::IsVectorAtCompileTime &&
+                                ((int(PlainObjectType::RowsAtCompileTime) == Eigen::Dynamic ||
+                                  int(Expression::ColsAtCompileTime) == Eigen::Dynamic ||
+                                  int(PlainObjectType::RowsAtCompileTime) == int(Expression::ColsAtCompileTime)) &&
+                                 (int(PlainObjectType::ColsAtCompileTime) == Eigen::Dynamic ||
+                                  int(Expression::RowsAtCompileTime) == Eigen::Dynamic ||
+                                  int(PlainObjectType::ColsAtCompileTime) == int(Expression::RowsAtCompileTime)))),
+                        YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES)
+
+    // Determine runtime rows and columns.
+    Index rows = expr.rows();
+    Index cols = expr.cols();
+    if (PlainObjectType::RowsAtCompileTime == 1) {
+      eigen_assert(expr.rows() == 1 || expr.cols() == 1);
+      rows = 1;
+      cols = expr.size();
+    } else if (PlainObjectType::ColsAtCompileTime == 1) {
+      eigen_assert(expr.rows() == 1 || expr.cols() == 1);
+      rows = expr.size();
+      cols = 1;
+    }
+    // Verify that the sizes are valid.
+    eigen_assert((PlainObjectType::RowsAtCompileTime == Dynamic) || (PlainObjectType::RowsAtCompileTime == rows));
+    eigen_assert((PlainObjectType::ColsAtCompileTime == Dynamic) || (PlainObjectType::ColsAtCompileTime == cols));
+
+    // If this is a vector, we might be transposing, which means that stride should swap.
+    const bool transpose = PlainObjectType::IsVectorAtCompileTime && (rows != expr.rows());
+    // If the storage format differs, we also need to swap the stride.
+    const bool row_major = ((PlainObjectType::Flags)&RowMajorBit) != 0;
+    const bool expr_row_major = (Expression::Flags & RowMajorBit) != 0;
+    const bool storage_differs = (row_major != expr_row_major);
+
+    const bool swap_stride = (transpose != storage_differs);
+
+    // Determine expr's actual strides, resolving any defaults if zero.
+    const Index expr_inner_actual = resolveInnerStride(expr.innerStride());
+    const Index expr_outer_actual = resolveOuterStride(expr_inner_actual, expr.outerStride(), expr.rows(), expr.cols(),
+                                                       Expression::IsVectorAtCompileTime != 0, expr_row_major);
+
+    // If this is a column-major row vector or row-major column vector, the inner-stride
+    // is arbitrary, so set it to either the compile-time inner stride or 1.
+    const bool row_vector = (rows == 1);
+    const bool col_vector = (cols == 1);
+    const Index inner_stride =
+        ((!row_major && row_vector) || (row_major && col_vector))
+            ? (StrideType::InnerStrideAtCompileTime > 0 ? Index(StrideType::InnerStrideAtCompileTime) : 1)
+        : swap_stride ? expr_outer_actual
+                      : expr_inner_actual;
+
+    // If this is a column-major column vector or row-major row vector, the outer-stride
+    // is arbitrary, so set it to either the compile-time outer stride or vector size.
+    const Index outer_stride =
+        ((!row_major && col_vector) || (row_major && row_vector))
+            ? (StrideType::OuterStrideAtCompileTime > 0 ? Index(StrideType::OuterStrideAtCompileTime)
+                                                        : rows * cols * inner_stride)
+        : swap_stride ? expr_inner_actual
+                      : expr_outer_actual;
+
+    // Check if given inner/outer strides are compatible with compile-time strides.
+    const bool inner_valid = (StrideType::InnerStrideAtCompileTime == Dynamic) ||
+                             (resolveInnerStride(Index(StrideType::InnerStrideAtCompileTime)) == inner_stride);
+    if (!inner_valid) {
+      return false;
+    }
+
+    const bool outer_valid =
+        (StrideType::OuterStrideAtCompileTime == Dynamic) ||
+        (resolveOuterStride(inner_stride, Index(StrideType::OuterStrideAtCompileTime), rows, cols,
+                            PlainObjectType::IsVectorAtCompileTime != 0, row_major) == outer_stride);
+    if (!outer_valid) {
+      return false;
+    }
+
+    internal::construct_at<Base>(this, expr.data(), rows, cols);
+    internal::construct_at(&m_stride, (StrideType::OuterStrideAtCompileTime == 0) ? 0 : outer_stride,
+                           (StrideType::InnerStrideAtCompileTime == 0) ? 0 : inner_stride);
+    return true;
+  }
+
+  StrideBase m_stride;
+};
+
+/** \class Ref
+ * \ingroup Core_Module
+ *
+ * \brief A matrix or vector expression mapping an existing expression
+ *
+ * \tparam PlainObjectType the equivalent matrix type of the mapped data
+ * \tparam Options specifies the pointer alignment in bytes. It can be: \c #Aligned128, , \c #Aligned64, \c #Aligned32,
+ * \c #Aligned16, \c #Aligned8 or \c #Unaligned. The default is \c #Unaligned. \tparam StrideType optionally specifies
+ * strides. By default, Ref implies a contiguous storage along the inner dimension (inner stride==1), but accepts a
+ * variable outer stride (leading dimension). This can be overridden by specifying strides. The type passed here must be
+ * a specialization of the Stride template, see examples below.
+ *
+ * This class provides a way to write non-template functions taking Eigen objects as parameters while limiting the
+ * number of copies. A Ref<> object can represent either a const expression or a l-value: \code
+ * // in-out argument:
+ * void foo1(Ref<VectorXf> x);
+ *
+ * // read-only const argument:
+ * void foo2(const Ref<const VectorXf>& x);
+ * \endcode
+ *
+ * In the in-out case, the input argument must satisfy the constraints of the actual Ref<> type, otherwise a compilation
+ * issue will be triggered. By default, a Ref<VectorXf> can reference any dense vector expression of float having a
+ * contiguous memory layout. Likewise, a Ref<MatrixXf> can reference any column-major dense matrix expression of float
+ * whose column's elements are contiguously stored with the possibility to have a constant space in-between each column,
+ * i.e. the inner stride must be equal to 1, but the outer stride (or leading dimension) can be greater than the number
+ * of rows.
+ *
+ * In the const case, if the input expression does not match the above requirement, then it is evaluated into a
+ * temporary before being passed to the function. Here are some examples: \code MatrixXf A; VectorXf a; foo1(a.head());
+ * // OK foo1(A.col());              // OK foo1(A.row());              // Compilation error because here innerstride!=1
+ * foo2(A.row());              // Compilation error because A.row() is a 1xN object while foo2 is expecting a Nx1 object
+ * foo2(A.row().transpose());  // The row is copied into a contiguous temporary
+ * foo2(2*a);                  // The expression is evaluated into a temporary
+ * foo2(A.col().segment(2,4)); // No temporary
+ * \endcode
+ *
+ * The range of inputs that can be referenced without temporary can be enlarged using the last two template parameters.
+ * Here is an example accepting an innerstride!=1:
+ * \code
+ * // in-out argument:
+ * void foo3(Ref<VectorXf,0,InnerStride<> > x);
+ * foo3(A.row());              // OK
+ * \endcode
+ * The downside here is that the function foo3 might be significantly slower than foo1 because it won't be able to
+ * exploit vectorization, and will involve more expensive address computations even if the input is contiguously stored
+ * in memory. To overcome this issue, one might propose to overload internally calling a template function, e.g.: \code
+ * // in the .h:
+ * void foo(const Ref<MatrixXf>& A);
+ * void foo(const Ref<MatrixXf,0,Stride<> >& A);
+ *
+ * // in the .cpp:
+ * template<typename TypeOfA> void foo_impl(const TypeOfA& A) {
+ *     ... // crazy code goes here
+ * }
+ * void foo(const Ref<MatrixXf>& A) { foo_impl(A); }
+ * void foo(const Ref<MatrixXf,0,Stride<> >& A) { foo_impl(A); }
+ * \endcode
+ *
+ * See also the following stackoverflow questions for further references:
+ *  - <a href="http://stackoverflow.com/questions/21132538/correct-usage-of-the-eigenref-class">Correct usage of the
+ * Eigen::Ref<> class</a>
+ *
+ * \sa PlainObjectBase::Map(), \ref TopicStorageOrders
+ */
+template <typename PlainObjectType, int Options, typename StrideType>
+class Ref : public RefBase<Ref<PlainObjectType, Options, StrideType> > {
+ private:
+  typedef internal::traits<Ref> Traits;
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC constexpr inline Ref(
+      const PlainObjectBase<Derived>& expr,
+      std::enable_if_t<bool(Traits::template match<Derived>::MatchAtCompileTime), Derived>* = 0);
+
+ public:
+  typedef RefBase<Ref> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC constexpr inline Ref(
+      PlainObjectBase<Derived>& expr,
+      std::enable_if_t<bool(Traits::template match<Derived>::MatchAtCompileTime), Derived>* = 0) {
+    EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+    // Construction must pass since we will not create temporary storage in the non-const case.
+    const bool success = Base::construct(expr.derived());
+    EIGEN_UNUSED_VARIABLE(success)
+    eigen_assert(success);
+  }
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC constexpr inline Ref(
+      const DenseBase<Derived>& expr,
+      std::enable_if_t<bool(Traits::template match<Derived>::MatchAtCompileTime), Derived>* = 0)
+#else
+  /** Implicit constructor from any dense expression */
+  template <typename Derived>
+  inline Ref(DenseBase<Derived>& expr)
+#endif
+  {
+    EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+    EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+    EIGEN_STATIC_ASSERT(!Derived::IsPlainObjectBase, THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+    // Construction must pass since we will not create temporary storage in the non-const case.
+    const bool success = Base::construct(expr.const_cast_derived());
+    EIGEN_UNUSED_VARIABLE(success)
+    eigen_assert(success);
+  }
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Ref)
+};
+
+// this is the const ref version
+template <typename TPlainObjectType, int Options, typename StrideType>
+class Ref<const TPlainObjectType, Options, StrideType>
+    : public RefBase<Ref<const TPlainObjectType, Options, StrideType> > {
+  typedef internal::traits<Ref> Traits;
+
+  static constexpr bool may_map_m_object_successfully =
+      (static_cast<int>(StrideType::InnerStrideAtCompileTime) == 0 ||
+       static_cast<int>(StrideType::InnerStrideAtCompileTime) == 1 ||
+       static_cast<int>(StrideType::InnerStrideAtCompileTime) == Dynamic) &&
+      (TPlainObjectType::IsVectorAtCompileTime || static_cast<int>(StrideType::OuterStrideAtCompileTime) == 0 ||
+       static_cast<int>(StrideType::OuterStrideAtCompileTime) == Dynamic ||
+       static_cast<int>(StrideType::OuterStrideAtCompileTime) ==
+           static_cast<int>(TPlainObjectType::InnerSizeAtCompileTime) ||
+       static_cast<int>(TPlainObjectType::InnerSizeAtCompileTime) == Dynamic);
+
+ public:
+  typedef RefBase<Ref> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
+
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC constexpr inline Ref(
+      const DenseBase<Derived>& expr,
+      std::enable_if_t<bool(Traits::template match<Derived>::ScalarTypeMatch), Derived>* = 0) {
+    //      std::cout << match_helper<Derived>::HasDirectAccess << "," << match_helper<Derived>::OuterStrideMatch << ","
+    //      << match_helper<Derived>::InnerStrideMatch << "\n"; std::cout << int(StrideType::OuterStrideAtCompileTime)
+    //      << " - " << int(Derived::OuterStrideAtCompileTime) << "\n"; std::cout <<
+    //      int(StrideType::InnerStrideAtCompileTime) << " - " << int(Derived::InnerStrideAtCompileTime) << "\n";
+    EIGEN_STATIC_ASSERT(Traits::template match<Derived>::type::value || may_map_m_object_successfully,
+                        STORAGE_LAYOUT_DOES_NOT_MATCH);
+    construct(expr.derived(), typename Traits::template match<Derived>::type());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline Ref(const Ref& other) : Base(other) {
+    // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline Ref(Ref&& other) {
+    if (other.data() == other.m_object.data()) {
+      m_object = std::move(other.m_object);
+      Base::construct(m_object);
+    } else
+      Base::construct(other);
+  }
+
+  template <typename OtherRef>
+  EIGEN_DEVICE_FUNC constexpr inline Ref(const RefBase<OtherRef>& other) {
+    EIGEN_STATIC_ASSERT(Traits::template match<OtherRef>::type::value || may_map_m_object_successfully,
+                        STORAGE_LAYOUT_DOES_NOT_MATCH);
+    construct(other.derived(), typename Traits::template match<OtherRef>::type());
+  }
+
+ protected:
+  template <typename Expression>
+  EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::true_type) {
+    // Check if we can use the underlying expr's storage directly, otherwise call the copy version.
+    if (!Base::construct(expr)) {
+      construct(expr, internal::false_type());
+    }
+  }
+
+  template <typename Expression>
+  EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type) {
+    internal::call_assignment_no_alias(m_object, expr, internal::assign_op<Scalar, Scalar>());
+    const bool success = Base::construct(m_object);
+    EIGEN_ONLY_USED_FOR_DEBUG(success)
+    eigen_assert(success);
+  }
+
+ protected:
+  TPlainObjectType m_object;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_REF_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Replicate.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Replicate.h
new file mode 100644
index 0000000..9bdc725
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Replicate.h
@@ -0,0 +1,130 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REPLICATE_H
+#define EIGEN_REPLICATE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType, int RowFactor, int ColFactor>
+struct traits<Replicate<MatrixType, RowFactor, ColFactor> > : traits<MatrixType> {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename traits<MatrixType>::StorageKind StorageKind;
+  typedef typename traits<MatrixType>::XprKind XprKind;
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+  enum {
+    RowsAtCompileTime = RowFactor == Dynamic || int(MatrixType::RowsAtCompileTime) == Dynamic
+                            ? Dynamic
+                            : RowFactor * MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = ColFactor == Dynamic || int(MatrixType::ColsAtCompileTime) == Dynamic
+                            ? Dynamic
+                            : ColFactor * MatrixType::ColsAtCompileTime,
+    // FIXME: propagate MaxRowsAtCompileTime and MaxColsAtCompileTime.
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    IsRowMajor = MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1   ? 1
+                 : MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1 ? 0
+                 : (MatrixType::Flags & RowMajorBit)                      ? 1
+                                                                          : 0,
+
+    // FIXME: consider enabling DirectAccess with negative strides.
+    Flags = IsRowMajor ? RowMajorBit : 0
+  };
+};
+}  // namespace internal
+
+/**
+ * \class Replicate
+ * \ingroup Core_Module
+ *
+ * \brief Expression of the multiple replication of a matrix or vector
+ *
+ * \tparam MatrixType the type of the object we are replicating
+ * \tparam RowFactor number of repetitions at compile time along the vertical direction, can be Dynamic.
+ * \tparam ColFactor number of repetitions at compile time along the horizontal direction, can be Dynamic.
+ *
+ * This class represents an expression of the multiple replication of a matrix or vector.
+ * It is the return type of DenseBase::replicate() and most of the time
+ * this is the only way it is used.
+ *
+ * \sa DenseBase::replicate()
+ */
+template <typename MatrixType, int RowFactor, int ColFactor>
+class Replicate : public internal::dense_xpr_base<Replicate<MatrixType, RowFactor, ColFactor> >::type {
+  typedef typename internal::traits<Replicate>::MatrixTypeNested MatrixTypeNested;
+  typedef typename internal::traits<Replicate>::MatrixTypeNested_ MatrixTypeNested_;
+
+ public:
+  typedef typename internal::dense_xpr_base<Replicate>::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Replicate)
+  typedef internal::remove_all_t<MatrixType> NestedExpression;
+
+  template <typename OriginalMatrixType>
+  EIGEN_DEVICE_FUNC constexpr inline explicit Replicate(const OriginalMatrixType& matrix)
+      : m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor) {
+    EIGEN_STATIC_ASSERT((internal::is_same<std::remove_const_t<MatrixType>, OriginalMatrixType>::value),
+                        THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
+    eigen_assert(RowFactor != Dynamic && ColFactor != Dynamic);
+  }
+
+  template <typename OriginalMatrixType>
+  EIGEN_DEVICE_FUNC constexpr inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
+      : m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor) {
+    EIGEN_STATIC_ASSERT((internal::is_same<std::remove_const_t<MatrixType>, OriginalMatrixType>::value),
+                        THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_matrix.rows() * m_rowFactor.value(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
+
+  EIGEN_DEVICE_FUNC constexpr const MatrixTypeNested_& nestedExpression() const { return m_matrix; }
+
+ protected:
+  MatrixTypeNested m_matrix;
+  const internal::variable_if_dynamic<Index, RowFactor> m_rowFactor;
+  const internal::variable_if_dynamic<Index, ColFactor> m_colFactor;
+};
+
+/**
+ * \return an expression of the replication of \c *this
+ *
+ * Example: \include MatrixBase_replicate.cpp
+ * Output: \verbinclude MatrixBase_replicate.out
+ *
+ * \sa VectorwiseOp::replicate(), DenseBase::replicate(Index,Index), class Replicate
+ */
+template <typename Derived>
+template <int RowFactor, int ColFactor>
+EIGEN_DEVICE_FUNC const Replicate<Derived, RowFactor, ColFactor> DenseBase<Derived>::replicate() const {
+  return Replicate<Derived, RowFactor, ColFactor>(derived());
+}
+
+/**
+ * \return an expression of the replication of each column (or row) of \c *this
+ *
+ * Example: \include DirectionWise_replicate_int.cpp
+ * Output: \verbinclude DirectionWise_replicate_int.out
+ *
+ * \sa VectorwiseOp::replicate(), DenseBase::replicate(), class Replicate
+ */
+template <typename ExpressionType, int Direction>
+EIGEN_DEVICE_FUNC const typename VectorwiseOp<ExpressionType, Direction>::ReplicateReturnType
+VectorwiseOp<ExpressionType, Direction>::replicate(Index factor) const {
+  return typename VectorwiseOp<ExpressionType, Direction>::ReplicateReturnType(
+      _expression(), Direction == Vertical ? factor : 1, Direction == Horizontal ? factor : 1);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_REPLICATE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Reshaped.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Reshaped.h
new file mode 100644
index 0000000..5763d6a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Reshaped.h
@@ -0,0 +1,363 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2014 yoco <peter.xiau@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RESHAPED_H
+#define EIGEN_RESHAPED_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class Reshaped
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a fixed-size or dynamic-size reshape
+ *
+ * \tparam XprType the type of the expression in which we are taking a reshape
+ * \tparam Rows the number of rows of the reshape we are taking at compile time (optional)
+ * \tparam Cols the number of columns of the reshape we are taking at compile time (optional)
+ * \tparam Order can be ColMajor or RowMajor, default is ColMajor.
+ *
+ * This class represents an expression of either a fixed-size or dynamic-size reshape.
+ * It is the return type of DenseBase::reshaped(NRowsType,NColsType) and
+ * most of the time this is the only way it is used.
+ *
+ * If you want to directly manipulate reshaped expressions,
+ * for instance if you want to write a function returning such an expression,
+ * it is advised to use the \em auto keyword for such use cases.
+ *
+ * Here is an example illustrating the dynamic case:
+ * \include class_Reshaped.cpp
+ * Output: \verbinclude class_Reshaped.out
+ *
+ * Here is an example illustrating the fixed-size case:
+ * \include class_FixedReshaped.cpp
+ * Output: \verbinclude class_FixedReshaped.out
+ *
+ * \sa DenseBase::reshaped(NRowsType,NColsType)
+ */
+
+namespace internal {
+
+template <typename XprType, int Rows, int Cols, int Order>
+struct traits<Reshaped<XprType, Rows, Cols, Order> > : traits<XprType> {
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef typename traits<XprType>::StorageKind StorageKind;
+  typedef typename traits<XprType>::XprKind XprKind;
+  enum {
+    MatrixRows = traits<XprType>::RowsAtCompileTime,
+    MatrixCols = traits<XprType>::ColsAtCompileTime,
+    RowsAtCompileTime = Rows,
+    ColsAtCompileTime = Cols,
+    MaxRowsAtCompileTime = Rows,
+    MaxColsAtCompileTime = Cols,
+    XpxStorageOrder = ((int(traits<XprType>::Flags) & RowMajorBit) == RowMajorBit) ? RowMajor : ColMajor,
+    ReshapedStorageOrder = (RowsAtCompileTime == 1 && ColsAtCompileTime != 1)   ? RowMajor
+                           : (ColsAtCompileTime == 1 && RowsAtCompileTime != 1) ? ColMajor
+                                                                                : XpxStorageOrder,
+    HasSameStorageOrderAsXprType = (ReshapedStorageOrder == XpxStorageOrder),
+    InnerSize = (ReshapedStorageOrder == int(RowMajor)) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
+    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time<XprType>::ret) : Dynamic,
+    OuterStrideAtCompileTime = Dynamic,
+
+    HasDirectAccess = internal::has_direct_access<XprType>::ret && (Order == int(XpxStorageOrder)) &&
+                      ((evaluator<XprType>::Flags & LinearAccessBit) == LinearAccessBit),
+
+    MaskPacketAccessBit =
+        (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0) && (InnerStrideAtCompileTime == 1)
+            ? PacketAccessBit
+            : 0,
+    // MaskAlignedBit = ((OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16)
+    // == 0)) ? AlignedBit : 0,
+    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
+    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
+    FlagsRowMajorBit = (ReshapedStorageOrder == int(RowMajor)) ? RowMajorBit : 0,
+    FlagsDirectAccessBit = HasDirectAccess ? DirectAccessBit : 0,
+    Flags0 = traits<XprType>::Flags & ((HereditaryBits & ~RowMajorBit) | MaskPacketAccessBit),
+
+    Flags = (Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit | FlagsDirectAccessBit)
+  };
+};
+
+template <typename XprType, int Rows, int Cols, int Order, bool HasDirectAccess>
+class ReshapedImpl_dense;
+
+}  // end namespace internal
+
+template <typename XprType, int Rows, int Cols, int Order, typename StorageKind>
+class ReshapedImpl;
+
+template <typename XprType, int Rows, int Cols, int Order>
+class Reshaped : public ReshapedImpl<XprType, Rows, Cols, Order, typename internal::traits<XprType>::StorageKind> {
+  typedef ReshapedImpl<XprType, Rows, Cols, Order, typename internal::traits<XprType>::StorageKind> Impl;
+
+ public:
+  // typedef typename Impl::Base Base;
+  typedef Impl Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(Reshaped)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reshaped)
+
+  /** Fixed-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr inline Reshaped(XprType& xpr) : Impl(xpr) {
+    EIGEN_STATIC_ASSERT(RowsAtCompileTime != Dynamic && ColsAtCompileTime != Dynamic,
+                        THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
+    eigen_assert(Rows * Cols == xpr.rows() * xpr.cols());
+  }
+
+  /** Dynamic-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr inline Reshaped(XprType& xpr, Index reshapeRows, Index reshapeCols)
+      : Impl(xpr, reshapeRows, reshapeCols) {
+    eigen_assert((RowsAtCompileTime == Dynamic || RowsAtCompileTime == reshapeRows) &&
+                 (ColsAtCompileTime == Dynamic || ColsAtCompileTime == reshapeCols));
+    eigen_assert(reshapeRows * reshapeCols == xpr.rows() * xpr.cols());
+  }
+};
+
+// The generic default implementation for dense reshape simply forward to the internal::ReshapedImpl_dense
+// that must be specialized for direct and non-direct access...
+template <typename XprType, int Rows, int Cols, int Order>
+class ReshapedImpl<XprType, Rows, Cols, Order, Dense>
+    : public internal::ReshapedImpl_dense<XprType, Rows, Cols, Order,
+                                          internal::traits<Reshaped<XprType, Rows, Cols, Order> >::HasDirectAccess> {
+  typedef internal::ReshapedImpl_dense<XprType, Rows, Cols, Order,
+                                       internal::traits<Reshaped<XprType, Rows, Cols, Order> >::HasDirectAccess>
+      Impl;
+
+ public:
+  typedef Impl Base;
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl)
+  EIGEN_DEVICE_FUNC constexpr inline ReshapedImpl(XprType& xpr) : Impl(xpr) {}
+  EIGEN_DEVICE_FUNC constexpr inline ReshapedImpl(XprType& xpr, Index reshapeRows, Index reshapeCols)
+      : Impl(xpr, reshapeRows, reshapeCols) {}
+};
+
+namespace internal {
+
+/** \internal Internal implementation of dense Reshaped in the general case. */
+template <typename XprType, int Rows, int Cols, int Order>
+class ReshapedImpl_dense<XprType, Rows, Cols, Order, false>
+    : public internal::dense_xpr_base<Reshaped<XprType, Rows, Cols, Order> >::type {
+  typedef Reshaped<XprType, Rows, Cols, Order> ReshapedType;
+
+ public:
+  typedef typename internal::dense_xpr_base<ReshapedType>::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(ReshapedType)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense)
+
+  typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
+  typedef internal::remove_all_t<XprType> NestedExpression;
+
+  class InnerIterator;
+
+  /** Fixed-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr inline ReshapedImpl_dense(XprType& xpr) : m_xpr(xpr), m_rows(Rows), m_cols(Cols) {}
+
+  /** Dynamic-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr inline ReshapedImpl_dense(XprType& xpr, Index nRows, Index nCols)
+      : m_xpr(xpr), m_rows(nRows), m_cols(nCols) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \sa MapBase::data() */
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const;
+  EIGEN_DEVICE_FUNC inline Index innerStride() const;
+  EIGEN_DEVICE_FUNC inline Index outerStride() const;
+#endif
+
+  /** \returns the nested expression */
+  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<XprType>& nestedExpression() const { return m_xpr; }
+
+  /** \returns the nested expression */
+  EIGEN_DEVICE_FUNC constexpr std::remove_reference_t<XprType>& nestedExpression() { return m_xpr; }
+
+ protected:
+  MatrixTypeNested m_xpr;
+  const internal::variable_if_dynamic<Index, Rows> m_rows;
+  const internal::variable_if_dynamic<Index, Cols> m_cols;
+};
+
+/** \internal Internal implementation of dense Reshaped in the direct access case. */
+template <typename XprType, int Rows, int Cols, int Order>
+class ReshapedImpl_dense<XprType, Rows, Cols, Order, true> : public MapBase<Reshaped<XprType, Rows, Cols, Order> > {
+  typedef Reshaped<XprType, Rows, Cols, Order> ReshapedType;
+  typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
+
+ public:
+  typedef MapBase<ReshapedType> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(ReshapedType)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense)
+
+  /** Fixed-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr inline ReshapedImpl_dense(XprType& xpr) : Base(xpr.data()), m_xpr(xpr) {}
+
+  /** Dynamic-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr inline ReshapedImpl_dense(XprType& xpr, Index nRows, Index nCols)
+      : Base(xpr.data(), nRows, nCols), m_xpr(xpr) {}
+
+  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<XprTypeNested>& nestedExpression() const { return m_xpr; }
+
+  EIGEN_DEVICE_FUNC constexpr XprType& nestedExpression() { return m_xpr; }
+
+  /** \sa MapBase::innerStride() */
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const { return m_xpr.innerStride(); }
+
+  /** \sa MapBase::outerStride() */
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const {
+    return (((Flags & RowMajorBit) == RowMajorBit) ? this->cols() : this->rows()) * m_xpr.innerStride();
+  }
+
+ protected:
+  XprTypeNested m_xpr;
+};
+
+// Evaluators
+template <typename ArgType, int Rows, int Cols, int Order, bool HasDirectAccess>
+struct reshaped_evaluator;
+
+template <typename ArgType, int Rows, int Cols, int Order>
+struct evaluator<Reshaped<ArgType, Rows, Cols, Order> >
+    : reshaped_evaluator<ArgType, Rows, Cols, Order, traits<Reshaped<ArgType, Rows, Cols, Order> >::HasDirectAccess> {
+  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
+  typedef typename XprType::Scalar Scalar;
+  // TODO: should check for smaller packet types
+  typedef typename packet_traits<Scalar>::type PacketScalar;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+    HasDirectAccess = traits<XprType>::HasDirectAccess,
+
+    //     RowsAtCompileTime = traits<XprType>::RowsAtCompileTime,
+    //     ColsAtCompileTime = traits<XprType>::ColsAtCompileTime,
+    //     MaxRowsAtCompileTime = traits<XprType>::MaxRowsAtCompileTime,
+    //     MaxColsAtCompileTime = traits<XprType>::MaxColsAtCompileTime,
+    //
+    //     InnerStrideAtCompileTime = traits<XprType>::HasSameStorageOrderAsXprType
+    //                              ? int(inner_stride_at_compile_time<ArgType>::ret)
+    //                              : Dynamic,
+    //     OuterStrideAtCompileTime = Dynamic,
+
+    FlagsLinearAccessBit =
+        (traits<XprType>::RowsAtCompileTime == 1 || traits<XprType>::ColsAtCompileTime == 1 || HasDirectAccess)
+            ? LinearAccessBit
+            : 0,
+    FlagsRowMajorBit = (traits<XprType>::ReshapedStorageOrder == int(RowMajor)) ? RowMajorBit : 0,
+    FlagsDirectAccessBit = HasDirectAccess ? DirectAccessBit : 0,
+    Flags0 = evaluator<ArgType>::Flags & (HereditaryBits & ~RowMajorBit),
+    Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit | FlagsDirectAccessBit,
+
+    PacketAlignment = unpacket_traits<PacketScalar>::alignment,
+    Alignment = evaluator<ArgType>::Alignment
+  };
+  typedef reshaped_evaluator<ArgType, Rows, Cols, Order, HasDirectAccess> reshaped_evaluator_type;
+  EIGEN_DEVICE_FUNC constexpr explicit evaluator(const XprType& xpr) : reshaped_evaluator_type(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+};
+
+template <typename ArgType, int Rows, int Cols, int Order>
+struct reshaped_evaluator<ArgType, Rows, Cols, Order, /* HasDirectAccess */ false>
+    : evaluator_base<Reshaped<ArgType, Rows, Cols, Order> > {
+  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost /* TODO + cost of index computations */,
+
+    Flags = (evaluator<ArgType>::Flags & (HereditaryBits /*| LinearAccessBit | DirectAccessBit*/)),
+
+    Alignment = 0
+  };
+
+  EIGEN_DEVICE_FUNC constexpr explicit reshaped_evaluator(const XprType& xpr)
+      : m_argImpl(xpr.nestedExpression()), m_xpr(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  typedef std::pair<Index, Index> RowCol;
+
+  EIGEN_DEVICE_FUNC constexpr inline RowCol index_remap(Index rowId, Index colId) const {
+    if (Order == ColMajor) {
+      const Index nth_elem_idx = colId * m_xpr.rows() + rowId;
+      return RowCol(nth_elem_idx % m_xpr.nestedExpression().rows(), nth_elem_idx / m_xpr.nestedExpression().rows());
+    } else {
+      const Index nth_elem_idx = colId + rowId * m_xpr.cols();
+      return RowCol(nth_elem_idx / m_xpr.nestedExpression().cols(), nth_elem_idx % m_xpr.nestedExpression().cols());
+    }
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline Scalar& coeffRef(Index rowId, Index colId) {
+    EIGEN_STATIC_ASSERT_LVALUE(XprType)
+    const RowCol row_col = index_remap(rowId, colId);
+    return m_argImpl.coeffRef(row_col.first, row_col.second);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline const Scalar& coeffRef(Index rowId, Index colId) const {
+    const RowCol row_col = index_remap(rowId, colId);
+    return m_argImpl.coeffRef(row_col.first, row_col.second);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const {
+    const RowCol row_col = index_remap(rowId, colId);
+    return m_argImpl.coeff(row_col.first, row_col.second);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline Scalar& coeffRef(Index index) {
+    EIGEN_STATIC_ASSERT_LVALUE(XprType)
+    const RowCol row_col = index_remap(Rows == 1 ? 0 : index, Rows == 1 ? index : 0);
+    return m_argImpl.coeffRef(row_col.first, row_col.second);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline const Scalar& coeffRef(Index index) const {
+    const RowCol row_col = index_remap(Rows == 1 ? 0 : index, Rows == 1 ? index : 0);
+    return m_argImpl.coeffRef(row_col.first, row_col.second);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr inline const CoeffReturnType coeff(Index index) const {
+    const RowCol row_col = index_remap(Rows == 1 ? 0 : index, Rows == 1 ? index : 0);
+    return m_argImpl.coeff(row_col.first, row_col.second);
+  }
+
+ protected:
+  evaluator<ArgType> m_argImpl;
+  const XprType& m_xpr;
+};
+
+template <typename ArgType, int Rows, int Cols, int Order>
+struct reshaped_evaluator<ArgType, Rows, Cols, Order, /* HasDirectAccess */ true>
+    : mapbase_evaluator<Reshaped<ArgType, Rows, Cols, Order>,
+                        typename Reshaped<ArgType, Rows, Cols, Order>::PlainObject> {
+  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
+  typedef typename XprType::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC constexpr explicit reshaped_evaluator(const XprType& xpr)
+      : mapbase_evaluator<XprType, typename XprType::PlainObject>(xpr) {
+    // TODO: for the 3.4 release, this should be turned to an internal assertion, but let's keep it as is for the beta
+    // lifetime
+    eigen_assert(((std::uintptr_t(xpr.data()) % plain_enum_max(1, evaluator<XprType>::Alignment)) == 0) &&
+                 "data is not aligned");
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_RESHAPED_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ReturnByValue.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ReturnByValue.h
new file mode 100644
index 0000000..410b77d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/ReturnByValue.h
@@ -0,0 +1,111 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RETURNBYVALUE_H
+#define EIGEN_RETURNBYVALUE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Derived>
+struct traits<ReturnByValue<Derived> > : public traits<typename traits<Derived>::ReturnType> {
+  enum {
+    // We're disabling the DirectAccess because e.g. the constructor of
+    // the Block-with-DirectAccess expression requires to have a coeffRef method.
+    // Also, this avoids having to implement stride support.
+    Flags = (traits<typename traits<Derived>::ReturnType>::Flags | EvalBeforeNestingBit) & ~DirectAccessBit
+  };
+};
+
+/* The ReturnByValue object doesn't even have a coeff() method.
+ * So the only way that nesting it in an expression can work, is by evaluating it into a plain matrix.
+ * So internal::nested always gives the plain return matrix type.
+ *
+ * FIXME: this specialization may be redundant with EvalBeforeNestingBit.
+ * Answer: EvalBeforeNestingBit should be deprecated since we have the evaluators
+ */
+template <typename Derived, int n, typename PlainObject>
+struct nested_eval<ReturnByValue<Derived>, n, PlainObject> {
+  typedef typename traits<Derived>::ReturnType type;
+};
+
+}  // end namespace internal
+
+/** \class ReturnByValue
+ * \ingroup Core_Module
+ *
+ */
+template <typename Derived>
+class ReturnByValue : public internal::dense_xpr_base<ReturnByValue<Derived> >::type, internal::no_assignment_operator {
+ public:
+  typedef typename internal::traits<Derived>::ReturnType ReturnType;
+
+  typedef typename internal::dense_xpr_base<ReturnByValue>::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(ReturnByValue)
+
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC inline void evalTo(Dest& dst) const {
+    static_cast<const Derived*>(this)->evalTo(dst);
+  }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return static_cast<const Derived*>(this)->rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return static_cast<const Derived*>(this)->cols(); }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+#define Unusable \
+  YOU_ARE_TRYING_TO_ACCESS_A_SINGLE_COEFFICIENT_IN_A_SPECIAL_EXPRESSION_WHERE_THAT_IS_NOT_ALLOWED_BECAUSE_THAT_WOULD_BE_INEFFICIENT
+  class Unusable {
+    Unusable(const Unusable&) {}
+    Unusable& operator=(const Unusable&) { return *this; }
+  };
+  const Unusable& coeff(Index) const { return *reinterpret_cast<const Unusable*>(this); }
+  const Unusable& coeff(Index, Index) const { return *reinterpret_cast<const Unusable*>(this); }
+  Unusable& coeffRef(Index) { return *reinterpret_cast<Unusable*>(this); }
+  Unusable& coeffRef(Index, Index) { return *reinterpret_cast<Unusable*>(this); }
+#undef Unusable
+#endif
+};
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC Derived& DenseBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other) {
+  other.evalTo(derived());
+  return derived();
+}
+
+namespace internal {
+
+// Expression is evaluated in a temporary; default implementation of Assignment is bypassed so that
+// when a ReturnByValue expression is assigned, the evaluator is not constructed.
+// TODO: Finalize port to new regime; ReturnByValue should not exist in the expression world
+
+template <typename Derived>
+struct evaluator<ReturnByValue<Derived> > : public evaluator<typename internal::traits<Derived>::ReturnType> {
+  typedef ReturnByValue<Derived> XprType;
+  typedef typename internal::traits<Derived>::ReturnType PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : m_result(xpr.rows(), xpr.cols()) {
+    internal::construct_at<Base>(this, m_result);
+    xpr.evalTo(m_result);
+  }
+
+ protected:
+  PlainObject m_result;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_RETURNBYVALUE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Reverse.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Reverse.h
new file mode 100644
index 0000000..a4af8d1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Reverse.h
@@ -0,0 +1,202 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Ricard Marxer <email@ricardmarxer.com>
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REVERSE_H
+#define EIGEN_REVERSE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename MatrixType, int Direction>
+struct traits<Reverse<MatrixType, Direction> > : traits<MatrixType> {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename traits<MatrixType>::StorageKind StorageKind;
+  typedef typename traits<MatrixType>::XprKind XprKind;
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    Flags = MatrixTypeNested_::Flags & (RowMajorBit | LvalueBit)
+  };
+};
+
+template <typename PacketType, bool ReversePacket>
+struct reverse_packet_cond {
+  static inline PacketType run(const PacketType& x) { return preverse(x); }
+};
+
+template <typename PacketType>
+struct reverse_packet_cond<PacketType, false> {
+  static inline PacketType run(const PacketType& x) { return x; }
+};
+
+}  // end namespace internal
+
+/** \class Reverse
+ * \ingroup Core_Module
+ *
+ * \brief Expression of the reverse of a vector or matrix
+ *
+ * \tparam MatrixType the type of the object of which we are taking the reverse
+ * \tparam Direction defines the direction of the reverse operation, can be Vertical, Horizontal, or BothDirections
+ *
+ * This class represents an expression of the reverse of a vector.
+ * It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse()
+ * and most of the time this is the only way it is used.
+ *
+ * \sa MatrixBase::reverse(), VectorwiseOp::reverse()
+ */
+template <typename MatrixType, int Direction>
+class Reverse : public internal::dense_xpr_base<Reverse<MatrixType, Direction> >::type {
+ public:
+  typedef typename internal::dense_xpr_base<Reverse>::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Reverse)
+  typedef internal::remove_all_t<MatrixType> NestedExpression;
+  using Base::IsRowMajor;
+
+ protected:
+  enum {
+    PacketSize = internal::packet_traits<Scalar>::size,
+    IsColMajor = !IsRowMajor,
+    ReverseRow = (Direction == Vertical) || (Direction == BothDirections),
+    ReverseCol = (Direction == Horizontal) || (Direction == BothDirections),
+    OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1,
+    OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1,
+    ReversePacket = (Direction == BothDirections) || ((Direction == Vertical) && IsColMajor) ||
+                    ((Direction == Horizontal) && IsRowMajor)
+  };
+  typedef internal::reverse_packet_cond<PacketScalar, ReversePacket> reverse_packet;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr explicit inline Reverse(const MatrixType& matrix) : m_matrix(matrix) {}
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reverse)
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_matrix.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_matrix.cols(); }
+
+  EIGEN_DEVICE_FUNC inline Index innerStride() const { return -m_matrix.innerStride(); }
+
+  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<typename MatrixType::Nested>& nestedExpression() const {
+    return m_matrix;
+  }
+
+ protected:
+  typename MatrixType::Nested m_matrix;
+};
+
+/** \returns an expression of the reverse of *this.
+ *
+ * Example: \include MatrixBase_reverse.cpp
+ * Output: \verbinclude MatrixBase_reverse.out
+ *
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::ReverseReturnType DenseBase<Derived>::reverse() {
+  return ReverseReturnType(derived());
+}
+
+// reverse const overload moved DenseBase.h due to a CUDA compiler bug
+
+/** This is the "in place" version of reverse: it reverses \c *this.
+ *
+ * In most cases it is probably better to simply use the reversed expression
+ * of a matrix. However, when reversing the matrix data itself is really needed,
+ * then this "in-place" version is probably the right choice because it provides
+ * the following additional benefits:
+ *  - less error prone: doing the same operation with .reverse() requires special care:
+ *    \code m = m.reverse().eval(); \endcode
+ *  - this API enables reverse operations without the need for a temporary
+ *  - it allows future optimizations (cache friendliness, etc.)
+ *
+ * \sa VectorwiseOp::reverseInPlace(), reverse() */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline void DenseBase<Derived>::reverseInPlace() {
+  constexpr int HalfRowsAtCompileTime = RowsAtCompileTime == Dynamic ? Dynamic : RowsAtCompileTime / 2;
+  constexpr int HalfColsAtCompileTime = ColsAtCompileTime == Dynamic ? Dynamic : ColsAtCompileTime / 2;
+  if (cols() > rows()) {
+    Index half = cols() / 2;
+    this->template leftCols<HalfColsAtCompileTime>(half).swap(
+        this->template rightCols<HalfColsAtCompileTime>(half).reverse());
+    if ((cols() % 2) == 1) {
+      Index half2 = rows() / 2;
+      col(half).template head<HalfRowsAtCompileTime>(half2).swap(
+          col(half).template tail<HalfRowsAtCompileTime>(half2).reverse());
+    }
+  } else {
+    Index half = rows() / 2;
+    this->template topRows<HalfRowsAtCompileTime>(half).swap(
+        this->template bottomRows<HalfRowsAtCompileTime>(half).reverse());
+    if ((rows() % 2) == 1) {
+      Index half2 = cols() / 2;
+      row(half).template head<HalfColsAtCompileTime>(half2).swap(
+          row(half).template tail<HalfColsAtCompileTime>(half2).reverse());
+    }
+  }
+}
+
+namespace internal {
+
+template <int Direction>
+struct vectorwise_reverse_inplace_impl;
+
+template <>
+struct vectorwise_reverse_inplace_impl<Vertical> {
+  template <typename ExpressionType>
+  static void run(ExpressionType& xpr) {
+    constexpr Index HalfAtCompileTime =
+        ExpressionType::RowsAtCompileTime == Dynamic ? Dynamic : ExpressionType::RowsAtCompileTime / 2;
+    Index half = xpr.rows() / 2;
+    xpr.template topRows<HalfAtCompileTime>(half).swap(
+        xpr.template bottomRows<HalfAtCompileTime>(half).colwise().reverse());
+  }
+};
+
+template <>
+struct vectorwise_reverse_inplace_impl<Horizontal> {
+  template <typename ExpressionType>
+  static void run(ExpressionType& xpr) {
+    constexpr Index HalfAtCompileTime =
+        ExpressionType::ColsAtCompileTime == Dynamic ? Dynamic : ExpressionType::ColsAtCompileTime / 2;
+    Index half = xpr.cols() / 2;
+    xpr.template leftCols<HalfAtCompileTime>(half).swap(
+        xpr.template rightCols<HalfAtCompileTime>(half).rowwise().reverse());
+  }
+};
+
+}  // end namespace internal
+
+/** This is the "in place" version of VectorwiseOp::reverse: it reverses each column or row of \c *this.
+ *
+ * In most cases it is probably better to simply use the reversed expression
+ * of a matrix. However, when reversing the matrix data itself is really needed,
+ * then this "in-place" version is probably the right choice because it provides
+ * the following additional benefits:
+ *  - less error prone: doing the same operation with .reverse() requires special care:
+ *    \code m = m.reverse().eval(); \endcode
+ *  - this API enables reverse operations without the need for a temporary
+ *
+ * \sa DenseBase::reverseInPlace(), reverse() */
+template <typename ExpressionType, int Direction>
+EIGEN_DEVICE_FUNC void VectorwiseOp<ExpressionType, Direction>::reverseInPlace() {
+  internal::vectorwise_reverse_inplace_impl<Direction>::run(m_matrix);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_REVERSE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Select.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Select.h
new file mode 100644
index 0000000..6ad290f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Select.h
@@ -0,0 +1,92 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELECT_H
+#define EIGEN_SELECT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \typedef Select
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a coefficient wise version of the C++ ternary operator ?:
+ *
+ * \tparam ConditionMatrixType the type of the \em condition expression which must be a boolean matrix
+ * \tparam ThenMatrixType the type of the \em then expression
+ * \tparam ElseMatrixType the type of the \em else expression
+ *
+ * This type represents an expression of a coefficient wise version of the C++ ternary operator ?:.
+ * It is the return type of DenseBase::select() and most of the time this is the only way it is used.
+ *
+ * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const
+ */
+template <typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
+using Select = CwiseTernaryOp<internal::scalar_boolean_select_op<typename DenseBase<ThenMatrixType>::Scalar,
+                                                                 typename DenseBase<ElseMatrixType>::Scalar,
+                                                                 typename DenseBase<ConditionMatrixType>::Scalar>,
+                              ThenMatrixType, ElseMatrixType, ConditionMatrixType>;
+
+/** \returns a matrix where each coefficient (i,j) is equal to \a thenMatrix(i,j)
+ * if \c *this(i,j) != Scalar(0), and \a elseMatrix(i,j) otherwise.
+ *
+ * Example: \include MatrixBase_select.cpp
+ * Output: \verbinclude MatrixBase_select.out
+ *
+ * \sa typedef Select
+ */
+template <typename Derived>
+template <typename ThenDerived, typename ElseDerived>
+inline EIGEN_DEVICE_FUNC constexpr CwiseTernaryOp<
+    internal::scalar_boolean_select_op<typename DenseBase<ThenDerived>::Scalar, typename DenseBase<ElseDerived>::Scalar,
+                                       typename DenseBase<Derived>::Scalar>,
+    ThenDerived, ElseDerived, Derived>
+DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix, const DenseBase<ElseDerived>& elseMatrix) const {
+  return Select<Derived, ThenDerived, ElseDerived>(thenMatrix.derived(), elseMatrix.derived(), derived());
+}
+/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with
+ * the \em else expression being a scalar value.
+ *
+ * \sa typedef Select
+ */
+template <typename Derived>
+template <typename ThenDerived>
+inline EIGEN_DEVICE_FUNC constexpr CwiseTernaryOp<
+    internal::scalar_boolean_select_op<typename DenseBase<ThenDerived>::Scalar, typename DenseBase<ThenDerived>::Scalar,
+                                       typename DenseBase<Derived>::Scalar>,
+    ThenDerived, typename DenseBase<ThenDerived>::ConstantReturnType, Derived>
+DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
+                           const typename DenseBase<ThenDerived>::Scalar& elseScalar) const {
+  using ElseConstantType = typename DenseBase<ThenDerived>::ConstantReturnType;
+  return Select<Derived, ThenDerived, ElseConstantType>(thenMatrix.derived(),
+                                                        ElseConstantType(rows(), cols(), elseScalar), derived());
+}
+/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with
+ * the \em then expression being a scalar value.
+ *
+ * \sa typedef Select
+ */
+template <typename Derived>
+template <typename ElseDerived>
+inline EIGEN_DEVICE_FUNC constexpr CwiseTernaryOp<
+    internal::scalar_boolean_select_op<typename DenseBase<ElseDerived>::Scalar, typename DenseBase<ElseDerived>::Scalar,
+                                       typename DenseBase<Derived>::Scalar>,
+    typename DenseBase<ElseDerived>::ConstantReturnType, ElseDerived, Derived>
+DenseBase<Derived>::select(const typename DenseBase<ElseDerived>::Scalar& thenScalar,
+                           const DenseBase<ElseDerived>& elseMatrix) const {
+  using ThenConstantType = typename DenseBase<ElseDerived>::ConstantReturnType;
+  return Select<Derived, ThenConstantType, ElseDerived>(ThenConstantType(rows(), cols(), thenScalar),
+                                                        elseMatrix.derived(), derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SELECT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SelfAdjointView.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SelfAdjointView.h
new file mode 100644
index 0000000..62d0729
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SelfAdjointView.h
@@ -0,0 +1,329 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINTMATRIX_H
+#define EIGEN_SELFADJOINTMATRIX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class SelfAdjointView
+ * \ingroup Core_Module
+ *
+ *
+ * \brief Expression of a selfadjoint matrix from a triangular part of a dense matrix
+ *
+ * \tparam MatrixType the type of the dense matrix storing the coefficients
+ * \tparam TriangularPart can be either \c #Lower or \c #Upper
+ *
+ * This class is an expression of a sefladjoint matrix from a triangular part of a matrix
+ * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
+ * and most of the time this is the only way that it is used.
+ *
+ * \sa class TriangularBase, MatrixBase::selfadjointView()
+ */
+
+namespace internal {
+template <typename MatrixType, unsigned int UpLo>
+struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType> {
+  typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
+  typedef remove_all_t<MatrixTypeNested> MatrixTypeNestedCleaned;
+  typedef MatrixType ExpressionType;
+  typedef typename MatrixType::PlainObject FullMatrixType;
+  enum {
+    Mode = UpLo | SelfAdjoint,
+    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
+    Flags = MatrixTypeNestedCleaned::Flags & (HereditaryBits | FlagsLvalueBit) &
+            (~(PacketAccessBit | DirectAccessBit | LinearAccessBit))  // FIXME these flags should be preserved
+  };
+};
+}  // namespace internal
+
+template <typename MatrixType_, unsigned int UpLo>
+class SelfAdjointView : public TriangularBase<SelfAdjointView<MatrixType_, UpLo> > {
+ public:
+  EIGEN_STATIC_ASSERT(UpLo == Lower || UpLo == Upper, SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY)
+
+  typedef MatrixType_ MatrixType;
+  typedef TriangularBase<SelfAdjointView> Base;
+  typedef typename internal::traits<SelfAdjointView>::MatrixTypeNested MatrixTypeNested;
+  typedef typename internal::traits<SelfAdjointView>::MatrixTypeNestedCleaned MatrixTypeNestedCleaned;
+  typedef MatrixTypeNestedCleaned NestedExpression;
+
+  /** \brief The type of coefficients in this matrix */
+  typedef typename internal::traits<SelfAdjointView>::Scalar Scalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef internal::remove_all_t<typename MatrixType::ConjugateReturnType> MatrixConjugateReturnType;
+  typedef SelfAdjointView<std::add_const_t<MatrixType>, UpLo> ConstSelfAdjointView;
+
+  enum {
+    Mode = internal::traits<SelfAdjointView>::Mode,
+    Flags = internal::traits<SelfAdjointView>::Flags,
+    TransposeMode = ((int(Mode) & int(Upper)) ? Lower : 0) | ((int(Mode) & int(Lower)) ? Upper : 0)
+  };
+  typedef typename MatrixType::PlainObject PlainObject;
+
+  EIGEN_DEVICE_FUNC explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_matrix.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_matrix.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return m_matrix.outerStride(); }
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_matrix.innerStride(); }
+
+  /** \sa MatrixBase::coeff()
+   * \warning the coordinates must fit into the referenced triangular part
+   */
+  EIGEN_DEVICE_FUNC inline Scalar coeff(Index row, Index col) const {
+    Base::check_coordinates_internal(row, col);
+    return m_matrix.coeff(row, col);
+  }
+
+  /** \sa MatrixBase::coeffRef()
+   * \warning the coordinates must fit into the referenced triangular part
+   */
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col) {
+    EIGEN_STATIC_ASSERT_LVALUE(SelfAdjointView);
+    Base::check_coordinates_internal(row, col);
+    return m_matrix.coeffRef(row, col);
+  }
+
+  /** \internal */
+  EIGEN_DEVICE_FUNC const MatrixTypeNestedCleaned& _expression() const { return m_matrix; }
+
+  EIGEN_DEVICE_FUNC const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; }
+  EIGEN_DEVICE_FUNC MatrixTypeNestedCleaned& nestedExpression() { return m_matrix; }
+
+  /** Efficient triangular matrix times vector/matrix product */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC const Product<SelfAdjointView, OtherDerived> operator*(const MatrixBase<OtherDerived>& rhs) const {
+    return Product<SelfAdjointView, OtherDerived>(*this, rhs.derived());
+  }
+
+  /** Efficient vector/matrix times triangular matrix product */
+  template <typename OtherDerived>
+  friend EIGEN_DEVICE_FUNC const Product<OtherDerived, SelfAdjointView> operator*(const MatrixBase<OtherDerived>& lhs,
+                                                                                  const SelfAdjointView& rhs) {
+    return Product<OtherDerived, SelfAdjointView>(lhs.derived(), rhs);
+  }
+
+  friend EIGEN_DEVICE_FUNC const
+      SelfAdjointView<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar, MatrixType, product), UpLo>
+      operator*(const Scalar& s, const SelfAdjointView& mat) {
+    return (s * mat.nestedExpression()).template selfadjointView<UpLo>();
+  }
+
+  /** Perform a symmetric rank 2 update of the selfadjoint matrix \c *this:
+   * \f$ this = this + \alpha u v^* + conj(\alpha) v u^* \f$
+   * \returns a reference to \c *this
+   *
+   * The vectors \a u and \c v \b must be column vectors, however they can be
+   * a adjoint expression without any overhead. Only the meaningful triangular
+   * part of the matrix is updated, the rest is left unchanged.
+   *
+   * \sa rankUpdate(const MatrixBase<DerivedU>&, Scalar)
+   */
+  template <typename DerivedU, typename DerivedV>
+  EIGEN_DEVICE_FUNC SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v,
+                                                const Scalar& alpha = Scalar(1));
+
+  /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
+   * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
+   *
+   * \returns a reference to \c *this
+   *
+   * Note that to perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
+   * call this function with u.adjoint().
+   *
+   * \sa rankUpdate(const MatrixBase<DerivedU>&, const MatrixBase<DerivedV>&, Scalar)
+   */
+  template <typename DerivedU>
+  EIGEN_DEVICE_FUNC SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
+
+  /** \returns an expression of a triangular view extracted from the current selfadjoint view of a given triangular part
+   *
+   * The parameter \a TriMode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper,
+   * \c #Lower, \c #StrictlyLower, \c #UnitLower.
+   *
+   * If \c TriMode references the same triangular part than \c *this, then this method simply return a \c TriangularView
+   * of the nested expression, otherwise, the nested expression is first transposed, thus returning a \c
+   * TriangularView<Transpose<MatrixType>> object.
+   *
+   * \sa MatrixBase::triangularView(), class TriangularView
+   */
+  template <unsigned int TriMode>
+  EIGEN_DEVICE_FUNC
+      std::conditional_t<(TriMode & (Upper | Lower)) == (UpLo & (Upper | Lower)), TriangularView<MatrixType, TriMode>,
+                         TriangularView<typename MatrixType::AdjointReturnType, TriMode> >
+      triangularView() const {
+    std::conditional_t<(TriMode & (Upper | Lower)) == (UpLo & (Upper | Lower)), MatrixType&,
+                       typename MatrixType::ConstTransposeReturnType>
+        tmp1(m_matrix);
+    std::conditional_t<(TriMode & (Upper | Lower)) == (UpLo & (Upper | Lower)), MatrixType&,
+                       typename MatrixType::AdjointReturnType>
+        tmp2(tmp1);
+    return std::conditional_t<(TriMode & (Upper | Lower)) == (UpLo & (Upper | Lower)),
+                              TriangularView<MatrixType, TriMode>,
+                              TriangularView<typename MatrixType::AdjointReturnType, TriMode> >(tmp2);
+  }
+
+  typedef SelfAdjointView<const MatrixConjugateReturnType, UpLo> ConjugateReturnType;
+  /** \sa MatrixBase::conjugate() const */
+  EIGEN_DEVICE_FUNC inline const ConjugateReturnType conjugate() const {
+    return ConjugateReturnType(m_matrix.conjugate());
+  }
+
+  /** \returns an expression of the complex conjugate of \c *this if Cond==true,
+   *           returns \c *this otherwise.
+   */
+  template <bool Cond>
+  EIGEN_DEVICE_FUNC inline std::conditional_t<Cond, ConjugateReturnType, ConstSelfAdjointView> conjugateIf() const {
+    typedef std::conditional_t<Cond, ConjugateReturnType, ConstSelfAdjointView> ReturnType;
+    return ReturnType(m_matrix.template conjugateIf<Cond>());
+  }
+
+  typedef SelfAdjointView<const typename MatrixType::AdjointReturnType, TransposeMode> AdjointReturnType;
+  /** \sa MatrixBase::adjoint() const */
+  EIGEN_DEVICE_FUNC inline const AdjointReturnType adjoint() const { return AdjointReturnType(m_matrix.adjoint()); }
+
+  typedef SelfAdjointView<typename MatrixType::TransposeReturnType, TransposeMode> TransposeReturnType;
+  /** \sa MatrixBase::transpose() */
+  template <class Dummy = int>
+  EIGEN_DEVICE_FUNC inline TransposeReturnType transpose(
+      std::enable_if_t<Eigen::internal::is_lvalue<MatrixType>::value, Dummy*> = nullptr) {
+    typename MatrixType::TransposeReturnType tmp(m_matrix);
+    return TransposeReturnType(tmp);
+  }
+
+  typedef SelfAdjointView<const typename MatrixType::ConstTransposeReturnType, TransposeMode> ConstTransposeReturnType;
+  /** \sa MatrixBase::transpose() const */
+  EIGEN_DEVICE_FUNC inline const ConstTransposeReturnType transpose() const {
+    return ConstTransposeReturnType(m_matrix.transpose());
+  }
+
+  /** \returns a const expression of the main diagonal of the matrix \c *this
+   *
+   * This method simply returns the diagonal of the nested expression, thus by-passing the SelfAdjointView decorator.
+   *
+   * \sa MatrixBase::diagonal(), class Diagonal */
+  EIGEN_DEVICE_FUNC typename MatrixType::ConstDiagonalReturnType diagonal() const {
+    return typename MatrixType::ConstDiagonalReturnType(m_matrix);
+  }
+
+  /////////// Cholesky module ///////////
+
+  LLT<PlainObject, UpLo> llt() const;
+  LDLT<PlainObject, UpLo> ldlt() const;
+
+  /////////// Eigenvalue module ///////////
+
+  /** Real part of #Scalar */
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  /** Return type of eigenvalues() */
+  typedef Matrix<RealScalar, internal::traits<MatrixType>::ColsAtCompileTime, 1> EigenvaluesReturnType;
+
+  EIGEN_DEVICE_FUNC EigenvaluesReturnType eigenvalues() const;
+  EIGEN_DEVICE_FUNC RealScalar operatorNorm() const;
+
+ protected:
+  MatrixTypeNested m_matrix;
+};
+
+// selfadjoint to dense matrix
+
+namespace internal {
+
+// TODO currently a selfadjoint expression has the form SelfAdjointView<.,.>
+//      in the future selfadjoint-ness should be defined by the expression traits
+//      such that Transpose<SelfAdjointView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to
+//      make it work)
+template <typename MatrixType, unsigned int Mode>
+struct evaluator_traits<SelfAdjointView<MatrixType, Mode> > {
+  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
+  typedef SelfAdjointShape Shape;
+};
+
+template <int UpLo, int SetOpposite, typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor,
+          int Version>
+class triangular_dense_assignment_kernel<UpLo, SelfAdjoint, SetOpposite, DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor,
+                                         Version>
+    : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> {
+ protected:
+  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> Base;
+  typedef typename Base::DstXprType DstXprType;
+  typedef typename Base::SrcXprType SrcXprType;
+  using Base::m_dst;
+  using Base::m_functor;
+  using Base::m_src;
+
+ public:
+  typedef typename Base::DstEvaluatorType DstEvaluatorType;
+  typedef typename Base::SrcEvaluatorType SrcEvaluatorType;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::AssignmentTraits AssignmentTraits;
+
+  EIGEN_DEVICE_FUNC triangular_dense_assignment_kernel(DstEvaluatorType& dst, const SrcEvaluatorType& src,
+                                                       const Functor& func, DstXprType& dstExpr)
+      : Base(dst, src, func, dstExpr) {}
+
+  EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col) {
+    eigen_internal_assert(row != col);
+    Scalar tmp = m_src.coeff(row, col);
+    m_functor.assignCoeff(m_dst.coeffRef(row, col), tmp);
+    m_functor.assignCoeff(m_dst.coeffRef(col, row), numext::conj(tmp));
+  }
+
+  // Override to ensure the SelfAdjoint assignCoeff (which mirrors conjugates) is called,
+  // not the base class version (which is a plain copy).
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeffByOuterInner(Index outer, Index inner) {
+    Index row = Base::rowIndexByOuterInner(outer, inner);
+    Index col = Base::colIndexByOuterInner(outer, inner);
+    assignCoeff(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC void assignDiagonalCoeff(Index id) { Base::assignCoeff(id, id); }
+
+  EIGEN_DEVICE_FUNC void assignOppositeCoeff(Index, Index) { eigen_internal_assert(false && "should never be called"); }
+};
+
+}  // end namespace internal
+
+/***************************************************************************
+ * Implementation of MatrixBase methods
+ ***************************************************************************/
+
+/** This is the const version of MatrixBase::selfadjointView() */
+template <typename Derived>
+template <unsigned int UpLo>
+EIGEN_DEVICE_FUNC constexpr typename MatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type
+MatrixBase<Derived>::selfadjointView() const {
+  return typename ConstSelfAdjointViewReturnType<UpLo>::Type(derived());
+}
+
+/** \returns an expression of a symmetric/self-adjoint view extracted from the upper or lower triangular part of the
+ * current matrix
+ *
+ * The parameter \a UpLo can be either \c #Upper or \c #Lower
+ *
+ * Example: \include MatrixBase_selfadjointView.cpp
+ * Output: \verbinclude MatrixBase_selfadjointView.out
+ *
+ * \sa class SelfAdjointView
+ */
+template <typename Derived>
+template <unsigned int UpLo>
+EIGEN_DEVICE_FUNC constexpr typename MatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type
+MatrixBase<Derived>::selfadjointView() {
+  return typename SelfAdjointViewReturnType<UpLo>::Type(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SELFADJOINTMATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SelfCwiseBinaryOp.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SelfCwiseBinaryOp.h
new file mode 100644
index 0000000..a887ad2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SelfCwiseBinaryOp.h
@@ -0,0 +1,50 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFCWISEBINARYOP_H
+#define EIGEN_SELFCWISEBINARYOP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator*=(const Scalar& other) {
+  using ConstantExpr = typename internal::plain_constant_type<Derived, Scalar>::type;
+  using Op = internal::mul_assign_op<Scalar>;
+  internal::call_assignment(derived(), ConstantExpr(rows(), cols(), other), Op());
+  return derived();
+}
+
+template <typename Derived>
+template <bool Enable, typename>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator*=(const RealScalar& other) {
+  realView() *= other;
+  return derived();
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator/=(const Scalar& other) {
+  using ConstantExpr = typename internal::plain_constant_type<Derived, Scalar>::type;
+  using Op = internal::div_assign_op<Scalar>;
+  internal::call_assignment(derived(), ConstantExpr(rows(), cols(), other), Op());
+  return derived();
+}
+
+template <typename Derived>
+template <bool Enable, typename>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator/=(const RealScalar& other) {
+  realView() /= other;
+  return derived();
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SELFCWISEBINARYOP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SkewSymmetricMatrix3.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SkewSymmetricMatrix3.h
new file mode 100644
index 0000000..a6ad143
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SkewSymmetricMatrix3.h
@@ -0,0 +1,382 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SKEWSYMMETRICMATRIX3_H
+#define EIGEN_SKEWSYMMETRICMATRIX3_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class SkewSymmetricBase
+ * \ingroup Core_Module
+ *
+ * \brief Base class for skew symmetric matrices and expressions
+ *
+ * This is the base class that is inherited by SkewSymmetricMatrix3 and related expression
+ * types, which internally use a three vector for storing the entries. SkewSymmetric
+ * types always represent square three times three matrices.
+ *
+ * This implementations follows class DiagonalMatrix
+ *
+ * \tparam Derived is the derived type, a SkewSymmetricMatrix3 or SkewSymmetricWrapper.
+ *
+ * \sa class SkewSymmetricMatrix3, class SkewSymmetricWrapper
+ */
+template <typename Derived>
+class SkewSymmetricBase : public EigenBase<Derived> {
+ public:
+  typedef typename internal::traits<Derived>::SkewSymmetricVectorType SkewSymmetricVectorType;
+  typedef typename SkewSymmetricVectorType::Scalar Scalar;
+  typedef typename SkewSymmetricVectorType::RealScalar RealScalar;
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+
+  enum {
+    RowsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
+    ColsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
+    MaxRowsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
+    MaxColsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
+    IsVectorAtCompileTime = 0,
+    Flags = NoPreferredStorageOrderBit
+  };
+
+  typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime>
+      DenseMatrixType;
+  typedef DenseMatrixType DenseType;
+  typedef SkewSymmetricMatrix3<Scalar> PlainObject;
+
+  /** \returns a reference to the derived object. */
+  EIGEN_DEVICE_FUNC inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+  /** \returns a const reference to the derived object. */
+  EIGEN_DEVICE_FUNC inline Derived& derived() { return *static_cast<Derived*>(this); }
+
+  /**
+   * Constructs a dense matrix from \c *this. Note, this directly returns a dense matrix type,
+   * not an expression.
+   * \returns A dense matrix, with its entries set from the derived object. */
+  EIGEN_DEVICE_FUNC DenseMatrixType toDenseMatrix() const { return derived(); }
+
+  /** Determinant vanishes */
+  EIGEN_DEVICE_FUNC constexpr Scalar determinant() const { return 0; }
+
+  /** A.transpose() = -A */
+  EIGEN_DEVICE_FUNC PlainObject transpose() const { return (-vector()).asSkewSymmetric(); }
+
+  /** \returns the exponential of this matrix using Rodrigues’ formula */
+  EIGEN_DEVICE_FUNC DenseMatrixType exponential() const {
+    DenseMatrixType retVal = DenseMatrixType::Identity();
+    const SkewSymmetricVectorType& v = vector();
+    if (v.isZero()) {
+      return retVal;
+    }
+    const Scalar norm2 = v.squaredNorm();
+    const Scalar norm = numext::sqrt(norm2);
+    retVal += ((((1 - numext::cos(norm)) / norm2) * derived()) * derived()) +
+              (numext::sin(norm) / norm) * derived().toDenseMatrix();
+    return retVal;
+  }
+
+  /** \returns a reference to the derived object's vector of coefficients. */
+  EIGEN_DEVICE_FUNC inline const SkewSymmetricVectorType& vector() const { return derived().vector(); }
+  /** \returns a const reference to the derived object's vector of coefficients. */
+  EIGEN_DEVICE_FUNC inline SkewSymmetricVectorType& vector() { return derived().vector(); }
+
+  /** \returns the number of rows. */
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return 3; }
+  /** \returns the number of columns. */
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return 3; }
+
+  /** \returns the matrix product of \c *this by the dense matrix, \a matrix */
+  template <typename MatrixDerived>
+  EIGEN_DEVICE_FUNC Product<Derived, MatrixDerived, LazyProduct> operator*(
+      const MatrixBase<MatrixDerived>& matrix) const {
+    return Product<Derived, MatrixDerived, LazyProduct>(derived(), matrix.derived());
+  }
+
+  /** \returns the matrix product of \c *this by the skew symmetric matrix, \a matrix */
+  template <typename MatrixDerived>
+  EIGEN_DEVICE_FUNC Product<Derived, MatrixDerived, LazyProduct> operator*(
+      const SkewSymmetricBase<MatrixDerived>& matrix) const {
+    return Product<Derived, MatrixDerived, LazyProduct>(derived(), matrix.derived());
+  }
+
+  template <typename OtherDerived>
+  using SkewSymmetricProductReturnType = SkewSymmetricWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+      SkewSymmetricVectorType, typename OtherDerived::SkewSymmetricVectorType, product)>;
+
+  /** \returns the wedge product of \c *this by the skew symmetric matrix \a other
+   *  A wedge B = AB - BA */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC SkewSymmetricProductReturnType<OtherDerived> wedge(
+      const SkewSymmetricBase<OtherDerived>& other) const {
+    return vector().cross(other.vector()).asSkewSymmetric();
+  }
+
+  using SkewSymmetricScaleReturnType =
+      SkewSymmetricWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(SkewSymmetricVectorType, Scalar, product)>;
+
+  /** \returns the product of \c *this by the scalar \a scalar */
+  EIGEN_DEVICE_FUNC inline SkewSymmetricScaleReturnType operator*(const Scalar& scalar) const {
+    return (vector() * scalar).asSkewSymmetric();
+  }
+
+  using ScaleSkewSymmetricReturnType =
+      SkewSymmetricWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar, SkewSymmetricVectorType, product)>;
+
+  /** \returns the product of a scalar and the skew symmetric matrix \a other */
+  EIGEN_DEVICE_FUNC friend inline ScaleSkewSymmetricReturnType operator*(const Scalar& scalar,
+                                                                         const SkewSymmetricBase& other) {
+    return (scalar * other.vector()).asSkewSymmetric();
+  }
+
+  template <typename OtherDerived>
+  using SkewSymmetricSumReturnType = SkewSymmetricWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+      SkewSymmetricVectorType, typename OtherDerived::SkewSymmetricVectorType, sum)>;
+
+  /** \returns the sum of \c *this and the skew symmetric matrix \a other */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline SkewSymmetricSumReturnType<OtherDerived> operator+(
+      const SkewSymmetricBase<OtherDerived>& other) const {
+    return (vector() + other.vector()).asSkewSymmetric();
+  }
+
+  template <typename OtherDerived>
+  using SkewSymmetricDifferenceReturnType = SkewSymmetricWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+      SkewSymmetricVectorType, typename OtherDerived::SkewSymmetricVectorType, difference)>;
+
+  /** \returns the difference of \c *this and the skew symmetric matrix \a other */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline SkewSymmetricDifferenceReturnType<OtherDerived> operator-(
+      const SkewSymmetricBase<OtherDerived>& other) const {
+    return (vector() - other.vector()).asSkewSymmetric();
+  }
+};
+
+/** \class SkewSymmetricMatrix3
+ * \ingroup Core_Module
+ *
+ * \brief Represents a 3x3 skew symmetric matrix with its storage
+ *
+ * \tparam Scalar_ the type of coefficients
+ *
+ * \sa class SkewSymmetricBase, class SkewSymmetricWrapper
+ */
+
+namespace internal {
+template <typename Scalar_>
+struct traits<SkewSymmetricMatrix3<Scalar_>> : traits<Matrix<Scalar_, 3, 3, 0, 3, 3>> {
+  typedef Matrix<Scalar_, 3, 1, 0, 3, 1> SkewSymmetricVectorType;
+  typedef SkewSymmetricShape StorageKind;
+  enum { Flags = LvalueBit | NoPreferredStorageOrderBit | NestByRefBit };
+};
+}  // namespace internal
+template <typename Scalar_>
+class SkewSymmetricMatrix3 : public SkewSymmetricBase<SkewSymmetricMatrix3<Scalar_>> {
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  typedef typename internal::traits<SkewSymmetricMatrix3>::SkewSymmetricVectorType SkewSymmetricVectorType;
+  typedef const SkewSymmetricMatrix3& Nested;
+  typedef Scalar_ Scalar;
+  typedef typename internal::traits<SkewSymmetricMatrix3>::StorageKind StorageKind;
+  typedef typename internal::traits<SkewSymmetricMatrix3>::StorageIndex StorageIndex;
+#endif
+
+ protected:
+  SkewSymmetricVectorType m_vector;
+
+ public:
+  /** const version of vector(). */
+  EIGEN_DEVICE_FUNC inline const SkewSymmetricVectorType& vector() const { return m_vector; }
+  /** \returns a reference to the stored vector of coefficients. */
+  EIGEN_DEVICE_FUNC inline SkewSymmetricVectorType& vector() { return m_vector; }
+
+  /** Default constructor without initialization */
+  EIGEN_DEVICE_FUNC inline SkewSymmetricMatrix3() {}
+
+  /** Constructor from three scalars */
+  EIGEN_DEVICE_FUNC inline SkewSymmetricMatrix3(const Scalar& x, const Scalar& y, const Scalar& z)
+      : m_vector(x, y, z) {}
+
+  /** \brief Constructs a SkewSymmetricMatrix3 from an r-value vector type */
+  EIGEN_DEVICE_FUNC explicit inline SkewSymmetricMatrix3(SkewSymmetricVectorType&& vec) : m_vector(std::move(vec)) {}
+
+  /** generic constructor from expression of the coefficients */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC explicit inline SkewSymmetricMatrix3(const MatrixBase<OtherDerived>& other) : m_vector(other) {}
+
+  /** Copy constructor. */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline SkewSymmetricMatrix3(const SkewSymmetricBase<OtherDerived>& other)
+      : m_vector(other.vector()) {}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
+  inline SkewSymmetricMatrix3(const SkewSymmetricMatrix3& other) : m_vector(other.vector()) {}
+#endif
+
+  /** Copy operator. */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC SkewSymmetricMatrix3& operator=(const SkewSymmetricBase<OtherDerived>& other) {
+    m_vector = other.vector();
+    return *this;
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** This is a special case of the templated operator=. Its purpose is to
+   * prevent a default operator= from hiding the templated operator=.
+   */
+  EIGEN_DEVICE_FUNC SkewSymmetricMatrix3& operator=(const SkewSymmetricMatrix3& other) {
+    m_vector = other.vector();
+    return *this;
+  }
+#endif
+
+  typedef SkewSymmetricWrapper<const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, SkewSymmetricVectorType>>
+      InitializeReturnType;
+
+  /** Initializes a skew symmetric matrix with coefficients set to zero */
+  EIGEN_DEVICE_FUNC static InitializeReturnType Zero() { return SkewSymmetricVectorType::Zero().asSkewSymmetric(); }
+
+  /** Sets all coefficients to zero. */
+  EIGEN_DEVICE_FUNC inline void setZero() { m_vector.setZero(); }
+};
+
+/** \class SkewSymmetricWrapper
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a skew symmetric matrix
+ *
+ * \tparam SkewSymmetricVectorType_ the type of the vector of coefficients
+ *
+ * This class is an expression of a skew symmetric matrix, but not storing its own vector of coefficients,
+ * instead wrapping an existing vector expression. It is the return type of MatrixBase::asSkewSymmetric()
+ * and most of the time this is the only way that it is used.
+ *
+ * \sa class SkewSymmetricMatrix3, class SkewSymmetricBase, MatrixBase::asSkewSymmetric()
+ */
+
+namespace internal {
+template <typename SkewSymmetricVectorType_>
+struct traits<SkewSymmetricWrapper<SkewSymmetricVectorType_>> {
+  typedef SkewSymmetricVectorType_ SkewSymmetricVectorType;
+  typedef typename SkewSymmetricVectorType::Scalar Scalar;
+  typedef typename SkewSymmetricVectorType::StorageIndex StorageIndex;
+  typedef SkewSymmetricShape StorageKind;
+  typedef typename traits<SkewSymmetricVectorType>::XprKind XprKind;
+  enum {
+    RowsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
+    ColsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
+    MaxRowsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
+    MaxColsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
+    Flags = (traits<SkewSymmetricVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
+  };
+};
+}  // namespace internal
+
+template <typename SkewSymmetricVectorType_>
+class SkewSymmetricWrapper : public SkewSymmetricBase<SkewSymmetricWrapper<SkewSymmetricVectorType_>>,
+                             internal::no_assignment_operator {
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  typedef SkewSymmetricVectorType_ SkewSymmetricVectorType;
+  typedef SkewSymmetricWrapper Nested;
+#endif
+
+  /** Constructor from expression of coefficients to wrap. */
+  EIGEN_DEVICE_FUNC explicit inline SkewSymmetricWrapper(SkewSymmetricVectorType& a_vector) : m_vector(a_vector) {}
+
+  /** \returns a const reference to the wrapped expression of coefficients. */
+  EIGEN_DEVICE_FUNC const SkewSymmetricVectorType& vector() const { return m_vector; }
+
+ protected:
+  typename SkewSymmetricVectorType::Nested m_vector;
+};
+
+/** \returns a pseudo-expression of a skew symmetric matrix with *this as vector of coefficients
+ *
+ * \only_for_vectors
+ *
+ * \sa class SkewSymmetricWrapper, class SkewSymmetricMatrix3, vector(), isSkewSymmetric()
+ **/
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr const SkewSymmetricWrapper<const Derived> MatrixBase<Derived>::asSkewSymmetric() const {
+  return SkewSymmetricWrapper<const Derived>(derived());
+}
+
+/** \returns true if *this is approximately equal to a skew symmetric matrix,
+ *          within the precision given by \a prec.
+ */
+template <typename Derived>
+bool MatrixBase<Derived>::isSkewSymmetric(const RealScalar& prec) const {
+  if (cols() != rows()) return false;
+  return (this->transpose() + *this).isZero(prec);
+}
+
+/** \returns the matrix product of \c *this by the skew symmetric matrix \a skew.
+ */
+template <typename Derived>
+template <typename SkewDerived>
+EIGEN_DEVICE_FUNC inline const Product<Derived, SkewDerived, LazyProduct> MatrixBase<Derived>::operator*(
+    const SkewSymmetricBase<SkewDerived>& skew) const {
+  return Product<Derived, SkewDerived, LazyProduct>(derived(), skew.derived());
+}
+
+namespace internal {
+
+template <>
+struct storage_kind_to_shape<SkewSymmetricShape> {
+  typedef SkewSymmetricShape Shape;
+};
+
+struct SkewSymmetric2Dense {};
+
+template <>
+struct AssignmentKind<DenseShape, SkewSymmetricShape> {
+  typedef SkewSymmetric2Dense Kind;
+};
+
+// SkewSymmetric matrix to Dense assignment
+template <typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, SkewSymmetric2Dense> {
+  EIGEN_DEVICE_FUNC static void run(
+      DstXprType& dst, const SrcXprType& src,
+      const internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
+    if ((dst.rows() != 3) || (dst.cols() != 3)) {
+      dst.resize(3, 3);
+    }
+    dst.diagonal().setZero();
+    const typename SrcXprType::SkewSymmetricVectorType v = src.vector();
+    dst(0, 1) = -v(2);
+    dst(1, 0) = v(2);
+    dst(0, 2) = v(1);
+    dst(2, 0) = -v(1);
+    dst(1, 2) = -v(0);
+    dst(2, 1) = v(0);
+  }
+  EIGEN_DEVICE_FUNC static void run(
+      DstXprType& dst, const SrcXprType& src,
+      const internal::add_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
+    dst.vector() += src.vector();
+  }
+
+  EIGEN_DEVICE_FUNC static void run(
+      DstXprType& dst, const SrcXprType& src,
+      const internal::sub_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
+    dst.vector() -= src.vector();
+  }
+};
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SKEWSYMMETRICMATRIX3_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Solve.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Solve.h
new file mode 100644
index 0000000..030b934
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Solve.h
@@ -0,0 +1,174 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SOLVE_H
+#define EIGEN_SOLVE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Decomposition, typename RhsType, typename StorageKind>
+class SolveImpl;
+
+/** \class Solve
+ * \ingroup Core_Module
+ *
+ * \brief Pseudo expression representing a solving operation
+ *
+ * \tparam Decomposition the type of the matrix or decomposition object
+ * \tparam Rhstype the type of the right-hand side
+ *
+ * This class represents an expression of A.solve(B)
+ * and most of the time this is the only way it is used.
+ *
+ */
+namespace internal {
+
+// this solve_traits class permits to determine the evaluation type with respect to storage kind (Dense vs Sparse)
+template <typename Decomposition, typename RhsType, typename StorageKind>
+struct solve_traits;
+
+template <typename Decomposition, typename RhsType>
+struct solve_traits<Decomposition, RhsType, Dense> {
+  typedef typename make_proper_matrix_type<typename RhsType::Scalar, Decomposition::ColsAtCompileTime,
+                                           RhsType::ColsAtCompileTime, RhsType::PlainObject::Options,
+                                           Decomposition::MaxColsAtCompileTime, RhsType::MaxColsAtCompileTime>::type
+      PlainObject;
+};
+
+template <typename Decomposition, typename RhsType>
+struct traits<Solve<Decomposition, RhsType> >
+    : traits<
+          typename solve_traits<Decomposition, RhsType, typename internal::traits<RhsType>::StorageKind>::PlainObject> {
+  typedef typename solve_traits<Decomposition, RhsType, typename internal::traits<RhsType>::StorageKind>::PlainObject
+      PlainObject;
+  typedef typename promote_index_type<typename Decomposition::StorageIndex, typename RhsType::StorageIndex>::type
+      StorageIndex;
+  typedef traits<PlainObject> BaseTraits;
+  enum { Flags = BaseTraits::Flags & RowMajorBit, CoeffReadCost = HugeCost };
+};
+
+}  // namespace internal
+
+template <typename Decomposition, typename RhsType>
+class Solve : public SolveImpl<Decomposition, RhsType, typename internal::traits<RhsType>::StorageKind> {
+ public:
+  typedef typename internal::traits<Solve>::PlainObject PlainObject;
+  typedef typename internal::traits<Solve>::StorageIndex StorageIndex;
+
+  Solve(const Decomposition &dec, const RhsType &rhs) : m_dec(dec), m_rhs(rhs) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_dec.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_rhs.cols(); }
+
+  EIGEN_DEVICE_FUNC constexpr const Decomposition &dec() const { return m_dec; }
+  EIGEN_DEVICE_FUNC constexpr const RhsType &rhs() const { return m_rhs; }
+
+ protected:
+  const Decomposition &m_dec;
+  const typename internal::ref_selector<RhsType>::type m_rhs;
+};
+
+// Specialization of the Solve expression for dense results
+template <typename Decomposition, typename RhsType>
+class SolveImpl<Decomposition, RhsType, Dense> : public MatrixBase<Solve<Decomposition, RhsType> > {
+  typedef Solve<Decomposition, RhsType> Derived;
+
+ public:
+  typedef MatrixBase<Solve<Decomposition, RhsType> > Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
+
+ private:
+  Scalar coeff(Index row, Index col) const;
+  Scalar coeff(Index i) const;
+};
+
+// Generic API dispatcher
+template <typename Decomposition, typename RhsType, typename StorageKind>
+class SolveImpl : public internal::generic_xpr_base<Solve<Decomposition, RhsType>, MatrixXpr, StorageKind>::type {
+ public:
+  typedef typename internal::generic_xpr_base<Solve<Decomposition, RhsType>, MatrixXpr, StorageKind>::type Base;
+};
+
+namespace internal {
+
+// Evaluator of Solve -> eval into a temporary
+template <typename Decomposition, typename RhsType>
+struct evaluator<Solve<Decomposition, RhsType> >
+    : public evaluator<typename Solve<Decomposition, RhsType>::PlainObject> {
+  typedef Solve<Decomposition, RhsType> SolveType;
+  typedef typename SolveType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  enum { Flags = Base::Flags | EvalBeforeNestingBit };
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const SolveType &solve) : m_result(solve.rows(), solve.cols()) {
+    internal::construct_at<Base>(this, m_result);
+    solve.dec()._solve_impl(solve.rhs(), m_result);
+  }
+
+ protected:
+  PlainObject m_result;
+};
+
+// Specialization for "dst = dec.solve(rhs)"
+// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse
+// specialization must exist somewhere
+template <typename DstXprType, typename DecType, typename RhsType, typename Scalar>
+struct Assignment<DstXprType, Solve<DecType, RhsType>, internal::assign_op<Scalar, Scalar>, Dense2Dense> {
+  typedef Solve<DecType, RhsType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar, Scalar> &) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    src.dec()._solve_impl(src.rhs(), dst);
+  }
+};
+
+// Specialization for "dst = dec.transpose().solve(rhs)"
+template <typename DstXprType, typename DecType, typename RhsType, typename Scalar>
+struct Assignment<DstXprType, Solve<Transpose<const DecType>, RhsType>, internal::assign_op<Scalar, Scalar>,
+                  Dense2Dense> {
+  typedef Solve<Transpose<const DecType>, RhsType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar, Scalar> &) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    src.dec().nestedExpression().template _solve_impl_transposed<false>(src.rhs(), dst);
+  }
+};
+
+// Specialization for "dst = dec.adjoint().solve(rhs)"
+template <typename DstXprType, typename DecType, typename RhsType, typename Scalar>
+struct Assignment<
+    DstXprType,
+    Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,
+          RhsType>,
+    internal::assign_op<Scalar, Scalar>, Dense2Dense> {
+  typedef Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,
+                RhsType>
+      SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar, Scalar> &) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    src.dec().nestedExpression().nestedExpression().template _solve_impl_transposed<true>(src.rhs(), dst);
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SOLVE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SolveTriangular.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SolveTriangular.h
new file mode 100644
index 0000000..9d31874
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SolveTriangular.h
@@ -0,0 +1,237 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SOLVETRIANGULAR_H
+#define EIGEN_SOLVETRIANGULAR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Forward declarations:
+// The following two routines are implemented in the products/TriangularSolver*.h files
+template <typename LhsScalar, typename RhsScalar, typename Index, int Side, int Mode, bool Conjugate, int StorageOrder>
+struct triangular_solve_vector;
+
+template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder,
+          int OtherStorageOrder, int OtherInnerStride>
+struct triangular_solve_matrix;
+
+// small helper struct extracting some traits on the underlying solver operation
+template <typename Lhs, typename Rhs, int Side>
+class trsolve_traits {
+ private:
+  enum { RhsIsVectorAtCompileTime = (Side == OnTheLeft ? Rhs::ColsAtCompileTime : Rhs::RowsAtCompileTime) == 1 };
+
+ public:
+  enum {
+    Unrolling = (RhsIsVectorAtCompileTime && Rhs::SizeAtCompileTime != Dynamic && Rhs::SizeAtCompileTime <= 8)
+                    ? CompleteUnrolling
+                    : NoUnrolling,
+    RhsVectors = RhsIsVectorAtCompileTime ? 1 : Dynamic
+  };
+};
+
+template <typename Lhs, typename Rhs,
+          int Side,  // can be OnTheLeft/OnTheRight
+          int Mode,  // can be Upper/Lower | UnitDiag
+          int Unrolling = trsolve_traits<Lhs, Rhs, Side>::Unrolling,
+          int RhsVectors = trsolve_traits<Lhs, Rhs, Side>::RhsVectors>
+struct triangular_solver_selector;
+
+template <typename Lhs, typename Rhs, int Side, int Mode>
+struct triangular_solver_selector<Lhs, Rhs, Side, Mode, NoUnrolling, 1> {
+  typedef typename Lhs::Scalar LhsScalar;
+  typedef typename Rhs::Scalar RhsScalar;
+  typedef blas_traits<Lhs> LhsProductTraits;
+  typedef typename LhsProductTraits::ExtractType ActualLhsType;
+  typedef Map<Matrix<RhsScalar, Dynamic, 1>, Aligned> MappedRhs;
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs) {
+    ActualLhsType actualLhs = LhsProductTraits::extract(lhs);
+
+    // FIXME find a way to allow an inner stride if packet_traits<Scalar>::size==1
+
+    bool useRhsDirectly = Rhs::InnerStrideAtCompileTime == 1 || rhs.innerStride() == 1;
+
+    ei_declare_aligned_stack_constructed_variable(RhsScalar, actualRhs, rhs.size(), (useRhsDirectly ? rhs.data() : 0));
+
+    if (!useRhsDirectly) MappedRhs(actualRhs, rhs.size()) = rhs;
+
+    triangular_solve_vector<LhsScalar, RhsScalar, Index, Side, Mode, LhsProductTraits::NeedToConjugate,
+                            (int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor>::run(actualLhs.cols(),
+                                                                                        actualLhs.data(),
+                                                                                        actualLhs.outerStride(),
+                                                                                        actualRhs);
+
+    if (!useRhsDirectly) rhs = MappedRhs(actualRhs, rhs.size());
+  }
+};
+
+// the rhs is a matrix
+template <typename Lhs, typename Rhs, int Side, int Mode>
+struct triangular_solver_selector<Lhs, Rhs, Side, Mode, NoUnrolling, Dynamic> {
+  typedef typename Rhs::Scalar Scalar;
+  typedef blas_traits<Lhs> LhsProductTraits;
+  typedef typename LhsProductTraits::DirectLinearAccessType ActualLhsType;
+
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs) {
+    add_const_on_value_type_t<ActualLhsType> actualLhs = LhsProductTraits::extract(lhs);
+
+    const Index size = lhs.rows();
+    const Index othersize = Side == OnTheLeft ? rhs.cols() : rhs.rows();
+
+    typedef internal::gemm_blocking_space<(Rhs::Flags & RowMajorBit) ? RowMajor : ColMajor, Scalar, Scalar,
+                                          Rhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime,
+                                          Lhs::MaxRowsAtCompileTime, 4>
+        BlockingType;
+
+    // Nothing to solve.
+    if (actualLhs.size() == 0 || rhs.size() == 0) {
+      return;
+    }
+
+    BlockingType blocking(rhs.rows(), rhs.cols(), size, 1, false);
+
+    triangular_solve_matrix<Scalar, Index, Side, Mode, LhsProductTraits::NeedToConjugate,
+                            (int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor,
+                            (Rhs::Flags & RowMajorBit) ? RowMajor : ColMajor,
+                            Rhs::InnerStrideAtCompileTime>::run(size, othersize, &actualLhs.coeffRef(0, 0),
+                                                                actualLhs.outerStride(), &rhs.coeffRef(0, 0),
+                                                                rhs.innerStride(), rhs.outerStride(), blocking);
+  }
+};
+
+/***************************************************************************
+ * meta-unrolling implementation
+ ***************************************************************************/
+
+template <typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size, bool Stop = LoopIndex == Size>
+struct triangular_solver_unroller;
+
+template <typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size>
+struct triangular_solver_unroller<Lhs, Rhs, Mode, LoopIndex, Size, false> {
+  enum {
+    IsLower = ((Mode & Lower) == Lower),
+    DiagIndex = IsLower ? LoopIndex : Size - LoopIndex - 1,
+    StartIndex = IsLower ? 0 : DiagIndex + 1
+  };
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs) {
+    if (LoopIndex > 0)
+      rhs.coeffRef(DiagIndex) -= lhs.row(DiagIndex)
+                                     .template segment<LoopIndex>(StartIndex)
+                                     .transpose()
+                                     .cwiseProduct(rhs.template segment<LoopIndex>(StartIndex))
+                                     .sum();
+
+    if (!(Mode & UnitDiag)) rhs.coeffRef(DiagIndex) /= lhs.coeff(DiagIndex, DiagIndex);
+
+    triangular_solver_unroller<Lhs, Rhs, Mode, LoopIndex + 1, Size>::run(lhs, rhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size>
+struct triangular_solver_unroller<Lhs, Rhs, Mode, LoopIndex, Size, true> {
+  static EIGEN_DEVICE_FUNC void run(const Lhs&, Rhs&) {}
+};
+
+template <typename Lhs, typename Rhs, int Mode>
+struct triangular_solver_selector<Lhs, Rhs, OnTheLeft, Mode, CompleteUnrolling, 1> {
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs) {
+    triangular_solver_unroller<Lhs, Rhs, Mode, 0, Rhs::SizeAtCompileTime>::run(lhs, rhs);
+  }
+};
+
+template <typename Lhs, typename Rhs, int Mode>
+struct triangular_solver_selector<Lhs, Rhs, OnTheRight, Mode, CompleteUnrolling, 1> {
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs) {
+    Transpose<const Lhs> trLhs(lhs);
+    Transpose<Rhs> trRhs(rhs);
+
+    triangular_solver_unroller<Transpose<const Lhs>, Transpose<Rhs>,
+                               ((Mode & Upper) == Upper ? Lower : Upper) | (Mode & UnitDiag), 0,
+                               Rhs::SizeAtCompileTime>::run(trLhs, trRhs);
+  }
+};
+
+}  // end namespace internal
+
+/***************************************************************************
+ * TriangularView methods
+ ***************************************************************************/
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType, unsigned int Mode>
+template <int Side, typename OtherDerived>
+EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType, Mode, Dense>::solveInPlace(
+    const MatrixBase<OtherDerived>& _other) const {
+  OtherDerived& other = _other.const_cast_derived();
+  eigen_assert(derived().cols() == derived().rows() && ((Side == OnTheLeft && derived().cols() == other.rows()) ||
+                                                        (Side == OnTheRight && derived().cols() == other.cols())));
+  eigen_assert((!(int(Mode) & int(ZeroDiag))) && bool(int(Mode) & (int(Upper) | int(Lower))));
+  // If solving for a 0x0 matrix, nothing to do, simply return.
+  if (derived().cols() == 0) return;
+
+  enum {
+    copy = (internal::traits<OtherDerived>::Flags & RowMajorBit) && OtherDerived::IsVectorAtCompileTime &&
+           OtherDerived::SizeAtCompileTime != 1
+  };
+  typedef std::conditional_t<copy, typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>
+      OtherCopy;
+  OtherCopy otherCopy(other);
+
+  internal::triangular_solver_selector<MatrixType, std::remove_reference_t<OtherCopy>, Side, Mode>::run(
+      derived().nestedExpression(), otherCopy);
+
+  if (copy) other = otherCopy;
+}
+
+template <typename Derived, unsigned int Mode>
+template <int Side, typename Other>
+const internal::triangular_solve_retval<Side, TriangularView<Derived, Mode>, Other>
+TriangularViewImpl<Derived, Mode, Dense>::solve(const MatrixBase<Other>& other) const {
+  return internal::triangular_solve_retval<Side, TriangularViewType, Other>(derived(), other.derived());
+}
+#endif
+
+namespace internal {
+
+template <int Side, typename TriangularType, typename Rhs>
+struct traits<triangular_solve_retval<Side, TriangularType, Rhs> > {
+  typedef typename internal::plain_matrix_type_column_major<Rhs>::type ReturnType;
+};
+
+template <int Side, typename TriangularType, typename Rhs>
+struct triangular_solve_retval : public ReturnByValue<triangular_solve_retval<Side, TriangularType, Rhs> > {
+  typedef remove_all_t<typename Rhs::Nested> RhsNestedCleaned;
+  typedef ReturnByValue<triangular_solve_retval> Base;
+
+  triangular_solve_retval(const TriangularType& tri, const Rhs& rhs) : m_triangularMatrix(tri), m_rhs(rhs) {}
+
+  constexpr Index rows() const noexcept { return m_rhs.rows(); }
+  constexpr Index cols() const noexcept { return m_rhs.cols(); }
+
+  template <typename Dest>
+  inline void evalTo(Dest& dst) const {
+    if (!is_same_dense(dst, m_rhs)) dst = m_rhs;
+    m_triangularMatrix.template solveInPlace<Side>(dst);
+  }
+
+ protected:
+  const TriangularType& m_triangularMatrix;
+  typename Rhs::Nested m_rhs;
+};
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SOLVETRIANGULAR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SolverBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SolverBase.h
new file mode 100644
index 0000000..a6ecbc9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/SolverBase.h
@@ -0,0 +1,167 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SOLVERBASE_H
+#define EIGEN_SOLVERBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Derived>
+struct solve_assertion {
+  template <bool Transpose_, typename Rhs>
+  static void run(const Derived& solver, const Rhs& b) {
+    solver.template _check_solve_assertion<Transpose_>(b);
+  }
+};
+
+template <typename Derived>
+struct solve_assertion<Transpose<Derived>> {
+  typedef Transpose<Derived> type;
+
+  template <bool Transpose_, typename Rhs>
+  static void run(const type& transpose, const Rhs& b) {
+    internal::solve_assertion<internal::remove_all_t<Derived>>::template run<true>(transpose.nestedExpression(), b);
+  }
+};
+
+template <typename Scalar, typename Derived>
+struct solve_assertion<CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>, const Transpose<Derived>>> {
+  typedef CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>, const Transpose<Derived>> type;
+
+  template <bool Transpose_, typename Rhs>
+  static void run(const type& adjoint, const Rhs& b) {
+    internal::solve_assertion<internal::remove_all_t<Transpose<Derived>>>::template run<true>(
+        adjoint.nestedExpression(), b);
+  }
+};
+}  // end namespace internal
+
+/** \class SolverBase
+ * \brief A base class for matrix decomposition and solvers
+ *
+ * \tparam Derived the actual type of the decomposition/solver.
+ *
+ * Any matrix decomposition inheriting this base class provide the following API:
+ *
+ * \code
+ * MatrixType A, b, x;
+ * DecompositionType dec(A);
+ * x = dec.solve(b);             // solve A   * x = b
+ * x = dec.transpose().solve(b); // solve A^T * x = b
+ * x = dec.adjoint().solve(b);   // solve A'  * x = b
+ * \endcode
+ *
+ * \warning Currently, any other usage of transpose() and adjoint() are not supported and will produce compilation
+ * errors.
+ *
+ * \sa class PartialPivLU, class FullPivLU, class HouseholderQR, class ColPivHouseholderQR, class FullPivHouseholderQR,
+ * class CompleteOrthogonalDecomposition, class LLT, class LDLT, class SVDBase
+ */
+template <typename Derived>
+class SolverBase : public EigenBase<Derived> {
+ public:
+  typedef EigenBase<Derived> Base;
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef Scalar CoeffReturnType;
+
+  template <typename Derived_>
+  friend struct internal::solve_assertion;
+
+  ComputationInfo info() const {
+    // CRTP static dispatch: Calls the 'info()' method on the derived class.
+    // Derived must implement 'ComputationInfo info() const'.
+    // If not implemented, name lookup falls back to this base method, causing
+    // infinite recursion (detectable by -Winfinite-recursion).
+    return derived().info();
+  }
+
+  enum {
+    RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+    ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+    SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
+    MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
+    MaxSizeAtCompileTime = internal::size_at_compile_time(internal::traits<Derived>::MaxRowsAtCompileTime,
+                                                          internal::traits<Derived>::MaxColsAtCompileTime),
+    IsVectorAtCompileTime =
+        internal::traits<Derived>::MaxRowsAtCompileTime == 1 || internal::traits<Derived>::MaxColsAtCompileTime == 1,
+    NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0
+                    : bool(IsVectorAtCompileTime)  ? 1
+                                                   : 2
+  };
+
+  /** Default constructor */
+  SolverBase() {}
+
+  ~SolverBase() {}
+
+  using Base::derived;
+
+  /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
+   */
+  template <typename Rhs>
+  inline Solve<Derived, Rhs> solve(const MatrixBase<Rhs>& b) const {
+    internal::solve_assertion<internal::remove_all_t<Derived>>::template run<false>(derived(), b);
+    return Solve<Derived, Rhs>(derived(), b.derived());
+  }
+
+  /** \internal the return type of transpose() */
+  typedef Transpose<const Derived> ConstTransposeReturnType;
+  /** \returns an expression of the transposed of the factored matrix.
+   *
+   * A typical usage is to solve for the transposed problem A^T x = b:
+   * \code x = dec.transpose().solve(b); \endcode
+   *
+   * \sa adjoint(), solve()
+   */
+  inline const ConstTransposeReturnType transpose() const { return ConstTransposeReturnType(derived()); }
+
+  /** \internal the return type of adjoint() */
+  typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+                             CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const ConstTransposeReturnType>,
+                             const ConstTransposeReturnType>
+      AdjointReturnType;
+  /** \returns an expression of the adjoint of the factored matrix
+   *
+   * A typical usage is to solve for the adjoint problem A' x = b:
+   * \code x = dec.adjoint().solve(b); \endcode
+   *
+   * For real scalar types, this function is equivalent to transpose().
+   *
+   * \sa transpose(), solve()
+   */
+  inline const AdjointReturnType adjoint() const { return AdjointReturnType(derived().transpose()); }
+
+ protected:
+  template <bool Transpose_, typename Rhs>
+  void _check_solve_assertion(const Rhs& b) const {
+    EIGEN_ONLY_USED_FOR_DEBUG(b);
+    eigen_assert(derived().m_isInitialized && "Solver is not initialized.");
+    eigen_assert((Transpose_ ? derived().cols() : derived().rows()) == b.rows() &&
+                 "SolverBase::solve(): invalid number of rows of the right hand side matrix b");
+  }
+};
+
+namespace internal {
+
+template <typename Derived>
+struct generic_xpr_base<Derived, MatrixXpr, SolverStorage> {
+  typedef SolverBase<Derived> type;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SOLVERBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/StableNorm.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/StableNorm.h
new file mode 100644
index 0000000..11abb77
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/StableNorm.h
@@ -0,0 +1,216 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STABLENORM_H
+#define EIGEN_STABLENORM_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename ExpressionType, typename Scalar>
+inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale) {
+  Scalar maxCoeff = bl.cwiseAbs().maxCoeff();
+
+  if (maxCoeff > scale) {
+    ssq = ssq * numext::abs2(scale / maxCoeff);
+    Scalar tmp = Scalar(1) / maxCoeff;
+    if (tmp > NumTraits<Scalar>::highest()) {
+      invScale = NumTraits<Scalar>::highest();
+      scale = Scalar(1) / invScale;
+    } else if (maxCoeff > NumTraits<Scalar>::highest())  // we got a INF
+    {
+      invScale = Scalar(1);
+      scale = maxCoeff;
+    } else {
+      scale = maxCoeff;
+      invScale = tmp;
+    }
+  } else if (maxCoeff != maxCoeff)  // we got a NaN
+  {
+    scale = maxCoeff;
+  }
+
+  // TODO: skip sub-vector when maxCoeff << current scale.
+  if (scale > Scalar(0))  // if scale==0, then bl is 0
+    ssq += (bl * invScale).squaredNorm();
+}
+
+template <typename VectorType, typename RealScalar>
+void stable_norm_impl_inner_step(const VectorType& vec, RealScalar& ssq, RealScalar& scale, RealScalar& invScale) {
+  const Index blockSize = 4096;
+
+  Index n = vec.size();
+  Index blockEnd = numext::round_down(n, blockSize);
+  for (Index i = 0; i < blockEnd; i += blockSize) {
+    internal::stable_norm_kernel(vec.template segment<blockSize>(i), ssq, scale, invScale);
+  }
+  if (n > blockEnd) {
+    internal::stable_norm_kernel(vec.tail(n - blockEnd), ssq, scale, invScale);
+  }
+}
+
+template <typename VectorType>
+typename VectorType::RealScalar stable_norm_impl(const VectorType& vec,
+                                                 std::enable_if_t<VectorType::IsVectorAtCompileTime>* = 0) {
+  using std::abs;
+  using std::sqrt;
+
+  Index n = vec.size();
+  if (EIGEN_PREDICT_FALSE(n == 1)) return abs(vec.coeff(0));
+
+  typedef typename VectorType::RealScalar RealScalar;
+  RealScalar scale(0);
+  RealScalar invScale(1);
+  RealScalar ssq(0);  // sum of squares
+
+  stable_norm_impl_inner_step(vec, ssq, scale, invScale);
+
+  return scale * sqrt(ssq);
+}
+
+template <typename MatrixType>
+typename MatrixType::RealScalar stable_norm_impl(const MatrixType& mat,
+                                                 std::enable_if_t<!MatrixType::IsVectorAtCompileTime>* = 0) {
+  using std::sqrt;
+
+  typedef typename MatrixType::RealScalar RealScalar;
+  RealScalar scale(0);
+  RealScalar invScale(1);
+  RealScalar ssq(0);  // sum of squares
+
+  for (Index j = 0; j < mat.outerSize(); ++j) stable_norm_impl_inner_step(mat.innerVector(j), ssq, scale, invScale);
+  return scale * sqrt(ssq);
+}
+
+template <typename Derived>
+inline typename NumTraits<typename traits<Derived>::Scalar>::Real blueNorm_impl(const EigenBase<Derived>& _vec) {
+  typedef typename Derived::RealScalar RealScalar;
+  using std::abs;
+  using std::pow;
+  using std::sqrt;
+
+  // This program calculates the machine-dependent constants
+  // bl, b2, slm, s2m, relerr overfl
+  // from the "basic" machine-dependent numbers
+  // nbig, ibeta, it, iemin, iemax, rbig.
+  // The following define the basic machine-dependent constants.
+  // For portability, the PORT subprograms "ilmaeh" and "rlmach"
+  // are used. For any specific computer, each of the assignment
+  // statements can be replaced
+  static const int ibeta = std::numeric_limits<RealScalar>::radix;  // base for floating-point numbers
+  static const int it = NumTraits<RealScalar>::digits();            // number of base-beta digits in mantissa
+  static const int iemin = NumTraits<RealScalar>::min_exponent();   // minimum exponent
+  static const int iemax = NumTraits<RealScalar>::max_exponent();   // maximum exponent
+  static const RealScalar rbig = NumTraits<RealScalar>::highest();  // largest floating-point number
+  static const RealScalar b1 =
+      RealScalar(pow(RealScalar(ibeta), RealScalar(-((1 - iemin) / 2))));  // lower boundary of midrange
+  static const RealScalar b2 =
+      RealScalar(pow(RealScalar(ibeta), RealScalar((iemax + 1 - it) / 2)));  // upper boundary of midrange
+  static const RealScalar s1m =
+      RealScalar(pow(RealScalar(ibeta), RealScalar((2 - iemin) / 2)));  // scaling factor for lower range
+  static const RealScalar s2m =
+      RealScalar(pow(RealScalar(ibeta), RealScalar(-((iemax + it) / 2))));  // scaling factor for upper range
+  static const RealScalar eps = RealScalar(pow(double(ibeta), 1 - it));
+  static const RealScalar relerr = sqrt(eps);  // tolerance for neglecting asml
+
+  const Derived& vec(_vec.derived());
+  Index n = vec.size();
+  RealScalar ab2 = b2 / RealScalar(n);
+  RealScalar asml = RealScalar(0);
+  RealScalar amed = RealScalar(0);
+  RealScalar abig = RealScalar(0);
+
+  for (Index j = 0; j < vec.outerSize(); ++j) {
+    for (typename Derived::InnerIterator iter(vec, j); iter; ++iter) {
+      RealScalar ax = abs(iter.value());
+      if (ax > ab2)
+        abig += numext::abs2(ax * s2m);
+      else if (ax < b1)
+        asml += numext::abs2(ax * s1m);
+      else
+        amed += numext::abs2(ax);
+    }
+  }
+  if (amed != amed) return amed;  // we got a NaN
+  if (abig > RealScalar(0)) {
+    abig = sqrt(abig);
+    if (abig > rbig)  // overflow, or *this contains INF values
+      return abig;    // return INF
+    if (amed > RealScalar(0)) {
+      abig = abig / s2m;
+      amed = sqrt(amed);
+    } else
+      return abig / s2m;
+  } else if (asml > RealScalar(0)) {
+    if (amed > RealScalar(0)) {
+      abig = sqrt(amed);
+      amed = sqrt(asml) / s1m;
+    } else
+      return sqrt(asml) / s1m;
+  } else
+    return sqrt(amed);
+  asml = numext::mini(abig, amed);
+  abig = numext::maxi(abig, amed);
+  if (asml <= abig * relerr)
+    return abig;
+  else
+    return abig * sqrt(RealScalar(1) + numext::abs2(asml / abig));
+}
+
+}  // end namespace internal
+
+/** \returns the \em l2 norm of \c *this avoiding underflow and overflow.
+ * This version use a blockwise two passes algorithm:
+ *  1 - find the absolute largest coefficient \c s
+ *  2 - compute \f$ s \Vert \frac{*this}{s} \Vert \f$ in a standard way
+ *
+ * For architecture/scalar types supporting vectorization, this version
+ * is faster than blueNorm(). Otherwise the blueNorm() is much faster.
+ *
+ * \sa norm(), blueNorm(), hypotNorm()
+ */
+template <typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::stableNorm() const {
+  return internal::stable_norm_impl(derived());
+}
+
+/** \returns the \em l2 norm of \c *this using the Blue's algorithm.
+ * A Portable Fortran Program to Find the Euclidean Norm of a Vector,
+ * ACM TOMS, Vol 4, Issue 1, 1978.
+ *
+ * For architecture/scalar types without vectorization, this version
+ * is much faster than stableNorm(). Otherwise the stableNorm() is faster.
+ *
+ * \sa norm(), stableNorm(), hypotNorm()
+ */
+template <typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::blueNorm() const {
+  return internal::blueNorm_impl(*this);
+}
+
+/** \returns the \em l2 norm of \c *this avoiding underflow and overflow.
+ * This version use a concatenation of hypot() calls, and it is very slow.
+ *
+ * \sa norm(), stableNorm()
+ */
+template <typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::hypotNorm() const {
+  if (size() == 1)
+    return numext::abs(coeff(0, 0));
+  else
+    return this->cwiseAbs().redux(internal::scalar_hypot_op<RealScalar>());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_STABLENORM_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/StlIterators.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/StlIterators.h
new file mode 100644
index 0000000..afa2ecb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/StlIterators.h
@@ -0,0 +1,618 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STLITERATORS_H
+#define EIGEN_STLITERATORS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename IteratorType>
+struct indexed_based_stl_iterator_traits;
+
+template <typename Derived>
+class indexed_based_stl_iterator_base {
+ protected:
+  typedef indexed_based_stl_iterator_traits<Derived> traits;
+  typedef typename traits::XprType XprType;
+  typedef indexed_based_stl_iterator_base<typename traits::non_const_iterator> non_const_iterator;
+  typedef indexed_based_stl_iterator_base<typename traits::const_iterator> const_iterator;
+  typedef std::conditional_t<internal::is_const<XprType>::value, non_const_iterator, const_iterator> other_iterator;
+
+  friend class indexed_based_stl_iterator_base<typename traits::const_iterator>;
+  friend class indexed_based_stl_iterator_base<typename traits::non_const_iterator>;
+
+ public:
+  typedef Index difference_type;
+  typedef std::random_access_iterator_tag iterator_category;
+
+  indexed_based_stl_iterator_base() noexcept : mp_xpr(0), m_index(0) {}
+  indexed_based_stl_iterator_base(XprType& xpr, Index index) noexcept : mp_xpr(&xpr), m_index(index) {}
+
+  indexed_based_stl_iterator_base(const non_const_iterator& other) noexcept
+      : mp_xpr(other.mp_xpr), m_index(other.m_index) {}
+
+  indexed_based_stl_iterator_base& operator=(const non_const_iterator& other) {
+    mp_xpr = other.mp_xpr;
+    m_index = other.m_index;
+    return *this;
+  }
+
+  Derived& operator++() {
+    ++m_index;
+    return derived();
+  }
+  Derived& operator--() {
+    --m_index;
+    return derived();
+  }
+
+  Derived operator++(int) {
+    Derived prev(derived());
+    operator++();
+    return prev;
+  }
+  Derived operator--(int) {
+    Derived prev(derived());
+    operator--();
+    return prev;
+  }
+
+  friend Derived operator+(const indexed_based_stl_iterator_base& a, Index b) {
+    Derived ret(a.derived());
+    ret += b;
+    return ret;
+  }
+  friend Derived operator-(const indexed_based_stl_iterator_base& a, Index b) {
+    Derived ret(a.derived());
+    ret -= b;
+    return ret;
+  }
+  friend Derived operator+(Index a, const indexed_based_stl_iterator_base& b) {
+    Derived ret(b.derived());
+    ret += a;
+    return ret;
+  }
+  friend Derived operator-(Index a, const indexed_based_stl_iterator_base& b) {
+    Derived ret(b.derived());
+    ret -= a;
+    return ret;
+  }
+
+  Derived& operator+=(Index b) {
+    m_index += b;
+    return derived();
+  }
+  Derived& operator-=(Index b) {
+    m_index -= b;
+    return derived();
+  }
+
+  difference_type operator-(const indexed_based_stl_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index - other.m_index;
+  }
+
+  difference_type operator-(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index - other.m_index;
+  }
+
+  bool operator==(const indexed_based_stl_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index == other.m_index;
+  }
+  bool operator!=(const indexed_based_stl_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index != other.m_index;
+  }
+  bool operator<(const indexed_based_stl_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index < other.m_index;
+  }
+  bool operator<=(const indexed_based_stl_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index <= other.m_index;
+  }
+  bool operator>(const indexed_based_stl_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index > other.m_index;
+  }
+  bool operator>=(const indexed_based_stl_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index >= other.m_index;
+  }
+
+  bool operator==(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index == other.m_index;
+  }
+  bool operator!=(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index != other.m_index;
+  }
+  bool operator<(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index < other.m_index;
+  }
+  bool operator<=(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index <= other.m_index;
+  }
+  bool operator>(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index > other.m_index;
+  }
+  bool operator>=(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index >= other.m_index;
+  }
+
+ protected:
+  Derived& derived() { return static_cast<Derived&>(*this); }
+  const Derived& derived() const { return static_cast<const Derived&>(*this); }
+
+  XprType* mp_xpr;
+  Index m_index;
+};
+
+template <typename Derived>
+class indexed_based_stl_reverse_iterator_base {
+ protected:
+  typedef indexed_based_stl_iterator_traits<Derived> traits;
+  typedef typename traits::XprType XprType;
+  typedef indexed_based_stl_reverse_iterator_base<typename traits::non_const_iterator> non_const_iterator;
+  typedef indexed_based_stl_reverse_iterator_base<typename traits::const_iterator> const_iterator;
+  typedef std::conditional_t<internal::is_const<XprType>::value, non_const_iterator, const_iterator> other_iterator;
+
+  friend class indexed_based_stl_reverse_iterator_base<typename traits::const_iterator>;
+  friend class indexed_based_stl_reverse_iterator_base<typename traits::non_const_iterator>;
+
+ public:
+  typedef Index difference_type;
+  typedef std::random_access_iterator_tag iterator_category;
+
+  indexed_based_stl_reverse_iterator_base() : mp_xpr(0), m_index(0) {}
+  indexed_based_stl_reverse_iterator_base(XprType& xpr, Index index) : mp_xpr(&xpr), m_index(index) {}
+
+  indexed_based_stl_reverse_iterator_base(const non_const_iterator& other)
+      : mp_xpr(other.mp_xpr), m_index(other.m_index) {}
+
+  indexed_based_stl_reverse_iterator_base& operator=(const non_const_iterator& other) {
+    mp_xpr = other.mp_xpr;
+    m_index = other.m_index;
+    return *this;
+  }
+
+  Derived& operator++() {
+    --m_index;
+    return derived();
+  }
+  Derived& operator--() {
+    ++m_index;
+    return derived();
+  }
+
+  Derived operator++(int) {
+    Derived prev(derived());
+    operator++();
+    return prev;
+  }
+  Derived operator--(int) {
+    Derived prev(derived());
+    operator--();
+    return prev;
+  }
+
+  friend Derived operator+(const indexed_based_stl_reverse_iterator_base& a, Index b) {
+    Derived ret(a.derived());
+    ret += b;
+    return ret;
+  }
+  friend Derived operator-(const indexed_based_stl_reverse_iterator_base& a, Index b) {
+    Derived ret(a.derived());
+    ret -= b;
+    return ret;
+  }
+  friend Derived operator+(Index a, const indexed_based_stl_reverse_iterator_base& b) {
+    Derived ret(b.derived());
+    ret += a;
+    return ret;
+  }
+  friend Derived operator-(Index a, const indexed_based_stl_reverse_iterator_base& b) {
+    Derived ret(b.derived());
+    ret -= a;
+    return ret;
+  }
+
+  Derived& operator+=(Index b) {
+    m_index -= b;
+    return derived();
+  }
+  Derived& operator-=(Index b) {
+    m_index += b;
+    return derived();
+  }
+
+  difference_type operator-(const indexed_based_stl_reverse_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return other.m_index - m_index;
+  }
+
+  difference_type operator-(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return other.m_index - m_index;
+  }
+
+  bool operator==(const indexed_based_stl_reverse_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index == other.m_index;
+  }
+  bool operator!=(const indexed_based_stl_reverse_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index != other.m_index;
+  }
+  bool operator<(const indexed_based_stl_reverse_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index > other.m_index;
+  }
+  bool operator<=(const indexed_based_stl_reverse_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index >= other.m_index;
+  }
+  bool operator>(const indexed_based_stl_reverse_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index < other.m_index;
+  }
+  bool operator>=(const indexed_based_stl_reverse_iterator_base& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index <= other.m_index;
+  }
+
+  bool operator==(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index == other.m_index;
+  }
+  bool operator!=(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index != other.m_index;
+  }
+  bool operator<(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index > other.m_index;
+  }
+  bool operator<=(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index >= other.m_index;
+  }
+  bool operator>(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index < other.m_index;
+  }
+  bool operator>=(const other_iterator& other) const {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index <= other.m_index;
+  }
+
+ protected:
+  Derived& derived() { return static_cast<Derived&>(*this); }
+  const Derived& derived() const { return static_cast<const Derived&>(*this); }
+
+  XprType* mp_xpr;
+  Index m_index;
+};
+
+template <typename XprType>
+class pointer_based_stl_iterator {
+  enum { is_lvalue = internal::is_lvalue<XprType>::value };
+  typedef pointer_based_stl_iterator<std::remove_const_t<XprType>> non_const_iterator;
+  typedef pointer_based_stl_iterator<std::add_const_t<XprType>> const_iterator;
+  typedef std::conditional_t<internal::is_const<XprType>::value, non_const_iterator, const_iterator> other_iterator;
+
+  friend class pointer_based_stl_iterator<std::add_const_t<XprType>>;
+  friend class pointer_based_stl_iterator<std::remove_const_t<XprType>>;
+
+ public:
+  typedef Index difference_type;
+  typedef typename XprType::Scalar value_type;
+#if EIGEN_COMP_CXXVER >= 20 && defined(__cpp_lib_concepts) && __cpp_lib_concepts >= 202002L
+  typedef std::conditional_t<XprType::InnerStrideAtCompileTime == 1, std::contiguous_iterator_tag,
+                             std::random_access_iterator_tag>
+      iterator_category;
+#else
+  typedef std::random_access_iterator_tag iterator_category;
+#endif
+  typedef std::conditional_t<bool(is_lvalue), value_type*, const value_type*> pointer;
+  typedef std::conditional_t<bool(is_lvalue), value_type&, const value_type&> reference;
+
+  pointer_based_stl_iterator() noexcept : m_ptr(0), m_incr(XprType::InnerStrideAtCompileTime) {}
+  pointer_based_stl_iterator(XprType& xpr, Index index) noexcept
+      : m_ptr(xpr.data() + index * xpr.innerStride()), m_incr(xpr.innerStride()) {}
+
+  pointer_based_stl_iterator(const non_const_iterator& other) noexcept : m_ptr(other.m_ptr), m_incr(other.m_incr) {}
+
+  pointer_based_stl_iterator& operator=(const non_const_iterator& other) noexcept {
+    m_ptr = other.m_ptr;
+    m_incr.setValue(other.m_incr);
+    return *this;
+  }
+
+  reference operator*() const { return *m_ptr; }
+  reference operator[](Index i) const { return *(m_ptr + i * m_incr.value()); }
+  pointer operator->() const { return m_ptr; }
+
+  pointer_based_stl_iterator& operator++() {
+    m_ptr += m_incr.value();
+    return *this;
+  }
+  pointer_based_stl_iterator& operator--() {
+    m_ptr -= m_incr.value();
+    return *this;
+  }
+
+  pointer_based_stl_iterator operator++(int) {
+    pointer_based_stl_iterator prev(*this);
+    operator++();
+    return prev;
+  }
+  pointer_based_stl_iterator operator--(int) {
+    pointer_based_stl_iterator prev(*this);
+    operator--();
+    return prev;
+  }
+
+  friend pointer_based_stl_iterator operator+(const pointer_based_stl_iterator& a, Index b) {
+    pointer_based_stl_iterator ret(a);
+    ret += b;
+    return ret;
+  }
+  friend pointer_based_stl_iterator operator-(const pointer_based_stl_iterator& a, Index b) {
+    pointer_based_stl_iterator ret(a);
+    ret -= b;
+    return ret;
+  }
+  friend pointer_based_stl_iterator operator+(Index a, const pointer_based_stl_iterator& b) {
+    pointer_based_stl_iterator ret(b);
+    ret += a;
+    return ret;
+  }
+  friend pointer_based_stl_iterator operator-(Index a, const pointer_based_stl_iterator& b) {
+    pointer_based_stl_iterator ret(b);
+    ret -= a;
+    return ret;
+  }
+
+  pointer_based_stl_iterator& operator+=(Index b) {
+    m_ptr += b * m_incr.value();
+    return *this;
+  }
+  pointer_based_stl_iterator& operator-=(Index b) {
+    m_ptr -= b * m_incr.value();
+    return *this;
+  }
+
+  difference_type operator-(const pointer_based_stl_iterator& other) const {
+    return (m_ptr - other.m_ptr) / m_incr.value();
+  }
+
+  difference_type operator-(const other_iterator& other) const { return (m_ptr - other.m_ptr) / m_incr.value(); }
+
+  bool operator==(const pointer_based_stl_iterator& other) const { return m_ptr == other.m_ptr; }
+  bool operator!=(const pointer_based_stl_iterator& other) const { return m_ptr != other.m_ptr; }
+  bool operator<(const pointer_based_stl_iterator& other) const { return m_ptr < other.m_ptr; }
+  bool operator<=(const pointer_based_stl_iterator& other) const { return m_ptr <= other.m_ptr; }
+  bool operator>(const pointer_based_stl_iterator& other) const { return m_ptr > other.m_ptr; }
+  bool operator>=(const pointer_based_stl_iterator& other) const { return m_ptr >= other.m_ptr; }
+
+  bool operator==(const other_iterator& other) const { return m_ptr == other.m_ptr; }
+  bool operator!=(const other_iterator& other) const { return m_ptr != other.m_ptr; }
+  bool operator<(const other_iterator& other) const { return m_ptr < other.m_ptr; }
+  bool operator<=(const other_iterator& other) const { return m_ptr <= other.m_ptr; }
+  bool operator>(const other_iterator& other) const { return m_ptr > other.m_ptr; }
+  bool operator>=(const other_iterator& other) const { return m_ptr >= other.m_ptr; }
+
+ protected:
+  pointer m_ptr;
+  internal::variable_if_dynamic<Index, XprType::InnerStrideAtCompileTime> m_incr;
+};
+
+template <typename XprType_>
+struct indexed_based_stl_iterator_traits<generic_randaccess_stl_iterator<XprType_>> {
+  typedef XprType_ XprType;
+  typedef generic_randaccess_stl_iterator<std::remove_const_t<XprType>> non_const_iterator;
+  typedef generic_randaccess_stl_iterator<std::add_const_t<XprType>> const_iterator;
+};
+
+template <typename XprType>
+class generic_randaccess_stl_iterator
+    : public indexed_based_stl_iterator_base<generic_randaccess_stl_iterator<XprType>> {
+ public:
+  typedef typename XprType::Scalar value_type;
+
+ protected:
+  enum {
+    has_direct_access = (internal::traits<XprType>::Flags & DirectAccessBit) ? 1 : 0,
+    is_lvalue = internal::is_lvalue<XprType>::value
+  };
+
+  typedef indexed_based_stl_iterator_base<generic_randaccess_stl_iterator> Base;
+  using Base::m_index;
+  using Base::mp_xpr;
+
+  // TODO: currently const Transpose/Reshape expressions never returns const references,
+  // so lets return by value too.
+  // typedef std::conditional_t<bool(has_direct_access), const value_type&, const value_type> read_only_ref_t;
+  typedef const value_type read_only_ref_t;
+
+ public:
+  typedef std::conditional_t<bool(is_lvalue), value_type*, const value_type*> pointer;
+  typedef std::conditional_t<bool(is_lvalue), value_type&, read_only_ref_t> reference;
+
+  generic_randaccess_stl_iterator() : Base() {}
+  generic_randaccess_stl_iterator(XprType& xpr, Index index) : Base(xpr, index) {}
+  generic_randaccess_stl_iterator(const typename Base::non_const_iterator& other) : Base(other) {}
+  using Base::operator=;
+
+  reference operator*() const { return (*mp_xpr)(m_index); }
+  reference operator[](Index i) const { return (*mp_xpr)(m_index + i); }
+  pointer operator->() const { return &((*mp_xpr)(m_index)); }
+};
+
+template <typename XprType_, DirectionType Direction>
+struct indexed_based_stl_iterator_traits<subvector_stl_iterator<XprType_, Direction>> {
+  typedef XprType_ XprType;
+  typedef subvector_stl_iterator<std::remove_const_t<XprType>, Direction> non_const_iterator;
+  typedef subvector_stl_iterator<std::add_const_t<XprType>, Direction> const_iterator;
+};
+
+template <typename XprType, DirectionType Direction>
+class subvector_stl_iterator : public indexed_based_stl_iterator_base<subvector_stl_iterator<XprType, Direction>> {
+ protected:
+  enum { is_lvalue = internal::is_lvalue<XprType>::value };
+
+  typedef indexed_based_stl_iterator_base<subvector_stl_iterator> Base;
+  using Base::m_index;
+  using Base::mp_xpr;
+
+  typedef std::conditional_t<Direction == Vertical, typename XprType::ColXpr, typename XprType::RowXpr> SubVectorType;
+  typedef std::conditional_t<Direction == Vertical, typename XprType::ConstColXpr, typename XprType::ConstRowXpr>
+      ConstSubVectorType;
+
+ public:
+  typedef std::conditional_t<bool(is_lvalue), SubVectorType, ConstSubVectorType> reference;
+  typedef typename reference::PlainObject value_type;
+
+ private:
+  class subvector_stl_iterator_ptr {
+   public:
+    subvector_stl_iterator_ptr(const reference& subvector) : m_subvector(subvector) {}
+    reference* operator->() { return &m_subvector; }
+
+   private:
+    reference m_subvector;
+  };
+
+ public:
+  typedef subvector_stl_iterator_ptr pointer;
+
+  subvector_stl_iterator() : Base() {}
+  subvector_stl_iterator(XprType& xpr, Index index) : Base(xpr, index) {}
+
+  reference operator*() const { return (*mp_xpr).template subVector<Direction>(m_index); }
+  reference operator[](Index i) const { return (*mp_xpr).template subVector<Direction>(m_index + i); }
+  pointer operator->() const { return (*mp_xpr).template subVector<Direction>(m_index); }
+};
+
+template <typename XprType_, DirectionType Direction>
+struct indexed_based_stl_iterator_traits<subvector_stl_reverse_iterator<XprType_, Direction>> {
+  typedef XprType_ XprType;
+  typedef subvector_stl_reverse_iterator<std::remove_const_t<XprType>, Direction> non_const_iterator;
+  typedef subvector_stl_reverse_iterator<std::add_const_t<XprType>, Direction> const_iterator;
+};
+
+template <typename XprType, DirectionType Direction>
+class subvector_stl_reverse_iterator
+    : public indexed_based_stl_reverse_iterator_base<subvector_stl_reverse_iterator<XprType, Direction>> {
+ protected:
+  enum { is_lvalue = internal::is_lvalue<XprType>::value };
+
+  typedef indexed_based_stl_reverse_iterator_base<subvector_stl_reverse_iterator> Base;
+  using Base::m_index;
+  using Base::mp_xpr;
+
+  typedef std::conditional_t<Direction == Vertical, typename XprType::ColXpr, typename XprType::RowXpr> SubVectorType;
+  typedef std::conditional_t<Direction == Vertical, typename XprType::ConstColXpr, typename XprType::ConstRowXpr>
+      ConstSubVectorType;
+
+ public:
+  typedef std::conditional_t<bool(is_lvalue), SubVectorType, ConstSubVectorType> reference;
+  typedef typename reference::PlainObject value_type;
+
+ private:
+  class subvector_stl_reverse_iterator_ptr {
+   public:
+    subvector_stl_reverse_iterator_ptr(const reference& subvector) : m_subvector(subvector) {}
+    reference* operator->() { return &m_subvector; }
+
+   private:
+    reference m_subvector;
+  };
+
+ public:
+  typedef subvector_stl_reverse_iterator_ptr pointer;
+
+  subvector_stl_reverse_iterator() : Base() {}
+  subvector_stl_reverse_iterator(XprType& xpr, Index index) : Base(xpr, index) {}
+
+  reference operator*() const { return (*mp_xpr).template subVector<Direction>(m_index); }
+  reference operator[](Index i) const { return (*mp_xpr).template subVector<Direction>(m_index + i); }
+  pointer operator->() const { return (*mp_xpr).template subVector<Direction>(m_index); }
+};
+
+}  // namespace internal
+
+/** returns an iterator to the first element of the 1D vector or array
+ * \only_for_vectors
+ * \sa end(), cbegin()
+ */
+template <typename Derived>
+inline typename DenseBase<Derived>::iterator DenseBase<Derived>::begin() {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return iterator(derived(), 0);
+}
+
+/** const version of begin() */
+template <typename Derived>
+inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::begin() const {
+  return cbegin();
+}
+
+/** returns a read-only const_iterator to the first element of the 1D vector or array
+ * \only_for_vectors
+ * \sa cend(), begin()
+ */
+template <typename Derived>
+inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::cbegin() const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return const_iterator(derived(), 0);
+}
+
+/** returns an iterator to the element following the last element of the 1D vector or array
+ * \only_for_vectors
+ * \sa begin(), cend()
+ */
+template <typename Derived>
+inline typename DenseBase<Derived>::iterator DenseBase<Derived>::end() {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return iterator(derived(), size());
+}
+
+/** const version of end() */
+template <typename Derived>
+inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::end() const {
+  return cend();
+}
+
+/** returns a read-only const_iterator to the element following the last element of the 1D vector or array
+ * \only_for_vectors
+ * \sa begin(), cend()
+ */
+template <typename Derived>
+inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::cend() const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return const_iterator(derived(), size());
+}
+
+}  // namespace Eigen
+
+#endif  // EIGEN_STLITERATORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Stride.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Stride.h
new file mode 100644
index 0000000..8957aa9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Stride.h
@@ -0,0 +1,115 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STRIDE_H
+#define EIGEN_STRIDE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class Stride
+ * \ingroup Core_Module
+ *
+ * \brief Holds strides information for Map
+ *
+ * This class holds the strides information for mapping arrays with strides with class Map.
+ *
+ * It holds two values: the inner stride and the outer stride.
+ *
+ * The inner stride is the pointer increment between two consecutive entries within a given row of a
+ * row-major matrix or within a given column of a column-major matrix.
+ *
+ * The outer stride is the pointer increment between two consecutive rows of a row-major matrix or
+ * between two consecutive columns of a column-major matrix.
+ *
+ * These two values can be passed either at compile-time as template parameters, or at runtime as
+ * arguments to the constructor.
+ *
+ * Indeed, this class takes two template parameters:
+ *  \tparam OuterStrideAtCompileTime_ the outer stride, or Dynamic if you want to specify it at runtime.
+ *  \tparam InnerStrideAtCompileTime_ the inner stride, or Dynamic if you want to specify it at runtime.
+ *
+ * Here is an example:
+ * \include Map_general_stride.cpp
+ * Output: \verbinclude Map_general_stride.out
+ *
+ * Both strides can be negative. However, a negative stride of -1 cannot be specified at compile time
+ * because of the ambiguity with Dynamic which is defined to -1 (historically, negative strides were
+ * not allowed).
+ *
+ * Note that for compile-time vectors (ColsAtCompileTime==1 or RowsAtCompile==1),
+ * the inner stride is the pointer increment between two consecutive elements,
+ * regardless of storage layout.
+ *
+ * \sa class InnerStride, class OuterStride, \ref TopicStorageOrders
+ */
+template <int OuterStrideAtCompileTime_, int InnerStrideAtCompileTime_>
+class Stride {
+ public:
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+  enum { InnerStrideAtCompileTime = InnerStrideAtCompileTime_, OuterStrideAtCompileTime = OuterStrideAtCompileTime_ };
+
+  /** Default constructor, for use when strides are fixed at compile time */
+  EIGEN_DEVICE_FUNC constexpr Stride() : m_outer(OuterStrideAtCompileTime), m_inner(InnerStrideAtCompileTime) {
+    // FIXME: for Eigen 4 we should use DynamicIndex instead of Dynamic.
+    // FIXME: for Eigen 4 we should also unify this API with fix<>
+    eigen_assert(InnerStrideAtCompileTime != Dynamic && OuterStrideAtCompileTime != Dynamic);
+  }
+
+  /** Constructor allowing to pass the strides at runtime */
+  EIGEN_DEVICE_FUNC constexpr Stride(Index outerStride, Index innerStride)
+      : m_outer(outerStride), m_inner(innerStride) {}
+
+  /** Copy constructor */
+  EIGEN_DEVICE_FUNC constexpr Stride(const Stride& other) : m_outer(other.outer()), m_inner(other.inner()) {}
+
+  /** Copy assignment operator */
+  EIGEN_DEVICE_FUNC constexpr Stride& operator=(const Stride& other) {
+    m_outer.setValue(other.outer());
+    m_inner.setValue(other.inner());
+    return *this;
+  }
+
+  /** \returns the outer stride */
+  EIGEN_DEVICE_FUNC constexpr Index outer() const { return m_outer.value(); }
+  /** \returns the inner stride */
+  EIGEN_DEVICE_FUNC constexpr Index inner() const { return m_inner.value(); }
+
+ protected:
+  internal::variable_if_dynamic<Index, OuterStrideAtCompileTime> m_outer;
+  internal::variable_if_dynamic<Index, InnerStrideAtCompileTime> m_inner;
+};
+
+/** \brief Convenience specialization of Stride to specify only an inner stride
+ * See class Map for some examples */
+template <int Value>
+class InnerStride : public Stride<0, Value> {
+  typedef Stride<0, Value> Base;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr InnerStride() : Base() {}
+  EIGEN_DEVICE_FUNC constexpr InnerStride(Index v) : Base(0, v) {}  // FIXME making this explicit could break valid code
+};
+
+/** \brief Convenience specialization of Stride to specify only an outer stride
+ * See class Map for some examples */
+template <int Value>
+class OuterStride : public Stride<Value, 0> {
+  typedef Stride<Value, 0> Base;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr OuterStride() : Base() {}
+  EIGEN_DEVICE_FUNC constexpr OuterStride(Index v) : Base(v, 0) {}  // FIXME making this explicit could break valid code
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_STRIDE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Swap.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Swap.h
new file mode 100644
index 0000000..6dc571e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Swap.h
@@ -0,0 +1,100 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SWAP_H
+#define EIGEN_SWAP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Overload default assignPacket behavior for swapping them
+template <typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT>
+class generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT,
+                                      swap_assign_op<typename DstEvaluatorTypeT::Scalar>, Specialized>
+    : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT,
+                                             swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn> {
+ protected:
+  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT,
+                                          swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>
+      Base;
+  using Base::m_dst;
+  using Base::m_functor;
+  using Base::m_src;
+
+ public:
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::DstXprType DstXprType;
+  typedef swap_assign_op<Scalar> Functor;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE generic_dense_assignment_kernel(DstEvaluatorTypeT &dst,
+                                                                                  const SrcEvaluatorTypeT &src,
+                                                                                  const Functor &func,
+                                                                                  DstXprType &dstExpr)
+      : Base(dst, src, func, dstExpr) {}
+
+  template <int StoreMode, int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE void assignPacket(Index row, Index col) {
+    PacketType tmp = m_src.template packet<LoadMode, PacketType>(row, col);
+    const_cast<SrcEvaluatorTypeT &>(m_src).template writePacket<LoadMode>(
+        row, col, m_dst.template packet<StoreMode, PacketType>(row, col));
+    m_dst.template writePacket<StoreMode>(row, col, tmp);
+  }
+
+  template <int StoreMode, int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE void assignPacket(Index index) {
+    PacketType tmp = m_src.template packet<LoadMode, PacketType>(index);
+    const_cast<SrcEvaluatorTypeT &>(m_src).template writePacket<LoadMode>(
+        index, m_dst.template packet<StoreMode, PacketType>(index));
+    m_dst.template writePacket<StoreMode>(index, tmp);
+  }
+
+  // TODO: find a simple way not to have to copy/paste this function from generic_dense_assignment_kernel, by simple I
+  // mean no CRTP (Gael)
+  template <int StoreMode, int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner) {
+    Index row = Base::rowIndexByOuterInner(outer, inner);
+    Index col = Base::colIndexByOuterInner(outer, inner);
+    assignPacket<StoreMode, LoadMode, PacketType>(row, col);
+  }
+
+  template <int StoreMode, int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE void assignPacketSegment(Index row, Index col, Index begin, Index count) {
+    PacketType tmp = m_src.template packetSegment<LoadMode, PacketType>(row, col, begin, count);
+    const_cast<SrcEvaluatorTypeT &>(m_src).template writePacketSegment<LoadMode>(
+        row, col, m_dst.template packetSegment<StoreMode, PacketType>(row, col, begin, count), begin, count);
+    m_dst.template writePacketSegment<StoreMode>(row, col, tmp, begin, count);
+  }
+
+  template <int StoreMode, int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE void assignPacketSegment(Index index, Index begin, Index count) {
+    PacketType tmp = m_src.template packetSegment<LoadMode, PacketType>(index, begin, count);
+    const_cast<SrcEvaluatorTypeT &>(m_src).template writePacketSegment<LoadMode>(
+        index, m_dst.template packetSegment<StoreMode, PacketType>(index, begin, count), begin, count);
+    m_dst.template writePacketSegment<StoreMode>(index, tmp, begin, count);
+  }
+
+  // TODO: find a simple way not to have to copy/paste this function from generic_dense_assignment_kernel, by simple I
+  // mean no CRTP (Gael)
+  template <int StoreMode, int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE void assignPacketSegmentByOuterInner(Index outer, Index inner, Index begin, Index count) {
+    Index row = Base::rowIndexByOuterInner(outer, inner);
+    Index col = Base::colIndexByOuterInner(outer, inner);
+    assignPacketSegment<StoreMode, LoadMode, PacketType>(row, col, begin, count);
+  }
+};
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SWAP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Transpose.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Transpose.h
new file mode 100644
index 0000000..2077c92
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Transpose.h
@@ -0,0 +1,428 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRANSPOSE_H
+#define EIGEN_TRANSPOSE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType>
+struct traits<Transpose<MatrixType> > : public traits<MatrixType> {
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNestedPlain;
+  enum {
+    RowsAtCompileTime = MatrixType::ColsAtCompileTime,
+    ColsAtCompileTime = MatrixType::RowsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
+    Flags0 = traits<MatrixTypeNestedPlain>::Flags & ~(LvalueBit | NestByRefBit),
+    Flags1 = Flags0 | FlagsLvalueBit,
+    Flags = Flags1 ^ RowMajorBit,
+    InnerStrideAtCompileTime = inner_stride_at_compile_time<MatrixType>::ret,
+    OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
+  };
+};
+}  // namespace internal
+
+template <typename MatrixType, typename StorageKind>
+class TransposeImpl;
+
+/** \class Transpose
+ * \ingroup Core_Module
+ *
+ * \brief Expression of the transpose of a matrix
+ *
+ * \tparam MatrixType the type of the object of which we are taking the transpose
+ *
+ * This class represents an expression of the transpose of a matrix.
+ * It is the return type of MatrixBase::transpose() and MatrixBase::adjoint()
+ * and most of the time this is the only way it is used.
+ *
+ * \sa MatrixBase::transpose(), MatrixBase::adjoint()
+ */
+template <typename MatrixType>
+class Transpose : public TransposeImpl<MatrixType, typename internal::traits<MatrixType>::StorageKind> {
+ public:
+  typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
+
+  typedef typename TransposeImpl<MatrixType, typename internal::traits<MatrixType>::StorageKind>::Base Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose)
+  typedef internal::remove_all_t<MatrixType> NestedExpression;
+
+  EIGEN_DEVICE_FUNC constexpr explicit EIGEN_STRONG_INLINE Transpose(MatrixType& matrix) : m_matrix(matrix) {}
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose)
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_matrix.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_matrix.rows(); }
+
+  /** \returns the nested expression */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const internal::remove_all_t<MatrixTypeNested>& nestedExpression()
+      const {
+    return m_matrix;
+  }
+
+  /** \returns the nested expression */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE std::remove_reference_t<MatrixTypeNested>& nestedExpression() {
+    return m_matrix;
+  }
+
+  /** \internal */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index nrows, Index ncols) { m_matrix.resize(ncols, nrows); }
+
+ protected:
+  typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
+};
+
+namespace internal {
+
+template <typename MatrixType, bool HasDirectAccess = has_direct_access<MatrixType>::ret>
+struct TransposeImpl_base {
+  typedef typename dense_xpr_base<Transpose<MatrixType> >::type type;
+};
+
+template <typename MatrixType>
+struct TransposeImpl_base<MatrixType, false> {
+  typedef typename dense_xpr_base<Transpose<MatrixType> >::type type;
+};
+
+}  // end namespace internal
+
+// Generic API dispatcher
+template <typename XprType, typename StorageKind>
+class TransposeImpl : public internal::generic_xpr_base<Transpose<XprType> >::type {
+ public:
+  typedef typename internal::generic_xpr_base<Transpose<XprType> >::type Base;
+};
+
+template <typename MatrixType>
+class TransposeImpl<MatrixType, Dense> : public internal::TransposeImpl_base<MatrixType>::type {
+ public:
+  typedef typename internal::TransposeImpl_base<MatrixType>::type Base;
+  using Base::coeffRef;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Transpose<MatrixType>)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TransposeImpl)
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Index innerStride() const {
+    return derived().nestedExpression().innerStride();
+  }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Index outerStride() const {
+    return derived().nestedExpression().outerStride();
+  }
+
+  typedef std::conditional_t<internal::is_lvalue<MatrixType>::value, Scalar, const Scalar> ScalarWithConstIfNotLvalue;
+
+  EIGEN_DEVICE_FUNC constexpr ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); }
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return derived().nestedExpression().data(); }
+
+  // FIXME: shall we keep the const version of coeffRef?
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const {
+    return derived().nestedExpression().coeffRef(colId, rowId);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const {
+    return derived().nestedExpression().coeffRef(index);
+  }
+
+ protected:
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TransposeImpl)
+};
+
+/** \returns an expression of the transpose of *this.
+ *
+ * Example: \include MatrixBase_transpose.cpp
+ * Output: \verbinclude MatrixBase_transpose.out
+ *
+ * \warning If you want to replace a matrix by its own transpose, do \b NOT do this:
+ * \code
+ * m = m.transpose(); // bug!!! caused by aliasing effect
+ * \endcode
+ * Instead, use the transposeInPlace() method:
+ * \code
+ * m.transposeInPlace();
+ * \endcode
+ * which gives Eigen good opportunities for optimization, or alternatively you can also do:
+ * \code
+ * m = m.transpose().eval();
+ * \endcode
+ *
+ * \sa transposeInPlace(), adjoint() */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename DenseBase<Derived>::TransposeReturnType DenseBase<Derived>::transpose() {
+  return TransposeReturnType(derived());
+}
+
+/** This is the const version of transpose().
+ *
+ * Make sure you read the warning for transpose() !
+ *
+ * \sa transposeInPlace(), adjoint() */
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstTransposeReturnType
+DenseBase<Derived>::transpose() const {
+  return ConstTransposeReturnType(derived());
+}
+
+/** \returns an expression of the adjoint (i.e. conjugate transpose) of *this.
+ *
+ * Example: \include MatrixBase_adjoint.cpp
+ * Output: \verbinclude MatrixBase_adjoint.out
+ *
+ * \warning If you want to replace a matrix by its own adjoint, do \b NOT do this:
+ * \code
+ * m = m.adjoint(); // bug!!! caused by aliasing effect
+ * \endcode
+ * Instead, use the adjointInPlace() method:
+ * \code
+ * m.adjointInPlace();
+ * \endcode
+ * which gives Eigen good opportunities for optimization, or alternatively you can also do:
+ * \code
+ * m = m.adjoint().eval();
+ * \endcode
+ *
+ * \sa adjointInPlace(), transpose(), conjugate(), class Transpose, class internal::scalar_conjugate_op */
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr const typename MatrixBase<Derived>::AdjointReturnType MatrixBase<Derived>::adjoint() const {
+  return AdjointReturnType(this->transpose());
+}
+
+/***************************************************************************
+ * "in place" transpose implementation
+ ***************************************************************************/
+
+namespace internal {
+
+template <typename MatrixType,
+          bool IsSquare = (MatrixType::RowsAtCompileTime == MatrixType::ColsAtCompileTime) &&
+                          MatrixType::RowsAtCompileTime != Dynamic,
+          bool MatchPacketSize =
+              (int(MatrixType::RowsAtCompileTime) == int(internal::packet_traits<typename MatrixType::Scalar>::size)) &&
+              (internal::evaluator<MatrixType>::Flags & PacketAccessBit)>
+struct inplace_transpose_selector;
+
+template <typename MatrixType>
+struct inplace_transpose_selector<MatrixType, true, false> {  // square matrix
+  static void run(MatrixType& m) {
+    m.matrix().template triangularView<StrictlyUpper>().swap(
+        m.matrix().transpose().template triangularView<StrictlyUpper>());
+  }
+};
+
+template <typename MatrixType>
+struct inplace_transpose_selector<MatrixType, true, true> {  // PacketSize x PacketSize
+  static void run(MatrixType& m) {
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename internal::packet_traits<typename MatrixType::Scalar>::type Packet;
+    const Index PacketSize = internal::packet_traits<Scalar>::size;
+    const Index Alignment = internal::evaluator<MatrixType>::Alignment;
+    PacketBlock<Packet> A;
+    for (Index i = 0; i < PacketSize; ++i) A.packet[i] = m.template packetByOuterInner<Alignment>(i, 0);
+    internal::ptranspose(A);
+    for (Index i = 0; i < PacketSize; ++i)
+      m.template writePacket<Alignment>(m.rowIndexByOuterInner(i, 0), m.colIndexByOuterInner(i, 0), A.packet[i]);
+  }
+};
+
+template <typename MatrixType, Index Alignment>
+void BlockedInPlaceTranspose(MatrixType& m) {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename internal::packet_traits<typename MatrixType::Scalar>::type Packet;
+  const Index PacketSize = internal::packet_traits<Scalar>::size;
+  eigen_assert(m.rows() == m.cols());
+  int row_start = 0;
+  for (; row_start + PacketSize <= m.rows(); row_start += PacketSize) {
+    for (int col_start = row_start; col_start + PacketSize <= m.cols(); col_start += PacketSize) {
+      PacketBlock<Packet> A;
+      if (row_start == col_start) {
+        for (Index i = 0; i < PacketSize; ++i)
+          A.packet[i] = m.template packetByOuterInner<Alignment>(row_start + i, col_start);
+        internal::ptranspose(A);
+        for (Index i = 0; i < PacketSize; ++i)
+          m.template writePacket<Alignment>(m.rowIndexByOuterInner(row_start + i, col_start),
+                                            m.colIndexByOuterInner(row_start + i, col_start), A.packet[i]);
+      } else {
+        PacketBlock<Packet> B;
+        for (Index i = 0; i < PacketSize; ++i) {
+          A.packet[i] = m.template packetByOuterInner<Alignment>(row_start + i, col_start);
+          B.packet[i] = m.template packetByOuterInner<Alignment>(col_start + i, row_start);
+        }
+        internal::ptranspose(A);
+        internal::ptranspose(B);
+        for (Index i = 0; i < PacketSize; ++i) {
+          m.template writePacket<Alignment>(m.rowIndexByOuterInner(row_start + i, col_start),
+                                            m.colIndexByOuterInner(row_start + i, col_start), B.packet[i]);
+          m.template writePacket<Alignment>(m.rowIndexByOuterInner(col_start + i, row_start),
+                                            m.colIndexByOuterInner(col_start + i, row_start), A.packet[i]);
+        }
+      }
+    }
+  }
+  for (Index row = row_start; row < m.rows(); ++row) {
+    m.matrix().row(row).head(row).swap(m.matrix().col(row).head(row).transpose());
+  }
+}
+
+template <typename MatrixType, bool MatchPacketSize>
+struct inplace_transpose_selector<MatrixType, false, MatchPacketSize> {  // non square or dynamic matrix
+  static void run(MatrixType& m) {
+    typedef typename MatrixType::Scalar Scalar;
+    if (m.rows() == m.cols()) {
+      const Index PacketSize = internal::packet_traits<Scalar>::size;
+      if (!NumTraits<Scalar>::IsComplex && m.rows() >= PacketSize) {
+        if ((m.rows() % PacketSize) == 0)
+          BlockedInPlaceTranspose<MatrixType, internal::evaluator<MatrixType>::Alignment>(m);
+        else
+          BlockedInPlaceTranspose<MatrixType, Unaligned>(m);
+      } else {
+        m.matrix().template triangularView<StrictlyUpper>().swap(
+            m.matrix().transpose().template triangularView<StrictlyUpper>());
+      }
+    } else {
+      m = m.transpose().eval();
+    }
+  }
+};
+
+}  // end namespace internal
+
+/** This is the "in place" version of transpose(): it replaces \c *this by its own transpose.
+ * Thus, doing
+ * \code
+ * m.transposeInPlace();
+ * \endcode
+ * has the same effect on m as doing
+ * \code
+ * m = m.transpose().eval();
+ * \endcode
+ * and is faster and also safer because in the latter line of code, forgetting the eval() results
+ * in a bug caused by \ref TopicAliasing "aliasing".
+ *
+ * Notice however that this method is only useful if you want to replace a matrix by its own transpose.
+ * If you just need the transpose of a matrix, use transpose().
+ *
+ * \note if the matrix is not square, then \c *this must be a resizable matrix.
+ * This excludes (non-square) fixed-size matrices, block-expressions and maps.
+ *
+ * \sa transpose(), adjoint(), adjointInPlace() */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline void DenseBase<Derived>::transposeInPlace() {
+  eigen_assert((rows() == cols() || (RowsAtCompileTime == Dynamic && ColsAtCompileTime == Dynamic)) &&
+               "transposeInPlace() called on a non-square non-resizable matrix");
+  internal::inplace_transpose_selector<Derived>::run(derived());
+}
+
+/***************************************************************************
+ * "in place" adjoint implementation
+ ***************************************************************************/
+
+/** This is the "in place" version of adjoint(): it replaces \c *this by its own transpose.
+ * Thus, doing
+ * \code
+ * m.adjointInPlace();
+ * \endcode
+ * has the same effect on m as doing
+ * \code
+ * m = m.adjoint().eval();
+ * \endcode
+ * and is faster and also safer because in the latter line of code, forgetting the eval() results
+ * in a bug caused by aliasing.
+ *
+ * Notice however that this method is only useful if you want to replace a matrix by its own adjoint.
+ * If you just need the adjoint of a matrix, use adjoint().
+ *
+ * \note if the matrix is not square, then \c *this must be a resizable matrix.
+ * This excludes (non-square) fixed-size matrices, block-expressions and maps.
+ *
+ * \sa transpose(), adjoint(), transposeInPlace() */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline void MatrixBase<Derived>::adjointInPlace() {
+  derived() = adjoint().eval();
+}
+
+#ifndef EIGEN_NO_DEBUG
+
+// The following is to detect aliasing problems in most common cases.
+
+namespace internal {
+
+template <bool DestIsTransposed, typename OtherDerived>
+struct check_transpose_aliasing_compile_time_selector {
+  enum { ret = bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed };
+};
+
+template <bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB>
+struct check_transpose_aliasing_compile_time_selector<DestIsTransposed, CwiseBinaryOp<BinOp, DerivedA, DerivedB> > {
+  enum {
+    ret = bool(blas_traits<DerivedA>::IsTransposed) != DestIsTransposed ||
+          bool(blas_traits<DerivedB>::IsTransposed) != DestIsTransposed
+  };
+};
+
+template <typename Scalar, bool DestIsTransposed, typename OtherDerived>
+struct check_transpose_aliasing_run_time_selector {
+  EIGEN_DEVICE_FUNC static bool run(const Scalar* dest, const OtherDerived& src) {
+    return (bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed) &&
+           (dest != 0 && dest == (const Scalar*)extract_data(src));
+  }
+};
+
+template <typename Scalar, bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB>
+struct check_transpose_aliasing_run_time_selector<Scalar, DestIsTransposed, CwiseBinaryOp<BinOp, DerivedA, DerivedB> > {
+  EIGEN_DEVICE_FUNC static bool run(const Scalar* dest, const CwiseBinaryOp<BinOp, DerivedA, DerivedB>& src) {
+    return ((blas_traits<DerivedA>::IsTransposed != DestIsTransposed) &&
+            (dest != 0 && dest == (const Scalar*)extract_data(src.lhs()))) ||
+           ((blas_traits<DerivedB>::IsTransposed != DestIsTransposed) &&
+            (dest != 0 && dest == (const Scalar*)extract_data(src.rhs())));
+  }
+};
+
+// the following selector, checkTransposeAliasing_impl, based on MightHaveTransposeAliasing,
+// is because when the condition controlling the assert is known at compile time, ICC emits a warning.
+// This is actually a good warning: in expressions that don't have any transposing, the condition is
+// known at compile time to be false, and using that, we can avoid generating the code of the assert again
+// and again for all these expressions that don't need it.
+
+template <typename Derived, typename OtherDerived,
+          bool MightHaveTransposeAliasing =
+              check_transpose_aliasing_compile_time_selector<blas_traits<Derived>::IsTransposed, OtherDerived>::ret>
+struct checkTransposeAliasing_impl {
+  EIGEN_DEVICE_FUNC static void run(const Derived& dst, const OtherDerived& other) {
+    eigen_assert(
+        (!check_transpose_aliasing_run_time_selector<typename Derived::Scalar, blas_traits<Derived>::IsTransposed,
+                                                     OtherDerived>::run(extract_data(dst), other)) &&
+        "aliasing detected during transposition, use transposeInPlace() "
+        "or evaluate the rhs into a temporary using .eval()");
+  }
+};
+
+template <typename Derived, typename OtherDerived>
+struct checkTransposeAliasing_impl<Derived, OtherDerived, false> {
+  EIGEN_DEVICE_FUNC static void run(const Derived&, const OtherDerived&) {}
+};
+
+template <typename Dst, typename Src>
+EIGEN_DEVICE_FUNC inline void check_for_aliasing(const Dst& dst, const Src& src) {
+  if ((!Dst::IsVectorAtCompileTime) && dst.rows() > 1 && dst.cols() > 1)
+    internal::checkTransposeAliasing_impl<Dst, Src>::run(dst, src);
+}
+
+}  // end namespace internal
+
+#endif  // EIGEN_NO_DEBUG
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRANSPOSE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Transpositions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Transpositions.h
new file mode 100644
index 0000000..f6dd258
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Transpositions.h
@@ -0,0 +1,323 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRANSPOSITIONS_H
+#define EIGEN_TRANSPOSITIONS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Derived>
+class TranspositionsBase {
+  typedef internal::traits<Derived> Traits;
+
+ public:
+  typedef typename Traits::IndicesType IndicesType;
+  typedef typename IndicesType::Scalar StorageIndex;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+
+  EIGEN_DEVICE_FUNC Derived& derived() { return *static_cast<Derived*>(this); }
+  EIGEN_DEVICE_FUNC const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  /** Copies the \a other transpositions into \c *this */
+  template <typename OtherDerived>
+  Derived& operator=(const TranspositionsBase<OtherDerived>& other) {
+    indices() = other.indices();
+    return derived();
+  }
+
+  /** \returns the number of transpositions */
+  EIGEN_DEVICE_FUNC Index size() const { return indices().size(); }
+  /** \returns the number of rows of the equivalent permutation matrix */
+  EIGEN_DEVICE_FUNC Index rows() const { return indices().size(); }
+  /** \returns the number of columns of the equivalent permutation matrix */
+  EIGEN_DEVICE_FUNC Index cols() const { return indices().size(); }
+
+  /** Direct access to the underlying index vector */
+  EIGEN_DEVICE_FUNC inline const StorageIndex& coeff(Index i) const { return indices().coeff(i); }
+  /** Direct access to the underlying index vector */
+  inline StorageIndex& coeffRef(Index i) { return indices().coeffRef(i); }
+  /** Direct access to the underlying index vector */
+  inline const StorageIndex& operator()(Index i) const { return indices()(i); }
+  /** Direct access to the underlying index vector */
+  inline StorageIndex& operator()(Index i) { return indices()(i); }
+  /** Direct access to the underlying index vector */
+  inline const StorageIndex& operator[](Index i) const { return indices()(i); }
+  /** Direct access to the underlying index vector */
+  inline StorageIndex& operator[](Index i) { return indices()(i); }
+
+  /** const version of indices(). */
+  EIGEN_DEVICE_FUNC const IndicesType& indices() const { return derived().indices(); }
+  /** \returns a reference to the stored array representing the transpositions. */
+  EIGEN_DEVICE_FUNC IndicesType& indices() { return derived().indices(); }
+
+  /** Resizes to given size. */
+  inline void resize(Index newSize) { indices().resize(newSize); }
+
+  /** Sets \c *this to represents an identity transformation */
+  void setIdentity() {
+    for (StorageIndex i = 0; i < indices().size(); ++i) coeffRef(i) = i;
+  }
+
+  // FIXME: do we want such methods ?
+  // might be useful when the target matrix expression is complex, e.g.:
+  // object.matrix().block(..,..,..,..) = trans * object.matrix().block(..,..,..,..);
+  /*
+  template<typename MatrixType>
+  void applyForwardToRows(MatrixType& mat) const
+  {
+    for(Index k=0 ; k<size() ; ++k)
+      if(m_indices(k)!=k)
+        mat.row(k).swap(mat.row(m_indices(k)));
+  }
+
+  template<typename MatrixType>
+  void applyBackwardToRows(MatrixType& mat) const
+  {
+    for(Index k=size()-1 ; k>=0 ; --k)
+      if(m_indices(k)!=k)
+        mat.row(k).swap(mat.row(m_indices(k)));
+  }
+  */
+
+  /** \returns the inverse transformation */
+  inline Transpose<TranspositionsBase> inverse() const { return Transpose<TranspositionsBase>(derived()); }
+
+  /** \returns the transpose transformation */
+  inline Transpose<TranspositionsBase> transpose() const { return Transpose<TranspositionsBase>(derived()); }
+
+ protected:
+};
+
+namespace internal {
+template <int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
+struct traits<Transpositions<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_> >
+    : traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_> > {
+  typedef Matrix<StorageIndex_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
+  typedef TranspositionsStorage StorageKind;
+};
+}  // namespace internal
+
+/** \class Transpositions
+ * \ingroup Core_Module
+ *
+ * \brief Represents a sequence of transpositions (row/column interchange)
+ *
+ * \tparam SizeAtCompileTime the number of transpositions, or Dynamic
+ * \tparam MaxSizeAtCompileTime the maximum number of transpositions, or Dynamic. This optional parameter defaults to
+ * SizeAtCompileTime. Most of the time, you should not have to specify it.
+ *
+ * This class represents a permutation transformation as a sequence of \em n transpositions
+ * \f$[T_{n-1} \ldots T_{i} \ldots T_{0}]\f$. It is internally stored as a vector of integers \c indices.
+ * Each transposition \f$ T_{i} \f$ applied on the left of a matrix (\f$ T_{i} M\f$) interchanges
+ * the rows \c i and \c indices[i] of the matrix \c M.
+ * A transposition applied on the right (e.g., \f$ M T_{i}\f$) yields a column interchange.
+ *
+ * Compared to the class PermutationMatrix, such a sequence of transpositions is what is
+ * computed during a decomposition with pivoting, and it is faster when applying the permutation in-place.
+ *
+ * To apply a sequence of transpositions to a matrix, simply use the operator * as in the following example:
+ * \code
+ * Transpositions tr;
+ * MatrixXf mat;
+ * mat = tr * mat;
+ * \endcode
+ * In this example, we detect that the matrix appears on both side, and so the transpositions
+ * are applied in-place without any temporary or extra copy.
+ *
+ * \sa class PermutationMatrix
+ */
+
+template <int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
+class Transpositions
+    : public TranspositionsBase<Transpositions<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_> > {
+  typedef internal::traits<Transpositions> Traits;
+
+ public:
+  typedef TranspositionsBase<Transpositions> Base;
+  typedef typename Traits::IndicesType IndicesType;
+  typedef typename IndicesType::Scalar StorageIndex;
+
+  inline Transpositions() {}
+
+  /** Copy constructor. */
+  template <typename OtherDerived>
+  inline Transpositions(const TranspositionsBase<OtherDerived>& other) : m_indices(other.indices()) {}
+
+  /** Generic constructor from expression of the transposition indices. */
+  template <typename Other>
+  explicit inline Transpositions(const MatrixBase<Other>& indices) : m_indices(indices) {}
+
+  /** Copies the \a other transpositions into \c *this */
+  template <typename OtherDerived>
+  Transpositions& operator=(const TranspositionsBase<OtherDerived>& other) {
+    return Base::operator=(other);
+  }
+
+  /** Constructs an uninitialized permutation matrix of given size.
+   */
+  inline Transpositions(Index size) : m_indices(size) {}
+
+  /** const version of indices(). */
+  EIGEN_DEVICE_FUNC const IndicesType& indices() const { return m_indices; }
+  /** \returns a reference to the stored array representing the transpositions. */
+  EIGEN_DEVICE_FUNC IndicesType& indices() { return m_indices; }
+
+ protected:
+  IndicesType m_indices;
+};
+
+namespace internal {
+template <int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess_>
+struct traits<Map<Transpositions<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>, PacketAccess_> >
+    : traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_> > {
+  typedef Map<const Matrix<StorageIndex_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, PacketAccess_> IndicesType;
+  typedef StorageIndex_ StorageIndex;
+  typedef TranspositionsStorage StorageKind;
+};
+}  // namespace internal
+
+template <int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess>
+class Map<Transpositions<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>, PacketAccess>
+    : public TranspositionsBase<
+          Map<Transpositions<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>, PacketAccess> > {
+  typedef internal::traits<Map> Traits;
+
+ public:
+  typedef TranspositionsBase<Map> Base;
+  typedef typename Traits::IndicesType IndicesType;
+  typedef typename IndicesType::Scalar StorageIndex;
+
+  explicit inline Map(const StorageIndex* indicesPtr) : m_indices(indicesPtr) {}
+
+  inline Map(const StorageIndex* indicesPtr, Index size) : m_indices(indicesPtr, size) {}
+
+  /** Copies the \a other transpositions into \c *this */
+  template <typename OtherDerived>
+  Map& operator=(const TranspositionsBase<OtherDerived>& other) {
+    return Base::operator=(other);
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** This is a special case of the templated operator=. Its purpose is to
+   * prevent a default operator= from hiding the templated operator=.
+   */
+  Map& operator=(const Map& other) {
+    m_indices = other.m_indices;
+    return *this;
+  }
+#endif
+
+  /** const version of indices(). */
+  EIGEN_DEVICE_FUNC const IndicesType& indices() const { return m_indices; }
+
+  /** \returns a reference to the stored array representing the transpositions. */
+  EIGEN_DEVICE_FUNC IndicesType& indices() { return m_indices; }
+
+ protected:
+  IndicesType m_indices;
+};
+
+namespace internal {
+template <typename IndicesType_>
+struct traits<TranspositionsWrapper<IndicesType_> > : traits<PermutationWrapper<IndicesType_> > {
+  typedef TranspositionsStorage StorageKind;
+};
+}  // namespace internal
+
+template <typename IndicesType_>
+class TranspositionsWrapper : public TranspositionsBase<TranspositionsWrapper<IndicesType_> > {
+  typedef internal::traits<TranspositionsWrapper> Traits;
+
+ public:
+  typedef TranspositionsBase<TranspositionsWrapper> Base;
+  typedef typename Traits::IndicesType IndicesType;
+  typedef typename IndicesType::Scalar StorageIndex;
+
+  explicit inline TranspositionsWrapper(IndicesType& indices) : m_indices(indices) {}
+
+  /** Copies the \a other transpositions into \c *this */
+  template <typename OtherDerived>
+  TranspositionsWrapper& operator=(const TranspositionsBase<OtherDerived>& other) {
+    return Base::operator=(other);
+  }
+
+  /** const version of indices(). */
+  EIGEN_DEVICE_FUNC const IndicesType& indices() const { return m_indices; }
+
+  /** \returns a reference to the stored array representing the transpositions. */
+  EIGEN_DEVICE_FUNC IndicesType& indices() { return m_indices; }
+
+ protected:
+  typename IndicesType::Nested m_indices;
+};
+
+/** \returns the \a matrix with the \a transpositions applied to the columns.
+ */
+template <typename MatrixDerived, typename TranspositionsDerived>
+EIGEN_DEVICE_FUNC const Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct> operator*(
+    const MatrixBase<MatrixDerived>& matrix, const TranspositionsBase<TranspositionsDerived>& transpositions) {
+  return Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>(matrix.derived(), transpositions.derived());
+}
+
+/** \returns the \a matrix with the \a transpositions applied to the rows.
+ */
+template <typename TranspositionsDerived, typename MatrixDerived>
+EIGEN_DEVICE_FUNC const Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct> operator*(
+    const TranspositionsBase<TranspositionsDerived>& transpositions, const MatrixBase<MatrixDerived>& matrix) {
+  return Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>(transpositions.derived(), matrix.derived());
+}
+
+// Template partial specialization for transposed/inverse transpositions
+
+namespace internal {
+
+template <typename Derived>
+struct traits<Transpose<TranspositionsBase<Derived> > > : traits<Derived> {};
+
+}  // end namespace internal
+
+template <typename TranspositionsDerived>
+class Transpose<TranspositionsBase<TranspositionsDerived> > {
+  typedef TranspositionsDerived TranspositionType;
+  typedef typename TranspositionType::IndicesType IndicesType;
+
+ public:
+  explicit Transpose(const TranspositionType& t) : m_transpositions(t) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index size() const noexcept { return m_transpositions.size(); }
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_transpositions.size(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_transpositions.size(); }
+
+  /** \returns the \a matrix with the inverse transpositions applied to the columns.
+   */
+  template <typename OtherDerived>
+  friend const Product<OtherDerived, Transpose, AliasFreeProduct> operator*(const MatrixBase<OtherDerived>& matrix,
+                                                                            const Transpose& trt) {
+    return Product<OtherDerived, Transpose, AliasFreeProduct>(matrix.derived(), trt);
+  }
+
+  /** \returns the \a matrix with the inverse transpositions applied to the rows.
+   */
+  template <typename OtherDerived>
+  const Product<Transpose, OtherDerived, AliasFreeProduct> operator*(const MatrixBase<OtherDerived>& matrix) const {
+    return Product<Transpose, OtherDerived, AliasFreeProduct>(*this, matrix.derived());
+  }
+
+  EIGEN_DEVICE_FUNC const TranspositionType& nestedExpression() const { return m_transpositions; }
+
+ protected:
+  const TranspositionType& m_transpositions;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRANSPOSITIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/TriangularMatrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/TriangularMatrix.h
new file mode 100644
index 0000000..e219e51
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/TriangularMatrix.h
@@ -0,0 +1,928 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIANGULARMATRIX_H
+#define EIGEN_TRIANGULARMATRIX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <int Side, typename TriangularType, typename Rhs>
+struct triangular_solve_retval;
+
+}
+
+/** \class TriangularBase
+ * \ingroup Core_Module
+ *
+ * \brief Base class for triangular part in a matrix
+ */
+template <typename Derived>
+class TriangularBase : public EigenBase<Derived> {
+ public:
+  enum {
+    Mode = internal::traits<Derived>::Mode,
+    RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+    ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+    MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
+
+    SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
+    /**< This is equal to the number of coefficients, i.e. the number of
+     * rows times the number of columns, or to \a Dynamic if this is not
+     * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
+
+    MaxSizeAtCompileTime = internal::size_at_compile_time(internal::traits<Derived>::MaxRowsAtCompileTime,
+                                                          internal::traits<Derived>::MaxColsAtCompileTime)
+
+  };
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+  typedef typename internal::traits<Derived>::FullMatrixType DenseMatrixType;
+  typedef DenseMatrixType DenseType;
+  typedef Derived const& Nested;
+
+  EIGEN_DEVICE_FUNC inline TriangularBase() {
+    eigen_assert(!((int(Mode) & int(UnitDiag)) && (int(Mode) & int(ZeroDiag))));
+  }
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return derived().rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return derived().cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return derived().outerStride(); }
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return derived().innerStride(); }
+
+  // dummy resize function
+  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) {
+    EIGEN_UNUSED_VARIABLE(rows);
+    EIGEN_UNUSED_VARIABLE(cols);
+    eigen_assert(rows == this->rows() && cols == this->cols());
+  }
+
+  EIGEN_DEVICE_FUNC inline Scalar coeff(Index row, Index col) const { return derived().coeff(row, col); }
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col) { return derived().coeffRef(row, col); }
+
+  /** \see MatrixBase::copyCoeff(row,col)
+   */
+  template <typename Other>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, Other& other) {
+    derived().coeffRef(row, col) = other.coeff(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC inline Scalar operator()(Index row, Index col) const {
+    check_coordinates(row, col);
+    return coeff(row, col);
+  }
+  EIGEN_DEVICE_FUNC inline Scalar& operator()(Index row, Index col) {
+    check_coordinates(row, col);
+    return coeffRef(row, col);
+  }
+
+#ifdef EIGEN_MULTIDIMENSIONAL_SUBSCRIPT
+  EIGEN_DEVICE_FUNC inline Scalar operator[](Index row, Index col) const { return operator()(row, col); }
+  EIGEN_DEVICE_FUNC inline Scalar& operator[](Index row, Index col) { return operator()(row, col); }
+#endif
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  EIGEN_DEVICE_FUNC inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+  EIGEN_DEVICE_FUNC inline Derived& derived() { return *static_cast<Derived*>(this); }
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+  template <typename DenseDerived>
+  EIGEN_DEVICE_FUNC void evalTo(MatrixBase<DenseDerived>& other) const;
+  template <typename DenseDerived>
+  EIGEN_DEVICE_FUNC void evalToLazy(MatrixBase<DenseDerived>& other) const;
+
+  EIGEN_DEVICE_FUNC DenseMatrixType toDenseMatrix() const {
+    DenseMatrixType res(rows(), cols());
+    evalToLazy(res);
+    return res;
+  }
+
+ protected:
+  void check_coordinates(Index row, Index col) const {
+    EIGEN_ONLY_USED_FOR_DEBUG(row);
+    EIGEN_ONLY_USED_FOR_DEBUG(col);
+    eigen_assert(col >= 0 && col < cols() && row >= 0 && row < rows());
+    const int mode = int(Mode) & ~SelfAdjoint;
+    EIGEN_ONLY_USED_FOR_DEBUG(mode);
+    eigen_assert((mode == Upper && col >= row) || (mode == Lower && col <= row) ||
+                 ((mode == StrictlyUpper || mode == UnitUpper) && col > row) ||
+                 ((mode == StrictlyLower || mode == UnitLower) && col < row));
+  }
+
+#ifdef EIGEN_INTERNAL_DEBUGGING
+  void check_coordinates_internal(Index row, Index col) const { check_coordinates(row, col); }
+#else
+  void check_coordinates_internal(Index, Index) const {}
+#endif
+};
+
+/** \class TriangularView
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a triangular part in a matrix
+ *
+ * \tparam MatrixType the type of the object in which we are taking the triangular part
+ * \tparam Mode the kind of triangular matrix expression to construct. Can be #Upper,
+ *             #Lower, #UnitUpper, #UnitLower, #StrictlyUpper, or #StrictlyLower.
+ *             This is in fact a bit field; it must have either #Upper or #Lower,
+ *             and additionally it may have #UnitDiag or #ZeroDiag or neither.
+ *
+ * This class represents a triangular part of a matrix, not necessarily square. Strictly speaking, for rectangular
+ * matrices one should speak of "trapezoid" parts. This class is the return type
+ * of MatrixBase::triangularView() and SparseMatrixBase::triangularView(), and most of the time this is the only way it
+ * is used.
+ *
+ * \sa MatrixBase::triangularView()
+ */
+namespace internal {
+template <typename MatrixType, unsigned int Mode_>
+struct traits<TriangularView<MatrixType, Mode_>> : traits<MatrixType> {
+  typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNestedNonRef;
+  typedef remove_all_t<MatrixTypeNested> MatrixTypeNestedCleaned;
+  typedef typename MatrixType::PlainObject FullMatrixType;
+  typedef MatrixType ExpressionType;
+  enum {
+    Mode = Mode_,
+    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
+    Flags = (MatrixTypeNestedCleaned::Flags & (HereditaryBits | FlagsLvalueBit) &
+             (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)))
+  };
+};
+}  // namespace internal
+
+template <typename MatrixType_, unsigned int Mode_, typename StorageKind>
+class TriangularViewImpl;
+
+template <typename MatrixType_, unsigned int Mode_>
+class TriangularView
+    : public TriangularViewImpl<MatrixType_, Mode_, typename internal::traits<MatrixType_>::StorageKind> {
+ public:
+  typedef TriangularViewImpl<MatrixType_, Mode_, typename internal::traits<MatrixType_>::StorageKind> Base;
+  typedef typename internal::traits<TriangularView>::Scalar Scalar;
+  typedef MatrixType_ MatrixType;
+
+ protected:
+  typedef typename internal::traits<TriangularView>::MatrixTypeNested MatrixTypeNested;
+  typedef typename internal::traits<TriangularView>::MatrixTypeNestedNonRef MatrixTypeNestedNonRef;
+
+  typedef internal::remove_all_t<typename MatrixType::ConjugateReturnType> MatrixConjugateReturnType;
+  typedef TriangularView<std::add_const_t<MatrixType>, Mode_> ConstTriangularView;
+
+ public:
+  typedef typename internal::traits<TriangularView>::StorageKind StorageKind;
+  typedef typename internal::traits<TriangularView>::MatrixTypeNestedCleaned NestedExpression;
+
+  enum {
+    Mode = Mode_,
+    Flags = internal::traits<TriangularView>::Flags,
+    TransposeMode = (int(Mode) & int(Upper) ? Lower : 0) | (int(Mode) & int(Lower) ? Upper : 0) |
+                    (int(Mode) & int(UnitDiag)) | (int(Mode) & int(ZeroDiag)),
+    IsVectorAtCompileTime = false
+  };
+
+  EIGEN_DEVICE_FUNC explicit inline TriangularView(MatrixType& matrix) : m_matrix(matrix) {}
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TriangularView)
+
+  /** \copydoc EigenBase::rows() */
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_matrix.rows(); }
+  /** \copydoc EigenBase::cols() */
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_matrix.cols(); }
+
+  /** \returns a const reference to the nested expression */
+  EIGEN_DEVICE_FUNC const NestedExpression& nestedExpression() const { return m_matrix; }
+
+  /** \returns a reference to the nested expression */
+  EIGEN_DEVICE_FUNC NestedExpression& nestedExpression() { return m_matrix; }
+
+  typedef TriangularView<const MatrixConjugateReturnType, Mode> ConjugateReturnType;
+  /** \sa MatrixBase::conjugate() const */
+  EIGEN_DEVICE_FUNC inline const ConjugateReturnType conjugate() const {
+    return ConjugateReturnType(m_matrix.conjugate());
+  }
+
+  /** \returns an expression of the complex conjugate of \c *this if Cond==true,
+   *           returns \c *this otherwise.
+   */
+  template <bool Cond>
+  EIGEN_DEVICE_FUNC inline std::conditional_t<Cond, ConjugateReturnType, ConstTriangularView> conjugateIf() const {
+    typedef std::conditional_t<Cond, ConjugateReturnType, ConstTriangularView> ReturnType;
+    return ReturnType(m_matrix.template conjugateIf<Cond>());
+  }
+
+  typedef TriangularView<const typename MatrixType::AdjointReturnType, TransposeMode> AdjointReturnType;
+  /** \sa MatrixBase::adjoint() const */
+  EIGEN_DEVICE_FUNC inline const AdjointReturnType adjoint() const { return AdjointReturnType(m_matrix.adjoint()); }
+
+  typedef TriangularView<typename MatrixType::TransposeReturnType, TransposeMode> TransposeReturnType;
+  /** \sa MatrixBase::transpose() */
+  template <class Dummy = int>
+  EIGEN_DEVICE_FUNC inline TransposeReturnType transpose(
+      std::enable_if_t<Eigen::internal::is_lvalue<MatrixType>::value, Dummy*> = nullptr) {
+    typename MatrixType::TransposeReturnType tmp(m_matrix);
+    return TransposeReturnType(tmp);
+  }
+
+  typedef TriangularView<const typename MatrixType::ConstTransposeReturnType, TransposeMode> ConstTransposeReturnType;
+  /** \sa MatrixBase::transpose() const */
+  EIGEN_DEVICE_FUNC inline const ConstTransposeReturnType transpose() const {
+    return ConstTransposeReturnType(m_matrix.transpose());
+  }
+
+  template <typename Other>
+  EIGEN_DEVICE_FUNC inline Solve<TriangularView, Other> solve(const MatrixBase<Other>& other) const {
+    return Solve<TriangularView, Other>(*this, other.derived());
+  }
+
+// workaround MSVC ICE
+#if EIGEN_COMP_MSVC
+  template <int Side, typename Other>
+  EIGEN_DEVICE_FUNC inline const internal::triangular_solve_retval<Side, TriangularView, Other> solve(
+      const MatrixBase<Other>& other) const {
+    return Base::template solve<Side>(other);
+  }
+#else
+  using Base::solve;
+#endif
+
+  /** \returns a selfadjoint view of the referenced triangular part which must be either \c #Upper or \c #Lower.
+   *
+   * This is a shortcut for \code this->nestedExpression().selfadjointView<(*this)::Mode>() \endcode
+   * \sa MatrixBase::selfadjointView() */
+  EIGEN_DEVICE_FUNC SelfAdjointView<MatrixTypeNestedNonRef, Mode> selfadjointView() {
+    EIGEN_STATIC_ASSERT((Mode & (UnitDiag | ZeroDiag)) == 0, PROGRAMMING_ERROR);
+    return SelfAdjointView<MatrixTypeNestedNonRef, Mode>(m_matrix);
+  }
+
+  /** This is the const version of selfadjointView() */
+  EIGEN_DEVICE_FUNC const SelfAdjointView<MatrixTypeNestedNonRef, Mode> selfadjointView() const {
+    EIGEN_STATIC_ASSERT((Mode & (UnitDiag | ZeroDiag)) == 0, PROGRAMMING_ERROR);
+    return SelfAdjointView<MatrixTypeNestedNonRef, Mode>(m_matrix);
+  }
+
+  /** \returns the determinant of the triangular matrix
+   * \sa MatrixBase::determinant() */
+  EIGEN_DEVICE_FUNC Scalar determinant() const {
+    if (Mode & UnitDiag)
+      return 1;
+    else if (Mode & ZeroDiag)
+      return 0;
+    else
+      return m_matrix.diagonal().prod();
+  }
+
+ protected:
+  MatrixTypeNested m_matrix;
+};
+
+/** \ingroup Core_Module
+ *
+ * \brief Base class for a triangular part in a \b dense matrix
+ *
+ * This class is an abstract base class of class TriangularView, and objects of type TriangularViewImpl cannot be
+ * instantiated. It extends class TriangularView with additional methods which available for dense expressions only.
+ *
+ * \sa class TriangularView, MatrixBase::triangularView()
+ */
+template <typename MatrixType_, unsigned int Mode_>
+class TriangularViewImpl<MatrixType_, Mode_, Dense> : public TriangularBase<TriangularView<MatrixType_, Mode_>> {
+ public:
+  typedef TriangularView<MatrixType_, Mode_> TriangularViewType;
+
+  typedef TriangularBase<TriangularViewType> Base;
+  typedef typename internal::traits<TriangularViewType>::Scalar Scalar;
+
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::PlainObject DenseMatrixType;
+  typedef DenseMatrixType PlainObject;
+
+ public:
+  using Base::derived;
+  using Base::evalToLazy;
+
+  typedef typename internal::traits<TriangularViewType>::StorageKind StorageKind;
+
+  enum { Mode = Mode_, Flags = internal::traits<TriangularViewType>::Flags };
+
+  /** \returns the outer-stride of the underlying dense matrix
+   * \sa DenseCoeffsBase::outerStride() */
+  EIGEN_DEVICE_FUNC inline Index outerStride() const { return derived().nestedExpression().outerStride(); }
+  /** \returns the inner-stride of the underlying dense matrix
+   * \sa DenseCoeffsBase::innerStride() */
+  EIGEN_DEVICE_FUNC inline Index innerStride() const { return derived().nestedExpression().innerStride(); }
+
+  /** \sa MatrixBase::operator+=() */
+  template <typename Other>
+  EIGEN_DEVICE_FUNC TriangularViewType& operator+=(const DenseBase<Other>& other) {
+    internal::call_assignment_no_alias(derived(), other.derived(),
+                                       internal::add_assign_op<Scalar, typename Other::Scalar>());
+    return derived();
+  }
+  /** \sa MatrixBase::operator-=() */
+  template <typename Other>
+  EIGEN_DEVICE_FUNC TriangularViewType& operator-=(const DenseBase<Other>& other) {
+    internal::call_assignment_no_alias(derived(), other.derived(),
+                                       internal::sub_assign_op<Scalar, typename Other::Scalar>());
+    return derived();
+  }
+
+  /** \sa MatrixBase::operator*=() */
+  EIGEN_DEVICE_FUNC TriangularViewType& operator*=(const typename internal::traits<MatrixType>::Scalar& other) {
+    return *this = derived().nestedExpression() * other;
+  }
+  /** \sa DenseBase::operator/=() */
+  EIGEN_DEVICE_FUNC TriangularViewType& operator/=(const typename internal::traits<MatrixType>::Scalar& other) {
+    return *this = derived().nestedExpression() / other;
+  }
+
+  /** \sa MatrixBase::fill() */
+  EIGEN_DEVICE_FUNC void fill(const Scalar& value) { setConstant(value); }
+  /** \sa MatrixBase::setConstant() */
+  EIGEN_DEVICE_FUNC TriangularViewType& setConstant(const Scalar& value) {
+    return *this = MatrixType::Constant(derived().rows(), derived().cols(), value);
+  }
+  /** \sa MatrixBase::setZero() */
+  EIGEN_DEVICE_FUNC TriangularViewType& setZero() { return setConstant(Scalar(0)); }
+  /** \sa MatrixBase::setOnes() */
+  EIGEN_DEVICE_FUNC TriangularViewType& setOnes() { return setConstant(Scalar(1)); }
+
+  /** \sa MatrixBase::coeff()
+   * \warning the coordinates must fit into the referenced triangular part
+   */
+  EIGEN_DEVICE_FUNC inline Scalar coeff(Index row, Index col) const {
+    Base::check_coordinates_internal(row, col);
+    return derived().nestedExpression().coeff(row, col);
+  }
+
+  /** \sa MatrixBase::coeffRef()
+   * \warning the coordinates must fit into the referenced triangular part
+   */
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col) {
+    EIGEN_STATIC_ASSERT_LVALUE(TriangularViewType);
+    Base::check_coordinates_internal(row, col);
+    return derived().nestedExpression().coeffRef(row, col);
+  }
+
+  /** Assigns a triangular matrix to a triangular part of a dense matrix */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC TriangularViewType& operator=(const TriangularBase<OtherDerived>& other);
+
+  /** Shortcut for\code *this = other.other.triangularView<(*this)::Mode>() \endcode */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC TriangularViewType& operator=(const MatrixBase<OtherDerived>& other);
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  EIGEN_DEVICE_FUNC TriangularViewType& operator=(const TriangularViewImpl& other) {
+    return *this = other.derived().nestedExpression();
+  }
+
+  template <typename OtherDerived>
+  /** \deprecated */
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC void lazyAssign(const TriangularBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  /** \deprecated */
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC void lazyAssign(const MatrixBase<OtherDerived>& other);
+#endif
+
+  /** Efficient triangular matrix times vector/matrix product */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC const Product<TriangularViewType, OtherDerived> operator*(
+      const MatrixBase<OtherDerived>& rhs) const {
+    return Product<TriangularViewType, OtherDerived>(derived(), rhs.derived());
+  }
+
+  /** Efficient vector/matrix times triangular matrix product */
+  template <typename OtherDerived>
+  friend EIGEN_DEVICE_FUNC const Product<OtherDerived, TriangularViewType> operator*(
+      const MatrixBase<OtherDerived>& lhs, const TriangularViewImpl& rhs) {
+    return Product<OtherDerived, TriangularViewType>(lhs.derived(), rhs.derived());
+  }
+
+  /** \returns the product of the inverse of \c *this with \a other, \a *this being triangular.
+   *
+   * This function computes the inverse-matrix matrix product inverse(\c *this) * \a other if
+   * \a Side==OnTheLeft (the default), or the right-inverse-multiply  \a other * inverse(\c *this) if
+   * \a Side==OnTheRight.
+   *
+   * Note that the template parameter \c Side can be omitted, in which case \c Side==OnTheLeft
+   *
+   * The matrix \c *this must be triangular and invertible (i.e., all the coefficients of the
+   * diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this
+   * is an upper (resp. lower) triangular matrix.
+   *
+   * Example: \include Triangular_solve.cpp
+   * Output: \verbinclude Triangular_solve.out
+   *
+   * This function returns an expression of the inverse-multiply and can works in-place if it is assigned
+   * to the same matrix or vector \a other.
+   *
+   * For users coming from BLAS, this function (and more specifically solveInPlace()) offer
+   * all the operations supported by the \c *TRSV and \c *TRSM BLAS routines.
+   *
+   * \sa TriangularView::solveInPlace()
+   */
+  template <int Side, typename Other>
+  inline const internal::triangular_solve_retval<Side, TriangularViewType, Other> solve(
+      const MatrixBase<Other>& other) const;
+
+  /** "in-place" version of TriangularView::solve() where the result is written in \a other
+   *
+   * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
+   * This function will const_cast it, so constness isn't honored here.
+   *
+   * Note that the template parameter \c Side can be omitted, in which case \c Side==OnTheLeft
+   *
+   * See TriangularView:solve() for the details.
+   */
+  template <int Side, typename OtherDerived>
+  EIGEN_DEVICE_FUNC void solveInPlace(const MatrixBase<OtherDerived>& other) const;
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC void solveInPlace(const MatrixBase<OtherDerived>& other) const {
+    return solveInPlace<OnTheLeft>(other);
+  }
+
+  /** Swaps the coefficients of the common triangular parts of two matrices */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+      void
+      swap(TriangularBase<OtherDerived>& other)
+#else
+      void
+      swap(TriangularBase<OtherDerived> const& other)
+#endif
+  {
+    EIGEN_STATIC_ASSERT_LVALUE(OtherDerived);
+    call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
+  }
+
+  /** Shortcut for \code (*this).swap(other.triangularView<(*this)::Mode>()) \endcode */
+  template <typename OtherDerived>
+  /** \deprecated */
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC void swap(MatrixBase<OtherDerived> const& other) {
+    EIGEN_STATIC_ASSERT_LVALUE(OtherDerived);
+    call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
+  }
+
+  template <typename RhsType, typename DstType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void _solve_impl(const RhsType& rhs, DstType& dst) const {
+    if (!internal::is_same_dense(dst, rhs)) dst = rhs;
+    this->solveInPlace(dst);
+  }
+
+  template <typename ProductType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TriangularViewType& _assignProduct(const ProductType& prod, const Scalar& alpha,
+                                                                           bool beta);
+
+ protected:
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(TriangularViewImpl)
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TriangularViewImpl)
+};
+
+/***************************************************************************
+ * Implementation of triangular evaluation/assignment
+ ***************************************************************************/
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+// FIXME should we keep that possibility
+template <typename MatrixType, unsigned int Mode>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline TriangularView<MatrixType, Mode>& TriangularViewImpl<MatrixType, Mode, Dense>::operator=(
+    const MatrixBase<OtherDerived>& other) {
+  internal::call_assignment_no_alias(derived(), other.derived(),
+                                     internal::assign_op<Scalar, typename OtherDerived::Scalar>());
+  return derived();
+}
+
+// FIXME should we keep that possibility
+template <typename MatrixType, unsigned int Mode>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType, Mode, Dense>::lazyAssign(const MatrixBase<OtherDerived>& other) {
+  internal::call_assignment_no_alias(derived(), other.template triangularView<Mode>());
+}
+
+template <typename MatrixType, unsigned int Mode>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline TriangularView<MatrixType, Mode>& TriangularViewImpl<MatrixType, Mode, Dense>::operator=(
+    const TriangularBase<OtherDerived>& other) {
+  eigen_assert(Mode == int(OtherDerived::Mode));
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename MatrixType, unsigned int Mode>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType, Mode, Dense>::lazyAssign(
+    const TriangularBase<OtherDerived>& other) {
+  eigen_assert(Mode == int(OtherDerived::Mode));
+  internal::call_assignment_no_alias(derived(), other.derived());
+}
+#endif
+
+/***************************************************************************
+ * Implementation of TriangularBase methods
+ ***************************************************************************/
+
+/** Assigns a triangular or selfadjoint matrix to a dense matrix.
+ * If the matrix is triangular, the opposite part is set to zero. */
+template <typename Derived>
+template <typename DenseDerived>
+EIGEN_DEVICE_FUNC void TriangularBase<Derived>::evalTo(MatrixBase<DenseDerived>& other) const {
+  evalToLazy(other.derived());
+}
+
+/***************************************************************************
+ * Implementation of TriangularView methods
+ ***************************************************************************/
+
+/***************************************************************************
+ * Implementation of MatrixBase methods
+ ***************************************************************************/
+
+/**
+ * \returns an expression of a triangular view extracted from the current matrix
+ *
+ * The parameter \a Mode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper,
+ * \c #Lower, \c #StrictlyLower, \c #UnitLower.
+ *
+ * Example: \include MatrixBase_triangularView.cpp
+ * Output: \verbinclude MatrixBase_triangularView.out
+ *
+ * \sa class TriangularView
+ */
+template <typename Derived>
+template <unsigned int Mode>
+EIGEN_DEVICE_FUNC constexpr typename MatrixBase<Derived>::template TriangularViewReturnType<Mode>::Type
+MatrixBase<Derived>::triangularView() {
+  return typename TriangularViewReturnType<Mode>::Type(derived());
+}
+
+/** This is the const version of MatrixBase::triangularView() */
+template <typename Derived>
+template <unsigned int Mode>
+EIGEN_DEVICE_FUNC constexpr typename MatrixBase<Derived>::template ConstTriangularViewReturnType<Mode>::Type
+MatrixBase<Derived>::triangularView() const {
+  return typename ConstTriangularViewReturnType<Mode>::Type(derived());
+}
+
+/** \returns true if *this is approximately equal to an upper triangular matrix,
+ *          within the precision given by \a prec.
+ *
+ * \sa isLowerTriangular()
+ */
+template <typename Derived>
+bool MatrixBase<Derived>::isUpperTriangular(const RealScalar& prec) const {
+  RealScalar maxAbsOnUpperPart = static_cast<RealScalar>(-1);
+  for (Index j = 0; j < cols(); ++j) {
+    Index maxi = numext::mini(j, rows() - 1);
+    for (Index i = 0; i <= maxi; ++i) {
+      RealScalar absValue = numext::abs(coeff(i, j));
+      if (absValue > maxAbsOnUpperPart) maxAbsOnUpperPart = absValue;
+    }
+  }
+  RealScalar threshold = maxAbsOnUpperPart * prec;
+  for (Index j = 0; j < cols(); ++j)
+    for (Index i = j + 1; i < rows(); ++i)
+      if (numext::abs(coeff(i, j)) > threshold) return false;
+  return true;
+}
+
+/** \returns true if *this is approximately equal to a lower triangular matrix,
+ *          within the precision given by \a prec.
+ *
+ * \sa isUpperTriangular()
+ */
+template <typename Derived>
+bool MatrixBase<Derived>::isLowerTriangular(const RealScalar& prec) const {
+  RealScalar maxAbsOnLowerPart = static_cast<RealScalar>(-1);
+  for (Index j = 0; j < cols(); ++j)
+    for (Index i = j; i < rows(); ++i) {
+      RealScalar absValue = numext::abs(coeff(i, j));
+      if (absValue > maxAbsOnLowerPart) maxAbsOnLowerPart = absValue;
+    }
+  RealScalar threshold = maxAbsOnLowerPart * prec;
+  for (Index j = 1; j < cols(); ++j) {
+    Index maxi = numext::mini(j, rows() - 1);
+    for (Index i = 0; i < maxi; ++i)
+      if (numext::abs(coeff(i, j)) > threshold) return false;
+  }
+  return true;
+}
+
+/***************************************************************************
+****************************************************************************
+* Evaluators and Assignment of triangular expressions
+***************************************************************************
+***************************************************************************/
+
+namespace internal {
+
+// TODO: currently a triangular expression has the form TriangularView<.,.>
+//      in the future triangular-ness should be defined by the expression traits
+//      such that Transpose<TriangularView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make
+//      it work)
+template <typename MatrixType, unsigned int Mode>
+struct evaluator_traits<TriangularView<MatrixType, Mode>> {
+  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
+  typedef typename glue_shapes<typename evaluator_traits<MatrixType>::Shape, TriangularShape>::type Shape;
+};
+
+template <typename MatrixType, unsigned int Mode>
+struct unary_evaluator<TriangularView<MatrixType, Mode>, IndexBased> : evaluator<internal::remove_all_t<MatrixType>> {
+  typedef TriangularView<MatrixType, Mode> XprType;
+  typedef evaluator<internal::remove_all_t<MatrixType>> Base;
+  EIGEN_DEVICE_FUNC unary_evaluator(const XprType& xpr) : Base(xpr.nestedExpression()) {}
+};
+
+// Additional assignment kinds:
+struct Triangular2Triangular {};
+struct Triangular2Dense {};
+struct Dense2Triangular {};
+
+template <typename Kernel, unsigned int Mode, int UnrollCount, bool ClearOpposite>
+struct triangular_assignment_loop;
+
+/** \internal Specialization of the dense assignment kernel for triangular matrices.
+ * The main difference is that the triangular, diagonal, and opposite parts are processed through three different
+ * functions. \tparam UpLo must be either Lower or Upper \tparam Mode must be either 0, UnitDiag, ZeroDiag, or
+ * SelfAdjoint
+ */
+template <int UpLo, int Mode, int SetOpposite, typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor,
+          int Version = Specialized>
+class triangular_dense_assignment_kernel
+    : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> {
+ protected:
+  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> Base;
+  typedef typename Base::DstXprType DstXprType;
+  typedef typename Base::SrcXprType SrcXprType;
+  using Base::m_dst;
+  using Base::m_functor;
+  using Base::m_src;
+
+ public:
+  typedef typename Base::DstEvaluatorType DstEvaluatorType;
+  typedef typename Base::SrcEvaluatorType SrcEvaluatorType;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::AssignmentTraits AssignmentTraits;
+
+  EIGEN_DEVICE_FUNC triangular_dense_assignment_kernel(DstEvaluatorType& dst, const SrcEvaluatorType& src,
+                                                       const Functor& func, DstXprType& dstExpr)
+      : Base(dst, src, func, dstExpr) {}
+
+#ifdef EIGEN_INTERNAL_DEBUGGING
+  EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col) {
+    eigen_internal_assert(row != col);
+    Base::assignCoeff(row, col);
+  }
+#else
+  using Base::assignCoeff;
+#endif
+
+  EIGEN_DEVICE_FUNC void assignDiagonalCoeff(Index id) {
+    if (Mode == UnitDiag && SetOpposite)
+      m_functor.assignCoeff(m_dst.coeffRef(id, id), Scalar(1));
+    else if (Mode == ZeroDiag && SetOpposite)
+      m_functor.assignCoeff(m_dst.coeffRef(id, id), Scalar(0));
+    else if (Mode == 0)
+      Base::assignCoeff(id, id);
+  }
+
+  EIGEN_DEVICE_FUNC void assignOppositeCoeff(Index row, Index col) {
+    eigen_internal_assert(row != col);
+    if (SetOpposite) m_functor.assignCoeff(m_dst.coeffRef(row, col), Scalar(0));
+  }
+};
+
+template <int Mode, bool SetOpposite, typename DstXprType, typename SrcXprType, typename Functor>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_triangular_assignment_loop(DstXprType& dst, const SrcXprType& src,
+                                                                           const Functor& func) {
+  typedef evaluator<DstXprType> DstEvaluatorType;
+  typedef evaluator<SrcXprType> SrcEvaluatorType;
+
+  SrcEvaluatorType srcEvaluator(src);
+
+  Index dstRows = src.rows();
+  Index dstCols = src.cols();
+  if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+  DstEvaluatorType dstEvaluator(dst);
+
+  typedef triangular_dense_assignment_kernel<Mode&(Lower | Upper), Mode&(UnitDiag | ZeroDiag | SelfAdjoint),
+                                             SetOpposite, DstEvaluatorType, SrcEvaluatorType, Functor>
+      Kernel;
+  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
+
+  enum {
+    unroll = DstXprType::SizeAtCompileTime != Dynamic && SrcEvaluatorType::CoeffReadCost < HugeCost &&
+             DstXprType::SizeAtCompileTime *
+                     (int(DstEvaluatorType::CoeffReadCost) + int(SrcEvaluatorType::CoeffReadCost)) / 2 <=
+                 EIGEN_UNROLLING_LIMIT
+  };
+
+  triangular_assignment_loop<Kernel, Mode, unroll ? int(DstXprType::SizeAtCompileTime) : Dynamic, SetOpposite>::run(
+      kernel);
+}
+
+template <int Mode, bool SetOpposite, typename DstXprType, typename SrcXprType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_triangular_assignment_loop(DstXprType& dst, const SrcXprType& src) {
+  call_triangular_assignment_loop<Mode, SetOpposite>(
+      dst, src, internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>());
+}
+
+template <>
+struct AssignmentKind<TriangularShape, TriangularShape> {
+  typedef Triangular2Triangular Kind;
+};
+template <>
+struct AssignmentKind<DenseShape, TriangularShape> {
+  typedef Triangular2Dense Kind;
+};
+template <>
+struct AssignmentKind<TriangularShape, DenseShape> {
+  typedef Dense2Triangular Kind;
+};
+
+template <typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Triangular> {
+  EIGEN_DEVICE_FUNC static void run(DstXprType& dst, const SrcXprType& src, const Functor& func) {
+    eigen_assert(int(DstXprType::Mode) == int(SrcXprType::Mode));
+
+    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);
+  }
+};
+
+template <typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Dense> {
+  EIGEN_DEVICE_FUNC static void run(DstXprType& dst, const SrcXprType& src, const Functor& func) {
+    call_triangular_assignment_loop<SrcXprType::Mode, (int(SrcXprType::Mode) & int(SelfAdjoint)) == 0>(dst, src, func);
+  }
+};
+
+template <typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Dense2Triangular> {
+  EIGEN_DEVICE_FUNC static void run(DstXprType& dst, const SrcXprType& src, const Functor& func) {
+    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);
+  }
+};
+
+template <typename Kernel, unsigned int Mode, int UnrollCount, bool SetOpposite>
+struct triangular_assignment_loop {
+  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
+  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
+  typedef typename DstEvaluatorType::XprType DstXprType;
+
+  enum {
+    col = (UnrollCount - 1) / DstXprType::RowsAtCompileTime,
+    row = (UnrollCount - 1) % DstXprType::RowsAtCompileTime
+  };
+
+  typedef typename Kernel::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC static inline void run(Kernel& kernel) {
+    triangular_assignment_loop<Kernel, Mode, UnrollCount - 1, SetOpposite>::run(kernel);
+
+    if (row == col)
+      kernel.assignDiagonalCoeff(row);
+    else if (((Mode & Lower) && row > col) || ((Mode & Upper) && row < col))
+      kernel.assignCoeff(row, col);
+    else if (SetOpposite)
+      kernel.assignOppositeCoeff(row, col);
+  }
+};
+
+// prevent buggy user code from causing an infinite recursion
+template <typename Kernel, unsigned int Mode, bool SetOpposite>
+struct triangular_assignment_loop<Kernel, Mode, 0, SetOpposite> {
+  EIGEN_DEVICE_FUNC static inline void run(Kernel&) {}
+};
+
+// TODO: experiment with a recursive assignment procedure splitting the current
+//       triangular part into one rectangular and two triangular parts.
+
+template <typename Kernel, unsigned int Mode, bool SetOpposite>
+struct triangular_assignment_loop<Kernel, Mode, Dynamic, SetOpposite> {
+  typedef typename Kernel::Scalar Scalar;
+  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
+  typedef typename Kernel::AssignmentTraits AssignmentTraits;
+
+  enum {
+    IsRowMajor = (int(DstEvaluatorType::Flags) & RowMajorBit) != 0,
+    // In col-major: inner=row, outer=col. Upper means row<col i.e. inner<outer -> active before diagonal.
+    // In row-major: inner=col, outer=row. Upper means row<col i.e. inner>outer -> active after diagonal.
+    // So ActiveBeforeDiag = (Upper XOR IsRowMajor).
+    ActiveBeforeDiag = (bool(Mode & Upper) != bool(IsRowMajor))
+  };
+
+  // Compile-time outer/inner to row/col mapping. These constant-fold away entirely:
+  // ColMajor: row(outer,i) -> i, col(outer,i) -> outer
+  // RowMajor: row(outer,i) -> outer, col(outer,i) -> i
+  static constexpr Index row(Index outer, Index inner) { return IsRowMajor ? outer : inner; }
+  static constexpr Index col(Index outer, Index inner) { return IsRowMajor ? inner : outer; }
+
+  // Iterates in outer/inner order matching the storage layout for cache friendliness.
+  // Unlike the old code (which always iterated outer=col, inner=row), this gives
+  // contiguous memory access for both ColMajor and RowMajor storage.
+  // Simple scalar loops allow GCC to recognize memcpy/memset idioms and Clang to auto-vectorize.
+  // Uses a single running index 'i' per column (not separate loop variables) so the compiler
+  // can track the continuous progression and optimize register allocation.
+  EIGEN_DEVICE_FUNC static inline void run(Kernel& kernel) {
+    const Index outerSize = IsRowMajor ? kernel.rows() : kernel.cols();
+    const Index innerSize = IsRowMajor ? kernel.cols() : kernel.rows();
+
+    for (Index outer = 0; outer < outerSize; ++outer) {
+      const Index maxi = numext::mini(outer, innerSize);
+      Index i = 0;
+
+      if (ActiveBeforeDiag) {
+        for (; i < maxi; ++i) kernel.assignCoeff(row(outer, i), col(outer, i));
+      } else if (SetOpposite) {
+        for (; i < maxi; ++i) kernel.assignOppositeCoeff(row(outer, i), col(outer, i));
+      } else {
+        i = maxi;
+      }
+
+      if (i < innerSize) kernel.assignDiagonalCoeff(i++);
+
+      if (!ActiveBeforeDiag) {
+        for (; i < innerSize; ++i) kernel.assignCoeff(row(outer, i), col(outer, i));
+      } else if (SetOpposite) {
+        for (; i < innerSize; ++i) kernel.assignOppositeCoeff(row(outer, i), col(outer, i));
+      }
+    }
+  }
+};
+
+}  // end namespace internal
+
+/** Assigns a triangular or selfadjoint matrix to a dense matrix.
+ * If the matrix is triangular, the opposite part is set to zero. */
+template <typename Derived>
+template <typename DenseDerived>
+EIGEN_DEVICE_FUNC void TriangularBase<Derived>::evalToLazy(MatrixBase<DenseDerived>& other) const {
+  other.derived().resize(this->rows(), this->cols());
+  internal::call_triangular_assignment_loop<Derived::Mode,
+                                            (int(Derived::Mode) & int(SelfAdjoint)) == 0 /* SetOpposite */>(
+      other.derived(), derived().nestedExpression());
+}
+
+namespace internal {
+
+// Triangular = Product
+template <typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs, Rhs, DefaultProduct>,
+                  internal::assign_op<Scalar, typename Product<Lhs, Rhs, DefaultProduct>::Scalar>, Dense2Triangular> {
+  typedef Product<Lhs, Rhs, DefaultProduct> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::assign_op<Scalar, typename SrcXprType::Scalar>&) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    dst._assignProduct(src, Scalar(1), false);
+  }
+};
+
+// Triangular += Product
+template <typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs, Rhs, DefaultProduct>,
+                  internal::add_assign_op<Scalar, typename Product<Lhs, Rhs, DefaultProduct>::Scalar>,
+                  Dense2Triangular> {
+  typedef Product<Lhs, Rhs, DefaultProduct> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::add_assign_op<Scalar, typename SrcXprType::Scalar>&) {
+    dst._assignProduct(src, Scalar(1), true);
+  }
+};
+
+// Triangular -= Product
+template <typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs, Rhs, DefaultProduct>,
+                  internal::sub_assign_op<Scalar, typename Product<Lhs, Rhs, DefaultProduct>::Scalar>,
+                  Dense2Triangular> {
+  typedef Product<Lhs, Rhs, DefaultProduct> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::sub_assign_op<Scalar, typename SrcXprType::Scalar>&) {
+    dst._assignProduct(src, Scalar(-1), true);
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRIANGULARMATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/VectorBlock.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/VectorBlock.h
new file mode 100644
index 0000000..1277e26
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/VectorBlock.h
@@ -0,0 +1,83 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_VECTORBLOCK_H
+#define EIGEN_VECTORBLOCK_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename VectorType, int Size>
+struct traits<VectorBlock<VectorType, Size> >
+    : public traits<Block<VectorType, traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
+                          traits<VectorType>::Flags & RowMajorBit ? Size : 1> > {};
+}  // namespace internal
+
+/** \class VectorBlock
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a fixed-size or dynamic-size sub-vector
+ *
+ * \tparam VectorType the type of the object in which we are taking a sub-vector
+ * \tparam Size size of the sub-vector we are taking at compile time (optional)
+ *
+ * This class represents an expression of either a fixed-size or dynamic-size sub-vector.
+ * It is the return type of DenseBase::segment(Index,Index) and DenseBase::segment<int>(Index) and
+ * most of the time this is the only way it is used.
+ *
+ * However, if you want to directly manipulate sub-vector expressions,
+ * for instance if you want to write a function returning such an expression, you
+ * will need to use this class.
+ *
+ * Here is an example illustrating the dynamic case:
+ * \include class_VectorBlock.cpp
+ * Output: \verbinclude class_VectorBlock.out
+ *
+ * \note Even though this expression has dynamic size, in the case where \a VectorType
+ * has fixed size, this expression inherits a fixed maximal size which means that evaluating
+ * it does not cause a dynamic memory allocation.
+ *
+ * Here is an example illustrating the fixed-size case:
+ * \include class_FixedVectorBlock.cpp
+ * Output: \verbinclude class_FixedVectorBlock.out
+ *
+ * \sa class Block, DenseBase::segment(Index,Index,Index,Index), DenseBase::segment(Index,Index)
+ */
+template <typename VectorType, int Size>
+class VectorBlock : public Block<VectorType, internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
+                                 internal::traits<VectorType>::Flags & RowMajorBit ? Size : 1> {
+  typedef Block<VectorType, internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
+                internal::traits<VectorType>::Flags & RowMajorBit ? Size : 1>
+      Base;
+  enum { IsColVector = !(internal::traits<VectorType>::Flags & RowMajorBit) };
+
+ public:
+  EIGEN_DENSE_PUBLIC_INTERFACE(VectorBlock)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(VectorBlock)
+
+  /** Dynamic-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE VectorBlock(VectorType& vector, Index start, Index size)
+      : Base(vector, IsColVector ? start : 0, IsColVector ? 0 : start, IsColVector ? size : 1, IsColVector ? 1 : size) {
+  }
+
+  /** Fixed-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE VectorBlock(VectorType& vector, Index start)
+      : Base(vector, IsColVector ? start : 0, IsColVector ? 0 : start) {}
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_VECTORBLOCK_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/VectorwiseOp.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/VectorwiseOp.h
new file mode 100644
index 0000000..9e34d8c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/VectorwiseOp.h
@@ -0,0 +1,735 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARTIAL_REDUX_H
+#define EIGEN_PARTIAL_REDUX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class PartialReduxExpr
+ * \ingroup Core_Module
+ *
+ * \brief Generic expression of a partially reduxed matrix
+ *
+ * \tparam MatrixType the type of the matrix we are applying the redux operation
+ * \tparam MemberOp type of the member functor
+ * \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal)
+ *
+ * This class represents an expression of a partial redux operator of a matrix.
+ * It is the return type of some VectorwiseOp functions,
+ * and most of the time this is the only way it is used.
+ *
+ * \sa class VectorwiseOp
+ */
+
+template <typename MatrixType, typename MemberOp, int Direction>
+class PartialReduxExpr;
+
+namespace internal {
+
+template <typename MatrixType, typename MemberOp, int Direction>
+struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> > : traits<MatrixType> {
+  typedef typename MemberOp::result_type Scalar;
+  typedef typename traits<MatrixType>::StorageKind StorageKind;
+  typedef typename traits<MatrixType>::XprKind XprKind;
+  typedef typename MatrixType::Scalar InputScalar;
+  enum {
+    RowsAtCompileTime = Direction == Vertical ? 1 : MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = Direction == Horizontal ? 1 : MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = Direction == Vertical ? 1 : MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = Direction == Horizontal ? 1 : MatrixType::MaxColsAtCompileTime,
+    Flags = RowsAtCompileTime == 1 ? RowMajorBit : 0,
+    TraversalSize = Direction == Vertical ? MatrixType::RowsAtCompileTime : MatrixType::ColsAtCompileTime
+  };
+};
+}  // namespace internal
+
+template <typename MatrixType, typename MemberOp, int Direction>
+class PartialReduxExpr : public internal::dense_xpr_base<PartialReduxExpr<MatrixType, MemberOp, Direction> >::type,
+                         internal::no_assignment_operator {
+ public:
+  typedef typename internal::dense_xpr_base<PartialReduxExpr>::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr)
+
+  EIGEN_DEVICE_FUNC explicit PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
+      : m_matrix(mat), m_functor(func) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return (Direction == Vertical ? 1 : m_matrix.rows()); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return (Direction == Horizontal ? 1 : m_matrix.cols()); }
+
+  EIGEN_DEVICE_FUNC typename MatrixType::Nested nestedExpression() const { return m_matrix; }
+
+  EIGEN_DEVICE_FUNC const MemberOp& functor() const { return m_functor; }
+
+ protected:
+  typename MatrixType::Nested m_matrix;
+  const MemberOp m_functor;
+};
+
+template <typename A, typename B>
+struct partial_redux_dummy_func;
+
+#define EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER, COST, VECTORIZABLE, BINARYOP)              \
+  template <typename ResultType, typename Scalar>                                           \
+  struct member_##MEMBER {                                                                  \
+    typedef ResultType result_type;                                                         \
+    typedef BINARYOP<Scalar, Scalar> BinaryOp;                                              \
+    template <int Size>                                                                     \
+    struct Cost {                                                                           \
+      enum { value = COST };                                                                \
+    };                                                                                      \
+    enum { Vectorizable = VECTORIZABLE };                                                   \
+    template <typename XprType>                                                             \
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType operator()(const XprType& mat) const { \
+      return mat.MEMBER();                                                                  \
+    }                                                                                       \
+    BinaryOp binaryFunc() const { return BinaryOp(); }                                      \
+  }
+
+#define EIGEN_MEMBER_FUNCTOR(MEMBER, COST) EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER, COST, 0, partial_redux_dummy_func)
+
+namespace internal {
+
+EIGEN_MEMBER_FUNCTOR(norm, (Size + 5) * NumTraits<Scalar>::MulCost + (Size - 1) * NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(stableNorm, (Size + 5) * NumTraits<Scalar>::MulCost + (Size - 1) * NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(blueNorm, (Size + 5) * NumTraits<Scalar>::MulCost + (Size - 1) * NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(hypotNorm, (Size - 1) * functor_traits<scalar_hypot_op<Scalar> >::Cost);
+EIGEN_MEMBER_FUNCTOR(all, (Size - 1) * NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(any, (Size - 1) * NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(count, (Size - 1) * NumTraits<Scalar>::AddCost);
+
+EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(sum, (Size - 1) * NumTraits<Scalar>::AddCost, 1, internal::scalar_sum_op);
+EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(minCoeff, (Size - 1) * NumTraits<Scalar>::AddCost, 1, internal::scalar_min_op);
+EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(maxCoeff, (Size - 1) * NumTraits<Scalar>::AddCost, 1, internal::scalar_max_op);
+EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(prod, (Size - 1) * NumTraits<Scalar>::MulCost, 1, internal::scalar_product_op);
+
+template <int p, typename ResultType, typename Scalar>
+struct member_lpnorm {
+  typedef ResultType result_type;
+  enum { Vectorizable = 0 };
+  template <int Size>
+  struct Cost {
+    enum { value = (Size + 5) * NumTraits<Scalar>::MulCost + (Size - 1) * NumTraits<Scalar>::AddCost };
+  };
+  EIGEN_DEVICE_FUNC member_lpnorm() {}
+  template <typename XprType>
+  EIGEN_DEVICE_FUNC inline ResultType operator()(const XprType& mat) const {
+    return mat.template lpNorm<p>();
+  }
+};
+
+template <typename BinaryOpT, typename Scalar>
+struct member_redux {
+  typedef BinaryOpT BinaryOp;
+  typedef typename result_of<BinaryOp(const Scalar&, const Scalar&)>::type result_type;
+
+  enum { Vectorizable = functor_traits<BinaryOp>::PacketAccess };
+  template <int Size>
+  struct Cost {
+    enum { value = (Size - 1) * functor_traits<BinaryOp>::Cost };
+  };
+  EIGEN_DEVICE_FUNC explicit member_redux(const BinaryOp func) : m_functor(func) {}
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline result_type operator()(const DenseBase<Derived>& mat) const {
+    return mat.redux(m_functor);
+  }
+  const BinaryOp& binaryFunc() const { return m_functor; }
+  const BinaryOp m_functor;
+};
+
+template <typename Scalar>
+struct scalar_replace_zero_with_one_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& x) const {
+    return numext::is_exactly_zero(x) ? Scalar(1) : x;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return pselect(pcmp_eq(x, pzero(x)), pset1<Packet>(Scalar(1)), x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_replace_zero_with_one_op<Scalar>> {
+  enum { Cost = 1, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+}  // namespace internal
+
+/** \class VectorwiseOp
+ * \ingroup Core_Module
+ *
+ * \brief Pseudo expression providing broadcasting and partial reduction operations
+ *
+ * \tparam ExpressionType the type of the object on which to do partial reductions
+ * \tparam Direction indicates whether to operate on columns (#Vertical) or rows (#Horizontal)
+ *
+ * This class represents a pseudo expression with broadcasting and partial reduction features.
+ * It is the return type of DenseBase::colwise() and DenseBase::rowwise()
+ * and most of the time this is the only way it is explicitly used.
+ *
+ * To understand the logic of rowwise/colwise expression, let's consider a generic case `A.colwise().foo()`
+ * where `foo` is any method of `VectorwiseOp`. This expression is equivalent to applying `foo()` to each
+ * column of `A` and then re-assemble the outputs in a matrix expression:
+ * \code [A.col(0).foo(), A.col(1).foo(), ..., A.col(A.cols()-1).foo()] \endcode
+ *
+ * Example: \include MatrixBase_colwise.cpp
+ * Output: \verbinclude MatrixBase_colwise.out
+ *
+ * The begin() and end() methods are obviously exceptions to the previous rule as they
+ * return STL-compatible begin/end iterators to the rows or columns of the nested expression.
+ * Typical use cases include for-range-loop and calls to STL algorithms:
+ *
+ * Example: \include MatrixBase_colwise_iterator_cxx11.cpp
+ * Output: \verbinclude MatrixBase_colwise_iterator_cxx11.out
+ *
+ * For a partial reduction on an empty input, some rules apply.
+ * For the sake of clarity, let's consider a vertical reduction:
+ *   - If the number of columns is zero, then a 1x0 row-major vector expression is returned.
+ *   - Otherwise, if the number of rows is zero, then
+ *       - a row vector of zeros is returned for sum-like reductions (sum, squaredNorm, norm, etc.)
+ *       - a row vector of ones is returned for a product reduction (e.g., <code>MatrixXd(n,0).colwise().prod()</code>)
+ *       - an assert is triggered for all other reductions (minCoeff,maxCoeff,redux(bin_op))
+ *
+ * \sa DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr
+ */
+template <typename ExpressionType, int Direction>
+class VectorwiseOp {
+ public:
+  typedef typename ExpressionType::Scalar Scalar;
+  typedef typename ExpressionType::RealScalar RealScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+  typedef typename internal::ref_selector<ExpressionType>::non_const_type ExpressionTypeNested;
+  typedef internal::remove_all_t<ExpressionTypeNested> ExpressionTypeNestedCleaned;
+
+  template <template <typename OutScalar, typename InputScalar> class Functor, typename ReturnScalar = Scalar>
+  struct ReturnType {
+    typedef PartialReduxExpr<ExpressionType, Functor<ReturnScalar, Scalar>, Direction> Type;
+  };
+
+  template <typename BinaryOp>
+  struct ReduxReturnType {
+    typedef PartialReduxExpr<ExpressionType, internal::member_redux<BinaryOp, Scalar>, Direction> Type;
+  };
+
+  enum { isVertical = (Direction == Vertical) ? 1 : 0, isHorizontal = (Direction == Horizontal) ? 1 : 0 };
+
+ protected:
+  template <typename OtherDerived>
+  struct ExtendedType {
+    typedef Replicate<OtherDerived, isVertical ? 1 : ExpressionType::RowsAtCompileTime,
+                      isHorizontal ? 1 : ExpressionType::ColsAtCompileTime>
+        Type;
+  };
+
+  /** \internal
+   * Replicates a vector to match the size of \c *this */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC typename ExtendedType<OtherDerived>::Type extendedTo(const DenseBase<OtherDerived>& other) const {
+    EIGEN_STATIC_ASSERT(internal::check_implication(isVertical, OtherDerived::MaxColsAtCompileTime == 1),
+                        YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
+    EIGEN_STATIC_ASSERT(internal::check_implication(isHorizontal, OtherDerived::MaxRowsAtCompileTime == 1),
+                        YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
+    return typename ExtendedType<OtherDerived>::Type(other.derived(), isVertical ? 1 : m_matrix.rows(),
+                                                     isHorizontal ? 1 : m_matrix.cols());
+  }
+
+  template <typename OtherDerived>
+  struct OppositeExtendedType {
+    typedef Replicate<OtherDerived, isHorizontal ? 1 : ExpressionType::RowsAtCompileTime,
+                      isVertical ? 1 : ExpressionType::ColsAtCompileTime>
+        Type;
+  };
+
+  /** \internal
+   * Replicates a vector in the opposite direction to match the size of \c *this */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC typename OppositeExtendedType<OtherDerived>::Type extendedToOpposite(
+      const DenseBase<OtherDerived>& other) const {
+    EIGEN_STATIC_ASSERT(internal::check_implication(isHorizontal, OtherDerived::MaxColsAtCompileTime == 1),
+                        YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
+    EIGEN_STATIC_ASSERT(internal::check_implication(isVertical, OtherDerived::MaxRowsAtCompileTime == 1),
+                        YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
+    return typename OppositeExtendedType<OtherDerived>::Type(other.derived(), isHorizontal ? 1 : m_matrix.rows(),
+                                                             isVertical ? 1 : m_matrix.cols());
+  }
+
+ public:
+  EIGEN_DEVICE_FUNC explicit inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
+
+  /** \internal */
+  EIGEN_DEVICE_FUNC inline const ExpressionType& _expression() const { return m_matrix; }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a>
+   * iterator type over the columns or rows as returned by the begin() and end() methods.
+   */
+  random_access_iterator_type iterator;
+  /** This is the const version of iterator (aka read-only) */
+  random_access_iterator_type const_iterator;
+#else
+  typedef internal::subvector_stl_iterator<ExpressionType, DirectionType(Direction)> iterator;
+  typedef internal::subvector_stl_iterator<const ExpressionType, DirectionType(Direction)> const_iterator;
+  typedef internal::subvector_stl_reverse_iterator<ExpressionType, DirectionType(Direction)> reverse_iterator;
+  typedef internal::subvector_stl_reverse_iterator<const ExpressionType, DirectionType(Direction)>
+      const_reverse_iterator;
+#endif
+
+  /** returns an iterator to the first row (rowwise) or column (colwise) of the nested expression.
+   * \sa end(), cbegin()
+   */
+  iterator begin() { return iterator(m_matrix, 0); }
+  /** const version of begin() */
+  const_iterator begin() const { return const_iterator(m_matrix, 0); }
+  /** const version of begin() */
+  const_iterator cbegin() const { return const_iterator(m_matrix, 0); }
+
+  /** returns a reverse iterator to the last row (rowwise) or column (colwise) of the nested expression.
+   * \sa rend(), crbegin()
+   */
+  reverse_iterator rbegin() {
+    return reverse_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>() - 1);
+  }
+  /** const version of rbegin() */
+  const_reverse_iterator rbegin() const {
+    return const_reverse_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>() - 1);
+  }
+  /** const version of rbegin() */
+  const_reverse_iterator crbegin() const {
+    return const_reverse_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>() - 1);
+  }
+
+  /** returns an iterator to the row (resp. column) following the last row (resp. column) of the nested expression
+   * \sa begin(), cend()
+   */
+  iterator end() { return iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
+  /** const version of end() */
+  const_iterator end() const {
+    return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>());
+  }
+  /** const version of end() */
+  const_iterator cend() const {
+    return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>());
+  }
+
+  /** returns a reverse iterator to the row (resp. column) before the first row (resp. column) of the nested expression
+   * \sa begin(), cend()
+   */
+  reverse_iterator rend() { return reverse_iterator(m_matrix, -1); }
+  /** const version of rend() */
+  const_reverse_iterator rend() const { return const_reverse_iterator(m_matrix, -1); }
+  /** const version of rend() */
+  const_reverse_iterator crend() const { return const_reverse_iterator(m_matrix, -1); }
+
+  /** \returns a row or column vector expression of \c *this reduxed by \a func
+   *
+   * The template parameter \a BinaryOp is the type of the functor
+   * of the custom redux operator. Note that func must be an associative operator.
+   *
+   * \warning the size along the reduction direction must be strictly positive,
+   *          otherwise an assertion is triggered.
+   *
+   * \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise()
+   */
+  template <typename BinaryOp>
+  EIGEN_DEVICE_FUNC const typename ReduxReturnType<BinaryOp>::Type redux(const BinaryOp& func = BinaryOp()) const {
+    eigen_assert(redux_length() > 0 && "you are using an empty matrix");
+    return typename ReduxReturnType<BinaryOp>::Type(_expression(), internal::member_redux<BinaryOp, Scalar>(func));
+  }
+
+  typedef typename ReturnType<internal::member_minCoeff>::Type MinCoeffReturnType;
+  typedef typename ReturnType<internal::member_maxCoeff>::Type MaxCoeffReturnType;
+  typedef PartialReduxExpr<const CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const ExpressionTypeNestedCleaned>,
+                           internal::member_sum<RealScalar, RealScalar>, Direction>
+      SquaredNormReturnType;
+  typedef CwiseUnaryOp<internal::scalar_sqrt_op<RealScalar>, const SquaredNormReturnType> NormReturnType;
+  typedef typename ReturnType<internal::member_blueNorm, RealScalar>::Type BlueNormReturnType;
+  typedef typename ReturnType<internal::member_stableNorm, RealScalar>::Type StableNormReturnType;
+  typedef typename ReturnType<internal::member_hypotNorm, RealScalar>::Type HypotNormReturnType;
+  typedef typename ReturnType<internal::member_sum>::Type SumReturnType;
+  typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(SumReturnType, Scalar, quotient) MeanReturnType;
+  typedef typename ReturnType<internal::member_all, bool>::Type AllReturnType;
+  typedef typename ReturnType<internal::member_any, bool>::Type AnyReturnType;
+  typedef PartialReduxExpr<ExpressionType, internal::member_count<Index, Scalar>, Direction> CountReturnType;
+  typedef typename ReturnType<internal::member_prod>::Type ProdReturnType;
+  typedef Reverse<const ExpressionType, Direction> ConstReverseReturnType;
+  typedef Reverse<ExpressionType, Direction> ReverseReturnType;
+
+  template <int p>
+  struct LpNormReturnType {
+    typedef PartialReduxExpr<ExpressionType, internal::member_lpnorm<p, RealScalar, Scalar>, Direction> Type;
+  };
+
+  /** \returns a row (or column) vector expression of the smallest coefficient
+   * of each column (or row) of the referenced expression.
+   *
+   * \warning the size along the reduction direction must be strictly positive,
+   *          otherwise an assertion is triggered.
+   *
+   * \warning the result is undefined if \c *this contains NaN.
+   *
+   * Example: \include PartialRedux_minCoeff.cpp
+   * Output: \verbinclude PartialRedux_minCoeff.out
+   *
+   * \sa DenseBase::minCoeff() */
+  EIGEN_DEVICE_FUNC const MinCoeffReturnType minCoeff() const {
+    eigen_assert(redux_length() > 0 && "you are using an empty matrix");
+    return MinCoeffReturnType(_expression());
+  }
+
+  /** \returns a row (or column) vector expression of the largest coefficient
+   * of each column (or row) of the referenced expression.
+   *
+   * \warning the size along the reduction direction must be strictly positive,
+   *          otherwise an assertion is triggered.
+   *
+   * \warning the result is undefined if \c *this contains NaN.
+   *
+   * Example: \include PartialRedux_maxCoeff.cpp
+   * Output: \verbinclude PartialRedux_maxCoeff.out
+   *
+   * \sa DenseBase::maxCoeff() */
+  EIGEN_DEVICE_FUNC const MaxCoeffReturnType maxCoeff() const {
+    eigen_assert(redux_length() > 0 && "you are using an empty matrix");
+    return MaxCoeffReturnType(_expression());
+  }
+
+  /** \returns a row (or column) vector expression of the squared norm
+   * of each column (or row) of the referenced expression.
+   * This is a vector with real entries, even if the original matrix has complex entries.
+   *
+   * Example: \include PartialRedux_squaredNorm.cpp
+   * Output: \verbinclude PartialRedux_squaredNorm.out
+   *
+   * \sa DenseBase::squaredNorm() */
+  EIGEN_DEVICE_FUNC const SquaredNormReturnType squaredNorm() const {
+    return SquaredNormReturnType(m_matrix.cwiseAbs2());
+  }
+
+  /** \returns a row (or column) vector expression of the norm
+   * of each column (or row) of the referenced expression.
+   * This is a vector with real entries, even if the original matrix has complex entries.
+   *
+   * Example: \include PartialRedux_norm.cpp
+   * Output: \verbinclude PartialRedux_norm.out
+   *
+   * \sa DenseBase::norm() */
+  EIGEN_DEVICE_FUNC const NormReturnType norm() const { return NormReturnType(squaredNorm()); }
+
+  /** \returns a row (or column) vector expression of the norm
+   * of each column (or row) of the referenced expression.
+   * This is a vector with real entries, even if the original matrix has complex entries.
+   *
+   * Example: \include PartialRedux_norm.cpp
+   * Output: \verbinclude PartialRedux_norm.out
+   *
+   * \sa DenseBase::norm() */
+  template <int p>
+  EIGEN_DEVICE_FUNC const typename LpNormReturnType<p>::Type lpNorm() const {
+    return typename LpNormReturnType<p>::Type(_expression());
+  }
+
+  /** \returns a row (or column) vector expression of the norm
+   * of each column (or row) of the referenced expression, using
+   * Blue's algorithm.
+   * This is a vector with real entries, even if the original matrix has complex entries.
+   *
+   * \sa DenseBase::blueNorm() */
+  EIGEN_DEVICE_FUNC const BlueNormReturnType blueNorm() const { return BlueNormReturnType(_expression()); }
+
+  /** \returns a row (or column) vector expression of the norm
+   * of each column (or row) of the referenced expression, avoiding
+   * underflow and overflow.
+   * This is a vector with real entries, even if the original matrix has complex entries.
+   *
+   * \sa DenseBase::stableNorm() */
+  EIGEN_DEVICE_FUNC const StableNormReturnType stableNorm() const { return StableNormReturnType(_expression()); }
+
+  /** \returns a row (or column) vector expression of the norm
+   * of each column (or row) of the referenced expression, avoiding
+   * underflow and overflow using a concatenation of hypot() calls.
+   * This is a vector with real entries, even if the original matrix has complex entries.
+   *
+   * \sa DenseBase::hypotNorm() */
+  EIGEN_DEVICE_FUNC const HypotNormReturnType hypotNorm() const { return HypotNormReturnType(_expression()); }
+
+  /** \returns a row (or column) vector expression of the sum
+   * of each column (or row) of the referenced expression.
+   *
+   * Example: \include PartialRedux_sum.cpp
+   * Output: \verbinclude PartialRedux_sum.out
+   *
+   * \sa DenseBase::sum() */
+  EIGEN_DEVICE_FUNC const SumReturnType sum() const { return SumReturnType(_expression()); }
+
+  /** \returns a row (or column) vector expression of the mean
+   * of each column (or row) of the referenced expression.
+   *
+   * \sa DenseBase::mean() */
+  EIGEN_DEVICE_FUNC const MeanReturnType mean() const {
+    return sum() / Scalar(Direction == Vertical ? m_matrix.rows() : m_matrix.cols());
+  }
+
+  /** \returns a row (or column) vector expression representing
+   * whether \b all coefficients of each respective column (or row) are \c true.
+   * This expression can be assigned to a vector with entries of type \c bool.
+   *
+   * \sa DenseBase::all() */
+  EIGEN_DEVICE_FUNC const AllReturnType all() const { return AllReturnType(_expression()); }
+
+  /** \returns a row (or column) vector expression representing
+   * whether \b at \b least one coefficient of each respective column (or row) is \c true.
+   * This expression can be assigned to a vector with entries of type \c bool.
+   *
+   * \sa DenseBase::any() */
+  EIGEN_DEVICE_FUNC const AnyReturnType any() const { return AnyReturnType(_expression()); }
+
+  /** \returns a row (or column) vector expression representing
+   * the number of \c true coefficients of each respective column (or row).
+   * This expression can be assigned to a vector whose entries have the same type as is used to
+   * index entries of the original matrix; for dense matrices, this is \c std::ptrdiff_t .
+   *
+   * Example: \include PartialRedux_count.cpp
+   * Output: \verbinclude PartialRedux_count.out
+   *
+   * \sa DenseBase::count() */
+  EIGEN_DEVICE_FUNC const CountReturnType count() const { return CountReturnType(_expression()); }
+
+  /** \returns a row (or column) vector expression of the product
+   * of each column (or row) of the referenced expression.
+   *
+   * Example: \include PartialRedux_prod.cpp
+   * Output: \verbinclude PartialRedux_prod.out
+   *
+   * \sa DenseBase::prod() */
+  EIGEN_DEVICE_FUNC const ProdReturnType prod() const { return ProdReturnType(_expression()); }
+
+  /** \returns a matrix expression
+   * where each column (or row) are reversed.
+   *
+   * Example: \include Vectorwise_reverse.cpp
+   * Output: \verbinclude Vectorwise_reverse.out
+   *
+   * \sa DenseBase::reverse() */
+  EIGEN_DEVICE_FUNC const ConstReverseReturnType reverse() const { return ConstReverseReturnType(_expression()); }
+
+  /** \returns a writable matrix expression
+   * where each column (or row) are reversed.
+   *
+   * \sa reverse() const */
+  EIGEN_DEVICE_FUNC ReverseReturnType reverse() { return ReverseReturnType(_expression()); }
+
+  typedef Replicate<ExpressionType, (isVertical ? Dynamic : 1), (isHorizontal ? Dynamic : 1)> ReplicateReturnType;
+  EIGEN_DEVICE_FUNC const ReplicateReturnType replicate(Index factor) const;
+
+  /**
+   * \return an expression of the replication of each column (or row) of \c *this
+   *
+   * Example: \include DirectionWise_replicate.cpp
+   * Output: \verbinclude DirectionWise_replicate.out
+   *
+   * \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate
+   */
+  // NOTE implemented here because of sunstudio's compilation errors
+  // isVertical*Factor+isHorizontal instead of (isVertical?Factor:1) to handle CUDA bug with ternary operator
+  template <int Factor>
+  const Replicate<ExpressionType, isVertical * Factor + isHorizontal,
+                  isHorizontal * Factor + isVertical> EIGEN_DEVICE_FUNC
+  replicate(Index factor = Factor) const {
+    return Replicate<ExpressionType, (isVertical ? Factor : 1), (isHorizontal ? Factor : 1)>(
+        _expression(), isVertical ? factor : 1, isHorizontal ? factor : 1);
+  }
+
+  /////////// Artithmetic operators ///////////
+
+  /** Copies the vector \a other to each subvector of \c *this */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC ExpressionType& operator=(const DenseBase<OtherDerived>& other) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+    EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+    // eigen_assert((m_matrix.isNull()) == (other.isNull())); FIXME
+    return m_matrix = extendedTo(other.derived());
+  }
+
+  /** Adds the vector \a other to each subvector of \c *this */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC ExpressionType& operator+=(const DenseBase<OtherDerived>& other) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+    EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+    return m_matrix += extendedTo(other.derived());
+  }
+
+  /** Subtracts the vector \a other to each subvector of \c *this */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC ExpressionType& operator-=(const DenseBase<OtherDerived>& other) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+    EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+    return m_matrix -= extendedTo(other.derived());
+  }
+
+  /** Multiplies each subvector of \c *this by the vector \a other */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC ExpressionType& operator*=(const DenseBase<OtherDerived>& other) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+    EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
+    EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+    m_matrix *= extendedTo(other.derived());
+    return m_matrix;
+  }
+
+  /** Divides each subvector of \c *this by the vector \a other */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC ExpressionType& operator/=(const DenseBase<OtherDerived>& other) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+    EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
+    EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+    m_matrix /= extendedTo(other.derived());
+    return m_matrix;
+  }
+
+  /** Returns the expression of the sum of the vector \a other to each subvector of \c *this */
+  template <typename OtherDerived>
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+      CwiseBinaryOp<internal::scalar_sum_op<Scalar, typename OtherDerived::Scalar>, const ExpressionTypeNestedCleaned,
+                    const typename ExtendedType<OtherDerived>::Type>
+      operator+(const DenseBase<OtherDerived>& other) const {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+    EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+    return m_matrix + extendedTo(other.derived());
+  }
+
+  /** Returns the expression of the difference between each subvector of \c *this and the vector \a other */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC CwiseBinaryOp<internal::scalar_difference_op<Scalar, typename OtherDerived::Scalar>,
+                                  const ExpressionTypeNestedCleaned, const typename ExtendedType<OtherDerived>::Type>
+  operator-(const DenseBase<OtherDerived>& other) const {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+    EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+    return m_matrix - extendedTo(other.derived());
+  }
+
+  /** Returns the expression where each subvector is the product of the vector \a other
+   * by the corresponding subvector of \c *this */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC CwiseBinaryOp<internal::scalar_product_op<Scalar, typename OtherDerived::Scalar>,
+                                  const ExpressionTypeNestedCleaned, const typename ExtendedType<OtherDerived>::Type>
+  operator*(const DenseBase<OtherDerived>& other) const {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+    EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
+    EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+    return m_matrix * extendedTo(other.derived());
+  }
+
+  /** Returns the expression where each subvector is the quotient of the corresponding
+   * subvector of \c *this by the vector \a other */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC CwiseBinaryOp<internal::scalar_quotient_op<Scalar, typename OtherDerived::Scalar>,
+                                  const ExpressionTypeNestedCleaned, const typename ExtendedType<OtherDerived>::Type>
+  operator/(const DenseBase<OtherDerived>& other) const {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+    EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
+    EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+    return m_matrix / extendedTo(other.derived());
+  }
+
+  using Normalized_NonzeroNormType =
+      CwiseUnaryOp<internal::scalar_replace_zero_with_one_op<Scalar>, const NormReturnType>;
+  using NormalizedReturnType = CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const ExpressionTypeNestedCleaned,
+                                             const typename OppositeExtendedType<Normalized_NonzeroNormType>::Type>;
+
+  /** \returns an expression where each column (or row) of the referenced matrix are normalized.
+   * The referenced matrix is \b not modified.
+   *
+   * \warning If the input columns (or rows) are too small (i.e., their norm equals to 0), they remain unchanged in the
+   *          resulting expression.
+   *
+   * \sa MatrixBase::normalized(), normalize()
+   */
+  EIGEN_DEVICE_FUNC NormalizedReturnType normalized() const {
+    return m_matrix.cwiseQuotient(extendedToOpposite(Normalized_NonzeroNormType(this->norm())));
+  }
+
+  /** Normalize in-place each row or columns of the referenced matrix.
+   *
+   * \warning If the input columns (or rows) are too small (i.e., their norm equals to 0), they are left unchanged.
+   *
+   * \sa MatrixBase::normalized(), normalize()
+   */
+  EIGEN_DEVICE_FUNC void normalize() { m_matrix = this->normalized(); }
+
+  EIGEN_DEVICE_FUNC inline void reverseInPlace();
+
+  /////////// Geometry module ///////////
+
+  typedef Homogeneous<ExpressionType, Direction> HomogeneousReturnType;
+  EIGEN_DEVICE_FUNC HomogeneousReturnType homogeneous() const;
+
+  typedef typename ExpressionType::PlainObject CrossReturnType;
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const;
+
+  enum {
+    HNormalized_Size = Direction == Vertical ? internal::traits<ExpressionType>::RowsAtCompileTime
+                                             : internal::traits<ExpressionType>::ColsAtCompileTime,
+    HNormalized_SizeMinusOne = HNormalized_Size == Dynamic ? Dynamic : HNormalized_Size - 1
+  };
+  typedef Block<const ExpressionType,
+                Direction == Vertical ? int(HNormalized_SizeMinusOne)
+                                      : int(internal::traits<ExpressionType>::RowsAtCompileTime),
+                Direction == Horizontal ? int(HNormalized_SizeMinusOne)
+                                        : int(internal::traits<ExpressionType>::ColsAtCompileTime)>
+      HNormalized_Block;
+  typedef Block<const ExpressionType,
+                Direction == Vertical ? 1 : int(internal::traits<ExpressionType>::RowsAtCompileTime),
+                Direction == Horizontal ? 1 : int(internal::traits<ExpressionType>::ColsAtCompileTime)>
+      HNormalized_Factors;
+  typedef CwiseBinaryOp<internal::scalar_quotient_op<typename internal::traits<ExpressionType>::Scalar>,
+                        const HNormalized_Block,
+                        const Replicate<HNormalized_Factors, Direction == Vertical ? HNormalized_SizeMinusOne : 1,
+                                        Direction == Horizontal ? HNormalized_SizeMinusOne : 1> >
+      HNormalizedReturnType;
+
+  EIGEN_DEVICE_FUNC const HNormalizedReturnType hnormalized() const;
+
+#ifdef EIGEN_VECTORWISEOP_PLUGIN
+#include EIGEN_VECTORWISEOP_PLUGIN
+#endif
+
+ protected:
+  EIGEN_DEVICE_FUNC Index redux_length() const { return Direction == Vertical ? m_matrix.rows() : m_matrix.cols(); }
+  ExpressionTypeNested m_matrix;
+};
+
+// const colwise moved to DenseBase.h due to CUDA compiler bug
+
+/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
+ *
+ * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::ColwiseReturnType DenseBase<Derived>::colwise() {
+  return ColwiseReturnType(derived());
+}
+
+// const rowwise moved to DenseBase.h due to CUDA compiler bug
+
+/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
+ *
+ * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::RowwiseReturnType DenseBase<Derived>::rowwise() {
+  return RowwiseReturnType(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PARTIAL_REDUX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Visitor.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Visitor.h
new file mode 100644
index 0000000..e1d2ca5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/Visitor.h
@@ -0,0 +1,537 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_VISITOR_H
+#define EIGEN_VISITOR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Visitor, typename Derived, int UnrollCount,
+          bool Vectorize = (Derived::PacketAccess && functor_traits<Visitor>::PacketAccess), bool LinearAccess = false,
+          bool ShortCircuitEvaluation = false>
+struct visitor_impl;
+
+template <typename Visitor, bool ShortCircuitEvaluation = false>
+struct short_circuit_eval_impl {
+  // if short circuit evaluation is not used, do nothing
+  static constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(const Visitor&) { return false; }
+};
+template <typename Visitor>
+struct short_circuit_eval_impl<Visitor, true> {
+  // if short circuit evaluation is used, check the visitor
+  static constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(const Visitor& visitor) { return visitor.done(); }
+};
+
+// unrolled inner-outer traversal
+template <typename Visitor, typename Derived, int UnrollCount, bool Vectorize, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, UnrollCount, Vectorize, false, ShortCircuitEvaluation> {
+  // don't use short circuit evaluation for unrolled version
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  static constexpr bool RowMajor = Derived::IsRowMajor;
+  static constexpr int RowsAtCompileTime = Derived::RowsAtCompileTime;
+  static constexpr int ColsAtCompileTime = Derived::ColsAtCompileTime;
+  static constexpr int PacketSize = packet_traits<Scalar>::size;
+
+  static constexpr bool CanVectorize(int K) {
+    constexpr int InnerSizeAtCompileTime = RowMajor ? ColsAtCompileTime : RowsAtCompileTime;
+    if (InnerSizeAtCompileTime < PacketSize) return false;
+    return Vectorize && (InnerSizeAtCompileTime - (K % InnerSizeAtCompileTime) >= PacketSize);
+  }
+
+  template <int K = 0, bool Empty = (K == UnrollCount), std::enable_if_t<Empty, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived&, Visitor&) {}
+
+  template <int K = 0, bool Empty = (K == UnrollCount), bool Initialize = (K == 0), bool DoVectorOp = CanVectorize(K),
+            std::enable_if_t<!Empty && Initialize && !DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    visitor.init(mat.coeff(0, 0), 0, 0);
+    run<1>(mat, visitor);
+  }
+
+  template <int K = 0, bool Empty = (K == UnrollCount), bool Initialize = (K == 0), bool DoVectorOp = CanVectorize(K),
+            std::enable_if_t<!Empty && !Initialize && !DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    static constexpr int R = RowMajor ? (K / ColsAtCompileTime) : (K % RowsAtCompileTime);
+    static constexpr int C = RowMajor ? (K % ColsAtCompileTime) : (K / RowsAtCompileTime);
+    visitor(mat.coeff(R, C), R, C);
+    run<K + 1>(mat, visitor);
+  }
+
+  template <int K = 0, bool Empty = (K == UnrollCount), bool Initialize = (K == 0), bool DoVectorOp = CanVectorize(K),
+            std::enable_if_t<!Empty && Initialize && DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    Packet P = mat.template packet<Packet>(0, 0);
+    visitor.initpacket(P, 0, 0);
+    run<PacketSize>(mat, visitor);
+  }
+
+  template <int K = 0, bool Empty = (K == UnrollCount), bool Initialize = (K == 0), bool DoVectorOp = CanVectorize(K),
+            std::enable_if_t<!Empty && !Initialize && DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    static constexpr int R = RowMajor ? (K / ColsAtCompileTime) : (K % RowsAtCompileTime);
+    static constexpr int C = RowMajor ? (K % ColsAtCompileTime) : (K / RowsAtCompileTime);
+    Packet P = mat.template packet<Packet>(R, C);
+    visitor.packet(P, R, C);
+    run<K + PacketSize>(mat, visitor);
+  }
+};
+
+// unrolled linear traversal
+template <typename Visitor, typename Derived, int UnrollCount, bool Vectorize, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, UnrollCount, Vectorize, true, ShortCircuitEvaluation> {
+  // don't use short circuit evaluation for unrolled version
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  static constexpr int PacketSize = packet_traits<Scalar>::size;
+
+  static constexpr bool CanVectorize(int K) { return Vectorize && ((UnrollCount - K) >= PacketSize); }
+
+  // empty
+  template <int K = 0, bool Empty = (K == UnrollCount), std::enable_if_t<Empty, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived&, Visitor&) {}
+
+  // scalar initialization
+  template <int K = 0, bool Empty = (K == UnrollCount), bool Initialize = (K == 0), bool DoVectorOp = CanVectorize(K),
+            std::enable_if_t<!Empty && Initialize && !DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    visitor.init(mat.coeff(0), 0);
+    run<1>(mat, visitor);
+  }
+
+  // scalar iteration
+  template <int K = 0, bool Empty = (K == UnrollCount), bool Initialize = (K == 0), bool DoVectorOp = CanVectorize(K),
+            std::enable_if_t<!Empty && !Initialize && !DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    visitor(mat.coeff(K), K);
+    run<K + 1>(mat, visitor);
+  }
+
+  // vector initialization
+  template <int K = 0, bool Empty = (K == UnrollCount), bool Initialize = (K == 0), bool DoVectorOp = CanVectorize(K),
+            std::enable_if_t<!Empty && Initialize && DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    Packet P = mat.template packet<Packet>(0);
+    visitor.initpacket(P, 0);
+    run<PacketSize>(mat, visitor);
+  }
+
+  // vector iteration
+  template <int K = 0, bool Empty = (K == UnrollCount), bool Initialize = (K == 0), bool DoVectorOp = CanVectorize(K),
+            std::enable_if_t<!Empty && !Initialize && DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    Packet P = mat.template packet<Packet>(K);
+    visitor.packet(P, K);
+    run<K + PacketSize>(mat, visitor);
+  }
+};
+
+// dynamic scalar outer-inner traversal
+template <typename Visitor, typename Derived, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, Dynamic, /*Vectorize=*/false, /*LinearAccess=*/false, ShortCircuitEvaluation> {
+  using short_circuit = short_circuit_eval_impl<Visitor, ShortCircuitEvaluation>;
+  static constexpr bool RowMajor = Derived::IsRowMajor;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    const Index innerSize = RowMajor ? mat.cols() : mat.rows();
+    const Index outerSize = RowMajor ? mat.rows() : mat.cols();
+    if (innerSize == 0 || outerSize == 0) return;
+    {
+      visitor.init(mat.coeff(0, 0), 0, 0);
+      if (short_circuit::run(visitor)) return;
+      for (Index i = 1; i < innerSize; ++i) {
+        Index r = RowMajor ? 0 : i;
+        Index c = RowMajor ? i : 0;
+        visitor(mat.coeff(r, c), r, c);
+        if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+      }
+    }
+    for (Index j = 1; j < outerSize; j++) {
+      for (Index i = 0; i < innerSize; ++i) {
+        Index r = RowMajor ? j : i;
+        Index c = RowMajor ? i : j;
+        visitor(mat.coeff(r, c), r, c);
+        if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+      }
+    }
+  }
+};
+
+// dynamic vectorized outer-inner traversal
+template <typename Visitor, typename Derived, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, Dynamic, /*Vectorize=*/true, /*LinearAccess=*/false, ShortCircuitEvaluation> {
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  static constexpr int PacketSize = packet_traits<Scalar>::size;
+  using short_circuit = short_circuit_eval_impl<Visitor, ShortCircuitEvaluation>;
+  static constexpr bool RowMajor = Derived::IsRowMajor;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    const Index innerSize = RowMajor ? mat.cols() : mat.rows();
+    const Index outerSize = RowMajor ? mat.rows() : mat.cols();
+    if (innerSize == 0 || outerSize == 0) return;
+    {
+      Index i = 0;
+      if (innerSize < PacketSize) {
+        visitor.init(mat.coeff(0, 0), 0, 0);
+        i = 1;
+      } else {
+        Packet p = mat.template packet<Packet>(0, 0);
+        visitor.initpacket(p, 0, 0);
+        i = PacketSize;
+      }
+      if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+      for (; i + PacketSize - 1 < innerSize; i += PacketSize) {
+        Index r = RowMajor ? 0 : i;
+        Index c = RowMajor ? i : 0;
+        Packet p = mat.template packet<Packet>(r, c);
+        visitor.packet(p, r, c);
+        if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+      }
+      for (; i < innerSize; ++i) {
+        Index r = RowMajor ? 0 : i;
+        Index c = RowMajor ? i : 0;
+        visitor(mat.coeff(r, c), r, c);
+        if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+      }
+    }
+    for (Index j = 1; j < outerSize; j++) {
+      Index i = 0;
+      for (; i + PacketSize - 1 < innerSize; i += PacketSize) {
+        Index r = RowMajor ? j : i;
+        Index c = RowMajor ? i : j;
+        Packet p = mat.template packet<Packet>(r, c);
+        visitor.packet(p, r, c);
+        if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+      }
+      for (; i < innerSize; ++i) {
+        Index r = RowMajor ? j : i;
+        Index c = RowMajor ? i : j;
+        visitor(mat.coeff(r, c), r, c);
+        if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+      }
+    }
+  }
+};
+
+// dynamic scalar linear traversal
+template <typename Visitor, typename Derived, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, Dynamic, /*Vectorize=*/false, /*LinearAccess=*/true, ShortCircuitEvaluation> {
+  using short_circuit = short_circuit_eval_impl<Visitor, ShortCircuitEvaluation>;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    const Index size = mat.size();
+    if (size == 0) return;
+    visitor.init(mat.coeff(0), 0);
+    if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+    for (Index k = 1; k < size; k++) {
+      visitor(mat.coeff(k), k);
+      if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+    }
+  }
+};
+
+// dynamic vectorized linear traversal
+template <typename Visitor, typename Derived, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, Dynamic, /*Vectorize=*/true, /*LinearAccess=*/true, ShortCircuitEvaluation> {
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  static constexpr int PacketSize = packet_traits<Scalar>::size;
+  using short_circuit = short_circuit_eval_impl<Visitor, ShortCircuitEvaluation>;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    const Index size = mat.size();
+    if (size == 0) return;
+    Index k = 0;
+    if (size < PacketSize) {
+      visitor.init(mat.coeff(0), 0);
+      k = 1;
+    } else {
+      Packet p = mat.template packet<Packet>(k);
+      visitor.initpacket(p, k);
+      k = PacketSize;
+    }
+    if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+    for (; k + PacketSize - 1 < size; k += PacketSize) {
+      Packet p = mat.template packet<Packet>(k);
+      visitor.packet(p, k);
+      if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+    }
+    for (; k < size; k++) {
+      visitor(mat.coeff(k), k);
+      if EIGEN_PREDICT_FALSE (short_circuit::run(visitor)) return;
+    }
+  }
+};
+
+// evaluator adaptor
+template <typename XprType>
+class visitor_evaluator {
+ public:
+  typedef evaluator<XprType> Evaluator;
+  typedef typename XprType::Scalar Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
+
+  static constexpr bool PacketAccess = static_cast<bool>(Evaluator::Flags & PacketAccessBit);
+  static constexpr bool LinearAccess = static_cast<bool>(Evaluator::Flags & LinearAccessBit);
+  static constexpr bool IsRowMajor = static_cast<bool>(XprType::IsRowMajor);
+  static constexpr int RowsAtCompileTime = XprType::RowsAtCompileTime;
+  static constexpr int ColsAtCompileTime = XprType::ColsAtCompileTime;
+  static constexpr int XprAlignment = Evaluator::Alignment;
+  static constexpr int CoeffReadCost = Evaluator::CoeffReadCost;
+
+  EIGEN_DEVICE_FUNC explicit visitor_evaluator(const XprType& xpr) : m_evaluator(xpr), m_xpr(xpr) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_xpr.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_xpr.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index size() const noexcept { return m_xpr.size(); }
+  // outer-inner access
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    return m_evaluator.coeff(row, col);
+  }
+  template <typename Packet, int Alignment = Unaligned>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packet(Index row, Index col) const {
+    return m_evaluator.template packet<Alignment, Packet>(row, col);
+  }
+  // linear access
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_evaluator.coeff(index); }
+  template <typename Packet, int Alignment = XprAlignment>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packet(Index index) const {
+    return m_evaluator.template packet<Alignment, Packet>(index);
+  }
+
+ protected:
+  Evaluator m_evaluator;
+  const XprType& m_xpr;
+};
+
+template <typename Derived, typename Visitor, bool ShortCircuitEvaulation>
+struct visit_impl {
+  using Evaluator = visitor_evaluator<Derived>;
+  using Scalar = typename DenseBase<Derived>::Scalar;
+
+  static constexpr bool IsRowMajor = DenseBase<Derived>::IsRowMajor;
+  static constexpr int SizeAtCompileTime = DenseBase<Derived>::SizeAtCompileTime;
+  static constexpr int RowsAtCompileTime = DenseBase<Derived>::RowsAtCompileTime;
+  static constexpr int ColsAtCompileTime = DenseBase<Derived>::ColsAtCompileTime;
+  static constexpr int InnerSizeAtCompileTime = IsRowMajor ? ColsAtCompileTime : RowsAtCompileTime;
+  static constexpr int OuterSizeAtCompileTime = IsRowMajor ? RowsAtCompileTime : ColsAtCompileTime;
+
+  static constexpr bool LinearAccess =
+      Evaluator::LinearAccess && static_cast<bool>(functor_traits<Visitor>::LinearAccess);
+  static constexpr bool Vectorize = Evaluator::PacketAccess && static_cast<bool>(functor_traits<Visitor>::PacketAccess);
+
+  static constexpr int PacketSize = packet_traits<Scalar>::size;
+  static constexpr int VectorOps =
+      Vectorize ? (LinearAccess ? (SizeAtCompileTime / PacketSize)
+                                : (OuterSizeAtCompileTime * (InnerSizeAtCompileTime / PacketSize)))
+                : 0;
+  static constexpr int ScalarOps = SizeAtCompileTime - (VectorOps * PacketSize);
+  // treat vector op and scalar op as same cost for unroll logic
+  static constexpr int TotalOps = VectorOps + ScalarOps;
+
+  static constexpr int UnrollCost = int(Evaluator::CoeffReadCost) + int(functor_traits<Visitor>::Cost);
+  static constexpr bool Unroll = (SizeAtCompileTime != Dynamic) && ((TotalOps * UnrollCost) <= EIGEN_UNROLLING_LIMIT);
+  static constexpr int UnrollCount = Unroll ? int(SizeAtCompileTime) : Dynamic;
+
+  using impl = visitor_impl<Visitor, Evaluator, UnrollCount, Vectorize, LinearAccess, ShortCircuitEvaulation>;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const DenseBase<Derived>& mat, Visitor& visitor) {
+    Evaluator evaluator(mat.derived());
+    impl::run(evaluator, visitor);
+  }
+};
+
+}  // end namespace internal
+
+/** Applies the visitor \a visitor to the whole coefficients of the matrix or vector.
+ *
+ * The template parameter \a Visitor is the type of the visitor and provides the following interface:
+ * \code
+ * struct MyVisitor {
+ *   // called for the first coefficient
+ *   void init(const Scalar& value, Index i, Index j);
+ *   // called for all other coefficients
+ *   void operator() (const Scalar& value, Index i, Index j);
+ * };
+ * \endcode
+ *
+ * \note compared to one or two \em for \em loops, visitors offer automatic
+ * unrolling for small fixed size matrix.
+ *
+ * \note if the matrix is empty, then the visitor is left unchanged.
+ *
+ * \sa minCoeff(Index*,Index*), maxCoeff(Index*,Index*), DenseBase::redux()
+ */
+template <typename Derived>
+template <typename Visitor>
+EIGEN_DEVICE_FUNC void DenseBase<Derived>::visit(Visitor& visitor) const {
+  using impl = internal::visit_impl<Derived, Visitor, /*ShortCircuitEvaulation*/ false>;
+  impl::run(derived(), visitor);
+}
+
+namespace internal {
+
+template <typename Scalar>
+struct all_visitor {
+  using result_type = bool;
+  using Packet = typename packet_traits<Scalar>::type;
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index, Index) { res = (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index) { res = (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline bool all_predux(const Packet& p) const { return !predux_any(pcmp_eq(p, pzero(p))); }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index, Index) { res = all_predux(p); }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index) { res = all_predux(p); }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index, Index) { res = res && (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index) { res = res && (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index, Index) { res = res && all_predux(p); }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index) { res = res && all_predux(p); }
+  EIGEN_DEVICE_FUNC inline bool done() const { return !res; }
+  bool res = true;
+};
+template <typename Scalar>
+struct functor_traits<all_visitor<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::ReadCost, LinearAccess = true, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+template <typename Scalar>
+struct any_visitor {
+  using result_type = bool;
+  using Packet = typename packet_traits<Scalar>::type;
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index, Index) { res = (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index) { res = (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline bool any_predux(const Packet& p) const {
+    return predux_any(pandnot(ptrue(p), pcmp_eq(p, pzero(p))));
+  }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index, Index) { res = any_predux(p); }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index) { res = any_predux(p); }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index, Index) { res = res || (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index) { res = res || (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index, Index) { res = res || any_predux(p); }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index) { res = res || any_predux(p); }
+  EIGEN_DEVICE_FUNC inline bool done() const { return res; }
+  bool res = false;
+};
+template <typename Scalar>
+struct functor_traits<any_visitor<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::ReadCost, LinearAccess = true, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+template <typename Scalar>
+struct count_visitor {
+  using result_type = Index;
+  using Packet = typename packet_traits<Scalar>::type;
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index, Index) { res = value != Scalar(0) ? 1 : 0; }
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index) { res = value != Scalar(0) ? 1 : 0; }
+  EIGEN_DEVICE_FUNC inline Index count_redux(const Packet& p) const {
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    Packet true_vals = pandnot(cst_one, pcmp_eq(p, pzero(p)));
+    Scalar num_true = predux(true_vals);
+    return static_cast<Index>(num_true);
+  }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index, Index) { res = count_redux(p); }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index) { res = count_redux(p); }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index, Index) {
+    if (value != Scalar(0)) res++;
+  }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index) {
+    if (value != Scalar(0)) res++;
+  }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index, Index) { res += count_redux(p); }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index) { res += count_redux(p); }
+  Index res = 0;
+};
+
+template <typename Scalar>
+struct functor_traits<count_visitor<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    LinearAccess = true,
+    // predux is problematic for bool
+    PacketAccess = packet_traits<Scalar>::HasCmp && packet_traits<Scalar>::HasAdd && !is_same<Scalar, bool>::value
+  };
+};
+
+template <typename Derived, bool AlwaysTrue = NumTraits<typename traits<Derived>::Scalar>::IsInteger>
+struct all_finite_impl {
+  static EIGEN_DEVICE_FUNC inline bool run(const Derived& /*derived*/) { return true; }
+};
+#if !defined(__FINITE_MATH_ONLY__) || !(__FINITE_MATH_ONLY__)
+template <typename Derived>
+struct all_finite_impl<Derived, false> {
+  static EIGEN_DEVICE_FUNC inline bool run(const Derived& derived) { return derived.array().isFiniteTyped().all(); }
+};
+#endif
+
+}  // end namespace internal
+
+/** \returns true if all coefficients are true
+ *
+ * Example: \include MatrixBase_all.cpp
+ * Output: \verbinclude MatrixBase_all.out
+ *
+ * \sa any(), Cwise::operator<()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::all() const {
+  using Visitor = internal::all_visitor<Scalar>;
+  using impl = internal::visit_impl<Derived, Visitor, /*ShortCircuitEvaulation*/ true>;
+  Visitor visitor;
+  impl::run(derived(), visitor);
+  return visitor.res;
+}
+
+/** \returns true if at least one coefficient is true
+ *
+ * \sa all()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::any() const {
+  using Visitor = internal::any_visitor<Scalar>;
+  using impl = internal::visit_impl<Derived, Visitor, /*ShortCircuitEvaulation*/ true>;
+  Visitor visitor;
+  impl::run(derived(), visitor);
+  return visitor.res;
+}
+
+/** \returns the number of coefficients which evaluate to true
+ *
+ * \sa all(), any()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC Index DenseBase<Derived>::count() const {
+  using Visitor = internal::count_visitor<Scalar>;
+  using impl = internal::visit_impl<Derived, Visitor, /*ShortCircuitEvaulation*/ false>;
+  Visitor visitor;
+  impl::run(derived(), visitor);
+  return visitor.res;
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::hasNaN() const {
+  return derived().cwiseTypedNotEqual(derived()).any();
+}
+
+/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values.
+ *
+ * \sa hasNaN()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::allFinite() const {
+  return internal::all_finite_impl<Derived>::run(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_VISITOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/Complex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/Complex.h
new file mode 100644
index 0000000..f891e0b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/Complex.h
@@ -0,0 +1,580 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_AVX_H
+#define EIGEN_COMPLEX_AVX_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- float ----------
+struct Packet4cf {
+  EIGEN_STRONG_INLINE Packet4cf() {}
+  EIGEN_STRONG_INLINE explicit Packet4cf(const __m256& a) : v(a) {}
+  __m256 v;
+};
+
+#ifndef EIGEN_VECTORIZE_AVX512
+template <>
+struct packet_traits<std::complex<float> > : default_packet_traits {
+  typedef Packet4cf type;
+  typedef Packet2cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet4cf> {
+  typedef std::complex<float> type;
+  typedef Packet2cf half;
+  typedef Packet8f as_real;
+  enum {
+    size = 4,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf padd<Packet4cf>(const Packet4cf& a, const Packet4cf& b) {
+  return Packet4cf(_mm256_add_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf psub<Packet4cf>(const Packet4cf& a, const Packet4cf& b) {
+  return Packet4cf(_mm256_sub_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pnegate(const Packet4cf& a) {
+  return Packet4cf(pnegate(a.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pconj(const Packet4cf& a) {
+  const __m256 mask = _mm256_castsi256_ps(_mm256_setr_epi32(0x00000000, SIGN_MASK_I32, 0x00000000, SIGN_MASK_I32,
+                                                            0x00000000, SIGN_MASK_I32, 0x00000000, SIGN_MASK_I32));
+  return Packet4cf(_mm256_xor_ps(a.v, mask));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf pmul(const Packet4cf& a, const Packet4cf& b) {
+  __m256 tmp1 = _mm256_mul_ps(_mm256_movehdup_ps(a.v), _mm256_permute_ps(b.v, _MM_SHUFFLE(2, 3, 0, 1)));
+  __m256 tmp2 = _mm256_moveldup_ps(a.v);
+#ifdef EIGEN_VECTORIZE_FMA
+  __m256 result = _mm256_fmaddsub_ps(tmp2, b.v, tmp1);
+#else
+  __m256 result = _mm256_addsub_ps(_mm256_mul_ps(tmp2, b.v), tmp1);
+#endif
+  return Packet4cf(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf pcmp_eq(const Packet4cf& a, const Packet4cf& b) {
+  __m256 eq = _mm256_cmp_ps(a.v, b.v, _CMP_EQ_OQ);
+  return Packet4cf(_mm256_and_ps(eq, _mm256_permute_ps(eq, 0xb1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf ptrue<Packet4cf>(const Packet4cf& a) {
+  return Packet4cf(ptrue(Packet8f(a.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pand<Packet4cf>(const Packet4cf& a, const Packet4cf& b) {
+  return Packet4cf(_mm256_and_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf por<Packet4cf>(const Packet4cf& a, const Packet4cf& b) {
+  return Packet4cf(_mm256_or_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pxor<Packet4cf>(const Packet4cf& a, const Packet4cf& b) {
+  return Packet4cf(_mm256_xor_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pandnot<Packet4cf>(const Packet4cf& a, const Packet4cf& b) {
+  return Packet4cf(_mm256_andnot_ps(b.v, a.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf pload<Packet4cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet4cf(_mm256_load_ps(&numext::real_ref(*from)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf ploadu<Packet4cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet4cf(_mm256_loadu_ps(&numext::real_ref(*from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf pset1<Packet4cf>(const std::complex<float>& from) {
+  const float re = std::real(from);
+  const float im = std::imag(from);
+  return Packet4cf(_mm256_set_ps(im, re, im, re, im, re, im, re));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf ploaddup<Packet4cf>(const std::complex<float>* from) {
+  // FIXME The following might be optimized using _mm256_movedup_pd
+  Packet2cf a = ploaddup<Packet2cf>(from);
+  Packet2cf b = ploaddup<Packet2cf>(from + 1);
+  return Packet4cf(_mm256_insertf128_ps(_mm256_castps128_ps256(a.v), b.v, 1));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<float> >(std::complex<float>* to, const Packet4cf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_ps(&numext::real_ref(*to), from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet4cf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_ps(&numext::real_ref(*to), from.v);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4cf pgather<std::complex<float>, Packet4cf>(const std::complex<float>* from,
+                                                                           Index stride) {
+  return Packet4cf(_mm256_set_ps(std::imag(from[3 * stride]), std::real(from[3 * stride]), std::imag(from[2 * stride]),
+                                 std::real(from[2 * stride]), std::imag(from[1 * stride]), std::real(from[1 * stride]),
+                                 std::imag(from[0 * stride]), std::real(from[0 * stride])));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet4cf>(std::complex<float>* to, const Packet4cf& from,
+                                                                       Index stride) {
+  __m128 low = _mm256_extractf128_ps(from.v, 0);
+  to[stride * 0] =
+      std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 0)), _mm_cvtss_f32(_mm_shuffle_ps(low, low, 1)));
+  to[stride * 1] =
+      std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 2)), _mm_cvtss_f32(_mm_shuffle_ps(low, low, 3)));
+
+  __m128 high = _mm256_extractf128_ps(from.v, 1);
+  to[stride * 2] =
+      std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 0)), _mm_cvtss_f32(_mm_shuffle_ps(high, high, 1)));
+  to[stride * 3] =
+      std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 2)), _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3)));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet4cf>(const Packet4cf& a) {
+  return pfirst(Packet2cf(_mm256_castps256_ps128(a.v)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf preverse(const Packet4cf& a) {
+  __m128 low = _mm256_extractf128_ps(a.v, 0);
+  __m128 high = _mm256_extractf128_ps(a.v, 1);
+  __m128d lowd = _mm_castps_pd(low);
+  __m128d highd = _mm_castps_pd(high);
+  low = _mm_castpd_ps(_mm_shuffle_pd(lowd, lowd, 0x1));
+  high = _mm_castpd_ps(_mm_shuffle_pd(highd, highd, 0x1));
+  __m256 result = _mm256_setzero_ps();
+  result = _mm256_insertf128_ps(result, low, 1);
+  result = _mm256_insertf128_ps(result, high, 0);
+  return Packet4cf(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet4cf>(const Packet4cf& a) {
+  return predux(padd(Packet2cf(_mm256_extractf128_ps(a.v, 0)), Packet2cf(_mm256_extractf128_ps(a.v, 1))));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet4cf>(const Packet4cf& a) {
+  return predux_mul(pmul(Packet2cf(_mm256_extractf128_ps(a.v, 0)), Packet2cf(_mm256_extractf128_ps(a.v, 1))));
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cf, Packet8f)
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf pdiv<Packet4cf>(const Packet4cf& a, const Packet4cf& b) {
+  return pdiv_complex(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf pcplxflip<Packet4cf>(const Packet4cf& x) {
+  return Packet4cf(_mm256_shuffle_ps(x.v, x.v, _MM_SHUFFLE(2, 3, 0, 1)));
+}
+
+//---------- double ----------
+struct Packet2cd {
+  EIGEN_STRONG_INLINE Packet2cd() {}
+  EIGEN_STRONG_INLINE explicit Packet2cd(const __m256d& a) : v(a) {}
+  __m256d v;
+};
+
+#ifndef EIGEN_VECTORIZE_AVX512
+template <>
+struct packet_traits<std::complex<double> > : default_packet_traits {
+  typedef Packet2cd type;
+  typedef Packet1cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 2,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet2cd> {
+  typedef std::complex<double> type;
+  typedef Packet1cd half;
+  typedef Packet4d as_real;
+  enum {
+    size = 2,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd padd<Packet2cd>(const Packet2cd& a, const Packet2cd& b) {
+  return Packet2cd(_mm256_add_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd psub<Packet2cd>(const Packet2cd& a, const Packet2cd& b) {
+  return Packet2cd(_mm256_sub_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pnegate(const Packet2cd& a) {
+  return Packet2cd(pnegate(a.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pconj(const Packet2cd& a) {
+  const __m256d mask =
+      _mm256_castsi256_pd(_mm256_set_epi32(SIGN_MASK_I32, 0x0, 0x0, 0x0, SIGN_MASK_I32, 0x0, 0x0, 0x0));
+  return Packet2cd(_mm256_xor_pd(a.v, mask));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd pmul(const Packet2cd& a, const Packet2cd& b) {
+  __m256d tmp1 = _mm256_mul_pd(_mm256_permute_pd(a.v, 0xF), _mm256_permute_pd(b.v, 0x5));
+  __m256d tmp2 = _mm256_movedup_pd(a.v);
+#ifdef EIGEN_VECTORIZE_FMA
+  __m256d result = _mm256_fmaddsub_pd(tmp2, b.v, tmp1);
+#else
+  __m256d result = _mm256_addsub_pd(_mm256_mul_pd(tmp2, b.v), tmp1);
+#endif
+  return Packet2cd(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd pcmp_eq(const Packet2cd& a, const Packet2cd& b) {
+  __m256d eq = _mm256_cmp_pd(a.v, b.v, _CMP_EQ_OQ);
+  return Packet2cd(pand(eq, _mm256_permute_pd(eq, 0x5)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd ptrue<Packet2cd>(const Packet2cd& a) {
+  return Packet2cd(ptrue(Packet4d(a.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pand<Packet2cd>(const Packet2cd& a, const Packet2cd& b) {
+  return Packet2cd(_mm256_and_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd por<Packet2cd>(const Packet2cd& a, const Packet2cd& b) {
+  return Packet2cd(_mm256_or_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pxor<Packet2cd>(const Packet2cd& a, const Packet2cd& b) {
+  return Packet2cd(_mm256_xor_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pandnot<Packet2cd>(const Packet2cd& a, const Packet2cd& b) {
+  return Packet2cd(_mm256_andnot_pd(b.v, a.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd pload<Packet2cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet2cd(_mm256_load_pd((const double*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd ploadu<Packet2cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cd(_mm256_loadu_pd((const double*)from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd pset1<Packet2cd>(const std::complex<double>& from) {
+  // in case casting to a __m128d* is really not safe, then we can still fallback to this version: (much slower though)
+  //   return Packet2cd(_mm256_loadu2_m128d((const double*)&from,(const double*)&from));
+  return Packet2cd(_mm256_broadcast_pd((const __m128d*)(const void*)&from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd ploaddup<Packet2cd>(const std::complex<double>* from) {
+  return pset1<Packet2cd>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<double> >(std::complex<double>* to, const Packet2cd& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_pd((double*)to, from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double>* to, const Packet2cd& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_pd((double*)to, from.v);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2cd pgather<std::complex<double>, Packet2cd>(const std::complex<double>* from,
+                                                                            Index stride) {
+  return Packet2cd(_mm256_set_pd(std::imag(from[1 * stride]), std::real(from[1 * stride]), std::imag(from[0 * stride]),
+                                 std::real(from[0 * stride])));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet2cd>(std::complex<double>* to, const Packet2cd& from,
+                                                                        Index stride) {
+  __m128d low = _mm256_extractf128_pd(from.v, 0);
+  to[stride * 0] = std::complex<double>(_mm_cvtsd_f64(low), _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1)));
+  __m128d high = _mm256_extractf128_pd(from.v, 1);
+  to[stride * 1] = std::complex<double>(_mm_cvtsd_f64(high), _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet2cd>(const Packet2cd& a) {
+  __m128d low = _mm256_extractf128_pd(a.v, 0);
+  EIGEN_ALIGN16 double res[2];
+  _mm_store_pd(res, low);
+  return std::complex<double>(res[0], res[1]);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd preverse(const Packet2cd& a) {
+  __m256d result = _mm256_permute2f128_pd(a.v, a.v, 1);
+  return Packet2cd(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux<Packet2cd>(const Packet2cd& a) {
+  return predux(padd(Packet1cd(_mm256_extractf128_pd(a.v, 0)), Packet1cd(_mm256_extractf128_pd(a.v, 1))));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet2cd>(const Packet2cd& a) {
+  return predux(pmul(Packet1cd(_mm256_extractf128_pd(a.v, 0)), Packet1cd(_mm256_extractf128_pd(a.v, 1))));
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cd, Packet4d)
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd pdiv<Packet2cd>(const Packet2cd& a, const Packet2cd& b) {
+  return pdiv_complex(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd pcplxflip<Packet2cd>(const Packet2cd& x) {
+  return Packet2cd(_mm256_shuffle_pd(x.v, x.v, 0x5));
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4cf, 4>& kernel) {
+  __m256d P0 = _mm256_castps_pd(kernel.packet[0].v);
+  __m256d P1 = _mm256_castps_pd(kernel.packet[1].v);
+  __m256d P2 = _mm256_castps_pd(kernel.packet[2].v);
+  __m256d P3 = _mm256_castps_pd(kernel.packet[3].v);
+
+  __m256d T0 = _mm256_shuffle_pd(P0, P1, 15);
+  __m256d T1 = _mm256_shuffle_pd(P0, P1, 0);
+  __m256d T2 = _mm256_shuffle_pd(P2, P3, 15);
+  __m256d T3 = _mm256_shuffle_pd(P2, P3, 0);
+
+  kernel.packet[1].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 32));
+  kernel.packet[3].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 49));
+  kernel.packet[0].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 32));
+  kernel.packet[2].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 49));
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cd, 2>& kernel) {
+  __m256d tmp = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 0 + (2 << 4));
+  kernel.packet[1].v = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 1 + (3 << 4));
+  kernel.packet[0].v = tmp;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd psqrt<Packet2cd>(const Packet2cd& a) {
+  return psqrt_complex<Packet2cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf psqrt<Packet4cf>(const Packet4cf& a) {
+  return psqrt_complex<Packet4cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd plog<Packet2cd>(const Packet2cd& a) {
+  return plog_complex<Packet2cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf plog<Packet4cf>(const Packet4cf& a) {
+  return plog_complex<Packet4cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd pexp<Packet2cd>(const Packet2cd& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return pexp_complex<Packet2cd>(a);
+#else
+  // Without AVX2, pexp_complex<Packet2cd> requires psincos_double<Packet4d> which needs
+  // 256-bit integer operations (Packet4l) not available on AVX-only targets.
+  // Process as two independent Packet1cd using the SSE implementation instead.
+  return Packet2cd(_mm256_insertf128_pd(_mm256_castpd128_pd256(pexp(Packet1cd(_mm256_castpd256_pd128(a.v))).v),
+                                        pexp(Packet1cd(_mm256_extractf128_pd(a.v, 1))).v, 1));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf pexp<Packet4cf>(const Packet4cf& a) {
+  return pexp_complex<Packet4cf>(a);
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+// std::complex<float>
+template <>
+EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet4cf& a, const Packet4cf& b, const Packet4cf& c) {
+  __m256 a_odd = _mm256_movehdup_ps(a.v);
+  __m256 a_even = _mm256_moveldup_ps(a.v);
+  __m256 b_swap = _mm256_permute_ps(b.v, _MM_SHUFFLE(2, 3, 0, 1));
+  __m256 result = _mm256_fmaddsub_ps(a_even, b.v, _mm256_fmaddsub_ps(a_odd, b_swap, c.v));
+  return Packet4cf(result);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pmsub(const Packet4cf& a, const Packet4cf& b, const Packet4cf& c) {
+  __m256 a_odd = _mm256_movehdup_ps(a.v);
+  __m256 a_even = _mm256_moveldup_ps(a.v);
+  __m256 b_swap = _mm256_permute_ps(b.v, _MM_SHUFFLE(2, 3, 0, 1));
+  __m256 result = _mm256_fmaddsub_ps(a_even, b.v, _mm256_fmsubadd_ps(a_odd, b_swap, c.v));
+  return Packet4cf(result);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pnmadd(const Packet4cf& a, const Packet4cf& b, const Packet4cf& c) {
+  return pnegate(pmsub(a, b, c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pnmsub(const Packet4cf& a, const Packet4cf& b, const Packet4cf& c) {
+  return pnegate(pmadd(a, b, c));
+}
+// std::complex<double>
+template <>
+EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet2cd& a, const Packet2cd& b, const Packet2cd& c) {
+  __m256d a_odd = _mm256_permute_pd(a.v, 0xF);
+  __m256d a_even = _mm256_movedup_pd(a.v);
+  __m256d b_swap = _mm256_permute_pd(b.v, 0x5);
+  __m256d result = _mm256_fmaddsub_pd(a_even, b.v, _mm256_fmaddsub_pd(a_odd, b_swap, c.v));
+  return Packet2cd(result);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pmsub(const Packet2cd& a, const Packet2cd& b, const Packet2cd& c) {
+  __m256d a_odd = _mm256_permute_pd(a.v, 0xF);
+  __m256d a_even = _mm256_movedup_pd(a.v);
+  __m256d b_swap = _mm256_permute_pd(b.v, 0x5);
+  __m256d result = _mm256_fmaddsub_pd(a_even, b.v, _mm256_fmsubadd_pd(a_odd, b_swap, c.v));
+  return Packet2cd(result);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pnmadd(const Packet2cd& a, const Packet2cd& b, const Packet2cd& c) {
+  return pnegate(pmsub(a, b, c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pnmsub(const Packet2cd& a, const Packet2cd& b, const Packet2cd& c) {
+  return pnegate(pmadd(a, b, c));
+}
+#endif
+
+/*---------------- load/store segment support ----------------*/
+
+/*---------------- std::complex<float> ----------------*/
+
+template <>
+struct has_packet_segment<Packet2cf> : std::true_type {};
+
+template <>
+struct has_packet_segment<Packet4cf> : std::true_type {};
+
+template <>
+inline Packet2cf ploaduSegment<Packet2cf>(const std::complex<float>* from, Index begin, Index count) {
+  return (Packet2cf)_mm_maskload_ps(&numext::real_ref(*from), segment_mask_2x64(begin, count));
+}
+
+template <>
+inline void pstoreuSegment<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index begin,
+                                                           Index count) {
+  _mm_maskstore_ps(&numext::real_ref(*to), segment_mask_2x64(begin, count), from.v);
+}
+
+template <>
+inline Packet4cf ploaduSegment<Packet4cf>(const std::complex<float>* from, Index begin, Index count) {
+  return (Packet4cf)_mm256_maskload_ps(&numext::real_ref(*from), segment_mask_4x64(begin, count));
+}
+
+template <>
+inline void pstoreuSegment<std::complex<float>, Packet4cf>(std::complex<float>* to, const Packet4cf& from, Index begin,
+                                                           Index count) {
+  _mm256_maskstore_ps(&numext::real_ref(*to), segment_mask_4x64(begin, count), from.v);
+}
+
+/*---------------- std::complex<double> ----------------*/
+
+template <>
+struct has_packet_segment<Packet2cd> : std::true_type {};
+
+template <>
+inline Packet2cd ploaduSegment<Packet2cd>(const std::complex<double>* from, Index begin, Index count) {
+  return (Packet2cd)_mm256_maskload_pd(&numext::real_ref(*from), segment_mask_4x64(2 * begin, 2 * count));
+}
+
+template <>
+inline void pstoreuSegment<std::complex<double>, Packet2cd>(std::complex<double>* to, const Packet2cd& from,
+                                                            Index begin, Index count) {
+  _mm256_maskstore_pd(&numext::real_ref(*to), segment_mask_4x64(2 * begin, 2 * count), from.v);
+}
+
+/*---------------- end load/store segment support ----------------*/
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_AVX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/MathFunctions.h
new file mode 100644
index 0000000..5a6cba3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/MathFunctions.h
@@ -0,0 +1,129 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_AVX_H
+#define EIGEN_MATH_FUNCTIONS_AVX_H
+
+/* The sin and cos functions of this file are loosely derived from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet8f)
+
+EIGEN_DOUBLE_PACKET_FUNCTION(atanh, Packet4d)
+EIGEN_DOUBLE_PACKET_FUNCTION(log, Packet4d)
+EIGEN_DOUBLE_PACKET_FUNCTION(exp, Packet4d)
+EIGEN_DOUBLE_PACKET_FUNCTION(log2, Packet4d)
+EIGEN_DOUBLE_PACKET_FUNCTION(tanh, Packet4d)
+EIGEN_DOUBLE_PACKET_FUNCTION(cbrt, Packet4d)
+#ifdef EIGEN_VECTORIZE_AVX2
+EIGEN_DOUBLE_PACKET_FUNCTION(sin, Packet4d)
+EIGEN_DOUBLE_PACKET_FUNCTION(cos, Packet4d)
+EIGEN_DOUBLE_PACKET_FUNCTION(tan, Packet4d)
+#else
+// Without AVX2, psincos_double<Packet4d> requires 256-bit integer operations (Packet4l)
+// that are not available. Process as two Packet2d halves using the SSE implementation.
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4d psin<Packet4d>(const Packet4d& x) {
+  return _mm256_insertf128_pd(_mm256_castpd128_pd256(psin(_mm256_castpd256_pd128(x))),
+                              psin(_mm256_extractf128_pd(x, 1)), 1);
+}
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4d pcos<Packet4d>(const Packet4d& x) {
+  return _mm256_insertf128_pd(_mm256_castpd128_pd256(pcos(_mm256_castpd256_pd128(x))),
+                              pcos(_mm256_extractf128_pd(x, 1)), 1);
+}
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4d ptan<Packet4d>(const Packet4d& x) {
+  return _mm256_insertf128_pd(_mm256_castpd128_pd256(ptan(_mm256_castpd256_pd128(x))),
+                              ptan(_mm256_extractf128_pd(x, 1)), 1);
+}
+#endif
+EIGEN_GENERIC_PACKET_FUNCTION(atan, Packet4d)
+EIGEN_GENERIC_PACKET_FUNCTION(exp2, Packet4d)
+EIGEN_GENERIC_PACKET_FUNCTION(expm1, Packet4d)
+EIGEN_GENERIC_PACKET_FUNCTION(log1p, Packet4d)
+
+// Notice that for newer processors, it is counterproductive to use Newton
+// iteration for square root. In particular, Skylake and Zen2 processors
+// have approximately doubled throughput of the _mm_sqrt_ps instruction
+// compared to their predecessors.
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f psqrt<Packet8f>(const Packet8f& _x) {
+  return _mm256_sqrt_ps(_x);
+}
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4d psqrt<Packet4d>(const Packet4d& _x) {
+  return _mm256_sqrt_pd(_x);
+}
+
+// Even on Skylake, using Newton iteration is a win for reciprocal square root.
+#if EIGEN_FAST_MATH
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f prsqrt<Packet8f>(const Packet8f& a) {
+  // _mm256_rsqrt_ps returns -inf for negative denormals.
+  // _mm512_rsqrt**_ps returns -NaN for negative denormals.  We may want
+  // consistency here.
+  // const Packet8f rsqrt = pselect(pcmp_lt(a, pzero(a)),
+  //                                pset1<Packet8f>(-NumTraits<float>::quiet_NaN()),
+  //                                _mm256_rsqrt_ps(a));
+  return generic_rsqrt_newton_step<Packet8f, /*Steps=*/1>::run(a, _mm256_rsqrt_ps(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f preciprocal<Packet8f>(const Packet8f& a) {
+  return generic_reciprocal_newton_step<Packet8f, /*Steps=*/1>::run(a, _mm256_rcp_ps(a));
+}
+
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pfrexp(const Packet8h& a, Packet8h& exponent) {
+  Packet8f fexponent;
+  const Packet8h out = float2half(pfrexp<Packet8f>(half2float(a), fexponent));
+  exponent = float2half(fexponent);
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pldexp(const Packet8h& a, const Packet8h& exponent) {
+  return float2half(pldexp<Packet8f>(half2float(a), half2float(exponent)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pfrexp(const Packet8bf& a, Packet8bf& exponent) {
+  Packet8f fexponent;
+  const Packet8bf out = F32ToBf16(pfrexp<Packet8f>(Bf16ToF32(a), fexponent));
+  exponent = F32ToBf16(fexponent);
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pldexp(const Packet8bf& a, const Packet8bf& exponent) {
+  return F32ToBf16(pldexp<Packet8f>(Bf16ToF32(a), Bf16ToF32(exponent)));
+}
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_BF16(Packet8f, Packet8bf)
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_F16(Packet8f, Packet8h)
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_AVX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/PacketMath.h
new file mode 100644
index 0000000..df94f70
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/PacketMath.h
@@ -0,0 +1,3045 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_AVX_H
+#define EIGEN_PACKET_MATH_AVX_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#if !defined(EIGEN_VECTORIZE_AVX512) && !defined(EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS)
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
+#endif
+
+#ifdef EIGEN_VECTORIZE_FMA
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+#endif
+
+typedef __m256 Packet8f;
+typedef eigen_packet_wrapper<__m256i, 0> Packet8i;
+typedef __m256d Packet4d;
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+typedef eigen_packet_wrapper<__m128i, 2> Packet8h;
+#endif
+typedef eigen_packet_wrapper<__m128i, 3> Packet8bf;
+typedef eigen_packet_wrapper<__m256i, 4> Packet8ui;
+
+#ifdef EIGEN_VECTORIZE_AVX2
+// Start from 3 to be compatible with AVX512
+typedef eigen_packet_wrapper<__m256i, 3> Packet4l;
+typedef eigen_packet_wrapper<__m256i, 5> Packet4ul;
+#endif
+
+#define SIGN_MASK_I64 static_cast<int64_t>(0x8000000000000000ULL)
+
+template <>
+struct is_arithmetic<__m256> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<__m256i> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<__m256d> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<Packet8i> {
+  enum { value = true };
+};
+// Note that `Packet8ui` uses the underlying type `__m256i`, which is
+// interpreted as a vector of _signed_ `int32`s, which breaks some arithmetic
+// operations used in `GenericPacketMath.h`.
+template <>
+struct is_arithmetic<Packet8ui> {
+  enum { value = false };
+};
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template <>
+struct is_arithmetic<Packet8h> {
+  enum { value = true };
+};
+#endif
+template <>
+struct is_arithmetic<Packet8bf> {
+  enum { value = true };
+};
+#ifdef EIGEN_VECTORIZE_AVX2
+template <>
+struct is_arithmetic<Packet4l> {
+  enum { value = true };
+};
+// Note that `Packet4ul` uses the underlying type `__m256i`, which is
+// interpreted as a vector of _signed_ `int32`s, which breaks some arithmetic
+// operations used in `GenericPacketMath.h`.
+template <>
+struct is_arithmetic<Packet4ul> {
+  enum { value = false };
+};
+#endif
+
+// Use the packet_traits defined in AVX512/PacketMath.h instead if we're going
+// to leverage AVX512 instructions.
+#ifndef EIGEN_VECTORIZE_AVX512
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet8f type;
+  typedef Packet4f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasReciprocal = EIGEN_FAST_MATH,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasPow = 1,
+    HasNdtri = 1,
+    HasBessel = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasCbrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasErfc = EIGEN_FAST_MATH,
+  };
+};
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet4d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp = 1,
+    HasDiv = 1,
+#ifdef EIGEN_VECTORIZE_AVX2
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+#endif
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = 1,
+    HasErfc = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasPow = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasCbrt = 1,
+    HasATan = 1,
+    HasATanh = 1,
+  };
+};
+
+template <>
+struct packet_traits<Eigen::half> : default_packet_traits {
+  typedef Packet8h type;
+  // There is no half-size packet for Packet8h.
+  typedef Packet8h half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasBessel = 1,
+    HasNdtri = 1
+  };
+};
+
+template <>
+struct packet_traits<bfloat16> : default_packet_traits {
+  typedef Packet8bf type;
+  // There is no half-size packet for current Packet8bf.
+  // TODO: support as SSE path.
+  typedef Packet8bf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasBessel = 1,
+    HasNdtri = 1
+  };
+};
+
+template <>
+struct packet_traits<int> : default_packet_traits {
+  typedef Packet8i type;
+  typedef Packet4i half;
+  enum { Vectorizable = 1, AlignedOnScalar = 1, HasCmp = 1, HasDiv = 1, size = 8 };
+};
+template <>
+struct packet_traits<uint32_t> : default_packet_traits {
+  typedef Packet8ui type;
+  typedef Packet4ui half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasDiv = 0,
+    HasNegate = 0,
+
+    HasCmp = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasShift = 1
+  };
+};
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template <>
+struct packet_traits<int64_t> : default_packet_traits {
+  typedef Packet4l type;
+  typedef Packet2l half;
+  enum { Vectorizable = 1, AlignedOnScalar = 1, HasCmp = 1, size = 4 };
+};
+template <>
+struct packet_traits<uint64_t> : default_packet_traits {
+  typedef Packet4ul type;
+  // There is no half-size packet for current Packet4ul.
+  // TODO: support as SSE path.
+  typedef Packet4ul half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasDiv = 0,
+    HasTranspose = 0,
+    HasNegate = 0,
+    HasCmp = 1,
+    HasShift = 1
+  };
+};
+#endif
+
+#endif
+
+template <>
+struct scalar_div_cost<float, true> {
+  enum { value = 14 };
+};
+template <>
+struct scalar_div_cost<double, true> {
+  enum { value = 16 };
+};
+
+template <>
+struct unpacket_traits<Packet8f> {
+  typedef float type;
+  typedef Packet4f half;
+  typedef Packet8i integer_packet;
+  typedef uint8_t mask_t;
+  enum {
+    size = 8,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = true,
+    masked_store_available = true
+#ifdef EIGEN_VECTORIZE_AVX512
+    ,
+    masked_fpops_available = true
+#endif
+  };
+};
+template <>
+struct unpacket_traits<Packet4d> {
+  typedef double type;
+  typedef Packet2d half;
+#ifdef EIGEN_VECTORIZE_AVX2
+  typedef Packet4l integer_packet;
+#endif
+  enum {
+    size = 4,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet8i> {
+  typedef int type;
+  typedef Packet4i half;
+  enum {
+    size = 8,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet8ui> {
+  typedef uint32_t type;
+  typedef Packet4ui half;
+  enum {
+    size = 8,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+#ifdef EIGEN_VECTORIZE_AVX2
+template <>
+struct unpacket_traits<Packet4l> {
+  typedef int64_t type;
+  typedef Packet2l half;
+  enum {
+    size = 4,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet4ul> {
+  typedef uint64_t type;
+  typedef Packet4ul half;
+  enum {
+    size = 4,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+#endif
+template <>
+struct unpacket_traits<Packet8bf> {
+  typedef bfloat16 type;
+  typedef Packet8bf half;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+// Helper function for bit packing snippet of low precision comparison.
+// It packs the flags from 16x16 to 8x16.
+EIGEN_STRONG_INLINE __m128i Pack16To8(Packet8f rf) {
+  return _mm_packs_epi32(_mm256_extractf128_si256(_mm256_castps_si256(rf), 0),
+                         _mm256_extractf128_si256(_mm256_castps_si256(rf), 1));
+}
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template <>
+EIGEN_STRONG_INLINE Packet4l pset1<Packet4l>(const int64_t& from) {
+  return _mm256_set1_epi64x(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pset1<Packet4ul>(const uint64_t& from) {
+  return _mm256_set1_epi64x(numext::bit_cast<uint64_t>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pzero(const Packet4l& /*a*/) {
+  return _mm256_setzero_si256();
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pzero(const Packet4ul& /*a*/) {
+  return _mm256_setzero_si256();
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l peven_mask(const Packet4l& /*a*/) {
+  return _mm256_set_epi64x(0ll, -1ll, 0ll, -1ll);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul peven_mask(const Packet4ul& /*a*/) {
+  return _mm256_set_epi64x(0ll, -1ll, 0ll, -1ll);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pload1<Packet4l>(const int64_t* from) {
+  return _mm256_set1_epi64x(*from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pload1<Packet4ul>(const uint64_t* from) {
+  return _mm256_set1_epi64x(*from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l padd<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_add_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul padd<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return _mm256_add_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l plset<Packet4l>(const int64_t& a) {
+  return padd(pset1<Packet4l>(a), Packet4l(_mm256_set_epi64x(3ll, 2ll, 1ll, 0ll)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul plset<Packet4ul>(const uint64_t& a) {
+  return padd(pset1<Packet4ul>(a), Packet4ul(_mm256_set_epi64x(3ll, 2ll, 1ll, 0ll)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l psub<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_sub_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul psub<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return _mm256_sub_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pnegate(const Packet4l& a) {
+  return psub(pzero(a), a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pconj(const Packet4l& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pcmp_le(const Packet4l& a, const Packet4l& b) {
+  return _mm256_xor_si256(_mm256_cmpgt_epi64(a, b), _mm256_set1_epi32(-1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pcmp_le(const Packet4ul& a, const Packet4ul& b) {
+  return (Packet4ul)pcmp_le((Packet4l)psub(a, pset1<Packet4ul>(0x8000000000000000UL)),
+                            (Packet4l)psub(b, pset1<Packet4ul>(0x8000000000000000UL)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pcmp_lt(const Packet4l& a, const Packet4l& b) {
+  return _mm256_cmpgt_epi64(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pcmp_lt(const Packet4ul& a, const Packet4ul& b) {
+  return (Packet4ul)pcmp_lt((Packet4l)psub(a, pset1<Packet4ul>(0x8000000000000000UL)),
+                            (Packet4l)psub(b, pset1<Packet4ul>(0x8000000000000000UL)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pcmp_eq(const Packet4l& a, const Packet4l& b) {
+  return _mm256_cmpeq_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pcmp_eq(const Packet4ul& a, const Packet4ul& b) {
+  return _mm256_cmpeq_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l ptrue<Packet4l>(const Packet4l& a) {
+  return _mm256_cmpeq_epi64(a, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul ptrue<Packet4ul>(const Packet4ul& a) {
+  return _mm256_cmpeq_epi64(a, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pand<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_and_si256(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l por<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_or_si256(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pxor<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_xor_si256(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pxor<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return _mm256_xor_si256(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pandnot<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_andnot_si256(b, a);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4l plogical_shift_right(Packet4l a) {
+  return _mm256_srli_epi64(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4l plogical_shift_left(Packet4l a) {
+  return _mm256_slli_epi64(a, N);
+}
+#ifdef EIGEN_VECTORIZE_AVX512FP16
+template <int N>
+EIGEN_STRONG_INLINE Packet4l parithmetic_shift_right(Packet4l a) {
+  return _mm256_srai_epi64(a, N);
+}
+#else
+template <int N>
+EIGEN_STRONG_INLINE std::enable_if_t<(N == 0), Packet4l> parithmetic_shift_right(Packet4l a) {
+  return a;
+}
+template <int N>
+EIGEN_STRONG_INLINE std::enable_if_t<(N > 0) && (N < 32), Packet4l> parithmetic_shift_right(Packet4l a) {
+  __m256i hi_word = _mm256_srai_epi32(a, N);
+  __m256i lo_word = _mm256_srli_epi64(a, N);
+  return _mm256_blend_epi32(hi_word, lo_word, 0b01010101);
+}
+template <int N>
+EIGEN_STRONG_INLINE std::enable_if_t<(N >= 32) && (N < 63), Packet4l> parithmetic_shift_right(Packet4l a) {
+  __m256i hi_word = _mm256_srai_epi32(a, 31);
+  __m256i lo_word = _mm256_shuffle_epi32(_mm256_srai_epi32(a, N - 32), (shuffle_mask<1, 1, 3, 3>::mask));
+  return _mm256_blend_epi32(hi_word, lo_word, 0b01010101);
+}
+template <int N>
+EIGEN_STRONG_INLINE std::enable_if_t<(N == 63), Packet4l> parithmetic_shift_right(Packet4l a) {
+  return _mm256_cmpgt_epi64(_mm256_setzero_si256(), a);
+}
+template <int N>
+EIGEN_STRONG_INLINE std::enable_if_t<(N < 0) || (N > 63), Packet4l> parithmetic_shift_right(Packet4l a) {
+  return parithmetic_shift_right<int(N & 63)>(a);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet4l pload<Packet4l>(const int64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pload<Packet4ul>(const uint64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l ploadu<Packet4l>(const int64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul ploadu<Packet4ul>(const uint64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+}
+// Loads 2 int64_ts from memory a returns the packet {a0, a0, a1, a1}
+template <>
+EIGEN_STRONG_INLINE Packet4l ploaddup<Packet4l>(const int64_t* from) {
+  const Packet4l a = _mm256_castsi128_si256(_mm_loadu_si128(reinterpret_cast<const __m128i*>(from)));
+  return _mm256_permutevar8x32_epi32(a, _mm256_setr_epi32(0, 1, 0, 1, 2, 3, 2, 3));
+}
+// Loads 2 uint64_ts from memory a returns the packet {a0, a0, a1, a1}
+template <>
+EIGEN_STRONG_INLINE Packet4ul ploaddup<Packet4ul>(const uint64_t* from) {
+  const Packet4ul a = _mm256_castsi128_si256(_mm_loadu_si128(reinterpret_cast<const __m128i*>(from)));
+  return _mm256_permutevar8x32_epi32(a, _mm256_setr_epi32(0, 1, 0, 1, 2, 3, 2, 3));
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int64_t>(int64_t* to, const Packet4l& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_si256(reinterpret_cast<__m256i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint64_t>(uint64_t* to, const Packet4ul& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_si256(reinterpret_cast<__m256i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int64_t>(int64_t* to, const Packet4l& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint64_t>(uint64_t* to, const Packet4ul& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet4l pgather<int64_t, Packet4l>(const int64_t* from, Index stride) {
+  return _mm256_set_epi64x(from[3 * stride], from[2 * stride], from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet4ul pgather<uint64_t, Packet4ul>(const uint64_t* from, Index stride) {
+  return _mm256_set_epi64x(from[3 * stride], from[2 * stride], from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<int64_t, Packet4l>(int64_t* to, const Packet4l& from, Index stride) {
+  __m128i low = _mm256_extractf128_si256(from, 0);
+  to[stride * 0] = _mm_extract_epi64_0(low);
+  to[stride * 1] = _mm_extract_epi64_1(low);
+
+  __m128i high = _mm256_extractf128_si256(from, 1);
+  to[stride * 2] = _mm_extract_epi64_0(high);
+  to[stride * 3] = _mm_extract_epi64_1(high);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<uint64_t, Packet4ul>(uint64_t* to, const Packet4ul& from, Index stride) {
+  __m128i low = _mm256_extractf128_si256(from, 0);
+  to[stride * 0] = _mm_extract_epi64_0(low);
+  to[stride * 1] = _mm_extract_epi64_1(low);
+
+  __m128i high = _mm256_extractf128_si256(from, 1);
+  to[stride * 2] = _mm_extract_epi64_0(high);
+  to[stride * 3] = _mm_extract_epi64_1(high);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet4l>(int64_t* to, const int64_t& a) {
+  Packet4l pa = pset1<Packet4l>(a);
+  pstore(to, pa);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet4ul>(uint64_t* to, const uint64_t& a) {
+  Packet4ul pa = pset1<Packet4ul>(a);
+  pstore(to, pa);
+}
+template <>
+EIGEN_STRONG_INLINE int64_t pfirst<Packet4l>(const Packet4l& a) {
+  return _mm_extract_epi64_0(_mm256_castsi256_si128(a));
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t pfirst<Packet4ul>(const Packet4ul& a) {
+  return _mm_extract_epi64_0(_mm256_castsi256_si128(a));
+}
+
+#define MM256_SHUFFLE_EPI64(A, B, M) _mm256_shuffle_pd(_mm256_castsi256_pd(A), _mm256_castsi256_pd(B), M)
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4l, 4>& kernel) {
+  __m256d T0 = MM256_SHUFFLE_EPI64(kernel.packet[0], kernel.packet[1], 15);
+  __m256d T1 = MM256_SHUFFLE_EPI64(kernel.packet[0], kernel.packet[1], 0);
+  __m256d T2 = MM256_SHUFFLE_EPI64(kernel.packet[2], kernel.packet[3], 15);
+  __m256d T3 = MM256_SHUFFLE_EPI64(kernel.packet[2], kernel.packet[3], 0);
+
+  kernel.packet[1] = _mm256_castpd_si256(_mm256_permute2f128_pd(T0, T2, 32));
+  kernel.packet[3] = _mm256_castpd_si256(_mm256_permute2f128_pd(T0, T2, 49));
+  kernel.packet[0] = _mm256_castpd_si256(_mm256_permute2f128_pd(T1, T3, 32));
+  kernel.packet[2] = _mm256_castpd_si256(_mm256_permute2f128_pd(T1, T3, 49));
+}
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4ul, 4>& kernel) {
+  ptranspose((PacketBlock<Packet4l, 4>&)kernel);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pmin<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  __m256i cmp = _mm256_cmpgt_epi64(a, b);
+  __m256i a_min = _mm256_andnot_si256(cmp, a);
+  __m256i b_min = _mm256_and_si256(cmp, b);
+  return Packet4l(_mm256_or_si256(a_min, b_min));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pmin<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return padd((Packet4ul)pmin((Packet4l)psub(a, pset1<Packet4ul>(0x8000000000000000UL)),
+                              (Packet4l)psub(b, pset1<Packet4ul>(0x8000000000000000UL))),
+              pset1<Packet4ul>(0x8000000000000000UL));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pmax<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  __m256i cmp = _mm256_cmpgt_epi64(a, b);
+  __m256i a_min = _mm256_and_si256(cmp, a);
+  __m256i b_min = _mm256_andnot_si256(cmp, b);
+  return Packet4l(_mm256_or_si256(a_min, b_min));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pmax<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return padd((Packet4ul)pmax((Packet4l)psub(a, pset1<Packet4ul>(0x8000000000000000UL)),
+                              (Packet4l)psub(b, pset1<Packet4ul>(0x8000000000000000UL))),
+              pset1<Packet4ul>(0x8000000000000000UL));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pabs<Packet4l>(const Packet4l& a) {
+  Packet4l pz = pzero<Packet4l>(a);
+  Packet4l cmp = _mm256_cmpgt_epi64(a, pz);
+  return psub(cmp, pxor(a, cmp));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pabs<Packet4ul>(const Packet4ul& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pmul<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  // 64-bit mul requires avx512, so do this with 32-bit multiplication
+  __m256i upper32_a = _mm256_srli_epi64(a, 32);
+  __m256i upper32_b = _mm256_srli_epi64(b, 32);
+
+  // upper * lower
+  __m256i mul1 = _mm256_mul_epu32(upper32_a, b);
+  __m256i mul2 = _mm256_mul_epu32(upper32_b, a);
+  // Gives us both upper*upper and lower*lower
+  __m256i mul3 = _mm256_mul_epu32(a, b);
+
+  __m256i high = _mm256_slli_epi64(_mm256_add_epi64(mul1, mul2), 32);
+  return _mm256_add_epi64(high, mul3);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pmul<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return (Packet4ul)pmul<Packet4l>((Packet4l)a, (Packet4l)b);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pset1<Packet8f>(const float& from) {
+  return _mm256_set1_ps(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pset1<Packet4d>(const double& from) {
+  return _mm256_set1_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pset1<Packet8i>(const int& from) {
+  return _mm256_set1_epi32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pset1<Packet8ui>(const uint32_t& from) {
+  return _mm256_set1_epi32(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pset1frombits<Packet8f>(unsigned int from) {
+  return _mm256_castsi256_ps(pset1<Packet8i>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pset1frombits<Packet4d>(uint64_t from) {
+  return _mm256_castsi256_pd(_mm256_set1_epi64x(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pzero(const Packet8f& /*a*/) {
+  return _mm256_setzero_ps();
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pzero(const Packet4d& /*a*/) {
+  return _mm256_setzero_pd();
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pzero(const Packet8i& /*a*/) {
+  return _mm256_setzero_si256();
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pzero(const Packet8ui& /*a*/) {
+  return _mm256_setzero_si256();
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f peven_mask(const Packet8f& /*a*/) {
+  return _mm256_castsi256_ps(_mm256_set_epi32(0, -1, 0, -1, 0, -1, 0, -1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i peven_mask(const Packet8i& /*a*/) {
+  return _mm256_set_epi32(0, -1, 0, -1, 0, -1, 0, -1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui peven_mask(const Packet8ui& /*a*/) {
+  return _mm256_set_epi32(0, -1, 0, -1, 0, -1, 0, -1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d peven_mask(const Packet4d& /*a*/) {
+  return _mm256_castsi256_pd(_mm256_set_epi32(0, 0, -1, -1, 0, 0, -1, -1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pload1<Packet8f>(const float* from) {
+  return _mm256_broadcast_ss(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pload1<Packet4d>(const double* from) {
+  return _mm256_broadcast_sd(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return _mm256_add_ps(a, b);
+}
+#ifdef EIGEN_VECTORIZE_AVX512
+template <>
+EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b, uint8_t umask) {
+  __mmask16 mask = static_cast<__mmask16>(umask & 0x00FF);
+  return _mm512_castps512_ps256(_mm512_maskz_add_ps(mask, _mm512_castps256_ps512(a), _mm512_castps256_ps512(b)));
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet4d padd<Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return _mm256_add_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i padd<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_add_epi32(a, b);
+#else
+  __m128i lo = _mm_add_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_add_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui padd<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_add_epi32(a, b);
+#else
+  __m128i lo = _mm_add_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_add_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f plset<Packet8f>(const float& a) {
+  return padd(pset1<Packet8f>(a), _mm256_set_ps(7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d plset<Packet4d>(const double& a) {
+  return padd(pset1<Packet4d>(a), _mm256_set_pd(3.0, 2.0, 1.0, 0.0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i plset<Packet8i>(const int& a) {
+  return padd(pset1<Packet8i>(a), (Packet8i)_mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui plset<Packet8ui>(const uint32_t& a) {
+  return padd(pset1<Packet8ui>(a), (Packet8ui)_mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f psub<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return _mm256_sub_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d psub<Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return _mm256_sub_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i psub<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_sub_epi32(a, b);
+#else
+  __m128i lo = _mm_sub_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_sub_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui psub<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_sub_epi32(a, b);
+#else
+  __m128i lo = _mm_sub_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_sub_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pnegate(const Packet8f& a) {
+  const Packet8f mask = _mm256_castsi256_ps(_mm256_set1_epi32(SIGN_MASK_I32));
+  return _mm256_xor_ps(a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pnegate(const Packet4d& a) {
+  const Packet4d mask = _mm256_castsi256_pd(_mm256_set1_epi64x(SIGN_MASK_I64));
+  return _mm256_xor_pd(a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pnegate(const Packet8i& a) {
+  return psub(pzero(a), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pconj(const Packet8f& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pconj(const Packet4d& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pconj(const Packet8i& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pmul<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return _mm256_mul_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pmul<Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return _mm256_mul_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pmul<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_mullo_epi32(a, b);
+#else
+  const __m128i lo = _mm_mullo_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  const __m128i hi = _mm_mullo_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pmul<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_mullo_epi32(a, b);
+#else
+  const __m128i lo = _mm_mullo_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  const __m128i hi = _mm_mullo_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pdiv<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return _mm256_div_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pdiv<Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return _mm256_div_pd(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8i pdiv<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX512
+  return _mm512_cvttpd_epi32(_mm512_div_pd(_mm512_cvtepi32_pd(a), _mm512_cvtepi32_pd(b)));
+#else
+  Packet4i lo = pdiv<Packet4i>(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  Packet4i hi = pdiv<Packet4i>(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1);
+#endif
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+template <>
+EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
+  return _mm256_fmadd_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
+  return _mm256_fmadd_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pmsub(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
+  return _mm256_fmsub_ps(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pmsub(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
+  return _mm256_fmsub_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pnmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
+  return _mm256_fnmadd_ps(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pnmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
+  return _mm256_fnmadd_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pnmsub(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
+  return _mm256_fnmsub_ps(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pnmsub(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
+  return _mm256_fnmsub_pd(a, b, c);
+}
+
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pcmp_le(const Packet8f& a, const Packet8f& b) {
+  return _mm256_cmp_ps(a, b, _CMP_LE_OQ);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pcmp_lt(const Packet8f& a, const Packet8f& b) {
+  return _mm256_cmp_ps(a, b, _CMP_LT_OQ);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pcmp_lt_or_nan(const Packet8f& a, const Packet8f& b) {
+  return _mm256_cmp_ps(a, b, _CMP_NGE_UQ);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pcmp_eq(const Packet8f& a, const Packet8f& b) {
+  return _mm256_cmp_ps(a, b, _CMP_EQ_OQ);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pisnan(const Packet8f& a) {
+  return _mm256_cmp_ps(a, a, _CMP_UNORD_Q);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pcmp_le(const Packet4d& a, const Packet4d& b) {
+  return _mm256_cmp_pd(a, b, _CMP_LE_OQ);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pcmp_lt(const Packet4d& a, const Packet4d& b) {
+  return _mm256_cmp_pd(a, b, _CMP_LT_OQ);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pcmp_lt_or_nan(const Packet4d& a, const Packet4d& b) {
+  return _mm256_cmp_pd(a, b, _CMP_NGE_UQ);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pcmp_eq(const Packet4d& a, const Packet4d& b) {
+  return _mm256_cmp_pd(a, b, _CMP_EQ_OQ);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8i pcmp_le(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_xor_si256(_mm256_cmpgt_epi32(a, b), _mm256_set1_epi32(-1));
+#else
+  __m128i lo = _mm_cmpgt_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  lo = _mm_xor_si128(lo, _mm_set1_epi32(-1));
+  __m128i hi = _mm_cmpgt_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  hi = _mm_xor_si128(hi, _mm_set1_epi32(-1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pcmp_lt(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_cmpgt_epi32(b, a);
+#else
+  __m128i lo = _mm_cmpgt_epi32(_mm256_extractf128_si256(b, 0), _mm256_extractf128_si256(a, 0));
+  __m128i hi = _mm_cmpgt_epi32(_mm256_extractf128_si256(b, 1), _mm256_extractf128_si256(a, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pcmp_eq(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_cmpeq_epi32(a, b);
+#else
+  __m128i lo = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pcmp_eq(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_cmpeq_epi32(a, b);
+#else
+  __m128i lo = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pmin<Packet8f>(const Packet8f& a, const Packet8f& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6, 3, 0)
+  // There appears to be a bug in GCC, by which the optimizer may flip
+  // the argument order in calls to _mm_min_ps/_mm_max_ps, so we have to
+  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
+  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
+  Packet8f res;
+  asm("vminps %[a], %[b], %[res]" : [res] "=x"(res) : [a] "x"(a), [b] "x"(b));
+  return res;
+#else
+  // Arguments are swapped to match NaN propagation behavior of std::min.
+  return _mm256_min_ps(b, a);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pmin<Packet4d>(const Packet4d& a, const Packet4d& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6, 3, 0)
+  // See pmin above
+  Packet4d res;
+  asm("vminpd %[a], %[b], %[res]" : [res] "=x"(res) : [a] "x"(a), [b] "x"(b));
+  return res;
+#else
+  // Arguments are swapped to match NaN propagation behavior of std::min.
+  return _mm256_min_pd(b, a);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pmin<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_min_epi32(a, b);
+#else
+  __m128i lo = _mm_min_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_min_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pmin<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_min_epu32(a, b);
+#else
+  __m128i lo = _mm_min_epu32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_min_epu32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pmax<Packet8f>(const Packet8f& a, const Packet8f& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6, 3, 0)
+  // See pmin above
+  Packet8f res;
+  asm("vmaxps %[a], %[b], %[res]" : [res] "=x"(res) : [a] "x"(a), [b] "x"(b));
+  return res;
+#else
+  // Arguments are swapped to match NaN propagation behavior of std::max.
+  return _mm256_max_ps(b, a);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pmax<Packet4d>(const Packet4d& a, const Packet4d& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6, 3, 0)
+  // See pmin above
+  Packet4d res;
+  asm("vmaxpd %[a], %[b], %[res]" : [res] "=x"(res) : [a] "x"(a), [b] "x"(b));
+  return res;
+#else
+  // Arguments are swapped to match NaN propagation behavior of std::max.
+  return _mm256_max_pd(b, a);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pmax<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_max_epi32(a, b);
+#else
+  __m128i lo = _mm_max_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_max_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pmax<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_max_epu32(a, b);
+#else
+  __m128i lo = _mm_max_epu32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_max_epu32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template <>
+EIGEN_STRONG_INLINE Packet8i psign(const Packet8i& a) {
+  return _mm256_sign_epi32(_mm256_set1_epi32(1), a);
+}
+#endif
+
+// Add specializations for min/max with prescribed NaN propagation.
+template <>
+EIGEN_STRONG_INLINE Packet8f pmin<PropagateNumbers, Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet8f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pmin<PropagateNumbers, Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet4d>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pmax<PropagateNumbers, Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet8f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pmax<PropagateNumbers, Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet4d>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pmin<PropagateNaN, Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet8f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pmin<PropagateNaN, Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet4d>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pmax<PropagateNaN, Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet8f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pmax<PropagateNaN, Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet4d>);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f print<Packet8f>(const Packet8f& a) {
+  return _mm256_round_ps(a, _MM_FROUND_CUR_DIRECTION);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d print<Packet4d>(const Packet4d& a) {
+  return _mm256_round_pd(a, _MM_FROUND_CUR_DIRECTION);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pceil<Packet8f>(const Packet8f& a) {
+  return _mm256_ceil_ps(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pceil<Packet4d>(const Packet4d& a) {
+  return _mm256_ceil_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pfloor<Packet8f>(const Packet8f& a) {
+  return _mm256_floor_ps(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pfloor<Packet4d>(const Packet4d& a) {
+  return _mm256_floor_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f ptrunc<Packet8f>(const Packet8f& a) {
+  return _mm256_round_ps(a, _MM_FROUND_TRUNC);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d ptrunc<Packet4d>(const Packet4d& a) {
+  return _mm256_round_pd(a, _MM_FROUND_TRUNC);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8i ptrue<Packet8i>(const Packet8i& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  // vpcmpeqd has lower latency than the more general vcmpps
+  return _mm256_cmpeq_epi32(a, a);
+#else
+  const __m256 b = _mm256_castsi256_ps(a);
+  return _mm256_castps_si256(_mm256_cmp_ps(b, b, _CMP_TRUE_UQ));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f ptrue<Packet8f>(const Packet8f& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  // vpcmpeqd has lower latency than the more general vcmpps
+  const __m256i b = _mm256_castps_si256(a);
+  return _mm256_castsi256_ps(_mm256_cmpeq_epi32(b, b));
+#else
+  return _mm256_cmp_ps(a, a, _CMP_TRUE_UQ);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d ptrue<Packet4d>(const Packet4d& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  // vpcmpeqq has lower latency than the more general vcmppd
+  const __m256i b = _mm256_castpd_si256(a);
+  return _mm256_castsi256_pd(_mm256_cmpeq_epi64(b, b));
+#else
+  return _mm256_cmp_pd(a, a, _CMP_TRUE_UQ);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pand<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return _mm256_and_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pand<Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return _mm256_and_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pand<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_and_si256(a, b);
+#else
+  return _mm256_castps_si256(_mm256_and_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pand<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_and_si256(a, b);
+#else
+  return _mm256_castps_si256(_mm256_and_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f por<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return _mm256_or_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d por<Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return _mm256_or_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i por<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_or_si256(a, b);
+#else
+  return _mm256_castps_si256(_mm256_or_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui por<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_or_si256(a, b);
+#else
+  return _mm256_castps_si256(_mm256_or_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pxor<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return _mm256_xor_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pxor<Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return _mm256_xor_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pxor<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_xor_si256(a, b);
+#else
+  return _mm256_castps_si256(_mm256_xor_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pxor<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_xor_si256(a, b);
+#else
+  return _mm256_castps_si256(_mm256_xor_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pandnot<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return _mm256_andnot_ps(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pandnot<Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return _mm256_andnot_pd(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pandnot<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_andnot_si256(b, a);
+#else
+  return _mm256_castps_si256(_mm256_andnot_ps(_mm256_castsi256_ps(b), _mm256_castsi256_ps(a)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pandnot<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_andnot_si256(b, a);
+#else
+  return _mm256_castps_si256(_mm256_andnot_ps(_mm256_castsi256_ps(b), _mm256_castsi256_ps(a)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8ui pcmp_lt(const Packet8ui& a, const Packet8ui& b) {
+  return pxor(pcmp_eq(a, pmax(a, b)), ptrue(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pcmp_le(const Packet8ui& a, const Packet8ui& b) {
+  return pcmp_eq(a, pmin(a, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pround<Packet8f>(const Packet8f& a) {
+  const Packet8f mask = pset1frombits<Packet8f>(static_cast<numext::uint32_t>(0x80000000u));
+  const Packet8f prev0dot5 = pset1frombits<Packet8f>(static_cast<numext::uint32_t>(0x3EFFFFFFu));
+  return _mm256_round_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pround<Packet4d>(const Packet4d& a) {
+  const Packet4d mask = pset1frombits<Packet4d>(static_cast<numext::uint64_t>(0x8000000000000000ull));
+  const Packet4d prev0dot5 = pset1frombits<Packet4d>(static_cast<numext::uint64_t>(0x3FDFFFFFFFFFFFFFull));
+  return _mm256_round_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pselect<Packet8f>(const Packet8f& mask, const Packet8f& a, const Packet8f& b) {
+  return _mm256_blendv_ps(b, a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pselect<Packet8i>(const Packet8i& mask, const Packet8i& a, const Packet8i& b) {
+  return _mm256_castps_si256(
+      _mm256_blendv_ps(_mm256_castsi256_ps(b), _mm256_castsi256_ps(a), _mm256_castsi256_ps(mask)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pselect<Packet8ui>(const Packet8ui& mask, const Packet8ui& a, const Packet8ui& b) {
+  return _mm256_castps_si256(
+      _mm256_blendv_ps(_mm256_castsi256_ps(b), _mm256_castsi256_ps(a), _mm256_castsi256_ps(mask)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pselect<Packet4d>(const Packet4d& mask, const Packet4d& a, const Packet4d& b) {
+  return _mm256_blendv_pd(b, a, mask);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8i parithmetic_shift_right(Packet8i a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_srai_epi32(a, N);
+#else
+  __m128i lo = _mm_srai_epi32(_mm256_extractf128_si256(a, 0), N);
+  __m128i hi = _mm_srai_epi32(_mm256_extractf128_si256(a, 1), N);
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8i plogical_shift_right(Packet8i a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_srli_epi32(a, N);
+#else
+  __m128i lo = _mm_srli_epi32(_mm256_extractf128_si256(a, 0), N);
+  __m128i hi = _mm_srli_epi32(_mm256_extractf128_si256(a, 1), N);
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8i plogical_shift_left(Packet8i a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_slli_epi32(a, N);
+#else
+  __m128i lo = _mm_slli_epi32(_mm256_extractf128_si256(a, 0), N);
+  __m128i hi = _mm_slli_epi32(_mm256_extractf128_si256(a, 1), N);
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8ui parithmetic_shift_right(Packet8ui a) {
+  return (Packet8ui)plogical_shift_right<N>((Packet8i)a);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8ui plogical_shift_right(Packet8ui a) {
+  return (Packet8ui)plogical_shift_right<N>((Packet8i)a);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8ui plogical_shift_left(Packet8ui a) {
+  return (Packet8ui)plogical_shift_left<N>((Packet8i)a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pload<Packet8f>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_ps(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pload<Packet4d>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pload<Packet8i>(const int* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pload<Packet8ui>(const uint32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_ps(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d ploadu<Packet4d>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i ploadu<Packet8i>(const int* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui ploadu<Packet8ui>(const uint32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from, uint8_t umask) {
+#ifdef EIGEN_VECTORIZE_AVX512
+  __mmask16 mask = static_cast<__mmask16>(umask & 0x00FF);
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_castps512_ps256(_mm512_maskz_loadu_ps(mask, from));
+#else
+  Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
+  const Packet8i bit_mask =
+      _mm256_set_epi32(0xffffff7f, 0xffffffbf, 0xffffffdf, 0xffffffef, 0xfffffff7, 0xfffffffb, 0xfffffffd, 0xfffffffe);
+  mask = por<Packet8i>(mask, bit_mask);
+  mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_maskload_ps(from, mask);
+#endif
+}
+
+// Loads 4 floats from memory a returns the packet {a0, a0  a1, a1, a2, a2, a3, a3}
+template <>
+EIGEN_STRONG_INLINE Packet8f ploaddup<Packet8f>(const float* from) {
+  // TODO try to find a way to avoid the need of a temporary register
+  //   Packet8f tmp  = _mm256_castps128_ps256(_mm_loadu_ps(from));
+  //   tmp = _mm256_insertf128_ps(tmp, _mm_movehl_ps(_mm256_castps256_ps128(tmp),_mm256_castps256_ps128(tmp)), 1);
+  //   return _mm256_unpacklo_ps(tmp,tmp);
+
+  // _mm256_insertf128_ps is very slow on Haswell, thus:
+  Packet8f tmp = _mm256_broadcast_ps((const __m128*)(const void*)from);
+  // mimic an "inplace" permutation of the lower 128bits using a blend
+  tmp = _mm256_blend_ps(
+      tmp, _mm256_castps128_ps256(_mm_permute_ps(_mm256_castps256_ps128(tmp), _MM_SHUFFLE(1, 0, 1, 0))), 15);
+  // then we can perform a consistent permutation on the global register to get everything in shape:
+  return _mm256_permute_ps(tmp, _MM_SHUFFLE(3, 3, 2, 2));
+}
+// Loads 2 doubles from memory a returns the packet {a0, a0, a1, a1}
+template <>
+EIGEN_STRONG_INLINE Packet4d ploaddup<Packet4d>(const double* from) {
+  Packet4d tmp = _mm256_broadcast_pd((const __m128d*)(const void*)from);
+  return _mm256_permute_pd(tmp, 3 << 2);
+}
+// Loads 4 integers from memory a returns the packet {a0, a0, a1, a1, a2, a2, a3, a3}
+template <>
+EIGEN_STRONG_INLINE Packet8i ploaddup<Packet8i>(const int* from) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  const Packet8i a = _mm256_castsi128_si256(ploadu<Packet4i>(from));
+  return _mm256_permutevar8x32_epi32(a, _mm256_setr_epi32(0, 0, 1, 1, 2, 2, 3, 3));
+#else
+  __m256 tmp = _mm256_broadcast_ps((const __m128*)(const void*)from);
+  // mimic an "inplace" permutation of the lower 128bits using a blend
+  tmp = _mm256_blend_ps(
+      tmp, _mm256_castps128_ps256(_mm_permute_ps(_mm256_castps256_ps128(tmp), _MM_SHUFFLE(1, 0, 1, 0))), 15);
+  // then we can perform a consistent permutation on the global register to get everything in shape:
+  return _mm256_castps_si256(_mm256_permute_ps(tmp, _MM_SHUFFLE(3, 3, 2, 2)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui ploaddup<Packet8ui>(const uint32_t* from) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  const Packet8ui a = _mm256_castsi128_si256(ploadu<Packet4ui>(from));
+  return _mm256_permutevar8x32_epi32(a, _mm256_setr_epi32(0, 0, 1, 1, 2, 2, 3, 3));
+#else
+  __m256 tmp = _mm256_broadcast_ps((const __m128*)(const void*)from);
+  // mimic an "inplace" permutation of the lower 128bits using a blend
+  tmp = _mm256_blend_ps(
+      tmp, _mm256_castps128_ps256(_mm_permute_ps(_mm256_castps256_ps128(tmp), _MM_SHUFFLE(1, 0, 1, 0))), 15);
+  // then we can perform a consistent permutation on the global register to get
+  // everything in shape:
+  return _mm256_castps_si256(_mm256_permute_ps(tmp, _MM_SHUFFLE(3, 3, 2, 2)));
+#endif
+}
+
+// Loads 2 floats from memory a returns the packet {a0, a0  a0, a0, a1, a1, a1, a1}
+template <>
+EIGEN_STRONG_INLINE Packet8f ploadquad<Packet8f>(const float* from) {
+  Packet8f tmp = _mm256_castps128_ps256(_mm_broadcast_ss(from));
+  return _mm256_insertf128_ps(tmp, _mm_broadcast_ss(from + 1), 1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i ploadquad<Packet8i>(const int* from) {
+  return _mm256_insertf128_si256(_mm256_set1_epi32(*from), _mm_set1_epi32(*(from + 1)), 1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui ploadquad<Packet8ui>(const uint32_t* from) {
+  return _mm256_insertf128_si256(_mm256_set1_epi32(*from), _mm_set1_epi32(*(from + 1)), 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet8f& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_ps(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet4d& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_pd(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet8i& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_si256(reinterpret_cast<__m256i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet8ui& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_si256(reinterpret_cast<__m256i*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet8f& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_ps(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet4d& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_pd(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet8i& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet8ui& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet8f& from, uint8_t umask) {
+#ifdef EIGEN_VECTORIZE_AVX512
+  __mmask16 mask = static_cast<__mmask16>(umask & 0x00FF);
+  EIGEN_DEBUG_UNALIGNED_STORE _mm512_mask_storeu_ps(to, mask, _mm512_castps256_ps512(from));
+#else
+  Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
+  const Packet8i bit_mask =
+      _mm256_set_epi32(0x7f7f7f7f, 0xbfbfbfbf, 0xdfdfdfdf, 0xefefefef, 0xf7f7f7f7, 0xfbfbfbfb, 0xfdfdfdfd, 0xfefefefe);
+  mask = por<Packet8i>(mask, bit_mask);
+  mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
+#if EIGEN_COMP_MSVC
+  // MSVC sometimes seems to use a bogus mask with maskstore.
+  const __m256i ifrom = _mm256_castps_si256(from);
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_maskmoveu_si128(_mm256_extractf128_si256(ifrom, 0), _mm256_extractf128_si256(mask, 0),
+                                                  reinterpret_cast<char*>(to));
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_maskmoveu_si128(_mm256_extractf128_si256(ifrom, 1), _mm256_extractf128_si256(mask, 1),
+                                                  reinterpret_cast<char*>(to + 4));
+#else
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_maskstore_ps(to, mask, from);
+#endif
+#endif
+}
+
+// NOTE: leverage _mm256_i32gather_ps and _mm256_i32gather_pd if AVX2 instructions are available
+// NOTE: for the record the following seems to be slower: return _mm256_i32gather_ps(from, _mm256_set1_epi32(stride),
+// 4);
+template <>
+EIGEN_DEVICE_FUNC inline Packet8f pgather<float, Packet8f>(const float* from, Index stride) {
+  return _mm256_set_ps(from[7 * stride], from[6 * stride], from[5 * stride], from[4 * stride], from[3 * stride],
+                       from[2 * stride], from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet4d pgather<double, Packet4d>(const double* from, Index stride) {
+  return _mm256_set_pd(from[3 * stride], from[2 * stride], from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet8i pgather<int, Packet8i>(const int* from, Index stride) {
+  return _mm256_set_epi32(from[7 * stride], from[6 * stride], from[5 * stride], from[4 * stride], from[3 * stride],
+                          from[2 * stride], from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet8ui pgather<uint32_t, Packet8ui>(const uint32_t* from, Index stride) {
+  return (Packet8ui)pgather<int, Packet8i>((int*)from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet8f>(float* to, const Packet8f& from, Index stride) {
+  __m128 low = _mm256_extractf128_ps(from, 0);
+  to[stride * 0] = _mm_cvtss_f32(low);
+  to[stride * 1] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 1));
+  to[stride * 2] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 2));
+  to[stride * 3] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 3));
+
+  __m128 high = _mm256_extractf128_ps(from, 1);
+  to[stride * 4] = _mm_cvtss_f32(high);
+  to[stride * 5] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 1));
+  to[stride * 6] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 2));
+  to[stride * 7] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3));
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet4d>(double* to, const Packet4d& from, Index stride) {
+  __m128d low = _mm256_extractf128_pd(from, 0);
+  to[stride * 0] = _mm_cvtsd_f64(low);
+  to[stride * 1] = _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1));
+  __m128d high = _mm256_extractf128_pd(from, 1);
+  to[stride * 2] = _mm_cvtsd_f64(high);
+  to[stride * 3] = _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1));
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<int, Packet8i>(int* to, const Packet8i& from, Index stride) {
+  __m128i low = _mm256_extractf128_si256(from, 0);
+  to[stride * 0] = _mm_extract_epi32(low, 0);
+  to[stride * 1] = _mm_extract_epi32(low, 1);
+  to[stride * 2] = _mm_extract_epi32(low, 2);
+  to[stride * 3] = _mm_extract_epi32(low, 3);
+
+  __m128i high = _mm256_extractf128_si256(from, 1);
+  to[stride * 4] = _mm_extract_epi32(high, 0);
+  to[stride * 5] = _mm_extract_epi32(high, 1);
+  to[stride * 6] = _mm_extract_epi32(high, 2);
+  to[stride * 7] = _mm_extract_epi32(high, 3);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<uint32_t, Packet8ui>(uint32_t* to, const Packet8ui& from, Index stride) {
+  pscatter<int, Packet8i>((int*)to, (Packet8i)from, stride);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet8f>(float* to, const float& a) {
+  Packet8f pa = pset1<Packet8f>(a);
+  pstore(to, pa);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet4d>(double* to, const double& a) {
+  Packet4d pa = pset1<Packet4d>(a);
+  pstore(to, pa);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet8i>(int* to, const int& a) {
+  Packet8i pa = pset1<Packet8i>(a);
+  pstore(to, pa);
+}
+
+#ifndef EIGEN_VECTORIZE_AVX512
+template <>
+EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<uint32_t>(const uint32_t* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet8f>(const Packet8f& a) {
+  return _mm_cvtss_f32(_mm256_castps256_ps128(a));
+}
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet4d>(const Packet4d& a) {
+  return _mm_cvtsd_f64(_mm256_castpd256_pd128(a));
+}
+template <>
+EIGEN_STRONG_INLINE int pfirst<Packet8i>(const Packet8i& a) {
+  return _mm_cvtsi128_si32(_mm256_castsi256_si128(a));
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t pfirst<Packet8ui>(const Packet8ui& a) {
+  return numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(_mm256_castsi256_si128(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f preverse(const Packet8f& a) {
+  __m256 tmp = _mm256_shuffle_ps(a, a, 0x1b);
+  return _mm256_permute2f128_ps(tmp, tmp, 1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d preverse(const Packet4d& a) {
+  __m256d tmp = _mm256_shuffle_pd(a, a, 5);
+  return _mm256_permute2f128_pd(tmp, tmp, 1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i preverse(const Packet8i& a) {
+  return _mm256_castps_si256(preverse(_mm256_castsi256_ps(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui preverse(const Packet8ui& a) {
+  return _mm256_castps_si256(preverse(_mm256_castsi256_ps(a)));
+}
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template <>
+EIGEN_STRONG_INLINE Packet4l preverse(const Packet4l& a) {
+  return _mm256_castpd_si256(preverse(_mm256_castsi256_pd(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul preverse(const Packet4ul& a) {
+  return _mm256_castpd_si256(preverse(_mm256_castsi256_pd(a)));
+}
+#endif
+
+// pabs should be ok
+template <>
+EIGEN_STRONG_INLINE Packet8f pabs(const Packet8f& a) {
+  const Packet8f mask = _mm256_castsi256_ps(_mm256_set1_epi32(0x7FFFFFFF));
+  return _mm256_and_ps(a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pabs(const Packet4d& a) {
+  const Packet4d mask = _mm256_castsi256_pd(_mm256_set1_epi64x(0x7FFFFFFFFFFFFFFF));
+  return _mm256_and_pd(a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i pabs(const Packet8i& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_abs_epi32(a);
+#else
+  __m128i lo = _mm_abs_epi32(_mm256_extractf128_si256(a, 0));
+  __m128i hi = _mm_abs_epi32(_mm256_extractf128_si256(a, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui pabs(const Packet8ui& a) {
+  return a;
+}
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template <>
+EIGEN_STRONG_INLINE Packet8h psignbit(const Packet8h& a) {
+  return _mm_cmpgt_epi16(_mm_setzero_si128(), a);
+}
+#endif  // EIGEN_VECTORIZE_AVX512FP16
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf psignbit(const Packet8bf& a) {
+  return _mm_cmpgt_epi16(_mm_setzero_si128(), a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f psignbit(const Packet8f& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_castsi256_ps(_mm256_cmpgt_epi32(_mm256_setzero_si256(), _mm256_castps_si256(a)));
+#else
+  return _mm256_castsi256_ps(parithmetic_shift_right<31>(Packet8i(_mm256_castps_si256(a))));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8ui psignbit(const Packet8ui& /*unused*/) {
+  return _mm256_setzero_si256();
+}
+#ifdef EIGEN_VECTORIZE_AVX2
+template <>
+EIGEN_STRONG_INLINE Packet4d psignbit(const Packet4d& a) {
+  return _mm256_castsi256_pd(_mm256_cmpgt_epi64(_mm256_setzero_si256(), _mm256_castpd_si256(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul psignbit(const Packet4ul& /*unused*/) {
+  return _mm256_setzero_si256();
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pfrexp<Packet8f>(const Packet8f& a, Packet8f& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+// Extract exponent without existence of Packet4l.
+template <>
+EIGEN_STRONG_INLINE Packet4d pfrexp_generic_get_biased_exponent(const Packet4d& a) {
+  const Packet4d cst_exp_mask = pset1frombits<Packet4d>(static_cast<uint64_t>(0x7ff0000000000000ull));
+  __m256i a_expo = _mm256_castpd_si256(pand(a, cst_exp_mask));
+#ifdef EIGEN_VECTORIZE_AVX2
+  a_expo = _mm256_srli_epi64(a_expo, 52);
+  __m128i lo = _mm256_extractf128_si256(a_expo, 0);
+  __m128i hi = _mm256_extractf128_si256(a_expo, 1);
+#else
+  __m128i lo = _mm256_extractf128_si256(a_expo, 0);
+  __m128i hi = _mm256_extractf128_si256(a_expo, 1);
+  lo = _mm_srli_epi64(lo, 52);
+  hi = _mm_srli_epi64(hi, 52);
+#endif
+  Packet2d exponent_lo = _mm_cvtepi32_pd(vec4i_swizzle1(lo, 0, 2, 1, 3));
+  Packet2d exponent_hi = _mm_cvtepi32_pd(vec4i_swizzle1(hi, 0, 2, 1, 3));
+  Packet4d exponent = _mm256_insertf128_pd(_mm256_setzero_pd(), exponent_lo, 0);
+  exponent = _mm256_insertf128_pd(exponent, exponent_hi, 1);
+  return exponent;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pfrexp<Packet4d>(const Packet4d& a, Packet4d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pldexp<Packet8f>(const Packet8f& a, const Packet8f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pldexp<Packet4d>(const Packet4d& a, const Packet4d& exponent) {
+  // Clamp exponent to [-2099, 2099]
+  const Packet4d max_exponent = pset1<Packet4d>(2099.0);
+  const Packet4i e = _mm256_cvtpd_epi32(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
+
+  // Split 2^e into four factors and multiply.
+  const Packet4i bias = pset1<Packet4i>(1023);
+  Packet4i b = parithmetic_shift_right<2>(e);  // floor(e/4)
+
+  // 2^b
+  Packet4i hi = vec4i_swizzle1(padd(b, bias), 0, 2, 1, 3);
+  Packet4i lo = _mm_slli_epi64(hi, 52);
+  hi = _mm_slli_epi64(_mm_srli_epi64(hi, 32), 52);
+  Packet4d c = _mm256_castsi256_pd(_mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1));
+  Packet4d out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
+
+  // 2^(e - 3b)
+  b = psub(psub(psub(e, b), b), b);  // e - 3b
+  hi = vec4i_swizzle1(padd(b, bias), 0, 2, 1, 3);
+  lo = _mm_slli_epi64(hi, 52);
+  hi = _mm_slli_epi64(_mm_srli_epi64(hi, 32), 52);
+  c = _mm256_castsi256_pd(_mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1));
+  out = pmul(out, c);  // a * 2^e
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pldexp_fast<Packet4d>(const Packet4d& a, const Packet4d& exponent) {
+  // Clamp exponent to [-1024, 1024]
+  const Packet4d min_exponent = pset1<Packet4d>(-1023.0);
+  const Packet4d max_exponent = pset1<Packet4d>(1024.0);
+  const Packet4i e = _mm256_cvtpd_epi32(pmin(pmax(exponent, min_exponent), max_exponent));
+  const Packet4i bias = pset1<Packet4i>(1023);
+
+  // 2^e
+  Packet4i hi = vec4i_swizzle1(padd(e, bias), 0, 2, 1, 3);
+  const Packet4i lo = _mm_slli_epi64(hi, 52);
+  hi = _mm_slli_epi64(_mm_srli_epi64(hi, 32), 52);
+  const Packet4d c = _mm256_castsi256_pd(_mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1));
+  return pmul(a, c);  // a * 2^e
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f predux_half<Packet8f>(const Packet8f& a) {
+  return _mm_add_ps(_mm256_castps256_ps128(a), _mm256_extractf128_ps(a, 1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i predux_half<Packet8i>(const Packet8i& a) {
+  return _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui predux_half<Packet8ui>(const Packet8ui& a) {
+  return _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1));
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8f, 8>& kernel) {
+  __m256 T0 = _mm256_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m256 T1 = _mm256_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m256 T2 = _mm256_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m256 T3 = _mm256_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+  __m256 T4 = _mm256_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
+  __m256 T5 = _mm256_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
+  __m256 T6 = _mm256_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
+  __m256 T7 = _mm256_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
+  __m256 S0 = _mm256_shuffle_ps(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256 S1 = _mm256_shuffle_ps(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m256 S2 = _mm256_shuffle_ps(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256 S3 = _mm256_shuffle_ps(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+  __m256 S4 = _mm256_shuffle_ps(T4, T6, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256 S5 = _mm256_shuffle_ps(T4, T6, _MM_SHUFFLE(3, 2, 3, 2));
+  __m256 S6 = _mm256_shuffle_ps(T5, T7, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256 S7 = _mm256_shuffle_ps(T5, T7, _MM_SHUFFLE(3, 2, 3, 2));
+  kernel.packet[0] = _mm256_permute2f128_ps(S0, S4, 0x20);
+  kernel.packet[1] = _mm256_permute2f128_ps(S1, S5, 0x20);
+  kernel.packet[2] = _mm256_permute2f128_ps(S2, S6, 0x20);
+  kernel.packet[3] = _mm256_permute2f128_ps(S3, S7, 0x20);
+  kernel.packet[4] = _mm256_permute2f128_ps(S0, S4, 0x31);
+  kernel.packet[5] = _mm256_permute2f128_ps(S1, S5, 0x31);
+  kernel.packet[6] = _mm256_permute2f128_ps(S2, S6, 0x31);
+  kernel.packet[7] = _mm256_permute2f128_ps(S3, S7, 0x31);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8f, 4>& kernel) {
+  __m256 T0 = _mm256_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m256 T1 = _mm256_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m256 T2 = _mm256_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m256 T3 = _mm256_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+
+  __m256 S0 = _mm256_shuffle_ps(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256 S1 = _mm256_shuffle_ps(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m256 S2 = _mm256_shuffle_ps(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256 S3 = _mm256_shuffle_ps(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+
+  kernel.packet[0] = _mm256_permute2f128_ps(S0, S1, 0x20);
+  kernel.packet[1] = _mm256_permute2f128_ps(S2, S3, 0x20);
+  kernel.packet[2] = _mm256_permute2f128_ps(S0, S1, 0x31);
+  kernel.packet[3] = _mm256_permute2f128_ps(S2, S3, 0x31);
+}
+
+#define MM256_SHUFFLE_EPI32(A, B, M) \
+  _mm256_castps_si256(_mm256_shuffle_ps(_mm256_castsi256_ps(A), _mm256_castsi256_ps(B), M))
+
+#ifndef EIGEN_VECTORIZE_AVX2
+#define MM256_UNPACKLO_EPI32(A, B) \
+  _mm256_castps_si256(_mm256_unpacklo_ps(_mm256_castsi256_ps(A), _mm256_castsi256_ps(B)))
+#define MM256_UNPACKHI_EPI32(A, B) \
+  _mm256_castps_si256(_mm256_unpackhi_ps(_mm256_castsi256_ps(A), _mm256_castsi256_ps(B)))
+#else
+#define MM256_UNPACKLO_EPI32(A, B) _mm256_unpacklo_epi32(A, B)
+#define MM256_UNPACKHI_EPI32(A, B) _mm256_unpackhi_epi32(A, B)
+#endif
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8i, 8>& kernel) {
+  __m256i T0 = MM256_UNPACKLO_EPI32(kernel.packet[0], kernel.packet[1]);
+  __m256i T1 = MM256_UNPACKHI_EPI32(kernel.packet[0], kernel.packet[1]);
+  __m256i T2 = MM256_UNPACKLO_EPI32(kernel.packet[2], kernel.packet[3]);
+  __m256i T3 = MM256_UNPACKHI_EPI32(kernel.packet[2], kernel.packet[3]);
+  __m256i T4 = MM256_UNPACKLO_EPI32(kernel.packet[4], kernel.packet[5]);
+  __m256i T5 = MM256_UNPACKHI_EPI32(kernel.packet[4], kernel.packet[5]);
+  __m256i T6 = MM256_UNPACKLO_EPI32(kernel.packet[6], kernel.packet[7]);
+  __m256i T7 = MM256_UNPACKHI_EPI32(kernel.packet[6], kernel.packet[7]);
+  __m256i S0 = MM256_SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256i S1 = MM256_SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m256i S2 = MM256_SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256i S3 = MM256_SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+  __m256i S4 = MM256_SHUFFLE_EPI32(T4, T6, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256i S5 = MM256_SHUFFLE_EPI32(T4, T6, _MM_SHUFFLE(3, 2, 3, 2));
+  __m256i S6 = MM256_SHUFFLE_EPI32(T5, T7, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256i S7 = MM256_SHUFFLE_EPI32(T5, T7, _MM_SHUFFLE(3, 2, 3, 2));
+  kernel.packet[0] = _mm256_permute2f128_si256(S0, S4, 0x20);
+  kernel.packet[1] = _mm256_permute2f128_si256(S1, S5, 0x20);
+  kernel.packet[2] = _mm256_permute2f128_si256(S2, S6, 0x20);
+  kernel.packet[3] = _mm256_permute2f128_si256(S3, S7, 0x20);
+  kernel.packet[4] = _mm256_permute2f128_si256(S0, S4, 0x31);
+  kernel.packet[5] = _mm256_permute2f128_si256(S1, S5, 0x31);
+  kernel.packet[6] = _mm256_permute2f128_si256(S2, S6, 0x31);
+  kernel.packet[7] = _mm256_permute2f128_si256(S3, S7, 0x31);
+}
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8ui, 8>& kernel) {
+  ptranspose((PacketBlock<Packet8i, 8>&)kernel);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8i, 4>& kernel) {
+  __m256i T0 = MM256_UNPACKLO_EPI32(kernel.packet[0], kernel.packet[1]);
+  __m256i T1 = MM256_UNPACKHI_EPI32(kernel.packet[0], kernel.packet[1]);
+  __m256i T2 = MM256_UNPACKLO_EPI32(kernel.packet[2], kernel.packet[3]);
+  __m256i T3 = MM256_UNPACKHI_EPI32(kernel.packet[2], kernel.packet[3]);
+
+  __m256i S0 = MM256_SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256i S1 = MM256_SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m256i S2 = MM256_SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m256i S3 = MM256_SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+
+  kernel.packet[0] = _mm256_permute2f128_si256(S0, S1, 0x20);
+  kernel.packet[1] = _mm256_permute2f128_si256(S2, S3, 0x20);
+  kernel.packet[2] = _mm256_permute2f128_si256(S0, S1, 0x31);
+  kernel.packet[3] = _mm256_permute2f128_si256(S2, S3, 0x31);
+}
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8ui, 4>& kernel) {
+  ptranspose((PacketBlock<Packet8i, 4>&)kernel);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4d, 4>& kernel) {
+  __m256d T0 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 15);
+  __m256d T1 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 0);
+  __m256d T2 = _mm256_shuffle_pd(kernel.packet[2], kernel.packet[3], 15);
+  __m256d T3 = _mm256_shuffle_pd(kernel.packet[2], kernel.packet[3], 0);
+
+  kernel.packet[1] = _mm256_permute2f128_pd(T0, T2, 32);
+  kernel.packet[3] = _mm256_permute2f128_pd(T0, T2, 49);
+  kernel.packet[0] = _mm256_permute2f128_pd(T1, T3, 32);
+  kernel.packet[2] = _mm256_permute2f128_pd(T1, T3, 49);
+}
+
+// Packet math for Eigen::half
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template <>
+struct unpacket_traits<Packet8h> {
+  typedef Eigen::half type;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet8h half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pset1<Packet8h>(const Eigen::half& from) {
+  return _mm_set1_epi16(numext::bit_cast<numext::uint16_t>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet8h>(const Packet8h& from) {
+  return numext::bit_cast<Eigen::half>(static_cast<numext::uint16_t>(_mm_extract_epi16(from, 0)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pload<Packet8h>(const Eigen::half* from) {
+  return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h ploadu<Packet8h>(const Eigen::half* from) {
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet8h& from) {
+  _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet8h& from) {
+  _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h ploaddup<Packet8h>(const Eigen::half* from) {
+  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
+  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
+  const numext::uint16_t c = numext::bit_cast<numext::uint16_t>(from[2]);
+  const numext::uint16_t d = numext::bit_cast<numext::uint16_t>(from[3]);
+  return _mm_set_epi16(d, d, c, c, b, b, a, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h ploadquad<Packet8h>(const Eigen::half* from) {
+  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
+  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
+  return _mm_set_epi16(b, b, b, b, a, a, a, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h ptrue(const Packet8h& a) {
+  return _mm_cmpeq_epi32(a, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pabs(const Packet8h& a) {
+  const __m128i sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm_andnot_si128(sign_mask, a);
+}
+
+EIGEN_STRONG_INLINE Packet8f half2float(const Packet8h& a) {
+#ifdef EIGEN_HAS_FP16_C
+  return _mm256_cvtph_ps(a);
+#else
+  Eigen::internal::Packet8f pp = _mm256_castsi256_ps(
+      _mm256_insertf128_si256(_mm256_castsi128_si256(half2floatsse(a)), half2floatsse(_mm_srli_si128(a, 8)), 1));
+  return pp;
+#endif
+}
+
+EIGEN_STRONG_INLINE Packet8h float2half(const Packet8f& a) {
+#ifdef EIGEN_HAS_FP16_C
+  return _mm256_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT);
+#else
+  __m128i lo = float2half(_mm256_extractf128_ps(a, 0));
+  __m128i hi = float2half(_mm256_extractf128_ps(a, 1));
+  return _mm_packus_epi32(lo, hi);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmin<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return float2half(pmin<Packet8f>(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmax<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return float2half(pmax<Packet8f>(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h plset<Packet8h>(const half& a) {
+  return float2half(plset<Packet8f>(static_cast<float>(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h por(const Packet8h& a, const Packet8h& b) {
+  // in some cases Packet4i is a wrapper around __m128i, so we either need to
+  // cast to Packet4i to directly call the intrinsics as below:
+  return _mm_or_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8h pxor(const Packet8h& a, const Packet8h& b) {
+  return _mm_xor_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8h pand(const Packet8h& a, const Packet8h& b) {
+  return _mm_and_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8h pandnot(const Packet8h& a, const Packet8h& b) {
+  return _mm_andnot_si128(b, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pselect(const Packet8h& mask, const Packet8h& a, const Packet8h& b) {
+  return _mm_blendv_epi8(b, a, mask);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pround<Packet8h>(const Packet8h& a) {
+  return float2half(pround<Packet8f>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h print<Packet8h>(const Packet8h& a) {
+  return float2half(print<Packet8f>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pceil<Packet8h>(const Packet8h& a) {
+  return float2half(pceil<Packet8f>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pfloor<Packet8h>(const Packet8h& a) {
+  return float2half(pfloor<Packet8f>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h ptrunc<Packet8h>(const Packet8h& a) {
+  return float2half(ptrunc<Packet8f>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pisinf<Packet8h>(const Packet8h& a) {
+  constexpr uint16_t kInf = ((1 << 5) - 1) << 10;
+  constexpr uint16_t kAbsMask = (1 << 15) - 1;
+  return _mm_cmpeq_epi16(_mm_and_si128(a.m_val, _mm_set1_epi16(kAbsMask)), _mm_set1_epi16(kInf));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pisnan<Packet8h>(const Packet8h& a) {
+  constexpr uint16_t kInf = ((1 << 5) - 1) << 10;
+  constexpr uint16_t kAbsMask = (1 << 15) - 1;
+  return _mm_cmpgt_epi16(_mm_and_si128(a.m_val, _mm_set1_epi16(kAbsMask)), _mm_set1_epi16(kInf));
+}
+
+// convert the sign-magnitude representation to two's complement
+EIGEN_STRONG_INLINE __m128i pmaptosigned(const __m128i& a) {
+  constexpr uint16_t kAbsMask = (1 << 15) - 1;
+  // if 'a' has the sign bit set, clear the sign bit and negate the result as if it were an integer
+  return _mm_sign_epi16(_mm_and_si128(a, _mm_set1_epi16(kAbsMask)), a);
+}
+
+// return true if both `a` and `b` are not NaN
+EIGEN_STRONG_INLINE Packet8h pisordered(const Packet8h& a, const Packet8h& b) {
+  constexpr uint16_t kInf = ((1 << 5) - 1) << 10;
+  constexpr uint16_t kAbsMask = (1 << 15) - 1;
+  __m128i abs_a = _mm_and_si128(a.m_val, _mm_set1_epi16(kAbsMask));
+  __m128i abs_b = _mm_and_si128(b.m_val, _mm_set1_epi16(kAbsMask));
+  // check if both `abs_a <= kInf` and `abs_b <= kInf` by checking if max(abs_a, abs_b) <= kInf
+  // SSE has no `lesser or equal` instruction for integers, but comparing against kInf + 1 accomplishes the same goal
+  return _mm_cmplt_epi16(_mm_max_epu16(abs_a, abs_b), _mm_set1_epi16(kInf + 1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pcmp_eq(const Packet8h& a, const Packet8h& b) {
+  __m128i isOrdered = pisordered(a, b);
+  __m128i isEqual = _mm_cmpeq_epi16(pmaptosigned(a.m_val), pmaptosigned(b.m_val));
+  return _mm_and_si128(isOrdered, isEqual);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pcmp_le(const Packet8h& a, const Packet8h& b) {
+  __m128i isOrdered = pisordered(a, b);
+  __m128i isGreater = _mm_cmpgt_epi16(pmaptosigned(a.m_val), pmaptosigned(b.m_val));
+  return _mm_andnot_si128(isGreater, isOrdered);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pcmp_lt(const Packet8h& a, const Packet8h& b) {
+  __m128i isOrdered = pisordered(a, b);
+  __m128i isLess = _mm_cmplt_epi16(pmaptosigned(a.m_val), pmaptosigned(b.m_val));
+  return _mm_and_si128(isOrdered, isLess);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pcmp_lt_or_nan(const Packet8h& a, const Packet8h& b) {
+  __m128i isUnordered = por(pisnan(a), pisnan(b));
+  __m128i isLess = _mm_cmplt_epi16(pmaptosigned(a.m_val), pmaptosigned(b.m_val));
+  return _mm_or_si128(isUnordered, isLess);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pconj(const Packet8h& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pnegate(const Packet8h& a) {
+  Packet8h sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm_xor_si128(a, sign_mask);
+}
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template <>
+EIGEN_STRONG_INLINE Packet8h padd<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  Packet8f af = half2float(a);
+  Packet8f bf = half2float(b);
+  Packet8f rf = padd(af, bf);
+  return float2half(rf);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h psub<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  Packet8f af = half2float(a);
+  Packet8f bf = half2float(b);
+  Packet8f rf = psub(af, bf);
+  return float2half(rf);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmul<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  Packet8f af = half2float(a);
+  Packet8f bf = half2float(b);
+  Packet8f rf = pmul(af, bf);
+  return float2half(rf);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmadd<Packet8h>(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return float2half(pmadd(half2float(a), half2float(b), half2float(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmsub<Packet8h>(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return float2half(pmsub(half2float(a), half2float(b), half2float(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pnmadd<Packet8h>(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return float2half(pnmadd(half2float(a), half2float(b), half2float(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pnmsub<Packet8h>(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return float2half(pnmsub(half2float(a), half2float(b), half2float(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pdiv<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  Packet8f af = half2float(a);
+  Packet8f bf = half2float(b);
+  Packet8f rf = pdiv(af, bf);
+  return float2half(rf);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pgather<Eigen::half, Packet8h>(const Eigen::half* from, Index stride) {
+  const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(from[0 * stride]);
+  const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(from[1 * stride]);
+  const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(from[2 * stride]);
+  const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(from[3 * stride]);
+  const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(from[4 * stride]);
+  const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(from[5 * stride]);
+  const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(from[6 * stride]);
+  const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(from[7 * stride]);
+  return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8h>(Eigen::half* to, const Packet8h& from, Index stride) {
+  EIGEN_ALIGN32 Eigen::half aux[8];
+  pstore(aux, from);
+  to[stride * 0] = aux[0];
+  to[stride * 1] = aux[1];
+  to[stride * 2] = aux[2];
+  to[stride * 3] = aux[3];
+  to[stride * 4] = aux[4];
+  to[stride * 5] = aux[5];
+  to[stride * 6] = aux[6];
+  to[stride * 7] = aux[7];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h preverse(const Packet8h& a) {
+  __m128i m = _mm_setr_epi8(14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1);
+  return _mm_shuffle_epi8(a, m);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8h, 8>& kernel) {
+  __m128i a = kernel.packet[0];
+  __m128i b = kernel.packet[1];
+  __m128i c = kernel.packet[2];
+  __m128i d = kernel.packet[3];
+  __m128i e = kernel.packet[4];
+  __m128i f = kernel.packet[5];
+  __m128i g = kernel.packet[6];
+  __m128i h = kernel.packet[7];
+
+  __m128i a03b03 = _mm_unpacklo_epi16(a, b);
+  __m128i c03d03 = _mm_unpacklo_epi16(c, d);
+  __m128i e03f03 = _mm_unpacklo_epi16(e, f);
+  __m128i g03h03 = _mm_unpacklo_epi16(g, h);
+  __m128i a47b47 = _mm_unpackhi_epi16(a, b);
+  __m128i c47d47 = _mm_unpackhi_epi16(c, d);
+  __m128i e47f47 = _mm_unpackhi_epi16(e, f);
+  __m128i g47h47 = _mm_unpackhi_epi16(g, h);
+
+  __m128i a01b01c01d01 = _mm_unpacklo_epi32(a03b03, c03d03);
+  __m128i a23b23c23d23 = _mm_unpackhi_epi32(a03b03, c03d03);
+  __m128i e01f01g01h01 = _mm_unpacklo_epi32(e03f03, g03h03);
+  __m128i e23f23g23h23 = _mm_unpackhi_epi32(e03f03, g03h03);
+  __m128i a45b45c45d45 = _mm_unpacklo_epi32(a47b47, c47d47);
+  __m128i a67b67c67d67 = _mm_unpackhi_epi32(a47b47, c47d47);
+  __m128i e45f45g45h45 = _mm_unpacklo_epi32(e47f47, g47h47);
+  __m128i e67f67g67h67 = _mm_unpackhi_epi32(e47f47, g47h47);
+
+  __m128i a0b0c0d0e0f0g0h0 = _mm_unpacklo_epi64(a01b01c01d01, e01f01g01h01);
+  __m128i a1b1c1d1e1f1g1h1 = _mm_unpackhi_epi64(a01b01c01d01, e01f01g01h01);
+  __m128i a2b2c2d2e2f2g2h2 = _mm_unpacklo_epi64(a23b23c23d23, e23f23g23h23);
+  __m128i a3b3c3d3e3f3g3h3 = _mm_unpackhi_epi64(a23b23c23d23, e23f23g23h23);
+  __m128i a4b4c4d4e4f4g4h4 = _mm_unpacklo_epi64(a45b45c45d45, e45f45g45h45);
+  __m128i a5b5c5d5e5f5g5h5 = _mm_unpackhi_epi64(a45b45c45d45, e45f45g45h45);
+  __m128i a6b6c6d6e6f6g6h6 = _mm_unpacklo_epi64(a67b67c67d67, e67f67g67h67);
+  __m128i a7b7c7d7e7f7g7h7 = _mm_unpackhi_epi64(a67b67c67d67, e67f67g67h67);
+
+  kernel.packet[0] = a0b0c0d0e0f0g0h0;
+  kernel.packet[1] = a1b1c1d1e1f1g1h1;
+  kernel.packet[2] = a2b2c2d2e2f2g2h2;
+  kernel.packet[3] = a3b3c3d3e3f3g3h3;
+  kernel.packet[4] = a4b4c4d4e4f4g4h4;
+  kernel.packet[5] = a5b5c5d5e5f5g5h5;
+  kernel.packet[6] = a6b6c6d6e6f6g6h6;
+  kernel.packet[7] = a7b7c7d7e7f7g7h7;
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8h, 4>& kernel) {
+  EIGEN_ALIGN32 Eigen::half in[4][8];
+  pstore<Eigen::half>(in[0], kernel.packet[0]);
+  pstore<Eigen::half>(in[1], kernel.packet[1]);
+  pstore<Eigen::half>(in[2], kernel.packet[2]);
+  pstore<Eigen::half>(in[3], kernel.packet[3]);
+
+  EIGEN_ALIGN32 Eigen::half out[4][8];
+
+  for (int i = 0; i < 4; ++i) {
+    for (int j = 0; j < 4; ++j) {
+      out[i][j] = in[j][2 * i];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j + 4] = in[j][2 * i + 1];
+    }
+  }
+
+  kernel.packet[0] = pload<Packet8h>(out[0]);
+  kernel.packet[1] = pload<Packet8h>(out[1]);
+  kernel.packet[2] = pload<Packet8h>(out[2]);
+  kernel.packet[3] = pload<Packet8h>(out[3]);
+}
+
+#endif
+
+// BFloat16 implementation.
+
+EIGEN_STRONG_INLINE Packet8f Bf16ToF32(const Packet8bf& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  __m256i extend = _mm256_cvtepu16_epi32(a);
+  return _mm256_castsi256_ps(_mm256_slli_epi32(extend, 16));
+#else
+  __m128i lo = _mm_cvtepu16_epi32(a);
+  __m128i hi = _mm_cvtepu16_epi32(_mm_srli_si128(a, 8));
+  __m128i lo_shift = _mm_slli_epi32(lo, 16);
+  __m128i hi_shift = _mm_slli_epi32(hi, 16);
+  return _mm256_castsi256_ps(_mm256_insertf128_si256(_mm256_castsi128_si256(lo_shift), hi_shift, 1));
+#endif
+}
+
+// Convert float to bfloat16 according to round-to-nearest-even/denormals algorithm.
+EIGEN_STRONG_INLINE Packet8bf F32ToBf16(const Packet8f& a) {
+  __m256i input = _mm256_castps_si256(a);
+
+#ifdef EIGEN_VECTORIZE_AVX2
+  // uint32_t lsb = (input >> 16);
+  __m256i t = _mm256_srli_epi32(input, 16);
+  // uint32_t lsb = lsb & 1;
+  t = _mm256_and_si256(t, _mm256_set1_epi32(1));
+  // uint32_t rounding_bias = 0x7fff + lsb;
+  t = _mm256_add_epi32(t, _mm256_set1_epi32(0x7fff));
+  // input += rounding_bias;
+  t = _mm256_add_epi32(t, input);
+  // input = input >> 16;
+  t = _mm256_srli_epi32(t, 16);
+  // Check NaN before converting back to bf16
+  __m256 mask = _mm256_cmp_ps(a, a, _CMP_ORD_Q);
+  __m256i nan = _mm256_set1_epi32(0x7fc0);
+  t = _mm256_blendv_epi8(nan, t, _mm256_castps_si256(mask));
+  // output = numext::bit_cast<uint16_t>(input);
+  return _mm_packus_epi32(_mm256_extractf128_si256(t, 0), _mm256_extractf128_si256(t, 1));
+#else
+  // uint32_t lsb = (input >> 16);
+  __m128i lo = _mm_srli_epi32(_mm256_extractf128_si256(input, 0), 16);
+  __m128i hi = _mm_srli_epi32(_mm256_extractf128_si256(input, 1), 16);
+  // uint32_t lsb = lsb & 1;
+  lo = _mm_and_si128(lo, _mm_set1_epi32(1));
+  hi = _mm_and_si128(hi, _mm_set1_epi32(1));
+  // uint32_t rounding_bias = 0x7fff + lsb;
+  lo = _mm_add_epi32(lo, _mm_set1_epi32(0x7fff));
+  hi = _mm_add_epi32(hi, _mm_set1_epi32(0x7fff));
+  // input += rounding_bias;
+  lo = _mm_add_epi32(lo, _mm256_extractf128_si256(input, 0));
+  hi = _mm_add_epi32(hi, _mm256_extractf128_si256(input, 1));
+  // input = input >> 16;
+  lo = _mm_srli_epi32(lo, 16);
+  hi = _mm_srli_epi32(hi, 16);
+  // Check NaN before converting back to bf16
+  __m256 mask = _mm256_cmp_ps(a, a, _CMP_ORD_Q);
+  __m128i nan = _mm_set1_epi32(0x7fc0);
+  lo = _mm_blendv_epi8(nan, lo, _mm_castps_si128(_mm256_castps256_ps128(mask)));
+  hi = _mm_blendv_epi8(nan, hi, _mm_castps_si128(_mm256_extractf128_ps(mask, 1)));
+  // output = numext::bit_cast<uint16_t>(input);
+  return _mm_packus_epi32(lo, hi);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pset1<Packet8bf>(const bfloat16& from) {
+  return _mm_set1_epi16(numext::bit_cast<numext::uint16_t>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 pfirst<Packet8bf>(const Packet8bf& from) {
+  return numext::bit_cast<bfloat16>(static_cast<numext::uint16_t>(_mm_extract_epi16(from, 0)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pload<Packet8bf>(const bfloat16* from) {
+  return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf ploadu<Packet8bf>(const bfloat16* from) {
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet8bf& from) {
+  _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet8bf& from) {
+  _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf ploaddup<Packet8bf>(const bfloat16* from) {
+  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
+  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
+  const numext::uint16_t c = numext::bit_cast<numext::uint16_t>(from[2]);
+  const numext::uint16_t d = numext::bit_cast<numext::uint16_t>(from[3]);
+  return _mm_set_epi16(d, d, c, c, b, b, a, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf ploadquad<Packet8bf>(const bfloat16* from) {
+  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
+  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
+  return _mm_set_epi16(b, b, b, b, a, a, a, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf ptrue(const Packet8bf& a) {
+  return _mm_cmpeq_epi32(a, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pabs(const Packet8bf& a) {
+  const __m128i sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm_andnot_si128(sign_mask, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmin<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return F32ToBf16(pmin<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmax<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return F32ToBf16(pmax<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf plset<Packet8bf>(const bfloat16& a) {
+  return F32ToBf16(plset<Packet8f>(static_cast<float>(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf por(const Packet8bf& a, const Packet8bf& b) {
+  return _mm_or_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pxor(const Packet8bf& a, const Packet8bf& b) {
+  return _mm_xor_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pand(const Packet8bf& a, const Packet8bf& b) {
+  return _mm_and_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pandnot(const Packet8bf& a, const Packet8bf& b) {
+  return _mm_andnot_si128(b, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pselect(const Packet8bf& mask, const Packet8bf& a, const Packet8bf& b) {
+  return _mm_blendv_epi8(b, a, mask);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pround<Packet8bf>(const Packet8bf& a) {
+  return F32ToBf16(pround<Packet8f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf print<Packet8bf>(const Packet8bf& a) {
+  return F32ToBf16(print<Packet8f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pceil<Packet8bf>(const Packet8bf& a) {
+  return F32ToBf16(pceil<Packet8f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pfloor<Packet8bf>(const Packet8bf& a) {
+  return F32ToBf16(pfloor<Packet8f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf ptrunc<Packet8bf>(const Packet8bf& a) {
+  return F32ToBf16(ptrunc<Packet8f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcmp_eq(const Packet8bf& a, const Packet8bf& b) {
+  return Pack16To8(pcmp_eq(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcmp_le(const Packet8bf& a, const Packet8bf& b) {
+  return Pack16To8(pcmp_le(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcmp_lt(const Packet8bf& a, const Packet8bf& b) {
+  return Pack16To8(pcmp_lt(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcmp_lt_or_nan(const Packet8bf& a, const Packet8bf& b) {
+  return Pack16To8(pcmp_lt_or_nan(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pconj(const Packet8bf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pnegate(const Packet8bf& a) {
+  Packet8bf sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm_xor_si128(a, sign_mask);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf padd<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return F32ToBf16(padd<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf psub<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return F32ToBf16(psub<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmul<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return F32ToBf16(pmul<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmadd<Packet8bf>(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
+  return F32ToBf16(pmadd(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmsub<Packet8bf>(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
+  return F32ToBf16(pmsub(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pnmadd<Packet8bf>(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
+  return F32ToBf16(pnmadd(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pnmsub<Packet8bf>(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
+  return F32ToBf16(pnmsub(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pdiv<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return F32ToBf16(pdiv<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pgather<bfloat16, Packet8bf>(const bfloat16* from, Index stride) {
+  const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(from[0 * stride]);
+  const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(from[1 * stride]);
+  const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(from[2 * stride]);
+  const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(from[3 * stride]);
+  const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(from[4 * stride]);
+  const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(from[5 * stride]);
+  const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(from[6 * stride]);
+  const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(from[7 * stride]);
+  return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet8bf>(bfloat16* to, const Packet8bf& from, Index stride) {
+  EIGEN_ALIGN32 bfloat16 aux[8];
+  pstore(aux, from);
+  to[stride * 0] = aux[0];
+  to[stride * 1] = aux[1];
+  to[stride * 2] = aux[2];
+  to[stride * 3] = aux[3];
+  to[stride * 4] = aux[4];
+  to[stride * 5] = aux[5];
+  to[stride * 6] = aux[6];
+  to[stride * 7] = aux[7];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf preverse(const Packet8bf& a) {
+  __m128i m = _mm_setr_epi8(14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1);
+  return _mm_shuffle_epi8(a, m);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8bf, 8>& kernel) {
+  __m128i a = kernel.packet[0];
+  __m128i b = kernel.packet[1];
+  __m128i c = kernel.packet[2];
+  __m128i d = kernel.packet[3];
+  __m128i e = kernel.packet[4];
+  __m128i f = kernel.packet[5];
+  __m128i g = kernel.packet[6];
+  __m128i h = kernel.packet[7];
+
+  __m128i a03b03 = _mm_unpacklo_epi16(a, b);
+  __m128i c03d03 = _mm_unpacklo_epi16(c, d);
+  __m128i e03f03 = _mm_unpacklo_epi16(e, f);
+  __m128i g03h03 = _mm_unpacklo_epi16(g, h);
+  __m128i a47b47 = _mm_unpackhi_epi16(a, b);
+  __m128i c47d47 = _mm_unpackhi_epi16(c, d);
+  __m128i e47f47 = _mm_unpackhi_epi16(e, f);
+  __m128i g47h47 = _mm_unpackhi_epi16(g, h);
+
+  __m128i a01b01c01d01 = _mm_unpacklo_epi32(a03b03, c03d03);
+  __m128i a23b23c23d23 = _mm_unpackhi_epi32(a03b03, c03d03);
+  __m128i e01f01g01h01 = _mm_unpacklo_epi32(e03f03, g03h03);
+  __m128i e23f23g23h23 = _mm_unpackhi_epi32(e03f03, g03h03);
+  __m128i a45b45c45d45 = _mm_unpacklo_epi32(a47b47, c47d47);
+  __m128i a67b67c67d67 = _mm_unpackhi_epi32(a47b47, c47d47);
+  __m128i e45f45g45h45 = _mm_unpacklo_epi32(e47f47, g47h47);
+  __m128i e67f67g67h67 = _mm_unpackhi_epi32(e47f47, g47h47);
+
+  kernel.packet[0] = _mm_unpacklo_epi64(a01b01c01d01, e01f01g01h01);
+  kernel.packet[1] = _mm_unpackhi_epi64(a01b01c01d01, e01f01g01h01);
+  kernel.packet[2] = _mm_unpacklo_epi64(a23b23c23d23, e23f23g23h23);
+  kernel.packet[3] = _mm_unpackhi_epi64(a23b23c23d23, e23f23g23h23);
+  kernel.packet[4] = _mm_unpacklo_epi64(a45b45c45d45, e45f45g45h45);
+  kernel.packet[5] = _mm_unpackhi_epi64(a45b45c45d45, e45f45g45h45);
+  kernel.packet[6] = _mm_unpacklo_epi64(a67b67c67d67, e67f67g67h67);
+  kernel.packet[7] = _mm_unpackhi_epi64(a67b67c67d67, e67f67g67h67);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8bf, 4>& kernel) {
+  __m128i a = kernel.packet[0];
+  __m128i b = kernel.packet[1];
+  __m128i c = kernel.packet[2];
+  __m128i d = kernel.packet[3];
+
+  __m128i ab_03 = _mm_unpacklo_epi16(a, b);
+  __m128i cd_03 = _mm_unpacklo_epi16(c, d);
+  __m128i ab_47 = _mm_unpackhi_epi16(a, b);
+  __m128i cd_47 = _mm_unpackhi_epi16(c, d);
+
+  kernel.packet[0] = _mm_unpacklo_epi32(ab_03, cd_03);
+  kernel.packet[1] = _mm_unpackhi_epi32(ab_03, cd_03);
+  kernel.packet[2] = _mm_unpacklo_epi32(ab_47, cd_47);
+  kernel.packet[3] = _mm_unpackhi_epi32(ab_47, cd_47);
+}
+
+/*---------------- load/store segment support ----------------*/
+
+// returns a mask of 8-bit elements (at most 4) that are all 1's in the range [begin, begin + count) and 0 elsewhere.
+inline __m128i segment_mask_4x8(Index begin, Index count) {
+  eigen_assert(begin >= 0 && begin + count <= 4);
+  long long mask = 1;
+  mask <<= CHAR_BIT * count;
+  mask--;
+  mask <<= CHAR_BIT * begin;
+#if !EIGEN_ARCH_x86_64
+  return _mm_loadl_epi64(reinterpret_cast<const __m128i*>(&mask));
+#else
+  return _mm_cvtsi64_si128(mask);
+#endif
+}
+
+// returns a mask of 8-bit elements (at most 8) that are all 1's in the range [begin, begin + count) and 0 elsewhere.
+inline __m128i segment_mask_8x8(Index begin, Index count) {
+  eigen_assert(begin >= 0 && begin + count <= 8);
+  long long mask = 1;
+  // avoid UB when count == 8
+  mask <<= (CHAR_BIT / 2) * count;
+  mask <<= (CHAR_BIT / 2) * count;
+  mask--;
+  mask <<= CHAR_BIT * begin;
+#if !EIGEN_ARCH_x86_64
+  return _mm_loadl_epi64(reinterpret_cast<const __m128i*>(&mask));
+#else
+  return _mm_cvtsi64_si128(mask);
+#endif
+}
+
+// returns a mask of 32-bit elements (at most 4) that are all 1's in the range [begin, begin + count) and 0 elsewhere.
+inline __m128i segment_mask_4x32(Index begin, Index count) {
+  eigen_assert(begin >= 0 && begin + count <= 4);
+  return _mm_cvtepi8_epi32(segment_mask_4x8(begin, count));
+}
+
+// returns a mask of 64-bit elements (at most 2) that are all 1's in the range [begin, begin + count) and 0 elsewhere.
+inline __m128i segment_mask_2x64(Index begin, Index count) {
+  eigen_assert(begin >= 0 && begin + count <= 2);
+  return _mm_cvtepi8_epi64(segment_mask_4x8(begin, count));
+}
+
+// returns a mask of 32-bit elements (at most 8) that are all 1's in the range [begin, begin + count) and 0 elsewhere.
+inline __m256i segment_mask_8x32(Index begin, Index count) {
+  __m128i mask_epi8 = segment_mask_8x8(begin, count);
+#ifdef EIGEN_VECTORIZE_AVX2
+  __m256i mask_epi32 = _mm256_cvtepi8_epi32(mask_epi8);
+#else
+  __m128i mask_epi32_lo = _mm_cvtepi8_epi32(mask_epi8);
+  __m128i mask_epi32_hi = _mm_cvtepi8_epi32(_mm_srli_epi64(mask_epi8, 32));
+  __m256i mask_epi32 = _mm256_insertf128_si256(_mm256_castsi128_si256(mask_epi32_lo), mask_epi32_hi, 1);
+#endif
+  return mask_epi32;
+}
+
+// returns a mask of 64-bit elements (at most 4) that are all 1's in the range [begin, begin + count) and 0 elsewhere.
+inline __m256i segment_mask_4x64(Index begin, Index count) {
+  __m128i mask_epi8 = segment_mask_4x8(begin, count);
+#ifdef EIGEN_VECTORIZE_AVX2
+  __m256i mask_epi64 = _mm256_cvtepi8_epi64(mask_epi8);
+#else
+  __m128i mask_epi64_lo = _mm_cvtepi8_epi64(mask_epi8);
+  __m128i mask_epi64_hi = _mm_cvtepi8_epi64(_mm_srli_epi64(mask_epi8, 16));
+  __m256i mask_epi64 = _mm256_insertf128_si256(_mm256_castsi128_si256(mask_epi64_lo), mask_epi64_hi, 1);
+#endif
+  return mask_epi64;
+}
+
+/*---------------- float ----------------*/
+
+template <>
+struct has_packet_segment<Packet4f> : std::true_type {};
+
+template <>
+struct has_packet_segment<Packet8f> : std::true_type {};
+
+template <>
+inline Packet4f ploaduSegment<Packet4f>(const float* from, Index begin, Index count) {
+  return _mm_maskload_ps(from, segment_mask_4x32(begin, count));
+}
+
+template <>
+inline void pstoreuSegment<float, Packet4f>(float* to, const Packet4f& from, Index begin, Index count) {
+  _mm_maskstore_ps(to, segment_mask_4x32(begin, count), from);
+}
+
+template <>
+inline Packet8f ploaduSegment<Packet8f>(const float* from, Index begin, Index count) {
+  return _mm256_maskload_ps(from, segment_mask_8x32(begin, count));
+}
+
+template <>
+inline void pstoreuSegment<float, Packet8f>(float* to, const Packet8f& from, Index begin, Index count) {
+  _mm256_maskstore_ps(to, segment_mask_8x32(begin, count), from);
+}
+
+/*---------------- int32 ----------------*/
+
+template <>
+struct has_packet_segment<Packet4i> : std::true_type {};
+
+template <>
+struct has_packet_segment<Packet8i> : std::true_type {};
+
+#ifdef EIGEN_VECTORIZE_AVX2
+
+template <>
+inline Packet4i ploaduSegment<Packet4i>(const int* from, Index begin, Index count) {
+  return _mm_maskload_epi32(from, segment_mask_4x32(begin, count));
+}
+
+template <>
+inline void pstoreuSegment<int, Packet4i>(int* to, const Packet4i& from, Index begin, Index count) {
+  _mm_maskstore_epi32(to, segment_mask_4x32(begin, count), from);
+}
+
+template <>
+inline Packet8i ploaduSegment<Packet8i>(const int* from, Index begin, Index count) {
+  return _mm256_maskload_epi32(from, segment_mask_8x32(begin, count));
+}
+
+template <>
+inline void pstoreuSegment<int, Packet8i>(int* to, const Packet8i& from, Index begin, Index count) {
+  _mm256_maskstore_epi32(to, segment_mask_8x32(begin, count), from);
+}
+
+#else
+
+template <>
+inline Packet4i ploaduSegment<Packet4i>(const int* from, Index begin, Index count) {
+  return _mm_castps_si128(ploaduSegment<Packet4f>(reinterpret_cast<const float*>(from), begin, count));
+}
+
+template <>
+inline void pstoreuSegment<int, Packet4i>(int* to, const Packet4i& from, Index begin, Index count) {
+  pstoreuSegment<float, Packet4f>(reinterpret_cast<float*>(to), _mm_castsi128_ps(from), begin, count);
+}
+
+template <>
+inline Packet8i ploaduSegment<Packet8i>(const int* from, Index begin, Index count) {
+  return _mm256_castps_si256(ploaduSegment<Packet8f>(reinterpret_cast<const float*>(from), begin, count));
+}
+
+template <>
+inline void pstoreuSegment<int, Packet8i>(int* to, const Packet8i& from, Index begin, Index count) {
+  pstoreuSegment<float, Packet8f>(reinterpret_cast<float*>(to), _mm256_castsi256_ps(from), begin, count);
+}
+
+#endif
+
+/*---------------- uint32 ----------------*/
+
+template <>
+struct has_packet_segment<Packet4ui> : std::true_type {};
+
+template <>
+struct has_packet_segment<Packet8ui> : std::true_type {};
+
+template <>
+inline Packet4ui ploaduSegment<Packet4ui>(const uint32_t* from, Index begin, Index count) {
+  return Packet4ui(ploaduSegment<Packet4i>(reinterpret_cast<const int*>(from), begin, count));
+}
+
+template <>
+inline void pstoreuSegment<uint32_t, Packet4ui>(uint32_t* to, const Packet4ui& from, Index begin, Index count) {
+  pstoreuSegment<int, Packet4i>(reinterpret_cast<int*>(to), Packet4i(from), begin, count);
+}
+
+template <>
+inline Packet8ui ploaduSegment<Packet8ui>(const uint32_t* from, Index begin, Index count) {
+  return Packet8ui(ploaduSegment<Packet8i>(reinterpret_cast<const int*>(from), begin, count));
+}
+
+template <>
+inline void pstoreuSegment<uint32_t, Packet8ui>(uint32_t* to, const Packet8ui& from, Index begin, Index count) {
+  pstoreuSegment<int, Packet8i>(reinterpret_cast<int*>(to), Packet8i(from), begin, count);
+}
+
+/*---------------- double ----------------*/
+
+template <>
+struct has_packet_segment<Packet2d> : std::true_type {};
+
+template <>
+struct has_packet_segment<Packet4d> : std::true_type {};
+
+template <>
+inline Packet2d ploaduSegment<Packet2d>(const double* from, Index begin, Index count) {
+  return _mm_maskload_pd(from, segment_mask_2x64(begin, count));
+}
+
+template <>
+inline void pstoreuSegment<double, Packet2d>(double* to, const Packet2d& from, Index begin, Index count) {
+  _mm_maskstore_pd(to, segment_mask_2x64(begin, count), from);
+}
+
+template <>
+inline Packet4d ploaduSegment<Packet4d>(const double* from, Index begin, Index count) {
+  return _mm256_maskload_pd(from, segment_mask_4x64(begin, count));
+}
+
+template <>
+inline void pstoreuSegment<double, Packet4d>(double* to, const Packet4d& from, Index begin, Index count) {
+  _mm256_maskstore_pd(to, segment_mask_4x64(begin, count), from);
+}
+
+#ifdef EIGEN_VECTORIZE_AVX2
+
+/*---------------- int64_t ----------------*/
+
+template <>
+struct has_packet_segment<Packet2l> : std::true_type {};
+
+template <>
+struct has_packet_segment<Packet4l> : std::true_type {};
+
+template <>
+inline Packet2l ploaduSegment<Packet2l>(const int64_t* from, Index begin, Index count) {
+  return _mm_maskload_epi64(reinterpret_cast<const long long*>(from), segment_mask_2x64(begin, count));
+}
+template <>
+inline void pstoreuSegment<int64_t, Packet2l>(int64_t* to, const Packet2l& from, Index begin, Index count) {
+  _mm_maskstore_epi64(reinterpret_cast<long long*>(to), segment_mask_2x64(begin, count), from);
+}
+template <>
+inline Packet4l ploaduSegment<Packet4l>(const int64_t* from, Index begin, Index count) {
+  return _mm256_maskload_epi64(reinterpret_cast<const long long*>(from), segment_mask_4x64(begin, count));
+}
+template <>
+inline void pstoreuSegment<int64_t, Packet4l>(int64_t* to, const Packet4l& from, Index begin, Index count) {
+  _mm256_maskstore_epi64(reinterpret_cast<long long*>(to), segment_mask_4x64(begin, count), from);
+}
+
+/*---------------- uint64_t ----------------*/
+
+template <>
+struct has_packet_segment<Packet4ul> : std::true_type {};
+
+template <>
+inline Packet4ul ploaduSegment<Packet4ul>(const uint64_t* from, Index begin, Index count) {
+  return Packet4ul(ploaduSegment<Packet4l>(reinterpret_cast<const int64_t*>(from), begin, count));
+}
+template <>
+inline void pstoreuSegment<uint64_t, Packet4ul>(uint64_t* to, const Packet4ul& from, Index begin, Index count) {
+  pstoreuSegment<int64_t, Packet4l>(reinterpret_cast<int64_t*>(to), Packet4l(from), begin, count);
+}
+#endif
+
+/*---------------- end load/store segment support ----------------*/
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_AVX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/Reductions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/Reductions.h
new file mode 100644
index 0000000..237617c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/Reductions.h
@@ -0,0 +1,353 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Charlie Schlosser <cs.schlosser@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REDUCTIONS_AVX_H
+#define EIGEN_REDUCTIONS_AVX_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet8i -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE int predux(const Packet8i& a) {
+  Packet4i lo = _mm256_castsi256_si128(a);
+  Packet4i hi = _mm256_extractf128_si256(a, 1);
+  return predux(padd(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_mul(const Packet8i& a) {
+  Packet4i lo = _mm256_castsi256_si128(a);
+  Packet4i hi = _mm256_extractf128_si256(a, 1);
+  return predux_mul(pmul(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_min(const Packet8i& a) {
+  Packet4i lo = _mm256_castsi256_si128(a);
+  Packet4i hi = _mm256_extractf128_si256(a, 1);
+  return predux_min(pmin(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_max(const Packet8i& a) {
+  Packet4i lo = _mm256_castsi256_si128(a);
+  Packet4i hi = _mm256_extractf128_si256(a, 1);
+  return predux_max(pmax(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet8i& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_movemask_epi8(a) != 0x0;
+#else
+  return _mm256_movemask_ps(_mm256_castsi256_ps(a)) != 0x0;
+#endif
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet8ui -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE uint32_t predux(const Packet8ui& a) {
+  Packet4ui lo = _mm256_castsi256_si128(a);
+  Packet4ui hi = _mm256_extractf128_si256(a, 1);
+  return predux(padd(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_mul(const Packet8ui& a) {
+  Packet4ui lo = _mm256_castsi256_si128(a);
+  Packet4ui hi = _mm256_extractf128_si256(a, 1);
+  return predux_mul(pmul(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_min(const Packet8ui& a) {
+  Packet4ui lo = _mm256_castsi256_si128(a);
+  Packet4ui hi = _mm256_extractf128_si256(a, 1);
+  return predux_min(pmin(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_max(const Packet8ui& a) {
+  Packet4ui lo = _mm256_castsi256_si128(a);
+  Packet4ui hi = _mm256_extractf128_si256(a, 1);
+  return predux_max(pmax(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet8ui& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_movemask_epi8(a) != 0x0;
+#else
+  return _mm256_movemask_ps(_mm256_castsi256_ps(a)) != 0x0;
+#endif
+}
+
+#ifdef EIGEN_VECTORIZE_AVX2
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet4l -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE int64_t predux(const Packet4l& a) {
+  Packet2l lo = _mm256_castsi256_si128(a);
+  Packet2l hi = _mm256_extractf128_si256(a, 1);
+  return predux(padd(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet4l& a) {
+  return _mm256_movemask_pd(_mm256_castsi256_pd(a)) != 0x0;
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet4ul -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE uint64_t predux(const Packet4ul& a) {
+  return static_cast<uint64_t>(predux(Packet4l(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet4ul& a) {
+  return _mm256_movemask_pd(_mm256_castsi256_pd(a)) != 0x0;
+}
+
+#endif
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet8f -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE float predux(const Packet8f& a) {
+  Packet4f lo = _mm256_castps256_ps128(a);
+  Packet4f hi = _mm256_extractf128_ps(a, 1);
+  return predux(padd(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_mul(const Packet8f& a) {
+  Packet4f lo = _mm256_castps256_ps128(a);
+  Packet4f hi = _mm256_extractf128_ps(a, 1);
+  return predux_mul(pmul(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min(const Packet8f& a) {
+  Packet4f lo = _mm256_castps256_ps128(a);
+  Packet4f hi = _mm256_extractf128_ps(a, 1);
+  return predux_min(pmin(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<PropagateNumbers>(const Packet8f& a) {
+  Packet4f lo = _mm256_castps256_ps128(a);
+  Packet4f hi = _mm256_extractf128_ps(a, 1);
+  return predux_min<PropagateNumbers>(pmin<PropagateNumbers>(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<PropagateNaN>(const Packet8f& a) {
+  Packet4f lo = _mm256_castps256_ps128(a);
+  Packet4f hi = _mm256_extractf128_ps(a, 1);
+  return predux_min<PropagateNaN>(pmin<PropagateNaN>(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max(const Packet8f& a) {
+  Packet4f lo = _mm256_castps256_ps128(a);
+  Packet4f hi = _mm256_extractf128_ps(a, 1);
+  return predux_max(pmax(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<PropagateNumbers>(const Packet8f& a) {
+  Packet4f lo = _mm256_castps256_ps128(a);
+  Packet4f hi = _mm256_extractf128_ps(a, 1);
+  return predux_max<PropagateNumbers>(pmax<PropagateNumbers>(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<PropagateNaN>(const Packet8f& a) {
+  Packet4f lo = _mm256_castps256_ps128(a);
+  Packet4f hi = _mm256_extractf128_ps(a, 1);
+  return predux_max<PropagateNaN>(pmax<PropagateNaN>(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet8f& a) {
+  return _mm256_movemask_ps(a) != 0x0;
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet4d -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE double predux(const Packet4d& a) {
+  Packet2d lo = _mm256_castpd256_pd128(a);
+  Packet2d hi = _mm256_extractf128_pd(a, 1);
+  return predux(padd(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_mul(const Packet4d& a) {
+  Packet2d lo = _mm256_castpd256_pd128(a);
+  Packet2d hi = _mm256_extractf128_pd(a, 1);
+  return predux_mul(pmul(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min(const Packet4d& a) {
+  Packet2d lo = _mm256_castpd256_pd128(a);
+  Packet2d hi = _mm256_extractf128_pd(a, 1);
+  return predux_min(pmin(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min<PropagateNumbers>(const Packet4d& a) {
+  Packet2d lo = _mm256_castpd256_pd128(a);
+  Packet2d hi = _mm256_extractf128_pd(a, 1);
+  return predux_min<PropagateNumbers>(pmin<PropagateNumbers>(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min<PropagateNaN>(const Packet4d& a) {
+  Packet2d lo = _mm256_castpd256_pd128(a);
+  Packet2d hi = _mm256_extractf128_pd(a, 1);
+  return predux_min<PropagateNaN>(pmin<PropagateNaN>(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max(const Packet4d& a) {
+  Packet2d lo = _mm256_castpd256_pd128(a);
+  Packet2d hi = _mm256_extractf128_pd(a, 1);
+  return predux_max(pmax(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max<PropagateNumbers>(const Packet4d& a) {
+  Packet2d lo = _mm256_castpd256_pd128(a);
+  Packet2d hi = _mm256_extractf128_pd(a, 1);
+  return predux_max<PropagateNumbers>(pmax<PropagateNumbers>(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max<PropagateNaN>(const Packet4d& a) {
+  Packet2d lo = _mm256_castpd256_pd128(a);
+  Packet2d hi = _mm256_extractf128_pd(a, 1);
+  return predux_max<PropagateNaN>(pmax<PropagateNaN>(lo, hi));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet4d& a) {
+  return _mm256_movemask_pd(a) != 0x0;
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet8h -- -- -- -- -- -- -- -- -- -- -- -- */
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+
+template <>
+EIGEN_STRONG_INLINE half predux(const Packet8h& a) {
+  return static_cast<half>(predux(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_mul(const Packet8h& a) {
+  return static_cast<half>(predux_mul(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_min(const Packet8h& a) {
+  return static_cast<half>(predux_min(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_min<PropagateNumbers>(const Packet8h& a) {
+  return static_cast<half>(predux_min<PropagateNumbers>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_min<PropagateNaN>(const Packet8h& a) {
+  return static_cast<half>(predux_min<PropagateNaN>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_max(const Packet8h& a) {
+  return static_cast<half>(predux_max(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_max<PropagateNumbers>(const Packet8h& a) {
+  return static_cast<half>(predux_max<PropagateNumbers>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_max<PropagateNaN>(const Packet8h& a) {
+  return static_cast<half>(predux_max<PropagateNaN>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet8h& a) {
+  return _mm_movemask_epi8(a) != 0;
+}
+#endif  // EIGEN_VECTORIZE_AVX512FP16
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet8bf -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux<Packet8f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_mul(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux_mul<Packet8f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux_min(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min<PropagateNumbers>(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux_min<PropagateNumbers>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min<PropagateNaN>(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux_min<PropagateNaN>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux_max<Packet8f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max<PropagateNumbers>(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux_max<PropagateNumbers>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max<PropagateNaN>(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux_max<PropagateNaN>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet8bf& a) {
+  return _mm_movemask_epi8(a) != 0;
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_REDUCTIONS_AVX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/TypeCasting.h
new file mode 100644
index 0000000..9feb38f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX/TypeCasting.h
@@ -0,0 +1,342 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_AVX_H
+#define EIGEN_TYPE_CASTING_AVX_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_VECTORIZE_AVX512
+template <>
+struct type_casting_traits<float, bool> : vectorized_type_casting_traits<float, bool> {};
+template <>
+struct type_casting_traits<bool, float> : vectorized_type_casting_traits<bool, float> {};
+
+template <>
+struct type_casting_traits<float, int> : vectorized_type_casting_traits<float, int> {};
+template <>
+struct type_casting_traits<int, float> : vectorized_type_casting_traits<int, float> {};
+
+template <>
+struct type_casting_traits<float, double> : vectorized_type_casting_traits<float, double> {};
+template <>
+struct type_casting_traits<double, float> : vectorized_type_casting_traits<double, float> {};
+
+template <>
+struct type_casting_traits<double, int> : vectorized_type_casting_traits<double, int> {};
+template <>
+struct type_casting_traits<int, double> : vectorized_type_casting_traits<int, double> {};
+
+template <>
+struct type_casting_traits<half, float> : vectorized_type_casting_traits<half, float> {};
+template <>
+struct type_casting_traits<float, half> : vectorized_type_casting_traits<float, half> {};
+
+template <>
+struct type_casting_traits<bfloat16, float> : vectorized_type_casting_traits<bfloat16, float> {};
+template <>
+struct type_casting_traits<float, bfloat16> : vectorized_type_casting_traits<float, bfloat16> {};
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template <>
+struct type_casting_traits<double, int64_t> : vectorized_type_casting_traits<double, int64_t> {};
+template <>
+struct type_casting_traits<int64_t, double> : vectorized_type_casting_traits<int64_t, double> {};
+#endif
+#endif
+
+EIGEN_STRONG_INLINE __m256 _eigen_mm256_set_m128(__m128 hi, __m128 lo) {
+#if EIGEN_COMP_GNUC && (EIGEN_COMP_CLANG < 1000 || EIGEN_COMP_GNUC < 810)
+  __m256 result = _mm256_castps128_ps256(lo);
+  return _mm256_insertf128_ps(result, hi, 1);
+#else
+  return _mm256_set_m128(hi, lo);
+#endif
+}
+
+EIGEN_STRONG_INLINE __m256d _eigen_mm256_set_m128d(__m128d hi, __m128d lo) {
+#if EIGEN_COMP_GNUC && (EIGEN_COMP_CLANG < 1000 || EIGEN_COMP_GNUC < 810)
+  __m256d result = _mm256_castpd128_pd256(lo);
+  return _mm256_insertf128_pd(result, hi, 1);
+#else
+  return _mm256_set_m128d(hi, lo);
+#endif
+}
+
+EIGEN_STRONG_INLINE __m256i _eigen_mm256_set_m128i(__m128i hi, __m128i lo) {
+#if EIGEN_COMP_GNUC && (EIGEN_COMP_CLANG < 1000 || EIGEN_COMP_GNUC < 810)
+#if defined(EIGEN_VECTORIZE_AVX2)
+  __m256i result = _mm256_castsi128_si256(lo);
+  return _mm256_inserti128_si256(result, hi, 1);
+#else
+  EIGEN_ALIGN32 int32_t tmp[8];
+  _mm_storeu_si128(reinterpret_cast<__m128i*>(tmp), lo);
+  _mm_storeu_si128(reinterpret_cast<__m128i*>(tmp + 4), hi);
+  return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(tmp));
+#endif
+#else
+  return _mm256_set_m128i(hi, lo);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16b pcast<Packet8f, Packet16b>(const Packet8f& a, const Packet8f& b) {
+  __m256 nonzero_a = _mm256_cmp_ps(a, pzero(a), _CMP_NEQ_UQ);
+  __m256 nonzero_b = _mm256_cmp_ps(b, pzero(b), _CMP_NEQ_UQ);
+  constexpr char kFF = '\255';
+#ifndef EIGEN_VECTORIZE_AVX2
+  __m128i shuffle_mask128_a_lo = _mm_set_epi8(kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, 12, 8, 4, 0);
+  __m128i shuffle_mask128_a_hi = _mm_set_epi8(kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, 12, 8, 4, 0, kFF, kFF, kFF, kFF);
+  __m128i shuffle_mask128_b_lo = _mm_set_epi8(kFF, kFF, kFF, kFF, 12, 8, 4, 0, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF);
+  __m128i shuffle_mask128_b_hi = _mm_set_epi8(12, 8, 4, 0, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF);
+  __m128i a_hi = _mm_shuffle_epi8(_mm256_extractf128_si256(_mm256_castps_si256(nonzero_a), 1), shuffle_mask128_a_hi);
+  __m128i a_lo = _mm_shuffle_epi8(_mm256_extractf128_si256(_mm256_castps_si256(nonzero_a), 0), shuffle_mask128_a_lo);
+  __m128i b_hi = _mm_shuffle_epi8(_mm256_extractf128_si256(_mm256_castps_si256(nonzero_b), 1), shuffle_mask128_b_hi);
+  __m128i b_lo = _mm_shuffle_epi8(_mm256_extractf128_si256(_mm256_castps_si256(nonzero_b), 0), shuffle_mask128_b_lo);
+  __m128i merged = _mm_or_si128(_mm_or_si128(b_lo, b_hi), _mm_or_si128(a_lo, a_hi));
+  return _mm_and_si128(merged, _mm_set1_epi8(1));
+#else
+  __m256i a_shuffle_mask = _mm256_set_epi8(kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, 12, 8, 4, 0, kFF, kFF, kFF, kFF, kFF,
+                                           kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, 12, 8, 4, 0);
+  __m256i b_shuffle_mask = _mm256_set_epi8(12, 8, 4, 0, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF,
+                                           kFF, kFF, kFF, 12, 8, 4, 0, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF);
+  __m256i a_shuff = _mm256_shuffle_epi8(_mm256_castps_si256(nonzero_a), a_shuffle_mask);
+  __m256i b_shuff = _mm256_shuffle_epi8(_mm256_castps_si256(nonzero_b), b_shuffle_mask);
+  __m256i a_or_b = _mm256_or_si256(a_shuff, b_shuff);
+  __m256i merged = _mm256_or_si256(a_or_b, _mm256_castsi128_si256(_mm256_extractf128_si256(a_or_b, 1)));
+  return _mm256_castsi256_si128(_mm256_and_si256(merged, _mm256_set1_epi8(1)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pcast<Packet16b, Packet8f>(const Packet16b& a) {
+  const __m256 cst_one = _mm256_set1_ps(1.0f);
+#ifdef EIGEN_VECTORIZE_AVX2
+  __m256i a_extended = _mm256_cvtepi8_epi32(a);
+  __m256i abcd_efgh = _mm256_cmpeq_epi32(a_extended, _mm256_setzero_si256());
+#else
+  __m128i abcd_efhg_ijkl_mnop = _mm_cmpeq_epi8(a, _mm_setzero_si128());
+  __m128i aabb_ccdd_eeff_gghh = _mm_unpacklo_epi8(abcd_efhg_ijkl_mnop, abcd_efhg_ijkl_mnop);
+  __m128i aaaa_bbbb_cccc_dddd = _mm_unpacklo_epi8(aabb_ccdd_eeff_gghh, aabb_ccdd_eeff_gghh);
+  __m128i eeee_ffff_gggg_hhhh = _mm_unpackhi_epi8(aabb_ccdd_eeff_gghh, aabb_ccdd_eeff_gghh);
+  __m256i abcd_efgh = _mm256_setr_m128i(aaaa_bbbb_cccc_dddd, eeee_ffff_gggg_hhhh);
+#endif
+  __m256 result = _mm256_andnot_ps(_mm256_castsi256_ps(abcd_efgh), cst_one);
+  return result;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8i pcast<Packet8f, Packet8i>(const Packet8f& a) {
+  return _mm256_cvttps_epi32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8i pcast<Packet4d, Packet8i>(const Packet4d& a, const Packet4d& b) {
+  return _eigen_mm256_set_m128i(_mm256_cvttpd_epi32(b), _mm256_cvttpd_epi32(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4d, Packet4i>(const Packet4d& a) {
+  return _mm256_cvttpd_epi32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pcast<Packet8i, Packet8f>(const Packet8i& a) {
+  return _mm256_cvtepi32_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pcast<Packet4d, Packet8f>(const Packet4d& a, const Packet4d& b) {
+  return _eigen_mm256_set_m128(_mm256_cvtpd_ps(b), _mm256_cvtpd_ps(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4d, Packet4f>(const Packet4d& a) {
+  return _mm256_cvtpd_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pcast<Packet8i, Packet4d>(const Packet8i& a) {
+  return _mm256_cvtepi32_pd(_mm256_castsi256_si128(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pcast<Packet4i, Packet4d>(const Packet4i& a) {
+  return _mm256_cvtepi32_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pcast<Packet8f, Packet4d>(const Packet8f& a) {
+  return _mm256_cvtps_pd(_mm256_castps256_ps128(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pcast<Packet4f, Packet4d>(const Packet4f& a) {
+  return _mm256_cvtps_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8i preinterpret<Packet8i, Packet8f>(const Packet8f& a) {
+  return _mm256_castps_si256(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f preinterpret<Packet8f, Packet8i>(const Packet8i& a) {
+  return _mm256_castsi256_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8ui preinterpret<Packet8ui, Packet8i>(const Packet8i& a) {
+  return Packet8ui(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8i preinterpret<Packet8i, Packet8ui>(const Packet8ui& a) {
+  return Packet8i(a);
+}
+
+// truncation operations
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet8f>(const Packet8f& a) {
+  return _mm256_castps256_ps128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4d>(const Packet4d& a) {
+  return _mm256_castpd256_pd128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet8i>(const Packet8i& a) {
+  return _mm256_castsi256_si128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet8ui>(const Packet8ui& a) {
+  return _mm256_castsi256_si128(a);
+}
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template <>
+EIGEN_STRONG_INLINE Packet4l pcast<Packet4d, Packet4l>(const Packet4d& a) {
+#if defined(EIGEN_VECTORIZE_AVX512DQ) && defined(EIGEN_VECTORIZE_AVS512VL)
+  return _mm256_cvttpd_epi64(a);
+#else
+
+  // if 'a' exceeds the numerical limits of int64_t, the behavior is undefined
+
+  // e <= 0 corresponds to |a| < 1, which should result in zero. incidentally, intel intrinsics with shift arguments
+  // greater than or equal to 64 produce zero. furthermore, negative shifts appear to be interpreted as large positive
+  // shifts (two's complement), which also result in zero. therefore, e does not need to be clamped to [0, 64)
+
+  constexpr int kTotalBits = sizeof(double) * CHAR_BIT, kMantissaBits = std::numeric_limits<double>::digits - 1,
+                kExponentBits = kTotalBits - kMantissaBits - 1, kBias = (1 << (kExponentBits - 1)) - 1;
+
+  const __m256i cst_one = _mm256_set1_epi64x(1);
+  const __m256i cst_total_bits = _mm256_set1_epi64x(kTotalBits);
+  const __m256i cst_bias = _mm256_set1_epi64x(kBias);
+
+  __m256i a_bits = _mm256_castpd_si256(a);
+  // shift left by 1 to clear the sign bit, and shift right by kMantissaBits + 1 to recover biased exponent
+  __m256i biased_e = _mm256_srli_epi64(_mm256_slli_epi64(a_bits, 1), kMantissaBits + 1);
+  __m256i e = _mm256_sub_epi64(biased_e, cst_bias);
+
+  // shift to the left by kExponentBits + 1 to clear the sign and exponent bits
+  __m256i shifted_mantissa = _mm256_slli_epi64(a_bits, kExponentBits + 1);
+  // shift to the right by kTotalBits - e to convert the significand to an integer
+  __m256i result_significand = _mm256_srlv_epi64(shifted_mantissa, _mm256_sub_epi64(cst_total_bits, e));
+
+  // add the implied bit
+  __m256i result_exponent = _mm256_sllv_epi64(cst_one, e);
+  // e <= 0 is interpreted as a large positive shift (2's complement), which also conveniently results in zero
+  __m256i result = _mm256_add_epi64(result_significand, result_exponent);
+  // handle negative arguments
+  __m256i sign_mask = _mm256_cmpgt_epi64(_mm256_setzero_si256(), a_bits);
+  result = _mm256_sub_epi64(_mm256_xor_si256(result, sign_mask), sign_mask);
+  return result;
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pcast<Packet4l, Packet4d>(const Packet4l& a) {
+#if defined(EIGEN_VECTORIZE_AVX512DQ) && defined(EIGEN_VECTORIZE_AVS512VL)
+  return _mm256_cvtepi64_pd(a);
+#else
+  int64_t aux[4];
+  pstoreu(aux, a);
+  return _mm256_set_pd(static_cast<double>(aux[3]), static_cast<double>(aux[2]), static_cast<double>(aux[1]),
+                       static_cast<double>(aux[0]));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pcast<Packet2l, Packet4d>(const Packet2l& a, const Packet2l& b) {
+  return _eigen_mm256_set_m128d((pcast<Packet2l, Packet2d>(b)), (pcast<Packet2l, Packet2d>(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4ul preinterpret<Packet4ul, Packet4l>(const Packet4l& a) {
+  return Packet4ul(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4l preinterpret<Packet4l, Packet4ul>(const Packet4ul& a) {
+  return Packet4l(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4l preinterpret<Packet4l, Packet4d>(const Packet4d& a) {
+  return _mm256_castpd_si256(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d preinterpret<Packet4d, Packet4l>(const Packet4l& a) {
+  return _mm256_castsi256_pd(a);
+}
+
+// truncation operations
+template <>
+EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet4l>(const Packet4l& a) {
+  return _mm256_castsi256_si128(a);
+}
+#endif
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template <>
+EIGEN_STRONG_INLINE Packet8f pcast<Packet8h, Packet8f>(const Packet8h& a) {
+  return half2float(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pcast<Packet8f, Packet8h>(const Packet8f& a) {
+  return float2half(a);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pcast<Packet8bf, Packet8f>(const Packet8bf& a) {
+  return Bf16ToF32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcast<Packet8f, Packet8bf>(const Packet8f& a) {
+  return F32ToBf16(a);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_AVX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/Complex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/Complex.h
new file mode 100644
index 0000000..ab426ac
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/Complex.h
@@ -0,0 +1,479 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_AVX512_H
+#define EIGEN_COMPLEX_AVX512_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- float ----------
+struct Packet8cf {
+  EIGEN_STRONG_INLINE Packet8cf() {}
+  EIGEN_STRONG_INLINE explicit Packet8cf(const __m512& a) : v(a) {}
+  __m512 v;
+};
+
+template <>
+struct packet_traits<std::complex<float> > : default_packet_traits {
+  typedef Packet8cf type;
+  typedef Packet4cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet8cf> {
+  typedef std::complex<float> type;
+  typedef Packet4cf half;
+  typedef Packet16f as_real;
+  enum {
+    size = 8,
+    alignment = unpacket_traits<Packet16f>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf ptrue<Packet8cf>(const Packet8cf& a) {
+  return Packet8cf(ptrue(Packet16f(a.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8cf padd<Packet8cf>(const Packet8cf& a, const Packet8cf& b) {
+  return Packet8cf(_mm512_add_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8cf psub<Packet8cf>(const Packet8cf& a, const Packet8cf& b) {
+  return Packet8cf(_mm512_sub_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8cf pnegate(const Packet8cf& a) {
+  return Packet8cf(pnegate(a.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8cf pconj(const Packet8cf& a) {
+  const __m512 mask = _mm512_castsi512_ps(_mm512_setr_epi32(
+      0x00000000, SIGN_MASK_I32, 0x00000000, SIGN_MASK_I32, 0x00000000, SIGN_MASK_I32, 0x00000000, SIGN_MASK_I32,
+      0x00000000, SIGN_MASK_I32, 0x00000000, SIGN_MASK_I32, 0x00000000, SIGN_MASK_I32, 0x00000000, SIGN_MASK_I32));
+  return Packet8cf(pxor(a.v, mask));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pmul<Packet8cf>(const Packet8cf& a, const Packet8cf& b) {
+  __m512 tmp2 = _mm512_mul_ps(_mm512_movehdup_ps(a.v), _mm512_permute_ps(b.v, _MM_SHUFFLE(2, 3, 0, 1)));
+  return Packet8cf(_mm512_fmaddsub_ps(_mm512_moveldup_ps(a.v), b.v, tmp2));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pand<Packet8cf>(const Packet8cf& a, const Packet8cf& b) {
+  return Packet8cf(pand(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8cf por<Packet8cf>(const Packet8cf& a, const Packet8cf& b) {
+  return Packet8cf(por(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8cf pxor<Packet8cf>(const Packet8cf& a, const Packet8cf& b) {
+  return Packet8cf(pxor(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8cf pandnot<Packet8cf>(const Packet8cf& a, const Packet8cf& b) {
+  return Packet8cf(pandnot(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pcmp_eq(const Packet8cf& a, const Packet8cf& b) {
+  __m512 eq = pcmp_eq<Packet16f>(a.v, b.v);
+  return Packet8cf(pand(eq, _mm512_permute_ps(eq, 0xB1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pload<Packet8cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet8cf(pload<Packet16f>(&numext::real_ref(*from)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8cf ploadu<Packet8cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet8cf(ploadu<Packet16f>(&numext::real_ref(*from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pset1<Packet8cf>(const std::complex<float>& from) {
+  const float re = std::real(from);
+  const float im = std::imag(from);
+  return Packet8cf(_mm512_set_ps(im, re, im, re, im, re, im, re, im, re, im, re, im, re, im, re));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf ploaddup<Packet8cf>(const std::complex<float>* from) {
+  return Packet8cf(_mm512_castpd_ps(ploaddup<Packet8d>((const double*)(const void*)from)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8cf ploadquad<Packet8cf>(const std::complex<float>* from) {
+  return Packet8cf(_mm512_castpd_ps(ploadquad<Packet8d>((const double*)(const void*)from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<float> >(std::complex<float>* to, const Packet8cf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet8cf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet8cf pgather<std::complex<float>, Packet8cf>(const std::complex<float>* from,
+                                                                           Index stride) {
+  return Packet8cf(_mm512_castpd_ps(pgather<double, Packet8d>((const double*)(const void*)from, stride)));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet8cf>(std::complex<float>* to, const Packet8cf& from,
+                                                                       Index stride) {
+  pscatter((double*)(void*)to, _mm512_castps_pd(from.v), stride);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet8cf>(const Packet8cf& a) {
+  return pfirst(Packet2cf(_mm512_castps512_ps128(a.v)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf preverse(const Packet8cf& a) {
+  return Packet8cf(_mm512_castsi512_ps(_mm512_permutexvar_epi64(
+      _mm512_set_epi32(0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7), _mm512_castps_si512(a.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet8cf>(const Packet8cf& a) {
+  return predux(padd(Packet4cf(extract256<0>(a.v)), Packet4cf(extract256<1>(a.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet8cf>(const Packet8cf& a) {
+  return predux_mul(pmul(Packet4cf(extract256<0>(a.v)), Packet4cf(extract256<1>(a.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf predux_half<Packet8cf>(const Packet8cf& a) {
+  __m256 lane0 = extract256<0>(a.v);
+  __m256 lane1 = extract256<1>(a.v);
+  __m256 res = _mm256_add_ps(lane0, lane1);
+  return Packet4cf(res);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet8cf, Packet16f)
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pdiv<Packet8cf>(const Packet8cf& a, const Packet8cf& b) {
+  return pdiv_complex(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pcplxflip<Packet8cf>(const Packet8cf& x) {
+  return Packet8cf(_mm512_shuffle_ps(x.v, x.v, _MM_SHUFFLE(2, 3, 0, 1)));
+}
+
+//---------- double ----------
+struct Packet4cd {
+  EIGEN_STRONG_INLINE Packet4cd() {}
+  EIGEN_STRONG_INLINE explicit Packet4cd(const __m512d& a) : v(a) {}
+  __m512d v;
+};
+
+template <>
+struct packet_traits<std::complex<double> > : default_packet_traits {
+  typedef Packet4cd type;
+  typedef Packet2cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 4,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4cd> {
+  typedef std::complex<double> type;
+  typedef Packet2cd half;
+  typedef Packet8d as_real;
+  enum {
+    size = 4,
+    alignment = unpacket_traits<Packet8d>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd padd<Packet4cd>(const Packet4cd& a, const Packet4cd& b) {
+  return Packet4cd(_mm512_add_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd psub<Packet4cd>(const Packet4cd& a, const Packet4cd& b) {
+  return Packet4cd(_mm512_sub_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd pnegate(const Packet4cd& a) {
+  return Packet4cd(pnegate(a.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd pconj(const Packet4cd& a) {
+  const __m512d mask =
+      _mm512_castsi512_pd(_mm512_set_epi32(SIGN_MASK_I32, 0x0, 0x0, 0x0, SIGN_MASK_I32, 0x0, 0x0, 0x0, SIGN_MASK_I32,
+                                           0x0, 0x0, 0x0, SIGN_MASK_I32, 0x0, 0x0, 0x0));
+  return Packet4cd(pxor(a.v, mask));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd pmul<Packet4cd>(const Packet4cd& a, const Packet4cd& b) {
+  __m512d tmp1 = _mm512_shuffle_pd(a.v, a.v, 0x0);
+  __m512d tmp2 = _mm512_shuffle_pd(a.v, a.v, 0xFF);
+  __m512d tmp3 = _mm512_shuffle_pd(b.v, b.v, 0x55);
+  __m512d odd = _mm512_mul_pd(tmp2, tmp3);
+  return Packet4cd(_mm512_fmaddsub_pd(tmp1, b.v, odd));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd ptrue<Packet4cd>(const Packet4cd& a) {
+  return Packet4cd(ptrue(Packet8d(a.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd pand<Packet4cd>(const Packet4cd& a, const Packet4cd& b) {
+  return Packet4cd(pand(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd por<Packet4cd>(const Packet4cd& a, const Packet4cd& b) {
+  return Packet4cd(por(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd pxor<Packet4cd>(const Packet4cd& a, const Packet4cd& b) {
+  return Packet4cd(pxor(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd pandnot<Packet4cd>(const Packet4cd& a, const Packet4cd& b) {
+  return Packet4cd(pandnot(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd pcmp_eq(const Packet4cd& a, const Packet4cd& b) {
+  __m512d eq = pcmp_eq<Packet8d>(a.v, b.v);
+  return Packet4cd(pand(eq, _mm512_permute_pd(eq, 0x55)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd pload<Packet4cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet4cd(pload<Packet8d>((const double*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd ploadu<Packet4cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet4cd(ploadu<Packet8d>((const double*)from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd pset1<Packet4cd>(const std::complex<double>& from) {
+  return Packet4cd(_mm512_castps_pd(_mm512_broadcast_f32x4(_mm_castpd_ps(pset1<Packet1cd>(from).v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd ploaddup<Packet4cd>(const std::complex<double>* from) {
+  return Packet4cd(
+      _mm512_insertf64x4(_mm512_castpd256_pd512(ploaddup<Packet2cd>(from).v), ploaddup<Packet2cd>(from + 1).v, 1));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<double> >(std::complex<double>* to, const Packet4cd& from) {
+  EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double>* to, const Packet4cd& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4cd pgather<std::complex<double>, Packet4cd>(const std::complex<double>* from,
+                                                                            Index stride) {
+  return Packet4cd(_mm512_insertf64x4(
+      _mm512_castpd256_pd512(_mm256_insertf128_pd(_mm256_castpd128_pd256(ploadu<Packet1cd>(from + 0 * stride).v),
+                                                  ploadu<Packet1cd>(from + 1 * stride).v, 1)),
+      _mm256_insertf128_pd(_mm256_castpd128_pd256(ploadu<Packet1cd>(from + 2 * stride).v),
+                           ploadu<Packet1cd>(from + 3 * stride).v, 1),
+      1));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet4cd>(std::complex<double>* to, const Packet4cd& from,
+                                                                        Index stride) {
+  __m512i fromi = _mm512_castpd_si512(from.v);
+  double* tod = (double*)(void*)to;
+  _mm_storeu_pd(tod + 0 * stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi, 0)));
+  _mm_storeu_pd(tod + 2 * stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi, 1)));
+  _mm_storeu_pd(tod + 4 * stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi, 2)));
+  _mm_storeu_pd(tod + 6 * stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi, 3)));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet4cd>(const Packet4cd& a) {
+  __m128d low = extract128<0>(a.v);
+  EIGEN_ALIGN16 double res[2];
+  _mm_store_pd(res, low);
+  return std::complex<double>(res[0], res[1]);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd preverse(const Packet4cd& a) {
+  return Packet4cd(_mm512_shuffle_f64x2(a.v, a.v, (shuffle_mask<3, 2, 1, 0>::mask)));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux<Packet4cd>(const Packet4cd& a) {
+  return predux(padd(Packet2cd(_mm512_extractf64x4_pd(a.v, 0)), Packet2cd(_mm512_extractf64x4_pd(a.v, 1))));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet4cd>(const Packet4cd& a) {
+  return predux_mul(pmul(Packet2cd(_mm512_extractf64x4_pd(a.v, 0)), Packet2cd(_mm512_extractf64x4_pd(a.v, 1))));
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cd, Packet8d)
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd pdiv<Packet4cd>(const Packet4cd& a, const Packet4cd& b) {
+  return pdiv_complex(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd pcplxflip<Packet4cd>(const Packet4cd& x) {
+  return Packet4cd(_mm512_permute_pd(x.v, 0x55));
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8cf, 4>& kernel) {
+  PacketBlock<Packet8d, 4> pb;
+
+  pb.packet[0] = _mm512_castps_pd(kernel.packet[0].v);
+  pb.packet[1] = _mm512_castps_pd(kernel.packet[1].v);
+  pb.packet[2] = _mm512_castps_pd(kernel.packet[2].v);
+  pb.packet[3] = _mm512_castps_pd(kernel.packet[3].v);
+  ptranspose(pb);
+  kernel.packet[0].v = _mm512_castpd_ps(pb.packet[0]);
+  kernel.packet[1].v = _mm512_castpd_ps(pb.packet[1]);
+  kernel.packet[2].v = _mm512_castpd_ps(pb.packet[2]);
+  kernel.packet[3].v = _mm512_castpd_ps(pb.packet[3]);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8cf, 8>& kernel) {
+  PacketBlock<Packet8d, 8> pb;
+
+  pb.packet[0] = _mm512_castps_pd(kernel.packet[0].v);
+  pb.packet[1] = _mm512_castps_pd(kernel.packet[1].v);
+  pb.packet[2] = _mm512_castps_pd(kernel.packet[2].v);
+  pb.packet[3] = _mm512_castps_pd(kernel.packet[3].v);
+  pb.packet[4] = _mm512_castps_pd(kernel.packet[4].v);
+  pb.packet[5] = _mm512_castps_pd(kernel.packet[5].v);
+  pb.packet[6] = _mm512_castps_pd(kernel.packet[6].v);
+  pb.packet[7] = _mm512_castps_pd(kernel.packet[7].v);
+  ptranspose(pb);
+  kernel.packet[0].v = _mm512_castpd_ps(pb.packet[0]);
+  kernel.packet[1].v = _mm512_castpd_ps(pb.packet[1]);
+  kernel.packet[2].v = _mm512_castpd_ps(pb.packet[2]);
+  kernel.packet[3].v = _mm512_castpd_ps(pb.packet[3]);
+  kernel.packet[4].v = _mm512_castpd_ps(pb.packet[4]);
+  kernel.packet[5].v = _mm512_castpd_ps(pb.packet[5]);
+  kernel.packet[6].v = _mm512_castpd_ps(pb.packet[6]);
+  kernel.packet[7].v = _mm512_castpd_ps(pb.packet[7]);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4cd, 4>& kernel) {
+  __m512d T0 =
+      _mm512_shuffle_f64x2(kernel.packet[0].v, kernel.packet[1].v, (shuffle_mask<0, 1, 0, 1>::mask));  // [a0 a1 b0 b1]
+  __m512d T1 =
+      _mm512_shuffle_f64x2(kernel.packet[0].v, kernel.packet[1].v, (shuffle_mask<2, 3, 2, 3>::mask));  // [a2 a3 b2 b3]
+  __m512d T2 =
+      _mm512_shuffle_f64x2(kernel.packet[2].v, kernel.packet[3].v, (shuffle_mask<0, 1, 0, 1>::mask));  // [c0 c1 d0 d1]
+  __m512d T3 =
+      _mm512_shuffle_f64x2(kernel.packet[2].v, kernel.packet[3].v, (shuffle_mask<2, 3, 2, 3>::mask));  // [c2 c3 d2 d3]
+
+  kernel.packet[3] = Packet4cd(_mm512_shuffle_f64x2(T1, T3, (shuffle_mask<1, 3, 1, 3>::mask)));  // [a3 b3 c3 d3]
+  kernel.packet[2] = Packet4cd(_mm512_shuffle_f64x2(T1, T3, (shuffle_mask<0, 2, 0, 2>::mask)));  // [a2 b2 c2 d2]
+  kernel.packet[1] = Packet4cd(_mm512_shuffle_f64x2(T0, T2, (shuffle_mask<1, 3, 1, 3>::mask)));  // [a1 b1 c1 d1]
+  kernel.packet[0] = Packet4cd(_mm512_shuffle_f64x2(T0, T2, (shuffle_mask<0, 2, 0, 2>::mask)));  // [a0 b0 c0 d0]
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd psqrt<Packet4cd>(const Packet4cd& a) {
+  return psqrt_complex<Packet4cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf psqrt<Packet8cf>(const Packet8cf& a) {
+  return psqrt_complex<Packet8cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd plog<Packet4cd>(const Packet4cd& a) {
+  return plog_complex<Packet4cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf plog<Packet8cf>(const Packet8cf& a) {
+  return plog_complex<Packet8cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd pexp<Packet4cd>(const Packet4cd& a) {
+  return pexp_complex<Packet4cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pexp<Packet8cf>(const Packet8cf& a) {
+  return pexp_complex<Packet8cf>(a);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_AVX512_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/GemmKernel.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/GemmKernel.h
new file mode 100644
index 0000000..fbc2d58
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/GemmKernel.h
@@ -0,0 +1,1249 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022 Intel Corporation
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_ARCH_AVX512_GEMM_KERNEL_H
+#define EIGEN_CORE_ARCH_AVX512_GEMM_KERNEL_H
+
+#if EIGEN_COMP_MSVC
+#include <intrin.h>
+#else
+#include <x86intrin.h>
+#endif
+#include <immintrin.h>
+#include <type_traits>
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+#if !defined(EIGEN_USE_AVX512_GEMM_KERNELS)
+#define EIGEN_USE_AVX512_GEMM_KERNELS 1
+#endif
+
+#define SECOND_FETCH (32)
+#if (EIGEN_COMP_GNUC_STRICT != 0) && !defined(EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_A_REGS)
+// Use less registers to load A elements to workaround compiler spills. Loose a
+// bit of performance (less than ~2%).
+#define EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_A_REGS
+#endif
+
+namespace Eigen {
+namespace internal {
+
+#if EIGEN_USE_AVX512_GEMM_KERNELS
+
+template <typename Scalar, bool is_unit_inc>
+class gemm_class {
+  using vec = typename packet_traits<Scalar>::type;
+  using vec_ymm = typename unpacket_traits<vec>::half;
+  using vec_xmm = typename unpacket_traits<vec_ymm>::half;
+  using umask_t = typename unpacket_traits<vec>::mask_t;
+
+  static constexpr bool is_f32 = sizeof(Scalar) == sizeof(float);
+  static constexpr bool is_f64 = sizeof(Scalar) == sizeof(double);
+
+#ifndef EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_A_REGS
+  static constexpr bool use_less_a_regs = !is_unit_inc;
+#else
+  static constexpr bool use_less_a_regs = true;
+#endif
+#ifndef EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_B_REGS
+  static constexpr bool use_less_b_regs = !is_unit_inc;
+#else
+  static constexpr bool use_less_b_regs = true;
+#endif
+
+  static constexpr int a_regs[] = {0, 1, 2, use_less_a_regs ? 0 : 3, use_less_a_regs ? 1 : 4, use_less_a_regs ? 2 : 5};
+  static constexpr int b_regs[] = {6, use_less_b_regs ? 6 : 7};
+  static constexpr int c_regs[] = {
+      8, 16, 24, 9, 17, 25, 10, 18, 26, 11, 19, 27, 12, 20, 28, 13, 21, 29, 14, 22, 30, 15, 23, 31,
+  };
+
+  static constexpr int alpha_load_reg = 0;
+  static constexpr int c_load_regs[] = {1, 2, 6};
+
+  static constexpr int a_shift = 128;
+  static constexpr int b_shift = 128;
+
+  static constexpr int nelems_in_cache_line = is_f32 ? 16 : 8;
+  static constexpr int a_prefetch_size = nelems_in_cache_line * 2;
+  static constexpr int b_prefetch_size = nelems_in_cache_line * 8;
+
+  vec zmm[32];
+  umask_t mask;
+
+  // gemm arguments.
+  Index m;
+  const Index n, k, ldc;
+  const Index inc;
+  const Scalar* alpha;
+
+  const Scalar *a, *b;
+  Scalar* c;
+
+  const bool is_alpha1;
+  const bool is_beta0;
+
+  const Index a_stride, b_stride;
+  const Index a_off, b_off;
+
+  EIGEN_ALWAYS_INLINE void prefetch_a(const Scalar* a_addr) {
+    _mm_prefetch((char*)(a_prefetch_size + a_addr - a_shift), _MM_HINT_T0);
+  }
+
+  EIGEN_ALWAYS_INLINE void prefetch_b(const Scalar* b_addr) {
+    _mm_prefetch((char*)(b_prefetch_size + b_addr - b_shift), _MM_HINT_T0);
+  }
+
+  EIGEN_ALWAYS_INLINE void prefetch_x(const Scalar* x_addr) { _mm_prefetch((char*)(x_addr - a_shift), _MM_HINT_T2); }
+
+  EIGEN_ALWAYS_INLINE void prefetch_c(const Scalar* c_addr) {
+#if defined(__PRFCHW__) && __PRFCHW__ == 1
+    _m_prefetchw((void*)c_addr);
+#else
+    _mm_prefetch((char*)c_addr, _MM_HINT_T0);
+#endif
+  }
+
+  template <int nelems>
+  EIGEN_ALWAYS_INLINE void a_load(vec& a_reg, const Scalar* a_addr) {
+    switch (nelems * sizeof(*a_addr) * 8) {
+      default:
+      case 512 * 3:
+        a_reg = ploadu<vec>(a_addr);
+        break;
+      case 512 * 2:
+        a_reg = ploadu<vec>(a_addr);
+        break;
+      case 512 * 1:
+        a_reg = ploadu<vec>(a_addr);
+        break;
+      case 256 * 1:
+        a_reg = preinterpret<vec>(_mm512_broadcast_f64x4(ploadu<Packet4d>(reinterpret_cast<const double*>(a_addr))));
+        break;
+      case 128 * 1:
+        a_reg = preinterpret<vec>(_mm512_broadcast_f32x4(ploadu<Packet4f>(reinterpret_cast<const float*>(a_addr))));
+        break;
+      case 64 * 1:
+        a_reg = preinterpret<vec>(pload1<Packet8d>(reinterpret_cast<const double*>(a_addr)));
+        break;
+      case 32 * 1:
+        a_reg = pload1<vec>(a_addr);
+        break;
+    }
+  }
+
+  EIGEN_ALWAYS_INLINE void b_load(vec& b_reg, const Scalar* b_addr) { b_reg = pload1<vec>(b_addr); }
+
+  template <int nelems>
+  EIGEN_ALWAYS_INLINE void c_store(Scalar* mem, vec& src) {
+    if (is_unit_inc) {
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          pstoreu(mem, src);
+          break;
+        case 512 * 2:
+          pstoreu(mem, src);
+          break;
+        case 512 * 1:
+          pstoreu(mem, src);
+          break;
+        case 256 * 1:
+          pstoreu(mem, preinterpret<vec_ymm>(src));
+          break;
+        case 128 * 1:
+          pstoreu(mem, preinterpret<vec_xmm>(src));
+          break;
+        case 64 * 1:
+          pstorel(mem, preinterpret<vec_xmm>(src));
+          break;
+        case 32 * 1:
+          pstores(mem, preinterpret<vec_xmm>(src));
+          break;
+      }
+    } else {
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          pscatter(mem, src, inc);
+          break;
+        case 512 * 2:
+          pscatter(mem, src, inc);
+          break;
+        case 512 * 1:
+          pscatter(mem, src, inc);
+          break;
+        case 256 * 1:
+          pscatter(mem, src, inc, mask);
+          break;
+        case 128 * 1:
+          pscatter(mem, src, inc, mask);
+          break;
+        case 64 * 1:
+          pscatter(mem, src, inc, mask);
+          break;
+        case 32 * 1:
+          pscatter(mem, src, inc, mask);
+          break;
+      }
+    }
+  }
+
+  template <int nelems>
+  EIGEN_ALWAYS_INLINE void vaddm(vec& dst, const Scalar* mem, vec& src, vec& reg) {
+    if (is_unit_inc) {
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          dst = padd(src, ploadu<vec>(mem));
+          break;
+        case 512 * 2:
+          dst = padd(src, ploadu<vec>(mem));
+          break;
+        case 512 * 1:
+          dst = padd(src, ploadu<vec>(mem));
+          break;
+        case 256 * 1:
+          dst = preinterpret<vec>(padd(preinterpret<vec_ymm>(src), ploadu<vec_ymm>(mem)));
+          break;
+        case 128 * 1:
+          dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), ploadu<vec_xmm>(mem)));
+          break;
+        case 64 * 1:
+          dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem)));
+          break;
+        case 32 * 1:
+          dst = preinterpret<vec>(padds(preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem)));
+          break;
+      }
+    } else {
+      // Zero out scratch register
+      reg = pzero(reg);
+
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = padd(src, reg);
+          break;
+        case 512 * 2:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = padd(src, reg);
+          break;
+        case 512 * 1:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = padd(src, reg);
+          break;
+        case 256 * 1:
+          reg = preinterpret<vec>(pgather<Scalar, vec_ymm>(mem, inc));
+          dst = preinterpret<vec>(padd(preinterpret<vec_ymm>(src), preinterpret<vec_ymm>(reg)));
+          break;
+        case 128 * 1:
+          reg = preinterpret<vec>(pgather<Scalar, vec_xmm>(mem, inc));
+          dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg)));
+          break;
+        case 64 * 1:
+          if (is_f32) {
+            reg = pgather(reg, mem, inc, mask);
+            dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg)));
+          } else {
+            dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem)));
+          }
+          break;
+        case 32 * 1:
+          dst = preinterpret<vec>(padds(preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem)));
+          break;
+      }
+    }
+  }
+
+  EIGEN_STRONG_INLINE void vfmadd(vec& dst, const vec& src1, const vec& src2) {
+    dst = pmadd(src1, src2, dst);
+
+#if (EIGEN_COMP_GNUC != 0) || (EIGEN_COMP_CLANG != 0)
+    // Workaround register spills for gcc and clang
+    __asm__("#" : [dst] "+v"(dst) : [src1] "%v"(src1), [src2] "v"(src2));
+#endif
+  }
+
+  template <int nelems>
+  EIGEN_ALWAYS_INLINE void vfmaddm(vec& dst, const Scalar* mem, vec& src, vec& scale, vec& reg) {
+    if (is_unit_inc) {
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          dst = pmadd(scale, src, ploadu<vec>(mem));
+          break;
+        case 512 * 2:
+          dst = pmadd(scale, src, ploadu<vec>(mem));
+          break;
+        case 512 * 1:
+          dst = pmadd(scale, src, ploadu<vec>(mem));
+          break;
+        case 256 * 1:
+          dst =
+              preinterpret<vec>(pmadd(preinterpret<vec_ymm>(scale), preinterpret<vec_ymm>(src), ploadu<vec_ymm>(mem)));
+          break;
+        case 128 * 1:
+          dst =
+              preinterpret<vec>(pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploadu<vec_xmm>(mem)));
+          break;
+        case 64 * 1:
+          dst =
+              preinterpret<vec>(pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem)));
+          break;
+        case 32 * 1:
+          dst =
+              preinterpret<vec>(pmadds(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem)));
+          break;
+      }
+    } else {
+      // Zero out scratch register
+      reg = pzero(reg);
+
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = pmadd(scale, src, reg);
+          break;
+        case 512 * 2:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = pmadd(scale, src, reg);
+          break;
+        case 512 * 1:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = pmadd(scale, src, reg);
+          break;
+        case 256 * 1:
+          reg = preinterpret<vec>(pgather<Scalar, vec_ymm>(mem, inc));
+          dst = preinterpret<vec>(
+              pmadd(preinterpret<vec_ymm>(scale), preinterpret<vec_ymm>(src), preinterpret<vec_ymm>(reg)));
+          break;
+        case 128 * 1:
+          reg = preinterpret<vec>(pgather<Scalar, vec_xmm>(mem, inc));
+          dst = preinterpret<vec>(
+              pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg)));
+          break;
+        case 64 * 1:
+          if (is_f32) {
+            reg = pgather(reg, mem, inc, mask);
+            dst = preinterpret<vec>(
+                pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg)));
+          } else {
+            dst = preinterpret<vec>(
+                pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem)));
+          }
+          break;
+        case 32 * 1:
+          dst =
+              preinterpret<vec>(pmadds(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem)));
+          break;
+      }
+    }
+  }
+
+  template <int j, int endX, int i, int endY, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(j > endX) || (i > endY)> a_loads(const Scalar* ao) {
+    EIGEN_UNUSED_VARIABLE(ao);
+  }
+
+  template <int j, int endX, int i, int endY, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(j <= endX) && (i <= endY)> a_loads(const Scalar* ao) {
+    if (j < endX) {
+      if (i < endY) {
+        auto& a_reg = zmm[a_regs[i + (j % 2) * 3]];
+        const Scalar* a_addr = ao + nelems * j + nelems_in_cache_line * i - a_shift;
+        a_load<nelems>(a_reg, a_addr);
+
+        a_loads<j, endX, i + 1, endY, nelems>(ao);
+      } else {
+        a_loads<j + 1, endX, 0, endY, nelems>(ao);
+      }
+    }
+  }
+
+  template <int un, int max_b_unroll, int i, int um_vecs, int a_unroll, int b_unroll>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(un > max_b_unroll) || (i > um_vecs)> prefetch_cs(const Scalar* co1,
+                                                                                         const Scalar* co2) {
+    EIGEN_UNUSED_VARIABLE(co1);
+    EIGEN_UNUSED_VARIABLE(co2);
+  }
+
+  /* C prefetch loop structure.
+   * for (int un = 0; un < 8; un++) {
+   *     if (b_unroll >= un + 1) {
+   *         if (un == 4) co2 = co1 + 4 * ldc;
+   *
+   *         for (int i = 0; i < um_vecs; i++) {
+   *             Scalar *co = (un + 1 <= 4) ? co1 : co2;
+   *             auto co_off = (un % 4) * ldc + a_unroll - 1 + i * nelems_in_cache_line * sizeof *co;
+   *             prefetch_c(co + co_off);
+   *         }
+   *     }
+   * }
+   */
+
+  template <int un, int max_b_unroll, int i, int um_vecs, int a_unroll, int b_unroll>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(un <= max_b_unroll) && (i <= um_vecs)> prefetch_cs(Scalar*& co1, Scalar*& co2) {
+    if (un < max_b_unroll) {
+      if (b_unroll >= un + 1) {
+        if (un == 4 && i == 0) co2 = co1 + 4 * ldc;
+
+        if (i < um_vecs) {
+          Scalar* co = (un + 1 <= 4) ? co1 : co2;
+          auto co_off = (un % 4) * ldc + a_unroll - 1 + i * nelems_in_cache_line * sizeof *co;
+          prefetch_c(co + co_off);
+
+          prefetch_cs<un, max_b_unroll, i + 1, um_vecs, a_unroll, b_unroll>(co1, co2);
+        } else {
+          prefetch_cs<un + 1, max_b_unroll, 0, um_vecs, a_unroll, b_unroll>(co1, co2);
+        }
+
+      } else {
+        prefetch_cs<un + 1, max_b_unroll, 0, um_vecs, a_unroll, b_unroll>(co1, co2);
+      }
+    }
+  }
+
+  // load_c
+  template <int i, int um_vecs, int idx, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(i > um_vecs)> scale_load_c(const Scalar* cox, vec& alpha_reg) {
+    EIGEN_UNUSED_VARIABLE(cox);
+    EIGEN_UNUSED_VARIABLE(alpha_reg);
+  }
+
+  template <int i, int um_vecs, int idx, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(i <= um_vecs)> scale_load_c(const Scalar* cox, vec& alpha_reg) {
+    if (i < um_vecs) {
+      auto& c_reg = zmm[c_regs[i + idx * 3]];
+      auto& c_load_reg = zmm[c_load_regs[i % 3]];
+      auto c_mem = cox;
+      if (is_unit_inc)
+        c_mem += i * nelems_in_cache_line;
+      else
+        c_mem += i * nelems_in_cache_line * inc;
+
+      if (!is_beta0 && is_alpha1)
+        vaddm<nelems>(c_reg, c_mem, c_reg, c_load_reg);
+      else if (!is_beta0 && !is_alpha1)
+        vfmaddm<nelems>(c_reg, c_mem, c_reg, alpha_reg, c_load_reg);
+      else if (is_beta0 && !is_alpha1)
+        c_reg = pmul(alpha_reg, c_reg);
+
+      scale_load_c<i + 1, um_vecs, idx, nelems>(cox, alpha_reg);
+    }
+  }
+
+  // store_c
+  template <int i, int um_vecs, int idx, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(i > um_vecs)> write_c(Scalar* cox) {
+    EIGEN_UNUSED_VARIABLE(cox);
+  }
+
+  template <int i, int um_vecs, int idx, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(i <= um_vecs)> write_c(Scalar* cox) {
+    if (i < um_vecs) {
+      auto& c_reg = zmm[c_regs[i + idx * 3]];
+      auto c_mem = cox;
+      if (is_unit_inc)
+        c_mem += i * nelems_in_cache_line;
+      else
+        c_mem += i * nelems_in_cache_line * inc;
+
+      c_store<nelems>(c_mem, c_reg);
+      c_reg = pzero(c_reg);
+
+      write_c<i + 1, um_vecs, idx, nelems>(cox);
+    }
+  }
+
+  /*  C update loop structure.
+   *  co2 = co1 + ldc;
+   *
+   *  auto &alpha_reg = zmm[alpha_load_reg];
+   *  if (!is_alpha1) alpha_reg = pload1<vec>(alpha);
+   *
+   *  int idx = 0;
+   *  for (pow = 1; pow <= 8; pow <<= 1) {
+   *
+   *      if (b_unroll >= pow) {
+   *          for (count = 1; count < (pow + 1) / 2 + 1;  count++) {
+   *              if (pow >= 4) co2 += ldc;
+   *
+   *              const Scalar *cox = (idx == 0) ? co1 : co2;
+   *
+   *              const int um_vecs = numext::div_ceil(a_unroll, nelems_in_cache_line);
+   *              scale_load_c<0, um_vecs, idx, a_unroll>(cox, alpha_reg);
+   *              write_c<0, um_vecs, idx, a_unroll>(cox);
+   *
+   *              idx++;
+   *          }
+   *      }
+   *  }
+   *
+   *  if (b_unroll == 1)
+   *      co1 += ldc;
+   *  else
+   *      co1 = co2 + ldc;
+   */
+
+  template <int pow, int a_unroll, int idx>
+  EIGEN_ALWAYS_INLINE void c_update_1count(Scalar*& cox) {
+    if (pow >= 4) cox += ldc;
+
+    const int um_vecs = numext::div_ceil(a_unroll, nelems_in_cache_line);
+    auto& alpha_reg = zmm[alpha_load_reg];
+
+    scale_load_c<0, um_vecs, idx, a_unroll>(cox, alpha_reg);
+    write_c<0, um_vecs, idx, a_unroll>(cox);
+  }
+
+  template <int pow, int a_unroll>
+  EIGEN_ALWAYS_INLINE void c_update_1pow(Scalar*& co1, Scalar*& co2) {
+    constexpr int idx = pow / 2;
+    Scalar*& cox = idx == 0 ? co1 : co2;
+
+    constexpr int max_count = (pow + 1) / 2;
+    static_assert(max_count <= 4, "Unsupported max_count.");
+
+    if (1 <= max_count) c_update_1count<pow, a_unroll, idx + 0>(cox);
+    if (2 <= max_count) c_update_1count<pow, a_unroll, idx + 1>(cox);
+    if (3 <= max_count) c_update_1count<pow, a_unroll, idx + 2>(cox);
+    if (4 <= max_count) c_update_1count<pow, a_unroll, idx + 3>(cox);
+  }
+
+  template <int max_b_unroll, int a_unroll, int b_unroll>
+  EIGEN_ALWAYS_INLINE void c_update(Scalar*& co1, Scalar*& co2) {
+    auto& alpha_reg = zmm[alpha_load_reg];
+
+    co2 = co1 + ldc;
+    if (!is_alpha1) alpha_reg = pload1<vec>(alpha);
+    if (!is_unit_inc && a_unroll < nelems_in_cache_line) mask = static_cast<umask_t>((1ull << a_unroll) - 1);
+
+    static_assert(max_b_unroll <= 8, "Unsupported max_b_unroll");
+
+    if (1 <= max_b_unroll && 1 <= b_unroll) c_update_1pow<1, a_unroll>(co1, co2);
+    if (2 <= max_b_unroll && 2 <= b_unroll) c_update_1pow<2, a_unroll>(co1, co2);
+    if (4 <= max_b_unroll && 4 <= b_unroll) c_update_1pow<4, a_unroll>(co1, co2);
+    if (8 <= max_b_unroll && 8 <= b_unroll) c_update_1pow<8, a_unroll>(co1, co2);
+
+    if (b_unroll == 1)
+      co1 += ldc;
+    else
+      co1 = co2 + ldc;
+  }
+
+  // compute
+  template <int um, int um_vecs, int idx, int uk, bool fetch_x, bool ktail>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(um > um_vecs)> compute(const Scalar* ao, const Scalar* bo, int& fetchA_idx,
+                                                               int& fetchB_idx, vec& b_reg) {
+    EIGEN_UNUSED_VARIABLE(ao);
+    EIGEN_UNUSED_VARIABLE(bo);
+    EIGEN_UNUSED_VARIABLE(fetchA_idx);
+    EIGEN_UNUSED_VARIABLE(fetchB_idx);
+    EIGEN_UNUSED_VARIABLE(b_reg);
+  }
+
+  template <int um, int um_vecs, int idx, int uk, bool fetch_x, bool ktail>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(um <= um_vecs)> compute(const Scalar* ao, const Scalar* bo, int& fetchA_idx,
+                                                                int& fetchB_idx, vec& b_reg) {
+    if (um < um_vecs) {
+      auto& c_reg = zmm[c_regs[um + idx * 3]];
+      auto& a_reg = zmm[a_regs[um + (uk % 2) * 3]];
+
+      vfmadd(c_reg, a_reg, b_reg);
+
+      if (!fetch_x && um == 0 &&
+          (((idx == 0 || idx == 6) && (uk % 2 == 0 || is_f64 || ktail)) ||
+           (idx == 3 && (uk % 2 == 1 || is_f64 || ktail)))) {
+        prefetch_a(ao + nelems_in_cache_line * fetchA_idx);
+        fetchA_idx++;
+      }
+
+      if (um == 0 && idx == 1 && (uk % 2 == 0 || is_f64 || ktail)) {
+        prefetch_b(bo + nelems_in_cache_line * fetchB_idx);
+        fetchB_idx++;
+      }
+
+      compute<um + 1, um_vecs, idx, uk, fetch_x, ktail>(ao, bo, fetchA_idx, fetchB_idx, b_reg);
+    }
+  }
+
+  // load_a
+  template <int um, int um_vecs, int uk, int nelems, bool ktail>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(um > um_vecs)> load_a(const Scalar* ao) {
+    EIGEN_UNUSED_VARIABLE(ao);
+  }
+
+  template <int um, int um_vecs, int uk, int nelems, bool ktail>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(um <= um_vecs)> load_a(const Scalar* ao) {
+    if (um < um_vecs) {
+      auto& a_reg = zmm[a_regs[um + (uk % 2) * 3]];
+      const Scalar* a_addr = ao + nelems * (1 + !ktail * !use_less_a_regs + uk) + nelems_in_cache_line * um - a_shift;
+      a_load<nelems>(a_reg, a_addr);
+
+      load_a<um + 1, um_vecs, uk, nelems, ktail>(ao);
+    }
+  }
+  template <int uk, int pow, int count, int um_vecs, int b_unroll, bool ktail, bool fetch_x, bool c_fetch>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(count > (pow + 1) / 2)> innerkernel_1pow(const Scalar*& aa,
+                                                                                 const Scalar* const& ao,
+                                                                                 const Scalar* const& bo, Scalar*& co2,
+                                                                                 int& fetchA_idx, int& fetchB_idx) {
+    EIGEN_UNUSED_VARIABLE(aa);
+    EIGEN_UNUSED_VARIABLE(ao);
+    EIGEN_UNUSED_VARIABLE(bo);
+    EIGEN_UNUSED_VARIABLE(co2);
+    EIGEN_UNUSED_VARIABLE(fetchA_idx);
+    EIGEN_UNUSED_VARIABLE(fetchB_idx);
+  }
+
+  template <int uk, int pow, int count, int um_vecs, int b_unroll, bool ktail, bool fetch_x, bool c_fetch>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(count <= (pow + 1) / 2)> innerkernel_1pow(const Scalar*& aa,
+                                                                                  const Scalar* const& ao,
+                                                                                  const Scalar* const& bo, Scalar*& co2,
+                                                                                  int& fetchA_idx, int& fetchB_idx) {
+    const int idx = (pow / 2) + count;
+
+    if (count < (pow + 1) / 2) {
+      auto& b_reg = zmm[b_regs[idx % 2]];
+
+      if (fetch_x && uk == 3 && idx == 0) prefetch_x(aa);
+      if (fetch_x && uk == 3 && idx == 4) aa += 8;
+
+      if (b_unroll >= pow) {
+        compute<0, um_vecs, idx, uk, fetch_x, ktail>(ao, bo, fetchA_idx, fetchB_idx, b_reg);
+
+        const Scalar* b_addr = bo + b_unroll * uk + idx + 1 + (b_unroll > 1) * !use_less_b_regs - b_shift;
+        b_load(b_reg, b_addr);
+      }
+
+      // Go to the next count.
+      innerkernel_1pow<uk, pow, count + 1, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx,
+                                                                                       fetchB_idx);
+
+    } else {
+      // Maybe prefetch C data after count-loop.
+      if (pow == 2 && c_fetch) {
+        if (uk % 3 == 0 && uk > 0) {
+          co2 += ldc;
+        } else {
+          prefetch_c(co2 + (uk % 3) * nelems_in_cache_line);
+        }
+      }
+    }
+  }
+
+  template <int uk, int max_b_unroll, int a_unroll, int b_unroll, bool ktail, bool fetch_x, bool c_fetch,
+            bool no_a_preload = false>
+  EIGEN_ALWAYS_INLINE void innerkernel_1uk(const Scalar*& aa, const Scalar* const& ao, const Scalar* const& bo,
+                                           Scalar*& co2, int& fetchA_idx, int& fetchB_idx) {
+    const int um_vecs = numext::div_ceil(a_unroll, nelems_in_cache_line);
+
+    if (max_b_unroll >= 1)
+      innerkernel_1pow<uk, 1, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+    if (max_b_unroll >= 2)
+      innerkernel_1pow<uk, 2, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+    if (max_b_unroll >= 4)
+      innerkernel_1pow<uk, 4, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+    if (max_b_unroll >= 8)
+      innerkernel_1pow<uk, 8, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+
+    // Load A after pow-loop. Skip this at the end to prevent running over the buffer
+    if (!no_a_preload) load_a<0, um_vecs, uk, a_unroll, ktail>(ao);
+  }
+
+  /*  Inner kernel loop structure.
+   *  for (int uk = 0; uk < kfactor; uk++) {
+   *      int idx = 0;
+   *
+   *      for (pow = 1; pow < max_b_unroll << 1; pow <<= 1) {
+   *          for (int count = 0; count < (pow + 1) / 2; count++) {
+   *              auto &b_reg = zmm[b_regs[idx % 2]];
+   *
+   *              if (fetch_x && uk == 3 && idx == 0) prefetch_x(aa);
+   *              if (fetch_x && uk == 3 && idx == 4) aa += 8;
+   *
+   *              if (b_unroll >= pow) {
+   *                  compute<0, um_vecs, idx, uk, fetchx, ktail>(ao, bo, fetchA_idx, fetchB_idx, b_reg);
+   *
+   *                  const Scalar *b_addr = bo + b_unroll * uk + idx + 1 + (b_unroll > 1) - b_shift ;
+   *                  b_load(b_reg, b_addr);
+   *              }
+   *              idx++;
+   *          }
+   *
+   *          Maybe prefetch C data.
+   *          if (pow == 2 && c_fetch) {
+   *              if (uk % 3 == 0 && uk > 0) {
+   *                  co2 += ldc;
+   *              } else {
+   *                  prefetch_c(co2 + (uk % 3) * nelems_in_cache_line);
+   *              }
+   *          }
+   *      }
+   *
+   *      Load A.
+   *      load_a<0, um_vecs, uk, ktail, a_unroll>(ao);
+   *  }
+   *
+   *  Advance A/B pointers after uk-loop.
+   *  ao += a_unroll * kfactor;
+   *  bo += b_unroll * kfactor;
+   */
+
+  template <int a_unroll, int b_unroll, int k_factor, int max_b_unroll, int max_k_factor, bool c_fetch,
+            bool no_a_preload = false>
+  EIGEN_ALWAYS_INLINE void innerkernel(const Scalar*& aa, const Scalar*& ao, const Scalar*& bo, Scalar*& co2) {
+    int fetchA_idx = 0;
+    int fetchB_idx = 0;
+
+    const bool fetch_x = k_factor == max_k_factor;
+    const bool ktail = k_factor == 1;
+
+    static_assert(k_factor <= 4 && k_factor > 0, "innerkernel maximum k_factor supported is 4");
+    static_assert(no_a_preload == false || (no_a_preload == true && k_factor == 1),
+                  "skipping a preload only allowed when k unroll is 1");
+
+    if (k_factor > 0)
+      innerkernel_1uk<0, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch, no_a_preload>(
+          aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+    if (k_factor > 1)
+      innerkernel_1uk<1, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch, no_a_preload>(
+          aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+    if (k_factor > 2)
+      innerkernel_1uk<2, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch, no_a_preload>(
+          aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+    if (k_factor > 3)
+      innerkernel_1uk<3, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch, no_a_preload>(
+          aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+
+    // Advance A/B pointers after uk-loop.
+    ao += a_unroll * k_factor;
+    bo += b_unroll * k_factor;
+  }
+
+  template <int a_unroll, int b_unroll, int max_b_unroll>
+  EIGEN_ALWAYS_INLINE void kloop(const Scalar*& aa, const Scalar*& ao, const Scalar*& bo, Scalar*& co1, Scalar*& co2) {
+    const int um_vecs = numext::div_ceil(a_unroll, nelems_in_cache_line);
+    if (!use_less_a_regs && k > 1)
+      a_loads<0, 2, 0, um_vecs, a_unroll>(ao);
+    else
+      a_loads<0, 1, 0, um_vecs, a_unroll>(ao);
+
+    b_load(zmm[b_regs[0]], bo - b_shift + 0);
+    if (!use_less_b_regs) b_load(zmm[b_regs[1]], bo - b_shift + 1);
+
+#ifndef SECOND_FETCH
+    prefetch_cs<0, max_b_unroll, 0, um_vecs, a_unroll, b_unroll>(co1, co2);
+#endif  // SECOND_FETCH
+
+    // Unrolling k-loop by a factor of 4.
+    const int max_k_factor = 4;
+    Index kRem = k % max_k_factor;
+    Index k_ = k - kRem;
+    if (k_ >= max_k_factor) {
+      k_ -= max_k_factor;
+      kRem += max_k_factor;
+    }
+    Index loop_count = k_ / max_k_factor;
+
+    if (loop_count > 0) {
+#ifdef SECOND_FETCH
+      loop_count -= SECOND_FETCH;
+#endif
+      while (loop_count > 0) {
+        innerkernel<a_unroll, b_unroll, max_k_factor, max_b_unroll, max_k_factor, 0>(aa, ao, bo, co2);
+        loop_count--;
+      }
+#ifdef SECOND_FETCH
+      co2 = co1 + nelems_in_cache_line - 1;
+
+      loop_count += b_unroll;
+      while (loop_count > 0) {
+        innerkernel<a_unroll, b_unroll, max_k_factor, max_b_unroll, max_k_factor, 1>(aa, ao, bo, co2);
+        loop_count--;
+      }
+
+      loop_count += SECOND_FETCH - b_unroll;
+      while (loop_count > 0) {
+        innerkernel<a_unroll, b_unroll, max_k_factor, max_b_unroll, max_k_factor, 0>(aa, ao, bo, co2);
+        loop_count--;
+      }
+#endif
+    }
+
+    // k-loop remainder handling.
+    loop_count = kRem;
+    while (loop_count > 1) {
+      innerkernel<a_unroll, b_unroll, 1, max_b_unroll, max_k_factor, 0>(aa, ao, bo, co2);
+      loop_count--;
+    }
+    if (loop_count > 0) {
+      innerkernel<a_unroll, b_unroll, 1, max_b_unroll, max_k_factor, 0, true>(aa, ao, bo, co2);
+    }
+
+    // Update C matrix.
+    c_update<max_b_unroll, a_unroll, b_unroll>(co1, co2);
+  }
+
+  template <int a_unroll, int b_unroll, int max_b_unroll>
+  EIGEN_ALWAYS_INLINE void nloop(const Scalar*& aa, const Scalar*& ao, const Scalar*& bo, Scalar*& co1, Scalar*& co2) {
+    // Set A matrix pointer.
+    ao = a + a_off * a_unroll;
+
+    // Set B matrix pointer if needed.
+    bo += b_unroll * b_off;
+
+    kloop<a_unroll, b_unroll, max_b_unroll>(aa, ao, bo, co1, co2);
+
+    // Advance B matrix pointer if needed.
+    bo += b_unroll * (b_stride - k - b_off);
+
+    // Advance prefetch A pointer.
+    aa += 16;
+  }
+
+  template <int a_unroll, int max_a_unroll, int max_b_unroll>
+  EIGEN_ALWAYS_INLINE void mloop(const Scalar*& ao, const Scalar*& bo, Scalar*& co1, Scalar*& co2) {
+    // Set prefetch A pointers.
+    const Scalar* aa = a + a_unroll * a_stride;
+
+    // Set C matrix pointers.
+    co1 = c;
+    if (a_unroll >= max_a_unroll) co2 = c + 2 * ldc;
+    if (is_unit_inc)
+      c += a_unroll;
+    else
+      c += a_unroll * inc;
+
+    // Set B matrix pointer.
+    bo = b;
+
+    // Main n-loop.
+    for (Index i = n / max_b_unroll; i > 0; i--) nloop<a_unroll, max_b_unroll, max_b_unroll>(aa, ao, bo, co1, co2);
+
+    // n-remainders.
+    if (n & 4 && max_b_unroll > 4) nloop<a_unroll, 4, max_b_unroll>(aa, ao, bo, co1, co2);
+    // Copy kernels don't support tails of n = 2 for single/double precision.
+    // Loop over ones.
+    int n_rem = 2 * ((n & 2) != 0) + 1 * ((n & 1) != 0);
+    while (n_rem > 0) {
+      nloop<a_unroll, 1, max_b_unroll>(aa, ao, bo, co1, co2);
+      n_rem--;
+    }
+
+    // Advance A matrix pointer.
+    a = ao + a_unroll * (a_stride - k - a_off);
+  }
+
+ public:
+  // Compute kernel unrolling C matrix by max_a_unroll x max_b_unroll.
+  template <int max_a_unroll, int max_b_unroll>
+  EIGEN_ALWAYS_INLINE void compute_kern() {
+    a -= -a_shift;
+    b -= -b_shift;
+
+    const Scalar* ao = nullptr;
+    const Scalar* bo = nullptr;
+    Scalar* co1 = nullptr;
+    Scalar* co2 = nullptr;
+
+    // Main m-loop.
+    for (; m >= max_a_unroll; m -= max_a_unroll) mloop<max_a_unroll, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+
+    // m-remainders.
+    if (m & 32 && max_a_unroll > 32) mloop<32, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+    if (m & 16 && max_a_unroll > 16) mloop<16, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+    if (m & 8 && max_a_unroll > 8) mloop<8, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+    if (m & 4 && max_a_unroll > 4) mloop<4, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+    if (m & 2 && max_a_unroll > 2 && is_f64) mloop<2, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+    if (m & 1 && max_a_unroll > 1 && is_f64) mloop<1, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+
+    // Copy kernels don't support tails of m = 2 for single precision.
+    // Loop over ones.
+    if (is_f32) {
+      int m_rem = 2 * ((m & 2) != 0) + 1 * ((m & 1) != 0);
+      while (m_rem > 0) {
+        mloop<1, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+        m_rem--;
+      }
+    }
+  }
+
+  gemm_class(Index m_, Index n_, Index k_, Index ldc_, Index inc_, const Scalar* alpha_, const Scalar* a_,
+             const Scalar* b_, Scalar* c_, bool is_alpha1_, bool is_beta0_, Index a_stride_, Index b_stride_,
+             Index a_off_, Index b_off_)
+      : m(m_),
+        n(n_),
+        k(k_),
+        ldc(ldc_),
+        inc(inc_),
+        alpha(alpha_),
+        a(a_),
+        b(b_),
+        c(c_),
+        is_alpha1(is_alpha1_),
+        is_beta0(is_beta0_),
+        a_stride(a_stride_),
+        b_stride(b_stride_),
+        a_off(a_off_),
+        b_off(b_off_) {
+    // Zero out all accumulation registers.
+    zmm[8] = pzero(zmm[8]);
+    zmm[9] = pzero(zmm[9]);
+    zmm[10] = pzero(zmm[10]);
+    zmm[11] = pzero(zmm[11]);
+    zmm[12] = pzero(zmm[12]);
+    zmm[13] = pzero(zmm[13]);
+    zmm[14] = pzero(zmm[14]);
+    zmm[15] = pzero(zmm[15]);
+    zmm[16] = pzero(zmm[16]);
+    zmm[17] = pzero(zmm[17]);
+    zmm[18] = pzero(zmm[18]);
+    zmm[19] = pzero(zmm[19]);
+    zmm[20] = pzero(zmm[20]);
+    zmm[21] = pzero(zmm[21]);
+    zmm[22] = pzero(zmm[22]);
+    zmm[23] = pzero(zmm[23]);
+    zmm[24] = pzero(zmm[24]);
+    zmm[25] = pzero(zmm[25]);
+    zmm[26] = pzero(zmm[26]);
+    zmm[27] = pzero(zmm[27]);
+    zmm[28] = pzero(zmm[28]);
+    zmm[29] = pzero(zmm[29]);
+    zmm[30] = pzero(zmm[30]);
+    zmm[31] = pzero(zmm[31]);
+  }
+};
+
+template <typename Scalar, bool is_unit_inc>
+const int gemm_class<Scalar, is_unit_inc>::a_regs[];
+
+template <typename Scalar, bool is_unit_inc>
+const int gemm_class<Scalar, is_unit_inc>::b_regs[];
+
+template <typename Scalar, bool is_unit_inc>
+const int gemm_class<Scalar, is_unit_inc>::c_regs[];
+
+// Compute kernel with max unroll support of:
+//   Single precision:
+//     max_a_unroll: 48, 32, 16, 8, 4, 2, 1
+//     max_b_unroll: 8, 4, 2, 1
+//   Double precision:
+//     max_a_unroll: 24, 16, 8, 4, 2, 1
+//     max_b_unroll: 8, 4, 2, 1
+template <typename Scalar, int max_a_unroll, int max_b_unroll, bool is_alpha1, bool is_beta0, bool is_unit_inc>
+EIGEN_DONT_INLINE void gemm_kern_avx512(Index m, Index n, Index k, Scalar* alpha, const Scalar* a, const Scalar* b,
+                                        Scalar* c, Index ldc, Index inc = 1, Index a_stride = -1, Index b_stride = -1,
+                                        Index a_off = 0, Index b_off = 0) {
+  if (a_stride == -1) a_stride = k;
+  if (b_stride == -1) b_stride = k;
+
+  gemm_class<Scalar, is_unit_inc> g(m, n, k, ldc, inc, alpha, a, b, c, is_alpha1, is_beta0, a_stride, b_stride, a_off,
+                                    b_off);
+  g.template compute_kern<max_a_unroll, max_b_unroll>();
+}
+
+// Template specializations of GEBP kernels with nr = 8.
+template <bool ConjLhs_, bool ConjRhs_, int PacketSize_>
+class gebp_traits<float, float, ConjLhs_, ConjRhs_, Architecture::Target, PacketSize_>
+    : public gebp_traits<float, float, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_> {
+  using Base = gebp_traits<float, float, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_>;
+
+ public:
+  enum { nr = Base::Vectorizable ? 8 : 4 };
+};
+
+template <bool ConjLhs_, bool ConjRhs_, int PacketSize_>
+class gebp_traits<double, double, ConjLhs_, ConjRhs_, Architecture::Target, PacketSize_>
+    : public gebp_traits<double, double, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_> {
+  using Base = gebp_traits<double, double, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_>;
+
+ public:
+  enum { nr = Base::Vectorizable ? 8 : 4 };
+};
+
+template <typename Scalar, typename Index, typename DataMapper, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<Scalar, Index, DataMapper, 8, ColMajor, Conjugate, PanelMode> {
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  enum { PacketSize = packet_traits<Scalar>::size };
+  EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0,
+                                    Index offset = 0);
+};
+
+template <typename Scalar, typename Index, typename DataMapper, bool Conjugate, bool PanelMode>
+EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, DataMapper, 8, ColMajor, Conjugate, PanelMode>::operator()(
+    Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  constexpr int nr = 8;
+  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
+  EIGEN_UNUSED_VARIABLE(stride);
+  EIGEN_UNUSED_VARIABLE(offset);
+  eigen_assert(((!PanelMode) && stride == 0 && offset == 0) || (PanelMode && stride >= depth && offset <= stride));
+  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+  Index packet_cols8 = nr >= 8 ? (cols / 8) * 8 : 0;
+  Index packet_cols4 = nr >= 4 ? (cols / 4) * 4 : 0;
+  Index count = 0;
+  const Index peeled_k = (depth / PacketSize) * PacketSize;
+  if (nr >= 8) {
+    for (Index j2 = 0; j2 < packet_cols8; j2 += 8) {
+      // skip what we have before
+      if (PanelMode) count += 8 * offset;
+      const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0);
+      const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1);
+      const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2);
+      const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3);
+      const LinearMapper dm4 = rhs.getLinearMapper(0, j2 + 4);
+      const LinearMapper dm5 = rhs.getLinearMapper(0, j2 + 5);
+      const LinearMapper dm6 = rhs.getLinearMapper(0, j2 + 6);
+      const LinearMapper dm7 = rhs.getLinearMapper(0, j2 + 7);
+      Index k = 0;
+      if ((PacketSize % 8) == 0)  // TODO enable vectorized transposition for PacketSize==4
+      {
+        for (; k < peeled_k; k += PacketSize) {
+          PacketBlock<Packet, (PacketSize % 8) == 0 ? 8 : PacketSize> kernel;
+
+          kernel.packet[0] = dm0.template loadPacket<Packet>(k);
+          kernel.packet[1] = dm1.template loadPacket<Packet>(k);
+          kernel.packet[2] = dm2.template loadPacket<Packet>(k);
+          kernel.packet[3] = dm3.template loadPacket<Packet>(k);
+          kernel.packet[4] = dm4.template loadPacket<Packet>(k);
+          kernel.packet[5] = dm5.template loadPacket<Packet>(k);
+          kernel.packet[6] = dm6.template loadPacket<Packet>(k);
+          kernel.packet[7] = dm7.template loadPacket<Packet>(k);
+
+          ptranspose(kernel);
+
+          pstoreu(blockB + count + 0 * PacketSize, cj.pconj(kernel.packet[0]));
+          pstoreu(blockB + count + 1 * PacketSize, cj.pconj(kernel.packet[1 % PacketSize]));
+          pstoreu(blockB + count + 2 * PacketSize, cj.pconj(kernel.packet[2 % PacketSize]));
+          pstoreu(blockB + count + 3 * PacketSize, cj.pconj(kernel.packet[3 % PacketSize]));
+          pstoreu(blockB + count + 4 * PacketSize, cj.pconj(kernel.packet[4 % PacketSize]));
+          pstoreu(blockB + count + 5 * PacketSize, cj.pconj(kernel.packet[5 % PacketSize]));
+          pstoreu(blockB + count + 6 * PacketSize, cj.pconj(kernel.packet[6 % PacketSize]));
+          pstoreu(blockB + count + 7 * PacketSize, cj.pconj(kernel.packet[7 % PacketSize]));
+          count += 8 * PacketSize;
+        }
+      }
+      for (; k < depth; k++) {
+        blockB[count + 0] = cj(dm0(k));
+        blockB[count + 1] = cj(dm1(k));
+        blockB[count + 2] = cj(dm2(k));
+        blockB[count + 3] = cj(dm3(k));
+        blockB[count + 4] = cj(dm4(k));
+        blockB[count + 5] = cj(dm5(k));
+        blockB[count + 6] = cj(dm6(k));
+        blockB[count + 7] = cj(dm7(k));
+        count += 8;
+      }
+      // skip what we have after
+      if (PanelMode) count += 8 * (stride - offset - depth);
+    }
+  }
+
+  if (nr >= 4) {
+    for (Index j2 = packet_cols8; j2 < packet_cols4; j2 += 4) {
+      // skip what we have before
+      if (PanelMode) count += 4 * offset;
+      const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0);
+      const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1);
+      const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2);
+      const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3);
+
+      Index k = 0;
+      if ((PacketSize % 4) == 0)  // TODO: enable vectorized transposition for PacketSize==2.
+      {
+        for (; k < peeled_k; k += PacketSize) {
+          PacketBlock<Packet, (PacketSize % 4) == 0 ? 4 : PacketSize> kernel;
+          kernel.packet[0] = dm0.template loadPacket<Packet>(k);
+          kernel.packet[1 % PacketSize] = dm1.template loadPacket<Packet>(k);
+          kernel.packet[2 % PacketSize] = dm2.template loadPacket<Packet>(k);
+          kernel.packet[3 % PacketSize] = dm3.template loadPacket<Packet>(k);
+          ptranspose(kernel);
+          pstoreu(blockB + count + 0 * PacketSize, cj.pconj(kernel.packet[0]));
+          pstoreu(blockB + count + 1 * PacketSize, cj.pconj(kernel.packet[1 % PacketSize]));
+          pstoreu(blockB + count + 2 * PacketSize, cj.pconj(kernel.packet[2 % PacketSize]));
+          pstoreu(blockB + count + 3 * PacketSize, cj.pconj(kernel.packet[3 % PacketSize]));
+          count += 4 * PacketSize;
+        }
+      }
+      for (; k < depth; k++) {
+        blockB[count + 0] = cj(dm0(k));
+        blockB[count + 1] = cj(dm1(k));
+        blockB[count + 2] = cj(dm2(k));
+        blockB[count + 3] = cj(dm3(k));
+        count += 4;
+      }
+      // skip what we have after
+      if (PanelMode) count += 4 * (stride - offset - depth);
+    }
+  }
+
+  // copy the remaining columns one at a time (nr==1)
+  for (Index j2 = packet_cols4; j2 < cols; ++j2) {
+    if (PanelMode) count += offset;
+    const LinearMapper dm0 = rhs.getLinearMapper(0, j2);
+    for (Index k = 0; k < depth; k++) {
+      blockB[count] = cj(dm0(k));
+      count += 1;
+    }
+    if (PanelMode) count += (stride - offset - depth);
+  }
+}
+
+template <typename Scalar, typename Index, typename DataMapper, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<Scalar, Index, DataMapper, 8, RowMajor, Conjugate, PanelMode> {
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename unpacket_traits<Packet>::half HalfPacket;
+  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  enum {
+    PacketSize = packet_traits<Scalar>::size,
+    HalfPacketSize = unpacket_traits<HalfPacket>::size,
+    QuarterPacketSize = unpacket_traits<QuarterPacket>::size
+  };
+  EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0,
+                                    Index offset = 0) {
+    constexpr int nr = 8;
+    EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
+    EIGEN_UNUSED_VARIABLE(stride);
+    EIGEN_UNUSED_VARIABLE(offset);
+    eigen_assert(((!PanelMode) && stride == 0 && offset == 0) || (PanelMode && stride >= depth && offset <= stride));
+    const bool HasHalf = (int)HalfPacketSize < (int)PacketSize;
+    const bool HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize;
+    conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+    Index packet_cols8 = nr >= 8 ? (cols / 8) * 8 : 0;
+    Index packet_cols4 = nr >= 4 ? (cols / 4) * 4 : 0;
+    Index count = 0;
+
+    if (nr >= 8) {
+      for (Index j2 = 0; j2 < packet_cols8; j2 += 8) {
+        // skip what we have before
+        if (PanelMode) count += 8 * offset;
+        for (Index k = 0; k < depth; k++) {
+          if (PacketSize == 8) {
+            // Packet A = ploadu<Packet>(&rhs.data()[k*rhs.stride() + j2]);
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+          } else if (HasHalf && HalfPacketSize == 8) {
+            HalfPacket A = rhs.template loadPacket<HalfPacket>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+          } else if (HasQuarter && QuarterPacketSize == 8) {
+            QuarterPacket A = rhs.template loadPacket<QuarterPacket>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+          } else if (PacketSize == 4) {
+            // Packet A = ploadu<Packet>(&rhs.data()[k*rhs.stride() + j2]);
+            // Packet B = ploadu<Packet>(&rhs.data()[k*rhs.stride() + j2 + PacketSize]);
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            Packet B = rhs.template loadPacket<Packet>(k, j2 + PacketSize);
+            pstoreu(blockB + count, cj.pconj(A));
+            pstoreu(blockB + count + PacketSize, cj.pconj(B));
+          } else {
+            // const Scalar* b0 = &rhs.data()[k*rhs.stride() + j2];
+            const LinearMapper dm0 = rhs.getLinearMapper(k, j2);
+            blockB[count + 0] = cj(dm0(0));
+            blockB[count + 1] = cj(dm0(1));
+            blockB[count + 2] = cj(dm0(2));
+            blockB[count + 3] = cj(dm0(3));
+            blockB[count + 4] = cj(dm0(4));
+            blockB[count + 5] = cj(dm0(5));
+            blockB[count + 6] = cj(dm0(6));
+            blockB[count + 7] = cj(dm0(7));
+          }
+          count += 8;
+        }
+        // skip what we have after
+        if (PanelMode) count += 8 * (stride - offset - depth);
+      }
+    }
+
+    if (nr >= 4) {
+      for (Index j2 = packet_cols8; j2 < packet_cols4; j2 += 4) {
+        // skip what we have before
+        if (PanelMode) count += 4 * offset;
+        for (Index k = 0; k < depth; k++) {
+          if (PacketSize == 4) {
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+            count += PacketSize;
+          } else if (HasHalf && HalfPacketSize == 4) {
+            HalfPacket A = rhs.template loadPacket<HalfPacket>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+            count += HalfPacketSize;
+          } else if (HasQuarter && QuarterPacketSize == 4) {
+            QuarterPacket A = rhs.template loadPacket<QuarterPacket>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+            count += QuarterPacketSize;
+          } else {
+            const LinearMapper dm0 = rhs.getLinearMapper(k, j2);
+            blockB[count + 0] = cj(dm0(0));
+            blockB[count + 1] = cj(dm0(1));
+            blockB[count + 2] = cj(dm0(2));
+            blockB[count + 3] = cj(dm0(3));
+            count += 4;
+          }
+        }
+        // skip what we have after
+        if (PanelMode) count += 4 * (stride - offset - depth);
+      }
+    }
+    // copy the remaining columns one at a time (nr==1)
+    for (Index j2 = packet_cols4; j2 < cols; ++j2) {
+      if (PanelMode) count += offset;
+      for (Index k = 0; k < depth; k++) {
+        blockB[count] = cj(rhs(k, j2));
+        count += 1;
+      }
+      if (PanelMode) count += stride - offset - depth;
+    }
+  }
+};
+
+template <typename Scalar, typename Index, typename DataMapper, int mr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<Scalar, Scalar, Index, DataMapper, mr, 8, ConjugateLhs, ConjugateRhs> {
+  EIGEN_ALWAYS_INLINE void operator()(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows,
+                                      Index depth, Index cols, Scalar alpha, Index strideA = -1, Index strideB = -1,
+                                      Index offsetA = 0, Index offsetB = 0);
+};
+
+template <typename Scalar, typename Index, typename DataMapper, int mr, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE void gebp_kernel<Scalar, Scalar, Index, DataMapper, mr, 8, ConjugateLhs, ConjugateRhs>::operator()(
+    const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols,
+    Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  if (res.incr() == 1) {
+    if (alpha == 1) {
+      gemm_kern_avx512<Scalar, mr, 8, true, false, true>(rows, cols, depth, &alpha, blockA, blockB, (Scalar*)res.data(),
+                                                         res.stride(), res.incr(), strideA, strideB, offsetA, offsetB);
+    } else {
+      gemm_kern_avx512<Scalar, mr, 8, false, false, true>(rows, cols, depth, &alpha, blockA, blockB,
+                                                          (Scalar*)res.data(), res.stride(), res.incr(), strideA,
+                                                          strideB, offsetA, offsetB);
+    }
+  } else {
+    if (alpha == 1) {
+      gemm_kern_avx512<Scalar, mr, 8, true, false, false>(rows, cols, depth, &alpha, blockA, blockB,
+                                                          (Scalar*)res.data(), res.stride(), res.incr(), strideA,
+                                                          strideB, offsetA, offsetB);
+    } else {
+      gemm_kern_avx512<Scalar, mr, 8, false, false, false>(rows, cols, depth, &alpha, blockA, blockB,
+                                                           (Scalar*)res.data(), res.stride(), res.incr(), strideA,
+                                                           strideB, offsetA, offsetB);
+    }
+  }
+}
+#endif  // EIGEN_USE_AVX512_GEMM_KERNELS
+
+}  // namespace internal
+}  // namespace Eigen
+
+#undef SECOND_FETCH
+
+#endif  // EIGEN_CORE_ARCH_AVX512_GEMM_KERNEL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/MathFunctions.h
new file mode 100644
index 0000000..2499019
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/MathFunctions.h
@@ -0,0 +1,119 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Pedro Gonnet (pedro.gonnet@gmail.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
+#define THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet16f)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet8d)
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pfrexp(const Packet16h& a, Packet16h& exponent) {
+  Packet16f fexponent;
+  const Packet16h out = float2half(pfrexp<Packet16f>(half2float(a), fexponent));
+  exponent = float2half(fexponent);
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pldexp(const Packet16h& a, const Packet16h& exponent) {
+  return float2half(pldexp<Packet16f>(half2float(a), half2float(exponent)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pfrexp(const Packet16bf& a, Packet16bf& exponent) {
+  Packet16f fexponent;
+  const Packet16bf out = F32ToBf16(pfrexp<Packet16f>(Bf16ToF32(a), fexponent));
+  exponent = F32ToBf16(fexponent);
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pldexp(const Packet16bf& a, const Packet16bf& exponent) {
+  return F32ToBf16(pldexp<Packet16f>(Bf16ToF32(a), Bf16ToF32(exponent)));
+}
+
+#if EIGEN_FAST_MATH
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f psqrt<Packet16f>(const Packet16f& x) {
+  return generic_sqrt_newton_step<Packet16f>::run(x, _mm512_rsqrt14_ps(x));
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8d psqrt<Packet8d>(const Packet8d& x) {
+#ifdef EIGEN_VECTORIZE_AVX512ER
+  return generic_sqrt_newton_step<Packet8d, /*Steps=*/1>::run(x, _mm512_rsqrt28_pd(x));
+#else
+  return generic_sqrt_newton_step<Packet8d, /*Steps=*/2>::run(x, _mm512_rsqrt14_pd(x));
+#endif
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet16f psqrt<Packet16f>(const Packet16f& x) {
+  return _mm512_sqrt_ps(x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d psqrt<Packet8d>(const Packet8d& x) {
+  return _mm512_sqrt_pd(x);
+}
+#endif
+
+// prsqrt for float.
+#if defined(EIGEN_VECTORIZE_AVX512ER)
+template <>
+EIGEN_STRONG_INLINE Packet16f prsqrt<Packet16f>(const Packet16f& x) {
+  return _mm512_rsqrt28_ps(x);
+}
+#elif EIGEN_FAST_MATH
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f prsqrt<Packet16f>(const Packet16f& x) {
+  return generic_rsqrt_newton_step<Packet16f, /*Steps=*/1>::run(x, _mm512_rsqrt14_ps(x));
+}
+#endif
+
+// prsqrt for double.
+#if EIGEN_FAST_MATH
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8d prsqrt<Packet8d>(const Packet8d& x) {
+#ifdef EIGEN_VECTORIZE_AVX512ER
+  return generic_rsqrt_newton_step<Packet8d, /*Steps=*/1>::run(x, _mm512_rsqrt28_pd(x));
+#else
+  return generic_rsqrt_newton_step<Packet8d, /*Steps=*/2>::run(x, _mm512_rsqrt14_pd(x));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f preciprocal<Packet16f>(const Packet16f& a) {
+#ifdef EIGEN_VECTORIZE_AVX512ER
+  return _mm512_rcp28_ps(a);
+#else
+  return generic_reciprocal_newton_step<Packet16f, /*Steps=*/1>::run(a, _mm512_rcp14_ps(a));
+#endif
+}
+#endif
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_BF16(Packet16f, Packet16bf)
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_F16(Packet16f, Packet16h)
+#endif  // EIGEN_VECTORIZE_AVX512FP16
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/MathFunctionsFP16.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/MathFunctionsFP16.h
new file mode 100644
index 0000000..240ade4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/MathFunctionsFP16.h
@@ -0,0 +1,75 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 The Eigen Authors.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_FP16_AVX512_H
+#define EIGEN_MATH_FUNCTIONS_FP16_AVX512_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+EIGEN_STRONG_INLINE Packet32h combine2Packet16h(const Packet16h& a, const Packet16h& b) {
+  __m512i result = _mm512_castsi256_si512(_mm256_castph_si256(a));
+  result = _mm512_inserti64x4(result, _mm256_castph_si256(b), 1);
+  return _mm512_castsi512_ph(result);
+}
+
+EIGEN_STRONG_INLINE void extract2Packet16h(const Packet32h& x, Packet16h& a, Packet16h& b) {
+  a = _mm256_castsi256_ph(_mm512_castsi512_si256(_mm512_castph_si512(x)));
+  b = _mm256_castsi256_ph(_mm512_extracti64x4_epi64(_mm512_castph_si512(x), 1));
+}
+
+#define _EIGEN_GENERATE_FP16_MATH_FUNCTION(func)                      \
+  template <>                                                         \
+  EIGEN_STRONG_INLINE Packet8h func<Packet8h>(const Packet8h& a) {    \
+    return float2half(func(half2float(a)));                           \
+  }                                                                   \
+                                                                      \
+  template <>                                                         \
+  EIGEN_STRONG_INLINE Packet16h func<Packet16h>(const Packet16h& a) { \
+    return float2half(func(half2float(a)));                           \
+  }                                                                   \
+                                                                      \
+  template <>                                                         \
+  EIGEN_STRONG_INLINE Packet32h func<Packet32h>(const Packet32h& a) { \
+    Packet16h low;                                                    \
+    Packet16h high;                                                   \
+    extract2Packet16h(a, low, high);                                  \
+    return combine2Packet16h(func(low), func(high));                  \
+  }
+
+_EIGEN_GENERATE_FP16_MATH_FUNCTION(psin)
+_EIGEN_GENERATE_FP16_MATH_FUNCTION(pcos)
+_EIGEN_GENERATE_FP16_MATH_FUNCTION(plog)
+_EIGEN_GENERATE_FP16_MATH_FUNCTION(plog2)
+_EIGEN_GENERATE_FP16_MATH_FUNCTION(plog1p)
+_EIGEN_GENERATE_FP16_MATH_FUNCTION(pexp)
+_EIGEN_GENERATE_FP16_MATH_FUNCTION(pexpm1)
+_EIGEN_GENERATE_FP16_MATH_FUNCTION(pexp2)
+_EIGEN_GENERATE_FP16_MATH_FUNCTION(ptanh)
+#undef _EIGEN_GENERATE_FP16_MATH_FUNCTION
+
+// pfrexp
+template <>
+EIGEN_STRONG_INLINE Packet32h pfrexp<Packet32h>(const Packet32h& a, Packet32h& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+// pldexp
+template <>
+EIGEN_STRONG_INLINE Packet32h pldexp<Packet32h>(const Packet32h& a, const Packet32h& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_FP16_AVX512_H
\ No newline at end of file
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/PacketMath.h
new file mode 100644
index 0000000..22271e1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/PacketMath.h
@@ -0,0 +1,3172 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner (benoit.steiner.goog@gmail.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_AVX512_H
+#define EIGEN_PACKET_MATH_AVX512_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#endif
+
+#ifdef EIGEN_VECTORIZE_FMA
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+#endif
+
+typedef __m512 Packet16f;
+typedef __m512i Packet16i;
+typedef __m512d Packet8d;
+typedef eigen_packet_wrapper<__m512i, 1> Packet8l;
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+typedef eigen_packet_wrapper<__m256i, 1> Packet16h;
+#endif
+typedef eigen_packet_wrapper<__m256i, 2> Packet16bf;
+
+typedef eigen_packet_wrapper<__m512i, 6> Packet32s;
+typedef eigen_packet_wrapper<__m256i, 6> Packet16s;
+typedef eigen_packet_wrapper<__m128i, 6> Packet8s;
+
+EIGEN_STRONG_INLINE Packet16i _eigen_mm512_loadu_epi32(const int* from) {
+#if EIGEN_COMP_GNUC && (EIGEN_COMP_CLANG < 1000 || EIGEN_COMP_GNUC < 1010)
+  return _mm512_loadu_si512(reinterpret_cast<const void*>(from));
+#else
+  return _mm512_loadu_epi32(from);
+#endif
+}
+
+EIGEN_STRONG_INLINE Packet16i _eigen_mm512_loadu_epi64(const int64_t* from) {
+#if EIGEN_COMP_GNUC && (EIGEN_COMP_CLANG < 1000 || EIGEN_COMP_GNUC < 1010)
+  return _mm512_loadu_si512(reinterpret_cast<const void*>(from));
+#else
+  return _mm512_loadu_epi64(from);
+#endif
+}
+
+EIGEN_STRONG_INLINE void _eigen_mm512_storeu_epi32(void* to, const Packet16i& from) {
+#if EIGEN_COMP_GNUC && (EIGEN_COMP_CLANG < 1000 || EIGEN_COMP_GNUC < 1010)
+  _mm512_storeu_si512(to, from);
+#else
+  _mm512_storeu_epi32(to, from);
+#endif
+}
+
+EIGEN_STRONG_INLINE void _eigen_mm512_storeu_epi64(void* to, const Packet16i& from) {
+#if EIGEN_COMP_GNUC && (EIGEN_COMP_CLANG < 1000 || EIGEN_COMP_GNUC < 1010)
+  _mm512_storeu_si512(to, from);
+#else
+  _mm512_storeu_epi64(to, from);
+#endif
+}
+
+EIGEN_STRONG_INLINE void _eigen_mm256_storeu_epi32(void* to, const __m256i& from) {
+#if EIGEN_COMP_GNUC && (EIGEN_COMP_CLANG < 1000 || EIGEN_COMP_GNUC < 1010)
+  _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
+#else
+  _mm256_storeu_epi32(to, from);
+#endif
+}
+
+EIGEN_STRONG_INLINE void _eigen_mm_storeu_epi32(void* to, const __m128i& from) {
+#if EIGEN_COMP_GNUC && (EIGEN_COMP_CLANG < 1000 || EIGEN_COMP_GNUC < 1010)
+  _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
+#else
+  _mm_storeu_epi32(to, from);
+#endif
+}
+
+template <>
+struct is_arithmetic<__m512> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<__m512i> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<__m512d> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<Packet8l> {
+  enum { value = true };
+};
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template <>
+struct is_arithmetic<Packet16h> {
+  enum { value = true };
+};
+
+template <>
+struct packet_traits<half> : default_packet_traits {
+  typedef Packet16h type;
+  // There is no half-size packet for Packet16h.
+  typedef Packet16h half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExp = 1,
+    HasExpm1 = 1,
+    HasBessel = 1,
+    HasNdtri = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+  };
+};
+#endif
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet16f type;
+  typedef Packet8f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasAbs = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasCbrt = 1,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasNdtri = 1,
+    HasBessel = 1,
+    HasExp = 1,
+    HasPow = 1,
+    HasReciprocal = EIGEN_FAST_MATH,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasErfc = EIGEN_FAST_MATH,
+    HasCmp = 1,
+    HasDiv = 1
+  };
+};
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet8d type;
+  typedef Packet4d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasCbrt = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasPow = 1,
+    HasATan = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasErfc = EIGEN_FAST_MATH,
+    HasATanh = 1,
+    HasCmp = 1,
+    HasDiv = 1
+  };
+};
+
+template <>
+struct packet_traits<int> : default_packet_traits {
+  typedef Packet16i type;
+  typedef Packet8i half;
+  enum { Vectorizable = 1, AlignedOnScalar = 1, HasCmp = 1, HasDiv = 1, size = 16 };
+};
+
+template <>
+struct packet_traits<int64_t> : default_packet_traits {
+  typedef Packet8l type;
+  typedef Packet4l half;
+  enum { Vectorizable = 1, AlignedOnScalar = 1, HasCmp = 1, size = 8 };
+};
+
+template <>
+struct unpacket_traits<Packet16f> {
+  typedef float type;
+  typedef Packet8f half;
+  typedef Packet16i integer_packet;
+  typedef uint16_t mask_t;
+  enum {
+    size = 16,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = true,
+    masked_store_available = true,
+    masked_fpops_available = true
+  };
+};
+template <>
+struct unpacket_traits<Packet8d> {
+  typedef double type;
+  typedef Packet4d half;
+  typedef Packet8l integer_packet;
+  typedef uint8_t mask_t;
+  enum {
+    size = 8,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = true,
+    masked_store_available = true,
+    masked_fpops_available = true
+  };
+};
+template <>
+struct unpacket_traits<Packet16i> {
+  typedef int type;
+  typedef Packet8i half;
+  enum {
+    size = 16,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet8l> {
+  typedef int64_t type;
+  typedef Packet4l half;
+  enum {
+    size = 8,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template <>
+struct unpacket_traits<Packet16h> {
+  typedef Eigen::half type;
+  typedef Packet8h half;
+  enum {
+    size = 16,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet32s> {
+  typedef numext::int16_t type;
+  typedef Packet16s half;
+  enum {
+    size = 32,
+    alignment = Aligned64,
+    vectorizable = false,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet16s> {
+  typedef numext::int16_t type;
+  typedef Packet8s half;
+  enum {
+    size = 16,
+    alignment = Aligned32,
+    vectorizable = false,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet8s> {
+  typedef numext::int16_t type;
+  typedef Packet8s half;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = false,
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pset1<Packet16f>(const float& from) {
+  return _mm512_set1_ps(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pset1<Packet8d>(const double& from) {
+  return _mm512_set1_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pset1<Packet16i>(const int& from) {
+  return _mm512_set1_epi32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l pset1<Packet8l>(const int64_t& from) {
+  return _mm512_set1_epi64(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pset1frombits<Packet16f>(unsigned int from) {
+  return _mm512_castsi512_ps(_mm512_set1_epi32(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pset1frombits<Packet8d>(const numext::uint64_t from) {
+  return _mm512_castsi512_pd(_mm512_set1_epi64(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pzero(const Packet16f& /*a*/) {
+  return _mm512_setzero_ps();
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pzero(const Packet8d& /*a*/) {
+  return _mm512_setzero_pd();
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pzero(const Packet16i& /*a*/) {
+  return _mm512_setzero_si512();
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l pzero(const Packet8l& /*a*/) {
+  return _mm512_setzero_si512();
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f peven_mask(const Packet16f& /*a*/) {
+  return _mm512_castsi512_ps(_mm512_set_epi32(0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i peven_mask(const Packet16i& /*a*/) {
+  return _mm512_set_epi32(0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d peven_mask(const Packet8d& /*a*/) {
+  return _mm512_castsi512_pd(_mm512_set_epi32(0, 0, -1, -1, 0, 0, -1, -1, 0, 0, -1, -1, 0, 0, -1, -1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l peven_mask(const Packet8l& /*a*/) {
+  return _mm512_set_epi32(0, 0, -1, -1, 0, 0, -1, -1, 0, 0, -1, -1, 0, 0, -1, -1);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pload1<Packet16f>(const float* from) {
+#if (EIGEN_COMP_GNUC != 0) || (EIGEN_COMP_CLANG != 0)
+  // Inline asm here helps reduce some register spilling in TRSM kernels.
+  // See note in unrolls::gemm::microKernel in TrsmKernel.h
+  Packet16f ret;
+  __asm__("vbroadcastss %[mem], %[dst]" : [dst] "=v"(ret) : [mem] "m"(*from));
+  return ret;
+#else
+  return _mm512_broadcastss_ps(_mm_load_ps1(from));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pload1<Packet8d>(const double* from) {
+#if (EIGEN_COMP_GNUC != 0) || (EIGEN_COMP_CLANG != 0)
+  Packet8d ret;
+  __asm__("vbroadcastsd %[mem], %[dst]" : [dst] "=v"(ret) : [mem] "m"(*from));
+  return ret;
+#else
+  return _mm512_set1_pd(*from);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f plset<Packet16f>(const float& a) {
+  return _mm512_add_ps(_mm512_set1_ps(a), _mm512_set_ps(15.0f, 14.0f, 13.0f, 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f,
+                                                        6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f, 0.0f));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d plset<Packet8d>(const double& a) {
+  return _mm512_add_pd(_mm512_set1_pd(a), _mm512_set_pd(7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i plset<Packet16i>(const int& a) {
+  return _mm512_add_epi32(_mm512_set1_epi32(a), _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l plset<Packet8l>(const int64_t& a) {
+  return _mm512_add_epi64(_mm512_set1_epi64(a), _mm512_set_epi64(7, 6, 5, 4, 3, 2, 1, 0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f padd<Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return _mm512_add_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d padd<Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return _mm512_add_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i padd<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_add_epi32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l padd<Packet8l>(const Packet8l& a, const Packet8l& b) {
+  return _mm512_add_epi64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f padd<Packet16f>(const Packet16f& a, const Packet16f& b, uint16_t umask) {
+  __mmask16 mask = static_cast<__mmask16>(umask);
+  return _mm512_maskz_add_ps(mask, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d padd<Packet8d>(const Packet8d& a, const Packet8d& b, uint8_t umask) {
+  __mmask8 mask = static_cast<__mmask8>(umask);
+  return _mm512_maskz_add_pd(mask, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f psub<Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return _mm512_sub_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d psub<Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return _mm512_sub_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i psub<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_sub_epi32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l psub<Packet8l>(const Packet8l& a, const Packet8l& b) {
+  return _mm512_sub_epi64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pnegate(const Packet16f& a) {
+  // NOTE: MSVC seems to struggle with _mm512_set1_epi32, leading to random results.
+  //       The intel docs give it a relatively high latency as well, so we're probably
+  //       better off with using _mm512_set_epi32 directly anyways.
+  const __m512i mask =
+      _mm512_set_epi32(SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32,
+                       SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32,
+                       SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32);
+  return _mm512_castsi512_ps(_mm512_xor_epi32(_mm512_castps_si512(a), mask));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pnegate(const Packet8d& a) {
+  const __m512i mask = _mm512_set_epi64(SIGN_MASK_I64, SIGN_MASK_I64, SIGN_MASK_I64, SIGN_MASK_I64, SIGN_MASK_I64,
+                                        SIGN_MASK_I64, SIGN_MASK_I64, SIGN_MASK_I64);
+  return _mm512_castsi512_pd(_mm512_xor_epi64(_mm512_castpd_si512(a), mask));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pnegate(const Packet16i& a) {
+  return _mm512_sub_epi32(_mm512_setzero_si512(), a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l pnegate(const Packet8l& a) {
+  return _mm512_sub_epi64(_mm512_setzero_si512(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pconj(const Packet16f& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pconj(const Packet8d& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pconj(const Packet16i& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l pconj(const Packet8l& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pmul<Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return _mm512_mul_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmul<Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return _mm512_mul_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pmul<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_mullo_epi32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l pmul<Packet8l>(const Packet8l& a, const Packet8l& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_mullo_epi64(a, b);
+#else
+  return _mm512_mullox_epi64(a, b);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pdiv<Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return _mm512_div_ps(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pdiv<Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return _mm512_div_pd(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pdiv<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  Packet8i q_lo = pdiv<Packet8i>(_mm512_extracti64x4_epi64(a, 0), _mm512_extracti64x4_epi64(b, 0));
+  Packet8i q_hi = pdiv<Packet8i>(_mm512_extracti64x4_epi64(a, 1), _mm512_extracti64x4_epi64(b, 1));
+  return _mm512_inserti64x4(_mm512_castsi256_si512(q_lo), q_hi, 1);
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+template <>
+EIGEN_STRONG_INLINE Packet16f pmadd(const Packet16f& a, const Packet16f& b, const Packet16f& c) {
+  return _mm512_fmadd_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmadd(const Packet8d& a, const Packet8d& b, const Packet8d& c) {
+  return _mm512_fmadd_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pmsub(const Packet16f& a, const Packet16f& b, const Packet16f& c) {
+  return _mm512_fmsub_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmsub(const Packet8d& a, const Packet8d& b, const Packet8d& c) {
+  return _mm512_fmsub_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pnmadd(const Packet16f& a, const Packet16f& b, const Packet16f& c) {
+  return _mm512_fnmadd_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pnmadd(const Packet8d& a, const Packet8d& b, const Packet8d& c) {
+  return _mm512_fnmadd_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pnmsub(const Packet16f& a, const Packet16f& b, const Packet16f& c) {
+  return _mm512_fnmsub_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pnmsub(const Packet8d& a, const Packet8d& b, const Packet8d& c) {
+  return _mm512_fnmsub_pd(a, b, c);
+}
+#endif
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet16f pselect(const Packet16f& mask, const Packet16f& a, const Packet16f& b) {
+  __mmask16 mask16 = _mm512_cmpeq_epi32_mask(_mm512_castps_si512(mask), _mm512_setzero_epi32());
+  return _mm512_mask_blend_ps(mask16, a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet16i pselect(const Packet16i& mask, const Packet16i& a, const Packet16i& b) {
+  __mmask16 mask16 = _mm512_cmpeq_epi32_mask(mask, _mm512_setzero_epi32());
+  return _mm512_mask_blend_epi32(mask16, a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet8l pselect(const Packet8l& mask, const Packet8l& a, const Packet8l& b) {
+  __mmask8 mask8 = _mm512_cmpeq_epi64_mask(mask, _mm512_setzero_si512());
+  return _mm512_mask_blend_epi64(mask8, a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet8d pselect(const Packet8d& mask, const Packet8d& a, const Packet8d& b) {
+  __mmask8 mask8 = _mm512_cmp_epi64_mask(_mm512_castpd_si512(mask), _mm512_setzero_epi32(), _MM_CMPINT_EQ);
+  return _mm512_mask_blend_pd(mask8, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pmin<Packet16f>(const Packet16f& a, const Packet16f& b) {
+  // Arguments are reversed to match NaN propagation behavior of std::min.
+  return _mm512_min_ps(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmin<Packet8d>(const Packet8d& a, const Packet8d& b) {
+  // Arguments are reversed to match NaN propagation behavior of std::min.
+  return _mm512_min_pd(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pmin<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_min_epi32(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l pmin<Packet8l>(const Packet8l& a, const Packet8l& b) {
+  return _mm512_min_epi64(b, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pmax<Packet16f>(const Packet16f& a, const Packet16f& b) {
+  // Arguments are reversed to match NaN propagation behavior of std::max.
+  return _mm512_max_ps(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmax<Packet8d>(const Packet8d& a, const Packet8d& b) {
+  // Arguments are reversed to match NaN propagation behavior of std::max.
+  return _mm512_max_pd(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pmax<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_max_epi32(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l pmax<Packet8l>(const Packet8l& a, const Packet8l& b) {
+  return _mm512_max_epi64(b, a);
+}
+
+// Add specializations for min/max with prescribed NaN propagation.
+template <>
+EIGEN_STRONG_INLINE Packet16f pmin<PropagateNumbers, Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet16f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmin<PropagateNumbers, Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet8d>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pmax<PropagateNumbers, Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet16f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmax<PropagateNumbers, Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet8d>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pmin<PropagateNaN, Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet16f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmin<PropagateNaN, Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet8d>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pmax<PropagateNaN, Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet16f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmax<PropagateNaN, Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet8d>);
+}
+
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+template <int I_>
+EIGEN_STRONG_INLINE Packet8f extract256(Packet16f x) {
+  return _mm512_extractf32x8_ps(x, I_);
+}
+template <int I_>
+EIGEN_STRONG_INLINE Packet2d extract128(Packet8d x) {
+  return _mm512_extractf64x2_pd(x, I_);
+}
+EIGEN_STRONG_INLINE Packet16f cat256(Packet8f a, Packet8f b) {
+  return _mm512_insertf32x8(_mm512_castps256_ps512(a), b, 1);
+}
+EIGEN_STRONG_INLINE Packet16i cat256i(Packet8i a, Packet8i b) {
+  return _mm512_inserti32x8(_mm512_castsi256_si512(a), b, 1);
+}
+#else
+// AVX512F does not define _mm512_extractf32x8_ps to extract _m256 from _m512
+template <int I_>
+EIGEN_STRONG_INLINE Packet8f extract256(Packet16f x) {
+  return _mm256_castsi256_ps(_mm512_extracti64x4_epi64(_mm512_castps_si512(x), I_));
+}
+
+// AVX512F does not define _mm512_extractf64x2_pd to extract _m128 from _m512
+template <int I_>
+EIGEN_STRONG_INLINE Packet2d extract128(Packet8d x) {
+  return _mm_castsi128_pd(_mm512_extracti32x4_epi32(_mm512_castpd_si512(x), I_));
+}
+
+EIGEN_STRONG_INLINE Packet16f cat256(Packet8f a, Packet8f b) {
+  return _mm512_castsi512_ps(
+      _mm512_inserti64x4(_mm512_castsi256_si512(_mm256_castps_si256(a)), _mm256_castps_si256(b), 1));
+}
+EIGEN_STRONG_INLINE Packet16i cat256i(Packet8i a, Packet8i b) {
+  return _mm512_inserti64x4(_mm512_castsi256_si512(a), b, 1);
+}
+#endif
+
+// Helper function for bit packing snippet of low precision comparison.
+// It packs the flags from 32x16 to 16x16.
+EIGEN_STRONG_INLINE __m256i Pack32To16(Packet16f rf) {
+  // Split data into small pieces and handle with AVX instructions
+  // to guarantee internal order of vector.
+  // Operation:
+  //   dst[15:0]    := Saturate16(rf[31:0])
+  //   dst[31:16]   := Saturate16(rf[63:32])
+  //   ...
+  //   dst[255:240] := Saturate16(rf[255:224])
+  __m256i lo = _mm256_castps_si256(extract256<0>(rf));
+  __m256i hi = _mm256_castps_si256(extract256<1>(rf));
+  __m128i result_lo = _mm_packs_epi32(_mm256_extractf128_si256(lo, 0), _mm256_extractf128_si256(lo, 1));
+  __m128i result_hi = _mm_packs_epi32(_mm256_extractf128_si256(hi, 0), _mm256_extractf128_si256(hi, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(result_lo), result_hi, 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pisnan(const Packet16f& a) {
+  __mmask16 mask = _mm512_cmp_ps_mask(a, a, _CMP_UNORD_Q);
+  return _mm512_castsi512_ps(_mm512_maskz_set1_epi32(mask, int32_t(-1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcmp_eq(const Packet16f& a, const Packet16f& b) {
+  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_EQ_OQ);
+  return _mm512_castsi512_ps(_mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, int32_t(-1)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pcmp_le(const Packet16f& a, const Packet16f& b) {
+  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_LE_OQ);
+  return _mm512_castsi512_ps(_mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, int32_t(-1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcmp_lt(const Packet16f& a, const Packet16f& b) {
+  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_LT_OQ);
+  return _mm512_castsi512_ps(_mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, int32_t(-1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcmp_lt_or_nan(const Packet16f& a, const Packet16f& b) {
+  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_NGE_UQ);
+  return _mm512_castsi512_ps(_mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, int32_t(-1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pcmp_eq(const Packet16i& a, const Packet16i& b) {
+  __mmask16 mask = _mm512_cmp_epi32_mask(a, b, _MM_CMPINT_EQ);
+  return _mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, int32_t(-1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pcmp_le(const Packet16i& a, const Packet16i& b) {
+  __mmask16 mask = _mm512_cmp_epi32_mask(a, b, _MM_CMPINT_LE);
+  return _mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, int32_t(-1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pcmp_lt(const Packet16i& a, const Packet16i& b) {
+  __mmask16 mask = _mm512_cmp_epi32_mask(a, b, _MM_CMPINT_LT);
+  return _mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, int32_t(-1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l pcmp_eq(const Packet8l& a, const Packet8l& b) {
+  __mmask8 mask = _mm512_cmp_epi64_mask(a, b, _MM_CMPINT_EQ);
+  return _mm512_mask_set1_epi64(_mm512_setzero_si512(), mask, int64_t(-1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l pcmp_le(const Packet8l& a, const Packet8l& b) {
+  __mmask8 mask = _mm512_cmp_epi64_mask(a, b, _MM_CMPINT_LE);
+  return _mm512_mask_set1_epi64(_mm512_setzero_si512(), mask, int64_t(-1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l pcmp_lt(const Packet8l& a, const Packet8l& b) {
+  __mmask8 mask = _mm512_cmp_epi64_mask(a, b, _MM_CMPINT_LT);
+  return _mm512_mask_set1_epi64(_mm512_setzero_si512(), mask, int64_t(-1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pcmp_eq(const Packet8d& a, const Packet8d& b) {
+  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_EQ_OQ);
+  return _mm512_castsi512_pd(_mm512_mask_set1_epi64(_mm512_setzero_epi32(), mask, int64_t(-1)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pcmp_le(const Packet8d& a, const Packet8d& b) {
+  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_LE_OQ);
+  return _mm512_castsi512_pd(_mm512_mask_set1_epi64(_mm512_setzero_epi32(), mask, int64_t(-1)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pcmp_lt(const Packet8d& a, const Packet8d& b) {
+  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_LT_OQ);
+  return _mm512_castsi512_pd(_mm512_mask_set1_epi64(_mm512_setzero_epi32(), mask, int64_t(-1)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pcmp_lt_or_nan(const Packet8d& a, const Packet8d& b) {
+  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_NGE_UQ);
+  return _mm512_castsi512_pd(_mm512_mask_set1_epi64(_mm512_setzero_epi32(), mask, int64_t(-1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f print<Packet16f>(const Packet16f& a) {
+  return _mm512_roundscale_ps(a, _MM_FROUND_CUR_DIRECTION);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d print<Packet8d>(const Packet8d& a) {
+  return _mm512_roundscale_pd(a, _MM_FROUND_CUR_DIRECTION);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pceil<Packet16f>(const Packet16f& a) {
+  return _mm512_roundscale_ps(a, _MM_FROUND_TO_POS_INF);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pceil<Packet8d>(const Packet8d& a) {
+  return _mm512_roundscale_pd(a, _MM_FROUND_TO_POS_INF);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pfloor<Packet16f>(const Packet16f& a) {
+  return _mm512_roundscale_ps(a, _MM_FROUND_TO_NEG_INF);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pfloor<Packet8d>(const Packet8d& a) {
+  return _mm512_roundscale_pd(a, _MM_FROUND_TO_NEG_INF);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f ptrunc<Packet16f>(const Packet16f& a) {
+  return _mm512_roundscale_ps(a, _MM_FROUND_TO_ZERO);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d ptrunc<Packet8d>(const Packet8d& a) {
+  return _mm512_roundscale_pd(a, _MM_FROUND_TO_ZERO);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i ptrue<Packet16i>(const Packet16i& /*a*/) {
+  return _mm512_set1_epi32(int32_t(-1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l ptrue<Packet8l>(const Packet8l& /*a*/) {
+  return _mm512_set1_epi64(int64_t(-1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f ptrue<Packet16f>(const Packet16f& a) {
+  return _mm512_castsi512_ps(ptrue<Packet16i>(_mm512_castps_si512(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d ptrue<Packet8d>(const Packet8d& a) {
+  return _mm512_castsi512_pd(ptrue<Packet16i>(_mm512_castpd_si512(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pand<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_and_si512(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l pand<Packet8l>(const Packet8l& a, const Packet8l& b) {
+  return _mm512_and_si512(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pand<Packet16f>(const Packet16f& a, const Packet16f& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_and_ps(a, b);
+#else
+  return _mm512_castsi512_ps(pand(_mm512_castps_si512(a), _mm512_castps_si512(b)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pand<Packet8d>(const Packet8d& a, const Packet8d& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_and_pd(a, b);
+#else
+  Packet8d res = _mm512_undefined_pd();
+  Packet4d lane0_a = _mm512_extractf64x4_pd(a, 0);
+  Packet4d lane0_b = _mm512_extractf64x4_pd(b, 0);
+  res = _mm512_insertf64x4(res, _mm256_and_pd(lane0_a, lane0_b), 0);
+
+  Packet4d lane1_a = _mm512_extractf64x4_pd(a, 1);
+  Packet4d lane1_b = _mm512_extractf64x4_pd(b, 1);
+  return _mm512_insertf64x4(res, _mm256_and_pd(lane1_a, lane1_b), 1);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i por<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_or_si512(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l por<Packet8l>(const Packet8l& a, const Packet8l& b) {
+  return _mm512_or_si512(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f por<Packet16f>(const Packet16f& a, const Packet16f& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_or_ps(a, b);
+#else
+  return _mm512_castsi512_ps(por(_mm512_castps_si512(a), _mm512_castps_si512(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d por<Packet8d>(const Packet8d& a, const Packet8d& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_or_pd(a, b);
+#else
+  return _mm512_castsi512_pd(por(_mm512_castpd_si512(a), _mm512_castpd_si512(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pxor<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_xor_si512(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l pxor<Packet8l>(const Packet8l& a, const Packet8l& b) {
+  return _mm512_xor_si512(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pxor<Packet16f>(const Packet16f& a, const Packet16f& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_xor_ps(a, b);
+#else
+  return _mm512_castsi512_ps(pxor(_mm512_castps_si512(a), _mm512_castps_si512(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pxor<Packet8d>(const Packet8d& a, const Packet8d& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_xor_pd(a, b);
+#else
+  return _mm512_castsi512_pd(pxor(_mm512_castpd_si512(a), _mm512_castpd_si512(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pandnot<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_andnot_si512(b, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l pandnot<Packet8l>(const Packet8l& a, const Packet8l& b) {
+  return _mm512_andnot_si512(b, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pandnot<Packet16f>(const Packet16f& a, const Packet16f& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_andnot_ps(b, a);
+#else
+  return _mm512_castsi512_ps(pandnot(_mm512_castps_si512(a), _mm512_castps_si512(b)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pandnot<Packet8d>(const Packet8d& a, const Packet8d& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_andnot_pd(b, a);
+#else
+  return _mm512_castsi512_pd(pandnot(_mm512_castpd_si512(a), _mm512_castpd_si512(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pround<Packet16f>(const Packet16f& a) {
+  // Work-around for default std::round rounding mode.
+  const Packet16f mask = pset1frombits<Packet16f>(static_cast<numext::uint32_t>(0x80000000u));
+  const Packet16f prev0dot5 = pset1frombits<Packet16f>(static_cast<numext::uint32_t>(0x3EFFFFFFu));
+  return _mm512_roundscale_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pround<Packet8d>(const Packet8d& a) {
+  // Work-around for default std::round rounding mode.
+  const Packet8d mask = pset1frombits<Packet8d>(static_cast<numext::uint64_t>(0x8000000000000000ull));
+  const Packet8d prev0dot5 = pset1frombits<Packet8d>(static_cast<numext::uint64_t>(0x3FDFFFFFFFFFFFFFull));
+  return _mm512_roundscale_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet16i parithmetic_shift_right(Packet16i a) {
+  return _mm512_srai_epi32(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet16i plogical_shift_right(Packet16i a) {
+  return _mm512_srli_epi32(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet16i plogical_shift_left(Packet16i a) {
+  return _mm512_slli_epi32(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8l parithmetic_shift_right(Packet8l a) {
+  return _mm512_srai_epi64(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8l plogical_shift_right(Packet8l a) {
+  return _mm512_srli_epi64(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8l plogical_shift_left(Packet8l a) {
+  return _mm512_slli_epi64(a, N);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pload<Packet16f>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_ps(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pload<Packet8d>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pload<Packet16i>(const int* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_epi64(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l pload<Packet8l>(const int64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_epi64(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f ploadu<Packet16f>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_ps(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d ploadu<Packet8d>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i ploadu<Packet16i>(const int* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _eigen_mm512_loadu_epi32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l ploadu<Packet8l>(const int64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _eigen_mm512_loadu_epi64(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f ploadu<Packet16f>(const float* from, uint16_t umask) {
+  __mmask16 mask = static_cast<__mmask16>(umask);
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_maskz_loadu_ps(mask, from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d ploadu<Packet8d>(const double* from, uint8_t umask) {
+  __mmask8 mask = static_cast<__mmask8>(umask);
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_maskz_loadu_pd(mask, from);
+}
+
+// Loads 8 floats from memory a returns the packet
+// {a0, a0  a1, a1, a2, a2, a3, a3, a4, a4, a5, a5, a6, a6, a7, a7}
+template <>
+EIGEN_STRONG_INLINE Packet16f ploaddup<Packet16f>(const float* from) {
+  // an unaligned load is required here as there is no requirement
+  // on the alignment of input pointer 'from'
+  __m256i low_half = _mm256_castps_si256(_mm256_loadu_ps(from));
+  __m512 even_elements = _mm512_castsi512_ps(_mm512_cvtepu32_epi64(low_half));
+  __m512 pairs = _mm512_permute_ps(even_elements, _MM_SHUFFLE(2, 2, 0, 0));
+  return pairs;
+}
+
+// Loads 4 doubles from memory a returns the packet {a0, a0,  a1, a1, a2, a2, a3,
+// a3}
+template <>
+EIGEN_STRONG_INLINE Packet8d ploaddup<Packet8d>(const double* from) {
+  Packet8d tmp = _mm512_castpd256_pd512(ploadu<Packet4d>(from));
+  const Packet8l scatter_mask = _mm512_set_epi64(3, 3, 2, 2, 1, 1, 0, 0);
+  return _mm512_permutexvar_pd(scatter_mask, tmp);
+}
+
+// Loads 4 int64_t from memory a returns the packet {a0, a0,  a1, a1, a2, a2, a3,
+// a3}
+template <>
+EIGEN_STRONG_INLINE Packet8l ploaddup<Packet8l>(const int64_t* from) {
+  Packet8l tmp = _mm512_castsi256_si512(ploadu<Packet4l>(from));
+  const Packet8l scatter_mask = _mm512_set_epi64(3, 3, 2, 2, 1, 1, 0, 0);
+  return _mm512_permutexvar_epi64(scatter_mask, tmp);
+}
+
+// Loads 8 integers from memory and returns the packet
+// {a0, a0  a1, a1, a2, a2, a3, a3, a4, a4, a5, a5, a6, a6, a7, a7}
+template <>
+EIGEN_STRONG_INLINE Packet16i ploaddup<Packet16i>(const int* from) {
+  __m256i low_half = _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+  __m512 even_elements = _mm512_castsi512_ps(_mm512_cvtepu32_epi64(low_half));
+  __m512 pairs = _mm512_permute_ps(even_elements, _MM_SHUFFLE(2, 2, 0, 0));
+  return _mm512_castps_si512(pairs);
+}
+
+// Loads 4 floats from memory a returns the packet
+// {a0, a0  a0, a0, a1, a1, a1, a1, a2, a2, a2, a2, a3, a3, a3, a3}
+template <>
+EIGEN_STRONG_INLINE Packet16f ploadquad<Packet16f>(const float* from) {
+  Packet16f tmp = _mm512_castps128_ps512(ploadu<Packet4f>(from));
+  const Packet16i scatter_mask = _mm512_set_epi32(3, 3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0);
+  return _mm512_permutexvar_ps(scatter_mask, tmp);
+}
+
+// Loads 2 doubles from memory a returns the packet
+// {a0, a0  a0, a0, a1, a1, a1, a1}
+template <>
+EIGEN_STRONG_INLINE Packet8d ploadquad<Packet8d>(const double* from) {
+  __m256d lane0 = _mm256_set1_pd(*from);
+  __m256d lane1 = _mm256_set1_pd(*(from + 1));
+  __m512d tmp = _mm512_undefined_pd();
+  tmp = _mm512_insertf64x4(tmp, lane0, 0);
+  return _mm512_insertf64x4(tmp, lane1, 1);
+}
+
+// Loads 2 int64_t from memory a returns the packet
+// {a0, a0  a0, a0, a1, a1, a1, a1}
+template <>
+EIGEN_STRONG_INLINE Packet8l ploadquad<Packet8l>(const int64_t* from) {
+  __m256i lane0 = _mm256_set1_epi64x(*from);
+  __m256i lane1 = _mm256_set1_epi64x(*(from + 1));
+  __m512i tmp = _mm512_undefined_epi32();
+  tmp = _mm512_inserti64x4(tmp, lane0, 0);
+  return _mm512_inserti64x4(tmp, lane1, 1);
+}
+
+// Loads 4 integers from memory and returns the packet
+// {a0, a0  a0, a0, a1, a1, a1, a1, a2, a2, a2, a2, a3, a3, a3, a3}
+template <>
+EIGEN_STRONG_INLINE Packet16i ploadquad<Packet16i>(const int* from) {
+  Packet16i tmp = _mm512_castsi128_si512(ploadu<Packet4i>(from));
+  const Packet16i scatter_mask = _mm512_set_epi32(3, 3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0);
+  return _mm512_permutexvar_epi32(scatter_mask, tmp);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet16f& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_ps(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet8d& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_pd(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet16i& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_epi32(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int64_t>(int64_t* to, const Packet8l& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_epi64(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet16f& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_ps(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet8d& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_pd(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet16i& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _eigen_mm512_storeu_epi32(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int64_t>(int64_t* to, const Packet8l& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _eigen_mm512_storeu_epi64(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet16f& from, uint16_t umask) {
+  __mmask16 mask = static_cast<__mmask16>(umask);
+  EIGEN_DEBUG_UNALIGNED_STORE return _mm512_mask_storeu_ps(to, mask, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet8d& from, uint8_t umask) {
+  __mmask8 mask = static_cast<__mmask8>(umask);
+  EIGEN_DEBUG_UNALIGNED_STORE return _mm512_mask_storeu_pd(to, mask, from);
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pgather(const Packet& src, const Scalar* from, Index stride,
+                                        typename unpacket_traits<Packet>::mask_t umask);
+template <>
+EIGEN_DEVICE_FUNC inline Packet16f pgather<float, Packet16f>(const Packet16f& src, const float* from, Index stride,
+                                                             uint16_t umask) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier = _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+  __mmask16 mask = static_cast<__mmask16>(umask);
+
+  return _mm512_mask_i32gather_ps(src, mask, indices, from, 4);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet8d pgather<double, Packet8d>(const Packet8d& src, const double* from, Index stride,
+                                                            uint8_t umask) {
+  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
+  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
+  __mmask8 mask = static_cast<__mmask8>(umask);
+
+  return _mm512_mask_i32gather_pd(src, mask, indices, from, 8);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet16f pgather<float, Packet16f>(const float* from, Index stride) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier = _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+
+  return _mm512_i32gather_ps(indices, from, 4);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet8d pgather<double, Packet8d>(const double* from, Index stride) {
+  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
+  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
+
+  return _mm512_i32gather_pd(indices, from, 8);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet8l pgather<int64_t, Packet8l>(const int64_t* from, Index stride) {
+  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
+  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
+
+  return _mm512_i32gather_epi64(indices, from, 8);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet16i pgather<int, Packet16i>(const int* from, Index stride) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier = _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+  return _mm512_i32gather_epi32(indices, from, 4);
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, Index stride,
+                                       typename unpacket_traits<Packet>::mask_t umask);
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet16f>(float* to, const Packet16f& from, Index stride,
+                                                         uint16_t umask) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier = _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+  __mmask16 mask = static_cast<__mmask16>(umask);
+  _mm512_mask_i32scatter_ps(to, mask, indices, from, 4);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet8d>(double* to, const Packet8d& from, Index stride,
+                                                         uint8_t umask) {
+  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
+  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
+  __mmask8 mask = static_cast<__mmask8>(umask);
+  _mm512_mask_i32scatter_pd(to, mask, indices, from, 8);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet16f>(float* to, const Packet16f& from, Index stride) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier = _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+  _mm512_i32scatter_ps(to, indices, from, 4);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet8d>(double* to, const Packet8d& from, Index stride) {
+  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
+  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
+  _mm512_i32scatter_pd(to, indices, from, 8);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<int64_t, Packet8l>(int64_t* to, const Packet8l& from, Index stride) {
+  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
+  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
+  _mm512_i32scatter_epi64(to, indices, from, 8);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<int, Packet16i>(int* to, const Packet16i& from, Index stride) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier = _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+  _mm512_i32scatter_epi32(to, indices, from, 4);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet16f>(float* to, const float& a) {
+  Packet16f pa = pset1<Packet16f>(a);
+  pstore(to, pa);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet8d>(double* to, const double& a) {
+  Packet8d pa = pset1<Packet8d>(a);
+  pstore(to, pa);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet16i>(int* to, const int& a) {
+  Packet16i pa = pset1<Packet16i>(a);
+  pstore(to, pa);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet8l>(int64_t* to, const int64_t& a) {
+  Packet8l pa = pset1<Packet8l>(a);
+  pstore(to, pa);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet16f>(const Packet16f& a) {
+  return _mm512_cvtss_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet8d>(const Packet8d& a) {
+  return _mm512_cvtsd_f64(a);
+}
+template <>
+EIGEN_STRONG_INLINE int64_t pfirst<Packet8l>(const Packet8l& a) {
+  int64_t x = _mm_extract_epi64_0(_mm512_extracti32x4_epi32(a, 0));
+  return x;
+}
+template <>
+EIGEN_STRONG_INLINE int pfirst<Packet16i>(const Packet16i& a) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(11, 0, 0)
+  return _mm_cvtsi128_si32(_mm512_castsi512_si128(a));
+#else
+  return _mm512_cvtsi512_si32(a);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f preverse(const Packet16f& a) {
+  return _mm512_permutexvar_ps(_mm512_set_epi32(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d preverse(const Packet8d& a) {
+  return _mm512_permutexvar_pd(_mm512_set_epi32(0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i preverse(const Packet16i& a) {
+  return _mm512_permutexvar_epi32(_mm512_set_epi32(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l preverse(const Packet8l& a) {
+  return _mm512_permutexvar_epi64(_mm512_set_epi64(0, 1, 2, 3, 4, 5, 6, 7), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pabs(const Packet16f& a) {
+  // _mm512_abs_ps intrinsic not found, so implement via bitwise AND with sign-bit mask.
+  return _mm512_castsi512_ps(_mm512_and_si512(_mm512_castps_si512(a), _mm512_set1_epi32(0x7fffffff)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pabs(const Packet8d& a) {
+  // _mm512_abs_pd intrinsic not found, so implement via bitwise AND with sign-bit mask.
+  return _mm512_castsi512_pd(_mm512_and_si512(_mm512_castpd_si512(a), _mm512_set1_epi64(0x7fffffffffffffff)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pabs(const Packet16i& a) {
+  return _mm512_abs_epi32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l pabs(const Packet8l& a) {
+  return _mm512_abs_epi64(a);
+}
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template <>
+EIGEN_STRONG_INLINE Packet16h psignbit(const Packet16h& a) {
+  return _mm256_srai_epi16(a, 15);
+}
+#endif  // EIGEN_VECTORIZE_AVX512FP16
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf psignbit(const Packet16bf& a) {
+  return _mm256_srai_epi16(a, 15);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f psignbit(const Packet16f& a) {
+  return _mm512_castsi512_ps(_mm512_srai_epi32(_mm512_castps_si512(a), 31));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d psignbit(const Packet8d& a) {
+  return _mm512_castsi512_pd(_mm512_srai_epi64(_mm512_castpd_si512(a), 63));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pfrexp<Packet16f>(const Packet16f& a, Packet16f& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+// Extract exponent without existence of Packet8l.
+template <>
+EIGEN_STRONG_INLINE Packet8d pfrexp_generic_get_biased_exponent(const Packet8d& a) {
+  const Packet8d cst_exp_mask = pset1frombits<Packet8d>(static_cast<uint64_t>(0x7ff0000000000000ull));
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_cvtepi64_pd(_mm512_srli_epi64(_mm512_castpd_si512(pand(a, cst_exp_mask)), 52));
+#else
+  return _mm512_cvtepi32_pd(_mm512_cvtepi64_epi32(_mm512_srli_epi64(_mm512_castpd_si512(pand(a, cst_exp_mask)), 52)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pfrexp<Packet8d>(const Packet8d& a, Packet8d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pldexp<Packet16f>(const Packet16f& a, const Packet16f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pldexp<Packet8d>(const Packet8d& a, const Packet8d& exponent) {
+  // Clamp exponent to [-2099, 2099]
+  const Packet8d max_exponent = pset1<Packet8d>(2099.0);
+  const Packet8i e = _mm512_cvtpd_epi32(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
+
+  // Split 2^e into four factors and multiply.
+  const Packet8i bias = pset1<Packet8i>(1023);
+  Packet8i b = parithmetic_shift_right<2>(e);  // floor(e/4)
+
+  // 2^b
+  const Packet8i permute_idx = _mm256_setr_epi32(0, 4, 1, 5, 2, 6, 3, 7);
+  Packet8i hi = _mm256_permutevar8x32_epi32(padd(b, bias), permute_idx);
+  Packet8i lo = _mm256_slli_epi64(hi, 52);
+  hi = _mm256_slli_epi64(_mm256_srli_epi64(hi, 32), 52);
+  Packet8d c = _mm512_castsi512_pd(_mm512_inserti64x4(_mm512_castsi256_si512(lo), hi, 1));
+  Packet8d out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
+
+  // 2^(e - 3b)
+  b = psub(psub(psub(e, b), b), b);  // e - 3b
+  hi = _mm256_permutevar8x32_epi32(padd(b, bias), permute_idx);
+  lo = _mm256_slli_epi64(hi, 52);
+  hi = _mm256_slli_epi64(_mm256_srli_epi64(hi, 32), 52);
+  c = _mm512_castsi512_pd(_mm512_inserti64x4(_mm512_castsi256_si512(lo), hi, 1));
+  out = pmul(out, c);  // a * 2^e
+  return out;
+}
+
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+// AVX512F does not define _mm512_extractf32x8_ps to extract _m256 from _m512
+#define EIGEN_EXTRACT_8f_FROM_16f(INPUT, OUTPUT)        \
+  __m256 OUTPUT##_0 = _mm512_extractf32x8_ps(INPUT, 0); \
+  __m256 OUTPUT##_1 = _mm512_extractf32x8_ps(INPUT, 1)
+
+// AVX512F does not define _mm512_extracti32x8_epi32 to extract _m256i from _m512i
+#define EIGEN_EXTRACT_8i_FROM_16i(INPUT, OUTPUT)            \
+  __m256i OUTPUT##_0 = _mm512_extracti32x8_epi32(INPUT, 0); \
+  __m256i OUTPUT##_1 = _mm512_extracti32x8_epi32(INPUT, 1)
+#else
+#define EIGEN_EXTRACT_8f_FROM_16f(INPUT, OUTPUT)                                                     \
+  __m256 OUTPUT##_0 = _mm256_insertf128_ps(_mm256_castps128_ps256(_mm512_extractf32x4_ps(INPUT, 0)), \
+                                           _mm512_extractf32x4_ps(INPUT, 1), 1);                     \
+  __m256 OUTPUT##_1 = _mm256_insertf128_ps(_mm256_castps128_ps256(_mm512_extractf32x4_ps(INPUT, 2)), \
+                                           _mm512_extractf32x4_ps(INPUT, 3), 1)
+
+#define EIGEN_EXTRACT_8i_FROM_16i(INPUT, OUTPUT)                                                            \
+  __m256i OUTPUT##_0 = _mm256_insertf128_si256(_mm256_castsi128_si256(_mm512_extracti32x4_epi32(INPUT, 0)), \
+                                               _mm512_extracti32x4_epi32(INPUT, 1), 1);                     \
+  __m256i OUTPUT##_1 = _mm256_insertf128_si256(_mm256_castsi128_si256(_mm512_extracti32x4_epi32(INPUT, 2)), \
+                                               _mm512_extracti32x4_epi32(INPUT, 3), 1)
+#endif
+
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+#define EIGEN_INSERT_8f_INTO_16f(OUTPUT, INPUTA, INPUTB) \
+  OUTPUT = _mm512_insertf32x8(_mm512_castps256_ps512(INPUTA), INPUTB, 1);
+
+#define EIGEN_INSERT_8i_INTO_16i(OUTPUT, INPUTA, INPUTB) \
+  OUTPUT = _mm512_inserti32x8(_mm512_castsi256_si512(INPUTA), INPUTB, 1);
+#else
+#define EIGEN_INSERT_8f_INTO_16f(OUTPUT, INPUTA, INPUTB)                    \
+  OUTPUT = _mm512_undefined_ps();                                           \
+  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTA, 0), 0); \
+  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTA, 1), 1); \
+  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTB, 0), 2); \
+  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTB, 1), 3);
+
+#define EIGEN_INSERT_8i_INTO_16i(OUTPUT, INPUTA, INPUTB)                       \
+  OUTPUT = _mm512_undefined_epi32();                                           \
+  OUTPUT = _mm512_inserti32x4(OUTPUT, _mm256_extractf128_si256(INPUTA, 0), 0); \
+  OUTPUT = _mm512_inserti32x4(OUTPUT, _mm256_extractf128_si256(INPUTA, 1), 1); \
+  OUTPUT = _mm512_inserti32x4(OUTPUT, _mm256_extractf128_si256(INPUTB, 0), 2); \
+  OUTPUT = _mm512_inserti32x4(OUTPUT, _mm256_extractf128_si256(INPUTB, 1), 3);
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8f predux_half<Packet16f>(const Packet16f& a) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  __m256 lane0 = _mm512_extractf32x8_ps(a, 0);
+  __m256 lane1 = _mm512_extractf32x8_ps(a, 1);
+  return _mm256_add_ps(lane0, lane1);
+#else
+  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
+  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
+  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
+  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
+  __m128 sum0 = _mm_add_ps(lane0, lane2);
+  __m128 sum1 = _mm_add_ps(lane1, lane3);
+  return _mm256_insertf128_ps(_mm256_castps128_ps256(sum0), sum1, 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d predux_half<Packet8d>(const Packet8d& a) {
+  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
+  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
+  return _mm256_add_pd(lane0, lane1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i predux_half<Packet16i>(const Packet16i& a) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  __m256i lane0 = _mm512_extracti32x8_epi32(a, 0);
+  __m256i lane1 = _mm512_extracti32x8_epi32(a, 1);
+  return _mm256_add_epi32(lane0, lane1);
+#else
+  __m128i lane0 = _mm512_extracti32x4_epi32(a, 0);
+  __m128i lane1 = _mm512_extracti32x4_epi32(a, 1);
+  __m128i lane2 = _mm512_extracti32x4_epi32(a, 2);
+  __m128i lane3 = _mm512_extracti32x4_epi32(a, 3);
+  __m128i sum0 = _mm_add_epi32(lane0, lane2);
+  __m128i sum1 = _mm_add_epi32(lane1, lane3);
+  return _mm256_inserti128_si256(_mm256_castsi128_si256(sum0), sum1, 1);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4l predux_half<Packet8l>(const Packet8l& a) {
+  __m256i lane0 = _mm512_extracti64x4_epi64(a, 0);
+  __m256i lane1 = _mm512_extracti64x4_epi64(a, 1);
+  return _mm256_add_epi64(lane0, lane1);
+}
+
+#define PACK_OUTPUT(OUTPUT, INPUT, INDEX, STRIDE) \
+  EIGEN_INSERT_8f_INTO_16f(OUTPUT[INDEX], INPUT[INDEX], INPUT[INDEX + STRIDE]);
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16f, 16>& kernel) {
+  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T4 = _mm512_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
+  __m512 T5 = _mm512_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
+  __m512 T6 = _mm512_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
+  __m512 T7 = _mm512_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
+  __m512 T8 = _mm512_unpacklo_ps(kernel.packet[8], kernel.packet[9]);
+  __m512 T9 = _mm512_unpackhi_ps(kernel.packet[8], kernel.packet[9]);
+  __m512 T10 = _mm512_unpacklo_ps(kernel.packet[10], kernel.packet[11]);
+  __m512 T11 = _mm512_unpackhi_ps(kernel.packet[10], kernel.packet[11]);
+  __m512 T12 = _mm512_unpacklo_ps(kernel.packet[12], kernel.packet[13]);
+  __m512 T13 = _mm512_unpackhi_ps(kernel.packet[12], kernel.packet[13]);
+  __m512 T14 = _mm512_unpacklo_ps(kernel.packet[14], kernel.packet[15]);
+  __m512 T15 = _mm512_unpackhi_ps(kernel.packet[14], kernel.packet[15]);
+  __m512 S0 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S1 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S2 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S3 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S4 = _mm512_shuffle_ps(T4, T6, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S5 = _mm512_shuffle_ps(T4, T6, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S6 = _mm512_shuffle_ps(T5, T7, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S7 = _mm512_shuffle_ps(T5, T7, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S8 = _mm512_shuffle_ps(T8, T10, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S9 = _mm512_shuffle_ps(T8, T10, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S10 = _mm512_shuffle_ps(T9, T11, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S11 = _mm512_shuffle_ps(T9, T11, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S12 = _mm512_shuffle_ps(T12, T14, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S13 = _mm512_shuffle_ps(T12, T14, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S14 = _mm512_shuffle_ps(T13, T15, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S15 = _mm512_shuffle_ps(T13, T15, _MM_SHUFFLE(3, 2, 3, 2));
+
+  EIGEN_EXTRACT_8f_FROM_16f(S0, S0);
+  EIGEN_EXTRACT_8f_FROM_16f(S1, S1);
+  EIGEN_EXTRACT_8f_FROM_16f(S2, S2);
+  EIGEN_EXTRACT_8f_FROM_16f(S3, S3);
+  EIGEN_EXTRACT_8f_FROM_16f(S4, S4);
+  EIGEN_EXTRACT_8f_FROM_16f(S5, S5);
+  EIGEN_EXTRACT_8f_FROM_16f(S6, S6);
+  EIGEN_EXTRACT_8f_FROM_16f(S7, S7);
+  EIGEN_EXTRACT_8f_FROM_16f(S8, S8);
+  EIGEN_EXTRACT_8f_FROM_16f(S9, S9);
+  EIGEN_EXTRACT_8f_FROM_16f(S10, S10);
+  EIGEN_EXTRACT_8f_FROM_16f(S11, S11);
+  EIGEN_EXTRACT_8f_FROM_16f(S12, S12);
+  EIGEN_EXTRACT_8f_FROM_16f(S13, S13);
+  EIGEN_EXTRACT_8f_FROM_16f(S14, S14);
+  EIGEN_EXTRACT_8f_FROM_16f(S15, S15);
+
+  PacketBlock<Packet8f, 32> tmp;
+
+  tmp.packet[0] = _mm256_permute2f128_ps(S0_0, S4_0, 0x20);
+  tmp.packet[1] = _mm256_permute2f128_ps(S1_0, S5_0, 0x20);
+  tmp.packet[2] = _mm256_permute2f128_ps(S2_0, S6_0, 0x20);
+  tmp.packet[3] = _mm256_permute2f128_ps(S3_0, S7_0, 0x20);
+  tmp.packet[4] = _mm256_permute2f128_ps(S0_0, S4_0, 0x31);
+  tmp.packet[5] = _mm256_permute2f128_ps(S1_0, S5_0, 0x31);
+  tmp.packet[6] = _mm256_permute2f128_ps(S2_0, S6_0, 0x31);
+  tmp.packet[7] = _mm256_permute2f128_ps(S3_0, S7_0, 0x31);
+
+  tmp.packet[8] = _mm256_permute2f128_ps(S0_1, S4_1, 0x20);
+  tmp.packet[9] = _mm256_permute2f128_ps(S1_1, S5_1, 0x20);
+  tmp.packet[10] = _mm256_permute2f128_ps(S2_1, S6_1, 0x20);
+  tmp.packet[11] = _mm256_permute2f128_ps(S3_1, S7_1, 0x20);
+  tmp.packet[12] = _mm256_permute2f128_ps(S0_1, S4_1, 0x31);
+  tmp.packet[13] = _mm256_permute2f128_ps(S1_1, S5_1, 0x31);
+  tmp.packet[14] = _mm256_permute2f128_ps(S2_1, S6_1, 0x31);
+  tmp.packet[15] = _mm256_permute2f128_ps(S3_1, S7_1, 0x31);
+
+  // Second set of _m256 outputs
+  tmp.packet[16] = _mm256_permute2f128_ps(S8_0, S12_0, 0x20);
+  tmp.packet[17] = _mm256_permute2f128_ps(S9_0, S13_0, 0x20);
+  tmp.packet[18] = _mm256_permute2f128_ps(S10_0, S14_0, 0x20);
+  tmp.packet[19] = _mm256_permute2f128_ps(S11_0, S15_0, 0x20);
+  tmp.packet[20] = _mm256_permute2f128_ps(S8_0, S12_0, 0x31);
+  tmp.packet[21] = _mm256_permute2f128_ps(S9_0, S13_0, 0x31);
+  tmp.packet[22] = _mm256_permute2f128_ps(S10_0, S14_0, 0x31);
+  tmp.packet[23] = _mm256_permute2f128_ps(S11_0, S15_0, 0x31);
+
+  tmp.packet[24] = _mm256_permute2f128_ps(S8_1, S12_1, 0x20);
+  tmp.packet[25] = _mm256_permute2f128_ps(S9_1, S13_1, 0x20);
+  tmp.packet[26] = _mm256_permute2f128_ps(S10_1, S14_1, 0x20);
+  tmp.packet[27] = _mm256_permute2f128_ps(S11_1, S15_1, 0x20);
+  tmp.packet[28] = _mm256_permute2f128_ps(S8_1, S12_1, 0x31);
+  tmp.packet[29] = _mm256_permute2f128_ps(S9_1, S13_1, 0x31);
+  tmp.packet[30] = _mm256_permute2f128_ps(S10_1, S14_1, 0x31);
+  tmp.packet[31] = _mm256_permute2f128_ps(S11_1, S15_1, 0x31);
+
+  // Pack them into the output
+  PACK_OUTPUT(kernel.packet, tmp.packet, 0, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 1, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 2, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 3, 16);
+
+  PACK_OUTPUT(kernel.packet, tmp.packet, 4, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 5, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 6, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 7, 16);
+
+  PACK_OUTPUT(kernel.packet, tmp.packet, 8, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 9, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 10, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 11, 16);
+
+  PACK_OUTPUT(kernel.packet, tmp.packet, 12, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 13, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 14, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 15, 16);
+}
+#define PACK_OUTPUT_2(OUTPUT, INPUT, INDEX, STRIDE) \
+  EIGEN_INSERT_8f_INTO_16f(OUTPUT[INDEX], INPUT[2 * INDEX], INPUT[2 * INDEX + STRIDE]);
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16f, 8>& kernel) {
+  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T4 = _mm512_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
+  __m512 T5 = _mm512_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
+  __m512 T6 = _mm512_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
+  __m512 T7 = _mm512_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
+
+  kernel.packet[0] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T0), _mm512_castps_pd(T2)));
+  kernel.packet[1] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T0), _mm512_castps_pd(T2)));
+  kernel.packet[2] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T1), _mm512_castps_pd(T3)));
+  kernel.packet[3] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T1), _mm512_castps_pd(T3)));
+  kernel.packet[4] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T4), _mm512_castps_pd(T6)));
+  kernel.packet[5] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T4), _mm512_castps_pd(T6)));
+  kernel.packet[6] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T5), _mm512_castps_pd(T7)));
+  kernel.packet[7] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T5), _mm512_castps_pd(T7)));
+
+  T0 = _mm512_shuffle_f32x4(kernel.packet[0], kernel.packet[4], 0x44);
+  T1 = _mm512_shuffle_f32x4(kernel.packet[0], kernel.packet[4], 0xee);
+  T2 = _mm512_shuffle_f32x4(kernel.packet[1], kernel.packet[5], 0x44);
+  T3 = _mm512_shuffle_f32x4(kernel.packet[1], kernel.packet[5], 0xee);
+  T4 = _mm512_shuffle_f32x4(kernel.packet[2], kernel.packet[6], 0x44);
+  T5 = _mm512_shuffle_f32x4(kernel.packet[2], kernel.packet[6], 0xee);
+  T6 = _mm512_shuffle_f32x4(kernel.packet[3], kernel.packet[7], 0x44);
+  T7 = _mm512_shuffle_f32x4(kernel.packet[3], kernel.packet[7], 0xee);
+
+  kernel.packet[0] = _mm512_shuffle_f32x4(T0, T2, 0x88);
+  kernel.packet[2] = _mm512_shuffle_f32x4(T0, T2, 0xdd);
+  kernel.packet[1] = _mm512_shuffle_f32x4(T4, T6, 0x88);
+  kernel.packet[3] = _mm512_shuffle_f32x4(T4, T6, 0xdd);
+  kernel.packet[4] = _mm512_shuffle_f32x4(T1, T3, 0x88);
+  kernel.packet[6] = _mm512_shuffle_f32x4(T1, T3, 0xdd);
+  kernel.packet[5] = _mm512_shuffle_f32x4(T5, T7, 0x88);
+  kernel.packet[7] = _mm512_shuffle_f32x4(T5, T7, 0xdd);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16f, 4>& kernel) {
+  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+
+  __m512 S0 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S1 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S2 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S3 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+
+  EIGEN_EXTRACT_8f_FROM_16f(S0, S0);
+  EIGEN_EXTRACT_8f_FROM_16f(S1, S1);
+  EIGEN_EXTRACT_8f_FROM_16f(S2, S2);
+  EIGEN_EXTRACT_8f_FROM_16f(S3, S3);
+
+  PacketBlock<Packet8f, 8> tmp;
+
+  tmp.packet[0] = _mm256_permute2f128_ps(S0_0, S1_0, 0x20);
+  tmp.packet[1] = _mm256_permute2f128_ps(S2_0, S3_0, 0x20);
+  tmp.packet[2] = _mm256_permute2f128_ps(S0_0, S1_0, 0x31);
+  tmp.packet[3] = _mm256_permute2f128_ps(S2_0, S3_0, 0x31);
+
+  tmp.packet[4] = _mm256_permute2f128_ps(S0_1, S1_1, 0x20);
+  tmp.packet[5] = _mm256_permute2f128_ps(S2_1, S3_1, 0x20);
+  tmp.packet[6] = _mm256_permute2f128_ps(S0_1, S1_1, 0x31);
+  tmp.packet[7] = _mm256_permute2f128_ps(S2_1, S3_1, 0x31);
+
+  PACK_OUTPUT_2(kernel.packet, tmp.packet, 0, 1);
+  PACK_OUTPUT_2(kernel.packet, tmp.packet, 1, 1);
+  PACK_OUTPUT_2(kernel.packet, tmp.packet, 2, 1);
+  PACK_OUTPUT_2(kernel.packet, tmp.packet, 3, 1);
+}
+
+#define PACK_OUTPUT_SQ_D(OUTPUT, INPUT, INDEX, STRIDE)                \
+  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[INDEX], 0); \
+  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[INDEX + STRIDE], 1);
+
+#define PACK_OUTPUT_D(OUTPUT, INPUT, INDEX, STRIDE)                         \
+  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[(2 * INDEX)], 0); \
+  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[(2 * INDEX) + STRIDE], 1);
+
+#define PACK_OUTPUT_L(OUTPUT, INPUT, INDEX, STRIDE)                         \
+  OUTPUT[INDEX] = _mm512_inserti64x4(OUTPUT[INDEX], INPUT[(2 * INDEX)], 0); \
+  OUTPUT[INDEX] = _mm512_inserti64x4(OUTPUT[INDEX], INPUT[(2 * INDEX) + STRIDE], 1);
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8d, 4>& kernel) {
+  __m512d T0 = _mm512_shuffle_pd(kernel.packet[0], kernel.packet[1], 0);
+  __m512d T1 = _mm512_shuffle_pd(kernel.packet[0], kernel.packet[1], 0xff);
+  __m512d T2 = _mm512_shuffle_pd(kernel.packet[2], kernel.packet[3], 0);
+  __m512d T3 = _mm512_shuffle_pd(kernel.packet[2], kernel.packet[3], 0xff);
+
+  PacketBlock<Packet4d, 8> tmp;
+
+  tmp.packet[0] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0), _mm512_extractf64x4_pd(T2, 0), 0x20);
+  tmp.packet[1] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0), _mm512_extractf64x4_pd(T3, 0), 0x20);
+  tmp.packet[2] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0), _mm512_extractf64x4_pd(T2, 0), 0x31);
+  tmp.packet[3] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0), _mm512_extractf64x4_pd(T3, 0), 0x31);
+
+  tmp.packet[4] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1), _mm512_extractf64x4_pd(T2, 1), 0x20);
+  tmp.packet[5] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1), _mm512_extractf64x4_pd(T3, 1), 0x20);
+  tmp.packet[6] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1), _mm512_extractf64x4_pd(T2, 1), 0x31);
+  tmp.packet[7] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1), _mm512_extractf64x4_pd(T3, 1), 0x31);
+
+  PACK_OUTPUT_D(kernel.packet, tmp.packet, 0, 1);
+  PACK_OUTPUT_D(kernel.packet, tmp.packet, 1, 1);
+  PACK_OUTPUT_D(kernel.packet, tmp.packet, 2, 1);
+  PACK_OUTPUT_D(kernel.packet, tmp.packet, 3, 1);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8d, 8>& kernel) {
+  __m512d T0 = _mm512_unpacklo_pd(kernel.packet[0], kernel.packet[1]);
+  __m512d T1 = _mm512_unpackhi_pd(kernel.packet[0], kernel.packet[1]);
+  __m512d T2 = _mm512_unpacklo_pd(kernel.packet[2], kernel.packet[3]);
+  __m512d T3 = _mm512_unpackhi_pd(kernel.packet[2], kernel.packet[3]);
+  __m512d T4 = _mm512_unpacklo_pd(kernel.packet[4], kernel.packet[5]);
+  __m512d T5 = _mm512_unpackhi_pd(kernel.packet[4], kernel.packet[5]);
+  __m512d T6 = _mm512_unpacklo_pd(kernel.packet[6], kernel.packet[7]);
+  __m512d T7 = _mm512_unpackhi_pd(kernel.packet[6], kernel.packet[7]);
+
+  kernel.packet[0] = _mm512_permutex_pd(T2, 0x4E);
+  kernel.packet[0] = _mm512_mask_blend_pd(0xCC, T0, kernel.packet[0]);
+  kernel.packet[2] = _mm512_permutex_pd(T0, 0x4E);
+  kernel.packet[2] = _mm512_mask_blend_pd(0xCC, kernel.packet[2], T2);
+  kernel.packet[1] = _mm512_permutex_pd(T3, 0x4E);
+  kernel.packet[1] = _mm512_mask_blend_pd(0xCC, T1, kernel.packet[1]);
+  kernel.packet[3] = _mm512_permutex_pd(T1, 0x4E);
+  kernel.packet[3] = _mm512_mask_blend_pd(0xCC, kernel.packet[3], T3);
+  kernel.packet[4] = _mm512_permutex_pd(T6, 0x4E);
+  kernel.packet[4] = _mm512_mask_blend_pd(0xCC, T4, kernel.packet[4]);
+  kernel.packet[6] = _mm512_permutex_pd(T4, 0x4E);
+  kernel.packet[6] = _mm512_mask_blend_pd(0xCC, kernel.packet[6], T6);
+  kernel.packet[5] = _mm512_permutex_pd(T7, 0x4E);
+  kernel.packet[5] = _mm512_mask_blend_pd(0xCC, T5, kernel.packet[5]);
+  kernel.packet[7] = _mm512_permutex_pd(T5, 0x4E);
+  kernel.packet[7] = _mm512_mask_blend_pd(0xCC, kernel.packet[7], T7);
+
+  T0 = _mm512_shuffle_f64x2(kernel.packet[4], kernel.packet[4], 0x4E);
+  T0 = _mm512_mask_blend_pd(0xF0, kernel.packet[0], T0);
+  T4 = _mm512_shuffle_f64x2(kernel.packet[0], kernel.packet[0], 0x4E);
+  T4 = _mm512_mask_blend_pd(0xF0, T4, kernel.packet[4]);
+  T1 = _mm512_shuffle_f64x2(kernel.packet[5], kernel.packet[5], 0x4E);
+  T1 = _mm512_mask_blend_pd(0xF0, kernel.packet[1], T1);
+  T5 = _mm512_shuffle_f64x2(kernel.packet[1], kernel.packet[1], 0x4E);
+  T5 = _mm512_mask_blend_pd(0xF0, T5, kernel.packet[5]);
+  T2 = _mm512_shuffle_f64x2(kernel.packet[6], kernel.packet[6], 0x4E);
+  T2 = _mm512_mask_blend_pd(0xF0, kernel.packet[2], T2);
+  T6 = _mm512_shuffle_f64x2(kernel.packet[2], kernel.packet[2], 0x4E);
+  T6 = _mm512_mask_blend_pd(0xF0, T6, kernel.packet[6]);
+  T3 = _mm512_shuffle_f64x2(kernel.packet[7], kernel.packet[7], 0x4E);
+  T3 = _mm512_mask_blend_pd(0xF0, kernel.packet[3], T3);
+  T7 = _mm512_shuffle_f64x2(kernel.packet[3], kernel.packet[3], 0x4E);
+  T7 = _mm512_mask_blend_pd(0xF0, T7, kernel.packet[7]);
+
+  kernel.packet[0] = T0;
+  kernel.packet[1] = T1;
+  kernel.packet[2] = T2;
+  kernel.packet[3] = T3;
+  kernel.packet[4] = T4;
+  kernel.packet[5] = T5;
+  kernel.packet[6] = T6;
+  kernel.packet[7] = T7;
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8l, 4>& kernel) {
+  __m512i T0 = _mm512_castpd_si512(
+      _mm512_shuffle_pd(_mm512_castsi512_pd(kernel.packet[0]), _mm512_castsi512_pd(kernel.packet[1]), 0));
+  __m512i T1 = _mm512_castpd_si512(
+      _mm512_shuffle_pd(_mm512_castsi512_pd(kernel.packet[0]), _mm512_castsi512_pd(kernel.packet[1]), 0xff));
+  __m512i T2 = _mm512_castpd_si512(
+      _mm512_shuffle_pd(_mm512_castsi512_pd(kernel.packet[2]), _mm512_castsi512_pd(kernel.packet[3]), 0));
+  __m512i T3 = _mm512_castpd_si512(
+      _mm512_shuffle_pd(_mm512_castsi512_pd(kernel.packet[2]), _mm512_castsi512_pd(kernel.packet[3]), 0xff));
+
+  PacketBlock<Packet4l, 8> tmp;
+
+  tmp.packet[0] = _mm256_permute2x128_si256(_mm512_extracti64x4_epi64(T0, 0), _mm512_extracti64x4_epi64(T2, 0), 0x20);
+  tmp.packet[1] = _mm256_permute2x128_si256(_mm512_extracti64x4_epi64(T1, 0), _mm512_extracti64x4_epi64(T3, 0), 0x20);
+  tmp.packet[2] = _mm256_permute2x128_si256(_mm512_extracti64x4_epi64(T0, 0), _mm512_extracti64x4_epi64(T2, 0), 0x31);
+  tmp.packet[3] = _mm256_permute2x128_si256(_mm512_extracti64x4_epi64(T1, 0), _mm512_extracti64x4_epi64(T3, 0), 0x31);
+
+  tmp.packet[4] = _mm256_permute2x128_si256(_mm512_extracti64x4_epi64(T0, 1), _mm512_extracti64x4_epi64(T2, 1), 0x20);
+  tmp.packet[5] = _mm256_permute2x128_si256(_mm512_extracti64x4_epi64(T1, 1), _mm512_extracti64x4_epi64(T3, 1), 0x20);
+  tmp.packet[6] = _mm256_permute2x128_si256(_mm512_extracti64x4_epi64(T0, 1), _mm512_extracti64x4_epi64(T2, 1), 0x31);
+  tmp.packet[7] = _mm256_permute2x128_si256(_mm512_extracti64x4_epi64(T1, 1), _mm512_extracti64x4_epi64(T3, 1), 0x31);
+
+  PACK_OUTPUT_L(kernel.packet, tmp.packet, 0, 1);
+  PACK_OUTPUT_L(kernel.packet, tmp.packet, 1, 1);
+  PACK_OUTPUT_L(kernel.packet, tmp.packet, 2, 1);
+  PACK_OUTPUT_L(kernel.packet, tmp.packet, 3, 1);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8l, 8>& kernel) {
+  __m512i T0 = _mm512_unpacklo_epi64(kernel.packet[0], kernel.packet[1]);
+  __m512i T1 = _mm512_unpackhi_epi64(kernel.packet[0], kernel.packet[1]);
+  __m512i T2 = _mm512_unpacklo_epi64(kernel.packet[2], kernel.packet[3]);
+  __m512i T3 = _mm512_unpackhi_epi64(kernel.packet[2], kernel.packet[3]);
+  __m512i T4 = _mm512_unpacklo_epi64(kernel.packet[4], kernel.packet[5]);
+  __m512i T5 = _mm512_unpackhi_epi64(kernel.packet[4], kernel.packet[5]);
+  __m512i T6 = _mm512_unpacklo_epi64(kernel.packet[6], kernel.packet[7]);
+  __m512i T7 = _mm512_unpackhi_epi64(kernel.packet[6], kernel.packet[7]);
+
+  kernel.packet[0] = _mm512_permutex_epi64(T2, 0x4E);
+  kernel.packet[0] = _mm512_mask_blend_epi64(0xCC, T0, kernel.packet[0]);
+  kernel.packet[2] = _mm512_permutex_epi64(T0, 0x4E);
+  kernel.packet[2] = _mm512_mask_blend_epi64(0xCC, kernel.packet[2], T2);
+  kernel.packet[1] = _mm512_permutex_epi64(T3, 0x4E);
+  kernel.packet[1] = _mm512_mask_blend_epi64(0xCC, T1, kernel.packet[1]);
+  kernel.packet[3] = _mm512_permutex_epi64(T1, 0x4E);
+  kernel.packet[3] = _mm512_mask_blend_epi64(0xCC, kernel.packet[3], T3);
+  kernel.packet[4] = _mm512_permutex_epi64(T6, 0x4E);
+  kernel.packet[4] = _mm512_mask_blend_epi64(0xCC, T4, kernel.packet[4]);
+  kernel.packet[6] = _mm512_permutex_epi64(T4, 0x4E);
+  kernel.packet[6] = _mm512_mask_blend_epi64(0xCC, kernel.packet[6], T6);
+  kernel.packet[5] = _mm512_permutex_epi64(T7, 0x4E);
+  kernel.packet[5] = _mm512_mask_blend_epi64(0xCC, T5, kernel.packet[5]);
+  kernel.packet[7] = _mm512_permutex_epi64(T5, 0x4E);
+  kernel.packet[7] = _mm512_mask_blend_epi64(0xCC, kernel.packet[7], T7);
+
+  T0 = _mm512_shuffle_i64x2(kernel.packet[4], kernel.packet[4], 0x4E);
+  T0 = _mm512_mask_blend_epi64(0xF0, kernel.packet[0], T0);
+  T4 = _mm512_shuffle_i64x2(kernel.packet[0], kernel.packet[0], 0x4E);
+  T4 = _mm512_mask_blend_epi64(0xF0, T4, kernel.packet[4]);
+  T1 = _mm512_shuffle_i64x2(kernel.packet[5], kernel.packet[5], 0x4E);
+  T1 = _mm512_mask_blend_epi64(0xF0, kernel.packet[1], T1);
+  T5 = _mm512_shuffle_i64x2(kernel.packet[1], kernel.packet[1], 0x4E);
+  T5 = _mm512_mask_blend_epi64(0xF0, T5, kernel.packet[5]);
+  T2 = _mm512_shuffle_i64x2(kernel.packet[6], kernel.packet[6], 0x4E);
+  T2 = _mm512_mask_blend_epi64(0xF0, kernel.packet[2], T2);
+  T6 = _mm512_shuffle_i64x2(kernel.packet[2], kernel.packet[2], 0x4E);
+  T6 = _mm512_mask_blend_epi64(0xF0, T6, kernel.packet[6]);
+  T3 = _mm512_shuffle_i64x2(kernel.packet[7], kernel.packet[7], 0x4E);
+  T3 = _mm512_mask_blend_epi64(0xF0, kernel.packet[3], T3);
+  T7 = _mm512_shuffle_i64x2(kernel.packet[3], kernel.packet[3], 0x4E);
+  T7 = _mm512_mask_blend_epi64(0xF0, T7, kernel.packet[7]);
+
+  kernel.packet[0] = T0;
+  kernel.packet[1] = T1;
+  kernel.packet[2] = T2;
+  kernel.packet[3] = T3;
+  kernel.packet[4] = T4;
+  kernel.packet[5] = T5;
+  kernel.packet[6] = T6;
+  kernel.packet[7] = T7;
+}
+
+#define PACK_OUTPUT_I32(OUTPUT, INPUT, INDEX, STRIDE) \
+  EIGEN_INSERT_8i_INTO_16i(OUTPUT[INDEX], INPUT[INDEX], INPUT[INDEX + STRIDE]);
+
+#define PACK_OUTPUT_I32_2(OUTPUT, INPUT, INDEX, STRIDE) \
+  EIGEN_INSERT_8i_INTO_16i(OUTPUT[INDEX], INPUT[2 * INDEX], INPUT[2 * INDEX + STRIDE]);
+
+#define SHUFFLE_EPI32(A, B, M) _mm512_castps_si512(_mm512_shuffle_ps(_mm512_castsi512_ps(A), _mm512_castsi512_ps(B), M))
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16i, 16>& kernel) {
+  __m512i T0 = _mm512_unpacklo_epi32(kernel.packet[0], kernel.packet[1]);
+  __m512i T1 = _mm512_unpackhi_epi32(kernel.packet[0], kernel.packet[1]);
+  __m512i T2 = _mm512_unpacklo_epi32(kernel.packet[2], kernel.packet[3]);
+  __m512i T3 = _mm512_unpackhi_epi32(kernel.packet[2], kernel.packet[3]);
+  __m512i T4 = _mm512_unpacklo_epi32(kernel.packet[4], kernel.packet[5]);
+  __m512i T5 = _mm512_unpackhi_epi32(kernel.packet[4], kernel.packet[5]);
+  __m512i T6 = _mm512_unpacklo_epi32(kernel.packet[6], kernel.packet[7]);
+  __m512i T7 = _mm512_unpackhi_epi32(kernel.packet[6], kernel.packet[7]);
+  __m512i T8 = _mm512_unpacklo_epi32(kernel.packet[8], kernel.packet[9]);
+  __m512i T9 = _mm512_unpackhi_epi32(kernel.packet[8], kernel.packet[9]);
+  __m512i T10 = _mm512_unpacklo_epi32(kernel.packet[10], kernel.packet[11]);
+  __m512i T11 = _mm512_unpackhi_epi32(kernel.packet[10], kernel.packet[11]);
+  __m512i T12 = _mm512_unpacklo_epi32(kernel.packet[12], kernel.packet[13]);
+  __m512i T13 = _mm512_unpackhi_epi32(kernel.packet[12], kernel.packet[13]);
+  __m512i T14 = _mm512_unpacklo_epi32(kernel.packet[14], kernel.packet[15]);
+  __m512i T15 = _mm512_unpackhi_epi32(kernel.packet[14], kernel.packet[15]);
+  __m512i S0 = SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S1 = SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S2 = SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S3 = SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S4 = SHUFFLE_EPI32(T4, T6, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S5 = SHUFFLE_EPI32(T4, T6, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S6 = SHUFFLE_EPI32(T5, T7, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S7 = SHUFFLE_EPI32(T5, T7, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S8 = SHUFFLE_EPI32(T8, T10, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S9 = SHUFFLE_EPI32(T8, T10, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S10 = SHUFFLE_EPI32(T9, T11, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S11 = SHUFFLE_EPI32(T9, T11, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S12 = SHUFFLE_EPI32(T12, T14, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S13 = SHUFFLE_EPI32(T12, T14, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S14 = SHUFFLE_EPI32(T13, T15, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S15 = SHUFFLE_EPI32(T13, T15, _MM_SHUFFLE(3, 2, 3, 2));
+
+  EIGEN_EXTRACT_8i_FROM_16i(S0, S0);
+  EIGEN_EXTRACT_8i_FROM_16i(S1, S1);
+  EIGEN_EXTRACT_8i_FROM_16i(S2, S2);
+  EIGEN_EXTRACT_8i_FROM_16i(S3, S3);
+  EIGEN_EXTRACT_8i_FROM_16i(S4, S4);
+  EIGEN_EXTRACT_8i_FROM_16i(S5, S5);
+  EIGEN_EXTRACT_8i_FROM_16i(S6, S6);
+  EIGEN_EXTRACT_8i_FROM_16i(S7, S7);
+  EIGEN_EXTRACT_8i_FROM_16i(S8, S8);
+  EIGEN_EXTRACT_8i_FROM_16i(S9, S9);
+  EIGEN_EXTRACT_8i_FROM_16i(S10, S10);
+  EIGEN_EXTRACT_8i_FROM_16i(S11, S11);
+  EIGEN_EXTRACT_8i_FROM_16i(S12, S12);
+  EIGEN_EXTRACT_8i_FROM_16i(S13, S13);
+  EIGEN_EXTRACT_8i_FROM_16i(S14, S14);
+  EIGEN_EXTRACT_8i_FROM_16i(S15, S15);
+
+  PacketBlock<Packet8i, 32> tmp;
+
+  tmp.packet[0] = _mm256_permute2f128_si256(S0_0, S4_0, 0x20);
+  tmp.packet[1] = _mm256_permute2f128_si256(S1_0, S5_0, 0x20);
+  tmp.packet[2] = _mm256_permute2f128_si256(S2_0, S6_0, 0x20);
+  tmp.packet[3] = _mm256_permute2f128_si256(S3_0, S7_0, 0x20);
+  tmp.packet[4] = _mm256_permute2f128_si256(S0_0, S4_0, 0x31);
+  tmp.packet[5] = _mm256_permute2f128_si256(S1_0, S5_0, 0x31);
+  tmp.packet[6] = _mm256_permute2f128_si256(S2_0, S6_0, 0x31);
+  tmp.packet[7] = _mm256_permute2f128_si256(S3_0, S7_0, 0x31);
+
+  tmp.packet[8] = _mm256_permute2f128_si256(S0_1, S4_1, 0x20);
+  tmp.packet[9] = _mm256_permute2f128_si256(S1_1, S5_1, 0x20);
+  tmp.packet[10] = _mm256_permute2f128_si256(S2_1, S6_1, 0x20);
+  tmp.packet[11] = _mm256_permute2f128_si256(S3_1, S7_1, 0x20);
+  tmp.packet[12] = _mm256_permute2f128_si256(S0_1, S4_1, 0x31);
+  tmp.packet[13] = _mm256_permute2f128_si256(S1_1, S5_1, 0x31);
+  tmp.packet[14] = _mm256_permute2f128_si256(S2_1, S6_1, 0x31);
+  tmp.packet[15] = _mm256_permute2f128_si256(S3_1, S7_1, 0x31);
+
+  // Second set of _m256 outputs
+  tmp.packet[16] = _mm256_permute2f128_si256(S8_0, S12_0, 0x20);
+  tmp.packet[17] = _mm256_permute2f128_si256(S9_0, S13_0, 0x20);
+  tmp.packet[18] = _mm256_permute2f128_si256(S10_0, S14_0, 0x20);
+  tmp.packet[19] = _mm256_permute2f128_si256(S11_0, S15_0, 0x20);
+  tmp.packet[20] = _mm256_permute2f128_si256(S8_0, S12_0, 0x31);
+  tmp.packet[21] = _mm256_permute2f128_si256(S9_0, S13_0, 0x31);
+  tmp.packet[22] = _mm256_permute2f128_si256(S10_0, S14_0, 0x31);
+  tmp.packet[23] = _mm256_permute2f128_si256(S11_0, S15_0, 0x31);
+
+  tmp.packet[24] = _mm256_permute2f128_si256(S8_1, S12_1, 0x20);
+  tmp.packet[25] = _mm256_permute2f128_si256(S9_1, S13_1, 0x20);
+  tmp.packet[26] = _mm256_permute2f128_si256(S10_1, S14_1, 0x20);
+  tmp.packet[27] = _mm256_permute2f128_si256(S11_1, S15_1, 0x20);
+  tmp.packet[28] = _mm256_permute2f128_si256(S8_1, S12_1, 0x31);
+  tmp.packet[29] = _mm256_permute2f128_si256(S9_1, S13_1, 0x31);
+  tmp.packet[30] = _mm256_permute2f128_si256(S10_1, S14_1, 0x31);
+  tmp.packet[31] = _mm256_permute2f128_si256(S11_1, S15_1, 0x31);
+
+  // Pack them into the output
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 0, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 1, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 2, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 3, 16);
+
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 4, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 5, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 6, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 7, 16);
+
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 8, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 9, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 10, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 11, 16);
+
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 12, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 13, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 14, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 15, 16);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16i, 4>& kernel) {
+  __m512i T0 = _mm512_unpacklo_epi32(kernel.packet[0], kernel.packet[1]);
+  __m512i T1 = _mm512_unpackhi_epi32(kernel.packet[0], kernel.packet[1]);
+  __m512i T2 = _mm512_unpacklo_epi32(kernel.packet[2], kernel.packet[3]);
+  __m512i T3 = _mm512_unpackhi_epi32(kernel.packet[2], kernel.packet[3]);
+
+  __m512i S0 = SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S1 = SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S2 = SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S3 = SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+
+  EIGEN_EXTRACT_8i_FROM_16i(S0, S0);
+  EIGEN_EXTRACT_8i_FROM_16i(S1, S1);
+  EIGEN_EXTRACT_8i_FROM_16i(S2, S2);
+  EIGEN_EXTRACT_8i_FROM_16i(S3, S3);
+
+  PacketBlock<Packet8i, 8> tmp;
+
+  tmp.packet[0] = _mm256_permute2f128_si256(S0_0, S1_0, 0x20);
+  tmp.packet[1] = _mm256_permute2f128_si256(S2_0, S3_0, 0x20);
+  tmp.packet[2] = _mm256_permute2f128_si256(S0_0, S1_0, 0x31);
+  tmp.packet[3] = _mm256_permute2f128_si256(S2_0, S3_0, 0x31);
+
+  tmp.packet[4] = _mm256_permute2f128_si256(S0_1, S1_1, 0x20);
+  tmp.packet[5] = _mm256_permute2f128_si256(S2_1, S3_1, 0x20);
+  tmp.packet[6] = _mm256_permute2f128_si256(S0_1, S1_1, 0x31);
+  tmp.packet[7] = _mm256_permute2f128_si256(S2_1, S3_1, 0x31);
+
+  PACK_OUTPUT_I32_2(kernel.packet, tmp.packet, 0, 1);
+  PACK_OUTPUT_I32_2(kernel.packet, tmp.packet, 1, 1);
+  PACK_OUTPUT_I32_2(kernel.packet, tmp.packet, 2, 1);
+  PACK_OUTPUT_I32_2(kernel.packet, tmp.packet, 3, 1);
+}
+
+// Packet math for Eigen::half
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template <>
+EIGEN_STRONG_INLINE Packet16h pset1<Packet16h>(const Eigen::half& from) {
+  return _mm256_set1_epi16(from.x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet16h>(const Packet16h& from) {
+  return half_impl::raw_uint16_to_half(static_cast<unsigned short>(_mm256_extract_epi16(from, 0)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pload<Packet16h>(const Eigen::half* from) {
+  return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h ploadu<Packet16h>(const Eigen::half* from) {
+  return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<half>(Eigen::half* to, const Packet16h& from) {
+  // (void*) -> workaround clang warning:
+  // cast from 'Eigen::half *' to '__m256i *' increases required alignment from 2 to 32
+  EIGEN_DEBUG_ALIGNED_STORE
+  _mm256_store_si256((__m256i*)(void*)to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<half>(Eigen::half* to, const Packet16h& from) {
+  // (void*) -> workaround clang warning:
+  // cast from 'Eigen::half *' to '__m256i *' increases required alignment from 2 to 32
+  EIGEN_DEBUG_UNALIGNED_STORE
+  _mm256_storeu_si256((__m256i*)(void*)to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h ploaddup<Packet16h>(const Eigen::half* from) {
+  unsigned short a = from[0].x;
+  unsigned short b = from[1].x;
+  unsigned short c = from[2].x;
+  unsigned short d = from[3].x;
+  unsigned short e = from[4].x;
+  unsigned short f = from[5].x;
+  unsigned short g = from[6].x;
+  unsigned short h = from[7].x;
+  return _mm256_set_epi16(h, h, g, g, f, f, e, e, d, d, c, c, b, b, a, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h ploadquad(const Eigen::half* from) {
+  unsigned short a = from[0].x;
+  unsigned short b = from[1].x;
+  unsigned short c = from[2].x;
+  unsigned short d = from[3].x;
+  return _mm256_set_epi16(d, d, d, d, c, c, c, c, b, b, b, b, a, a, a, a);
+}
+
+EIGEN_STRONG_INLINE Packet16f half2float(const Packet16h& a) { return _mm512_cvtph_ps(a); }
+
+EIGEN_STRONG_INLINE Packet16h float2half(const Packet16f& a) {
+  return _mm512_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h ptrue(const Packet16h& a) {
+  return Packet16h(ptrue(Packet8i(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pabs(const Packet16h& a) {
+  const __m256i sign_mask = _mm256_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm256_andnot_si256(sign_mask, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmin<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return float2half(pmin<Packet16f>(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmax<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return float2half(pmax<Packet16f>(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h plset<Packet16h>(const half& a) {
+  return float2half(plset<Packet16f>(static_cast<float>(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h por(const Packet16h& a, const Packet16h& b) {
+  // in some cases Packet8i is a wrapper around __m256i, so we need to
+  // cast to Packet8i to call the correct overload.
+  return Packet16h(por(Packet8i(a), Packet8i(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16h pxor(const Packet16h& a, const Packet16h& b) {
+  return Packet16h(pxor(Packet8i(a), Packet8i(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16h pand(const Packet16h& a, const Packet16h& b) {
+  return Packet16h(pand(Packet8i(a), Packet8i(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16h pandnot(const Packet16h& a, const Packet16h& b) {
+  return Packet16h(pandnot(Packet8i(a), Packet8i(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pselect(const Packet16h& mask, const Packet16h& a, const Packet16h& b) {
+  return _mm256_blendv_epi8(b, a, mask);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pround<Packet16h>(const Packet16h& a) {
+  return float2half(pround<Packet16f>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h print<Packet16h>(const Packet16h& a) {
+  return float2half(print<Packet16f>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pceil<Packet16h>(const Packet16h& a) {
+  return float2half(pceil<Packet16f>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pfloor<Packet16h>(const Packet16h& a) {
+  return float2half(pfloor<Packet16f>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h ptrunc<Packet16h>(const Packet16h& a) {
+  return float2half(ptrunc<Packet16f>(half2float(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcmp_eq(const Packet16h& a, const Packet16h& b) {
+  Packet16f af = half2float(a);
+  Packet16f bf = half2float(b);
+  return Pack32To16(pcmp_eq(af, bf));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcmp_le(const Packet16h& a, const Packet16h& b) {
+  return Pack32To16(pcmp_le(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcmp_lt(const Packet16h& a, const Packet16h& b) {
+  return Pack32To16(pcmp_lt(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcmp_lt_or_nan(const Packet16h& a, const Packet16h& b) {
+  return Pack32To16(pcmp_lt_or_nan(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pconj(const Packet16h& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pnegate(const Packet16h& a) {
+  Packet16h sign_mask = _mm256_set1_epi16(static_cast<unsigned short>(0x8000));
+  return _mm256_xor_si256(a, sign_mask);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h padd<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  Packet16f af = half2float(a);
+  Packet16f bf = half2float(b);
+  Packet16f rf = padd(af, bf);
+  return float2half(rf);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h psub<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  Packet16f af = half2float(a);
+  Packet16f bf = half2float(b);
+  Packet16f rf = psub(af, bf);
+  return float2half(rf);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmul<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  Packet16f af = half2float(a);
+  Packet16f bf = half2float(b);
+  Packet16f rf = pmul(af, bf);
+  return float2half(rf);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pdiv<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  Packet16f af = half2float(a);
+  Packet16f bf = half2float(b);
+  Packet16f rf = pdiv(af, bf);
+  return float2half(rf);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmadd<Packet16h>(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return float2half(pmadd(half2float(a), half2float(b), half2float(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmsub<Packet16h>(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return float2half(pmsub(half2float(a), half2float(b), half2float(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pnmadd<Packet16h>(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return float2half(pnmadd(half2float(a), half2float(b), half2float(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pnmsub<Packet16h>(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return float2half(pnmsub(half2float(a), half2float(b), half2float(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h predux_half<Packet16h>(const Packet16h& a) {
+  Packet8h lane0 = _mm256_extractf128_si256(a, 0);
+  Packet8h lane1 = _mm256_extractf128_si256(a, 1);
+  return padd<Packet8h>(lane0, lane1);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h preverse(const Packet16h& a) {
+  __m128i m = _mm_setr_epi8(14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1);
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(_mm_shuffle_epi8(_mm256_extractf128_si256(a, 1), m)),
+                                 _mm_shuffle_epi8(_mm256_extractf128_si256(a, 0), m), 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pgather<Eigen::half, Packet16h>(const Eigen::half* from, Index stride) {
+  return _mm256_set_epi16(from[15 * stride].x, from[14 * stride].x, from[13 * stride].x, from[12 * stride].x,
+                          from[11 * stride].x, from[10 * stride].x, from[9 * stride].x, from[8 * stride].x,
+                          from[7 * stride].x, from[6 * stride].x, from[5 * stride].x, from[4 * stride].x,
+                          from[3 * stride].x, from[2 * stride].x, from[1 * stride].x, from[0 * stride].x);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pscatter<half, Packet16h>(half* to, const Packet16h& from, Index stride) {
+  EIGEN_ALIGN64 half aux[16];
+  pstore(aux, from);
+  to[stride * 0] = aux[0];
+  to[stride * 1] = aux[1];
+  to[stride * 2] = aux[2];
+  to[stride * 3] = aux[3];
+  to[stride * 4] = aux[4];
+  to[stride * 5] = aux[5];
+  to[stride * 6] = aux[6];
+  to[stride * 7] = aux[7];
+  to[stride * 8] = aux[8];
+  to[stride * 9] = aux[9];
+  to[stride * 10] = aux[10];
+  to[stride * 11] = aux[11];
+  to[stride * 12] = aux[12];
+  to[stride * 13] = aux[13];
+  to[stride * 14] = aux[14];
+  to[stride * 15] = aux[15];
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16h, 16>& kernel) {
+  __m256i a = kernel.packet[0];
+  __m256i b = kernel.packet[1];
+  __m256i c = kernel.packet[2];
+  __m256i d = kernel.packet[3];
+  __m256i e = kernel.packet[4];
+  __m256i f = kernel.packet[5];
+  __m256i g = kernel.packet[6];
+  __m256i h = kernel.packet[7];
+  __m256i i = kernel.packet[8];
+  __m256i j = kernel.packet[9];
+  __m256i k = kernel.packet[10];
+  __m256i l = kernel.packet[11];
+  __m256i m = kernel.packet[12];
+  __m256i n = kernel.packet[13];
+  __m256i o = kernel.packet[14];
+  __m256i p = kernel.packet[15];
+
+  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
+  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
+  __m256i ef_07 = _mm256_unpacklo_epi16(e, f);
+  __m256i gh_07 = _mm256_unpacklo_epi16(g, h);
+  __m256i ij_07 = _mm256_unpacklo_epi16(i, j);
+  __m256i kl_07 = _mm256_unpacklo_epi16(k, l);
+  __m256i mn_07 = _mm256_unpacklo_epi16(m, n);
+  __m256i op_07 = _mm256_unpacklo_epi16(o, p);
+
+  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
+  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
+  __m256i ef_8f = _mm256_unpackhi_epi16(e, f);
+  __m256i gh_8f = _mm256_unpackhi_epi16(g, h);
+  __m256i ij_8f = _mm256_unpackhi_epi16(i, j);
+  __m256i kl_8f = _mm256_unpackhi_epi16(k, l);
+  __m256i mn_8f = _mm256_unpackhi_epi16(m, n);
+  __m256i op_8f = _mm256_unpackhi_epi16(o, p);
+
+  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
+  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
+  __m256i efgh_03 = _mm256_unpacklo_epi32(ef_07, gh_07);
+  __m256i efgh_47 = _mm256_unpackhi_epi32(ef_07, gh_07);
+  __m256i ijkl_03 = _mm256_unpacklo_epi32(ij_07, kl_07);
+  __m256i ijkl_47 = _mm256_unpackhi_epi32(ij_07, kl_07);
+  __m256i mnop_03 = _mm256_unpacklo_epi32(mn_07, op_07);
+  __m256i mnop_47 = _mm256_unpackhi_epi32(mn_07, op_07);
+
+  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
+  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
+  __m256i efgh_8b = _mm256_unpacklo_epi32(ef_8f, gh_8f);
+  __m256i efgh_cf = _mm256_unpackhi_epi32(ef_8f, gh_8f);
+  __m256i ijkl_8b = _mm256_unpacklo_epi32(ij_8f, kl_8f);
+  __m256i ijkl_cf = _mm256_unpackhi_epi32(ij_8f, kl_8f);
+  __m256i mnop_8b = _mm256_unpacklo_epi32(mn_8f, op_8f);
+  __m256i mnop_cf = _mm256_unpackhi_epi32(mn_8f, op_8f);
+
+  __m256i abcdefgh_01 = _mm256_unpacklo_epi64(abcd_03, efgh_03);
+  __m256i abcdefgh_23 = _mm256_unpackhi_epi64(abcd_03, efgh_03);
+  __m256i ijklmnop_01 = _mm256_unpacklo_epi64(ijkl_03, mnop_03);
+  __m256i ijklmnop_23 = _mm256_unpackhi_epi64(ijkl_03, mnop_03);
+  __m256i abcdefgh_45 = _mm256_unpacklo_epi64(abcd_47, efgh_47);
+  __m256i abcdefgh_67 = _mm256_unpackhi_epi64(abcd_47, efgh_47);
+  __m256i ijklmnop_45 = _mm256_unpacklo_epi64(ijkl_47, mnop_47);
+  __m256i ijklmnop_67 = _mm256_unpackhi_epi64(ijkl_47, mnop_47);
+  __m256i abcdefgh_89 = _mm256_unpacklo_epi64(abcd_8b, efgh_8b);
+  __m256i abcdefgh_ab = _mm256_unpackhi_epi64(abcd_8b, efgh_8b);
+  __m256i ijklmnop_89 = _mm256_unpacklo_epi64(ijkl_8b, mnop_8b);
+  __m256i ijklmnop_ab = _mm256_unpackhi_epi64(ijkl_8b, mnop_8b);
+  __m256i abcdefgh_cd = _mm256_unpacklo_epi64(abcd_cf, efgh_cf);
+  __m256i abcdefgh_ef = _mm256_unpackhi_epi64(abcd_cf, efgh_cf);
+  __m256i ijklmnop_cd = _mm256_unpacklo_epi64(ijkl_cf, mnop_cf);
+  __m256i ijklmnop_ef = _mm256_unpackhi_epi64(ijkl_cf, mnop_cf);
+
+  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
+  __m256i a_p_0 = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x20);
+  __m256i a_p_1 = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x20);
+  __m256i a_p_2 = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x20);
+  __m256i a_p_3 = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x20);
+  __m256i a_p_4 = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x20);
+  __m256i a_p_5 = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x20);
+  __m256i a_p_6 = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x20);
+  __m256i a_p_7 = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x20);
+  __m256i a_p_8 = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x31);
+  __m256i a_p_9 = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x31);
+  __m256i a_p_a = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x31);
+  __m256i a_p_b = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x31);
+  __m256i a_p_c = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x31);
+  __m256i a_p_d = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x31);
+  __m256i a_p_e = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x31);
+  __m256i a_p_f = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x31);
+
+  kernel.packet[0] = a_p_0;
+  kernel.packet[1] = a_p_1;
+  kernel.packet[2] = a_p_2;
+  kernel.packet[3] = a_p_3;
+  kernel.packet[4] = a_p_4;
+  kernel.packet[5] = a_p_5;
+  kernel.packet[6] = a_p_6;
+  kernel.packet[7] = a_p_7;
+  kernel.packet[8] = a_p_8;
+  kernel.packet[9] = a_p_9;
+  kernel.packet[10] = a_p_a;
+  kernel.packet[11] = a_p_b;
+  kernel.packet[12] = a_p_c;
+  kernel.packet[13] = a_p_d;
+  kernel.packet[14] = a_p_e;
+  kernel.packet[15] = a_p_f;
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16h, 8>& kernel) {
+  EIGEN_ALIGN64 half in[8][16];
+  pstore<half>(in[0], kernel.packet[0]);
+  pstore<half>(in[1], kernel.packet[1]);
+  pstore<half>(in[2], kernel.packet[2]);
+  pstore<half>(in[3], kernel.packet[3]);
+  pstore<half>(in[4], kernel.packet[4]);
+  pstore<half>(in[5], kernel.packet[5]);
+  pstore<half>(in[6], kernel.packet[6]);
+  pstore<half>(in[7], kernel.packet[7]);
+
+  EIGEN_ALIGN64 half out[8][16];
+
+  for (int i = 0; i < 8; ++i) {
+    for (int j = 0; j < 8; ++j) {
+      out[i][j] = in[j][2 * i];
+    }
+    for (int j = 0; j < 8; ++j) {
+      out[i][j + 8] = in[j][2 * i + 1];
+    }
+  }
+
+  kernel.packet[0] = pload<Packet16h>(out[0]);
+  kernel.packet[1] = pload<Packet16h>(out[1]);
+  kernel.packet[2] = pload<Packet16h>(out[2]);
+  kernel.packet[3] = pload<Packet16h>(out[3]);
+  kernel.packet[4] = pload<Packet16h>(out[4]);
+  kernel.packet[5] = pload<Packet16h>(out[5]);
+  kernel.packet[6] = pload<Packet16h>(out[6]);
+  kernel.packet[7] = pload<Packet16h>(out[7]);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16h, 4>& kernel) {
+  EIGEN_ALIGN64 half in[4][16];
+  pstore<half>(in[0], kernel.packet[0]);
+  pstore<half>(in[1], kernel.packet[1]);
+  pstore<half>(in[2], kernel.packet[2]);
+  pstore<half>(in[3], kernel.packet[3]);
+
+  EIGEN_ALIGN64 half out[4][16];
+
+  for (int i = 0; i < 4; ++i) {
+    for (int j = 0; j < 4; ++j) {
+      out[i][j] = in[j][4 * i];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j + 4] = in[j][4 * i + 1];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j + 8] = in[j][4 * i + 2];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j + 12] = in[j][4 * i + 3];
+    }
+  }
+
+  kernel.packet[0] = pload<Packet16h>(out[0]);
+  kernel.packet[1] = pload<Packet16h>(out[1]);
+  kernel.packet[2] = pload<Packet16h>(out[2]);
+  kernel.packet[3] = pload<Packet16h>(out[3]);
+}
+
+#endif  // EIGEN_VECTORIZE_AVX512FP16
+
+template <>
+struct is_arithmetic<Packet16bf> {
+  enum { value = true };
+};
+
+template <>
+struct packet_traits<bfloat16> : default_packet_traits {
+  typedef Packet16bf type;
+  typedef Packet8bf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+    HasInsert = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+    HasLog = 1,  // Currently fails test with bad accuracy.
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasNdtri = 1,
+    HasBessel = 1,
+#endif
+    HasExp = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasCmp = 1,
+    HasDiv = 1
+  };
+};
+
+template <>
+struct unpacket_traits<Packet16bf> {
+  typedef bfloat16 type;
+  enum {
+    size = 16,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet8bf half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pset1<Packet16bf>(const bfloat16& from) {
+  return _mm256_set1_epi16(from.value);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 pfirst<Packet16bf>(const Packet16bf& from) {
+  bfloat16 t;
+  t.value = static_cast<unsigned short>(_mm256_extract_epi16(from, 0));
+  return t;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pload<Packet16bf>(const bfloat16* from) {
+  return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf ploadu<Packet16bf>(const bfloat16* from) {
+  return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet16bf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE
+  _mm256_store_si256(reinterpret_cast<__m256i*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet16bf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE
+  _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf ploaddup<Packet16bf>(const bfloat16* from) {
+  unsigned short a = from[0].value;
+  unsigned short b = from[1].value;
+  unsigned short c = from[2].value;
+  unsigned short d = from[3].value;
+  unsigned short e = from[4].value;
+  unsigned short f = from[5].value;
+  unsigned short g = from[6].value;
+  unsigned short h = from[7].value;
+  return _mm256_set_epi16(h, h, g, g, f, f, e, e, d, d, c, c, b, b, a, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf ploadquad(const bfloat16* from) {
+  unsigned short a = from[0].value;
+  unsigned short b = from[1].value;
+  unsigned short c = from[2].value;
+  unsigned short d = from[3].value;
+  return _mm256_set_epi16(d, d, d, d, c, c, c, c, b, b, b, b, a, a, a, a);
+}
+
+EIGEN_STRONG_INLINE Packet16f Bf16ToF32(const Packet16bf& a) {
+  return _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(a), 16));
+}
+
+// Convert float to bfloat16 according to round-to-nearest-even/denormals algorithm.
+EIGEN_STRONG_INLINE Packet16bf F32ToBf16(const Packet16f& a) {
+  Packet16bf r;
+
+#if defined(EIGEN_VECTORIZE_AVX512BF16) && EIGEN_GNUC_STRICT_AT_LEAST(10, 1, 0)
+  // Since GCC 10.1 supports avx512bf16 and C style explicit cast
+  // (C++ static_cast is not supported yet), do conversion via intrinsic
+  // and register path for performance.
+  r = (__m256i)(_mm512_cvtneps_pbh(a));
+
+#else
+  __m512i t;
+  __m512i input = _mm512_castps_si512(a);
+  __m512i nan = _mm512_set1_epi32(0x7fc0);
+
+  // uint32_t lsb = (input >> 16) & 1;
+  t = _mm512_and_si512(_mm512_srli_epi32(input, 16), _mm512_set1_epi32(1));
+  // uint32_t rounding_bias = 0x7fff + lsb;
+  t = _mm512_add_epi32(t, _mm512_set1_epi32(0x7fff));
+  // input += rounding_bias;
+  t = _mm512_add_epi32(t, input);
+  // input = input >> 16;
+  t = _mm512_srli_epi32(t, 16);
+
+  // Check NaN before converting back to bf16
+  __mmask16 mask = _mm512_cmp_ps_mask(a, a, _CMP_ORD_Q);
+
+  t = _mm512_mask_blend_epi32(mask, nan, t);
+  // output.value = static_cast<uint16_t>(input);
+  r = _mm512_cvtepi32_epi16(t);
+#endif  // EIGEN_VECTORIZE_AVX512BF16
+
+  return r;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf ptrue(const Packet16bf& a) {
+  return Packet16bf(ptrue<Packet8i>(Packet8i(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf por(const Packet16bf& a, const Packet16bf& b) {
+  return Packet16bf(por<Packet8i>(Packet8i(a), Packet8i(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pxor(const Packet16bf& a, const Packet16bf& b) {
+  return Packet16bf(pxor<Packet8i>(Packet8i(a), Packet8i(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pand(const Packet16bf& a, const Packet16bf& b) {
+  return Packet16bf(pand<Packet8i>(Packet8i(a), Packet8i(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pandnot(const Packet16bf& a, const Packet16bf& b) {
+  return Packet16bf(pandnot<Packet8i>(Packet8i(a), Packet8i(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pselect(const Packet16bf& mask, const Packet16bf& a, const Packet16bf& b) {
+  // Input mask is expected to be all 0/1, handle it with 8-bit
+  // intrinsic for performance.
+  return _mm256_blendv_epi8(b, a, mask);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pround<Packet16bf>(const Packet16bf& a) {
+  return F32ToBf16(pround<Packet16f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf print<Packet16bf>(const Packet16bf& a) {
+  return F32ToBf16(print<Packet16f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pceil<Packet16bf>(const Packet16bf& a) {
+  return F32ToBf16(pceil<Packet16f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pfloor<Packet16bf>(const Packet16bf& a) {
+  return F32ToBf16(pfloor<Packet16f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf ptrunc<Packet16bf>(const Packet16bf& a) {
+  return F32ToBf16(ptrunc<Packet16f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pcmp_eq(const Packet16bf& a, const Packet16bf& b) {
+  return Pack32To16(pcmp_eq(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pcmp_le(const Packet16bf& a, const Packet16bf& b) {
+  return Pack32To16(pcmp_le(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pcmp_lt(const Packet16bf& a, const Packet16bf& b) {
+  return Pack32To16(pcmp_lt(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pcmp_lt_or_nan(const Packet16bf& a, const Packet16bf& b) {
+  return Pack32To16(pcmp_lt_or_nan(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pnegate(const Packet16bf& a) {
+  Packet16bf sign_mask = _mm256_set1_epi16(static_cast<unsigned short>(0x8000));
+  return _mm256_xor_si256(a, sign_mask);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pconj(const Packet16bf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pabs(const Packet16bf& a) {
+  const __m256i sign_mask = _mm256_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm256_andnot_si256(sign_mask, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf padd<Packet16bf>(const Packet16bf& a, const Packet16bf& b) {
+  return F32ToBf16(padd<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf psub<Packet16bf>(const Packet16bf& a, const Packet16bf& b) {
+  return F32ToBf16(psub<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pmul<Packet16bf>(const Packet16bf& a, const Packet16bf& b) {
+  return F32ToBf16(pmul(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pmadd<Packet16bf>(const Packet16bf& a, const Packet16bf& b, const Packet16bf& c) {
+  return F32ToBf16(pmadd(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pmsub<Packet16bf>(const Packet16bf& a, const Packet16bf& b, const Packet16bf& c) {
+  return F32ToBf16(pmsub(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pnmadd<Packet16bf>(const Packet16bf& a, const Packet16bf& b, const Packet16bf& c) {
+  return F32ToBf16(pnmadd(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pnmsub<Packet16bf>(const Packet16bf& a, const Packet16bf& b, const Packet16bf& c) {
+  return F32ToBf16(pnmsub(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pdiv<Packet16bf>(const Packet16bf& a, const Packet16bf& b) {
+  return F32ToBf16(pdiv<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pmin<Packet16bf>(const Packet16bf& a, const Packet16bf& b) {
+  return F32ToBf16(pmin<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pmax<Packet16bf>(const Packet16bf& a, const Packet16bf& b) {
+  return F32ToBf16(pmax<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf plset<Packet16bf>(const bfloat16& a) {
+  return F32ToBf16(plset<Packet16f>(static_cast<float>(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf predux_half<Packet16bf>(const Packet16bf& a) {
+  Packet8bf lane0 = _mm256_extractf128_si256(a, 0);
+  Packet8bf lane1 = _mm256_extractf128_si256(a, 1);
+  return padd<Packet8bf>(lane0, lane1);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf preverse(const Packet16bf& a) {
+  __m256i m = _mm256_setr_epi8(14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1, 14, 15, 12, 13, 10, 11, 8, 9, 6, 7,
+                               4, 5, 2, 3, 0, 1);
+
+  Packet16bf res;
+  // Swap hi and lo first because shuffle is in 128-bit lanes.
+  res = _mm256_permute2x128_si256(a, a, 1);
+  // Shuffle 8-bit values in src within 2*128-bit lanes.
+  return _mm256_shuffle_epi8(res, m);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pgather<bfloat16, Packet16bf>(const bfloat16* from, Index stride) {
+  return _mm256_set_epi16(
+      from[15 * stride].value, from[14 * stride].value, from[13 * stride].value, from[12 * stride].value,
+      from[11 * stride].value, from[10 * stride].value, from[9 * stride].value, from[8 * stride].value,
+      from[7 * stride].value, from[6 * stride].value, from[5 * stride].value, from[4 * stride].value,
+      from[3 * stride].value, from[2 * stride].value, from[1 * stride].value, from[0 * stride].value);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet16bf>(bfloat16* to, const Packet16bf& from, Index stride) {
+  EIGEN_ALIGN64 bfloat16 aux[16];
+  pstore(aux, from);
+  to[stride * 0] = aux[0];
+  to[stride * 1] = aux[1];
+  to[stride * 2] = aux[2];
+  to[stride * 3] = aux[3];
+  to[stride * 4] = aux[4];
+  to[stride * 5] = aux[5];
+  to[stride * 6] = aux[6];
+  to[stride * 7] = aux[7];
+  to[stride * 8] = aux[8];
+  to[stride * 9] = aux[9];
+  to[stride * 10] = aux[10];
+  to[stride * 11] = aux[11];
+  to[stride * 12] = aux[12];
+  to[stride * 13] = aux[13];
+  to[stride * 14] = aux[14];
+  to[stride * 15] = aux[15];
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16bf, 16>& kernel) {
+  __m256i a = kernel.packet[0];
+  __m256i b = kernel.packet[1];
+  __m256i c = kernel.packet[2];
+  __m256i d = kernel.packet[3];
+  __m256i e = kernel.packet[4];
+  __m256i f = kernel.packet[5];
+  __m256i g = kernel.packet[6];
+  __m256i h = kernel.packet[7];
+  __m256i i = kernel.packet[8];
+  __m256i j = kernel.packet[9];
+  __m256i k = kernel.packet[10];
+  __m256i l = kernel.packet[11];
+  __m256i m = kernel.packet[12];
+  __m256i n = kernel.packet[13];
+  __m256i o = kernel.packet[14];
+  __m256i p = kernel.packet[15];
+
+  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
+  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
+  __m256i ef_07 = _mm256_unpacklo_epi16(e, f);
+  __m256i gh_07 = _mm256_unpacklo_epi16(g, h);
+  __m256i ij_07 = _mm256_unpacklo_epi16(i, j);
+  __m256i kl_07 = _mm256_unpacklo_epi16(k, l);
+  __m256i mn_07 = _mm256_unpacklo_epi16(m, n);
+  __m256i op_07 = _mm256_unpacklo_epi16(o, p);
+
+  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
+  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
+  __m256i ef_8f = _mm256_unpackhi_epi16(e, f);
+  __m256i gh_8f = _mm256_unpackhi_epi16(g, h);
+  __m256i ij_8f = _mm256_unpackhi_epi16(i, j);
+  __m256i kl_8f = _mm256_unpackhi_epi16(k, l);
+  __m256i mn_8f = _mm256_unpackhi_epi16(m, n);
+  __m256i op_8f = _mm256_unpackhi_epi16(o, p);
+
+  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
+  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
+  __m256i efgh_03 = _mm256_unpacklo_epi32(ef_07, gh_07);
+  __m256i efgh_47 = _mm256_unpackhi_epi32(ef_07, gh_07);
+  __m256i ijkl_03 = _mm256_unpacklo_epi32(ij_07, kl_07);
+  __m256i ijkl_47 = _mm256_unpackhi_epi32(ij_07, kl_07);
+  __m256i mnop_03 = _mm256_unpacklo_epi32(mn_07, op_07);
+  __m256i mnop_47 = _mm256_unpackhi_epi32(mn_07, op_07);
+
+  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
+  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
+  __m256i efgh_8b = _mm256_unpacklo_epi32(ef_8f, gh_8f);
+  __m256i efgh_cf = _mm256_unpackhi_epi32(ef_8f, gh_8f);
+  __m256i ijkl_8b = _mm256_unpacklo_epi32(ij_8f, kl_8f);
+  __m256i ijkl_cf = _mm256_unpackhi_epi32(ij_8f, kl_8f);
+  __m256i mnop_8b = _mm256_unpacklo_epi32(mn_8f, op_8f);
+  __m256i mnop_cf = _mm256_unpackhi_epi32(mn_8f, op_8f);
+
+  __m256i abcdefgh_01 = _mm256_unpacklo_epi64(abcd_03, efgh_03);
+  __m256i abcdefgh_23 = _mm256_unpackhi_epi64(abcd_03, efgh_03);
+  __m256i ijklmnop_01 = _mm256_unpacklo_epi64(ijkl_03, mnop_03);
+  __m256i ijklmnop_23 = _mm256_unpackhi_epi64(ijkl_03, mnop_03);
+  __m256i abcdefgh_45 = _mm256_unpacklo_epi64(abcd_47, efgh_47);
+  __m256i abcdefgh_67 = _mm256_unpackhi_epi64(abcd_47, efgh_47);
+  __m256i ijklmnop_45 = _mm256_unpacklo_epi64(ijkl_47, mnop_47);
+  __m256i ijklmnop_67 = _mm256_unpackhi_epi64(ijkl_47, mnop_47);
+  __m256i abcdefgh_89 = _mm256_unpacklo_epi64(abcd_8b, efgh_8b);
+  __m256i abcdefgh_ab = _mm256_unpackhi_epi64(abcd_8b, efgh_8b);
+  __m256i ijklmnop_89 = _mm256_unpacklo_epi64(ijkl_8b, mnop_8b);
+  __m256i ijklmnop_ab = _mm256_unpackhi_epi64(ijkl_8b, mnop_8b);
+  __m256i abcdefgh_cd = _mm256_unpacklo_epi64(abcd_cf, efgh_cf);
+  __m256i abcdefgh_ef = _mm256_unpackhi_epi64(abcd_cf, efgh_cf);
+  __m256i ijklmnop_cd = _mm256_unpacklo_epi64(ijkl_cf, mnop_cf);
+  __m256i ijklmnop_ef = _mm256_unpackhi_epi64(ijkl_cf, mnop_cf);
+
+  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
+  kernel.packet[0] = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x20);
+  kernel.packet[1] = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x20);
+  kernel.packet[2] = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x20);
+  kernel.packet[3] = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x20);
+  kernel.packet[4] = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x20);
+  kernel.packet[5] = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x20);
+  kernel.packet[6] = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x20);
+  kernel.packet[7] = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x20);
+  kernel.packet[8] = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x31);
+  kernel.packet[9] = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x31);
+  kernel.packet[10] = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x31);
+  kernel.packet[11] = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x31);
+  kernel.packet[12] = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x31);
+  kernel.packet[13] = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x31);
+  kernel.packet[14] = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x31);
+  kernel.packet[15] = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x31);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16bf, 4>& kernel) {
+  __m256i a = kernel.packet[0];
+  __m256i b = kernel.packet[1];
+  __m256i c = kernel.packet[2];
+  __m256i d = kernel.packet[3];
+
+  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
+  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
+  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
+  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
+
+  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
+  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
+  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
+  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
+
+  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
+  kernel.packet[0] = _mm256_permute2x128_si256(abcd_03, abcd_47, 0x20);
+  kernel.packet[1] = _mm256_permute2x128_si256(abcd_8b, abcd_cf, 0x20);
+  kernel.packet[2] = _mm256_permute2x128_si256(abcd_03, abcd_47, 0x31);
+  kernel.packet[3] = _mm256_permute2x128_si256(abcd_8b, abcd_cf, 0x31);
+}
+
+// Minimal implementation of 16-bit int packets for use in pfrexp, pldexp.
+
+template <>
+EIGEN_STRONG_INLINE Packet32s pset1<Packet32s>(const numext::int16_t& x) {
+  return _mm512_set1_epi16(x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16s pset1<Packet16s>(const numext::int16_t& x) {
+  return _mm256_set1_epi16(x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8s pset1<Packet8s>(const numext::int16_t& x) {
+  return _mm_set1_epi16(x);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int16_t, Packet32s>(numext::int16_t* out, const Packet32s& x) {
+  EIGEN_DEBUG_ALIGNED_STORE
+  _mm512_store_epi32(out, x);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int16_t, Packet16s>(numext::int16_t* out, const Packet16s& x) {
+  EIGEN_DEBUG_ALIGNED_STORE
+#if defined(EIGEN_VECTORIZE_AVX512F) && defined(EIGEN_VECTORIZE_AVX512VL)
+  _mm256_store_epi32(out, x);
+#else
+  _mm256_store_si256(reinterpret_cast<__m256i*>(out), x);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int16_t, Packet8s>(numext::int16_t* out, const Packet8s& x) {
+  EIGEN_DEBUG_ALIGNED_STORE
+#if defined(EIGEN_VECTORIZE_AVX512F) && defined(EIGEN_VECTORIZE_AVX512VL)
+  _mm256_store_epi32(out, x);
+#else
+  _mm_store_si128(reinterpret_cast<__m128i*>(out), x);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int16_t, Packet32s>(numext::int16_t* out, const Packet32s& x) {
+  EIGEN_DEBUG_UNALIGNED_STORE
+  _eigen_mm512_storeu_epi32(out, x);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int16_t, Packet16s>(numext::int16_t* out, const Packet16s& x) {
+  EIGEN_DEBUG_UNALIGNED_STORE
+  _eigen_mm256_storeu_epi32(out, x);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int16_t, Packet8s>(numext::int16_t* out, const Packet8s& x) {
+  EIGEN_DEBUG_UNALIGNED_STORE
+  _eigen_mm_storeu_epi32(out, x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32s padd(const Packet32s& a, const Packet32s& b) {
+  return _mm512_add_epi16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16s padd(const Packet16s& a, const Packet16s& b) {
+  return _mm256_add_epi16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8s padd(const Packet8s& a, const Packet8s& b) {
+  return _mm_add_epi16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32s psub(const Packet32s& a, const Packet32s& b) {
+  return _mm512_sub_epi16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16s psub(const Packet16s& a, const Packet16s& b) {
+  return _mm256_sub_epi16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8s psub(const Packet8s& a, const Packet8s& b) {
+  return _mm_sub_epi16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32s pmul(const Packet32s& a, const Packet32s& b) {
+  return _mm512_mullo_epi16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16s pmul(const Packet16s& a, const Packet16s& b) {
+  return _mm256_mullo_epi16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8s pmul(const Packet8s& a, const Packet8s& b) {
+  return _mm_mullo_epi16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32s pnegate(const Packet32s& a) {
+  return _mm512_sub_epi16(_mm512_setzero_si512(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16s pnegate(const Packet16s& a) {
+  return _mm256_sub_epi16(_mm256_setzero_si256(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8s pnegate(const Packet8s& a) {
+  return _mm_sub_epi16(_mm_setzero_si128(), a);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet32s parithmetic_shift_right(Packet32s a) {
+  return _mm512_srai_epi16(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet16s parithmetic_shift_right(Packet16s a) {
+  return _mm256_srai_epi16(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8s parithmetic_shift_right(Packet8s a) {
+  return _mm_srai_epi16(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet32s plogical_shift_left(Packet32s a) {
+  return _mm512_slli_epi16(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet16s plogical_shift_left(Packet16s a) {
+  return _mm256_slli_epi16(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8s plogical_shift_left(Packet8s a) {
+  return _mm_slli_epi16(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet32s plogical_shift_right(Packet32s a) {
+  return _mm512_srli_epi16(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet16s plogical_shift_right(Packet16s a) {
+  return _mm256_srli_epi16(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8s plogical_shift_right(Packet8s a) {
+  return _mm_srli_epi16(a, N);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_AVX512_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/PacketMathFP16.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/PacketMathFP16.h
new file mode 100644
index 0000000..370fb83
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/PacketMathFP16.h
@@ -0,0 +1,1411 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 The Eigen Authors.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_FP16_AVX512_H
+#define EIGEN_PACKET_MATH_FP16_AVX512_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+typedef __m512h Packet32h;
+typedef __m256h Packet16h;
+typedef __m128h Packet8h;
+
+template <>
+struct is_arithmetic<Packet8h> {
+  enum { value = true };
+};
+
+template <>
+struct packet_traits<half> : default_packet_traits {
+  typedef Packet32h type;
+  typedef Packet16h half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 32,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExp = 1,
+    HasExpm1 = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    // These ones should be implemented in future
+    HasBessel = 0,
+    HasNdtri = 0,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = 0,  // EIGEN_FAST_MATH,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet32h> {
+  typedef Eigen::half type;
+  typedef Packet16h half;
+  typedef Packet32s integer_packet;
+  enum {
+    size = 32,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet16h> {
+  typedef Eigen::half type;
+  typedef Packet8h half;
+  typedef Packet16s integer_packet;
+  enum {
+    size = 16,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet8h> {
+  typedef Eigen::half type;
+  typedef Packet8h half;
+  typedef Packet8s integer_packet;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+// Conversions
+
+EIGEN_STRONG_INLINE Packet16f half2float(const Packet16h& a) { return _mm512_cvtxph_ps(a); }
+
+EIGEN_STRONG_INLINE Packet8f half2float(const Packet8h& a) { return _mm256_cvtxph_ps(a); }
+
+EIGEN_STRONG_INLINE Packet16h float2half(const Packet16f& a) { return _mm512_cvtxps_ph(a); }
+
+EIGEN_STRONG_INLINE Packet8h float2half(const Packet8f& a) { return _mm256_cvtxps_ph(a); }
+
+// Memory functions
+
+// pset1
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pset1<Packet32h>(const Eigen::half& from) {
+  return _mm512_set1_ph(from.x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pset1<Packet16h>(const Eigen::half& from) {
+  return _mm256_set1_ph(from.x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pset1<Packet8h>(const Eigen::half& from) {
+  return _mm_set1_ph(from.x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pzero(const Packet32h& /*a*/) {
+  return _mm512_setzero_ph();
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pzero(const Packet16h& /*a*/) {
+  return _mm256_setzero_ph();
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pzero(const Packet8h& /*a*/) {
+  return _mm_setzero_ph();
+}
+
+// pset1frombits
+template <>
+EIGEN_STRONG_INLINE Packet32h pset1frombits<Packet32h>(unsigned short from) {
+  return _mm512_castsi512_ph(_mm512_set1_epi16(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pset1frombits<Packet16h>(unsigned short from) {
+  return _mm256_castsi256_ph(_mm256_set1_epi16(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pset1frombits<Packet8h>(unsigned short from) {
+  return _mm_castsi128_ph(_mm_set1_epi16(from));
+}
+
+// pfirst
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet32h>(const Packet32h& from) {
+  return Eigen::half(_mm512_cvtsh_h(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet16h>(const Packet16h& from) {
+  return Eigen::half(_mm256_cvtsh_h(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet8h>(const Packet8h& from) {
+  return Eigen::half(_mm_cvtsh_h(from));
+}
+
+// pload
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pload<Packet32h>(const Eigen::half* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_ph(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pload<Packet16h>(const Eigen::half* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_ph(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pload<Packet8h>(const Eigen::half* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_ph(from);
+}
+
+// ploadu
+
+template <>
+EIGEN_STRONG_INLINE Packet32h ploadu<Packet32h>(const Eigen::half* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_ph(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h ploadu<Packet16h>(const Eigen::half* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_ph(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h ploadu<Packet8h>(const Eigen::half* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm_loadu_ph(from);
+}
+
+// pstore
+
+template <>
+EIGEN_STRONG_INLINE void pstore<half>(Eigen::half* to, const Packet32h& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_ph(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<half>(Eigen::half* to, const Packet16h& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_ph(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<half>(Eigen::half* to, const Packet8h& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm_store_ph(to, from);
+}
+
+// pstoreu
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<half>(Eigen::half* to, const Packet32h& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_ph(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<half>(Eigen::half* to, const Packet16h& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_ph(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<half>(Eigen::half* to, const Packet8h& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_ph(to, from);
+}
+
+// ploaddup
+template <>
+EIGEN_STRONG_INLINE Packet32h ploaddup<Packet32h>(const Eigen::half* from) {
+  __m512h a = _mm512_castph256_ph512(_mm256_loadu_ph(from));
+  return _mm512_permutexvar_ph(_mm512_set_epi16(15, 15, 14, 14, 13, 13, 12, 12, 11, 11, 10, 10, 9, 9, 8, 8, 7, 7, 6, 6,
+                                                5, 5, 4, 4, 3, 3, 2, 2, 1, 1, 0, 0),
+                               a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h ploaddup<Packet16h>(const Eigen::half* from) {
+  __m256h a = _mm256_castph128_ph256(_mm_loadu_ph(from));
+  return _mm256_permutexvar_ph(_mm256_set_epi16(7, 7, 6, 6, 5, 5, 4, 4, 3, 3, 2, 2, 1, 1, 0, 0), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h ploaddup<Packet8h>(const Eigen::half* from) {
+  return _mm_set_ph(from[3].x, from[3].x, from[2].x, from[2].x, from[1].x, from[1].x, from[0].x, from[0].x);
+}
+
+// ploadquad
+template <>
+EIGEN_STRONG_INLINE Packet32h ploadquad<Packet32h>(const Eigen::half* from) {
+  __m512h a = _mm512_castph128_ph512(_mm_loadu_ph(from));
+  return _mm512_permutexvar_ph(
+      _mm512_set_epi16(7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0),
+      a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h ploadquad<Packet16h>(const Eigen::half* from) {
+  return _mm256_set_ph(from[3].x, from[3].x, from[3].x, from[3].x, from[2].x, from[2].x, from[2].x, from[2].x,
+                       from[1].x, from[1].x, from[1].x, from[1].x, from[0].x, from[0].x, from[0].x, from[0].x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h ploadquad<Packet8h>(const Eigen::half* from) {
+  return _mm_set_ph(from[1].x, from[1].x, from[1].x, from[1].x, from[0].x, from[0].x, from[0].x, from[0].x);
+}
+
+// pabs
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pabs<Packet32h>(const Packet32h& a) {
+  return _mm512_abs_ph(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pabs<Packet16h>(const Packet16h& a) {
+  return _mm256_abs_ph(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pabs<Packet8h>(const Packet8h& a) {
+  return _mm_abs_ph(a);
+}
+
+// psignbit
+
+template <>
+EIGEN_STRONG_INLINE Packet32h psignbit<Packet32h>(const Packet32h& a) {
+  return _mm512_castsi512_ph(_mm512_srai_epi16(_mm512_castph_si512(a), 15));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h psignbit<Packet16h>(const Packet16h& a) {
+  return _mm256_castsi256_ph(_mm256_srai_epi16(_mm256_castph_si256(a), 15));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h psignbit<Packet8h>(const Packet8h& a) {
+  return _mm_castsi128_ph(_mm_srai_epi16(_mm_castph_si128(a), 15));
+}
+
+// pmin
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pmin<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_min_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmin<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return _mm256_min_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmin<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return _mm_min_ph(a, b);
+}
+
+// pmax
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pmax<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_max_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmax<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return _mm256_max_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmax<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return _mm_max_ph(a, b);
+}
+
+// plset
+template <>
+EIGEN_STRONG_INLINE Packet32h plset<Packet32h>(const half& a) {
+  return _mm512_add_ph(pset1<Packet32h>(a), _mm512_set_ph(31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17,
+                                                          16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h plset<Packet16h>(const half& a) {
+  return _mm256_add_ph(pset1<Packet16h>(a), _mm256_set_ph(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h plset<Packet8h>(const half& a) {
+  return _mm_add_ph(pset1<Packet8h>(a), _mm_set_ph(7, 6, 5, 4, 3, 2, 1, 0));
+}
+
+// por
+
+template <>
+EIGEN_STRONG_INLINE Packet32h por(const Packet32h& a, const Packet32h& b) {
+  return _mm512_castsi512_ph(_mm512_or_si512(_mm512_castph_si512(a), _mm512_castph_si512(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h por(const Packet16h& a, const Packet16h& b) {
+  return _mm256_castsi256_ph(_mm256_or_si256(_mm256_castph_si256(a), _mm256_castph_si256(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h por(const Packet8h& a, const Packet8h& b) {
+  return _mm_castsi128_ph(_mm_or_si128(_mm_castph_si128(a), _mm_castph_si128(b)));
+}
+
+// pxor
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pxor(const Packet32h& a, const Packet32h& b) {
+  return _mm512_castsi512_ph(_mm512_xor_si512(_mm512_castph_si512(a), _mm512_castph_si512(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pxor(const Packet16h& a, const Packet16h& b) {
+  return _mm256_castsi256_ph(_mm256_xor_si256(_mm256_castph_si256(a), _mm256_castph_si256(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pxor(const Packet8h& a, const Packet8h& b) {
+  return _mm_castsi128_ph(_mm_xor_si128(_mm_castph_si128(a), _mm_castph_si128(b)));
+}
+
+// pand
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pand(const Packet32h& a, const Packet32h& b) {
+  return _mm512_castsi512_ph(_mm512_and_si512(_mm512_castph_si512(a), _mm512_castph_si512(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pand(const Packet16h& a, const Packet16h& b) {
+  return _mm256_castsi256_ph(_mm256_and_si256(_mm256_castph_si256(a), _mm256_castph_si256(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pand(const Packet8h& a, const Packet8h& b) {
+  return _mm_castsi128_ph(_mm_and_si128(_mm_castph_si128(a), _mm_castph_si128(b)));
+}
+
+// pandnot
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pandnot(const Packet32h& a, const Packet32h& b) {
+  return _mm512_castsi512_ph(_mm512_andnot_si512(_mm512_castph_si512(b), _mm512_castph_si512(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pandnot(const Packet16h& a, const Packet16h& b) {
+  return _mm256_castsi256_ph(_mm256_andnot_si256(_mm256_castph_si256(b), _mm256_castph_si256(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pandnot(const Packet8h& a, const Packet8h& b) {
+  return _mm_castsi128_ph(_mm_andnot_si128(_mm_castph_si128(b), _mm_castph_si128(a)));
+}
+
+// pselect
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet32h pselect(const Packet32h& mask, const Packet32h& a, const Packet32h& b) {
+  __mmask32 mask32 = _mm512_cmp_epi16_mask(_mm512_castph_si512(mask), _mm512_setzero_epi32(), _MM_CMPINT_EQ);
+  return _mm512_mask_blend_ph(mask32, a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet16h pselect(const Packet16h& mask, const Packet16h& a, const Packet16h& b) {
+  __mmask16 mask16 = _mm256_cmp_epi16_mask(_mm256_castph_si256(mask), _mm256_setzero_si256(), _MM_CMPINT_EQ);
+  return _mm256_mask_blend_ph(mask16, a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet8h pselect(const Packet8h& mask, const Packet8h& a, const Packet8h& b) {
+  __mmask8 mask8 = _mm_cmp_epi16_mask(_mm_castph_si128(mask), _mm_setzero_si128(), _MM_CMPINT_EQ);
+  return _mm_mask_blend_ph(mask8, a, b);
+}
+
+// pcmp_eq
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcmp_eq(const Packet32h& a, const Packet32h& b) {
+  __mmask32 mask = _mm512_cmp_ph_mask(a, b, _CMP_EQ_OQ);
+  return _mm512_castsi512_ph(_mm512_mask_set1_epi16(_mm512_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcmp_eq(const Packet16h& a, const Packet16h& b) {
+  __mmask16 mask = _mm256_cmp_ph_mask(a, b, _CMP_EQ_OQ);
+  return _mm256_castsi256_ph(_mm256_mask_set1_epi16(_mm256_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pcmp_eq(const Packet8h& a, const Packet8h& b) {
+  __mmask8 mask = _mm_cmp_ph_mask(a, b, _CMP_EQ_OQ);
+  return _mm_castsi128_ph(_mm_mask_set1_epi16(_mm_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+// pcmp_le
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcmp_le(const Packet32h& a, const Packet32h& b) {
+  __mmask32 mask = _mm512_cmp_ph_mask(a, b, _CMP_LE_OQ);
+  return _mm512_castsi512_ph(_mm512_mask_set1_epi16(_mm512_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcmp_le(const Packet16h& a, const Packet16h& b) {
+  __mmask16 mask = _mm256_cmp_ph_mask(a, b, _CMP_LE_OQ);
+  return _mm256_castsi256_ph(_mm256_mask_set1_epi16(_mm256_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pcmp_le(const Packet8h& a, const Packet8h& b) {
+  __mmask8 mask = _mm_cmp_ph_mask(a, b, _CMP_LE_OQ);
+  return _mm_castsi128_ph(_mm_mask_set1_epi16(_mm_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+// pcmp_lt
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcmp_lt(const Packet32h& a, const Packet32h& b) {
+  __mmask32 mask = _mm512_cmp_ph_mask(a, b, _CMP_LT_OQ);
+  return _mm512_castsi512_ph(_mm512_mask_set1_epi16(_mm512_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcmp_lt(const Packet16h& a, const Packet16h& b) {
+  __mmask16 mask = _mm256_cmp_ph_mask(a, b, _CMP_LT_OQ);
+  return _mm256_castsi256_ph(_mm256_mask_set1_epi16(_mm256_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pcmp_lt(const Packet8h& a, const Packet8h& b) {
+  __mmask8 mask = _mm_cmp_ph_mask(a, b, _CMP_LT_OQ);
+  return _mm_castsi128_ph(_mm_mask_set1_epi16(_mm_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+// pcmp_lt_or_nan
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcmp_lt_or_nan(const Packet32h& a, const Packet32h& b) {
+  __mmask32 mask = _mm512_cmp_ph_mask(a, b, _CMP_NGE_UQ);
+  return _mm512_castsi512_ph(_mm512_mask_set1_epi16(_mm512_set1_epi16(0), mask, static_cast<short>(0xffffu)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcmp_lt_or_nan(const Packet16h& a, const Packet16h& b) {
+  __mmask16 mask = _mm256_cmp_ph_mask(a, b, _CMP_NGE_UQ);
+  return _mm256_castsi256_ph(_mm256_mask_set1_epi16(_mm256_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pcmp_lt_or_nan(const Packet8h& a, const Packet8h& b) {
+  __mmask8 mask = _mm_cmp_ph_mask(a, b, _CMP_NGE_UQ);
+  return _mm_castsi128_ph(_mm_mask_set1_epi16(_mm_set1_epi32(0), mask, static_cast<short>(0xffffu)));
+}
+
+// padd
+
+template <>
+EIGEN_STRONG_INLINE Packet32h padd<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_add_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h padd<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return _mm256_add_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h padd<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return _mm_add_ph(a, b);
+}
+
+// psub
+
+template <>
+EIGEN_STRONG_INLINE Packet32h psub<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_sub_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h psub<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return _mm256_sub_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h psub<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return _mm_sub_ph(a, b);
+}
+
+// pmul
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pmul<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_mul_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmul<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return _mm256_mul_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmul<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return _mm_mul_ph(a, b);
+}
+
+// pdiv
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pdiv<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_div_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pdiv<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return _mm256_div_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pdiv<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return _mm_div_ph(a, b);
+  ;
+}
+
+// pround
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pround<Packet32h>(const Packet32h& a) {
+  // Work-around for default std::round rounding mode.
+
+  // Mask for the sign bit.
+  const Packet32h signMask =
+      pset1frombits<Packet32h>(static_cast<numext::uint16_t>(static_cast<std::uint16_t>(0x8000u)));
+  // The largest half-precision float less than 0.5.
+  const Packet32h prev0dot5 = pset1frombits<Packet32h>(static_cast<numext::uint16_t>(0x37FFu));
+
+  return _mm512_roundscale_ph(padd(por(pand(a, signMask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pround<Packet16h>(const Packet16h& a) {
+  // Work-around for default std::round rounding mode.
+
+  // Mask for the sign bit.
+  const Packet16h signMask =
+      pset1frombits<Packet16h>(static_cast<numext::uint16_t>(static_cast<std::uint16_t>(0x8000u)));
+  // The largest half-precision float less than 0.5.
+  const Packet16h prev0dot5 = pset1frombits<Packet16h>(static_cast<numext::uint16_t>(0x37FFu));
+
+  return _mm256_roundscale_ph(padd(por(pand(a, signMask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pround<Packet8h>(const Packet8h& a) {
+  // Work-around for default std::round rounding mode.
+
+  // Mask for the sign bit.
+  const Packet8h signMask = pset1frombits<Packet8h>(static_cast<numext::uint16_t>(static_cast<std::uint16_t>(0x8000u)));
+  // The largest half-precision float less than 0.5.
+  const Packet8h prev0dot5 = pset1frombits<Packet8h>(static_cast<numext::uint16_t>(0x37FFu));
+
+  return _mm_roundscale_ph(padd(por(pand(a, signMask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+// print
+
+template <>
+EIGEN_STRONG_INLINE Packet32h print<Packet32h>(const Packet32h& a) {
+  return _mm512_roundscale_ph(a, _MM_FROUND_CUR_DIRECTION);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h print<Packet16h>(const Packet16h& a) {
+  return _mm256_roundscale_ph(a, _MM_FROUND_CUR_DIRECTION);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h print<Packet8h>(const Packet8h& a) {
+  return _mm_roundscale_ph(a, _MM_FROUND_CUR_DIRECTION);
+}
+
+// pceil
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pceil<Packet32h>(const Packet32h& a) {
+  return _mm512_roundscale_ph(a, _MM_FROUND_TO_POS_INF);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pceil<Packet16h>(const Packet16h& a) {
+  return _mm256_roundscale_ph(a, _MM_FROUND_TO_POS_INF);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pceil<Packet8h>(const Packet8h& a) {
+  return _mm_roundscale_ph(a, _MM_FROUND_TO_POS_INF);
+}
+
+// pfloor
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pfloor<Packet32h>(const Packet32h& a) {
+  return _mm512_roundscale_ph(a, _MM_FROUND_TO_NEG_INF);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pfloor<Packet16h>(const Packet16h& a) {
+  return _mm256_roundscale_ph(a, _MM_FROUND_TO_NEG_INF);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pfloor<Packet8h>(const Packet8h& a) {
+  return _mm_roundscale_ph(a, _MM_FROUND_TO_NEG_INF);
+}
+
+// ptrunc
+
+template <>
+EIGEN_STRONG_INLINE Packet32h ptrunc<Packet32h>(const Packet32h& a) {
+  return _mm512_roundscale_ph(a, _MM_FROUND_TO_ZERO);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h ptrunc<Packet16h>(const Packet16h& a) {
+  return _mm256_roundscale_ph(a, _MM_FROUND_TO_ZERO);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h ptrunc<Packet8h>(const Packet8h& a) {
+  return _mm_roundscale_ph(a, _MM_FROUND_TO_ZERO);
+}
+
+// predux
+template <>
+EIGEN_STRONG_INLINE half predux<Packet32h>(const Packet32h& a) {
+  return half(_mm512_reduce_add_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux<Packet16h>(const Packet16h& a) {
+  return half(_mm256_reduce_add_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux<Packet8h>(const Packet8h& a) {
+  return half(_mm_reduce_add_ph(a));
+}
+
+// predux_half
+template <>
+EIGEN_STRONG_INLINE Packet16h predux_half<Packet32h>(const Packet32h& a) {
+  const __m512i bits = _mm512_castph_si512(a);
+  Packet16h lo = _mm256_castsi256_ph(_mm512_castsi512_si256(bits));
+  Packet16h hi = _mm256_castsi256_ph(_mm512_extracti64x4_epi64(bits, 1));
+  return padd(lo, hi);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h predux_half<Packet16h>(const Packet16h& a) {
+  Packet8h lo = _mm_castsi128_ph(_mm256_castsi256_si128(_mm256_castph_si256(a)));
+  Packet8h hi = _mm_castps_ph(_mm256_extractf128_ps(_mm256_castph_ps(a), 1));
+  return padd(lo, hi);
+}
+
+// predux_max
+
+template <>
+EIGEN_STRONG_INLINE half predux_max<Packet32h>(const Packet32h& a) {
+  return half(_mm512_reduce_max_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_max<Packet16h>(const Packet16h& a) {
+  return half(_mm256_reduce_max_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_max<Packet8h>(const Packet8h& a) {
+  return half(_mm_reduce_max_ph(a));
+}
+
+// predux_min
+
+template <>
+EIGEN_STRONG_INLINE half predux_min<Packet32h>(const Packet32h& a) {
+  return half(_mm512_reduce_min_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_min<Packet16h>(const Packet16h& a) {
+  return half(_mm256_reduce_min_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_min<Packet8h>(const Packet8h& a) {
+  return half(_mm_reduce_min_ph(a));
+}
+
+// predux_mul
+
+template <>
+EIGEN_STRONG_INLINE half predux_mul<Packet32h>(const Packet32h& a) {
+  return half(_mm512_reduce_mul_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_mul<Packet16h>(const Packet16h& a) {
+  return half(_mm256_reduce_mul_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_mul<Packet8h>(const Packet8h& a) {
+  return half(_mm_reduce_mul_ph(a));
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+
+// pmadd
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pmadd(const Packet32h& a, const Packet32h& b, const Packet32h& c) {
+  return _mm512_fmadd_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmadd(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return _mm256_fmadd_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmadd(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return _mm_fmadd_ph(a, b, c);
+}
+
+// pmsub
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pmsub(const Packet32h& a, const Packet32h& b, const Packet32h& c) {
+  return _mm512_fmsub_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmsub(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return _mm256_fmsub_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmsub(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return _mm_fmsub_ph(a, b, c);
+}
+
+// pnmadd
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pnmadd(const Packet32h& a, const Packet32h& b, const Packet32h& c) {
+  return _mm512_fnmadd_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pnmadd(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return _mm256_fnmadd_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pnmadd(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return _mm_fnmadd_ph(a, b, c);
+}
+
+// pnmsub
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pnmsub(const Packet32h& a, const Packet32h& b, const Packet32h& c) {
+  return _mm512_fnmsub_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pnmsub(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return _mm256_fnmsub_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pnmsub(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return _mm_fnmsub_ph(a, b, c);
+}
+
+#endif
+
+// pnegate
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pnegate<Packet32h>(const Packet32h& a) {
+  return _mm512_castsi512_ph(
+      _mm512_xor_si512(_mm512_castph_si512(a), _mm512_set1_epi16(static_cast<std::uint16_t>(0x8000u))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pnegate<Packet16h>(const Packet16h& a) {
+  return _mm256_castsi256_ph(
+      _mm256_xor_si256(_mm256_castph_si256(a), _mm256_set1_epi16(static_cast<std::uint16_t>(0x8000u))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pnegate<Packet8h>(const Packet8h& a) {
+  return _mm_castsi128_ph(_mm_xor_si128(_mm_castph_si128(a), _mm_set1_epi16(static_cast<std::uint16_t>(0x8000u))));
+}
+
+// pconj
+
+// Nothing, packets are real.
+
+// psqrt
+
+template <>
+EIGEN_STRONG_INLINE Packet32h psqrt<Packet32h>(const Packet32h& a) {
+  return generic_sqrt_newton_step<Packet32h>::run(a, _mm512_rsqrt_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h psqrt<Packet16h>(const Packet16h& a) {
+  return generic_sqrt_newton_step<Packet16h>::run(a, _mm256_rsqrt_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h psqrt<Packet8h>(const Packet8h& a) {
+  return generic_sqrt_newton_step<Packet8h>::run(a, _mm_rsqrt_ph(a));
+}
+
+// prsqrt
+
+template <>
+EIGEN_STRONG_INLINE Packet32h prsqrt<Packet32h>(const Packet32h& a) {
+  return generic_rsqrt_newton_step<Packet32h, /*Steps=*/1>::run(a, _mm512_rsqrt_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h prsqrt<Packet16h>(const Packet16h& a) {
+  return generic_rsqrt_newton_step<Packet16h, /*Steps=*/1>::run(a, _mm256_rsqrt_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h prsqrt<Packet8h>(const Packet8h& a) {
+  return generic_rsqrt_newton_step<Packet8h, /*Steps=*/1>::run(a, _mm_rsqrt_ph(a));
+}
+
+// preciprocal
+
+template <>
+EIGEN_STRONG_INLINE Packet32h preciprocal<Packet32h>(const Packet32h& a) {
+  return generic_reciprocal_newton_step<Packet32h, /*Steps=*/1>::run(a, _mm512_rcp_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h preciprocal<Packet16h>(const Packet16h& a) {
+  return generic_reciprocal_newton_step<Packet16h, /*Steps=*/1>::run(a, _mm256_rcp_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h preciprocal<Packet8h>(const Packet8h& a) {
+  return generic_reciprocal_newton_step<Packet8h, /*Steps=*/1>::run(a, _mm_rcp_ph(a));
+}
+
+// ptranspose
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet32h, 32>& a) {
+  __m512i t[32];
+
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 16; i++) {
+    t[2 * i] = _mm512_unpacklo_epi16(_mm512_castph_si512(a.packet[2 * i]), _mm512_castph_si512(a.packet[2 * i + 1]));
+    t[2 * i + 1] =
+        _mm512_unpackhi_epi16(_mm512_castph_si512(a.packet[2 * i]), _mm512_castph_si512(a.packet[2 * i + 1]));
+  }
+
+  __m512i p[32];
+
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 8; i++) {
+    p[4 * i] = _mm512_unpacklo_epi32(t[4 * i], t[4 * i + 2]);
+    p[4 * i + 1] = _mm512_unpackhi_epi32(t[4 * i], t[4 * i + 2]);
+    p[4 * i + 2] = _mm512_unpacklo_epi32(t[4 * i + 1], t[4 * i + 3]);
+    p[4 * i + 3] = _mm512_unpackhi_epi32(t[4 * i + 1], t[4 * i + 3]);
+  }
+
+  __m512i q[32];
+
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 4; i++) {
+    q[8 * i] = _mm512_unpacklo_epi64(p[8 * i], p[8 * i + 4]);
+    q[8 * i + 1] = _mm512_unpackhi_epi64(p[8 * i], p[8 * i + 4]);
+    q[8 * i + 2] = _mm512_unpacklo_epi64(p[8 * i + 1], p[8 * i + 5]);
+    q[8 * i + 3] = _mm512_unpackhi_epi64(p[8 * i + 1], p[8 * i + 5]);
+    q[8 * i + 4] = _mm512_unpacklo_epi64(p[8 * i + 2], p[8 * i + 6]);
+    q[8 * i + 5] = _mm512_unpackhi_epi64(p[8 * i + 2], p[8 * i + 6]);
+    q[8 * i + 6] = _mm512_unpacklo_epi64(p[8 * i + 3], p[8 * i + 7]);
+    q[8 * i + 7] = _mm512_unpackhi_epi64(p[8 * i + 3], p[8 * i + 7]);
+  }
+
+  __m512i f[32];
+
+#define PACKET32H_TRANSPOSE_HELPER(X, Y)                                                            \
+  do {                                                                                              \
+    f[Y * 8] = _mm512_inserti32x4(f[Y * 8], _mm512_extracti32x4_epi32(q[X * 8], Y), X);             \
+    f[Y * 8 + 1] = _mm512_inserti32x4(f[Y * 8 + 1], _mm512_extracti32x4_epi32(q[X * 8 + 1], Y), X); \
+    f[Y * 8 + 2] = _mm512_inserti32x4(f[Y * 8 + 2], _mm512_extracti32x4_epi32(q[X * 8 + 2], Y), X); \
+    f[Y * 8 + 3] = _mm512_inserti32x4(f[Y * 8 + 3], _mm512_extracti32x4_epi32(q[X * 8 + 3], Y), X); \
+    f[Y * 8 + 4] = _mm512_inserti32x4(f[Y * 8 + 4], _mm512_extracti32x4_epi32(q[X * 8 + 4], Y), X); \
+    f[Y * 8 + 5] = _mm512_inserti32x4(f[Y * 8 + 5], _mm512_extracti32x4_epi32(q[X * 8 + 5], Y), X); \
+    f[Y * 8 + 6] = _mm512_inserti32x4(f[Y * 8 + 6], _mm512_extracti32x4_epi32(q[X * 8 + 6], Y), X); \
+    f[Y * 8 + 7] = _mm512_inserti32x4(f[Y * 8 + 7], _mm512_extracti32x4_epi32(q[X * 8 + 7], Y), X); \
+  } while (false);
+
+  PACKET32H_TRANSPOSE_HELPER(0, 0);
+  PACKET32H_TRANSPOSE_HELPER(1, 1);
+  PACKET32H_TRANSPOSE_HELPER(2, 2);
+  PACKET32H_TRANSPOSE_HELPER(3, 3);
+
+  PACKET32H_TRANSPOSE_HELPER(1, 0);
+  PACKET32H_TRANSPOSE_HELPER(2, 0);
+  PACKET32H_TRANSPOSE_HELPER(3, 0);
+  PACKET32H_TRANSPOSE_HELPER(2, 1);
+  PACKET32H_TRANSPOSE_HELPER(3, 1);
+  PACKET32H_TRANSPOSE_HELPER(3, 2);
+
+  PACKET32H_TRANSPOSE_HELPER(0, 1);
+  PACKET32H_TRANSPOSE_HELPER(0, 2);
+  PACKET32H_TRANSPOSE_HELPER(0, 3);
+  PACKET32H_TRANSPOSE_HELPER(1, 2);
+  PACKET32H_TRANSPOSE_HELPER(1, 3);
+  PACKET32H_TRANSPOSE_HELPER(2, 3);
+
+#undef PACKET32H_TRANSPOSE_HELPER
+
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 32; i++) {
+    a.packet[i] = _mm512_castsi512_ph(f[i]);
+  }
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet32h, 4>& a) {
+  __m512i p0, p1, p2, p3, t0, t1, t2, t3, a0, a1, a2, a3;
+  t0 = _mm512_unpacklo_epi16(_mm512_castph_si512(a.packet[0]), _mm512_castph_si512(a.packet[1]));
+  t1 = _mm512_unpackhi_epi16(_mm512_castph_si512(a.packet[0]), _mm512_castph_si512(a.packet[1]));
+  t2 = _mm512_unpacklo_epi16(_mm512_castph_si512(a.packet[2]), _mm512_castph_si512(a.packet[3]));
+  t3 = _mm512_unpackhi_epi16(_mm512_castph_si512(a.packet[2]), _mm512_castph_si512(a.packet[3]));
+
+  p0 = _mm512_unpacklo_epi32(t0, t2);
+  p1 = _mm512_unpackhi_epi32(t0, t2);
+  p2 = _mm512_unpacklo_epi32(t1, t3);
+  p3 = _mm512_unpackhi_epi32(t1, t3);
+
+  a0 = p0;
+  a1 = p1;
+  a2 = p2;
+  a3 = p3;
+
+  a0 = _mm512_inserti32x4(a0, _mm512_extracti32x4_epi32(p1, 0), 1);
+  a1 = _mm512_inserti32x4(a1, _mm512_extracti32x4_epi32(p0, 1), 0);
+
+  a0 = _mm512_inserti32x4(a0, _mm512_extracti32x4_epi32(p2, 0), 2);
+  a2 = _mm512_inserti32x4(a2, _mm512_extracti32x4_epi32(p0, 2), 0);
+
+  a0 = _mm512_inserti32x4(a0, _mm512_extracti32x4_epi32(p3, 0), 3);
+  a3 = _mm512_inserti32x4(a3, _mm512_extracti32x4_epi32(p0, 3), 0);
+
+  a1 = _mm512_inserti32x4(a1, _mm512_extracti32x4_epi32(p2, 1), 2);
+  a2 = _mm512_inserti32x4(a2, _mm512_extracti32x4_epi32(p1, 2), 1);
+
+  a2 = _mm512_inserti32x4(a2, _mm512_extracti32x4_epi32(p3, 2), 3);
+  a3 = _mm512_inserti32x4(a3, _mm512_extracti32x4_epi32(p2, 3), 2);
+
+  a1 = _mm512_inserti32x4(a1, _mm512_extracti32x4_epi32(p3, 1), 3);
+  a3 = _mm512_inserti32x4(a3, _mm512_extracti32x4_epi32(p1, 3), 1);
+
+  a.packet[0] = _mm512_castsi512_ph(a0);
+  a.packet[1] = _mm512_castsi512_ph(a1);
+  a.packet[2] = _mm512_castsi512_ph(a2);
+  a.packet[3] = _mm512_castsi512_ph(a3);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16h, 16>& kernel) {
+  __m256i a = _mm256_castph_si256(kernel.packet[0]);
+  __m256i b = _mm256_castph_si256(kernel.packet[1]);
+  __m256i c = _mm256_castph_si256(kernel.packet[2]);
+  __m256i d = _mm256_castph_si256(kernel.packet[3]);
+  __m256i e = _mm256_castph_si256(kernel.packet[4]);
+  __m256i f = _mm256_castph_si256(kernel.packet[5]);
+  __m256i g = _mm256_castph_si256(kernel.packet[6]);
+  __m256i h = _mm256_castph_si256(kernel.packet[7]);
+  __m256i i = _mm256_castph_si256(kernel.packet[8]);
+  __m256i j = _mm256_castph_si256(kernel.packet[9]);
+  __m256i k = _mm256_castph_si256(kernel.packet[10]);
+  __m256i l = _mm256_castph_si256(kernel.packet[11]);
+  __m256i m = _mm256_castph_si256(kernel.packet[12]);
+  __m256i n = _mm256_castph_si256(kernel.packet[13]);
+  __m256i o = _mm256_castph_si256(kernel.packet[14]);
+  __m256i p = _mm256_castph_si256(kernel.packet[15]);
+
+  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
+  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
+  __m256i ef_07 = _mm256_unpacklo_epi16(e, f);
+  __m256i gh_07 = _mm256_unpacklo_epi16(g, h);
+  __m256i ij_07 = _mm256_unpacklo_epi16(i, j);
+  __m256i kl_07 = _mm256_unpacklo_epi16(k, l);
+  __m256i mn_07 = _mm256_unpacklo_epi16(m, n);
+  __m256i op_07 = _mm256_unpacklo_epi16(o, p);
+
+  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
+  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
+  __m256i ef_8f = _mm256_unpackhi_epi16(e, f);
+  __m256i gh_8f = _mm256_unpackhi_epi16(g, h);
+  __m256i ij_8f = _mm256_unpackhi_epi16(i, j);
+  __m256i kl_8f = _mm256_unpackhi_epi16(k, l);
+  __m256i mn_8f = _mm256_unpackhi_epi16(m, n);
+  __m256i op_8f = _mm256_unpackhi_epi16(o, p);
+
+  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
+  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
+  __m256i efgh_03 = _mm256_unpacklo_epi32(ef_07, gh_07);
+  __m256i efgh_47 = _mm256_unpackhi_epi32(ef_07, gh_07);
+  __m256i ijkl_03 = _mm256_unpacklo_epi32(ij_07, kl_07);
+  __m256i ijkl_47 = _mm256_unpackhi_epi32(ij_07, kl_07);
+  __m256i mnop_03 = _mm256_unpacklo_epi32(mn_07, op_07);
+  __m256i mnop_47 = _mm256_unpackhi_epi32(mn_07, op_07);
+
+  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
+  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
+  __m256i efgh_8b = _mm256_unpacklo_epi32(ef_8f, gh_8f);
+  __m256i efgh_cf = _mm256_unpackhi_epi32(ef_8f, gh_8f);
+  __m256i ijkl_8b = _mm256_unpacklo_epi32(ij_8f, kl_8f);
+  __m256i ijkl_cf = _mm256_unpackhi_epi32(ij_8f, kl_8f);
+  __m256i mnop_8b = _mm256_unpacklo_epi32(mn_8f, op_8f);
+  __m256i mnop_cf = _mm256_unpackhi_epi32(mn_8f, op_8f);
+
+  __m256i abcdefgh_01 = _mm256_unpacklo_epi64(abcd_03, efgh_03);
+  __m256i abcdefgh_23 = _mm256_unpackhi_epi64(abcd_03, efgh_03);
+  __m256i ijklmnop_01 = _mm256_unpacklo_epi64(ijkl_03, mnop_03);
+  __m256i ijklmnop_23 = _mm256_unpackhi_epi64(ijkl_03, mnop_03);
+  __m256i abcdefgh_45 = _mm256_unpacklo_epi64(abcd_47, efgh_47);
+  __m256i abcdefgh_67 = _mm256_unpackhi_epi64(abcd_47, efgh_47);
+  __m256i ijklmnop_45 = _mm256_unpacklo_epi64(ijkl_47, mnop_47);
+  __m256i ijklmnop_67 = _mm256_unpackhi_epi64(ijkl_47, mnop_47);
+  __m256i abcdefgh_89 = _mm256_unpacklo_epi64(abcd_8b, efgh_8b);
+  __m256i abcdefgh_ab = _mm256_unpackhi_epi64(abcd_8b, efgh_8b);
+  __m256i ijklmnop_89 = _mm256_unpacklo_epi64(ijkl_8b, mnop_8b);
+  __m256i ijklmnop_ab = _mm256_unpackhi_epi64(ijkl_8b, mnop_8b);
+  __m256i abcdefgh_cd = _mm256_unpacklo_epi64(abcd_cf, efgh_cf);
+  __m256i abcdefgh_ef = _mm256_unpackhi_epi64(abcd_cf, efgh_cf);
+  __m256i ijklmnop_cd = _mm256_unpacklo_epi64(ijkl_cf, mnop_cf);
+  __m256i ijklmnop_ef = _mm256_unpackhi_epi64(ijkl_cf, mnop_cf);
+
+  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
+  __m256i a_p_0 = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x20);
+  __m256i a_p_1 = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x20);
+  __m256i a_p_2 = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x20);
+  __m256i a_p_3 = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x20);
+  __m256i a_p_4 = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x20);
+  __m256i a_p_5 = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x20);
+  __m256i a_p_6 = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x20);
+  __m256i a_p_7 = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x20);
+  __m256i a_p_8 = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x31);
+  __m256i a_p_9 = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x31);
+  __m256i a_p_a = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x31);
+  __m256i a_p_b = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x31);
+  __m256i a_p_c = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x31);
+  __m256i a_p_d = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x31);
+  __m256i a_p_e = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x31);
+  __m256i a_p_f = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x31);
+
+  kernel.packet[0] = _mm256_castsi256_ph(a_p_0);
+  kernel.packet[1] = _mm256_castsi256_ph(a_p_1);
+  kernel.packet[2] = _mm256_castsi256_ph(a_p_2);
+  kernel.packet[3] = _mm256_castsi256_ph(a_p_3);
+  kernel.packet[4] = _mm256_castsi256_ph(a_p_4);
+  kernel.packet[5] = _mm256_castsi256_ph(a_p_5);
+  kernel.packet[6] = _mm256_castsi256_ph(a_p_6);
+  kernel.packet[7] = _mm256_castsi256_ph(a_p_7);
+  kernel.packet[8] = _mm256_castsi256_ph(a_p_8);
+  kernel.packet[9] = _mm256_castsi256_ph(a_p_9);
+  kernel.packet[10] = _mm256_castsi256_ph(a_p_a);
+  kernel.packet[11] = _mm256_castsi256_ph(a_p_b);
+  kernel.packet[12] = _mm256_castsi256_ph(a_p_c);
+  kernel.packet[13] = _mm256_castsi256_ph(a_p_d);
+  kernel.packet[14] = _mm256_castsi256_ph(a_p_e);
+  kernel.packet[15] = _mm256_castsi256_ph(a_p_f);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16h, 8>& kernel) {
+  EIGEN_ALIGN64 half in[8][16];
+  pstore<half>(in[0], kernel.packet[0]);
+  pstore<half>(in[1], kernel.packet[1]);
+  pstore<half>(in[2], kernel.packet[2]);
+  pstore<half>(in[3], kernel.packet[3]);
+  pstore<half>(in[4], kernel.packet[4]);
+  pstore<half>(in[5], kernel.packet[5]);
+  pstore<half>(in[6], kernel.packet[6]);
+  pstore<half>(in[7], kernel.packet[7]);
+
+  EIGEN_ALIGN64 half out[8][16];
+
+  for (int i = 0; i < 8; ++i) {
+    for (int j = 0; j < 8; ++j) {
+      out[i][j] = in[j][2 * i];
+    }
+    for (int j = 0; j < 8; ++j) {
+      out[i][j + 8] = in[j][2 * i + 1];
+    }
+  }
+
+  kernel.packet[0] = pload<Packet16h>(out[0]);
+  kernel.packet[1] = pload<Packet16h>(out[1]);
+  kernel.packet[2] = pload<Packet16h>(out[2]);
+  kernel.packet[3] = pload<Packet16h>(out[3]);
+  kernel.packet[4] = pload<Packet16h>(out[4]);
+  kernel.packet[5] = pload<Packet16h>(out[5]);
+  kernel.packet[6] = pload<Packet16h>(out[6]);
+  kernel.packet[7] = pload<Packet16h>(out[7]);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16h, 4>& kernel) {
+  EIGEN_ALIGN64 half in[4][16];
+  pstore<half>(in[0], kernel.packet[0]);
+  pstore<half>(in[1], kernel.packet[1]);
+  pstore<half>(in[2], kernel.packet[2]);
+  pstore<half>(in[3], kernel.packet[3]);
+
+  EIGEN_ALIGN64 half out[4][16];
+
+  for (int i = 0; i < 4; ++i) {
+    for (int j = 0; j < 4; ++j) {
+      out[i][j] = in[j][4 * i];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j + 4] = in[j][4 * i + 1];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j + 8] = in[j][4 * i + 2];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j + 12] = in[j][4 * i + 3];
+    }
+  }
+
+  kernel.packet[0] = pload<Packet16h>(out[0]);
+  kernel.packet[1] = pload<Packet16h>(out[1]);
+  kernel.packet[2] = pload<Packet16h>(out[2]);
+  kernel.packet[3] = pload<Packet16h>(out[3]);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8h, 8>& kernel) {
+  __m128i a = _mm_castph_si128(kernel.packet[0]);
+  __m128i b = _mm_castph_si128(kernel.packet[1]);
+  __m128i c = _mm_castph_si128(kernel.packet[2]);
+  __m128i d = _mm_castph_si128(kernel.packet[3]);
+  __m128i e = _mm_castph_si128(kernel.packet[4]);
+  __m128i f = _mm_castph_si128(kernel.packet[5]);
+  __m128i g = _mm_castph_si128(kernel.packet[6]);
+  __m128i h = _mm_castph_si128(kernel.packet[7]);
+
+  __m128i a03b03 = _mm_unpacklo_epi16(a, b);
+  __m128i c03d03 = _mm_unpacklo_epi16(c, d);
+  __m128i e03f03 = _mm_unpacklo_epi16(e, f);
+  __m128i g03h03 = _mm_unpacklo_epi16(g, h);
+  __m128i a47b47 = _mm_unpackhi_epi16(a, b);
+  __m128i c47d47 = _mm_unpackhi_epi16(c, d);
+  __m128i e47f47 = _mm_unpackhi_epi16(e, f);
+  __m128i g47h47 = _mm_unpackhi_epi16(g, h);
+
+  __m128i a01b01c01d01 = _mm_unpacklo_epi32(a03b03, c03d03);
+  __m128i a23b23c23d23 = _mm_unpackhi_epi32(a03b03, c03d03);
+  __m128i e01f01g01h01 = _mm_unpacklo_epi32(e03f03, g03h03);
+  __m128i e23f23g23h23 = _mm_unpackhi_epi32(e03f03, g03h03);
+  __m128i a45b45c45d45 = _mm_unpacklo_epi32(a47b47, c47d47);
+  __m128i a67b67c67d67 = _mm_unpackhi_epi32(a47b47, c47d47);
+  __m128i e45f45g45h45 = _mm_unpacklo_epi32(e47f47, g47h47);
+  __m128i e67f67g67h67 = _mm_unpackhi_epi32(e47f47, g47h47);
+
+  __m128i a0b0c0d0e0f0g0h0 = _mm_unpacklo_epi64(a01b01c01d01, e01f01g01h01);
+  __m128i a1b1c1d1e1f1g1h1 = _mm_unpackhi_epi64(a01b01c01d01, e01f01g01h01);
+  __m128i a2b2c2d2e2f2g2h2 = _mm_unpacklo_epi64(a23b23c23d23, e23f23g23h23);
+  __m128i a3b3c3d3e3f3g3h3 = _mm_unpackhi_epi64(a23b23c23d23, e23f23g23h23);
+  __m128i a4b4c4d4e4f4g4h4 = _mm_unpacklo_epi64(a45b45c45d45, e45f45g45h45);
+  __m128i a5b5c5d5e5f5g5h5 = _mm_unpackhi_epi64(a45b45c45d45, e45f45g45h45);
+  __m128i a6b6c6d6e6f6g6h6 = _mm_unpacklo_epi64(a67b67c67d67, e67f67g67h67);
+  __m128i a7b7c7d7e7f7g7h7 = _mm_unpackhi_epi64(a67b67c67d67, e67f67g67h67);
+
+  kernel.packet[0] = _mm_castsi128_ph(a0b0c0d0e0f0g0h0);
+  kernel.packet[1] = _mm_castsi128_ph(a1b1c1d1e1f1g1h1);
+  kernel.packet[2] = _mm_castsi128_ph(a2b2c2d2e2f2g2h2);
+  kernel.packet[3] = _mm_castsi128_ph(a3b3c3d3e3f3g3h3);
+  kernel.packet[4] = _mm_castsi128_ph(a4b4c4d4e4f4g4h4);
+  kernel.packet[5] = _mm_castsi128_ph(a5b5c5d5e5f5g5h5);
+  kernel.packet[6] = _mm_castsi128_ph(a6b6c6d6e6f6g6h6);
+  kernel.packet[7] = _mm_castsi128_ph(a7b7c7d7e7f7g7h7);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8h, 4>& kernel) {
+  EIGEN_ALIGN32 Eigen::half in[4][8];
+  pstore<Eigen::half>(in[0], kernel.packet[0]);
+  pstore<Eigen::half>(in[1], kernel.packet[1]);
+  pstore<Eigen::half>(in[2], kernel.packet[2]);
+  pstore<Eigen::half>(in[3], kernel.packet[3]);
+
+  EIGEN_ALIGN32 Eigen::half out[4][8];
+
+  for (int i = 0; i < 4; ++i) {
+    for (int j = 0; j < 4; ++j) {
+      out[i][j] = in[j][2 * i];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j + 4] = in[j][2 * i + 1];
+    }
+  }
+
+  kernel.packet[0] = pload<Packet8h>(out[0]);
+  kernel.packet[1] = pload<Packet8h>(out[1]);
+  kernel.packet[2] = pload<Packet8h>(out[2]);
+  kernel.packet[3] = pload<Packet8h>(out[3]);
+}
+
+// preverse
+
+template <>
+EIGEN_STRONG_INLINE Packet32h preverse(const Packet32h& a) {
+  return _mm512_permutexvar_ph(_mm512_set_epi16(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                                                20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31),
+                               a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h preverse(const Packet16h& a) {
+  __m128i m = _mm_setr_epi8(14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1);
+  return _mm256_castsi256_ph(_mm256_insertf128_si256(
+      _mm256_castsi128_si256(_mm_shuffle_epi8(_mm256_extractf128_si256(_mm256_castph_si256(a), 1), m)),
+      _mm_shuffle_epi8(_mm256_extractf128_si256(_mm256_castph_si256(a), 0), m), 1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h preverse(const Packet8h& a) {
+  __m128i m = _mm_setr_epi8(14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1);
+  return _mm_castsi128_ph(_mm_shuffle_epi8(_mm_castph_si128(a), m));
+}
+
+// pscatter
+
+template <>
+EIGEN_STRONG_INLINE void pscatter<half, Packet32h>(half* to, const Packet32h& from, Index stride) {
+  EIGEN_ALIGN64 half aux[32];
+  pstore(aux, from);
+
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 32; i++) {
+    to[stride * i] = aux[i];
+  }
+}
+template <>
+EIGEN_STRONG_INLINE void pscatter<half, Packet16h>(half* to, const Packet16h& from, Index stride) {
+  EIGEN_ALIGN64 half aux[16];
+  pstore(aux, from);
+  to[stride * 0] = aux[0];
+  to[stride * 1] = aux[1];
+  to[stride * 2] = aux[2];
+  to[stride * 3] = aux[3];
+  to[stride * 4] = aux[4];
+  to[stride * 5] = aux[5];
+  to[stride * 6] = aux[6];
+  to[stride * 7] = aux[7];
+  to[stride * 8] = aux[8];
+  to[stride * 9] = aux[9];
+  to[stride * 10] = aux[10];
+  to[stride * 11] = aux[11];
+  to[stride * 12] = aux[12];
+  to[stride * 13] = aux[13];
+  to[stride * 14] = aux[14];
+  to[stride * 15] = aux[15];
+}
+
+template <>
+EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8h>(Eigen::half* to, const Packet8h& from, Index stride) {
+  EIGEN_ALIGN32 Eigen::half aux[8];
+  pstore(aux, from);
+  to[stride * 0] = aux[0];
+  to[stride * 1] = aux[1];
+  to[stride * 2] = aux[2];
+  to[stride * 3] = aux[3];
+  to[stride * 4] = aux[4];
+  to[stride * 5] = aux[5];
+  to[stride * 6] = aux[6];
+  to[stride * 7] = aux[7];
+}
+
+// pgather
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pgather<Eigen::half, Packet32h>(const Eigen::half* from, Index stride) {
+  return _mm512_set_ph(from[31 * stride].x, from[30 * stride].x, from[29 * stride].x, from[28 * stride].x,
+                       from[27 * stride].x, from[26 * stride].x, from[25 * stride].x, from[24 * stride].x,
+                       from[23 * stride].x, from[22 * stride].x, from[21 * stride].x, from[20 * stride].x,
+                       from[19 * stride].x, from[18 * stride].x, from[17 * stride].x, from[16 * stride].x,
+                       from[15 * stride].x, from[14 * stride].x, from[13 * stride].x, from[12 * stride].x,
+                       from[11 * stride].x, from[10 * stride].x, from[9 * stride].x, from[8 * stride].x,
+                       from[7 * stride].x, from[6 * stride].x, from[5 * stride].x, from[4 * stride].x,
+                       from[3 * stride].x, from[2 * stride].x, from[1 * stride].x, from[0 * stride].x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pgather<Eigen::half, Packet16h>(const Eigen::half* from, Index stride) {
+  return _mm256_set_ph(from[15 * stride].x, from[14 * stride].x, from[13 * stride].x, from[12 * stride].x,
+                       from[11 * stride].x, from[10 * stride].x, from[9 * stride].x, from[8 * stride].x,
+                       from[7 * stride].x, from[6 * stride].x, from[5 * stride].x, from[4 * stride].x,
+                       from[3 * stride].x, from[2 * stride].x, from[1 * stride].x, from[0 * stride].x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pgather<Eigen::half, Packet8h>(const Eigen::half* from, Index stride) {
+  return _mm_set_ph(from[7 * stride].x, from[6 * stride].x, from[5 * stride].x, from[4 * stride].x, from[3 * stride].x,
+                    from[2 * stride].x, from[1 * stride].x, from[0 * stride].x);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_FP16_AVX512_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/Reductions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/Reductions.h
new file mode 100644
index 0000000..f59b78e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/Reductions.h
@@ -0,0 +1,297 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Charlie Schlosser <cs.schlosser@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REDUCTIONS_AVX512_H
+#define EIGEN_REDUCTIONS_AVX512_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet16i -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE int predux(const Packet16i& a) {
+  return _mm512_reduce_add_epi32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_mul(const Packet16i& a) {
+  return _mm512_reduce_mul_epi32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_min(const Packet16i& a) {
+  return _mm512_reduce_min_epi32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_max(const Packet16i& a) {
+  return _mm512_reduce_max_epi32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet16i& a) {
+  return _mm512_reduce_or_epi32(a) != 0;
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet8l -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE int64_t predux(const Packet8l& a) {
+  return _mm512_reduce_add_epi64(a);
+}
+
+#if EIGEN_COMP_MSVC
+// MSVC's _mm512_reduce_mul_epi64 is borked, at least up to and including 1939.
+//    alignas(64) int64_t data[] = { 1,1,-1,-1,1,-1,-1,-1 };
+//    int64_t out = _mm512_reduce_mul_epi64(_mm512_load_epi64(data));
+// produces garbage: 4294967295.  This occurs when the result should be negative.
+// Fall back to a manual approach:
+template <>
+EIGEN_STRONG_INLINE int64_t predux_mul(const Packet8l& a) {
+  Packet4l lane0 = _mm512_extracti64x4_epi64(a, 0);
+  Packet4l lane1 = _mm512_extracti64x4_epi64(a, 1);
+  return predux_mul(pmul(lane0, lane1));
+}
+#else
+template <>
+EIGEN_STRONG_INLINE int64_t predux_mul<Packet8l>(const Packet8l& a) {
+  return _mm512_reduce_mul_epi64(a);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE int64_t predux_min(const Packet8l& a) {
+  return _mm512_reduce_min_epi64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE int64_t predux_max(const Packet8l& a) {
+  return _mm512_reduce_max_epi64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet8l& a) {
+  return _mm512_reduce_or_epi64(a) != 0;
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet16f -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE float predux(const Packet16f& a) {
+  return _mm512_reduce_add_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_mul(const Packet16f& a) {
+  return _mm512_reduce_mul_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min(const Packet16f& a) {
+  return _mm512_reduce_min_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<PropagateNumbers>(const Packet16f& a) {
+  Packet8f lane0 = _mm512_extractf32x8_ps(a, 0);
+  Packet8f lane1 = _mm512_extractf32x8_ps(a, 1);
+  return predux_min<PropagateNumbers>(pmin<PropagateNumbers>(lane0, lane1));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<PropagateNaN>(const Packet16f& a) {
+  Packet8f lane0 = _mm512_extractf32x8_ps(a, 0);
+  Packet8f lane1 = _mm512_extractf32x8_ps(a, 1);
+  return predux_min<PropagateNaN>(pmin<PropagateNaN>(lane0, lane1));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max(const Packet16f& a) {
+  return _mm512_reduce_max_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<PropagateNumbers>(const Packet16f& a) {
+  Packet8f lane0 = _mm512_extractf32x8_ps(a, 0);
+  Packet8f lane1 = _mm512_extractf32x8_ps(a, 1);
+  return predux_max<PropagateNumbers>(pmax<PropagateNumbers>(lane0, lane1));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<PropagateNaN>(const Packet16f& a) {
+  Packet8f lane0 = _mm512_extractf32x8_ps(a, 0);
+  Packet8f lane1 = _mm512_extractf32x8_ps(a, 1);
+  return predux_max<PropagateNaN>(pmax<PropagateNaN>(lane0, lane1));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet16f& a) {
+  return _mm512_reduce_or_epi32(_mm512_castps_si512(a)) != 0;
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet8d -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE double predux(const Packet8d& a) {
+  return _mm512_reduce_add_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_mul(const Packet8d& a) {
+  return _mm512_reduce_mul_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min(const Packet8d& a) {
+  return _mm512_reduce_min_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min<PropagateNumbers>(const Packet8d& a) {
+  Packet4d lane0 = _mm512_extractf64x4_pd(a, 0);
+  Packet4d lane1 = _mm512_extractf64x4_pd(a, 1);
+  return predux_min<PropagateNumbers>(pmin<PropagateNumbers>(lane0, lane1));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min<PropagateNaN>(const Packet8d& a) {
+  Packet4d lane0 = _mm512_extractf64x4_pd(a, 0);
+  Packet4d lane1 = _mm512_extractf64x4_pd(a, 1);
+  return predux_min<PropagateNaN>(pmin<PropagateNaN>(lane0, lane1));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max(const Packet8d& a) {
+  return _mm512_reduce_max_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max<PropagateNumbers>(const Packet8d& a) {
+  Packet4d lane0 = _mm512_extractf64x4_pd(a, 0);
+  Packet4d lane1 = _mm512_extractf64x4_pd(a, 1);
+  return predux_max<PropagateNumbers>(pmax<PropagateNumbers>(lane0, lane1));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max<PropagateNaN>(const Packet8d& a) {
+  Packet4d lane0 = _mm512_extractf64x4_pd(a, 0);
+  Packet4d lane1 = _mm512_extractf64x4_pd(a, 1);
+  return predux_max<PropagateNaN>(pmax<PropagateNaN>(lane0, lane1));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet8d& a) {
+  return _mm512_reduce_or_epi64(_mm512_castpd_si512(a)) != 0;
+}
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet16h -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE half predux(const Packet16h& from) {
+  return half(predux(half2float(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_mul(const Packet16h& from) {
+  return half(predux_mul(half2float(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_min(const Packet16h& from) {
+  return half(predux_min(half2float(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_min<PropagateNumbers>(const Packet16h& from) {
+  return half(predux_min<PropagateNumbers>(half2float(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_min<PropagateNaN>(const Packet16h& from) {
+  return half(predux_min<PropagateNaN>(half2float(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_max(const Packet16h& from) {
+  return half(predux_max(half2float(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_max<PropagateNumbers>(const Packet16h& from) {
+  return half(predux_max<PropagateNumbers>(half2float(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux_max<PropagateNaN>(const Packet16h& from) {
+  return half(predux_max<PropagateNaN>(half2float(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet16h& a) {
+  return predux_any<Packet8i>(a.m_val);
+}
+#endif
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet16bf -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux<Packet16f>(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_mul(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux_mul<Packet16f>(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux_min<Packet16f>(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min<PropagateNumbers>(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux_min<PropagateNumbers>(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min<PropagateNaN>(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux_min<PropagateNaN>(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux_max(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max<PropagateNumbers>(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux_max<PropagateNumbers>(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max<PropagateNaN>(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux_max<PropagateNaN>(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet16bf& a) {
+  return predux_any<Packet8i>(a.m_val);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_REDUCTIONS_AVX512_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TrsmKernel.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TrsmKernel.h
new file mode 100644
index 0000000..11c0bd7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TrsmKernel.h
@@ -0,0 +1,1171 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022 Intel Corporation
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_ARCH_AVX512_TRSM_KERNEL_H
+#define EIGEN_CORE_ARCH_AVX512_TRSM_KERNEL_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+#if !defined(EIGEN_USE_AVX512_TRSM_KERNELS)
+#define EIGEN_USE_AVX512_TRSM_KERNELS 1
+#endif
+
+// TRSM kernels currently unconditionally rely on malloc with AVX512.
+// Disable them if malloc is explicitly disabled at compile-time.
+#ifdef EIGEN_NO_MALLOC
+#undef EIGEN_USE_AVX512_TRSM_KERNELS
+#define EIGEN_USE_AVX512_TRSM_KERNELS 0
+#endif
+
+#if EIGEN_USE_AVX512_TRSM_KERNELS
+#if !defined(EIGEN_USE_AVX512_TRSM_R_KERNELS)
+#define EIGEN_USE_AVX512_TRSM_R_KERNELS 1
+#endif
+#if !defined(EIGEN_USE_AVX512_TRSM_L_KERNELS)
+#define EIGEN_USE_AVX512_TRSM_L_KERNELS 1
+#endif
+#else  // EIGEN_USE_AVX512_TRSM_KERNELS == 0
+#define EIGEN_USE_AVX512_TRSM_R_KERNELS 0
+#define EIGEN_USE_AVX512_TRSM_L_KERNELS 0
+#endif
+
+// Need this for some std::min calls.
+#ifdef min
+#undef min
+#endif
+
+namespace Eigen {
+namespace internal {
+
+#if (EIGEN_USE_AVX512_TRSM_KERNELS)
+
+#define EIGEN_AVX_MAX_NUM_ACC (int64_t(24))
+#define EIGEN_AVX_MAX_NUM_ROW (int64_t(8))  // Denoted L in code.
+#define EIGEN_AVX_MAX_K_UNROL (int64_t(4))
+#define EIGEN_AVX_B_LOAD_SETS (int64_t(2))
+#define EIGEN_AVX_MAX_A_BCAST (int64_t(2))
+typedef Packet16f vecFullFloat;
+typedef Packet8d vecFullDouble;
+typedef Packet8f vecHalfFloat;
+typedef Packet4d vecHalfDouble;
+
+// Compile-time unrolls are implemented here.
+// Note: this depends on macros and typedefs above.
+#include "TrsmUnrolls.inc"
+
+#if (EIGEN_COMP_CLANG != 0)
+
+/**
+ * For smaller problem sizes, and certain compilers, using the optimized kernels trsmKernelL/R directly
+ * is faster than the packed versions in TriangularSolverMatrix.h.
+ *
+ * The current heuristic is based on having having all arrays used in the largest gemm-update
+ * in triSolve fit in roughly L2Cap (percentage) of the L2 cache. These cutoffs are a bit conservative and could be
+ * larger for some trsm cases.
+ * The formula:
+ *
+ *   (L*M + M*N + L*N)*sizeof(Scalar) < L2Cache*L2Cap
+ *
+ *  L = number of rows to solve at a time
+ *  N = number of rhs
+ *  M = Dimension of triangular matrix
+ *
+ */
+#if !defined(EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS)
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS 1
+#endif
+
+#if EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS
+
+#if EIGEN_USE_AVX512_TRSM_R_KERNELS
+#if !defined(EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS)
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS 1
+#endif  // !defined(EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS)
+#endif
+
+#if EIGEN_USE_AVX512_TRSM_L_KERNELS
+#if !defined(EIGEN_ENABLE_AVX512_NOCOPY_TRSM_L_CUTOFFS)
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_L_CUTOFFS 1
+#endif
+#endif  // EIGEN_USE_AVX512_TRSM_L_KERNELS
+
+#else  // EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS == 0
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS 0
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_L_CUTOFFS 0
+#endif  // EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS
+
+template <typename Scalar>
+int64_t avx512_trsm_cutoff(int64_t L2Size, int64_t N, double L2Cap) {
+  const int64_t U3 = 3 * packet_traits<Scalar>::size;
+  const int64_t MaxNb = 5 * U3;
+  int64_t Nb = std::min(MaxNb, N);
+  double cutoff_d =
+      (((L2Size * L2Cap) / (sizeof(Scalar))) - (EIGEN_AVX_MAX_NUM_ROW)*Nb) / ((EIGEN_AVX_MAX_NUM_ROW) + Nb);
+  int64_t cutoff_l = static_cast<int64_t>(cutoff_d);
+  return (cutoff_l / EIGEN_AVX_MAX_NUM_ROW) * EIGEN_AVX_MAX_NUM_ROW;
+}
+#else  // !(EIGEN_USE_AVX512_TRSM_KERNELS) || !(EIGEN_COMP_CLANG != 0)
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS 0
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS 0
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_L_CUTOFFS 0
+#endif
+
+/**
+ * Used by gemmKernel for the case A/B row-major and C col-major.
+ */
+template <typename Scalar, typename vec, int64_t unrollM, int64_t unrollN, bool remM, bool remN>
+EIGEN_ALWAYS_INLINE void transStoreC(PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS>& zmm, Scalar* C_arr,
+                                     int64_t LDC, int64_t remM_ = 0, int64_t remN_ = 0) {
+  EIGEN_UNUSED_VARIABLE(remN_);
+  EIGEN_UNUSED_VARIABLE(remM_);
+  using urolls = unrolls::trans<Scalar>;
+
+  constexpr int64_t U3 = urolls::PacketSize * 3;
+  constexpr int64_t U2 = urolls::PacketSize * 2;
+  constexpr int64_t U1 = urolls::PacketSize * 1;
+
+  static_assert(unrollN == U1 || unrollN == U2 || unrollN == U3, "unrollN should be a multiple of PacketSize");
+  static_assert(unrollM == EIGEN_AVX_MAX_NUM_ROW, "unrollM should be equal to EIGEN_AVX_MAX_NUM_ROW");
+
+  urolls::template transpose<unrollN, 0>(zmm);
+  EIGEN_IF_CONSTEXPR(unrollN > U2) urolls::template transpose<unrollN, 2>(zmm);
+  EIGEN_IF_CONSTEXPR(unrollN > U1) urolls::template transpose<unrollN, 1>(zmm);
+
+  static_assert((remN && unrollN == U1) || !remN, "When handling N remainder set unrollN=U1");
+  EIGEN_IF_CONSTEXPR(!remN) {
+    urolls::template storeC<std::min(unrollN, U1), unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+    EIGEN_IF_CONSTEXPR(unrollN > U1) {
+      constexpr int64_t unrollN_ = std::min(unrollN - U1, U1);
+      urolls::template storeC<unrollN_, unrollN, 1, remM>(C_arr + U1 * LDC, LDC, zmm, remM_);
+    }
+    EIGEN_IF_CONSTEXPR(unrollN > U2) {
+      constexpr int64_t unrollN_ = std::min(unrollN - U2, U1);
+      urolls::template storeC<unrollN_, unrollN, 2, remM>(C_arr + U2 * LDC, LDC, zmm, remM_);
+    }
+  }
+  else {
+    EIGEN_IF_CONSTEXPR((std::is_same<Scalar, float>::value)) {
+      // Note: without "if constexpr" this section of code will also be
+      // parsed by the compiler so each of the storeC will still be instantiated.
+      // We use enable_if in aux_storeC to set it to an empty function for
+      // these cases.
+      if (remN_ == 15)
+        urolls::template storeC<15, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 14)
+        urolls::template storeC<14, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 13)
+        urolls::template storeC<13, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 12)
+        urolls::template storeC<12, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 11)
+        urolls::template storeC<11, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 10)
+        urolls::template storeC<10, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 9)
+        urolls::template storeC<9, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 8)
+        urolls::template storeC<8, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 7)
+        urolls::template storeC<7, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 6)
+        urolls::template storeC<6, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 5)
+        urolls::template storeC<5, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 4)
+        urolls::template storeC<4, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 3)
+        urolls::template storeC<3, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 2)
+        urolls::template storeC<2, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 1)
+        urolls::template storeC<1, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+    }
+    else {
+      if (remN_ == 7)
+        urolls::template storeC<7, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 6)
+        urolls::template storeC<6, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 5)
+        urolls::template storeC<5, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 4)
+        urolls::template storeC<4, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 3)
+        urolls::template storeC<3, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 2)
+        urolls::template storeC<2, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 1)
+        urolls::template storeC<1, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+    }
+  }
+}
+
+/**
+ * GEMM like operation for trsm panel updates.
+ * Computes: C -= A*B
+ * K must be multiple of 4.
+ *
+ * Unrolls used are {1,2,4,8}x{U1,U2,U3};
+ * For good performance we want K to be large with M/N relatively small, but also large enough
+ * to use the {8,U3} unroll block.
+ *
+ * isARowMajor: is A_arr row-major?
+ * isCRowMajor: is C_arr row-major? (B_arr is assumed to be row-major).
+ * isAdd: C += A*B or C -= A*B (used by trsm)
+ * handleKRem: Handle arbitrary K? This is not needed for trsm.
+ */
+template <typename Scalar, bool isARowMajor, bool isCRowMajor, bool isAdd, bool handleKRem>
+void gemmKernel(Scalar* A_arr, Scalar* B_arr, Scalar* C_arr, int64_t M, int64_t N, int64_t K, int64_t LDA, int64_t LDB,
+                int64_t LDC) {
+  using urolls = unrolls::gemm<Scalar, isAdd>;
+  constexpr int64_t U3 = urolls::PacketSize * 3;
+  constexpr int64_t U2 = urolls::PacketSize * 2;
+  constexpr int64_t U1 = urolls::PacketSize * 1;
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  int64_t N_ = (N / U3) * U3;
+  int64_t M_ = (M / EIGEN_AVX_MAX_NUM_ROW) * EIGEN_AVX_MAX_NUM_ROW;
+  int64_t K_ = (K / EIGEN_AVX_MAX_K_UNROL) * EIGEN_AVX_MAX_K_UNROL;
+  int64_t j = 0;
+  for (; j < N_; j += U3) {
+    constexpr int64_t EIGEN_AVX_MAX_B_LOAD = EIGEN_AVX_B_LOAD_SETS * 3;
+    int64_t i = 0;
+    for (; i < M_; i += EIGEN_AVX_MAX_NUM_ROW) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)], *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<3, EIGEN_AVX_MAX_NUM_ROW>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 3, EIGEN_AVX_MAX_NUM_ROW, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 3, EIGEN_AVX_MAX_NUM_ROW, 1, EIGEN_AVX_B_LOAD_SETS * 3,
+                                       EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<3, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<3, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U3, false, false>(zmm, &C_arr[i + j * LDC], LDC);
+      }
+    }
+    if (M - i >= 4) {  // Note: this block assumes EIGEN_AVX_MAX_NUM_ROW = 8. Should be removed otherwise
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<3, 4>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 3, 4, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_B_LOAD_SETS * 3,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 3, 4, 1, EIGEN_AVX_B_LOAD_SETS * 3, EIGEN_AVX_MAX_A_BCAST>(
+              B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<3, 4>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<3, 4>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U3, true, false>(zmm, &C_arr[i + j * LDC], LDC, 4);
+      }
+      i += 4;
+    }
+    if (M - i >= 2) {
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<3, 2>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 3, 2, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_B_LOAD_SETS * 3,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 3, 2, 1, EIGEN_AVX_B_LOAD_SETS * 3, EIGEN_AVX_MAX_A_BCAST>(
+              B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<3, 2>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<3, 2>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U3, true, false>(zmm, &C_arr[i + j * LDC], LDC, 2);
+      }
+      i += 2;
+    }
+    if (M - i > 0) {
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<3, 1>(zmm);
+      {
+        for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+          urolls::template microKernel<isARowMajor, 3, 1, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_B_LOAD_SETS * 3, 1>(
+              B_t, A_t, LDB, LDA, zmm);
+          B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+          else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+        }
+        EIGEN_IF_CONSTEXPR(handleKRem) {
+          for (int64_t k = K_; k < K; k++) {
+            urolls::template microKernel<isARowMajor, 3, 1, 1, EIGEN_AVX_B_LOAD_SETS * 3, 1>(B_t, A_t, LDB, LDA, zmm);
+            B_t += LDB;
+            EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+            else A_t += LDA;
+          }
+        }
+        EIGEN_IF_CONSTEXPR(isCRowMajor) {
+          urolls::template updateC<3, 1>(&C_arr[i * LDC + j], LDC, zmm);
+          urolls::template storeC<3, 1>(&C_arr[i * LDC + j], LDC, zmm);
+        }
+        else {
+          transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U3, true, false>(zmm, &C_arr[i + j * LDC], LDC, 1);
+        }
+      }
+    }
+  }
+  if (N - j >= U2) {
+    constexpr int64_t EIGEN_AVX_MAX_B_LOAD = EIGEN_AVX_B_LOAD_SETS * 2;
+    int64_t i = 0;
+    for (; i < M_; i += EIGEN_AVX_MAX_NUM_ROW) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)], *B_t = &B_arr[0 * LDB + j];
+      EIGEN_IF_CONSTEXPR(isCRowMajor) B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<2, EIGEN_AVX_MAX_NUM_ROW>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 2, EIGEN_AVX_MAX_NUM_ROW, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 2, EIGEN_AVX_MAX_NUM_ROW, 1, EIGEN_AVX_MAX_B_LOAD,
+                                       EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<2, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<2, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U2, false, false>(zmm, &C_arr[i + j * LDC], LDC);
+      }
+    }
+    if (M - i >= 4) {  // Note: this block assumes EIGEN_AVX_MAX_NUM_ROW = 8. Should be removed otherwise
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<2, 4>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 2, 4, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 2, 4, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB,
+                                                                                                          LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<2, 4>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<2, 4>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U2, true, false>(zmm, &C_arr[i + j * LDC], LDC, 4);
+      }
+      i += 4;
+    }
+    if (M - i >= 2) {
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<2, 2>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 2, 2, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 2, 2, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB,
+                                                                                                          LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<2, 2>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<2, 2>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U2, true, false>(zmm, &C_arr[i + j * LDC], LDC, 2);
+      }
+      i += 2;
+    }
+    if (M - i > 0) {
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<2, 1>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 2, 1, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD, 1>(B_t, A_t, LDB,
+                                                                                                        LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 2, 1, 1, EIGEN_AVX_MAX_B_LOAD, 1>(B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<2, 1>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<2, 1>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U2, true, false>(zmm, &C_arr[i + j * LDC], LDC, 1);
+      }
+    }
+    j += U2;
+  }
+  if (N - j >= U1) {
+    constexpr int64_t EIGEN_AVX_MAX_B_LOAD = EIGEN_AVX_B_LOAD_SETS * 1;
+    int64_t i = 0;
+    for (; i < M_; i += EIGEN_AVX_MAX_NUM_ROW) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)], *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, EIGEN_AVX_MAX_NUM_ROW>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, EIGEN_AVX_MAX_NUM_ROW, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, EIGEN_AVX_MAX_NUM_ROW, 1, EIGEN_AVX_B_LOAD_SETS * 1,
+                                       EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<1, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, false, false>(zmm, &C_arr[i + j * LDC], LDC);
+      }
+    }
+    if (M - i >= 4) {  // Note: this block assumes EIGEN_AVX_MAX_NUM_ROW = 8. Should be removed otherwise
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 4>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, 4, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, 4, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB,
+                                                                                                          LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, 4>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<1, 4>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, false>(zmm, &C_arr[i + j * LDC], LDC, 4);
+      }
+      i += 4;
+    }
+    if (M - i >= 2) {
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 2>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, 2, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, 2, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB,
+                                                                                                          LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, 2>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<1, 2>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, false>(zmm, &C_arr[i + j * LDC], LDC, 2);
+      }
+      i += 2;
+    }
+    if (M - i > 0) {
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 1>(zmm);
+      {
+        for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+          urolls::template microKernel<isARowMajor, 1, 1, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD, 1>(B_t, A_t, LDB,
+                                                                                                          LDA, zmm);
+          B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+          else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+        }
+        EIGEN_IF_CONSTEXPR(handleKRem) {
+          for (int64_t k = K_; k < K; k++) {
+            urolls::template microKernel<isARowMajor, 1, 1, 1, EIGEN_AVX_B_LOAD_SETS * 1, 1>(B_t, A_t, LDB, LDA, zmm);
+            B_t += LDB;
+            EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+            else A_t += LDA;
+          }
+        }
+        EIGEN_IF_CONSTEXPR(isCRowMajor) {
+          urolls::template updateC<1, 1>(&C_arr[i * LDC + j], LDC, zmm);
+          urolls::template storeC<1, 1>(&C_arr[i * LDC + j], LDC, zmm);
+        }
+        else {
+          transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, false>(zmm, &C_arr[i + j * LDC], LDC, 1);
+        }
+      }
+    }
+    j += U1;
+  }
+  if (N - j > 0) {
+    constexpr int64_t EIGEN_AVX_MAX_B_LOAD = EIGEN_AVX_B_LOAD_SETS * 1;
+    int64_t i = 0;
+    for (; i < M_; i += EIGEN_AVX_MAX_NUM_ROW) {
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, EIGEN_AVX_MAX_NUM_ROW>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, EIGEN_AVX_MAX_NUM_ROW, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST, true>(B_t, A_t, LDB, LDA, zmm, N - j);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, EIGEN_AVX_MAX_NUM_ROW, 1, EIGEN_AVX_MAX_B_LOAD,
+                                       EIGEN_AVX_MAX_A_BCAST, true>(B_t, A_t, LDB, LDA, zmm, N - j);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, EIGEN_AVX_MAX_NUM_ROW, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+        urolls::template storeC<1, EIGEN_AVX_MAX_NUM_ROW, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, false, true>(zmm, &C_arr[i + j * LDC], LDC, 0, N - j);
+      }
+    }
+    if (M - i >= 4) {  // Note: this block assumes EIGEN_AVX_MAX_NUM_ROW = 8. Should be removed otherwise
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 4>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, 4, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST, true>(B_t, A_t, LDB, LDA, zmm, N - j);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, 4, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST, true>(
+              B_t, A_t, LDB, LDA, zmm, N - j);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, 4, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+        urolls::template storeC<1, 4, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, true>(zmm, &C_arr[i + j * LDC], LDC, 4, N - j);
+      }
+      i += 4;
+    }
+    if (M - i >= 2) {
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 2>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, 2, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST, true>(B_t, A_t, LDB, LDA, zmm, N - j);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, 2, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST, true>(
+              B_t, A_t, LDB, LDA, zmm, N - j);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, 2, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+        urolls::template storeC<1, 2, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, true>(zmm, &C_arr[i + j * LDC], LDC, 2, N - j);
+      }
+      i += 2;
+    }
+    if (M - i > 0) {
+      Scalar* A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar* B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 1>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, 1, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD, 1, true>(
+            B_t, A_t, LDB, LDA, zmm, N - j);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, 1, 1, EIGEN_AVX_MAX_B_LOAD, 1, true>(B_t, A_t, LDB, LDA, zmm,
+                                                                                            N - j);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, 1, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+        urolls::template storeC<1, 1, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, true>(zmm, &C_arr[i + j * LDC], LDC, 1, N - j);
+      }
+    }
+  }
+}
+
+/**
+ * Triangular solve kernel with A on left with K number of rhs. dim(A) = unrollM
+ *
+ * unrollM: dimension of A matrix (triangular matrix). unrollM should be <= EIGEN_AVX_MAX_NUM_ROW
+ * isFWDSolve: is forward solve?
+ * isUnitDiag: is the diagonal of A all ones?
+ * The B matrix (RHS) is assumed to be row-major
+ */
+template <typename Scalar, typename vec, int64_t unrollM, bool isARowMajor, bool isFWDSolve, bool isUnitDiag>
+EIGEN_ALWAYS_INLINE void triSolveKernel(Scalar* A_arr, Scalar* B_arr, int64_t K, int64_t LDA, int64_t LDB) {
+  static_assert(unrollM <= EIGEN_AVX_MAX_NUM_ROW, "unrollM should be equal to EIGEN_AVX_MAX_NUM_ROW");
+  using urolls = unrolls::trsm<Scalar>;
+  constexpr int64_t U3 = urolls::PacketSize * 3;
+  constexpr int64_t U2 = urolls::PacketSize * 2;
+  constexpr int64_t U1 = urolls::PacketSize * 1;
+
+  PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> RHSInPacket;
+  PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> AInPacket;
+
+  int64_t k = 0;
+  while (K - k >= U3) {
+    urolls::template loadRHS<isFWDSolve, unrollM, 3>(B_arr + k, LDB, RHSInPacket);
+    urolls::template triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, unrollM, 3>(A_arr, LDA, RHSInPacket,
+                                                                                          AInPacket);
+    urolls::template storeRHS<isFWDSolve, unrollM, 3>(B_arr + k, LDB, RHSInPacket);
+    k += U3;
+  }
+  if (K - k >= U2) {
+    urolls::template loadRHS<isFWDSolve, unrollM, 2>(B_arr + k, LDB, RHSInPacket);
+    urolls::template triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, unrollM, 2>(A_arr, LDA, RHSInPacket,
+                                                                                          AInPacket);
+    urolls::template storeRHS<isFWDSolve, unrollM, 2>(B_arr + k, LDB, RHSInPacket);
+    k += U2;
+  }
+  if (K - k >= U1) {
+    urolls::template loadRHS<isFWDSolve, unrollM, 1>(B_arr + k, LDB, RHSInPacket);
+    urolls::template triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, unrollM, 1>(A_arr, LDA, RHSInPacket,
+                                                                                          AInPacket);
+    urolls::template storeRHS<isFWDSolve, unrollM, 1>(B_arr + k, LDB, RHSInPacket);
+    k += U1;
+  }
+  if (K - k > 0) {
+    // Handle remaining number of RHS
+    urolls::template loadRHS<isFWDSolve, unrollM, 1, true>(B_arr + k, LDB, RHSInPacket, K - k);
+    urolls::template triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, unrollM, 1>(A_arr, LDA, RHSInPacket,
+                                                                                          AInPacket);
+    urolls::template storeRHS<isFWDSolve, unrollM, 1, true>(B_arr + k, LDB, RHSInPacket, K - k);
+  }
+}
+
+/**
+ * Triangular solve routine with A on left and dimension of at most L with K number of rhs. This is essentially
+ * a wrapper for triSolveMicrokernel for M = {1,2,3,4,5,6,7,8}.
+ *
+ * isFWDSolve: is forward solve?
+ * isUnitDiag: is the diagonal of A all ones?
+ * The B matrix (RHS) is assumed to be row-major
+ */
+template <typename Scalar, bool isARowMajor, bool isFWDSolve, bool isUnitDiag>
+void triSolveKernelLxK(Scalar* A_arr, Scalar* B_arr, int64_t M, int64_t K, int64_t LDA, int64_t LDB) {
+  // Note: this assumes EIGEN_AVX_MAX_NUM_ROW = 8. Unrolls should be adjusted
+  // accordingly if EIGEN_AVX_MAX_NUM_ROW is smaller.
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  if (M == 8)
+    triSolveKernel<Scalar, vec, 8, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 7)
+    triSolveKernel<Scalar, vec, 7, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 6)
+    triSolveKernel<Scalar, vec, 6, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 5)
+    triSolveKernel<Scalar, vec, 5, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 4)
+    triSolveKernel<Scalar, vec, 4, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 3)
+    triSolveKernel<Scalar, vec, 3, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 2)
+    triSolveKernel<Scalar, vec, 2, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 1)
+    triSolveKernel<Scalar, vec, 1, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  return;
+}
+
+/**
+ * This routine is used to copy B to/from a temporary array (row-major) for cases where B is column-major.
+ *
+ * toTemp: true => copy to temporary array, false => copy from temporary array
+ * remM: true = need to handle remainder values for M (M < EIGEN_AVX_MAX_NUM_ROW)
+ *
+ */
+template <typename Scalar, bool toTemp = true, bool remM = false>
+EIGEN_ALWAYS_INLINE void copyBToRowMajor(Scalar* B_arr, int64_t LDB, int64_t K, Scalar* B_temp, int64_t LDB_,
+                                         int64_t remM_ = 0) {
+  EIGEN_UNUSED_VARIABLE(remM_);
+  using urolls = unrolls::transB<Scalar>;
+  using vecHalf = typename std::conditional<std::is_same<Scalar, float>::value, vecHalfFloat, vecFullDouble>::type;
+  PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> ymm;
+  constexpr int64_t U3 = urolls::PacketSize * 3;
+  constexpr int64_t U2 = urolls::PacketSize * 2;
+  constexpr int64_t U1 = urolls::PacketSize * 1;
+  int64_t K_ = K / U3 * U3;
+  int64_t k = 0;
+
+  for (; k < K_; k += U3) {
+    urolls::template transB_kernel<U3, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+    B_temp += U3;
+  }
+  if (K - k >= U2) {
+    urolls::template transB_kernel<U2, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+    B_temp += U2;
+    k += U2;
+  }
+  if (K - k >= U1) {
+    urolls::template transB_kernel<U1, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+    B_temp += U1;
+    k += U1;
+  }
+  EIGEN_IF_CONSTEXPR(U1 > 8) {
+    // Note: without "if constexpr" this section of code will also be
+    // parsed by the compiler so there is an additional check in {load/store}BBlock
+    // to make sure the counter is not non-negative.
+    if (K - k >= 8) {
+      urolls::template transB_kernel<8, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+      B_temp += 8;
+      k += 8;
+    }
+  }
+  EIGEN_IF_CONSTEXPR(U1 > 4) {
+    // Note: without "if constexpr" this section of code will also be
+    // parsed by the compiler so there is an additional check in {load/store}BBlock
+    // to make sure the counter is not non-negative.
+    if (K - k >= 4) {
+      urolls::template transB_kernel<4, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+      B_temp += 4;
+      k += 4;
+    }
+  }
+  if (K - k >= 2) {
+    urolls::template transB_kernel<2, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+    B_temp += 2;
+    k += 2;
+  }
+  if (K - k >= 1) {
+    urolls::template transB_kernel<1, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+    B_temp += 1;
+    k += 1;
+  }
+}
+
+/**
+ * Main triangular solve driver
+ *
+ * Triangular solve with A on the left.
+ * Scalar:    Scalar precision, only float/double is supported.
+ * isARowMajor:  is A row-major?
+ * isBRowMajor:  is B row-major?
+ * isFWDSolve:   is this forward solve or backward (true => forward)?
+ * isUnitDiag: is diagonal of A unit or nonunit (true => A has unit diagonal)?
+ *
+ * M: dimension of A
+ * numRHS: number of right hand sides (coincides with K dimension for gemm updates)
+ *
+ * Here are the mapping between the different TRSM cases (col-major) and triSolve:
+ *
+ * LLN (left , lower, A non-transposed) ::  isARowMajor=false, isBRowMajor=false, isFWDSolve=true
+ * LUT (left , upper, A transposed)     ::  isARowMajor=true,  isBRowMajor=false, isFWDSolve=true
+ * LUN (left , upper, A non-transposed) ::  isARowMajor=false, isBRowMajor=false, isFWDSolve=false
+ * LLT (left , lower, A transposed)     ::  isARowMajor=true,  isBRowMajor=false, isFWDSolve=false
+ * RUN (right, upper, A non-transposed) ::  isARowMajor=true,  isBRowMajor=true,  isFWDSolve=true
+ * RLT (right, lower, A transposed)     ::  isARowMajor=false, isBRowMajor=true,  isFWDSolve=true
+ * RUT (right, upper, A transposed)     ::  isARowMajor=false, isBRowMajor=true,  isFWDSolve=false
+ * RLN (right, lower, A non-transposed) ::  isARowMajor=true,  isBRowMajor=true,  isFWDSolve=false
+ *
+ * Note: For RXX cases M,numRHS should be swapped.
+ *
+ */
+template <typename Scalar, bool isARowMajor = true, bool isBRowMajor = true, bool isFWDSolve = true,
+          bool isUnitDiag = false>
+void triSolve(Scalar* A_arr, Scalar* B_arr, int64_t M, int64_t numRHS, int64_t LDA, int64_t LDB) {
+  constexpr int64_t psize = packet_traits<Scalar>::size;
+  /**
+   * The values for kB, numM were determined experimentally.
+   * kB: Number of RHS we process at a time.
+   * numM: number of rows of B we will store in a temporary array (see below.) This should be a multiple of L.
+   *
+   * kB was determined by initially setting kB = numRHS and benchmarking triSolve (TRSM-RUN case)
+   * performance with M=numRHS.
+   * It was observed that performance started to drop around M=numRHS=240. This is likely machine dependent.
+   *
+   * numM was chosen "arbitrarily". It should be relatively small so B_temp is not too large, but it should be
+   * large enough to allow GEMM updates to have larger "K"s (see below.) No benchmarking has been done so far to
+   * determine optimal values for numM.
+   */
+  constexpr int64_t kB = (3 * psize) * 5;  // 5*U3
+  constexpr int64_t numM = 8 * EIGEN_AVX_MAX_NUM_ROW;
+
+  int64_t sizeBTemp = 0;
+  Scalar* B_temp = NULL;
+  EIGEN_IF_CONSTEXPR(!isBRowMajor) {
+    /**
+     * If B is col-major, we copy it to a fixed-size temporary array of size at most ~numM*kB and
+     * transpose it to row-major. Call the solve routine, and copy+transpose it back to the original array.
+     * The updated row-major copy of B is reused in the GEMM updates.
+     */
+    sizeBTemp = (((std::min(kB, numRHS) + psize - 1) / psize + 4) * psize) * numM;
+  }
+
+  EIGEN_IF_CONSTEXPR(!isBRowMajor) B_temp = (Scalar*)handmade_aligned_malloc(sizeof(Scalar) * sizeBTemp, 64);
+
+  for (int64_t k = 0; k < numRHS; k += kB) {
+    int64_t bK = numRHS - k > kB ? kB : numRHS - k;
+    int64_t M_ = (M / EIGEN_AVX_MAX_NUM_ROW) * EIGEN_AVX_MAX_NUM_ROW, gemmOff = 0;
+
+    // bK rounded up to next multiple of L=EIGEN_AVX_MAX_NUM_ROW. When B_temp is used, we solve for bkL RHS
+    // instead of bK RHS in triSolveKernelLxK.
+    int64_t bkL = ((bK + (EIGEN_AVX_MAX_NUM_ROW - 1)) / EIGEN_AVX_MAX_NUM_ROW) * EIGEN_AVX_MAX_NUM_ROW;
+    const int64_t numScalarPerCache = 64 / sizeof(Scalar);
+    // Leading dimension of B_temp, will be a multiple of the cache line size.
+    int64_t LDT = ((bkL + (numScalarPerCache - 1)) / numScalarPerCache) * numScalarPerCache;
+    int64_t offsetBTemp = 0;
+    for (int64_t i = 0; i < M_; i += EIGEN_AVX_MAX_NUM_ROW) {
+      EIGEN_IF_CONSTEXPR(!isBRowMajor) {
+        int64_t indA_i = isFWDSolve ? i : M - 1 - i;
+        int64_t indB_i = isFWDSolve ? i : M - (i + EIGEN_AVX_MAX_NUM_ROW);
+        int64_t offB_1 = isFWDSolve ? offsetBTemp : sizeBTemp - EIGEN_AVX_MAX_NUM_ROW * LDT - offsetBTemp;
+        int64_t offB_2 = isFWDSolve ? offsetBTemp : sizeBTemp - LDT - offsetBTemp;
+        // Copy values from B to B_temp.
+        copyBToRowMajor<Scalar, true, false>(B_arr + indB_i + k * LDB, LDB, bK, B_temp + offB_1, LDT);
+        // Triangular solve with a small block of A and long horizontal blocks of B (or B_temp if B col-major)
+        triSolveKernelLxK<Scalar, isARowMajor, isFWDSolve, isUnitDiag>(
+            &A_arr[idA<isARowMajor>(indA_i, indA_i, LDA)], B_temp + offB_2, EIGEN_AVX_MAX_NUM_ROW, bkL, LDA, LDT);
+        // Copy values from B_temp back to B. B_temp will be reused in gemm call below.
+        copyBToRowMajor<Scalar, false, false>(B_arr + indB_i + k * LDB, LDB, bK, B_temp + offB_1, LDT);
+
+        offsetBTemp += EIGEN_AVX_MAX_NUM_ROW * LDT;
+      }
+      else {
+        int64_t ind = isFWDSolve ? i : M - 1 - i;
+        triSolveKernelLxK<Scalar, isARowMajor, isFWDSolve, isUnitDiag>(
+            &A_arr[idA<isARowMajor>(ind, ind, LDA)], B_arr + k + ind * LDB, EIGEN_AVX_MAX_NUM_ROW, bK, LDA, LDB);
+      }
+      if (i + EIGEN_AVX_MAX_NUM_ROW < M_) {
+        /**
+         * For the GEMM updates, we want "K" (K=i+8 in this case) to be large as soon as possible
+         * to reuse the accumulators in GEMM as much as possible. So we only update 8xbK blocks of
+         * B as follows:
+         *
+         *        A             B
+         *     __
+         *    |__|__           |__|
+         *    |__|__|__        |__|
+         *    |__|__|__|__     |__|
+         *    |********|__|    |**|
+         */
+        EIGEN_IF_CONSTEXPR(isBRowMajor) {
+          int64_t indA_i = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : M - (i + 2 * EIGEN_AVX_MAX_NUM_ROW);
+          int64_t indA_j = isFWDSolve ? 0 : M - (i + EIGEN_AVX_MAX_NUM_ROW);
+          int64_t indB_i = isFWDSolve ? 0 : M - (i + EIGEN_AVX_MAX_NUM_ROW);
+          int64_t indB_i2 = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : M - (i + 2 * EIGEN_AVX_MAX_NUM_ROW);
+          gemmKernel<Scalar, isARowMajor, isBRowMajor, false, false>(
+              &A_arr[idA<isARowMajor>(indA_i, indA_j, LDA)], B_arr + k + indB_i * LDB, B_arr + k + indB_i2 * LDB,
+              EIGEN_AVX_MAX_NUM_ROW, bK, i + EIGEN_AVX_MAX_NUM_ROW, LDA, LDB, LDB);
+        }
+        else {
+          if (offsetBTemp + EIGEN_AVX_MAX_NUM_ROW * LDT > sizeBTemp) {
+            /**
+             * Similar idea as mentioned above, but here we are limited by the number of updated values of B
+             * that can be stored (row-major) in B_temp.
+             *
+             * If there is not enough space to store the next batch of 8xbK of B in B_temp, we call GEMM
+             * update and partially update the remaining old values of B which depends on the new values
+             * of B stored in B_temp. These values are then no longer needed and can be overwritten.
+             */
+            int64_t indA_i = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : 0;
+            int64_t indA_j = isFWDSolve ? gemmOff : M - (i + EIGEN_AVX_MAX_NUM_ROW);
+            int64_t indB_i = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : 0;
+            int64_t offB_1 = isFWDSolve ? 0 : sizeBTemp - offsetBTemp;
+            gemmKernel<Scalar, isARowMajor, isBRowMajor, false, false>(
+                &A_arr[idA<isARowMajor>(indA_i, indA_j, LDA)], B_temp + offB_1, B_arr + indB_i + (k)*LDB,
+                M - (i + EIGEN_AVX_MAX_NUM_ROW), bK, i + EIGEN_AVX_MAX_NUM_ROW - gemmOff, LDA, LDT, LDB);
+            offsetBTemp = 0;
+            gemmOff = i + EIGEN_AVX_MAX_NUM_ROW;
+          } else {
+            /**
+             * If there is enough space in B_temp, we only update the next 8xbK values of B.
+             */
+            int64_t indA_i = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : M - (i + 2 * EIGEN_AVX_MAX_NUM_ROW);
+            int64_t indA_j = isFWDSolve ? gemmOff : M - (i + EIGEN_AVX_MAX_NUM_ROW);
+            int64_t indB_i = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : M - (i + 2 * EIGEN_AVX_MAX_NUM_ROW);
+            int64_t offB_1 = isFWDSolve ? 0 : sizeBTemp - offsetBTemp;
+            gemmKernel<Scalar, isARowMajor, isBRowMajor, false, false>(
+                &A_arr[idA<isARowMajor>(indA_i, indA_j, LDA)], B_temp + offB_1, B_arr + indB_i + (k)*LDB,
+                EIGEN_AVX_MAX_NUM_ROW, bK, i + EIGEN_AVX_MAX_NUM_ROW - gemmOff, LDA, LDT, LDB);
+          }
+        }
+      }
+    }
+    // Handle M remainder..
+    int64_t bM = M - M_;
+    if (bM > 0) {
+      if (M_ > 0) {
+        EIGEN_IF_CONSTEXPR(isBRowMajor) {
+          int64_t indA_i = isFWDSolve ? M_ : 0;
+          int64_t indA_j = isFWDSolve ? 0 : bM;
+          int64_t indB_i = isFWDSolve ? 0 : bM;
+          int64_t indB_i2 = isFWDSolve ? M_ : 0;
+          gemmKernel<Scalar, isARowMajor, isBRowMajor, false, false>(
+              &A_arr[idA<isARowMajor>(indA_i, indA_j, LDA)], B_arr + k + indB_i * LDB, B_arr + k + indB_i2 * LDB, bM,
+              bK, M_, LDA, LDB, LDB);
+        }
+        else {
+          int64_t indA_i = isFWDSolve ? M_ : 0;
+          int64_t indA_j = isFWDSolve ? gemmOff : bM;
+          int64_t indB_i = isFWDSolve ? M_ : 0;
+          int64_t offB_1 = isFWDSolve ? 0 : sizeBTemp - offsetBTemp;
+          gemmKernel<Scalar, isARowMajor, isBRowMajor, false, false>(&A_arr[idA<isARowMajor>(indA_i, indA_j, LDA)],
+                                                                     B_temp + offB_1, B_arr + indB_i + (k)*LDB, bM, bK,
+                                                                     M_ - gemmOff, LDA, LDT, LDB);
+        }
+      }
+      EIGEN_IF_CONSTEXPR(!isBRowMajor) {
+        int64_t indA_i = isFWDSolve ? M_ : M - 1 - M_;
+        int64_t indB_i = isFWDSolve ? M_ : 0;
+        int64_t offB_1 = isFWDSolve ? 0 : (bM - 1) * bkL;
+        copyBToRowMajor<Scalar, true, true>(B_arr + indB_i + k * LDB, LDB, bK, B_temp, bkL, bM);
+        triSolveKernelLxK<Scalar, isARowMajor, isFWDSolve, isUnitDiag>(&A_arr[idA<isARowMajor>(indA_i, indA_i, LDA)],
+                                                                       B_temp + offB_1, bM, bkL, LDA, bkL);
+        copyBToRowMajor<Scalar, false, true>(B_arr + indB_i + k * LDB, LDB, bK, B_temp, bkL, bM);
+      }
+      else {
+        int64_t ind = isFWDSolve ? M_ : M - 1 - M_;
+        triSolveKernelLxK<Scalar, isARowMajor, isFWDSolve, isUnitDiag>(&A_arr[idA<isARowMajor>(ind, ind, LDA)],
+                                                                       B_arr + k + ind * LDB, bM, bK, LDA, LDB);
+      }
+    }
+  }
+
+  EIGEN_IF_CONSTEXPR(!isBRowMajor) handmade_aligned_free(B_temp);
+}
+
+// Template specializations of trsmKernelL/R for float/double and inner strides of 1.
+#if (EIGEN_USE_AVX512_TRSM_R_KERNELS)
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride,
+          bool Specialized>
+struct trsmKernelR;
+
+template <typename Index, int Mode, int TriStorageOrder>
+struct trsmKernelR<float, Index, Mode, false, TriStorageOrder, 1, true> {
+  static void kernel(Index size, Index otherSize, const float* _tri, Index triStride, float* _other, Index otherIncr,
+                     Index otherStride);
+};
+
+template <typename Index, int Mode, int TriStorageOrder>
+struct trsmKernelR<double, Index, Mode, false, TriStorageOrder, 1, true> {
+  static void kernel(Index size, Index otherSize, const double* _tri, Index triStride, double* _other, Index otherIncr,
+                     Index otherStride);
+};
+
+template <typename Index, int Mode, int TriStorageOrder>
+EIGEN_DONT_INLINE void trsmKernelR<float, Index, Mode, false, TriStorageOrder, 1, true>::kernel(
+    Index size, Index otherSize, const float* _tri, Index triStride, float* _other, Index otherIncr,
+    Index otherStride) {
+  EIGEN_UNUSED_VARIABLE(otherIncr);
+#ifdef EIGEN_RUNTIME_NO_MALLOC
+  if (!is_malloc_allowed()) {
+    trsmKernelR<float, Index, Mode, false, TriStorageOrder, 1, /*Specialized=*/false>::kernel(
+        size, otherSize, _tri, triStride, _other, otherIncr, otherStride);
+    return;
+  }
+#endif
+  triSolve<float, TriStorageOrder != RowMajor, true, (Mode & Lower) != Lower, (Mode & UnitDiag) != 0>(
+      const_cast<float*>(_tri), _other, size, otherSize, triStride, otherStride);
+}
+
+template <typename Index, int Mode, int TriStorageOrder>
+EIGEN_DONT_INLINE void trsmKernelR<double, Index, Mode, false, TriStorageOrder, 1, true>::kernel(
+    Index size, Index otherSize, const double* _tri, Index triStride, double* _other, Index otherIncr,
+    Index otherStride) {
+  EIGEN_UNUSED_VARIABLE(otherIncr);
+#ifdef EIGEN_RUNTIME_NO_MALLOC
+  if (!is_malloc_allowed()) {
+    trsmKernelR<double, Index, Mode, false, TriStorageOrder, 1, /*Specialized=*/false>::kernel(
+        size, otherSize, _tri, triStride, _other, otherIncr, otherStride);
+    return;
+  }
+#endif
+  triSolve<double, TriStorageOrder != RowMajor, true, (Mode & Lower) != Lower, (Mode & UnitDiag) != 0>(
+      const_cast<double*>(_tri), _other, size, otherSize, triStride, otherStride);
+}
+#endif  // (EIGEN_USE_AVX512_TRSM_R_KERNELS)
+
+// These trsm kernels require temporary memory allocation
+#if (EIGEN_USE_AVX512_TRSM_L_KERNELS)
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride,
+          bool Specialized = true>
+struct trsmKernelL;
+
+template <typename Index, int Mode, int TriStorageOrder>
+struct trsmKernelL<float, Index, Mode, false, TriStorageOrder, 1, true> {
+  static void kernel(Index size, Index otherSize, const float* _tri, Index triStride, float* _other, Index otherIncr,
+                     Index otherStride);
+};
+
+template <typename Index, int Mode, int TriStorageOrder>
+struct trsmKernelL<double, Index, Mode, false, TriStorageOrder, 1, true> {
+  static void kernel(Index size, Index otherSize, const double* _tri, Index triStride, double* _other, Index otherIncr,
+                     Index otherStride);
+};
+
+template <typename Index, int Mode, int TriStorageOrder>
+EIGEN_DONT_INLINE void trsmKernelL<float, Index, Mode, false, TriStorageOrder, 1, true>::kernel(
+    Index size, Index otherSize, const float* _tri, Index triStride, float* _other, Index otherIncr,
+    Index otherStride) {
+  EIGEN_UNUSED_VARIABLE(otherIncr);
+#ifdef EIGEN_RUNTIME_NO_MALLOC
+  if (!is_malloc_allowed()) {
+    trsmKernelL<float, Index, Mode, false, TriStorageOrder, 1, /*Specialized=*/false>::kernel(
+        size, otherSize, _tri, triStride, _other, otherIncr, otherStride);
+    return;
+  }
+#endif
+  triSolve<float, TriStorageOrder == RowMajor, false, (Mode & Lower) == Lower, (Mode & UnitDiag) != 0>(
+      const_cast<float*>(_tri), _other, size, otherSize, triStride, otherStride);
+}
+
+template <typename Index, int Mode, int TriStorageOrder>
+EIGEN_DONT_INLINE void trsmKernelL<double, Index, Mode, false, TriStorageOrder, 1, true>::kernel(
+    Index size, Index otherSize, const double* _tri, Index triStride, double* _other, Index otherIncr,
+    Index otherStride) {
+  EIGEN_UNUSED_VARIABLE(otherIncr);
+#ifdef EIGEN_RUNTIME_NO_MALLOC
+  if (!is_malloc_allowed()) {
+    trsmKernelL<double, Index, Mode, false, TriStorageOrder, 1, /*Specialized=*/false>::kernel(
+        size, otherSize, _tri, triStride, _other, otherIncr, otherStride);
+    return;
+  }
+#endif
+  triSolve<double, TriStorageOrder == RowMajor, false, (Mode & Lower) == Lower, (Mode & UnitDiag) != 0>(
+      const_cast<double*>(_tri), _other, size, otherSize, triStride, otherStride);
+}
+#endif  // EIGEN_USE_AVX512_TRSM_L_KERNELS
+
+#endif  // EIGEN_USE_AVX512_TRSM_KERNELS
+
+}  // namespace internal
+}  // namespace Eigen
+#endif  // EIGEN_CORE_ARCH_AVX512_TRSM_KERNEL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TrsmUnrolls.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TrsmUnrolls.inc
new file mode 100644
index 0000000..3a5f68e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TrsmUnrolls.inc
@@ -0,0 +1,1219 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022 Intel Corporation
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_ARCH_AVX512_TRSM_UNROLLS_H
+#define EIGEN_CORE_ARCH_AVX512_TRSM_UNROLLS_H
+
+template <bool isARowMajor = true>
+EIGEN_ALWAYS_INLINE int64_t idA(int64_t i, int64_t j, int64_t LDA) {
+  EIGEN_IF_CONSTEXPR(isARowMajor) return i * LDA + j;
+  else return i + j * LDA;
+}
+
+/**
+ * This namespace contains various classes used to generate compile-time unrolls which are
+ * used throughout the trsm/gemm kernels. The unrolls are characterized as for-loops (1-D), nested
+ * for-loops (2-D), or triple nested for-loops (3-D). Unrolls are generated using template recursion
+ *
+ * Example, the 2-D for-loop is unrolled recursively by first flattening to a 1-D loop.
+ *
+ * for(startI = 0; startI < endI; startI++)             for(startC = 0; startC < endI*endJ; startC++)
+ *   for(startJ = 0; startJ < endJ; startJ++)  ---->      startI = (startC)/(endJ)
+ *     func(startI,startJ)                                startJ = (startC)%(endJ)
+ *                                                        func(...)
+ *
+ * The 1-D loop can be unrolled recursively by using enable_if and defining an auxiliary function
+ * with a template parameter used as a counter.
+ *
+ * template <endI, endJ, counter>
+ * std::enable_if_t<(counter <= 0)>  <---- tail case.
+ * aux_func {}
+ *
+ * template <endI, endJ, counter>
+ * std::enable_if_t<(counter > 0)>   <---- actual for-loop
+ * aux_func {
+ *   startC = endI*endJ - counter
+ *   startI = (startC)/(endJ)
+ *   startJ = (startC)%(endJ)
+ *   func(startI, startJ)
+ *   aux_func<endI, endJ, counter-1>()
+ * }
+ *
+ * Note: Additional wrapper functions are provided for aux_func which hides the counter template
+ * parameter since counter usually depends on endI, endJ, etc...
+ *
+ * Conventions:
+ * 1) endX: specifies the terminal value for the for-loop, (ex: for(startX = 0; startX < endX; startX++))
+ *
+ * 2) rem, remM, remK template parameters are used for deciding whether to use masked operations for
+ *    handling remaining tails (when sizes are not multiples of PacketSize or EIGEN_AVX_MAX_NUM_ROW)
+ */
+namespace unrolls {
+
+template <int64_t N>
+EIGEN_ALWAYS_INLINE auto remMask(int64_t m) {
+  EIGEN_IF_CONSTEXPR(N == 16) { return 0xFFFF >> (16 - m); }
+  else EIGEN_IF_CONSTEXPR(N == 8) {
+    return 0xFF >> (8 - m);
+  }
+  else EIGEN_IF_CONSTEXPR(N == 4) {
+    return 0x0F >> (4 - m);
+  }
+  return 0;
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void trans8x8blocks(PacketBlock<Packet, 8> &kernel);
+
+template <>
+EIGEN_ALWAYS_INLINE void trans8x8blocks(PacketBlock<Packet16f, 8> &kernel) {
+  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T4 = _mm512_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
+  __m512 T5 = _mm512_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
+  __m512 T6 = _mm512_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
+  __m512 T7 = _mm512_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
+
+  kernel.packet[0] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T0), _mm512_castps_pd(T2)));
+  kernel.packet[1] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T0), _mm512_castps_pd(T2)));
+  kernel.packet[2] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T1), _mm512_castps_pd(T3)));
+  kernel.packet[3] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T1), _mm512_castps_pd(T3)));
+  kernel.packet[4] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T4), _mm512_castps_pd(T6)));
+  kernel.packet[5] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T4), _mm512_castps_pd(T6)));
+  kernel.packet[6] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T5), _mm512_castps_pd(T7)));
+  kernel.packet[7] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T5), _mm512_castps_pd(T7)));
+
+  T0 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[4]), 0x4E));
+  T0 = _mm512_mask_blend_ps(0xF0F0, kernel.packet[0], T0);
+  T4 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[0]), 0x4E));
+  T4 = _mm512_mask_blend_ps(0xF0F0, T4, kernel.packet[4]);
+  T1 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[5]), 0x4E));
+  T1 = _mm512_mask_blend_ps(0xF0F0, kernel.packet[1], T1);
+  T5 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[1]), 0x4E));
+  T5 = _mm512_mask_blend_ps(0xF0F0, T5, kernel.packet[5]);
+  T2 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[6]), 0x4E));
+  T2 = _mm512_mask_blend_ps(0xF0F0, kernel.packet[2], T2);
+  T6 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[2]), 0x4E));
+  T6 = _mm512_mask_blend_ps(0xF0F0, T6, kernel.packet[6]);
+  T3 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[7]), 0x4E));
+  T3 = _mm512_mask_blend_ps(0xF0F0, kernel.packet[3], T3);
+  T7 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[3]), 0x4E));
+  T7 = _mm512_mask_blend_ps(0xF0F0, T7, kernel.packet[7]);
+
+  kernel.packet[0] = T0;
+  kernel.packet[1] = T1;
+  kernel.packet[2] = T2;
+  kernel.packet[3] = T3;
+  kernel.packet[4] = T4;
+  kernel.packet[5] = T5;
+  kernel.packet[6] = T6;
+  kernel.packet[7] = T7;
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void trans8x8blocks(PacketBlock<Packet8d, 8> &kernel) {
+  ptranspose(kernel);
+}
+
+/***
+ * Unrolls for transposed C stores
+ */
+template <typename Scalar>
+class trans {
+ public:
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  using vecHalf = typename std::conditional<std::is_same<Scalar, float>::value, vecHalfFloat, vecFullDouble>::type;
+  static constexpr int64_t PacketSize = packet_traits<Scalar>::size;
+
+  /***********************************
+   * Auxiliary Functions for:
+   *  - storeC
+   ***********************************
+   */
+
+  /**
+   * aux_storeC
+   *
+   * 1-D unroll
+   *      for(startN = 0; startN < endN; startN++)
+   *
+   * (endN <= PacketSize) is required to handle the fp32 case, see comments in transStoreC
+   *
+   **/
+  template <int64_t endN, int64_t counter, int64_t unrollN, int64_t packetIndexOffset, bool remM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0 && endN <= PacketSize)> aux_storeC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t remM_ = 0) {
+    constexpr int64_t counterReverse = endN - counter;
+    constexpr int64_t startN = counterReverse;
+
+    EIGEN_IF_CONSTEXPR(startN < EIGEN_AVX_MAX_NUM_ROW) {
+      EIGEN_IF_CONSTEXPR(remM) {
+        pstoreu<Scalar>(
+            C_arr + LDC * startN,
+            padd(ploadu<vecHalf>((const Scalar *)C_arr + LDC * startN, remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_)),
+                 preinterpret<vecHalf>(zmm.packet[packetIndexOffset + (unrollN / PacketSize) * startN]),
+                 remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_)),
+            remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_));
+      }
+      else {
+        pstoreu<Scalar>(C_arr + LDC * startN,
+                        padd(ploadu<vecHalf>((const Scalar *)C_arr + LDC * startN),
+                             preinterpret<vecHalf>(zmm.packet[packetIndexOffset + (unrollN / PacketSize) * startN])));
+      }
+    }
+    else {  // This block is only needed for fp32 case
+      // Reinterpret as __m512 for _mm512_shuffle_f32x4
+      vecFullFloat zmm2vecFullFloat = preinterpret<vecFullFloat>(
+          zmm.packet[packetIndexOffset + (unrollN / PacketSize) * (startN - EIGEN_AVX_MAX_NUM_ROW)]);
+      // Swap lower and upper half of avx register.
+      zmm.packet[packetIndexOffset + (unrollN / PacketSize) * (startN - EIGEN_AVX_MAX_NUM_ROW)] =
+          preinterpret<vec>(_mm512_shuffle_f32x4(zmm2vecFullFloat, zmm2vecFullFloat, 0b01001110));
+
+      EIGEN_IF_CONSTEXPR(remM) {
+        pstoreu<Scalar>(
+            C_arr + LDC * startN,
+            padd(ploadu<vecHalf>((const Scalar *)C_arr + LDC * startN, remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_)),
+                 preinterpret<vecHalf>(
+                     zmm.packet[packetIndexOffset + (unrollN / PacketSize) * (startN - EIGEN_AVX_MAX_NUM_ROW)])),
+            remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_));
+      }
+      else {
+        pstoreu<Scalar>(
+            C_arr + LDC * startN,
+            padd(ploadu<vecHalf>((const Scalar *)C_arr + LDC * startN),
+                 preinterpret<vecHalf>(
+                     zmm.packet[packetIndexOffset + (unrollN / PacketSize) * (startN - EIGEN_AVX_MAX_NUM_ROW)])));
+      }
+    }
+    aux_storeC<endN, counter - 1, unrollN, packetIndexOffset, remM>(C_arr, LDC, zmm, remM_);
+  }
+
+  template <int64_t endN, int64_t counter, int64_t unrollN, int64_t packetIndexOffset, bool remM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<!(counter > 0 && endN <= PacketSize)> aux_storeC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t remM_ = 0) {
+    EIGEN_UNUSED_VARIABLE(C_arr);
+    EIGEN_UNUSED_VARIABLE(LDC);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(remM_);
+  }
+
+  template <int64_t endN, int64_t unrollN, int64_t packetIndexOffset, bool remM>
+  static EIGEN_ALWAYS_INLINE void storeC(Scalar *C_arr, int64_t LDC,
+                                         PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                         int64_t remM_ = 0) {
+    aux_storeC<endN, endN, unrollN, packetIndexOffset, remM>(C_arr, LDC, zmm, remM_);
+  }
+
+  /**
+   * Transposes LxunrollN row major block of matrices stored `EIGEN_AVX_MAX_NUM_ACC` zmm registers to
+   * "unrollN"xL ymm registers to be stored col-major into C.
+   *
+   *  For 8x48, the 8x48 block (row-major) is stored in zmm as follows:
+   *
+   *  ```
+   *  row0: zmm0 zmm1 zmm2
+   *  row1: zmm3 zmm4 zmm5
+   *    .
+   *    .
+   *  row7: zmm21 zmm22 zmm23
+   *
+   *  For 8x32, the 8x32 block (row-major) is stored in zmm as follows:
+   *
+   *  row0: zmm0 zmm1
+   *  row1: zmm2 zmm3
+   *    .
+   *    .
+   *  row7: zmm14 zmm15
+   * ```
+   *
+   * In general we will have {1,2,3} groups of avx registers each of size
+   * `EIGEN_AVX_MAX_NUM_ROW`. packetIndexOffset is used to select which "block" of
+   * avx registers are being transposed.
+   */
+  template <int64_t unrollN, int64_t packetIndexOffset>
+  static EIGEN_ALWAYS_INLINE void transpose(PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    // Note: this assumes EIGEN_AVX_MAX_NUM_ROW = 8. Unrolls should be adjusted
+    // accordingly if EIGEN_AVX_MAX_NUM_ROW is smaller.
+    constexpr int64_t zmmStride = unrollN / PacketSize;
+    PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> r;
+    r.packet[0] = zmm.packet[packetIndexOffset + zmmStride * 0];
+    r.packet[1] = zmm.packet[packetIndexOffset + zmmStride * 1];
+    r.packet[2] = zmm.packet[packetIndexOffset + zmmStride * 2];
+    r.packet[3] = zmm.packet[packetIndexOffset + zmmStride * 3];
+    r.packet[4] = zmm.packet[packetIndexOffset + zmmStride * 4];
+    r.packet[5] = zmm.packet[packetIndexOffset + zmmStride * 5];
+    r.packet[6] = zmm.packet[packetIndexOffset + zmmStride * 6];
+    r.packet[7] = zmm.packet[packetIndexOffset + zmmStride * 7];
+    trans8x8blocks(r);
+    zmm.packet[packetIndexOffset + zmmStride * 0] = r.packet[0];
+    zmm.packet[packetIndexOffset + zmmStride * 1] = r.packet[1];
+    zmm.packet[packetIndexOffset + zmmStride * 2] = r.packet[2];
+    zmm.packet[packetIndexOffset + zmmStride * 3] = r.packet[3];
+    zmm.packet[packetIndexOffset + zmmStride * 4] = r.packet[4];
+    zmm.packet[packetIndexOffset + zmmStride * 5] = r.packet[5];
+    zmm.packet[packetIndexOffset + zmmStride * 6] = r.packet[6];
+    zmm.packet[packetIndexOffset + zmmStride * 7] = r.packet[7];
+  }
+};
+
+/**
+ * Unrolls for copyBToRowMajor
+ *
+ * Idea:
+ *  1) Load a block of right-hand sides to registers (using loadB).
+ *  2) Convert the block from column-major to row-major (transposeLxL)
+ *  3) Store the blocks from register either to a temp array (toTemp == true), or back to B (toTemp == false).
+ *
+ *  We use at most EIGEN_AVX_MAX_NUM_ACC avx registers to store the blocks of B. The remaining registers are
+ *  used as temps for transposing.
+ *
+ *  Blocks will be of size Lx{U1,U2,U3}. packetIndexOffset is used to index between these subblocks
+ *  For fp32, PacketSize = 2*EIGEN_AVX_MAX_NUM_ROW, so we reinterpret packets as packets half the size (zmm -> ymm).
+ */
+template <typename Scalar>
+class transB {
+ public:
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  using vecHalf = typename std::conditional<std::is_same<Scalar, float>::value, vecHalfFloat, vecFullDouble>::type;
+  static constexpr int64_t PacketSize = packet_traits<Scalar>::size;
+
+  /***********************************
+   * Auxiliary Functions for:
+   *  - loadB
+   *  - storeB
+   *  - loadBBlock
+   *  - storeBBlock
+   ***********************************
+   */
+
+  /**
+   * aux_loadB
+   *
+   * 1-D unroll
+   *      for(startN = 0; startN < endN; startN++)
+   **/
+  template <int64_t endN, int64_t counter, int64_t packetIndexOffset, bool remM, int64_t remN_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_loadB(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+      int64_t remM_ = 0) {
+    constexpr int64_t counterReverse = endN - counter;
+    constexpr int64_t startN = counterReverse;
+
+    EIGEN_IF_CONSTEXPR(remM) {
+      ymm.packet[packetIndexOffset + startN] =
+          ploadu<vecHalf>((const Scalar *)&B_arr[startN * LDB], remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_));
+    }
+    else {
+      EIGEN_IF_CONSTEXPR(remN_ == 0) {
+        ymm.packet[packetIndexOffset + startN] = ploadu<vecHalf>((const Scalar *)&B_arr[startN * LDB]);
+      }
+      else ymm.packet[packetIndexOffset + startN] =
+          ploadu<vecHalf>((const Scalar *)&B_arr[startN * LDB], remMask<EIGEN_AVX_MAX_NUM_ROW>(remN_));
+    }
+
+    aux_loadB<endN, counter - 1, packetIndexOffset, remM, remN_>(B_arr, LDB, ymm, remM_);
+  }
+
+  template <int64_t endN, int64_t counter, int64_t packetIndexOffset, bool remM, int64_t remN_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_loadB(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+      int64_t remM_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(ymm);
+    EIGEN_UNUSED_VARIABLE(remM_);
+  }
+
+  /**
+   * aux_storeB
+   *
+   * 1-D unroll
+   *      for(startN = 0; startN < endN; startN++)
+   **/
+  template <int64_t endN, int64_t counter, int64_t packetIndexOffset, bool remK, bool remM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_storeB(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t rem_ = 0) {
+    constexpr int64_t counterReverse = endN - counter;
+    constexpr int64_t startN = counterReverse;
+
+    EIGEN_IF_CONSTEXPR(remK || remM) {
+      pstoreu<Scalar>(&B_arr[startN * LDB], ymm.packet[packetIndexOffset + startN],
+                      remMask<EIGEN_AVX_MAX_NUM_ROW>(rem_));
+    }
+    else {
+      pstoreu<Scalar>(&B_arr[startN * LDB], ymm.packet[packetIndexOffset + startN]);
+    }
+
+    aux_storeB<endN, counter - 1, packetIndexOffset, remK, remM>(B_arr, LDB, ymm, rem_);
+  }
+
+  template <int64_t endN, int64_t counter, int64_t packetIndexOffset, bool remK, bool remM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_storeB(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(ymm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /**
+   * aux_loadBBlock
+   *
+   * 1-D unroll
+   *      for(startN = 0; startN < endN; startN += EIGEN_AVX_MAX_NUM_ROW)
+   **/
+  template <int64_t endN, int64_t counter, bool toTemp, bool remM, int64_t remN_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_loadBBlock(
+      Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+      PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t remM_ = 0) {
+    constexpr int64_t counterReverse = endN - counter;
+    constexpr int64_t startN = counterReverse;
+    transB::template loadB<EIGEN_AVX_MAX_NUM_ROW, startN, false, (toTemp ? 0 : remN_)>(&B_temp[startN], LDB_, ymm);
+    aux_loadBBlock<endN, counter - EIGEN_AVX_MAX_NUM_ROW, toTemp, remM, remN_>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+  }
+
+  template <int64_t endN, int64_t counter, bool toTemp, bool remM, int64_t remN_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_loadBBlock(
+      Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+      PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t remM_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(B_temp);
+    EIGEN_UNUSED_VARIABLE(LDB_);
+    EIGEN_UNUSED_VARIABLE(ymm);
+    EIGEN_UNUSED_VARIABLE(remM_);
+  }
+
+  /**
+   * aux_storeBBlock
+   *
+   * 1-D unroll
+   *      for(startN = 0; startN < endN; startN += EIGEN_AVX_MAX_NUM_ROW)
+   **/
+  template <int64_t endN, int64_t counter, bool toTemp, bool remM, int64_t remK_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_storeBBlock(
+      Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+      PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t remM_ = 0) {
+    constexpr int64_t counterReverse = endN - counter;
+    constexpr int64_t startN = counterReverse;
+
+    EIGEN_IF_CONSTEXPR(toTemp) {
+      transB::template storeB<EIGEN_AVX_MAX_NUM_ROW, startN, remK_ != 0, false>(&B_temp[startN], LDB_, ymm, remK_);
+    }
+    else {
+      transB::template storeB<std::min(EIGEN_AVX_MAX_NUM_ROW, endN), startN, false, remM>(&B_arr[0 + startN * LDB], LDB,
+                                                                                          ymm, remM_);
+    }
+    aux_storeBBlock<endN, counter - EIGEN_AVX_MAX_NUM_ROW, toTemp, remM, remK_>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+  }
+
+  template <int64_t endN, int64_t counter, bool toTemp, bool remM, int64_t remK_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_storeBBlock(
+      Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+      PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t remM_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(B_temp);
+    EIGEN_UNUSED_VARIABLE(LDB_);
+    EIGEN_UNUSED_VARIABLE(ymm);
+    EIGEN_UNUSED_VARIABLE(remM_);
+  }
+
+  /********************************************************
+   * Wrappers for aux_XXXX to hide counter parameter
+   ********************************************************/
+
+  template <int64_t endN, int64_t packetIndexOffset, bool remM, int64_t remN_>
+  static EIGEN_ALWAYS_INLINE void loadB(Scalar *B_arr, int64_t LDB,
+                                        PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+                                        int64_t remM_ = 0) {
+    aux_loadB<endN, endN, packetIndexOffset, remM, remN_>(B_arr, LDB, ymm, remM_);
+  }
+
+  template <int64_t endN, int64_t packetIndexOffset, bool remK, bool remM>
+  static EIGEN_ALWAYS_INLINE void storeB(Scalar *B_arr, int64_t LDB,
+                                         PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+                                         int64_t rem_ = 0) {
+    aux_storeB<endN, endN, packetIndexOffset, remK, remM>(B_arr, LDB, ymm, rem_);
+  }
+
+  template <int64_t unrollN, bool toTemp, bool remM, int64_t remN_ = 0>
+  static EIGEN_ALWAYS_INLINE void loadBBlock(Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+                                             PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+                                             int64_t remM_ = 0) {
+    EIGEN_IF_CONSTEXPR(toTemp) { transB::template loadB<unrollN, 0, remM, 0>(&B_arr[0], LDB, ymm, remM_); }
+    else {
+      aux_loadBBlock<unrollN, unrollN, toTemp, remM, remN_>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+  }
+
+  template <int64_t unrollN, bool toTemp, bool remM, int64_t remK_>
+  static EIGEN_ALWAYS_INLINE void storeBBlock(Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+                                              PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+                                              int64_t remM_ = 0) {
+    aux_storeBBlock<unrollN, unrollN, toTemp, remM, remK_>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+  }
+
+  template <int64_t packetIndexOffset>
+  static EIGEN_ALWAYS_INLINE void transposeLxL(PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm) {
+    // Note: this assumes EIGEN_AVX_MAX_NUM_ROW = 8. Unrolls should be adjusted
+    // accordingly if EIGEN_AVX_MAX_NUM_ROW is smaller.
+    PacketBlock<vecHalf, EIGEN_AVX_MAX_NUM_ROW> r;
+    r.packet[0] = ymm.packet[packetIndexOffset + 0];
+    r.packet[1] = ymm.packet[packetIndexOffset + 1];
+    r.packet[2] = ymm.packet[packetIndexOffset + 2];
+    r.packet[3] = ymm.packet[packetIndexOffset + 3];
+    r.packet[4] = ymm.packet[packetIndexOffset + 4];
+    r.packet[5] = ymm.packet[packetIndexOffset + 5];
+    r.packet[6] = ymm.packet[packetIndexOffset + 6];
+    r.packet[7] = ymm.packet[packetIndexOffset + 7];
+    ptranspose(r);
+    ymm.packet[packetIndexOffset + 0] = r.packet[0];
+    ymm.packet[packetIndexOffset + 1] = r.packet[1];
+    ymm.packet[packetIndexOffset + 2] = r.packet[2];
+    ymm.packet[packetIndexOffset + 3] = r.packet[3];
+    ymm.packet[packetIndexOffset + 4] = r.packet[4];
+    ymm.packet[packetIndexOffset + 5] = r.packet[5];
+    ymm.packet[packetIndexOffset + 6] = r.packet[6];
+    ymm.packet[packetIndexOffset + 7] = r.packet[7];
+  }
+
+  template <int64_t unrollN, bool toTemp, bool remM>
+  static EIGEN_ALWAYS_INLINE void transB_kernel(Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+                                                PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+                                                int64_t remM_ = 0) {
+    constexpr int64_t U3 = PacketSize * 3;
+    constexpr int64_t U2 = PacketSize * 2;
+    constexpr int64_t U1 = PacketSize * 1;
+    /**
+     *  Unrolls needed for each case:
+     *   - AVX512 fp32 48 32 16 8 4 2 1
+     *   - AVX512 fp64 24 16 8  4 2 1
+     *
+     *  For fp32 L and U1 are 1:2 so for U3/U2 cases the loads/stores need to be split up.
+     */
+    EIGEN_IF_CONSTEXPR(unrollN == U3) {
+      // load LxU3 B col major, transpose LxU3 row major
+      constexpr int64_t maxUBlock = std::min(3 * EIGEN_AVX_MAX_NUM_ROW, U3);
+      transB::template loadBBlock<maxUBlock, toTemp, remM>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      transB::template transposeLxL<1 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      transB::template transposeLxL<2 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      transB::template storeBBlock<maxUBlock, toTemp, remM, 0>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+
+      EIGEN_IF_CONSTEXPR(maxUBlock < U3) {
+        transB::template loadBBlock<maxUBlock, toTemp, remM>(&B_arr[maxUBlock * LDB], LDB, &B_temp[maxUBlock], LDB_,
+                                                             ymm, remM_);
+        transB::template transposeLxL<0 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+        transB::template transposeLxL<1 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+        transB::template transposeLxL<2 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+        transB::template storeBBlock<maxUBlock, toTemp, remM, 0>(&B_arr[maxUBlock * LDB], LDB, &B_temp[maxUBlock], LDB_,
+                                                                 ymm, remM_);
+      }
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == U2) {
+      // load LxU2 B col major, transpose LxU2 row major
+      constexpr int64_t maxUBlock = std::min(3 * EIGEN_AVX_MAX_NUM_ROW, U2);
+      transB::template loadBBlock<maxUBlock, toTemp, remM>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      transB::template transposeLxL<1 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      EIGEN_IF_CONSTEXPR(maxUBlock < U2) transB::template transposeLxL<2 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      transB::template storeBBlock<maxUBlock, toTemp, remM, 0>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+
+      EIGEN_IF_CONSTEXPR(maxUBlock < U2) {
+        transB::template loadBBlock<EIGEN_AVX_MAX_NUM_ROW, toTemp, remM>(&B_arr[maxUBlock * LDB], LDB,
+                                                                         &B_temp[maxUBlock], LDB_, ymm, remM_);
+        transB::template transposeLxL<0>(ymm);
+        transB::template storeBBlock<EIGEN_AVX_MAX_NUM_ROW, toTemp, remM, 0>(&B_arr[maxUBlock * LDB], LDB,
+                                                                             &B_temp[maxUBlock], LDB_, ymm, remM_);
+      }
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == U1) {
+      // load LxU1 B col major, transpose LxU1 row major
+      transB::template loadBBlock<U1, toTemp, remM>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0>(ymm);
+      EIGEN_IF_CONSTEXPR(EIGEN_AVX_MAX_NUM_ROW < U1) { transB::template transposeLxL<1 * EIGEN_AVX_MAX_NUM_ROW>(ymm); }
+      transB::template storeBBlock<U1, toTemp, remM, 0>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == 8 && U1 > 8) {
+      // load Lx4 B col major, transpose Lx4 row major
+      transB::template loadBBlock<8, toTemp, remM>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0>(ymm);
+      transB::template storeBBlock<8, toTemp, remM, 8>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == 4 && U1 > 4) {
+      // load Lx4 B col major, transpose Lx4 row major
+      transB::template loadBBlock<4, toTemp, remM>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0>(ymm);
+      transB::template storeBBlock<4, toTemp, remM, 4>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == 2) {
+      // load Lx2 B col major, transpose Lx2 row major
+      transB::template loadBBlock<2, toTemp, remM, 2>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0>(ymm);
+      transB::template storeBBlock<2, toTemp, remM, 2>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == 1) {
+      // load Lx1 B col major, transpose Lx1 row major
+      transB::template loadBBlock<1, toTemp, remM, 1>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0>(ymm);
+      transB::template storeBBlock<1, toTemp, remM, 1>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+  }
+};
+
+/**
+ * Unrolls for triSolveKernel
+ *
+ * Idea:
+ *  1) Load a block of right-hand sides to registers in RHSInPacket (using loadRHS).
+ *  2) Do triangular solve with RHSInPacket and a small block of A (triangular matrix)
+ *     stored in AInPacket (using triSolveMicroKernel).
+ *  3) Store final results (in avx registers) back into memory (using storeRHS).
+ *
+ *  RHSInPacket uses at most EIGEN_AVX_MAX_NUM_ACC avx registers and AInPacket uses at most
+ *  EIGEN_AVX_MAX_NUM_ROW registers.
+ */
+template <typename Scalar>
+class trsm {
+ public:
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  static constexpr int64_t PacketSize = packet_traits<Scalar>::size;
+
+  /***********************************
+   * Auxiliary Functions for:
+   *  - loadRHS
+   *  - storeRHS
+   *  - divRHSByDiag
+   *  - updateRHS
+   *  - triSolveMicroKernel
+   ************************************/
+  /**
+   * aux_loadRHS
+   *
+   * 2-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startK = 0; startK < endK; startK++)
+   **/
+  template <bool isFWDSolve, int64_t endM, int64_t endK, int64_t counter, bool krem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_loadRHS(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    constexpr int64_t counterReverse = endM * endK - counter;
+    constexpr int64_t startM = counterReverse / (endK);
+    constexpr int64_t startK = counterReverse % endK;
+
+    constexpr int64_t packetIndex = startM * endK + startK;
+    constexpr int64_t startM_ = isFWDSolve ? startM : -startM;
+    const int64_t rhsIndex = (startK * PacketSize) + startM_ * LDB;
+    EIGEN_IF_CONSTEXPR(krem) {
+      RHSInPacket.packet[packetIndex] = ploadu<vec>(&B_arr[rhsIndex], remMask<PacketSize>(rem));
+    }
+    else {
+      RHSInPacket.packet[packetIndex] = ploadu<vec>(&B_arr[rhsIndex]);
+    }
+    aux_loadRHS<isFWDSolve, endM, endK, counter - 1, krem>(B_arr, LDB, RHSInPacket, rem);
+  }
+
+  template <bool isFWDSolve, int64_t endM, int64_t endK, int64_t counter, bool krem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_loadRHS(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(RHSInPacket);
+    EIGEN_UNUSED_VARIABLE(rem);
+  }
+
+  /**
+   * aux_storeRHS
+   *
+   * 2-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startK = 0; startK < endK; startK++)
+   **/
+  template <bool isFWDSolve, int64_t endM, int64_t endK, int64_t counter, bool krem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_storeRHS(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    constexpr int64_t counterReverse = endM * endK - counter;
+    constexpr int64_t startM = counterReverse / (endK);
+    constexpr int64_t startK = counterReverse % endK;
+
+    constexpr int64_t packetIndex = startM * endK + startK;
+    constexpr int64_t startM_ = isFWDSolve ? startM : -startM;
+    const int64_t rhsIndex = (startK * PacketSize) + startM_ * LDB;
+    EIGEN_IF_CONSTEXPR(krem) {
+      pstoreu<Scalar>(&B_arr[rhsIndex], RHSInPacket.packet[packetIndex], remMask<PacketSize>(rem));
+    }
+    else {
+      pstoreu<Scalar>(&B_arr[rhsIndex], RHSInPacket.packet[packetIndex]);
+    }
+    aux_storeRHS<isFWDSolve, endM, endK, counter - 1, krem>(B_arr, LDB, RHSInPacket, rem);
+  }
+
+  template <bool isFWDSolve, int64_t endM, int64_t endK, int64_t counter, bool krem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_storeRHS(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(RHSInPacket);
+    EIGEN_UNUSED_VARIABLE(rem);
+  }
+
+  /**
+   * aux_divRHSByDiag
+   *
+   * currM may be -1, (currM >=0) in enable_if checks for this
+   *
+   * 1-D unroll
+   *      for(startK = 0; startK < endK; startK++)
+   **/
+  template <int64_t currM, int64_t endK, int64_t counter>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0 && currM >= 0)> aux_divRHSByDiag(
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    constexpr int64_t counterReverse = endK - counter;
+    constexpr int64_t startK = counterReverse;
+
+    constexpr int64_t packetIndex = currM * endK + startK;
+    RHSInPacket.packet[packetIndex] = pmul(AInPacket.packet[currM], RHSInPacket.packet[packetIndex]);
+    aux_divRHSByDiag<currM, endK, counter - 1>(RHSInPacket, AInPacket);
+  }
+
+  template <int64_t currM, int64_t endK, int64_t counter>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<!(counter > 0 && currM >= 0)> aux_divRHSByDiag(
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    EIGEN_UNUSED_VARIABLE(RHSInPacket);
+    EIGEN_UNUSED_VARIABLE(AInPacket);
+  }
+
+  /**
+   * aux_updateRHS
+   *
+   * 2-D unroll
+   *      for(startM = initM; startM < endM; startM++)
+   *        for(startK = 0; startK < endK; startK++)
+   **/
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t initM, int64_t endM, int64_t endK,
+            int64_t counter, int64_t currentM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_updateRHS(
+      Scalar *A_arr, int64_t LDA, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    constexpr int64_t counterReverse = (endM - initM) * endK - counter;
+    constexpr int64_t startM = initM + counterReverse / (endK);
+    constexpr int64_t startK = counterReverse % endK;
+
+    // For each row of A, first update all corresponding RHS
+    constexpr int64_t packetIndex = startM * endK + startK;
+    EIGEN_IF_CONSTEXPR(currentM > 0) {
+      RHSInPacket.packet[packetIndex] =
+          pnmadd(AInPacket.packet[startM], RHSInPacket.packet[(currentM - 1) * endK + startK],
+                 RHSInPacket.packet[packetIndex]);
+    }
+
+    EIGEN_IF_CONSTEXPR(startK == endK - 1) {
+      // Once all RHS for previous row of A is updated, we broadcast the next element in the column A_{i, currentM}.
+      EIGEN_IF_CONSTEXPR(startM == currentM && !isUnitDiag) {
+        // If diagonal is not unit, we broadcast reciprocals of diagonals AinPacket.packet[currentM].
+        // This will be used in divRHSByDiag
+        EIGEN_IF_CONSTEXPR(isFWDSolve)
+        AInPacket.packet[currentM] = pset1<vec>(Scalar(1) / A_arr[idA<isARowMajor>(currentM, currentM, LDA)]);
+        else AInPacket.packet[currentM] = pset1<vec>(Scalar(1) / A_arr[idA<isARowMajor>(-currentM, -currentM, LDA)]);
+      }
+      else {
+        // Broadcast next off diagonal element of A
+        EIGEN_IF_CONSTEXPR(isFWDSolve)
+        AInPacket.packet[startM] = pset1<vec>(A_arr[idA<isARowMajor>(startM, currentM, LDA)]);
+        else AInPacket.packet[startM] = pset1<vec>(A_arr[idA<isARowMajor>(-startM, -currentM, LDA)]);
+      }
+    }
+
+    aux_updateRHS<isARowMajor, isFWDSolve, isUnitDiag, initM, endM, endK, counter - 1, currentM>(
+        A_arr, LDA, RHSInPacket, AInPacket);
+  }
+
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t initM, int64_t endM, int64_t endK,
+            int64_t counter, int64_t currentM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_updateRHS(
+      Scalar *A_arr, int64_t LDA, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    EIGEN_UNUSED_VARIABLE(A_arr);
+    EIGEN_UNUSED_VARIABLE(LDA);
+    EIGEN_UNUSED_VARIABLE(RHSInPacket);
+    EIGEN_UNUSED_VARIABLE(AInPacket);
+  }
+
+  /**
+   * aux_triSolverMicroKernel
+   *
+   * 1-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   **/
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t endM, int64_t counter, int64_t numK>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_triSolveMicroKernel(
+      Scalar *A_arr, int64_t LDA, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    constexpr int64_t counterReverse = endM - counter;
+    constexpr int64_t startM = counterReverse;
+
+    constexpr int64_t currentM = startM;
+    // Divides the right-hand side in row startM, by digonal value of A
+    // broadcasted to AInPacket.packet[startM-1] in the previous iteration.
+    //
+    // Without "if constexpr" the compiler instantiates the case <-1, numK>
+    // this is handled with enable_if to prevent out-of-bound warnings
+    // from the compiler
+    EIGEN_IF_CONSTEXPR(!isUnitDiag && startM > 0)
+    trsm::template divRHSByDiag<startM - 1, numK>(RHSInPacket, AInPacket);
+
+    // After division, the rhs corresponding to subsequent rows of A can be partially updated
+    // We also broadcast the reciprocal of the next diagonal to AInPacket.packet[currentM] (if needed)
+    // to be used in the next iteration.
+    trsm::template updateRHS<isARowMajor, isFWDSolve, isUnitDiag, startM, endM, numK, currentM>(A_arr, LDA, RHSInPacket,
+                                                                                                AInPacket);
+
+    // Handle division for the RHS corresponding to the final row of A.
+    EIGEN_IF_CONSTEXPR(!isUnitDiag && startM == endM - 1)
+    trsm::template divRHSByDiag<startM, numK>(RHSInPacket, AInPacket);
+
+    aux_triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, endM, counter - 1, numK>(A_arr, LDA, RHSInPacket,
+                                                                                          AInPacket);
+  }
+
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t endM, int64_t counter, int64_t numK>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_triSolveMicroKernel(
+      Scalar *A_arr, int64_t LDA, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    EIGEN_UNUSED_VARIABLE(A_arr);
+    EIGEN_UNUSED_VARIABLE(LDA);
+    EIGEN_UNUSED_VARIABLE(RHSInPacket);
+    EIGEN_UNUSED_VARIABLE(AInPacket);
+  }
+
+  /********************************************************
+   * Wrappers for aux_XXXX to hide counter parameter
+   ********************************************************/
+
+  /**
+   * Load endMxendK block of B to RHSInPacket
+   * Masked loads are used for cases where endK is not a multiple of PacketSize
+   */
+  template <bool isFWDSolve, int64_t endM, int64_t endK, bool krem = false>
+  static EIGEN_ALWAYS_INLINE void loadRHS(Scalar *B_arr, int64_t LDB,
+                                          PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    aux_loadRHS<isFWDSolve, endM, endK, endM * endK, krem>(B_arr, LDB, RHSInPacket, rem);
+  }
+
+  /**
+   * Load endMxendK block of B to RHSInPacket
+   * Masked loads are used for cases where endK is not a multiple of PacketSize
+   */
+  template <bool isFWDSolve, int64_t endM, int64_t endK, bool krem = false>
+  static EIGEN_ALWAYS_INLINE void storeRHS(Scalar *B_arr, int64_t LDB,
+                                           PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    aux_storeRHS<isFWDSolve, endM, endK, endM * endK, krem>(B_arr, LDB, RHSInPacket, rem);
+  }
+
+  /**
+   * Only used if Triangular matrix has non-unit diagonal values
+   */
+  template <int64_t currM, int64_t endK>
+  static EIGEN_ALWAYS_INLINE void divRHSByDiag(PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+                                               PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    aux_divRHSByDiag<currM, endK, endK>(RHSInPacket, AInPacket);
+  }
+
+  /**
+   * Update right-hand sides (stored in avx registers)
+   * Traversing along the column A_{i,currentM}, where currentM <= i <= endM, and broadcasting each value to AInPacket.
+   **/
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t startM, int64_t endM, int64_t endK,
+            int64_t currentM>
+  static EIGEN_ALWAYS_INLINE void updateRHS(Scalar *A_arr, int64_t LDA,
+                                            PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+                                            PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    aux_updateRHS<isARowMajor, isFWDSolve, isUnitDiag, startM, endM, endK, (endM - startM) * endK, currentM>(
+        A_arr, LDA, RHSInPacket, AInPacket);
+  }
+
+  /**
+   * endM: dimension of A. 1 <= endM <= EIGEN_AVX_MAX_NUM_ROW
+   * numK: number of avx registers to use for each row of B (ex fp32: 48 rhs => 3 avx reg used). 1 <= endK <= 3.
+   * isFWDSolve: true => forward substitution, false => backwards substitution
+   * isUnitDiag: true => triangular matrix has unit diagonal.
+   */
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t endM, int64_t numK>
+  static EIGEN_ALWAYS_INLINE void triSolveMicroKernel(Scalar *A_arr, int64_t LDA,
+                                                      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+                                                      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    static_assert(numK >= 1 && numK <= 3, "numK out of range");
+    aux_triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, endM, endM, numK>(A_arr, LDA, RHSInPacket, AInPacket);
+  }
+};
+
+/**
+ * Unrolls for gemm kernel
+ *
+ * isAdd: true => C += A*B, false => C -= A*B
+ */
+template <typename Scalar, bool isAdd>
+class gemm {
+ public:
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  static constexpr int64_t PacketSize = packet_traits<Scalar>::size;
+
+  /***********************************
+   * Auxiliary Functions for:
+   *  - setzero
+   *  - updateC
+   *  - storeC
+   *  - startLoadB
+   *  - triSolveMicroKernel
+   ************************************/
+
+  /**
+   * aux_setzero
+   *
+   * 2-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startN = 0; startN < endN; startN++)
+   **/
+  template <int64_t endM, int64_t endN, int64_t counter>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_setzero(
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    constexpr int64_t counterReverse = endM * endN - counter;
+    constexpr int64_t startM = counterReverse / (endN);
+    constexpr int64_t startN = counterReverse % endN;
+
+    zmm.packet[startN * endM + startM] = pzero(zmm.packet[startN * endM + startM]);
+    aux_setzero<endM, endN, counter - 1>(zmm);
+  }
+
+  template <int64_t endM, int64_t endN, int64_t counter>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_setzero(
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    EIGEN_UNUSED_VARIABLE(zmm);
+  }
+
+  /**
+   * aux_updateC
+   *
+   * 2-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startN = 0; startN < endN; startN++)
+   **/
+  template <int64_t endM, int64_t endN, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_updateC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    constexpr int64_t counterReverse = endM * endN - counter;
+    constexpr int64_t startM = counterReverse / (endN);
+    constexpr int64_t startN = counterReverse % endN;
+
+    EIGEN_IF_CONSTEXPR(rem)
+    zmm.packet[startN * endM + startM] =
+        padd(ploadu<vec>(&C_arr[(startN)*LDC + startM * PacketSize], remMask<PacketSize>(rem_)),
+             zmm.packet[startN * endM + startM], remMask<PacketSize>(rem_));
+    else zmm.packet[startN * endM + startM] =
+        padd(ploadu<vec>(&C_arr[(startN)*LDC + startM * PacketSize]), zmm.packet[startN * endM + startM]);
+    aux_updateC<endM, endN, counter - 1, rem>(C_arr, LDC, zmm, rem_);
+  }
+
+  template <int64_t endM, int64_t endN, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_updateC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(C_arr);
+    EIGEN_UNUSED_VARIABLE(LDC);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /**
+   * aux_storeC
+   *
+   * 2-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startN = 0; startN < endN; startN++)
+   **/
+  template <int64_t endM, int64_t endN, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_storeC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    constexpr int64_t counterReverse = endM * endN - counter;
+    constexpr int64_t startM = counterReverse / (endN);
+    constexpr int64_t startN = counterReverse % endN;
+
+    EIGEN_IF_CONSTEXPR(rem)
+    pstoreu<Scalar>(&C_arr[(startN)*LDC + startM * PacketSize], zmm.packet[startN * endM + startM],
+                    remMask<PacketSize>(rem_));
+    else pstoreu<Scalar>(&C_arr[(startN)*LDC + startM * PacketSize], zmm.packet[startN * endM + startM]);
+    aux_storeC<endM, endN, counter - 1, rem>(C_arr, LDC, zmm, rem_);
+  }
+
+  template <int64_t endM, int64_t endN, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_storeC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(C_arr);
+    EIGEN_UNUSED_VARIABLE(LDC);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /**
+   * aux_startLoadB
+   *
+   * 1-D unroll
+   *      for(startL = 0; startL < endL; startL++)
+   **/
+  template <int64_t unrollM, int64_t unrollN, int64_t endL, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_startLoadB(
+      Scalar *B_t, int64_t LDB, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    constexpr int64_t counterReverse = endL - counter;
+    constexpr int64_t startL = counterReverse;
+
+    EIGEN_IF_CONSTEXPR(rem)
+    zmm.packet[unrollM * unrollN + startL] =
+        ploadu<vec>(&B_t[(startL / unrollM) * LDB + (startL % unrollM) * PacketSize], remMask<PacketSize>(rem_));
+    else zmm.packet[unrollM * unrollN + startL] =
+        ploadu<vec>(&B_t[(startL / unrollM) * LDB + (startL % unrollM) * PacketSize]);
+
+    aux_startLoadB<unrollM, unrollN, endL, counter - 1, rem>(B_t, LDB, zmm, rem_);
+  }
+
+  template <int64_t unrollM, int64_t unrollN, int64_t endL, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_startLoadB(
+      Scalar *B_t, int64_t LDB, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_t);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /**
+   * aux_startBCastA
+   *
+   * 1-D unroll
+   *      for(startB = 0; startB < endB; startB++)
+   **/
+  template <bool isARowMajor, int64_t unrollM, int64_t unrollN, int64_t endB, int64_t counter, int64_t numLoad>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_startBCastA(
+      Scalar *A_t, int64_t LDA, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    constexpr int64_t counterReverse = endB - counter;
+    constexpr int64_t startB = counterReverse;
+
+    zmm.packet[unrollM * unrollN + numLoad + startB] = pload1<vec>(&A_t[idA<isARowMajor>(startB, 0, LDA)]);
+
+    aux_startBCastA<isARowMajor, unrollM, unrollN, endB, counter - 1, numLoad>(A_t, LDA, zmm);
+  }
+
+  template <bool isARowMajor, int64_t unrollM, int64_t unrollN, int64_t endB, int64_t counter, int64_t numLoad>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_startBCastA(
+      Scalar *A_t, int64_t LDA, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    EIGEN_UNUSED_VARIABLE(A_t);
+    EIGEN_UNUSED_VARIABLE(LDA);
+    EIGEN_UNUSED_VARIABLE(zmm);
+  }
+
+  /**
+   * aux_loadB
+   * currK: current K
+   *
+   * 1-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   **/
+  template <int64_t endM, int64_t counter, int64_t unrollN, int64_t currK, int64_t unrollK, int64_t numLoad,
+            int64_t numBCast, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_loadB(
+      Scalar *B_t, int64_t LDB, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    if ((numLoad / endM + currK < unrollK)) {
+      constexpr int64_t counterReverse = endM - counter;
+      constexpr int64_t startM = counterReverse;
+
+      EIGEN_IF_CONSTEXPR(rem) {
+        zmm.packet[endM * unrollN + (startM + currK * endM) % numLoad] =
+            ploadu<vec>(&B_t[(numLoad / endM + currK) * LDB + startM * PacketSize], remMask<PacketSize>(rem_));
+      }
+      else {
+        zmm.packet[endM * unrollN + (startM + currK * endM) % numLoad] =
+            ploadu<vec>(&B_t[(numLoad / endM + currK) * LDB + startM * PacketSize]);
+      }
+
+      aux_loadB<endM, counter - 1, unrollN, currK, unrollK, numLoad, numBCast, rem>(B_t, LDB, zmm, rem_);
+    }
+  }
+
+  template <int64_t endM, int64_t counter, int64_t unrollN, int64_t currK, int64_t unrollK, int64_t numLoad,
+            int64_t numBCast, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_loadB(
+      Scalar *B_t, int64_t LDB, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_t);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /**
+   * aux_microKernel
+   *
+   * 3-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startN = 0; startN < endN; startN++)
+   *          for(startK = 0; startK < endK; startK++)
+   **/
+  template <bool isARowMajor, int64_t endM, int64_t endN, int64_t endK, int64_t counter, int64_t numLoad,
+            int64_t numBCast, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_microKernel(
+      Scalar *B_t, Scalar *A_t, int64_t LDB, int64_t LDA, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+      int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    constexpr int64_t counterReverse = endM * endN * endK - counter;
+    constexpr int startK = counterReverse / (endM * endN);
+    constexpr int startN = (counterReverse / (endM)) % endN;
+    constexpr int startM = counterReverse % endM;
+
+    EIGEN_IF_CONSTEXPR(startK == 0 && startM == 0 && startN == 0) {
+      gemm::template startLoadB<endM, endN, numLoad, rem>(B_t, LDB, zmm, rem_);
+      gemm::template startBCastA<isARowMajor, endM, endN, numBCast, numLoad>(A_t, LDA, zmm);
+    }
+
+    {
+      // Interleave FMA and Bcast
+      EIGEN_IF_CONSTEXPR(isAdd) {
+        zmm.packet[startN * endM + startM] =
+            pmadd(zmm.packet[endM * endN + numLoad + (startN + startK * endN) % numBCast],
+                  zmm.packet[endM * endN + (startM + startK * endM) % numLoad], zmm.packet[startN * endM + startM]);
+      }
+      else {
+        zmm.packet[startN * endM + startM] =
+            pnmadd(zmm.packet[endM * endN + numLoad + (startN + startK * endN) % numBCast],
+                   zmm.packet[endM * endN + (startM + startK * endM) % numLoad], zmm.packet[startN * endM + startM]);
+      }
+      // Bcast
+      EIGEN_IF_CONSTEXPR(startM == endM - 1 && (numBCast + startN + startK * endN < endK * endN)) {
+        zmm.packet[endM * endN + numLoad + (startN + startK * endN) % numBCast] = pload1<vec>(&A_t[idA<isARowMajor>(
+            (numBCast + startN + startK * endN) % endN, (numBCast + startN + startK * endN) / endN, LDA)]);
+      }
+    }
+
+    // We have updated all accumulators, time to load next set of B's
+    EIGEN_IF_CONSTEXPR((startN == endN - 1) && (startM == endM - 1)) {
+      gemm::template loadB<endM, endN, startK, endK, numLoad, numBCast, rem>(B_t, LDB, zmm, rem_);
+    }
+    aux_microKernel<isARowMajor, endM, endN, endK, counter - 1, numLoad, numBCast, rem>(B_t, A_t, LDB, LDA, zmm, rem_);
+  }
+
+  template <bool isARowMajor, int64_t endM, int64_t endN, int64_t endK, int64_t counter, int64_t numLoad,
+            int64_t numBCast, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_microKernel(
+      Scalar *B_t, Scalar *A_t, int64_t LDB, int64_t LDA, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+      int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_t);
+    EIGEN_UNUSED_VARIABLE(A_t);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(LDA);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /********************************************************
+   * Wrappers for aux_XXXX to hide counter parameter
+   ********************************************************/
+
+  template <int64_t endM, int64_t endN>
+  static EIGEN_ALWAYS_INLINE void setzero(PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    aux_setzero<endM, endN, endM * endN>(zmm);
+  }
+
+  /**
+   * Ideally the compiler folds these into vaddp{s,d} with an embedded memory load.
+   */
+  template <int64_t endM, int64_t endN, bool rem = false>
+  static EIGEN_ALWAYS_INLINE void updateC(Scalar *C_arr, int64_t LDC,
+                                          PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                          int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    aux_updateC<endM, endN, endM * endN, rem>(C_arr, LDC, zmm, rem_);
+  }
+
+  template <int64_t endM, int64_t endN, bool rem = false>
+  static EIGEN_ALWAYS_INLINE void storeC(Scalar *C_arr, int64_t LDC,
+                                         PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                         int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    aux_storeC<endM, endN, endM * endN, rem>(C_arr, LDC, zmm, rem_);
+  }
+
+  /**
+   * Use numLoad registers for loading B at start of microKernel
+   */
+  template <int64_t unrollM, int64_t unrollN, int64_t endL, bool rem>
+  static EIGEN_ALWAYS_INLINE void startLoadB(Scalar *B_t, int64_t LDB,
+                                             PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                             int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    aux_startLoadB<unrollM, unrollN, endL, endL, rem>(B_t, LDB, zmm, rem_);
+  }
+
+  /**
+   * Use numBCast registers for broadcasting A at start of microKernel
+   */
+  template <bool isARowMajor, int64_t unrollM, int64_t unrollN, int64_t endB, int64_t numLoad>
+  static EIGEN_ALWAYS_INLINE void startBCastA(Scalar *A_t, int64_t LDA,
+                                              PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    aux_startBCastA<isARowMajor, unrollM, unrollN, endB, endB, numLoad>(A_t, LDA, zmm);
+  }
+
+  /**
+   * Loads next set of B into vector registers between each K unroll.
+   */
+  template <int64_t endM, int64_t unrollN, int64_t currK, int64_t unrollK, int64_t numLoad, int64_t numBCast, bool rem>
+  static EIGEN_ALWAYS_INLINE void loadB(Scalar *B_t, int64_t LDB,
+                                        PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                        int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    aux_loadB<endM, endM, unrollN, currK, unrollK, numLoad, numBCast, rem>(B_t, LDB, zmm, rem_);
+  }
+
+  /**
+   * Generates a microkernel for gemm (row-major) with unrolls {1,2,4,8}x{U1,U2,U3} to compute C -= A*B.
+   * A matrix can be row/col-major. B matrix is assumed row-major.
+   *
+   * isARowMajor: is A row major
+   * endM: Number registers per row
+   * endN: Number of rows
+   * endK: Loop unroll for K.
+   * numLoad: Number of registers for loading B.
+   * numBCast: Number of registers for broadcasting A.
+   *
+   * Ex: microkernel<isARowMajor,0,3,0,4,0,4,6,2>: 8x48 unroll (24 accumulators), k unrolled 4 times,
+   * 6 register for loading B, 2 for broadcasting A.
+   *
+   * Note: Ideally the microkernel should not have any register spilling.
+   * The avx instruction counts should be:
+   *   - endK*endN vbroadcasts{s,d}
+   *   - endK*endM vmovup{s,d}
+   *   - endK*endN*endM FMAs
+   *
+   * From testing, there are no register spills with clang. There are register spills with GNU, which
+   * causes a performance hit.
+   */
+  template <bool isARowMajor, int64_t endM, int64_t endN, int64_t endK, int64_t numLoad, int64_t numBCast,
+            bool rem = false>
+  static EIGEN_ALWAYS_INLINE void microKernel(Scalar *B_t, Scalar *A_t, int64_t LDB, int64_t LDA,
+                                              PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                              int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    aux_microKernel<isARowMajor, endM, endN, endK, endM * endN * endK, numLoad, numBCast, rem>(B_t, A_t, LDB, LDA, zmm,
+                                                                                               rem_);
+  }
+};
+}  // namespace unrolls
+
+#endif  // EIGEN_CORE_ARCH_AVX512_TRSM_UNROLLS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TypeCasting.h
new file mode 100644
index 0000000..e970d43
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TypeCasting.h
@@ -0,0 +1,285 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2019 Rasmus Munk Larsen <rmlarsen@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_AVX512_H
+#define EIGEN_TYPE_CASTING_AVX512_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <>
+struct type_casting_traits<float, bool> : vectorized_type_casting_traits<float, bool> {};
+template <>
+struct type_casting_traits<bool, float> : vectorized_type_casting_traits<bool, float> {};
+
+template <>
+struct type_casting_traits<float, int> : vectorized_type_casting_traits<float, int> {};
+template <>
+struct type_casting_traits<int, float> : vectorized_type_casting_traits<int, float> {};
+
+template <>
+struct type_casting_traits<float, double> : vectorized_type_casting_traits<float, double> {};
+template <>
+struct type_casting_traits<double, float> : vectorized_type_casting_traits<double, float> {};
+
+template <>
+struct type_casting_traits<double, int> : vectorized_type_casting_traits<double, int> {};
+template <>
+struct type_casting_traits<int, double> : vectorized_type_casting_traits<int, double> {};
+
+template <>
+struct type_casting_traits<double, int64_t> : vectorized_type_casting_traits<double, int64_t> {};
+template <>
+struct type_casting_traits<int64_t, double> : vectorized_type_casting_traits<int64_t, double> {};
+
+template <>
+struct type_casting_traits<half, float> : vectorized_type_casting_traits<half, float> {};
+template <>
+struct type_casting_traits<float, half> : vectorized_type_casting_traits<float, half> {};
+
+template <>
+struct type_casting_traits<bfloat16, float> : vectorized_type_casting_traits<bfloat16, float> {};
+template <>
+struct type_casting_traits<float, bfloat16> : vectorized_type_casting_traits<float, bfloat16> {};
+
+EIGEN_STRONG_INLINE __mmask16 _eigen_mm512_cmpneq_ps_mask(__m512 a, __m512 b) {
+#if EIGEN_COMP_GNUC && (EIGEN_COMP_CLANG < 1000 || EIGEN_COMP_GNUC < 810)
+  return _mm512_cmp_ps_mask(a, b, _CMP_NEQ_UQ);
+#else
+  return _mm512_cmpneq_ps_mask(a, b);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16b pcast<Packet16f, Packet16b>(const Packet16f& a) {
+  __mmask16 mask = _eigen_mm512_cmpneq_ps_mask(a, pzero(a));
+  return _mm512_maskz_cvtepi32_epi8(mask, _mm512_set1_epi32(1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcast<Packet16b, Packet16f>(const Packet16b& a) {
+  return _mm512_cvtepi32_ps(_mm512_and_si512(_mm512_cvtepi8_epi32(a), _mm512_set1_epi32(1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pcast<Packet16f, Packet16i>(const Packet16f& a) {
+  return _mm512_cvttps_epi32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pcast<Packet16f, Packet8d>(const Packet16f& a) {
+  return _mm512_cvtps_pd(_mm512_castps512_ps256(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pcast<Packet8f, Packet8d>(const Packet8f& a) {
+  return _mm512_cvtps_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l pcast<Packet8d, Packet8l>(const Packet8d& a) {
+#if defined(EIGEN_VECTORIZE_AVX512DQ) && defined(EIGEN_VECTORIZE_AVX512VL)
+  return _mm512_cvttpd_epi64(a);
+#else
+  constexpr int kTotalBits = sizeof(double) * CHAR_BIT, kMantissaBits = std::numeric_limits<double>::digits - 1,
+                kExponentBits = kTotalBits - kMantissaBits - 1, kBias = (1 << (kExponentBits - 1)) - 1;
+
+  const __m512i cst_one = _mm512_set1_epi64(1);
+  const __m512i cst_total_bits = _mm512_set1_epi64(kTotalBits);
+  const __m512i cst_bias = _mm512_set1_epi64(kBias);
+
+  __m512i a_bits = _mm512_castpd_si512(a);
+  // shift left by 1 to clear the sign bit, and shift right by kMantissaBits + 1 to recover biased exponent
+  __m512i biased_e = _mm512_srli_epi64(_mm512_slli_epi64(a_bits, 1), kMantissaBits + 1);
+  __m512i e = _mm512_sub_epi64(biased_e, cst_bias);
+
+  // shift to the left by kExponentBits + 1 to clear the sign and exponent bits
+  __m512i shifted_mantissa = _mm512_slli_epi64(a_bits, kExponentBits + 1);
+  // shift to the right by kTotalBits - e to convert the significand to an integer
+  __m512i result_significand = _mm512_srlv_epi64(shifted_mantissa, _mm512_sub_epi64(cst_total_bits, e));
+
+  // add the implied bit
+  __m512i result_exponent = _mm512_sllv_epi64(cst_one, e);
+  // e <= 0 is interpreted as a large positive shift (2's complement), which also conveniently results in zero
+  __m512i result = _mm512_add_epi64(result_significand, result_exponent);
+  // handle negative arguments
+  __mmask8 sign_mask = _mm512_cmplt_epi64_mask(a_bits, _mm512_setzero_si512());
+  result = _mm512_mask_sub_epi64(result, sign_mask, _mm512_setzero_si512(), result);
+  return result;
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcast<Packet16i, Packet16f>(const Packet16i& a) {
+  return _mm512_cvtepi32_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pcast<Packet16i, Packet8d>(const Packet16i& a) {
+  return _mm512_cvtepi32_pd(_mm512_castsi512_si256(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pcast<Packet8i, Packet8d>(const Packet8i& a) {
+  return _mm512_cvtepi32_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pcast<Packet8l, Packet8d>(const Packet8l& a) {
+#if defined(EIGEN_VECTORIZE_AVX512DQ) && defined(EIGEN_VECTORIZE_AVX512VL)
+  return _mm512_cvtepi64_pd(a);
+#else
+  EIGEN_ALIGN64 int64_t aux[8];
+  pstore(aux, a);
+  return _mm512_set_pd(static_cast<double>(aux[7]), static_cast<double>(aux[6]), static_cast<double>(aux[5]),
+                       static_cast<double>(aux[4]), static_cast<double>(aux[3]), static_cast<double>(aux[2]),
+                       static_cast<double>(aux[1]), static_cast<double>(aux[0]));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcast<Packet8d, Packet16f>(const Packet8d& a, const Packet8d& b) {
+  return cat256(_mm512_cvtpd_ps(a), _mm512_cvtpd_ps(b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pcast<Packet8d, Packet16i>(const Packet8d& a, const Packet8d& b) {
+  return cat256i(_mm512_cvttpd_epi32(a), _mm512_cvttpd_epi32(b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8i pcast<Packet8d, Packet8i>(const Packet8d& a) {
+  return _mm512_cvtpd_epi32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pcast<Packet8d, Packet8f>(const Packet8d& a) {
+  return _mm512_cvtpd_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i preinterpret<Packet16i, Packet16f>(const Packet16f& a) {
+  return _mm512_castps_si512(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet16i>(const Packet16i& a) {
+  return _mm512_castsi512_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d preinterpret<Packet8d, Packet16f>(const Packet16f& a) {
+  return _mm512_castps_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d preinterpret<Packet8d, Packet8l>(const Packet8l& a) {
+  return _mm512_castsi512_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l preinterpret<Packet8l, Packet8d>(const Packet8d& a) {
+  return _mm512_castpd_si512(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet8d>(const Packet8d& a) {
+  return _mm512_castpd_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f preinterpret<Packet8f, Packet16f>(const Packet16f& a) {
+  return _mm512_castps512_ps256(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet16f>(const Packet16f& a) {
+  return _mm512_castps512_ps128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d preinterpret<Packet4d, Packet8d>(const Packet8d& a) {
+  return _mm512_castpd512_pd256(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet8d>(const Packet8d& a) {
+  return _mm512_castpd512_pd128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet8f>(const Packet8f& a) {
+  return _mm512_castps256_ps512(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet4f>(const Packet4f& a) {
+  return _mm512_castps128_ps512(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d preinterpret<Packet8d, Packet4d>(const Packet4d& a) {
+  return _mm512_castpd256_pd512(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d preinterpret<Packet8d, Packet2d>(const Packet2d& a) {
+  return _mm512_castpd128_pd512(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8i preinterpret<Packet8i, Packet16i>(const Packet16i& a) {
+  return _mm512_castsi512_si256(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet16i>(const Packet16i& a) {
+  return _mm512_castsi512_si128(a);
+}
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template <>
+EIGEN_STRONG_INLINE Packet8h preinterpret<Packet8h, Packet16h>(const Packet16h& a) {
+  return _mm256_castsi256_si128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcast<Packet16h, Packet16f>(const Packet16h& a) {
+  return half2float(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcast<Packet16f, Packet16h>(const Packet16f& a) {
+  return float2half(a);
+}
+
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf preinterpret<Packet8bf, Packet16bf>(const Packet16bf& a) {
+  return _mm256_castsi256_si128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcast<Packet16bf, Packet16f>(const Packet16bf& a) {
+  return Bf16ToF32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pcast<Packet16f, Packet16bf>(const Packet16f& a) {
+  return F32ToBf16(a);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_AVX512_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TypeCastingFP16.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TypeCastingFP16.h
new file mode 100644
index 0000000..f06f13d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AVX512/TypeCastingFP16.h
@@ -0,0 +1,130 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 The Eigen Authors.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_FP16_AVX512_H
+#define EIGEN_TYPE_CASTING_FP16_AVX512_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <>
+EIGEN_STRONG_INLINE Packet32s preinterpret<Packet32s, Packet32h>(const Packet32h& a) {
+  return _mm512_castph_si512(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16s preinterpret<Packet16s, Packet16h>(const Packet16h& a) {
+  return _mm256_castph_si256(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s preinterpret<Packet8s, Packet8h>(const Packet8h& a) {
+  return _mm_castph_si128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32h preinterpret<Packet32h, Packet32s>(const Packet32s& a) {
+  return _mm512_castsi512_ph(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16h preinterpret<Packet16h, Packet16s>(const Packet16s& a) {
+  return _mm256_castsi256_ph(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8h preinterpret<Packet8h, Packet8s>(const Packet8s& a) {
+  return _mm_castsi128_ph(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcast<Packet16h, Packet16f>(const Packet16h& a) {
+  return half2float(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pcast<Packet8h, Packet8f>(const Packet8h& a) {
+  return half2float(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcast<Packet16f, Packet16h>(const Packet16f& a) {
+  return float2half(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8h pcast<Packet8f, Packet8h>(const Packet8f& a) {
+  return float2half(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcast<Packet32h, Packet16f>(const Packet32h& a) {
+  // Discard second-half of input.
+  Packet16h low = _mm256_castpd_ph(_mm512_extractf64x4_pd(_mm512_castph_pd(a), 0));
+  return _mm512_cvtxph_ps(low);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pcast<Packet16h, Packet8f>(const Packet16h& a) {
+  // Discard second-half of input.
+  Packet8h low = _mm_castps_ph(_mm256_extractf32x4_ps(_mm256_castph_ps(a), 0));
+  return _mm256_cvtxph_ps(low);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet8h, Packet4f>(const Packet8h& a) {
+  Packet8f full = _mm256_cvtxph_ps(a);
+  // Discard second-half of input.
+  return _mm256_extractf32x4_ps(full, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcast<Packet16f, Packet32h>(const Packet16f& a, const Packet16f& b) {
+  __m512 result = _mm512_castsi512_ps(_mm512_castsi256_si512(_mm256_castph_si256(_mm512_cvtxps_ph(a))));
+  result = _mm512_insertf32x8(result, _mm256_castph_ps(_mm512_cvtxps_ph(b)), 1);
+  return _mm512_castps_ph(result);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16h pcast<Packet8f, Packet16h>(const Packet8f& a, const Packet8f& b) {
+  __m256 result = _mm256_castsi256_ps(_mm256_castsi128_si256(_mm_castph_si128(_mm256_cvtxps_ph(a))));
+  result = _mm256_insertf32x4(result, _mm_castph_ps(_mm256_cvtxps_ph(b)), 1);
+  return _mm256_castps_ph(result);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8h pcast<Packet4f, Packet8h>(const Packet4f& a, const Packet4f& b) {
+  __m256 result = _mm256_castsi256_ps(_mm256_castsi128_si256(_mm_castps_si128(a)));
+  result = _mm256_insertf128_ps(result, b, 1);
+  return _mm256_cvtxps_ph(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32s pcast<Packet32h, Packet32s>(const Packet32h& a) {
+  return _mm512_cvtph_epi16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16s pcast<Packet16h, Packet16s>(const Packet16h& a) {
+  return _mm256_cvtph_epi16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet8h, Packet8s>(const Packet8h& a) {
+  return _mm_cvtph_epi16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcast<Packet32s, Packet32h>(const Packet32s& a) {
+  return _mm512_cvtepi16_ph(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16h pcast<Packet16s, Packet16h>(const Packet16s& a) {
+  return _mm256_cvtepi16_ph(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8h pcast<Packet8s, Packet8h>(const Packet8s& a) {
+  return _mm_cvtepi16_ph(a);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_FP16_AVX512_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/Complex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/Complex.h
new file mode 100644
index 0000000..d49f136
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/Complex.h
@@ -0,0 +1,639 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010-2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX32_ALTIVEC_H
+#define EIGEN_COMPLEX32_ALTIVEC_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+inline Packet4ui p4ui_CONJ_XOR() {
+  return vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);  //{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
+}
+#ifdef EIGEN_VECTORIZE_VSX
+#if defined(_BIG_ENDIAN)
+inline Packet2ul p2ul_CONJ_XOR1() {
+  return (Packet2ul)vec_sld((Packet4ui)p2d_MZERO, (Packet4ui)p2l_ZERO,
+                            8);  //{ 0x8000000000000000, 0x0000000000000000 };
+}
+inline Packet2ul p2ul_CONJ_XOR2() {
+  return (Packet2ul)vec_sld((Packet4ui)p2l_ZERO, (Packet4ui)p2d_MZERO,
+                            8);  //{ 0x8000000000000000, 0x0000000000000000 };
+}
+#else
+inline Packet2ul p2ul_CONJ_XOR1() {
+  return (Packet2ul)vec_sld((Packet4ui)p2l_ZERO, (Packet4ui)p2d_MZERO,
+                            8);  //{ 0x8000000000000000, 0x0000000000000000 };
+}
+inline Packet2ul p2ul_CONJ_XOR2() {
+  return (Packet2ul)vec_sld((Packet4ui)p2d_MZERO, (Packet4ui)p2l_ZERO,
+                            8);  //{ 0x8000000000000000, 0x0000000000000000 };
+}
+#endif
+#endif
+
+//---------- float ----------
+struct Packet2cf {
+  EIGEN_STRONG_INLINE explicit Packet2cf() {}
+  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
+
+  EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) {
+    Packet4f v1, v2;
+
+    // Permute and multiply the real parts of a and b
+    v1 = vec_perm(a.v, a.v, p16uc_PSET32_WODD);
+    // Get the imaginary parts of a
+    v2 = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN);
+    // multiply a_re * b
+    v1 = vec_madd(v1, b.v, p4f_ZERO);
+    // multiply a_im * b and get the conjugate result
+    v2 = vec_madd(v2, b.v, p4f_ZERO);
+    v2 = reinterpret_cast<Packet4f>(pxor(v2, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR())));
+    // permute back to a proper order
+    v2 = vec_perm(v2, v2, p16uc_COMPLEX32_REV);
+
+    return Packet2cf(padd<Packet4f>(v1, v2));
+  }
+
+  EIGEN_STRONG_INLINE Packet2cf& operator*=(const Packet2cf& b) {
+    v = pmul(Packet2cf(*this), b).v;
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator*(const Packet2cf& b) const { return Packet2cf(*this) *= b; }
+
+  EIGEN_STRONG_INLINE Packet2cf& operator+=(const Packet2cf& b) {
+    v = padd(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator+(const Packet2cf& b) const { return Packet2cf(*this) += b; }
+  EIGEN_STRONG_INLINE Packet2cf& operator-=(const Packet2cf& b) {
+    v = psub(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator-(const Packet2cf& b) const { return Packet2cf(*this) -= b; }
+  EIGEN_STRONG_INLINE Packet2cf operator-(void) const { return Packet2cf(-v); }
+
+  Packet4f v;
+};
+
+template <>
+struct packet_traits<std::complex<float> > : default_packet_traits {
+  typedef Packet2cf type;
+  typedef Packet2cf half;
+  typedef Packet4f as_real;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2cf> {
+  typedef std::complex<float> type;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet2cf half;
+  typedef Packet4f as_real;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) {
+  Packet2cf res;
+#ifdef EIGEN_VECTORIZE_VSX
+  // Load a single std::complex<float> from memory and duplicate
+  //
+  // Using pload would read past the end of the reference in this case
+  // Using vec_xl_len + vec_splat, generates poor assembly
+  __asm__("lxvdsx %x0,%y1" : "=wa"(res.v) : "Z"(from));
+#else
+  if ((std::ptrdiff_t(&from) % 16) == 0)
+    res.v = pload<Packet4f>((const float*)&from);
+  else
+    res.v = ploadu<Packet4f>((const float*)&from);
+  res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI);
+#endif
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) {
+  return Packet2cf(pload<Packet4f>((const float*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) {
+  return Packet2cf(ploadu<Packet4f>((const float*)from));
+}
+template <>
+EIGEN_ALWAYS_INLINE Packet2cf pload_partial<Packet2cf>(const std::complex<float>* from, const Index n,
+                                                       const Index offset) {
+  return Packet2cf(pload_partial<Packet4f>((const float*)from, n * 2, offset * 2));
+}
+template <>
+EIGEN_ALWAYS_INLINE Packet2cf ploadu_partial<Packet2cf>(const std::complex<float>* from, const Index n,
+                                                        const Index offset) {
+  return Packet2cf(ploadu_partial<Packet4f>((const float*)from, n * 2, offset * 2));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) {
+  return pset1<Packet2cf>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<float> >(std::complex<float>* to, const Packet2cf& from) {
+  pstore((float*)to, from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet2cf& from) {
+  pstoreu((float*)to, from.v);
+}
+template <>
+EIGEN_ALWAYS_INLINE void pstore_partial<std::complex<float> >(std::complex<float>* to, const Packet2cf& from,
+                                                              const Index n, const Index offset) {
+  pstore_partial((float*)to, from.v, n * 2, offset * 2);
+}
+template <>
+EIGEN_ALWAYS_INLINE void pstoreu_partial<std::complex<float> >(std::complex<float>* to, const Packet2cf& from,
+                                                               const Index n, const Index offset) {
+  pstoreu_partial((float*)to, from.v, n * 2, offset * 2);
+}
+
+EIGEN_STRONG_INLINE Packet2cf pload2(const std::complex<float>& from0, const std::complex<float>& from1) {
+  Packet4f res0, res1;
+#ifdef EIGEN_VECTORIZE_VSX
+  // Load two std::complex<float> from memory and combine
+  __asm__("lxsdx %x0,%y1" : "=wa"(res0) : "Z"(from0));
+  __asm__("lxsdx %x0,%y1" : "=wa"(res1) : "Z"(from1));
+#ifdef _BIG_ENDIAN
+  __asm__("xxpermdi %x0, %x1, %x2, 0" : "=wa"(res0) : "wa"(res0), "wa"(res1));
+#else
+  __asm__("xxpermdi %x0, %x2, %x1, 0" : "=wa"(res0) : "wa"(res0), "wa"(res1));
+#endif
+#else
+  *reinterpret_cast<std::complex<float>*>(&res0) = from0;
+  *reinterpret_cast<std::complex<float>*>(&res1) = from1;
+  res0 = vec_perm(res0, res1, p16uc_TRANSPOSE64_HI);
+#endif
+  return Packet2cf(res0);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet2cf pload_ignore<Packet2cf>(const std::complex<float>* from) {
+  Packet2cf res;
+  res.v = pload_ignore<Packet4f>(reinterpret_cast<const float*>(from));
+  return res;
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pgather_complex_size2(const Scalar* from, Index stride,
+                                                                   const Index n = 2) {
+  eigen_internal_assert(n <= unpacket_traits<Packet>::size && "number of elements will gather past end of packet");
+  EIGEN_ALIGN16 Scalar af[2];
+  for (Index i = 0; i < n; i++) {
+    af[i] = from[i * stride];
+  }
+  return pload_ignore<Packet>(af);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from,
+                                                                                        Index stride) {
+  return pgather_complex_size2<std::complex<float>, Packet2cf>(from, stride);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet2cf
+pgather_partial<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride, const Index n) {
+  return pgather_complex_size2<std::complex<float>, Packet2cf>(from, stride, n);
+}
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_complex_size2(Scalar* to, const Packet& from, Index stride,
+                                                                  const Index n = 2) {
+  eigen_internal_assert(n <= unpacket_traits<Packet>::size && "number of elements will scatter past end of packet");
+  EIGEN_ALIGN16 Scalar af[2];
+  pstore<Scalar>((Scalar*)af, from);
+  for (Index i = 0; i < n; i++) {
+    to[i * stride] = af[i];
+  }
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to,
+                                                                                    const Packet2cf& from,
+                                                                                    Index stride) {
+  pscatter_complex_size2<std::complex<float>, Packet2cf>(to, from, stride);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<std::complex<float>, Packet2cf>(std::complex<float>* to,
+                                                                                            const Packet2cf& from,
+                                                                                            Index stride,
+                                                                                            const Index n) {
+  pscatter_complex_size2<std::complex<float>, Packet2cf>(to, from, stride, n);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(a.v + b.v);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(a.v - b.v);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) {
+  return Packet2cf(pnegate(a.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) {
+  return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR())));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(pand<Packet4f>(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(por<Packet4f>(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(pxor<Packet4f>(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(pandnot<Packet4f>(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float>* addr) {
+  EIGEN_PPC_PREFETCH(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) {
+  EIGEN_ALIGN16 std::complex<float> res[2];
+  pstore((float*)&res, a.v);
+
+  return res[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) {
+  Packet4f rev_a;
+  rev_a = vec_sld(a.v, a.v, 8);
+  return Packet2cf(rev_a);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) {
+  Packet4f b;
+  b = vec_sld(a.v, a.v, 8);
+  b = padd<Packet4f>(a.v, b);
+  return pfirst<Packet2cf>(Packet2cf(b));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) {
+  Packet4f b;
+  Packet2cf prod;
+  b = vec_sld(a.v, a.v, 8);
+  prod = pmul<Packet2cf>(a, Packet2cf(b));
+
+  return pfirst<Packet2cf>(prod);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf, Packet4f)
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return pdiv_complex(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x) {
+  return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf, 2>& kernel) {
+#ifdef EIGEN_VECTORIZE_VSX
+  Packet4f tmp = reinterpret_cast<Packet4f>(
+      vec_mergeh(reinterpret_cast<Packet2d>(kernel.packet[0].v), reinterpret_cast<Packet2d>(kernel.packet[1].v)));
+  kernel.packet[1].v = reinterpret_cast<Packet4f>(
+      vec_mergel(reinterpret_cast<Packet2d>(kernel.packet[0].v), reinterpret_cast<Packet2d>(kernel.packet[1].v)));
+#else
+  Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
+  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
+#endif
+  kernel.packet[0].v = tmp;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
+  Packet4f eq = reinterpret_cast<Packet4f>(vec_cmpeq(a.v, b.v));
+  return Packet2cf(vec_and(eq, vec_perm(eq, eq, p16uc_COMPLEX32_REV)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
+  return psqrt_complex<Packet2cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf plog<Packet2cf>(const Packet2cf& a) {
+  return plog_complex<Packet2cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pexp<Packet2cf>(const Packet2cf& a) {
+  return pexp_complex<Packet2cf>(a);
+}
+
+//---------- double ----------
+#ifdef EIGEN_VECTORIZE_VSX
+struct Packet1cd {
+  EIGEN_STRONG_INLINE Packet1cd() {}
+  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
+
+  EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) {
+    Packet2d a_re, a_im, v1, v2;
+
+    // Permute and multiply the real parts of a and b
+    a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI);
+    // Get the imaginary parts of a
+    a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO);
+    // multiply a_re * b
+    v1 = vec_madd(a_re, b.v, p2d_ZERO);
+    // multiply a_im * b and get the conjugate result
+    v2 = vec_madd(a_im, b.v, p2d_ZERO);
+    v2 = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(v2), reinterpret_cast<Packet4ui>(v2), 8));
+    v2 = pxor(v2, reinterpret_cast<Packet2d>(p2ul_CONJ_XOR1()));
+
+    return Packet1cd(padd<Packet2d>(v1, v2));
+  }
+
+  EIGEN_STRONG_INLINE Packet1cd& operator*=(const Packet1cd& b) {
+    v = pmul(Packet1cd(*this), b).v;
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator*(const Packet1cd& b) const { return Packet1cd(*this) *= b; }
+
+  EIGEN_STRONG_INLINE Packet1cd& operator+=(const Packet1cd& b) {
+    v = padd(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator+(const Packet1cd& b) const { return Packet1cd(*this) += b; }
+  EIGEN_STRONG_INLINE Packet1cd& operator-=(const Packet1cd& b) {
+    v = psub(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator-(const Packet1cd& b) const { return Packet1cd(*this) -= b; }
+  EIGEN_STRONG_INLINE Packet1cd operator-(void) const { return Packet1cd(-v); }
+
+  Packet2d v;
+};
+
+template <>
+struct packet_traits<std::complex<double> > : default_packet_traits {
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  typedef Packet2d as_real;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 1,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet1cd> {
+  typedef std::complex<double> type;
+  enum {
+    size = 1,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet1cd half;
+  typedef Packet2d as_real;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) {
+  return Packet1cd(pload<Packet2d>((const double*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) {
+  return Packet1cd(ploadu<Packet2d>((const double*)from));
+}
+template <>
+EIGEN_ALWAYS_INLINE Packet1cd pload_partial<Packet1cd>(const std::complex<double>* from, const Index n,
+                                                       const Index offset) {
+  return Packet1cd(pload_partial<Packet2d>((const double*)from, n * 2, offset * 2));
+}
+template <>
+EIGEN_ALWAYS_INLINE Packet1cd ploadu_partial<Packet1cd>(const std::complex<double>* from, const Index n,
+                                                        const Index offset) {
+  return Packet1cd(ploadu_partial<Packet2d>((const double*)from, n * 2, offset * 2));
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<double> >(std::complex<double>* to, const Packet1cd& from) {
+  pstore((double*)to, from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double>* to, const Packet1cd& from) {
+  pstoreu((double*)to, from.v);
+}
+template <>
+EIGEN_ALWAYS_INLINE void pstore_partial<std::complex<double> >(std::complex<double>* to, const Packet1cd& from,
+                                                               const Index n, const Index offset) {
+  pstore_partial((double*)to, from.v, n * 2, offset * 2);
+}
+template <>
+EIGEN_ALWAYS_INLINE void pstoreu_partial<std::complex<double> >(std::complex<double>* to, const Packet1cd& from,
+                                                                const Index n, const Index offset) {
+  pstoreu_partial((double*)to, from.v, n * 2, offset * 2);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd
+pset1<Packet1cd>(const std::complex<double>& from) { /* here we really have to use unaligned loads :( */
+  return ploadu<Packet1cd>(&from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet1cd
+pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index) {
+  return pload<Packet1cd>(from);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet1cd
+pgather_partial<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index, const Index) {
+  return pload<Packet1cd>(from);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to,
+                                                                                     const Packet1cd& from, Index) {
+  pstore<std::complex<double> >(to, from);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<std::complex<double>, Packet1cd>(std::complex<double>* to,
+                                                                                             const Packet1cd& from,
+                                                                                             Index, const Index) {
+  pstore<std::complex<double> >(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(a.v + b.v);
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(a.v - b.v);
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) {
+  return Packet1cd(pnegate(Packet2d(a.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) {
+  return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p2ul_CONJ_XOR2())));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(pand(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(por(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(pxor(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(pandnot(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) {
+  return pset1<Packet1cd>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double>* addr) {
+  EIGEN_PPC_PREFETCH(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) {
+  EIGEN_ALIGN16 std::complex<double> res[1];
+  pstore<std::complex<double> >(res, a);
+
+  return res[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) {
+  return pfirst(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) {
+  return pfirst(a);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd, Packet2d)
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return pdiv_complex(a, b);
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip /*<Packet1cd>*/ (const Packet1cd& x) {
+  return Packet1cd(preverse(Packet2d(x.v)));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd, 2>& kernel) {
+  Packet2d tmp = vec_mergeh(kernel.packet[0].v, kernel.packet[1].v);
+  kernel.packet[1].v = vec_mergel(kernel.packet[0].v, kernel.packet[1].v);
+  kernel.packet[0].v = tmp;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
+  // Compare real and imaginary parts of a and b to get the mask vector:
+  // [re(a)==re(b), im(a)==im(b)]
+  Packet2d eq = reinterpret_cast<Packet2d>(vec_cmpeq(a.v, b.v));
+  // Swap real/imag elements in the mask in to get:
+  // [im(a)==im(b), re(a)==re(b)]
+  Packet2d eq_swapped =
+      reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(eq), reinterpret_cast<Packet4ui>(eq), 8));
+  // Return re(a)==re(b) & im(a)==im(b) by computing bitwise AND of eq and eq_swapped
+  return Packet1cd(vec_and(eq, eq_swapped));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
+  return psqrt_complex<Packet1cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd plog<Packet1cd>(const Packet1cd& a) {
+  return plog_complex<Packet1cd>(a);
+}
+
+#endif  // __VSX__
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX32_ALTIVEC_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MathFunctions.h
new file mode 100644
index 0000000..c95ee38
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MathFunctions.h
@@ -0,0 +1,81 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_ALTIVEC_H
+#define EIGEN_MATH_FUNCTIONS_ALTIVEC_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet4f)
+#ifdef EIGEN_VECTORIZE_VSX
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet2d)
+#endif
+
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f psqrt<Packet4f>(const Packet4f& x) {
+  return vec_sqrt(x);
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet2d psqrt<Packet2d>(const Packet2d& x) {
+  return vec_sqrt(x);
+}
+
+#if !EIGEN_COMP_CLANG
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f prsqrt<Packet4f>(const Packet4f& x) {
+  return pset1<Packet4f>(1.0f) / psqrt<Packet4f>(x);
+  //  vec_rsqrt returns different results from the generic version
+  //  return  vec_rsqrt(x);
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet2d prsqrt<Packet2d>(const Packet2d& x) {
+  return pset1<Packet2d>(1.0) / psqrt<Packet2d>(x);
+  //  vec_rsqrt returns different results from the generic version
+  //  return  vec_rsqrt(x);
+}
+
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf psqrt<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(psqrt<Packet4f>, a);
+}
+
+#if !EIGEN_COMP_CLANG
+template <>
+EIGEN_STRONG_INLINE Packet8bf prsqrt<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(prsqrt<Packet4f>, a);
+}
+#endif
+#else
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f psqrt<Packet4f>(const Packet4f& x) {
+  Packet4f a;
+  for (Index i = 0; i < packet_traits<float>::size; i++) {
+    a[i] = numext::sqrt(x[i]);
+  }
+  return a;
+}
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_ALTIVEC_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProduct.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
new file mode 100644
index 0000000..4725526
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
@@ -0,0 +1,3686 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020 Everton Constantino (everton.constantino@ibm.com)
+// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_PRODUCT_ALTIVEC_H
+#define EIGEN_MATRIX_PRODUCT_ALTIVEC_H
+
+#ifndef EIGEN_ALTIVEC_USE_CUSTOM_PACK
+#define EIGEN_ALTIVEC_USE_CUSTOM_PACK 1
+#endif
+
+#if !defined(EIGEN_ALTIVEC_DISABLE_MMA)
+#define EIGEN_ALTIVEC_DISABLE_MMA 0
+#endif
+
+// Check for MMA builtin support.
+#if !EIGEN_ALTIVEC_DISABLE_MMA && defined(__has_builtin)
+#if __has_builtin(__builtin_mma_assemble_acc)
+#define EIGEN_ALTIVEC_MMA_SUPPORT
+#endif
+#endif
+
+// Check if and how we should actually use MMA if supported.
+#if defined(EIGEN_ALTIVEC_MMA_SUPPORT)
+
+#if !defined(EIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH)
+#define EIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH 0
+#endif
+
+// Check if we want to enable dynamic dispatch. Not supported by LLVM.
+#if EIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH && !EIGEN_COMP_LLVM
+#define EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH 1
+// Otherwise, use MMA by default if available.
+#elif defined(__MMA__)
+#define EIGEN_ALTIVEC_MMA_ONLY 1
+#endif
+
+#endif  // EIGEN_ALTIVEC_MMA_SUPPORT
+
+#include "MatrixProductCommon.h"
+
+#if defined(EIGEN_ALTIVEC_MMA_ONLY) || defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+#include "MatrixProductMMA.h"
+#endif
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**************************
+ * Constants and typedefs *
+ **************************/
+template <typename Scalar>
+struct quad_traits {
+  typedef typename packet_traits<Scalar>::type vectortype;
+  typedef PacketBlock<vectortype, 4> type;
+  typedef vectortype rhstype;
+  enum { vectorsize = packet_traits<Scalar>::size, size = 4, rows = 4 };
+};
+
+template <>
+struct quad_traits<double> {
+  typedef Packet2d vectortype;
+  typedef PacketBlock<vectortype, 4> type;
+  typedef PacketBlock<Packet2d, 2> rhstype;
+  enum { vectorsize = packet_traits<double>::size, size = 2, rows = 4 };
+};
+
+template <>
+struct quad_traits<bfloat16> {
+  typedef Packet8bf vectortype;
+  typedef PacketBlock<vectortype, 4> type;
+  typedef vectortype rhstype;
+  enum { vectorsize = packet_traits<bfloat16>::size, size = 8, rows = 4 };
+};
+
+// MatrixProduct decomposes real/imaginary vectors into a real vector and an imaginary vector, this turned out
+// to be faster than Eigen's usual approach of having real/imaginary pairs on a single vector. This constants then
+// are responsible to extract from convert between Eigen's and MatrixProduct approach.
+
+const static Packet16uc p16uc_GETREAL32 = {0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27};
+
+const static Packet16uc p16uc_GETIMAG32 = {4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31};
+
+const static Packet16uc p16uc_GETREAL32b = {0, 1, 2, 3, 16, 17, 18, 19, 8, 9, 10, 11, 24, 25, 26, 27};
+
+const static Packet16uc p16uc_GETIMAG32b = {4, 5, 6, 7, 20, 21, 22, 23, 12, 13, 14, 15, 28, 29, 30, 31};
+
+/*********************************************
+ * Single precision real and complex packing *
+ * *******************************************/
+
+/**
+ * Symm packing is related to packing of symmetric adjoint blocks, as expected the packing leaves
+ * the diagonal real, whatever is below it is copied from the respective upper diagonal element and
+ * conjugated. There's no PanelMode available for symm packing.
+ *
+ * Packing in general is supposed to leave the lhs block and the rhs block easy to be read by gemm using
+ * its respective rank-update instructions. The float32/64 versions are different because at this moment
+ * the size of the accumulator is fixed at 512-bits so you can't have a 4x4 accumulator of 64-bit elements.
+ *
+ * As mentioned earlier MatrixProduct breaks complex numbers into a real vector and a complex vector so packing has
+ * to take that into account, at the moment, we run pack the real part and then the imaginary part, this is the main
+ * reason why packing for complex is broken down into several different parts, also the reason why we endup having a
+ * float32/64 and complex float32/64 version.
+ **/
+template <typename Scalar, int StorageOrder>
+EIGEN_ALWAYS_INLINE std::complex<Scalar> getAdjointVal(
+    Index i, Index j, const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder>& dt) {
+  std::complex<Scalar> v;
+  if (i < j) {
+    v.real(dt(j, i).real());
+    v.imag(-dt(j, i).imag());
+  } else if (i > j) {
+    v.real(dt(i, j).real());
+    v.imag(dt(i, j).imag());
+  } else {
+    v.real(dt(i, j).real());
+    v.imag((Scalar)0.0);
+  }
+  return v;
+}
+
+template <typename Scalar, int StorageOrder, int N>
+EIGEN_STRONG_INLINE void symm_pack_complex_rhs_helper(std::complex<Scalar>* blockB, const std::complex<Scalar>* _rhs,
+                                                      Index rhsStride, Index rows, Index cols, Index k2) {
+  const Index depth = k2 + rows;
+  const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder> rhs(_rhs, rhsStride);
+  const Index vectorSize = N * quad_traits<Scalar>::vectorsize;
+  const Index vectorDelta = vectorSize * rows;
+  Scalar* blockBf = reinterpret_cast<Scalar*>(blockB);
+
+  Index rir = 0, rii, j = 0;
+  for (; j + vectorSize <= cols; j += vectorSize) {
+    rii = rir + vectorDelta;
+
+    for (Index i = k2; i < depth; i++) {
+      for (Index k = 0; k < vectorSize; k++) {
+        std::complex<Scalar> v = getAdjointVal<Scalar, StorageOrder>(i, j + k, rhs);
+
+        blockBf[rir + k] = v.real();
+        blockBf[rii + k] = v.imag();
+      }
+      rir += vectorSize;
+      rii += vectorSize;
+    }
+
+    rir += vectorDelta;
+  }
+
+  for (; j < cols; j++) {
+    rii = rir + rows;
+
+    for (Index i = k2; i < depth; i++) {
+      std::complex<Scalar> v = getAdjointVal<Scalar, StorageOrder>(i, j, rhs);
+
+      blockBf[rir] = v.real();
+      blockBf[rii] = v.imag();
+
+      rir += 1;
+      rii += 1;
+    }
+
+    rir += rows;
+  }
+}
+
+template <typename Scalar, int StorageOrder>
+EIGEN_STRONG_INLINE void symm_pack_complex_lhs_helper(std::complex<Scalar>* blockA, const std::complex<Scalar>* _lhs,
+                                                      Index lhsStride, Index cols, Index rows) {
+  const Index depth = cols;
+  const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder> lhs(_lhs, lhsStride);
+  const Index vectorSize = quad_traits<Scalar>::vectorsize;
+  const Index vectorDelta = vectorSize * depth;
+  Scalar* blockAf = reinterpret_cast<Scalar*>(blockA);
+
+  Index rir = 0, rii, j = 0;
+  for (; j + vectorSize <= rows; j += vectorSize) {
+    rii = rir + vectorDelta;
+
+    for (Index i = 0; i < depth; i++) {
+      for (Index k = 0; k < vectorSize; k++) {
+        std::complex<Scalar> v = getAdjointVal<Scalar, StorageOrder>(j + k, i, lhs);
+
+        blockAf[rir + k] = v.real();
+        blockAf[rii + k] = v.imag();
+      }
+      rir += vectorSize;
+      rii += vectorSize;
+    }
+
+    rir += vectorDelta;
+  }
+
+  if (j < rows) {
+    rii = rir + ((rows - j) * depth);
+
+    for (Index i = 0; i < depth; i++) {
+      Index k = j;
+      for (; k < rows; k++) {
+        std::complex<Scalar> v = getAdjointVal<Scalar, StorageOrder>(k, i, lhs);
+
+        blockAf[rir] = v.real();
+        blockAf[rii] = v.imag();
+
+        rir += 1;
+        rii += 1;
+      }
+    }
+  }
+}
+
+template <typename Scalar, int StorageOrder, int N>
+EIGEN_STRONG_INLINE void symm_pack_rhs_helper(Scalar* blockB, const Scalar* _rhs, Index rhsStride, Index rows,
+                                              Index cols, Index k2) {
+  const Index depth = k2 + rows;
+  const_blas_data_mapper<Scalar, Index, StorageOrder> rhs(_rhs, rhsStride);
+  const Index vectorSize = quad_traits<Scalar>::vectorsize;
+
+  Index ri = 0, j = 0;
+  for (; j + N * vectorSize <= cols; j += N * vectorSize) {
+    Index i = k2;
+    for (; i < depth; i++) {
+      for (Index k = 0; k < N * vectorSize; k++) {
+        if (i <= j + k)
+          blockB[ri + k] = rhs(j + k, i);
+        else
+          blockB[ri + k] = rhs(i, j + k);
+      }
+      ri += N * vectorSize;
+    }
+  }
+
+  for (; j < cols; j++) {
+    for (Index i = k2; i < depth; i++) {
+      if (j <= i)
+        blockB[ri] = rhs(i, j);
+      else
+        blockB[ri] = rhs(j, i);
+      ri += 1;
+    }
+  }
+}
+
+template <typename Scalar, int StorageOrder>
+EIGEN_STRONG_INLINE void symm_pack_lhs_helper(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols,
+                                              Index rows) {
+  const Index depth = cols;
+  const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs, lhsStride);
+  const Index vectorSize = quad_traits<Scalar>::vectorsize;
+
+  Index ri = 0, j = 0;
+  for (; j + vectorSize <= rows; j += vectorSize) {
+    Index i = 0;
+
+    for (; i < depth; i++) {
+      for (Index k = 0; k < vectorSize; k++) {
+        if (i <= j + k)
+          blockA[ri + k] = lhs(j + k, i);
+        else
+          blockA[ri + k] = lhs(i, j + k);
+      }
+      ri += vectorSize;
+    }
+  }
+
+  if (j < rows) {
+    for (Index i = 0; i < depth; i++) {
+      Index k = j;
+      for (; k < rows; k++) {
+        if (i <= k)
+          blockA[ri] = lhs(k, i);
+        else
+          blockA[ri] = lhs(i, k);
+        ri += 1;
+      }
+    }
+  }
+}
+
+template <typename Index, int nr, int StorageOrder>
+struct symm_pack_rhs<std::complex<float>, Index, nr, StorageOrder> {
+  void operator()(std::complex<float>* blockB, const std::complex<float>* _rhs, Index rhsStride, Index rows, Index cols,
+                  Index k2) {
+    symm_pack_complex_rhs_helper<float, StorageOrder, 1>(blockB, _rhs, rhsStride, rows, cols, k2);
+  }
+};
+
+template <typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
+struct symm_pack_lhs<std::complex<float>, Index, Pack1, Pack2_dummy, StorageOrder> {
+  void operator()(std::complex<float>* blockA, const std::complex<float>* _lhs, Index lhsStride, Index cols,
+                  Index rows) {
+    symm_pack_complex_lhs_helper<float, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
+  }
+};
+
+// *********** symm_pack std::complex<float64> ***********
+
+template <typename Index, int nr, int StorageOrder>
+struct symm_pack_rhs<std::complex<double>, Index, nr, StorageOrder> {
+  void operator()(std::complex<double>* blockB, const std::complex<double>* _rhs, Index rhsStride, Index rows,
+                  Index cols, Index k2) {
+    symm_pack_complex_rhs_helper<double, StorageOrder, 2>(blockB, _rhs, rhsStride, rows, cols, k2);
+  }
+};
+
+template <typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
+struct symm_pack_lhs<std::complex<double>, Index, Pack1, Pack2_dummy, StorageOrder> {
+  void operator()(std::complex<double>* blockA, const std::complex<double>* _lhs, Index lhsStride, Index cols,
+                  Index rows) {
+    symm_pack_complex_lhs_helper<double, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
+  }
+};
+
+// *********** symm_pack float32 ***********
+template <typename Index, int nr, int StorageOrder>
+struct symm_pack_rhs<float, Index, nr, StorageOrder> {
+  void operator()(float* blockB, const float* _rhs, Index rhsStride, Index rows, Index cols, Index k2) {
+    symm_pack_rhs_helper<float, StorageOrder, 1>(blockB, _rhs, rhsStride, rows, cols, k2);
+  }
+};
+
+template <typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
+struct symm_pack_lhs<float, Index, Pack1, Pack2_dummy, StorageOrder> {
+  void operator()(float* blockA, const float* _lhs, Index lhsStride, Index cols, Index rows) {
+    symm_pack_lhs_helper<float, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
+  }
+};
+
+// *********** symm_pack float64 ***********
+template <typename Index, int nr, int StorageOrder>
+struct symm_pack_rhs<double, Index, nr, StorageOrder> {
+  void operator()(double* blockB, const double* _rhs, Index rhsStride, Index rows, Index cols, Index k2) {
+    symm_pack_rhs_helper<double, StorageOrder, 2>(blockB, _rhs, rhsStride, rows, cols, k2);
+  }
+};
+
+template <typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
+struct symm_pack_lhs<double, Index, Pack1, Pack2_dummy, StorageOrder> {
+  void operator()(double* blockA, const double* _lhs, Index lhsStride, Index cols, Index rows) {
+    symm_pack_lhs_helper<double, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
+  }
+};
+
+/**
+ * PanelMode
+ * Packing might be called several times before being multiplied by gebp_kernel, this happens because
+ * on special occasions it fills part of block with other parts of the matrix. Two variables control
+ * how PanelMode should behave: offset and stride. The idea is that those variables represent whatever
+ * is going to be the real offset and stride in the future and this is what you should obey. The process
+ * is to behave as you would with normal packing but leave the start of each part with the correct offset
+ * and the end as well respecting the real stride the block will have. Gebp is aware of both blocks stride
+ * and offset and behaves accordingly.
+ **/
+
+template <typename Scalar, typename Packet, int N>
+EIGEN_ALWAYS_INLINE void storeBlock(Scalar* to, PacketBlock<Packet, N>& block) {
+  const Index size = 16 / sizeof(Scalar);
+  pstore<Scalar>(to + (0 * size), block.packet[0]);
+  pstore<Scalar>(to + (1 * size), block.packet[1]);
+  if (N > 2) {
+    pstore<Scalar>(to + (2 * size), block.packet[2]);
+  }
+  if (N > 3) {
+    pstore<Scalar>(to + (3 * size), block.packet[3]);
+  }
+}
+
+// General template for lhs & rhs complex packing.
+template <typename Scalar, typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate,
+          bool PanelMode, bool UseLhs>
+struct dhs_cpack {
+  template <bool transpose>
+  EIGEN_ALWAYS_INLINE void dhs_cblock(PacketBlock<PacketC, 8>& cblock, PacketBlock<Packet, 4>& block,
+                                      Packet16uc permute) {
+    if (transpose) {
+      block.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, permute);
+      block.packet[1] = vec_perm(cblock.packet[2].v, cblock.packet[3].v, permute);
+      block.packet[2] = vec_perm(cblock.packet[4].v, cblock.packet[5].v, permute);
+      block.packet[3] = vec_perm(cblock.packet[6].v, cblock.packet[7].v, permute);
+
+      Packet4f t0, t1, t2, t3;
+#ifdef EIGEN_VECTORIZE_VSX
+      t0 = reinterpret_cast<Packet>(
+          vec_mergeh(reinterpret_cast<Packet2ul>(block.packet[0]), reinterpret_cast<Packet2ul>(block.packet[1])));
+      t1 = reinterpret_cast<Packet>(
+          vec_mergel(reinterpret_cast<Packet2ul>(block.packet[0]), reinterpret_cast<Packet2ul>(block.packet[1])));
+      t2 = reinterpret_cast<Packet>(
+          vec_mergeh(reinterpret_cast<Packet2ul>(block.packet[2]), reinterpret_cast<Packet2ul>(block.packet[3])));
+      t3 = reinterpret_cast<Packet>(
+          vec_mergel(reinterpret_cast<Packet2ul>(block.packet[2]), reinterpret_cast<Packet2ul>(block.packet[3])));
+#else
+      t0 = reinterpret_cast<Packet>(vec_perm(block.packet[0], block.packet[1], p16uc_TRANSPOSE64_HI));
+      t1 = reinterpret_cast<Packet>(vec_perm(block.packet[0], block.packet[1], p16uc_TRANSPOSE64_LO));
+      t2 = reinterpret_cast<Packet>(vec_perm(block.packet[2], block.packet[3], p16uc_TRANSPOSE64_HI));
+      t3 = reinterpret_cast<Packet>(vec_perm(block.packet[2], block.packet[3], p16uc_TRANSPOSE64_LO));
+#endif
+
+      block.packet[0] = t0;
+      block.packet[1] = t1;
+      block.packet[2] = t2;
+      block.packet[3] = t3;
+    } else {
+      block.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[4].v, permute);
+      block.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[5].v, permute);
+      block.packet[2] = vec_perm(cblock.packet[2].v, cblock.packet[6].v, permute);
+      block.packet[3] = vec_perm(cblock.packet[3].v, cblock.packet[7].v, permute);
+    }
+  }
+
+  EIGEN_ALWAYS_INLINE void dhs_ccopy(Scalar* blockAt, const DataMapper& lhs2, Index& i, Index& rir, Index& rii,
+                                     Index depth, const Index vectorSize) {
+    PacketBlock<Packet, 4> blockr, blocki;
+    PacketBlock<PacketC, 8> cblock;
+
+    for (; i + vectorSize <= depth; i += vectorSize) {
+      if (UseLhs) {
+        bload<DataMapper, PacketC, 2, StorageOrder, true, 4>(cblock, lhs2, 0, i);
+      } else {
+        bload<DataMapper, PacketC, 2, StorageOrder, true, 4>(cblock, lhs2, i, 0);
+      }
+
+      if (((StorageOrder == RowMajor) && UseLhs) || (((StorageOrder == ColMajor) && !UseLhs))) {
+        dhs_cblock<true>(cblock, blockr, p16uc_GETREAL32b);
+        dhs_cblock<true>(cblock, blocki, p16uc_GETIMAG32b);
+      } else {
+        dhs_cblock<false>(cblock, blockr, p16uc_GETREAL32);
+        dhs_cblock<false>(cblock, blocki, p16uc_GETIMAG32);
+      }
+
+      if (Conjugate) {
+        blocki.packet[0] = -blocki.packet[0];
+        blocki.packet[1] = -blocki.packet[1];
+        blocki.packet[2] = -blocki.packet[2];
+        blocki.packet[3] = -blocki.packet[3];
+      }
+
+      storeBlock<Scalar, Packet, 4>(blockAt + rir, blockr);
+      storeBlock<Scalar, Packet, 4>(blockAt + rii, blocki);
+
+      rir += 4 * vectorSize;
+      rii += 4 * vectorSize;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void operator()(std::complex<Scalar>* blockA, const DataMapper& lhs, Index depth, Index rows,
+                                      Index stride, Index offset) {
+    const Index vectorSize = quad_traits<Scalar>::vectorsize;
+    const Index vectorDelta = vectorSize * ((PanelMode) ? stride : depth);
+    Index rir = ((PanelMode) ? (vectorSize * offset) : 0), rii;
+    Scalar* blockAt = reinterpret_cast<Scalar*>(blockA);
+    Index j = 0;
+
+    for (; j + vectorSize <= rows; j += vectorSize) {
+      const DataMapper lhs2 = UseLhs ? lhs.getSubMapper(j, 0) : lhs.getSubMapper(0, j);
+      Index i = 0;
+
+      rii = rir + vectorDelta;
+
+      dhs_ccopy(blockAt, lhs2, i, rir, rii, depth, vectorSize);
+
+      for (; i < depth; i++) {
+        PacketBlock<Packet, 1> blockr, blocki;
+        PacketBlock<PacketC, 2> cblock;
+
+        if (((StorageOrder == ColMajor) && UseLhs) || (((StorageOrder == RowMajor) && !UseLhs))) {
+          if (UseLhs) {
+            cblock.packet[0] = lhs2.template loadPacket<PacketC>(0, i);
+            cblock.packet[1] = lhs2.template loadPacket<PacketC>(2, i);
+          } else {
+            cblock.packet[0] = lhs2.template loadPacket<PacketC>(i, 0);
+            cblock.packet[1] = lhs2.template loadPacket<PacketC>(i, 2);
+          }
+        } else {
+          if (UseLhs) {
+            cblock.packet[0] = pload2(lhs2(0, i), lhs2(1, i));
+            cblock.packet[1] = pload2(lhs2(2, i), lhs2(3, i));
+          } else {
+            cblock.packet[0] = pload2(lhs2(i, 0), lhs2(i, 1));
+            cblock.packet[1] = pload2(lhs2(i, 2), lhs2(i, 3));
+          }
+        }
+
+        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL32);
+        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETIMAG32);
+
+        if (Conjugate) {
+          blocki.packet[0] = -blocki.packet[0];
+        }
+
+        pstore<Scalar>(blockAt + rir, blockr.packet[0]);
+        pstore<Scalar>(blockAt + rii, blocki.packet[0]);
+
+        rir += vectorSize;
+        rii += vectorSize;
+      }
+
+      rir += ((PanelMode) ? (vectorSize * (2 * stride - depth)) : vectorDelta);
+    }
+
+    if (!UseLhs) {
+      if (PanelMode) rir -= (offset * (vectorSize - 1));
+
+      for (; j < rows; j++) {
+        const DataMapper lhs2 = lhs.getSubMapper(0, j);
+        rii = rir + ((PanelMode) ? stride : depth);
+
+        for (Index i = 0; i < depth; i++) {
+          blockAt[rir] = lhs2(i, 0).real();
+
+          if (Conjugate)
+            blockAt[rii] = -lhs2(i, 0).imag();
+          else
+            blockAt[rii] = lhs2(i, 0).imag();
+
+          rir += 1;
+          rii += 1;
+        }
+
+        rir += ((PanelMode) ? (2 * stride - depth) : depth);
+      }
+    } else {
+      if (j < rows) {
+        if (PanelMode) rir += (offset * (rows - j - vectorSize));
+        rii = rir + (((PanelMode) ? stride : depth) * (rows - j));
+
+        for (Index i = 0; i < depth; i++) {
+          Index k = j;
+          for (; k < rows; k++) {
+            blockAt[rir] = lhs(k, i).real();
+
+            if (Conjugate)
+              blockAt[rii] = -lhs(k, i).imag();
+            else
+              blockAt[rii] = lhs(k, i).imag();
+
+            rir += 1;
+            rii += 1;
+          }
+        }
+      }
+    }
+  }
+};
+
+// General template for lhs & rhs packing.
+template <typename Scalar, typename DataMapper, typename Packet, int StorageOrder, bool PanelMode, bool UseLhs>
+struct dhs_pack {
+  template <Index n>
+  EIGEN_ALWAYS_INLINE void dhs_copy(Scalar* blockA, const DataMapper& lhs2, Index& i, Index& ri, Index depth,
+                                    const Index vectorSize) {
+    PacketBlock<Packet, 4> block[n];
+
+    for (; i + n * vectorSize <= depth; i += n * vectorSize) {
+      for (Index k = 0; k < n; k++) {
+        if (UseLhs) {
+          bload<DataMapper, Packet, 4, StorageOrder, false, 4>(block[k], lhs2, 0, i + k * vectorSize);
+        } else {
+          bload<DataMapper, Packet, 4, StorageOrder, false, 4>(block[k], lhs2, i + k * vectorSize, 0);
+        }
+      }
+
+      if (((StorageOrder == RowMajor) && UseLhs) || ((StorageOrder == ColMajor) && !UseLhs)) {
+        for (Index k = 0; k < n; k++) {
+          ptranspose(block[k]);
+        }
+      }
+
+      for (Index k = 0; k < n; k++) {
+        storeBlock<Scalar, Packet, 4>(blockA + ri + k * 4 * vectorSize, block[k]);
+      }
+
+      ri += n * 4 * vectorSize;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride,
+                                      Index offset) {
+    const Index vectorSize = quad_traits<Scalar>::vectorsize;
+    Index ri = 0, j = 0;
+
+    for (; j + vectorSize <= rows; j += vectorSize) {
+      const DataMapper lhs2 = UseLhs ? lhs.getSubMapper(j, 0) : lhs.getSubMapper(0, j);
+      Index i = 0;
+
+      if (PanelMode) ri += vectorSize * offset;
+
+      dhs_copy<4>(blockA, lhs2, i, ri, depth, vectorSize);
+      dhs_copy<2>(blockA, lhs2, i, ri, depth, vectorSize);
+      dhs_copy<1>(blockA, lhs2, i, ri, depth, vectorSize);
+
+      for (; i < depth; i++) {
+        if (((StorageOrder == RowMajor) && UseLhs) || ((StorageOrder == ColMajor) && !UseLhs)) {
+          if (UseLhs) {
+            blockA[ri + 0] = lhs2(0, i);
+            blockA[ri + 1] = lhs2(1, i);
+            blockA[ri + 2] = lhs2(2, i);
+            blockA[ri + 3] = lhs2(3, i);
+          } else {
+            blockA[ri + 0] = lhs2(i, 0);
+            blockA[ri + 1] = lhs2(i, 1);
+            blockA[ri + 2] = lhs2(i, 2);
+            blockA[ri + 3] = lhs2(i, 3);
+          }
+        } else {
+          Packet lhsV;
+          if (UseLhs) {
+            lhsV = lhs2.template loadPacket<Packet>(0, i);
+          } else {
+            lhsV = lhs2.template loadPacket<Packet>(i, 0);
+          }
+          pstore<Scalar>(blockA + ri, lhsV);
+        }
+
+        ri += vectorSize;
+      }
+
+      if (PanelMode) ri += vectorSize * (stride - offset - depth);
+    }
+
+    if (!UseLhs) {
+      if (PanelMode) ri += offset;
+
+      for (; j < rows; j++) {
+        const DataMapper lhs2 = lhs.getSubMapper(0, j);
+        for (Index i = 0; i < depth; i++) {
+          blockA[ri] = lhs2(i, 0);
+          ri += 1;
+        }
+
+        if (PanelMode) ri += stride - depth;
+      }
+    } else {
+      if (j < rows) {
+        if (PanelMode) ri += offset * (rows - j);
+
+        for (Index i = 0; i < depth; i++) {
+          Index k = j;
+          for (; k < rows; k++) {
+            blockA[ri] = lhs(k, i);
+            ri += 1;
+          }
+        }
+      }
+    }
+  }
+};
+
+// General template for lhs packing, float64 specialization.
+template <typename DataMapper, int StorageOrder, bool PanelMode>
+struct dhs_pack<double, DataMapper, Packet2d, StorageOrder, PanelMode, true> {
+  template <Index n>
+  EIGEN_ALWAYS_INLINE void dhs_copy(double* blockA, const DataMapper& lhs2, Index& i, Index& ri, Index depth,
+                                    const Index vectorSize) {
+    PacketBlock<Packet2d, 2> block[n];
+
+    for (; i + n * vectorSize <= depth; i += n * vectorSize) {
+      for (Index k = 0; k < n; k++) {
+        if (StorageOrder == RowMajor) {
+          block[k].packet[0] = lhs2.template loadPacket<Packet2d>(0, i + k * vectorSize);
+          block[k].packet[1] = lhs2.template loadPacket<Packet2d>(1, i + k * vectorSize);
+        } else {
+          block[k].packet[0] = lhs2.template loadPacket<Packet2d>(0, i + k * vectorSize + 0);
+          block[k].packet[1] = lhs2.template loadPacket<Packet2d>(0, i + k * vectorSize + 1);
+        }
+      }
+
+      if (StorageOrder == RowMajor) {
+        for (Index k = 0; k < n; k++) {
+          ptranspose(block[k]);
+        }
+      }
+
+      for (Index k = 0; k < n; k++) {
+        storeBlock<double, Packet2d, 2>(blockA + ri + k * 2 * vectorSize, block[k]);
+      }
+
+      ri += n * 2 * vectorSize;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride,
+                                      Index offset) {
+    const Index vectorSize = quad_traits<double>::vectorsize;
+    Index ri = 0, j = 0;
+
+    for (; j + vectorSize <= rows; j += vectorSize) {
+      const DataMapper lhs2 = lhs.getSubMapper(j, 0);
+      Index i = 0;
+
+      if (PanelMode) ri += vectorSize * offset;
+
+      dhs_copy<4>(blockA, lhs2, i, ri, depth, vectorSize);
+      dhs_copy<2>(blockA, lhs2, i, ri, depth, vectorSize);
+      dhs_copy<1>(blockA, lhs2, i, ri, depth, vectorSize);
+
+      for (; i < depth; i++) {
+        if (StorageOrder == RowMajor) {
+          blockA[ri + 0] = lhs2(0, i);
+          blockA[ri + 1] = lhs2(1, i);
+        } else {
+          Packet2d lhsV = lhs2.template loadPacket<Packet2d>(0, i);
+          pstore<double>(blockA + ri, lhsV);
+        }
+
+        ri += vectorSize;
+      }
+
+      if (PanelMode) ri += vectorSize * (stride - offset - depth);
+    }
+
+    if (j < rows) {
+      if (PanelMode) ri += offset * (rows - j);
+
+      for (Index i = 0; i < depth; i++) {
+        Index k = j;
+        for (; k < rows; k++) {
+          blockA[ri] = lhs(k, i);
+          ri += 1;
+        }
+      }
+    }
+  }
+};
+
+// General template for rhs packing, float64 specialization.
+template <typename DataMapper, int StorageOrder, bool PanelMode>
+struct dhs_pack<double, DataMapper, Packet2d, StorageOrder, PanelMode, false> {
+  template <Index n>
+  EIGEN_ALWAYS_INLINE void dhs_copy(double* blockB, const DataMapper& rhs2, Index& i, Index& ri, Index depth,
+                                    const Index vectorSize) {
+    PacketBlock<Packet2d, 2> block1[n], block2[n];
+    PacketBlock<Packet2d, 4> block3[n];
+
+    for (; i + n * vectorSize <= depth; i += n * vectorSize) {
+      for (Index k = 0; k < n; k++) {
+        if (StorageOrder == ColMajor) {
+          block1[k].packet[0] = rhs2.template loadPacket<Packet2d>(i + k * vectorSize, 0);
+          block1[k].packet[1] = rhs2.template loadPacket<Packet2d>(i + k * vectorSize, 1);
+          block2[k].packet[0] = rhs2.template loadPacket<Packet2d>(i + k * vectorSize, 2);
+          block2[k].packet[1] = rhs2.template loadPacket<Packet2d>(i + k * vectorSize, 3);
+        } else {
+          block3[k].packet[0] = rhs2.template loadPacket<Packet2d>(i + k * vectorSize + 0, 0);  //[a1 a2]
+          block3[k].packet[1] = rhs2.template loadPacket<Packet2d>(i + k * vectorSize + 0, 2);  //[a3 a4]
+          block3[k].packet[2] = rhs2.template loadPacket<Packet2d>(i + k * vectorSize + 1, 0);  //[b1 b2]
+          block3[k].packet[3] = rhs2.template loadPacket<Packet2d>(i + k * vectorSize + 1, 2);  //[b3 b4]
+        }
+      }
+
+      if (StorageOrder == ColMajor) {
+        for (Index k = 0; k < n; k++) {
+          ptranspose(block1[k]);
+          ptranspose(block2[k]);
+        }
+      }
+
+      for (Index k = 0; k < n; k++) {
+        if (StorageOrder == ColMajor) {
+          pstore<double>(blockB + ri + k * 4 * vectorSize, block1[k].packet[0]);
+          pstore<double>(blockB + ri + k * 4 * vectorSize + 2, block2[k].packet[0]);
+          pstore<double>(blockB + ri + k * 4 * vectorSize + 4, block1[k].packet[1]);
+          pstore<double>(blockB + ri + k * 4 * vectorSize + 6, block2[k].packet[1]);
+        } else {
+          storeBlock<double, Packet2d, 4>(blockB + ri + k * 4 * vectorSize, block3[k]);
+        }
+      }
+
+      ri += n * 4 * vectorSize;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride,
+                                      Index offset) {
+    const Index vectorSize = quad_traits<double>::vectorsize;
+    Index ri = 0, j = 0;
+
+    for (; j + 2 * vectorSize <= cols; j += 2 * vectorSize) {
+      const DataMapper rhs2 = rhs.getSubMapper(0, j);
+      Index i = 0;
+
+      if (PanelMode) ri += offset * (2 * vectorSize);
+
+      dhs_copy<4>(blockB, rhs2, i, ri, depth, vectorSize);
+      dhs_copy<2>(blockB, rhs2, i, ri, depth, vectorSize);
+      dhs_copy<1>(blockB, rhs2, i, ri, depth, vectorSize);
+
+      for (; i < depth; i++) {
+        if (StorageOrder == ColMajor) {
+          blockB[ri + 0] = rhs2(i, 0);
+          blockB[ri + 1] = rhs2(i, 1);
+
+          ri += vectorSize;
+
+          blockB[ri + 0] = rhs2(i, 2);
+          blockB[ri + 1] = rhs2(i, 3);
+        } else {
+          Packet2d rhsV = rhs2.template loadPacket<Packet2d>(i, 0);
+          pstore<double>(blockB + ri, rhsV);
+
+          ri += vectorSize;
+
+          rhsV = rhs2.template loadPacket<Packet2d>(i, 2);
+          pstore<double>(blockB + ri, rhsV);
+        }
+        ri += vectorSize;
+      }
+
+      if (PanelMode) ri += (2 * vectorSize) * (stride - offset - depth);
+    }
+
+    if (PanelMode) ri += offset;
+
+    for (; j < cols; j++) {
+      const DataMapper rhs2 = rhs.getSubMapper(0, j);
+      for (Index i = 0; i < depth; i++) {
+        blockB[ri] = rhs2(i, 0);
+        ri += 1;
+      }
+
+      if (PanelMode) ri += stride - depth;
+    }
+  }
+};
+
+// General template for lhs packing, bfloat16 specialization.
+template <typename DataMapper, int StorageOrder, bool PanelMode>
+struct dhs_pack<bfloat16, DataMapper, Packet8bf, StorageOrder, PanelMode, true> {
+  EIGEN_STRONG_INLINE void operator()(bfloat16* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride,
+                                      Index offset) {
+    const Index vectorSize = quad_traits<bfloat16>::vectorsize;
+    Index ri = 0, j = 0;
+
+    for (; j + 2 * vectorSize <= rows; j += 2 * vectorSize) {
+      const DataMapper lhs2 = lhs.getSubMapper(j, 0);
+      Index i = 0;
+
+      if (PanelMode) ri += 2 * vectorSize * offset;
+
+      if (StorageOrder == ColMajor) {
+        for (; i + 2 <= depth; i += 2) {
+          PacketBlock<Packet8bf, 4> block;
+
+          block.packet[0] = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 0);
+          block.packet[1] = lhs2.template loadPacket<Packet8bf>(1 * vectorSize, i + 0);
+          block.packet[2] = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 1);
+          block.packet[3] = lhs2.template loadPacket<Packet8bf>(1 * vectorSize, i + 1);
+
+          Packet8bf t0, t1;
+          t0 = vec_mergeh(block.packet[0].m_val, block.packet[2].m_val);
+          t1 = vec_mergel(block.packet[0].m_val, block.packet[2].m_val);
+          block.packet[2] = vec_mergeh(block.packet[1].m_val, block.packet[3].m_val);
+          block.packet[3] = vec_mergel(block.packet[1].m_val, block.packet[3].m_val);
+          block.packet[0] = t0;
+          block.packet[1] = t1;
+
+          storeBlock<bfloat16, Packet8bf, 4>(blockA + ri, block);
+
+          ri += 2 * 2 * vectorSize;
+        }
+        if (depth & 1) {
+          PacketBlock<Packet8bf, 2> block;
+
+          block.packet[0] = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 0);
+          block.packet[1] = lhs2.template loadPacket<Packet8bf>(1 * vectorSize, i + 0);
+
+          storeBlock<bfloat16, Packet8bf, 2>(blockA + ri, block);
+
+          ri += 2 * vectorSize;
+        }
+      } else {
+        for (; i + vectorSize <= depth; i += vectorSize) {
+          PacketBlock<Packet8bf, 8> block1, block2;
+
+          bload<DataMapper, Packet8bf, 8, StorageOrder, false, 8>(block1, lhs2, 0 * vectorSize, i);
+          bload<DataMapper, Packet8bf, 8, StorageOrder, false, 8>(block2, lhs2, 1 * vectorSize, i);
+
+          Packet4ui v1[8], v2[8];
+
+          v1[0] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[0].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[1].m_val));
+          v1[1] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[0].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[1].m_val));
+          v1[2] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[2].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[3].m_val));
+          v1[3] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[2].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[3].m_val));
+          v1[4] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[4].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[5].m_val));
+          v1[5] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[4].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[5].m_val));
+          v1[6] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[6].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[7].m_val));
+          v1[7] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[6].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[7].m_val));
+          v2[0] = vec_mergeh(reinterpret_cast<Packet4ui>(block2.packet[0].m_val),
+                             reinterpret_cast<Packet4ui>(block2.packet[1].m_val));
+          v2[1] = vec_mergel(reinterpret_cast<Packet4ui>(block2.packet[0].m_val),
+                             reinterpret_cast<Packet4ui>(block2.packet[1].m_val));
+          v2[2] = vec_mergeh(reinterpret_cast<Packet4ui>(block2.packet[2].m_val),
+                             reinterpret_cast<Packet4ui>(block2.packet[3].m_val));
+          v2[3] = vec_mergel(reinterpret_cast<Packet4ui>(block2.packet[2].m_val),
+                             reinterpret_cast<Packet4ui>(block2.packet[3].m_val));
+          v2[4] = vec_mergeh(reinterpret_cast<Packet4ui>(block2.packet[4].m_val),
+                             reinterpret_cast<Packet4ui>(block2.packet[5].m_val));
+          v2[5] = vec_mergel(reinterpret_cast<Packet4ui>(block2.packet[4].m_val),
+                             reinterpret_cast<Packet4ui>(block2.packet[5].m_val));
+          v2[6] = vec_mergeh(reinterpret_cast<Packet4ui>(block2.packet[6].m_val),
+                             reinterpret_cast<Packet4ui>(block2.packet[7].m_val));
+          v2[7] = vec_mergel(reinterpret_cast<Packet4ui>(block2.packet[6].m_val),
+                             reinterpret_cast<Packet4ui>(block2.packet[7].m_val));
+
+#ifdef EIGEN_VECTORIZE_VSX
+          block1.packet[0] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v1[0]), reinterpret_cast<Packet2ul>(v1[2])));
+          block1.packet[2] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v1[0]), reinterpret_cast<Packet2ul>(v1[2])));
+          block1.packet[4] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v1[1]), reinterpret_cast<Packet2ul>(v1[3])));
+          block1.packet[6] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v1[1]), reinterpret_cast<Packet2ul>(v1[3])));
+          block1.packet[1] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v1[4]), reinterpret_cast<Packet2ul>(v1[6])));
+          block1.packet[3] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v1[4]), reinterpret_cast<Packet2ul>(v1[6])));
+          block1.packet[5] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v1[5]), reinterpret_cast<Packet2ul>(v1[7])));
+          block1.packet[7] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v1[5]), reinterpret_cast<Packet2ul>(v1[7])));
+          block2.packet[0] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v2[0]), reinterpret_cast<Packet2ul>(v2[2])));
+          block2.packet[2] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v2[0]), reinterpret_cast<Packet2ul>(v2[2])));
+          block2.packet[4] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v2[1]), reinterpret_cast<Packet2ul>(v2[3])));
+          block2.packet[6] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v2[1]), reinterpret_cast<Packet2ul>(v2[3])));
+          block2.packet[1] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v2[4]), reinterpret_cast<Packet2ul>(v2[6])));
+          block2.packet[3] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v2[4]), reinterpret_cast<Packet2ul>(v2[6])));
+          block2.packet[5] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v2[5]), reinterpret_cast<Packet2ul>(v2[7])));
+          block2.packet[7] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v2[5]), reinterpret_cast<Packet2ul>(v2[7])));
+#else
+          block1.packet[0] = reinterpret_cast<Packet8us>(vec_perm(v1[0], v1[2], p16uc_TRANSPOSE64_HI));
+          block1.packet[2] = reinterpret_cast<Packet8us>(vec_perm(v1[0], v1[2], p16uc_TRANSPOSE64_LO));
+          block1.packet[4] = reinterpret_cast<Packet8us>(vec_perm(v1[1], v1[3], p16uc_TRANSPOSE64_HI));
+          block1.packet[6] = reinterpret_cast<Packet8us>(vec_perm(v1[1], v1[3], p16uc_TRANSPOSE64_LO));
+          block1.packet[1] = reinterpret_cast<Packet8us>(vec_perm(v1[4], v1[6], p16uc_TRANSPOSE64_HI));
+          block1.packet[3] = reinterpret_cast<Packet8us>(vec_perm(v1[4], v1[6], p16uc_TRANSPOSE64_LO));
+          block1.packet[5] = reinterpret_cast<Packet8us>(vec_perm(v1[5], v1[7], p16uc_TRANSPOSE64_HI));
+          block1.packet[7] = reinterpret_cast<Packet8us>(vec_perm(v1[5], v1[7], p16uc_TRANSPOSE64_LO));
+          block2.packet[0] = reinterpret_cast<Packet8us>(vec_perm(v2[0], v2[2], p16uc_TRANSPOSE64_HI));
+          block2.packet[2] = reinterpret_cast<Packet8us>(vec_perm(v2[0], v2[2], p16uc_TRANSPOSE64_LO));
+          block2.packet[4] = reinterpret_cast<Packet8us>(vec_perm(v2[1], v2[3], p16uc_TRANSPOSE64_HI));
+          block2.packet[6] = reinterpret_cast<Packet8us>(vec_perm(v2[1], v2[3], p16uc_TRANSPOSE64_LO));
+          block2.packet[1] = reinterpret_cast<Packet8us>(vec_perm(v2[4], v2[6], p16uc_TRANSPOSE64_HI));
+          block2.packet[3] = reinterpret_cast<Packet8us>(vec_perm(v2[4], v2[6], p16uc_TRANSPOSE64_LO));
+          block2.packet[5] = reinterpret_cast<Packet8us>(vec_perm(v2[5], v2[7], p16uc_TRANSPOSE64_HI));
+          block2.packet[7] = reinterpret_cast<Packet8us>(vec_perm(v2[5], v2[7], p16uc_TRANSPOSE64_LO));
+#endif
+
+          for (Index M = 0; M < 8; M += 2) {
+            pstore<bfloat16>(blockA + ri + (0 * vectorSize) + (2 * vectorSize * M), block1.packet[M + 0]);
+            pstore<bfloat16>(blockA + ri + (1 * vectorSize) + (2 * vectorSize * M), block1.packet[M + 1]);
+            pstore<bfloat16>(blockA + ri + (2 * vectorSize) + (2 * vectorSize * M), block2.packet[M + 0]);
+            pstore<bfloat16>(blockA + ri + (3 * vectorSize) + (2 * vectorSize * M), block2.packet[M + 1]);
+          }
+
+          ri += 2 * vectorSize * vectorSize;
+        }
+        for (; i + 2 <= depth; i += 2) {
+          for (Index M = 0; M < 2 * vectorSize; M++) {
+            blockA[ri + (M * 2) + 0] = lhs2(M, i + 0);
+            blockA[ri + (M * 2) + 1] = lhs2(M, i + 1);
+          }
+
+          ri += 2 * 2 * vectorSize;
+        }
+        if (depth & 1) {
+          for (Index M = 0; M < 2 * vectorSize; M++) {
+            blockA[ri + M] = lhs2(M, i);
+          }
+          ri += 2 * vectorSize;
+        }
+      }
+
+      if (PanelMode) ri += 2 * vectorSize * (stride - offset - depth);
+    }
+    for (; j + vectorSize <= rows; j += vectorSize) {
+      const DataMapper lhs2 = lhs.getSubMapper(j, 0);
+      Index i = 0;
+
+      if (PanelMode) ri += vectorSize * offset;
+
+      if (StorageOrder == ColMajor) {
+        for (; i + 2 <= depth; i += 2) {
+          PacketBlock<Packet8bf, 2> block;
+
+          block.packet[0] = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 0);
+          block.packet[1] = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 1);
+
+          Packet8bf t0;
+          t0 = vec_mergeh(block.packet[0].m_val, block.packet[1].m_val);
+          block.packet[1] = vec_mergel(block.packet[0].m_val, block.packet[1].m_val);
+          block.packet[0] = t0;
+
+          storeBlock<bfloat16, Packet8bf, 2>(blockA + ri, block);
+
+          ri += 2 * vectorSize;
+        }
+        if (depth & 1) {
+          Packet8bf lhsV = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 0);
+          pstore<bfloat16>(blockA + ri, lhsV);
+
+          ri += vectorSize;
+        }
+      } else {
+        for (; i + vectorSize <= depth; i += vectorSize) {
+          PacketBlock<Packet8bf, 8> block1;
+
+          bload<DataMapper, Packet8bf, 8, StorageOrder, false, 8>(block1, lhs2, 0 * vectorSize, i);
+
+          Packet4ui v1[8];
+
+          // This is transposing and interleaving data
+          v1[0] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[0].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[1].m_val));
+          v1[1] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[0].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[1].m_val));
+          v1[2] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[2].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[3].m_val));
+          v1[3] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[2].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[3].m_val));
+          v1[4] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[4].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[5].m_val));
+          v1[5] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[4].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[5].m_val));
+          v1[6] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[6].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[7].m_val));
+          v1[7] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[6].m_val),
+                             reinterpret_cast<Packet4ui>(block1.packet[7].m_val));
+
+#ifdef EIGEN_VECTORIZE_VSX
+          block1.packet[0] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v1[0]), reinterpret_cast<Packet2ul>(v1[2])));
+          block1.packet[2] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v1[0]), reinterpret_cast<Packet2ul>(v1[2])));
+          block1.packet[4] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v1[1]), reinterpret_cast<Packet2ul>(v1[3])));
+          block1.packet[6] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v1[1]), reinterpret_cast<Packet2ul>(v1[3])));
+          block1.packet[1] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v1[4]), reinterpret_cast<Packet2ul>(v1[6])));
+          block1.packet[3] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v1[4]), reinterpret_cast<Packet2ul>(v1[6])));
+          block1.packet[5] = reinterpret_cast<Packet8us>(
+              vec_mergeh(reinterpret_cast<Packet2ul>(v1[5]), reinterpret_cast<Packet2ul>(v1[7])));
+          block1.packet[7] = reinterpret_cast<Packet8us>(
+              vec_mergel(reinterpret_cast<Packet2ul>(v1[5]), reinterpret_cast<Packet2ul>(v1[7])));
+#else
+          block1.packet[0] = reinterpret_cast<Packet8us>(vec_perm(v1[0], v1[2], p16uc_TRANSPOSE64_HI));
+          block1.packet[2] = reinterpret_cast<Packet8us>(vec_perm(v1[0], v1[2], p16uc_TRANSPOSE64_LO));
+          block1.packet[4] = reinterpret_cast<Packet8us>(vec_perm(v1[1], v1[3], p16uc_TRANSPOSE64_HI));
+          block1.packet[6] = reinterpret_cast<Packet8us>(vec_perm(v1[1], v1[3], p16uc_TRANSPOSE64_LO));
+          block1.packet[1] = reinterpret_cast<Packet8us>(vec_perm(v1[4], v1[6], p16uc_TRANSPOSE64_HI));
+          block1.packet[3] = reinterpret_cast<Packet8us>(vec_perm(v1[4], v1[6], p16uc_TRANSPOSE64_LO));
+          block1.packet[5] = reinterpret_cast<Packet8us>(vec_perm(v1[5], v1[7], p16uc_TRANSPOSE64_HI));
+          block1.packet[7] = reinterpret_cast<Packet8us>(vec_perm(v1[5], v1[7], p16uc_TRANSPOSE64_LO));
+#endif
+
+          for (Index M = 0; M < 8; M++) {
+            pstore<bfloat16>(blockA + ri + (vectorSize * M), block1.packet[M]);
+          }
+
+          ri += vectorSize * vectorSize;
+        }
+        for (; i + 2 <= depth; i += 2) {
+          for (Index M = 0; M < vectorSize; M++) {
+            blockA[ri + (M * 2) + 0] = lhs2(M, i + 0);
+            blockA[ri + (M * 2) + 1] = lhs2(M, i + 1);
+          }
+
+          ri += 2 * vectorSize;
+        }
+        if (depth & 1) {
+          for (Index M = 0; M < vectorSize; M++) {
+            blockA[ri + M] = lhs2(M, i);
+          }
+
+          ri += vectorSize;
+        }
+      }
+
+      if (PanelMode) ri += vectorSize * (stride - offset - depth);
+    }
+    if (j + 4 <= rows) {
+      const DataMapper lhs2 = lhs.getSubMapper(j, 0);
+      Index i = 0;
+
+      if (PanelMode) ri += 4 * offset;
+
+      for (; i + 2 <= depth; i += 2) {
+        if (StorageOrder == ColMajor) {
+          PacketBlock<Packet8bf, 2> block;
+
+          block.packet[0] = lhs2.template loadPacketPartial<Packet8bf>(0, i + 0, 4);
+          block.packet[1] = lhs2.template loadPacketPartial<Packet8bf>(0, i + 1, 4);
+
+          block.packet[0] = vec_mergeh(block.packet[0].m_val, block.packet[1].m_val);
+
+          pstore<bfloat16>(blockA + ri, block.packet[0]);
+        } else {
+          blockA[ri + 0] = lhs2(0, i + 0);
+          blockA[ri + 1] = lhs2(0, i + 1);
+          blockA[ri + 2] = lhs2(1, i + 0);
+          blockA[ri + 3] = lhs2(1, i + 1);
+          blockA[ri + 4] = lhs2(2, i + 0);
+          blockA[ri + 5] = lhs2(2, i + 1);
+          blockA[ri + 6] = lhs2(3, i + 0);
+          blockA[ri + 7] = lhs2(3, i + 1);
+        }
+
+        ri += 2 * 4;
+      }
+      if (depth & 1) {
+        if (StorageOrder == ColMajor) {
+          Packet8bf lhsV = lhs2.template loadPacketPartial<Packet8bf>(0, i + 0, 4);
+
+          pstore_partial<bfloat16>(blockA + ri, lhsV, 4);
+        } else {
+          blockA[ri + 0] = lhs2(0, i);
+          blockA[ri + 1] = lhs2(1, i);
+          blockA[ri + 2] = lhs2(2, i);
+          blockA[ri + 3] = lhs2(3, i);
+        }
+
+        ri += 4;
+      }
+
+      if (PanelMode) ri += 4 * (stride - offset - depth);
+      j += 4;
+    }
+
+    if (j < rows) {
+      if (PanelMode) ri += offset * (rows - j);
+
+      Index i = 0;
+      for (; i + 2 <= depth; i += 2) {
+        Index k = j;
+        for (; k < rows; k++) {
+          blockA[ri + 0] = lhs(k, i + 0);
+          blockA[ri + 1] = lhs(k, i + 1);
+          ri += 2;
+        }
+      }
+      if (depth & 1) {
+        for (; j < rows; j++) {
+          blockA[ri] = lhs(j, i);
+          ri += 1;
+        }
+      }
+    }
+  }
+};
+
+// General template for rhs packing, bfloat16 specialization.
+template <typename DataMapper, int StorageOrder, bool PanelMode>
+struct dhs_pack<bfloat16, DataMapper, Packet8bf, StorageOrder, PanelMode, false> {
+  EIGEN_STRONG_INLINE void operator()(bfloat16* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride,
+                                      Index offset) {
+    const Index vectorSize = quad_traits<bfloat16>::vectorsize;
+    Index ri = 0, j = 0;
+
+    for (; j + 4 <= cols; j += 4) {
+      const DataMapper rhs2 = rhs.getSubMapper(0, j);
+      Index i = 0;
+
+      if (PanelMode) ri += 4 * offset;
+
+      for (; i + vectorSize <= depth; i += vectorSize) {
+        if (StorageOrder == ColMajor) {
+          PacketBlock<Packet8bf, 4> block;
+
+          bload<DataMapper, Packet8bf, 4, StorageOrder, false, 4>(block, rhs2, i, 0);
+
+          Packet4ui t0, t1, t2, t3;
+
+          t0 = vec_mergeh(reinterpret_cast<Packet4ui>(block.packet[0].m_val),
+                          reinterpret_cast<Packet4ui>(block.packet[1].m_val));
+          t1 = vec_mergel(reinterpret_cast<Packet4ui>(block.packet[0].m_val),
+                          reinterpret_cast<Packet4ui>(block.packet[1].m_val));
+          t2 = vec_mergeh(reinterpret_cast<Packet4ui>(block.packet[2].m_val),
+                          reinterpret_cast<Packet4ui>(block.packet[3].m_val));
+          t3 = vec_mergel(reinterpret_cast<Packet4ui>(block.packet[2].m_val),
+                          reinterpret_cast<Packet4ui>(block.packet[3].m_val));
+
+#ifdef EIGEN_VECTORIZE_VSX
+          block.packet[0] =
+              reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(t0), reinterpret_cast<Packet2ul>(t2)));
+          block.packet[1] =
+              reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(t0), reinterpret_cast<Packet2ul>(t2)));
+          block.packet[2] =
+              reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(t1), reinterpret_cast<Packet2ul>(t3)));
+          block.packet[3] =
+              reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(t1), reinterpret_cast<Packet2ul>(t3)));
+#else
+          block.packet[0] = reinterpret_cast<Packet8us>(vec_perm(t0, t2, p16uc_TRANSPOSE64_HI));
+          block.packet[1] = reinterpret_cast<Packet8us>(vec_perm(t0, t2, p16uc_TRANSPOSE64_LO));
+          block.packet[2] = reinterpret_cast<Packet8us>(vec_perm(t1, t3, p16uc_TRANSPOSE64_HI));
+          block.packet[3] = reinterpret_cast<Packet8us>(vec_perm(t1, t3, p16uc_TRANSPOSE64_LO));
+#endif
+
+          storeBlock<bfloat16, Packet8bf, 4>(blockB + ri, block);
+        } else {
+          PacketBlock<Packet8bf, 8> block;
+
+          for (int M = 0; M < 8; M++) {
+            block.packet[M] = rhs2.template loadPacketPartial<Packet8bf>(i + M, 0, 4);
+          }
+
+          block.packet[0] = vec_mergeh(block.packet[0].m_val, block.packet[1].m_val);
+          block.packet[1] = vec_mergeh(block.packet[2].m_val, block.packet[3].m_val);
+          block.packet[2] = vec_mergeh(block.packet[4].m_val, block.packet[5].m_val);
+          block.packet[3] = vec_mergeh(block.packet[6].m_val, block.packet[7].m_val);
+
+          const Index size = 16 / sizeof(bfloat16);
+
+          for (int M = 0; M < 4; M++) {
+            pstore<bfloat16>(blockB + ri + (M * size), block.packet[M]);
+          }
+        }
+
+        ri += 4 * vectorSize;
+      }
+      for (; i + 2 <= depth; i += 2) {
+        if (StorageOrder == ColMajor) {
+          blockB[ri + 0] = rhs2(i + 0, 0);
+          blockB[ri + 1] = rhs2(i + 1, 0);
+          blockB[ri + 2] = rhs2(i + 0, 1);
+          blockB[ri + 3] = rhs2(i + 1, 1);
+          blockB[ri + 4] = rhs2(i + 0, 2);
+          blockB[ri + 5] = rhs2(i + 1, 2);
+          blockB[ri + 6] = rhs2(i + 0, 3);
+          blockB[ri + 7] = rhs2(i + 1, 3);
+        } else {
+          PacketBlock<Packet8bf, 2> block;
+
+          for (int M = 0; M < 2; M++) {
+            block.packet[M] = rhs2.template loadPacketPartial<Packet8bf>(i + M, 0, 4);
+          }
+
+          block.packet[0] = vec_mergeh(block.packet[0].m_val, block.packet[1].m_val);
+
+          pstore<bfloat16>(blockB + ri, block.packet[0]);
+        }
+
+        ri += 4 * 2;
+      }
+      if (depth & 1) {
+        blockB[ri + 0] = rhs2(i, 0);
+        blockB[ri + 1] = rhs2(i, 1);
+        blockB[ri + 2] = rhs2(i, 2);
+        blockB[ri + 3] = rhs2(i, 3);
+
+        ri += 4;
+      }
+
+      if (PanelMode) ri += 4 * (stride - offset - depth);
+    }
+
+    if (j < cols) {
+      if (PanelMode) ri += offset * (cols - j);
+
+      Index i = 0;
+      for (; i + 2 <= depth; i += 2) {
+        Index k = j;
+        for (; k < cols; k++) {
+          blockB[ri + 0] = rhs(i + 0, k);
+          blockB[ri + 1] = rhs(i + 1, k);
+          ri += 2;
+        }
+      }
+      if (depth & 1) {
+        for (; j < cols; j++) {
+          blockB[ri] = rhs(i, j);
+          ri += 1;
+        }
+      }
+    }
+  }
+};
+
+// General template for lhs complex packing, float64 specialization.
+template <typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode>
+struct dhs_cpack<double, DataMapper, Packet, PacketC, StorageOrder, Conjugate, PanelMode, true> {
+  EIGEN_ALWAYS_INLINE void dhs_ccopy(double* blockAt, const DataMapper& lhs2, Index& i, Index& rir, Index& rii,
+                                     Index depth, const Index vectorSize) {
+    PacketBlock<Packet, 2> blockr, blocki;
+    PacketBlock<PacketC, 4> cblock;
+
+    for (; i + vectorSize <= depth; i += vectorSize) {
+      if (StorageOrder == ColMajor) {
+        cblock.packet[0] = lhs2.template loadPacket<PacketC>(0, i + 0);  //[a1 a1i]
+        cblock.packet[1] = lhs2.template loadPacket<PacketC>(0, i + 1);  //[b1 b1i]
+
+        cblock.packet[2] = lhs2.template loadPacket<PacketC>(1, i + 0);  //[a2 a2i]
+        cblock.packet[3] = lhs2.template loadPacket<PacketC>(1, i + 1);  //[b2 b2i]
+
+        blockr.packet[0] = vec_mergeh(cblock.packet[0].v, cblock.packet[2].v);  //[a1 a2]
+        blockr.packet[1] = vec_mergeh(cblock.packet[1].v, cblock.packet[3].v);  //[b1 b2]
+
+        blocki.packet[0] = vec_mergel(cblock.packet[0].v, cblock.packet[2].v);
+        blocki.packet[1] = vec_mergel(cblock.packet[1].v, cblock.packet[3].v);
+      } else {
+        cblock.packet[0] = lhs2.template loadPacket<PacketC>(0, i);  //[a1 a1i]
+        cblock.packet[1] = lhs2.template loadPacket<PacketC>(1, i);  //[a2 a2i]
+
+        cblock.packet[2] = lhs2.template loadPacket<PacketC>(0, i + 1);  //[b1 b1i]
+        cblock.packet[3] = lhs2.template loadPacket<PacketC>(1, i + 1);  //[b2 b2i
+
+        blockr.packet[0] = vec_mergeh(cblock.packet[0].v, cblock.packet[1].v);  //[a1 a2]
+        blockr.packet[1] = vec_mergeh(cblock.packet[2].v, cblock.packet[3].v);  //[b1 b2]
+
+        blocki.packet[0] = vec_mergel(cblock.packet[0].v, cblock.packet[1].v);
+        blocki.packet[1] = vec_mergel(cblock.packet[2].v, cblock.packet[3].v);
+      }
+
+      if (Conjugate) {
+        blocki.packet[0] = -blocki.packet[0];
+        blocki.packet[1] = -blocki.packet[1];
+      }
+
+      storeBlock<double, Packet, 2>(blockAt + rir, blockr);
+      storeBlock<double, Packet, 2>(blockAt + rii, blocki);
+
+      rir += 2 * vectorSize;
+      rii += 2 * vectorSize;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows,
+                                      Index stride, Index offset) {
+    const Index vectorSize = quad_traits<double>::vectorsize;
+    const Index vectorDelta = vectorSize * ((PanelMode) ? stride : depth);
+    Index rir = ((PanelMode) ? (vectorSize * offset) : 0), rii;
+    double* blockAt = reinterpret_cast<double*>(blockA);
+    Index j = 0;
+
+    for (; j + vectorSize <= rows; j += vectorSize) {
+      const DataMapper lhs2 = lhs.getSubMapper(j, 0);
+      Index i = 0;
+
+      rii = rir + vectorDelta;
+
+      dhs_ccopy(blockAt, lhs2, i, rir, rii, depth, vectorSize);
+
+      for (; i < depth; i++) {
+        PacketBlock<Packet, 1> blockr, blocki;
+        PacketBlock<PacketC, 2> cblock;
+
+        cblock.packet[0] = lhs2.template loadPacket<PacketC>(0, i);
+        cblock.packet[1] = lhs2.template loadPacket<PacketC>(1, i);
+
+        blockr.packet[0] = vec_mergeh(cblock.packet[0].v, cblock.packet[1].v);
+        blocki.packet[0] = vec_mergel(cblock.packet[0].v, cblock.packet[1].v);
+
+        if (Conjugate) {
+          blocki.packet[0] = -blocki.packet[0];
+        }
+
+        pstore<double>(blockAt + rir, blockr.packet[0]);
+        pstore<double>(blockAt + rii, blocki.packet[0]);
+
+        rir += vectorSize;
+        rii += vectorSize;
+      }
+
+      rir += ((PanelMode) ? (vectorSize * (2 * stride - depth)) : vectorDelta);
+    }
+
+    if (j < rows) {
+      if (PanelMode) rir += (offset * (rows - j - vectorSize));
+      rii = rir + (((PanelMode) ? stride : depth) * (rows - j));
+
+      for (Index i = 0; i < depth; i++) {
+        Index k = j;
+        for (; k < rows; k++) {
+          blockAt[rir] = lhs(k, i).real();
+
+          if (Conjugate)
+            blockAt[rii] = -lhs(k, i).imag();
+          else
+            blockAt[rii] = lhs(k, i).imag();
+
+          rir += 1;
+          rii += 1;
+        }
+      }
+    }
+  }
+};
+
+// General template for rhs complex packing, float64 specialization.
+template <typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode>
+struct dhs_cpack<double, DataMapper, Packet, PacketC, StorageOrder, Conjugate, PanelMode, false> {
+  EIGEN_ALWAYS_INLINE void dhs_ccopy(double* blockBt, const DataMapper& rhs2, Index& i, Index& rir, Index& rii,
+                                     Index depth, const Index vectorSize) {
+    for (; i < depth; i++) {
+      PacketBlock<PacketC, 4> cblock;
+      PacketBlock<Packet, 2> blockr, blocki;
+
+      bload<DataMapper, PacketC, 2, ColMajor, false, 4>(cblock, rhs2, i, 0);
+
+      blockr.packet[0] = vec_mergeh(cblock.packet[0].v, cblock.packet[1].v);
+      blockr.packet[1] = vec_mergeh(cblock.packet[2].v, cblock.packet[3].v);
+
+      blocki.packet[0] = vec_mergel(cblock.packet[0].v, cblock.packet[1].v);
+      blocki.packet[1] = vec_mergel(cblock.packet[2].v, cblock.packet[3].v);
+
+      if (Conjugate) {
+        blocki.packet[0] = -blocki.packet[0];
+        blocki.packet[1] = -blocki.packet[1];
+      }
+
+      storeBlock<double, Packet, 2>(blockBt + rir, blockr);
+      storeBlock<double, Packet, 2>(blockBt + rii, blocki);
+
+      rir += 2 * vectorSize;
+      rii += 2 * vectorSize;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols,
+                                      Index stride, Index offset) {
+    const Index vectorSize = quad_traits<double>::vectorsize;
+    const Index vectorDelta = 2 * vectorSize * ((PanelMode) ? stride : depth);
+    Index rir = ((PanelMode) ? (2 * vectorSize * offset) : 0), rii;
+    double* blockBt = reinterpret_cast<double*>(blockB);
+    Index j = 0;
+
+    for (; j + 2 * vectorSize <= cols; j += 2 * vectorSize) {
+      const DataMapper rhs2 = rhs.getSubMapper(0, j);
+      Index i = 0;
+
+      rii = rir + vectorDelta;
+
+      dhs_ccopy(blockBt, rhs2, i, rir, rii, depth, vectorSize);
+
+      rir += ((PanelMode) ? (2 * vectorSize * (2 * stride - depth)) : vectorDelta);
+    }
+
+    if (PanelMode) rir -= (offset * (2 * vectorSize - 1));
+
+    for (; j < cols; j++) {
+      const DataMapper rhs2 = rhs.getSubMapper(0, j);
+      rii = rir + ((PanelMode) ? stride : depth);
+
+      for (Index i = 0; i < depth; i++) {
+        blockBt[rir] = rhs2(i, 0).real();
+
+        if (Conjugate)
+          blockBt[rii] = -rhs2(i, 0).imag();
+        else
+          blockBt[rii] = rhs2(i, 0).imag();
+
+        rir += 1;
+        rii += 1;
+      }
+
+      rir += ((PanelMode) ? (2 * stride - depth) : depth);
+    }
+  }
+};
+
+/**************
+ * GEMM utils *
+ **************/
+
+// 512-bits rank1-update of acc. It can either positive or negative accumulate (useful for complex gemm).
+template <typename Packet, bool NegativeAccumulate, int N>
+EIGEN_ALWAYS_INLINE void pger_common(PacketBlock<Packet, N>* acc, const Packet& lhsV, const Packet* rhsV) {
+  if (NegativeAccumulate) {
+    for (int M = 0; M < N; M++) {
+      acc->packet[M] = vec_nmsub(lhsV, rhsV[M], acc->packet[M]);
+    }
+  } else {
+    for (int M = 0; M < N; M++) {
+      acc->packet[M] = vec_madd(lhsV, rhsV[M], acc->packet[M]);
+    }
+  }
+}
+
+template <int N, typename Scalar, typename Packet, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger(PacketBlock<Packet, N>* acc, const Scalar* lhs, const Packet* rhsV) {
+  Packet lhsV = pload<Packet>(lhs);
+
+  pger_common<Packet, NegativeAccumulate, N>(acc, lhsV, rhsV);
+}
+
+// 512-bits rank1-update of complex acc. It takes decoupled accumulators as entries. It also takes cares of mixed types
+// real * complex and complex * real.
+template <int N, typename Packet, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void pgerc_common(PacketBlock<Packet, N>* accReal, PacketBlock<Packet, N>* accImag,
+                                      const Packet& lhsV, Packet& lhsVi, const Packet* rhsV, const Packet* rhsVi) {
+  pger_common<Packet, false, N>(accReal, lhsV, rhsV);
+  if (LhsIsReal) {
+    pger_common<Packet, ConjugateRhs, N>(accImag, lhsV, rhsVi);
+    EIGEN_UNUSED_VARIABLE(lhsVi);
+  } else {
+    if (!RhsIsReal) {
+      pger_common<Packet, ConjugateLhs == ConjugateRhs, N>(accReal, lhsVi, rhsVi);
+      pger_common<Packet, ConjugateRhs, N>(accImag, lhsV, rhsVi);
+    } else {
+      EIGEN_UNUSED_VARIABLE(rhsVi);
+    }
+    pger_common<Packet, ConjugateLhs, N>(accImag, lhsVi, rhsV);
+  }
+}
+
+template <int N, typename Scalar, typename Packet, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void pgerc(PacketBlock<Packet, N>* accReal, PacketBlock<Packet, N>* accImag, const Scalar* lhs_ptr,
+                               const Scalar* lhs_ptr_imag, const Packet* rhsV, const Packet* rhsVi) {
+  Packet lhsV = ploadLhs<Packet>(lhs_ptr);
+  Packet lhsVi;
+  if (!LhsIsReal)
+    lhsVi = ploadLhs<Packet>(lhs_ptr_imag);
+  else
+    EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
+
+  pgerc_common<N, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(accReal, accImag, lhsV, lhsVi, rhsV, rhsVi);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet ploadLhs(const __UNPACK_TYPE__(Packet) * lhs) {
+  return ploadu<Packet>(lhs);
+}
+
+// Zero the accumulator on PacketBlock.
+template <typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bsetzero(PacketBlock<Packet, N>& acc) {
+  for (int M = 0; M < N; M++) {
+    acc.packet[M] = pset1<Packet>((__UNPACK_TYPE__(Packet))0);
+  }
+}
+
+template <typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bscalec_common(PacketBlock<Packet, N>& acc, PacketBlock<Packet, N>& accZ,
+                                        const Packet& pAlpha) {
+  for (int M = 0; M < N; M++) {
+    acc.packet[M] = vec_mul(accZ.packet[M], pAlpha);
+  }
+}
+
+template <typename Packet, int N>
+EIGEN_ALWAYS_INLINE void band(PacketBlock<Packet, N>& acc, const Packet& pMask) {
+  for (int M = 0; M < N; M++) {
+    acc.packet[M] = pand<Packet>(acc.packet[M], pMask);
+  }
+}
+
+// Complex version of PacketBlock scaling.
+template <typename Packet, int N, bool mask>
+EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet, N>& aReal, PacketBlock<Packet, N>& aImag, const Packet& bReal,
+                                 const Packet& bImag, PacketBlock<Packet, N>& cReal, PacketBlock<Packet, N>& cImag,
+                                 const Packet& pMask) {
+  if (mask && (sizeof(__UNPACK_TYPE__(Packet)) == sizeof(float))) {
+    band<Packet, N>(aReal, pMask);
+    band<Packet, N>(aImag, pMask);
+  } else {
+    EIGEN_UNUSED_VARIABLE(pMask);
+  }
+
+  bscalec_common<Packet, N>(cReal, aReal, bReal);
+
+  bscalec_common<Packet, N>(cImag, aImag, bReal);
+
+  pger_common<Packet, true, N>(&cReal, bImag, aImag.packet);
+
+  pger_common<Packet, false, N>(&cImag, bImag, aReal.packet);
+}
+
+// Load a PacketBlock, the N parameters make tuning gemm easier so we can add more accumulators as needed.
+//
+// full = operate (load) on the entire PacketBlock or only half
+template <typename DataMapper, typename Packet, const Index accCols, int StorageOrder, bool Complex, int N, bool full>
+EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet, N*(Complex ? 2 : 1)>& acc, const DataMapper& res, Index row,
+                               Index col) {
+  if (StorageOrder == RowMajor) {
+    for (int M = 0; M < N; M++) {
+      acc.packet[M] = res.template loadPacket<Packet>(row + M, col);
+    }
+    if (Complex) {
+      for (int M = 0; M < N; M++) {
+        acc.packet[M + N] = res.template loadPacket<Packet>(row + M, col + accCols);
+      }
+    }
+  } else {
+    for (int M = 0; M < N; M++) {
+      acc.packet[M] = res.template loadPacket<Packet>(row, col + M);
+    }
+    if (Complex && full) {
+      for (int M = 0; M < N; M++) {
+        acc.packet[M + N] = res.template loadPacket<Packet>(row + accCols, col + M);
+      }
+    }
+  }
+}
+
+template <typename DataMapper, typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bstore(PacketBlock<Packet, N>& acc, const DataMapper& res, Index row) {
+  for (int M = 0; M < N; M++) {
+    res.template storePacket<Packet>(row, M, acc.packet[M]);
+  }
+}
+
+#ifdef USE_PARTIAL_PACKETS
+template <typename DataMapper, typename Packet, const Index accCols, bool Complex, Index N, bool full>
+EIGEN_ALWAYS_INLINE void bload_partial(PacketBlock<Packet, N*(Complex ? 2 : 1)>& acc, const DataMapper& res, Index row,
+                                       Index elements) {
+  for (Index M = 0; M < N; M++) {
+    acc.packet[M] = res.template loadPacketPartial<Packet>(row, M, elements);
+  }
+  if (Complex && full) {
+    for (Index M = 0; M < N; M++) {
+      acc.packet[M + N] = res.template loadPacketPartial<Packet>(row + accCols, M, elements);
+    }
+  }
+}
+
+template <typename DataMapper, typename Packet, Index N>
+EIGEN_ALWAYS_INLINE void bstore_partial(PacketBlock<Packet, N>& acc, const DataMapper& res, Index row, Index elements) {
+  for (Index M = 0; M < N; M++) {
+    res.template storePacketPartial<Packet>(row, M, acc.packet[M], elements);
+  }
+}
+#endif
+
+#ifdef _ARCH_PWR10
+#define USE_P10_AND_PVIPR2_0 (EIGEN_COMP_LLVM || (__GNUC__ >= 11))
+#else
+#define USE_P10_AND_PVIPR2_0 0
+#endif
+
+#if !USE_P10_AND_PVIPR2_0
+const static Packet4i mask4[4] = {{0, 0, 0, 0}, {-1, 0, 0, 0}, {-1, -1, 0, 0}, {-1, -1, -1, 0}};
+#endif
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet bmask(const Index remaining_rows) {
+#if USE_P10_AND_PVIPR2_0
+#ifdef _BIG_ENDIAN
+  return Packet(vec_reve(vec_genwm((1 << remaining_rows) - 1)));
+#else
+  return Packet(vec_genwm((1 << remaining_rows) - 1));
+#endif
+#else
+  return Packet(mask4[remaining_rows]);
+#endif
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet2d bmask<Packet2d>(const Index remaining_rows) {
+#if USE_P10_AND_PVIPR2_0
+  Packet2d mask2 = Packet2d(vec_gendm(remaining_rows));
+#ifdef _BIG_ENDIAN
+  return preverse(mask2);
+#else
+  return mask2;
+#endif
+#else
+  Packet2l ret = {-remaining_rows, 0};
+  return Packet2d(ret);
+#endif
+}
+
+template <typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet, N>& acc, PacketBlock<Packet, N>& accZ, const Packet& pAlpha) {
+  for (int M = 0; M < N; M++) {
+    acc.packet[M] = pmadd<Packet>(pAlpha, accZ.packet[M], acc.packet[M]);
+  }
+}
+
+// Scale the PacketBlock vectors by alpha.
+template <typename Packet, int N, bool mask>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet, N>& acc, PacketBlock<Packet, N>& accZ, const Packet& pAlpha,
+                                const Packet& pMask) {
+  if (mask) {
+    band<Packet, N>(accZ, pMask);
+  } else {
+    EIGEN_UNUSED_VARIABLE(pMask);
+  }
+
+  bscale<Packet, N>(acc, accZ, pAlpha);
+}
+
+template <typename Packet, int N, bool real>
+EIGEN_ALWAYS_INLINE void pbroadcastN(const __UNPACK_TYPE__(Packet) * ap0, const __UNPACK_TYPE__(Packet) * ap1,
+                                     const __UNPACK_TYPE__(Packet) * ap2, Packet& a0, Packet& a1, Packet& a2,
+                                     Packet& a3) {
+  a0 = pset1<Packet>(ap0[0]);
+  if (N == 4) {
+    a1 = pset1<Packet>(ap0[1]);
+    a2 = pset1<Packet>(ap0[2]);
+    a3 = pset1<Packet>(ap0[3]);
+    EIGEN_UNUSED_VARIABLE(ap1);
+    EIGEN_UNUSED_VARIABLE(ap2);
+  } else {
+    if (N > 1) {
+      a1 = pset1<Packet>(ap1[0]);
+    } else {
+      EIGEN_UNUSED_VARIABLE(a1);
+      EIGEN_UNUSED_VARIABLE(ap1);
+    }
+    if (N > 2) {
+      a2 = pset1<Packet>(ap2[0]);
+    } else {
+      EIGEN_UNUSED_VARIABLE(a2);
+      EIGEN_UNUSED_VARIABLE(ap2);
+    }
+  }
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pbroadcastN<Packet4f, 4, true>(const float* ap0, const float*, const float*, Packet4f& a0,
+                                                        Packet4f& a1, Packet4f& a2, Packet4f& a3) {
+  pbroadcast4<Packet4f>(ap0, a0, a1, a2, a3);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pbroadcastN<Packet4f, 4, false>(const float* ap0, const float* ap1, const float* ap2,
+                                                         Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3) {
+  pbroadcastN<Packet4f, 4, true>(ap0, ap1, ap2, a0, a1, a2, a3);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pbroadcastN<Packet2d, 4, false>(const double* ap0, const double*, const double*, Packet2d& a0,
+                                                         Packet2d& a1, Packet2d& a2, Packet2d& a3) {
+  a1 = pload<Packet2d>(ap0);
+  a3 = pload<Packet2d>(ap0 + 2);
+  a0 = vec_splat(a1, 0);
+  a1 = vec_splat(a1, 1);
+  a2 = vec_splat(a3, 0);
+  a3 = vec_splat(a3, 1);
+}
+
+// Grab two decouples real/imaginary PacketBlocks and return two coupled (real/imaginary pairs) PacketBlocks.
+template <typename Packet, typename Packetc, int N, bool full>
+EIGEN_ALWAYS_INLINE void bcouple_common(PacketBlock<Packet, N>& taccReal, PacketBlock<Packet, N>& taccImag,
+                                        PacketBlock<Packetc, N>& acc1, PacketBlock<Packetc, N>& acc2) {
+  for (int M = 0; M < N; M++) {
+    acc1.packet[M].v = vec_mergeh(taccReal.packet[M], taccImag.packet[M]);
+  }
+
+  if (full) {
+    for (int M = 0; M < N; M++) {
+      acc2.packet[M].v = vec_mergel(taccReal.packet[M], taccImag.packet[M]);
+    }
+  }
+}
+
+template <typename Packet, typename Packetc, int N, bool full>
+EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet, N>& taccReal, PacketBlock<Packet, N>& taccImag,
+                                 PacketBlock<Packetc, N * 2>& tRes, PacketBlock<Packetc, N>& acc1,
+                                 PacketBlock<Packetc, N>& acc2) {
+  bcouple_common<Packet, Packetc, N, full>(taccReal, taccImag, acc1, acc2);
+
+  for (int M = 0; M < N; M++) {
+    acc1.packet[M] = padd<Packetc>(tRes.packet[M], acc1.packet[M]);
+  }
+
+  if (full) {
+    for (int M = 0; M < N; M++) {
+      acc2.packet[M] = padd<Packetc>(tRes.packet[M + N], acc2.packet[M]);
+    }
+  }
+}
+
+// PEEL loop factor.
+#define PEEL 7
+#define PEEL_ROW 7
+
+#define MICRO_UNROLL(func) func(0) func(1) func(2) func(3) func(4) func(5) func(6) func(7)
+
+#define MICRO_NORMAL_ROWS accRows == quad_traits<Scalar>::rows || accRows == 1
+
+#define MICRO_NEW_ROWS ((MICRO_NORMAL_ROWS) ? accRows : 1)
+
+#define MICRO_RHS(ptr, N) rhs_##ptr##N
+
+#define MICRO_ZERO_PEEL(peel)                 \
+  if ((PEEL_ROW > peel) && (peel != 0)) {     \
+    bsetzero<Packet, accRows>(accZero##peel); \
+  } else {                                    \
+    EIGEN_UNUSED_VARIABLE(accZero##peel);     \
+  }
+
+#define MICRO_ADD(ptr, N)               \
+  if (MICRO_NORMAL_ROWS) {              \
+    MICRO_RHS(ptr, 0) += (accRows * N); \
+  } else {                              \
+    MICRO_RHS(ptr, 0) += N;             \
+    MICRO_RHS(ptr, 1) += N;             \
+    if (accRows == 3) {                 \
+      MICRO_RHS(ptr, 2) += N;           \
+    }                                   \
+  }
+
+#define MICRO_ADD_ROWS(N) MICRO_ADD(ptr, N)
+
+#define MICRO_BROADCAST1(peel, ptr, rhsV, real)                                                                      \
+  if (MICRO_NORMAL_ROWS) {                                                                                           \
+    pbroadcastN<Packet, accRows, real>(MICRO_RHS(ptr, 0) + (accRows * peel), MICRO_RHS(ptr, 0), MICRO_RHS(ptr, 0),   \
+                                       rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]);                  \
+  } else {                                                                                                           \
+    pbroadcastN<Packet, accRows, real>(MICRO_RHS(ptr, 0) + peel, MICRO_RHS(ptr, 1) + peel, MICRO_RHS(ptr, 2) + peel, \
+                                       rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]);                  \
+  }
+
+#define MICRO_BROADCAST(peel) MICRO_BROADCAST1(peel, ptr, rhsV, true)
+
+#define MICRO_BROADCAST_EXTRA1(ptr, rhsV, real)                                                                 \
+  pbroadcastN<Packet, accRows, real>(MICRO_RHS(ptr, 0), MICRO_RHS(ptr, 1), MICRO_RHS(ptr, 2), rhsV[0], rhsV[1], \
+                                     rhsV[2], rhsV[3]);
+
+#define MICRO_BROADCAST_EXTRA             \
+  Packet rhsV[4];                         \
+  MICRO_BROADCAST_EXTRA1(ptr, rhsV, true) \
+  MICRO_ADD_ROWS(1)
+
+#define MICRO_SRC2(ptr, N, M)                   \
+  if (MICRO_NORMAL_ROWS) {                      \
+    EIGEN_UNUSED_VARIABLE(strideB);             \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr, 1));   \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr, 2));   \
+  } else {                                      \
+    MICRO_RHS(ptr, 1) = rhs_base + N + M;       \
+    if (accRows == 3) {                         \
+      MICRO_RHS(ptr, 2) = rhs_base + N * 2 + M; \
+    } else {                                    \
+      EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr, 2)); \
+    }                                           \
+  }
+
+#define MICRO_SRC2_PTR MICRO_SRC2(ptr, strideB, 0)
+
+#define MICRO_ZERO_PEEL_ROW MICRO_UNROLL(MICRO_ZERO_PEEL)
+
+#define MICRO_WORK_PEEL(peel)                                                                            \
+  if (PEEL_ROW > peel) {                                                                                 \
+    MICRO_BROADCAST(peel)                                                                                \
+    pger<accRows, Scalar, Packet, false>(&accZero##peel, lhs_ptr + (remaining_rows * peel), rhsV##peel); \
+  } else {                                                                                               \
+    EIGEN_UNUSED_VARIABLE(rhsV##peel);                                                                   \
+  }
+
+#define MICRO_WORK_PEEL_ROW                                                              \
+  Packet rhsV0[4], rhsV1[4], rhsV2[4], rhsV3[4], rhsV4[4], rhsV5[4], rhsV6[4], rhsV7[4]; \
+  MICRO_UNROLL(MICRO_WORK_PEEL)                                                          \
+  lhs_ptr += (remaining_rows * PEEL_ROW);                                                \
+  MICRO_ADD_ROWS(PEEL_ROW)
+
+#define MICRO_ADD_PEEL(peel, sum)                        \
+  if (PEEL_ROW > peel) {                                 \
+    for (Index i = 0; i < accRows; i++) {                \
+      accZero##sum.packet[i] += accZero##peel.packet[i]; \
+    }                                                    \
+  }
+
+#define MICRO_ADD_PEEL_ROW \
+  MICRO_ADD_PEEL(4, 0)     \
+  MICRO_ADD_PEEL(5, 1)     \
+  MICRO_ADD_PEEL(6, 2) MICRO_ADD_PEEL(7, 3) MICRO_ADD_PEEL(2, 0) MICRO_ADD_PEEL(3, 1) MICRO_ADD_PEEL(1, 0)
+
+#define MICRO_PREFETCHN1(ptr, N)               \
+  EIGEN_POWER_PREFETCH(MICRO_RHS(ptr, 0));     \
+  if (N == 2 || N == 3) {                      \
+    EIGEN_POWER_PREFETCH(MICRO_RHS(ptr, 1));   \
+    if (N == 3) {                              \
+      EIGEN_POWER_PREFETCH(MICRO_RHS(ptr, 2)); \
+    }                                          \
+  }
+
+#define MICRO_PREFETCHN(N) MICRO_PREFETCHN1(ptr, N)
+
+#define MICRO_COMPLEX_PREFETCHN(N) \
+  MICRO_PREFETCHN1(ptr_real, N);   \
+  if (!RhsIsReal) {                \
+    MICRO_PREFETCHN1(ptr_imag, N); \
+  }
+
+template <typename Scalar, typename Packet, const Index accRows, const Index remaining_rows>
+EIGEN_ALWAYS_INLINE void MICRO_EXTRA_ROW(const Scalar*& lhs_ptr, const Scalar*& rhs_ptr0, const Scalar*& rhs_ptr1,
+                                         const Scalar*& rhs_ptr2, PacketBlock<Packet, accRows>& accZero) {
+  MICRO_BROADCAST_EXTRA
+  pger<accRows, Scalar, Packet, false>(&accZero, lhs_ptr, rhsV);
+  lhs_ptr += remaining_rows;
+}
+
+template <typename Scalar, typename Packet, typename DataMapper, const Index accRows, const Index accCols,
+          const Index remaining_rows>
+EIGEN_ALWAYS_INLINE void gemm_unrolled_row_iteration(const DataMapper& res, const Scalar* lhs_base,
+                                                     const Scalar* rhs_base, Index depth, Index strideA, Index offsetA,
+                                                     Index strideB, Index row, Index rows, const Packet& pAlpha,
+                                                     const Packet& pMask) {
+  const Scalar *rhs_ptr0 = rhs_base, *rhs_ptr1 = NULL, *rhs_ptr2 = NULL;
+  const Scalar* lhs_ptr = lhs_base + row * strideA + remaining_rows * offsetA;
+  PacketBlock<Packet, accRows> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7, acc;
+
+  MICRO_SRC2_PTR
+  bsetzero<Packet, accRows>(accZero0);
+
+  Index remaining_depth = depth & -quad_traits<Scalar>::rows;
+  Index k = 0;
+  if (remaining_depth >= PEEL_ROW) {
+    MICRO_ZERO_PEEL_ROW
+    do {
+      MICRO_PREFETCHN(accRows)
+      EIGEN_POWER_PREFETCH(lhs_ptr);
+      MICRO_WORK_PEEL_ROW
+    } while ((k += PEEL_ROW) + PEEL_ROW <= remaining_depth);
+    MICRO_ADD_PEEL_ROW
+  }
+  for (; k < depth; k++) {
+    MICRO_EXTRA_ROW<Scalar, Packet, accRows, remaining_rows>(lhs_ptr, rhs_ptr0, rhs_ptr1, rhs_ptr2, accZero0);
+  }
+
+#ifdef USE_PARTIAL_PACKETS
+  EIGEN_UNUSED_VARIABLE(rows);
+  EIGEN_UNUSED_VARIABLE(pMask);
+  bload_partial<DataMapper, Packet, 0, false, accRows>(acc, res, row, remaining_rows);
+  bscale<Packet, accRows>(acc, accZero0, pAlpha);
+  bstore_partial<DataMapper, Packet, accRows>(acc, res, row, remaining_rows);
+#else
+  bload<DataMapper, Packet, 0, ColMajor, false, accRows>(acc, res, row, 0);
+  if ((accRows == 1) || (rows >= accCols)) {
+    bscale<Packet, accRows, true>(acc, accZero0, pAlpha, pMask);
+    bstore<DataMapper, Packet, accRows>(acc, res, row);
+  } else {
+    bscale<Packet, accRows, false>(acc, accZero0, pAlpha, pMask);
+    for (Index j = 0; j < accRows; j++) {
+      for (Index i = 0; i < remaining_rows; i++) {
+        res(row + i, j) = acc.packet[j][i];
+      }
+    }
+  }
+#endif
+}
+
+#define MICRO_EXTRA(MICRO_EXTRA_UNROLL, value, is_col)   \
+  switch (value) {                                       \
+    default:                                             \
+      MICRO_EXTRA_UNROLL(1)                              \
+      break;                                             \
+    case 2:                                              \
+      if (is_col || (sizeof(Scalar) == sizeof(float))) { \
+        MICRO_EXTRA_UNROLL(2)                            \
+      }                                                  \
+      break;                                             \
+    case 3:                                              \
+      if (is_col || (sizeof(Scalar) == sizeof(float))) { \
+        MICRO_EXTRA_UNROLL(3)                            \
+      }                                                  \
+      break;                                             \
+  }
+
+#define MICRO_EXTRA_ROWS(N)                                                     \
+  gemm_unrolled_row_iteration<Scalar, Packet, DataMapper, accRows, accCols, N>( \
+      res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, rows, pAlpha, pMask);
+
+template <typename Scalar, typename Packet, typename DataMapper, const Index accRows, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemm_extra_row(const DataMapper& res, const Scalar* lhs_base, const Scalar* rhs_base,
+                                        Index depth, Index strideA, Index offsetA, Index strideB, Index row, Index rows,
+                                        Index remaining_rows, const Packet& pAlpha, const Packet& pMask) {
+  MICRO_EXTRA(MICRO_EXTRA_ROWS, remaining_rows, false)
+}
+
+#define MICRO_UNROLL_WORK(func, func2, peel) \
+  MICRO_UNROLL(func2);                       \
+  func(0, peel) func(1, peel) func(2, peel) func(3, peel) func(4, peel) func(5, peel) func(6, peel) func(7, peel)
+
+#define MICRO_WORK_ONE(iter, peel)                                               \
+  if (unroll_factor > iter) {                                                    \
+    pger_common<Packet, false, accRows>(&accZero##iter, lhsV##iter, rhsV##peel); \
+  }
+
+#define MICRO_TYPE_PEEL4(func, func2, peel)                        \
+  if (PEEL > peel) {                                               \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
+    MICRO_BROADCAST(peel)                                          \
+    MICRO_UNROLL_WORK(func, func2, peel)                           \
+  } else {                                                         \
+    EIGEN_UNUSED_VARIABLE(rhsV##peel);                             \
+  }
+
+#define MICRO_UNROLL_TYPE_PEEL(M, func, func1, func2)                                                           \
+  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M], rhsV4[M], rhsV5[M], rhsV6[M], rhsV7[M];                        \
+  func(func1, func2, 0) func(func1, func2, 1) func(func1, func2, 2) func(func1, func2, 3) func(func1, func2, 4) \
+      func(func1, func2, 5) func(func1, func2, 6) func(func1, func2, 7)
+
+#define MICRO_UNROLL_TYPE_ONE(M, func, func1, func2) \
+  Packet rhsV0[M];                                   \
+  func(func1, func2, 0)
+
+#define MICRO_UNROLL_TYPE(MICRO_TYPE, size)                       \
+  MICRO_TYPE(4, MICRO_TYPE_PEEL4, MICRO_WORK_ONE, MICRO_LOAD_ONE) \
+  MICRO_ADD_ROWS(size)
+
+#define MICRO_ONE_PEEL4 MICRO_UNROLL_TYPE(MICRO_UNROLL_TYPE_PEEL, PEEL)
+
+#define MICRO_ONE4 MICRO_UNROLL_TYPE(MICRO_UNROLL_TYPE_ONE, 1)
+
+#define MICRO_DST_PTR_ONE(iter)               \
+  if (unroll_factor > iter) {                 \
+    bsetzero<Packet, accRows>(accZero##iter); \
+  } else {                                    \
+    EIGEN_UNUSED_VARIABLE(accZero##iter);     \
+  }
+
+#define MICRO_DST_PTR MICRO_UNROLL(MICRO_DST_PTR_ONE)
+
+#define MICRO_SRC_PTR MICRO_UNROLL(MICRO_SRC_PTR_ONE)
+
+#define MICRO_PREFETCH MICRO_UNROLL(MICRO_PREFETCH_ONE)
+
+#ifdef USE_PARTIAL_PACKETS
+#define MICRO_STORE_ONE(iter)                                                                         \
+  if (unroll_factor > iter) {                                                                         \
+    if (MICRO_NORMAL_PARTIAL(iter)) {                                                                 \
+      bload<DataMapper, Packet, 0, ColMajor, false, accRows>(acc, res, row + iter * accCols, 0);      \
+      bscale<Packet, accRows>(acc, accZero##iter, pAlpha);                                            \
+      bstore<DataMapper, Packet, accRows>(acc, res, row + iter * accCols);                            \
+    } else {                                                                                          \
+      bload_partial<DataMapper, Packet, 0, false, accRows>(acc, res, row + iter * accCols, accCols2); \
+      bscale<Packet, accRows>(acc, accZero##iter, pAlpha);                                            \
+      bstore_partial<DataMapper, Packet, accRows>(acc, res, row + iter * accCols, accCols2);          \
+    }                                                                                                 \
+  }
+#else
+#define MICRO_STORE_ONE(iter)                                                                  \
+  if (unroll_factor > iter) {                                                                  \
+    bload<DataMapper, Packet, 0, ColMajor, false, accRows>(acc, res, row + iter * accCols, 0); \
+    bscale<Packet, accRows, !(MICRO_NORMAL(iter))>(acc, accZero##iter, pAlpha, pMask);         \
+    bstore<DataMapper, Packet, accRows>(acc, res, row + iter * accCols);                       \
+  }
+#endif
+
+#define MICRO_STORE MICRO_UNROLL(MICRO_STORE_ONE)
+
+#ifdef USE_PARTIAL_PACKETS
+template <int unroll_factor, typename Scalar, typename Packet, typename DataMapper, const Index accRows,
+          const Index accCols, bool full>
+#else
+template <int unroll_factor, typename Scalar, typename Packet, typename DataMapper, const Index accRows,
+          const Index accCols, const Index accCols2>
+#endif
+EIGEN_ALWAYS_INLINE void gemm_unrolled_iteration(const DataMapper& res, const Scalar* lhs_base, const Scalar* rhs_base,
+                                                 Index depth, Index strideA, Index offsetA, Index strideB, Index& row,
+                                                 const Packet& pAlpha,
+#ifdef USE_PARTIAL_PACKETS
+                                                 Index accCols2
+#else
+                                                 const Packet& pMask
+#endif
+) {
+  const Scalar *rhs_ptr0 = rhs_base, *rhs_ptr1 = NULL, *rhs_ptr2 = NULL;
+  const Scalar *lhs_ptr0 = NULL, *lhs_ptr1 = NULL, *lhs_ptr2 = NULL, *lhs_ptr3 = NULL, *lhs_ptr4 = NULL,
+               *lhs_ptr5 = NULL, *lhs_ptr6 = NULL, *lhs_ptr7 = NULL;
+  PacketBlock<Packet, accRows> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
+  PacketBlock<Packet, accRows> acc;
+
+  MICRO_SRC2_PTR
+  MICRO_SRC_PTR
+  MICRO_DST_PTR
+
+  Index k = 0;
+  for (; k + PEEL <= depth; k += PEEL) {
+    MICRO_PREFETCHN(accRows)
+    MICRO_PREFETCH
+    MICRO_ONE_PEEL4
+  }
+  for (; k < depth; k++) {
+    MICRO_ONE4
+  }
+  MICRO_STORE
+
+  MICRO_UPDATE
+}
+
+#ifdef USE_PARTIAL_PACKETS
+#define MICRO_UNROLL_ITER2(N, M)                                                                              \
+  gemm_unrolled_iteration<N + ((M) ? 1 : 0), Scalar, Packet, DataMapper, accRows, accCols, !M>(               \
+      res3, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, pAlpha, M ? remaining_rows : accCols); \
+  if (M) return;
+#else
+#define MICRO_UNROLL_ITER2(N, M)                                                                             \
+  gemm_unrolled_iteration<N + ((M) ? 1 : 0), Scalar, Packet, DataMapper, accRows, accCols, M ? M : accCols>( \
+      res3, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, pAlpha, pMask);                       \
+  if (M) return;
+#endif
+
+template <typename Scalar, typename Packet, typename DataMapper, const Index accRows, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemm_cols(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index depth,
+                                   Index strideA, Index offsetA, Index strideB, Index offsetB, Index col, Index rows,
+                                   Index remaining_rows, const Packet& pAlpha, const Packet& pMask) {
+  const DataMapper res3 = res.getSubMapper(0, col);
+
+  const Scalar* rhs_base = blockB + col * strideB + MICRO_NEW_ROWS * offsetB;
+  const Scalar* lhs_base = blockA + accCols * offsetA;
+  Index row = 0;
+
+#define MAX_UNROLL 7
+  while (row + MAX_UNROLL * accCols <= rows) {
+    MICRO_UNROLL_ITER2(MAX_UNROLL, 0);
+  }
+  switch ((rows - row) / accCols) {
+#if MAX_UNROLL > 7
+    case 7:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 7)
+      break;
+#endif
+#if MAX_UNROLL > 6
+    case 6:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 6)
+      break;
+#endif
+#if MAX_UNROLL > 5
+    case 5:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 5)
+      break;
+#endif
+#if MAX_UNROLL > 4
+    case 4:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 4)
+      break;
+#endif
+#if MAX_UNROLL > 3
+    case 3:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 3)
+      break;
+#endif
+#if MAX_UNROLL > 2
+    case 2:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 2)
+      break;
+#endif
+#if MAX_UNROLL > 1
+    case 1:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 1)
+      break;
+#endif
+    default:
+      break;
+  }
+#undef MAX_UNROLL
+
+  if (remaining_rows > 0) {
+    gemm_extra_row<Scalar, Packet, DataMapper, accRows, accCols>(res3, blockA, rhs_base, depth, strideA, offsetA,
+                                                                 strideB, row, rows, remaining_rows, pAlpha, pMask);
+  }
+}
+
+#define MICRO_EXTRA_COLS(N)                                                                                         \
+  gemm_cols<Scalar, Packet, DataMapper, N, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, \
+                                                    col, rows, remaining_rows, pAlpha, pMask);
+
+template <typename Scalar, typename Packet, typename DataMapper, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemm_extra_cols(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index depth,
+                                         Index strideA, Index offsetA, Index strideB, Index offsetB, Index col,
+                                         Index rows, Index cols, Index remaining_rows, const Packet& pAlpha,
+                                         const Packet& pMask) {
+  MICRO_EXTRA(MICRO_EXTRA_COLS, cols - col, true)
+}
+
+/****************
+ * GEMM kernels *
+ * **************/
+template <typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows,
+          const Index accCols>
+EIGEN_STRONG_INLINE void gemm(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows,
+                              Index depth, Index cols, Scalar alpha, Index strideA, Index strideB, Index offsetA,
+                              Index offsetB) {
+  const Index remaining_rows = rows % accCols;
+
+  if (strideA == -1) strideA = depth;
+  if (strideB == -1) strideB = depth;
+
+  const Packet pAlpha = pset1<Packet>(alpha);
+  const Packet pMask = bmask<Packet>(remaining_rows);
+
+  Index col = 0;
+  for (; col + accRows <= cols; col += accRows) {
+    gemm_cols<Scalar, Packet, DataMapper, accRows, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB,
+                                                            offsetB, col, rows, remaining_rows, pAlpha, pMask);
+  }
+
+  if (col != cols) {
+    gemm_extra_cols<Scalar, Packet, DataMapper, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB,
+                                                         col, rows, cols, remaining_rows, pAlpha, pMask);
+  }
+}
+
+#define accColsC (accCols / 2)
+#define advanceRows ((LhsIsReal) ? 1 : 2)
+#define advanceCols ((RhsIsReal) ? 1 : 2)
+
+// PEEL_COMPLEX loop factor.
+#define PEEL_COMPLEX 3
+#define PEEL_COMPLEX_ROW 3
+
+#define MICRO_COMPLEX_UNROLL(func) func(0) func(1) func(2) func(3)
+
+#define MICRO_COMPLEX_ZERO_PEEL(peel)             \
+  if ((PEEL_COMPLEX_ROW > peel) && (peel != 0)) { \
+    bsetzero<Packet, accRows>(accReal##peel);     \
+    bsetzero<Packet, accRows>(accImag##peel);     \
+  } else {                                        \
+    EIGEN_UNUSED_VARIABLE(accReal##peel);         \
+    EIGEN_UNUSED_VARIABLE(accImag##peel);         \
+  }
+
+#define MICRO_COMPLEX_ADD_ROWS(N, used)            \
+  MICRO_ADD(ptr_real, N)                           \
+  if (!RhsIsReal) {                                \
+    MICRO_ADD(ptr_imag, N)                         \
+  } else if (used) {                               \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag, 0)); \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag, 1)); \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag, 2)); \
+  }
+
+#define MICRO_COMPLEX_BROADCAST(peel)              \
+  MICRO_BROADCAST1(peel, ptr_real, rhsV, false)    \
+  if (!RhsIsReal) {                                \
+    MICRO_BROADCAST1(peel, ptr_imag, rhsVi, false) \
+  } else {                                         \
+    EIGEN_UNUSED_VARIABLE(rhsVi##peel);            \
+  }
+
+#define MICRO_COMPLEX_BROADCAST_EXTRA              \
+  Packet rhsV[4], rhsVi[4];                        \
+  MICRO_BROADCAST_EXTRA1(ptr_real, rhsV, false)    \
+  if (!RhsIsReal) {                                \
+    MICRO_BROADCAST_EXTRA1(ptr_imag, rhsVi, false) \
+  } else {                                         \
+    EIGEN_UNUSED_VARIABLE(rhsVi);                  \
+  }                                                \
+  MICRO_COMPLEX_ADD_ROWS(1, true)
+
+#define MICRO_COMPLEX_SRC2_PTR                                    \
+  MICRO_SRC2(ptr_real, strideB* advanceCols, 0)                   \
+  if (!RhsIsReal) {                                               \
+    MICRO_RHS(ptr_imag, 0) = rhs_base + MICRO_NEW_ROWS * strideB; \
+    MICRO_SRC2(ptr_imag, strideB* advanceCols, strideB)           \
+  } else {                                                        \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag, 0));                \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag, 1));                \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag, 2));                \
+  }
+
+#define MICRO_COMPLEX_ZERO_PEEL_ROW MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_ZERO_PEEL)
+
+#define MICRO_COMPLEX_WORK_PEEL(peel)                                                 \
+  if (PEEL_COMPLEX_ROW > peel) {                                                      \
+    MICRO_COMPLEX_BROADCAST(peel)                                                     \
+    pgerc<accRows, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>( \
+        &accReal##peel, &accImag##peel, lhs_ptr_real + (remaining_rows * peel),       \
+        lhs_ptr_imag + (remaining_rows * peel), rhsV##peel, rhsVi##peel);             \
+  } else {                                                                            \
+    EIGEN_UNUSED_VARIABLE(rhsV##peel);                                                \
+    EIGEN_UNUSED_VARIABLE(rhsVi##peel);                                               \
+  }
+
+#define MICRO_COMPLEX_ADD_COLS(size)         \
+  lhs_ptr_real += (remaining_rows * size);   \
+  if (!LhsIsReal)                            \
+    lhs_ptr_imag += (remaining_rows * size); \
+  else                                       \
+    EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
+
+#define MICRO_COMPLEX_WORK_PEEL_ROW                  \
+  Packet rhsV0[4], rhsV1[4], rhsV2[4], rhsV3[4];     \
+  Packet rhsVi0[4], rhsVi1[4], rhsVi2[4], rhsVi3[4]; \
+  MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_WORK_PEEL)      \
+  MICRO_COMPLEX_ADD_COLS(PEEL_COMPLEX_ROW)           \
+  MICRO_COMPLEX_ADD_ROWS(PEEL_COMPLEX_ROW, false)
+
+#define MICRO_COMPLEX_ADD_PEEL(peel, sum)                \
+  if (PEEL_COMPLEX_ROW > peel) {                         \
+    for (Index i = 0; i < accRows; i++) {                \
+      accReal##sum.packet[i] += accReal##peel.packet[i]; \
+      accImag##sum.packet[i] += accImag##peel.packet[i]; \
+    }                                                    \
+  }
+
+#define MICRO_COMPLEX_ADD_PEEL_ROW \
+  MICRO_COMPLEX_ADD_PEEL(2, 0) MICRO_COMPLEX_ADD_PEEL(3, 1) MICRO_COMPLEX_ADD_PEEL(1, 0)
+
+template <typename Scalar, typename Packet, const Index accRows, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal,
+          bool RhsIsReal, const Index remaining_rows>
+EIGEN_ALWAYS_INLINE void MICRO_COMPLEX_EXTRA_ROW(const Scalar*& lhs_ptr_real, const Scalar*& lhs_ptr_imag,
+                                                 const Scalar*& rhs_ptr_real0, const Scalar*& rhs_ptr_real1,
+                                                 const Scalar*& rhs_ptr_real2, const Scalar*& rhs_ptr_imag0,
+                                                 const Scalar*& rhs_ptr_imag1, const Scalar*& rhs_ptr_imag2,
+                                                 PacketBlock<Packet, accRows>& accReal,
+                                                 PacketBlock<Packet, accRows>& accImag) {
+  MICRO_COMPLEX_BROADCAST_EXTRA
+  pgerc<accRows, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real,
+                                                                                   lhs_ptr_imag, rhsV, rhsVi);
+  MICRO_COMPLEX_ADD_COLS(1)
+}
+
+template <typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accRows,
+          const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal,
+          const Index remaining_rows>
+EIGEN_ALWAYS_INLINE void gemm_unrolled_complex_row_iteration(const DataMapper& res, const Scalar* lhs_base,
+                                                             const Scalar* rhs_base, Index depth, Index strideA,
+                                                             Index offsetA, Index strideB, Index row, Index rows,
+                                                             const Packet& pAlphaReal, const Packet& pAlphaImag,
+                                                             const Packet& pMask) {
+  const Scalar *rhs_ptr_real0 = rhs_base, *rhs_ptr_real1 = NULL, *rhs_ptr_real2 = NULL;
+  const Scalar *rhs_ptr_imag0 = NULL, *rhs_ptr_imag1 = NULL, *rhs_ptr_imag2 = NULL;
+  const Scalar* lhs_ptr_real = lhs_base + advanceRows * row * strideA + remaining_rows * offsetA;
+  const Scalar* lhs_ptr_imag = NULL;
+  if (!LhsIsReal)
+    lhs_ptr_imag = lhs_ptr_real + remaining_rows * strideA;
+  else
+    EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
+  PacketBlock<Packet, accRows> accReal0, accImag0, accReal1, accImag1, accReal2, accImag2, accReal3, accImag3;
+  PacketBlock<Packet, accRows> taccReal, taccImag;
+  PacketBlock<Packetc, accRows> acc0, acc1;
+  PacketBlock<Packetc, accRows * 2> tRes;
+
+  MICRO_COMPLEX_SRC2_PTR
+
+  bsetzero<Packet, accRows>(accReal0);
+  bsetzero<Packet, accRows>(accImag0);
+
+  Index remaining_depth = depth & -quad_traits<Scalar>::rows;
+  Index k = 0;
+  if (remaining_depth >= PEEL_COMPLEX_ROW) {
+    MICRO_COMPLEX_ZERO_PEEL_ROW
+    do {
+      MICRO_COMPLEX_PREFETCHN(accRows)
+      EIGEN_POWER_PREFETCH(lhs_ptr_real);
+      if (!LhsIsReal) {
+        EIGEN_POWER_PREFETCH(lhs_ptr_imag);
+      }
+      MICRO_COMPLEX_WORK_PEEL_ROW
+    } while ((k += PEEL_COMPLEX_ROW) + PEEL_COMPLEX_ROW <= remaining_depth);
+    MICRO_COMPLEX_ADD_PEEL_ROW
+  }
+  for (; k < depth; k++) {
+    MICRO_COMPLEX_EXTRA_ROW<Scalar, Packet, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal, remaining_rows>(
+        lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real0, rhs_ptr_real1, rhs_ptr_real2, rhs_ptr_imag0, rhs_ptr_imag1,
+        rhs_ptr_imag2, accReal0, accImag0);
+  }
+
+  constexpr bool full = (remaining_rows > accColsC);
+  bload<DataMapper, Packetc, accColsC, ColMajor, true, accRows, full>(tRes, res, row, 0);
+  if ((accRows == 1) || (rows >= accCols)) {
+    bscalec<Packet, accRows, true>(accReal0, accImag0, pAlphaReal, pAlphaImag, taccReal, taccImag, pMask);
+    bcouple<Packet, Packetc, accRows, full>(taccReal, taccImag, tRes, acc0, acc1);
+    bstore<DataMapper, Packetc, accRows>(acc0, res, row + 0);
+    if (full) {
+      bstore<DataMapper, Packetc, accRows>(acc1, res, row + accColsC);
+    }
+  } else {
+    bscalec<Packet, accRows, false>(accReal0, accImag0, pAlphaReal, pAlphaImag, taccReal, taccImag, pMask);
+    bcouple<Packet, Packetc, accRows, full>(taccReal, taccImag, tRes, acc0, acc1);
+
+    if ((sizeof(Scalar) == sizeof(float)) && (remaining_rows == 1)) {
+      for (Index j = 0; j < accRows; j++) {
+        res(row + 0, j) = pfirst<Packetc>(acc0.packet[j]);
+      }
+    } else {
+      bstore<DataMapper, Packetc, accRows>(acc0, res, row + 0);
+      if (full) {
+        for (Index j = 0; j < accRows; j++) {
+          res(row + accColsC, j) = pfirst<Packetc>(acc1.packet[j]);
+        }
+      }
+    }
+  }
+}
+
+#define MICRO_COMPLEX_EXTRA_ROWS(N)                                                                        \
+  gemm_unrolled_complex_row_iteration<Scalar, Packet, Packetc, DataMapper, accRows, accCols, ConjugateLhs, \
+                                      ConjugateRhs, LhsIsReal, RhsIsReal, N>(                              \
+      res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, rows, pAlphaReal, pAlphaImag, pMask);
+
+template <typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accRows,
+          const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(const DataMapper& res, const Scalar* lhs_base, const Scalar* rhs_base,
+                                                Index depth, Index strideA, Index offsetA, Index strideB, Index row,
+                                                Index rows, Index remaining_rows, const Packet& pAlphaReal,
+                                                const Packet& pAlphaImag, const Packet& pMask) {
+  MICRO_EXTRA(MICRO_COMPLEX_EXTRA_ROWS, remaining_rows, false)
+}
+
+#define MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
+  MICRO_COMPLEX_UNROLL(func2);                       \
+  func(0, peel) func(1, peel) func(2, peel) func(3, peel)
+
+#define MICRO_COMPLEX_WORK_ONE4(iter, peel)                                                \
+  if (unroll_factor > iter) {                                                              \
+    pgerc_common<accRows, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(       \
+        &accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
+  }
+
+#define MICRO_COMPLEX_TYPE_PEEL4(func, func2, peel) \
+  if (PEEL_COMPLEX > peel) {                        \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3;              \
+    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3;          \
+    MICRO_COMPLEX_BROADCAST(peel)                   \
+    MICRO_COMPLEX_UNROLL_WORK(func, func2, peel)    \
+  } else {                                          \
+    EIGEN_UNUSED_VARIABLE(rhsV##peel);              \
+    EIGEN_UNUSED_VARIABLE(rhsVi##peel);             \
+  }
+
+#define MICRO_COMPLEX_UNROLL_TYPE_PEEL(M, func, func1, func2) \
+  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M];              \
+  Packet rhsVi0[M], rhsVi1[M], rhsVi2[M], rhsVi3[M];          \
+  func(func1, func2, 0) func(func1, func2, 1) func(func1, func2, 2) func(func1, func2, 3)
+
+#define MICRO_COMPLEX_UNROLL_TYPE_ONE(M, func, func1, func2) \
+  Packet rhsV0[M], rhsVi0[M];                                \
+  func(func1, func2, 0)
+
+#define MICRO_COMPLEX_UNROLL_TYPE(MICRO_COMPLEX_TYPE, size)                                        \
+  MICRO_COMPLEX_TYPE(4, MICRO_COMPLEX_TYPE_PEEL4, MICRO_COMPLEX_WORK_ONE4, MICRO_COMPLEX_LOAD_ONE) \
+  MICRO_COMPLEX_ADD_ROWS(size, false)
+
+#define MICRO_COMPLEX_ONE_PEEL4 MICRO_COMPLEX_UNROLL_TYPE(MICRO_COMPLEX_UNROLL_TYPE_PEEL, PEEL_COMPLEX)
+
+#define MICRO_COMPLEX_ONE4 MICRO_COMPLEX_UNROLL_TYPE(MICRO_COMPLEX_UNROLL_TYPE_ONE, 1)
+
+#define MICRO_COMPLEX_DST_PTR_ONE(iter)       \
+  if (unroll_factor > iter) {                 \
+    bsetzero<Packet, accRows>(accReal##iter); \
+    bsetzero<Packet, accRows>(accImag##iter); \
+  } else {                                    \
+    EIGEN_UNUSED_VARIABLE(accReal##iter);     \
+    EIGEN_UNUSED_VARIABLE(accImag##iter);     \
+  }
+
+#define MICRO_COMPLEX_DST_PTR MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_DST_PTR_ONE)
+
+#define MICRO_COMPLEX_SRC_PTR MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_SRC_PTR_ONE)
+
+#define MICRO_COMPLEX_PREFETCH MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_PREFETCH_ONE)
+
+#define MICRO_COMPLEX_STORE_ONE(iter)                                                                               \
+  if (unroll_factor > iter) {                                                                                       \
+    constexpr bool full = ((MICRO_NORMAL(iter)) || (accCols2 > accColsC));                                          \
+    bload<DataMapper, Packetc, accColsC, ColMajor, true, accRows, full>(tRes, res, row + iter * accCols, 0);        \
+    bscalec<Packet, accRows, !(MICRO_NORMAL(iter))>(accReal##iter, accImag##iter, pAlphaReal, pAlphaImag, taccReal, \
+                                                    taccImag, pMask);                                               \
+    bcouple<Packet, Packetc, accRows, full>(taccReal, taccImag, tRes, acc0, acc1);                                  \
+    bstore<DataMapper, Packetc, accRows>(acc0, res, row + iter * accCols + 0);                                      \
+    if (full) {                                                                                                     \
+      bstore<DataMapper, Packetc, accRows>(acc1, res, row + iter * accCols + accColsC);                             \
+    }                                                                                                               \
+  }
+
+#define MICRO_COMPLEX_STORE MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_STORE_ONE)
+
+template <int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename DataMapper,
+          const Index accRows, const Index accCols, const Index accCols2, bool ConjugateLhs, bool ConjugateRhs,
+          bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_unrolled_iteration(const DataMapper& res, const Scalar* lhs_base,
+                                                         const Scalar* rhs_base, Index depth, Index strideA,
+                                                         Index offsetA, Index strideB, Index& row,
+                                                         const Packet& pAlphaReal, const Packet& pAlphaImag,
+                                                         const Packet& pMask) {
+  const Scalar *rhs_ptr_real0 = rhs_base, *rhs_ptr_real1 = NULL, *rhs_ptr_real2 = NULL;
+  const Scalar *rhs_ptr_imag0 = NULL, *rhs_ptr_imag1 = NULL, *rhs_ptr_imag2 = NULL;
+  const Index imag_delta = accCols * strideA;
+  const Index imag_delta2 = accCols2 * strideA;
+  const Scalar *lhs_ptr_real0 = NULL, *lhs_ptr_real1 = NULL;
+  const Scalar *lhs_ptr_real2 = NULL, *lhs_ptr_real3 = NULL;
+  PacketBlock<Packet, accRows> accReal0, accImag0, accReal1, accImag1;
+  PacketBlock<Packet, accRows> accReal2, accImag2, accReal3, accImag3;
+  PacketBlock<Packet, accRows> taccReal, taccImag;
+  PacketBlock<Packetc, accRows> acc0, acc1;
+  PacketBlock<Packetc, accRows * 2> tRes;
+
+  MICRO_COMPLEX_SRC2_PTR
+  MICRO_COMPLEX_SRC_PTR
+  MICRO_COMPLEX_DST_PTR
+
+  Index k = 0;
+  for (; k + PEEL_COMPLEX <= depth; k += PEEL_COMPLEX) {
+    MICRO_COMPLEX_PREFETCHN(accRows)
+    MICRO_COMPLEX_PREFETCH
+    MICRO_COMPLEX_ONE_PEEL4
+  }
+  for (; k < depth; k++) {
+    MICRO_COMPLEX_ONE4
+  }
+  MICRO_COMPLEX_STORE
+
+  MICRO_COMPLEX_UPDATE
+}
+
+#define MICRO_COMPLEX_UNROLL_ITER2(N, M)                                                                  \
+  gemm_complex_unrolled_iteration<N + (M ? 1 : 0), Scalar, Packet, Packetc, DataMapper, accRows, accCols, \
+                                  M ? M : accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(     \
+      res3, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, pAlphaReal, pAlphaImag, pMask);    \
+  if (M) return;
+
+template <typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accRows,
+          const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_cols(const DataMapper& res, const Scalar* blockA, const Scalar* blockB,
+                                           Index depth, Index strideA, Index offsetA, Index strideB, Index offsetB,
+                                           Index col, Index rows, Index remaining_rows, const Packet& pAlphaReal,
+                                           const Packet& pAlphaImag, const Packet& pMask) {
+  const DataMapper res3 = res.getSubMapper(0, col);
+
+  const Scalar* rhs_base = blockB + advanceCols * col * strideB + MICRO_NEW_ROWS * offsetB;
+  const Scalar* lhs_base = blockA + accCols * offsetA;
+  Index row = 0;
+
+#define MAX_COMPLEX_UNROLL 4
+  while (row + MAX_COMPLEX_UNROLL * accCols <= rows) {
+    MICRO_COMPLEX_UNROLL_ITER2(MAX_COMPLEX_UNROLL, 0);
+  }
+  switch ((rows - row) / accCols) {
+#if MAX_COMPLEX_UNROLL > 4
+    case 4:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_UNROLL_ITER2, 4)
+      break;
+#endif
+#if MAX_COMPLEX_UNROLL > 3
+    case 3:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_UNROLL_ITER2, 3)
+      break;
+#endif
+#if MAX_COMPLEX_UNROLL > 2
+    case 2:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_UNROLL_ITER2, 2)
+      break;
+#endif
+#if MAX_COMPLEX_UNROLL > 1
+    case 1:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_UNROLL_ITER2, 1)
+      break;
+#endif
+    default:
+      break;
+  }
+#undef MAX_COMPLEX_UNROLL
+
+  if (remaining_rows > 0) {
+    gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal,
+                           RhsIsReal>(res3, blockA, rhs_base, depth, strideA, offsetA, strideB, row, rows,
+                                      remaining_rows, pAlphaReal, pAlphaImag, pMask);
+  }
+}
+
+#define MICRO_COMPLEX_EXTRA_COLS(N)                                                                         \
+  gemm_complex_cols<Scalar, Packet, Packetc, DataMapper, N, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, \
+                    RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows,   \
+                               remaining_rows, pAlphaReal, pAlphaImag, pMask);
+
+template <typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accCols,
+          bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_extra_cols(const DataMapper& res, const Scalar* blockA, const Scalar* blockB,
+                                                 Index depth, Index strideA, Index offsetA, Index strideB,
+                                                 Index offsetB, Index col, Index rows, Index cols, Index remaining_rows,
+                                                 const Packet& pAlphaReal, const Packet& pAlphaImag,
+                                                 const Packet& pMask) {
+  MICRO_EXTRA(MICRO_COMPLEX_EXTRA_COLS, cols - col, true)
+}
+
+template <typename LhsScalar, typename RhsScalar, typename Scalarc, typename Scalar, typename Packet, typename Packetc,
+          typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs,
+          bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_STRONG_INLINE void gemm_complex(const DataMapper& res, const LhsScalar* blockAc, const RhsScalar* blockBc,
+                                      Index rows, Index depth, Index cols, Scalarc alpha, Index strideA, Index strideB,
+                                      Index offsetA, Index offsetB) {
+  const Index remaining_rows = rows % accCols;
+
+  if (strideA == -1) strideA = depth;
+  if (strideB == -1) strideB = depth;
+
+  const Packet pAlphaReal = pset1<Packet>(alpha.real());
+  const Packet pAlphaImag = pset1<Packet>(alpha.imag());
+  const Packet pMask = bmask<Packet>(remaining_rows);
+
+  const Scalar* blockA = (Scalar*)blockAc;
+  const Scalar* blockB = (Scalar*)blockBc;
+
+  Index col = 0;
+  for (; col + accRows <= cols; col += accRows) {
+    gemm_complex_cols<Scalar, Packet, Packetc, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal,
+                      RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows,
+                                 remaining_rows, pAlphaReal, pAlphaImag, pMask);
+  }
+
+  if (col != cols) {
+    gemm_complex_extra_cols<Scalar, Packet, Packetc, DataMapper, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal,
+                            RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols,
+                                       remaining_rows, pAlphaReal, pAlphaImag, pMask);
+  }
+}
+
+#undef accColsC
+#undef advanceCols
+#undef advanceRows
+
+EIGEN_ALWAYS_INLINE bool supportsMMA() {
+#if defined(EIGEN_ALTIVEC_MMA_ONLY)
+  return true;
+#elif defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH) && defined(__BUILTIN_CPU_SUPPORTS__)
+  return __builtin_cpu_supports("arch_3_1") && __builtin_cpu_supports("mma");
+#else
+  return false;  // No dynamic dispatch for LLVM or older GCC
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet4f loadAndMultiplyF32(Packet4f acc, const Packet4f pAlpha, float* result) {
+  Packet4f result_block = ploadu<Packet4f>(result);
+  return pmadd(acc, pAlpha, result_block);
+}
+
+template <bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void storeF32(float*& result, Packet4f result_block, Index rows, Index extra_rows) {
+  if (lhsExtraRows) {
+    pstoreu_partial(result, result_block, extra_rows);
+  } else {
+    pstoreu(result, result_block);
+  }
+  result += rows;
+}
+
+template <bool rhsExtraCols, bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void storeResults(Packet4f (&acc)[4], Index rows, const Packet4f pAlpha, float* result,
+                                      Index extra_cols, Index extra_rows) {
+  Index x = 0;
+  if (rhsExtraCols) {
+    do {
+      Packet4f result_block = loadAndMultiplyF32(acc[x], pAlpha, result);
+      storeF32<lhsExtraRows>(result, result_block, rows, extra_rows);
+    } while (++x < extra_cols);
+  } else {
+    Packet4f result_block[4];
+    float* result2 = result;
+    do {
+      result_block[x] = loadAndMultiplyF32(acc[x], pAlpha, result);
+      result += rows;
+    } while (++x < 4);
+    x = 0;
+    do {
+      storeF32<lhsExtraRows>(result2, result_block[x], rows, extra_rows);
+    } while (++x < 4);
+  }
+}
+
+EIGEN_ALWAYS_INLINE Packet4f oneConvertBF16Hi(Packet8us data) {
+  Packet8us z = pset1<Packet8us>(0);
+#ifdef _BIG_ENDIAN
+  return reinterpret_cast<Packet4f>(vec_mergeh(data, z));
+#else
+  return reinterpret_cast<Packet4f>(vec_mergeh(z, data));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet4f oneConvertBF16Lo(Packet8us data) {
+  Packet8us z = pset1<Packet8us>(0);
+#ifdef _BIG_ENDIAN
+  return reinterpret_cast<Packet4f>(vec_mergel(data, z));
+#else
+  return reinterpret_cast<Packet4f>(vec_mergel(z, data));
+#endif
+}
+
+template <Index N, Index M>
+EIGEN_ALWAYS_INLINE void storeConvertTwoBF16(float* to, PacketBlock<Packet8bf, (N + 7) / 8>& block, Index extra = 0) {
+  if (N < 4) {
+    pstoreu_partial(to + 0, oneConvertBF16Hi(block.packet[0].m_val), extra);
+  } else if (N >= (M * 8 + 4)) {
+    pstoreu(to + 0, oneConvertBF16Hi(block.packet[M].m_val));
+    if (N >= 8) {
+      pstoreu(to + 4, oneConvertBF16Lo(block.packet[M].m_val));
+    }
+  }
+}
+
+template <Index N>
+EIGEN_ALWAYS_INLINE void storeConvertBlockBF16(float* to, PacketBlock<Packet8bf, (N + 7) / 8>& block, Index extra) {
+  storeConvertTwoBF16<N, 0>(to + 0, block, extra);
+  if (N >= 16) {
+    storeConvertTwoBF16<N, 1>(to + 8, block);
+  }
+  if (N >= 32) {
+    storeConvertTwoBF16<N, 2>(to + 16, block);
+    storeConvertTwoBF16<N, 3>(to + 24, block);
+  }
+}
+
+template <bool non_unit_stride, Index delta>
+EIGEN_ALWAYS_INLINE Packet8bf loadBF16fromResult(bfloat16* src, Index resInc) {
+  if (non_unit_stride) {
+    return pgather<bfloat16, Packet8bf>(src + delta * resInc, resInc);
+  } else {
+    return ploadu<Packet8bf>(src + delta);
+  }
+}
+
+static Packet16uc p16uc_MERGE16_32_1 = {0, 1, 16, 17, 2, 3, 18, 19, 0, 1, 16, 17, 2, 3, 18, 19};
+static Packet16uc p16uc_MERGE16_32_2 = {4, 5, 20, 21, 6, 7, 22, 23, 4, 5, 20, 21, 6, 7, 22, 23};
+static Packet16uc p16uc_MERGE16_32_3 = {8, 9, 24, 25, 10, 11, 26, 27, 8, 9, 24, 25, 10, 11, 26, 27};
+static Packet16uc p16uc_MERGE16_32_4 = {12, 13, 28, 29, 14, 15, 30, 31, 12, 13, 28, 29, 14, 15, 30, 31};
+
+static Packet16uc p16uc_MERGE16_32_5 = {0, 1, 16, 17, 16, 17, 16, 17, 0, 1, 16, 17, 16, 17, 16, 17};
+static Packet16uc p16uc_MERGE16_32_6 = {2, 3, 18, 19, 18, 19, 18, 19, 2, 3, 18, 19, 18, 19, 18, 19};
+static Packet16uc p16uc_MERGE16_32_7 = {4, 5, 20, 21, 20, 21, 20, 21, 4, 5, 20, 21, 20, 21, 20, 21};
+static Packet16uc p16uc_MERGE16_32_8 = {6, 7, 22, 23, 22, 23, 22, 23, 6, 7, 22, 23, 22, 23, 22, 23};
+
+EIGEN_ALWAYS_INLINE Packet4f oneConvertBF16Perm(Packet8us data, Packet16uc mask) {
+  Packet8us z = pset1<Packet8us>(0);
+#ifdef _BIG_ENDIAN
+  return reinterpret_cast<Packet4f>(vec_perm(data, z, mask));
+#else
+  return reinterpret_cast<Packet4f>(vec_perm(z, data, mask));
+#endif
+}
+
+template <bool lhsExtraRows, bool odd, Index size>
+EIGEN_ALWAYS_INLINE void convertArrayPointerBF16toF32DupOne(float* result, Index rows, const bfloat16* src,
+                                                            Index extra_rows) {
+  Packet4f dup[4 * 4];
+  Packet8bf data[4];
+
+  for (Index i = 0; i < size; i++) {
+    data[i] = ploadu<Packet8bf>(src + rows * i);
+  }
+
+  for (Index i = 0, j = 0; i < size; i++, j += 4) {
+    dup[j + 0] = oneConvertBF16Perm(data[i].m_val, odd ? p16uc_MERGE16_32_5 : p16uc_MERGE16_32_1);
+    dup[j + 1] = oneConvertBF16Perm(data[i].m_val, odd ? p16uc_MERGE16_32_6 : p16uc_MERGE16_32_2);
+    dup[j + 2] = oneConvertBF16Perm(data[i].m_val, odd ? p16uc_MERGE16_32_7 : p16uc_MERGE16_32_3);
+    dup[j + 3] = oneConvertBF16Perm(data[i].m_val, odd ? p16uc_MERGE16_32_8 : p16uc_MERGE16_32_4);
+  }
+
+  for (Index j = 0; j < 4 * size; j += 4) {
+    if (lhsExtraRows) {
+      Packet4f z = pset1<Packet4f>(float(0));
+      Index i = 0;
+      do {
+        pstoreu(result + (j + i) * 4, dup[j + i]);
+      } while (++i < extra_rows);
+      do {
+        pstoreu(result + (j + i) * 4, z);
+      } while (++i < 4);
+    } else {
+      for (Index i = 0; i < 4; i++) {
+        pstoreu(result + (j + i) * 4, dup[j + i]);
+      }
+    }
+  }
+}
+
+template <bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void convertArrayPointerBF16toF32Dup(float* result, Index cols, Index rows, const bfloat16* src,
+                                                         Index delta, Index extra_rows) {
+  Index col = 0;
+  src += delta * 2;
+  for (; col + 4 * 2 <= cols; col += 4 * 2, result += 4 * 4 * 4, src += 4 * rows) {
+    convertArrayPointerBF16toF32DupOne<lhsExtraRows, false, 4>(result, rows, src, extra_rows);
+  }
+  for (; col + 2 <= cols; col += 2, result += 4 * 4, src += rows) {
+    convertArrayPointerBF16toF32DupOne<lhsExtraRows, false, 1>(result, rows, src, extra_rows);
+  }
+  if (cols & 1) {
+    convertArrayPointerBF16toF32DupOne<lhsExtraRows, true, 1>(result, rows, src - delta, extra_rows);
+  }
+}
+
+template <const Index size, bool non_unit_stride>
+EIGEN_ALWAYS_INLINE void convertPointerBF16toF32(Index& i, float* result, Index rows, bfloat16*& src, Index resInc) {
+  constexpr Index extra = ((size < 4) ? 4 : size);
+  while (i + size <= rows) {
+    PacketBlock<Packet8bf, (size + 7) / 8> r32;
+    r32.packet[0] = loadBF16fromResult<non_unit_stride, 0>(src, resInc);
+    if (size >= 16) {
+      r32.packet[1] = loadBF16fromResult<non_unit_stride, 8>(src, resInc);
+    }
+    if (size >= 32) {
+      r32.packet[2] = loadBF16fromResult<non_unit_stride, 16>(src, resInc);
+      r32.packet[3] = loadBF16fromResult<non_unit_stride, 24>(src, resInc);
+    }
+    storeConvertBlockBF16<size>(result + i, r32, rows & 3);
+    i += extra;
+    src += extra * resInc;
+    if (size != 32) break;
+  }
+}
+
+template <bool non_unit_stride>
+EIGEN_ALWAYS_INLINE void convertArrayPointerBF16toF32(float* result, Index cols, Index rows, bfloat16* src,
+                                                      Index resInc) {
+  for (Index col = 0; col < cols; col++, src += (rows * resInc), result += rows) {
+    Index i = 0;
+    bfloat16* src2 = src;
+    convertPointerBF16toF32<32, non_unit_stride>(i, result, rows, src2, resInc);
+    convertPointerBF16toF32<16, non_unit_stride>(i, result, rows, src2, resInc);
+    convertPointerBF16toF32<8, non_unit_stride>(i, result, rows, src2, resInc);
+    convertPointerBF16toF32<4, non_unit_stride>(i, result, rows, src2, resInc);
+    convertPointerBF16toF32<1, non_unit_stride>(i, result, rows, src2, resInc);
+  }
+}
+
+template <Index num_acc, Index size = 4>
+EIGEN_ALWAYS_INLINE void zeroAccumulators(Packet4f (&acc)[num_acc][size]) {
+  Packet4f z = pset1<Packet4f>(float(0));
+
+  for (Index k = 0; k < num_acc; k++) {
+    for (Index j = 0; j < size; j++) {
+      acc[k][j] = z;
+    }
+  }
+}
+
+template <Index num_acc>
+EIGEN_ALWAYS_INLINE void tranposeResults(Packet4f (&acc)[num_acc][4]) {
+  for (Index i = 0; i < num_acc; i++) {
+    Packet4ui t0, t1, t2, t3;
+    t0 = vec_mergeh(reinterpret_cast<Packet4ui>(acc[i][0]), reinterpret_cast<Packet4ui>(acc[i][2]));
+    t1 = vec_mergel(reinterpret_cast<Packet4ui>(acc[i][0]), reinterpret_cast<Packet4ui>(acc[i][2]));
+    t2 = vec_mergeh(reinterpret_cast<Packet4ui>(acc[i][1]), reinterpret_cast<Packet4ui>(acc[i][3]));
+    t3 = vec_mergel(reinterpret_cast<Packet4ui>(acc[i][1]), reinterpret_cast<Packet4ui>(acc[i][3]));
+    acc[i][0] = reinterpret_cast<Packet4f>(vec_mergeh(t0, t2));
+    acc[i][1] = reinterpret_cast<Packet4f>(vec_mergel(t0, t2));
+    acc[i][2] = reinterpret_cast<Packet4f>(vec_mergeh(t1, t3));
+    acc[i][3] = reinterpret_cast<Packet4f>(vec_mergel(t1, t3));
+  }
+}
+
+template <Index num_acc>
+EIGEN_ALWAYS_INLINE void addResults(Packet4f (&acc)[num_acc][4]) {
+  for (Index i = 0, j = 0; j < num_acc; i++, j += 2) {
+    for (Index x = 0, y = 0; x < 2; x++, y += 2) {
+      for (Index w = 0, z = 0; w < 2; w++, z += 2) {
+        acc[i][y + w] = acc[j + x][z + 0] + acc[j + x][z + 1];
+      }
+    }
+  }
+}
+
+template <Index num_acc, bool rhsExtraCols, bool lhsExtraRows, Index num_rhs>
+EIGEN_ALWAYS_INLINE void outputResultsVSX(Packet4f (&acc)[num_acc][4], Index rows, const Packet4f pAlpha, float* result,
+                                          const Index extra_cols, Index extra_rows) {
+  tranposeResults<num_acc>(acc);
+  addResults<num_acc>(acc);
+
+  constexpr Index real_rhs = ((num_rhs / 2) - (rhsExtraCols ? 1 : 0));
+  Index k = 0;
+  for (Index i = 0; i < real_rhs; i++, result += 4 * rows, k++) {
+    storeResults<false, lhsExtraRows>(acc[k], rows, pAlpha, result, extra_cols, extra_rows);
+  }
+  if (rhsExtraCols) {
+    storeResults<rhsExtraCols, lhsExtraRows>(acc[k], rows, pAlpha, result, extra_cols, extra_rows);
+  }
+}
+
+template <bool zero>
+EIGEN_ALWAYS_INLINE void loadTwoRhsFloat32(const float* block, Index strideB, Index i, Packet4f& dhs0, Packet4f& dhs1) {
+  dhs0 = ploadu<Packet4f>(block + strideB * i + 0);
+  if (zero) {
+    Packet4f dhs2 = pset1<Packet4f>(float(0));
+    dhs1 = vec_mergel(dhs0, dhs2);
+    dhs0 = vec_mergeh(dhs0, dhs2);
+  } else {
+    dhs1 = ploadu<Packet4f>(block + strideB * i + 4);
+  }
+}
+
+template <Index num_acc, bool zero, bool rhsExtraCols, Index num_rhs>
+EIGEN_ALWAYS_INLINE void KLoop(const float* indexA, const float* indexB, Packet4f (&acc)[num_acc][4], Index strideB,
+                               Index k, Index offsetB, Index extra_cols) {
+  constexpr Index num_lhs = 4;
+  Packet4f lhs[num_lhs], rhs[num_rhs];
+
+  constexpr Index real_rhs = (num_rhs - (rhsExtraCols ? 2 : 0));
+  for (Index i = 0; i < real_rhs; i += 2) {
+    loadTwoRhsFloat32<zero>(indexB + k * 4, strideB, i, rhs[i + 0], rhs[i + 1]);
+  }
+  if (rhsExtraCols) {
+    loadTwoRhsFloat32<zero>(indexB + k * extra_cols - offsetB, strideB, real_rhs, rhs[real_rhs + 0], rhs[real_rhs + 1]);
+  }
+
+  indexA += 2 * k * 4;
+  for (Index j = 0; j < num_lhs; j++) {
+    lhs[j] = ploadu<Packet4f>(indexA + j * 4);
+  }
+
+  for (Index j = 0; j < num_rhs; j++) {
+    for (Index i = 0; i < num_lhs; i++) {
+      acc[j][i] = pmadd(rhs[j], lhs[i], acc[j][i]);
+    }
+  }
+}
+
+template <const Index num_acc, bool rhsExtraCols, bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void colVSXLoopBodyIter(Index depth, Index rows, const Packet4f pAlpha, const float* indexA,
+                                            const float* indexB, Index strideB, Index offsetB, float* result,
+                                            const Index extra_cols, const Index extra_rows) {
+  constexpr Index num_rhs = num_acc;
+
+  Packet4f acc[num_acc][4];
+
+  zeroAccumulators<num_acc>(acc);
+
+  Index k;
+  for (k = 0; k + 2 <= depth; k += 2) {
+    KLoop<num_acc, false, rhsExtraCols, num_rhs>(indexA, indexB, acc, strideB, k, offsetB, extra_cols);
+  }
+  if (depth & 1) {
+    KLoop<num_acc, true, rhsExtraCols, num_rhs>(indexA, indexB, acc, strideB, k, offsetB, extra_cols);
+  }
+
+  outputResultsVSX<num_acc, rhsExtraCols, lhsExtraRows, num_rhs>(acc, rows, pAlpha, result, extra_cols, extra_rows);
+}
+
+// No more than 4 (uses 2X the accumulators or 8X the number of VSX registers)
+#define MAX_BFLOAT16_ACC_VSX 4
+
+template <const Index num_acc, bool rhsExtraCols, bool lhsExtraRows>
+void colVSXLoopBody(Index& col, Index depth, Index cols, Index rows, const Packet4f pAlpha, const float* indexA,
+                    const float* indexB, Index strideB, Index offsetB, float* result) {
+  constexpr Index step = (num_acc * 4);  // each accumulator has 4 elements
+  const Index extra_cols = (rhsExtraCols) ? (cols & 3) : 0;
+  const Index extra_rows = (lhsExtraRows) ? (rows & 3) : 0;
+  constexpr bool multiIters = !rhsExtraCols && (num_acc == MAX_BFLOAT16_ACC_VSX);
+
+  do {
+    colVSXLoopBodyIter<num_acc * 2, rhsExtraCols, lhsExtraRows>(depth, rows, pAlpha, indexA, indexB, strideB, offsetB,
+                                                                result, extra_cols, extra_rows);
+
+    indexB += strideB * (num_acc * 2);
+    result += rows * step;
+  } while (multiIters && (step <= cols - (col += step)));
+}
+
+template <const Index num_acc, bool rhsExtraCols, bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void colVSXLoopBodyExtraN(Index col, Index depth, Index cols, Index rows, const Packet4f pAlpha,
+                                              const float* indexA, const float* blockB, Index strideB, Index offsetB,
+                                              float* result) {
+  if (MAX_BFLOAT16_ACC_VSX > num_acc) {
+    colVSXLoopBody<num_acc + (rhsExtraCols ? 1 : 0), rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA,
+                                                                                 blockB, strideB, offsetB, result);
+  }
+}
+
+template <bool rhsExtraCols, bool lhsExtraRows>
+void colVSXLoopBodyExtra(Index col, Index depth, Index cols, Index rows, const Packet4f pAlpha, const float* indexA,
+                         const float* blockB, Index strideB, Index offsetB, float* result) {
+  switch ((cols - col) >> 2) {
+    case 3:
+      colVSXLoopBodyExtraN<3, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB,
+                                                          offsetB, result);
+      break;
+    case 2:
+      colVSXLoopBodyExtraN<2, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB,
+                                                          offsetB, result);
+      break;
+    case 1:
+      colVSXLoopBodyExtraN<1, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB,
+                                                          offsetB, result);
+      break;
+    default:
+      if (rhsExtraCols) {
+        colVSXLoopBody<1, true, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+      }
+      break;
+  }
+}
+
+template <Index size, bool lhsExtraRows = false>
+EIGEN_ALWAYS_INLINE void colVSXLoops(Index depth, Index cols, Index rows, const Packet4f pAlpha, const bfloat16* indexA,
+                                     const float* indexA2, const float* blockB2, Index strideA, Index strideB,
+                                     Index offsetB, float* result2) {
+  Index delta_rows = 2 * (lhsExtraRows ? (rows & 3) : size);
+  for (Index row = 0; row < size; row += 4) {
+    convertArrayPointerBF16toF32Dup<lhsExtraRows>(const_cast<float*>(indexA2), strideA, delta_rows, indexA, row,
+                                                  rows & 3);
+
+    const float* blockB = blockB2;
+    float* result = result2 + row;
+
+    Index col = 0;
+    if (cols >= (MAX_BFLOAT16_ACC_VSX * 4)) {
+      colVSXLoopBody<MAX_BFLOAT16_ACC_VSX, false, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA2, blockB,
+                                                                strideB, 0, result);
+      blockB += (strideB >> 1) * col;
+      result += rows * col;
+    }
+    if (cols & 3) {
+      colVSXLoopBodyExtra<true, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA2, blockB, strideB, offsetB,
+                                              result);
+    } else {
+      colVSXLoopBodyExtra<false, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA2, blockB, strideB, 0, result);
+    }
+  }
+}
+
+template <Index size>
+EIGEN_ALWAYS_INLINE void calcVSXColLoops(const bfloat16*& indexA, const float* indexA2, Index& row, Index depth,
+                                         Index cols, Index rows, const Packet4f pAlpha, const float* indexB,
+                                         Index strideA, Index strideB, Index offsetA, Index offsetB, Index bigSuffix,
+                                         float* result) {
+  if ((size == 16) || (rows & size)) {
+    indexA += size * offsetA;
+    colVSXLoops<size>(depth, cols, rows, pAlpha, indexA, indexA2, indexB, strideA, strideB, offsetB, result + row);
+    row += size;
+    indexA += bigSuffix * size / 16;
+  }
+}
+
+template <const Index size, typename DataMapper>
+EIGEN_ALWAYS_INLINE void convertBF16toF32(Index& i, float* result, Index rows, const DataMapper& src) {
+  constexpr Index extra = ((size < 4) ? 4 : size);
+  while (i + size <= rows) {
+    PacketBlock<Packet8bf, (size + 7) / 8> r32;
+    r32.packet[0] = src.template loadPacket<Packet8bf>(i + 0);
+    if (size >= 16) {
+      r32.packet[1] = src.template loadPacket<Packet8bf>(i + 8);
+    }
+    if (size >= 32) {
+      r32.packet[2] = src.template loadPacket<Packet8bf>(i + 16);
+      r32.packet[3] = src.template loadPacket<Packet8bf>(i + 24);
+    }
+    storeConvertBlockBF16<size>(result + i, r32, rows & 3);
+    i += extra;
+    if (size != 32) break;
+  }
+}
+
+template <typename DataMapper>
+EIGEN_ALWAYS_INLINE void convertArrayBF16toF32(float* result, Index cols, Index rows, const DataMapper& src) {
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  for (Index j = 0; j < cols; j++, result += rows) {
+    const LinearMapper src2 = src.getLinearMapper(0, j);
+    Index i = 0;
+    convertBF16toF32<32, LinearMapper>(i, result, rows, src2);
+    convertBF16toF32<16, LinearMapper>(i, result, rows, src2);
+    convertBF16toF32<8, LinearMapper>(i, result, rows, src2);
+    convertBF16toF32<4, LinearMapper>(i, result, rows, src2);
+    convertBF16toF32<1, LinearMapper>(i, result, rows, src2);
+  }
+}
+
+EIGEN_ALWAYS_INLINE Packet8bf convertF32toBF16VSX(const float* res) {
+  return F32ToBf16Both(ploadu<Packet4f>(res + 0), ploadu<Packet4f>(res + 4));
+}
+
+template <typename DataMapper, const Index size>
+EIGEN_ALWAYS_INLINE void convertArrayF32toBF16ColVSX(float* result, Index col, Index rows, const DataMapper& res) {
+  const DataMapper res2 = res.getSubMapper(0, col);
+  Index row;
+  float* result2 = result + col * rows;
+  for (row = 0; row + 8 <= rows; row += 8, result2 += 8) {
+    // get and save block
+    PacketBlock<Packet8bf, size> block;
+    for (Index j = 0; j < size; j++) {
+      block.packet[j] = convertF32toBF16VSX(result2 + j * rows);
+    }
+    res2.template storePacketBlock<Packet8bf, size>(row, 0, block);
+  }
+  // extra rows
+  if (row < rows) {
+    for (Index j = 0; j < size; j++) {
+      Packet8bf fp16 = convertF32toBF16VSX(result2 + j * rows);
+      res2.template storePacketPartial<Packet8bf>(row, j, fp16, rows & 7);
+    }
+  }
+}
+
+template <typename DataMapper>
+EIGEN_ALWAYS_INLINE void convertArrayF32toBF16VSX(float* result, Index cols, Index rows, const DataMapper& res) {
+  Index col;
+  for (col = 0; col + 4 <= cols; col += 4) {
+    convertArrayF32toBF16ColVSX<DataMapper, 4>(result, col, rows, res);
+  }
+  // extra cols
+  switch (cols - col) {
+    case 1:
+      convertArrayF32toBF16ColVSX<DataMapper, 1>(result, col, rows, res);
+      break;
+    case 2:
+      convertArrayF32toBF16ColVSX<DataMapper, 2>(result, col, rows, res);
+      break;
+    case 3:
+      convertArrayF32toBF16ColVSX<DataMapper, 3>(result, col, rows, res);
+      break;
+  }
+}
+
+template <typename DataMapper>
+void gemmbfloat16(const DataMapper& res, const bfloat16* indexA, const bfloat16* indexB, Index rows, Index depth,
+                  Index cols, bfloat16 alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  const Packet4f pAlpha = pset1<Packet4f>(falpha);
+
+  if (strideA == -1) strideA = depth;
+  if (strideB == -1) strideB = depth;
+
+  ei_declare_aligned_stack_constructed_variable(float, result, cols* rows, 0);
+  ei_declare_aligned_stack_constructed_variable(float, indexB2, strideB* cols, 0);
+  ei_declare_aligned_stack_constructed_variable(float, indexA2, ((strideA + 1) & -2) * 4 * 2, 0);
+
+  convertArrayBF16toF32<DataMapper>(result, cols, rows, res);
+  convertArrayPointerBF16toF32(indexB2, cols, strideB, const_cast<bfloat16*>(indexB));
+
+  Index bigSuffix = 2 * 8 * (strideA - offsetA);
+  float* indexBF32 = indexB2 + 4 * offsetB;
+  offsetB *= 3;
+  strideB *= 2;
+
+  Index row = 0;
+  // LHS (8x16) block
+  while (row + 16 <= rows) {
+    calcVSXColLoops<16>(indexA, indexA2, row, depth, cols, rows, pAlpha, indexBF32, strideA, strideB, offsetA, offsetB,
+                        bigSuffix, result);
+  }
+  // LHS (8x8) block
+  calcVSXColLoops<8>(indexA, indexA2, row, depth, cols, rows, pAlpha, indexBF32, strideA, strideB, offsetA, offsetB,
+                     bigSuffix, result);
+  // LHS (8x4) block
+  calcVSXColLoops<4>(indexA, indexA2, row, depth, cols, rows, pAlpha, indexBF32, strideA, strideB, offsetA, offsetB,
+                     bigSuffix, result);
+  // extra rows
+  if (rows & 3) {
+    // This index is the beginning of remaining block.
+    colVSXLoops<4, true>(depth, cols, rows, pAlpha, indexA, indexA2, indexBF32, strideA, strideB, offsetB,
+                         result + row);
+  }
+
+  // Convert back to bfloat16
+  convertArrayF32toBF16VSX<DataMapper>(result, cols, rows, res);
+}
+
+#undef MAX_BFLOAT16_ACC_VSX
+
+#include "MatrixVectorProduct.inc"
+
+/************************************
+ * ppc64le template specializations *
+ * **********************************/
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode> {
+  void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>::operator()(
+    double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset) {
+  dhs_pack<double, DataMapper, Packet2d, ColMajor, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode> {
+  void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>::operator()(
+    double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset) {
+  dhs_pack<double, DataMapper, Packet2d, RowMajor, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+#if EIGEN_ALTIVEC_USE_CUSTOM_PACK
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<double, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode> {
+  void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<double, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>::operator()(
+    double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  dhs_pack<double, DataMapper, Packet2d, ColMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<double, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode> {
+  void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<double, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>::operator()(
+    double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  dhs_pack<double, DataMapper, Packet2d, RowMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<bfloat16, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode> {
+  void operator()(bfloat16* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<bfloat16, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>::operator()(
+    bfloat16* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  dhs_pack<bfloat16, DataMapper, Packet8bf, ColMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<bfloat16, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode> {
+  void operator()(bfloat16* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<bfloat16, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>::operator()(
+    bfloat16* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  dhs_pack<bfloat16, DataMapper, Packet8bf, RowMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+#endif
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<bfloat16, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode> {
+  void operator()(bfloat16* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<bfloat16, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>::operator()(
+    bfloat16* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset) {
+  dhs_pack<bfloat16, DataMapper, Packet8bf, ColMajor, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<bfloat16, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode> {
+  void operator()(bfloat16* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<bfloat16, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>::operator()(
+    bfloat16* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset) {
+  dhs_pack<bfloat16, DataMapper, Packet8bf, RowMajor, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode> {
+  void operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>::operator()(
+    float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset) {
+  dhs_pack<float, DataMapper, Packet4f, RowMajor, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode> {
+  void operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>::operator()(
+    float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset) {
+  dhs_pack<float, DataMapper, Packet4f, ColMajor, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode> {
+  void operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0,
+                  Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate,
+                   PanelMode>::operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows,
+                                          Index stride, Index offset) {
+  dhs_cpack<float, DataMapper, Packet4f, Packet2cf, RowMajor, Conjugate, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode> {
+  void operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0,
+                  Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate,
+                   PanelMode>::operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows,
+                                          Index stride, Index offset) {
+  dhs_cpack<float, DataMapper, Packet4f, Packet2cf, ColMajor, Conjugate, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+#if EIGEN_ALTIVEC_USE_CUSTOM_PACK
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<float, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode> {
+  void operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<float, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>::operator()(
+    float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  dhs_pack<float, DataMapper, Packet4f, ColMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<float, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode> {
+  void operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0, Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<float, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>::operator()(
+    float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  dhs_pack<float, DataMapper, Packet4f, RowMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+#endif
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode> {
+  void operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0,
+                  Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>::operator()(
+    std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  dhs_cpack<float, DataMapper, Packet4f, Packet2cf, ColMajor, Conjugate, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode> {
+  void operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0,
+                  Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>::operator()(
+    std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  dhs_cpack<float, DataMapper, Packet4f, Packet2cf, RowMajor, Conjugate, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode> {
+  void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0,
+                  Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate,
+                   PanelMode>::operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows,
+                                          Index stride, Index offset) {
+  dhs_cpack<double, DataMapper, Packet2d, Packet1cd, RowMajor, Conjugate, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode> {
+  void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0,
+                  Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate,
+                   PanelMode>::operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows,
+                                          Index stride, Index offset) {
+  dhs_cpack<double, DataMapper, Packet2d, Packet1cd, ColMajor, Conjugate, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode> {
+  void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0,
+                  Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>::operator()(
+    std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  dhs_cpack<double, DataMapper, Packet2d, Packet1cd, ColMajor, Conjugate, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode> {
+  void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0,
+                  Index offset = 0);
+};
+
+template <typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>::operator()(
+    std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  dhs_cpack<double, DataMapper, Packet2d, Packet1cd, RowMajor, Conjugate, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+// ********* gebp specializations *********
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<float, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> {
+  typedef typename quad_traits<float>::vectortype Packet;
+  typedef typename quad_traits<float>::rhstype RhsPacket;
+
+  void operator()(const DataMapper& res, const float* blockA, const float* blockB, Index rows, Index depth, Index cols,
+                  float alpha, Index strideA = -1, Index strideB = -1, Index offsetA = 0, Index offsetB = 0);
+};
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<float, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>::operator()(
+    const DataMapper& res, const float* blockA, const float* blockB, Index rows, Index depth, Index cols, float alpha,
+    Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  const Index accRows = quad_traits<float>::rows;
+  const Index accCols = quad_traits<float>::size;
+  static void (*gemm_function)(const DataMapper&, const float*, const float*, Index, Index, Index, float, Index, Index,
+                               Index, Index) =
+#ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ? &Eigen::internal::gemmMMA<float, Packet, RhsPacket, DataMapper, accRows, accCols> :
+#endif
+                      &Eigen::internal::gemm<float, Packet, RhsPacket, DataMapper, accRows, accCols>;
+  gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+}
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<std::complex<float>, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> {
+  typedef Packet4f Packet;
+  typedef Packet2cf Packetc;
+  typedef Packet4f RhsPacket;
+
+  void operator()(const DataMapper& res, const std::complex<float>* blockA, const std::complex<float>* blockB,
+                  Index rows, Index depth, Index cols, std::complex<float> alpha, Index strideA = -1,
+                  Index strideB = -1, Index offsetA = 0, Index offsetB = 0);
+};
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<std::complex<float>, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs,
+                 ConjugateRhs>::operator()(const DataMapper& res, const std::complex<float>* blockA,
+                                           const std::complex<float>* blockB, Index rows, Index depth, Index cols,
+                                           std::complex<float> alpha, Index strideA, Index strideB, Index offsetA,
+                                           Index offsetB) {
+  const Index accRows = quad_traits<float>::rows;
+  const Index accCols = quad_traits<float>::size;
+  static void (*gemm_function)(const DataMapper&, const std::complex<float>*, const std::complex<float>*, Index, Index,
+                               Index, std::complex<float>, Index, Index, Index, Index) =
+#ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ? &Eigen::internal::gemm_complexMMA<std::complex<float>, std::complex<float>, std::complex<float>,
+                                                          float, Packet, Packetc, RhsPacket, DataMapper, accRows,
+                                                          accCols, ConjugateLhs, ConjugateRhs, false, false>
+                      :
+#endif
+                      &Eigen::internal::gemm_complex<std::complex<float>, std::complex<float>, std::complex<float>,
+                                                     float, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols,
+                                                     ConjugateLhs, ConjugateRhs, false, false>;
+  gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+}
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<float, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> {
+  typedef Packet4f Packet;
+  typedef Packet2cf Packetc;
+  typedef Packet4f RhsPacket;
+
+  void operator()(const DataMapper& res, const float* blockA, const std::complex<float>* blockB, Index rows,
+                  Index depth, Index cols, std::complex<float> alpha, Index strideA = -1, Index strideB = -1,
+                  Index offsetA = 0, Index offsetB = 0);
+};
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<float, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>::operator()(
+    const DataMapper& res, const float* blockA, const std::complex<float>* blockB, Index rows, Index depth, Index cols,
+    std::complex<float> alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  const Index accRows = quad_traits<float>::rows;
+  const Index accCols = quad_traits<float>::size;
+  static void (*gemm_function)(const DataMapper&, const float*, const std::complex<float>*, Index, Index, Index,
+                               std::complex<float>, Index, Index, Index, Index) =
+#ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ? &Eigen::internal::gemm_complexMMA<float, std::complex<float>, std::complex<float>, float,
+                                                          Packet, Packetc, RhsPacket, DataMapper, accRows, accCols,
+                                                          ConjugateLhs, ConjugateRhs, true, false>
+                      :
+#endif
+                      &Eigen::internal::gemm_complex<float, std::complex<float>, std::complex<float>, float, Packet,
+                                                     Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs,
+                                                     ConjugateRhs, true, false>;
+  gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+}
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<std::complex<float>, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> {
+  typedef Packet4f Packet;
+  typedef Packet2cf Packetc;
+  typedef Packet4f RhsPacket;
+
+  void operator()(const DataMapper& res, const std::complex<float>* blockA, const float* blockB, Index rows,
+                  Index depth, Index cols, std::complex<float> alpha, Index strideA = -1, Index strideB = -1,
+                  Index offsetA = 0, Index offsetB = 0);
+};
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<std::complex<float>, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>::operator()(
+    const DataMapper& res, const std::complex<float>* blockA, const float* blockB, Index rows, Index depth, Index cols,
+    std::complex<float> alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  const Index accRows = quad_traits<float>::rows;
+  const Index accCols = quad_traits<float>::size;
+  static void (*gemm_function)(const DataMapper&, const std::complex<float>*, const float*, Index, Index, Index,
+                               std::complex<float>, Index, Index, Index, Index) =
+#ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ? &Eigen::internal::gemm_complexMMA<std::complex<float>, float, std::complex<float>, float,
+                                                          Packet, Packetc, RhsPacket, DataMapper, accRows, accCols,
+                                                          ConjugateLhs, ConjugateRhs, false, true>
+                      :
+#endif
+                      &Eigen::internal::gemm_complex<std::complex<float>, float, std::complex<float>, float, Packet,
+                                                     Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs,
+                                                     ConjugateRhs, false, true>;
+  gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+}
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<double, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> {
+  typedef typename quad_traits<double>::vectortype Packet;
+  typedef typename quad_traits<double>::rhstype RhsPacket;
+
+  void operator()(const DataMapper& res, const double* blockA, const double* blockB, Index rows, Index depth,
+                  Index cols, double alpha, Index strideA = -1, Index strideB = -1, Index offsetA = 0,
+                  Index offsetB = 0);
+};
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<double, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>::operator()(
+    const DataMapper& res, const double* blockA, const double* blockB, Index rows, Index depth, Index cols,
+    double alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  const Index accRows = quad_traits<double>::rows;
+  const Index accCols = quad_traits<double>::size;
+  static void (*gemm_function)(const DataMapper&, const double*, const double*, Index, Index, Index, double, Index,
+                               Index, Index, Index) =
+#ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ? &Eigen::internal::gemmMMA<double, Packet, RhsPacket, DataMapper, accRows, accCols> :
+#endif
+                      &Eigen::internal::gemm<double, Packet, RhsPacket, DataMapper, accRows, accCols>;
+  gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+}
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<std::complex<double>, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> {
+  typedef quad_traits<double>::vectortype Packet;
+  typedef Packet1cd Packetc;
+  typedef quad_traits<double>::rhstype RhsPacket;
+
+  void operator()(const DataMapper& res, const std::complex<double>* blockA, const std::complex<double>* blockB,
+                  Index rows, Index depth, Index cols, std::complex<double> alpha, Index strideA = -1,
+                  Index strideB = -1, Index offsetA = 0, Index offsetB = 0);
+};
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<std::complex<double>, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs,
+                 ConjugateRhs>::operator()(const DataMapper& res, const std::complex<double>* blockA,
+                                           const std::complex<double>* blockB, Index rows, Index depth, Index cols,
+                                           std::complex<double> alpha, Index strideA, Index strideB, Index offsetA,
+                                           Index offsetB) {
+  const Index accRows = quad_traits<double>::rows;
+  const Index accCols = quad_traits<double>::size;
+  static void (*gemm_function)(const DataMapper&, const std::complex<double>*, const std::complex<double>*, Index,
+                               Index, Index, std::complex<double>, Index, Index, Index, Index) =
+#ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA())
+          ? &Eigen::internal::gemm_complexMMA<std::complex<double>, std::complex<double>, std::complex<double>, double,
+                                              Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs,
+                                              ConjugateRhs, false, false>
+          :
+#endif
+          &Eigen::internal::gemm_complex<std::complex<double>, std::complex<double>, std::complex<double>, double,
+                                         Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs,
+                                         ConjugateRhs, false, false>;
+  gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+}
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<std::complex<double>, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> {
+  typedef quad_traits<double>::vectortype Packet;
+  typedef Packet1cd Packetc;
+  typedef quad_traits<double>::rhstype RhsPacket;
+
+  void operator()(const DataMapper& res, const std::complex<double>* blockA, const double* blockB, Index rows,
+                  Index depth, Index cols, std::complex<double> alpha, Index strideA = -1, Index strideB = -1,
+                  Index offsetA = 0, Index offsetB = 0);
+};
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<std::complex<double>, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>::operator()(
+    const DataMapper& res, const std::complex<double>* blockA, const double* blockB, Index rows, Index depth,
+    Index cols, std::complex<double> alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  const Index accRows = quad_traits<double>::rows;
+  const Index accCols = quad_traits<double>::size;
+  static void (*gemm_function)(const DataMapper&, const std::complex<double>*, const double*, Index, Index, Index,
+                               std::complex<double>, Index, Index, Index, Index) =
+#ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ? &Eigen::internal::gemm_complexMMA<std::complex<double>, double, std::complex<double>, double,
+                                                          Packet, Packetc, RhsPacket, DataMapper, accRows, accCols,
+                                                          ConjugateLhs, ConjugateRhs, false, true>
+                      :
+#endif
+                      &Eigen::internal::gemm_complex<std::complex<double>, double, std::complex<double>, double, Packet,
+                                                     Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs,
+                                                     ConjugateRhs, false, true>;
+  gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+}
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<double, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> {
+  typedef quad_traits<double>::vectortype Packet;
+  typedef Packet1cd Packetc;
+  typedef quad_traits<double>::rhstype RhsPacket;
+
+  void operator()(const DataMapper& res, const double* blockA, const std::complex<double>* blockB, Index rows,
+                  Index depth, Index cols, std::complex<double> alpha, Index strideA = -1, Index strideB = -1,
+                  Index offsetA = 0, Index offsetB = 0);
+};
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<double, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>::operator()(
+    const DataMapper& res, const double* blockA, const std::complex<double>* blockB, Index rows, Index depth,
+    Index cols, std::complex<double> alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  const Index accRows = quad_traits<double>::rows;
+  const Index accCols = quad_traits<double>::size;
+  static void (*gemm_function)(const DataMapper&, const double*, const std::complex<double>*, Index, Index, Index,
+                               std::complex<double>, Index, Index, Index, Index) =
+#ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ? &Eigen::internal::gemm_complexMMA<double, std::complex<double>, std::complex<double>, double,
+                                                          Packet, Packetc, RhsPacket, DataMapper, accRows, accCols,
+                                                          ConjugateLhs, ConjugateRhs, true, false>
+                      :
+#endif
+                      &Eigen::internal::gemm_complex<double, std::complex<double>, std::complex<double>, double, Packet,
+                                                     Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs,
+                                                     ConjugateRhs, true, false>;
+  gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+}
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<bfloat16, bfloat16, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> {
+  typedef typename quad_traits<bfloat16>::vectortype Packet;
+  typedef typename quad_traits<bfloat16>::rhstype RhsPacket;
+
+  void operator()(const DataMapper& res, const bfloat16* blockA, const bfloat16* blockB, Index rows, Index depth,
+                  Index cols, bfloat16 alpha, Index strideA = -1, Index strideB = -1, Index offsetA = 0,
+                  Index offsetB = 0);
+};
+
+template <typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<bfloat16, bfloat16, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>::operator()(
+    const DataMapper& res, const bfloat16* blockA, const bfloat16* blockB, Index rows, Index depth, Index cols,
+    bfloat16 alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  static void (*gemm_function)(const DataMapper&, const bfloat16*, const bfloat16*, Index, Index, Index, bfloat16,
+                               Index, Index, Index, Index) =
+#ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ? &Eigen::internal::gemmMMAbfloat16<DataMapper> :
+#endif
+                      &Eigen::internal::gemmbfloat16<DataMapper>;
+  gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+}
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATRIX_PRODUCT_ALTIVEC_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
new file mode 100644
index 0000000..e78ca5a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
@@ -0,0 +1,205 @@
+// #define EIGEN_POWER_USE_PREFETCH  // Use prefetching in gemm routines
+#ifdef EIGEN_POWER_USE_PREFETCH
+#define EIGEN_POWER_PREFETCH(p) prefetch(p)
+#else
+#define EIGEN_POWER_PREFETCH(p)
+#endif
+
+#if defined(_ARCH_PWR9) || defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+#define USE_PARTIAL_PACKETS
+#endif
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar, typename Packet, typename DataMapper, const Index accRows, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemm_extra_row(const DataMapper& res, const Scalar* lhs_base, const Scalar* rhs_base,
+                                        Index depth, Index strideA, Index offsetA, Index strideB, Index row, Index rows,
+                                        Index remaining_rows, const Packet& pAlpha, const Packet& pMask);
+
+template <typename Scalar, typename Packet, typename DataMapper, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemm_extra_cols(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index depth,
+                                         Index strideA, Index offsetA, Index strideB, Index offsetB, Index col,
+                                         Index rows, Index cols, Index remaining_rows, const Packet& pAlpha,
+                                         const Packet& pMask);
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet bmask(const Index remaining_rows);
+
+template <typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accRows,
+          const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(const DataMapper& res, const Scalar* lhs_base, const Scalar* rhs_base,
+                                                Index depth, Index strideA, Index offsetA, Index strideB, Index row,
+                                                Index rows, Index remaining_rows, const Packet& pAlphaReal,
+                                                const Packet& pAlphaImag, const Packet& pMask);
+
+template <typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accCols,
+          bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_extra_cols(const DataMapper& res, const Scalar* blockA, const Scalar* blockB,
+                                                 Index depth, Index strideA, Index offsetA, Index strideB,
+                                                 Index offsetB, Index col, Index rows, Index cols, Index remaining_rows,
+                                                 const Packet& pAlphaReal, const Packet& pAlphaImag,
+                                                 const Packet& pMask);
+
+template <typename DataMapper>
+EIGEN_ALWAYS_INLINE void convertArrayBF16toF32(float* result, Index cols, Index rows, const DataMapper& src);
+
+template <const Index size, bool non_unit_stride, Index delta>
+EIGEN_ALWAYS_INLINE void storeBF16fromResult(bfloat16* dst, Packet8bf data, Index resInc, Index extra = 0);
+
+template <bool non_unit_stride = false>
+EIGEN_ALWAYS_INLINE void convertArrayPointerBF16toF32(float* result, Index cols, Index rows, bfloat16* src,
+                                                      Index resInc = 1);
+
+template <bool rhsExtraCols, bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void storeResults(Packet4f (&acc)[4], Index rows, const Packet4f pAlpha, float* result,
+                                      Index extra_cols, Index extra_rows);
+
+template <Index num_acc, bool extraRows, Index size = 4>
+EIGEN_ALWAYS_INLINE void outputVecColResults(Packet4f (&acc)[num_acc][size], float* result, Packet4f pAlpha,
+                                             Index extra_rows);
+
+template <Index num_acc, Index size = 4>
+EIGEN_ALWAYS_INLINE void outputVecResults(Packet4f (&acc)[num_acc][size], float* result, Packet4f pAlpha);
+
+template <typename RhsMapper, bool linear>
+EIGEN_ALWAYS_INLINE Packet8bf loadColData(RhsMapper& rhs, Index j);
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet ploadLhs(const __UNPACK_TYPE__(Packet) * lhs);
+
+template <typename DataMapper, typename Packet, const Index accCols, int StorageOrder, bool Complex, int N,
+          bool full = true>
+EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet, N*(Complex ? 2 : 1)>& acc, const DataMapper& res, Index row,
+                               Index col);
+
+template <typename DataMapper, typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bstore(PacketBlock<Packet, N>& acc, const DataMapper& res, Index row);
+
+#ifdef USE_PARTIAL_PACKETS
+template <typename DataMapper, typename Packet, const Index accCols, bool Complex, Index N, bool full = true>
+EIGEN_ALWAYS_INLINE void bload_partial(PacketBlock<Packet, N*(Complex ? 2 : 1)>& acc, const DataMapper& res, Index row,
+                                       Index elements);
+
+template <typename DataMapper, typename Packet, Index N>
+EIGEN_ALWAYS_INLINE void bstore_partial(PacketBlock<Packet, N>& acc, const DataMapper& res, Index row, Index elements);
+#endif
+
+template <typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet, N>& acc, PacketBlock<Packet, N>& accZ, const Packet& pAlpha);
+
+template <typename Packet, int N, bool mask>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet, N>& acc, PacketBlock<Packet, N>& accZ, const Packet& pAlpha,
+                                const Packet& pMask);
+
+template <typename Packet, int N, bool mask>
+EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet, N>& aReal, PacketBlock<Packet, N>& aImag, const Packet& bReal,
+                                 const Packet& bImag, PacketBlock<Packet, N>& cReal, PacketBlock<Packet, N>& cImag,
+                                 const Packet& pMask);
+
+template <typename Packet, typename Packetc, int N, bool full>
+EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet, N>& taccReal, PacketBlock<Packet, N>& taccImag,
+                                 PacketBlock<Packetc, N * 2>& tRes, PacketBlock<Packetc, N>& acc1,
+                                 PacketBlock<Packetc, N>& acc2);
+
+#define MICRO_NORMAL(iter) (accCols == accCols2) || (unroll_factor != (iter + 1))
+
+#define MICRO_UNROLL_ITER1(func, N)          \
+  switch (remaining_rows) {                  \
+    default:                                 \
+      func(N, 0) break;                      \
+    case 1:                                  \
+      func(N, 1) break;                      \
+    case 2:                                  \
+      if (sizeof(Scalar) == sizeof(float)) { \
+        func(N, 2)                           \
+      }                                      \
+      break;                                 \
+    case 3:                                  \
+      if (sizeof(Scalar) == sizeof(float)) { \
+        func(N, 3)                           \
+      }                                      \
+      break;                                 \
+  }
+
+#ifdef USE_PARTIAL_PACKETS
+#define MICRO_UNROLL_ITER(func, N) \
+  if (remaining_rows) {            \
+    func(N, true);                 \
+  } else {                         \
+    func(N, false);                \
+  }
+
+#define MICRO_NORMAL_PARTIAL(iter) full || (unroll_factor != (iter + 1))
+#else
+#define MICRO_UNROLL_ITER(func, N) MICRO_UNROLL_ITER1(func, N)
+#endif
+
+#define MICRO_COMPLEX_UNROLL_ITER(func, N) MICRO_UNROLL_ITER1(func, N)
+
+#define MICRO_NORMAL_COLS(iter, a, b) ((MICRO_NORMAL(iter)) ? a : b)
+
+#define MICRO_LOAD1(lhs_ptr, iter)                               \
+  if (unroll_factor > iter) {                                    \
+    lhsV##iter = ploadLhs<Packet>(lhs_ptr##iter);                \
+    lhs_ptr##iter += MICRO_NORMAL_COLS(iter, accCols, accCols2); \
+  } else {                                                       \
+    EIGEN_UNUSED_VARIABLE(lhsV##iter);                           \
+  }
+
+#define MICRO_LOAD_ONE(iter) MICRO_LOAD1(lhs_ptr, iter)
+
+#define MICRO_COMPLEX_LOAD_ONE(iter)                                                                       \
+  if (!LhsIsReal && (unroll_factor > iter)) {                                                              \
+    lhsVi##iter = ploadLhs<Packet>(lhs_ptr_real##iter + MICRO_NORMAL_COLS(iter, imag_delta, imag_delta2)); \
+  } else {                                                                                                 \
+    EIGEN_UNUSED_VARIABLE(lhsVi##iter);                                                                    \
+  }                                                                                                        \
+  MICRO_LOAD1(lhs_ptr_real, iter)
+
+#define MICRO_SRC_PTR1(lhs_ptr, advRows, iter)                                  \
+  if (unroll_factor > iter) {                                                   \
+    lhs_ptr##iter = lhs_base + (row + (iter * accCols)) * strideA * advRows -   \
+                    MICRO_NORMAL_COLS(iter, 0, (accCols - accCols2) * offsetA); \
+  } else {                                                                      \
+    EIGEN_UNUSED_VARIABLE(lhs_ptr##iter);                                       \
+  }
+
+#define MICRO_SRC_PTR_ONE(iter) MICRO_SRC_PTR1(lhs_ptr, 1, iter)
+
+#define MICRO_COMPLEX_SRC_PTR_ONE(iter) MICRO_SRC_PTR1(lhs_ptr_real, advanceRows, iter)
+
+#define MICRO_PREFETCH1(lhs_ptr, iter)   \
+  if (unroll_factor > iter) {            \
+    EIGEN_POWER_PREFETCH(lhs_ptr##iter); \
+  }
+
+#define MICRO_PREFETCH_ONE(iter) MICRO_PREFETCH1(lhs_ptr, iter)
+
+#define MICRO_COMPLEX_PREFETCH_ONE(iter) MICRO_PREFETCH1(lhs_ptr_real, iter)
+
+#ifdef USE_PARTIAL_PACKETS
+#define MICRO_UPDATE_MASK
+#else
+#define MICRO_UPDATE_MASK EIGEN_UNUSED_VARIABLE(pMask);
+#endif
+
+#define MICRO_UPDATE                \
+  if (accCols == accCols2) {        \
+    MICRO_UPDATE_MASK               \
+    EIGEN_UNUSED_VARIABLE(offsetA); \
+    row += unroll_factor * accCols; \
+  }
+
+#define MICRO_COMPLEX_UPDATE                \
+  MICRO_UPDATE                              \
+  if (LhsIsReal || (accCols == accCols2)) { \
+    EIGEN_UNUSED_VARIABLE(imag_delta2);     \
+  }
+
+}  // end namespace internal
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
new file mode 100644
index 0000000..ab02d32
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
@@ -0,0 +1,900 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020 Everton Constantino (everton.constantino@ibm.com)
+// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+#define EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+
+// If using dynamic dispatch, set the CPU target.
+#if defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+#pragma GCC push_options
+#pragma GCC target("cpu=power10,htm")
+#endif
+
+#ifdef __has_builtin
+#if !__has_builtin(__builtin_vsx_assemble_pair)
+#define __builtin_vsx_assemble_pair __builtin_mma_assemble_pair
+#endif
+#if !__has_builtin(__builtin_vsx_disassemble_pair)
+#define __builtin_vsx_disassemble_pair __builtin_mma_disassemble_pair
+#endif
+#endif
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+#include "MatrixProductMMAbfloat16.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#define accColsC (accCols / 2)
+
+EIGEN_ALWAYS_INLINE void bsetzeroMMA(__vector_quad* acc) { __builtin_mma_xxsetaccz(acc); }
+
+template <typename DataMapper, typename Packet, bool full>
+EIGEN_ALWAYS_INLINE void storeAccumulator(Index i, const DataMapper& data, const Packet& alpha, const Index elements,
+                                          __vector_quad* acc) {
+  PacketBlock<Packet, 4> result;
+  __builtin_mma_disassemble_acc(&result.packet, acc);
+
+  PacketBlock<Packet, 4> tRes;
+  if (full) {
+    EIGEN_UNUSED_VARIABLE(elements);
+    bload<DataMapper, Packet, 0, ColMajor, false, 4>(tRes, data, i, 0);
+    bscale<Packet, 4>(tRes, result, alpha);
+    bstore<DataMapper, Packet, 4>(tRes, data, i);
+  } else {
+    bload_partial<DataMapper, Packet, 0, false, 4>(tRes, data, i, elements);
+    bscale<Packet, 4>(tRes, result, alpha);
+    bstore_partial<DataMapper, Packet, 4>(tRes, data, i, elements);
+  }
+}
+
+template <typename DataMapper, typename Packet, typename Packetc, const Index accCols, const Index accCols2>
+EIGEN_ALWAYS_INLINE void storeComplexAccumulator(Index i, const DataMapper& data, const Packet& alphaReal,
+                                                 const Packet& alphaImag, const Packet& pMask, __vector_quad* accReal,
+                                                 __vector_quad* accImag) {
+  constexpr bool full = (accCols2 > accColsC);
+  PacketBlock<Packet, 4> resultReal, resultImag;
+  __builtin_mma_disassemble_acc(&resultReal.packet, accReal);
+  __builtin_mma_disassemble_acc(&resultImag.packet, accImag);
+
+  PacketBlock<Packetc, 8> tRes;
+  bload<DataMapper, Packetc, accColsC, ColMajor, true, 4, full>(tRes, data, i, 0);
+
+  PacketBlock<Packet, 4> taccReal, taccImag;
+  bscalec<Packet, 4, (accCols != accCols2)>(resultReal, resultImag, alphaReal, alphaImag, taccReal, taccImag, pMask);
+
+  PacketBlock<Packetc, 4> acc1, acc2;
+  bcouple<Packet, Packetc, 4, full>(taccReal, taccImag, tRes, acc1, acc2);
+
+  bstore<DataMapper, Packetc, 4>(acc1, data, i);
+  if (full) {
+    bstore<DataMapper, Packetc, 4>(acc2, data, i + accColsC);
+  }
+}
+
+template <typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad* acc, const RhsPacket& a, const LhsPacket& b) {
+  if (NegativeAccumulate) {
+    __builtin_mma_xvf32gernp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+  } else {
+    __builtin_mma_xvf32gerpp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+  }
+}
+
+template <typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad* acc, const __vector_pair& a, const Packet2d& b) {
+  if (NegativeAccumulate) {
+    __builtin_mma_xvf64gernp(acc, (__vector_pair)a, (__vector unsigned char)b);
+  } else {
+    __builtin_mma_xvf64gerpp(acc, (__vector_pair)a, (__vector unsigned char)b);
+  }
+}
+
+template <typename Packet, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void pgercMMA(__vector_quad* accReal, __vector_quad* accImag, const Packet& lhsV, Packet& lhsVi,
+                                  const RhsPacket& rhsV, RhsPacket& rhsVi) {
+  pgerMMA<Packet, RhsPacket, false>(accReal, rhsV, lhsV);
+  if (LhsIsReal) {
+    pgerMMA<Packet, RhsPacket, ConjugateRhs>(accImag, rhsVi, lhsV);
+    EIGEN_UNUSED_VARIABLE(lhsVi);
+  } else {
+    if (!RhsIsReal) {
+      pgerMMA<Packet, RhsPacket, ConjugateLhs == ConjugateRhs>(accReal, rhsVi, lhsVi);
+      pgerMMA<Packet, RhsPacket, ConjugateRhs>(accImag, rhsVi, lhsV);
+    } else {
+      EIGEN_UNUSED_VARIABLE(rhsVi);
+    }
+    pgerMMA<Packet, RhsPacket, ConjugateLhs>(accImag, rhsV, lhsVi);
+  }
+}
+
+// This is necessary because ploadRhs for double returns a pair of vectors when MMA is enabled.
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet ploadRhs(const __UNPACK_TYPE__(Packet) * rhs) {
+  return ploadu<Packet>(rhs);
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_ALWAYS_INLINE void ploadRhsMMA(const Scalar* rhs, Packet& rhsV) {
+  rhsV = ploadRhs<Packet>(rhs);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void ploadRhsMMA(const double* rhs, __vector_pair& rhsV) {
+#if EIGEN_COMP_LLVM
+  __builtin_vsx_assemble_pair(
+      &rhsV, reinterpret_cast<__vector unsigned char>(ploadRhs<Packet2d>(rhs + (sizeof(Packet2d) / sizeof(double)))),
+      reinterpret_cast<__vector unsigned char>(ploadRhs<Packet2d>(rhs)));
+#else
+  rhsV = *reinterpret_cast<__vector_pair*>(const_cast<double*>(rhs));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE void ploadLhsMMA(const double* lhs, __vector_pair& lhsV) { ploadRhsMMA(lhs, lhsV); }
+
+#define GEMM_MULTIPLE_COLS
+
+// Disable in GCC until unnecessary register moves are fixed
+// #if (EIGEN_COMP_LLVM || (__GNUC__ >= 11))
+#if EIGEN_COMP_LLVM
+#define VECTOR_PAIR_LOADS_LHS
+#endif
+
+// PEEL_MMA loop factor.
+#ifdef GEMM_MULTIPLE_COLS
+#define PEEL_MMA 8
+#else
+// Register spillage with GCC12+
+#if EIGEN_COMP_LLVM || (__GNUC__ < 12) || defined(VECTOR_PAIR_LOADS_LHS)
+#define PEEL_MMA 7
+#else
+#define PEEL_MMA 6
+#endif
+#endif
+
+#define MICRO_MMA_UNROLL(func) func(0) func(1) func(2) func(3) func(4) func(5) func(6) func(7)
+
+#define MICRO_MMA_WORK(func, type, peel)                                                                        \
+  if (accItr == 1) {                                                                                            \
+    func(0, type, peel, 0, 0) func(1, type, peel, 1, 0) func(2, type, peel, 2, 0) func(3, type, peel, 3, 0)     \
+        func(4, type, peel, 4, 0) func(5, type, peel, 5, 0) func(6, type, peel, 6, 0) func(7, type, peel, 7, 0) \
+  } else if (accItr == 2) {                                                                                     \
+    func(0, type, peel, 0, 0) func(1, type, peel, 0, 1) func(2, type, peel, 1, 0) func(3, type, peel, 1, 1)     \
+        func(4, type, peel, 2, 0) func(5, type, peel, 2, 1) func(6, type, peel, 3, 0) func(7, type, peel, 3, 1) \
+  } else {                                                                                                      \
+    func(0, type, peel, 0, 0) func(1, type, peel, 0, 1) func(2, type, peel, 0, 2) func(3, type, peel, 0, 3)     \
+        func(4, type, peel, 1, 0) func(5, type, peel, 1, 1) func(6, type, peel, 1, 2) func(7, type, peel, 1, 3) \
+  }
+
+#define MICRO_MMA_WORK_ONE(iter, type, peel, left, right)                        \
+  if (unroll_factor > left) {                                                    \
+    pgerMMA<Packet, type, false>(&accZero##iter, rhsV##right[peel], lhsV##left); \
+  }
+
+#ifdef VECTOR_PAIR_LOADS_LHS
+#define MICRO_MMA_WORK_TWO(iter, type, peel, left, right)                                          \
+  if (unroll_factor > left) {                                                                      \
+    pgerMMA<Packet, type, false>(&accZero##iter, rhsV##right[peel], lhsV2##left.packet[peel & 1]); \
+  }
+
+#define MICRO_MMA_LOAD1_TWO(lhs_ptr, left)                                                        \
+  if (unroll_factor > left) {                                                                     \
+    if (MICRO_NORMAL(left)) {                                                                     \
+      ploadLhsMMA(reinterpret_cast<const double*>(lhs_ptr##left), plhsV##left);                   \
+      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(&lhsV2##left.packet), &plhsV##left); \
+      lhs_ptr##left += accCols * 2;                                                               \
+    } else {                                                                                      \
+      lhsV2##left.packet[0] = ploadLhs<Packet>(lhs_ptr##left);                                    \
+      lhsV2##left.packet[1] = ploadLhs<Packet>(lhs_ptr##left + accCols2);                         \
+      lhs_ptr##left += accCols2 * 2;                                                              \
+      EIGEN_UNUSED_VARIABLE(plhsV##left);                                                         \
+    }                                                                                             \
+  } else {                                                                                        \
+    EIGEN_UNUSED_VARIABLE(lhsV2##left);                                                           \
+    EIGEN_UNUSED_VARIABLE(plhsV##left);                                                           \
+  }
+
+#define MICRO_MMA_LOAD_TWO(left) MICRO_MMA_LOAD1_TWO(lhs_ptr, left)
+#endif
+
+#define MICRO_MMA_UNROLL_ITER(func, val)                       \
+  func(val, 0) if (accItr > 1) {                               \
+    func(val, 1) if (accItr > 2) { func(val, 2) func(val, 3) } \
+  }
+
+#define MICRO_MMA_LOAD_ONE_RHS1(peel, right) ploadRhsMMA(rhs_ptr##right + (accRows * peel), rhsV##right[peel]);
+
+#define MICRO_MMA_LOAD_ONE_RHS(peel) MICRO_MMA_UNROLL_ITER(MICRO_MMA_LOAD_ONE_RHS1, peel)
+
+#define MICRO_MMA_TYPE_PEEL(funcw, funcl, type, peel)              \
+  if (PEEL_MMA > peel) {                                           \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
+    MICRO_MMA_LOAD_ONE_RHS(peel)                                   \
+    MICRO_MMA_UNROLL(funcl)                                        \
+    MICRO_MMA_WORK(funcw, type, peel)                              \
+  }
+
+#ifndef VECTOR_PAIR_LOADS_LHS
+#define MICRO_MMA_UNROLL_TYPE_PEEL(funcw, funcl, type)                                                  \
+  type rhsV0[8], rhsV1[(accItr > 1) ? 8 : 1], rhsV2[(accItr > 2) ? 8 : 1], rhsV3[(accItr > 2) ? 8 : 1]; \
+  MICRO_MMA_TYPE_PEEL(funcw, funcl, type, 0)                                                            \
+  MICRO_MMA_TYPE_PEEL(funcw, funcl, type, 1)                                                            \
+  MICRO_MMA_TYPE_PEEL(funcw, funcl, type, 2)                                                            \
+  MICRO_MMA_TYPE_PEEL(funcw, funcl, type, 3)                                                            \
+  MICRO_MMA_TYPE_PEEL(funcw, funcl, type, 4)                                                            \
+  MICRO_MMA_TYPE_PEEL(funcw, funcl, type, 5)                                                            \
+  MICRO_MMA_TYPE_PEEL(funcw, funcl, type, 6) MICRO_MMA_TYPE_PEEL(funcw, funcl, type, 7)
+#else
+#define MICRO_MMA_LOAD_TWO_RHS(peel1, right)                                                      \
+  ploadRhsMMA(reinterpret_cast<const double*>(rhs_ptr##right + (accRows * peel1)), prhsV##peel1); \
+  __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(&rhsV##right[peel1]), &prhsV##peel1);
+
+#define MICRO_MMA_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type, peel1, peel2)           \
+  if (PEEL_MMA > peel2) {                                                                  \
+    PacketBlock<Packet, 2> lhsV20, lhsV21, lhsV22, lhsV23, lhsV24, lhsV25, lhsV26, lhsV27; \
+    __vector_pair plhsV0, plhsV1, plhsV2, plhsV3, plhsV4, plhsV5, plhsV6, plhsV7;          \
+    if (sizeof(type) == 16) {                                                              \
+      MICRO_MMA_UNROLL_ITER(MICRO_MMA_LOAD_TWO_RHS, peel1)                                 \
+    } else {                                                                               \
+      EIGEN_UNUSED_VARIABLE(prhsV##peel1);                                                 \
+      MICRO_MMA_LOAD_ONE_RHS(peel1)                                                        \
+      MICRO_MMA_LOAD_ONE_RHS(peel2)                                                        \
+    }                                                                                      \
+    MICRO_MMA_UNROLL(funcl2)                                                               \
+    MICRO_MMA_WORK(funcw2, type, peel1)                                                    \
+    MICRO_MMA_WORK(funcw2, type, peel2)                                                    \
+  } else {                                                                                 \
+    EIGEN_UNUSED_VARIABLE(prhsV##peel1);                                                   \
+    MICRO_MMA_TYPE_PEEL(funcw1, funcl1, type, peel1)                                       \
+  }
+
+#define MICRO_MMA_UNROLL_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type)                               \
+  type rhsV0[8], rhsV1[(accItr > 1) ? 8 : 1], rhsV2[(accItr > 2) ? 8 : 1], rhsV3[(accItr > 2) ? 8 : 1]; \
+  __vector_pair prhsV0, prhsV2, prhsV4, prhsV6;                                                         \
+  MICRO_MMA_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type, 0, 1)                                      \
+  MICRO_MMA_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type, 2, 3)                                      \
+  MICRO_MMA_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type, 4, 5)                                      \
+  MICRO_MMA_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type, 6, 7)
+#endif
+
+#define MICRO_MMA_UNROLL_TYPE_ONE(funcw, funcl, type) \
+  type rhsV0[1], rhsV1[1], rhsV2[1], rhsV3[1];        \
+  MICRO_MMA_TYPE_PEEL(funcw, funcl, type, 0)
+
+#define MICRO_MMA_UPDATE_RHS1(size, right) rhs_ptr##right += (accRows * size);
+
+#define MICRO_MMA_UPDATE_RHS(size) MICRO_MMA_UNROLL_ITER(MICRO_MMA_UPDATE_RHS1, size)
+
+#define MICRO_MMA_UNROLL_TYPE(MICRO_MMA_TYPE, size)             \
+  MICRO_MMA_TYPE(MICRO_MMA_WORK_ONE, MICRO_LOAD_ONE, RhsPacket) \
+  MICRO_MMA_UPDATE_RHS(size)
+
+#ifndef VECTOR_PAIR_LOADS_LHS
+#define MICRO_MMA_ONE_PEEL MICRO_MMA_UNROLL_TYPE(MICRO_MMA_UNROLL_TYPE_PEEL, PEEL_MMA)
+#else
+#define MICRO_MMA_UNROLL_TYPE2(MICRO_MMA_TYPE, size)                                                    \
+  MICRO_MMA_TYPE(MICRO_MMA_WORK_ONE, MICRO_LOAD_ONE, MICRO_MMA_WORK_TWO, MICRO_MMA_LOAD_TWO, RhsPacket) \
+  MICRO_MMA_UPDATE_RHS(size)
+
+#define MICRO_MMA_ONE_PEEL MICRO_MMA_UNROLL_TYPE2(MICRO_MMA_UNROLL_TYPE_PEEL2, PEEL_MMA)
+#endif
+
+#define MICRO_MMA_ONE MICRO_MMA_UNROLL_TYPE(MICRO_MMA_UNROLL_TYPE_ONE, 1)
+
+#define MICRO_MMA_DST_PTR_ONE(iter)       \
+  if (unroll_factor * accItr > iter) {    \
+    bsetzeroMMA(&accZero##iter);          \
+  } else {                                \
+    EIGEN_UNUSED_VARIABLE(accZero##iter); \
+  }
+
+#define MICRO_MMA_DST_PTR MICRO_MMA_UNROLL(MICRO_MMA_DST_PTR_ONE)
+
+#define MICRO_MMA_SRC_PTR MICRO_MMA_UNROLL(MICRO_SRC_PTR_ONE)
+
+#define MICRO_MMA_PREFETCH MICRO_MMA_UNROLL(MICRO_PREFETCH_ONE)
+
+#define MICRO_MMA_STORE_ONE(iter, left, right)                                                                 \
+  if (unroll_factor > left) {                                                                                  \
+    storeAccumulator<DataMapper, Packet, MICRO_NORMAL_PARTIAL(left)>(row + left * accCols, res##right, pAlpha, \
+                                                                     accCols2, &accZero##iter);                \
+  }
+
+#define MICRO_MMA_ITER_UNROLL(func)                                                                                 \
+  if (accItr == 1) {                                                                                                \
+    func(0, 0, 0) func(1, 1, 0) func(2, 2, 0) func(3, 3, 0) func(4, 4, 0) func(5, 5, 0) func(6, 6, 0) func(7, 7, 0) \
+  } else if (accItr == 2) {                                                                                         \
+    func(0, 0, 0) func(1, 0, 1) func(2, 1, 0) func(3, 1, 1) func(4, 2, 0) func(5, 2, 1) func(6, 3, 0) func(7, 3, 1) \
+  } else {                                                                                                          \
+    func(0, 0, 0) func(1, 0, 1) func(2, 0, 2) func(3, 0, 3) func(4, 1, 0) func(5, 1, 1) func(6, 1, 2) func(7, 1, 3) \
+  }
+
+#define MICRO_MMA_STORE MICRO_MMA_ITER_UNROLL(MICRO_MMA_STORE_ONE)
+
+#define MICRO_MMA_EXTRA_ROWS(right)                                                                           \
+  gemm_extra_row<Scalar, Packet, DataMapper, accRows, accCols>(                                               \
+      res3##right, blockA, rhs_base + right * accRows * strideB, depth, strideA, offsetA, strideB, row, rows, \
+      remaining_rows, pAlpha, pMask);
+
+#define MICRO_MMA_EXTRA_ROWS1(val, right) MICRO_MMA_EXTRA_ROWS(right);
+
+template <int unroll_factor, typename Scalar, typename Packet, typename RhsPacket, typename DataMapper,
+          const Index accRows, const Index accCols, bool full, const Index accItr>
+EIGEN_ALWAYS_INLINE void gemm_unrolled_MMA_iteration(const DataMapper& res0, const DataMapper& res1,
+                                                     const DataMapper& res2, const DataMapper& res3,
+                                                     const Scalar* lhs_base, const Scalar* rhs_base, Index depth,
+                                                     Index strideA, Index strideB, Index offsetA, Index& row,
+                                                     const Packet& pAlpha, Index accCols2) {
+  const Scalar *rhs_ptr0 = rhs_base, *rhs_ptr1 = NULL, *rhs_ptr2 = NULL, *rhs_ptr3 = NULL;
+  const Scalar *lhs_ptr0 = NULL, *lhs_ptr1 = NULL, *lhs_ptr2 = NULL, *lhs_ptr3 = NULL, *lhs_ptr4 = NULL,
+               *lhs_ptr5 = NULL, *lhs_ptr6 = NULL, *lhs_ptr7 = NULL;
+  __vector_quad accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
+
+  if (accItr > 1) {
+    rhs_ptr1 = rhs_base + (accRows * strideB);
+  } else {
+    EIGEN_UNUSED_VARIABLE(strideB);
+    EIGEN_UNUSED_VARIABLE(rhs_ptr1);
+    EIGEN_UNUSED_VARIABLE(res1);
+  }
+  if (accItr > 2) {
+    rhs_ptr2 = rhs_base + (2 * accRows * strideB);
+    rhs_ptr3 = rhs_base + (3 * accRows * strideB);
+  } else {
+    EIGEN_UNUSED_VARIABLE(rhs_ptr2);
+    EIGEN_UNUSED_VARIABLE(rhs_ptr3);
+    EIGEN_UNUSED_VARIABLE(res2);
+    EIGEN_UNUSED_VARIABLE(res3);
+  }
+
+  MICRO_MMA_SRC_PTR
+  MICRO_MMA_DST_PTR
+
+  Index k = 0, depth2 = depth - PEEL_MMA;
+  for (; k <= depth2; k += PEEL_MMA) {
+    EIGEN_POWER_PREFETCH(rhs_ptr);
+    MICRO_MMA_PREFETCH
+    MICRO_MMA_ONE_PEEL
+  }
+  for (; k < depth; k++) {
+    MICRO_MMA_ONE
+  }
+  MICRO_MMA_STORE
+
+  MICRO_UPDATE
+}
+
+#define MICRO_MMA_UNROLL_ITER2(N, M)                                                                                 \
+  gemm_unrolled_MMA_iteration<N + (M ? 1 : 0), Scalar, Packet, RhsPacket, DataMapper, accRows, accCols, !M, accItr>( \
+      res30, res31, res32, res33, lhs_base, rhs_base, depth, strideA, strideB, offsetA, row, pAlpha,                 \
+      M ? remaining_rows : accCols);                                                                                 \
+  if (M) return;
+
+#define MICRO_MMA_ROWS(n)             \
+  while (row + n * accCols <= rows) { \
+    MICRO_MMA_UNROLL_ITER2(n, 0);     \
+  }
+
+template <typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows,
+          const Index accCols, const Index accItr>
+EIGEN_ALWAYS_INLINE void gemmMMA_cols(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index depth,
+                                      Index strideA, Index offsetA, Index strideB, Index offsetB, Index col, Index rows,
+                                      Index remaining_rows, const Packet& pAlpha, const Packet& pMask) {
+  const DataMapper res30 = res.getSubMapper(0, col);
+  const DataMapper res31 = (accItr > 1) ? res30.getSubMapper(0, accRows * 1) : res30;
+  const DataMapper res32 = (accItr > 2) ? res30.getSubMapper(0, accRows * 2) : res30;
+  const DataMapper res33 = (accItr > 2) ? res30.getSubMapper(0, accRows * 3) : res30;
+
+  const Scalar* rhs_base = blockB + col * strideB + accRows * offsetB;
+  const Scalar* lhs_base = blockA + accCols * offsetA;
+  Index row = 0;
+
+#define MAX_MMA_UNROLL 7
+
+#if MAX_MMA_UNROLL < 2
+  if (1) {
+#elif MAX_MMA_UNROLL < 4
+  if (accItr <= 2) {
+#else
+  if (accItr == 1) {
+#endif
+    MICRO_MMA_ROWS(MAX_MMA_UNROLL);
+  } else if (accItr == 2) {
+    MICRO_MMA_ROWS(4);
+  } else {
+    MICRO_MMA_ROWS(2);
+  }
+  switch ((rows - row) / accCols) {
+#if MAX_MMA_UNROLL > 7
+    case 7:
+      if (accItr == 1) {
+        MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 7)
+      }
+      break;
+#endif
+#if MAX_MMA_UNROLL > 6
+    case 6:
+      if (accItr == 1) {
+        MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 6)
+      }
+      break;
+#endif
+#if MAX_MMA_UNROLL > 5
+    case 5:
+      if (accItr == 1) {
+        MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 5)
+      }
+      break;
+#endif
+#if MAX_MMA_UNROLL > 4
+    case 4:
+      if (accItr == 1) {
+        MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 4)
+      }
+      break;
+#endif
+#if MAX_MMA_UNROLL > 3
+    case 3:
+      if (accItr <= 2) {
+        MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 3)
+      }
+      break;
+#endif
+#if MAX_MMA_UNROLL > 2
+    case 2:
+      if (accItr <= 2) {
+        MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 2)
+      }
+      break;
+#endif
+#if MAX_MMA_UNROLL > 1
+    case 1:
+      MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 1)
+      break;
+#endif
+    default:
+      break;
+  }
+#undef MAX_MMA_UNROLL
+
+  if (remaining_rows > 0) {
+    MICRO_MMA_UNROLL_ITER(MICRO_MMA_EXTRA_ROWS1, 0)
+  }
+}
+
+#define MICRO_MMA_COLS(n)                                                                                          \
+  for (; col + n * accRows <= cols; col += n * accRows) {                                                          \
+    gemmMMA_cols<Scalar, Packet, RhsPacket2, DataMapper, accRows, accCols, n>(                                     \
+        res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, remaining_rows, pAlpha, pMask); \
+  }
+
+template <typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows,
+          const Index accCols>
+void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols,
+             Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  const Index remaining_rows = rows % accCols;
+
+  if (strideA == -1) strideA = depth;
+  if (strideB == -1) strideB = depth;
+
+  const Packet pAlpha = pset1<Packet>(alpha);
+  const Packet pMask = bmask<Packet>(remaining_rows);
+
+  typedef typename std::conditional_t<(sizeof(Scalar) == sizeof(float)), RhsPacket, __vector_pair> RhsPacket2;
+
+  Index col = 0;
+#ifdef GEMM_MULTIPLE_COLS
+  MICRO_MMA_COLS(4);
+  MICRO_MMA_COLS(2);
+#endif
+  MICRO_MMA_COLS(1);
+
+  if (col != cols) {
+    gemm_extra_cols<Scalar, Packet, DataMapper, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB,
+                                                         col, rows, cols, remaining_rows, pAlpha, pMask);
+  }
+}
+
+#define advanceRows ((LhsIsReal) ? 1 : 2)
+#define advanceCols ((RhsIsReal) ? 1 : 2)
+
+// PEEL_COMPLEX_MMA loop factor.
+#ifdef GEMM_MULTIPLE_COLS
+#define PEEL_COMPLEX_MMA 4
+#else
+#define PEEL_COMPLEX_MMA 3
+#endif
+
+#define MICRO_COMPLEX_MMA_UNROLL(func) func(0) func(1) func(2) func(3)
+
+#define MICRO_COMPLEX_MMA_WORK(func, type, peel)                                                            \
+  if (accItr == 1) {                                                                                        \
+    func(0, type, peel, 0, 0) func(1, type, peel, 1, 0) func(2, type, peel, 2, 0) func(3, type, peel, 3, 0) \
+  } else if (accItr == 2) {                                                                                 \
+    func(0, type, peel, 0, 0) func(1, type, peel, 0, 1) func(2, type, peel, 1, 0) func(3, type, peel, 1, 1) \
+  } else {                                                                                                  \
+    func(0, type, peel, 0, 0) func(1, type, peel, 0, 1) func(2, type, peel, 0, 2) func(3, type, peel, 0, 3) \
+  }
+
+#define MICRO_COMPLEX_MMA_WORK_ONE(iter, type, peel, left, right)                                        \
+  if (unroll_factor > left) {                                                                            \
+    pgercMMA<Packet, type, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(                            \
+        &accReal##iter, &accImag##iter, lhsV##left, lhsVi##left, rhsV##right[peel], rhsVi##right[peel]); \
+  }
+
+#ifdef VECTOR_PAIR_LOADS_LHS
+#define MICRO_COMPLEX_MMA_WORK_TWO(iter, type, peel, left, right)                                    \
+  if (unroll_factor > left) {                                                                        \
+    pgercMMA<Packet, type, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(                        \
+        &accReal##iter, &accImag##iter, lhsV2##left.packet[peel & 1], lhsVi2##left.packet[peel & 1], \
+        rhsV##right[peel], rhsVi##right[peel]);                                                      \
+  }
+
+#define MICRO_COMPLEX_MMA_LOAD1_TWO(lhs_ptr, left)                                                  \
+  if (!LhsIsReal && (unroll_factor > left)) {                                                       \
+    if (MICRO_NORMAL(left)) {                                                                       \
+      ploadLhsMMA(reinterpret_cast<const double*>(lhs_ptr_real##left + imag_delta), plhsVi##left);  \
+      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(&lhsVi2##left.packet), &plhsVi##left); \
+    } else {                                                                                        \
+      lhsVi2##left.packet[0] = ploadLhs<Packet>(lhs_ptr_real##left + imag_delta2);                  \
+      lhsVi2##left.packet[1] = ploadLhs<Packet>(lhs_ptr_real##left + imag_delta2 + accCols2);       \
+      EIGEN_UNUSED_VARIABLE(plhsVi##left);                                                          \
+    }                                                                                               \
+  } else {                                                                                          \
+    EIGEN_UNUSED_VARIABLE(lhsVi2##left);                                                            \
+    EIGEN_UNUSED_VARIABLE(plhsVi##left);                                                            \
+  }                                                                                                 \
+  MICRO_MMA_LOAD1_TWO(lhs_ptr_real, left)
+
+#define MICRO_COMPLEX_MMA_LOAD_TWO(left) MICRO_COMPLEX_MMA_LOAD1_TWO(lhs_ptr, left)
+#endif
+
+#define MICRO_COMPLEX_MMA_LOAD_RHS1(peel, right)                             \
+  ploadRhsMMA(rhs_ptr_real##right + (accRows * peel), rhsV##right[peel]);    \
+  if (!RhsIsReal) {                                                          \
+    ploadRhsMMA(rhs_ptr_imag##right + (accRows * peel), rhsVi##right[peel]); \
+  }
+
+#define MICRO_COMPLEX_MMA_LOAD_ONE_RHS(peel) MICRO_MMA_UNROLL_ITER(MICRO_COMPLEX_MMA_LOAD_RHS1, peel)
+
+#define MICRO_COMPLEX_MMA_TYPE_PEEL(funcw, funcl, type, peel) \
+  if (PEEL_COMPLEX_MMA > peel) {                              \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3;                        \
+    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3;                    \
+    MICRO_COMPLEX_MMA_LOAD_ONE_RHS(peel)                      \
+    MICRO_COMPLEX_MMA_UNROLL(funcl)                           \
+    MICRO_COMPLEX_MMA_WORK(funcw, type, peel)                 \
+  }
+
+#ifndef VECTOR_PAIR_LOADS_LHS
+#define MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL(funcw, funcl, type)                                                      \
+  type rhsV0[4], rhsVi0[4], rhsV1[(accItr > 1) ? 4 : 1], rhsVi1[(accItr > 1) ? 4 : 1], rhsV2[(accItr > 2) ? 4 : 1], \
+      rhsVi2[(accItr > 2) ? 4 : 1], rhsV3[(accItr > 2) ? 4 : 1], rhsVi3[(accItr > 2) ? 4 : 1];                      \
+  MICRO_COMPLEX_MMA_TYPE_PEEL(funcw, funcl, type, 0)                                                                \
+  MICRO_COMPLEX_MMA_TYPE_PEEL(funcw, funcl, type, 1)                                                                \
+  MICRO_COMPLEX_MMA_TYPE_PEEL(funcw, funcl, type, 2) MICRO_COMPLEX_MMA_TYPE_PEEL(funcw, funcl, type, 3)
+#else
+#define MICRO_COMPLEX_MMA_LOAD_TWO_RHS(peel1, right)                                                      \
+  ploadRhsMMA(reinterpret_cast<const double*>(rhs_ptr_real##right + (accRows * peel1)), prhsV##peel1);    \
+  __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(&rhsV##right[peel1]), &prhsV##peel1);            \
+  if (!RhsIsReal) {                                                                                       \
+    ploadRhsMMA(reinterpret_cast<const double*>(rhs_ptr_imag##right + (accRows * peel1)), prhsVi##peel1); \
+    __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(&rhsVi##right[peel1]), &prhsVi##peel1);        \
+  } else {                                                                                                \
+    EIGEN_UNUSED_VARIABLE(prhsVi##peel1);                                                                 \
+  }
+
+#define MICRO_COMPLEX_MMA_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type, peel1, peel2) \
+  if (PEEL_COMPLEX_MMA > peel2) {                                                        \
+    PacketBlock<Packet, 2> lhsV20, lhsV21, lhsV22, lhsV23;                               \
+    PacketBlock<Packet, 2> lhsVi20, lhsVi21, lhsVi22, lhsVi23;                           \
+    __vector_pair plhsV0, plhsV1, plhsV2, plhsV3;                                        \
+    __vector_pair plhsVi0, plhsVi1, plhsVi2, plhsVi3;                                    \
+    if (sizeof(type) == 16) {                                                            \
+      MICRO_MMA_UNROLL_ITER(MICRO_COMPLEX_MMA_LOAD_TWO_RHS, peel1)                       \
+    } else {                                                                             \
+      EIGEN_UNUSED_VARIABLE(prhsV##peel1);                                               \
+      EIGEN_UNUSED_VARIABLE(prhsVi##peel1);                                              \
+      MICRO_COMPLEX_MMA_LOAD_ONE_RHS(peel1);                                             \
+      MICRO_COMPLEX_MMA_LOAD_ONE_RHS(peel2);                                             \
+    }                                                                                    \
+    MICRO_COMPLEX_MMA_UNROLL(funcl2)                                                     \
+    MICRO_COMPLEX_MMA_WORK(funcw2, type, peel1)                                          \
+    MICRO_COMPLEX_MMA_WORK(funcw2, type, peel2)                                          \
+  } else {                                                                               \
+    EIGEN_UNUSED_VARIABLE(prhsV##peel1);                                                 \
+    EIGEN_UNUSED_VARIABLE(prhsVi##peel1);                                                \
+    MICRO_COMPLEX_MMA_TYPE_PEEL(funcw1, funcl1, type, peel1)                             \
+  }
+
+#define MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type)                                   \
+  type rhsV0[4], rhsVi0[4], rhsV1[(accItr > 1) ? 4 : 1], rhsVi1[(accItr > 1) ? 4 : 1], rhsV2[(accItr > 2) ? 4 : 1], \
+      rhsVi2[(accItr > 2) ? 4 : 1], rhsV3[(accItr > 2) ? 4 : 1], rhsVi3[(accItr > 2) ? 4 : 1];                      \
+  __vector_pair prhsV0, prhsV2;                                                                                     \
+  __vector_pair prhsVi0, prhsVi2;                                                                                   \
+  MICRO_COMPLEX_MMA_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type, 0, 1)                                          \
+  MICRO_COMPLEX_MMA_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type, 2, 3)
+#endif
+
+#define MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE(funcw, funcl, type)                              \
+  type rhsV0[1], rhsVi0[1], rhsV1[1], rhsVi1[1], rhsV2[1], rhsVi2[1], rhsV3[1], rhsVi3[1]; \
+  MICRO_COMPLEX_MMA_TYPE_PEEL(funcw, funcl, type, 0)
+
+#define MICRO_COMPLEX_MMA_UPDATE_RHS1(size, right) \
+  rhs_ptr_real##right += (accRows * size);         \
+  if (!RhsIsReal) rhs_ptr_imag##right += (accRows * size);
+
+#define MICRO_COMPLEX_MMA_UPDATE_RHS(size) MICRO_MMA_UNROLL_ITER(MICRO_COMPLEX_MMA_UPDATE_RHS1, size)
+
+#define MICRO_COMPLEX_MMA_UNROLL_TYPE(MICRO_COMPLEX_MMA_TYPE, size)                     \
+  MICRO_COMPLEX_MMA_TYPE(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_LOAD_ONE, RhsPacket) \
+  MICRO_COMPLEX_MMA_UPDATE_RHS(size);
+
+#ifndef VECTOR_PAIR_LOADS_LHS
+#define MICRO_COMPLEX_MMA_ONE_PEEL MICRO_COMPLEX_MMA_UNROLL_TYPE(MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL, PEEL_COMPLEX_MMA)
+#else
+#define MICRO_COMPLEX_MMA_UNROLL_TYPE2(MICRO_COMPLEX_MMA_TYPE, size)                                     \
+  MICRO_COMPLEX_MMA_TYPE(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_LOAD_ONE, MICRO_COMPLEX_MMA_WORK_TWO, \
+                         MICRO_COMPLEX_MMA_LOAD_TWO, RhsPacket)                                          \
+  MICRO_COMPLEX_MMA_UPDATE_RHS(size);
+
+#define MICRO_COMPLEX_MMA_ONE_PEEL MICRO_COMPLEX_MMA_UNROLL_TYPE2(MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL2, PEEL_COMPLEX_MMA)
+#endif
+
+#define MICRO_COMPLEX_MMA_ONE MICRO_COMPLEX_MMA_UNROLL_TYPE(MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE, 1)
+
+#define MICRO_COMPLEX_MMA_DST_PTR_ONE(iter) \
+  if (unroll_factor * accItr > iter) {      \
+    bsetzeroMMA(&accReal##iter);            \
+    bsetzeroMMA(&accImag##iter);            \
+  } else {                                  \
+    EIGEN_UNUSED_VARIABLE(accReal##iter);   \
+    EIGEN_UNUSED_VARIABLE(accImag##iter);   \
+  }
+
+#define MICRO_COMPLEX_MMA_DST_PTR MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_DST_PTR_ONE)
+
+#define MICRO_COMPLEX_MMA_SRC_PTR MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_SRC_PTR_ONE)
+
+#define MICRO_COMPLEX_MMA_PREFETCH MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_PREFETCH_ONE)
+
+#define MICRO_COMPLEX_MMA_STORE_ONE(iter, left, right)                                                                 \
+  if (unroll_factor > left) {                                                                                          \
+    storeComplexAccumulator<DataMapper, Packet, Packetc, accCols, (unroll_factor != (left + 1)) ? accCols : accCols2>( \
+        row + left * accCols, res##right, pAlphaReal, pAlphaImag, pMask, &accReal##iter, &accImag##iter);              \
+  }
+
+#define MICRO_COMPLEX_MMA_ITER_UNROLL(func)                 \
+  if (accItr == 1) {                                        \
+    func(0, 0, 0) func(1, 1, 0) func(2, 2, 0) func(3, 3, 0) \
+  } else if (accItr == 2) {                                 \
+    func(0, 0, 0) func(1, 0, 1) func(2, 1, 0) func(3, 1, 1) \
+  } else {                                                  \
+    func(0, 0, 0) func(1, 0, 1) func(2, 0, 2) func(3, 0, 3) \
+  }
+
+#define MICRO_COMPLEX_MMA_STORE MICRO_COMPLEX_MMA_ITER_UNROLL(MICRO_COMPLEX_MMA_STORE_ONE)
+
+#define MICRO_COMPLEX_MMA_EXTRA_ROWS(right)                                                                            \
+  gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, \
+                         RhsIsReal>(res3##right, blockA, rhs_base + right * accRows * (RhsIsReal ? 1 : 2) * strideB,   \
+                                    depth, strideA, offsetA, strideB, row, rows, remaining_rows, pAlphaReal,           \
+                                    pAlphaImag, pMask);
+
+#define MICRO_COMPLEX_MMA_EXTRA_ROWS1(val, right) MICRO_COMPLEX_MMA_EXTRA_ROWS(right);
+
+template <int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename RhsPacket,
+          typename DataMapper, const Index accRows, const Index accCols, const Index accCols2, bool ConjugateLhs,
+          bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal, const Index accItr>
+EIGEN_ALWAYS_INLINE void gemm_complex_unrolled_MMA_iteration(const DataMapper& res0, const DataMapper& res1,
+                                                             const DataMapper& res2, const DataMapper& res3,
+                                                             const Scalar* lhs_base, const Scalar* rhs_base,
+                                                             Index depth, Index strideA, Index offsetA, Index strideB,
+                                                             Index& row, const Packet& pAlphaReal,
+                                                             const Packet& pAlphaImag, const Packet& pMask) {
+  const Scalar *rhs_ptr_real0 = rhs_base, *rhs_ptr_real1 = NULL, *rhs_ptr_real2 = NULL, *rhs_ptr_real3 = NULL;
+  const Scalar *rhs_ptr_imag0 = NULL, *rhs_ptr_imag1 = NULL, *rhs_ptr_imag2 = NULL, *rhs_ptr_imag3 = NULL;
+  const Index imag_delta = accCols * strideA;
+  const Index imag_delta2 = accCols2 * strideA;
+
+  if (!RhsIsReal) {
+    rhs_ptr_imag0 = rhs_base + accRows * strideB;
+  } else {
+    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag0);
+  }
+  if (accItr > 1) {
+    if (!RhsIsReal) {
+      rhs_ptr_real1 = rhs_base + (2 * accRows * strideB);
+      rhs_ptr_imag1 = rhs_base + (3 * accRows * strideB);
+    } else {
+      rhs_ptr_real1 = rhs_base + accRows * strideB;
+      EIGEN_UNUSED_VARIABLE(rhs_ptr_imag1);
+    }
+  } else {
+    EIGEN_UNUSED_VARIABLE(rhs_ptr_real1);
+    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag1);
+    EIGEN_UNUSED_VARIABLE(res1);
+  }
+  if (accItr > 2) {
+    if (!RhsIsReal) {
+      rhs_ptr_real2 = rhs_base + (4 * accRows * strideB);
+      rhs_ptr_imag2 = rhs_base + (5 * accRows * strideB);
+      rhs_ptr_real3 = rhs_base + (6 * accRows * strideB);
+      rhs_ptr_imag3 = rhs_base + (7 * accRows * strideB);
+    } else {
+      rhs_ptr_real2 = rhs_base + (2 * accRows * strideB);
+      rhs_ptr_real3 = rhs_base + (3 * accRows * strideB);
+      EIGEN_UNUSED_VARIABLE(rhs_ptr_imag2);
+      EIGEN_UNUSED_VARIABLE(rhs_ptr_imag3);
+    }
+  } else {
+    EIGEN_UNUSED_VARIABLE(rhs_ptr_real2);
+    EIGEN_UNUSED_VARIABLE(rhs_ptr_real3);
+    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag2);
+    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag3);
+    EIGEN_UNUSED_VARIABLE(res2);
+    EIGEN_UNUSED_VARIABLE(res3);
+  }
+  const Scalar *lhs_ptr_real0 = NULL, *lhs_ptr_real1 = NULL;
+  const Scalar *lhs_ptr_real2 = NULL, *lhs_ptr_real3 = NULL;
+  __vector_quad accReal0, accImag0, accReal1, accImag1, accReal2, accImag2, accReal3, accImag3;
+
+  MICRO_COMPLEX_MMA_SRC_PTR
+  MICRO_COMPLEX_MMA_DST_PTR
+
+  Index k = 0, depth2 = depth - PEEL_COMPLEX_MMA;
+  for (; k <= depth2; k += PEEL_COMPLEX_MMA) {
+    EIGEN_POWER_PREFETCH(rhs_ptr_real);
+    if (!RhsIsReal) {
+      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
+    }
+    MICRO_COMPLEX_MMA_PREFETCH
+    MICRO_COMPLEX_MMA_ONE_PEEL
+  }
+  for (; k < depth; k++) {
+    MICRO_COMPLEX_MMA_ONE
+  }
+  MICRO_COMPLEX_MMA_STORE
+
+  MICRO_COMPLEX_UPDATE
+}
+
+#define MICRO_COMPLEX_MMA_UNROLL_ITER2(N, M)                                                                           \
+  gemm_complex_unrolled_MMA_iteration<N + (M ? 1 : 0), Scalar, Packet, Packetc, RhsPacket, DataMapper, accRows,        \
+                                      accCols, M ? M : accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal,      \
+                                      accItr>(res30, res31, res32, res33, lhs_base, rhs_base, depth, strideA, offsetA, \
+                                              strideB, row, pAlphaReal, pAlphaImag, pMask);                            \
+  if (M) return;
+
+#define MICRO_COMPLEX_MMA_ROWS(n)         \
+  while (row + n * accCols <= rows) {     \
+    MICRO_COMPLEX_MMA_UNROLL_ITER2(n, 0); \
+  }
+
+template <typename Scalar, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper,
+          const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal,
+          bool RhsIsReal, const Index accItr>
+EIGEN_ALWAYS_INLINE void gemmMMA_complex_cols(const DataMapper& res, const Scalar* blockA, const Scalar* blockB,
+                                              Index depth, Index strideA, Index offsetA, Index strideB, Index offsetB,
+                                              Index col, Index rows, Index remaining_rows, const Packet& pAlphaReal,
+                                              const Packet& pAlphaImag, const Packet& pMask) {
+  const DataMapper res30 = res.getSubMapper(0, col);
+  const DataMapper res31 = (accItr > 1) ? res30.getSubMapper(0, accRows * 1) : res30;
+  const DataMapper res32 = (accItr > 2) ? res30.getSubMapper(0, accRows * 2) : res30;
+  const DataMapper res33 = (accItr > 2) ? res30.getSubMapper(0, accRows * 3) : res30;
+
+  const Scalar* rhs_base = blockB + advanceCols * col * strideB + accRows * offsetB;
+  const Scalar* lhs_base = blockA + accCols * offsetA;
+  Index row = 0;
+
+#define MAX_COMPLEX_MMA_UNROLL 4
+
+#if MAX_COMPLEX_MMA_UNROLL < 2
+  if (1) {
+#elif MAX_COMPLEX_MMA_UNROLL < 4
+  if (accItr <= 2) {
+#else
+  if (accItr == 1) {
+#endif
+    MICRO_COMPLEX_MMA_ROWS(MAX_COMPLEX_MMA_UNROLL);
+  } else if (accItr == 2) {
+    MICRO_COMPLEX_MMA_ROWS(2);
+  } else {
+    MICRO_COMPLEX_MMA_ROWS(1);
+  }
+  switch ((rows - row) / accCols) {
+#if MAX_COMPLEX_MMA_UNROLL > 3
+    case 3:
+      if (accItr == 1) {
+        MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_MMA_UNROLL_ITER2, 3)
+      }
+      break;
+#endif
+#if MAX_COMPLEX_MMA_UNROLL > 2
+    case 2:
+      if (accItr == 1) {
+        MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_MMA_UNROLL_ITER2, 2)
+      }
+      break;
+#endif
+#if MAX_COMPLEX_MMA_UNROLL > 1
+    case 1:
+      if (accItr <= 2) {
+        MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_MMA_UNROLL_ITER2, 1)
+      }
+      break;
+#endif
+    default:
+      break;
+  }
+#undef MAX_COMPLEX_MMA_UNROLL
+
+  if (remaining_rows > 0) {
+    MICRO_MMA_UNROLL_ITER(MICRO_COMPLEX_MMA_EXTRA_ROWS1, 0)
+  }
+}
+
+#define MICRO_COMPLEX_MMA_COLS(n)                                                                                      \
+  for (; col + n * accRows <= cols; col += n * accRows) {                                                              \
+    gemmMMA_complex_cols<Scalar, Packet, Packetc, RhsPacket2, DataMapper, accRows, accCols, ConjugateLhs,              \
+                         ConjugateRhs, LhsIsReal, RhsIsReal, n>(res, blockA, blockB, depth, strideA, offsetA, strideB, \
+                                                                offsetB, col, rows, remaining_rows, pAlphaReal,        \
+                                                                pAlphaImag, pMask);                                    \
+  }
+
+template <typename LhsScalar, typename RhsScalar, typename Scalarc, typename Scalar, typename Packet, typename Packetc,
+          typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs,
+          bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+void gemm_complexMMA(const DataMapper& res, const LhsScalar* blockAc, const RhsScalar* blockBc, Index rows, Index depth,
+                     Index cols, Scalarc alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  const Index remaining_rows = rows % accCols;
+
+  if (strideA == -1) strideA = depth;
+  if (strideB == -1) strideB = depth;
+
+  const Packet pAlphaReal = pset1<Packet>(alpha.real());
+  const Packet pAlphaImag = pset1<Packet>(alpha.imag());
+  const Packet pMask = bmask<Packet>(remaining_rows);
+
+  const Scalar* blockA = (Scalar*)blockAc;
+  const Scalar* blockB = (Scalar*)blockBc;
+
+  typedef typename std::conditional_t<(sizeof(Scalar) == sizeof(float)), RhsPacket, __vector_pair> RhsPacket2;
+
+  Index col = 0;
+#ifdef GEMM_MULTIPLE_COLS
+  MICRO_COMPLEX_MMA_COLS(4);
+  MICRO_COMPLEX_MMA_COLS(2);
+#endif
+  MICRO_COMPLEX_MMA_COLS(1);
+
+  if (col != cols) {
+    gemm_complex_extra_cols<Scalar, Packet, Packetc, DataMapper, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal,
+                            RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols,
+                                       remaining_rows, pAlphaReal, pAlphaImag, pMask);
+  }
+}
+
+#undef accColsC
+#undef advanceRows
+#undef advanceCols
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#if defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+#pragma GCC pop_options
+#endif
+
+#endif  // EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProductMMAbfloat16.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProductMMAbfloat16.h
new file mode 100644
index 0000000..6ecec0e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixProductMMAbfloat16.h
@@ -0,0 +1,742 @@
+#ifndef EIGEN_MATRIX_PRODUCT_MMA_BFLOAT16_ALTIVEC_H
+#define EIGEN_MATRIX_PRODUCT_MMA_BFLOAT16_ALTIVEC_H
+
+#if EIGEN_COMP_LLVM
+#define BFLOAT16_UNROLL _Pragma("unroll 8")
+#else
+#define BFLOAT16_UNROLL _Pragma("GCC unroll(8)")
+#endif
+
+namespace Eigen {
+
+namespace internal {
+
+template <bool zero>
+EIGEN_ALWAYS_INLINE Packet8bf loadBfloat16(const bfloat16* indexA) {
+  Packet8bf lhs1 = ploadu<Packet8bf>(indexA);
+  if (zero) {
+    Packet8bf lhs2 = pset1<Packet8bf>(Eigen::bfloat16(0));
+    return vec_mergeh(lhs1.m_val, lhs2.m_val);
+  } else {
+    return lhs1;
+  }
+}
+
+template <bool zero>
+EIGEN_ALWAYS_INLINE Packet8bf loadRhsBfloat16(const bfloat16* blockB, Index strideB, Index i) {
+  return loadBfloat16<zero>(blockB + strideB * i);
+}
+
+template <Index num_acc, Index num_packets, bool zero, bool rhsExtraCols, bool lhsExtraRows, Index num_rhs,
+          Index num_lhs>
+EIGEN_ALWAYS_INLINE void KLoop(const bfloat16* indexA, const bfloat16* indexB, __vector_quad (&quad_acc)[num_acc],
+                               Index strideB, Index k, Index offsetB, Index extra_cols, Index extra_rows) {
+  Packet8bf lhs[num_lhs], rhs[num_rhs];
+
+  BFLOAT16_UNROLL
+  for (Index i = 0; i < (num_rhs - (rhsExtraCols ? 1 : 0)); i++) {
+    rhs[i] = loadRhsBfloat16<zero>(indexB + k * 4, strideB, i);
+  }
+  if (rhsExtraCols) {
+    rhs[num_rhs - 1] = loadRhsBfloat16<zero>(indexB + k * extra_cols - offsetB, strideB, num_rhs - 1);
+  }
+
+  indexA += k * (lhsExtraRows ? extra_rows : num_packets);
+  if (num_lhs == 1) {
+    lhs[0] = loadBfloat16<zero>(indexA);
+  } else {
+    BFLOAT16_UNROLL
+    for (Index j = 0; j < num_lhs; j += 2) {
+      Packet8bf lhs1 = ploadu<Packet8bf>(indexA + (j + 0) * (zero ? 4 : 8));
+      if (zero) {
+        Packet8bf lhs2 = pset1<Packet8bf>(Eigen::bfloat16(0));
+        lhs[j + 0] = vec_mergeh(lhs1.m_val, lhs2.m_val);
+        lhs[j + 1] = vec_mergel(lhs1.m_val, lhs2.m_val);
+      } else {
+        lhs[j + 0] = lhs1;
+        lhs[j + 1] = ploadu<Packet8bf>(indexA + (j + 1) * 8);
+      }
+    }
+  }
+
+  BFLOAT16_UNROLL
+  for (Index i = 0, x = 0; i < num_rhs; i++) {
+    BFLOAT16_UNROLL
+    for (Index j = 0; j < num_lhs; j++, x++) {
+      __builtin_mma_xvbf16ger2pp(&(quad_acc[x]), reinterpret_cast<Packet16uc>(rhs[i].m_val),
+                                 reinterpret_cast<Packet16uc>(lhs[j].m_val));
+    }
+  }
+}
+
+template <Index num_acc>
+EIGEN_ALWAYS_INLINE void zeroAccumulators(__vector_quad (&quad_acc)[num_acc]) {
+  BFLOAT16_UNROLL
+  for (Index k = 0; k < num_acc; k++) __builtin_mma_xxsetaccz(&(quad_acc[k]));
+}
+
+template <Index num_acc>
+EIGEN_ALWAYS_INLINE void disassembleAccumulators(__vector_quad (&quad_acc)[num_acc], Packet4f (&acc)[num_acc][4]) {
+  BFLOAT16_UNROLL
+  for (Index k = 0; k < num_acc; k++) __builtin_mma_disassemble_acc((void*)acc[k], &(quad_acc[k]));
+}
+
+template <Index num_acc, bool rhsExtraCols, bool lhsExtraRows, Index num_rhs, Index num_lhs>
+EIGEN_ALWAYS_INLINE void outputResults(Packet4f (&acc)[num_acc][4], Index rows, const Packet4f pAlpha, float* result,
+                                       const Index extra_cols, Index extra_rows) {
+  BFLOAT16_UNROLL
+  for (Index i = 0, k = 0; i < num_rhs - (rhsExtraCols ? 1 : 0); i++, result += 4 * rows) {
+    BFLOAT16_UNROLL
+    for (Index j = 0; j < num_lhs; j++, k++) {
+      storeResults<false, lhsExtraRows>(acc[k], rows, pAlpha, result + j * 4, extra_cols, extra_rows);
+    }
+  }
+  if (rhsExtraCols) {
+    storeResults<rhsExtraCols, lhsExtraRows>(acc[num_acc - 1], rows, pAlpha, result, extra_cols, extra_rows);
+  }
+}
+
+template <const Index num_acc, const Index num_packets, bool rhsExtraCols, bool lhsExtraRows, bool multiIter = false>
+EIGEN_ALWAYS_INLINE void colLoopBodyIter(Index depth, Index rows, const Packet4f pAlpha, const bfloat16* indexA,
+                                         const bfloat16* indexB, Index strideB, Index offsetB, float* result,
+                                         const Index extra_cols, const Index extra_rows) {
+  constexpr Index num_lhs = multiIter ? (num_packets / 4) : 1;
+  constexpr Index num_rhs = (num_acc + num_lhs - 1) / num_lhs;
+
+  for (Index offset_row = 0; offset_row < num_packets; offset_row += 4, indexA += (multiIter ? 0 : 8),
+             indexB += (multiIter ? (num_rhs * strideB) : 0), result += (multiIter ? (4 * rows * num_rhs) : 4)) {
+    Packet4f acc[num_acc][4];
+    __vector_quad quad_acc[num_acc];
+
+    zeroAccumulators<num_acc>(quad_acc);
+
+    Index k;
+    for (k = 0; k + 2 <= depth; k += 2) {
+      KLoop<num_acc, num_packets, false, rhsExtraCols, lhsExtraRows, num_rhs, num_lhs>(
+          indexA, indexB, quad_acc, strideB, k, offsetB, extra_cols, extra_rows);
+    }
+    if (depth & 1) {
+      KLoop<num_acc, num_packets, true, rhsExtraCols, lhsExtraRows, num_rhs, num_lhs>(
+          indexA - (multiIter ? 0 : offset_row), indexB, quad_acc, strideB, k, offsetB, extra_cols, extra_rows);
+    }
+
+    disassembleAccumulators<num_acc>(quad_acc, acc);
+
+    outputResults<num_acc, rhsExtraCols, lhsExtraRows, num_rhs, num_lhs>(acc, rows, pAlpha, result, extra_cols,
+                                                                         extra_rows);
+  }
+}
+
+#define MAX_BFLOAT16_ACC 8
+
+template <const Index num_acc, const Index num_packets, bool rhsExtraCols, bool lhsExtraRows>
+void colLoopBody(Index& col, Index depth, Index cols, Index rows, const Packet4f pAlpha, const bfloat16* indexA,
+                 const bfloat16* indexB, Index strideB, Index offsetB, float* result) {
+  constexpr Index step = (num_acc * 4);  // each accumulator has 4 elements
+  const Index extra_cols = (rhsExtraCols) ? (cols & 3) : 0;
+  const Index extra_rows = (lhsExtraRows) ? (rows & 3) : 0;
+  constexpr bool multiIters = !rhsExtraCols && (num_acc == MAX_BFLOAT16_ACC);
+  constexpr bool normIters = multiIters && ((num_acc % (num_packets / 4)) == 0);
+
+  do {
+    colLoopBodyIter<num_acc, num_packets, rhsExtraCols, lhsExtraRows, normIters>(
+        depth, rows, pAlpha, indexA, indexB, strideB, offsetB, result, extra_cols, extra_rows);
+
+    indexB += strideB * num_acc;
+    result += rows * step;
+  } while (multiIters && (step <= cols - (col += step)));
+}
+
+template <const Index num_acc, const Index num_packets, bool rhsExtraCols, bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void colLoopBodyExtraN(Index col, Index depth, Index cols, Index rows, const Packet4f pAlpha,
+                                           const bfloat16* indexA, const bfloat16* blockB, Index strideB, Index offsetB,
+                                           float* result) {
+  if (MAX_BFLOAT16_ACC > num_acc) {
+    colLoopBody<num_acc + (rhsExtraCols ? 1 : 0), num_packets, rhsExtraCols, lhsExtraRows>(
+        col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+  }
+}
+
+template <const Index num_packets, bool rhsExtraCols, bool lhsExtraRows>
+void colLoopBodyExtra(Index col, Index depth, Index cols, Index rows, const Packet4f pAlpha, const bfloat16* indexA,
+                      const bfloat16* blockB, Index strideB, Index offsetB, float* result) {
+  switch ((cols - col) >> 2) {
+    case 7:
+      colLoopBodyExtraN<7, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB,
+                                                                    strideB, offsetB, result);
+      break;
+    case 6:
+      colLoopBodyExtraN<6, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB,
+                                                                    strideB, offsetB, result);
+      break;
+    case 5:
+      colLoopBodyExtraN<5, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB,
+                                                                    strideB, offsetB, result);
+      break;
+    case 4:
+      colLoopBodyExtraN<4, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB,
+                                                                    strideB, offsetB, result);
+      break;
+    case 3:
+      colLoopBodyExtraN<3, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB,
+                                                                    strideB, offsetB, result);
+      break;
+    case 2:
+      colLoopBodyExtraN<2, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB,
+                                                                    strideB, offsetB, result);
+      break;
+    case 1:
+      colLoopBodyExtraN<1, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB,
+                                                                    strideB, offsetB, result);
+      break;
+    default:
+      if (rhsExtraCols) {
+        colLoopBody<1, num_packets, true, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB,
+                                                        offsetB, result);
+      }
+      break;
+  }
+}
+
+template <const Index num_packets, bool lhsExtraRows = false>
+EIGEN_ALWAYS_INLINE void colLoops(Index depth, Index cols, Index rows, const Packet4f pAlpha, const bfloat16* indexA,
+                                  const bfloat16* blockB, Index strideB, Index offsetB, float* result) {
+  Index col = 0;
+  if (cols >= (MAX_BFLOAT16_ACC * 4)) {
+    colLoopBody<MAX_BFLOAT16_ACC, num_packets, false, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB,
+                                                                    strideB, 0, result);
+    blockB += (strideB >> 2) * col;
+    result += rows * col;
+  }
+  if (cols & 3) {
+    colLoopBodyExtra<num_packets, true, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB,
+                                                      result);
+  } else {
+    colLoopBodyExtra<num_packets, false, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, 0,
+                                                       result);
+  }
+}
+
+EIGEN_ALWAYS_INLINE Packet8bf convertF32toBF16(const float* res) {
+  Packet16uc fp16[2];
+  __vector_pair fp16_vp = *reinterpret_cast<__vector_pair*>(const_cast<float*>(res));
+  __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(fp16), &fp16_vp);
+  fp16[0] = __builtin_vsx_xvcvspbf16(fp16[0]);
+  fp16[1] = __builtin_vsx_xvcvspbf16(fp16[1]);
+  return vec_pack(reinterpret_cast<Packet4ui>(fp16[0]), reinterpret_cast<Packet4ui>(fp16[1]));
+}
+
+template <typename DataMapper, const Index size>
+EIGEN_ALWAYS_INLINE void convertArrayF32toBF16Col(float* result, Index col, Index rows, const DataMapper& res) {
+  const DataMapper res2 = res.getSubMapper(0, col);
+  Index row;
+  float* result2 = result + col * rows;
+  for (row = 0; row + 8 <= rows; row += 8, result2 += 8) {
+    // get and save block
+    PacketBlock<Packet8bf, size> block;
+    BFLOAT16_UNROLL
+    for (Index j = 0; j < size; j++) {
+      block.packet[j] = convertF32toBF16(result2 + j * rows);
+    }
+    res2.template storePacketBlock<Packet8bf, size>(row, 0, block);
+  }
+  // extra rows
+  if (row < rows) {
+    BFLOAT16_UNROLL
+    for (Index j = 0; j < size; j++) {
+      Packet8bf fp16 = convertF32toBF16(result2 + j * rows);
+      res2.template storePacketPartial<Packet8bf>(row, j, fp16, rows & 7);
+    }
+  }
+}
+
+template <const Index size, bool non_unit_stride = false>
+EIGEN_ALWAYS_INLINE void convertPointerF32toBF16(Index& i, float* result, Index rows, bfloat16*& dst,
+                                                 Index resInc = 1) {
+  constexpr Index extra = ((size < 8) ? 8 : size);
+  while (i + size <= rows) {
+    PacketBlock<Packet8bf, (size + 7) / 8> r32;
+    r32.packet[0] = convertF32toBF16(result + i + 0);
+    if (size >= 16) {
+      r32.packet[1] = convertF32toBF16(result + i + 8);
+    }
+    if (size >= 32) {
+      r32.packet[2] = convertF32toBF16(result + i + 16);
+      r32.packet[3] = convertF32toBF16(result + i + 24);
+    }
+    storeBF16fromResult<size, non_unit_stride, 0>(dst, r32.packet[0], resInc, rows & 7);
+    if (size >= 16) {
+      storeBF16fromResult<size, non_unit_stride, 8>(dst, r32.packet[1], resInc);
+    }
+    if (size >= 32) {
+      storeBF16fromResult<size, non_unit_stride, 16>(dst, r32.packet[2], resInc);
+      storeBF16fromResult<size, non_unit_stride, 24>(dst, r32.packet[3], resInc);
+    }
+    i += extra;
+    dst += extra * resInc;
+    if (size != 32) break;
+  }
+}
+
+template <bool non_unit_stride = false>
+EIGEN_ALWAYS_INLINE void convertArrayPointerF32toBF16(float* result, Index rows, bfloat16* dst, Index resInc = 1) {
+  Index i = 0;
+  convertPointerF32toBF16<32, non_unit_stride>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16<16, non_unit_stride>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16<8, non_unit_stride>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16<1, non_unit_stride>(i, result, rows, dst, resInc);
+}
+
+template <typename DataMapper>
+EIGEN_ALWAYS_INLINE void convertArrayF32toBF16(float* result, Index cols, Index rows, const DataMapper& res) {
+  Index col;
+  for (col = 0; col + 4 <= cols; col += 4) {
+    convertArrayF32toBF16Col<DataMapper, 4>(result, col, rows, res);
+  }
+  // extra cols
+  switch (cols - col) {
+    case 1:
+      convertArrayF32toBF16Col<DataMapper, 1>(result, col, rows, res);
+      break;
+    case 2:
+      convertArrayF32toBF16Col<DataMapper, 2>(result, col, rows, res);
+      break;
+    case 3:
+      convertArrayF32toBF16Col<DataMapper, 3>(result, col, rows, res);
+      break;
+  }
+}
+
+template <Index size>
+EIGEN_ALWAYS_INLINE void calcColLoops(const bfloat16*& indexA, Index& row, Index depth, Index cols, Index rows,
+                                      const Packet4f pAlpha, const bfloat16* indexB, Index strideB, Index offsetA,
+                                      Index offsetB, Index bigSuffix, float* result) {
+  if ((size == 16) || (rows & size)) {
+    indexA += size * offsetA;
+    colLoops<size>(depth, cols, rows, pAlpha, indexA, indexB, strideB, offsetB, result + row);
+    row += size;
+    indexA += bigSuffix * size / 16;
+  }
+}
+
+template <typename DataMapper>
+void gemmMMAbfloat16(const DataMapper& res, const bfloat16* indexA, const bfloat16* indexB, Index rows, Index depth,
+                     Index cols, bfloat16 alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  const Packet4f pAlpha = pset1<Packet4f>(falpha);
+  ei_declare_aligned_stack_constructed_variable(float, result, cols* rows, 0);
+
+  convertArrayBF16toF32<DataMapper>(result, cols, rows, res);
+
+  if (strideA == -1) strideA = depth;
+  if (strideB == -1) strideB = depth;
+  // Packing is done in blocks.
+  // There's 4 possible sizes of blocks
+  // Blocks of 8 columns with 16 elements (8x16)
+  // Blocks of 8 columns with 8 elements (8x8). This happens when there's 16 > rows >= 8
+  // Blocks of 8 columns with 4 elements (8x4). This happens when there's 8 > rows >= 4
+  // Blocks of 8 columns with < 4 elements. This happens when there's less than 4 remaining rows
+
+  // Loop for LHS standard block (8x16)
+  Index bigSuffix = (2 * 8) * (strideA - offsetA);
+  indexB += 4 * offsetB;
+  strideB *= 4;
+  offsetB *= 3;
+
+  Index row = 0;
+  while (row + 16 <= rows) {
+    calcColLoops<16>(indexA, row, depth, cols, rows, pAlpha, indexB, strideB, offsetA, offsetB, bigSuffix, result);
+  }
+  // LHS (8x8) block
+  calcColLoops<8>(indexA, row, depth, cols, rows, pAlpha, indexB, strideB, offsetA, offsetB, bigSuffix, result);
+  // LHS (8x4) block
+  calcColLoops<4>(indexA, row, depth, cols, rows, pAlpha, indexB, strideB, offsetA, offsetB, bigSuffix, result);
+  // extra rows
+  if (rows & 3) {
+    // This index is the beginning of remaining block.
+    colLoops<4, true>(depth, cols, rows, pAlpha, indexA, indexB, strideB, offsetB, result + row);
+  }
+
+  // Convert back to bfloat16
+  convertArrayF32toBF16<DataMapper>(result, cols, rows, res);
+}
+
+#undef MAX_BFLOAT16_ACC
+
+#if !EIGEN_ALTIVEC_DISABLE_MMA
+template <Index num_acc, typename LhsMapper, bool zero>
+EIGEN_ALWAYS_INLINE void loadVecLoop(Index k, LhsMapper& lhs, Packet8bf (&a0)[num_acc], Packet8bf b1) {
+  a0[k + 0] = lhs.template loadPacket<Packet8bf>(k * 4, 0);
+  if (!zero) {
+    b1 = lhs.template loadPacket<Packet8bf>(k * 4, 1);
+  }
+  if (num_acc > (k + 1)) {
+    a0[k + 1] = vec_mergel(a0[k + 0].m_val, b1.m_val);
+  }
+  a0[k + 0] = vec_mergeh(a0[k + 0].m_val, b1.m_val);
+}
+
+template <Index num_acc>
+EIGEN_ALWAYS_INLINE void multVec(__vector_quad (&quad_acc)[num_acc], Packet8bf (&a0)[num_acc], Packet8bf b0) {
+  BFLOAT16_UNROLL
+  for (Index k = 0; k < num_acc; k++) {
+    __builtin_mma_xvbf16ger2pp(&(quad_acc[k]), reinterpret_cast<Packet16uc>(b0.m_val),
+                               reinterpret_cast<Packet16uc>(a0[k].m_val));
+  }
+}
+
+template <Index num_acc, typename LhsMapper, typename RhsMapper, bool zero, bool linear>
+EIGEN_ALWAYS_INLINE void vecColLoop(Index j, LhsMapper& lhs, RhsMapper& rhs, __vector_quad (&quad_acc)[num_acc]) {
+  Packet8bf a0[num_acc];
+  Packet8bf b1 = pset1<Packet8bf>(Eigen::bfloat16(0));
+  Packet8bf b0 = loadColData<RhsMapper, linear>(rhs, j);
+
+  if (zero) {
+    b0 = vec_mergeh(b0.m_val, b1.m_val);
+  }
+
+  using LhsSubMapper = typename LhsMapper::SubMapper;
+
+  LhsSubMapper lhs2 = lhs.getSubMapper(0, j);
+  BFLOAT16_UNROLL
+  for (Index k = 0; k < num_acc; k += 2) {
+    loadVecLoop<num_acc, LhsSubMapper, zero>(k, lhs2, a0, b1);
+  }
+
+  multVec<num_acc>(quad_acc, a0, b0);
+}
+
+#define MAX_BFLOAT16_VEC_ACC 8
+
+template <const Index num_acc, typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+void colVecColLoopBody(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha,
+                       float* result) {
+  constexpr Index step = (num_acc * 4);
+  const Index extra_rows = (extraRows) ? (rows & 3) : 0;
+  constexpr bool multiIters = !extraRows && (num_acc == MAX_BFLOAT16_VEC_ACC);
+
+  do {
+    Packet4f acc[num_acc][4];
+    __vector_quad quad_acc[num_acc];
+
+    zeroAccumulators<num_acc>(quad_acc);
+
+    using LhsSubMapper = typename LhsMapper::SubMapper;
+
+    LhsSubMapper lhs2 = lhs.getSubMapper(row, 0);
+    for (Index j = 0; j + 2 <= cend; j += 2) {
+      vecColLoop<num_acc, LhsSubMapper, RhsMapper, false, linear>(j, lhs2, rhs, quad_acc);
+    }
+    if (cend & 1) {
+      vecColLoop<num_acc, LhsSubMapper, RhsMapper, true, linear>(cend - 1, lhs2, rhs, quad_acc);
+    }
+
+    disassembleAccumulators<num_acc>(quad_acc, acc);
+
+    outputVecColResults<num_acc, extraRows>(acc, result, pAlpha, extra_rows);
+
+    result += step;
+  } while (multiIters && (step <= rows - (row += step)));
+}
+
+template <const Index num_acc, typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+EIGEN_ALWAYS_INLINE void colVecColLoopBodyExtraN(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs,
+                                                 const Packet4f pAlpha, float* result) {
+  if (MAX_BFLOAT16_VEC_ACC > num_acc) {
+    colVecColLoopBody<num_acc + (extraRows ? 1 : 0), LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs,
+                                                                                              pAlpha, result);
+  }
+}
+
+template <typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+EIGEN_ALWAYS_INLINE void colVecColLoopBodyExtra(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs,
+                                                const Packet4f pAlpha, float* result) {
+  switch ((rows - row) >> 2) {
+    case 7:
+      colVecColLoopBodyExtraN<7, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 6:
+      colVecColLoopBodyExtraN<6, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 5:
+      colVecColLoopBodyExtraN<5, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 4:
+      colVecColLoopBodyExtraN<4, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 3:
+      colVecColLoopBodyExtraN<3, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 2:
+      colVecColLoopBodyExtraN<2, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 1:
+      colVecColLoopBodyExtraN<1, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    default:
+      if (extraRows) {
+        colVecColLoopBody<1, LhsMapper, RhsMapper, true, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      }
+      break;
+  }
+}
+
+template <typename LhsMapper, typename RhsMapper, bool linear>
+EIGEN_ALWAYS_INLINE void calcVecColLoops(Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha,
+                                         float* result) {
+  Index row = 0;
+  if (rows >= (MAX_BFLOAT16_VEC_ACC * 4)) {
+    colVecColLoopBody<MAX_BFLOAT16_VEC_ACC, LhsMapper, RhsMapper, false, linear>(row, cend, rows, lhs, rhs, pAlpha,
+                                                                                 result);
+    result += row;
+  }
+  if (rows & 3) {
+    colVecColLoopBodyExtra<LhsMapper, RhsMapper, true, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+  } else {
+    colVecColLoopBodyExtra<LhsMapper, RhsMapper, false, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+  }
+}
+
+template <typename RhsMapper, typename LhsMapper, typename = void>
+struct UseMMAStride : std::false_type {
+  static EIGEN_ALWAYS_INLINE void run(Index j2, Index jend, Index rows, LhsMapper& lhs, RhsMapper& rhs, Packet4f pAlpha,
+                                      float* result) {
+    using RhsSubMapper = typename RhsMapper::SubMapper;
+
+    RhsSubMapper rhs2 = rhs.getSubMapper(j2, 0);
+    calcVecColLoops<LhsMapper, RhsSubMapper, false>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+  }
+};
+
+template <typename RhsMapper, typename LhsMapper>
+struct UseMMAStride<RhsMapper, LhsMapper,
+                    std::enable_if_t<std::is_member_function_pointer<decltype(&RhsMapper::stride)>::value>>
+    : std::true_type {
+  static EIGEN_ALWAYS_INLINE void run(Index j2, Index jend, Index rows, LhsMapper& lhs, RhsMapper& rhs, Packet4f pAlpha,
+                                      float* result) {
+    using RhsSubMapper = typename RhsMapper::SubMapper;
+
+    RhsSubMapper rhs2 = rhs.getSubMapper(j2, 0);
+    if (rhs.stride() == 1) {
+      calcVecColLoops<LhsMapper, RhsSubMapper, true>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+    } else {
+      calcVecColLoops<LhsMapper, RhsSubMapper, false>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+    }
+  }
+};
+
+template <typename LhsMapper, typename RhsMapper>
+void gemvMMA_bfloat16_col(Index rows, Index cols, const LhsMapper& alhs, const RhsMapper& rhs, bfloat16* res,
+                          Index resIncr, bfloat16 alpha) {
+  EIGEN_UNUSED_VARIABLE(resIncr);
+  eigen_internal_assert(resIncr == 1);
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  RhsMapper rhs2(rhs);
+
+  const Index lhsStride = lhs.stride();
+
+  // TODO: improve the following heuristic:
+  const Index block_cols = cols < 128 ? cols : (lhsStride * sizeof(bfloat16) < 16000 ? 16 : 8);
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  Packet4f pAlpha = pset1<Packet4f>(falpha);
+
+  ei_declare_aligned_stack_constructed_variable(float, result, rows, 0);
+
+  convertArrayPointerBF16toF32(result, 1, rows, res);
+
+  for (Index j2 = 0; j2 < cols; j2 += block_cols) {
+    Index jend = numext::mini(j2 + block_cols, cols);
+
+    using LhsSubMapper = typename LhsMapper::SubMapper;
+
+    LhsSubMapper lhs2 = lhs.getSubMapper(0, j2);
+    UseMMAStride<RhsMapper, LhsSubMapper>::run(j2, jend, rows, lhs2, rhs2, pAlpha, result);
+  }
+
+  convertArrayPointerF32toBF16(result, rows, res);
+}
+
+static Packet16uc p16uc_ELEMENT_VEC3 = {0x0c, 0x0d, 0x0e, 0x0f, 0x1c, 0x1d, 0x1e, 0x1f,
+                                        0x0c, 0x0d, 0x0e, 0x0f, 0x1c, 0x1d, 0x1e, 0x1f};
+
+template <Index num_acc>
+EIGEN_ALWAYS_INLINE void preduxVecResults2(Packet4f (&acc)[num_acc][4], Index k) {
+  if (num_acc > (k + 1)) {
+    acc[k][0] = vec_mergeh(acc[k][0], acc[k + 1][0]);
+    acc[k][1] = vec_mergeo(acc[k][1], acc[k + 1][1]);
+    acc[k][2] = vec_mergel(acc[k][2], acc[k + 1][2]);
+    acc[k][3] = vec_perm(acc[k][3], acc[k + 1][3], p16uc_ELEMENT_VEC3);
+
+    acc[k][0] = (acc[k][0] + acc[k][2]) + (acc[k][1] + acc[k][3]);
+  } else {
+    acc[k][0] = vec_mergeh(acc[k][0], acc[k][1]);
+    acc[k][0] += vec_mergel(acc[k][2], acc[k][3]);
+#ifdef _BIG_ENDIAN
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 12);
+#else
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 4);
+#endif
+  }
+}
+
+template <Index num_acc>
+EIGEN_ALWAYS_INLINE void preduxVecResults(Packet4f (&acc)[num_acc][4]) {
+  BFLOAT16_UNROLL
+  for (Index k = 0; k < num_acc; k += 4) {
+    preduxVecResults2<num_acc>(acc, k + 0);
+    if (num_acc > (k + 2)) {
+      preduxVecResults2<num_acc>(acc, k + 2);
+      acc[k + 0][0] = reinterpret_cast<Packet4f>(
+          vec_mergeh(reinterpret_cast<Packet2ul>(acc[k + 0][0]), reinterpret_cast<Packet2ul>(acc[k + 2][0])));
+    }
+  }
+}
+
+template <Index num_acc, typename LhsMapper, typename RhsMapper, bool extra>
+EIGEN_ALWAYS_INLINE void multVecLoop(__vector_quad (&quad_acc)[num_acc], const LhsMapper& lhs, RhsMapper& rhs, Index j,
+                                     Index extra_cols) {
+  Packet8bf a0[num_acc], b0;
+
+  if (extra) {
+    b0 = rhs.template loadPacketPartial<Packet8bf>(j, extra_cols);
+  } else {
+    b0 = rhs.template loadPacket<Packet8bf>(j);
+  }
+
+  const LhsMapper lhs2 = lhs.getSubMapper(0, j);
+  BFLOAT16_UNROLL
+  for (Index k = 0; k < num_acc; k++) {
+    if (extra) {
+      a0[k] = lhs2.template loadPacketPartial<Packet8bf>(k, 0, extra_cols);
+    } else {
+      a0[k] = lhs2.template loadPacket<Packet8bf>(k, 0);
+    }
+  }
+
+  multVec<num_acc>(quad_acc, a0, b0);
+}
+
+template <Index num_acc, typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void vecLoop(Index cols, const LhsMapper& lhs, RhsMapper& rhs, __vector_quad (&quad_acc)[num_acc],
+                                 Index extra_cols) {
+  Index j = 0;
+  for (; j + 8 <= cols; j += 8) {
+    multVecLoop<num_acc, LhsMapper, RhsMapper, false>(quad_acc, lhs, rhs, j, extra_cols);
+  }
+
+  if (extra_cols) {
+    multVecLoop<num_acc, LhsMapper, RhsMapper, true>(quad_acc, lhs, rhs, j, extra_cols);
+  }
+}
+
+template <const Index num_acc, typename LhsMapper, typename RhsMapper>
+void colVecLoopBody(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha,
+                    float* result) {
+  constexpr bool multiIters = (num_acc == MAX_BFLOAT16_VEC_ACC);
+  const Index extra_cols = (cols & 7);
+
+  do {
+    Packet4f acc[num_acc][4];
+    __vector_quad quad_acc[num_acc];
+
+    zeroAccumulators<num_acc>(quad_acc);
+
+    const LhsMapper lhs2 = lhs.getSubMapper(row, 0);
+    vecLoop<num_acc, LhsMapper, RhsMapper>(cols, lhs2, rhs, quad_acc, extra_cols);
+
+    disassembleAccumulators<num_acc>(quad_acc, acc);
+
+    preduxVecResults<num_acc>(acc);
+
+    outputVecResults<num_acc>(acc, result, pAlpha);
+
+    result += num_acc;
+  } while (multiIters && (num_acc <= rows - (row += num_acc)));
+}
+
+template <const Index num_acc, typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void colVecLoopBodyExtraN(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs,
+                                              const Packet4f pAlpha, float* result) {
+  if (MAX_BFLOAT16_VEC_ACC > num_acc) {
+    colVecLoopBody<num_acc, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+  }
+}
+
+template <typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void colVecLoopBodyExtra(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs,
+                                             const Packet4f pAlpha, float* result) {
+  switch (rows - row) {
+    case 7:
+      colVecLoopBodyExtraN<7, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 6:
+      colVecLoopBodyExtraN<6, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 5:
+      colVecLoopBodyExtraN<5, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 4:
+      colVecLoopBodyExtraN<4, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 3:
+      colVecLoopBodyExtraN<3, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 2:
+      colVecLoopBodyExtraN<2, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 1:
+      colVecLoopBodyExtraN<1, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+  }
+}
+
+template <typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void calcVecLoops(Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha,
+                                      float* result) {
+  Index row = 0;
+  if (rows >= MAX_BFLOAT16_VEC_ACC) {
+    colVecLoopBody<MAX_BFLOAT16_VEC_ACC, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    result += row;
+  }
+  colVecLoopBodyExtra<LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+}
+
+template <typename LhsMapper, typename RhsMapper>
+EIGEN_STRONG_INLINE void gemvMMA_bfloat16_row(Index rows, Index cols, const LhsMapper& alhs, const RhsMapper& rhs,
+                                              bfloat16* res, Index resIncr, bfloat16 alpha) {
+  typedef typename RhsMapper::LinearMapper LinearMapper;
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  LinearMapper rhs2 = rhs.getLinearMapper(0, 0);
+
+  eigen_internal_assert(rhs.stride() == 1);
+
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  const Packet4f pAlpha = pset1<Packet4f>(falpha);
+
+  ei_declare_aligned_stack_constructed_variable(float, result, rows, 0);
+  if (resIncr == 1) {
+    convertArrayPointerBF16toF32(result, 1, rows, res);
+  } else {
+    convertArrayPointerBF16toF32<true>(result, 1, rows, res, resIncr);
+  }
+  calcVecLoops<LhsMapper, LinearMapper>(cols, rows, lhs, rhs2, pAlpha, result);
+  if (resIncr == 1) {
+    convertArrayPointerF32toBF16(result, rows, res);
+  } else {
+    convertArrayPointerF32toBF16<true>(result, rows, res, resIncr);
+  }
+}
+#endif
+
+#undef MAX_BFLOAT16_VEC_ACC
+#undef BFLOAT16_UNROLL
+
+}  // namespace internal
+}  // namespace Eigen
+#endif  // EIGEN_MATRIX_PRODUCT_MMA_BFLOAT16_ALTIVEC_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.inc
new file mode 100644
index 0000000..90c0d39
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.inc
@@ -0,0 +1,2818 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_VECTOR_PRODUCT_ALTIVEC_H
+#define EIGEN_MATRIX_VECTOR_PRODUCT_ALTIVEC_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+#if defined(__MMA__) && !EIGEN_ALTIVEC_DISABLE_MMA
+#if EIGEN_COMP_LLVM || (__GNUC__ > 10 || __GNUC_MINOR__ >= 3)
+#define USE_GEMV_MMA
+#endif
+
+#if !EIGEN_COMP_LLVM && (__GNUC__ < 11)
+// Only allow one vector_pair in buggy gcc - gcc 10.x has a bug
+#define GCC_ONE_VECTORPAIR_BUG
+#endif
+#endif
+
+// #define USE_SLOWER_GEMV_MMA   // MMA is currently not as fast as VSX in complex double GEMV (revisit when gcc is
+// improved)
+
+// #define EIGEN_POWER_USE_GEMV_PREFETCH
+#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
+#define EIGEN_POWER_GEMV_PREFETCH(p) prefetch(p)
+#else
+#define EIGEN_POWER_GEMV_PREFETCH(p)
+#endif
+
+#ifdef __has_builtin
+#if !__has_builtin(__builtin_vsx_assemble_pair)
+#define __builtin_vsx_assemble_pair __builtin_mma_assemble_pair
+#endif
+#if !__has_builtin(__builtin_vsx_disassemble_pair)
+#define __builtin_vsx_disassemble_pair __builtin_mma_disassemble_pair
+#endif
+#endif
+
+#if EIGEN_COMP_LLVM
+#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
+  __builtin_vsx_assemble_pair(&dst, (__vector unsigned char)src2, (__vector unsigned char)src1)
+#else
+#if (__GNUC__ <= 10)
+#if (__GNUC_MINOR__ > 3)
+#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
+  __builtin_vsx_assemble_pair(&dst, (__vector unsigned char)src2, (__vector unsigned char)src1)
+#else
+#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
+  __builtin_vsx_assemble_pair(&dst, (__vector unsigned char)src1, (__vector unsigned char)src2)
+#endif
+#else
+#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
+  __builtin_vsx_build_pair(&dst, (__vector unsigned char)src1, (__vector unsigned char)src2)
+#endif
+#endif
+
+#define GEMV_IS_COMPLEX_COMPLEX ((sizeof(LhsPacket) == 16) && (sizeof(RhsPacket) == 16))
+#define GEMV_IS_FLOAT (ResPacketSize == (16 / sizeof(float)))
+#define GEMV_IS_SCALAR (sizeof(ResPacket) != 16)
+#define GEMV_IS_COMPLEX_FLOAT (ResPacketSize == (16 / sizeof(std::complex<float>)))
+
+/** \internal multiply and add and store results */
+template <typename ResPacket, typename ResScalar>
+EIGEN_ALWAYS_INLINE void storeMaddData(ResScalar* res, ResPacket& palpha, ResPacket& data) {
+  pstoreu(res, pmadd(data, palpha, ploadu<ResPacket>(res)));
+}
+
+template <typename ResScalar>
+EIGEN_ALWAYS_INLINE void storeMaddData(ResScalar* res, ResScalar& alpha, ResScalar& data) {
+  *res += (alpha * data);
+}
+
+#define GEMV_UNROLL(func, N) func(0, N) func(1, N) func(2, N) func(3, N) func(4, N) func(5, N) func(6, N) func(7, N)
+
+#define GEMV_UNROLL_HALF(func, N) func(0, 0, 1, N) func(1, 2, 3, N) func(2, 4, 5, N) func(3, 6, 7, N)
+
+#define GEMV_GETN(N) (((N) * ResPacketSize) >> 2)
+
+#define GEMV_LOADPACKET_COL(iter) lhs.template load<LhsPacket, LhsAlignment>(i + ((iter) * LhsPacketSize), j)
+
+#ifdef USE_GEMV_MMA
+#define GEMV_UNROLL3(func, N, which)                                                                          \
+  func(0, N, which) func(1, N, which) func(2, N, which) func(3, N, which) func(4, N, which) func(5, N, which) \
+      func(6, N, which) func(7, N, which)
+
+#define GEMV_UNUSED_VAR(iter, N, which) \
+  if (GEMV_GETN(N) <= iter) {           \
+    EIGEN_UNUSED_VARIABLE(which##iter); \
+  }
+
+#define GEMV_UNUSED_EXTRA_VAR(iter, N, which) \
+  if (N <= iter) {                            \
+    EIGEN_UNUSED_VARIABLE(which##iter);       \
+  }
+
+#define GEMV_UNUSED_EXTRA(N, which) GEMV_UNROLL3(GEMV_UNUSED_EXTRA_VAR, N, which)
+
+#define GEMV_UNUSED(N, which) GEMV_UNROLL3(GEMV_UNUSED_VAR, N, which)
+
+#define GEMV_INIT_MMA(iter, N)         \
+  if (GEMV_GETN(N) > iter) {           \
+    __builtin_mma_xxsetaccz(&e##iter); \
+  }
+
+#if EIGEN_COMP_LLVM
+#define GEMV_LOADPAIR_COL_MMA(iter1, iter2) \
+  GEMV_BUILDPAIR_MMA(b##iter1, GEMV_LOADPACKET_COL(iter2), GEMV_LOADPACKET_COL((iter2) + 1));
+#else
+#define GEMV_LOADPAIR_COL_MMA(iter1, iter2)                                     \
+  const LhsScalar& src##iter1 = lhs(i + ((iter1 * 32) / sizeof(LhsScalar)), j); \
+  b##iter1 = *reinterpret_cast<__vector_pair*>(const_cast<LhsScalar*>(&src##iter1));
+#endif
+
+#define GEMV_LOAD1A_COL_MMA(iter, N)         \
+  if (GEMV_GETN(N) > iter) {                 \
+    if (GEMV_IS_FLOAT) {                     \
+      g##iter = GEMV_LOADPACKET_COL(iter);   \
+      EIGEN_UNUSED_VARIABLE(b##iter);        \
+    } else {                                 \
+      GEMV_LOADPAIR_COL_MMA(iter, iter << 1) \
+      EIGEN_UNUSED_VARIABLE(g##iter);        \
+    }                                        \
+  } else {                                   \
+    EIGEN_UNUSED_VARIABLE(b##iter);          \
+    EIGEN_UNUSED_VARIABLE(g##iter);          \
+  }
+
+#define GEMV_WORK1A_COL_MMA(iter, N)                                      \
+  if (GEMV_GETN(N) > iter) {                                              \
+    if (GEMV_IS_FLOAT) {                                                  \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter, a0, g##iter); \
+    } else {                                                              \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter, b##iter, a0); \
+    }                                                                     \
+  }
+
+#define GEMV_LOAD1B_COL_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN(N) > iter1) {                       \
+    if (GEMV_IS_FLOAT) {                            \
+      GEMV_LOADPAIR_COL_MMA(iter2, iter2)           \
+      EIGEN_UNUSED_VARIABLE(b##iter3);              \
+    } else {                                        \
+      GEMV_LOADPAIR_COL_MMA(iter2, iter2 << 1)      \
+      GEMV_LOADPAIR_COL_MMA(iter3, iter3 << 1)      \
+    }                                               \
+  } else {                                          \
+    EIGEN_UNUSED_VARIABLE(b##iter2);                \
+    EIGEN_UNUSED_VARIABLE(b##iter3);                \
+  }                                                 \
+  EIGEN_UNUSED_VARIABLE(g##iter2);                  \
+  EIGEN_UNUSED_VARIABLE(g##iter3);
+
+#define GEMV_WORK1B_COL_MMA(iter1, iter2, iter3, N)                          \
+  if (GEMV_GETN(N) > iter1) {                                                \
+    if (GEMV_IS_FLOAT) {                                                     \
+      LhsPacket h[2];                                                        \
+      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(h), &b##iter2); \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter2, a0, h[0]);      \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter3, a0, h[1]);      \
+    } else {                                                                 \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter2, b##iter2, a0);  \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter3, b##iter3, a0);  \
+    }                                                                        \
+  }
+
+#if EIGEN_COMP_LLVM
+#define GEMV_LOAD_COL_MMA(N)                        \
+  if (GEMV_GETN(N) > 1) {                           \
+    GEMV_UNROLL_HALF(GEMV_LOAD1B_COL_MMA, (N >> 1)) \
+  } else {                                          \
+    GEMV_UNROLL(GEMV_LOAD1A_COL_MMA, N)             \
+  }
+
+#define GEMV_WORK_COL_MMA(N)                        \
+  if (GEMV_GETN(N) > 1) {                           \
+    GEMV_UNROLL_HALF(GEMV_WORK1B_COL_MMA, (N >> 1)) \
+  } else {                                          \
+    GEMV_UNROLL(GEMV_WORK1A_COL_MMA, N)             \
+  }
+#else
+#define GEMV_LOAD_COL_MMA(N) GEMV_UNROLL(GEMV_LOAD1A_COL_MMA, N)
+
+#define GEMV_WORK_COL_MMA(N) GEMV_UNROLL(GEMV_WORK1A_COL_MMA, N)
+#endif
+
+#define GEMV_DISASSEMBLE_MMA(iter, N)                              \
+  if (GEMV_GETN(N) > iter) {                                       \
+    __builtin_mma_disassemble_acc(&result##iter.packet, &e##iter); \
+    if (!GEMV_IS_FLOAT) {                                          \
+      result##iter.packet[0][1] = result##iter.packet[1][0];       \
+      result##iter.packet[2][1] = result##iter.packet[3][0];       \
+    }                                                              \
+  }
+
+#define GEMV_LOADPAIR2_COL_MMA(iter1, iter2) \
+  b##iter1 = *reinterpret_cast<__vector_pair*>(res + i + ((iter2) * ResPacketSize));
+
+#define GEMV_LOAD2_COL_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN(N) > iter1) {                      \
+    if (GEMV_IS_FLOAT) {                           \
+      GEMV_LOADPAIR2_COL_MMA(iter2, iter2);        \
+      EIGEN_UNUSED_VARIABLE(b##iter3);             \
+    } else {                                       \
+      GEMV_LOADPAIR2_COL_MMA(iter2, iter2 << 1);   \
+      GEMV_LOADPAIR2_COL_MMA(iter3, iter3 << 1);   \
+    }                                              \
+  } else {                                         \
+    EIGEN_UNUSED_VARIABLE(b##iter2);               \
+    EIGEN_UNUSED_VARIABLE(b##iter3);               \
+  }
+
+#if EIGEN_COMP_LLVM
+#define GEMV_WORKPAIR2_COL_MMA(iter2, iter3, iter4)                                         \
+  ResPacket f##iter2[2];                                                                    \
+  __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(f##iter2), &b##iter2);             \
+  f##iter2[0] = pmadd(result##iter2.packet[0], palpha, f##iter2[0]);                        \
+  f##iter2[1] = pmadd(result##iter3.packet[(iter2 == iter3) ? 2 : 0], palpha, f##iter2[1]); \
+  GEMV_BUILDPAIR_MMA(b##iter2, f##iter2[0], f##iter2[1]);
+#else
+#define GEMV_WORKPAIR2_COL_MMA(iter2, iter3, iter4)                                        \
+  if (GEMV_IS_FLOAT) {                                                                     \
+    __asm__("xvmaddasp %0,%x1,%x3\n\txvmaddasp %L0,%x2,%x3"                                \
+            : "+&d"(b##iter2)                                                              \
+            : "wa"(result##iter3.packet[0]), "wa"(result##iter2.packet[0]), "wa"(palpha)); \
+  } else {                                                                                 \
+    __asm__("xvmaddadp %0,%x1,%x3\n\txvmaddadp %L0,%x2,%x3"                                \
+            : "+&d"(b##iter2)                                                              \
+            : "wa"(result##iter2.packet[2]), "wa"(result##iter2.packet[0]), "wa"(palpha)); \
+  }
+#endif
+
+#define GEMV_WORK2_COL_MMA(iter1, iter2, iter3, N)      \
+  if (GEMV_GETN(N) > iter1) {                           \
+    if (GEMV_IS_FLOAT) {                                \
+      GEMV_WORKPAIR2_COL_MMA(iter2, iter3, iter2);      \
+    } else {                                            \
+      GEMV_WORKPAIR2_COL_MMA(iter2, iter2, iter2 << 1); \
+      GEMV_WORKPAIR2_COL_MMA(iter3, iter3, iter3 << 1); \
+    }                                                   \
+  }
+
+#define GEMV_STOREPAIR2_COL_MMA(iter1, iter2) \
+  *reinterpret_cast<__vector_pair*>(res + i + ((iter2) * ResPacketSize)) = b##iter1;
+
+#define GEMV_STORE_COL_MMA(iter, N)                                                                          \
+  if (GEMV_GETN(N) > iter) {                                                                                 \
+    if (GEMV_IS_FLOAT) {                                                                                     \
+      storeMaddData<ResPacket, ResScalar>(res + i + (iter * ResPacketSize), palpha, result##iter.packet[0]); \
+    } else {                                                                                                 \
+      GEMV_LOADPAIR2_COL_MMA(iter, iter << 1)                                                                \
+      GEMV_WORKPAIR2_COL_MMA(iter, iter, iter << 1)                                                          \
+      GEMV_STOREPAIR2_COL_MMA(iter, iter << 1)                                                               \
+    }                                                                                                        \
+  }
+
+#define GEMV_STORE2_COL_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN(N) > iter1) {                       \
+    if (GEMV_IS_FLOAT) {                            \
+      GEMV_STOREPAIR2_COL_MMA(iter2, iter2);        \
+    } else {                                        \
+      GEMV_STOREPAIR2_COL_MMA(iter2, iter2 << 1)    \
+      GEMV_STOREPAIR2_COL_MMA(iter3, iter3 << 1)    \
+    }                                               \
+  }
+
+#define GEMV_PROCESS_COL_ONE_MMA(N)                 \
+  GEMV_UNROLL(GEMV_INIT_MMA, N)                     \
+  Index j = j2;                                     \
+  __vector_pair b0, b1, b2, b3, b4, b5, b6, b7;     \
+  do {                                              \
+    LhsPacket g0, g1, g2, g3, g4, g5, g6, g7;       \
+    RhsPacket a0 = pset1<RhsPacket>(rhs2(j, 0));    \
+    GEMV_UNROLL(GEMV_PREFETCH, N)                   \
+    GEMV_LOAD_COL_MMA(N)                            \
+    GEMV_WORK_COL_MMA(N)                            \
+  } while (++j < jend);                             \
+  GEMV_UNROLL(GEMV_DISASSEMBLE_MMA, N)              \
+  if (GEMV_GETN(N) <= 1) {                          \
+    GEMV_UNROLL(GEMV_STORE_COL_MMA, N)              \
+  } else {                                          \
+    GEMV_UNROLL_HALF(GEMV_LOAD2_COL_MMA, (N >> 1))  \
+    GEMV_UNROLL_HALF(GEMV_WORK2_COL_MMA, (N >> 1))  \
+    GEMV_UNROLL_HALF(GEMV_STORE2_COL_MMA, (N >> 1)) \
+  }                                                 \
+  i += (ResPacketSize * N);
+#endif
+
+#define GEMV_INIT(iter, N)                    \
+  if (N > iter) {                             \
+    c##iter = pset1<ResPacket>(ResScalar(0)); \
+  } else {                                    \
+    EIGEN_UNUSED_VARIABLE(c##iter);           \
+  }
+
+#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
+#define GEMV_PREFETCH(iter, N)                                   \
+  if (GEMV_GETN(N) > ((iter >> 1) + ((N >> 1) * (iter & 1)))) {  \
+    lhs.prefetch(i + (iter * LhsPacketSize) + prefetch_dist, j); \
+  }
+#else
+#define GEMV_PREFETCH(iter, N)
+#endif
+
+#define GEMV_WORK_COL(iter, N)                                   \
+  if (N > iter) {                                                \
+    c##iter = pcj.pmadd(GEMV_LOADPACKET_COL(iter), a0, c##iter); \
+  }
+
+#define GEMV_STORE_COL(iter, N)                                                           \
+  if (N > iter) {                                                                         \
+    pstoreu(res + i + (iter * ResPacketSize),                                             \
+            pmadd(c##iter, palpha, ploadu<ResPacket>(res + i + (iter * ResPacketSize)))); \
+  }
+
+/** \internal main macro for gemv_col - initialize accumulators, multiply and add inputs, and store results */
+#define GEMV_PROCESS_COL_ONE(N)                  \
+  GEMV_UNROLL(GEMV_INIT, N)                      \
+  Index j = j2;                                  \
+  do {                                           \
+    RhsPacket a0 = pset1<RhsPacket>(rhs2(j, 0)); \
+    GEMV_UNROLL(GEMV_PREFETCH, N)                \
+    GEMV_UNROLL(GEMV_WORK_COL, N)                \
+  } while (++j < jend);                          \
+  GEMV_UNROLL(GEMV_STORE_COL, N)                 \
+  i += (ResPacketSize * N);
+
+#ifdef USE_GEMV_MMA
+#define GEMV_PROCESS_COL(N) GEMV_PROCESS_COL_ONE_MMA(N)
+#else
+#define GEMV_PROCESS_COL(N) GEMV_PROCESS_COL_ONE(N)
+#endif
+
+/** \internal perform a matrix multiply and accumulate of packet a and packet b */
+#ifdef USE_GEMV_MMA
+template <typename LhsPacket, typename RhsPacket, bool accumulate>
+EIGEN_ALWAYS_INLINE void pger_vecMMA_acc(__vector_quad* acc, const RhsPacket& a, const LhsPacket& b) {
+  if (accumulate) {
+    __builtin_mma_xvf32gerpp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+  } else {
+    __builtin_mma_xvf32ger(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+  }
+}
+
+/** \internal perform a matrix multiply and accumulate of vector_pair a and packet b */
+template <typename LhsPacket, typename RhsPacket, bool accumulate>
+EIGEN_ALWAYS_INLINE void pger_vecMMA_acc(__vector_quad* acc, __vector_pair& a, const LhsPacket& b) {
+  if (accumulate) {
+    __builtin_mma_xvf64gerpp(acc, a, (__vector unsigned char)b);
+  } else {
+    __builtin_mma_xvf64ger(acc, a, (__vector unsigned char)b);
+  }
+}
+#endif
+
+template <typename LhsScalar, typename LhsMapper, typename RhsScalar, typename RhsMapper, typename ResScalar>
+EIGEN_STRONG_INLINE void gemv_col(Index rows, Index cols, const LhsMapper& alhs, const RhsMapper& rhs, ResScalar* res,
+                                  Index resIncr, ResScalar alpha) {
+  typedef gemv_traits<LhsScalar, RhsScalar> Traits;
+
+  typedef typename Traits::LhsPacket LhsPacket;
+  typedef typename Traits::RhsPacket RhsPacket;
+  typedef typename Traits::ResPacket ResPacket;
+
+  EIGEN_UNUSED_VARIABLE(resIncr);
+  eigen_internal_assert(resIncr == 1);
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  RhsMapper rhs2(rhs);
+
+  conj_helper<LhsScalar, RhsScalar, false, false> cj;
+  conj_helper<LhsPacket, RhsPacket, false, false> pcj;
+
+  const Index lhsStride = lhs.stride();
+  // TODO: for padded aligned inputs, we could enable aligned reads
+  enum {
+    LhsAlignment = Unaligned,
+    ResPacketSize = Traits::ResPacketSize,
+    LhsPacketSize = Traits::LhsPacketSize,
+    RhsPacketSize = Traits::RhsPacketSize,
+  };
+
+#ifndef GCC_ONE_VECTORPAIR_BUG
+  const Index n8 = rows - 8 * ResPacketSize + 1;
+  const Index n4 = rows - 4 * ResPacketSize + 1;
+  const Index n2 = rows - 2 * ResPacketSize + 1;
+#endif
+  const Index n1 = rows - 1 * ResPacketSize + 1;
+#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
+  const Index prefetch_dist = 64 * LhsPacketSize;
+#endif
+
+  // TODO: improve the following heuristic:
+  const Index block_cols = cols < 128 ? cols : (lhsStride * sizeof(LhsScalar) < 16000 ? 16 : 8);
+  ResPacket palpha = pset1<ResPacket>(alpha);
+
+  for (Index j2 = 0; j2 < cols; j2 += block_cols) {
+    Index jend = numext::mini(j2 + block_cols, cols);
+    Index i = 0;
+    ResPacket c0, c1, c2, c3, c4, c5, c6, c7;
+#ifdef USE_GEMV_MMA
+    __vector_quad e0, e1, e2, e3, e4, e5, e6, e7;
+    PacketBlock<ResPacket, 4> result0, result1, result2, result3, result4, result5, result6, result7;
+    GEMV_UNUSED(8, e)
+    GEMV_UNUSED(8, result)
+    GEMV_UNUSED_EXTRA(1, c)
+#endif
+#ifndef GCC_ONE_VECTORPAIR_BUG
+    while (i < n8) {
+      GEMV_PROCESS_COL(8)
+    }
+    if (i < n4) {
+      GEMV_PROCESS_COL(4)
+    }
+    if (i < n2) {
+      GEMV_PROCESS_COL(2)
+    }
+    if (i < n1)
+#else
+    while (i < n1)
+#endif
+    {
+      GEMV_PROCESS_COL_ONE(1)
+    }
+    for (; i < rows; ++i) {
+      ResScalar d0(0);
+      Index j = j2;
+      do {
+        d0 += cj.pmul(lhs(i, j), rhs2(j, 0));
+      } while (++j < jend);
+      res[i] += alpha * d0;
+    }
+  }
+}
+
+template <bool extraRows>
+EIGEN_ALWAYS_INLINE void outputVecCol(Packet4f acc, float* result, Packet4f pAlpha, Index extra_rows) {
+  Packet4f d0 = ploadu<Packet4f>(result);
+  d0 = pmadd(acc, pAlpha, d0);
+  if (extraRows) {
+    pstoreu_partial(result, d0, extra_rows);
+  } else {
+    pstoreu(result, d0);
+  }
+}
+
+template <Index num_acc, bool extraRows, Index size>
+EIGEN_ALWAYS_INLINE void outputVecColResults(Packet4f (&acc)[num_acc][size], float* result, Packet4f pAlpha,
+                                             Index extra_rows) {
+  constexpr Index real_acc = (num_acc - (extraRows ? 1 : 0));
+  for (Index k = 0; k < real_acc; k++) {
+    outputVecCol<false>(acc[k][0], result + k * 4, pAlpha, extra_rows);
+  }
+  if (extraRows) {
+    outputVecCol<true>(acc[real_acc][0], result + real_acc * 4, pAlpha, extra_rows);
+  }
+}
+
+static Packet16uc p16uc_MERGE16_32_V1 = {0, 1, 16, 17, 0, 1, 16, 17, 0, 1, 16, 17, 0, 1, 16, 17};
+static Packet16uc p16uc_MERGE16_32_V2 = {2, 3, 18, 19, 2, 3, 18, 19, 2, 3, 18, 19, 2, 3, 18, 19};
+
+template <Index num_acc, typename LhsMapper, bool zero>
+EIGEN_ALWAYS_INLINE void loadVecLoopVSX(Index k, LhsMapper& lhs, Packet4f (&a0)[num_acc][2]) {
+  Packet8bf c0 = lhs.template loadPacket<Packet8bf>(k * 4, 0);
+  Packet8bf b1;
+  if (!zero) {
+    b1 = lhs.template loadPacket<Packet8bf>(k * 4, 1);
+
+    a0[k + 0][1] = oneConvertBF16Hi(b1.m_val);
+  }
+  a0[k + 0][0] = oneConvertBF16Hi(c0.m_val);
+
+  if (num_acc > (k + 1)) {
+    a0[k + 1][0] = oneConvertBF16Lo(c0.m_val);
+    if (!zero) {
+      a0[k + 1][1] = oneConvertBF16Lo(b1.m_val);
+    }
+  }
+}
+
+template <Index num_acc, bool zero>
+EIGEN_ALWAYS_INLINE void multVecVSX(Packet4f (&acc)[num_acc][2], Packet4f (&a0)[num_acc][2], Packet4f (&b0)[2]) {
+  for (Index k = 0; k < num_acc; k++) {
+    for (Index i = 0; i < (zero ? 1 : 2); i++) {
+      acc[k][i] = pmadd(b0[i], a0[k][i], acc[k][i]);
+    }
+  }
+}
+
+template <typename RhsMapper, bool linear>
+struct loadColData_impl {
+  // linear == false
+  static EIGEN_ALWAYS_INLINE Packet8bf run(RhsMapper& rhs, Index j) {
+    const Index n = unpacket_traits<Packet8bf>::size;
+    EIGEN_ALIGN16 bfloat16 to[n];
+    LOAD_STORE_UNROLL_16
+    for (Index i = 0; i < n; i++) {
+      to[i] = rhs(j + i, 0);
+    }
+    return pload<Packet8bf>(to);
+  }
+};
+
+template <typename RhsMapper>
+struct loadColData_impl<RhsMapper, true> {
+  // linear == true
+  static EIGEN_ALWAYS_INLINE Packet8bf run(RhsMapper& rhs, Index j) {
+    return rhs.template loadPacket<Packet8bf>(j + 0, 0);
+  }
+};
+
+template <typename RhsMapper, bool linear>
+EIGEN_ALWAYS_INLINE Packet8bf loadColData(RhsMapper& rhs, Index j) {
+  return loadColData_impl<RhsMapper, linear>::run(rhs, j);
+}
+
+template <Index num_acc, typename LhsMapper, typename RhsMapper, bool zero, bool linear>
+EIGEN_ALWAYS_INLINE void vecColLoopVSX(Index j, LhsMapper& lhs, RhsMapper& rhs, Packet4f (&acc)[num_acc][2]) {
+  Packet4f a0[num_acc][2], b0[2];
+  Packet8bf b2 = loadColData<RhsMapper, linear>(rhs, j);
+
+  b0[0] = oneConvertBF16Perm(b2.m_val, p16uc_MERGE16_32_V1);
+  if (!zero) {
+    b0[1] = oneConvertBF16Perm(b2.m_val, p16uc_MERGE16_32_V2);
+  }
+
+  using LhsSubMapper = typename LhsMapper::SubMapper;
+
+  LhsSubMapper lhs2 = lhs.getSubMapper(0, j);
+  for (Index k = 0; k < num_acc; k += 2) {
+    loadVecLoopVSX<num_acc, LhsSubMapper, zero>(k, lhs2, a0);
+  }
+
+  multVecVSX<num_acc, zero>(acc, a0, b0);
+}
+
+template <Index num_acc>
+EIGEN_ALWAYS_INLINE void addResultsVSX(Packet4f (&acc)[num_acc][2]) {
+  for (Index i = 0; i < num_acc; i++) {
+    acc[i][0] = acc[i][0] + acc[i][1];
+  }
+}
+
+// Uses 2X the accumulators or 4X the number of VSX registers
+#define MAX_BFLOAT16_VEC_ACC_VSX 8
+
+template <const Index num_acc, typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+void colVSXVecColLoopBody(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha,
+                          float* result) {
+  constexpr Index step = (num_acc * 4);
+  const Index extra_rows = (extraRows) ? (rows & 3) : 0;
+  constexpr bool multiIters = !extraRows && (num_acc == MAX_BFLOAT16_VEC_ACC_VSX);
+
+  do {
+    Packet4f acc[num_acc][2];
+
+    zeroAccumulators<num_acc, 2>(acc);
+
+    using LhsSubMapper = typename LhsMapper::SubMapper;
+
+    LhsSubMapper lhs2 = lhs.getSubMapper(row, 0);
+    for (Index j = 0; j + 2 <= cend; j += 2) {
+      vecColLoopVSX<num_acc, LhsSubMapper, RhsMapper, false, linear>(j, lhs2, rhs, acc);
+    }
+    if (cend & 1) {
+      vecColLoopVSX<num_acc, LhsSubMapper, RhsMapper, true, linear>(cend - 1, lhs2, rhs, acc);
+    }
+
+    addResultsVSX<num_acc>(acc);
+
+    outputVecColResults<num_acc, extraRows, 2>(acc, result, pAlpha, extra_rows);
+
+    result += step;
+  } while (multiIters && (step <= rows - (row += step)));
+}
+
+template <const Index num_acc, typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+EIGEN_ALWAYS_INLINE void colVSXVecColLoopBodyExtraN(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs,
+                                                    const Packet4f pAlpha, float* result) {
+  if (MAX_BFLOAT16_VEC_ACC_VSX > num_acc) {
+    colVSXVecColLoopBody<num_acc + (extraRows ? 1 : 0), LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs,
+                                                                                                 rhs, pAlpha, result);
+  }
+}
+
+template <typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+EIGEN_ALWAYS_INLINE void colVSXVecColLoopBodyExtra(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs,
+                                                   const Packet4f pAlpha, float* result) {
+  switch ((rows - row) >> 2) {
+    case 7:
+      colVSXVecColLoopBodyExtraN<7, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 6:
+      colVSXVecColLoopBodyExtraN<6, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 5:
+      colVSXVecColLoopBodyExtraN<5, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 4:
+      colVSXVecColLoopBodyExtraN<4, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 3:
+      colVSXVecColLoopBodyExtraN<3, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 2:
+      colVSXVecColLoopBodyExtraN<2, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 1:
+      colVSXVecColLoopBodyExtraN<1, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      break;
+    default:
+      if (extraRows) {
+        colVSXVecColLoopBody<1, LhsMapper, RhsMapper, true, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+      }
+      break;
+  }
+}
+
+template <typename LhsMapper, typename RhsMapper, bool linear>
+EIGEN_ALWAYS_INLINE void calcVSXVecColLoops(Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs,
+                                            const Packet4f pAlpha, float* result) {
+  Index row = 0;
+  if (rows >= (MAX_BFLOAT16_VEC_ACC_VSX * 4)) {
+    colVSXVecColLoopBody<MAX_BFLOAT16_VEC_ACC_VSX, LhsMapper, RhsMapper, false, linear>(row, cend, rows, lhs, rhs,
+                                                                                        pAlpha, result);
+    result += row;
+  }
+  if (rows & 3) {
+    colVSXVecColLoopBodyExtra<LhsMapper, RhsMapper, true, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+  } else {
+    colVSXVecColLoopBodyExtra<LhsMapper, RhsMapper, false, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+  }
+}
+
+template <const Index size, bool inc, Index delta>
+EIGEN_ALWAYS_INLINE void storeBF16fromResult(bfloat16* dst, Packet8bf data, Index resInc, Index extra) {
+  if (inc) {
+    if (size < 8) {
+      pscatter_partial(dst + delta * resInc, data, resInc, extra);
+    } else {
+      pscatter(dst + delta * resInc, data, resInc);
+    }
+  } else {
+    if (size < 8) {
+      pstoreu_partial(dst + delta, data, extra);
+    } else {
+      pstoreu(dst + delta, data);
+    }
+  }
+}
+
+template <const Index size, bool inc = false>
+EIGEN_ALWAYS_INLINE void convertPointerF32toBF16VSX(Index& i, float* result, Index rows, bfloat16*& dst,
+                                                    Index resInc = 1) {
+  constexpr Index extra = ((size < 8) ? 8 : size);
+  while (i + size <= rows) {
+    PacketBlock<Packet8bf, (size + 7) / 8> r32;
+    r32.packet[0] = convertF32toBF16VSX(result + i + 0);
+    if (size >= 16) {
+      r32.packet[1] = convertF32toBF16VSX(result + i + 8);
+    }
+    if (size >= 32) {
+      r32.packet[2] = convertF32toBF16VSX(result + i + 16);
+      r32.packet[3] = convertF32toBF16VSX(result + i + 24);
+    }
+    storeBF16fromResult<size, inc, 0>(dst, r32.packet[0], resInc, rows & 7);
+    if (size >= 16) {
+      storeBF16fromResult<size, inc, 8>(dst, r32.packet[1], resInc);
+    }
+    if (size >= 32) {
+      storeBF16fromResult<size, inc, 16>(dst, r32.packet[2], resInc);
+      storeBF16fromResult<size, inc, 24>(dst, r32.packet[3], resInc);
+    }
+    i += extra;
+    dst += extra * resInc;
+    if (size != 32) break;
+  }
+}
+
+template <bool inc = false>
+EIGEN_ALWAYS_INLINE void convertArrayPointerF32toBF16VSX(float* result, Index rows, bfloat16* dst, Index resInc = 1) {
+  Index i = 0;
+  convertPointerF32toBF16VSX<32, inc>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16VSX<16, inc>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16VSX<8, inc>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16VSX<1, inc>(i, result, rows, dst, resInc);
+}
+
+template <typename RhsMapper, typename LhsMapper, typename = void>
+struct UseStride : std::false_type {
+  static EIGEN_ALWAYS_INLINE void run(Index j2, Index jend, Index rows, LhsMapper& lhs, RhsMapper& rhs, Packet4f pAlpha,
+                                      float* result) {
+    using RhsSubMapper = typename RhsMapper::SubMapper;
+
+    RhsSubMapper rhs2 = rhs.getSubMapper(j2, 0);
+    calcVSXVecColLoops<LhsMapper, RhsSubMapper, false>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+  }
+};
+
+template <typename RhsMapper, typename LhsMapper>
+struct UseStride<RhsMapper, LhsMapper,
+                 std::enable_if_t<std::is_member_function_pointer<decltype(&RhsMapper::stride)>::value>>
+    : std::true_type {
+  static EIGEN_ALWAYS_INLINE void run(Index j2, Index jend, Index rows, LhsMapper& lhs, RhsMapper& rhs, Packet4f pAlpha,
+                                      float* result) {
+    using RhsSubMapper = typename RhsMapper::SubMapper;
+
+    RhsSubMapper rhs2 = rhs.getSubMapper(j2, 0);
+    if (rhs.stride() == 1) {
+      calcVSXVecColLoops<LhsMapper, RhsSubMapper, true>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+    } else {
+      calcVSXVecColLoops<LhsMapper, RhsSubMapper, false>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+    }
+  }
+};
+
+template <typename LhsMapper, typename RhsMapper>
+void gemv_bfloat16_col(Index rows, Index cols, const LhsMapper& alhs, const RhsMapper& rhs, bfloat16* res,
+                       Index resIncr, bfloat16 alpha) {
+  EIGEN_UNUSED_VARIABLE(resIncr);
+  eigen_internal_assert(resIncr == 1);
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  RhsMapper rhs2(rhs);
+
+  const Index lhsStride = lhs.stride();
+
+  // TODO: improve the following heuristic:
+  const Index block_cols = cols < 128 ? cols : (lhsStride * sizeof(bfloat16) < 16000 ? 16 : 8);
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  Packet4f pAlpha = pset1<Packet4f>(falpha);
+
+  ei_declare_aligned_stack_constructed_variable(float, result, rows, 0);
+
+  convertArrayPointerBF16toF32(result, 1, rows, res);
+
+  for (Index j2 = 0; j2 < cols; j2 += block_cols) {
+    Index jend = numext::mini(j2 + block_cols, cols);
+
+    using LhsSubMapper = typename LhsMapper::SubMapper;
+
+    LhsSubMapper lhs2 = lhs.getSubMapper(0, j2);
+    UseStride<RhsMapper, LhsSubMapper>::run(j2, jend, rows, lhs2, rhs2, pAlpha, result);
+  }
+
+  convertArrayPointerF32toBF16VSX(result, rows, res);
+}
+
+template <Index num_acc, Index size>
+EIGEN_ALWAYS_INLINE void outputVecResults(Packet4f (&acc)[num_acc][size], float* result, Packet4f pAlpha) {
+  constexpr Index extra = num_acc & 3;
+
+  for (Index k = 0; k < num_acc; k += 4) {
+    Packet4f d0 = ploadu<Packet4f>(result + k);
+    d0 = pmadd(acc[k + 0][0], pAlpha, d0);
+
+    if (num_acc > (k + 3)) {
+      pstoreu(result + k, d0);
+    } else {
+      if (extra == 3) {
+        pstoreu_partial(result + k, d0, extra);
+      } else {
+        memcpy((void*)(result + k), (void*)(&d0), sizeof(float) * extra);
+      }
+    }
+  }
+}
+
+template <Index num_acc>
+EIGEN_ALWAYS_INLINE void preduxVecResults2VSX(Packet4f (&acc)[num_acc][2], Index k) {
+  if (num_acc > (k + 1)) {
+    acc[k][1] = vec_mergel(acc[k + 0][0], acc[k + 1][0]);
+    acc[k][0] = vec_mergeh(acc[k + 0][0], acc[k + 1][0]);
+    acc[k][0] = acc[k][0] + acc[k][1];
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 8);
+  } else {
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 8);
+#ifdef _BIG_ENDIAN
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 12);
+#else
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 4);
+#endif
+  }
+}
+
+template <Index num_acc>
+EIGEN_ALWAYS_INLINE void preduxVecResultsVSX(Packet4f (&acc)[num_acc][2]) {
+  for (Index k = 0; k < num_acc; k += 4) {
+    preduxVecResults2VSX<num_acc>(acc, k + 0);
+    if (num_acc > (k + 2)) {
+      preduxVecResults2VSX<num_acc>(acc, k + 2);
+#ifdef EIGEN_VECTORIZE_VSX
+      acc[k + 0][0] = reinterpret_cast<Packet4f>(
+          vec_mergeh(reinterpret_cast<Packet2ul>(acc[k + 0][0]), reinterpret_cast<Packet2ul>(acc[k + 2][0])));
+#else
+      acc[k + 0][0] = reinterpret_cast<Packet4f>(vec_perm(acc[k + 0][0], acc[k + 2][0], p16uc_TRANSPOSE64_HI));
+#endif
+    }
+  }
+}
+
+#ifndef _ARCH_PWR9
+EIGEN_ALWAYS_INLINE Packet8us loadPacketPartialZero(Packet8us data, Index extra_cols) {
+  Packet16uc shift = pset1<Packet16uc>(8 * 2 * (8 - extra_cols));
+#ifdef _BIG_ENDIAN
+  return reinterpret_cast<Packet8us>(vec_slo(vec_sro(reinterpret_cast<Packet16uc>(data), shift), shift));
+#else
+  return reinterpret_cast<Packet8us>(vec_sro(vec_slo(reinterpret_cast<Packet16uc>(data), shift), shift));
+#endif
+}
+#endif
+
+template <Index num_acc, typename LhsMapper, typename RhsMapper, bool extra>
+EIGEN_ALWAYS_INLINE void multVSXVecLoop(Packet4f (&acc)[num_acc][2], const LhsMapper& lhs, RhsMapper& rhs, Index j,
+                                        Index extra_cols) {
+  Packet4f a0[num_acc][2], b0[2];
+  Packet8bf a1, b1;
+
+  if (extra) {
+    b1 = rhs.template loadPacketPartial<Packet8bf>(j, extra_cols);
+#ifndef _ARCH_PWR9
+    b1 = loadPacketPartialZero(b1.m_val, extra_cols);
+#endif
+  } else {
+    b1 = rhs.template loadPacket<Packet8bf>(j);
+  }
+  b0[0] = oneConvertBF16Hi(b1.m_val);
+  b0[1] = oneConvertBF16Lo(b1.m_val);
+
+  const LhsMapper lhs2 = lhs.getSubMapper(0, j);
+  for (Index k = 0; k < num_acc; k++) {
+    if (extra) {
+      a1 = lhs2.template loadPacketPartial<Packet8bf>(k, 0, extra_cols);
+#ifndef _ARCH_PWR9
+      a1 = loadPacketPartialZero(a1.m_val, extra_cols);
+#endif
+    } else {
+      a1 = lhs2.template loadPacket<Packet8bf>(k, 0);
+    }
+    a0[k][0] = oneConvertBF16Hi(a1.m_val);
+    a0[k][1] = oneConvertBF16Lo(a1.m_val);
+  }
+
+  multVecVSX<num_acc, false>(acc, a0, b0);
+}
+
+template <Index num_acc, typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void vecVSXLoop(Index cols, const LhsMapper& lhs, RhsMapper& rhs, Packet4f (&acc)[num_acc][2],
+                                    Index extra_cols) {
+  Index j = 0;
+  for (; j + 8 <= cols; j += 8) {
+    multVSXVecLoop<num_acc, LhsMapper, RhsMapper, false>(acc, lhs, rhs, j, extra_cols);
+  }
+
+  if (extra_cols) {
+    multVSXVecLoop<num_acc, LhsMapper, RhsMapper, true>(acc, lhs, rhs, j, extra_cols);
+  }
+}
+
+template <const Index num_acc, typename LhsMapper, typename RhsMapper>
+void colVSXVecLoopBody(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha,
+                       float* result) {
+  constexpr bool multiIters = (num_acc == MAX_BFLOAT16_VEC_ACC_VSX);
+  const Index extra_cols = (cols & 7);
+
+  do {
+    Packet4f acc[num_acc][2];
+
+    zeroAccumulators<num_acc, 2>(acc);
+
+    const LhsMapper lhs2 = lhs.getSubMapper(row, 0);
+    vecVSXLoop<num_acc, LhsMapper, RhsMapper>(cols, lhs2, rhs, acc, extra_cols);
+
+    addResultsVSX<num_acc>(acc);
+
+    preduxVecResultsVSX<num_acc>(acc);
+
+    outputVecResults<num_acc, 2>(acc, result, pAlpha);
+
+    result += num_acc;
+  } while (multiIters && (num_acc <= rows - (row += num_acc)));
+}
+
+template <const Index num_acc, typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void colVSXVecLoopBodyExtraN(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs,
+                                                 const Packet4f pAlpha, float* result) {
+  if (MAX_BFLOAT16_VEC_ACC_VSX > num_acc) {
+    colVSXVecLoopBody<num_acc, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+  }
+}
+
+template <typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void colVSXVecLoopBodyExtra(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs,
+                                                const Packet4f pAlpha, float* result) {
+  switch (rows - row) {
+    case 7:
+      colVSXVecLoopBodyExtraN<7, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 6:
+      colVSXVecLoopBodyExtraN<6, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 5:
+      colVSXVecLoopBodyExtraN<5, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 4:
+      colVSXVecLoopBodyExtraN<4, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 3:
+      colVSXVecLoopBodyExtraN<3, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 2:
+      colVSXVecLoopBodyExtraN<2, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+    case 1:
+      colVSXVecLoopBodyExtraN<1, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+      break;
+  }
+}
+
+template <typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void calcVSXVecLoops(Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha,
+                                         float* result) {
+  Index row = 0;
+  if (rows >= MAX_BFLOAT16_VEC_ACC_VSX) {
+    colVSXVecLoopBody<MAX_BFLOAT16_VEC_ACC_VSX, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    result += row;
+  }
+  colVSXVecLoopBodyExtra<LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+}
+
+template <typename LhsMapper, typename RhsMapper>
+EIGEN_STRONG_INLINE void gemv_bfloat16_row(Index rows, Index cols, const LhsMapper& alhs, const RhsMapper& rhs,
+                                           bfloat16* res, Index resIncr, bfloat16 alpha) {
+  typedef typename RhsMapper::LinearMapper LinearMapper;
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  LinearMapper rhs2 = rhs.getLinearMapper(0, 0);
+
+  eigen_internal_assert(rhs.stride() == 1);
+
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  const Packet4f pAlpha = pset1<Packet4f>(falpha);
+
+  ei_declare_aligned_stack_constructed_variable(float, result, rows, 0);
+  if (resIncr == 1) {
+    convertArrayPointerBF16toF32(result, 1, rows, res);
+  } else {
+    convertArrayPointerBF16toF32<true>(result, 1, rows, res, resIncr);
+  }
+  calcVSXVecLoops<LhsMapper, LinearMapper>(cols, rows, lhs, rhs2, pAlpha, result);
+  if (resIncr == 1) {
+    convertArrayPointerF32toBF16VSX(result, rows, res);
+  } else {
+    convertArrayPointerF32toBF16VSX<true>(result, rows, res, resIncr);
+  }
+}
+
+#undef MAX_BFLOAT16_VEC_ACC_VSX
+
+const Packet16uc p16uc_COMPLEX32_XORFLIP = {0x44, 0x55, 0x66, 0x77, 0x00, 0x11, 0x22, 0x33,
+                                            0xcc, 0xdd, 0xee, 0xff, 0x88, 0x99, 0xaa, 0xbb};
+const Packet16uc p16uc_COMPLEX64_XORFLIP = {0x88, 0x99, 0xaa, 0xbb, 0xcc, 0xdd, 0xee, 0xff,
+                                            0x00, 0x11, 0x22, 0x33, 0x44, 0x55, 0x66, 0x77};
+
+#ifdef _BIG_ENDIAN
+const Packet16uc p16uc_COMPLEX32_CONJ_XOR = {0x00, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00,
+                                             0x00, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00};
+const Packet16uc p16uc_COMPLEX64_CONJ_XOR = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                                             0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+const Packet16uc p16uc_COMPLEX32_CONJ_XOR2 = {0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                                              0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+const Packet16uc p16uc_COMPLEX64_CONJ_XOR2 = {0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                                              0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+const Packet16uc p16uc_COMPLEX32_NEGATE = {0x80, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00,
+                                           0x80, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00};
+const Packet16uc p16uc_COMPLEX64_NEGATE = {0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                                           0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+#else
+const Packet16uc p16uc_COMPLEX32_CONJ_XOR = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80,
+                                             0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80};
+const Packet16uc p16uc_COMPLEX64_CONJ_XOR = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+                                             0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80};
+const Packet16uc p16uc_COMPLEX32_CONJ_XOR2 = {0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x00,
+                                              0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x00};
+const Packet16uc p16uc_COMPLEX64_CONJ_XOR2 = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80,
+                                              0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+const Packet16uc p16uc_COMPLEX32_NEGATE = {0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x80,
+                                           0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x80};
+const Packet16uc p16uc_COMPLEX64_NEGATE = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80,
+                                           0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80};
+#endif
+
+#ifdef _BIG_ENDIAN
+#define COMPLEX_DELTA 0
+#else
+#define COMPLEX_DELTA 2
+#endif
+
+/** \internal packet conjugate (same as pconj but uses the constants in pcplxflipconj for better code generation) */
+EIGEN_ALWAYS_INLINE Packet2cf pconj2(const Packet2cf& a) {
+  return Packet2cf(pxor(a.v, reinterpret_cast<Packet4f>(p16uc_COMPLEX32_CONJ_XOR)));
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pconj2(const Packet1cd& a) {
+  return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p16uc_COMPLEX64_CONJ_XOR)));
+}
+
+/** \internal packet conjugate with real & imaginary operation inverted */
+EIGEN_ALWAYS_INLINE Packet2cf pconjinv(const Packet2cf& a) {
+#ifdef __POWER8_VECTOR__
+  return Packet2cf(Packet4f(vec_neg(Packet2d(a.v))));
+#else
+  return Packet2cf(pxor(a.v, reinterpret_cast<Packet4f>(p16uc_COMPLEX32_CONJ_XOR2)));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pconjinv(const Packet1cd& a) {
+  return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p16uc_COMPLEX64_CONJ_XOR2)));
+}
+
+#if defined(_ARCH_PWR8) && (!EIGEN_COMP_LLVM || __clang_major__ >= 12)
+#define PERMXOR_GOOD  // Clang had a bug with vec_permxor and endianness prior to version 12
+#endif
+
+/** \internal flip the real & imaginary results and packet conjugate */
+EIGEN_ALWAYS_INLINE Packet2cf pcplxflipconj(Packet2cf a) {
+#ifdef PERMXOR_GOOD
+  return Packet2cf(Packet4f(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX32_CONJ_XOR, p16uc_COMPLEX32_XORFLIP)));
+#else
+  return pcplxflip(pconj2(a));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pcplxflipconj(Packet1cd a) {
+#ifdef PERMXOR_GOOD
+  return Packet1cd(Packet2d(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX64_CONJ_XOR, p16uc_COMPLEX64_XORFLIP)));
+#else
+  return pcplxflip(pconj2(a));
+#endif
+}
+
+/** \internal packet conjugate and flip the real & imaginary results */
+EIGEN_ALWAYS_INLINE Packet2cf pcplxconjflip(Packet2cf a) {
+#ifdef PERMXOR_GOOD
+  return Packet2cf(Packet4f(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX32_CONJ_XOR2, p16uc_COMPLEX32_XORFLIP)));
+#else
+  return pconj2(pcplxflip(a));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pcplxconjflip(Packet1cd a) {
+#ifdef PERMXOR_GOOD
+  return Packet1cd(Packet2d(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX64_CONJ_XOR2, p16uc_COMPLEX64_XORFLIP)));
+#else
+  return pconj2(pcplxflip(a));
+#endif
+}
+
+/** \internal packet negate */
+EIGEN_ALWAYS_INLINE Packet2cf pnegate2(Packet2cf a) {
+#ifdef __POWER8_VECTOR__
+  return Packet2cf(vec_neg(a.v));
+#else
+  return Packet2cf(pxor(a.v, reinterpret_cast<Packet4f>(p16uc_COMPLEX32_NEGATE)));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pnegate2(Packet1cd a) {
+#ifdef __POWER8_VECTOR__
+  return Packet1cd(vec_neg(a.v));
+#else
+  return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p16uc_COMPLEX64_NEGATE)));
+#endif
+}
+
+/** \internal flip the real & imaginary results and negate */
+EIGEN_ALWAYS_INLINE Packet2cf pcplxflipnegate(Packet2cf a) {
+#ifdef PERMXOR_GOOD
+  return Packet2cf(Packet4f(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX32_NEGATE, p16uc_COMPLEX32_XORFLIP)));
+#else
+  return pcplxflip(pnegate2(a));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pcplxflipnegate(Packet1cd a) {
+#ifdef PERMXOR_GOOD
+  return Packet1cd(Packet2d(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX64_NEGATE, p16uc_COMPLEX64_XORFLIP)));
+#else
+  return pcplxflip(pnegate2(a));
+#endif
+}
+
+/** \internal flip the real & imaginary results */
+EIGEN_ALWAYS_INLINE Packet2cf pcplxflip2(Packet2cf a) {
+  return Packet2cf(Packet4f(vec_perm(Packet16uc(a.v), Packet16uc(a.v), p16uc_COMPLEX32_XORFLIP)));
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pcplxflip2(Packet1cd a) {
+#ifdef EIGEN_VECTORIZE_VSX
+  return Packet1cd(__builtin_vsx_xxpermdi(a.v, a.v, 2));
+#else
+  return Packet1cd(Packet2d(vec_perm(Packet16uc(a.v), Packet16uc(a.v), p16uc_COMPLEX64_XORFLIP)));
+#endif
+}
+
+/** \internal load half a vector with one complex value */
+EIGEN_ALWAYS_INLINE Packet4f pload_complex_half(std::complex<float>* src) {
+  Packet4f t;
+#ifdef EIGEN_VECTORIZE_VSX
+  // Load float64/two float32 (doubleword alignment)
+  __asm__("lxsdx %x0,%y1" : "=wa"(t) : "Z"(*src));
+#else
+  *reinterpret_cast<std::complex<float>*>(reinterpret_cast<float*>(&t) + COMPLEX_DELTA) = *src;
+#endif
+  return t;
+}
+
+/** \internal load two vectors from the real and imaginary portions of a complex value */
+template <typename RhsScalar>
+EIGEN_ALWAYS_INLINE void pload_realimag(RhsScalar* src, Packet4f& r, Packet4f& i) {
+#ifdef _ARCH_PWR9
+  __asm__("lxvwsx %x0,%y1" : "=wa"(r) : "Z"(*(reinterpret_cast<float*>(src) + 0)));
+  __asm__("lxvwsx %x0,%y1" : "=wa"(i) : "Z"(*(reinterpret_cast<float*>(src) + 1)));
+#else
+  Packet4f t = pload_complex_half(src);
+  r = vec_splat(t, COMPLEX_DELTA + 0);
+  i = vec_splat(t, COMPLEX_DELTA + 1);
+#endif
+}
+
+template <typename RhsScalar>
+EIGEN_ALWAYS_INLINE void pload_realimag(RhsScalar* src, Packet2d& r, Packet2d& i) {
+#ifdef EIGEN_VECTORIZE_VSX
+  __asm__("lxvdsx %x0,%y1" : "=wa"(r) : "Z"(*(reinterpret_cast<double*>(src) + 0)));
+  __asm__("lxvdsx %x0,%y1" : "=wa"(i) : "Z"(*(reinterpret_cast<double*>(src) + 1)));
+#else
+  Packet2d t = ploadu<Packet2d>(reinterpret_cast<double*>(src));
+  r = vec_splat(t, 0);
+  i = vec_splat(t, 1);
+#endif
+}
+
+#ifndef __POWER8_VECTOR__
+const Packet16uc p16uc_MERGEE = {0x00, 0x01, 0x02, 0x03, 0x10, 0x11, 0x12, 0x13,
+                                 0x08, 0x09, 0x0A, 0x0B, 0x18, 0x19, 0x1A, 0x1B};
+
+const Packet16uc p16uc_MERGEO = {0x04, 0x05, 0x06, 0x07, 0x14, 0x15, 0x16, 0x17,
+                                 0x0C, 0x0D, 0x0E, 0x0F, 0x1C, 0x1D, 0x1E, 0x1F};
+#endif
+
+/** \internal load two vectors from the interleaved real & imaginary values of src */
+template <typename RhsScalar>
+EIGEN_ALWAYS_INLINE void pload_realimag_row(RhsScalar* src, Packet4f& r, Packet4f& i) {
+  Packet4f t = ploadu<Packet4f>(reinterpret_cast<float*>(src));
+#ifdef __POWER8_VECTOR__
+  r = vec_mergee(t, t);
+  i = vec_mergeo(t, t);
+#else
+  r = vec_perm(t, t, p16uc_MERGEE);
+  i = vec_perm(t, t, p16uc_MERGEO);
+#endif
+}
+
+template <typename RhsScalar>
+EIGEN_ALWAYS_INLINE void pload_realimag_row(RhsScalar* src, Packet2d& r, Packet2d& i) {
+  return pload_realimag(src, r, i);
+}
+
+/** \internal load and splat a complex value into a vector - column-wise */
+EIGEN_ALWAYS_INLINE Packet4f pload_realimag_combine(std::complex<float>* src) {
+#ifdef EIGEN_VECTORIZE_VSX
+  Packet4f ret;
+  __asm__("lxvdsx %x0,%y1" : "=wa"(ret) : "Z"(*(reinterpret_cast<double*>(src) + 0)));
+  return ret;
+#else
+  return Packet4f(ploaddup<Packet2d>(reinterpret_cast<double*>(src)));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_realimag_combine(std::complex<double>* src) { return ploadu<Packet1cd>(src).v; }
+
+/** \internal load a complex value into a vector - row-wise */
+EIGEN_ALWAYS_INLINE Packet4f pload_realimag_combine_row(std::complex<float>* src) { return ploadu<Packet2cf>(src).v; }
+
+EIGEN_ALWAYS_INLINE Packet2d pload_realimag_combine_row(std::complex<double>* src) { return ploadu<Packet1cd>(src).v; }
+
+/** \internal load a scalar or a vector from complex location */
+template <typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet4f pload_complex(std::complex<float>* src) {
+  if (GEMV_IS_SCALAR) {
+    return pload_complex_half(src);
+  } else {
+    return ploadu<Packet4f>(reinterpret_cast<float*>(src));
+  }
+}
+
+template <typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet2d pload_complex(std::complex<double>* src) {
+  return ploadu<Packet2d>(reinterpret_cast<double*>(src));
+}
+
+/** \internal load from a complex vector and convert to a real vector */
+template <typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet4f pload_complex(Packet2cf* src) {
+  return src->v;
+}
+
+template <typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet2d pload_complex(Packet1cd* src) {
+  return src->v;
+}
+
+/** \internal load a full vector from complex location - column-wise */
+EIGEN_ALWAYS_INLINE Packet4f pload_complex_full(std::complex<float>* src) {
+  return Packet4f(ploaddup<Packet2d>(reinterpret_cast<double*>(src)));
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_complex_full(std::complex<double>* src) { return ploadu<Packet1cd>(src).v; }
+
+/** \internal load a full vector from complex location - row-wise */
+EIGEN_ALWAYS_INLINE Packet4f pload_complex_full_row(std::complex<float>* src) { return ploadu<Packet2cf>(src).v; }
+
+EIGEN_ALWAYS_INLINE Packet2d pload_complex_full_row(std::complex<double>* src) { return pload_complex_full(src); }
+
+/** \internal load a vector from a real-only scalar location - column-wise */
+EIGEN_ALWAYS_INLINE Packet4f pload_real(float* src) { return pset1<Packet4f>(*src); }
+
+EIGEN_ALWAYS_INLINE Packet2d pload_real(double* src) { return pset1<Packet2d>(*src); }
+
+EIGEN_ALWAYS_INLINE Packet4f pload_real(Packet4f& src) { return src; }
+
+EIGEN_ALWAYS_INLINE Packet2d pload_real(Packet2d& src) { return src; }
+
+/** \internal load a vector from a real-only vector location */
+EIGEN_ALWAYS_INLINE Packet4f pload_real_full(float* src) {
+  Packet4f ret = ploadu<Packet4f>(src);
+  return vec_mergeh(ret, ret);
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_real_full(double* src) { return pload_real(src); }
+
+EIGEN_ALWAYS_INLINE Packet4f pload_real_full(std::complex<float>* src) {
+  return pload_complex_full(src);  // Just for compilation
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_real_full(std::complex<double>* src) {
+  return pload_complex_full(src);  // Just for compilation
+}
+
+/** \internal load a vector from a real-only scalar location - row-wise */
+template <typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet4f pload_real_row(float* src) {
+  if (GEMV_IS_SCALAR) {
+    return pload_real_full(src);
+  } else {
+    return ploadu<Packet4f>(src);
+  }
+}
+
+template <typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet2d pload_real_row(double* src) {
+  return pload_real(src);
+}
+
+EIGEN_ALWAYS_INLINE Packet2cf padd(Packet2cf& a, std::complex<float>& b) {
+  EIGEN_UNUSED_VARIABLE(b);
+  return a;  // Just for compilation
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd padd(Packet1cd& a, std::complex<double>& b) {
+  EIGEN_UNUSED_VARIABLE(b);
+  return a;  // Just for compilation
+}
+
+/** \internal set a scalar from complex location */
+template <typename Scalar, typename ResScalar>
+EIGEN_ALWAYS_INLINE Scalar pset1_realimag(ResScalar& alpha, int which, int conj) {
+  return (which) ? ((conj) ? -alpha.real() : alpha.real()) : ((conj) ? -alpha.imag() : alpha.imag());
+}
+
+/** \internal set a vector from complex location */
+template <typename Scalar, typename ResScalar, typename ResPacket, int which>
+EIGEN_ALWAYS_INLINE Packet2cf pset1_complex(std::complex<float>& alpha) {
+  Packet2cf ret;
+  ret.v[COMPLEX_DELTA + 0] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x01), (which & 0x04));
+  ret.v[COMPLEX_DELTA + 1] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x02), (which & 0x08));
+  ret.v[2 - COMPLEX_DELTA] = ret.v[COMPLEX_DELTA + 0];
+  ret.v[3 - COMPLEX_DELTA] = ret.v[COMPLEX_DELTA + 1];
+  return ret;
+}
+
+template <typename Scalar, typename ResScalar, typename ResPacket, int which>
+EIGEN_ALWAYS_INLINE Packet1cd pset1_complex(std::complex<double>& alpha) {
+  Packet1cd ret;
+  ret.v[0] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x01), (which & 0x04));
+  ret.v[1] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x02), (which & 0x08));
+  return ret;
+}
+
+/** \internal zero out a vector for real or complex forms */
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet pset_zero() {
+  return pset1<Packet>(__UNPACK_TYPE__(Packet)(0));
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet2cf pset_zero<Packet2cf>() {
+  return Packet2cf(pset1<Packet4f>(float(0)));
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet1cd pset_zero<Packet1cd>() {
+  return Packet1cd(pset1<Packet2d>(double(0)));
+}
+
+/** \internal initialize a vector from another vector */
+template <typename Packet, typename LhsPacket, typename RhsPacket>
+EIGEN_ALWAYS_INLINE Packet pset_init(Packet& c1) {
+  if (GEMV_IS_COMPLEX_COMPLEX) {
+    EIGEN_UNUSED_VARIABLE(c1);
+    return pset_zero<Packet>();
+  } else {
+    return c1;  // Intentionally left uninitialized
+  }
+}
+
+template <typename PResPacket, typename ResPacket, typename ResScalar, typename Scalar>
+struct alpha_store {
+  alpha_store(ResScalar& alpha) {
+    separate.r = pset1_complex<Scalar, ResScalar, ResPacket, 0x3>(alpha);
+    separate.i = pset1_complex<Scalar, ResScalar, ResPacket, 0x0>(alpha);
+  }
+  struct ri {
+    PResPacket r;
+    PResPacket i;
+  } separate;
+};
+
+/** \internal multiply and add for complex math */
+template <typename ScalarPacket, typename AlphaData>
+EIGEN_ALWAYS_INLINE ScalarPacket pmadd_complex(ScalarPacket& c0, ScalarPacket& c2, ScalarPacket& c4, AlphaData& b0) {
+  return pmadd(c2, b0.separate.i.v, pmadd(c0, b0.separate.r.v, c4));
+}
+
+/** \internal store and madd for complex math */
+template <typename Scalar, typename ScalarPacket, typename PResPacket, typename ResPacket, typename ResScalar,
+          typename AlphaData>
+EIGEN_ALWAYS_INLINE void pstoreu_pmadd_complex(PResPacket& c0, AlphaData& b0, ResScalar* res) {
+  PResPacket c2 = pcplxflipconj(c0);
+  if (GEMV_IS_SCALAR) {
+    ScalarPacket c4 = ploadu<ScalarPacket>(reinterpret_cast<Scalar*>(res));
+    ScalarPacket c3 = pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c4, b0);
+    pstoreu(reinterpret_cast<Scalar*>(res), c3);
+  } else {
+    ScalarPacket c4 = pload_complex<ResPacket>(res);
+    PResPacket c3 = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c4, b0));
+    pstoreu(res, c3);
+  }
+}
+
+template <typename ScalarPacket, typename PResPacket, typename ResPacket, typename ResScalar, typename AlphaData,
+          Index ResPacketSize, Index iter2>
+EIGEN_ALWAYS_INLINE void pstoreu_pmadd_complex(PResPacket& c0, PResPacket& c1, AlphaData& b0, ResScalar* res) {
+  PResPacket c2 = pcplxflipconj(c0);
+  PResPacket c3 = pcplxflipconj(c1);
+#if !defined(_ARCH_PWR10)
+  ScalarPacket c4 = pload_complex<ResPacket>(res + (iter2 * ResPacketSize));
+  ScalarPacket c5 = pload_complex<ResPacket>(res + ((iter2 + 1) * ResPacketSize));
+  PResPacket c6 = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c4, b0));
+  PResPacket c7 = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c1.v, c3.v, c5, b0));
+  pstoreu(res + (iter2 * ResPacketSize), c6);
+  pstoreu(res + ((iter2 + 1) * ResPacketSize), c7);
+#else
+  __vector_pair a = *reinterpret_cast<__vector_pair*>(res + (iter2 * ResPacketSize));
+#if EIGEN_COMP_LLVM
+  PResPacket c6[2];
+  __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(c6), &a);
+  c6[0] = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c6[0].v, b0));
+  c6[1] = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c1.v, c3.v, c6[1].v, b0));
+  GEMV_BUILDPAIR_MMA(a, c6[0].v, c6[1].v);
+#else
+  if (GEMV_IS_COMPLEX_FLOAT) {
+    __asm__("xvmaddasp %L0,%x1,%x2\n\txvmaddasp %0,%x1,%x3" : "+&d"(a) : "wa"(b0.separate.r.v), "wa"(c0.v), "wa"(c1.v));
+    __asm__("xvmaddasp %L0,%x1,%x2\n\txvmaddasp %0,%x1,%x3" : "+&d"(a) : "wa"(b0.separate.i.v), "wa"(c2.v), "wa"(c3.v));
+  } else {
+    __asm__("xvmaddadp %L0,%x1,%x2\n\txvmaddadp %0,%x1,%x3" : "+&d"(a) : "wa"(b0.separate.r.v), "wa"(c0.v), "wa"(c1.v));
+    __asm__("xvmaddadp %L0,%x1,%x2\n\txvmaddadp %0,%x1,%x3" : "+&d"(a) : "wa"(b0.separate.i.v), "wa"(c2.v), "wa"(c3.v));
+  }
+#endif
+  *reinterpret_cast<__vector_pair*>(res + (iter2 * ResPacketSize)) = a;
+#endif
+}
+
+/** \internal load lhs packet */
+template <typename Scalar, typename LhsScalar, typename LhsMapper, typename LhsPacket>
+EIGEN_ALWAYS_INLINE LhsPacket loadLhsPacket(LhsMapper& lhs, Index i, Index j) {
+  if (sizeof(Scalar) == sizeof(LhsScalar)) {
+    const LhsScalar& src = lhs(i + 0, j);
+    return LhsPacket(pload_real_full(const_cast<LhsScalar*>(&src)));
+  }
+  return lhs.template load<LhsPacket, Unaligned>(i + 0, j);
+}
+
+/** \internal madd for complex times complex */
+template <typename ComplexPacket, typename RealPacket, bool ConjugateLhs, bool ConjugateRhs, bool Negate>
+EIGEN_ALWAYS_INLINE RealPacket pmadd_complex_complex(RealPacket& a, RealPacket& b, RealPacket& c) {
+  if (ConjugateLhs && ConjugateRhs) {
+    return vec_madd(a, pconj2(ComplexPacket(b)).v, c);
+  } else if (Negate && !ConjugateLhs && ConjugateRhs) {
+    return vec_nmsub(a, b, c);
+  } else {
+    return vec_madd(a, b, c);
+  }
+}
+
+/** \internal madd for complex times real */
+template <typename ComplexPacket, typename RealPacket, bool Conjugate>
+EIGEN_ALWAYS_INLINE RealPacket pmadd_complex_real(RealPacket& a, RealPacket& b, RealPacket& c) {
+  if (Conjugate) {
+    return vec_madd(a, pconj2(ComplexPacket(b)).v, c);
+  } else {
+    return vec_madd(a, b, c);
+  }
+}
+
+template <typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, bool ConjugateLhs,
+          bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_generic(LhsPacket& a0, RhsScalar* b, PResPacket& c0) {
+  conj_helper<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs> pcj;
+  RhsPacket b0;
+  if (StorageOrder == ColMajor) {
+    b0 = pset1<RhsPacket>(*b);
+  } else {
+    b0 = ploadu<RhsPacket>(b);
+  }
+  c0 = pcj.pmadd(a0, b0, c0);
+}
+
+/** \internal core multiply operation for vectors - complex times complex */
+template <typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket,
+          typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_complex(LhsPacket& a0, RhsScalar* b, PResPacket& c0, ResPacket& c1) {
+  ScalarPacket br, bi;
+  if (StorageOrder == ColMajor) {
+    pload_realimag<RhsScalar>(b, br, bi);
+  } else {
+    pload_realimag_row<RhsScalar>(b, br, bi);
+  }
+  if (ConjugateLhs && !ConjugateRhs) a0 = pconj2(a0);
+  LhsPacket a1 = pcplxflipconj(a0);
+  ScalarPacket cr = pmadd_complex_complex<LhsPacket, ScalarPacket, ConjugateLhs, ConjugateRhs, false>(a0.v, br, c0.v);
+  ScalarPacket ci = pmadd_complex_complex<LhsPacket, ScalarPacket, ConjugateLhs, ConjugateRhs, true>(a1.v, bi, c1.v);
+  c1 = ResPacket(ci);
+  c0 = PResPacket(cr);
+}
+
+/** \internal core multiply operation for vectors - real times complex */
+template <typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket,
+          typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_real_complex(LhsPacket& a0, RhsScalar* b, PResPacket& c0) {
+  ScalarPacket b0;
+  if (StorageOrder == ColMajor) {
+    b0 = pload_complex_full(b);
+  } else {
+    b0 = pload_complex_full_row(b);
+  }
+  ScalarPacket cri = pmadd_complex_real<PResPacket, ScalarPacket, ConjugateRhs>(a0, b0, c0.v);
+  c0 = PResPacket(cri);
+}
+
+/** \internal core multiply operation for vectors - complex times real */
+template <typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket,
+          typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_real(LhsPacket& a0, RhsScalar* b, PResPacket& c0) {
+  ScalarPacket a1 = pload_complex<ResPacket>(&a0);
+  ScalarPacket b0;
+  if (StorageOrder == ColMajor) {
+    b0 = pload_real(b);
+  } else {
+    b0 = pload_real_row<ResPacket>(b);
+  }
+  ScalarPacket cri = pmadd_complex_real<PResPacket, ScalarPacket, ConjugateLhs>(a1, b0, c0.v);
+  c0 = PResPacket(cri);
+}
+
+#define GEMV_MULT_COMPLEX_COMPLEX(LhsType, RhsType, ResType)                                                        \
+  template <typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, \
+            typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>                             \
+  EIGEN_ALWAYS_INLINE void gemv_mult_complex(LhsType& a0, RhsType* b, ResType& c0, ResType& c1) {                   \
+    gemv_mult_complex_complex<ScalarPacket, LhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs,   \
+                              ConjugateRhs, StorageOrder>(a0, b, c0, c1);                                           \
+  }
+
+GEMV_MULT_COMPLEX_COMPLEX(Packet2cf, std::complex<float>, Packet2cf)
+GEMV_MULT_COMPLEX_COMPLEX(Packet1cd, std::complex<double>, Packet1cd)
+
+#define GEMV_MULT_REAL_COMPLEX(LhsType, RhsType, ResType)                                                           \
+  template <typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, \
+            typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>                             \
+  EIGEN_ALWAYS_INLINE void gemv_mult_complex(LhsType& a0, RhsType* b, ResType& c0, RhsType&) {                      \
+    gemv_mult_real_complex<ScalarPacket, LhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs,      \
+                           ConjugateRhs, StorageOrder>(a0, b, c0);                                                  \
+  }
+
+GEMV_MULT_REAL_COMPLEX(float, std::complex<float>, Packet2cf)
+GEMV_MULT_REAL_COMPLEX(double, std::complex<double>, Packet1cd)
+GEMV_MULT_REAL_COMPLEX(Packet4f, std::complex<float>, Packet2cf)
+GEMV_MULT_REAL_COMPLEX(Packet2d, std::complex<double>, Packet1cd)
+
+#define GEMV_MULT_COMPLEX_REAL(LhsType, RhsType, ResType1, ResType2)                                                \
+  template <typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, \
+            typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>                             \
+  EIGEN_ALWAYS_INLINE void gemv_mult_complex(LhsType& a0, RhsType* b, ResType1& c0, ResType2&) {                    \
+    gemv_mult_complex_real<ScalarPacket, LhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs,      \
+                           ConjugateRhs, StorageOrder>(a0, b, c0);                                                  \
+  }
+
+GEMV_MULT_COMPLEX_REAL(Packet2cf, float, Packet2cf, std::complex<float>)
+GEMV_MULT_COMPLEX_REAL(Packet1cd, double, Packet1cd, std::complex<double>)
+GEMV_MULT_COMPLEX_REAL(std::complex<float>, float, Packet2cf, std::complex<float>)
+GEMV_MULT_COMPLEX_REAL(std::complex<double>, double, Packet1cd, std::complex<double>)
+
+#ifdef USE_GEMV_MMA
+/** \internal convert packet to real form */
+template <typename T>
+EIGEN_ALWAYS_INLINE T convertReal(T a) {
+  return a;
+}
+
+EIGEN_ALWAYS_INLINE Packet4f convertReal(Packet2cf a) { return a.v; }
+
+EIGEN_ALWAYS_INLINE Packet2d convertReal(Packet1cd a) { return a.v; }
+
+/** \internal convert packet to complex form */
+template <typename T>
+EIGEN_ALWAYS_INLINE T convertComplex(T a) {
+  return a;
+}
+
+EIGEN_ALWAYS_INLINE Packet2cf convertComplex(Packet4f a) { return Packet2cf(a); }
+
+EIGEN_ALWAYS_INLINE Packet1cd convertComplex(Packet2d a) { return Packet1cd(a); }
+
+/** \internal load a vector from a complex location (for MMA version) */
+template <typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename ResPacket>
+EIGEN_ALWAYS_INLINE void pload_complex_MMA(SLhsPacket& a) {
+  a = SLhsPacket(pload_complex<ResPacket>(&a));
+}
+
+template <typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename ResPacket>
+EIGEN_ALWAYS_INLINE void pload_complex_MMA(__vector_pair&) {
+  // Pass thru
+}
+
+/** \internal perform a matrix multiply and accumulate (positive and negative) of packet a and packet b */
+template <typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger_vecMMA(__vector_quad* acc, RhsPacket& a, LhsPacket& b) {
+  if (NegativeAccumulate) {
+    __builtin_mma_xvf32gernp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+  } else {
+    __builtin_mma_xvf32gerpp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+  }
+}
+
+/** \internal perform a matrix multiply and accumulate (positive and negative) of vector_pair a and packet b */
+template <typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger_vecMMA(__vector_quad* acc, __vector_pair& a, Packet2d& b) {
+  if (NegativeAccumulate) {
+    __builtin_mma_xvf64gernp(acc, (__vector_pair)a, (__vector unsigned char)b);
+  } else {
+    __builtin_mma_xvf64gerpp(acc, (__vector_pair)a, (__vector unsigned char)b);
+  }
+}
+
+template <typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger_vecMMA(__vector_quad*, __vector_pair&, Packet4f&) {
+  // Just for compilation
+}
+
+/** \internal madd for complex times complex (MMA version) */
+template <typename RealPacket, typename LhsPacket, bool ConjugateLhs, bool ConjugateRhs, bool Negate>
+EIGEN_ALWAYS_INLINE void pmadd_complex_complex_MMA(LhsPacket& a, RealPacket& b, __vector_quad* c) {
+  if (ConjugateLhs && ConjugateRhs) {
+    RealPacket b2 = pconj2(convertComplex(b)).v;
+    return pger_vecMMA<RealPacket, RealPacket, false>(c, b2, a.v);
+  } else if (Negate && !ConjugateLhs && ConjugateRhs) {
+    return pger_vecMMA<RealPacket, RealPacket, true>(c, b, a.v);
+  } else {
+    return pger_vecMMA<RealPacket, RealPacket, false>(c, b, a.v);
+  }
+}
+
+template <typename RealPacket, typename LhsPacket, bool ConjugateLhs, bool ConjugateRhs, bool Negate>
+EIGEN_ALWAYS_INLINE void pmadd_complex_complex_MMA(__vector_pair& a, RealPacket& b, __vector_quad* c) {
+  if (ConjugateLhs && ConjugateRhs) {
+    RealPacket b2 = pconj2(convertComplex(b)).v;
+    return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b2);
+  } else if (Negate && !ConjugateLhs && ConjugateRhs) {
+    return pger_vecMMA<RealPacket, __vector_pair, true>(c, a, b);
+  } else {
+    return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b);
+  }
+}
+
+/** \internal madd for complex times real (MMA version) */
+template <typename RealPacket, typename LhsPacket, bool Conjugate, int StorageOrder>
+EIGEN_ALWAYS_INLINE void pmadd_complex_real_MMA(LhsPacket& a, RealPacket& b, __vector_quad* c) {
+  RealPacket a2 = convertReal(a);
+  if (Conjugate) {
+    RealPacket b2 = pconj2(convertComplex(b)).v;
+    if (StorageOrder == ColMajor) {
+      return pger_vecMMA<RealPacket, RealPacket, false>(c, b2, a2);
+    } else {
+      return pger_vecMMA<RealPacket, RealPacket, false>(c, a2, b2);
+    }
+  } else {
+    if (StorageOrder == ColMajor) {
+      return pger_vecMMA<RealPacket, RealPacket, false>(c, b, a2);
+    } else {
+      return pger_vecMMA<RealPacket, RealPacket, false>(c, a2, b);
+    }
+  }
+}
+
+/** \internal madd for real times complex (MMA version) */
+template <typename RealPacket, typename LhsPacket, bool Conjugate, int StorageOrder>
+EIGEN_ALWAYS_INLINE void pmadd_complex_real_MMA(__vector_pair& a, RealPacket& b, __vector_quad* c) {
+  if (Conjugate) {
+    RealPacket b2 = pconj2(convertComplex(b)).v;
+    return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b2);
+  } else {
+    return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b);
+  }
+}
+
+/** \internal core multiply operation for vectors (MMA version) - complex times complex */
+template <typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename ResPacket,
+          bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_complex_MMA(SLhsPacket& a0, RhsScalar* b, __vector_quad* c0) {
+  ScalarPacket b0;
+  if (StorageOrder == ColMajor) {
+    b0 = pload_realimag_combine(b);
+  } else {
+    b0 = pload_realimag_combine_row(b);
+  }
+  pmadd_complex_complex_MMA<ScalarPacket, LhsPacket, ConjugateLhs, ConjugateRhs, false>(a0, b0, c0);
+}
+
+/** \internal core multiply operation for vectors (MMA version) - complex times real */
+template <typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename ResPacket,
+          bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_real_MMA(SLhsPacket& a0, RhsScalar* b, __vector_quad* c0) {
+  pload_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, ResPacket>(a0);
+  ScalarPacket b0;
+  if (StorageOrder == ColMajor) {
+    b0 = pload_real(b);
+  } else {
+    b0 = pload_real_row<ResPacket>(b);
+  }
+  pmadd_complex_real_MMA<ScalarPacket, LhsPacket, ConjugateLhs, ColMajor>(a0, b0, c0);
+}
+
+/** \internal core multiply operation for vectors (MMA version) - real times complex */
+template <typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename ResPacket,
+          bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_real_complex_MMA(SLhsPacket& a0, RhsScalar* b, __vector_quad* c0) {
+  ScalarPacket b0;
+  if (StorageOrder == ColMajor) {
+    b0 = pload_complex_full(b);
+  } else {
+    b0 = pload_complex_full_row(b);
+  }
+  pmadd_complex_real_MMA<ScalarPacket, LhsPacket, ConjugateRhs,
+                         (sizeof(RhsScalar) == sizeof(std::complex<float>)) ? StorageOrder : ColMajor>(a0, b0, c0);
+}
+
+#define GEMV_MULT_COMPLEX_COMPLEX_MMA(LhsType, RhsType)                                                             \
+  template <typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar, \
+            typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>         \
+  EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(LhsType& a0, RhsType* b, __vector_quad* c0) {                      \
+    gemv_mult_complex_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs,          \
+                                  ConjugateRhs, StorageOrder>(a0, b, c0);                                           \
+  }
+
+GEMV_MULT_COMPLEX_COMPLEX_MMA(Packet2cf, std::complex<float>)
+GEMV_MULT_COMPLEX_COMPLEX_MMA(__vector_pair, std::complex<float>)
+GEMV_MULT_COMPLEX_COMPLEX_MMA(Packet1cd, std::complex<double>)
+
+/** \internal core multiply operation for vectors (MMA version) - complex times complex */
+template <typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar,
+          typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(__vector_pair& a0, std::complex<double>* b, __vector_quad* c0) {
+  if (sizeof(LhsScalar) == 16) {
+    gemv_mult_complex_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs,
+                                  StorageOrder>(a0, b, c0);
+  } else {
+    gemv_mult_real_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs,
+                               StorageOrder>(a0, b, c0);
+  }
+}
+
+#define GEMV_MULT_REAL_COMPLEX_MMA(LhsType, RhsType)                                                                  \
+  template <typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar,   \
+            typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>           \
+  EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(LhsType& a0, RhsType* b, __vector_quad* c0) {                        \
+    gemv_mult_real_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, \
+                               StorageOrder>(a0, b, c0);                                                              \
+  }
+
+GEMV_MULT_REAL_COMPLEX_MMA(Packet4f, std::complex<float>)
+GEMV_MULT_REAL_COMPLEX_MMA(Packet2d, std::complex<double>)
+
+#define GEMV_MULT_COMPLEX_REAL_MMA(LhsType, RhsType)                                                                  \
+  template <typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar,   \
+            typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>           \
+  EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(LhsType& a0, RhsType* b, __vector_quad* c0) {                        \
+    gemv_mult_complex_real_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, \
+                               StorageOrder>(a0, b, c0);                                                              \
+  }
+
+GEMV_MULT_COMPLEX_REAL_MMA(Packet2cf, float)
+GEMV_MULT_COMPLEX_REAL_MMA(Packet1cd, double)
+GEMV_MULT_COMPLEX_REAL_MMA(__vector_pair, float)
+GEMV_MULT_COMPLEX_REAL_MMA(__vector_pair, double)
+
+/** \internal disassemble MMA accumulator results into packets */
+template <typename Scalar, typename ScalarPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs,
+          bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE void disassembleResults2(__vector_quad* c0, PacketBlock<ScalarPacket, 4>& result0) {
+  __builtin_mma_disassemble_acc(&result0.packet, c0);
+  if (sizeof(LhsPacket) == 16) {
+    if (sizeof(RhsPacket) == 16) {
+      ScalarPacket tmp0, tmp2;
+      tmp2 = vec_mergeh(result0.packet[2], result0.packet[3]);
+      tmp0 = vec_mergeh(result0.packet[0], result0.packet[1]);
+      result0.packet[3] = vec_mergel(result0.packet[3], result0.packet[2]);
+      result0.packet[1] = vec_mergel(result0.packet[1], result0.packet[0]);
+      result0.packet[2] = tmp2;
+      result0.packet[0] = tmp0;
+
+      if (ConjugateLhs) {
+        result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+        result0.packet[2] = pconj2(convertComplex(result0.packet[2])).v;
+      } else if (ConjugateRhs) {
+        result0.packet[1] = pconj2(convertComplex(result0.packet[1])).v;
+        result0.packet[3] = pconj2(convertComplex(result0.packet[3])).v;
+      } else {
+        result0.packet[1] = pconjinv(convertComplex(result0.packet[1])).v;
+        result0.packet[3] = pconjinv(convertComplex(result0.packet[3])).v;
+      }
+      result0.packet[0] = vec_add(result0.packet[0], result0.packet[1]);
+      result0.packet[2] = vec_add(result0.packet[2], result0.packet[3]);
+    } else {
+      result0.packet[0][1] = result0.packet[1][1];
+      result0.packet[2][1] = result0.packet[3][1];
+    }
+  }
+}
+
+template <typename Scalar, typename ScalarPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs,
+          bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE void disassembleResults4(__vector_quad* c0, PacketBlock<ScalarPacket, 4>& result0) {
+  __builtin_mma_disassemble_acc(&result0.packet, c0);
+  if (GEMV_IS_COMPLEX_COMPLEX) {
+    if (ConjugateLhs) {
+      result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+      result0.packet[1] = pcplxflip2(convertComplex(result0.packet[1])).v;
+    } else {
+      if (ConjugateRhs) {
+        result0.packet[1] = pcplxconjflip(convertComplex(result0.packet[1])).v;
+      } else {
+        result0.packet[1] = pcplxflipconj(convertComplex(result0.packet[1])).v;
+      }
+    }
+    result0.packet[0] = vec_add(result0.packet[0], result0.packet[1]);
+  } else if (sizeof(LhsPacket) == sizeof(std::complex<float>)) {
+    if (ConjugateLhs) {
+      result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+    }
+  } else {
+    result0.packet[0] = vec_mergee(result0.packet[0], result0.packet[1]);
+  }
+}
+
+template <typename Scalar, typename ScalarPacket, int ResPacketSize, typename LhsPacket, typename RhsPacket,
+          bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE void disassembleResults(__vector_quad* c0, PacketBlock<ScalarPacket, 4>& result0) {
+  if (!GEMV_IS_COMPLEX_FLOAT) {
+    disassembleResults2<Scalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(c0, result0);
+  } else {
+    disassembleResults4<Scalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(c0, result0);
+  }
+}
+#endif
+
+#define GEMV_GETN_COMPLEX(N) (((N) * ResPacketSize) >> 1)
+
+#define GEMV_LOADPACKET_COL_COMPLEX(iter) \
+  loadLhsPacket<Scalar, LhsScalar, LhsMapper, PLhsPacket>(lhs, i + ((iter) * ResPacketSize), j)
+
+#define GEMV_LOADPACKET_COL_COMPLEX_DATA(iter) convertReal(GEMV_LOADPACKET_COL_COMPLEX(iter))
+
+#ifdef USE_GEMV_MMA
+#define GEMV_INIT_COL_COMPLEX_MMA(iter, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter) {       \
+    __builtin_mma_xxsetaccz(&e0##iter);    \
+  }
+
+#if EIGEN_COMP_LLVM
+#define GEMV_LOADPAIR_COL_COMPLEX_MMA(iter1, iter2)                     \
+  GEMV_BUILDPAIR_MMA(a##iter1, GEMV_LOADPACKET_COL_COMPLEX_DATA(iter2), \
+                     GEMV_LOADPACKET_COL_COMPLEX_DATA((iter2) + 1));    \
+  EIGEN_UNUSED_VARIABLE(f##iter1);
+#else
+#define GEMV_LOADPAIR_COL_COMPLEX_MMA(iter1, iter2)                                                         \
+  if (sizeof(LhsPacket) == 16) {                                                                            \
+    const LhsScalar& src = lhs(i + ((32 * iter1) / sizeof(LhsScalar)), j);                                  \
+    a##iter1 = *reinterpret_cast<__vector_pair*>(const_cast<LhsScalar*>(&src));                             \
+    EIGEN_UNUSED_VARIABLE(f##iter1);                                                                        \
+  } else {                                                                                                  \
+    f##iter1 = lhs.template load<PLhsPacket, Unaligned>(i + ((iter2) * ResPacketSize), j);                  \
+    GEMV_BUILDPAIR_MMA(a##iter1, vec_splat(convertReal(f##iter1), 0), vec_splat(convertReal(f##iter1), 1)); \
+  }
+#endif
+
+#define GEMV_LOAD1_COL_COMPLEX_MMA(iter, N)          \
+  if (GEMV_GETN_COMPLEX(N) > iter) {                 \
+    if (GEMV_IS_COMPLEX_FLOAT) {                     \
+      f##iter = GEMV_LOADPACKET_COL_COMPLEX(iter);   \
+      EIGEN_UNUSED_VARIABLE(a##iter);                \
+    } else {                                         \
+      GEMV_LOADPAIR_COL_COMPLEX_MMA(iter, iter << 1) \
+    }                                                \
+  } else {                                           \
+    EIGEN_UNUSED_VARIABLE(a##iter);                  \
+    EIGEN_UNUSED_VARIABLE(f##iter);                  \
+  }
+
+#define GEMV_WORK1_COL_COMPLEX_MMA(iter, N)                                                                      \
+  if (GEMV_GETN_COMPLEX(N) > iter) {                                                                             \
+    if (GEMV_IS_COMPLEX_FLOAT) {                                                                                 \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket,    \
+                            ConjugateLhs, ConjugateRhs, ColMajor>(f##iter, b, &e0##iter);                        \
+    } else {                                                                                                     \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, \
+                            ConjugateLhs, ConjugateRhs, ColMajor>(a##iter, b, &e0##iter);                        \
+    }                                                                                                            \
+  }
+
+#define GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter1, iter2) \
+  GEMV_BUILDPAIR_MMA(a##iter1, GEMV_LOADPACKET_COL_COMPLEX_DATA(iter2), GEMV_LOADPACKET_COL_COMPLEX_DATA((iter2) + 1));
+
+#define GEMV_LOAD2_COL_COMPLEX_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter1) {                      \
+    if (GEMV_IS_COMPLEX_FLOAT) {                           \
+      GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter2, iter2);        \
+      EIGEN_UNUSED_VARIABLE(a##iter3)                      \
+    } else {                                               \
+      GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter2, iter2 << 1);   \
+      GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter3, iter3 << 1);   \
+    }                                                      \
+  } else {                                                 \
+    EIGEN_UNUSED_VARIABLE(a##iter2);                       \
+    EIGEN_UNUSED_VARIABLE(a##iter3);                       \
+  }                                                        \
+  EIGEN_UNUSED_VARIABLE(f##iter2);                         \
+  EIGEN_UNUSED_VARIABLE(f##iter3);
+
+#define GEMV_WORK2_COL_COMPLEX_MMA(iter1, iter2, iter3, N)                                                       \
+  if (GEMV_GETN_COMPLEX(N) > iter1) {                                                                            \
+    if (GEMV_IS_COMPLEX_FLOAT) {                                                                                 \
+      PLhsPacket g[2];                                                                                           \
+      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(g), &a##iter2);                                     \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket,    \
+                            ConjugateLhs, ConjugateRhs, ColMajor>(g[0], b, &e0##iter2);                          \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket,    \
+                            ConjugateLhs, ConjugateRhs, ColMajor>(g[1], b, &e0##iter3);                          \
+    } else {                                                                                                     \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, \
+                            ConjugateLhs, ConjugateRhs, ColMajor>(a##iter2, b, &e0##iter2);                      \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, \
+                            ConjugateLhs, ConjugateRhs, ColMajor>(a##iter3, b, &e0##iter3);                      \
+    }                                                                                                            \
+  }
+
+#if EIGEN_COMP_LLVM
+#define GEMV_LOAD_COL_COMPLEX_MMA(N)                       \
+  if (GEMV_GETN_COMPLEX(N) > 1) {                          \
+    GEMV_UNROLL_HALF(GEMV_LOAD2_COL_COMPLEX_MMA, (N >> 1)) \
+  } else {                                                 \
+    GEMV_UNROLL(GEMV_LOAD1_COL_COMPLEX_MMA, N)             \
+  }
+
+#define GEMV_WORK_COL_COMPLEX_MMA(N)                       \
+  if (GEMV_GETN_COMPLEX(N) > 1) {                          \
+    GEMV_UNROLL_HALF(GEMV_WORK2_COL_COMPLEX_MMA, (N >> 1)) \
+  } else {                                                 \
+    GEMV_UNROLL(GEMV_WORK1_COL_COMPLEX_MMA, N)             \
+  }
+#else
+#define GEMV_LOAD_COL_COMPLEX_MMA(N) GEMV_UNROLL(GEMV_LOAD1_COL_COMPLEX_MMA, N)
+
+#define GEMV_WORK_COL_COMPLEX_MMA(N) GEMV_UNROLL(GEMV_WORK1_COL_COMPLEX_MMA, N)
+#endif
+
+#define GEMV_DISASSEMBLE_COMPLEX_MMA(iter)                                                                   \
+  disassembleResults<Scalar, ScalarPacket, ResPacketSize, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>( \
+      &e0##iter, result0##iter);
+
+#define GEMV_STORE_COL_COMPLEX_MMA(iter, N)                                                     \
+  if (GEMV_GETN_COMPLEX(N) > iter) {                                                            \
+    GEMV_DISASSEMBLE_COMPLEX_MMA(iter);                                                         \
+    c0##iter = PResPacket(result0##iter.packet[0]);                                             \
+    if (GEMV_IS_COMPLEX_FLOAT) {                                                                \
+      pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>( \
+          c0##iter, alpha_data, res + i + (iter * ResPacketSize));                              \
+    } else {                                                                                    \
+      pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>( \
+          c0##iter, alpha_data, res + i + ((iter << 1) * ResPacketSize));                       \
+      c0##iter = PResPacket(result0##iter.packet[2]);                                           \
+      pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>( \
+          c0##iter, alpha_data, res + i + (((iter << 1) + 1) * ResPacketSize));                 \
+    }                                                                                           \
+  }
+
+#define GEMV_STORE2_COL_COMPLEX_MMA(iter1, iter2, iter3, N)                                                        \
+  if (GEMV_GETN_COMPLEX(N) > iter1) {                                                                              \
+    GEMV_DISASSEMBLE_COMPLEX_MMA(iter2);                                                                           \
+    GEMV_DISASSEMBLE_COMPLEX_MMA(iter3);                                                                           \
+    c0##iter2 = PResPacket(result0##iter2.packet[0]);                                                              \
+    if (GEMV_IS_COMPLEX_FLOAT) {                                                                                   \
+      c0##iter3 = PResPacket(result0##iter3.packet[0]);                                                            \
+      pstoreu_pmadd_complex<ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData, ResPacketSize, iter2>(      \
+          c0##iter2, c0##iter3, alpha_data, res + i);                                                              \
+    } else {                                                                                                       \
+      c0##iter3 = PResPacket(result0##iter2.packet[2]);                                                            \
+      pstoreu_pmadd_complex<ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData, ResPacketSize, iter2 << 1>( \
+          c0##iter2, c0##iter3, alpha_data, res + i);                                                              \
+      c0##iter2 = PResPacket(result0##iter3.packet[0]);                                                            \
+      c0##iter3 = PResPacket(result0##iter3.packet[2]);                                                            \
+      pstoreu_pmadd_complex<ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData, ResPacketSize, iter3 << 1>( \
+          c0##iter2, c0##iter3, alpha_data, res + i);                                                              \
+    }                                                                                                              \
+  }
+
+#define GEMV_PROCESS_COL_COMPLEX_ONE_MMA(N)                 \
+  GEMV_UNROLL(GEMV_INIT_COL_COMPLEX_MMA, N)                 \
+  Index j = j2;                                             \
+  do {                                                      \
+    const RhsScalar& b1 = rhs2(j, 0);                       \
+    RhsScalar* b = const_cast<RhsScalar*>(&b1);             \
+    GEMV_UNROLL(GEMV_PREFETCH, N)                           \
+    GEMV_LOAD_COL_COMPLEX_MMA(N)                            \
+    GEMV_WORK_COL_COMPLEX_MMA(N)                            \
+  } while (++j < jend);                                     \
+  if (GEMV_GETN(N) <= 2) {                                  \
+    GEMV_UNROLL(GEMV_STORE_COL_COMPLEX_MMA, N)              \
+  } else {                                                  \
+    GEMV_UNROLL_HALF(GEMV_STORE2_COL_COMPLEX_MMA, (N >> 1)) \
+  }                                                         \
+  i += (ResPacketSize * N);
+#endif
+
+#define GEMV_INIT_COMPLEX(iter, N)                                   \
+  if (N > iter) {                                                    \
+    c0##iter = pset_zero<PResPacket>();                              \
+    c1##iter = pset_init<ResPacket, LhsPacket, RhsPacket>(c1##iter); \
+  } else {                                                           \
+    EIGEN_UNUSED_VARIABLE(c0##iter);                                 \
+    EIGEN_UNUSED_VARIABLE(c1##iter);                                 \
+  }
+
+#define GEMV_WORK_COL_COMPLEX(iter, N)                                                                     \
+  if (N > iter) {                                                                                          \
+    f##iter = GEMV_LOADPACKET_COL_COMPLEX(iter);                                                           \
+    gemv_mult_complex<ScalarPacket, PLhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, \
+                      ConjugateRhs, ColMajor>(f##iter, b, c0##iter, c1##iter);                             \
+  } else {                                                                                                 \
+    EIGEN_UNUSED_VARIABLE(f##iter);                                                                        \
+  }
+
+#define GEMV_STORE_COL_COMPLEX(iter, N)                                                       \
+  if (N > iter) {                                                                             \
+    if (GEMV_IS_COMPLEX_COMPLEX) {                                                            \
+      c0##iter = padd(c0##iter, c1##iter);                                                    \
+    }                                                                                         \
+    pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>( \
+        c0##iter, alpha_data, res + i + (iter * ResPacketSize));                              \
+  }
+
+/** \internal main macro for gemv_complex_col - initialize accumulators, multiply and add inputs, and store results */
+#define GEMV_PROCESS_COL_COMPLEX_ONE(N)         \
+  GEMV_UNROLL(GEMV_INIT_COMPLEX, N)             \
+  Index j = j2;                                 \
+  do {                                          \
+    const RhsScalar& b1 = rhs2(j, 0);           \
+    RhsScalar* b = const_cast<RhsScalar*>(&b1); \
+    GEMV_UNROLL(GEMV_PREFETCH, N)               \
+    GEMV_UNROLL(GEMV_WORK_COL_COMPLEX, N)       \
+  } while (++j < jend);                         \
+  GEMV_UNROLL(GEMV_STORE_COL_COMPLEX, N)        \
+  i += (ResPacketSize * N);
+
+#if defined(USE_GEMV_MMA) && (EIGEN_COMP_LLVM || defined(USE_SLOWER_GEMV_MMA))
+#define USE_GEMV_COL_COMPLEX_MMA
+#endif
+
+#ifdef USE_GEMV_COL_COMPLEX_MMA
+#define GEMV_PROCESS_COL_COMPLEX(N) GEMV_PROCESS_COL_COMPLEX_ONE_MMA(N)
+#else
+#if defined(USE_GEMV_MMA) && (__GNUC__ > 10)
+#define GEMV_PROCESS_COL_COMPLEX(N)          \
+  if (sizeof(Scalar) != sizeof(LhsPacket)) { \
+    GEMV_PROCESS_COL_COMPLEX_ONE_MMA(N)      \
+  } else {                                   \
+    GEMV_PROCESS_COL_COMPLEX_ONE(N)          \
+  }
+#else
+#define GEMV_PROCESS_COL_COMPLEX(N) GEMV_PROCESS_COL_COMPLEX_ONE(N)
+#endif
+#endif
+
+template <typename Scalar, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, bool LhsIsReal,
+          typename RhsScalar, typename RhsMapper, bool ConjugateRhs, bool RhsIsReal, typename ResScalar>
+EIGEN_STRONG_INLINE void gemv_complex_col(Index rows, Index cols, const LhsMapper& alhs, const RhsMapper& rhs,
+                                          ResScalar* res, Index resIncr, ResScalar alpha) {
+  typedef gemv_traits<LhsScalar, RhsScalar> Traits;
+
+  typedef typename Traits::LhsPacket LhsPacket;
+  typedef typename Traits::RhsPacket RhsPacket;
+  typedef typename Traits::ResPacket ResPacket;
+
+  typedef typename packet_traits<Scalar>::type ScalarPacket;
+  typedef typename packet_traits<LhsScalar>::type PLhsPacket;
+  typedef typename packet_traits<ResScalar>::type PResPacket;
+  typedef gemv_traits<ResPacket, ResPacket> PTraits;
+
+  EIGEN_UNUSED_VARIABLE(resIncr);
+  eigen_internal_assert(resIncr == 1);
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  RhsMapper rhs2(rhs);
+
+  conj_helper<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs> cj;
+
+  const Index lhsStride = lhs.stride();
+  // TODO: for padded aligned inputs, we could enable aligned reads
+  enum {
+    LhsAlignment = Unaligned,
+    ResPacketSize = PTraits::ResPacketSize,
+    LhsPacketSize = PTraits::LhsPacketSize,
+    RhsPacketSize = PTraits::RhsPacketSize,
+  };
+#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
+  const Index prefetch_dist = 64 * LhsPacketSize;
+#endif
+
+#ifndef GCC_ONE_VECTORPAIR_BUG
+  const Index n8 = rows - 8 * ResPacketSize + 1;
+  const Index n4 = rows - 4 * ResPacketSize + 1;
+  const Index n2 = rows - 2 * ResPacketSize + 1;
+#endif
+  const Index n1 = rows - 1 * ResPacketSize + 1;
+
+  // TODO: improve the following heuristic:
+  const Index block_cols = cols < 128 ? cols : (lhsStride * sizeof(LhsScalar) < 16000 ? 16 : 8);
+
+  typedef alpha_store<PResPacket, ResPacket, ResScalar, Scalar> AlphaData;
+  AlphaData alpha_data(alpha);
+
+  for (Index j2 = 0; j2 < cols; j2 += block_cols) {
+    Index jend = numext::mini(j2 + block_cols, cols);
+    Index i = 0;
+    PResPacket c00, c01, c02, c03, c04, c05, c06, c07;
+    ResPacket c10, c11, c12, c13, c14, c15, c16, c17;
+    PLhsPacket f0, f1, f2, f3, f4, f5, f6, f7;
+#ifdef USE_GEMV_MMA
+    __vector_quad e00, e01, e02, e03, e04, e05, e06, e07;
+    __vector_pair a0, a1, a2, a3, a4, a5, a6, a7;
+    PacketBlock<ScalarPacket, 4> result00, result01, result02, result03, result04, result05, result06, result07;
+    GEMV_UNUSED(8, e0)
+    GEMV_UNUSED(8, result0)
+    GEMV_UNUSED(8, a)
+    GEMV_UNUSED(8, f)
+#if !defined(GCC_ONE_VECTORPAIR_BUG) && defined(USE_GEMV_COL_COMPLEX_MMA)
+    if (GEMV_IS_COMPLEX_COMPLEX || !GEMV_IS_COMPLEX_FLOAT)
+#endif
+#endif
+#ifndef GCC_ONE_VECTORPAIR_BUG
+    {
+      while (i < n8) {
+        GEMV_PROCESS_COL_COMPLEX(8)
+      }
+    }
+    while (i < n4) {
+      GEMV_PROCESS_COL_COMPLEX(4)
+    }
+    if (i < n2) {
+      GEMV_PROCESS_COL_COMPLEX(2)
+    }
+    if (i < n1)
+#else
+    while (i < n1)
+#endif
+    {
+      GEMV_PROCESS_COL_COMPLEX_ONE(1)
+    }
+    for (; i < rows; ++i) {
+      ResScalar d0(0);
+      Index j = j2;
+      do {
+        d0 += cj.pmul(lhs(i, j), rhs2(j, 0));
+      } while (++j < jend);
+      res[i] += alpha * d0;
+    }
+  }
+}
+
+template <typename Scalar, int N>
+struct ScalarBlock {
+  Scalar scalar[N];
+};
+
+#ifdef USE_GEMV_MMA
+static Packet16uc p16uc_ELEMENT_3 = {0x0c, 0x0d, 0x0e, 0x0f, 0x1c, 0x1d, 0x1e, 0x1f,
+                                     0x0c, 0x0d, 0x0e, 0x0f, 0x1c, 0x1d, 0x1e, 0x1f};
+
+/** \internal predux (add elements of a vector) from a MMA accumulator - real results */
+template <typename ResScalar, typename ResPacket>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_real(__vector_quad* acc0, __vector_quad* acc1) {
+  PacketBlock<ResPacket, 4> result0, result1;
+  __builtin_mma_disassemble_acc(&result0.packet, acc0);
+  __builtin_mma_disassemble_acc(&result1.packet, acc1);
+  result0.packet[0] = vec_mergeh(result0.packet[0], result1.packet[0]);
+  result0.packet[1] = vec_mergeo(result0.packet[1], result1.packet[1]);
+  result0.packet[2] = vec_mergel(result0.packet[2], result1.packet[2]);
+  result0.packet[3] = vec_perm(result0.packet[3], result1.packet[3], p16uc_ELEMENT_3);
+  result0.packet[0] =
+      vec_add(vec_add(result0.packet[0], result0.packet[2]), vec_add(result0.packet[1], result0.packet[3]));
+  return *reinterpret_cast<ScalarBlock<ResScalar, 2>*>(&result0.packet[0]);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE ScalarBlock<double, 2> predux_real<double, Packet2d>(__vector_quad* acc0, __vector_quad* acc1) {
+  PacketBlock<Packet2d, 4> result0, result1;
+  __builtin_mma_disassemble_acc(&result0.packet, acc0);
+  __builtin_mma_disassemble_acc(&result1.packet, acc1);
+  result0.packet[0] =
+      vec_add(vec_mergeh(result0.packet[0], result1.packet[0]), vec_mergel(result0.packet[1], result1.packet[1]));
+  return *reinterpret_cast<ScalarBlock<double, 2>*>(&result0.packet[0]);
+}
+
+/** \internal add complex results together */
+template <typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE ScalarBlock<std::complex<float>, 2> addComplexResults(PacketBlock<Packet4f, 4>& result0,
+                                                                          PacketBlock<Packet4f, 4>& result1) {
+  ScalarBlock<std::complex<float>, 2> cc0;
+  result0.packet[0] = reinterpret_cast<Packet4f>(
+      vec_mergeh(reinterpret_cast<Packet2d>(result0.packet[0]), reinterpret_cast<Packet2d>(result1.packet[0])));
+  result0.packet[2] = reinterpret_cast<Packet4f>(
+      vec_mergel(reinterpret_cast<Packet2d>(result0.packet[2]), reinterpret_cast<Packet2d>(result1.packet[2])));
+  result0.packet[0] = vec_add(result0.packet[0], result0.packet[2]);
+  if (GEMV_IS_COMPLEX_COMPLEX) {
+    result0.packet[1] = reinterpret_cast<Packet4f>(
+        vec_mergeh(reinterpret_cast<Packet2d>(result0.packet[1]), reinterpret_cast<Packet2d>(result1.packet[1])));
+    result0.packet[3] = reinterpret_cast<Packet4f>(
+        vec_mergel(reinterpret_cast<Packet2d>(result0.packet[3]), reinterpret_cast<Packet2d>(result1.packet[3])));
+    result0.packet[1] = vec_add(result0.packet[1], result0.packet[3]);
+    if (ConjugateLhs) {
+      result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+      result0.packet[1] = pcplxflip2(convertComplex(result0.packet[1])).v;
+    } else if (ConjugateRhs) {
+      result0.packet[1] = pcplxconjflip(convertComplex(result0.packet[1])).v;
+    } else {
+      result0.packet[1] = pcplxflipconj(convertComplex(result0.packet[1])).v;
+    }
+    result0.packet[0] = vec_add(result0.packet[0], result0.packet[1]);
+  } else {
+    if (ConjugateLhs && (sizeof(LhsPacket) == sizeof(std::complex<float>))) {
+      result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+    }
+  }
+  cc0.scalar[0].real(result0.packet[0][0]);
+  cc0.scalar[0].imag(result0.packet[0][1]);
+  cc0.scalar[1].real(result0.packet[0][2]);
+  cc0.scalar[1].imag(result0.packet[0][3]);
+  return cc0;
+}
+
+template <typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE ScalarBlock<std::complex<double>, 2> addComplexResults(PacketBlock<Packet2d, 4>&,
+                                                                           PacketBlock<Packet2d, 4>&) {
+  ScalarBlock<std::complex<double>, 2> cc0;
+  EIGEN_UNUSED_VARIABLE(cc0);
+  return cc0;  // Just for compilation
+}
+
+/** \internal predux (add elements of a vector) from a MMA accumulator - complex results */
+template <typename ResScalar, typename ResPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs,
+          bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(__vector_quad* acc0, __vector_quad* acc1) {
+  PacketBlock<ResPacket, 4> result0, result1;
+  __builtin_mma_disassemble_acc(&result0.packet, acc0);
+  __builtin_mma_disassemble_acc(&result1.packet, acc1);
+  return addComplexResults<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(result0, result1);
+}
+
+template <typename ResScalar, typename ResPacket>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_real(__vector_quad* acc0) {
+  PacketBlock<ResPacket, 4> result0;
+  __builtin_mma_disassemble_acc(&result0.packet, acc0);
+  result0.packet[0] =
+      vec_add(vec_mergeh(result0.packet[0], result0.packet[2]), vec_mergel(result0.packet[1], result0.packet[3]));
+  return *reinterpret_cast<ScalarBlock<ResScalar, 2>*>(&result0.packet[0]);
+}
+
+template <typename ResScalar, typename ResPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs,
+          bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(__vector_quad* acc0) {
+  ScalarBlock<ResScalar, 2> cc0;
+  PacketBlock<ResPacket, 4> result0;
+  __builtin_mma_disassemble_acc(&result0.packet, acc0);
+  if (GEMV_IS_COMPLEX_COMPLEX) {
+    if (ConjugateLhs) {
+      result0.packet[1] = pconjinv(convertComplex(result0.packet[1])).v;
+      result0.packet[3] = pconjinv(convertComplex(result0.packet[3])).v;
+    } else if (ConjugateRhs) {
+      result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+      result0.packet[2] = pconj2(convertComplex(result0.packet[2])).v;
+    } else {
+      result0.packet[1] = pconj2(convertComplex(result0.packet[1])).v;
+      result0.packet[3] = pconj2(convertComplex(result0.packet[3])).v;
+    }
+    result0.packet[0] = vec_add(result0.packet[0], __builtin_vsx_xxpermdi(result0.packet[1], result0.packet[1], 2));
+    result0.packet[2] = vec_add(result0.packet[2], __builtin_vsx_xxpermdi(result0.packet[3], result0.packet[3], 2));
+  } else {
+    result0.packet[0] = __builtin_vsx_xxpermdi(result0.packet[0], result0.packet[1], 1);
+    result0.packet[2] = __builtin_vsx_xxpermdi(result0.packet[2], result0.packet[3], 1);
+  }
+  cc0.scalar[0].real(result0.packet[0][0]);
+  cc0.scalar[0].imag(result0.packet[0][1]);
+  cc0.scalar[1].real(result0.packet[2][0]);
+  cc0.scalar[1].imag(result0.packet[2][1]);
+  return cc0;
+}
+#endif
+
+template <typename ResScalar, typename ResPacket>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_real(ResPacket& a, ResPacket& b) {
+  ScalarBlock<ResScalar, 2> cc0;
+  cc0.scalar[0] = predux(a);
+  cc0.scalar[1] = predux(b);
+  return cc0;
+}
+
+template <typename ResScalar, typename ResPacket>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(ResPacket& a, ResPacket& b) {
+  return predux_real<ResScalar, ResPacket>(a, b);
+}
+
+#define GEMV_UNROLL_ROW(func, N) func(0, N) func(1, N) func(2, N) func(3, N) func(4, N) func(5, N) func(6, N) func(7, N)
+
+#define GEMV_UNROLL_ROW_HALF(func, N) func(0, 0, 1, N) func(1, 2, 3, N) func(2, 4, 5, N) func(3, 6, 7, N)
+
+#define GEMV_LOADPACKET_ROW(iter) lhs.template load<LhsPacket, Unaligned>(i + (iter), j)
+
+#ifdef USE_GEMV_MMA
+#define GEMV_UNROLL3_ROW(func, N, which)                                                                      \
+  func(0, N, which) func(1, N, which) func(2, N, which) func(3, N, which) func(4, N, which) func(5, N, which) \
+      func(6, N, which) func(7, N, which)
+
+#define GEMV_UNUSED_ROW(N, which) GEMV_UNROLL3_ROW(GEMV_UNUSED_VAR, N, which)
+
+#define GEMV_INIT_ROW(iter, N)         \
+  if (GEMV_GETN(N) > iter) {           \
+    __builtin_mma_xxsetaccz(&c##iter); \
+  }
+
+#define GEMV_LOADPAIR_ROW(iter1, iter2) \
+  GEMV_BUILDPAIR_MMA(b##iter1, GEMV_LOADPACKET_ROW(iter2), GEMV_LOADPACKET_ROW((iter2) + 1));
+
+#define GEMV_WORK_ROW(iter, N)                                                              \
+  if (GEMV_GETN(N) > iter) {                                                                \
+    if (GEMV_IS_FLOAT) {                                                                    \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&c##iter, a0, GEMV_LOADPACKET_ROW(iter)); \
+    } else {                                                                                \
+      __vector_pair b##iter;                                                                \
+      GEMV_LOADPAIR_ROW(iter, iter << 1)                                                    \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&c##iter, b##iter, a0);                   \
+    }                                                                                       \
+  }
+
+#define GEMV_PREDUX2(iter1, iter2, iter3, N)                               \
+  if (N > iter1) {                                                         \
+    if (GEMV_IS_FLOAT) {                                                   \
+      cc##iter1 = predux_real<ResScalar, ResPacket>(&c##iter2, &c##iter3); \
+    } else {                                                               \
+      cc##iter1 = predux_real<ResScalar, ResPacket>(&c##iter1);            \
+    }                                                                      \
+  } else {                                                                 \
+    EIGEN_UNUSED_VARIABLE(cc##iter1);                                      \
+  }
+#else
+#define GEMV_INIT_ROW(iter, N)                \
+  if (N > iter) {                             \
+    c##iter = pset1<ResPacket>(ResScalar(0)); \
+  } else {                                    \
+    EIGEN_UNUSED_VARIABLE(c##iter);           \
+  }
+
+#define GEMV_WORK_ROW(iter, N)                                   \
+  if (N > iter) {                                                \
+    c##iter = pcj.pmadd(GEMV_LOADPACKET_ROW(iter), a0, c##iter); \
+  }
+
+#define GEMV_PREDUX2(iter1, iter2, iter3, N)                           \
+  if (N > iter1) {                                                     \
+    cc##iter1 = predux_real<ResScalar, ResPacket>(c##iter2, c##iter3); \
+  } else {                                                             \
+    EIGEN_UNUSED_VARIABLE(cc##iter1);                                  \
+  }
+#endif
+
+#define GEMV_MULT(iter1, iter2, iter3, N)                  \
+  if (N > iter1) {                                         \
+    cc##iter1.scalar[0] += cj.pmul(lhs(i + iter2, j), a0); \
+    cc##iter1.scalar[1] += cj.pmul(lhs(i + iter3, j), a0); \
+  }
+
+#define GEMV_STORE_ROW(iter1, iter2, iter3, N)                                           \
+  if (N > iter1) {                                                                       \
+    storeMaddData<ResScalar>(res + ((i + iter2) * resIncr), alpha, cc##iter1.scalar[0]); \
+    storeMaddData<ResScalar>(res + ((i + iter3) * resIncr), alpha, cc##iter1.scalar[1]); \
+  }
+
+/** \internal main macro for gemv_row - initialize accumulators, multiply and add inputs, predux and store results */
+#define GEMV_PROCESS_ROW(N)                                       \
+  for (; i < n##N; i += N) {                                      \
+    GEMV_UNROLL_ROW(GEMV_INIT_ROW, N)                             \
+    Index j = 0;                                                  \
+    for (; j + LhsPacketSize <= cols; j += LhsPacketSize) {       \
+      RhsPacket a0 = rhs2.template load<RhsPacket, Unaligned>(j); \
+      GEMV_UNROLL_ROW(GEMV_WORK_ROW, N)                           \
+    }                                                             \
+    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX2, (N >> 1))                  \
+    for (; j < cols; ++j) {                                       \
+      RhsScalar a0 = rhs2(j);                                     \
+      GEMV_UNROLL_ROW_HALF(GEMV_MULT, (N >> 1))                   \
+    }                                                             \
+    GEMV_UNROLL_ROW_HALF(GEMV_STORE_ROW, (N >> 1))                \
+  }
+
+template <typename LhsScalar, typename LhsMapper, typename RhsScalar, typename RhsMapper, typename ResScalar>
+EIGEN_STRONG_INLINE void gemv_row(Index rows, Index cols, const LhsMapper& alhs, const RhsMapper& rhs, ResScalar* res,
+                                  Index resIncr, ResScalar alpha) {
+  typedef gemv_traits<LhsScalar, RhsScalar> Traits;
+
+  typedef typename Traits::LhsPacket LhsPacket;
+  typedef typename Traits::RhsPacket RhsPacket;
+  typedef typename Traits::ResPacket ResPacket;
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  typename RhsMapper::LinearMapper rhs2 = rhs.getLinearMapper(0, 0);
+
+  eigen_internal_assert(rhs.stride() == 1);
+  conj_helper<LhsScalar, RhsScalar, false, false> cj;
+  conj_helper<LhsPacket, RhsPacket, false, false> pcj;
+
+  // TODO: fine tune the following heuristic. The rationale is that if the matrix is very large,
+  //       processing 8 rows at once might be counter productive wrt cache.
+#ifndef GCC_ONE_VECTORPAIR_BUG
+  const Index n8 = lhs.stride() * sizeof(LhsScalar) > 32000 ? (rows - 7) : (rows - 7);
+  const Index n4 = rows - 3;
+  const Index n2 = rows - 1;
+#endif
+
+  // TODO: for padded aligned inputs, we could enable aligned reads
+  enum {
+    LhsAlignment = Unaligned,
+    ResPacketSize = Traits::ResPacketSize,
+    LhsPacketSize = Traits::LhsPacketSize,
+    RhsPacketSize = Traits::RhsPacketSize,
+  };
+
+  Index i = 0;
+#ifdef USE_GEMV_MMA
+  __vector_quad c0, c1, c2, c3, c4, c5, c6, c7;
+  GEMV_UNUSED_ROW(8, c)
+#else
+  ResPacket c0, c1, c2, c3, c4, c5, c6, c7;
+#endif
+#ifndef GCC_ONE_VECTORPAIR_BUG
+  ScalarBlock<ResScalar, 2> cc0, cc1, cc2, cc3;
+  GEMV_PROCESS_ROW(8)
+  GEMV_PROCESS_ROW(4)
+  GEMV_PROCESS_ROW(2)
+#endif
+  for (; i < rows; ++i) {
+    ResPacket d0 = pset1<ResPacket>(ResScalar(0));
+    Index j = 0;
+    for (; j + LhsPacketSize <= cols; j += LhsPacketSize) {
+      RhsPacket b0 = rhs2.template load<RhsPacket, Unaligned>(j);
+
+      d0 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 0, j), b0, d0);
+    }
+    ResScalar dd0 = predux(d0);
+    for (; j < cols; ++j) {
+      dd0 += cj.pmul(lhs(i, j), rhs2(j));
+    }
+    res[i * resIncr] += alpha * dd0;
+  }
+}
+
+#define EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL(Scalar)                                                                   \
+  template <typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+  struct general_matrix_vector_product<Index, Scalar, LhsMapper, ColMajor, ConjugateLhs, Scalar, RhsMapper,            \
+                                       ConjugateRhs, Version> {                                                        \
+    typedef typename ScalarBinaryOpTraits<Scalar, Scalar>::ReturnType ResScalar;                                       \
+                                                                                                                       \
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(Index rows, Index cols, const LhsMapper& lhs,                  \
+                                                        const RhsMapper& rhs, ResScalar* res, Index resIncr,           \
+                                                        ResScalar alpha) {                                             \
+      gemv_col<Scalar, LhsMapper, Scalar, RhsMapper, ResScalar>(rows, cols, lhs, rhs, res, resIncr, alpha);            \
+    }                                                                                                                  \
+  };
+
+#define EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW(Scalar)                                                                   \
+  template <typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+  struct general_matrix_vector_product<Index, Scalar, LhsMapper, RowMajor, ConjugateLhs, Scalar, RhsMapper,            \
+                                       ConjugateRhs, Version> {                                                        \
+    typedef typename ScalarBinaryOpTraits<Scalar, Scalar>::ReturnType ResScalar;                                       \
+                                                                                                                       \
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(Index rows, Index cols, const LhsMapper& lhs,                  \
+                                                        const RhsMapper& rhs, ResScalar* res, Index resIncr,           \
+                                                        ResScalar alpha) {                                             \
+      gemv_row<Scalar, LhsMapper, Scalar, RhsMapper, ResScalar>(rows, cols, lhs, rhs, res, resIncr, alpha);            \
+    }                                                                                                                  \
+  };
+
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL(float)
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL(double)
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW(float)
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW(double)
+
+#ifdef USE_GEMV_MMA
+#define gemv_bf16_col gemvMMA_bfloat16_col
+#define gemv_bf16_row gemvMMA_bfloat16_row
+#else
+#define gemv_bf16_col gemv_bfloat16_col
+#define gemv_bf16_row gemv_bfloat16_row
+#endif
+
+#define EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL_BFLOAT16()                                                                \
+  template <typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+  struct general_matrix_vector_product<Index, bfloat16, LhsMapper, ColMajor, ConjugateLhs, bfloat16, RhsMapper,        \
+                                       ConjugateRhs, Version> {                                                        \
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(Index rows, Index cols, const LhsMapper& lhs,                  \
+                                                        const RhsMapper& rhs, bfloat16* res, Index resIncr,            \
+                                                        bfloat16 alpha) {                                              \
+      gemv_bf16_col<LhsMapper, RhsMapper>(rows, cols, lhs, rhs, res, resIncr, alpha);                                  \
+    }                                                                                                                  \
+  };
+
+#define EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW_BFLOAT16()                                                                \
+  template <typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+  struct general_matrix_vector_product<Index, bfloat16, LhsMapper, RowMajor, ConjugateLhs, bfloat16, RhsMapper,        \
+                                       ConjugateRhs, Version> {                                                        \
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(Index rows, Index cols, const LhsMapper& lhs,                  \
+                                                        const RhsMapper& rhs, bfloat16* res, Index resIncr,            \
+                                                        bfloat16 alpha) {                                              \
+      gemv_bf16_row<LhsMapper, RhsMapper>(rows, cols, lhs, rhs, res, resIncr, alpha);                                  \
+    }                                                                                                                  \
+  };
+
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL_BFLOAT16()
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW_BFLOAT16()
+
+template <typename ResScalar, typename PResPacket, typename ResPacket, typename LhsPacket, typename RhsPacket>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(PResPacket& a0, PResPacket& b0, ResPacket& a1,
+                                                             ResPacket& b1) {
+  if (GEMV_IS_COMPLEX_COMPLEX) {
+    a0 = padd(a0, a1);
+    b0 = padd(b0, b1);
+  }
+  return predux_complex<ResScalar, PResPacket>(a0, b0);
+}
+
+#define GEMV_LOADPACKET_ROW_COMPLEX(iter) loadLhsPacket<Scalar, LhsScalar, LhsMapper, PLhsPacket>(lhs, i + (iter), j)
+
+#define GEMV_LOADPACKET_ROW_COMPLEX_DATA(iter) convertReal(GEMV_LOADPACKET_ROW_COMPLEX(iter))
+
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_WORK(which, N)    \
+  j = 0;                                                  \
+  for (; j + LhsPacketSize <= cols; j += LhsPacketSize) { \
+    const RhsScalar& b1 = rhs2(j);                        \
+    RhsScalar* b = const_cast<RhsScalar*>(&b1);           \
+    GEMV_UNROLL_ROW(which, N)                             \
+  }
+
+#define GEMV_PROCESS_END_ROW_COMPLEX(N)               \
+  for (; j < cols; ++j) {                             \
+    RhsScalar b0 = rhs2(j);                           \
+    GEMV_UNROLL_ROW_HALF(GEMV_MULT_COMPLEX, (N >> 1)) \
+  }                                                   \
+  GEMV_UNROLL_ROW_HALF(GEMV_STORE_ROW_COMPLEX, (N >> 1))
+
+#ifdef USE_GEMV_MMA
+#define GEMV_INIT_ROW_COMPLEX_MMA(iter, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter) {       \
+    __builtin_mma_xxsetaccz(&e0##iter);    \
+  }
+
+#define GEMV_LOADPAIR_ROW_COMPLEX_MMA(iter1, iter2) \
+  GEMV_BUILDPAIR_MMA(a##iter1, GEMV_LOADPACKET_ROW_COMPLEX_DATA(iter2), GEMV_LOADPACKET_ROW_COMPLEX_DATA((iter2) + 1));
+
+#define GEMV_WORK_ROW_COMPLEX_MMA(iter, N)                                                                       \
+  if (GEMV_GETN_COMPLEX(N) > iter) {                                                                             \
+    if (GEMV_IS_COMPLEX_FLOAT) {                                                                                 \
+      PLhsPacket a##iter = GEMV_LOADPACKET_ROW_COMPLEX(iter);                                                    \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket,    \
+                            ConjugateLhs, ConjugateRhs, RowMajor>(a##iter, b, &e0##iter);                        \
+    } else {                                                                                                     \
+      __vector_pair a##iter;                                                                                     \
+      GEMV_LOADPAIR_ROW_COMPLEX_MMA(iter, iter << 1)                                                             \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, \
+                            ConjugateLhs, ConjugateRhs, RowMajor>(a##iter, b, &e0##iter);                        \
+    }                                                                                                            \
+  }
+
+#define GEMV_PREDUX4_COMPLEX_MMA(iter1, iter2, iter3, N)                                                         \
+  if (N > iter1) {                                                                                               \
+    if (GEMV_IS_COMPLEX_FLOAT) {                                                                                 \
+      cc##iter1 = predux_complex<ResScalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(     \
+          &e0##iter2, &e0##iter3);                                                                               \
+    } else {                                                                                                     \
+      cc##iter1 =                                                                                                \
+          predux_complex<ResScalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(&e0##iter1); \
+    }                                                                                                            \
+  } else {                                                                                                       \
+    EIGEN_UNUSED_VARIABLE(cc##iter1);                                                                            \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_MMA(N)  \
+  GEMV_UNROLL_ROW(GEMV_INIT_ROW_COMPLEX_MMA, N) \
+  GEMV_PROCESS_ROW_COMPLEX_SINGLE_WORK(GEMV_WORK_ROW_COMPLEX_MMA, N)
+
+#define GEMV_PROCESS_ROW_COMPLEX_ONE_MMA(N)                  \
+  for (; i < n##N; i += N) {                                 \
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE_MMA(N)                   \
+    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX4_COMPLEX_MMA, (N >> 1)) \
+    GEMV_PROCESS_END_ROW_COMPLEX(N);                         \
+  }
+#endif
+
+#define GEMV_WORK_ROW_COMPLEX(iter, N)                                                                     \
+  if (N > iter) {                                                                                          \
+    PLhsPacket a##iter = GEMV_LOADPACKET_ROW_COMPLEX(iter);                                                \
+    gemv_mult_complex<ScalarPacket, PLhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, \
+                      ConjugateRhs, RowMajor>(a##iter, b, c0##iter, c1##iter);                             \
+  }
+
+#define GEMV_PREDUX4_COMPLEX(iter1, iter2, iter3, N)                                                          \
+  if (N > iter1) {                                                                                            \
+    cc##iter1 = predux_complex<ResScalar, PResPacket, ResPacket, LhsPacket, RhsPacket>(c0##iter2, c0##iter3,  \
+                                                                                       c1##iter2, c1##iter3); \
+  } else {                                                                                                    \
+    EIGEN_UNUSED_VARIABLE(cc##iter1);                                                                         \
+  }
+
+#define GEMV_MULT_COMPLEX(iter1, iter2, iter3, N)          \
+  if (N > iter1) {                                         \
+    cc##iter1.scalar[0] += cj.pmul(lhs(i + iter2, j), b0); \
+    cc##iter1.scalar[1] += cj.pmul(lhs(i + iter3, j), b0); \
+  }
+
+#define GEMV_STORE_ROW_COMPLEX(iter1, iter2, iter3, N)                                   \
+  if (N > iter1) {                                                                       \
+    storeMaddData<ResScalar>(res + ((i + iter2) * resIncr), alpha, cc##iter1.scalar[0]); \
+    storeMaddData<ResScalar>(res + ((i + iter3) * resIncr), alpha, cc##iter1.scalar[1]); \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N) \
+  GEMV_UNROLL_ROW(GEMV_INIT_COMPLEX, N)        \
+  GEMV_PROCESS_ROW_COMPLEX_SINGLE_WORK(GEMV_WORK_ROW_COMPLEX, N)
+
+/** \internal main macro for gemv_complex_row - initialize accumulators, multiply and add inputs, predux and store
+ * results */
+#define GEMV_PROCESS_ROW_COMPLEX_ONE_NEW(N)              \
+  for (; i < n##N; i += N) {                             \
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N)               \
+    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX4_COMPLEX, (N >> 1)) \
+    GEMV_PROCESS_END_ROW_COMPLEX(N);                     \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_PREDUX_NEW(iter) \
+  if (GEMV_IS_COMPLEX_COMPLEX) {                  \
+    c0##iter = padd(c0##iter, c1##iter);          \
+  }                                               \
+  dd0 = predux(c0##iter);
+
+#if EIGEN_COMP_LLVM
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE(N) GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N)
+
+#define GEMV_PROCESS_ROW_COMPLEX_ONE(N) GEMV_PROCESS_ROW_COMPLEX_ONE_NEW(N)
+
+#define GEMV_PROCESS_ROW_COMPLEX_PREDUX(iter) GEMV_PROCESS_ROW_COMPLEX_PREDUX_NEW(iter)
+#else
+// gcc seems to be reading and writing registers unnecessarily to memory.
+// Use the old way for complex double until it is fixed.
+
+#define GEMV_LOADPACKET_ROW_COMPLEX_OLD(iter) lhs.template load<LhsPacket, LhsAlignment>(i + (iter), j)
+
+#define GEMV_INIT_COMPLEX_OLD(iter, N) \
+  EIGEN_UNUSED_VARIABLE(c0##iter);     \
+  if (N > iter) {                      \
+    c1##iter = pset_zero<ResPacket>(); \
+  } else {                             \
+    EIGEN_UNUSED_VARIABLE(c1##iter);   \
+  }
+
+#define GEMV_WORK_ROW_COMPLEX_OLD(iter, N)                     \
+  if (N > iter) {                                              \
+    LhsPacket a##iter = GEMV_LOADPACKET_ROW_COMPLEX_OLD(iter); \
+    c1##iter = pcj.pmadd(a##iter, b0, c1##iter);               \
+  }
+
+#define GEMV_PREDUX4_COMPLEX_OLD(iter1, iter2, iter3, N) \
+  if (N > iter1) {                                       \
+    cc##iter1.scalar[0] = predux(c1##iter2);             \
+    cc##iter1.scalar[1] = predux(c1##iter3);             \
+  } else {                                               \
+    EIGEN_UNUSED_VARIABLE(cc##iter1);                    \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_OLD(N)                  \
+  GEMV_UNROLL_ROW(GEMV_INIT_COMPLEX_OLD, N)                     \
+  j = 0;                                                        \
+  for (; j + LhsPacketSize <= cols; j += LhsPacketSize) {       \
+    RhsPacket b0 = rhs2.template load<RhsPacket, Unaligned>(j); \
+    GEMV_UNROLL_ROW(GEMV_WORK_ROW_COMPLEX_OLD, N)               \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_ONE_OLD(N)                  \
+  for (; i < n##N; i += N) {                                 \
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE_OLD(N)                   \
+    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX4_COMPLEX_OLD, (N >> 1)) \
+    GEMV_PROCESS_END_ROW_COMPLEX(N)                          \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_PREDUX_OLD(iter) dd0 = predux(c1##iter);
+
+#if (__GNUC__ > 10)
+#define GEMV_PROCESS_ROW_COMPLEX_IS_NEW 1
+#else
+#define GEMV_PROCESS_ROW_COMPLEX_IS_NEW (sizeof(Scalar) == sizeof(float)) || GEMV_IS_COMPLEX_COMPLEX
+#endif
+
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE(N) \
+  if (GEMV_PROCESS_ROW_COMPLEX_IS_NEW) {   \
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N) \
+  } else {                                 \
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE_OLD(N) \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_ONE(N)  \
+  if (GEMV_PROCESS_ROW_COMPLEX_IS_NEW) { \
+    GEMV_PROCESS_ROW_COMPLEX_ONE_NEW(N)  \
+  } else {                               \
+    GEMV_PROCESS_ROW_COMPLEX_ONE_OLD(N)  \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_PREDUX(iter) \
+  if (GEMV_PROCESS_ROW_COMPLEX_IS_NEW) {      \
+    GEMV_PROCESS_ROW_COMPLEX_PREDUX_NEW(iter) \
+  } else {                                    \
+    GEMV_PROCESS_ROW_COMPLEX_PREDUX_OLD(iter) \
+  }
+#endif
+
+#ifdef USE_GEMV_MMA
+#define GEMV_PROCESS_ROW_COMPLEX(N) GEMV_PROCESS_ROW_COMPLEX_ONE_MMA(N)
+#else
+#define GEMV_PROCESS_ROW_COMPLEX(N) GEMV_PROCESS_ROW_COMPLEX_ONE(N)
+#endif
+
+template <typename Scalar, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, bool LhsIsReal,
+          typename RhsScalar, typename RhsMapper, bool ConjugateRhs, bool RhsIsReal, typename ResScalar>
+EIGEN_STRONG_INLINE void gemv_complex_row(Index rows, Index cols, const LhsMapper& alhs, const RhsMapper& rhs,
+                                          ResScalar* res, Index resIncr, ResScalar alpha) {
+  typedef gemv_traits<LhsScalar, RhsScalar> Traits;
+
+  typedef typename Traits::LhsPacket LhsPacket;
+  typedef typename Traits::RhsPacket RhsPacket;
+  typedef typename Traits::ResPacket ResPacket;
+
+  typedef typename packet_traits<Scalar>::type ScalarPacket;
+  typedef typename packet_traits<LhsScalar>::type PLhsPacket;
+  typedef typename packet_traits<ResScalar>::type PResPacket;
+  typedef gemv_traits<ResPacket, ResPacket> PTraits;
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  typename RhsMapper::LinearMapper rhs2 = rhs.getLinearMapper(0, 0);
+
+  eigen_internal_assert(rhs.stride() == 1);
+  conj_helper<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs> cj;
+#if !EIGEN_COMP_LLVM
+  conj_helper<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs> pcj;
+#endif
+
+  // TODO: fine tune the following heuristic. The rationale is that if the matrix is very large,
+  //       processing 8 rows at once might be counter productive wrt cache.
+#ifndef GCC_ONE_VECTORPAIR_BUG
+  const Index n8 = lhs.stride() * sizeof(LhsScalar) > 32000 ? (rows - 7) : (rows - 7);
+  const Index n4 = rows - 3;
+  const Index n2 = rows - 1;
+#endif
+
+  // TODO: for padded aligned inputs, we could enable aligned reads
+  enum {
+    LhsAlignment = Unaligned,
+    ResPacketSize = PTraits::ResPacketSize,
+    LhsPacketSize = PTraits::LhsPacketSize,
+    RhsPacketSize = PTraits::RhsPacketSize,
+  };
+
+  Index i = 0, j;
+  PResPacket c00, c01, c02, c03, c04, c05, c06, c07;
+  ResPacket c10, c11, c12, c13, c14, c15, c16, c17;
+#ifdef USE_GEMV_MMA
+  __vector_quad e00, e01, e02, e03, e04, e05, e06, e07;
+  GEMV_UNUSED_ROW(8, e0)
+  GEMV_UNUSED_EXTRA(1, c0)
+  GEMV_UNUSED_EXTRA(1, c1)
+#endif
+  ResScalar dd0;
+#ifndef GCC_ONE_VECTORPAIR_BUG
+  ScalarBlock<ResScalar, 2> cc0, cc1, cc2, cc3;
+#ifdef USE_GEMV_MMA
+  if (!GEMV_IS_COMPLEX_COMPLEX)
+#endif
+  {
+    GEMV_PROCESS_ROW_COMPLEX(8)
+  }
+  GEMV_PROCESS_ROW_COMPLEX(4)
+  GEMV_PROCESS_ROW_COMPLEX(2)
+#endif
+  for (; i < rows; ++i) {
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE(1)
+    GEMV_PROCESS_ROW_COMPLEX_PREDUX(0)
+    for (; j < cols; ++j) {
+      dd0 += cj.pmul(lhs(i, j), rhs2(j));
+    }
+    res[i * resIncr] += alpha * dd0;
+  }
+}
+
+#define EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(Scalar, LhsScalar, RhsScalar)                                          \
+  template <typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+  struct general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, ConjugateLhs, RhsScalar, RhsMapper,      \
+                                       ConjugateRhs, Version> {                                                        \
+    typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;                                 \
+                                                                                                                       \
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(Index rows, Index cols, const LhsMapper& lhs,                  \
+                                                        const RhsMapper& rhs, ResScalar* res, Index resIncr,           \
+                                                        ResScalar alpha) {                                             \
+      gemv_complex_col<Scalar, LhsScalar, LhsMapper, ConjugateLhs, sizeof(Scalar) == sizeof(LhsScalar), RhsScalar,     \
+                       RhsMapper, ConjugateRhs, sizeof(Scalar) == sizeof(RhsScalar), ResScalar>(rows, cols, lhs, rhs,  \
+                                                                                                res, resIncr, alpha);  \
+    }                                                                                                                  \
+  };
+
+#define EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(Scalar, LhsScalar, RhsScalar)                                          \
+  template <typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+  struct general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, ConjugateLhs, RhsScalar, RhsMapper,      \
+                                       ConjugateRhs, Version> {                                                        \
+    typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;                                 \
+                                                                                                                       \
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(Index rows, Index cols, const LhsMapper& lhs,                  \
+                                                        const RhsMapper& rhs, ResScalar* res, Index resIncr,           \
+                                                        ResScalar alpha) {                                             \
+      gemv_complex_row<Scalar, LhsScalar, LhsMapper, ConjugateLhs, sizeof(Scalar) == sizeof(LhsScalar), RhsScalar,     \
+                       RhsMapper, ConjugateRhs, sizeof(Scalar) == sizeof(RhsScalar), ResScalar>(rows, cols, lhs, rhs,  \
+                                                                                                res, resIncr, alpha);  \
+    }                                                                                                                  \
+  };
+
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(float, float, std::complex<float>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(float, std::complex<float>, float)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(float, std::complex<float>, std::complex<float>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(double, double, std::complex<double>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(double, std::complex<double>, double)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(double, std::complex<double>, std::complex<double>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(float, float, std::complex<float>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(float, std::complex<float>, float)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(float, std::complex<float>, std::complex<float>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(double, double, std::complex<double>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(double, std::complex<double>, double)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(double, std::complex<double>, std::complex<double>)
+
+#endif  // EIGEN_MATRIX_VECTOR_PRODUCT_ALTIVEC_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/PacketMath.h
new file mode 100644
index 0000000..8d5a351
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/PacketMath.h
@@ -0,0 +1,3651 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_ALTIVEC_H
+#define EIGEN_PACKET_MATH_ALTIVEC_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 4
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+// NOTE Altivec has 32 registers, but Eigen only accepts a value of 8 or 16
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#endif
+
+typedef __vector float Packet4f;
+typedef __vector int Packet4i;
+typedef __vector unsigned int Packet4ui;
+typedef __vector __bool int Packet4bi;
+typedef __vector short int Packet8s;
+typedef __vector unsigned short int Packet8us;
+typedef __vector __bool short Packet8bi;
+typedef __vector signed char Packet16c;
+typedef __vector unsigned char Packet16uc;
+typedef eigen_packet_wrapper<__vector unsigned short int, 0> Packet8bf;
+
+// To avoid repeating the same code, but we need to reuse the constants
+// and it doesn't really work to declare them global, so we define macros instead
+#define EIGEN_DECLARE_CONST_FAST_Packet4f(NAME, X) Packet4f p4f_##NAME = {X, X, X, X}
+
+#define EIGEN_DECLARE_CONST_FAST_Packet4i(NAME, X) Packet4i p4i_##NAME = vec_splat_s32(X)
+
+#define EIGEN_DECLARE_CONST_FAST_Packet4ui(NAME, X) Packet4ui p4ui_##NAME = {X, X, X, X}
+
+#define EIGEN_DECLARE_CONST_FAST_Packet8us(NAME, X) Packet8us p8us_##NAME = {X, X, X, X, X, X, X, X}
+
+#define EIGEN_DECLARE_CONST_FAST_Packet16uc(NAME, X) \
+  Packet16uc p16uc_##NAME = {X, X, X, X, X, X, X, X, X, X, X, X, X, X, X, X}
+
+#define EIGEN_DECLARE_CONST_Packet4f(NAME, X) Packet4f p4f_##NAME = pset1<Packet4f>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4i(NAME, X) Packet4i p4i_##NAME = pset1<Packet4i>(X)
+
+#define EIGEN_DECLARE_CONST_Packet2d(NAME, X) Packet2d p2d_##NAME = pset1<Packet2d>(X)
+
+#define EIGEN_DECLARE_CONST_Packet2l(NAME, X) Packet2l p2l_##NAME = pset1<Packet2l>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME, X) \
+  const Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(pset1<Packet4i>(X))
+
+#define DST_CHAN 1
+#define DST_CTRL(size, count, stride) (((size) << 24) | ((count) << 16) | (stride))
+#define __UNPACK_TYPE__(PACKETNAME) typename unpacket_traits<PACKETNAME>::type
+
+// These constants are endian-agnostic
+static EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0);       //{ 0.0, 0.0, 0.0, 0.0}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0);       //{ 0, 0, 0, 0,}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(ONE, 1);        //{ 1, 1, 1, 1}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS16, -16);  //{ -16, -16, -16, -16}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1, -1);    //{ -1, -1, -1, -1}
+static EIGEN_DECLARE_CONST_FAST_Packet4ui(SIGN, 0x80000000u);
+static EIGEN_DECLARE_CONST_FAST_Packet4ui(PREV0DOT5, 0x3EFFFFFFu);
+static EIGEN_DECLARE_CONST_FAST_Packet8us(ONE, 1);  //{ 1, 1, 1, 1, 1, 1, 1, 1}
+static Packet4f p4f_MZERO =
+    (Packet4f)vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1);  //{ 0x80000000, 0x80000000, 0x80000000, 0x80000000}
+#ifndef __VSX__
+static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0);  //{ 1.0, 1.0, 1.0, 1.0}
+#endif
+
+static Packet4f p4f_COUNTDOWN = {0.0, 1.0, 2.0, 3.0};
+static Packet4i p4i_COUNTDOWN = {0, 1, 2, 3};
+static Packet8s p8s_COUNTDOWN = {0, 1, 2, 3, 4, 5, 6, 7};
+static Packet8us p8us_COUNTDOWN = {0, 1, 2, 3, 4, 5, 6, 7};
+
+static Packet16c p16c_COUNTDOWN = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+static Packet16uc p16uc_COUNTDOWN = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+
+static Packet16uc p16uc_REVERSE32 = {12, 13, 14, 15, 8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3};
+static Packet16uc p16uc_REVERSE16 = {14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1};
+static Packet16uc p16uc_REVERSE8 = {15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
+
+#ifdef _BIG_ENDIAN
+static Packet16uc p16uc_DUPLICATE32_HI = {0, 1, 2, 3, 0, 1, 2, 3, 4, 5, 6, 7, 4, 5, 6, 7};
+#endif
+static const Packet16uc p16uc_DUPLICATE16_EVEN = {0, 1, 0, 1, 4, 5, 4, 5, 8, 9, 8, 9, 12, 13, 12, 13};
+static const Packet16uc p16uc_DUPLICATE16_ODD = {2, 3, 2, 3, 6, 7, 6, 7, 10, 11, 10, 11, 14, 15, 14, 15};
+
+static Packet16uc p16uc_QUADRUPLICATE16_HI = {0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3};
+static Packet16uc p16uc_QUADRUPLICATE16 = {0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3};
+
+static Packet16uc p16uc_MERGEE16 = {0, 1, 16, 17, 4, 5, 20, 21, 8, 9, 24, 25, 12, 13, 28, 29};
+static Packet16uc p16uc_MERGEO16 = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31};
+#ifdef _BIG_ENDIAN
+static Packet16uc p16uc_MERGEH16 = {0, 1, 4, 5, 8, 9, 12, 13, 16, 17, 20, 21, 24, 25, 28, 29};
+#else
+static Packet16uc p16uc_MERGEL16 = {2, 3, 6, 7, 10, 11, 14, 15, 18, 19, 22, 23, 26, 27, 30, 31};
+#endif
+
+// Handle endianness properly while loading constants
+// Define global static constants:
+#ifdef _BIG_ENDIAN
+static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0);
+static Packet16uc p16uc_PSET32_WODD =
+    vec_sld((Packet16uc)vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc)vec_splat((Packet4ui)p16uc_FORWARD, 2),
+            8);  //{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
+static Packet16uc p16uc_PSET32_WEVEN = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc)vec_splat((Packet4ui)p16uc_FORWARD, 3),
+                                               8);  //{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
+static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc)vec_abs(p4i_MINUS16), 3),
+                                              8);  //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
+#else
+static Packet16uc p16uc_FORWARD = p16uc_REVERSE32;
+static Packet16uc p16uc_PSET32_WODD =
+    vec_sld((Packet16uc)vec_splat((Packet4ui)p16uc_FORWARD, 1), (Packet16uc)vec_splat((Packet4ui)p16uc_FORWARD, 3),
+            8);  //{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
+static Packet16uc p16uc_PSET32_WEVEN =
+    vec_sld((Packet16uc)vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc)vec_splat((Packet4ui)p16uc_FORWARD, 2),
+            8);  //{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
+static Packet16uc p16uc_HALF64_0_16 = vec_sld(vec_splat((Packet16uc)vec_abs(p4i_MINUS16), 0), (Packet16uc)p4i_ZERO,
+                                              8);  //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
+#endif  // _BIG_ENDIAN
+
+static Packet16uc p16uc_PSET64_HI = (Packet16uc)vec_mergeh(
+    (Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);  //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
+static Packet16uc p16uc_PSET64_LO = (Packet16uc)vec_mergel(
+    (Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);  //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
+static Packet16uc p16uc_TRANSPOSE64_HI =
+    p16uc_PSET64_HI + p16uc_HALF64_0_16;  //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
+static Packet16uc p16uc_TRANSPOSE64_LO =
+    p16uc_PSET64_LO + p16uc_HALF64_0_16;  //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
+
+static Packet16uc p16uc_COMPLEX32_REV =
+    vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8);  //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
+
+#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
+#define EIGEN_PPC_PREFETCH(ADDR) __builtin_prefetch(ADDR);
+#else
+#define EIGEN_PPC_PREFETCH(ADDR) asm("   dcbt [%[addr]]\n" ::[addr] "r"(ADDR) : "cc");
+#endif
+
+#if EIGEN_COMP_LLVM
+#define LOAD_STORE_UNROLL_16 _Pragma("unroll 16")
+#else
+#define LOAD_STORE_UNROLL_16 _Pragma("GCC unroll(16)")
+#endif
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4f type;
+  typedef Packet4f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasAbs = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+#ifdef EIGEN_VECTORIZE_VSX
+    HasCmp = 1,
+    HasPow = 1,
+    HasSqrt = 1,
+    HasCbrt = 1,
+#if !EIGEN_COMP_CLANG
+    HasRsqrt = 1,
+#else
+    HasRsqrt = 0,
+#endif
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasErfc = EIGEN_FAST_MATH,
+#else
+    HasSqrt = 0,
+    HasRsqrt = 0,
+    HasTanh = 0,
+    HasErf = 0,
+#endif
+    HasNegate = 1,
+  };
+};
+template <>
+struct packet_traits<bfloat16> : default_packet_traits {
+  typedef Packet8bf type;
+  typedef Packet8bf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasAbs = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+#ifdef EIGEN_VECTORIZE_VSX
+    HasSqrt = 1,
+#if !EIGEN_COMP_CLANG
+    HasRsqrt = 1,
+#else
+    HasRsqrt = 0,
+#endif
+#else
+    HasSqrt = 0,
+    HasRsqrt = 0,
+#endif
+    HasTanh = 0,
+    HasErf = 0,
+    HasNegate = 1,
+  };
+};
+
+template <>
+struct packet_traits<int> : default_packet_traits {
+  typedef Packet4i type;
+  typedef Packet4i half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+#if defined(_ARCH_PWR10) && (EIGEN_COMP_LLVM || EIGEN_GNUC_STRICT_AT_LEAST(11, 0, 0))
+    HasDiv = 1,
+#else
+    HasDiv = 0,
+#endif
+    HasCmp = 1
+  };
+};
+
+template <>
+struct packet_traits<short int> : default_packet_traits {
+  typedef Packet8s type;
+  typedef Packet8s half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 0,
+    HasCmp = 1
+  };
+};
+
+template <>
+struct packet_traits<unsigned short int> : default_packet_traits {
+  typedef Packet8us type;
+  typedef Packet8us half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 0,
+    HasCmp = 1
+  };
+};
+
+template <>
+struct packet_traits<signed char> : default_packet_traits {
+  typedef Packet16c type;
+  typedef Packet16c half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 0,
+    HasCmp = 1
+  };
+};
+
+template <>
+struct packet_traits<unsigned char> : default_packet_traits {
+  typedef Packet16uc type;
+  typedef Packet16uc half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 0,
+    HasCmp = 1
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4f> {
+  typedef float type;
+  typedef Packet4f half;
+  typedef Packet4i integer_packet;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet4i> {
+  typedef int type;
+  typedef Packet4i half;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet8s> {
+  typedef short int type;
+  typedef Packet8s half;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet8us> {
+  typedef unsigned short int type;
+  typedef Packet8us half;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet16c> {
+  typedef signed char type;
+  typedef Packet16c half;
+  enum {
+    size = 16,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet16uc> {
+  typedef unsigned char type;
+  typedef Packet16uc half;
+  enum {
+    size = 16,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet8bf> {
+  typedef bfloat16 type;
+  typedef Packet8bf half;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet pload_common(const __UNPACK_TYPE__(Packet) * from) {
+  // some versions of GCC throw "unused-but-set-parameter".
+  // ignoring these warnings for now.
+  EIGEN_UNUSED_VARIABLE(from);
+  EIGEN_DEBUG_ALIGNED_LOAD
+#ifdef EIGEN_VECTORIZE_VSX
+  return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
+#else
+  return vec_ld(0, from);
+#endif
+}
+
+// Need to define them first or we get specialization after instantiation errors
+template <>
+EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) {
+  return pload_common<Packet4f>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) {
+  return pload_common<Packet4i>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8s pload<Packet8s>(const short int* from) {
+  return pload_common<Packet8s>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8us pload<Packet8us>(const unsigned short int* from) {
+  return pload_common<Packet8us>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c pload<Packet16c>(const signed char* from) {
+  return pload_common<Packet16c>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16uc pload<Packet16uc>(const unsigned char* from) {
+  return pload_common<Packet16uc>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pload<Packet8bf>(const bfloat16* from) {
+  return pload_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet pload_ignore(const __UNPACK_TYPE__(Packet) * from) {
+  // some versions of GCC throw "unused-but-set-parameter".
+  // ignoring these warnings for now.
+  EIGEN_UNUSED_VARIABLE(from);
+  EIGEN_DEBUG_ALIGNED_LOAD
+  // Ignore partial input memory initialized
+#if !EIGEN_COMP_LLVM
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+#endif
+#ifdef EIGEN_VECTORIZE_VSX
+  return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
+#else
+  return vec_ld(0, from);
+#endif
+#if !EIGEN_COMP_LLVM
+#pragma GCC diagnostic pop
+#endif
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet8bf pload_ignore<Packet8bf>(const bfloat16* from) {
+  return pload_ignore<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet pload_partial_common(const __UNPACK_TYPE__(Packet) * from, const Index n,
+                                                const Index offset) {
+  // some versions of GCC throw "unused-but-set-parameter".
+  // ignoring these warnings for now.
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_internal_assert(n + offset <= packet_size && "number of elements plus offset will read past end of packet");
+  const Index size = sizeof(__UNPACK_TYPE__(Packet));
+#ifdef _ARCH_PWR9
+  EIGEN_UNUSED_VARIABLE(packet_size);
+  EIGEN_DEBUG_ALIGNED_LOAD
+  EIGEN_UNUSED_VARIABLE(from);
+  Packet load = vec_xl_len(const_cast<__UNPACK_TYPE__(Packet)*>(from), n * size);
+  if (offset) {
+    Packet16uc shift = pset1<Packet16uc>(offset * 8 * size);
+#ifdef _BIG_ENDIAN
+    load = Packet(vec_sro(Packet16uc(load), shift));
+#else
+    load = Packet(vec_slo(Packet16uc(load), shift));
+#endif
+  }
+  return load;
+#else
+  if (n) {
+    EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) load[packet_size];
+    unsigned char* load2 = reinterpret_cast<unsigned char*>(load + offset);
+    unsigned char* from2 = reinterpret_cast<unsigned char*>(const_cast<__UNPACK_TYPE__(Packet)*>(from));
+    Index n2 = n * size;
+    if (16 <= n2) {
+      pstoreu(load2, ploadu<Packet16uc>(from2));
+    } else {
+      memcpy((void*)load2, (void*)from2, n2);
+    }
+    return pload_ignore<Packet>(load);
+  } else {
+    return Packet(pset1<Packet16uc>(0));
+  }
+#endif
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet4f pload_partial<Packet4f>(const float* from, const Index n, const Index offset) {
+  return pload_partial_common<Packet4f>(from, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet4i pload_partial<Packet4i>(const int* from, const Index n, const Index offset) {
+  return pload_partial_common<Packet4i>(from, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet8s pload_partial<Packet8s>(const short int* from, const Index n, const Index offset) {
+  return pload_partial_common<Packet8s>(from, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet8us pload_partial<Packet8us>(const unsigned short int* from, const Index n,
+                                                       const Index offset) {
+  return pload_partial_common<Packet8us>(from, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet8bf pload_partial<Packet8bf>(const bfloat16* from, const Index n, const Index offset) {
+  return pload_partial_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from), n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet16c pload_partial<Packet16c>(const signed char* from, const Index n, const Index offset) {
+  return pload_partial_common<Packet16c>(from, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet16uc pload_partial<Packet16uc>(const unsigned char* from, const Index n, const Index offset) {
+  return pload_partial_common<Packet16uc>(from, n, offset);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE void pstore_common(__UNPACK_TYPE__(Packet) * to, const Packet& from) {
+  // some versions of GCC throw "unused-but-set-parameter" (float *to).
+  // ignoring these warnings for now.
+  EIGEN_UNUSED_VARIABLE(to);
+  EIGEN_DEBUG_ALIGNED_STORE
+#ifdef EIGEN_VECTORIZE_VSX
+  vec_xst(from, 0, to);
+#else
+  vec_st(from, 0, to);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) {
+  pstore_common<Packet4f>(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) {
+  pstore_common<Packet4i>(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<short int>(short int* to, const Packet8s& from) {
+  pstore_common<Packet8s>(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<unsigned short int>(unsigned short int* to, const Packet8us& from) {
+  pstore_common<Packet8us>(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet8bf& from) {
+  pstore_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from.m_val);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<signed char>(signed char* to, const Packet16c& from) {
+  pstore_common<Packet16c>(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<unsigned char>(unsigned char* to, const Packet16uc& from) {
+  pstore_common<Packet16uc>(to, from);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void pstore_partial_common(__UNPACK_TYPE__(Packet) * to, const Packet& from, const Index n,
+                                               const Index offset) {
+  // some versions of GCC throw "unused-but-set-parameter" (float *to).
+  // ignoring these warnings for now.
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_internal_assert(n + offset <= packet_size && "number of elements plus offset will write past end of packet");
+  const Index size = sizeof(__UNPACK_TYPE__(Packet));
+#ifdef _ARCH_PWR9
+  EIGEN_UNUSED_VARIABLE(packet_size);
+  EIGEN_UNUSED_VARIABLE(to);
+  EIGEN_DEBUG_ALIGNED_STORE
+  Packet store = from;
+  if (offset) {
+    Packet16uc shift = pset1<Packet16uc>(offset * 8 * size);
+#ifdef _BIG_ENDIAN
+    store = Packet(vec_slo(Packet16uc(store), shift));
+#else
+    store = Packet(vec_sro(Packet16uc(store), shift));
+#endif
+  }
+  vec_xst_len(store, to, n * size);
+#else
+  if (n) {
+    EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) store[packet_size];
+    pstore(store, from);
+    unsigned char* store2 = reinterpret_cast<unsigned char*>(store + offset);
+    unsigned char* to2 = reinterpret_cast<unsigned char*>(to);
+    Index n2 = n * size;
+    if (16 <= n2) {
+      pstore(to2, ploadu<Packet16uc>(store2));
+    } else {
+      memcpy((void*)to2, (void*)store2, n2);
+    }
+  }
+#endif
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pstore_partial<float>(float* to, const Packet4f& from, const Index n, const Index offset) {
+  pstore_partial_common<Packet4f>(to, from, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pstore_partial<int>(int* to, const Packet4i& from, const Index n, const Index offset) {
+  pstore_partial_common<Packet4i>(to, from, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pstore_partial<short int>(short int* to, const Packet8s& from, const Index n,
+                                                   const Index offset) {
+  pstore_partial_common<Packet8s>(to, from, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pstore_partial<unsigned short int>(unsigned short int* to, const Packet8us& from,
+                                                            const Index n, const Index offset) {
+  pstore_partial_common<Packet8us>(to, from, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pstore_partial<bfloat16>(bfloat16* to, const Packet8bf& from, const Index n,
+                                                  const Index offset) {
+  pstore_partial_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from.m_val, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pstore_partial<signed char>(signed char* to, const Packet16c& from, const Index n,
+                                                     const Index offset) {
+  pstore_partial_common<Packet16c>(to, from, n, offset);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pstore_partial<unsigned char>(unsigned char* to, const Packet16uc& from, const Index n,
+                                                       const Index offset) {
+  pstore_partial_common<Packet16uc>(to, from, n, offset);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet pset1_size4(const __UNPACK_TYPE__(Packet) & from) {
+  Packet v = {from, from, from, from};
+  return v;
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet pset1_size8(const __UNPACK_TYPE__(Packet) & from) {
+  Packet v = {from, from, from, from, from, from, from, from};
+  return v;
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet pset1_size16(const __UNPACK_TYPE__(Packet) & from) {
+  Packet v = {from, from, from, from, from, from, from, from, from, from, from, from, from, from, from, from};
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
+  return pset1_size4<Packet4f>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) {
+  return pset1_size4<Packet4i>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8s pset1<Packet8s>(const short int& from) {
+  return pset1_size8<Packet8s>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8us pset1<Packet8us>(const unsigned short int& from) {
+  return pset1_size8<Packet8us>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c pset1<Packet16c>(const signed char& from) {
+  return pset1_size16<Packet16c>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16uc pset1<Packet16uc>(const unsigned char& from) {
+  return pset1_size16<Packet16uc>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(unsigned int from) {
+  return reinterpret_cast<Packet4f>(pset1<Packet4i>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pset1<Packet8bf>(const bfloat16& from) {
+  return pset1_size8<Packet8us>(reinterpret_cast<const unsigned short int&>(from));
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE void pbroadcast4_common(const __UNPACK_TYPE__(Packet) * a, Packet& a0, Packet& a1, Packet& a2,
+                                            Packet& a3) {
+  a3 = pload<Packet>(a);
+  a0 = vec_splat(a3, 0);
+  a1 = vec_splat(a3, 1);
+  a2 = vec_splat(a3, 2);
+  a3 = vec_splat(a3, 3);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet4f>(const float* a, Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3) {
+  pbroadcast4_common<Packet4f>(a, a0, a1, a2, a3);
+}
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet4i>(const int* a, Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3) {
+  pbroadcast4_common<Packet4i>(a, a0, a1, a2, a3);
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pgather_common(const __UNPACK_TYPE__(Packet) * from, Index stride,
+                                                            const Index n = unpacket_traits<Packet>::size) {
+  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[unpacket_traits<Packet>::size];
+  eigen_internal_assert(n <= unpacket_traits<Packet>::size && "number of elements will gather past end of packet");
+  if (stride == 1) {
+    if (n == unpacket_traits<Packet>::size) {
+      return ploadu<Packet>(from);
+    } else {
+      return ploadu_partial<Packet>(from, n);
+    }
+  } else {
+    LOAD_STORE_UNROLL_16
+    for (Index i = 0; i < n; i++) {
+      a[i] = from[i * stride];
+    }
+    // Leave rest of the array uninitialized
+    return pload_ignore<Packet>(a);
+  }
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4f pgather<float, Packet4f>(const float* from, Index stride) {
+  return pgather_common<Packet4f>(from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4i pgather<int, Packet4i>(const int* from, Index stride) {
+  return pgather_common<Packet4i>(from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8s pgather<short int, Packet8s>(const short int* from, Index stride) {
+  return pgather_common<Packet8s>(from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8us pgather<unsigned short int, Packet8us>(const unsigned short int* from,
+                                                                                       Index stride) {
+  return pgather_common<Packet8us>(from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8bf pgather<bfloat16, Packet8bf>(const bfloat16* from, Index stride) {
+  return pgather_common<Packet8bf>(from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet16c pgather<signed char, Packet16c>(const signed char* from, Index stride) {
+  return pgather_common<Packet16c>(from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet16uc pgather<unsigned char, Packet16uc>(const unsigned char* from,
+                                                                                    Index stride) {
+  return pgather_common<Packet16uc>(from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4f pgather_partial<float, Packet4f>(const float* from, Index stride,
+                                                                                const Index n) {
+  return pgather_common<Packet4f>(from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4i pgather_partial<int, Packet4i>(const int* from, Index stride,
+                                                                              const Index n) {
+  return pgather_common<Packet4i>(from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8s pgather_partial<short int, Packet8s>(const short int* from, Index stride,
+                                                                                    const Index n) {
+  return pgather_common<Packet8s>(from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8us
+pgather_partial<unsigned short int, Packet8us>(const unsigned short int* from, Index stride, const Index n) {
+  return pgather_common<Packet8us>(from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8bf pgather_partial<bfloat16, Packet8bf>(const bfloat16* from, Index stride,
+                                                                                     const Index n) {
+  return pgather_common<Packet8bf>(from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet16c pgather_partial<signed char, Packet16c>(const signed char* from,
+                                                                                        Index stride, const Index n) {
+  return pgather_common<Packet16c>(from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet16uc pgather_partial<unsigned char, Packet16uc>(const unsigned char* from,
+                                                                                            Index stride,
+                                                                                            const Index n) {
+  return pgather_common<Packet16uc>(from, stride, n);
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_common(__UNPACK_TYPE__(Packet) * to, const Packet& from,
+                                                           Index stride,
+                                                           const Index n = unpacket_traits<Packet>::size) {
+  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[unpacket_traits<Packet>::size];
+  eigen_internal_assert(n <= unpacket_traits<Packet>::size && "number of elements will scatter past end of packet");
+  if (stride == 1) {
+    if (n == unpacket_traits<Packet>::size) {
+      return pstoreu(to, from);
+    } else {
+      return pstoreu_partial(to, from, n);
+    }
+  } else {
+    pstore<__UNPACK_TYPE__(Packet)>(a, from);
+    LOAD_STORE_UNROLL_16
+    for (Index i = 0; i < n; i++) {
+      to[i * stride] = a[i];
+    }
+  }
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride) {
+  pscatter_common<Packet4f>(to, from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride) {
+  pscatter_common<Packet4i>(to, from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<short int, Packet8s>(short int* to, const Packet8s& from,
+                                                                         Index stride) {
+  pscatter_common<Packet8s>(to, from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<unsigned short int, Packet8us>(unsigned short int* to,
+                                                                                   const Packet8us& from,
+                                                                                   Index stride) {
+  pscatter_common<Packet8us>(to, from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<bfloat16, Packet8bf>(bfloat16* to, const Packet8bf& from,
+                                                                         Index stride) {
+  pscatter_common<Packet8bf>(to, from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<signed char, Packet16c>(signed char* to, const Packet16c& from,
+                                                                            Index stride) {
+  pscatter_common<Packet16c>(to, from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<unsigned char, Packet16uc>(unsigned char* to,
+                                                                               const Packet16uc& from, Index stride) {
+  pscatter_common<Packet16uc>(to, from, stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<float, Packet4f>(float* to, const Packet4f& from,
+                                                                             Index stride, const Index n) {
+  pscatter_common<Packet4f>(to, from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<int, Packet4i>(int* to, const Packet4i& from, Index stride,
+                                                                           const Index n) {
+  pscatter_common<Packet4i>(to, from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<short int, Packet8s>(short int* to, const Packet8s& from,
+                                                                                 Index stride, const Index n) {
+  pscatter_common<Packet8s>(to, from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<unsigned short int, Packet8us>(unsigned short int* to,
+                                                                                           const Packet8us& from,
+                                                                                           Index stride,
+                                                                                           const Index n) {
+  pscatter_common<Packet8us>(to, from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<bfloat16, Packet8bf>(bfloat16* to, const Packet8bf& from,
+                                                                                 Index stride, const Index n) {
+  pscatter_common<Packet8bf>(to, from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<signed char, Packet16c>(signed char* to,
+                                                                                    const Packet16c& from, Index stride,
+                                                                                    const Index n) {
+  pscatter_common<Packet16c>(to, from, stride, n);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<unsigned char, Packet16uc>(unsigned char* to,
+                                                                                       const Packet16uc& from,
+                                                                                       Index stride, const Index n) {
+  pscatter_common<Packet16uc>(to, from, stride, n);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) {
+  return pset1<Packet4f>(a) + p4f_COUNTDOWN;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a) {
+  return pset1<Packet4i>(a) + p4i_COUNTDOWN;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s plset<Packet8s>(const short int& a) {
+  return pset1<Packet8s>(a) + p8s_COUNTDOWN;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us plset<Packet8us>(const unsigned short int& a) {
+  return pset1<Packet8us>(a) + p8us_COUNTDOWN;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c plset<Packet16c>(const signed char& a) {
+  return pset1<Packet16c>(a) + p16c_COUNTDOWN;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc plset<Packet16uc>(const unsigned char& a) {
+  return pset1<Packet16uc>(a) + p16uc_COUNTDOWN;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return a + b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return a + b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui padd<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return a + b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s padd<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return a + b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us padd<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return a + b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c padd<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return a + b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc padd<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return a + b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return a - b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return a - b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s psub<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return a - b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us psub<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return a - b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c psub<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return a - b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc psub<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return a - b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) {
+#ifdef __POWER8_VECTOR__
+  return vec_neg(a);
+#else
+  return vec_xor(a, p4f_MZERO);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pnegate(const Packet16c& a) {
+#ifdef __POWER8_VECTOR__
+  return vec_neg(a);
+#else
+  return reinterpret_cast<Packet16c>(p4i_ZERO) - a;
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pnegate(const Packet8s& a) {
+#ifdef __POWER8_VECTOR__
+  return vec_neg(a);
+#else
+  return reinterpret_cast<Packet8s>(p4i_ZERO) - a;
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) {
+#ifdef __POWER8_VECTOR__
+  return vec_neg(a);
+#else
+  return p4i_ZERO - a;
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_madd(a, b, p4f_MZERO);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return a * b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmul<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vec_mul(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmul<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vec_mul(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pmul<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vec_mul(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmul<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vec_mul(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
+#ifndef __VSX__  // VSX actually provides a div instruction
+  Packet4f t, y_0, y_1;
+
+  // Altivec does not offer a divide instruction, we have to do a reciprocal approximation
+  y_0 = vec_re(b);
+
+  // Do one Newton-Raphson iteration to get the needed accuracy
+  t = vec_nmsub(y_0, b, p4f_ONE);
+  y_1 = vec_madd(y_0, t, y_0);
+
+  return vec_madd(a, y_1, p4f_MZERO);
+#else
+  return vec_div(a, b);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) {
+#if defined(_ARCH_PWR10) && (EIGEN_COMP_LLVM || EIGEN_GNUC_STRICT_AT_LEAST(11, 0, 0))
+  return vec_div(a, b);
+#else
+  EIGEN_UNUSED_VARIABLE(a);
+  EIGEN_UNUSED_VARIABLE(b);
+  eigen_assert(false && "packet integer division are not supported by AltiVec");
+  return pset1<Packet4i>(0);
+#endif
+}
+
+// This overload is required for integer packet types.
+template <>
+EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return vec_madd(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
+  return a * b + c;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmadd(const Packet8s& a, const Packet8s& b, const Packet8s& c) {
+  return vec_madd(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmadd(const Packet8us& a, const Packet8us& b, const Packet8us& c) {
+  return vec_madd(a, b, c);
+}
+
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+EIGEN_STRONG_INLINE Packet4f pmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return vec_msub(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return vec_nmsub(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return vec_nmadd(a, b, c);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
+#ifdef EIGEN_VECTORIZE_VSX
+  // NOTE: about 10% slower than vec_min, but consistent with std::min and SSE regarding NaN
+  Packet4f ret;
+  __asm__("xvcmpgesp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa"(ret) : "wa"(a), "wa"(b));
+  return ret;
+#else
+  return vec_min(a, b);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_min(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmin<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vec_min(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmin<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vec_min(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pmin<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vec_min(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmin<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vec_min(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
+#ifdef EIGEN_VECTORIZE_VSX
+  // NOTE: about 10% slower than vec_max, but consistent with std::max and SSE regarding NaN
+  Packet4f ret;
+  __asm__("xvcmpgtsp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa"(ret) : "wa"(a), "wa"(b));
+  return ret;
+#else
+  return vec_max(a, b);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_max(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmax<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vec_max(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmax<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vec_max(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pmax<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vec_max(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmax<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vec_max(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_le(const Packet4f& a, const Packet4f& b) {
+  return reinterpret_cast<Packet4f>(vec_cmple(a, b));
+}
+// To fix bug with vec_cmplt on older versions
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_lt(const Packet4f& a, const Packet4f& b) {
+  return reinterpret_cast<Packet4f>(vec_cmplt(a, b));
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_eq(const Packet4f& a, const Packet4f& b) {
+  return reinterpret_cast<Packet4f>(vec_cmpeq(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan(const Packet4f& a, const Packet4f& b) {
+  Packet4f c = reinterpret_cast<Packet4f>(vec_cmpge(a, b));
+  return vec_nor(c, c);
+}
+
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_le(const Packet4i& a, const Packet4i& b) {
+  return reinterpret_cast<Packet4i>(vec_cmple(a, b));
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_lt(const Packet4i& a, const Packet4i& b) {
+  return reinterpret_cast<Packet4i>(vec_cmplt(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_eq(const Packet4i& a, const Packet4i& b) {
+  return reinterpret_cast<Packet4i>(vec_cmpeq(a, b));
+}
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+EIGEN_STRONG_INLINE Packet8s pcmp_le(const Packet8s& a, const Packet8s& b) {
+  return reinterpret_cast<Packet8s>(vec_cmple(a, b));
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet8s pcmp_lt(const Packet8s& a, const Packet8s& b) {
+  return reinterpret_cast<Packet8s>(vec_cmplt(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcmp_eq(const Packet8s& a, const Packet8s& b) {
+  return reinterpret_cast<Packet8s>(vec_cmpeq(a, b));
+}
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+EIGEN_STRONG_INLINE Packet8us pcmp_le(const Packet8us& a, const Packet8us& b) {
+  return reinterpret_cast<Packet8us>(vec_cmple(a, b));
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet8us pcmp_lt(const Packet8us& a, const Packet8us& b) {
+  return reinterpret_cast<Packet8us>(vec_cmplt(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcmp_eq(const Packet8us& a, const Packet8us& b) {
+  return reinterpret_cast<Packet8us>(vec_cmpeq(a, b));
+}
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+EIGEN_STRONG_INLINE Packet16c pcmp_le(const Packet16c& a, const Packet16c& b) {
+  return reinterpret_cast<Packet16c>(vec_cmple(a, b));
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet16c pcmp_lt(const Packet16c& a, const Packet16c& b) {
+  return reinterpret_cast<Packet16c>(vec_cmplt(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcmp_eq(const Packet16c& a, const Packet16c& b) {
+  return reinterpret_cast<Packet16c>(vec_cmpeq(a, b));
+}
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcmp_le(const Packet16uc& a, const Packet16uc& b) {
+  return reinterpret_cast<Packet16uc>(vec_cmple(a, b));
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcmp_lt(const Packet16uc& a, const Packet16uc& b) {
+  return reinterpret_cast<Packet16uc>(vec_cmplt(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcmp_eq(const Packet16uc& a, const Packet16uc& b) {
+  return reinterpret_cast<Packet16uc>(vec_cmpeq(a, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_and(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_and(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vec_and(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pand<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vec_and(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pand<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return pand<Packet8us>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_or(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_or(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s por<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vec_or(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us por<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vec_or(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf por<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return por<Packet8us>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_xor(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_xor(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pxor<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vec_xor(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pxor<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return pxor<Packet8us>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_andc(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_andc(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b) {
+  return vec_sel(b, a, reinterpret_cast<Packet4ui>(mask));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a) {
+  Packet4f t = vec_add(
+      reinterpret_cast<Packet4f>(vec_or(vec_and(reinterpret_cast<Packet4ui>(a), p4ui_SIGN), p4ui_PREV0DOT5)), a);
+  Packet4f res;
+
+#ifdef EIGEN_VECTORIZE_VSX
+  __asm__("xvrspiz %x0, %x1\n\t" : "=&wa"(res) : "wa"(t));
+#else
+  __asm__("vrfiz %0, %1\n\t" : "=v"(res) : "v"(t));
+#endif
+
+  return res;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) {
+  return vec_ceil(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) {
+  return vec_floor(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f ptrunc<Packet4f>(const Packet4f& a) {
+  return vec_trunc(a);
+}
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a) {
+  Packet4f res;
+
+  __asm__("xvrspic %x0, %x1\n\t" : "=&wa"(res) : "wa"(a));
+
+  return res;
+}
+#endif
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet ploadu_common(const __UNPACK_TYPE__(Packet) * from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+#if defined(EIGEN_VECTORIZE_VSX)
+  return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
+#else
+  Packet16uc MSQ = vec_ld(0, (unsigned char*)from);   // most significant quadword
+  Packet16uc LSQ = vec_ld(15, (unsigned char*)from);  // least significant quadword
+  Packet16uc mask = vec_lvsl(0, from);                // create the permute mask
+  // TODO: Add static_cast here
+  return (Packet)vec_perm(MSQ, LSQ, mask);  // align the data
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
+  return ploadu_common<Packet4f>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from) {
+  return ploadu_common<Packet4i>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s ploadu<Packet8s>(const short int* from) {
+  return ploadu_common<Packet8s>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us ploadu<Packet8us>(const unsigned short int* from) {
+  return ploadu_common<Packet8us>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf ploadu<Packet8bf>(const bfloat16* from) {
+  return ploadu_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c ploadu<Packet16c>(const signed char* from) {
+  return ploadu_common<Packet16c>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc ploadu<Packet16uc>(const unsigned char* from) {
+  return ploadu_common<Packet16uc>(from);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet ploadu_partial_common(const __UNPACK_TYPE__(Packet) * from, const Index n,
+                                                 const Index offset) {
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_internal_assert(n + offset <= packet_size && "number of elements plus offset will read past end of packet");
+  const Index size = sizeof(__UNPACK_TYPE__(Packet));
+#ifdef _ARCH_PWR9
+  EIGEN_UNUSED_VARIABLE(packet_size);
+  EIGEN_DEBUG_ALIGNED_LOAD
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  Packet load = vec_xl_len(const_cast<__UNPACK_TYPE__(Packet)*>(from), n * size);
+  if (offset) {
+    Packet16uc shift = pset1<Packet16uc>(offset * 8 * size);
+#ifdef _BIG_ENDIAN
+    load = Packet(vec_sro(Packet16uc(load), shift));
+#else
+    load = Packet(vec_slo(Packet16uc(load), shift));
+#endif
+  }
+  return load;
+#else
+  if (n) {
+    EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) load[packet_size];
+    unsigned char* load2 = reinterpret_cast<unsigned char*>(load + offset);
+    unsigned char* from2 = reinterpret_cast<unsigned char*>(const_cast<__UNPACK_TYPE__(Packet)*>(from));
+    Index n2 = n * size;
+    if (16 <= n2) {
+      pstoreu(load2, ploadu<Packet16uc>(from2));
+    } else {
+      memcpy((void*)load2, (void*)from2, n2);
+    }
+    return pload_ignore<Packet>(load);
+  } else {
+    return Packet(pset1<Packet16uc>(0));
+  }
+#endif
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet4f ploadu_partial<Packet4f>(const float* from, const Index n, const Index offset) {
+  return ploadu_partial_common<Packet4f>(from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE Packet4i ploadu_partial<Packet4i>(const int* from, const Index n, const Index offset) {
+  return ploadu_partial_common<Packet4i>(from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE Packet8s ploadu_partial<Packet8s>(const short int* from, const Index n, const Index offset) {
+  return ploadu_partial_common<Packet8s>(from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE Packet8us ploadu_partial<Packet8us>(const unsigned short int* from, const Index n,
+                                                        const Index offset) {
+  return ploadu_partial_common<Packet8us>(from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE Packet8bf ploadu_partial<Packet8bf>(const bfloat16* from, const Index n, const Index offset) {
+  return ploadu_partial_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from), n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE Packet16c ploadu_partial<Packet16c>(const signed char* from, const Index n, const Index offset) {
+  return ploadu_partial_common<Packet16c>(from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE Packet16uc ploadu_partial<Packet16uc>(const unsigned char* from, const Index n,
+                                                          const Index offset) {
+  return ploadu_partial_common<Packet16uc>(from, n, offset);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet ploaddup_common(const __UNPACK_TYPE__(Packet) * from) {
+  Packet p;
+  if ((std::ptrdiff_t(from) % 16) == 0)
+    p = pload<Packet>(from);
+  else
+    p = ploadu<Packet>(from);
+  return vec_mergeh(p, p);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) {
+  return ploaddup_common<Packet4f>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int* from) {
+  return ploaddup_common<Packet4i>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8s ploaddup<Packet8s>(const short int* from) {
+  Packet8s p;
+  if ((std::ptrdiff_t(from) % 16) == 0)
+    p = pload<Packet8s>(from);
+  else
+    p = ploadu<Packet8s>(from);
+  return vec_mergeh(p, p);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8us ploaddup<Packet8us>(const unsigned short int* from) {
+  Packet8us p;
+  if ((std::ptrdiff_t(from) % 16) == 0)
+    p = pload<Packet8us>(from);
+  else
+    p = ploadu<Packet8us>(from);
+  return vec_mergeh(p, p);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8s ploadquad<Packet8s>(const short int* from) {
+  Packet8s p;
+  if ((std::ptrdiff_t(from) % 16) == 0)
+    p = pload<Packet8s>(from);
+  else
+    p = ploadu<Packet8s>(from);
+  return vec_perm(p, p, p16uc_QUADRUPLICATE16_HI);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8us ploadquad<Packet8us>(const unsigned short int* from) {
+  Packet8us p;
+  if ((std::ptrdiff_t(from) % 16) == 0)
+    p = pload<Packet8us>(from);
+  else
+    p = ploadu<Packet8us>(from);
+  return vec_perm(p, p, p16uc_QUADRUPLICATE16_HI);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf ploadquad<Packet8bf>(const bfloat16* from) {
+  return ploadquad<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c ploaddup<Packet16c>(const signed char* from) {
+  Packet16c p;
+  if ((std::ptrdiff_t(from) % 16) == 0)
+    p = pload<Packet16c>(from);
+  else
+    p = ploadu<Packet16c>(from);
+  return vec_mergeh(p, p);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16uc ploaddup<Packet16uc>(const unsigned char* from) {
+  Packet16uc p;
+  if ((std::ptrdiff_t(from) % 16) == 0)
+    p = pload<Packet16uc>(from);
+  else
+    p = ploadu<Packet16uc>(from);
+  return vec_mergeh(p, p);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c ploadquad<Packet16c>(const signed char* from) {
+  Packet16c p;
+  if ((std::ptrdiff_t(from) % 16) == 0)
+    p = pload<Packet16c>(from);
+  else
+    p = ploadu<Packet16c>(from);
+  return vec_perm(p, p, p16uc_QUADRUPLICATE16);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16uc ploadquad<Packet16uc>(const unsigned char* from) {
+  Packet16uc p;
+  if ((std::ptrdiff_t(from) % 16) == 0)
+    p = pload<Packet16uc>(from);
+  else
+    p = ploadu<Packet16uc>(from);
+  return vec_perm(p, p, p16uc_QUADRUPLICATE16);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE void pstoreu_common(__UNPACK_TYPE__(Packet) * to, const Packet& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE
+#if defined(EIGEN_VECTORIZE_VSX)
+  vec_xst(from, 0, to);
+#else
+  // Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html
+  // Warning: not thread safe!
+  Packet16uc MSQ, LSQ, edges;
+  Packet16uc edgeAlign, align;
+
+  MSQ = vec_ld(0, (unsigned char*)to);             // most significant quadword
+  LSQ = vec_ld(15, (unsigned char*)to);            // least significant quadword
+  edgeAlign = vec_lvsl(0, to);                     // permute map to extract edges
+  edges = vec_perm(LSQ, MSQ, edgeAlign);           // extract the edges
+  align = vec_lvsr(0, to);                         // permute map to misalign data
+  MSQ = vec_perm(edges, (Packet16uc)from, align);  // misalign the data (MSQ)
+  LSQ = vec_perm((Packet16uc)from, edges, align);  // misalign the data (LSQ)
+  vec_st(LSQ, 15, (unsigned char*)to);             // Store the LSQ part first
+  vec_st(MSQ, 0, (unsigned char*)to);              // Store the MSQ part second
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) {
+  pstoreu_common<Packet4f>(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from) {
+  pstoreu_common<Packet4i>(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<short int>(short int* to, const Packet8s& from) {
+  pstoreu_common<Packet8s>(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<unsigned short int>(unsigned short int* to, const Packet8us& from) {
+  pstoreu_common<Packet8us>(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet8bf& from) {
+  pstoreu_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from.m_val);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<signed char>(signed char* to, const Packet16c& from) {
+  pstoreu_common<Packet16c>(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<unsigned char>(unsigned char* to, const Packet16uc& from) {
+  pstoreu_common<Packet16uc>(to, from);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void pstoreu_partial_common(__UNPACK_TYPE__(Packet) * to, const Packet& from, const Index n,
+                                                const Index offset) {
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_internal_assert(n + offset <= packet_size && "number of elements plus offset will write past end of packet");
+  const Index size = sizeof(__UNPACK_TYPE__(Packet));
+#ifdef _ARCH_PWR9
+  EIGEN_UNUSED_VARIABLE(packet_size);
+  EIGEN_DEBUG_UNALIGNED_STORE
+  Packet store = from;
+  if (offset) {
+    Packet16uc shift = pset1<Packet16uc>(offset * 8 * size);
+#ifdef _BIG_ENDIAN
+    store = Packet(vec_slo(Packet16uc(store), shift));
+#else
+    store = Packet(vec_sro(Packet16uc(store), shift));
+#endif
+  }
+  vec_xst_len(store, to, n * size);
+#else
+  if (n) {
+    EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) store[packet_size];
+    pstore(store, from);
+    unsigned char* store2 = reinterpret_cast<unsigned char*>(store + offset);
+    unsigned char* to2 = reinterpret_cast<unsigned char*>(to);
+    Index n2 = n * size;
+    if (16 <= n2) {
+      pstoreu(to2, ploadu<Packet16uc>(store2));
+    } else {
+      memcpy((void*)to2, (void*)store2, n2);
+    }
+  }
+#endif
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pstoreu_partial<float>(float* to, const Packet4f& from, const Index n, const Index offset) {
+  pstoreu_partial_common<Packet4f>(to, from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE void pstoreu_partial<int>(int* to, const Packet4i& from, const Index n, const Index offset) {
+  pstoreu_partial_common<Packet4i>(to, from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE void pstoreu_partial<short int>(short int* to, const Packet8s& from, const Index n,
+                                                    const Index offset) {
+  pstoreu_partial_common<Packet8s>(to, from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE void pstoreu_partial<unsigned short int>(unsigned short int* to, const Packet8us& from,
+                                                             const Index n, const Index offset) {
+  pstoreu_partial_common<Packet8us>(to, from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE void pstoreu_partial<bfloat16>(bfloat16* to, const Packet8bf& from, const Index n,
+                                                   const Index offset) {
+  pstoreu_partial_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE void pstoreu_partial<signed char>(signed char* to, const Packet16c& from, const Index n,
+                                                      const Index offset) {
+  pstoreu_partial_common<Packet16c>(to, from, n, offset);
+}
+template <>
+EIGEN_ALWAYS_INLINE void pstoreu_partial<unsigned char>(unsigned char* to, const Packet16uc& from, const Index n,
+                                                        const Index offset) {
+  pstoreu_partial_common<Packet16uc>(to, from, n, offset);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
+  EIGEN_PPC_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) {
+  EIGEN_PPC_PREFETCH(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
+  EIGEN_ALIGN16 float x;
+  vec_ste(a, 0, &x);
+  return x;
+}
+template <>
+EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) {
+  EIGEN_ALIGN16 int x;
+  vec_ste(a, 0, &x);
+  return x;
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) pfirst_common(const Packet& a) {
+  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) x;
+  vec_ste(a, 0, &x);
+  return x;
+}
+
+template <>
+EIGEN_STRONG_INLINE short int pfirst<Packet8s>(const Packet8s& a) {
+  return pfirst_common<Packet8s>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE unsigned short int pfirst<Packet8us>(const Packet8us& a) {
+  return pfirst_common<Packet8us>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE signed char pfirst<Packet16c>(const Packet16c& a) {
+  return pfirst_common<Packet16c>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE unsigned char pfirst<Packet16uc>(const Packet16uc& a) {
+  return pfirst_common<Packet16uc>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) {
+  return reinterpret_cast<Packet4f>(
+      vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) {
+  return reinterpret_cast<Packet4i>(
+      vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s preverse(const Packet8s& a) {
+  return reinterpret_cast<Packet8s>(
+      vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us preverse(const Packet8us& a) {
+  return reinterpret_cast<Packet8us>(
+      vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c preverse(const Packet16c& a) {
+  return vec_perm(a, a, p16uc_REVERSE8);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc preverse(const Packet16uc& a) {
+  return vec_perm(a, a, p16uc_REVERSE8);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf preverse(const Packet8bf& a) {
+  return preverse<Packet8us>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) {
+  return vec_abs(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) {
+  return vec_abs(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pabs(const Packet8s& a) {
+  return vec_abs(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pabs(const Packet8us& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pabs(const Packet16c& a) {
+  return vec_abs(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pabs(const Packet16uc& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pabs(const Packet8bf& a) {
+  EIGEN_DECLARE_CONST_FAST_Packet8us(abs_mask, 0x7FFF);
+  return pand<Packet8us>(p8us_abs_mask, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf psignbit(const Packet8bf& a) {
+  return vec_sra(a.m_val, vec_splat_u16(15));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f psignbit(const Packet4f& a) {
+  return (Packet4f)vec_sra((Packet4i)a, vec_splats((unsigned int)(31)));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(const Packet4i& a) {
+  return vec_sra(a, reinterpret_cast<Packet4ui>(pset1<Packet4i>(N)));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i plogical_shift_right(const Packet4i& a) {
+  return vec_sr(a, reinterpret_cast<Packet4ui>(pset1<Packet4i>(N)));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i plogical_shift_left(const Packet4i& a) {
+  return vec_sl(a, reinterpret_cast<Packet4ui>(pset1<Packet4i>(N)));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4f plogical_shift_left(const Packet4f& a) {
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
+  Packet4ui r = vec_sl(reinterpret_cast<Packet4ui>(a), p4ui_mask);
+  return reinterpret_cast<Packet4f>(r);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4f plogical_shift_right(const Packet4f& a) {
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
+  Packet4ui r = vec_sr(reinterpret_cast<Packet4ui>(a), p4ui_mask);
+  return reinterpret_cast<Packet4f>(r);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui plogical_shift_right(const Packet4ui& a) {
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
+  return vec_sr(a, p4ui_mask);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui plogical_shift_left(const Packet4ui& a) {
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
+  return vec_sl(a, p4ui_mask);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet8us plogical_shift_left(const Packet8us& a) {
+  const EIGEN_DECLARE_CONST_FAST_Packet8us(mask, N);
+  return vec_sl(a, p8us_mask);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8us plogical_shift_right(const Packet8us& a) {
+  const EIGEN_DECLARE_CONST_FAST_Packet8us(mask, N);
+  return vec_sr(a, p8us_mask);
+}
+
+EIGEN_STRONG_INLINE Packet4f Bf16ToF32Even(const Packet8bf& bf) {
+  return plogical_shift_left<16>(reinterpret_cast<Packet4f>(bf.m_val));
+}
+
+EIGEN_STRONG_INLINE Packet4f Bf16ToF32Odd(const Packet8bf& bf) {
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(high_mask, 0xFFFF0000);
+  return pand<Packet4f>(reinterpret_cast<Packet4f>(bf.m_val), reinterpret_cast<Packet4f>(p4ui_high_mask));
+}
+
+EIGEN_ALWAYS_INLINE Packet8us pmerge(Packet4ui even, Packet4ui odd) {
+#ifdef _BIG_ENDIAN
+  return vec_perm(reinterpret_cast<Packet8us>(odd), reinterpret_cast<Packet8us>(even), p16uc_MERGEO16);
+#else
+  return vec_perm(reinterpret_cast<Packet8us>(even), reinterpret_cast<Packet8us>(odd), p16uc_MERGEE16);
+#endif
+}
+
+// Simple interleaving of bool masks, prevents true values from being
+// converted to NaNs.
+EIGEN_STRONG_INLINE Packet8bf F32ToBf16Bool(Packet4f even, Packet4f odd) {
+  return pmerge(reinterpret_cast<Packet4ui>(even), reinterpret_cast<Packet4ui>(odd));
+}
+
+// #define SUPPORT_BF16_SUBNORMALS
+
+#ifndef __VEC_CLASS_FP_NAN
+#define __VEC_CLASS_FP_NAN (1 << 6)
+#endif
+
+#if defined(SUPPORT_BF16_SUBNORMALS) && !defined(__VEC_CLASS_FP_SUBNORMAL)
+#define __VEC_CLASS_FP_SUBNORMAL_P (1 << 1)
+#define __VEC_CLASS_FP_SUBNORMAL_N (1 << 0)
+
+#define __VEC_CLASS_FP_SUBNORMAL (__VEC_CLASS_FP_SUBNORMAL_P | __VEC_CLASS_FP_SUBNORMAL_N)
+#endif
+
+EIGEN_STRONG_INLINE Packet8bf F32ToBf16(Packet4f p4f) {
+#ifdef _ARCH_PWR10
+  return reinterpret_cast<Packet8us>(__builtin_vsx_xvcvspbf16(reinterpret_cast<Packet16uc>(p4f)));
+#else
+  Packet4ui input = reinterpret_cast<Packet4ui>(p4f);
+  Packet4ui lsb = plogical_shift_right<16>(input);
+  lsb = pand<Packet4ui>(lsb, reinterpret_cast<Packet4ui>(p4i_ONE));
+
+  EIGEN_DECLARE_CONST_FAST_Packet4ui(BIAS, 0x7FFFu);
+  Packet4ui rounding_bias = padd<Packet4ui>(lsb, p4ui_BIAS);
+  input = padd<Packet4ui>(input, rounding_bias);
+
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(nan, 0x7FC00000);
+#if defined(_ARCH_PWR9) && defined(EIGEN_VECTORIZE_VSX)
+  Packet4bi nan_selector = vec_test_data_class(p4f, __VEC_CLASS_FP_NAN);
+  input = vec_sel(input, p4ui_nan, nan_selector);
+
+#ifdef SUPPORT_BF16_SUBNORMALS
+  Packet4bi subnormal_selector = vec_test_data_class(p4f, __VEC_CLASS_FP_SUBNORMAL);
+  input = vec_sel(input, reinterpret_cast<Packet4ui>(p4f), subnormal_selector);
+#endif
+#else
+#ifdef SUPPORT_BF16_SUBNORMALS
+  // Test NaN and Subnormal
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(exp_mask, 0x7F800000);
+  Packet4ui exp = pand<Packet4ui>(p4ui_exp_mask, reinterpret_cast<Packet4ui>(p4f));
+
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(mantissa_mask, 0x7FFFFF);
+  Packet4ui mantissa = pand<Packet4ui>(p4ui_mantissa_mask, reinterpret_cast<Packet4ui>(p4f));
+
+  Packet4bi is_max_exp = vec_cmpeq(exp, p4ui_exp_mask);
+  Packet4bi is_mant_zero = vec_cmpeq(mantissa, reinterpret_cast<Packet4ui>(p4i_ZERO));
+
+  Packet4ui nan_selector =
+      pandnot<Packet4ui>(reinterpret_cast<Packet4ui>(is_max_exp), reinterpret_cast<Packet4ui>(is_mant_zero));
+
+  Packet4bi is_zero_exp = vec_cmpeq(exp, reinterpret_cast<Packet4ui>(p4i_ZERO));
+
+  Packet4ui subnormal_selector =
+      pandnot<Packet4ui>(reinterpret_cast<Packet4ui>(is_zero_exp), reinterpret_cast<Packet4ui>(is_mant_zero));
+
+  input = vec_sel(input, p4ui_nan, nan_selector);
+  input = vec_sel(input, reinterpret_cast<Packet4ui>(p4f), subnormal_selector);
+#else
+  // Test only NaN
+  Packet4bi nan_selector = vec_cmpeq(p4f, p4f);
+
+  input = vec_sel(p4ui_nan, input, nan_selector);
+#endif
+#endif
+
+  input = plogical_shift_right<16>(input);
+  return reinterpret_cast<Packet8us>(input);
+#endif
+}
+
+#ifdef _BIG_ENDIAN
+/**
+ * Pack the high portion of two float Packets into one bfloat16 Packet
+ *
+ * @tparam lohi to expect either a low & high OR odd & even order
+ */
+template <bool lohi>
+EIGEN_ALWAYS_INLINE Packet8bf Bf16PackHigh(Packet4f lo, Packet4f hi) {
+  if (lohi) {
+    return vec_perm(reinterpret_cast<Packet8us>(lo), reinterpret_cast<Packet8us>(hi), p16uc_MERGEH16);
+  } else {
+    return vec_perm(reinterpret_cast<Packet8us>(hi), reinterpret_cast<Packet8us>(lo), p16uc_MERGEE16);
+  }
+}
+
+/**
+ * Pack the low portion of two float Packets into one bfloat16 Packet
+ *
+ * @param lohi to expect either a low & high OR odd & even order
+ */
+template <bool lohi>
+EIGEN_ALWAYS_INLINE Packet8bf Bf16PackLow(Packet4f lo, Packet4f hi) {
+  if (lohi) {
+    return vec_pack(reinterpret_cast<Packet4ui>(lo), reinterpret_cast<Packet4ui>(hi));
+  } else {
+    return vec_perm(reinterpret_cast<Packet8us>(hi), reinterpret_cast<Packet8us>(lo), p16uc_MERGEO16);
+  }
+}
+#else
+template <bool lohi>
+EIGEN_ALWAYS_INLINE Packet8bf Bf16PackLow(Packet4f hi, Packet4f lo) {
+  if (lohi) {
+    return vec_pack(reinterpret_cast<Packet4ui>(hi), reinterpret_cast<Packet4ui>(lo));
+  } else {
+    return vec_perm(reinterpret_cast<Packet8us>(hi), reinterpret_cast<Packet8us>(lo), p16uc_MERGEE16);
+  }
+}
+
+template <bool lohi>
+EIGEN_ALWAYS_INLINE Packet8bf Bf16PackHigh(Packet4f hi, Packet4f lo) {
+  if (lohi) {
+    return vec_perm(reinterpret_cast<Packet8us>(hi), reinterpret_cast<Packet8us>(lo), p16uc_MERGEL16);
+  } else {
+    return vec_perm(reinterpret_cast<Packet8us>(hi), reinterpret_cast<Packet8us>(lo), p16uc_MERGEO16);
+  }
+}
+#endif
+
+/**
+ * Convert and pack two float Packets into one bfloat16 Packet
+ *
+ * @tparam lohi to expect either a low & high OR odd & even order
+ */
+template <bool lohi = true>
+EIGEN_ALWAYS_INLINE Packet8bf F32ToBf16Two(Packet4f lo, Packet4f hi) {
+  Packet8us p4f = Bf16PackHigh<lohi>(lo, hi);
+  Packet8us p4f2 = Bf16PackLow<lohi>(lo, hi);
+
+  Packet8us lsb = pand<Packet8us>(p4f, p8us_ONE);
+  EIGEN_DECLARE_CONST_FAST_Packet8us(BIAS, 0x7FFFu);
+  lsb = padd<Packet8us>(lsb, p8us_BIAS);
+  lsb = padd<Packet8us>(lsb, p4f2);
+
+  Packet8bi rounding_bias = vec_cmplt(lsb, p4f2);
+  Packet8us input = psub<Packet8us>(p4f, reinterpret_cast<Packet8us>(rounding_bias));
+
+#if defined(_ARCH_PWR9) && defined(EIGEN_VECTORIZE_VSX)
+  Packet4bi nan_selector_lo = vec_test_data_class(lo, __VEC_CLASS_FP_NAN);
+  Packet4bi nan_selector_hi = vec_test_data_class(hi, __VEC_CLASS_FP_NAN);
+  Packet8us nan_selector =
+      Bf16PackLow<lohi>(reinterpret_cast<Packet4f>(nan_selector_lo), reinterpret_cast<Packet4f>(nan_selector_hi));
+
+  input = vec_sel(input, p8us_BIAS, nan_selector);
+
+#ifdef SUPPORT_BF16_SUBNORMALS
+  Packet4bi subnormal_selector_lo = vec_test_data_class(lo, __VEC_CLASS_FP_SUBNORMAL);
+  Packet4bi subnormal_selector_hi = vec_test_data_class(hi, __VEC_CLASS_FP_SUBNORMAL);
+  Packet8us subnormal_selector = Bf16PackLow<lohi>(reinterpret_cast<Packet4f>(subnormal_selector_lo),
+                                                   reinterpret_cast<Packet4f>(subnormal_selector_hi));
+
+  input = vec_sel(input, reinterpret_cast<Packet8us>(p4f), subnormal_selector);
+#endif
+#else
+#ifdef SUPPORT_BF16_SUBNORMALS
+  // Test NaN and Subnormal
+  const EIGEN_DECLARE_CONST_FAST_Packet8us(exp_mask, 0x7F80);
+  Packet8us exp = pand<Packet8us>(p8us_exp_mask, p4f);
+
+  const EIGEN_DECLARE_CONST_FAST_Packet8us(mantissa_mask, 0x7Fu);
+  Packet8us mantissa = pand<Packet8us>(p8us_mantissa_mask, p4f);
+
+  Packet8bi is_max_exp = vec_cmpeq(exp, p8us_exp_mask);
+  Packet8bi is_mant_zero = vec_cmpeq(mantissa, reinterpret_cast<Packet8us>(p4i_ZERO));
+
+  Packet8us nan_selector =
+      pandnot<Packet8us>(reinterpret_cast<Packet8us>(is_max_exp), reinterpret_cast<Packet8us>(is_mant_zero));
+
+  Packet8bi is_zero_exp = vec_cmpeq(exp, reinterpret_cast<Packet8us>(p4i_ZERO));
+
+  Packet8us subnormal_selector =
+      pandnot<Packet8us>(reinterpret_cast<Packet8us>(is_zero_exp), reinterpret_cast<Packet8us>(is_mant_zero));
+
+  // Using BIAS as NaN (since any or all of the last 7 bits can be set)
+  input = vec_sel(input, p8us_BIAS, nan_selector);
+  input = vec_sel(input, reinterpret_cast<Packet8us>(p4f), subnormal_selector);
+#else
+  // Test only NaN
+  Packet4bi nan_selector_lo = vec_cmpeq(lo, lo);
+  Packet4bi nan_selector_hi = vec_cmpeq(hi, hi);
+  Packet8us nan_selector =
+      Bf16PackLow<lohi>(reinterpret_cast<Packet4f>(nan_selector_lo), reinterpret_cast<Packet4f>(nan_selector_hi));
+
+  input = vec_sel(p8us_BIAS, input, nan_selector);
+#endif
+#endif
+
+  return input;
+}
+
+/**
+ * Convert and pack two float Packets into one bfloat16 Packet - low & high order
+ */
+EIGEN_STRONG_INLINE Packet8bf F32ToBf16Both(Packet4f lo, Packet4f hi) {
+#ifdef _ARCH_PWR10
+  Packet8bf fp16_0 = F32ToBf16(lo);
+  Packet8bf fp16_1 = F32ToBf16(hi);
+  return vec_pack(reinterpret_cast<Packet4ui>(fp16_0.m_val), reinterpret_cast<Packet4ui>(fp16_1.m_val));
+#else
+  return F32ToBf16Two(lo, hi);
+#endif
+}
+
+/**
+ * Convert and pack two float Packets into one bfloat16 Packet - odd & even order
+ */
+EIGEN_STRONG_INLINE Packet8bf F32ToBf16(Packet4f even, Packet4f odd) {
+#ifdef _ARCH_PWR10
+  return pmerge(reinterpret_cast<Packet4ui>(F32ToBf16(even).m_val), reinterpret_cast<Packet4ui>(F32ToBf16(odd).m_val));
+#else
+  return F32ToBf16Two<false>(even, odd);
+#endif
+}
+#define BF16_TO_F32_UNARY_OP_WRAPPER(OP, A) \
+  Packet4f a_even = Bf16ToF32Even(A);       \
+  Packet4f a_odd = Bf16ToF32Odd(A);         \
+  Packet4f op_even = OP(a_even);            \
+  Packet4f op_odd = OP(a_odd);              \
+  return F32ToBf16(op_even, op_odd);
+
+#define BF16_TO_F32_BINARY_OP_WRAPPER(OP, A, B) \
+  Packet4f a_even = Bf16ToF32Even(A);           \
+  Packet4f a_odd = Bf16ToF32Odd(A);             \
+  Packet4f b_even = Bf16ToF32Even(B);           \
+  Packet4f b_odd = Bf16ToF32Odd(B);             \
+  Packet4f op_even = OP(a_even, b_even);        \
+  Packet4f op_odd = OP(a_odd, b_odd);           \
+  return F32ToBf16(op_even, op_odd);
+
+#define BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(OP, A, B) \
+  Packet4f a_even = Bf16ToF32Even(A);                \
+  Packet4f a_odd = Bf16ToF32Odd(A);                  \
+  Packet4f b_even = Bf16ToF32Even(B);                \
+  Packet4f b_odd = Bf16ToF32Odd(B);                  \
+  Packet4f op_even = OP(a_even, b_even);             \
+  Packet4f op_odd = OP(a_odd, b_odd);                \
+  return F32ToBf16Bool(op_even, op_odd);
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf padd<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(padd<Packet4f>, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmul<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(pmul<Packet4f>, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pdiv<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(pdiv<Packet4f>, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pnegate<Packet8bf>(const Packet8bf& a) {
+  EIGEN_DECLARE_CONST_FAST_Packet8us(neg_mask, 0x8000);
+  return pxor<Packet8us>(p8us_neg_mask, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf psub<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(psub<Packet4f>, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pexp<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(pexp_float, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pexp2<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(generic_exp2, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pldexp<Packet8bf>(const Packet8bf& a, const Packet8bf& exponent) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(pldexp<Packet4f>, a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pfrexp<Packet8bf>(const Packet8bf& a, Packet8bf& e) {
+  Packet4f a_even = Bf16ToF32Even(a);
+  Packet4f a_odd = Bf16ToF32Odd(a);
+  Packet4f e_even;
+  Packet4f e_odd;
+  Packet4f op_even = pfrexp<Packet4f>(a_even, e_even);
+  Packet4f op_odd = pfrexp<Packet4f>(a_odd, e_odd);
+  e = F32ToBf16(e_even, e_odd);
+  return F32ToBf16(op_even, op_odd);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf psin<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(psin_float, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcos<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(pcos_float, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf plog<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(plog_float, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pfloor<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(pfloor<Packet4f>, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pceil<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(pceil<Packet4f>, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pround<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(pround<Packet4f>, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf ptrunc<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(ptrunc<Packet4f>, a);
+}
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+EIGEN_STRONG_INLINE Packet8bf print<Packet8bf>(const Packet8bf& a) {
+  BF16_TO_F32_UNARY_OP_WRAPPER(print<Packet4f>, a);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmadd(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
+  Packet4f a_even = Bf16ToF32Even(a);
+  Packet4f a_odd = Bf16ToF32Odd(a);
+  Packet4f b_even = Bf16ToF32Even(b);
+  Packet4f b_odd = Bf16ToF32Odd(b);
+  Packet4f c_even = Bf16ToF32Even(c);
+  Packet4f c_odd = Bf16ToF32Odd(c);
+  Packet4f pmadd_even = pmadd<Packet4f>(a_even, b_even, c_even);
+  Packet4f pmadd_odd = pmadd<Packet4f>(a_odd, b_odd, c_odd);
+  return F32ToBf16(pmadd_even, pmadd_odd);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmsub(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
+  Packet4f a_even = Bf16ToF32Even(a);
+  Packet4f a_odd = Bf16ToF32Odd(a);
+  Packet4f b_even = Bf16ToF32Even(b);
+  Packet4f b_odd = Bf16ToF32Odd(b);
+  Packet4f c_even = Bf16ToF32Even(c);
+  Packet4f c_odd = Bf16ToF32Odd(c);
+  Packet4f pmadd_even = pmsub<Packet4f>(a_even, b_even, c_even);
+  Packet4f pmadd_odd = pmsub<Packet4f>(a_odd, b_odd, c_odd);
+  return F32ToBf16(pmadd_even, pmadd_odd);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pnmadd(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
+  Packet4f a_even = Bf16ToF32Even(a);
+  Packet4f a_odd = Bf16ToF32Odd(a);
+  Packet4f b_even = Bf16ToF32Even(b);
+  Packet4f b_odd = Bf16ToF32Odd(b);
+  Packet4f c_even = Bf16ToF32Even(c);
+  Packet4f c_odd = Bf16ToF32Odd(c);
+  Packet4f pmadd_even = pnmadd<Packet4f>(a_even, b_even, c_even);
+  Packet4f pmadd_odd = pnmadd<Packet4f>(a_odd, b_odd, c_odd);
+  return F32ToBf16(pmadd_even, pmadd_odd);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pnmsub(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
+  Packet4f a_even = Bf16ToF32Even(a);
+  Packet4f a_odd = Bf16ToF32Odd(a);
+  Packet4f b_even = Bf16ToF32Even(b);
+  Packet4f b_odd = Bf16ToF32Odd(b);
+  Packet4f c_even = Bf16ToF32Even(c);
+  Packet4f c_odd = Bf16ToF32Odd(c);
+  Packet4f pmadd_even = pnmsub<Packet4f>(a_even, b_even, c_even);
+  Packet4f pmadd_odd = pnmsub<Packet4f>(a_odd, b_odd, c_odd);
+  return F32ToBf16(pmadd_even, pmadd_odd);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmin<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(pmin<Packet4f>, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmax<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(pmax<Packet4f>, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcmp_lt(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_lt<Packet4f>, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcmp_lt_or_nan(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_lt_or_nan<Packet4f>, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcmp_le(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_le<Packet4f>, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcmp_eq(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_eq<Packet4f>, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 pfirst(const Packet8bf& a) {
+  return Eigen::bfloat16_impl::raw_uint16_to_bfloat16((pfirst<Packet8us>(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf ploaddup<Packet8bf>(const bfloat16* from) {
+  return ploaddup<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf plset<Packet8bf>(const bfloat16& a) {
+  bfloat16 countdown[8] = {bfloat16(0), bfloat16(1), bfloat16(2), bfloat16(3),
+                           bfloat16(4), bfloat16(5), bfloat16(6), bfloat16(7)};
+  return padd<Packet8bf>(pset1<Packet8bf>(a), pload<Packet8bf>(countdown));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) {
+  Packet4f b, sum;
+  b = vec_sld(a, a, 8);
+  sum = a + b;
+  b = vec_sld(sum, sum, 4);
+  sum += b;
+  return pfirst(sum);
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a) {
+  Packet4i b, sum;
+  b = vec_sld(a, a, 8);
+  sum = a + b;
+  b = vec_sld(sum, sum, 4);
+  sum += b;
+  return pfirst(sum);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux<Packet8bf>(const Packet8bf& a) {
+  float redux_even = predux<Packet4f>(Bf16ToF32Even(a));
+  float redux_odd = predux<Packet4f>(Bf16ToF32Odd(a));
+  float f32_result = redux_even + redux_odd;
+  return bfloat16(f32_result);
+}
+template <typename Packet>
+EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_size8(const Packet& a) {
+  union {
+    Packet v;
+    __UNPACK_TYPE__(Packet) n[8];
+  } vt;
+  vt.v = a;
+
+  EIGEN_ALIGN16 int first_loader[4] = {vt.n[0], vt.n[1], vt.n[2], vt.n[3]};
+  EIGEN_ALIGN16 int second_loader[4] = {vt.n[4], vt.n[5], vt.n[6], vt.n[7]};
+  Packet4i first_half = pload<Packet4i>(first_loader);
+  Packet4i second_half = pload<Packet4i>(second_loader);
+
+  return static_cast<__UNPACK_TYPE__(Packet)>(predux(first_half) + predux(second_half));
+}
+
+template <>
+EIGEN_STRONG_INLINE short int predux<Packet8s>(const Packet8s& a) {
+  return predux_size8<Packet8s>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE unsigned short int predux<Packet8us>(const Packet8us& a) {
+  return predux_size8<Packet8us>(a);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_size16(const Packet& a) {
+  union {
+    Packet v;
+    __UNPACK_TYPE__(Packet) n[16];
+  } vt;
+  vt.v = a;
+
+  EIGEN_ALIGN16 int first_loader[4] = {vt.n[0], vt.n[1], vt.n[2], vt.n[3]};
+  EIGEN_ALIGN16 int second_loader[4] = {vt.n[4], vt.n[5], vt.n[6], vt.n[7]};
+  EIGEN_ALIGN16 int third_loader[4] = {vt.n[8], vt.n[9], vt.n[10], vt.n[11]};
+  EIGEN_ALIGN16 int fourth_loader[4] = {vt.n[12], vt.n[13], vt.n[14], vt.n[15]};
+
+  Packet4i first_quarter = pload<Packet4i>(first_loader);
+  Packet4i second_quarter = pload<Packet4i>(second_loader);
+  Packet4i third_quarter = pload<Packet4i>(third_loader);
+  Packet4i fourth_quarter = pload<Packet4i>(fourth_loader);
+
+  return static_cast<__UNPACK_TYPE__(Packet)>(predux(first_quarter) + predux(second_quarter) + predux(third_quarter) +
+                                              predux(fourth_quarter));
+}
+
+template <>
+EIGEN_STRONG_INLINE signed char predux<Packet16c>(const Packet16c& a) {
+  return predux_size16<Packet16c>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE unsigned char predux<Packet16uc>(const Packet16uc& a) {
+  return predux_size16<Packet16uc>(a);
+}
+
+// Other reduction functions:
+// mul
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) {
+  Packet4f prod;
+  prod = pmul(a, vec_sld(a, a, 8));
+  return pfirst(pmul(prod, vec_sld(prod, prod, 4)));
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a) {
+  EIGEN_ALIGN16 int aux[4];
+  pstore(aux, a);
+  return aux[0] * aux[1] * aux[2] * aux[3];
+}
+
+template <>
+EIGEN_STRONG_INLINE short int predux_mul<Packet8s>(const Packet8s& a) {
+  Packet8s pair, quad, octo;
+
+  pair = vec_mul(a, vec_sld(a, a, 8));
+  quad = vec_mul(pair, vec_sld(pair, pair, 4));
+  octo = vec_mul(quad, vec_sld(quad, quad, 2));
+
+  return pfirst(octo);
+}
+
+template <>
+EIGEN_STRONG_INLINE unsigned short int predux_mul<Packet8us>(const Packet8us& a) {
+  Packet8us pair, quad, octo;
+
+  pair = vec_mul(a, vec_sld(a, a, 8));
+  quad = vec_mul(pair, vec_sld(pair, pair, 4));
+  octo = vec_mul(quad, vec_sld(quad, quad, 2));
+
+  return pfirst(octo);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet8bf>(const Packet8bf& a) {
+  float redux_even = predux_mul<Packet4f>(Bf16ToF32Even(a));
+  float redux_odd = predux_mul<Packet4f>(Bf16ToF32Odd(a));
+  float f32_result = redux_even * redux_odd;
+  return bfloat16(f32_result);
+}
+
+template <>
+EIGEN_STRONG_INLINE signed char predux_mul<Packet16c>(const Packet16c& a) {
+  Packet16c pair, quad, octo, result;
+
+  pair = vec_mul(a, vec_sld(a, a, 8));
+  quad = vec_mul(pair, vec_sld(pair, pair, 4));
+  octo = vec_mul(quad, vec_sld(quad, quad, 2));
+  result = vec_mul(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE unsigned char predux_mul<Packet16uc>(const Packet16uc& a) {
+  Packet16uc pair, quad, octo, result;
+
+  pair = vec_mul(a, vec_sld(a, a, 8));
+  quad = vec_mul(pair, vec_sld(pair, pair, 4));
+  octo = vec_mul(quad, vec_sld(quad, quad, 2));
+  result = vec_mul(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+
+// min
+template <typename Packet>
+EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_min4(const Packet& a) {
+  Packet b, res;
+  b = vec_min(a, vec_sld(a, a, 8));
+  res = vec_min(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) {
+  return predux_min4<Packet4f>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a) {
+  return predux_min4<Packet4i>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min<Packet8bf>(const Packet8bf& a) {
+  float redux_even = predux_min<Packet4f>(Bf16ToF32Even(a));
+  float redux_odd = predux_min<Packet4f>(Bf16ToF32Odd(a));
+  float f32_result = (std::min)(redux_even, redux_odd);
+  return bfloat16(f32_result);
+}
+
+template <>
+EIGEN_STRONG_INLINE short int predux_min<Packet8s>(const Packet8s& a) {
+  Packet8s pair, quad, octo;
+
+  // pair = { Min(a0,a4), Min(a1,a5), Min(a2,a6), Min(a3,a7) }
+  pair = vec_min(a, vec_sld(a, a, 8));
+
+  // quad = { Min(a0, a4, a2, a6), Min(a1, a5, a3, a7) }
+  quad = vec_min(pair, vec_sld(pair, pair, 4));
+
+  // octo = { Min(a0, a4, a2, a6, a1, a5, a3, a7) }
+  octo = vec_min(quad, vec_sld(quad, quad, 2));
+  return pfirst(octo);
+}
+
+template <>
+EIGEN_STRONG_INLINE unsigned short int predux_min<Packet8us>(const Packet8us& a) {
+  Packet8us pair, quad, octo;
+
+  // pair = { Min(a0,a4), Min(a1,a5), Min(a2,a6), Min(a3,a7) }
+  pair = vec_min(a, vec_sld(a, a, 8));
+
+  // quad = { Min(a0, a4, a2, a6), Min(a1, a5, a3, a7) }
+  quad = vec_min(pair, vec_sld(pair, pair, 4));
+
+  // octo = { Min(a0, a4, a2, a6, a1, a5, a3, a7) }
+  octo = vec_min(quad, vec_sld(quad, quad, 2));
+  return pfirst(octo);
+}
+
+template <>
+EIGEN_STRONG_INLINE signed char predux_min<Packet16c>(const Packet16c& a) {
+  Packet16c pair, quad, octo, result;
+
+  pair = vec_min(a, vec_sld(a, a, 8));
+  quad = vec_min(pair, vec_sld(pair, pair, 4));
+  octo = vec_min(quad, vec_sld(quad, quad, 2));
+  result = vec_min(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE unsigned char predux_min<Packet16uc>(const Packet16uc& a) {
+  Packet16uc pair, quad, octo, result;
+
+  pair = vec_min(a, vec_sld(a, a, 8));
+  quad = vec_min(pair, vec_sld(pair, pair, 4));
+  octo = vec_min(quad, vec_sld(quad, quad, 2));
+  result = vec_min(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+// max
+template <typename Packet>
+EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_max4(const Packet& a) {
+  Packet b, res;
+  b = vec_max(a, vec_sld(a, a, 8));
+  res = vec_max(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) {
+  return predux_max4<Packet4f>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a) {
+  return predux_max4<Packet4i>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max<Packet8bf>(const Packet8bf& a) {
+  float redux_even = predux_max<Packet4f>(Bf16ToF32Even(a));
+  float redux_odd = predux_max<Packet4f>(Bf16ToF32Odd(a));
+  float f32_result = (std::max)(redux_even, redux_odd);
+  return bfloat16(f32_result);
+}
+
+template <>
+EIGEN_STRONG_INLINE short int predux_max<Packet8s>(const Packet8s& a) {
+  Packet8s pair, quad, octo;
+
+  // pair = { Max(a0,a4), Max(a1,a5), Max(a2,a6), Max(a3,a7) }
+  pair = vec_max(a, vec_sld(a, a, 8));
+
+  // quad = { Max(a0, a4, a2, a6), Max(a1, a5, a3, a7) }
+  quad = vec_max(pair, vec_sld(pair, pair, 4));
+
+  // octo = { Max(a0, a4, a2, a6, a1, a5, a3, a7) }
+  octo = vec_max(quad, vec_sld(quad, quad, 2));
+  return pfirst(octo);
+}
+
+template <>
+EIGEN_STRONG_INLINE unsigned short int predux_max<Packet8us>(const Packet8us& a) {
+  Packet8us pair, quad, octo;
+
+  // pair = { Max(a0,a4), Max(a1,a5), Max(a2,a6), Max(a3,a7) }
+  pair = vec_max(a, vec_sld(a, a, 8));
+
+  // quad = { Max(a0, a4, a2, a6), Max(a1, a5, a3, a7) }
+  quad = vec_max(pair, vec_sld(pair, pair, 4));
+
+  // octo = { Max(a0, a4, a2, a6, a1, a5, a3, a7) }
+  octo = vec_max(quad, vec_sld(quad, quad, 2));
+  return pfirst(octo);
+}
+
+template <>
+EIGEN_STRONG_INLINE signed char predux_max<Packet16c>(const Packet16c& a) {
+  Packet16c pair, quad, octo, result;
+
+  pair = vec_max(a, vec_sld(a, a, 8));
+  quad = vec_max(pair, vec_sld(pair, pair, 4));
+  octo = vec_max(quad, vec_sld(quad, quad, 2));
+  result = vec_max(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE unsigned char predux_max<Packet16uc>(const Packet16uc& a) {
+  Packet16uc pair, quad, octo, result;
+
+  pair = vec_max(a, vec_sld(a, a, 8));
+  quad = vec_max(pair, vec_sld(pair, pair, 4));
+  octo = vec_max(quad, vec_sld(quad, quad, 2));
+  result = vec_max(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x) {
+  return vec_any_ne(x, pzero(x));
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC inline void ptranpose_common(PacketBlock<T, 4>& kernel) {
+  T t0, t1, t2, t3;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f, 4>& kernel) { ptranpose_common<Packet4f>(kernel); }
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4i, 4>& kernel) { ptranpose_common<Packet4i>(kernel); }
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8s, 4>& kernel) {
+  Packet8s t0, t1, t2, t3;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8us, 4>& kernel) {
+  Packet8us t0, t1, t2, t3;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8bf, 4>& kernel) {
+  Packet8us t0, t1, t2, t3;
+
+  t0 = vec_mergeh(kernel.packet[0].m_val, kernel.packet[2].m_val);
+  t1 = vec_mergel(kernel.packet[0].m_val, kernel.packet[2].m_val);
+  t2 = vec_mergeh(kernel.packet[1].m_val, kernel.packet[3].m_val);
+  t3 = vec_mergel(kernel.packet[1].m_val, kernel.packet[3].m_val);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16c, 4>& kernel) {
+  Packet16c t0, t1, t2, t3;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16uc, 4>& kernel) {
+  Packet16uc t0, t1, t2, t3;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8s, 8>& kernel) {
+  Packet8s v[8], sum[8];
+
+  v[0] = vec_mergeh(kernel.packet[0], kernel.packet[4]);
+  v[1] = vec_mergel(kernel.packet[0], kernel.packet[4]);
+  v[2] = vec_mergeh(kernel.packet[1], kernel.packet[5]);
+  v[3] = vec_mergel(kernel.packet[1], kernel.packet[5]);
+  v[4] = vec_mergeh(kernel.packet[2], kernel.packet[6]);
+  v[5] = vec_mergel(kernel.packet[2], kernel.packet[6]);
+  v[6] = vec_mergeh(kernel.packet[3], kernel.packet[7]);
+  v[7] = vec_mergel(kernel.packet[3], kernel.packet[7]);
+  sum[0] = vec_mergeh(v[0], v[4]);
+  sum[1] = vec_mergel(v[0], v[4]);
+  sum[2] = vec_mergeh(v[1], v[5]);
+  sum[3] = vec_mergel(v[1], v[5]);
+  sum[4] = vec_mergeh(v[2], v[6]);
+  sum[5] = vec_mergel(v[2], v[6]);
+  sum[6] = vec_mergeh(v[3], v[7]);
+  sum[7] = vec_mergel(v[3], v[7]);
+
+  kernel.packet[0] = vec_mergeh(sum[0], sum[4]);
+  kernel.packet[1] = vec_mergel(sum[0], sum[4]);
+  kernel.packet[2] = vec_mergeh(sum[1], sum[5]);
+  kernel.packet[3] = vec_mergel(sum[1], sum[5]);
+  kernel.packet[4] = vec_mergeh(sum[2], sum[6]);
+  kernel.packet[5] = vec_mergel(sum[2], sum[6]);
+  kernel.packet[6] = vec_mergeh(sum[3], sum[7]);
+  kernel.packet[7] = vec_mergel(sum[3], sum[7]);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8us, 8>& kernel) {
+  Packet8us v[8], sum[8];
+
+  v[0] = vec_mergeh(kernel.packet[0], kernel.packet[4]);
+  v[1] = vec_mergel(kernel.packet[0], kernel.packet[4]);
+  v[2] = vec_mergeh(kernel.packet[1], kernel.packet[5]);
+  v[3] = vec_mergel(kernel.packet[1], kernel.packet[5]);
+  v[4] = vec_mergeh(kernel.packet[2], kernel.packet[6]);
+  v[5] = vec_mergel(kernel.packet[2], kernel.packet[6]);
+  v[6] = vec_mergeh(kernel.packet[3], kernel.packet[7]);
+  v[7] = vec_mergel(kernel.packet[3], kernel.packet[7]);
+  sum[0] = vec_mergeh(v[0], v[4]);
+  sum[1] = vec_mergel(v[0], v[4]);
+  sum[2] = vec_mergeh(v[1], v[5]);
+  sum[3] = vec_mergel(v[1], v[5]);
+  sum[4] = vec_mergeh(v[2], v[6]);
+  sum[5] = vec_mergel(v[2], v[6]);
+  sum[6] = vec_mergeh(v[3], v[7]);
+  sum[7] = vec_mergel(v[3], v[7]);
+
+  kernel.packet[0] = vec_mergeh(sum[0], sum[4]);
+  kernel.packet[1] = vec_mergel(sum[0], sum[4]);
+  kernel.packet[2] = vec_mergeh(sum[1], sum[5]);
+  kernel.packet[3] = vec_mergel(sum[1], sum[5]);
+  kernel.packet[4] = vec_mergeh(sum[2], sum[6]);
+  kernel.packet[5] = vec_mergel(sum[2], sum[6]);
+  kernel.packet[6] = vec_mergeh(sum[3], sum[7]);
+  kernel.packet[7] = vec_mergel(sum[3], sum[7]);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8bf, 8>& kernel) {
+  Packet8bf v[8], sum[8];
+
+  v[0] = vec_mergeh(kernel.packet[0].m_val, kernel.packet[4].m_val);
+  v[1] = vec_mergel(kernel.packet[0].m_val, kernel.packet[4].m_val);
+  v[2] = vec_mergeh(kernel.packet[1].m_val, kernel.packet[5].m_val);
+  v[3] = vec_mergel(kernel.packet[1].m_val, kernel.packet[5].m_val);
+  v[4] = vec_mergeh(kernel.packet[2].m_val, kernel.packet[6].m_val);
+  v[5] = vec_mergel(kernel.packet[2].m_val, kernel.packet[6].m_val);
+  v[6] = vec_mergeh(kernel.packet[3].m_val, kernel.packet[7].m_val);
+  v[7] = vec_mergel(kernel.packet[3].m_val, kernel.packet[7].m_val);
+  sum[0] = vec_mergeh(v[0].m_val, v[4].m_val);
+  sum[1] = vec_mergel(v[0].m_val, v[4].m_val);
+  sum[2] = vec_mergeh(v[1].m_val, v[5].m_val);
+  sum[3] = vec_mergel(v[1].m_val, v[5].m_val);
+  sum[4] = vec_mergeh(v[2].m_val, v[6].m_val);
+  sum[5] = vec_mergel(v[2].m_val, v[6].m_val);
+  sum[6] = vec_mergeh(v[3].m_val, v[7].m_val);
+  sum[7] = vec_mergel(v[3].m_val, v[7].m_val);
+
+  kernel.packet[0] = vec_mergeh(sum[0].m_val, sum[4].m_val);
+  kernel.packet[1] = vec_mergel(sum[0].m_val, sum[4].m_val);
+  kernel.packet[2] = vec_mergeh(sum[1].m_val, sum[5].m_val);
+  kernel.packet[3] = vec_mergel(sum[1].m_val, sum[5].m_val);
+  kernel.packet[4] = vec_mergeh(sum[2].m_val, sum[6].m_val);
+  kernel.packet[5] = vec_mergel(sum[2].m_val, sum[6].m_val);
+  kernel.packet[6] = vec_mergeh(sum[3].m_val, sum[7].m_val);
+  kernel.packet[7] = vec_mergel(sum[3].m_val, sum[7].m_val);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16c, 16>& kernel) {
+  Packet16c step1[16], step2[16], step3[16];
+
+  step1[0] = vec_mergeh(kernel.packet[0], kernel.packet[8]);
+  step1[1] = vec_mergel(kernel.packet[0], kernel.packet[8]);
+  step1[2] = vec_mergeh(kernel.packet[1], kernel.packet[9]);
+  step1[3] = vec_mergel(kernel.packet[1], kernel.packet[9]);
+  step1[4] = vec_mergeh(kernel.packet[2], kernel.packet[10]);
+  step1[5] = vec_mergel(kernel.packet[2], kernel.packet[10]);
+  step1[6] = vec_mergeh(kernel.packet[3], kernel.packet[11]);
+  step1[7] = vec_mergel(kernel.packet[3], kernel.packet[11]);
+  step1[8] = vec_mergeh(kernel.packet[4], kernel.packet[12]);
+  step1[9] = vec_mergel(kernel.packet[4], kernel.packet[12]);
+  step1[10] = vec_mergeh(kernel.packet[5], kernel.packet[13]);
+  step1[11] = vec_mergel(kernel.packet[5], kernel.packet[13]);
+  step1[12] = vec_mergeh(kernel.packet[6], kernel.packet[14]);
+  step1[13] = vec_mergel(kernel.packet[6], kernel.packet[14]);
+  step1[14] = vec_mergeh(kernel.packet[7], kernel.packet[15]);
+  step1[15] = vec_mergel(kernel.packet[7], kernel.packet[15]);
+
+  step2[0] = vec_mergeh(step1[0], step1[8]);
+  step2[1] = vec_mergel(step1[0], step1[8]);
+  step2[2] = vec_mergeh(step1[1], step1[9]);
+  step2[3] = vec_mergel(step1[1], step1[9]);
+  step2[4] = vec_mergeh(step1[2], step1[10]);
+  step2[5] = vec_mergel(step1[2], step1[10]);
+  step2[6] = vec_mergeh(step1[3], step1[11]);
+  step2[7] = vec_mergel(step1[3], step1[11]);
+  step2[8] = vec_mergeh(step1[4], step1[12]);
+  step2[9] = vec_mergel(step1[4], step1[12]);
+  step2[10] = vec_mergeh(step1[5], step1[13]);
+  step2[11] = vec_mergel(step1[5], step1[13]);
+  step2[12] = vec_mergeh(step1[6], step1[14]);
+  step2[13] = vec_mergel(step1[6], step1[14]);
+  step2[14] = vec_mergeh(step1[7], step1[15]);
+  step2[15] = vec_mergel(step1[7], step1[15]);
+
+  step3[0] = vec_mergeh(step2[0], step2[8]);
+  step3[1] = vec_mergel(step2[0], step2[8]);
+  step3[2] = vec_mergeh(step2[1], step2[9]);
+  step3[3] = vec_mergel(step2[1], step2[9]);
+  step3[4] = vec_mergeh(step2[2], step2[10]);
+  step3[5] = vec_mergel(step2[2], step2[10]);
+  step3[6] = vec_mergeh(step2[3], step2[11]);
+  step3[7] = vec_mergel(step2[3], step2[11]);
+  step3[8] = vec_mergeh(step2[4], step2[12]);
+  step3[9] = vec_mergel(step2[4], step2[12]);
+  step3[10] = vec_mergeh(step2[5], step2[13]);
+  step3[11] = vec_mergel(step2[5], step2[13]);
+  step3[12] = vec_mergeh(step2[6], step2[14]);
+  step3[13] = vec_mergel(step2[6], step2[14]);
+  step3[14] = vec_mergeh(step2[7], step2[15]);
+  step3[15] = vec_mergel(step2[7], step2[15]);
+
+  kernel.packet[0] = vec_mergeh(step3[0], step3[8]);
+  kernel.packet[1] = vec_mergel(step3[0], step3[8]);
+  kernel.packet[2] = vec_mergeh(step3[1], step3[9]);
+  kernel.packet[3] = vec_mergel(step3[1], step3[9]);
+  kernel.packet[4] = vec_mergeh(step3[2], step3[10]);
+  kernel.packet[5] = vec_mergel(step3[2], step3[10]);
+  kernel.packet[6] = vec_mergeh(step3[3], step3[11]);
+  kernel.packet[7] = vec_mergel(step3[3], step3[11]);
+  kernel.packet[8] = vec_mergeh(step3[4], step3[12]);
+  kernel.packet[9] = vec_mergel(step3[4], step3[12]);
+  kernel.packet[10] = vec_mergeh(step3[5], step3[13]);
+  kernel.packet[11] = vec_mergel(step3[5], step3[13]);
+  kernel.packet[12] = vec_mergeh(step3[6], step3[14]);
+  kernel.packet[13] = vec_mergel(step3[6], step3[14]);
+  kernel.packet[14] = vec_mergeh(step3[7], step3[15]);
+  kernel.packet[15] = vec_mergel(step3[7], step3[15]);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16uc, 16>& kernel) {
+  Packet16uc step1[16], step2[16], step3[16];
+
+  step1[0] = vec_mergeh(kernel.packet[0], kernel.packet[8]);
+  step1[1] = vec_mergel(kernel.packet[0], kernel.packet[8]);
+  step1[2] = vec_mergeh(kernel.packet[1], kernel.packet[9]);
+  step1[3] = vec_mergel(kernel.packet[1], kernel.packet[9]);
+  step1[4] = vec_mergeh(kernel.packet[2], kernel.packet[10]);
+  step1[5] = vec_mergel(kernel.packet[2], kernel.packet[10]);
+  step1[6] = vec_mergeh(kernel.packet[3], kernel.packet[11]);
+  step1[7] = vec_mergel(kernel.packet[3], kernel.packet[11]);
+  step1[8] = vec_mergeh(kernel.packet[4], kernel.packet[12]);
+  step1[9] = vec_mergel(kernel.packet[4], kernel.packet[12]);
+  step1[10] = vec_mergeh(kernel.packet[5], kernel.packet[13]);
+  step1[11] = vec_mergel(kernel.packet[5], kernel.packet[13]);
+  step1[12] = vec_mergeh(kernel.packet[6], kernel.packet[14]);
+  step1[13] = vec_mergel(kernel.packet[6], kernel.packet[14]);
+  step1[14] = vec_mergeh(kernel.packet[7], kernel.packet[15]);
+  step1[15] = vec_mergel(kernel.packet[7], kernel.packet[15]);
+
+  step2[0] = vec_mergeh(step1[0], step1[8]);
+  step2[1] = vec_mergel(step1[0], step1[8]);
+  step2[2] = vec_mergeh(step1[1], step1[9]);
+  step2[3] = vec_mergel(step1[1], step1[9]);
+  step2[4] = vec_mergeh(step1[2], step1[10]);
+  step2[5] = vec_mergel(step1[2], step1[10]);
+  step2[6] = vec_mergeh(step1[3], step1[11]);
+  step2[7] = vec_mergel(step1[3], step1[11]);
+  step2[8] = vec_mergeh(step1[4], step1[12]);
+  step2[9] = vec_mergel(step1[4], step1[12]);
+  step2[10] = vec_mergeh(step1[5], step1[13]);
+  step2[11] = vec_mergel(step1[5], step1[13]);
+  step2[12] = vec_mergeh(step1[6], step1[14]);
+  step2[13] = vec_mergel(step1[6], step1[14]);
+  step2[14] = vec_mergeh(step1[7], step1[15]);
+  step2[15] = vec_mergel(step1[7], step1[15]);
+
+  step3[0] = vec_mergeh(step2[0], step2[8]);
+  step3[1] = vec_mergel(step2[0], step2[8]);
+  step3[2] = vec_mergeh(step2[1], step2[9]);
+  step3[3] = vec_mergel(step2[1], step2[9]);
+  step3[4] = vec_mergeh(step2[2], step2[10]);
+  step3[5] = vec_mergel(step2[2], step2[10]);
+  step3[6] = vec_mergeh(step2[3], step2[11]);
+  step3[7] = vec_mergel(step2[3], step2[11]);
+  step3[8] = vec_mergeh(step2[4], step2[12]);
+  step3[9] = vec_mergel(step2[4], step2[12]);
+  step3[10] = vec_mergeh(step2[5], step2[13]);
+  step3[11] = vec_mergel(step2[5], step2[13]);
+  step3[12] = vec_mergeh(step2[6], step2[14]);
+  step3[13] = vec_mergel(step2[6], step2[14]);
+  step3[14] = vec_mergeh(step2[7], step2[15]);
+  step3[15] = vec_mergel(step2[7], step2[15]);
+
+  kernel.packet[0] = vec_mergeh(step3[0], step3[8]);
+  kernel.packet[1] = vec_mergel(step3[0], step3[8]);
+  kernel.packet[2] = vec_mergeh(step3[1], step3[9]);
+  kernel.packet[3] = vec_mergel(step3[1], step3[9]);
+  kernel.packet[4] = vec_mergeh(step3[2], step3[10]);
+  kernel.packet[5] = vec_mergel(step3[2], step3[10]);
+  kernel.packet[6] = vec_mergeh(step3[3], step3[11]);
+  kernel.packet[7] = vec_mergel(step3[3], step3[11]);
+  kernel.packet[8] = vec_mergeh(step3[4], step3[12]);
+  kernel.packet[9] = vec_mergel(step3[4], step3[12]);
+  kernel.packet[10] = vec_mergeh(step3[5], step3[13]);
+  kernel.packet[11] = vec_mergel(step3[5], step3[13]);
+  kernel.packet[12] = vec_mergeh(step3[6], step3[14]);
+  kernel.packet[13] = vec_mergel(step3[6], step3[14]);
+  kernel.packet[14] = vec_mergeh(step3[7], step3[15]);
+  kernel.packet[15] = vec_mergel(step3[7], step3[15]);
+}
+
+//---------- double ----------
+#ifdef EIGEN_VECTORIZE_VSX
+typedef __vector double Packet2d;
+typedef __vector unsigned long long Packet2ul;
+typedef __vector long long Packet2l;
+#if EIGEN_COMP_CLANG
+typedef Packet2ul Packet2bl;
+#else
+typedef __vector __bool long Packet2bl;
+#endif
+
+static Packet2l p2l_ZERO = reinterpret_cast<Packet2l>(p4i_ZERO);
+static Packet2ul p2ul_SIGN = {0x8000000000000000ull, 0x8000000000000000ull};
+static Packet2ul p2ul_PREV0DOT5 = {0x3FDFFFFFFFFFFFFFull, 0x3FDFFFFFFFFFFFFFull};
+static Packet2d p2d_ONE = {1.0, 1.0};
+static Packet2d p2d_ZERO = reinterpret_cast<Packet2d>(p4f_ZERO);
+static Packet2d p2d_MZERO = {numext::bit_cast<double>(0x8000000000000000ull),
+                             numext::bit_cast<double>(0x8000000000000000ull)};
+
+#ifdef _BIG_ENDIAN
+static Packet2d p2d_COUNTDOWN =
+    reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ZERO), reinterpret_cast<Packet4f>(p2d_ONE), 8));
+#else
+static Packet2d p2d_COUNTDOWN =
+    reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ONE), reinterpret_cast<Packet4f>(p2d_ZERO), 8));
+#endif
+
+template <int index>
+Packet2d vec_splat_dbl(Packet2d& a) {
+  return vec_splat(a, index);
+}
+
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasAbs = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasErfc = EIGEN_FAST_MATH,
+    HasATanh = 1,
+    HasATan = 0,
+    HasCmp = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasSqrt = 1,
+    HasCbrt = 1,
+#if !EIGEN_COMP_CLANG
+    HasRsqrt = 1,
+#else
+    HasRsqrt = 0,
+#endif
+    HasNegate = 1,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2d> {
+  typedef double type;
+  typedef Packet2l integer_packet;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet2d half;
+};
+template <>
+struct unpacket_traits<Packet2l> {
+  typedef int64_t type;
+  typedef Packet2l half;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = false,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+inline std::ostream& operator<<(std::ostream& s, const Packet2l& v) {
+  union {
+    Packet2l v;
+    int64_t n[2];
+  } vt;
+  vt.v = v;
+  s << vt.n[0] << ", " << vt.n[1];
+  return s;
+}
+
+inline std::ostream& operator<<(std::ostream& s, const Packet2d& v) {
+  union {
+    Packet2d v;
+    double n[2];
+  } vt;
+  vt.v = v;
+  s << vt.n[0] << ", " << vt.n[1];
+  return s;
+}
+
+// Need to define them first or we get specialization after instantiation errors
+template <>
+EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD
+  return vec_xl(0, const_cast<double*>(from));  // cast needed by Clang
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet2d pload_partial<Packet2d>(const double* from, const Index n, const Index offset) {
+  return pload_partial_common<Packet2d>(from, n, offset);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_ALIGNED_STORE
+  vec_xst(from, 0, to);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pstore_partial<double>(double* to, const Packet2d& from, const Index n, const Index offset) {
+  pstore_partial_common<Packet2d>(to, from, n, offset);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
+  Packet2d v = {from, from};
+  return v;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pset1<Packet2l>(const int64_t& from) {
+  Packet2l v = {from, from};
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(unsigned long from) {
+  Packet2l v = {static_cast<long long>(from), static_cast<long long>(from)};
+  return reinterpret_cast<Packet2d>(v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet2d>(const double* a, Packet2d& a0, Packet2d& a1, Packet2d& a2,
+                                               Packet2d& a3) {
+  // This way is faster than vec_splat (at least for doubles in Power 9)
+  a0 = pset1<Packet2d>(a[0]);
+  a1 = pset1<Packet2d>(a[1]);
+  a2 = pset1<Packet2d>(a[2]);
+  a3 = pset1<Packet2d>(a[3]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet2d pgather<double, Packet2d>(const double* from, Index stride) {
+  return pgather_common<Packet2d>(from, stride);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet2d pgather_partial<double, Packet2d>(const double* from, Index stride,
+                                                                                 const Index n) {
+  return pgather_common<Packet2d>(from, stride, n);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride) {
+  pscatter_common<Packet2d>(to, from, stride);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<double, Packet2d>(double* to, const Packet2d& from,
+                                                                              Index stride, const Index n) {
+  pscatter_common<Packet2d>(to, from, stride, n);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) {
+  return pset1<Packet2d>(a) + p2d_COUNTDOWN;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return a + b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return a - b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) {
+#ifdef __POWER8_VECTOR__
+  return vec_neg(a);
+#else
+  return vec_xor(a, p2d_MZERO);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_madd(a, b, p2d_MZERO);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_div(a, b);
+}
+
+// This overload is required for integer packet types.
+template <>
+EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return vec_madd(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return vec_msub(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return vec_nmsub(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return vec_nmadd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  // NOTE: about 10% slower than vec_min, but consistent with std::min and SSE regarding NaN
+  Packet2d ret;
+  __asm__("xvcmpgedp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa"(ret) : "wa"(a), "wa"(b));
+  return ret;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  // NOTE: about 10% slower than vec_max, but consistent with std::max and SSE regarding NaN
+  Packet2d ret;
+  __asm__("xvcmpgtdp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa"(ret) : "wa"(a), "wa"(b));
+  return ret;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b) {
+  return reinterpret_cast<Packet2d>(vec_cmple(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b) {
+  return reinterpret_cast<Packet2d>(vec_cmplt(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b) {
+  return reinterpret_cast<Packet2d>(vec_cmpeq(a, b));
+}
+template <>
+#ifdef __POWER8_VECTOR__
+EIGEN_STRONG_INLINE Packet2l pcmp_eq(const Packet2l& a, const Packet2l& b) {
+  return reinterpret_cast<Packet2l>(vec_cmpeq(a, b));
+}
+#else
+EIGEN_STRONG_INLINE Packet2l pcmp_eq(const Packet2l& a, const Packet2l& b) {
+  Packet4i halves = reinterpret_cast<Packet4i>(vec_cmpeq(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(b)));
+  Packet4i flipped = vec_perm(halves, halves, p16uc_COMPLEX32_REV);
+  return reinterpret_cast<Packet2l>(pand(halves, flipped));
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b) {
+  Packet2d c = reinterpret_cast<Packet2d>(vec_cmpge(a, b));
+  return vec_nor(c, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_and(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_or(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_xor(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_and(a, vec_nor(b, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) {
+  Packet2d t = vec_add(
+      reinterpret_cast<Packet2d>(vec_or(vec_and(reinterpret_cast<Packet2ul>(a), p2ul_SIGN), p2ul_PREV0DOT5)), a);
+  Packet2d res;
+
+  __asm__("xvrdpiz %x0, %x1\n\t" : "=&wa"(res) : "wa"(t));
+
+  return res;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) {
+  return vec_ceil(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) {
+  return vec_floor(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d ptrunc<Packet2d>(const Packet2d& a) {
+  return vec_trunc(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a) {
+  Packet2d res;
+
+  __asm__("xvrdpic %x0, %x1\n\t" : "=&wa"(res) : "wa"(a));
+
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return vec_xl(0, const_cast<double*>(from));
+}
+
+template <>
+EIGEN_ALWAYS_INLINE Packet2d ploadu_partial<Packet2d>(const double* from, const Index n, const Index offset) {
+  return ploadu_partial_common<Packet2d>(from, n, offset);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) {
+  Packet2d p;
+  if ((std::ptrdiff_t(from) % 16) == 0)
+    p = pload<Packet2d>(from);
+  else
+    p = ploadu<Packet2d>(from);
+  return vec_splat_dbl<0>(p);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE
+  vec_xst(from, 0, to);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void pstoreu_partial<double>(double* to, const Packet2d& from, const Index n, const Index offset) {
+  pstoreu_partial_common<Packet2d>(to, from, n, offset);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
+  EIGEN_PPC_PREFETCH(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
+  EIGEN_ALIGN16 double x[2];
+  pstore<double>(x, a);
+  return x[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) {
+  return vec_sld(a, a, 8);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) {
+  return vec_abs(a);
+}
+#ifdef __POWER8_VECTOR__
+template <>
+EIGEN_STRONG_INLINE Packet2d psignbit(const Packet2d& a) {
+  return (Packet2d)vec_sra((Packet2l)a, vec_splats((unsigned long long)(63)));
+}
+#else
+#ifdef _BIG_ENDIAN
+static Packet16uc p16uc_DUPSIGN = {0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8};
+#else
+static Packet16uc p16uc_DUPSIGN = {7, 7, 7, 7, 7, 7, 7, 7, 15, 15, 15, 15, 15, 15, 15, 15};
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet2d psignbit(const Packet2d& a) {
+  Packet16c tmp = vec_sra(reinterpret_cast<Packet16c>(a), vec_splats((unsigned char)(7)));
+  return reinterpret_cast<Packet2d>(vec_perm(tmp, tmp, p16uc_DUPSIGN));
+}
+#endif
+
+template <>
+inline Packet2l pcast<Packet2d, Packet2l>(const Packet2d& x);
+
+template <>
+inline Packet2d pcast<Packet2l, Packet2d>(const Packet2l& x);
+
+// Packet2l shifts.
+// For POWER8 we simply use vec_sr/l.
+//
+// Things are more complicated for POWER7. There is actually a
+// vec_xxsxdi intrinsic but it is not supported by some gcc versions.
+// So we need to shift by N % 32 and rearrage bytes.
+#ifdef __POWER8_VECTOR__
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
+  const Packet2ul shift = {N, N};
+  return vec_sl(a, shift);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
+  const Packet2ul shift = {N, N};
+  return vec_sr(a, shift);
+}
+
+#else
+
+// Shifts [A, B, C, D] to [B, 0, D, 0].
+// Used to implement left shifts for Packet2l.
+EIGEN_ALWAYS_INLINE Packet4i shift_even_left(const Packet4i& a) {
+  static const Packet16uc perm = {0x14, 0x15, 0x16, 0x17, 0x00, 0x01, 0x02, 0x03,
+                                  0x1c, 0x1d, 0x1e, 0x1f, 0x08, 0x09, 0x0a, 0x0b};
+#ifdef _BIG_ENDIAN
+  return vec_perm(p4i_ZERO, a, perm);
+#else
+  return vec_perm(a, p4i_ZERO, perm);
+#endif
+}
+
+// Shifts [A, B, C, D] to [0, A, 0, C].
+// Used to implement right shifts for Packet2l.
+EIGEN_ALWAYS_INLINE Packet4i shift_odd_right(const Packet4i& a) {
+  static const Packet16uc perm = {0x04, 0x05, 0x06, 0x07, 0x10, 0x11, 0x12, 0x13,
+                                  0x0c, 0x0d, 0x0e, 0x0f, 0x18, 0x19, 0x1a, 0x1b};
+#ifdef _BIG_ENDIAN
+  return vec_perm(p4i_ZERO, a, perm);
+#else
+  return vec_perm(a, p4i_ZERO, perm);
+#endif
+}
+
+template <int N, typename EnableIf = void>
+struct plogical_shift_left_impl;
+
+template <int N>
+struct plogical_shift_left_impl<N, std::enable_if_t<(N < 32) && (N >= 0)> > {
+  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
+    static const unsigned n = static_cast<unsigned>(N);
+    const Packet4ui shift = {n, n, n, n};
+    const Packet4i ai = reinterpret_cast<Packet4i>(a);
+    static const unsigned m = static_cast<unsigned>(32 - N);
+    const Packet4ui shift_right = {m, m, m, m};
+    const Packet4i out_hi = vec_sl(ai, shift);
+    const Packet4i out_lo = shift_even_left(vec_sr(ai, shift_right));
+    return reinterpret_cast<Packet2l>(por<Packet4i>(out_hi, out_lo));
+  }
+};
+
+template <int N>
+struct plogical_shift_left_impl<N, std::enable_if_t<(N >= 32)> > {
+  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
+    static const unsigned m = static_cast<unsigned>(N - 32);
+    const Packet4ui shift = {m, m, m, m};
+    const Packet4i ai = reinterpret_cast<Packet4i>(a);
+    return reinterpret_cast<Packet2l>(shift_even_left(vec_sl(ai, shift)));
+  }
+};
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
+  return plogical_shift_left_impl<N>::run(a);
+}
+
+template <int N, typename EnableIf = void>
+struct plogical_shift_right_impl;
+
+template <int N>
+struct plogical_shift_right_impl<N, std::enable_if_t<(N < 32) && (N >= 0)> > {
+  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
+    static const unsigned n = static_cast<unsigned>(N);
+    const Packet4ui shift = {n, n, n, n};
+    const Packet4i ai = reinterpret_cast<Packet4i>(a);
+    static const unsigned m = static_cast<unsigned>(32 - N);
+    const Packet4ui shift_left = {m, m, m, m};
+    const Packet4i out_lo = vec_sr(ai, shift);
+    const Packet4i out_hi = shift_odd_right(vec_sl(ai, shift_left));
+    return reinterpret_cast<Packet2l>(por<Packet4i>(out_hi, out_lo));
+  }
+};
+
+template <int N>
+struct plogical_shift_right_impl<N, std::enable_if_t<(N >= 32)> > {
+  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
+    static const unsigned m = static_cast<unsigned>(N - 32);
+    const Packet4ui shift = {m, m, m, m};
+    const Packet4i ai = reinterpret_cast<Packet4i>(a);
+    return reinterpret_cast<Packet2l>(shift_odd_right(vec_sr(ai, shift)));
+  }
+};
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
+  return plogical_shift_right_impl<N>::run(a);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
+  // Clamp exponent to [-2099, 2099]
+  const Packet2d max_exponent = pset1<Packet2d>(2099.0);
+  const Packet2l e = pcast<Packet2d, Packet2l>(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
+
+  // Split 2^e into four factors and multiply:
+  const Packet2l bias = {1023, 1023};
+  Packet2l b = plogical_shift_right<2>(e);  // floor(e/4)
+  Packet2d c = reinterpret_cast<Packet2d>(plogical_shift_left<52>(b + bias));
+  Packet2d out = pmul(pmul(pmul(a, c), c), c);                        // a * 2^(3b)
+  b = psub(psub(psub(e, b), b), b);                                   // e - 3b
+  c = reinterpret_cast<Packet2d>(plogical_shift_left<52>(b + bias));  // 2^(e - 3b)
+  out = pmul(out, c);                                                 // a * 2^e
+  return out;
+}
+
+// Extract exponent without existence of Packet2l.
+template <>
+EIGEN_STRONG_INLINE Packet2d pfrexp_generic_get_biased_exponent(const Packet2d& a) {
+  return pcast<Packet2l, Packet2d>(plogical_shift_right<52>(reinterpret_cast<Packet2l>(pabs(a))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) {
+  Packet2d b, sum;
+  b = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(a), reinterpret_cast<Packet4f>(a), 8));
+  sum = a + b;
+  return pfirst<Packet2d>(sum);
+}
+
+// Other reduction functions:
+// mul
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) {
+  return pfirst(
+      pmul(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
+}
+
+// min
+template <>
+EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) {
+  return pfirst(
+      pmin(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
+}
+
+// max
+template <>
+EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) {
+  return pfirst(
+      pmax(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2d, 2>& kernel) {
+  Packet2d t0, t1;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[1]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[0] = t0;
+  kernel.packet[1] = t1;
+}
+
+#endif  // __VSX__
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_ALTIVEC_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/TypeCasting.h
new file mode 100644
index 0000000..439339e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/AltiVec/TypeCasting.h
@@ -0,0 +1,153 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2019 Rasmus Munk Larsen <rmlarsen@google.com>
+// Copyright (C) 2023 Chip Kerchner (chip.kerchner@ibm.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_ALTIVEC_H
+#define EIGEN_TYPE_CASTING_ALTIVEC_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <>
+struct type_casting_traits<float, int> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+struct type_casting_traits<int, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+struct type_casting_traits<bfloat16, unsigned short int> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+struct type_casting_traits<unsigned short int, bfloat16> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
+  return vec_cts(a, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4f, Packet4ui>(const Packet4f& a) {
+  return vec_ctu(a, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
+  return vec_ctf(a, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4ui, Packet4f>(const Packet4ui& a) {
+  return vec_ctf(a, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet8bf, Packet8us>(const Packet8bf& a) {
+  Packet4f float_even = Bf16ToF32Even(a);
+  Packet4f float_odd = Bf16ToF32Odd(a);
+  Packet4ui int_even = pcast<Packet4f, Packet4ui>(float_even);
+  Packet4ui int_odd = pcast<Packet4f, Packet4ui>(float_odd);
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(low_mask, 0x0000FFFF);
+  Packet4ui low_even = pand<Packet4ui>(int_even, p4ui_low_mask);
+  Packet4ui low_odd = pand<Packet4ui>(int_odd, p4ui_low_mask);
+
+  // Check values that are bigger than USHRT_MAX (0xFFFF)
+  Packet4bi overflow_selector;
+  if (vec_any_gt(int_even, p4ui_low_mask)) {
+    overflow_selector = vec_cmpgt(int_even, p4ui_low_mask);
+    low_even = vec_sel(low_even, p4ui_low_mask, overflow_selector);
+  }
+  if (vec_any_gt(int_odd, p4ui_low_mask)) {
+    overflow_selector = vec_cmpgt(int_odd, p4ui_low_mask);
+    low_odd = vec_sel(low_even, p4ui_low_mask, overflow_selector);
+  }
+
+  return pmerge(low_even, low_odd);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcast<Packet8us, Packet8bf>(const Packet8us& a) {
+  // short -> int -> float -> bfloat16
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(low_mask, 0x0000FFFF);
+  Packet4ui int_cast = reinterpret_cast<Packet4ui>(a);
+  Packet4ui int_even = pand<Packet4ui>(int_cast, p4ui_low_mask);
+  Packet4ui int_odd = plogical_shift_right<16>(int_cast);
+  Packet4f float_even = pcast<Packet4ui, Packet4f>(int_even);
+  Packet4f float_odd = pcast<Packet4ui, Packet4f>(int_odd);
+  return F32ToBf16(float_even, float_odd);
+}
+
+template <>
+struct type_casting_traits<bfloat16, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet8bf, Packet4f>(const Packet8bf& a) {
+  Packet8us z = pset1<Packet8us>(0);
+#ifdef _BIG_ENDIAN
+  return reinterpret_cast<Packet4f>(vec_mergeh(a.m_val, z));
+#else
+  return reinterpret_cast<Packet4f>(vec_mergeh(z, a.m_val));
+#endif
+}
+
+template <>
+struct type_casting_traits<float, bfloat16> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pcast<Packet4f, Packet8bf>(const Packet4f& a, const Packet4f& b) {
+  return F32ToBf16Both(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4f>(const Packet4f& a) {
+  return reinterpret_cast<Packet4i>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4i>(const Packet4i& a) {
+  return reinterpret_cast<Packet4f>(a);
+}
+
+#ifdef EIGEN_VECTORIZE_VSX
+template <>
+inline Packet2l pcast<Packet2d, Packet2l>(const Packet2d& x) {
+  EIGEN_ALIGN_MAX double dtmp[2];
+  pstore(dtmp, x);
+  EIGEN_ALIGN_MAX long long itmp[2] = {static_cast<long long>(dtmp[0]), static_cast<long long>(dtmp[1])};
+  return vec_xl(0, itmp);
+}
+
+template <>
+inline Packet2d pcast<Packet2l, Packet2d>(const Packet2l& x) {
+  EIGEN_ALIGN_MAX long long itmp[2];
+  vec_xst(x, 0, itmp);
+  EIGEN_ALIGN_MAX double dtmp[2] = {static_cast<double>(itmp[0]), static_cast<double>(itmp[1])};
+  return pload<Packet2d>(dtmp);
+}
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_ALTIVEC_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/BFloat16.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/BFloat16.h
new file mode 100644
index 0000000..8f1f092
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/BFloat16.h
@@ -0,0 +1,903 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef EIGEN_BFLOAT16_H
+#define EIGEN_BFLOAT16_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+#if defined(EIGEN_HAS_HIP_BF16)
+// When compiling with GPU support, the "hip_bfloat16" base class as well as
+// some other routines are defined in the GPU compiler header files
+// (hip_bfloat16.h), and they are not tagged constexpr
+// As a consequence, we get compile failures when compiling Eigen with
+// GPU support. Hence the need to disable EIGEN_CONSTEXPR when building
+// Eigen with GPU support
+#pragma push_macro("EIGEN_CONSTEXPR")
+#undef EIGEN_CONSTEXPR
+#define EIGEN_CONSTEXPR
+#endif
+
+#define BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, METHOD)                                         \
+  template <>                                                                                       \
+  EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED PACKET_BF16 METHOD<PACKET_BF16>( \
+      const PACKET_BF16& _x) {                                                                      \
+    return F32ToBf16(METHOD<PACKET_F>(Bf16ToF32(_x)));                                              \
+  }
+
+#define EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_BF16(PACKET_F, PACKET_BF16) \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pcos)                      \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, psin)                      \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pexp)                      \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pexp2)                     \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pexpm1)                    \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, plog)                      \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, plog1p)                    \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, plog2)                     \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, preciprocal)               \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, prsqrt)                    \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pcbrt)                     \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, psqrt)                     \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, ptanh)
+
+// BF16 wrappers for unsupported/SpecialFunctions.
+#define EIGEN_INSTANTIATE_SPECIAL_FUNCS_BF16(PACKET_F, PACKET_BF16) \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, perf)                 \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pndtri)
+
+#define EIGEN_INSTANTIATE_BESSEL_FUNCS_BF16(PACKET_F, PACKET_BF16) \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_i0)          \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_i0e)         \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_i1)          \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_i1e)         \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_j0)          \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_j1)          \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_k0)          \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_k0e)         \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_k1)          \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_k1e)         \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_y0)          \
+  BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, pbessel_y1)
+
+// Only use HIP GPU bf16 in kernels
+#if defined(EIGEN_HAS_HIP_BF16) && defined(EIGEN_GPU_COMPILE_PHASE)
+#define EIGEN_USE_HIP_BF16
+#endif
+
+namespace Eigen {
+
+struct bfloat16;
+
+namespace numext {
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bit_cast<Eigen::bfloat16, uint16_t>(const uint16_t& src);
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC uint16_t bit_cast<uint16_t, Eigen::bfloat16>(const Eigen::bfloat16& src);
+}  // namespace numext
+namespace bfloat16_impl {
+
+#if defined(EIGEN_USE_HIP_BF16)
+
+struct __bfloat16_raw : public hip_bfloat16 {
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw() {}
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw(hip_bfloat16 hb) : hip_bfloat16(hb) {}
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw(unsigned short raw) : hip_bfloat16(raw) {}
+};
+
+#else
+
+// Make our own __bfloat16_raw definition.
+struct __bfloat16_raw {
+#if defined(EIGEN_HAS_HIP_BF16) && !defined(EIGEN_GPU_COMPILE_PHASE)
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw() {}
+#else
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw() : value(0) {}
+#endif
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw(unsigned short raw) : value(raw) {}
+  unsigned short value;
+};
+
+#endif  // defined(EIGEN_USE_HIP_BF16)
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw raw_uint16_to_bfloat16(unsigned short value);
+template <bool AssumeArgumentIsNormalOrInfinityOrZero>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne(float ff);
+// Forward declarations of template specializations, to avoid Visual C++ 2019 errors, saying:
+// > error C2908: explicit specialization; 'float_to_bfloat16_rtne' has already been instantiated
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<false>(float ff);
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<true>(float ff);
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float bfloat16_to_float(__bfloat16_raw h);
+
+struct bfloat16_base : public __bfloat16_raw {
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16_base() {}
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16_base(const __bfloat16_raw& h) : __bfloat16_raw(h) {}
+};
+
+}  // namespace bfloat16_impl
+
+// Class definition.
+struct bfloat16 : public bfloat16_impl::bfloat16_base {
+  typedef bfloat16_impl::__bfloat16_raw __bfloat16_raw;
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(const __bfloat16_raw& h) : bfloat16_impl::bfloat16_base(h) {}
+
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(bool b)
+      : bfloat16_impl::bfloat16_base(bfloat16_impl::raw_uint16_to_bfloat16(b ? 0x3f80 : 0)) {}
+
+  template <class T>
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(T val)
+      : bfloat16_impl::bfloat16_base(
+            bfloat16_impl::float_to_bfloat16_rtne<internal::is_integral<T>::value>(static_cast<float>(val))) {}
+
+  explicit EIGEN_DEVICE_FUNC bfloat16(float f)
+      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<false>(f)) {}
+
+  // Following the convention of numpy, converting between complex and
+  // float will lead to loss of imag value.
+  template <typename RealScalar>
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(const std::complex<RealScalar>& val)
+      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<false>(static_cast<float>(val.real()))) {}
+
+  EIGEN_DEVICE_FUNC operator float() const {  // NOLINT: Allow implicit conversion to float, because it is lossless.
+    return bfloat16_impl::bfloat16_to_float(*this);
+  }
+};
+
+// TODO(majnemer): Get rid of this once we can rely on C++17 inline variables do
+// solve the ODR issue.
+namespace bfloat16_impl {
+template <typename = void>
+struct numeric_limits_bfloat16_impl {
+  static EIGEN_CONSTEXPR const bool is_specialized = true;
+  static EIGEN_CONSTEXPR const bool is_signed = true;
+  static EIGEN_CONSTEXPR const bool is_integer = false;
+  static EIGEN_CONSTEXPR const bool is_exact = false;
+  static EIGEN_CONSTEXPR const bool has_infinity = true;
+  static EIGEN_CONSTEXPR const bool has_quiet_NaN = true;
+  static EIGEN_CONSTEXPR const bool has_signaling_NaN = true;
+  EIGEN_DIAGNOSTICS(push)
+  EIGEN_DISABLE_DEPRECATED_WARNING
+  static EIGEN_CONSTEXPR const std::float_denorm_style has_denorm = std::denorm_present;
+  static EIGEN_CONSTEXPR const bool has_denorm_loss = false;
+  EIGEN_DIAGNOSTICS(pop)
+  static EIGEN_CONSTEXPR const std::float_round_style round_style = std::numeric_limits<float>::round_style;
+  static EIGEN_CONSTEXPR const bool is_iec559 = true;
+  // The C++ standard defines this as "true if the set of values representable
+  // by the type is finite." BFloat16 has finite precision.
+  static EIGEN_CONSTEXPR const bool is_bounded = true;
+  static EIGEN_CONSTEXPR const bool is_modulo = false;
+  static EIGEN_CONSTEXPR const int digits = 8;
+  static EIGEN_CONSTEXPR const int digits10 = 2;
+  static EIGEN_CONSTEXPR const int max_digits10 = 4;
+  static EIGEN_CONSTEXPR const int radix = std::numeric_limits<float>::radix;
+  static EIGEN_CONSTEXPR const int min_exponent = std::numeric_limits<float>::min_exponent;
+  static EIGEN_CONSTEXPR const int min_exponent10 = std::numeric_limits<float>::min_exponent10;
+  static EIGEN_CONSTEXPR const int max_exponent = std::numeric_limits<float>::max_exponent;
+  static EIGEN_CONSTEXPR const int max_exponent10 = std::numeric_limits<float>::max_exponent10;
+  static EIGEN_CONSTEXPR const bool traps = std::numeric_limits<float>::traps;
+  // IEEE754: "The implementer shall choose how tininess is detected, but shall
+  // detect tininess in the same way for all operations in radix two"
+  static EIGEN_CONSTEXPR const bool tinyness_before = std::numeric_limits<float>::tinyness_before;
+
+  static EIGEN_CONSTEXPR Eigen::bfloat16(min)() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x0080); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 lowest() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0xff7f); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16(max)() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7f7f); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 epsilon() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x3c00); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 round_error() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x3f00); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 infinity() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7f80); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 quiet_NaN() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7fc0); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 signaling_NaN() {
+    return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7fa0);
+  }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 denorm_min() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x0001); }
+};
+
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_specialized;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_signed;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_integer;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_exact;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::has_infinity;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::has_quiet_NaN;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::has_signaling_NaN;
+EIGEN_DIAGNOSTICS(push)
+EIGEN_DISABLE_DEPRECATED_WARNING
+template <typename T>
+EIGEN_CONSTEXPR const std::float_denorm_style numeric_limits_bfloat16_impl<T>::has_denorm;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::has_denorm_loss;
+EIGEN_DIAGNOSTICS(pop)
+template <typename T>
+EIGEN_CONSTEXPR const std::float_round_style numeric_limits_bfloat16_impl<T>::round_style;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_iec559;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_bounded;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_modulo;
+template <typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::digits;
+template <typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::digits10;
+template <typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::max_digits10;
+template <typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::radix;
+template <typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::min_exponent;
+template <typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::min_exponent10;
+template <typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::max_exponent;
+template <typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::max_exponent10;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::traps;
+template <typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::tinyness_before;
+}  // end namespace bfloat16_impl
+}  // end namespace Eigen
+
+namespace std {
+// If std::numeric_limits<T> is specialized, should also specialize
+// std::numeric_limits<const T>, std::numeric_limits<volatile T>, and
+// std::numeric_limits<const volatile T>
+// https://stackoverflow.com/a/16519653/
+template <>
+class numeric_limits<Eigen::bfloat16> : public Eigen::bfloat16_impl::numeric_limits_bfloat16_impl<> {};
+template <>
+class numeric_limits<const Eigen::bfloat16> : public numeric_limits<Eigen::bfloat16> {};
+template <>
+class numeric_limits<volatile Eigen::bfloat16> : public numeric_limits<Eigen::bfloat16> {};
+template <>
+class numeric_limits<const volatile Eigen::bfloat16> : public numeric_limits<Eigen::bfloat16> {};
+}  // end namespace std
+
+namespace Eigen {
+
+namespace bfloat16_impl {
+
+// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
+// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
+// of the functions, while the latter can only deal with one of them.
+#if !defined(EIGEN_HAS_NATIVE_BF16) || (EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)  // Emulate support for bfloat16 floats
+
+#if EIGEN_COMP_CLANG && defined(EIGEN_CUDACC)
+// We need to provide emulated *host-side* BF16 operators for clang.
+#pragma push_macro("EIGEN_DEVICE_FUNC")
+#undef EIGEN_DEVICE_FUNC
+#if (defined(EIGEN_HAS_GPU_BF16) && defined(EIGEN_HAS_NATIVE_BF16))
+#define EIGEN_DEVICE_FUNC __host__
+#else  // both host and device need emulated ops.
+#define EIGEN_DEVICE_FUNC __host__ __device__
+#endif
+#endif
+
+// Definitions for CPUs, mostly working through conversion
+// to/from fp32.
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator+(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(float(a) + float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator+(const bfloat16& a, const int& b) {
+  return bfloat16(float(a) + static_cast<float>(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator+(const int& a, const bfloat16& b) {
+  return bfloat16(static_cast<float>(a) + float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator*(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(float(a) * float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator-(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(float(a) - float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator/(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(float(a) / float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator-(const bfloat16& a) {
+  numext::uint16_t x = numext::bit_cast<uint16_t>(a) ^ 0x8000;
+  return numext::bit_cast<bfloat16>(x);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator+=(bfloat16& a, const bfloat16& b) {
+  a = bfloat16(float(a) + float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator*=(bfloat16& a, const bfloat16& b) {
+  a = bfloat16(float(a) * float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator-=(bfloat16& a, const bfloat16& b) {
+  a = bfloat16(float(a) - float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator/=(bfloat16& a, const bfloat16& b) {
+  a = bfloat16(float(a) / float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator++(bfloat16& a) {
+  a += bfloat16(1);
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator--(bfloat16& a) {
+  a -= bfloat16(1);
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator++(bfloat16& a, int) {
+  bfloat16 original_value = a;
+  ++a;
+  return original_value;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator--(bfloat16& a, int) {
+  bfloat16 original_value = a;
+  --a;
+  return original_value;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator==(const bfloat16& a, const bfloat16& b) {
+  return numext::equal_strict(float(a), float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator!=(const bfloat16& a, const bfloat16& b) {
+  return numext::not_equal_strict(float(a), float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator<(const bfloat16& a, const bfloat16& b) {
+  return float(a) < float(b);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator<=(const bfloat16& a, const bfloat16& b) {
+  return float(a) <= float(b);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator>(const bfloat16& a, const bfloat16& b) {
+  return float(a) > float(b);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator>=(const bfloat16& a, const bfloat16& b) {
+  return float(a) >= float(b);
+}
+
+#if EIGEN_COMP_CLANG && defined(EIGEN_CUDACC)
+#pragma pop_macro("EIGEN_DEVICE_FUNC")
+#endif
+#endif  // Emulate support for bfloat16 floats
+
+// Division by an index. Do it in full float precision to avoid accuracy
+// issues in converting the denominator to bfloat16.
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator/(const bfloat16& a, Index b) {
+  return bfloat16(static_cast<float>(a) / static_cast<float>(b));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw truncate_to_bfloat16(const float v) {
+#if defined(EIGEN_USE_HIP_BF16)
+  return __bfloat16_raw(__bfloat16_raw::round_to_bfloat16(v, __bfloat16_raw::truncate));
+#else
+  __bfloat16_raw output;
+  if (numext::isnan EIGEN_NOT_A_MACRO(v)) {
+    output.value = std::signbit(v) ? 0xFFC0 : 0x7FC0;
+    return output;
+  }
+  output.value = static_cast<numext::uint16_t>(numext::bit_cast<numext::uint32_t>(v) >> 16);
+  return output;
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw raw_uint16_to_bfloat16(numext::uint16_t value) {
+#if defined(EIGEN_USE_HIP_BF16)
+  __bfloat16_raw bf;
+  bf.data = value;
+  return bf;
+#else
+  return __bfloat16_raw(value);
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR numext::uint16_t raw_bfloat16_as_uint16(
+    const __bfloat16_raw& bf) {
+#if defined(EIGEN_USE_HIP_BF16)
+  return bf.data;
+#else
+  return bf.value;
+#endif
+}
+
+// float_to_bfloat16_rtne template specialization that does not make any
+// assumption about the value of its function argument (ff).
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<false>(float ff) {
+#if defined(EIGEN_USE_HIP_BF16)
+  return __bfloat16_raw(__bfloat16_raw::round_to_bfloat16(ff));
+#else
+  __bfloat16_raw output;
+
+  if (numext::isnan EIGEN_NOT_A_MACRO(ff)) {
+    // If the value is a NaN, squash it to a qNaN with msb of fraction set,
+    // this makes sure after truncation we don't end up with an inf.
+    //
+    // qNaN magic: All exponent bits set + most significant bit of fraction
+    // set.
+    output.value = std::signbit(ff) ? 0xFFC0 : 0x7FC0;
+  } else {
+    // Fast rounding algorithm that rounds a half value to nearest even. This
+    // reduces expected error when we convert a large number of floats. Here
+    // is how it works:
+    //
+    // Definitions:
+    // To convert a float 32 to bfloat16, a float 32 can be viewed as 32 bits
+    // with the following tags:
+    //
+    // Sign |  Exp (8 bits) | Frac (23 bits)
+    //  S     EEEEEEEE         FFFFFFLRTTTTTTTTTTTTTTT
+    //
+    //  S: Sign bit.
+    //  E: Exponent bits.
+    //  F: First 6 bits of fraction.
+    //  L: Least significant bit of resulting bfloat16 if we truncate away the
+    //  rest of the float32. This is also the 7th bit of fraction
+    //  R: Rounding bit, 8th bit of fraction.
+    //  T: Sticky bits, rest of fraction, 15 bits.
+    //
+    // To round half to nearest even, there are 3 cases where we want to round
+    // down (simply truncate the result of the bits away, which consists of
+    // rounding bit and sticky bits) and two cases where we want to round up
+    // (truncate then add one to the result).
+    //
+    // The fast converting algorithm simply adds lsb (L) to 0x7fff (15 bits of
+    // 1s) as the rounding bias, adds the rounding bias to the input, then
+    // truncates the last 16 bits away.
+    //
+    // To understand how it works, we can analyze this algorithm case by case:
+    //
+    // 1. L = 0, R = 0:
+    //   Expect: round down, this is less than half value.
+    //
+    //   Algorithm:
+    //   - Rounding bias: 0x7fff + 0 = 0x7fff
+    //   - Adding rounding bias to input may create any carry, depending on
+    //   whether there is any value set to 1 in T bits.
+    //   - R may be set to 1 if there is a carry.
+    //   - L remains 0.
+    //   - Note that this case also handles Inf and -Inf, where all fraction
+    //   bits, including L, R and Ts are all 0. The output remains Inf after
+    //   this algorithm.
+    //
+    // 2. L = 1, R = 0:
+    //   Expect: round down, this is less than half value.
+    //
+    //   Algorithm:
+    //   - Rounding bias: 0x7fff + 1 = 0x8000
+    //   - Adding rounding bias to input doesn't change sticky bits but
+    //   adds 1 to rounding bit.
+    //   - L remains 1.
+    //
+    // 3. L = 0, R = 1, all of T are 0:
+    //   Expect: round down, this is exactly at half, the result is already
+    //   even (L=0).
+    //
+    //   Algorithm:
+    //   - Rounding bias: 0x7fff + 0 = 0x7fff
+    //   - Adding rounding bias to input sets all sticky bits to 1, but
+    //   doesn't create a carry.
+    //   - R remains 1.
+    //   - L remains 0.
+    //
+    // 4. L = 1, R = 1:
+    //   Expect: round up, this is exactly at half, the result needs to be
+    //   round to the next even number.
+    //
+    //   Algorithm:
+    //   - Rounding bias: 0x7fff + 1 = 0x8000
+    //   - Adding rounding bias to input doesn't change sticky bits, but
+    //   creates a carry from rounding bit.
+    //   - The carry sets L to 0, creates another carry bit and propagate
+    //   forward to F bits.
+    //   - If all the F bits are 1, a carry then propagates to the exponent
+    //   bits, which then creates the minimum value with the next exponent
+    //   value. Note that we won't have the case where exponents are all 1,
+    //   since that's either a NaN (handled in the other if condition) or inf
+    //   (handled in case 1).
+    //
+    // 5. L = 0, R = 1, any of T is 1:
+    //   Expect: round up, this is greater than half.
+    //
+    //   Algorithm:
+    //   - Rounding bias: 0x7fff + 0 = 0x7fff
+    //   - Adding rounding bias to input creates a carry from sticky bits,
+    //   sets rounding bit to 0, then create another carry.
+    //   - The second carry sets L to 1.
+    //
+    // Examples:
+    //
+    //  Exact half value that is already even:
+    //    Input:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
+    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
+    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0     1000000000000000
+    //
+    //     This falls into case 3. We truncate the rest of 16 bits and no
+    //     carry is created into F and L:
+    //
+    //    Output:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
+    //     S     E E E E E E E E      F F F F F F L
+    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0
+    //
+    //  Exact half value, round to next even number:
+    //    Input:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
+    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
+    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 0 1     1000000000000000
+    //
+    //     This falls into case 4. We create a carry from R and T,
+    //     which then propagates into L and F:
+    //
+    //    Output:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
+    //     S     E E E E E E E E      F F F F F F L
+    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0
+    //
+    //
+    //  Max denormal value round to min normal value:
+    //    Input:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
+    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
+    //     0     0 0 0 0 0 0 0 0      1 1 1 1 1 1 1     1111111111111111
+    //
+    //     This falls into case 4. We create a carry from R and T,
+    //     propagate into L and F, which then propagates into exponent
+    //     bits:
+    //
+    //    Output:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
+    //     S     E E E E E E E E      F F F F F F L
+    //     0     0 0 0 0 0 0 0 1      0 0 0 0 0 0 0
+    //
+    //  Max normal value round to Inf:
+    //    Input:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
+    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
+    //     0     1 1 1 1 1 1 1 0      1 1 1 1 1 1 1     1111111111111111
+    //
+    //     This falls into case 4. We create a carry from R and T,
+    //     propagate into L and F, which then propagates into exponent
+    //     bits:
+    //
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
+    //     S     E E E E E E E E      F F F F F F L
+    //     0     1 1 1 1 1 1 1 1      0 0 0 0 0 0 0
+
+    // At this point, ff must be either a normal float, or +/-infinity.
+    output = float_to_bfloat16_rtne<true>(ff);
+  }
+  return output;
+#endif
+}
+
+// float_to_bfloat16_rtne template specialization that assumes that its function
+// argument (ff) is either a normal floating point number, or +/-infinity, or
+// zero. Used to improve the runtime performance of conversion from an integer
+// type to bfloat16.
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<true>(float ff) {
+#if defined(EIGEN_USE_HIP_BF16)
+  return __bfloat16_raw(__bfloat16_raw::round_to_bfloat16(ff));
+#else
+  numext::uint32_t input = numext::bit_cast<numext::uint32_t>(ff);
+  __bfloat16_raw output;
+
+  // Least significant bit of resulting bfloat.
+  numext::uint32_t lsb = (input >> 16) & 1;
+  numext::uint32_t rounding_bias = 0x7fff + lsb;
+  input += rounding_bias;
+  output.value = static_cast<numext::uint16_t>(input >> 16);
+  return output;
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float bfloat16_to_float(__bfloat16_raw h) {
+#if defined(EIGEN_USE_HIP_BF16)
+  return static_cast<float>(h);
+#else
+  return numext::bit_cast<float>(static_cast<numext::uint32_t>(h.value) << 16);
+#endif
+}
+
+// --- standard functions ---
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool(isinf)(const bfloat16& a) {
+  EIGEN_USING_STD(isinf);
+#if defined(EIGEN_USE_HIP_BF16)
+  return (isinf)(a);  // Uses HIP hip_bfloat16 isinf operator
+#else
+  return (isinf)(float(a));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool(isnan)(const bfloat16& a) {
+  EIGEN_USING_STD(isnan);
+#if defined(EIGEN_USE_HIP_BF16)
+  return (isnan)(a);  // Uses HIP hip_bfloat16 isnan operator
+#else
+  return (isnan)(float(a));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool(isfinite)(const bfloat16& a) {
+  return !(isinf EIGEN_NOT_A_MACRO(a)) && !(isnan EIGEN_NOT_A_MACRO(a));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 abs(const bfloat16& a) {
+  numext::uint16_t x = numext::bit_cast<numext::uint16_t>(a) & 0x7FFF;
+  return numext::bit_cast<bfloat16>(x);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 exp(const bfloat16& a) { return bfloat16(::expf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 exp2(const bfloat16& a) { return bfloat16(::exp2f(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 expm1(const bfloat16& a) { return bfloat16(numext::expm1(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log(const bfloat16& a) { return bfloat16(::logf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log1p(const bfloat16& a) { return bfloat16(numext::log1p(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log10(const bfloat16& a) { return bfloat16(::log10f(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log2(const bfloat16& a) {
+  return bfloat16(static_cast<float>(EIGEN_LOG2E) * ::logf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sqrt(const bfloat16& a) { return bfloat16(::sqrtf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 cbrt(const bfloat16& a) { return bfloat16(::cbrtf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 pow(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(::powf(float(a), float(b)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 atan2(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(::atan2f(float(a), float(b)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sin(const bfloat16& a) { return bfloat16(::sinf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 cos(const bfloat16& a) { return bfloat16(::cosf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 tan(const bfloat16& a) { return bfloat16(::tanf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 asin(const bfloat16& a) { return bfloat16(::asinf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 acos(const bfloat16& a) { return bfloat16(::acosf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 atan(const bfloat16& a) { return bfloat16(::atanf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sinh(const bfloat16& a) { return bfloat16(::sinhf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 cosh(const bfloat16& a) { return bfloat16(::coshf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 tanh(const bfloat16& a) { return bfloat16(::tanhf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 asinh(const bfloat16& a) { return bfloat16(::asinhf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 acosh(const bfloat16& a) { return bfloat16(::acoshf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 atanh(const bfloat16& a) { return bfloat16(::atanhf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 floor(const bfloat16& a) { return bfloat16(::floorf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 ceil(const bfloat16& a) { return bfloat16(::ceilf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 rint(const bfloat16& a) { return bfloat16(::rintf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 round(const bfloat16& a) { return bfloat16(::roundf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 trunc(const bfloat16& a) { return bfloat16(::truncf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmod(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(::fmodf(float(a), float(b)));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16(min)(const bfloat16& a, const bfloat16& b) {
+  const float f1 = static_cast<float>(a);
+  const float f2 = static_cast<float>(b);
+  return f2 < f1 ? b : a;
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16(max)(const bfloat16& a, const bfloat16& b) {
+  const float f1 = static_cast<float>(a);
+  const float f2 = static_cast<float>(b);
+  return f1 < f2 ? b : a;
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmin(const bfloat16& a, const bfloat16& b) {
+  const float f1 = static_cast<float>(a);
+  const float f2 = static_cast<float>(b);
+  return bfloat16(::fminf(f1, f2));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmax(const bfloat16& a, const bfloat16& b) {
+  const float f1 = static_cast<float>(a);
+  const float f2 = static_cast<float>(b);
+  return bfloat16(::fmaxf(f1, f2));
+}
+
+EIGEN_DEVICE_FUNC inline bfloat16 fma(const bfloat16& a, const bfloat16& b, const bfloat16& c) {
+  // Emulate FMA via float.
+  return bfloat16(numext::fma(static_cast<float>(a), static_cast<float>(b), static_cast<float>(c)));
+}
+
+#ifndef EIGEN_NO_IO
+EIGEN_ALWAYS_INLINE std::ostream& operator<<(std::ostream& os, const bfloat16& v) {
+  os << static_cast<float>(v);
+  return os;
+}
+#endif
+
+}  // namespace bfloat16_impl
+
+namespace internal {
+
+template <>
+struct is_arithmetic<bfloat16> {
+  enum { value = true };
+};
+
+template <>
+struct random_impl<bfloat16> {
+  enum : int { MantissaBits = 7 };
+  using Impl = random_impl<float>;
+  static EIGEN_DEVICE_FUNC inline bfloat16 run(const bfloat16& x, const bfloat16& y) {
+    float result = Impl::run(x, y, MantissaBits);
+    return bfloat16(result);
+  }
+  static EIGEN_DEVICE_FUNC inline bfloat16 run() {
+    float result = Impl::run(MantissaBits);
+    return bfloat16(result);
+  }
+};
+
+}  // namespace internal
+
+template <>
+struct NumTraits<Eigen::bfloat16> : GenericNumTraits<Eigen::bfloat16> {
+  enum { IsSigned = true, IsInteger = false, IsComplex = false, RequireInitialization = false };
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 epsilon() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0x3c00);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 dummy_precision() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0x3D4D);  // bfloat16(5e-2f);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 highest() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0x7F7F);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 lowest() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0xFF7F);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 infinity() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0x7f80);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 quiet_NaN() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0x7fc0);
+  }
+};
+
+}  // namespace Eigen
+
+#if defined(EIGEN_HAS_HIP_BF16)
+#pragma pop_macro("EIGEN_CONSTEXPR")
+#endif
+
+namespace Eigen {
+namespace numext {
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isnan)(const Eigen::bfloat16& h) {
+  return (bfloat16_impl::isnan)(h);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isinf)(const Eigen::bfloat16& h) {
+  return (bfloat16_impl::isinf)(h);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isfinite)(const Eigen::bfloat16& h) {
+  return (bfloat16_impl::isfinite)(h);
+}
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bit_cast<Eigen::bfloat16, uint16_t>(const uint16_t& src) {
+  return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(src);
+}
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC uint16_t bit_cast<uint16_t, Eigen::bfloat16>(const Eigen::bfloat16& src) {
+  return Eigen::bfloat16_impl::raw_bfloat16_as_uint16(src);
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 nextafter(const bfloat16& from, const bfloat16& to) {
+  if (numext::isnan EIGEN_NOT_A_MACRO(from)) {
+    return from;
+  }
+  if (numext::isnan EIGEN_NOT_A_MACRO(to)) {
+    return to;
+  }
+  if (from == to) {
+    return to;
+  }
+  uint16_t from_bits = numext::bit_cast<uint16_t>(from);
+  bool from_sign = from_bits >> 15;
+  // Whether we are adjusting toward the infinity with the same sign as from.
+  bool toward_inf = (to > from) == !from_sign;
+  if (toward_inf) {
+    ++from_bits;
+  } else if ((from_bits & 0x7fff) == 0) {
+    // Adjusting away from inf, but from is zero, so just toggle the sign.
+    from_bits ^= 0x8000;
+  } else {
+    --from_bits;
+  }
+  return numext::bit_cast<bfloat16>(from_bits);
+}
+
+// Specialize multiply-add to match packet operations and reduce conversions to/from float.
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 madd<Eigen::bfloat16>(const Eigen::bfloat16& x,
+                                                                            const Eigen::bfloat16& y,
+                                                                            const Eigen::bfloat16& z) {
+  return Eigen::bfloat16(static_cast<float>(x) * static_cast<float>(y) + static_cast<float>(z));
+}
+
+}  // namespace numext
+}  // namespace Eigen
+
+#if EIGEN_HAS_STD_HASH
+namespace std {
+template <>
+struct hash<Eigen::bfloat16> {
+  EIGEN_STRONG_INLINE std::size_t operator()(const Eigen::bfloat16& a) const {
+    return static_cast<std::size_t>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(a));
+  }
+};
+}  // namespace std
+#endif
+
+// Add the missing shfl* intrinsics.
+// The __shfl* functions are only valid on HIP or _CUDA_ARCH_ >= 300.
+//   CUDA defines them for (__CUDA_ARCH__ >= 300 || !defined(__CUDA_ARCH__))
+//
+// HIP and CUDA prior to SDK 9.0 define
+//    __shfl, __shfl_up, __shfl_down, __shfl_xor for int and float
+// CUDA since 9.0 deprecates those and instead defines
+//    __shfl_sync, __shfl_up_sync, __shfl_down_sync, __shfl_xor_sync,
+//    with native support for __half and __nv_bfloat16
+//
+// Note that the following are __device__ - only functions.
+#if defined(EIGEN_HIPCC)
+
+#if defined(EIGEN_HAS_HIP_BF16)
+
+__device__ EIGEN_STRONG_INLINE Eigen::bfloat16 __shfl(Eigen::bfloat16 var, int srcLane, int width = warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::bfloat16>(static_cast<Eigen::numext::uint16_t>(__shfl(ivar, srcLane, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::bfloat16 __shfl_up(Eigen::bfloat16 var, unsigned int delta,
+                                                         int width = warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::bfloat16>(static_cast<Eigen::numext::uint16_t>(__shfl_up(ivar, delta, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::bfloat16 __shfl_down(Eigen::bfloat16 var, unsigned int delta,
+                                                           int width = warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::bfloat16>(
+      static_cast<Eigen::numext::uint16_t>(__shfl_down(ivar, delta, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::bfloat16 __shfl_xor(Eigen::bfloat16 var, int laneMask, int width = warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::bfloat16>(
+      static_cast<Eigen::numext::uint16_t>(__shfl_xor(ivar, laneMask, width)));
+}
+
+#endif  // HIP
+
+#endif  // __shfl*
+
+#if defined(EIGEN_HIPCC)
+EIGEN_STRONG_INLINE __device__ Eigen::bfloat16 __ldg(const Eigen::bfloat16* ptr) {
+  return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(
+      __ldg(Eigen::numext::bit_cast<const Eigen::numext::uint16_t*>(ptr)));
+}
+#endif  // __ldg
+
+#endif  // EIGEN_BFLOAT16_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/ConjHelper.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/ConjHelper.h
new file mode 100644
index 0000000..137e7fb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/ConjHelper.h
@@ -0,0 +1,146 @@
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARCH_CONJ_HELPER_H
+#define EIGEN_ARCH_CONJ_HELPER_H
+
+#define EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(PACKET_CPLX, PACKET_REAL)                                                  \
+  template <>                                                                                                       \
+  struct conj_helper<PACKET_REAL, PACKET_CPLX, false, false> {                                                      \
+    EIGEN_STRONG_INLINE PACKET_CPLX pmadd(const PACKET_REAL& x, const PACKET_CPLX& y, const PACKET_CPLX& c) const { \
+      return padd(c, this->pmul(x, y));                                                                             \
+    }                                                                                                               \
+    EIGEN_STRONG_INLINE PACKET_CPLX pmsub(const PACKET_REAL& x, const PACKET_CPLX& y, const PACKET_CPLX& c) const { \
+      return psub(this->pmul(x, y), c);                                                                             \
+    }                                                                                                               \
+    EIGEN_STRONG_INLINE PACKET_CPLX pmul(const PACKET_REAL& x, const PACKET_CPLX& y) const {                        \
+      return PACKET_CPLX(Eigen::internal::pmul<PACKET_REAL>(x, y.v));                                               \
+    }                                                                                                               \
+  };                                                                                                                \
+                                                                                                                    \
+  template <>                                                                                                       \
+  struct conj_helper<PACKET_CPLX, PACKET_REAL, false, false> {                                                      \
+    EIGEN_STRONG_INLINE PACKET_CPLX pmadd(const PACKET_CPLX& x, const PACKET_REAL& y, const PACKET_CPLX& c) const { \
+      return padd(c, this->pmul(x, y));                                                                             \
+    }                                                                                                               \
+    EIGEN_STRONG_INLINE PACKET_CPLX pmsub(const PACKET_CPLX& x, const PACKET_REAL& y, const PACKET_CPLX& c) const { \
+      return psub(this->pmul(x, y), c);                                                                             \
+    }                                                                                                               \
+    EIGEN_STRONG_INLINE PACKET_CPLX pmul(const PACKET_CPLX& x, const PACKET_REAL& y) const {                        \
+      return PACKET_CPLX(Eigen::internal::pmul<PACKET_REAL>(x.v, y));                                               \
+    }                                                                                                               \
+  };
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <bool Conjugate>
+struct conj_if;
+
+template <>
+struct conj_if<true> {
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const {
+    return numext::conj(x);
+  }
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T pconj(const T& x) const {
+    return internal::pconj(x);
+  }
+};
+
+template <>
+struct conj_if<false> {
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& operator()(const T& x) const {
+    return x;
+  }
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& pconj(const T& x) const {
+    return x;
+  }
+};
+
+// Generic Implementation, assume scalars since the packet-version is
+// specialized below.
+template <typename LhsType, typename RhsType, bool ConjLhs, bool ConjRhs>
+struct conj_helper {
+  typedef typename ScalarBinaryOpTraits<LhsType, RhsType>::ReturnType ResultType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType pmadd(const LhsType& x, const RhsType& y,
+                                                         const ResultType& c) const {
+    return this->pmul(x, y) + c;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType pmsub(const LhsType& x, const RhsType& y,
+                                                         const ResultType& c) const {
+    return this->pmul(x, y) - c;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType pmul(const LhsType& x, const RhsType& y) const {
+    return conj_if<ConjLhs>()(x) * conj_if<ConjRhs>()(y);
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar>
+struct conj_helper<LhsScalar, RhsScalar, true, true> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResultType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType pmadd(const LhsScalar& x, const RhsScalar& y,
+                                                         const ResultType& c) const {
+    return this->pmul(x, y) + c;
+  }
+
+  // We save a conjuation by using the identity conj(a)*conj(b) = conj(a*b).
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType pmul(const LhsScalar& x, const RhsScalar& y) const {
+    return numext::conj(x * y);
+  }
+};
+
+// Implementation with equal type, use packet operations.
+template <typename Packet, bool ConjLhs, bool ConjRhs>
+struct conj_helper<Packet, Packet, ConjLhs, ConjRhs> {
+  typedef Packet ResultType;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmadd(const Packet& x, const Packet& y, const Packet& c) const {
+    return Eigen::internal::pmadd(conj_if<ConjLhs>().pconj(x), conj_if<ConjRhs>().pconj(y), c);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmsub(const Packet& x, const Packet& y, const Packet& c) const {
+    return Eigen::internal::pmsub(conj_if<ConjLhs>().pconj(x), conj_if<ConjRhs>().pconj(y), c);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmul(const Packet& x, const Packet& y) const {
+    return Eigen::internal::pmul(conj_if<ConjLhs>().pconj(x), conj_if<ConjRhs>().pconj(y));
+  }
+};
+
+template <typename Packet>
+struct conj_helper<Packet, Packet, true, true> {
+  typedef Packet ResultType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmadd(const Packet& x, const Packet& y, const Packet& c) const {
+    return Eigen::internal::pmadd(pconj(x), pconj(y), c);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmsub(const Packet& x, const Packet& y, const Packet& c) const {
+    return Eigen::internal::pmsub(pconj(x), pconj(y), c);
+  }
+  // We save a conjuation by using the identity conj(a)*conj(b) = conj(a*b).
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmul(const Packet& x, const Packet& y) const {
+    return pconj(Eigen::internal::pmul(x, y));
+  }
+};
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_ARCH_CONJ_HELPER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathComplex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathComplex.h
new file mode 100644
index 0000000..6363d9e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathComplex.h
@@ -0,0 +1,282 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2018-2025 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_COMPLEX_H
+#define EIGEN_ARCH_GENERIC_PACKET_MATH_COMPLEX_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+//----------------------------------------------------------------------
+// Complex Arithmetic and Functions
+//----------------------------------------------------------------------
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pdiv_complex(const Packet& x, const Packet& y) {
+  typedef typename unpacket_traits<Packet>::as_real RealPacket;
+  typedef typename unpacket_traits<RealPacket>::type RealScalar;
+  // In the following we annotate the code for the case where the inputs
+  // are a pair length-2 SIMD vectors representing a single pair of complex
+  // numbers x = a + i*b, y = c + i*d.
+  const RealPacket one = pset1<RealPacket>(RealScalar(1));
+  const RealPacket y_flip = pcplxflip(y).v;
+  // We need to avoid dividing by  Inf/Inf, so use a mask to carefully
+  // apply the scale.
+  const RealPacket mask = pcmp_lt(pabs(y.v), pabs(y_flip));  // |c| < |d|
+  const RealPacket y_scaled = pselect(mask, pdiv(y.v, y_flip), one);
+  RealPacket denom = pmul(y.v, y_scaled);
+  denom = padd(denom, pcplxflip(Packet(denom)).v);  // c * c' + d * d'
+  Packet num = pmul(x, pconj(Packet(y_scaled)));    // a * c' + b * d', -a * d + b * c
+  return Packet(pdiv(num.v, denom));
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pmul_complex(const Packet& x, const Packet& y) {
+  // In the following we annotate the code for the case where the inputs
+  // are a pair length-2 SIMD vectors representing a single pair of complex
+  // numbers x = a + i*b, y = c + i*d.
+  Packet x_re = pdupreal(x);                  // a, a
+  Packet x_im = pdupimag(x);                  // b, b
+  Packet tmp_re = Packet(pmul(x_re.v, y.v));  // a*c, a*d
+  Packet tmp_im = Packet(pmul(x_im.v, y.v));  // b*c, b*d
+  tmp_im = pcplxflip(pconj(tmp_im));          // -b*d, d*c
+  return padd(tmp_im, tmp_re);                // a*c - b*d, a*d + b*c
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog_complex(const Packet& x) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename Scalar::value_type RealScalar;
+  typedef typename unpacket_traits<Packet>::as_real RealPacket;
+
+  // Real part
+  RealPacket x_flip = pcplxflip(x).v;  // b, a
+  Packet x_norm = phypot_complex(x);   // sqrt(a^2 + b^2), sqrt(a^2 + b^2)
+  RealPacket xlogr = plog(x_norm.v);   // log(sqrt(a^2 + b^2)), log(sqrt(a^2 + b^2))
+
+  // Imag part
+  RealPacket ximg = patan2(x.v, x_flip);  // atan2(a, b), atan2(b, a)
+
+  const RealPacket cst_pos_inf = pset1<RealPacket>(NumTraits<RealScalar>::infinity());
+  RealPacket x_abs = pabs(x.v);
+  RealPacket is_x_pos_inf = pcmp_eq(x_abs, cst_pos_inf);
+  RealPacket is_y_pos_inf = pcplxflip(Packet(is_x_pos_inf)).v;
+  RealPacket is_any_inf = por(is_x_pos_inf, is_y_pos_inf);
+  RealPacket xreal = pselect(is_any_inf, cst_pos_inf, xlogr);
+
+  return Packet(pselect(peven_mask(xreal), xreal, ximg));  // log(sqrt(a^2 + b^2)), atan2(b, a)
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pexp_complex(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::as_real RealPacket;
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename Scalar::value_type RealScalar;
+  const RealPacket even_mask = peven_mask(a.v);
+  const RealPacket odd_mask = pcplxflip(Packet(even_mask)).v;
+
+  // Let a = x + iy.
+  // exp(a) = exp(x) * cis(y), plus some special edge-case handling.
+
+  // exp(x):
+  RealPacket x = pand(a.v, even_mask);
+  x = por(x, pcplxflip(Packet(x)).v);
+  RealPacket expx = pexp(x);  // exp(x);
+
+  // cis(y):
+  RealPacket y = pand(odd_mask, a.v);
+  y = por(y, pcplxflip(Packet(y)).v);
+  RealPacket cisy = psincos_selector<RealPacket>(y);
+  cisy = pcplxflip(Packet(cisy)).v;  // cos(y) + i * sin(y)
+
+  const RealPacket cst_pos_inf = pset1<RealPacket>(NumTraits<RealScalar>::infinity());
+  const RealPacket cst_neg_inf = pset1<RealPacket>(-NumTraits<RealScalar>::infinity());
+
+  // If x is -inf, we know that cossin(y) is bounded,
+  //   so the result is (0, +/-0), where the sign of the imaginary part comes
+  //   from the sign of cossin(y).
+  RealPacket cisy_sign = por(pandnot(cisy, pabs(cisy)), pset1<RealPacket>(RealScalar(1)));
+  cisy = pselect(pcmp_eq(x, cst_neg_inf), cisy_sign, cisy);
+
+  // If x is inf, and cos(y) has unknown sign (y is inf or NaN), the result
+  // is (+/-inf, NaN), where the signs are undetermined (take the sign of y).
+  RealPacket y_sign = por(pandnot(y, pabs(y)), pset1<RealPacket>(RealScalar(1)));
+  cisy = pselect(pand(pcmp_eq(x, cst_pos_inf), pisnan(cisy)), pand(y_sign, even_mask), cisy);
+
+  // If exp(x) is +inf and y is finite, replace cisy with copysign(1, cisy) to
+  // prevent inf * 0 = NaN. The vectorized sincos may compute exact zero
+  // for near-zero values like cos(pi/2), and inf * +-1 = +-inf is correct.
+  // The y=0 case is handled separately below.
+  RealPacket cisy_sign_one = por(pand(cisy, pset1<RealPacket>(RealScalar(-0.0))), pset1<RealPacket>(RealScalar(1)));
+  RealPacket expx_inf_y_finite = pand(pcmp_eq(expx, cst_pos_inf), pcmp_lt(pabs(y), cst_pos_inf));
+  cisy = pselect(expx_inf_y_finite, cisy_sign_one, cisy);
+
+  Packet result = Packet(pmul(expx, cisy));
+
+  // If y is +/- 0, the input is real, so take the real result for consistency.
+  result = pselect(Packet(pcmp_eq(y, pzero(y))), Packet(por(pand(expx, even_mask), pand(y, odd_mask))), result);
+
+  return result;
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psqrt_complex(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename Scalar::value_type RealScalar;
+  typedef typename unpacket_traits<Packet>::as_real RealPacket;
+
+  // Computes the principal sqrt of the complex numbers in the input.
+  //
+  // For example, for packets containing 2 complex numbers stored in interleaved format
+  //    a = [a0, a1] = [x0, y0, x1, y1],
+  // where x0 = real(a0), y0 = imag(a0) etc., this function returns
+  //    b = [b0, b1] = [u0, v0, u1, v1],
+  // such that b0^2 = a0, b1^2 = a1.
+  //
+  // To derive the formula for the complex square roots, let's consider the equation for
+  // a single complex square root of the number x + i*y. We want to find real numbers
+  // u and v such that
+  //    (u + i*v)^2 = x + i*y  <=>
+  //    u^2 - v^2 + i*2*u*v = x + i*v.
+  // By equating the real and imaginary parts we get:
+  //    u^2 - v^2 = x
+  //    2*u*v = y.
+  //
+  // For x >= 0, this has the numerically stable solution
+  //    u = sqrt(0.5 * (x + sqrt(x^2 + y^2)))
+  //    v = 0.5 * (y / u)
+  // and for x < 0,
+  //    v = sign(y) * sqrt(0.5 * (-x + sqrt(x^2 + y^2)))
+  //    u = 0.5 * (y / v)
+  //
+  //  To avoid unnecessary over- and underflow, we compute sqrt(x^2 + y^2) as
+  //     l = max(|x|, |y|) * sqrt(1 + (min(|x|, |y|) / max(|x|, |y|))^2) ,
+
+  // In the following, without lack of generality, we have annotated the code, assuming
+  // that the input is a packet of 2 complex numbers.
+  //
+  // Step 1. Compute l = [l0, l0, l1, l1], where
+  //    l0 = sqrt(x0^2 + y0^2),  l1 = sqrt(x1^2 + y1^2)
+  // To avoid over- and underflow, we use the stable formula for each hypotenuse
+  //    l0 = (min0 == 0 ? max0 : max0 * sqrt(1 + (min0/max0)**2)),
+  // where max0 = max(|x0|, |y0|), min0 = min(|x0|, |y0|), and similarly for l1.
+
+  RealPacket a_abs = pabs(a.v);                        // [|x0|, |y0|, |x1|, |y1|]
+  RealPacket a_abs_flip = pcplxflip(Packet(a_abs)).v;  // [|y0|, |x0|, |y1|, |x1|]
+  RealPacket a_max = pmax(a_abs, a_abs_flip);
+  RealPacket a_min = pmin(a_abs, a_abs_flip);
+  RealPacket a_min_zero_mask = pcmp_eq(a_min, pzero(a_min));
+  RealPacket a_max_zero_mask = pcmp_eq(a_max, pzero(a_max));
+  RealPacket r = pdiv(a_min, a_max);
+  const RealPacket cst_one = pset1<RealPacket>(RealScalar(1));
+  RealPacket l = pmul(a_max, psqrt(padd(cst_one, pmul(r, r))));  // [l0, l0, l1, l1]
+  // Set l to a_max if a_min is zero.
+  l = pselect(a_min_zero_mask, a_max, l);
+
+  // Step 2. Compute [rho0, *, rho1, *], where
+  // rho0 = sqrt(0.5 * (l0 + |x0|)), rho1 =  sqrt(0.5 * (l1 + |x1|))
+  // We don't care about the imaginary parts computed here. They will be overwritten later.
+  const RealPacket cst_half = pset1<RealPacket>(RealScalar(0.5));
+  Packet rho;
+  rho.v = psqrt(pmul(cst_half, padd(a_abs, l)));
+
+  // Step 3. Compute [rho0, eta0, rho1, eta1], where
+  // eta0 = (y0 / l0) / 2, and eta1 = (y1 / l1) / 2.
+  // set eta = 0 of input is 0 + i0.
+  RealPacket eta = pandnot(pmul(cst_half, pdiv(a.v, pcplxflip(rho).v)), a_max_zero_mask);
+  RealPacket real_mask = peven_mask(a.v);
+  Packet positive_real_result;
+  // Compute result for inputs with positive real part.
+  positive_real_result.v = pselect(real_mask, rho.v, eta);
+
+  // Step 4. Compute solution for inputs with negative real part:
+  //         [|eta0|, sign(y0)*rho0, |eta1|, sign(y1)*rho1]
+  const RealPacket cst_imag_sign_mask = pset1<Packet>(Scalar(RealScalar(0.0), RealScalar(-0.0))).v;
+  RealPacket imag_signs = pand(a.v, cst_imag_sign_mask);
+  Packet negative_real_result;
+  // Notice that rho is positive, so taking its absolute value is a noop.
+  negative_real_result.v = por(pabs(pcplxflip(positive_real_result).v), imag_signs);
+
+  // Step 5. Select solution branch based on the sign of the real parts.
+  Packet negative_real_mask;
+  negative_real_mask.v = pcmp_lt(pand(real_mask, a.v), pzero(a.v));
+  negative_real_mask.v = por(negative_real_mask.v, pcplxflip(negative_real_mask).v);
+  Packet result = pselect(negative_real_mask, negative_real_result, positive_real_result);
+
+  // Step 6. Handle special cases for infinities:
+  // * If z is (x,+∞), the result is (+∞,+∞) even if x is NaN
+  // * If z is (x,-∞), the result is (+∞,-∞) even if x is NaN
+  // * If z is (-∞,y), the result is (0*|y|,+∞) for finite or NaN y
+  // * If z is (+∞,y), the result is (+∞,0*|y|) for finite or NaN y
+  const RealPacket cst_pos_inf = pset1<RealPacket>(NumTraits<RealScalar>::infinity());
+  Packet is_inf;
+  is_inf.v = pcmp_eq(a_abs, cst_pos_inf);
+  Packet is_real_inf;
+  is_real_inf.v = pand(is_inf.v, real_mask);
+  is_real_inf = por(is_real_inf, pcplxflip(is_real_inf));
+  // prepare packet of (+∞,0*|y|) or (0*|y|,+∞), depending on the sign of the infinite real part.
+  Packet real_inf_result;
+  real_inf_result.v = pmul(a_abs, pset1<Packet>(Scalar(RealScalar(1.0), RealScalar(0.0))).v);
+  real_inf_result.v = pselect(negative_real_mask.v, pcplxflip(real_inf_result).v, real_inf_result.v);
+  // prepare packet of (+∞,+∞) or (+∞,-∞), depending on the sign of the infinite imaginary part.
+  Packet is_imag_inf;
+  is_imag_inf.v = pandnot(is_inf.v, real_mask);
+  is_imag_inf = por(is_imag_inf, pcplxflip(is_imag_inf));
+  Packet imag_inf_result;
+  imag_inf_result.v = por(pand(cst_pos_inf, real_mask), pandnot(a.v, real_mask));
+  // unless otherwise specified, if either the real or imaginary component is nan, the entire result is nan
+  Packet result_is_nan = pisnan(result);
+  result = por(result_is_nan, result);
+
+  return pselect(is_imag_inf, imag_inf_result, pselect(is_real_inf, real_inf_result, result));
+}
+
+// \internal \returns the norm of a complex number z = x + i*y, defined as sqrt(x^2 + y^2).
+// Implemented using the hypot(a,b) algorithm from https://doi.org/10.48550/arXiv.1904.09481
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet phypot_complex(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename Scalar::value_type RealScalar;
+  typedef typename unpacket_traits<Packet>::as_real RealPacket;
+
+  const RealPacket cst_zero_rp = pset1<RealPacket>(static_cast<RealScalar>(0.0));
+  const RealPacket cst_minus_one_rp = pset1<RealPacket>(static_cast<RealScalar>(-1.0));
+  const RealPacket cst_two_rp = pset1<RealPacket>(static_cast<RealScalar>(2.0));
+  const RealPacket evenmask = peven_mask(a.v);
+
+  RealPacket a_abs = pabs(a.v);
+  RealPacket a_flip = pcplxflip(Packet(a_abs)).v;       // |b|, |a|
+  RealPacket a_all = pselect(evenmask, a_abs, a_flip);  // |a|, |a|
+  RealPacket b_all = pselect(evenmask, a_flip, a_abs);  // |b|, |b|
+
+  RealPacket a2 = pmul(a.v, a.v);                    // |a^2, b^2|
+  RealPacket a2_flip = pcplxflip(Packet(a2)).v;      // |b^2, a^2|
+  RealPacket h = psqrt(padd(a2, a2_flip));           // |sqrt(a^2 + b^2), sqrt(a^2 + b^2)|
+  RealPacket h_sq = pmul(h, h);                      // |a^2 + b^2, a^2 + b^2|
+  RealPacket a_sq = pselect(evenmask, a2, a2_flip);  // |a^2, a^2|
+  RealPacket m_h_sq = pmul(h_sq, cst_minus_one_rp);
+  RealPacket m_a_sq = pmul(a_sq, cst_minus_one_rp);
+  RealPacket x = psub(psub(pmadd(h, h, m_h_sq), pmadd(b_all, b_all, psub(a_sq, h_sq))), pmadd(a_all, a_all, m_a_sq));
+  h = psub(h, pdiv(x, pmul(cst_two_rp, h)));  // |h - x/(2*h), h - x/(2*h)|
+
+  // handle zero-case
+  RealPacket iszero = pcmp_eq(por(a_abs, a_flip), cst_zero_rp);
+
+  h = pandnot(h, iszero);  // |sqrt(a^2+b^2), sqrt(a^2+b^2)|
+  return Packet(h);        // |sqrt(a^2+b^2), sqrt(a^2+b^2)|
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARCH_GENERIC_PACKET_MATH_COMPLEX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathDoubleWord.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathDoubleWord.h
new file mode 100644
index 0000000..5cb677f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathDoubleWord.h
@@ -0,0 +1,208 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018-2025 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_DOUBLE_WORD_H
+#define EIGEN_ARCH_GENERIC_PACKET_MATH_DOUBLE_WORD_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+// This function splits x into the nearest integer n and fractional part r,
+// such that x = n + r holds exactly.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void absolute_split(const Packet& x, Packet& n, Packet& r) {
+  n = pround(x);
+  r = psub(x, n);
+}
+
+// This function computes the sum {s, r}, such that x + y = s_hi + s_lo
+// holds exactly, and s_hi = fl(x+y), if |x| >= |y|.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void fast_twosum(const Packet& x, const Packet& y, Packet& s_hi, Packet& s_lo) {
+  s_hi = padd(x, y);
+  const Packet t = psub(s_hi, x);
+  s_lo = psub(y, t);
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+// This function implements the extended precision product of
+// a pair of floating point numbers. Given {x, y}, it computes the pair
+// {p_hi, p_lo} such that x * y = p_hi + p_lo holds exactly and
+// p_hi = fl(x * y).
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void twoprod(const Packet& x, const Packet& y, Packet& p_hi, Packet& p_lo) {
+  p_hi = pmul(x, y);
+  p_lo = pmsub(x, y, p_hi);
+}
+
+// A version of twoprod that takes x, y, and fl(x*y) as input and returns the p_lo such that
+// x * y = xy + p_lo holds exactly.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet twoprod_low(const Packet& x, const Packet& y, const Packet& xy) {
+  return pmsub(x, y, xy);
+}
+
+#else
+
+// This function implements the Veltkamp splitting. Given a floating point
+// number x it returns the pair {x_hi, x_lo} such that x_hi + x_lo = x holds
+// exactly and that half of the significant of x fits in x_hi.
+// This is Algorithm 3 from Jean-Michel Muller, "Elementary Functions",
+// 3rd edition, Birkh\"auser, 2016.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void veltkamp_splitting(const Packet& x, Packet& x_hi, Packet& x_lo) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  constexpr int shift = (NumTraits<Scalar>::digits() + 1) / 2;
+  const Scalar shift_scale = Scalar(uint64_t(1) << shift);  // Scalar constructor not necessarily constexpr.
+  const Packet gamma = pmul(pset1<Packet>(shift_scale + Scalar(1)), x);
+  Packet rho = psub(x, gamma);
+  x_hi = padd(rho, gamma);
+  x_lo = psub(x, x_hi);
+}
+
+// This function implements Dekker's algorithm for products x * y.
+// Given floating point numbers {x, y} computes the pair
+// {p_hi, p_lo} such that x * y = p_hi + p_lo holds exactly and
+// p_hi = fl(x * y).
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void twoprod(const Packet& x, const Packet& y, Packet& p_hi, Packet& p_lo) {
+  Packet x_hi, x_lo, y_hi, y_lo;
+  veltkamp_splitting(x, x_hi, x_lo);
+  veltkamp_splitting(y, y_hi, y_lo);
+
+  p_hi = pmul(x, y);
+  p_lo = pmadd(x_hi, y_hi, pnegate(p_hi));
+  p_lo = pmadd(x_hi, y_lo, p_lo);
+  p_lo = pmadd(x_lo, y_hi, p_lo);
+  p_lo = pmadd(x_lo, y_lo, p_lo);
+}
+
+// A version of twoprod that takes x, y, and fl(x*y) as input and returns the p_lo such that
+// x * y = xy + p_lo holds exactly.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet twoprod_low(const Packet& x, const Packet& y, const Packet& xy) {
+  Packet x_hi, x_lo, y_hi, y_lo;
+  veltkamp_splitting(x, x_hi, x_lo);
+  veltkamp_splitting(y, y_hi, y_lo);
+
+  Packet p_lo = pmadd(x_hi, y_hi, pnegate(xy));
+  p_lo = pmadd(x_hi, y_lo, p_lo);
+  p_lo = pmadd(x_lo, y_hi, p_lo);
+  p_lo = pmadd(x_lo, y_lo, p_lo);
+  return p_lo;
+}
+
+#endif  // EIGEN_VECTORIZE_FMA
+
+// This function implements Dekker's algorithm for the addition
+// of two double word numbers represented by {x_hi, x_lo} and {y_hi, y_lo}.
+// It returns the result as a pair {s_hi, s_lo} such that
+// x_hi + x_lo + y_hi + y_lo = s_hi + s_lo holds exactly.
+// This is Algorithm 5 from Jean-Michel Muller, "Elementary Functions",
+// 3rd edition, Birkh\"auser, 2016.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void twosum(const Packet& x_hi, const Packet& x_lo, const Packet& y_hi,
+                                                  const Packet& y_lo, Packet& s_hi, Packet& s_lo) {
+  const Packet x_greater_mask = pcmp_lt(pabs(y_hi), pabs(x_hi));
+  Packet r_hi_1, r_lo_1;
+  fast_twosum(x_hi, y_hi, r_hi_1, r_lo_1);
+  Packet r_hi_2, r_lo_2;
+  fast_twosum(y_hi, x_hi, r_hi_2, r_lo_2);
+  const Packet r_hi = pselect(x_greater_mask, r_hi_1, r_hi_2);
+
+  const Packet s1 = padd(padd(y_lo, r_lo_1), x_lo);
+  const Packet s2 = padd(padd(x_lo, r_lo_2), y_lo);
+  const Packet s = pselect(x_greater_mask, s1, s2);
+
+  fast_twosum(r_hi, s, s_hi, s_lo);
+}
+
+// This is a version of twosum for double word numbers,
+// which assumes that |x_hi| >= |y_hi|.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void fast_twosum(const Packet& x_hi, const Packet& x_lo, const Packet& y_hi,
+                                                       const Packet& y_lo, Packet& s_hi, Packet& s_lo) {
+  Packet r_hi, r_lo;
+  fast_twosum(x_hi, y_hi, r_hi, r_lo);
+  const Packet s = padd(padd(y_lo, r_lo), x_lo);
+  fast_twosum(r_hi, s, s_hi, s_lo);
+}
+
+// This is a version of twosum for adding a floating point number x to
+// double word number {y_hi, y_lo} number, with the assumption
+// that |x| >= |y_hi|.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void fast_twosum(const Packet& x, const Packet& y_hi, const Packet& y_lo,
+                                                       Packet& s_hi, Packet& s_lo) {
+  Packet r_hi, r_lo;
+  fast_twosum(x, y_hi, r_hi, r_lo);
+  const Packet s = padd(y_lo, r_lo);
+  fast_twosum(r_hi, s, s_hi, s_lo);
+}
+
+// This function implements the multiplication of a double word
+// number represented by {x_hi, x_lo} by a floating point number y.
+// It returns the result as a pair {p_hi, p_lo} such that
+// (x_hi + x_lo) * y = p_hi + p_lo hold with a relative error
+// of less than 2*2^{-2p}, where p is the number of significand bit
+// in the floating point type.
+// This is Algorithm 7 from Jean-Michel Muller, "Elementary Functions",
+// 3rd edition, Birkh\"auser, 2016.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void twoprod(const Packet& x_hi, const Packet& x_lo, const Packet& y,
+                                                   Packet& p_hi, Packet& p_lo) {
+  Packet c_hi, c_lo1;
+  twoprod(x_hi, y, c_hi, c_lo1);
+  const Packet c_lo2 = pmul(x_lo, y);
+  Packet t_hi, t_lo1;
+  fast_twosum(c_hi, c_lo2, t_hi, t_lo1);
+  const Packet t_lo2 = padd(t_lo1, c_lo1);
+  fast_twosum(t_hi, t_lo2, p_hi, p_lo);
+}
+
+// This function implements the multiplication of two double word
+// numbers represented by {x_hi, x_lo} and {y_hi, y_lo}.
+// It returns the result as a pair {p_hi, p_lo} such that
+// (x_hi + x_lo) * (y_hi + y_lo) = p_hi + p_lo holds with a relative error
+// of less than 2*2^{-2p}, where p is the number of significand bit
+// in the floating point type.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void twoprod(const Packet& x_hi, const Packet& x_lo, const Packet& y_hi,
+                                                   const Packet& y_lo, Packet& p_hi, Packet& p_lo) {
+  Packet p_hi_hi, p_hi_lo;
+  twoprod(x_hi, x_lo, y_hi, p_hi_hi, p_hi_lo);
+  Packet p_lo_hi, p_lo_lo;
+  twoprod(x_hi, x_lo, y_lo, p_lo_hi, p_lo_lo);
+  fast_twosum(p_hi_hi, p_hi_lo, p_lo_hi, p_lo_lo, p_hi, p_lo);
+}
+
+// This function implements the division of double word {x_hi, x_lo}
+// by float y. This is Algorithm 15 from "Tight and rigorous error bounds
+// for basic building blocks of double-word arithmetic", Joldes, Muller, & Popescu,
+// 2017. https://hal.archives-ouvertes.fr/hal-01351529
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void doubleword_div_fp(const Packet& x_hi, const Packet& x_lo, const Packet& y,
+                                                             Packet& z_hi, Packet& z_lo) {
+  const Packet t_hi = pdiv(x_hi, y);
+  Packet pi_hi, pi_lo;
+  twoprod(t_hi, y, pi_hi, pi_lo);
+  const Packet delta_hi = psub(x_hi, pi_hi);
+  const Packet delta_t = psub(delta_hi, pi_lo);
+  const Packet delta = padd(delta_t, x_lo);
+  const Packet t_lo = pdiv(delta, y);
+  fast_twosum(t_hi, t_lo, z_hi, z_lo);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARCH_GENERIC_PACKET_MATH_DOUBLE_WORD_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathFrexpLdexp.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathFrexpLdexp.h
new file mode 100644
index 0000000..978818e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathFrexpLdexp.h
@@ -0,0 +1,162 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2018-2025 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_FREXP_LDEXP_H
+#define EIGEN_ARCH_GENERIC_PACKET_MATH_FREXP_LDEXP_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+// Creates a Scalar integer type with same bit-width.
+template <typename T>
+struct make_integer;
+template <>
+struct make_integer<float> {
+  typedef numext::int32_t type;
+};
+template <>
+struct make_integer<double> {
+  typedef numext::int64_t type;
+};
+template <>
+struct make_integer<half> {
+  typedef numext::int16_t type;
+};
+template <>
+struct make_integer<bfloat16> {
+  typedef numext::int16_t type;
+};
+
+template <typename Packet>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Packet pfrexp_generic_get_biased_exponent(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
+  static constexpr int mantissa_bits = numext::numeric_limits<Scalar>::digits - 1;
+  return pcast<PacketI, Packet>(plogical_shift_right<mantissa_bits>(preinterpret<PacketI>(pabs(a))));
+}
+
+// Safely applies frexp, correctly handles denormals.
+// Assumes IEEE floating point format.
+template <typename Packet>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Packet pfrexp_generic(const Packet& a, Packet& exponent) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename make_unsigned<typename make_integer<Scalar>::type>::type ScalarUI;
+  static constexpr int TotalBits = sizeof(Scalar) * CHAR_BIT, MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
+                       ExponentBits = TotalBits - MantissaBits - 1;
+
+  constexpr ScalarUI scalar_sign_mantissa_mask =
+      ~(((ScalarUI(1) << ExponentBits) - ScalarUI(1)) << MantissaBits);  // ~0x7f800000
+  const Packet sign_mantissa_mask = pset1frombits<Packet>(static_cast<ScalarUI>(scalar_sign_mantissa_mask));
+  const Packet half = pset1<Packet>(Scalar(0.5));
+  const Packet zero = pzero(a);
+  const Packet normal_min = pset1<Packet>((numext::numeric_limits<Scalar>::min)());  // Minimum normal value, 2^-126
+
+  // To handle denormals, normalize by multiplying by 2^(int(MantissaBits)+1).
+  const Packet is_denormal = pcmp_lt(pabs(a), normal_min);
+  constexpr ScalarUI scalar_normalization_offset = ScalarUI(MantissaBits + 1);  // 24
+  // The following cannot be constexpr because bfloat16(uint16_t) is not constexpr.
+  const Scalar scalar_normalization_factor = Scalar(ScalarUI(1) << int(scalar_normalization_offset));  // 2^24
+  const Packet normalization_factor = pset1<Packet>(scalar_normalization_factor);
+  const Packet normalized_a = pselect(is_denormal, pmul(a, normalization_factor), a);
+
+  // Determine exponent offset: -126 if normal, -126-24 if denormal
+  const Scalar scalar_exponent_offset = -Scalar((ScalarUI(1) << (ExponentBits - 1)) - ScalarUI(2));  // -126
+  Packet exponent_offset = pset1<Packet>(scalar_exponent_offset);
+  const Packet normalization_offset = pset1<Packet>(-Scalar(scalar_normalization_offset));  // -24
+  exponent_offset = pselect(is_denormal, padd(exponent_offset, normalization_offset), exponent_offset);
+
+  // Determine exponent and mantissa from normalized_a.
+  exponent = pfrexp_generic_get_biased_exponent(normalized_a);
+  // Zero, Inf and NaN return 'a' unmodified, exponent is zero
+  // (technically the exponent is unspecified for inf/NaN, but GCC/Clang set it to zero)
+  const Scalar scalar_non_finite_exponent = Scalar((ScalarUI(1) << ExponentBits) - ScalarUI(1));  // 255
+  const Packet non_finite_exponent = pset1<Packet>(scalar_non_finite_exponent);
+  const Packet is_zero_or_not_finite = por(pcmp_eq(a, zero), pcmp_eq(exponent, non_finite_exponent));
+  const Packet m = pselect(is_zero_or_not_finite, a, por(pand(normalized_a, sign_mantissa_mask), half));
+  exponent = pselect(is_zero_or_not_finite, zero, padd(exponent, exponent_offset));
+  return m;
+}
+
+// Safely applies ldexp, correctly handles overflows, underflows and denormals.
+// Assumes IEEE floating point format.
+template <typename Packet>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Packet pldexp_generic(const Packet& a, const Packet& exponent) {
+  // We want to return a * 2^exponent, allowing for all possible integer
+  // exponents without overflowing or underflowing in intermediate
+  // computations.
+  //
+  // Since 'a' and the output can be denormal, the maximum range of 'exponent'
+  // to consider for a float is:
+  //   -255-23 -> 255+23
+  // Below -278 any finite float 'a' will become zero, and above +278 any
+  // finite float will become inf, including when 'a' is the smallest possible
+  // denormal.
+  //
+  // Unfortunately, 2^(278) cannot be represented using either one or two
+  // finite normal floats, so we must split the scale factor into at least
+  // three parts. It turns out to be faster to split 'exponent' into four
+  // factors, since [exponent>>2] is much faster to compute that [exponent/3].
+  //
+  // Set e = min(max(exponent, -278), 278);
+  //     b = floor(e/4);
+  //   out = ((((a * 2^(b)) * 2^(b)) * 2^(b)) * 2^(e-3*b))
+  //
+  // This will avoid any intermediate overflows and correctly handle 0, inf,
+  // NaN cases.
+  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<PacketI>::type ScalarI;
+  static constexpr int TotalBits = sizeof(Scalar) * CHAR_BIT, MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
+                       ExponentBits = TotalBits - MantissaBits - 1;
+
+  const Packet max_exponent = pset1<Packet>(Scalar((ScalarI(1) << ExponentBits) + ScalarI(MantissaBits - 1)));  // 278
+  const PacketI bias = pset1<PacketI>((ScalarI(1) << (ExponentBits - 1)) - ScalarI(1));                         // 127
+  const PacketI e = pcast<Packet, PacketI>(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
+  PacketI b = parithmetic_shift_right<2>(e);                                          // floor(e/4);
+  Packet c = preinterpret<Packet>(plogical_shift_left<MantissaBits>(padd(b, bias)));  // 2^b
+  Packet out = pmul(pmul(pmul(a, c), c), c);                                          // a * 2^(3b)
+  b = pnmadd(pset1<PacketI>(3), b, e);                                                // e - 3b
+  c = preinterpret<Packet>(plogical_shift_left<MantissaBits>(padd(b, bias)));         // 2^(e-3*b)
+  out = pmul(out, c);
+  return out;
+}
+
+// Explicitly multiplies
+//    a * (2^e)
+// clamping e to the range
+// [NumTraits<Scalar>::min_exponent()-2, NumTraits<Scalar>::max_exponent()]
+//
+// This is approx 7x faster than pldexp_impl, but will prematurely over/underflow
+// if 2^e doesn't fit into a normal floating-point Scalar.
+//
+// Assumes IEEE floating point format
+template <typename Packet>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Packet pldexp_fast(const Packet& a, const Packet& exponent) {
+  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<PacketI>::type ScalarI;
+  static constexpr int TotalBits = sizeof(Scalar) * CHAR_BIT, MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
+                       ExponentBits = TotalBits - MantissaBits - 1;
+
+  const Packet bias = pset1<Packet>(Scalar((ScalarI(1) << (ExponentBits - 1)) - ScalarI(1)));  // 127
+  const Packet limit = pset1<Packet>(Scalar((ScalarI(1) << ExponentBits) - ScalarI(1)));       // 255
+  // restrict biased exponent between 0 and 255 for float.
+  const PacketI e = pcast<Packet, PacketI>(pmin(pmax(padd(exponent, bias), pzero(limit)), limit));  // exponent + 127
+  // return a * (2^e)
+  return pmul(a, preinterpret<Packet>(plogical_shift_left<MantissaBits>(e)));
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARCH_GENERIC_PACKET_MATH_FREXP_LDEXP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
new file mode 100644
index 0000000..2ab1847
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
@@ -0,0 +1,608 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
+// Copyright (C) 2009-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2018-2025 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* The exp and log functions of this file initially come from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
+#define EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+#include "GenericPacketMathPolynomials.h"
+#include "GenericPacketMathFrexpLdexp.h"
+#include "GenericPacketMathDoubleWord.h"
+
+namespace Eigen {
+namespace internal {
+
+//----------------------------------------------------------------------
+// Exponential and Logarithmic Functions
+//----------------------------------------------------------------------
+
+// Natural or base 2 logarithm.
+// Computes log(x) as log(2^e * m) = C*e + log(m), where the constant C =log(2)
+// and m is in the range [sqrt(1/2),sqrt(2)). In this range, the logarithm can
+// be easily approximated by a polynomial centered on m=1 for stability.
+// TODO(gonnet): Further reduce the interval allowing for lower-degree
+//               polynomial interpolants -> ... -> profit!
+template <typename Packet, bool base2>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog_impl_float(const Packet _x) {
+  const Packet cst_1 = pset1<Packet>(1.0f);
+  const Packet cst_minus_inf = pset1frombits<Packet>(static_cast<Eigen::numext::uint32_t>(0xff800000u));
+  const Packet cst_pos_inf = pset1frombits<Packet>(static_cast<Eigen::numext::uint32_t>(0x7f800000u));
+
+  const Packet cst_cephes_SQRTHF = pset1<Packet>(0.707106781186547524f);
+  Packet e, x;
+  // extract significant in the range [0.5,1) and exponent
+  x = pfrexp(_x, e);
+
+  // part2: Shift the inputs from the range [0.5,1) to [sqrt(1/2),sqrt(2))
+  // and shift by -1. The values are then centered around 0, which improves
+  // the stability of the polynomial evaluation.
+  //   if( x < SQRTHF ) {
+  //     e -= 1;
+  //     x = x + x - 1.0;
+  //   } else { x = x - 1.0; }
+  Packet mask = pcmp_lt(x, cst_cephes_SQRTHF);
+  Packet tmp = pand(x, mask);
+  x = psub(x, cst_1);
+  e = psub(e, pand(cst_1, mask));
+  x = padd(x, tmp);
+
+  // Polynomial coefficients for rational r(x) = p(x)/q(x)
+  // approximating log(1+x) on [sqrt(0.5)-1;sqrt(2)-1].
+  constexpr float alpha[] = {0.18256296349849254f, 1.0000000190281063f, 1.0000000190281136f};
+  constexpr float beta[] = {0.049616247954120038f, 0.59923249590823520f, 1.4999999999999927f, 1.0f};
+
+  Packet p = ppolevl<Packet, 2>::run(x, alpha);
+  p = pmul(x, p);
+  Packet q = ppolevl<Packet, 3>::run(x, beta);
+  x = pdiv(p, q);
+
+  // Add the logarithm of the exponent back to the result of the interpolation.
+  if (base2) {
+    const Packet cst_log2e = pset1<Packet>(static_cast<float>(EIGEN_LOG2E));
+    x = pmadd(x, cst_log2e, e);
+  } else {
+    const Packet cst_ln2 = pset1<Packet>(static_cast<float>(EIGEN_LN2));
+    x = pmadd(e, cst_ln2, x);
+  }
+
+  Packet invalid_mask = pcmp_lt_or_nan(_x, pzero(_x));
+  Packet iszero_mask = pcmp_eq(_x, pzero(_x));
+  Packet pos_inf_mask = pcmp_eq(_x, cst_pos_inf);
+  // Filter out invalid inputs, i.e.:
+  //  - negative arg will be NAN
+  //  - 0 will be -INF
+  //  - +INF will be +INF
+  return pselect(iszero_mask, cst_minus_inf, por(pselect(pos_inf_mask, cst_pos_inf, x), invalid_mask));
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog_float(const Packet _x) {
+  return plog_impl_float<Packet, /* base2 */ false>(_x);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog2_float(const Packet _x) {
+  return plog_impl_float<Packet, /* base2 */ true>(_x);
+}
+
+/* Returns the base e (2.718...) or base 2 logarithm of x.
+ * The argument is separated into its exponent and fractional parts.
+ * The logarithm of the fraction in the interval [sqrt(1/2), sqrt(2)],
+ * is approximated by
+ *
+ *     log(1+x) = x - 0.5 x**2 + x**3 P(x)/Q(x).
+ *
+ * for more detail see: http://www.netlib.org/cephes/
+ */
+template <typename Packet, bool base2>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog_impl_double(const Packet _x) {
+  Packet x = _x;
+
+  const Packet cst_1 = pset1<Packet>(1.0);
+  const Packet cst_neg_half = pset1<Packet>(-0.5);
+  const Packet cst_minus_inf = pset1frombits<Packet>(static_cast<uint64_t>(0xfff0000000000000ull));
+  const Packet cst_pos_inf = pset1frombits<Packet>(static_cast<uint64_t>(0x7ff0000000000000ull));
+
+  // Polynomial Coefficients for log(1+x) = x - x**2/2 + x**3 P(x)/Q(x)
+  //                             1/sqrt(2) <= x < sqrt(2)
+  const Packet cst_cephes_SQRTHF = pset1<Packet>(0.70710678118654752440E0);
+  const Packet cst_cephes_log_p0 = pset1<Packet>(1.01875663804580931796E-4);
+  const Packet cst_cephes_log_p1 = pset1<Packet>(4.97494994976747001425E-1);
+  const Packet cst_cephes_log_p2 = pset1<Packet>(4.70579119878881725854E0);
+  const Packet cst_cephes_log_p3 = pset1<Packet>(1.44989225341610930846E1);
+  const Packet cst_cephes_log_p4 = pset1<Packet>(1.79368678507819816313E1);
+  const Packet cst_cephes_log_p5 = pset1<Packet>(7.70838733755885391666E0);
+
+  const Packet cst_cephes_log_q0 = pset1<Packet>(1.0);
+  const Packet cst_cephes_log_q1 = pset1<Packet>(1.12873587189167450590E1);
+  const Packet cst_cephes_log_q2 = pset1<Packet>(4.52279145837532221105E1);
+  const Packet cst_cephes_log_q3 = pset1<Packet>(8.29875266912776603211E1);
+  const Packet cst_cephes_log_q4 = pset1<Packet>(7.11544750618563894466E1);
+  const Packet cst_cephes_log_q5 = pset1<Packet>(2.31251620126765340583E1);
+
+  Packet e;
+  // extract significant in the range [0.5,1) and exponent
+  x = pfrexp(x, e);
+
+  // Shift the inputs from the range [0.5,1) to [sqrt(1/2),sqrt(2))
+  // and shift by -1. The values are then centered around 0, which improves
+  // the stability of the polynomial evaluation.
+  //   if( x < SQRTHF ) {
+  //     e -= 1;
+  //     x = x + x - 1.0;
+  //   } else { x = x - 1.0; }
+  Packet mask = pcmp_lt(x, cst_cephes_SQRTHF);
+  Packet tmp = pand(x, mask);
+  x = psub(x, cst_1);
+  e = psub(e, pand(cst_1, mask));
+  x = padd(x, tmp);
+
+  Packet x2 = pmul(x, x);
+  Packet x3 = pmul(x2, x);
+
+  // Evaluate the polynomial in factored form for better instruction-level parallelism.
+  // y = x - 0.5*x^2 + x^3 * polevl( x, P, 5 ) / p1evl( x, Q, 5 ) );
+  Packet y, y1, y_;
+  y = pmadd(cst_cephes_log_p0, x, cst_cephes_log_p1);
+  y1 = pmadd(cst_cephes_log_p3, x, cst_cephes_log_p4);
+  y = pmadd(y, x, cst_cephes_log_p2);
+  y1 = pmadd(y1, x, cst_cephes_log_p5);
+  y_ = pmadd(y, x3, y1);
+
+  y = pmadd(cst_cephes_log_q0, x, cst_cephes_log_q1);
+  y1 = pmadd(cst_cephes_log_q3, x, cst_cephes_log_q4);
+  y = pmadd(y, x, cst_cephes_log_q2);
+  y1 = pmadd(y1, x, cst_cephes_log_q5);
+  y = pmadd(y, x3, y1);
+
+  y_ = pmul(y_, x3);
+  y = pdiv(y_, y);
+
+  y = pmadd(cst_neg_half, x2, y);
+  x = padd(x, y);
+
+  // Add the logarithm of the exponent back to the result of the interpolation.
+  if (base2) {
+    const Packet cst_log2e = pset1<Packet>(static_cast<double>(EIGEN_LOG2E));
+    x = pmadd(x, cst_log2e, e);
+  } else {
+    const Packet cst_ln2 = pset1<Packet>(static_cast<double>(EIGEN_LN2));
+    x = pmadd(e, cst_ln2, x);
+  }
+
+  Packet invalid_mask = pcmp_lt_or_nan(_x, pzero(_x));
+  Packet iszero_mask = pcmp_eq(_x, pzero(_x));
+  Packet pos_inf_mask = pcmp_eq(_x, cst_pos_inf);
+  // Filter out invalid inputs, i.e.:
+  //  - negative arg will be NAN
+  //  - 0 will be -INF
+  //  - +INF will be +INF
+  return pselect(iszero_mask, cst_minus_inf, por(pselect(pos_inf_mask, cst_pos_inf, x), invalid_mask));
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog_double(const Packet _x) {
+  return plog_impl_double<Packet, /* base2 */ false>(_x);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog2_double(const Packet _x) {
+  return plog_impl_double<Packet, /* base2 */ true>(_x);
+}
+
+/** \internal \returns log(1 + x) computed using W. Kahan's formula.
+    See: http://www.plunk.org/~hatch/rightway.php
+ */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_log1p(const Packet& x) {
+  typedef typename unpacket_traits<Packet>::type ScalarType;
+  const Packet one = pset1<Packet>(ScalarType(1));
+  Packet xp1 = padd(x, one);
+  Packet small_mask = pcmp_eq(xp1, one);
+  Packet log1 = plog(xp1);
+  Packet inf_mask = pcmp_eq(xp1, log1);
+  Packet log_large = pmul(x, pdiv(log1, psub(xp1, one)));
+  return pselect(por(small_mask, inf_mask), x, log_large);
+}
+
+/** \internal \returns exp(x)-1 computed using W. Kahan's formula.
+    See: http://www.plunk.org/~hatch/rightway.php
+ */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_expm1(const Packet& x) {
+  typedef typename unpacket_traits<Packet>::type ScalarType;
+  const Packet one = pset1<Packet>(ScalarType(1));
+  const Packet neg_one = pset1<Packet>(ScalarType(-1));
+  Packet u = pexp(x);
+  Packet one_mask = pcmp_eq(u, one);
+  Packet u_minus_one = psub(u, one);
+  Packet neg_one_mask = pcmp_eq(u_minus_one, neg_one);
+  Packet logu = plog(u);
+  // The following comparison is to catch the case where
+  // exp(x) = +inf. It is written in this way to avoid having
+  // to form the constant +inf, which depends on the packet
+  // type.
+  Packet pos_inf_mask = pcmp_eq(logu, u);
+  Packet expm1 = pmul(u_minus_one, pdiv(x, logu));
+  expm1 = pselect(pos_inf_mask, u, expm1);
+  return pselect(one_mask, x, pselect(neg_one_mask, neg_one, expm1));
+}
+
+// Exponential function. Works by writing "x = m*log(2) + r" where
+// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
+// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
+// exp(r) is computed using a 6th order minimax polynomial approximation.
+template <typename Packet, bool IsFinite>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pexp_float(const Packet _x) {
+  const Packet cst_zero = pset1<Packet>(0.0f);
+  const Packet cst_one = pset1<Packet>(1.0f);
+  const Packet cst_half = pset1<Packet>(0.5f);
+  const Packet cst_exp_hi = pset1<Packet>(88.723f);
+  const Packet cst_exp_lo = pset1<Packet>(-104.f);
+  const Packet cst_pldexp_threshold = pset1<Packet>(87.0);
+
+  const Packet cst_cephes_LOG2EF = pset1<Packet>(1.44269504088896341f);
+  const Packet cst_p2 = pset1<Packet>(0.49999988079071044921875f);
+  const Packet cst_p3 = pset1<Packet>(0.16666518151760101318359375f);
+  const Packet cst_p4 = pset1<Packet>(4.166965186595916748046875e-2f);
+  const Packet cst_p5 = pset1<Packet>(8.36894474923610687255859375e-3f);
+  const Packet cst_p6 = pset1<Packet>(1.37449637986719608306884765625e-3f);
+  Packet zero_mask;
+  Packet x;
+  if (!IsFinite) {
+    // Clamp x.
+    zero_mask = pcmp_lt(_x, cst_exp_lo);
+    x = pmin(_x, cst_exp_hi);
+  } else {
+    x = _x;
+  }
+
+  // Express exp(x) as exp(m*ln(2) + r), start by extracting
+  // m = floor(x/ln(2) + 0.5).
+  Packet m = pfloor(pmadd(x, cst_cephes_LOG2EF, cst_half));
+
+  // Get r = x - m*ln(2). If no FMA instructions are available, m*ln(2) is
+  // subtracted out in two parts, m*C1+m*C2 = m*ln(2), to avoid accumulating
+  // truncation errors.
+  const Packet cst_cephes_exp_C1 = pset1<Packet>(-0.693359375f);
+  const Packet cst_cephes_exp_C2 = pset1<Packet>(2.12194440e-4f);
+  Packet r = pmadd(m, cst_cephes_exp_C1, x);
+  r = pmadd(m, cst_cephes_exp_C2, r);
+
+  // Evaluate the 6th order polynomial approximation to exp(r)
+  // with r in the interval [-ln(2)/2;ln(2)/2].
+  const Packet r2 = pmul(r, r);
+  Packet p_even = pmadd(r2, cst_p6, cst_p4);
+  const Packet p_odd = pmadd(r2, cst_p5, cst_p3);
+  p_even = pmadd(r2, p_even, cst_p2);
+  const Packet p_low = padd(r, cst_one);
+  Packet y = pmadd(r, p_odd, p_even);
+  y = pmadd(r2, y, p_low);
+  if (IsFinite) {
+    return pldexp_fast(y, m);
+  }
+  // Return 2^m * exp(r).
+  const Packet fast_pldexp_unsafe = pcmp_lt(cst_pldexp_threshold, pabs(x));
+  if (!predux_any(fast_pldexp_unsafe)) {
+    // For |x| <= 87, we know the result is not zero or inf, and we can safely use
+    // the fast version of pldexp.
+    return pmax(pldexp_fast(y, m), _x);
+  }
+  return pselect(zero_mask, cst_zero, pmax(pldexp(y, m), _x));
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pexp_double(const Packet _x) {
+  const Packet cst_zero = pset1<Packet>(0.0);
+  const Packet cst_1 = pset1<Packet>(1.0);
+  const Packet cst_2 = pset1<Packet>(2.0);
+  const Packet cst_half = pset1<Packet>(0.5);
+
+  const Packet cst_exp_hi = pset1<Packet>(709.784);
+  const Packet cst_exp_lo = pset1<Packet>(-745.519);
+  const Packet cst_pldexp_threshold = pset1<Packet>(708.0);
+  const Packet cst_cephes_LOG2EF = pset1<Packet>(1.4426950408889634073599);
+  const Packet cst_cephes_exp_p0 = pset1<Packet>(1.26177193074810590878e-4);
+  const Packet cst_cephes_exp_p1 = pset1<Packet>(3.02994407707441961300e-2);
+  const Packet cst_cephes_exp_p2 = pset1<Packet>(9.99999999999999999910e-1);
+  const Packet cst_cephes_exp_q0 = pset1<Packet>(3.00198505138664455042e-6);
+  const Packet cst_cephes_exp_q1 = pset1<Packet>(2.52448340349684104192e-3);
+  const Packet cst_cephes_exp_q2 = pset1<Packet>(2.27265548208155028766e-1);
+  const Packet cst_cephes_exp_q3 = pset1<Packet>(2.00000000000000000009e0);
+  const Packet cst_cephes_exp_C1 = pset1<Packet>(0.693145751953125);
+  const Packet cst_cephes_exp_C2 = pset1<Packet>(1.42860682030941723212e-6);
+
+  Packet tmp, fx;
+
+  // clamp x
+  Packet zero_mask = pcmp_lt(_x, cst_exp_lo);
+  Packet x = pmin(_x, cst_exp_hi);
+
+  // Express exp(x) as exp(g + n*log(2)).
+  fx = pmadd(cst_cephes_LOG2EF, x, cst_half);
+
+  // Get the integer modulus of log(2), i.e. the "n" described above.
+  fx = pfloor(fx);
+
+  // Get the remainder modulo log(2), i.e. the "g" described above. Subtract
+  // n*log(2) out in two steps, i.e. n*C1 + n*C2, C1+C2=log2 to get the last
+  // digits right.
+  tmp = pmul(fx, cst_cephes_exp_C1);
+  Packet z = pmul(fx, cst_cephes_exp_C2);
+  x = psub(x, tmp);
+  x = psub(x, z);
+
+  Packet x2 = pmul(x, x);
+
+  // Evaluate the numerator polynomial of the rational interpolant.
+  Packet px = cst_cephes_exp_p0;
+  px = pmadd(px, x2, cst_cephes_exp_p1);
+  px = pmadd(px, x2, cst_cephes_exp_p2);
+  px = pmul(px, x);
+
+  // Evaluate the denominator polynomial of the rational interpolant.
+  Packet qx = cst_cephes_exp_q0;
+  qx = pmadd(qx, x2, cst_cephes_exp_q1);
+  qx = pmadd(qx, x2, cst_cephes_exp_q2);
+  qx = pmadd(qx, x2, cst_cephes_exp_q3);
+
+  // I don't really get this bit, copied from the SSE2 routines, so...
+  // TODO(gonnet): Figure out what is going on here, perhaps find a better
+  // rational interpolant?
+  x = pdiv(px, psub(qx, px));
+  x = pmadd(cst_2, x, cst_1);
+
+  // Construct the result 2^n * exp(g) = e * x. The max is used to catch
+  // non-finite values in the input.
+  const Packet fast_pldexp_unsafe = pcmp_lt(cst_pldexp_threshold, pabs(_x));
+  if (!predux_any(fast_pldexp_unsafe)) {
+    // For |x| <= 708, we know the result is not zero or inf, and we can safely use
+    // the fast version of pldexp.
+    return pmax(pldexp_fast(x, fx), _x);
+  }
+  return pselect(zero_mask, cst_zero, pmax(pldexp(x, fx), _x));
+}
+
+// This function computes exp2(x) = exp(ln(2) * x).
+// To improve accuracy, the product ln(2)*x is computed using the twoprod
+// algorithm, such that ln(2) * x = p_hi + p_lo holds exactly. Then exp2(x) is
+// computed as exp2(x) = exp(p_hi) * exp(p_lo) ~= exp(p_hi) * (1 + p_lo). This
+// correction step this reduces the maximum absolute error as follows:
+//
+// type   | max error (simple product) | max error (twoprod) |
+// -----------------------------------------------------------
+// float  |       35 ulps              |       4 ulps        |
+// double |      363 ulps              |     110 ulps        |
+//
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_exp2(const Packet& _x) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  constexpr int max_exponent = std::numeric_limits<Scalar>::max_exponent;
+  constexpr int digits = std::numeric_limits<Scalar>::digits;
+  constexpr Scalar max_cap = Scalar(max_exponent + 1);
+  constexpr Scalar min_cap = -Scalar(max_exponent + digits - 1);
+  Packet x = pmax(pmin(_x, pset1<Packet>(max_cap)), pset1<Packet>(min_cap));
+  Packet p_hi, p_lo;
+  twoprod(pset1<Packet>(Scalar(EIGEN_LN2)), x, p_hi, p_lo);
+  Packet exp2_hi = pexp(p_hi);
+  Packet exp2_lo = padd(pset1<Packet>(Scalar(1)), p_lo);
+  return pmul(exp2_hi, exp2_lo);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+// Include the split-out sections. Order matters: Pow depends on exp/log and FrexpLdexp,
+// Trig depends on exp (for ptanh_float), Complex depends on Trig (for psincos_selector).
+#include "GenericPacketMathPow.h"
+#include "GenericPacketMathTrig.h"
+#include "GenericPacketMathComplex.h"
+
+namespace Eigen {
+namespace internal {
+
+//----------------------------------------------------------------------
+// Sign Function
+//----------------------------------------------------------------------
+
+template <typename Packet>
+struct psign_impl<Packet, std::enable_if_t<!is_scalar<Packet>::value &&
+                                           !NumTraits<typename unpacket_traits<Packet>::type>::IsComplex &&
+                                           !NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>> {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    const Packet cst_zero = pzero(a);
+
+    const Packet abs_a = pabs(a);
+    const Packet sign_mask = pandnot(a, abs_a);
+    const Packet nonzero_mask = pcmp_lt(cst_zero, abs_a);
+
+    return pselect(nonzero_mask, por(sign_mask, cst_one), abs_a);
+  }
+};
+
+template <typename Packet>
+struct psign_impl<Packet, std::enable_if_t<!is_scalar<Packet>::value &&
+                                           !NumTraits<typename unpacket_traits<Packet>::type>::IsComplex &&
+                                           NumTraits<typename unpacket_traits<Packet>::type>::IsSigned &&
+                                           NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>> {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    const Packet cst_minus_one = pset1<Packet>(Scalar(-1));
+    const Packet cst_zero = pzero(a);
+
+    const Packet positive_mask = pcmp_lt(cst_zero, a);
+    const Packet positive = pand(positive_mask, cst_one);
+    const Packet negative_mask = pcmp_lt(a, cst_zero);
+    const Packet negative = pand(negative_mask, cst_minus_one);
+
+    return por(positive, negative);
+  }
+};
+
+template <typename Packet>
+struct psign_impl<Packet, std::enable_if_t<!is_scalar<Packet>::value &&
+                                           !NumTraits<typename unpacket_traits<Packet>::type>::IsComplex &&
+                                           !NumTraits<typename unpacket_traits<Packet>::type>::IsSigned &&
+                                           NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>> {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    const Packet cst_zero = pzero(a);
+
+    const Packet zero_mask = pcmp_eq(cst_zero, a);
+    return pandnot(cst_one, zero_mask);
+  }
+};
+
+// \internal \returns the sign of a complex number z, defined as z / abs(z).
+template <typename Packet>
+struct psign_impl<Packet, std::enable_if_t<!is_scalar<Packet>::value &&
+                                           NumTraits<typename unpacket_traits<Packet>::type>::IsComplex &&
+                                           unpacket_traits<Packet>::vectorizable>> {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) {
+    typedef typename unpacket_traits<Packet>::type Scalar;
+    typedef typename Scalar::value_type RealScalar;
+    typedef typename unpacket_traits<Packet>::as_real RealPacket;
+
+    // Step 1. Compute (for each element z = x + i*y in a)
+    //     l = abs(z) = sqrt(x^2 + y^2).
+    // To avoid over- and underflow, we use the stable formula for each hypotenuse
+    //    l = (zmin == 0 ? zmax : zmax * sqrt(1 + (zmin/zmax)**2)),
+    // where zmax = max(|x|, |y|), zmin = min(|x|, |y|),
+    RealPacket a_abs = pabs(a.v);
+    RealPacket a_abs_flip = pcplxflip(Packet(a_abs)).v;
+    RealPacket a_max = pmax(a_abs, a_abs_flip);
+    RealPacket a_min = pmin(a_abs, a_abs_flip);
+    RealPacket a_min_zero_mask = pcmp_eq(a_min, pzero(a_min));
+    RealPacket a_max_zero_mask = pcmp_eq(a_max, pzero(a_max));
+    RealPacket r = pdiv(a_min, a_max);
+    const RealPacket cst_one = pset1<RealPacket>(RealScalar(1));
+    RealPacket l = pmul(a_max, psqrt(padd(cst_one, pmul(r, r))));  // [l0, l0, l1, l1]
+    // Set l to a_max if a_min is zero, since the roundtrip sqrt(a_max^2) may be
+    // lossy.
+    l = pselect(a_min_zero_mask, a_max, l);
+    // Step 2 compute a / abs(a).
+    RealPacket sign_as_real = pandnot(pdiv(a.v, l), a_max_zero_mask);
+    Packet sign;
+    sign.v = sign_as_real;
+    return sign;
+  }
+};
+
+//----------------------------------------------------------------------
+// Rounding Functions
+//----------------------------------------------------------------------
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_rint(const Packet& a) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  using IntType = typename numext::get_integer_by_size<sizeof(Scalar)>::signed_type;
+  // Adds and subtracts signum(a) * 2^kMantissaBits to force rounding.
+  const IntType kLimit = IntType(1) << (NumTraits<Scalar>::digits() - 1);
+  const Packet cst_limit = pset1<Packet>(static_cast<Scalar>(kLimit));
+  Packet abs_a = pabs(a);
+  Packet sign_a = pandnot(a, abs_a);
+  Packet rint_a = padd(abs_a, cst_limit);
+  // Don't compile-away addition and subtraction.
+  EIGEN_OPTIMIZATION_BARRIER(rint_a);
+  rint_a = psub(rint_a, cst_limit);
+  rint_a = por(rint_a, sign_a);
+  // If greater than limit (or NaN), simply return a.
+  Packet mask = pcmp_lt(abs_a, cst_limit);
+  Packet result = pselect(mask, rint_a, a);
+  return result;
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_floor(const Packet& a) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  const Packet cst_1 = pset1<Packet>(Scalar(1));
+  Packet rint_a = generic_rint(a);
+  // if a < rint(a), then rint(a) == ceil(a)
+  Packet mask = pcmp_lt(a, rint_a);
+  Packet offset = pand(cst_1, mask);
+  Packet result = psub(rint_a, offset);
+  return result;
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_ceil(const Packet& a) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  const Packet cst_1 = pset1<Packet>(Scalar(1));
+  const Packet sign_mask = pset1<Packet>(static_cast<Scalar>(-0.0));
+  Packet rint_a = generic_rint(a);
+  // if rint(a) < a, then rint(a) == floor(a)
+  Packet mask = pcmp_lt(rint_a, a);
+  Packet offset = pand(cst_1, mask);
+  Packet result = padd(rint_a, offset);
+  // Signed zero must remain signed (e.g. ceil(-0.02) == -0).
+  result = por(result, pand(sign_mask, a));
+  return result;
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_trunc(const Packet& a) {
+  Packet abs_a = pabs(a);
+  Packet sign_a = pandnot(a, abs_a);
+  Packet floor_abs_a = generic_floor(abs_a);
+  Packet result = por(floor_abs_a, sign_a);
+  return result;
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_round(const Packet& a) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  const Packet cst_half = pset1<Packet>(Scalar(0.5));
+  const Packet cst_1 = pset1<Packet>(Scalar(1));
+  Packet abs_a = pabs(a);
+  Packet sign_a = pandnot(a, abs_a);
+  Packet floor_abs_a = generic_floor(abs_a);
+  Packet diff = psub(abs_a, floor_abs_a);
+  Packet mask = pcmp_le(cst_half, diff);
+  Packet offset = pand(cst_1, mask);
+  Packet result = padd(floor_abs_a, offset);
+  result = por(result, sign_a);
+  return result;
+}
+
+template <typename Packet>
+struct nearest_integer_packetop_impl<Packet, /*IsScalar*/ false, /*IsInteger*/ false> {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  static_assert(packet_traits<Scalar>::HasRound, "Generic nearest integer functions are disabled for this type.");
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_floor(const Packet& x) { return generic_floor(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_ceil(const Packet& x) { return generic_ceil(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_rint(const Packet& x) { return generic_rint(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_round(const Packet& x) { return generic_round(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_trunc(const Packet& x) { return generic_trunc(x); }
+};
+
+template <typename Packet>
+struct nearest_integer_packetop_impl<Packet, /*IsScalar*/ false, /*IsInteger*/ true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_floor(const Packet& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_ceil(const Packet& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_rint(const Packet& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_round(const Packet& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run_trunc(const Packet& x) { return x; }
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
new file mode 100644
index 0000000..fb9d710
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
@@ -0,0 +1,243 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
+#define EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+// Forward declarations of the generic math functions
+// implemented in GenericPacketMathFunctions.h
+// This is needed to workaround a circular dependency.
+
+/***************************************************************************
+ * Some generic implementations to be used by implementers
+ ***************************************************************************/
+
+/** Default implementation of pfrexp.
+ * It is expected to be called by implementers of template<> pfrexp.
+ */
+template <typename Packet>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Packet pfrexp_generic(const Packet& a, Packet& exponent);
+
+// Extracts the biased exponent value from Packet p, and casts the results to
+// a floating-point Packet type. Used by pfrexp_generic. Override this if
+// there is no unpacket_traits<Packet>::integer_packet.
+template <typename Packet>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Packet pfrexp_generic_get_biased_exponent(const Packet& p);
+
+/** Default implementation of pldexp.
+ * It is expected to be called by implementers of template<> pldexp.
+ */
+template <typename Packet>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Packet pldexp_generic(const Packet& a, const Packet& exponent);
+
+// Explicitly multiplies
+//    a * (2^e)
+// clamping e to the range
+// [NumTraits<Scalar>::min_exponent()-2, NumTraits<Scalar>::max_exponent()]
+//
+// This is approx 7x faster than pldexp_impl, but will prematurely over/underflow
+// if 2^e doesn't fit into a normal floating-point Scalar.
+//
+// Assumes IEEE floating point format
+template <typename Packet>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Packet pldexp_fast(const Packet& a, const Packet& exponent);
+
+/** \internal \returns cbrt(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcbrt_float(const Packet& x_in);
+
+/** \internal \returns cbrt(x) for double precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcbrt_double(const Packet& x_in);
+
+/** \internal \returns log(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog_float(const Packet _x);
+
+/** \internal \returns log2(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog2_float(const Packet _x);
+
+/** \internal \returns log(x) for double precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog_double(const Packet _x);
+
+/** \internal \returns log2(x) for double precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog2_double(const Packet _x);
+
+/** \internal \returns log(1 + x) */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_log1p(const Packet& x);
+
+/** \internal \returns exp(x)-1 */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_expm1(const Packet& x);
+
+/** \internal \returns atan(x) */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_atan(const Packet& x);
+
+/** \internal \returns exp2(x) */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_exp2(const Packet& x);
+
+/** \internal \returns exp(x) for single precision float */
+template <typename Packet, bool IsFinite = false>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pexp_float(const Packet _x);
+
+/** \internal \returns exp(x) for double precision real numbers */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pexp_double(const Packet _x);
+
+/** \internal \returns sin(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psin_float(const Packet& x);
+
+/** \internal \returns cos(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcos_float(const Packet& x);
+
+/** \internal \returns tan(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet ptan_float(const Packet& x);
+
+/** \internal \returns sin(x) for double precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psin_double(const Packet& x);
+
+/** \internal \returns cos(x) for double precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcos_double(const Packet& x);
+
+/** \internal \returns tan(x) for double precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet ptan_double(const Packet& x);
+
+/** \internal \returns asin(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pasin_float(const Packet& x);
+
+/** \internal \returns acos(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pacos_float(const Packet& x);
+
+/** \internal \returns tanh(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet ptanh_float(const Packet& x);
+
+/** \internal \returns tanh(x) for double precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet ptanh_double(const Packet& x);
+
+/** \internal \returns atanh(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet patanh_float(const Packet& x);
+
+/** \internal \returns atanh(x) for double precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet patanh_double(const Packet& x);
+
+/** \internal \returns sqrt(x) for complex types */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psqrt_complex(const Packet& a);
+
+/** \internal \returns x / y for complex types */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pdiv_complex(const Packet& x, const Packet& y);
+
+/** \internal \returns x * y for complex types */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pmul_complex(const Packet& x, const Packet& y);
+
+template <typename Packet, int N>
+struct ppolevl;
+
+/** \internal \returns log(x) for complex types */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog_complex(const Packet& x);
+
+/** \internal \returns exp(x) for complex types */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pexp_complex(const Packet& x);
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_rint(const Packet& a);
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_floor(const Packet& a);
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_ceil(const Packet& a);
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_trunc(const Packet& a);
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_round(const Packet& a);
+
+// Macros for instantiating these generic functions for different backends.
+#define EIGEN_PACKET_FUNCTION(METHOD, SCALAR, PACKET)                                             \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_UNUSED PACKET p##METHOD<PACKET>(const PACKET& _x) { \
+    return p##METHOD##_##SCALAR(_x);                                                              \
+  }
+
+// Macros for instantiating these generic functions for different backends.
+#define EIGEN_GENERIC_PACKET_FUNCTION(METHOD, PACKET)                                             \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_UNUSED PACKET p##METHOD<PACKET>(const PACKET& _x) { \
+    return generic_##METHOD(_x);                                                                  \
+  }
+
+#define EIGEN_FLOAT_PACKET_FUNCTION(METHOD, PACKET) EIGEN_PACKET_FUNCTION(METHOD, float, PACKET)
+#define EIGEN_DOUBLE_PACKET_FUNCTION(METHOD, PACKET) EIGEN_PACKET_FUNCTION(METHOD, double, PACKET)
+
+#define EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(PACKET) \
+  EIGEN_FLOAT_PACKET_FUNCTION(sin, PACKET)                 \
+  EIGEN_FLOAT_PACKET_FUNCTION(cos, PACKET)                 \
+  EIGEN_FLOAT_PACKET_FUNCTION(tan, PACKET)                 \
+  EIGEN_FLOAT_PACKET_FUNCTION(asin, PACKET)                \
+  EIGEN_FLOAT_PACKET_FUNCTION(acos, PACKET)                \
+  EIGEN_FLOAT_PACKET_FUNCTION(tanh, PACKET)                \
+  EIGEN_FLOAT_PACKET_FUNCTION(atanh, PACKET)               \
+  EIGEN_FLOAT_PACKET_FUNCTION(log, PACKET)                 \
+  EIGEN_FLOAT_PACKET_FUNCTION(log2, PACKET)                \
+  EIGEN_FLOAT_PACKET_FUNCTION(exp, PACKET)                 \
+  EIGEN_FLOAT_PACKET_FUNCTION(cbrt, PACKET)                \
+  EIGEN_GENERIC_PACKET_FUNCTION(expm1, PACKET)             \
+  EIGEN_GENERIC_PACKET_FUNCTION(exp2, PACKET)              \
+  EIGEN_GENERIC_PACKET_FUNCTION(log1p, PACKET)             \
+  EIGEN_GENERIC_PACKET_FUNCTION(atan, PACKET)
+
+#define EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(PACKET) \
+  EIGEN_DOUBLE_PACKET_FUNCTION(sin, PACKET)                 \
+  EIGEN_DOUBLE_PACKET_FUNCTION(cos, PACKET)                 \
+  EIGEN_DOUBLE_PACKET_FUNCTION(tan, PACKET)                 \
+  EIGEN_DOUBLE_PACKET_FUNCTION(log, PACKET)                 \
+  EIGEN_DOUBLE_PACKET_FUNCTION(log2, PACKET)                \
+  EIGEN_DOUBLE_PACKET_FUNCTION(exp, PACKET)                 \
+  EIGEN_DOUBLE_PACKET_FUNCTION(tanh, PACKET)                \
+  EIGEN_DOUBLE_PACKET_FUNCTION(atanh, PACKET)               \
+  EIGEN_DOUBLE_PACKET_FUNCTION(cbrt, PACKET)                \
+  EIGEN_GENERIC_PACKET_FUNCTION(expm1, PACKET)              \
+  EIGEN_GENERIC_PACKET_FUNCTION(exp2, PACKET)               \
+  EIGEN_GENERIC_PACKET_FUNCTION(log1p, PACKET)              \
+  EIGEN_GENERIC_PACKET_FUNCTION(atan, PACKET)
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathPolynomials.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathPolynomials.h
new file mode 100644
index 0000000..a8ec6ae
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathPolynomials.h
@@ -0,0 +1,151 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2009-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_POLYNOMIALS_H
+#define EIGEN_ARCH_GENERIC_PACKET_MATH_POLYNOMIALS_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/* polevl (modified for Eigen)
+ *
+ *      Evaluate polynomial
+ *
+ *
+ *
+ * SYNOPSIS:
+ *
+ * int N;
+ * Scalar x, y, coef[N+1];
+ *
+ * y = polevl<decltype(x), N>( x, coef);
+ *
+ *
+ *
+ * DESCRIPTION:
+ *
+ * Evaluates polynomial of degree N:
+ *
+ *                     2          N
+ * y  =  C  + C x + C x  +...+ C x
+ *        0    1     2          N
+ *
+ * Coefficients are stored in reverse order:
+ *
+ * coef[0] = C  , ..., coef[N] = C  .
+ *            N                   0
+ *
+ *  The function p1evl() assumes that coef[N] = 1.0 and is
+ * omitted from the array.  Its calling arguments are
+ * otherwise the same as polevl().
+ *
+ *
+ * The Eigen implementation is templatized.  For best speed, store
+ * coef as a const array (constexpr), e.g.
+ *
+ * const double coef[] = {1.0, 2.0, 3.0, ...};
+ *
+ */
+template <typename Packet, int N>
+struct ppolevl {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x,
+                                                          const typename unpacket_traits<Packet>::type coeff[]) {
+    EIGEN_STATIC_ASSERT((N > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return pmadd(ppolevl<Packet, N - 1>::run(x, coeff), x, pset1<Packet>(coeff[N]));
+  }
+};
+
+template <typename Packet>
+struct ppolevl<Packet, 0> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x,
+                                                          const typename unpacket_traits<Packet>::type coeff[]) {
+    EIGEN_UNUSED_VARIABLE(x);
+    return pset1<Packet>(coeff[0]);
+  }
+};
+
+/* chbevl (modified for Eigen)
+ *
+ *     Evaluate Chebyshev series
+ *
+ *
+ *
+ * SYNOPSIS:
+ *
+ * int N;
+ * Scalar x, y, coef[N], chebevl();
+ *
+ * y = chbevl( x, coef, N );
+ *
+ *
+ *
+ * DESCRIPTION:
+ *
+ * Evaluates the series
+ *
+ *        N-1
+ *         - '
+ *  y  =   >   coef[i] T (x/2)
+ *         -            i
+ *        i=0
+ *
+ * of Chebyshev polynomials Ti at argument x/2.
+ *
+ * Coefficients are stored in reverse order, i.e. the zero
+ * order term is last in the array.  Note N is the number of
+ * coefficients, not the order.
+ *
+ * If coefficients are for the interval a to b, x must
+ * have been transformed to x -> 2(2x - b - a)/(b-a) before
+ * entering the routine.  This maps x from (a, b) to (-1, 1),
+ * over which the Chebyshev polynomials are defined.
+ *
+ * If the coefficients are for the inverted interval, in
+ * which (a, b) is mapped to (1/b, 1/a), the transformation
+ * required is x -> 2(2ab/x - b - a)/(b-a).  If b is infinity,
+ * this becomes x -> 4a/x - 1.
+ *
+ *
+ *
+ * SPEED:
+ *
+ * Taking advantage of the recurrence properties of the
+ * Chebyshev polynomials, the routine requires one more
+ * addition per loop than evaluating a nested polynomial of
+ * the same degree.
+ *
+ */
+
+template <typename Packet, int N>
+struct pchebevl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet run(Packet x,
+                                                          const typename unpacket_traits<Packet>::type coef[]) {
+    typedef typename unpacket_traits<Packet>::type Scalar;
+    Packet b0 = pset1<Packet>(coef[0]);
+    Packet b1 = pset1<Packet>(static_cast<Scalar>(0.f));
+    Packet b2;
+
+    for (int i = 1; i < N; i++) {
+      b2 = b1;
+      b1 = b0;
+      b0 = psub(pmadd(x, b1, pset1<Packet>(coef[i])), b2);
+    }
+
+    return pmul(pset1<Packet>(static_cast<Scalar>(0.5f)), psub(b0, b2));
+  }
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARCH_GENERIC_PACKET_MATH_POLYNOMIALS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathPow.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathPow.h
new file mode 100644
index 0000000..08686f8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathPow.h
@@ -0,0 +1,710 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018-2025 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_POW_H
+#define EIGEN_ARCH_GENERIC_PACKET_MATH_POW_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+//----------------------------------------------------------------------
+// Cubic Root Functions
+//----------------------------------------------------------------------
+
+// This function implements a single step of Halley's iteration for
+// computing x = y^(1/3):
+//   x_{k+1} = x_k - (x_k^3 - y) x_k / (2x_k^3 + y)
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet cbrt_halley_iteration_step(const Packet& x_k,
+                                                                                      const Packet& y) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  Packet x_k_cb = pmul(x_k, pmul(x_k, x_k));
+  Packet denom = pmadd(pset1<Packet>(Scalar(2)), x_k_cb, y);
+  Packet num = psub(x_k_cb, y);
+  Packet r = pdiv(num, denom);
+  return pnmadd(x_k, r, x_k);
+}
+
+// Decompose the input such that x^(1/3) = y^(1/3) * 2^e_div3, and y is in the
+// interval [0.125,1].
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet cbrt_decompose(const Packet& x, Packet& e_div3) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  // Extract the significant s in the range [0.5,1) and exponent e, such that
+  // x = 2^e * s.
+  Packet e, s;
+  s = pfrexp(x, e);
+
+  // Split the exponent into a part divisible by 3 and the remainder.
+  // e = 3*e_div3 + e_mod3.
+  constexpr Scalar kOneThird = Scalar(1) / 3;
+  e_div3 = pceil(pmul(e, pset1<Packet>(kOneThird)));
+  Packet e_mod3 = pnmadd(pset1<Packet>(Scalar(3)), e_div3, e);
+
+  // Replace s by y = (s * 2^e_mod3).
+  return pldexp_fast(s, e_mod3);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet cbrt_special_cases_and_sign(const Packet& x,
+                                                                                       const Packet& abs_root) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+
+  // Set sign.
+  const Packet sign_mask = pset1<Packet>(Scalar(-0.0));
+  const Packet x_sign = pand(sign_mask, x);
+  Packet root = por(x_sign, abs_root);
+
+  // Pass non-finite and zero values of x straight through.
+  const Packet is_not_finite = por(pisinf(x), pisnan(x));
+  const Packet is_zero = pcmp_eq(pzero(x), x);
+  const Packet use_x = por(is_not_finite, is_zero);
+  return pselect(use_x, x, root);
+}
+
+// Generic implementation of cbrt(x) for float.
+//
+// The algorithm computes the cubic root of the input by first
+// decomposing it into a exponent and significant
+//   x = s * 2^e.
+//
+// We can then write the cube root as
+//
+//   x^(1/3) = 2^(e/3) * s^(1/3)
+//           = 2^((3*e_div3 + e_mod3)/3) * s^(1/3)
+//           = 2^(e_div3) * 2^(e_mod3/3) * s^(1/3)
+//           = 2^(e_div3) * (s * 2^e_mod3)^(1/3)
+//
+// where e_div3 = ceil(e/3) and e_mod3 = e - 3*e_div3.
+//
+// The cube root of the second term y = (s * 2^e_mod3)^(1/3) is coarsely
+// approximated using a cubic polynomial and subsequently refined using a
+// single step of Halley's iteration, and finally the two terms are combined
+// using pldexp_fast.
+//
+// Note: Many alternatives exist for implementing cbrt. See, for example,
+// the excellent discussion in Kahan's note:
+//   https://csclub.uwaterloo.ca/~pbarfuss/qbrt.pdf
+// This particular implementation was found to be very fast and accurate
+// among several alternatives tried, but is probably not "optimal" on all
+// platforms.
+//
+// This is accurate to 2 ULP.
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcbrt_float(const Packet& x) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, float>::value, "Scalar type must be float");
+
+  // Decompose the input such that x^(1/3) = y^(1/3) * 2^e_div3, and y is in the
+  // interval [0.125,1].
+  Packet e_div3;
+  const Packet y = cbrt_decompose(pabs(x), e_div3);
+
+  // Compute initial approximation accurate to 5.22e-3.
+  // The polynomial was computed using Rminimax.
+  constexpr float alpha[] = {5.9220016002655029296875e-01f, -1.3859539031982421875e+00f, 1.4581282138824462890625e+00f,
+                             3.408401906490325927734375e-01f};
+  Packet r = ppolevl<Packet, 3>::run(y, alpha);
+
+  // Take one step of Halley's iteration.
+  r = cbrt_halley_iteration_step(r, y);
+
+  // Finally multiply by 2^(e_div3)
+  r = pldexp_fast(r, e_div3);
+
+  return cbrt_special_cases_and_sign(x, r);
+}
+
+// Generic implementation of cbrt(x) for double.
+//
+// The algorithm is identical to the one for float except that a different initial
+// approximation is used for y^(1/3) and two Halley iteration steps are performed.
+//
+// This is accurate to 1 ULP.
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcbrt_double(const Packet& x) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, double>::value, "Scalar type must be double");
+
+  // Decompose the input such that x^(1/3) = y^(1/3) * 2^e_div3, and y is in the
+  // interval [0.125,1].
+  Packet e_div3;
+  const Packet y = cbrt_decompose(pabs(x), e_div3);
+
+  // Compute initial approximation accurate to 0.016.
+  // The polynomial was computed using Rminimax.
+  constexpr double alpha[] = {-4.69470621553356115551736138513660989701747894287109375e-01,
+                              1.072314636518546304699839311069808900356292724609375e+00,
+                              3.81249427609571867048288140722434036433696746826171875e-01};
+  Packet r = ppolevl<Packet, 2>::run(y, alpha);
+
+  // Take two steps of Halley's iteration.
+  r = cbrt_halley_iteration_step(r, y);
+  r = cbrt_halley_iteration_step(r, y);
+
+  // Finally multiply by 2^(e_div3).
+  r = pldexp_fast(r, e_div3);
+  return cbrt_special_cases_and_sign(x, r);
+}
+
+//----------------------------------------------------------------------
+// Power Functions (accurate_log2, generic_pow, unary_pow)
+//----------------------------------------------------------------------
+
+// This function computes log2(x) and returns the result as a double word.
+template <typename Scalar>
+struct accurate_log2 {
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(const Packet& x, Packet& log2_x_hi, Packet& log2_x_lo) {
+    log2_x_hi = plog2(x);
+    log2_x_lo = pzero(x);
+  }
+};
+
+// This specialization uses a more accurate algorithm to compute log2(x) for
+// floats in [1/sqrt(2);sqrt(2)] with a relative accuracy of ~6.56508e-10.
+// This additional accuracy is needed to counter the error-magnification
+// inherent in multiplying by a potentially large exponent in pow(x,y).
+// The minimax polynomial used was calculated using the Rminimax tool,
+// see https://gitlab.inria.fr/sfilip/rminimax.
+// Command line:
+//   $ ratapprox --function="log2(1+x)/x"  --dom='[-0.2929,0.41422]'
+//   --type=[10,0]
+//       --numF="[D,D,SG]" --denF="[SG]" --log --dispCoeff="dec"
+//
+// The resulting implementation of pow(x,y) is accurate to 3 ulps.
+template <>
+struct accurate_log2<float> {
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(const Packet& z, Packet& log2_x_hi, Packet& log2_x_lo) {
+    // Split the two lowest order constant coefficient into double-word representation.
+    constexpr double kC0 = 1.442695041742110273474963832995854318141937255859375e+00;
+    constexpr float kC0_hi = static_cast<float>(kC0);
+    constexpr float kC0_lo = static_cast<float>(kC0 - static_cast<double>(kC0_hi));
+    const Packet c0_hi = pset1<Packet>(kC0_hi);
+    const Packet c0_lo = pset1<Packet>(kC0_lo);
+
+    constexpr double kC1 = -7.2134751588268664068692714863573201000690460205078125e-01;
+    constexpr float kC1_hi = static_cast<float>(kC1);
+    constexpr float kC1_lo = static_cast<float>(kC1 - static_cast<double>(kC1_hi));
+    const Packet c1_hi = pset1<Packet>(kC1_hi);
+    const Packet c1_lo = pset1<Packet>(kC1_lo);
+
+    constexpr float c[] = {
+        9.7010828554630279541015625e-02,  -1.6896486282348632812500000e-01, 1.7200836539268493652343750e-01,
+        -1.7892272770404815673828125e-01, 2.0505344867706298828125000e-01,  -2.4046677350997924804687500e-01,
+        2.8857553005218505859375000e-01,  -3.6067414283752441406250000e-01, 4.8089790344238281250000000e-01};
+
+    // Evaluate the higher order terms in the polynomial using
+    // standard arithmetic.
+    const Packet one = pset1<Packet>(1.0f);
+    const Packet x = psub(z, one);
+    Packet p = ppolevl<Packet, 8>::run(x, c);
+    // Evaluate the final two step in Horner's rule using double-word
+    // arithmetic.
+    Packet p_hi, p_lo;
+    twoprod(x, p, p_hi, p_lo);
+    fast_twosum(c1_hi, c1_lo, p_hi, p_lo, p_hi, p_lo);
+    twoprod(p_hi, p_lo, x, p_hi, p_lo);
+    fast_twosum(c0_hi, c0_lo, p_hi, p_lo, p_hi, p_lo);
+    // Multiply by x to recover log2(z).
+    twoprod(p_hi, p_lo, x, log2_x_hi, log2_x_lo);
+  }
+};
+
+// This specialization uses a more accurate algorithm to compute log2(x) for
+// floats in [1/sqrt(2);sqrt(2)] with a relative accuracy of ~1.27e-18.
+// This additional accuracy is needed to counter the error-magnification
+// inherent in multiplying by a potentially large exponent in pow(x,y).
+// The minimax polynomial used was calculated using the Sollya tool.
+// See sollya.org.
+
+template <>
+struct accurate_log2<double> {
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(const Packet& x, Packet& log2_x_hi, Packet& log2_x_lo) {
+    // We use a transformation of variables:
+    //    r = c * (x-1) / (x+1),
+    // such that
+    //    log2(x) = log2((1 + r/c) / (1 - r/c)) = f(r).
+    // The function f(r) can be approximated well using an odd polynomial
+    // of the form
+    //   P(r) = ((Q(r^2) * r^2 + C) * r^2 + 1) * r,
+    // For the implementation of log2<double> here, Q is of degree 6 with
+    // coefficient represented in working precision (double), while C is a
+    // constant represented in extra precision as a double word to achieve
+    // full accuracy.
+    //
+    // The polynomial coefficients were computed by the Sollya script:
+    //
+    // c = 2 / log(2);
+    // trans = c * (x-1)/(x+1);
+    // itrans = (1+x/c)/(1-x/c);
+    // interval=[trans(sqrt(0.5)); trans(sqrt(2))];
+    // print(interval);
+    // f = log2(itrans(x));
+    // p=fpminimax(f,[|1,3,5,7,9,11,13,15,17|],[|1,DD,double...|],interval,relative,floating);
+    const Packet q12 = pset1<Packet>(2.87074255468000586e-9);
+    const Packet q10 = pset1<Packet>(2.38957980901884082e-8);
+    const Packet q8 = pset1<Packet>(2.31032094540014656e-7);
+    const Packet q6 = pset1<Packet>(2.27279857398537278e-6);
+    const Packet q4 = pset1<Packet>(2.31271023278625638e-5);
+    const Packet q2 = pset1<Packet>(2.47556738444535513e-4);
+    const Packet q0 = pset1<Packet>(2.88543873228900172e-3);
+    const Packet C_hi = pset1<Packet>(0.0400377511598501157);
+    const Packet C_lo = pset1<Packet>(-4.77726582251425391e-19);
+    const Packet one = pset1<Packet>(1.0);
+
+    const Packet cst_2_log2e_hi = pset1<Packet>(2.88539008177792677);
+    const Packet cst_2_log2e_lo = pset1<Packet>(4.07660016854549667e-17);
+    // c * (x - 1)
+    Packet t_hi, t_lo;
+    // t = c * (x-1)
+    twoprod(cst_2_log2e_hi, cst_2_log2e_lo, psub(x, one), t_hi, t_lo);
+    // r = c * (x-1) / (x+1),
+    Packet r_hi, r_lo;
+    doubleword_div_fp(t_hi, t_lo, padd(x, one), r_hi, r_lo);
+
+    // r2 = r * r
+    Packet r2_hi, r2_lo;
+    twoprod(r_hi, r_lo, r_hi, r_lo, r2_hi, r2_lo);
+    // r4 = r2 * r2
+    Packet r4_hi, r4_lo;
+    twoprod(r2_hi, r2_lo, r2_hi, r2_lo, r4_hi, r4_lo);
+
+    // Evaluate Q(r^2) in working precision. We evaluate it in two parts
+    // (even and odd in r^2) to improve instruction level parallelism.
+    Packet q_even = pmadd(q12, r4_hi, q8);
+    Packet q_odd = pmadd(q10, r4_hi, q6);
+    q_even = pmadd(q_even, r4_hi, q4);
+    q_odd = pmadd(q_odd, r4_hi, q2);
+    q_even = pmadd(q_even, r4_hi, q0);
+    Packet q = pmadd(q_odd, r2_hi, q_even);
+
+    // Now evaluate the low order terms of P(x) in double word precision.
+    // In the following, due to the increasing magnitude of the coefficients
+    // and r being constrained to [-0.5, 0.5] we can use fast_twosum instead
+    // of the slower twosum.
+    // Q(r^2) * r^2
+    Packet p_hi, p_lo;
+    twoprod(r2_hi, r2_lo, q, p_hi, p_lo);
+    // Q(r^2) * r^2 + C
+    Packet p1_hi, p1_lo;
+    fast_twosum(C_hi, C_lo, p_hi, p_lo, p1_hi, p1_lo);
+    // (Q(r^2) * r^2 + C) * r^2
+    Packet p2_hi, p2_lo;
+    twoprod(r2_hi, r2_lo, p1_hi, p1_lo, p2_hi, p2_lo);
+    // ((Q(r^2) * r^2 + C) * r^2 + 1)
+    Packet p3_hi, p3_lo;
+    fast_twosum(one, p2_hi, p2_lo, p3_hi, p3_lo);
+
+    // log(z) ~= ((Q(r^2) * r^2 + C) * r^2 + 1) * r
+    twoprod(p3_hi, p3_lo, r_hi, r_lo, log2_x_hi, log2_x_lo);
+  }
+};
+
+// This function implements the non-trivial case of pow(x,y) where x is
+// positive and y is (possibly) non-integer.
+// Formally, pow(x,y) = exp2(y * log2(x)), where exp2(x) is shorthand for 2^x.
+// TODO(rmlarsen): We should probably add this as a packet op 'ppow', to make it
+// easier to specialize or turn off for specific types and/or backends.
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet generic_pow_impl(const Packet& x, const Packet& y) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  // Split x into exponent e_x and mantissa m_x.
+  Packet e_x;
+  Packet m_x = pfrexp(x, e_x);
+
+  // Adjust m_x to lie in [1/sqrt(2):sqrt(2)] to minimize absolute error in log2(m_x).
+  constexpr Scalar sqrt_half = Scalar(0.70710678118654752440);
+  const Packet m_x_scale_mask = pcmp_lt(m_x, pset1<Packet>(sqrt_half));
+  m_x = pselect(m_x_scale_mask, pmul(pset1<Packet>(Scalar(2)), m_x), m_x);
+  e_x = pselect(m_x_scale_mask, psub(e_x, pset1<Packet>(Scalar(1))), e_x);
+
+  // Compute log2(m_x) with 6 extra bits of accuracy.
+  Packet rx_hi, rx_lo;
+  accurate_log2<Scalar>()(m_x, rx_hi, rx_lo);
+
+  // Compute the two terms {y * e_x, y * r_x} in f = y * log2(x) with doubled
+  // precision using double word arithmetic.
+  Packet f1_hi, f1_lo, f2_hi, f2_lo;
+  twoprod(e_x, y, f1_hi, f1_lo);
+  twoprod(rx_hi, rx_lo, y, f2_hi, f2_lo);
+  // Sum the two terms in f using double word arithmetic. We know
+  // that |e_x| > |log2(m_x)|, except for the case where e_x==0.
+  // This means that we can use fast_twosum(f1,f2).
+  // In the case e_x == 0, e_x * y = f1 = 0, so we don't lose any
+  // accuracy by violating the assumption of fast_twosum, because
+  // it's a no-op.
+  Packet f_hi, f_lo;
+  fast_twosum(f1_hi, f1_lo, f2_hi, f2_lo, f_hi, f_lo);
+
+  // Split f into integer and fractional parts.
+  Packet n_z, r_z;
+  absolute_split(f_hi, n_z, r_z);
+  r_z = padd(r_z, f_lo);
+  Packet n_r;
+  absolute_split(r_z, n_r, r_z);
+  n_z = padd(n_z, n_r);
+
+  // We now have an accurate split of f = n_z + r_z and can compute
+  //   x^y = 2**{n_z + r_z) = exp2(r_z) * 2**{n_z}.
+  // Multiplication by the second factor can be done exactly using pldexp(), since
+  // it is an integer power of 2.
+  const Packet e_r = generic_exp2(r_z);
+
+  // Since we know that e_r is in [1/sqrt(2); sqrt(2)], we can use the fast version
+  // of pldexp to multiply by 2**{n_z} when |n_z| is sufficiently small.
+  constexpr Scalar kPldExpThresh = std::numeric_limits<Scalar>::max_exponent - 2;
+  const Packet pldexp_fast_unsafe = pcmp_lt(pset1<Packet>(kPldExpThresh), pabs(n_z));
+  if (predux_any(pldexp_fast_unsafe)) {
+    return pldexp(e_r, n_z);
+  }
+  return pldexp_fast(e_r, n_z);
+}
+
+// Generic implementation of pow(x,y).
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS std::enable_if_t<!is_scalar<Packet>::value, Packet> generic_pow(
+    const Packet& x, const Packet& y) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+
+  const Packet cst_inf = pset1<Packet>(NumTraits<Scalar>::infinity());
+  const Packet cst_zero = pset1<Packet>(Scalar(0));
+  const Packet cst_one = pset1<Packet>(Scalar(1));
+  const Packet cst_nan = pset1<Packet>(NumTraits<Scalar>::quiet_NaN());
+
+  const Packet x_abs = pabs(x);
+  Packet pow = generic_pow_impl(x_abs, y);
+
+  // In the following we enforce the special case handling prescribed in
+  // https://en.cppreference.com/w/cpp/numeric/math/pow.
+
+  // Predicates for sign and magnitude of x.
+  const Packet x_is_negative = pcmp_lt(x, cst_zero);
+  const Packet x_is_zero = pcmp_eq(x, cst_zero);
+  const Packet x_is_one = pcmp_eq(x, cst_one);
+  const Packet x_has_signbit = psignbit(x);
+  const Packet x_abs_gt_one = pcmp_lt(cst_one, x_abs);
+  const Packet x_abs_is_inf = pcmp_eq(x_abs, cst_inf);
+
+  // Predicates for sign and magnitude of y.
+  const Packet y_abs = pabs(y);
+  const Packet y_abs_is_inf = pcmp_eq(y_abs, cst_inf);
+  const Packet y_is_negative = pcmp_lt(y, cst_zero);
+  const Packet y_is_zero = pcmp_eq(y, cst_zero);
+  const Packet y_is_one = pcmp_eq(y, cst_one);
+  // Predicates for whether y is integer and odd/even.
+  const Packet y_is_int = pandnot(pcmp_eq(pfloor(y), y), y_abs_is_inf);
+  const Packet y_div_2 = pmul(y, pset1<Packet>(Scalar(0.5)));
+  const Packet y_is_even = pcmp_eq(pround(y_div_2), y_div_2);
+  const Packet y_is_odd_int = pandnot(y_is_int, y_is_even);
+  // Smallest exponent for which (1 + epsilon) overflows to infinity.
+  constexpr Scalar huge_exponent =
+      (NumTraits<Scalar>::max_exponent() * Scalar(EIGEN_LN2)) / NumTraits<Scalar>::epsilon();
+  const Packet y_abs_is_huge = pcmp_le(pset1<Packet>(huge_exponent), y_abs);
+
+  // *  pow(base, exp) returns NaN if base is finite and negative
+  //    and exp is finite and non-integer.
+  pow = pselect(pandnot(x_is_negative, y_is_int), cst_nan, pow);
+
+  // * pow(±0, exp), where exp is negative, finite, and is an even integer or
+  // a non-integer, returns +∞
+  // * pow(±0, exp), where exp is positive non-integer or a positive even
+  // integer, returns +0
+  // * pow(+0, exp), where exp is a negative odd integer, returns +∞
+  // * pow(-0, exp), where exp is a negative odd integer, returns -∞
+  // * pow(+0, exp), where exp is a positive odd integer, returns +0
+  // * pow(-0, exp), where exp is a positive odd integer, returns -0
+  // Sign is flipped by the rule below.
+  pow = pselect(x_is_zero, pselect(y_is_negative, cst_inf, cst_zero), pow);
+
+  // pow(base, exp) returns -pow(abs(base), exp) if base has the sign bit set,
+  // and exp is an odd integer exponent.
+  pow = pselect(pand(x_has_signbit, y_is_odd_int), pnegate(pow), pow);
+
+  // * pow(base, -∞) returns +∞ for any |base|<1
+  // * pow(base, -∞) returns +0 for any |base|>1
+  // * pow(base, +∞) returns +0 for any |base|<1
+  // * pow(base, +∞) returns +∞ for any |base|>1
+  // * pow(±0, -∞) returns +∞
+  // * pow(-1, +-∞) = 1
+  Packet inf_y_val = pselect(por(pand(y_is_negative, x_is_zero), pxor(y_is_negative, x_abs_gt_one)), cst_inf, cst_zero);
+  inf_y_val = pselect(pcmp_eq(x, pset1<Packet>(Scalar(-1.0))), cst_one, inf_y_val);
+  pow = pselect(y_abs_is_huge, inf_y_val, pow);
+
+  // * pow(+∞, exp) returns +0 for any negative exp
+  // * pow(+∞, exp) returns +∞ for any positive exp
+  // * pow(-∞, exp) returns -0 if exp is a negative odd integer.
+  // * pow(-∞, exp) returns +0 if exp is a negative non-integer or negative
+  //     even integer.
+  // * pow(-∞, exp) returns -∞ if exp is a positive odd integer.
+  // * pow(-∞, exp) returns +∞ if exp is a positive non-integer or positive
+  //     even integer.
+  auto x_pos_inf_value = pselect(y_is_negative, cst_zero, cst_inf);
+  auto x_neg_inf_value = pselect(y_is_odd_int, pnegate(x_pos_inf_value), x_pos_inf_value);
+  pow = pselect(x_abs_is_inf, pselect(x_is_negative, x_neg_inf_value, x_pos_inf_value), pow);
+
+  // All cases of NaN inputs return NaN, except the two below.
+  pow = pselect(por(pisnan(x), pisnan(y)), cst_nan, pow);
+
+  // * pow(base, 1) returns base.
+  // * pow(base, +/-0) returns 1, regardless of base, even NaN.
+  // * pow(+1, exp) returns 1, regardless of exponent, even NaN.
+  pow = pselect(y_is_one, x, pselect(por(x_is_one, y_is_zero), cst_one, pow));
+
+  return pow;
+}
+
+template <typename Scalar>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS std::enable_if_t<is_scalar<Scalar>::value, Scalar> generic_pow(
+    const Scalar& x, const Scalar& y) {
+  return numext::pow(x, y);
+}
+
+namespace unary_pow {
+
+template <typename ScalarExponent, bool IsInteger = NumTraits<ScalarExponent>::IsInteger>
+struct exponent_helper {
+  using safe_abs_type = ScalarExponent;
+  static constexpr ScalarExponent one_half = ScalarExponent(0.5);
+  // these routines assume that exp is an integer stored as a floating point type
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScalarExponent safe_abs(const ScalarExponent& exp) {
+    return numext::abs(exp);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool is_odd(const ScalarExponent& exp) {
+    eigen_assert(((numext::isfinite)(exp) && exp == numext::floor(exp)) && "exp must be an integer");
+    ScalarExponent exp_div_2 = exp * one_half;
+    ScalarExponent floor_exp_div_2 = numext::floor(exp_div_2);
+    return exp_div_2 != floor_exp_div_2;
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScalarExponent floor_div_two(const ScalarExponent& exp) {
+    ScalarExponent exp_div_2 = exp * one_half;
+    return numext::floor(exp_div_2);
+  }
+};
+
+template <typename ScalarExponent>
+struct exponent_helper<ScalarExponent, true> {
+  // if `exp` is a signed integer type, cast it to its unsigned counterpart to safely store its absolute value
+  // consider the (rare) case where `exp` is an int32_t: abs(-2147483648) != 2147483648
+  using safe_abs_type = typename numext::get_integer_by_size<sizeof(ScalarExponent)>::unsigned_type;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE safe_abs_type safe_abs(const ScalarExponent& exp) {
+    ScalarExponent mask = numext::signbit(exp);
+    safe_abs_type result = safe_abs_type(exp ^ mask);
+    return result + safe_abs_type(ScalarExponent(1) & mask);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool is_odd(const safe_abs_type& exp) {
+    return exp % safe_abs_type(2) != safe_abs_type(0);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE safe_abs_type floor_div_two(const safe_abs_type& exp) {
+    return exp >> safe_abs_type(1);
+  }
+};
+
+template <typename Packet, typename ScalarExponent,
+          bool ReciprocateIfExponentIsNegative =
+              !NumTraits<typename unpacket_traits<Packet>::type>::IsInteger && NumTraits<ScalarExponent>::IsSigned>
+struct reciprocate {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent& exponent) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet cst_pos_one = pset1<Packet>(Scalar(1));
+    return exponent < 0 ? pdiv(cst_pos_one, x) : x;
+  }
+};
+
+template <typename Packet, typename ScalarExponent>
+struct reciprocate<Packet, ScalarExponent, false> {
+  // pdiv not defined, nor necessary for integer base types
+  // if the exponent is unsigned, then the exponent cannot be negative
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent&) { return x; }
+};
+
+template <typename Packet, typename ScalarExponent>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet int_pow(const Packet& x, const ScalarExponent& exponent) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  using ExponentHelper = exponent_helper<ScalarExponent>;
+  using AbsExponentType = typename ExponentHelper::safe_abs_type;
+  const Packet cst_pos_one = pset1<Packet>(Scalar(1));
+  if (exponent == ScalarExponent(0)) return cst_pos_one;
+
+  Packet result = reciprocate<Packet, ScalarExponent>::run(x, exponent);
+  Packet y = cst_pos_one;
+  AbsExponentType m = ExponentHelper::safe_abs(exponent);
+
+  while (m > 1) {
+    bool odd = ExponentHelper::is_odd(m);
+    if (odd) y = pmul(y, result);
+    result = pmul(result, result);
+    m = ExponentHelper::floor_div_two(m);
+  }
+
+  return pmul(y, result);
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<!is_scalar<Packet>::value, Packet> gen_pow(
+    const Packet& x, const typename unpacket_traits<Packet>::type& exponent) {
+  const Packet exponent_packet = pset1<Packet>(exponent);
+  return generic_pow_impl(x, exponent_packet);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<is_scalar<Scalar>::value, Scalar> gen_pow(
+    const Scalar& x, const Scalar& exponent) {
+  return numext::pow(x, exponent);
+}
+
+template <typename Packet, typename ScalarExponent>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet handle_nonint_nonint_errors(const Packet& x, const Packet& powx,
+                                                                         const ScalarExponent& exponent) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+
+  // non-integer base and exponent case
+  const Packet cst_pos_zero = pzero(x);
+  const Packet cst_pos_one = pset1<Packet>(Scalar(1));
+  const Packet cst_pos_inf = pset1<Packet>(NumTraits<Scalar>::infinity());
+  const Packet cst_true = ptrue<Packet>(x);
+
+  const bool exponent_is_not_fin = !(numext::isfinite)(exponent);
+  const bool exponent_is_neg = exponent < ScalarExponent(0);
+  const bool exponent_is_pos = exponent > ScalarExponent(0);
+
+  const Packet exp_is_not_fin = exponent_is_not_fin ? cst_true : cst_pos_zero;
+  const Packet exp_is_neg = exponent_is_neg ? cst_true : cst_pos_zero;
+  const Packet exp_is_pos = exponent_is_pos ? cst_true : cst_pos_zero;
+  const Packet exp_is_inf = pand(exp_is_not_fin, por(exp_is_neg, exp_is_pos));
+  const Packet exp_is_nan = pandnot(exp_is_not_fin, por(exp_is_neg, exp_is_pos));
+
+  const Packet x_is_le_zero = pcmp_le(x, cst_pos_zero);
+  const Packet x_is_ge_zero = pcmp_le(cst_pos_zero, x);
+  const Packet x_is_zero = pand(x_is_le_zero, x_is_ge_zero);
+
+  const Packet abs_x = pabs(x);
+  const Packet abs_x_is_le_one = pcmp_le(abs_x, cst_pos_one);
+  const Packet abs_x_is_ge_one = pcmp_le(cst_pos_one, abs_x);
+  const Packet abs_x_is_inf = pcmp_eq(abs_x, cst_pos_inf);
+  const Packet abs_x_is_one = pand(abs_x_is_le_one, abs_x_is_ge_one);
+
+  Packet pow_is_inf_if_exp_is_neg = por(x_is_zero, pand(abs_x_is_le_one, exp_is_inf));
+  Packet pow_is_inf_if_exp_is_pos = por(abs_x_is_inf, pand(abs_x_is_ge_one, exp_is_inf));
+  Packet pow_is_one = pand(abs_x_is_one, por(exp_is_inf, x_is_ge_zero));
+
+  Packet result = powx;
+  result = por(x_is_le_zero, result);
+  result = pselect(pow_is_inf_if_exp_is_neg, pand(cst_pos_inf, exp_is_neg), result);
+  result = pselect(pow_is_inf_if_exp_is_pos, pand(cst_pos_inf, exp_is_pos), result);
+  result = por(exp_is_nan, result);
+  result = pselect(pow_is_one, cst_pos_one, result);
+  return result;
+}
+
+template <typename Packet, typename ScalarExponent,
+          std::enable_if_t<NumTraits<typename unpacket_traits<Packet>::type>::IsSigned, bool> = true>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet handle_negative_exponent(const Packet& x, const ScalarExponent& exponent) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+
+  // signed integer base, signed integer exponent case
+
+  // This routine handles negative exponents.
+  // The return value is either 0, 1, or -1.
+  const Packet cst_pos_one = pset1<Packet>(Scalar(1));
+  const bool exponent_is_odd = exponent % ScalarExponent(2) != ScalarExponent(0);
+  const Packet exp_is_odd = exponent_is_odd ? ptrue<Packet>(x) : pzero<Packet>(x);
+
+  const Packet abs_x = pabs(x);
+  const Packet abs_x_is_one = pcmp_eq(abs_x, cst_pos_one);
+
+  Packet result = pselect(exp_is_odd, x, abs_x);
+  result = pselect(abs_x_is_one, result, pzero<Packet>(x));
+  return result;
+}
+
+template <typename Packet, typename ScalarExponent,
+          std::enable_if_t<!NumTraits<typename unpacket_traits<Packet>::type>::IsSigned, bool> = true>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet handle_negative_exponent(const Packet& x, const ScalarExponent&) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+
+  // unsigned integer base, signed integer exponent case
+
+  // This routine handles negative exponents.
+  // The return value is either 0 or 1
+
+  const Scalar pos_one = Scalar(1);
+
+  const Packet cst_pos_one = pset1<Packet>(pos_one);
+
+  const Packet x_is_one = pcmp_eq(x, cst_pos_one);
+
+  return pand(x_is_one, x);
+}
+
+}  // end namespace unary_pow
+
+template <typename Packet, typename ScalarExponent,
+          bool BaseIsIntegerType = NumTraits<typename unpacket_traits<Packet>::type>::IsInteger,
+          bool ExponentIsIntegerType = NumTraits<ScalarExponent>::IsInteger,
+          bool ExponentIsSigned = NumTraits<ScalarExponent>::IsSigned>
+struct unary_pow_impl;
+
+template <typename Packet, typename ScalarExponent, bool ExponentIsSigned>
+struct unary_pow_impl<Packet, ScalarExponent, false, false, ExponentIsSigned> {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent& exponent) {
+    const bool exponent_is_integer = (numext::isfinite)(exponent) && numext::round(exponent) == exponent;
+    if (exponent_is_integer) {
+      // The simple recursive doubling implementation is only accurate to 3 ulps
+      // for integer exponents in [-3:7]. Since this is a common case, we
+      // specialize it here.
+      bool use_repeated_squaring =
+          (exponent <= ScalarExponent(7) && (!ExponentIsSigned || exponent >= ScalarExponent(-3)));
+      return use_repeated_squaring ? unary_pow::int_pow(x, exponent) : generic_pow(x, pset1<Packet>(exponent));
+    } else {
+      Packet result = unary_pow::gen_pow(x, exponent);
+      result = unary_pow::handle_nonint_nonint_errors(x, result, exponent);
+      return result;
+    }
+  }
+};
+
+template <typename Packet, typename ScalarExponent, bool ExponentIsSigned>
+struct unary_pow_impl<Packet, ScalarExponent, false, true, ExponentIsSigned> {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent& exponent) {
+    return unary_pow::int_pow(x, exponent);
+  }
+};
+
+template <typename Packet, typename ScalarExponent>
+struct unary_pow_impl<Packet, ScalarExponent, true, true, true> {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent& exponent) {
+    if (exponent < ScalarExponent(0)) {
+      return unary_pow::handle_negative_exponent(x, exponent);
+    } else {
+      return unary_pow::int_pow(x, exponent);
+    }
+  }
+};
+
+template <typename Packet, typename ScalarExponent>
+struct unary_pow_impl<Packet, ScalarExponent, true, true, false> {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent& exponent) {
+    return unary_pow::int_pow(x, exponent);
+  }
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARCH_GENERIC_PACKET_MATH_POW_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathTrig.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathTrig.h
new file mode 100644
index 0000000..1d7c594
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/GenericPacketMathTrig.h
@@ -0,0 +1,833 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2009-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2018-2025 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_TRIG_H
+#define EIGEN_ARCH_GENERIC_PACKET_MATH_TRIG_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+//----------------------------------------------------------------------
+// Trigonometric Functions
+//----------------------------------------------------------------------
+
+// Enum for selecting which function to compute. SinCos is intended to compute
+// pairs of Sin and Cos of the even entries in the packet, e.g.
+// SinCos([a, *, b, *]) = [sin(a), cos(a), sin(b), cos(b)].
+enum class TrigFunction : uint8_t { Sin, Cos, Tan, SinCos };
+
+// The following code is inspired by the following stack-overflow answer:
+//   https://stackoverflow.com/questions/30463616/payne-hanek-algorithm-implementation-in-c/30465751#30465751
+// It has been largely optimized:
+//  - By-pass calls to frexp.
+//  - Aligned loads of required 96 bits of 2/pi. This is accomplished by
+//    (1) balancing the mantissa and exponent to the required bits of 2/pi are
+//    aligned on 8-bits, and (2) replicating the storage of the bits of 2/pi.
+//  - Avoid a branch in rounding and extraction of the remaining fractional part.
+// Overall, I measured a speed up higher than x2 on x86-64.
+inline float trig_reduce_huge(float xf, Eigen::numext::int32_t* quadrant) {
+  using Eigen::numext::int32_t;
+  using Eigen::numext::int64_t;
+  using Eigen::numext::uint32_t;
+  using Eigen::numext::uint64_t;
+
+  const double pio2_62 = 3.4061215800865545e-19;     // pi/2 * 2^-62
+  const uint64_t zero_dot_five = uint64_t(1) << 61;  // 0.5 in 2.62-bit fixed-point format
+
+  // 192 bits of 2/pi for Payne-Hanek reduction
+  // Bits are introduced by packet of 8 to enable aligned reads.
+  static const uint32_t two_over_pi[] = {
+      0x00000028, 0x000028be, 0x0028be60, 0x28be60db, 0xbe60db93, 0x60db9391, 0xdb939105, 0x9391054a, 0x91054a7f,
+      0x054a7f09, 0x4a7f09d5, 0x7f09d5f4, 0x09d5f47d, 0xd5f47d4d, 0xf47d4d37, 0x7d4d3770, 0x4d377036, 0x377036d8,
+      0x7036d8a5, 0x36d8a566, 0xd8a5664f, 0xa5664f10, 0x664f10e4, 0x4f10e410, 0x10e41000, 0xe4100000};
+
+  uint32_t xi = numext::bit_cast<uint32_t>(xf);
+  // Below, -118 = -126 + 8.
+  //   -126 is to get the exponent,
+  //   +8 is to enable alignment of 2/pi's bits on 8 bits.
+  // This is possible because the fractional part of x as only 24 meaningful bits.
+  uint32_t e = (xi >> 23) - 118;
+  // Extract the mantissa and shift it to align it wrt the exponent
+  xi = ((xi & 0x007fffffu) | 0x00800000u) << (e & 0x7);
+
+  uint32_t i = e >> 3;
+  uint32_t twoopi_1 = two_over_pi[i - 1];
+  uint32_t twoopi_2 = two_over_pi[i + 3];
+  uint32_t twoopi_3 = two_over_pi[i + 7];
+
+  // Compute x * 2/pi in 2.62-bit fixed-point format.
+  uint64_t p;
+  p = uint64_t(xi) * twoopi_3;
+  p = uint64_t(xi) * twoopi_2 + (p >> 32);
+  p = (uint64_t(xi * twoopi_1) << 32) + p;
+
+  // Round to nearest: add 0.5 and extract integral part.
+  uint64_t q = (p + zero_dot_five) >> 62;
+  *quadrant = int(q);
+  // Now it remains to compute "r = x - q*pi/2" with high accuracy,
+  // since we have p=x/(pi/2) with high accuracy, we can more efficiently compute r as:
+  //   r = (p-q)*pi/2,
+  // where the product can be be carried out with sufficient accuracy using double precision.
+  p -= q << 62;
+  return float(double(int64_t(p)) * pio2_62);
+}
+
+template <TrigFunction Func, typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+#if EIGEN_COMP_GNUC_STRICT
+    __attribute__((optimize("-fno-unsafe-math-optimizations")))
+#endif
+    Packet
+    psincos_float(const Packet& _x) {
+  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
+
+  const Packet cst_2oPI = pset1<Packet>(0.636619746685028076171875f);  // 2/PI
+  const Packet cst_rounding_magic = pset1<Packet>(12582912);           // 2^23 for rounding
+  const PacketI csti_1 = pset1<PacketI>(1);
+  const Packet cst_sign_mask = pset1frombits<Packet>(static_cast<Eigen::numext::uint32_t>(0x80000000u));
+
+  Packet x = pabs(_x);
+
+  // Scale x by 2/Pi to find x's octant.
+  Packet y = pmul(x, cst_2oPI);
+
+  // Rounding trick to find nearest integer:
+  Packet y_round = padd(y, cst_rounding_magic);
+  EIGEN_OPTIMIZATION_BARRIER(y_round)
+  PacketI y_int = preinterpret<PacketI>(y_round);  // last 23 digits represent integer (if abs(x)<2^24)
+  y = psub(y_round, cst_rounding_magic);           // nearest integer to x * (2/pi)
+
+// Subtract y * Pi/2 to reduce x to the interval -Pi/4 <= x <= +Pi/4
+// using "Extended precision modular arithmetic"
+#if defined(EIGEN_VECTORIZE_FMA)
+  // This version requires true FMA for high accuracy.
+  // It provides a max error of 1ULP up to (with absolute_error < 5.9605e-08):
+  constexpr float huge_th = (Func == TrigFunction::Sin) ? 117435.992f : 71476.0625f;
+  x = pmadd(y, pset1<Packet>(-1.57079601287841796875f), x);
+  x = pmadd(y, pset1<Packet>(-3.1391647326017846353352069854736328125e-07f), x);
+  x = pmadd(y, pset1<Packet>(-5.390302529957764765544681040410068817436695098876953125e-15f), x);
+#else
+  // Without true FMA, the previous set of coefficients maintain 1ULP accuracy
+  // up to x<15.7 (for sin), but accuracy is immediately lost for x>15.7.
+  // We thus use one more iteration to maintain 2ULPs up to reasonably large inputs.
+
+  // The following set of coefficients maintain 1ULP up to 9.43 and 14.16 for sin and cos respectively.
+  // and 2 ULP up to:
+  constexpr float huge_th = (Func == TrigFunction::Sin) ? 25966.f : 18838.f;
+  x = pmadd(y, pset1<Packet>(-1.5703125), x);  // = 0xbfc90000
+  EIGEN_OPTIMIZATION_BARRIER(x)
+  x = pmadd(y, pset1<Packet>(-0.000483989715576171875), x);  // = 0xb9fdc000
+  EIGEN_OPTIMIZATION_BARRIER(x)
+  x = pmadd(y, pset1<Packet>(1.62865035235881805419921875e-07), x);                      // = 0x342ee000
+  x = pmadd(y, pset1<Packet>(5.5644315544167710640977020375430583953857421875e-11), x);  // = 0x2e74b9ee
+
+// For the record, the following set of coefficients maintain 2ULP up
+// to a slightly larger range:
+// const float huge_th = ComputeSine ? 51981.f : 39086.125f;
+// but it slightly fails to maintain 1ULP for two values of sin below pi.
+// x = pmadd(y, pset1<Packet>(-3.140625/2.), x);
+// x = pmadd(y, pset1<Packet>(-0.00048351287841796875), x);
+// x = pmadd(y, pset1<Packet>(-3.13855707645416259765625e-07), x);
+// x = pmadd(y, pset1<Packet>(-6.0771006282767103812147979624569416046142578125e-11), x);
+
+// For the record, with only 3 iterations it is possible to maintain
+// 1 ULP up to 3PI (maybe more) and 2ULP up to 255.
+// The coefficients are: 0xbfc90f80, 0xb7354480, 0x2e74b9ee
+#endif
+
+  if (predux_any(pcmp_le(pset1<Packet>(huge_th), pabs(_x)))) {
+    const int PacketSize = unpacket_traits<Packet>::size;
+    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) float vals[PacketSize];
+    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) float x_cpy[PacketSize];
+    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Eigen::numext::int32_t y_int2[PacketSize];
+    pstoreu(vals, pabs(_x));
+    pstoreu(x_cpy, x);
+    pstoreu(y_int2, y_int);
+    for (int k = 0; k < PacketSize; ++k) {
+      float val = vals[k];
+      if (val >= huge_th && (numext::isfinite)(val)) x_cpy[k] = trig_reduce_huge(val, &y_int2[k]);
+    }
+    x = ploadu<Packet>(x_cpy);
+    y_int = ploadu<PacketI>(y_int2);
+  }
+
+  // Get the polynomial selection mask from the second bit of y_int
+  // We'll calculate both (sin and cos) polynomials and then select from the two.
+  Packet poly_mask = preinterpret<Packet>(pcmp_eq(pand(y_int, csti_1), pzero(y_int)));
+
+  Packet x2 = pmul(x, x);
+
+  // Evaluate the cos(x) polynomial. (-Pi/4 <= x <= Pi/4)
+  Packet y1 = pset1<Packet>(2.4372266125283204019069671630859375e-05f);
+  y1 = pmadd(y1, x2, pset1<Packet>(-0.00138865201734006404876708984375f));
+  y1 = pmadd(y1, x2, pset1<Packet>(0.041666619479656219482421875f));
+  y1 = pmadd(y1, x2, pset1<Packet>(-0.5f));
+  y1 = pmadd(y1, x2, pset1<Packet>(1.f));
+
+  // Evaluate the sin(x) polynomial. (Pi/4 <= x <= Pi/4)
+  // octave/matlab code to compute those coefficients:
+  //    x = (0:0.0001:pi/4)';
+  //    A = [x.^3 x.^5 x.^7];
+  //    w = ((1.-(x/(pi/4)).^2).^5)*2000+1;         # weights trading relative accuracy
+  //    c = (A'*diag(w)*A)\(A'*diag(w)*(sin(x)-x)); # weighted LS, linear coeff forced to 1
+  //    printf('%.64f\n %.64f\n%.64f\n', c(3), c(2), c(1))
+  //
+  Packet y2 = pset1<Packet>(-0.0001959234114083702898469196984621021329076029360294342041015625f);
+  y2 = pmadd(y2, x2, pset1<Packet>(0.0083326873655616851693794799871284340042620897293090820312500000f));
+  y2 = pmadd(y2, x2, pset1<Packet>(-0.1666666203982298255503735617821803316473960876464843750000000000f));
+  y2 = pmul(y2, x2);
+  y2 = pmadd(y2, x, x);
+
+  // Select the correct result from the two polynomials.
+  // Compute the sign to apply to the polynomial.
+  // sin: sign = second_bit(y_int) xor signbit(_x)
+  // cos: sign = second_bit(y_int+1)
+  Packet sign_bit = (Func == TrigFunction::Sin) ? pxor(_x, preinterpret<Packet>(plogical_shift_left<30>(y_int)))
+                                                : preinterpret<Packet>(plogical_shift_left<30>(padd(y_int, csti_1)));
+  sign_bit = pand(sign_bit, cst_sign_mask);  // clear all but left most bit
+
+  if ((Func == TrigFunction::SinCos) || (Func == TrigFunction::Tan)) {
+    // TODO(rmlarsen): Add single polynomial for tan(x) instead of paying for sin+cos+div.
+    Packet peven = peven_mask(x);
+    Packet ysin = pselect(poly_mask, y2, y1);
+    Packet ycos = pselect(poly_mask, y1, y2);
+    Packet sign_bit_sin = pxor(_x, preinterpret<Packet>(plogical_shift_left<30>(y_int)));
+    Packet sign_bit_cos = preinterpret<Packet>(plogical_shift_left<30>(padd(y_int, csti_1)));
+    sign_bit_sin = pand(sign_bit_sin, cst_sign_mask);  // clear all but left most bit
+    sign_bit_cos = pand(sign_bit_cos, cst_sign_mask);  // clear all but left most bit
+    y = (Func == TrigFunction::SinCos) ? pselect(peven, pxor(ysin, sign_bit_sin), pxor(ycos, sign_bit_cos))
+                                       : pdiv(pxor(ysin, sign_bit_sin), pxor(ycos, sign_bit_cos));
+  } else {
+    y = (Func == TrigFunction::Sin) ? pselect(poly_mask, y2, y1) : pselect(poly_mask, y1, y2);
+    y = pxor(y, sign_bit);
+  }
+  return y;
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psin_float(const Packet& x) {
+  return psincos_float<TrigFunction::Sin>(x);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcos_float(const Packet& x) {
+  return psincos_float<TrigFunction::Cos>(x);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet ptan_float(const Packet& x) {
+  return psincos_float<TrigFunction::Tan>(x);
+}
+
+// Trigonometric argument reduction for double for inputs smaller than 15.
+// Reduces trigonometric arguments for double inputs where x < 15. Given an argument x and its corresponding quadrant
+// count n, the function computes and returns the reduced argument t such that x = n * pi/2 + t.
+template <typename Packet>
+Packet trig_reduce_small_double(const Packet& x, const Packet& q) {
+  // Pi/2 split into 2 values
+  const Packet cst_pio2_a = pset1<Packet>(-1.570796325802803);
+  const Packet cst_pio2_b = pset1<Packet>(-9.920935184482005e-10);
+
+  Packet t;
+  t = pmadd(cst_pio2_a, q, x);
+  t = pmadd(cst_pio2_b, q, t);
+  return t;
+}
+
+// Trigonometric argument reduction for double for inputs smaller than 1e14.
+// Reduces trigonometric arguments for double inputs where x < 1e14. Given an argument x and its corresponding quadrant
+// count n, the function computes and returns the reduced argument t such that x = n * pi/2 + t.
+template <typename Packet>
+Packet trig_reduce_medium_double(const Packet& x, const Packet& q_high, const Packet& q_low) {
+  // Pi/2 split into 4 values
+  const Packet cst_pio2_a = pset1<Packet>(-1.570796325802803);
+  const Packet cst_pio2_b = pset1<Packet>(-9.920935184482005e-10);
+  const Packet cst_pio2_c = pset1<Packet>(-6.123234014771656e-17);
+  const Packet cst_pio2_d = pset1<Packet>(1.903488962019325e-25);
+
+  Packet t;
+  t = pmadd(cst_pio2_a, q_high, x);
+  t = pmadd(cst_pio2_a, q_low, t);
+  t = pmadd(cst_pio2_b, q_high, t);
+  t = pmadd(cst_pio2_b, q_low, t);
+  t = pmadd(cst_pio2_c, q_high, t);
+  t = pmadd(cst_pio2_c, q_low, t);
+  t = pmadd(cst_pio2_d, padd(q_low, q_high), t);
+  return t;
+}
+
+template <TrigFunction Func, typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+#if EIGEN_COMP_GNUC_STRICT
+    __attribute__((optimize("-fno-unsafe-math-optimizations")))
+#endif
+    Packet
+    psincos_double(const Packet& x) {
+  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
+  typedef typename unpacket_traits<PacketI>::type ScalarI;
+
+  const Packet cst_sign_mask = pset1frombits<Packet>(static_cast<Eigen::numext::uint64_t>(0x8000000000000000u));
+
+  // If the argument is smaller than this value, use a simpler argument reduction
+  const double small_th = 15;
+  // If the argument is bigger than this value, use the non-vectorized std version
+  const double huge_th = 1e14;
+
+  const Packet cst_2oPI = pset1<Packet>(0.63661977236758134307553505349006);  // 2/PI
+  // Integer Packet constants
+  const PacketI cst_one = pset1<PacketI>(ScalarI(1));
+  // Constant for splitting
+  const Packet cst_split = pset1<Packet>(1 << 24);
+
+  Packet x_abs = pabs(x);
+
+  // Scale x by 2/Pi
+  PacketI q_int;
+  Packet s;
+
+  // TODO Implement huge angle argument reduction
+  if (EIGEN_PREDICT_FALSE(predux_any(pcmp_le(pset1<Packet>(small_th), x_abs)))) {
+    Packet q_high = pmul(pfloor(pmul(x_abs, pdiv(cst_2oPI, cst_split))), cst_split);
+    Packet q_low_noround = psub(pmul(x_abs, cst_2oPI), q_high);
+    q_int = pcast<Packet, PacketI>(padd(q_low_noround, pset1<Packet>(0.5)));
+    Packet q_low = pcast<PacketI, Packet>(q_int);
+    s = trig_reduce_medium_double(x_abs, q_high, q_low);
+  } else {
+    Packet qval_noround = pmul(x_abs, cst_2oPI);
+    q_int = pcast<Packet, PacketI>(padd(qval_noround, pset1<Packet>(0.5)));
+    Packet q = pcast<PacketI, Packet>(q_int);
+    s = trig_reduce_small_double(x_abs, q);
+  }
+
+  // All the upcoming approximating polynomials have even exponents
+  Packet ss = pmul(s, s);
+
+  // Padé approximant of cos(x)
+  // Assuring < 1 ULP error on the interval [-pi/4, pi/4]
+  // cos(x) ~= (80737373*x^8 - 13853547000*x^6 + 727718024880*x^4 - 11275015752000*x^2 + 23594700729600)/(147173*x^8 +
+  // 39328920*x^6 + 5772800880*x^4 + 522334612800*x^2 + 23594700729600)
+  // MATLAB code to compute those coefficients:
+  //    syms x;
+  //    cosf = @(x) cos(x);
+  //    pade_cosf = pade(cosf(x), x, 0, 'Order', 8)
+  const Packet cn4 = pset1<Packet>(80737373);
+  const Packet cn3 = pset1<Packet>(-13853547000);
+  const Packet cn2 = pset1<Packet>(727718024880);
+  const Packet cn1 = pset1<Packet>(-11275015752000);
+  const Packet cn0 = pset1<Packet>(23594700729600);  // shared with cd0
+  const Packet cd3 = pset1<Packet>(147173);
+  const Packet cd2 = pset1<Packet>(39328920);
+  const Packet cd1 = pset1<Packet>(5772800880);
+  const Packet cd0 = pset1<Packet>(522334612800);
+  Packet sc1_num = pmadd(ss, cn4, cn3);
+  Packet sc2_num = pmadd(sc1_num, ss, cn2);
+  Packet sc3_num = pmadd(sc2_num, ss, cn1);
+  Packet sc4_num = pmadd(sc3_num, ss, cn0);
+  Packet sc1_denum = pmadd(ss, cd3, cd2);
+  Packet sc2_denum = pmadd(sc1_denum, ss, cd1);
+  Packet sc3_denum = pmadd(sc2_denum, ss, cd0);
+  Packet sc4_denum = pmadd(sc3_denum, ss, cn0);
+  Packet scos = pdiv(sc4_num, sc4_denum);
+
+  // Padé approximant of sin(x)
+  // Assuring < 1 ULP error on the interval [-pi/4, pi/4]
+  // sin(x) ~= (x*(4585922449*x^8 - 1066023933480*x^6 + 83284044283440*x^4 - 2303682236856000*x^2 +
+  // 15605159573203200))/(45*(1029037*x^8 + 345207016*x^6 + 61570292784*x^4 + 6603948711360*x^2 + 346781323848960))
+  // MATLAB code to compute those coefficients:
+  //    syms x;
+  //    sinf = @(x) sin(x);
+  //    pade_sinf = pade(sinf(x), x, 0, 'Order', 8, 'OrderMode', 'relative')
+  const Packet sn4 = pset1<Packet>(4585922449);
+  const Packet sn3 = pset1<Packet>(-1066023933480);
+  const Packet sn2 = pset1<Packet>(83284044283440);
+  const Packet sn1 = pset1<Packet>(-2303682236856000);
+  const Packet sn0 = pset1<Packet>(15605159573203200);
+  const Packet sd3 = pset1<Packet>(1029037);
+  const Packet sd2 = pset1<Packet>(345207016);
+  const Packet sd1 = pset1<Packet>(61570292784);
+  const Packet sd0_inner = pset1<Packet>(6603948711360);
+  const Packet sd0 = pset1<Packet>(346781323848960);
+  const Packet cst_45 = pset1<Packet>(45);
+  Packet ss1_num = pmadd(ss, sn4, sn3);
+  Packet ss2_num = pmadd(ss1_num, ss, sn2);
+  Packet ss3_num = pmadd(ss2_num, ss, sn1);
+  Packet ss4_num = pmadd(ss3_num, ss, sn0);
+  Packet ss1_denum = pmadd(ss, sd3, sd2);
+  Packet ss2_denum = pmadd(ss1_denum, ss, sd1);
+  Packet ss3_denum = pmadd(ss2_denum, ss, sd0_inner);
+  Packet ss4_denum = pmadd(ss3_denum, ss, sd0);
+  Packet ssin = pdiv(pmul(s, ss4_num), pmul(cst_45, ss4_denum));
+
+  Packet poly_mask = preinterpret<Packet>(pcmp_eq(pand(q_int, cst_one), pzero(q_int)));
+
+  Packet sign_sin = pxor(x, preinterpret<Packet>(plogical_shift_left<62>(q_int)));
+  Packet sign_cos = preinterpret<Packet>(plogical_shift_left<62>(padd(q_int, cst_one)));
+  Packet sign_bit, sFinalRes;
+  if (Func == TrigFunction::Sin) {
+    sign_bit = sign_sin;
+    sFinalRes = pselect(poly_mask, ssin, scos);
+  } else if (Func == TrigFunction::Cos) {
+    sign_bit = sign_cos;
+    sFinalRes = pselect(poly_mask, scos, ssin);
+  } else if (Func == TrigFunction::Tan) {
+    // TODO(rmlarsen): Add single polynomial for tan(x) instead of paying for sin+cos+div.
+    sign_bit = pxor(sign_sin, sign_cos);
+    sFinalRes = pdiv(pselect(poly_mask, ssin, scos), pselect(poly_mask, scos, ssin));
+  } else if (Func == TrigFunction::SinCos) {
+    Packet peven = peven_mask(x);
+    sign_bit = pselect(peven, sign_sin, sign_cos);
+    sFinalRes = pselect(pxor(peven, poly_mask), scos, ssin);
+  }
+  sign_bit = pand(sign_bit, cst_sign_mask);  // clear all but left most bit
+  sFinalRes = pxor(sFinalRes, sign_bit);
+
+  // If the inputs values are higher than that a value that the argument reduction can currently address, compute them
+  // using the C++ standard library.
+  // TODO Remove it when huge angle argument reduction is implemented
+  if (EIGEN_PREDICT_FALSE(predux_any(pcmp_le(pset1<Packet>(huge_th), x_abs)))) {
+    const int PacketSize = unpacket_traits<Packet>::size;
+    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) double sincos_vals[PacketSize];
+    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) double x_cpy[PacketSize];
+    pstoreu(x_cpy, x);
+    pstoreu(sincos_vals, sFinalRes);
+    for (int k = 0; k < PacketSize; ++k) {
+      double val = x_cpy[k];
+      if (std::abs(val) > huge_th && (numext::isfinite)(val)) {
+        if (Func == TrigFunction::Sin) {
+          sincos_vals[k] = std::sin(val);
+        } else if (Func == TrigFunction::Cos) {
+          sincos_vals[k] = std::cos(val);
+        } else if (Func == TrigFunction::Tan) {
+          sincos_vals[k] = std::tan(val);
+        } else if (Func == TrigFunction::SinCos) {
+          sincos_vals[k] = k % 2 == 0 ? std::sin(val) : std::cos(val);
+        }
+      }
+    }
+    sFinalRes = ploadu<Packet>(sincos_vals);
+  }
+  return sFinalRes;
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psin_double(const Packet& x) {
+  return psincos_double<TrigFunction::Sin>(x);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcos_double(const Packet& x) {
+  return psincos_double<TrigFunction::Cos>(x);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet ptan_double(const Packet& x) {
+  return psincos_double<TrigFunction::Tan>(x);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+    std::enable_if_t<std::is_same<typename unpacket_traits<Packet>::type, float>::value, Packet>
+    psincos_selector(const Packet& x) {
+  return psincos_float<TrigFunction::SinCos, Packet>(x);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+    std::enable_if_t<std::is_same<typename unpacket_traits<Packet>::type, double>::value, Packet>
+    psincos_selector(const Packet& x) {
+  return psincos_double<TrigFunction::SinCos, Packet>(x);
+}
+
+//----------------------------------------------------------------------
+// Inverse Trigonometric Functions
+//----------------------------------------------------------------------
+
+// Generic implementation of acos(x).
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pacos_float(const Packet& x_in) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, float>::value, "Scalar type must be float");
+
+  const Packet cst_one = pset1<Packet>(Scalar(1));
+  const Packet cst_pi = pset1<Packet>(Scalar(EIGEN_PI));
+  const Packet p6 = pset1<Packet>(Scalar(2.36423197202384471893310546875e-3));
+  const Packet p5 = pset1<Packet>(Scalar(-1.1368644423782825469970703125e-2));
+  const Packet p4 = pset1<Packet>(Scalar(2.717843465507030487060546875e-2));
+  const Packet p3 = pset1<Packet>(Scalar(-4.8969544470310211181640625e-2));
+  const Packet p2 = pset1<Packet>(Scalar(8.8804088532924652099609375e-2));
+  const Packet p1 = pset1<Packet>(Scalar(-0.214591205120086669921875));
+  const Packet p0 = pset1<Packet>(Scalar(1.57079637050628662109375));
+
+  // For x in [0:1], we approximate acos(x)/sqrt(1-x), which is a smooth
+  // function, by a 6'th order polynomial.
+  // For x in [-1:0) we use that acos(-x) = pi - acos(x).
+  const Packet neg_mask = psignbit(x_in);
+  const Packet abs_x = pabs(x_in);
+
+  // Evaluate the polynomial using Horner's rule:
+  //   P(x) = p0 + x * (p1 +  x * (p2 + ... (p5 + x * p6)) ... ) .
+  // We evaluate even and odd terms independently to increase
+  // instruction level parallelism.
+  Packet x2 = pmul(x_in, x_in);
+  Packet p_even = pmadd(p6, x2, p4);
+  Packet p_odd = pmadd(p5, x2, p3);
+  p_even = pmadd(p_even, x2, p2);
+  p_odd = pmadd(p_odd, x2, p1);
+  p_even = pmadd(p_even, x2, p0);
+  Packet p = pmadd(p_odd, abs_x, p_even);
+
+  // The polynomial approximates acos(x)/sqrt(1-x), so
+  // multiply by sqrt(1-x) to get acos(x).
+  // Conveniently returns NaN for arguments outside [-1:1].
+  Packet denom = psqrt(psub(cst_one, abs_x));
+  Packet result = pmul(denom, p);
+  // Undo mapping for negative arguments.
+  return pselect(neg_mask, psub(cst_pi, result), result);
+}
+
+// Generic implementation of asin(x).
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pasin_float(const Packet& x_in) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, float>::value, "Scalar type must be float");
+
+  constexpr float kPiOverTwo = static_cast<float>(EIGEN_PI / 2);
+
+  const Packet cst_half = pset1<Packet>(0.5f);
+  const Packet cst_one = pset1<Packet>(1.0f);
+  const Packet cst_two = pset1<Packet>(2.0f);
+  const Packet cst_pi_over_two = pset1<Packet>(kPiOverTwo);
+
+  const Packet abs_x = pabs(x_in);
+  const Packet sign_mask = pandnot(x_in, abs_x);
+  const Packet invalid_mask = pcmp_lt(cst_one, abs_x);
+
+  // For arguments |x| > 0.5, we map x back to [0:0.5] using
+  // the transformation x_large = sqrt(0.5*(1-x)), and use the
+  // identity
+  //   asin(x) = pi/2 - 2 * asin( sqrt( 0.5 * (1 - x)))
+
+  const Packet x_large = psqrt(pnmadd(cst_half, abs_x, cst_half));
+  const Packet large_mask = pcmp_lt(cst_half, abs_x);
+  const Packet x = pselect(large_mask, x_large, abs_x);
+  const Packet x2 = pmul(x, x);
+
+  // For |x| < 0.5 approximate asin(x)/x by an 8th order polynomial with
+  // even terms only.
+  constexpr float alpha[] = {5.08838854730129241943359375e-2f, 3.95139865577220916748046875e-2f,
+                             7.550220191478729248046875e-2f, 0.16664917767047882080078125f, 1.00000011920928955078125f};
+  Packet p = ppolevl<Packet, 4>::run(x2, alpha);
+  p = pmul(p, x);
+
+  const Packet p_large = pnmadd(cst_two, p, cst_pi_over_two);
+  p = pselect(large_mask, p_large, p);
+  // Flip the sign for negative arguments.
+  p = pxor(p, sign_mask);
+  // Return NaN for arguments outside [-1:1].
+  return por(invalid_mask, p);
+}
+
+template <typename Scalar>
+struct patan_reduced {
+  template <typename Packet>
+  static EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet run(const Packet& x);
+};
+
+template <>
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet patan_reduced<double>::run(const Packet& x) {
+  constexpr double alpha[] = {2.6667153866462208e-05, 3.0917513112462781e-03, 5.2574296781008604e-02,
+                              3.0409318473444424e-01, 7.5365702534987022e-01, 8.2704055405494614e-01,
+                              3.3004361289279920e-01};
+
+  constexpr double beta[] = {
+      2.7311202462436667e-04, 1.0899150928962708e-02, 1.1548932646420353e-01, 4.9716458728465573e-01, 1.0,
+      9.3705509168587852e-01, 3.3004361289279920e-01};
+
+  Packet x2 = pmul(x, x);
+  Packet p = ppolevl<Packet, 6>::run(x2, alpha);
+  Packet q = ppolevl<Packet, 6>::run(x2, beta);
+  return pmul(x, pdiv(p, q));
+}
+
+// Computes elementwise atan(x) for x in [-1:1] with 2 ulp accuracy.
+template <>
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet patan_reduced<float>::run(const Packet& x) {
+  constexpr float alpha[] = {1.12026982009410858154296875e-01f, 7.296695709228515625e-01f, 8.109951019287109375e-01f};
+
+  constexpr float beta[] = {1.00917108356952667236328125e-02f, 2.8318560123443603515625e-01f, 1.0f,
+                            8.109951019287109375e-01f};
+
+  Packet x2 = pmul(x, x);
+  Packet p = ppolevl<Packet, 2>::run(x2, alpha);
+  Packet q = ppolevl<Packet, 3>::run(x2, beta);
+  return pmul(x, pdiv(p, q));
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_atan(const Packet& x_in) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+
+  constexpr Scalar kPiOverTwo = static_cast<Scalar>(EIGEN_PI / 2);
+
+  const Packet cst_signmask = pset1<Packet>(Scalar(-0.0));
+  const Packet cst_one = pset1<Packet>(Scalar(1));
+  const Packet cst_pi_over_two = pset1<Packet>(kPiOverTwo);
+
+  //   "Large": For |x| > 1, use atan(1/x) = sign(x)*pi/2 - atan(x).
+  //   "Small": For |x| <= 1, approximate atan(x) directly by a polynomial
+  //            calculated using Rminimax.
+
+  const Packet abs_x = pabs(x_in);
+  const Packet x_signmask = pand(x_in, cst_signmask);
+  const Packet large_mask = pcmp_lt(cst_one, abs_x);
+  const Packet x = pselect(large_mask, preciprocal(abs_x), abs_x);
+  const Packet p = patan_reduced<Scalar>::run(x);
+  // Apply transformations according to the range reduction masks.
+  Packet result = pselect(large_mask, psub(cst_pi_over_two, p), p);
+  // Return correct sign
+  return pxor(result, x_signmask);
+}
+
+//----------------------------------------------------------------------
+// Hyperbolic Functions
+//----------------------------------------------------------------------
+
+#ifdef EIGEN_FAST_MATH
+
+/** \internal \returns the hyperbolic tan of \a a (coeff-wise)
+    Doesn't do anything fancy, just a 9/8-degree rational interpolant which
+    is accurate up to a couple of ulps in the (approximate) range [-8, 8],
+    outside of which tanh(x) = +/-1 in single precision. The input is clamped
+    to the range [-c, c]. The value c is chosen as the smallest value where
+    the approximation evaluates to exactly 1.
+
+    This implementation works on both scalars and packets.
+*/
+template <typename T>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS T ptanh_float(const T& a_x) {
+  // Clamp the inputs to the range [-c, c] and set everything
+  // outside that range to 1.0. The value c is chosen as the smallest
+  // floating point argument such that the approximation is exactly 1.
+  // This saves clamping the value at the end.
+#ifdef EIGEN_VECTORIZE_FMA
+  const T plus_clamp = pset1<T>(8.01773357391357422f);
+  const T minus_clamp = pset1<T>(-8.01773357391357422f);
+#else
+  const T plus_clamp = pset1<T>(7.90738964080810547f);
+  const T minus_clamp = pset1<T>(-7.90738964080810547f);
+#endif
+  const T x = pmax(pmin(a_x, plus_clamp), minus_clamp);
+
+  // The following rational approximation was generated by rminimax
+  // (https://gitlab.inria.fr/sfilip/rminimax) using the following
+  // command:
+  // $ ratapprox --function="tanh(x)" --dom='[-8.67,8.67]' --num="odd"
+  //   --den="even" --type="[9,8]" --numF="[SG]" --denF="[SG]" --log
+  //   --output=tanhf.sollya --dispCoeff="dec"
+
+  // The monomial coefficients of the numerator polynomial (odd).
+  constexpr float alpha[] = {1.394553628e-8f, 2.102733560e-5f, 3.520756727e-3f, 1.340216100e-1f};
+
+  // The monomial coefficients of the denominator polynomial (even).
+  constexpr float beta[] = {8.015776984e-7f, 3.326951409e-4f, 2.597254514e-2f, 4.673548340e-1f, 1.0f};
+
+  // Since the polynomials are odd/even, we need x^2.
+  const T x2 = pmul(x, x);
+  const T x3 = pmul(x2, x);
+
+  T p = ppolevl<T, 3>::run(x2, alpha);
+  T q = ppolevl<T, 4>::run(x2, beta);
+  // Take advantage of the fact that the constant term in p is 1 to compute
+  // x*(x^2*p + 1) = x^3 * p + x.
+  p = pmadd(x3, p, x);
+
+  // Divide the numerator by the denominator.
+  return pdiv(p, q);
+}
+
+#else
+
+/** \internal \returns the hyperbolic tan of \a a (coeff-wise).
+    On the domain [-1.25:1.25] we use an approximation of the form
+    tanh(x) ~= x^3 * (P(x) / Q(x)) + x, where P and Q are polynomials in x^2.
+    For |x| > 1.25, tanh is implemented as tanh(x) = 1 - (2 / (1 + exp(2*x))).
+
+    This implementation has a maximum error of 1 ULP (measured with AVX2+FMA).
+
+    This implementation works on both scalars and packets.
+*/
+template <typename T>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS T ptanh_float(const T& x) {
+  // The polynomial coefficients were computed using Rminimax:
+  // % ./ratapprox --function="tanh(x)-x" --dom='[-1.25,1.25]' --num="[x^3,x^5]" --den="even"
+  //     --type="[3,4]" --numF="[SG]" --denF="[SG]" --log --dispCoeff="dec" --output=tanhf.solly
+  constexpr float alpha[] = {-1.46725140511989593505859375e-02f, -3.333333432674407958984375e-01f};
+  constexpr float beta[] = {1.570280082523822784423828125e-02, 4.4401752948760986328125e-01, 1.0f};
+  const T x2 = pmul(x, x);
+  const T x3 = pmul(x2, x);
+  const T p = ppolevl<T, 1>::run(x2, alpha);
+  const T q = ppolevl<T, 2>::run(x2, beta);
+  const T small_tanh = pmadd(x3, pdiv(p, q), x);
+
+  const T sign_mask = pset1<T>(-0.0f);
+  const T abs_x = pandnot(x, sign_mask);
+  constexpr float kSmallThreshold = 1.25f;
+  const T large_mask = pcmp_lt(pset1<T>(kSmallThreshold), abs_x);
+  // Fast exit if all elements are small.
+  if (!predux_any(large_mask)) {
+    return small_tanh;
+  }
+
+  //  Compute as 1 - (2 / (1 + exp(2*x)))
+  const T one = pset1<T>(1.0f);
+  const T two = pset1<T>(2.0f);
+  const T s = pexp_float<T, true>(pmul(two, abs_x));
+  const T abs_tanh = psub(one, pdiv(two, padd(s, one)));
+
+  // Handle infinite inputs and set sign bit.
+  constexpr float kHugeThreshold = 16.0f;
+  const T huge_mask = pcmp_lt(pset1<T>(kHugeThreshold), abs_x);
+  const T x_sign = pand(sign_mask, x);
+  const T large_tanh = por(x_sign, pselect(huge_mask, one, abs_tanh));
+  return pselect(large_mask, large_tanh, small_tanh);
+}
+
+#endif  // EIGEN_FAST_MATH
+
+/** \internal \returns the hyperbolic tan of \a a (coeff-wise)
+    This uses a 19/18-degree rational interpolant which
+    is accurate up to a couple of ulps in the (approximate) range [-18.7, 18.7],
+    outside of which tanh(x) = +/-1 in single precision. The input is clamped
+    to the range [-c, c]. The value c is chosen as the smallest value where
+    the approximation evaluates to exactly 1.
+
+    This implementation works on both scalars and packets.
+*/
+template <typename T>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS T ptanh_double(const T& a_x) {
+  // Clamp the inputs to the range [-c, c] and set everything
+  // outside that range to 1.0. The value c is chosen as the smallest
+  // floating point argument such that the approximation is exactly 1.
+  // This saves clamping the value at the end.
+#ifdef EIGEN_VECTORIZE_FMA
+  const T plus_clamp = pset1<T>(17.6610191624600077);
+  const T minus_clamp = pset1<T>(-17.6610191624600077);
+#else
+  const T plus_clamp = pset1<T>(17.714196154005176);
+  const T minus_clamp = pset1<T>(-17.714196154005176);
+#endif
+  const T x = pmax(pmin(a_x, plus_clamp), minus_clamp);
+  // The following rational approximation was generated by rminimax
+  // (https://gitlab.inria.fr/sfilip/rminimax) using the following
+  // command:
+  // $ ./ratapprox --function="tanh(x)" --dom='[-18.72,18.72]'
+  //   --num="odd" --den="even" --type="[19,18]" --numF="[D]"
+  //   --denF="[D]" --log --output=tanh.sollya --dispCoeff="dec"
+
+  // The monomial coefficients of the numerator polynomial (odd).
+  constexpr double alpha[] = {2.6158007860482230e-23, 7.6534862268749319e-19, 3.1309488231386680e-15,
+                              4.2303918148209176e-12, 2.4618379131293676e-09, 6.8644367682497074e-07,
+                              9.3839087674268880e-05, 5.9809711724441161e-03, 1.5184719640284322e-01};
+
+  // The monomial coefficients of the denominator polynomial (even).
+  constexpr double beta[] = {6.463747022670968018e-21, 5.782506856739003571e-17,
+                             1.293019623712687916e-13, 1.123643448069621992e-10,
+                             4.492975677839633985e-08, 8.785185266237658698e-06,
+                             8.295161192716231542e-04, 3.437448108450402717e-02,
+                             4.851805297361760360e-01, 1.0};
+
+  // Since the polynomials are odd/even, we need x^2.
+  const T x2 = pmul(x, x);
+  const T x3 = pmul(x2, x);
+
+  // Interleave the evaluation of the numerator polynomial p and
+  // denominator polynomial q.
+  T p = ppolevl<T, 8>::run(x2, alpha);
+  T q = ppolevl<T, 9>::run(x2, beta);
+  // Take advantage of the fact that the constant term in p is 1 to compute
+  // x*(x^2*p + 1) = x^3 * p + x.
+  p = pmadd(x3, p, x);
+
+  // Divide the numerator by the denominator.
+  return pdiv(p, q);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet patanh_float(const Packet& x) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, float>::value, "Scalar type must be float");
+
+  // For |x| in [0:0.5] we use a polynomial approximation of the form
+  // P(x) = x + x^3*(alpha[4] + x^2 * (alpha[3] + x^2 * (... x^2 * alpha[0]) ... )).
+  constexpr float alpha[] = {0.1819281280040740966796875f, 8.2311116158962249755859375e-2f,
+                             0.14672131836414337158203125f, 0.1997792422771453857421875f, 0.3333373963832855224609375f};
+  const Packet x2 = pmul(x, x);
+  const Packet x3 = pmul(x, x2);
+  Packet p = ppolevl<Packet, 4>::run(x2, alpha);
+  p = pmadd(x3, p, x);
+
+  // For |x| in ]0.5:1.0] we use atanh = 0.5*ln((1+x)/(1-x));
+  const Packet half = pset1<Packet>(0.5f);
+  const Packet one = pset1<Packet>(1.0f);
+  Packet r = pdiv(padd(one, x), psub(one, x));
+  r = pmul(half, plog(r));
+
+  const Packet x_gt_half = pcmp_le(half, pabs(x));
+  const Packet x_eq_one = pcmp_eq(one, pabs(x));
+  const Packet x_gt_one = pcmp_lt(one, pabs(x));
+  const Packet sign_mask = pset1<Packet>(-0.0f);
+  const Packet x_sign = pand(sign_mask, x);
+  const Packet inf = pset1<Packet>(std::numeric_limits<float>::infinity());
+  return por(x_gt_one, pselect(x_eq_one, por(x_sign, inf), pselect(x_gt_half, r, p)));
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet patanh_double(const Packet& x) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, double>::value, "Scalar type must be double");
+  // For x in [-0.5:0.5] we use a rational approximation of the form
+  // R(x) = x + x^3*P(x^2)/Q(x^2), where P is or order 4 and Q is of order 5.
+  constexpr double alpha[] = {3.3071338469301391e-03, -4.7129526768798737e-02, 1.8185306179826699e-01,
+                              -2.5949536095445679e-01, 1.2306328729812676e-01};
+
+  constexpr double beta[] = {-3.8679974580640881e-03, 7.6391885763341910e-02,  -4.2828141436397615e-01,
+                             9.8733495886883648e-01,  -1.0000000000000000e+00, 3.6918986189438030e-01};
+
+  const Packet x2 = pmul(x, x);
+  const Packet x3 = pmul(x, x2);
+  Packet p = ppolevl<Packet, 4>::run(x2, alpha);
+  Packet q = ppolevl<Packet, 5>::run(x2, beta);
+  Packet y_small = pmadd(x3, pdiv(p, q), x);
+
+  // For |x| in ]0.5:1.0] we use atanh = 0.5*ln((1+x)/(1-x));
+  const Packet half = pset1<Packet>(0.5);
+  const Packet one = pset1<Packet>(1.0);
+  Packet y_large = pdiv(padd(one, x), psub(one, x));
+  y_large = pmul(half, plog(y_large));
+
+  const Packet x_gt_half = pcmp_le(half, pabs(x));
+  const Packet x_eq_one = pcmp_eq(one, pabs(x));
+  const Packet x_gt_one = pcmp_lt(one, pabs(x));
+  const Packet sign_mask = pset1<Packet>(-0.0);
+  const Packet x_sign = pand(sign_mask, x);
+  const Packet inf = pset1<Packet>(std::numeric_limits<double>::infinity());
+  return por(x_gt_one, pselect(x_eq_one, por(x_sign, inf), pselect(x_gt_half, y_large, y_small)));
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARCH_GENERIC_PACKET_MATH_TRIG_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/Half.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/Half.h
new file mode 100644
index 0000000..671a9dc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/Half.h
@@ -0,0 +1,1126 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+//
+// The conversion routines are Copyright (c) Fabian Giesen, 2016.
+// The original license follows:
+//
+// Copyright (c) Fabian Giesen, 2016
+// All rights reserved.
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted.
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+// Standard 16-bit float type, mostly useful for GPUs. Defines a new
+// type Eigen::half (inheriting either from CUDA's or HIP's __half struct) with
+// operator overloads such that it behaves basically as an arithmetic
+// type. It will be quite slow on CPUs (so it is recommended to stay
+// in fp32 for CPUs, except for simple parameter conversions, I/O
+// to disk and the likes), but fast on GPUs.
+
+#ifndef EIGEN_HALF_H
+#define EIGEN_HALF_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+// When compiling with GPU support, the "__half_raw" base class as well as
+// some other routines are defined in the GPU compiler header files
+// (cuda_fp16.h, hip_fp16.h), and they are not tagged constexpr
+// As a consequence, we get compile failures when compiling Eigen with
+// GPU support. Hence the need to disable EIGEN_CONSTEXPR when building
+// Eigen with GPU support.
+// Any functions that require `numext::bit_cast` may also not be constexpr,
+// including any native types when setting via raw bit values.
+#if defined(EIGEN_HAS_GPU_FP16) || defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC) || defined(EIGEN_HAS_BUILTIN_FLOAT16)
+#define _EIGEN_MAYBE_CONSTEXPR
+#else
+#define _EIGEN_MAYBE_CONSTEXPR constexpr
+#endif
+
+#define F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, METHOD)                                                  \
+  template <>                                                                                              \
+  EIGEN_UNUSED EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC PACKET_F16 METHOD<PACKET_F16>(const PACKET_F16& _x) { \
+    return float2half(METHOD<PACKET_F>(half2float(_x)));                                                   \
+  }
+
+#define EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_F16(PACKET_F, PACKET_F16) \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pcos)                      \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, psin)                      \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pexp)                      \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pexp2)                     \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pexpm1)                    \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, plog)                      \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, plog1p)                    \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, plog2)                     \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, preciprocal)               \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, prsqrt)                    \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pcbrt)                     \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, psqrt)                     \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, ptanh)
+
+// F16 wrappers for unsupported/SpecialFunctions.
+#define EIGEN_INSTANTIATE_SPECIAL_FUNCS_F16(PACKET_F, PACKET_F16) \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, perf)                 \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pndtri)
+
+#define EIGEN_INSTANTIATE_BESSEL_FUNCS_F16(PACKET_F, PACKET_F16) \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_i0)          \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_i0e)         \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_i1)          \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_i1e)         \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_j0)          \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_j1)          \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_k0)          \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_k0e)         \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_k1)          \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_k1e)         \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_y0)          \
+  F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, pbessel_y1)
+
+namespace Eigen {
+
+struct half;
+
+namespace half_impl {
+
+// We want to use the __half_raw struct from the HIP header file only during the device compile phase.
+// This is required because of a quirk in the way TensorFlow GPU builds are done.
+// When compiling TensorFlow source code with GPU support, files that
+//  * contain GPU kernels (i.e. *.cu.cc files) are compiled via hipcc
+//  * do not contain GPU kernels ( i.e. *.cc files) are compiled via gcc (typically)
+//
+// Tensorflow uses the Eigen::half type as its FP16 type, and there are functions that
+//  * are defined in a file that gets compiled via hipcc AND
+//  * have Eigen::half as a pass-by-value argument AND
+//  * are called in a file that gets compiled via gcc
+//
+// In the scenario described above the caller and callee will see different versions
+// of the Eigen::half base class __half_raw, and they will be compiled by different compilers
+//
+// There appears to be an ABI mismatch between gcc and clang (which is called by hipcc) that results in
+// the callee getting corrupted values for the Eigen::half argument.
+//
+// Making the host side compile phase of hipcc use the same Eigen::half impl, as the gcc compile, resolves
+// this error, and hence the following convoluted #if condition
+#if !defined(EIGEN_HAS_GPU_FP16) || !defined(EIGEN_GPU_COMPILE_PHASE)
+
+// Make our own __half_raw definition that is similar to CUDA's.
+struct __half_raw {
+  struct construct_from_rep_tag {};
+#if (defined(EIGEN_HAS_GPU_FP16) && !defined(EIGEN_GPU_COMPILE_PHASE))
+  // Eigen::half can be used as the datatype for shared memory declarations (in Eigen and TF)
+  // The element type for shared memory cannot have non-trivial constructors
+  // and hence the following special casing (which skips the zero-initilization).
+  // Note that this check gets done even in the host compilation phase, and
+  // hence the need for this
+  EIGEN_DEVICE_FUNC __half_raw() {}
+#else
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR __half_raw() : x(0) {}
+#endif
+
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  explicit EIGEN_DEVICE_FUNC __half_raw(numext::uint16_t raw) : x(numext::bit_cast<__fp16>(raw)) {}
+  EIGEN_DEVICE_FUNC constexpr __half_raw(construct_from_rep_tag, __fp16 rep) : x{rep} {}
+  __fp16 x;
+#elif defined(EIGEN_HAS_BUILTIN_FLOAT16)
+  explicit EIGEN_DEVICE_FUNC __half_raw(numext::uint16_t raw) : x(numext::bit_cast<_Float16>(raw)) {}
+  EIGEN_DEVICE_FUNC constexpr __half_raw(construct_from_rep_tag, _Float16 rep) : x{rep} {}
+  _Float16 x;
+#else
+  explicit EIGEN_DEVICE_FUNC constexpr __half_raw(numext::uint16_t raw) : x(raw) {}
+  EIGEN_DEVICE_FUNC constexpr __half_raw(construct_from_rep_tag, numext::uint16_t rep) : x{rep} {}
+  numext::uint16_t x;
+#endif
+};
+
+#elif defined(EIGEN_HAS_HIP_FP16)
+// HIP GPU compile phase: nothing to do here.
+// HIP fp16 header file has a definition for __half_raw
+#elif defined(EIGEN_HAS_CUDA_FP16)
+
+// CUDA GPU compile phase.
+#if EIGEN_CUDA_SDK_VER < 90000
+// In CUDA < 9.0, __half is the equivalent of CUDA 9's __half_raw
+typedef __half __half_raw;
+#endif  // defined(EIGEN_HAS_CUDA_FP16)
+
+#elif defined(SYCL_DEVICE_ONLY)
+typedef cl::sycl::half __half_raw;
+#endif
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR __half_raw raw_uint16_to_half(numext::uint16_t x);
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff);
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h);
+
+struct half_base : public __half_raw {
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half_base() {}
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half_base(const __half_raw& h) : __half_raw(h) {}
+
+#if defined(EIGEN_HAS_GPU_FP16)
+#if defined(EIGEN_HAS_HIP_FP16)
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half_base(const __half& h) { x = __half_as_ushort(h); }
+#elif defined(EIGEN_HAS_CUDA_FP16)
+#if EIGEN_CUDA_SDK_VER >= 90000
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half_base(const __half& h) : __half_raw(*(__half_raw*)&h) {}
+#endif
+#endif
+#endif
+};
+
+}  // namespace half_impl
+
+// Class definition.
+struct half : public half_impl::half_base {
+  // Writing this out as separate #if-else blocks to make the code easier to follow
+  // The same applies to most #if-else blocks in this file
+#if !defined(EIGEN_HAS_GPU_FP16) || !defined(EIGEN_GPU_COMPILE_PHASE)
+  // Use the same base class for the following two scenarios
+  // * when compiling without GPU support enabled
+  // * during host compile phase when compiling with GPU support enabled
+  typedef half_impl::__half_raw __half_raw;
+#elif defined(EIGEN_HAS_HIP_FP16)
+  // Nothing to do here
+  // HIP fp16 header file has a definition for __half_raw
+#elif defined(EIGEN_HAS_CUDA_FP16)
+// Note that EIGEN_CUDA_SDK_VER is set to 0 even when compiling with HIP, so
+// (EIGEN_CUDA_SDK_VER < 90000) is true even for HIP!  So keeping this within
+// #if defined(EIGEN_HAS_CUDA_FP16) is needed
+#if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
+  typedef half_impl::__half_raw __half_raw;
+#endif
+#endif
+
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half() {}
+
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half(const __half_raw& h) : half_impl::half_base(h) {}
+
+#if defined(EIGEN_HAS_GPU_FP16)
+#if defined(EIGEN_HAS_HIP_FP16)
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half(const __half& h) : half_impl::half_base(h) {}
+#elif defined(EIGEN_HAS_CUDA_FP16)
+#if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half(const __half& h) : half_impl::half_base(h) {}
+#endif
+#endif
+#endif
+
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  explicit EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half(__fp16 b)
+      : half(__half_raw(__half_raw::construct_from_rep_tag(), b)) {}
+#elif defined(EIGEN_HAS_BUILTIN_FLOAT16)
+  explicit EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half(_Float16 b)
+      : half(__half_raw(__half_raw::construct_from_rep_tag(), b)) {}
+#endif
+
+  explicit EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR half(bool b)
+      : half_impl::half_base(half_impl::raw_uint16_to_half(b ? 0x3c00 : 0)) {}
+  template <class T>
+  explicit EIGEN_DEVICE_FUNC half(T val)
+      : half_impl::half_base(half_impl::float_to_half_rtne(static_cast<float>(val))) {}
+  explicit EIGEN_DEVICE_FUNC half(float f) : half_impl::half_base(half_impl::float_to_half_rtne(f)) {}
+
+  // Following the convention of numpy, converting between complex and
+  // float will lead to loss of imag value.
+  template <typename RealScalar>
+  explicit EIGEN_DEVICE_FUNC half(std::complex<RealScalar> c)
+      : half_impl::half_base(half_impl::float_to_half_rtne(static_cast<float>(c.real()))) {}
+
+  EIGEN_DEVICE_FUNC operator float() const {  // NOLINT: Allow implicit conversion to float, because it is lossless.
+    return half_impl::half_to_float(*this);
+  }
+
+#if defined(EIGEN_HAS_GPU_FP16) && !defined(EIGEN_GPU_COMPILE_PHASE)
+  EIGEN_DEVICE_FUNC operator __half() const {
+    ::__half_raw hr;
+    hr.x = x;
+    return __half(hr);
+  }
+#endif
+};
+
+// TODO(majnemer): Get rid of this once we can rely on C++17 inline variables do
+// solve the ODR issue.
+namespace half_impl {
+template <typename = void>
+struct numeric_limits_half_impl {
+  static constexpr const bool is_specialized = true;
+  static constexpr const bool is_signed = true;
+  static constexpr const bool is_integer = false;
+  static constexpr const bool is_exact = false;
+  static constexpr const bool has_infinity = true;
+  static constexpr const bool has_quiet_NaN = true;
+  static constexpr const bool has_signaling_NaN = true;
+  EIGEN_DIAGNOSTICS(push)
+  EIGEN_DISABLE_DEPRECATED_WARNING
+  static constexpr const std::float_denorm_style has_denorm = std::denorm_present;
+  static constexpr const bool has_denorm_loss = false;
+  EIGEN_DIAGNOSTICS(pop)
+  static constexpr const std::float_round_style round_style = std::round_to_nearest;
+  static constexpr const bool is_iec559 = true;
+  // The C++ standard defines this as "true if the set of values representable
+  // by the type is finite." Half has finite precision.
+  static constexpr const bool is_bounded = true;
+  static constexpr const bool is_modulo = false;
+  static constexpr const int digits = 11;
+  static constexpr const int digits10 =
+      3;  // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
+  static constexpr const int max_digits10 =
+      5;  // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
+  static constexpr const int radix = std::numeric_limits<float>::radix;
+  static constexpr const int min_exponent = -13;
+  static constexpr const int min_exponent10 = -4;
+  static constexpr const int max_exponent = 16;
+  static constexpr const int max_exponent10 = 4;
+  static constexpr const bool traps = std::numeric_limits<float>::traps;
+  // IEEE754: "The implementer shall choose how tininess is detected, but shall
+  // detect tininess in the same way for all operations in radix two"
+  static constexpr const bool tinyness_before = std::numeric_limits<float>::tinyness_before;
+
+  static _EIGEN_MAYBE_CONSTEXPR Eigen::half(min)() { return Eigen::half_impl::raw_uint16_to_half(0x0400); }
+  static _EIGEN_MAYBE_CONSTEXPR Eigen::half lowest() { return Eigen::half_impl::raw_uint16_to_half(0xfbff); }
+  static _EIGEN_MAYBE_CONSTEXPR Eigen::half(max)() { return Eigen::half_impl::raw_uint16_to_half(0x7bff); }
+  static _EIGEN_MAYBE_CONSTEXPR Eigen::half epsilon() { return Eigen::half_impl::raw_uint16_to_half(0x1400); }
+  static _EIGEN_MAYBE_CONSTEXPR Eigen::half round_error() { return Eigen::half_impl::raw_uint16_to_half(0x3800); }
+  static _EIGEN_MAYBE_CONSTEXPR Eigen::half infinity() { return Eigen::half_impl::raw_uint16_to_half(0x7c00); }
+  static _EIGEN_MAYBE_CONSTEXPR Eigen::half quiet_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7e00); }
+  static _EIGEN_MAYBE_CONSTEXPR Eigen::half signaling_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7d00); }
+  static _EIGEN_MAYBE_CONSTEXPR Eigen::half denorm_min() { return Eigen::half_impl::raw_uint16_to_half(0x0001); }
+};
+
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::is_specialized;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::is_signed;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::is_integer;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::is_exact;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::has_infinity;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::has_quiet_NaN;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::has_signaling_NaN;
+EIGEN_DIAGNOSTICS(push)
+EIGEN_DISABLE_DEPRECATED_WARNING
+template <typename T>
+constexpr const std::float_denorm_style numeric_limits_half_impl<T>::has_denorm;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::has_denorm_loss;
+EIGEN_DIAGNOSTICS(pop)
+template <typename T>
+constexpr const std::float_round_style numeric_limits_half_impl<T>::round_style;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::is_iec559;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::is_bounded;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::is_modulo;
+template <typename T>
+constexpr const int numeric_limits_half_impl<T>::digits;
+template <typename T>
+constexpr const int numeric_limits_half_impl<T>::digits10;
+template <typename T>
+constexpr const int numeric_limits_half_impl<T>::max_digits10;
+template <typename T>
+constexpr const int numeric_limits_half_impl<T>::radix;
+template <typename T>
+constexpr const int numeric_limits_half_impl<T>::min_exponent;
+template <typename T>
+constexpr const int numeric_limits_half_impl<T>::min_exponent10;
+template <typename T>
+constexpr const int numeric_limits_half_impl<T>::max_exponent;
+template <typename T>
+constexpr const int numeric_limits_half_impl<T>::max_exponent10;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::traps;
+template <typename T>
+constexpr const bool numeric_limits_half_impl<T>::tinyness_before;
+}  // end namespace half_impl
+}  // end namespace Eigen
+
+namespace std {
+// If std::numeric_limits<T> is specialized, should also specialize
+// std::numeric_limits<const T>, std::numeric_limits<volatile T>, and
+// std::numeric_limits<const volatile T>
+// https://stackoverflow.com/a/16519653/
+template <>
+class numeric_limits<Eigen::half> : public Eigen::half_impl::numeric_limits_half_impl<> {};
+template <>
+class numeric_limits<const Eigen::half> : public numeric_limits<Eigen::half> {};
+template <>
+class numeric_limits<volatile Eigen::half> : public numeric_limits<Eigen::half> {};
+template <>
+class numeric_limits<const volatile Eigen::half> : public numeric_limits<Eigen::half> {};
+}  // end namespace std
+
+namespace Eigen {
+
+namespace half_impl {
+
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(HIP_DEVICE_COMPILE))
+// Note: We deliberately do *not* define this to 1 even if we have Arm's native
+// fp16 type since GPU half types are rather different from native CPU half types.
+#define EIGEN_HAS_NATIVE_GPU_FP16
+#endif
+
+// Intrinsics for native fp16 support. Note that on current hardware,
+// these are no faster than fp32 arithmetic (you need to use the half2
+// versions to get the ALU speed increased), but you do save the
+// conversion steps back and forth.
+
+#if defined(EIGEN_HAS_NATIVE_GPU_FP16)
+EIGEN_STRONG_INLINE __device__ half operator+(const half& a, const half& b) {
+#if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
+  return __hadd(::__half(a), ::__half(b));
+#else
+  return __hadd(a, b);
+#endif
+}
+EIGEN_STRONG_INLINE __device__ half operator*(const half& a, const half& b) { return __hmul(a, b); }
+EIGEN_STRONG_INLINE __device__ half operator-(const half& a, const half& b) { return __hsub(a, b); }
+EIGEN_STRONG_INLINE __device__ half operator/(const half& a, const half& b) {
+#if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
+  return __hdiv(a, b);
+#else
+  float num = __half2float(a);
+  float denom = __half2float(b);
+  return __float2half(num / denom);
+#endif
+}
+EIGEN_STRONG_INLINE __device__ half operator-(const half& a) { return __hneg(a); }
+EIGEN_STRONG_INLINE __device__ half& operator+=(half& a, const half& b) {
+  a = a + b;
+  return a;
+}
+EIGEN_STRONG_INLINE __device__ half& operator*=(half& a, const half& b) {
+  a = a * b;
+  return a;
+}
+EIGEN_STRONG_INLINE __device__ half& operator-=(half& a, const half& b) {
+  a = a - b;
+  return a;
+}
+EIGEN_STRONG_INLINE __device__ half& operator/=(half& a, const half& b) {
+  a = a / b;
+  return a;
+}
+EIGEN_STRONG_INLINE __device__ bool operator==(const half& a, const half& b) { return __heq(a, b); }
+EIGEN_STRONG_INLINE __device__ bool operator!=(const half& a, const half& b) { return __hne(a, b); }
+EIGEN_STRONG_INLINE __device__ bool operator<(const half& a, const half& b) { return __hlt(a, b); }
+EIGEN_STRONG_INLINE __device__ bool operator<=(const half& a, const half& b) { return __hle(a, b); }
+EIGEN_STRONG_INLINE __device__ bool operator>(const half& a, const half& b) { return __hgt(a, b); }
+EIGEN_STRONG_INLINE __device__ bool operator>=(const half& a, const half& b) { return __hge(a, b); }
+
+#endif  // EIGEN_HAS_NATIVE_GPU_FP16
+
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC) && !defined(EIGEN_GPU_COMPILE_PHASE)
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator+(const half& a, const half& b) { return half(vaddh_f16(a.x, b.x)); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator*(const half& a, const half& b) { return half(vmulh_f16(a.x, b.x)); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator-(const half& a, const half& b) { return half(vsubh_f16(a.x, b.x)); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator/(const half& a, const half& b) { return half(vdivh_f16(a.x, b.x)); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator-(const half& a) { return half(vnegh_f16(a.x)); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator+=(half& a, const half& b) {
+  a = half(vaddh_f16(a.x, b.x));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator*=(half& a, const half& b) {
+  a = half(vmulh_f16(a.x, b.x));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator-=(half& a, const half& b) {
+  a = half(vsubh_f16(a.x, b.x));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator/=(half& a, const half& b) {
+  a = half(vdivh_f16(a.x, b.x));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator==(const half& a, const half& b) { return vceqh_f16(a.x, b.x); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator!=(const half& a, const half& b) { return !vceqh_f16(a.x, b.x); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator<(const half& a, const half& b) { return vclth_f16(a.x, b.x); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator<=(const half& a, const half& b) { return vcleh_f16(a.x, b.x); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator>(const half& a, const half& b) { return vcgth_f16(a.x, b.x); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator>=(const half& a, const half& b) { return vcgeh_f16(a.x, b.x); }
+
+#elif defined(EIGEN_HAS_BUILTIN_FLOAT16) && !defined(EIGEN_GPU_COMPILE_PHASE)
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator+(const half& a, const half& b) { return half(a.x + b.x); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator*(const half& a, const half& b) { return half(a.x * b.x); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator-(const half& a, const half& b) { return half(a.x - b.x); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator/(const half& a, const half& b) { return half(a.x / b.x); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator-(const half& a) { return half(-a.x); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator+=(half& a, const half& b) {
+  a = a + b;
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator*=(half& a, const half& b) {
+  a = a * b;
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator-=(half& a, const half& b) {
+  a = a - b;
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator/=(half& a, const half& b) {
+  a = a / b;
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator==(const half& a, const half& b) { return a.x == b.x; }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator!=(const half& a, const half& b) { return a.x != b.x; }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator<(const half& a, const half& b) { return a.x < b.x; }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator<=(const half& a, const half& b) { return a.x <= b.x; }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator>(const half& a, const half& b) { return a.x > b.x; }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator>=(const half& a, const half& b) { return a.x >= b.x; }
+
+// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
+// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
+// of the functions, while the latter can only deal with one of them.
+#elif !defined(EIGEN_HAS_NATIVE_GPU_FP16) || (EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)  // Emulate support for half floats
+
+#if EIGEN_COMP_CLANG && defined(EIGEN_GPUCC)
+// We need to provide emulated *host-side* FP16 operators for clang.
+#pragma push_macro("EIGEN_DEVICE_FUNC")
+#undef EIGEN_DEVICE_FUNC
+#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_HAS_NATIVE_GPU_FP16)
+#define EIGEN_DEVICE_FUNC __host__
+#else  // both host and device need emulated ops.
+#define EIGEN_DEVICE_FUNC __host__ __device__
+#endif
+#endif
+
+// Definitions for CPUs and older HIP+CUDA, mostly working through conversion
+// to/from fp32.
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator+(const half& a, const half& b) { return half(float(a) + float(b)); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator*(const half& a, const half& b) { return half(float(a) * float(b)); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator-(const half& a, const half& b) { return half(float(a) - float(b)); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator/(const half& a, const half& b) { return half(float(a) / float(b)); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator-(const half& a) {
+  half result;
+  result.x = a.x ^ 0x8000;
+  return result;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator+=(half& a, const half& b) {
+  a = half(float(a) + float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator*=(half& a, const half& b) {
+  a = half(float(a) * float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator-=(half& a, const half& b) {
+  a = half(float(a) - float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator/=(half& a, const half& b) {
+  a = half(float(a) / float(b));
+  return a;
+}
+
+// Non-negative floating point numbers have a monotonic mapping to non-negative integers.
+// This property allows floating point numbers to be reinterpreted as integers for comparisons, which is useful if there
+// is no native floating point comparison operator. Floating point signedness is handled by the sign-magnitude
+// representation, whereas integers typically use two's complement. Converting the bit pattern from sign-magnitude to
+// two's complement allows the transformed bit patterns be compared as signed integers. All edge cases (+/-0 and +/-
+// infinity) are handled automatically, except NaN.
+//
+// fp16 uses 1 sign bit, 5 exponent bits, and 10 mantissa bits. The bit pattern conveys NaN when all the exponent
+// bits (5) are set, and at least one mantissa bit is set. The sign bit is irrelevant for determining NaN. To check for
+// NaN, clear the sign bit and check if the integral representation is greater than 01111100000000. To test
+// for non-NaN, clear the sign bit and check if the integral representation is less than or equal to 01111100000000.
+
+// convert sign-magnitude representation to two's complement
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC int16_t mapToSigned(uint16_t a) {
+  constexpr uint16_t kAbsMask = (1 << 15) - 1;
+  // If the sign bit is set, clear the sign bit and return the (integer) negation. Otherwise, return the input.
+  return (a >> 15) ? -(a & kAbsMask) : a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool isOrdered(const half& a, const half& b) {
+  constexpr uint16_t kInf = ((1 << 5) - 1) << 10;
+  constexpr uint16_t kAbsMask = (1 << 15) - 1;
+  return numext::maxi(a.x & kAbsMask, b.x & kAbsMask) <= kInf;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator==(const half& a, const half& b) {
+  bool result = mapToSigned(a.x) == mapToSigned(b.x);
+  result &= isOrdered(a, b);
+  return result;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator!=(const half& a, const half& b) { return !(a == b); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator<(const half& a, const half& b) {
+  bool result = mapToSigned(a.x) < mapToSigned(b.x);
+  result &= isOrdered(a, b);
+  return result;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator<=(const half& a, const half& b) {
+  bool result = mapToSigned(a.x) <= mapToSigned(b.x);
+  result &= isOrdered(a, b);
+  return result;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator>(const half& a, const half& b) {
+  bool result = mapToSigned(a.x) > mapToSigned(b.x);
+  result &= isOrdered(a, b);
+  return result;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator>=(const half& a, const half& b) {
+  bool result = mapToSigned(a.x) >= mapToSigned(b.x);
+  result &= isOrdered(a, b);
+  return result;
+}
+
+#if EIGEN_COMP_CLANG && defined(EIGEN_GPUCC)
+#pragma pop_macro("EIGEN_DEVICE_FUNC")
+#endif
+
+#endif  // Emulate support for half floats
+
+// Division by an index. Do it in full float precision to avoid accuracy
+// issues in converting the denominator to half.
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator/(const half& a, Index b) {
+  return half(static_cast<float>(a) / static_cast<float>(b));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator++(half& a) {
+  a += half(1);
+  return a;
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator--(half& a) {
+  a -= half(1);
+  return a;
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator++(half& a, int) {
+  half original_value = a;
+  ++a;
+  return original_value;
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator--(half& a, int) {
+  half original_value = a;
+  --a;
+  return original_value;
+}
+
+// Conversion routines, including fallbacks for the host or older CUDA.
+// Note that newer Intel CPUs (Haswell or newer) have vectorized versions of
+// these in hardware. If we need more performance on older/other CPUs, they are
+// also possible to vectorize directly.
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR __half_raw raw_uint16_to_half(numext::uint16_t x) {
+  // We cannot simply do a "return __half_raw(x)" here, because __half_raw is union type
+  // in the hip_fp16 header file, and that will trigger a compile error
+  // On the other hand, having anything but a return statement also triggers a compile error
+  // because this is constexpr function.
+  // Fortunately, since we need to disable EIGEN_CONSTEXPR for GPU anyway, we can get out
+  // of this catch22 by having separate bodies for GPU / non GPU
+#if defined(EIGEN_HAS_GPU_FP16)
+  __half_raw h;
+  h.x = x;
+  return h;
+#else
+  return __half_raw(x);
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC numext::uint16_t raw_half_as_uint16(const __half_raw& h) {
+  // HIP/CUDA/Default have a member 'x' of type uint16_t.
+  // For ARM64 native half, the member 'x' is of type __fp16, so we need to bit-cast.
+  // For SYCL, cl::sycl::half is _Float16, so cast directly.
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  return numext::bit_cast<numext::uint16_t>(h.x);
+#elif defined(EIGEN_HAS_BUILTIN_FLOAT16)
+  return numext::bit_cast<numext::uint16_t>(h.x);
+#elif defined(SYCL_DEVICE_ONLY)
+  return numext::bit_cast<numext::uint16_t>(h);
+#else
+  return h.x;
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  __half tmp_ff = __float2half(ff);
+  return *(__half_raw*)&tmp_ff;
+
+#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  __half_raw h;
+  h.x = static_cast<__fp16>(ff);
+  return h;
+
+#elif defined(EIGEN_HAS_BUILTIN_FLOAT16)
+  __half_raw h;
+  h.x = static_cast<_Float16>(ff);
+  return h;
+
+#elif defined(EIGEN_HAS_FP16_C)
+  __half_raw h;
+#if EIGEN_COMP_MSVC
+  // MSVC does not have scalar instructions.
+  h.x = _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(ff), 0), 0);
+#else
+  h.x = _cvtss_sh(ff, 0);
+#endif
+  return h;
+
+#else
+  uint32_t f_bits = Eigen::numext::bit_cast<uint32_t>(ff);
+  const uint32_t f32infty_bits = {255 << 23};
+  const uint32_t f16max_bits = {(127 + 16) << 23};
+  const uint32_t denorm_magic_bits = {((127 - 15) + (23 - 10) + 1) << 23};
+  const uint32_t sign_mask = 0x80000000u;
+  __half_raw o;
+  o.x = static_cast<uint16_t>(0x0u);
+
+  const uint32_t sign = f_bits & sign_mask;
+  f_bits ^= sign;
+
+  // NOTE all the integer compares in this function can be safely
+  // compiled into signed compares since all operands are below
+  // 0x80000000. Important if you want fast straight SSE2 code
+  // (since there's no unsigned PCMPGTD).
+
+  if (f_bits >= f16max_bits) {                         // result is Inf or NaN (all exponent bits set)
+    o.x = (f_bits > f32infty_bits) ? 0x7e00 : 0x7c00;  // NaN->qNaN and Inf->Inf
+  } else {                                             // (De)normalized number or zero
+    if (f_bits < (113 << 23)) {                        // resulting FP16 is subnormal or zero
+      // use a magic value to align our 10 mantissa bits at the bottom of
+      // the float. as long as FP addition is round-to-nearest-even this
+      // just works.
+      f_bits = Eigen::numext::bit_cast<uint32_t>(Eigen::numext::bit_cast<float>(f_bits) +
+                                                 Eigen::numext::bit_cast<float>(denorm_magic_bits));
+
+      // and one integer subtract of the bias later, we have our final float!
+      o.x = static_cast<numext::uint16_t>(f_bits - denorm_magic_bits);
+    } else {
+      const uint32_t mant_odd = (f_bits >> 13) & 1;  // resulting mantissa is odd
+
+      // update exponent, rounding bias part 1
+      // Equivalent to `f.u += ((unsigned int)(15 - 127) << 23) + 0xfff`, but
+      // without arithmetic overflow.
+      f_bits += 0xc8000fffU;
+      // rounding bias part 2
+      f_bits += mant_odd;
+      // take the bits!
+      o.x = static_cast<numext::uint16_t>(f_bits >> 13);
+    }
+  }
+
+  o.x |= static_cast<numext::uint16_t>(sign >> 16);
+  return o;
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  return __half2float(h);
+#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC) || defined(EIGEN_HAS_BUILTIN_FLOAT16)
+  return static_cast<float>(h.x);
+#elif defined(EIGEN_HAS_FP16_C)
+#if EIGEN_COMP_MSVC
+  // MSVC does not have scalar instructions.
+  return _mm_cvtss_f32(_mm_cvtph_ps(_mm_set1_epi16(h.x)));
+#else
+  return _cvtsh_ss(h.x);
+#endif
+#else
+  const float magic = Eigen::numext::bit_cast<float>(static_cast<uint32_t>(113 << 23));
+  const uint32_t shifted_exp = 0x7c00 << 13;  // exponent mask after shift
+  uint32_t o_bits = (h.x & 0x7fff) << 13;     // exponent/mantissa bits
+  const uint32_t exp = shifted_exp & o_bits;  // just the exponent
+  o_bits += (127 - 15) << 23;                 // exponent adjust
+
+  // handle exponent special cases
+  if (exp == shifted_exp) {      // Inf/NaN?
+    o_bits += (128 - 16) << 23;  // extra exp adjust
+  } else if (exp == 0) {         // Zero/Denormal?
+    o_bits += 1 << 23;           // extra exp adjust
+    // renormalize
+    o_bits = Eigen::numext::bit_cast<uint32_t>(Eigen::numext::bit_cast<float>(o_bits) - magic);
+  }
+
+  o_bits |= (h.x & 0x8000u) << 16;  // sign bit
+  return Eigen::numext::bit_cast<float>(o_bits);
+#endif
+}
+
+// --- standard functions ---
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool(isinf)(const half& a) {
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC) || defined(EIGEN_HAS_BUILTIN_FLOAT16)
+  return (numext::bit_cast<numext::uint16_t>(a.x) & 0x7fff) == 0x7c00;
+#else
+  return (a.x & 0x7fff) == 0x7c00;
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool(isnan)(const half& a) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  return __hisnan(a);
+#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC) || defined(EIGEN_HAS_BUILTIN_FLOAT16)
+  return (numext::bit_cast<numext::uint16_t>(a.x) & 0x7fff) > 0x7c00;
+#else
+  return (a.x & 0x7fff) > 0x7c00;
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool(isfinite)(const half& a) {
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC) || defined(EIGEN_HAS_BUILTIN_FLOAT16)
+  return (numext::bit_cast<numext::uint16_t>(a.x) & 0x7fff) < 0x7c00;
+#else
+  return (a.x & 0x7fff) < 0x7c00;
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half abs(const half& a) {
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  return half(vabsh_f16(a.x));
+#elif defined(EIGEN_HAS_BUILTIN_FLOAT16)
+  half result;
+  result.x =
+      numext::bit_cast<_Float16>(static_cast<numext::uint16_t>(numext::bit_cast<numext::uint16_t>(a.x) & 0x7FFF));
+  return result;
+#else
+  half result;
+  result.x = a.x & 0x7FFF;
+  return result;
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half exp(const half& a) {
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530) || \
+    defined(EIGEN_HIP_DEVICE_COMPILE)
+  return half(hexp(a));
+#else
+  return half(::expf(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half exp2(const half& a) {
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530) || \
+    defined(EIGEN_HIP_DEVICE_COMPILE)
+  return half(hexp2(a));
+#else
+  return half(::exp2f(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half expm1(const half& a) { return half(numext::expm1(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log(const half& a) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && EIGEN_CUDA_SDK_VER >= 80000 && defined(EIGEN_CUDA_ARCH) && \
+     EIGEN_CUDA_ARCH >= 530) ||                                                                 \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  return half(hlog(a));
+#else
+  return half(::logf(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log1p(const half& a) { return half(numext::log1p(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log10(const half& a) { return half(::log10f(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log2(const half& a) {
+  return half(static_cast<float>(EIGEN_LOG2E) * ::logf(float(a)));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sqrt(const half& a) {
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530) || \
+    defined(EIGEN_HIP_DEVICE_COMPILE)
+  return half(hsqrt(a));
+#else
+  return half(::sqrtf(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half pow(const half& a, const half& b) {
+  return half(::powf(float(a), float(b)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half atan2(const half& a, const half& b) {
+  return half(::atan2f(float(a), float(b)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sin(const half& a) { return half(::sinf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half cos(const half& a) { return half(::cosf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tan(const half& a) { return half(::tanf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tanh(const half& a) { return half(::tanhf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half asin(const half& a) { return half(::asinf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half acos(const half& a) { return half(::acosf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half atan(const half& a) { return half(::atanf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half atanh(const half& a) { return half(::atanhf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half floor(const half& a) {
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 300) || \
+    defined(EIGEN_HIP_DEVICE_COMPILE)
+  return half(hfloor(a));
+#else
+  return half(::floorf(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half ceil(const half& a) {
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 300) || \
+    defined(EIGEN_HIP_DEVICE_COMPILE)
+  return half(hceil(a));
+#else
+  return half(::ceilf(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half rint(const half& a) { return half(::rintf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half round(const half& a) { return half(::roundf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half trunc(const half& a) { return half(::truncf(float(a))); }
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half fmod(const half& a, const half& b) {
+  return half(::fmodf(float(a), float(b)));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half(min)(const half& a, const half& b) { return b < a ? b : a; }
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half(max)(const half& a, const half& b) { return a < b ? b : a; }
+
+EIGEN_DEVICE_FUNC inline half fma(const half& a, const half& b, const half& c) {
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  return half(vfmah_f16(c.x, a.x, b.x));
+#elif defined(EIGEN_VECTORIZE_AVX512FP16)
+  // Reduces to vfmadd213sh.
+  return half(_mm_cvtsh_h(_mm_fmadd_ph(_mm_set_sh(a.x), _mm_set_sh(b.x), _mm_set_sh(c.x))));
+#else
+  // Emulate FMA via float.
+  return half(numext::fma(static_cast<float>(a), static_cast<float>(b), static_cast<float>(c)));
+#endif
+}
+
+#ifndef EIGEN_NO_IO
+EIGEN_ALWAYS_INLINE std::ostream& operator<<(std::ostream& os, const half& v) {
+  os << static_cast<float>(v);
+  return os;
+}
+#endif
+
+}  // end namespace half_impl
+
+// import Eigen::half_impl::half into Eigen namespace
+// using half_impl::half;
+
+namespace internal {
+
+template <>
+struct is_arithmetic<half> {
+  enum { value = true };
+};
+
+template <>
+struct random_impl<half> {
+  enum : int { MantissaBits = 10 };
+  using Impl = random_impl<float>;
+  static EIGEN_DEVICE_FUNC inline half run(const half& x, const half& y) {
+    float result = Impl::run(x, y, MantissaBits);
+    return half(result);
+  }
+  static EIGEN_DEVICE_FUNC inline half run() {
+    float result = Impl::run(MantissaBits);
+    return half(result);
+  }
+};
+
+}  // end namespace internal
+
+template <>
+struct NumTraits<Eigen::half> : GenericNumTraits<Eigen::half> {
+  enum { IsSigned = true, IsInteger = false, IsComplex = false, RequireInitialization = false };
+
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half epsilon() {
+    return half_impl::raw_uint16_to_half(0x0800);
+  }
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half dummy_precision() {
+    return half_impl::raw_uint16_to_half(0x211f);  //  Eigen::half(1e-2f);
+  }
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half highest() {
+    return half_impl::raw_uint16_to_half(0x7bff);
+  }
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half lowest() {
+    return half_impl::raw_uint16_to_half(0xfbff);
+  }
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half infinity() {
+    return half_impl::raw_uint16_to_half(0x7c00);
+  }
+  EIGEN_DEVICE_FUNC _EIGEN_MAYBE_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half quiet_NaN() {
+    return half_impl::raw_uint16_to_half(0x7e00);
+  }
+};
+
+}  // end namespace Eigen
+
+#undef _EIGEN_MAYBE_CONSTEXPR
+
+namespace Eigen {
+namespace numext {
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isnan)(const Eigen::half& h) {
+  return (half_impl::isnan)(h);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isinf)(const Eigen::half& h) {
+  return (half_impl::isinf)(h);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isfinite)(const Eigen::half& h) {
+  return (half_impl::isfinite)(h);
+}
+
+#endif
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bit_cast<Eigen::half, uint16_t>(const uint16_t& src) {
+  return Eigen::half(Eigen::half_impl::raw_uint16_to_half(src));
+}
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC uint16_t bit_cast<uint16_t, Eigen::half>(const Eigen::half& src) {
+  return Eigen::half_impl::raw_half_as_uint16(src);
+}
+
+// Specialize multiply-add to match packet operations and reduce conversions to/from float.
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half madd<Eigen::half>(const Eigen::half& x, const Eigen::half& y,
+                                                                    const Eigen::half& z) {
+  return Eigen::half(static_cast<float>(x) * static_cast<float>(y) + static_cast<float>(z));
+}
+
+}  // namespace numext
+}  // namespace Eigen
+
+// Add the missing shfl* intrinsics.
+// The __shfl* functions are only valid on HIP or _CUDA_ARCH_ >= 300.
+//   CUDA defines them for (__CUDA_ARCH__ >= 300 || !defined(__CUDA_ARCH__))
+//
+// HIP and CUDA prior to SDK 9.0 define
+//    __shfl, __shfl_up, __shfl_down, __shfl_xor for int and float
+// CUDA since 9.0 deprecates those and instead defines
+//    __shfl_sync, __shfl_up_sync, __shfl_down_sync, __shfl_xor_sync,
+//    with native support for __half and __nv_bfloat16
+//
+// Note that the following are __device__ - only functions.
+#if (defined(EIGEN_CUDACC) && (!defined(EIGEN_CUDA_ARCH) || EIGEN_CUDA_ARCH >= 300)) || defined(EIGEN_HIPCC)
+
+#if defined(EIGEN_HAS_CUDA_FP16) && EIGEN_CUDA_SDK_VER >= 90000
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_sync(unsigned mask, Eigen::half var, int srcLane,
+                                                       int width = warpSize) {
+  const __half h = var;
+  return static_cast<Eigen::half>(__shfl_sync(mask, h, srcLane, width));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_up_sync(unsigned mask, Eigen::half var, unsigned int delta,
+                                                          int width = warpSize) {
+  const __half h = var;
+  return static_cast<Eigen::half>(__shfl_up_sync(mask, h, delta, width));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_down_sync(unsigned mask, Eigen::half var, unsigned int delta,
+                                                            int width = warpSize) {
+  const __half h = var;
+  return static_cast<Eigen::half>(__shfl_down_sync(mask, h, delta, width));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_xor_sync(unsigned mask, Eigen::half var, int laneMask,
+                                                           int width = warpSize) {
+  const __half h = var;
+  return static_cast<Eigen::half>(__shfl_xor_sync(mask, h, laneMask, width));
+}
+
+#else  // HIP or CUDA SDK < 9.0
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl(Eigen::half var, int srcLane, int width = warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl(ivar, srcLane, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_up(Eigen::half var, unsigned int delta, int width = warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_up(ivar, delta, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_down(Eigen::half var, unsigned int delta, int width = warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_down(ivar, delta, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_xor(Eigen::half var, int laneMask, int width = warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_xor(ivar, laneMask, width)));
+}
+
+#endif  // HIP vs CUDA
+#endif  // __shfl*
+
+// ldg() has an overload for __half_raw, but we also need one for Eigen::half.
+#if (defined(EIGEN_CUDACC) && (!defined(EIGEN_CUDA_ARCH) || EIGEN_CUDA_ARCH >= 350)) || defined(EIGEN_HIPCC)
+EIGEN_STRONG_INLINE __device__ Eigen::half __ldg(const Eigen::half* ptr) {
+  return Eigen::half_impl::raw_uint16_to_half(__ldg(reinterpret_cast<const Eigen::numext::uint16_t*>(ptr)));
+}
+#endif  // __ldg
+
+#if EIGEN_HAS_STD_HASH
+namespace std {
+template <>
+struct hash<Eigen::half> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t operator()(const Eigen::half& a) const {
+    return static_cast<std::size_t>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(a));
+  }
+};
+}  // end namespace std
+#endif
+
+namespace Eigen {
+namespace internal {
+
+template <>
+struct cast_impl<float, half> {
+  EIGEN_DEVICE_FUNC static inline half run(const float& a) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+    return __float2half(a);
+#else
+    return half(a);
+#endif
+  }
+};
+
+template <>
+struct cast_impl<int, half> {
+  EIGEN_DEVICE_FUNC static inline half run(const int& a) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+    return __float2half(static_cast<float>(a));
+#else
+    return half(static_cast<float>(a));
+#endif
+  }
+};
+
+template <>
+struct cast_impl<half, float> {
+  EIGEN_DEVICE_FUNC static inline float run(const half& a) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+    return __half2float(a);
+#else
+    return static_cast<float>(a);
+#endif
+  }
+};
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_HALF_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/Settings.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/Settings.h
new file mode 100644
index 0000000..7e3a970
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/Default/Settings.h
@@ -0,0 +1,47 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* All the parameters defined in this file can be specialized in the
+ * architecture specific files, and/or by the user.
+ * More to come... */
+
+#ifndef EIGEN_DEFAULT_SETTINGS_H
+#define EIGEN_DEFAULT_SETTINGS_H
+
+/** Defines the maximal loop size to enable meta unrolling of loops.
+ * Note that the value here is expressed in Eigen's own notion of "number of FLOPS",
+ * it does not correspond to the number of iterations or the number of instructions
+ */
+#ifndef EIGEN_UNROLLING_LIMIT
+#define EIGEN_UNROLLING_LIMIT 110
+#endif
+
+/** Defines the threshold between a "small" and a "large" matrix.
+ * This threshold is mainly used to select the proper product implementation.
+ */
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+/** Defines the maximal width of the blocks used in the triangular product and solver
+ * for vectors (level 2 blas xTRMV and xTRSV). The default is 8.
+ */
+#ifndef EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH
+#define EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH 8
+#endif
+
+/** Defines the default number of registers available for that architecture.
+ * Currently it must be 8 or 16. Other values will fail.
+ */
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 8
+#endif
+
+#endif  // EIGEN_DEFAULT_SETTINGS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/Complex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/Complex.h
new file mode 100644
index 0000000..be4c218
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/Complex.h
@@ -0,0 +1,196 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2021 C. Antonio Sanchez <cantonios@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_GPU_H
+#define EIGEN_COMPLEX_GPU_H
+
+// Many std::complex methods such as operator+, operator-, operator* and
+// operator/ are not constexpr. Due to this, GCC and older versions of clang do
+// not treat them as device functions and thus Eigen functors making use of
+// these operators fail to compile. Here, we manually specialize these
+// operators and functors for complex types when building for CUDA to enable
+// their use on-device.
+//
+// NOTES:
+//  - Compound assignment operators +=,-=,*=,/=(Scalar) will not work on device,
+//    since they are already specialized in the standard. Using them will result
+//    in silent kernel failures.
+//  - Compiling with MSVC and using +=,-=,*=,/=(std::complex<Scalar>) will lead
+//    to duplicate definition errors, since these are already specialized in
+//    Visual Studio's <complex> header (contrary to the standard).  This is
+//    preferable to removing such definitions, which will lead to silent kernel
+//    failures.
+//  - Compiling with ICC requires defining _USE_COMPLEX_SPECIALIZATION_ prior
+//    to the first inclusion of <complex>.
+
+#if defined(EIGEN_GPUCC) && defined(EIGEN_GPU_COMPILE_PHASE)
+
+// ICC already specializes std::complex<float> and std::complex<double>
+// operators, preventing us from making them device functions here.
+// This will lead to silent runtime errors if the operators are used on device.
+//
+// To allow std::complex operator use on device, define _OVERRIDE_COMPLEX_SPECIALIZATION_
+// prior to first inclusion of <complex>.  This prevents ICC from adding
+// its own specializations, so our custom ones below can be used instead.
+#if !(EIGEN_COMP_ICC && defined(_USE_COMPLEX_SPECIALIZATION_))
+
+// Import Eigen's internal operator specializations.
+#define EIGEN_USING_STD_COMPLEX_OPERATORS           \
+  using Eigen::complex_operator_detail::operator+;  \
+  using Eigen::complex_operator_detail::operator-;  \
+  using Eigen::complex_operator_detail::operator*;  \
+  using Eigen::complex_operator_detail::operator/;  \
+  using Eigen::complex_operator_detail::operator+=; \
+  using Eigen::complex_operator_detail::operator-=; \
+  using Eigen::complex_operator_detail::operator*=; \
+  using Eigen::complex_operator_detail::operator/=; \
+  using Eigen::complex_operator_detail::operator==; \
+  using Eigen::complex_operator_detail::operator!=;
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// Specialized std::complex overloads.
+namespace complex_operator_detail {
+
+// NOTE: We cannot specialize compound assignment operators with Scalar T,
+//         (i.e.  operator@=(const T&), for @=+,-,*,/)
+//       since they are already specialized for float/double/long double within
+//       the standard <complex> header. We also do not specialize the stream
+//       operators.
+#define EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(T)                                                        \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator+(const std::complex<T>& a) { return a; }           \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator-(const std::complex<T>& a) {                       \
+    return std::complex<T>(-numext::real(a), -numext::imag(a));                                                     \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator+(const std::complex<T>& a,                         \
+                                                                  const std::complex<T>& b) {                       \
+    return std::complex<T>(numext::real(a) + numext::real(b), numext::imag(a) + numext::imag(b));                   \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator+(const std::complex<T>& a, const T& b) {           \
+    return std::complex<T>(numext::real(a) + b, numext::imag(a));                                                   \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator+(const T& a, const std::complex<T>& b) {           \
+    return std::complex<T>(a + numext::real(b), numext::imag(b));                                                   \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator-(const std::complex<T>& a,                         \
+                                                                  const std::complex<T>& b) {                       \
+    return std::complex<T>(numext::real(a) - numext::real(b), numext::imag(a) - numext::imag(b));                   \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator-(const std::complex<T>& a, const T& b) {           \
+    return std::complex<T>(numext::real(a) - b, numext::imag(a));                                                   \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator-(const T& a, const std::complex<T>& b) {           \
+    return std::complex<T>(a - numext::real(b), -numext::imag(b));                                                  \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator*(const std::complex<T>& a,                         \
+                                                                  const std::complex<T>& b) {                       \
+    return internal::complex_multiply(a, b);                                                                        \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator*(const std::complex<T>& a, const T& b) {           \
+    return std::complex<T>(numext::real(a) * b, numext::imag(a) * b);                                               \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator*(const T& a, const std::complex<T>& b) {           \
+    return std::complex<T>(a * numext::real(b), a * numext::imag(b));                                               \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator/(const std::complex<T>& a,                         \
+                                                                  const std::complex<T>& b) {                       \
+    return internal::complex_divide(a, b);                                                                          \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator/(const std::complex<T>& a, const T& b) {           \
+    return std::complex<T>(numext::real(a) / b, numext::imag(a) / b);                                               \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator/(const T& a, const std::complex<T>& b) {           \
+    return internal::complex_divide(std::complex<T>(a, 0), b);                                                      \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T>& operator+=(std::complex<T>& a, const std::complex<T>& b) { \
+    numext::real_ref(a) += numext::real(b);                                                                         \
+    numext::imag_ref(a) += numext::imag(b);                                                                         \
+    return a;                                                                                                       \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T>& operator-=(std::complex<T>& a, const std::complex<T>& b) { \
+    numext::real_ref(a) -= numext::real(b);                                                                         \
+    numext::imag_ref(a) -= numext::imag(b);                                                                         \
+    return a;                                                                                                       \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T>& operator*=(std::complex<T>& a, const std::complex<T>& b) { \
+    a = internal::complex_multiply(a, b);                                                                           \
+    return a;                                                                                                       \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T>& operator/=(std::complex<T>& a, const std::complex<T>& b) { \
+    a = internal::complex_divide(a, b);                                                                             \
+    return a;                                                                                                       \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator==(const std::complex<T>& a, const std::complex<T>& b) {       \
+    return numext::real(a) == numext::real(b) && numext::imag(a) == numext::imag(b);                                \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator==(const std::complex<T>& a, const T& b) {                     \
+    return numext::real(a) == b && numext::imag(a) == 0;                                                            \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator==(const T& a, const std::complex<T>& b) {                     \
+    return a == numext::real(b) && 0 == numext::imag(b);                                                            \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator!=(const std::complex<T>& a, const std::complex<T>& b) {       \
+    return !(a == b);                                                                                               \
+  }                                                                                                                 \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator!=(const std::complex<T>& a, const T& b) { return !(a == b); } \
+                                                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator!=(const T& a, const std::complex<T>& b) { return !(a == b); }
+
+// Do not specialize for long double, since that reduces to double on device.
+EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(float)
+EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(double)
+
+#undef EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS
+
+}  // namespace complex_operator_detail
+
+EIGEN_USING_STD_COMPLEX_OPERATORS
+
+namespace numext {
+EIGEN_USING_STD_COMPLEX_OPERATORS
+}  // namespace numext
+
+namespace internal {
+EIGEN_USING_STD_COMPLEX_OPERATORS
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // !(EIGEN_COMP_ICC && _USE_COMPLEX_SPECIALIZATION_)
+
+#endif  // EIGEN_GPUCC && EIGEN_GPU_COMPILE_PHASE
+
+#endif  // EIGEN_COMPLEX_GPU_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/MathFunctions.h
new file mode 100644
index 0000000..81bc8bb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/MathFunctions.h
@@ -0,0 +1,104 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_GPU_H
+#define EIGEN_MATH_FUNCTIONS_GPU_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Make sure this is only available when targeting a GPU: we don't want to
+// introduce conflicts between these packet_traits definitions and the ones
+// we'll use on the host side (SSE, AVX, ...)
+#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 plog<float4>(const float4& a) {
+  return make_float4(logf(a.x), logf(a.y), logf(a.z), logf(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 plog<double2>(const double2& a) {
+  using ::log;
+  return make_double2(log(a.x), log(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 plog1p<float4>(const float4& a) {
+  return make_float4(log1pf(a.x), log1pf(a.y), log1pf(a.z), log1pf(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 plog1p<double2>(const double2& a) {
+  return make_double2(log1p(a.x), log1p(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pexp<float4>(const float4& a) {
+  return make_float4(expf(a.x), expf(a.y), expf(a.z), expf(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pexp<double2>(const double2& a) {
+  using ::exp;
+  return make_double2(exp(a.x), exp(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pexp2<float4>(const float4& a) {
+  return make_float4(exp2f(a.x), exp2f(a.y), exp2f(a.z), exp2f(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pexp2<double2>(const double2& a) {
+  using ::exp;
+  return make_double2(exp2(a.x), exp2(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pexpm1<float4>(const float4& a) {
+  return make_float4(expm1f(a.x), expm1f(a.y), expm1f(a.z), expm1f(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pexpm1<double2>(const double2& a) {
+  return make_double2(expm1(a.x), expm1(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 psqrt<float4>(const float4& a) {
+  return make_float4(sqrtf(a.x), sqrtf(a.y), sqrtf(a.z), sqrtf(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 psqrt<double2>(const double2& a) {
+  using ::sqrt;
+  return make_double2(sqrt(a.x), sqrt(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 prsqrt<float4>(const float4& a) {
+  return make_float4(rsqrtf(a.x), rsqrtf(a.y), rsqrtf(a.z), rsqrtf(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 prsqrt<double2>(const double2& a) {
+  return make_double2(rsqrt(a.x), rsqrt(a.y));
+}
+
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_GPU_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/PacketMath.h
new file mode 100644
index 0000000..7d062f3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/PacketMath.h
@@ -0,0 +1,1710 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_GPU_H
+#define EIGEN_PACKET_MATH_GPU_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Read-only data cached load available.
+#if defined(EIGEN_HIP_DEVICE_COMPILE) || (defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 350)
+#define EIGEN_GPU_HAS_LDG 1
+#endif
+
+// FP16 math available.
+#if (defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530)
+#define EIGEN_CUDA_HAS_FP16_ARITHMETIC 1
+#endif
+
+#if defined(EIGEN_HIP_DEVICE_COMPILE) || defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
+#define EIGEN_GPU_HAS_FP16_ARITHMETIC 1
+#endif
+
+// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
+// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
+// of the functions, while the latter can only deal with one of them.
+#if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
+#define EIGEN_HAS_GPU_DEVICE_FUNCTIONS 1
+#else
+#define EIGEN_HAS_GPU_DEVICE_FUNCTIONS 0
+#endif
+
+// Make sure this is only available when targeting a GPU: we don't want to
+// introduce conflicts between these packet_traits definitions and the ones
+// we'll use on the host side (SSE, AVX, ...)
+#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
+
+template <>
+struct is_arithmetic<float4> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<double2> {
+  enum { value = true };
+};
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef float4 type;
+  typedef float4 half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasDiv = 1,
+    HasSin = 0,
+    HasCos = 0,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasLGamma = 1,
+    HasDiGamma = 1,
+    HasZeta = 1,
+    HasPolygamma = 1,
+    HasErf = 1,
+    HasErfc = 1,
+    HasNdtri = 1,
+    HasBessel = 1,
+    HasIGamma = 1,
+    HasIGammaDerA = 1,
+    HasGammaSampleDerAlpha = 1,
+    HasIGammac = 1,
+    HasBetaInc = 1,
+
+    HasFloor = 1,
+    HasCmp = EIGEN_HAS_GPU_DEVICE_FUNCTIONS
+  };
+};
+
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef double2 type;
+  typedef double2 half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasDiv = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasLGamma = 1,
+    HasDiGamma = 1,
+    HasZeta = 1,
+    HasPolygamma = 1,
+    HasErf = 1,
+    HasErfc = 1,
+    HasNdtri = 1,
+    HasBessel = 1,
+    HasIGamma = 1,
+    HasIGammaDerA = 1,
+    HasGammaSampleDerAlpha = 1,
+    HasIGammac = 1,
+    HasBetaInc = 1,
+  };
+};
+
+template <>
+struct unpacket_traits<float4> {
+  typedef float type;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef float4 half;
+};
+template <>
+struct unpacket_traits<double2> {
+  typedef double type;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef double2 half;
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pset1<float4>(const float& from) {
+  return make_float4(from, from, from, from);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pset1<double2>(const double& from) {
+  return make_double2(from, from);
+}
+
+#if EIGEN_HAS_GPU_DEVICE_FUNCTIONS
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_and(const float& a, const float& b) {
+  return __int_as_float(__float_as_int(a) & __float_as_int(b));
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_and(const double& a, const double& b) {
+  return __longlong_as_double(__double_as_longlong(a) & __double_as_longlong(b));
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_or(const float& a, const float& b) {
+  return __int_as_float(__float_as_int(a) | __float_as_int(b));
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_or(const double& a, const double& b) {
+  return __longlong_as_double(__double_as_longlong(a) | __double_as_longlong(b));
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_xor(const float& a, const float& b) {
+  return __int_as_float(__float_as_int(a) ^ __float_as_int(b));
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_xor(const double& a, const double& b) {
+  return __longlong_as_double(__double_as_longlong(a) ^ __double_as_longlong(b));
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_andnot(const float& a, const float& b) {
+  return __int_as_float(__float_as_int(a) & ~__float_as_int(b));
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_andnot(const double& a, const double& b) {
+  return __longlong_as_double(__double_as_longlong(a) & ~__double_as_longlong(b));
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float eq_mask(const float& a, const float& b) {
+  return __int_as_float(a == b ? 0xffffffffu : 0u);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double eq_mask(const double& a, const double& b) {
+  return __longlong_as_double(a == b ? 0xffffffffffffffffull : 0ull);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float lt_mask(const float& a, const float& b) {
+  return __int_as_float(a < b ? 0xffffffffu : 0u);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double lt_mask(const double& a, const double& b) {
+  return __longlong_as_double(a < b ? 0xffffffffffffffffull : 0ull);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float le_mask(const float& a, const float& b) {
+  return __int_as_float(a <= b ? 0xffffffffu : 0u);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double le_mask(const double& a, const double& b) {
+  return __longlong_as_double(a <= b ? 0xffffffffffffffffull : 0ull);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pand<float4>(const float4& a, const float4& b) {
+  return make_float4(bitwise_and(a.x, b.x), bitwise_and(a.y, b.y), bitwise_and(a.z, b.z), bitwise_and(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pand<double2>(const double2& a, const double2& b) {
+  return make_double2(bitwise_and(a.x, b.x), bitwise_and(a.y, b.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 por<float4>(const float4& a, const float4& b) {
+  return make_float4(bitwise_or(a.x, b.x), bitwise_or(a.y, b.y), bitwise_or(a.z, b.z), bitwise_or(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 por<double2>(const double2& a, const double2& b) {
+  return make_double2(bitwise_or(a.x, b.x), bitwise_or(a.y, b.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pxor<float4>(const float4& a, const float4& b) {
+  return make_float4(bitwise_xor(a.x, b.x), bitwise_xor(a.y, b.y), bitwise_xor(a.z, b.z), bitwise_xor(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pxor<double2>(const double2& a, const double2& b) {
+  return make_double2(bitwise_xor(a.x, b.x), bitwise_xor(a.y, b.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pandnot<float4>(const float4& a, const float4& b) {
+  return make_float4(bitwise_andnot(a.x, b.x), bitwise_andnot(a.y, b.y), bitwise_andnot(a.z, b.z),
+                     bitwise_andnot(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pandnot<double2>(const double2& a, const double2& b) {
+  return make_double2(bitwise_andnot(a.x, b.x), bitwise_andnot(a.y, b.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcmp_eq<float4>(const float4& a, const float4& b) {
+  return make_float4(eq_mask(a.x, b.x), eq_mask(a.y, b.y), eq_mask(a.z, b.z), eq_mask(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcmp_lt<float4>(const float4& a, const float4& b) {
+  return make_float4(lt_mask(a.x, b.x), lt_mask(a.y, b.y), lt_mask(a.z, b.z), lt_mask(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcmp_le<float4>(const float4& a, const float4& b) {
+  return make_float4(le_mask(a.x, b.x), le_mask(a.y, b.y), le_mask(a.z, b.z), le_mask(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pcmp_eq<double2>(const double2& a, const double2& b) {
+  return make_double2(eq_mask(a.x, b.x), eq_mask(a.y, b.y));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pcmp_lt<double2>(const double2& a, const double2& b) {
+  return make_double2(lt_mask(a.x, b.x), lt_mask(a.y, b.y));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pcmp_le<double2>(const double2& a, const double2& b) {
+  return make_double2(le_mask(a.x, b.x), le_mask(a.y, b.y));
+}
+#endif  // EIGEN_HAS_GPU_DEVICE_FUNCTIONS
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 plset<float4>(const float& a) {
+  return make_float4(a, a + 1, a + 2, a + 3);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 plset<double2>(const double& a) {
+  return make_double2(a, a + 1);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 padd<float4>(const float4& a, const float4& b) {
+  return make_float4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 padd<double2>(const double2& a, const double2& b) {
+  return make_double2(a.x + b.x, a.y + b.y);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 psub<float4>(const float4& a, const float4& b) {
+  return make_float4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 psub<double2>(const double2& a, const double2& b) {
+  return make_double2(a.x - b.x, a.y - b.y);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pnegate(const float4& a) {
+  return make_float4(-a.x, -a.y, -a.z, -a.w);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pnegate(const double2& a) {
+  return make_double2(-a.x, -a.y);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pconj(const float4& a) {
+  return a;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pconj(const double2& a) {
+  return a;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmul<float4>(const float4& a, const float4& b) {
+  return make_float4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmul<double2>(const double2& a, const double2& b) {
+  return make_double2(a.x * b.x, a.y * b.y);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pdiv<float4>(const float4& a, const float4& b) {
+  return make_float4(a.x / b.x, a.y / b.y, a.z / b.z, a.w / b.w);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pdiv<double2>(const double2& a, const double2& b) {
+  return make_double2(a.x / b.x, a.y / b.y);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmin<float4>(const float4& a, const float4& b) {
+  return make_float4(fminf(a.x, b.x), fminf(a.y, b.y), fminf(a.z, b.z), fminf(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmin<double2>(const double2& a, const double2& b) {
+  return make_double2(fmin(a.x, b.x), fmin(a.y, b.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmax<float4>(const float4& a, const float4& b) {
+  return make_float4(fmaxf(a.x, b.x), fmaxf(a.y, b.y), fmaxf(a.z, b.z), fmaxf(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmax<double2>(const double2& a, const double2& b) {
+  return make_double2(fmax(a.x, b.x), fmax(a.y, b.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pload<float4>(const float* from) {
+  return *reinterpret_cast<const float4*>(from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pload<double2>(const double* from) {
+  return *reinterpret_cast<const double2*>(from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ploadu<float4>(const float* from) {
+  return make_float4(from[0], from[1], from[2], from[3]);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ploadu<double2>(const double* from) {
+  return make_double2(from[0], from[1]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ploaddup<float4>(const float* from) {
+  return make_float4(from[0], from[0], from[1], from[1]);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ploaddup<double2>(const double* from) {
+  return make_double2(from[0], from[0]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<float>(float* to, const float4& from) {
+  *reinterpret_cast<float4*>(to) = from;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<double>(double* to, const double2& from) {
+  *reinterpret_cast<double2*>(to) = from;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const float4& from) {
+  to[0] = from.x;
+  to[1] = from.y;
+  to[2] = from.z;
+  to[3] = from.w;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const double2& from) {
+  to[0] = from.x;
+  to[1] = from.y;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Aligned>(const float* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  return __ldg(reinterpret_cast<const float4*>(from));
+#else
+  return make_float4(from[0], from[1], from[2], from[3]);
+#endif
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Aligned>(const double* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  return __ldg(reinterpret_cast<const double2*>(from));
+#else
+  return make_double2(from[0], from[1]);
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Unaligned>(const float* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  return make_float4(__ldg(from + 0), __ldg(from + 1), __ldg(from + 2), __ldg(from + 3));
+#else
+  return make_float4(from[0], from[1], from[2], from[3]);
+#endif
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Unaligned>(const double* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  return make_double2(__ldg(from + 0), __ldg(from + 1));
+#else
+  return make_double2(from[0], from[1]);
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float4 pgather<float, float4>(const float* from, Index stride) {
+  return make_float4(from[0 * stride], from[1 * stride], from[2 * stride], from[3 * stride]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline double2 pgather<double, double2>(const double* from, Index stride) {
+  return make_double2(from[0 * stride], from[1 * stride]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, float4>(float* to, const float4& from, Index stride) {
+  to[stride * 0] = from.x;
+  to[stride * 1] = from.y;
+  to[stride * 2] = from.z;
+  to[stride * 3] = from.w;
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, double2>(double* to, const double2& from, Index stride) {
+  to[stride * 0] = from.x;
+  to[stride * 1] = from.y;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float pfirst<float4>(const float4& a) {
+  return a.x;
+}
+template <>
+EIGEN_DEVICE_FUNC inline double pfirst<double2>(const double2& a) {
+  return a.x;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float predux<float4>(const float4& a) {
+  return a.x + a.y + a.z + a.w;
+}
+template <>
+EIGEN_DEVICE_FUNC inline double predux<double2>(const double2& a) {
+  return a.x + a.y;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float predux_max<float4>(const float4& a) {
+  return fmaxf(fmaxf(a.x, a.y), fmaxf(a.z, a.w));
+}
+template <>
+EIGEN_DEVICE_FUNC inline double predux_max<double2>(const double2& a) {
+  return fmax(a.x, a.y);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float predux_min<float4>(const float4& a) {
+  return fminf(fminf(a.x, a.y), fminf(a.z, a.w));
+}
+template <>
+EIGEN_DEVICE_FUNC inline double predux_min<double2>(const double2& a) {
+  return fmin(a.x, a.y);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float predux_mul<float4>(const float4& a) {
+  return a.x * a.y * a.z * a.w;
+}
+template <>
+EIGEN_DEVICE_FUNC inline double predux_mul<double2>(const double2& a) {
+  return a.x * a.y;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float4 pabs<float4>(const float4& a) {
+  return make_float4(fabsf(a.x), fabsf(a.y), fabsf(a.z), fabsf(a.w));
+}
+template <>
+EIGEN_DEVICE_FUNC inline double2 pabs<double2>(const double2& a) {
+  return make_double2(fabs(a.x), fabs(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float4 pfloor<float4>(const float4& a) {
+  return make_float4(floorf(a.x), floorf(a.y), floorf(a.z), floorf(a.w));
+}
+template <>
+EIGEN_DEVICE_FUNC inline double2 pfloor<double2>(const double2& a) {
+  return make_double2(floor(a.x), floor(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float4 pceil<float4>(const float4& a) {
+  return make_float4(ceilf(a.x), ceilf(a.y), ceilf(a.z), ceilf(a.w));
+}
+template <>
+EIGEN_DEVICE_FUNC inline double2 pceil<double2>(const double2& a) {
+  return make_double2(ceil(a.x), ceil(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float4 print<float4>(const float4& a) {
+  return make_float4(rintf(a.x), rintf(a.y), rintf(a.z), rintf(a.w));
+}
+template <>
+EIGEN_DEVICE_FUNC inline double2 print<double2>(const double2& a) {
+  return make_double2(rint(a.x), rint(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline float4 ptrunc<float4>(const float4& a) {
+  return make_float4(truncf(a.x), truncf(a.y), truncf(a.z), truncf(a.w));
+}
+template <>
+EIGEN_DEVICE_FUNC inline double2 ptrunc<double2>(const double2& a) {
+  return make_double2(trunc(a.x), trunc(a.y));
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<float4, 4>& kernel) {
+  float tmp = kernel.packet[0].y;
+  kernel.packet[0].y = kernel.packet[1].x;
+  kernel.packet[1].x = tmp;
+
+  tmp = kernel.packet[0].z;
+  kernel.packet[0].z = kernel.packet[2].x;
+  kernel.packet[2].x = tmp;
+
+  tmp = kernel.packet[0].w;
+  kernel.packet[0].w = kernel.packet[3].x;
+  kernel.packet[3].x = tmp;
+
+  tmp = kernel.packet[1].z;
+  kernel.packet[1].z = kernel.packet[2].y;
+  kernel.packet[2].y = tmp;
+
+  tmp = kernel.packet[1].w;
+  kernel.packet[1].w = kernel.packet[3].y;
+  kernel.packet[3].y = tmp;
+
+  tmp = kernel.packet[2].w;
+  kernel.packet[2].w = kernel.packet[3].z;
+  kernel.packet[3].z = tmp;
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<double2, 2>& kernel) {
+  double tmp = kernel.packet[0].y;
+  kernel.packet[0].y = kernel.packet[1].x;
+  kernel.packet[1].x = tmp;
+}
+
+#endif  // defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
+
+// Half-packet functions are not available on the host for CUDA 9.0-9.2, only
+// on device. There is no benefit to using them on the host anyways, since they are
+// emulated.
+#if (defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)) && defined(EIGEN_GPU_COMPILE_PHASE)
+
+typedef ulonglong2 Packet4h2;
+template <>
+struct unpacket_traits<Packet4h2> {
+  typedef Eigen::half type;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet4h2 half;
+};
+template <>
+struct is_arithmetic<Packet4h2> {
+  enum { value = true };
+};
+
+template <>
+struct unpacket_traits<half2> {
+  typedef Eigen::half type;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef half2 half;
+};
+template <>
+struct is_arithmetic<half2> {
+  enum { value = true };
+};
+
+template <>
+struct packet_traits<Eigen::half> : default_packet_traits {
+  typedef Packet4h2 type;
+  typedef Packet4h2 half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasExp = 1,
+    HasExpm1 = 1,
+    HasLog = 1,
+    HasLog1p = 1
+  };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pset1<half2>(const Eigen::half& from) {
+  return __half2half2(from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pset1<Packet4h2>(const Eigen::half& from) {
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = pset1<half2>(from);
+  p_alias[1] = pset1<half2>(from);
+  p_alias[2] = pset1<half2>(from);
+  p_alias[3] = pset1<half2>(from);
+  return r;
+}
+
+namespace {
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pload(const Eigen::half* from) {
+  return *reinterpret_cast<const half2*>(from);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploadu(const Eigen::half* from) { return __halves2half2(from[0], from[1]); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploaddup(const Eigen::half* from) {
+  return __halves2half2(from[0], from[0]);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore(Eigen::half* to, const half2& from) {
+  *reinterpret_cast<half2*>(to) = from;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu(Eigen::half* to, const half2& from) {
+  to[0] = __low2half(from);
+  to[1] = __high2half(from);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro_aligned(const Eigen::half* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  // Input is guaranteed to be properly aligned.
+  return __ldg(reinterpret_cast<const half2*>(from));
+#else
+  return __halves2half2(*(from + 0), *(from + 1));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro_unaligned(const Eigen::half* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  return __halves2half2(__ldg(from + 0), __ldg(from + 1));
+#else
+  return __halves2half2(*(from + 0), *(from + 1));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pgather(const Eigen::half* from, Index stride) {
+  return __halves2half2(from[0 * stride], from[1 * stride]);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter(Eigen::half* to, const half2& from, Index stride) {
+  to[stride * 0] = __low2half(from);
+  to[stride * 1] = __high2half(from);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half pfirst(const half2& a) { return __low2half(a); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pabs(const half2& a) {
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half result1 = half_impl::raw_uint16_to_half(a1.x & 0x7FFF);
+  half result2 = half_impl::raw_uint16_to_half(a2.x & 0x7FFF);
+  return __halves2half2(result1, result2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ptrue(const half2& /*a*/) {
+  half true_half = half_impl::raw_uint16_to_half(0xffffu);
+  return pset1<half2>(true_half);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pzero(const half2& /*a*/) {
+  half false_half = half_impl::raw_uint16_to_half(0x0000u);
+  return pset1<half2>(false_half);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<half2, 2>& kernel) {
+  __half a1 = __low2half(kernel.packet[0]);
+  __half a2 = __high2half(kernel.packet[0]);
+  __half b1 = __low2half(kernel.packet[1]);
+  __half b2 = __high2half(kernel.packet[1]);
+  kernel.packet[0] = __halves2half2(a1, b1);
+  kernel.packet[1] = __halves2half2(a2, b2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plset(const Eigen::half& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __halves2half2(a, __hadd(a, __float2half(1.0f)));
+#else
+  float f = __half2float(a) + 1.0f;
+  return __halves2half2(a, __float2half(f));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pselect(const half2& mask, const half2& a, const half2& b) {
+  half mask_low = __low2half(mask);
+  half mask_high = __high2half(mask);
+  half result_low = mask_low == half(0) ? __low2half(b) : __low2half(a);
+  half result_high = mask_high == half(0) ? __high2half(b) : __high2half(a);
+  return __halves2half2(result_low, result_high);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_eq(const half2& a, const half2& b) {
+  half true_half = half_impl::raw_uint16_to_half(0xffffu);
+  half false_half = half_impl::raw_uint16_to_half(0x0000u);
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half eq1 = __half2float(a1) == __half2float(b1) ? true_half : false_half;
+  half eq2 = __half2float(a2) == __half2float(b2) ? true_half : false_half;
+  return __halves2half2(eq1, eq2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_lt(const half2& a, const half2& b) {
+  half true_half = half_impl::raw_uint16_to_half(0xffffu);
+  half false_half = half_impl::raw_uint16_to_half(0x0000u);
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half eq1 = __half2float(a1) < __half2float(b1) ? true_half : false_half;
+  half eq2 = __half2float(a2) < __half2float(b2) ? true_half : false_half;
+  return __halves2half2(eq1, eq2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_le(const half2& a, const half2& b) {
+  half true_half = half_impl::raw_uint16_to_half(0xffffu);
+  half false_half = half_impl::raw_uint16_to_half(0x0000u);
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half eq1 = __half2float(a1) <= __half2float(b1) ? true_half : false_half;
+  half eq2 = __half2float(a2) <= __half2float(b2) ? true_half : false_half;
+  return __halves2half2(eq1, eq2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pand(const half2& a, const half2& b) {
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half result1 = half_impl::raw_uint16_to_half(a1.x & b1.x);
+  half result2 = half_impl::raw_uint16_to_half(a2.x & b2.x);
+  return __halves2half2(result1, result2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 por(const half2& a, const half2& b) {
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half result1 = half_impl::raw_uint16_to_half(a1.x | b1.x);
+  half result2 = half_impl::raw_uint16_to_half(a2.x | b2.x);
+  return __halves2half2(result1, result2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pxor(const half2& a, const half2& b) {
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half result1 = half_impl::raw_uint16_to_half(a1.x ^ b1.x);
+  half result2 = half_impl::raw_uint16_to_half(a2.x ^ b2.x);
+  return __halves2half2(result1, result2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pandnot(const half2& a, const half2& b) {
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half result1 = half_impl::raw_uint16_to_half(a1.x & ~b1.x);
+  half result2 = half_impl::raw_uint16_to_half(a2.x & ~b2.x);
+  return __halves2half2(result1, result2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 padd(const half2& a, const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hadd2(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 + b1;
+  float r2 = a2 + b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 psub(const half2& a, const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hsub2(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 - b1;
+  float r2 = a2 - b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pnegate(const half2& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hneg2(a);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  return __floats2half2_rn(-a1, -a2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pconj(const half2& a) { return a; }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmul(const half2& a, const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hmul2(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 * b1;
+  float r2 = a2 * b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmadd(const half2& a, const half2& b, const half2& c) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hfma2(a, b, c);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float c1 = __low2float(c);
+  float c2 = __high2float(c);
+  float r1 = a1 * b1 + c1;
+  float r2 = a2 * b2 + c2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pdiv(const half2& a, const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __h2div(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 / b1;
+  float r2 = a2 / b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin(const half2& a, const half2& b) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  __half r1 = a1 < b1 ? __low2half(a) : __low2half(b);
+  __half r2 = a2 < b2 ? __high2half(a) : __high2half(b);
+  return __halves2half2(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax(const half2& a, const half2& b) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  __half r1 = a1 > b1 ? __low2half(a) : __low2half(b);
+  __half r2 = a2 > b2 ? __high2half(a) : __high2half(b);
+  return __halves2half2(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux(const half2& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hadd(__low2half(a), __high2half(a));
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  return Eigen::half(__float2half(a1 + a2));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_max(const half2& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  __half first = __low2half(a);
+  __half second = __high2half(a);
+  return __hgt(first, second) ? first : second;
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  return a1 > a2 ? __low2half(a) : __high2half(a);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_min(const half2& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  __half first = __low2half(a);
+  __half second = __high2half(a);
+  return __hlt(first, second) ? first : second;
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  return a1 < a2 ? __low2half(a) : __high2half(a);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_mul(const half2& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hmul(__low2half(a), __high2half(a));
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  return Eigen::half(__float2half(a1 * a2));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plog1p(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = log1pf(a1);
+  float r2 = log1pf(a2);
+  return __floats2half2_rn(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pexpm1(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = expm1f(a1);
+  float r2 = expm1f(a2);
+  return __floats2half2_rn(r1, r2);
+}
+
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)) || defined(EIGEN_HIP_DEVICE_COMPILE)
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plog(const half2& a) { return h2log(a); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pexp(const half2& a) { return h2exp(a); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 psqrt(const half2& a) { return h2sqrt(a); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 prsqrt(const half2& a) { return h2rsqrt(a); }
+
+#else
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plog(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = logf(a1);
+  float r2 = logf(a2);
+  return __floats2half2_rn(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pexp(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = expf(a1);
+  float r2 = expf(a2);
+  return __floats2half2_rn(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 psqrt(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = sqrtf(a1);
+  float r2 = sqrtf(a2);
+  return __floats2half2_rn(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 prsqrt(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = rsqrtf(a1);
+  float r2 = rsqrtf(a2);
+  return __floats2half2_rn(r1, r2);
+}
+#endif
+}  // namespace
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pload<Packet4h2>(const Eigen::half* from) {
+  return *reinterpret_cast<const Packet4h2*>(from);
+}
+
+// unaligned load;
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 ploadu<Packet4h2>(const Eigen::half* from) {
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = ploadu(from + 0);
+  p_alias[1] = ploadu(from + 2);
+  p_alias[2] = ploadu(from + 4);
+  p_alias[3] = ploadu(from + 6);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 ploaddup<Packet4h2>(const Eigen::half* from) {
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = ploaddup(from + 0);
+  p_alias[1] = ploaddup(from + 1);
+  p_alias[2] = ploaddup(from + 2);
+  p_alias[3] = ploaddup(from + 3);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet4h2& from) {
+  *reinterpret_cast<Packet4h2*>(to) = from;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet4h2& from) {
+  const half2* from_alias = reinterpret_cast<const half2*>(&from);
+  pstoreu(to + 0, from_alias[0]);
+  pstoreu(to + 2, from_alias[1]);
+  pstoreu(to + 4, from_alias[2]);
+  pstoreu(to + 6, from_alias[3]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4h2 ploadt_ro<Packet4h2, Aligned>(const Eigen::half* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  Packet4h2 r;
+  r = __ldg(reinterpret_cast<const Packet4h2*>(from));
+  return r;
+#else
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  r_alias[0] = ploadt_ro_aligned(from + 0);
+  r_alias[1] = ploadt_ro_aligned(from + 2);
+  r_alias[2] = ploadt_ro_aligned(from + 4);
+  r_alias[3] = ploadt_ro_aligned(from + 6);
+  return r;
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4h2 ploadt_ro<Packet4h2, Unaligned>(const Eigen::half* from) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  r_alias[0] = ploadt_ro_unaligned(from + 0);
+  r_alias[1] = ploadt_ro_unaligned(from + 2);
+  r_alias[2] = ploadt_ro_unaligned(from + 4);
+  r_alias[3] = ploadt_ro_unaligned(from + 6);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pgather<Eigen::half, Packet4h2>(const Eigen::half* from, Index stride) {
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = __halves2half2(from[0 * stride], from[1 * stride]);
+  p_alias[1] = __halves2half2(from[2 * stride], from[3 * stride]);
+  p_alias[2] = __halves2half2(from[4 * stride], from[5 * stride]);
+  p_alias[3] = __halves2half2(from[6 * stride], from[7 * stride]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4h2>(Eigen::half* to, const Packet4h2& from,
+                                                                            Index stride) {
+  const half2* from_alias = reinterpret_cast<const half2*>(&from);
+  pscatter(to + stride * 0, from_alias[0], stride);
+  pscatter(to + stride * 2, from_alias[1], stride);
+  pscatter(to + stride * 4, from_alias[2], stride);
+  pscatter(to + stride * 6, from_alias[3], stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4h2>(const Packet4h2& a) {
+  return pfirst(*(reinterpret_cast<const half2*>(&a)));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pabs<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  p_alias[0] = pabs(a_alias[0]);
+  p_alias[1] = pabs(a_alias[1]);
+  p_alias[2] = pabs(a_alias[2]);
+  p_alias[3] = pabs(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 ptrue<Packet4h2>(const Packet4h2& /*a*/) {
+  half true_half = half_impl::raw_uint16_to_half(0xffffu);
+  return pset1<Packet4h2>(true_half);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pzero<Packet4h2>(const Packet4h2& /*a*/) {
+  half false_half = half_impl::raw_uint16_to_half(0x0000u);
+  return pset1<Packet4h2>(false_half);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose_double(double* d_row0, double* d_row1, double* d_row2,
+                                                             double* d_row3, double* d_row4, double* d_row5,
+                                                             double* d_row6, double* d_row7) {
+  double d_tmp;
+  d_tmp = d_row0[1];
+  d_row0[1] = d_row4[0];
+  d_row4[0] = d_tmp;
+
+  d_tmp = d_row1[1];
+  d_row1[1] = d_row5[0];
+  d_row5[0] = d_tmp;
+
+  d_tmp = d_row2[1];
+  d_row2[1] = d_row6[0];
+  d_row6[0] = d_tmp;
+
+  d_tmp = d_row3[1];
+  d_row3[1] = d_row7[0];
+  d_row7[0] = d_tmp;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose_half2(half2* f_row0, half2* f_row1, half2* f_row2,
+                                                            half2* f_row3) {
+  half2 f_tmp;
+  f_tmp = f_row0[1];
+  f_row0[1] = f_row2[0];
+  f_row2[0] = f_tmp;
+
+  f_tmp = f_row1[1];
+  f_row1[1] = f_row3[0];
+  f_row3[0] = f_tmp;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose_half(half2& f0, half2& f1) {
+  __half a1 = __low2half(f0);
+  __half a2 = __high2half(f0);
+  __half b1 = __low2half(f1);
+  __half b2 = __high2half(f1);
+  f0 = __halves2half2(a1, b1);
+  f1 = __halves2half2(a2, b2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4h2, 8>& kernel) {
+  double* d_row0 = reinterpret_cast<double*>(&kernel.packet[0]);
+  double* d_row1 = reinterpret_cast<double*>(&kernel.packet[1]);
+  double* d_row2 = reinterpret_cast<double*>(&kernel.packet[2]);
+  double* d_row3 = reinterpret_cast<double*>(&kernel.packet[3]);
+  double* d_row4 = reinterpret_cast<double*>(&kernel.packet[4]);
+  double* d_row5 = reinterpret_cast<double*>(&kernel.packet[5]);
+  double* d_row6 = reinterpret_cast<double*>(&kernel.packet[6]);
+  double* d_row7 = reinterpret_cast<double*>(&kernel.packet[7]);
+  ptranspose_double(d_row0, d_row1, d_row2, d_row3, d_row4, d_row5, d_row6, d_row7);
+
+  half2* f_row0 = reinterpret_cast<half2*>(d_row0);
+  half2* f_row1 = reinterpret_cast<half2*>(d_row1);
+  half2* f_row2 = reinterpret_cast<half2*>(d_row2);
+  half2* f_row3 = reinterpret_cast<half2*>(d_row3);
+  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
+  ptranspose_half(f_row0[0], f_row1[0]);
+  ptranspose_half(f_row0[1], f_row1[1]);
+  ptranspose_half(f_row2[0], f_row3[0]);
+  ptranspose_half(f_row2[1], f_row3[1]);
+
+  f_row0 = reinterpret_cast<half2*>(d_row0 + 1);
+  f_row1 = reinterpret_cast<half2*>(d_row1 + 1);
+  f_row2 = reinterpret_cast<half2*>(d_row2 + 1);
+  f_row3 = reinterpret_cast<half2*>(d_row3 + 1);
+  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
+  ptranspose_half(f_row0[0], f_row1[0]);
+  ptranspose_half(f_row0[1], f_row1[1]);
+  ptranspose_half(f_row2[0], f_row3[0]);
+  ptranspose_half(f_row2[1], f_row3[1]);
+
+  f_row0 = reinterpret_cast<half2*>(d_row4);
+  f_row1 = reinterpret_cast<half2*>(d_row5);
+  f_row2 = reinterpret_cast<half2*>(d_row6);
+  f_row3 = reinterpret_cast<half2*>(d_row7);
+  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
+  ptranspose_half(f_row0[0], f_row1[0]);
+  ptranspose_half(f_row0[1], f_row1[1]);
+  ptranspose_half(f_row2[0], f_row3[0]);
+  ptranspose_half(f_row2[1], f_row3[1]);
+
+  f_row0 = reinterpret_cast<half2*>(d_row4 + 1);
+  f_row1 = reinterpret_cast<half2*>(d_row5 + 1);
+  f_row2 = reinterpret_cast<half2*>(d_row6 + 1);
+  f_row3 = reinterpret_cast<half2*>(d_row7 + 1);
+  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
+  ptranspose_half(f_row0[0], f_row1[0]);
+  ptranspose_half(f_row0[1], f_row1[1]);
+  ptranspose_half(f_row2[0], f_row3[0]);
+  ptranspose_half(f_row2[1], f_row3[1]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 plset<Packet4h2>(const Eigen::half& a) {
+#if defined(EIGEN_HIP_DEVICE_COMPILE)
+
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = __halves2half2(a, __hadd(a, __float2half(1.0f)));
+  p_alias[1] = __halves2half2(__hadd(a, __float2half(2.0f)), __hadd(a, __float2half(3.0f)));
+  p_alias[2] = __halves2half2(__hadd(a, __float2half(4.0f)), __hadd(a, __float2half(5.0f)));
+  p_alias[3] = __halves2half2(__hadd(a, __float2half(6.0f)), __hadd(a, __float2half(7.0f)));
+  return r;
+#elif defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+
+  half2 b = pset1<half2>(a);
+  half2 c;
+  half2 half_offset0 = __halves2half2(__float2half(0.0f), __float2half(2.0f));
+  half2 half_offset1 = __halves2half2(__float2half(4.0f), __float2half(6.0f));
+
+  c = __hadd2(b, half_offset0);
+  r_alias[0] = plset(__low2half(c));
+  r_alias[1] = plset(__high2half(c));
+
+  c = __hadd2(b, half_offset1);
+  r_alias[2] = plset(__low2half(c));
+  r_alias[3] = plset(__high2half(c));
+
+  return r;
+
+#else
+  float f = __half2float(a);
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = __halves2half2(a, __float2half(f + 1.0f));
+  p_alias[1] = __halves2half2(__float2half(f + 2.0f), __float2half(f + 3.0f));
+  p_alias[2] = __halves2half2(__float2half(f + 4.0f), __float2half(f + 5.0f));
+  p_alias[3] = __halves2half2(__float2half(f + 6.0f), __float2half(f + 7.0f));
+  return r;
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pselect<Packet4h2>(const Packet4h2& mask, const Packet4h2& a,
+                                                                   const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* mask_alias = reinterpret_cast<const half2*>(&mask);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pselect(mask_alias[0], a_alias[0], b_alias[0]);
+  r_alias[1] = pselect(mask_alias[1], a_alias[1], b_alias[1]);
+  r_alias[2] = pselect(mask_alias[2], a_alias[2], b_alias[2]);
+  r_alias[3] = pselect(mask_alias[3], a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pcmp_eq<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pcmp_eq(a_alias[0], b_alias[0]);
+  r_alias[1] = pcmp_eq(a_alias[1], b_alias[1]);
+  r_alias[2] = pcmp_eq(a_alias[2], b_alias[2]);
+  r_alias[3] = pcmp_eq(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pcmp_lt<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pcmp_lt(a_alias[0], b_alias[0]);
+  r_alias[1] = pcmp_lt(a_alias[1], b_alias[1]);
+  r_alias[2] = pcmp_lt(a_alias[2], b_alias[2]);
+  r_alias[3] = pcmp_lt(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pcmp_le<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pcmp_le(a_alias[0], b_alias[0]);
+  r_alias[1] = pcmp_le(a_alias[1], b_alias[1]);
+  r_alias[2] = pcmp_le(a_alias[2], b_alias[2]);
+  r_alias[3] = pcmp_le(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pand<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pand(a_alias[0], b_alias[0]);
+  r_alias[1] = pand(a_alias[1], b_alias[1]);
+  r_alias[2] = pand(a_alias[2], b_alias[2]);
+  r_alias[3] = pand(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 por<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = por(a_alias[0], b_alias[0]);
+  r_alias[1] = por(a_alias[1], b_alias[1]);
+  r_alias[2] = por(a_alias[2], b_alias[2]);
+  r_alias[3] = por(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pxor<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pxor(a_alias[0], b_alias[0]);
+  r_alias[1] = pxor(a_alias[1], b_alias[1]);
+  r_alias[2] = pxor(a_alias[2], b_alias[2]);
+  r_alias[3] = pxor(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pandnot<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pandnot(a_alias[0], b_alias[0]);
+  r_alias[1] = pandnot(a_alias[1], b_alias[1]);
+  r_alias[2] = pandnot(a_alias[2], b_alias[2]);
+  r_alias[3] = pandnot(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 padd<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = padd(a_alias[0], b_alias[0]);
+  r_alias[1] = padd(a_alias[1], b_alias[1]);
+  r_alias[2] = padd(a_alias[2], b_alias[2]);
+  r_alias[3] = padd(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 psub<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = psub(a_alias[0], b_alias[0]);
+  r_alias[1] = psub(a_alias[1], b_alias[1]);
+  r_alias[2] = psub(a_alias[2], b_alias[2]);
+  r_alias[3] = psub(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pnegate(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = pnegate(a_alias[0]);
+  r_alias[1] = pnegate(a_alias[1]);
+  r_alias[2] = pnegate(a_alias[2]);
+  r_alias[3] = pnegate(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pconj(const Packet4h2& a) {
+  return a;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmul<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pmul(a_alias[0], b_alias[0]);
+  r_alias[1] = pmul(a_alias[1], b_alias[1]);
+  r_alias[2] = pmul(a_alias[2], b_alias[2]);
+  r_alias[3] = pmul(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmadd<Packet4h2>(const Packet4h2& a, const Packet4h2& b,
+                                                                 const Packet4h2& c) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  const half2* c_alias = reinterpret_cast<const half2*>(&c);
+  r_alias[0] = pmadd(a_alias[0], b_alias[0], c_alias[0]);
+  r_alias[1] = pmadd(a_alias[1], b_alias[1], c_alias[1]);
+  r_alias[2] = pmadd(a_alias[2], b_alias[2], c_alias[2]);
+  r_alias[3] = pmadd(a_alias[3], b_alias[3], c_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pdiv<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pdiv(a_alias[0], b_alias[0]);
+  r_alias[1] = pdiv(a_alias[1], b_alias[1]);
+  r_alias[2] = pdiv(a_alias[2], b_alias[2]);
+  r_alias[3] = pdiv(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmin<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pmin(a_alias[0], b_alias[0]);
+  r_alias[1] = pmin(a_alias[1], b_alias[1]);
+  r_alias[2] = pmin(a_alias[2], b_alias[2]);
+  r_alias[3] = pmin(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmax<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pmax(a_alias[0], b_alias[0]);
+  r_alias[1] = pmax(a_alias[1], b_alias[1]);
+  r_alias[2] = pmax(a_alias[2], b_alias[2]);
+  r_alias[3] = pmax(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux<Packet4h2>(const Packet4h2& a) {
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+
+  return predux(a_alias[0]) + predux(a_alias[1]) + predux(a_alias[2]) + predux(a_alias[3]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_max<Packet4h2>(const Packet4h2& a) {
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  half2 m0 = __halves2half2(predux_max(a_alias[0]), predux_max(a_alias[1]));
+  half2 m1 = __halves2half2(predux_max(a_alias[2]), predux_max(a_alias[3]));
+  __half first = predux_max(m0);
+  __half second = predux_max(m1);
+#if defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
+  return (__hgt(first, second) ? first : second);
+#else
+  float ffirst = __half2float(first);
+  float fsecond = __half2float(second);
+  return (ffirst > fsecond) ? first : second;
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_min<Packet4h2>(const Packet4h2& a) {
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  half2 m0 = __halves2half2(predux_min(a_alias[0]), predux_min(a_alias[1]));
+  half2 m1 = __halves2half2(predux_min(a_alias[2]), predux_min(a_alias[3]));
+  __half first = predux_min(m0);
+  __half second = predux_min(m1);
+#if defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
+  return (__hlt(first, second) ? first : second);
+#else
+  float ffirst = __half2float(first);
+  float fsecond = __half2float(second);
+  return (ffirst < fsecond) ? first : second;
+#endif
+}
+
+// likely overflow/underflow
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet4h2>(const Packet4h2& a) {
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  return predux_mul(pmul(pmul(a_alias[0], a_alias[1]), pmul(a_alias[2], a_alias[3])));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 plog1p<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = plog1p(a_alias[0]);
+  r_alias[1] = plog1p(a_alias[1]);
+  r_alias[2] = plog1p(a_alias[2]);
+  r_alias[3] = plog1p(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pexpm1<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = pexpm1(a_alias[0]);
+  r_alias[1] = pexpm1(a_alias[1]);
+  r_alias[2] = pexpm1(a_alias[2]);
+  r_alias[3] = pexpm1(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 plog<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = plog(a_alias[0]);
+  r_alias[1] = plog(a_alias[1]);
+  r_alias[2] = plog(a_alias[2]);
+  r_alias[3] = plog(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pexp<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = pexp(a_alias[0]);
+  r_alias[1] = pexp(a_alias[1]);
+  r_alias[2] = pexp(a_alias[2]);
+  r_alias[3] = pexp(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 psqrt<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = psqrt(a_alias[0]);
+  r_alias[1] = psqrt(a_alias[1]);
+  r_alias[2] = psqrt(a_alias[2]);
+  r_alias[3] = psqrt(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 prsqrt<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = prsqrt(a_alias[0]);
+  r_alias[1] = prsqrt(a_alias[1]);
+  r_alias[2] = prsqrt(a_alias[2]);
+  r_alias[3] = prsqrt(a_alias[3]);
+  return r;
+}
+
+// The following specialized padd, pmul, pdiv, pmin, pmax, pset1 are needed for
+// the implementation of GPU half reduction.
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 padd<half2>(const half2& a, const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hadd2(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 + b1;
+  float r2 = a2 + b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmul<half2>(const half2& a, const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hmul2(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 * b1;
+  float r2 = a2 * b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pdiv<half2>(const half2& a, const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __h2div(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 / b1;
+  float r2 = a2 / b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin<half2>(const half2& a, const half2& b) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  __half r1 = a1 < b1 ? __low2half(a) : __low2half(b);
+  __half r2 = a2 < b2 ? __high2half(a) : __high2half(b);
+  return __halves2half2(r1, r2);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax<half2>(const half2& a, const half2& b) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  __half r1 = a1 > b1 ? __low2half(a) : __low2half(b);
+  __half r2 = a2 > b2 ? __high2half(a) : __high2half(b);
+  return __halves2half2(r1, r2);
+}
+
+#endif  // (defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)) && defined(EIGEN_GPU_COMPILE_PHASE)
+
+#undef EIGEN_GPU_HAS_LDG
+#undef EIGEN_CUDA_HAS_FP16_ARITHMETIC
+#undef EIGEN_GPU_HAS_FP16_ARITHMETIC
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_GPU_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/Tuple.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/Tuple.h
new file mode 100644
index 0000000..0e74487
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/Tuple.h
@@ -0,0 +1,268 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2021 The Eigen Team
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TUPLE_GPU
+#define EIGEN_TUPLE_GPU
+
+#include <type_traits>
+#include <utility>
+
+// This is a replacement of std::tuple that can be used in device code.
+
+namespace Eigen {
+namespace internal {
+namespace tuple_impl {
+
+// Internal tuple implementation.
+template <size_t N, typename... Types>
+class TupleImpl;
+
+// Generic recursive tuple.
+template <size_t N, typename T1, typename... Ts>
+class TupleImpl<N, T1, Ts...> {
+ public:
+  // Tuple may contain Eigen types.
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
+
+  // Default constructor, enable if all types are default-constructible.
+  template <typename U1 = T1,
+            typename EnableIf = std::enable_if_t<std::is_default_constructible<U1>::value &&
+                                                 reduce_all<std::is_default_constructible<Ts>::value...>::value>>
+  constexpr EIGEN_DEVICE_FUNC TupleImpl() : m_head{}, m_tail{} {}
+
+  // Element constructor.
+  template <typename U1, typename... Us,
+            // Only enable if...
+            typename EnableIf = std::enable_if_t<
+                // the number of input arguments match, and ...
+                sizeof...(Us) == sizeof...(Ts) && (
+                                                      // this does not look like a copy/move constructor.
+                                                      N > 1 || std::is_convertible<U1, T1>::value)>>
+  constexpr EIGEN_DEVICE_FUNC TupleImpl(U1&& arg1, Us&&... args)
+      : m_head(std::forward<U1>(arg1)), m_tail(std::forward<Us>(args)...) {}
+
+  // The first stored value.
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T1& head() { return m_head; }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const T1& head() const { return m_head; }
+
+  // The tail values.
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE TupleImpl<N - 1, Ts...>& tail() { return m_tail; }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const TupleImpl<N - 1, Ts...>& tail() const { return m_tail; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void swap(TupleImpl& other) {
+    using numext::swap;
+    swap(m_head, other.m_head);
+    swap(m_tail, other.m_tail);
+  }
+
+  template <typename... UTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TupleImpl& operator=(const TupleImpl<N, UTypes...>& other) {
+    m_head = other.m_head;
+    m_tail = other.m_tail;
+    return *this;
+  }
+
+  template <typename... UTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TupleImpl& operator=(TupleImpl<N, UTypes...>&& other) {
+    m_head = std::move(other.m_head);
+    m_tail = std::move(other.m_tail);
+    return *this;
+  }
+
+ private:
+  // Allow related tuples to reference m_head/m_tail.
+  template <size_t M, typename... UTypes>
+  friend class TupleImpl;
+
+  T1 m_head;
+  TupleImpl<N - 1, Ts...> m_tail;
+};
+
+// Empty tuple specialization.
+template <>
+class TupleImpl<size_t(0)> {};
+
+template <typename TupleType>
+struct is_tuple : std::false_type {};
+
+template <typename... Types>
+struct is_tuple<TupleImpl<sizeof...(Types), Types...>> : std::true_type {};
+
+// Gets an element from a tuple.
+template <size_t Idx, typename T1, typename... Ts>
+struct tuple_get_impl {
+  using TupleType = TupleImpl<sizeof...(Ts) + 1, T1, Ts...>;
+  using ReturnType = typename tuple_get_impl<Idx - 1, Ts...>::ReturnType;
+
+  static constexpr EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ReturnType& run(TupleType& tuple) {
+    return tuple_get_impl<Idx - 1, Ts...>::run(tuple.tail());
+  }
+
+  static constexpr EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const ReturnType& run(const TupleType& tuple) {
+    return tuple_get_impl<Idx - 1, Ts...>::run(tuple.tail());
+  }
+};
+
+// Base case, getting the head element.
+template <typename T1, typename... Ts>
+struct tuple_get_impl<0, T1, Ts...> {
+  using TupleType = TupleImpl<sizeof...(Ts) + 1, T1, Ts...>;
+  using ReturnType = T1;
+
+  static constexpr EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T1& run(TupleType& tuple) { return tuple.head(); }
+
+  static constexpr EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const T1& run(const TupleType& tuple) { return tuple.head(); }
+};
+
+// Concatenates N Tuples.
+template <size_t NTuples, typename... Tuples>
+struct tuple_cat_impl;
+
+template <size_t NTuples, size_t N1, typename... Args1, size_t N2, typename... Args2, typename... Tuples>
+struct tuple_cat_impl<NTuples, TupleImpl<N1, Args1...>, TupleImpl<N2, Args2...>, Tuples...> {
+  using TupleType1 = TupleImpl<N1, Args1...>;
+  using TupleType2 = TupleImpl<N2, Args2...>;
+  using MergedTupleType = TupleImpl<N1 + N2, Args1..., Args2...>;
+
+  using ReturnType = typename tuple_cat_impl<NTuples - 1, MergedTupleType, Tuples...>::ReturnType;
+
+  // Uses the index sequences to extract and merge elements from tuple1 and tuple2,
+  // then recursively calls again.
+  template <typename Tuple1, size_t... I1s, typename Tuple2, size_t... I2s, typename... MoreTuples>
+  static constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ReturnType run(Tuple1&& tuple1, std::index_sequence<I1s...>,
+                                                                        Tuple2&& tuple2, std::index_sequence<I2s...>,
+                                                                        MoreTuples&&... tuples) {
+    return tuple_cat_impl<NTuples - 1, MergedTupleType, Tuples...>::run(
+        MergedTupleType(tuple_get_impl<I1s, Args1...>::run(std::forward<Tuple1>(tuple1))...,
+                        tuple_get_impl<I2s, Args2...>::run(std::forward<Tuple2>(tuple2))...),
+        std::forward<MoreTuples>(tuples)...);
+  }
+
+  // Concatenates the first two tuples.
+  template <typename Tuple1, typename Tuple2, typename... MoreTuples>
+  static constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ReturnType run(Tuple1&& tuple1, Tuple2&& tuple2,
+                                                                        MoreTuples&&... tuples) {
+    return run(std::forward<Tuple1>(tuple1), std::make_index_sequence<N1>{}, std::forward<Tuple2>(tuple2),
+               std::make_index_sequence<N2>{}, std::forward<MoreTuples>(tuples)...);
+  }
+};
+
+// Base case with a single tuple.
+template <size_t N, typename... Args>
+struct tuple_cat_impl<1, TupleImpl<N, Args...>> {
+  using ReturnType = TupleImpl<N, Args...>;
+
+  template <typename Tuple1>
+  static constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ReturnType run(Tuple1&& tuple1) {
+    return tuple1;
+  }
+};
+
+// Special case of no tuples.
+template <>
+struct tuple_cat_impl<0> {
+  using ReturnType = TupleImpl<0>;
+  static constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ReturnType run() { return ReturnType{}; }
+};
+
+// For use in make_tuple, unwraps a reference_wrapper.
+template <typename T>
+struct unwrap_reference_wrapper {
+  using type = T;
+};
+
+template <typename T>
+struct unwrap_reference_wrapper<std::reference_wrapper<T>> {
+  using type = T&;
+};
+
+// For use in make_tuple, decays a type and unwraps a reference_wrapper.
+template <typename T>
+struct unwrap_decay {
+  using type = typename unwrap_reference_wrapper<typename std::decay<T>::type>::type;
+};
+
+/**
+ * Utility for determining a tuple's size.
+ */
+template <typename Tuple>
+struct tuple_size;
+
+template <typename... Types>
+struct tuple_size<TupleImpl<sizeof...(Types), Types...>> : std::integral_constant<size_t, sizeof...(Types)> {};
+
+/**
+ * Gets an element of a tuple.
+ * \tparam Idx index of the element.
+ * \tparam Types ... tuple element types.
+ * \param tuple the tuple.
+ * \return a reference to the desired element.
+ */
+template <size_t Idx, typename... Types>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename tuple_get_impl<Idx, Types...>::ReturnType& get(
+    const TupleImpl<sizeof...(Types), Types...>& tuple) {
+  return tuple_get_impl<Idx, Types...>::run(tuple);
+}
+
+template <size_t Idx, typename... Types>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename tuple_get_impl<Idx, Types...>::ReturnType& get(
+    TupleImpl<sizeof...(Types), Types...>& tuple) {
+  return tuple_get_impl<Idx, Types...>::run(tuple);
+}
+
+/**
+ * Concatenate multiple tuples.
+ * \param tuples ... list of tuples.
+ * \return concatenated tuple.
+ */
+template <typename... Tuples, typename EnableIf = std::enable_if_t<
+                                  internal::reduce_all<is_tuple<typename std::decay<Tuples>::type>::value...>::value>>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    typename tuple_cat_impl<sizeof...(Tuples), typename std::decay<Tuples>::type...>::ReturnType
+    tuple_cat(Tuples&&... tuples) {
+  return tuple_cat_impl<sizeof...(Tuples), typename std::decay<Tuples>::type...>::run(std::forward<Tuples>(tuples)...);
+}
+
+/**
+ * Tie arguments together into a tuple.
+ */
+template <typename... Args, typename ReturnType = TupleImpl<sizeof...(Args), Args&...>>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ReturnType tie(Args&... args) noexcept {
+  return ReturnType{args...};
+}
+
+/**
+ * Create a tuple of l-values with the supplied arguments.
+ */
+template <typename... Args, typename ReturnType = TupleImpl<sizeof...(Args), typename unwrap_decay<Args>::type...>>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ReturnType make_tuple(Args&&... args) {
+  return ReturnType{std::forward<Args>(args)...};
+}
+
+/**
+ * Forward a set of arguments as a tuple.
+ */
+template <typename... Args>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TupleImpl<sizeof...(Args), Args...> forward_as_tuple(Args&&... args) {
+  return TupleImpl<sizeof...(Args), Args...>(std::forward<Args>(args)...);
+}
+
+/**
+ * Alternative to std::tuple that can be used on device.
+ */
+template <typename... Types>
+using tuple = TupleImpl<sizeof...(Types), Types...>;
+
+}  // namespace tuple_impl
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_TUPLE_GPU
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/TypeCasting.h
new file mode 100644
index 0000000..ae43f8e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/GPU/TypeCasting.h
@@ -0,0 +1,77 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_GPU_H
+#define EIGEN_TYPE_CASTING_GPU_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+
+template <>
+struct type_casting_traits<Eigen::half, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcast<half2, float4>(const half2& a, const half2& b) {
+  float2 r1 = __half22float2(a);
+  float2 r2 = __half22float2(b);
+  return make_float4(r1.x, r1.y, r2.x, r2.y);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pcast<float4, Packet4h2>(const float4& a, const float4& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  r_alias[0] = __floats2half2_rn(a.x, a.y);
+  r_alias[1] = __floats2half2_rn(a.z, a.w);
+  r_alias[2] = __floats2half2_rn(b.x, b.y);
+  r_alias[3] = __floats2half2_rn(b.z, b.w);
+  return r;
+}
+
+template <>
+struct type_casting_traits<float, Eigen::half> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcast<Packet4h2, float4>(const Packet4h2& a) {
+  // Simply discard the second half of the input
+  float4 r;
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  float2 r1 = __half22float2(a_alias[0]);
+  float2 r2 = __half22float2(a_alias[1]);
+  r.x = static_cast<float>(r1.x);
+  r.y = static_cast<float>(r1.y);
+  r.z = static_cast<float>(r2.x);
+  r.w = static_cast<float>(r2.y);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcast<float4, half2>(const float4& a) {
+  // Simply discard the second half of the input
+  return __floats2half2_rn(a.x, a.y);
+}
+
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_GPU_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/HIP/hcc/math_constants.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/HIP/hcc/math_constants.h
new file mode 100644
index 0000000..99dd3ae
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/HIP/hcc/math_constants.h
@@ -0,0 +1,23 @@
+/*
+ * math_constants.h -
+ *  HIP equivalent of the CUDA header of the same name
+ */
+
+#ifndef __MATH_CONSTANTS_H__
+#define __MATH_CONSTANTS_H__
+
+/* single precision constants */
+
+#define HIPRT_INF_F __int_as_float(0x7f800000)
+#define HIPRT_NAN_F __int_as_float(0x7fffffff)
+#define HIPRT_MIN_DENORM_F __int_as_float(0x00000001)
+#define HIPRT_MAX_NORMAL_F __int_as_float(0x7f7fffff)
+#define HIPRT_NEG_ZERO_F __int_as_float(0x80000000)
+#define HIPRT_ZERO_F 0.0f
+#define HIPRT_ONE_F 1.0f
+
+/* double precision constants */
+#define HIPRT_INF __hiloint2double(0x7ff00000, 0x00000000)
+#define HIPRT_NAN __hiloint2double(0xfff80000, 0x00000000)
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/HVX/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/HVX/PacketMath.h
new file mode 100644
index 0000000..1249426
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/HVX/PacketMath.h
@@ -0,0 +1,1092 @@
+
+#ifndef EIGEN_HVX_PACKET_MATH_H
+#define EIGEN_HVX_PACKET_MATH_H
+
+// Only support 128B HVX now.
+// Floating-point operations are supported only since V68.
+#if defined __HVX__ && (__HVX_LENGTH__ == 128) && __HVX_ARCH__ >= 68
+
+// All the floating-point operations do not support IEEE standard.
+// From HVX document:
+//   There is no concept of infinity or NaN. QFloat saturates to maximum
+//   exponent with maximum positive or minimum negative significand.
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#endif
+
+namespace Eigen {
+namespace internal {
+
+// HVX utilities.
+
+template <int D>
+EIGEN_STRONG_INLINE HVX_Vector HVX_vmem(const void* m) {
+  HVX_Vector v;
+#if EIGEN_COMP_CLANG
+  // Use inlined assembly for aligned vmem load on unaligned memory.
+  // Use type cast to HVX_Vector* may mess up with compiler data alignment.
+  __asm__("%0 = vmem(%1+#%2)" : "=v"(v) : "r"(m), "i"(D) : "memory");
+#else
+  void* aligned_mem =
+      reinterpret_cast<void*>((reinterpret_cast<uintptr_t>(m) & ~(__HVX_LENGTH__ - 1)) + D * __HVX_LENGTH__);
+  memcpy(&v, aligned_mem, __HVX_LENGTH__);
+#endif
+  return v;
+}
+
+template <typename T>
+EIGEN_STRONG_INLINE HVX_Vector HVX_load(const T* mem) {
+  HVX_Vector v;
+  memcpy(&v, reinterpret_cast<const HVX_Vector*>(mem), __HVX_LENGTH__);
+  return v;
+}
+
+template <typename T>
+EIGEN_STRONG_INLINE HVX_Vector HVX_loadu(const T* mem) {
+  HVX_Vector v;
+  memcpy(&v, mem, __HVX_LENGTH__);
+  return v;
+}
+
+template <size_t Size, size_t Alignment, typename T>
+EIGEN_STRONG_INLINE HVX_Vector HVX_load_partial(const T* mem) {
+#if defined(EIGEN_HVX_FAST_PARTIAL_VECTOR_LOAD)
+  // Fast partial vector load through aligned vmem load.
+  // The load may past end of array but is aligned to prevent memory fault.
+  HVX_Vector v0 = HVX_vmem<0>(mem);
+  HVX_Vector v1 = v0;
+  uintptr_t mem_addr = reinterpret_cast<uintptr_t>(mem);
+  EIGEN_IF_CONSTEXPR(Size * sizeof(T) <= Alignment) {
+    // Data size less than alignment will never cross multiple aligned vectors.
+    v1 = v0;
+  }
+  else {
+    uintptr_t left_off = mem_addr & (__HVX_LENGTH__ - 1);
+    if (left_off + Size * sizeof(T) > __HVX_LENGTH__) {
+      v1 = HVX_vmem<1>(mem);
+    } else {
+      v1 = v0;
+    }
+  }
+  return Q6_V_valign_VVR(v1, v0, mem_addr);
+#else
+  HVX_Vector v;
+  memcpy(&v, mem, Size * sizeof(T));
+  return v;
+#endif
+}
+
+template <typename T>
+EIGEN_STRONG_INLINE void HVX_store(T* mem, HVX_Vector v) {
+  memcpy(reinterpret_cast<HVX_Vector*>(mem), &v, __HVX_LENGTH__);
+}
+
+template <typename T>
+EIGEN_STRONG_INLINE void HVX_storeu(T* mem, HVX_Vector v) {
+  memcpy(mem, &v, __HVX_LENGTH__);
+}
+
+template <size_t Size, size_t Alignment, typename T>
+EIGEN_STRONG_INLINE void HVX_store_partial(T* mem, HVX_Vector v) {
+  uintptr_t mem_addr = reinterpret_cast<uintptr_t>(mem);
+  HVX_Vector value = Q6_V_vlalign_VVR(v, v, mem_addr);
+  uintptr_t left_off = mem_addr & (__HVX_LENGTH__ - 1);
+  uintptr_t right_off = left_off + Size * sizeof(T);
+
+  HVX_VectorPred ql_not = Q6_Q_vsetq_R(mem_addr);
+  HVX_VectorPred qr = Q6_Q_vsetq2_R(right_off);
+
+  EIGEN_IF_CONSTEXPR(Size * sizeof(T) > Alignment) {
+    if (right_off > __HVX_LENGTH__) {
+      Q6_vmem_QRIV(qr, mem + __HVX_LENGTH__ / sizeof(T), value);
+      qr = Q6_Q_vcmp_eq_VbVb(value, value);
+    }
+  }
+
+  ql_not = Q6_Q_or_QQn(ql_not, qr);
+  Q6_vmem_QnRIV(ql_not, mem, value);
+}
+
+// Packet definitions.
+enum class HVXPacketSize {
+  Full,
+  Half,
+  Quarter,
+};
+
+// Hexagon compiler uses same HVX_Vector to represent all HVX vector types.
+// Wrap different vector type (float32, int32, etc) to different class with
+// explicit constructor and casting back-and-force to HVX_Vector.
+template <HVXPacketSize T>
+class HVXPacket {
+ public:
+  HVXPacket() = default;
+  static HVXPacket Create(HVX_Vector v) { return HVXPacket(v); }
+  HVX_Vector Get() const { return m_val; }
+
+ private:
+  explicit HVXPacket(HVX_Vector v) : m_val(v) {}
+  HVX_Vector m_val = Q6_V_vzero();
+};
+
+typedef HVXPacket<HVXPacketSize::Full> Packet32f;
+typedef HVXPacket<HVXPacketSize::Half> Packet16f;
+typedef HVXPacket<HVXPacketSize::Quarter> Packet8f;
+
+// Packet traits.
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet32f type;
+  typedef Packet16f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 32,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 0,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbsDiff = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 0,
+    HasSetLinear = 0,
+    HasDiv = 0,
+    HasSin = 0,
+    HasCos = 0,
+    HasACos = 0,
+    HasASin = 0,
+    HasATan = 0,
+    HasATanh = 0,
+    HasLog = 0,
+    HasExp = 0,
+    HasSqrt = 0,
+    HasRsqrt = 0,
+    HasTanh = 0,
+    HasErf = 0,
+    HasBessel = 0,
+    HasNdtri = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet32f> {
+  typedef float type;
+  typedef Packet16f half;
+  enum {
+    size = 32,
+    alignment = Aligned128,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet16f> {
+  typedef float type;
+  typedef Packet8f half;
+  enum {
+    size = 16,
+    // Many code assume alignment on packet size instead of following trait
+    // So we do not use Aligned128 to optimize aligned load/store,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet8f> {
+  typedef float type;
+  typedef Packet8f half;
+  enum {
+    size = 8,
+    // Many code assume alignment on packet size instead of following trait
+    // So we do not use Aligned128 to optimize aligned load/store,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+// float32 operations.
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pzero_hvx(const HVXPacket<T>&) {
+  return HVXPacket<T>::Create(Q6_V_vzero());
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pzero<Packet32f>(const Packet32f&) {
+  return pzero_hvx(Packet32f());
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pzero<Packet16f>(const Packet16f&) {
+  return pzero_hvx(Packet16f());
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pzero<Packet8f>(const Packet8f&) {
+  return pzero_hvx(Packet8f());
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE typename unpacket_traits<HVXPacket<T>>::half predux_half_hvx(const HVXPacket<T>& a) {
+  const Index packet_size = unpacket_traits<HVXPacket<T>>::size;
+  return unpacket_traits<HVXPacket<T>>::half::Create(
+      Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_vror_VR(a.Get(), sizeof(float) * packet_size / 2), a.Get())));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f predux_half(const Packet32f& a) {
+  return predux_half_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f predux_half(const Packet16f& a) {
+  return predux_half_hvx(a);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pset1_hvx(const float& from) {
+  union {
+    float f;
+    int32_t i;
+  } u;
+  u.f = from;
+  return HVXPacket<T>::Create(Q6_V_vsplat_R(u.i));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pset1<Packet32f>(const float& from) {
+  return pset1_hvx<HVXPacketSize::Full>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pset1<Packet16f>(const float& from) {
+  return pset1_hvx<HVXPacketSize::Half>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pset1<Packet8f>(const float& from) {
+  return pset1_hvx<HVXPacketSize::Quarter>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pload<Packet32f>(const float* from) {
+  return Packet32f::Create(HVX_load(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pload<Packet16f>(const float* from) {
+  return Packet16f::Create(
+      HVX_load_partial<unpacket_traits<Packet16f>::size, unpacket_traits<Packet16f>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pload<Packet8f>(const float* from) {
+  return Packet8f::Create(
+      HVX_load_partial<unpacket_traits<Packet8f>::size, unpacket_traits<Packet8f>::alignment>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f ploadu<Packet32f>(const float* from) {
+  return Packet32f::Create(HVX_loadu(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f ploadu<Packet16f>(const float* from) {
+  return Packet16f::Create(HVX_load_partial<unpacket_traits<Packet16f>::size, 0>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from) {
+  return Packet8f::Create(HVX_load_partial<unpacket_traits<Packet8f>::size, 0>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet32f& from) {
+  HVX_store(to, from.Get());
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet16f& from) {
+  HVX_store_partial<unpacket_traits<Packet16f>::size, unpacket_traits<Packet16f>::alignment>(to, from.Get());
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet8f& from) {
+  HVX_store_partial<unpacket_traits<Packet8f>::size, unpacket_traits<Packet8f>::alignment>(to, from.Get());
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet32f& from) {
+  HVX_storeu(to, from.Get());
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet16f& from) {
+  HVX_store_partial<unpacket_traits<Packet16f>::size, 0>(to, from.Get());
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet8f& from) {
+  HVX_store_partial<unpacket_traits<Packet8f>::size, 0>(to, from.Get());
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pmul_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  return HVXPacket<T>::Create(Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a.Get(), b.Get())));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pmul<Packet32f>(const Packet32f& a, const Packet32f& b) {
+  return pmul_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pmul<Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return pmul_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pmul<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return pmul_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> padd_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  return HVXPacket<T>::Create(Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a.Get(), b.Get())));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f padd<Packet32f>(const Packet32f& a, const Packet32f& b) {
+  return padd_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f padd<Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return padd_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return padd_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> psub_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  return HVXPacket<T>::Create(Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(a.Get(), b.Get())));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f psub<Packet32f>(const Packet32f& a, const Packet32f& b) {
+  return psub_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f psub<Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return psub_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f psub<Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return psub_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pnegate_hvx(const HVXPacket<T>& a) {
+  return HVXPacket<T>::Create(a.Get() ^ Q6_V_vsplat_R(0x80000000));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pnegate(const Packet32f& a) {
+  return pnegate_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pnegate(const Packet16f& a) {
+  return pnegate_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pnegate(const Packet8f& a) {
+  return pnegate_hvx(a);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> ptrue_hvx(const HVXPacket<T>& a) {
+  return HVXPacket<T>::Create(Q6_V_vsplat_R(0x3f800000));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f ptrue(const Packet32f& a) {
+  return ptrue_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f ptrue(const Packet16f& a) {
+  return ptrue_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f ptrue(const Packet8f& a) {
+  return ptrue_hvx(a);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pcmp_le_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  HVX_Vector v_true = ptrue(a).Get();
+  HVX_VectorPred pred = Q6_Q_vcmp_gt_VsfVsf(a.Get(), b.Get());
+  return HVXPacket<T>::Create(Q6_V_vmux_QVV(pred, Q6_V_vzero(), v_true));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pcmp_le(const Packet32f& a, const Packet32f& b) {
+  return pcmp_le_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pcmp_le(const Packet16f& a, const Packet16f& b) {
+  return pcmp_le_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pcmp_le(const Packet8f& a, const Packet8f& b) {
+  return pcmp_le_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pcmp_eq_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  HVX_Vector v_true = ptrue(a).Get();
+  HVX_VectorPred pred = Q6_Q_vcmp_eq_VwVw(a.Get(), b.Get());
+  return HVXPacket<T>::Create(Q6_V_vmux_QVV(pred, v_true, Q6_V_vzero()));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pcmp_eq(const Packet32f& a, const Packet32f& b) {
+  return pcmp_eq_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pcmp_eq(const Packet16f& a, const Packet16f& b) {
+  return pcmp_eq_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pcmp_eq(const Packet8f& a, const Packet8f& b) {
+  return pcmp_eq_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pcmp_lt_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  HVX_Vector v_true = ptrue(a).Get();
+  HVX_VectorPred pred = Q6_Q_vcmp_gt_VsfVsf(b.Get(), a.Get());
+  return HVXPacket<T>::Create(Q6_V_vmux_QVV(pred, v_true, Q6_V_vzero()));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pcmp_lt(const Packet32f& a, const Packet32f& b) {
+  return pcmp_lt_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pcmp_lt(const Packet16f& a, const Packet16f& b) {
+  return pcmp_lt_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pcmp_lt(const Packet8f& a, const Packet8f& b) {
+  return pcmp_lt_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pcmp_lt_or_nan_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  HVX_Vector v_true = ptrue(a).Get();
+  HVX_VectorPred pred = Q6_Q_vcmp_gt_VsfVsf(b.Get(), a.Get());
+  return HVXPacket<T>::Create(Q6_V_vmux_QVV(pred, v_true, Q6_V_vzero()));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pcmp_lt_or_nan(const Packet32f& a, const Packet32f& b) {
+  return pcmp_lt_or_nan_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pcmp_lt_or_nan(const Packet16f& a, const Packet16f& b) {
+  return pcmp_lt_or_nan_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pcmp_lt_or_nan(const Packet8f& a, const Packet8f& b) {
+  return pcmp_lt_or_nan_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pabs_hvx(const HVXPacket<T>& a) {
+  return HVXPacket<T>::Create(a.Get() & Q6_V_vsplat_R(0x7FFFFFFF));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pabs(const Packet32f& a) {
+  return pabs_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pabs(const Packet16f& a) {
+  return pabs_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pabs(const Packet8f& a) {
+  return pabs_hvx(a);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE float pfirst_hvx(const HVXPacket<T>& a) {
+  union {
+    float array[1];
+    HVX_Vector vector;
+  } HVX_and_array;
+  HVX_and_array.vector = a.Get();
+  return HVX_and_array.array[0];
+}
+template <>
+EIGEN_STRONG_INLINE float pfirst(const Packet32f& a) {
+  return pfirst_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE float pfirst(const Packet16f& a) {
+  return pfirst_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE float pfirst(const Packet8f& a) {
+  return pfirst_hvx(a);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet32f, 4>& kernel) {
+  // Shuffle the 32-bit lanes.
+  HVX_VectorPair v_0_1_0 = Q6_W_vshuff_VVR(kernel.packet[1].Get(), kernel.packet[0].Get(), -4);
+  HVX_VectorPair v_0_3_2 = Q6_W_vshuff_VVR(kernel.packet[3].Get(), kernel.packet[2].Get(), -4);
+
+  // Shuffle the 64-bit lanes.
+  HVX_VectorPair v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_3_2), HEXAGON_HVX_GET_V0(v_0_1_0), -8);
+  HVX_VectorPair v_1_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_3_2), HEXAGON_HVX_GET_V1(v_0_1_0), -8);
+  kernel.packet[0] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_1_1_0));
+  kernel.packet[1] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_1_1_0));
+  kernel.packet[2] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_1_3_2));
+  kernel.packet[3] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_1_3_2));
+}
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16f, 4>& kernel) {
+  // Shuffle the 32-bit lanes.
+  HVX_VectorPair v_0_1_0 = Q6_W_vshuff_VVR(kernel.packet[1].Get(), kernel.packet[0].Get(), -4);
+  HVX_VectorPair v_0_3_2 = Q6_W_vshuff_VVR(kernel.packet[3].Get(), kernel.packet[2].Get(), -4);
+
+  // Shuffle the 64-bit lanes.
+  HVX_VectorPair v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_3_2), HEXAGON_HVX_GET_V0(v_0_1_0), -8);
+
+  kernel.packet[0] = Packet16f::Create(HEXAGON_HVX_GET_V0(v_1_1_0));
+  kernel.packet[1] = Packet16f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_1_1_0), HEXAGON_HVX_GET_V0(v_1_1_0), 64));
+  kernel.packet[2] = Packet16f::Create(HEXAGON_HVX_GET_V1(v_1_1_0));
+  kernel.packet[3] = Packet16f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V1(v_1_1_0), HEXAGON_HVX_GET_V1(v_1_1_0), 64));
+}
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8f, 4>& kernel) {
+  // Shuffle the 32-bit lanes.
+  HVX_VectorPair v_0_1_0 = Q6_W_vshuff_VVR(kernel.packet[1].Get(), kernel.packet[0].Get(), -4);
+  HVX_VectorPair v_0_3_2 = Q6_W_vshuff_VVR(kernel.packet[3].Get(), kernel.packet[2].Get(), -4);
+
+  // Shuffle the 64-bit lanes.
+  HVX_VectorPair v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_3_2), HEXAGON_HVX_GET_V0(v_0_1_0), -8);
+
+  kernel.packet[0] = Packet8f::Create(HEXAGON_HVX_GET_V0(v_1_1_0));
+  kernel.packet[1] = Packet8f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_1_1_0), HEXAGON_HVX_GET_V0(v_1_1_0), 32));
+  kernel.packet[2] = Packet8f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_1_1_0), HEXAGON_HVX_GET_V0(v_1_1_0), 64));
+  kernel.packet[3] = Packet8f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_1_1_0), HEXAGON_HVX_GET_V0(v_1_1_0), 96));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8f, 8>& kernel) {
+  // Shuffle the 32-bit lanes.
+  HVX_VectorPair v_0_1_0 = Q6_W_vshuff_VVR(kernel.packet[1].Get(), kernel.packet[0].Get(), -4);
+  HVX_VectorPair v_0_3_2 = Q6_W_vshuff_VVR(kernel.packet[3].Get(), kernel.packet[2].Get(), -4);
+  HVX_VectorPair v_0_5_4 = Q6_W_vshuff_VVR(kernel.packet[5].Get(), kernel.packet[4].Get(), -4);
+  HVX_VectorPair v_0_7_6 = Q6_W_vshuff_VVR(kernel.packet[7].Get(), kernel.packet[6].Get(), -4);
+
+  // Shuffle the 64-bit lanes.
+  HVX_VectorPair v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_3_2), HEXAGON_HVX_GET_V0(v_0_1_0), -8);
+  HVX_VectorPair v_1_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_7_6), HEXAGON_HVX_GET_V0(v_0_5_4), -8);
+
+  // Shuffle the 128-bit lanes.
+  v_0_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_3_2), HEXAGON_HVX_GET_V0(v_1_1_0), -16);
+
+  kernel.packet[0] = Packet8f::Create(HEXAGON_HVX_GET_V0(v_0_1_0));
+  kernel.packet[1] = Packet8f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_0_1_0), HEXAGON_HVX_GET_V0(v_0_1_0), 32));
+  kernel.packet[2] = Packet8f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_0_1_0), HEXAGON_HVX_GET_V0(v_0_1_0), 64));
+  kernel.packet[3] = Packet8f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_0_1_0), HEXAGON_HVX_GET_V0(v_0_1_0), 96));
+  kernel.packet[4] = Packet8f::Create(HEXAGON_HVX_GET_V1(v_0_1_0));
+  kernel.packet[5] = Packet8f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V1(v_0_1_0), HEXAGON_HVX_GET_V1(v_0_1_0), 32));
+  kernel.packet[6] = Packet8f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V1(v_0_1_0), HEXAGON_HVX_GET_V1(v_0_1_0), 64));
+  kernel.packet[7] = Packet8f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V1(v_0_1_0), HEXAGON_HVX_GET_V1(v_0_1_0), 96));
+}
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16f, 16>& kernel) {
+  // Shuffle the 32-bit lanes.
+  HVX_VectorPair v_0_1_0 = Q6_W_vshuff_VVR(kernel.packet[1].Get(), kernel.packet[0].Get(), -4);
+  HVX_VectorPair v_0_3_2 = Q6_W_vshuff_VVR(kernel.packet[3].Get(), kernel.packet[2].Get(), -4);
+  HVX_VectorPair v_0_5_4 = Q6_W_vshuff_VVR(kernel.packet[5].Get(), kernel.packet[4].Get(), -4);
+  HVX_VectorPair v_0_7_6 = Q6_W_vshuff_VVR(kernel.packet[7].Get(), kernel.packet[6].Get(), -4);
+  HVX_VectorPair v_0_9_8 = Q6_W_vshuff_VVR(kernel.packet[9].Get(), kernel.packet[8].Get(), -4);
+  HVX_VectorPair v_0_11_10 = Q6_W_vshuff_VVR(kernel.packet[11].Get(), kernel.packet[10].Get(), -4);
+  HVX_VectorPair v_0_13_12 = Q6_W_vshuff_VVR(kernel.packet[13].Get(), kernel.packet[12].Get(), -4);
+  HVX_VectorPair v_0_15_14 = Q6_W_vshuff_VVR(kernel.packet[15].Get(), kernel.packet[14].Get(), -4);
+
+  // Shuffle the 64-bit lanes.
+  HVX_VectorPair v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_3_2), HEXAGON_HVX_GET_V0(v_0_1_0), -8);
+  HVX_VectorPair v_1_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_7_6), HEXAGON_HVX_GET_V0(v_0_5_4), -8);
+  HVX_VectorPair v_1_5_4 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_11_10), HEXAGON_HVX_GET_V0(v_0_9_8), -8);
+  HVX_VectorPair v_1_7_6 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_15_14), HEXAGON_HVX_GET_V0(v_0_13_12), -8);
+
+  // Shuffle the 128-bit lanes.
+  v_0_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_3_2), HEXAGON_HVX_GET_V0(v_1_1_0), -16);
+  v_0_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_3_2), HEXAGON_HVX_GET_V1(v_1_1_0), -16);
+  v_0_9_8 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_7_6), HEXAGON_HVX_GET_V0(v_1_5_4), -16);
+  v_0_11_10 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_7_6), HEXAGON_HVX_GET_V1(v_1_5_4), -16);
+
+  // Shuffle the 256-bit lanes.
+  v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_9_8), HEXAGON_HVX_GET_V0(v_0_1_0), -32);
+  v_1_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_9_8), HEXAGON_HVX_GET_V1(v_0_1_0), -32);
+  v_1_5_4 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_11_10), HEXAGON_HVX_GET_V0(v_0_3_2), -32);
+  v_1_7_6 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_11_10), HEXAGON_HVX_GET_V1(v_0_3_2), -32);
+
+  kernel.packet[0] = Packet16f::Create(HEXAGON_HVX_GET_V0(v_1_1_0));
+  kernel.packet[1] = Packet16f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_1_1_0), HEXAGON_HVX_GET_V0(v_1_1_0), 64));
+  kernel.packet[2] = Packet16f::Create(HEXAGON_HVX_GET_V1(v_1_1_0));
+  kernel.packet[3] = Packet16f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V1(v_1_1_0), HEXAGON_HVX_GET_V1(v_1_1_0), 64));
+  kernel.packet[4] = Packet16f::Create(HEXAGON_HVX_GET_V0(v_1_3_2));
+  kernel.packet[5] = Packet16f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_1_3_2), HEXAGON_HVX_GET_V0(v_1_3_2), 64));
+  kernel.packet[6] = Packet16f::Create(HEXAGON_HVX_GET_V1(v_1_3_2));
+  kernel.packet[7] = Packet16f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V1(v_1_3_2), HEXAGON_HVX_GET_V1(v_1_3_2), 64));
+  kernel.packet[8] = Packet16f::Create(HEXAGON_HVX_GET_V0(v_1_5_4));
+  kernel.packet[9] = Packet16f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_1_5_4), HEXAGON_HVX_GET_V0(v_1_5_4), 64));
+  kernel.packet[10] = Packet16f::Create(HEXAGON_HVX_GET_V1(v_1_5_4));
+  kernel.packet[11] = Packet16f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V1(v_1_5_4), HEXAGON_HVX_GET_V1(v_1_5_4), 64));
+  kernel.packet[12] = Packet16f::Create(HEXAGON_HVX_GET_V0(v_1_7_6));
+  kernel.packet[13] = Packet16f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V0(v_1_7_6), HEXAGON_HVX_GET_V0(v_1_7_6), 64));
+  kernel.packet[14] = Packet16f::Create(HEXAGON_HVX_GET_V1(v_1_7_6));
+  kernel.packet[15] = Packet16f::Create(Q6_V_valign_VVR(HEXAGON_HVX_GET_V1(v_1_7_6), HEXAGON_HVX_GET_V1(v_1_7_6), 64));
+}
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet32f, 32>& kernel) {
+  // Shuffle the 32-bit lanes.
+  HVX_VectorPair v_0_1_0 = Q6_W_vshuff_VVR(kernel.packet[1].Get(), kernel.packet[0].Get(), -4);
+  HVX_VectorPair v_0_3_2 = Q6_W_vshuff_VVR(kernel.packet[3].Get(), kernel.packet[2].Get(), -4);
+  HVX_VectorPair v_0_5_4 = Q6_W_vshuff_VVR(kernel.packet[5].Get(), kernel.packet[4].Get(), -4);
+  HVX_VectorPair v_0_7_6 = Q6_W_vshuff_VVR(kernel.packet[7].Get(), kernel.packet[6].Get(), -4);
+  HVX_VectorPair v_0_9_8 = Q6_W_vshuff_VVR(kernel.packet[9].Get(), kernel.packet[8].Get(), -4);
+  HVX_VectorPair v_0_11_10 = Q6_W_vshuff_VVR(kernel.packet[11].Get(), kernel.packet[10].Get(), -4);
+  HVX_VectorPair v_0_13_12 = Q6_W_vshuff_VVR(kernel.packet[13].Get(), kernel.packet[12].Get(), -4);
+  HVX_VectorPair v_0_15_14 = Q6_W_vshuff_VVR(kernel.packet[15].Get(), kernel.packet[14].Get(), -4);
+  HVX_VectorPair v_0_17_16 = Q6_W_vshuff_VVR(kernel.packet[17].Get(), kernel.packet[16].Get(), -4);
+  HVX_VectorPair v_0_19_18 = Q6_W_vshuff_VVR(kernel.packet[19].Get(), kernel.packet[18].Get(), -4);
+  HVX_VectorPair v_0_21_20 = Q6_W_vshuff_VVR(kernel.packet[21].Get(), kernel.packet[20].Get(), -4);
+  HVX_VectorPair v_0_23_22 = Q6_W_vshuff_VVR(kernel.packet[23].Get(), kernel.packet[22].Get(), -4);
+  HVX_VectorPair v_0_25_24 = Q6_W_vshuff_VVR(kernel.packet[25].Get(), kernel.packet[24].Get(), -4);
+  HVX_VectorPair v_0_27_26 = Q6_W_vshuff_VVR(kernel.packet[27].Get(), kernel.packet[26].Get(), -4);
+  HVX_VectorPair v_0_29_28 = Q6_W_vshuff_VVR(kernel.packet[29].Get(), kernel.packet[28].Get(), -4);
+  HVX_VectorPair v_0_31_30 = Q6_W_vshuff_VVR(kernel.packet[31].Get(), kernel.packet[30].Get(), -4);
+
+  // Shuffle the 64-bit lanes.
+  HVX_VectorPair v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_3_2), HEXAGON_HVX_GET_V0(v_0_1_0), -8);
+  HVX_VectorPair v_1_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_3_2), HEXAGON_HVX_GET_V1(v_0_1_0), -8);
+  HVX_VectorPair v_1_5_4 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_7_6), HEXAGON_HVX_GET_V0(v_0_5_4), -8);
+  HVX_VectorPair v_1_7_6 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_7_6), HEXAGON_HVX_GET_V1(v_0_5_4), -8);
+  HVX_VectorPair v_1_9_8 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_11_10), HEXAGON_HVX_GET_V0(v_0_9_8), -8);
+  HVX_VectorPair v_1_11_10 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_11_10), HEXAGON_HVX_GET_V1(v_0_9_8), -8);
+  HVX_VectorPair v_1_13_12 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_15_14), HEXAGON_HVX_GET_V0(v_0_13_12), -8);
+  HVX_VectorPair v_1_15_14 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_15_14), HEXAGON_HVX_GET_V1(v_0_13_12), -8);
+  HVX_VectorPair v_1_17_16 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_19_18), HEXAGON_HVX_GET_V0(v_0_17_16), -8);
+  HVX_VectorPair v_1_19_18 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_19_18), HEXAGON_HVX_GET_V1(v_0_17_16), -8);
+  HVX_VectorPair v_1_21_20 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_23_22), HEXAGON_HVX_GET_V0(v_0_21_20), -8);
+  HVX_VectorPair v_1_23_22 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_23_22), HEXAGON_HVX_GET_V1(v_0_21_20), -8);
+  HVX_VectorPair v_1_25_24 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_27_26), HEXAGON_HVX_GET_V0(v_0_25_24), -8);
+  HVX_VectorPair v_1_27_26 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_27_26), HEXAGON_HVX_GET_V1(v_0_25_24), -8);
+  HVX_VectorPair v_1_29_28 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_31_30), HEXAGON_HVX_GET_V0(v_0_29_28), -8);
+  HVX_VectorPair v_1_31_30 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_31_30), HEXAGON_HVX_GET_V1(v_0_29_28), -8);
+
+  // Shuffle the 128-bit lanes.
+  v_0_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_5_4), HEXAGON_HVX_GET_V0(v_1_1_0), -16);
+  v_0_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_5_4), HEXAGON_HVX_GET_V1(v_1_1_0), -16);
+  v_0_5_4 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_7_6), HEXAGON_HVX_GET_V0(v_1_3_2), -16);
+  v_0_7_6 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_7_6), HEXAGON_HVX_GET_V1(v_1_3_2), -16);
+  v_0_9_8 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_13_12), HEXAGON_HVX_GET_V0(v_1_9_8), -16);
+  v_0_11_10 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_13_12), HEXAGON_HVX_GET_V1(v_1_9_8), -16);
+  v_0_13_12 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_15_14), HEXAGON_HVX_GET_V0(v_1_11_10), -16);
+  v_0_15_14 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_15_14), HEXAGON_HVX_GET_V1(v_1_11_10), -16);
+  v_0_17_16 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_21_20), HEXAGON_HVX_GET_V0(v_1_17_16), -16);
+  v_0_19_18 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_21_20), HEXAGON_HVX_GET_V1(v_1_17_16), -16);
+  v_0_21_20 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_23_22), HEXAGON_HVX_GET_V0(v_1_19_18), -16);
+  v_0_23_22 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_23_22), HEXAGON_HVX_GET_V1(v_1_19_18), -16);
+  v_0_25_24 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_29_28), HEXAGON_HVX_GET_V0(v_1_25_24), -16);
+  v_0_27_26 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_29_28), HEXAGON_HVX_GET_V1(v_1_25_24), -16);
+  v_0_29_28 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_31_30), HEXAGON_HVX_GET_V0(v_1_27_26), -16);
+  v_0_31_30 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_31_30), HEXAGON_HVX_GET_V1(v_1_27_26), -16);
+
+  // Shuffle the 256-bit lanes.
+  v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_9_8), HEXAGON_HVX_GET_V0(v_0_1_0), -32);
+  v_1_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_9_8), HEXAGON_HVX_GET_V1(v_0_1_0), -32);
+  v_1_5_4 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_11_10), HEXAGON_HVX_GET_V0(v_0_3_2), -32);
+  v_1_7_6 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_11_10), HEXAGON_HVX_GET_V1(v_0_3_2), -32);
+  v_1_9_8 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_13_12), HEXAGON_HVX_GET_V0(v_0_5_4), -32);
+  v_1_11_10 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_13_12), HEXAGON_HVX_GET_V1(v_0_5_4), -32);
+  v_1_13_12 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_15_14), HEXAGON_HVX_GET_V0(v_0_7_6), -32);
+  v_1_15_14 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_15_14), HEXAGON_HVX_GET_V1(v_0_7_6), -32);
+  v_1_17_16 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_25_24), HEXAGON_HVX_GET_V0(v_0_17_16), -32);
+  v_1_19_18 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_25_24), HEXAGON_HVX_GET_V1(v_0_17_16), -32);
+  v_1_21_20 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_27_26), HEXAGON_HVX_GET_V0(v_0_19_18), -32);
+  v_1_23_22 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_27_26), HEXAGON_HVX_GET_V1(v_0_19_18), -32);
+  v_1_25_24 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_29_28), HEXAGON_HVX_GET_V0(v_0_21_20), -32);
+  v_1_27_26 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_29_28), HEXAGON_HVX_GET_V1(v_0_21_20), -32);
+  v_1_29_28 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_31_30), HEXAGON_HVX_GET_V0(v_0_23_22), -32);
+  v_1_31_30 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_31_30), HEXAGON_HVX_GET_V1(v_0_23_22), -32);
+
+  // Shuffle the 512-bit lanes.
+  v_0_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_17_16), HEXAGON_HVX_GET_V0(v_1_1_0), -64);
+  v_0_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_17_16), HEXAGON_HVX_GET_V1(v_1_1_0), -64);
+  v_0_5_4 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_19_18), HEXAGON_HVX_GET_V0(v_1_3_2), -64);
+  v_0_7_6 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_19_18), HEXAGON_HVX_GET_V1(v_1_3_2), -64);
+  v_0_9_8 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_21_20), HEXAGON_HVX_GET_V0(v_1_5_4), -64);
+  v_0_11_10 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_21_20), HEXAGON_HVX_GET_V1(v_1_5_4), -64);
+  v_0_13_12 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_23_22), HEXAGON_HVX_GET_V0(v_1_7_6), -64);
+  v_0_15_14 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_23_22), HEXAGON_HVX_GET_V1(v_1_7_6), -64);
+  v_0_17_16 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_25_24), HEXAGON_HVX_GET_V0(v_1_9_8), -64);
+  v_0_19_18 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_25_24), HEXAGON_HVX_GET_V1(v_1_9_8), -64);
+  v_0_21_20 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_27_26), HEXAGON_HVX_GET_V0(v_1_11_10), -64);
+  v_0_23_22 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_27_26), HEXAGON_HVX_GET_V1(v_1_11_10), -64);
+  v_0_25_24 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_29_28), HEXAGON_HVX_GET_V0(v_1_13_12), -64);
+  v_0_27_26 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_29_28), HEXAGON_HVX_GET_V1(v_1_13_12), -64);
+  v_0_29_28 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_31_30), HEXAGON_HVX_GET_V0(v_1_15_14), -64);
+  v_0_31_30 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_31_30), HEXAGON_HVX_GET_V1(v_1_15_14), -64);
+
+  kernel.packet[0] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_1_0));
+  kernel.packet[1] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_1_0));
+  kernel.packet[2] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_3_2));
+  kernel.packet[3] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_3_2));
+  kernel.packet[4] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_5_4));
+  kernel.packet[5] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_5_4));
+  kernel.packet[6] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_7_6));
+  kernel.packet[7] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_7_6));
+  kernel.packet[8] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_9_8));
+  kernel.packet[9] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_9_8));
+  kernel.packet[10] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_11_10));
+  kernel.packet[11] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_11_10));
+  kernel.packet[12] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_13_12));
+  kernel.packet[13] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_13_12));
+  kernel.packet[14] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_15_14));
+  kernel.packet[15] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_15_14));
+  kernel.packet[16] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_17_16));
+  kernel.packet[17] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_17_16));
+  kernel.packet[18] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_19_18));
+  kernel.packet[19] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_19_18));
+  kernel.packet[20] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_21_20));
+  kernel.packet[21] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_21_20));
+  kernel.packet[22] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_23_22));
+  kernel.packet[23] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_23_22));
+  kernel.packet[24] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_25_24));
+  kernel.packet[25] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_25_24));
+  kernel.packet[26] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_27_26));
+  kernel.packet[27] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_27_26));
+  kernel.packet[28] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_29_28));
+  kernel.packet[29] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_29_28));
+  kernel.packet[30] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_31_30));
+  kernel.packet[31] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_31_30));
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE float predux_hvx(const HVXPacket<T>& a) {
+  const Index packet_size = unpacket_traits<HVXPacket<T>>::size;
+#if __HVX_ARCH__ >= 79
+  HVX_Vector vsum = Q6_Vsf_vadd_VsfVsf(a.Get(), Q6_V_vror_VR(a.Get(), sizeof(float)));
+  for (int i = 2; i < packet_size; i <<= 1) {
+    vsum = Q6_Vsf_vadd_VsfVsf(vsum, Q6_V_vror_VR(vsum, i * sizeof(float)));
+  }
+  return pfirst(HVXPacket<T>::Create(vsum));
+#else
+  HVX_Vector vsum = Q6_Vqf32_vadd_VsfVsf(a.Get(), Q6_V_vror_VR(a.Get(), sizeof(float)));
+  for (int i = 2; i < packet_size; i <<= 1) {
+    vsum = Q6_Vqf32_vadd_Vqf32Vqf32(vsum, Q6_V_vror_VR(vsum, i * sizeof(float)));
+  }
+  return pfirst(HVXPacket<T>::Create(Q6_Vsf_equals_Vqf32(vsum)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE float predux<Packet32f>(const Packet32f& a) {
+  return predux_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE float predux<Packet16f>(const Packet16f& a) {
+  return predux_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE float predux<Packet8f>(const Packet8f& a) {
+  return predux_hvx(a);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> ploaddup_hvx(const float* from) {
+  constexpr Index size = unpacket_traits<HVXPacket<T>>::size / 2;
+  HVX_Vector load = HVX_load_partial<size, 0>(from);
+  HVX_VectorPair dup = Q6_W_vshuff_VVR(load, load, -4);
+  return HVXPacket<T>::Create(HEXAGON_HVX_GET_V0(dup));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f ploaddup(const float* from) {
+  return ploaddup_hvx<HVXPacketSize::Full>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f ploaddup(const float* from) {
+  return ploaddup_hvx<HVXPacketSize::Half>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f ploaddup(const float* from) {
+  return ploaddup_hvx<HVXPacketSize::Quarter>(from);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> ploadquad_hvx(const float* from) {
+  constexpr Index size = unpacket_traits<HVXPacket<T>>::size / 4;
+  HVX_Vector load = HVX_load_partial<size, 0>(from);
+  HVX_VectorPair dup = Q6_W_vshuff_VVR(load, load, -4);
+  HVX_VectorPair quad = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(dup), HEXAGON_HVX_GET_V0(dup), -8);
+  return HVXPacket<T>::Create(HEXAGON_HVX_GET_V0(quad));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f ploadquad(const float* from) {
+  return ploadquad_hvx<HVXPacketSize::Full>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f ploadquad(const float* from) {
+  return ploadquad_hvx<HVXPacketSize::Half>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f ploadquad(const float* from) {
+  return ploadquad_hvx<HVXPacketSize::Quarter>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f preverse(const Packet32f& a) {
+  HVX_Vector delta = Q6_Vb_vsplat_R(0x7c);
+  return Packet32f::Create(Q6_V_vdelta_VV(a.Get(), delta));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f preverse(const Packet16f& a) {
+  HVX_Vector delta = Q6_Vb_vsplat_R(0x3c);
+  return Packet16f::Create(Q6_V_vdelta_VV(a.Get(), delta));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f preverse(const Packet8f& a) {
+  HVX_Vector delta = Q6_Vb_vsplat_R(0x1c);
+  return Packet8f::Create(Q6_V_vdelta_VV(a.Get(), delta));
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pmin_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  return HVXPacket<T>::Create(Q6_Vsf_vmin_VsfVsf(a.Get(), b.Get()));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pmin(const Packet32f& a, const Packet32f& b) {
+  return pmin_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pmin(const Packet16f& a, const Packet16f& b) {
+  return pmin_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pmin(const Packet8f& a, const Packet8f& b) {
+  return pmin_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pmax_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  return HVXPacket<T>::Create(Q6_Vsf_vmax_VsfVsf(a.Get(), b.Get()));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pmax(const Packet32f& a, const Packet32f& b) {
+  return pmax_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pmax(const Packet16f& a, const Packet16f& b) {
+  return pmax_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pmax(const Packet8f& a, const Packet8f& b) {
+  return pmax_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pand_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  return HVXPacket<T>::Create(a.Get() & b.Get());
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pand(const Packet32f& a, const Packet32f& b) {
+  return pand_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pand(const Packet16f& a, const Packet16f& b) {
+  return pand_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pand(const Packet8f& a, const Packet8f& b) {
+  return pand_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> por_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  return HVXPacket<T>::Create(a.Get() | b.Get());
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f por(const Packet32f& a, const Packet32f& b) {
+  return por_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f por(const Packet16f& a, const Packet16f& b) {
+  return por_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f por(const Packet8f& a, const Packet8f& b) {
+  return por_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pxor_hvx(const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  return HVXPacket<T>::Create(a.Get() ^ b.Get());
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pxor(const Packet32f& a, const Packet32f& b) {
+  return pxor_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pxor(const Packet16f& a, const Packet16f& b) {
+  return pxor_hvx(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pxor(const Packet8f& a, const Packet8f& b) {
+  return pxor_hvx(a, b);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pnot_hvx(const HVXPacket<T>& a) {
+  return HVXPacket<T>::Create(~a.Get());
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pnot(const Packet32f& a) {
+  return pnot_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pnot(const Packet16f& a) {
+  return pnot_hvx(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pnot(const Packet8f& a) {
+  return pnot_hvx(a);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pselect_hvx(const HVXPacket<T>& mask, const HVXPacket<T>& a, const HVXPacket<T>& b) {
+  HVX_VectorPred pred = Q6_Q_vcmp_eq_VwVw(mask.Get(), Q6_V_vzero());
+  return HVXPacket<T>::Create(Q6_V_vmux_QVV(pred, b.Get(), a.Get()));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pselect(const Packet32f& mask, const Packet32f& a, const Packet32f& b) {
+  return pselect_hvx(mask, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pselect(const Packet16f& mask, const Packet16f& a, const Packet16f& b) {
+  return pselect_hvx(mask, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pselect(const Packet8f& mask, const Packet8f& a, const Packet8f& b) {
+  return pselect_hvx(mask, a, b);
+}
+
+template <HVXPacketSize T, typename Op>
+EIGEN_STRONG_INLINE float predux_generic(const HVXPacket<T>& a, Op op) {
+  const Index packet_size = unpacket_traits<HVXPacket<T>>::size;
+  HVXPacket<T> vredux = a;
+  for (int i = 1; i < packet_size; i <<= 1) {
+    vredux = op(vredux, HVXPacket<T>::Create(Q6_V_vror_VR(vredux.Get(), i * sizeof(float))));
+  }
+  return pfirst(vredux);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max(const Packet32f& a) {
+  return predux_generic(a, pmax<Packet32f>);
+}
+template <>
+EIGEN_STRONG_INLINE float predux_max(const Packet16f& a) {
+  return predux_generic(a, pmax<Packet16f>);
+}
+template <>
+EIGEN_STRONG_INLINE float predux_max(const Packet8f& a) {
+  return predux_generic(a, pmax<Packet8f>);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min(const Packet32f& a) {
+  return predux_generic(a, pmin<Packet32f>);
+}
+template <>
+EIGEN_STRONG_INLINE float predux_min(const Packet16f& a) {
+  return predux_generic(a, pmin<Packet16f>);
+}
+template <>
+EIGEN_STRONG_INLINE float predux_min(const Packet8f& a) {
+  return predux_generic(a, pmin<Packet8f>);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet32f& a) {
+  return predux_generic(a, por<Packet32f>) != 0.0f;
+}
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet16f& a) {
+  return predux_generic(a, por<Packet16f>) != 0.0f;
+}
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet8f& a) {
+  return predux_generic(a, por<Packet8f>) != 0.0f;
+}
+
+static const float index_vsf[32]
+    __attribute__((aligned(__HVX_LENGTH__))) = {0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
+                                                16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> plset_hvx(const float& a) {
+  return padd(pload<HVXPacket<T>>(index_vsf), pset1<HVXPacket<T>>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f plset(const float& a) {
+  return plset_hvx<HVXPacketSize::Full>(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f plset(const float& a) {
+  return plset_hvx<HVXPacketSize::Half>(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f plset(const float& a) {
+  return plset_hvx<HVXPacketSize::Quarter>(a);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE void pscatter_hvx(float* to, const HVXPacket<T>& from, Index stride) {
+  const Index packet_size = unpacket_traits<HVXPacket<T>>::size;
+  float elements[packet_size] __attribute__((aligned(__HVX_LENGTH__)));
+  pstore<float>(elements, from);
+  for (Index i = 0; i < packet_size; ++i) {
+    to[i * stride] = elements[i];
+  }
+}
+template <>
+EIGEN_STRONG_INLINE void pscatter<float, Packet32f>(float* to, const Packet32f& from, Index stride) {
+  pscatter_hvx(to, from, stride);
+}
+template <>
+EIGEN_STRONG_INLINE void pscatter<float, Packet16f>(float* to, const Packet16f& from, Index stride) {
+  pscatter_hvx(to, from, stride);
+}
+template <>
+EIGEN_STRONG_INLINE void pscatter<float, Packet8f>(float* to, const Packet8f& from, Index stride) {
+  pscatter_hvx(to, from, stride);
+}
+
+template <HVXPacketSize T>
+EIGEN_STRONG_INLINE HVXPacket<T> pgather_hvx(const float* from, Index stride) {
+  const Index packet_size = unpacket_traits<HVXPacket<T>>::size;
+  float elements[packet_size] __attribute__((aligned(__HVX_LENGTH__)));
+  for (Index i = 0; i < packet_size; i++) {
+    elements[i] = from[i * stride];
+  }
+  return pload<HVXPacket<T>>(elements);
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f pgather<float, Packet32f>(const float* from, Index stride) {
+  return pgather_hvx<HVXPacketSize::Full>(from, stride);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pgather<float, Packet16f>(const float* from, Index stride) {
+  return pgather_hvx<HVXPacketSize::Half>(from, stride);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8f pgather<float, Packet8f>(const float* from, Index stride) {
+  return pgather_hvx<HVXPacketSize::Quarter>(from, stride);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // __HVX__ && (__HVX_LENGTH__ == 128) && __HVX_ARCH__ >= 68
+
+#endif  // EIGEN_HVX_PACKET_MATH_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/Complex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/Complex.h
new file mode 100644
index 0000000..43ba989
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/Complex.h
@@ -0,0 +1,520 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// copyright (c) 2023 zang ruochen <zangruochen@loongson.cn>
+// copyright (c) 2024 XiWei Gu <guxiwei-hf@loongson.cn>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_LSX_H
+#define EIGEN_COMPLEX_LSX_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- float ----------
+struct Packet2cf {
+  EIGEN_STRONG_INLINE Packet2cf() {}
+  EIGEN_STRONG_INLINE explicit Packet2cf(const __m128& a) : v(a) {}
+  Packet4f v;
+};
+
+template <>
+struct packet_traits<std::complex<float> > : default_packet_traits {
+  typedef Packet2cf type;
+  typedef Packet2cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasSqrt = 1,
+    HasExp = 1,
+    HasAbs = 0,
+    HasLog = 1,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2cf> {
+  typedef std::complex<float> type;
+  typedef Packet2cf half;
+  typedef Packet4f as_real;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(__lsx_vfadd_s(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(__lsx_vfsub_s(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) {
+  const uint32_t b[4] = {0x80000000u, 0x80000000u, 0x80000000u, 0x80000000u};
+  Packet4i mask = (Packet4i)__lsx_vld(b, 0);
+  Packet2cf res;
+  res.v = (Packet4f)__lsx_vxor_v((__m128i)a.v, mask);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) {
+  const uint32_t b[4] = {0x00000000u, 0x80000000u, 0x00000000u, 0x80000000u};
+  Packet4i mask = (__m128i)__lsx_vld(b, 0);
+  Packet2cf res;
+  res.v = (Packet4f)__lsx_vxor_v((__m128i)a.v, mask);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  Packet4f part0_tmp = (Packet4f)__lsx_vfmul_s(a.v, b.v);
+  Packet4f part0 = __lsx_vfsub_s(part0_tmp, (__m128)__lsx_vshuf4i_w(part0_tmp, 0x31));
+  Packet4f part1_tmp = __lsx_vfmul_s((__m128)__lsx_vshuf4i_w(a.v, 0xb1), b.v);
+  Packet4f part1 = __lsx_vfadd_s(part1_tmp, (__m128)__lsx_vshuf4i_w(part1_tmp, 0x31));
+  Packet2cf res;
+  res.v = (Packet4f)__lsx_vpackev_w((__m128i)part1, (__m128i)part0);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf ptrue<Packet2cf>(const Packet2cf& a) {
+  return Packet2cf(ptrue(Packet4f(a.v)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  Packet2cf res;
+  res.v = (Packet4f)__lsx_vand_v((__m128i)a.v, (__m128i)b.v);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  Packet2cf res;
+  res.v = (Packet4f)__lsx_vor_v((__m128i)a.v, (__m128i)b.v);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  Packet2cf res;
+  res.v = (Packet4f)__lsx_vxor_v((__m128i)a.v, (__m128i)b.v);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  Packet2cf res;
+  res.v = (Packet4f)__lsx_vandn_v((__m128i)b.v, (__m128i)a.v);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(&numext::real_ref(*from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(&numext::real_ref(*from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) {
+  float f0 = from.real(), f1 = from.imag();
+  Packet4f re = {f0, f0, f0, f0};
+  Packet4f im = {f1, f1, f1, f1};
+  return Packet2cf((Packet4f)__lsx_vilvl_w((__m128i)im, (__m128i)re));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) {
+  return pset1<Packet2cf>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<float> >(std::complex<float>* to, const Packet2cf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), Packet4f(from.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet2cf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), Packet4f(from.v));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from,
+                                                                           Index stride) {
+  Packet2cf res;
+  __m128i tmp = __lsx_vldrepl_d(from, 0);
+  __m128i tmp1 = __lsx_vldrepl_d(from + stride, 0);
+  tmp = __lsx_vilvl_d(tmp1, tmp);
+  res.v = (__m128)tmp;
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from,
+                                                                       Index stride) {
+  __lsx_vstelm_d((__m128i)from.v, to, 0, 0);
+  __lsx_vstelm_d((__m128i)from.v, to + stride, 0, 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float>* addr) {
+  __builtin_prefetch(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) {
+  EIGEN_ALIGN16 std::complex<float> res[2];
+  __lsx_vst(a.v, res, 0);
+  return res[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) {
+  Packet2cf res;
+  res.v = (Packet4f)__lsx_vshuf4i_w(a.v, 0x4e);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) {
+  return pfirst(Packet2cf(__lsx_vfadd_s(a.v, vec4f_movehl(a.v, a.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) {
+  return pfirst(pmul(a, Packet2cf(vec4f_movehl(a.v, a.v))));
+}
+
+EIGEN_STRONG_INLINE Packet2cf pcplxflip /* <Packet2cf> */ (const Packet2cf& x) {
+  return Packet2cf(vec4f_swizzle1(x.v, 1, 0, 3, 2));
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf, Packet4f)
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return pdiv_complex(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf plog<Packet2cf>(const Packet2cf& a) {
+  return plog_complex(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pzero(const Packet2cf& /* a */) {
+  __m128 v = {0.0f, 0.0f, 0.0f, 0.0f};
+  return (Packet2cf)v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmadd<Packet2cf>(const Packet2cf& a, const Packet2cf& b, const Packet2cf& c) {
+  Packet2cf result, t0, t1, t2;
+  t1 = pzero(t1);
+  t0.v = (__m128)__lsx_vpackev_w((__m128i)a.v, (__m128i)a.v);
+  t2.v = __lsx_vfmadd_s(t0.v, b.v, c.v);
+  result.v = __lsx_vfadd_s(t2.v, t1.v);
+  t1.v = __lsx_vfsub_s(t1.v, a.v);
+  t1.v = (__m128)__lsx_vpackod_w((__m128i)a.v, (__m128i)t1.v);
+  t2.v = (__m128)__lsx_vshuf4i_w((__m128i)b.v, 0xb1);
+  result.v = __lsx_vfmadd_s(t1.v, t2.v, result.v);
+  return result;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pexp<Packet2cf>(const Packet2cf& a) {
+  return pexp_complex(a);
+}
+
+//---------- double ----------
+struct Packet1cd {
+  EIGEN_STRONG_INLINE Packet1cd() {}
+  EIGEN_STRONG_INLINE explicit Packet1cd(const __m128d& a) : v(a) {}
+  Packet2d v;
+};
+
+template <>
+struct packet_traits<std::complex<double> > : default_packet_traits {
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 1,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasSqrt = 1,
+    HasAbs = 0,
+    HasLog = 1,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet1cd> {
+  typedef std::complex<double> type;
+  typedef Packet1cd half;
+  typedef Packet2d as_real;
+  enum {
+    size = 1,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(__lsx_vfadd_d(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(__lsx_vfsub_d(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) {
+  return Packet1cd(pnegate(Packet2d(a.v)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) {
+  const uint64_t tmp[2] = {0x0000000000000000u, 0x8000000000000000u};
+  __m128i mask = __lsx_vld(tmp, 0);
+  Packet1cd res;
+  res.v = (Packet2d)__lsx_vxor_v((__m128i)a.v, mask);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  Packet2d tmp_real = __lsx_vfmul_d(a.v, b.v);
+  Packet2d real = __lsx_vfsub_d(tmp_real, preverse(tmp_real));
+
+  Packet2d tmp_imag = __lsx_vfmul_d(preverse(a.v), b.v);
+  Packet2d imag = (__m128d)__lsx_vfadd_d((__m128d)tmp_imag, preverse(tmp_imag));
+  Packet1cd res;
+  res.v = (__m128d)__lsx_vilvl_d((__m128i)imag, (__m128i)real);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ptrue<Packet1cd>(const Packet1cd& a) {
+  return Packet1cd(ptrue(Packet2d(a.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  Packet1cd res;
+  res.v = (Packet2d)__lsx_vand_v((__m128i)a.v, (__m128i)b.v);
+  return res;
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  Packet1cd res;
+  res.v = (Packet2d)__lsx_vor_v((__m128i)a.v, (__m128i)b.v);
+  return res;
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  Packet1cd res;
+  res.v = (Packet2d)__lsx_vxor_v((__m128i)a.v, (__m128i)b.v);
+  return res;
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  Packet1cd res;
+  res.v = (Packet2d)__lsx_vandn_v((__m128i)b.v, (__m128i)a.v);
+  return res;
+}
+
+// FIXME: force unaligned load, this is a temporary fix
+template <>
+EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd
+pset1<Packet1cd>(const std::complex<double>& from) { /* here we really have to use unaligned loads :( */
+  return ploadu<Packet1cd>(&from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) {
+  return pset1<Packet1cd>(*from);
+}
+
+// FIXME: force unaligned store, this is a temporary fix
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<double> >(std::complex<double>* to, const Packet1cd& from) {
+  EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, Packet2d(from.v));
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double>* to, const Packet1cd& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, Packet2d(from.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double>* addr) {
+  __builtin_prefetch(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) {
+  EIGEN_ALIGN16 double res[2];
+  __lsx_vst(a.v, res, 0);
+  return std::complex<double>(res[0], res[1]);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) {
+  return pfirst(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) {
+  return pfirst(a);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd, Packet2d)
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return pdiv_complex(a, b);
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip /* <Packet1cd> */ (const Packet1cd& x) {
+  return Packet1cd(preverse(Packet2d(x.v)));
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cf, 2>& kernel) {
+  Packet4f tmp1 = (Packet4f)__lsx_vilvl_w((__m128i)kernel.packet[1].v, (__m128i)kernel.packet[0].v);
+  Packet4f tmp2 = (Packet4f)__lsx_vilvh_w((__m128i)kernel.packet[1].v, (__m128i)kernel.packet[0].v);
+  kernel.packet[0].v = (Packet4f)__lsx_vshuf4i_w(tmp1, 0xd8);
+  kernel.packet[1].v = (Packet4f)__lsx_vshuf4i_w(tmp2, 0xd8);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
+  Packet4f eq = (Packet4f)__lsx_vfcmp_ceq_s(a.v, b.v);
+  return Packet2cf(pand<Packet4f>(eq, vec4f_swizzle1(eq, 1, 0, 3, 2)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
+  Packet2d eq = (Packet2d)__lsx_vfcmp_ceq_d(a.v, b.v);
+  return Packet1cd(pand<Packet2d>(eq, preverse(eq)));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2cf pselect(const Packet2cf& mask, const Packet2cf& a, const Packet2cf& b) {
+  Packet2cf res;
+  res.v = (Packet4f)__lsx_vbitsel_v((__m128i)b.v, (__m128i)a.v, (__m128i)mask.v);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
+  return psqrt_complex<Packet1cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
+  return psqrt_complex<Packet2cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd plog<Packet1cd>(const Packet1cd& a) {
+  return plog_complex(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pzero<Packet1cd>(const Packet1cd& /* a */) {
+  __m128d v = {0.0, 0.0};
+  return (Packet1cd)v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmadd<Packet1cd>(const Packet1cd& a, const Packet1cd& b, const Packet1cd& c) {
+  Packet1cd result, t0, t1, t2;
+  t1 = pzero(t1);
+  t0.v = (__m128d)__lsx_vpackev_d((__m128i)a.v, (__m128i)a.v);
+  t2.v = __lsx_vfmadd_d(t0.v, b.v, c.v);
+  result.v = __lsx_vfadd_d(t2.v, t1.v);
+  t1.v = __lsx_vfsub_d(t1.v, a.v);
+  t1.v = (__m128d)__lsx_vpackod_d((__m128i)a.v, (__m128i)t1.v);
+  t2.v = (__m128d)__lsx_vshuf4i_d((__m128i)t2.v, (__m128i)b.v, 0xb);
+  result.v = __lsx_vfmadd_d(t1.v, t2.v, result.v);
+  return result;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from,
+                                                                            Index /* stride */) {
+  Packet1cd res;
+  __m128i tmp = __lsx_vld((void*)from, 0);
+  res.v = (__m128d)tmp;
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from,
+                                                                        Index /* stride */) {
+  __lsx_vst((__m128i)from.v, (void*)to, 0);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd, 2>& kernel) {
+  Packet2d tmp = (__m128d)__lsx_vilvl_d((__m128i)kernel.packet[1].v, (__m128i)kernel.packet[0].v);
+  kernel.packet[1].v = (__m128d)__lsx_vilvh_d((__m128i)kernel.packet[1].v, (__m128i)kernel.packet[0].v);
+  kernel.packet[0].v = tmp;
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_LSX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/GeneralBlockPanelKernel.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/GeneralBlockPanelKernel.h
new file mode 100644
index 0000000..4b07062
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/GeneralBlockPanelKernel.h
@@ -0,0 +1,23 @@
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+#ifndef EIGEN_LSX_GEBP_NR
+#define EIGEN_LSX_GEBP_NR 8
+#endif
+
+template <>
+struct gebp_traits<float, float, false, false, Architecture::LSX, GEBPPacketFull>
+    : gebp_traits<float, float, false, false, Architecture::Generic, GEBPPacketFull> {
+  enum { nr = EIGEN_LSX_GEBP_NR };
+};
+
+template <>
+struct gebp_traits<double, double, false, false, Architecture::LSX, GEBPPacketFull>
+    : gebp_traits<double, double, false, false, Architecture::Generic, GEBPPacketFull> {
+  enum { nr = EIGEN_LSX_GEBP_NR };
+};
+}  // namespace internal
+}  // namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/MathFunctions.h
new file mode 100644
index 0000000..cead463
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/MathFunctions.h
@@ -0,0 +1,43 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 XiWei Gu (guxiwei-hf@loongson.cn)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_LSX_H
+#define EIGEN_MATH_FUNCTIONS_LSX_H
+
+/* The sin and cos functions of this file are loosely derived from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+EIGEN_DOUBLE_PACKET_FUNCTION(atanh, Packet2d)
+EIGEN_DOUBLE_PACKET_FUNCTION(log, Packet2d)
+EIGEN_DOUBLE_PACKET_FUNCTION(log2, Packet2d)
+EIGEN_DOUBLE_PACKET_FUNCTION(tanh, Packet2d)
+
+EIGEN_FLOAT_PACKET_FUNCTION(atanh, Packet4f)
+EIGEN_FLOAT_PACKET_FUNCTION(log, Packet4f)
+EIGEN_FLOAT_PACKET_FUNCTION(log2, Packet4f)
+EIGEN_FLOAT_PACKET_FUNCTION(tanh, Packet4f)
+
+EIGEN_GENERIC_PACKET_FUNCTION(atan, Packet2d)
+EIGEN_GENERIC_PACKET_FUNCTION(atan, Packet4f)
+EIGEN_GENERIC_PACKET_FUNCTION(exp2, Packet2d)
+EIGEN_GENERIC_PACKET_FUNCTION(exp2, Packet4f)
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_LSX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/PacketMath.h
new file mode 100644
index 0000000..366399f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/PacketMath.h
@@ -0,0 +1,2846 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2023 Zang Ruochen <zangruochen@loongson.cn>
+// Copyright (C) 2024 XiWei Gu <guxiwei-hf@loongson.cn>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_LSX_H
+#define EIGEN_PACKET_MATH_LSX_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#if EIGEN_ARCH_LOONGARCH64
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#endif
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+typedef __m128 Packet4f;
+typedef __m128d Packet2d;
+
+typedef eigen_packet_wrapper<__m128i, 0> Packet16c;
+typedef eigen_packet_wrapper<__m128i, 1> Packet8s;
+typedef eigen_packet_wrapper<__m128i, 2> Packet4i;
+typedef eigen_packet_wrapper<__m128i, 3> Packet2l;
+typedef eigen_packet_wrapper<__m128i, 4> Packet16uc;
+typedef eigen_packet_wrapper<__m128i, 5> Packet8us;
+typedef eigen_packet_wrapper<__m128i, 6> Packet4ui;
+typedef eigen_packet_wrapper<__m128i, 7> Packet2ul;
+
+template <>
+struct is_arithmetic<__m128> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<__m128i> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<__m128d> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<Packet16c> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<Packet8s> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<Packet4i> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<Packet2l> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<Packet16uc> {
+  enum { value = false };
+};
+template <>
+struct is_arithmetic<Packet8us> {
+  enum { value = false };
+};
+template <>
+struct is_arithmetic<Packet4ui> {
+  enum { value = false };
+};
+template <>
+struct is_arithmetic<Packet2ul> {
+  enum { value = false };
+};
+
+EIGEN_ALWAYS_INLINE Packet4f make_packet4f(float a, float b, float c, float d) {
+  float from[4] = {a, b, c, d};
+  return (Packet4f)__lsx_vld(from, 0);
+}
+
+EIGEN_STRONG_INLINE Packet4f shuffle1(const Packet4f& m, int mask) {
+  const float* a = reinterpret_cast<const float*>(&m);
+  Packet4f res =
+      make_packet4f(*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3)), *(a + ((mask >> 6) & 3)));
+  return res;
+}
+
+template <bool interleave>
+EIGEN_STRONG_INLINE Packet4f shuffle2(const Packet4f& m, const Packet4f& n, int mask) {
+  const float* a = reinterpret_cast<const float*>(&m);
+  const float* b = reinterpret_cast<const float*>(&n);
+  Packet4f res =
+      make_packet4f(*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(b + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3)));
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f shuffle2<true>(const Packet4f& m, const Packet4f& n, int mask) {
+  const float* a = reinterpret_cast<const float*>(&m);
+  const float* b = reinterpret_cast<const float*>(&n);
+  Packet4f res =
+      make_packet4f(*(a + (mask & 3)), *(b + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3)));
+  return res;
+}
+
+EIGEN_STRONG_INLINE static int eigen_lsx_shuffle_mask(int p, int q, int r, int s) {
+  return ((s) << 6 | (r) << 4 | (q) << 2 | (p));
+}
+
+EIGEN_STRONG_INLINE Packet4f vec4f_swizzle1(const Packet4f& a, int p, int q, int r, int s) {
+  return shuffle1(a, eigen_lsx_shuffle_mask(p, q, r, s));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_swizzle2(const Packet4f& a, const Packet4f& b, int p, int q, int r, int s) {
+  return shuffle2<false>(a, b, eigen_lsx_shuffle_mask(p, q, r, s));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_movelh(const Packet4f& a, const Packet4f& b) {
+  return shuffle2<false>(a, b, eigen_lsx_shuffle_mask(0, 1, 0, 1));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_movehl(const Packet4f& a, const Packet4f& b) {
+  return shuffle2<false>(b, a, eigen_lsx_shuffle_mask(2, 3, 2, 3));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_unpacklo(const Packet4f& a, const Packet4f& b) {
+  return shuffle2<true>(a, b, eigen_lsx_shuffle_mask(0, 0, 1, 1));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b) {
+  return shuffle2<true>(a, b, eigen_lsx_shuffle_mask(2, 2, 3, 3));
+}
+
+EIGEN_ALWAYS_INLINE Packet2d make_packet2d(double a, double b) {
+  double from[2] = {a, b};
+  return (Packet2d)__lsx_vld(from, 0);
+}
+
+EIGEN_STRONG_INLINE Packet2d shuffle(const Packet2d& m, const Packet2d& n, int mask) {
+  const double* a = reinterpret_cast<const double*>(&m);
+  const double* b = reinterpret_cast<const double*>(&n);
+  Packet2d res = make_packet2d(*(a + (mask & 1)), *(b + ((mask >> 1) & 1)));
+  return res;
+}
+
+EIGEN_STRONG_INLINE Packet2d vec2d_swizzle2(const Packet2d& a, const Packet2d& b, int mask) {
+  return shuffle(a, b, mask);
+}
+EIGEN_STRONG_INLINE Packet2d vec2d_unpacklo(const Packet2d& a, const Packet2d& b) { return shuffle(a, b, 0); }
+EIGEN_STRONG_INLINE Packet2d vec2d_unpackhi(const Packet2d& a, const Packet2d& b) { return shuffle(a, b, 3); }
+
+template <>
+struct packet_traits<int8_t> : default_packet_traits {
+  typedef Packet16c type;
+  typedef Packet16c half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasSetLinear = 0,
+    HasCmp = 1,
+  };
+};
+
+template <>
+struct packet_traits<int16_t> : default_packet_traits {
+  typedef Packet8s type;
+  typedef Packet8s half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasSetLinear = 0,
+    HasCmp = 1,
+    HasDiv = 1,
+  };
+};
+
+template <>
+struct packet_traits<int32_t> : default_packet_traits {
+  typedef Packet4i type;
+  typedef Packet4i half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasSetLinear = 0,
+    HasCmp = 1,
+    HasDiv = 1,
+  };
+};
+
+template <>
+struct packet_traits<int64_t> : default_packet_traits {
+  typedef Packet2l type;
+  typedef Packet2l half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasSetLinear = 0,
+    HasCmp = 1,
+    HasDiv = 1,
+  };
+};
+
+template <>
+struct packet_traits<uint8_t> : default_packet_traits {
+  typedef Packet16uc type;
+  typedef Packet16uc half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasSetLinear = 0,
+    HasNegate = 0,
+    HasCmp = 1,
+  };
+};
+
+template <>
+struct packet_traits<uint16_t> : default_packet_traits {
+  typedef Packet8us type;
+  typedef Packet8us half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasSetLinear = 0,
+    HasNegate = 0,
+    HasCmp = 1,
+    HasDiv = 1,
+  };
+};
+
+template <>
+struct packet_traits<uint32_t> : default_packet_traits {
+  typedef Packet4ui type;
+  typedef Packet4ui half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasSetLinear = 0,
+    HasNegate = 0,
+    HasCmp = 1,
+    HasDiv = 1,
+  };
+};
+
+template <>
+struct packet_traits<uint64_t> : default_packet_traits {
+  typedef Packet2ul type;
+  typedef Packet2ul half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasSetLinear = 0,
+    HasNegate = 0,
+    HasCmp = 1,
+    HasDiv = 1,
+  };
+};
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4f type;
+  typedef Packet4f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasSetLinear = 0,
+    HasSign = 0,
+    HasDiv = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasRsqrt = 1
+  };
+};
+
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasSetLinear = 0,
+    HasSign = 0,
+    HasDiv = 1,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasRsqrt = 1
+  };
+};
+
+template <>
+struct unpacket_traits<Packet16c> {
+  typedef int8_t type;
+  typedef Packet16c half;
+  enum {
+    size = 16,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet8s> {
+  typedef int16_t type;
+  typedef Packet8s half;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet4i> {
+  typedef int32_t type;
+  typedef Packet4i half;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet2l> {
+  typedef int64_t type;
+  typedef Packet2l half;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet16uc> {
+  typedef uint8_t type;
+  typedef Packet16uc half;
+  enum {
+    size = 16,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet8us> {
+  typedef uint16_t type;
+  typedef Packet8us half;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet4ui> {
+  typedef uint32_t type;
+  typedef Packet4ui half;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet2ul> {
+  typedef uint64_t type;
+  typedef Packet2ul half;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet4f> {
+  typedef float type;
+  typedef Packet4f half;
+  typedef Packet4i integer_packet;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet2d> {
+  typedef double type;
+  typedef Packet2d half;
+  typedef Packet2l integer_packet;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet16c pset1<Packet16c>(const int8_t& from) {
+  return __lsx_vreplgr2vr_b(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pset1<Packet8s>(const int16_t& from) {
+  return __lsx_vreplgr2vr_h(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int32_t& from) {
+  return __lsx_vreplgr2vr_w(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pset1<Packet2l>(const int64_t& from) {
+  return __lsx_vreplgr2vr_d(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pset1<Packet16uc>(const uint8_t& from) {
+  return __lsx_vreplgr2vr_b(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pset1<Packet8us>(const uint16_t& from) {
+  return __lsx_vreplgr2vr_h(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pset1<Packet4ui>(const uint32_t& from) {
+  return __lsx_vreplgr2vr_w(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pset1<Packet2ul>(const uint64_t& from) {
+  return __lsx_vreplgr2vr_d(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
+  Packet4f v = {from, from, from, from};
+  return v;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
+  Packet2d v = {from, from};
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(uint32_t from) {
+  return reinterpret_cast<__m128>((__m128i)pset1<Packet4ui>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(uint64_t from) {
+  return reinterpret_cast<__m128d>((__m128i)pset1<Packet2ul>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c plset<Packet16c>(const int8_t& a) {
+  const int8_t countdown[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+  return __lsx_vadd_b(pset1<Packet16c>(a), __lsx_vld(countdown, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s plset<Packet8s>(const int16_t& a) {
+  const int16_t countdown[] = {0, 1, 2, 3, 4, 5, 6, 7};
+  return __lsx_vadd_h(pset1<Packet8s>(a), __lsx_vld(countdown, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int32_t& a) {
+  const int32_t countdown[] = {0, 1, 2, 3};
+  return __lsx_vadd_w(pset1<Packet4i>(a), __lsx_vld(countdown, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l plset<Packet2l>(const int64_t& a) {
+  const int64_t countdown[] = {0, 1};
+  return __lsx_vadd_d(pset1<Packet2l>(a), __lsx_vld(countdown, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc plset<Packet16uc>(const uint8_t& a) {
+  const uint8_t countdown[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+  return __lsx_vadd_b(pset1<Packet16uc>(a), __lsx_vld(countdown, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us plset<Packet8us>(const uint16_t& a) {
+  const uint16_t countdown[] = {0, 1, 2, 3, 4, 5, 6, 7};
+  return __lsx_vadd_h(pset1<Packet8us>(a), __lsx_vld(countdown, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui plset<Packet4ui>(const uint32_t& a) {
+  const uint32_t countdown[] = {0, 1, 2, 3};
+  return __lsx_vadd_w(pset1<Packet4ui>(a), __lsx_vld(countdown, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul plset<Packet2ul>(const uint64_t& a) {
+  const uint64_t countdown[] = {0, 1};
+  return __lsx_vadd_d(pset1<Packet2ul>(a), __lsx_vld(countdown, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) {
+  static const Packet4f countdown = {0.0f, 1.0f, 2.0f, 3.0f};
+  return __lsx_vfadd_s(pset1<Packet4f>(a), countdown);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) {
+  static const Packet2d countdown = {0.0f, 1.0f};
+  return __lsx_vfadd_d(pset1<Packet2d>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c padd<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vadd_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s padd<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vadd_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vadd_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l padd<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vadd_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc padd<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vadd_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us padd<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vadd_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui padd<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vadd_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul padd<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vadd_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return __lsx_vfadd_s(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return __lsx_vfadd_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c psub<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vsub_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s psub<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vsub_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vsub_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l psub<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vsub_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc psub<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vsub_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us psub<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vsub_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui psub<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vsub_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul psub<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vsub_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return __lsx_vfsub_s(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return __lsx_vfsub_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b);
+template <>
+EIGEN_STRONG_INLINE Packet4f paddsub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  const Packet4f mask =
+      make_packet4f(numext::bit_cast<float>(0x80000000u), 0.0f, numext::bit_cast<float>(0x80000000u), 0.0f);
+  return padd(a, pxor(mask, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b);
+template <>
+EIGEN_STRONG_INLINE Packet2d paddsub<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  const Packet2d mask = make_packet2d(numext::bit_cast<double>(0x8000000000000000ull), 0.0);
+  return padd(a, pxor(mask, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) {
+  Packet4f mask = make_packet4f(numext::bit_cast<float>(0x80000000), numext::bit_cast<float>(0x80000000),
+                                numext::bit_cast<float>(0x80000000), numext::bit_cast<float>(0x80000000));
+  return (Packet4f)__lsx_vxor_v(numext::bit_cast<__m128i>(mask), numext::bit_cast<__m128i>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) {
+  Packet2d mask =
+      make_packet2d(numext::bit_cast<double>(0x8000000000000000), numext::bit_cast<double>(0x8000000000000000));
+  return (Packet2d)__lsx_vxor_v(numext::bit_cast<__m128i>(mask), numext::bit_cast<__m128i>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pnegate(const Packet16c& a) {
+  return __lsx_vneg_b(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pnegate(const Packet8s& a) {
+  return __lsx_vneg_h(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) {
+  return __lsx_vneg_w(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pnegate(const Packet2l& a) {
+  return __lsx_vneg_d(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pconj(const Packet16c& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pconj(const Packet8s& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pconj(const Packet2l& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pconj(const Packet16uc& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pconj(const Packet8us& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pconj(const Packet4ui& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pconj(const Packet2ul& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return __lsx_vfmul_s(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return __lsx_vfmul_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pmul<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vmul_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmul<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vmul_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vmul_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmul<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vmul_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmul<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vmul_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmul<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vmul_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmul<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vmul_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pmul<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vmul_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return __lsx_vfdiv_s(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return __lsx_vfdiv_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pdiv<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vdiv_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vdiv_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pdiv<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vdiv_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pdiv<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vdiv_hu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pdiv<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vdiv_wu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pdiv<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vdiv_du(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return __lsx_vfmadd_s(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return __lsx_vfmadd_d(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return __lsx_vfmsub_s(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return __lsx_vfmsub_d(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return __lsx_vfnmsub_s(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return __lsx_vfnmsub_d(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return __lsx_vfnmadd_s(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return __lsx_vfnmadd_d(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pmadd(const Packet16c& a, const Packet16c& b, const Packet16c& c) {
+  return __lsx_vmadd_b(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmadd(const Packet8s& a, const Packet8s& b, const Packet8s& c) {
+  return __lsx_vmadd_h(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
+  return __lsx_vmadd_w(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmadd(const Packet2l& a, const Packet2l& b, const Packet2l& c) {
+  return __lsx_vmadd_d(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmadd(const Packet16uc& a, const Packet16uc& b, const Packet16uc& c) {
+  return __lsx_vmadd_b(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmadd(const Packet8us& a, const Packet8us& b, const Packet8us& c) {
+  return __lsx_vmadd_h(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmadd(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c) {
+  return __lsx_vmadd_w(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pmadd(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c) {
+  return __lsx_vmadd_d(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (Packet4f)__lsx_vand_v((__m128i)a, (__m128i)b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (Packet2d)__lsx_vand_v((__m128i)a, (__m128i)b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pand<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vand_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pand<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vand_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vand_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pand<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vand_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pand<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vand_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pand<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vand_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vand_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pand<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vand_v(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (Packet4f)__lsx_vor_v((__m128i)a, (__m128i)b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (Packet2d)__lsx_vor_v((__m128i)a, (__m128i)b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c por<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s por<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l por<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc por<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us por<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui por<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul por<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vor_v(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (Packet4f)__lsx_vxor_v((__m128i)a, (__m128i)b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (Packet2d)__lsx_vxor_v((__m128i)a, (__m128i)b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pxor<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vxor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pxor<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vxor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vxor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pxor<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vxor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pxor<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vxor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pxor<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vxor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pxor<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vxor_v(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pxor<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vxor_v(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (Packet4f)__lsx_vandn_v((__m128i)b, (__m128i)a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (Packet2d)__lsx_vandn_v((__m128i)b, (__m128i)a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pandnot<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vandn_v(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pandnot<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vandn_v(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vandn_v(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pandnot<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vandn_v(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pandnot<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vandn_v(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pandnot<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vandn_v(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pandnot<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vandn_v(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pandnot<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vandn_v(b, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_le<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (Packet4f)__lsx_vfcmp_cle_s(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_le<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (Packet2d)__lsx_vfcmp_cle_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcmp_le<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vsle_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcmp_le<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vsle_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_le<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vsle_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcmp_le<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vsle_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcmp_le<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vsle_bu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcmp_le<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vsle_hu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcmp_le<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vsle_wu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcmp_le<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vsle_du(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_lt<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (Packet4f)__lsx_vfcmp_clt_s(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_lt<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (Packet2d)__lsx_vfcmp_clt_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcmp_lt<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vslt_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcmp_lt<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vslt_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_lt<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vslt_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcmp_lt<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vslt_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcmp_lt<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vslt_bu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcmp_lt<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vslt_hu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcmp_lt<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vslt_wu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcmp_lt<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vslt_du(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (Packet4f)__lsx_vfcmp_sult_s(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (Packet2d)__lsx_vfcmp_sult_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_eq<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (Packet4f)__lsx_vfcmp_seq_s(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_eq<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (Packet2d)__lsx_vfcmp_seq_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcmp_eq<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vseq_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcmp_eq<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vseq_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_eq<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vseq_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcmp_eq<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vseq_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcmp_eq<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vseq_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcmp_eq<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vseq_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcmp_eq<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vseq_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcmp_eq<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vseq_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c pmin<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vmin_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmin<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vmin_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vmin_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmin<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vmin_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmin<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vmin_bu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmin<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vmin_hu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmin<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vmin_wu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pmin<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vmin_du(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c pmax<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vmax_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmax<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vmax_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vmax_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmax<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vmax_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmax<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vmax_bu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmax<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vmax_hu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmax<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vmax_wu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pmax<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vmax_du(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4i aNaN = __lsx_vfcmp_cun_s(a, a);
+  Packet4i aMinOrNaN = por<Packet4i>(__lsx_vfcmp_clt_s(a, b), aNaN);
+  return (Packet4f)__lsx_vbitsel_v((__m128i)b, (__m128i)a, aMinOrNaN);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  Packet2l aNaN = __lsx_vfcmp_cun_d(a, a);
+  Packet2l aMinOrNaN = por<Packet2l>(__lsx_vfcmp_clt_d(a, b), aNaN);
+  return (Packet2d)__lsx_vbitsel_v((__m128i)b, (__m128i)a, aMinOrNaN);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4i aNaN = __lsx_vfcmp_cun_s(a, a);
+  Packet4i aMaxOrNaN = por<Packet4i>(__lsx_vfcmp_clt_s(b, a), aNaN);
+  return (Packet4f)__lsx_vbitsel_v((__m128i)b, (__m128i)a, aMaxOrNaN);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  Packet2l aNaN = __lsx_vfcmp_cun_d(a, a);
+  Packet2l aMaxOrNaN = por<Packet2l>(__lsx_vfcmp_clt_d(b, a), aNaN);
+  return (Packet2d)__lsx_vbitsel_v((__m128i)b, (__m128i)a, aMaxOrNaN);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet16c parithmetic_shift_right(const Packet16c& a) {
+  return __lsx_vsrai_b((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8s parithmetic_shift_right(const Packet8s& a) {
+  return __lsx_vsrai_h((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(const Packet4i& a) {
+  return __lsx_vsrai_w((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2l parithmetic_shift_right(const Packet2l& a) {
+  return __lsx_vsrai_d((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet16uc parithmetic_shift_right(const Packet16uc& a) {
+  return __lsx_vsrli_b((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8us parithmetic_shift_right(const Packet8us& a) {
+  return __lsx_vsrli_h((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui parithmetic_shift_right(const Packet4ui& a) {
+  return __lsx_vsrli_w((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2ul parithmetic_shift_right(const Packet2ul& a) {
+  return __lsx_vsrli_d((__m128i)a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet16c plogical_shift_right(const Packet16c& a) {
+  return __lsx_vsrli_b((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8s plogical_shift_right(const Packet8s& a) {
+  return __lsx_vsrli_h((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i plogical_shift_right(const Packet4i& a) {
+  return __lsx_vsrli_w((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
+  return __lsx_vsrli_d((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet16uc plogical_shift_right(const Packet16uc& a) {
+  return __lsx_vsrli_b((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8us plogical_shift_right(const Packet8us& a) {
+  return __lsx_vsrli_h((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui plogical_shift_right(const Packet4ui& a) {
+  return __lsx_vsrli_w((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2ul plogical_shift_right(const Packet2ul& a) {
+  return __lsx_vsrli_d((__m128i)a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet16c plogical_shift_left(const Packet16c& a) {
+  return __lsx_vslli_b((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8s plogical_shift_left(const Packet8s& a) {
+  return __lsx_vslli_h((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i plogical_shift_left(const Packet4i& a) {
+  return __lsx_vslli_w((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
+  return __lsx_vslli_d((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet16uc plogical_shift_left(const Packet16uc& a) {
+  return __lsx_vslli_b((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8us plogical_shift_left(const Packet8us& a) {
+  return __lsx_vslli_h((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui plogical_shift_left(const Packet4ui& a) {
+  return __lsx_vslli_w((__m128i)a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2ul plogical_shift_left(const Packet2ul& a) {
+  return __lsx_vslli_d((__m128i)a, N);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) {
+  return (Packet4f)__lsx_vbitclri_w((__m128i)a, 31);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) {
+  return (Packet2d)__lsx_vbitclri_d((__m128i)a, 63);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pabs(const Packet16c& a) {
+  return __lsx_vabsd_b(a, pzero(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pabs(const Packet8s& a) {
+  return __lsx_vabsd_h(a, pzero(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) {
+  return __lsx_vabsd_w(a, pzero(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pabs(const Packet2l& a) {
+  return __lsx_vabsd_d(a, pzero(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pabs(const Packet16uc& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pabs(const Packet8us& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pabs(const Packet4ui& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pabs(const Packet2ul& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c pabsdiff(const Packet16c& a, const Packet16c& b) {
+  return __lsx_vabsd_b(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pabsdiff(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vabsd_h(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pabsdiff(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vabsd_w(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pabsdiff(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vabsd_d(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pabsdiff(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vabsd_bu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pabsdiff(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vabsd_hu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pabsdiff(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vabsd_wu(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pabsdiff(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vabsd_du(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pabsdiff(const Packet4f& a, const Packet4f& b) {
+  return pabs(psub(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pabsdiff(const Packet2d& a, const Packet2d& b) {
+  return pabs(psub(a, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return (Packet4f)__lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return (Packet2d)__lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pload<Packet16c>(const int8_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pload<Packet8s>(const int16_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pload<Packet2l>(const int64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pload<Packet16uc>(const uint8_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pload<Packet8us>(const uint16_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pload<Packet4ui>(const uint32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pload<Packet2ul>(const uint64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __lsx_vld(from, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet4f)__lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet2d)__lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c ploadu<Packet16c>(const int8_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s ploadu<Packet8s>(const int16_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l ploadu<Packet2l>(const int64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc ploadu<Packet16uc>(const uint8_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us ploadu<Packet8us>(const uint16_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui ploadu<Packet4ui>(const uint32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __lsx_vld(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul ploadu<Packet2ul>(const uint64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __lsx_vld(from, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) {
+  float f0 = from[0], f1 = from[1];
+  return make_packet4f(f0, f0, f1, f1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) {
+  return pset1<Packet2d>(from[0]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c ploaddup<Packet16c>(const int8_t* from) {
+  Packet16c tmp = pload<Packet16c>(from);
+  return __lsx_vilvl_b(tmp, tmp);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s ploaddup<Packet8s>(const int16_t* from) {
+  Packet8s tmp = pload<Packet8s>(from);
+  return __lsx_vilvl_h(tmp, tmp);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int32_t* from) {
+  Packet4i tmp = pload<Packet4i>(from);
+  return __lsx_vilvl_w(tmp, tmp);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l ploaddup<Packet2l>(const int64_t* from) {
+  return pset1<Packet2l>(from[0]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc ploaddup<Packet16uc>(const uint8_t* from) {
+  Packet16uc tmp = pload<Packet16uc>(from);
+  return __lsx_vilvl_b(tmp, tmp);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us ploaddup<Packet8us>(const uint16_t* from) {
+  Packet8us tmp = pload<Packet8us>(from);
+  return __lsx_vilvl_h(tmp, tmp);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui ploaddup<Packet4ui>(const uint32_t* from) {
+  Packet4ui tmp = pload<Packet4ui>(from);
+  return __lsx_vilvl_w(tmp, tmp);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul ploaddup<Packet2ul>(const uint64_t* from) {
+  return pset1<Packet2ul>(from[0]);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __lsx_vst(from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __lsx_vst(from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet16c& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int16_t>(int16_t* to, const Packet8s& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet4i& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int64_t>(int64_t* to, const Packet2l& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet16uc& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint16_t>(uint16_t* to, const Packet8us& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet4ui& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint64_t>(uint64_t* to, const Packet2ul& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __lsx_vst(from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __lsx_vst(from, to, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet16c& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int16_t>(int16_t* to, const Packet8s& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet4i& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int64_t>(int64_t* to, const Packet2l& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet16uc& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint16_t>(uint16_t* to, const Packet8us& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet4ui& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint64_t>(uint64_t* to, const Packet2ul& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __lsx_vst((__m128i)from, to, 0);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4f pgather<float, Packet4f>(const float* from, Index stride) {
+  Packet4f v = {from[0], from[stride], from[2 * stride], from[3 * stride]};
+  return v;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2d pgather<double, Packet2d>(const double* from, Index stride) {
+  Packet2d v = {from[0], from[stride]};
+  return v;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16c pgather<int8_t, Packet16c>(const int8_t* from, Index stride) {
+  int8_t v[16] __attribute__((aligned(16)));
+  v[0] = from[0];
+  v[1] = from[stride];
+  v[2] = from[2 * stride];
+  v[3] = from[3 * stride];
+  v[4] = from[4 * stride];
+  v[5] = from[5 * stride];
+  v[6] = from[6 * stride];
+  v[7] = from[7 * stride];
+  v[8] = from[8 * stride];
+  v[9] = from[9 * stride];
+  v[10] = from[10 * stride];
+  v[11] = from[11 * stride];
+  v[12] = from[12 * stride];
+  v[13] = from[13 * stride];
+  v[14] = from[14 * stride];
+  v[15] = from[15 * stride];
+  return __lsx_vld(v, 0);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8s pgather<int16_t, Packet8s>(const int16_t* from, Index stride) {
+  int16_t v[8] __attribute__((aligned(16)));
+  v[0] = from[0];
+  v[1] = from[stride];
+  v[2] = from[2 * stride];
+  v[3] = from[3 * stride];
+  v[4] = from[4 * stride];
+  v[5] = from[5 * stride];
+  v[6] = from[6 * stride];
+  v[7] = from[7 * stride];
+  return __lsx_vld(v, 0);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4i pgather<int32_t, Packet4i>(const int32_t* from, Index stride) {
+  int32_t v[4] __attribute__((aligned(16)));
+  v[0] = from[0];
+  v[1] = from[stride];
+  v[2] = from[2 * stride];
+  v[3] = from[3 * stride];
+  return __lsx_vld(v, 0);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2l pgather<int64_t, Packet2l>(const int64_t* from, Index stride) {
+  int64_t v[2] __attribute__((aligned(16)));
+  v[0] = from[0];
+  v[1] = from[stride];
+  return __lsx_vld(v, 0);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16uc pgather<uint8_t, Packet16uc>(const uint8_t* from, Index stride) {
+  uint8_t v[16] __attribute__((aligned(16)));
+  v[0] = from[0];
+  v[1] = from[stride];
+  v[2] = from[2 * stride];
+  v[3] = from[3 * stride];
+  v[4] = from[4 * stride];
+  v[5] = from[5 * stride];
+  v[6] = from[6 * stride];
+  v[7] = from[7 * stride];
+  v[8] = from[8 * stride];
+  v[9] = from[9 * stride];
+  v[10] = from[10 * stride];
+  v[11] = from[11 * stride];
+  v[12] = from[12 * stride];
+  v[13] = from[13 * stride];
+  v[14] = from[14 * stride];
+  v[15] = from[15 * stride];
+  return __lsx_vld(v, 0);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8us pgather<uint16_t, Packet8us>(const uint16_t* from, Index stride) {
+  uint16_t v[8] __attribute__((aligned(16)));
+  v[0] = from[0];
+  v[1] = from[stride];
+  v[2] = from[2 * stride];
+  v[3] = from[3 * stride];
+  v[4] = from[4 * stride];
+  v[5] = from[5 * stride];
+  v[6] = from[6 * stride];
+  v[7] = from[7 * stride];
+  return __lsx_vld(v, 0);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4ui pgather<uint32_t, Packet4ui>(const uint32_t* from, Index stride) {
+  uint32_t v[4] __attribute__((aligned(16)));
+  v[0] = from[0];
+  v[1] = from[stride];
+  v[2] = from[2 * stride];
+  v[3] = from[3 * stride];
+  return __lsx_vld(v, 0);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ul pgather<uint64_t, Packet2ul>(const uint64_t* from, Index stride) {
+  uint64_t v[2] __attribute__((aligned(16)));
+  v[0] = from[0];
+  v[1] = from[stride];
+  return __lsx_vld(v, 0);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride) {
+  __lsx_vstelm_w(from, to, 0, 0);
+  __lsx_vstelm_w(from, to + stride * 1, 0, 1);
+  __lsx_vstelm_w(from, to + stride * 2, 0, 2);
+  __lsx_vstelm_w(from, to + stride * 3, 0, 3);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride) {
+  __lsx_vstelm_d(from, to, 0, 0);
+  __lsx_vstelm_d(from, to + stride, 0, 1);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet16c>(int8_t* to, const Packet16c& from,
+                                                                       Index stride) {
+  __lsx_vstelm_b((__m128i)from, to, 0, 0);
+  __lsx_vstelm_b((__m128i)from, to + stride * 1, 0, 1);
+  __lsx_vstelm_b((__m128i)from, to + stride * 2, 0, 2);
+  __lsx_vstelm_b((__m128i)from, to + stride * 3, 0, 3);
+  __lsx_vstelm_b((__m128i)from, to + stride * 4, 0, 4);
+  __lsx_vstelm_b((__m128i)from, to + stride * 5, 0, 5);
+  __lsx_vstelm_b((__m128i)from, to + stride * 6, 0, 6);
+  __lsx_vstelm_b((__m128i)from, to + stride * 7, 0, 7);
+  __lsx_vstelm_b((__m128i)from, to + stride * 8, 0, 8);
+  __lsx_vstelm_b((__m128i)from, to + stride * 9, 0, 9);
+  __lsx_vstelm_b((__m128i)from, to + stride * 10, 0, 10);
+  __lsx_vstelm_b((__m128i)from, to + stride * 11, 0, 11);
+  __lsx_vstelm_b((__m128i)from, to + stride * 12, 0, 12);
+  __lsx_vstelm_b((__m128i)from, to + stride * 13, 0, 13);
+  __lsx_vstelm_b((__m128i)from, to + stride * 14, 0, 14);
+  __lsx_vstelm_b((__m128i)from, to + stride * 15, 0, 15);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int16_t, Packet8s>(int16_t* to, const Packet8s& from,
+                                                                       Index stride) {
+  __lsx_vstelm_h((__m128i)from, to, 0, 0);
+  __lsx_vstelm_h((__m128i)from, to + stride * 1, 0, 1);
+  __lsx_vstelm_h((__m128i)from, to + stride * 2, 0, 2);
+  __lsx_vstelm_h((__m128i)from, to + stride * 3, 0, 3);
+  __lsx_vstelm_h((__m128i)from, to + stride * 4, 0, 4);
+  __lsx_vstelm_h((__m128i)from, to + stride * 5, 0, 5);
+  __lsx_vstelm_h((__m128i)from, to + stride * 6, 0, 6);
+  __lsx_vstelm_h((__m128i)from, to + stride * 7, 0, 7);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int32_t, Packet4i>(int32_t* to, const Packet4i& from,
+                                                                       Index stride) {
+  __lsx_vstelm_w((__m128i)from, to, 0, 0);
+  __lsx_vstelm_w((__m128i)from, to + stride * 1, 0, 1);
+  __lsx_vstelm_w((__m128i)from, to + stride * 2, 0, 2);
+  __lsx_vstelm_w((__m128i)from, to + stride * 3, 0, 3);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int64_t, Packet2l>(int64_t* to, const Packet2l& from,
+                                                                       Index stride) {
+  __lsx_vstelm_d((__m128i)from, to, 0, 0);
+  __lsx_vstelm_d((__m128i)from, to + stride * 1, 0, 1);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet16uc>(uint8_t* to, const Packet16uc& from,
+                                                                         Index stride) {
+  __lsx_vstelm_b((__m128i)from, to, 0, 0);
+  __lsx_vstelm_b((__m128i)from, to + stride * 1, 0, 1);
+  __lsx_vstelm_b((__m128i)from, to + stride * 2, 0, 2);
+  __lsx_vstelm_b((__m128i)from, to + stride * 3, 0, 3);
+  __lsx_vstelm_b((__m128i)from, to + stride * 4, 0, 4);
+  __lsx_vstelm_b((__m128i)from, to + stride * 5, 0, 5);
+  __lsx_vstelm_b((__m128i)from, to + stride * 6, 0, 6);
+  __lsx_vstelm_b((__m128i)from, to + stride * 7, 0, 7);
+  __lsx_vstelm_b((__m128i)from, to + stride * 8, 0, 8);
+  __lsx_vstelm_b((__m128i)from, to + stride * 9, 0, 9);
+  __lsx_vstelm_b((__m128i)from, to + stride * 10, 0, 10);
+  __lsx_vstelm_b((__m128i)from, to + stride * 11, 0, 11);
+  __lsx_vstelm_b((__m128i)from, to + stride * 12, 0, 12);
+  __lsx_vstelm_b((__m128i)from, to + stride * 13, 0, 13);
+  __lsx_vstelm_b((__m128i)from, to + stride * 14, 0, 14);
+  __lsx_vstelm_b((__m128i)from, to + stride * 15, 0, 15);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint16_t, Packet8us>(uint16_t* to, const Packet8us& from,
+                                                                         Index stride) {
+  __lsx_vstelm_h((__m128i)from, to, 0, 0);
+  __lsx_vstelm_h((__m128i)from, to + stride * 1, 0, 1);
+  __lsx_vstelm_h((__m128i)from, to + stride * 2, 0, 2);
+  __lsx_vstelm_h((__m128i)from, to + stride * 3, 0, 3);
+  __lsx_vstelm_h((__m128i)from, to + stride * 4, 0, 4);
+  __lsx_vstelm_h((__m128i)from, to + stride * 5, 0, 5);
+  __lsx_vstelm_h((__m128i)from, to + stride * 6, 0, 6);
+  __lsx_vstelm_h((__m128i)from, to + stride * 7, 0, 7);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint32_t, Packet4ui>(uint32_t* to, const Packet4ui& from,
+                                                                         Index stride) {
+  __lsx_vstelm_w((__m128i)from, to, 0, 0);
+  __lsx_vstelm_w((__m128i)from, to + stride * 1, 0, 1);
+  __lsx_vstelm_w((__m128i)from, to + stride * 2, 0, 2);
+  __lsx_vstelm_w((__m128i)from, to + stride * 3, 0, 3);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint64_t, Packet2ul>(uint64_t* to, const Packet2ul& from,
+                                                                         Index stride) {
+  __lsx_vstelm_d((__m128i)from, to, 0, 0);
+  __lsx_vstelm_d((__m128i)from, to + stride * 1, 0, 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
+  __builtin_prefetch(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
+  __builtin_prefetch(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int8_t>(const int8_t* addr) {
+  __builtin_prefetch(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int16_t>(const int16_t* addr) {
+  __builtin_prefetch(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int32_t>(const int32_t* addr) {
+  __builtin_prefetch(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int64_t>(const int64_t* addr) {
+  __builtin_prefetch(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<uint8_t>(const uint8_t* addr) {
+  __builtin_prefetch(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<uint16_t>(const uint16_t* addr) {
+  __builtin_prefetch(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<uint32_t>(const uint32_t* addr) {
+  __builtin_prefetch(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<uint64_t>(const uint64_t* addr) {
+  __builtin_prefetch(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
+  float v;
+  __lsx_vstelm_w(a, &v, 0, 0);
+  return v;
+}
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
+  double v;
+  __lsx_vstelm_d(a, &v, 0, 0);
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE int8_t pfirst<Packet16c>(const Packet16c& a) {
+  return (int8_t)__lsx_vpickve2gr_b((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t pfirst<Packet8s>(const Packet8s& a) {
+  return (int16_t)__lsx_vpickve2gr_h((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t pfirst<Packet4i>(const Packet4i& a) {
+  return __lsx_vpickve2gr_w((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int64_t pfirst<Packet2l>(const Packet2l& a) {
+  return __lsx_vpickve2gr_d((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t pfirst<Packet16uc>(const Packet16uc& a) {
+  return (uint8_t)__lsx_vpickve2gr_bu((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t pfirst<Packet8us>(const Packet8us& a) {
+  return (uint16_t)__lsx_vpickve2gr_hu((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t pfirst<Packet4ui>(const Packet4ui& a) {
+  return __lsx_vpickve2gr_wu((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t pfirst<Packet2ul>(const Packet2ul& a) {
+  return __lsx_vpickve2gr_du((__m128i)a, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) {
+  return (Packet4f)__lsx_vshuf4i_w(a, 0x1B);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) {
+  return (Packet2d)__lsx_vshuf4i_d(a, a, 0x1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c preverse(const Packet16c& a) {
+  return __lsx_vshuf4i_b(__lsx_vshuf4i_w((__m128i)a, 0x1B), 0x1B);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s preverse(const Packet8s& a) {
+  return __lsx_vshuf4i_h(__lsx_vshuf4i_d((__m128i)a, (__m128i)a, 0x1), 0x1B);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) {
+  return __lsx_vshuf4i_w((__m128i)a, 0x1B);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l preverse(const Packet2l& a) {
+  return __lsx_vshuf4i_d((__m128i)a, (__m128i)a, 0x1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc preverse(const Packet16uc& a) {
+  return __lsx_vshuf4i_b(__lsx_vshuf4i_w((__m128i)a, 0x1B), 0x1B);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us preverse(const Packet8us& a) {
+  return __lsx_vshuf4i_h(__lsx_vshuf4i_d((__m128i)a, (__m128i)a, 0x1), 0x1B);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preverse(const Packet4ui& a) {
+  return __lsx_vshuf4i_w((__m128i)a, 0x1B);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul preverse(const Packet2ul& a) {
+  return __lsx_vshuf4i_d((__m128i)a, (__m128i)a, 0x1);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) {
+  Packet4f tmp = __lsx_vfadd_s(a, vec4f_swizzle1(a, 2, 3, 2, 3));
+  return pfirst<Packet4f>(__lsx_vfadd_s(tmp, vec4f_swizzle1(tmp, 1, 1, 1, 1)));
+}
+template <>
+EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) {
+  return pfirst<Packet2d>(__lsx_vfadd_d(a, preverse(a)));
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux<Packet16c>(const Packet16c& a) {
+  Packet8s tmp1 = __lsx_vhaddw_h_b(a, a);
+  Packet4i tmp2 = __lsx_vhaddw_w_h(tmp1, tmp1);
+  Packet2l tmp3 = __lsx_vhaddw_d_w(tmp2, tmp2);
+  return (int8_t)__lsx_vpickve2gr_d(__lsx_vhaddw_q_d(tmp3, tmp3), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux<Packet8s>(const Packet8s& a) {
+  Packet4i tmp1 = __lsx_vhaddw_w_h(a, a);
+  Packet2l tmp2 = __lsx_vhaddw_d_w(tmp1, tmp1);
+  return (int16_t)__lsx_vpickve2gr_d(__lsx_vhaddw_q_d(tmp2, tmp2), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a) {
+  Packet2l tmp = __lsx_vhaddw_d_w(a, a);
+  return (int32_t)__lsx_vpickve2gr_d(__lsx_vhaddw_q_d(tmp, tmp), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int64_t predux<Packet2l>(const Packet2l& a) {
+  return (int64_t)__lsx_vpickve2gr_d(__lsx_vhaddw_q_d(a, a), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux<Packet16uc>(const Packet16uc& a) {
+  Packet8us tmp1 = __lsx_vhaddw_hu_bu(a, a);
+  Packet4ui tmp2 = __lsx_vhaddw_wu_hu(tmp1, tmp1);
+  Packet2ul tmp3 = __lsx_vhaddw_du_wu(tmp2, tmp2);
+  return (uint8_t)__lsx_vpickve2gr_d(__lsx_vhaddw_qu_du(tmp3, tmp3), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux<Packet8us>(const Packet8us& a) {
+  Packet4ui tmp1 = __lsx_vhaddw_wu_hu(a, a);
+  Packet2ul tmp2 = __lsx_vhaddw_du_wu(tmp1, tmp1);
+  return (uint16_t)__lsx_vpickve2gr_d(__lsx_vhaddw_qu_du(tmp2, tmp2), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux<Packet4ui>(const Packet4ui& a) {
+  Packet2ul tmp = __lsx_vhaddw_du_wu(a, a);
+  return (uint32_t)__lsx_vpickve2gr_d(__lsx_vhaddw_qu_du(tmp, tmp), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t predux<Packet2ul>(const Packet2ul& a) {
+  return (uint64_t)__lsx_vpickve2gr_d(__lsx_vhaddw_qu_du(a, a), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) {
+  Packet4f tmp = __lsx_vfmul_s(a, vec4f_swizzle1(a, 2, 3, 2, 3));
+  return pfirst<Packet4f>(__lsx_vfmul_s(tmp, vec4f_swizzle1(tmp, 1, 1, 1, 1)));
+}
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) {
+  return pfirst<Packet2d>(__lsx_vfmul_d(a, preverse(a)));
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux_mul<Packet16c>(const Packet16c& a) {
+  Packet8s tmp1 = __lsx_vmulwev_h_b(a, preverse(a));
+  Packet4i tmp2 = __lsx_vmulwev_w_h(tmp1, preverse(tmp1));
+  Packet2l tmp3 = __lsx_vmulwev_d_w(tmp2, preverse(tmp2));
+  return (int8_t)__lsx_vpickve2gr_d(__lsx_vmulwev_q_d(tmp3, preverse(tmp3)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_mul<Packet8s>(const Packet8s& a) {
+  Packet4i tmp1 = __lsx_vmulwev_w_h(a, preverse(a));
+  Packet2l tmp2 = __lsx_vmulwev_d_w(tmp1, preverse(tmp1));
+  return (int16_t)__lsx_vpickve2gr_d(__lsx_vmulwev_q_d(tmp2, preverse(tmp2)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_mul<Packet4i>(const Packet4i& a) {
+  Packet2l tmp = __lsx_vmulwev_d_w(a, preverse(a));
+  return (int32_t)__lsx_vpickve2gr_d(__lsx_vmulwev_q_d(tmp, preverse(tmp)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int64_t predux_mul<Packet2l>(const Packet2l& a) {
+  return (int64_t)__lsx_vpickve2gr_d(__lsx_vmulwev_q_d(a, preverse(a)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_mul<Packet16uc>(const Packet16uc& a) {
+  Packet8us tmp1 = __lsx_vmulwev_h_bu(a, preverse(a));
+  Packet4ui tmp2 = __lsx_vmulwev_w_h(tmp1, preverse(tmp1));
+  Packet2ul tmp3 = __lsx_vmulwev_d_w(tmp2, preverse(tmp2));
+  return (uint8_t)__lsx_vpickve2gr_d(__lsx_vmulwev_q_d(tmp3, preverse(tmp3)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_mul<Packet8us>(const Packet8us& a) {
+  Packet4ui tmp1 = __lsx_vmulwev_w_hu(a, preverse(a));
+  Packet2ul tmp2 = __lsx_vmulwev_d_w(tmp1, preverse(tmp1));
+  return (uint16_t)__lsx_vpickve2gr_d(__lsx_vmulwev_q_d(tmp2, preverse(tmp2)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_mul<Packet4ui>(const Packet4ui& a) {
+  Packet2ul tmp = __lsx_vmulwev_d_wu(a, preverse(a));
+  return (uint32_t)__lsx_vpickve2gr_d(__lsx_vmulwev_q_d(tmp, preverse(tmp)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t predux_mul<Packet2ul>(const Packet2ul& a) {
+  return (uint64_t)__lsx_vpickve2gr_d(__lsx_vmulwev_q_du(a, preverse(a)), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) {
+  Packet4f tmp = __lsx_vfmin_s(a, (Packet4f)__lsx_vshuf4i_w(a, 0x4E));
+  return pfirst(__lsx_vfmin_s(tmp, (Packet4f)__lsx_vshuf4i_w(tmp, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) {
+  return pfirst(__lsx_vfmin_d(a, preverse(a)));
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux_min<Packet16c>(const Packet16c& a) {
+  Packet16c tmp1 = __lsx_vmin_b(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  Packet16c tmp2 = __lsx_vmin_b(tmp1, __lsx_vshuf4i_h((__m128i)tmp1, 0x4E));
+  Packet16c tmp3 = __lsx_vmin_b(tmp2, __lsx_vshuf4i_b((__m128i)tmp2, 0x4E));
+  return pfirst((Packet16c)__lsx_vmin_b(tmp3, __lsx_vshuf4i_b((__m128i)tmp3, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_min<Packet8s>(const Packet8s& a) {
+  Packet8s tmp1 = __lsx_vmin_h(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  Packet8s tmp2 = __lsx_vmin_h(tmp1, __lsx_vshuf4i_h((__m128i)tmp1, 0x4E));
+  return pfirst((Packet8s)__lsx_vmin_h(tmp2, __lsx_vshuf4i_h((__m128i)tmp2, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a) {
+  Packet4i tmp = __lsx_vmin_w(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  return pfirst((Packet4i)__lsx_vmin_w(tmp, __lsx_vshuf4i_w((__m128i)tmp, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE int64_t predux_min<Packet2l>(const Packet2l& a) {
+  return pfirst((Packet2l)__lsx_vmin_d(a, preverse(a)));
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_min<Packet16uc>(const Packet16uc& a) {
+  Packet16uc tmp1 = __lsx_vmin_bu(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  Packet16uc tmp2 = __lsx_vmin_bu(tmp1, __lsx_vshuf4i_h((__m128i)tmp1, 0x4E));
+  Packet16uc tmp3 = __lsx_vmin_bu(tmp2, __lsx_vshuf4i_b((__m128i)tmp2, 0x4E));
+  return pfirst((Packet16uc)__lsx_vmin_bu(tmp3, __lsx_vshuf4i_b((__m128i)tmp3, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_min<Packet8us>(const Packet8us& a) {
+  Packet8us tmp1 = __lsx_vmin_hu(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  Packet8us tmp2 = __lsx_vmin_hu(tmp1, __lsx_vshuf4i_h((__m128i)tmp1, 0x4E));
+  return pfirst((Packet8us)__lsx_vmin_hu(tmp2, __lsx_vshuf4i_h((__m128i)tmp2, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_min<Packet4ui>(const Packet4ui& a) {
+  Packet4ui tmp = __lsx_vmin_wu(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  return pfirst((Packet4ui)__lsx_vmin_wu(tmp, __lsx_vshuf4i_w((__m128i)tmp, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t predux_min<Packet2ul>(const Packet2ul& a) {
+  return pfirst((Packet2ul)__lsx_vmin_du(a, preverse(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) {
+  Packet4f tmp = __lsx_vfmax_s(a, (Packet4f)__lsx_vshuf4i_w(a, 0x4E));
+  return pfirst(__lsx_vfmax_s(tmp, (Packet4f)__lsx_vshuf4i_w(tmp, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) {
+  return pfirst(__lsx_vfmax_d(a, preverse(a)));
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux_max<Packet16c>(const Packet16c& a) {
+  Packet16c tmp1 = __lsx_vmax_b(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  Packet16c tmp2 = __lsx_vmax_b(tmp1, __lsx_vshuf4i_h((__m128i)tmp1, 0x4E));
+  Packet16c tmp3 = __lsx_vmax_b(tmp2, __lsx_vshuf4i_b((__m128i)tmp2, 0x4E));
+  return pfirst((Packet16c)__lsx_vmax_b(tmp3, __lsx_vshuf4i_b((__m128i)tmp3, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_max<Packet8s>(const Packet8s& a) {
+  Packet8s tmp1 = __lsx_vmax_h(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  Packet8s tmp2 = __lsx_vmax_h(tmp1, __lsx_vshuf4i_h((__m128i)tmp1, 0x4E));
+  return pfirst((Packet8s)__lsx_vmax_h(tmp2, __lsx_vshuf4i_h((__m128i)tmp2, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a) {
+  Packet4i tmp = __lsx_vmax_w(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  return pfirst((Packet4i)__lsx_vmax_w(tmp, __lsx_vshuf4i_w((__m128i)tmp, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE int64_t predux_max<Packet2l>(const Packet2l& a) {
+  return pfirst((Packet2l)__lsx_vmax_d(a, preverse(a)));
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_max<Packet16uc>(const Packet16uc& a) {
+  Packet16uc tmp1 = __lsx_vmax_bu(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  Packet16uc tmp2 = __lsx_vmax_bu(tmp1, __lsx_vshuf4i_h((__m128i)tmp1, 0x4E));
+  Packet16uc tmp3 = __lsx_vmax_bu(tmp2, __lsx_vshuf4i_b((__m128i)tmp2, 0x4E));
+  return pfirst((Packet16uc)__lsx_vmax_bu(tmp3, __lsx_vshuf4i_b((__m128i)tmp3, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_max<Packet8us>(const Packet8us& a) {
+  Packet8us tmp1 = __lsx_vmax_hu(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  Packet8us tmp2 = __lsx_vmax_hu(tmp1, __lsx_vshuf4i_h((__m128i)tmp1, 0x4E));
+  return pfirst((Packet8us)__lsx_vmax_hu(tmp2, __lsx_vshuf4i_h((__m128i)tmp2, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_max<Packet4ui>(const Packet4ui& a) {
+  Packet4ui tmp = __lsx_vmax_wu(a, __lsx_vshuf4i_w((__m128i)a, 0x4E));
+  return pfirst((Packet4ui)__lsx_vmax_wu(tmp, __lsx_vshuf4i_w((__m128i)tmp, 0xB1)));
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t predux_max<Packet2ul>(const Packet2ul& a) {
+  return pfirst((Packet2ul)__lsx_vmax_du(a, preverse(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
+  return __lsx_vfsqrt_s(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d psqrt(const Packet2d& a) {
+  return __lsx_vfsqrt_d(a);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
+  Packet4f T0 = (Packet4f)__lsx_vilvl_w((__m128i)kernel.packet[1], (__m128i)kernel.packet[0]);
+  Packet4f T1 = (Packet4f)__lsx_vilvh_w((__m128i)kernel.packet[1], (__m128i)kernel.packet[0]);
+  Packet4f T2 = (Packet4f)__lsx_vilvl_w((__m128i)kernel.packet[3], (__m128i)kernel.packet[2]);
+  Packet4f T3 = (Packet4f)__lsx_vilvh_w((__m128i)kernel.packet[3], (__m128i)kernel.packet[2]);
+
+  kernel.packet[0] = (Packet4f)__lsx_vilvl_d((__m128i)T2, (__m128i)T0);
+  kernel.packet[1] = (Packet4f)__lsx_vilvh_d((__m128i)T2, (__m128i)T0);
+  kernel.packet[2] = (Packet4f)__lsx_vilvl_d((__m128i)T3, (__m128i)T1);
+  kernel.packet[3] = (Packet4f)__lsx_vilvh_d((__m128i)T3, (__m128i)T1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2d, 2>& kernel) {
+  Packet2d tmp = (Packet2d)__lsx_vilvh_d((__m128i)kernel.packet[1], (__m128i)kernel.packet[0]);
+  kernel.packet[0] = (Packet2d)__lsx_vilvl_d((__m128i)kernel.packet[1], (__m128i)kernel.packet[0]);
+  kernel.packet[1] = tmp;
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 16>& kernel) {
+  __m128i t0 = __lsx_vilvl_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t1 = __lsx_vilvh_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t2 = __lsx_vilvl_b(kernel.packet[3], kernel.packet[2]);
+  __m128i t3 = __lsx_vilvh_b(kernel.packet[3], kernel.packet[2]);
+  __m128i t4 = __lsx_vilvl_b(kernel.packet[5], kernel.packet[4]);
+  __m128i t5 = __lsx_vilvh_b(kernel.packet[5], kernel.packet[4]);
+  __m128i t6 = __lsx_vilvl_b(kernel.packet[7], kernel.packet[6]);
+  __m128i t7 = __lsx_vilvh_b(kernel.packet[7], kernel.packet[6]);
+  __m128i t8 = __lsx_vilvl_b(kernel.packet[9], kernel.packet[8]);
+  __m128i t9 = __lsx_vilvh_b(kernel.packet[9], kernel.packet[8]);
+  __m128i ta = __lsx_vilvl_b(kernel.packet[11], kernel.packet[10]);
+  __m128i tb = __lsx_vilvh_b(kernel.packet[11], kernel.packet[10]);
+  __m128i tc = __lsx_vilvl_b(kernel.packet[13], kernel.packet[12]);
+  __m128i td = __lsx_vilvh_b(kernel.packet[13], kernel.packet[12]);
+  __m128i te = __lsx_vilvl_b(kernel.packet[15], kernel.packet[14]);
+  __m128i tf = __lsx_vilvh_b(kernel.packet[15], kernel.packet[14]);
+
+  __m128i s0 = __lsx_vilvl_h(t2, t0);
+  __m128i s1 = __lsx_vilvh_h(t2, t0);
+  __m128i s2 = __lsx_vilvl_h(t3, t1);
+  __m128i s3 = __lsx_vilvh_h(t3, t1);
+  __m128i s4 = __lsx_vilvl_h(t6, t4);
+  __m128i s5 = __lsx_vilvh_h(t6, t4);
+  __m128i s6 = __lsx_vilvl_h(t7, t5);
+  __m128i s7 = __lsx_vilvh_h(t7, t5);
+  __m128i s8 = __lsx_vilvl_h(ta, t8);
+  __m128i s9 = __lsx_vilvh_h(ta, t8);
+  __m128i sa = __lsx_vilvl_h(tb, t9);
+  __m128i sb = __lsx_vilvh_h(tb, t9);
+  __m128i sc = __lsx_vilvl_h(te, tc);
+  __m128i sd = __lsx_vilvh_h(te, tc);
+  __m128i se = __lsx_vilvl_h(tf, td);
+  __m128i sf = __lsx_vilvh_h(tf, td);
+
+  __m128i u0 = __lsx_vilvl_w(s4, s0);
+  __m128i u1 = __lsx_vilvh_w(s4, s0);
+  __m128i u2 = __lsx_vilvl_w(s5, s1);
+  __m128i u3 = __lsx_vilvh_w(s5, s1);
+  __m128i u4 = __lsx_vilvl_w(s6, s2);
+  __m128i u5 = __lsx_vilvh_w(s6, s2);
+  __m128i u6 = __lsx_vilvl_w(s7, s3);
+  __m128i u7 = __lsx_vilvh_w(s7, s3);
+  __m128i u8 = __lsx_vilvl_w(sc, s8);
+  __m128i u9 = __lsx_vilvh_w(sc, s8);
+  __m128i ua = __lsx_vilvl_w(sd, s9);
+  __m128i ub = __lsx_vilvh_w(sd, s9);
+  __m128i uc = __lsx_vilvl_w(se, sa);
+  __m128i ud = __lsx_vilvh_w(se, sa);
+  __m128i ue = __lsx_vilvl_w(sf, sb);
+  __m128i uf = __lsx_vilvh_w(sf, sb);
+
+  kernel.packet[0] = __lsx_vilvl_d(u8, u0);
+  kernel.packet[1] = __lsx_vilvh_d(u8, u0);
+  kernel.packet[2] = __lsx_vilvl_d(u9, u1);
+  kernel.packet[3] = __lsx_vilvh_d(u9, u1);
+  kernel.packet[4] = __lsx_vilvl_d(ua, u2);
+  kernel.packet[5] = __lsx_vilvh_d(ua, u2);
+  kernel.packet[6] = __lsx_vilvl_d(ub, u3);
+  kernel.packet[7] = __lsx_vilvh_d(ub, u3);
+  kernel.packet[8] = __lsx_vilvl_d(uc, u4);
+  kernel.packet[9] = __lsx_vilvh_d(uc, u4);
+  kernel.packet[10] = __lsx_vilvl_d(ud, u5);
+  kernel.packet[11] = __lsx_vilvh_d(ud, u5);
+  kernel.packet[12] = __lsx_vilvl_d(ue, u6);
+  kernel.packet[13] = __lsx_vilvh_d(ue, u6);
+  kernel.packet[14] = __lsx_vilvl_d(uf, u7);
+  kernel.packet[15] = __lsx_vilvh_d(uf, u7);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 8>& kernel) {
+  __m128i t0 = __lsx_vilvl_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t1 = __lsx_vilvh_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t2 = __lsx_vilvl_b(kernel.packet[3], kernel.packet[2]);
+  __m128i t3 = __lsx_vilvh_b(kernel.packet[3], kernel.packet[2]);
+  __m128i t4 = __lsx_vilvl_b(kernel.packet[5], kernel.packet[4]);
+  __m128i t5 = __lsx_vilvh_b(kernel.packet[5], kernel.packet[4]);
+  __m128i t6 = __lsx_vilvl_b(kernel.packet[7], kernel.packet[6]);
+  __m128i t7 = __lsx_vilvh_b(kernel.packet[7], kernel.packet[6]);
+
+  __m128i s0 = __lsx_vilvl_h(t2, t0);
+  __m128i s1 = __lsx_vilvh_h(t2, t0);
+  __m128i s2 = __lsx_vilvl_h(t3, t1);
+  __m128i s3 = __lsx_vilvh_h(t3, t1);
+  __m128i s4 = __lsx_vilvl_h(t6, t4);
+  __m128i s5 = __lsx_vilvh_h(t6, t4);
+  __m128i s6 = __lsx_vilvl_h(t7, t5);
+  __m128i s7 = __lsx_vilvh_h(t7, t5);
+
+  kernel.packet[0] = __lsx_vilvl_w(s4, s0);
+  kernel.packet[1] = __lsx_vilvh_w(s4, s0);
+  kernel.packet[2] = __lsx_vilvl_w(s5, s1);
+  kernel.packet[3] = __lsx_vilvh_w(s5, s1);
+  kernel.packet[4] = __lsx_vilvl_w(s6, s2);
+  kernel.packet[5] = __lsx_vilvh_w(s6, s2);
+  kernel.packet[6] = __lsx_vilvl_w(s7, s3);
+  kernel.packet[7] = __lsx_vilvh_w(s7, s3);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 4>& kernel) {
+  __m128i t0 = __lsx_vilvl_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t1 = __lsx_vilvh_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t2 = __lsx_vilvl_b(kernel.packet[3], kernel.packet[2]);
+  __m128i t3 = __lsx_vilvh_b(kernel.packet[3], kernel.packet[2]);
+
+  kernel.packet[0] = __lsx_vilvl_h(t2, t0);
+  kernel.packet[1] = __lsx_vilvh_h(t2, t0);
+  kernel.packet[2] = __lsx_vilvl_h(t3, t1);
+  kernel.packet[3] = __lsx_vilvh_h(t3, t1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8s, 8>& kernel) {
+  __m128i t0 = __lsx_vilvl_h(kernel.packet[1], kernel.packet[0]);
+  __m128i t1 = __lsx_vilvh_h(kernel.packet[1], kernel.packet[0]);
+  __m128i t2 = __lsx_vilvl_h(kernel.packet[3], kernel.packet[2]);
+  __m128i t3 = __lsx_vilvh_h(kernel.packet[3], kernel.packet[2]);
+  __m128i t4 = __lsx_vilvl_h(kernel.packet[5], kernel.packet[4]);
+  __m128i t5 = __lsx_vilvh_h(kernel.packet[5], kernel.packet[4]);
+  __m128i t6 = __lsx_vilvl_h(kernel.packet[7], kernel.packet[6]);
+  __m128i t7 = __lsx_vilvh_h(kernel.packet[7], kernel.packet[6]);
+
+  __m128i s0 = __lsx_vilvl_w(t2, t0);
+  __m128i s1 = __lsx_vilvh_w(t2, t0);
+  __m128i s2 = __lsx_vilvl_w(t3, t1);
+  __m128i s3 = __lsx_vilvh_w(t3, t1);
+  __m128i s4 = __lsx_vilvl_w(t6, t4);
+  __m128i s5 = __lsx_vilvh_w(t6, t4);
+  __m128i s6 = __lsx_vilvl_w(t7, t5);
+  __m128i s7 = __lsx_vilvh_w(t7, t5);
+
+  kernel.packet[0] = __lsx_vilvl_d(s4, s0);
+  kernel.packet[1] = __lsx_vilvh_d(s4, s0);
+  kernel.packet[2] = __lsx_vilvl_d(s5, s1);
+  kernel.packet[3] = __lsx_vilvh_d(s5, s1);
+  kernel.packet[4] = __lsx_vilvl_d(s6, s2);
+  kernel.packet[5] = __lsx_vilvh_d(s6, s2);
+  kernel.packet[6] = __lsx_vilvl_d(s7, s3);
+  kernel.packet[7] = __lsx_vilvh_d(s7, s3);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8s, 4>& kernel) {
+  __m128i t0 = __lsx_vilvl_h(kernel.packet[1], kernel.packet[0]);
+  __m128i t1 = __lsx_vilvh_h(kernel.packet[1], kernel.packet[0]);
+  __m128i t2 = __lsx_vilvl_h(kernel.packet[3], kernel.packet[2]);
+  __m128i t3 = __lsx_vilvh_h(kernel.packet[3], kernel.packet[2]);
+
+  kernel.packet[0] = __lsx_vilvl_w(t2, t0);
+  kernel.packet[1] = __lsx_vilvh_w(t2, t0);
+  kernel.packet[2] = __lsx_vilvl_w(t3, t1);
+  kernel.packet[3] = __lsx_vilvh_w(t3, t1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
+  __m128i T0 = __lsx_vilvl_w(kernel.packet[1], kernel.packet[0]);
+  __m128i T1 = __lsx_vilvh_w(kernel.packet[1], kernel.packet[0]);
+  __m128i T2 = __lsx_vilvl_w(kernel.packet[3], kernel.packet[2]);
+  __m128i T3 = __lsx_vilvh_w(kernel.packet[3], kernel.packet[2]);
+
+  kernel.packet[0] = __lsx_vilvl_d(T2, T0);
+  kernel.packet[1] = __lsx_vilvh_d(T2, T0);
+  kernel.packet[2] = __lsx_vilvl_d(T3, T1);
+  kernel.packet[3] = __lsx_vilvh_d(T3, T1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2l, 2>& kernel) {
+  __m128i tmp = __lsx_vilvh_d(kernel.packet[1], kernel.packet[0]);
+  kernel.packet[0] = __lsx_vilvl_d(kernel.packet[1], kernel.packet[0]);
+  kernel.packet[1] = tmp;
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 16>& kernel) {
+  __m128i t0 = __lsx_vilvl_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t1 = __lsx_vilvh_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t2 = __lsx_vilvl_b(kernel.packet[3], kernel.packet[2]);
+  __m128i t3 = __lsx_vilvh_b(kernel.packet[3], kernel.packet[2]);
+  __m128i t4 = __lsx_vilvl_b(kernel.packet[5], kernel.packet[4]);
+  __m128i t5 = __lsx_vilvh_b(kernel.packet[5], kernel.packet[4]);
+  __m128i t6 = __lsx_vilvl_b(kernel.packet[7], kernel.packet[6]);
+  __m128i t7 = __lsx_vilvh_b(kernel.packet[7], kernel.packet[6]);
+  __m128i t8 = __lsx_vilvl_b(kernel.packet[9], kernel.packet[8]);
+  __m128i t9 = __lsx_vilvh_b(kernel.packet[9], kernel.packet[8]);
+  __m128i ta = __lsx_vilvl_b(kernel.packet[11], kernel.packet[10]);
+  __m128i tb = __lsx_vilvh_b(kernel.packet[11], kernel.packet[10]);
+  __m128i tc = __lsx_vilvl_b(kernel.packet[13], kernel.packet[12]);
+  __m128i td = __lsx_vilvh_b(kernel.packet[13], kernel.packet[12]);
+  __m128i te = __lsx_vilvl_b(kernel.packet[15], kernel.packet[14]);
+  __m128i tf = __lsx_vilvh_b(kernel.packet[15], kernel.packet[14]);
+
+  __m128i s0 = __lsx_vilvl_h(t2, t0);
+  __m128i s1 = __lsx_vilvh_h(t2, t0);
+  __m128i s2 = __lsx_vilvl_h(t3, t1);
+  __m128i s3 = __lsx_vilvh_h(t3, t1);
+  __m128i s4 = __lsx_vilvl_h(t6, t4);
+  __m128i s5 = __lsx_vilvh_h(t6, t4);
+  __m128i s6 = __lsx_vilvl_h(t7, t5);
+  __m128i s7 = __lsx_vilvh_h(t7, t5);
+  __m128i s8 = __lsx_vilvl_h(ta, t8);
+  __m128i s9 = __lsx_vilvh_h(ta, t8);
+  __m128i sa = __lsx_vilvl_h(tb, t9);
+  __m128i sb = __lsx_vilvh_h(tb, t9);
+  __m128i sc = __lsx_vilvl_h(te, tc);
+  __m128i sd = __lsx_vilvh_h(te, tc);
+  __m128i se = __lsx_vilvl_h(tf, td);
+  __m128i sf = __lsx_vilvh_h(tf, td);
+
+  __m128i u0 = __lsx_vilvl_w(s4, s0);
+  __m128i u1 = __lsx_vilvh_w(s4, s0);
+  __m128i u2 = __lsx_vilvl_w(s5, s1);
+  __m128i u3 = __lsx_vilvh_w(s5, s1);
+  __m128i u4 = __lsx_vilvl_w(s6, s2);
+  __m128i u5 = __lsx_vilvh_w(s6, s2);
+  __m128i u6 = __lsx_vilvl_w(s7, s3);
+  __m128i u7 = __lsx_vilvh_w(s7, s3);
+  __m128i u8 = __lsx_vilvl_w(sc, s8);
+  __m128i u9 = __lsx_vilvh_w(sc, s8);
+  __m128i ua = __lsx_vilvl_w(sd, s9);
+  __m128i ub = __lsx_vilvh_w(sd, s9);
+  __m128i uc = __lsx_vilvl_w(se, sa);
+  __m128i ud = __lsx_vilvh_w(se, sa);
+  __m128i ue = __lsx_vilvl_w(sf, sb);
+  __m128i uf = __lsx_vilvh_w(sf, sb);
+
+  kernel.packet[0] = __lsx_vilvl_d(u8, u0);
+  kernel.packet[1] = __lsx_vilvh_d(u8, u0);
+  kernel.packet[2] = __lsx_vilvl_d(u9, u1);
+  kernel.packet[3] = __lsx_vilvh_d(u9, u1);
+  kernel.packet[4] = __lsx_vilvl_d(ua, u2);
+  kernel.packet[5] = __lsx_vilvh_d(ua, u2);
+  kernel.packet[6] = __lsx_vilvl_d(ub, u3);
+  kernel.packet[7] = __lsx_vilvh_d(ub, u3);
+  kernel.packet[8] = __lsx_vilvl_d(uc, u4);
+  kernel.packet[9] = __lsx_vilvh_d(uc, u4);
+  kernel.packet[10] = __lsx_vilvl_d(ud, u5);
+  kernel.packet[11] = __lsx_vilvh_d(ud, u5);
+  kernel.packet[12] = __lsx_vilvl_d(ue, u6);
+  kernel.packet[13] = __lsx_vilvh_d(ue, u6);
+  kernel.packet[14] = __lsx_vilvl_d(uf, u7);
+  kernel.packet[15] = __lsx_vilvh_d(uf, u7);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 8>& kernel) {
+  __m128i t0 = __lsx_vilvl_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t1 = __lsx_vilvh_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t2 = __lsx_vilvl_b(kernel.packet[3], kernel.packet[2]);
+  __m128i t3 = __lsx_vilvh_b(kernel.packet[3], kernel.packet[2]);
+  __m128i t4 = __lsx_vilvl_b(kernel.packet[5], kernel.packet[4]);
+  __m128i t5 = __lsx_vilvh_b(kernel.packet[5], kernel.packet[4]);
+  __m128i t6 = __lsx_vilvl_b(kernel.packet[7], kernel.packet[6]);
+  __m128i t7 = __lsx_vilvh_b(kernel.packet[7], kernel.packet[6]);
+
+  __m128i s0 = __lsx_vilvl_h(t2, t0);
+  __m128i s1 = __lsx_vilvh_h(t2, t0);
+  __m128i s2 = __lsx_vilvl_h(t3, t1);
+  __m128i s3 = __lsx_vilvh_h(t3, t1);
+  __m128i s4 = __lsx_vilvl_h(t6, t4);
+  __m128i s5 = __lsx_vilvh_h(t6, t4);
+  __m128i s6 = __lsx_vilvl_h(t7, t5);
+  __m128i s7 = __lsx_vilvh_h(t7, t5);
+
+  kernel.packet[0] = __lsx_vilvl_w(s4, s0);
+  kernel.packet[1] = __lsx_vilvh_w(s4, s0);
+  kernel.packet[2] = __lsx_vilvl_w(s5, s1);
+  kernel.packet[3] = __lsx_vilvh_w(s5, s1);
+  kernel.packet[4] = __lsx_vilvl_w(s6, s2);
+  kernel.packet[5] = __lsx_vilvh_w(s6, s2);
+  kernel.packet[6] = __lsx_vilvl_w(s7, s3);
+  kernel.packet[7] = __lsx_vilvh_w(s7, s3);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 4>& kernel) {
+  __m128i t0 = __lsx_vilvl_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t1 = __lsx_vilvh_b(kernel.packet[1], kernel.packet[0]);
+  __m128i t2 = __lsx_vilvl_b(kernel.packet[3], kernel.packet[2]);
+  __m128i t3 = __lsx_vilvh_b(kernel.packet[3], kernel.packet[2]);
+
+  kernel.packet[0] = __lsx_vilvl_h(t2, t0);
+  kernel.packet[1] = __lsx_vilvh_h(t2, t0);
+  kernel.packet[2] = __lsx_vilvl_h(t3, t1);
+  kernel.packet[3] = __lsx_vilvh_h(t3, t1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8us, 8>& kernel) {
+  __m128i t0 = __lsx_vilvl_h(kernel.packet[1], kernel.packet[0]);
+  __m128i t1 = __lsx_vilvh_h(kernel.packet[1], kernel.packet[0]);
+  __m128i t2 = __lsx_vilvl_h(kernel.packet[3], kernel.packet[2]);
+  __m128i t3 = __lsx_vilvh_h(kernel.packet[3], kernel.packet[2]);
+  __m128i t4 = __lsx_vilvl_h(kernel.packet[5], kernel.packet[4]);
+  __m128i t5 = __lsx_vilvh_h(kernel.packet[5], kernel.packet[4]);
+  __m128i t6 = __lsx_vilvl_h(kernel.packet[7], kernel.packet[6]);
+  __m128i t7 = __lsx_vilvh_h(kernel.packet[7], kernel.packet[6]);
+
+  __m128i s0 = __lsx_vilvl_w(t2, t0);
+  __m128i s1 = __lsx_vilvh_w(t2, t0);
+  __m128i s2 = __lsx_vilvl_w(t3, t1);
+  __m128i s3 = __lsx_vilvh_w(t3, t1);
+  __m128i s4 = __lsx_vilvl_w(t6, t4);
+  __m128i s5 = __lsx_vilvh_w(t6, t4);
+  __m128i s6 = __lsx_vilvl_w(t7, t5);
+  __m128i s7 = __lsx_vilvh_w(t7, t5);
+
+  kernel.packet[0] = __lsx_vilvl_d(s4, s0);
+  kernel.packet[1] = __lsx_vilvh_d(s4, s0);
+  kernel.packet[2] = __lsx_vilvl_d(s5, s1);
+  kernel.packet[3] = __lsx_vilvh_d(s5, s1);
+  kernel.packet[4] = __lsx_vilvl_d(s6, s2);
+  kernel.packet[5] = __lsx_vilvh_d(s6, s2);
+  kernel.packet[6] = __lsx_vilvl_d(s7, s3);
+  kernel.packet[7] = __lsx_vilvh_d(s7, s3);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8us, 4>& kernel) {
+  __m128i t0 = __lsx_vilvl_h(kernel.packet[1], kernel.packet[0]);
+  __m128i t1 = __lsx_vilvh_h(kernel.packet[1], kernel.packet[0]);
+  __m128i t2 = __lsx_vilvl_h(kernel.packet[3], kernel.packet[2]);
+  __m128i t3 = __lsx_vilvh_h(kernel.packet[3], kernel.packet[2]);
+
+  kernel.packet[0] = __lsx_vilvl_w(t2, t0);
+  kernel.packet[1] = __lsx_vilvh_w(t2, t0);
+  kernel.packet[2] = __lsx_vilvl_w(t3, t1);
+  kernel.packet[3] = __lsx_vilvh_w(t3, t1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4ui, 4>& kernel) {
+  __m128i T0 = __lsx_vilvl_w(kernel.packet[1], kernel.packet[0]);
+  __m128i T1 = __lsx_vilvh_w(kernel.packet[1], kernel.packet[0]);
+  __m128i T2 = __lsx_vilvl_w(kernel.packet[3], kernel.packet[2]);
+  __m128i T3 = __lsx_vilvh_w(kernel.packet[3], kernel.packet[2]);
+
+  kernel.packet[0] = __lsx_vilvl_d(T2, T0);
+  kernel.packet[1] = __lsx_vilvh_d(T2, T0);
+  kernel.packet[2] = __lsx_vilvl_d(T3, T1);
+  kernel.packet[3] = __lsx_vilvh_d(T3, T1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2ul, 2>& kernel) {
+  __m128i tmp = __lsx_vilvh_d(kernel.packet[1], kernel.packet[0]);
+  kernel.packet[0] = __lsx_vilvl_d(kernel.packet[1], kernel.packet[0]);
+  kernel.packet[1] = tmp;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f prsqrt(const Packet4f& a) {
+  return __lsx_vfrsqrt_s(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d prsqrt(const Packet2d& a) {
+  return __lsx_vfrsqrt_d(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pfloor(const Packet4f& a) {
+  return __lsx_vfrintrm_s(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pfloor(const Packet2d& a) {
+  return __lsx_vfrintrm_d(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pceil(const Packet4f& a) {
+  return __lsx_vfrintrp_s(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pceil(const Packet2d& a) {
+  return __lsx_vfrintrp_d(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pround(const Packet4f& a) {
+  const Packet4f mask = pset1frombits<Packet4f>(static_cast<numext::uint32_t>(0x80000000u));
+  const Packet4f prev0dot5 = pset1frombits<Packet4f>(static_cast<numext::uint32_t>(0x3EFFFFFFu));
+  return __lsx_vfrintrz_s(padd(pxor(pand(a, mask), prev0dot5), a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pround(const Packet2d& a) {
+  const Packet2d mask = pset1frombits<Packet2d>(static_cast<numext::uint64_t>(0x8000000000000000ull));
+  const Packet2d prev0dot5 = pset1frombits<Packet2d>(static_cast<numext::uint64_t>(0x3FDFFFFFFFFFFFFFull));
+  return __lsx_vfrintrz_d(padd(por(pand(a, mask), prev0dot5), a));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b) {
+  return (Packet4f)__lsx_vbitsel_v((__m128i)b, (__m128i)a, (__m128i)mask);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16c pselect(const Packet16c& mask, const Packet16c& a, const Packet16c& b) {
+  return (Packet16c)__lsx_vbitsel_v((__m128i)b, (__m128i)a, (__m128i)mask);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c ploadquad<Packet16c>(const int8_t* from) {
+  int8_t tmp[16] = {*from,       *from,       *from,       *from,       *(from + 1), *(from + 1),
+                    *(from + 1), *(from + 1), *(from + 2), *(from + 2), *(from + 2), *(from + 2),
+                    *(from + 3), *(from + 3), *(from + 3), *(from + 3)};
+  return __lsx_vld(tmp, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc ploadquad<Packet16uc>(const uint8_t* from) {
+  uint8_t tmp[16] = {*from,       *from,       *from,       *from,       *(from + 1), *(from + 1),
+                     *(from + 1), *(from + 1), *(from + 2), *(from + 2), *(from + 2), *(from + 2),
+                     *(from + 3), *(from + 3), *(from + 3), *(from + 3)};
+  return __lsx_vld(tmp, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s ploadquad<Packet8s>(const int16_t* from) {
+  int16_t tmp[8] = {*from, *from, *from, *from, *(from + 1), *(from + 1), *(from + 1), *(from + 1)};
+  return __lsx_vld(tmp, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us ploadquad<Packet8us>(const uint16_t* from) {
+  uint16_t tmp[8] = {*from, *from, *from, *from, *(from + 1), *(from + 1), *(from + 1), *(from + 1)};
+  return __lsx_vld(tmp, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ploadquad<Packet4i>(const int32_t* from) {
+  int32_t tmp[4] = {*from, *from, *from, *from};
+  return __lsx_vld(tmp, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui ploadquad<Packet4ui>(const uint32_t* from) {
+  uint32_t tmp[4] = {*from, *from, *from, *from};
+  return __lsx_vld(tmp, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c pnmsub(const Packet16c& a, const Packet16c& b, const Packet16c& c) {
+  return __lsx_vmsub_b(pnegate(c), a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pnmsub(const Packet8s& a, const Packet8s& b, const Packet8s& c) {
+  return __lsx_vmsub_h(pnegate(c), a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pnmsub(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
+  return __lsx_vmsub_w(pnegate(c), a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pnmsub(const Packet2l& a, const Packet2l& b, const Packet2l& c) {
+  return __lsx_vmsub_d(pnegate(c), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c pmsub(const Packet16c& a, const Packet16c& b, const Packet16c& c) {
+  return __lsx_vmadd_b(pnegate(c), a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmsub(const Packet8s& a, const Packet8s& b, const Packet8s& c) {
+  return __lsx_vmadd_h(pnegate(c), a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmsub(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
+  return __lsx_vmadd_w(pnegate(c), a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmsub(const Packet2l& a, const Packet2l& b, const Packet2l& c) {
+  return __lsx_vmadd_d(pnegate(c), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16c pnmadd(const Packet16c& a, const Packet16c& b, const Packet16c& c) {
+  return __lsx_vmsub_b(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pnmadd(const Packet8s& a, const Packet8s& b, const Packet8s& c) {
+  return __lsx_vmsub_h(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pnmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
+  return __lsx_vmsub_w(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pnmadd(const Packet2l& a, const Packet2l& b, const Packet2l& c) {
+  return __lsx_vmsub_d(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pexp(const Packet4f& _x) {
+  return pexp_float(_x);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pexp(const Packet2d& _x) {
+  return pexp_double(_x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pzero(const Packet4f& /* a */) {
+  Packet4f v = {0.0f, 0.0f, 0.0f, 0.0f};
+  return v;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pmin<Packet4f>(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pmax<Packet4f>(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadquad<Packet4f>(const float* from) {
+  return (__m128)__lsx_vldrepl_w(from, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f psignbit(const Packet4f& a) {
+  return (__m128)__lsx_vsrai_w((__m128i)a, 31);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a) {
+  return __lsx_vfrintrne_s(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f ptrunc<Packet4f>(const Packet4f& a) {
+  return __lsx_vfrintrz_s(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f preciprocal<Packet4f>(const Packet4f& a) {
+  return __lsx_vfrecip_s(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pzero(const Packet2d& /* a */) {
+  Packet2d v = {0.0, 0.0};
+  return v;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pmin<Packet2d>(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pmax<Packet2d>(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d psignbit(const Packet2d& a) {
+  return (__m128d)(__lsx_vsrai_d((__m128i)a, 63));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2d pselect(const Packet2d& mask, const Packet2d& a, const Packet2d& b) {
+  return (Packet2d)__lsx_vbitsel_v((__m128i)b, (__m128i)a, (__m128i)mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a) {
+  return __lsx_vfrintrne_d(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d ptrunc<Packet2d>(const Packet2d& a) {
+  return __lsx_vfrintrz_d(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
+  return pldexp_generic(a, exponent);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8s pselect(const Packet8s& mask, const Packet8s& a, const Packet8s& b) {
+  return __lsx_vbitsel_v(b, a, mask);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4i pselect(const Packet4i& mask, const Packet4i& a, const Packet4i& b) {
+  return __lsx_vbitsel_v(b, a, mask);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2l pselect(const Packet2l& mask, const Packet2l& a, const Packet2l& b) {
+  return __lsx_vbitsel_v(b, a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pdiv<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return __lsx_vdiv_bu(a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16uc pselect(const Packet16uc& mask, const Packet16uc& a,
+                                                         const Packet16uc& b) {
+  return __lsx_vbitsel_v(b, a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc psqrt(const Packet16uc& a) {
+  __m128i res = {0, 0};
+  __m128i add = {0x0808080808080808, 0x0808080808080808};
+  for (int i = 0; i < 4; i++) {
+    const __m128i temp = __lsx_vor_v(res, add);
+    const __m128i tmul = __lsx_vpackev_b(__lsx_vmulwod_h_bu(temp, temp), __lsx_vmulwev_h_bu(temp, temp));
+    res = __lsx_vbitsel_v(res, temp, __lsx_vsle_bu(tmul, a));
+    add = __lsx_vsrli_b(add, 1);
+  }
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8us pselect(const Packet8us& mask, const Packet8us& a, const Packet8us& b) {
+  return __lsx_vbitsel_v(b, a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us psqrt(const Packet8us& a) {
+  __m128i res = {0, 0};
+  __m128i add = {0x0080008000800080, 0x0080008000800080};
+  for (int i = 0; i < 4; i++) {
+    const __m128i temp = __lsx_vor_v(res, add);
+    const __m128i tmul = __lsx_vpackev_h(__lsx_vmulwod_w_hu(temp, temp), __lsx_vmulwev_w_hu(temp, temp));
+    res = __lsx_vbitsel_v(res, temp, __lsx_vsle_hu(tmul, a));
+    add = __lsx_vsrli_h(add, 1);
+  }
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4ui pselect(const Packet4ui& mask, const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vbitsel_v(b, a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui psqrt(const Packet4ui& a) {
+  __m128i res = {0, 0};
+  __m128i add = {0x0000800000008000, 0x0000800000008000};
+  for (int i = 0; i < 4; i++) {
+    const __m128i temp = __lsx_vor_v(res, add);
+    const __m128i tmul = __lsx_vpackev_w(__lsx_vmulwod_d_wu(temp, temp), __lsx_vmulwev_d_wu(temp, temp));
+    res = __lsx_vbitsel_v(res, temp, __lsx_vsle_wu(tmul, a));
+    add = __lsx_vsrli_w(add, 1);
+  }
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ul pselect(const Packet2ul& mask, const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vbitsel_v(b, a, mask);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/TypeCasting.h
new file mode 100644
index 0000000..cda8680
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/LSX/TypeCasting.h
@@ -0,0 +1,526 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2023 Zang Ruochen <zangruochen@loongson.cn>
+// Copyright (C) 2024 XiWei Gu <guxiwei-hf@loongson.cn>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_LSX_H
+#define EIGEN_TYPE_CASTING_LSX_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//==============================================================================
+// preinterpret
+//==============================================================================
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4i>(const Packet4i& a) {
+  return (__m128)((__m128i)a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4ui>(const Packet4ui& a) {
+  return (__m128)((__m128i)a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2l>(const Packet2l& a) {
+  return (__m128d)((__m128i)a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2ul>(const Packet2ul& a) {
+  return (__m128d)((__m128i)a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4i>(const Packet4i& a) {
+  return (__m128d)((__m128i)a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c preinterpret<Packet16c, Packet16uc>(const Packet16uc& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s preinterpret<Packet8s, Packet8us>(const Packet8us& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4f>(const Packet4f& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4ui>(const Packet4ui& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet2d>(const Packet2d& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2d>(const Packet2d& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc preinterpret<Packet16uc, Packet16c>(const Packet16c& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us preinterpret<Packet8us, Packet8s>(const Packet8s& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4f>(const Packet4f& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4i>(const Packet4i& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2d>(const Packet2d& a) {
+  return (__m128i)a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2l>(const Packet2l& a) {
+  return (__m128i)a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4f, Packet2l>(const Packet4f& a) {
+  Packet2d tmp = __lsx_vfcvtl_d_s(a);
+  return __lsx_vftint_l_d(tmp);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4f, Packet2ul>(const Packet4f& a) {
+  Packet2d tmp = __lsx_vfcvtl_d_s(a);
+  return __lsx_vftint_lu_d(tmp);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
+  return __lsx_vftint_w_s(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4f, Packet4ui>(const Packet4f& a) {
+  return __lsx_vftint_wu_s(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet4f, Packet8s>(const Packet4f& a, const Packet4f& b) {
+  return __lsx_vssrlni_h_w(__lsx_vftint_w_s(a), __lsx_vftint_w_s(b), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet4f, Packet8us>(const Packet4f& a, const Packet4f& b) {
+  return __lsx_vssrlni_hu_w(__lsx_vftint_wu_s(a), __lsx_vftint_wu_s(b), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet4f, Packet16c>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
+                                                         const Packet4f& d) {
+  Packet8s tmp1 = __lsx_vssrlni_h_w(__lsx_vftint_w_s(a), __lsx_vftint_w_s(b), 0);
+  Packet8s tmp2 = __lsx_vssrlni_h_w(__lsx_vftint_w_s(c), __lsx_vftint_w_s(d), 0);
+  return __lsx_vssrlni_b_h((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet4f, Packet16uc>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
+                                                           const Packet4f& d) {
+  Packet8us tmp1 = __lsx_vssrlni_hu_w(__lsx_vftint_wu_s(a), __lsx_vftint_wu_s(b), 0);
+  Packet8us tmp2 = __lsx_vssrlni_hu_w(__lsx_vftint_wu_s(c), __lsx_vftint_wu_s(d), 0);
+  return __lsx_vssrlni_bu_h((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet16c, Packet4f>(const Packet16c& a) {
+  Packet8s tmp1 = __lsx_vsllwil_h_b((__m128i)a, 0);
+  Packet4i tmp2 = __lsx_vsllwil_w_h((__m128i)tmp1, 0);
+  return __lsx_vffint_s_w(tmp2);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet16c, Packet2l>(const Packet16c& a) {
+  Packet8s tmp1 = __lsx_vsllwil_h_b((__m128i)a, 0);
+  Packet4i tmp2 = __lsx_vsllwil_w_h((__m128i)tmp1, 0);
+  return __lsx_vsllwil_d_w((__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet16c, Packet2ul>(const Packet16c& a) {
+  Packet8s tmp1 = __lsx_vsllwil_h_b((__m128i)a, 0);
+  Packet4i tmp2 = __lsx_vsllwil_w_h((__m128i)tmp1, 0);
+  return (Packet2ul)__lsx_vsllwil_d_w((__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet16c, Packet4i>(const Packet16c& a) {
+  Packet8s tmp1 = __lsx_vsllwil_h_b((__m128i)a, 0);
+  return __lsx_vsllwil_w_h((__m128i)tmp1, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet16c, Packet4ui>(const Packet16c& a) {
+  Packet8s tmp1 = __lsx_vsllwil_h_b((__m128i)a, 0);
+  return (Packet4ui)__lsx_vsllwil_w_h((__m128i)tmp1, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet16c, Packet8s>(const Packet16c& a) {
+  return __lsx_vsllwil_h_b((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet16c, Packet8us>(const Packet16c& a) {
+  return (Packet8us)__lsx_vsllwil_h_b((__m128i)a, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet16uc, Packet4f>(const Packet16uc& a) {
+  Packet8us tmp1 = __lsx_vsllwil_hu_bu((__m128i)a, 0);
+  Packet4ui tmp2 = __lsx_vsllwil_wu_hu((__m128i)tmp1, 0);
+  return __lsx_vffint_s_wu(tmp2);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet16uc, Packet2ul>(const Packet16uc& a) {
+  Packet8us tmp1 = __lsx_vsllwil_hu_bu((__m128i)a, 0);
+  Packet4ui tmp2 = __lsx_vsllwil_wu_hu((__m128i)tmp1, 0);
+  return __lsx_vsllwil_du_wu((__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet16uc, Packet2l>(const Packet16uc& a) {
+  Packet8us tmp1 = __lsx_vsllwil_hu_bu((__m128i)a, 0);
+  Packet4ui tmp2 = __lsx_vsllwil_wu_hu((__m128i)tmp1, 0);
+  return (Packet2l)__lsx_vsllwil_du_wu((__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet16uc, Packet4ui>(const Packet16uc& a) {
+  Packet8us tmp1 = __lsx_vsllwil_hu_bu((__m128i)a, 0);
+  return __lsx_vsllwil_wu_hu((__m128i)tmp1, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet16uc, Packet4i>(const Packet16uc& a) {
+  Packet8us tmp1 = __lsx_vsllwil_hu_bu((__m128i)a, 0);
+  return (Packet4i)__lsx_vsllwil_wu_hu((__m128i)tmp1, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet16uc, Packet8us>(const Packet16uc& a) {
+  return __lsx_vsllwil_hu_bu((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet16uc, Packet8s>(const Packet16uc& a) {
+  return (Packet8s)__lsx_vsllwil_hu_bu((__m128i)a, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet8s, Packet4f>(const Packet8s& a) {
+  Packet4i tmp1 = __lsx_vsllwil_w_h((__m128i)a, 0);
+  return __lsx_vffint_s_w(tmp1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet8s, Packet2l>(const Packet8s& a) {
+  Packet4i tmp1 = __lsx_vsllwil_w_h((__m128i)a, 0);
+  return __lsx_vsllwil_d_w((__m128i)tmp1, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet8s, Packet2ul>(const Packet8s& a) {
+  Packet4i tmp1 = __lsx_vsllwil_w_h((__m128i)a, 0);
+  return (Packet2ul)__lsx_vsllwil_d_w((__m128i)tmp1, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet8s, Packet4i>(const Packet8s& a) {
+  return __lsx_vsllwil_w_h((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet8s, Packet4ui>(const Packet8s& a) {
+  return (Packet4ui)__lsx_vsllwil_w_h((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet8s, Packet16c>(const Packet8s& a, const Packet8s& b) {
+  return __lsx_vssrlni_b_h((__m128i)a, (__m128i)b, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet8s, Packet16uc>(const Packet8s& a, const Packet8s& b) {
+  return (Packet16uc)__lsx_vssrlni_b_h((__m128i)a, (__m128i)b, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet8us, Packet4f>(const Packet8us& a) {
+  Packet4ui tmp1 = __lsx_vsllwil_wu_hu((__m128i)a, 0);
+  return __lsx_vffint_s_wu(tmp1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet8us, Packet2ul>(const Packet8us& a) {
+  Packet4ui tmp1 = __lsx_vsllwil_wu_hu((__m128i)a, 0);
+  return __lsx_vsllwil_du_wu((__m128i)tmp1, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet8us, Packet2l>(const Packet8us& a) {
+  Packet4ui tmp1 = __lsx_vsllwil_wu_hu((__m128i)a, 0);
+  return (Packet2l)__lsx_vsllwil_du_wu((__m128i)tmp1, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet8us, Packet4ui>(const Packet8us& a) {
+  return __lsx_vsllwil_wu_hu((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet8us, Packet4i>(const Packet8us& a) {
+  return (Packet4i)__lsx_vsllwil_wu_hu((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet8us, Packet16uc>(const Packet8us& a, const Packet8us& b) {
+  return __lsx_vssrlni_bu_h((__m128i)a, (__m128i)b, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet8us, Packet16c>(const Packet8us& a, const Packet8us& b) {
+  return (Packet16c)__lsx_vssrlni_bu_h((__m128i)a, (__m128i)b, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
+  return __lsx_vffint_s_w(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4i, Packet2l>(const Packet4i& a) {
+  return __lsx_vsllwil_d_w((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4i, Packet2ul>(const Packet4i& a) {
+  return (Packet2ul)__lsx_vsllwil_d_w((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet4i, Packet8s>(const Packet4i& a, const Packet4i& b) {
+  return __lsx_vssrlni_h_w((__m128i)a, (__m128i)b, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet4i, Packet8us>(const Packet4i& a, const Packet4i& b) {
+  return (Packet8us)__lsx_vssrlni_h_w((__m128i)a, (__m128i)b, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet4i, Packet16c>(const Packet4i& a, const Packet4i& b, const Packet4i& c,
+                                                         const Packet4i& d) {
+  Packet8s tmp1 = __lsx_vssrlni_h_w((__m128i)a, (__m128i)b, 0);
+  Packet8s tmp2 = __lsx_vssrlni_h_w((__m128i)c, (__m128i)d, 0);
+  return __lsx_vssrlni_b_h((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet4i, Packet16uc>(const Packet4i& a, const Packet4i& b, const Packet4i& c,
+                                                           const Packet4i& d) {
+  Packet8s tmp1 = __lsx_vssrlni_h_w((__m128i)a, (__m128i)b, 0);
+  Packet8s tmp2 = __lsx_vssrlni_h_w((__m128i)c, (__m128i)d, 0);
+  return (Packet16uc)__lsx_vssrlni_b_h((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4ui, Packet4f>(const Packet4ui& a) {
+  return __lsx_vffint_s_wu(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4ui, Packet2ul>(const Packet4ui& a) {
+  return __lsx_vsllwil_du_wu((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4ui, Packet2l>(const Packet4ui& a) {
+  return (Packet2l)__lsx_vsllwil_du_wu((__m128i)a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet4ui, Packet8us>(const Packet4ui& a, const Packet4ui& b) {
+  return __lsx_vssrlni_hu_w((__m128i)a, (__m128i)b, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet4ui, Packet8s>(const Packet4ui& a, const Packet4ui& b) {
+  return (Packet8s)__lsx_vssrlni_hu_w((__m128i)a, (__m128i)b, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet4ui, Packet16uc>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
+                                                            const Packet4ui& d) {
+  Packet8us tmp1 = __lsx_vssrlni_hu_w((__m128i)a, (__m128i)b, 0);
+  Packet8us tmp2 = __lsx_vssrlni_hu_w((__m128i)c, (__m128i)d, 0);
+  return __lsx_vssrlni_bu_h((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet4ui, Packet16c>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
+                                                          const Packet4ui& d) {
+  Packet8us tmp1 = __lsx_vssrlni_hu_w((__m128i)a, (__m128i)b, 0);
+  Packet8us tmp2 = __lsx_vssrlni_hu_w((__m128i)c, (__m128i)d, 0);
+  return (Packet16c)__lsx_vssrlni_bu_h((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet2l, Packet4f>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vffint_s_w(__lsx_vssrlni_w_d((__m128i)a, (__m128i)b, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet2l, Packet4i>(const Packet2l& a, const Packet2l& b) {
+  return __lsx_vssrlni_w_d((__m128i)a, (__m128i)b, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet2l, Packet4ui>(const Packet2l& a, const Packet2l& b) {
+  return (Packet4ui)__lsx_vssrlni_w_d((__m128i)a, (__m128i)b, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet2l, Packet8s>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                       const Packet2l& d) {
+  Packet4i tmp1 = __lsx_vssrlni_w_d((__m128i)a, (__m128i)b, 0);
+  Packet4i tmp2 = __lsx_vssrlni_w_d((__m128i)c, (__m128i)d, 0);
+  return __lsx_vssrlni_h_w((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet2l, Packet8us>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                         const Packet2l& d) {
+  Packet4i tmp1 = __lsx_vssrlni_w_d((__m128i)a, (__m128i)b, 0);
+  Packet4i tmp2 = __lsx_vssrlni_w_d((__m128i)c, (__m128i)d, 0);
+  return (Packet8us)__lsx_vssrlni_h_w((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet2l, Packet16c>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                         const Packet2l& d, const Packet2l& e, const Packet2l& f,
+                                                         const Packet2l& g, const Packet2l& h) {
+  const Packet8s abcd = pcast<Packet2l, Packet8s>(a, b, c, d);
+  const Packet8s efgh = pcast<Packet2l, Packet8s>(e, f, g, h);
+  return __lsx_vssrlni_b_h((__m128i)abcd, (__m128i)efgh, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet2l, Packet16uc>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                           const Packet2l& d, const Packet2l& e, const Packet2l& f,
+                                                           const Packet2l& g, const Packet2l& h) {
+  const Packet8us abcd = pcast<Packet2l, Packet8us>(a, b, c, d);
+  const Packet8us efgh = pcast<Packet2l, Packet8us>(e, f, g, h);
+  return __lsx_vssrlni_bu_h((__m128i)abcd, (__m128i)efgh, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet2ul, Packet4f>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vffint_s_wu(__lsx_vssrlni_w_d((__m128i)a, (__m128i)b, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet2ul, Packet4ui>(const Packet2ul& a, const Packet2ul& b) {
+  return __lsx_vssrlni_wu_d((__m128i)a, (__m128i)b, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet2ul, Packet4i>(const Packet2ul& a, const Packet2ul& b) {
+  return (Packet4i)__lsx_vssrlni_wu_d((__m128i)a, (__m128i)b, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet2ul, Packet8us>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                          const Packet2ul& d) {
+  Packet4ui tmp1 = __lsx_vssrlni_wu_d((__m128i)a, (__m128i)b, 0);
+  Packet4ui tmp2 = __lsx_vssrlni_wu_d((__m128i)c, (__m128i)d, 0);
+  return __lsx_vssrlni_hu_w((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet2ul, Packet8s>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                        const Packet2ul& d) {
+  Packet4ui tmp1 = __lsx_vssrlni_wu_d((__m128i)a, (__m128i)b, 0);
+  Packet4ui tmp2 = __lsx_vssrlni_wu_d((__m128i)c, (__m128i)d, 0);
+  return (Packet8s)__lsx_vssrlni_hu_w((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet2ul, Packet16uc>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                            const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
+                                                            const Packet2ul& g, const Packet2ul& h) {
+  const Packet8s abcd = pcast<Packet2ul, Packet8s>(a, b, c, d);
+  const Packet8s efgh = pcast<Packet2ul, Packet8s>(e, f, g, h);
+  return __lsx_vssrlni_b_h((__m128i)abcd, (__m128i)efgh, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet2ul, Packet16c>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                          const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
+                                                          const Packet2ul& g, const Packet2ul& h) {
+  const Packet8us abcd = pcast<Packet2ul, Packet8us>(a, b, c, d);
+  const Packet8us efgh = pcast<Packet2ul, Packet8us>(e, f, g, h);
+  return __lsx_vssrlni_bu_h((__m128i)abcd, (__m128i)efgh, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
+  return __lsx_vfcvt_s_d(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2d, Packet2l>(const Packet2d& a) {
+  return __lsx_vftint_l_d(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2d, Packet2ul>(const Packet2d& a) {
+  return __lsx_vftint_lu_d(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet2d, Packet4i>(const Packet2d& a, const Packet2d& b) {
+  return __lsx_vssrlni_w_d(__lsx_vftint_l_d(a), __lsx_vftint_l_d(b), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet2d, Packet4ui>(const Packet2d& a, const Packet2d& b) {
+  return __lsx_vssrlni_wu_d(__lsx_vftint_lu_d(a), __lsx_vftint_lu_d(b), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet2d, Packet8s>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                       const Packet2d& d) {
+  Packet4i tmp1 = __lsx_vssrlni_w_d(__lsx_vftint_l_d(a), __lsx_vftint_l_d(b), 0);
+  Packet4i tmp2 = __lsx_vssrlni_w_d(__lsx_vftint_l_d(c), __lsx_vftint_l_d(d), 0);
+  return __lsx_vssrlni_h_w((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet2d, Packet8us>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                         const Packet2d& d) {
+  Packet4ui tmp1 = __lsx_vssrlni_wu_d(__lsx_vftint_lu_d(a), __lsx_vftint_lu_d(b), 0);
+  Packet4ui tmp2 = __lsx_vssrlni_wu_d(__lsx_vftint_lu_d(c), __lsx_vftint_lu_d(d), 0);
+  return __lsx_vssrlni_hu_w((__m128i)tmp1, (__m128i)tmp2, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet2d, Packet16c>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                         const Packet2d& d, const Packet2d& e, const Packet2d& f,
+                                                         const Packet2d& g, const Packet2d& h) {
+  const Packet8s abcd = pcast<Packet2d, Packet8s>(a, b, c, d);
+  const Packet8s efgh = pcast<Packet2d, Packet8s>(e, f, g, h);
+  return __lsx_vssrlni_b_h((__m128i)abcd, (__m128i)efgh, 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet2d, Packet16uc>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                           const Packet2d& d, const Packet2d& e, const Packet2d& f,
+                                                           const Packet2d& g, const Packet2d& h) {
+  const Packet8us abcd = pcast<Packet2d, Packet8us>(a, b, c, d);
+  const Packet8us efgh = pcast<Packet2d, Packet8us>(e, f, g, h);
+  return __lsx_vssrlni_bu_h((__m128i)abcd, (__m128i)efgh, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) {
+  return __lsx_vfcvtl_d_s(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet16c, Packet2d>(const Packet16c& a) {
+  Packet8s tmp1 = __lsx_vsllwil_h_b((__m128i)a, 0);
+  Packet4i tmp2 = __lsx_vsllwil_w_h((__m128i)tmp1, 0);
+  return __lsx_vffint_d_l(__lsx_vsllwil_d_w((__m128i)tmp2, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet16uc, Packet2d>(const Packet16uc& a) {
+  Packet8us tmp1 = __lsx_vsllwil_hu_bu((__m128i)a, 0);
+  Packet4ui tmp2 = __lsx_vsllwil_wu_hu((__m128i)tmp1, 0);
+  return __lsx_vffint_d_lu(__lsx_vsllwil_du_wu((__m128i)tmp2, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet8s, Packet2d>(const Packet8s& a) {
+  Packet4i tmp = __lsx_vsllwil_w_h((__m128i)a, 0);
+  return __lsx_vffint_d_l(__lsx_vsllwil_d_w((__m128i)tmp, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet8us, Packet2d>(const Packet8us& a) {
+  Packet4ui tmp = __lsx_vsllwil_wu_hu((__m128i)a, 0);
+  return __lsx_vffint_d_lu(__lsx_vsllwil_du_wu((__m128i)tmp, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4i, Packet2d>(const Packet4i& a) {
+  return __lsx_vffint_d_l(__lsx_vsllwil_d_w((__m128i)a, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4ui, Packet2d>(const Packet4ui& a) {
+  return __lsx_vffint_d_lu(__lsx_vsllwil_du_wu((__m128i)a, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2l, Packet2d>(const Packet2l& a) {
+  return __lsx_vffint_d_l(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2ul, Packet2d>(const Packet2ul& a) {
+  return __lsx_vffint_d_lu(a);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_LSX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/MSA/Complex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/MSA/Complex.h
new file mode 100644
index 0000000..fbba642
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/MSA/Complex.h
@@ -0,0 +1,613 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Wave Computing, Inc.
+// Written by:
+//   Chris Larsen
+//   Alexey Frunze (afrunze@wavecomp.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_MSA_H
+#define EIGEN_COMPLEX_MSA_H
+
+#include <iostream>
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- float ----------
+struct Packet2cf {
+  EIGEN_STRONG_INLINE Packet2cf() {}
+  EIGEN_STRONG_INLINE explicit Packet2cf(const std::complex<float>& a, const std::complex<float>& b) {
+    Packet4f t = {std::real(a), std::imag(a), std::real(b), std::imag(b)};
+    v = t;
+  }
+  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
+  EIGEN_STRONG_INLINE Packet2cf(const Packet2cf& a) : v(a.v) {}
+  EIGEN_STRONG_INLINE Packet2cf& operator=(const Packet2cf& b) {
+    v = b.v;
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf conjugate(void) const {
+    return Packet2cf((Packet4f)__builtin_msa_bnegi_d((v2u64)v, 63));
+  }
+  EIGEN_STRONG_INLINE Packet2cf& operator*=(const Packet2cf& b) {
+    Packet4f v1, v2;
+
+    // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
+    v1 = (Packet4f)__builtin_msa_ilvev_w((v4i32)v, (v4i32)v);
+    // Get the imag values of a | a1_im | a1_im | a2_im | a2_im |
+    v2 = (Packet4f)__builtin_msa_ilvod_w((v4i32)v, (v4i32)v);
+    // Multiply the real a with b
+    v1 = pmul(v1, b.v);
+    // Multiply the imag a with b
+    v2 = pmul(v2, b.v);
+    // Conjugate v2
+    v2 = Packet2cf(v2).conjugate().v;
+    // Swap real/imag elements in v2.
+    v2 = (Packet4f)__builtin_msa_shf_w((v4i32)v2, EIGEN_MSA_SHF_I8(1, 0, 3, 2));
+    // Add and return the result
+    v = padd(v1, v2);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator*(const Packet2cf& b) const { return Packet2cf(*this) *= b; }
+  EIGEN_STRONG_INLINE Packet2cf& operator+=(const Packet2cf& b) {
+    v = padd(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator+(const Packet2cf& b) const { return Packet2cf(*this) += b; }
+  EIGEN_STRONG_INLINE Packet2cf& operator-=(const Packet2cf& b) {
+    v = psub(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator-(const Packet2cf& b) const { return Packet2cf(*this) -= b; }
+  EIGEN_STRONG_INLINE Packet2cf operator/(const Packet2cf& b) const { return pdiv_complex(Packet2cf(*this), b); }
+  EIGEN_STRONG_INLINE Packet2cf& operator/=(const Packet2cf& b) {
+    *this = Packet2cf(*this) / b;
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator-(void) const { return Packet2cf(pnegate(v)); }
+
+  Packet4f v;
+};
+
+inline std::ostream& operator<<(std::ostream& os, const Packet2cf& value) {
+  os << "[ (" << value.v[0] << ", " << value.v[1]
+     << "i),"
+        "  ("
+     << value.v[2] << ", " << value.v[3] << "i) ]";
+  return os;
+}
+
+template <>
+struct packet_traits<std::complex<float> > : default_packet_traits {
+  typedef Packet2cf type;
+  typedef Packet2cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2cf> {
+  typedef std::complex<float> type;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet2cf half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) {
+  EIGEN_MSA_DEBUG;
+
+  float f0 = from.real(), f1 = from.imag();
+  Packet4f v0 = {f0, f0, f0, f0};
+  Packet4f v1 = {f1, f1, f1, f1};
+  return Packet2cf((Packet4f)__builtin_msa_ilvr_w((Packet4i)v1, (Packet4i)v0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a + b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a - b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return -a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a.conjugate();
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a * b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf(pand(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf(por(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf(pxor(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf(pandnot(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_MSA_DEBUG;
+
+  return pset1<Packet2cf>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<float> >(std::complex<float>* to, const Packet2cf& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_STORE pstore<float>((float*)to, from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet2cf& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu<float>((float*)to, from.v);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from,
+                                                                           Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf(from[0 * stride], from[1 * stride]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from,
+                                                                       Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  *to = std::complex<float>(from.v[0], from.v[1]);
+  to += stride;
+  *to = std::complex<float>(from.v[2], from.v[3]);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float>* addr) {
+  EIGEN_MSA_DEBUG;
+
+  prefetch(reinterpret_cast<const float*>(addr));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return std::complex<float>(a.v[0], a.v[1]);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf((Packet4f)__builtin_msa_shf_w((v4i32)a.v, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf((Packet4f)__builtin_msa_shf_w((v4i32)a.v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4f value = (Packet4f)preverse((Packet2d)a.v);
+  value += a.v;
+  return std::complex<float>(value[0], value[1]);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return std::complex<float>((a.v[0] * a.v[2]) - (a.v[1] * a.v[3]), (a.v[0] * a.v[3]) + (a.v[1] * a.v[2]));
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf, Packet4f)
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a / b;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet2cf, 2>& value) {
+  os << "[ " << value.packet[0] << ", " << std::endl << "  " << value.packet[1] << " ]";
+  return os;
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cf, 2>& kernel) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4f tmp = (Packet4f)__builtin_msa_ilvl_d((v2i64)kernel.packet[1].v, (v2i64)kernel.packet[0].v);
+  kernel.packet[0].v = (Packet4f)__builtin_msa_ilvr_d((v2i64)kernel.packet[1].v, (v2i64)kernel.packet[0].v);
+  kernel.packet[1].v = tmp;
+}
+
+//---------- double ----------
+
+struct Packet1cd {
+  EIGEN_STRONG_INLINE Packet1cd() {}
+  EIGEN_STRONG_INLINE explicit Packet1cd(const std::complex<double>& a) {
+    v[0] = std::real(a);
+    v[1] = std::imag(a);
+  }
+  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
+  EIGEN_STRONG_INLINE Packet1cd(const Packet1cd& a) : v(a.v) {}
+  EIGEN_STRONG_INLINE Packet1cd& operator=(const Packet1cd& b) {
+    v = b.v;
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd conjugate(void) const {
+    static const v2u64 p2ul_CONJ_XOR = {0x0, 0x8000000000000000};
+    return (Packet1cd)pxor(v, (Packet2d)p2ul_CONJ_XOR);
+  }
+  EIGEN_STRONG_INLINE Packet1cd& operator*=(const Packet1cd& b) {
+    Packet2d v1, v2;
+
+    // Get the real values of a | a1_re | a1_re
+    v1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)v, (v2i64)v);
+    // Get the imag values of a | a1_im | a1_im
+    v2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)v, (v2i64)v);
+    // Multiply the real a with b
+    v1 = pmul(v1, b.v);
+    // Multiply the imag a with b
+    v2 = pmul(v2, b.v);
+    // Conjugate v2
+    v2 = Packet1cd(v2).conjugate().v;
+    // Swap real/imag elements in v2.
+    v2 = (Packet2d)__builtin_msa_shf_w((v4i32)v2, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+    // Add and return the result
+    v = padd(v1, v2);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator*(const Packet1cd& b) const { return Packet1cd(*this) *= b; }
+  EIGEN_STRONG_INLINE Packet1cd& operator+=(const Packet1cd& b) {
+    v = padd(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator+(const Packet1cd& b) const { return Packet1cd(*this) += b; }
+  EIGEN_STRONG_INLINE Packet1cd& operator-=(const Packet1cd& b) {
+    v = psub(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator-(const Packet1cd& b) const { return Packet1cd(*this) -= b; }
+  EIGEN_STRONG_INLINE Packet1cd& operator/=(const Packet1cd& b) {
+    *this *= b.conjugate();
+    Packet2d s = pmul<Packet2d>(b.v, b.v);
+    s = padd(s, preverse<Packet2d>(s));
+    v = pdiv(v, s);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator/(const Packet1cd& b) const { return Packet1cd(*this) /= b; }
+  EIGEN_STRONG_INLINE Packet1cd operator-(void) const { return Packet1cd(pnegate(v)); }
+
+  Packet2d v;
+};
+
+inline std::ostream& operator<<(std::ostream& os, const Packet1cd& value) {
+  os << "[ (" << value.v[0] << ", " << value.v[1] << "i) ]";
+  return os;
+}
+
+template <>
+struct packet_traits<std::complex<double> > : default_packet_traits {
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 1,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet1cd> {
+  typedef std::complex<double> type;
+  enum {
+    size = 1,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet1cd half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a + b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a - b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return -a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a.conjugate();
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a * b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(pand(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(por(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(pxor(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(pandnot(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_MSA_DEBUG;
+
+  return pset1<Packet1cd>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<double> >(std::complex<double>* to, const Packet1cd& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_STORE pstore<double>((double*)to, from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double>* to, const Packet1cd& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu<double>((double*)to, from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double>* addr) {
+  EIGEN_MSA_DEBUG;
+
+  prefetch(reinterpret_cast<const double*>(addr));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from,
+                                                                            Index stride __attribute__((unused))) {
+  EIGEN_MSA_DEBUG;
+
+  Packet1cd res;
+  res.v[0] = std::real(from[0]);
+  res.v[1] = std::imag(from[0]);
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from,
+                                                                        Index stride __attribute__((unused))) {
+  EIGEN_MSA_DEBUG;
+
+  pstore(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return std::complex<double>(a.v[0], a.v[1]);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return pfirst(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return pfirst(a);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd, Packet2d)
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a / b;
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip /*<Packet1cd>*/ (const Packet1cd& x) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(preverse(Packet2d(x.v)));
+}
+
+inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet1cd, 2>& value) {
+  os << "[ " << value.packet[0] << ", " << std::endl << "  " << value.packet[1] << " ]";
+  return os;
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd, 2>& kernel) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d v1, v2;
+
+  v1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)kernel.packet[0].v, (v2i64)kernel.packet[1].v);
+  // Get the imag values of a
+  v2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)kernel.packet[0].v, (v2i64)kernel.packet[1].v);
+
+  kernel.packet[0].v = v1;
+  kernel.packet[1].v = v2;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_MSA_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/MSA/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/MSA/MathFunctions.h
new file mode 100644
index 0000000..f68d254
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/MSA/MathFunctions.h
@@ -0,0 +1,379 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Copyright (C) 2018 Wave Computing, Inc.
+// Written by:
+//   Chris Larsen
+//   Alexey Frunze (afrunze@wavecomp.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* The sin, cos, exp, and log functions of this file come from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+/* The tanh function of this file is an adaptation of
+ * template<typename T> T generic_fast_tanh_float(const T&)
+ * from MathFunctionsImpl.h.
+ */
+
+#ifndef EIGEN_MATH_FUNCTIONS_MSA_H
+#define EIGEN_MATH_FUNCTIONS_MSA_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f plog<Packet4f>(const Packet4f& _x) {
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292e-2f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, -1.1514610310e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, -1.2420140846e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, +1.4249322787e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, -1.6668057665e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, +2.0000714765e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, -2.4999993993e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, +3.3333331174e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
+  static EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
+  static EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
+
+  // Convert negative argument into NAN (quiet negative, to be specific).
+  Packet4f zero = (Packet4f)__builtin_msa_ldi_w(0);
+  Packet4i neg_mask = __builtin_msa_fclt_w(_x, zero);
+  Packet4i zero_mask = __builtin_msa_fceq_w(_x, zero);
+  Packet4f non_neg_x_or_nan = padd(_x, (Packet4f)neg_mask);  // Add 0.0 or NAN.
+  Packet4f x = non_neg_x_or_nan;
+
+  // Extract exponent from x = mantissa * 2**exponent, where 1.0 <= mantissa < 2.0.
+  // N.B. the exponent is one less of what frexpf() would return.
+  Packet4i e_int = __builtin_msa_ftint_s_w(__builtin_msa_flog2_w(x));
+  // Multiply x by 2**(-exponent-1) to get 0.5 <= x < 1.0 as from frexpf().
+  x = __builtin_msa_fexp2_w(x, (Packet4i)__builtin_msa_nori_b((v16u8)e_int, 0));
+
+  /*
+     if (x < SQRTHF) {
+       x = x + x - 1.0;
+     } else {
+       e += 1;
+       x = x - 1.0;
+     }
+  */
+  Packet4f xx = padd(x, x);
+  Packet4i ge_mask = __builtin_msa_fcle_w(p4f_cephes_SQRTHF, x);
+  e_int = psub(e_int, ge_mask);
+  x = (Packet4f)__builtin_msa_bsel_v((v16u8)ge_mask, (v16u8)xx, (v16u8)x);
+  x = psub(x, p4f_1);
+  Packet4f e = __builtin_msa_ffint_s_w(e_int);
+
+  Packet4f x2 = pmul(x, x);
+  Packet4f x3 = pmul(x2, x);
+
+  Packet4f y, y1, y2;
+  y = pmadd(p4f_cephes_log_p0, x, p4f_cephes_log_p1);
+  y1 = pmadd(p4f_cephes_log_p3, x, p4f_cephes_log_p4);
+  y2 = pmadd(p4f_cephes_log_p6, x, p4f_cephes_log_p7);
+  y = pmadd(y, x, p4f_cephes_log_p2);
+  y1 = pmadd(y1, x, p4f_cephes_log_p5);
+  y2 = pmadd(y2, x, p4f_cephes_log_p8);
+  y = pmadd(y, x3, y1);
+  y = pmadd(y, x3, y2);
+  y = pmul(y, x3);
+
+  y = pmadd(e, p4f_cephes_log_q1, y);
+  x = __builtin_msa_fmsub_w(x, x2, p4f_half);
+  x = padd(x, y);
+  x = pmadd(e, p4f_cephes_log_q2, x);
+
+  // x is now the logarithm result candidate. We still need to handle the
+  // extreme arguments of zero and positive infinity, though.
+  // N.B. if the argument is +INFINITY, x is NAN because the polynomial terms
+  // contain infinities of both signs (see the coefficients and code above).
+  // INFINITY - INFINITY is NAN.
+
+  // If the argument is +INFINITY, make it the new result candidate.
+  // To achieve that we choose the smaller of the result candidate and the
+  // argument.
+  // This is correct for all finite pairs of values (the logarithm is smaller
+  // than the argument).
+  // This is also correct in the special case when the argument is +INFINITY
+  // and the result candidate is NAN. This is because the fmin.df instruction
+  // prefers non-NANs to NANs.
+  x = __builtin_msa_fmin_w(x, non_neg_x_or_nan);
+
+  // If the argument is zero (including -0.0), the result becomes -INFINITY.
+  Packet4i neg_infs = __builtin_msa_slli_w(zero_mask, 23);
+  x = (Packet4f)__builtin_msa_bsel_v((v16u8)zero_mask, (v16u8)x, (v16u8)neg_infs);
+
+  return x;
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f pexp<Packet4f>(const Packet4f& _x) {
+  // Limiting single-precision pexp's argument to [-128, +128] lets pexp
+  // reach 0 and INFINITY naturally.
+  static EIGEN_DECLARE_CONST_Packet4f(exp_lo, -128.0f);
+  static EIGEN_DECLARE_CONST_Packet4f(exp_hi, +128.0f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894e-2f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
+  static EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
+
+  Packet4f x = _x;
+
+  // Clamp x.
+  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(x, p4f_exp_lo), (v16u8)x, (v16u8)p4f_exp_lo);
+  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(p4f_exp_hi, x), (v16u8)x, (v16u8)p4f_exp_hi);
+
+  // Round to nearest integer by adding 0.5 (with x's sign) and truncating.
+  Packet4f x2_add = (Packet4f)__builtin_msa_binsli_w((v4u32)p4f_half, (v4u32)x, 0);
+  Packet4f x2 = pmadd(x, p4f_cephes_LOG2EF, x2_add);
+  Packet4i x2_int = __builtin_msa_ftrunc_s_w(x2);
+  Packet4f x2_int_f = __builtin_msa_ffint_s_w(x2_int);
+
+  x = __builtin_msa_fmsub_w(x, x2_int_f, p4f_cephes_exp_C1);
+  x = __builtin_msa_fmsub_w(x, x2_int_f, p4f_cephes_exp_C2);
+
+  Packet4f z = pmul(x, x);
+
+  Packet4f y = p4f_cephes_exp_p0;
+  y = pmadd(y, x, p4f_cephes_exp_p1);
+  y = pmadd(y, x, p4f_cephes_exp_p2);
+  y = pmadd(y, x, p4f_cephes_exp_p3);
+  y = pmadd(y, x, p4f_cephes_exp_p4);
+  y = pmadd(y, x, p4f_cephes_exp_p5);
+  y = pmadd(y, z, x);
+  y = padd(y, p4f_1);
+
+  // y *= 2**exponent.
+  y = __builtin_msa_fexp2_w(y, x2_int);
+
+  return y;
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f ptanh<Packet4f>(const Packet4f& _x) {
+  static EIGEN_DECLARE_CONST_Packet4f(tanh_tiny, 1e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(tanh_hi, 9.0f);
+  // The monomial coefficients of the numerator polynomial (odd).
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_1, 4.89352455891786e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_3, 6.37261928875436e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_5, 1.48572235717979e-5f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_7, 5.12229709037114e-8f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_9, -8.60467152213735e-11f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_11, 2.00018790482477e-13f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_13, -2.76076847742355e-16f);
+  // The monomial coefficients of the denominator polynomial (even).
+  static EIGEN_DECLARE_CONST_Packet4f(beta_0, 4.89352518554385e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(beta_2, 2.26843463243900e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(beta_4, 1.18534705686654e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(beta_6, 1.19825839466702e-6f);
+
+  Packet4f x = pabs(_x);
+  Packet4i tiny_mask = __builtin_msa_fclt_w(x, p4f_tanh_tiny);
+
+  // Clamp the inputs to the range [-9, 9] since anything outside
+  // this range is -/+1.0f in single-precision.
+  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(p4f_tanh_hi, x), (v16u8)x, (v16u8)p4f_tanh_hi);
+
+  // Since the polynomials are odd/even, we need x**2.
+  Packet4f x2 = pmul(x, x);
+
+  // Evaluate the numerator polynomial p.
+  Packet4f p = pmadd(x2, p4f_alpha_13, p4f_alpha_11);
+  p = pmadd(x2, p, p4f_alpha_9);
+  p = pmadd(x2, p, p4f_alpha_7);
+  p = pmadd(x2, p, p4f_alpha_5);
+  p = pmadd(x2, p, p4f_alpha_3);
+  p = pmadd(x2, p, p4f_alpha_1);
+  p = pmul(x, p);
+
+  // Evaluate the denominator polynomial q.
+  Packet4f q = pmadd(x2, p4f_beta_6, p4f_beta_4);
+  q = pmadd(x2, q, p4f_beta_2);
+  q = pmadd(x2, q, p4f_beta_0);
+
+  // Divide the numerator by the denominator.
+  p = pdiv(p, q);
+
+  // Reinstate the sign.
+  p = (Packet4f)__builtin_msa_binsli_w((v4u32)p, (v4u32)_x, 0);
+
+  // When the argument is very small in magnitude it's more accurate to just return it.
+  p = (Packet4f)__builtin_msa_bsel_v((v16u8)tiny_mask, (v16u8)p, (v16u8)_x);
+
+  return p;
+}
+
+template <bool sine>
+Packet4f psincos_inner_msa_float(const Packet4f& _x) {
+  static EIGEN_DECLARE_CONST_Packet4f(sincos_max_arg, 13176795.0f);  // Approx. (2**24) / (4/Pi).
+  static EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP1, -0.78515625f);
+  static EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP2, -2.4187564849853515625e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP3, -3.77489497744594108e-8f);
+  static EIGEN_DECLARE_CONST_Packet4f(sincof_p0, -1.9515295891e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(sincof_p1, 8.3321608736e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(sincof_p2, -1.6666654611e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(coscof_p0, 2.443315711809948e-5f);
+  static EIGEN_DECLARE_CONST_Packet4f(coscof_p1, -1.388731625493765e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(coscof_p2, 4.166664568298827e-2f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_FOPI, 1.27323954473516f);  // 4/Pi.
+  static EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
+  static EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
+
+  Packet4f x = pabs(_x);
+
+  // Translate infinite arguments into NANs.
+  Packet4f zero_or_nan_if_inf = psub(_x, _x);
+  x = padd(x, zero_or_nan_if_inf);
+  // Prevent sin/cos from generating values larger than 1.0 in magnitude
+  // for very large arguments by setting x to 0.0.
+  Packet4i small_or_nan_mask = __builtin_msa_fcult_w(x, p4f_sincos_max_arg);
+  x = pand(x, (Packet4f)small_or_nan_mask);
+
+  // Scale x by 4/Pi to find x's octant.
+  Packet4f y = pmul(x, p4f_cephes_FOPI);
+  // Get the octant. We'll reduce x by this number of octants or by one more than it.
+  Packet4i y_int = __builtin_msa_ftrunc_s_w(y);
+  // x's from even-numbered octants will translate to octant 0: [0, +Pi/4].
+  // x's from odd-numbered octants will translate to octant -1: [-Pi/4, 0].
+  // Adjustment for odd-numbered octants: octant = (octant + 1) & (~1).
+  Packet4i y_int1 = __builtin_msa_addvi_w(y_int, 1);
+  Packet4i y_int2 = (Packet4i)__builtin_msa_bclri_w((Packet4ui)y_int1, 0);  // bclri = bit-clear
+  y = __builtin_msa_ffint_s_w(y_int2);
+
+  // Compute the sign to apply to the polynomial.
+  Packet4i sign_mask = sine ? pxor(__builtin_msa_slli_w(y_int1, 29), (Packet4i)_x)
+                            : __builtin_msa_slli_w(__builtin_msa_addvi_w(y_int, 3), 29);
+
+  // Get the polynomial selection mask.
+  // We'll calculate both (sin and cos) polynomials and then select from the two.
+  Packet4i poly_mask = __builtin_msa_ceqi_w(__builtin_msa_slli_w(y_int2, 30), 0);
+
+  // Reduce x by y octants to get: -Pi/4 <= x <= +Pi/4.
+  // The magic pass: "Extended precision modular arithmetic"
+  // x = ((x - y * DP1) - y * DP2) - y * DP3
+  Packet4f tmp1 = pmul(y, p4f_minus_cephes_DP1);
+  Packet4f tmp2 = pmul(y, p4f_minus_cephes_DP2);
+  Packet4f tmp3 = pmul(y, p4f_minus_cephes_DP3);
+  x = padd(x, tmp1);
+  x = padd(x, tmp2);
+  x = padd(x, tmp3);
+
+  // Evaluate the cos(x) polynomial.
+  y = p4f_coscof_p0;
+  Packet4f z = pmul(x, x);
+  y = pmadd(y, z, p4f_coscof_p1);
+  y = pmadd(y, z, p4f_coscof_p2);
+  y = pmul(y, z);
+  y = pmul(y, z);
+  y = __builtin_msa_fmsub_w(y, z, p4f_half);
+  y = padd(y, p4f_1);
+
+  // Evaluate the sin(x) polynomial.
+  Packet4f y2 = p4f_sincof_p0;
+  y2 = pmadd(y2, z, p4f_sincof_p1);
+  y2 = pmadd(y2, z, p4f_sincof_p2);
+  y2 = pmul(y2, z);
+  y2 = pmadd(y2, x, x);
+
+  // Select the correct result from the two polynomials.
+  y = sine ? (Packet4f)__builtin_msa_bsel_v((v16u8)poly_mask, (v16u8)y, (v16u8)y2)
+           : (Packet4f)__builtin_msa_bsel_v((v16u8)poly_mask, (v16u8)y2, (v16u8)y);
+
+  // Update the sign.
+  sign_mask = pxor(sign_mask, (Packet4i)y);
+  y = (Packet4f)__builtin_msa_binsli_w((v4u32)y, (v4u32)sign_mask, 0);  // binsli = bit-insert-left
+  return y;
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f psin<Packet4f>(const Packet4f& x) {
+  return psincos_inner_msa_float</* sine */ true>(x);
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f pcos<Packet4f>(const Packet4f& x) {
+  return psincos_inner_msa_float</* sine */ false>(x);
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet2d pexp<Packet2d>(const Packet2d& _x) {
+  // Limiting double-precision pexp's argument to [-1024, +1024] lets pexp
+  // reach 0 and INFINITY naturally.
+  static EIGEN_DECLARE_CONST_Packet2d(exp_lo, -1024.0);
+  static EIGEN_DECLARE_CONST_Packet2d(exp_hi, +1024.0);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
+  static EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
+  static EIGEN_DECLARE_CONST_Packet2d(1, 1.0);
+  static EIGEN_DECLARE_CONST_Packet2d(2, 2.0);
+
+  Packet2d x = _x;
+
+  // Clamp x.
+  x = (Packet2d)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_d(x, p2d_exp_lo), (v16u8)x, (v16u8)p2d_exp_lo);
+  x = (Packet2d)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_d(p2d_exp_hi, x), (v16u8)x, (v16u8)p2d_exp_hi);
+
+  // Round to nearest integer by adding 0.5 (with x's sign) and truncating.
+  Packet2d x2_add = (Packet2d)__builtin_msa_binsli_d((v2u64)p2d_half, (v2u64)x, 0);
+  Packet2d x2 = pmadd(x, p2d_cephes_LOG2EF, x2_add);
+  Packet2l x2_long = __builtin_msa_ftrunc_s_d(x2);
+  Packet2d x2_long_d = __builtin_msa_ffint_s_d(x2_long);
+
+  x = __builtin_msa_fmsub_d(x, x2_long_d, p2d_cephes_exp_C1);
+  x = __builtin_msa_fmsub_d(x, x2_long_d, p2d_cephes_exp_C2);
+
+  x2 = pmul(x, x);
+
+  Packet2d px = p2d_cephes_exp_p0;
+  px = pmadd(px, x2, p2d_cephes_exp_p1);
+  px = pmadd(px, x2, p2d_cephes_exp_p2);
+  px = pmul(px, x);
+
+  Packet2d qx = p2d_cephes_exp_q0;
+  qx = pmadd(qx, x2, p2d_cephes_exp_q1);
+  qx = pmadd(qx, x2, p2d_cephes_exp_q2);
+  qx = pmadd(qx, x2, p2d_cephes_exp_q3);
+
+  x = pdiv(px, psub(qx, px));
+  x = pmadd(p2d_2, x, p2d_1);
+
+  // x *= 2**exponent.
+  x = __builtin_msa_fexp2_d(x, x2_long);
+
+  return x;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_MSA_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/MSA/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/MSA/PacketMath.h
new file mode 100644
index 0000000..558893e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/MSA/PacketMath.h
@@ -0,0 +1,1199 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Wave Computing, Inc.
+// Written by:
+//   Chris Larsen
+//   Alexey Frunze (afrunze@wavecomp.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_MSA_H
+#define EIGEN_PACKET_MATH_MSA_H
+
+#include <iostream>
+#include <string>
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#endif
+
+#define EIGEN_MSA_DEBUG
+
+#define EIGEN_MSA_SHF_I8(a, b, c, d) (((d) << 6) | ((c) << 4) | ((b) << 2) | (a))
+
+typedef v4f32 Packet4f;
+typedef v4i32 Packet4i;
+typedef v4u32 Packet4ui;
+
+#define EIGEN_DECLARE_CONST_Packet4f(NAME, X) const Packet4f p4f_##NAME = {X, X, X, X}
+#define EIGEN_DECLARE_CONST_Packet4i(NAME, X) const Packet4i p4i_##NAME = {X, X, X, X}
+#define EIGEN_DECLARE_CONST_Packet4ui(NAME, X) const Packet4ui p4ui_##NAME = {X, X, X, X}
+
+inline std::ostream& operator<<(std::ostream& os, const Packet4f& value) {
+  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
+  return os;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const Packet4i& value) {
+  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
+  return os;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const Packet4ui& value) {
+  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
+  return os;
+}
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4f type;
+  typedef Packet4f half;  // Packet2f intrinsics not implemented yet
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+    // FIXME: verify the Has* flags.
+    HasDiv = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+  };
+};
+
+template <>
+struct packet_traits<int32_t> : default_packet_traits {
+  typedef Packet4i type;
+  typedef Packet4i half;  // Packet2i intrinsics not implemented yet
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+    // FIXME: verify the Has* flags.
+    HasDiv = 1,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4f> {
+  typedef float type;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet4f half;
+};
+
+template <>
+struct unpacket_traits<Packet4i> {
+  typedef int32_t type;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet4i half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4f v = {from, from, from, from};
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int32_t& from) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fill_w(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pload1<Packet4f>(const float* from) {
+  EIGEN_MSA_DEBUG;
+
+  float f = *from;
+  Packet4f v = {f, f, f, f};
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pload1<Packet4i>(const int32_t* from) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fill_w(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fadd_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_addv_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) {
+  EIGEN_MSA_DEBUG;
+
+  static const Packet4f countdown = {0.0f, 1.0f, 2.0f, 3.0f};
+  return padd(pset1<Packet4f>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int32_t& a) {
+  EIGEN_MSA_DEBUG;
+
+  static const Packet4i countdown = {0, 1, 2, 3};
+  return padd(pset1<Packet4i>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fsub_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_subv_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_bnegi_w((v4u32)a, 31);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_addvi_w((v4i32)__builtin_msa_nori_b((v16u8)a, 0), 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fmul_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_mulv_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fdiv_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_div_s_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fmadd_w(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
+  EIGEN_MSA_DEBUG;
+
+  // Use "asm" construct to avoid __builtin_msa_maddv_w GNU C bug.
+  Packet4i value = c;
+  __asm__("maddv.w %w[value], %w[a], %w[b]\n"
+          // Outputs
+          : [value] "+f"(value)
+          // Inputs
+          : [a] "f"(a), [b] "f"(b));
+  return value;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_and_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4i)__builtin_msa_and_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_or_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4i)__builtin_msa_or_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4i)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return pand(a, (Packet4f)__builtin_msa_xori_b((v16u8)b, 255));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return pand(a, (Packet4i)__builtin_msa_xori_b((v16u8)b, 255));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  // This prefers numbers to NaNs.
+  return __builtin_msa_fmin_w(a, b);
+#else
+  // This prefers NaNs to numbers.
+  Packet4i aNaN = __builtin_msa_fcun_w(a, a);
+  Packet4i aMinOrNaN = por(__builtin_msa_fclt_w(a, b), aNaN);
+  return (Packet4f)__builtin_msa_bsel_v((v16u8)aMinOrNaN, (v16u8)b, (v16u8)a);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_min_s_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  // This prefers numbers to NaNs.
+  return __builtin_msa_fmax_w(a, b);
+#else
+  // This prefers NaNs to numbers.
+  Packet4i aNaN = __builtin_msa_fcun_w(a, a);
+  Packet4i aMaxOrNaN = por(__builtin_msa_fclt_w(b, a), aNaN);
+  return (Packet4f)__builtin_msa_bsel_v((v16u8)aMaxOrNaN, (v16u8)b, (v16u8)a);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_max_s_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_LOAD return (Packet4f)__builtin_msa_ld_w(const_cast<float*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int32_t* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_LOAD return __builtin_msa_ld_w(const_cast<int32_t*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet4f)__builtin_msa_ld_w(const_cast<float*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int32_t* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet4i)__builtin_msa_ld_w(const_cast<int32_t*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) {
+  EIGEN_MSA_DEBUG;
+
+  float f0 = from[0], f1 = from[1];
+  Packet4f v0 = {f0, f0, f0, f0};
+  Packet4f v1 = {f1, f1, f1, f1};
+  return (Packet4f)__builtin_msa_ilvr_d((v2i64)v1, (v2i64)v0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int32_t* from) {
+  EIGEN_MSA_DEBUG;
+
+  int32_t i0 = from[0], i1 = from[1];
+  Packet4i v0 = {i0, i0, i0, i0};
+  Packet4i v1 = {i1, i1, i1, i1};
+  return (Packet4i)__builtin_msa_ilvr_d((v2i64)v1, (v2i64)v0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_w((Packet4i)from, to, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet4i& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_w(from, to, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_w((Packet4i)from, to, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet4i& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_w(from, to, 0);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  float f = *from;
+  Packet4f v = {f, f, f, f};
+  v[1] = from[stride];
+  v[2] = from[2 * stride];
+  v[3] = from[3 * stride];
+  return v;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4i pgather<int32_t, Packet4i>(const int32_t* from, Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  int32_t i = *from;
+  Packet4i v = {i, i, i, i};
+  v[1] = from[stride];
+  v[2] = from[2 * stride];
+  v[3] = from[3 * stride];
+  return v;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  *to = from[0];
+  to += stride;
+  *to = from[1];
+  to += stride;
+  *to = from[2];
+  to += stride;
+  *to = from[3];
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<int32_t, Packet4i>(int32_t* to, const Packet4i& from, Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  *to = from[0];
+  to += stride;
+  *to = from[1];
+  to += stride;
+  *to = from[2];
+  to += stride;
+  *to = from[3];
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
+  EIGEN_MSA_DEBUG;
+
+  __builtin_prefetch(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<int32_t>(const int32_t* addr) {
+  EIGEN_MSA_DEBUG;
+
+  __builtin_prefetch(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE int32_t pfirst<Packet4i>(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(3, 2, 1, 0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(3, 2, 1, 0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_bclri_w((v4u32)a, 31);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4i zero = __builtin_msa_ldi_w(0);
+  return __builtin_msa_add_a_w(zero, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4f s = padd(a, (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  s = padd(s, (Packet4f)__builtin_msa_shf_w((v4i32)s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return s[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4i s = padd(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  s = padd(s, __builtin_msa_shf_w(s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return s[0];
+}
+
+// Other reduction functions:
+// mul
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4f p = pmul(a, (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  p = pmul(p, (Packet4f)__builtin_msa_shf_w((v4i32)p, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return p[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE int32_t predux_mul<Packet4i>(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4i p = pmul(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  p = pmul(p, __builtin_msa_shf_w(p, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return p[0];
+}
+
+// min
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  // Swap 64-bit halves of a.
+  Packet4f swapped = (Packet4f)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+#if !EIGEN_FAST_MATH
+  // Detect presence of NaNs from pairs a[0]-a[2] and a[1]-a[3] as two 32-bit
+  // masks of all zeroes/ones in low 64 bits.
+  v16u8 unord = (v16u8)__builtin_msa_fcun_w(a, swapped);
+  // Combine the two masks into one: 64 ones if no NaNs, otherwise 64 zeroes.
+  unord = (v16u8)__builtin_msa_ceqi_d((v2i64)unord, 0);
+#endif
+  // Continue with min computation.
+  Packet4f v = __builtin_msa_fmin_w(a, swapped);
+  v = __builtin_msa_fmin_w(v, (Packet4f)__builtin_msa_shf_w((Packet4i)v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+#if !EIGEN_FAST_MATH
+  // Based on the mask select between v and 4 qNaNs.
+  v16u8 qnans = (v16u8)__builtin_msa_fill_w(0x7FC00000);
+  v = (Packet4f)__builtin_msa_bsel_v(unord, qnans, (v16u8)v);
+#endif
+  return v[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4i m = pmin(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  m = pmin(m, __builtin_msa_shf_w(m, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return m[0];
+}
+
+// max
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  // Swap 64-bit halves of a.
+  Packet4f swapped = (Packet4f)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+#if !EIGEN_FAST_MATH
+  // Detect presence of NaNs from pairs a[0]-a[2] and a[1]-a[3] as two 32-bit
+  // masks of all zeroes/ones in low 64 bits.
+  v16u8 unord = (v16u8)__builtin_msa_fcun_w(a, swapped);
+  // Combine the two masks into one: 64 ones if no NaNs, otherwise 64 zeroes.
+  unord = (v16u8)__builtin_msa_ceqi_d((v2i64)unord, 0);
+#endif
+  // Continue with max computation.
+  Packet4f v = __builtin_msa_fmax_w(a, swapped);
+  v = __builtin_msa_fmax_w(v, (Packet4f)__builtin_msa_shf_w((Packet4i)v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+#if !EIGEN_FAST_MATH
+  // Based on the mask select between v and 4 qNaNs.
+  v16u8 qnans = (v16u8)__builtin_msa_fill_w(0x7FC00000);
+  v = (Packet4f)__builtin_msa_bsel_v(unord, qnans, (v16u8)v);
+#endif
+  return v[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4i m = pmax(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  m = pmax(m, __builtin_msa_shf_w(m, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return m[0];
+}
+
+inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet4f, 4>& value) {
+  os << "[ " << value.packet[0] << "," << std::endl
+     << "  " << value.packet[1] << "," << std::endl
+     << "  " << value.packet[2] << "," << std::endl
+     << "  " << value.packet[3] << " ]";
+  return os;
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
+  EIGEN_MSA_DEBUG;
+
+  v4i32 tmp1, tmp2, tmp3, tmp4;
+
+  tmp1 = __builtin_msa_ilvr_w((v4i32)kernel.packet[1], (v4i32)kernel.packet[0]);
+  tmp2 = __builtin_msa_ilvr_w((v4i32)kernel.packet[3], (v4i32)kernel.packet[2]);
+  tmp3 = __builtin_msa_ilvl_w((v4i32)kernel.packet[1], (v4i32)kernel.packet[0]);
+  tmp4 = __builtin_msa_ilvl_w((v4i32)kernel.packet[3], (v4i32)kernel.packet[2]);
+
+  kernel.packet[0] = (Packet4f)__builtin_msa_ilvr_d((v2i64)tmp2, (v2i64)tmp1);
+  kernel.packet[1] = (Packet4f)__builtin_msa_ilvod_d((v2i64)tmp2, (v2i64)tmp1);
+  kernel.packet[2] = (Packet4f)__builtin_msa_ilvr_d((v2i64)tmp4, (v2i64)tmp3);
+  kernel.packet[3] = (Packet4f)__builtin_msa_ilvod_d((v2i64)tmp4, (v2i64)tmp3);
+}
+
+inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet4i, 4>& value) {
+  os << "[ " << value.packet[0] << "," << std::endl
+     << "  " << value.packet[1] << "," << std::endl
+     << "  " << value.packet[2] << "," << std::endl
+     << "  " << value.packet[3] << " ]";
+  return os;
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
+  EIGEN_MSA_DEBUG;
+
+  v4i32 tmp1, tmp2, tmp3, tmp4;
+
+  tmp1 = __builtin_msa_ilvr_w(kernel.packet[1], kernel.packet[0]);
+  tmp2 = __builtin_msa_ilvr_w(kernel.packet[3], kernel.packet[2]);
+  tmp3 = __builtin_msa_ilvl_w(kernel.packet[1], kernel.packet[0]);
+  tmp4 = __builtin_msa_ilvl_w(kernel.packet[3], kernel.packet[2]);
+
+  kernel.packet[0] = (Packet4i)__builtin_msa_ilvr_d((v2i64)tmp2, (v2i64)tmp1);
+  kernel.packet[1] = (Packet4i)__builtin_msa_ilvod_d((v2i64)tmp2, (v2i64)tmp1);
+  kernel.packet[2] = (Packet4i)__builtin_msa_ilvr_d((v2i64)tmp4, (v2i64)tmp3);
+  kernel.packet[3] = (Packet4i)__builtin_msa_ilvod_d((v2i64)tmp4, (v2i64)tmp3);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fsqrt_w(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f prsqrt(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  return __builtin_msa_frsqrt_w(a);
+#else
+  Packet4f ones = __builtin_msa_ffint_s_w(__builtin_msa_ldi_w(1));
+  return pdiv(ones, psqrt(a));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) {
+  Packet4f v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"  // 3 = round towards -INFINITY.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.w %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) {
+  Packet4f v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"
+      "xori    %[new_mode], %[new_mode], 1\n"  // 2 = round towards +INFINITY.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.w %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a) {
+  Packet4f v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"
+      "xori    %[new_mode], %[new_mode], 3\n"  // 0 = round to nearest, ties to even.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.w %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+//---------- double ----------
+
+typedef v2f64 Packet2d;
+typedef v2i64 Packet2l;
+typedef v2u64 Packet2ul;
+
+#define EIGEN_DECLARE_CONST_Packet2d(NAME, X) const Packet2d p2d_##NAME = {X, X}
+#define EIGEN_DECLARE_CONST_Packet2l(NAME, X) const Packet2l p2l_##NAME = {X, X}
+#define EIGEN_DECLARE_CONST_Packet2ul(NAME, X) const Packet2ul p2ul_##NAME = {X, X}
+
+inline std::ostream& operator<<(std::ostream& os, const Packet2d& value) {
+  os << "[ " << value[0] << ", " << value[1] << " ]";
+  return os;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const Packet2l& value) {
+  os << "[ " << value[0] << ", " << value[1] << " ]";
+  return os;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const Packet2ul& value) {
+  os << "[ " << value[0] << ", " << value[1] << " ]";
+  return os;
+}
+
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+    // FIXME: verify the Has* flags.
+    HasDiv = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2d> {
+  typedef double type;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet2d half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d value = {from, from};
+  return value;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fadd_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) {
+  EIGEN_MSA_DEBUG;
+
+  static const Packet2d countdown = {0.0, 1.0};
+  return padd(pset1<Packet2d>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fsub_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_bnegi_d((v2u64)a, 63);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fmul_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fdiv_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fmadd_d(c, a, b);
+}
+
+// Logical Operations are not supported for float, so we have to reinterpret casts using MSA
+// intrinsics
+template <>
+EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_and_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_or_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return pand(a, (Packet2d)__builtin_msa_xori_b((v16u8)b, 255));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet2d)__builtin_msa_ld_d(const_cast<double*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  // This prefers numbers to NaNs.
+  return __builtin_msa_fmin_d(a, b);
+#else
+  // This prefers NaNs to numbers.
+  v2i64 aNaN = __builtin_msa_fcun_d(a, a);
+  v2i64 aMinOrNaN = por(__builtin_msa_fclt_d(a, b), aNaN);
+  return (Packet2d)__builtin_msa_bsel_v((v16u8)aMinOrNaN, (v16u8)b, (v16u8)a);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  // This prefers numbers to NaNs.
+  return __builtin_msa_fmax_d(a, b);
+#else
+  // This prefers NaNs to numbers.
+  v2i64 aNaN = __builtin_msa_fcun_d(a, a);
+  v2i64 aMaxOrNaN = por(__builtin_msa_fclt_d(b, a), aNaN);
+  return (Packet2d)__builtin_msa_bsel_v((v16u8)aMaxOrNaN, (v16u8)b, (v16u8)a);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet2d)__builtin_msa_ld_d(const_cast<double*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d value = {*from, *from};
+  return value;
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_d((v2i64)from, to, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_d((v2i64)from, to, 0);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d value;
+  value[0] = *from;
+  from += stride;
+  value[1] = *from;
+  return value;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  *to = from[0];
+  to += stride;
+  *to = from[1];
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
+  EIGEN_MSA_DEBUG;
+
+  __builtin_prefetch(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_bclri_d((v2u64)a, 63);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d s = padd(a, preverse(a));
+  return s[0];
+}
+
+// Other reduction functions:
+// mul
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d p = pmul(a, preverse(a));
+  return p[0];
+}
+
+// min
+template <>
+EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  Packet2d swapped = (Packet2d)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+  Packet2d v = __builtin_msa_fmin_d(a, swapped);
+  return v[0];
+#else
+  double a0 = a[0], a1 = a[1];
+  return ((numext::isnan)(a0) || a0 < a1) ? a0 : a1;
+#endif
+}
+
+// max
+template <>
+EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  Packet2d swapped = (Packet2d)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+  Packet2d v = __builtin_msa_fmax_d(a, swapped);
+  return v[0];
+#else
+  double a0 = a[0], a1 = a[1];
+  return ((numext::isnan)(a0) || a0 > a1) ? a0 : a1;
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d psqrt(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fsqrt_d(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d prsqrt(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  return __builtin_msa_frsqrt_d(a);
+#else
+  Packet2d ones = __builtin_msa_ffint_s_d(__builtin_msa_ldi_d(1));
+  return pdiv(ones, psqrt(a));
+#endif
+}
+
+inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet2d, 2>& value) {
+  os << "[ " << value.packet[0] << "," << std::endl << "  " << value.packet[1] << " ]";
+  return os;
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2d, 2>& kernel) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d trn1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)kernel.packet[1], (v2i64)kernel.packet[0]);
+  Packet2d trn2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)kernel.packet[1], (v2i64)kernel.packet[0]);
+  kernel.packet[0] = trn1;
+  kernel.packet[1] = trn2;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) {
+  Packet2d v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"  // 3 = round towards -INFINITY.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.d %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) {
+  Packet2d v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"
+      "xori    %[new_mode], %[new_mode], 1\n"  // 2 = round towards +INFINITY.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.d %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) {
+  Packet2d v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"
+      "xori    %[new_mode], %[new_mode], 3\n"  // 0 = round to nearest, ties to even.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.d %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_MSA_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/Complex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/Complex.h
new file mode 100644
index 0000000..9b476e0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/Complex.h
@@ -0,0 +1,738 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_NEON_H
+#define EIGEN_COMPLEX_NEON_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+inline uint32x4_t p4ui_CONJ_XOR() {
+// See bug 1325, clang fails to call vld1q_u64.
+#if EIGEN_COMP_CLANG || EIGEN_COMP_CASTXML
+  uint32x4_t ret = {0x00000000, 0x80000000, 0x00000000, 0x80000000};
+  return ret;
+#else
+  static const uint32_t conj_XOR_DATA[] = {0x00000000, 0x80000000, 0x00000000, 0x80000000};
+  return vld1q_u32(conj_XOR_DATA);
+#endif
+}
+
+inline uint32x2_t p2ui_CONJ_XOR() {
+  static const uint32_t conj_XOR_DATA[] = {0x00000000, 0x80000000};
+  return vld1_u32(conj_XOR_DATA);
+}
+
+//---------- float ----------
+
+struct Packet1cf {
+  EIGEN_STRONG_INLINE Packet1cf() {}
+  EIGEN_STRONG_INLINE explicit Packet1cf(const Packet2f& a) : v(a) {}
+  Packet2f v;
+};
+struct Packet2cf {
+  EIGEN_STRONG_INLINE Packet2cf() {}
+  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
+  Packet4f v;
+};
+
+template <>
+struct packet_traits<std::complex<float>> : default_packet_traits {
+  typedef Packet2cf type;
+  typedef Packet1cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet1cf> : neon_unpacket_default<Packet1cf, std::complex<float>> {
+  using as_real = Packet2f;
+};
+template <>
+struct unpacket_traits<Packet2cf> : neon_unpacket_default<Packet2cf, std::complex<float>> {
+  using half = Packet1cf;
+  using as_real = Packet4f;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pcast<float, Packet1cf>(const float& a) {
+  return Packet1cf(vset_lane_f32(a, vdup_n_f32(0.f), 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcast<Packet2f, Packet2cf>(const Packet2f& a) {
+  return Packet2cf(vreinterpretq_f32_u64(vmovl_u32(vreinterpret_u32_f32(a))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pzero(const Packet1cf& /*a*/) {
+  return Packet1cf(vdup_n_f32(0.0f));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pzero(const Packet2cf& /*a*/) {
+  return Packet2cf(vdupq_n_f32(0.0f));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pset1<Packet1cf>(const std::complex<float>& from) {
+  return Packet1cf(vld1_f32(reinterpret_cast<const float*>(&from)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) {
+  const float32x2_t r64 = vld1_f32(reinterpret_cast<const float*>(&from));
+  return Packet2cf(vcombine_f32(r64, r64));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf padd<Packet1cf>(const Packet1cf& a, const Packet1cf& b) {
+  return Packet1cf(padd<Packet2f>(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(padd<Packet4f>(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf psub<Packet1cf>(const Packet1cf& a, const Packet1cf& b) {
+  return Packet1cf(psub<Packet2f>(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(psub<Packet4f>(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pnegate(const Packet1cf& a) {
+  return Packet1cf(pnegate<Packet2f>(a.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) {
+  return Packet2cf(pnegate<Packet4f>(a.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pconj(const Packet1cf& a) {
+  const Packet2ui b = Packet2ui(vreinterpret_u32_f32(a.v));
+  return Packet1cf(vreinterpret_f32_u32(veor_u32(b, p2ui_CONJ_XOR())));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) {
+  const Packet4ui b = Packet4ui(vreinterpretq_u32_f32(a.v));
+  return Packet2cf(vreinterpretq_f32_u32(veorq_u32(b, p4ui_CONJ_XOR())));
+}
+
+#ifdef __ARM_FEATURE_COMPLEX
+template <>
+EIGEN_STRONG_INLINE Packet1cf pmadd<Packet1cf>(const Packet1cf& a, const Packet1cf& b, const Packet1cf& c) {
+  Packet1cf result;
+  result.v = vcmla_f32(c.v, a.v, b.v);
+  result.v = vcmla_rot90_f32(result.v, a.v, b.v);
+  return result;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pmul<Packet1cf>(const Packet1cf& a, const Packet1cf& b) {
+  return pmadd(a, b, pzero(a));
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet1cf pmul<Packet1cf>(const Packet1cf& a, const Packet1cf& b) {
+  Packet2f v1, v2;
+
+  // Get the real values of a | a1_re | a1_re |
+  v1 = vdup_lane_f32(a.v, 0);
+  // Get the imag values of a | a1_im | a1_im |
+  v2 = vdup_lane_f32(a.v, 1);
+  // Multiply the real a with b
+  v1 = vmul_f32(v1, b.v);
+  // Multiply the imag a with b
+  v2 = vmul_f32(v2, b.v);
+  // Conjugate v2
+  v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR()));
+  // Swap real/imag elements in v2.
+  v2 = vrev64_f32(v2);
+  // Add and return the result
+  return Packet1cf(vadd_f32(v1, v2));
+}
+#endif
+
+#ifdef __ARM_FEATURE_COMPLEX
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmadd<Packet2cf>(const Packet2cf& a, const Packet2cf& b, const Packet2cf& c) {
+  Packet2cf result;
+  result.v = vcmlaq_f32(c.v, a.v, b.v);
+  result.v = vcmlaq_rot90_f32(result.v, a.v, b.v);
+  return result;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return pmadd(a, b, pzero(a));
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  Packet4f v1, v2;
+
+  // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
+  v1 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 0), vdup_lane_f32(vget_high_f32(a.v), 0));
+  // Get the imag values of a | a1_im | a1_im | a2_im | a2_im |
+  v2 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 1), vdup_lane_f32(vget_high_f32(a.v), 1));
+  // Multiply the real a with b
+  v1 = vmulq_f32(v1, b.v);
+  // Multiply the imag a with b
+  v2 = vmulq_f32(v2, b.v);
+  // Conjugate v2
+  v2 = vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(v2), p4ui_CONJ_XOR()));
+  // Swap real/imag elements in v2.
+  v2 = vrev64q_f32(v2);
+  // Add and return the result
+  return Packet2cf(vaddq_f32(v1, v2));
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pcmp_eq(const Packet1cf& a, const Packet1cf& b) {
+  // Compare real and imaginary parts of a and b to get the mask vector:
+  // [re(a[0])==re(b[0]), im(a[0])==im(b[0])]
+  Packet2f eq = pcmp_eq<Packet2f>(a.v, b.v);
+  // Swap real/imag elements in the mask in to get:
+  // [im(a[0])==im(b[0]), re(a[0])==re(b[0])]
+  Packet2f eq_swapped = vrev64_f32(eq);
+  // Return re(a)==re(b) && im(a)==im(b) by computing bitwise AND of eq and eq_swapped
+  return Packet1cf(pand<Packet2f>(eq, eq_swapped));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
+  // Compare real and imaginary parts of a and b to get the mask vector:
+  // [re(a[0])==re(b[0]), im(a[0])==im(b[0]), re(a[1])==re(b[1]), im(a[1])==im(b[1])]
+  Packet4f eq = pcmp_eq<Packet4f>(a.v, b.v);
+  // Swap real/imag elements in the mask in to get:
+  // [im(a[0])==im(b[0]), re(a[0])==re(b[0]), im(a[1])==im(b[1]), re(a[1])==re(b[1])]
+  Packet4f eq_swapped = vrev64q_f32(eq);
+  // Return re(a)==re(b) && im(a)==im(b) by computing bitwise AND of eq and eq_swapped
+  return Packet2cf(pand<Packet4f>(eq, eq_swapped));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pand<Packet1cf>(const Packet1cf& a, const Packet1cf& b) {
+  return Packet1cf(vreinterpret_f32_u32(vand_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf por<Packet1cf>(const Packet1cf& a, const Packet1cf& b) {
+  return Packet1cf(vreinterpret_f32_u32(vorr_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pxor<Packet1cf>(const Packet1cf& a, const Packet1cf& b) {
+  return Packet1cf(vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pandnot<Packet1cf>(const Packet1cf& a, const Packet1cf& b) {
+  return Packet1cf(vreinterpret_f32_u32(vbic_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pload<Packet1cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet1cf(
+      pload<Packet2f>(assume_aligned<unpacket_traits<Packet1cf>::alignment>(reinterpret_cast<const float*>(from))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(
+      pload<Packet4f>(assume_aligned<unpacket_traits<Packet2cf>::alignment>(reinterpret_cast<const float*>(from))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf ploadu<Packet1cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cf(ploadu<Packet2f>((const float*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(reinterpret_cast<const float*>(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf ploaddup<Packet1cf>(const std::complex<float>* from) {
+  return pset1<Packet1cf>(*from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) {
+  return pset1<Packet2cf>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<float>>(std::complex<float>* to, const Packet1cf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE pstore(assume_aligned<unpacket_traits<Packet1cf>::alignment>(reinterpret_cast<float*>(to)),
+                                   from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<float>>(std::complex<float>* to, const Packet2cf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE pstore(assume_aligned<unpacket_traits<Packet2cf>::alignment>(reinterpret_cast<float*>(to)),
+                                   from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<float>>(std::complex<float>* to, const Packet1cf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<float>>(std::complex<float>* to, const Packet2cf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu(reinterpret_cast<float*>(to), from.v);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1cf pgather<std::complex<float>, Packet1cf>(const std::complex<float>* from,
+                                                                           Index stride) {
+  const Packet2f tmp = vdup_n_f32(std::real(from[0 * stride]));
+  return Packet1cf(vset_lane_f32(std::imag(from[0 * stride]), tmp, 1));
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from,
+                                                                           Index stride) {
+  Packet4f res = vdupq_n_f32(std::real(from[0 * stride]));
+  res = vsetq_lane_f32(std::imag(from[0 * stride]), res, 1);
+  res = vsetq_lane_f32(std::real(from[1 * stride]), res, 2);
+  res = vsetq_lane_f32(std::imag(from[1 * stride]), res, 3);
+  return Packet2cf(res);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet1cf>(std::complex<float>* to, const Packet1cf& from,
+                                                                       Index stride) {
+  to[stride * 0] = std::complex<float>(vget_lane_f32(from.v, 0), vget_lane_f32(from.v, 1));
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from,
+                                                                       Index stride) {
+  to[stride * 0] = std::complex<float>(vgetq_lane_f32(from.v, 0), vgetq_lane_f32(from.v, 1));
+  to[stride * 1] = std::complex<float>(vgetq_lane_f32(from.v, 2), vgetq_lane_f32(from.v, 3));
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<float>>(const std::complex<float>* addr) {
+  EIGEN_ARM_PREFETCH(reinterpret_cast<const float*>(addr));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet1cf>(const Packet1cf& a) {
+  EIGEN_ALIGN16 std::complex<float> x;
+  vst1_f32(reinterpret_cast<float*>(&x), a.v);
+  return x;
+}
+template <>
+EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) {
+  EIGEN_ALIGN16 std::complex<float> x[2];
+  vst1q_f32(reinterpret_cast<float*>(x), a.v);
+  return x[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf preverse(const Packet1cf& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) {
+  return Packet2cf(vcombine_f32(vget_high_f32(a.v), vget_low_f32(a.v)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pcplxflip<Packet1cf>(const Packet1cf& a) {
+  return Packet1cf(vrev64_f32(a.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& a) {
+  return Packet2cf(vrev64q_f32(a.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet1cf>(const Packet1cf& a) {
+  std::complex<float> s;
+  vst1_f32((float*)&s, a.v);
+  return s;
+}
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) {
+  std::complex<float> s;
+  vst1_f32(reinterpret_cast<float*>(&s), vadd_f32(vget_low_f32(a.v), vget_high_f32(a.v)));
+  return s;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet1cf>(const Packet1cf& a) {
+  std::complex<float> s;
+  vst1_f32((float*)&s, a.v);
+  return s;
+}
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) {
+  float32x2_t a1, a2, v1, v2, prod;
+  std::complex<float> s;
+
+  a1 = vget_low_f32(a.v);
+  a2 = vget_high_f32(a.v);
+  // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
+  v1 = vdup_lane_f32(a1, 0);
+  // Get the real values of a | a1_im | a1_im | a2_im | a2_im |
+  v2 = vdup_lane_f32(a1, 1);
+  // Multiply the real a with b
+  v1 = vmul_f32(v1, a2);
+  // Multiply the imag a with b
+  v2 = vmul_f32(v2, a2);
+  // Conjugate v2
+  v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR()));
+  // Swap real/imag elements in v2.
+  v2 = vrev64_f32(v2);
+  // Add v1, v2
+  prod = vadd_f32(v1, v2);
+
+  vst1_f32(reinterpret_cast<float*>(&s), prod);
+
+  return s;
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cf, Packet2f)
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf, Packet4f)
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pdiv<Packet1cf>(const Packet1cf& a, const Packet1cf& b) {
+  return pdiv_complex(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return pdiv_complex(a, b);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1cf, 1>& /*kernel*/) {}
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cf, 2>& kernel) {
+  Packet4f tmp = vcombine_f32(vget_high_f32(kernel.packet[0].v), vget_high_f32(kernel.packet[1].v));
+  kernel.packet[0].v = vcombine_f32(vget_low_f32(kernel.packet[0].v), vget_low_f32(kernel.packet[1].v));
+  kernel.packet[1].v = tmp;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf psqrt<Packet1cf>(const Packet1cf& a) {
+  return psqrt_complex<Packet1cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
+  return psqrt_complex<Packet2cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf plog<Packet1cf>(const Packet1cf& a) {
+  return plog_complex(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf plog<Packet2cf>(const Packet2cf& a) {
+  return plog_complex(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cf pexp<Packet1cf>(const Packet1cf& a) {
+  return pexp_complex(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pexp<Packet2cf>(const Packet2cf& a) {
+  return pexp_complex(a);
+}
+
+//---------- double ----------
+#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+
+inline uint64x2_t p2ul_CONJ_XOR() {
+  static const uint64_t p2ul_conj_XOR_DATA[] = {0x0, 0x8000000000000000};
+  return vld1q_u64(p2ul_conj_XOR_DATA);
+}
+
+struct Packet1cd {
+  EIGEN_STRONG_INLINE Packet1cd() {}
+  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
+  Packet2d v;
+};
+
+template <>
+struct packet_traits<std::complex<double>> : default_packet_traits {
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 1,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet1cd> : neon_unpacket_default<Packet1cd, std::complex<double>> {
+  using as_real = Packet2d;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(
+      pload<Packet2d>(assume_aligned<unpacket_traits<Packet1cd>::alignment>(reinterpret_cast<const double*>(from))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>(reinterpret_cast<const double*>(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pzero<Packet1cd>(const Packet1cd& /*a*/) {
+  return Packet1cd(vdupq_n_f64(0.0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from) {
+  /* here we really have to use unaligned loads :( */
+  return ploadu<Packet1cd>(&from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(padd<Packet2d>(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(psub<Packet2d>(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) {
+  return Packet1cd(pnegate<Packet2d>(a.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) {
+  return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v), p2ul_CONJ_XOR())));
+}
+
+#ifdef __ARM_FEATURE_COMPLEX
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmadd<Packet1cd>(const Packet1cd& a, const Packet1cd& b, const Packet1cd& c) {
+  Packet1cd result;
+  result.v = vcmlaq_f64(c.v, a.v, b.v);
+  result.v = vcmlaq_rot90_f64(result.v, a.v, b.v);
+  return result;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return pmadd(a, b, pzero(a));
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  Packet2d v1, v2;
+
+  // Get the real values of a
+  v1 = vdupq_lane_f64(vget_low_f64(a.v), 0);
+  // Get the imag values of a
+  v2 = vdupq_lane_f64(vget_high_f64(a.v), 0);
+  // Multiply the real a with b
+  v1 = vmulq_f64(v1, b.v);
+  // Multiply the imag a with b
+  v2 = vmulq_f64(v2, b.v);
+  // Conjugate v2
+  v2 = vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(v2), p2ul_CONJ_XOR()));
+  // Swap real/imag elements in v2.
+  v2 = preverse<Packet2d>(v2);
+  // Add and return the result
+  return Packet1cd(vaddq_f64(v1, v2));
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
+  // Compare real and imaginary parts of a and b to get the mask vector:
+  // [re(a)==re(b), im(a)==im(b)]
+  Packet2d eq = pcmp_eq<Packet2d>(a.v, b.v);
+  // Swap real/imag elements in the mask in to get:
+  // [im(a)==im(b), re(a)==re(b)]
+  Packet2d eq_swapped = vreinterpretq_f64_u32(vrev64q_u32(vreinterpretq_u32_f64(eq)));
+  // Return re(a)==re(b) & im(a)==im(b) by computing bitwise AND of eq and eq_swapped
+  return Packet1cd(pand<Packet2d>(eq, eq_swapped));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a.v), vreinterpretq_u64_f64(b.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a.v), vreinterpretq_u64_f64(b.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v), vreinterpretq_u64_f64(b.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a.v), vreinterpretq_u64_f64(b.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) {
+  return pset1<Packet1cd>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<double>>(std::complex<double>* to, const Packet1cd& from) {
+  EIGEN_DEBUG_ALIGNED_STORE pstore(assume_aligned<unpacket_traits<Packet1cd>::alignment>(reinterpret_cast<double*>(to)),
+                                   from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<double>>(std::complex<double>* to, const Packet1cd& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu(reinterpret_cast<double*>(to), from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<double>>(const std::complex<double>* addr) {
+  EIGEN_ARM_PREFETCH(reinterpret_cast<const double*>(addr));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from,
+                                                                            Index stride) {
+  Packet2d res = pset1<Packet2d>(0.0);
+  res = vsetq_lane_f64(std::real(from[0 * stride]), res, 0);
+  res = vsetq_lane_f64(std::imag(from[0 * stride]), res, 1);
+  return Packet1cd(res);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from,
+                                                                        Index stride) {
+  to[stride * 0] = std::complex<double>(vgetq_lane_f64(from.v, 0), vgetq_lane_f64(from.v, 1));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) {
+  EIGEN_ALIGN16 std::complex<double> res;
+  pstore<std::complex<double>>(&res, a);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) {
+  return pfirst(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) {
+  return pfirst(a);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd, Packet2d)
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return pdiv_complex(a, b);
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip /*<Packet1cd>*/ (const Packet1cd& x) {
+  return Packet1cd(preverse(Packet2d(x.v)));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd, 2>& kernel) {
+  Packet2d tmp = vcombine_f64(vget_high_f64(kernel.packet[0].v), vget_high_f64(kernel.packet[1].v));
+  kernel.packet[0].v = vcombine_f64(vget_low_f64(kernel.packet[0].v), vget_low_f64(kernel.packet[1].v));
+  kernel.packet[1].v = tmp;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
+  return psqrt_complex<Packet1cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd plog<Packet1cd>(const Packet1cd& a) {
+  return plog_complex(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pexp<Packet1cd>(const Packet1cd& a) {
+  return pexp_complex<Packet1cd>(a);
+}
+
+#endif  // EIGEN_ARCH_ARM64
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_NEON_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
new file mode 100644
index 0000000..4ecf7d1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
@@ -0,0 +1,243 @@
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+#if EIGEN_ARCH_ARM && EIGEN_COMP_CLANG
+
+// Clang seems to excessively spill registers in the GEBP kernel on 32-bit arm.
+// Here we specialize gebp_traits to eliminate these register spills.
+// See #2138.
+template <>
+struct gebp_traits<float, float, false, false, Architecture::NEON, GEBPPacketFull>
+    : gebp_traits<float, float, false, false, Architecture::Generic, GEBPPacketFull> {
+  EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const {
+    // This volatile inline ASM both acts as a barrier to prevent reordering,
+    // as well as enforces strict register use.
+    asm volatile("vmla.f32 %q[r], %q[c], %q[alpha]" : [r] "+w"(r) : [c] "w"(c), [alpha] "w"(alpha) :);
+  }
+
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const Packet4f& a, const Packet4f& b, Packet4f& c, Packet4f&, const LaneIdType&) const {
+    acc(a, b, c);
+  }
+
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const Packet4f& a, const QuadPacket<Packet4f>& b, Packet4f& c, Packet4f& tmp,
+                                const LaneIdType& lane) const {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+};
+
+#endif  // EIGEN_ARCH_ARM && EIGEN_COMP_CLANG
+
+#if EIGEN_ARCH_ARM64
+
+#ifndef EIGEN_NEON_GEBP_NR
+#define EIGEN_NEON_GEBP_NR 8
+#endif
+
+template <>
+struct gebp_traits<float, float, false, false, Architecture::NEON, GEBPPacketFull>
+    : gebp_traits<float, float, false, false, Architecture::Generic, GEBPPacketFull> {
+  typedef float RhsPacket;
+  typedef float32x4_t RhsPacketx4;
+  enum { nr = EIGEN_NEON_GEBP_NR };
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const { dest = *b; }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const { dest = vld1q_f32(b); }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const { dest = *b; }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const { loadRhs(b, dest); }
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    c = vfmaq_n_f32(c, a, b);
+  }
+  // NOTE: Template parameter inference failed when compiled with Android NDK:
+  // "candidate template ignored: could not match 'FixedInt<N>' against 'Eigen::internal::FixedInt<0>".
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    madd_helper<0>(a, b, c);
+  }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<1>&) const {
+    madd_helper<1>(a, b, c);
+  }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<2>&) const {
+    madd_helper<2>(a, b, c);
+  }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<3>&) const {
+    madd_helper<3>(a, b, c);
+  }
+
+ private:
+  template <int LaneID>
+  EIGEN_STRONG_INLINE void madd_helper(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c) const {
+#if EIGEN_GNUC_STRICT_LESS_THAN(9, 0, 0)
+    // 1. workaround gcc issue https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89101
+    //    vfmaq_laneq_f32 is implemented through a costly dup, which was fixed in gcc9
+    // 2. workaround the gcc register split problem on arm64-neon
+    if (LaneID == 0)
+      asm("fmla %0.4s, %1.4s, %2.s[0]\n" : "+w"(c) : "w"(a), "w"(b) :);
+    else if (LaneID == 1)
+      asm("fmla %0.4s, %1.4s, %2.s[1]\n" : "+w"(c) : "w"(a), "w"(b) :);
+    else if (LaneID == 2)
+      asm("fmla %0.4s, %1.4s, %2.s[2]\n" : "+w"(c) : "w"(a), "w"(b) :);
+    else if (LaneID == 3)
+      asm("fmla %0.4s, %1.4s, %2.s[3]\n" : "+w"(c) : "w"(a), "w"(b) :);
+#else
+    c = vfmaq_laneq_f32(c, a, b, LaneID);
+#endif
+  }
+};
+
+template <>
+struct gebp_traits<double, double, false, false, Architecture::NEON>
+    : gebp_traits<double, double, false, false, Architecture::Generic> {
+  typedef double RhsPacket;
+  enum { nr = EIGEN_NEON_GEBP_NR };
+  struct RhsPacketx4 {
+    float64x2_t B_0, B_1;
+  };
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const { dest = *b; }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const {
+    dest.B_0 = vld1q_f64(b);
+    dest.B_1 = vld1q_f64(b + 2);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const { loadRhs(b, dest); }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const { loadRhs(b, dest); }
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    c = vfmaq_n_f64(c, a, b);
+  }
+
+  // NOTE: Template parameter inference failed when compiled with Android NDK:
+  // "candidate template ignored: could not match 'FixedInt<N>' against 'Eigen::internal::FixedInt<0>".
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    madd_helper<0>(a, b, c);
+  }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<1>&) const {
+    madd_helper<1>(a, b, c);
+  }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<2>&) const {
+    madd_helper<2>(a, b, c);
+  }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<3>&) const {
+    madd_helper<3>(a, b, c);
+  }
+
+ private:
+  template <int LaneID>
+  EIGEN_STRONG_INLINE void madd_helper(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c) const {
+#if EIGEN_GNUC_STRICT_LESS_THAN(9, 0, 0)
+    // 1. workaround gcc issue https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89101
+    //    vfmaq_laneq_f64 is implemented through a costly dup, which was fixed in gcc9
+    // 2. workaround the gcc register split problem on arm64-neon
+    if (LaneID == 0)
+      asm("fmla %0.2d, %1.2d, %2.d[0]\n" : "+w"(c) : "w"(a), "w"(b.B_0) :);
+    else if (LaneID == 1)
+      asm("fmla %0.2d, %1.2d, %2.d[1]\n" : "+w"(c) : "w"(a), "w"(b.B_0) :);
+    else if (LaneID == 2)
+      asm("fmla %0.2d, %1.2d, %2.d[0]\n" : "+w"(c) : "w"(a), "w"(b.B_1) :);
+    else if (LaneID == 3)
+      asm("fmla %0.2d, %1.2d, %2.d[1]\n" : "+w"(c) : "w"(a), "w"(b.B_1) :);
+#else
+    if (LaneID == 0)
+      c = vfmaq_laneq_f64(c, a, b.B_0, 0);
+    else if (LaneID == 1)
+      c = vfmaq_laneq_f64(c, a, b.B_0, 1);
+    else if (LaneID == 2)
+      c = vfmaq_laneq_f64(c, a, b.B_1, 0);
+    else if (LaneID == 3)
+      c = vfmaq_laneq_f64(c, a, b.B_1, 1);
+#endif
+  }
+};
+
+// The register at operand 3 of fmla for data type half must be v0~v15, the compiler may not
+// allocate a required register for the '%2' of inline asm 'fmla %0.8h, %1.8h, %2.h[id]',
+// so inline assembly can't be used here to advoid the bug that vfmaq_lane_f16 is implemented
+// through a costly dup in gcc compiler.
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC && EIGEN_COMP_CLANG
+
+template <>
+struct gebp_traits<half, half, false, false, Architecture::NEON>
+    : gebp_traits<half, half, false, false, Architecture::Generic> {
+  typedef half RhsPacket;
+  typedef float16x4_t RhsPacketx4;
+  typedef float16x4_t PacketHalf;
+  enum { nr = EIGEN_NEON_GEBP_NR };
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const { dest = *b; }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const { dest = vld1_f16((const __fp16*)b); }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const { dest = *b; }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar*, RhsPacket&) const {
+    // If LHS is a Packet8h, we cannot correctly mimic a ploadquad of the RHS
+    // using a single scalar value.
+    eigen_assert(false && "Cannot loadRhsQuad for a scalar RHS.");
+  }
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    c = vfmaq_n_f16(c, a, b);
+  }
+  EIGEN_STRONG_INLINE void madd(const PacketHalf& a, const RhsPacket& b, PacketHalf& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    c = vfma_n_f16(c, a, b);
+  }
+
+  // NOTE: Template parameter inference failed when compiled with Android NDK:
+  // "candidate template ignored: could not match 'FixedInt<N>' against 'Eigen::internal::FixedInt<0>".
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    madd_helper<0>(a, b, c);
+  }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<1>&) const {
+    madd_helper<1>(a, b, c);
+  }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<2>&) const {
+    madd_helper<2>(a, b, c);
+  }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<3>&) const {
+    madd_helper<3>(a, b, c);
+  }
+
+ private:
+  template <int LaneID>
+  EIGEN_STRONG_INLINE void madd_helper(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c) const {
+    c = vfmaq_lane_f16(c, a, b, LaneID);
+  }
+};
+#endif  // EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC && EIGEN_COMP_CLANG
+#endif  // EIGEN_ARCH_ARM64
+
+}  // namespace internal
+}  // namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/MathFunctions.h
new file mode 100644
index 0000000..5c48db8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/MathFunctions.h
@@ -0,0 +1,63 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_NEON_H
+#define EIGEN_MATH_FUNCTIONS_NEON_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet2f)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet4f)
+
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Packet4hf ptanh<Packet4hf>(const Packet4hf& x) {
+  // Convert to float, call the float ptanh, and then convert back.
+  return vcvt_f16_f32(ptanh<Packet4f>(vcvt_f32_f16(x)));
+}
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Packet8hf ptanh<Packet8hf>(const Packet8hf& x) {
+  // Convert each 4 half types to float, call the float ptanh, and then convert back.
+  return vcombine_f16(vcvt_f16_f32(ptanh<Packet4f>(vcvt_f32_f16(vget_low_f16(x)))),
+                      vcvt_f16_f32(ptanh<Packet4f>(vcvt_high_f32_f16(x))));
+}
+#endif  // EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_BF16(Packet4f, Packet4bf)
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pfrexp(const Packet4bf& a, Packet4bf& exponent) {
+  Packet4f fexponent;
+  const Packet4bf out = F32ToBf16(pfrexp<Packet4f>(Bf16ToF32(a), fexponent));
+  exponent = F32ToBf16(fexponent);
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pldexp(const Packet4bf& a, const Packet4bf& exponent) {
+  return F32ToBf16(pldexp<Packet4f>(Bf16ToF32(a), Bf16ToF32(exponent)));
+}
+
+//---------- double ----------
+
+#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet2d)
+
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_NEON_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/PacketMath.h
new file mode 100644
index 0000000..50a25cf
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/PacketMath.h
@@ -0,0 +1,6100 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Konstantinos Margaritis <markos@freevec.org>
+// Heavily based on Gael's SSE version.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_NEON_H
+#define EIGEN_PACKET_MATH_NEON_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#if EIGEN_ARCH_ARM64
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#else
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
+#endif
+#endif
+
+#if EIGEN_COMP_MSVC_STRICT
+
+// In MSVC's arm_neon.h header file, all NEON vector types
+// are aliases to the same underlying type __n128.
+// We thus have to wrap them to make them different C++ types.
+// (See also bug 1428)
+typedef eigen_packet_wrapper<float32x2_t, 0> Packet2f;
+typedef eigen_packet_wrapper<float32x4_t, 1> Packet4f;
+typedef eigen_packet_wrapper<int32_t, 2> Packet4c;
+typedef eigen_packet_wrapper<int8x8_t, 3> Packet8c;
+typedef eigen_packet_wrapper<int8x16_t, 4> Packet16c;
+typedef eigen_packet_wrapper<uint32_t, 5> Packet4uc;
+typedef eigen_packet_wrapper<uint8x8_t, 6> Packet8uc;
+typedef eigen_packet_wrapper<uint8x16_t, 7> Packet16uc;
+typedef eigen_packet_wrapper<int16x4_t, 8> Packet4s;
+typedef eigen_packet_wrapper<int16x8_t, 9> Packet8s;
+typedef eigen_packet_wrapper<uint16x4_t, 10> Packet4us;
+typedef eigen_packet_wrapper<uint16x8_t, 11> Packet8us;
+typedef eigen_packet_wrapper<int32x2_t, 12> Packet2i;
+typedef eigen_packet_wrapper<int32x4_t, 13> Packet4i;
+typedef eigen_packet_wrapper<uint32x2_t, 14> Packet2ui;
+typedef eigen_packet_wrapper<uint32x4_t, 15> Packet4ui;
+typedef eigen_packet_wrapper<int64x2_t, 16> Packet2l;
+typedef eigen_packet_wrapper<uint64x2_t, 17> Packet2ul;
+
+EIGEN_ALWAYS_INLINE Packet4f make_packet4f(float a, float b, float c, float d) {
+  float from[4] = {a, b, c, d};
+  return vld1q_f32(from);
+}
+
+EIGEN_ALWAYS_INLINE Packet2f make_packet2f(float a, float b) {
+  float from[2] = {a, b};
+  return vld1_f32(from);
+}
+
+#else
+
+typedef float32x2_t Packet2f;
+typedef float32x4_t Packet4f;
+typedef eigen_packet_wrapper<int32_t, 2> Packet4c;
+typedef int8x8_t Packet8c;
+typedef int8x16_t Packet16c;
+typedef eigen_packet_wrapper<uint32_t, 5> Packet4uc;
+typedef uint8x8_t Packet8uc;
+typedef uint8x16_t Packet16uc;
+typedef int16x4_t Packet4s;
+typedef int16x8_t Packet8s;
+typedef uint16x4_t Packet4us;
+typedef uint16x8_t Packet8us;
+typedef int32x2_t Packet2i;
+typedef int32x4_t Packet4i;
+typedef uint32x2_t Packet2ui;
+typedef uint32x4_t Packet4ui;
+typedef int64x2_t Packet2l;
+typedef uint64x2_t Packet2ul;
+
+EIGEN_ALWAYS_INLINE Packet4f make_packet4f(float a, float b, float c, float d) { return Packet4f{a, b, c, d}; }
+EIGEN_ALWAYS_INLINE Packet2f make_packet2f(float a, float b) { return Packet2f{a, b}; }
+
+#endif  // EIGEN_COMP_MSVC_STRICT
+
+EIGEN_STRONG_INLINE Packet4f shuffle1(const Packet4f& m, int mask) {
+  const float* a = reinterpret_cast<const float*>(&m);
+  Packet4f res =
+      make_packet4f(*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3)), *(a + ((mask >> 6) & 3)));
+  return res;
+}
+
+// functionally equivalent to _mm_shuffle_ps in SSE when interleave
+// == false (i.e. shuffle<false>(m, n, mask) equals _mm_shuffle_ps(m, n, mask)),
+// interleave m and n when interleave == true. Currently used in LU/arch/InverseSize4.h
+// to enable a shared implementation for fast inversion of matrices of size 4.
+template <bool interleave>
+EIGEN_STRONG_INLINE Packet4f shuffle2(const Packet4f& m, const Packet4f& n, int mask) {
+  const float* a = reinterpret_cast<const float*>(&m);
+  const float* b = reinterpret_cast<const float*>(&n);
+  Packet4f res =
+      make_packet4f(*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(b + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3)));
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f shuffle2<true>(const Packet4f& m, const Packet4f& n, int mask) {
+  const float* a = reinterpret_cast<const float*>(&m);
+  const float* b = reinterpret_cast<const float*>(&n);
+  Packet4f res =
+      make_packet4f(*(a + (mask & 3)), *(b + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3)));
+  return res;
+}
+
+EIGEN_STRONG_INLINE static int eigen_neon_shuffle_mask(int p, int q, int r, int s) {
+  return ((s) << 6 | (r) << 4 | (q) << 2 | (p));
+}
+
+EIGEN_STRONG_INLINE Packet4f vec4f_swizzle1(const Packet4f& a, int p, int q, int r, int s) {
+  return shuffle1(a, eigen_neon_shuffle_mask(p, q, r, s));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_swizzle2(const Packet4f& a, const Packet4f& b, int p, int q, int r, int s) {
+  return shuffle2<false>(a, b, eigen_neon_shuffle_mask(p, q, r, s));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_movelh(const Packet4f& a, const Packet4f& b) {
+  return shuffle2<false>(a, b, eigen_neon_shuffle_mask(0, 1, 0, 1));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_movehl(const Packet4f& a, const Packet4f& b) {
+  return shuffle2<false>(b, a, eigen_neon_shuffle_mask(2, 3, 2, 3));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_unpacklo(const Packet4f& a, const Packet4f& b) {
+  return shuffle2<true>(a, b, eigen_neon_shuffle_mask(0, 0, 1, 1));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b) {
+  return shuffle2<true>(a, b, eigen_neon_shuffle_mask(2, 2, 3, 3));
+}
+#define vec4f_duplane(a, p) Packet4f(vdupq_lane_f32(vget_low_f32(a), p))
+
+#define EIGEN_DECLARE_CONST_Packet4f(NAME, X) const Packet4f p4f_##NAME = pset1<Packet4f>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME, X) \
+  const Packet4f p4f_##NAME = vreinterpretq_f32_u32(pset1<int32_t>(X))
+
+#define EIGEN_DECLARE_CONST_Packet4i(NAME, X) const Packet4i p4i_##NAME = pset1<Packet4i>(X)
+
+#if EIGEN_ARCH_ARM64 && EIGEN_COMP_GNUC
+// __builtin_prefetch tends to do nothing on ARM64 compilers because the
+// prefetch instructions there are too detailed for __builtin_prefetch to map
+// meaningfully to them.
+#define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__("prfm pldl1keep, [%[addr]]\n" ::[addr] "r"(ADDR) :);
+#elif EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
+#define EIGEN_ARM_PREFETCH(ADDR) __builtin_prefetch(ADDR);
+#elif defined __pld
+#define EIGEN_ARM_PREFETCH(ADDR) __pld(ADDR)
+#elif EIGEN_ARCH_ARM
+#define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__("pld [%[addr]]\n" ::[addr] "r"(ADDR) :);
+#else
+// by default no explicit prefetching
+#define EIGEN_ARM_PREFETCH(ADDR)
+#endif
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4f type;
+  typedef Packet2f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbsDiff = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+    HasDiv = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasPow = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasCbrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasErfc = EIGEN_FAST_MATH,
+    HasBessel = 0,  // Issues with accuracy.
+    HasNdtri = 0
+  };
+};
+
+template <>
+struct packet_traits<int8_t> : default_packet_traits {
+  typedef Packet16c type;
+  typedef Packet8c half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasAbsDiff = 1,
+    HasArg = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+  };
+};
+
+template <>
+struct packet_traits<uint8_t> : default_packet_traits {
+  typedef Packet16uc type;
+  typedef Packet8uc half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 0,
+    HasAbs = 1,
+    HasAbsDiff = 1,
+    HasArg = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+
+    HasSqrt = 1
+  };
+};
+
+template <>
+struct packet_traits<int16_t> : default_packet_traits {
+  typedef Packet8s type;
+  typedef Packet4s half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasAbsDiff = 1,
+    HasArg = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+  };
+};
+
+template <>
+struct packet_traits<uint16_t> : default_packet_traits {
+  typedef Packet8us type;
+  typedef Packet4us half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 0,
+    HasAbs = 1,
+    HasAbsDiff = 1,
+    HasArg = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+    HasSqrt = 1
+  };
+};
+
+template <>
+struct packet_traits<int32_t> : default_packet_traits {
+  typedef Packet4i type;
+  typedef Packet2i half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbsDiff = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+  };
+};
+
+template <>
+struct packet_traits<uint32_t> : default_packet_traits {
+  typedef Packet4ui type;
+  typedef Packet2ui half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 0,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbsDiff = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+
+    HasSqrt = 1
+  };
+};
+
+template <>
+struct packet_traits<int64_t> : default_packet_traits {
+  typedef Packet2l type;
+  typedef Packet2l half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbsDiff = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+  };
+};
+
+template <>
+struct packet_traits<uint64_t> : default_packet_traits {
+  typedef Packet2ul type;
+  typedef Packet2ul half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 0,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbsDiff = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+  };
+};
+
+template <typename Packet, typename Scalar>
+struct neon_unpacket_default {
+  using type = Scalar;
+  using half = Packet;
+  static constexpr int size = sizeof(Packet) / sizeof(Scalar);
+  static constexpr int alignment = sizeof(Packet);
+  static constexpr bool vectorizable = true;
+  static constexpr bool masked_load_available = false;
+  static constexpr bool masked_store_available = false;
+};
+
+template <>
+struct unpacket_traits<Packet2f> : neon_unpacket_default<Packet2f, float> {
+  using integer_packet = Packet2i;
+};
+template <>
+struct unpacket_traits<Packet4f> : neon_unpacket_default<Packet4f, float> {
+  using half = Packet2f;
+  using integer_packet = Packet4i;
+};
+template <>
+struct unpacket_traits<Packet4c> : neon_unpacket_default<Packet4c, int8_t> {};
+template <>
+struct unpacket_traits<Packet8c> : neon_unpacket_default<Packet8c, int8_t> {
+  using half = Packet4c;
+};
+template <>
+struct unpacket_traits<Packet16c> : neon_unpacket_default<Packet16c, int8_t> {
+  using half = Packet8c;
+};
+template <>
+struct unpacket_traits<Packet4uc> : neon_unpacket_default<Packet4uc, uint8_t> {};
+template <>
+struct unpacket_traits<Packet8uc> : neon_unpacket_default<Packet8uc, uint8_t> {
+  using half = Packet4uc;
+};
+template <>
+struct unpacket_traits<Packet16uc> : neon_unpacket_default<Packet16uc, uint8_t> {
+  using half = Packet8uc;
+};
+template <>
+struct unpacket_traits<Packet4s> : neon_unpacket_default<Packet4s, int16_t> {};
+template <>
+struct unpacket_traits<Packet8s> : neon_unpacket_default<Packet8s, int16_t> {
+  using half = Packet4s;
+};
+template <>
+struct unpacket_traits<Packet4us> : neon_unpacket_default<Packet4us, uint16_t> {};
+template <>
+struct unpacket_traits<Packet8us> : neon_unpacket_default<Packet8us, uint16_t> {
+  using half = Packet4us;
+};
+template <>
+struct unpacket_traits<Packet2i> : neon_unpacket_default<Packet2i, int32_t> {};
+template <>
+struct unpacket_traits<Packet4i> : neon_unpacket_default<Packet4i, int32_t> {
+  using half = Packet2i;
+};
+template <>
+struct unpacket_traits<Packet2ui> : neon_unpacket_default<Packet2ui, uint32_t> {};
+template <>
+struct unpacket_traits<Packet4ui> : neon_unpacket_default<Packet4ui, uint32_t> {
+  using half = Packet2ui;
+};
+template <>
+struct unpacket_traits<Packet2l> : neon_unpacket_default<Packet2l, int64_t> {};
+template <>
+struct unpacket_traits<Packet2ul> : neon_unpacket_default<Packet2ul, uint64_t> {};
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pzero(const Packet2f& /*a*/) {
+  return vdup_n_f32(0.0f);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pzero(const Packet4f& /*a*/) {
+  return vdupq_n_f32(0.0f);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pset1<Packet2f>(const float& from) {
+  return vdup_n_f32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
+  return vdupq_n_f32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pset1<Packet4c>(const int8_t& from) {
+  return vget_lane_s32(vreinterpret_s32_s8(vdup_n_s8(from)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pset1<Packet8c>(const int8_t& from) {
+  return vdup_n_s8(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pset1<Packet16c>(const int8_t& from) {
+  return vdupq_n_s8(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pset1<Packet4uc>(const uint8_t& from) {
+  return vget_lane_u32(vreinterpret_u32_u8(vdup_n_u8(from)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pset1<Packet8uc>(const uint8_t& from) {
+  return vdup_n_u8(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pset1<Packet16uc>(const uint8_t& from) {
+  return vdupq_n_u8(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pset1<Packet4s>(const int16_t& from) {
+  return vdup_n_s16(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pset1<Packet8s>(const int16_t& from) {
+  return vdupq_n_s16(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pset1<Packet4us>(const uint16_t& from) {
+  return vdup_n_u16(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pset1<Packet8us>(const uint16_t& from) {
+  return vdupq_n_u16(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pset1<Packet2i>(const int32_t& from) {
+  return vdup_n_s32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int32_t& from) {
+  return vdupq_n_s32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pset1<Packet2ui>(const uint32_t& from) {
+  return vdup_n_u32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pset1<Packet4ui>(const uint32_t& from) {
+  return vdupq_n_u32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pset1<Packet2l>(const int64_t& from) {
+  return vdupq_n_s64(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pset1<Packet2ul>(const uint64_t& from) {
+  return vdupq_n_u64(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pset1frombits<Packet2f>(uint32_t from) {
+  return vreinterpret_f32_u32(vdup_n_u32(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(uint32_t from) {
+  return vreinterpretq_f32_u32(vdupq_n_u32(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f plset<Packet2f>(const float& a) {
+  const float c[] = {0.0f, 1.0f};
+  return vadd_f32(pset1<Packet2f>(a), vld1_f32(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) {
+  const float c[] = {0.0f, 1.0f, 2.0f, 3.0f};
+  return vaddq_f32(pset1<Packet4f>(a), vld1q_f32(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c plset<Packet4c>(const int8_t& a) {
+  return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(vreinterpret_s8_u32(vdup_n_u32(0x03020100)), vdup_n_s8(a))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c plset<Packet8c>(const int8_t& a) {
+  const int8_t c[] = {0, 1, 2, 3, 4, 5, 6, 7};
+  return vadd_s8(pset1<Packet8c>(a), vld1_s8(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c plset<Packet16c>(const int8_t& a) {
+  const int8_t c[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+  return vaddq_s8(pset1<Packet16c>(a), vld1q_s8(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc plset<Packet4uc>(const uint8_t& a) {
+  return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(vreinterpret_u8_u32(vdup_n_u32(0x03020100)), vdup_n_u8(a))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc plset<Packet8uc>(const uint8_t& a) {
+  const uint8_t c[] = {0, 1, 2, 3, 4, 5, 6, 7};
+  return vadd_u8(pset1<Packet8uc>(a), vld1_u8(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc plset<Packet16uc>(const uint8_t& a) {
+  const uint8_t c[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+  return vaddq_u8(pset1<Packet16uc>(a), vld1q_u8(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s plset<Packet4s>(const int16_t& a) {
+  const int16_t c[] = {0, 1, 2, 3};
+  return vadd_s16(pset1<Packet4s>(a), vld1_s16(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us plset<Packet4us>(const uint16_t& a) {
+  const uint16_t c[] = {0, 1, 2, 3};
+  return vadd_u16(pset1<Packet4us>(a), vld1_u16(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s plset<Packet8s>(const int16_t& a) {
+  const int16_t c[] = {0, 1, 2, 3, 4, 5, 6, 7};
+  return vaddq_s16(pset1<Packet8s>(a), vld1q_s16(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us plset<Packet8us>(const uint16_t& a) {
+  const uint16_t c[] = {0, 1, 2, 3, 4, 5, 6, 7};
+  return vaddq_u16(pset1<Packet8us>(a), vld1q_u16(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i plset<Packet2i>(const int32_t& a) {
+  const int32_t c[] = {0, 1};
+  return vadd_s32(pset1<Packet2i>(a), vld1_s32(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int32_t& a) {
+  const int32_t c[] = {0, 1, 2, 3};
+  return vaddq_s32(pset1<Packet4i>(a), vld1q_s32(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui plset<Packet2ui>(const uint32_t& a) {
+  const uint32_t c[] = {0, 1};
+  return vadd_u32(pset1<Packet2ui>(a), vld1_u32(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui plset<Packet4ui>(const uint32_t& a) {
+  const uint32_t c[] = {0, 1, 2, 3};
+  return vaddq_u32(pset1<Packet4ui>(a), vld1q_u32(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l plset<Packet2l>(const int64_t& a) {
+  const int64_t c[] = {0, 1};
+  return vaddq_s64(pset1<Packet2l>(a), vld1q_s64(c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul plset<Packet2ul>(const uint64_t& a) {
+  const uint64_t c[] = {0, 1};
+  return vaddq_u64(pset1<Packet2ul>(a), vld1q_u64(c));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f padd<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vadd_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vaddq_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c padd<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return vget_lane_s32(
+      vreinterpret_s32_s8(vadd_s8(vreinterpret_s8_s32(vdup_n_s32(a)), vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c padd<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vadd_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c padd<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vaddq_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc padd<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return vget_lane_u32(
+      vreinterpret_u32_u8(vadd_u8(vreinterpret_u8_u32(vdup_n_u32(a)), vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc padd<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vadd_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc padd<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vaddq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s padd<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vadd_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s padd<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vaddq_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us padd<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vadd_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us padd<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vaddq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i padd<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vadd_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vaddq_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui padd<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vadd_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui padd<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vaddq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l padd<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vaddq_s64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul padd<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vaddq_u64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f psub<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vsub_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vsubq_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c psub<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return vget_lane_s32(
+      vreinterpret_s32_s8(vsub_s8(vreinterpret_s8_s32(vdup_n_s32(a)), vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c psub<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vsub_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c psub<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vsubq_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc psub<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return vget_lane_u32(
+      vreinterpret_u32_u8(vsub_u8(vreinterpret_u8_u32(vdup_n_u32(a)), vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc psub<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vsub_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc psub<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vsubq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s psub<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vsub_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s psub<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vsubq_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us psub<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vsub_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us psub<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vsubq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i psub<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vsub_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vsubq_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui psub<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vsub_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui psub<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vsubq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l psub<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vsubq_s64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul psub<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vsubq_u64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pxor<Packet2f>(const Packet2f& a, const Packet2f& b);
+template <>
+EIGEN_STRONG_INLINE Packet2f paddsub<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  Packet2f mask = make_packet2f(numext::bit_cast<float>(0x80000000u), 0.0f);
+  return padd(a, pxor(mask, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b);
+template <>
+EIGEN_STRONG_INLINE Packet4f paddsub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f mask = make_packet4f(numext::bit_cast<float>(0x80000000u), 0.0f, numext::bit_cast<float>(0x80000000u), 0.0f);
+  return padd(a, pxor(mask, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pnegate(const Packet2f& a) {
+  return vneg_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) {
+  return vnegq_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pnegate(const Packet4c& a) {
+  return vget_lane_s32(vreinterpret_s32_s8(vneg_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pnegate(const Packet8c& a) {
+  return vneg_s8(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pnegate(const Packet16c& a) {
+  return vnegq_s8(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pnegate(const Packet4s& a) {
+  return vneg_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pnegate(const Packet8s& a) {
+  return vnegq_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pnegate(const Packet2i& a) {
+  return vneg_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) {
+  return vnegq_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pnegate(const Packet2l& a) {
+#if EIGEN_ARCH_ARM64
+  return vnegq_s64(a);
+#else
+  return vcombine_s64(vdup_n_s64(-vgetq_lane_s64(a, 0)), vdup_n_s64(-vgetq_lane_s64(a, 1)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pconj(const Packet2f& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pconj(const Packet4c& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pconj(const Packet8c& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pconj(const Packet16c& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pconj(const Packet4uc& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pconj(const Packet8uc& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pconj(const Packet16uc& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pconj(const Packet4s& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pconj(const Packet8s& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pconj(const Packet4us& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pconj(const Packet8us& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pconj(const Packet2i& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pconj(const Packet2ui& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pconj(const Packet4ui& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pconj(const Packet2l& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pconj(const Packet2ul& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pmul<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vmul_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vmulq_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pmul<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return vget_lane_s32(
+      vreinterpret_s32_s8(vmul_s8(vreinterpret_s8_s32(vdup_n_s32(a)), vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pmul<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vmul_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pmul<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vmulq_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pmul<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return vget_lane_u32(
+      vreinterpret_u32_u8(vmul_u8(vreinterpret_u8_u32(vdup_n_u32(a)), vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pmul<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vmul_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmul<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vmulq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pmul<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vmul_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmul<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vmulq_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pmul<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vmul_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmul<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vmulq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pmul<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vmul_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vmulq_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pmul<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vmul_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmul<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vmulq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmul<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vcombine_s64(vdup_n_s64(vgetq_lane_s64(a, 0) * vgetq_lane_s64(b, 0)),
+                      vdup_n_s64(vgetq_lane_s64(a, 1) * vgetq_lane_s64(b, 1)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pmul<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vcombine_u64(vdup_n_u64(vgetq_lane_u64(a, 0) * vgetq_lane_u64(b, 0)),
+                      vdup_n_u64(vgetq_lane_u64(a, 1) * vgetq_lane_u64(b, 1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4c pdiv<Packet4c>(const Packet4c& /*a*/, const Packet4c& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4c>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pdiv<Packet8c>(const Packet8c& /*a*/, const Packet8c& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet8c>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pdiv<Packet16c>(const Packet16c& /*a*/, const Packet16c& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet16c>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pdiv<Packet4uc>(const Packet4uc& /*a*/, const Packet4uc& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4uc>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pdiv<Packet8uc>(const Packet8uc& /*a*/, const Packet8uc& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet8uc>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pdiv<Packet16uc>(const Packet16uc& /*a*/, const Packet16uc& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet16uc>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pdiv<Packet4s>(const Packet4s& /*a*/, const Packet4s& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4s>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pdiv<Packet8s>(const Packet8s& /*a*/, const Packet8s& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet8s>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pdiv<Packet4us>(const Packet4us& /*a*/, const Packet4us& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4us>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pdiv<Packet8us>(const Packet8us& /*a*/, const Packet8us& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet8us>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pdiv<Packet2i>(const Packet2i& /*a*/, const Packet2i& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet2i>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4i>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pdiv<Packet2ui>(const Packet2ui& /*a*/, const Packet2ui& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet2ui>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pdiv<Packet4ui>(const Packet4ui& /*a*/, const Packet4ui& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4ui>(0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pdiv<Packet2l>(const Packet2l& /*a*/, const Packet2l& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet2l>(0LL);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pdiv<Packet2ul>(const Packet2ul& /*a*/, const Packet2ul& /*b*/) {
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet2ul>(0ULL);
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+template <>
+EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return vfmaq_f32(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c) {
+  return vfma_f32(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return vfmsq_f32(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pnmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c) {
+  return vfms_f32(c, a, b);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return vmlaq_f32(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c) {
+  return vmla_f32(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return vmlsq_f32(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pnmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c) {
+  return vmls_f32(c, a, b);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet4f pmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return pnegate(pnmadd(a, b, c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pmsub(const Packet2f& a, const Packet2f& b, const Packet2f& c) {
+  return pnegate(pnmadd(a, b, c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return pnegate(pmadd(a, b, c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pnmsub(const Packet2f& a, const Packet2f& b, const Packet2f& c) {
+  return pnegate(pmadd(a, b, c));
+}
+
+// No FMA instruction for int, so use MLA unconditionally.
+template <>
+EIGEN_STRONG_INLINE Packet4c pmadd(const Packet4c& a, const Packet4c& b, const Packet4c& c) {
+  return vget_lane_s32(
+      vreinterpret_s32_s8(vmla_s8(vreinterpret_s8_s32(vdup_n_s32(c)), vreinterpret_s8_s32(vdup_n_s32(a)),
+                                  vreinterpret_s8_s32(vdup_n_s32(b)))),
+      0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pmadd(const Packet8c& a, const Packet8c& b, const Packet8c& c) {
+  return vmla_s8(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pmadd(const Packet16c& a, const Packet16c& b, const Packet16c& c) {
+  return vmlaq_s8(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pmadd(const Packet4uc& a, const Packet4uc& b, const Packet4uc& c) {
+  return vget_lane_u32(
+      vreinterpret_u32_u8(vmla_u8(vreinterpret_u8_u32(vdup_n_u32(c)), vreinterpret_u8_u32(vdup_n_u32(a)),
+                                  vreinterpret_u8_u32(vdup_n_u32(b)))),
+      0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pmadd(const Packet8uc& a, const Packet8uc& b, const Packet8uc& c) {
+  return vmla_u8(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmadd(const Packet16uc& a, const Packet16uc& b, const Packet16uc& c) {
+  return vmlaq_u8(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pmadd(const Packet4s& a, const Packet4s& b, const Packet4s& c) {
+  return vmla_s16(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmadd(const Packet8s& a, const Packet8s& b, const Packet8s& c) {
+  return vmlaq_s16(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pmadd(const Packet4us& a, const Packet4us& b, const Packet4us& c) {
+  return vmla_u16(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmadd(const Packet8us& a, const Packet8us& b, const Packet8us& c) {
+  return vmlaq_u16(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pmadd(const Packet2i& a, const Packet2i& b, const Packet2i& c) {
+  return vmla_s32(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
+  return vmlaq_s32(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pmadd(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c) {
+  return vmla_u32(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmadd(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c) {
+  return vmlaq_u32(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pabsdiff<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vabd_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pabsdiff<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vabdq_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pabsdiff<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return vget_lane_s32(
+      vreinterpret_s32_s8(vabd_s8(vreinterpret_s8_s32(vdup_n_s32(a)), vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pabsdiff<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vabd_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pabsdiff<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vabdq_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pabsdiff<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return vget_lane_u32(
+      vreinterpret_u32_u8(vabd_u8(vreinterpret_u8_u32(vdup_n_u32(a)), vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pabsdiff<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vabd_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pabsdiff<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vabdq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pabsdiff<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vabd_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pabsdiff<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vabdq_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pabsdiff<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vabd_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pabsdiff<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vabdq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pabsdiff<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vabd_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pabsdiff<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vabdq_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pabsdiff<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vabd_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pabsdiff<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vabdq_u32(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pmin<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vmin_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vminq_f32(a, b);
+}
+
+#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
+// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8
+// systems).
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vminnmq_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pmin<PropagateNumbers, Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vminnm_f32(a, b);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pmin<Packet4f>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pmin<PropagateNaN, Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return pmin<Packet2f>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4c pmin<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return vget_lane_s32(
+      vreinterpret_s32_s8(vmin_s8(vreinterpret_s8_s32(vdup_n_s32(a)), vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pmin<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vmin_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pmin<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vminq_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pmin<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return vget_lane_u32(
+      vreinterpret_u32_u8(vmin_u8(vreinterpret_u8_u32(vdup_n_u32(a)), vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pmin<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vmin_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmin<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vminq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pmin<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vmin_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmin<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vminq_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pmin<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vmin_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmin<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vminq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pmin<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vmin_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vminq_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pmin<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vmin_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmin<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vminq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmin<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vcombine_s64(vdup_n_s64((std::min)(vgetq_lane_s64(a, 0), vgetq_lane_s64(b, 0))),
+                      vdup_n_s64((std::min)(vgetq_lane_s64(a, 1), vgetq_lane_s64(b, 1))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pmin<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vcombine_u64(vdup_n_u64((std::min)(vgetq_lane_u64(a, 0), vgetq_lane_u64(b, 0))),
+                      vdup_n_u64((std::min)(vgetq_lane_u64(a, 1), vgetq_lane_u64(b, 1))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pmax<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vmax_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vmaxq_f32(a, b);
+}
+
+#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
+// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8
+// systems).
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vmaxnmq_f32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pmax<PropagateNumbers, Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vmaxnm_f32(a, b);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pmax<Packet4f>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pmax<PropagateNaN, Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return pmax<Packet2f>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4c pmax<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return vget_lane_s32(
+      vreinterpret_s32_s8(vmax_s8(vreinterpret_s8_s32(vdup_n_s32(a)), vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pmax<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vmax_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pmax<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vmaxq_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pmax<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return vget_lane_u32(
+      vreinterpret_u32_u8(vmax_u8(vreinterpret_u8_u32(vdup_n_u32(a)), vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pmax<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vmax_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pmax<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vmaxq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pmax<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vmax_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pmax<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vmaxq_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pmax<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vmax_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pmax<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vmaxq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pmax<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vmax_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vmaxq_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pmax<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vmax_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmax<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vmaxq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmax<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vcombine_s64(vdup_n_s64((std::max)(vgetq_lane_s64(a, 0), vgetq_lane_s64(b, 0))),
+                      vdup_n_s64((std::max)(vgetq_lane_s64(a, 1), vgetq_lane_s64(b, 1))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pmax<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vcombine_u64(vdup_n_u64((std::max)(vgetq_lane_u64(a, 0), vgetq_lane_u64(b, 0))),
+                      vdup_n_u64((std::max)(vgetq_lane_u64(a, 1), vgetq_lane_u64(b, 1))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pcmp_le<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vreinterpret_f32_u32(vcle_f32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_le<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vreinterpretq_f32_u32(vcleq_f32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcmp_le<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return vget_lane_s32(
+      vreinterpret_s32_u8(vcle_s8(vreinterpret_s8_s32(vdup_n_s32(a)), vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcmp_le<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vreinterpret_s8_u8(vcle_s8(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcmp_le<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vreinterpretq_s8_u8(vcleq_s8(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcmp_le<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return vget_lane_u32(
+      vreinterpret_u32_u8(vcle_u8(vreinterpret_u8_u32(vdup_n_u32(a)), vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcmp_le<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vcle_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcmp_le<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vcleq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcmp_le<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vreinterpret_s16_u16(vcle_s16(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcmp_le<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vreinterpretq_s16_u16(vcleq_s16(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcmp_le<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vcle_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcmp_le<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vcleq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcmp_le<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vreinterpret_s32_u32(vcle_s32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_le<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vreinterpretq_s32_u32(vcleq_s32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcmp_le<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vcle_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcmp_le<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vcleq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcmp_le<Packet2l>(const Packet2l& a, const Packet2l& b) {
+#if EIGEN_ARCH_ARM64
+  return vreinterpretq_s64_u64(vcleq_s64(a, b));
+#else
+  return vcombine_s64(vdup_n_s64(vgetq_lane_s64(a, 0) <= vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
+                      vdup_n_s64(vgetq_lane_s64(a, 1) <= vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcmp_le<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+#if EIGEN_ARCH_ARM64
+  return vcleq_u64(a, b);
+#else
+  return vcombine_u64(vdup_n_u64(vgetq_lane_u64(a, 0) <= vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
+                      vdup_n_u64(vgetq_lane_u64(a, 1) <= vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pcmp_lt<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vreinterpret_f32_u32(vclt_f32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_lt<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vreinterpretq_f32_u32(vcltq_f32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcmp_lt<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return vget_lane_s32(
+      vreinterpret_s32_u8(vclt_s8(vreinterpret_s8_s32(vdup_n_s32(a)), vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcmp_lt<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vreinterpret_s8_u8(vclt_s8(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcmp_lt<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vreinterpretq_s8_u8(vcltq_s8(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcmp_lt<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return vget_lane_u32(
+      vreinterpret_u32_u8(vclt_u8(vreinterpret_u8_u32(vdup_n_u32(a)), vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcmp_lt<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vclt_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcmp_lt<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vcltq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcmp_lt<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vreinterpret_s16_u16(vclt_s16(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcmp_lt<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vreinterpretq_s16_u16(vcltq_s16(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcmp_lt<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vclt_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcmp_lt<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vcltq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcmp_lt<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vreinterpret_s32_u32(vclt_s32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_lt<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vreinterpretq_s32_u32(vcltq_s32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcmp_lt<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vclt_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcmp_lt<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vcltq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcmp_lt<Packet2l>(const Packet2l& a, const Packet2l& b) {
+#if EIGEN_ARCH_ARM64
+  return vreinterpretq_s64_u64(vcltq_s64(a, b));
+#else
+  return vcombine_s64(vdup_n_s64(vgetq_lane_s64(a, 0) < vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
+                      vdup_n_s64(vgetq_lane_s64(a, 1) < vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcmp_lt<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+#if EIGEN_ARCH_ARM64
+  return vcltq_u64(a, b);
+#else
+  return vcombine_u64(vdup_n_u64(vgetq_lane_u64(a, 0) < vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
+                      vdup_n_u64(vgetq_lane_u64(a, 1) < vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pcmp_eq<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vreinterpret_f32_u32(vceq_f32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_eq<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vreinterpretq_f32_u32(vceqq_f32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcmp_eq<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return vget_lane_s32(
+      vreinterpret_s32_u8(vceq_s8(vreinterpret_s8_s32(vdup_n_s32(a)), vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcmp_eq<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vreinterpret_s8_u8(vceq_s8(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pcmp_eq<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vreinterpretq_s8_u8(vceqq_s8(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcmp_eq<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return vget_lane_u32(
+      vreinterpret_u32_u8(vceq_u8(vreinterpret_u8_u32(vdup_n_u32(a)), vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcmp_eq<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vceq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcmp_eq<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vceqq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcmp_eq<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vreinterpret_s16_u16(vceq_s16(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcmp_eq<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vreinterpretq_s16_u16(vceqq_s16(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcmp_eq<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vceq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcmp_eq<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vceqq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcmp_eq<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vreinterpret_s32_u32(vceq_s32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_eq<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vreinterpretq_s32_u32(vceqq_s32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcmp_eq<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vceq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcmp_eq<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vceqq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcmp_eq<Packet2l>(const Packet2l& a, const Packet2l& b) {
+#if EIGEN_ARCH_ARM64
+  return vreinterpretq_s64_u64(vceqq_s64(a, b));
+#else
+  return vcombine_s64(vdup_n_s64(vgetq_lane_s64(a, 0) == vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
+                      vdup_n_s64(vgetq_lane_s64(a, 1) == vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcmp_eq<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+#if EIGEN_ARCH_ARM64
+  return vceqq_u64(a, b);
+#else
+  return vcombine_u64(vdup_n_u64(vgetq_lane_u64(a, 0) == vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
+                      vdup_n_u64(vgetq_lane_u64(a, 1) == vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pcmp_lt_or_nan<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vreinterpret_f32_u32(vmvn_u32(vcge_f32(a, b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vreinterpretq_f32_u32(vmvnq_u32(vcgeq_f32(a, b)));
+}
+
+// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
+template <>
+EIGEN_STRONG_INLINE Packet2f pand<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vreinterpret_f32_u32(vand_u32(vreinterpret_u32_f32(a), vreinterpret_u32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a), vreinterpretq_u32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pand<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return a & b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pand<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vand_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pand<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vandq_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pand<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return a & b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pand<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vand_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pand<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vandq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pand<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vand_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pand<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vandq_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pand<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vand_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pand<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vandq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pand<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vand_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vandq_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pand<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vand_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vandq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pand<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vandq_s64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pand<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vandq_u64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f por<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vreinterpret_f32_u32(vorr_u32(vreinterpret_u32_f32(a), vreinterpret_u32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a), vreinterpretq_u32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c por<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return a | b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c por<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vorr_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c por<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vorrq_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc por<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return a | b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc por<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vorr_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc por<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vorrq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s por<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vorr_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s por<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vorrq_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us por<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vorr_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us por<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vorrq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i por<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vorr_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vorrq_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui por<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vorr_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui por<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vorrq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l por<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vorrq_s64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul por<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vorrq_u64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pxor<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(a), vreinterpret_u32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(a), vreinterpretq_u32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pxor<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return a ^ b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pxor<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return veor_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pxor<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return veorq_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pxor<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return a ^ b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pxor<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return veor_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pxor<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return veorq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pxor<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return veor_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pxor<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return veorq_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pxor<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return veor_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pxor<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return veorq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pxor<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return veor_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return veorq_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pxor<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return veor_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pxor<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return veorq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pxor<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return veorq_s64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pxor<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return veorq_u64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pandnot<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return vreinterpret_f32_u32(vbic_u32(vreinterpret_u32_f32(a), vreinterpret_u32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(a), vreinterpretq_u32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pandnot<Packet4c>(const Packet4c& a, const Packet4c& b) {
+  return a & ~b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pandnot<Packet8c>(const Packet8c& a, const Packet8c& b) {
+  return vbic_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pandnot<Packet16c>(const Packet16c& a, const Packet16c& b) {
+  return vbicq_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pandnot<Packet4uc>(const Packet4uc& a, const Packet4uc& b) {
+  return a & ~b;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pandnot<Packet8uc>(const Packet8uc& a, const Packet8uc& b) {
+  return vbic_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pandnot<Packet16uc>(const Packet16uc& a, const Packet16uc& b) {
+  return vbicq_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pandnot<Packet4s>(const Packet4s& a, const Packet4s& b) {
+  return vbic_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pandnot<Packet8s>(const Packet8s& a, const Packet8s& b) {
+  return vbicq_s16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pandnot<Packet4us>(const Packet4us& a, const Packet4us& b) {
+  return vbic_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pandnot<Packet8us>(const Packet8us& a, const Packet8us& b) {
+  return vbicq_u16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pandnot<Packet2i>(const Packet2i& a, const Packet2i& b) {
+  return vbic_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vbicq_s32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pandnot<Packet2ui>(const Packet2ui& a, const Packet2ui& b) {
+  return vbic_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pandnot<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return vbicq_u32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pandnot<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vbicq_s64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pandnot<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vbicq_u64(a, b);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4c parithmetic_shift_right(Packet4c& a) {
+  return vget_lane_s32(vreinterpret_s32_s8(vshr_n_s8(vreinterpret_s8_s32(vdup_n_s32(a)), N)), 0);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8c parithmetic_shift_right(Packet8c a) {
+  return vshr_n_s8(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet16c parithmetic_shift_right(Packet16c a) {
+  return vshrq_n_s8(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4uc parithmetic_shift_right(Packet4uc& a) {
+  return vget_lane_u32(vreinterpret_u32_u8(vshr_n_u8(vreinterpret_u8_u32(vdup_n_u32(a)), N)), 0);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8uc parithmetic_shift_right(Packet8uc a) {
+  return vshr_n_u8(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet16uc parithmetic_shift_right(Packet16uc a) {
+  return vshrq_n_u8(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4s parithmetic_shift_right(Packet4s a) {
+  return vshr_n_s16(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8s parithmetic_shift_right(Packet8s a) {
+  return vshrq_n_s16(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4us parithmetic_shift_right(Packet4us a) {
+  return vshr_n_u16(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8us parithmetic_shift_right(Packet8us a) {
+  return vshrq_n_u16(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2i parithmetic_shift_right(Packet2i a) {
+  return vshr_n_s32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(Packet4i a) {
+  return vshrq_n_s32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2ui parithmetic_shift_right(Packet2ui a) {
+  return vshr_n_u32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui parithmetic_shift_right(Packet4ui a) {
+  return vshrq_n_u32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2l parithmetic_shift_right(Packet2l a) {
+  return vshrq_n_s64(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2ul parithmetic_shift_right(Packet2ul a) {
+  return vshrq_n_u64(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4c plogical_shift_right(Packet4c& a) {
+  return vget_lane_s32(vreinterpret_s32_u8(vshr_n_u8(vreinterpret_u8_s32(vdup_n_s32(a)), N)), 0);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8c plogical_shift_right(Packet8c a) {
+  return vreinterpret_s8_u8(vshr_n_u8(vreinterpret_u8_s8(a), N));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet16c plogical_shift_right(Packet16c a) {
+  return vreinterpretq_s8_u8(vshrq_n_u8(vreinterpretq_u8_s8(a), N));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4uc plogical_shift_right(Packet4uc& a) {
+  return vget_lane_u32(vreinterpret_u32_s8(vshr_n_s8(vreinterpret_s8_u32(vdup_n_u32(a)), N)), 0);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8uc plogical_shift_right(Packet8uc a) {
+  return vshr_n_u8(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet16uc plogical_shift_right(Packet16uc a) {
+  return vshrq_n_u8(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4s plogical_shift_right(Packet4s a) {
+  return vreinterpret_s16_u16(vshr_n_u16(vreinterpret_u16_s16(a), N));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8s plogical_shift_right(Packet8s a) {
+  return vreinterpretq_s16_u16(vshrq_n_u16(vreinterpretq_u16_s16(a), N));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4us plogical_shift_right(Packet4us a) {
+  return vshr_n_u16(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8us plogical_shift_right(Packet8us a) {
+  return vshrq_n_u16(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2i plogical_shift_right(Packet2i a) {
+  return vreinterpret_s32_u32(vshr_n_u32(vreinterpret_u32_s32(a), N));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i plogical_shift_right(Packet4i a) {
+  return vreinterpretq_s32_u32(vshrq_n_u32(vreinterpretq_u32_s32(a), N));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2ui plogical_shift_right(Packet2ui a) {
+  return vshr_n_u32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui plogical_shift_right(Packet4ui a) {
+  return vshrq_n_u32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_right(Packet2l a) {
+  return vreinterpretq_s64_u64(vshrq_n_u64(vreinterpretq_u64_s64(a), N));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2ul plogical_shift_right(Packet2ul a) {
+  return vshrq_n_u64(a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4c plogical_shift_left(Packet4c& a) {
+  return vget_lane_s32(vreinterpret_s32_s8(vshl_n_s8(vreinterpret_s8_s32(vdup_n_s32(a)), N)), 0);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8c plogical_shift_left(Packet8c a) {
+  return vshl_n_s8(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet16c plogical_shift_left(Packet16c a) {
+  return vshlq_n_s8(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4uc plogical_shift_left(Packet4uc& a) {
+  return vget_lane_u32(vreinterpret_u32_u8(vshl_n_u8(vreinterpret_u8_u32(vdup_n_u32(a)), N)), 0);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8uc plogical_shift_left(Packet8uc a) {
+  return vshl_n_u8(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet16uc plogical_shift_left(Packet16uc a) {
+  return vshlq_n_u8(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4s plogical_shift_left(Packet4s a) {
+  return vshl_n_s16(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8s plogical_shift_left(Packet8s a) {
+  return vshlq_n_s16(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4us plogical_shift_left(Packet4us a) {
+  return vshl_n_u16(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet8us plogical_shift_left(Packet8us a) {
+  return vshlq_n_u16(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2i plogical_shift_left(Packet2i a) {
+  return vshl_n_s32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i plogical_shift_left(Packet4i a) {
+  return vshlq_n_s32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2ui plogical_shift_left(Packet2ui a) {
+  return vshl_n_u32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui plogical_shift_left(Packet4ui a) {
+  return vshlq_n_u32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_left(Packet2l a) {
+  return vshlq_n_s64(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2ul plogical_shift_left(Packet2ul a) {
+  return vshlq_n_u64(a, N);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pload<Packet2f>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1_f32(assume_aligned<unpacket_traits<Packet2f>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f32(assume_aligned<unpacket_traits<Packet4f>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pload<Packet4c>(const int8_t* from) {
+  Packet4c res;
+  memcpy(&res, from, sizeof(Packet4c));
+  return res;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pload<Packet8c>(const int8_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1_s8(assume_aligned<unpacket_traits<Packet8c>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pload<Packet16c>(const int8_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s8(assume_aligned<unpacket_traits<Packet16c>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pload<Packet4uc>(const uint8_t* from) {
+  Packet4uc res;
+  memcpy(&res, from, sizeof(Packet4uc));
+  return res;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pload<Packet8uc>(const uint8_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1_u8(assume_aligned<unpacket_traits<Packet8uc>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pload<Packet16uc>(const uint8_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u8(assume_aligned<unpacket_traits<Packet16uc>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pload<Packet4s>(const int16_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1_s16(assume_aligned<unpacket_traits<Packet4s>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pload<Packet8s>(const int16_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s16(assume_aligned<unpacket_traits<Packet8s>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pload<Packet4us>(const uint16_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1_u16(assume_aligned<unpacket_traits<Packet4us>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pload<Packet8us>(const uint16_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u16(assume_aligned<unpacket_traits<Packet8us>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pload<Packet2i>(const int32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1_s32(assume_aligned<unpacket_traits<Packet2i>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s32(assume_aligned<unpacket_traits<Packet4i>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pload<Packet2ui>(const uint32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1_u32(assume_aligned<unpacket_traits<Packet2ui>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pload<Packet4ui>(const uint32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u32(assume_aligned<unpacket_traits<Packet4ui>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pload<Packet2l>(const int64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s64(assume_aligned<unpacket_traits<Packet2l>::alignment>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pload<Packet2ul>(const uint64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u64(assume_aligned<unpacket_traits<Packet2ul>::alignment>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f ploadu<Packet2f>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_f32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c ploadu<Packet4c>(const int8_t* from) {
+  Packet4c res;
+  memcpy(&res, from, sizeof(Packet4c));
+  return res;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c ploadu<Packet8c>(const int8_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s8(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c ploadu<Packet16c>(const int8_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s8(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc ploadu<Packet4uc>(const uint8_t* from) {
+  Packet4uc res;
+  memcpy(&res, from, sizeof(Packet4uc));
+  return res;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc ploadu<Packet8uc>(const uint8_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u8(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc ploadu<Packet16uc>(const uint8_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u8(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s ploadu<Packet4s>(const int16_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s16(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s ploadu<Packet8s>(const int16_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s16(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us ploadu<Packet4us>(const uint16_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u16(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us ploadu<Packet8us>(const uint16_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u16(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i ploadu<Packet2i>(const int32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui ploadu<Packet2ui>(const uint32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui ploadu<Packet4ui>(const uint32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l ploadu<Packet2l>(const int64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s64(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul ploadu<Packet2ul>(const uint64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u64(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f ploaddup<Packet2f>(const float* from) {
+  return vld1_dup_f32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) {
+  return vcombine_f32(vld1_dup_f32(from), vld1_dup_f32(from + 1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c ploaddup<Packet4c>(const int8_t* from) {
+  const int8x8_t a = vreinterpret_s8_s32(vdup_n_s32(pload<Packet4c>(from)));
+  return vget_lane_s32(vreinterpret_s32_s8(vzip_s8(a, a).val[0]), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c ploaddup<Packet8c>(const int8_t* from) {
+  const int8x8_t a = vld1_s8(from);
+  return vzip_s8(a, a).val[0];
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c ploaddup<Packet16c>(const int8_t* from) {
+  const int8x8_t a = vld1_s8(from);
+  const int8x8x2_t b = vzip_s8(a, a);
+  return vcombine_s8(b.val[0], b.val[1]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc ploaddup<Packet4uc>(const uint8_t* from) {
+  const uint8x8_t a = vreinterpret_u8_u32(vdup_n_u32(pload<Packet4uc>(from)));
+  return vget_lane_u32(vreinterpret_u32_u8(vzip_u8(a, a).val[0]), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc ploaddup<Packet8uc>(const uint8_t* from) {
+  const uint8x8_t a = vld1_u8(from);
+  return vzip_u8(a, a).val[0];
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc ploaddup<Packet16uc>(const uint8_t* from) {
+  const uint8x8_t a = vld1_u8(from);
+  const uint8x8x2_t b = vzip_u8(a, a);
+  return vcombine_u8(b.val[0], b.val[1]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s ploaddup<Packet4s>(const int16_t* from) {
+  return vreinterpret_s16_u32(
+      vzip_u32(vreinterpret_u32_s16(vld1_dup_s16(from)), vreinterpret_u32_s16(vld1_dup_s16(from + 1))).val[0]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s ploaddup<Packet8s>(const int16_t* from) {
+  const int16x4_t a = vld1_s16(from);
+  const int16x4x2_t b = vzip_s16(a, a);
+  return vcombine_s16(b.val[0], b.val[1]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us ploaddup<Packet4us>(const uint16_t* from) {
+  return vreinterpret_u16_u32(
+      vzip_u32(vreinterpret_u32_u16(vld1_dup_u16(from)), vreinterpret_u32_u16(vld1_dup_u16(from + 1))).val[0]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us ploaddup<Packet8us>(const uint16_t* from) {
+  const uint16x4_t a = vld1_u16(from);
+  const uint16x4x2_t b = vzip_u16(a, a);
+  return vcombine_u16(b.val[0], b.val[1]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i ploaddup<Packet2i>(const int32_t* from) {
+  return vld1_dup_s32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int32_t* from) {
+  return vcombine_s32(vld1_dup_s32(from), vld1_dup_s32(from + 1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui ploaddup<Packet2ui>(const uint32_t* from) {
+  return vld1_dup_u32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui ploaddup<Packet4ui>(const uint32_t* from) {
+  return vcombine_u32(vld1_dup_u32(from), vld1_dup_u32(from + 1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l ploaddup<Packet2l>(const int64_t* from) {
+  return vld1q_dup_s64(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul ploaddup<Packet2ul>(const uint64_t* from) {
+  return vld1q_dup_u64(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadquad<Packet4f>(const float* from) {
+  return vld1q_dup_f32(from);
+}
+
+// WORKAROUND: Apple Clang 17.0.0 (and Homebrew Clang 21.1.8) at -O0 optimization
+// generate incorrect code for vld1_dup_[su]8, ignoring the pointer offset.
+// We use vdup_n_s8(*from) to force a safe scalar load before broadcast.
+EIGEN_ALWAYS_INLINE int8x8_t eigen_vld1_dup_s8(const int8_t* ptr) {
+#if EIGEN_COMP_CLANGAPPLE && EIGEN_ARCH_ARM64
+  return vdup_n_s8(*ptr);
+#else
+  return vld1_dup_s8(ptr);
+#endif
+}
+
+EIGEN_ALWAYS_INLINE uint8x8_t eigen_vld1_dup_u8(const uint8_t* ptr) {
+#if EIGEN_COMP_CLANGAPPLE && EIGEN_ARCH_ARM64
+  return vdup_n_u8(*ptr);
+#else
+  return vld1_dup_u8(ptr);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4c ploadquad<Packet4c>(const int8_t* from) {
+  return vget_lane_s32(vreinterpret_s32_s8(eigen_vld1_dup_s8(from)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c ploadquad<Packet8c>(const int8_t* from) {
+  return vreinterpret_s8_u32(
+      vzip_u32(vreinterpret_u32_s8(eigen_vld1_dup_s8(from)), vreinterpret_u32_s8(eigen_vld1_dup_s8(from + 1))).val[0]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c ploadquad<Packet16c>(const int8_t* from) {
+  const int8x8_t a = vreinterpret_s8_u32(
+      vzip_u32(vreinterpret_u32_s8(eigen_vld1_dup_s8(from)), vreinterpret_u32_s8(eigen_vld1_dup_s8(from + 1))).val[0]);
+  const int8x8_t b = vreinterpret_s8_u32(
+      vzip_u32(vreinterpret_u32_s8(eigen_vld1_dup_s8(from + 2)), vreinterpret_u32_s8(eigen_vld1_dup_s8(from + 3)))
+          .val[0]);
+  return vcombine_s8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc ploadquad<Packet4uc>(const uint8_t* from) {
+  return vget_lane_u32(vreinterpret_u32_u8(eigen_vld1_dup_u8(from)), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc ploadquad<Packet8uc>(const uint8_t* from) {
+  return vreinterpret_u8_u32(
+      vzip_u32(vreinterpret_u32_u8(eigen_vld1_dup_u8(from)), vreinterpret_u32_u8(eigen_vld1_dup_u8(from + 1))).val[0]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc ploadquad<Packet16uc>(const uint8_t* from) {
+  const uint8x8_t a = vreinterpret_u8_u32(
+      vzip_u32(vreinterpret_u32_u8(eigen_vld1_dup_u8(from)), vreinterpret_u32_u8(eigen_vld1_dup_u8(from + 1))).val[0]);
+  const uint8x8_t b = vreinterpret_u8_u32(
+      vzip_u32(vreinterpret_u32_u8(eigen_vld1_dup_u8(from + 2)), vreinterpret_u32_u8(eigen_vld1_dup_u8(from + 3)))
+          .val[0]);
+  return vcombine_u8(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s ploadquad<Packet8s>(const int16_t* from) {
+  return vcombine_s16(vld1_dup_s16(from), vld1_dup_s16(from + 1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us ploadquad<Packet8us>(const uint16_t* from) {
+  return vcombine_u16(vld1_dup_u16(from), vld1_dup_u16(from + 1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ploadquad<Packet4i>(const int32_t* from) {
+  return vld1q_dup_s32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui ploadquad<Packet4ui>(const uint32_t* from) {
+  return vld1q_dup_u32(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet2f& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1_f32(assume_aligned<unpacket_traits<Packet2f>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_f32(assume_aligned<unpacket_traits<Packet4f>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet4c& from) {
+  memcpy(to, &from, sizeof(from));
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet8c& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1_s8(assume_aligned<unpacket_traits<Packet8c>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet16c& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_s8(assume_aligned<unpacket_traits<Packet16c>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet4uc& from) {
+  memcpy(to, &from, sizeof(from));
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet8uc& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1_u8(assume_aligned<unpacket_traits<Packet8uc>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet16uc& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_u8(assume_aligned<unpacket_traits<Packet16uc>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int16_t>(int16_t* to, const Packet4s& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1_s16(assume_aligned<unpacket_traits<Packet4s>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int16_t>(int16_t* to, const Packet8s& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_s16(assume_aligned<unpacket_traits<Packet8s>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint16_t>(uint16_t* to, const Packet4us& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1_u16(assume_aligned<unpacket_traits<Packet4us>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint16_t>(uint16_t* to, const Packet8us& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_u16(assume_aligned<unpacket_traits<Packet8us>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet2i& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1_s32(assume_aligned<unpacket_traits<Packet2i>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet4i& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_s32(assume_aligned<unpacket_traits<Packet4i>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet2ui& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1_u32(assume_aligned<unpacket_traits<Packet2ui>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet4ui& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_u32(assume_aligned<unpacket_traits<Packet4ui>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int64_t>(int64_t* to, const Packet2l& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_s64(assume_aligned<unpacket_traits<Packet2l>::alignment>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint64_t>(uint64_t* to, const Packet2ul& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_u64(assume_aligned<unpacket_traits<Packet2ul>::alignment>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet2f& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_f32(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_f32(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet4c& from) {
+  memcpy(to, &from, sizeof(from));
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet8c& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_s8(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet16c& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_s8(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet4uc& from) {
+  memcpy(to, &from, sizeof(from));
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet8uc& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_u8(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet16uc& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_u8(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int16_t>(int16_t* to, const Packet4s& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_s16(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int16_t>(int16_t* to, const Packet8s& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_s16(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint16_t>(uint16_t* to, const Packet4us& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_u16(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint16_t>(uint16_t* to, const Packet8us& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_u16(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet2i& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_s32(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet4i& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_s32(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet2ui& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_u32(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet4ui& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_u32(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int64_t>(int64_t* to, const Packet2l& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_s64(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint64_t>(uint64_t* to, const Packet2ul& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_u64(to, from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2f pgather<float, Packet2f>(const float* from, Index stride) {
+  Packet2f res = vld1_dup_f32(from);
+  res = vld1_lane_f32(from + 1 * stride, res, 1);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4f pgather<float, Packet4f>(const float* from, Index stride) {
+  Packet4f res = vld1q_dup_f32(from);
+  res = vld1q_lane_f32(from + 1 * stride, res, 1);
+  res = vld1q_lane_f32(from + 2 * stride, res, 2);
+  res = vld1q_lane_f32(from + 3 * stride, res, 3);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4c pgather<int8_t, Packet4c>(const int8_t* from, Index stride) {
+  Packet4c res;
+  for (int i = 0; i != 4; i++) reinterpret_cast<int8_t*>(&res)[i] = *(from + i * stride);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c pgather<int8_t, Packet8c>(const int8_t* from, Index stride) {
+  Packet8c res = vld1_dup_s8(from);
+  res = vld1_lane_s8(from + 1 * stride, res, 1);
+  res = vld1_lane_s8(from + 2 * stride, res, 2);
+  res = vld1_lane_s8(from + 3 * stride, res, 3);
+  res = vld1_lane_s8(from + 4 * stride, res, 4);
+  res = vld1_lane_s8(from + 5 * stride, res, 5);
+  res = vld1_lane_s8(from + 6 * stride, res, 6);
+  res = vld1_lane_s8(from + 7 * stride, res, 7);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16c pgather<int8_t, Packet16c>(const int8_t* from, Index stride) {
+  Packet16c res = vld1q_dup_s8(from);
+  res = vld1q_lane_s8(from + 1 * stride, res, 1);
+  res = vld1q_lane_s8(from + 2 * stride, res, 2);
+  res = vld1q_lane_s8(from + 3 * stride, res, 3);
+  res = vld1q_lane_s8(from + 4 * stride, res, 4);
+  res = vld1q_lane_s8(from + 5 * stride, res, 5);
+  res = vld1q_lane_s8(from + 6 * stride, res, 6);
+  res = vld1q_lane_s8(from + 7 * stride, res, 7);
+  res = vld1q_lane_s8(from + 8 * stride, res, 8);
+  res = vld1q_lane_s8(from + 9 * stride, res, 9);
+  res = vld1q_lane_s8(from + 10 * stride, res, 10);
+  res = vld1q_lane_s8(from + 11 * stride, res, 11);
+  res = vld1q_lane_s8(from + 12 * stride, res, 12);
+  res = vld1q_lane_s8(from + 13 * stride, res, 13);
+  res = vld1q_lane_s8(from + 14 * stride, res, 14);
+  res = vld1q_lane_s8(from + 15 * stride, res, 15);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4uc pgather<uint8_t, Packet4uc>(const uint8_t* from, Index stride) {
+  Packet4uc res;
+  for (int i = 0; i != 4; i++) reinterpret_cast<uint8_t*>(&res)[i] = *(from + i * stride);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc pgather<uint8_t, Packet8uc>(const uint8_t* from, Index stride) {
+  Packet8uc res = vld1_dup_u8(from);
+  res = vld1_lane_u8(from + 1 * stride, res, 1);
+  res = vld1_lane_u8(from + 2 * stride, res, 2);
+  res = vld1_lane_u8(from + 3 * stride, res, 3);
+  res = vld1_lane_u8(from + 4 * stride, res, 4);
+  res = vld1_lane_u8(from + 5 * stride, res, 5);
+  res = vld1_lane_u8(from + 6 * stride, res, 6);
+  res = vld1_lane_u8(from + 7 * stride, res, 7);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16uc pgather<uint8_t, Packet16uc>(const uint8_t* from, Index stride) {
+  Packet16uc res = vld1q_dup_u8(from);
+  res = vld1q_lane_u8(from + 1 * stride, res, 1);
+  res = vld1q_lane_u8(from + 2 * stride, res, 2);
+  res = vld1q_lane_u8(from + 3 * stride, res, 3);
+  res = vld1q_lane_u8(from + 4 * stride, res, 4);
+  res = vld1q_lane_u8(from + 5 * stride, res, 5);
+  res = vld1q_lane_u8(from + 6 * stride, res, 6);
+  res = vld1q_lane_u8(from + 7 * stride, res, 7);
+  res = vld1q_lane_u8(from + 8 * stride, res, 8);
+  res = vld1q_lane_u8(from + 9 * stride, res, 9);
+  res = vld1q_lane_u8(from + 10 * stride, res, 10);
+  res = vld1q_lane_u8(from + 11 * stride, res, 11);
+  res = vld1q_lane_u8(from + 12 * stride, res, 12);
+  res = vld1q_lane_u8(from + 13 * stride, res, 13);
+  res = vld1q_lane_u8(from + 14 * stride, res, 14);
+  res = vld1q_lane_u8(from + 15 * stride, res, 15);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s pgather<int16_t, Packet4s>(const int16_t* from, Index stride) {
+  Packet4s res = vld1_dup_s16(from);
+  res = vld1_lane_s16(from + 1 * stride, res, 1);
+  res = vld1_lane_s16(from + 2 * stride, res, 2);
+  res = vld1_lane_s16(from + 3 * stride, res, 3);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8s pgather<int16_t, Packet8s>(const int16_t* from, Index stride) {
+  Packet8s res = vld1q_dup_s16(from);
+  res = vld1q_lane_s16(from + 1 * stride, res, 1);
+  res = vld1q_lane_s16(from + 2 * stride, res, 2);
+  res = vld1q_lane_s16(from + 3 * stride, res, 3);
+  res = vld1q_lane_s16(from + 4 * stride, res, 4);
+  res = vld1q_lane_s16(from + 5 * stride, res, 5);
+  res = vld1q_lane_s16(from + 6 * stride, res, 6);
+  res = vld1q_lane_s16(from + 7 * stride, res, 7);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us pgather<uint16_t, Packet4us>(const uint16_t* from, Index stride) {
+  Packet4us res = vld1_dup_u16(from);
+  res = vld1_lane_u16(from + 1 * stride, res, 1);
+  res = vld1_lane_u16(from + 2 * stride, res, 2);
+  res = vld1_lane_u16(from + 3 * stride, res, 3);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8us pgather<uint16_t, Packet8us>(const uint16_t* from, Index stride) {
+  Packet8us res = vld1q_dup_u16(from);
+  res = vld1q_lane_u16(from + 1 * stride, res, 1);
+  res = vld1q_lane_u16(from + 2 * stride, res, 2);
+  res = vld1q_lane_u16(from + 3 * stride, res, 3);
+  res = vld1q_lane_u16(from + 4 * stride, res, 4);
+  res = vld1q_lane_u16(from + 5 * stride, res, 5);
+  res = vld1q_lane_u16(from + 6 * stride, res, 6);
+  res = vld1q_lane_u16(from + 7 * stride, res, 7);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2i pgather<int32_t, Packet2i>(const int32_t* from, Index stride) {
+  Packet2i res = vld1_dup_s32(from);
+  res = vld1_lane_s32(from + 1 * stride, res, 1);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4i pgather<int32_t, Packet4i>(const int32_t* from, Index stride) {
+  Packet4i res = vld1q_dup_s32(from);
+  res = vld1q_lane_s32(from + 1 * stride, res, 1);
+  res = vld1q_lane_s32(from + 2 * stride, res, 2);
+  res = vld1q_lane_s32(from + 3 * stride, res, 3);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ui pgather<uint32_t, Packet2ui>(const uint32_t* from, Index stride) {
+  Packet2ui res = vld1_dup_u32(from);
+  res = vld1_lane_u32(from + 1 * stride, res, 1);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4ui pgather<uint32_t, Packet4ui>(const uint32_t* from, Index stride) {
+  Packet4ui res = vld1q_dup_u32(from);
+  res = vld1q_lane_u32(from + 1 * stride, res, 1);
+  res = vld1q_lane_u32(from + 2 * stride, res, 2);
+  res = vld1q_lane_u32(from + 3 * stride, res, 3);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2l pgather<int64_t, Packet2l>(const int64_t* from, Index stride) {
+  Packet2l res = vld1q_dup_s64(from);
+  res = vld1q_lane_s64(from + 1 * stride, res, 1);
+  return res;
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ul pgather<uint64_t, Packet2ul>(const uint64_t* from, Index stride) {
+  Packet2ul res = vld1q_dup_u64(from);
+  res = vld1q_lane_u64(from + 1 * stride, res, 1);
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<float, Packet2f>(float* to, const Packet2f& from, Index stride) {
+  vst1_lane_f32(to + stride * 0, from, 0);
+  vst1_lane_f32(to + stride * 1, from, 1);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride) {
+  vst1q_lane_f32(to + stride * 0, from, 0);
+  vst1q_lane_f32(to + stride * 1, from, 1);
+  vst1q_lane_f32(to + stride * 2, from, 2);
+  vst1q_lane_f32(to + stride * 3, from, 3);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet4c>(int8_t* to, const Packet4c& from, Index stride) {
+  for (int i = 0; i != 4; i++) *(to + i * stride) = reinterpret_cast<const int8_t*>(&from)[i];
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet8c>(int8_t* to, const Packet8c& from, Index stride) {
+  vst1_lane_s8(to + stride * 0, from, 0);
+  vst1_lane_s8(to + stride * 1, from, 1);
+  vst1_lane_s8(to + stride * 2, from, 2);
+  vst1_lane_s8(to + stride * 3, from, 3);
+  vst1_lane_s8(to + stride * 4, from, 4);
+  vst1_lane_s8(to + stride * 5, from, 5);
+  vst1_lane_s8(to + stride * 6, from, 6);
+  vst1_lane_s8(to + stride * 7, from, 7);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet16c>(int8_t* to, const Packet16c& from,
+                                                                       Index stride) {
+  vst1q_lane_s8(to + stride * 0, from, 0);
+  vst1q_lane_s8(to + stride * 1, from, 1);
+  vst1q_lane_s8(to + stride * 2, from, 2);
+  vst1q_lane_s8(to + stride * 3, from, 3);
+  vst1q_lane_s8(to + stride * 4, from, 4);
+  vst1q_lane_s8(to + stride * 5, from, 5);
+  vst1q_lane_s8(to + stride * 6, from, 6);
+  vst1q_lane_s8(to + stride * 7, from, 7);
+  vst1q_lane_s8(to + stride * 8, from, 8);
+  vst1q_lane_s8(to + stride * 9, from, 9);
+  vst1q_lane_s8(to + stride * 10, from, 10);
+  vst1q_lane_s8(to + stride * 11, from, 11);
+  vst1q_lane_s8(to + stride * 12, from, 12);
+  vst1q_lane_s8(to + stride * 13, from, 13);
+  vst1q_lane_s8(to + stride * 14, from, 14);
+  vst1q_lane_s8(to + stride * 15, from, 15);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet4uc>(uint8_t* to, const Packet4uc& from,
+                                                                        Index stride) {
+  for (int i = 0; i != 4; i++) *(to + i * stride) = reinterpret_cast<const uint8_t*>(&from)[i];
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet8uc>(uint8_t* to, const Packet8uc& from,
+                                                                        Index stride) {
+  vst1_lane_u8(to + stride * 0, from, 0);
+  vst1_lane_u8(to + stride * 1, from, 1);
+  vst1_lane_u8(to + stride * 2, from, 2);
+  vst1_lane_u8(to + stride * 3, from, 3);
+  vst1_lane_u8(to + stride * 4, from, 4);
+  vst1_lane_u8(to + stride * 5, from, 5);
+  vst1_lane_u8(to + stride * 6, from, 6);
+  vst1_lane_u8(to + stride * 7, from, 7);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet16uc>(uint8_t* to, const Packet16uc& from,
+                                                                         Index stride) {
+  vst1q_lane_u8(to + stride * 0, from, 0);
+  vst1q_lane_u8(to + stride * 1, from, 1);
+  vst1q_lane_u8(to + stride * 2, from, 2);
+  vst1q_lane_u8(to + stride * 3, from, 3);
+  vst1q_lane_u8(to + stride * 4, from, 4);
+  vst1q_lane_u8(to + stride * 5, from, 5);
+  vst1q_lane_u8(to + stride * 6, from, 6);
+  vst1q_lane_u8(to + stride * 7, from, 7);
+  vst1q_lane_u8(to + stride * 8, from, 8);
+  vst1q_lane_u8(to + stride * 9, from, 9);
+  vst1q_lane_u8(to + stride * 10, from, 10);
+  vst1q_lane_u8(to + stride * 11, from, 11);
+  vst1q_lane_u8(to + stride * 12, from, 12);
+  vst1q_lane_u8(to + stride * 13, from, 13);
+  vst1q_lane_u8(to + stride * 14, from, 14);
+  vst1q_lane_u8(to + stride * 15, from, 15);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int16_t, Packet4s>(int16_t* to, const Packet4s& from,
+                                                                       Index stride) {
+  vst1_lane_s16(to + stride * 0, from, 0);
+  vst1_lane_s16(to + stride * 1, from, 1);
+  vst1_lane_s16(to + stride * 2, from, 2);
+  vst1_lane_s16(to + stride * 3, from, 3);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int16_t, Packet8s>(int16_t* to, const Packet8s& from,
+                                                                       Index stride) {
+  vst1q_lane_s16(to + stride * 0, from, 0);
+  vst1q_lane_s16(to + stride * 1, from, 1);
+  vst1q_lane_s16(to + stride * 2, from, 2);
+  vst1q_lane_s16(to + stride * 3, from, 3);
+  vst1q_lane_s16(to + stride * 4, from, 4);
+  vst1q_lane_s16(to + stride * 5, from, 5);
+  vst1q_lane_s16(to + stride * 6, from, 6);
+  vst1q_lane_s16(to + stride * 7, from, 7);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint16_t, Packet4us>(uint16_t* to, const Packet4us& from,
+                                                                         Index stride) {
+  vst1_lane_u16(to + stride * 0, from, 0);
+  vst1_lane_u16(to + stride * 1, from, 1);
+  vst1_lane_u16(to + stride * 2, from, 2);
+  vst1_lane_u16(to + stride * 3, from, 3);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint16_t, Packet8us>(uint16_t* to, const Packet8us& from,
+                                                                         Index stride) {
+  vst1q_lane_u16(to + stride * 0, from, 0);
+  vst1q_lane_u16(to + stride * 1, from, 1);
+  vst1q_lane_u16(to + stride * 2, from, 2);
+  vst1q_lane_u16(to + stride * 3, from, 3);
+  vst1q_lane_u16(to + stride * 4, from, 4);
+  vst1q_lane_u16(to + stride * 5, from, 5);
+  vst1q_lane_u16(to + stride * 6, from, 6);
+  vst1q_lane_u16(to + stride * 7, from, 7);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int32_t, Packet2i>(int32_t* to, const Packet2i& from,
+                                                                       Index stride) {
+  vst1_lane_s32(to + stride * 0, from, 0);
+  vst1_lane_s32(to + stride * 1, from, 1);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int32_t, Packet4i>(int32_t* to, const Packet4i& from,
+                                                                       Index stride) {
+  vst1q_lane_s32(to + stride * 0, from, 0);
+  vst1q_lane_s32(to + stride * 1, from, 1);
+  vst1q_lane_s32(to + stride * 2, from, 2);
+  vst1q_lane_s32(to + stride * 3, from, 3);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint32_t, Packet2ui>(uint32_t* to, const Packet2ui& from,
+                                                                         Index stride) {
+  vst1_lane_u32(to + stride * 0, from, 0);
+  vst1_lane_u32(to + stride * 1, from, 1);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint32_t, Packet4ui>(uint32_t* to, const Packet4ui& from,
+                                                                         Index stride) {
+  vst1q_lane_u32(to + stride * 0, from, 0);
+  vst1q_lane_u32(to + stride * 1, from, 1);
+  vst1q_lane_u32(to + stride * 2, from, 2);
+  vst1q_lane_u32(to + stride * 3, from, 3);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int64_t, Packet2l>(int64_t* to, const Packet2l& from,
+                                                                       Index stride) {
+  vst1q_lane_s64(to + stride * 0, from, 0);
+  vst1q_lane_s64(to + stride * 1, from, 1);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint64_t, Packet2ul>(uint64_t* to, const Packet2ul& from,
+                                                                         Index stride) {
+  vst1q_lane_u64(to + stride * 0, from, 0);
+  vst1q_lane_u64(to + stride * 1, from, 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int8_t>(const int8_t* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<uint8_t>(const uint8_t* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int16_t>(const int16_t* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<uint16_t>(const uint16_t* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int32_t>(const int32_t* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<uint32_t>(const uint32_t* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int64_t>(const int64_t* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<uint64_t>(const uint64_t* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet2f>(const Packet2f& a) {
+  return vget_lane_f32(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
+  return vgetq_lane_f32(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t pfirst<Packet4c>(const Packet4c& a) {
+  return static_cast<int8_t>(a & 0xff);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t pfirst<Packet8c>(const Packet8c& a) {
+  return vget_lane_s8(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t pfirst<Packet16c>(const Packet16c& a) {
+  return vgetq_lane_s8(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t pfirst<Packet4uc>(const Packet4uc& a) {
+  return static_cast<uint8_t>(a & 0xff);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t pfirst<Packet8uc>(const Packet8uc& a) {
+  return vget_lane_u8(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t pfirst<Packet16uc>(const Packet16uc& a) {
+  return vgetq_lane_u8(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t pfirst<Packet4s>(const Packet4s& a) {
+  return vget_lane_s16(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t pfirst<Packet8s>(const Packet8s& a) {
+  return vgetq_lane_s16(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t pfirst<Packet4us>(const Packet4us& a) {
+  return vget_lane_u16(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t pfirst<Packet8us>(const Packet8us& a) {
+  return vgetq_lane_u16(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t pfirst<Packet2i>(const Packet2i& a) {
+  return vget_lane_s32(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t pfirst<Packet4i>(const Packet4i& a) {
+  return vgetq_lane_s32(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t pfirst<Packet2ui>(const Packet2ui& a) {
+  return vget_lane_u32(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t pfirst<Packet4ui>(const Packet4ui& a) {
+  return vgetq_lane_u32(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int64_t pfirst<Packet2l>(const Packet2l& a) {
+  return vgetq_lane_s64(a, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t pfirst<Packet2ul>(const Packet2ul& a) {
+  return vgetq_lane_u64(a, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f preverse(const Packet2f& a) {
+  return vrev64_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) {
+  const float32x4_t a_r64 = vrev64q_f32(a);
+  return vcombine_f32(vget_high_f32(a_r64), vget_low_f32(a_r64));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c preverse(const Packet4c& a) {
+  return vget_lane_s32(vreinterpret_s32_s8(vrev64_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c preverse(const Packet8c& a) {
+  return vrev64_s8(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c preverse(const Packet16c& a) {
+  const int8x16_t a_r64 = vrev64q_s8(a);
+  return vcombine_s8(vget_high_s8(a_r64), vget_low_s8(a_r64));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc preverse(const Packet4uc& a) {
+  return vget_lane_u32(vreinterpret_u32_u8(vrev64_u8(vreinterpret_u8_u32(vdup_n_u32(a)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc preverse(const Packet8uc& a) {
+  return vrev64_u8(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc preverse(const Packet16uc& a) {
+  const uint8x16_t a_r64 = vrev64q_u8(a);
+  return vcombine_u8(vget_high_u8(a_r64), vget_low_u8(a_r64));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s preverse(const Packet4s& a) {
+  return vrev64_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s preverse(const Packet8s& a) {
+  const int16x8_t a_r64 = vrev64q_s16(a);
+  return vcombine_s16(vget_high_s16(a_r64), vget_low_s16(a_r64));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us preverse(const Packet4us& a) {
+  return vrev64_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us preverse(const Packet8us& a) {
+  const uint16x8_t a_r64 = vrev64q_u16(a);
+  return vcombine_u16(vget_high_u16(a_r64), vget_low_u16(a_r64));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i preverse(const Packet2i& a) {
+  return vrev64_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) {
+  const int32x4_t a_r64 = vrev64q_s32(a);
+  return vcombine_s32(vget_high_s32(a_r64), vget_low_s32(a_r64));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui preverse(const Packet2ui& a) {
+  return vrev64_u32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preverse(const Packet4ui& a) {
+  const uint32x4_t a_r64 = vrev64q_u32(a);
+  return vcombine_u32(vget_high_u32(a_r64), vget_low_u32(a_r64));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l preverse(const Packet2l& a) {
+  return vcombine_s64(vget_high_s64(a), vget_low_s64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul preverse(const Packet2ul& a) {
+  return vcombine_u64(vget_high_u64(a), vget_low_u64(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pabs(const Packet2f& a) {
+  return vabs_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) {
+  return vabsq_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pabs<Packet4c>(const Packet4c& a) {
+  return vget_lane_s32(vreinterpret_s32_s8(vabs_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pabs(const Packet8c& a) {
+  return vabs_s8(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c pabs(const Packet16c& a) {
+  return vabsq_s8(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pabs(const Packet4uc& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pabs(const Packet8uc& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc pabs(const Packet16uc& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pabs(const Packet4s& a) {
+  return vabs_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pabs(const Packet8s& a) {
+  return vabsq_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pabs(const Packet4us& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pabs(const Packet8us& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pabs(const Packet2i& a) {
+  return vabs_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) {
+  return vabsq_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pabs(const Packet2ui& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pabs(const Packet4ui& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pabs(const Packet2l& a) {
+#if EIGEN_ARCH_ARM64
+  return vabsq_s64(a);
+#else
+  return vcombine_s64(vdup_n_s64((std::abs)(vgetq_lane_s64(a, 0))), vdup_n_s64((std::abs)(vgetq_lane_s64(a, 1))));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pabs(const Packet2ul& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f psignbit(const Packet2f& a) {
+  return vreinterpret_f32_s32(vshr_n_s32(vreinterpret_s32_f32(a), 31));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f psignbit(const Packet4f& a) {
+  return vreinterpretq_f32_s32(vshrq_n_s32(vreinterpretq_s32_f32(a), 31));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pfrexp<Packet2f>(const Packet2f& a, Packet2f& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pldexp<Packet2f>(const Packet2f& a, const Packet2f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE float predux<Packet2f>(const Packet2f& a) {
+  return vaddv_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) {
+  return vaddvq_f32(a);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE float predux<Packet2f>(const Packet2f& a) {
+  return vget_lane_f32(vpadd_f32(a, a), 0);
+}
+template <>
+EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) {
+  const float32x2_t sum = vadd_f32(vget_low_f32(a), vget_high_f32(a));
+  return vget_lane_f32(vpadd_f32(sum, sum), 0);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE int8_t predux<Packet4c>(const Packet4c& a) {
+  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
+  int8x8_t sum = vpadd_s8(a_dup, a_dup);
+  sum = vpadd_s8(sum, sum);
+  return vget_lane_s8(sum, 0);
+}
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE int8_t predux<Packet8c>(const Packet8c& a) {
+  return vaddv_s8(a);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux<Packet16c>(const Packet16c& a) {
+  return vaddvq_s8(a);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE int8_t predux<Packet8c>(const Packet8c& a) {
+  int8x8_t sum = vpadd_s8(a, a);
+  sum = vpadd_s8(sum, sum);
+  sum = vpadd_s8(sum, sum);
+  return vget_lane_s8(sum, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux<Packet16c>(const Packet16c& a) {
+  int8x8_t sum = vadd_s8(vget_low_s8(a), vget_high_s8(a));
+  sum = vpadd_s8(sum, sum);
+  sum = vpadd_s8(sum, sum);
+  sum = vpadd_s8(sum, sum);
+  return vget_lane_s8(sum, 0);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE uint8_t predux<Packet4uc>(const Packet4uc& a) {
+  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
+  uint8x8_t sum = vpadd_u8(a_dup, a_dup);
+  sum = vpadd_u8(sum, sum);
+  return vget_lane_u8(sum, 0);
+}
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE uint8_t predux<Packet8uc>(const Packet8uc& a) {
+  return vaddv_u8(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux<Packet16uc>(const Packet16uc& a) {
+  return vaddvq_u8(a);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux<Packet4s>(const Packet4s& a) {
+  return vaddv_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux<Packet8s>(const Packet8s& a) {
+  return vaddvq_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux<Packet4us>(const Packet4us& a) {
+  return vaddv_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux<Packet8us>(const Packet8us& a) {
+  return vaddvq_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux<Packet2i>(const Packet2i& a) {
+  return vaddv_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a) {
+  return vaddvq_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux<Packet2ui>(const Packet2ui& a) {
+  return vaddv_u32(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux<Packet4ui>(const Packet4ui& a) {
+  return vaddvq_u32(a);
+}
+template <>
+EIGEN_STRONG_INLINE int64_t predux<Packet2l>(const Packet2l& a) {
+  return vaddvq_s64(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t predux<Packet2ul>(const Packet2ul& a) {
+  return vaddvq_u64(a);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE uint8_t predux<Packet8uc>(const Packet8uc& a) {
+  uint8x8_t sum = vpadd_u8(a, a);
+  sum = vpadd_u8(sum, sum);
+  sum = vpadd_u8(sum, sum);
+  return vget_lane_u8(sum, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux<Packet16uc>(const Packet16uc& a) {
+  uint8x8_t sum = vadd_u8(vget_low_u8(a), vget_high_u8(a));
+  sum = vpadd_u8(sum, sum);
+  sum = vpadd_u8(sum, sum);
+  sum = vpadd_u8(sum, sum);
+  return vget_lane_u8(sum, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux<Packet4s>(const Packet4s& a) {
+  const int16x4_t sum = vpadd_s16(a, a);
+  return vget_lane_s16(vpadd_s16(sum, sum), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux<Packet8s>(const Packet8s& a) {
+  int16x4_t sum = vadd_s16(vget_low_s16(a), vget_high_s16(a));
+  sum = vpadd_s16(sum, sum);
+  sum = vpadd_s16(sum, sum);
+  return vget_lane_s16(sum, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux<Packet4us>(const Packet4us& a) {
+  const uint16x4_t sum = vpadd_u16(a, a);
+  return vget_lane_u16(vpadd_u16(sum, sum), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux<Packet8us>(const Packet8us& a) {
+  uint16x4_t sum = vadd_u16(vget_low_u16(a), vget_high_u16(a));
+  sum = vpadd_u16(sum, sum);
+  sum = vpadd_u16(sum, sum);
+  return vget_lane_u16(sum, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux<Packet2i>(const Packet2i& a) {
+  return vget_lane_s32(vpadd_s32(a, a), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a) {
+  const int32x2_t sum = vadd_s32(vget_low_s32(a), vget_high_s32(a));
+  return vget_lane_s32(vpadd_s32(sum, sum), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux<Packet2ui>(const Packet2ui& a) {
+  return vget_lane_u32(vpadd_u32(a, a), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux<Packet4ui>(const Packet4ui& a) {
+  const uint32x2_t sum = vadd_u32(vget_low_u32(a), vget_high_u32(a));
+  return vget_lane_u32(vpadd_u32(sum, sum), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int64_t predux<Packet2l>(const Packet2l& a) {
+  return vgetq_lane_s64(a, 0) + vgetq_lane_s64(a, 1);
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t predux<Packet2ul>(const Packet2ul& a) {
+  return vgetq_lane_u64(a, 0) + vgetq_lane_u64(a, 1);
+}
+#endif
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4c predux_half(const Packet8c& a) {
+  return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(a, vreinterpret_s8_s32(vrev64_s32(vreinterpret_s32_s8(a))))), 0);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c predux_half(const Packet16c& a) {
+  return vadd_s8(vget_high_s8(a), vget_low_s8(a));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4uc predux_half(const Packet8uc& a) {
+  return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(a, vreinterpret_u8_u32(vrev64_u32(vreinterpret_u32_u8(a))))), 0);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc predux_half(const Packet16uc& a) {
+  return vadd_u8(vget_high_u8(a), vget_low_u8(a));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s predux_half(const Packet8s& a) {
+  return vadd_s16(vget_high_s16(a), vget_low_s16(a));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us predux_half(const Packet8us& a) {
+  return vadd_u16(vget_high_u16(a), vget_low_u16(a));
+}
+
+// Other reduction functions:
+// mul
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet2f>(const Packet2f& a) {
+  return vget_lane_f32(a, 0) * vget_lane_f32(a, 1);
+}
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) {
+  return predux_mul<Packet2f>(vmul_f32(vget_low_f32(a), vget_high_f32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux_mul<Packet4c>(const Packet4c& a) {
+  int8x8_t prod = vreinterpret_s8_s32(vdup_n_s32(a));
+  prod = vmul_s8(prod, vrev16_s8(prod));
+  return vget_lane_s8(prod, 0) * vget_lane_s8(prod, 2);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux_mul<Packet8c>(const Packet8c& a) {
+  int8x8_t prod = vmul_s8(a, vrev16_s8(a));
+  prod = vmul_s8(prod, vrev32_s8(prod));
+  return vget_lane_s8(prod, 0) * vget_lane_s8(prod, 4);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux_mul<Packet16c>(const Packet16c& a) {
+  return predux_mul<Packet8c>(vmul_s8(vget_low_s8(a), vget_high_s8(a)));
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_mul<Packet4uc>(const Packet4uc& a) {
+  uint8x8_t prod = vreinterpret_u8_u32(vdup_n_u32(a));
+  prod = vmul_u8(prod, vrev16_u8(prod));
+  return vget_lane_u8(prod, 0) * vget_lane_u8(prod, 2);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_mul<Packet8uc>(const Packet8uc& a) {
+  uint8x8_t prod = vmul_u8(a, vrev16_u8(a));
+  prod = vmul_u8(prod, vrev32_u8(prod));
+  return vget_lane_u8(prod, 0) * vget_lane_u8(prod, 4);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_mul<Packet16uc>(const Packet16uc& a) {
+  return predux_mul<Packet8uc>(vmul_u8(vget_low_u8(a), vget_high_u8(a)));
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_mul<Packet4s>(const Packet4s& a) {
+  const int16x4_t prod = vmul_s16(a, vrev32_s16(a));
+  return vget_lane_s16(prod, 0) * vget_lane_s16(prod, 2);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_mul<Packet8s>(const Packet8s& a) {
+  int16x4_t prod;
+
+  // Get the product of a_lo * a_hi -> |a1*a5|a2*a6|a3*a7|a4*a8|
+  prod = vmul_s16(vget_low_s16(a), vget_high_s16(a));
+  // Swap and multiply |a1*a5*a2*a6|a3*a7*a4*a8|
+  prod = vmul_s16(prod, vrev32_s16(prod));
+  // Multiply |a1*a5*a2*a6*a3*a7*a4*a8|
+  return vget_lane_s16(prod, 0) * vget_lane_s16(prod, 2);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_mul<Packet4us>(const Packet4us& a) {
+  const uint16x4_t prod = vmul_u16(a, vrev32_u16(a));
+  return vget_lane_u16(prod, 0) * vget_lane_u16(prod, 2);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_mul<Packet8us>(const Packet8us& a) {
+  uint16x4_t prod;
+
+  // Get the product of a_lo * a_hi -> |a1*a5|a2*a6|a3*a7|a4*a8|
+  prod = vmul_u16(vget_low_u16(a), vget_high_u16(a));
+  // Swap and multiply |a1*a5*a2*a6|a3*a7*a4*a8|
+  prod = vmul_u16(prod, vrev32_u16(prod));
+  // Multiply |a1*a5*a2*a6*a3*a7*a4*a8|
+  return vget_lane_u16(prod, 0) * vget_lane_u16(prod, 2);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_mul<Packet2i>(const Packet2i& a) {
+  return vget_lane_s32(a, 0) * vget_lane_s32(a, 1);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_mul<Packet4i>(const Packet4i& a) {
+  return predux_mul<Packet2i>(vmul_s32(vget_low_s32(a), vget_high_s32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_mul<Packet2ui>(const Packet2ui& a) {
+  return vget_lane_u32(a, 0) * vget_lane_u32(a, 1);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_mul<Packet4ui>(const Packet4ui& a) {
+  return predux_mul<Packet2ui>(vmul_u32(vget_low_u32(a), vget_high_u32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE int64_t predux_mul<Packet2l>(const Packet2l& a) {
+  return vgetq_lane_s64(a, 0) * vgetq_lane_s64(a, 1);
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t predux_mul<Packet2ul>(const Packet2ul& a) {
+  return vgetq_lane_u64(a, 0) * vgetq_lane_u64(a, 1);
+}
+
+// min
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet2f>(const Packet2f& a) {
+  return vminv_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) {
+  return vminvq_f32(a);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet2f>(const Packet2f& a) {
+  return vget_lane_f32(vpmin_f32(a, a), 0);
+}
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) {
+  const float32x2_t min = vmin_f32(vget_low_f32(a), vget_high_f32(a));
+  return vget_lane_f32(vpmin_f32(min, min), 0);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE int8_t predux_min<Packet4c>(const Packet4c& a) {
+  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
+  int8x8_t min = vpmin_s8(a_dup, a_dup);
+  min = vpmin_s8(min, min);
+  return vget_lane_s8(min, 0);
+}
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE int8_t predux_min<Packet8c>(const Packet8c& a) {
+  return vminv_s8(a);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux_min<Packet16c>(const Packet16c& a) {
+  return vminvq_s8(a);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE int8_t predux_min<Packet8c>(const Packet8c& a) {
+  int8x8_t min = vpmin_s8(a, a);
+  min = vpmin_s8(min, min);
+  min = vpmin_s8(min, min);
+  return vget_lane_s8(min, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux_min<Packet16c>(const Packet16c& a) {
+  int8x8_t min = vmin_s8(vget_low_s8(a), vget_high_s8(a));
+  min = vpmin_s8(min, min);
+  min = vpmin_s8(min, min);
+  min = vpmin_s8(min, min);
+  return vget_lane_s8(min, 0);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_min<Packet4uc>(const Packet4uc& a) {
+  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
+  uint8x8_t min = vpmin_u8(a_dup, a_dup);
+  min = vpmin_u8(min, min);
+  return vget_lane_u8(min, 0);
+}
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_min<Packet8uc>(const Packet8uc& a) {
+  return vminv_u8(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_min<Packet16uc>(const Packet16uc& a) {
+  return vminvq_u8(a);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_min<Packet4s>(const Packet4s& a) {
+  return vminv_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_min<Packet8s>(const Packet8s& a) {
+  return vminvq_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_min<Packet4us>(const Packet4us& a) {
+  return vminv_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_min<Packet8us>(const Packet8us& a) {
+  return vminvq_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_min<Packet2i>(const Packet2i& a) {
+  return vminv_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a) {
+  return vminvq_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_min<Packet2ui>(const Packet2ui& a) {
+  return vminv_u32(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_min<Packet4ui>(const Packet4ui& a) {
+  return vminvq_u32(a);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_min<Packet8uc>(const Packet8uc& a) {
+  uint8x8_t min = vpmin_u8(a, a);
+  min = vpmin_u8(min, min);
+  min = vpmin_u8(min, min);
+  return vget_lane_u8(min, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_min<Packet16uc>(const Packet16uc& a) {
+  uint8x8_t min = vmin_u8(vget_low_u8(a), vget_high_u8(a));
+  min = vpmin_u8(min, min);
+  min = vpmin_u8(min, min);
+  min = vpmin_u8(min, min);
+  return vget_lane_u8(min, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_min<Packet4s>(const Packet4s& a) {
+  const int16x4_t min = vpmin_s16(a, a);
+  return vget_lane_s16(vpmin_s16(min, min), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_min<Packet8s>(const Packet8s& a) {
+  int16x4_t min = vmin_s16(vget_low_s16(a), vget_high_s16(a));
+  min = vpmin_s16(min, min);
+  min = vpmin_s16(min, min);
+  return vget_lane_s16(min, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_min<Packet4us>(const Packet4us& a) {
+  const uint16x4_t min = vpmin_u16(a, a);
+  return vget_lane_u16(vpmin_u16(min, min), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_min<Packet8us>(const Packet8us& a) {
+  uint16x4_t min = vmin_u16(vget_low_u16(a), vget_high_u16(a));
+  min = vpmin_u16(min, min);
+  min = vpmin_u16(min, min);
+  return vget_lane_u16(min, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_min<Packet2i>(const Packet2i& a) {
+  return vget_lane_s32(vpmin_s32(a, a), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a) {
+  const int32x2_t min = vmin_s32(vget_low_s32(a), vget_high_s32(a));
+  return vget_lane_s32(vpmin_s32(min, min), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_min<Packet2ui>(const Packet2ui& a) {
+  return vget_lane_u32(vpmin_u32(a, a), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_min<Packet4ui>(const Packet4ui& a) {
+  const uint32x2_t min = vmin_u32(vget_low_u32(a), vget_high_u32(a));
+  return vget_lane_u32(vpmin_u32(min, min), 0);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE int64_t predux_min<Packet2l>(const Packet2l& a) {
+  return (std::min)(vgetq_lane_s64(a, 0), vgetq_lane_s64(a, 1));
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t predux_min<Packet2ul>(const Packet2ul& a) {
+  return (std::min)(vgetq_lane_u64(a, 0), vgetq_lane_u64(a, 1));
+}
+
+// max
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet2f>(const Packet2f& a) {
+  return vmaxv_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) {
+  return vmaxvq_f32(a);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet2f>(const Packet2f& a) {
+  return vget_lane_f32(vpmax_f32(a, a), 0);
+}
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) {
+  const float32x2_t max = vmax_f32(vget_low_f32(a), vget_high_f32(a));
+  return vget_lane_f32(vpmax_f32(max, max), 0);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE int8_t predux_max<Packet4c>(const Packet4c& a) {
+  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
+  int8x8_t max = vpmax_s8(a_dup, a_dup);
+  max = vpmax_s8(max, max);
+  return vget_lane_s8(max, 0);
+}
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE int8_t predux_max<Packet8c>(const Packet8c& a) {
+  return vmaxv_s8(a);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux_max<Packet16c>(const Packet16c& a) {
+  return vmaxvq_s8(a);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE int8_t predux_max<Packet8c>(const Packet8c& a) {
+  int8x8_t max = vpmax_s8(a, a);
+  max = vpmax_s8(max, max);
+  max = vpmax_s8(max, max);
+  return vget_lane_s8(max, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int8_t predux_max<Packet16c>(const Packet16c& a) {
+  int8x8_t max = vmax_s8(vget_low_s8(a), vget_high_s8(a));
+  max = vpmax_s8(max, max);
+  max = vpmax_s8(max, max);
+  max = vpmax_s8(max, max);
+  return vget_lane_s8(max, 0);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_max<Packet4uc>(const Packet4uc& a) {
+  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
+  uint8x8_t max = vpmax_u8(a_dup, a_dup);
+  max = vpmax_u8(max, max);
+  return vget_lane_u8(max, 0);
+}
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_max<Packet8uc>(const Packet8uc& a) {
+  return vmaxv_u8(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_max<Packet16uc>(const Packet16uc& a) {
+  return vmaxvq_u8(a);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_max<Packet4s>(const Packet4s& a) {
+  return vmaxv_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_max<Packet8s>(const Packet8s& a) {
+  return vmaxvq_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_max<Packet4us>(const Packet4us& a) {
+  return vmaxv_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_max<Packet8us>(const Packet8us& a) {
+  return vmaxvq_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_max<Packet2i>(const Packet2i& a) {
+  return vmaxv_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a) {
+  return vmaxvq_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_max<Packet2ui>(const Packet2ui& a) {
+  return vmaxv_u32(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_max<Packet4ui>(const Packet4ui& a) {
+  return vmaxvq_u32(a);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_max<Packet8uc>(const Packet8uc& a) {
+  uint8x8_t max = vpmax_u8(a, a);
+  max = vpmax_u8(max, max);
+  max = vpmax_u8(max, max);
+  return vget_lane_u8(max, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint8_t predux_max<Packet16uc>(const Packet16uc& a) {
+  uint8x8_t max = vmax_u8(vget_low_u8(a), vget_high_u8(a));
+  max = vpmax_u8(max, max);
+  max = vpmax_u8(max, max);
+  max = vpmax_u8(max, max);
+  return vget_lane_u8(max, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_max<Packet4s>(const Packet4s& a) {
+  const int16x4_t max = vpmax_s16(a, a);
+  return vget_lane_s16(vpmax_s16(max, max), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int16_t predux_max<Packet8s>(const Packet8s& a) {
+  int16x4_t max = vmax_s16(vget_low_s16(a), vget_high_s16(a));
+  max = vpmax_s16(max, max);
+  max = vpmax_s16(max, max);
+  return vget_lane_s16(max, 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_max<Packet4us>(const Packet4us& a) {
+  const uint16x4_t max = vpmax_u16(a, a);
+  return vget_lane_u16(vpmax_u16(max, max), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint16_t predux_max<Packet8us>(const Packet8us& a) {
+  uint16x4_t max = vmax_u16(vget_low_u16(a), vget_high_u16(a));
+  max = vpmax_u16(max, max);
+  max = vpmax_u16(max, max);
+  return vget_lane_u16(max, 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_max<Packet2i>(const Packet2i& a) {
+  return vget_lane_s32(vpmax_s32(a, a), 0);
+}
+template <>
+EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a) {
+  const int32x2_t max = vmax_s32(vget_low_s32(a), vget_high_s32(a));
+  return vget_lane_s32(vpmax_s32(max, max), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_max<Packet2ui>(const Packet2ui& a) {
+  return vget_lane_u32(vpmax_u32(a, a), 0);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_max<Packet4ui>(const Packet4ui& a) {
+  const uint32x2_t max = vmax_u32(vget_low_u32(a), vget_high_u32(a));
+  return vget_lane_u32(vpmax_u32(max, max), 0);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE int64_t predux_max<Packet2l>(const Packet2l& a) {
+  return (std::max)(vgetq_lane_s64(a, 0), vgetq_lane_s64(a, 1));
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t predux_max<Packet2ul>(const Packet2ul& a) {
+  return (std::max)(vgetq_lane_u64(a, 0), vgetq_lane_u64(a, 1));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x) {
+  uint32x2_t tmp = vorr_u32(vget_low_u32(vreinterpretq_u32_f32(x)), vget_high_u32(vreinterpretq_u32_f32(x)));
+  return vget_lane_u32(vpmax_u32(tmp, tmp), 0);
+}
+
+// Helpers for ptranspose.
+namespace detail {
+
+template <typename Packet>
+void zip_in_place(Packet& p1, Packet& p2);
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet2f>(Packet2f& p1, Packet2f& p2) {
+  const float32x2x2_t tmp = vzip_f32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4f>(Packet4f& p1, Packet4f& p2) {
+  const float32x4x2_t tmp = vzipq_f32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8c>(Packet8c& p1, Packet8c& p2) {
+  const int8x8x2_t tmp = vzip_s8(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet16c>(Packet16c& p1, Packet16c& p2) {
+  const int8x16x2_t tmp = vzipq_s8(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8uc>(Packet8uc& p1, Packet8uc& p2) {
+  const uint8x8x2_t tmp = vzip_u8(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet16uc>(Packet16uc& p1, Packet16uc& p2) {
+  const uint8x16x2_t tmp = vzipq_u8(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet2i>(Packet2i& p1, Packet2i& p2) {
+  const int32x2x2_t tmp = vzip_s32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4i>(Packet4i& p1, Packet4i& p2) {
+  const int32x4x2_t tmp = vzipq_s32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet2ui>(Packet2ui& p1, Packet2ui& p2) {
+  const uint32x2x2_t tmp = vzip_u32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4ui>(Packet4ui& p1, Packet4ui& p2) {
+  const uint32x4x2_t tmp = vzipq_u32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4s>(Packet4s& p1, Packet4s& p2) {
+  const int16x4x2_t tmp = vzip_s16(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8s>(Packet8s& p1, Packet8s& p2) {
+  const int16x8x2_t tmp = vzipq_s16(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4us>(Packet4us& p1, Packet4us& p2) {
+  const uint16x4x2_t tmp = vzip_u16(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8us>(Packet8us& p1, Packet8us& p2) {
+  const uint16x8x2_t tmp = vzipq_u16(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 2>& kernel) {
+  zip_in_place(kernel.packet[0], kernel.packet[1]);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 4>& kernel) {
+  zip_in_place(kernel.packet[0], kernel.packet[2]);
+  zip_in_place(kernel.packet[1], kernel.packet[3]);
+  zip_in_place(kernel.packet[0], kernel.packet[1]);
+  zip_in_place(kernel.packet[2], kernel.packet[3]);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 8>& kernel) {
+  zip_in_place(kernel.packet[0], kernel.packet[4]);
+  zip_in_place(kernel.packet[1], kernel.packet[5]);
+  zip_in_place(kernel.packet[2], kernel.packet[6]);
+  zip_in_place(kernel.packet[3], kernel.packet[7]);
+
+  zip_in_place(kernel.packet[0], kernel.packet[2]);
+  zip_in_place(kernel.packet[1], kernel.packet[3]);
+  zip_in_place(kernel.packet[4], kernel.packet[6]);
+  zip_in_place(kernel.packet[5], kernel.packet[7]);
+
+  zip_in_place(kernel.packet[0], kernel.packet[1]);
+  zip_in_place(kernel.packet[2], kernel.packet[3]);
+  zip_in_place(kernel.packet[4], kernel.packet[5]);
+  zip_in_place(kernel.packet[6], kernel.packet[7]);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 16>& kernel) {
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 4; ++i) {
+    const int m = (1 << i);
+    EIGEN_UNROLL_LOOP
+    for (int j = 0; j < m; ++j) {
+      const int n = (1 << (3 - i));
+      EIGEN_UNROLL_LOOP
+      for (int k = 0; k < n; ++k) {
+        const int idx = 2 * j * n + k;
+        zip_in_place(kernel.packet[idx], kernel.packet[idx + n]);
+      }
+    }
+  }
+}
+
+}  // namespace detail
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2f, 2>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4c, 4>& kernel) {
+  const int8x8_t a = vreinterpret_s8_s32(vset_lane_s32(kernel.packet[2], vdup_n_s32(kernel.packet[0]), 1));
+  const int8x8_t b = vreinterpret_s8_s32(vset_lane_s32(kernel.packet[3], vdup_n_s32(kernel.packet[1]), 1));
+
+  const int8x8x2_t zip8 = vzip_s8(a, b);
+  const int16x4x2_t zip16 = vzip_s16(vreinterpret_s16_s8(zip8.val[0]), vreinterpret_s16_s8(zip8.val[1]));
+
+  kernel.packet[0] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[0]), 0);
+  kernel.packet[1] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[0]), 1);
+  kernel.packet[2] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[1]), 0);
+  kernel.packet[3] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[1]), 1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8c, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8c, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 16>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4uc, 4>& kernel) {
+  const uint8x8_t a = vreinterpret_u8_u32(vset_lane_u32(kernel.packet[2], vdup_n_u32(kernel.packet[0]), 1));
+  const uint8x8_t b = vreinterpret_u8_u32(vset_lane_u32(kernel.packet[3], vdup_n_u32(kernel.packet[1]), 1));
+
+  const uint8x8x2_t zip8 = vzip_u8(a, b);
+  const uint16x4x2_t zip16 = vzip_u16(vreinterpret_u16_u8(zip8.val[0]), vreinterpret_u16_u8(zip8.val[1]));
+
+  kernel.packet[0] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[0]), 0);
+  kernel.packet[1] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[0]), 1);
+  kernel.packet[2] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[1]), 0);
+  kernel.packet[3] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[1]), 1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8uc, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8uc, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 16>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4s, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8s, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8s, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4us, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8us, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8us, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2i, 2>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2ui, 2>& kernel) {
+  detail::zip_in_place(kernel.packet[0], kernel.packet[1]);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4ui, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2l, 2>& kernel) {
+#if EIGEN_ARCH_ARM64
+  const int64x2_t tmp1 = vzip1q_s64(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[1] = vzip2q_s64(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[0] = tmp1;
+#else
+  const int64x1_t tmp[2][2] = {{vget_low_s64(kernel.packet[0]), vget_high_s64(kernel.packet[0])},
+                               {vget_low_s64(kernel.packet[1]), vget_high_s64(kernel.packet[1])}};
+
+  kernel.packet[0] = vcombine_s64(tmp[0][0], tmp[1][0]);
+  kernel.packet[1] = vcombine_s64(tmp[0][1], tmp[1][1]);
+#endif
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2ul, 2>& kernel) {
+#if EIGEN_ARCH_ARM64
+  const uint64x2_t tmp1 = vzip1q_u64(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[1] = vzip2q_u64(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[0] = tmp1;
+#else
+  const uint64x1_t tmp[2][2] = {{vget_low_u64(kernel.packet[0]), vget_high_u64(kernel.packet[0])},
+                                {vget_low_u64(kernel.packet[1]), vget_high_u64(kernel.packet[1])}};
+
+  kernel.packet[0] = vcombine_u64(tmp[0][0], tmp[1][0]);
+  kernel.packet[1] = vcombine_u64(tmp[0][1], tmp[1][1]);
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2f pselect(const Packet2f& mask, const Packet2f& a, const Packet2f& b) {
+  return vbsl_f32(vreinterpret_u32_f32(mask), a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b) {
+  return vbslq_f32(vreinterpretq_u32_f32(mask), a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c pselect(const Packet8c& mask, const Packet8c& a, const Packet8c& b) {
+  return vbsl_s8(vreinterpret_u8_s8(mask), a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16c pselect(const Packet16c& mask, const Packet16c& a, const Packet16c& b) {
+  return vbslq_s8(vreinterpretq_u8_s8(mask), a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc pselect(const Packet8uc& mask, const Packet8uc& a, const Packet8uc& b) {
+  return vbsl_u8(mask, a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16uc pselect(const Packet16uc& mask, const Packet16uc& a,
+                                                         const Packet16uc& b) {
+  return vbslq_u8(mask, a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s pselect(const Packet4s& mask, const Packet4s& a, const Packet4s& b) {
+  return vbsl_s16(vreinterpret_u16_s16(mask), a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8s pselect(const Packet8s& mask, const Packet8s& a, const Packet8s& b) {
+  return vbslq_s16(vreinterpretq_u16_s16(mask), a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us pselect(const Packet4us& mask, const Packet4us& a, const Packet4us& b) {
+  return vbsl_u16(mask, a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8us pselect(const Packet8us& mask, const Packet8us& a, const Packet8us& b) {
+  return vbslq_u16(mask, a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2i pselect(const Packet2i& mask, const Packet2i& a, const Packet2i& b) {
+  return vbsl_s32(vreinterpret_u32_s32(mask), a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4i pselect(const Packet4i& mask, const Packet4i& a, const Packet4i& b) {
+  return vbslq_s32(vreinterpretq_u32_s32(mask), a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ui pselect(const Packet2ui& mask, const Packet2ui& a, const Packet2ui& b) {
+  return vbsl_u32(mask, a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4ui pselect(const Packet4ui& mask, const Packet4ui& a, const Packet4ui& b) {
+  return vbslq_u32(mask, a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2l pselect(const Packet2l& mask, const Packet2l& a, const Packet2l& b) {
+  return vbslq_s64(vreinterpretq_u64_s64(mask), a, b);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ul pselect(const Packet2ul& mask, const Packet2ul& a, const Packet2ul& b) {
+  return vbslq_u64(mask, a, b);
+}
+
+// Use armv8 rounding intinsics if available.
+#if EIGEN_ARCH_ARMV8
+template <>
+EIGEN_STRONG_INLINE Packet2f print<Packet2f>(const Packet2f& a) {
+  return vrndn_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a) {
+  return vrndnq_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pfloor<Packet2f>(const Packet2f& a) {
+  return vrndm_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) {
+  return vrndmq_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pceil<Packet2f>(const Packet2f& a) {
+  return vrndp_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) {
+  return vrndpq_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pround<Packet2f>(const Packet2f& a) {
+  return vrnda_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a) {
+  return vrndaq_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f ptrunc<Packet2f>(const Packet2f& a) {
+  return vrnd_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ptrunc<Packet4f>(const Packet4f& a) {
+  return vrndq_f32(a);
+}
+#endif
+
+/**
+ * Computes the integer square root
+ * @remarks The calculation is performed using an algorithm which iterates through each binary digit of the result
+ *   and tests whether setting that digit to 1 would cause the square of the value to be greater than the argument
+ *   value. The algorithm is described in detail here: http://ww1.microchip.com/downloads/en/AppNotes/91040a.pdf .
+ */
+template <>
+EIGEN_STRONG_INLINE Packet4uc psqrt(const Packet4uc& a) {
+  uint8x8_t x = vreinterpret_u8_u32(vdup_n_u32(a));
+  uint8x8_t res = vdup_n_u8(0);
+  uint8x8_t add = vdup_n_u8(0x8);
+  for (int i = 0; i < 4; i++) {
+    const uint8x8_t temp = vorr_u8(res, add);
+    res = vbsl_u8(vcge_u8(x, vmul_u8(temp, temp)), temp, res);
+    add = vshr_n_u8(add, 1);
+  }
+  return vget_lane_u32(vreinterpret_u32_u8(res), 0);
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template <>
+EIGEN_STRONG_INLINE Packet8uc psqrt(const Packet8uc& a) {
+  uint8x8_t res = vdup_n_u8(0);
+  uint8x8_t add = vdup_n_u8(0x8);
+  for (int i = 0; i < 4; i++) {
+    const uint8x8_t temp = vorr_u8(res, add);
+    res = vbsl_u8(vcge_u8(a, vmul_u8(temp, temp)), temp, res);
+    add = vshr_n_u8(add, 1);
+  }
+  return res;
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template <>
+EIGEN_STRONG_INLINE Packet16uc psqrt(const Packet16uc& a) {
+  uint8x16_t res = vdupq_n_u8(0);
+  uint8x16_t add = vdupq_n_u8(0x8);
+  for (int i = 0; i < 4; i++) {
+    const uint8x16_t temp = vorrq_u8(res, add);
+    res = vbslq_u8(vcgeq_u8(a, vmulq_u8(temp, temp)), temp, res);
+    add = vshrq_n_u8(add, 1);
+  }
+  return res;
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template <>
+EIGEN_STRONG_INLINE Packet4us psqrt(const Packet4us& a) {
+  uint16x4_t res = vdup_n_u16(0);
+  uint16x4_t add = vdup_n_u16(0x80);
+  for (int i = 0; i < 8; i++) {
+    const uint16x4_t temp = vorr_u16(res, add);
+    res = vbsl_u16(vcge_u16(a, vmul_u16(temp, temp)), temp, res);
+    add = vshr_n_u16(add, 1);
+  }
+  return res;
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template <>
+EIGEN_STRONG_INLINE Packet8us psqrt(const Packet8us& a) {
+  uint16x8_t res = vdupq_n_u16(0);
+  uint16x8_t add = vdupq_n_u16(0x80);
+  for (int i = 0; i < 8; i++) {
+    const uint16x8_t temp = vorrq_u16(res, add);
+    res = vbslq_u16(vcgeq_u16(a, vmulq_u16(temp, temp)), temp, res);
+    add = vshrq_n_u16(add, 1);
+  }
+  return res;
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template <>
+EIGEN_STRONG_INLINE Packet2ui psqrt(const Packet2ui& a) {
+  uint32x2_t res = vdup_n_u32(0);
+  uint32x2_t add = vdup_n_u32(0x8000);
+  for (int i = 0; i < 16; i++) {
+    const uint32x2_t temp = vorr_u32(res, add);
+    res = vbsl_u32(vcge_u32(a, vmul_u32(temp, temp)), temp, res);
+    add = vshr_n_u32(add, 1);
+  }
+  return res;
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template <>
+EIGEN_STRONG_INLINE Packet4ui psqrt(const Packet4ui& a) {
+  uint32x4_t res = vdupq_n_u32(0);
+  uint32x4_t add = vdupq_n_u32(0x8000);
+  for (int i = 0; i < 16; i++) {
+    const uint32x4_t temp = vorrq_u32(res, add);
+    res = vbslq_u32(vcgeq_u32(a, vmulq_u32(temp, temp)), temp, res);
+    add = vshrq_n_u32(add, 1);
+  }
+  return res;
+}
+
+EIGEN_STRONG_INLINE Packet4f prsqrt_float_unsafe(const Packet4f& a) {
+  // Compute approximate reciprocal sqrt.
+  // Does not correctly handle +/- 0 or +inf
+  float32x4_t result = vrsqrteq_f32(a);
+  result = vmulq_f32(vrsqrtsq_f32(vmulq_f32(a, result), result), result);
+  result = vmulq_f32(vrsqrtsq_f32(vmulq_f32(a, result), result), result);
+  return result;
+}
+
+EIGEN_STRONG_INLINE Packet2f prsqrt_float_unsafe(const Packet2f& a) {
+  // Compute approximate reciprocal sqrt.
+  // Does not correctly handle +/- 0 or +inf
+  float32x2_t result = vrsqrte_f32(a);
+  result = vmul_f32(vrsqrts_f32(vmul_f32(a, result), result), result);
+  result = vmul_f32(vrsqrts_f32(vmul_f32(a, result), result), result);
+  return result;
+}
+
+template <typename Packet>
+Packet prsqrt_float_common(const Packet& a) {
+  const Packet cst_zero = pzero(a);
+  const Packet cst_inf = pset1<Packet>(NumTraits<float>::infinity());
+  Packet return_zero = pcmp_eq(a, cst_inf);
+  Packet return_inf = pcmp_eq(a, cst_zero);
+  Packet result = prsqrt_float_unsafe(a);
+  result = pselect(return_inf, por(cst_inf, a), result);
+  result = pandnot(result, return_zero);
+  return result;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f prsqrt(const Packet4f& a) {
+  return prsqrt_float_common(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f prsqrt(const Packet2f& a) {
+  return prsqrt_float_common(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preciprocal<Packet4f>(const Packet4f& a) {
+  // Compute approximate reciprocal.
+  float32x4_t result = vrecpeq_f32(a);
+  result = vmulq_f32(vrecpsq_f32(a, result), result);
+  result = vmulq_f32(vrecpsq_f32(a, result), result);
+  return result;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f preciprocal<Packet2f>(const Packet2f& a) {
+  // Compute approximate reciprocal.
+  float32x2_t result = vrecpe_f32(a);
+  result = vmul_f32(vrecps_f32(a, result), result);
+  result = vmul_f32(vrecps_f32(a, result), result);
+  return result;
+}
+
+// Unfortunately vsqrt_f32 is only available for A64.
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
+  return vsqrtq_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& a) {
+  return vsqrt_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pdiv(const Packet4f& a, const Packet4f& b) {
+  return vdivq_f32(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pdiv(const Packet2f& a, const Packet2f& b) {
+  return vdiv_f32(a, b);
+}
+#else
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet psqrt_float_common(const Packet& a) {
+  const Packet cst_zero = pzero(a);
+  const Packet cst_inf = pset1<Packet>(NumTraits<float>::infinity());
+
+  Packet result = pmul(a, prsqrt_float_unsafe(a));
+  Packet a_is_zero = pcmp_eq(a, cst_zero);
+  Packet a_is_inf = pcmp_eq(a, cst_inf);
+  Packet return_a = por(a_is_zero, a_is_inf);
+
+  result = pselect(return_a, a, result);
+  return result;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
+  return psqrt_float_common(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& a) {
+  return psqrt_float_common(a);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet pdiv_float_common(const Packet& a, const Packet& b) {
+  // if b is large, NEON intrinsics will flush preciprocal(b) to zero
+  // avoid underflow with the following manipulation:
+  // a / b = f * (a * reciprocal(f * b))
+
+  const Packet cst_one = pset1<Packet>(1.0f);
+  const Packet cst_quarter = pset1<Packet>(0.25f);
+  const Packet cst_thresh = pset1<Packet>(NumTraits<float>::highest() / 4.0f);
+
+  Packet b_will_underflow = pcmp_le(cst_thresh, pabs(b));
+  Packet f = pselect(b_will_underflow, cst_quarter, cst_one);
+  Packet result = pmul(f, pmul(a, preciprocal(pmul(b, f))));
+  return result;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pdiv_float_common(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pdiv<Packet2f>(const Packet2f& a, const Packet2f& b) {
+  return pdiv_float_common(a, b);
+}
+#endif
+
+//---------- bfloat16 ----------
+// TODO: Add support for native armv8.6-a bfloat16_t
+
+// TODO: Guard if we have native bfloat16 support
+typedef eigen_packet_wrapper<uint16x4_t, 19> Packet4bf;
+
+template <>
+struct is_arithmetic<Packet4bf> {
+  enum { value = true };
+};
+
+template <>
+struct packet_traits<bfloat16> : default_packet_traits {
+  typedef Packet4bf type;
+  typedef Packet4bf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbsDiff = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+    HasDiv = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 0,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasBessel = 0,  // Issues with accuracy.
+    HasNdtri = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4bf> : neon_unpacket_default<Packet4bf, bfloat16> {};
+
+namespace detail {
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4bf>(Packet4bf& p1, Packet4bf& p2) {
+  const uint16x4x2_t tmp = vzip_u16(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+}  // namespace detail
+
+EIGEN_STRONG_INLINE Packet4bf F32ToBf16(const Packet4f& p) {
+  // See the scalar implementation in BFloat16.h for a comprehensible explanation
+  // of this fast rounding algorithm
+  Packet4ui input = Packet4ui(vreinterpretq_u32_f32(p));
+
+  // lsb = (input >> 16) & 1
+  Packet4ui lsb = vandq_u32(vshrq_n_u32(input, 16), vdupq_n_u32(1));
+
+  // rounding_bias = 0x7fff + lsb
+  Packet4ui rounding_bias = vaddq_u32(lsb, vdupq_n_u32(0x7fff));
+
+  // input += rounding_bias
+  input = vaddq_u32(input, rounding_bias);
+
+  // input = input >> 16
+  input = vshrq_n_u32(input, 16);
+
+  // Replace float-nans by bfloat16-nans, that is 0x7fc0
+  const Packet4ui bf16_nan = vdupq_n_u32(0x7fc0);
+  const Packet4ui mask = vceqq_f32(p, p);
+  input = vbslq_u32(mask, input, bf16_nan);
+
+  // output = static_cast<uint16_t>(input)
+  return vmovn_u32(input);
+}
+
+EIGEN_STRONG_INLINE Packet4f Bf16ToF32(const Packet4bf& p) {
+  return Packet4f(vreinterpretq_f32_u32(vshlq_n_u32(vmovl_u16(p), 16)));
+}
+
+EIGEN_STRONG_INLINE Packet4bf F32MaskToBf16Mask(const Packet4f& p) { return vmovn_u32(vreinterpretq_u32_f32(p)); }
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pset1<Packet4bf>(const bfloat16& from) {
+  return Packet4bf(pset1<Packet4us>(from.value));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 pfirst<Packet4bf>(const Packet4bf& from) {
+  return bfloat16_impl::raw_uint16_to_bfloat16(static_cast<uint16_t>(pfirst<Packet4us>(Packet4us(from))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pload<Packet4bf>(const bfloat16* from) {
+  return Packet4bf(
+      pload<Packet4us>(reinterpret_cast<const uint16_t*>(assume_aligned<unpacket_traits<Packet4bf>::alignment>(from))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf ploadu<Packet4bf>(const bfloat16* from) {
+  return Packet4bf(ploadu<Packet4us>(reinterpret_cast<const uint16_t*>(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet4bf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1_u16(
+      reinterpret_cast<uint16_t*>(assume_aligned<unpacket_traits<Packet4bf>::alignment>(to)), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet4bf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_u16(reinterpret_cast<uint16_t*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf ploaddup<Packet4bf>(const bfloat16* from) {
+  return Packet4bf(ploaddup<Packet4us>(reinterpret_cast<const uint16_t*>(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pabs(const Packet4bf& a) {
+  return F32ToBf16(pabs<Packet4f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pmin<PropagateNumbers, Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pmin<PropagateNumbers, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4bf pmin<PropagateNaN, Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pmin<PropagateNaN, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pmin<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pmin<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pmax<PropagateNumbers, Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pmax<PropagateNumbers, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4bf pmax<PropagateNaN, Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pmax<PropagateNaN, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pmax<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pmax<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf plset<Packet4bf>(const bfloat16& a) {
+  return F32ToBf16(plset<Packet4f>(static_cast<float>(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf por(const Packet4bf& a, const Packet4bf& b) {
+  return Packet4bf(por<Packet4us>(Packet4us(a), Packet4us(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pxor(const Packet4bf& a, const Packet4bf& b) {
+  return Packet4bf(pxor<Packet4us>(Packet4us(a), Packet4us(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pand(const Packet4bf& a, const Packet4bf& b) {
+  return Packet4bf(pand<Packet4us>(Packet4us(a), Packet4us(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pandnot(const Packet4bf& a, const Packet4bf& b) {
+  return Packet4bf(pandnot<Packet4us>(Packet4us(a), Packet4us(b)));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4bf pselect(const Packet4bf& mask, const Packet4bf& a, const Packet4bf& b) {
+  return Packet4bf(pselect<Packet4us>(Packet4us(mask), Packet4us(a), Packet4us(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf print<Packet4bf>(const Packet4bf& a) {
+  return F32ToBf16(print<Packet4f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pfloor<Packet4bf>(const Packet4bf& a) {
+  return F32ToBf16(pfloor<Packet4f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pceil<Packet4bf>(const Packet4bf& a) {
+  return F32ToBf16(pceil<Packet4f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pround<Packet4bf>(const Packet4bf& a) {
+  return F32ToBf16(pround<Packet4f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf ptrunc<Packet4bf>(const Packet4bf& a) {
+  return F32ToBf16(ptrunc<Packet4f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pconj(const Packet4bf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf padd<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(padd<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf psub<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(psub<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pmul<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pmul<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pmadd<Packet4bf>(const Packet4bf& a, const Packet4bf& b, const Packet4bf& c) {
+  return F32ToBf16(pmadd<Packet4f>(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pmsub<Packet4bf>(const Packet4bf& a, const Packet4bf& b, const Packet4bf& c) {
+  return F32ToBf16(pmsub<Packet4f>(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pnmadd<Packet4bf>(const Packet4bf& a, const Packet4bf& b, const Packet4bf& c) {
+  return F32ToBf16(pnmadd<Packet4f>(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pnmsub<Packet4bf>(const Packet4bf& a, const Packet4bf& b, const Packet4bf& c) {
+  return F32ToBf16(pnmsub<Packet4f>(Bf16ToF32(a), Bf16ToF32(b), Bf16ToF32(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pdiv<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pdiv<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pgather<bfloat16, Packet4bf>(const bfloat16* from, Index stride) {
+  return Packet4bf(pgather<uint16_t, Packet4us>(reinterpret_cast<const uint16_t*>(from), stride));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet4bf>(bfloat16* to, const Packet4bf& from, Index stride) {
+  pscatter<uint16_t, Packet4us>(reinterpret_cast<uint16_t*>(to), Packet4us(from), stride);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux<Packet4bf>(const Packet4bf& a) {
+  return static_cast<bfloat16>(predux<Packet4f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max<Packet4bf>(const Packet4bf& a) {
+  return static_cast<bfloat16>(predux_max<Packet4f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min<Packet4bf>(const Packet4bf& a) {
+  return static_cast<bfloat16>(predux_min<Packet4f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet4bf>(const Packet4bf& a) {
+  return static_cast<bfloat16>(predux_mul<Packet4f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf preverse<Packet4bf>(const Packet4bf& a) {
+  return Packet4bf(preverse<Packet4us>(Packet4us(a)));
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4bf, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pabsdiff<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pabsdiff<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pcmp_eq<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32MaskToBf16Mask(pcmp_eq<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pcmp_lt<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32MaskToBf16Mask(pcmp_lt<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pcmp_lt_or_nan<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32MaskToBf16Mask(pcmp_lt_or_nan<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pcmp_le<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32MaskToBf16Mask(pcmp_le<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pnegate<Packet4bf>(const Packet4bf& a) {
+  return Packet4bf(pxor<Packet4us>(Packet4us(a), pset1<Packet4us>(static_cast<uint16_t>(0x8000))));
+}
+
+//---------- double ----------
+
+// Clang 3.5 in the iOS toolchain has an ICE triggered by NEON intrinsics for double.
+// Confirmed at least with __apple_build_version__ = 6000054.
+#if EIGEN_COMP_CLANGAPPLE
+// Let's hope that by the time __apple_build_version__ hits the 601* range, the bug will be fixed.
+// https://gist.github.com/yamaya/2924292 suggests that the 3 first digits are only updated with
+// major toolchain updates.
+#define EIGEN_APPLE_DOUBLE_NEON_BUG (EIGEN_COMP_CLANGAPPLE < 6010000)
+#else
+#define EIGEN_APPLE_DOUBLE_NEON_BUG 0
+#endif
+
+#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+
+#if EIGEN_COMP_GNUC
+// Bug 907: workaround missing declarations of the following two functions in the ADK
+// Defining these functions as templates ensures that if these intrinsics are
+// already defined in arm_neon.h, then our workaround doesn't cause a conflict
+// and has lower priority in overload resolution.
+// This doesn't work with MSVC though, since the function names are macros.
+template <typename T>
+uint64x2_t vreinterpretq_u64_f64(T a) {
+  return (uint64x2_t)a;
+}
+
+template <typename T>
+float64x2_t vreinterpretq_f64_u64(T a) {
+  return (float64x2_t)a;
+}
+#endif
+
+#if EIGEN_COMP_MSVC_STRICT
+typedef eigen_packet_wrapper<float64x2_t, 18> Packet2d;
+typedef eigen_packet_wrapper<float64x1_t, 19> Packet1d;
+
+EIGEN_ALWAYS_INLINE Packet2d make_packet2d(double a, double b) {
+  double from[2] = {a, b};
+  return vld1q_f64(from);
+}
+
+#else
+typedef float64x2_t Packet2d;
+typedef float64x1_t Packet1d;
+
+EIGEN_ALWAYS_INLINE Packet2d make_packet2d(double a, double b) { return Packet2d{a, b}; }
+#endif
+
+// functionally equivalent to _mm_shuffle_pd in SSE (i.e. shuffle(m, n, mask) equals _mm_shuffle_pd(m,n,mask))
+// Currently used in LU/arch/InverseSize4.h to enable a shared implementation
+// for fast inversion of matrices of size 4.
+EIGEN_STRONG_INLINE Packet2d shuffle(const Packet2d& m, const Packet2d& n, int mask) {
+  const double* a = reinterpret_cast<const double*>(&m);
+  const double* b = reinterpret_cast<const double*>(&n);
+  Packet2d res = make_packet2d(*(a + (mask & 1)), *(b + ((mask >> 1) & 1)));
+  return res;
+}
+
+EIGEN_STRONG_INLINE Packet2d vec2d_swizzle2(const Packet2d& a, const Packet2d& b, int mask) {
+  return shuffle(a, b, mask);
+}
+EIGEN_STRONG_INLINE Packet2d vec2d_unpacklo(const Packet2d& a, const Packet2d& b) { return shuffle(a, b, 0); }
+EIGEN_STRONG_INLINE Packet2d vec2d_unpackhi(const Packet2d& a, const Packet2d& b) { return shuffle(a, b, 3); }
+#define vec2d_duplane(a, p) Packet2d(vdupq_laneq_f64(a, p))
+
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbsDiff = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+
+    HasDiv = 1,
+
+#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+    HasExp = 1,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasPow = 1,
+    HasATan = 1,
+    HasATanh = 1,
+#endif
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasCbrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasErfc = EIGEN_FAST_MATH
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2d> : neon_unpacket_default<Packet2d, double> {
+  using integer_packet = Packet2l;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pzero<Packet2d>(const Packet2d& /*a*/) {
+  return vdupq_n_f64(0.0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
+  return vdupq_n_f64(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) {
+  const double c[] = {0.0, 1.0};
+  return vaddq_f64(pset1<Packet2d>(a), vld1q_f64(c));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vaddq_f64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vsubq_f64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d&, const Packet2d&);
+template <>
+EIGEN_STRONG_INLINE Packet2d paddsub<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  const Packet2d mask = make_packet2d(numext::bit_cast<double>(0x8000000000000000ull), 0.0);
+  return padd(a, pxor(mask, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) {
+  return vnegq_f64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vmulq_f64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vdivq_f64(a, b);
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+// See bug 936. See above comment about FMA for float.
+template <>
+EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return vfmaq_f64(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return vfmsq_f64(c, a, b);
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return vmlaq_f64(c, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return vmlsq_f64(c, a, b);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet2d pmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return pnegate(pnmadd(a, b, c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return pnegate(pmadd(a, b, c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vminq_f64(a, b);
+}
+
+#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
+// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8
+// systems).
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vminnmq_f64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vmaxnmq_f64(a, b);
+}
+
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pmin<Packet2d>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vmaxq_f64(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pmax<Packet2d>(a, b);
+}
+
+// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
+template <>
+EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a), vreinterpretq_u64_f64(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a), vreinterpretq_u64_f64(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a), vreinterpretq_u64_f64(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a), vreinterpretq_u64_f64(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b) {
+  return vreinterpretq_f64_u64(vcleq_f64(a, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b) {
+  return vreinterpretq_f64_u64(vcltq_f64(a, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b) {
+  return vreinterpretq_f64_u32(vmvnq_u32(vreinterpretq_u32_u64(vcgeq_f64(a, b))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b) {
+  return vreinterpretq_f64_u64(vceqq_f64(a, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f64(assume_aligned<unpacket_traits<Packet2d>::alignment>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f64(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) {
+  return vld1q_dup_f64(from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_f64(assume_aligned<unpacket_traits<Packet2d>::alignment>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_f64(to, from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2d pgather<double, Packet2d>(const double* from, Index stride) {
+  Packet2d res = pset1<Packet2d>(0.0);
+  res = vld1q_lane_f64(from + 0 * stride, res, 0);
+  res = vld1q_lane_f64(from + 1 * stride, res, 1);
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride) {
+  vst1q_lane_f64(to + stride * 0, from, 0);
+  vst1q_lane_f64(to + stride * 1, from, 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+
+// FIXME only store the 2 first elements ?
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
+  return vgetq_lane_f64(a, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) {
+  return vcombine_f64(vget_high_f64(a), vget_low_f64(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) {
+  return vabsq_f64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d psignbit(const Packet2d& a) {
+  return vreinterpretq_f64_s64(vshrq_n_s64(vreinterpretq_s64_f64(a), 63));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) {
+  return vaddvq_f64(a);
+}
+
+// Other reduction functions:
+// mul
+#if EIGEN_COMP_CLANGAPPLE
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) {
+  return (vget_low_f64(a) * vget_high_f64(a))[0];
+}
+#else
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) {
+  return vget_lane_f64(vmul_f64(vget_low_f64(a), vget_high_f64(a)), 0);
+}
+#endif
+
+// min
+template <>
+EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) {
+  return vminvq_f64(a);
+}
+
+// max
+template <>
+EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) {
+  return vmaxvq_f64(a);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2d, 2>& kernel) {
+  const float64x2_t tmp1 = vzip1q_f64(kernel.packet[0], kernel.packet[1]);
+  const float64x2_t tmp2 = vzip2q_f64(kernel.packet[0], kernel.packet[1]);
+
+  kernel.packet[0] = tmp1;
+  kernel.packet[1] = tmp2;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2d pselect(const Packet2d& mask, const Packet2d& a, const Packet2d& b) {
+  return vbslq_f64(vreinterpretq_u64_f64(mask), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a) {
+  return vrndnq_f64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) {
+  return vrndmq_f64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) {
+  return vrndpq_f64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) {
+  return vrndaq_f64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ptrunc<Packet2d>(const Packet2d& a) {
+  return vrndq_f64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(uint64_t from) {
+  return vreinterpretq_f64_u64(vdupq_n_u64(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d prsqrt(const Packet2d& a) {
+  // Do Newton iterations for 1/sqrt(x).
+  return generic_rsqrt_newton_step<Packet2d, /*Steps=*/3>::run(a, vrsqrteq_f64(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d psqrt(const Packet2d& _x) {
+  return vsqrtq_f64(_x);
+}
+
+#endif  // EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+
+// Do we have an fp16 types and supporting Neon intrinsics?
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+typedef float16x4_t Packet4hf;
+typedef float16x8_t Packet8hf;
+
+template <>
+struct packet_traits<Eigen::half> : default_packet_traits {
+  typedef Packet8hf type;
+  typedef Packet4hf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp = 1,
+    HasCast = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbsDiff = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+    HasInsert = 1,
+    HasReduxp = 1,
+    HasDiv = 1,
+    HasSin = 0,
+    HasCos = 0,
+    HasLog = 0,
+    HasExp = 0,
+    HasTanh = packet_traits<float>::HasTanh,  // tanh<half> calls tanh<float>
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasErf = EIGEN_FAST_MATH,
+    HasBessel = 0,  // Issues with accuracy.
+    HasNdtri = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4hf> : neon_unpacket_default<Packet4hf, half> {};
+template <>
+struct unpacket_traits<Packet8hf> : neon_unpacket_default<Packet8hf, half> {
+  using half = Packet4hf;
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf predux_half<Packet8hf>(const Packet8hf& a) {
+  return vadd_f16(vget_low_f16(a), vget_high_f16(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pset1<Packet8hf>(const Eigen::half& from) {
+  return vdupq_n_f16(from.x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pset1<Packet4hf>(const Eigen::half& from) {
+  return vdup_n_f16(from.x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf plset<Packet8hf>(const Eigen::half& a) {
+  const float16_t f[] = {0, 1, 2, 3, 4, 5, 6, 7};
+  Packet8hf countdown = vld1q_f16(f);
+  return vaddq_f16(pset1<Packet8hf>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf plset<Packet4hf>(const Eigen::half& a) {
+  const float16_t f[] = {0, 1, 2, 3};
+  Packet4hf countdown = vld1_f16(f);
+  return vadd_f16(pset1<Packet4hf>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf padd<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vaddq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf padd<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vadd_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf psub<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vsubq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf psub<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vsub_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pnegate(const Packet8hf& a) {
+  return vnegq_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pnegate(const Packet4hf& a) {
+  return vneg_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pconj(const Packet8hf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pconj(const Packet4hf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmul<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vmulq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmul<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vmul_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pdiv<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vdivq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pdiv<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vdiv_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmadd(const Packet8hf& a, const Packet8hf& b, const Packet8hf& c) {
+  return vfmaq_f16(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmadd(const Packet4hf& a, const Packet4hf& b, const Packet4hf& c) {
+  return vfma_f16(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pnmadd(const Packet8hf& a, const Packet8hf& b, const Packet8hf& c) {
+  return vfmsq_f16(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pnmadd(const Packet4hf& a, const Packet4hf& b, const Packet4hf& c) {
+  return vfms_f16(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmsub(const Packet8hf& a, const Packet8hf& b, const Packet8hf& c) {
+  return pnegate(pnmadd(a, b, c));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmsub(const Packet4hf& a, const Packet4hf& b, const Packet4hf& c) {
+  return pnegate(pnmadd(a, b, c));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pnmsub(const Packet8hf& a, const Packet8hf& b, const Packet8hf& c) {
+  return pnegate(pmadd(a, b, c));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pnmsub(const Packet4hf& a, const Packet4hf& b, const Packet4hf& c) {
+  return pnegate(pmadd(a, b, c));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmin<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vminq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmin<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vmin_f16(a, b);
+}
+
+#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
+// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8
+// systems).
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmin<PropagateNumbers, Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vminnm_f16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmin<PropagateNumbers, Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vminnmq_f16(a, b);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmin<PropagateNaN, Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return pmin<Packet4hf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmin<PropagateNaN, Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return pmin<Packet8hf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmax<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vmaxq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmax<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vmax_f16(a, b);
+}
+
+#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
+// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8
+// systems).
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmax<PropagateNumbers, Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vmaxnm_f16(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmax<PropagateNumbers, Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vmaxnmq_f16(a, b);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmax<PropagateNaN, Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return pmax<Packet4hf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmax<PropagateNaN, Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return pmax<Packet8hf>(a, b);
+}
+
+#define EIGEN_MAKE_ARM_FP16_CMP_8(name)                                               \
+  template <>                                                                         \
+  EIGEN_STRONG_INLINE Packet8hf pcmp_##name(const Packet8hf& a, const Packet8hf& b) { \
+    return vreinterpretq_f16_u16(vc##name##q_f16(a, b));                              \
+  }
+
+#define EIGEN_MAKE_ARM_FP16_CMP_4(name)                                               \
+  template <>                                                                         \
+  EIGEN_STRONG_INLINE Packet4hf pcmp_##name(const Packet4hf& a, const Packet4hf& b) { \
+    return vreinterpret_f16_u16(vc##name##_f16(a, b));                                \
+  }
+
+EIGEN_MAKE_ARM_FP16_CMP_8(eq)
+EIGEN_MAKE_ARM_FP16_CMP_8(lt)
+EIGEN_MAKE_ARM_FP16_CMP_8(le)
+
+EIGEN_MAKE_ARM_FP16_CMP_4(eq)
+EIGEN_MAKE_ARM_FP16_CMP_4(lt)
+EIGEN_MAKE_ARM_FP16_CMP_4(le)
+
+#undef EIGEN_MAKE_ARM_FP16_CMP_8
+#undef EIGEN_MAKE_ARM_FP16_CMP_4
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pcmp_lt_or_nan<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vreinterpretq_f16_u16(vmvnq_u16(vcgeq_f16(a, b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pcmp_lt_or_nan<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vreinterpret_f16_u16(vmvn_u16(vcge_f16(a, b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf print<Packet8hf>(const Packet8hf& a) {
+  return vrndnq_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf print<Packet4hf>(const Packet4hf& a) {
+  return vrndn_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pfloor<Packet8hf>(const Packet8hf& a) {
+  return vrndmq_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pfloor<Packet4hf>(const Packet4hf& a) {
+  return vrndm_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pceil<Packet8hf>(const Packet8hf& a) {
+  return vrndpq_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pceil<Packet4hf>(const Packet4hf& a) {
+  return vrndp_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pround<Packet8hf>(const Packet8hf& a) {
+  return vrndaq_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pround<Packet4hf>(const Packet4hf& a) {
+  return vrnda_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf ptrunc<Packet8hf>(const Packet8hf& a) {
+  return vrndq_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf ptrunc<Packet4hf>(const Packet4hf& a) {
+  return vrnd_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf psqrt<Packet8hf>(const Packet8hf& a) {
+  return vsqrtq_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf psqrt<Packet4hf>(const Packet4hf& a) {
+  return vsqrt_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pand<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vreinterpretq_f16_u16(vandq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pand<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vreinterpret_f16_u16(vand_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf por<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vreinterpretq_f16_u16(vorrq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf por<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vreinterpret_f16_u16(vorr_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pxor<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vreinterpretq_f16_u16(veorq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pxor<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vreinterpret_f16_u16(veor_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pandnot<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vreinterpretq_f16_u16(vbicq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pandnot<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vreinterpret_f16_u16(vbic_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pload<Packet8hf>(const Eigen::half* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f16(
+      reinterpret_cast<const float16_t*>(assume_aligned<unpacket_traits<Packet8hf>::alignment>(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pload<Packet4hf>(const Eigen::half* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1_f16(
+      reinterpret_cast<const float16_t*>(assume_aligned<unpacket_traits<Packet4hf>::alignment>(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf ploadu<Packet8hf>(const Eigen::half* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f16(reinterpret_cast<const float16_t*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf ploadu<Packet4hf>(const Eigen::half* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_f16(reinterpret_cast<const float16_t*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf ploaddup<Packet8hf>(const Eigen::half* from) {
+  Packet8hf packet;
+  packet[0] = from[0].x;
+  packet[1] = from[0].x;
+  packet[2] = from[1].x;
+  packet[3] = from[1].x;
+  packet[4] = from[2].x;
+  packet[5] = from[2].x;
+  packet[6] = from[3].x;
+  packet[7] = from[3].x;
+  return packet;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf ploaddup<Packet4hf>(const Eigen::half* from) {
+  float16x4_t packet;
+  float16_t* tmp;
+  tmp = (float16_t*)&packet;
+  tmp[0] = from[0].x;
+  tmp[1] = from[0].x;
+  tmp[2] = from[1].x;
+  tmp[3] = from[1].x;
+  return packet;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf ploadquad<Packet8hf>(const Eigen::half* from) {
+  Packet4hf lo, hi;
+  lo = vld1_dup_f16(reinterpret_cast<const float16_t*>(from));
+  hi = vld1_dup_f16(reinterpret_cast<const float16_t*>(from + 1));
+  return vcombine_f16(lo, hi);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pinsertfirst(const Packet8hf& a, Eigen::half b) {
+  return vsetq_lane_f16(b.x, a, 0);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pinsertfirst(const Packet4hf& a, Eigen::half b) {
+  return vset_lane_f16(b.x, a, 0);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pselect(const Packet8hf& mask, const Packet8hf& a, const Packet8hf& b) {
+  return vbslq_f16(vreinterpretq_u16_f16(mask), a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pselect(const Packet4hf& mask, const Packet4hf& a, const Packet4hf& b) {
+  return vbsl_f16(vreinterpret_u16_f16(mask), a, b);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pinsertlast(const Packet8hf& a, Eigen::half b) {
+  return vsetq_lane_f16(b.x, a, 7);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pinsertlast(const Packet4hf& a, Eigen::half b) {
+  return vset_lane_f16(b.x, a, 3);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet8hf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_f16(
+      reinterpret_cast<float16_t*>(assume_aligned<unpacket_traits<Packet8hf>::alignment>(to)), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet4hf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1_f16(
+      reinterpret_cast<float16_t*>(assume_aligned<unpacket_traits<Packet4hf>::alignment>(to)), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet8hf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_f16(reinterpret_cast<float16_t*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet4hf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_f16(reinterpret_cast<float16_t*>(to), from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pgather<Eigen::half, Packet8hf>(const Eigen::half* from, Index stride) {
+  Packet8hf res = pset1<Packet8hf>(Eigen::half(0.f));
+  res = vsetq_lane_f16(from[0 * stride].x, res, 0);
+  res = vsetq_lane_f16(from[1 * stride].x, res, 1);
+  res = vsetq_lane_f16(from[2 * stride].x, res, 2);
+  res = vsetq_lane_f16(from[3 * stride].x, res, 3);
+  res = vsetq_lane_f16(from[4 * stride].x, res, 4);
+  res = vsetq_lane_f16(from[5 * stride].x, res, 5);
+  res = vsetq_lane_f16(from[6 * stride].x, res, 6);
+  res = vsetq_lane_f16(from[7 * stride].x, res, 7);
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pgather<Eigen::half, Packet4hf>(const Eigen::half* from, Index stride) {
+  Packet4hf res = pset1<Packet4hf>(Eigen::half(0.f));
+  res = vset_lane_f16(from[0 * stride].x, res, 0);
+  res = vset_lane_f16(from[1 * stride].x, res, 1);
+  res = vset_lane_f16(from[2 * stride].x, res, 2);
+  res = vset_lane_f16(from[3 * stride].x, res, 3);
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8hf>(Eigen::half* to, const Packet8hf& from,
+                                                                            Index stride) {
+  to[stride * 0].x = vgetq_lane_f16(from, 0);
+  to[stride * 1].x = vgetq_lane_f16(from, 1);
+  to[stride * 2].x = vgetq_lane_f16(from, 2);
+  to[stride * 3].x = vgetq_lane_f16(from, 3);
+  to[stride * 4].x = vgetq_lane_f16(from, 4);
+  to[stride * 5].x = vgetq_lane_f16(from, 5);
+  to[stride * 6].x = vgetq_lane_f16(from, 6);
+  to[stride * 7].x = vgetq_lane_f16(from, 7);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4hf>(Eigen::half* to, const Packet4hf& from,
+                                                                            Index stride) {
+  to[stride * 0].x = vget_lane_f16(from, 0);
+  to[stride * 1].x = vget_lane_f16(from, 1);
+  to[stride * 2].x = vget_lane_f16(from, 2);
+  to[stride * 3].x = vget_lane_f16(from, 3);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<Eigen::half>(const Eigen::half* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet8hf>(const Packet8hf& a) {
+  float16_t x[8];
+  vst1q_f16(x, a);
+  Eigen::half h;
+  h.x = x[0];
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4hf>(const Packet4hf& a) {
+  float16_t x[4];
+  vst1_f16(x, a);
+  Eigen::half h;
+  h.x = x[0];
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf preverse(const Packet8hf& a) {
+  float16x4_t a_lo, a_hi;
+  Packet8hf a_r64;
+
+  a_r64 = vrev64q_f16(a);
+  a_lo = vget_low_f16(a_r64);
+  a_hi = vget_high_f16(a_r64);
+  return vcombine_f16(a_hi, a_lo);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf preverse<Packet4hf>(const Packet4hf& a) {
+  return vrev64_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pabs<Packet8hf>(const Packet8hf& a) {
+  return vabsq_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf psignbit(const Packet8hf& a) {
+  return vreinterpretq_f16_s16(vshrq_n_s16(vreinterpretq_s16_f16(a), 15));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pabs<Packet4hf>(const Packet4hf& a) {
+  return vabs_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf psignbit(const Packet4hf& a) {
+  return vreinterpret_f16_s16(vshr_n_s16(vreinterpret_s16_f16(a), 15));
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux<Packet8hf>(const Packet8hf& a) {
+  float16x4_t a_lo, a_hi, sum;
+
+  a_lo = vget_low_f16(a);
+  a_hi = vget_high_f16(a);
+  sum = vpadd_f16(a_lo, a_hi);
+  sum = vpadd_f16(sum, sum);
+  sum = vpadd_f16(sum, sum);
+
+  Eigen::half h;
+  h.x = vget_lane_f16(sum, 0);
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux<Packet4hf>(const Packet4hf& a) {
+  float16x4_t sum;
+
+  sum = vpadd_f16(a, a);
+  sum = vpadd_f16(sum, sum);
+  Eigen::half h;
+  h.x = vget_lane_f16(sum, 0);
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet8hf>(const Packet8hf& a) {
+  float16x4_t a_lo, a_hi, prod;
+
+  a_lo = vget_low_f16(a);
+  a_hi = vget_high_f16(a);
+  prod = vmul_f16(a_lo, a_hi);
+  prod = vmul_f16(prod, vrev64_f16(prod));
+
+  Eigen::half h;
+  h.x = vmulh_f16(vget_lane_f16(prod, 0), vget_lane_f16(prod, 1));
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet4hf>(const Packet4hf& a) {
+  float16x4_t prod;
+  prod = vmul_f16(a, vrev64_f16(a));
+  Eigen::half h;
+  h.x = vmulh_f16(vget_lane_f16(prod, 0), vget_lane_f16(prod, 1));
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_min<Packet8hf>(const Packet8hf& a) {
+  Eigen::half h;
+  h.x = vminvq_f16(a);
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_min<Packet4hf>(const Packet4hf& a) {
+  Eigen::half h;
+  h.x = vminv_f16(a);
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_max<Packet8hf>(const Packet8hf& a) {
+  Eigen::half h;
+  h.x = vmaxvq_f16(a);
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_max<Packet4hf>(const Packet4hf& a) {
+  Eigen::half h;
+  h.x = vmaxv_f16(a);
+  return h;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8hf, 4>& kernel) {
+  const float16x8x2_t zip16_1 = vzipq_f16(kernel.packet[0], kernel.packet[1]);
+  const float16x8x2_t zip16_2 = vzipq_f16(kernel.packet[2], kernel.packet[3]);
+
+  const float32x4x2_t zip32_1 = vzipq_f32(vreinterpretq_f32_f16(zip16_1.val[0]), vreinterpretq_f32_f16(zip16_2.val[0]));
+  const float32x4x2_t zip32_2 = vzipq_f32(vreinterpretq_f32_f16(zip16_1.val[1]), vreinterpretq_f32_f16(zip16_2.val[1]));
+
+  kernel.packet[0] = vreinterpretq_f16_f32(zip32_1.val[0]);
+  kernel.packet[1] = vreinterpretq_f16_f32(zip32_1.val[1]);
+  kernel.packet[2] = vreinterpretq_f16_f32(zip32_2.val[0]);
+  kernel.packet[3] = vreinterpretq_f16_f32(zip32_2.val[1]);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4hf, 4>& kernel) {
+  EIGEN_ALIGN16 float16x4x4_t tmp_x4;
+  float16_t* tmp = (float16_t*)&kernel;
+  tmp_x4 = vld4_f16(tmp);
+
+  kernel.packet[0] = tmp_x4.val[0];
+  kernel.packet[1] = tmp_x4.val[1];
+  kernel.packet[2] = tmp_x4.val[2];
+  kernel.packet[3] = tmp_x4.val[3];
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8hf, 8>& kernel) {
+  float16x8x2_t T_1[4];
+
+  T_1[0] = vuzpq_f16(kernel.packet[0], kernel.packet[1]);
+  T_1[1] = vuzpq_f16(kernel.packet[2], kernel.packet[3]);
+  T_1[2] = vuzpq_f16(kernel.packet[4], kernel.packet[5]);
+  T_1[3] = vuzpq_f16(kernel.packet[6], kernel.packet[7]);
+
+  float16x8x2_t T_2[4];
+  T_2[0] = vuzpq_f16(T_1[0].val[0], T_1[1].val[0]);
+  T_2[1] = vuzpq_f16(T_1[0].val[1], T_1[1].val[1]);
+  T_2[2] = vuzpq_f16(T_1[2].val[0], T_1[3].val[0]);
+  T_2[3] = vuzpq_f16(T_1[2].val[1], T_1[3].val[1]);
+
+  float16x8x2_t T_3[4];
+  T_3[0] = vuzpq_f16(T_2[0].val[0], T_2[2].val[0]);
+  T_3[1] = vuzpq_f16(T_2[0].val[1], T_2[2].val[1]);
+  T_3[2] = vuzpq_f16(T_2[1].val[0], T_2[3].val[0]);
+  T_3[3] = vuzpq_f16(T_2[1].val[1], T_2[3].val[1]);
+
+  kernel.packet[0] = T_3[0].val[0];
+  kernel.packet[1] = T_3[2].val[0];
+  kernel.packet[2] = T_3[1].val[0];
+  kernel.packet[3] = T_3[3].val[0];
+  kernel.packet[4] = T_3[0].val[1];
+  kernel.packet[5] = T_3[2].val[1];
+  kernel.packet[6] = T_3[1].val[1];
+  kernel.packet[7] = T_3[3].val[1];
+}
+#endif  // end EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_NEON_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/TypeCasting.h
new file mode 100644
index 0000000..58d7b8c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/TypeCasting.h
@@ -0,0 +1,1642 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Rasmus Munk Larsen <rmlarsen@google.com>
+// Copyright (C) 2020 Antonio Sanchez <cantonios@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_NEON_H
+#define EIGEN_TYPE_CASTING_NEON_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//==============================================================================
+// preinterpret (truncation operations)
+//==============================================================================
+
+template <>
+EIGEN_STRONG_INLINE Packet8c preinterpret<Packet8c, Packet16c>(const Packet16c& a) {
+  return Packet8c(vget_low_s8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c preinterpret<Packet4c, Packet8c>(const Packet8c& a) {
+  return Packet4c(vget_lane_s32(vreinterpret_s32_s8(a), 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c preinterpret<Packet4c, Packet16c>(const Packet16c& a) {
+  return preinterpret<Packet4c>(preinterpret<Packet8c>(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8uc preinterpret<Packet8uc, Packet16uc>(const Packet16uc& a) {
+  return Packet8uc(vget_low_u8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc preinterpret<Packet4uc, Packet8uc>(const Packet8uc& a) {
+  return Packet4uc(vget_lane_u32(vreinterpret_u32_u8(a), 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc preinterpret<Packet4uc, Packet16uc>(const Packet16uc& a) {
+  return preinterpret<Packet4uc>(preinterpret<Packet8uc>(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4s preinterpret<Packet4s, Packet8s>(const Packet8s& a) {
+  return Packet4s(vget_low_s16(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4us preinterpret<Packet4us, Packet8us>(const Packet8us& a) {
+  return Packet4us(vget_low_u16(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet4i>(const Packet4i& a) {
+  return Packet2i(vget_low_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet4ui>(const Packet4ui& a) {
+  return Packet2ui(vget_low_u32(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet4f>(const Packet4f& a) {
+  return Packet2f(vget_low_f32(a));
+}
+
+//==============================================================================
+// preinterpret
+//==============================================================================
+template <>
+EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2i>(const Packet2i& a) {
+  return Packet2f(vreinterpret_f32_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2ui>(const Packet2ui& a) {
+  return Packet2f(vreinterpret_f32_u32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4i>(const Packet4i& a) {
+  return Packet4f(vreinterpretq_f32_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4ui>(const Packet4ui& a) {
+  return Packet4f(vreinterpretq_f32_u32(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4c preinterpret<Packet4c, Packet4uc>(const Packet4uc& a) {
+  return static_cast<Packet4c>(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c preinterpret<Packet8c, Packet8uc>(const Packet8uc& a) {
+  return Packet8c(vreinterpret_s8_u8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c preinterpret<Packet16c, Packet16uc>(const Packet16uc& a) {
+  return Packet16c(vreinterpretq_s8_u8(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4uc preinterpret<Packet4uc, Packet4c>(const Packet4c& a) {
+  return static_cast<Packet4uc>(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc preinterpret<Packet8uc, Packet8c>(const Packet8c& a) {
+  return Packet8uc(vreinterpret_u8_s8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc preinterpret<Packet16uc, Packet16c>(const Packet16c& a) {
+  return Packet16uc(vreinterpretq_u8_s8(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4s preinterpret<Packet4s, Packet4us>(const Packet4us& a) {
+  return Packet4s(vreinterpret_s16_u16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s preinterpret<Packet8s, Packet8us>(const Packet8us& a) {
+  return Packet8s(vreinterpretq_s16_u16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us preinterpret<Packet4us, Packet4s>(const Packet4s& a) {
+  return Packet4us(vreinterpret_u16_s16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us preinterpret<Packet8us, Packet8s>(const Packet8s& a) {
+  return Packet8us(vreinterpretq_u16_s16(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2f>(const Packet2f& a) {
+  return Packet2i(vreinterpret_s32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2ui>(const Packet2ui& a) {
+  return Packet2i(vreinterpret_s32_u32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4f>(const Packet4f& a) {
+  return Packet4i(vreinterpretq_s32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4ui>(const Packet4ui& a) {
+  return Packet4i(vreinterpretq_s32_u32(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2f>(const Packet2f& a) {
+  return Packet2ui(vreinterpret_u32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2i>(const Packet2i& a) {
+  return Packet2ui(vreinterpret_u32_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4f>(const Packet4f& a) {
+  return Packet4ui(vreinterpretq_u32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4i>(const Packet4i& a) {
+  return Packet4ui(vreinterpretq_u32_s32(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2ul>(const Packet2ul& a) {
+  return Packet2l(vreinterpretq_s64_u64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2l>(const Packet2l& a) {
+  return Packet2ul(vreinterpretq_u64_s64(a));
+}
+
+//==============================================================================
+// pcast, SrcType = float
+//==============================================================================
+
+template <>
+struct type_casting_traits<float, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+struct type_casting_traits<float, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+// If float64 exists, first convert to that to keep as much precision as possible.
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4f, Packet2l>(const Packet4f& a) {
+  // Discard second half of input.
+  return vcvtq_s64_f64(vcvt_f64_f32(vget_low_f32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2f, Packet2l>(const Packet2f& a) {
+  return vcvtq_s64_f64(vcvt_f64_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4f, Packet2ul>(const Packet4f& a) {
+  // Discard second half of input.
+  return vcvtq_u64_f64(vcvt_f64_f32(vget_low_f32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2f, Packet2ul>(const Packet2f& a) {
+  return vcvtq_u64_f64(vcvt_f64_f32(a));
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4f, Packet2l>(const Packet4f& a) {
+  // Discard second half of input.
+  return vmovl_s32(vget_low_s32(vcvtq_s32_f32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2f, Packet2l>(const Packet2f& a) {
+  return vmovl_s32(vcvt_s32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4f, Packet2ul>(const Packet4f& a) {
+  // Discard second half of input.
+  return vmovl_u32(vget_low_u32(vcvtq_u32_f32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2f, Packet2ul>(const Packet2f& a) {
+  // Discard second half of input.
+  return vmovl_u32(vcvt_u32_f32(a));
+}
+#endif  // EIGEN_ARCH_ARM64
+
+template <>
+struct type_casting_traits<float, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
+  return vcvtq_s32_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet2f, Packet2i>(const Packet2f& a) {
+  return vcvt_s32_f32(a);
+}
+
+template <>
+struct type_casting_traits<float, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4f, Packet4ui>(const Packet4f& a) {
+  return vcvtq_u32_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet2f, Packet2ui>(const Packet2f& a) {
+  return vcvt_u32_f32(a);
+}
+
+template <>
+struct type_casting_traits<float, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet4f, Packet8s>(const Packet4f& a, const Packet4f& b) {
+  return vcombine_s16(vmovn_s32(vcvtq_s32_f32(a)), vmovn_s32(vcvtq_s32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet4f, Packet4s>(const Packet4f& a) {
+  return vmovn_s32(vcvtq_s32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2f, Packet4s>(const Packet2f& a, const Packet2f& b) {
+  return vmovn_s32(vcombine_s32(vcvt_s32_f32(a), vcvt_s32_f32(b)));
+}
+
+template <>
+struct type_casting_traits<float, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet4f, Packet8us>(const Packet4f& a, const Packet4f& b) {
+  return vcombine_u16(vmovn_u32(vcvtq_u32_f32(a)), vmovn_u32(vcvtq_u32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet4f, Packet4us>(const Packet4f& a) {
+  return vmovn_u32(vcvtq_u32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2f, Packet4us>(const Packet2f& a, const Packet2f& b) {
+  return vmovn_u32(vcombine_u32(vcvt_u32_f32(a), vcvt_u32_f32(b)));
+}
+
+template <>
+struct type_casting_traits<float, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet4f, Packet16c>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
+                                                         const Packet4f& d) {
+  const int16x8_t ab_s16 = pcast<Packet4f, Packet8s>(a, b);
+  const int16x8_t cd_s16 = pcast<Packet4f, Packet8s>(c, d);
+  return vcombine_s8(vmovn_s16(ab_s16), vmovn_s16(cd_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet4f, Packet8c>(const Packet4f& a, const Packet4f& b) {
+  const int16x8_t ab_s16 = pcast<Packet4f, Packet8s>(a, b);
+  return vmovn_s16(ab_s16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2f, Packet8c>(const Packet2f& a, const Packet2f& b, const Packet2f& c,
+                                                       const Packet2f& d) {
+  const int16x4_t ab_s16 = pcast<Packet2f, Packet4s>(a, b);
+  const int16x4_t cd_s16 = pcast<Packet2f, Packet4s>(c, d);
+  return vmovn_s16(vcombine_s16(ab_s16, cd_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet4f, Packet4c>(const Packet4f& a) {
+  const int32x4_t a_s32x4 = vcvtq_s32_f32(a);
+  const int16x4_t a_s16x4 = vmovn_s32(a_s32x4);
+  const int16x8_t aa_s16x8 = vcombine_s16(a_s16x4, a_s16x4);
+  const int8x8_t aa_s8x8 = vmovn_s16(aa_s16x8);
+  return vget_lane_s32(vreinterpret_s32_s8(aa_s8x8), 0);
+}
+
+template <>
+struct type_casting_traits<float, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet4f, Packet16uc>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
+                                                           const Packet4f& d) {
+  return preinterpret<Packet16uc>(pcast<Packet4f, Packet16c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet4f, Packet8uc>(const Packet4f& a, const Packet4f& b) {
+  return preinterpret<Packet8uc>(pcast<Packet4f, Packet8c>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2f, Packet8uc>(const Packet2f& a, const Packet2f& b, const Packet2f& c,
+                                                         const Packet2f& d) {
+  return preinterpret<Packet8uc>(pcast<Packet2f, Packet8c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet4f, Packet4uc>(const Packet4f& a) {
+  return static_cast<Packet4uc>(pcast<Packet4f, Packet4c>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = int8_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::int8_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet16c, Packet4f>(const Packet16c& a) {
+  // Discard all but first 4 bytes.
+  return vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a)))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4c, Packet4f>(const Packet4c& a) {
+  return vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8(vreinterpret_s8_s32(vdup_n_s32(a))))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet8c, Packet2f>(const Packet8c& a) {
+  // Discard all but first 2 bytes.
+  return vcvt_f32_s32(vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(a)))));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet16c, Packet2l>(const Packet16c& a) {
+  // Discard all but first two bytes.
+  return vmovl_s32(vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a))))));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet16c, Packet2ul>(const Packet16c& a) {
+  return preinterpret<Packet2ul>(pcast<Packet16c, Packet2l>(a));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet16c, Packet4i>(const Packet16c& a) {
+  // Discard all but first 4 bytes.
+  return vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet8c, Packet4i>(const Packet8c& a) {
+  return vmovl_s16(vget_low_s16(vmovl_s8(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4c, Packet4i>(const Packet4c& a) {
+  return pcast<Packet8c, Packet4i>(vreinterpret_s8_s32(vdup_n_s32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet8c, Packet2i>(const Packet8c& a) {
+  // Discard all but first 2 bytes.
+  return vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(a))));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet16c, Packet4ui>(const Packet16c& a) {
+  return preinterpret<Packet4ui>(pcast<Packet16c, Packet4i>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet8c, Packet2ui>(const Packet8c& a) {
+  return preinterpret<Packet2ui>(pcast<Packet8c, Packet2i>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4c, Packet4ui>(const Packet4c& a) {
+  return preinterpret<Packet4ui>(pcast<Packet4c, Packet4i>(a));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet16c, Packet8s>(const Packet16c& a) {
+  // Discard second half of input.
+  return vmovl_s8(vget_low_s8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet8c, Packet8s>(const Packet8c& a) {
+  return vmovl_s8(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet8c, Packet4s>(const Packet8c& a) {
+  // Discard second half of input.
+  return vget_low_s16(vmovl_s8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet4c, Packet4s>(const Packet4c& a) {
+  return pcast<Packet8c, Packet4s>(vreinterpret_s8_s32(vdup_n_s32(a)));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet16c, Packet8us>(const Packet16c& a) {
+  return preinterpret<Packet8us>(pcast<Packet16c, Packet8s>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet8c, Packet8us>(const Packet8c& a) {
+  return preinterpret<Packet8us>(pcast<Packet8c, Packet8s>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet8c, Packet4us>(const Packet8c& a) {
+  return preinterpret<Packet4us>(pcast<Packet8c, Packet4s>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet4c, Packet4us>(const Packet4c& a) {
+  return preinterpret<Packet4us>(pcast<Packet4c, Packet4s>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = uint8_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::uint8_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet16uc, Packet4f>(const Packet16uc& a) {
+  // Discard all but first 4 bytes.
+  return vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a)))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4uc, Packet4f>(const Packet4uc& a) {
+  return vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vreinterpret_u8_u32(vdup_n_u32(a))))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet8uc, Packet2f>(const Packet8uc& a) {
+  // Discard all but first 2 bytes.
+  return vcvt_f32_u32(vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(a)))));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet16uc, Packet2ul>(const Packet16uc& a) {
+  // Discard all but first two bytes.
+  return vmovl_u32(vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))))));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet16uc, Packet2l>(const Packet16uc& a) {
+  return preinterpret<Packet2l>(pcast<Packet16uc, Packet2ul>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet16uc, Packet4ui>(const Packet16uc& a) {
+  // Discard all but first 4 bytes.
+  return vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet8uc, Packet4ui>(const Packet8uc& a) {
+  return vmovl_u16(vget_low_u16(vmovl_u8(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet8uc, Packet2ui>(const Packet8uc& a) {
+  // Discard all but first 2 bytes.
+  return vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(a))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4uc, Packet4ui>(const Packet4uc& a) {
+  return pcast<Packet8uc, Packet4ui>(vreinterpret_u8_u32(vdup_n_u32(a)));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet16uc, Packet4i>(const Packet16uc& a) {
+  return preinterpret<Packet4i>(pcast<Packet16uc, Packet4ui>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet8uc, Packet2i>(const Packet8uc& a) {
+  return preinterpret<Packet2i>(pcast<Packet8uc, Packet2ui>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4uc, Packet4i>(const Packet4uc& a) {
+  return preinterpret<Packet4i>(pcast<Packet4uc, Packet4ui>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet16uc, Packet8us>(const Packet16uc& a) {
+  // Discard second half of input.
+  return vmovl_u8(vget_low_u8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet8uc, Packet8us>(const Packet8uc& a) {
+  return vmovl_u8(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet4uc, Packet4us>(const Packet4uc& a) {
+  return vget_low_u16(vmovl_u8(vreinterpret_u8_u32(vdup_n_u32(a))));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet16uc, Packet8s>(const Packet16uc& a) {
+  return preinterpret<Packet8s>(pcast<Packet16uc, Packet8us>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet8uc, Packet8s>(const Packet8uc& a) {
+  return preinterpret<Packet8s>(pcast<Packet8uc, Packet8us>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet4uc, Packet4s>(const Packet4uc& a) {
+  return preinterpret<Packet4s>(pcast<Packet4uc, Packet4us>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = int16_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::int16_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet8s, Packet4f>(const Packet8s& a) {
+  // Discard second half of input.
+  return vcvtq_f32_s32(vmovl_s16(vget_low_s16(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4s, Packet4f>(const Packet4s& a) {
+  return vcvtq_f32_s32(vmovl_s16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet4s, Packet2f>(const Packet4s& a) {
+  // Discard second half of input.
+  return vcvt_f32_s32(vget_low_s32(vmovl_s16(a)));
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet8s, Packet2l>(const Packet8s& a) {
+  // Discard all but first two values.
+  return vmovl_s32(vget_low_s32(vmovl_s16(vget_low_s16(a))));
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet8s, Packet2ul>(const Packet8s& a) {
+  return preinterpret<Packet2ul>(pcast<Packet8s, Packet2l>(a));
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet8s, Packet4i>(const Packet8s& a) {
+  // Discard second half of input.
+  return vmovl_s16(vget_low_s16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4s, Packet4i>(const Packet4s& a) {
+  return vmovl_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet4s, Packet2i>(const Packet4s& a) {
+  // Discard second half of input.
+  return vget_low_s32(vmovl_s16(a));
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet8s, Packet4ui>(const Packet8s& a) {
+  return preinterpret<Packet4ui>(pcast<Packet8s, Packet4i>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4s, Packet4ui>(const Packet4s& a) {
+  return preinterpret<Packet4ui>(pcast<Packet4s, Packet4i>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet4s, Packet2ui>(const Packet4s& a) {
+  return preinterpret<Packet2ui>(pcast<Packet4s, Packet2i>(a));
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet8s, Packet16c>(const Packet8s& a, const Packet8s& b) {
+  return vcombine_s8(vmovn_s16(a), vmovn_s16(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet8s, Packet8c>(const Packet8s& a) {
+  return vmovn_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet4s, Packet8c>(const Packet4s& a, const Packet4s& b) {
+  return vmovn_s16(vcombine_s16(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet4s, Packet4c>(const Packet4s& a) {
+  const int8x8_t aa_s8x8 = pcast<Packet4s, Packet8c>(a, a);
+  return vget_lane_s32(vreinterpret_s32_s8(aa_s8x8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet8s, Packet16uc>(const Packet8s& a, const Packet8s& b) {
+  return preinterpret<Packet16uc>(pcast<Packet8s, Packet16c>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet8s, Packet8uc>(const Packet8s& a) {
+  return preinterpret<Packet8uc>(pcast<Packet8s, Packet8c>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet4s, Packet8uc>(const Packet4s& a, const Packet4s& b) {
+  return preinterpret<Packet8uc>(pcast<Packet4s, Packet8c>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet4s, Packet4uc>(const Packet4s& a) {
+  return static_cast<Packet4uc>(pcast<Packet4s, Packet4c>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = uint16_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::uint16_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet8us, Packet4f>(const Packet8us& a) {
+  // Discard second half of input.
+  return vcvtq_f32_u32(vmovl_u16(vget_low_u16(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4us, Packet4f>(const Packet4us& a) {
+  return vcvtq_f32_u32(vmovl_u16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet4us, Packet2f>(const Packet4us& a) {
+  // Discard second half of input.
+  return vcvt_f32_u32(vget_low_u32(vmovl_u16(a)));
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet8us, Packet2ul>(const Packet8us& a) {
+  // Discard all but first two values.
+  return vmovl_u32(vget_low_u32(vmovl_u16(vget_low_u16(a))));
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet8us, Packet2l>(const Packet8us& a) {
+  return preinterpret<Packet2l>(pcast<Packet8us, Packet2ul>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet8us, Packet4ui>(const Packet8us& a) {
+  // Discard second half of input.
+  return vmovl_u16(vget_low_u16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4us, Packet4ui>(const Packet4us& a) {
+  return vmovl_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet4us, Packet2ui>(const Packet4us& a) {
+  // Discard second half of input.
+  return vget_low_u32(vmovl_u16(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet8us, Packet4i>(const Packet8us& a) {
+  return preinterpret<Packet4i>(pcast<Packet8us, Packet4ui>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4us, Packet4i>(const Packet4us& a) {
+  return preinterpret<Packet4i>(pcast<Packet4us, Packet4ui>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet4us, Packet2i>(const Packet4us& a) {
+  return preinterpret<Packet2i>(pcast<Packet4us, Packet2ui>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet8us, Packet16uc>(const Packet8us& a, const Packet8us& b) {
+  return vcombine_u8(vmovn_u16(a), vmovn_u16(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet8us, Packet8uc>(const Packet8us& a) {
+  return vmovn_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet4us, Packet8uc>(const Packet4us& a, const Packet4us& b) {
+  return vmovn_u16(vcombine_u16(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet4us, Packet4uc>(const Packet4us& a) {
+  uint8x8_t aa_u8x8 = pcast<Packet4us, Packet8uc>(a, a);
+  return vget_lane_u32(vreinterpret_u32_u8(aa_u8x8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet8us, Packet16c>(const Packet8us& a, const Packet8us& b) {
+  return preinterpret<Packet16c>(pcast<Packet8us, Packet16uc>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet8us, Packet8c>(const Packet8us& a) {
+  return preinterpret<Packet8c>(pcast<Packet8us, Packet8uc>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet4us, Packet8c>(const Packet4us& a, const Packet4us& b) {
+  return preinterpret<Packet8c>(pcast<Packet4us, Packet8uc>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet4us, Packet4c>(const Packet4us& a) {
+  return static_cast<Packet4c>(pcast<Packet4us, Packet4uc>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = int32_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::int32_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
+  return vcvtq_f32_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet2i, Packet2f>(const Packet2i& a) {
+  return vcvt_f32_s32(a);
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4i, Packet2l>(const Packet4i& a) {
+  // Discard second half of input.
+  return vmovl_s32(vget_low_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2i, Packet2l>(const Packet2i& a) {
+  return vmovl_s32(a);
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4i, Packet2ul>(const Packet4i& a) {
+  return preinterpret<Packet2ul>(pcast<Packet4i, Packet2l>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2i, Packet2ul>(const Packet2i& a) {
+  return preinterpret<Packet2ul>(pcast<Packet2i, Packet2l>(a));
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet4i, Packet8s>(const Packet4i& a, const Packet4i& b) {
+  return vcombine_s16(vmovn_s32(a), vmovn_s32(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet4i, Packet4s>(const Packet4i& a) {
+  return vmovn_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2i, Packet4s>(const Packet2i& a, const Packet2i& b) {
+  return vmovn_s32(vcombine_s32(a, b));
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet4i, Packet8us>(const Packet4i& a, const Packet4i& b) {
+  return vcombine_u16(vmovn_u32(vreinterpretq_u32_s32(a)), vmovn_u32(vreinterpretq_u32_s32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet4i, Packet4us>(const Packet4i& a) {
+  return vmovn_u32(vreinterpretq_u32_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2i, Packet4us>(const Packet2i& a, const Packet2i& b) {
+  return vmovn_u32(vreinterpretq_u32_s32(vcombine_s32(a, b)));
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet4i, Packet16c>(const Packet4i& a, const Packet4i& b, const Packet4i& c,
+                                                         const Packet4i& d) {
+  const int16x8_t ab_s16 = pcast<Packet4i, Packet8s>(a, b);
+  const int16x8_t cd_s16 = pcast<Packet4i, Packet8s>(c, d);
+  return vcombine_s8(vmovn_s16(ab_s16), vmovn_s16(cd_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet4i, Packet8c>(const Packet4i& a, const Packet4i& b) {
+  const int16x8_t ab_s16 = pcast<Packet4i, Packet8s>(a, b);
+  return vmovn_s16(ab_s16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2i, Packet8c>(const Packet2i& a, const Packet2i& b, const Packet2i& c,
+                                                       const Packet2i& d) {
+  const int16x4_t ab_s16 = vmovn_s32(vcombine_s32(a, b));
+  const int16x4_t cd_s16 = vmovn_s32(vcombine_s32(c, d));
+  return vmovn_s16(vcombine_s16(ab_s16, cd_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet4i, Packet4c>(const Packet4i& a) {
+  const int16x4_t a_s16x4 = vmovn_s32(a);
+  const int16x8_t aa_s16x8 = vcombine_s16(a_s16x4, a_s16x4);
+  const int8x8_t aa_s8x8 = vmovn_s16(aa_s16x8);
+  return vget_lane_s32(vreinterpret_s32_s8(aa_s8x8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet4i, Packet16uc>(const Packet4i& a, const Packet4i& b, const Packet4i& c,
+                                                           const Packet4i& d) {
+  return preinterpret<Packet16uc>(pcast<Packet4i, Packet16c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet4i, Packet8uc>(const Packet4i& a, const Packet4i& b) {
+  return preinterpret<Packet8uc>(pcast<Packet4i, Packet8c>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2i, Packet8uc>(const Packet2i& a, const Packet2i& b, const Packet2i& c,
+                                                         const Packet2i& d) {
+  return preinterpret<Packet8uc>(pcast<Packet2i, Packet8c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet4i, Packet4uc>(const Packet4i& a) {
+  return static_cast<Packet4uc>(pcast<Packet4i, Packet4c>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = uint32_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::uint32_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4ui, Packet4f>(const Packet4ui& a) {
+  return vcvtq_f32_u32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet2ui, Packet2f>(const Packet2ui& a) {
+  return vcvt_f32_u32(a);
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4ui, Packet2ul>(const Packet4ui& a) {
+  // Discard second half of input.
+  return vmovl_u32(vget_low_u32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2ui, Packet2ul>(const Packet2ui& a) {
+  return vmovl_u32(a);
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4ui, Packet2l>(const Packet4ui& a) {
+  return preinterpret<Packet2l>(pcast<Packet4ui, Packet2ul>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2ui, Packet2l>(const Packet2ui& a) {
+  return preinterpret<Packet2l>(pcast<Packet2ui, Packet2ul>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet4ui, Packet8us>(const Packet4ui& a, const Packet4ui& b) {
+  return vcombine_u16(vmovn_u32(a), vmovn_u32(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2ui, Packet4us>(const Packet2ui& a, const Packet2ui& b) {
+  return vmovn_u32(vcombine_u32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet4ui, Packet4us>(const Packet4ui& a) {
+  return vmovn_u32(a);
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet4ui, Packet8s>(const Packet4ui& a, const Packet4ui& b) {
+  return preinterpret<Packet8s>(pcast<Packet4ui, Packet8us>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2ui, Packet4s>(const Packet2ui& a, const Packet2ui& b) {
+  return preinterpret<Packet4s>(pcast<Packet2ui, Packet4us>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet4ui, Packet4s>(const Packet4ui& a) {
+  return preinterpret<Packet4s>(pcast<Packet4ui, Packet4us>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet4ui, Packet16uc>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
+                                                            const Packet4ui& d) {
+  const uint16x8_t ab_u16 = vcombine_u16(vmovn_u32(a), vmovn_u32(b));
+  const uint16x8_t cd_u16 = vcombine_u16(vmovn_u32(c), vmovn_u32(d));
+  return vcombine_u8(vmovn_u16(ab_u16), vmovn_u16(cd_u16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet4ui, Packet8uc>(const Packet4ui& a, const Packet4ui& b) {
+  const uint16x8_t ab_u16 = vcombine_u16(vmovn_u32(a), vmovn_u32(b));
+  return vmovn_u16(ab_u16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2ui, Packet8uc>(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c,
+                                                          const Packet2ui& d) {
+  const uint16x4_t ab_u16 = vmovn_u32(vcombine_u32(a, b));
+  const uint16x4_t cd_u16 = vmovn_u32(vcombine_u32(c, d));
+  return vmovn_u16(vcombine_u16(ab_u16, cd_u16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet4ui, Packet4uc>(const Packet4ui& a) {
+  const uint16x4_t a_u16x4 = vmovn_u32(a);
+  const uint16x8_t aa_u16x8 = vcombine_u16(a_u16x4, a_u16x4);
+  const uint8x8_t aa_u8x8 = vmovn_u16(aa_u16x8);
+  return vget_lane_u32(vreinterpret_u32_u8(aa_u8x8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet4ui, Packet16c>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
+                                                          const Packet4ui& d) {
+  return preinterpret<Packet16c>(pcast<Packet4ui, Packet16uc>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet4ui, Packet8c>(const Packet4ui& a, const Packet4ui& b) {
+  return preinterpret<Packet8c>(pcast<Packet4ui, Packet8uc>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2ui, Packet8c>(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c,
+                                                        const Packet2ui& d) {
+  return preinterpret<Packet8c>(pcast<Packet2ui, Packet8uc>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet4ui, Packet4c>(const Packet4ui& a) {
+  return static_cast<Packet4c>(pcast<Packet4ui, Packet4uc>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = int64_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::int64_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet2l, Packet4f>(const Packet2l& a, const Packet2l& b) {
+#if EIGEN_ARCH_ARM64
+  return vcombine_f32(vcvt_f32_f64(vcvtq_f64_s64(a)), vcvt_f32_f64(vcvtq_f64_s64(b)));
+#else
+  EIGEN_ALIGN_MAX int64_t lvals[4];
+  pstore(lvals, a);
+  pstore(lvals + 2, b);
+  EIGEN_ALIGN_MAX float fvals[4] = {static_cast<float>(lvals[0]), static_cast<float>(lvals[1]),
+                                    static_cast<float>(lvals[2]), static_cast<float>(lvals[3])};
+  return pload<Packet4f>(fvals);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet2l, Packet2f>(const Packet2l& a) {
+#if EIGEN_ARCH_ARM64
+  return vcvt_f32_f64(vcvtq_f64_s64(a));
+#else
+  EIGEN_ALIGN_MAX int64_t lvals[2];
+  pstore(lvals, a);
+  EIGEN_ALIGN_MAX float fvals[2] = {static_cast<float>(lvals[0]), static_cast<float>(lvals[1])};
+  return pload<Packet2f>(fvals);
+#endif
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet2l, Packet4i>(const Packet2l& a, const Packet2l& b) {
+  return vcombine_s32(vmovn_s64(a), vmovn_s64(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet2l, Packet2i>(const Packet2l& a) {
+  return vmovn_s64(a);
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet2l, Packet4ui>(const Packet2l& a, const Packet2l& b) {
+  return vcombine_u32(vmovn_u64(vreinterpretq_u64_s64(a)), vmovn_u64(vreinterpretq_u64_s64(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet2l, Packet2ui>(const Packet2l& a) {
+  return vmovn_u64(vreinterpretq_u64_s64(a));
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet2l, Packet8s>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                       const Packet2l& d) {
+  const int32x4_t ab_s32 = pcast<Packet2l, Packet4i>(a, b);
+  const int32x4_t cd_s32 = pcast<Packet2l, Packet4i>(c, d);
+  return vcombine_s16(vmovn_s32(ab_s32), vmovn_s32(cd_s32));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2l, Packet4s>(const Packet2l& a, const Packet2l& b) {
+  const int32x4_t ab_s32 = pcast<Packet2l, Packet4i>(a, b);
+  return vmovn_s32(ab_s32);
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet2l, Packet8us>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                         const Packet2l& d) {
+  return preinterpret<Packet8us>(pcast<Packet2l, Packet8s>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2l, Packet4us>(const Packet2l& a, const Packet2l& b) {
+  return preinterpret<Packet4us>(pcast<Packet2l, Packet4s>(a, b));
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet2l, Packet16c>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                         const Packet2l& d, const Packet2l& e, const Packet2l& f,
+                                                         const Packet2l& g, const Packet2l& h) {
+  const int16x8_t abcd_s16 = pcast<Packet2l, Packet8s>(a, b, c, d);
+  const int16x8_t efgh_s16 = pcast<Packet2l, Packet8s>(e, f, g, h);
+  return vcombine_s8(vmovn_s16(abcd_s16), vmovn_s16(efgh_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2l, Packet8c>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                       const Packet2l& d) {
+  const int16x8_t abcd_s16 = pcast<Packet2l, Packet8s>(a, b, c, d);
+  return vmovn_s16(abcd_s16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet2l, Packet4c>(const Packet2l& a, const Packet2l& b) {
+  const int16x4_t ab_s16 = pcast<Packet2l, Packet4s>(a, b);
+  const int16x8_t abab_s16 = vcombine_s16(ab_s16, ab_s16);
+  const int8x8_t abab_s8 = vmovn_s16(abab_s16);
+  return vget_lane_s32(vreinterpret_s32_s8(abab_s8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet2l, Packet16uc>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                           const Packet2l& d, const Packet2l& e, const Packet2l& f,
+                                                           const Packet2l& g, const Packet2l& h) {
+  const uint16x8_t abcd_u16 = pcast<Packet2l, Packet8us>(a, b, c, d);
+  const uint16x8_t efgh_u16 = pcast<Packet2l, Packet8us>(e, f, g, h);
+  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2l, Packet8uc>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                         const Packet2l& d) {
+  return preinterpret<Packet8uc>(pcast<Packet2l, Packet8c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet2l, Packet4uc>(const Packet2l& a, const Packet2l& b) {
+  return static_cast<Packet4uc>(pcast<Packet2l, Packet4c>(a, b));
+}
+
+//==============================================================================
+// pcast, SrcType = uint64_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::uint64_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet2ul, Packet4f>(const Packet2ul& a, const Packet2ul& b) {
+#if EIGEN_ARCH_ARM64
+  return vcombine_f32(vcvt_f32_f64(vcvtq_f64_u64(a)), vcvt_f32_f64(vcvtq_f64_u64(b)));
+#else
+  EIGEN_ALIGN_MAX uint64_t uvals[4];
+  pstore(uvals, a);
+  pstore(uvals + 2, b);
+  EIGEN_ALIGN_MAX float fvals[4] = {static_cast<float>(uvals[0]), static_cast<float>(uvals[1]),
+                                    static_cast<float>(uvals[2]), static_cast<float>(uvals[3])};
+  return pload<Packet4f>(fvals);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet2ul, Packet2f>(const Packet2ul& a) {
+#if EIGEN_ARCH_ARM64
+  return vcvt_f32_f64(vcvtq_f64_u64(a));
+#else
+  EIGEN_ALIGN_MAX uint64_t uvals[2];
+  pstore(uvals, a);
+  EIGEN_ALIGN_MAX float fvals[2] = {static_cast<float>(uvals[0]), static_cast<float>(uvals[1])};
+  return pload<Packet2f>(fvals);
+#endif
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet2ul, Packet4ui>(const Packet2ul& a, const Packet2ul& b) {
+  return vcombine_u32(vmovn_u64(a), vmovn_u64(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet2ul, Packet2ui>(const Packet2ul& a) {
+  return vmovn_u64(a);
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet2ul, Packet4i>(const Packet2ul& a, const Packet2ul& b) {
+  return preinterpret<Packet4i>(pcast<Packet2ul, Packet4ui>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet2ul, Packet2i>(const Packet2ul& a) {
+  return preinterpret<Packet2i>(pcast<Packet2ul, Packet2ui>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet2ul, Packet8us>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                          const Packet2ul& d) {
+  const uint16x4_t ab_u16 = vmovn_u32(vcombine_u32(vmovn_u64(a), vmovn_u64(b)));
+  const uint16x4_t cd_u16 = vmovn_u32(vcombine_u32(vmovn_u64(c), vmovn_u64(d)));
+  return vcombine_u16(ab_u16, cd_u16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2ul, Packet4us>(const Packet2ul& a, const Packet2ul& b) {
+  return vmovn_u32(vcombine_u32(vmovn_u64(a), vmovn_u64(b)));
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet2ul, Packet8s>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                        const Packet2ul& d) {
+  return preinterpret<Packet8s>(pcast<Packet2ul, Packet8us>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2ul, Packet4s>(const Packet2ul& a, const Packet2ul& b) {
+  return preinterpret<Packet4s>(pcast<Packet2ul, Packet4us>(a, b));
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet2ul, Packet16uc>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                            const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
+                                                            const Packet2ul& g, const Packet2ul& h) {
+  const uint16x8_t abcd_u16 = pcast<Packet2ul, Packet8us>(a, b, c, d);
+  const uint16x8_t efgh_u16 = pcast<Packet2ul, Packet8us>(e, f, g, h);
+  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2ul, Packet8uc>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                          const Packet2ul& d) {
+  const uint16x8_t abcd_u16 = pcast<Packet2ul, Packet8us>(a, b, c, d);
+  return vmovn_u16(abcd_u16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet2ul, Packet4uc>(const Packet2ul& a, const Packet2ul& b) {
+  const uint16x4_t ab_u16 = pcast<Packet2ul, Packet4us>(a, b);
+  const uint16x8_t abab_u16 = vcombine_u16(ab_u16, ab_u16);
+  const uint8x8_t abab_u8 = vmovn_u16(abab_u16);
+  return vget_lane_u32(vreinterpret_u32_u8(abab_u8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet2ul, Packet16c>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                          const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
+                                                          const Packet2ul& g, const Packet2ul& h) {
+  return preinterpret<Packet16c>(pcast<Packet2ul, Packet16uc>(a, b, c, d, e, f, g, h));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2ul, Packet8c>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                        const Packet2ul& d) {
+  return preinterpret<Packet8c>(pcast<Packet2ul, Packet8uc>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet2ul, Packet4c>(const Packet2ul& a, const Packet2ul& b) {
+  return static_cast<Packet4c>(pcast<Packet2ul, Packet4uc>(a, b));
+}
+
+#if EIGEN_ARCH_ARM64
+
+//==============================================================================
+// pcast/preinterpret, Double
+//==============================================================================
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2l>(const Packet2l& a) {
+  return Packet2d(vreinterpretq_f64_s64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2ul>(const Packet2ul& a) {
+  return Packet2d(vreinterpretq_f64_u64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2d>(const Packet2d& a) {
+  return Packet2l(vreinterpretq_s64_f64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2d>(const Packet2d& a) {
+  return Packet2ul(vreinterpretq_u64_f64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4i>(const Packet4i& a) {
+  return Packet2d(vreinterpretq_f64_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet2d>(const Packet2d& a) {
+  return Packet4i(vreinterpretq_s32_f64(a));
+}
+
+template <>
+struct type_casting_traits<double, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
+  return vcombine_f32(vcvt_f32_f64(a), vcvt_f32_f64(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet2d, Packet2f>(const Packet2d& a) {
+  return vcvt_f32_f64(a);
+}
+
+template <>
+struct type_casting_traits<double, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2d, Packet2l>(const Packet2d& a) {
+  return vcvtq_s64_f64(a);
+}
+
+template <>
+struct type_casting_traits<double, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2d, Packet2ul>(const Packet2d& a) {
+  return vcvtq_u64_f64(a);
+}
+
+template <>
+struct type_casting_traits<double, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet2d, Packet4i>(const Packet2d& a, const Packet2d& b) {
+  return vcombine_s32(vmovn_s64(vcvtq_s64_f64(a)), vmovn_s64(vcvtq_s64_f64(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet2d, Packet2i>(const Packet2d& a) {
+  return vmovn_s64(vcvtq_s64_f64(a));
+}
+
+template <>
+struct type_casting_traits<double, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet2d, Packet4ui>(const Packet2d& a, const Packet2d& b) {
+  return vcombine_u32(vmovn_u64(vcvtq_u64_f64(a)), vmovn_u64(vcvtq_u64_f64(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet2d, Packet2ui>(const Packet2d& a) {
+  return vmovn_u64(vcvtq_u64_f64(a));
+}
+
+template <>
+struct type_casting_traits<double, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet2d, Packet8s>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                       const Packet2d& d) {
+  const int32x4_t ab_s32 = pcast<Packet2d, Packet4i>(a, b);
+  const int32x4_t cd_s32 = pcast<Packet2d, Packet4i>(c, d);
+  return vcombine_s16(vmovn_s32(ab_s32), vmovn_s32(cd_s32));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2d, Packet4s>(const Packet2d& a, const Packet2d& b) {
+  const int32x4_t ab_s32 = pcast<Packet2d, Packet4i>(a, b);
+  return vmovn_s32(ab_s32);
+}
+
+template <>
+struct type_casting_traits<double, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet2d, Packet8us>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                         const Packet2d& d) {
+  return preinterpret<Packet8us>(pcast<Packet2d, Packet8s>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2d, Packet4us>(const Packet2d& a, const Packet2d& b) {
+  return preinterpret<Packet4us>(pcast<Packet2d, Packet4s>(a, b));
+}
+
+template <>
+struct type_casting_traits<double, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet2d, Packet16c>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                         const Packet2d& d, const Packet2d& e, const Packet2d& f,
+                                                         const Packet2d& g, const Packet2d& h) {
+  const int16x8_t abcd_s16 = pcast<Packet2d, Packet8s>(a, b, c, d);
+  const int16x8_t efgh_s16 = pcast<Packet2d, Packet8s>(e, f, g, h);
+  return vcombine_s8(vmovn_s16(abcd_s16), vmovn_s16(efgh_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2d, Packet8c>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                       const Packet2d& d) {
+  const int16x8_t abcd_s16 = pcast<Packet2d, Packet8s>(a, b, c, d);
+  return vmovn_s16(abcd_s16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet2d, Packet4c>(const Packet2d& a, const Packet2d& b) {
+  const int32x4_t ab_s32 = pcast<Packet2d, Packet4i>(a, b);
+  return pcast<Packet4i, Packet4c>(ab_s32);
+}
+
+template <>
+struct type_casting_traits<double, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet2d, Packet16uc>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                           const Packet2d& d, const Packet2d& e, const Packet2d& f,
+                                                           const Packet2d& g, const Packet2d& h) {
+  const uint16x8_t abcd_u16 = pcast<Packet2d, Packet8us>(a, b, c, d);
+  const uint16x8_t efgh_u16 = pcast<Packet2d, Packet8us>(e, f, g, h);
+  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2d, Packet8uc>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                         const Packet2d& d) {
+  return preinterpret<Packet8uc>(pcast<Packet2d, Packet8c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet2d, Packet4uc>(const Packet2d& a, const Packet2d& b) {
+  return static_cast<Packet4uc>(pcast<Packet2d, Packet4c>(a, b));
+}
+
+template <>
+struct type_casting_traits<float, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) {
+  // Discard second-half of input.
+  return vcvt_f64_f32(vget_low_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2f, Packet2d>(const Packet2f& a) {
+  return vcvt_f64_f32(a);
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet16c, Packet2d>(const Packet16c& a) {
+  // Discard all but first two values.
+  // MSVC defines most intrinsics as macros, so we need to do this in two lines for portability.
+  Packet2f tmp = pcast<Packet8c, Packet2f>(vget_low_s8(a));
+  return vcvt_f64_f32(tmp);
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet16uc, Packet2d>(const Packet16uc& a) {
+  // Discard all but first two values.
+  Packet2f tmp = pcast<Packet8uc, Packet2f>(vget_low_u8(a));
+  return vcvt_f64_f32(tmp);
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet8s, Packet2d>(const Packet8s& a) {
+  // Discard all but first two values.
+  Packet2f tmp = pcast<Packet4s, Packet2f>(vget_low_s16(a));
+  return vcvt_f64_f32(tmp);
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet8us, Packet2d>(const Packet8us& a) {
+  // Discard all but first two values.
+  Packet2f tmp = pcast<Packet4us, Packet2f>(vget_low_u16(a));
+  return vcvt_f64_f32(tmp);
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4i, Packet2d>(const Packet4i& a) {
+  // Discard second half of input.
+  return vcvtq_f64_s64(vmovl_s32(vget_low_s32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2i, Packet2d>(const Packet2i& a) {
+  return vcvtq_f64_s64(vmovl_s32(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4ui, Packet2d>(const Packet4ui& a) {
+  // Discard second half of input.
+  return vcvtq_f64_u64(vmovl_u32(vget_low_u32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2ui, Packet2d>(const Packet2ui& a) {
+  return vcvtq_f64_u64(vmovl_u32(a));
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2l, Packet2d>(const Packet2l& a) {
+  return vcvtq_f64_s64(a);
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2ul, Packet2d>(const Packet2ul& a) {
+  return vcvtq_f64_u64(a);
+}
+
+#endif  // EIGEN_ARCH_ARM64
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_NEON_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/UnaryFunctors.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/UnaryFunctors.h
new file mode 100644
index 0000000..8be5bb0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/NEON/UnaryFunctors.h
@@ -0,0 +1,57 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NEON_UNARY_FUNCTORS_H
+#define EIGEN_NEON_UNARY_FUNCTORS_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+/** \internal
+ * \brief Template specialization of the logistic function for Eigen::half.
+ */
+template <>
+struct scalar_logistic_op<Eigen::half> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half operator()(const Eigen::half& x) const {
+    // Convert to float and call scalar_logistic_op<float>.
+    const scalar_logistic_op<float> float_op;
+    return Eigen::half(float_op(float(x)));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half packetOp(const Eigen::half& x) const { return this->operator()(x); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf packetOp(const Packet4hf& x) const {
+    const scalar_logistic_op<float> float_op;
+    return vcvt_f16_f32(float_op.packetOp(vcvt_f32_f16(x)));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf packetOp(const Packet8hf& x) const {
+    const scalar_logistic_op<float> float_op;
+    return vcombine_f16(vcvt_f16_f32(float_op.packetOp(vcvt_f32_f16(vget_low_f16(x)))),
+                        vcvt_f16_f32(float_op.packetOp(vcvt_high_f32_f16(x))));
+  }
+};
+
+template <>
+struct functor_traits<scalar_logistic_op<Eigen::half>> {
+  enum {
+    Cost = functor_traits<scalar_logistic_op<float>>::Cost,
+    PacketAccess = functor_traits<scalar_logistic_op<float>>::PacketAccess,
+  };
+};
+#endif  // EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_NEON_UNARY_FUNCTORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/GeneralBlockPanelKernel.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/GeneralBlockPanelKernel.h
new file mode 100644
index 0000000..026e1db
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/GeneralBlockPanelKernel.h
@@ -0,0 +1,236 @@
+// This file is part of Eigen, a lightweight C template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Kseniya Zaytseva <kseniya.zaytseva@syntacore.com>
+// Copyright (C) 2025 Chip Kerchner <ckerchner@tenstorrent.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RVV10_GENERAL_BLOCK_KERNEL_H
+#define EIGEN_RVV10_GENERAL_BLOCK_KERNEL_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/********************************* real ************************************/
+
+template <>
+struct gebp_traits<float, float, false, false, Architecture::RVV10, GEBPPacketFull>
+    : gebp_traits<float, float, false, false, Architecture::Generic, GEBPPacketFull> {
+  typedef float RhsPacket;
+  typedef QuadPacket<float> RhsPacketx4;
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const { dest = pset1<RhsPacket>(*b); }
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const {
+    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const { loadRhs(b, dest); }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const { dest = ploadquad<RhsPacket>(b); }
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+    c = __riscv_vfmadd_vf_f32m1(a, b, c, unpacket_traits<AccPacket>::size);
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 2
+    c = __riscv_vfmadd_vf_f32m2(a, b, c, unpacket_traits<AccPacket>::size);
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 4
+    c = __riscv_vfmadd_vf_f32m4(a, b, c, unpacket_traits<AccPacket>::size);
+#endif
+  }
+
+#if EIGEN_RISCV64_DEFAULT_LMUL >= 2
+  EIGEN_STRONG_INLINE void madd(const Packet1Xf& a, const RhsPacket& b, Packet1Xf& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    c = __riscv_vfmadd_vf_f32m1(a, b, c, unpacket_traits<Packet1Xf>::size);
+  }
+#endif
+#if EIGEN_RISCV64_DEFAULT_LMUL == 4
+  EIGEN_STRONG_INLINE void madd(const Packet2Xf& a, const RhsPacket& b, Packet2Xf& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    c = __riscv_vfmadd_vf_f32m2(a, b, c, unpacket_traits<Packet2Xf>::size);
+  }
+#endif
+
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const LaneIdType& lane) const {
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+    c = __riscv_vfmadd_vf_f32m1(a, b.get(lane), c, unpacket_traits<AccPacket>::size);
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 2
+    c = __riscv_vfmadd_vf_f32m2(a, b.get(lane), c, unpacket_traits<AccPacket>::size);
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 4
+    c = __riscv_vfmadd_vf_f32m4(a, b.get(lane), c, unpacket_traits<AccPacket>::size);
+#endif
+  }
+};
+
+template <>
+struct gebp_traits<double, double, false, false, Architecture::RVV10, GEBPPacketFull>
+    : gebp_traits<double, double, false, false, Architecture::Generic, GEBPPacketFull> {
+  typedef double RhsPacket;
+  typedef QuadPacket<double> RhsPacketx4;
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const { dest = pset1<RhsPacket>(*b); }
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const {
+    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const { loadRhs(b, dest); }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const { dest = ploadquad<RhsPacket>(b); }
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+    c = __riscv_vfmadd_vf_f64m1(a, b, c, unpacket_traits<AccPacket>::size);
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 2
+    c = __riscv_vfmadd_vf_f64m2(a, b, c, unpacket_traits<AccPacket>::size);
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 4
+    c = __riscv_vfmadd_vf_f64m4(a, b, c, unpacket_traits<AccPacket>::size);
+#endif
+  }
+
+#if EIGEN_RISCV64_DEFAULT_LMUL >= 2
+  EIGEN_STRONG_INLINE void madd(const Packet1Xd& a, const RhsPacket& b, Packet1Xd& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    c = __riscv_vfmadd_vf_f64m1(a, b, c, unpacket_traits<Packet1Xd>::size);
+  }
+#endif
+#if EIGEN_RISCV64_DEFAULT_LMUL == 4
+  EIGEN_STRONG_INLINE void madd(const Packet2Xd& a, const RhsPacket& b, Packet2Xd& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    c = __riscv_vfmadd_vf_f64m2(a, b, c, unpacket_traits<Packet2Xd>::size);
+  }
+#endif
+
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const LaneIdType& lane) const {
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+    c = __riscv_vfmadd_vf_f64m1(a, b.get(lane), c, unpacket_traits<AccPacket>::size);
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 2
+    c = __riscv_vfmadd_vf_f64m2(a, b.get(lane), c, unpacket_traits<AccPacket>::size);
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 4
+    c = __riscv_vfmadd_vf_f64m4(a, b.get(lane), c, unpacket_traits<AccPacket>::size);
+#endif
+  }
+};
+
+#if defined(EIGEN_VECTORIZE_RVV10FP16)
+
+template <>
+struct gebp_traits<half, half, false, false, Architecture::RVV10>
+    : gebp_traits<half, half, false, false, Architecture::Generic> {
+  typedef half RhsPacket;
+  typedef PacketXh LhsPacket;
+  typedef PacketXh AccPacket;
+  typedef QuadPacket<half> RhsPacketx4;
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const { dest = pset1<RhsPacket>(*b); }
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const {
+    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const { loadRhs(b, dest); }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const { dest = pload<RhsPacket>(b); }
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+    c = __riscv_vfmadd_vf_f16m1(a, numext::bit_cast<_Float16>(b), c, unpacket_traits<AccPacket>::size);
+#else
+    c = __riscv_vfmadd_vf_f16m2(a, numext::bit_cast<_Float16>(b), c, unpacket_traits<AccPacket>::size);
+#endif
+  }
+
+#if EIGEN_RISCV64_DEFAULT_LMUL >= 2
+  EIGEN_STRONG_INLINE void madd(const Packet1Xh& a, const RhsPacket& b, Packet1Xh& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    c = __riscv_vfmadd_vf_f16m1(a, numext::bit_cast<_Float16>(b), c, unpacket_traits<Packet1Xh>::size);
+  }
+#endif
+
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const LaneIdType& lane) const {
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+    c = __riscv_vfmadd_vf_f16m1(a, numext::bit_cast<_Float16>(b.get(lane)), c, unpacket_traits<AccPacket>::size);
+#else
+    c = __riscv_vfmadd_vf_f16m2(a, numext::bit_cast<_Float16>(b.get(lane)), c, unpacket_traits<AccPacket>::size);
+#endif
+  }
+};
+
+#endif
+
+#if defined(EIGEN_VECTORIZE_RVV10BF16)
+
+template <>
+struct gebp_traits<bfloat16, bfloat16, false, false, Architecture::RVV10>
+    : gebp_traits<bfloat16, bfloat16, false, false, Architecture::Generic> {
+  typedef bfloat16 RhsPacket;
+  typedef PacketXbf LhsPacket;
+  typedef PacketXbf AccPacket;
+  typedef QuadPacket<bfloat16> RhsPacketx4;
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const { dest = pset1<RhsPacket>(*b); }
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const {
+    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const { loadRhs(b, dest); }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const { dest = pload<RhsPacket>(b); }
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+    c = F32ToBf16(
+        __riscv_vfwmaccbf16_vf_f32m2(Bf16ToF32(c), numext::bit_cast<__bf16>(b), a, unpacket_traits<AccPacket>::size));
+#else
+    c = F32ToBf16(
+        __riscv_vfwmaccbf16_vf_f32m4(Bf16ToF32(c), numext::bit_cast<__bf16>(b), a, unpacket_traits<AccPacket>::size));
+#endif
+  }
+
+#if EIGEN_RISCV64_DEFAULT_LMUL >= 2
+  EIGEN_STRONG_INLINE void madd(const Packet1Xbf& a, const RhsPacket& b, Packet1Xbf& c, RhsPacket& /*tmp*/,
+                                const FixedInt<0>&) const {
+    c = F32ToBf16(
+        __riscv_vfwmaccbf16_vf_f32m2(Bf16ToF32(c), numext::bit_cast<__bf16>(b), a, unpacket_traits<Packet1Xbf>::size));
+  }
+#endif
+
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/,
+                                const LaneIdType& lane) const {
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+    c = F32ToBf16(__riscv_vfwmaccbf16_vf_f32m2(Bf16ToF32(c), numext::bit_cast<__bf16>(b.get(lane)), a,
+                                               unpacket_traits<AccPacket>::size));
+#else
+    c = F32ToBf16(__riscv_vfwmaccbf16_vf_f32m4(Bf16ToF32(c), numext::bit_cast<__bf16>(b.get(lane)), a,
+                                               unpacket_traits<AccPacket>::size));
+#endif
+  }
+};
+
+#endif
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_RVV10_GENERAL_BLOCK_KERNEL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/MathFunctions.h
new file mode 100644
index 0000000..10a70c4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/MathFunctions.h
@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Kseniya Zaytseva <kseniya.zaytseva@syntacore.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_RVV10_H
+#define EIGEN_MATH_FUNCTIONS_RVV10_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet1Xf)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet2Xf)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet4Xf)
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet1Xd)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet2Xd)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet4Xd)
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_RVV10_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMath.h
new file mode 100644
index 0000000..679d5c1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMath.h
@@ -0,0 +1,2442 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Kseniya Zaytseva <kseniya.zaytseva@syntacore.com>
+// Copyright (C) 2025 Chip Kerchner <ckerchner@tenstorrent.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_RVV10_H
+#define EIGEN_PACKET_MATH_RVV10_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+
+template <typename Scalar, std::size_t VectorLength, std::size_t VectorLMul>
+struct rvv_packet_size_selector {
+  enum { size = VectorLength * VectorLMul / (sizeof(Scalar) * CHAR_BIT) };
+};
+
+template <std::size_t VectorLength, std::size_t VectorLMul>
+struct rvv_packet_alignment_selector {
+  enum {
+    alignment =
+        (VectorLength * VectorLMul) >= 1024
+            ? Aligned128
+            : ((VectorLength * VectorLMul) >= 512 ? Aligned64
+                                                  : ((VectorLength * VectorLMul) >= 256 ? Aligned32 : Aligned16))
+  };
+};
+
+typedef vbool64_t PacketMask64;
+typedef vbool32_t PacketMask32;
+typedef vbool16_t PacketMask16;
+typedef vbool8_t PacketMask8;
+typedef vbool4_t PacketMask4;
+
+/********************************* int32 **************************************/
+typedef eigen_packet_wrapper<vint32m1_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL))), 0> Packet1Xi;
+typedef eigen_packet_wrapper<vuint32m1_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL))), 1> Packet1Xu;
+
+typedef eigen_packet_wrapper<vint32m2_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 2))), 2> Packet2Xi;
+typedef eigen_packet_wrapper<vuint32m2_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 2))), 3> Packet2Xu;
+
+typedef eigen_packet_wrapper<vint32m4_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 4))), 4> Packet4Xi;
+typedef eigen_packet_wrapper<vuint32m4_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 4))), 5> Packet4Xu;
+
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+typedef Packet1Xi PacketXi;
+typedef Packet1Xu PacketXu;
+
+template <>
+struct packet_traits<numext::int32_t> : default_packet_traits {
+  typedef Packet1Xi type;
+  typedef Packet1Xi half;  // Half not implemented yet
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<numext::int32_t, EIGEN_RISCV64_RVV_VL, 1>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0
+  };
+};
+
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 2
+typedef Packet2Xi PacketXi;
+typedef Packet2Xu PacketXu;
+
+template <>
+struct packet_traits<numext::int32_t> : default_packet_traits {
+  typedef Packet2Xi type;
+  typedef Packet1Xi half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<numext::int32_t, EIGEN_RISCV64_RVV_VL, 2>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0
+  };
+};
+
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 4
+typedef Packet4Xi PacketXi;
+typedef Packet4Xu PacketXu;
+
+template <>
+struct packet_traits<numext::int32_t> : default_packet_traits {
+  typedef Packet4Xi type;
+  typedef Packet2Xi half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<numext::int32_t, EIGEN_RISCV64_RVV_VL, 4>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet1Xi> {
+  typedef numext::int32_t type;
+  typedef Packet1Xi half;  // Half not yet implemented
+  typedef numext::uint8_t mask_t;
+  enum {
+    size = rvv_packet_size_selector<numext::int32_t, EIGEN_RISCV64_RVV_VL, 1>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 1>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2Xi> {
+  typedef numext::int32_t type;
+  typedef Packet1Xi half;
+  typedef numext::uint8_t mask_t;
+  enum {
+    size = rvv_packet_size_selector<numext::int32_t, EIGEN_RISCV64_RVV_VL, 2>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 2>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4Xi> {
+  typedef numext::int32_t type;
+  typedef Packet2Xi half;
+  typedef numext::uint8_t mask_t;
+  enum {
+    size = rvv_packet_size_selector<numext::int32_t, EIGEN_RISCV64_RVV_VL, 4>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 4>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<numext::int32_t>(const numext::int32_t* addr) {
+#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
+  __builtin_prefetch(addr);
+#endif
+}
+
+/********************************* Packet1Xi ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pset1<Packet1Xi>(const numext::int32_t& from) {
+  return __riscv_vmv_v_x_i32m1(from, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi plset<Packet1Xi>(const numext::int32_t& a) {
+  Packet1Xi idx = __riscv_vreinterpret_v_u32m1_i32m1(__riscv_vid_v_u32m1(unpacket_traits<Packet1Xi>::size));
+  return __riscv_vadd_vx_i32m1(idx, a, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pzero<Packet1Xi>(const Packet1Xi& /*a*/) {
+  return __riscv_vmv_v_x_i32m1(0, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi padd<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vadd_vv_i32m1(a, b, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi psub<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vsub(a, b, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pnegate(const Packet1Xi& a) {
+  return __riscv_vneg(a, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pconj(const Packet1Xi& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pmul<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vmul(a, b, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pdiv<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vdiv(a, b, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pmadd(const Packet1Xi& a, const Packet1Xi& b, const Packet1Xi& c) {
+  return __riscv_vmadd(a, b, c, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pmsub(const Packet1Xi& a, const Packet1Xi& b, const Packet1Xi& c) {
+  return __riscv_vmadd(a, b, pnegate(c), unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pnmadd(const Packet1Xi& a, const Packet1Xi& b, const Packet1Xi& c) {
+  return __riscv_vnmsub_vv_i32m1(a, b, c, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pnmsub(const Packet1Xi& a, const Packet1Xi& b, const Packet1Xi& c) {
+  return __riscv_vnmsub_vv_i32m1(a, b, pnegate(c), unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pmin<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vmin(a, b, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pmax<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vmax(a, b, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pcmp_le<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  PacketMask32 mask = __riscv_vmsle_vv_i32m1_b32(a, b, unpacket_traits<Packet1Xi>::size);
+  return __riscv_vmerge_vxm_i32m1(pzero(a), 0xffffffff, mask, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pcmp_lt<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  PacketMask32 mask = __riscv_vmslt_vv_i32m1_b32(a, b, unpacket_traits<Packet1Xi>::size);
+  return __riscv_vmerge_vxm_i32m1(pzero(a), 0xffffffff, mask, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pcmp_eq<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  PacketMask32 mask = __riscv_vmseq_vv_i32m1_b32(a, b, unpacket_traits<Packet1Xi>::size);
+  return __riscv_vmerge_vxm_i32m1(pzero(a), 0xffffffff, mask, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi ptrue<Packet1Xi>(const Packet1Xi& /*a*/) {
+  return __riscv_vmv_v_x_i32m1(0xffffffffu, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pand<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vand_vv_i32m1(a, b, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi por<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vor_vv_i32m1(a, b, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pxor<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vxor_vv_i32m1(a, b, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pandnot<Packet1Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vand_vv_i32m1(a, __riscv_vnot_v_i32m1(b, unpacket_traits<Packet1Xi>::size),
+                               unpacket_traits<Packet1Xi>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet1Xi parithmetic_shift_right(Packet1Xi a) {
+  return __riscv_vsra_vx_i32m1(a, N, unpacket_traits<Packet1Xi>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet1Xi plogical_shift_right(Packet1Xi a) {
+  return __riscv_vreinterpret_i32m1(
+      __riscv_vsrl_vx_u32m1(__riscv_vreinterpret_u32m1(a), N, unpacket_traits<Packet1Xi>::size));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet1Xi plogical_shift_left(Packet1Xi a) {
+  return __riscv_vsll_vx_i32m1(a, N, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pload<Packet1Xi>(const numext::int32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle32_v_i32m1(from, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi ploadu<Packet1Xi>(const numext::int32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle32_v_i32m1(from, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi ploaddup<Packet1Xi>(const numext::int32_t* from) {
+  Packet1Xu data = __riscv_vreinterpret_v_i32m1_u32m1(pload<Packet1Xi>(from));
+  return __riscv_vreinterpret_v_i64m1_i32m1(__riscv_vreinterpret_v_u64m1_i64m1(__riscv_vlmul_trunc_v_u64m2_u64m1(
+      __riscv_vwmaccu_vx_u64m2(__riscv_vwaddu_vv_u64m2(data, data, unpacket_traits<Packet1Xi>::size), 0xffffffffu, data,
+                               unpacket_traits<Packet1Xi>::size))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi ploadquad<Packet1Xi>(const numext::int32_t* from) {
+  Packet1Xu idx =
+      __riscv_vsrl_vx_u32m1(__riscv_vid_v_u32m1(unpacket_traits<Packet1Xi>::size), 2, unpacket_traits<Packet1Xi>::size);
+  return __riscv_vrgather_vv_i32m1(pload<Packet1Xi>(from), idx, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int32_t>(numext::int32_t* to, const Packet1Xi& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse32_v_i32m1(to, from, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int32_t>(numext::int32_t* to, const Packet1Xi& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse32_v_i32m1(to, from, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1Xi pgather<numext::int32_t, Packet1Xi>(const numext::int32_t* from, Index stride) {
+  return __riscv_vlse32_v_i32m1(from, stride * sizeof(numext::int32_t), unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int32_t, Packet1Xi>(numext::int32_t* to, const Packet1Xi& from,
+                                                                   Index stride) {
+  __riscv_vsse32(to, stride * sizeof(numext::int32_t), from, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t pfirst<Packet1Xi>(const Packet1Xi& a) {
+  return __riscv_vmv_x_s_i32m1_i32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi preverse(const Packet1Xi& a) {
+  Packet1Xu idx = __riscv_vrsub_vx_u32m1(__riscv_vid_v_u32m1(unpacket_traits<Packet1Xi>::size),
+                                         unpacket_traits<Packet1Xi>::size - 1, unpacket_traits<Packet1Xi>::size);
+  return __riscv_vrgather_vv_i32m1(a, idx, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pabs(const Packet1Xi& a) {
+  Packet1Xi mask = __riscv_vsra_vx_i32m1(a, 31, unpacket_traits<Packet1Xi>::size);
+  return __riscv_vsub_vv_i32m1(__riscv_vxor_vv_i32m1(a, mask, unpacket_traits<Packet1Xi>::size), mask,
+                               unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux<Packet1Xi>(const Packet1Xi& a) {
+  return __riscv_vmv_x(__riscv_vredsum_vs_i32m1_i32m1(a, __riscv_vmv_v_x_i32m1(0, unpacket_traits<Packet1Xi>::size),
+                                                      unpacket_traits<Packet1Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_mul<Packet1Xi>(const Packet1Xi& a) {
+  // Multiply the vector by its reverse
+  Packet1Xi prod = __riscv_vmul_vv_i32m1(preverse(a), a, unpacket_traits<Packet1Xi>::size);
+  Packet1Xi half_prod;
+
+  if (EIGEN_RISCV64_RVV_VL >= 1024) {
+    half_prod = __riscv_vslidedown_vx_i32m1(prod, 8, unpacket_traits<Packet1Xi>::size);
+    prod = __riscv_vmul_vv_i32m1(prod, half_prod, unpacket_traits<Packet1Xi>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 512) {
+    half_prod = __riscv_vslidedown_vx_i32m1(prod, 4, unpacket_traits<Packet1Xi>::size);
+    prod = __riscv_vmul_vv_i32m1(prod, half_prod, unpacket_traits<Packet1Xi>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 256) {
+    half_prod = __riscv_vslidedown_vx_i32m1(prod, 2, unpacket_traits<Packet1Xi>::size);
+    prod = __riscv_vmul_vv_i32m1(prod, half_prod, unpacket_traits<Packet1Xi>::size);
+  }
+  // Last reduction
+  half_prod = __riscv_vslidedown_vx_i32m1(prod, 1, unpacket_traits<Packet1Xi>::size);
+  prod = __riscv_vmul_vv_i32m1(prod, half_prod, unpacket_traits<Packet1Xi>::size);
+
+  // The reduction is done to the first element.
+  return pfirst(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_min<Packet1Xi>(const Packet1Xi& a) {
+  return __riscv_vmv_x(__riscv_vredmin_vs_i32m1_i32m1(
+      a, __riscv_vmv_v_x_i32m1((std::numeric_limits<numext::int32_t>::max)(), unpacket_traits<Packet1Xi>::size),
+      unpacket_traits<Packet1Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_max<Packet1Xi>(const Packet1Xi& a) {
+  return __riscv_vmv_x(__riscv_vredmax_vs_i32m1_i32m1(
+      a, __riscv_vmv_v_x_i32m1((std::numeric_limits<numext::int32_t>::min)(), unpacket_traits<Packet1Xi>::size),
+      unpacket_traits<Packet1Xi>::size));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1Xi, N>& kernel) {
+  numext::int32_t buffer[unpacket_traits<Packet1Xi>::size * N] = {0};
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse32(&buffer[i], N * sizeof(numext::int32_t), kernel.packet[i], unpacket_traits<Packet1Xi>::size);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle32_v_i32m1(&buffer[i * unpacket_traits<Packet1Xi>::size], unpacket_traits<Packet1Xi>::size);
+  }
+}
+
+/********************************* float32 ************************************/
+
+typedef eigen_packet_wrapper<vfloat32m1_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL))), 6> Packet1Xf;
+typedef eigen_packet_wrapper<vfloat32m2_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 2))), 7>
+    Packet2Xf;
+typedef eigen_packet_wrapper<vfloat32m4_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 4))), 8>
+    Packet4Xf;
+
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+typedef Packet1Xf PacketXf;
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet1Xf type;
+  typedef Packet1Xf half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<float, EIGEN_RISCV64_RVV_VL, 1>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasRound = 1,
+
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH
+  };
+};
+
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 2
+typedef Packet2Xf PacketXf;
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet2Xf type;
+  typedef Packet1Xf half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<float, EIGEN_RISCV64_RVV_VL, 2>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasRound = 1,
+
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH
+  };
+};
+
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 4
+typedef Packet4Xf PacketXf;
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4Xf type;
+  typedef Packet2Xf half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<float, EIGEN_RISCV64_RVV_VL, 4>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasRound = 1,
+
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet1Xf> {
+  typedef float type;
+  typedef Packet1Xf half;  // Half not yet implemented
+  typedef Packet1Xi integer_packet;
+  typedef numext::uint8_t mask_t;
+  typedef PacketMask32 packet_mask;
+
+  enum {
+    size = rvv_packet_size_selector<float, EIGEN_RISCV64_RVV_VL, 1>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 1>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2Xf> {
+  typedef float type;
+  typedef Packet1Xf half;
+  typedef Packet2Xi integer_packet;
+  typedef numext::uint8_t mask_t;
+  typedef PacketMask16 packet_mask;
+
+  enum {
+    size = rvv_packet_size_selector<float, EIGEN_RISCV64_RVV_VL, 2>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 2>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4Xf> {
+  typedef float type;
+  typedef Packet2Xf half;
+  typedef Packet4Xi integer_packet;
+  typedef numext::uint8_t mask_t;
+  typedef PacketMask8 packet_mask;
+
+  enum {
+    size = rvv_packet_size_selector<float, EIGEN_RISCV64_RVV_VL, 4>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 4>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+/********************************* Packet1Xf ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf ptrue<Packet1Xf>(const Packet1Xf& /*a*/) {
+  return __riscv_vreinterpret_f32m1(__riscv_vmv_v_x_u32m1(0xffffffffu, unpacket_traits<Packet1Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pzero<Packet1Xf>(const Packet1Xf& /*a*/) {
+  return __riscv_vfmv_v_f_f32m1(0.0f, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pabs(const Packet1Xf& a) {
+  return __riscv_vfabs_v_f32m1(a, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pabsdiff(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vfabs_v_f32m1(__riscv_vfsub_vv_f32m1(a, b, unpacket_traits<Packet1Xf>::size),
+                               unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pset1<Packet1Xf>(const float& from) {
+  return __riscv_vfmv_v_f_f32m1(from, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pset1frombits<Packet1Xf>(numext::uint32_t from) {
+  return __riscv_vreinterpret_f32m1(__riscv_vmv_v_x_u32m1(from, unpacket_traits<Packet1Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf plset<Packet1Xf>(const float& a) {
+  Packet1Xf idx = __riscv_vfcvt_f_x_v_f32m1(
+      __riscv_vreinterpret_v_u32m1_i32m1(__riscv_vid_v_u32m1(unpacket_traits<Packet1Xi>::size)),
+      unpacket_traits<Packet1Xf>::size);
+  return __riscv_vfadd_vf_f32m1(idx, a, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet1Xf>(const float* a, Packet1Xf& a0, Packet1Xf& a1, Packet1Xf& a2,
+                                                Packet1Xf& a3) {
+  vfloat32m1_t aa = __riscv_vle32_v_f32m1(a, 4);
+  a0 = __riscv_vrgather_vx_f32m1(aa, 0, unpacket_traits<Packet1Xf>::size);
+  a1 = __riscv_vrgather_vx_f32m1(aa, 1, unpacket_traits<Packet1Xf>::size);
+  a2 = __riscv_vrgather_vx_f32m1(aa, 2, unpacket_traits<Packet1Xf>::size);
+  a3 = __riscv_vrgather_vx_f32m1(aa, 3, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf padd<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vfadd_vv_f32m1(a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf psub<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vfsub_vv_f32m1(a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pnegate(const Packet1Xf& a) {
+  return __riscv_vfneg_v_f32m1(a, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf psignbit(const Packet1Xf& a) {
+  return __riscv_vreinterpret_v_i32m1_f32m1(
+      __riscv_vsra_vx_i32m1(__riscv_vreinterpret_v_f32m1_i32m1(a), 31, unpacket_traits<Packet1Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pconj(const Packet1Xf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pmul<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vfmul_vv_f32m1(a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pdiv<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vfdiv_vv_f32m1(a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pmadd(const Packet1Xf& a, const Packet1Xf& b, const Packet1Xf& c) {
+  return __riscv_vfmadd_vv_f32m1(a, b, c, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pmsub(const Packet1Xf& a, const Packet1Xf& b, const Packet1Xf& c) {
+  return __riscv_vfmsub_vv_f32m1(a, b, c, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pnmadd(const Packet1Xf& a, const Packet1Xf& b, const Packet1Xf& c) {
+  return __riscv_vfnmsub_vv_f32m1(a, b, c, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pnmsub(const Packet1Xf& a, const Packet1Xf& b, const Packet1Xf& c) {
+  return __riscv_vfnmadd_vv_f32m1(a, b, c, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pmin<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  Packet1Xf nans = __riscv_vfmv_v_f_f32m1((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet1Xf>::size);
+  PacketMask32 mask = __riscv_vmfeq_vv_f32m1_b32(a, a, unpacket_traits<Packet1Xf>::size);
+  PacketMask32 mask2 = __riscv_vmfeq_vv_f32m1_b32(b, b, unpacket_traits<Packet1Xf>::size);
+  mask = __riscv_vmand_mm_b32(mask, mask2, unpacket_traits<Packet1Xf>::size);
+
+  return __riscv_vfmin_vv_f32m1_tumu(mask, nans, a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pmin<PropagateNaN, Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return pmin<Packet1Xf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pmin<PropagateNumbers, Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vfmin_vv_f32m1(a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pmax<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  Packet1Xf nans = __riscv_vfmv_v_f_f32m1((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet1Xf>::size);
+  PacketMask32 mask = __riscv_vmfeq_vv_f32m1_b32(a, a, unpacket_traits<Packet1Xf>::size);
+  PacketMask32 mask2 = __riscv_vmfeq_vv_f32m1_b32(b, b, unpacket_traits<Packet1Xf>::size);
+  mask = __riscv_vmand_mm_b32(mask, mask2, unpacket_traits<Packet1Xf>::size);
+
+  return __riscv_vfmax_vv_f32m1_tumu(mask, nans, a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pmax<PropagateNaN, Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return pmax<Packet1Xf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pmax<PropagateNumbers, Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vfmax_vv_f32m1(a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pcmp_le<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  PacketMask32 mask = __riscv_vmfle_vv_f32m1_b32(a, b, unpacket_traits<Packet1Xf>::size);
+  return __riscv_vmerge_vvm_f32m1(pzero<Packet1Xf>(a), ptrue<Packet1Xf>(a), mask, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pcmp_lt<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  PacketMask32 mask = __riscv_vmflt_vv_f32m1_b32(a, b, unpacket_traits<Packet1Xf>::size);
+  return __riscv_vmerge_vvm_f32m1(pzero<Packet1Xf>(a), ptrue<Packet1Xf>(a), mask, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pcmp_eq<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  PacketMask32 mask = __riscv_vmfeq_vv_f32m1_b32(a, b, unpacket_traits<Packet1Xf>::size);
+  return __riscv_vmerge_vvm_f32m1(pzero<Packet1Xf>(a), ptrue<Packet1Xf>(a), mask, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pcmp_lt_or_nan<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  PacketMask32 mask = __riscv_vmfge_vv_f32m1_b32(a, b, unpacket_traits<Packet1Xf>::size);
+  return __riscv_vfmerge_vfm_f32m1(ptrue<Packet1Xf>(a), 0.0f, mask, unpacket_traits<Packet1Xf>::size);
+}
+
+// Logical Operations are not supported for float, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pand<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vreinterpret_v_u32m1_f32m1(__riscv_vand_vv_u32m1(
+      __riscv_vreinterpret_v_f32m1_u32m1(a), __riscv_vreinterpret_v_f32m1_u32m1(b), unpacket_traits<Packet1Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf por<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vreinterpret_v_u32m1_f32m1(__riscv_vor_vv_u32m1(
+      __riscv_vreinterpret_v_f32m1_u32m1(a), __riscv_vreinterpret_v_f32m1_u32m1(b), unpacket_traits<Packet1Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pxor<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vreinterpret_v_u32m1_f32m1(__riscv_vxor_vv_u32m1(
+      __riscv_vreinterpret_v_f32m1_u32m1(a), __riscv_vreinterpret_v_f32m1_u32m1(b), unpacket_traits<Packet1Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pandnot<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vreinterpret_v_u32m1_f32m1(__riscv_vand_vv_u32m1(
+      __riscv_vreinterpret_v_f32m1_u32m1(a),
+      __riscv_vnot_v_u32m1(__riscv_vreinterpret_v_f32m1_u32m1(b), unpacket_traits<Packet1Xf>::size),
+      unpacket_traits<Packet1Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pload<Packet1Xf>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle32_v_f32m1(from, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf ploadu<Packet1Xf>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle32_v_f32m1(from, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf ploaddup<Packet1Xf>(const float* from) {
+  Packet1Xu data = __riscv_vreinterpret_v_f32m1_u32m1(pload<Packet1Xf>(from));
+  return __riscv_vreinterpret_v_i32m1_f32m1(
+      __riscv_vreinterpret_v_i64m1_i32m1(__riscv_vreinterpret_v_u64m1_i64m1(__riscv_vlmul_trunc_v_u64m2_u64m1(
+          __riscv_vwmaccu_vx_u64m2(__riscv_vwaddu_vv_u64m2(data, data, unpacket_traits<Packet1Xi>::size), 0xffffffffu,
+                                   data, unpacket_traits<Packet1Xi>::size)))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf ploadquad<Packet1Xf>(const float* from) {
+  Packet1Xu idx =
+      __riscv_vsrl_vx_u32m1(__riscv_vid_v_u32m1(unpacket_traits<Packet1Xf>::size), 2, unpacket_traits<Packet1Xf>::size);
+  return __riscv_vrgather_vv_f32m1(pload<Packet1Xf>(from), idx, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet1Xf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse32_v_f32m1(to, from, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet1Xf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse32_v_f32m1(to, from, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1Xf pgather<float, Packet1Xf>(const float* from, Index stride) {
+  return __riscv_vlse32_v_f32m1(from, stride * sizeof(float), unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet1Xf>(float* to, const Packet1Xf& from, Index stride) {
+  __riscv_vsse32(to, stride * sizeof(float), from, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet1Xf>(const Packet1Xf& a) {
+  return __riscv_vfmv_f_s_f32m1_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf psqrt(const Packet1Xf& a) {
+  return __riscv_vfsqrt_v_f32m1(a, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf print<Packet1Xf>(const Packet1Xf& a) {
+  const Packet1Xf limit = pset1<Packet1Xf>(static_cast<float>(1 << 23));
+  const Packet1Xf abs_a = pabs(a);
+
+  PacketMask32 mask = __riscv_vmfne_vv_f32m1_b32(a, a, unpacket_traits<Packet1Xf>::size);
+  const Packet1Xf x = __riscv_vfadd_vv_f32m1_tumu(mask, a, a, a, unpacket_traits<Packet1Xf>::size);
+  const Packet1Xf new_x = __riscv_vfcvt_f_x_v_f32m1(__riscv_vfcvt_x_f_v_i32m1(a, unpacket_traits<Packet1Xf>::size),
+                                                    unpacket_traits<Packet1Xf>::size);
+
+  mask = __riscv_vmflt_vv_f32m1_b32(abs_a, limit, unpacket_traits<Packet1Xf>::size);
+  Packet1Xf signed_x = __riscv_vfsgnj_vv_f32m1(new_x, x, unpacket_traits<Packet1Xf>::size);
+  return __riscv_vmerge_vvm_f32m1(x, signed_x, mask, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pfloor<Packet1Xf>(const Packet1Xf& a) {
+  Packet1Xf tmp = print<Packet1Xf>(a);
+  // If greater, subtract one.
+  PacketMask32 mask = __riscv_vmflt_vv_f32m1_b32(a, tmp, unpacket_traits<Packet1Xf>::size);
+  return __riscv_vfsub_vf_f32m1_tumu(mask, tmp, tmp, 1.0f, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf preverse(const Packet1Xf& a) {
+  Packet1Xu idx = __riscv_vrsub_vx_u32m1(__riscv_vid_v_u32m1(unpacket_traits<Packet1Xf>::size),
+                                         unpacket_traits<Packet1Xf>::size - 1, unpacket_traits<Packet1Xf>::size);
+  return __riscv_vrgather_vv_f32m1(a, idx, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pfrexp<Packet1Xf>(const Packet1Xf& a, Packet1Xf& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet1Xf>(const Packet1Xf& a) {
+  return __riscv_vfmv_f(__riscv_vfredusum_vs_f32m1_f32m1(
+      a, __riscv_vfmv_v_f_f32m1(0.0, unpacket_traits<Packet1Xf>::size), unpacket_traits<Packet1Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet1Xf>(const Packet1Xf& a) {
+  // Multiply the vector by its reverse
+  Packet1Xf prod = __riscv_vfmul_vv_f32m1(preverse(a), a, unpacket_traits<Packet1Xf>::size);
+  Packet1Xf half_prod;
+
+  if (EIGEN_RISCV64_RVV_VL >= 1024) {
+    half_prod = __riscv_vslidedown_vx_f32m1(prod, 8, unpacket_traits<Packet1Xf>::size);
+    prod = __riscv_vfmul_vv_f32m1(prod, half_prod, unpacket_traits<Packet1Xf>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 512) {
+    half_prod = __riscv_vslidedown_vx_f32m1(prod, 4, unpacket_traits<Packet1Xf>::size);
+    prod = __riscv_vfmul_vv_f32m1(prod, half_prod, unpacket_traits<Packet1Xf>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 256) {
+    half_prod = __riscv_vslidedown_vx_f32m1(prod, 2, unpacket_traits<Packet1Xf>::size);
+    prod = __riscv_vfmul_vv_f32m1(prod, half_prod, unpacket_traits<Packet1Xf>::size);
+  }
+  // Last reduction
+  half_prod = __riscv_vslidedown_vx_f32m1(prod, 1, unpacket_traits<Packet1Xf>::size);
+  prod = __riscv_vfmul_vv_f32m1(prod, half_prod, unpacket_traits<Packet1Xf>::size);
+
+  // The reduction is done to the first element.
+  return pfirst(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet1Xf>(const Packet1Xf& a) {
+  return (std::min)(
+      __riscv_vfmv_f(__riscv_vfredmin_vs_f32m1_f32m1(
+          a, __riscv_vfmv_v_f_f32m1((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet1Xf>::size),
+          unpacket_traits<Packet1Xf>::size)),
+      (std::numeric_limits<float>::max)());
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet1Xf>(const Packet1Xf& a) {
+  return (std::max)(
+      __riscv_vfmv_f(__riscv_vfredmax_vs_f32m1_f32m1(
+          a, __riscv_vfmv_v_f_f32m1((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet1Xf>::size),
+          unpacket_traits<Packet1Xf>::size)),
+      -(std::numeric_limits<float>::max)());
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1Xf, N>& kernel) {
+  float buffer[unpacket_traits<Packet1Xf>::size * N];
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse32(&buffer[i], N * sizeof(float), kernel.packet[i], unpacket_traits<Packet1Xf>::size);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle32_v_f32m1(&buffer[i * unpacket_traits<Packet1Xf>::size], unpacket_traits<Packet1Xf>::size);
+  }
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pldexp<Packet1Xf>(const Packet1Xf& a, const Packet1Xf& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketMask32 por(const PacketMask32& a, const PacketMask32& b) {
+  return __riscv_vmor_mm_b32(a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketMask32 pand(const PacketMask32& a, const PacketMask32& b) {
+  return __riscv_vmand_mm_b32(a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+EIGEN_STRONG_INLINE PacketMask32 pcmp_eq_mask(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vmfeq_vv_f32m1_b32(a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+EIGEN_STRONG_INLINE PacketMask32 pcmp_lt_mask(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vmflt_vv_f32m1_b32(a, b, unpacket_traits<Packet1Xf>::size);
+}
+
+EIGEN_STRONG_INLINE Packet1Xf pselect(const PacketMask32& mask, const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vmerge_vvm_f32m1(b, a, mask, unpacket_traits<Packet1Xf>::size);
+}
+
+EIGEN_STRONG_INLINE Packet1Xf pselect(const Packet1Xf& mask, const Packet1Xf& a, const Packet1Xf& b) {
+  PacketMask32 mask2 =
+      __riscv_vmsne_vx_i32m1_b32(__riscv_vreinterpret_v_f32m1_i32m1(mask), 0, unpacket_traits<Packet1Xf>::size);
+  return __riscv_vmerge_vvm_f32m1(b, a, mask2, unpacket_traits<Packet1Xf>::size);
+}
+
+/********************************* int64 **************************************/
+
+typedef eigen_packet_wrapper<vint64m1_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL))), 9> Packet1Xl;
+typedef eigen_packet_wrapper<vuint64m1_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL))), 10> Packet1Xul;
+
+typedef eigen_packet_wrapper<vint64m2_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 2))), 11> Packet2Xl;
+typedef eigen_packet_wrapper<vuint64m2_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 2))), 12>
+    Packet2Xul;
+
+typedef eigen_packet_wrapper<vint64m4_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 4))), 13> Packet4Xl;
+typedef eigen_packet_wrapper<vuint64m4_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 4))), 14>
+    Packet4Xul;
+
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+typedef Packet1Xl PacketXl;
+typedef Packet1Xul PacketXul;
+
+template <>
+struct packet_traits<numext::int64_t> : default_packet_traits {
+  typedef Packet1Xl type;
+  typedef Packet1Xl half;  // Half not implemented yet
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<numext::int64_t, EIGEN_RISCV64_RVV_VL, 1>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0
+  };
+};
+
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 2
+typedef Packet2Xl PacketXl;
+typedef Packet2Xul PacketXul;
+
+template <>
+struct packet_traits<numext::int64_t> : default_packet_traits {
+  typedef Packet2Xl type;
+  typedef Packet1Xl half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<numext::int64_t, EIGEN_RISCV64_RVV_VL, 2>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0
+  };
+};
+
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 4
+typedef Packet4Xl PacketXl;
+typedef Packet4Xul PacketXul;
+
+template <>
+struct packet_traits<numext::int64_t> : default_packet_traits {
+  typedef Packet4Xl type;
+  typedef Packet2Xl half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<numext::int64_t, EIGEN_RISCV64_RVV_VL, 4>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet1Xl> {
+  typedef numext::int64_t type;
+  typedef Packet1Xl half;  // Half not yet implemented
+  typedef numext::uint8_t mask_t;
+  enum {
+    size = rvv_packet_size_selector<numext::int64_t, EIGEN_RISCV64_RVV_VL, 1>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 1>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2Xl> {
+  typedef numext::int64_t type;
+  typedef Packet1Xl half;
+  typedef numext::uint8_t mask_t;
+  enum {
+    size = rvv_packet_size_selector<numext::int64_t, EIGEN_RISCV64_RVV_VL, 2>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 2>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4Xl> {
+  typedef numext::int64_t type;
+  typedef Packet2Xl half;
+  typedef numext::uint8_t mask_t;
+  enum {
+    size = rvv_packet_size_selector<numext::int64_t, EIGEN_RISCV64_RVV_VL, 4>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 4>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<numext::int64_t>(const numext::int64_t* addr) {
+#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
+  __builtin_prefetch(addr);
+#endif
+}
+
+/********************************* Packet1Xl ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pset1<Packet1Xl>(const numext::int64_t& from) {
+  return __riscv_vmv_v_x_i64m1(from, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl plset<Packet1Xl>(const numext::int64_t& a) {
+  Packet1Xl idx = __riscv_vreinterpret_v_u64m1_i64m1(__riscv_vid_v_u64m1(unpacket_traits<Packet1Xl>::size));
+  return __riscv_vadd_vx_i64m1(idx, a, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pzero<Packet1Xl>(const Packet1Xl& /*a*/) {
+  return __riscv_vmv_v_x_i64m1(0, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl padd<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vadd_vv_i64m1(a, b, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl psub<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vsub(a, b, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pnegate(const Packet1Xl& a) {
+  return __riscv_vneg(a, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pconj(const Packet1Xl& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pmul<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vmul(a, b, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pdiv<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vdiv(a, b, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pmadd(const Packet1Xl& a, const Packet1Xl& b, const Packet1Xl& c) {
+  return __riscv_vmadd(a, b, c, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pmsub(const Packet1Xl& a, const Packet1Xl& b, const Packet1Xl& c) {
+  return __riscv_vmadd(a, b, pnegate(c), unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pnmadd(const Packet1Xl& a, const Packet1Xl& b, const Packet1Xl& c) {
+  return __riscv_vnmsub_vv_i64m1(a, b, c, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pnmsub(const Packet1Xl& a, const Packet1Xl& b, const Packet1Xl& c) {
+  return __riscv_vnmsub_vv_i64m1(a, b, pnegate(c), unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pmin<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vmin(a, b, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pmax<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vmax(a, b, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pcmp_le<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  PacketMask64 mask = __riscv_vmsle_vv_i64m1_b64(a, b, unpacket_traits<Packet1Xl>::size);
+  return __riscv_vmerge_vxm_i64m1(pzero(a), 0xffffffffffffffff, mask, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pcmp_lt<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  PacketMask64 mask = __riscv_vmslt_vv_i64m1_b64(a, b, unpacket_traits<Packet1Xl>::size);
+  return __riscv_vmerge_vxm_i64m1(pzero(a), 0xffffffffffffffff, mask, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pcmp_eq<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  PacketMask64 mask = __riscv_vmseq_vv_i64m1_b64(a, b, unpacket_traits<Packet1Xl>::size);
+  return __riscv_vmerge_vxm_i64m1(pzero(a), 0xffffffffffffffff, mask, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl ptrue<Packet1Xl>(const Packet1Xl& /*a*/) {
+  return __riscv_vmv_v_x_i64m1(0xffffffffffffffffu, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pand<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vand_vv_i64m1(a, b, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl por<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vor_vv_i64m1(a, b, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pxor<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vxor_vv_i64m1(a, b, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pandnot<Packet1Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vand_vv_i64m1(a, __riscv_vnot_v_i64m1(b, unpacket_traits<Packet1Xl>::size),
+                               unpacket_traits<Packet1Xl>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet1Xl parithmetic_shift_right(Packet1Xl a) {
+  return __riscv_vsra_vx_i64m1(a, N, unpacket_traits<Packet1Xl>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet1Xl plogical_shift_right(Packet1Xl a) {
+  return __riscv_vreinterpret_i64m1(
+      __riscv_vsrl_vx_u64m1(__riscv_vreinterpret_u64m1(a), N, unpacket_traits<Packet1Xl>::size));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet1Xl plogical_shift_left(Packet1Xl a) {
+  return __riscv_vsll_vx_i64m1(a, N, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pload<Packet1Xl>(const numext::int64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle64_v_i64m1(from, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl ploadu<Packet1Xl>(const numext::int64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle64_v_i64m1(from, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl ploaddup<Packet1Xl>(const numext::int64_t* from) {
+  Packet1Xul idx =
+      __riscv_vsrl_vx_u64m1(__riscv_vid_v_u64m1(unpacket_traits<Packet1Xl>::size), 1, unpacket_traits<Packet1Xl>::size);
+  return __riscv_vrgather_vv_i64m1(pload<Packet1Xl>(from), idx, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl ploadquad<Packet1Xl>(const numext::int64_t* from) {
+  Packet1Xul idx =
+      __riscv_vsrl_vx_u64m1(__riscv_vid_v_u64m1(unpacket_traits<Packet1Xl>::size), 2, unpacket_traits<Packet1Xl>::size);
+  return __riscv_vrgather_vv_i64m1(pload<Packet1Xl>(from), idx, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int64_t>(numext::int64_t* to, const Packet1Xl& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse64_v_i64m1(to, from, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int64_t>(numext::int64_t* to, const Packet1Xl& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse64_v_i64m1(to, from, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1Xl pgather<numext::int64_t, Packet1Xl>(const numext::int64_t* from, Index stride) {
+  return __riscv_vlse64_v_i64m1(from, stride * sizeof(numext::int64_t), unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int64_t, Packet1Xl>(numext::int64_t* to, const Packet1Xl& from,
+                                                                   Index stride) {
+  __riscv_vsse64(to, stride * sizeof(numext::int64_t), from, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t pfirst<Packet1Xl>(const Packet1Xl& a) {
+  return __riscv_vmv_x_s_i64m1_i64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl preverse(const Packet1Xl& a) {
+  Packet1Xul idx = __riscv_vrsub_vx_u64m1(__riscv_vid_v_u64m1(unpacket_traits<Packet1Xl>::size),
+                                          unpacket_traits<Packet1Xl>::size - 1, unpacket_traits<Packet1Xl>::size);
+  return __riscv_vrgather_vv_i64m1(a, idx, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pabs(const Packet1Xl& a) {
+  Packet1Xl mask = __riscv_vsra_vx_i64m1(a, 63, unpacket_traits<Packet1Xl>::size);
+  return __riscv_vsub_vv_i64m1(__riscv_vxor_vv_i64m1(a, mask, unpacket_traits<Packet1Xl>::size), mask,
+                               unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux<Packet1Xl>(const Packet1Xl& a) {
+  return __riscv_vmv_x(__riscv_vredsum_vs_i64m1_i64m1(a, __riscv_vmv_v_x_i64m1(0, unpacket_traits<Packet1Xl>::size),
+                                                      unpacket_traits<Packet1Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux_mul<Packet1Xl>(const Packet1Xl& a) {
+  // Multiply the vector by its reverse
+  Packet1Xl prod = __riscv_vmul_vv_i64m1(preverse(a), a, unpacket_traits<Packet1Xl>::size);
+  Packet1Xl half_prod;
+
+  if (EIGEN_RISCV64_RVV_VL >= 1024) {
+    half_prod = __riscv_vslidedown_vx_i64m1(prod, 4, unpacket_traits<Packet1Xl>::size);
+    prod = __riscv_vmul_vv_i64m1(prod, half_prod, unpacket_traits<Packet1Xl>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 512) {
+    half_prod = __riscv_vslidedown_vx_i64m1(prod, 2, unpacket_traits<Packet1Xl>::size);
+    prod = __riscv_vmul_vv_i64m1(prod, half_prod, unpacket_traits<Packet1Xl>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 256) {
+    half_prod = __riscv_vslidedown_vx_i64m1(prod, 1, unpacket_traits<Packet1Xl>::size);
+    prod = __riscv_vmul_vv_i64m1(prod, half_prod, unpacket_traits<Packet1Xl>::size);
+  }
+
+  // The reduction is done to the first element.
+  return pfirst(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux_min<Packet1Xl>(const Packet1Xl& a) {
+  return __riscv_vmv_x(__riscv_vredmin_vs_i64m1_i64m1(
+      a, __riscv_vmv_v_x_i64m1((std::numeric_limits<numext::int64_t>::max)(), unpacket_traits<Packet1Xl>::size),
+      unpacket_traits<Packet1Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux_max<Packet1Xl>(const Packet1Xl& a) {
+  return __riscv_vmv_x(__riscv_vredmax_vs_i64m1_i64m1(
+      a, __riscv_vmv_v_x_i64m1((std::numeric_limits<numext::int64_t>::min)(), unpacket_traits<Packet1Xl>::size),
+      unpacket_traits<Packet1Xl>::size));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1Xl, N>& kernel) {
+  numext::int64_t buffer[unpacket_traits<Packet1Xl>::size * N] = {0};
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse64(&buffer[i], N * sizeof(numext::int64_t), kernel.packet[i], unpacket_traits<Packet1Xl>::size);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle64_v_i64m1(&buffer[i * unpacket_traits<Packet1Xl>::size], unpacket_traits<Packet1Xl>::size);
+  }
+}
+
+/********************************* double ************************************/
+
+typedef eigen_packet_wrapper<vfloat64m1_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL))), 15> Packet1Xd;
+typedef eigen_packet_wrapper<vfloat64m2_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 2))), 16>
+    Packet2Xd;
+typedef eigen_packet_wrapper<vfloat64m4_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 4))), 17>
+    Packet4Xd;
+
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+typedef Packet1Xd PacketXd;
+
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet1Xd type;
+  typedef Packet1Xd half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<double, EIGEN_RISCV64_RVV_VL, 1>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasRound = 1,
+
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1
+  };
+};
+
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 2
+typedef Packet2Xd PacketXd;
+
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet2Xd type;
+  typedef Packet1Xd half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<double, EIGEN_RISCV64_RVV_VL, 2>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasRound = 1,
+
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1
+  };
+};
+
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 4
+typedef Packet4Xd PacketXd;
+
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet4Xd type;
+  typedef Packet2Xd half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<double, EIGEN_RISCV64_RVV_VL, 4>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasRound = 1,
+
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet1Xd> {
+  typedef double type;
+  typedef Packet1Xd half;  // Half not yet implemented
+  typedef Packet1Xl integer_packet;
+  typedef numext::uint8_t mask_t;
+  typedef PacketMask64 packet_mask;
+
+  enum {
+    size = rvv_packet_size_selector<double, EIGEN_RISCV64_RVV_VL, 1>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 1>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2Xd> {
+  typedef double type;
+  typedef Packet1Xd half;
+  typedef Packet2Xl integer_packet;
+  typedef numext::uint8_t mask_t;
+  typedef PacketMask32 packet_mask;
+
+  enum {
+    size = rvv_packet_size_selector<double, EIGEN_RISCV64_RVV_VL, 2>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 2>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4Xd> {
+  typedef double type;
+  typedef Packet2Xd half;
+  typedef Packet4Xl integer_packet;
+  typedef numext::uint8_t mask_t;
+  typedef PacketMask16 packet_mask;
+
+  enum {
+    size = rvv_packet_size_selector<double, EIGEN_RISCV64_RVV_VL, 4>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 4>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+/********************************* Packet1Xd ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd ptrue<Packet1Xd>(const Packet1Xd& /*a*/) {
+  return __riscv_vreinterpret_f64m1(__riscv_vmv_v_x_u64m1(0xffffffffffffffffu, unpacket_traits<Packet1Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pzero<Packet1Xd>(const Packet1Xd& /*a*/) {
+  return __riscv_vfmv_v_f_f64m1(0.0, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pabs(const Packet1Xd& a) {
+  return __riscv_vfabs_v_f64m1(a, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pabsdiff(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vfabs_v_f64m1(__riscv_vfsub_vv_f64m1(a, b, unpacket_traits<Packet1Xd>::size),
+                               unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pset1<Packet1Xd>(const double& from) {
+  return __riscv_vfmv_v_f_f64m1(from, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pset1frombits<Packet1Xd>(numext::uint64_t from) {
+  return __riscv_vreinterpret_f64m1(__riscv_vmv_v_x_u64m1(from, unpacket_traits<Packet1Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd plset<Packet1Xd>(const double& a) {
+  Packet1Xd idx = __riscv_vfcvt_f_x_v_f64m1(
+      __riscv_vreinterpret_v_u64m1_i64m1(__riscv_vid_v_u64m1(unpacket_traits<Packet1Xl>::size)),
+      unpacket_traits<Packet1Xd>::size);
+  return __riscv_vfadd_vf_f64m1(idx, a, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet1Xd>(const double* a, Packet1Xd& a0, Packet1Xd& a1, Packet1Xd& a2,
+                                                Packet1Xd& a3) {
+  if (EIGEN_RISCV64_RVV_VL >= 256) {
+    vfloat64m1_t aa = __riscv_vle64_v_f64m1(a, 4);
+    a0 = __riscv_vrgather_vx_f64m1(aa, 0, unpacket_traits<Packet1Xd>::size);
+    a1 = __riscv_vrgather_vx_f64m1(aa, 1, unpacket_traits<Packet1Xd>::size);
+    a2 = __riscv_vrgather_vx_f64m1(aa, 2, unpacket_traits<Packet1Xd>::size);
+    a3 = __riscv_vrgather_vx_f64m1(aa, 3, unpacket_traits<Packet1Xd>::size);
+  } else {
+    vfloat64m1_t aa0 = __riscv_vle64_v_f64m1(a + 0, 2);
+    vfloat64m1_t aa1 = __riscv_vle64_v_f64m1(a + 2, 2);
+    a0 = __riscv_vrgather_vx_f64m1(aa0, 0, unpacket_traits<Packet1Xd>::size);
+    a1 = __riscv_vrgather_vx_f64m1(aa0, 1, unpacket_traits<Packet1Xd>::size);
+    a2 = __riscv_vrgather_vx_f64m1(aa1, 0, unpacket_traits<Packet1Xd>::size);
+    a3 = __riscv_vrgather_vx_f64m1(aa1, 1, unpacket_traits<Packet1Xd>::size);
+  }
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd padd<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vfadd_vv_f64m1(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd psub<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vfsub_vv_f64m1(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pnegate(const Packet1Xd& a) {
+  return __riscv_vfneg_v_f64m1(a, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd psignbit(const Packet1Xd& a) {
+  return __riscv_vreinterpret_v_i64m1_f64m1(
+      __riscv_vsra_vx_i64m1(__riscv_vreinterpret_v_f64m1_i64m1(a), 63, unpacket_traits<Packet1Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pconj(const Packet1Xd& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pmul<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vfmul_vv_f64m1(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pdiv<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vfdiv_vv_f64m1(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pmadd(const Packet1Xd& a, const Packet1Xd& b, const Packet1Xd& c) {
+  return __riscv_vfmadd_vv_f64m1(a, b, c, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pmsub(const Packet1Xd& a, const Packet1Xd& b, const Packet1Xd& c) {
+  return __riscv_vfmsub_vv_f64m1(a, b, c, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pnmadd(const Packet1Xd& a, const Packet1Xd& b, const Packet1Xd& c) {
+  return __riscv_vfnmsub_vv_f64m1(a, b, c, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pnmsub(const Packet1Xd& a, const Packet1Xd& b, const Packet1Xd& c) {
+  return __riscv_vfnmadd_vv_f64m1(a, b, c, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pmin<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  Packet1Xd nans = __riscv_vfmv_v_f_f64m1((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet1Xd>::size);
+  PacketMask64 mask = __riscv_vmfeq_vv_f64m1_b64(a, a, unpacket_traits<Packet1Xd>::size);
+  PacketMask64 mask2 = __riscv_vmfeq_vv_f64m1_b64(b, b, unpacket_traits<Packet1Xd>::size);
+  mask = __riscv_vmand_mm_b64(mask, mask2, unpacket_traits<Packet1Xd>::size);
+
+  return __riscv_vfmin_vv_f64m1_tumu(mask, nans, a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pmin<PropagateNaN, Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return pmin<Packet1Xd>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pmin<PropagateNumbers, Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vfmin_vv_f64m1(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pmax<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  Packet1Xd nans = __riscv_vfmv_v_f_f64m1((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet1Xd>::size);
+  PacketMask64 mask = __riscv_vmfeq_vv_f64m1_b64(a, a, unpacket_traits<Packet1Xd>::size);
+  PacketMask64 mask2 = __riscv_vmfeq_vv_f64m1_b64(b, b, unpacket_traits<Packet1Xd>::size);
+  mask = __riscv_vmand_mm_b64(mask, mask2, unpacket_traits<Packet1Xd>::size);
+
+  return __riscv_vfmax_vv_f64m1_tumu(mask, nans, a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pmax<PropagateNaN, Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return pmax<Packet1Xd>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pmax<PropagateNumbers, Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vfmax_vv_f64m1(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pcmp_le<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  PacketMask64 mask = __riscv_vmfle_vv_f64m1_b64(a, b, unpacket_traits<Packet1Xd>::size);
+  return __riscv_vmerge_vvm_f64m1(pzero<Packet1Xd>(a), ptrue<Packet1Xd>(a), mask, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pcmp_lt<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  PacketMask64 mask = __riscv_vmflt_vv_f64m1_b64(a, b, unpacket_traits<Packet1Xd>::size);
+  return __riscv_vmerge_vvm_f64m1(pzero<Packet1Xd>(a), ptrue<Packet1Xd>(a), mask, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pcmp_eq<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  PacketMask64 mask = __riscv_vmfeq_vv_f64m1_b64(a, b, unpacket_traits<Packet1Xd>::size);
+  return __riscv_vmerge_vvm_f64m1(pzero<Packet1Xd>(a), ptrue<Packet1Xd>(a), mask, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pcmp_lt_or_nan<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  PacketMask64 mask = __riscv_vmfge_vv_f64m1_b64(a, b, unpacket_traits<Packet1Xd>::size);
+  return __riscv_vfmerge_vfm_f64m1(ptrue<Packet1Xd>(a), 0.0, mask, unpacket_traits<Packet1Xd>::size);
+}
+
+// Logical Operations are not supported for double, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pand<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vreinterpret_v_u64m1_f64m1(__riscv_vand_vv_u64m1(
+      __riscv_vreinterpret_v_f64m1_u64m1(a), __riscv_vreinterpret_v_f64m1_u64m1(b), unpacket_traits<Packet1Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd por<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vreinterpret_v_u64m1_f64m1(__riscv_vor_vv_u64m1(
+      __riscv_vreinterpret_v_f64m1_u64m1(a), __riscv_vreinterpret_v_f64m1_u64m1(b), unpacket_traits<Packet1Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pxor<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vreinterpret_v_u64m1_f64m1(__riscv_vxor_vv_u64m1(
+      __riscv_vreinterpret_v_f64m1_u64m1(a), __riscv_vreinterpret_v_f64m1_u64m1(b), unpacket_traits<Packet1Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pandnot<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vreinterpret_v_u64m1_f64m1(__riscv_vand_vv_u64m1(
+      __riscv_vreinterpret_v_f64m1_u64m1(a),
+      __riscv_vnot_v_u64m1(__riscv_vreinterpret_v_f64m1_u64m1(b), unpacket_traits<Packet1Xd>::size),
+      unpacket_traits<Packet1Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pload<Packet1Xd>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle64_v_f64m1(from, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd ploadu<Packet1Xd>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle64_v_f64m1(from, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd ploaddup<Packet1Xd>(const double* from) {
+  Packet1Xul idx =
+      __riscv_vsrl_vx_u64m1(__riscv_vid_v_u64m1(unpacket_traits<Packet1Xd>::size), 1, unpacket_traits<Packet1Xd>::size);
+  return __riscv_vrgather_vv_f64m1(pload<Packet1Xd>(from), idx, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd ploadquad<Packet1Xd>(const double* from) {
+  Packet1Xul idx =
+      __riscv_vsrl_vx_u64m1(__riscv_vid_v_u64m1(unpacket_traits<Packet1Xd>::size), 2, unpacket_traits<Packet1Xd>::size);
+  return __riscv_vrgather_vv_f64m1(pload<Packet1Xd>(from), idx, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet1Xd& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse64_v_f64m1(to, from, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet1Xd& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse64_v_f64m1(to, from, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1Xd pgather<double, Packet1Xd>(const double* from, Index stride) {
+  return __riscv_vlse64_v_f64m1(from, stride * sizeof(double), unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet1Xd>(double* to, const Packet1Xd& from, Index stride) {
+  __riscv_vsse64(to, stride * sizeof(double), from, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet1Xd>(const Packet1Xd& a) {
+  return __riscv_vfmv_f_s_f64m1_f64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd psqrt(const Packet1Xd& a) {
+  return __riscv_vfsqrt_v_f64m1(a, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd print<Packet1Xd>(const Packet1Xd& a) {
+  const Packet1Xd limit = pset1<Packet1Xd>(static_cast<double>(1ull << 52));
+  const Packet1Xd abs_a = pabs(a);
+
+  PacketMask64 mask = __riscv_vmfne_vv_f64m1_b64(a, a, unpacket_traits<Packet1Xd>::size);
+  const Packet1Xd x = __riscv_vfadd_vv_f64m1_tumu(mask, a, a, a, unpacket_traits<Packet1Xd>::size);
+  const Packet1Xd new_x = __riscv_vfcvt_f_x_v_f64m1(__riscv_vfcvt_x_f_v_i64m1(a, unpacket_traits<Packet1Xd>::size),
+                                                    unpacket_traits<Packet1Xd>::size);
+
+  mask = __riscv_vmflt_vv_f64m1_b64(abs_a, limit, unpacket_traits<Packet1Xd>::size);
+  Packet1Xd signed_x = __riscv_vfsgnj_vv_f64m1(new_x, x, unpacket_traits<Packet1Xd>::size);
+  return __riscv_vmerge_vvm_f64m1(x, signed_x, mask, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pfloor<Packet1Xd>(const Packet1Xd& a) {
+  Packet1Xd tmp = print<Packet1Xd>(a);
+  // If greater, subtract one.
+  PacketMask64 mask = __riscv_vmflt_vv_f64m1_b64(a, tmp, unpacket_traits<Packet1Xd>::size);
+  return __riscv_vfsub_vf_f64m1_tumu(mask, tmp, tmp, 1.0, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd preverse(const Packet1Xd& a) {
+  Packet1Xul idx = __riscv_vrsub_vx_u64m1(__riscv_vid_v_u64m1(unpacket_traits<Packet1Xd>::size),
+                                          unpacket_traits<Packet1Xd>::size - 1, unpacket_traits<Packet1Xd>::size);
+  return __riscv_vrgather_vv_f64m1(a, idx, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pfrexp<Packet1Xd>(const Packet1Xd& a, Packet1Xd& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux<Packet1Xd>(const Packet1Xd& a) {
+  return __riscv_vfmv_f(__riscv_vfredusum_vs_f64m1_f64m1(
+      a, __riscv_vfmv_v_f_f64m1(0.0, unpacket_traits<Packet1Xd>::size), unpacket_traits<Packet1Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet1Xd>(const Packet1Xd& a) {
+  // Multiply the vector by its reverse
+  Packet1Xd prod = __riscv_vfmul_vv_f64m1(preverse(a), a, unpacket_traits<Packet1Xd>::size);
+  Packet1Xd half_prod;
+
+  if (EIGEN_RISCV64_RVV_VL >= 1024) {
+    half_prod = __riscv_vslidedown_vx_f64m1(prod, 4, unpacket_traits<Packet1Xd>::size);
+    prod = __riscv_vfmul_vv_f64m1(prod, half_prod, unpacket_traits<Packet1Xd>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 512) {
+    half_prod = __riscv_vslidedown_vx_f64m1(prod, 2, unpacket_traits<Packet1Xd>::size);
+    prod = __riscv_vfmul_vv_f64m1(prod, half_prod, unpacket_traits<Packet1Xd>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 256) {
+    half_prod = __riscv_vslidedown_vx_f64m1(prod, 1, unpacket_traits<Packet1Xd>::size);
+    prod = __riscv_vfmul_vv_f64m1(prod, half_prod, unpacket_traits<Packet1Xd>::size);
+  }
+
+  // The reduction is done to the first element.
+  return pfirst(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min<Packet1Xd>(const Packet1Xd& a) {
+  return (std::min)(
+      __riscv_vfmv_f(__riscv_vfredmin_vs_f64m1_f64m1(
+          a, __riscv_vfmv_v_f_f64m1((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet1Xd>::size),
+          unpacket_traits<Packet1Xd>::size)),
+      (std::numeric_limits<double>::max)());
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max<Packet1Xd>(const Packet1Xd& a) {
+  return (std::max)(
+      __riscv_vfmv_f(__riscv_vfredmax_vs_f64m1_f64m1(
+          a, __riscv_vfmv_v_f_f64m1((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet1Xd>::size),
+          unpacket_traits<Packet1Xd>::size)),
+      -(std::numeric_limits<double>::max)());
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1Xd, N>& kernel) {
+  double buffer[unpacket_traits<Packet1Xd>::size * N];
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse64(&buffer[i], N * sizeof(double), kernel.packet[i], unpacket_traits<Packet1Xd>::size);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle64_v_f64m1(&buffer[i * unpacket_traits<Packet1Xd>::size], unpacket_traits<Packet1Xd>::size);
+  }
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pldexp<Packet1Xd>(const Packet1Xd& a, const Packet1Xd& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketMask64 por(const PacketMask64& a, const PacketMask64& b) {
+  return __riscv_vmor_mm_b64(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketMask64 pandnot(const PacketMask64& a, const PacketMask64& b) {
+  return __riscv_vmor_mm_b64(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketMask64 pand(const PacketMask64& a, const PacketMask64& b) {
+  return __riscv_vmand_mm_b64(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+EIGEN_STRONG_INLINE PacketMask64 pcmp_eq_mask(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vmfeq_vv_f64m1_b64(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+EIGEN_STRONG_INLINE PacketMask64 pcmp_lt_mask(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vmflt_vv_f64m1_b64(a, b, unpacket_traits<Packet1Xd>::size);
+}
+
+EIGEN_STRONG_INLINE Packet1Xd pselect(const PacketMask64& mask, const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vmerge_vvm_f64m1(b, a, mask, unpacket_traits<Packet1Xd>::size);
+}
+
+EIGEN_STRONG_INLINE Packet1Xd pselect(const Packet1Xd& mask, const Packet1Xd& a, const Packet1Xd& b) {
+  PacketMask64 mask2 =
+      __riscv_vmsne_vx_i64m1_b64(__riscv_vreinterpret_v_f64m1_i64m1(mask), 0, unpacket_traits<Packet1Xd>::size);
+  return __riscv_vmerge_vvm_f64m1(b, a, mask2, unpacket_traits<Packet1Xd>::size);
+}
+
+/********************************* short **************************************/
+
+typedef eigen_packet_wrapper<vint16m1_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL))), 18> Packet1Xs;
+typedef eigen_packet_wrapper<vuint16m1_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL))), 19> Packet1Xsu;
+
+typedef eigen_packet_wrapper<vint16m2_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 2))), 20> Packet2Xs;
+typedef eigen_packet_wrapper<vuint16m2_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 2))), 21>
+    Packet2Xsu;
+
+typedef eigen_packet_wrapper<vint16m4_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 4))), 22> Packet4Xs;
+typedef eigen_packet_wrapper<vuint16m4_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 4))), 23>
+    Packet4Xsu;
+
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+typedef Packet1Xs PacketXs;
+typedef Packet1Xsu PacketXsu;
+
+template <>
+struct packet_traits<numext::int16_t> : default_packet_traits {
+  typedef Packet1Xs type;
+  typedef Packet1Xs half;  // Half not implemented yet
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<numext::int16_t, EIGEN_RISCV64_RVV_VL, 1>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0
+  };
+};
+
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 2
+typedef Packet2Xs PacketXs;
+typedef Packet2Xsu PacketXsu;
+
+template <>
+struct packet_traits<numext::int16_t> : default_packet_traits {
+  typedef Packet2Xs type;
+  typedef Packet1Xs half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<numext::int16_t, EIGEN_RISCV64_RVV_VL, 2>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0
+  };
+};
+
+#elif EIGEN_RISCV64_DEFAULT_LMUL == 4
+typedef Packet4Xs PacketXs;
+typedef Packet4Xsu PacketXsu;
+
+template <>
+struct packet_traits<numext::int16_t> : default_packet_traits {
+  typedef Packet4Xs type;
+  typedef Packet2Xs half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<numext::int16_t, EIGEN_RISCV64_RVV_VL, 4>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet1Xs> {
+  typedef numext::int16_t type;
+  typedef Packet1Xs half;  // Half not yet implemented
+  typedef numext::uint8_t mask_t;
+  enum {
+    size = rvv_packet_size_selector<numext::int16_t, EIGEN_RISCV64_RVV_VL, 1>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 1>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2Xs> {
+  typedef numext::int16_t type;
+  typedef Packet1Xs half;
+  typedef numext::uint8_t mask_t;
+  enum {
+    size = rvv_packet_size_selector<numext::int16_t, EIGEN_RISCV64_RVV_VL, 2>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 2>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4Xs> {
+  typedef numext::int16_t type;
+  typedef Packet2Xs half;
+  typedef numext::uint8_t mask_t;
+  enum {
+    size = rvv_packet_size_selector<numext::int16_t, EIGEN_RISCV64_RVV_VL, 4>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 4>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<numext::int16_t>(const numext::int16_t* addr) {
+#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
+  __builtin_prefetch(addr);
+#endif
+}
+
+/********************************* Packet1Xs ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pset1<Packet1Xs>(const numext::int16_t& from) {
+  return __riscv_vmv_v_x_i16m1(from, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs plset<Packet1Xs>(const numext::int16_t& a) {
+  Packet1Xs idx = __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vid_v_u16m1(unpacket_traits<Packet1Xs>::size));
+  return __riscv_vadd_vx_i16m1(idx, a, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pzero<Packet1Xs>(const Packet1Xs& /*a*/) {
+  return __riscv_vmv_v_x_i16m1(0, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs padd<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vadd_vv_i16m1(a, b, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs psub<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vsub(a, b, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pnegate(const Packet1Xs& a) {
+  return __riscv_vneg(a, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pconj(const Packet1Xs& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pmul<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vmul(a, b, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pdiv<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vdiv(a, b, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pmadd(const Packet1Xs& a, const Packet1Xs& b, const Packet1Xs& c) {
+  return __riscv_vmadd(a, b, c, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pmsub(const Packet1Xs& a, const Packet1Xs& b, const Packet1Xs& c) {
+  return __riscv_vmadd(a, b, pnegate(c), unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pnmadd(const Packet1Xs& a, const Packet1Xs& b, const Packet1Xs& c) {
+  return __riscv_vnmsub_vv_i16m1(a, b, c, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pnmsub(const Packet1Xs& a, const Packet1Xs& b, const Packet1Xs& c) {
+  return __riscv_vnmsub_vv_i16m1(a, b, pnegate(c), unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pmin<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vmin(a, b, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pmax<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vmax(a, b, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pcmp_le<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  PacketMask16 mask = __riscv_vmsle_vv_i16m1_b16(a, b, unpacket_traits<Packet1Xs>::size);
+  return __riscv_vmerge_vxm_i16m1(pzero(a), static_cast<short>(0xffff), mask, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pcmp_lt<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  PacketMask16 mask = __riscv_vmslt_vv_i16m1_b16(a, b, unpacket_traits<Packet1Xs>::size);
+  return __riscv_vmerge_vxm_i16m1(pzero(a), static_cast<short>(0xffff), mask, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pcmp_eq<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  PacketMask16 mask = __riscv_vmseq_vv_i16m1_b16(a, b, unpacket_traits<Packet1Xs>::size);
+  return __riscv_vmerge_vxm_i16m1(pzero(a), static_cast<short>(0xffff), mask, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs ptrue<Packet1Xs>(const Packet1Xs& /*a*/) {
+  return __riscv_vmv_v_x_i16m1(static_cast<unsigned short>(0xffffu), unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pand<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vand_vv_i16m1(a, b, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs por<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vor_vv_i16m1(a, b, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pxor<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vxor_vv_i16m1(a, b, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pandnot<Packet1Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vand_vv_i16m1(a, __riscv_vnot_v_i16m1(b, unpacket_traits<Packet1Xs>::size),
+                               unpacket_traits<Packet1Xs>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet1Xs parithmetic_shift_right(Packet1Xs a) {
+  return __riscv_vsra_vx_i16m1(a, N, unpacket_traits<Packet1Xs>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet1Xs plogical_shift_right(Packet1Xs a) {
+  return __riscv_vreinterpret_i16m1(
+      __riscv_vsrl_vx_u16m1(__riscv_vreinterpret_u16m1(a), N, unpacket_traits<Packet1Xs>::size));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet1Xs plogical_shift_left(Packet1Xs a) {
+  return __riscv_vsll_vx_i16m1(a, N, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pload<Packet1Xs>(const numext::int16_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle16_v_i16m1(from, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs ploadu<Packet1Xs>(const numext::int16_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle16_v_i16m1(from, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs ploaddup<Packet1Xs>(const numext::int16_t* from) {
+  Packet1Xsu data = __riscv_vreinterpret_v_i16m1_u16m1(pload<Packet1Xs>(from));
+  return __riscv_vreinterpret_v_i32m1_i16m1(__riscv_vreinterpret_v_u32m1_i32m1(__riscv_vlmul_trunc_v_u32m2_u32m1(
+      __riscv_vwmaccu_vx_u32m2(__riscv_vwaddu_vv_u32m2(data, data, unpacket_traits<Packet1Xs>::size), 0xffffu, data,
+                               unpacket_traits<Packet1Xs>::size))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs ploadquad<Packet1Xs>(const numext::int16_t* from) {
+  Packet1Xsu idx =
+      __riscv_vsrl_vx_u16m1(__riscv_vid_v_u16m1(unpacket_traits<Packet1Xs>::size), 2, unpacket_traits<Packet1Xs>::size);
+  return __riscv_vrgather_vv_i16m1(pload<Packet1Xs>(from), idx, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int16_t>(numext::int16_t* to, const Packet1Xs& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse16_v_i16m1(to, from, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int16_t>(numext::int16_t* to, const Packet1Xs& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse16_v_i16m1(to, from, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1Xs pgather<numext::int16_t, Packet1Xs>(const numext::int16_t* from, Index stride) {
+  return __riscv_vlse16_v_i16m1(from, stride * sizeof(numext::int16_t), unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int16_t, Packet1Xs>(numext::int16_t* to, const Packet1Xs& from,
+                                                                   Index stride) {
+  __riscv_vsse16(to, stride * sizeof(numext::int16_t), from, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t pfirst<Packet1Xs>(const Packet1Xs& a) {
+  return __riscv_vmv_x_s_i16m1_i16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs preverse(const Packet1Xs& a) {
+  Packet1Xsu idx = __riscv_vrsub_vx_u16m1(__riscv_vid_v_u16m1(unpacket_traits<Packet1Xs>::size),
+                                          unpacket_traits<Packet1Xs>::size - 1, unpacket_traits<Packet1Xs>::size);
+  return __riscv_vrgather_vv_i16m1(a, idx, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pabs(const Packet1Xs& a) {
+  Packet1Xs mask = __riscv_vsra_vx_i16m1(a, 15, unpacket_traits<Packet1Xs>::size);
+  return __riscv_vsub_vv_i16m1(__riscv_vxor_vv_i16m1(a, mask, unpacket_traits<Packet1Xs>::size), mask,
+                               unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux<Packet1Xs>(const Packet1Xs& a) {
+  return __riscv_vmv_x(__riscv_vredsum_vs_i16m1_i16m1(a, __riscv_vmv_v_x_i16m1(0, unpacket_traits<Packet1Xs>::size),
+                                                      unpacket_traits<Packet1Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux_mul<Packet1Xs>(const Packet1Xs& a) {
+  // Multiply the vector by its reverse
+  Packet1Xs prod = __riscv_vmul_vv_i16m1(preverse(a), a, unpacket_traits<Packet1Xs>::size);
+  Packet1Xs half_prod;
+
+  if (EIGEN_RISCV64_RVV_VL >= 1024) {
+    half_prod = __riscv_vslidedown_vx_i16m1(prod, 16, unpacket_traits<Packet1Xs>::size);
+    prod = __riscv_vmul_vv_i16m1(prod, half_prod, unpacket_traits<Packet1Xs>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 512) {
+    half_prod = __riscv_vslidedown_vx_i16m1(prod, 8, unpacket_traits<Packet1Xs>::size);
+    prod = __riscv_vmul_vv_i16m1(prod, half_prod, unpacket_traits<Packet1Xs>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 256) {
+    half_prod = __riscv_vslidedown_vx_i16m1(prod, 4, unpacket_traits<Packet1Xs>::size);
+    prod = __riscv_vmul_vv_i16m1(prod, half_prod, unpacket_traits<Packet1Xs>::size);
+  }
+  // Last reduction
+  half_prod = __riscv_vslidedown_vx_i16m1(prod, 2, unpacket_traits<Packet1Xs>::size);
+  prod = __riscv_vmul_vv_i16m1(prod, half_prod, unpacket_traits<Packet1Xs>::size);
+
+  half_prod = __riscv_vslidedown_vx_i16m1(prod, 1, unpacket_traits<Packet1Xs>::size);
+  prod = __riscv_vmul_vv_i16m1(prod, half_prod, unpacket_traits<Packet1Xs>::size);
+
+  // The reduction is done to the first element.
+  return pfirst(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux_min<Packet1Xs>(const Packet1Xs& a) {
+  return __riscv_vmv_x(__riscv_vredmin_vs_i16m1_i16m1(
+      a, __riscv_vmv_v_x_i16m1((std::numeric_limits<numext::int16_t>::max)(), unpacket_traits<Packet1Xs>::size),
+      unpacket_traits<Packet1Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux_max<Packet1Xs>(const Packet1Xs& a) {
+  return __riscv_vmv_x(__riscv_vredmax_vs_i16m1_i16m1(
+      a, __riscv_vmv_v_x_i16m1((std::numeric_limits<numext::int16_t>::min)(), unpacket_traits<Packet1Xs>::size),
+      unpacket_traits<Packet1Xs>::size));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1Xs, N>& kernel) {
+  numext::int16_t buffer[unpacket_traits<Packet1Xs>::size * N] = {0};
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse16(&buffer[i], N * sizeof(numext::int16_t), kernel.packet[i], unpacket_traits<Packet1Xs>::size);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle16_v_i16m1(&buffer[i * unpacket_traits<Packet1Xs>::size], unpacket_traits<Packet1Xs>::size);
+  }
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_RVV10_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMath2.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMath2.h
new file mode 100644
index 0000000..d99a154
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMath2.h
@@ -0,0 +1,1537 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Kseniya Zaytseva <kseniya.zaytseva@syntacore.com>
+// Copyright (C) 2025 Chip Kerchner <ckerchner@tenstorrent.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET2_MATH_RVV10_H
+#define EIGEN_PACKET2_MATH_RVV10_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/********************************* Packet2Xi ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pset1<Packet2Xi>(const numext::int32_t& from) {
+  return __riscv_vmv_v_x_i32m2(from, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi plset<Packet2Xi>(const numext::int32_t& a) {
+  Packet2Xi idx = __riscv_vreinterpret_v_u32m2_i32m2(__riscv_vid_v_u32m2(unpacket_traits<Packet2Xi>::size));
+  return __riscv_vadd_vx_i32m2(idx, a, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pzero<Packet2Xi>(const Packet2Xi& /*a*/) {
+  return __riscv_vmv_v_x_i32m2(0, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi padd<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  return __riscv_vadd_vv_i32m2(a, b, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi psub<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  return __riscv_vsub(a, b, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pnegate(const Packet2Xi& a) {
+  return __riscv_vneg(a, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pconj(const Packet2Xi& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pmul<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  return __riscv_vmul(a, b, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pdiv<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  return __riscv_vdiv(a, b, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pmadd(const Packet2Xi& a, const Packet2Xi& b, const Packet2Xi& c) {
+  return __riscv_vmadd(a, b, c, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pmsub(const Packet2Xi& a, const Packet2Xi& b, const Packet2Xi& c) {
+  return __riscv_vmadd(a, b, pnegate(c), unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pnmadd(const Packet2Xi& a, const Packet2Xi& b, const Packet2Xi& c) {
+  return __riscv_vnmsub_vv_i32m2(a, b, c, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pnmsub(const Packet2Xi& a, const Packet2Xi& b, const Packet2Xi& c) {
+  return __riscv_vnmsub_vv_i32m2(a, b, pnegate(c), unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pmin<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  return __riscv_vmin(a, b, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pmax<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  return __riscv_vmax(a, b, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pcmp_le<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  PacketMask16 mask = __riscv_vmsle_vv_i32m2_b16(a, b, unpacket_traits<Packet2Xi>::size);
+  return __riscv_vmerge_vxm_i32m2(pzero(a), 0xffffffff, mask, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pcmp_lt<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  PacketMask16 mask = __riscv_vmslt_vv_i32m2_b16(a, b, unpacket_traits<Packet2Xi>::size);
+  return __riscv_vmerge_vxm_i32m2(pzero(a), 0xffffffff, mask, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pcmp_eq<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  PacketMask16 mask = __riscv_vmseq_vv_i32m2_b16(a, b, unpacket_traits<Packet2Xi>::size);
+  return __riscv_vmerge_vxm_i32m2(pzero(a), 0xffffffff, mask, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi ptrue<Packet2Xi>(const Packet2Xi& /*a*/) {
+  return __riscv_vmv_v_x_i32m2(0xffffffffu, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pand<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  return __riscv_vand_vv_i32m2(a, b, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi por<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  return __riscv_vor_vv_i32m2(a, b, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pxor<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  return __riscv_vxor_vv_i32m2(a, b, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pandnot<Packet2Xi>(const Packet2Xi& a, const Packet2Xi& b) {
+  return __riscv_vand_vv_i32m2(a, __riscv_vnot_v_i32m2(b, unpacket_traits<Packet2Xi>::size),
+                               unpacket_traits<Packet2Xi>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2Xi parithmetic_shift_right(Packet2Xi a) {
+  return __riscv_vsra_vx_i32m2(a, N, unpacket_traits<Packet2Xi>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2Xi plogical_shift_right(Packet2Xi a) {
+  return __riscv_vreinterpret_i32m2(
+      __riscv_vsrl_vx_u32m2(__riscv_vreinterpret_u32m2(a), N, unpacket_traits<Packet2Xi>::size));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2Xi plogical_shift_left(Packet2Xi a) {
+  return __riscv_vsll_vx_i32m2(a, N, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pload<Packet2Xi>(const numext::int32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle32_v_i32m2(from, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi ploadu<Packet2Xi>(const numext::int32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle32_v_i32m2(from, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi ploaddup<Packet2Xi>(const numext::int32_t* from) {
+  Packet2Xu data = __riscv_vreinterpret_v_i32m2_u32m2(pload<Packet2Xi>(from));
+  return __riscv_vreinterpret_v_i64m2_i32m2(__riscv_vreinterpret_v_u64m2_i64m2(__riscv_vlmul_trunc_v_u64m4_u64m2(
+      __riscv_vwmaccu_vx_u64m4(__riscv_vwaddu_vv_u64m4(data, data, unpacket_traits<Packet2Xi>::size), 0xffffffffu, data,
+                               unpacket_traits<Packet2Xi>::size))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi ploadquad<Packet2Xi>(const numext::int32_t* from) {
+  Packet2Xu idx =
+      __riscv_vsrl_vx_u32m2(__riscv_vid_v_u32m2(unpacket_traits<Packet2Xi>::size), 2, unpacket_traits<Packet2Xi>::size);
+  return __riscv_vrgather_vv_i32m2(pload<Packet2Xi>(from), idx, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int32_t>(numext::int32_t* to, const Packet2Xi& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse32_v_i32m2(to, from, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int32_t>(numext::int32_t* to, const Packet2Xi& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse32_v_i32m2(to, from, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2Xi pgather<numext::int32_t, Packet2Xi>(const numext::int32_t* from, Index stride) {
+  return __riscv_vlse32_v_i32m2(from, stride * sizeof(numext::int32_t), unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int32_t, Packet2Xi>(numext::int32_t* to, const Packet2Xi& from,
+                                                                   Index stride) {
+  __riscv_vsse32(to, stride * sizeof(numext::int32_t), from, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t pfirst<Packet2Xi>(const Packet2Xi& a) {
+  return __riscv_vmv_x_s_i32m2_i32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi preverse(const Packet2Xi& a) {
+  Packet2Xu idx = __riscv_vrsub_vx_u32m2(__riscv_vid_v_u32m2(unpacket_traits<Packet2Xi>::size),
+                                         unpacket_traits<Packet2Xi>::size - 1, unpacket_traits<Packet2Xi>::size);
+  return __riscv_vrgather_vv_i32m2(a, idx, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pabs(const Packet2Xi& a) {
+  Packet2Xi mask = __riscv_vsra_vx_i32m2(a, 31, unpacket_traits<Packet2Xi>::size);
+  return __riscv_vsub_vv_i32m2(__riscv_vxor_vv_i32m2(a, mask, unpacket_traits<Packet2Xi>::size), mask,
+                               unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux<Packet2Xi>(const Packet2Xi& a) {
+  return __riscv_vmv_x(__riscv_vredsum_vs_i32m2_i32m1(a, __riscv_vmv_v_x_i32m1(0, unpacket_traits<Packet2Xi>::size / 2),
+                                                      unpacket_traits<Packet2Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_mul<Packet2Xi>(const Packet2Xi& a) {
+  return predux_mul<Packet1Xi>(__riscv_vmul_vv_i32m1(__riscv_vget_v_i32m2_i32m1(a, 0), __riscv_vget_v_i32m2_i32m1(a, 1),
+                                                     unpacket_traits<Packet1Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_min<Packet2Xi>(const Packet2Xi& a) {
+  return __riscv_vmv_x(__riscv_vredmin_vs_i32m2_i32m1(
+      a, __riscv_vmv_v_x_i32m1((std::numeric_limits<numext::int32_t>::max)(), unpacket_traits<Packet2Xi>::size / 2),
+      unpacket_traits<Packet2Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_max<Packet2Xi>(const Packet2Xi& a) {
+  return __riscv_vmv_x(__riscv_vredmax_vs_i32m2_i32m1(
+      a, __riscv_vmv_v_x_i32m1((std::numeric_limits<numext::int32_t>::min)(), unpacket_traits<Packet2Xi>::size / 2),
+      unpacket_traits<Packet2Xi>::size));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2Xi, N>& kernel) {
+  numext::int32_t buffer[unpacket_traits<Packet2Xi>::size * N] = {0};
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse32(&buffer[i], N * sizeof(numext::int32_t), kernel.packet[i], unpacket_traits<Packet2Xi>::size);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle32_v_i32m2(&buffer[i * unpacket_traits<Packet2Xi>::size], unpacket_traits<Packet2Xi>::size);
+  }
+}
+
+template <typename Packet = Packet4Xi>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet4Xi>::value && (unpacket_traits<Packet4Xi>::size % 8) == 0,
+                            Packet2Xi>::type
+    predux_half(const Packet4Xi& a) {
+  return __riscv_vadd_vv_i32m2(__riscv_vget_v_i32m4_i32m2(a, 0), __riscv_vget_v_i32m4_i32m2(a, 1),
+                               unpacket_traits<Packet2Xi>::size);
+}
+
+template <typename Packet = Packet2Xi>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet2Xi>::value && (unpacket_traits<Packet2Xi>::size % 8) == 0,
+                            Packet1Xi>::type
+    predux_half(const Packet2Xi& a) {
+  return __riscv_vadd_vv_i32m1(__riscv_vget_v_i32m2_i32m1(a, 0), __riscv_vget_v_i32m2_i32m1(a, 1),
+                               unpacket_traits<Packet1Xi>::size);
+}
+
+/********************************* Packet2Xf ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf ptrue<Packet2Xf>(const Packet2Xf& /*a*/) {
+  return __riscv_vreinterpret_f32m2(__riscv_vmv_v_x_u32m2(0xffffffffu, unpacket_traits<Packet2Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pzero<Packet2Xf>(const Packet2Xf& /*a*/) {
+  return __riscv_vfmv_v_f_f32m2(0.0f, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pabs(const Packet2Xf& a) {
+  return __riscv_vfabs_v_f32m2(a, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pabsdiff(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vfabs_v_f32m2(__riscv_vfsub_vv_f32m2(a, b, unpacket_traits<Packet2Xf>::size),
+                               unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pset1<Packet2Xf>(const float& from) {
+  return __riscv_vfmv_v_f_f32m2(from, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pset1frombits<Packet2Xf>(numext::uint32_t from) {
+  return __riscv_vreinterpret_f32m2(__riscv_vmv_v_x_u32m2(from, unpacket_traits<Packet2Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf plset<Packet2Xf>(const float& a) {
+  Packet2Xf idx = __riscv_vfcvt_f_x_v_f32m2(
+      __riscv_vreinterpret_v_u32m2_i32m2(__riscv_vid_v_u32m2(unpacket_traits<Packet4Xi>::size)),
+      unpacket_traits<Packet2Xf>::size);
+  return __riscv_vfadd_vf_f32m2(idx, a, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet2Xf>(const float* a, Packet2Xf& a0, Packet2Xf& a1, Packet2Xf& a2,
+                                                Packet2Xf& a3) {
+  vfloat32m2_t aa = __riscv_vle32_v_f32m2(a, 4);
+  a0 = __riscv_vrgather_vx_f32m2(aa, 0, unpacket_traits<Packet2Xf>::size);
+  a1 = __riscv_vrgather_vx_f32m2(aa, 1, unpacket_traits<Packet2Xf>::size);
+  a2 = __riscv_vrgather_vx_f32m2(aa, 2, unpacket_traits<Packet2Xf>::size);
+  a3 = __riscv_vrgather_vx_f32m2(aa, 3, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf padd<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vfadd_vv_f32m2(a, b, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf psub<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vfsub_vv_f32m2(a, b, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pnegate(const Packet2Xf& a) {
+  return __riscv_vfneg_v_f32m2(a, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf psignbit(const Packet2Xf& a) {
+  return __riscv_vreinterpret_v_i32m2_f32m2(
+      __riscv_vsra_vx_i32m2(__riscv_vreinterpret_v_f32m2_i32m2(a), 31, unpacket_traits<Packet2Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pconj(const Packet2Xf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pmul<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vfmul_vv_f32m2(a, b, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pdiv<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vfdiv_vv_f32m2(a, b, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pmadd(const Packet2Xf& a, const Packet2Xf& b, const Packet2Xf& c) {
+  return __riscv_vfmadd_vv_f32m2(a, b, c, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pmsub(const Packet2Xf& a, const Packet2Xf& b, const Packet2Xf& c) {
+  return __riscv_vfmsub_vv_f32m2(a, b, c, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pnmadd(const Packet2Xf& a, const Packet2Xf& b, const Packet2Xf& c) {
+  return __riscv_vfnmsub_vv_f32m2(a, b, c, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pnmsub(const Packet2Xf& a, const Packet2Xf& b, const Packet2Xf& c) {
+  return __riscv_vfnmadd_vv_f32m2(a, b, c, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pmin<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  Packet2Xf nans = __riscv_vfmv_v_f_f32m2((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet2Xf>::size);
+  PacketMask16 mask = __riscv_vmfeq_vv_f32m2_b16(a, a, unpacket_traits<Packet2Xf>::size);
+  PacketMask16 mask2 = __riscv_vmfeq_vv_f32m2_b16(b, b, unpacket_traits<Packet2Xf>::size);
+  mask = __riscv_vmand_mm_b16(mask, mask2, unpacket_traits<Packet2Xf>::size);
+
+  return __riscv_vfmin_vv_f32m2_tumu(mask, nans, a, b, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pmin<PropagateNaN, Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return pmin<Packet2Xf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pmin<PropagateNumbers, Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vfmin_vv_f32m2(a, b, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pmax<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  Packet2Xf nans = __riscv_vfmv_v_f_f32m2((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet2Xf>::size);
+  PacketMask16 mask = __riscv_vmfeq_vv_f32m2_b16(a, a, unpacket_traits<Packet2Xf>::size);
+  PacketMask16 mask2 = __riscv_vmfeq_vv_f32m2_b16(b, b, unpacket_traits<Packet2Xf>::size);
+  mask = __riscv_vmand_mm_b16(mask, mask2, unpacket_traits<Packet2Xf>::size);
+
+  return __riscv_vfmax_vv_f32m2_tumu(mask, nans, a, b, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pmax<PropagateNaN, Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return pmax<Packet2Xf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pmax<PropagateNumbers, Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vfmax_vv_f32m2(a, b, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pcmp_le<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  PacketMask16 mask = __riscv_vmfle_vv_f32m2_b16(a, b, unpacket_traits<Packet2Xf>::size);
+  return __riscv_vmerge_vvm_f32m2(pzero<Packet2Xf>(a), ptrue<Packet2Xf>(a), mask, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pcmp_lt<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  PacketMask16 mask = __riscv_vmflt_vv_f32m2_b16(a, b, unpacket_traits<Packet2Xf>::size);
+  return __riscv_vmerge_vvm_f32m2(pzero<Packet2Xf>(a), ptrue<Packet2Xf>(a), mask, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pcmp_eq<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  PacketMask16 mask = __riscv_vmfeq_vv_f32m2_b16(a, b, unpacket_traits<Packet2Xf>::size);
+  return __riscv_vmerge_vvm_f32m2(pzero<Packet2Xf>(a), ptrue<Packet2Xf>(a), mask, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pcmp_lt_or_nan<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  PacketMask16 mask = __riscv_vmfge_vv_f32m2_b16(a, b, unpacket_traits<Packet2Xf>::size);
+  return __riscv_vfmerge_vfm_f32m2(ptrue<Packet2Xf>(a), 0.0f, mask, unpacket_traits<Packet2Xf>::size);
+}
+
+EIGEN_STRONG_INLINE Packet2Xf pselect(const PacketMask16& mask, const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vmerge_vvm_f32m2(b, a, mask, unpacket_traits<Packet2Xf>::size);
+}
+
+EIGEN_STRONG_INLINE Packet2Xf pselect(const Packet2Xf& mask, const Packet2Xf& a, const Packet2Xf& b) {
+  PacketMask16 mask2 =
+      __riscv_vmsne_vx_i32m2_b16(__riscv_vreinterpret_v_f32m2_i32m2(mask), 0, unpacket_traits<Packet2Xf>::size);
+  return __riscv_vmerge_vvm_f32m2(b, a, mask2, unpacket_traits<Packet2Xf>::size);
+}
+
+// Logical Operations are not supported for float, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pand<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vand_vv_u32m2(
+      __riscv_vreinterpret_v_f32m2_u32m2(a), __riscv_vreinterpret_v_f32m2_u32m2(b), unpacket_traits<Packet2Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf por<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vor_vv_u32m2(
+      __riscv_vreinterpret_v_f32m2_u32m2(a), __riscv_vreinterpret_v_f32m2_u32m2(b), unpacket_traits<Packet2Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pxor<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vxor_vv_u32m2(
+      __riscv_vreinterpret_v_f32m2_u32m2(a), __riscv_vreinterpret_v_f32m2_u32m2(b), unpacket_traits<Packet2Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pandnot<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& b) {
+  return __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vand_vv_u32m2(
+      __riscv_vreinterpret_v_f32m2_u32m2(a),
+      __riscv_vnot_v_u32m2(__riscv_vreinterpret_v_f32m2_u32m2(b), unpacket_traits<Packet2Xf>::size),
+      unpacket_traits<Packet2Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pload<Packet2Xf>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle32_v_f32m2(from, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf ploadu<Packet2Xf>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle32_v_f32m2(from, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf ploaddup<Packet2Xf>(const float* from) {
+  Packet2Xu data = __riscv_vreinterpret_v_f32m2_u32m2(pload<Packet2Xf>(from));
+  return __riscv_vreinterpret_v_i32m2_f32m2(
+      __riscv_vreinterpret_v_i64m2_i32m2(__riscv_vreinterpret_v_u64m2_i64m2(__riscv_vlmul_trunc_v_u64m4_u64m2(
+          __riscv_vwmaccu_vx_u64m4(__riscv_vwaddu_vv_u64m4(data, data, unpacket_traits<Packet2Xi>::size), 0xffffffffu,
+                                   data, unpacket_traits<Packet2Xi>::size)))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf ploadquad<Packet2Xf>(const float* from) {
+  Packet2Xu idx =
+      __riscv_vsrl_vx_u32m2(__riscv_vid_v_u32m2(unpacket_traits<Packet2Xf>::size), 2, unpacket_traits<Packet2Xf>::size);
+  return __riscv_vrgather_vv_f32m2(pload<Packet2Xf>(from), idx, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet2Xf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse32_v_f32m2(to, from, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet2Xf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse32_v_f32m2(to, from, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2Xf pgather<float, Packet2Xf>(const float* from, Index stride) {
+  return __riscv_vlse32_v_f32m2(from, stride * sizeof(float), unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet2Xf>(float* to, const Packet2Xf& from, Index stride) {
+  __riscv_vsse32(to, stride * sizeof(float), from, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet2Xf>(const Packet2Xf& a) {
+  return __riscv_vfmv_f_s_f32m2_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf psqrt(const Packet2Xf& a) {
+  return __riscv_vfsqrt_v_f32m2(a, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf print<Packet2Xf>(const Packet2Xf& a) {
+  const Packet2Xf limit = pset1<Packet2Xf>(static_cast<float>(1 << 23));
+  const Packet2Xf abs_a = pabs(a);
+
+  PacketMask16 mask = __riscv_vmfne_vv_f32m2_b16(a, a, unpacket_traits<Packet2Xf>::size);
+  const Packet2Xf x = __riscv_vfadd_vv_f32m2_tumu(mask, a, a, a, unpacket_traits<Packet2Xf>::size);
+  const Packet2Xf new_x = __riscv_vfcvt_f_x_v_f32m2(__riscv_vfcvt_x_f_v_i32m2(a, unpacket_traits<Packet2Xf>::size),
+                                                    unpacket_traits<Packet2Xf>::size);
+
+  mask = __riscv_vmflt_vv_f32m2_b16(abs_a, limit, unpacket_traits<Packet2Xf>::size);
+  Packet2Xf signed_x = __riscv_vfsgnj_vv_f32m2(new_x, x, unpacket_traits<Packet2Xf>::size);
+  return __riscv_vmerge_vvm_f32m2(x, signed_x, mask, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pfloor<Packet2Xf>(const Packet2Xf& a) {
+  Packet2Xf tmp = print<Packet2Xf>(a);
+  // If greater, subtract one.
+  PacketMask16 mask = __riscv_vmflt_vv_f32m2_b16(a, tmp, unpacket_traits<Packet2Xf>::size);
+  return __riscv_vfsub_vf_f32m2_tumu(mask, tmp, tmp, 1.0f, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf preverse(const Packet2Xf& a) {
+  Packet2Xu idx = __riscv_vrsub_vx_u32m2(__riscv_vid_v_u32m2(unpacket_traits<Packet2Xf>::size),
+                                         unpacket_traits<Packet2Xf>::size - 1, unpacket_traits<Packet2Xf>::size);
+  return __riscv_vrgather_vv_f32m2(a, idx, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pfrexp<Packet2Xf>(const Packet2Xf& a, Packet2Xf& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet2Xf>(const Packet2Xf& a) {
+  return __riscv_vfmv_f(__riscv_vfredusum_vs_f32m2_f32m1(
+      a, __riscv_vfmv_v_f_f32m1(0.0, unpacket_traits<Packet2Xf>::size / 2), unpacket_traits<Packet2Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet2Xf>(const Packet2Xf& a) {
+  return predux_mul<Packet1Xf>(__riscv_vfmul_vv_f32m1(
+      __riscv_vget_v_f32m2_f32m1(a, 0), __riscv_vget_v_f32m2_f32m1(a, 1), unpacket_traits<Packet1Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet2Xf>(const Packet2Xf& a) {
+  return (std::min)(
+      __riscv_vfmv_f(__riscv_vfredmin_vs_f32m2_f32m1(
+          a, __riscv_vfmv_v_f_f32m1((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet2Xf>::size / 2),
+          unpacket_traits<Packet2Xf>::size)),
+      (std::numeric_limits<float>::max)());
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet2Xf>(const Packet2Xf& a) {
+  return (std::max)(
+      __riscv_vfmv_f(__riscv_vfredmax_vs_f32m2_f32m1(
+          a, __riscv_vfmv_v_f_f32m1((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet2Xf>::size / 2),
+          unpacket_traits<Packet2Xf>::size)),
+      -(std::numeric_limits<float>::max)());
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2Xf, N>& kernel) {
+  float buffer[unpacket_traits<Packet2Xf>::size * N];
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse32(&buffer[i], N * sizeof(float), kernel.packet[i], unpacket_traits<Packet2Xf>::size);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle32_v_f32m2(&buffer[i * unpacket_traits<Packet2Xf>::size], unpacket_traits<Packet2Xf>::size);
+  }
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pldexp<Packet2Xf>(const Packet2Xf& a, const Packet2Xf& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <typename Packet = Packet4Xf>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet4Xf>::value && (unpacket_traits<Packet4Xf>::size % 8) == 0,
+                            Packet2Xf>::type
+    predux_half(const Packet4Xf& a) {
+  return __riscv_vfadd_vv_f32m2(__riscv_vget_v_f32m4_f32m2(a, 0), __riscv_vget_v_f32m4_f32m2(a, 1),
+                                unpacket_traits<Packet2Xf>::size);
+}
+
+template <typename Packet = Packet2Xf>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet2Xf>::value && (unpacket_traits<Packet2Xf>::size % 8) == 0,
+                            Packet1Xf>::type
+    predux_half(const Packet2Xf& a) {
+  return __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m2_f32m1(a, 0), __riscv_vget_v_f32m2_f32m1(a, 1),
+                                unpacket_traits<Packet1Xf>::size);
+}
+
+/********************************* Packet2Xl ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pset1<Packet2Xl>(const numext::int64_t& from) {
+  return __riscv_vmv_v_x_i64m2(from, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl plset<Packet2Xl>(const numext::int64_t& a) {
+  Packet2Xl idx = __riscv_vreinterpret_v_u64m2_i64m2(__riscv_vid_v_u64m2(unpacket_traits<Packet2Xl>::size));
+  return __riscv_vadd_vx_i64m2(idx, a, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pzero<Packet2Xl>(const Packet2Xl& /*a*/) {
+  return __riscv_vmv_v_x_i64m2(0, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl padd<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  return __riscv_vadd_vv_i64m2(a, b, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl psub<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  return __riscv_vsub(a, b, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pnegate(const Packet2Xl& a) {
+  return __riscv_vneg(a, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pconj(const Packet2Xl& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pmul<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  return __riscv_vmul(a, b, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pdiv<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  return __riscv_vdiv(a, b, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pmadd(const Packet2Xl& a, const Packet2Xl& b, const Packet2Xl& c) {
+  return __riscv_vmadd(a, b, c, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pmsub(const Packet2Xl& a, const Packet2Xl& b, const Packet2Xl& c) {
+  return __riscv_vmadd(a, b, pnegate(c), unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pnmadd(const Packet2Xl& a, const Packet2Xl& b, const Packet2Xl& c) {
+  return __riscv_vnmsub_vv_i64m2(a, b, c, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pnmsub(const Packet2Xl& a, const Packet2Xl& b, const Packet2Xl& c) {
+  return __riscv_vnmsub_vv_i64m2(a, b, pnegate(c), unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pmin<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  return __riscv_vmin(a, b, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pmax<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  return __riscv_vmax(a, b, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pcmp_le<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  PacketMask32 mask = __riscv_vmsle_vv_i64m2_b32(a, b, unpacket_traits<Packet2Xl>::size);
+  return __riscv_vmerge_vxm_i64m2(pzero(a), 0xffffffffffffffff, mask, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pcmp_lt<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  PacketMask32 mask = __riscv_vmslt_vv_i64m2_b32(a, b, unpacket_traits<Packet2Xl>::size);
+  return __riscv_vmerge_vxm_i64m2(pzero(a), 0xffffffffffffffff, mask, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pcmp_eq<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  PacketMask32 mask = __riscv_vmseq_vv_i64m2_b32(a, b, unpacket_traits<Packet2Xl>::size);
+  return __riscv_vmerge_vxm_i64m2(pzero(a), 0xffffffffffffffff, mask, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl ptrue<Packet2Xl>(const Packet2Xl& /*a*/) {
+  return __riscv_vmv_v_x_i64m2(0xffffffffffffffffu, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pand<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  return __riscv_vand_vv_i64m2(a, b, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl por<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  return __riscv_vor_vv_i64m2(a, b, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pxor<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  return __riscv_vxor_vv_i64m2(a, b, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pandnot<Packet2Xl>(const Packet2Xl& a, const Packet2Xl& b) {
+  return __riscv_vand_vv_i64m2(a, __riscv_vnot_v_i64m2(b, unpacket_traits<Packet2Xl>::size),
+                               unpacket_traits<Packet2Xl>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2Xl parithmetic_shift_right(Packet2Xl a) {
+  return __riscv_vsra_vx_i64m2(a, N, unpacket_traits<Packet2Xl>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2Xl plogical_shift_right(Packet2Xl a) {
+  return __riscv_vreinterpret_i64m2(
+      __riscv_vsrl_vx_u64m2(__riscv_vreinterpret_u64m2(a), N, unpacket_traits<Packet2Xl>::size));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2Xl plogical_shift_left(Packet2Xl a) {
+  return __riscv_vsll_vx_i64m2(a, N, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pload<Packet2Xl>(const numext::int64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle64_v_i64m2(from, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl ploadu<Packet2Xl>(const numext::int64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle64_v_i64m2(from, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl ploaddup<Packet2Xl>(const numext::int64_t* from) {
+  Packet2Xul idx =
+      __riscv_vsrl_vx_u64m2(__riscv_vid_v_u64m2(unpacket_traits<Packet2Xl>::size), 1, unpacket_traits<Packet2Xl>::size);
+  return __riscv_vrgather_vv_i64m2(pload<Packet2Xl>(from), idx, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl ploadquad<Packet2Xl>(const numext::int64_t* from) {
+  Packet2Xul idx =
+      __riscv_vsrl_vx_u64m2(__riscv_vid_v_u64m2(unpacket_traits<Packet2Xl>::size), 2, unpacket_traits<Packet2Xl>::size);
+  return __riscv_vrgather_vv_i64m2(pload<Packet2Xl>(from), idx, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int64_t>(numext::int64_t* to, const Packet2Xl& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse64_v_i64m2(to, from, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int64_t>(numext::int64_t* to, const Packet2Xl& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse64_v_i64m2(to, from, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2Xl pgather<numext::int64_t, Packet2Xl>(const numext::int64_t* from, Index stride) {
+  return __riscv_vlse64_v_i64m2(from, stride * sizeof(numext::int64_t), unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int64_t, Packet2Xl>(numext::int64_t* to, const Packet2Xl& from,
+                                                                   Index stride) {
+  __riscv_vsse64(to, stride * sizeof(numext::int64_t), from, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t pfirst<Packet2Xl>(const Packet2Xl& a) {
+  return __riscv_vmv_x_s_i64m2_i64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl preverse(const Packet2Xl& a) {
+  Packet2Xul idx = __riscv_vrsub_vx_u64m2(__riscv_vid_v_u64m2(unpacket_traits<Packet2Xl>::size),
+                                          unpacket_traits<Packet2Xl>::size - 1, unpacket_traits<Packet2Xl>::size);
+  return __riscv_vrgather_vv_i64m2(a, idx, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pabs(const Packet2Xl& a) {
+  Packet2Xl mask = __riscv_vsra_vx_i64m2(a, 63, unpacket_traits<Packet2Xl>::size);
+  return __riscv_vsub_vv_i64m2(__riscv_vxor_vv_i64m2(a, mask, unpacket_traits<Packet2Xl>::size), mask,
+                               unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux<Packet2Xl>(const Packet2Xl& a) {
+  return __riscv_vmv_x(__riscv_vredsum_vs_i64m2_i64m1(a, __riscv_vmv_v_x_i64m1(0, unpacket_traits<Packet2Xl>::size / 2),
+                                                      unpacket_traits<Packet2Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux_mul<Packet2Xl>(const Packet2Xl& a) {
+  return predux_mul<Packet1Xl>(__riscv_vmul_vv_i64m1(__riscv_vget_v_i64m2_i64m1(a, 0), __riscv_vget_v_i64m2_i64m1(a, 1),
+                                                     unpacket_traits<Packet1Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux_min<Packet2Xl>(const Packet2Xl& a) {
+  return __riscv_vmv_x(__riscv_vredmin_vs_i64m2_i64m1(
+      a, __riscv_vmv_v_x_i64m1((std::numeric_limits<numext::int64_t>::max)(), unpacket_traits<Packet2Xl>::size / 2),
+      unpacket_traits<Packet2Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux_max<Packet2Xl>(const Packet2Xl& a) {
+  return __riscv_vmv_x(__riscv_vredmax_vs_i64m2_i64m1(
+      a, __riscv_vmv_v_x_i64m1((std::numeric_limits<numext::int64_t>::min)(), unpacket_traits<Packet2Xl>::size / 2),
+      unpacket_traits<Packet2Xl>::size));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2Xl, N>& kernel) {
+  numext::int64_t buffer[unpacket_traits<Packet2Xl>::size * N] = {0};
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse64(&buffer[i], N * sizeof(numext::int64_t), kernel.packet[i], unpacket_traits<Packet2Xl>::size);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle64_v_i64m2(&buffer[i * unpacket_traits<Packet2Xl>::size], unpacket_traits<Packet2Xl>::size);
+  }
+}
+
+template <typename Packet = Packet4Xl>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet4Xl>::value && (unpacket_traits<Packet4Xl>::size % 8) == 0,
+                            Packet2Xl>::type
+    predux_half(const Packet4Xl& a) {
+  return __riscv_vadd_vv_i64m2(__riscv_vget_v_i64m4_i64m2(a, 0), __riscv_vget_v_i64m4_i64m2(a, 1),
+                               unpacket_traits<Packet2Xl>::size);
+}
+
+template <typename Packet = Packet2Xl>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet2Xl>::value && (unpacket_traits<Packet2Xl>::size % 8) == 0,
+                            Packet1Xl>::type
+    predux_half(const Packet2Xl& a) {
+  return __riscv_vadd_vv_i64m1(__riscv_vget_v_i64m2_i64m1(a, 0), __riscv_vget_v_i64m2_i64m1(a, 1),
+                               unpacket_traits<Packet1Xl>::size);
+}
+
+/********************************* Packet2Xd ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd ptrue<Packet2Xd>(const Packet2Xd& /*a*/) {
+  return __riscv_vreinterpret_f64m2(__riscv_vmv_v_x_u64m2(0xffffffffffffffffu, unpacket_traits<Packet2Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pzero<Packet2Xd>(const Packet2Xd& /*a*/) {
+  return __riscv_vfmv_v_f_f64m2(0.0, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pabs(const Packet2Xd& a) {
+  return __riscv_vfabs_v_f64m2(a, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pabsdiff(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vfabs_v_f64m2(__riscv_vfsub_vv_f64m2(a, b, unpacket_traits<Packet2Xd>::size),
+                               unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pset1<Packet2Xd>(const double& from) {
+  return __riscv_vfmv_v_f_f64m2(from, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pset1frombits<Packet2Xd>(numext::uint64_t from) {
+  return __riscv_vreinterpret_f64m2(__riscv_vmv_v_x_u64m2(from, unpacket_traits<Packet2Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd plset<Packet2Xd>(const double& a) {
+  Packet2Xd idx = __riscv_vfcvt_f_x_v_f64m2(
+      __riscv_vreinterpret_v_u64m2_i64m2(__riscv_vid_v_u64m2(unpacket_traits<Packet4Xi>::size)),
+      unpacket_traits<Packet2Xd>::size);
+  return __riscv_vfadd_vf_f64m2(idx, a, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet2Xd>(const double* a, Packet2Xd& a0, Packet2Xd& a1, Packet2Xd& a2,
+                                                Packet2Xd& a3) {
+  vfloat64m2_t aa = __riscv_vle64_v_f64m2(a, 4);
+  a0 = __riscv_vrgather_vx_f64m2(aa, 0, unpacket_traits<Packet2Xd>::size);
+  a1 = __riscv_vrgather_vx_f64m2(aa, 1, unpacket_traits<Packet2Xd>::size);
+  a2 = __riscv_vrgather_vx_f64m2(aa, 2, unpacket_traits<Packet2Xd>::size);
+  a3 = __riscv_vrgather_vx_f64m2(aa, 3, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd padd<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vfadd_vv_f64m2(a, b, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd psub<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vfsub_vv_f64m2(a, b, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pnegate(const Packet2Xd& a) {
+  return __riscv_vfneg_v_f64m2(a, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd psignbit(const Packet2Xd& a) {
+  return __riscv_vreinterpret_v_i64m2_f64m2(
+      __riscv_vsra_vx_i64m2(__riscv_vreinterpret_v_f64m2_i64m2(a), 63, unpacket_traits<Packet2Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pconj(const Packet2Xd& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pmul<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vfmul_vv_f64m2(a, b, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pdiv<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vfdiv_vv_f64m2(a, b, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pmadd(const Packet2Xd& a, const Packet2Xd& b, const Packet2Xd& c) {
+  return __riscv_vfmadd_vv_f64m2(a, b, c, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pmsub(const Packet2Xd& a, const Packet2Xd& b, const Packet2Xd& c) {
+  return __riscv_vfmsub_vv_f64m2(a, b, c, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pnmadd(const Packet2Xd& a, const Packet2Xd& b, const Packet2Xd& c) {
+  return __riscv_vfnmsub_vv_f64m2(a, b, c, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pnmsub(const Packet2Xd& a, const Packet2Xd& b, const Packet2Xd& c) {
+  return __riscv_vfnmadd_vv_f64m2(a, b, c, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pmin<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  Packet2Xd nans = __riscv_vfmv_v_f_f64m2((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet2Xd>::size);
+  PacketMask32 mask = __riscv_vmfeq_vv_f64m2_b32(a, a, unpacket_traits<Packet2Xd>::size);
+  PacketMask32 mask2 = __riscv_vmfeq_vv_f64m2_b32(b, b, unpacket_traits<Packet2Xd>::size);
+  mask = __riscv_vmand_mm_b32(mask, mask2, unpacket_traits<Packet2Xd>::size);
+
+  return __riscv_vfmin_vv_f64m2_tumu(mask, nans, a, b, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pmin<PropagateNaN, Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return pmin<Packet2Xd>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pmin<PropagateNumbers, Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vfmin_vv_f64m2(a, b, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pmax<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  Packet2Xd nans = __riscv_vfmv_v_f_f64m2((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet2Xd>::size);
+  PacketMask32 mask = __riscv_vmfeq_vv_f64m2_b32(a, a, unpacket_traits<Packet2Xd>::size);
+  PacketMask32 mask2 = __riscv_vmfeq_vv_f64m2_b32(b, b, unpacket_traits<Packet2Xd>::size);
+  mask = __riscv_vmand_mm_b32(mask, mask2, unpacket_traits<Packet2Xd>::size);
+
+  return __riscv_vfmax_vv_f64m2_tumu(mask, nans, a, b, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pmax<PropagateNaN, Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return pmax<Packet2Xd>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pmax<PropagateNumbers, Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vfmax_vv_f64m2(a, b, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pcmp_le<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  PacketMask32 mask = __riscv_vmfle_vv_f64m2_b32(a, b, unpacket_traits<Packet2Xd>::size);
+  return __riscv_vmerge_vvm_f64m2(pzero<Packet2Xd>(a), ptrue<Packet2Xd>(a), mask, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pcmp_lt<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  PacketMask32 mask = __riscv_vmflt_vv_f64m2_b32(a, b, unpacket_traits<Packet2Xd>::size);
+  return __riscv_vmerge_vvm_f64m2(pzero<Packet2Xd>(a), ptrue<Packet2Xd>(a), mask, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pcmp_eq<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  PacketMask32 mask = __riscv_vmfeq_vv_f64m2_b32(a, b, unpacket_traits<Packet2Xd>::size);
+  return __riscv_vmerge_vvm_f64m2(pzero<Packet2Xd>(a), ptrue<Packet2Xd>(a), mask, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pcmp_lt_or_nan<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  PacketMask32 mask = __riscv_vmfge_vv_f64m2_b32(a, b, unpacket_traits<Packet2Xd>::size);
+  return __riscv_vfmerge_vfm_f64m2(ptrue<Packet2Xd>(a), 0.0, mask, unpacket_traits<Packet2Xd>::size);
+}
+
+EIGEN_STRONG_INLINE Packet2Xd pselect(const PacketMask32& mask, const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vmerge_vvm_f64m2(b, a, mask, unpacket_traits<Packet2Xd>::size);
+}
+
+EIGEN_STRONG_INLINE Packet2Xd pselect(const Packet2Xd& mask, const Packet2Xd& a, const Packet2Xd& b) {
+  PacketMask32 mask2 =
+      __riscv_vmsne_vx_i64m2_b32(__riscv_vreinterpret_v_f64m2_i64m2(mask), 0, unpacket_traits<Packet2Xd>::size);
+  return __riscv_vmerge_vvm_f64m2(b, a, mask2, unpacket_traits<Packet2Xd>::size);
+}
+
+// Logical Operations are not supported for double, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pand<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vreinterpret_v_u64m2_f64m2(__riscv_vand_vv_u64m2(
+      __riscv_vreinterpret_v_f64m2_u64m2(a), __riscv_vreinterpret_v_f64m2_u64m2(b), unpacket_traits<Packet2Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd por<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vreinterpret_v_u64m2_f64m2(__riscv_vor_vv_u64m2(
+      __riscv_vreinterpret_v_f64m2_u64m2(a), __riscv_vreinterpret_v_f64m2_u64m2(b), unpacket_traits<Packet2Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pxor<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vreinterpret_v_u64m2_f64m2(__riscv_vxor_vv_u64m2(
+      __riscv_vreinterpret_v_f64m2_u64m2(a), __riscv_vreinterpret_v_f64m2_u64m2(b), unpacket_traits<Packet2Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pandnot<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& b) {
+  return __riscv_vreinterpret_v_u64m2_f64m2(__riscv_vand_vv_u64m2(
+      __riscv_vreinterpret_v_f64m2_u64m2(a),
+      __riscv_vnot_v_u64m2(__riscv_vreinterpret_v_f64m2_u64m2(b), unpacket_traits<Packet2Xd>::size),
+      unpacket_traits<Packet2Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pload<Packet2Xd>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle64_v_f64m2(from, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd ploadu<Packet2Xd>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle64_v_f64m2(from, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd ploaddup<Packet2Xd>(const double* from) {
+  Packet2Xul idx =
+      __riscv_vsrl_vx_u64m2(__riscv_vid_v_u64m2(unpacket_traits<Packet2Xd>::size), 1, unpacket_traits<Packet2Xd>::size);
+  return __riscv_vrgather_vv_f64m2(pload<Packet2Xd>(from), idx, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd ploadquad<Packet2Xd>(const double* from) {
+  Packet2Xul idx =
+      __riscv_vsrl_vx_u64m2(__riscv_vid_v_u64m2(unpacket_traits<Packet2Xd>::size), 2, unpacket_traits<Packet2Xd>::size);
+  return __riscv_vrgather_vv_f64m2(pload<Packet2Xd>(from), idx, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2Xd& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse64_v_f64m2(to, from, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2Xd& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse64_v_f64m2(to, from, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2Xd pgather<double, Packet2Xd>(const double* from, Index stride) {
+  return __riscv_vlse64_v_f64m2(from, stride * sizeof(double), unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2Xd>(double* to, const Packet2Xd& from, Index stride) {
+  __riscv_vsse64(to, stride * sizeof(double), from, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet2Xd>(const Packet2Xd& a) {
+  return __riscv_vfmv_f_s_f64m2_f64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd psqrt(const Packet2Xd& a) {
+  return __riscv_vfsqrt_v_f64m2(a, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd print<Packet2Xd>(const Packet2Xd& a) {
+  const Packet2Xd limit = pset1<Packet2Xd>(static_cast<double>(1ull << 52));
+  const Packet2Xd abs_a = pabs(a);
+
+  PacketMask32 mask = __riscv_vmfne_vv_f64m2_b32(a, a, unpacket_traits<Packet2Xd>::size);
+  const Packet2Xd x = __riscv_vfadd_vv_f64m2_tumu(mask, a, a, a, unpacket_traits<Packet2Xd>::size);
+  const Packet2Xd new_x = __riscv_vfcvt_f_x_v_f64m2(__riscv_vfcvt_x_f_v_i64m2(a, unpacket_traits<Packet2Xd>::size),
+                                                    unpacket_traits<Packet2Xd>::size);
+
+  mask = __riscv_vmflt_vv_f64m2_b32(abs_a, limit, unpacket_traits<Packet2Xd>::size);
+  Packet2Xd signed_x = __riscv_vfsgnj_vv_f64m2(new_x, x, unpacket_traits<Packet2Xd>::size);
+  return __riscv_vmerge_vvm_f64m2(x, signed_x, mask, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pfloor<Packet2Xd>(const Packet2Xd& a) {
+  Packet2Xd tmp = print<Packet2Xd>(a);
+  // If greater, subtract one.
+  PacketMask32 mask = __riscv_vmflt_vv_f64m2_b32(a, tmp, unpacket_traits<Packet2Xd>::size);
+  return __riscv_vfsub_vf_f64m2_tumu(mask, tmp, tmp, 1.0, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd preverse(const Packet2Xd& a) {
+  Packet2Xul idx = __riscv_vrsub_vx_u64m2(__riscv_vid_v_u64m2(unpacket_traits<Packet2Xd>::size),
+                                          unpacket_traits<Packet2Xd>::size - 1, unpacket_traits<Packet2Xd>::size);
+  return __riscv_vrgather_vv_f64m2(a, idx, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pfrexp<Packet2Xd>(const Packet2Xd& a, Packet2Xd& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux<Packet2Xd>(const Packet2Xd& a) {
+  return __riscv_vfmv_f(__riscv_vfredusum_vs_f64m2_f64m1(
+      a, __riscv_vfmv_v_f_f64m1(0.0, unpacket_traits<Packet2Xd>::size / 2), unpacket_traits<Packet2Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet2Xd>(const Packet2Xd& a) {
+  return predux_mul<Packet1Xd>(__riscv_vfmul_vv_f64m1(
+      __riscv_vget_v_f64m2_f64m1(a, 0), __riscv_vget_v_f64m2_f64m1(a, 1), unpacket_traits<Packet1Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min<Packet2Xd>(const Packet2Xd& a) {
+  return (std::min)(
+      __riscv_vfmv_f(__riscv_vfredmin_vs_f64m2_f64m1(
+          a, __riscv_vfmv_v_f_f64m1((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet2Xd>::size / 2),
+          unpacket_traits<Packet2Xd>::size)),
+      (std::numeric_limits<double>::max)());
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max<Packet2Xd>(const Packet2Xd& a) {
+  return (std::max)(
+      __riscv_vfmv_f(__riscv_vfredmax_vs_f64m2_f64m1(
+          a, __riscv_vfmv_v_f_f64m1((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet2Xd>::size / 2),
+          unpacket_traits<Packet2Xd>::size)),
+      -(std::numeric_limits<double>::max)());
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2Xd, N>& kernel) {
+  double buffer[unpacket_traits<Packet2Xd>::size * N];
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse64(&buffer[i], N * sizeof(double), kernel.packet[i], unpacket_traits<Packet2Xd>::size);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle64_v_f64m2(&buffer[i * unpacket_traits<Packet2Xd>::size], unpacket_traits<Packet2Xd>::size);
+  }
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pldexp<Packet2Xd>(const Packet2Xd& a, const Packet2Xd& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <typename Packet = Packet4Xd>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet4Xd>::value && (unpacket_traits<Packet4Xd>::size % 8) == 0,
+                            Packet2Xd>::type
+    predux_half(const Packet4Xd& a) {
+  return __riscv_vfadd_vv_f64m2(__riscv_vget_v_f64m4_f64m2(a, 0), __riscv_vget_v_f64m4_f64m2(a, 1),
+                                unpacket_traits<Packet2Xd>::size);
+}
+
+template <typename Packet = Packet2Xd>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet2Xd>::value && (unpacket_traits<Packet2Xd>::size % 8) == 0,
+                            Packet1Xd>::type
+    predux_half(const Packet2Xd& a) {
+  return __riscv_vfadd_vv_f64m1(__riscv_vget_v_f64m2_f64m1(a, 0), __riscv_vget_v_f64m2_f64m1(a, 1),
+                                unpacket_traits<Packet1Xd>::size);
+}
+
+/********************************* Packet2Xs ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pset1<Packet2Xs>(const numext::int16_t& from) {
+  return __riscv_vmv_v_x_i16m2(from, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs plset<Packet2Xs>(const numext::int16_t& a) {
+  Packet2Xs idx = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vid_v_u16m2(unpacket_traits<Packet2Xs>::size));
+  return __riscv_vadd_vx_i16m2(idx, a, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pzero<Packet2Xs>(const Packet2Xs& /*a*/) {
+  return __riscv_vmv_v_x_i16m2(0, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs padd<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  return __riscv_vadd_vv_i16m2(a, b, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs psub<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  return __riscv_vsub(a, b, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pnegate(const Packet2Xs& a) {
+  return __riscv_vneg(a, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pconj(const Packet2Xs& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pmul<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  return __riscv_vmul(a, b, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pdiv<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  return __riscv_vdiv(a, b, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pmadd(const Packet2Xs& a, const Packet2Xs& b, const Packet2Xs& c) {
+  return __riscv_vmadd(a, b, c, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pmsub(const Packet2Xs& a, const Packet2Xs& b, const Packet2Xs& c) {
+  return __riscv_vmadd(a, b, pnegate(c), unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pnmadd(const Packet2Xs& a, const Packet2Xs& b, const Packet2Xs& c) {
+  return __riscv_vnmsub_vv_i16m2(a, b, c, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pnmsub(const Packet2Xs& a, const Packet2Xs& b, const Packet2Xs& c) {
+  return __riscv_vnmsub_vv_i16m2(a, b, pnegate(c), unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pmin<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  return __riscv_vmin(a, b, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pmax<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  return __riscv_vmax(a, b, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pcmp_le<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  PacketMask8 mask = __riscv_vmsle_vv_i16m2_b8(a, b, unpacket_traits<Packet2Xs>::size);
+  return __riscv_vmerge_vxm_i16m2(pzero(a), static_cast<short>(0xffff), mask, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pcmp_lt<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  PacketMask8 mask = __riscv_vmslt_vv_i16m2_b8(a, b, unpacket_traits<Packet2Xs>::size);
+  return __riscv_vmerge_vxm_i16m2(pzero(a), static_cast<short>(0xffff), mask, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pcmp_eq<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  PacketMask8 mask = __riscv_vmseq_vv_i16m2_b8(a, b, unpacket_traits<Packet2Xs>::size);
+  return __riscv_vmerge_vxm_i16m2(pzero(a), static_cast<short>(0xffff), mask, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs ptrue<Packet2Xs>(const Packet2Xs& /*a*/) {
+  return __riscv_vmv_v_x_i16m2(static_cast<unsigned short>(0xffffu), unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pand<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  return __riscv_vand_vv_i16m2(a, b, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs por<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  return __riscv_vor_vv_i16m2(a, b, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pxor<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  return __riscv_vxor_vv_i16m2(a, b, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pandnot<Packet2Xs>(const Packet2Xs& a, const Packet2Xs& b) {
+  return __riscv_vand_vv_i16m2(a, __riscv_vnot_v_i16m2(b, unpacket_traits<Packet2Xs>::size),
+                               unpacket_traits<Packet2Xs>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2Xs parithmetic_shift_right(Packet2Xs a) {
+  return __riscv_vsra_vx_i16m2(a, N, unpacket_traits<Packet2Xs>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2Xs plogical_shift_right(Packet2Xs a) {
+  return __riscv_vreinterpret_i16m2(
+      __riscv_vsrl_vx_u16m2(__riscv_vreinterpret_u16m2(a), N, unpacket_traits<Packet2Xs>::size));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2Xs plogical_shift_left(Packet2Xs a) {
+  return __riscv_vsll_vx_i16m2(a, N, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pload<Packet2Xs>(const numext::int16_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle16_v_i16m2(from, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs ploadu<Packet2Xs>(const numext::int16_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle16_v_i16m2(from, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs ploaddup<Packet2Xs>(const numext::int16_t* from) {
+  Packet2Xsu data = __riscv_vreinterpret_v_i16m2_u16m2(pload<Packet2Xs>(from));
+  return __riscv_vreinterpret_v_i32m2_i16m2(__riscv_vreinterpret_v_u32m2_i32m2(__riscv_vlmul_trunc_v_u32m4_u32m2(
+      __riscv_vwmaccu_vx_u32m4(__riscv_vwaddu_vv_u32m4(data, data, unpacket_traits<Packet2Xs>::size), 0xffffu, data,
+                               unpacket_traits<Packet2Xs>::size))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs ploadquad<Packet2Xs>(const numext::int16_t* from) {
+  Packet2Xsu idx =
+      __riscv_vsrl_vx_u16m2(__riscv_vid_v_u16m2(unpacket_traits<Packet2Xs>::size), 2, unpacket_traits<Packet2Xs>::size);
+  return __riscv_vrgather_vv_i16m2(pload<Packet2Xs>(from), idx, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int16_t>(numext::int16_t* to, const Packet2Xs& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse16_v_i16m2(to, from, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int16_t>(numext::int16_t* to, const Packet2Xs& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse16_v_i16m2(to, from, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2Xs pgather<numext::int16_t, Packet2Xs>(const numext::int16_t* from, Index stride) {
+  return __riscv_vlse16_v_i16m2(from, stride * sizeof(numext::int16_t), unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int16_t, Packet2Xs>(numext::int16_t* to, const Packet2Xs& from,
+                                                                   Index stride) {
+  __riscv_vsse16(to, stride * sizeof(numext::int16_t), from, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t pfirst<Packet2Xs>(const Packet2Xs& a) {
+  return __riscv_vmv_x_s_i16m2_i16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs preverse(const Packet2Xs& a) {
+  Packet2Xsu idx = __riscv_vrsub_vx_u16m2(__riscv_vid_v_u16m2(unpacket_traits<Packet2Xs>::size),
+                                          unpacket_traits<Packet2Xs>::size - 1, unpacket_traits<Packet2Xs>::size);
+  return __riscv_vrgather_vv_i16m2(a, idx, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pabs(const Packet2Xs& a) {
+  Packet2Xs mask = __riscv_vsra_vx_i16m2(a, 15, unpacket_traits<Packet2Xs>::size);
+  return __riscv_vsub_vv_i16m2(__riscv_vxor_vv_i16m2(a, mask, unpacket_traits<Packet2Xs>::size), mask,
+                               unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux<Packet2Xs>(const Packet2Xs& a) {
+  return __riscv_vmv_x(__riscv_vredsum_vs_i16m2_i16m1(a, __riscv_vmv_v_x_i16m1(0, unpacket_traits<Packet2Xs>::size / 2),
+                                                      unpacket_traits<Packet2Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux_mul<Packet2Xs>(const Packet2Xs& a) {
+  return predux_mul<Packet1Xs>(__riscv_vmul_vv_i16m1(__riscv_vget_v_i16m2_i16m1(a, 0), __riscv_vget_v_i16m2_i16m1(a, 1),
+                                                     unpacket_traits<Packet1Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux_min<Packet2Xs>(const Packet2Xs& a) {
+  return __riscv_vmv_x(__riscv_vredmin_vs_i16m2_i16m1(
+      a, __riscv_vmv_v_x_i16m1((std::numeric_limits<numext::int16_t>::max)(), unpacket_traits<Packet2Xs>::size / 2),
+      unpacket_traits<Packet2Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux_max<Packet2Xs>(const Packet2Xs& a) {
+  return __riscv_vmv_x(__riscv_vredmax_vs_i16m2_i16m1(
+      a, __riscv_vmv_v_x_i16m1((std::numeric_limits<numext::int16_t>::min)(), unpacket_traits<Packet2Xs>::size / 2),
+      unpacket_traits<Packet2Xs>::size));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2Xs, N>& kernel) {
+  numext::int16_t buffer[unpacket_traits<Packet2Xs>::size * N] = {0};
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse16(&buffer[i], N * sizeof(numext::int16_t), kernel.packet[i], unpacket_traits<Packet2Xs>::size);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle16_v_i16m2(&buffer[i * unpacket_traits<Packet2Xs>::size], unpacket_traits<Packet2Xs>::size);
+  }
+}
+
+template <typename Packet = Packet4Xs>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet4Xs>::value && (unpacket_traits<Packet4Xs>::size % 8) == 0,
+                            Packet2Xs>::type
+    predux_half(const Packet4Xs& a) {
+  return __riscv_vadd_vv_i16m2(__riscv_vget_v_i16m4_i16m2(a, 0), __riscv_vget_v_i16m4_i16m2(a, 1),
+                               unpacket_traits<Packet2Xs>::size);
+}
+
+template <typename Packet = Packet2Xs>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet2Xs>::value && (unpacket_traits<Packet2Xs>::size % 8) == 0,
+                            Packet1Xs>::type
+    predux_half(const Packet2Xs& a) {
+  return __riscv_vadd_vv_i16m1(__riscv_vget_v_i16m2_i16m1(a, 0), __riscv_vget_v_i16m2_i16m1(a, 1),
+                               unpacket_traits<Packet1Xs>::size);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_PACKET2_MATH_RVV10_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMath4.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMath4.h
new file mode 100644
index 0000000..249dadf
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMath4.h
@@ -0,0 +1,1462 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Kseniya Zaytseva <kseniya.zaytseva@syntacore.com>
+// Copyright (C) 2025 Chip Kerchner <ckerchner@tenstorrent.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET4_MATH_RVV10_H
+#define EIGEN_PACKET4_MATH_RVV10_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/********************************* Packet4Xi ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pset1<Packet4Xi>(const numext::int32_t& from) {
+  return __riscv_vmv_v_x_i32m4(from, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi plset<Packet4Xi>(const numext::int32_t& a) {
+  Packet4Xi idx = __riscv_vreinterpret_v_u32m4_i32m4(__riscv_vid_v_u32m4(unpacket_traits<Packet4Xi>::size));
+  return __riscv_vadd_vx_i32m4(idx, a, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pzero<Packet4Xi>(const Packet4Xi& /*a*/) {
+  return __riscv_vmv_v_x_i32m4(0, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi padd<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  return __riscv_vadd_vv_i32m4(a, b, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi psub<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  return __riscv_vsub(a, b, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pnegate(const Packet4Xi& a) {
+  return __riscv_vneg(a, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pconj(const Packet4Xi& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pmul<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  return __riscv_vmul(a, b, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pdiv<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  return __riscv_vdiv(a, b, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pmadd(const Packet4Xi& a, const Packet4Xi& b, const Packet4Xi& c) {
+  return __riscv_vmadd(a, b, c, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pmsub(const Packet4Xi& a, const Packet4Xi& b, const Packet4Xi& c) {
+  return __riscv_vmadd(a, b, pnegate(c), unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pnmadd(const Packet4Xi& a, const Packet4Xi& b, const Packet4Xi& c) {
+  return __riscv_vnmsub_vv_i32m4(a, b, c, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pnmsub(const Packet4Xi& a, const Packet4Xi& b, const Packet4Xi& c) {
+  return __riscv_vnmsub_vv_i32m4(a, b, pnegate(c), unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pmin<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  return __riscv_vmin(a, b, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pmax<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  return __riscv_vmax(a, b, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pcmp_le<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  PacketMask8 mask = __riscv_vmsle_vv_i32m4_b8(a, b, unpacket_traits<Packet4Xi>::size);
+  return __riscv_vmerge_vxm_i32m4(pzero(a), 0xffffffff, mask, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pcmp_lt<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  PacketMask8 mask = __riscv_vmslt_vv_i32m4_b8(a, b, unpacket_traits<Packet4Xi>::size);
+  return __riscv_vmerge_vxm_i32m4(pzero(a), 0xffffffff, mask, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pcmp_eq<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  PacketMask8 mask = __riscv_vmseq_vv_i32m4_b8(a, b, unpacket_traits<Packet4Xi>::size);
+  return __riscv_vmerge_vxm_i32m4(pzero(a), 0xffffffff, mask, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi ptrue<Packet4Xi>(const Packet4Xi& /*a*/) {
+  return __riscv_vmv_v_x_i32m4(0xffffffffu, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pand<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  return __riscv_vand_vv_i32m4(a, b, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi por<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  return __riscv_vor_vv_i32m4(a, b, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pxor<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  return __riscv_vxor_vv_i32m4(a, b, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pandnot<Packet4Xi>(const Packet4Xi& a, const Packet4Xi& b) {
+  return __riscv_vand_vv_i32m4(a, __riscv_vnot_v_i32m4(b, unpacket_traits<Packet4Xi>::size),
+                               unpacket_traits<Packet4Xi>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4Xi parithmetic_shift_right(Packet4Xi a) {
+  return __riscv_vsra_vx_i32m4(a, N, unpacket_traits<Packet4Xi>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4Xi plogical_shift_right(Packet4Xi a) {
+  return __riscv_vreinterpret_i32m4(
+      __riscv_vsrl_vx_u32m4(__riscv_vreinterpret_u32m4(a), N, unpacket_traits<Packet4Xi>::size));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4Xi plogical_shift_left(Packet4Xi a) {
+  return __riscv_vsll_vx_i32m4(a, N, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pload<Packet4Xi>(const numext::int32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle32_v_i32m4(from, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi ploadu<Packet4Xi>(const numext::int32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle32_v_i32m4(from, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi ploaddup<Packet4Xi>(const numext::int32_t* from) {
+  Packet4Xu data = __riscv_vreinterpret_v_i32m4_u32m4(pload<Packet4Xi>(from));
+  return __riscv_vreinterpret_v_i64m4_i32m4(__riscv_vreinterpret_v_u64m4_i64m4(__riscv_vlmul_trunc_v_u64m8_u64m4(
+      __riscv_vwmaccu_vx_u64m8(__riscv_vwaddu_vv_u64m8(data, data, unpacket_traits<Packet4Xi>::size), 0xffffffffu, data,
+                               unpacket_traits<Packet4Xi>::size))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi ploadquad<Packet4Xi>(const numext::int32_t* from) {
+  Packet4Xu idx =
+      __riscv_vsrl_vx_u32m4(__riscv_vid_v_u32m4(unpacket_traits<Packet4Xi>::size), 2, unpacket_traits<Packet4Xi>::size);
+  return __riscv_vrgather_vv_i32m4(pload<Packet4Xi>(from), idx, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int32_t>(numext::int32_t* to, const Packet4Xi& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse32_v_i32m4(to, from, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int32_t>(numext::int32_t* to, const Packet4Xi& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse32_v_i32m4(to, from, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4Xi pgather<numext::int32_t, Packet4Xi>(const numext::int32_t* from, Index stride) {
+  return __riscv_vlse32_v_i32m4(from, stride * sizeof(numext::int32_t), unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int32_t, Packet4Xi>(numext::int32_t* to, const Packet4Xi& from,
+                                                                   Index stride) {
+  __riscv_vsse32(to, stride * sizeof(numext::int32_t), from, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t pfirst<Packet4Xi>(const Packet4Xi& a) {
+  return __riscv_vmv_x_s_i32m4_i32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi preverse(const Packet4Xi& a) {
+  Packet4Xu idx = __riscv_vrsub_vx_u32m4(__riscv_vid_v_u32m4(unpacket_traits<Packet4Xi>::size),
+                                         unpacket_traits<Packet4Xi>::size - 1, unpacket_traits<Packet4Xi>::size);
+  return __riscv_vrgather_vv_i32m4(a, idx, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pabs(const Packet4Xi& a) {
+  Packet4Xi mask = __riscv_vsra_vx_i32m4(a, 31, unpacket_traits<Packet4Xi>::size);
+  return __riscv_vsub_vv_i32m4(__riscv_vxor_vv_i32m4(a, mask, unpacket_traits<Packet4Xi>::size), mask,
+                               unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux<Packet4Xi>(const Packet4Xi& a) {
+  return __riscv_vmv_x(__riscv_vredsum_vs_i32m4_i32m1(a, __riscv_vmv_v_x_i32m1(0, unpacket_traits<Packet4Xi>::size / 4),
+                                                      unpacket_traits<Packet4Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_mul<Packet4Xi>(const Packet4Xi& a) {
+  Packet1Xi half1 = __riscv_vmul_vv_i32m1(__riscv_vget_v_i32m4_i32m1(a, 0), __riscv_vget_v_i32m4_i32m1(a, 1),
+                                          unpacket_traits<Packet1Xi>::size);
+  Packet1Xi half2 = __riscv_vmul_vv_i32m1(__riscv_vget_v_i32m4_i32m1(a, 2), __riscv_vget_v_i32m4_i32m1(a, 3),
+                                          unpacket_traits<Packet1Xi>::size);
+  return predux_mul<Packet1Xi>(__riscv_vmul_vv_i32m1(half1, half2, unpacket_traits<Packet1Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_min<Packet4Xi>(const Packet4Xi& a) {
+  return __riscv_vmv_x(__riscv_vredmin_vs_i32m4_i32m1(
+      a, __riscv_vmv_v_x_i32m1((std::numeric_limits<numext::int32_t>::max)(), unpacket_traits<Packet4Xi>::size / 4),
+      unpacket_traits<Packet4Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_max<Packet4Xi>(const Packet4Xi& a) {
+  return __riscv_vmv_x(__riscv_vredmax_vs_i32m4_i32m1(
+      a, __riscv_vmv_v_x_i32m1((std::numeric_limits<numext::int32_t>::min)(), unpacket_traits<Packet4Xi>::size / 4),
+      unpacket_traits<Packet4Xi>::size));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4Xi, N>& kernel) {
+  numext::int32_t buffer[unpacket_traits<Packet4Xi>::size * N] = {0};
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse32(&buffer[i], N * sizeof(numext::int32_t), kernel.packet[i], unpacket_traits<Packet4Xi>::size);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle32_v_i32m4(&buffer[i * unpacket_traits<Packet4Xi>::size], unpacket_traits<Packet4Xi>::size);
+  }
+}
+
+/********************************* Packet4Xf ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf ptrue<Packet4Xf>(const Packet4Xf& /*a*/) {
+  return __riscv_vreinterpret_f32m4(__riscv_vmv_v_x_u32m4(0xffffffffu, unpacket_traits<Packet4Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pzero<Packet4Xf>(const Packet4Xf& /*a*/) {
+  return __riscv_vfmv_v_f_f32m4(0.0f, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pabs(const Packet4Xf& a) {
+  return __riscv_vfabs_v_f32m4(a, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pabsdiff(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vfabs_v_f32m4(__riscv_vfsub_vv_f32m4(a, b, unpacket_traits<Packet4Xf>::size),
+                               unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pset1<Packet4Xf>(const float& from) {
+  return __riscv_vfmv_v_f_f32m4(from, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pset1frombits<Packet4Xf>(numext::uint32_t from) {
+  return __riscv_vreinterpret_f32m4(__riscv_vmv_v_x_u32m4(from, unpacket_traits<Packet4Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf plset<Packet4Xf>(const float& a) {
+  Packet4Xf idx = __riscv_vfcvt_f_x_v_f32m4(
+      __riscv_vreinterpret_v_u32m4_i32m4(__riscv_vid_v_u32m4(unpacket_traits<Packet4Xi>::size)),
+      unpacket_traits<Packet4Xf>::size);
+  return __riscv_vfadd_vf_f32m4(idx, a, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet4Xf>(const float* a, Packet4Xf& a0, Packet4Xf& a1, Packet4Xf& a2,
+                                                Packet4Xf& a3) {
+  vfloat32m4_t aa = __riscv_vle32_v_f32m4(a, 4);
+  a0 = __riscv_vrgather_vx_f32m4(aa, 0, unpacket_traits<Packet4Xf>::size);
+  a1 = __riscv_vrgather_vx_f32m4(aa, 1, unpacket_traits<Packet4Xf>::size);
+  a2 = __riscv_vrgather_vx_f32m4(aa, 2, unpacket_traits<Packet4Xf>::size);
+  a3 = __riscv_vrgather_vx_f32m4(aa, 3, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf padd<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vfadd_vv_f32m4(a, b, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf psub<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vfsub_vv_f32m4(a, b, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pnegate(const Packet4Xf& a) {
+  return __riscv_vfneg_v_f32m4(a, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf psignbit(const Packet4Xf& a) {
+  return __riscv_vreinterpret_v_i32m4_f32m4(
+      __riscv_vsra_vx_i32m4(__riscv_vreinterpret_v_f32m4_i32m4(a), 31, unpacket_traits<Packet4Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pconj(const Packet4Xf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pmul<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vfmul_vv_f32m4(a, b, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pdiv<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vfdiv_vv_f32m4(a, b, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pmadd(const Packet4Xf& a, const Packet4Xf& b, const Packet4Xf& c) {
+  return __riscv_vfmadd_vv_f32m4(a, b, c, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pmsub(const Packet4Xf& a, const Packet4Xf& b, const Packet4Xf& c) {
+  return __riscv_vfmsub_vv_f32m4(a, b, c, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pnmadd(const Packet4Xf& a, const Packet4Xf& b, const Packet4Xf& c) {
+  return __riscv_vfnmsub_vv_f32m4(a, b, c, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pnmsub(const Packet4Xf& a, const Packet4Xf& b, const Packet4Xf& c) {
+  return __riscv_vfnmadd_vv_f32m4(a, b, c, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pmin<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  Packet4Xf nans = __riscv_vfmv_v_f_f32m4((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet4Xf>::size);
+  PacketMask8 mask = __riscv_vmfeq_vv_f32m4_b8(a, a, unpacket_traits<Packet4Xf>::size);
+  PacketMask8 mask2 = __riscv_vmfeq_vv_f32m4_b8(b, b, unpacket_traits<Packet4Xf>::size);
+  mask = __riscv_vmand_mm_b8(mask, mask2, unpacket_traits<Packet4Xf>::size);
+
+  return __riscv_vfmin_vv_f32m4_tumu(mask, nans, a, b, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pmin<PropagateNaN, Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return pmin<Packet4Xf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pmin<PropagateNumbers, Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vfmin_vv_f32m4(a, b, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pmax<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  Packet4Xf nans = __riscv_vfmv_v_f_f32m4((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet4Xf>::size);
+  PacketMask8 mask = __riscv_vmfeq_vv_f32m4_b8(a, a, unpacket_traits<Packet4Xf>::size);
+  PacketMask8 mask2 = __riscv_vmfeq_vv_f32m4_b8(b, b, unpacket_traits<Packet4Xf>::size);
+  mask = __riscv_vmand_mm_b8(mask, mask2, unpacket_traits<Packet4Xf>::size);
+
+  return __riscv_vfmax_vv_f32m4_tumu(mask, nans, a, b, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pmax<PropagateNaN, Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return pmax<Packet4Xf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pmax<PropagateNumbers, Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vfmax_vv_f32m4(a, b, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pcmp_le<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  PacketMask8 mask = __riscv_vmfle_vv_f32m4_b8(a, b, unpacket_traits<Packet4Xf>::size);
+  return __riscv_vmerge_vvm_f32m4(pzero<Packet4Xf>(a), ptrue<Packet4Xf>(a), mask, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pcmp_lt<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  PacketMask8 mask = __riscv_vmflt_vv_f32m4_b8(a, b, unpacket_traits<Packet4Xf>::size);
+  return __riscv_vmerge_vvm_f32m4(pzero<Packet4Xf>(a), ptrue<Packet4Xf>(a), mask, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pcmp_eq<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  PacketMask8 mask = __riscv_vmfeq_vv_f32m4_b8(a, b, unpacket_traits<Packet4Xf>::size);
+  return __riscv_vmerge_vvm_f32m4(pzero<Packet4Xf>(a), ptrue<Packet4Xf>(a), mask, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pcmp_lt_or_nan<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  PacketMask8 mask = __riscv_vmfge_vv_f32m4_b8(a, b, unpacket_traits<Packet4Xf>::size);
+  return __riscv_vfmerge_vfm_f32m4(ptrue<Packet4Xf>(a), 0.0f, mask, unpacket_traits<Packet4Xf>::size);
+}
+
+EIGEN_STRONG_INLINE Packet4Xf pselect(const PacketMask8& mask, const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vmerge_vvm_f32m4(b, a, mask, unpacket_traits<Packet4Xf>::size);
+}
+
+EIGEN_STRONG_INLINE Packet4Xf pselect(const Packet4Xf& mask, const Packet4Xf& a, const Packet4Xf& b) {
+  PacketMask8 mask2 =
+      __riscv_vmsne_vx_i32m4_b8(__riscv_vreinterpret_v_f32m4_i32m4(mask), 0, unpacket_traits<Packet4Xf>::size);
+  return __riscv_vmerge_vvm_f32m4(b, a, mask2, unpacket_traits<Packet4Xf>::size);
+}
+
+// Logical Operations are not supported for float, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pand<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vreinterpret_v_u32m4_f32m4(__riscv_vand_vv_u32m4(
+      __riscv_vreinterpret_v_f32m4_u32m4(a), __riscv_vreinterpret_v_f32m4_u32m4(b), unpacket_traits<Packet4Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf por<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vreinterpret_v_u32m4_f32m4(__riscv_vor_vv_u32m4(
+      __riscv_vreinterpret_v_f32m4_u32m4(a), __riscv_vreinterpret_v_f32m4_u32m4(b), unpacket_traits<Packet4Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pxor<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vreinterpret_v_u32m4_f32m4(__riscv_vxor_vv_u32m4(
+      __riscv_vreinterpret_v_f32m4_u32m4(a), __riscv_vreinterpret_v_f32m4_u32m4(b), unpacket_traits<Packet4Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pandnot<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& b) {
+  return __riscv_vreinterpret_v_u32m4_f32m4(__riscv_vand_vv_u32m4(
+      __riscv_vreinterpret_v_f32m4_u32m4(a),
+      __riscv_vnot_v_u32m4(__riscv_vreinterpret_v_f32m4_u32m4(b), unpacket_traits<Packet4Xf>::size),
+      unpacket_traits<Packet4Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pload<Packet4Xf>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle32_v_f32m4(from, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf ploadu<Packet4Xf>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle32_v_f32m4(from, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf ploaddup<Packet4Xf>(const float* from) {
+  Packet4Xu data = __riscv_vreinterpret_v_f32m4_u32m4(pload<Packet4Xf>(from));
+  return __riscv_vreinterpret_v_i32m4_f32m4(
+      __riscv_vreinterpret_v_i64m4_i32m4(__riscv_vreinterpret_v_u64m4_i64m4(__riscv_vlmul_trunc_v_u64m8_u64m4(
+          __riscv_vwmaccu_vx_u64m8(__riscv_vwaddu_vv_u64m8(data, data, unpacket_traits<Packet4Xi>::size), 0xffffffffu,
+                                   data, unpacket_traits<Packet4Xi>::size)))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf ploadquad<Packet4Xf>(const float* from) {
+  Packet4Xu idx =
+      __riscv_vsrl_vx_u32m4(__riscv_vid_v_u32m4(unpacket_traits<Packet4Xf>::size), 2, unpacket_traits<Packet4Xf>::size);
+  return __riscv_vrgather_vv_f32m4(pload<Packet4Xf>(from), idx, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4Xf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse32_v_f32m4(to, from, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4Xf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse32_v_f32m4(to, from, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4Xf pgather<float, Packet4Xf>(const float* from, Index stride) {
+  return __riscv_vlse32_v_f32m4(from, stride * sizeof(float), unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4Xf>(float* to, const Packet4Xf& from, Index stride) {
+  __riscv_vsse32(to, stride * sizeof(float), from, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4Xf>(const Packet4Xf& a) {
+  return __riscv_vfmv_f_s_f32m4_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf psqrt(const Packet4Xf& a) {
+  return __riscv_vfsqrt_v_f32m4(a, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf print<Packet4Xf>(const Packet4Xf& a) {
+  const Packet4Xf limit = pset1<Packet4Xf>(static_cast<float>(1 << 23));
+  const Packet4Xf abs_a = pabs(a);
+
+  PacketMask8 mask = __riscv_vmfne_vv_f32m4_b8(a, a, unpacket_traits<Packet4Xf>::size);
+  const Packet4Xf x = __riscv_vfadd_vv_f32m4_tumu(mask, a, a, a, unpacket_traits<Packet4Xf>::size);
+  const Packet4Xf new_x = __riscv_vfcvt_f_x_v_f32m4(__riscv_vfcvt_x_f_v_i32m4(a, unpacket_traits<Packet4Xf>::size),
+                                                    unpacket_traits<Packet4Xf>::size);
+
+  mask = __riscv_vmflt_vv_f32m4_b8(abs_a, limit, unpacket_traits<Packet4Xf>::size);
+  Packet4Xf signed_x = __riscv_vfsgnj_vv_f32m4(new_x, x, unpacket_traits<Packet4Xf>::size);
+  return __riscv_vmerge_vvm_f32m4(x, signed_x, mask, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pfloor<Packet4Xf>(const Packet4Xf& a) {
+  Packet4Xf tmp = print<Packet4Xf>(a);
+  // If greater, subtract one.
+  PacketMask8 mask = __riscv_vmflt_vv_f32m4_b8(a, tmp, unpacket_traits<Packet4Xf>::size);
+  return __riscv_vfsub_vf_f32m4_tumu(mask, tmp, tmp, 1.0f, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf preverse(const Packet4Xf& a) {
+  Packet4Xu idx = __riscv_vrsub_vx_u32m4(__riscv_vid_v_u32m4(unpacket_traits<Packet4Xf>::size),
+                                         unpacket_traits<Packet4Xf>::size - 1, unpacket_traits<Packet4Xf>::size);
+  return __riscv_vrgather_vv_f32m4(a, idx, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pfrexp<Packet4Xf>(const Packet4Xf& a, Packet4Xf& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet4Xf>(const Packet4Xf& a) {
+  return __riscv_vfmv_f(__riscv_vfredusum_vs_f32m4_f32m1(
+      a, __riscv_vfmv_v_f_f32m1(0.0, unpacket_traits<Packet4Xf>::size / 4), unpacket_traits<Packet4Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet4Xf>(const Packet4Xf& a) {
+  Packet1Xf half1 = __riscv_vfmul_vv_f32m1(__riscv_vget_v_f32m4_f32m1(a, 0), __riscv_vget_v_f32m4_f32m1(a, 1),
+                                           unpacket_traits<Packet1Xf>::size);
+  Packet1Xf half2 = __riscv_vfmul_vv_f32m1(__riscv_vget_v_f32m4_f32m1(a, 2), __riscv_vget_v_f32m4_f32m1(a, 3),
+                                           unpacket_traits<Packet1Xf>::size);
+  return predux_mul<Packet1Xf>(__riscv_vfmul_vv_f32m1(half1, half2, unpacket_traits<Packet1Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet4Xf>(const Packet4Xf& a) {
+  return (std::min)(
+      __riscv_vfmv_f(__riscv_vfredmin_vs_f32m4_f32m1(
+          a, __riscv_vfmv_v_f_f32m1((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet4Xf>::size / 4),
+          unpacket_traits<Packet4Xf>::size)),
+      (std::numeric_limits<float>::max)());
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet4Xf>(const Packet4Xf& a) {
+  return (std::max)(
+      __riscv_vfmv_f(__riscv_vfredmax_vs_f32m4_f32m1(
+          a, __riscv_vfmv_v_f_f32m1((std::numeric_limits<float>::quiet_NaN)(), unpacket_traits<Packet4Xf>::size / 4),
+          unpacket_traits<Packet4Xf>::size)),
+      -(std::numeric_limits<float>::max)());
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4Xf, N>& kernel) {
+  float buffer[unpacket_traits<Packet4Xf>::size * N];
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse32(&buffer[i], N * sizeof(float), kernel.packet[i], unpacket_traits<Packet4Xf>::size);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle32_v_f32m4(&buffer[i * unpacket_traits<Packet4Xf>::size], unpacket_traits<Packet4Xf>::size);
+  }
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pldexp<Packet4Xf>(const Packet4Xf& a, const Packet4Xf& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+/********************************* Packet4Xl ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pset1<Packet4Xl>(const numext::int64_t& from) {
+  return __riscv_vmv_v_x_i64m4(from, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl plset<Packet4Xl>(const numext::int64_t& a) {
+  Packet4Xl idx = __riscv_vreinterpret_v_u64m4_i64m4(__riscv_vid_v_u64m4(unpacket_traits<Packet4Xl>::size));
+  return __riscv_vadd_vx_i64m4(idx, a, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pzero<Packet4Xl>(const Packet4Xl& /*a*/) {
+  return __riscv_vmv_v_x_i64m4(0, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl padd<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  return __riscv_vadd_vv_i64m4(a, b, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl psub<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  return __riscv_vsub(a, b, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pnegate(const Packet4Xl& a) {
+  return __riscv_vneg(a, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pconj(const Packet4Xl& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pmul<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  return __riscv_vmul(a, b, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pdiv<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  return __riscv_vdiv(a, b, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pmadd(const Packet4Xl& a, const Packet4Xl& b, const Packet4Xl& c) {
+  return __riscv_vmadd(a, b, c, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pmsub(const Packet4Xl& a, const Packet4Xl& b, const Packet4Xl& c) {
+  return __riscv_vmadd(a, b, pnegate(c), unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pnmadd(const Packet4Xl& a, const Packet4Xl& b, const Packet4Xl& c) {
+  return __riscv_vnmsub_vv_i64m4(a, b, c, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pnmsub(const Packet4Xl& a, const Packet4Xl& b, const Packet4Xl& c) {
+  return __riscv_vnmsub_vv_i64m4(a, b, pnegate(c), unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pmin<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  return __riscv_vmin(a, b, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pmax<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  return __riscv_vmax(a, b, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pcmp_le<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  PacketMask16 mask = __riscv_vmsle_vv_i64m4_b16(a, b, unpacket_traits<Packet4Xl>::size);
+  return __riscv_vmerge_vxm_i64m4(pzero(a), 0xffffffffffffffff, mask, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pcmp_lt<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  PacketMask16 mask = __riscv_vmslt_vv_i64m4_b16(a, b, unpacket_traits<Packet4Xl>::size);
+  return __riscv_vmerge_vxm_i64m4(pzero(a), 0xffffffffffffffff, mask, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pcmp_eq<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  PacketMask16 mask = __riscv_vmseq_vv_i64m4_b16(a, b, unpacket_traits<Packet4Xl>::size);
+  return __riscv_vmerge_vxm_i64m4(pzero(a), 0xffffffffffffffff, mask, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl ptrue<Packet4Xl>(const Packet4Xl& /*a*/) {
+  return __riscv_vmv_v_x_i64m4(0xffffffffffffffffu, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pand<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  return __riscv_vand_vv_i64m4(a, b, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl por<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  return __riscv_vor_vv_i64m4(a, b, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pxor<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  return __riscv_vxor_vv_i64m4(a, b, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pandnot<Packet4Xl>(const Packet4Xl& a, const Packet4Xl& b) {
+  return __riscv_vand_vv_i64m4(a, __riscv_vnot_v_i64m4(b, unpacket_traits<Packet4Xl>::size),
+                               unpacket_traits<Packet4Xl>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4Xl parithmetic_shift_right(Packet4Xl a) {
+  return __riscv_vsra_vx_i64m4(a, N, unpacket_traits<Packet4Xl>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4Xl plogical_shift_right(Packet4Xl a) {
+  return __riscv_vreinterpret_i64m4(
+      __riscv_vsrl_vx_u64m4(__riscv_vreinterpret_u64m4(a), N, unpacket_traits<Packet4Xl>::size));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4Xl plogical_shift_left(Packet4Xl a) {
+  return __riscv_vsll_vx_i64m4(a, N, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pload<Packet4Xl>(const numext::int64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle64_v_i64m4(from, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl ploadu<Packet4Xl>(const numext::int64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle64_v_i64m4(from, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl ploaddup<Packet4Xl>(const numext::int64_t* from) {
+  Packet4Xul idx =
+      __riscv_vsrl_vx_u64m4(__riscv_vid_v_u64m4(unpacket_traits<Packet4Xl>::size), 1, unpacket_traits<Packet4Xl>::size);
+  return __riscv_vrgather_vv_i64m4(pload<Packet4Xl>(from), idx, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl ploadquad<Packet4Xl>(const numext::int64_t* from) {
+  Packet4Xul idx =
+      __riscv_vsrl_vx_u64m4(__riscv_vid_v_u64m4(unpacket_traits<Packet4Xl>::size), 2, unpacket_traits<Packet4Xl>::size);
+  return __riscv_vrgather_vv_i64m4(pload<Packet4Xl>(from), idx, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int64_t>(numext::int64_t* to, const Packet4Xl& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse64_v_i64m4(to, from, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int64_t>(numext::int64_t* to, const Packet4Xl& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse64_v_i64m4(to, from, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4Xl pgather<numext::int64_t, Packet4Xl>(const numext::int64_t* from, Index stride) {
+  return __riscv_vlse64_v_i64m4(from, stride * sizeof(numext::int64_t), unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int64_t, Packet4Xl>(numext::int64_t* to, const Packet4Xl& from,
+                                                                   Index stride) {
+  __riscv_vsse64(to, stride * sizeof(numext::int64_t), from, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t pfirst<Packet4Xl>(const Packet4Xl& a) {
+  return __riscv_vmv_x_s_i64m4_i64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl preverse(const Packet4Xl& a) {
+  Packet4Xul idx = __riscv_vrsub_vx_u64m4(__riscv_vid_v_u64m4(unpacket_traits<Packet4Xl>::size),
+                                          unpacket_traits<Packet4Xl>::size - 1, unpacket_traits<Packet4Xl>::size);
+  return __riscv_vrgather_vv_i64m4(a, idx, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pabs(const Packet4Xl& a) {
+  Packet4Xl mask = __riscv_vsra_vx_i64m4(a, 63, unpacket_traits<Packet4Xl>::size);
+  return __riscv_vsub_vv_i64m4(__riscv_vxor_vv_i64m4(a, mask, unpacket_traits<Packet4Xl>::size), mask,
+                               unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux<Packet4Xl>(const Packet4Xl& a) {
+  return __riscv_vmv_x(__riscv_vredsum_vs_i64m4_i64m1(a, __riscv_vmv_v_x_i64m1(0, unpacket_traits<Packet4Xl>::size / 4),
+                                                      unpacket_traits<Packet4Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux_mul<Packet4Xl>(const Packet4Xl& a) {
+  Packet1Xl half1 = __riscv_vmul_vv_i64m1(__riscv_vget_v_i64m4_i64m1(a, 0), __riscv_vget_v_i64m4_i64m1(a, 1),
+                                          unpacket_traits<Packet1Xl>::size);
+  Packet1Xl half2 = __riscv_vmul_vv_i64m1(__riscv_vget_v_i64m4_i64m1(a, 2), __riscv_vget_v_i64m4_i64m1(a, 3),
+                                          unpacket_traits<Packet1Xl>::size);
+  return predux_mul<Packet1Xl>(__riscv_vmul_vv_i64m1(half1, half2, unpacket_traits<Packet1Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux_min<Packet4Xl>(const Packet4Xl& a) {
+  return __riscv_vmv_x(__riscv_vredmin_vs_i64m4_i64m1(
+      a, __riscv_vmv_v_x_i64m1((std::numeric_limits<numext::int64_t>::max)(), unpacket_traits<Packet4Xl>::size / 4),
+      unpacket_traits<Packet4Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int64_t predux_max<Packet4Xl>(const Packet4Xl& a) {
+  return __riscv_vmv_x(__riscv_vredmax_vs_i64m4_i64m1(
+      a, __riscv_vmv_v_x_i64m1((std::numeric_limits<numext::int64_t>::min)(), unpacket_traits<Packet4Xl>::size / 4),
+      unpacket_traits<Packet4Xl>::size));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4Xl, N>& kernel) {
+  numext::int64_t buffer[unpacket_traits<Packet4Xl>::size * N] = {0};
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse64(&buffer[i], N * sizeof(numext::int64_t), kernel.packet[i], unpacket_traits<Packet4Xl>::size);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle64_v_i64m4(&buffer[i * unpacket_traits<Packet4Xl>::size], unpacket_traits<Packet4Xl>::size);
+  }
+}
+
+/********************************* Packet4Xd ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd ptrue<Packet4Xd>(const Packet4Xd& /*a*/) {
+  return __riscv_vreinterpret_f64m4(__riscv_vmv_v_x_u64m4(0xffffffffffffffffu, unpacket_traits<Packet4Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pzero<Packet4Xd>(const Packet4Xd& /*a*/) {
+  return __riscv_vfmv_v_f_f64m4(0.0, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pabs(const Packet4Xd& a) {
+  return __riscv_vfabs_v_f64m4(a, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pabsdiff(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vfabs_v_f64m4(__riscv_vfsub_vv_f64m4(a, b, unpacket_traits<Packet4Xd>::size),
+                               unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pset1<Packet4Xd>(const double& from) {
+  return __riscv_vfmv_v_f_f64m4(from, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pset1frombits<Packet4Xd>(numext::uint64_t from) {
+  return __riscv_vreinterpret_f64m4(__riscv_vmv_v_x_u64m4(from, unpacket_traits<Packet4Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd plset<Packet4Xd>(const double& a) {
+  Packet4Xd idx = __riscv_vfcvt_f_x_v_f64m4(
+      __riscv_vreinterpret_v_u64m4_i64m4(__riscv_vid_v_u64m4(unpacket_traits<Packet4Xi>::size)),
+      unpacket_traits<Packet4Xd>::size);
+  return __riscv_vfadd_vf_f64m4(idx, a, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet4Xd>(const double* a, Packet4Xd& a0, Packet4Xd& a1, Packet4Xd& a2,
+                                                Packet4Xd& a3) {
+  vfloat64m4_t aa = __riscv_vle64_v_f64m4(a, 4);
+  a0 = __riscv_vrgather_vx_f64m4(aa, 0, unpacket_traits<Packet4Xd>::size);
+  a1 = __riscv_vrgather_vx_f64m4(aa, 1, unpacket_traits<Packet4Xd>::size);
+  a2 = __riscv_vrgather_vx_f64m4(aa, 2, unpacket_traits<Packet4Xd>::size);
+  a3 = __riscv_vrgather_vx_f64m4(aa, 3, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd padd<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vfadd_vv_f64m4(a, b, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd psub<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vfsub_vv_f64m4(a, b, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pnegate(const Packet4Xd& a) {
+  return __riscv_vfneg_v_f64m4(a, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd psignbit(const Packet4Xd& a) {
+  return __riscv_vreinterpret_v_i64m4_f64m4(
+      __riscv_vsra_vx_i64m4(__riscv_vreinterpret_v_f64m4_i64m4(a), 63, unpacket_traits<Packet4Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pconj(const Packet4Xd& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pmul<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vfmul_vv_f64m4(a, b, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pdiv<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vfdiv_vv_f64m4(a, b, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pmadd(const Packet4Xd& a, const Packet4Xd& b, const Packet4Xd& c) {
+  return __riscv_vfmadd_vv_f64m4(a, b, c, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pmsub(const Packet4Xd& a, const Packet4Xd& b, const Packet4Xd& c) {
+  return __riscv_vfmsub_vv_f64m4(a, b, c, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pnmadd(const Packet4Xd& a, const Packet4Xd& b, const Packet4Xd& c) {
+  return __riscv_vfnmsub_vv_f64m4(a, b, c, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pnmsub(const Packet4Xd& a, const Packet4Xd& b, const Packet4Xd& c) {
+  return __riscv_vfnmadd_vv_f64m4(a, b, c, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pmin<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  Packet4Xd nans = __riscv_vfmv_v_f_f64m4((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet4Xd>::size);
+  PacketMask16 mask = __riscv_vmfeq_vv_f64m4_b16(a, a, unpacket_traits<Packet4Xd>::size);
+  PacketMask16 mask2 = __riscv_vmfeq_vv_f64m4_b16(b, b, unpacket_traits<Packet4Xd>::size);
+  mask = __riscv_vmand_mm_b16(mask, mask2, unpacket_traits<Packet4Xd>::size);
+
+  return __riscv_vfmin_vv_f64m4_tumu(mask, nans, a, b, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pmin<PropagateNaN, Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return pmin<Packet4Xd>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pmin<PropagateNumbers, Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vfmin_vv_f64m4(a, b, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pmax<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  Packet4Xd nans = __riscv_vfmv_v_f_f64m4((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet4Xd>::size);
+  PacketMask16 mask = __riscv_vmfeq_vv_f64m4_b16(a, a, unpacket_traits<Packet4Xd>::size);
+  PacketMask16 mask2 = __riscv_vmfeq_vv_f64m4_b16(b, b, unpacket_traits<Packet4Xd>::size);
+  mask = __riscv_vmand_mm_b16(mask, mask2, unpacket_traits<Packet4Xd>::size);
+
+  return __riscv_vfmax_vv_f64m4_tumu(mask, nans, a, b, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pmax<PropagateNaN, Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return pmax<Packet4Xd>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pmax<PropagateNumbers, Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vfmax_vv_f64m4(a, b, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pcmp_le<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  PacketMask16 mask = __riscv_vmfle_vv_f64m4_b16(a, b, unpacket_traits<Packet4Xd>::size);
+  return __riscv_vmerge_vvm_f64m4(pzero<Packet4Xd>(a), ptrue<Packet4Xd>(a), mask, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pcmp_lt<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  PacketMask16 mask = __riscv_vmflt_vv_f64m4_b16(a, b, unpacket_traits<Packet4Xd>::size);
+  return __riscv_vmerge_vvm_f64m4(pzero<Packet4Xd>(a), ptrue<Packet4Xd>(a), mask, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pcmp_eq<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  PacketMask16 mask = __riscv_vmfeq_vv_f64m4_b16(a, b, unpacket_traits<Packet4Xd>::size);
+  return __riscv_vmerge_vvm_f64m4(pzero<Packet4Xd>(a), ptrue<Packet4Xd>(a), mask, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pcmp_lt_or_nan<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  PacketMask16 mask = __riscv_vmfge_vv_f64m4_b16(a, b, unpacket_traits<Packet4Xd>::size);
+  return __riscv_vfmerge_vfm_f64m4(ptrue<Packet4Xd>(a), 0.0, mask, unpacket_traits<Packet4Xd>::size);
+}
+
+EIGEN_STRONG_INLINE Packet4Xd pselect(const PacketMask16& mask, const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vmerge_vvm_f64m4(b, a, mask, unpacket_traits<Packet4Xd>::size);
+}
+
+EIGEN_STRONG_INLINE Packet4Xd pselect(const Packet4Xd& mask, const Packet4Xd& a, const Packet4Xd& b) {
+  PacketMask16 mask2 =
+      __riscv_vmsne_vx_i64m4_b16(__riscv_vreinterpret_v_f64m4_i64m4(mask), 0, unpacket_traits<Packet4Xd>::size);
+  return __riscv_vmerge_vvm_f64m4(b, a, mask2, unpacket_traits<Packet4Xd>::size);
+}
+
+// Logical Operations are not supported for double, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pand<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vreinterpret_v_u64m4_f64m4(__riscv_vand_vv_u64m4(
+      __riscv_vreinterpret_v_f64m4_u64m4(a), __riscv_vreinterpret_v_f64m4_u64m4(b), unpacket_traits<Packet4Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd por<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vreinterpret_v_u64m4_f64m4(__riscv_vor_vv_u64m4(
+      __riscv_vreinterpret_v_f64m4_u64m4(a), __riscv_vreinterpret_v_f64m4_u64m4(b), unpacket_traits<Packet4Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pxor<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vreinterpret_v_u64m4_f64m4(__riscv_vxor_vv_u64m4(
+      __riscv_vreinterpret_v_f64m4_u64m4(a), __riscv_vreinterpret_v_f64m4_u64m4(b), unpacket_traits<Packet4Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pandnot<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& b) {
+  return __riscv_vreinterpret_v_u64m4_f64m4(__riscv_vand_vv_u64m4(
+      __riscv_vreinterpret_v_f64m4_u64m4(a),
+      __riscv_vnot_v_u64m4(__riscv_vreinterpret_v_f64m4_u64m4(b), unpacket_traits<Packet4Xd>::size),
+      unpacket_traits<Packet4Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pload<Packet4Xd>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle64_v_f64m4(from, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd ploadu<Packet4Xd>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle64_v_f64m4(from, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd ploaddup<Packet4Xd>(const double* from) {
+  Packet4Xul idx =
+      __riscv_vsrl_vx_u64m4(__riscv_vid_v_u64m4(unpacket_traits<Packet4Xd>::size), 1, unpacket_traits<Packet4Xd>::size);
+  return __riscv_vrgather_vv_f64m4(pload<Packet4Xd>(from), idx, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd ploadquad<Packet4Xd>(const double* from) {
+  Packet4Xul idx =
+      __riscv_vsrl_vx_u64m4(__riscv_vid_v_u64m4(unpacket_traits<Packet4Xd>::size), 2, unpacket_traits<Packet4Xd>::size);
+  return __riscv_vrgather_vv_f64m4(pload<Packet4Xd>(from), idx, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet4Xd& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse64_v_f64m4(to, from, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet4Xd& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse64_v_f64m4(to, from, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4Xd pgather<double, Packet4Xd>(const double* from, Index stride) {
+  return __riscv_vlse64_v_f64m4(from, stride * sizeof(double), unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet4Xd>(double* to, const Packet4Xd& from, Index stride) {
+  __riscv_vsse64(to, stride * sizeof(double), from, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet4Xd>(const Packet4Xd& a) {
+  return __riscv_vfmv_f_s_f64m4_f64(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd psqrt(const Packet4Xd& a) {
+  return __riscv_vfsqrt_v_f64m4(a, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd print<Packet4Xd>(const Packet4Xd& a) {
+  const Packet4Xd limit = pset1<Packet4Xd>(static_cast<double>(1ull << 52));
+  const Packet4Xd abs_a = pabs(a);
+
+  PacketMask16 mask = __riscv_vmfne_vv_f64m4_b16(a, a, unpacket_traits<Packet4Xd>::size);
+  const Packet4Xd x = __riscv_vfadd_vv_f64m4_tumu(mask, a, a, a, unpacket_traits<Packet4Xd>::size);
+  const Packet4Xd new_x = __riscv_vfcvt_f_x_v_f64m4(__riscv_vfcvt_x_f_v_i64m4(a, unpacket_traits<Packet4Xd>::size),
+                                                    unpacket_traits<Packet4Xd>::size);
+
+  mask = __riscv_vmflt_vv_f64m4_b16(abs_a, limit, unpacket_traits<Packet4Xd>::size);
+  Packet4Xd signed_x = __riscv_vfsgnj_vv_f64m4(new_x, x, unpacket_traits<Packet4Xd>::size);
+  return __riscv_vmerge_vvm_f64m4(x, signed_x, mask, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pfloor<Packet4Xd>(const Packet4Xd& a) {
+  Packet4Xd tmp = print<Packet4Xd>(a);
+  // If greater, subtract one.
+  PacketMask16 mask = __riscv_vmflt_vv_f64m4_b16(a, tmp, unpacket_traits<Packet4Xd>::size);
+  return __riscv_vfsub_vf_f64m4_tumu(mask, tmp, tmp, 1.0, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd preverse(const Packet4Xd& a) {
+  Packet4Xul idx = __riscv_vrsub_vx_u64m4(__riscv_vid_v_u64m4(unpacket_traits<Packet4Xd>::size),
+                                          unpacket_traits<Packet4Xd>::size - 1, unpacket_traits<Packet4Xd>::size);
+  return __riscv_vrgather_vv_f64m4(a, idx, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pfrexp<Packet4Xd>(const Packet4Xd& a, Packet4Xd& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux<Packet4Xd>(const Packet4Xd& a) {
+  return __riscv_vfmv_f(__riscv_vfredusum_vs_f64m4_f64m1(
+      a, __riscv_vfmv_v_f_f64m1(0.0, unpacket_traits<Packet4Xd>::size / 4), unpacket_traits<Packet4Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet4Xd>(const Packet4Xd& a) {
+  Packet1Xd half1 = __riscv_vfmul_vv_f64m1(__riscv_vget_v_f64m4_f64m1(a, 0), __riscv_vget_v_f64m4_f64m1(a, 1),
+                                           unpacket_traits<Packet1Xd>::size);
+  Packet1Xd half2 = __riscv_vfmul_vv_f64m1(__riscv_vget_v_f64m4_f64m1(a, 2), __riscv_vget_v_f64m4_f64m1(a, 3),
+                                           unpacket_traits<Packet1Xd>::size);
+  return predux_mul<Packet1Xd>(__riscv_vfmul_vv_f64m1(half1, half2, unpacket_traits<Packet1Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min<Packet4Xd>(const Packet4Xd& a) {
+  return (std::min)(
+      __riscv_vfmv_f(__riscv_vfredmin_vs_f64m4_f64m1(
+          a, __riscv_vfmv_v_f_f64m1((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet4Xd>::size / 4),
+          unpacket_traits<Packet4Xd>::size)),
+      (std::numeric_limits<double>::max)());
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max<Packet4Xd>(const Packet4Xd& a) {
+  return (std::max)(
+      __riscv_vfmv_f(__riscv_vfredmax_vs_f64m4_f64m1(
+          a, __riscv_vfmv_v_f_f64m1((std::numeric_limits<double>::quiet_NaN)(), unpacket_traits<Packet4Xd>::size / 4),
+          unpacket_traits<Packet4Xd>::size)),
+      -(std::numeric_limits<double>::max)());
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4Xd, N>& kernel) {
+  double buffer[unpacket_traits<Packet4Xd>::size * N];
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse64(&buffer[i], N * sizeof(double), kernel.packet[i], unpacket_traits<Packet4Xd>::size);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle64_v_f64m4(&buffer[i * unpacket_traits<Packet4Xd>::size], unpacket_traits<Packet4Xd>::size);
+  }
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pldexp<Packet4Xd>(const Packet4Xd& a, const Packet4Xd& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+/********************************* Packet4Xs ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pset1<Packet4Xs>(const numext::int16_t& from) {
+  return __riscv_vmv_v_x_i16m4(from, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs plset<Packet4Xs>(const numext::int16_t& a) {
+  Packet4Xs idx = __riscv_vreinterpret_v_u16m4_i16m4(__riscv_vid_v_u16m4(unpacket_traits<Packet4Xs>::size));
+  return __riscv_vadd_vx_i16m4(idx, a, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pzero<Packet4Xs>(const Packet4Xs& /*a*/) {
+  return __riscv_vmv_v_x_i16m4(0, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs padd<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  return __riscv_vadd_vv_i16m4(a, b, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs psub<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  return __riscv_vsub(a, b, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pnegate(const Packet4Xs& a) {
+  return __riscv_vneg(a, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pconj(const Packet4Xs& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pmul<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  return __riscv_vmul(a, b, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pdiv<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  return __riscv_vdiv(a, b, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pmadd(const Packet4Xs& a, const Packet4Xs& b, const Packet4Xs& c) {
+  return __riscv_vmadd(a, b, c, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pmsub(const Packet4Xs& a, const Packet4Xs& b, const Packet4Xs& c) {
+  return __riscv_vmadd(a, b, pnegate(c), unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pnmadd(const Packet4Xs& a, const Packet4Xs& b, const Packet4Xs& c) {
+  return __riscv_vnmsub_vv_i16m4(a, b, c, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pnmsub(const Packet4Xs& a, const Packet4Xs& b, const Packet4Xs& c) {
+  return __riscv_vnmsub_vv_i16m4(a, b, pnegate(c), unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pmin<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  return __riscv_vmin(a, b, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pmax<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  return __riscv_vmax(a, b, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pcmp_le<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  PacketMask4 mask = __riscv_vmsle_vv_i16m4_b4(a, b, unpacket_traits<Packet4Xs>::size);
+  return __riscv_vmerge_vxm_i16m4(pzero(a), static_cast<short>(0xffff), mask, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pcmp_lt<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  PacketMask4 mask = __riscv_vmslt_vv_i16m4_b4(a, b, unpacket_traits<Packet4Xs>::size);
+  return __riscv_vmerge_vxm_i16m4(pzero(a), static_cast<short>(0xffff), mask, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pcmp_eq<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  PacketMask4 mask = __riscv_vmseq_vv_i16m4_b4(a, b, unpacket_traits<Packet4Xs>::size);
+  return __riscv_vmerge_vxm_i16m4(pzero(a), static_cast<short>(0xffff), mask, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs ptrue<Packet4Xs>(const Packet4Xs& /*a*/) {
+  return __riscv_vmv_v_x_i16m4(static_cast<unsigned short>(0xffffu), unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pand<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  return __riscv_vand_vv_i16m4(a, b, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs por<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  return __riscv_vor_vv_i16m4(a, b, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pxor<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  return __riscv_vxor_vv_i16m4(a, b, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pandnot<Packet4Xs>(const Packet4Xs& a, const Packet4Xs& b) {
+  return __riscv_vand_vv_i16m4(a, __riscv_vnot_v_i16m4(b, unpacket_traits<Packet4Xs>::size),
+                               unpacket_traits<Packet4Xs>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4Xs parithmetic_shift_right(Packet4Xs a) {
+  return __riscv_vsra_vx_i16m4(a, N, unpacket_traits<Packet4Xs>::size);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4Xs plogical_shift_right(Packet4Xs a) {
+  return __riscv_vreinterpret_i16m4(
+      __riscv_vsrl_vx_u16m4(__riscv_vreinterpret_u16m4(a), N, unpacket_traits<Packet4Xs>::size));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet4Xs plogical_shift_left(Packet4Xs a) {
+  return __riscv_vsll_vx_i16m4(a, N, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pload<Packet4Xs>(const numext::int16_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle16_v_i16m4(from, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs ploadu<Packet4Xs>(const numext::int16_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle16_v_i16m4(from, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs ploaddup<Packet4Xs>(const numext::int16_t* from) {
+  Packet4Xsu data = __riscv_vreinterpret_v_i16m4_u16m4(pload<Packet4Xs>(from));
+  return __riscv_vreinterpret_v_i32m4_i16m4(__riscv_vreinterpret_v_u32m4_i32m4(__riscv_vlmul_trunc_v_u32m8_u32m4(
+      __riscv_vwmaccu_vx_u32m8(__riscv_vwaddu_vv_u32m8(data, data, unpacket_traits<Packet4Xs>::size), 0xffffu, data,
+                               unpacket_traits<Packet4Xs>::size))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs ploadquad<Packet4Xs>(const numext::int16_t* from) {
+  Packet4Xsu idx =
+      __riscv_vsrl_vx_u16m4(__riscv_vid_v_u16m4(unpacket_traits<Packet4Xs>::size), 2, unpacket_traits<Packet4Xs>::size);
+  return __riscv_vrgather_vv_i16m4(pload<Packet4Xs>(from), idx, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int16_t>(numext::int16_t* to, const Packet4Xs& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse16_v_i16m4(to, from, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int16_t>(numext::int16_t* to, const Packet4Xs& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse16_v_i16m4(to, from, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4Xs pgather<numext::int16_t, Packet4Xs>(const numext::int16_t* from, Index stride) {
+  return __riscv_vlse16_v_i16m4(from, stride * sizeof(numext::int16_t), unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int16_t, Packet4Xs>(numext::int16_t* to, const Packet4Xs& from,
+                                                                   Index stride) {
+  __riscv_vsse16(to, stride * sizeof(numext::int16_t), from, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t pfirst<Packet4Xs>(const Packet4Xs& a) {
+  return __riscv_vmv_x_s_i16m4_i16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs preverse(const Packet4Xs& a) {
+  Packet4Xsu idx = __riscv_vrsub_vx_u16m4(__riscv_vid_v_u16m4(unpacket_traits<Packet4Xs>::size),
+                                          unpacket_traits<Packet4Xs>::size - 1, unpacket_traits<Packet4Xs>::size);
+  return __riscv_vrgather_vv_i16m4(a, idx, unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pabs(const Packet4Xs& a) {
+  Packet4Xs mask = __riscv_vsra_vx_i16m4(a, 15, unpacket_traits<Packet4Xs>::size);
+  return __riscv_vsub_vv_i16m4(__riscv_vxor_vv_i16m4(a, mask, unpacket_traits<Packet4Xs>::size), mask,
+                               unpacket_traits<Packet4Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux<Packet4Xs>(const Packet4Xs& a) {
+  return __riscv_vmv_x(__riscv_vredsum_vs_i16m4_i16m1(a, __riscv_vmv_v_x_i16m1(0, unpacket_traits<Packet4Xs>::size / 4),
+                                                      unpacket_traits<Packet4Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux_mul<Packet4Xs>(const Packet4Xs& a) {
+  Packet1Xs half1 = __riscv_vmul_vv_i16m1(__riscv_vget_v_i16m4_i16m1(a, 0), __riscv_vget_v_i16m4_i16m1(a, 1),
+                                          unpacket_traits<Packet1Xs>::size);
+  Packet1Xs half2 = __riscv_vmul_vv_i16m1(__riscv_vget_v_i16m4_i16m1(a, 2), __riscv_vget_v_i16m4_i16m1(a, 3),
+                                          unpacket_traits<Packet1Xs>::size);
+  return predux_mul<Packet1Xs>(__riscv_vmul_vv_i16m1(half1, half2, unpacket_traits<Packet1Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux_min<Packet4Xs>(const Packet4Xs& a) {
+  return __riscv_vmv_x(__riscv_vredmin_vs_i16m4_i16m1(
+      a, __riscv_vmv_v_x_i16m1((std::numeric_limits<numext::int16_t>::max)(), unpacket_traits<Packet4Xs>::size / 4),
+      unpacket_traits<Packet4Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int16_t predux_max<Packet4Xs>(const Packet4Xs& a) {
+  return __riscv_vmv_x(__riscv_vredmax_vs_i16m4_i16m1(
+      a, __riscv_vmv_v_x_i16m1((std::numeric_limits<numext::int16_t>::min)(), unpacket_traits<Packet4Xs>::size / 4),
+      unpacket_traits<Packet4Xs>::size));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4Xs, N>& kernel) {
+  numext::int16_t buffer[unpacket_traits<Packet4Xs>::size * N] = {0};
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse16(&buffer[i], N * sizeof(numext::int16_t), kernel.packet[i], unpacket_traits<Packet4Xs>::size);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] =
+        __riscv_vle16_v_i16m4(&buffer[i * unpacket_traits<Packet4Xs>::size], unpacket_traits<Packet4Xs>::size);
+  }
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_PACKET4_MATH_RVV10_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMathBF16.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMathBF16.h
new file mode 100644
index 0000000..2522efd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMathBF16.h
@@ -0,0 +1,839 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Chip Kerchner <ckerchner@tenstorrent.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_BF16_RVV10_H
+#define EIGEN_PACKET_MATH_BF16_RVV10_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+typedef eigen_packet_wrapper<vbfloat16m1_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL))), 26> Packet1Xbf;
+typedef eigen_packet_wrapper<vbfloat16m2_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 2))), 27>
+    Packet2Xbf;
+
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+typedef Packet1Xbf PacketXbf;
+
+template <>
+struct packet_traits<bfloat16> : default_packet_traits {
+  typedef Packet1Xbf type;
+  typedef Packet1Xbf half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<bfloat16, EIGEN_RISCV64_RVV_VL, 1>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,
+    HasSign = 0,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasRound = 0,
+
+    HasSin = 0,
+    HasCos = 0,
+    HasLog = 0,
+    HasExp = 0,
+    HasSqrt = 1,
+    HasTanh = 0,
+    HasErf = 0
+  };
+};
+
+#else
+typedef Packet2Xbf PacketXbf;
+
+template <>
+struct packet_traits<bfloat16> : default_packet_traits {
+  typedef Packet2Xbf type;
+  typedef Packet1Xbf half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<bfloat16, EIGEN_RISCV64_RVV_VL, 2>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,
+    HasSign = 0,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasRound = 0,
+
+    HasSin = 0,
+    HasCos = 0,
+    HasLog = 0,
+    HasExp = 0,
+    HasSqrt = 1,
+    HasTanh = 0,
+    HasErf = 0
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet1Xbf> : default_unpacket_traits {
+  typedef bfloat16 type;
+  typedef Packet1Xbf half;  // Half not yet implemented
+  typedef Packet1Xs integer_packet;
+  typedef numext::uint8_t mask_t;
+
+  enum {
+    size = rvv_packet_size_selector<bfloat16, EIGEN_RISCV64_RVV_VL, 1>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 1>::alignment,
+    vectorizable = true
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2Xbf> : default_unpacket_traits {
+  typedef bfloat16 type;
+  typedef Packet1Xbf half;
+  typedef Packet2Xs integer_packet;
+  typedef numext::uint8_t mask_t;
+
+  enum {
+    size = rvv_packet_size_selector<bfloat16, EIGEN_RISCV64_RVV_VL, 2>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 2>::alignment,
+    vectorizable = true
+  };
+};
+
+/********************************* Packet1Xbf ************************************/
+
+EIGEN_STRONG_INLINE Packet2Xf Bf16ToF32(const Packet1Xbf& a) {
+  return __riscv_vfwcvtbf16_f_f_v_f32m2(a, unpacket_traits<Packet1Xbf>::size);
+}
+
+EIGEN_STRONG_INLINE Packet1Xbf F32ToBf16(const Packet2Xf& a) {
+  return __riscv_vfncvtbf16_f_f_w_bf16m1(a, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf ptrue<Packet1Xbf>(const Packet1Xbf& /*a*/) {
+  return __riscv_vreinterpret_bf16m1(
+      __riscv_vmv_v_x_u16m1(static_cast<numext::uint16_t>(0xffffu), unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pzero<Packet1Xbf>(const Packet1Xbf& /*a*/) {
+  return __riscv_vreinterpret_bf16m1(
+      __riscv_vmv_v_x_i16m1(numext::bit_cast<int16_t>(static_cast<__bf16>(0.0)), unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pabs(const Packet1Xbf& a) {
+  return __riscv_vreinterpret_v_u16m1_bf16m1(__riscv_vand_vx_u16m1(__riscv_vreinterpret_v_bf16m1_u16m1(a),
+                                                                   static_cast<numext::uint16_t>(0x7fffu),
+                                                                   unpacket_traits<Packet1Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pset1<Packet1Xbf>(const bfloat16& from) {
+  return __riscv_vreinterpret_bf16m1(
+      __riscv_vmv_v_x_i16m1(numext::bit_cast<int16_t>(from), unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pset1frombits<Packet1Xbf>(numext::uint16_t from) {
+  return __riscv_vreinterpret_bf16m1(__riscv_vmv_v_x_u16m1(from, unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf plset<Packet1Xbf>(const bfloat16& a) {
+  return F32ToBf16(plset<Packet2Xf>(static_cast<float>(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet1Xbf>(const bfloat16* a, Packet1Xbf& a0, Packet1Xbf& a1, Packet1Xbf& a2,
+                                                 Packet1Xbf& a3) {
+  vint16m1_t aa = __riscv_vle16_v_i16m1(reinterpret_cast<const int16_t*>(a), 4);
+  a0 = __riscv_vreinterpret_bf16m1(__riscv_vrgather_vx_i16m1(aa, 0, unpacket_traits<Packet1Xs>::size));
+  a1 = __riscv_vreinterpret_bf16m1(__riscv_vrgather_vx_i16m1(aa, 1, unpacket_traits<Packet1Xs>::size));
+  a2 = __riscv_vreinterpret_bf16m1(__riscv_vrgather_vx_i16m1(aa, 2, unpacket_traits<Packet1Xs>::size));
+  a3 = __riscv_vreinterpret_bf16m1(__riscv_vrgather_vx_i16m1(aa, 3, unpacket_traits<Packet1Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf padd<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  // b + (1 * a)
+  return F32ToBf16(__riscv_vfwmaccbf16_vf_f32m2(Bf16ToF32(b),
+                                                numext::bit_cast<__bf16>(static_cast<numext::int16_t>(0x3f80u)), a,
+                                                unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf psub<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  // a + (-1 * b)
+  return F32ToBf16(__riscv_vfwmaccbf16_vf_f32m2(Bf16ToF32(a),
+                                                numext::bit_cast<__bf16>(static_cast<numext::int16_t>(0xbf80u)), b,
+                                                unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pabsdiff(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return pabs<Packet1Xbf>(psub<Packet1Xbf>(a, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pnegate(const Packet1Xbf& a) {
+  return __riscv_vreinterpret_v_u16m1_bf16m1(__riscv_vxor_vx_u16m1(__riscv_vreinterpret_v_bf16m1_u16m1(a),
+                                                                   static_cast<numext::uint16_t>(0x8000u),
+                                                                   unpacket_traits<Packet1Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf psignbit(const Packet1Xbf& a) {
+  return __riscv_vreinterpret_v_i16m1_bf16m1(
+      __riscv_vsra_vx_i16m1(__riscv_vreinterpret_v_bf16m1_i16m1(a), 15, unpacket_traits<Packet1Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pconj(const Packet1Xbf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pmul<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  Packet2Xf c;
+  return F32ToBf16(__riscv_vfwmaccbf16_vv_f32m2(pzero<Packet2Xf>(c), a, b, unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pdiv<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pdiv<Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pmadd(const Packet1Xbf& a, const Packet1Xbf& b, const Packet1Xbf& c) {
+  return F32ToBf16(__riscv_vfwmaccbf16_vv_f32m2(Bf16ToF32(c), a, b, unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pmsub(const Packet1Xbf& a, const Packet1Xbf& b, const Packet1Xbf& c) {
+  return F32ToBf16(
+      __riscv_vfwmaccbf16_vv_f32m2(Bf16ToF32(pnegate<Packet1Xbf>(c)), a, b, unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pnmadd(const Packet1Xbf& a, const Packet1Xbf& b, const Packet1Xbf& c) {
+  return F32ToBf16(
+      __riscv_vfwmaccbf16_vv_f32m2(Bf16ToF32(c), pnegate<Packet1Xbf>(a), b, unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pnmsub(const Packet1Xbf& a, const Packet1Xbf& b, const Packet1Xbf& c) {
+  return pnegate<Packet1Xbf>(
+      F32ToBf16(__riscv_vfwmaccbf16_vv_f32m2(Bf16ToF32(c), a, b, unpacket_traits<Packet1Xbf>::size)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pmin<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pmin<Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pmin<PropagateNaN, Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pmin<PropagateNaN, Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pmin<PropagateNumbers, Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pmin<PropagateNumbers, Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pmax<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pmax<Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pmax<PropagateNaN, Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pmax<PropagateNaN, Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pmax<PropagateNumbers, Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pmax<PropagateNumbers, Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pcmp_le<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pcmp_le<Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pcmp_lt<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pcmp_lt<Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pcmp_eq<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pcmp_eq<Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pcmp_lt_or_nan<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return F32ToBf16(pcmp_lt_or_nan<Packet2Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+EIGEN_STRONG_INLINE Packet1Xbf pselect(const PacketMask16& mask, const Packet1Xbf& a, const Packet1Xbf& b) {
+  return __riscv_vreinterpret_v_i16m1_bf16m1(__riscv_vmerge_vvm_i16m1(__riscv_vreinterpret_v_bf16m1_i16m1(b),
+                                                                      __riscv_vreinterpret_v_bf16m1_i16m1(a), mask,
+                                                                      unpacket_traits<Packet1Xbf>::size));
+}
+
+EIGEN_STRONG_INLINE Packet1Xbf pselect(const Packet1Xbf& mask, const Packet1Xbf& a, const Packet1Xbf& b) {
+  PacketMask16 mask2 =
+      __riscv_vmsne_vx_i16m1_b16(__riscv_vreinterpret_v_bf16m1_i16m1(mask), 0, unpacket_traits<Packet1Xbf>::size);
+  return __riscv_vreinterpret_v_i16m1_bf16m1(__riscv_vmerge_vvm_i16m1(__riscv_vreinterpret_v_bf16m1_i16m1(b),
+                                                                      __riscv_vreinterpret_v_bf16m1_i16m1(a), mask2,
+                                                                      unpacket_traits<Packet1Xbf>::size));
+}
+
+// Logical Operations are not supported for bfloat16, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pand<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return __riscv_vreinterpret_v_u16m1_bf16m1(__riscv_vand_vv_u16m1(__riscv_vreinterpret_v_bf16m1_u16m1(a),
+                                                                   __riscv_vreinterpret_v_bf16m1_u16m1(b),
+                                                                   unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf por<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return __riscv_vreinterpret_v_u16m1_bf16m1(__riscv_vor_vv_u16m1(__riscv_vreinterpret_v_bf16m1_u16m1(a),
+                                                                  __riscv_vreinterpret_v_bf16m1_u16m1(b),
+                                                                  unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pxor<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return __riscv_vreinterpret_v_u16m1_bf16m1(__riscv_vxor_vv_u16m1(__riscv_vreinterpret_v_bf16m1_u16m1(a),
+                                                                   __riscv_vreinterpret_v_bf16m1_u16m1(b),
+                                                                   unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pandnot<Packet1Xbf>(const Packet1Xbf& a, const Packet1Xbf& b) {
+  return __riscv_vreinterpret_v_u16m1_bf16m1(__riscv_vand_vv_u16m1(
+      __riscv_vreinterpret_v_bf16m1_u16m1(a),
+      __riscv_vnot_v_u16m1(__riscv_vreinterpret_v_bf16m1_u16m1(b), unpacket_traits<Packet1Xbf>::size),
+      unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pload<Packet1Xbf>(const bfloat16* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle16_v_bf16m1(reinterpret_cast<const __bf16*>(from),
+                                                         unpacket_traits<Packet1Xbf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf ploadu<Packet1Xbf>(const bfloat16* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle16_v_bf16m1(reinterpret_cast<const __bf16*>(from),
+                                                           unpacket_traits<Packet1Xbf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf ploaddup<Packet1Xbf>(const bfloat16* from) {
+  Packet1Xsu data = __riscv_vreinterpret_v_bf16m1_u16m1(pload<Packet1Xbf>(from));
+  return __riscv_vreinterpret_v_i16m1_bf16m1(
+      __riscv_vreinterpret_v_i32m1_i16m1(__riscv_vreinterpret_v_u32m1_i32m1(__riscv_vlmul_trunc_v_u32m2_u32m1(
+          __riscv_vwmaccu_vx_u32m2(__riscv_vwaddu_vv_u32m2(data, data, unpacket_traits<Packet1Xs>::size), 0xffffu, data,
+                                   unpacket_traits<Packet1Xs>::size)))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf ploadquad<Packet1Xbf>(const bfloat16* from) {
+  Packet1Xsu idx = __riscv_vsrl_vx_u16m1(__riscv_vid_v_u16m1(unpacket_traits<Packet1Xbf>::size), 2,
+                                         unpacket_traits<Packet1Xbf>::size);
+  return __riscv_vreinterpret_v_i16m1_bf16m1(__riscv_vrgather_vv_i16m1(
+      pload<Packet1Xs>(reinterpret_cast<const short*>(from)), idx, unpacket_traits<Packet1Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet1Xbf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse16_v_bf16m1(reinterpret_cast<__bf16*>(to), from,
+                                                   unpacket_traits<Packet1Xbf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet1Xbf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse16_v_bf16m1(reinterpret_cast<__bf16*>(to), from,
+                                                     unpacket_traits<Packet1Xbf>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1Xbf pgather<bfloat16, Packet1Xbf>(const bfloat16* from, Index stride) {
+  return __riscv_vlse16_v_bf16m1(reinterpret_cast<const __bf16*>(from), stride * sizeof(bfloat16),
+                                 unpacket_traits<Packet1Xbf>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<bfloat16, Packet1Xbf>(bfloat16* to, const Packet1Xbf& from, Index stride) {
+  __riscv_vsse16(reinterpret_cast<__bf16*>(to), stride * sizeof(bfloat16), from, unpacket_traits<Packet1Xbf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 pfirst<Packet1Xbf>(const Packet1Xbf& a) {
+  return numext::bit_cast<bfloat16>(__riscv_vmv_x_s_i16m1_i16(__riscv_vreinterpret_v_bf16m1_i16m1(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf psqrt(const Packet1Xbf& a) {
+  return F32ToBf16(psqrt<Packet2Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf print<Packet1Xbf>(const Packet1Xbf& a) {
+  return F32ToBf16(print<Packet2Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pfloor<Packet1Xbf>(const Packet1Xbf& a) {
+  return F32ToBf16(pfloor<Packet2Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf preverse(const Packet1Xbf& a) {
+  return __riscv_vreinterpret_v_i16m1_bf16m1(preverse<Packet1Xs>(__riscv_vreinterpret_v_bf16m1_i16m1(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux<Packet1Xbf>(const Packet1Xbf& a) {
+  return static_cast<bfloat16>(predux<Packet2Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet1Xbf>(const Packet1Xbf& a) {
+  return static_cast<bfloat16>(predux_mul<Packet2Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min<Packet1Xbf>(const Packet1Xbf& a) {
+  return static_cast<bfloat16>(predux_min<Packet2Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max<Packet1Xbf>(const Packet1Xbf& a) {
+  return static_cast<bfloat16>(predux_max<Packet2Xf>(Bf16ToF32(a)));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1Xbf, N>& kernel) {
+  bfloat16 buffer[unpacket_traits<Packet1Xbf>::size * N];
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse16(reinterpret_cast<__bf16*>(&buffer[i]), N * sizeof(bfloat16), kernel.packet[i],
+                   unpacket_traits<Packet1Xbf>::size);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] = __riscv_vle16_v_bf16m1(reinterpret_cast<__bf16*>(&buffer[i * unpacket_traits<Packet1Xbf>::size]),
+                                              unpacket_traits<Packet1Xbf>::size);
+  }
+}
+
+/********************************* Packet2Xbf ************************************/
+
+EIGEN_STRONG_INLINE Packet4Xf Bf16ToF32(const Packet2Xbf& a) {
+  return __riscv_vfwcvtbf16_f_f_v_f32m4(a, unpacket_traits<Packet2Xbf>::size);
+}
+
+EIGEN_STRONG_INLINE Packet2Xbf F32ToBf16(const Packet4Xf& a) {
+  return __riscv_vfncvtbf16_f_f_w_bf16m2(a, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf ptrue<Packet2Xbf>(const Packet2Xbf& /*a*/) {
+  return __riscv_vreinterpret_bf16m2(
+      __riscv_vmv_v_x_u16m2(static_cast<numext::uint16_t>(0xffffu), unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pzero<Packet2Xbf>(const Packet2Xbf& /*a*/) {
+  return __riscv_vreinterpret_bf16m2(
+      __riscv_vmv_v_x_i16m2(numext::bit_cast<int16_t>(static_cast<__bf16>(0.0)), unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pabs(const Packet2Xbf& a) {
+  return __riscv_vreinterpret_v_u16m2_bf16m2(__riscv_vand_vx_u16m2(__riscv_vreinterpret_v_bf16m2_u16m2(a),
+                                                                   static_cast<numext::uint16_t>(0x7fffu),
+                                                                   unpacket_traits<Packet2Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pset1<Packet2Xbf>(const bfloat16& from) {
+  return __riscv_vreinterpret_bf16m2(
+      __riscv_vmv_v_x_i16m2(numext::bit_cast<int16_t>(from), unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pset1frombits<Packet2Xbf>(numext::uint16_t from) {
+  return __riscv_vreinterpret_bf16m2(__riscv_vmv_v_x_u16m2(from, unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf plset<Packet2Xbf>(const bfloat16& a) {
+  return F32ToBf16(plset<Packet4Xf>(static_cast<float>(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet2Xbf>(const bfloat16* a, Packet2Xbf& a0, Packet2Xbf& a1, Packet2Xbf& a2,
+                                                 Packet2Xbf& a3) {
+  vint16m2_t aa = __riscv_vle16_v_i16m2(reinterpret_cast<const int16_t*>(a), 4);
+  a0 = __riscv_vreinterpret_bf16m2(__riscv_vrgather_vx_i16m2(aa, 0, unpacket_traits<Packet2Xs>::size));
+  a1 = __riscv_vreinterpret_bf16m2(__riscv_vrgather_vx_i16m2(aa, 1, unpacket_traits<Packet2Xs>::size));
+  a2 = __riscv_vreinterpret_bf16m2(__riscv_vrgather_vx_i16m2(aa, 2, unpacket_traits<Packet2Xs>::size));
+  a3 = __riscv_vreinterpret_bf16m2(__riscv_vrgather_vx_i16m2(aa, 3, unpacket_traits<Packet2Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf padd<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  // b + (1 * a)
+  return F32ToBf16(__riscv_vfwmaccbf16_vf_f32m4(Bf16ToF32(b),
+                                                numext::bit_cast<__bf16>(static_cast<numext::int16_t>(0x3f80u)), a,
+                                                unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf psub<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  // a + (-1 * b)
+  return F32ToBf16(__riscv_vfwmaccbf16_vf_f32m4(Bf16ToF32(a),
+                                                numext::bit_cast<__bf16>(static_cast<numext::int16_t>(0xbf80u)), b,
+                                                unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pabsdiff(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return pabs<Packet2Xbf>(psub<Packet2Xbf>(a, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pnegate(const Packet2Xbf& a) {
+  return __riscv_vreinterpret_v_u16m2_bf16m2(__riscv_vxor_vx_u16m2(__riscv_vreinterpret_v_bf16m2_u16m2(a),
+                                                                   static_cast<numext::uint16_t>(0x8000u),
+                                                                   unpacket_traits<Packet2Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf psignbit(const Packet2Xbf& a) {
+  return __riscv_vreinterpret_v_i16m2_bf16m2(
+      __riscv_vsra_vx_i16m2(__riscv_vreinterpret_v_bf16m2_i16m2(a), 15, unpacket_traits<Packet2Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pconj(const Packet2Xbf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pmul<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  Packet4Xf c;
+  return F32ToBf16(__riscv_vfwmaccbf16_vv_f32m4(pzero<Packet4Xf>(c), a, b, unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pdiv<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pdiv<Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pmadd(const Packet2Xbf& a, const Packet2Xbf& b, const Packet2Xbf& c) {
+  return F32ToBf16(__riscv_vfwmaccbf16_vv_f32m4(Bf16ToF32(c), a, b, unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pmsub(const Packet2Xbf& a, const Packet2Xbf& b, const Packet2Xbf& c) {
+  return F32ToBf16(
+      __riscv_vfwmaccbf16_vv_f32m4(Bf16ToF32(pnegate<Packet2Xbf>(c)), a, b, unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pnmadd(const Packet2Xbf& a, const Packet2Xbf& b, const Packet2Xbf& c) {
+  return F32ToBf16(
+      __riscv_vfwmaccbf16_vv_f32m4(Bf16ToF32(c), pnegate<Packet2Xbf>(a), b, unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pnmsub(const Packet2Xbf& a, const Packet2Xbf& b, const Packet2Xbf& c) {
+  return pnegate<Packet2Xbf>(
+      F32ToBf16(__riscv_vfwmaccbf16_vv_f32m4(Bf16ToF32(c), a, b, unpacket_traits<Packet2Xbf>::size)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pmin<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pmin<Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pmin<PropagateNaN, Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pmin<PropagateNaN, Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pmin<PropagateNumbers, Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pmin<PropagateNumbers, Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pmax<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pmax<Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pmax<PropagateNaN, Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pmax<PropagateNaN, Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pmax<PropagateNumbers, Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pmax<PropagateNumbers, Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pcmp_le<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pcmp_le<Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pcmp_lt<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pcmp_lt<Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pcmp_eq<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pcmp_eq<Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pcmp_lt_or_nan<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return F32ToBf16(pcmp_lt_or_nan<Packet4Xf>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+EIGEN_STRONG_INLINE Packet2Xbf pselect(const PacketMask8& mask, const Packet2Xbf& a, const Packet2Xbf& b) {
+  return __riscv_vreinterpret_v_i16m2_bf16m2(__riscv_vmerge_vvm_i16m2(__riscv_vreinterpret_v_bf16m2_i16m2(b),
+                                                                      __riscv_vreinterpret_v_bf16m2_i16m2(a), mask,
+                                                                      unpacket_traits<Packet2Xbf>::size));
+}
+
+EIGEN_STRONG_INLINE Packet2Xbf pselect(const Packet2Xbf& mask, const Packet2Xbf& a, const Packet2Xbf& b) {
+  PacketMask8 mask2 =
+      __riscv_vmsne_vx_i16m2_b8(__riscv_vreinterpret_v_bf16m2_i16m2(mask), 0, unpacket_traits<Packet2Xbf>::size);
+  return __riscv_vreinterpret_v_i16m2_bf16m2(__riscv_vmerge_vvm_i16m2(__riscv_vreinterpret_v_bf16m2_i16m2(b),
+                                                                      __riscv_vreinterpret_v_bf16m2_i16m2(a), mask2,
+                                                                      unpacket_traits<Packet2Xbf>::size));
+}
+
+// Logical Operations are not supported for bflaot16, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pand<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return __riscv_vreinterpret_v_u16m2_bf16m2(__riscv_vand_vv_u16m2(__riscv_vreinterpret_v_bf16m2_u16m2(a),
+                                                                   __riscv_vreinterpret_v_bf16m2_u16m2(b),
+                                                                   unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf por<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return __riscv_vreinterpret_v_u16m2_bf16m2(__riscv_vor_vv_u16m2(__riscv_vreinterpret_v_bf16m2_u16m2(a),
+                                                                  __riscv_vreinterpret_v_bf16m2_u16m2(b),
+                                                                  unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pxor<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return __riscv_vreinterpret_v_u16m2_bf16m2(__riscv_vxor_vv_u16m2(__riscv_vreinterpret_v_bf16m2_u16m2(a),
+                                                                   __riscv_vreinterpret_v_bf16m2_u16m2(b),
+                                                                   unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pandnot<Packet2Xbf>(const Packet2Xbf& a, const Packet2Xbf& b) {
+  return __riscv_vreinterpret_v_u16m2_bf16m2(__riscv_vand_vv_u16m2(
+      __riscv_vreinterpret_v_bf16m2_u16m2(a),
+      __riscv_vnot_v_u16m2(__riscv_vreinterpret_v_bf16m2_u16m2(b), unpacket_traits<Packet2Xbf>::size),
+      unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pload<Packet2Xbf>(const bfloat16* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle16_v_bf16m2(reinterpret_cast<const __bf16*>(from),
+                                                         unpacket_traits<Packet2Xbf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf ploadu<Packet2Xbf>(const bfloat16* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle16_v_bf16m2(reinterpret_cast<const __bf16*>(from),
+                                                           unpacket_traits<Packet2Xbf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf ploaddup<Packet2Xbf>(const bfloat16* from) {
+  Packet2Xsu data = __riscv_vreinterpret_v_bf16m2_u16m2(pload<Packet2Xbf>(from));
+  return __riscv_vreinterpret_v_i16m2_bf16m2(
+      __riscv_vreinterpret_v_i32m2_i16m2(__riscv_vreinterpret_v_u32m2_i32m2(__riscv_vlmul_trunc_v_u32m4_u32m2(
+          __riscv_vwmaccu_vx_u32m4(__riscv_vwaddu_vv_u32m4(data, data, unpacket_traits<Packet2Xs>::size), 0xffffu, data,
+                                   unpacket_traits<Packet2Xs>::size)))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf ploadquad<Packet2Xbf>(const bfloat16* from) {
+  Packet2Xsu idx = __riscv_vsrl_vx_u16m2(__riscv_vid_v_u16m2(unpacket_traits<Packet2Xbf>::size), 2,
+                                         unpacket_traits<Packet2Xbf>::size);
+  return __riscv_vreinterpret_v_i16m2_bf16m2(__riscv_vrgather_vv_i16m2(
+      pload<Packet2Xs>(reinterpret_cast<const short*>(from)), idx, unpacket_traits<Packet2Xbf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet2Xbf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse16_v_bf16m2(reinterpret_cast<__bf16*>(to), from,
+                                                   unpacket_traits<Packet2Xbf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet2Xbf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse16_v_bf16m2(reinterpret_cast<__bf16*>(to), from,
+                                                     unpacket_traits<Packet2Xbf>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2Xbf pgather<bfloat16, Packet2Xbf>(const bfloat16* from, Index stride) {
+  return __riscv_vlse16_v_bf16m2(reinterpret_cast<const __bf16*>(from), stride * sizeof(bfloat16),
+                                 unpacket_traits<Packet2Xbf>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<bfloat16, Packet2Xbf>(bfloat16* to, const Packet2Xbf& from, Index stride) {
+  __riscv_vsse16(reinterpret_cast<__bf16*>(to), stride * sizeof(bfloat16), from, unpacket_traits<Packet2Xbf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 pfirst<Packet2Xbf>(const Packet2Xbf& a) {
+  return numext::bit_cast<bfloat16>(__riscv_vmv_x_s_i16m2_i16(__riscv_vreinterpret_v_bf16m2_i16m2(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf psqrt(const Packet2Xbf& a) {
+  return F32ToBf16(psqrt<Packet4Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf print<Packet2Xbf>(const Packet2Xbf& a) {
+  return F32ToBf16(print<Packet4Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pfloor<Packet2Xbf>(const Packet2Xbf& a) {
+  return F32ToBf16(pfloor<Packet4Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf preverse(const Packet2Xbf& a) {
+  return __riscv_vreinterpret_v_i16m2_bf16m2(preverse<Packet2Xs>(__riscv_vreinterpret_v_bf16m2_i16m2(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux<Packet2Xbf>(const Packet2Xbf& a) {
+  return static_cast<bfloat16>(predux<Packet4Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet2Xbf>(const Packet2Xbf& a) {
+  return static_cast<bfloat16>(predux_mul<Packet4Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min<Packet2Xbf>(const Packet2Xbf& a) {
+  return static_cast<bfloat16>(predux_min<Packet4Xf>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max<Packet2Xbf>(const Packet2Xbf& a) {
+  return static_cast<bfloat16>(predux_max<Packet4Xf>(Bf16ToF32(a)));
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2Xbf, N>& kernel) {
+  bfloat16 buffer[unpacket_traits<Packet2Xbf>::size * N];
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse16(reinterpret_cast<__bf16*>(&buffer[i]), N * sizeof(bfloat16), kernel.packet[i],
+                   unpacket_traits<Packet2Xbf>::size);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] = __riscv_vle16_v_bf16m2(reinterpret_cast<__bf16*>(&buffer[i * unpacket_traits<Packet2Xbf>::size]),
+                                              unpacket_traits<Packet2Xbf>::size);
+  }
+}
+
+template <typename Packet = Packet2Xbf>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet2Xbf>::value && (unpacket_traits<Packet2Xbf>::size % 8) == 0,
+                            Packet1Xbf>::type
+    predux_half(const Packet2Xbf& a) {
+  return padd<Packet1Xbf>(__riscv_vget_v_bf16m2_bf16m1(a, 0), __riscv_vget_v_bf16m2_bf16m1(a, 1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xbf pcast<Packet1Xs, Packet1Xbf>(const Packet1Xs& a) {
+  return __riscv_vreinterpret_v_i16m1_bf16m1(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xbf pcast<Packet2Xs, Packet2Xbf>(const Packet2Xs& a) {
+  return __riscv_vreinterpret_v_i16m2_bf16m2(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pcast<Packet1Xbf, Packet1Xs>(const Packet1Xbf& a) {
+  return __riscv_vreinterpret_v_bf16m1_i16m1(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pcast<Packet2Xbf, Packet2Xs>(const Packet2Xbf& a) {
+  return __riscv_vreinterpret_v_bf16m2_i16m2(a);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_BF16_RVV10_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMathFP16.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMathFP16.h
new file mode 100644
index 0000000..73118a3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/PacketMathFP16.h
@@ -0,0 +1,999 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Kseniya Zaytseva <kseniya.zaytseva@syntacore.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_FP16_RVV10_H
+#define EIGEN_PACKET_MATH_FP16_RVV10_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+typedef eigen_packet_wrapper<vfloat16m1_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL))), 24> Packet1Xh;
+typedef eigen_packet_wrapper<vfloat16m2_t __attribute__((riscv_rvv_vector_bits(EIGEN_RISCV64_RVV_VL * 2))), 25>
+    Packet2Xh;
+
+#if EIGEN_RISCV64_DEFAULT_LMUL == 1
+typedef Packet1Xh PacketXh;
+
+template <>
+struct packet_traits<Eigen::half> : default_packet_traits {
+  typedef Packet1Xh type;
+  typedef Packet1Xh half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<Eigen::half, EIGEN_RISCV64_RVV_VL, 1>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasRound = 1,
+
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasLog = 0,
+    HasExp = 0,
+    HasSqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = 0
+  };
+};
+
+#else
+typedef Packet2Xh PacketXh;
+
+template <>
+struct packet_traits<Eigen::half> : default_packet_traits {
+  typedef Packet2Xh type;
+  typedef Packet1Xh half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = rvv_packet_size_selector<Eigen::half, EIGEN_RISCV64_RVV_VL, 2>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasRound = 1,
+
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasLog = 0,
+    HasExp = 0,
+    HasSqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = 0
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet1Xh> {
+  typedef Eigen::half type;
+  typedef Packet1Xh half;  // Half not yet implemented
+  typedef Packet1Xs integer_packet;
+  typedef numext::uint8_t mask_t;
+
+  enum {
+    size = rvv_packet_size_selector<Eigen::half, EIGEN_RISCV64_RVV_VL, 1>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 1>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2Xh> {
+  typedef Eigen::half type;
+  typedef Packet1Xh half;
+  typedef Packet2Xs integer_packet;
+  typedef numext::uint8_t mask_t;
+
+  enum {
+    size = rvv_packet_size_selector<Eigen::half, EIGEN_RISCV64_RVV_VL, 2>::size,
+    alignment = rvv_packet_alignment_selector<EIGEN_RISCV64_RVV_VL, 2>::alignment,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+/********************************* Packet1Xh ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh ptrue<Packet1Xh>(const Packet1Xh& /*a*/) {
+  return __riscv_vreinterpret_f16m1(__riscv_vmv_v_x_u16m1(0xffffu, unpacket_traits<Packet1Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pzero<Packet1Xh>(const Packet1Xh& /*a*/) {
+  return __riscv_vfmv_v_f_f16m1(static_cast<_Float16>(0.0), unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pabs(const Packet1Xh& a) {
+  return __riscv_vfabs_v_f16m1(a, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pabsdiff(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vfabs_v_f16m1(__riscv_vfsub_vv_f16m1(a, b, unpacket_traits<Packet1Xh>::size),
+                               unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pset1<Packet1Xh>(const Eigen::half& from) {
+  return __riscv_vfmv_v_f_f16m1(numext::bit_cast<_Float16>(from), unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pset1frombits<Packet1Xh>(numext::uint16_t from) {
+  return __riscv_vreinterpret_f16m1(__riscv_vmv_v_x_u16m1(from, unpacket_traits<Packet1Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh plset<Packet1Xh>(const Eigen::half& a) {
+  Packet1Xh idx = __riscv_vfcvt_f_x_v_f16m1(
+      __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vid_v_u16m1(unpacket_traits<Packet1Xs>::size)),
+      unpacket_traits<Packet1Xh>::size);
+  return __riscv_vfadd_vf_f16m1(idx, numext::bit_cast<_Float16>(a), unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet1Xh>(const Eigen::half* a, Packet1Xh& a0, Packet1Xh& a1, Packet1Xh& a2,
+                                                Packet1Xh& a3) {
+  vfloat16m1_t aa = __riscv_vle16_v_f16m1(reinterpret_cast<const _Float16*>(a), 4);
+  a0 = __riscv_vrgather_vx_f16m1(aa, 0, unpacket_traits<Packet1Xh>::size);
+  a1 = __riscv_vrgather_vx_f16m1(aa, 1, unpacket_traits<Packet1Xh>::size);
+  a2 = __riscv_vrgather_vx_f16m1(aa, 2, unpacket_traits<Packet1Xh>::size);
+  a3 = __riscv_vrgather_vx_f16m1(aa, 3, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh padd<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vfadd_vv_f16m1(a, b, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh psub<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vfsub_vv_f16m1(a, b, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pnegate(const Packet1Xh& a) {
+  return __riscv_vfneg_v_f16m1(a, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh psignbit(const Packet1Xh& a) {
+  return __riscv_vreinterpret_v_i16m1_f16m1(
+      __riscv_vsra_vx_i16m1(__riscv_vreinterpret_v_f16m1_i16m1(a), 15, unpacket_traits<Packet1Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pconj(const Packet1Xh& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pmul<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vfmul_vv_f16m1(a, b, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pdiv<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vfdiv_vv_f16m1(a, b, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pmadd(const Packet1Xh& a, const Packet1Xh& b, const Packet1Xh& c) {
+  return __riscv_vfmadd_vv_f16m1(a, b, c, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pmsub(const Packet1Xh& a, const Packet1Xh& b, const Packet1Xh& c) {
+  return __riscv_vfmsub_vv_f16m1(a, b, c, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pnmadd(const Packet1Xh& a, const Packet1Xh& b, const Packet1Xh& c) {
+  return __riscv_vfnmsub_vv_f16m1(a, b, c, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pnmsub(const Packet1Xh& a, const Packet1Xh& b, const Packet1Xh& c) {
+  return __riscv_vfnmadd_vv_f16m1(a, b, c, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pmin<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  const Eigen::half nan = (std::numeric_limits<Eigen::half>::quiet_NaN)();
+  Packet1Xh nans = __riscv_vfmv_v_f_f16m1(numext::bit_cast<_Float16>(nan), unpacket_traits<Packet1Xh>::size);
+  PacketMask16 mask = __riscv_vmfeq_vv_f16m1_b16(a, a, unpacket_traits<Packet1Xh>::size);
+  PacketMask16 mask2 = __riscv_vmfeq_vv_f16m1_b16(b, b, unpacket_traits<Packet1Xh>::size);
+  mask = __riscv_vmand_mm_b16(mask, mask2, unpacket_traits<Packet1Xh>::size);
+
+  return __riscv_vfmin_vv_f16m1_tumu(mask, nans, a, b, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pmin<PropagateNaN, Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return pmin<Packet1Xh>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pmin<PropagateNumbers, Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vfmin_vv_f16m1(a, b, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pmax<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  const Eigen::half nan = (std::numeric_limits<Eigen::half>::quiet_NaN)();
+  Packet1Xh nans = __riscv_vfmv_v_f_f16m1(numext::bit_cast<_Float16>(nan), unpacket_traits<Packet1Xh>::size);
+  PacketMask16 mask = __riscv_vmfeq_vv_f16m1_b16(a, a, unpacket_traits<Packet1Xh>::size);
+  PacketMask16 mask2 = __riscv_vmfeq_vv_f16m1_b16(b, b, unpacket_traits<Packet1Xh>::size);
+  mask = __riscv_vmand_mm_b16(mask, mask2, unpacket_traits<Packet1Xh>::size);
+
+  return __riscv_vfmax_vv_f16m1_tumu(mask, nans, a, b, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pmax<PropagateNaN, Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return pmax<Packet1Xh>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pmax<PropagateNumbers, Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vfmax_vv_f16m1(a, b, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pcmp_le<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  PacketMask16 mask = __riscv_vmfle_vv_f16m1_b16(a, b, unpacket_traits<Packet1Xh>::size);
+  return __riscv_vmerge_vvm_f16m1(pzero<Packet1Xh>(a), ptrue<Packet1Xh>(a), mask, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pcmp_lt<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  PacketMask16 mask = __riscv_vmflt_vv_f16m1_b16(a, b, unpacket_traits<Packet1Xh>::size);
+  return __riscv_vmerge_vvm_f16m1(pzero<Packet1Xh>(a), ptrue<Packet1Xh>(a), mask, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pcmp_eq<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  PacketMask16 mask = __riscv_vmfeq_vv_f16m1_b16(a, b, unpacket_traits<Packet1Xh>::size);
+  return __riscv_vmerge_vvm_f16m1(pzero<Packet1Xh>(a), ptrue<Packet1Xh>(a), mask, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pcmp_lt_or_nan<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  PacketMask16 mask = __riscv_vmfge_vv_f16m1_b16(a, b, unpacket_traits<Packet1Xh>::size);
+  return __riscv_vfmerge_vfm_f16m1(ptrue<Packet1Xh>(a), static_cast<_Float16>(0.0), mask,
+                                   unpacket_traits<Packet1Xh>::size);
+}
+
+EIGEN_STRONG_INLINE Packet1Xh pselect(const PacketMask16& mask, const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vmerge_vvm_f16m1(b, a, mask, unpacket_traits<Packet1Xh>::size);
+}
+
+EIGEN_STRONG_INLINE Packet1Xh pselect(const Packet1Xh& mask, const Packet1Xh& a, const Packet1Xh& b) {
+  PacketMask16 mask2 =
+      __riscv_vmsne_vx_i16m1_b16(__riscv_vreinterpret_v_f16m1_i16m1(mask), 0, unpacket_traits<Packet1Xh>::size);
+  return __riscv_vreinterpret_v_i16m1_f16m1(__riscv_vmerge_vvm_i16m1(__riscv_vreinterpret_v_f16m1_i16m1(b),
+                                                                     __riscv_vreinterpret_v_f16m1_i16m1(a), mask2,
+                                                                     unpacket_traits<Packet1Xh>::size));
+}
+
+// Logical Operations are not supported for half, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pand<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vreinterpret_v_u16m1_f16m1(__riscv_vand_vv_u16m1(
+      __riscv_vreinterpret_v_f16m1_u16m1(a), __riscv_vreinterpret_v_f16m1_u16m1(b), unpacket_traits<Packet1Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh por<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vreinterpret_v_u16m1_f16m1(__riscv_vor_vv_u16m1(
+      __riscv_vreinterpret_v_f16m1_u16m1(a), __riscv_vreinterpret_v_f16m1_u16m1(b), unpacket_traits<Packet1Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pxor<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vreinterpret_v_u16m1_f16m1(__riscv_vxor_vv_u16m1(
+      __riscv_vreinterpret_v_f16m1_u16m1(a), __riscv_vreinterpret_v_f16m1_u16m1(b), unpacket_traits<Packet1Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pandnot<Packet1Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vreinterpret_v_u16m1_f16m1(__riscv_vand_vv_u16m1(
+      __riscv_vreinterpret_v_f16m1_u16m1(a),
+      __riscv_vnot_v_u16m1(__riscv_vreinterpret_v_f16m1_u16m1(b), unpacket_traits<Packet1Xh>::size),
+      unpacket_traits<Packet1Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pload<Packet1Xh>(const Eigen::half* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle16_v_f16m1(reinterpret_cast<const _Float16*>(from),
+                                                        unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh ploadu<Packet1Xh>(const Eigen::half* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle16_v_f16m1(reinterpret_cast<const _Float16*>(from),
+                                                          unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh ploaddup<Packet1Xh>(const Eigen::half* from) {
+  Packet1Xsu data = __riscv_vreinterpret_v_f16m1_u16m1(pload<Packet1Xh>(from));
+  return __riscv_vreinterpret_v_i16m1_f16m1(
+      __riscv_vreinterpret_v_i32m1_i16m1(__riscv_vreinterpret_v_u32m1_i32m1(__riscv_vlmul_trunc_v_u32m2_u32m1(
+          __riscv_vwmaccu_vx_u32m2(__riscv_vwaddu_vv_u32m2(data, data, unpacket_traits<Packet1Xs>::size), 0xffffu, data,
+                                   unpacket_traits<Packet1Xs>::size)))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh ploadquad<Packet1Xh>(const Eigen::half* from) {
+  Packet1Xsu idx =
+      __riscv_vsrl_vx_u16m1(__riscv_vid_v_u16m1(unpacket_traits<Packet1Xh>::size), 2, unpacket_traits<Packet1Xh>::size);
+  return __riscv_vrgather_vv_f16m1(pload<Packet1Xh>(from), idx, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet1Xh& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse16_v_f16m1(reinterpret_cast<_Float16*>(to), from,
+                                                  unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet1Xh& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse16_v_f16m1(reinterpret_cast<_Float16*>(to), from,
+                                                    unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1Xh pgather<Eigen::half, Packet1Xh>(const Eigen::half* from, Index stride) {
+  return __riscv_vlse16_v_f16m1(reinterpret_cast<const _Float16*>(from), stride * sizeof(Eigen::half),
+                                unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<Eigen::half, Packet1Xh>(Eigen::half* to, const Packet1Xh& from, Index stride) {
+  __riscv_vsse16(reinterpret_cast<_Float16*>(to), stride * sizeof(Eigen::half), from, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet1Xh>(const Packet1Xh& a) {
+  return static_cast<Eigen::half>(__riscv_vfmv_f_s_f16m1_f16(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh psqrt(const Packet1Xh& a) {
+  return __riscv_vfsqrt_v_f16m1(a, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh print<Packet1Xh>(const Packet1Xh& a) {
+  const Packet1Xh limit = pset1<Packet1Xh>(static_cast<Eigen::half>(1 << 10));
+  const Packet1Xh abs_a = pabs(a);
+
+  PacketMask16 mask = __riscv_vmfne_vv_f16m1_b16(a, a, unpacket_traits<Packet1Xh>::size);
+  const Packet1Xh x = __riscv_vfadd_vv_f16m1_tumu(mask, a, a, a, unpacket_traits<Packet1Xh>::size);
+  const Packet1Xh new_x = __riscv_vfcvt_f_x_v_f16m1(__riscv_vfcvt_x_f_v_i16m1(a, unpacket_traits<Packet1Xh>::size),
+                                                    unpacket_traits<Packet1Xh>::size);
+
+  mask = __riscv_vmflt_vv_f16m1_b16(abs_a, limit, unpacket_traits<Packet1Xh>::size);
+  Packet1Xh signed_x = __riscv_vfsgnj_vv_f16m1(new_x, x, unpacket_traits<Packet1Xh>::size);
+  return __riscv_vmerge_vvm_f16m1(x, signed_x, mask, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pfloor<Packet1Xh>(const Packet1Xh& a) {
+  Packet1Xh tmp = print<Packet1Xh>(a);
+  // If greater, subtract one.
+  PacketMask16 mask = __riscv_vmflt_vv_f16m1_b16(a, tmp, unpacket_traits<Packet1Xh>::size);
+  return __riscv_vfsub_vf_f16m1_tumu(mask, tmp, tmp, static_cast<_Float16>(1.0), unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh preverse(const Packet1Xh& a) {
+  Packet1Xsu idx = __riscv_vrsub_vx_u16m1(__riscv_vid_v_u16m1(unpacket_traits<Packet1Xh>::size),
+                                          unpacket_traits<Packet1Xh>::size - 1, unpacket_traits<Packet1Xh>::size);
+  return __riscv_vrgather_vv_f16m1(a, idx, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux<Packet1Xh>(const Packet1Xh& a) {
+  return static_cast<Eigen::half>(__riscv_vfmv_f(__riscv_vfredusum_vs_f16m1_f16m1(
+      a, __riscv_vfmv_v_f_f16m1(static_cast<_Float16>(0.0), unpacket_traits<Packet1Xh>::size),
+      unpacket_traits<Packet1Xh>::size)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet1Xh>(const Packet1Xh& a) {
+  // Multiply the vector by its reverse
+  Packet1Xh prod = __riscv_vfmul_vv_f16m1(preverse(a), a, unpacket_traits<Packet1Xh>::size);
+  Packet1Xh half_prod;
+
+  if (EIGEN_RISCV64_RVV_VL >= 1024) {
+    half_prod = __riscv_vslidedown_vx_f16m1(prod, 16, unpacket_traits<Packet1Xh>::size);
+    prod = __riscv_vfmul_vv_f16m1(prod, half_prod, unpacket_traits<Packet1Xh>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 512) {
+    half_prod = __riscv_vslidedown_vx_f16m1(prod, 8, unpacket_traits<Packet1Xh>::size);
+    prod = __riscv_vfmul_vv_f16m1(prod, half_prod, unpacket_traits<Packet1Xh>::size);
+  }
+  if (EIGEN_RISCV64_RVV_VL >= 256) {
+    half_prod = __riscv_vslidedown_vx_f16m1(prod, 4, unpacket_traits<Packet1Xh>::size);
+    prod = __riscv_vfmul_vv_f16m1(prod, half_prod, unpacket_traits<Packet1Xh>::size);
+  }
+  // Last reduction
+  half_prod = __riscv_vslidedown_vx_f16m1(prod, 2, unpacket_traits<Packet1Xh>::size);
+  prod = __riscv_vfmul_vv_f16m1(prod, half_prod, unpacket_traits<Packet1Xh>::size);
+
+  half_prod = __riscv_vslidedown_vx_f16m1(prod, 1, unpacket_traits<Packet1Xh>::size);
+  prod = __riscv_vfmul_vv_f16m1(prod, half_prod, unpacket_traits<Packet1Xh>::size);
+
+  // The reduction is done to the first element.
+  return pfirst(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_min<Packet1Xh>(const Packet1Xh& a) {
+  const Eigen::half max = (std::numeric_limits<Eigen::half>::max)();
+  const Eigen::half nan = (std::numeric_limits<Eigen::half>::quiet_NaN)();
+  return (std::min)(static_cast<Eigen::half>(__riscv_vfmv_f(__riscv_vfredmin_vs_f16m1_f16m1(
+                        a, __riscv_vfmv_v_f_f16m1(numext::bit_cast<_Float16>(nan), unpacket_traits<Packet1Xh>::size),
+                        unpacket_traits<Packet1Xh>::size))),
+                    max);
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_max<Packet1Xh>(const Packet1Xh& a) {
+  const Eigen::half min = -(std::numeric_limits<Eigen::half>::max)();
+  const Eigen::half nan = (std::numeric_limits<Eigen::half>::quiet_NaN)();
+  return (std::max)(static_cast<Eigen::half>(__riscv_vfmv_f(__riscv_vfredmax_vs_f16m1_f16m1(
+                        a, __riscv_vfmv_v_f_f16m1(numext::bit_cast<_Float16>(nan), unpacket_traits<Packet1Xh>::size),
+                        unpacket_traits<Packet1Xh>::size))),
+                    min);
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1Xh, N>& kernel) {
+  Eigen::half buffer[unpacket_traits<Packet1Xh>::size * N];
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse16(reinterpret_cast<_Float16*>(&buffer[i]), N * sizeof(Eigen::half), kernel.packet[i],
+                   unpacket_traits<Packet1Xh>::size);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] = __riscv_vle16_v_f16m1(reinterpret_cast<_Float16*>(&buffer[i * unpacket_traits<Packet1Xh>::size]),
+                                             unpacket_traits<Packet1Xh>::size);
+  }
+}
+
+EIGEN_STRONG_INLINE Packet2Xf half2float(const Packet1Xh& a) {
+  return __riscv_vfwcvt_f_f_v_f32m2(a, unpacket_traits<Packet2Xf>::size);
+}
+
+EIGEN_STRONG_INLINE Packet1Xh float2half(const Packet2Xf& a) {
+  return __riscv_vfncvt_f_f_w_f16m1(a, unpacket_traits<Packet1Xh>::size);
+}
+
+/********************************* Packet2Xh ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh ptrue<Packet2Xh>(const Packet2Xh& /*a*/) {
+  return __riscv_vreinterpret_f16m2(__riscv_vmv_v_x_u16m2(0xffffu, unpacket_traits<Packet2Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pzero<Packet2Xh>(const Packet2Xh& /*a*/) {
+  return __riscv_vfmv_v_f_f16m2(static_cast<_Float16>(0.0), unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pabs(const Packet2Xh& a) {
+  return __riscv_vfabs_v_f16m2(a, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pabsdiff(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vfabs_v_f16m2(__riscv_vfsub_vv_f16m2(a, b, unpacket_traits<Packet2Xh>::size),
+                               unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pset1<Packet2Xh>(const Eigen::half& from) {
+  return __riscv_vfmv_v_f_f16m2(numext::bit_cast<_Float16>(from), unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pset1frombits<Packet2Xh>(numext::uint16_t from) {
+  return __riscv_vreinterpret_f16m2(__riscv_vmv_v_x_u16m2(from, unpacket_traits<Packet2Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh plset<Packet2Xh>(const Eigen::half& a) {
+  Packet2Xh idx = __riscv_vfcvt_f_x_v_f16m2(
+      __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vid_v_u16m2(unpacket_traits<Packet4Xs>::size)),
+      unpacket_traits<Packet2Xh>::size);
+  return __riscv_vfadd_vf_f16m2(idx, numext::bit_cast<_Float16>(a), unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet2Xh>(const Eigen::half* a, Packet2Xh& a0, Packet2Xh& a1, Packet2Xh& a2,
+                                                Packet2Xh& a3) {
+  vfloat16m2_t aa = __riscv_vle16_v_f16m2(reinterpret_cast<const _Float16*>(a), 4);
+  a0 = __riscv_vrgather_vx_f16m2(aa, 0, unpacket_traits<Packet2Xh>::size);
+  a1 = __riscv_vrgather_vx_f16m2(aa, 1, unpacket_traits<Packet2Xh>::size);
+  a2 = __riscv_vrgather_vx_f16m2(aa, 2, unpacket_traits<Packet2Xh>::size);
+  a3 = __riscv_vrgather_vx_f16m2(aa, 3, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh padd<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vfadd_vv_f16m2(a, b, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh psub<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vfsub_vv_f16m2(a, b, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pnegate(const Packet2Xh& a) {
+  return __riscv_vfneg_v_f16m2(a, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh psignbit(const Packet2Xh& a) {
+  return __riscv_vreinterpret_v_i16m2_f16m2(
+      __riscv_vsra_vx_i16m2(__riscv_vreinterpret_v_f16m2_i16m2(a), 15, unpacket_traits<Packet2Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pconj(const Packet2Xh& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pmul<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vfmul_vv_f16m2(a, b, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pdiv<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vfdiv_vv_f16m2(a, b, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pmadd(const Packet2Xh& a, const Packet2Xh& b, const Packet2Xh& c) {
+  return __riscv_vfmadd_vv_f16m2(a, b, c, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pmsub(const Packet2Xh& a, const Packet2Xh& b, const Packet2Xh& c) {
+  return __riscv_vfmsub_vv_f16m2(a, b, c, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pnmadd(const Packet2Xh& a, const Packet2Xh& b, const Packet2Xh& c) {
+  return __riscv_vfnmsub_vv_f16m2(a, b, c, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pnmsub(const Packet2Xh& a, const Packet2Xh& b, const Packet2Xh& c) {
+  return __riscv_vfnmadd_vv_f16m2(a, b, c, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pmin<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  const Eigen::half nan = (std::numeric_limits<Eigen::half>::quiet_NaN)();
+  Packet2Xh nans = __riscv_vfmv_v_f_f16m2(numext::bit_cast<_Float16>(nan), unpacket_traits<Packet2Xh>::size);
+  PacketMask8 mask = __riscv_vmfeq_vv_f16m2_b8(a, a, unpacket_traits<Packet2Xh>::size);
+  PacketMask8 mask2 = __riscv_vmfeq_vv_f16m2_b8(b, b, unpacket_traits<Packet2Xh>::size);
+  mask = __riscv_vmand_mm_b8(mask, mask2, unpacket_traits<Packet2Xh>::size);
+
+  return __riscv_vfmin_vv_f16m2_tumu(mask, nans, a, b, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pmin<PropagateNaN, Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return pmin<Packet2Xh>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pmin<PropagateNumbers, Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vfmin_vv_f16m2(a, b, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pmax<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  const Eigen::half nan = (std::numeric_limits<Eigen::half>::quiet_NaN)();
+  Packet2Xh nans = __riscv_vfmv_v_f_f16m2(numext::bit_cast<_Float16>(nan), unpacket_traits<Packet2Xh>::size);
+  PacketMask8 mask = __riscv_vmfeq_vv_f16m2_b8(a, a, unpacket_traits<Packet2Xh>::size);
+  PacketMask8 mask2 = __riscv_vmfeq_vv_f16m2_b8(b, b, unpacket_traits<Packet2Xh>::size);
+  mask = __riscv_vmand_mm_b8(mask, mask2, unpacket_traits<Packet2Xh>::size);
+
+  return __riscv_vfmax_vv_f16m2_tumu(mask, nans, a, b, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pmax<PropagateNaN, Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return pmax<Packet2Xh>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pmax<PropagateNumbers, Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vfmax_vv_f16m2(a, b, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pcmp_le<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  PacketMask8 mask = __riscv_vmfle_vv_f16m2_b8(a, b, unpacket_traits<Packet2Xh>::size);
+  return __riscv_vmerge_vvm_f16m2(pzero<Packet2Xh>(a), ptrue<Packet2Xh>(a), mask, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pcmp_lt<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  PacketMask8 mask = __riscv_vmflt_vv_f16m2_b8(a, b, unpacket_traits<Packet2Xh>::size);
+  return __riscv_vmerge_vvm_f16m2(pzero<Packet2Xh>(a), ptrue<Packet2Xh>(a), mask, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pcmp_eq<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  PacketMask8 mask = __riscv_vmfeq_vv_f16m2_b8(a, b, unpacket_traits<Packet2Xh>::size);
+  return __riscv_vmerge_vvm_f16m2(pzero<Packet2Xh>(a), ptrue<Packet2Xh>(a), mask, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pcmp_lt_or_nan<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  PacketMask8 mask = __riscv_vmfge_vv_f16m2_b8(a, b, unpacket_traits<Packet2Xh>::size);
+  return __riscv_vfmerge_vfm_f16m2(ptrue<Packet2Xh>(a), static_cast<_Float16>(0.0), mask,
+                                   unpacket_traits<Packet2Xh>::size);
+}
+
+EIGEN_STRONG_INLINE Packet2Xh pselect(const PacketMask8& mask, const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vmerge_vvm_f16m2(b, a, mask, unpacket_traits<Packet2Xh>::size);
+}
+
+EIGEN_STRONG_INLINE Packet2Xh pselect(const Packet2Xh& mask, const Packet2Xh& a, const Packet2Xh& b) {
+  PacketMask8 mask2 =
+      __riscv_vmsne_vx_i16m2_b8(__riscv_vreinterpret_v_f16m2_i16m2(mask), 0, unpacket_traits<Packet2Xh>::size);
+  return __riscv_vreinterpret_v_i16m2_f16m2(__riscv_vmerge_vvm_i16m2(__riscv_vreinterpret_v_f16m2_i16m2(b),
+                                                                     __riscv_vreinterpret_v_f16m2_i16m2(a), mask2,
+                                                                     unpacket_traits<Packet2Xh>::size));
+}
+
+// Logical Operations are not supported for half, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pand<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vreinterpret_v_u16m2_f16m2(__riscv_vand_vv_u16m2(
+      __riscv_vreinterpret_v_f16m2_u16m2(a), __riscv_vreinterpret_v_f16m2_u16m2(b), unpacket_traits<Packet2Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh por<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vreinterpret_v_u16m2_f16m2(__riscv_vor_vv_u16m2(
+      __riscv_vreinterpret_v_f16m2_u16m2(a), __riscv_vreinterpret_v_f16m2_u16m2(b), unpacket_traits<Packet2Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pxor<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vreinterpret_v_u16m2_f16m2(__riscv_vxor_vv_u16m2(
+      __riscv_vreinterpret_v_f16m2_u16m2(a), __riscv_vreinterpret_v_f16m2_u16m2(b), unpacket_traits<Packet2Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pandnot<Packet2Xh>(const Packet2Xh& a, const Packet2Xh& b) {
+  return __riscv_vreinterpret_v_u16m2_f16m2(__riscv_vand_vv_u16m2(
+      __riscv_vreinterpret_v_f16m2_u16m2(a),
+      __riscv_vnot_v_u16m2(__riscv_vreinterpret_v_f16m2_u16m2(b), unpacket_traits<Packet2Xh>::size),
+      unpacket_traits<Packet2Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pload<Packet2Xh>(const Eigen::half* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return __riscv_vle16_v_f16m2(reinterpret_cast<const _Float16*>(from),
+                                                        unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh ploadu<Packet2Xh>(const Eigen::half* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return __riscv_vle16_v_f16m2(reinterpret_cast<const _Float16*>(from),
+                                                          unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh ploaddup<Packet2Xh>(const Eigen::half* from) {
+  Packet2Xsu data = __riscv_vreinterpret_v_f16m2_u16m2(pload<Packet2Xh>(from));
+  return __riscv_vreinterpret_v_i16m2_f16m2(
+      __riscv_vreinterpret_v_i32m2_i16m2(__riscv_vreinterpret_v_u32m2_i32m2(__riscv_vlmul_trunc_v_u32m4_u32m2(
+          __riscv_vwmaccu_vx_u32m4(__riscv_vwaddu_vv_u32m4(data, data, unpacket_traits<Packet2Xs>::size), 0xffffu, data,
+                                   unpacket_traits<Packet2Xs>::size)))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh ploadquad<Packet2Xh>(const Eigen::half* from) {
+  Packet2Xsu idx =
+      __riscv_vsrl_vx_u16m2(__riscv_vid_v_u16m2(unpacket_traits<Packet2Xh>::size), 2, unpacket_traits<Packet2Xh>::size);
+  return __riscv_vrgather_vv_f16m2(pload<Packet2Xh>(from), idx, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet2Xh& from) {
+  EIGEN_DEBUG_ALIGNED_STORE __riscv_vse16_v_f16m2(reinterpret_cast<_Float16*>(to), from,
+                                                  unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet2Xh& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE __riscv_vse16_v_f16m2(reinterpret_cast<_Float16*>(to), from,
+                                                    unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2Xh pgather<Eigen::half, Packet2Xh>(const Eigen::half* from, Index stride) {
+  return __riscv_vlse16_v_f16m2(reinterpret_cast<const _Float16*>(from), stride * sizeof(Eigen::half),
+                                unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<Eigen::half, Packet2Xh>(Eigen::half* to, const Packet2Xh& from, Index stride) {
+  __riscv_vsse16(reinterpret_cast<_Float16*>(to), stride * sizeof(Eigen::half), from, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet2Xh>(const Packet2Xh& a) {
+  return numext::bit_cast<Eigen::half>(__riscv_vfmv_f_s_f16m2_f16(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh psqrt(const Packet2Xh& a) {
+  return __riscv_vfsqrt_v_f16m2(a, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh print<Packet2Xh>(const Packet2Xh& a) {
+  const Packet2Xh limit = pset1<Packet2Xh>(static_cast<Eigen::half>(1 << 10));
+  const Packet2Xh abs_a = pabs(a);
+
+  PacketMask8 mask = __riscv_vmfne_vv_f16m2_b8(a, a, unpacket_traits<Packet2Xh>::size);
+  const Packet2Xh x = __riscv_vfadd_vv_f16m2_tumu(mask, a, a, a, unpacket_traits<Packet2Xh>::size);
+  const Packet2Xh new_x = __riscv_vfcvt_f_x_v_f16m2(__riscv_vfcvt_x_f_v_i16m2(a, unpacket_traits<Packet2Xh>::size),
+                                                    unpacket_traits<Packet2Xh>::size);
+
+  mask = __riscv_vmflt_vv_f16m2_b8(abs_a, limit, unpacket_traits<Packet2Xh>::size);
+  Packet2Xh signed_x = __riscv_vfsgnj_vv_f16m2(new_x, x, unpacket_traits<Packet2Xh>::size);
+  return __riscv_vmerge_vvm_f16m2(x, signed_x, mask, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pfloor<Packet2Xh>(const Packet2Xh& a) {
+  Packet2Xh tmp = print<Packet2Xh>(a);
+  // If greater, subtract one.
+  PacketMask8 mask = __riscv_vmflt_vv_f16m2_b8(a, tmp, unpacket_traits<Packet2Xh>::size);
+  return __riscv_vfsub_vf_f16m2_tumu(mask, tmp, tmp, static_cast<_Float16>(1.0), unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh preverse(const Packet2Xh& a) {
+  Packet2Xsu idx = __riscv_vrsub_vx_u16m2(__riscv_vid_v_u16m2(unpacket_traits<Packet2Xh>::size),
+                                          unpacket_traits<Packet2Xh>::size - 1, unpacket_traits<Packet2Xh>::size);
+  return __riscv_vrgather_vv_f16m2(a, idx, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux<Packet2Xh>(const Packet2Xh& a) {
+  return static_cast<Eigen::half>(__riscv_vfmv_f(__riscv_vfredusum_vs_f16m2_f16m1(
+      a, __riscv_vfmv_v_f_f16m1(static_cast<_Float16>(0.0), unpacket_traits<Packet2Xh>::size / 2),
+      unpacket_traits<Packet2Xh>::size)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet2Xh>(const Packet2Xh& a) {
+  return predux_mul<Packet1Xh>(__riscv_vfmul_vv_f16m1(
+      __riscv_vget_v_f16m2_f16m1(a, 0), __riscv_vget_v_f16m2_f16m1(a, 1), unpacket_traits<Packet1Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_min<Packet2Xh>(const Packet2Xh& a) {
+  const Eigen::half max = (std::numeric_limits<Eigen::half>::max)();
+  const Eigen::half nan = (std::numeric_limits<Eigen::half>::quiet_NaN)();
+  return (std::min)(
+      static_cast<Eigen::half>(__riscv_vfmv_f(__riscv_vfredmin_vs_f16m2_f16m1(
+          a, __riscv_vfmv_v_f_f16m1(numext::bit_cast<_Float16>(nan), unpacket_traits<Packet2Xh>::size / 2),
+          unpacket_traits<Packet2Xh>::size))),
+      max);
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_max<Packet2Xh>(const Packet2Xh& a) {
+  const Eigen::half min = -(std::numeric_limits<Eigen::half>::max)();
+  const Eigen::half nan = (std::numeric_limits<Eigen::half>::quiet_NaN)();
+  return (std::max)(
+      static_cast<Eigen::half>(__riscv_vfmv_f(__riscv_vfredmax_vs_f16m2_f16m1(
+          a, __riscv_vfmv_v_f_f16m1(numext::bit_cast<_Float16>(nan), unpacket_traits<Packet2Xh>::size / 2),
+          unpacket_traits<Packet2Xh>::size))),
+      min);
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2Xh, N>& kernel) {
+  Eigen::half buffer[unpacket_traits<Packet2Xh>::size * N];
+  int i = 0;
+
+  for (i = 0; i < N; i++) {
+    __riscv_vsse16(reinterpret_cast<_Float16*>(&buffer[i]), N * sizeof(Eigen::half), kernel.packet[i],
+                   unpacket_traits<Packet2Xh>::size);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] = __riscv_vle16_v_f16m2(reinterpret_cast<_Float16*>(&buffer[i * unpacket_traits<Packet2Xh>::size]),
+                                             unpacket_traits<Packet2Xh>::size);
+  }
+}
+
+EIGEN_STRONG_INLINE Packet4Xf half2float(const Packet2Xh& a) {
+  return __riscv_vfwcvt_f_f_v_f32m4(a, unpacket_traits<Packet4Xf>::size);
+}
+
+EIGEN_STRONG_INLINE Packet2Xh float2half(const Packet4Xf& a) {
+  return __riscv_vfncvt_f_f_w_f16m2(a, unpacket_traits<Packet2Xh>::size);
+}
+
+template <typename Packet = Packet2Xh>
+EIGEN_STRONG_INLINE
+    typename std::enable_if<std::is_same<Packet, Packet2Xh>::value && (unpacket_traits<Packet2Xh>::size % 8) == 0,
+                            Packet1Xh>::type
+    predux_half(const Packet2Xh& a) {
+  return __riscv_vfadd_vv_f16m1(__riscv_vget_v_f16m2_f16m1(a, 0), __riscv_vget_v_f16m2_f16m1(a, 1),
+                                unpacket_traits<Packet1Xh>::size);
+}
+
+F16_PACKET_FUNCTION(Packet2Xf, Packet1Xh, pcos)
+F16_PACKET_FUNCTION(Packet2Xf, Packet1Xh, pexp)
+F16_PACKET_FUNCTION(Packet2Xf, Packet1Xh, pexpm1)
+F16_PACKET_FUNCTION(Packet2Xf, Packet1Xh, plog)
+F16_PACKET_FUNCTION(Packet2Xf, Packet1Xh, plog1p)
+F16_PACKET_FUNCTION(Packet2Xf, Packet1Xh, plog2)
+F16_PACKET_FUNCTION(Packet2Xf, Packet1Xh, preciprocal)
+F16_PACKET_FUNCTION(Packet2Xf, Packet1Xh, prsqrt)
+F16_PACKET_FUNCTION(Packet2Xf, Packet1Xh, psin)
+F16_PACKET_FUNCTION(Packet2Xf, Packet1Xh, ptanh)
+
+F16_PACKET_FUNCTION(Packet4Xf, Packet2Xh, pcos)
+F16_PACKET_FUNCTION(Packet4Xf, Packet2Xh, pexp)
+F16_PACKET_FUNCTION(Packet4Xf, Packet2Xh, pexpm1)
+F16_PACKET_FUNCTION(Packet4Xf, Packet2Xh, plog)
+F16_PACKET_FUNCTION(Packet4Xf, Packet2Xh, plog1p)
+F16_PACKET_FUNCTION(Packet4Xf, Packet2Xh, plog2)
+F16_PACKET_FUNCTION(Packet4Xf, Packet2Xh, preciprocal)
+F16_PACKET_FUNCTION(Packet4Xf, Packet2Xh, prsqrt)
+F16_PACKET_FUNCTION(Packet4Xf, Packet2Xh, psin)
+F16_PACKET_FUNCTION(Packet4Xf, Packet2Xh, ptanh)
+
+/********************************* casting ************************************/
+
+template <>
+struct type_casting_traits<_Float16, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+struct type_casting_traits<numext::int16_t, _Float16> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh pcast<Packet1Xs, Packet1Xh>(const Packet1Xs& a) {
+  return __riscv_vfcvt_f_x_v_f16m1(a, unpacket_traits<Packet1Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs pcast<Packet1Xh, Packet1Xs>(const Packet1Xh& a) {
+  return __riscv_vfcvt_rtz_x_f_v_i16m1(a, unpacket_traits<Packet1Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xh preinterpret<Packet1Xh, Packet1Xs>(const Packet1Xs& a) {
+  return __riscv_vreinterpret_v_i16m1_f16m1(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xs preinterpret<Packet1Xs, Packet1Xh>(const Packet1Xh& a) {
+  return __riscv_vreinterpret_v_f16m1_i16m1(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pcast<Packet2Xs, Packet2Xh>(const Packet2Xs& a) {
+  return __riscv_vfcvt_f_x_v_f16m2(a, unpacket_traits<Packet2Xs>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pcast<Packet2Xh, Packet2Xs>(const Packet2Xh& a) {
+  return __riscv_vfcvt_rtz_x_f_v_i16m2(a, unpacket_traits<Packet2Xh>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh preinterpret<Packet2Xh, Packet2Xs>(const Packet2Xs& a) {
+  return __riscv_vreinterpret_v_i16m2_f16m2(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs preinterpret<Packet2Xs, Packet2Xh>(const Packet2Xh& a) {
+  return __riscv_vreinterpret_v_f16m2_i16m2(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pcast<Packet1Xh, Packet4Xs>(const Packet1Xh& a, const Packet1Xh& b, const Packet1Xh& c,
+                                                          const Packet1Xh& d) {
+  return __riscv_vcreate_v_i16m1_i16m4(__riscv_vfcvt_rtz_x_f_v_i16m1(a, unpacket_traits<Packet1Xh>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i16m1(b, unpacket_traits<Packet1Xh>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i16m1(c, unpacket_traits<Packet1Xh>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i16m1(d, unpacket_traits<Packet1Xh>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pcast<Packet1Xs, Packet2Xh>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vcreate_v_f16m1_f16m2(__riscv_vfcvt_f_x_v_f16m1(a, unpacket_traits<Packet1Xs>::size),
+                                       __riscv_vfcvt_f_x_v_f16m1(b, unpacket_traits<Packet1Xs>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xh pcast<Packet1Xh, Packet2Xh>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vcreate_v_f16m1_f16m2(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pcast<Packet1Xh, Packet2Xs>(const Packet1Xh& a, const Packet1Xh& b) {
+  return __riscv_vcreate_v_i16m1_i16m2(__riscv_vfcvt_rtz_x_f_v_i16m1(a, unpacket_traits<Packet1Xh>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i16m1(b, unpacket_traits<Packet1Xh>::size));
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_FP16_RVV10_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/TypeCasting.h
new file mode 100644
index 0000000..2b0d3db
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/RVV10/TypeCasting.h
@@ -0,0 +1,284 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Kseniya Zaytseva <kseniya.zaytseva@syntacore.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_RVV10_H
+#define EIGEN_TYPE_CASTING_RVV10_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/********************************* 32 bits ************************************/
+
+template <>
+struct type_casting_traits<float, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+struct type_casting_traits<numext::int32_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf pcast<Packet1Xi, Packet1Xf>(const Packet1Xi& a) {
+  return __riscv_vfcvt_f_x_v_f32m1(a, unpacket_traits<Packet1Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi pcast<Packet1Xf, Packet1Xi>(const Packet1Xf& a) {
+  return __riscv_vfcvt_rtz_x_f_v_i32m1(a, unpacket_traits<Packet1Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xf preinterpret<Packet1Xf, Packet1Xi>(const Packet1Xi& a) {
+  return __riscv_vreinterpret_v_i32m1_f32m1(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xi preinterpret<Packet1Xi, Packet1Xf>(const Packet1Xf& a) {
+  return __riscv_vreinterpret_v_f32m1_i32m1(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pcast<Packet4Xi, Packet4Xf>(const Packet4Xi& a) {
+  return __riscv_vfcvt_f_x_v_f32m4(a, unpacket_traits<Packet4Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pcast<Packet4Xf, Packet4Xi>(const Packet4Xf& a) {
+  return __riscv_vfcvt_rtz_x_f_v_i32m4(a, unpacket_traits<Packet4Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf preinterpret<Packet4Xf, Packet4Xi>(const Packet4Xi& a) {
+  return __riscv_vreinterpret_v_i32m4_f32m4(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi preinterpret<Packet4Xi, Packet4Xf>(const Packet4Xf& a) {
+  return __riscv_vreinterpret_v_f32m4_i32m4(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pcast<Packet2Xi, Packet2Xf>(const Packet2Xi& a) {
+  return __riscv_vfcvt_f_x_v_f32m2(a, unpacket_traits<Packet2Xi>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pcast<Packet2Xf, Packet2Xi>(const Packet2Xf& a) {
+  return __riscv_vfcvt_rtz_x_f_v_i32m2(a, unpacket_traits<Packet2Xf>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf preinterpret<Packet2Xf, Packet2Xi>(const Packet2Xi& a) {
+  return __riscv_vreinterpret_v_i32m2_f32m2(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi preinterpret<Packet2Xi, Packet2Xf>(const Packet2Xf& a) {
+  return __riscv_vreinterpret_v_f32m2_i32m2(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pcast<Packet1Xi, Packet4Xi>(const Packet1Xi& a, const Packet1Xi& b, const Packet1Xi& c,
+                                                               const Packet1Xi& d) {
+  return __riscv_vcreate_v_i32m1_i32m4(a, b, c, d);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pcast<Packet1Xi, Packet4Xf>(const Packet1Xi& a, const Packet1Xi& b, const Packet1Xi& c,
+                                                               const Packet1Xi& d) {
+  return __riscv_vcreate_v_f32m1_f32m4(__riscv_vfcvt_f_x_v_f32m1(a, unpacket_traits<Packet1Xi>::size),
+                                       __riscv_vfcvt_f_x_v_f32m1(b, unpacket_traits<Packet1Xi>::size),
+                                       __riscv_vfcvt_f_x_v_f32m1(c, unpacket_traits<Packet1Xi>::size),
+                                       __riscv_vfcvt_f_x_v_f32m1(d, unpacket_traits<Packet1Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xf pcast<Packet1Xf, Packet4Xf>(const Packet1Xf& a, const Packet1Xf& b, const Packet1Xf& c,
+                                                               const Packet1Xf& d) {
+  return __riscv_vcreate_v_f32m1_f32m4(a, b, c, d);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xi pcast<Packet1Xf, Packet4Xi>(const Packet1Xf& a, const Packet1Xf& b, const Packet1Xf& c,
+                                                               const Packet1Xf& d) {
+  return __riscv_vcreate_v_i32m1_i32m4(__riscv_vfcvt_rtz_x_f_v_i32m1(a, unpacket_traits<Packet1Xf>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i32m1(b, unpacket_traits<Packet1Xf>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i32m1(c, unpacket_traits<Packet1Xf>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i32m1(d, unpacket_traits<Packet1Xf>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pcast<Packet1Xi, Packet2Xi>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vcreate_v_i32m1_i32m2(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pcast<Packet1Xi, Packet2Xf>(const Packet1Xi& a, const Packet1Xi& b) {
+  return __riscv_vcreate_v_f32m1_f32m2(__riscv_vfcvt_f_x_v_f32m1(a, unpacket_traits<Packet1Xi>::size),
+                                       __riscv_vfcvt_f_x_v_f32m1(b, unpacket_traits<Packet1Xi>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xf pcast<Packet1Xf, Packet2Xf>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vcreate_v_f32m1_f32m2(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xi pcast<Packet1Xf, Packet2Xi>(const Packet1Xf& a, const Packet1Xf& b) {
+  return __riscv_vcreate_v_i32m1_i32m2(__riscv_vfcvt_rtz_x_f_v_i32m1(a, unpacket_traits<Packet1Xf>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i32m1(b, unpacket_traits<Packet1Xf>::size));
+}
+
+/********************************* 64 bits ************************************/
+
+template <>
+struct type_casting_traits<double, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+struct type_casting_traits<numext::int64_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd pcast<Packet1Xl, Packet1Xd>(const Packet1Xl& a) {
+  return __riscv_vfcvt_f_x_v_f64m1(a, unpacket_traits<Packet1Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl pcast<Packet1Xd, Packet1Xl>(const Packet1Xd& a) {
+  return __riscv_vfcvt_rtz_x_f_v_i64m1(a, unpacket_traits<Packet1Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xd preinterpret<Packet1Xd, Packet1Xl>(const Packet1Xl& a) {
+  return __riscv_vreinterpret_v_i64m1_f64m1(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1Xl preinterpret<Packet1Xl, Packet1Xd>(const Packet1Xd& a) {
+  return __riscv_vreinterpret_v_f64m1_i64m1(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pcast<Packet4Xl, Packet4Xd>(const Packet4Xl& a) {
+  return __riscv_vfcvt_f_x_v_f64m4(a, unpacket_traits<Packet4Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pcast<Packet4Xd, Packet4Xl>(const Packet4Xd& a) {
+  return __riscv_vfcvt_rtz_x_f_v_i64m4(a, unpacket_traits<Packet4Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd preinterpret<Packet4Xd, Packet4Xl>(const Packet4Xl& a) {
+  return __riscv_vreinterpret_v_i64m4_f64m4(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl preinterpret<Packet4Xl, Packet4Xd>(const Packet4Xd& a) {
+  return __riscv_vreinterpret_v_f64m4_i64m4(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pcast<Packet2Xl, Packet2Xd>(const Packet2Xl& a) {
+  return __riscv_vfcvt_f_x_v_f64m2(a, unpacket_traits<Packet2Xl>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pcast<Packet2Xd, Packet2Xl>(const Packet2Xd& a) {
+  return __riscv_vfcvt_rtz_x_f_v_i64m2(a, unpacket_traits<Packet2Xd>::size);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd preinterpret<Packet2Xd, Packet2Xl>(const Packet2Xl& a) {
+  return __riscv_vreinterpret_v_i64m2_f64m2(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl preinterpret<Packet2Xl, Packet2Xd>(const Packet2Xd& a) {
+  return __riscv_vreinterpret_v_f64m2_i64m2(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pcast<Packet1Xl, Packet4Xl>(const Packet1Xl& a, const Packet1Xl& b, const Packet1Xl& c,
+                                                               const Packet1Xl& d) {
+  return __riscv_vcreate_v_i64m1_i64m4(a, b, c, d);
+  ;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pcast<Packet1Xl, Packet4Xd>(const Packet1Xl& a, const Packet1Xl& b, const Packet1Xl& c,
+                                                               const Packet1Xl& d) {
+  return __riscv_vcreate_v_f64m1_f64m4(__riscv_vfcvt_f_x_v_f64m1(a, unpacket_traits<Packet1Xl>::size),
+                                       __riscv_vfcvt_f_x_v_f64m1(b, unpacket_traits<Packet1Xl>::size),
+                                       __riscv_vfcvt_f_x_v_f64m1(c, unpacket_traits<Packet1Xl>::size),
+                                       __riscv_vfcvt_f_x_v_f64m1(d, unpacket_traits<Packet1Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xd pcast<Packet1Xd, Packet4Xd>(const Packet1Xd& a, const Packet1Xd& b, const Packet1Xd& c,
+                                                               const Packet1Xd& d) {
+  return __riscv_vcreate_v_f64m1_f64m4(a, b, c, d);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xl pcast<Packet1Xd, Packet4Xl>(const Packet1Xd& a, const Packet1Xd& b, const Packet1Xd& c,
+                                                               const Packet1Xd& d) {
+  return __riscv_vcreate_v_i64m1_i64m4(__riscv_vfcvt_rtz_x_f_v_i64m1(a, unpacket_traits<Packet1Xd>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i64m1(b, unpacket_traits<Packet1Xd>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i64m1(c, unpacket_traits<Packet1Xd>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i64m1(d, unpacket_traits<Packet1Xd>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pcast<Packet1Xl, Packet2Xl>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vcreate_v_i64m1_i64m2(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pcast<Packet1Xl, Packet2Xd>(const Packet1Xl& a, const Packet1Xl& b) {
+  return __riscv_vcreate_v_f64m1_f64m2(__riscv_vfcvt_f_x_v_f64m1(a, unpacket_traits<Packet1Xl>::size),
+                                       __riscv_vfcvt_f_x_v_f64m1(b, unpacket_traits<Packet1Xl>::size));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xd pcast<Packet1Xd, Packet2Xd>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vcreate_v_f64m1_f64m2(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xl pcast<Packet1Xd, Packet2Xl>(const Packet1Xd& a, const Packet1Xd& b) {
+  return __riscv_vcreate_v_i64m1_i64m2(__riscv_vfcvt_rtz_x_f_v_i64m1(a, unpacket_traits<Packet1Xd>::size),
+                                       __riscv_vfcvt_rtz_x_f_v_i64m1(b, unpacket_traits<Packet1Xd>::size));
+}
+
+/********************************* 16 bits ************************************/
+
+template <>
+EIGEN_STRONG_INLINE Packet2Xs pcast<Packet1Xs, Packet2Xs>(const Packet1Xs& a, const Packet1Xs& b) {
+  return __riscv_vcreate_v_i16m1_i16m2(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4Xs pcast<Packet1Xs, Packet4Xs>(const Packet1Xs& a, const Packet1Xs& b, const Packet1Xs& c,
+                                                               const Packet1Xs& d) {
+  return __riscv_vcreate_v_i16m1_i16m4(a, b, c, d);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_RVV10_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/Complex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/Complex.h
new file mode 100644
index 0000000..484ec4c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/Complex.h
@@ -0,0 +1,501 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_SSE_H
+#define EIGEN_COMPLEX_SSE_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- float ----------
+struct Packet2cf {
+  EIGEN_STRONG_INLINE Packet2cf() {}
+  EIGEN_STRONG_INLINE explicit Packet2cf(const __m128& a) : v(a) {}
+  Packet4f v;
+};
+
+// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
+// to leverage AVX instructions.
+#ifndef EIGEN_VECTORIZE_AVX
+template <>
+struct packet_traits<std::complex<float> > : default_packet_traits {
+  typedef Packet2cf type;
+  typedef Packet2cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0,
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet2cf> {
+  typedef std::complex<float> type;
+  typedef Packet2cf half;
+  typedef Packet4f as_real;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(_mm_add_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(_mm_sub_ps(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) {
+  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000));
+  return Packet2cf(_mm_xor_ps(a.v, mask));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) {
+  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x00000000, 0x80000000, 0x00000000, 0x80000000));
+  return Packet2cf(_mm_xor_ps(a.v, mask));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) {
+#ifdef EIGEN_VECTORIZE_SSE3
+  __m128 tmp1 = _mm_mul_ps(_mm_movehdup_ps(a.v), vec4f_swizzle1(b.v, 1, 0, 3, 2));
+  __m128 tmp2 = _mm_moveldup_ps(a.v);
+#else
+  __m128 tmp1 = _mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3), vec4f_swizzle1(b.v, 1, 0, 3, 2));
+  __m128 tmp2 = vec4f_swizzle1(a.v, 0, 0, 2, 2);
+#endif
+#ifdef EIGEN_VECTORIZE_FMA
+  __m128 result = _mm_fmaddsub_ps(tmp2, b.v, tmp1);
+#else
+#ifdef EIGEN_VECTORIZE_SSE3
+  __m128 result = _mm_addsub_ps(_mm_mul_ps(tmp2, b.v), tmp1);
+#else
+  const __m128 mask = _mm_setr_ps(-0.0f, 0.0f, -0.0f, 0.0f);
+  __m128 result = _mm_add_ps(_mm_mul_ps(tmp2, b.v), _mm_xor_ps(tmp1, mask));
+#endif
+#endif
+  return Packet2cf(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf ptrue<Packet2cf>(const Packet2cf& a) {
+  return Packet2cf(ptrue(Packet4f(a.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(_mm_and_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(_mm_or_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(_mm_xor_ps(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(_mm_andnot_ps(b.v, a.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(_mm_load_ps(&numext::real_ref(*from)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(_mm_loadu_ps(&numext::real_ref(*from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) {
+  const float re = std::real(from);
+  const float im = std::imag(from);
+  return Packet2cf(_mm_set_ps(im, re, im, re));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) {
+  return pset1<Packet2cf>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<float> >(std::complex<float>* to, const Packet2cf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm_store_ps(&numext::real_ref(*to), from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet2cf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_ps(&numext::real_ref(*to), from.v);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from,
+                                                                           Index stride) {
+  return Packet2cf(_mm_set_ps(std::imag(from[1 * stride]), std::real(from[1 * stride]), std::imag(from[0 * stride]),
+                              std::real(from[0 * stride])));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from,
+                                                                       Index stride) {
+  to[stride * 0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 0)),
+                                       _mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 1)));
+  to[stride * 1] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 2)),
+                                       _mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 3)));
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float>* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) {
+  alignas(alignof(__m64)) std::complex<float> res;
+  _mm_storel_pi((__m64*)&res, a.v);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) {
+  return Packet2cf(_mm_castpd_ps(preverse(Packet2d(_mm_castps_pd(a.v)))));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) {
+  return pfirst(Packet2cf(_mm_add_ps(a.v, _mm_movehl_ps(a.v, a.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) {
+  return pfirst(pmul(a, Packet2cf(_mm_movehl_ps(a.v, a.v))));
+}
+
+EIGEN_STRONG_INLINE Packet2cf pcplxflip /* <Packet2cf> */ (const Packet2cf& x) {
+  return Packet2cf(vec4f_swizzle1(x.v, 1, 0, 3, 2));
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf, Packet4f)
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return pdiv_complex(a, b);
+}
+
+//---------- double ----------
+struct Packet1cd {
+  EIGEN_STRONG_INLINE Packet1cd() {}
+  EIGEN_STRONG_INLINE explicit Packet1cd(const __m128d& a) : v(a) {}
+  Packet2d v;
+};
+
+// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
+// to leverage AVX instructions.
+#ifndef EIGEN_VECTORIZE_AVX
+template <>
+struct packet_traits<std::complex<double> > : default_packet_traits {
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 1,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasSqrt = 1,
+    HasLog = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0,
+    HasExp = 1
+  };
+};
+#endif
+
+template <>
+struct unpacket_traits<Packet1cd> {
+  typedef std::complex<double> type;
+  typedef Packet1cd half;
+  typedef Packet2d as_real;
+  enum {
+    size = 1,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(_mm_add_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(_mm_sub_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) {
+  return Packet1cd(pnegate(Packet2d(a.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) {
+  const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(static_cast<int32_t>(0x80000000), 0x0, 0x0, 0x0));
+  return Packet1cd(_mm_xor_pd(a.v, mask));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) {
+  __m128d tmp1 = _mm_mul_pd(_mm_unpackhi_pd(a.v, a.v), vec2d_swizzle1(b.v, 1, 0));
+#ifdef EIGEN_VECTORIZE_SSE3
+  __m128d tmp2 = _mm_movedup_pd(a.v);
+#else
+  __m128d tmp2 = _mm_unpacklo_pd(a.v, a.v);
+#endif
+#ifdef EIGEN_VECTORIZE_FMA
+  __m128d result = _mm_fmaddsub_pd(tmp2, b.v, tmp1);
+#else
+#ifdef EIGEN_VECTORIZE_SSE3
+  __m128d result = _mm_addsub_pd(_mm_mul_pd(tmp2, b.v), tmp1);
+#else
+  const __m128d mask = _mm_setr_pd(-0.0, 0.0);
+  __m128d result = _mm_add_pd(_mm_mul_pd(tmp2, b.v), _mm_xor_pd(tmp1, mask));
+#endif
+#endif
+  return Packet1cd(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ptrue<Packet1cd>(const Packet1cd& a) {
+  return Packet1cd(ptrue(Packet2d(a.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(_mm_and_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(_mm_or_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(_mm_xor_pd(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(_mm_andnot_pd(b.v, a.v));
+}
+
+// FIXME force unaligned load, this is a temporary fix
+template <>
+EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(_mm_load_pd((const double*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(_mm_loadu_pd((const double*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd
+pset1<Packet1cd>(const std::complex<double>& from) { /* here we really have to use unaligned loads :( */
+  return ploadu<Packet1cd>(&from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) {
+  return pset1<Packet1cd>(*from);
+}
+
+// FIXME force unaligned store, this is a temporary fix
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<double> >(std::complex<double>* to, const Packet1cd& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm_store_pd((double*)to, from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double>* to, const Packet1cd& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_pd((double*)to, from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double>* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) {
+  EIGEN_ALIGN16 double res[2];
+  _mm_store_pd(res, a.v);
+  return std::complex<double>(res[0], res[1]);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) {
+  return pfirst(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) {
+  return pfirst(a);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd, Packet2d)
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return pdiv_complex(a, b);
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip /* <Packet1cd> */ (const Packet1cd& x) {
+  return Packet1cd(preverse(Packet2d(x.v)));
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cf, 2>& kernel) {
+  __m128d w1 = _mm_castps_pd(kernel.packet[0].v);
+  __m128d w2 = _mm_castps_pd(kernel.packet[1].v);
+
+  __m128 tmp = _mm_castpd_ps(_mm_unpackhi_pd(w1, w2));
+  kernel.packet[0].v = _mm_castpd_ps(_mm_unpacklo_pd(w1, w2));
+  kernel.packet[1].v = tmp;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
+  __m128 eq = _mm_cmpeq_ps(a.v, b.v);
+  return Packet2cf(pand<Packet4f>(eq, vec4f_swizzle1(eq, 1, 0, 3, 2)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
+  __m128d eq = _mm_cmpeq_pd(a.v, b.v);
+  return Packet1cd(pand<Packet2d>(eq, vec2d_swizzle1(eq, 1, 0)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
+  return psqrt_complex<Packet1cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
+  return psqrt_complex<Packet2cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd plog<Packet1cd>(const Packet1cd& a) {
+  return plog_complex<Packet1cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf plog<Packet2cf>(const Packet2cf& a) {
+  return plog_complex<Packet2cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pexp<Packet1cd>(const Packet1cd& a) {
+  return pexp_complex<Packet1cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pexp<Packet2cf>(const Packet2cf& a) {
+  return pexp_complex<Packet2cf>(a);
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+// std::complex<float>
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& a, const Packet2cf& b, const Packet2cf& c) {
+  __m128 a_odd = _mm_movehdup_ps(a.v);
+  __m128 a_even = _mm_moveldup_ps(a.v);
+  __m128 b_swap = _mm_permute_ps(b.v, _MM_SHUFFLE(2, 3, 0, 1));
+  __m128 result = _mm_fmaddsub_ps(a_even, b.v, _mm_fmaddsub_ps(a_odd, b_swap, c.v));
+  return Packet2cf(result);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmsub(const Packet2cf& a, const Packet2cf& b, const Packet2cf& c) {
+  __m128 a_odd = _mm_movehdup_ps(a.v);
+  __m128 a_even = _mm_moveldup_ps(a.v);
+  __m128 b_swap = _mm_permute_ps(b.v, _MM_SHUFFLE(2, 3, 0, 1));
+  __m128 result = _mm_fmaddsub_ps(a_even, b.v, _mm_fmsubadd_ps(a_odd, b_swap, c.v));
+  return Packet2cf(result);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pnmadd(const Packet2cf& a, const Packet2cf& b, const Packet2cf& c) {
+  return pnegate(pmsub(a, b, c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pnmsub(const Packet2cf& a, const Packet2cf& b, const Packet2cf& c) {
+  return pnegate(pmadd(a, b, c));
+}
+// std::complex<double>
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& a, const Packet1cd& b, const Packet1cd& c) {
+  __m128d a_odd = _mm_permute_pd(a.v, 0x3);
+  __m128d a_even = _mm_movedup_pd(a.v);
+  __m128d b_swap = _mm_permute_pd(b.v, 0x1);
+  __m128d result = _mm_fmaddsub_pd(a_even, b.v, _mm_fmaddsub_pd(a_odd, b_swap, c.v));
+  return Packet1cd(result);
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmsub(const Packet1cd& a, const Packet1cd& b, const Packet1cd& c) {
+  __m128d a_odd = _mm_permute_pd(a.v, 0x3);
+  __m128d a_even = _mm_movedup_pd(a.v);
+  __m128d b_swap = _mm_permute_pd(b.v, 0x1);
+  __m128d result = _mm_fmaddsub_pd(a_even, b.v, _mm_fmsubadd_pd(a_odd, b_swap, c.v));
+  return Packet1cd(result);
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pnmadd(const Packet1cd& a, const Packet1cd& b, const Packet1cd& c) {
+  return pnegate(pmsub(a, b, c));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pnmsub(const Packet1cd& a, const Packet1cd& b, const Packet1cd& c) {
+  return pnegate(pmadd(a, b, c));
+}
+#endif
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_SSE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/MathFunctions.h
new file mode 100644
index 0000000..30c1f07
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/MathFunctions.h
@@ -0,0 +1,88 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* The sin and cos and functions of this file come from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+#ifndef EIGEN_MATH_FUNCTIONS_SSE_H
+#define EIGEN_MATH_FUNCTIONS_SSE_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet4f)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet2d)
+
+// Notice that for newer processors, it is counterproductive to use Newton
+// iteration for square root. In particular, Skylake and Zen2 processors
+// have approximately doubled throughput of the _mm_sqrt_ps instruction
+// compared to their predecessors.
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f psqrt<Packet4f>(const Packet4f& x) {
+  return _mm_sqrt_ps(x);
+}
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet2d psqrt<Packet2d>(const Packet2d& x) {
+  return _mm_sqrt_pd(x);
+}
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16b psqrt<Packet16b>(const Packet16b& x) {
+  return x;
+}
+
+#if EIGEN_FAST_MATH
+// Even on Skylake, using Newton iteration is a win for reciprocal square root.
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f prsqrt<Packet4f>(const Packet4f& x) {
+  return generic_rsqrt_newton_step<Packet4f, /*Steps=*/1>::run(x, _mm_rsqrt_ps(x));
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+// Trying to speed up reciprocal using Newton-Raphson is counterproductive
+// unless FMA is available. Without FMA pdiv(pset1<Packet>(Scalar(1),a)) is
+// 30% faster.
+template <>
+EIGEN_STRONG_INLINE Packet4f preciprocal<Packet4f>(const Packet4f& x) {
+  return generic_reciprocal_newton_step<Packet4f, /*Steps=*/1>::run(x, _mm_rcp_ps(x));
+}
+#endif
+
+#endif
+
+}  // end namespace internal
+
+namespace numext {
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float sqrt(const float& x) {
+  return internal::pfirst(internal::Packet4f(_mm_sqrt_ss(_mm_set_ss(x))));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double sqrt(const double& x) {
+#if EIGEN_COMP_GNUC_STRICT
+  // This works around a GCC bug generating poor code for _mm_sqrt_pd
+  // See https://gitlab.com/libeigen/eigen/commit/8dca9f97e38970
+  return internal::pfirst(internal::Packet2d(__builtin_ia32_sqrtsd(_mm_set_sd(x))));
+#else
+  return internal::pfirst(internal::Packet2d(_mm_sqrt_pd(_mm_set_sd(x))));
+#endif
+}
+
+}  // namespace numext
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_SSE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/PacketMath.h
new file mode 100644
index 0000000..e7039d4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/PacketMath.h
@@ -0,0 +1,2144 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_SSE_H
+#define EIGEN_PACKET_MATH_SSE_H
+
+#include <cstdint>
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#if !defined(EIGEN_VECTORIZE_AVX) && !defined(EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS)
+// 32 bits =>  8 registers
+// 64 bits => 16 registers
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS (2 * sizeof(void*))
+#endif
+
+#ifdef EIGEN_VECTORIZE_FMA
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+#endif
+
+#if ((defined EIGEN_VECTORIZE_AVX) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_MINGW || EIGEN_COMP_LCC) && \
+     (__GXX_ABI_VERSION < 1004)) ||                                                                     \
+    EIGEN_OS_QNX
+// With GCC's default ABI version, a __m128 or __m256 are the same types and therefore we cannot
+// have overloads for both types without linking error.
+// One solution is to increase ABI version using -fabi-version=4 (or greater).
+// Otherwise, we workaround this inconvenience by wrapping 128bit types into the following helper
+// structure:
+typedef eigen_packet_wrapper<__m128> Packet4f;
+typedef eigen_packet_wrapper<__m128d> Packet2d;
+#else
+typedef __m128 Packet4f;
+typedef __m128d Packet2d;
+#endif
+
+typedef eigen_packet_wrapper<__m128i, 0> Packet4i;
+typedef eigen_packet_wrapper<__m128i, 1> Packet16b;
+typedef eigen_packet_wrapper<__m128i, 4> Packet4ui;
+typedef eigen_packet_wrapper<__m128i, 5> Packet2l;
+
+template <>
+struct is_arithmetic<__m128> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<__m128i> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<__m128d> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<Packet4i> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<Packet2l> {
+  enum { value = true };
+};
+// Note that `Packet4ui` uses the underlying type `__m128i`, which is
+// interpreted as a vector of _signed_ `int32`s, which breaks some arithmetic
+// operations used in `GenericPacketMath.h`.
+template <>
+struct is_arithmetic<Packet4ui> {
+  enum { value = false };
+};
+template <>
+struct is_arithmetic<Packet16b> {
+  enum { value = true };
+};
+
+template <int p, int q, int r, int s>
+struct shuffle_mask {
+  enum { mask = (s) << 6 | (r) << 4 | (q) << 2 | (p) };
+};
+
+#define SIGN_MASK_I32 static_cast<int32_t>(0x80000000)
+
+// TODO: change the implementation of all swizzle* ops from macro to template,
+#define vec4f_swizzle1(v, p, q, r, s) \
+  Packet4f(_mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(v), (shuffle_mask<p, q, r, s>::mask))))
+
+#define vec4i_swizzle1(v, p, q, r, s) Packet4i(_mm_shuffle_epi32(v, (shuffle_mask<p, q, r, s>::mask)))
+
+#define vec4ui_swizzle1(v, p, q, r, s) Packet4ui(vec4i_swizzle1(v, p, q, r, s))
+
+#define vec2d_swizzle1(v, p, q) \
+  Packet2d(_mm_castsi128_pd(    \
+      _mm_shuffle_epi32(_mm_castpd_si128(v), (shuffle_mask<2 * p, 2 * p + 1, 2 * q, 2 * q + 1>::mask))))
+
+#define vec4f_swizzle2(a, b, p, q, r, s) Packet4f(_mm_shuffle_ps((a), (b), (shuffle_mask<p, q, r, s>::mask)))
+
+#define vec4i_swizzle2(a, b, p, q, r, s) \
+  Packet4i(                              \
+      _mm_castps_si128((_mm_shuffle_ps(_mm_castsi128_ps(a), _mm_castsi128_ps(b), (shuffle_mask<p, q, r, s>::mask)))))
+
+#define vec4ui_swizzle2(a, b, p, q, r, s) Packet4i(vec4i_swizzle2(a, b, p, q, r, s))
+
+EIGEN_STRONG_INLINE Packet4f vec4f_movelh(const Packet4f& a, const Packet4f& b) {
+  return Packet4f(_mm_movelh_ps(a, b));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_movehl(const Packet4f& a, const Packet4f& b) {
+  return Packet4f(_mm_movehl_ps(a, b));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_unpacklo(const Packet4f& a, const Packet4f& b) {
+  return Packet4f(_mm_unpacklo_ps(a, b));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b) {
+  return Packet4f(_mm_unpackhi_ps(a, b));
+}
+#define vec4f_duplane(a, p) vec4f_swizzle2(a, a, p, p, p, p)
+
+#define vec2d_swizzle2(a, b, mask) Packet2d(_mm_shuffle_pd(a, b, mask))
+
+EIGEN_STRONG_INLINE Packet2d vec2d_unpacklo(const Packet2d& a, const Packet2d& b) {
+  return Packet2d(_mm_unpacklo_pd(a, b));
+}
+EIGEN_STRONG_INLINE Packet2d vec2d_unpackhi(const Packet2d& a, const Packet2d& b) {
+  return Packet2d(_mm_unpackhi_pd(a, b));
+}
+#define vec2d_duplane(a, p) vec2d_swizzle2(a, a, (p << 1) | p)
+
+#define EIGEN_DECLARE_CONST_Packet4f(NAME, X) const Packet4f p4f_##NAME = pset1<Packet4f>(X)
+
+#define EIGEN_DECLARE_CONST_Packet2d(NAME, X) const Packet2d p2d_##NAME = pset1<Packet2d>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME, X) const Packet4f p4f_##NAME = pset1frombits<Packet4f>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4i(NAME, X) const Packet4i p4i_##NAME = pset1<Packet4i>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4ui(NAME, X) const Packet4ui p4ui_##NAME = pset1<Packet4ui>(X)
+
+// Work around lack of extract/cvt for epi64 when compiling for 32-bit.
+#if EIGEN_ARCH_x86_64
+EIGEN_ALWAYS_INLINE int64_t _mm_extract_epi64_0(const __m128i& a) { return _mm_cvtsi128_si64(a); }
+#ifdef EIGEN_VECTORIZE_SSE4_1
+EIGEN_ALWAYS_INLINE int64_t _mm_extract_epi64_1(const __m128i& a) { return _mm_extract_epi64(a, 1); }
+#else
+EIGEN_ALWAYS_INLINE int64_t _mm_extract_epi64_1(const __m128i& a) {
+  return _mm_cvtsi128_si64(_mm_castpd_si128(_mm_shuffle_pd(_mm_castsi128_pd(a), _mm_castsi128_pd(a), 0x1)));
+}
+#endif
+#else
+// epi64 instructions are not available.  The following seems to generate the same instructions
+// with -O2 in GCC/Clang.
+EIGEN_ALWAYS_INLINE int64_t _mm_extract_epi64_0(const __m128i& a) {
+  return numext::bit_cast<int64_t>(_mm_cvtsd_f64(_mm_castsi128_pd(a)));
+}
+EIGEN_ALWAYS_INLINE int64_t _mm_extract_epi64_1(const __m128i& a) {
+  return numext::bit_cast<int64_t>(_mm_cvtsd_f64(_mm_shuffle_pd(_mm_castsi128_pd(a), _mm_castsi128_pd(a), 0x1)));
+}
+#endif
+
+// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
+// to leverage AVX instructions.
+#ifndef EIGEN_VECTORIZE_AVX
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4f type;
+  typedef Packet4f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasReciprocal = EIGEN_FAST_MATH,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasNdtri = 1,
+    HasExp = 1,
+    HasPow = 1,
+    HasBessel = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasCbrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasErfc = EIGEN_FAST_MATH,
+    HasSign = 0  // The manually vectorized version is slightly slower for SSE.
+  };
+};
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasErfc = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasPow = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasCbrt = 1,
+    HasATan = 1,
+    HasATanh = 1,
+  };
+};
+template <>
+struct packet_traits<int> : default_packet_traits {
+  typedef Packet4i type;
+  typedef Packet4i half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp = 1,
+    HasDiv = 1,
+    HasShift = 1,
+  };
+};
+template <>
+struct packet_traits<uint32_t> : default_packet_traits {
+  typedef Packet4ui type;
+  typedef Packet4ui half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasNegate = 0,
+    HasCmp = 1,
+    HasShift = 1,
+  };
+};
+template <>
+struct packet_traits<int64_t> : default_packet_traits {
+  typedef Packet2l type;
+  typedef Packet2l half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasCmp = 1,
+    HasShift = 1,
+  };
+};
+#endif
+template <>
+struct packet_traits<bool> : default_packet_traits {
+  typedef Packet16b type;
+  typedef Packet16b half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasCmp = 1,
+    HasShift = 0,
+    HasAbs = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasConj = 0,
+    HasSqrt = 1,
+    HasNegate = 0,
+    HasSign = 0  // Don't try to vectorize psign<bool> = identity.
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4f> {
+  typedef float type;
+  typedef Packet4f half;
+  typedef Packet4i integer_packet;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet2d> {
+  typedef double type;
+  typedef Packet2d half;
+  typedef Packet2l integer_packet;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet2l> {
+  typedef int64_t type;
+  typedef Packet2l half;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet4i> {
+  typedef int type;
+  typedef Packet4i half;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet4ui> {
+  typedef uint32_t type;
+  typedef Packet4ui half;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template <>
+struct unpacket_traits<Packet16b> {
+  typedef bool type;
+  typedef Packet16b half;
+  enum {
+    size = 16,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+#ifndef EIGEN_VECTORIZE_AVX
+template <>
+struct scalar_div_cost<float, true> {
+  enum { value = 7 };
+};
+template <>
+struct scalar_div_cost<double, true> {
+  enum { value = 8 };
+};
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
+  return _mm_set_ps1(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
+  return _mm_set1_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pset1<Packet2l>(const int64_t& from) {
+  return _mm_set1_epi64x(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) {
+  return _mm_set1_epi32(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pset1<Packet4ui>(const uint32_t& from) {
+  return _mm_set1_epi32(numext::bit_cast<int32_t>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b pset1<Packet16b>(const bool& from) {
+  return _mm_set1_epi8(static_cast<char>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(unsigned int from) {
+  return _mm_castsi128_ps(pset1<Packet4i>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(uint64_t from) {
+  return _mm_castsi128_pd(_mm_set1_epi64x(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f peven_mask(const Packet4f& /*a*/) {
+  return _mm_castsi128_ps(_mm_set_epi32(0, -1, 0, -1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l peven_mask(const Packet2l& /*a*/) {
+  return _mm_set_epi32(0, 0, -1, -1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i peven_mask(const Packet4i& /*a*/) {
+  return _mm_set_epi32(0, -1, 0, -1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui peven_mask(const Packet4ui& /*a*/) {
+  return _mm_set_epi32(0, -1, 0, -1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d peven_mask(const Packet2d& /*a*/) {
+  return _mm_castsi128_pd(_mm_set_epi32(0, 0, -1, -1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pzero(const Packet4f& /*a*/) {
+  return _mm_setzero_ps();
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pzero(const Packet2d& /*a*/) {
+  return _mm_setzero_pd();
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pzero(const Packet2l& /*a*/) {
+  return _mm_setzero_si128();
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pzero(const Packet4i& /*a*/) {
+  return _mm_setzero_si128();
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pzero(const Packet4ui& /*a*/) {
+  return _mm_setzero_si128();
+}
+
+// GCC generates a shufps instruction for _mm_set1_ps/_mm_load1_ps instead of the more efficient pshufd instruction.
+// However, using inrinsics for pset1 makes gcc to generate crappy code in some cases (see bug 203)
+// Using inline assembly is also not an option because then gcc fails to reorder properly the instructions.
+// Therefore, we introduced the pload1 functions to be used in product kernels for which bug 203 does not apply.
+// Also note that with AVX, we want it to generate a vbroadcastss.
+#if EIGEN_COMP_GNUC_STRICT && (!defined __AVX__)
+template <>
+EIGEN_STRONG_INLINE Packet4f pload1<Packet4f>(const float* from) {
+  return vec4f_swizzle1(_mm_load_ss(from), 0, 0, 0, 0);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) {
+  return _mm_add_ps(pset1<Packet4f>(a), _mm_set_ps(3, 2, 1, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) {
+  return _mm_add_pd(pset1<Packet2d>(a), _mm_set_pd(1, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l plset<Packet2l>(const int64_t& a) {
+  return _mm_add_epi32(pset1<Packet2l>(a), _mm_set_epi64x(1, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a) {
+  return _mm_add_epi32(pset1<Packet4i>(a), _mm_set_epi32(3, 2, 1, 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui plset<Packet4ui>(const uint32_t& a) {
+  return _mm_add_epi32(pset1<Packet4ui>(a), _mm_set_epi32(3, 2, 1, 0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return _mm_add_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return _mm_add_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l padd<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return _mm_add_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return _mm_add_epi32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui padd<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return _mm_add_epi32(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16b padd<Packet16b>(const Packet16b& a, const Packet16b& b) {
+  return _mm_or_si128(a, b);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet padds(const Packet& a, const Packet& b);
+template <>
+EIGEN_STRONG_INLINE Packet4f padds<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return _mm_add_ss(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d padds<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return _mm_add_sd(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return _mm_sub_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return _mm_sub_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l psub<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return _mm_sub_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return _mm_sub_epi32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui psub<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return _mm_sub_epi32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b psub<Packet16b>(const Packet16b& a, const Packet16b& b) {
+  return _mm_xor_si128(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b);
+template <>
+EIGEN_STRONG_INLINE Packet4f paddsub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+#ifdef EIGEN_VECTORIZE_SSE3
+  return _mm_addsub_ps(a, b);
+#else
+  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(SIGN_MASK_I32, 0x0, SIGN_MASK_I32, 0x0));
+  return padd(a, pxor(mask, b));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d&, const Packet2d&);
+template <>
+EIGEN_STRONG_INLINE Packet2d paddsub<Packet2d>(const Packet2d& a, const Packet2d& b) {
+#ifdef EIGEN_VECTORIZE_SSE3
+  return _mm_addsub_pd(a, b);
+#else
+  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0x0, SIGN_MASK_I32, 0x0, 0x0));
+  return padd(a, pxor(mask, b));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) {
+  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32, SIGN_MASK_I32));
+  return _mm_xor_ps(a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) {
+  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0x0, SIGN_MASK_I32, 0x0, SIGN_MASK_I32));
+  return _mm_xor_pd(a, mask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pnegate(const Packet2l& a) {
+  return psub(pzero(a), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) {
+  return psub(pzero(a), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pconj(const Packet2l& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return _mm_mul_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return _mm_mul_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmul<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  // 64-bit mul requires avx512, so do this with 32-bit multiplication
+  __m128i upper32_a = _mm_srli_epi64(a, 32);
+  __m128i upper32_b = _mm_srli_epi64(b, 32);
+
+  // upper * lower
+  __m128i mul1 = _mm_mul_epu32(upper32_a, b);
+  __m128i mul2 = _mm_mul_epu32(upper32_b, a);
+  // Gives us both upper*upper and lower*lower
+  __m128i mul3 = _mm_mul_epu32(a, b);
+
+  __m128i high = _mm_slli_epi64(_mm_add_epi64(mul1, mul2), 32);
+  return _mm_add_epi64(high, mul3);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_mullo_epi32(a, b);
+#else
+  // this version is slightly faster than 4 scalar products
+  return vec4i_swizzle1(
+      vec4i_swizzle2(_mm_mul_epu32(a, b), _mm_mul_epu32(vec4i_swizzle1(a, 1, 0, 3, 2), vec4i_swizzle1(b, 1, 0, 3, 2)),
+                     0, 2, 0, 2),
+      0, 2, 1, 3);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmul<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_mullo_epi32(a, b);
+#else
+  // this version is slightly faster than 4 scalar products
+  return vec4ui_swizzle1(
+      vec4ui_swizzle2(_mm_mul_epu32(a, b),
+                      _mm_mul_epu32(vec4ui_swizzle1(a, 1, 0, 3, 2), vec4ui_swizzle1(b, 1, 0, 3, 2)), 0, 2, 0, 2),
+      0, 2, 1, 3);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16b pmul<Packet16b>(const Packet16b& a, const Packet16b& b) {
+  return _mm_and_si128(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return _mm_div_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return _mm_div_pd(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) {
+#ifdef EIGEN_VECTORIZE_AVX
+  return _mm256_cvttpd_epi32(_mm256_div_pd(_mm256_cvtepi32_pd(a), _mm256_cvtepi32_pd(b)));
+#else
+  __m128i q_lo = _mm_cvttpd_epi32(_mm_div_pd(_mm_cvtepi32_pd(a), _mm_cvtepi32_pd(b)));
+  __m128i q_hi = _mm_cvttpd_epi32(
+      _mm_div_pd(_mm_cvtepi32_pd(vec4i_swizzle1(a, 2, 3, 0, 1)), _mm_cvtepi32_pd(vec4i_swizzle1(b, 2, 3, 0, 1))));
+  return vec4i_swizzle1(_mm_unpacklo_epi32(q_lo, q_hi), 0, 2, 1, 3);
+#endif
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+template <>
+EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return _mm_fmadd_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return _mm_fmadd_pd(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return _mm_fmsub_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return _mm_fmsub_pd(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return _mm_fnmadd_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return _mm_fnmadd_pd(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return _mm_fnmsub_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return _mm_fnmsub_pd(a, b, c);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet pmadds(const Packet& a, const Packet& b, const Packet& c);
+template <>
+EIGEN_STRONG_INLINE Packet4f pmadds<Packet4f>(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return _mm_fmadd_ss(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmadds<Packet2d>(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return _mm_fmadd_sd(a, b, c);
+}
+#endif
+
+#ifdef EIGEN_VECTORIZE_SSE4_1
+template <>
+EIGEN_STRONG_INLINE Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b) {
+  return _mm_blendv_ps(b, a, mask);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2l pselect(const Packet2l& mask, const Packet2l& a, const Packet2l& b) {
+  return _mm_castpd_si128(_mm_blendv_pd(_mm_castsi128_pd(b), _mm_castsi128_pd(a), _mm_castsi128_pd(mask)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pselect(const Packet4i& mask, const Packet4i& a, const Packet4i& b) {
+  return _mm_castps_si128(_mm_blendv_ps(_mm_castsi128_ps(b), _mm_castsi128_ps(a), _mm_castsi128_ps(mask)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4ui pselect(const Packet4ui& mask, const Packet4ui& a, const Packet4ui& b) {
+  return _mm_castps_si128(_mm_blendv_ps(_mm_castsi128_ps(b), _mm_castsi128_ps(a), _mm_castsi128_ps(mask)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pselect(const Packet2d& mask, const Packet2d& a, const Packet2d& b) {
+  return _mm_blendv_pd(b, a, mask);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet2l ptrue<Packet2l>(const Packet2l& a) {
+  return _mm_cmpeq_epi32(a, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ptrue<Packet4i>(const Packet4i& a) {
+  return _mm_cmpeq_epi32(a, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b ptrue<Packet16b>(const Packet16b& /*a*/) {
+  return pset1<Packet16b>(true);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f ptrue<Packet4f>(const Packet4f& a) {
+  Packet4i b = _mm_castps_si128(a);
+  return _mm_castsi128_ps(_mm_cmpeq_epi32(b, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d ptrue<Packet2d>(const Packet2d& a) {
+  Packet4i b = _mm_castpd_si128(a);
+  return _mm_castsi128_pd(_mm_cmpeq_epi32(b, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return _mm_and_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return _mm_and_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pand<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return _mm_and_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return _mm_and_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return _mm_and_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b pand<Packet16b>(const Packet16b& a, const Packet16b& b) {
+  return _mm_and_si128(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return _mm_or_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return _mm_or_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l por<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return _mm_or_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return _mm_or_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui por<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return _mm_or_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b por<Packet16b>(const Packet16b& a, const Packet16b& b) {
+  return _mm_or_si128(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return _mm_xor_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return _mm_xor_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pxor<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return _mm_xor_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return _mm_xor_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pxor<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return _mm_xor_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b pxor<Packet16b>(const Packet16b& a, const Packet16b& b) {
+  return _mm_xor_si128(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return _mm_andnot_ps(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return _mm_andnot_pd(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pandnot<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return _mm_andnot_si128(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return _mm_andnot_si128(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pandnot<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+  return _mm_andnot_si128(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b pandnot<Packet16b>(const Packet16b& a, const Packet16b& b) {
+  return _mm_andnot_si128(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b pcmp_lt(const Packet16b& a, const Packet16b& b) {
+  return _mm_andnot_si128(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_le(const Packet4f& a, const Packet4f& b) {
+  return _mm_cmple_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_lt(const Packet4f& a, const Packet4f& b) {
+  return _mm_cmplt_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan(const Packet4f& a, const Packet4f& b) {
+  return _mm_cmpnge_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcmp_eq(const Packet4f& a, const Packet4f& b) {
+  return _mm_cmpeq_ps(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b) {
+  return _mm_cmple_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b) {
+  return _mm_cmplt_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b) {
+  return _mm_cmpnge_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b) {
+  return _mm_cmpeq_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_lt(const Packet4i& a, const Packet4i& b) {
+  return _mm_cmplt_epi32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_eq(const Packet4i& a, const Packet4i& b) {
+  return _mm_cmpeq_epi32(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcmp_le(const Packet4i& a, const Packet4i& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_cmpeq_epi32(a, _mm_min_epi32(a, b));
+#else
+  return por(pcmp_lt(a, b), pcmp_eq(a, b));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcmp_lt(const Packet2l& a, const Packet2l& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_2
+  return _mm_cmpgt_epi64(b, a);
+#else
+  Packet4i eq = pcmp_eq<Packet4i>(Packet4i(a), Packet4i(b));
+  Packet2l hi_eq = Packet2l(_mm_shuffle_epi32(eq, (shuffle_mask<1, 1, 3, 3>::mask)));
+  Packet4i lt = pcmp_lt<Packet4i>(Packet4i(a), Packet4i(b));
+  Packet2l hi_lt = Packet2l(_mm_shuffle_epi32(lt, (shuffle_mask<1, 1, 3, 3>::mask)));
+  Packet2l lo_lt = Packet2l(_mm_shuffle_epi32(lt, (shuffle_mask<0, 0, 2, 2>::mask)));
+  // return hi(a) < hi(b) || (hi(a) == hi(b) && lo(a) < lo(b))
+  return por(hi_lt, pand(hi_eq, lo_lt));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcmp_eq(const Packet2l& a, const Packet2l& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_cmpeq_epi64(a, b);
+#else
+  Packet4i tmp = pcmp_eq<Packet4i>(Packet4i(a), Packet4i(b));
+  return Packet2l(pand<Packet4i>(tmp, _mm_shuffle_epi32(tmp, (shuffle_mask<1, 0, 3, 2>::mask))));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcmp_le(const Packet2l& a, const Packet2l& b) {
+  return por(pcmp_lt(a, b), pcmp_eq(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b pcmp_eq(const Packet16b& a, const Packet16b& b) {
+  // Mask out invalid bool bits to avoid UB.
+  const Packet16b kBoolMask = pset1<Packet16b>(true);
+  return _mm_and_si128(_mm_cmpeq_epi8(a, b), kBoolMask);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcmp_eq(const Packet4ui& a, const Packet4ui& b) {
+  return _mm_cmpeq_epi32(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6, 3, 0)
+// There appears to be a bug in GCC, by which the optimizer may
+// flip the argument order in calls to _mm_min_ps, so we have to
+// resort to inline ASM here. This is supposed to be fixed in gcc6.3,
+// see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
+#ifdef EIGEN_VECTORIZE_AVX
+  Packet4f res;
+  asm("vminps %[a], %[b], %[res]" : [res] "=x"(res) : [a] "x"(a), [b] "x"(b));
+#else
+  Packet4f res = b;
+  asm("minps %[a], %[res]" : [res] "+x"(res) : [a] "x"(a));
+#endif
+  return res;
+#else
+  // Arguments are reversed to match NaN propagation behavior of std::min.
+  return _mm_min_ps(b, a);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6, 3, 0)
+// There appears to be a bug in GCC, by which the optimizer may
+// flip the argument order in calls to _mm_min_pd, so we have to
+// resort to inline ASM here. This is supposed to be fixed in gcc6.3,
+// see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
+#ifdef EIGEN_VECTORIZE_AVX
+  Packet2d res;
+  asm("vminpd %[a], %[b], %[res]" : [res] "=x"(res) : [a] "x"(a), [b] "x"(b));
+#else
+  Packet2d res = b;
+  asm("minpd %[a], %[res]" : [res] "+x"(res) : [a] "x"(a));
+#endif
+  return res;
+#else
+  // Arguments are reversed to match NaN propagation behavior of std::min.
+  return _mm_min_pd(b, a);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmin<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  Packet2l a_lt_mask = pcmp_lt(a, b);
+  return por(pandnot(b, a_lt_mask), pand(a, a_lt_mask));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_min_epi32(a, b);
+#else
+  // after some bench, this version *is* faster than a scalar implementation
+  Packet4i mask = _mm_cmplt_epi32(a, b);
+  return _mm_or_si128(_mm_and_si128(mask, a), _mm_andnot_si128(mask, b));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmin<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_min_epu32(a, b);
+#else
+  return padd((Packet4ui)pmin((Packet4i)psub(a, pset1<Packet4ui>(0x80000000UL)),
+                              (Packet4i)psub(b, pset1<Packet4ui>(0x80000000UL))),
+              pset1<Packet4ui>(0x80000000UL));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6, 3, 0)
+// There appears to be a bug in GCC, by which the optimizer may
+// flip the argument order in calls to _mm_max_ps, so we have to
+// resort to inline ASM here. This is supposed to be fixed in gcc6.3,
+// see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
+#ifdef EIGEN_VECTORIZE_AVX
+  Packet4f res;
+  asm("vmaxps %[a], %[b], %[res]" : [res] "=x"(res) : [a] "x"(a), [b] "x"(b));
+#else
+  Packet4f res = b;
+  asm("maxps %[a], %[res]" : [res] "+x"(res) : [a] "x"(a));
+#endif
+  return res;
+#else
+  // Arguments are reversed to match NaN propagation behavior of std::max.
+  return _mm_max_ps(b, a);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6, 3, 0)
+// There appears to be a bug in GCC, by which the optimizer may
+// flip the argument order in calls to _mm_max_pd, so we have to
+// resort to inline ASM here. This is supposed to be fixed in gcc6.3,
+// see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
+#ifdef EIGEN_VECTORIZE_AVX
+  Packet2d res;
+  asm("vmaxpd %[a], %[b], %[res]" : [res] "=x"(res) : [a] "x"(a), [b] "x"(b));
+#else
+  Packet2d res = b;
+  asm("maxpd %[a], %[res]" : [res] "+x"(res) : [a] "x"(a));
+#endif
+  return res;
+#else
+  // Arguments are reversed to match NaN propagation behavior of std::max.
+  return _mm_max_pd(b, a);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pmax<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  Packet2l a_lt_mask = pcmp_lt(a, b);
+  return por(pandnot(a, a_lt_mask), pand(b, a_lt_mask));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_max_epi32(a, b);
+#else
+  // after some bench, this version *is* faster than a scalar implementation
+  Packet4i mask = _mm_cmpgt_epi32(a, b);
+  return _mm_or_si128(_mm_and_si128(mask, a), _mm_andnot_si128(mask, b));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pmax<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_max_epu32(a, b);
+#else
+  return padd((Packet4ui)pmax((Packet4i)psub(a, pset1<Packet4ui>(0x80000000UL)),
+                              (Packet4i)psub(b, pset1<Packet4ui>(0x80000000UL))),
+              pset1<Packet4ui>(0x80000000UL));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcmp_lt(const Packet4ui& a, const Packet4ui& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return pxor(pcmp_eq(a, pmax(a, b)), ptrue(a));
+#else
+  return (Packet4ui)pcmp_lt((Packet4i)psub(a, pset1<Packet4ui>(0x80000000UL)),
+                            (Packet4i)psub(b, pset1<Packet4ui>(0x80000000UL)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcmp_le(const Packet4ui& a, const Packet4ui& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return pcmp_eq(a, pmin(a, b));
+#else
+  return (Packet4ui)pcmp_le((Packet4i)psub(a, pset1<Packet4ui>(0x80000000UL)),
+                            (Packet4i)psub(b, pset1<Packet4ui>(0x80000000UL)));
+#endif
+}
+
+template <typename Packet, typename Op>
+EIGEN_STRONG_INLINE Packet pminmax_propagate_numbers(const Packet& a, const Packet& b, Op op) {
+  // In this implementation, we take advantage of the fact that pmin/pmax for SSE
+  // always return a if either a or b is NaN.
+  Packet not_nan_mask_a = pcmp_eq(a, a);
+  Packet m = op(a, b);
+  return pselect<Packet>(not_nan_mask_a, m, b);
+}
+
+template <typename Packet, typename Op>
+EIGEN_STRONG_INLINE Packet pminmax_propagate_nan(const Packet& a, const Packet& b, Op op) {
+  // In this implementation, we take advantage of the fact that pmin/pmax for SSE
+  // always return a if either a or b is NaN.
+  Packet not_nan_mask_a = pcmp_eq(a, a);
+  Packet m = op(b, a);
+  return pselect<Packet>(not_nan_mask_a, m, a);
+}
+
+// Add specializations for min/max with prescribed NaN propagation.
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet4f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet2d>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet4f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet2d>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet4f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet2d>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet4f>);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet2d>);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f psignbit(const Packet4f& a) {
+  return _mm_castsi128_ps(_mm_srai_epi32(_mm_castps_si128(a), 31));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d psignbit(const Packet2d& a) {
+  Packet4f tmp = psignbit<Packet4f>(_mm_castpd_ps(a));
+#ifdef EIGEN_VECTORIZE_AVX
+  return _mm_castps_pd(_mm_permute_ps(tmp, (shuffle_mask<1, 1, 3, 3>::mask)));
+#else
+  return _mm_castps_pd(_mm_shuffle_ps(tmp, tmp, (shuffle_mask<1, 1, 3, 3>::mask)));
+#endif  // EIGEN_VECTORIZE_AVX
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i psignbit(const Packet4i& a) {
+  return _mm_srai_epi32(a, 31);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui psignbit(const Packet4ui& a) {
+  return pzero(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l psignbit(const Packet2l& a) {
+  Packet4i tmp = psignbit<Packet4i>(Packet4i(a));
+  return Packet2l(_mm_shuffle_epi32(tmp, (shuffle_mask<1, 1, 3, 3>::mask)));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2l parithmetic_shift_right(const Packet2l& a) {
+  Packet2l signbit = psignbit(a);
+  return por(_mm_slli_epi64(signbit, 64 - N), _mm_srli_epi64(a, N));
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
+  return _mm_srli_epi64(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
+  return _mm_slli_epi64(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(const Packet4i& a) {
+  return _mm_srai_epi32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i plogical_shift_right(const Packet4i& a) {
+  return _mm_srli_epi32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i plogical_shift_left(const Packet4i& a) {
+  return _mm_slli_epi32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui parithmetic_shift_right(const Packet4ui& a) {
+  return _mm_srli_epi32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui plogical_shift_right(const Packet4ui& a) {
+  return _mm_srli_epi32(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4ui plogical_shift_left(const Packet4ui& a) {
+  return _mm_slli_epi32(a, N);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) {
+  const __m128i mask = _mm_setr_epi32(0x7FFFFFFF, 0x7FFFFFFF, 0x7FFFFFFF, 0x7FFFFFFF);
+  return _mm_castsi128_ps(_mm_and_si128(mask, _mm_castps_si128(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) {
+  const __m128i mask = _mm_setr_epi32(-1, 0x7FFFFFFF, -1, 0x7FFFFFFF);
+  return _mm_castsi128_pd(_mm_and_si128(mask, _mm_castpd_si128(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pabs(const Packet2l& a) {
+  Packet2l signbit = psignbit(a);
+  return _mm_sub_epi64(_mm_xor_si128(a, signbit), signbit);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) {
+#ifdef EIGEN_VECTORIZE_SSSE3
+  return _mm_abs_epi32(a);
+#else
+  Packet4i signbit = psignbit(a);
+  return _mm_sub_epi32(_mm_xor_si128(a, signbit), signbit);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pabs(const Packet4ui& a) {
+  return a;
+}
+
+#ifdef EIGEN_VECTORIZE_SSE4_1
+template <>
+EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a) {
+  // Unfortunately _mm_round_ps doesn't have a rounding mode to implement numext::round.
+  const Packet4f mask = pset1frombits<Packet4f>(0x80000000u);
+  const Packet4f prev0dot5 = pset1frombits<Packet4f>(0x3EFFFFFFu);
+  return _mm_round_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) {
+  const Packet2d mask = _mm_castsi128_pd(_mm_set_epi64x(0x8000000000000000ull, 0x8000000000000000ull));
+  const Packet2d prev0dot5 = _mm_castsi128_pd(_mm_set_epi64x(0x3FDFFFFFFFFFFFFFull, 0x3FDFFFFFFFFFFFFFull));
+  return _mm_round_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a) {
+  return _mm_round_ps(a, _MM_FROUND_CUR_DIRECTION);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a) {
+  return _mm_round_pd(a, _MM_FROUND_CUR_DIRECTION);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) {
+  return _mm_ceil_ps(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) {
+  return _mm_ceil_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) {
+  return _mm_floor_ps(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) {
+  return _mm_floor_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ptrunc<Packet4f>(const Packet4f& a) {
+  return _mm_round_ps(a, _MM_FROUND_TRUNC);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d ptrunc<Packet2d>(const Packet2d& a) {
+  return _mm_round_pd(a, _MM_FROUND_TRUNC);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_ps(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pload<Packet2l>(const int64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pload<Packet4ui>(const uint32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b pload<Packet16b>(const bool* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+#if EIGEN_COMP_MSVC
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_ps(from);
+}
+#else
+// NOTE: with the code below, MSVC's compiler crashes!
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_ps(from);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l ploadu<Packet2l>(const int64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui ploadu<Packet4ui>(const uint32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b ploadu<Packet16b>(const bool* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+// Load lower part of packet zero extending.
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet ploadl(const typename unpacket_traits<Packet>::type* from);
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadl<Packet4f>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm_castpd_ps(_mm_load_sd(reinterpret_cast<const double*>(from)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d ploadl<Packet2d>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm_load_sd(from);
+}
+
+// Load scalar
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet ploads(const typename unpacket_traits<Packet>::type* from);
+template <>
+EIGEN_STRONG_INLINE Packet4f ploads<Packet4f>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm_load_ss(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d ploads<Packet2d>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm_load_sd(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) {
+  return vec4f_swizzle1(_mm_castpd_ps(_mm_load_sd(reinterpret_cast<const double*>(from))), 0, 0, 1, 1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) {
+  return pset1<Packet2d>(from[0]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l ploaddup<Packet2l>(const int64_t* from) {
+  return pset1<Packet2l>(from[0]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int* from) {
+  Packet4i tmp;
+  tmp = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(from));
+  return vec4i_swizzle1(tmp, 0, 0, 1, 1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui ploaddup<Packet4ui>(const uint32_t* from) {
+  Packet4ui tmp;
+  tmp = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(from));
+  return vec4ui_swizzle1(tmp, 0, 0, 1, 1);
+}
+
+// Loads 8 bools from memory and returns the packet
+// {b0, b0, b1, b1, b2, b2, b3, b3, b4, b4, b5, b5, b6, b6, b7, b7}
+template <>
+EIGEN_STRONG_INLINE Packet16b ploaddup<Packet16b>(const bool* from) {
+  __m128i tmp = _mm_castpd_si128(pload1<Packet2d>(reinterpret_cast<const double*>(from)));
+  return _mm_unpacklo_epi8(tmp, tmp);
+}
+
+// Loads 4 bools from memory and returns the packet
+// {b0, b0  b0, b0, b1, b1, b1, b1, b2, b2, b2, b2, b3, b3, b3, b3}
+template <>
+EIGEN_STRONG_INLINE Packet16b ploadquad<Packet16b>(const bool* from) {
+  __m128i tmp = _mm_castps_si128(pload1<Packet4f>(reinterpret_cast<const float*>(from)));
+  tmp = _mm_unpacklo_epi8(tmp, tmp);
+  return _mm_unpacklo_epi16(tmp, tmp);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm_store_ps(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm_store_pd(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int64_t>(int64_t* to, const Packet2l& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet4ui& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<bool>(bool* to, const Packet16b& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_pd(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_ps(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int64_t>(int64_t* to, const Packet2l& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet4ui& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<bool>(bool* to, const Packet16b& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_STRONG_INLINE void pstorel(Scalar* to, const Packet& from);
+template <>
+EIGEN_STRONG_INLINE void pstorel(float* to, const Packet4f& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storel_pi(reinterpret_cast<__m64*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstorel(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_storel_pd(to, from);
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_STRONG_INLINE void pstores(Scalar* to, const Packet& from);
+template <>
+EIGEN_STRONG_INLINE void pstores(float* to, const Packet4f& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_store_ss(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstores(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_store_sd(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) {
+  return _mm_shuffle_ps(a, a, 0x1B);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) {
+  return _mm_shuffle_pd(a, a, 0x1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l preverse(const Packet2l& a) {
+  return _mm_castpd_si128(preverse(_mm_castsi128_pd(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) {
+  return _mm_shuffle_epi32(a, 0x1B);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preverse(const Packet4ui& a) {
+  return _mm_shuffle_epi32(a, 0x1B);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16b preverse(const Packet16b& a) {
+#ifdef EIGEN_VECTORIZE_SSSE3
+  __m128i mask = _mm_set_epi8(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15);
+  return _mm_shuffle_epi8(a, mask);
+#else
+  Packet16b tmp = _mm_shuffle_epi32(a, _MM_SHUFFLE(0, 1, 2, 3));
+  tmp = _mm_shufflehi_epi16(_mm_shufflelo_epi16(tmp, _MM_SHUFFLE(2, 3, 0, 1)), _MM_SHUFFLE(2, 3, 0, 1));
+  return _mm_or_si128(_mm_slli_epi16(tmp, 8), _mm_srli_epi16(tmp, 8));
+#endif
+}
+
+#if EIGEN_COMP_MSVC_STRICT && EIGEN_OS_WIN64
+// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010
+// Direct of the struct members fixed bug #62.
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
+  return a.m128_f32[0];
+}
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
+  return a.m128d_f64[0];
+}
+template <>
+EIGEN_STRONG_INLINE int64_t pfirst<Packet2l>(const Packet2l& a) {
+  int64_t x = _mm_extract_epi64_0(a);
+  return x;
+}
+template <>
+EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) {
+  int x = _mm_cvtsi128_si32(a);
+  return x;
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t pfirst<Packet4ui>(const Packet4ui& a) {
+  uint32_t x = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(a));
+  return x;
+}
+#elif EIGEN_COMP_MSVC_STRICT
+// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
+  float x = _mm_cvtss_f32(a);
+  return x;
+}
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
+  double x = _mm_cvtsd_f64(a);
+  return x;
+}
+template <>
+EIGEN_STRONG_INLINE int64_t pfirst<Packet2l>(const Packet2l& a) {
+  int64_t x = _mm_extract_epi64_0(a);
+  return x;
+}
+template <>
+EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) {
+  int x = _mm_cvtsi128_si32(a);
+  return x;
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t pfirst<Packet4ui>(const Packet4ui& a) {
+  uint32_t x = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(a));
+  return x;
+}
+#else
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
+  return _mm_cvtss_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
+  return _mm_cvtsd_f64(a);
+}
+template <>
+EIGEN_STRONG_INLINE int64_t pfirst<Packet2l>(const Packet2l& a) {
+  return _mm_extract_epi64_0(a);
+}
+template <>
+EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) {
+  return _mm_cvtsi128_si32(a);
+}
+template <>
+EIGEN_STRONG_INLINE uint32_t pfirst<Packet4ui>(const Packet4ui& a) {
+  return numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(a));
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE bool pfirst<Packet16b>(const Packet16b& a) {
+  int x = _mm_cvtsi128_si32(a);
+  return static_cast<bool>(x & 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pgather<float, Packet4f>(const float* from, Index stride) {
+  return _mm_set_ps(from[3 * stride], from[2 * stride], from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pgather<double, Packet2d>(const double* from, Index stride) {
+  return _mm_set_pd(from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pgather<int64_t, Packet2l>(const int64_t* from, Index stride) {
+  return _mm_set_epi64x(from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pgather<int, Packet4i>(const int* from, Index stride) {
+  return _mm_set_epi32(from[3 * stride], from[2 * stride], from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pgather<uint32_t, Packet4ui>(const uint32_t* from, Index stride) {
+  return _mm_set_epi32(numext::bit_cast<int32_t>(from[3 * stride]), numext::bit_cast<int32_t>(from[2 * stride]),
+                       numext::bit_cast<int32_t>(from[1 * stride]), numext::bit_cast<int32_t>(from[0 * stride]));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16b pgather<bool, Packet16b>(const bool* from, Index stride) {
+  return _mm_set_epi8(from[15 * stride], from[14 * stride], from[13 * stride], from[12 * stride], from[11 * stride],
+                      from[10 * stride], from[9 * stride], from[8 * stride], from[7 * stride], from[6 * stride],
+                      from[5 * stride], from[4 * stride], from[3 * stride], from[2 * stride], from[1 * stride],
+                      from[0 * stride]);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride) {
+  to[stride * 0] = pfirst(from);
+  to[stride * 1] = pfirst(Packet4f(_mm_shuffle_ps(from, from, 1)));
+  to[stride * 2] = pfirst(Packet4f(_mm_shuffle_ps(from, from, 2)));
+  to[stride * 3] = pfirst(Packet4f(_mm_shuffle_ps(from, from, 3)));
+}
+template <>
+EIGEN_STRONG_INLINE void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride) {
+  to[stride * 0] = pfirst(from);
+  to[stride * 1] = pfirst(preverse(from));
+}
+template <>
+EIGEN_STRONG_INLINE void pscatter<int64_t, Packet2l>(int64_t* to, const Packet2l& from, Index stride) {
+  to[stride * 0] = pfirst(from);
+  to[stride * 1] = pfirst(preverse(from));
+}
+template <>
+EIGEN_STRONG_INLINE void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride) {
+  to[stride * 0] = _mm_cvtsi128_si32(from);
+  to[stride * 1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 1));
+  to[stride * 2] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 2));
+  to[stride * 3] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 3));
+}
+template <>
+EIGEN_STRONG_INLINE void pscatter<uint32_t, Packet4ui>(uint32_t* to, const Packet4ui& from, Index stride) {
+  to[stride * 0] = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(from));
+  to[stride * 1] = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(_mm_shuffle_epi32(from, 1)));
+  to[stride * 2] = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(_mm_shuffle_epi32(from, 2)));
+  to[stride * 3] = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(_mm_shuffle_epi32(from, 3)));
+}
+template <>
+EIGEN_STRONG_INLINE void pscatter<bool, Packet16b>(bool* to, const Packet16b& from, Index stride) {
+  EIGEN_ALIGN16 bool tmp[16];
+  pstore(tmp, from);
+  to[stride * 0] = tmp[0];
+  to[stride * 1] = tmp[1];
+  to[stride * 2] = tmp[2];
+  to[stride * 3] = tmp[3];
+  to[stride * 4] = tmp[4];
+  to[stride * 5] = tmp[5];
+  to[stride * 6] = tmp[6];
+  to[stride * 7] = tmp[7];
+  to[stride * 8] = tmp[8];
+  to[stride * 9] = tmp[9];
+  to[stride * 10] = tmp[10];
+  to[stride * 11] = tmp[11];
+  to[stride * 12] = tmp[12];
+  to[stride * 13] = tmp[13];
+  to[stride * 14] = tmp[14];
+  to[stride * 15] = tmp[15];
+}
+
+// some compilers might be tempted to perform multiple moves instead of using a vector path.
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet4f>(float* to, const float& a) {
+  Packet4f pa = _mm_set_ss(a);
+  pstore(to, Packet4f(vec4f_swizzle1(pa, 0, 0, 0, 0)));
+}
+// some compilers might be tempted to perform multiple moves instead of using a vector path.
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet2d>(double* to, const double& a) {
+  Packet2d pa = _mm_set_sd(a);
+  pstore(to, Packet2d(vec2d_swizzle1(pa, 0, 0)));
+}
+
+#if EIGEN_COMP_PGI && EIGEN_COMP_PGI < 1900
+typedef const void* SsePrefetchPtrType;
+#else
+typedef const char* SsePrefetchPtrType;
+#endif
+
+#ifndef EIGEN_VECTORIZE_AVX
+template <>
+EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<int64_t>(const int64_t* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<uint32_t>(const uint32_t* addr) {
+  _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+// Extract exponent without existence of Packet2l.
+template <>
+EIGEN_STRONG_INLINE Packet2d pfrexp_generic_get_biased_exponent(const Packet2d& a) {
+  const Packet2d cst_exp_mask = pset1frombits<Packet2d>(static_cast<uint64_t>(0x7ff0000000000000ull));
+  __m128i a_expo = _mm_srli_epi64(_mm_castpd_si128(pand(a, cst_exp_mask)), 52);
+  return _mm_cvtepi32_pd(vec4i_swizzle1(a_expo, 0, 2, 1, 3));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+// We specialize pldexp here, since the generic implementation uses Packet2l, which is not well
+// supported by SSE, and has more range than is needed for exponents.
+template <>
+EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
+  // Clamp exponent to [-2099, 2099]
+  const Packet2d max_exponent = pset1<Packet2d>(2099.0);
+  const Packet2d e = pmin(pmax(exponent, pnegate(max_exponent)), max_exponent);
+
+  // Convert e to integer and swizzle to low-order bits.
+  const Packet4i ei = vec4i_swizzle1(_mm_cvtpd_epi32(e), 0, 3, 1, 3);
+
+  // Split 2^e into four factors and multiply:
+  const Packet4i bias = _mm_set_epi32(0, 1023, 0, 1023);
+  Packet4i b = parithmetic_shift_right<2>(ei);                       // floor(e/4)
+  Packet2d c = _mm_castsi128_pd(_mm_slli_epi64(padd(b, bias), 52));  // 2^b
+  Packet2d out = pmul(pmul(pmul(a, c), c), c);                       // a * 2^(3b)
+  b = psub(psub(psub(ei, b), b), b);                                 // e - 3b
+  c = _mm_castsi128_pd(_mm_slli_epi64(padd(b, bias), 52));           // 2^(e - 3b)
+  out = pmul(out, c);                                                // a * 2^e
+  return out;
+}
+
+// We specialize pldexp here, since the generic implementation uses Packet2l, which is not well
+// supported by SSE, and has more range than is needed for exponents.
+template <>
+EIGEN_STRONG_INLINE Packet2d pldexp_fast<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
+  // Clamp exponent to [-1023, 1024]
+  const Packet2d min_exponent = pset1<Packet2d>(-1023.0);
+  const Packet2d max_exponent = pset1<Packet2d>(1024.0);
+  const Packet2d e = pmin(pmax(exponent, min_exponent), max_exponent);
+
+  // Convert e to integer and swizzle to low-order bits.
+  const Packet4i ei = vec4i_swizzle1(_mm_cvtpd_epi32(e), 0, 3, 1, 3);
+
+  // Compute 2^e multiply:
+  const Packet4i bias = _mm_set_epi32(0, 1023, 0, 1023);
+  const Packet2d c = _mm_castsi128_pd(_mm_slli_epi64(padd(ei, bias), 52));  // 2^e
+  return pmul(a, c);
+}
+
+// with AVX, the default implementations based on pload1 are faster
+#ifndef __AVX__
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet4f>(const float* a, Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3) {
+  a3 = pload<Packet4f>(a);
+  a0 = vec4f_swizzle1(a3, 0, 0, 0, 0);
+  a1 = vec4f_swizzle1(a3, 1, 1, 1, 1);
+  a2 = vec4f_swizzle1(a3, 2, 2, 2, 2);
+  a3 = vec4f_swizzle1(a3, 3, 3, 3, 3);
+}
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet2d>(const double* a, Packet2d& a0, Packet2d& a1, Packet2d& a2,
+                                               Packet2d& a3) {
+#ifdef EIGEN_VECTORIZE_SSE3
+  a0 = _mm_loaddup_pd(a + 0);
+  a1 = _mm_loaddup_pd(a + 1);
+  a2 = _mm_loaddup_pd(a + 2);
+  a3 = _mm_loaddup_pd(a + 3);
+#else
+  a1 = pload<Packet2d>(a);
+  a0 = vec2d_swizzle1(a1, 0, 0);
+  a1 = vec2d_swizzle1(a1, 1, 1);
+  a3 = pload<Packet2d>(a + 2);
+  a2 = vec2d_swizzle1(a3, 0, 0);
+  a3 = vec2d_swizzle1(a3, 1, 1);
+#endif
+}
+#endif
+
+EIGEN_STRONG_INLINE void punpackp(Packet4f* vecs) {
+  vecs[1] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0x55));
+  vecs[2] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0xAA));
+  vecs[3] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0xFF));
+  vecs[0] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0x00));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
+  _MM_TRANSPOSE4_PS(kernel.packet[0], kernel.packet[1], kernel.packet[2], kernel.packet[3]);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2d, 2>& kernel) {
+  __m128d tmp = _mm_unpackhi_pd(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[0] = _mm_unpacklo_pd(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[1] = tmp;
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2l, 2>& kernel) {
+  __m128i tmp = _mm_unpackhi_epi64(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[0] = _mm_unpacklo_epi64(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[1] = tmp;
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
+  __m128i T0 = _mm_unpacklo_epi32(kernel.packet[0], kernel.packet[1]);
+  __m128i T1 = _mm_unpacklo_epi32(kernel.packet[2], kernel.packet[3]);
+  __m128i T2 = _mm_unpackhi_epi32(kernel.packet[0], kernel.packet[1]);
+  __m128i T3 = _mm_unpackhi_epi32(kernel.packet[2], kernel.packet[3]);
+
+  kernel.packet[0] = _mm_unpacklo_epi64(T0, T1);
+  kernel.packet[1] = _mm_unpackhi_epi64(T0, T1);
+  kernel.packet[2] = _mm_unpacklo_epi64(T2, T3);
+  kernel.packet[3] = _mm_unpackhi_epi64(T2, T3);
+}
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4ui, 4>& kernel) {
+  ptranspose((PacketBlock<Packet4i, 4>&)kernel);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16b, 4>& kernel) {
+  __m128i T0 = _mm_unpacklo_epi8(kernel.packet[0], kernel.packet[1]);
+  __m128i T1 = _mm_unpackhi_epi8(kernel.packet[0], kernel.packet[1]);
+  __m128i T2 = _mm_unpacklo_epi8(kernel.packet[2], kernel.packet[3]);
+  __m128i T3 = _mm_unpackhi_epi8(kernel.packet[2], kernel.packet[3]);
+  kernel.packet[0] = _mm_unpacklo_epi16(T0, T2);
+  kernel.packet[1] = _mm_unpackhi_epi16(T0, T2);
+  kernel.packet[2] = _mm_unpacklo_epi16(T1, T3);
+  kernel.packet[3] = _mm_unpackhi_epi16(T1, T3);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16b, 16>& kernel) {
+  // If we number the elements in the input thus:
+  // kernel.packet[ 0] = {00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 0a, 0b, 0c, 0d, 0e, 0f}
+  // kernel.packet[ 1] = {10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1a, 1b, 1c, 1d, 1e, 1f}
+  // ...
+  // kernel.packet[15] = {f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, fa, fb, fc, fd, fe, ff},
+  //
+  // the desired output is:
+  // kernel.packet[ 0] = {00, 10, 20, 30, 40, 50, 60, 70, 80, 90, a0, b0, c0, d0, e0, f0}
+  // kernel.packet[ 1] = {01, 11, 21, 31, 41, 51, 61, 71, 81, 91, a1, b1, c1, d1, e1, f1}
+  // ...
+  // kernel.packet[15] = {0f, 1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f, 9f, af, bf, cf, df, ef, ff},
+  __m128i t0 =
+      _mm_unpacklo_epi8(kernel.packet[0], kernel.packet[1]);  // 00 10 01 11 02 12 03 13 04 14 05 15 06 16 07 17
+  __m128i t1 =
+      _mm_unpackhi_epi8(kernel.packet[0], kernel.packet[1]);  // 08 18 09 19 0a 1a 0b 1b 0c 1c 0d 1d 0e 1e 0f 1f
+  __m128i t2 =
+      _mm_unpacklo_epi8(kernel.packet[2], kernel.packet[3]);  // 20 30 21 31 22 32 ...                     27 37
+  __m128i t3 =
+      _mm_unpackhi_epi8(kernel.packet[2], kernel.packet[3]);  // 28 38 29 39 2a 3a ...                     2f 3f
+  __m128i t4 =
+      _mm_unpacklo_epi8(kernel.packet[4], kernel.packet[5]);  // 40 50 41 51 42 52                         47 57
+  __m128i t5 = _mm_unpackhi_epi8(kernel.packet[4], kernel.packet[5]);  // 48 58 49 59 4a 5a
+  __m128i t6 = _mm_unpacklo_epi8(kernel.packet[6], kernel.packet[7]);
+  __m128i t7 = _mm_unpackhi_epi8(kernel.packet[6], kernel.packet[7]);
+  __m128i t8 = _mm_unpacklo_epi8(kernel.packet[8], kernel.packet[9]);
+  __m128i t9 = _mm_unpackhi_epi8(kernel.packet[8], kernel.packet[9]);
+  __m128i ta = _mm_unpacklo_epi8(kernel.packet[10], kernel.packet[11]);
+  __m128i tb = _mm_unpackhi_epi8(kernel.packet[10], kernel.packet[11]);
+  __m128i tc = _mm_unpacklo_epi8(kernel.packet[12], kernel.packet[13]);
+  __m128i td = _mm_unpackhi_epi8(kernel.packet[12], kernel.packet[13]);
+  __m128i te = _mm_unpacklo_epi8(kernel.packet[14], kernel.packet[15]);
+  __m128i tf = _mm_unpackhi_epi8(kernel.packet[14], kernel.packet[15]);
+
+  __m128i s0 = _mm_unpacklo_epi16(t0, t2);  // 00 10 20 30 01 11 21 31 02 12 22 32 03 13 23 33
+  __m128i s1 = _mm_unpackhi_epi16(t0, t2);  // 04 14 24 34
+  __m128i s2 = _mm_unpacklo_epi16(t1, t3);  // 08 18 28 38 ...
+  __m128i s3 = _mm_unpackhi_epi16(t1, t3);  // 0c 1c 2c 3c ...
+  __m128i s4 = _mm_unpacklo_epi16(t4, t6);  // 40 50 60 70 41 51 61 71 42 52 62 72 43 53 63 73
+  __m128i s5 = _mm_unpackhi_epi16(t4, t6);  // 44 54 64 74 ...
+  __m128i s6 = _mm_unpacklo_epi16(t5, t7);
+  __m128i s7 = _mm_unpackhi_epi16(t5, t7);
+  __m128i s8 = _mm_unpacklo_epi16(t8, ta);
+  __m128i s9 = _mm_unpackhi_epi16(t8, ta);
+  __m128i sa = _mm_unpacklo_epi16(t9, tb);
+  __m128i sb = _mm_unpackhi_epi16(t9, tb);
+  __m128i sc = _mm_unpacklo_epi16(tc, te);
+  __m128i sd = _mm_unpackhi_epi16(tc, te);
+  __m128i se = _mm_unpacklo_epi16(td, tf);
+  __m128i sf = _mm_unpackhi_epi16(td, tf);
+
+  __m128i u0 = _mm_unpacklo_epi32(s0, s4);  // 00 10 20 30 40 50 60 70 01 11 21 31 41 51 61 71
+  __m128i u1 = _mm_unpackhi_epi32(s0, s4);  // 02 12 22 32 42 52 62 72 03 13 23 33 43 53 63 73
+  __m128i u2 = _mm_unpacklo_epi32(s1, s5);
+  __m128i u3 = _mm_unpackhi_epi32(s1, s5);
+  __m128i u4 = _mm_unpacklo_epi32(s2, s6);
+  __m128i u5 = _mm_unpackhi_epi32(s2, s6);
+  __m128i u6 = _mm_unpacklo_epi32(s3, s7);
+  __m128i u7 = _mm_unpackhi_epi32(s3, s7);
+  __m128i u8 = _mm_unpacklo_epi32(s8, sc);
+  __m128i u9 = _mm_unpackhi_epi32(s8, sc);
+  __m128i ua = _mm_unpacklo_epi32(s9, sd);
+  __m128i ub = _mm_unpackhi_epi32(s9, sd);
+  __m128i uc = _mm_unpacklo_epi32(sa, se);
+  __m128i ud = _mm_unpackhi_epi32(sa, se);
+  __m128i ue = _mm_unpacklo_epi32(sb, sf);
+  __m128i uf = _mm_unpackhi_epi32(sb, sf);
+
+  kernel.packet[0] = _mm_unpacklo_epi64(u0, u8);
+  kernel.packet[1] = _mm_unpackhi_epi64(u0, u8);
+  kernel.packet[2] = _mm_unpacklo_epi64(u1, u9);
+  kernel.packet[3] = _mm_unpackhi_epi64(u1, u9);
+  kernel.packet[4] = _mm_unpacklo_epi64(u2, ua);
+  kernel.packet[5] = _mm_unpackhi_epi64(u2, ua);
+  kernel.packet[6] = _mm_unpacklo_epi64(u3, ub);
+  kernel.packet[7] = _mm_unpackhi_epi64(u3, ub);
+  kernel.packet[8] = _mm_unpacklo_epi64(u4, uc);
+  kernel.packet[9] = _mm_unpackhi_epi64(u4, uc);
+  kernel.packet[10] = _mm_unpacklo_epi64(u5, ud);
+  kernel.packet[11] = _mm_unpackhi_epi64(u5, ud);
+  kernel.packet[12] = _mm_unpacklo_epi64(u6, ue);
+  kernel.packet[13] = _mm_unpackhi_epi64(u6, ue);
+  kernel.packet[14] = _mm_unpacklo_epi64(u7, uf);
+  kernel.packet[15] = _mm_unpackhi_epi64(u7, uf);
+}
+
+// Scalar path for pmadd with FMA to ensure consistency with vectorized path.
+#if defined(EIGEN_VECTORIZE_FMA)
+template <>
+EIGEN_STRONG_INLINE float pmadd(const float& a, const float& b, const float& c) {
+  return std::fmaf(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE double pmadd(const double& a, const double& b, const double& c) {
+  return std::fma(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE float pmsub(const float& a, const float& b, const float& c) {
+  return std::fmaf(a, b, -c);
+}
+template <>
+EIGEN_STRONG_INLINE double pmsub(const double& a, const double& b, const double& c) {
+  return std::fma(a, b, -c);
+}
+template <>
+EIGEN_STRONG_INLINE float pnmadd(const float& a, const float& b, const float& c) {
+  return std::fmaf(-a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE double pnmadd(const double& a, const double& b, const double& c) {
+  return std::fma(-a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE float pnmsub(const float& a, const float& b, const float& c) {
+  return std::fmaf(-a, b, -c);
+}
+template <>
+EIGEN_STRONG_INLINE double pnmsub(const double& a, const double& b, const double& c) {
+  return std::fma(-a, b, -c);
+}
+#endif
+
+#ifdef EIGEN_VECTORIZE_SSE4_1
+// Helpers for half->float and float->half conversions.
+// Currently only used by the AVX code.
+EIGEN_STRONG_INLINE __m128i half2floatsse(__m128i h) {
+  __m128i input = _mm_cvtepu16_epi32(h);
+
+  // Direct vectorization of half_to_float, C parts in the comments.
+  __m128i shifted_exp = _mm_set1_epi32(0x7c00 << 13);
+  // o.u = (h.x & 0x7fff) << 13; // exponent/mantissa bits
+  __m128i ou = _mm_slli_epi32(_mm_and_si128(input, _mm_set1_epi32(0x7fff)), 13);
+  // exp = shifted_exp & o.u;   // just the exponent
+  __m128i exp = _mm_and_si128(ou, shifted_exp);
+  // o.u += (127 - 15) << 23;
+  ou = _mm_add_epi32(ou, _mm_set1_epi32((127 - 15) << 23));
+
+  // Inf/NaN?
+  __m128i naninf_mask = _mm_cmpeq_epi32(exp, shifted_exp);
+  // Inf/NaN adjust
+  __m128i naninf_adj = _mm_and_si128(_mm_set1_epi32((128 - 16) << 23), naninf_mask);
+  // extra exp adjust for  Inf/NaN
+  ou = _mm_add_epi32(ou, naninf_adj);
+
+  // Zero/Denormal?
+  __m128i zeroden_mask = _mm_cmpeq_epi32(exp, _mm_setzero_si128());
+  __m128i zeroden_adj = _mm_and_si128(zeroden_mask, _mm_set1_epi32(1 << 23));
+  // o.u += 1 << 23;
+  ou = _mm_add_epi32(ou, zeroden_adj);
+  // magic.u = 113 << 23
+  __m128i magic = _mm_and_si128(zeroden_mask, _mm_set1_epi32(113 << 23));
+  // o.f -= magic.f
+  ou = _mm_castps_si128(_mm_sub_ps(_mm_castsi128_ps(ou), _mm_castsi128_ps(magic)));
+
+  __m128i sign = _mm_slli_epi32(_mm_and_si128(input, _mm_set1_epi32(0x8000)), 16);
+  // o.u |= (h.x & 0x8000) << 16;    // sign bit
+  ou = _mm_or_si128(ou, sign);
+  // return o.f;
+  // We are actually returning uint version, to make
+  // _mm256_insertf128_si256 work.
+  return ou;
+}
+
+EIGEN_STRONG_INLINE __m128i float2half(__m128 f) {
+  // unsigned int sign_mask = 0x80000000u;
+  __m128i sign = _mm_set1_epi32(0x80000000u);
+  // unsigned int sign = f.u & sign_mask;
+  sign = _mm_and_si128(sign, _mm_castps_si128(f));
+  // f.u ^= sign;
+  f = _mm_xor_ps(f, _mm_castsi128_ps(sign));
+
+  __m128i fu = _mm_castps_si128(f);
+
+  __m128i f16max = _mm_set1_epi32((127 + 16) << 23);
+  __m128i f32infty = _mm_set1_epi32(255 << 23);
+  // if (f.u >= f16max.u) // result is Inf or NaN (all exponent bits set)
+  // there is no _mm_cmpge_epi32, so use lt and swap operands
+  __m128i infnan_mask = _mm_cmplt_epi32(f16max, _mm_castps_si128(f));
+  __m128i inf_mask = _mm_cmpgt_epi32(_mm_castps_si128(f), f32infty);
+  __m128i nan_mask = _mm_andnot_si128(inf_mask, infnan_mask);
+  __m128i inf_value = _mm_and_si128(inf_mask, _mm_set1_epi32(0x7e00));
+  __m128i nan_value = _mm_and_si128(nan_mask, _mm_set1_epi32(0x7c00));
+  // o.x = (f.u > f32infty.u) ? 0x7e00 : 0x7c00; // NaN->qNaN and Inf->Inf
+  __m128i naninf_value = _mm_or_si128(inf_value, nan_value);
+
+  __m128i denorm_magic = _mm_set1_epi32(((127 - 15) + (23 - 10) + 1) << 23);
+  __m128i subnorm_mask = _mm_cmplt_epi32(_mm_castps_si128(f), _mm_set1_epi32(113 << 23));
+  //  f.f += denorm_magic.f;
+  f = _mm_add_ps(f, _mm_castsi128_ps(denorm_magic));
+  // f.u - denorm_magic.u
+  __m128i o = _mm_sub_epi32(_mm_castps_si128(f), denorm_magic);
+  o = _mm_and_si128(o, subnorm_mask);
+  // Correct result for inf/nan/zero/subnormal, 0 otherwise
+  o = _mm_or_si128(o, naninf_value);
+
+  __m128i mask = _mm_or_si128(infnan_mask, subnorm_mask);
+  o = _mm_and_si128(o, mask);
+
+  // mant_odd = (f.u >> 13) & 1;
+  __m128i mand_odd = _mm_and_si128(_mm_srli_epi32(fu, 13), _mm_set1_epi32(0x1));
+  // f.u += 0xc8000fffU;
+  fu = _mm_add_epi32(fu, _mm_set1_epi32(0xc8000fffU));
+  // f.u += mant_odd;
+  fu = _mm_add_epi32(fu, mand_odd);
+  fu = _mm_andnot_si128(mask, fu);
+  // f.u >> 13
+  fu = _mm_srli_epi32(fu, 13);
+  o = _mm_or_si128(fu, o);
+
+  // o.x |= static_cast<numext::uint16_t>(sign >> 16);
+  o = _mm_or_si128(o, _mm_srli_epi32(sign, 16));
+
+  // 16 bit values
+  return _mm_and_si128(o, _mm_set1_epi32(0xffff));
+}
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#if EIGEN_COMP_PGI && EIGEN_COMP_PGI < 1900
+// PGI++ does not define the following intrinsics in C++ mode.
+static inline __m128 _mm_castpd_ps(__m128d x) { return reinterpret_cast<__m128&>(x); }
+static inline __m128i _mm_castpd_si128(__m128d x) { return reinterpret_cast<__m128i&>(x); }
+static inline __m128d _mm_castps_pd(__m128 x) { return reinterpret_cast<__m128d&>(x); }
+static inline __m128i _mm_castps_si128(__m128 x) { return reinterpret_cast<__m128i&>(x); }
+static inline __m128 _mm_castsi128_ps(__m128i x) { return reinterpret_cast<__m128&>(x); }
+static inline __m128d _mm_castsi128_pd(__m128i x) { return reinterpret_cast<__m128d&>(x); }
+#endif
+
+#endif  // EIGEN_PACKET_MATH_SSE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/Reductions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/Reductions.h
new file mode 100644
index 0000000..f38df4e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/Reductions.h
@@ -0,0 +1,324 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Charlie Schlosser <cs.schlosser@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REDUCTIONS_SSE_H
+#define EIGEN_REDUCTIONS_SSE_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Packet>
+struct sse_add_wrapper {
+  static EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) { return padd<Packet>(a, b); }
+};
+
+template <typename Packet>
+struct sse_mul_wrapper {
+  static EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) { return pmul<Packet>(a, b); }
+};
+
+template <typename Packet>
+struct sse_min_wrapper {
+  static EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) { return pmin<Packet>(a, b); }
+};
+
+template <int NaNPropagation, typename Packet>
+struct sse_min_prop_wrapper {
+  static EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) {
+    return pmin<NaNPropagation, Packet>(a, b);
+  }
+};
+
+template <typename Packet>
+struct sse_max_wrapper {
+  static EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) { return pmax<Packet>(a, b); }
+};
+
+template <int NaNPropagation, typename Packet>
+struct sse_max_prop_wrapper {
+  static EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) {
+    return pmax<NaNPropagation, Packet>(a, b);
+  }
+};
+
+template <typename Packet, typename Op>
+struct sse_predux_common;
+
+template <typename Packet>
+struct sse_predux_impl : sse_predux_common<Packet, sse_add_wrapper<Packet>> {};
+
+template <typename Packet>
+struct sse_predux_mul_impl : sse_predux_common<Packet, sse_mul_wrapper<Packet>> {};
+
+template <typename Packet>
+struct sse_predux_min_impl : sse_predux_common<Packet, sse_min_wrapper<Packet>> {};
+
+template <int NaNPropagation, typename Packet>
+struct sse_predux_min_prop_impl : sse_predux_common<Packet, sse_min_prop_wrapper<NaNPropagation, Packet>> {};
+
+template <typename Packet>
+struct sse_predux_max_impl : sse_predux_common<Packet, sse_max_wrapper<Packet>> {};
+
+template <int NaNPropagation, typename Packet>
+struct sse_predux_max_prop_impl : sse_predux_common<Packet, sse_max_prop_wrapper<NaNPropagation, Packet>> {};
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet16b -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <>
+EIGEN_STRONG_INLINE bool predux(const Packet16b& a) {
+  Packet4i tmp = _mm_or_si128(a, _mm_unpackhi_epi64(a, a));
+  return (pfirst(tmp) != 0) || (pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1)) != 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_mul(const Packet16b& a) {
+  Packet4i tmp = _mm_and_si128(a, _mm_unpackhi_epi64(a, a));
+  return ((pfirst<Packet4i>(tmp) == 0x01010101) && (pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1)) == 0x01010101));
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_min(const Packet16b& a) {
+  return predux_mul(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_max(const Packet16b& a) {
+  return predux(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet16b& a) {
+  return predux(a);
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet4i -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <typename Op>
+struct sse_predux_common<Packet4i, Op> {
+  static EIGEN_STRONG_INLINE int run(const Packet4i& a) {
+    Packet4i tmp;
+    tmp = Op::packetOp(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0, 1, 2, 3)));
+    tmp = Op::packetOp(tmp, _mm_unpackhi_epi32(tmp, tmp));
+    return _mm_cvtsi128_si32(tmp);
+  }
+};
+
+template <>
+EIGEN_STRONG_INLINE int predux(const Packet4i& a) {
+  return sse_predux_impl<Packet4i>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_mul(const Packet4i& a) {
+  return sse_predux_mul_impl<Packet4i>::run(a);
+}
+
+#ifdef EIGEN_VECTORIZE_SSE4_1
+template <>
+EIGEN_STRONG_INLINE int predux_min(const Packet4i& a) {
+  return sse_predux_min_impl<Packet4i>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux_max(const Packet4i& a) {
+  return sse_predux_max_impl<Packet4i>::run(a);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet4i& a) {
+  return _mm_movemask_ps(_mm_castsi128_ps(a)) != 0x0;
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet4ui -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <typename Op>
+struct sse_predux_common<Packet4ui, Op> {
+  static EIGEN_STRONG_INLINE uint32_t run(const Packet4ui& a) {
+    Packet4ui tmp;
+    tmp = Op::packetOp(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0, 1, 2, 3)));
+    tmp = Op::packetOp(tmp, _mm_unpackhi_epi32(tmp, tmp));
+    return static_cast<uint32_t>(_mm_cvtsi128_si32(tmp));
+  }
+};
+
+template <>
+EIGEN_STRONG_INLINE uint32_t predux(const Packet4ui& a) {
+  return sse_predux_impl<Packet4ui>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_mul(const Packet4ui& a) {
+  return sse_predux_mul_impl<Packet4ui>::run(a);
+}
+
+#ifdef EIGEN_VECTORIZE_SSE4_1
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_min(const Packet4ui& a) {
+  return sse_predux_min_impl<Packet4ui>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE uint32_t predux_max(const Packet4ui& a) {
+  return sse_predux_max_impl<Packet4ui>::run(a);
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet4ui& a) {
+  return _mm_movemask_ps(_mm_castsi128_ps(a)) != 0x0;
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet2l -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <typename Op>
+struct sse_predux_common<Packet2l, Op> {
+  static EIGEN_STRONG_INLINE int64_t run(const Packet2l& a) {
+    Packet2l tmp;
+    tmp = Op::packetOp(a, _mm_unpackhi_epi64(a, a));
+    return pfirst(tmp);
+  }
+};
+
+template <>
+EIGEN_STRONG_INLINE int64_t predux(const Packet2l& a) {
+  return sse_predux_impl<Packet2l>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet2l& a) {
+  return _mm_movemask_pd(_mm_castsi128_pd(a)) != 0x0;
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet4f -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <typename Op>
+struct sse_predux_common<Packet4f, Op> {
+  static EIGEN_STRONG_INLINE float run(const Packet4f& a) {
+    Packet4f tmp;
+    tmp = Op::packetOp(a, _mm_movehl_ps(a, a));
+#ifdef EIGEN_VECTORIZE_SSE3
+    tmp = Op::packetOp(tmp, _mm_movehdup_ps(tmp));
+#else
+    tmp = Op::packetOp(tmp, _mm_shuffle_ps(tmp, tmp, 1));
+#endif
+    return _mm_cvtss_f32(tmp);
+  }
+};
+
+template <>
+EIGEN_STRONG_INLINE float predux(const Packet4f& a) {
+  return sse_predux_impl<Packet4f>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_mul(const Packet4f& a) {
+  return sse_predux_mul_impl<Packet4f>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min(const Packet4f& a) {
+  return sse_predux_min_impl<Packet4f>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<PropagateNumbers>(const Packet4f& a) {
+  return sse_predux_min_prop_impl<PropagateNumbers, Packet4f>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<PropagateNaN>(const Packet4f& a) {
+  return sse_predux_min_prop_impl<PropagateNaN, Packet4f>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max(const Packet4f& a) {
+  return sse_predux_max_impl<Packet4f>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<PropagateNumbers>(const Packet4f& a) {
+  return sse_predux_max_prop_impl<PropagateNumbers, Packet4f>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<PropagateNaN>(const Packet4f& a) {
+  return sse_predux_max_prop_impl<PropagateNaN, Packet4f>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet4f& a) {
+  return _mm_movemask_ps(a) != 0x0;
+}
+
+/* -- -- -- -- -- -- -- -- -- -- -- -- Packet2d -- -- -- -- -- -- -- -- -- -- -- -- */
+
+template <typename Op>
+struct sse_predux_common<Packet2d, Op> {
+  static EIGEN_STRONG_INLINE double run(const Packet2d& a) {
+    Packet2d tmp;
+    tmp = Op::packetOp(a, _mm_unpackhi_pd(a, a));
+    return _mm_cvtsd_f64(tmp);
+  }
+};
+
+template <>
+EIGEN_STRONG_INLINE double predux(const Packet2d& a) {
+  return sse_predux_impl<Packet2d>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_mul(const Packet2d& a) {
+  return sse_predux_mul_impl<Packet2d>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min(const Packet2d& a) {
+  return sse_predux_min_impl<Packet2d>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min<PropagateNumbers>(const Packet2d& a) {
+  return sse_predux_min_prop_impl<PropagateNumbers, Packet2d>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min<PropagateNaN>(const Packet2d& a) {
+  return sse_predux_min_prop_impl<PropagateNaN, Packet2d>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max(const Packet2d& a) {
+  return sse_predux_max_impl<Packet2d>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max<PropagateNumbers>(const Packet2d& a) {
+  return sse_predux_max_prop_impl<PropagateNumbers, Packet2d>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max<PropagateNaN>(const Packet2d& a) {
+  return sse_predux_max_prop_impl<PropagateNaN, Packet2d>::run(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet2d& a) {
+  return _mm_movemask_pd(a) != 0x0;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_REDUCTIONS_SSE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/TypeCasting.h
new file mode 100644
index 0000000..8eebc02
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SSE/TypeCasting.h
@@ -0,0 +1,180 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_SSE_H
+#define EIGEN_TYPE_CASTING_SSE_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_VECTORIZE_AVX
+template <>
+struct type_casting_traits<float, bool> : vectorized_type_casting_traits<float, bool> {};
+template <>
+struct type_casting_traits<bool, float> : vectorized_type_casting_traits<bool, float> {};
+
+template <>
+struct type_casting_traits<float, int> : vectorized_type_casting_traits<float, int> {};
+template <>
+struct type_casting_traits<int, float> : vectorized_type_casting_traits<int, float> {};
+
+template <>
+struct type_casting_traits<float, double> : vectorized_type_casting_traits<float, double> {};
+template <>
+struct type_casting_traits<double, float> : vectorized_type_casting_traits<double, float> {};
+
+template <>
+struct type_casting_traits<double, int> : vectorized_type_casting_traits<double, int> {};
+template <>
+struct type_casting_traits<int, double> : vectorized_type_casting_traits<int, double> {};
+
+#ifndef EIGEN_VECTORIZE_AVX2
+template <>
+struct type_casting_traits<double, int64_t> : vectorized_type_casting_traits<double, int64_t> {};
+template <>
+struct type_casting_traits<int64_t, double> : vectorized_type_casting_traits<int64_t, double> {};
+#endif
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet16b pcast<Packet4f, Packet16b>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
+                                                         const Packet4f& d) {
+  __m128 zero = pzero(a);
+  __m128 nonzero_a = _mm_cmpneq_ps(a, zero);
+  __m128 nonzero_b = _mm_cmpneq_ps(b, zero);
+  __m128 nonzero_c = _mm_cmpneq_ps(c, zero);
+  __m128 nonzero_d = _mm_cmpneq_ps(d, zero);
+  __m128i ab_bytes = _mm_packs_epi32(_mm_castps_si128(nonzero_a), _mm_castps_si128(nonzero_b));
+  __m128i cd_bytes = _mm_packs_epi32(_mm_castps_si128(nonzero_c), _mm_castps_si128(nonzero_d));
+  __m128i merged = _mm_packs_epi16(ab_bytes, cd_bytes);
+  return _mm_and_si128(merged, _mm_set1_epi8(1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet16b, Packet4f>(const Packet16b& a) {
+  const __m128 cst_one = _mm_set_ps1(1.0f);
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  __m128i a_extended = _mm_cvtepi8_epi32(a);
+  __m128i abcd = _mm_cmpeq_epi32(a_extended, _mm_setzero_si128());
+#else
+  __m128i abcd_efhg_ijkl_mnop = _mm_cmpeq_epi8(a, _mm_setzero_si128());
+  __m128i aabb_ccdd_eeff_gghh = _mm_unpacklo_epi8(abcd_efhg_ijkl_mnop, abcd_efhg_ijkl_mnop);
+  __m128i abcd = _mm_unpacklo_epi8(aabb_ccdd_eeff_gghh, aabb_ccdd_eeff_gghh);
+#endif
+  __m128 result = _mm_andnot_ps(_mm_castsi128_ps(abcd), cst_one);
+  return result;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
+  return _mm_cvttps_epi32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet2d, Packet4i>(const Packet2d& a, const Packet2d& b) {
+  return _mm_castps_si128(_mm_shuffle_ps(_mm_castsi128_ps(_mm_cvttpd_epi32(a)), _mm_castsi128_ps(_mm_cvttpd_epi32(b)),
+                                         (1 << 2) | (1 << 6)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2d, Packet2l>(const Packet2d& a) {
+#if EIGEN_ARCH_x86_64
+  return _mm_set_epi64x(_mm_cvttsd_si64(preverse(a)), _mm_cvttsd_si64(a));
+#else
+  return _mm_set_epi64x(static_cast<int64_t>(pfirst(preverse(a))), static_cast<int64_t>(pfirst(a)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2l, Packet2d>(const Packet2l& a) {
+  EIGEN_ALIGN16 int64_t aux[2];
+  pstore(aux, a);
+  return _mm_set_pd(static_cast<double>(aux[1]), static_cast<double>(aux[0]));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
+  return _mm_cvtepi32_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
+  return _mm_shuffle_ps(_mm_cvtpd_ps(a), _mm_cvtpd_ps(b), (1 << 2) | (1 << 6));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4i, Packet2d>(const Packet4i& a) {
+  // Simply discard the second half of the input
+  return _mm_cvtepi32_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) {
+  // Simply discard the second half of the input
+  return _mm_cvtps_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4f>(const Packet4f& a) {
+  return _mm_castps_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet2d>(const Packet2d& a) {
+  return _mm_castpd_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4f>(const Packet4f& a) {
+  return _mm_castps_si128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4i>(const Packet4i& a) {
+  return _mm_castsi128_ps(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4i>(const Packet4i& a) {
+  return _mm_castsi128_pd(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2l>(const Packet2l& a) {
+  return _mm_castsi128_pd(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2d>(const Packet2d& a) {
+  return _mm_castpd_si128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet2d>(const Packet2d& a) {
+  return _mm_castpd_si128(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4i>(const Packet4i& a) {
+  return Packet4ui(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4ui>(const Packet4ui& a) {
+  return Packet4i(a);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_SSE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SVE/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SVE/MathFunctions.h
new file mode 100644
index 0000000..a547d39
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SVE/MathFunctions.h
@@ -0,0 +1,24 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020, Arm Limited and Contributors
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_SVE_H
+#define EIGEN_MATH_FUNCTIONS_SVE_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(PacketXf)
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_SVE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SVE/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SVE/PacketMath.h
new file mode 100644
index 0000000..310a40c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SVE/PacketMath.h
@@ -0,0 +1,678 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020, Arm Limited and Contributors
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_SVE_H
+#define EIGEN_PACKET_MATH_SVE_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+
+template <typename Scalar, int SVEVectorLength>
+struct sve_packet_size_selector {
+  enum { size = SVEVectorLength / (sizeof(Scalar) * CHAR_BIT) };
+};
+
+/********************************* int32 **************************************/
+typedef svint32_t PacketXi __attribute__((arm_sve_vector_bits(EIGEN_ARM64_SVE_VL)));
+
+template <>
+struct packet_traits<numext::int32_t> : default_packet_traits {
+  typedef PacketXi type;
+  typedef PacketXi half;  // Half not implemented yet
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = sve_packet_size_selector<numext::int32_t, EIGEN_ARM64_SVE_VL>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasReduxp = 0  // Not implemented in SVE
+  };
+};
+
+template <>
+struct unpacket_traits<PacketXi> {
+  typedef numext::int32_t type;
+  typedef PacketXi half;  // Half not yet implemented
+  enum {
+    size = sve_packet_size_selector<numext::int32_t, EIGEN_ARM64_SVE_VL>::size,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<numext::int32_t>(const numext::int32_t* addr) {
+  svprfw(svptrue_b32(), addr, SV_PLDL1KEEP);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pset1<PacketXi>(const numext::int32_t& from) {
+  return svdup_n_s32(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi plset<PacketXi>(const numext::int32_t& a) {
+  numext::int32_t c[packet_traits<numext::int32_t>::size];
+  for (int i = 0; i < packet_traits<numext::int32_t>::size; i++) c[i] = i;
+  return svadd_s32_x(svptrue_b32(), pset1<PacketXi>(a), svld1_s32(svptrue_b32(), c));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi padd<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svadd_s32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi psub<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svsub_s32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pnegate(const PacketXi& a) {
+  return svneg_s32_x(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pconj(const PacketXi& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pmul<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svmul_s32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pdiv<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svdiv_s32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pmadd(const PacketXi& a, const PacketXi& b, const PacketXi& c) {
+  return svmla_s32_x(svptrue_b32(), c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pmin<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svmin_s32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pmax<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svmax_s32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pcmp_le<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svdup_n_s32_z(svcmple_s32(svptrue_b32(), a, b), 0xffffffffu);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pcmp_lt<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svdup_n_s32_z(svcmplt_s32(svptrue_b32(), a, b), 0xffffffffu);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pcmp_eq<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svdup_n_s32_z(svcmpeq_s32(svptrue_b32(), a, b), 0xffffffffu);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi ptrue<PacketXi>(const PacketXi& /*a*/) {
+  return svdup_n_s32_x(svptrue_b32(), 0xffffffffu);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pzero<PacketXi>(const PacketXi& /*a*/) {
+  return svdup_n_s32_x(svptrue_b32(), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pand<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svand_s32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi por<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svorr_s32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pxor<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return sveor_s32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pandnot<PacketXi>(const PacketXi& a, const PacketXi& b) {
+  return svbic_s32_x(svptrue_b32(), a, b);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE PacketXi parithmetic_shift_right(PacketXi a) {
+  return svasrd_n_s32_x(svptrue_b32(), a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE PacketXi plogical_shift_right(PacketXi a) {
+  return svreinterpret_s32_u32(svlsr_n_u32_x(svptrue_b32(), svreinterpret_u32_s32(a), N));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE PacketXi plogical_shift_left(PacketXi a) {
+  return svlsl_n_s32_x(svptrue_b32(), a, N);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pload<PacketXi>(const numext::int32_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return svld1_s32(svptrue_b32(), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi ploadu<PacketXi>(const numext::int32_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return svld1_s32(svptrue_b32(), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi ploaddup<PacketXi>(const numext::int32_t* from) {
+  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
+  return svld1_gather_u32index_s32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi ploadquad<PacketXi>(const numext::int32_t* from) {
+  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a0, a0, a1, a1, a1, a1, ...}
+  return svld1_gather_u32index_s32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int32_t>(numext::int32_t* to, const PacketXi& from) {
+  EIGEN_DEBUG_ALIGNED_STORE svst1_s32(svptrue_b32(), to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int32_t>(numext::int32_t* to, const PacketXi& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE svst1_s32(svptrue_b32(), to, from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline PacketXi pgather<numext::int32_t, PacketXi>(const numext::int32_t* from, Index stride) {
+  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
+  svint32_t indices = svindex_s32(0, stride);
+  return svld1_gather_s32index_s32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int32_t, PacketXi>(numext::int32_t* to, const PacketXi& from,
+                                                                  Index stride) {
+  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
+  svint32_t indices = svindex_s32(0, stride);
+  svst1_scatter_s32index_s32(svptrue_b32(), to, indices, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t pfirst<PacketXi>(const PacketXi& a) {
+  // svlasta returns the first element if all predicate bits are 0
+  return svlasta_s32(svpfalse_b(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi preverse(const PacketXi& a) {
+  return svrev_s32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pabs(const PacketXi& a) {
+  return svabs_s32_x(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux<PacketXi>(const PacketXi& a) {
+  return static_cast<numext::int32_t>(svaddv_s32(svptrue_b32(), a));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_mul<PacketXi>(const PacketXi& a) {
+  EIGEN_STATIC_ASSERT((EIGEN_ARM64_SVE_VL % 128 == 0), EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT);
+
+  // Multiply the vector by its reverse
+  svint32_t prod = svmul_s32_x(svptrue_b32(), a, svrev_s32(a));
+  svint32_t half_prod;
+
+  // Extract the high half of the vector. Depending on the VL more reductions need to be done
+  if (EIGEN_ARM64_SVE_VL >= 2048) {
+    half_prod = svtbl_s32(prod, svindex_u32(32, 1));
+    prod = svmul_s32_x(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 1024) {
+    half_prod = svtbl_s32(prod, svindex_u32(16, 1));
+    prod = svmul_s32_x(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 512) {
+    half_prod = svtbl_s32(prod, svindex_u32(8, 1));
+    prod = svmul_s32_x(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 256) {
+    half_prod = svtbl_s32(prod, svindex_u32(4, 1));
+    prod = svmul_s32_x(svptrue_b32(), prod, half_prod);
+  }
+  // Last reduction
+  half_prod = svtbl_s32(prod, svindex_u32(2, 1));
+  prod = svmul_s32_x(svptrue_b32(), prod, half_prod);
+
+  // The reduction is done to the first element.
+  return pfirst<PacketXi>(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_min<PacketXi>(const PacketXi& a) {
+  return svminv_s32(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_max<PacketXi>(const PacketXi& a) {
+  return svmaxv_s32(svptrue_b32(), a);
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<PacketXi, N>& kernel) {
+  int buffer[packet_traits<numext::int32_t>::size * N] = {0};
+  int i = 0;
+
+  PacketXi stride_index = svindex_s32(0, N);
+
+  for (i = 0; i < N; i++) {
+    svst1_scatter_s32index_s32(svptrue_b32(), buffer + i, stride_index, kernel.packet[i]);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] = svld1_s32(svptrue_b32(), buffer + i * packet_traits<numext::int32_t>::size);
+  }
+}
+
+/********************************* float32 ************************************/
+
+typedef svfloat32_t PacketXf __attribute__((arm_sve_vector_bits(EIGEN_ARM64_SVE_VL)));
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef PacketXf type;
+  typedef PacketXf half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = sve_packet_size_selector<float, EIGEN_ARM64_SVE_VL>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasReduxp = 0,  // Not implemented in SVE
+
+    HasDiv = 1,
+
+    HasCmp = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTan = EIGEN_FAST_MATH,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasExp = 1,
+    HasPow = 1,
+    HasSqrt = 1,
+    HasCbrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasErfc = EIGEN_FAST_MATH
+  };
+};
+
+template <>
+struct unpacket_traits<PacketXf> {
+  typedef float type;
+  typedef PacketXf half;  // Half not yet implemented
+  typedef PacketXi integer_packet;
+
+  enum {
+    size = sve_packet_size_selector<float, EIGEN_ARM64_SVE_VL>::size,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pset1<PacketXf>(const float& from) {
+  return svdup_n_f32(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pset1frombits<PacketXf>(numext::uint32_t from) {
+  return svreinterpret_f32_u32(svdup_n_u32_x(svptrue_b32(), from));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf plset<PacketXf>(const float& a) {
+  float c[packet_traits<float>::size];
+  for (int i = 0; i < packet_traits<float>::size; i++) c[i] = i;
+  return svadd_f32_x(svptrue_b32(), pset1<PacketXf>(a), svld1_f32(svptrue_b32(), c));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf padd<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svadd_f32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf psub<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svsub_f32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pnegate(const PacketXf& a) {
+  return svneg_f32_x(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pconj(const PacketXf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmul<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svmul_f32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pdiv<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svdiv_f32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmadd(const PacketXf& a, const PacketXf& b, const PacketXf& c) {
+  return svmla_f32_x(svptrue_b32(), c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmin<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svmin_f32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmin<PropagateNaN, PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return pmin<PacketXf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmin<PropagateNumbers, PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svminnm_f32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmax<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svmax_f32_x(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmax<PropagateNaN, PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return pmax<PacketXf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmax<PropagateNumbers, PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svmaxnm_f32_x(svptrue_b32(), a, b);
+}
+
+// Float comparisons in SVE return svbool (predicate). Use svdup to set active
+// lanes to 1 (0xffffffffu) and inactive lanes to 0.
+template <>
+EIGEN_STRONG_INLINE PacketXf pcmp_le<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svreinterpret_f32_u32(svdup_n_u32_z(svcmple_f32(svptrue_b32(), a, b), 0xffffffffu));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pcmp_lt<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svreinterpret_f32_u32(svdup_n_u32_z(svcmplt_f32(svptrue_b32(), a, b), 0xffffffffu));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pcmp_eq<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svreinterpret_f32_u32(svdup_n_u32_z(svcmpeq_f32(svptrue_b32(), a, b), 0xffffffffu));
+}
+
+// Do a predicate inverse (svnot_b_z) on the predicate resulted from the
+// greater/equal comparison (svcmpge_f32). Then fill a float vector with the
+// active elements.
+template <>
+EIGEN_STRONG_INLINE PacketXf pcmp_lt_or_nan<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svreinterpret_f32_u32(svdup_n_u32_z(svnot_b_z(svptrue_b32(), svcmpge_f32(svptrue_b32(), a, b)), 0xffffffffu));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pfloor<PacketXf>(const PacketXf& a) {
+  return svrintm_f32_x(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf ptrue<PacketXf>(const PacketXf& /*a*/) {
+  return svreinterpret_f32_u32(svdup_n_u32_x(svptrue_b32(), 0xffffffffu));
+}
+
+// Logical Operations are not supported for float, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE PacketXf pand<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svreinterpret_f32_u32(svand_u32_x(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf por<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svreinterpret_f32_u32(svorr_u32_x(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pxor<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svreinterpret_f32_u32(sveor_u32_x(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pandnot<PacketXf>(const PacketXf& a, const PacketXf& b) {
+  return svreinterpret_f32_u32(svbic_u32_x(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pload<PacketXf>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return svld1_f32(svptrue_b32(), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf ploadu<PacketXf>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return svld1_f32(svptrue_b32(), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf ploaddup<PacketXf>(const float* from) {
+  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
+  return svld1_gather_u32index_f32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf ploadquad<PacketXf>(const float* from) {
+  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a0, a0, a1, a1, a1, a1, ...}
+  return svld1_gather_u32index_f32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const PacketXf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE svst1_f32(svptrue_b32(), to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const PacketXf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE svst1_f32(svptrue_b32(), to, from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline PacketXf pgather<float, PacketXf>(const float* from, Index stride) {
+  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
+  svint32_t indices = svindex_s32(0, stride);
+  return svld1_gather_s32index_f32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, PacketXf>(float* to, const PacketXf& from, Index stride) {
+  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
+  svint32_t indices = svindex_s32(0, stride);
+  svst1_scatter_s32index_f32(svptrue_b32(), to, indices, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<PacketXf>(const PacketXf& a) {
+  // svlasta returns the first element if all predicate bits are 0
+  return svlasta_f32(svpfalse_b(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf preverse(const PacketXf& a) {
+  return svrev_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pabs(const PacketXf& a) {
+  return svabs_f32_x(svptrue_b32(), a);
+}
+
+// TODO(tellenbach): Should this go into MathFunctions.h? If so, change for
+// all vector extensions and the generic version.
+template <>
+EIGEN_STRONG_INLINE PacketXf pfrexp<PacketXf>(const PacketXf& a, PacketXf& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<PacketXf>(const PacketXf& a) {
+  return svaddv_f32(svptrue_b32(), a);
+}
+
+// Other reduction functions:
+// mul
+// Only works for SVE Vls multiple of 128
+template <>
+EIGEN_STRONG_INLINE float predux_mul<PacketXf>(const PacketXf& a) {
+  EIGEN_STATIC_ASSERT((EIGEN_ARM64_SVE_VL % 128 == 0), EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT);
+  // Multiply the vector by its reverse
+  svfloat32_t prod = svmul_f32_x(svptrue_b32(), a, svrev_f32(a));
+  svfloat32_t half_prod;
+
+  // Extract the high half of the vector. Depending on the VL more reductions need to be done
+  if (EIGEN_ARM64_SVE_VL >= 2048) {
+    half_prod = svtbl_f32(prod, svindex_u32(32, 1));
+    prod = svmul_f32_x(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 1024) {
+    half_prod = svtbl_f32(prod, svindex_u32(16, 1));
+    prod = svmul_f32_x(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 512) {
+    half_prod = svtbl_f32(prod, svindex_u32(8, 1));
+    prod = svmul_f32_x(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 256) {
+    half_prod = svtbl_f32(prod, svindex_u32(4, 1));
+    prod = svmul_f32_x(svptrue_b32(), prod, half_prod);
+  }
+  // Last reduction
+  half_prod = svtbl_f32(prod, svindex_u32(2, 1));
+  prod = svmul_f32_x(svptrue_b32(), prod, half_prod);
+
+  // The reduction is done to the first element.
+  return pfirst<PacketXf>(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<PacketXf>(const PacketXf& a) {
+  return svminv_f32(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<PacketXf>(const PacketXf& a) {
+  return svmaxv_f32(svptrue_b32(), a);
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<PacketXf, N>& kernel) {
+  float buffer[packet_traits<float>::size * N] = {0};
+  int i = 0;
+
+  PacketXi stride_index = svindex_s32(0, N);
+
+  for (i = 0; i < N; i++) {
+    svst1_scatter_s32index_f32(svptrue_b32(), buffer + i, stride_index, kernel.packet[i]);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] = svld1_f32(svptrue_b32(), buffer + i * packet_traits<float>::size);
+  }
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pldexp<PacketXf>(const PacketXf& a, const PacketXf& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf psqrt<PacketXf>(const PacketXf& a) {
+  return svsqrt_f32_x(svptrue_b32(), a);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_SVE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SVE/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SVE/TypeCasting.h
new file mode 100644
index 0000000..c7027b3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SVE/TypeCasting.h
@@ -0,0 +1,52 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020, Arm Limited and Contributors
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_SVE_H
+#define EIGEN_TYPE_CASTING_SVE_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <>
+struct type_casting_traits<float, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+struct type_casting_traits<numext::int32_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pcast<PacketXi, PacketXf>(const PacketXi& a) {
+  return svcvt_f32_s32_x(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pcast<PacketXf, PacketXi>(const PacketXf& a) {
+  return svcvt_s32_f32_x(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf preinterpret<PacketXf, PacketXi>(const PacketXi& a) {
+  return svreinterpret_f32_s32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi preinterpret<PacketXi, PacketXf>(const PacketXf& a) {
+  return svreinterpret_s32_f32(a);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_SVE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/InteropHeaders.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/InteropHeaders.h
new file mode 100644
index 0000000..0bdf825
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/InteropHeaders.h
@@ -0,0 +1,226 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * InteropHeaders.h
+ *
+ * \brief:
+ *  InteropHeaders
+ *
+ *****************************************************************/
+
+#ifndef EIGEN_INTEROP_HEADERS_SYCL_H
+#define EIGEN_INTEROP_HEADERS_SYCL_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+#if !defined(EIGEN_DONT_VECTORIZE_SYCL)
+
+namespace internal {
+
+template <int lengths>
+struct sycl_packet_traits : default_packet_traits {
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = lengths,
+    HasDiv = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasSin = 1,
+    HasCos = 1,
+    HasTan = 1,
+    HasASin = 1,
+    HasACos = 1,
+    HasATan = 1,
+    HasSinh = 1,
+    HasCosh = 1,
+    HasTanh = 1,
+    HasLGamma = 0,
+    HasDiGamma = 0,
+    HasZeta = 0,
+    HasPolygamma = 0,
+    HasErf = 0,
+    HasErfc = 0,
+    HasNdtri = 0,
+    HasIGamma = 0,
+    HasIGammac = 0,
+    HasBetaInc = 0,
+    // This flag is used to indicate whether packet comparison is supported.
+    // pcmp_eq, pcmp_lt and pcmp_le should be defined for it to be true.
+    HasCmp = 1,
+    HasMax = 1,
+    HasMin = 1,
+    HasMul = 1,
+    HasAdd = 1,
+    HasFloor = 1,
+    HasRound = 1,
+    HasRint = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasCeil = 1,
+  };
+};
+
+#ifdef SYCL_DEVICE_ONLY
+#define SYCL_PACKET_TRAITS(packet_type, unpacket_type, lengths)       \
+  template <>                                                         \
+  struct packet_traits<unpacket_type> : sycl_packet_traits<lengths> { \
+    typedef packet_type type;                                         \
+    typedef packet_type half;                                         \
+  };
+
+SYCL_PACKET_TRAITS(cl::sycl::cl_half8, Eigen::half, 8)
+SYCL_PACKET_TRAITS(cl::sycl::cl_half8, const Eigen::half, 8)
+SYCL_PACKET_TRAITS(cl::sycl::cl_float4, float, 4)
+SYCL_PACKET_TRAITS(cl::sycl::cl_float4, const float, 4)
+SYCL_PACKET_TRAITS(cl::sycl::cl_double2, double, 2)
+SYCL_PACKET_TRAITS(cl::sycl::cl_double2, const double, 2)
+#undef SYCL_PACKET_TRAITS
+
+// Make sure this is only available when targeting a GPU: we don't want to
+// introduce conflicts between these packet_traits definitions and the ones
+// we'll use on the host side (SSE, AVX, ...)
+#define SYCL_ARITHMETIC(packet_type)  \
+  template <>                         \
+  struct is_arithmetic<packet_type> { \
+    enum { value = true };            \
+  };
+SYCL_ARITHMETIC(cl::sycl::cl_half8)
+SYCL_ARITHMETIC(cl::sycl::cl_float4)
+SYCL_ARITHMETIC(cl::sycl::cl_double2)
+#undef SYCL_ARITHMETIC
+
+#define SYCL_UNPACKET_TRAITS(packet_type, unpacket_type, lengths)        \
+  template <>                                                            \
+  struct unpacket_traits<packet_type> {                                  \
+    typedef unpacket_type type;                                          \
+    enum { size = lengths, vectorizable = true, alignment = Aligned16 }; \
+    typedef packet_type half;                                            \
+  };
+SYCL_UNPACKET_TRAITS(cl::sycl::cl_half8, Eigen::half, 8)
+SYCL_UNPACKET_TRAITS(cl::sycl::cl_float4, float, 4)
+SYCL_UNPACKET_TRAITS(cl::sycl::cl_double2, double, 2)
+
+#undef SYCL_UNPACKET_TRAITS
+#endif
+
+}  // end namespace internal
+
+#endif
+
+namespace TensorSycl {
+namespace internal {
+
+template <typename PacketReturnType, int PacketSize>
+struct PacketWrapper;
+// This function should never get called on the device
+#ifndef SYCL_DEVICE_ONLY
+template <typename PacketReturnType, int PacketSize>
+struct PacketWrapper {
+  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type Scalar;
+  template <typename Index>
+  EIGEN_DEVICE_FUNC static Scalar scalarize(Index, PacketReturnType&) {
+    eigen_assert(false && "THERE IS NO PACKETIZE VERSION FOR  THE CHOSEN TYPE");
+    abort();
+  }
+  EIGEN_DEVICE_FUNC static PacketReturnType convert_to_packet_type(Scalar in, Scalar) {
+    return ::Eigen::internal::template plset<PacketReturnType>(in);
+  }
+  EIGEN_DEVICE_FUNC static void set_packet(PacketReturnType, Scalar*) {
+    eigen_assert(false && "THERE IS NO PACKETIZE VERSION FOR  THE CHOSEN TYPE");
+    abort();
+  }
+};
+
+#elif defined(SYCL_DEVICE_ONLY)
+template <typename PacketReturnType>
+struct PacketWrapper<PacketReturnType, 4> {
+  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type Scalar;
+  template <typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index index, PacketReturnType& in) {
+    switch (index) {
+      case 0:
+        return in.x();
+      case 1:
+        return in.y();
+      case 2:
+        return in.z();
+      case 3:
+        return in.w();
+      default:
+        // INDEX MUST BE BETWEEN 0 and 3.There is no abort function in SYCL kernel. so we cannot use abort here.
+        //  The code will never reach here
+        __builtin_unreachable();
+    }
+    __builtin_unreachable();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(Scalar in, Scalar other) {
+    return PacketReturnType(in, other, other, other);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType& lhs, Scalar* rhs) {
+    lhs = PacketReturnType(rhs[0], rhs[1], rhs[2], rhs[3]);
+  }
+};
+
+template <typename PacketReturnType>
+struct PacketWrapper<PacketReturnType, 1> {
+  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type Scalar;
+  template <typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index, PacketReturnType& in) {
+    return in;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(Scalar in, Scalar) {
+    return PacketReturnType(in);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType& lhs, Scalar* rhs) { lhs = rhs[0]; }
+};
+
+template <typename PacketReturnType>
+struct PacketWrapper<PacketReturnType, 2> {
+  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type Scalar;
+  template <typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index index, PacketReturnType& in) {
+    switch (index) {
+      case 0:
+        return in.x();
+      case 1:
+        return in.y();
+      default:
+        // INDEX MUST BE BETWEEN 0 and 1.There is no abort function in SYCL kernel. so we cannot use abort here.
+        // The code will never reach here
+        __builtin_unreachable();
+    }
+    __builtin_unreachable();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(Scalar in, Scalar other) {
+    return PacketReturnType(in, other);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType& lhs, Scalar* rhs) {
+    lhs = PacketReturnType(rhs[0], rhs[1]);
+  }
+};
+
+#endif
+
+}  // end namespace internal
+}  // end namespace TensorSycl
+}  // end namespace Eigen
+
+#endif  // EIGEN_INTEROP_HEADERS_SYCL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/MathFunctions.h
new file mode 100644
index 0000000..90d6066
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/MathFunctions.h
@@ -0,0 +1,113 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * MathFunctions.h
+ *
+ * \brief:
+ *  MathFunctions
+ *
+ *****************************************************************/
+
+#ifndef EIGEN_MATH_FUNCTIONS_SYCL_H
+#define EIGEN_MATH_FUNCTIONS_SYCL_H
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Make sure this is only available when targeting a GPU: we don't want to
+// introduce conflicts between these packet_traits definitions and the ones
+// we'll use on the host side (SSE, AVX, ...)
+#if defined(SYCL_DEVICE_ONLY)
+
+// Generic macro for unary SYCL math functions.
+#define SYCL_PACKET_FUNCTION(EIGEN_FUNC, SYCL_FUNC, PACKET)                          \
+  template <>                                                                        \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PACKET EIGEN_FUNC<PACKET>(const PACKET& a) { \
+    return cl::sycl::SYCL_FUNC(a);                                                   \
+  }
+
+// Instantiate a unary function for the standard set of SYCL vector types.
+#define SYCL_UNARY_FUNCTION(EIGEN_FUNC, SYCL_FUNC)                 \
+  SYCL_PACKET_FUNCTION(EIGEN_FUNC, SYCL_FUNC, cl::sycl::cl_half8)  \
+  SYCL_PACKET_FUNCTION(EIGEN_FUNC, SYCL_FUNC, cl::sycl::cl_float4) \
+  SYCL_PACKET_FUNCTION(EIGEN_FUNC, SYCL_FUNC, cl::sycl::cl_double2)
+
+SYCL_UNARY_FUNCTION(plog, log)
+SYCL_UNARY_FUNCTION(plog1p, log1p)
+SYCL_UNARY_FUNCTION(plog10, log10)
+SYCL_UNARY_FUNCTION(pexpm1, expm1)
+SYCL_UNARY_FUNCTION(psqrt, sqrt)
+SYCL_UNARY_FUNCTION(prsqrt, rsqrt)
+SYCL_UNARY_FUNCTION(psin, sin)
+SYCL_UNARY_FUNCTION(pcos, cos)
+SYCL_UNARY_FUNCTION(ptan, tan)
+SYCL_UNARY_FUNCTION(pasin, asin)
+SYCL_UNARY_FUNCTION(pacos, acos)
+SYCL_UNARY_FUNCTION(patan, atan)
+SYCL_UNARY_FUNCTION(psinh, sinh)
+SYCL_UNARY_FUNCTION(pcosh, cosh)
+SYCL_UNARY_FUNCTION(ptanh, tanh)
+SYCL_UNARY_FUNCTION(pround, round)
+SYCL_UNARY_FUNCTION(print, rint)
+SYCL_UNARY_FUNCTION(pfloor, floor)
+
+// pexp has additional scalar type instantiations.
+SYCL_UNARY_FUNCTION(pexp, exp)
+SYCL_PACKET_FUNCTION(pexp, exp, cl::sycl::cl_half)
+SYCL_PACKET_FUNCTION(pexp, exp, cl::sycl::cl_float)
+
+// pceil uses cl_half (scalar) instead of cl_half8 (vector) — preserving original behavior.
+SYCL_PACKET_FUNCTION(pceil, ceil, cl::sycl::cl_half)
+SYCL_PACKET_FUNCTION(pceil, ceil, cl::sycl::cl_float4)
+SYCL_PACKET_FUNCTION(pceil, ceil, cl::sycl::cl_double2)
+
+#undef SYCL_UNARY_FUNCTION
+#undef SYCL_PACKET_FUNCTION
+
+// Binary min/max functions.
+#define SYCL_BINARY_FUNCTION(EIGEN_FUNC, SYCL_FUNC, PACKET)                                           \
+  template <>                                                                                         \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PACKET EIGEN_FUNC<PACKET>(const PACKET& a, const PACKET& b) { \
+    return cl::sycl::SYCL_FUNC(a, b);                                                                 \
+  }
+
+SYCL_BINARY_FUNCTION(pmin, fmin, cl::sycl::cl_half8)
+SYCL_BINARY_FUNCTION(pmin, fmin, cl::sycl::cl_float4)
+SYCL_BINARY_FUNCTION(pmin, fmin, cl::sycl::cl_double2)
+SYCL_BINARY_FUNCTION(pmax, fmax, cl::sycl::cl_half8)
+SYCL_BINARY_FUNCTION(pmax, fmax, cl::sycl::cl_float4)
+SYCL_BINARY_FUNCTION(pmax, fmax, cl::sycl::cl_double2)
+
+#undef SYCL_BINARY_FUNCTION
+
+// pldexp requires integer conversion of the exponent.
+#define SYCL_PLDEXP(packet_type)                                                                                  \
+  template <>                                                                                                     \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pldexp(const packet_type& a, const packet_type& exponent) {   \
+    return cl::sycl::ldexp(a, exponent.template convert<cl::sycl::cl_int, cl::sycl::rounding_mode::automatic>()); \
+  }
+
+SYCL_PLDEXP(cl::sycl::cl_half8)
+SYCL_PLDEXP(cl::sycl::cl_float4)
+SYCL_PLDEXP(cl::sycl::cl_double2)
+#undef SYCL_PLDEXP
+
+#endif
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_SYCL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/PacketMath.h
new file mode 100644
index 0000000..e5dad3c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/PacketMath.h
@@ -0,0 +1,551 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * PacketMath.h
+ *
+ * \brief:
+ *  PacketMath
+ *
+ *****************************************************************/
+
+#ifndef EIGEN_PACKET_MATH_SYCL_H
+#define EIGEN_PACKET_MATH_SYCL_H
+#include <type_traits>
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+#ifdef SYCL_DEVICE_ONLY
+#define SYCL_PLOAD(packet_type, AlignedType)                                                                           \
+  template <>                                                                                                          \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pload##AlignedType<packet_type>(                                   \
+      const typename unpacket_traits<packet_type>::type* from) {                                                       \
+    auto ptr =                                                                                                         \
+        cl::sycl::address_space_cast<cl::sycl::access::address_space::generic_space, cl::sycl::access::decorated::no>( \
+            from);                                                                                                     \
+    packet_type res{};                                                                                                 \
+    res.load(0, ptr);                                                                                                  \
+    return res;                                                                                                        \
+  }
+
+SYCL_PLOAD(cl::sycl::cl_float4, u)
+SYCL_PLOAD(cl::sycl::cl_float4, )
+SYCL_PLOAD(cl::sycl::cl_double2, u)
+SYCL_PLOAD(cl::sycl::cl_double2, )
+#undef SYCL_PLOAD
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_half8 pload<cl::sycl::cl_half8>(
+    const typename unpacket_traits<cl::sycl::cl_half8>::type* from) {
+  auto ptr =
+      cl::sycl::address_space_cast<cl::sycl::access::address_space::generic_space, cl::sycl::access::decorated::no>(
+          reinterpret_cast<const cl::sycl::cl_half*>(from));
+  cl::sycl::cl_half8 res{};
+  res.load(0, ptr);
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_half8 ploadu<cl::sycl::cl_half8>(
+    const typename unpacket_traits<cl::sycl::cl_half8>::type* from) {
+  auto ptr =
+      cl::sycl::address_space_cast<cl::sycl::access::address_space::generic_space, cl::sycl::access::decorated::no>(
+          reinterpret_cast<const cl::sycl::cl_half*>(from));
+  cl::sycl::cl_half8 res{};
+  res.load(0, ptr);
+  return res;
+}
+
+#define SYCL_PSTORE(scalar, packet_type, alignment)                                                                    \
+  template <>                                                                                                          \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstore##alignment(scalar* to, const packet_type& from) {                  \
+    auto ptr =                                                                                                         \
+        cl::sycl::address_space_cast<cl::sycl::access::address_space::generic_space, cl::sycl::access::decorated::no>( \
+            to);                                                                                                       \
+    from.store(0, ptr);                                                                                                \
+  }
+
+SYCL_PSTORE(float, cl::sycl::cl_float4, )
+SYCL_PSTORE(float, cl::sycl::cl_float4, u)
+SYCL_PSTORE(double, cl::sycl::cl_double2, )
+SYCL_PSTORE(double, cl::sycl::cl_double2, u)
+#undef SYCL_PSTORE
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoreu(Eigen::half* to, const cl::sycl::cl_half8& from) {
+  auto ptr =
+      cl::sycl::address_space_cast<cl::sycl::access::address_space::generic_space, cl::sycl::access::decorated::no>(
+          reinterpret_cast<cl::sycl::cl_half*>(to));
+  from.store(0, ptr);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstore(Eigen::half* to, const cl::sycl::cl_half8& from) {
+  auto ptr =
+      cl::sycl::address_space_cast<cl::sycl::access::address_space::generic_space, cl::sycl::access::decorated::no>(
+          reinterpret_cast<cl::sycl::cl_half*>(to));
+  from.store(0, ptr);
+}
+
+#define SYCL_PSET1(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pset1<packet_type>( \
+      const typename unpacket_traits<packet_type>::type& from) {        \
+    return packet_type(from);                                           \
+  }
+
+// global space
+SYCL_PSET1(cl::sycl::cl_half8)
+SYCL_PSET1(cl::sycl::cl_float4)
+SYCL_PSET1(cl::sycl::cl_double2)
+
+#undef SYCL_PSET1
+
+template <typename packet_type>
+struct get_base_packet {
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type get_ploaddup(sycl_multi_pointer) {}
+
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type get_pgather(sycl_multi_pointer, Index) {}
+};
+
+template <>
+struct get_base_packet<cl::sycl::cl_half8> {
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_half8 get_ploaddup(sycl_multi_pointer from) {
+    return cl::sycl::cl_half8(static_cast<cl::sycl::half>(from[0]), static_cast<cl::sycl::half>(from[0]),
+                              static_cast<cl::sycl::half>(from[1]), static_cast<cl::sycl::half>(from[1]),
+                              static_cast<cl::sycl::half>(from[2]), static_cast<cl::sycl::half>(from[2]),
+                              static_cast<cl::sycl::half>(from[3]), static_cast<cl::sycl::half>(from[3]));
+  }
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_half8 get_pgather(sycl_multi_pointer from, Index stride) {
+    return cl::sycl::cl_half8(
+        static_cast<cl::sycl::half>(from[0 * stride]), static_cast<cl::sycl::half>(from[1 * stride]),
+        static_cast<cl::sycl::half>(from[2 * stride]), static_cast<cl::sycl::half>(from[3 * stride]),
+        static_cast<cl::sycl::half>(from[4 * stride]), static_cast<cl::sycl::half>(from[5 * stride]),
+        static_cast<cl::sycl::half>(from[6 * stride]), static_cast<cl::sycl::half>(from[7 * stride]));
+  }
+
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_pscatter(sycl_multi_pointer to, const cl::sycl::cl_half8& from,
+                                                                 Index stride) {
+    auto tmp = stride;
+    to[0] = Eigen::half(from.s0());
+    to[tmp] = Eigen::half(from.s1());
+    to[tmp += stride] = Eigen::half(from.s2());
+    to[tmp += stride] = Eigen::half(from.s3());
+    to[tmp += stride] = Eigen::half(from.s4());
+    to[tmp += stride] = Eigen::half(from.s5());
+    to[tmp += stride] = Eigen::half(from.s6());
+    to[tmp += stride] = Eigen::half(from.s7());
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_half8 set_plset(const cl::sycl::half& a) {
+    return cl::sycl::cl_half8(static_cast<cl::sycl::half>(a), static_cast<cl::sycl::half>(a + 1),
+                              static_cast<cl::sycl::half>(a + 2), static_cast<cl::sycl::half>(a + 3),
+                              static_cast<cl::sycl::half>(a + 4), static_cast<cl::sycl::half>(a + 5),
+                              static_cast<cl::sycl::half>(a + 6), static_cast<cl::sycl::half>(a + 7));
+  }
+};
+
+template <>
+struct get_base_packet<cl::sycl::cl_float4> {
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 get_ploaddup(sycl_multi_pointer from) {
+    return cl::sycl::cl_float4(from[0], from[0], from[1], from[1]);
+  }
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 get_pgather(sycl_multi_pointer from, Index stride) {
+    return cl::sycl::cl_float4(from[0 * stride], from[1 * stride], from[2 * stride], from[3 * stride]);
+  }
+
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_pscatter(sycl_multi_pointer to, const cl::sycl::cl_float4& from,
+                                                                 Index stride) {
+    auto tmp = stride;
+    to[0] = from.x();
+    to[tmp] = from.y();
+    to[tmp += stride] = from.z();
+    to[tmp += stride] = from.w();
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 set_plset(const float& a) {
+    return cl::sycl::cl_float4(static_cast<float>(a), static_cast<float>(a + 1), static_cast<float>(a + 2),
+                               static_cast<float>(a + 3));
+  }
+};
+
+template <>
+struct get_base_packet<cl::sycl::cl_double2> {
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2 get_ploaddup(const sycl_multi_pointer from) {
+    return cl::sycl::cl_double2(from[0], from[0]);
+  }
+
+  template <typename sycl_multi_pointer, typename Index>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2 get_pgather(const sycl_multi_pointer from,
+                                                                                Index stride) {
+    return cl::sycl::cl_double2(from[0 * stride], from[1 * stride]);
+  }
+
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_pscatter(sycl_multi_pointer to,
+                                                                 const cl::sycl::cl_double2& from, Index stride) {
+    to[0] = from.x();
+    to[stride] = from.y();
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2 set_plset(const double& a) {
+    return cl::sycl::cl_double2(static_cast<double>(a), static_cast<double>(a + 1));
+  }
+};
+
+#define SYCL_PLOAD_DUP_SPECILIZE(packet_type)                              \
+  template <>                                                              \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ploaddup<packet_type>( \
+      const typename unpacket_traits<packet_type>::type* from) {           \
+    return get_base_packet<packet_type>::get_ploaddup(from);               \
+  }
+
+SYCL_PLOAD_DUP_SPECILIZE(cl::sycl::cl_half8)
+SYCL_PLOAD_DUP_SPECILIZE(cl::sycl::cl_float4)
+SYCL_PLOAD_DUP_SPECILIZE(cl::sycl::cl_double2)
+
+#undef SYCL_PLOAD_DUP_SPECILIZE
+
+#define SYCL_PLSET(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type plset<packet_type>( \
+      const typename unpacket_traits<packet_type>::type& a) {           \
+    return get_base_packet<packet_type>::set_plset(a);                  \
+  }
+SYCL_PLSET(cl::sycl::cl_float4)
+SYCL_PLSET(cl::sycl::cl_double2)
+#undef SYCL_PLSET
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_half8 plset<cl::sycl::cl_half8>(
+    const typename unpacket_traits<cl::sycl::cl_half8>::type& a) {
+  return get_base_packet<cl::sycl::cl_half8>::set_plset((const cl::sycl::half&)a);
+}
+
+#define SYCL_PGATHER_SPECILIZE(scalar, packet_type)                               \
+  template <>                                                                     \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pgather<scalar, packet_type>( \
+      const typename unpacket_traits<packet_type>::type* from, Index stride) {    \
+    return get_base_packet<packet_type>::get_pgather(from, stride);               \
+  }
+
+SYCL_PGATHER_SPECILIZE(Eigen::half, cl::sycl::cl_half8)
+SYCL_PGATHER_SPECILIZE(float, cl::sycl::cl_float4)
+SYCL_PGATHER_SPECILIZE(double, cl::sycl::cl_double2)
+#undef SYCL_PGATHER_SPECILIZE
+
+#define SYCL_PSCATTER_SPECILIZE(scalar, packet_type)                                             \
+  template <>                                                                                    \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<scalar, packet_type>(                      \
+      typename unpacket_traits<packet_type>::type * to, const packet_type& from, Index stride) { \
+    get_base_packet<packet_type>::set_pscatter(to, from, stride);                                \
+  }
+
+SYCL_PSCATTER_SPECILIZE(Eigen::half, cl::sycl::cl_half8)
+SYCL_PSCATTER_SPECILIZE(float, cl::sycl::cl_float4)
+SYCL_PSCATTER_SPECILIZE(double, cl::sycl::cl_double2)
+
+#undef SYCL_PSCATTER_SPECILIZE
+
+#define SYCL_PMAD(packet_type)                                                                        \
+  template <>                                                                                         \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pmadd(const packet_type& a, const packet_type& b, \
+                                                          const packet_type& c) {                     \
+    return cl::sycl::mad(a, b, c);                                                                    \
+  }
+
+SYCL_PMAD(cl::sycl::cl_half8)
+SYCL_PMAD(cl::sycl::cl_float4)
+SYCL_PMAD(cl::sycl::cl_double2)
+#undef SYCL_PMAD
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Eigen::half pfirst<cl::sycl::cl_half8>(const cl::sycl::cl_half8& a) {
+  return Eigen::half(a.s0());
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float pfirst<cl::sycl::cl_float4>(const cl::sycl::cl_float4& a) {
+  return a.x();
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double pfirst<cl::sycl::cl_double2>(const cl::sycl::cl_double2& a) {
+  return a.x();
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Eigen::half predux<cl::sycl::cl_half8>(const cl::sycl::cl_half8& a) {
+  return Eigen::half(a.s0() + a.s1() + a.s2() + a.s3() + a.s4() + a.s5() + a.s6() + a.s7());
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux<cl::sycl::cl_float4>(const cl::sycl::cl_float4& a) {
+  return a.x() + a.y() + a.z() + a.w();
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux<cl::sycl::cl_double2>(const cl::sycl::cl_double2& a) {
+  return a.x() + a.y();
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Eigen::half predux_max<cl::sycl::cl_half8>(const cl::sycl::cl_half8& a) {
+  return Eigen::half(cl::sycl::fmax(cl::sycl::fmax(cl::sycl::fmax(a.s0(), a.s1()), cl::sycl::fmax(a.s2(), a.s3())),
+                                    cl::sycl::fmax(cl::sycl::fmax(a.s4(), a.s5()), cl::sycl::fmax(a.s6(), a.s7()))));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_max<cl::sycl::cl_float4>(const cl::sycl::cl_float4& a) {
+  return cl::sycl::fmax(cl::sycl::fmax(a.x(), a.y()), cl::sycl::fmax(a.z(), a.w()));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_max<cl::sycl::cl_double2>(const cl::sycl::cl_double2& a) {
+  return cl::sycl::fmax(a.x(), a.y());
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Eigen::half predux_min<cl::sycl::cl_half8>(const cl::sycl::cl_half8& a) {
+  return Eigen::half(cl::sycl::fmin(cl::sycl::fmin(cl::sycl::fmin(a.s0(), a.s1()), cl::sycl::fmin(a.s2(), a.s3())),
+                                    cl::sycl::fmin(cl::sycl::fmin(a.s4(), a.s5()), cl::sycl::fmin(a.s6(), a.s7()))));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_min<cl::sycl::cl_float4>(const cl::sycl::cl_float4& a) {
+  return cl::sycl::fmin(cl::sycl::fmin(a.x(), a.y()), cl::sycl::fmin(a.z(), a.w()));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_min<cl::sycl::cl_double2>(const cl::sycl::cl_double2& a) {
+  return cl::sycl::fmin(a.x(), a.y());
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Eigen::half predux_mul<cl::sycl::cl_half8>(const cl::sycl::cl_half8& a) {
+  return Eigen::half(a.s0() * a.s1() * a.s2() * a.s3() * a.s4() * a.s5() * a.s6() * a.s7());
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_mul<cl::sycl::cl_float4>(const cl::sycl::cl_float4& a) {
+  return a.x() * a.y() * a.z() * a.w();
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_mul<cl::sycl::cl_double2>(const cl::sycl::cl_double2& a) {
+  return a.x() * a.y();
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_half8 pabs<cl::sycl::cl_half8>(const cl::sycl::cl_half8& a) {
+  return cl::sycl::cl_half8(cl::sycl::fabs(a.s0()), cl::sycl::fabs(a.s1()), cl::sycl::fabs(a.s2()),
+                            cl::sycl::fabs(a.s3()), cl::sycl::fabs(a.s4()), cl::sycl::fabs(a.s5()),
+                            cl::sycl::fabs(a.s6()), cl::sycl::fabs(a.s7()));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4 pabs<cl::sycl::cl_float4>(const cl::sycl::cl_float4& a) {
+  return cl::sycl::cl_float4(cl::sycl::fabs(a.x()), cl::sycl::fabs(a.y()), cl::sycl::fabs(a.z()),
+                             cl::sycl::fabs(a.w()));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_double2 pabs<cl::sycl::cl_double2>(const cl::sycl::cl_double2& a) {
+  return cl::sycl::cl_double2(cl::sycl::fabs(a.x()), cl::sycl::fabs(a.y()));
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_le(const Packet& a, const Packet& b) {
+  return (a <= b).template as<Packet>();
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_lt(const Packet& a, const Packet& b) {
+  return (a < b).template as<Packet>();
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_eq(const Packet& a, const Packet& b) {
+  return (a == b).template as<Packet>();
+}
+
+#define SYCL_PCMP(OP, TYPE)                                                                  \
+  template <>                                                                                \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE TYPE pcmp_##OP<TYPE>(const TYPE& a, const TYPE& b) { \
+    return sycl_pcmp_##OP<TYPE>(a, b);                                                       \
+  }
+
+SYCL_PCMP(le, cl::sycl::cl_half8)
+SYCL_PCMP(lt, cl::sycl::cl_half8)
+SYCL_PCMP(eq, cl::sycl::cl_half8)
+SYCL_PCMP(le, cl::sycl::cl_float4)
+SYCL_PCMP(lt, cl::sycl::cl_float4)
+SYCL_PCMP(eq, cl::sycl::cl_float4)
+SYCL_PCMP(le, cl::sycl::cl_double2)
+SYCL_PCMP(lt, cl::sycl::cl_double2)
+SYCL_PCMP(eq, cl::sycl::cl_double2)
+#undef SYCL_PCMP
+
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void ptranspose(PacketBlock<cl::sycl::cl_half8, 8>& kernel) {
+  cl::sycl::cl_half tmp = kernel.packet[0].s1();
+  kernel.packet[0].s1() = kernel.packet[1].s0();
+  kernel.packet[1].s0() = tmp;
+
+  tmp = kernel.packet[0].s2();
+  kernel.packet[0].s2() = kernel.packet[2].s0();
+  kernel.packet[2].s0() = tmp;
+
+  tmp = kernel.packet[0].s3();
+  kernel.packet[0].s3() = kernel.packet[3].s0();
+  kernel.packet[3].s0() = tmp;
+
+  tmp = kernel.packet[0].s4();
+  kernel.packet[0].s4() = kernel.packet[4].s0();
+  kernel.packet[4].s0() = tmp;
+
+  tmp = kernel.packet[0].s5();
+  kernel.packet[0].s5() = kernel.packet[5].s0();
+  kernel.packet[5].s0() = tmp;
+
+  tmp = kernel.packet[0].s6();
+  kernel.packet[0].s6() = kernel.packet[6].s0();
+  kernel.packet[6].s0() = tmp;
+
+  tmp = kernel.packet[0].s7();
+  kernel.packet[0].s7() = kernel.packet[7].s0();
+  kernel.packet[7].s0() = tmp;
+
+  tmp = kernel.packet[1].s2();
+  kernel.packet[1].s2() = kernel.packet[2].s1();
+  kernel.packet[2].s1() = tmp;
+
+  tmp = kernel.packet[1].s3();
+  kernel.packet[1].s3() = kernel.packet[3].s1();
+  kernel.packet[3].s1() = tmp;
+
+  tmp = kernel.packet[1].s4();
+  kernel.packet[1].s4() = kernel.packet[4].s1();
+  kernel.packet[4].s1() = tmp;
+
+  tmp = kernel.packet[1].s5();
+  kernel.packet[1].s5() = kernel.packet[5].s1();
+  kernel.packet[5].s1() = tmp;
+
+  tmp = kernel.packet[1].s6();
+  kernel.packet[1].s6() = kernel.packet[6].s1();
+  kernel.packet[6].s1() = tmp;
+
+  tmp = kernel.packet[1].s7();
+  kernel.packet[1].s7() = kernel.packet[7].s1();
+  kernel.packet[7].s1() = tmp;
+
+  tmp = kernel.packet[2].s3();
+  kernel.packet[2].s3() = kernel.packet[3].s2();
+  kernel.packet[3].s2() = tmp;
+
+  tmp = kernel.packet[2].s4();
+  kernel.packet[2].s4() = kernel.packet[4].s2();
+  kernel.packet[4].s2() = tmp;
+
+  tmp = kernel.packet[2].s5();
+  kernel.packet[2].s5() = kernel.packet[5].s2();
+  kernel.packet[5].s2() = tmp;
+
+  tmp = kernel.packet[2].s6();
+  kernel.packet[2].s6() = kernel.packet[6].s2();
+  kernel.packet[6].s2() = tmp;
+
+  tmp = kernel.packet[2].s7();
+  kernel.packet[2].s7() = kernel.packet[7].s2();
+  kernel.packet[7].s2() = tmp;
+
+  tmp = kernel.packet[3].s4();
+  kernel.packet[3].s4() = kernel.packet[4].s3();
+  kernel.packet[4].s3() = tmp;
+
+  tmp = kernel.packet[3].s5();
+  kernel.packet[3].s5() = kernel.packet[5].s3();
+  kernel.packet[5].s3() = tmp;
+
+  tmp = kernel.packet[3].s6();
+  kernel.packet[3].s6() = kernel.packet[6].s3();
+  kernel.packet[6].s3() = tmp;
+
+  tmp = kernel.packet[3].s7();
+  kernel.packet[3].s7() = kernel.packet[7].s3();
+  kernel.packet[7].s3() = tmp;
+
+  tmp = kernel.packet[4].s5();
+  kernel.packet[4].s5() = kernel.packet[5].s4();
+  kernel.packet[5].s4() = tmp;
+
+  tmp = kernel.packet[4].s6();
+  kernel.packet[4].s6() = kernel.packet[6].s4();
+  kernel.packet[6].s4() = tmp;
+
+  tmp = kernel.packet[4].s7();
+  kernel.packet[4].s7() = kernel.packet[7].s4();
+  kernel.packet[7].s4() = tmp;
+
+  tmp = kernel.packet[5].s6();
+  kernel.packet[5].s6() = kernel.packet[6].s5();
+  kernel.packet[6].s5() = tmp;
+
+  tmp = kernel.packet[5].s7();
+  kernel.packet[5].s7() = kernel.packet[7].s5();
+  kernel.packet[7].s5() = tmp;
+
+  tmp = kernel.packet[6].s7();
+  kernel.packet[6].s7() = kernel.packet[7].s6();
+  kernel.packet[7].s6() = tmp;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void ptranspose(PacketBlock<cl::sycl::cl_float4, 4>& kernel) {
+  float tmp = kernel.packet[0].y();
+  kernel.packet[0].y() = kernel.packet[1].x();
+  kernel.packet[1].x() = tmp;
+
+  tmp = kernel.packet[0].z();
+  kernel.packet[0].z() = kernel.packet[2].x();
+  kernel.packet[2].x() = tmp;
+
+  tmp = kernel.packet[0].w();
+  kernel.packet[0].w() = kernel.packet[3].x();
+  kernel.packet[3].x() = tmp;
+
+  tmp = kernel.packet[1].z();
+  kernel.packet[1].z() = kernel.packet[2].y();
+  kernel.packet[2].y() = tmp;
+
+  tmp = kernel.packet[1].w();
+  kernel.packet[1].w() = kernel.packet[3].y();
+  kernel.packet[3].y() = tmp;
+
+  tmp = kernel.packet[2].w();
+  kernel.packet[2].w() = kernel.packet[3].z();
+  kernel.packet[3].z() = tmp;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void ptranspose(PacketBlock<cl::sycl::cl_double2, 2>& kernel) {
+  double tmp = kernel.packet[0].y();
+  kernel.packet[0].y() = kernel.packet[1].x();
+  kernel.packet[1].x() = tmp;
+}
+
+#endif  // SYCL_DEVICE_ONLY
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_SYCL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/TypeCasting.h
new file mode 100644
index 0000000..6e3fa4f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/SYCL/TypeCasting.h
@@ -0,0 +1,83 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * TypeCasting.h
+ *
+ * \brief:
+ *  TypeCasting
+ *
+ *****************************************************************/
+
+#ifndef EIGEN_TYPE_CASTING_SYCL_H
+#define EIGEN_TYPE_CASTING_SYCL_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+#ifdef SYCL_DEVICE_ONLY
+template <>
+struct type_casting_traits<float, int> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_int4 pcast<cl::sycl::cl_float4, cl::sycl::cl_int4>(
+    const cl::sycl::cl_float4& a) {
+  return a.template convert<cl::sycl::cl_int, cl::sycl::rounding_mode::automatic>();
+}
+
+template <>
+struct type_casting_traits<int, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4 pcast<cl::sycl::cl_int4, cl::sycl::cl_float4>(
+    const cl::sycl::cl_int4& a) {
+  return a.template convert<cl::sycl::cl_float, cl::sycl::rounding_mode::automatic>();
+}
+
+template <>
+struct type_casting_traits<double, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4 pcast<cl::sycl::cl_double2, cl::sycl::cl_float4>(
+    const cl::sycl::cl_double2& a, const cl::sycl::cl_double2& b) {
+  auto a1 = a.template convert<cl::sycl::cl_float, cl::sycl::rounding_mode::automatic>();
+  auto b1 = b.template convert<cl::sycl::cl_float, cl::sycl::rounding_mode::automatic>();
+  return cl::sycl::cl_float4(a1.x(), a1.y(), b1.x(), b1.y());
+}
+
+template <>
+struct type_casting_traits<float, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_double2 pcast<cl::sycl::cl_float4, cl::sycl::cl_double2>(
+    const cl::sycl::cl_float4& a) {
+  // Simply discard the second half of the input
+  return cl::sycl::cl_double2(a.x(), a.y());
+}
+
+#endif
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_SYCL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/ZVector/Complex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/ZVector/Complex.h
new file mode 100644
index 0000000..dfcdcab
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/ZVector/Complex.h
@@ -0,0 +1,553 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX32_ZVECTOR_H
+#define EIGEN_COMPLEX32_ZVECTOR_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+inline Packet4ui p4ui_CONJ_XOR() {
+  return Packet4ui {0x00000000, 0x80000000, 0x00000000, 0x80000000};  // vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);
+}
+#endif
+
+inline Packet2ul p2ul_CONJ_XOR1() {
+  return (Packet2ul)vec_sld((Packet4ui)p2d_ZERO_, (Packet4ui)p2l_ZERO,
+                            8);  //{ 0x8000000000000000, 0x0000000000000000 };
+}
+inline Packet2ul p2ul_CONJ_XOR2() {
+  return (Packet2ul)vec_sld((Packet4ui)p2l_ZERO, (Packet4ui)p2d_ZERO_,
+                            8);  //{ 0x8000000000000000, 0x0000000000000000 };
+}
+
+struct Packet1cd {
+  EIGEN_STRONG_INLINE Packet1cd() {}
+  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
+  Packet2d v;
+};
+
+struct Packet2cf {
+  EIGEN_STRONG_INLINE Packet2cf() {}
+  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
+  union {
+    Packet4f v;
+    Packet1cd cd[2];
+  };
+#else
+  Packet4f v;
+#endif
+};
+
+template <>
+struct packet_traits<std::complex<float> > : default_packet_traits {
+  typedef Packet2cf type;
+  typedef Packet2cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasNegate = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct packet_traits<std::complex<double> > : default_packet_traits {
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 1,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasLog = 1,
+    HasNegate = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2cf> {
+  typedef std::complex<float> type;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet2cf half;
+  typedef Packet4f as_real;
+};
+template <>
+struct unpacket_traits<Packet1cd> {
+  typedef std::complex<double> type;
+  enum {
+    size = 1,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet1cd half;
+  typedef Packet2d as_real;
+};
+
+/* Forward declaration */
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf, 2>& kernel);
+
+/* complex<double> first */
+template <>
+EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from));
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<double> >(std::complex<double>* to, const Packet1cd& from) {
+  EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double>* to, const Packet1cd& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd
+pset1<Packet1cd>(const std::complex<double>& from) { /* here we really have to use unaligned loads :( */
+  return ploadu<Packet1cd>(&from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from,
+                                                                            Index stride EIGEN_UNUSED) {
+  return pload<Packet1cd>(from);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from,
+                                                                        Index stride EIGEN_UNUSED) {
+  pstore<std::complex<double> >(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(a.v + b.v);
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(a.v - b.v);
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) {
+  return Packet1cd(pnegate(Packet2d(a.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) {
+  return Packet1cd((Packet2d)vec_xor((Packet2d)a.v, (Packet2d)p2ul_CONJ_XOR2()));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  Packet2d a_re, a_im, v1, v2;
+
+  // Permute and multiply the real parts of a and b
+  a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI);
+  // Get the imaginary parts of a
+  a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO);
+  // multiply a_re * b
+  v1 = vec_madd(a_re, b.v, p2d_ZERO);
+  // multiply a_im * b and get the conjugate result
+  v2 = vec_madd(a_im, b.v, p2d_ZERO);
+  v2 = (Packet2d)vec_sld((Packet4ui)v2, (Packet4ui)v2, 8);
+  v2 = (Packet2d)vec_xor((Packet2d)v2, (Packet2d)p2ul_CONJ_XOR1());
+
+  return Packet1cd(v1 + v2);
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(vec_and(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(vec_or(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(vec_xor(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return Packet1cd(vec_and(a.v, vec_nor(b.v, b.v)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) {
+  return pset1<Packet1cd>(*from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
+  Packet2d eq = vec_cmpeq(a.v, b.v);
+  Packet2d tmp = {eq[1], eq[0]};
+  return (Packet1cd)pand<Packet2d>(eq, tmp);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double>* addr) {
+  EIGEN_ZVECTOR_PREFETCH(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) {
+  EIGEN_ALIGN16 std::complex<double> res;
+  pstore<std::complex<double> >(&res, a);
+
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) {
+  return pfirst(a);
+}
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) {
+  return pfirst(a);
+}
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd, Packet2d)
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  return pdiv_complex(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
+  return psqrt_complex<Packet1cd>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
+  return psqrt_complex<Packet2cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd plog<Packet1cd>(const Packet1cd& a) {
+  return plog_complex<Packet1cd>(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf plog<Packet2cf>(const Packet2cf& a) {
+  return plog_complex<Packet2cf>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pexp<Packet2cf>(const Packet2cf& a) {
+  return pexp_complex(a);
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip /*<Packet1cd>*/ (const Packet1cd& x) {
+  return Packet1cd(preverse(Packet2d(x.v)));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd, 2>& kernel) {
+  Packet2d tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
+  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
+  kernel.packet[0].v = tmp;
+}
+
+/* complex<float> follows */
+template <>
+EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from));
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<float> >(std::complex<float>* to, const Packet2cf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet2cf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) {
+  EIGEN_ALIGN16 std::complex<float> res[2];
+  pstore<std::complex<float> >(res, a);
+
+  return res[0];
+}
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
+template <>
+EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) {
+  Packet2cf res;
+  res.cd[0] = Packet1cd(vec_ld2f((const float*)&from));
+  res.cd[1] = res.cd[0];
+  return res;
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) {
+  Packet2cf res;
+  if ((std::ptrdiff_t(&from) % 16) == 0)
+    res.v = pload<Packet4f>((const float*)&from);
+  else
+    res.v = ploadu<Packet4f>((const float*)&from);
+  res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI);
+  return res;
+}
+#endif
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from,
+                                                                           Index stride) {
+  EIGEN_ALIGN16 std::complex<float> af[2];
+  af[0] = from[0 * stride];
+  af[1] = from[1 * stride];
+  return pload<Packet2cf>(af);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from,
+                                                                       Index stride) {
+  EIGEN_ALIGN16 std::complex<float> af[2];
+  pstore<std::complex<float> >((std::complex<float>*)af, from);
+  to[0 * stride] = af[0];
+  to[1 * stride] = af[1];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(padd<Packet4f>(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(psub<Packet4f>(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) {
+  return Packet2cf(pnegate(Packet4f(a.v)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(pand<Packet4f>(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(por<Packet4f>(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(pxor<Packet4f>(a.v, b.v));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return Packet2cf(pandnot<Packet4f>(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) {
+  return pset1<Packet2cf>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float>* addr) {
+  EIGEN_ZVECTOR_PREFETCH(addr);
+}
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
+  Packet4f eq = pcmp_eq<Packet4f>(a.v, b.v);
+  Packet2cf res;
+  Packet2d tmp1 = {eq.v4f[0][1], eq.v4f[0][0]};
+  Packet2d tmp2 = {eq.v4f[1][1], eq.v4f[1][0]};
+  res.v.v4f[0] = pand<Packet2d>(eq.v4f[0], tmp1);
+  res.v.v4f[1] = pand<Packet2d>(eq.v4f[1], tmp2);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) {
+  Packet2cf res;
+  res.v.v4f[0] = pconj(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[0]))).v;
+  res.v.v4f[1] = pconj(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[1]))).v;
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  Packet2cf res;
+  res.v.v4f[0] =
+      pmul(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[0])), Packet1cd(reinterpret_cast<Packet2d>(b.v.v4f[0]))).v;
+  res.v.v4f[1] =
+      pmul(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[1])), Packet1cd(reinterpret_cast<Packet2d>(b.v.v4f[1]))).v;
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) {
+  Packet2cf res;
+  res.cd[0] = a.cd[1];
+  res.cd[1] = a.cd[0];
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) {
+  std::complex<float> res;
+  Packet1cd b = padd<Packet1cd>(a.cd[0], a.cd[1]);
+  vec_st2f(b.v, (float*)&res);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) {
+  std::complex<float> res;
+  Packet1cd b = pmul<Packet1cd>(a.cd[0], a.cd[1]);
+  vec_st2f(b.v, (float*)&res);
+  return res;
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf, Packet4f)
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return pdiv_complex(a, b);
+}
+
+EIGEN_STRONG_INLINE Packet2cf pcplxflip /*<Packet2cf>*/ (const Packet2cf& x) {
+  Packet2cf res;
+  res.cd[0] = pcplxflip(x.cd[0]);
+  res.cd[1] = pcplxflip(x.cd[1]);
+  return res;
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf, 2>& kernel) {
+  Packet1cd tmp = kernel.packet[0].cd[1];
+  kernel.packet[0].cd[1] = kernel.packet[1].cd[0];
+  kernel.packet[1].cd[0] = tmp;
+}
+
+#else
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
+  Packet4f eq = vec_cmpeq(a.v, b.v);
+  Packet4f tmp = {eq[1], eq[0], eq[3], eq[2]};
+  return (Packet2cf)pand<Packet4f>(eq, tmp);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) {
+  return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR())));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  Packet4f a_re, a_im, prod, prod_im;
+
+  // Permute and multiply the real parts of a and b
+  a_re = vec_perm(a.v, a.v, p16uc_PSET32_WODD);
+
+  // Get the imaginary parts of a
+  a_im = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN);
+
+  // multiply a_im * b and get the conjugate result
+  prod_im = a_im * b.v;
+  prod_im = pxor<Packet4f>(prod_im, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR()));
+  // permute back to a proper order
+  prod_im = vec_perm(prod_im, prod_im, p16uc_COMPLEX32_REV);
+
+  // multiply a_re * b, add prod_im
+  prod = pmadd<Packet4f>(a_re, b.v, prod_im);
+
+  return Packet2cf(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) {
+  Packet4f rev_a;
+  rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX32_REV2);
+  return Packet2cf(rev_a);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) {
+  Packet4f b;
+  b = vec_sld(a.v, a.v, 8);
+  b = padd<Packet4f>(a.v, b);
+  return pfirst<Packet2cf>(Packet2cf(b));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) {
+  Packet4f b;
+  Packet2cf prod;
+  b = vec_sld(a.v, a.v, 8);
+  prod = pmul<Packet2cf>(a, Packet2cf(b));
+
+  return pfirst<Packet2cf>(prod);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf, Packet4f)
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  return pdiv_complex(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x) {
+  return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf, 2>& kernel) {
+  Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
+  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
+  kernel.packet[0].v = tmp;
+}
+
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX32_ZVECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/ZVector/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/ZVector/MathFunctions.h
new file mode 100644
index 0000000..348d643
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/ZVector/MathFunctions.h
@@ -0,0 +1,244 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* The sin, cos, exp, and log functions of this file come from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+#ifndef EIGEN_MATH_FUNCTIONS_ZVECTOR_H
+#define EIGEN_MATH_FUNCTIONS_ZVECTOR_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+EIGEN_DOUBLE_PACKET_FUNCTION(atanh, Packet2d)
+EIGEN_DOUBLE_PACKET_FUNCTION(log, Packet2d)
+EIGEN_DOUBLE_PACKET_FUNCTION(log2, Packet2d)
+EIGEN_DOUBLE_PACKET_FUNCTION(tanh, Packet2d)
+
+EIGEN_FLOAT_PACKET_FUNCTION(atanh, Packet4f)
+EIGEN_FLOAT_PACKET_FUNCTION(log, Packet4f)
+EIGEN_FLOAT_PACKET_FUNCTION(log2, Packet4f)
+
+EIGEN_GENERIC_PACKET_FUNCTION(atan, Packet2d)
+EIGEN_GENERIC_PACKET_FUNCTION(atan, Packet4f)
+EIGEN_GENERIC_PACKET_FUNCTION(exp2, Packet2d)
+EIGEN_GENERIC_PACKET_FUNCTION(exp2, Packet4f)
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+static EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
+static EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
+static EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f);
+static EIGEN_DECLARE_CONST_Packet4i(23, 23);
+
+static EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inv_mant_mask, ~0x7f800000);
+
+/* the smallest non denormalized float number */
+static EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos, 0x00800000);
+static EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf, 0xff800000);  // -1.f/0.f
+static EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_nan, 0xffffffff);
+
+/* natural logarithm computed for 4 simultaneous float
+  return NaN for x <= 0
+*/
+static EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292E-2f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, -1.1514610310E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, -1.2420140846E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, +1.4249322787E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, -1.6668057665E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, +2.0000714765E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, -2.4999993993E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, +3.3333331174E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
+
+static EIGEN_DECLARE_CONST_Packet4f(exp_hi, 88.3762626647950f);
+static EIGEN_DECLARE_CONST_Packet4f(exp_lo, -88.3762626647949f);
+
+static EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
+
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500E-4f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507E-3f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073E-3f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894E-2f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201E-1f);
+#endif
+
+static EIGEN_DECLARE_CONST_Packet2d(1, 1.0);
+static EIGEN_DECLARE_CONST_Packet2d(2, 2.0);
+static EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
+
+static EIGEN_DECLARE_CONST_Packet2d(exp_hi, 709.437);
+static EIGEN_DECLARE_CONST_Packet2d(exp_lo, -709.436139303);
+
+static EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
+
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
+
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
+
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet2d pexp<Packet2d>(const Packet2d& _x) {
+  Packet2d x = _x;
+
+  Packet2d tmp, fx;
+  Packet2l emm0;
+
+  // clamp x
+  x = pmax(pmin(x, p2d_exp_hi), p2d_exp_lo);
+  /* express exp(x) as exp(g + n*log(2)) */
+  fx = pmadd(p2d_cephes_LOG2EF, x, p2d_half);
+
+  fx = vec_floor(fx);
+
+  tmp = pmul(fx, p2d_cephes_exp_C1);
+  Packet2d z = pmul(fx, p2d_cephes_exp_C2);
+  x = psub(x, tmp);
+  x = psub(x, z);
+
+  Packet2d x2 = pmul(x, x);
+
+  Packet2d px = p2d_cephes_exp_p0;
+  px = pmadd(px, x2, p2d_cephes_exp_p1);
+  px = pmadd(px, x2, p2d_cephes_exp_p2);
+  px = pmul(px, x);
+
+  Packet2d qx = p2d_cephes_exp_q0;
+  qx = pmadd(qx, x2, p2d_cephes_exp_q1);
+  qx = pmadd(qx, x2, p2d_cephes_exp_q2);
+  qx = pmadd(qx, x2, p2d_cephes_exp_q3);
+
+  x = pdiv(px, psub(qx, px));
+  x = pmadd(p2d_2, x, p2d_1);
+
+  // build 2^n
+  emm0 = vec_ctsl(fx, 0);
+
+  static const Packet2l p2l_1023 = {1023, 1023};
+  static const Packet2ul p2ul_52 = {52, 52};
+
+  emm0 = emm0 + p2l_1023;
+  emm0 = emm0 << reinterpret_cast<Packet2l>(p2ul_52);
+
+  // Altivec's max & min operators just drop silent NaNs. Check NaNs in
+  // inputs and return them unmodified.
+  Packet2ul isnumber_mask = reinterpret_cast<Packet2ul>(vec_cmpeq(_x, _x));
+  return vec_sel(_x, pmax(pmul(x, reinterpret_cast<Packet2d>(emm0)), _x), isnumber_mask);
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f pexp<Packet4f>(const Packet4f& _x) {
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+  Packet4f x = _x;
+
+  Packet4f tmp, fx;
+  Packet4i emm0;
+
+  // clamp x
+  x = pmax(pmin(x, p4f_exp_hi), p4f_exp_lo);
+
+  // express exp(x) as exp(g + n*log(2))
+  fx = pmadd(x, p4f_cephes_LOG2EF, p4f_half);
+
+  fx = pfloor(fx);
+
+  tmp = pmul(fx, p4f_cephes_exp_C1);
+  Packet4f z = pmul(fx, p4f_cephes_exp_C2);
+  x = psub(x, tmp);
+  x = psub(x, z);
+
+  z = pmul(x, x);
+
+  Packet4f y = p4f_cephes_exp_p0;
+  y = pmadd(y, x, p4f_cephes_exp_p1);
+  y = pmadd(y, x, p4f_cephes_exp_p2);
+  y = pmadd(y, x, p4f_cephes_exp_p3);
+  y = pmadd(y, x, p4f_cephes_exp_p4);
+  y = pmadd(y, x, p4f_cephes_exp_p5);
+  y = pmadd(y, z, x);
+  y = padd(y, p4f_1);
+
+  // build 2^n
+  emm0 = Packet4i{(int)fx[0], (int)fx[1], (int)fx[2], (int)fx[3]};
+  emm0 = emm0 + p4i_0x7f;
+  emm0 = emm0 << reinterpret_cast<Packet4i>(p4i_23);
+
+  return pmax(pmul(y, reinterpret_cast<Packet4f>(emm0)), _x);
+#else
+  Packet4f res;
+  res.v4f[0] = pexp<Packet2d>(_x.v4f[0]);
+  res.v4f[1] = pexp<Packet2d>(_x.v4f[1]);
+  return res;
+#endif
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet2d psqrt<Packet2d>(const Packet2d& x) {
+  return vec_sqrt(x);
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f psqrt<Packet4f>(const Packet4f& x) {
+  Packet4f res;
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+  res = vec_sqrt(x);
+#else
+  res.v4f[0] = psqrt<Packet2d>(x.v4f[0]);
+  res.v4f[1] = psqrt<Packet2d>(x.v4f[1]);
+#endif
+  return res;
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet2d prsqrt<Packet2d>(const Packet2d& x) {
+  return pset1<Packet2d>(1.0) / psqrt<Packet2d>(x);
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f prsqrt<Packet4f>(const Packet4f& x) {
+  Packet4f res;
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+  res = pset1<Packet4f>(1.0) / psqrt<Packet4f>(x);
+#else
+  res.v4f[0] = prsqrt<Packet2d>(x.v4f[0]);
+  res.v4f[1] = prsqrt<Packet2d>(x.v4f[1]);
+#endif
+  return res;
+}
+
+// Hyperbolic Tangent function.
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f ptanh<Packet4f>(const Packet4f& x) {
+  return ptanh_float(x);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_ZVECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/ZVector/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/ZVector/PacketMath.h
new file mode 100644
index 0000000..4ccda87
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/ZVector/PacketMath.h
@@ -0,0 +1,1353 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_ZVECTOR_H
+#define EIGEN_PACKET_MATH_ZVECTOR_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 16
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#endif
+
+typedef __vector int Packet4i;
+typedef __vector unsigned int Packet4ui;
+typedef __vector __bool int Packet4bi;
+typedef __vector short int Packet8i;
+typedef __vector unsigned char Packet16uc;
+typedef __vector double Packet2d;
+typedef __vector unsigned long long Packet2ul;
+typedef __vector long long Packet2l;
+
+// Z14 has builtin support for float vectors
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+typedef __vector float Packet4f;
+#else
+typedef struct {
+  Packet2d v4f[2];
+} Packet4f;
+#endif
+
+typedef union {
+  numext::int32_t i[4];
+  numext::uint32_t ui[4];
+  numext::int64_t l[2];
+  numext::uint64_t ul[2];
+  double d[2];
+  float f[4];
+  Packet4i v4i;
+  Packet4ui v4ui;
+  Packet2l v2l;
+  Packet2ul v2ul;
+  Packet2d v2d;
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+  Packet4f v4f;
+#endif
+} Packet;
+
+// We don't want to write the same code all the time, but we need to reuse the constants
+// and it doesn't really work to declare them global, so we define macros instead
+
+#define EIGEN_DECLARE_CONST_FAST_Packet4i(NAME, X) Packet4i p4i_##NAME = reinterpret_cast<Packet4i>(vec_splat_s32(X))
+
+#define EIGEN_DECLARE_CONST_FAST_Packet2d(NAME, X) Packet2d p2d_##NAME = reinterpret_cast<Packet2d>(vec_splat_s64(X))
+
+#define EIGEN_DECLARE_CONST_FAST_Packet2l(NAME, X) Packet2l p2l_##NAME = reinterpret_cast<Packet2l>(vec_splat_s64(X))
+
+#define EIGEN_DECLARE_CONST_Packet4i(NAME, X) Packet4i p4i_##NAME = pset1<Packet4i>(X)
+
+#define EIGEN_DECLARE_CONST_Packet2d(NAME, X) Packet2d p2d_##NAME = pset1<Packet2d>(X)
+
+#define EIGEN_DECLARE_CONST_Packet2l(NAME, X) Packet2l p2l_##NAME = pset1<Packet2l>(X)
+
+// These constants are endian-agnostic
+static EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0);  //{ 0, 0, 0, 0,}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(ONE, 1);   //{ 1, 1, 1, 1}
+
+static EIGEN_DECLARE_CONST_FAST_Packet2d(ZERO, 0);
+static EIGEN_DECLARE_CONST_FAST_Packet2l(ZERO, 0);
+static EIGEN_DECLARE_CONST_FAST_Packet2l(ONE, 1);
+
+static Packet2d p2d_ONE = {1.0, 1.0};
+static Packet2d p2d_ZERO_ = {numext::bit_cast<double>(0x8000000000000000ull),
+                             numext::bit_cast<double>(0x8000000000000000ull)};
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+#define EIGEN_DECLARE_CONST_FAST_Packet4f(NAME, X) Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(vec_splat_s32(X))
+
+#define EIGEN_DECLARE_CONST_Packet4f(NAME, X) Packet4f p4f_##NAME = pset1<Packet4f>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME, X) \
+  const Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(pset1<Packet4i>(X))
+
+static EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0);     //{ 0.0, 0.0, 0.0, 0.0}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1, -1);  //{ -1, -1, -1, -1}
+static Packet4f p4f_MZERO = {0x80000000, 0x80000000, 0x80000000, 0x80000000};
+#endif
+
+static Packet4i p4i_COUNTDOWN = {0, 1, 2, 3};
+static Packet4f p4f_COUNTDOWN = {0.0, 1.0, 2.0, 3.0};
+static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(
+    vec_sld(reinterpret_cast<Packet16uc>(p2d_ZERO), reinterpret_cast<Packet16uc>(p2d_ONE), 8));
+
+static Packet16uc p16uc_PSET64_HI = {0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7};
+static Packet16uc p16uc_DUPLICATE32_HI = {0, 1, 2, 3, 0, 1, 2, 3, 4, 5, 6, 7, 4, 5, 6, 7};
+
+// Mask alignment
+#define EIGEN_MASK_ALIGNMENT 0xfffffffffffffff0
+
+#define EIGEN_ALIGNED_PTR(x) ((std::ptrdiff_t)(x) & EIGEN_MASK_ALIGNMENT)
+
+// Handle endianness properly while loading constants
+// Define global static constants:
+
+static Packet16uc p16uc_FORWARD = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+static Packet16uc p16uc_REVERSE32 = {12, 13, 14, 15, 8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3};
+static Packet16uc p16uc_REVERSE64 = {8, 9, 10, 11, 12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6, 7};
+
+static Packet16uc p16uc_PSET32_WODD =
+    vec_sld((Packet16uc)vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc)vec_splat((Packet4ui)p16uc_FORWARD, 2),
+            8);  //{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
+static Packet16uc p16uc_PSET32_WEVEN = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc)vec_splat((Packet4ui)p16uc_FORWARD, 3),
+                                               8);  //{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
+/*static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3),
+8);      //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
+
+static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD,
+(Packet4ui)p16uc_PSET32_WEVEN);     //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };*/
+static Packet16uc p16uc_PSET64_LO = (Packet16uc)vec_mergel(
+    (Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);  //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
+/*static Packet16uc p16uc_TRANSPOSE64_HI = vec_add(p16uc_PSET64_HI, p16uc_HALF64_0_16); //{ 0,1,2,3, 4,5,6,7,
+16,17,18,19, 20,21,22,23}; static Packet16uc p16uc_TRANSPOSE64_LO = vec_add(p16uc_PSET64_LO, p16uc_HALF64_0_16); //{
+8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};*/
+static Packet16uc p16uc_TRANSPOSE64_HI = {0, 1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 19, 20, 21, 22, 23};
+static Packet16uc p16uc_TRANSPOSE64_LO = {8, 9, 10, 11, 12, 13, 14, 15, 24, 25, 26, 27, 28, 29, 30, 31};
+
+static Packet16uc p16uc_COMPLEX32_REV =
+    vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8);  //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
+
+static Packet16uc p16uc_COMPLEX32_REV2 =
+    vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8);  //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
+
+#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
+#define EIGEN_ZVECTOR_PREFETCH(ADDR) __builtin_prefetch(ADDR);
+#else
+#define EIGEN_ZVECTOR_PREFETCH(ADDR) asm("   pfd [%[addr]]\n" ::[addr] "r"(ADDR) : "cc");
+#endif
+
+template <>
+struct packet_traits<int> : default_packet_traits {
+  typedef Packet4i type;
+  typedef Packet4i half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+  };
+};
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4f type;
+  typedef Packet4f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasAbs = 1,
+    HasSin = 0,
+    HasCos = 0,
+    HasLog = 0,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasTanh = 1,
+    HasErf = 1,
+    HasNegate = 1,
+  };
+};
+
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasAbs = 1,
+    HasSin = 0,
+    HasCos = 0,
+    HasLog = 0,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasNegate = 1,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4i> {
+  typedef int type;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet4i half;
+};
+template <>
+struct unpacket_traits<Packet4f> {
+  typedef float type;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet4f half;
+  typedef Packet4i integer_packet;
+};
+template <>
+struct unpacket_traits<Packet2d> {
+  typedef double type;
+  enum {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+  typedef Packet2d half;
+  typedef Packet2l integer_packet;
+};
+
+/* Forward declaration */
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f, 4>& kernel);
+
+inline std::ostream& operator<<(std::ostream& s, const Packet4i& v) {
+  Packet vt;
+  vt.v4i = v;
+  s << vt.i[0] << ", " << vt.i[1] << ", " << vt.i[2] << ", " << vt.i[3];
+  return s;
+}
+
+inline std::ostream& operator<<(std::ostream& s, const Packet4ui& v) {
+  Packet vt;
+  vt.v4ui = v;
+  s << vt.ui[0] << ", " << vt.ui[1] << ", " << vt.ui[2] << ", " << vt.ui[3];
+  return s;
+}
+
+inline std::ostream& operator<<(std::ostream& s, const Packet2l& v) {
+  Packet vt;
+  vt.v2l = v;
+  s << vt.l[0] << ", " << vt.l[1];
+  return s;
+}
+
+inline std::ostream& operator<<(std::ostream& s, const Packet2ul& v) {
+  Packet vt;
+  vt.v2ul = v;
+  s << vt.ul[0] << ", " << vt.ul[1];
+  return s;
+}
+
+inline std::ostream& operator<<(std::ostream& s, const Packet2d& v) {
+  Packet vt;
+  vt.v2d = v;
+  s << vt.d[0] << ", " << vt.d[1];
+  return s;
+}
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+inline std::ostream& operator<<(std::ostream& s, const Packet4f& v) {
+  Packet vt;
+  vt.v4f = v;
+  s << vt.f[0] << ", " << vt.f[1] << ", " << vt.f[2] << ", " << vt.f[3];
+  return s;
+}
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD
+  return vec_xl(0, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD
+  return vec_xl(0, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) {
+  EIGEN_DEBUG_ALIGNED_STORE
+  vec_xst(from, 0, to);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) {
+  EIGEN_DEBUG_ALIGNED_STORE
+  vec_xst(from, 0, to);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) {
+  return vec_splats(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
+  return vec_splats(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet4i>(const int* a, Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3) {
+  a3 = pload<Packet4i>(a);
+  a0 = vec_splat(a3, 0);
+  a1 = vec_splat(a3, 1);
+  a2 = vec_splat(a3, 2);
+  a3 = vec_splat(a3, 3);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet2d>(const double* a, Packet2d& a0, Packet2d& a1, Packet2d& a2,
+                                               Packet2d& a3) {
+  a1 = pload<Packet2d>(a);
+  a0 = vec_splat(a1, 0);
+  a1 = vec_splat(a1, 1);
+  a3 = pload<Packet2d>(a + 2);
+  a2 = vec_splat(a3, 0);
+  a3 = vec_splat(a3, 1);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride) {
+  EIGEN_ALIGN16 int ai[4];
+  ai[0] = from[0 * stride];
+  ai[1] = from[1 * stride];
+  ai[2] = from[2 * stride];
+  ai[3] = from[3 * stride];
+  return pload<Packet4i>(ai);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride) {
+  EIGEN_ALIGN16 double af[2];
+  af[0] = from[0 * stride];
+  af[1] = from[1 * stride];
+  return pload<Packet2d>(af);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride) {
+  EIGEN_ALIGN16 int ai[4];
+  pstore<int>((int*)ai, from);
+  to[0 * stride] = ai[0];
+  to[1 * stride] = ai[1];
+  to[2 * stride] = ai[2];
+  to[3 * stride] = ai[3];
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride) {
+  EIGEN_ALIGN16 double af[2];
+  pstore<double>(af, from);
+  to[0 * stride] = af[0];
+  to[1 * stride] = af[1];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return (a + b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (a + b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return (a - b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (a - b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return (a * b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (a * b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return (a / b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return (a / b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) {
+  return (-a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) {
+  return (-a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
+  return padd<Packet4i>(pmul<Packet4i>(a, b), c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  return vec_madd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a) {
+  return padd<Packet4i>(pset1<Packet4i>(a), p4i_COUNTDOWN);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) {
+  return padd<Packet2d>(pset1<Packet2d>(a), p2d_COUNTDOWN);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_min(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_min(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_max(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_max(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_and(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_and(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_or(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_or(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return vec_xor(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_xor(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  return pand<Packet4i>(a, vec_nor(b, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return vec_and(a, vec_nor(b, b));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) {
+  /* Uses non-default rounding for vec_round */
+  return __builtin_s390_vfidb(a, 0, 1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) {
+  return vec_ceil(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) {
+  return vec_floor(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from) {
+  return pload<Packet4i>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) {
+  return pload<Packet2d>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int* from) {
+  Packet4i p = pload<Packet4i>(from);
+  return vec_perm(p, p, p16uc_DUPLICATE32_HI);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) {
+  Packet2d p = pload<Packet2d>(from);
+  return vec_perm(p, p, p16uc_PSET64_HI);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from) {
+  pstore<int>(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) {
+  pstore<double>(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) {
+  EIGEN_ZVECTOR_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
+  EIGEN_ZVECTOR_PREFETCH(addr);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2l parithmetic_shift_right(const Packet2l& a) {
+  return Packet2l{parithmetic_shift_right<N>(a[0]), parithmetic_shift_right<N>(a[1])};
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(const Packet4i& a) {
+  return Packet4i{parithmetic_shift_right<N>(a[0]), parithmetic_shift_right<N>(a[1]), parithmetic_shift_right<N>(a[2]),
+                  parithmetic_shift_right<N>(a[3])};
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
+  return Packet2l{plogical_shift_right<N>(a[0]), plogical_shift_right<N>(a[1])};
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i plogical_shift_right(const Packet4i& a) {
+  return Packet4i{plogical_shift_right<N>(a[0]), plogical_shift_right<N>(a[1]), plogical_shift_right<N>(a[2]),
+                  plogical_shift_right<N>(a[3])};
+}
+
+template <int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
+  return Packet2l{plogical_shift_left<N>(a[0]), plogical_shift_left<N>(a[1])};
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4i plogical_shift_left(const Packet4i& a) {
+  return Packet4i{plogical_shift_left<N>(a[0]), plogical_shift_left<N>(a[1]), plogical_shift_left<N>(a[2]),
+                  plogical_shift_left<N>(a[3])};
+}
+
+template <>
+EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) {
+  EIGEN_ALIGN16 int x[4];
+  pstore(x, a);
+  return x[0];
+}
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
+  EIGEN_ALIGN16 double x[2];
+  pstore(x, a);
+  return x[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) {
+  return reinterpret_cast<Packet4i>(
+      vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) {
+  return reinterpret_cast<Packet2d>(
+      vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE64));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pabs<Packet4i>(const Packet4i& a) {
+  return vec_abs(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pabs<Packet2d>(const Packet2d& a) {
+  return vec_abs(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a) {
+  Packet4i b, sum;
+  b = vec_sld(a, a, 8);
+  sum = padd<Packet4i>(a, b);
+  b = vec_sld(sum, sum, 4);
+  sum = padd<Packet4i>(sum, b);
+  return pfirst(sum);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) {
+  Packet2d b, sum;
+  b = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8));
+  sum = padd<Packet2d>(a, b);
+  return pfirst(sum);
+}
+
+// Other reduction functions:
+// mul
+template <>
+EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a) {
+  EIGEN_ALIGN16 int aux[4];
+  pstore(aux, a);
+  return aux[0] * aux[1] * aux[2] * aux[3];
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) {
+  return pfirst(
+      pmul(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
+}
+
+// min
+template <>
+EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a) {
+  Packet4i b, res;
+  b = pmin<Packet4i>(a, vec_sld(a, a, 8));
+  res = pmin<Packet4i>(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) {
+  return pfirst(pmin<Packet2d>(
+      a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
+}
+
+// max
+template <>
+EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a) {
+  Packet4i b, res;
+  b = pmax<Packet4i>(a, vec_sld(a, a, 8));
+  res = pmax<Packet4i>(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+// max
+template <>
+EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) {
+  return pfirst(pmax<Packet2d>(
+      a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
+  Packet4i t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  Packet4i t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  Packet4i t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  Packet4i t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2d, 2>& kernel) {
+  Packet2d t0 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_HI);
+  Packet2d t1 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_LO);
+  kernel.packet[0] = t0;
+  kernel.packet[1] = t1;
+}
+
+/* z13 has no vector float support so we emulate that with double
+   z14 has proper vector float support.
+*/
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
+/* Helper function to simulate a vec_splat_packet4f
+ */
+template <int element>
+EIGEN_STRONG_INLINE Packet4f vec_splat_packet4f(const Packet4f& from) {
+  Packet4f splat;
+  switch (element) {
+    case 0:
+      splat.v4f[0] = vec_splat(from.v4f[0], 0);
+      splat.v4f[1] = splat.v4f[0];
+      break;
+    case 1:
+      splat.v4f[0] = vec_splat(from.v4f[0], 1);
+      splat.v4f[1] = splat.v4f[0];
+      break;
+    case 2:
+      splat.v4f[0] = vec_splat(from.v4f[1], 0);
+      splat.v4f[1] = splat.v4f[0];
+      break;
+    case 3:
+      splat.v4f[0] = vec_splat(from.v4f[1], 1);
+      splat.v4f[1] = splat.v4f[0];
+      break;
+  }
+  return splat;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) {
+  // FIXME: No intrinsic yet
+  EIGEN_DEBUG_ALIGNED_LOAD
+  Packet4f vfrom;
+  vfrom.v4f[0] = vec_ld2f(&from[0]);
+  vfrom.v4f[1] = vec_ld2f(&from[2]);
+  return vfrom;
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) {
+  // FIXME: No intrinsic yet
+  EIGEN_DEBUG_ALIGNED_STORE
+  vec_st2f(from.v4f[0], &to[0]);
+  vec_st2f(from.v4f[1], &to[2]);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
+  Packet4f to;
+  to.v4f[0] = pset1<Packet2d>(static_cast<const double&>(from));
+  to.v4f[1] = to.v4f[0];
+  return to;
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet4f>(const float* a, Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3) {
+  a3 = pload<Packet4f>(a);
+  a0 = vec_splat_packet4f<0>(a3);
+  a1 = vec_splat_packet4f<1>(a3);
+  a2 = vec_splat_packet4f<2>(a3);
+  a3 = vec_splat_packet4f<3>(a3);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride) {
+  EIGEN_ALIGN16 float ai[4];
+  ai[0] = from[0 * stride];
+  ai[1] = from[1 * stride];
+  ai[2] = from[2 * stride];
+  ai[3] = from[3 * stride];
+  return pload<Packet4f>(ai);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride) {
+  EIGEN_ALIGN16 float ai[4];
+  pstore<float>((float*)ai, from);
+  to[0 * stride] = ai[0];
+  to[1 * stride] = ai[1];
+  to[2 * stride] = ai[2];
+  to[3 * stride] = ai[3];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f c;
+  c.v4f[0] = a.v4f[0] + b.v4f[0];
+  c.v4f[1] = a.v4f[1] + b.v4f[1];
+  return c;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f c;
+  c.v4f[0] = a.v4f[0] - b.v4f[0];
+  c.v4f[1] = a.v4f[1] - b.v4f[1];
+  return c;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f c;
+  c.v4f[0] = a.v4f[0] * b.v4f[0];
+  c.v4f[1] = a.v4f[1] * b.v4f[1];
+  return c;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f c;
+  c.v4f[0] = a.v4f[0] / b.v4f[0];
+  c.v4f[1] = a.v4f[1] / b.v4f[1];
+  return c;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) {
+  Packet4f c;
+  c.v4f[0] = -a.v4f[0];
+  c.v4f[1] = -a.v4f[1];
+  return c;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  Packet4f res;
+  res.v4f[0] = vec_madd(a.v4f[0], b.v4f[0], c.v4f[0]);
+  res.v4f[1] = vec_madd(a.v4f[1], b.v4f[1], c.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f res;
+  res.v4f[0] = pmin(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pmin(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f res;
+  res.v4f[0] = pmax(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pmax(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f res;
+  res.v4f[0] = pand(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pand(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f res;
+  res.v4f[0] = por(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = por(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f res;
+  res.v4f[0] = pxor(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pxor(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f res;
+  res.v4f[0] = pandnot(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pandnot(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a) {
+  Packet4f res;
+  res.v4f[0] = generic_round(a.v4f[0]);
+  res.v4f[1] = generic_round(a.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) {
+  Packet4f res;
+  res.v4f[0] = vec_ceil(a.v4f[0]);
+  res.v4f[1] = vec_ceil(a.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) {
+  Packet4f res;
+  res.v4f[0] = vec_floor(a.v4f[0]);
+  res.v4f[1] = vec_floor(a.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) {
+  Packet4f p = pload<Packet4f>(from);
+  p.v4f[1] = vec_splat(p.v4f[0], 1);
+  p.v4f[0] = vec_splat(p.v4f[0], 0);
+  return p;
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
+  EIGEN_ALIGN16 float x[2];
+  vec_st2f(a.v4f[0], &x[0]);
+  return x[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) {
+  Packet4f rev;
+  rev.v4f[0] = preverse<Packet2d>(a.v4f[1]);
+  rev.v4f[1] = preverse<Packet2d>(a.v4f[0]);
+  return rev;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pabs<Packet4f>(const Packet4f& a) {
+  Packet4f res;
+  res.v4f[0] = pabs(a.v4f[0]);
+  res.v4f[1] = pabs(a.v4f[1]);
+  return res;
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) {
+  Packet2d sum;
+  sum = padd<Packet2d>(a.v4f[0], a.v4f[1]);
+  double first = predux<Packet2d>(sum);
+  return static_cast<float>(first);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) {
+  // Return predux_mul<Packet2d> of the subvectors product
+  return static_cast<float>(pfirst(predux_mul(pmul(a.v4f[0], a.v4f[1]))));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) {
+  Packet2d b, res;
+  b = pmin<Packet2d>(a.v4f[0], a.v4f[1]);
+  res = pmin<Packet2d>(
+      b, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(b), reinterpret_cast<Packet4i>(b), 8)));
+  return static_cast<float>(pfirst(res));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) {
+  Packet2d b, res;
+  b = pmax<Packet2d>(a.v4f[0], a.v4f[1]);
+  res = pmax<Packet2d>(
+      b, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(b), reinterpret_cast<Packet4i>(b), 8)));
+  return static_cast<float>(pfirst(res));
+}
+
+/* Split the Packet4f PacketBlock into 4 Packet2d PacketBlocks and transpose each one
+ */
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
+  PacketBlock<Packet2d, 2> t0, t1, t2, t3;
+  // copy top-left 2x2 Packet2d block
+  t0.packet[0] = kernel.packet[0].v4f[0];
+  t0.packet[1] = kernel.packet[1].v4f[0];
+
+  // copy top-right 2x2 Packet2d block
+  t1.packet[0] = kernel.packet[0].v4f[1];
+  t1.packet[1] = kernel.packet[1].v4f[1];
+
+  // copy bottom-left 2x2 Packet2d block
+  t2.packet[0] = kernel.packet[2].v4f[0];
+  t2.packet[1] = kernel.packet[3].v4f[0];
+
+  // copy bottom-right 2x2 Packet2d block
+  t3.packet[0] = kernel.packet[2].v4f[1];
+  t3.packet[1] = kernel.packet[3].v4f[1];
+
+  // Transpose all 2x2 blocks
+  ptranspose(t0);
+  ptranspose(t1);
+  ptranspose(t2);
+  ptranspose(t3);
+
+  // Copy back transposed blocks, but exchange t1 and t2 due to transposition
+  kernel.packet[0].v4f[0] = t0.packet[0];
+  kernel.packet[0].v4f[1] = t2.packet[0];
+  kernel.packet[1].v4f[0] = t0.packet[1];
+  kernel.packet[1].v4f[1] = t2.packet[1];
+  kernel.packet[2].v4f[0] = t1.packet[0];
+  kernel.packet[2].v4f[1] = t3.packet[0];
+  kernel.packet[3].v4f[0] = t1.packet[1];
+  kernel.packet[3].v4f[1] = t3.packet[1];
+}
+
+template <>
+Packet4f EIGEN_STRONG_INLINE pcmp_le<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f res;
+  res.v4f[0] = pcmp_le(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pcmp_le(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template <>
+Packet4f EIGEN_STRONG_INLINE pcmp_lt<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f res;
+  res.v4f[0] = pcmp_lt(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pcmp_lt(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template <>
+Packet4f EIGEN_STRONG_INLINE pcmp_eq<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f res;
+  res.v4f[0] = pcmp_eq(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pcmp_eq(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+#else
+template <>
+EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD
+  return vec_xl(0, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) {
+  EIGEN_DEBUG_ALIGNED_STORE
+  vec_xst(from, 0, to);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
+  return vec_splats(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pbroadcast4<Packet4f>(const float* a, Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3) {
+  a3 = pload<Packet4f>(a);
+  a0 = vec_splat(a3, 0);
+  a1 = vec_splat(a3, 1);
+  a2 = vec_splat(a3, 2);
+  a3 = vec_splat(a3, 3);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride) {
+  EIGEN_ALIGN16 float af[4];
+  af[0] = from[0 * stride];
+  af[1] = from[1 * stride];
+  af[2] = from[2 * stride];
+  af[3] = from[3 * stride];
+  return pload<Packet4f>(af);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride) {
+  EIGEN_ALIGN16 float af[4];
+  pstore<float>((float*)af, from);
+  to[0 * stride] = af[0];
+  to[1 * stride] = af[1];
+  to[2 * stride] = af[2];
+  to[3 * stride] = af[3];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (a + b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (a - b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (a * b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return (a / b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pnegate<Packet4f>(const Packet4f& a) {
+  return (-a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pconj<Packet4f>(const Packet4f& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmadd<Packet4f>(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  return vec_madd(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_min(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_max(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_and(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_or(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_xor(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return vec_and(a, vec_nor(b, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a) {
+  /* Uses non-default rounding for vec_round */
+  return __builtin_s390_vfisb(a, 0, 1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) {
+  return vec_ceil(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) {
+  return vec_floor(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pabs<Packet4f>(const Packet4f& a) {
+  return vec_abs(a);
+}
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
+  EIGEN_ALIGN16 float x[4];
+  pstore(x, a);
+  return x[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) {
+  Packet4f p = pload<Packet4f>(from);
+  return vec_perm(p, p, p16uc_DUPLICATE32_HI);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) {
+  return reinterpret_cast<Packet4f>(
+      vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) {
+  Packet4f b, sum;
+  b = vec_sld(a, a, 8);
+  sum = padd<Packet4f>(a, b);
+  b = vec_sld(sum, sum, 4);
+  sum = padd<Packet4f>(sum, b);
+  return pfirst(sum);
+}
+
+// Other reduction functions:
+// mul
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) {
+  Packet4f prod;
+  prod = pmul(a, vec_sld(a, a, 8));
+  return pfirst(pmul(prod, vec_sld(prod, prod, 4)));
+}
+
+// min
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) {
+  Packet4f b, res;
+  b = pmin<Packet4f>(a, vec_sld(a, a, 8));
+  res = pmin<Packet4f>(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+// max
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) {
+  Packet4f b, res;
+  b = pmax<Packet4f>(a, vec_sld(a, a, 8));
+  res = pmax<Packet4f>(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
+  Packet4f t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  Packet4f t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  Packet4f t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  Packet4f t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
+  // Clamp exponent to [-2099, 2099]
+  const Packet2d max_exponent = pset1<Packet2d>(2099.0);
+  const Packet2l e = pcast<Packet2d, Packet2l>(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
+
+  // Split 2^e into four factors and multiply:
+  const Packet2l bias = {1023, 1023};
+  Packet2l b = plogical_shift_right<2>(e);  // floor(e/4)
+  Packet2d c = reinterpret_cast<Packet2d>(plogical_shift_left<52>(b + bias));
+  Packet2d out = pmul(pmul(pmul(a, c), c), c);                        // a * 2^(3b)
+  b = psub(psub(psub(e, b), b), b);                                   // e - 3b
+  c = reinterpret_cast<Packet2d>(plogical_shift_left<52>(b + bias));  // 2^(e - 3b)
+  out = pmul(out, c);                                                 // a * 2^e
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
+  EIGEN_ZVECTOR_PREFETCH(addr);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
+  return pload<Packet4f>(from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) {
+  pstore<float>(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) {
+  return padd<Packet4f>(pset1<Packet4f>(a), p4f_COUNTDOWN);
+}
+
+#if !defined(vec_float) || !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 13)
+#pragma GCC warning "float->int and int->float conversion is simulated. compile for z15 for improved performance"
+template <>
+struct cast_impl<Packet4i, Packet4f> {
+  EIGEN_DEVICE_FUNC static inline Packet4f run(const Packet4i& a) {
+    return Packet4f{float(a[0]), float(a[1]), float(a[2]), float(a[3])};
+  }
+};
+
+template <>
+struct cast_impl<Packet4f, Packet4i> {
+  EIGEN_DEVICE_FUNC static inline Packet4i run(const Packet4f& a) {
+    return Packet4i{int(a[0]), int(a[1]), int(a[2]), int(a[3])};
+  }
+};
+
+template <>
+struct cast_impl<Packet2l, Packet2d> {
+  EIGEN_DEVICE_FUNC static inline Packet2d run(const Packet2l& a) { return Packet2d{double(a[0]), double(a[1])}; }
+};
+
+template <>
+struct cast_impl<Packet2d, Packet2l> {
+  EIGEN_DEVICE_FUNC static inline Packet2l run(const Packet2d& a) {
+    return Packet2l{(long long)(a[0]), (long long)(a[1])};
+  }
+};
+#else
+template <>
+struct cast_impl<Packet4i, Packet4f> {
+  EIGEN_DEVICE_FUNC static inline Packet4f run(const Packet4i& a) { return vec_float(a); }
+};
+
+template <>
+struct cast_impl<Packet4f, Packet4i> {
+  EIGEN_DEVICE_FUNC static inline Packet4i run(const Packet4f& a) { return vec_signed(a); }
+};
+
+template <>
+struct cast_impl<Packet2l, Packet2d> {
+  EIGEN_DEVICE_FUNC static inline Packet2d run(const Packet2l& a) { return vec_double(a); }
+};
+
+template <>
+struct cast_impl<Packet2d, Packet2l> {
+  EIGEN_DEVICE_FUNC static inline Packet2l run(const Packet2d& a) { return vec_signed(a); }
+};
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(uint32_t from) {
+  return pset1<Packet4f>(Eigen::numext::bit_cast<float>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(uint64_t from) {
+  return pset1<Packet2d>(Eigen::numext::bit_cast<double>(from));
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_ZVECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/Complex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/Complex.h
new file mode 100644
index 0000000..edc9693
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/Complex.h
@@ -0,0 +1,406 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Rasmus Munk Larsen
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_CLANG_H
+#define EIGEN_COMPLEX_CLANG_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <typename RealScalar, int N>
+struct complex_packet_wrapper {
+  using RealPacketT = detail::VectorType<RealScalar, 2 * N>;
+  complex_packet_wrapper() = default;
+  EIGEN_STRONG_INLINE explicit complex_packet_wrapper(const RealPacketT& a) : v(a) {}
+  EIGEN_STRONG_INLINE constexpr std::complex<RealScalar> operator[](Index i) const {
+    return std::complex<RealScalar>(v[2 * i], v[2 * i + 1]);
+  }
+  RealPacketT v;
+};
+
+using Packet8cf = complex_packet_wrapper<float, 8>;
+using Packet4cf = complex_packet_wrapper<float, 4>;
+using Packet2cf = complex_packet_wrapper<float, 2>;
+using Packet4cd = complex_packet_wrapper<double, 4>;
+using Packet2cd = complex_packet_wrapper<double, 2>;
+
+struct generic_complex_packet_traits : default_packet_traits {
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasArg = 0,
+    HasSetLinear = 0,
+    HasConj = 1,
+    // Math functions
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+  };
+};
+
+template <>
+struct packet_traits<std::complex<float>> : generic_complex_packet_traits {
+  using type = Packet8cf;
+  using half = Packet8cf;
+  enum {
+    size = 8,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet8cf> : generic_unpacket_traits {
+  using type = std::complex<float>;
+  using half = Packet8cf;
+  using as_real = Packet16f;
+  enum {
+    size = 8,
+  };
+};
+
+template <>
+struct packet_traits<std::complex<double>> : generic_complex_packet_traits {
+  using type = Packet4cd;
+  using half = Packet4cd;
+  enum {
+    size = 4,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4cd> : generic_unpacket_traits {
+  using type = std::complex<double>;
+  using half = Packet4cd;
+  using as_real = Packet8d;
+  enum {
+    size = 4,
+  };
+};
+
+// ------------ Load and store ops ----------
+#define EIGEN_CLANG_COMPLEX_LOAD_STORE(PACKET_TYPE)                                                       \
+  template <>                                                                                             \
+  EIGEN_STRONG_INLINE PACKET_TYPE ploadu<PACKET_TYPE>(const unpacket_traits<PACKET_TYPE>::type* from) {   \
+    return PACKET_TYPE(ploadu<typename unpacket_traits<PACKET_TYPE>::as_real>(&numext::real_ref(*from))); \
+  }                                                                                                       \
+  template <>                                                                                             \
+  EIGEN_STRONG_INLINE PACKET_TYPE pload<PACKET_TYPE>(const unpacket_traits<PACKET_TYPE>::type* from) {    \
+    return PACKET_TYPE(pload<typename unpacket_traits<PACKET_TYPE>::as_real>(&numext::real_ref(*from)));  \
+  }                                                                                                       \
+  template <>                                                                                             \
+  EIGEN_STRONG_INLINE void pstoreu<typename unpacket_traits<PACKET_TYPE>::type, PACKET_TYPE>(             \
+      typename unpacket_traits<PACKET_TYPE>::type * to, const PACKET_TYPE& from) {                        \
+    pstoreu(&numext::real_ref(*to), from.v);                                                              \
+  }                                                                                                       \
+  template <>                                                                                             \
+  EIGEN_STRONG_INLINE void pstore<typename unpacket_traits<PACKET_TYPE>::type, PACKET_TYPE>(              \
+      typename unpacket_traits<PACKET_TYPE>::type * to, const PACKET_TYPE& from) {                        \
+    pstore(&numext::real_ref(*to), from.v);                                                               \
+  }
+
+EIGEN_CLANG_COMPLEX_LOAD_STORE(Packet8cf);
+EIGEN_CLANG_COMPLEX_LOAD_STORE(Packet4cd);
+#undef EIGEN_CLANG_COMPLEX_LOAD_STORE
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pset1<Packet8cf>(const std::complex<float>& from) {
+  const float re = numext::real(from);
+  const float im = numext::imag(from);
+  return Packet8cf(Packet16f{re, im, re, im, re, im, re, im, re, im, re, im, re, im, re, im});
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd pset1<Packet4cd>(const std::complex<double>& from) {
+  const double re = numext::real(from);
+  const double im = numext::imag(from);
+  return Packet4cd(Packet8d{re, im, re, im, re, im, re, im});
+}
+
+// ----------- Unary ops ------------------
+#define DELEGATE_UNARY_TO_REAL_OP(PACKET_TYPE, OP)                        \
+  template <>                                                             \
+  EIGEN_STRONG_INLINE PACKET_TYPE OP<PACKET_TYPE>(const PACKET_TYPE& a) { \
+    return PACKET_TYPE(OP(a.v));                                          \
+  }
+
+#define EIGEN_CLANG_COMPLEX_UNARY_CWISE_OPS(PACKET_TYPE)                                             \
+  DELEGATE_UNARY_TO_REAL_OP(PACKET_TYPE, pnegate)                                                    \
+  DELEGATE_UNARY_TO_REAL_OP(PACKET_TYPE, pzero)                                                      \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE unpacket_traits<PACKET_TYPE>::type pfirst<PACKET_TYPE>(const PACKET_TYPE& a) { \
+    return a[0];                                                                                     \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pexp<PACKET_TYPE>(const PACKET_TYPE& a) {                          \
+    return pexp_complex(a);                                                                          \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE plog<PACKET_TYPE>(const PACKET_TYPE& a) {                          \
+    return plog_complex(a);                                                                          \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE psqrt<PACKET_TYPE>(const PACKET_TYPE& a) {                         \
+    return psqrt_complex(a);                                                                         \
+  }
+
+EIGEN_CLANG_COMPLEX_UNARY_CWISE_OPS(Packet8cf);
+EIGEN_CLANG_COMPLEX_UNARY_CWISE_OPS(Packet4cd);
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pconj<Packet8cf>(const Packet8cf& a) {
+  return Packet8cf(__builtin_shufflevector(a.v, -a.v, 0, 17, 2, 19, 4, 21, 6, 23, 8, 25, 10, 27, 12, 29, 14, 31));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pconj<Packet4cf>(const Packet4cf& a) {
+  return Packet4cf(__builtin_shufflevector(a.v, -a.v, 0, 9, 2, 11, 4, 13, 6, 15));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pconj<Packet2cf>(const Packet2cf& a) {
+  return Packet2cf(__builtin_shufflevector(a.v, -a.v, 0, 5, 2, 7));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd pconj<Packet4cd>(const Packet4cd& a) {
+  return Packet4cd(__builtin_shufflevector(a.v, -a.v, 0, 9, 2, 11, 4, 13, 6, 15));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pconj<Packet2cd>(const Packet2cd& a) {
+  return Packet2cd(__builtin_shufflevector(a.v, -a.v, 0, 5, 2, 7));
+}
+
+#undef DELEGATE_UNARY_TO_REAL_OP
+#undef EIGEN_CLANG_COMPLEX_UNARY_CWISE_OPS
+
+// Flip real and imaginary parts, i.e.  {re(a), im(a)} -> {im(a), re(a)}.
+template <>
+EIGEN_STRONG_INLINE Packet8cf pcplxflip<Packet8cf>(const Packet8cf& a) {
+  return Packet8cf(__builtin_shufflevector(a.v, a.v, 1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10, 13, 12, 15, 14));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pcplxflip<Packet4cf>(const Packet4cf& a) {
+  return Packet4cf(__builtin_shufflevector(a.v, a.v, 1, 0, 3, 2, 5, 4, 7, 6));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& a) {
+  return Packet2cf(__builtin_shufflevector(a.v, a.v, 1, 0, 3, 2));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd pcplxflip<Packet4cd>(const Packet4cd& a) {
+  return Packet4cd(__builtin_shufflevector(a.v, a.v, 1, 0, 3, 2, 5, 4, 7, 6));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pcplxflip<Packet2cd>(const Packet2cd& a) {
+  return Packet2cd(__builtin_shufflevector(a.v, a.v, 1, 0, 3, 2));
+}
+
+// Copy real to imaginary part, i.e. {re(a), im(a)} -> {re(a), re(a)}.
+template <>
+EIGEN_STRONG_INLINE Packet8cf pdupreal<Packet8cf>(const Packet8cf& a) {
+  return Packet8cf(__builtin_shufflevector(a.v, a.v, 0, 0, 2, 2, 4, 4, 6, 6, 8, 8, 10, 10, 12, 12, 14, 14));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pdupreal<Packet4cf>(const Packet4cf& a) {
+  return Packet4cf(__builtin_shufflevector(a.v, a.v, 0, 0, 2, 2, 4, 4, 6, 6));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pdupreal<Packet2cf>(const Packet2cf& a) {
+  return Packet2cf(__builtin_shufflevector(a.v, a.v, 0, 0, 2, 2));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd pdupreal<Packet4cd>(const Packet4cd& a) {
+  return Packet4cd(__builtin_shufflevector(a.v, a.v, 0, 0, 2, 2, 4, 4, 6, 6));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pdupreal<Packet2cd>(const Packet2cd& a) {
+  return Packet2cd(__builtin_shufflevector(a.v, a.v, 0, 0, 2, 2));
+}
+
+// Copy imaginary to real part, i.e. {re(a), im(a)} -> {im(a), im(a)}.
+template <>
+EIGEN_STRONG_INLINE Packet8cf pdupimag<Packet8cf>(const Packet8cf& a) {
+  return Packet8cf(__builtin_shufflevector(a.v, a.v, 1, 1, 3, 3, 5, 5, 7, 7, 9, 9, 11, 11, 13, 13, 15, 15));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cf pdupimag<Packet4cf>(const Packet4cf& a) {
+  return Packet4cf(__builtin_shufflevector(a.v, a.v, 1, 1, 3, 3, 5, 5, 7, 7));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cf pdupimag<Packet2cf>(const Packet2cf& a) {
+  return Packet2cf(__builtin_shufflevector(a.v, a.v, 1, 1, 3, 3));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd pdupimag<Packet4cd>(const Packet4cd& a) {
+  return Packet4cd(__builtin_shufflevector(a.v, a.v, 1, 1, 3, 3, 5, 5, 7, 7));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2cd pdupimag<Packet2cd>(const Packet2cd& a) {
+  return Packet2cd(__builtin_shufflevector(a.v, a.v, 1, 1, 3, 3));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf ploaddup<Packet8cf>(const std::complex<float>* from) {
+  return Packet8cf(Packet16f{std::real(from[0]), std::imag(from[0]), std::real(from[0]), std::imag(from[0]),
+                             std::real(from[1]), std::imag(from[1]), std::real(from[1]), std::imag(from[1]),
+                             std::real(from[2]), std::imag(from[2]), std::real(from[2]), std::imag(from[2]),
+                             std::real(from[3]), std::imag(from[3]), std::real(from[3]), std::imag(from[3])});
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd ploaddup<Packet4cd>(const std::complex<double>* from) {
+  return Packet4cd(Packet8d{std::real(from[0]), std::imag(from[0]), std::real(from[0]), std::imag(from[0]),
+                            std::real(from[1]), std::imag(from[1]), std::real(from[1]), std::imag(from[1])});
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf ploadquad<Packet8cf>(const std::complex<float>* from) {
+  return Packet8cf(Packet16f{std::real(from[0]), std::imag(from[0]), std::real(from[0]), std::imag(from[0]),
+                             std::real(from[0]), std::imag(from[0]), std::real(from[0]), std::imag(from[0]),
+                             std::real(from[1]), std::imag(from[1]), std::real(from[1]), std::imag(from[1]),
+                             std::real(from[1]), std::imag(from[1]), std::real(from[1]), std::imag(from[1])});
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd ploadquad<Packet4cd>(const std::complex<double>* from) {
+  return pset1<Packet4cd>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf preverse<Packet8cf>(const Packet8cf& a) {
+  return Packet8cf(reinterpret_cast<Packet16f>(preverse(reinterpret_cast<Packet8d>(a.v))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4cd preverse<Packet4cd>(const Packet4cd& a) {
+  return Packet4cd(__builtin_shufflevector(a.v, a.v, 6, 7, 4, 5, 2, 3, 0, 1));
+}
+
+// ----------- Binary ops ------------------
+#define DELEGATE_BINARY_TO_REAL_OP(PACKET_TYPE, OP)                                             \
+  template <>                                                                                   \
+  EIGEN_STRONG_INLINE PACKET_TYPE OP<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) { \
+    return PACKET_TYPE(OP(a.v, b.v));                                                           \
+  }
+
+#define EIGEN_CLANG_COMPLEX_BINARY_CWISE_OPS(PACKET_TYPE)                                            \
+  DELEGATE_BINARY_TO_REAL_OP(PACKET_TYPE, psub)                                                      \
+  DELEGATE_BINARY_TO_REAL_OP(PACKET_TYPE, pand)                                                      \
+  DELEGATE_BINARY_TO_REAL_OP(PACKET_TYPE, por)                                                       \
+  DELEGATE_BINARY_TO_REAL_OP(PACKET_TYPE, pxor)                                                      \
+  DELEGATE_BINARY_TO_REAL_OP(PACKET_TYPE, pandnot)                                                   \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pdiv<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {    \
+    return pdiv_complex(a, b);                                                                       \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pcmp_eq<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) { \
+    const PACKET_TYPE t = PACKET_TYPE(pcmp_eq(a.v, b.v));                                            \
+    return PACKET_TYPE(pand(pdupreal(t).v, pdupimag(t).v));                                          \
+  }
+
+EIGEN_CLANG_COMPLEX_BINARY_CWISE_OPS(Packet8cf);
+EIGEN_CLANG_COMPLEX_BINARY_CWISE_OPS(Packet4cd);
+
+// Binary ops that are needed on sub-packets for predux and predux_mul.
+#define EIGEN_CLANG_COMPLEX_REDUCER_BINARY_CWISE_OPS(PACKET_TYPE)                                 \
+  DELEGATE_BINARY_TO_REAL_OP(PACKET_TYPE, padd)                                                   \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmul<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) { \
+    return pmul_complex(a, b);                                                                    \
+  }
+
+EIGEN_CLANG_COMPLEX_REDUCER_BINARY_CWISE_OPS(Packet8cf);
+EIGEN_CLANG_COMPLEX_REDUCER_BINARY_CWISE_OPS(Packet4cf);
+EIGEN_CLANG_COMPLEX_REDUCER_BINARY_CWISE_OPS(Packet2cf);
+EIGEN_CLANG_COMPLEX_REDUCER_BINARY_CWISE_OPS(Packet4cd);
+EIGEN_CLANG_COMPLEX_REDUCER_BINARY_CWISE_OPS(Packet2cd);
+
+#define EIGEN_CLANG_PACKET_SCATTER_GATHER(PACKET_TYPE)                                                               \
+  template <>                                                                                                        \
+  EIGEN_STRONG_INLINE void pscatter(unpacket_traits<PACKET_TYPE>::type* to, const PACKET_TYPE& from, Index stride) { \
+    constexpr int size = unpacket_traits<PACKET_TYPE>::size;                                                         \
+    for (int i = 0; i < size; ++i) {                                                                                 \
+      to[i * stride] = from[i];                                                                                      \
+    }                                                                                                                \
+  }                                                                                                                  \
+  template <>                                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pgather<typename unpacket_traits<PACKET_TYPE>::type, PACKET_TYPE>(                 \
+      const unpacket_traits<PACKET_TYPE>::type* from, Index stride) {                                                \
+    constexpr int size = unpacket_traits<PACKET_TYPE>::size;                                                         \
+    PACKET_TYPE result;                                                                                              \
+    for (int i = 0; i < size; ++i) {                                                                                 \
+      const unpacket_traits<PACKET_TYPE>::type from_i = from[i * stride];                                            \
+      result.v[2 * i] = numext::real(from_i);                                                                        \
+      result.v[2 * i + 1] = numext::imag(from_i);                                                                    \
+    }                                                                                                                \
+    return result;                                                                                                   \
+  }
+
+EIGEN_CLANG_PACKET_SCATTER_GATHER(Packet8cf);
+EIGEN_CLANG_PACKET_SCATTER_GATHER(Packet4cd);
+#undef EIGEN_CLANG_PACKET_SCATTER_GATHER
+
+#undef DELEGATE_BINARY_TO_REAL_OP
+#undef EIGEN_CLANG_COMPLEX_BINARY_CWISE_OPS
+#undef EIGEN_CLANG_COMPLEX_REDUCER_BINARY_CWISE_OPS
+
+// ------------ ternary ops -------------
+template <>
+EIGEN_STRONG_INLINE Packet8cf pselect<Packet8cf>(const Packet8cf& mask, const Packet8cf& a, const Packet8cf& b) {
+  return Packet8cf(reinterpret_cast<Packet16f>(
+      pselect(reinterpret_cast<Packet8d>(mask.v), reinterpret_cast<Packet8d>(a.v), reinterpret_cast<Packet8d>(b.v))));
+}
+
+namespace detail {
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8cf>(Packet8cf& p1, Packet8cf& p2) {
+  Packet16f tmp = __builtin_shufflevector(p1.v, p2.v, 0, 1, 16, 17, 2, 3, 18, 19, 4, 5, 20, 21, 6, 7, 22, 23);
+  p2.v = __builtin_shufflevector(p1.v, p2.v, 8, 9, 24, 25, 10, 11, 26, 27, 12, 13, 28, 29, 14, 15, 30, 31);
+  p1.v = tmp;
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4cd>(Packet4cd& p1, Packet4cd& p2) {
+  Packet8d tmp = __builtin_shufflevector(p1.v, p2.v, 0, 1, 8, 9, 2, 3, 10, 11);
+  p2.v = __builtin_shufflevector(p1.v, p2.v, 4, 5, 12, 13, 6, 7, 14, 15);
+  p1.v = tmp;
+}
+}  // namespace detail
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8cf, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8cf, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8cf, 2>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4cd, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4cd, 2>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet8cf, Packet16f)
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cd, Packet8d)
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_CLANG_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/MathFunctions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/MathFunctions.h
new file mode 100644
index 0000000..706a870
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/MathFunctions.h
@@ -0,0 +1,47 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Rasmus Munk Larsen
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_CLANG_H
+#define EIGEN_MATH_FUNCTIONS_CLANG_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pfrexp<Packet16f>(const Packet16f& a, Packet16f& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pfrexp<Packet8d>(const Packet8d& a, Packet8d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pldexp<Packet16f>(const Packet16f& a, const Packet16f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pldexp<Packet8d>(const Packet8d& a, const Packet8d& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet16f)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet8d)
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_CLANG_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/PacketMath.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/PacketMath.h
new file mode 100644
index 0000000..4beadfe
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/PacketMath.h
@@ -0,0 +1,875 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Rasmus Munk Larsen
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_CLANG_H
+#define EIGEN_PACKET_MATH_CLANG_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+namespace detail {
+// namespace detail contains implementation details specific to this
+// file, while namespace internal contains internal APIs used elsewhere
+// in Eigen.
+template <typename ScalarT, int n>
+using VectorType = ScalarT __attribute__((ext_vector_type(n), aligned(n * sizeof(ScalarT))));
+}  // namespace detail
+
+// --- Primary packet type definitions (fixed at 64 bytes) ---
+
+// TODO(rmlarsen): Generalize to other vector sizes.
+static_assert(EIGEN_GENERIC_VECTOR_SIZE_BYTES == 64, "We currently assume the full vector size is 64 bytes");
+using Packet16f = detail::VectorType<float, 16>;
+using Packet8d = detail::VectorType<double, 8>;
+using Packet16i = detail::VectorType<int32_t, 16>;
+using Packet8l = detail::VectorType<int64_t, 8>;
+
+// --- packet_traits specializations ---
+struct generic_float_packet_traits : default_packet_traits {
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasRound = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasCmp = 1,
+    HasSet1 = 1,
+    HasCast = 1,
+    HasBitwise = 1,
+    HasRedux = 1,
+    HasSign = 1,
+    HasArg = 0,
+    HasConj = 1,
+    // Math functions
+    HasReciprocal = 1,
+    HasSin = 1,
+    HasCos = 1,
+    HasTan = 1,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasExp = 1,
+    HasPow = 1,
+    HasNdtri = 1,
+    HasBessel = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasCbrt = 1,
+    HasTanh = 1,
+    HasErf = 1,
+    HasErfc = 1
+  };
+};
+
+template <>
+struct packet_traits<float> : generic_float_packet_traits {
+  using type = Packet16f;
+  using half = Packet16f;
+  enum {
+    size = 16,
+  };
+};
+
+template <>
+struct packet_traits<double> : generic_float_packet_traits {
+  using type = Packet8d;
+  using half = Packet8d;
+  // Generic double-precision acos/asin are not yet implemented in
+  // GenericPacketMathFunctions.h (only float versions exist).
+  enum { size = 8, HasACos = 0, HasASin = 0 };
+};
+
+struct generic_integer_packet_traits : default_packet_traits {
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasCmp = 1,
+    HasSet1 = 1,
+    HasCast = 1,
+    HasBitwise = 1,
+    HasRedux = 1,
+    // Set remaining to 0
+    HasRound = 1,
+    HasSqrt = 0,
+    HasRsqrt = 0,
+    HasReciprocal = 0,
+    HasArg = 0,
+    HasConj = 1,
+    HasExp = 0,
+    HasLog = 0,
+    HasSin = 0,
+    HasCos = 0,
+  };
+};
+
+template <>
+struct packet_traits<int32_t> : generic_integer_packet_traits {
+  using type = Packet16i;
+  using half = Packet16i;
+  enum {
+    size = 16,
+  };
+};
+
+template <>
+struct packet_traits<int64_t> : generic_integer_packet_traits {
+  using type = Packet8l;
+  using half = Packet8l;
+  enum {
+    size = 8,
+  };
+};
+
+// --- unpacket_traits specializations ---
+struct generic_unpacket_traits : default_unpacket_traits {
+  enum {
+    alignment = EIGEN_GENERIC_VECTOR_SIZE_BYTES,
+    vectorizable = true,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet16f> : generic_unpacket_traits {
+  using type = float;
+  using half = Packet16f;
+  using integer_packet = Packet16i;
+  enum {
+    size = 16,
+  };
+};
+template <>
+struct unpacket_traits<Packet8d> : generic_unpacket_traits {
+  using type = double;
+  using half = Packet8d;
+  using integer_packet = Packet8l;
+  enum {
+    size = 8,
+  };
+};
+template <>
+struct unpacket_traits<Packet16i> : generic_unpacket_traits {
+  using type = int32_t;
+  using half = Packet16i;
+  enum {
+    size = 16,
+  };
+};
+template <>
+struct unpacket_traits<Packet8l> : generic_unpacket_traits {
+  using type = int64_t;
+  using half = Packet8l;
+  enum {
+    size = 8,
+  };
+};
+
+namespace detail {
+// --- vector type helpers ---
+template <typename VectorT>
+struct ScalarTypeOfVector {
+  using type = std::remove_all_extents_t<std::remove_reference_t<decltype(VectorT()[0])>>;
+};
+
+template <typename VectorT>
+using scalar_type_of_vector_t = typename ScalarTypeOfVector<VectorT>::type;
+
+template <typename VectorType>
+struct UnsignedVectorHelper {
+  static VectorType v;
+  static constexpr int n = __builtin_vectorelements(v);
+  using UnsignedScalar = std::make_unsigned_t<scalar_type_of_vector_t<VectorType>>;
+  using type = UnsignedScalar __attribute__((ext_vector_type(n), aligned(n * sizeof(UnsignedScalar))));
+};
+
+template <typename VectorT>
+using unsigned_vector_t = typename UnsignedVectorHelper<VectorT>::type;
+
+template <typename VectorT>
+using HalfPacket = VectorType<typename unpacket_traits<VectorT>::type, unpacket_traits<VectorT>::size / 2>;
+
+template <typename VectorT>
+using QuarterPacket = VectorType<typename unpacket_traits<VectorT>::type, unpacket_traits<VectorT>::size / 4>;
+
+// load and store helpers.
+template <typename VectorT>
+EIGEN_STRONG_INLINE VectorT load_vector_unaligned(const scalar_type_of_vector_t<VectorT>* from) {
+  VectorT to;
+  __builtin_memcpy(&to, from, sizeof(VectorT));
+  return to;
+}
+
+template <typename VectorT>
+EIGEN_STRONG_INLINE VectorT load_vector_aligned(const scalar_type_of_vector_t<VectorT>* from) {
+  return *reinterpret_cast<const VectorT*>(assume_aligned<EIGEN_GENERIC_VECTOR_SIZE_BYTES>(from));
+}
+
+template <typename VectorT>
+EIGEN_STRONG_INLINE void store_vector_unaligned(scalar_type_of_vector_t<VectorT>* to, const VectorT& from) {
+  __builtin_memcpy(to, &from, sizeof(VectorT));
+}
+
+template <typename VectorT>
+EIGEN_STRONG_INLINE void store_vector_aligned(scalar_type_of_vector_t<VectorT>* to, const VectorT& from) {
+  *reinterpret_cast<VectorT*>(assume_aligned<EIGEN_GENERIC_VECTOR_SIZE_BYTES>(to)) = from;
+}
+
+}  // namespace detail
+
+// --- Intrinsic-like specializations ---
+
+// --- Load/Store operations ---
+#define EIGEN_CLANG_PACKET_LOAD_STORE_PACKET(PACKET_TYPE)                                                         \
+  template <>                                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE ploadu<PACKET_TYPE>(const detail::scalar_type_of_vector_t<PACKET_TYPE>* from) { \
+    return detail::load_vector_unaligned<PACKET_TYPE>(from);                                                      \
+  }                                                                                                               \
+  template <>                                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE pload<PACKET_TYPE>(const detail::scalar_type_of_vector_t<PACKET_TYPE>* from) {  \
+    return detail::load_vector_aligned<PACKET_TYPE>(from);                                                        \
+  }                                                                                                               \
+  template <>                                                                                                     \
+  EIGEN_STRONG_INLINE void pstoreu<detail::scalar_type_of_vector_t<PACKET_TYPE>, PACKET_TYPE>(                    \
+      detail::scalar_type_of_vector_t<PACKET_TYPE> * to, const PACKET_TYPE& from) {                               \
+    detail::store_vector_unaligned<PACKET_TYPE>(to, from);                                                        \
+  }                                                                                                               \
+  template <>                                                                                                     \
+  EIGEN_STRONG_INLINE void pstore<detail::scalar_type_of_vector_t<PACKET_TYPE>, PACKET_TYPE>(                     \
+      detail::scalar_type_of_vector_t<PACKET_TYPE> * to, const PACKET_TYPE& from) {                               \
+    detail::store_vector_aligned<PACKET_TYPE>(to, from);                                                          \
+  }
+
+EIGEN_CLANG_PACKET_LOAD_STORE_PACKET(Packet16f)
+EIGEN_CLANG_PACKET_LOAD_STORE_PACKET(Packet8d)
+EIGEN_CLANG_PACKET_LOAD_STORE_PACKET(Packet16i)
+EIGEN_CLANG_PACKET_LOAD_STORE_PACKET(Packet8l)
+#undef EIGEN_CLANG_PACKET_LOAD_STORE_PACKET
+
+// --- Broadcast operation ---
+template <>
+EIGEN_STRONG_INLINE Packet16f pset1frombits<Packet16f>(uint32_t from) {
+  return Packet16f(numext::bit_cast<float>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pset1frombits<Packet8d>(uint64_t from) {
+  return Packet8d(numext::bit_cast<double>(from));
+}
+
+#define EIGEN_CLANG_PACKET_SET1(PACKET_TYPE)                                                            \
+  template <>                                                                                           \
+  EIGEN_STRONG_INLINE PACKET_TYPE pset1<PACKET_TYPE>(const unpacket_traits<PACKET_TYPE>::type& from) {  \
+    return PACKET_TYPE(from);                                                                           \
+  }                                                                                                     \
+  template <>                                                                                           \
+  EIGEN_STRONG_INLINE unpacket_traits<PACKET_TYPE>::type pfirst<PACKET_TYPE>(const PACKET_TYPE& from) { \
+    return from[0];                                                                                     \
+  }
+
+EIGEN_CLANG_PACKET_SET1(Packet16f)
+EIGEN_CLANG_PACKET_SET1(Packet8d)
+EIGEN_CLANG_PACKET_SET1(Packet16i)
+EIGEN_CLANG_PACKET_SET1(Packet8l)
+#undef EIGEN_CLANG_PACKET_SET1
+
+// --- Arithmetic operations ---
+#define EIGEN_CLANG_PACKET_ARITHMETIC(PACKET_TYPE)                             \
+  template <>                                                                  \
+  EIGEN_STRONG_INLINE PACKET_TYPE pisnan<PACKET_TYPE>(const PACKET_TYPE& a) {  \
+    return reinterpret_cast<PACKET_TYPE>(a != a);                              \
+  }                                                                            \
+  template <>                                                                  \
+  EIGEN_STRONG_INLINE PACKET_TYPE pnegate<PACKET_TYPE>(const PACKET_TYPE& a) { \
+    return -a;                                                                 \
+  }
+
+EIGEN_CLANG_PACKET_ARITHMETIC(Packet16f)
+EIGEN_CLANG_PACKET_ARITHMETIC(Packet8d)
+EIGEN_CLANG_PACKET_ARITHMETIC(Packet16i)
+EIGEN_CLANG_PACKET_ARITHMETIC(Packet8l)
+#undef EIGEN_CLANG_PACKET_ARITHMETIC
+
+// --- Bitwise operations (via casting) ---
+
+namespace detail {
+
+// Reinterpret-cast helpers, equivalent to preinterpret<> but defined here
+// because PacketMath.h is included before TypeCasting.h.
+EIGEN_STRONG_INLINE Packet16i preinterpret_float_to_int(const Packet16f& a) { return reinterpret_cast<Packet16i>(a); }
+EIGEN_STRONG_INLINE Packet16f preinterpret_int_to_float(const Packet16i& a) { return reinterpret_cast<Packet16f>(a); }
+EIGEN_STRONG_INLINE Packet8l preinterpret_double_to_long(const Packet8d& a) { return reinterpret_cast<Packet8l>(a); }
+EIGEN_STRONG_INLINE Packet8d preinterpret_long_to_double(const Packet8l& a) { return reinterpret_cast<Packet8d>(a); }
+
+}  // namespace detail
+
+// Bitwise ops for integer packets
+#define EIGEN_CLANG_PACKET_BITWISE_INT(PACKET_TYPE)                                                  \
+  template <>                                                                                        \
+  constexpr EIGEN_STRONG_INLINE PACKET_TYPE pzero<PACKET_TYPE>(const PACKET_TYPE& /*unused*/) {      \
+    return PACKET_TYPE(0);                                                                           \
+  }                                                                                                  \
+  template <>                                                                                        \
+  constexpr EIGEN_STRONG_INLINE PACKET_TYPE ptrue<PACKET_TYPE>(const PACKET_TYPE& /*unused*/) {      \
+    return numext::bit_cast<PACKET_TYPE>(PACKET_TYPE(0) == PACKET_TYPE(0));                          \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pand<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {    \
+    return a & b;                                                                                    \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE por<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {     \
+    return a | b;                                                                                    \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pxor<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {    \
+    return a ^ b;                                                                                    \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pandnot<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) { \
+    return a & ~b;                                                                                   \
+  }                                                                                                  \
+  template <int N>                                                                                   \
+  EIGEN_STRONG_INLINE PACKET_TYPE parithmetic_shift_right(const PACKET_TYPE& a) {                    \
+    return a >> N;                                                                                   \
+  }                                                                                                  \
+  template <int N>                                                                                   \
+  EIGEN_STRONG_INLINE PACKET_TYPE plogical_shift_right(const PACKET_TYPE& a) {                       \
+    using UnsignedT = detail::unsigned_vector_t<PACKET_TYPE>;                                        \
+    return reinterpret_cast<PACKET_TYPE>(reinterpret_cast<UnsignedT>(a) >> N);                       \
+  }                                                                                                  \
+  template <int N>                                                                                   \
+  EIGEN_STRONG_INLINE PACKET_TYPE plogical_shift_left(const PACKET_TYPE& a) {                        \
+    return a << N;                                                                                   \
+  }
+
+EIGEN_CLANG_PACKET_BITWISE_INT(Packet16i)
+EIGEN_CLANG_PACKET_BITWISE_INT(Packet8l)
+#undef EIGEN_CLANG_PACKET_BITWISE_INT
+
+// Bitwise ops for floating point packets
+#define EIGEN_CLANG_PACKET_BITWISE_FLOAT(PACKET_TYPE, CAST_TO_INT, CAST_FROM_INT)                    \
+  template <>                                                                                        \
+  constexpr EIGEN_STRONG_INLINE PACKET_TYPE pzero<PACKET_TYPE>(const PACKET_TYPE& /*unused*/) {      \
+    using Scalar = detail::scalar_type_of_vector_t<PACKET_TYPE>;                                     \
+    return PACKET_TYPE(Scalar(0));                                                                   \
+  }                                                                                                  \
+  template <>                                                                                        \
+  constexpr EIGEN_STRONG_INLINE PACKET_TYPE ptrue<PACKET_TYPE>(const PACKET_TYPE& /* unused */) {    \
+    using Scalar = detail::scalar_type_of_vector_t<PACKET_TYPE>;                                     \
+    return numext::bit_cast<PACKET_TYPE>(PACKET_TYPE(Scalar(0)) == PACKET_TYPE(Scalar(0)));          \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pand<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {    \
+    return CAST_FROM_INT(CAST_TO_INT(a) & CAST_TO_INT(b));                                           \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE por<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {     \
+    return CAST_FROM_INT(CAST_TO_INT(a) | CAST_TO_INT(b));                                           \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pxor<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {    \
+    return CAST_FROM_INT(CAST_TO_INT(a) ^ CAST_TO_INT(b));                                           \
+  }                                                                                                  \
+  template <>                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pandnot<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) { \
+    return CAST_FROM_INT(CAST_TO_INT(a) & ~CAST_TO_INT(b));                                          \
+  }
+
+EIGEN_CLANG_PACKET_BITWISE_FLOAT(Packet16f, detail::preinterpret_float_to_int, detail::preinterpret_int_to_float)
+EIGEN_CLANG_PACKET_BITWISE_FLOAT(Packet8d, detail::preinterpret_double_to_long, detail::preinterpret_long_to_double)
+#undef EIGEN_CLANG_PACKET_BITWISE_FLOAT
+
+// --- Comparison operations ---
+// Clang vector extensions perform comparisons in the original type (float/double),
+// returning an int vector with all-ones (-1) for true and all-zeros for false.
+// The bit_cast reinterprets those int bitmasks as float packets, which is the
+// format expected by pselect and other Eigen packet operations.
+#define EIGEN_CLANG_PACKET_CMP(PACKET_TYPE, INT_PACKET_TYPE)                                                \
+  template <>                                                                                               \
+  EIGEN_STRONG_INLINE PACKET_TYPE pcmp_eq<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {        \
+    return numext::bit_cast<PACKET_TYPE>(INT_PACKET_TYPE(a == b));                                          \
+  }                                                                                                         \
+  template <>                                                                                               \
+  EIGEN_STRONG_INLINE PACKET_TYPE pcmp_lt<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {        \
+    return numext::bit_cast<PACKET_TYPE>(INT_PACKET_TYPE(a < b));                                           \
+  }                                                                                                         \
+  template <>                                                                                               \
+  EIGEN_STRONG_INLINE PACKET_TYPE pcmp_le<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {        \
+    return numext::bit_cast<PACKET_TYPE>(INT_PACKET_TYPE(a <= b));                                          \
+  }                                                                                                         \
+  template <>                                                                                               \
+  EIGEN_STRONG_INLINE PACKET_TYPE pcmp_lt_or_nan<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) { \
+    return numext::bit_cast<PACKET_TYPE>(INT_PACKET_TYPE(!(a >= b)));                                       \
+  }
+
+EIGEN_CLANG_PACKET_CMP(Packet16f, Packet16i)
+EIGEN_CLANG_PACKET_CMP(Packet8d, Packet8l)
+#undef EIGEN_CLANG_PACKET_CMP
+
+// --- Min/Max operations ---
+#if EIGEN_HAS_BUILTIN(__builtin_elementwise_min) && EIGEN_HAS_BUILTIN(__builtin_elementwise_max) && \
+    EIGEN_HAS_BUILTIN(__builtin_elementwise_abs)
+#define EIGEN_CLANG_PACKET_ELEMENTWISE(PACKET_TYPE)                                                                 \
+  template <>                                                                                                       \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmin<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {                   \
+    /* Match NaN propagation of std::min. */                                                                        \
+    return a == a ? __builtin_elementwise_min(a, b) : a;                                                            \
+  }                                                                                                                 \
+  template <>                                                                                                       \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmax<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {                   \
+    /* Match NaN propagation of std::max. */                                                                        \
+    return a == a ? __builtin_elementwise_max(a, b) : a;                                                            \
+  }                                                                                                                 \
+  template <>                                                                                                       \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmin<PropagateNumbers, PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) { \
+    return __builtin_elementwise_min(a, b);                                                                         \
+  }                                                                                                                 \
+  template <>                                                                                                       \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmax<PropagateNumbers, PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) { \
+    return __builtin_elementwise_max(a, b);                                                                         \
+  }                                                                                                                 \
+  template <>                                                                                                       \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmin<PropagateNaN, PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {     \
+    return a != a ? a : (b != b ? b : __builtin_elementwise_min(a, b));                                             \
+  }                                                                                                                 \
+  template <>                                                                                                       \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmax<PropagateNaN, PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b) {     \
+    return a != a ? a : (b != b ? b : __builtin_elementwise_max(a, b));                                             \
+  }                                                                                                                 \
+  template <>                                                                                                       \
+  EIGEN_STRONG_INLINE PACKET_TYPE pabs<PACKET_TYPE>(const PACKET_TYPE& a) {                                         \
+    return __builtin_elementwise_abs(a);                                                                            \
+  }                                                                                                                 \
+  template <>                                                                                                       \
+  EIGEN_STRONG_INLINE PACKET_TYPE pselect<PACKET_TYPE>(const PACKET_TYPE& mask, const PACKET_TYPE& a,               \
+                                                       const PACKET_TYPE& b) {                                      \
+    return mask != 0 ? a : b;                                                                                       \
+  }
+
+EIGEN_CLANG_PACKET_ELEMENTWISE(Packet16f)
+EIGEN_CLANG_PACKET_ELEMENTWISE(Packet8d)
+EIGEN_CLANG_PACKET_ELEMENTWISE(Packet16i)
+EIGEN_CLANG_PACKET_ELEMENTWISE(Packet8l)
+#undef EIGEN_CLANG_PACKET_ELEMENTWISE
+#endif
+
+// --- Math functions (float/double only) ---
+
+#if EIGEN_HAS_BUILTIN(__builtin_elementwise_floor) && EIGEN_HAS_BUILTIN(__builtin_elementwise_ceil) &&      \
+    EIGEN_HAS_BUILTIN(__builtin_elementwise_round) && EIGEN_HAS_BUILTIN(__builtin_elementwise_roundeven) && \
+    EIGEN_HAS_BUILTIN(__builtin_elementwise_trunc) && EIGEN_HAS_BUILTIN(__builtin_elementwise_sqrt)
+#define EIGEN_CLANG_PACKET_MATH_FLOAT(PACKET_TYPE)                            \
+  template <>                                                                 \
+  EIGEN_STRONG_INLINE PACKET_TYPE pfloor<PACKET_TYPE>(const PACKET_TYPE& a) { \
+    return __builtin_elementwise_floor(a);                                    \
+  }                                                                           \
+  template <>                                                                 \
+  EIGEN_STRONG_INLINE PACKET_TYPE pceil<PACKET_TYPE>(const PACKET_TYPE& a) {  \
+    return __builtin_elementwise_ceil(a);                                     \
+  }                                                                           \
+  template <>                                                                 \
+  EIGEN_STRONG_INLINE PACKET_TYPE pround<PACKET_TYPE>(const PACKET_TYPE& a) { \
+    return __builtin_elementwise_round(a);                                    \
+  }                                                                           \
+  template <>                                                                 \
+  EIGEN_STRONG_INLINE PACKET_TYPE print<PACKET_TYPE>(const PACKET_TYPE& a) {  \
+    return __builtin_elementwise_roundeven(a);                                \
+  }                                                                           \
+  template <>                                                                 \
+  EIGEN_STRONG_INLINE PACKET_TYPE ptrunc<PACKET_TYPE>(const PACKET_TYPE& a) { \
+    return __builtin_elementwise_trunc(a);                                    \
+  }                                                                           \
+  template <>                                                                 \
+  EIGEN_STRONG_INLINE PACKET_TYPE psqrt<PACKET_TYPE>(const PACKET_TYPE& a) {  \
+    return __builtin_elementwise_sqrt(a);                                     \
+  }
+
+EIGEN_CLANG_PACKET_MATH_FLOAT(Packet16f)
+EIGEN_CLANG_PACKET_MATH_FLOAT(Packet8d)
+#undef EIGEN_CLANG_PACKET_MATH_FLOAT
+#endif
+
+// --- Fused Multiply-Add (MADD) ---
+#if defined(__FMA__) && EIGEN_HAS_BUILTIN(__builtin_elementwise_fma)
+#define EIGEN_CLANG_PACKET_MADD(PACKET_TYPE)                                                      \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmadd<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b,  \
+                                                     const PACKET_TYPE& c) {                      \
+    return __builtin_elementwise_fma(a, b, c);                                                    \
+  }                                                                                               \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmsub<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b,  \
+                                                     const PACKET_TYPE& c) {                      \
+    return __builtin_elementwise_fma(a, b, -c);                                                   \
+  }                                                                                               \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE pnmadd<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b, \
+                                                      const PACKET_TYPE& c) {                     \
+    return __builtin_elementwise_fma(-a, b, c);                                                   \
+  }                                                                                               \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE pnmsub<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b, \
+                                                      const PACKET_TYPE& c) {                     \
+    return -(__builtin_elementwise_fma(a, b, c));                                                 \
+  }
+#else
+// Fallback if FMA builtin is not available
+#define EIGEN_CLANG_PACKET_MADD(PACKET_TYPE)                                                      \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmadd<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b,  \
+                                                     const PACKET_TYPE& c) {                      \
+    return (a * b) + c;                                                                           \
+  }                                                                                               \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE pmsub<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b,  \
+                                                     const PACKET_TYPE& c) {                      \
+    return (a * b) - c;                                                                           \
+  }                                                                                               \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE pnmadd<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b, \
+                                                      const PACKET_TYPE& c) {                     \
+    return c - (a * b);                                                                           \
+  }                                                                                               \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE PACKET_TYPE pnmsub<PACKET_TYPE>(const PACKET_TYPE& a, const PACKET_TYPE& b, \
+                                                      const PACKET_TYPE& c) {                     \
+    return -((a * b) + c);                                                                        \
+  }
+#endif
+
+EIGEN_CLANG_PACKET_MADD(Packet16f)
+EIGEN_CLANG_PACKET_MADD(Packet8d)
+#undef EIGEN_CLANG_PACKET_MADD
+
+#define EIGEN_CLANG_PACKET_SCATTER_GATHER(PACKET_TYPE)                                                               \
+  template <>                                                                                                        \
+  EIGEN_STRONG_INLINE void pscatter(unpacket_traits<PACKET_TYPE>::type* to, const PACKET_TYPE& from, Index stride) { \
+    constexpr int size = unpacket_traits<PACKET_TYPE>::size;                                                         \
+    for (int i = 0; i < size; ++i) {                                                                                 \
+      to[i * stride] = from[i];                                                                                      \
+    }                                                                                                                \
+  }                                                                                                                  \
+  template <>                                                                                                        \
+  EIGEN_STRONG_INLINE PACKET_TYPE pgather<typename unpacket_traits<PACKET_TYPE>::type, PACKET_TYPE>(                 \
+      const unpacket_traits<PACKET_TYPE>::type* from, Index stride) {                                                \
+    constexpr int size = unpacket_traits<PACKET_TYPE>::size;                                                         \
+    PACKET_TYPE result;                                                                                              \
+    for (int i = 0; i < size; ++i) {                                                                                 \
+      result[i] = from[i * stride];                                                                                  \
+    }                                                                                                                \
+    return result;                                                                                                   \
+  }
+
+EIGEN_CLANG_PACKET_SCATTER_GATHER(Packet16f)
+EIGEN_CLANG_PACKET_SCATTER_GATHER(Packet8d)
+EIGEN_CLANG_PACKET_SCATTER_GATHER(Packet16i)
+EIGEN_CLANG_PACKET_SCATTER_GATHER(Packet8l)
+
+#undef EIGEN_CLANG_PACKET_SCATTER_GATHER
+
+// ---- Various operations that depend on __builtin_shufflevector.
+#if EIGEN_HAS_BUILTIN(__builtin_shufflevector)
+namespace detail {
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet preverse_impl_8(const Packet& a) {
+  return __builtin_shufflevector(a, a, 7, 6, 5, 4, 3, 2, 1, 0);
+}
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet preverse_impl_16(const Packet& a) {
+  return __builtin_shufflevector(a, a, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+}
+}  // namespace detail
+
+#define EIGEN_CLANG_PACKET_REVERSE(PACKET_TYPE, SIZE)                           \
+  template <>                                                                   \
+  EIGEN_STRONG_INLINE PACKET_TYPE preverse<PACKET_TYPE>(const PACKET_TYPE& a) { \
+    return detail::preverse_impl_##SIZE(a);                                     \
+  }
+
+EIGEN_CLANG_PACKET_REVERSE(Packet16f, 16)
+EIGEN_CLANG_PACKET_REVERSE(Packet8d, 8)
+EIGEN_CLANG_PACKET_REVERSE(Packet16i, 16)
+EIGEN_CLANG_PACKET_REVERSE(Packet8l, 8)
+#undef EIGEN_CLANG_PACKET_REVERSE
+
+namespace detail {
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet ploaddup16(const typename unpacket_traits<Packet>::type* from) {
+  static_assert((unpacket_traits<Packet>::size) % 2 == 0, "Packet size must be a multiple of 2");
+  using HalfPacket = HalfPacket<Packet>;
+  HalfPacket a = load_vector_unaligned<HalfPacket>(from);
+  return __builtin_shufflevector(a, a, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet ploadquad16(const typename unpacket_traits<Packet>::type* from) {
+  static_assert((unpacket_traits<Packet>::size) % 4 == 0, "Packet size must be a multiple of 4");
+  using QuarterPacket = QuarterPacket<Packet>;
+  QuarterPacket a = load_vector_unaligned<QuarterPacket>(from);
+  return __builtin_shufflevector(a, a, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet ploaddup8(const typename unpacket_traits<Packet>::type* from) {
+  static_assert((unpacket_traits<Packet>::size) % 2 == 0, "Packet size must be a multiple of 2");
+  using HalfPacket = HalfPacket<Packet>;
+  HalfPacket a = load_vector_unaligned<HalfPacket>(from);
+  return __builtin_shufflevector(a, a, 0, 0, 1, 1, 2, 2, 3, 3);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet ploadquad8(const typename unpacket_traits<Packet>::type* from) {
+  static_assert((unpacket_traits<Packet>::size) % 4 == 0, "Packet size must be a multiple of 4");
+  using QuarterPacket = QuarterPacket<Packet>;
+  QuarterPacket a = load_vector_unaligned<QuarterPacket>(from);
+  return __builtin_shufflevector(a, a, 0, 0, 0, 0, 1, 1, 1, 1);
+}
+
+}  // namespace detail
+
+template <>
+EIGEN_STRONG_INLINE Packet16f ploaddup<Packet16f>(const float* from) {
+  return detail::ploaddup16<Packet16f>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d ploaddup<Packet8d>(const double* from) {
+  return detail::ploaddup8<Packet8d>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i ploaddup<Packet16i>(const int32_t* from) {
+  return detail::ploaddup16<Packet16i>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l ploaddup<Packet8l>(const int64_t* from) {
+  return detail::ploaddup8<Packet8l>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f ploadquad<Packet16f>(const float* from) {
+  return detail::ploadquad16<Packet16f>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d ploadquad<Packet8d>(const double* from) {
+  return detail::ploadquad8<Packet8d>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i ploadquad<Packet16i>(const int32_t* from) {
+  return detail::ploadquad16<Packet16i>(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l ploadquad<Packet8l>(const int64_t* from) {
+  return detail::ploadquad8<Packet8l>(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f plset<Packet16f>(const float& a) {
+  Packet16f x{a + 0.0f, a + 1.0f, a + 2.0f,  a + 3.0f,  a + 4.0f,  a + 5.0f,  a + 6.0f,  a + 7.0f,
+              a + 8.0f, a + 9.0f, a + 10.0f, a + 11.0f, a + 12.0f, a + 13.0f, a + 14.0f, a + 15.0f};
+  return x;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d plset<Packet8d>(const double& a) {
+  return Packet8d{a + 0.0, a + 1.0, a + 2.0, a + 3.0, a + 4.0, a + 5.0, a + 6.0, a + 7.0};
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i plset<Packet16i>(const int32_t& a) {
+  return Packet16i{a + 0, a + 1, a + 2,  a + 3,  a + 4,  a + 5,  a + 6,  a + 7,
+                   a + 8, a + 9, a + 10, a + 11, a + 12, a + 13, a + 14, a + 15};
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l plset<Packet8l>(const int64_t& a) {
+  return Packet8l{a + 0, a + 1, a + 2, a + 3, a + 4, a + 5, a + 6, a + 7};
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f peven_mask(const Packet16f& /* unused */) {
+  float kTrue = numext::bit_cast<float>(int32_t(-1));
+  float kFalse = 0.0f;
+  return Packet16f{kTrue, kFalse, kTrue, kFalse, kTrue, kFalse, kTrue, kFalse,
+                   kTrue, kFalse, kTrue, kFalse, kTrue, kFalse, kTrue, kFalse};
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d peven_mask(const Packet8d& /* unused */) {
+  double kTrue = numext::bit_cast<double>(int64_t(-1l));
+  double kFalse = 0.0;
+  return Packet8d{kTrue, kFalse, kTrue, kFalse, kTrue, kFalse, kTrue, kFalse};
+}
+
+// Helpers for ptranspose.
+namespace detail {
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void zip_in_place16(Packet& p1, Packet& p2) {
+  Packet tmp = __builtin_shufflevector(p1, p2, 0, 16, 1, 17, 2, 18, 3, 19, 4, 20, 5, 21, 6, 22, 7, 23);
+  p2 = __builtin_shufflevector(p1, p2, 8, 24, 9, 25, 10, 26, 11, 27, 12, 28, 13, 29, 14, 30, 15, 31);
+  p1 = tmp;
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void zip_in_place8(Packet& p1, Packet& p2) {
+  Packet tmp = __builtin_shufflevector(p1, p2, 0, 8, 1, 9, 2, 10, 3, 11);
+  p2 = __builtin_shufflevector(p1, p2, 4, 12, 5, 13, 6, 14, 7, 15);
+  p1 = tmp;
+}
+
+template <typename Packet>
+void zip_in_place(Packet& p1, Packet& p2);
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet16f>(Packet16f& p1, Packet16f& p2) {
+  zip_in_place16(p1, p2);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8d>(Packet8d& p1, Packet8d& p2) {
+  zip_in_place8(p1, p2);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet16i>(Packet16i& p1, Packet16i& p2) {
+  zip_in_place16(p1, p2);
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8l>(Packet8l& p1, Packet8l& p2) {
+  zip_in_place8(p1, p2);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 2>& kernel) {
+  zip_in_place(kernel.packet[0], kernel.packet[1]);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 4>& kernel) {
+  zip_in_place(kernel.packet[0], kernel.packet[2]);
+  zip_in_place(kernel.packet[1], kernel.packet[3]);
+  zip_in_place(kernel.packet[0], kernel.packet[1]);
+  zip_in_place(kernel.packet[2], kernel.packet[3]);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 8>& kernel) {
+  zip_in_place(kernel.packet[0], kernel.packet[4]);
+  zip_in_place(kernel.packet[1], kernel.packet[5]);
+  zip_in_place(kernel.packet[2], kernel.packet[6]);
+  zip_in_place(kernel.packet[3], kernel.packet[7]);
+
+  zip_in_place(kernel.packet[0], kernel.packet[2]);
+  zip_in_place(kernel.packet[1], kernel.packet[3]);
+  zip_in_place(kernel.packet[4], kernel.packet[6]);
+  zip_in_place(kernel.packet[5], kernel.packet[7]);
+
+  zip_in_place(kernel.packet[0], kernel.packet[1]);
+  zip_in_place(kernel.packet[2], kernel.packet[3]);
+  zip_in_place(kernel.packet[4], kernel.packet[5]);
+  zip_in_place(kernel.packet[6], kernel.packet[7]);
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 16>& kernel) {
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 4; ++i) {
+    const int m = (1 << i);
+    EIGEN_UNROLL_LOOP
+    for (int j = 0; j < m; ++j) {
+      const int n = (1 << (3 - i));
+      EIGEN_UNROLL_LOOP
+      for (int k = 0; k < n; ++k) {
+        const int idx = 2 * j * n + k;
+        zip_in_place(kernel.packet[idx], kernel.packet[idx + n]);
+      }
+    }
+  }
+}
+
+}  // namespace detail
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16f, 16>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16f, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16f, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16f, 2>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8d, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8d, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8d, 2>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16i, 16>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16i, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16i, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16i, 2>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8l, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8l, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8l, 2>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+#endif
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_CLANG_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/Reductions.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/Reductions.h
new file mode 100644
index 0000000..defedf9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/Reductions.h
@@ -0,0 +1,162 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Rasmus Munk Larsen
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REDUCTIONS_CLANG_H
+#define EIGEN_REDUCTIONS_CLANG_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+// --- Reductions ---
+#if EIGEN_HAS_BUILTIN(__builtin_reduce_min) && EIGEN_HAS_BUILTIN(__builtin_reduce_max) && \
+    EIGEN_HAS_BUILTIN(__builtin_reduce_or)
+#define EIGEN_CLANG_PACKET_REDUX_MINMAX(PACKET_TYPE)                                        \
+  template <>                                                                               \
+  EIGEN_STRONG_INLINE unpacket_traits<PACKET_TYPE>::type predux_min(const PACKET_TYPE& a) { \
+    return __builtin_reduce_min(a);                                                         \
+  }                                                                                         \
+  template <>                                                                               \
+  EIGEN_STRONG_INLINE unpacket_traits<PACKET_TYPE>::type predux_max(const PACKET_TYPE& a) { \
+    return __builtin_reduce_max(a);                                                         \
+  }                                                                                         \
+  template <>                                                                               \
+  EIGEN_STRONG_INLINE bool predux_any(const PACKET_TYPE& a) {                               \
+    return __builtin_reduce_or(a != 0) != 0;                                                \
+  }
+
+EIGEN_CLANG_PACKET_REDUX_MINMAX(Packet16f)
+EIGEN_CLANG_PACKET_REDUX_MINMAX(Packet8d)
+EIGEN_CLANG_PACKET_REDUX_MINMAX(Packet16i)
+EIGEN_CLANG_PACKET_REDUX_MINMAX(Packet8l)
+#undef EIGEN_CLANG_PACKET_REDUX_MINMAX
+#endif
+
+#if EIGEN_HAS_BUILTIN(__builtin_reduce_add) && EIGEN_HAS_BUILTIN(__builtin_reduce_mul)
+#define EIGEN_CLANG_PACKET_REDUX_INT(PACKET_TYPE)                                                        \
+  template <>                                                                                            \
+  EIGEN_STRONG_INLINE unpacket_traits<PACKET_TYPE>::type predux<PACKET_TYPE>(const PACKET_TYPE& a) {     \
+    return __builtin_reduce_add(a);                                                                      \
+  }                                                                                                      \
+  template <>                                                                                            \
+  EIGEN_STRONG_INLINE unpacket_traits<PACKET_TYPE>::type predux_mul<PACKET_TYPE>(const PACKET_TYPE& a) { \
+    return __builtin_reduce_mul(a);                                                                      \
+  }
+
+// __builtin_reduce_{mul,add} are only defined for integer types.
+EIGEN_CLANG_PACKET_REDUX_INT(Packet16i)
+EIGEN_CLANG_PACKET_REDUX_INT(Packet8l)
+#undef EIGEN_CLANG_PACKET_REDUX_INT
+#endif
+
+#if EIGEN_HAS_BUILTIN(__builtin_shufflevector)
+namespace detail {
+template <typename VectorT>
+EIGEN_STRONG_INLINE std::pair<scalar_type_of_vector_t<VectorT>, scalar_type_of_vector_t<VectorT>> ReduceAdd16(
+    const VectorT& a) {
+  const auto t1 = __builtin_shufflevector(a, a, 0, 1, 2, 3, 4, 5, 6, 7) +
+                  __builtin_shufflevector(a, a, 8, 9, 10, 11, 12, 13, 14, 15);
+  const auto t2 = __builtin_shufflevector(t1, t1, 0, 1, 2, 3) + __builtin_shufflevector(t1, t1, 4, 5, 6, 7);
+  const auto t3 = __builtin_shufflevector(t2, t2, 0, 1) + __builtin_shufflevector(t2, t2, 2, 3);
+  return {t3[0], t3[1]};
+}
+
+template <typename VectorT>
+EIGEN_STRONG_INLINE std::pair<scalar_type_of_vector_t<VectorT>, scalar_type_of_vector_t<VectorT>> ReduceAdd8(
+    const VectorT& a) {
+  const auto t1 = __builtin_shufflevector(a, a, 0, 1, 2, 3) + __builtin_shufflevector(a, a, 4, 5, 6, 7);
+  const auto t2 = __builtin_shufflevector(t1, t1, 0, 1) + __builtin_shufflevector(t1, t1, 2, 3);
+  return {t2[0], t2[1]};
+}
+
+template <typename VectorT>
+EIGEN_STRONG_INLINE std::pair<scalar_type_of_vector_t<VectorT>, scalar_type_of_vector_t<VectorT>> ReduceMul16(
+    const VectorT& a) {
+  const auto t1 = __builtin_shufflevector(a, a, 0, 1, 2, 3, 4, 5, 6, 7) *
+                  __builtin_shufflevector(a, a, 8, 9, 10, 11, 12, 13, 14, 15);
+  const auto t2 = __builtin_shufflevector(t1, t1, 0, 1, 2, 3) * __builtin_shufflevector(t1, t1, 4, 5, 6, 7);
+  const auto t3 = __builtin_shufflevector(t2, t2, 0, 1) * __builtin_shufflevector(t2, t2, 2, 3);
+  return {t3[0], t3[1]};
+}
+
+template <typename VectorT>
+EIGEN_STRONG_INLINE std::pair<scalar_type_of_vector_t<VectorT>, scalar_type_of_vector_t<VectorT>> ReduceMul8(
+    const VectorT& a) {
+  const auto t1 = __builtin_shufflevector(a, a, 0, 1, 2, 3) * __builtin_shufflevector(a, a, 4, 5, 6, 7);
+  const auto t2 = __builtin_shufflevector(t1, t1, 0, 1) * __builtin_shufflevector(t1, t1, 2, 3);
+  return {t2[0], t2[1]};
+}
+}  // namespace detail
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet16f>(const Packet16f& a) {
+  float even, odd;
+  std::tie(even, odd) = detail::ReduceAdd16(a);
+  return even + odd;
+}
+template <>
+EIGEN_STRONG_INLINE double predux<Packet8d>(const Packet8d& a) {
+  double even, odd;
+  std::tie(even, odd) = detail::ReduceAdd8(a);
+  return even + odd;
+}
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet16f>(const Packet16f& a) {
+  float even, odd;
+  std::tie(even, odd) = detail::ReduceMul16(a);
+  return even * odd;
+}
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet8d>(const Packet8d& a) {
+  double even, odd;
+  std::tie(even, odd) = detail::ReduceMul8(a);
+  return even * odd;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet8cf>(const Packet8cf& a) {
+  float re, im;
+  std::tie(re, im) = detail::ReduceAdd16(a.v);
+  return std::complex<float>(re, im);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux<Packet4cd>(const Packet4cd& a) {
+  double re, im;
+  std::tie(re, im) = detail::ReduceAdd8(a.v);
+  return std::complex<double>(re, im);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet8cf>(const Packet8cf& a) {
+  const Packet4cf lower4 = Packet4cf(__builtin_shufflevector(a.v, a.v, 0, 1, 2, 3, 4, 5, 6, 7));
+  const Packet4cf upper4 = Packet4cf(__builtin_shufflevector(a.v, a.v, 8, 9, 10, 11, 12, 13, 14, 15));
+  const Packet4cf prod4 = pmul<Packet4cf>(lower4, upper4);
+  const Packet2cf lower2 = Packet2cf(__builtin_shufflevector(prod4.v, prod4.v, 0, 1, 2, 3));
+  const Packet2cf upper2 = Packet2cf(__builtin_shufflevector(prod4.v, prod4.v, 4, 5, 6, 7));
+  const Packet2cf prod2 = pmul<Packet2cf>(lower2, upper2);
+  return prod2[0] * prod2[1];
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet4cd>(const Packet4cd& a) {
+  const Packet2cd lower2 = Packet2cd(__builtin_shufflevector(a.v, a.v, 0, 1, 2, 3));
+  const Packet2cd upper2 = Packet2cd(__builtin_shufflevector(a.v, a.v, 4, 5, 6, 7));
+  const Packet2cd prod2 = pmul<Packet2cd>(lower2, upper2);
+  return prod2[0] * prod2[1];
+}
+
+#endif
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_REDUCTIONS_CLANG_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/TypeCasting.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/TypeCasting.h
new file mode 100644
index 0000000..87ac9ea
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/arch/clang/TypeCasting.h
@@ -0,0 +1,100 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Rasmus Munk Larsen
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_CLANG_H
+#define EIGEN_TYPE_CASTING_CLANG_H
+
+// IWYU pragma: private
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+//==============================================================================
+// preinterpret
+//==============================================================================
+template <>
+EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet16i>(const Packet16i& a) {
+  return reinterpret_cast<Packet16f>(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i preinterpret<Packet16i, Packet16f>(const Packet16f& a) {
+  return reinterpret_cast<Packet16i>(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d preinterpret<Packet8d, Packet8l>(const Packet8l& a) {
+  return reinterpret_cast<Packet8d>(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8l preinterpret<Packet8l, Packet8d>(const Packet8d& a) {
+  return reinterpret_cast<Packet8l>(a);
+}
+
+//==============================================================================
+// pcast
+//==============================================================================
+#if EIGEN_HAS_BUILTIN(__builtin_convertvector)
+template <>
+EIGEN_STRONG_INLINE Packet16i pcast<Packet16f, Packet16i>(const Packet16f& a) {
+  return __builtin_convertvector(a, Packet16i);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16f pcast<Packet16i, Packet16f>(const Packet16i& a) {
+  return __builtin_convertvector(a, Packet16f);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8l pcast<Packet8d, Packet8l>(const Packet8d& a) {
+  return __builtin_convertvector(a, Packet8l);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pcast<Packet8l, Packet8d>(const Packet8l& a) {
+  return __builtin_convertvector(a, Packet8d);
+}
+
+// float -> double: converts lower 8 floats to 8 doubles
+template <>
+EIGEN_STRONG_INLINE Packet8d pcast<Packet16f, Packet8d>(const Packet16f& a) {
+  using HalfFloat = detail::VectorType<float, 8>;
+  HalfFloat lo = __builtin_shufflevector(a, a, 0, 1, 2, 3, 4, 5, 6, 7);
+  return __builtin_convertvector(lo, Packet8d);
+}
+
+// double -> float: converts two Packet8d to one Packet16f
+template <>
+EIGEN_STRONG_INLINE Packet16f pcast<Packet8d, Packet16f>(const Packet8d& a, const Packet8d& b) {
+  using HalfFloat = detail::VectorType<float, 8>;
+  HalfFloat lo = __builtin_convertvector(a, HalfFloat);
+  HalfFloat hi = __builtin_convertvector(b, HalfFloat);
+  return __builtin_shufflevector(lo, hi, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15);
+}
+
+// int32 -> int64: converts lower 8 int32s to 8 int64s
+template <>
+EIGEN_STRONG_INLINE Packet8l pcast<Packet16i, Packet8l>(const Packet16i& a) {
+  using HalfInt = detail::VectorType<int32_t, 8>;
+  HalfInt lo = __builtin_shufflevector(a, a, 0, 1, 2, 3, 4, 5, 6, 7);
+  return __builtin_convertvector(lo, Packet8l);
+}
+
+// int64 -> int32: converts two Packet8l to one Packet16i
+template <>
+EIGEN_STRONG_INLINE Packet16i pcast<Packet8l, Packet16i>(const Packet8l& a, const Packet8l& b) {
+  using HalfInt = detail::VectorType<int32_t, 8>;
+  HalfInt lo = __builtin_convertvector(a, HalfInt);
+  HalfInt hi = __builtin_convertvector(b, HalfInt);
+  return __builtin_shufflevector(lo, hi, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15);
+}
+#endif
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_CLANG_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/AssignmentFunctors.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/AssignmentFunctors.h
new file mode 100644
index 0000000..8f7ad13
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/AssignmentFunctors.h
@@ -0,0 +1,174 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ASSIGNMENT_FUNCTORS_H
+#define EIGEN_ASSIGNMENT_FUNCTORS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * \brief Template functor for scalar/packet assignment
+ *
+ */
+template <typename DstScalar, typename SrcScalar>
+struct assign_op {
+  EIGEN_DEVICE_FUNC constexpr void assignCoeff(DstScalar& a, const SrcScalar& b) const { a = b; }
+
+  template <int Alignment, typename Packet>
+  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const {
+    pstoret<DstScalar, Packet, Alignment>(a, b);
+  }
+
+  template <int Alignment, typename Packet>
+  EIGEN_STRONG_INLINE void assignPacketSegment(DstScalar* a, const Packet& b, Index begin, Index count) const {
+    pstoretSegment<DstScalar, Packet, Alignment>(a, b, begin, count);
+  }
+};
+
+// Empty overload for void type (used by PermutationMatrix)
+template <typename DstScalar>
+struct assign_op<DstScalar, void> {};
+
+template <typename DstScalar, typename SrcScalar>
+struct functor_traits<assign_op<DstScalar, SrcScalar>> {
+  enum {
+    Cost = NumTraits<DstScalar>::ReadCost,
+    PacketAccess = is_same<DstScalar, SrcScalar>::value && packet_traits<DstScalar>::Vectorizable &&
+                   packet_traits<SrcScalar>::Vectorizable
+  };
+};
+
+/** \internal
+ * \brief Template functor for scalar/packet compound assignment
+ *
+ */
+template <typename DstScalar, typename SrcScalar, typename Func>
+struct compound_assign_op {
+  using traits = functor_traits<compound_assign_op<DstScalar, SrcScalar, Func>>;
+  EIGEN_DEVICE_FUNC constexpr void assignCoeff(DstScalar& a, const SrcScalar& b) const {
+    assign_op<DstScalar, DstScalar>().assignCoeff(a, Func().operator()(a, b));
+  }
+
+  template <int Alignment, typename Packet>
+  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const {
+    assign_op<DstScalar, DstScalar>().template assignPacket<Alignment, Packet>(
+        a, Func().packetOp(ploadt<Packet, Alignment>(a), b));
+  }
+
+  template <int Alignment, typename Packet>
+  EIGEN_STRONG_INLINE void assignPacketSegment(DstScalar* a, const Packet& b, Index begin, Index count) const {
+    assign_op<DstScalar, DstScalar>().template assignPacketSegment<Alignment, Packet>(
+        a, Func().packetOp(ploadtSegment<Packet, Alignment>(a, begin, count), b), begin, count);
+  }
+};
+
+template <typename DstScalar, typename SrcScalar, typename Func>
+struct functor_traits<compound_assign_op<DstScalar, SrcScalar, Func>> {
+  enum {
+    Cost = int(functor_traits<assign_op<DstScalar, DstScalar>>::Cost) + int(functor_traits<Func>::Cost),
+    PacketAccess = functor_traits<assign_op<DstScalar, DstScalar>>::PacketAccess && functor_traits<Func>::PacketAccess
+  };
+};
+
+/** \internal
+ * \brief Template functor for scalar/packet assignment with addition
+ *
+ */
+template <typename DstScalar, typename SrcScalar = DstScalar>
+struct add_assign_op : compound_assign_op<DstScalar, SrcScalar, scalar_sum_op<DstScalar, SrcScalar>> {};
+
+template <typename DstScalar, typename SrcScalar>
+struct functor_traits<add_assign_op<DstScalar, SrcScalar>> : add_assign_op<DstScalar, SrcScalar>::traits {};
+
+/** \internal
+ * \brief Template functor for scalar/packet assignment with subtraction
+ *
+ */
+template <typename DstScalar, typename SrcScalar = DstScalar>
+struct sub_assign_op : compound_assign_op<DstScalar, SrcScalar, scalar_difference_op<DstScalar, SrcScalar>> {};
+
+template <typename DstScalar, typename SrcScalar>
+struct functor_traits<sub_assign_op<DstScalar, SrcScalar>> : sub_assign_op<DstScalar, SrcScalar>::traits {};
+
+/** \internal
+ * \brief Template functor for scalar/packet assignment with multiplication
+ *
+ */
+template <typename DstScalar, typename SrcScalar = DstScalar>
+struct mul_assign_op : compound_assign_op<DstScalar, SrcScalar, scalar_product_op<DstScalar, SrcScalar>> {};
+
+template <typename DstScalar, typename SrcScalar>
+struct functor_traits<mul_assign_op<DstScalar, SrcScalar>> : mul_assign_op<DstScalar, SrcScalar>::traits {};
+
+/** \internal
+ * \brief Template functor for scalar/packet assignment with dividing
+ *
+ */
+template <typename DstScalar, typename SrcScalar = DstScalar>
+struct div_assign_op : compound_assign_op<DstScalar, SrcScalar, scalar_quotient_op<DstScalar, SrcScalar>> {};
+
+template <typename DstScalar, typename SrcScalar>
+struct functor_traits<div_assign_op<DstScalar, SrcScalar>> : div_assign_op<DstScalar, SrcScalar>::traits {};
+
+/** \internal
+ * \brief Template functor for scalar/packet assignment with swapping
+ *
+ * It works as follow. For a non-vectorized evaluation loop, we have:
+ *   for(i) func(A.coeffRef(i), B.coeff(i));
+ * where B is a SwapWrapper expression. The trick is to make SwapWrapper::coeff behaves like a non-const coeffRef.
+ * Actually, SwapWrapper might not even be needed since even if B is a plain expression, since it has to be writable
+ * B.coeff already returns a const reference to the underlying scalar value.
+ *
+ * The case of a vectorized loop is more tricky:
+ *   for(i,j) func.assignPacket<A_Align>(&A.coeffRef(i,j), B.packet<B_Align>(i,j));
+ * Here, B must be a SwapWrapper whose packet function actually returns a proxy object holding a Scalar*,
+ * the actual alignment and Packet type.
+ *
+ */
+template <typename Scalar>
+struct swap_assign_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const {
+#ifdef EIGEN_GPUCC
+    // FIXME: check whether cuda::swap exists.
+    Scalar t = b;
+    const_cast<Scalar&>(b) = a;
+    a = t;
+#else
+    using std::swap;
+    swap(a, const_cast<Scalar&>(b));
+#endif
+  }
+};
+template <typename Scalar>
+struct functor_traits<swap_assign_op<Scalar>> {
+  enum {
+    Cost = 3 * NumTraits<Scalar>::ReadCost,
+    PacketAccess =
+#if defined(EIGEN_VECTORIZE_AVX) && (EIGEN_CLANG_STRICT_LESS_THAN(8, 0, 0) || EIGEN_COMP_CLANGAPPLE)
+        // This is a partial workaround for a bug in clang generating bad code
+        // when mixing 256/512 bits loads and 128 bits moves.
+        // See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1684
+        //     https://bugs.llvm.org/show_bug.cgi?id=40815
+    0
+#else
+        packet_traits<Scalar>::Vectorizable
+#endif
+  };
+};
+
+}  // namespace internal
+
+}  // namespace Eigen
+
+#endif  // EIGEN_ASSIGNMENT_FUNCTORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/BinaryFunctors.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/BinaryFunctors.h
new file mode 100644
index 0000000..bbaab11
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/BinaryFunctors.h
@@ -0,0 +1,748 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BINARY_FUNCTORS_H
+#define EIGEN_BINARY_FUNCTORS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- associative binary functors ----------
+
+template <typename Arg1, typename Arg2>
+struct binary_op_base {
+  typedef Arg1 first_argument_type;
+  typedef Arg2 second_argument_type;
+};
+
+/** \internal
+ * \brief Template functor to compute the sum of two scalars
+ *
+ * \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, DenseBase::sum()
+ */
+template <typename LhsScalar, typename RhsScalar>
+struct scalar_sum_op : binary_op_base<LhsScalar, RhsScalar> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar, scalar_sum_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_sum_op(){EIGEN_SCALAR_BINARY_OP_PLUGIN}
+#endif
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type
+  operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a + b;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return internal::padd(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const {
+    return internal::predux(a);
+  }
+};
+template <typename LhsScalar, typename RhsScalar>
+struct functor_traits<scalar_sum_op<LhsScalar, RhsScalar>> {
+  enum {
+    Cost = (int(NumTraits<LhsScalar>::AddCost) + int(NumTraits<RhsScalar>::AddCost)) / 2,  // rough estimate!
+    PacketAccess =
+        is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasAdd && packet_traits<RhsScalar>::HasAdd
+    // TODO: vectorize mixed sum
+  };
+};
+
+template <>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE bool scalar_sum_op<bool, bool>::operator()(const bool& a,
+                                                                                           const bool& b) const {
+  return a || b;
+}
+
+/** \internal
+ * \brief Template functor to compute the product of two scalars
+ *
+ * \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux()
+ */
+template <typename LhsScalar, typename RhsScalar>
+struct scalar_product_op : binary_op_base<LhsScalar, RhsScalar> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar, scalar_product_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_product_op(){EIGEN_SCALAR_BINARY_OP_PLUGIN}
+#endif
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type
+  operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a * b;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return internal::pmul(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const {
+    return internal::predux_mul(a);
+  }
+};
+template <typename LhsScalar, typename RhsScalar>
+struct functor_traits<scalar_product_op<LhsScalar, RhsScalar>> {
+  enum {
+    Cost = (int(NumTraits<LhsScalar>::MulCost) + int(NumTraits<RhsScalar>::MulCost)) / 2,  // rough estimate!
+    PacketAccess =
+        is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
+    // TODO: vectorize mixed product
+  };
+};
+
+template <>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE bool scalar_product_op<bool, bool>::operator()(const bool& a,
+                                                                                               const bool& b) const {
+  return a && b;
+}
+
+/** \internal
+ * \brief Template functor to compute the conjugate product of two scalars
+ *
+ * This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x
+ * * conj(y)
+ */
+template <typename LhsScalar, typename RhsScalar>
+struct scalar_conj_product_op : binary_op_base<LhsScalar, RhsScalar> {
+  enum { Conj = NumTraits<LhsScalar>::IsComplex };
+
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar, scalar_conj_product_op>::ReturnType result_type;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return conj_helper<LhsScalar, RhsScalar, Conj, false>().pmul(a, b);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return conj_helper<Packet, Packet, Conj, false>().pmul(a, b);
+  }
+};
+template <typename LhsScalar, typename RhsScalar>
+struct functor_traits<scalar_conj_product_op<LhsScalar, RhsScalar>> {
+  enum {
+    Cost = NumTraits<LhsScalar>::MulCost,
+    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the min of two scalars
+ *
+ * \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff()
+ */
+template <typename LhsScalar, typename RhsScalar, int NaNPropagation>
+struct scalar_min_op : binary_op_base<LhsScalar, RhsScalar> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar, scalar_min_op>::ReturnType result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return internal::pmin<NaNPropagation>(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return internal::pmin<NaNPropagation>(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const {
+    return internal::predux_min<NaNPropagation>(a);
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, int NaNPropagation>
+struct functor_traits<scalar_min_op<LhsScalar, RhsScalar, NaNPropagation>> {
+  enum {
+    Cost = (NumTraits<LhsScalar>::AddCost + NumTraits<RhsScalar>::AddCost) / 2,
+    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMin
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the max of two scalars
+ *
+ * \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff()
+ */
+template <typename LhsScalar, typename RhsScalar, int NaNPropagation>
+struct scalar_max_op : binary_op_base<LhsScalar, RhsScalar> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar, scalar_max_op>::ReturnType result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return internal::pmax<NaNPropagation>(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return internal::pmax<NaNPropagation>(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const {
+    return internal::predux_max<NaNPropagation>(a);
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, int NaNPropagation>
+struct functor_traits<scalar_max_op<LhsScalar, RhsScalar, NaNPropagation>> {
+  enum {
+    Cost = (NumTraits<LhsScalar>::AddCost + NumTraits<RhsScalar>::AddCost) / 2,
+    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMax
+  };
+};
+
+/** \internal
+ * \brief Template functors for comparison of two scalars
+ * \todo Implement packet-comparisons
+ */
+template <typename LhsScalar, typename RhsScalar, ComparisonName cmp, bool UseTypedComparators = false>
+struct scalar_cmp_op;
+
+template <typename LhsScalar, typename RhsScalar, ComparisonName cmp, bool UseTypedComparators>
+struct functor_traits<scalar_cmp_op<LhsScalar, RhsScalar, cmp, UseTypedComparators>> {
+  enum {
+    Cost = (NumTraits<LhsScalar>::AddCost + NumTraits<RhsScalar>::AddCost) / 2,
+    PacketAccess = (UseTypedComparators || is_same<LhsScalar, bool>::value) && is_same<LhsScalar, RhsScalar>::value &&
+                   packet_traits<LhsScalar>::HasCmp
+  };
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_EQ, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = std::conditional_t<UseTypedComparators, LhsScalar, bool>;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a == b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pand(pcmp_eq(a, b), cst_one);
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_LT, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = std::conditional_t<UseTypedComparators, LhsScalar, bool>;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a < b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pand(pcmp_lt(a, b), cst_one);
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_LE, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = std::conditional_t<UseTypedComparators, LhsScalar, bool>;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a <= b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pand(cst_one, pcmp_le(a, b));
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_GT, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = std::conditional_t<UseTypedComparators, LhsScalar, bool>;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a > b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pand(cst_one, pcmp_lt(b, a));
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_GE, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = std::conditional_t<UseTypedComparators, LhsScalar, bool>;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a >= b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pand(cst_one, pcmp_le(b, a));
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_UNORD, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = std::conditional_t<UseTypedComparators, LhsScalar, bool>;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return !(a <= b || b <= a) ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pandnot(cst_one, por(pcmp_le(a, b), pcmp_le(b, a)));
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_NEQ, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = std::conditional_t<UseTypedComparators, LhsScalar, bool>;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a != b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pandnot(cst_one, pcmp_eq(a, b));
+  }
+};
+
+/** \internal
+ * \brief Template functor to compute the hypot of two \b positive \b and \b real scalars
+ *
+ * \sa MatrixBase::stableNorm(), class Redux
+ */
+template <typename Scalar>
+struct scalar_hypot_op<Scalar, Scalar> : binary_op_base<Scalar, Scalar> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& x, const Scalar& y) const {
+    // This functor is used by hypotNorm only for which it is faster to first apply abs
+    // on all coefficients prior to reduction through hypot.
+    // This way we avoid calling abs on positive and real entries, and this also permits
+    // to seamlessly handle complexes. Otherwise we would have to handle both real and complexes
+    // through the same functor...
+    return internal::positive_real_hypot(x, y);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_hypot_op<Scalar, Scalar>> {
+  enum {
+    Cost = 3 * NumTraits<Scalar>::AddCost + 2 * NumTraits<Scalar>::MulCost + 2 * scalar_div_cost<Scalar, false>::value,
+    PacketAccess = false
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the pow of two scalars
+ * See the specification of pow in https://en.cppreference.com/w/cpp/numeric/math/pow
+ */
+template <typename Scalar, typename Exponent>
+struct scalar_pow_op : binary_op_base<Scalar, Exponent> {
+  typedef typename ScalarBinaryOpTraits<Scalar, Exponent, scalar_pow_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_pow_op() {
+    typedef Scalar LhsScalar;
+    typedef Exponent RhsScalar;
+    EIGEN_SCALAR_BINARY_OP_PLUGIN
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC constexpr inline result_type operator()(const Scalar& a, const Exponent& b) const {
+    return numext::pow(a, b);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return generic_pow(a, b);
+  }
+};
+
+template <typename Scalar, typename Exponent>
+struct functor_traits<scalar_pow_op<Scalar, Exponent>> {
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = (!NumTraits<Scalar>::IsComplex && !NumTraits<Scalar>::IsInteger && packet_traits<Scalar>::HasPow)
+  };
+};
+
+//---------- non associative binary functors ----------
+
+/** \internal
+ * \brief Template functor to compute the difference of two scalars
+ *
+ * \sa class CwiseBinaryOp, MatrixBase::operator-
+ */
+template <typename LhsScalar, typename RhsScalar>
+struct scalar_difference_op : binary_op_base<LhsScalar, RhsScalar> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar, scalar_difference_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_difference_op(){EIGEN_SCALAR_BINARY_OP_PLUGIN}
+#endif
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type
+  operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a - b;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return internal::psub(a, b);
+  }
+};
+template <typename LhsScalar, typename RhsScalar>
+struct functor_traits<scalar_difference_op<LhsScalar, RhsScalar>> {
+  enum {
+    Cost = (int(NumTraits<LhsScalar>::AddCost) + int(NumTraits<RhsScalar>::AddCost)) / 2,
+    PacketAccess =
+        is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasSub && packet_traits<RhsScalar>::HasSub
+  };
+};
+
+template <typename Packet, bool IsInteger = NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>
+struct maybe_raise_div_by_zero {
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void run(Packet x) { EIGEN_UNUSED_VARIABLE(x); }
+};
+
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template <typename Packet>
+struct maybe_raise_div_by_zero<Packet, true> {
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE void run(Packet x) {
+    if (EIGEN_PREDICT_FALSE(predux_any(pcmp_eq(x, pzero(x))))) {
+      // Use volatile variables to force a division by zero, which will
+      // result in the default platform behaviour (usually SIGFPE).
+      volatile typename unpacket_traits<Packet>::type zero = 0;
+      volatile typename unpacket_traits<Packet>::type val = 1;
+      val = val / zero;
+    }
+  }
+};
+#endif
+
+/** \internal
+ * \brief Template functor to compute the quotient of two scalars
+ *
+ * \sa class CwiseBinaryOp, Cwise::operator/()
+ */
+template <typename LhsScalar, typename RhsScalar>
+struct scalar_quotient_op : binary_op_base<LhsScalar, RhsScalar> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar, scalar_quotient_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_quotient_op(){EIGEN_SCALAR_BINARY_OP_PLUGIN}
+#endif
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type
+  operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a / b;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return internal::pdiv(a, b);
+  }
+};
+template <typename LhsScalar, typename RhsScalar>
+struct functor_traits<scalar_quotient_op<LhsScalar, RhsScalar>> {
+  typedef typename scalar_quotient_op<LhsScalar, RhsScalar>::result_type result_type;
+  enum {
+    PacketAccess =
+        is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv,
+    Cost = scalar_div_cost<result_type, PacketAccess>::value
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the and of two scalars as if they were booleans
+ *
+ * \sa class CwiseBinaryOp, ArrayBase::operator&&
+ */
+template <typename Scalar>
+struct scalar_boolean_and_op {
+  using result_type = Scalar;
+  // `false` any value `a` that satisfies `a == Scalar(0)`
+  // `true` is the complement of `false`
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return (a != Scalar(0)) && (b != Scalar(0)) ? Scalar(1) : Scalar(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    // and(a,b) == !or(!a,!b)
+    Packet not_a = pcmp_eq(a, pzero(a));
+    Packet not_b = pcmp_eq(b, pzero(b));
+    Packet a_nand_b = por(not_a, not_b);
+    return pandnot(cst_one, a_nand_b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_boolean_and_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+/** \internal
+ * \brief Template functor to compute the or of two scalars as if they were booleans
+ *
+ * \sa class CwiseBinaryOp, ArrayBase::operator||
+ */
+template <typename Scalar>
+struct scalar_boolean_or_op {
+  using result_type = Scalar;
+  // `false` any value `a` that satisfies `a == Scalar(0)`
+  // `true` is the complement of `false`
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return (a != Scalar(0)) || (b != Scalar(0)) ? Scalar(1) : Scalar(0);
+  }
+  template <typename Packet>
+  EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    // if or(a,b) == 0, then a == 0 and b == 0
+    // or(a,b) == !nor(a,b)
+    Packet a_nor_b = pcmp_eq(por(a, b), pzero(a));
+    return pandnot(cst_one, a_nor_b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_boolean_or_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+/** \internal
+ * \brief Template functor to compute the xor of two scalars as if they were booleans
+ *
+ * \sa class CwiseBinaryOp, ArrayBase::operator^
+ */
+template <typename Scalar>
+struct scalar_boolean_xor_op {
+  using result_type = Scalar;
+  // `false` any value `a` that satisfies `a == Scalar(0)`
+  // `true` is the complement of `false`
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return (a != Scalar(0)) != (b != Scalar(0)) ? Scalar(1) : Scalar(0);
+  }
+  template <typename Packet>
+  EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    // xor(a,b) == xor(!a,!b)
+    Packet not_a = pcmp_eq(a, pzero(a));
+    Packet not_b = pcmp_eq(b, pzero(b));
+    Packet a_xor_b = pxor(not_a, not_b);
+    return pand(cst_one, a_xor_b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_boolean_xor_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+template <typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct bitwise_binary_impl {
+  static constexpr size_t Size = sizeof(Scalar);
+  using uint_t = typename numext::get_integer_by_size<Size>::unsigned_type;
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar run_and(const Scalar& a, const Scalar& b) {
+    uint_t a_as_uint = numext::bit_cast<uint_t, Scalar>(a);
+    uint_t b_as_uint = numext::bit_cast<uint_t, Scalar>(b);
+    uint_t result = a_as_uint & b_as_uint;
+    return numext::bit_cast<Scalar, uint_t>(result);
+  }
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar run_or(const Scalar& a, const Scalar& b) {
+    uint_t a_as_uint = numext::bit_cast<uint_t, Scalar>(a);
+    uint_t b_as_uint = numext::bit_cast<uint_t, Scalar>(b);
+    uint_t result = a_as_uint | b_as_uint;
+    return numext::bit_cast<Scalar, uint_t>(result);
+  }
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar run_xor(const Scalar& a, const Scalar& b) {
+    uint_t a_as_uint = numext::bit_cast<uint_t, Scalar>(a);
+    uint_t b_as_uint = numext::bit_cast<uint_t, Scalar>(b);
+    uint_t result = a_as_uint ^ b_as_uint;
+    return numext::bit_cast<Scalar, uint_t>(result);
+  }
+};
+
+template <typename Scalar>
+struct bitwise_binary_impl<Scalar, true> {
+  using Real = typename NumTraits<Scalar>::Real;
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar run_and(const Scalar& a, const Scalar& b) {
+    Real real_result = bitwise_binary_impl<Real>::run_and(numext::real(a), numext::real(b));
+    Real imag_result = bitwise_binary_impl<Real>::run_and(numext::imag(a), numext::imag(b));
+    return Scalar(real_result, imag_result);
+  }
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar run_or(const Scalar& a, const Scalar& b) {
+    Real real_result = bitwise_binary_impl<Real>::run_or(numext::real(a), numext::real(b));
+    Real imag_result = bitwise_binary_impl<Real>::run_or(numext::imag(a), numext::imag(b));
+    return Scalar(real_result, imag_result);
+  }
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar run_xor(const Scalar& a, const Scalar& b) {
+    Real real_result = bitwise_binary_impl<Real>::run_xor(numext::real(a), numext::real(b));
+    Real imag_result = bitwise_binary_impl<Real>::run_xor(numext::imag(a), numext::imag(b));
+    return Scalar(real_result, imag_result);
+  }
+};
+
+/** \internal
+ * \brief Template functor to compute the bitwise and of two scalars
+ *
+ * \sa class CwiseBinaryOp, ArrayBase::operator&
+ */
+template <typename Scalar>
+struct scalar_bitwise_and_op {
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::RequireInitialization,
+                      BITWISE OPERATIONS MAY ONLY BE PERFORMED ON PLAIN DATA TYPES)
+  EIGEN_STATIC_ASSERT((!internal::is_same<Scalar, bool>::value), DONT USE BITWISE OPS ON BOOLEAN TYPES)
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return bitwise_binary_impl<Scalar>::run_and(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return pand(a, b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bitwise_and_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = true };
+};
+
+/** \internal
+ * \brief Template functor to compute the bitwise or of two scalars
+ *
+ * \sa class CwiseBinaryOp, ArrayBase::operator|
+ */
+template <typename Scalar>
+struct scalar_bitwise_or_op {
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::RequireInitialization,
+                      BITWISE OPERATIONS MAY ONLY BE PERFORMED ON PLAIN DATA TYPES)
+  EIGEN_STATIC_ASSERT((!internal::is_same<Scalar, bool>::value), DONT USE BITWISE OPS ON BOOLEAN TYPES)
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return bitwise_binary_impl<Scalar>::run_or(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return por(a, b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bitwise_or_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = true };
+};
+
+/** \internal
+ * \brief Template functor to compute the bitwise xor of two scalars
+ *
+ * \sa class CwiseBinaryOp, ArrayBase::operator^
+ */
+template <typename Scalar>
+struct scalar_bitwise_xor_op {
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::RequireInitialization,
+                      BITWISE OPERATIONS MAY ONLY BE PERFORMED ON PLAIN DATA TYPES)
+  EIGEN_STATIC_ASSERT((!internal::is_same<Scalar, bool>::value), DONT USE BITWISE OPS ON BOOLEAN TYPES)
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return bitwise_binary_impl<Scalar>::run_xor(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return pxor(a, b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bitwise_xor_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = true };
+};
+
+/** \internal
+ * \brief Template functor to compute the absolute difference of two scalars
+ *
+ * \sa class CwiseBinaryOp, MatrixBase::absolute_difference
+ */
+template <typename LhsScalar, typename RhsScalar>
+struct scalar_absolute_difference_op : binary_op_base<LhsScalar, RhsScalar> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar, scalar_absolute_difference_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_absolute_difference_op(){EIGEN_SCALAR_BINARY_OP_PLUGIN}
+#endif
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type
+  operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return numext::absdiff(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return internal::pabsdiff(a, b);
+  }
+};
+template <typename LhsScalar, typename RhsScalar>
+struct functor_traits<scalar_absolute_difference_op<LhsScalar, RhsScalar>> {
+  enum {
+    Cost = (NumTraits<LhsScalar>::AddCost + NumTraits<RhsScalar>::AddCost) / 2,
+    PacketAccess = is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasAbsDiff
+  };
+};
+
+template <typename LhsScalar, typename RhsScalar>
+struct scalar_atan2_op {
+  using Scalar = LhsScalar;
+
+  static constexpr bool Enable =
+      is_same<LhsScalar, RhsScalar>::value && !NumTraits<Scalar>::IsInteger && !NumTraits<Scalar>::IsComplex;
+  EIGEN_STATIC_ASSERT(Enable, "LhsScalar and RhsScalar must be the same non-integer, non-complex type")
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& y, const Scalar& x) const {
+    return numext::atan2(y, x);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& y, const Packet& x) const {
+    return internal::patan2(y, x);
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar>
+struct functor_traits<scalar_atan2_op<LhsScalar, RhsScalar>> {
+  using Scalar = LhsScalar;
+  enum {
+    PacketAccess = is_same<LhsScalar, RhsScalar>::value && packet_traits<Scalar>::HasATan &&
+                   packet_traits<Scalar>::HasDiv && !NumTraits<Scalar>::IsInteger && !NumTraits<Scalar>::IsComplex,
+    Cost = int(scalar_div_cost<Scalar, PacketAccess>::value) + int(functor_traits<scalar_atan_op<Scalar>>::Cost)
+  };
+};
+
+//---------- binary functors bound to a constant, thus appearing as a unary functor ----------
+
+// The following two classes permits to turn any binary functor into a unary one with one argument bound to a constant
+// value. They are analogues to std::binder1st/binder2nd but with the following differences:
+//  - they are compatible with packetOp
+//  - they are portable across C++ versions (the std::binder* are deprecated in C++11)
+template <typename BinaryOp>
+struct bind1st_op : BinaryOp {
+  typedef typename BinaryOp::first_argument_type first_argument_type;
+  typedef typename BinaryOp::second_argument_type second_argument_type;
+  typedef typename BinaryOp::result_type result_type;
+
+  EIGEN_DEVICE_FUNC constexpr explicit bind1st_op(const first_argument_type& val) : m_value(val) {}
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const second_argument_type& b) const {
+    return BinaryOp::operator()(m_value, b);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& b) const {
+    return BinaryOp::packetOp(internal::pset1<Packet>(m_value), b);
+  }
+
+  first_argument_type m_value;
+};
+template <typename BinaryOp>
+struct functor_traits<bind1st_op<BinaryOp>> : functor_traits<BinaryOp> {};
+
+template <typename BinaryOp>
+struct bind2nd_op : BinaryOp {
+  typedef typename BinaryOp::first_argument_type first_argument_type;
+  typedef typename BinaryOp::second_argument_type second_argument_type;
+  typedef typename BinaryOp::result_type result_type;
+
+  EIGEN_DEVICE_FUNC constexpr explicit bind2nd_op(const second_argument_type& val) : m_value(val) {}
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const first_argument_type& a) const {
+    return BinaryOp::operator()(a, m_value);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return BinaryOp::packetOp(a, internal::pset1<Packet>(m_value));
+  }
+
+  second_argument_type m_value;
+};
+template <typename BinaryOp>
+struct functor_traits<bind2nd_op<BinaryOp>> : functor_traits<BinaryOp> {};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BINARY_FUNCTORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/NullaryFunctors.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/NullaryFunctors.h
new file mode 100644
index 0000000..d8fc78a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/NullaryFunctors.h
@@ -0,0 +1,274 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NULLARY_FUNCTORS_H
+#define EIGEN_NULLARY_FUNCTORS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+struct scalar_constant_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) {}
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()() const { return m_other; }
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetOp() const {
+    return internal::pset1<PacketType>(m_other);
+  }
+  const Scalar m_other;
+};
+template <typename Scalar>
+struct functor_traits<scalar_constant_op<Scalar>> {
+  enum {
+    Cost = 0 /* as the constant value should be loaded in register only once for the whole expression */,
+    PacketAccess = packet_traits<Scalar>::Vectorizable,
+    IsRepeatable = true
+  };
+};
+
+template <typename Scalar>
+struct scalar_zero_op {
+  EIGEN_DEVICE_FUNC scalar_zero_op() = default;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()() const { return Scalar(0); }
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketType packetOp() const {
+    return internal::pzero<PacketType>(PacketType());
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_zero_op<Scalar>> : functor_traits<scalar_constant_op<Scalar>> {};
+
+template <typename Scalar>
+struct scalar_identity_op {
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(IndexType row, IndexType col) const {
+    return row == col ? Scalar(1) : Scalar(0);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_identity_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true };
+};
+
+template <typename Scalar, bool IsInteger>
+struct linspaced_op_impl;
+
+template <typename Scalar>
+struct linspaced_op_impl<Scalar, /*IsInteger*/ false> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  EIGEN_DEVICE_FUNC constexpr linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps)
+      : m_low(low),
+        m_high(high),
+        m_size1(num_steps == 1 ? 1 : num_steps - 1),
+        m_step(num_steps == 1 ? Scalar() : Scalar((high - low) / RealScalar(num_steps - 1))),
+        m_flip(numext::abs(high) < numext::abs(low)) {}
+
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(IndexType i) const {
+    if (m_flip)
+      return (i == 0) ? m_low : Scalar(m_high - RealScalar(m_size1 - i) * m_step);
+    else
+      return (i == m_size1) ? m_high : Scalar(m_low + RealScalar(i) * m_step);
+  }
+
+  template <typename Packet, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(IndexType i) const {
+    // Principle:
+    // [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) )
+    Packet low = pset1<Packet>(m_low);
+    Packet high = pset1<Packet>(m_high);
+    Packet step = pset1<Packet>(m_step);
+    if (m_flip) {
+      Packet pi = plset<Packet>(Scalar(i - m_size1));
+      Packet res = pmadd(step, pi, high);
+      Packet mask = pcmp_lt(pzero(res), plset<Packet>(Scalar(i)));
+      return pselect<Packet>(mask, res, low);
+    } else {
+      Packet pi = plset<Packet>(Scalar(i));
+      Packet res = pmadd(step, pi, low);
+      Packet mask = pcmp_lt(pi, pset1<Packet>(Scalar(m_size1)));
+      return pselect<Packet>(mask, res, high);
+    }
+  }
+
+  const Scalar m_low;
+  const Scalar m_high;
+  const Index m_size1;
+  const Scalar m_step;
+  const bool m_flip;
+};
+
+template <typename Scalar>
+struct linspaced_op_impl<Scalar, /*IsInteger*/ true> {
+  EIGEN_DEVICE_FUNC constexpr linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps)
+      : m_low(low),
+        m_multiplier((high - low) / convert_index<Scalar>(num_steps <= 1 ? 1 : num_steps - 1)),
+        m_divisor(convert_index<Scalar>((high >= low ? num_steps : -num_steps) + (high - low)) /
+                  ((numext::abs(high - low) + 1) == 0 ? 1 : (numext::abs(high - low) + 1))),
+        m_use_divisor(num_steps > 1 && (numext::abs(high - low) + 1) < num_steps) {}
+
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(IndexType i) const {
+    if (m_use_divisor)
+      return m_low + convert_index<Scalar>(i) / m_divisor;
+    else
+      return m_low + convert_index<Scalar>(i) * m_multiplier;
+  }
+
+  const Scalar m_low;
+  const Scalar m_multiplier;
+  const Scalar m_divisor;
+  const bool m_use_divisor;
+};
+
+// ----- Linspace functor ----------------------------------------------------------------
+
+// Forward declaration (we default to random access which does not really give
+// us a speed gain when using packet access but it allows to use the functor in
+// nested expressions).
+template <typename Scalar>
+struct linspaced_op;
+template <typename Scalar>
+struct functor_traits<linspaced_op<Scalar>> {
+  enum {
+    Cost = 1,
+    PacketAccess = (!NumTraits<Scalar>::IsInteger) && packet_traits<Scalar>::HasSetLinear,
+    /*&& ((!NumTraits<Scalar>::IsInteger) || packet_traits<Scalar>::HasDiv),*/  // <- vectorization for integer is
+                                                                                // currently disabled
+    IsRepeatable = true
+  };
+};
+template <typename Scalar>
+struct linspaced_op {
+  EIGEN_DEVICE_FUNC constexpr linspaced_op(const Scalar& low, const Scalar& high, Index num_steps)
+      : impl((num_steps == 1 ? high : low), high, num_steps) {}
+
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(IndexType i) const {
+    return impl(i);
+  }
+
+  template <typename Packet, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(IndexType i) const {
+    return impl.template packetOp<Packet>(i);
+  }
+
+  // This proxy object handles the actual required temporaries and the different
+  // implementations (integer vs. floating point).
+  const linspaced_op_impl<Scalar, NumTraits<Scalar>::IsInteger> impl;
+};
+
+template <typename Scalar>
+struct equalspaced_op {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  EIGEN_DEVICE_FUNC constexpr equalspaced_op(const Scalar& start, const Scalar& step) : m_start(start), m_step(step) {}
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(IndexType i) const {
+    return m_start + m_step * static_cast<Scalar>(i);
+  }
+  template <typename Packet, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(IndexType i) const {
+    const Packet cst_start = pset1<Packet>(m_start);
+    const Packet cst_step = pset1<Packet>(m_step);
+    const Packet cst_lin0 = plset<Packet>(Scalar(0));
+    const Packet cst_offset = pmadd(cst_lin0, cst_step, cst_start);
+
+    Packet i_packet = pset1<Packet>(static_cast<Scalar>(i));
+    return pmadd(i_packet, cst_step, cst_offset);
+  }
+  const Scalar m_start;
+  const Scalar m_step;
+};
+
+template <typename Scalar>
+struct functor_traits<equalspaced_op<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::AddCost + NumTraits<Scalar>::MulCost,
+    PacketAccess =
+        packet_traits<Scalar>::HasSetLinear && packet_traits<Scalar>::HasMul && packet_traits<Scalar>::HasAdd,
+    IsRepeatable = true
+  };
+};
+
+// Linear access is automatically determined from the operator() prototypes available for the given functor.
+// If it exposes an operator()(i,j), then we assume the i and j coefficients are required independently
+// and linear access is not possible. In all other cases, linear access is enabled.
+// Users should not have to deal with this structure.
+template <typename Functor>
+struct functor_has_linear_access {
+  enum { ret = !has_binary_operator<Functor>::value };
+};
+
+// For unreliable compilers, let's specialize the has_*ary_operator
+// helpers so that at least built-in nullary functors work fine.
+#if !(EIGEN_COMP_MSVC || EIGEN_COMP_GNUC || (EIGEN_COMP_ICC >= 1600))
+template <typename Scalar, typename IndexType>
+struct has_nullary_operator<scalar_constant_op<Scalar>, IndexType> {
+  enum { value = 1 };
+};
+template <typename Scalar, typename IndexType>
+struct has_unary_operator<scalar_constant_op<Scalar>, IndexType> {
+  enum { value = 0 };
+};
+template <typename Scalar, typename IndexType>
+struct has_binary_operator<scalar_constant_op<Scalar>, IndexType> {
+  enum { value = 0 };
+};
+
+template <typename Scalar, typename IndexType>
+struct has_nullary_operator<scalar_identity_op<Scalar>, IndexType> {
+  enum { value = 0 };
+};
+template <typename Scalar, typename IndexType>
+struct has_unary_operator<scalar_identity_op<Scalar>, IndexType> {
+  enum { value = 0 };
+};
+template <typename Scalar, typename IndexType>
+struct has_binary_operator<scalar_identity_op<Scalar>, IndexType> {
+  enum { value = 1 };
+};
+
+template <typename Scalar, typename IndexType>
+struct has_nullary_operator<linspaced_op<Scalar>, IndexType> {
+  enum { value = 0 };
+};
+template <typename Scalar, typename IndexType>
+struct has_unary_operator<linspaced_op<Scalar>, IndexType> {
+  enum { value = 1 };
+};
+template <typename Scalar, typename IndexType>
+struct has_binary_operator<linspaced_op<Scalar>, IndexType> {
+  enum { value = 0 };
+};
+
+template <typename Scalar, typename IndexType>
+struct has_nullary_operator<scalar_random_op<Scalar>, IndexType> {
+  enum { value = 1 };
+};
+template <typename Scalar, typename IndexType>
+struct has_unary_operator<scalar_random_op<Scalar>, IndexType> {
+  enum { value = 0 };
+};
+template <typename Scalar, typename IndexType>
+struct has_binary_operator<scalar_random_op<Scalar>, IndexType> {
+  enum { value = 0 };
+};
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_NULLARY_FUNCTORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/StlFunctors.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/StlFunctors.h
new file mode 100644
index 0000000..0599ce3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/StlFunctors.h
@@ -0,0 +1,149 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STL_FUNCTORS_H
+#define EIGEN_STL_FUNCTORS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+// default functor traits for STL functors:
+
+template <typename T>
+struct functor_traits<std::multiplies<T> > {
+  enum { Cost = NumTraits<T>::MulCost, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::divides<T> > {
+  enum { Cost = NumTraits<T>::MulCost, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::plus<T> > {
+  enum { Cost = NumTraits<T>::AddCost, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::minus<T> > {
+  enum { Cost = NumTraits<T>::AddCost, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::negate<T> > {
+  enum { Cost = NumTraits<T>::AddCost, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::logical_or<T> > {
+  enum { Cost = 1, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::logical_and<T> > {
+  enum { Cost = 1, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::logical_not<T> > {
+  enum { Cost = 1, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::greater<T> > {
+  enum { Cost = 1, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::less<T> > {
+  enum { Cost = 1, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::greater_equal<T> > {
+  enum { Cost = 1, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::less_equal<T> > {
+  enum { Cost = 1, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::equal_to<T> > {
+  enum { Cost = 1, PacketAccess = false };
+};
+
+template <typename T>
+struct functor_traits<std::not_equal_to<T> > {
+  enum { Cost = 1, PacketAccess = false };
+};
+
+#if (EIGEN_COMP_CXXVER < 17)
+// std::unary_negate is deprecated since c++17 and will be removed in c++20
+template <typename T>
+struct functor_traits<std::unary_negate<T> > {
+  enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false };
+};
+
+// std::binary_negate is deprecated since c++17 and will be removed in c++20
+template <typename T>
+struct functor_traits<std::binary_negate<T> > {
+  enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false };
+};
+#endif
+
+#ifdef EIGEN_STDEXT_SUPPORT
+
+template <typename T0, typename T1>
+struct functor_traits<std::project1st<T0, T1> > {
+  enum { Cost = 0, PacketAccess = false };
+};
+
+template <typename T0, typename T1>
+struct functor_traits<std::project2nd<T0, T1> > {
+  enum { Cost = 0, PacketAccess = false };
+};
+
+template <typename T0, typename T1>
+struct functor_traits<std::select2nd<std::pair<T0, T1> > > {
+  enum { Cost = 0, PacketAccess = false };
+};
+
+template <typename T0, typename T1>
+struct functor_traits<std::select1st<std::pair<T0, T1> > > {
+  enum { Cost = 0, PacketAccess = false };
+};
+
+template <typename T0, typename T1>
+struct functor_traits<std::unary_compose<T0, T1> > {
+  enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost, PacketAccess = false };
+};
+
+template <typename T0, typename T1, typename T2>
+struct functor_traits<std::binary_compose<T0, T1, T2> > {
+  enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost + functor_traits<T2>::Cost, PacketAccess = false };
+};
+
+#endif  // EIGEN_STDEXT_SUPPORT
+
+// allow to add new functors and specializations of functor_traits from outside Eigen.
+// this macro is really needed because functor_traits must be specialized after it is declared but before it is used...
+#ifdef EIGEN_FUNCTORS_PLUGIN
+#include EIGEN_FUNCTORS_PLUGIN
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_STL_FUNCTORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/TernaryFunctors.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/TernaryFunctors.h
new file mode 100644
index 0000000..9a4ea48
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/TernaryFunctors.h
@@ -0,0 +1,53 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TERNARY_FUNCTORS_H
+#define EIGEN_TERNARY_FUNCTORS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- associative ternary functors ----------
+
+template <typename ThenScalar, typename ElseScalar, typename ConditionScalar>
+struct scalar_boolean_select_op {
+  static constexpr bool ThenElseAreSame =
+      is_same<std::remove_const_t<ThenScalar>, std::remove_const_t<ElseScalar>>::value;
+  EIGEN_STATIC_ASSERT(ThenElseAreSame, THEN AND ELSE MUST BE SAME TYPE)
+  using Scalar = ThenScalar;
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const ThenScalar& a, const ElseScalar& b,
+                                                                    const ConditionScalar& cond) const {
+    return cond == ConditionScalar(0) ? b : a;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b, const Packet& cond) const {
+    return pselect(pcmp_eq(cond, pzero(cond)), b, a);
+  }
+};
+
+template <typename ThenScalar, typename ElseScalar, typename ConditionScalar>
+struct functor_traits<scalar_boolean_select_op<ThenScalar, ElseScalar, ConditionScalar>> {
+  using Scalar = ThenScalar;
+  enum {
+    Cost = 1,
+    PacketAccess = is_same<ThenScalar, ElseScalar>::value && is_same<ConditionScalar, Scalar>::value &&
+                   packet_traits<Scalar>::HasCmp
+  };
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TERNARY_FUNCTORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/UnaryFunctors.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/UnaryFunctors.h
new file mode 100644
index 0000000..99cda3d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/functors/UnaryFunctors.h
@@ -0,0 +1,1421 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_UNARY_FUNCTORS_H
+#define EIGEN_UNARY_FUNCTORS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * \brief Template functor to compute the opposite of a scalar
+ *
+ * \sa class CwiseUnaryOp, MatrixBase::operator-
+ */
+template <typename Scalar>
+struct scalar_opposite_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const { return numext::negate(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return internal::pnegate(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_opposite_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasNegate };
+};
+
+/** \internal
+ * \brief Template functor to compute the absolute value of a scalar
+ *
+ * \sa class CwiseUnaryOp, Cwise::abs
+ */
+template <typename Scalar>
+struct scalar_abs_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const Scalar& a) const {
+    return numext::abs(a);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return internal::pabs(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_abs_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAbs };
+};
+
+/** \internal
+ * \brief Template functor to compute the score of a scalar, to chose a pivot
+ *
+ * \sa class CwiseUnaryOp
+ */
+template <typename Scalar>
+struct scalar_score_coeff_op : scalar_abs_op<Scalar> {
+  typedef void Score_is_abs;
+};
+template <typename Scalar>
+struct functor_traits<scalar_score_coeff_op<Scalar>> : functor_traits<scalar_abs_op<Scalar>> {};
+
+/* Avoid recomputing abs when we know the score and they are the same. Not a true Eigen functor.  */
+template <typename Scalar, typename = void>
+struct abs_knowing_score {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  template <typename Score>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const Scalar& a, const Score&) const {
+    return numext::abs(a);
+  }
+};
+template <typename Scalar>
+struct abs_knowing_score<Scalar, typename scalar_score_coeff_op<Scalar>::Score_is_abs> {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  template <typename Scal>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const Scal&, const result_type& a) const {
+    return a;
+  }
+};
+
+/** \internal
+ * \brief Template functor to compute the squared absolute value of a scalar
+ *
+ * \sa class CwiseUnaryOp, Cwise::abs2
+ */
+template <typename Scalar>
+struct scalar_abs2_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const Scalar& a) const {
+    return numext::abs2(a);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return internal::pmul(a, a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_abs2_op<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasMul && !NumTraits<Scalar>::IsComplex
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the conjugate of a complex value
+ *
+ * \sa class CwiseUnaryOp, MatrixBase::conjugate()
+ */
+template <typename Scalar>
+struct scalar_conjugate_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const { return numext::conj(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return internal::pconj(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_conjugate_op<Scalar>> {
+  enum {
+    Cost = 0,
+    // Yes the cost is zero even for complexes because in most cases for which
+    // the cost is used, conjugation turns to be a no-op. Some examples:
+    //   cost(a*conj(b)) == cost(a*b)
+    //   cost(a+conj(b)) == cost(a+b)
+    //   <etc.
+    // If we don't set it to zero, then:
+    //   A.conjugate().lazyProduct(B.conjugate())
+    // will bake its operands. We definitely don't want that!
+    PacketAccess = packet_traits<Scalar>::HasConj
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the phase angle of a complex
+ *
+ * \sa class CwiseUnaryOp, Cwise::arg
+ */
+template <typename Scalar>
+struct scalar_arg_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const Scalar& a) const {
+    return numext::arg(a);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return internal::parg(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_arg_op<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::IsComplex ? 5 * NumTraits<Scalar>::MulCost : NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasArg
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the complex argument, returned as a complex type
+ *
+ * \sa class CwiseUnaryOp, Cwise::carg
+ */
+template <typename Scalar>
+struct scalar_carg_op {
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    return Scalar(numext::arg(a));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return pcarg(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_carg_op<Scalar>> {
+  using RealScalar = typename NumTraits<Scalar>::Real;
+  enum { Cost = functor_traits<scalar_atan2_op<RealScalar>>::Cost, PacketAccess = packet_traits<RealScalar>::HasATan };
+};
+
+/** \internal
+ * \brief Template functor to cast a scalar to another type
+ *
+ * \sa class CwiseUnaryOp, MatrixBase::cast()
+ */
+template <typename Scalar, typename NewType>
+struct scalar_cast_op {
+  typedef NewType result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE NewType operator()(const Scalar& a) const {
+    return cast<Scalar, NewType>(a);
+  }
+};
+
+template <typename Scalar, typename NewType>
+struct functor_traits<scalar_cast_op<Scalar, NewType>> {
+  enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false };
+};
+
+/** \internal
+ * `core_cast_op` serves to distinguish the vectorized implementation from that of the legacy `scalar_cast_op` for
+ * backwards compatibility. The manner in which packet ops are handled is defined by the specialized unary_evaluator:
+ * `unary_evaluator<CwiseUnaryOp<core_cast_op<SrcType, DstType>, ArgType>, IndexBased>` in CoreEvaluators.h
+ * Otherwise, the non-vectorized behavior is identical to that of `scalar_cast_op`
+ */
+template <typename SrcType, typename DstType>
+struct core_cast_op : scalar_cast_op<SrcType, DstType> {};
+
+template <typename SrcType, typename DstType>
+struct functor_traits<core_cast_op<SrcType, DstType>> {
+  using CastingTraits = type_casting_traits<SrcType, DstType>;
+  enum {
+    Cost = is_same<SrcType, DstType>::value ? 0 : NumTraits<DstType>::AddCost,
+    PacketAccess = CastingTraits::VectorizedCast && (CastingTraits::SrcCoeffRatio <= 8)
+  };
+};
+
+/** \internal
+ * \brief Template functor to arithmetically shift a scalar right by a number of bits
+ *
+ * \sa class CwiseUnaryOp, MatrixBase::shift_right()
+ */
+template <typename Scalar, int N>
+struct scalar_shift_right_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    return numext::arithmetic_shift_right(a);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return internal::parithmetic_shift_right<N>(a);
+  }
+};
+template <typename Scalar, int N>
+struct functor_traits<scalar_shift_right_op<Scalar, N>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasShift };
+};
+
+/** \internal
+ * \brief Template functor to logically shift a scalar left by a number of bits
+ *
+ * \sa class CwiseUnaryOp, MatrixBase::shift_left()
+ */
+template <typename Scalar, int N>
+struct scalar_shift_left_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    return numext::logical_shift_left(a);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return internal::plogical_shift_left<N>(a);
+  }
+};
+template <typename Scalar, int N>
+struct functor_traits<scalar_shift_left_op<Scalar, N>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasShift };
+};
+
+/** \internal
+ * \brief Template functor to extract the real part of a complex
+ *
+ * \sa class CwiseUnaryOp, MatrixBase::real()
+ */
+template <typename Scalar>
+struct scalar_real_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const Scalar& a) const {
+    return numext::real(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_real_op<Scalar>> {
+  enum { Cost = 0, PacketAccess = false };
+};
+
+/** \internal
+ * \brief Template functor to extract the imaginary part of a complex
+ *
+ * \sa class CwiseUnaryOp, MatrixBase::imag()
+ */
+template <typename Scalar>
+struct scalar_imag_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const Scalar& a) const {
+    return numext::imag(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_imag_op<Scalar>> {
+  enum { Cost = 0, PacketAccess = false };
+};
+
+/** \internal
+ * \brief Template functor to extract the real part of a complex as a reference
+ *
+ * \sa class CwiseUnaryOp, MatrixBase::real()
+ */
+template <typename Scalar>
+struct scalar_real_ref_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const result_type& operator()(const Scalar& a) const {
+    return numext::real_ref(a);
+  }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type& operator()(Scalar& a) const {
+    return numext::real_ref(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_real_ref_op<Scalar>> {
+  enum { Cost = 0, PacketAccess = false };
+};
+
+/** \internal
+ * \brief Template functor to extract the imaginary part of a complex as a reference
+ *
+ * \sa class CwiseUnaryOp, MatrixBase::imag()
+ */
+template <typename Scalar>
+struct scalar_imag_ref_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type& operator()(Scalar& a) const {
+    return numext::imag_ref(a);
+  }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const result_type& operator()(const Scalar& a) const {
+    return numext::imag_ref(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_imag_ref_op<Scalar>> {
+  enum { Cost = 0, PacketAccess = false };
+};
+
+/** \internal
+ *
+ * \brief Template functor to compute the exponential of a scalar
+ *
+ * \sa class CwiseUnaryOp, Cwise::exp()
+ */
+template <typename Scalar>
+struct scalar_exp_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return internal::pexp(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pexp(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_exp_op<Scalar>> {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasExp,
+  // The following numbers are based on the AVX implementation.
+#ifdef EIGEN_VECTORIZE_FMA
+    // Haswell can issue 2 add/mul/madd per cycle.
+    Cost = (sizeof(Scalar) == 4
+                // float: 8 pmadd, 4 pmul, 2 padd/psub, 6 other
+                ? (8 * NumTraits<Scalar>::AddCost + 6 * NumTraits<Scalar>::MulCost)
+                // double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div,  13 other
+                : (14 * NumTraits<Scalar>::AddCost + 6 * NumTraits<Scalar>::MulCost +
+                   scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value))
+#else
+    Cost = (sizeof(Scalar) == 4
+                // float: 7 pmadd, 6 pmul, 4 padd/psub, 10 other
+                ? (21 * NumTraits<Scalar>::AddCost + 13 * NumTraits<Scalar>::MulCost)
+                // double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div,  13 other
+                : (23 * NumTraits<Scalar>::AddCost + 12 * NumTraits<Scalar>::MulCost +
+                   scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value))
+#endif
+  };
+};
+
+template <typename Scalar>
+struct scalar_exp2_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return internal::pexp2(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pexp2(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_exp2_op<Scalar>> {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasExp,
+    Cost = functor_traits<scalar_exp_op<Scalar>>::Cost  // TODO: measure cost of exp2
+  };
+};
+
+/** \internal
+ *
+ * \brief Template functor to compute the exponential of a scalar - 1.
+ *
+ * \sa class CwiseUnaryOp, ArrayBase::expm1()
+ */
+template <typename Scalar>
+struct scalar_expm1_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::expm1(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pexpm1(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_expm1_op<Scalar>> {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasExpm1,
+    Cost = functor_traits<scalar_exp_op<Scalar>>::Cost  // TODO: measure cost of expm1
+  };
+};
+
+/** \internal
+ *
+ * \brief Template functor to compute the logarithm of a scalar
+ *
+ * \sa class CwiseUnaryOp, ArrayBase::log()
+ */
+template <typename Scalar>
+struct scalar_log_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::log(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::plog(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_log_op<Scalar>> {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasLog,
+    Cost = (PacketAccess
+  // The following numbers are based on the AVX implementation.
+#ifdef EIGEN_VECTORIZE_FMA
+                // 8 pmadd, 6 pmul, 8 padd/psub, 16 other, can issue 2 add/mul/madd per cycle.
+                ? (20 * NumTraits<Scalar>::AddCost + 7 * NumTraits<Scalar>::MulCost)
+#else
+                // 8 pmadd, 6 pmul, 8 padd/psub, 20 other
+                ? (36 * NumTraits<Scalar>::AddCost + 14 * NumTraits<Scalar>::MulCost)
+#endif
+                // Measured cost of std::log.
+                : sizeof(Scalar) == 4 ? 40 : 85)
+  };
+};
+
+/** \internal
+ *
+ * \brief Template functor to compute the logarithm of 1 plus a scalar value
+ *
+ * \sa class CwiseUnaryOp, ArrayBase::log1p()
+ */
+template <typename Scalar>
+struct scalar_log1p_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::log1p(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::plog1p(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_log1p_op<Scalar>> {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasLog1p,
+    Cost = functor_traits<scalar_log_op<Scalar>>::Cost  // TODO: measure cost of log1p
+  };
+};
+
+/** \internal
+ *
+ * \brief Template functor to compute the base-10 logarithm of a scalar
+ *
+ * \sa class CwiseUnaryOp, Cwise::log10()
+ */
+template <typename Scalar>
+struct scalar_log10_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const {
+    EIGEN_USING_STD(log10) return log10(a);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::plog10(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_log10_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog10 };
+};
+
+/** \internal
+ *
+ * \brief Template functor to compute the base-2 logarithm of a scalar
+ *
+ * \sa class CwiseUnaryOp, Cwise::log2()
+ */
+template <typename Scalar>
+struct scalar_log2_op {
+  using RealScalar = typename NumTraits<Scalar>::Real;
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const {
+    return Scalar(RealScalar(EIGEN_LOG2E)) * numext::log(a);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::plog2(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_log2_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog };
+};
+
+/** \internal
+ * \brief Template functor to compute the square root of a scalar
+ * \sa class CwiseUnaryOp, Cwise::sqrt()
+ */
+template <typename Scalar>
+struct scalar_sqrt_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::sqrt(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::psqrt(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_sqrt_op<Scalar>> {
+  enum {
+#if EIGEN_FAST_MATH
+    // The following numbers are based on the AVX implementation.
+    Cost = (sizeof(Scalar) == 8 ? 28
+                                // 4 pmul, 1 pmadd, 3 other
+                                : (3 * NumTraits<Scalar>::AddCost + 5 * NumTraits<Scalar>::MulCost)),
+#else
+    // The following numbers are based on min VSQRT throughput on Haswell.
+    Cost = (sizeof(Scalar) == 8 ? 28 : 14),
+#endif
+    PacketAccess = packet_traits<Scalar>::HasSqrt
+  };
+};
+
+// Boolean specialization to eliminate -Wimplicit-conversion-floating-point-to-bool warnings.
+template <>
+struct scalar_sqrt_op<bool> {
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator()(const bool& a) const { return a; }
+  template <typename Packet>
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return a;
+  }
+};
+template <>
+struct functor_traits<scalar_sqrt_op<bool>> {
+  enum { Cost = 1, PacketAccess = packet_traits<bool>::Vectorizable };
+};
+
+/** \internal
+ * \brief Template functor to compute the cube root of a scalar
+ * \sa class CwiseUnaryOp, Cwise::sqrt()
+ */
+template <typename Scalar>
+struct scalar_cbrt_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::cbrt(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pcbrt(a);
+  }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_cbrt_op<Scalar>> {
+  enum { Cost = 20 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasCbrt };
+};
+
+/** \internal
+ * \brief Template functor to compute the reciprocal square root of a scalar
+ * \sa class CwiseUnaryOp, Cwise::rsqrt()
+ */
+template <typename Scalar>
+struct scalar_rsqrt_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::rsqrt(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::prsqrt(a);
+  }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_rsqrt_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasRsqrt };
+};
+
+/** \internal
+ * \brief Template functor to compute the cosine of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::cos()
+ */
+template <typename Scalar>
+struct scalar_cos_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::cos(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pcos(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_cos_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasCos };
+};
+
+/** \internal
+ * \brief Template functor to compute the sine of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::sin()
+ */
+template <typename Scalar>
+struct scalar_sin_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::sin(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::psin(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_sin_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasSin };
+};
+
+/** \internal
+ * \brief Template functor to compute the tan of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::tan()
+ */
+template <typename Scalar>
+struct scalar_tan_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::tan(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::ptan(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_tan_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasTan };
+};
+
+/** \internal
+ * \brief Template functor to compute the arc cosine of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::acos()
+ */
+template <typename Scalar>
+struct scalar_acos_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::acos(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pacos(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_acos_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasACos };
+};
+
+/** \internal
+ * \brief Template functor to compute the arc sine of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::asin()
+ */
+template <typename Scalar>
+struct scalar_asin_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::asin(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pasin(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_asin_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasASin };
+};
+
+/** \internal
+ * \brief Template functor to compute the atan of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::atan()
+ */
+template <typename Scalar>
+struct scalar_atan_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::atan(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::patan(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_atan_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasATan };
+};
+
+/** \internal
+ * \brief Template functor to compute the tanh of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::tanh()
+ */
+template <typename Scalar>
+struct scalar_tanh_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::tanh(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& x) const {
+    return ptanh(x);
+  }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_tanh_op<Scalar>> {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasTanh,
+    Cost = ((EIGEN_FAST_MATH && is_same<Scalar, float>::value)
+// The following numbers are based on the AVX implementation,
+#ifdef EIGEN_VECTORIZE_FMA
+                // Haswell can issue 2 add/mul/madd per cycle.
+                // 9 pmadd, 2 pmul, 1 div, 2 other
+                ? (2 * NumTraits<Scalar>::AddCost + 6 * NumTraits<Scalar>::MulCost +
+                   scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value)
+#else
+                ? (11 * NumTraits<Scalar>::AddCost + 11 * NumTraits<Scalar>::MulCost +
+                   scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value)
+#endif
+                // This number assumes a naive implementation of tanh
+                : (6 * NumTraits<Scalar>::AddCost + 3 * NumTraits<Scalar>::MulCost +
+                   2 * scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value +
+                   functor_traits<scalar_exp_op<Scalar>>::Cost))
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the atanh of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::atanh()
+ */
+template <typename Scalar>
+struct scalar_atanh_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::atanh(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& x) const {
+    return patanh(x);
+  }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_atanh_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasATanh };
+};
+
+/** \internal
+ * \brief Template functor to compute the sinh of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::sinh()
+ */
+template <typename Scalar>
+struct scalar_sinh_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::sinh(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::psinh(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_sinh_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasSinh };
+};
+
+/** \internal
+ * \brief Template functor to compute the asinh of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::asinh()
+ */
+template <typename Scalar>
+struct scalar_asinh_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::asinh(a); }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_asinh_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
+};
+
+/** \internal
+ * \brief Template functor to compute the cosh of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::cosh()
+ */
+template <typename Scalar>
+struct scalar_cosh_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::cosh(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pcosh(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_cosh_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasCosh };
+};
+
+/** \internal
+ * \brief Template functor to compute the acosh of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::acosh()
+ */
+template <typename Scalar>
+struct scalar_acosh_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::acosh(a); }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_acosh_op<Scalar>> {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
+};
+
+/** \internal
+ * \brief Template functor to compute the inverse of a scalar
+ * \sa class CwiseUnaryOp, Cwise::inverse()
+ */
+template <typename Scalar>
+struct scalar_inverse_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return Scalar(1) / a; }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::preciprocal(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_inverse_op<Scalar>> {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasDiv,
+    // If packet_traits<Scalar>::HasReciprocal then the Estimated cost is that
+    // of computing an approximation plus a single Newton-Raphson step, which
+    // consists of 1 pmul + 1 pmadd.
+    Cost = (packet_traits<Scalar>::HasReciprocal ? 4 * NumTraits<Scalar>::MulCost
+                                                 : scalar_div_cost<Scalar, PacketAccess>::value)
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the square of a scalar
+ * \sa class CwiseUnaryOp, Cwise::square()
+ */
+template <typename Scalar>
+struct scalar_square_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return a * a; }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pmul(a, a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_square_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul };
+};
+
+// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
+template <>
+struct scalar_square_op<bool> {
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator()(const bool& a) const { return a; }
+  template <typename Packet>
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return a;
+  }
+};
+template <>
+struct functor_traits<scalar_square_op<bool>> {
+  enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable };
+};
+
+/** \internal
+ * \brief Template functor to compute the cube of a scalar
+ * \sa class CwiseUnaryOp, Cwise::cube()
+ */
+template <typename Scalar>
+struct scalar_cube_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return a * a * a; }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pmul(a, pmul(a, a));
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_cube_op<Scalar>> {
+  enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul };
+};
+
+// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
+template <>
+struct scalar_cube_op<bool> {
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator()(const bool& a) const { return a; }
+  template <typename Packet>
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return a;
+  }
+};
+template <>
+struct functor_traits<scalar_cube_op<bool>> {
+  enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable };
+};
+
+/** \internal
+ * \brief Template functor to compute the rounded value of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::round()
+ */
+template <typename Scalar>
+struct scalar_round_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const { return numext::round(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pround(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_round_op<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasRound || NumTraits<Scalar>::IsInteger
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the floor of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::floor()
+ */
+template <typename Scalar>
+struct scalar_floor_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const { return numext::floor(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pfloor(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_floor_op<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasRound || NumTraits<Scalar>::IsInteger
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the rounded (with current rounding mode)  value of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::rint()
+ */
+template <typename Scalar>
+struct scalar_rint_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const { return numext::rint(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::print(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_rint_op<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasRound || NumTraits<Scalar>::IsInteger
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the ceil of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::ceil()
+ */
+template <typename Scalar>
+struct scalar_ceil_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const { return numext::ceil(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::pceil(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_ceil_op<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasRound || NumTraits<Scalar>::IsInteger
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the truncation of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::floor()
+ */
+template <typename Scalar>
+struct scalar_trunc_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const { return numext::trunc(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::ptrunc(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_trunc_op<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasRound || NumTraits<Scalar>::IsInteger
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute whether a scalar is NaN
+ * \sa class CwiseUnaryOp, ArrayBase::isnan()
+ */
+template <typename Scalar, bool UseTypedPredicate = false>
+struct scalar_isnan_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE bool operator()(const Scalar& a) const {
+#if defined(SYCL_DEVICE_ONLY)
+    return numext::isnan(a);
+#else
+    return numext::isnan EIGEN_NOT_A_MACRO(a);
+#endif
+  }
+};
+
+template <typename Scalar>
+struct scalar_isnan_op<Scalar, true> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+#if defined(SYCL_DEVICE_ONLY)
+    return (numext::isnan(a) ? ptrue(a) : pzero(a));
+#else
+    return (numext::isnan EIGEN_NOT_A_MACRO(a) ? ptrue(a) : pzero(a));
+#endif
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return pisnan(a);
+  }
+};
+
+template <typename Scalar, bool UseTypedPredicate>
+struct functor_traits<scalar_isnan_op<Scalar, UseTypedPredicate>> {
+  enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasCmp && UseTypedPredicate };
+};
+
+/** \internal
+ * \brief Template functor to check whether a scalar is +/-inf
+ * \sa class CwiseUnaryOp, ArrayBase::isinf()
+ */
+template <typename Scalar, bool UseTypedPredicate = false>
+struct scalar_isinf_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE bool operator()(const Scalar& a) const {
+#if defined(SYCL_DEVICE_ONLY)
+    return numext::isinf(a);
+#else
+    return (numext::isinf)(a);
+#endif
+  }
+};
+
+template <typename Scalar>
+struct scalar_isinf_op<Scalar, true> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+#if defined(SYCL_DEVICE_ONLY)
+    return (numext::isinf(a) ? ptrue(a) : pzero(a));
+#else
+    return (numext::isinf EIGEN_NOT_A_MACRO(a) ? ptrue(a) : pzero(a));
+#endif
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return pisinf(a);
+  }
+};
+template <typename Scalar, bool UseTypedPredicate>
+struct functor_traits<scalar_isinf_op<Scalar, UseTypedPredicate>> {
+  enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasCmp && UseTypedPredicate };
+};
+
+/** \internal
+ * \brief Template functor to check whether a scalar has a finite value
+ * \sa class CwiseUnaryOp, ArrayBase::isfinite()
+ */
+template <typename Scalar, bool UseTypedPredicate = false>
+struct scalar_isfinite_op {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE bool operator()(const Scalar& a) const {
+#if defined(SYCL_DEVICE_ONLY)
+    return numext::isfinite(a);
+#else
+    return (numext::isfinite)(a);
+#endif
+  }
+};
+
+template <typename Scalar>
+struct scalar_isfinite_op<Scalar, true> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+#if defined(SYCL_DEVICE_ONLY)
+    return (numext::isfinite(a) ? ptrue(a) : pzero(a));
+#else
+    return (numext::isfinite EIGEN_NOT_A_MACRO(a) ? ptrue(a) : pzero(a));
+#endif
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    constexpr Scalar inf = NumTraits<Scalar>::infinity();
+    return pcmp_lt(pabs(a), pset1<Packet>(inf));
+  }
+};
+template <typename Scalar, bool UseTypedPredicate>
+struct functor_traits<scalar_isfinite_op<Scalar, UseTypedPredicate>> {
+  enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasCmp && UseTypedPredicate };
+};
+
+/** \internal
+ * \brief Template functor to compute the logical not of a scalar as if it were a boolean
+ *
+ * \sa class CwiseUnaryOp, ArrayBase::operator!
+ */
+template <typename Scalar>
+struct scalar_boolean_not_op {
+  using result_type = Scalar;
+  // `false` any value `a` that satisfies `a == Scalar(0)`
+  // `true` is the complement of `false`
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    return a == Scalar(0) ? Scalar(1) : Scalar(0);
+  }
+  template <typename Packet>
+  EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    Packet not_a = pcmp_eq(a, pzero(a));
+    return pand(not_a, cst_one);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_boolean_not_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+template <typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct bitwise_unary_impl {
+  static constexpr size_t Size = sizeof(Scalar);
+  using uint_t = typename numext::get_integer_by_size<Size>::unsigned_type;
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar run_not(const Scalar& a) {
+    uint_t a_as_uint = numext::bit_cast<uint_t, Scalar>(a);
+    uint_t result = ~a_as_uint;
+    return numext::bit_cast<Scalar, uint_t>(result);
+  }
+};
+
+template <typename Scalar>
+struct bitwise_unary_impl<Scalar, true> {
+  using Real = typename NumTraits<Scalar>::Real;
+  static EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar run_not(const Scalar& a) {
+    Real real_result = bitwise_unary_impl<Real>::run_not(numext::real(a));
+    Real imag_result = bitwise_unary_impl<Real>::run_not(numext::imag(a));
+    return Scalar(real_result, imag_result);
+  }
+};
+
+/** \internal
+ * \brief Template functor to compute the bitwise not of a scalar
+ *
+ * \sa class CwiseUnaryOp, ArrayBase::operator~
+ */
+template <typename Scalar>
+struct scalar_bitwise_not_op {
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::RequireInitialization,
+                      BITWISE OPERATIONS MAY ONLY BE PERFORMED ON PLAIN DATA TYPES)
+  EIGEN_STATIC_ASSERT((!internal::is_same<Scalar, bool>::value), DONT USE BITWISE OPS ON BOOLEAN TYPES)
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    return bitwise_unary_impl<Scalar>::run_not(a);
+  }
+  template <typename Packet>
+  EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return pandnot(ptrue(a), a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bitwise_not_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = true };
+};
+
+/** \internal
+ * \brief Template functor to compute the signum of a scalar
+ * \sa class CwiseUnaryOp, Cwise::sign()
+ */
+template <typename Scalar>
+struct scalar_sign_op {
+  EIGEN_DEVICE_FUNC constexpr inline Scalar operator()(const Scalar& a) const { return numext::sign(a); }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::psign(a);
+  }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_sign_op<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::IsComplex ? (8 * NumTraits<Scalar>::MulCost)  // roughly
+                                        : (3 * NumTraits<Scalar>::AddCost),
+    PacketAccess = packet_traits<Scalar>::HasSign && packet_traits<Scalar>::Vectorizable
+  };
+};
+
+// Real-valued implementation.
+template <typename T, typename EnableIf = void>
+struct scalar_logistic_op_impl {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const { return packetOp(x); }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    const Packet one = pset1<Packet>(T(1));
+    const Packet inf = pset1<Packet>(NumTraits<T>::infinity());
+    const Packet e = pexp(x);
+    const Packet inf_mask = pcmp_eq(e, inf);
+    return pselect(inf_mask, one, pdiv(e, padd(one, e)));
+  }
+};
+
+// Complex-valud implementation.
+template <typename T>
+struct scalar_logistic_op_impl<T, std::enable_if_t<NumTraits<T>::IsComplex>> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE T operator()(const T& x) const {
+    const T e = numext::exp(x);
+    return (numext::isinf)(numext::real(e)) ? T(1) : e / (e + T(1));
+  }
+};
+
+/** \internal
+ * \brief Template functor to compute the logistic function of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::logistic()
+ */
+template <typename T>
+struct scalar_logistic_op : scalar_logistic_op_impl<T> {};
+
+// TODO(rmlarsen): Enable the following on host when integer_packet is defined
+// for the relevant packet types.
+#ifndef EIGEN_GPUCC
+
+/** \internal
+ * \brief Template specialization of the logistic function for float.
+ * Computes S(x) = exp(x) / (1 + exp(x)), where exp(x) is implemented
+ * using an algorithm partly adopted from the implementation of
+ * pexp_float. See the individual steps described in the code below.
+ * Note that compared to pexp, we use an additional outer multiplicative
+ * range reduction step using the identity exp(x) = exp(x/2)^2.
+ * This prevert us from having to call ldexp on values that could produce
+ * a denormal result, which allows us to call the faster implementation in
+ * pldexp_fast_impl<Packet>::run(p, m).
+ * The final squaring, however, doubles the error bound on the final
+ * approximation. Exhaustive testing shows that we have a worst case error
+ * of 4.5 ulps (compared to computing S(x) in double precision), which is
+ * acceptable.
+ */
+template <>
+struct scalar_logistic_op<float> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator()(const float& x) const {
+    // Truncate at the first point where the interpolant is exactly one.
+    const float cst_exp_hi = 16.6355324f;
+    const float e = numext::exp(numext::mini(x, cst_exp_hi));
+    return e / (1.0f + e);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& _x) const {
+    const Packet cst_zero = pset1<Packet>(0.0f);
+    const Packet cst_one = pset1<Packet>(1.0f);
+    const Packet cst_half = pset1<Packet>(0.5f);
+    // Truncate at the first point where the interpolant is exactly one.
+    const Packet cst_exp_hi = pset1<Packet>(16.6355324f);
+    const Packet cst_exp_lo = pset1<Packet>(-104.f);
+
+    // Clamp x to the non-trivial range where S(x). Outside this
+    // interval the correctly rounded value of S(x) is either zero
+    // or one.
+    Packet zero_mask = pcmp_lt(_x, cst_exp_lo);
+    Packet x = pmin(_x, cst_exp_hi);
+
+    // 1. Multiplicative range reduction:
+    // Reduce the range of x by a factor of 2. This avoids having
+    // to compute exp(x) accurately where the result is a denormalized
+    // value.
+    x = pmul(x, cst_half);
+
+    // 2. Subtractive range reduction:
+    // Express exp(x) as exp(m*ln(2) + r) = 2^m*exp(r), start by extracting
+    // m = floor(x/ln(2) + 0.5), such that x = m*ln(2) + r.
+    const Packet cst_cephes_LOG2EF = pset1<Packet>(1.44269504088896341f);
+    Packet m = pfloor(pmadd(x, cst_cephes_LOG2EF, cst_half));
+    // Get r = x - m*ln(2). We use a trick from Cephes where the term
+    // m*ln(2) is subtracted out in two parts, m*C1+m*C2 = m*ln(2),
+    // to avoid accumulating truncation errors.
+    const Packet cst_cephes_exp_C1 = pset1<Packet>(-0.693359375f);
+    const Packet cst_cephes_exp_C2 = pset1<Packet>(2.12194440e-4f);
+    Packet r = pmadd(m, cst_cephes_exp_C1, x);
+    r = pmadd(m, cst_cephes_exp_C2, r);
+
+    // 3. Compute an approximation to exp(r) using a degree 5 minimax polynomial.
+    // We compute even and odd terms separately to increase instruction level
+    // parallelism.
+    Packet r2 = pmul(r, r);
+    const Packet cst_p2 = pset1<Packet>(0.49999141693115234375f);
+    const Packet cst_p3 = pset1<Packet>(0.16666877269744873046875f);
+    const Packet cst_p4 = pset1<Packet>(4.1898667812347412109375e-2f);
+    const Packet cst_p5 = pset1<Packet>(8.33471305668354034423828125e-3f);
+
+    const Packet p_even = pmadd(r2, cst_p4, cst_p2);
+    const Packet p_odd = pmadd(r2, cst_p5, cst_p3);
+    const Packet p_low = padd(r, cst_one);
+    Packet p = pmadd(r, p_odd, p_even);
+    p = pmadd(r2, p, p_low);
+
+    // 4. Undo subtractive range reduction exp(m*ln(2) + r) = 2^m * exp(r).
+    Packet e = pldexp_fast(p, m);
+
+    // 5. Undo multiplicative range reduction by using exp(r) = exp(r/2)^2.
+    e = pmul(e, e);
+
+    // Return exp(x) / (1 + exp(x))
+    return pselect(zero_mask, cst_zero, pdiv(e, padd(cst_one, e)));
+  }
+};
+#endif  // #ifndef EIGEN_GPU_COMPILE_PHASE
+
+template <typename T>
+struct functor_traits<scalar_logistic_op<T>> {
+  enum {
+    // The cost estimate for float here here is for the common(?) case where
+    // all arguments are greater than -9.
+    Cost = scalar_div_cost<T, packet_traits<T>::HasDiv>::value +
+           (internal::is_same<T, float>::value ? NumTraits<T>::AddCost * 15 + NumTraits<T>::MulCost * 11
+                                               : NumTraits<T>::AddCost * 2 + functor_traits<scalar_exp_op<T>>::Cost),
+    PacketAccess = !NumTraits<T>::IsComplex && packet_traits<T>::HasAdd && packet_traits<T>::HasDiv &&
+                   (internal::is_same<T, float>::value
+                        ? packet_traits<T>::HasMul && packet_traits<T>::HasMax && packet_traits<T>::HasMin
+                        : packet_traits<T>::HasNegate && packet_traits<T>::HasExp)
+  };
+};
+
+template <typename Scalar, typename ExponentScalar, bool IsBaseInteger = NumTraits<Scalar>::IsInteger,
+          bool IsExponentInteger = NumTraits<ExponentScalar>::IsInteger,
+          bool IsBaseComplex = NumTraits<Scalar>::IsComplex,
+          bool IsExponentComplex = NumTraits<ExponentScalar>::IsComplex>
+struct scalar_unary_pow_op {
+  typedef typename internal::promote_scalar_arg<
+      Scalar, ExponentScalar,
+      internal::has_ReturnType<ScalarBinaryOpTraits<Scalar, ExponentScalar, scalar_unary_pow_op>>::value>::type
+      PromotedExponent;
+  typedef typename ScalarBinaryOpTraits<Scalar, PromotedExponent, scalar_unary_pow_op>::ReturnType result_type;
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE scalar_unary_pow_op(const ExponentScalar& exponent)
+      : m_exponent(exponent) {}
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE result_type operator()(const Scalar& a) const {
+    EIGEN_USING_STD(pow);
+    return static_cast<result_type>(pow(a, m_exponent));
+  }
+
+ private:
+  const ExponentScalar m_exponent;
+  scalar_unary_pow_op() {}
+};
+
+template <typename T>
+constexpr int exponent_digits() {
+  return CHAR_BIT * sizeof(T) - NumTraits<T>::digits() - NumTraits<T>::IsSigned;
+}
+
+template <typename From, typename To>
+struct is_floating_exactly_representable {
+  // TODO(rmlarsen): Add radix to NumTraits and enable this check.
+  // (NumTraits<To>::radix == NumTraits<From>::radix) &&
+  static constexpr bool value =
+      (exponent_digits<To>() >= exponent_digits<From>() && NumTraits<To>::digits() >= NumTraits<From>::digits());
+};
+
+// Specialization for real, non-integer types, non-complex types.
+template <typename Scalar, typename ExponentScalar>
+struct scalar_unary_pow_op<Scalar, ExponentScalar, false, false, false, false> {
+  template <bool IsExactlyRepresentable = is_floating_exactly_representable<ExponentScalar, Scalar>::value>
+  std::enable_if_t<IsExactlyRepresentable, void> check_is_representable() const {}
+
+  // Issue a deprecation warning if we do a narrowing conversion on the exponent.
+  template <bool IsExactlyRepresentable = is_floating_exactly_representable<ExponentScalar, Scalar>::value>
+  EIGEN_DEPRECATED std::enable_if_t<!IsExactlyRepresentable, void> check_is_representable() const {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_unary_pow_op(const ExponentScalar& exponent)
+      : m_exponent(static_cast<Scalar>(exponent)) {
+    check_is_representable();
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    EIGEN_USING_STD(pow);
+    return static_cast<Scalar>(pow(a, m_exponent));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return unary_pow_impl<Packet, Scalar>::run(a, m_exponent);
+  }
+
+ private:
+  const Scalar m_exponent;
+  scalar_unary_pow_op() {}
+};
+
+template <typename Scalar, typename ExponentScalar, bool BaseIsInteger>
+struct scalar_unary_pow_op<Scalar, ExponentScalar, BaseIsInteger, true, false, false> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE scalar_unary_pow_op(const ExponentScalar& exponent)
+      : m_exponent(exponent) {}
+  // TODO: error handling logic for complex^real_integer
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    return unary_pow_impl<Scalar, ExponentScalar>::run(a, m_exponent);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return unary_pow_impl<Packet, ExponentScalar>::run(a, m_exponent);
+  }
+
+ private:
+  const ExponentScalar m_exponent;
+  scalar_unary_pow_op() {}
+};
+
+template <typename Scalar, typename ExponentScalar>
+struct functor_traits<scalar_unary_pow_op<Scalar, ExponentScalar>> {
+  enum {
+    GenPacketAccess = functor_traits<scalar_pow_op<Scalar, ExponentScalar>>::PacketAccess,
+    IntPacketAccess = !NumTraits<Scalar>::IsComplex && packet_traits<Scalar>::HasMul &&
+                      (packet_traits<Scalar>::HasDiv || NumTraits<Scalar>::IsInteger) && packet_traits<Scalar>::HasCmp,
+    PacketAccess = NumTraits<ExponentScalar>::IsInteger ? IntPacketAccess : (IntPacketAccess && GenPacketAccess),
+    Cost = functor_traits<scalar_pow_op<Scalar, ExponentScalar>>::Cost
+  };
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_FUNCTORS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h
new file mode 100644
index 0000000..cb32270
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h
@@ -0,0 +1,2355 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERAL_BLOCK_PANEL_H
+#define EIGEN_GENERAL_BLOCK_PANEL_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+enum GEBPPacketSizeType { GEBPPacketFull = 0, GEBPPacketHalf, GEBPPacketQuarter };
+
+template <typename LhsScalar_, typename RhsScalar_, bool ConjLhs_ = false, bool ConjRhs_ = false,
+          int Arch = Architecture::Target, int PacketSize_ = GEBPPacketFull>
+class gebp_traits;
+
+/** \internal \returns b if a<=0, and returns a otherwise. */
+inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b) { return a <= 0 ? b : a; }
+
+#if defined(EIGEN_DEFAULT_L1_CACHE_SIZE)
+#define EIGEN_SET_DEFAULT_L1_CACHE_SIZE(val) EIGEN_DEFAULT_L1_CACHE_SIZE
+#else
+#define EIGEN_SET_DEFAULT_L1_CACHE_SIZE(val) val
+#endif  // defined(EIGEN_DEFAULT_L1_CACHE_SIZE)
+
+#if defined(EIGEN_DEFAULT_L2_CACHE_SIZE)
+#define EIGEN_SET_DEFAULT_L2_CACHE_SIZE(val) EIGEN_DEFAULT_L2_CACHE_SIZE
+#else
+#define EIGEN_SET_DEFAULT_L2_CACHE_SIZE(val) val
+#endif  // defined(EIGEN_DEFAULT_L2_CACHE_SIZE)
+
+#if defined(EIGEN_DEFAULT_L3_CACHE_SIZE)
+#define EIGEN_SET_DEFAULT_L3_CACHE_SIZE(val) EIGEN_DEFAULT_L3_CACHE_SIZE
+#else
+#define EIGEN_SET_DEFAULT_L3_CACHE_SIZE(val) val
+#endif  // defined(EIGEN_DEFAULT_L3_CACHE_SIZE)
+
+#if EIGEN_ARCH_i386_OR_x86_64
+const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(32 * 1024);
+const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(256 * 1024);
+const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(2 * 1024 * 1024);
+#elif EIGEN_ARCH_PPC
+const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(64 * 1024);
+#ifdef _ARCH_PWR10
+const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(2 * 1024 * 1024);
+const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(8 * 1024 * 1024);
+#else
+const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(512 * 1024);
+const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(4 * 1024 * 1024);
+#endif
+#else
+const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(16 * 1024);
+const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(512 * 1024);
+const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(512 * 1024);
+#endif
+
+#undef EIGEN_SET_DEFAULT_L1_CACHE_SIZE
+#undef EIGEN_SET_DEFAULT_L2_CACHE_SIZE
+#undef EIGEN_SET_DEFAULT_L3_CACHE_SIZE
+
+/** \internal */
+struct CacheSizes {
+  CacheSizes() : m_l1(-1), m_l2(-1), m_l3(-1) {
+    int l1CacheSize, l2CacheSize, l3CacheSize;
+    queryCacheSizes(l1CacheSize, l2CacheSize, l3CacheSize);
+    m_l1 = manage_caching_sizes_helper(l1CacheSize, defaultL1CacheSize);
+    m_l2 = manage_caching_sizes_helper(l2CacheSize, defaultL2CacheSize);
+    m_l3 = manage_caching_sizes_helper(l3CacheSize, defaultL3CacheSize);
+  }
+
+  std::ptrdiff_t m_l1;
+  std::ptrdiff_t m_l2;
+  std::ptrdiff_t m_l3;
+};
+
+/** \internal */
+inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1, std::ptrdiff_t* l2, std::ptrdiff_t* l3) {
+  static CacheSizes m_cacheSizes;
+
+  if (action == SetAction) {
+    // set the cpu cache size and cache all block sizes from a global cache size in byte
+    eigen_internal_assert(l1 != 0 && l2 != 0);
+    m_cacheSizes.m_l1 = *l1;
+    m_cacheSizes.m_l2 = *l2;
+    m_cacheSizes.m_l3 = *l3;
+  } else if (action == GetAction) {
+    eigen_internal_assert(l1 != 0 && l2 != 0);
+    *l1 = m_cacheSizes.m_l1;
+    *l2 = m_cacheSizes.m_l2;
+    *l3 = m_cacheSizes.m_l3;
+  } else {
+    eigen_internal_assert(false);
+  }
+}
+
+/* Helper for computeProductBlockingSizes.
+ *
+ * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar,
+ * this function computes the blocking size parameters along the respective dimensions
+ * for matrix products and related algorithms. The blocking sizes depends on various
+ * parameters:
+ * - the L1 and L2 cache sizes,
+ * - the register level blocking sizes defined by gebp_traits,
+ * - the number of scalars that fit into a packet (when vectorization is enabled).
+ *
+ * \sa setCpuCacheSizes */
+
+template <typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
+void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index num_threads = 1) {
+  typedef gebp_traits<LhsScalar, RhsScalar> Traits;
+
+  // Explanations:
+  // Let's recall that the product algorithms form mc x kc vertical panels A' on the lhs and
+  // kc x nc blocks B' on the rhs. B' has to fit into L2/L3 cache. Moreover, A' is processed
+  // per mr x kc horizontal small panels where mr is the blocking size along the m dimension
+  // at the register level. This small horizontal panel has to stay within L1 cache.
+  std::ptrdiff_t l1, l2, l3;
+  manage_caching_sizes(GetAction, &l1, &l2, &l3);
+#ifdef EIGEN_VECTORIZE_AVX512
+  const std::ptrdiff_t phys_l1 = l1;
+  // We need to find a rationale for that, but without this adjustment,
+  // performance with AVX512 is pretty bad, like -20% slower.
+  // One reason is that with increasing packet-size, the blocking size k
+  // has to become pretty small if we want that 1 lhs panel fit within L1.
+  // For instance, with the 3pX4 kernel and double, the size of the lhs+rhs panels are:
+  //   k*(3*64 + 4*8) Bytes, with l1=32kBytes, and k%8=0, we have k=144.
+  // This is quite small for a good reuse of the accumulation registers.
+  l1 *= 4;
+#endif
+
+  if (num_threads > 1) {
+    typedef typename Traits::ResScalar ResScalar;
+    enum {
+      kdiv = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
+      ksub = Traits::mr * (Traits::nr * sizeof(ResScalar)),
+      kr = 8,
+      mr = Traits::mr,
+      nr = Traits::nr
+    };
+    // Increasing k gives us more time to prefetch the content of the "C"
+    // registers. However once the latency is hidden there is no point in
+    // increasing the value of k, so we'll cap it at 320 (value determined
+    // experimentally).
+    // To avoid that k vanishes, we make k_cache at least as big as kr
+    const Index k_cache = numext::maxi<Index>(kr, (numext::mini<Index>)((l1 - ksub) / kdiv, 320));
+    if (k_cache < k) {
+      k = k_cache - (k_cache % kr);
+      eigen_internal_assert(k > 0);
+    }
+
+    const Index n_cache = (l2 - l1) / (nr * sizeof(RhsScalar) * k);
+    const Index n_per_thread = numext::div_ceil(n, num_threads);
+    if (n_cache <= n_per_thread) {
+      // Don't exceed the capacity of the l2 cache.
+      eigen_internal_assert(n_cache >= static_cast<Index>(nr));
+      n = n_cache - (n_cache % nr);
+      eigen_internal_assert(n > 0);
+    } else {
+      n = (numext::mini<Index>)(n, (n_per_thread + nr - 1) - ((n_per_thread + nr - 1) % nr));
+    }
+
+    if (l3 > l2) {
+      // l3 is shared between all cores, so we'll give each thread its own chunk of l3.
+      const Index m_cache = (l3 - l2) / (sizeof(LhsScalar) * k * num_threads);
+      const Index m_per_thread = numext::div_ceil(m, num_threads);
+      if (m_cache < m_per_thread && m_cache >= static_cast<Index>(mr)) {
+        m = m_cache - (m_cache % mr);
+        eigen_internal_assert(m > 0);
+      } else {
+        m = (numext::mini<Index>)(m, (m_per_thread + mr - 1) - ((m_per_thread + mr - 1) % mr));
+      }
+    }
+  } else {
+    // In unit tests we do not want to use extra large matrices,
+    // so we reduce the cache size to check the blocking strategy is not flawed
+#ifdef EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS
+    l1 = 9 * 1024;
+    l2 = 32 * 1024;
+    l3 = 512 * 1024;
+#endif
+
+    // Early return for small problems because the computation below are time consuming for small problems.
+    // Perhaps it would make more sense to consider k*n*m?
+    // Note that for very tiny problem, this function should be bypassed anyway
+    // because we use the coefficient-based implementation for them.
+    if ((numext::maxi)(k, (numext::maxi)(m, n)) < 48) return;
+
+    typedef typename Traits::ResScalar ResScalar;
+    enum {
+      k_peeling = 8,
+      k_div = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
+      k_sub = Traits::mr * (Traits::nr * sizeof(ResScalar))
+    };
+
+    // ---- 1st level of blocking on L1, yields kc ----
+
+    // Blocking on the third dimension (i.e., k) is chosen so that an horizontal panel
+    // of size mr x kc of the lhs plus a vertical panel of kc x nr of the rhs both fits within L1 cache.
+    // We also include a register-level block of the result (mx x nr).
+    // (In an ideal world only the lhs panel would stay in L1)
+    // Moreover, kc has to be a multiple of 8 to be compatible with loop peeling, leading to a maximum blocking size of:
+    const Index max_kc = numext::maxi<Index>(((l1 - k_sub) / k_div) & (~(k_peeling - 1)), 1);
+    const Index old_k = k;
+    if (k > max_kc) {
+      // We are really blocking on the third dimension:
+      // -> reduce blocking size to make sure the last block is as large as possible
+      //    while keeping the same number of sweeps over the result.
+      k = (k % max_kc) == 0 ? max_kc
+                            : max_kc - k_peeling * ((max_kc - 1 - (k % max_kc)) / (k_peeling * (k / max_kc + 1)));
+
+      eigen_internal_assert(((old_k / k) == (old_k / max_kc)) && "the number of sweeps has to remain the same");
+    }
+
+#ifdef EIGEN_VECTORIZE_AVX512
+    // The l1 *= 4 inflation above allows larger kc for better accumulator reuse,
+    // but can overfill the physical L1. Recompute max_kc using 85% of actual L1
+    // to leave headroom for RHS streaming, prefetch buffers, and stack.
+    {
+      const Index phys_l1_eff = phys_l1 * 85 / 100;
+      const Index max_kc_phys = numext::maxi<Index>(((phys_l1_eff - k_sub) / k_div) & (~(k_peeling - 1)), k_peeling);
+      if (max_kc_phys < k) {
+        k = (old_k % max_kc_phys) == 0 ? max_kc_phys
+                                       : max_kc_phys - k_peeling * ((max_kc_phys - 1 - (old_k % max_kc_phys)) /
+                                                                    (k_peeling * (old_k / max_kc_phys + 1)));
+      }
+    }
+#endif
+
+// ---- 2nd level of blocking on max(L2,L3), yields nc ----
+
+// Estimate the effective per-core L2 capacity for 2nd-level blocking.
+// Use 1.5x the runtime-detected L2 size. The extra 50% accounts for data
+// that spills to L3 but remains accessible with low latency. This matches
+// the empirically-tuned constant (1.5MB) previously used when L2 was 1MB.
+#ifdef EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS
+    const Index actual_l2 = l3;
+#else
+    const Index actual_l2 = l2 * 3 / 2;
+#endif
+
+    // Here, nc is chosen such that a block of kc x nc of the rhs fit within half of L2.
+    // The second half is implicitly reserved to access the result and lhs coefficients.
+    // When k<max_kc, then nc can grow without bound. In practice, it seems to be fruitful
+    // to limit this growth: we bound nc growth to a factor of 1.5x.
+    // However, if the entire lhs block fit within L1, then we are not going to block on the rows at all,
+    // and it becomes fruitful to keep the packed rhs blocks in L1 if there is enough remaining space.
+    Index max_nc;
+    const Index lhs_bytes = m * k * sizeof(LhsScalar);
+    const Index remaining_l1 = l1 - k_sub - lhs_bytes;
+    if (remaining_l1 >= Index(Traits::nr * sizeof(RhsScalar)) * k) {
+      // L1 blocking
+      max_nc = remaining_l1 / (k * sizeof(RhsScalar));
+    } else {
+      // L2 blocking
+      max_nc = (3 * actual_l2) / (2 * 2 * max_kc * sizeof(RhsScalar));
+    }
+    // WARNING Below, we assume that Traits::nr is a power of two.
+    Index nc = numext::mini<Index>(actual_l2 / (2 * k * sizeof(RhsScalar)), max_nc) & (~(Traits::nr - 1));
+    if (n > nc) {
+      // We are really blocking over the columns:
+      // -> reduce blocking size to make sure the last block is as large as possible
+      //    while keeping the same number of sweeps over the packed lhs.
+      //    Here we allow one more sweep if this gives us a perfect match, thus the commented "-1"
+      n = (n % nc) == 0 ? nc : (nc - Traits::nr * ((nc /*-1*/ - (n % nc)) / (Traits::nr * (n / nc + 1))));
+    } else if (old_k == k) {
+      // So far, no blocking at all, i.e., kc==k, and nc==n.
+      // In this case, let's perform a blocking over the rows such that the packed lhs data is kept in cache L1/L2
+      // TODO: part of this blocking strategy is now implemented within the kernel itself, so the L1-based heuristic
+      // here should be obsolete.
+      Index problem_size = k * n * sizeof(LhsScalar);
+      Index actual_lm = actual_l2;
+      Index max_mc = m;
+      if (problem_size <= 1024) {
+        // problem is small enough to keep in L1
+        // Let's choose m such that lhs's block fit in 1/3 of L1
+        actual_lm = l1;
+      } else if (l3 != 0 && problem_size <= 32768) {
+        // we have both L2 and L3, and problem is small enough to be kept in L2
+        // Let's choose m such that lhs's block fit in 1/3 of L2
+        actual_lm = l2;
+        max_mc = (numext::mini<Index>)(576, max_mc);
+      }
+      Index mc = (numext::mini<Index>)(actual_lm / (3 * k * sizeof(LhsScalar)), max_mc);
+      if (mc > Traits::mr)
+        mc -= mc % Traits::mr;
+      else if (mc == 0)
+        return;
+      m = (m % mc) == 0 ? mc : (mc - Traits::mr * ((mc /*-1*/ - (m % mc)) / (Traits::mr * (m / mc + 1))));
+    }
+  }
+}
+
+template <typename Index>
+inline bool useSpecificBlockingSizes(Index& k, Index& m, Index& n) {
+#ifdef EIGEN_TEST_SPECIFIC_BLOCKING_SIZES
+  if (EIGEN_TEST_SPECIFIC_BLOCKING_SIZES) {
+    k = numext::mini<Index>(k, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_K);
+    m = numext::mini<Index>(m, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_M);
+    n = numext::mini<Index>(n, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_N);
+    return true;
+  }
+#else
+  EIGEN_UNUSED_VARIABLE(k)
+  EIGEN_UNUSED_VARIABLE(m)
+  EIGEN_UNUSED_VARIABLE(n)
+#endif
+  return false;
+}
+
+/** \brief Computes the blocking parameters for a m x k times k x n matrix product
+ *
+ * \param[in,out] k Input: the third dimension of the product. Output: the blocking size along the same dimension.
+ * \param[in,out] m Input: the number of rows of the left hand side. Output: the blocking size along the same dimension.
+ * \param[in,out] n Input: the number of columns of the right hand side. Output: the blocking size along the same
+ *                         dimension.
+ * \param[in] num_threads Input: the number of threads used for the computation.
+ *
+ * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar,
+ * this function computes the blocking size parameters along the respective dimensions
+ * for matrix products and related algorithms.
+ *
+ * The blocking size parameters may be evaluated:
+ *   - either by a heuristic based on cache sizes;
+ *   - or using fixed prescribed values (for testing purposes).
+ *
+ * \sa setCpuCacheSizes */
+
+template <typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
+void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1) {
+  if (!useSpecificBlockingSizes(k, m, n)) {
+    evaluateProductBlockingSizesHeuristic<LhsScalar, RhsScalar, KcFactor, Index>(k, m, n, num_threads);
+  }
+}
+
+template <typename LhsScalar, typename RhsScalar, typename Index>
+inline void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1) {
+  computeProductBlockingSizes<LhsScalar, RhsScalar, 1, Index>(k, m, n, num_threads);
+}
+
+template <typename RhsPacket, typename RhsPacketx4, int registers_taken>
+struct RhsPanelHelper {
+ private:
+  static constexpr int remaining_registers =
+      (std::max)(int(EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS) - registers_taken, 0);
+
+ public:
+  typedef std::conditional_t<remaining_registers >= 4, RhsPacketx4, RhsPacket> type;
+};
+
+template <typename Packet>
+struct QuadPacket {
+  Packet B_0, B1, B2, B3;
+  const Packet& get(const FixedInt<0>&) const { return B_0; }
+  const Packet& get(const FixedInt<1>&) const { return B1; }
+  const Packet& get(const FixedInt<2>&) const { return B2; }
+  const Packet& get(const FixedInt<3>&) const { return B3; }
+};
+
+template <int N, typename T1, typename T2, typename T3>
+struct packet_conditional {
+  typedef T3 type;
+};
+
+template <typename T1, typename T2, typename T3>
+struct packet_conditional<GEBPPacketFull, T1, T2, T3> {
+  typedef T1 type;
+};
+
+template <typename T1, typename T2, typename T3>
+struct packet_conditional<GEBPPacketHalf, T1, T2, T3> {
+  typedef T2 type;
+};
+
+#define PACKET_DECL_COND_POSTFIX(postfix, name, packet_size)                                               \
+  typedef typename packet_conditional<                                                                     \
+      packet_size, typename packet_traits<name##Scalar>::type, typename packet_traits<name##Scalar>::half, \
+      typename unpacket_traits<typename packet_traits<name##Scalar>::half>::half>::type name##Packet##postfix
+
+#define PACKET_DECL_COND(name, packet_size)                                                                \
+  typedef typename packet_conditional<                                                                     \
+      packet_size, typename packet_traits<name##Scalar>::type, typename packet_traits<name##Scalar>::half, \
+      typename unpacket_traits<typename packet_traits<name##Scalar>::half>::half>::type name##Packet
+
+#define PACKET_DECL_COND_SCALAR_POSTFIX(postfix, packet_size)                                  \
+  typedef typename packet_conditional<                                                         \
+      packet_size, typename packet_traits<Scalar>::type, typename packet_traits<Scalar>::half, \
+      typename unpacket_traits<typename packet_traits<Scalar>::half>::half>::type ScalarPacket##postfix
+
+#define PACKET_DECL_COND_SCALAR(packet_size)                                                   \
+  typedef typename packet_conditional<                                                         \
+      packet_size, typename packet_traits<Scalar>::type, typename packet_traits<Scalar>::half, \
+      typename unpacket_traits<typename packet_traits<Scalar>::half>::half>::type ScalarPacket
+
+/* Vectorization logic
+ *  real*real: unpack rhs to constant packets, ...
+ *
+ *  cd*cd : unpack rhs to (b_r,b_r), (b_i,b_i), mul to get (a_r b_r,a_i b_r) (a_r b_i,a_i b_i),
+ *          storing each res packet into two packets (2x2),
+ *          at the end combine them: swap the second and addsub them
+ *  cf*cf : same but with 2x4 blocks
+ *  cplx*real : unpack rhs to constant packets, ...
+ *  real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual
+ */
+template <typename LhsScalar_, typename RhsScalar_, bool ConjLhs_, bool ConjRhs_, int Arch, int PacketSize_>
+class gebp_traits {
+ public:
+  typedef LhsScalar_ LhsScalar;
+  typedef RhsScalar_ RhsScalar;
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+
+  PACKET_DECL_COND_POSTFIX(_, Lhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Rhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Res, PacketSize_);
+
+  enum {
+    ConjLhs = ConjLhs_,
+    ConjRhs = ConjRhs_,
+    Vectorizable = unpacket_traits<LhsPacket_>::vectorizable && unpacket_traits<RhsPacket_>::vectorizable,
+    LhsPacketSize = Vectorizable ? unpacket_traits<LhsPacket_>::size : 1,
+    RhsPacketSize = Vectorizable ? unpacket_traits<RhsPacket_>::size : 1,
+    ResPacketSize = Vectorizable ? unpacket_traits<ResPacket_>::size : 1,
+
+    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
+
+    // register block size along the N direction must be 1 or 4
+    nr = 4,
+
+    // register block size along the M direction (currently, this one cannot be modified)
+    default_mr = (plain_enum_min(16, NumberOfRegisters) / 2 / nr) * LhsPacketSize,
+#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && \
+    !defined(EIGEN_VECTORIZE_VSX) && ((!EIGEN_COMP_MSVC) || (EIGEN_COMP_MSVC >= 1914))
+    // we assume 16 registers or more
+    // See bug 992, if the scalar type is not vectorizable but that EIGEN_HAS_SINGLE_INSTRUCTION_MADD is defined,
+    // then using 3*LhsPacketSize triggers non-implemented paths in syrk.
+    // Bug 1515: MSVC prior to v19.14 yields to register spilling.
+    mr = Vectorizable ? 3 * LhsPacketSize : default_mr,
+#else
+    mr = default_mr,
+#endif
+
+    LhsProgress = LhsPacketSize,
+    RhsProgress = 1
+  };
+
+  typedef std::conditional_t<Vectorizable, LhsPacket_, LhsScalar> LhsPacket;
+  typedef std::conditional_t<Vectorizable, RhsPacket_, RhsScalar> RhsPacket;
+  typedef std::conditional_t<Vectorizable, ResPacket_, ResScalar> ResPacket;
+  typedef LhsPacket LhsPacket4Packing;
+
+  typedef QuadPacket<RhsPacket> RhsPacketx4;
+  typedef ResPacket AccPacket;
+
+  EIGEN_STRONG_INLINE void initAcc(AccPacket& p) { p = pset1<ResPacket>(ResScalar(0)); }
+
+  template <typename RhsPacketType>
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const {
+    dest = pset1<RhsPacketType>(*b);
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const {
+    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
+  }
+
+  template <typename RhsPacketType>
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const {
+    loadRhs(b, dest);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const { dest = ploadquad<RhsPacket>(b); }
+
+  template <typename LhsPacketType>
+  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacketType& dest) const {
+    dest = pload<LhsPacketType>(a);
+  }
+
+  template <typename LhsPacketType>
+  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const {
+    dest = ploadu<LhsPacketType>(a);
+  }
+
+  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp,
+                                const LaneIdType&) const {
+    conj_helper<LhsPacketType, RhsPacketType, ConjLhs, ConjRhs> cj;
+    // It would be a lot cleaner to call pmadd all the time. Unfortunately if we
+    // let gcc allocate the register in which to store the result of the pmul
+    // (in the case where there is no FMA) gcc fails to figure out how to avoid
+    // spilling register.
+#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+    EIGEN_UNUSED_VARIABLE(tmp);
+    c = cj.pmadd(a, b, c);
+#else
+    tmp = b;
+    tmp = cj.pmul(a, tmp);
+    c = padd(c, tmp);
+#endif
+  }
+
+  template <typename LhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp,
+                                const LaneIdType& lane) const {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+
+  EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const {
+    r = pmadd(c, alpha, r);
+  }
+
+  template <typename ResPacketHalf>
+  EIGEN_STRONG_INLINE void acc(const ResPacketHalf& c, const ResPacketHalf& alpha, ResPacketHalf& r) const {
+    r = pmadd(c, alpha, r);
+  }
+};
+
+template <typename RealScalar, bool ConjLhs_, int Arch, int PacketSize_>
+class gebp_traits<std::complex<RealScalar>, RealScalar, ConjLhs_, false, Arch, PacketSize_> {
+ public:
+  typedef std::complex<RealScalar> LhsScalar;
+  typedef RealScalar RhsScalar;
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+
+  PACKET_DECL_COND_POSTFIX(_, Lhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Rhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Res, PacketSize_);
+
+  enum {
+    ConjLhs = ConjLhs_,
+    ConjRhs = false,
+    Vectorizable = unpacket_traits<LhsPacket_>::vectorizable && unpacket_traits<RhsPacket_>::vectorizable,
+    LhsPacketSize = Vectorizable ? unpacket_traits<LhsPacket_>::size : 1,
+    RhsPacketSize = Vectorizable ? unpacket_traits<RhsPacket_>::size : 1,
+    ResPacketSize = Vectorizable ? unpacket_traits<ResPacket_>::size : 1,
+
+    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
+    nr = 4,
+#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX)
+    // we assume 16 registers
+    mr = 3 * LhsPacketSize,
+#else
+    mr = (plain_enum_min(16, NumberOfRegisters) / 2 / nr) * LhsPacketSize,
+#endif
+
+    LhsProgress = LhsPacketSize,
+    RhsProgress = 1
+  };
+
+  typedef std::conditional_t<Vectorizable, LhsPacket_, LhsScalar> LhsPacket;
+  typedef std::conditional_t<Vectorizable, RhsPacket_, RhsScalar> RhsPacket;
+  typedef std::conditional_t<Vectorizable, ResPacket_, ResScalar> ResPacket;
+  typedef LhsPacket LhsPacket4Packing;
+
+  typedef QuadPacket<RhsPacket> RhsPacketx4;
+
+  typedef ResPacket AccPacket;
+
+  EIGEN_STRONG_INLINE void initAcc(AccPacket& p) { p = pset1<ResPacket>(ResScalar(0)); }
+
+  template <typename RhsPacketType>
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const {
+    dest = pset1<RhsPacketType>(*b);
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const {
+    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
+  }
+
+  template <typename RhsPacketType>
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const {
+    loadRhs(b, dest);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const {
+    loadRhsQuad_impl(b, dest, std::conditional_t<RhsPacketSize == 16, true_type, false_type>());
+  }
+
+  EIGEN_STRONG_INLINE void loadRhsQuad_impl(const RhsScalar* b, RhsPacket& dest, const true_type&) const {
+    // FIXME: replace with a dedicated ploadheight operation for more efficient quad loading.
+    RhsScalar tmp[4] = {b[0], b[0], b[1], b[1]};
+    dest = ploadquad<RhsPacket>(tmp);
+  }
+
+  EIGEN_STRONG_INLINE void loadRhsQuad_impl(const RhsScalar* b, RhsPacket& dest, const false_type&) const {
+    eigen_internal_assert(RhsPacketSize <= 8);
+    dest = pset1<RhsPacket>(*b);
+  }
+
+  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const { dest = pload<LhsPacket>(a); }
+
+  template <typename LhsPacketType>
+  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const {
+    dest = ploadu<LhsPacketType>(a);
+  }
+
+  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp,
+                                const LaneIdType&) const {
+    madd_impl(a, b, c, tmp, std::conditional_t<Vectorizable, true_type, false_type>());
+  }
+
+  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType>
+  EIGEN_STRONG_INLINE void madd_impl(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c,
+                                     RhsPacketType& tmp, const true_type&) const {
+#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+    EIGEN_UNUSED_VARIABLE(tmp);
+    c.v = pmadd(a.v, b, c.v);
+#else
+    tmp = b;
+    tmp = pmul(a.v, tmp);
+    c.v = padd(c.v, tmp);
+#endif
+  }
+
+  EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/,
+                                     const false_type&) const {
+    c += a * b;
+  }
+
+  template <typename LhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp,
+                                const LaneIdType& lane) const {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+
+  template <typename ResPacketType, typename AccPacketType>
+  EIGEN_STRONG_INLINE void acc(const AccPacketType& c, const ResPacketType& alpha, ResPacketType& r) const {
+    conj_helper<ResPacketType, ResPacketType, ConjLhs, false> cj;
+    r = cj.pmadd(c, alpha, r);
+  }
+
+ protected:
+};
+
+template <typename Packet>
+struct DoublePacket {
+  Packet first;
+  Packet second;
+};
+
+template <typename Packet>
+DoublePacket<Packet> padd(const DoublePacket<Packet>& a, const DoublePacket<Packet>& b) {
+  DoublePacket<Packet> res;
+  res.first = padd(a.first, b.first);
+  res.second = padd(a.second, b.second);
+  return res;
+}
+
+template <typename Packet>
+const DoublePacket<Packet>& predux_half(const DoublePacket<Packet>& a,
+                                        std::enable_if_t<unpacket_traits<Packet>::size <= 8>* = 0) {
+  return a;
+}
+
+template <typename Packet>
+DoublePacket<typename unpacket_traits<Packet>::half> predux_half(
+    const DoublePacket<Packet>& a,
+    std::enable_if_t<unpacket_traits<Packet>::size >= 16 &&
+                     !NumTraits<typename unpacket_traits<Packet>::type>::IsComplex>* = 0) {
+  // Workaround: reduce real packets to half size by reinterpreting as complex.
+  DoublePacket<typename unpacket_traits<Packet>::half> res;
+  typedef std::complex<typename unpacket_traits<Packet>::type> Cplx;
+  typedef typename packet_traits<Cplx>::type CplxPacket;
+  res.first = predux_half(CplxPacket(a.first)).v;
+  res.second = predux_half(CplxPacket(a.second)).v;
+  return res;
+}
+
+// same here, "quad" actually means "8" in terms of real coefficients
+template <typename Scalar, typename RealPacket>
+void loadQuadToDoublePacket(const Scalar* b, DoublePacket<RealPacket>& dest,
+                            std::enable_if_t<unpacket_traits<RealPacket>::size <= 8>* = 0) {
+  dest.first = pset1<RealPacket>(numext::real(*b));
+  dest.second = pset1<RealPacket>(numext::imag(*b));
+}
+
+template <typename Scalar, typename RealPacket>
+void loadQuadToDoublePacket(const Scalar* b, DoublePacket<RealPacket>& dest,
+                            std::enable_if_t<unpacket_traits<RealPacket>::size == 16>* = 0) {
+  // Workaround: load quad elements by reinterpreting real packets as complex.
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  RealScalar r[4] = {numext::real(b[0]), numext::real(b[0]), numext::real(b[1]), numext::real(b[1])};
+  RealScalar i[4] = {numext::imag(b[0]), numext::imag(b[0]), numext::imag(b[1]), numext::imag(b[1])};
+  dest.first = ploadquad<RealPacket>(r);
+  dest.second = ploadquad<RealPacket>(i);
+}
+
+template <typename Packet>
+struct unpacket_traits<DoublePacket<Packet>> {
+  typedef DoublePacket<typename unpacket_traits<Packet>::half> half;
+  enum { size = 2 * unpacket_traits<Packet>::size };
+};
+// template<typename Packet>
+// DoublePacket<Packet> pmadd(const DoublePacket<Packet> &a, const DoublePacket<Packet> &b)
+// {
+//   DoublePacket<Packet> res;
+//   res.first  = padd(a.first, b.first);
+//   res.second = padd(a.second,b.second);
+//   return res;
+// }
+
+template <typename RealScalar, bool ConjLhs_, bool ConjRhs_, int Arch, int PacketSize_>
+class gebp_traits<std::complex<RealScalar>, std::complex<RealScalar>, ConjLhs_, ConjRhs_, Arch, PacketSize_> {
+ public:
+  typedef std::complex<RealScalar> Scalar;
+  typedef std::complex<RealScalar> LhsScalar;
+  typedef std::complex<RealScalar> RhsScalar;
+  typedef std::complex<RealScalar> ResScalar;
+
+  PACKET_DECL_COND_POSTFIX(_, Lhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Rhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Res, PacketSize_);
+  PACKET_DECL_COND(Real, PacketSize_);
+  PACKET_DECL_COND_SCALAR(PacketSize_);
+
+  enum {
+    ConjLhs = ConjLhs_,
+    ConjRhs = ConjRhs_,
+    Vectorizable = unpacket_traits<RealPacket>::vectorizable && unpacket_traits<ScalarPacket>::vectorizable,
+    ResPacketSize = Vectorizable ? unpacket_traits<ResPacket_>::size : 1,
+    LhsPacketSize = Vectorizable ? unpacket_traits<LhsPacket_>::size : 1,
+    RhsPacketSize = Vectorizable ? unpacket_traits<RhsScalar>::size : 1,
+    RealPacketSize = Vectorizable ? unpacket_traits<RealPacket>::size : 1,
+    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
+
+    nr = 4,
+    mr = (plain_enum_min(16, NumberOfRegisters) / 2 / nr) * ResPacketSize,
+
+    LhsProgress = ResPacketSize,
+    RhsProgress = 1
+  };
+
+  typedef DoublePacket<RealPacket> DoublePacketType;
+
+  typedef std::conditional_t<Vectorizable, ScalarPacket, Scalar> LhsPacket4Packing;
+  typedef std::conditional_t<Vectorizable, RealPacket, Scalar> LhsPacket;
+  typedef std::conditional_t<Vectorizable, DoublePacketType, Scalar> RhsPacket;
+  typedef std::conditional_t<Vectorizable, ScalarPacket, Scalar> ResPacket;
+  typedef std::conditional_t<Vectorizable, DoublePacketType, Scalar> AccPacket;
+
+  // this actually holds 8 packets!
+  typedef QuadPacket<RhsPacket> RhsPacketx4;
+
+  EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); }
+
+  EIGEN_STRONG_INLINE void initAcc(DoublePacketType& p) {
+    p.first = pset1<RealPacket>(RealScalar(0));
+    p.second = pset1<RealPacket>(RealScalar(0));
+  }
+
+  // Scalar path
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ScalarPacket& dest) const { dest = pset1<ScalarPacket>(*b); }
+
+  // Vectorized path
+  template <typename RealPacketType>
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacket<RealPacketType>& dest) const {
+    dest.first = pset1<RealPacketType>(numext::real(*b));
+    dest.second = pset1<RealPacketType>(numext::imag(*b));
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const {
+    loadRhs(b, dest.B_0);
+    loadRhs(b + 1, dest.B1);
+    loadRhs(b + 2, dest.B2);
+    loadRhs(b + 3, dest.B3);
+  }
+
+  // Scalar path
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, ScalarPacket& dest) const { loadRhs(b, dest); }
+
+  // Vectorized path
+  template <typename RealPacketType>
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, DoublePacket<RealPacketType>& dest) const {
+    loadRhs(b, dest);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, ResPacket& dest) const { loadRhs(b, dest); }
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, DoublePacketType& dest) const {
+    loadQuadToDoublePacket(b, dest);
+  }
+
+  // nothing special here
+  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const {
+    dest = pload<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a));
+  }
+
+  template <typename LhsPacketType>
+  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const {
+    dest = ploadu<LhsPacketType>((const typename unpacket_traits<LhsPacketType>::type*)(a));
+  }
+
+  template <typename LhsPacketType, typename RhsPacketType, typename ResPacketType, typename TmpType,
+            typename LaneIdType>
+  EIGEN_STRONG_INLINE std::enable_if_t<!is_same<RhsPacketType, RhsPacketx4>::value> madd(const LhsPacketType& a,
+                                                                                         const RhsPacketType& b,
+                                                                                         DoublePacket<ResPacketType>& c,
+                                                                                         TmpType& /*tmp*/,
+                                                                                         const LaneIdType&) const {
+    c.first = pmadd(a, b.first, c.first);
+    c.second = pmadd(a, b.second, c.second);
+  }
+
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/,
+                                const LaneIdType&) const {
+    c = cj.pmadd(a, b, c);
+  }
+
+  template <typename LhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp,
+                                const LaneIdType& lane) const {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+
+  EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; }
+
+  template <typename RealPacketType, typename ResPacketType>
+  EIGEN_STRONG_INLINE void acc(const DoublePacket<RealPacketType>& c, const ResPacketType& alpha,
+                               ResPacketType& r) const {
+    // assemble c
+    ResPacketType tmp;
+    if ((!ConjLhs) && (!ConjRhs)) {
+      tmp = pcplxflip(pconj(ResPacketType(c.second)));
+      tmp = padd(ResPacketType(c.first), tmp);
+    } else if ((!ConjLhs) && (ConjRhs)) {
+      tmp = pconj(pcplxflip(ResPacketType(c.second)));
+      tmp = padd(ResPacketType(c.first), tmp);
+    } else if ((ConjLhs) && (!ConjRhs)) {
+      tmp = pcplxflip(ResPacketType(c.second));
+      tmp = padd(pconj(ResPacketType(c.first)), tmp);
+    } else if ((ConjLhs) && (ConjRhs)) {
+      tmp = pcplxflip(ResPacketType(c.second));
+      tmp = psub(pconj(ResPacketType(c.first)), tmp);
+    }
+
+    r = pmadd(tmp, alpha, r);
+  }
+
+ protected:
+  conj_helper<LhsScalar, RhsScalar, ConjLhs, ConjRhs> cj;
+};
+
+template <typename RealScalar, bool ConjRhs_, int Arch, int PacketSize_>
+class gebp_traits<RealScalar, std::complex<RealScalar>, false, ConjRhs_, Arch, PacketSize_> {
+ public:
+  typedef std::complex<RealScalar> Scalar;
+  typedef RealScalar LhsScalar;
+  typedef Scalar RhsScalar;
+  typedef Scalar ResScalar;
+
+  PACKET_DECL_COND_POSTFIX(_, Lhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Rhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Res, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Real, PacketSize_);
+  PACKET_DECL_COND_SCALAR_POSTFIX(_, PacketSize_);
+
+#undef PACKET_DECL_COND_SCALAR_POSTFIX
+#undef PACKET_DECL_COND_POSTFIX
+#undef PACKET_DECL_COND_SCALAR
+#undef PACKET_DECL_COND
+
+  enum {
+    ConjLhs = false,
+    ConjRhs = ConjRhs_,
+    Vectorizable = unpacket_traits<RealPacket_>::vectorizable && unpacket_traits<ScalarPacket_>::vectorizable,
+    LhsPacketSize = Vectorizable ? unpacket_traits<LhsPacket_>::size : 1,
+    RhsPacketSize = Vectorizable ? unpacket_traits<RhsPacket_>::size : 1,
+    ResPacketSize = Vectorizable ? unpacket_traits<ResPacket_>::size : 1,
+
+    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
+    // FIXME: should depend on NumberOfRegisters
+    nr = 4,
+    mr = (plain_enum_min(16, NumberOfRegisters) / 2 / nr) * ResPacketSize,
+
+    LhsProgress = ResPacketSize,
+    RhsProgress = 1
+  };
+
+  typedef std::conditional_t<Vectorizable, LhsPacket_, LhsScalar> LhsPacket;
+  typedef std::conditional_t<Vectorizable, RhsPacket_, RhsScalar> RhsPacket;
+  typedef std::conditional_t<Vectorizable, ResPacket_, ResScalar> ResPacket;
+  typedef LhsPacket LhsPacket4Packing;
+  typedef QuadPacket<RhsPacket> RhsPacketx4;
+  typedef ResPacket AccPacket;
+
+  EIGEN_STRONG_INLINE void initAcc(AccPacket& p) { p = pset1<ResPacket>(ResScalar(0)); }
+
+  template <typename RhsPacketType>
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const {
+    dest = pset1<RhsPacketType>(*b);
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const {
+    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
+  }
+
+  template <typename RhsPacketType>
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const {
+    loadRhs(b, dest);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+
+  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const { dest = ploaddup<LhsPacket>(a); }
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const { dest = ploadquad<RhsPacket>(b); }
+
+  template <typename LhsPacketType>
+  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const {
+    dest = ploaddup<LhsPacketType>(a);
+  }
+
+  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp,
+                                const LaneIdType&) const {
+    madd_impl(a, b, c, tmp, std::conditional_t<Vectorizable, true_type, false_type>());
+  }
+
+  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType>
+  EIGEN_STRONG_INLINE void madd_impl(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c,
+                                     RhsPacketType& tmp, const true_type&) const {
+#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+    EIGEN_UNUSED_VARIABLE(tmp);
+    c.v = pmadd(a, b.v, c.v);
+#else
+    tmp = b;
+    tmp.v = pmul(a, tmp.v);
+    c = padd(c, tmp);
+#endif
+  }
+
+  EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/,
+                                     const false_type&) const {
+    c += a * b;
+  }
+
+  template <typename LhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp,
+                                const LaneIdType& lane) const {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+
+  template <typename ResPacketType, typename AccPacketType>
+  EIGEN_STRONG_INLINE void acc(const AccPacketType& c, const ResPacketType& alpha, ResPacketType& r) const {
+    conj_helper<ResPacketType, ResPacketType, false, ConjRhs> cj;
+    r = cj.pmadd(alpha, c, r);
+  }
+
+ protected:
+};
+
+/* optimized General packed Block * packed Panel product kernel
+ *
+ * Mixing type logic: C += A * B
+ *  |  A  |  B  | comments
+ *  |real |cplx | no vectorization yet, would require to pack A with duplication
+ *  |cplx |real | easy vectorization
+ */
+template <typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr,
+          bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel {
+  typedef gebp_traits<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs, Architecture::Target> Traits;
+  typedef gebp_traits<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs, Architecture::Target, GEBPPacketHalf>
+      HalfTraits;
+  typedef gebp_traits<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs, Architecture::Target, GEBPPacketQuarter>
+      QuarterTraits;
+
+  typedef typename Traits::ResScalar ResScalar;
+  typedef typename Traits::LhsPacket LhsPacket;
+  typedef typename Traits::RhsPacket RhsPacket;
+  typedef typename Traits::ResPacket ResPacket;
+  typedef typename Traits::AccPacket AccPacket;
+  typedef typename Traits::RhsPacketx4 RhsPacketx4;
+
+  typedef typename RhsPanelHelper<RhsPacket, RhsPacketx4, 15>::type RhsPanel15;
+  typedef typename RhsPanelHelper<RhsPacket, RhsPacketx4, 27>::type RhsPanel27;
+
+  typedef gebp_traits<RhsScalar, LhsScalar, ConjugateRhs, ConjugateLhs, Architecture::Target> SwappedTraits;
+
+  typedef typename SwappedTraits::ResScalar SResScalar;
+  typedef typename SwappedTraits::LhsPacket SLhsPacket;
+  typedef typename SwappedTraits::RhsPacket SRhsPacket;
+  typedef typename SwappedTraits::ResPacket SResPacket;
+  typedef typename SwappedTraits::AccPacket SAccPacket;
+
+  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
+  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
+  typedef typename HalfTraits::ResPacket ResPacketHalf;
+  typedef typename HalfTraits::AccPacket AccPacketHalf;
+
+  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
+  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
+  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
+  typedef typename QuarterTraits::AccPacket AccPacketQuarter;
+
+  typedef typename DataMapper::LinearMapper LinearMapper;
+
+  enum {
+    Vectorizable = Traits::Vectorizable,
+    LhsProgress = Traits::LhsProgress,
+    LhsProgressHalf = HalfTraits::LhsProgress,
+    LhsProgressQuarter = QuarterTraits::LhsProgress,
+    RhsProgress = Traits::RhsProgress,
+    RhsProgressHalf = HalfTraits::RhsProgress,
+    RhsProgressQuarter = QuarterTraits::RhsProgress,
+    ResPacketSize = Traits::ResPacketSize
+  };
+
+  EIGEN_DONT_INLINE void operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB, Index rows,
+                                    Index depth, Index cols, ResScalar alpha, Index strideA = -1, Index strideB = -1,
+                                    Index offsetA = 0, Index offsetB = 0);
+};
+
+template <typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr,
+          bool ConjugateLhs, bool ConjugateRhs,
+          int SwappedLhsProgress =
+              gebp_traits<RhsScalar, LhsScalar, ConjugateRhs, ConjugateLhs, Architecture::Target>::LhsProgress>
+struct last_row_process_16_packets {
+  typedef gebp_traits<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs, Architecture::Target> Traits;
+  typedef gebp_traits<RhsScalar, LhsScalar, ConjugateRhs, ConjugateLhs, Architecture::Target> SwappedTraits;
+
+  typedef typename Traits::ResScalar ResScalar;
+  typedef typename SwappedTraits::LhsPacket SLhsPacket;
+  typedef typename SwappedTraits::RhsPacket SRhsPacket;
+  typedef typename SwappedTraits::ResPacket SResPacket;
+  typedef typename SwappedTraits::AccPacket SAccPacket;
+
+  EIGEN_STRONG_INLINE void operator()(const DataMapper& res, SwappedTraits& straits, const LhsScalar* blA,
+                                      const RhsScalar* blB, Index depth, const Index endk, Index i, Index j2,
+                                      ResScalar alpha, SAccPacket& C0) {
+    EIGEN_UNUSED_VARIABLE(res);
+    EIGEN_UNUSED_VARIABLE(straits);
+    EIGEN_UNUSED_VARIABLE(blA);
+    EIGEN_UNUSED_VARIABLE(blB);
+    EIGEN_UNUSED_VARIABLE(depth);
+    EIGEN_UNUSED_VARIABLE(endk);
+    EIGEN_UNUSED_VARIABLE(i);
+    EIGEN_UNUSED_VARIABLE(j2);
+    EIGEN_UNUSED_VARIABLE(alpha);
+    EIGEN_UNUSED_VARIABLE(C0);
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr,
+          bool ConjugateLhs, bool ConjugateRhs>
+struct last_row_process_16_packets<LhsScalar, RhsScalar, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs, 16> {
+  typedef gebp_traits<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs, Architecture::Target> Traits;
+  typedef gebp_traits<RhsScalar, LhsScalar, ConjugateRhs, ConjugateLhs, Architecture::Target> SwappedTraits;
+
+  typedef typename Traits::ResScalar ResScalar;
+  typedef typename SwappedTraits::LhsPacket SLhsPacket;
+  typedef typename SwappedTraits::RhsPacket SRhsPacket;
+  typedef typename SwappedTraits::ResPacket SResPacket;
+  typedef typename SwappedTraits::AccPacket SAccPacket;
+
+  EIGEN_STRONG_INLINE void operator()(const DataMapper& res, SwappedTraits& straits, const LhsScalar* blA,
+                                      const RhsScalar* blB, Index depth, const Index endk, Index i, Index j2,
+                                      ResScalar alpha, SAccPacket& C0) {
+    typedef typename unpacket_traits<typename unpacket_traits<SResPacket>::half>::half SResPacketQuarter;
+    typedef typename unpacket_traits<typename unpacket_traits<SLhsPacket>::half>::half SLhsPacketQuarter;
+    typedef typename unpacket_traits<typename unpacket_traits<SRhsPacket>::half>::half SRhsPacketQuarter;
+    typedef typename unpacket_traits<typename unpacket_traits<SAccPacket>::half>::half SAccPacketQuarter;
+
+    SResPacketQuarter R = res.template gatherPacket<SResPacketQuarter>(i, j2);
+    SResPacketQuarter alphav = pset1<SResPacketQuarter>(alpha);
+
+    if (depth - endk > 0) {
+      // We have to handle the last row(s) of the rhs, which
+      // correspond to a half-packet
+      SAccPacketQuarter c0 = predux_half(predux_half(C0));
+
+      for (Index kk = endk; kk < depth; kk++) {
+        SLhsPacketQuarter a0;
+        SRhsPacketQuarter b0;
+        straits.loadLhsUnaligned(blB, a0);
+        straits.loadRhs(blA, b0);
+        straits.madd(a0, b0, c0, b0, fix<0>);
+        blB += SwappedTraits::LhsProgress / 4;
+        blA += 1;
+      }
+      straits.acc(c0, alphav, R);
+    } else {
+      straits.acc(predux_half(predux_half(C0)), alphav, R);
+    }
+    res.scatterPacket(i, j2, R);
+  }
+};
+
+// Compile-time recursive helper: processes RHS columns J..NrCols-1 for gebp_micro_onestep.
+// For each column, loads/updates the RHS panel and does madd for all MrPackets LHS packets.
+// The bool partial specialization terminates the recursion without requiring if constexpr.
+template <int J, int MrPackets, int NrCols, bool Continue = (J < NrCols)>
+struct gebp_rhs_cols;
+
+// Base case: J >= NrCols, do nothing.
+template <int J, int MrPackets, int NrCols>
+struct gebp_rhs_cols<J, MrPackets, NrCols, false> {
+  template <typename GEBPTraits, typename LhsArray, typename RhsPanelType, typename RhsPacketType, typename AccArray,
+            typename RhsScalar>
+  static EIGEN_ALWAYS_INLINE void run(GEBPTraits&, const RhsScalar*, Index, LhsArray&, RhsPanelType&, RhsPacketType&,
+                                      AccArray&) {}
+};
+
+// Active case: J < NrCols.
+template <int J, int MrPackets, int NrCols>
+struct gebp_rhs_cols<J, MrPackets, NrCols, true> {
+  template <typename GEBPTraits, typename LhsArray, typename RhsPanelType, typename RhsPacketType, typename AccArray,
+            typename RhsScalar>
+  static EIGEN_ALWAYS_INLINE void run(GEBPTraits& traits, const RhsScalar* blB, Index rhs_offset, LhsArray& A,
+                                      RhsPanelType& rhs_panel, RhsPacketType& T0, AccArray& C) {
+    constexpr int lane = J % 4;
+    EIGEN_IF_CONSTEXPR(lane == 0)
+    traits.loadRhs(blB + (J + rhs_offset) * GEBPTraits::RhsProgress, rhs_panel);
+    else traits.updateRhs(blB + (J + rhs_offset) * GEBPTraits::RhsProgress, rhs_panel);
+
+    EIGEN_IF_CONSTEXPR(MrPackets >= 1) traits.madd(A[0], rhs_panel, C[J + 0 * NrCols], T0, fix<lane>);
+    EIGEN_IF_CONSTEXPR(MrPackets >= 2) traits.madd(A[1], rhs_panel, C[J + 1 * NrCols], T0, fix<lane>);
+    EIGEN_IF_CONSTEXPR(MrPackets >= 3) traits.madd(A[2], rhs_panel, C[J + 2 * NrCols], T0, fix<lane>);
+
+    gebp_rhs_cols<J + 1, MrPackets, NrCols>::run(traits, blB, rhs_offset, A, rhs_panel, T0, C);
+  }
+};
+
+// One step of the micro-kernel: loads MrPackets LHS packets at step K,
+// then processes NrCols RHS columns via gebp_rhs_cols.
+template <int K, int MrPackets, int NrCols>
+struct gebp_micro_step {
+  template <typename GEBPTraits, typename LhsScalar_, typename RhsScalar_, typename LhsArray, typename RhsPanelType,
+            typename RhsPacketType, typename AccArray>
+  static EIGEN_ALWAYS_INLINE void run(GEBPTraits& traits, const LhsScalar_* blA, const RhsScalar_* blB, LhsArray& A,
+                                      RhsPanelType& rhs_panel, RhsPacketType& T0, AccArray& C) {
+    constexpr int LhsProg = GEBPTraits::LhsProgress;
+
+    EIGEN_IF_CONSTEXPR(MrPackets >= 1) traits.loadLhs(&blA[(0 + MrPackets * K) * LhsProg], A[0]);
+    EIGEN_IF_CONSTEXPR(MrPackets >= 2) traits.loadLhs(&blA[(1 + MrPackets * K) * LhsProg], A[1]);
+    EIGEN_IF_CONSTEXPR(MrPackets >= 3) traits.loadLhs(&blA[(2 + MrPackets * K) * LhsProg], A[2]);
+
+    gebp_rhs_cols<0, MrPackets, NrCols>::run(traits, blB, Index(NrCols * K), A, rhs_panel, T0, C);
+  }
+};
+// Compiler workaround macros used inside gebp_peeled_loop.
+#if EIGEN_ARCH_ARM64 && defined(EIGEN_VECTORIZE_NEON) && EIGEN_GNUC_STRICT_LESS_THAN(9, 0, 0)
+#define EIGEN_GEBP_ARM64_3P_WORKAROUND(MrPackets, A, LhsArray, FullLhsPacket)                               \
+  EIGEN_IF_CONSTEXPR(                                                                                       \
+      (MrPackets == 3 &&                                                                                    \
+       std::is_same<std::remove_all_extents_t<std::remove_reference_t<LhsArray>>, FullLhsPacket>::value)) { \
+    __asm__("" : "+w,m"(A[0]), "+w,m"(A[1]), "+w,m"(A[2]));                                                 \
+  }
+#else
+#define EIGEN_GEBP_ARM64_3P_WORKAROUND(MrPackets, A, LhsArray, FullLhsPacket)
+#endif
+
+// GCC's register allocator can fail to keep array-based accumulators in XMM
+// registers, causing excessive spilling to the stack. Pin accumulators using
+// inline asm "+x" constraints.  The ACC pinning only works for plain SSE
+// vector types (not DoublePacket used by complex), so it requires if constexpr
+// (C++17) to safely discard the dead branch for complex types.
+// See Eigen bugs 935 and 1637.
+#if EIGEN_GNUC_STRICT_AT_LEAST(6, 0, 0) && defined(EIGEN_VECTORIZE_SSE)
+#ifdef EIGEN_HAS_CXX17_IFCONSTEXPR
+// C++17: pin accumulators when they're plain SSE vectors (sizeof matches FullLhsPacket).
+// For complex types, AccPacket is a struct (DoublePacket) and the asm is safely discarded.
+#define EIGEN_GEBP_SSE_SPILLING_WORKAROUND(MrPackets, NrCols, A, ACC, LhsArray, FullLhsPacket)                    \
+  EIGEN_IF_CONSTEXPR(                                                                                             \
+      (MrPackets <= 2 && NrCols >= 4 &&                                                                           \
+       std::is_same<std::remove_all_extents_t<std::remove_reference_t<LhsArray>>, FullLhsPacket>::value &&        \
+       sizeof(ACC[0]) == sizeof(FullLhsPacket))) {                                                                \
+    EIGEN_IF_CONSTEXPR(MrPackets == 2 && NrCols == 4) {                                                           \
+      __asm__(""                                                                                                  \
+              : "+x"(ACC[0]), "+x"(ACC[1]), "+x"(ACC[2]), "+x"(ACC[3]), "+x"(ACC[4]), "+x"(ACC[5]), "+x"(ACC[6]), \
+                "+x"(ACC[7]));                                                                                    \
+    }                                                                                                             \
+    EIGEN_GEBP_SSE_1P_WORKAROUND(MrPackets, NrCols, A, ACC)                                                       \
+  }
+#else
+// C++14: only pin LHS packets (A), not accumulators, to avoid asm errors with complex types.
+#define EIGEN_GEBP_SSE_SPILLING_WORKAROUND(MrPackets, NrCols, A, ACC, LhsArray, FullLhsPacket)              \
+  EIGEN_IF_CONSTEXPR(                                                                                       \
+      (MrPackets <= 2 && NrCols >= 4 &&                                                                     \
+       std::is_same<std::remove_all_extents_t<std::remove_reference_t<LhsArray>>, FullLhsPacket>::value)) { \
+    EIGEN_IF_CONSTEXPR(MrPackets == 2) { __asm__("" : "+x,m"(A[0]), "+x,m"(A[1])); }                        \
+    EIGEN_GEBP_SSE_1P_WORKAROUND(MrPackets, NrCols, A, ACC)                                                 \
+  }
+#endif
+#if !(EIGEN_COMP_LCC)
+#define EIGEN_GEBP_SSE_1P_WORKAROUND(MrPackets, NrCols, A, ACC) \
+  EIGEN_IF_CONSTEXPR(MrPackets == 1 && NrCols == 1) { __asm__("" : "+x,m"(A[0])); }
+#else
+#define EIGEN_GEBP_SSE_1P_WORKAROUND(MrPackets, NrCols, A, ACC)
+#endif
+#else
+#define EIGEN_GEBP_SSE_SPILLING_WORKAROUND(MrPackets, NrCols, A, ACC, LhsArray, FullLhsPacket)
+#endif
+
+// Unrolled peeled loop body: calls gebp_micro_step for K=0..7, handling
+// double-accumulation for 1pX4, prefetches, and compiler workarounds.
+template <int MrPackets, int NrCols>
+struct gebp_peeled_loop {
+  template <typename GEBPTraits, typename LhsScalar_, typename RhsScalar_, typename LhsArray, typename RhsPanelType,
+            typename RhsPacketType, typename AccArray, typename AccArrayD, typename FullLhsPacket>
+  static EIGEN_ALWAYS_INLINE void run(GEBPTraits& traits, const LhsScalar_* blA, const RhsScalar_* blB, LhsArray& A,
+                                      RhsPanelType& rhs_panel, RhsPacketType& T0, AccArray& C, AccArrayD& D) {
+    constexpr bool use_double_accum = (MrPackets == 1 && NrCols == 4);
+
+    // Prefetch for 4-col paths
+    EIGEN_IF_CONSTEXPR(NrCols == 4) { internal::prefetch(blB + (48 + 0)); }
+
+    // Helper to do one step with workarounds
+#define EIGEN_GEBP_DO_STEP(KVAL, ACC)                                                       \
+  do {                                                                                      \
+    gebp_micro_step<KVAL, MrPackets, NrCols>::run(traits, blA, blB, A, rhs_panel, T0, ACC); \
+    /* ARM64 NEON register alloc workaround for 3-packet paths */                           \
+    EIGEN_GEBP_ARM64_3P_WORKAROUND(MrPackets, A, LhsArray, FullLhsPacket)                   \
+    /* GCC SSE spilling workaround: pin LHS packets and accumulators in registers */        \
+    EIGEN_GEBP_SSE_SPILLING_WORKAROUND(MrPackets, NrCols, A, ACC, LhsArray, FullLhsPacket)  \
+    /* LHS prefetch for 2pX4 and 3pX4 */                                                    \
+    EIGEN_IF_CONSTEXPR((MrPackets == 2 || MrPackets == 3) && NrCols == 4) {                 \
+      internal::prefetch(blA + (MrPackets * KVAL + 16) * GEBPTraits::LhsProgress);          \
+      if (EIGEN_ARCH_ARM || EIGEN_ARCH_MIPS) {                                              \
+        internal::prefetch(blB + (NrCols * KVAL + 16) * GEBPTraits::RhsProgress);           \
+      }                                                                                     \
+    }                                                                                       \
+  } while (false)
+
+    EIGEN_IF_CONSTEXPR(use_double_accum) {
+      EIGEN_GEBP_DO_STEP(0, C);
+      EIGEN_GEBP_DO_STEP(1, D);
+      EIGEN_GEBP_DO_STEP(2, C);
+      EIGEN_GEBP_DO_STEP(3, D);
+      EIGEN_IF_CONSTEXPR(NrCols == 4) { internal::prefetch(blB + (48 + 16)); }
+      EIGEN_GEBP_DO_STEP(4, C);
+      EIGEN_GEBP_DO_STEP(5, D);
+      EIGEN_GEBP_DO_STEP(6, C);
+      EIGEN_GEBP_DO_STEP(7, D);
+    }
+    else {
+      EIGEN_GEBP_DO_STEP(0, C);
+      EIGEN_GEBP_DO_STEP(1, C);
+      EIGEN_GEBP_DO_STEP(2, C);
+      EIGEN_GEBP_DO_STEP(3, C);
+      EIGEN_IF_CONSTEXPR(NrCols == 4 && MrPackets == 2) { internal::prefetch(blB + (48 + 16)); }
+      EIGEN_GEBP_DO_STEP(4, C);
+      EIGEN_GEBP_DO_STEP(5, C);
+      EIGEN_GEBP_DO_STEP(6, C);
+      EIGEN_GEBP_DO_STEP(7, C);
+    }
+
+#undef EIGEN_GEBP_DO_STEP
+  }
+};
+
+// Unified micro-panel function: handles a MrPackets x NrCols register block.
+// GEBPTraits determines the packet types (supports full/half/quarter sizes).
+// Accumulator layout: C[j + p * NrCols] for column j, LHS packet p.
+template <int MrPackets, int NrCols, typename GEBPTraits, typename LhsScalar_, typename RhsScalar_, typename ResScalar_,
+          typename Index_, typename DataMapper_, typename LinearMapper_, typename FullLhsPacket>
+EIGEN_ALWAYS_INLINE void gebp_micro_panel_impl(GEBPTraits& traits, const DataMapper_& res, const LhsScalar_* blockA,
+                                               const RhsScalar_* blockB, ResScalar_ alpha, Index_ i, Index_ j2,
+                                               Index_ depth, Index_ strideA, Index_ strideB, Index_ offsetA,
+                                               Index_ offsetB, int prefetch_res_offset, Index_ peeled_kc, int pk) {
+  using LhsPacketLocal = typename GEBPTraits::LhsPacket;
+  using RhsPacketLocal = typename GEBPTraits::RhsPacket;
+  using ResPacketLocal = typename GEBPTraits::ResPacket;
+  using AccPacketLocal = typename GEBPTraits::AccPacket;
+  using RhsPacketx4Local = typename GEBPTraits::RhsPacketx4;
+  constexpr int LhsProg = GEBPTraits::LhsProgress;
+  constexpr int RhsProg = GEBPTraits::RhsProgress;
+  constexpr int ResPacketSz = GEBPTraits::ResPacketSize;
+
+  // Determine RhsPanel type based on register pressure
+  using RhsPanelType = std::conditional_t<
+      NrCols == 1, RhsPacketLocal,
+      typename RhsPanelHelper<RhsPacketLocal, RhsPacketx4Local, MrPackets * NrCols + MrPackets>::type>;
+
+  const LhsScalar_* blA = &blockA[i * strideA + offsetA * (MrPackets * LhsProg)];
+  prefetch(&blA[0]);
+
+  // Accumulators: C[j + p * NrCols] for column j, LHS packet p.
+  // With if constexpr (C++17) we use exact sizes; with plain if (C++14) we pad
+  // to 3*NrCols so dead-branch array accesses in gebp_rhs_cols remain valid.
+#ifdef EIGEN_HAS_CXX17_IFCONSTEXPR
+  constexpr int CSize = MrPackets * NrCols;
+#else
+  constexpr int CSize = 3 * NrCols > MrPackets * NrCols ? 3 * NrCols : MrPackets * NrCols;
+#endif
+  AccPacketLocal C[CSize];
+  for (int n = 0; n < MrPackets * NrCols; ++n) traits.initAcc(C[n]);
+
+  // Double-accumulation trick for 1pX4 path to break FMA dependency chains
+  constexpr bool use_double_accum = (MrPackets == 1 && NrCols == 4);
+#ifdef EIGEN_HAS_CXX17_IFCONSTEXPR
+  AccPacketLocal D[use_double_accum ? NrCols : 1];
+#else
+  // Without if constexpr, we must allocate a larger array to satisfy the
+  // compiler that D[n] is always in bounds for the use_double_accum path.
+  AccPacketLocal D[CSize];
+#endif
+  EIGEN_IF_CONSTEXPR(use_double_accum) {
+    for (int n = 0; n < NrCols; ++n) traits.initAcc(D[n]);
+  }
+
+  // Prefetch result memory
+  for (int j = 0; j < NrCols; ++j) res.getLinearMapper(i, j2 + j).prefetch(NrCols > 1 ? prefetch_res_offset : 0);
+
+  // RHS pointer
+  const RhsScalar_* blB = &blockB[j2 * strideB + offsetB * NrCols];
+  prefetch(&blB[0]);
+
+  // LHS packet staging area. With if constexpr (C++17) we use exact sizes.
+#ifdef EIGEN_HAS_CXX17_IFCONSTEXPR
+  LhsPacketLocal A[MrPackets];
+#else
+  LhsPacketLocal A[3];
+#endif
+
+  // ---- Peeled k-loop (pk=8 unrolled) ----
+  for (Index_ k = 0; k < peeled_kc; k += pk) {
+    RhsPanelType rhs_panel;
+    RhsPacketLocal T0;
+
+    gebp_peeled_loop<MrPackets, NrCols>::template run<GEBPTraits, LhsScalar_, RhsScalar_, decltype(A), RhsPanelType,
+                                                      RhsPacketLocal, decltype(C), decltype(D), FullLhsPacket>(
+        traits, blA, blB, A, rhs_panel, T0, C, D);
+
+    blB += pk * NrCols * RhsProg;
+    blA += pk * MrPackets * LhsProg;
+  }
+
+  // Merge double accumulators
+  EIGEN_IF_CONSTEXPR(use_double_accum) {
+    for (int n = 0; n < NrCols; ++n) C[n] = padd(C[n], D[n]);
+  }
+
+  // ---- Remainder k-loop ----
+  for (Index_ k = peeled_kc; k < depth; k++) {
+    RhsPanelType rhs_panel;
+    RhsPacketLocal T0;
+
+    gebp_micro_step<0, MrPackets, NrCols>::run(traits, blA, blB, A, rhs_panel, T0, C);
+
+    blB += NrCols * RhsProg;
+    blA += MrPackets * LhsProg;
+  }
+
+  // ---- Store results: C[j + p * NrCols] -> res(i + p*ResPacketSz, j2 + j) ----
+  ResPacketLocal alphav = pset1<ResPacketLocal>(alpha);
+  for (int j = 0; j < NrCols; ++j) {
+    LinearMapper_ r = res.getLinearMapper(i, j2 + j);
+    for (int p = 0; p < MrPackets; ++p) {
+      ResPacketLocal R = r.template loadPacket<ResPacketLocal>(p * ResPacketSz);
+      traits.acc(C[j + p * NrCols], alphav, R);
+      r.storePacket(p * ResPacketSz, R);
+    }
+  }
+}
+
+template <typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr,
+          bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_DONT_INLINE void gebp_kernel<LhsScalar, RhsScalar, Index, DataMapper, mr, nr, ConjugateLhs,
+                                   ConjugateRhs>::operator()(const DataMapper& res, const LhsScalar* blockA,
+                                                             const RhsScalar* blockB, Index rows, Index depth,
+                                                             Index cols, ResScalar alpha, Index strideA, Index strideB,
+                                                             Index offsetA, Index offsetB) {
+  Traits traits;
+  SwappedTraits straits;
+
+  if (strideA == -1) strideA = depth;
+  if (strideB == -1) strideB = depth;
+  conj_helper<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs> cj;
+  Index packet_cols4 = nr >= 4 ? (cols / 4) * 4 : 0;
+  Index packet_cols8 = nr >= 8 ? (cols / 8) * 8 : 0;
+  const Index peeled_mc3 = mr >= 3 * Traits::LhsProgress ? (rows / (3 * LhsProgress)) * (3 * LhsProgress) : 0;
+  const Index peeled_mc2 =
+      mr >= 2 * Traits::LhsProgress ? peeled_mc3 + ((rows - peeled_mc3) / (2 * LhsProgress)) * (2 * LhsProgress) : 0;
+  const Index peeled_mc1 =
+      mr >= 1 * Traits::LhsProgress ? peeled_mc2 + ((rows - peeled_mc2) / (1 * LhsProgress)) * (1 * LhsProgress) : 0;
+  const Index peeled_mc_half =
+      mr >= LhsProgressHalf ? peeled_mc1 + ((rows - peeled_mc1) / (LhsProgressHalf)) * (LhsProgressHalf) : 0;
+  const Index peeled_mc_quarter =
+      mr >= LhsProgressQuarter
+          ? peeled_mc_half + ((rows - peeled_mc_half) / (LhsProgressQuarter)) * (LhsProgressQuarter)
+          : 0;
+  enum { pk = 8 };  // NOTE Such a large peeling factor is important for large matrices (~ +5% when >1000 on Haswell)
+  const Index peeled_kc = depth & ~(pk - 1);
+  const int prefetch_res_offset = 32 / sizeof(ResScalar);
+
+  // Helper to invoke gebp_micro_panel_impl with the right types.
+  // The always_inline attribute is critical: without it GCC outlines each
+  // template instantiation of this generic lambda as a separate function,
+  // adding call overhead that causes 10-17 % regressions in LLT/TRSM
+  // for small-to-medium matrix sizes.
+  auto micro_panel = [&](auto mrp_tag, auto nrc_tag, auto& local_traits, Index i, Index j2) EIGEN_LAMBDA_ALWAYS_INLINE {
+    constexpr int MrP = decltype(mrp_tag)::value;
+    constexpr int NrC = decltype(nrc_tag)::value;
+    using LTraits = std::remove_reference_t<decltype(local_traits)>;
+    gebp_micro_panel_impl<MrP, NrC, LTraits, LhsScalar, RhsScalar, ResScalar, Index, DataMapper, LinearMapper,
+                          LhsPacket>(local_traits, res, blockA, blockB, alpha, i, j2, depth, strideA, strideB, offsetA,
+                                     offsetB, prefetch_res_offset, peeled_kc, pk);
+  };
+
+  //---------- Process 3 * LhsProgress rows at once ----------
+  EIGEN_IF_CONSTEXPR(mr >= 3 * Traits::LhsProgress) {
+    std::ptrdiff_t l1, l2, l3;
+    manage_caching_sizes(GetAction, &l1, &l2, &l3);
+    const Index actual_panel_rows =
+        (3 * LhsProgress) * std::max<Index>(1, ((l1 - sizeof(ResScalar) * mr * nr - depth * nr * sizeof(RhsScalar)) /
+                                                (depth * sizeof(LhsScalar) * 3 * LhsProgress)));
+    for (Index i1 = 0; i1 < peeled_mc3; i1 += actual_panel_rows) {
+      const Index actual_panel_end = (std::min)(i1 + actual_panel_rows, peeled_mc3);
+#if EIGEN_ARCH_ARM64 || EIGEN_ARCH_LOONGARCH64
+      EIGEN_IF_CONSTEXPR(nr >= 8) {
+        for (Index j2 = 0; j2 < packet_cols8; j2 += 8) {
+          for (Index i = i1; i < actual_panel_end; i += 3 * LhsProgress) {
+            micro_panel(fix<3>, fix<8>, traits, i, j2);
+          }
+        }
+      }
+#endif
+      for (Index j2 = packet_cols8; j2 < packet_cols4; j2 += 4) {
+        for (Index i = i1; i < actual_panel_end; i += 3 * LhsProgress) {
+          micro_panel(fix<3>, fix<4>, traits, i, j2);
+        }
+      }
+      for (Index j2 = packet_cols4; j2 < cols; j2++) {
+        for (Index i = i1; i < actual_panel_end; i += 3 * LhsProgress) {
+          micro_panel(fix<3>, fix<1>, traits, i, j2);
+        }
+      }
+    }
+  }
+
+  //---------- Process 2 * LhsProgress rows at once ----------
+  EIGEN_IF_CONSTEXPR(mr >= 2 * Traits::LhsProgress) {
+    std::ptrdiff_t l1, l2, l3;
+    manage_caching_sizes(GetAction, &l1, &l2, &l3);
+    Index actual_panel_rows =
+        (2 * LhsProgress) * std::max<Index>(1, ((l1 - sizeof(ResScalar) * mr * nr - depth * nr * sizeof(RhsScalar)) /
+                                                (depth * sizeof(LhsScalar) * 2 * LhsProgress)));
+    for (Index i1 = peeled_mc3; i1 < peeled_mc2; i1 += actual_panel_rows) {
+      Index actual_panel_end = (std::min)(i1 + actual_panel_rows, peeled_mc2);
+#if EIGEN_ARCH_ARM64 || EIGEN_ARCH_LOONGARCH64
+      EIGEN_IF_CONSTEXPR(nr >= 8) {
+        for (Index j2 = 0; j2 < packet_cols8; j2 += 8) {
+          for (Index i = i1; i < actual_panel_end; i += 2 * LhsProgress) {
+            micro_panel(fix<2>, fix<8>, traits, i, j2);
+          }
+        }
+      }
+#endif
+      for (Index j2 = packet_cols8; j2 < packet_cols4; j2 += 4) {
+        for (Index i = i1; i < actual_panel_end; i += 2 * LhsProgress) {
+          micro_panel(fix<2>, fix<4>, traits, i, j2);
+        }
+      }
+      for (Index j2 = packet_cols4; j2 < cols; j2++) {
+        for (Index i = i1; i < actual_panel_end; i += 2 * LhsProgress) {
+          micro_panel(fix<2>, fix<1>, traits, i, j2);
+        }
+      }
+    }
+  }
+
+  //---------- Process 1 * LhsProgress rows at once ----------
+  EIGEN_IF_CONSTEXPR(mr >= 1 * Traits::LhsProgress) {
+    for (Index i = peeled_mc2; i < peeled_mc1; i += LhsProgress) {
+#if EIGEN_ARCH_ARM64 || EIGEN_ARCH_LOONGARCH64
+      EIGEN_IF_CONSTEXPR(nr >= 8) {
+        for (Index j2 = 0; j2 < packet_cols8; j2 += 8) {
+          micro_panel(fix<1>, fix<8>, traits, i, j2);
+        }
+      }
+#endif
+      for (Index j2 = packet_cols8; j2 < packet_cols4; j2 += 4) {
+        micro_panel(fix<1>, fix<4>, traits, i, j2);
+      }
+      for (Index j2 = packet_cols4; j2 < cols; j2++) {
+        micro_panel(fix<1>, fix<1>, traits, i, j2);
+      }
+    }
+  }
+
+  //---------- Process LhsProgressHalf rows at once ----------
+  EIGEN_IF_CONSTEXPR((LhsProgressHalf < LhsProgress) && mr >= LhsProgressHalf) {
+    HalfTraits half_traits;
+    for (Index i = peeled_mc1; i < peeled_mc_half; i += LhsProgressHalf) {
+      for (Index j2 = 0; j2 < packet_cols4; j2 += 4) {
+        gebp_micro_panel_impl<1, 4, HalfTraits, LhsScalar, RhsScalar, ResScalar, Index, DataMapper, LinearMapper,
+                              LhsPacket>(half_traits, res, blockA, blockB, alpha, i, j2, depth, strideA, strideB,
+                                         offsetA, offsetB, prefetch_res_offset, peeled_kc, pk);
+      }
+      for (Index j2 = packet_cols4; j2 < cols; j2++) {
+        gebp_micro_panel_impl<1, 1, HalfTraits, LhsScalar, RhsScalar, ResScalar, Index, DataMapper, LinearMapper,
+                              LhsPacket>(half_traits, res, blockA, blockB, alpha, i, j2, depth, strideA, strideB,
+                                         offsetA, offsetB, prefetch_res_offset, peeled_kc, pk);
+      }
+    }
+  }
+
+  //---------- Process LhsProgressQuarter rows at once ----------
+  EIGEN_IF_CONSTEXPR((LhsProgressQuarter < LhsProgressHalf) && mr >= LhsProgressQuarter) {
+    QuarterTraits quarter_traits;
+    for (Index i = peeled_mc_half; i < peeled_mc_quarter; i += LhsProgressQuarter) {
+      for (Index j2 = 0; j2 < packet_cols4; j2 += 4) {
+        gebp_micro_panel_impl<1, 4, QuarterTraits, LhsScalar, RhsScalar, ResScalar, Index, DataMapper, LinearMapper,
+                              LhsPacket>(quarter_traits, res, blockA, blockB, alpha, i, j2, depth, strideA, strideB,
+                                         offsetA, offsetB, prefetch_res_offset, peeled_kc, pk);
+      }
+      for (Index j2 = packet_cols4; j2 < cols; j2++) {
+        gebp_micro_panel_impl<1, 1, QuarterTraits, LhsScalar, RhsScalar, ResScalar, Index, DataMapper, LinearMapper,
+                              LhsPacket>(quarter_traits, res, blockA, blockB, alpha, i, j2, depth, strideA, strideB,
+                                         offsetA, offsetB, prefetch_res_offset, peeled_kc, pk);
+      }
+    }
+  }
+
+  //---------- Process remaining rows, 1 at once ----------
+  if (peeled_mc_quarter < rows) {
+#if EIGEN_ARCH_ARM64 || EIGEN_ARCH_LOONGARCH64
+    EIGEN_IF_CONSTEXPR(nr >= 8) {
+      // loop on each panel of the rhs
+      for (Index j2 = 0; j2 < packet_cols8; j2 += 8) {
+        // loop on each row of the lhs (1*LhsProgress x depth)
+        for (Index i = peeled_mc_quarter; i < rows; i += 1) {
+          const LhsScalar* blA = &blockA[i * strideA + offsetA];
+          prefetch(&blA[0]);
+          // gets a 1 x 1 res block as registers
+          ResScalar C0(0), C1(0), C2(0), C3(0), C4(0), C5(0), C6(0), C7(0);
+          const RhsScalar* blB = &blockB[j2 * strideB + offsetB * 8];
+          for (Index k = 0; k < depth; k++) {
+            LhsScalar A0 = blA[k];
+            RhsScalar B_0;
+
+            B_0 = blB[0];
+            C0 = cj.pmadd(A0, B_0, C0);
+
+            B_0 = blB[1];
+            C1 = cj.pmadd(A0, B_0, C1);
+
+            B_0 = blB[2];
+            C2 = cj.pmadd(A0, B_0, C2);
+
+            B_0 = blB[3];
+            C3 = cj.pmadd(A0, B_0, C3);
+
+            B_0 = blB[4];
+            C4 = cj.pmadd(A0, B_0, C4);
+
+            B_0 = blB[5];
+            C5 = cj.pmadd(A0, B_0, C5);
+
+            B_0 = blB[6];
+            C6 = cj.pmadd(A0, B_0, C6);
+
+            B_0 = blB[7];
+            C7 = cj.pmadd(A0, B_0, C7);
+
+            blB += 8;
+          }
+          res(i, j2 + 0) += alpha * C0;
+          res(i, j2 + 1) += alpha * C1;
+          res(i, j2 + 2) += alpha * C2;
+          res(i, j2 + 3) += alpha * C3;
+          res(i, j2 + 4) += alpha * C4;
+          res(i, j2 + 5) += alpha * C5;
+          res(i, j2 + 6) += alpha * C6;
+          res(i, j2 + 7) += alpha * C7;
+        }
+      }
+    }
+#endif
+
+    for (Index j2 = packet_cols8; j2 < packet_cols4; j2 += 4) {
+      // loop on each row of the lhs (1*LhsProgress x depth)
+      for (Index i = peeled_mc_quarter; i < rows; i += 1) {
+        const LhsScalar* blA = &blockA[i * strideA + offsetA];
+        prefetch(&blA[0]);
+        const RhsScalar* blB = &blockB[j2 * strideB + offsetB * 4];
+
+        // If LhsProgress is 8 or 16, it assumes that there is a
+        // half or quarter packet, respectively, of the same size as
+        // nr (which is currently 4) for the return type.
+        const int SResPacketHalfSize = unpacket_traits<typename unpacket_traits<SResPacket>::half>::size;
+        const int SResPacketQuarterSize =
+            unpacket_traits<typename unpacket_traits<typename unpacket_traits<SResPacket>::half>::half>::size;
+        // The following code assumes we can load SRhsPacket in such a way that
+        // it multiplies blocks of 4 elements in SLhsPacket.  This is not the
+        // case for some customized kernels (i.e. NEON fp16).  If the assumption
+        // fails, drop down to the scalar path.
+        constexpr bool kCanLoadSRhsQuad =
+            (unpacket_traits<SLhsPacket>::size < 4) ||
+            (unpacket_traits<SRhsPacket>::size % ((std::max<int>)(unpacket_traits<SLhsPacket>::size, 4) / 4)) == 0;
+        if (kCanLoadSRhsQuad && (SwappedTraits::LhsProgress % 4) == 0 && (SwappedTraits::LhsProgress <= 16) &&
+            (SwappedTraits::LhsProgress != 8 || SResPacketHalfSize == nr) &&
+            (SwappedTraits::LhsProgress != 16 || SResPacketQuarterSize == nr)) {
+          SAccPacket C0, C1, C2, C3;
+          straits.initAcc(C0);
+          straits.initAcc(C1);
+          straits.initAcc(C2);
+          straits.initAcc(C3);
+
+          const Index spk = (std::max)(1, SwappedTraits::LhsProgress / 4);
+          const Index endk = (depth / spk) * spk;
+          const Index endk4 = (depth / (spk * 4)) * (spk * 4);
+
+          Index k = 0;
+          for (; k < endk4; k += 4 * spk) {
+            SLhsPacket A0, A1;
+            SRhsPacket B_0, B_1;
+
+            straits.loadLhsUnaligned(blB + 0 * SwappedTraits::LhsProgress, A0);
+            straits.loadLhsUnaligned(blB + 1 * SwappedTraits::LhsProgress, A1);
+
+            straits.loadRhsQuad(blA + 0 * spk, B_0);
+            straits.loadRhsQuad(blA + 1 * spk, B_1);
+            straits.madd(A0, B_0, C0, B_0, fix<0>);
+            straits.madd(A1, B_1, C1, B_1, fix<0>);
+
+            straits.loadLhsUnaligned(blB + 2 * SwappedTraits::LhsProgress, A0);
+            straits.loadLhsUnaligned(blB + 3 * SwappedTraits::LhsProgress, A1);
+            straits.loadRhsQuad(blA + 2 * spk, B_0);
+            straits.loadRhsQuad(blA + 3 * spk, B_1);
+            straits.madd(A0, B_0, C2, B_0, fix<0>);
+            straits.madd(A1, B_1, C3, B_1, fix<0>);
+
+            blB += 4 * SwappedTraits::LhsProgress;
+            blA += 4 * spk;
+          }
+          C0 = padd(padd(C0, C1), padd(C2, C3));
+          for (; k < endk; k += spk) {
+            SLhsPacket A0;
+            SRhsPacket B_0;
+
+            straits.loadLhsUnaligned(blB, A0);
+            straits.loadRhsQuad(blA, B_0);
+            straits.madd(A0, B_0, C0, B_0, fix<0>);
+
+            blB += SwappedTraits::LhsProgress;
+            blA += spk;
+          }
+          if (SwappedTraits::LhsProgress == 8) {
+            // Special case where we have to first reduce the accumulation register C0
+            typedef std::conditional_t<SwappedTraits::LhsProgress >= 8, typename unpacket_traits<SResPacket>::half,
+                                       SResPacket>
+                SResPacketHalf;
+            typedef std::conditional_t<SwappedTraits::LhsProgress >= 8, typename unpacket_traits<SLhsPacket>::half,
+                                       SLhsPacket>
+                SLhsPacketHalf;
+            typedef std::conditional_t<SwappedTraits::LhsProgress >= 8, typename unpacket_traits<SRhsPacket>::half,
+                                       SRhsPacket>
+                SRhsPacketHalf;
+            typedef std::conditional_t<SwappedTraits::LhsProgress >= 8, typename unpacket_traits<SAccPacket>::half,
+                                       SAccPacket>
+                SAccPacketHalf;
+
+            SResPacketHalf R = res.template gatherPacket<SResPacketHalf>(i, j2);
+            SResPacketHalf alphav = pset1<SResPacketHalf>(alpha);
+
+            if (depth - endk > 0) {
+              // We have to handle the last row of the rhs which corresponds to a half-packet
+              SLhsPacketHalf a0;
+              SRhsPacketHalf b0;
+              straits.loadLhsUnaligned(blB, a0);
+              straits.loadRhs(blA, b0);
+              SAccPacketHalf c0 = predux_half(C0);
+              straits.madd(a0, b0, c0, b0, fix<0>);
+              straits.acc(c0, alphav, R);
+            } else {
+              straits.acc(predux_half(C0), alphav, R);
+            }
+            res.scatterPacket(i, j2, R);
+          } else if (SwappedTraits::LhsProgress == 16) {
+            // Special case where we have to first reduce the
+            // accumulation register C0. We specialize the block in
+            // template form, so that LhsProgress < 16 paths don't
+            // fail to compile
+            last_row_process_16_packets<LhsScalar, RhsScalar, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> p;
+            p(res, straits, blA, blB, depth, endk, i, j2, alpha, C0);
+          } else {
+            SResPacket R = res.template gatherPacket<SResPacket>(i, j2);
+            SResPacket alphav = pset1<SResPacket>(alpha);
+            straits.acc(C0, alphav, R);
+            res.scatterPacket(i, j2, R);
+          }
+        } else  // scalar path
+        {
+          // get a 1 x 4 res block as registers
+          ResScalar C0(0), C1(0), C2(0), C3(0);
+
+          for (Index k = 0; k < depth; k++) {
+            LhsScalar A0;
+            RhsScalar B_0, B_1;
+
+            A0 = blA[k];
+
+            B_0 = blB[0];
+            B_1 = blB[1];
+            C0 = cj.pmadd(A0, B_0, C0);
+            C1 = cj.pmadd(A0, B_1, C1);
+
+            B_0 = blB[2];
+            B_1 = blB[3];
+            C2 = cj.pmadd(A0, B_0, C2);
+            C3 = cj.pmadd(A0, B_1, C3);
+
+            blB += 4;
+          }
+          res(i, j2 + 0) += alpha * C0;
+          res(i, j2 + 1) += alpha * C1;
+          res(i, j2 + 2) += alpha * C2;
+          res(i, j2 + 3) += alpha * C3;
+        }
+      }
+    }
+    // remaining columns
+    for (Index j2 = packet_cols4; j2 < cols; j2++) {
+      // loop on each row of the lhs (1*LhsProgress x depth)
+      for (Index i = peeled_mc_quarter; i < rows; i += 1) {
+        const LhsScalar* blA = &blockA[i * strideA + offsetA];
+        prefetch(&blA[0]);
+        // gets a 1 x 1 res block as registers
+        ResScalar C0(0);
+        const RhsScalar* blB = &blockB[j2 * strideB + offsetB];
+        for (Index k = 0; k < depth; k++) {
+          LhsScalar A0 = blA[k];
+          RhsScalar B_0 = blB[k];
+          C0 = cj.pmadd(A0, B_0, C0);
+        }
+        res(i, j2) += alpha * C0;
+      }
+    }
+  }
+}
+
+// pack a block of the lhs
+// The traversal is as follow (mr==4):
+//   0  4  8 12 ...
+//   1  5  9 13 ...
+//   2  6 10 14 ...
+//   3  7 11 15 ...
+//
+//  16 20 24 28 ...
+//  17 21 25 29 ...
+//  18 22 26 30 ...
+//  19 23 27 31 ...
+//
+//  32 33 34 35 ...
+//  36 36 38 39 ...
+template <typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate,
+          bool PanelMode>
+struct gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode> {
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  EIGEN_DONT_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0,
+                                    Index offset = 0);
+};
+
+template <typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate,
+          bool PanelMode>
+EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate,
+                                     PanelMode>::operator()(Scalar* blockA, const DataMapper& lhs, Index depth,
+                                                            Index rows, Index stride, Index offset) {
+  typedef typename unpacket_traits<Packet>::half HalfPacket;
+  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
+  enum {
+    PacketSize = unpacket_traits<Packet>::size,
+    HalfPacketSize = unpacket_traits<HalfPacket>::size,
+    QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
+    HasHalf = (int)HalfPacketSize < (int)PacketSize,
+    HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize
+  };
+
+  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
+  EIGEN_UNUSED_VARIABLE(stride);
+  EIGEN_UNUSED_VARIABLE(offset);
+  eigen_assert(((!PanelMode) && stride == 0 && offset == 0) || (PanelMode && stride >= depth && offset <= stride));
+  eigen_assert(((Pack1 % PacketSize) == 0 && Pack1 <= 4 * PacketSize) || (Pack1 <= 4) || (Pack1 < PacketSize));
+  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+  Index count = 0;
+
+  const Index peeled_mc3 = Pack1 >= 3 * PacketSize ? (rows / (3 * PacketSize)) * (3 * PacketSize) : 0;
+  const Index peeled_mc2 =
+      Pack1 >= 2 * PacketSize ? peeled_mc3 + ((rows - peeled_mc3) / (2 * PacketSize)) * (2 * PacketSize) : 0;
+  const Index peeled_mc1 =
+      Pack1 >= 1 * PacketSize ? peeled_mc2 + ((rows - peeled_mc2) / (1 * PacketSize)) * (1 * PacketSize) : 0;
+  const Index peeled_mc_half =
+      Pack1 >= HalfPacketSize ? peeled_mc1 + ((rows - peeled_mc1) / (HalfPacketSize)) * (HalfPacketSize) : 0;
+  const Index peeled_mc_quarter = Pack1 >= QuarterPacketSize ? (rows / (QuarterPacketSize)) * (QuarterPacketSize) : 0;
+  const Index last_lhs_progress =
+      rows > peeled_mc_quarter ? (Pack2 > 1 ? Pack2 : ((rows - peeled_mc_quarter) & ~1)) : 0;
+  const Index peeled_mc0 = Pack2 >= PacketSize              ? peeled_mc_quarter
+                           : Pack2 > 1 && last_lhs_progress ? (rows / last_lhs_progress) * last_lhs_progress
+                                                            : 0;
+
+  Index i = 0;
+
+  // Pack 3 packets
+  if (Pack1 >= 3 * PacketSize) {
+    for (; i < peeled_mc3; i += 3 * PacketSize) {
+      if (PanelMode) count += (3 * PacketSize) * offset;
+
+      for (Index k = 0; k < depth; k++) {
+        Packet A, B, C;
+        A = lhs.template loadPacket<Packet>(i + 0 * PacketSize, k);
+        B = lhs.template loadPacket<Packet>(i + 1 * PacketSize, k);
+        C = lhs.template loadPacket<Packet>(i + 2 * PacketSize, k);
+        pstore(blockA + count, cj.pconj(A));
+        count += PacketSize;
+        pstore(blockA + count, cj.pconj(B));
+        count += PacketSize;
+        pstore(blockA + count, cj.pconj(C));
+        count += PacketSize;
+      }
+      if (PanelMode) count += (3 * PacketSize) * (stride - offset - depth);
+    }
+  }
+  // Pack 2 packets
+  if (Pack1 >= 2 * PacketSize) {
+    for (; i < peeled_mc2; i += 2 * PacketSize) {
+      if (PanelMode) count += (2 * PacketSize) * offset;
+
+      for (Index k = 0; k < depth; k++) {
+        Packet A, B;
+        A = lhs.template loadPacket<Packet>(i + 0 * PacketSize, k);
+        B = lhs.template loadPacket<Packet>(i + 1 * PacketSize, k);
+        pstore(blockA + count, cj.pconj(A));
+        count += PacketSize;
+        pstore(blockA + count, cj.pconj(B));
+        count += PacketSize;
+      }
+      if (PanelMode) count += (2 * PacketSize) * (stride - offset - depth);
+    }
+  }
+  // Pack 1 packets
+  if (Pack1 >= 1 * PacketSize) {
+    for (; i < peeled_mc1; i += 1 * PacketSize) {
+      if (PanelMode) count += (1 * PacketSize) * offset;
+
+      for (Index k = 0; k < depth; k++) {
+        Packet A;
+        A = lhs.template loadPacket<Packet>(i + 0 * PacketSize, k);
+        pstore(blockA + count, cj.pconj(A));
+        count += PacketSize;
+      }
+      if (PanelMode) count += (1 * PacketSize) * (stride - offset - depth);
+    }
+  }
+  // Pack half packets
+  if (HasHalf && Pack1 >= HalfPacketSize) {
+    for (; i < peeled_mc_half; i += HalfPacketSize) {
+      if (PanelMode) count += (HalfPacketSize)*offset;
+
+      for (Index k = 0; k < depth; k++) {
+        HalfPacket A;
+        A = lhs.template loadPacket<HalfPacket>(i + 0 * (HalfPacketSize), k);
+        pstoreu(blockA + count, cj.pconj(A));
+        count += HalfPacketSize;
+      }
+      if (PanelMode) count += (HalfPacketSize) * (stride - offset - depth);
+    }
+  }
+  // Pack quarter packets
+  if (HasQuarter && Pack1 >= QuarterPacketSize) {
+    for (; i < peeled_mc_quarter; i += QuarterPacketSize) {
+      if (PanelMode) count += (QuarterPacketSize)*offset;
+
+      for (Index k = 0; k < depth; k++) {
+        QuarterPacket A;
+        A = lhs.template loadPacket<QuarterPacket>(i + 0 * (QuarterPacketSize), k);
+        pstoreu(blockA + count, cj.pconj(A));
+        count += QuarterPacketSize;
+      }
+      if (PanelMode) count += (QuarterPacketSize) * (stride - offset - depth);
+    }
+  }
+  // Pack2 may be *smaller* than PacketSize—that happens for
+  // products like real * complex, where we have to go half the
+  // progress on the lhs in order to duplicate those operands to
+  // address both real & imaginary parts on the rhs. This portion will
+  // pack those half ones until they match the number expected on the
+  // last peeling loop at this point (for the rhs).
+  if (Pack2 < PacketSize && Pack2 > 1) {
+    for (; i < peeled_mc0; i += last_lhs_progress) {
+      if (PanelMode) count += last_lhs_progress * offset;
+
+      for (Index k = 0; k < depth; k++)
+        for (Index w = 0; w < last_lhs_progress; w++) blockA[count++] = cj(lhs(i + w, k));
+
+      if (PanelMode) count += last_lhs_progress * (stride - offset - depth);
+    }
+  }
+  // Pack scalars
+  for (; i < rows; i++) {
+    if (PanelMode) count += offset;
+    for (Index k = 0; k < depth; k++) blockA[count++] = cj(lhs(i, k));
+    if (PanelMode) count += (stride - offset - depth);
+  }
+}
+
+template <typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate,
+          bool PanelMode>
+struct gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode> {
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  EIGEN_DONT_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride = 0,
+                                    Index offset = 0);
+};
+
+template <typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate,
+          bool PanelMode>
+EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate,
+                                     PanelMode>::operator()(Scalar* blockA, const DataMapper& lhs, Index depth,
+                                                            Index rows, Index stride, Index offset) {
+  typedef typename unpacket_traits<Packet>::half HalfPacket;
+  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
+  enum {
+    PacketSize = unpacket_traits<Packet>::size,
+    HalfPacketSize = unpacket_traits<HalfPacket>::size,
+    QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
+    HasHalf = (int)HalfPacketSize < (int)PacketSize,
+    HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize
+  };
+
+  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
+  EIGEN_UNUSED_VARIABLE(stride);
+  EIGEN_UNUSED_VARIABLE(offset);
+  eigen_assert(((!PanelMode) && stride == 0 && offset == 0) || (PanelMode && stride >= depth && offset <= stride));
+  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+  Index count = 0;
+  bool gone_half = false, gone_quarter = false, gone_last = false;
+
+  Index i = 0;
+  Index pack = Pack1;
+  Index psize = PacketSize;
+  while (pack > 0) {
+    Index remaining_rows = rows - i;
+    Index peeled_mc = gone_last ? Pack2 > 1 ? (rows / pack) * pack : 0 : i + (remaining_rows / pack) * pack;
+    Index starting_pos = i;
+    for (; i < peeled_mc; i += pack) {
+      if (PanelMode) count += pack * offset;
+
+      Index k = 0;
+      if (pack >= psize && psize >= QuarterPacketSize) {
+        const Index peeled_k = (depth / psize) * psize;
+        for (; k < peeled_k; k += psize) {
+          for (Index m = 0; m < pack; m += psize) {
+            if (psize == PacketSize) {
+              PacketBlock<Packet> kernel;
+              for (Index p = 0; p < psize; ++p) kernel.packet[p] = lhs.template loadPacket<Packet>(i + p + m, k);
+              ptranspose(kernel);
+              for (Index p = 0; p < psize; ++p) pstore(blockA + count + m + (pack)*p, cj.pconj(kernel.packet[p]));
+            } else if (HasHalf && psize == HalfPacketSize) {
+              gone_half = true;
+              PacketBlock<HalfPacket> kernel_half;
+              for (Index p = 0; p < psize; ++p)
+                kernel_half.packet[p] = lhs.template loadPacket<HalfPacket>(i + p + m, k);
+              ptranspose(kernel_half);
+              for (Index p = 0; p < psize; ++p) pstore(blockA + count + m + (pack)*p, cj.pconj(kernel_half.packet[p]));
+            } else if (HasQuarter && psize == QuarterPacketSize) {
+              gone_quarter = true;
+              PacketBlock<QuarterPacket> kernel_quarter;
+              for (Index p = 0; p < psize; ++p)
+                kernel_quarter.packet[p] = lhs.template loadPacket<QuarterPacket>(i + p + m, k);
+              ptranspose(kernel_quarter);
+              for (Index p = 0; p < psize; ++p)
+                pstore(blockA + count + m + (pack)*p, cj.pconj(kernel_quarter.packet[p]));
+            }
+          }
+          count += psize * pack;
+        }
+      }
+
+      for (; k < depth; k++) {
+        Index w = 0;
+        for (; w < pack - 3; w += 4) {
+          Scalar a(cj(lhs(i + w + 0, k))), b(cj(lhs(i + w + 1, k))), c(cj(lhs(i + w + 2, k))), d(cj(lhs(i + w + 3, k)));
+          blockA[count++] = a;
+          blockA[count++] = b;
+          blockA[count++] = c;
+          blockA[count++] = d;
+        }
+        if (pack % 4)
+          for (; w < pack; ++w) blockA[count++] = cj(lhs(i + w, k));
+      }
+
+      if (PanelMode) count += pack * (stride - offset - depth);
+    }
+
+    pack -= psize;
+    Index left = rows - i;
+    if (pack <= 0) {
+      if (!gone_last && (starting_pos == i || left >= psize / 2 || left >= psize / 4) &&
+          ((psize / 2 == HalfPacketSize && HasHalf && !gone_half) ||
+           (psize / 2 == QuarterPacketSize && HasQuarter && !gone_quarter))) {
+        psize /= 2;
+        pack = psize;
+        continue;
+      }
+      // Pack2 may be *smaller* than PacketSize—that happens for
+      // products like real * complex, where we have to go half the
+      // progress on the lhs in order to duplicate those operands to
+      // address both real & imaginary parts on the rhs. This portion will
+      // pack those half ones until they match the number expected on the
+      // last peeling loop at this point (for the rhs).
+      if (Pack2 < PacketSize && !gone_last) {
+        gone_last = true;
+        psize = pack = Pack2;
+      }
+    }
+  }
+
+  for (; i < rows; i++) {
+    if (PanelMode) count += offset;
+    for (Index k = 0; k < depth; k++) blockA[count++] = cj(lhs(i, k));
+    if (PanelMode) count += (stride - offset - depth);
+  }
+}
+
+// copy a complete panel of the rhs
+// this version is optimized for column major matrices
+// The traversal order is as follow: (nr==4):
+//  0  1  2  3   12 13 14 15   24 27
+//  4  5  6  7   16 17 18 19   25 28
+//  8  9 10 11   20 21 22 23   26 29
+//  .  .  .  .    .  .  .  .    .  .
+template <typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode> {
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  enum { PacketSize = packet_traits<Scalar>::size };
+  EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0,
+                                    Index offset = 0);
+};
+
+template <typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>::operator()(
+    Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) {
+  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
+  EIGEN_UNUSED_VARIABLE(stride);
+  EIGEN_UNUSED_VARIABLE(offset);
+  eigen_assert(((!PanelMode) && stride == 0 && offset == 0) || (PanelMode && stride >= depth && offset <= stride));
+  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+  Index packet_cols8 = nr >= 8 ? (cols / 8) * 8 : 0;
+  Index packet_cols4 = nr >= 4 ? (cols / 4) * 4 : 0;
+  Index count = 0;
+  const Index peeled_k = (depth / PacketSize) * PacketSize;
+
+#if EIGEN_ARCH_ARM64 || EIGEN_ARCH_LOONGARCH64
+  EIGEN_IF_CONSTEXPR(nr >= 8) {
+    for (Index j2 = 0; j2 < packet_cols8; j2 += 8) {
+      // skip what we have before
+      if (PanelMode) count += 8 * offset;
+      const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0);
+      const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1);
+      const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2);
+      const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3);
+      const LinearMapper dm4 = rhs.getLinearMapper(0, j2 + 4);
+      const LinearMapper dm5 = rhs.getLinearMapper(0, j2 + 5);
+      const LinearMapper dm6 = rhs.getLinearMapper(0, j2 + 6);
+      const LinearMapper dm7 = rhs.getLinearMapper(0, j2 + 7);
+      Index k = 0;
+      if (PacketSize % 2 == 0 && PacketSize <= 8)  // 2 4 8
+      {
+        for (; k < peeled_k; k += PacketSize) {
+          if (PacketSize == 2) {
+            PacketBlock<Packet, PacketSize == 2 ? 2 : PacketSize> kernel0, kernel1, kernel2, kernel3;
+            kernel0.packet[0 % PacketSize] = dm0.template loadPacket<Packet>(k);
+            kernel0.packet[1 % PacketSize] = dm1.template loadPacket<Packet>(k);
+            kernel1.packet[0 % PacketSize] = dm2.template loadPacket<Packet>(k);
+            kernel1.packet[1 % PacketSize] = dm3.template loadPacket<Packet>(k);
+            kernel2.packet[0 % PacketSize] = dm4.template loadPacket<Packet>(k);
+            kernel2.packet[1 % PacketSize] = dm5.template loadPacket<Packet>(k);
+            kernel3.packet[0 % PacketSize] = dm6.template loadPacket<Packet>(k);
+            kernel3.packet[1 % PacketSize] = dm7.template loadPacket<Packet>(k);
+            ptranspose(kernel0);
+            ptranspose(kernel1);
+            ptranspose(kernel2);
+            ptranspose(kernel3);
+
+            pstoreu(blockB + count + 0 * PacketSize, cj.pconj(kernel0.packet[0 % PacketSize]));
+            pstoreu(blockB + count + 1 * PacketSize, cj.pconj(kernel1.packet[0 % PacketSize]));
+            pstoreu(blockB + count + 2 * PacketSize, cj.pconj(kernel2.packet[0 % PacketSize]));
+            pstoreu(blockB + count + 3 * PacketSize, cj.pconj(kernel3.packet[0 % PacketSize]));
+
+            pstoreu(blockB + count + 4 * PacketSize, cj.pconj(kernel0.packet[1 % PacketSize]));
+            pstoreu(blockB + count + 5 * PacketSize, cj.pconj(kernel1.packet[1 % PacketSize]));
+            pstoreu(blockB + count + 6 * PacketSize, cj.pconj(kernel2.packet[1 % PacketSize]));
+            pstoreu(blockB + count + 7 * PacketSize, cj.pconj(kernel3.packet[1 % PacketSize]));
+            count += 8 * PacketSize;
+          } else if (PacketSize == 4) {
+            PacketBlock<Packet, PacketSize == 4 ? 4 : PacketSize> kernel0, kernel1;
+
+            kernel0.packet[0 % PacketSize] = dm0.template loadPacket<Packet>(k);
+            kernel0.packet[1 % PacketSize] = dm1.template loadPacket<Packet>(k);
+            kernel0.packet[2 % PacketSize] = dm2.template loadPacket<Packet>(k);
+            kernel0.packet[3 % PacketSize] = dm3.template loadPacket<Packet>(k);
+            kernel1.packet[0 % PacketSize] = dm4.template loadPacket<Packet>(k);
+            kernel1.packet[1 % PacketSize] = dm5.template loadPacket<Packet>(k);
+            kernel1.packet[2 % PacketSize] = dm6.template loadPacket<Packet>(k);
+            kernel1.packet[3 % PacketSize] = dm7.template loadPacket<Packet>(k);
+            ptranspose(kernel0);
+            ptranspose(kernel1);
+
+            pstoreu(blockB + count + 0 * PacketSize, cj.pconj(kernel0.packet[0 % PacketSize]));
+            pstoreu(blockB + count + 1 * PacketSize, cj.pconj(kernel1.packet[0 % PacketSize]));
+            pstoreu(blockB + count + 2 * PacketSize, cj.pconj(kernel0.packet[1 % PacketSize]));
+            pstoreu(blockB + count + 3 * PacketSize, cj.pconj(kernel1.packet[1 % PacketSize]));
+            pstoreu(blockB + count + 4 * PacketSize, cj.pconj(kernel0.packet[2 % PacketSize]));
+            pstoreu(blockB + count + 5 * PacketSize, cj.pconj(kernel1.packet[2 % PacketSize]));
+            pstoreu(blockB + count + 6 * PacketSize, cj.pconj(kernel0.packet[3 % PacketSize]));
+            pstoreu(blockB + count + 7 * PacketSize, cj.pconj(kernel1.packet[3 % PacketSize]));
+            count += 8 * PacketSize;
+          } else if (PacketSize == 8) {
+            PacketBlock<Packet, PacketSize == 8 ? 8 : PacketSize> kernel0;
+
+            kernel0.packet[0 % PacketSize] = dm0.template loadPacket<Packet>(k);
+            kernel0.packet[1 % PacketSize] = dm1.template loadPacket<Packet>(k);
+            kernel0.packet[2 % PacketSize] = dm2.template loadPacket<Packet>(k);
+            kernel0.packet[3 % PacketSize] = dm3.template loadPacket<Packet>(k);
+            kernel0.packet[4 % PacketSize] = dm4.template loadPacket<Packet>(k);
+            kernel0.packet[5 % PacketSize] = dm5.template loadPacket<Packet>(k);
+            kernel0.packet[6 % PacketSize] = dm6.template loadPacket<Packet>(k);
+            kernel0.packet[7 % PacketSize] = dm7.template loadPacket<Packet>(k);
+            ptranspose(kernel0);
+
+            pstoreu(blockB + count + 0 * PacketSize, cj.pconj(kernel0.packet[0 % PacketSize]));
+            pstoreu(blockB + count + 1 * PacketSize, cj.pconj(kernel0.packet[1 % PacketSize]));
+            pstoreu(blockB + count + 2 * PacketSize, cj.pconj(kernel0.packet[2 % PacketSize]));
+            pstoreu(blockB + count + 3 * PacketSize, cj.pconj(kernel0.packet[3 % PacketSize]));
+            pstoreu(blockB + count + 4 * PacketSize, cj.pconj(kernel0.packet[4 % PacketSize]));
+            pstoreu(blockB + count + 5 * PacketSize, cj.pconj(kernel0.packet[5 % PacketSize]));
+            pstoreu(blockB + count + 6 * PacketSize, cj.pconj(kernel0.packet[6 % PacketSize]));
+            pstoreu(blockB + count + 7 * PacketSize, cj.pconj(kernel0.packet[7 % PacketSize]));
+            count += 8 * PacketSize;
+          }
+        }
+      }
+
+      for (; k < depth; k++) {
+        blockB[count + 0] = cj(dm0(k));
+        blockB[count + 1] = cj(dm1(k));
+        blockB[count + 2] = cj(dm2(k));
+        blockB[count + 3] = cj(dm3(k));
+        blockB[count + 4] = cj(dm4(k));
+        blockB[count + 5] = cj(dm5(k));
+        blockB[count + 6] = cj(dm6(k));
+        blockB[count + 7] = cj(dm7(k));
+        count += 8;
+      }
+      // skip what we have after
+      if (PanelMode) count += 8 * (stride - offset - depth);
+    }
+  }
+#endif
+
+  EIGEN_IF_CONSTEXPR(nr >= 4) {
+    for (Index j2 = packet_cols8; j2 < packet_cols4; j2 += 4) {
+      // skip what we have before
+      if (PanelMode) count += 4 * offset;
+      const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0);
+      const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1);
+      const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2);
+      const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3);
+
+      Index k = 0;
+      if ((PacketSize % 4) == 0)  // TODO: enable vectorized transposition for PacketSize==2.
+      {
+        for (; k < peeled_k; k += PacketSize) {
+          PacketBlock<Packet, (PacketSize % 4) == 0 ? 4 : PacketSize> kernel;
+          kernel.packet[0] = dm0.template loadPacket<Packet>(k);
+          kernel.packet[1 % PacketSize] = dm1.template loadPacket<Packet>(k);
+          kernel.packet[2 % PacketSize] = dm2.template loadPacket<Packet>(k);
+          kernel.packet[3 % PacketSize] = dm3.template loadPacket<Packet>(k);
+          ptranspose(kernel);
+          pstoreu(blockB + count + 0 * PacketSize, cj.pconj(kernel.packet[0]));
+          pstoreu(blockB + count + 1 * PacketSize, cj.pconj(kernel.packet[1 % PacketSize]));
+          pstoreu(blockB + count + 2 * PacketSize, cj.pconj(kernel.packet[2 % PacketSize]));
+          pstoreu(blockB + count + 3 * PacketSize, cj.pconj(kernel.packet[3 % PacketSize]));
+          count += 4 * PacketSize;
+        }
+      }
+      for (; k < depth; k++) {
+        blockB[count + 0] = cj(dm0(k));
+        blockB[count + 1] = cj(dm1(k));
+        blockB[count + 2] = cj(dm2(k));
+        blockB[count + 3] = cj(dm3(k));
+        count += 4;
+      }
+      // skip what we have after
+      if (PanelMode) count += 4 * (stride - offset - depth);
+    }
+  }
+
+  // copy the remaining columns one at a time (nr==1)
+  for (Index j2 = packet_cols4; j2 < cols; ++j2) {
+    if (PanelMode) count += offset;
+    const LinearMapper dm0 = rhs.getLinearMapper(0, j2);
+    for (Index k = 0; k < depth; k++) {
+      blockB[count] = cj(dm0(k));
+      count += 1;
+    }
+    if (PanelMode) count += (stride - offset - depth);
+  }
+}
+
+// this version is optimized for row major matrices
+template <typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<Scalar, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode> {
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename unpacket_traits<Packet>::half HalfPacket;
+  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  enum {
+    PacketSize = packet_traits<Scalar>::size,
+    HalfPacketSize = unpacket_traits<HalfPacket>::size,
+    QuarterPacketSize = unpacket_traits<QuarterPacket>::size
+  };
+  EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride = 0,
+                                    Index offset = 0) {
+    EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
+    EIGEN_UNUSED_VARIABLE(stride);
+    EIGEN_UNUSED_VARIABLE(offset);
+    eigen_assert(((!PanelMode) && stride == 0 && offset == 0) || (PanelMode && stride >= depth && offset <= stride));
+    const bool HasHalf = (int)HalfPacketSize < (int)PacketSize;
+    const bool HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize;
+    conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+    Index packet_cols8 = nr >= 8 ? (cols / 8) * 8 : 0;
+    Index packet_cols4 = nr >= 4 ? (cols / 4) * 4 : 0;
+    Index count = 0;
+
+#if EIGEN_ARCH_ARM64 || EIGEN_ARCH_LOONGARCH64
+    EIGEN_IF_CONSTEXPR(nr >= 8) {
+      for (Index j2 = 0; j2 < packet_cols8; j2 += 8) {
+        // skip what we have before
+        if (PanelMode) count += 8 * offset;
+        for (Index k = 0; k < depth; k++) {
+          if (PacketSize == 8) {
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+            count += PacketSize;
+          } else if (PacketSize == 4) {
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            Packet B = rhs.template loadPacket<Packet>(k, j2 + 4);
+            pstoreu(blockB + count, cj.pconj(A));
+            pstoreu(blockB + count + PacketSize, cj.pconj(B));
+            count += 2 * PacketSize;
+          } else {
+            const LinearMapper dm0 = rhs.getLinearMapper(k, j2);
+            blockB[count + 0] = cj(dm0(0));
+            blockB[count + 1] = cj(dm0(1));
+            blockB[count + 2] = cj(dm0(2));
+            blockB[count + 3] = cj(dm0(3));
+            blockB[count + 4] = cj(dm0(4));
+            blockB[count + 5] = cj(dm0(5));
+            blockB[count + 6] = cj(dm0(6));
+            blockB[count + 7] = cj(dm0(7));
+            count += 8;
+          }
+        }
+        // skip what we have after
+        if (PanelMode) count += 8 * (stride - offset - depth);
+      }
+    }
+#endif
+
+    if (nr >= 4) {
+      for (Index j2 = packet_cols8; j2 < packet_cols4; j2 += 4) {
+        // skip what we have before
+        if (PanelMode) count += 4 * offset;
+        for (Index k = 0; k < depth; k++) {
+          if (PacketSize == 4) {
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+            count += PacketSize;
+          } else if (HasHalf && HalfPacketSize == 4) {
+            HalfPacket A = rhs.template loadPacket<HalfPacket>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+            count += HalfPacketSize;
+          } else if (HasQuarter && QuarterPacketSize == 4) {
+            QuarterPacket A = rhs.template loadPacket<QuarterPacket>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+            count += QuarterPacketSize;
+          } else {
+            const LinearMapper dm0 = rhs.getLinearMapper(k, j2);
+            blockB[count + 0] = cj(dm0(0));
+            blockB[count + 1] = cj(dm0(1));
+            blockB[count + 2] = cj(dm0(2));
+            blockB[count + 3] = cj(dm0(3));
+            count += 4;
+          }
+        }
+        // skip what we have after
+        if (PanelMode) count += 4 * (stride - offset - depth);
+      }
+    }
+    // copy the remaining columns one at a time (nr==1)
+    for (Index j2 = packet_cols4; j2 < cols; ++j2) {
+      if (PanelMode) count += offset;
+      for (Index k = 0; k < depth; k++) {
+        blockB[count] = cj(rhs(k, j2));
+        count += 1;
+      }
+      if (PanelMode) count += stride - offset - depth;
+    }
+  }
+};
+
+}  // end namespace internal
+
+/** \returns the currently set level 1 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
+ * \sa setCpuCacheSize */
+inline std::ptrdiff_t l1CacheSize() {
+  std::ptrdiff_t l1, l2, l3;
+  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
+  return l1;
+}
+
+/** \returns the currently set level 2 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
+ * \sa setCpuCacheSize */
+inline std::ptrdiff_t l2CacheSize() {
+  std::ptrdiff_t l1, l2, l3;
+  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
+  return l2;
+}
+
+/** \returns the currently set level 3 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
+ * \sa setCpuCacheSize */
+inline std::ptrdiff_t l3CacheSize() {
+  std::ptrdiff_t l1, l2, l3;
+  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
+  return l3;
+}
+
+/** Set the cpu L1 and L2 cache sizes (in bytes).
+ * These values are use to adjust the size of the blocks
+ * for the algorithms working per blocks.
+ *
+ * \sa computeProductBlockingSizes */
+inline void setCpuCacheSizes(std::ptrdiff_t l1, std::ptrdiff_t l2, std::ptrdiff_t l3) {
+  internal::manage_caching_sizes(SetAction, &l1, &l2, &l3);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GENERAL_BLOCK_PANEL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrix.h
new file mode 100644
index 0000000..2b45dd2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrix.h
@@ -0,0 +1,459 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERAL_MATRIX_MATRIX_H
+#define EIGEN_GENERAL_MATRIX_MATRIX_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename LhsScalar_, typename RhsScalar_>
+class level3_blocking;
+
+/* Specialization for a row-major destination matrix => simple transposition of the product */
+template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
+          int RhsStorageOrder, bool ConjugateRhs, int ResInnerStride>
+struct general_matrix_matrix_product<Index, LhsScalar, LhsStorageOrder, ConjugateLhs, RhsScalar, RhsStorageOrder,
+                                     ConjugateRhs, RowMajor, ResInnerStride> {
+  typedef gebp_traits<RhsScalar, LhsScalar> Traits;
+
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static EIGEN_STRONG_INLINE void run(Index rows, Index cols, Index depth, const LhsScalar* lhs, Index lhsStride,
+                                      const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resIncr,
+                                      Index resStride, ResScalar alpha, level3_blocking<RhsScalar, LhsScalar>& blocking,
+                                      GemmParallelInfo<Index>* info = 0) {
+    // transpose the product such that the result is column major
+    general_matrix_matrix_product<Index, RhsScalar, RhsStorageOrder == RowMajor ? ColMajor : RowMajor, ConjugateRhs,
+                                  LhsScalar, LhsStorageOrder == RowMajor ? ColMajor : RowMajor, ConjugateLhs, ColMajor,
+                                  ResInnerStride>::run(cols, rows, depth, rhs, rhsStride, lhs, lhsStride, res, resIncr,
+                                                       resStride, alpha, blocking, info);
+  }
+};
+
+/*  Specialization for a col-major destination matrix
+ *    => Blocking algorithm following Goto's paper */
+template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
+          int RhsStorageOrder, bool ConjugateRhs, int ResInnerStride>
+struct general_matrix_matrix_product<Index, LhsScalar, LhsStorageOrder, ConjugateLhs, RhsScalar, RhsStorageOrder,
+                                     ConjugateRhs, ColMajor, ResInnerStride> {
+  typedef gebp_traits<LhsScalar, RhsScalar> Traits;
+
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static void run(Index rows, Index cols, Index depth, const LhsScalar* lhs_, Index lhsStride, const RhsScalar* rhs_,
+                  Index rhsStride, ResScalar* res_, Index resIncr, Index resStride, ResScalar alpha,
+                  level3_blocking<LhsScalar, RhsScalar>& blocking, GemmParallelInfo<Index>* info = 0) {
+    typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
+    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+    LhsMapper lhs(lhs_, lhsStride);
+    RhsMapper rhs(rhs_, rhsStride);
+    ResMapper res(res_, resStride, resIncr);
+
+    Index kc = blocking.kc();                    // cache block size along the K direction
+    Index mc = (std::min)(rows, blocking.mc());  // cache block size along the M direction
+    Index nc = (std::min)(cols, blocking.nc());  // cache block size along the N direction
+
+    gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
+                  LhsStorageOrder>
+        pack_lhs;
+    gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
+    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;
+
+#if !defined(EIGEN_USE_BLAS) && (defined(EIGEN_HAS_OPENMP) || defined(EIGEN_GEMM_THREADPOOL))
+    if (info) {
+      // this is the parallel version!
+      int tid = info->logical_thread_id;
+      int threads = info->num_threads;
+
+      LhsScalar* blockA = blocking.blockA();
+      eigen_internal_assert(blockA != 0);
+
+      std::size_t sizeB = kc * nc;
+      ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, 0);
+
+      // For each horizontal panel of the rhs, and corresponding vertical panel of the lhs...
+      for (Index k = 0; k < depth; k += kc) {
+        const Index actual_kc = (std::min)(k + kc, depth) - k;  // => rows of B', and cols of the A'
+
+        // In order to reduce the chance that a thread has to wait for the other,
+        // let's start by packing B'.
+        pack_rhs(blockB, rhs.getSubMapper(k, 0), actual_kc, nc);
+
+        // Pack A_k to A' in a parallel fashion:
+        // each thread packs the sub block A_k,i to A'_i where i is the thread id.
+
+        // However, before copying to A'_i, we have to make sure that no other thread is still using it,
+        // i.e., we test that info->task_info[tid].users equals 0.
+        // Then, we set info->task_info[tid].users to the number of threads to mark that all other threads are going to
+        // use it.
+        while (info->task_info[tid].users != 0) {
+          std::this_thread::yield();
+        }
+        info->task_info[tid].users = threads;
+
+        pack_lhs(blockA + info->task_info[tid].lhs_start * actual_kc,
+                 lhs.getSubMapper(info->task_info[tid].lhs_start, k), actual_kc, info->task_info[tid].lhs_length);
+
+        // Notify the other threads that the part A'_i is ready to go.
+        info->task_info[tid].sync = k;
+
+        // Computes C_i += A' * B' per A'_i
+        for (int shift = 0; shift < threads; ++shift) {
+          int i = (tid + shift) % threads;
+
+          // At this point we have to make sure that A'_i has been updated by the thread i,
+          // we use testAndSetOrdered to mimic a volatile access.
+          // However, no need to wait for the B' part which has been updated by the current thread!
+          if (shift > 0) {
+            while (info->task_info[i].sync != k) {
+              std::this_thread::yield();
+            }
+          }
+
+          gebp(res.getSubMapper(info->task_info[i].lhs_start, 0), blockA + info->task_info[i].lhs_start * actual_kc,
+               blockB, info->task_info[i].lhs_length, actual_kc, nc, alpha);
+        }
+
+        // Then keep going as usual with the remaining B'
+        for (Index j = nc; j < cols; j += nc) {
+          const Index actual_nc = (std::min)(j + nc, cols) - j;
+
+          // pack B_k,j to B'
+          pack_rhs(blockB, rhs.getSubMapper(k, j), actual_kc, actual_nc);
+
+          // C_j += A' * B'
+          gebp(res.getSubMapper(0, j), blockA, blockB, rows, actual_kc, actual_nc, alpha);
+        }
+
+        // Release all the sub blocks A'_i of A' for the current thread,
+        // i.e., we simply decrement the number of users by 1
+        for (Index i = 0; i < threads; ++i) info->task_info[i].users -= 1;
+      }
+    } else
+#endif  // defined(EIGEN_HAS_OPENMP) || defined(EIGEN_GEMM_THREADPOOL)
+    {
+      EIGEN_UNUSED_VARIABLE(info);
+
+      // this is the sequential version!
+      std::size_t sizeA = kc * mc;
+      std::size_t sizeB = kc * nc;
+
+      ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
+      ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());
+
+      const bool pack_rhs_once = mc != rows && kc == depth && nc == cols;
+
+      // For each horizontal panel of the rhs, and corresponding panel of the lhs...
+      for (Index i2 = 0; i2 < rows; i2 += mc) {
+        const Index actual_mc = (std::min)(i2 + mc, rows) - i2;
+
+        for (Index k2 = 0; k2 < depth; k2 += kc) {
+          const Index actual_kc = (std::min)(k2 + kc, depth) - k2;
+
+          // OK, here we have selected one horizontal panel of rhs and one vertical panel of lhs.
+          // => Pack lhs's panel into a sequential chunk of memory (L2/L3 caching)
+          // Note that this panel will be read as many times as the number of blocks in the rhs's
+          // horizontal panel which is, in practice, a very low number.
+          pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+          // For each kc x nc block of the rhs's horizontal panel...
+          for (Index j2 = 0; j2 < cols; j2 += nc) {
+            const Index actual_nc = (std::min)(j2 + nc, cols) - j2;
+
+            // We pack the rhs's block into a sequential chunk of memory (L2 caching)
+            // Note that this block will be read a very high number of times, which is equal to the number of
+            // micro horizontal panel of the large rhs's panel (e.g., rows/12 times).
+            if ((!pack_rhs_once) || i2 == 0) pack_rhs(blockB, rhs.getSubMapper(k2, j2), actual_kc, actual_nc);
+
+            // Everything is packed, we can now call the panel * block kernel:
+            gebp(res.getSubMapper(i2, j2), blockA, blockB, actual_mc, actual_kc, actual_nc, alpha);
+          }
+        }
+      }
+    }
+  }
+};
+
+/*********************************************************************************
+ *  Specialization of generic_product_impl for "large" GEMM, i.e.,
+ *  implementation of the high level wrapper to general_matrix_matrix_product
+ **********************************************************************************/
+
+template <typename Scalar, typename Index, typename Gemm, typename Lhs, typename Rhs, typename Dest,
+          typename BlockingType>
+struct gemm_functor {
+  gemm_functor(const Lhs& lhs, const Rhs& rhs, Dest& dest, const Scalar& actualAlpha, BlockingType& blocking)
+      : m_lhs(lhs), m_rhs(rhs), m_dest(dest), m_actualAlpha(actualAlpha), m_blocking(blocking) {}
+
+  void initParallelSession(Index num_threads) const {
+    m_blocking.initParallel(m_lhs.rows(), m_rhs.cols(), m_lhs.cols(), num_threads);
+    m_blocking.allocateA();
+  }
+
+  void operator()(Index row, Index rows, Index col = 0, Index cols = -1, GemmParallelInfo<Index>* info = 0) const {
+    if (cols == -1) cols = m_rhs.cols();
+
+    Gemm::run(rows, cols, m_lhs.cols(), &m_lhs.coeffRef(row, 0), m_lhs.outerStride(), &m_rhs.coeffRef(0, col),
+              m_rhs.outerStride(), (Scalar*)&(m_dest.coeffRef(row, col)), m_dest.innerStride(), m_dest.outerStride(),
+              m_actualAlpha, m_blocking, info);
+  }
+
+  typedef typename Gemm::Traits Traits;
+
+ protected:
+  const Lhs& m_lhs;
+  const Rhs& m_rhs;
+  Dest& m_dest;
+  Scalar m_actualAlpha;
+  BlockingType& m_blocking;
+};
+
+template <int StorageOrder, typename LhsScalar, typename RhsScalar, int MaxRows, int MaxCols, int MaxDepth,
+          int KcFactor = 1, bool FiniteAtCompileTime = MaxRows != Dynamic && MaxCols != Dynamic && MaxDepth != Dynamic>
+class gemm_blocking_space;
+
+template <typename LhsScalar_, typename RhsScalar_>
+class level3_blocking {
+  typedef LhsScalar_ LhsScalar;
+  typedef RhsScalar_ RhsScalar;
+
+ protected:
+  LhsScalar* m_blockA;
+  RhsScalar* m_blockB;
+
+  Index m_mc;
+  Index m_nc;
+  Index m_kc;
+
+ public:
+  level3_blocking() : m_blockA(0), m_blockB(0), m_mc(0), m_nc(0), m_kc(0) {}
+
+  inline Index mc() const { return m_mc; }
+  inline Index nc() const { return m_nc; }
+  inline Index kc() const { return m_kc; }
+
+  inline LhsScalar* blockA() { return m_blockA; }
+  inline RhsScalar* blockB() { return m_blockB; }
+};
+
+template <int StorageOrder, typename LhsScalar_, typename RhsScalar_, int MaxRows, int MaxCols, int MaxDepth,
+          int KcFactor>
+class gemm_blocking_space<StorageOrder, LhsScalar_, RhsScalar_, MaxRows, MaxCols, MaxDepth, KcFactor,
+                          true /* == FiniteAtCompileTime */>
+    : public level3_blocking<std::conditional_t<StorageOrder == RowMajor, RhsScalar_, LhsScalar_>,
+                             std::conditional_t<StorageOrder == RowMajor, LhsScalar_, RhsScalar_>> {
+  enum {
+    Transpose = StorageOrder == RowMajor,
+    ActualRows = Transpose ? MaxCols : MaxRows,
+    ActualCols = Transpose ? MaxRows : MaxCols
+  };
+  typedef std::conditional_t<Transpose, RhsScalar_, LhsScalar_> LhsScalar;
+  typedef std::conditional_t<Transpose, LhsScalar_, RhsScalar_> RhsScalar;
+  enum { SizeA = ActualRows * MaxDepth, SizeB = ActualCols * MaxDepth };
+
+#if EIGEN_MAX_STATIC_ALIGN_BYTES >= EIGEN_DEFAULT_ALIGN_BYTES
+  EIGEN_ALIGN_MAX LhsScalar m_staticA[SizeA];
+  EIGEN_ALIGN_MAX RhsScalar m_staticB[SizeB];
+#else
+  EIGEN_ALIGN_MAX char m_staticA[SizeA * sizeof(LhsScalar) + EIGEN_DEFAULT_ALIGN_BYTES - 1];
+  EIGEN_ALIGN_MAX char m_staticB[SizeB * sizeof(RhsScalar) + EIGEN_DEFAULT_ALIGN_BYTES - 1];
+#endif
+
+ public:
+  gemm_blocking_space(Index /*rows*/, Index /*cols*/, Index /*depth*/, Index /*num_threads*/,
+                      bool /*full_rows = false*/) {
+    this->m_mc = ActualRows;
+    this->m_nc = ActualCols;
+    this->m_kc = MaxDepth;
+#if EIGEN_MAX_STATIC_ALIGN_BYTES >= EIGEN_DEFAULT_ALIGN_BYTES
+    this->m_blockA = m_staticA;
+    this->m_blockB = m_staticB;
+#else
+    this->m_blockA = reinterpret_cast<LhsScalar*>((std::uintptr_t(m_staticA) + (EIGEN_DEFAULT_ALIGN_BYTES - 1)) &
+                                                  ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES - 1));
+    this->m_blockB = reinterpret_cast<RhsScalar*>((std::uintptr_t(m_staticB) + (EIGEN_DEFAULT_ALIGN_BYTES - 1)) &
+                                                  ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES - 1));
+#endif
+  }
+
+  void initParallel(Index, Index, Index, Index) {}
+
+  inline void allocateA() {}
+  inline void allocateB() {}
+  inline void allocateAll() {}
+};
+
+template <int StorageOrder, typename LhsScalar_, typename RhsScalar_, int MaxRows, int MaxCols, int MaxDepth,
+          int KcFactor>
+class gemm_blocking_space<StorageOrder, LhsScalar_, RhsScalar_, MaxRows, MaxCols, MaxDepth, KcFactor, false>
+    : public level3_blocking<std::conditional_t<StorageOrder == RowMajor, RhsScalar_, LhsScalar_>,
+                             std::conditional_t<StorageOrder == RowMajor, LhsScalar_, RhsScalar_>> {
+  enum { Transpose = StorageOrder == RowMajor };
+  typedef std::conditional_t<Transpose, RhsScalar_, LhsScalar_> LhsScalar;
+  typedef std::conditional_t<Transpose, LhsScalar_, RhsScalar_> RhsScalar;
+
+  Index m_sizeA;
+  Index m_sizeB;
+
+ public:
+  gemm_blocking_space(Index rows, Index cols, Index depth, Index num_threads, bool l3_blocking) {
+    this->m_mc = Transpose ? cols : rows;
+    this->m_nc = Transpose ? rows : cols;
+    this->m_kc = depth;
+
+    if (l3_blocking) {
+      computeProductBlockingSizes<LhsScalar, RhsScalar, KcFactor>(this->m_kc, this->m_mc, this->m_nc, num_threads);
+    } else  // no l3 blocking
+    {
+      Index n = this->m_nc;
+      computeProductBlockingSizes<LhsScalar, RhsScalar, KcFactor>(this->m_kc, this->m_mc, n, num_threads);
+    }
+
+    m_sizeA = this->m_mc * this->m_kc;
+    m_sizeB = this->m_kc * this->m_nc;
+  }
+
+  void initParallel(Index rows, Index cols, Index depth, Index num_threads) {
+    this->m_mc = Transpose ? cols : rows;
+    this->m_nc = Transpose ? rows : cols;
+    this->m_kc = depth;
+
+    eigen_internal_assert(this->m_blockA == 0 && this->m_blockB == 0);
+    Index m = this->m_mc;
+    computeProductBlockingSizes<LhsScalar, RhsScalar, KcFactor>(this->m_kc, m, this->m_nc, num_threads);
+    m_sizeA = this->m_mc * this->m_kc;
+    m_sizeB = this->m_kc * this->m_nc;
+  }
+
+  void allocateA() {
+    if (this->m_blockA == 0) this->m_blockA = aligned_new<LhsScalar>(m_sizeA);
+  }
+
+  void allocateB() {
+    if (this->m_blockB == 0) this->m_blockB = aligned_new<RhsScalar>(m_sizeB);
+  }
+
+  void allocateAll() {
+    allocateA();
+    allocateB();
+  }
+
+  ~gemm_blocking_space() {
+    aligned_delete(this->m_blockA, m_sizeA);
+    aligned_delete(this->m_blockB, m_sizeB);
+  }
+};
+
+}  // end namespace internal
+
+namespace internal {
+
+template <typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, GemmProduct>
+    : generic_product_impl_base<Lhs, Rhs, generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, GemmProduct>> {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+  typedef typename Lhs::Scalar LhsScalar;
+  typedef typename Rhs::Scalar RhsScalar;
+
+  typedef internal::blas_traits<Lhs> LhsBlasTraits;
+  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+  typedef internal::remove_all_t<ActualLhsType> ActualLhsTypeCleaned;
+
+  typedef internal::blas_traits<Rhs> RhsBlasTraits;
+  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+  typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
+
+  enum { MaxDepthAtCompileTime = min_size_prefer_fixed(Lhs::MaxColsAtCompileTime, Rhs::MaxRowsAtCompileTime) };
+
+  typedef generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, CoeffBasedProductMode> lazyproduct;
+
+  template <typename Dst>
+  static void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    // See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=404 for a discussion and helper program
+    // to determine the following heuristic.
+    // EIGEN_GEMM_TO_COEFFBASED_THRESHOLD is typically defined to 20 in GeneralProduct.h,
+    // unless it has been specialized by the user or for a given architecture.
+    // Note that the condition rhs.rows()>0 was required because lazy product did not handle empty inputs
+    // correctly. It is unclear whether this guard is still necessary.
+    if ((rhs.rows() + dst.rows() + dst.cols()) < EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows() > 0)
+      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::assign_op<typename Dst::Scalar, Scalar>());
+    else {
+      dst.setZero();
+      scaleAndAddTo(dst, lhs, rhs, Scalar(1));
+    }
+  }
+
+  template <typename Dst>
+  static void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    if ((rhs.rows() + dst.rows() + dst.cols()) < EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows() > 0)
+      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::add_assign_op<typename Dst::Scalar, Scalar>());
+    else
+      scaleAndAddTo(dst, lhs, rhs, Scalar(1));
+  }
+
+  template <typename Dst>
+  static void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
+    if ((rhs.rows() + dst.rows() + dst.cols()) < EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows() > 0)
+      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::sub_assign_op<typename Dst::Scalar, Scalar>());
+    else
+      scaleAndAddTo(dst, lhs, rhs, Scalar(-1));
+  }
+
+  template <typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha) {
+    eigen_assert(dst.rows() == a_lhs.rows() && dst.cols() == a_rhs.cols());
+    if (a_lhs.cols() == 0 || a_lhs.rows() == 0 || a_rhs.cols() == 0) return;
+
+    if (dst.cols() == 1) {
+      // Fallback to GEMV if either the lhs or rhs is a runtime vector
+      typename Dest::ColXpr dst_vec(dst.col(0));
+      return internal::generic_product_impl<Lhs, typename Rhs::ConstColXpr, DenseShape, DenseShape,
+                                            GemvProduct>::scaleAndAddTo(dst_vec, a_lhs, a_rhs.col(0), alpha);
+    } else if (dst.rows() == 1) {
+      // Fallback to GEMV if either the lhs or rhs is a runtime vector
+      typename Dest::RowXpr dst_vec(dst.row(0));
+      return internal::generic_product_impl<typename Lhs::ConstRowXpr, Rhs, DenseShape, DenseShape,
+                                            GemvProduct>::scaleAndAddTo(dst_vec, a_lhs.row(0), a_rhs, alpha);
+    }
+
+    add_const_on_value_type_t<ActualLhsType> lhs = LhsBlasTraits::extract(a_lhs);
+    add_const_on_value_type_t<ActualRhsType> rhs = RhsBlasTraits::extract(a_rhs);
+
+    Scalar actualAlpha = combine_scalar_factors(alpha, a_lhs, a_rhs);
+
+    typedef internal::gemm_blocking_space<(Dest::Flags & RowMajorBit) ? RowMajor : ColMajor, LhsScalar, RhsScalar,
+                                          Dest::MaxRowsAtCompileTime, Dest::MaxColsAtCompileTime, MaxDepthAtCompileTime>
+        BlockingType;
+
+    typedef internal::gemm_functor<
+        Scalar, Index,
+        internal::general_matrix_matrix_product<
+            Index, LhsScalar, (ActualLhsTypeCleaned::Flags & RowMajorBit) ? RowMajor : ColMajor,
+            bool(LhsBlasTraits::NeedToConjugate), RhsScalar,
+            (ActualRhsTypeCleaned::Flags & RowMajorBit) ? RowMajor : ColMajor, bool(RhsBlasTraits::NeedToConjugate),
+            (Dest::Flags & RowMajorBit) ? RowMajor : ColMajor, Dest::InnerStrideAtCompileTime>,
+        ActualLhsTypeCleaned, ActualRhsTypeCleaned, Dest, BlockingType>
+        GemmFunctor;
+
+    BlockingType blocking(dst.rows(), dst.cols(), lhs.cols(), 1, true);
+    internal::parallelize_gemm<(Dest::MaxRowsAtCompileTime > 32 || Dest::MaxRowsAtCompileTime == Dynamic)>(
+        GemmFunctor(lhs, rhs, dst, actualAlpha, blocking), a_lhs.rows(), a_rhs.cols(), a_lhs.cols(),
+        Dest::Flags & RowMajorBit);
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GENERAL_MATRIX_MATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
new file mode 100644
index 0000000..adae3dd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
@@ -0,0 +1,330 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
+#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjLhs, bool ConjRhs>
+struct selfadjoint_rank1_update;
+
+namespace internal {
+
+/**********************************************************************
+ * This file implements a general A * B product while
+ * evaluating only one triangular part of the product.
+ * This is a more general version of self adjoint product (C += A A^T)
+ * as the level 3 SYRK Blas routine.
+ **********************************************************************/
+
+// forward declarations (defined at the end of this file)
+template <typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs,
+          int ResInnerStride, int UpLo>
+struct tribb_kernel;
+
+/* Optimized matrix-matrix product evaluating only one triangular half */
+template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
+          int RhsStorageOrder, bool ConjugateRhs, int ResStorageOrder, int ResInnerStride, int UpLo,
+          int Version = Specialized>
+struct general_matrix_matrix_triangular_product;
+
+// as usual if the result is row major => we transpose the product
+template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
+          int RhsStorageOrder, bool ConjugateRhs, int ResInnerStride, int UpLo, int Version>
+struct general_matrix_matrix_triangular_product<Index, LhsScalar, LhsStorageOrder, ConjugateLhs, RhsScalar,
+                                                RhsStorageOrder, ConjugateRhs, RowMajor, ResInnerStride, UpLo,
+                                                Version> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth, const LhsScalar* lhs, Index lhsStride,
+                                      const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resIncr,
+                                      Index resStride, const ResScalar& alpha,
+                                      level3_blocking<RhsScalar, LhsScalar>& blocking) {
+    general_matrix_matrix_triangular_product<Index, RhsScalar, RhsStorageOrder == RowMajor ? ColMajor : RowMajor,
+                                             ConjugateRhs, LhsScalar, LhsStorageOrder == RowMajor ? ColMajor : RowMajor,
+                                             ConjugateLhs, ColMajor, ResInnerStride,
+                                             UpLo == Lower ? Upper : Lower>::run(size, depth, rhs, rhsStride, lhs,
+                                                                                 lhsStride, res, resIncr, resStride,
+                                                                                 alpha, blocking);
+  }
+};
+
+template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
+          int RhsStorageOrder, bool ConjugateRhs, int ResInnerStride, int UpLo, int Version>
+struct general_matrix_matrix_triangular_product<Index, LhsScalar, LhsStorageOrder, ConjugateLhs, RhsScalar,
+                                                RhsStorageOrder, ConjugateRhs, ColMajor, ResInnerStride, UpLo,
+                                                Version> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth, const LhsScalar* lhs_, Index lhsStride,
+                                      const RhsScalar* rhs_, Index rhsStride, ResScalar* res_, Index resIncr,
+                                      Index resStride, const ResScalar& alpha,
+                                      level3_blocking<LhsScalar, RhsScalar>& blocking) {
+    if (size == 0) {
+      return;
+    }
+
+    typedef gebp_traits<LhsScalar, RhsScalar> Traits;
+
+    typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
+    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+    LhsMapper lhs(lhs_, lhsStride);
+    RhsMapper rhs(rhs_, rhsStride);
+    ResMapper res(res_, resStride, resIncr);
+
+    Index kc = blocking.kc();
+    // Ensure that mc >= nr and <= size
+    Index mc = (std::min)(size, (std::max)(static_cast<decltype(blocking.mc())>(Traits::nr), blocking.mc()));
+
+    // !!! mc must be a multiple of nr
+    if (mc > Traits::nr) {
+      using UnsignedIndex = std::make_unsigned_t<Index>;
+      mc = (UnsignedIndex(mc) / Traits::nr) * Traits::nr;
+    }
+
+    std::size_t sizeA = kc * mc;
+    std::size_t sizeB = kc * size;
+
+    ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
+    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());
+
+    gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
+                  LhsStorageOrder>
+        pack_lhs;
+    gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
+    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;
+    tribb_kernel<LhsScalar, RhsScalar, Index, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs, ResInnerStride, UpLo>
+        sybb;
+
+    for (Index k2 = 0; k2 < depth; k2 += kc) {
+      const Index actual_kc = (std::min)(k2 + kc, depth) - k2;
+
+      // note that the actual rhs is the transpose/adjoint of mat
+      pack_rhs(blockB, rhs.getSubMapper(k2, 0), actual_kc, size);
+
+      for (Index i2 = 0; i2 < size; i2 += mc) {
+        const Index actual_mc = (std::min)(i2 + mc, size) - i2;
+
+        pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+        // the selected actual_mc * size panel of res is split into three different part:
+        //  1 - before the diagonal => processed with gebp or skipped
+        //  2 - the actual_mc x actual_mc symmetric block => processed with a special kernel
+        //  3 - after the diagonal => processed with gebp or skipped
+        if (UpLo == Lower)
+          gebp(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, (std::min)(size, i2), alpha, -1, -1, 0,
+               0);
+
+        sybb(res_ + resStride * i2 + resIncr * i2, resIncr, resStride, blockA, blockB + actual_kc * i2, actual_mc,
+             actual_kc, alpha);
+
+        if (UpLo == Upper) {
+          Index j2 = i2 + actual_mc;
+          gebp(res.getSubMapper(i2, j2), blockA, blockB + actual_kc * j2, actual_mc, actual_kc,
+               (std::max)(Index(0), size - j2), alpha, -1, -1, 0, 0);
+        }
+      }
+    }
+  }
+};
+
+// Optimized packed Block * packed Block product kernel evaluating only one given triangular part
+// This kernel is built on top of the gebp kernel:
+// - the current destination block is processed per panel of actual_mc x BlockSize
+//   where BlockSize is set to the minimal value allowing gebp to be as fast as possible
+// - then, as usual, each panel is split into three parts along the diagonal,
+//   the sub blocks above and below the diagonal are processed as usual,
+//   while the triangular block overlapping the diagonal is evaluated into a
+//   small temporary buffer which is then accumulated into the result using a
+//   triangular traversal.
+template <typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs,
+          int ResInnerStride, int UpLo>
+struct tribb_kernel {
+  typedef gebp_traits<LhsScalar, RhsScalar, ConjLhs, ConjRhs> Traits;
+  typedef typename Traits::ResScalar ResScalar;
+
+  enum { BlockSize = meta_least_common_multiple<plain_enum_max(mr, nr), plain_enum_min(mr, nr)>::ret };
+  void operator()(ResScalar* res_, Index resIncr, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB,
+                  Index size, Index depth, const ResScalar& alpha) {
+    typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+    typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned> BufferMapper;
+    ResMapper res(res_, resStride, resIncr);
+    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel1;
+    gebp_kernel<LhsScalar, RhsScalar, Index, BufferMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel2;
+
+    Matrix<ResScalar, BlockSize, BlockSize, ColMajor> buffer;
+
+    // let's process the block per panel of actual_mc x BlockSize,
+    // again, each is split into three parts, etc.
+    for (Index j = 0; j < size; j += BlockSize) {
+      Index actualBlockSize = std::min<Index>(BlockSize, size - j);
+      const RhsScalar* actual_b = blockB + j * depth;
+
+      if (UpLo == Upper)
+        gebp_kernel1(res.getSubMapper(0, j), blockA, actual_b, j, depth, actualBlockSize, alpha, -1, -1, 0, 0);
+
+      // selfadjoint micro block
+      {
+        Index i = j;
+        buffer.setZero();
+        // 1 - apply the kernel on the temporary buffer
+        gebp_kernel2(BufferMapper(buffer.data(), BlockSize), blockA + depth * i, actual_b, actualBlockSize, depth,
+                     actualBlockSize, alpha, -1, -1, 0, 0);
+
+        // 2 - triangular accumulation
+        for (Index j1 = 0; j1 < actualBlockSize; ++j1) {
+          typename ResMapper::LinearMapper r = res.getLinearMapper(i, j + j1);
+          for (Index i1 = UpLo == Lower ? j1 : 0; UpLo == Lower ? i1 < actualBlockSize : i1 <= j1; ++i1)
+            r(i1) += buffer(i1, j1);
+        }
+      }
+
+      if (UpLo == Lower) {
+        Index i = j + actualBlockSize;
+        gebp_kernel1(res.getSubMapper(i, j), blockA + depth * i, actual_b, size - i, depth, actualBlockSize, alpha, -1,
+                     -1, 0, 0);
+      }
+    }
+  }
+};
+
+}  // end namespace internal
+
+// high level API
+
+template <typename MatrixType, typename ProductType, int UpLo, bool IsOuterProduct>
+struct general_product_to_triangular_selector;
+
+template <typename MatrixType, typename ProductType, int UpLo>
+struct general_product_to_triangular_selector<MatrixType, ProductType, UpLo, true> {
+  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta) {
+    typedef typename MatrixType::Scalar Scalar;
+
+    typedef internal::remove_all_t<typename ProductType::LhsNested> Lhs;
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
+    typedef internal::remove_all_t<ActualLhs> ActualLhs_;
+    internal::add_const_on_value_type_t<ActualLhs> actualLhs = LhsBlasTraits::extract(prod.lhs());
+
+    typedef internal::remove_all_t<typename ProductType::RhsNested> Rhs;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
+    typedef internal::remove_all_t<ActualRhs> ActualRhs_;
+    internal::add_const_on_value_type_t<ActualRhs> actualRhs = RhsBlasTraits::extract(prod.rhs());
+
+    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) *
+                         RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
+
+    if (!beta) mat.template triangularView<UpLo>().setZero();
+
+    enum {
+      StorageOrder = (internal::traits<MatrixType>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+      UseLhsDirectly = ActualLhs_::InnerStrideAtCompileTime == 1,
+      UseRhsDirectly = ActualRhs_::InnerStrideAtCompileTime == 1
+    };
+
+    internal::gemv_static_vector_if<Scalar, Lhs::SizeAtCompileTime, Lhs::MaxSizeAtCompileTime, !UseLhsDirectly>
+        static_lhs;
+    ei_declare_aligned_stack_constructed_variable(
+        Scalar, actualLhsPtr, actualLhs.size(),
+        (UseLhsDirectly ? const_cast<Scalar*>(actualLhs.data()) : static_lhs.data()));
+    if (!UseLhsDirectly) Map<typename ActualLhs_::PlainObject>(actualLhsPtr, actualLhs.size()) = actualLhs;
+
+    internal::gemv_static_vector_if<Scalar, Rhs::SizeAtCompileTime, Rhs::MaxSizeAtCompileTime, !UseRhsDirectly>
+        static_rhs;
+    ei_declare_aligned_stack_constructed_variable(
+        Scalar, actualRhsPtr, actualRhs.size(),
+        (UseRhsDirectly ? const_cast<Scalar*>(actualRhs.data()) : static_rhs.data()));
+    if (!UseRhsDirectly) Map<typename ActualRhs_::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
+
+    selfadjoint_rank1_update<
+        Scalar, Index, StorageOrder, UpLo, LhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex,
+        RhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex>::run(actualLhs.size(), mat.data(),
+                                                                             mat.outerStride(), actualLhsPtr,
+                                                                             actualRhsPtr, actualAlpha);
+  }
+};
+
+template <typename MatrixType, typename ProductType, int UpLo>
+struct general_product_to_triangular_selector<MatrixType, ProductType, UpLo, false> {
+  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta) {
+    typedef internal::remove_all_t<typename ProductType::LhsNested> Lhs;
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
+    typedef internal::remove_all_t<ActualLhs> ActualLhs_;
+    internal::add_const_on_value_type_t<ActualLhs> actualLhs = LhsBlasTraits::extract(prod.lhs());
+
+    typedef internal::remove_all_t<typename ProductType::RhsNested> Rhs;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
+    typedef internal::remove_all_t<ActualRhs> ActualRhs_;
+    internal::add_const_on_value_type_t<ActualRhs> actualRhs = RhsBlasTraits::extract(prod.rhs());
+
+    typename ProductType::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) *
+                                               RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
+
+    if (!beta) mat.template triangularView<UpLo>().setZero();
+
+    enum {
+      IsRowMajor = (internal::traits<MatrixType>::Flags & RowMajorBit) ? 1 : 0,
+      LhsIsRowMajor = ActualLhs_::Flags & RowMajorBit ? 1 : 0,
+      RhsIsRowMajor = ActualRhs_::Flags & RowMajorBit ? 1 : 0,
+      SkipDiag = (UpLo & (UnitDiag | ZeroDiag)) != 0
+    };
+
+    Index size = mat.cols();
+    if (SkipDiag) size--;
+    Index depth = actualLhs.cols();
+
+    typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor, typename Lhs::Scalar, typename Rhs::Scalar,
+                                          MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime,
+                                          ActualRhs_::MaxColsAtCompileTime>
+        BlockingType;
+
+    BlockingType blocking(size, size, depth, 1, false);
+
+    internal::general_matrix_matrix_triangular_product<
+        Index, typename Lhs::Scalar, LhsIsRowMajor ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
+        typename Rhs::Scalar, RhsIsRowMajor ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
+        IsRowMajor ? RowMajor : ColMajor, MatrixType::InnerStrideAtCompileTime,
+        UpLo&(Lower | Upper)>::run(size, depth, &actualLhs.coeffRef(SkipDiag && (UpLo & Lower) == Lower ? 1 : 0, 0),
+                                   actualLhs.outerStride(),
+                                   &actualRhs.coeffRef(0, SkipDiag && (UpLo & Upper) == Upper ? 1 : 0),
+                                   actualRhs.outerStride(),
+                                   mat.data() +
+                                       (SkipDiag ? (bool(IsRowMajor) != ((UpLo & Lower) == Lower) ? mat.innerStride()
+                                                                                                  : mat.outerStride())
+                                                 : 0),
+                                   mat.innerStride(), mat.outerStride(), actualAlpha, blocking);
+  }
+};
+
+template <typename MatrixType_, unsigned int Mode_>
+template <typename ProductType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename TriangularViewImpl<MatrixType_, Mode_, Dense>::TriangularViewType&
+TriangularViewImpl<MatrixType_, Mode_, Dense>::_assignProduct(
+    const ProductType& prod, const typename TriangularViewImpl<MatrixType_, Mode_, Dense>::Scalar& alpha, bool beta) {
+  EIGEN_STATIC_ASSERT((Mode_ & UnitDiag) == 0, WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED);
+  eigen_assert(derived().nestedExpression().rows() == prod.rows() && derived().cols() == prod.cols());
+
+  general_product_to_triangular_selector<MatrixType_, ProductType, Mode_,
+                                         internal::traits<ProductType>::InnerSize == 1>::run(derived()
+                                                                                                 .nestedExpression()
+                                                                                                 .const_cast_derived(),
+                                                                                             prod, alpha, beta);
+
+  return derived();
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
new file mode 100644
index 0000000..e14aa61
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
@@ -0,0 +1,150 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Level 3 BLAS SYRK/HERK implementation.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
+#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Index, typename Scalar, int AStorageOrder, bool ConjugateA, int ResStorageOrder, int UpLo>
+struct general_matrix_matrix_rankupdate
+    : general_matrix_matrix_triangular_product<Index, Scalar, AStorageOrder, ConjugateA, Scalar, AStorageOrder,
+                                               ConjugateA, ResStorageOrder, 1, UpLo, BuiltIn> {};
+
+// try to go to BLAS specialization
+#define EIGEN_BLAS_RANKUPDATE_SPECIALIZE(Scalar)                                                                      \
+  template <typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder, bool ConjugateRhs, int UpLo> \
+  struct general_matrix_matrix_triangular_product<Index, Scalar, LhsStorageOrder, ConjugateLhs, Scalar,               \
+                                                  RhsStorageOrder, ConjugateRhs, ColMajor, 1, UpLo, Specialized> {    \
+    static EIGEN_STRONG_INLINE void run(Index size, Index depth, const Scalar* lhs, Index lhsStride,                  \
+                                        const Scalar* rhs, Index rhsStride, Scalar* res, Index resIncr,               \
+                                        Index resStride, Scalar alpha, level3_blocking<Scalar, Scalar>& blocking) {   \
+      if (lhs == rhs && ((UpLo & (Lower | Upper)) == UpLo)) {                                                         \
+        general_matrix_matrix_rankupdate<Index, Scalar, LhsStorageOrder, ConjugateLhs, ColMajor, UpLo>::run(          \
+            size, depth, lhs, lhsStride, rhs, rhsStride, res, resStride, alpha, blocking);                            \
+      } else {                                                                                                        \
+        general_matrix_matrix_triangular_product<Index, Scalar, LhsStorageOrder, ConjugateLhs, Scalar,                \
+                                                 RhsStorageOrder, ConjugateRhs, ColMajor, 1, UpLo,                    \
+                                                 BuiltIn>::run(size, depth, lhs, lhsStride, rhs, rhsStride, res,      \
+                                                               resIncr, resStride, alpha, blocking);                  \
+      }                                                                                                               \
+    }                                                                                                                 \
+  };
+
+EIGEN_BLAS_RANKUPDATE_SPECIALIZE(double)
+EIGEN_BLAS_RANKUPDATE_SPECIALIZE(float)
+// TODO: handle complex cases
+// EIGEN_BLAS_RANKUPDATE_SPECIALIZE(dcomplex)
+// EIGEN_BLAS_RANKUPDATE_SPECIALIZE(scomplex)
+
+// SYRK for float/double
+#define EIGEN_BLAS_RANKUPDATE_R(EIGTYPE, BLASTYPE, BLASFUNC)                                                        \
+  template <typename Index, int AStorageOrder, bool ConjugateA, int UpLo>                                           \
+  struct general_matrix_matrix_rankupdate<Index, EIGTYPE, AStorageOrder, ConjugateA, ColMajor, UpLo> {              \
+    enum {                                                                                                          \
+      IsLower = (UpLo & Lower) == Lower,                                                                            \
+      LowUp = IsLower ? Lower : Upper,                                                                              \
+      conjA = ((AStorageOrder == ColMajor) && ConjugateA) ? 1 : 0                                                   \
+    };                                                                                                              \
+    static EIGEN_STRONG_INLINE void run(Index size, Index depth, const EIGTYPE* lhs, Index lhsStride,               \
+                                        const EIGTYPE* /*rhs*/, Index /*rhsStride*/, EIGTYPE* res, Index resStride, \
+                                        EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) {           \
+      /* typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs;*/                                    \
+      if (size == 0 || depth == 0) return;                                                                          \
+      BlasIndex lda = convert_index<BlasIndex>(lhsStride), ldc = convert_index<BlasIndex>(resStride),               \
+                n = convert_index<BlasIndex>(size), k = convert_index<BlasIndex>(depth);                            \
+      char uplo = ((IsLower) ? 'L' : 'U'), trans = ((AStorageOrder == RowMajor) ? 'T' : 'N');                       \
+      EIGTYPE beta(1);                                                                                              \
+      BLASFUNC(&uplo, &trans, &n, &k, (const BLASTYPE*)&numext::real_ref(alpha), lhs, &lda,                         \
+               (const BLASTYPE*)&numext::real_ref(beta), res, &ldc);                                                \
+    }                                                                                                               \
+  };
+
+// HERK for complex data
+#define EIGEN_BLAS_RANKUPDATE_C(EIGTYPE, BLASTYPE, RTYPE, BLASFUNC)                                                 \
+  template <typename Index, int AStorageOrder, bool ConjugateA, int UpLo>                                           \
+  struct general_matrix_matrix_rankupdate<Index, EIGTYPE, AStorageOrder, ConjugateA, ColMajor, UpLo> {              \
+    enum {                                                                                                          \
+      IsLower = (UpLo & Lower) == Lower,                                                                            \
+      LowUp = IsLower ? Lower : Upper,                                                                              \
+      conjA = (((AStorageOrder == ColMajor) && ConjugateA) || ((AStorageOrder == RowMajor) && !ConjugateA)) ? 1 : 0 \
+    };                                                                                                              \
+    static EIGEN_STRONG_INLINE void run(Index size, Index depth, const EIGTYPE* lhs, Index lhsStride,               \
+                                        const EIGTYPE* /*rhs*/, Index /*rhsStride*/, EIGTYPE* res, Index resStride, \
+                                        EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) {           \
+      typedef Matrix<EIGTYPE, Dynamic, Dynamic, AStorageOrder> MatrixType;                                          \
+      if (size == 0 || depth == 0) return;                                                                          \
+      BlasIndex lda = convert_index<BlasIndex>(lhsStride), ldc = convert_index<BlasIndex>(resStride),               \
+                n = convert_index<BlasIndex>(size), k = convert_index<BlasIndex>(depth);                            \
+      char uplo = ((IsLower) ? 'L' : 'U'), trans = ((AStorageOrder == RowMajor) ? 'C' : 'N');                       \
+      RTYPE alpha_, beta_;                                                                                          \
+      const EIGTYPE* a_ptr;                                                                                         \
+                                                                                                                    \
+      alpha_ = alpha.real();                                                                                        \
+      beta_ = 1.0;                                                                                                  \
+      /* Copy with conjugation in some cases*/                                                                      \
+      MatrixType a;                                                                                                 \
+      if (conjA) {                                                                                                  \
+        Map<const MatrixType, 0, OuterStride<> > mapA(lhs, n, k, OuterStride<>(lhsStride));                         \
+        a = mapA.conjugate();                                                                                       \
+        lda = a.outerStride();                                                                                      \
+        a_ptr = a.data();                                                                                           \
+      } else                                                                                                        \
+        a_ptr = lhs;                                                                                                \
+      BLASFUNC(&uplo, &trans, &n, &k, &alpha_, (BLASTYPE*)a_ptr, &lda, &beta_, (BLASTYPE*)res, &ldc);               \
+    }                                                                                                               \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_RANKUPDATE_R(double, double, dsyrk)
+EIGEN_BLAS_RANKUPDATE_R(float, float, ssyrk)
+#else
+EIGEN_BLAS_RANKUPDATE_R(double, double, dsyrk_)
+EIGEN_BLAS_RANKUPDATE_R(float, float, ssyrk_)
+#endif
+
+// TODO: handle complex cases
+// EIGEN_BLAS_RANKUPDATE_C(dcomplex, double, double, zherk_)
+// EIGEN_BLAS_RANKUPDATE_C(scomplex, float,  float, cherk_)
+
+#undef EIGEN_BLAS_RANKUPDATE_SPECIALIZE
+#undef EIGEN_BLAS_RANKUPDATE_R
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
new file mode 100644
index 0000000..11c29b6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
@@ -0,0 +1,206 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   General matrix-matrix product functionality based on ?GEMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
+#define EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**********************************************************************
+ * This file implements general matrix-matrix multiplication using BLAS
+ * gemm function via partial specialization of
+ * general_matrix_matrix_product::run(..) method for float, double,
+ * std::complex<float> and std::complex<double> types
+ **********************************************************************/
+
+// gemm specialization
+
+#define EIGEN_BLAS_GEMM_SPECIALIZATION(EIGTYPE, EIGPREFIX, BLASTYPE, BLASFUNC)                                      \
+  template <typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder, bool ConjugateRhs>         \
+  struct general_matrix_matrix_product<Index, EIGTYPE, LhsStorageOrder, ConjugateLhs, EIGTYPE, RhsStorageOrder,     \
+                                       ConjugateRhs, ColMajor, 1> {                                                 \
+    typedef gebp_traits<EIGTYPE, EIGTYPE> Traits;                                                                   \
+                                                                                                                    \
+    static void run(Index rows, Index cols, Index depth, const EIGTYPE* lhs_, Index lhsStride, const EIGTYPE* rhs_, \
+                    Index rhsStride, EIGTYPE* res, Index resIncr, Index resStride, EIGTYPE alpha,                   \
+                    level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/, GemmParallelInfo<Index>* /*info = 0*/) {       \
+      using std::conj;                                                                                              \
+      if (rows == 0 || cols == 0 || depth == 0) return;                                                             \
+      EIGEN_ONLY_USED_FOR_DEBUG(resIncr);                                                                           \
+      eigen_assert(resIncr == 1);                                                                                   \
+      char transa, transb;                                                                                          \
+      BlasIndex m, n, k, lda, ldb, ldc;                                                                             \
+      const EIGTYPE *a, *b;                                                                                         \
+      EIGTYPE beta(1);                                                                                              \
+      MatrixX##EIGPREFIX a_tmp, b_tmp;                                                                              \
+                                                                                                                    \
+      /* Set transpose options */                                                                                   \
+      transa = (LhsStorageOrder == RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N';                                  \
+      transb = (RhsStorageOrder == RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N';                                  \
+                                                                                                                    \
+      /* Set m, n, k */                                                                                             \
+      m = convert_index<BlasIndex>(rows);                                                                           \
+      n = convert_index<BlasIndex>(cols);                                                                           \
+      k = convert_index<BlasIndex>(depth);                                                                          \
+                                                                                                                    \
+      /* Set lda, ldb, ldc */                                                                                       \
+      lda = convert_index<BlasIndex>(lhsStride);                                                                    \
+      ldb = convert_index<BlasIndex>(rhsStride);                                                                    \
+      ldc = convert_index<BlasIndex>(resStride);                                                                    \
+                                                                                                                    \
+      /* Set a, b, c */                                                                                             \
+      if ((LhsStorageOrder == ColMajor) && (ConjugateLhs)) {                                                        \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(lhs_, m, k, OuterStride<>(lhsStride));                 \
+        a_tmp = lhs.conjugate();                                                                                    \
+        a = a_tmp.data();                                                                                           \
+        lda = convert_index<BlasIndex>(a_tmp.outerStride());                                                        \
+      } else                                                                                                        \
+        a = lhs_;                                                                                                   \
+                                                                                                                    \
+      if ((RhsStorageOrder == ColMajor) && (ConjugateRhs)) {                                                        \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(rhs_, k, n, OuterStride<>(rhsStride));                 \
+        b_tmp = rhs.conjugate();                                                                                    \
+        b = b_tmp.data();                                                                                           \
+        ldb = convert_index<BlasIndex>(b_tmp.outerStride());                                                        \
+      } else                                                                                                        \
+        b = rhs_;                                                                                                   \
+                                                                                                                    \
+      BLASFUNC(&transa, &transb, &m, &n, &k, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda,   \
+               (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc);           \
+    }                                                                                                               \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_GEMM_SPECIALIZATION(double, d, double, dgemm)
+EIGEN_BLAS_GEMM_SPECIALIZATION(float, f, float, sgemm)
+EIGEN_BLAS_GEMM_SPECIALIZATION(dcomplex, cd, MKL_Complex16, zgemm)
+EIGEN_BLAS_GEMM_SPECIALIZATION(scomplex, cf, MKL_Complex8, cgemm)
+#else
+EIGEN_BLAS_GEMM_SPECIALIZATION(double, d, double, dgemm_)
+EIGEN_BLAS_GEMM_SPECIALIZATION(float, f, float, sgemm_)
+EIGEN_BLAS_GEMM_SPECIALIZATION(dcomplex, cd, double, zgemm_)
+EIGEN_BLAS_GEMM_SPECIALIZATION(scomplex, cf, float, cgemm_)
+#endif
+
+// If OpenBLAS with BUILD_BFLOAT16=1 support is available,
+// use sbgemm for bfloat16.
+#if EIGEN_USE_OPENBLAS_BFLOAT16
+
+extern "C" {
+// OpenBLAS prototype.
+void sbgemm_(const char* trans_a, const char* trans_b, const int* M, const int* N, const int* K, const float* alpha,
+             const Eigen::bfloat16* A, const int* lda, const Eigen::bfloat16* B, const int* ldb, const float* beta,
+             float* C, const int* ldc);
+}  // extern "C"
+
+template <typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder, bool ConjugateRhs>
+struct general_matrix_matrix_product<Index, Eigen::bfloat16, LhsStorageOrder, ConjugateLhs, Eigen::bfloat16,
+                                     RhsStorageOrder, ConjugateRhs, ColMajor, 1> {
+  typedef gebp_traits<Eigen::bfloat16, Eigen::bfloat16> Traits;
+
+  static void run(Index rows, Index cols, Index depth, const Eigen::bfloat16* lhs_, Index lhsStride,
+                  const Eigen::bfloat16* rhs_, Index rhsStride, Eigen::bfloat16* res, Index resIncr, Index resStride,
+                  Eigen::bfloat16 alpha, level3_blocking<Eigen::bfloat16, Eigen::bfloat16>& /*blocking*/,
+                  GemmParallelInfo<Index>* /*info = 0*/) {
+    using std::conj;
+    if (rows == 0 || cols == 0 || depth == 0) return;
+    EIGEN_ONLY_USED_FOR_DEBUG(resIncr);
+    eigen_assert(resIncr == 1);
+    char transa, transb;
+    BlasIndex m, n, k, lda, ldb, ldc;
+    const Eigen::bfloat16 *a, *b;
+
+    float falpha = static_cast<float>(alpha);
+    float fbeta = float(1.0);
+
+    using MatrixXbf16 = Matrix<Eigen::bfloat16, Dynamic, Dynamic>;
+    MatrixXbf16 a_tmp, b_tmp;
+    MatrixXf r_tmp;
+
+    /* Set transpose options */
+    transa = (LhsStorageOrder == RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N';
+    transb = (RhsStorageOrder == RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N';
+
+    /* Set m, n, k */
+    m = convert_index<BlasIndex>(rows);
+    n = convert_index<BlasIndex>(cols);
+    k = convert_index<BlasIndex>(depth);
+
+    /* Set lda, ldb, ldc */
+    lda = convert_index<BlasIndex>(lhsStride);
+    ldb = convert_index<BlasIndex>(rhsStride);
+    ldc = convert_index<BlasIndex>(m);
+
+    /* Set a, b, c */
+    if ((LhsStorageOrder == ColMajor) && (ConjugateLhs)) {
+      Map<const MatrixXbf16, 0, OuterStride<> > lhs(lhs_, m, k, OuterStride<>(lhsStride));
+      a_tmp = lhs.conjugate();
+      a = a_tmp.data();
+      lda = convert_index<BlasIndex>(a_tmp.outerStride());
+    } else {
+      a = lhs_;
+    }
+
+    if ((RhsStorageOrder == ColMajor) && (ConjugateRhs)) {
+      Map<const MatrixXbf16, 0, OuterStride<> > rhs(rhs_, k, n, OuterStride<>(rhsStride));
+      b_tmp = rhs.conjugate();
+      b = b_tmp.data();
+      ldb = convert_index<BlasIndex>(b_tmp.outerStride());
+    } else {
+      b = rhs_;
+    }
+
+    // Evaluate to a temporary intermediate array.
+    r_tmp.resize(m, n);
+
+    sbgemm_(&transa, &transb, &m, &n, &k, (const float*)&numext::real_ref(falpha), a, &lda, b, &ldb,
+            (const float*)&numext::real_ref(fbeta), r_tmp.data(), &ldc);
+
+    // Cast to the output.
+    Map<MatrixXbf16, 0, OuterStride<> > result(res, m, n, OuterStride<>(resStride));
+    result = r_tmp.cast<Eigen::bfloat16>();
+  }
+};
+
+#endif  // EIGEN_USE_OPENBLAS_SBGEMM
+
+#undef EIGEN_BLAS_GEMM_SPECIALIZATION
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixVector.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixVector.h
new file mode 100644
index 0000000..5f74e37
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixVector.h
@@ -0,0 +1,674 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERAL_MATRIX_VECTOR_H
+#define EIGEN_GENERAL_MATRIX_VECTOR_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+enum GEMVPacketSizeType { GEMVPacketFull = 0, GEMVPacketHalf, GEMVPacketQuarter };
+
+template <int N, typename T1, typename T2, typename T3>
+struct gemv_packet_cond {
+  typedef T3 type;
+};
+
+template <typename T1, typename T2, typename T3>
+struct gemv_packet_cond<GEMVPacketFull, T1, T2, T3> {
+  typedef T1 type;
+};
+
+template <typename T1, typename T2, typename T3>
+struct gemv_packet_cond<GEMVPacketHalf, T1, T2, T3> {
+  typedef T2 type;
+};
+
+template <typename LhsScalar, typename RhsScalar, int PacketSize_ = GEMVPacketFull>
+class gemv_traits {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+
+#define PACKET_DECL_COND_POSTFIX(postfix, name, packet_size)                                               \
+  typedef typename gemv_packet_cond<                                                                       \
+      packet_size, typename packet_traits<name##Scalar>::type, typename packet_traits<name##Scalar>::half, \
+      typename unpacket_traits<typename packet_traits<name##Scalar>::half>::half>::type name##Packet##postfix
+
+  PACKET_DECL_COND_POSTFIX(_, Lhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Rhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Res, PacketSize_);
+#undef PACKET_DECL_COND_POSTFIX
+
+ public:
+  enum {
+    Vectorizable = unpacket_traits<LhsPacket_>::vectorizable && unpacket_traits<RhsPacket_>::vectorizable &&
+                   int(unpacket_traits<LhsPacket_>::size) == int(unpacket_traits<RhsPacket_>::size),
+    LhsPacketSize = Vectorizable ? unpacket_traits<LhsPacket_>::size : 1,
+    RhsPacketSize = Vectorizable ? unpacket_traits<RhsPacket_>::size : 1,
+    ResPacketSize = Vectorizable ? unpacket_traits<ResPacket_>::size : 1
+  };
+
+  typedef std::conditional_t<Vectorizable, LhsPacket_, LhsScalar> LhsPacket;
+  typedef std::conditional_t<Vectorizable, RhsPacket_, RhsScalar> RhsPacket;
+  typedef std::conditional_t<Vectorizable, ResPacket_, ResScalar> ResPacket;
+};
+
+/* Optimized col-major matrix * vector product:
+ * This algorithm processes the matrix per vertical panels,
+ * which are then processed horizontally per chunk of 8*PacketSize x 1 vertical segments.
+ *
+ * Mixing type logic: C += alpha * A * B
+ *  |  A  |  B  |alpha| comments
+ *  |real |cplx |cplx | no vectorization
+ *  |real |cplx |real | alpha is converted to a cplx when calling the run function, no vectorization
+ *  |cplx |real |cplx | invalid, the caller has to do tmp: = A * B; C += alpha*tmp
+ *  |cplx |real |real | optimal case, vectorization possible via real-cplx mul
+ *
+ * The same reasoning apply for the transposed case.
+ */
+template <typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar,
+          typename RhsMapper, bool ConjugateRhs, int Version>
+struct general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, ConjugateLhs, RhsScalar, RhsMapper,
+                                     ConjugateRhs, Version> {
+  typedef gemv_traits<LhsScalar, RhsScalar> Traits;
+  typedef gemv_traits<LhsScalar, RhsScalar, GEMVPacketHalf> HalfTraits;
+  typedef gemv_traits<LhsScalar, RhsScalar, GEMVPacketQuarter> QuarterTraits;
+
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+
+  typedef typename Traits::LhsPacket LhsPacket;
+  typedef typename Traits::RhsPacket RhsPacket;
+  typedef typename Traits::ResPacket ResPacket;
+
+  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
+  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
+  typedef typename HalfTraits::ResPacket ResPacketHalf;
+
+  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
+  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
+  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
+
+  EIGEN_DEVICE_FUNC inline static void run(Index rows, Index cols, const LhsMapper& lhs, const RhsMapper& rhs,
+                                           ResScalar* res, Index resIncr, RhsScalar alpha);
+
+  template <int N>
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE void process_rows(
+      Index i, Index j2, Index jend, const LhsMapper& lhs, const RhsMapper& rhs, ResScalar* res,
+      const ResPacket& palpha, conj_helper<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>& pcj);
+};
+
+// Recursive template unroller for col-major GEMV full-packet row blocks.
+// Unrolls the packet dimension (K = 0..N-1) at compile time, guaranteeing
+// that each accumulator lives in its own register variable.
+template <int K, int N>
+struct gemv_colmajor_unroller {
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void init_zero(Packet* c) {
+    gemv_colmajor_unroller<K - 1, N>::init_zero(c);
+    c[K] = pzero(Packet{});
+  }
+
+  template <typename LhsPacket, int LhsStride, int Alignment, typename AccPacket, typename RhsPacket,
+            typename ConjHelper, typename LhsMapper, typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void madd(AccPacket* c, const LhsMapper& lhs, Index i, Index j,
+                                                         const RhsPacket& b0, ConjHelper& pcj) {
+    gemv_colmajor_unroller<K - 1, N>::template madd<LhsPacket, LhsStride, Alignment>(c, lhs, i, j, b0, pcj);
+    c[K] = pcj.pmadd(lhs.template load<LhsPacket, Alignment>(i + LhsStride * K, j), b0, c[K]);
+  }
+
+  template <typename ResPacket, int ResStride, typename ResScalar>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void store(const ResPacket* c, ResScalar* res, Index i,
+                                                          const ResPacket& palpha) {
+    gemv_colmajor_unroller<K - 1, N>::template store<ResPacket, ResStride>(c, res, i, palpha);
+    pstoreu(res + i + ResStride * K, pmadd(c[K], palpha, ploadu<ResPacket>(res + i + ResStride * K)));
+  }
+};
+
+template <int N>
+struct gemv_colmajor_unroller<0, N> {
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void init_zero(Packet* c) {
+    c[0] = pzero(Packet{});
+  }
+
+  template <typename LhsPacket, int LhsStride, int Alignment, typename AccPacket, typename RhsPacket,
+            typename ConjHelper, typename LhsMapper, typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void madd(AccPacket* c, const LhsMapper& lhs, Index i, Index j,
+                                                         const RhsPacket& b0, ConjHelper& pcj) {
+    c[0] = pcj.pmadd(lhs.template load<LhsPacket, Alignment>(i, j), b0, c[0]);
+  }
+
+  template <typename ResPacket, int ResStride, typename ResScalar>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void store(const ResPacket* c, ResScalar* res, Index i,
+                                                          const ResPacket& palpha) {
+    pstoreu(res + i, pmadd(c[0], palpha, ploadu<ResPacket>(res + i)));
+  }
+};
+
+template <typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar,
+          typename RhsMapper, bool ConjugateRhs, int Version>
+template <int N>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void general_matrix_vector_product<
+    Index, LhsScalar, LhsMapper, ColMajor, ConjugateLhs, RhsScalar, RhsMapper, ConjugateRhs,
+    Version>::process_rows(Index i, Index j2, Index jend, const LhsMapper& lhs, const RhsMapper& rhs, ResScalar* res,
+                           const ResPacket& palpha,
+                           conj_helper<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>& pcj) {
+  enum { LhsAlignment = Unaligned, LhsPacketSize = Traits::LhsPacketSize, ResPacketSize = Traits::ResPacketSize };
+  using Unroller = gemv_colmajor_unroller<N - 1, N>;
+
+  ResPacket c[N];
+  Unroller::init_zero(c);
+  for (Index j = j2; j < jend; ++j) {
+    RhsPacket b0 = pset1<RhsPacket>(rhs(j, 0));
+    Unroller::template madd<LhsPacket, LhsPacketSize, LhsAlignment>(c, lhs, i, j, b0, pcj);
+  }
+  Unroller::template store<ResPacket, ResPacketSize>(c, res, i, palpha);
+}
+
+template <typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar,
+          typename RhsMapper, bool ConjugateRhs, int Version>
+EIGEN_DEVICE_FUNC inline void
+general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, ConjugateLhs, RhsScalar, RhsMapper, ConjugateRhs,
+                              Version>::run(Index rows, Index cols, const LhsMapper& alhs, const RhsMapper& rhs,
+                                            ResScalar* res, Index resIncr, RhsScalar alpha) {
+  EIGEN_UNUSED_VARIABLE(resIncr);
+  eigen_internal_assert(resIncr == 1);
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+
+  conj_helper<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs> cj;
+  conj_helper<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs> pcj;
+  conj_helper<LhsPacketHalf, RhsPacketHalf, ConjugateLhs, ConjugateRhs> pcj_half;
+  conj_helper<LhsPacketQuarter, RhsPacketQuarter, ConjugateLhs, ConjugateRhs> pcj_quarter;
+
+  const Index lhsStride = lhs.stride();
+  // TODO: for padded aligned inputs, we could enable aligned reads
+  enum {
+    LhsAlignment = Unaligned,
+    ResPacketSize = Traits::ResPacketSize,
+    ResPacketSizeHalf = HalfTraits::ResPacketSize,
+    ResPacketSizeQuarter = QuarterTraits::ResPacketSize,
+    LhsPacketSize = Traits::LhsPacketSize,
+    HasHalf = (int)ResPacketSizeHalf < (int)ResPacketSize,
+    HasQuarter = (int)ResPacketSizeQuarter < (int)ResPacketSizeHalf
+  };
+
+  const Index n8 = rows - 8 * ResPacketSize + 1;
+  const Index n4 = rows - 4 * ResPacketSize + 1;
+  const Index n3 = rows - 3 * ResPacketSize + 1;
+  const Index n2 = rows - 2 * ResPacketSize + 1;
+  const Index n1 = rows - 1 * ResPacketSize + 1;
+  const Index n_half = rows - 1 * ResPacketSizeHalf + 1;
+  const Index n_quarter = rows - 1 * ResPacketSizeQuarter + 1;
+
+  // TODO: improve the following heuristic:
+  const Index block_cols = cols < 128 ? cols : (lhsStride * Index(sizeof(LhsScalar)) < 32000 ? Index(16) : Index(4));
+  ResPacket palpha = pset1<ResPacket>(alpha);
+  ResPacketHalf palpha_half = pset1<ResPacketHalf>(alpha);
+  ResPacketQuarter palpha_quarter = pset1<ResPacketQuarter>(alpha);
+
+  for (Index j2 = 0; j2 < cols; j2 += block_cols) {
+    Index jend = numext::mini(j2 + block_cols, cols);
+    Index i = 0;
+    for (; i < n8; i += ResPacketSize * 8) process_rows<8>(i, j2, jend, lhs, rhs, res, palpha, pcj);
+    if (i < n4) {
+      process_rows<4>(i, j2, jend, lhs, rhs, res, palpha, pcj);
+      i += ResPacketSize * 4;
+    }
+    if (i < n3) {
+      process_rows<3>(i, j2, jend, lhs, rhs, res, palpha, pcj);
+      i += ResPacketSize * 3;
+    }
+    if (i < n2) {
+      process_rows<2>(i, j2, jend, lhs, rhs, res, palpha, pcj);
+      i += ResPacketSize * 2;
+    }
+    if (i < n1) {
+      process_rows<1>(i, j2, jend, lhs, rhs, res, palpha, pcj);
+      i += ResPacketSize;
+    }
+    if (HasHalf && i < n_half) {
+      ResPacketHalf c0 = pzero(ResPacketHalf{});
+      for (Index j = j2; j < jend; j += 1) {
+        RhsPacketHalf b0 = pset1<RhsPacketHalf>(rhs(j, 0));
+        c0 = pcj_half.pmadd(lhs.template load<LhsPacketHalf, LhsAlignment>(i + 0, j), b0, c0);
+      }
+      pstoreu(res + i + ResPacketSizeHalf * 0,
+              pmadd(c0, palpha_half, ploadu<ResPacketHalf>(res + i + ResPacketSizeHalf * 0)));
+      i += ResPacketSizeHalf;
+    }
+    if (HasQuarter && i < n_quarter) {
+      ResPacketQuarter c0 = pzero(ResPacketQuarter{});
+      for (Index j = j2; j < jend; j += 1) {
+        RhsPacketQuarter b0 = pset1<RhsPacketQuarter>(rhs(j, 0));
+        c0 = pcj_quarter.pmadd(lhs.template load<LhsPacketQuarter, LhsAlignment>(i + 0, j), b0, c0);
+      }
+      pstoreu(res + i + ResPacketSizeQuarter * 0,
+              pmadd(c0, palpha_quarter, ploadu<ResPacketQuarter>(res + i + ResPacketSizeQuarter * 0)));
+      i += ResPacketSizeQuarter;
+    }
+    for (; i < rows; ++i) {
+      ResScalar c0(0);
+      for (Index j = j2; j < jend; j += 1) c0 += cj.pmul(lhs(i, j), rhs(j, 0));
+      res[i] += alpha * c0;
+    }
+  }
+}
+
+/* Optimized row-major matrix * vector product:
+ * This algorithm processes 4 rows at once that allows to both reduce
+ * the number of load/stores of the result by a factor 4 and to reduce
+ * the instruction dependency. Moreover, we know that all bands have the
+ * same alignment pattern.
+ *
+ * Mixing type logic:
+ *  - alpha is always a complex (or converted to a complex)
+ *  - no vectorization
+ */
+template <typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar,
+          typename RhsMapper, bool ConjugateRhs, int Version>
+struct general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, ConjugateLhs, RhsScalar, RhsMapper,
+                                     ConjugateRhs, Version> {
+  typedef gemv_traits<LhsScalar, RhsScalar> Traits;
+  typedef gemv_traits<LhsScalar, RhsScalar, GEMVPacketHalf> HalfTraits;
+  typedef gemv_traits<LhsScalar, RhsScalar, GEMVPacketQuarter> QuarterTraits;
+
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+
+  typedef typename Traits::LhsPacket LhsPacket;
+  typedef typename Traits::RhsPacket RhsPacket;
+  typedef typename Traits::ResPacket ResPacket;
+
+  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
+  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
+  typedef typename HalfTraits::ResPacket ResPacketHalf;
+
+  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
+  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
+  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
+
+  EIGEN_DEVICE_FUNC static inline void run(Index rows, Index cols, const LhsMapper& lhs, const RhsMapper& rhs,
+                                           ResScalar* res, Index resIncr, ResScalar alpha);
+
+  // Specialized path for when cols < full packet size. Kept noinline to avoid
+  // bloating the main run() function and causing icache pressure.
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run_small_cols(Index rows, Index cols, const LhsMapper& lhs,
+                                                                 const RhsMapper& rhs, ResScalar* res, Index resIncr,
+                                                                 ResScalar alpha);
+
+  // Templated helper that processes N rows in run_small_cols. N is a compile-time
+  // constant; row-dimension unrolling is done via recursive templates to guarantee
+  // full unrolling regardless of compiler heuristics.
+  template <int N>
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE void process_rows_small_cols(Index i, Index cols, const LhsMapper& lhs,
+                                                                            const RhsMapper& rhs, ResScalar* res,
+                                                                            Index resIncr, ResScalar alpha,
+                                                                            Index halfColBlockEnd,
+                                                                            Index quarterColBlockEnd);
+};
+
+template <typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar,
+          typename RhsMapper, bool ConjugateRhs, int Version>
+EIGEN_DEVICE_FUNC inline void
+general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, ConjugateLhs, RhsScalar, RhsMapper, ConjugateRhs,
+                              Version>::run(Index rows, Index cols, const LhsMapper& alhs, const RhsMapper& rhs,
+                                            ResScalar* res, Index resIncr, ResScalar alpha) {
+  // When cols < full packet size, the main vectorized loops are empty.
+  // Dispatch to a separate noinline function to avoid polluting the icache.
+  // Only dispatch when cols is large enough that half or quarter packets can be used;
+  // otherwise the helper would just do scalar work with extra function call overhead.
+  enum {
+    LhsPacketSize_ = Traits::LhsPacketSize,
+    MinUsefulCols_ =
+        ((int)QuarterTraits::LhsPacketSize < (int)HalfTraits::LhsPacketSize)
+            ? (int)QuarterTraits::LhsPacketSize
+            : (((int)HalfTraits::LhsPacketSize < (int)Traits::LhsPacketSize) ? (int)HalfTraits::LhsPacketSize
+                                                                             : (int)Traits::LhsPacketSize),
+    HasSubPackets_ = (int)MinUsefulCols_ < (int)LhsPacketSize_
+  };
+  if (HasSubPackets_ && cols >= MinUsefulCols_ && cols < LhsPacketSize_) {
+    run_small_cols(rows, cols, alhs, rhs, res, resIncr, alpha);
+    return;
+  }
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+
+  eigen_internal_assert(rhs.stride() == 1);
+  conj_helper<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs> cj;
+  conj_helper<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs> pcj;
+  conj_helper<LhsPacketHalf, RhsPacketHalf, ConjugateLhs, ConjugateRhs> pcj_half;
+  conj_helper<LhsPacketQuarter, RhsPacketQuarter, ConjugateLhs, ConjugateRhs> pcj_quarter;
+
+  // TODO: fine tune the following heuristic. The rationale is that if the matrix is very large,
+  //       processing 8 rows at once might be counter productive wrt cache.
+  const Index n8 = lhs.stride() * sizeof(LhsScalar) > 32000 ? 0 : rows - 7;
+  const Index n4 = rows - 3;
+  const Index n2 = rows - 1;
+
+  // TODO: for padded aligned inputs, we could enable aligned reads
+  enum {
+    LhsAlignment = Unaligned,
+    ResPacketSize = Traits::ResPacketSize,
+    ResPacketSizeHalf = HalfTraits::ResPacketSize,
+    ResPacketSizeQuarter = QuarterTraits::ResPacketSize,
+    LhsPacketSize = Traits::LhsPacketSize,
+    LhsPacketSizeHalf = HalfTraits::LhsPacketSize,
+    LhsPacketSizeQuarter = QuarterTraits::LhsPacketSize,
+    HasHalf = (int)ResPacketSizeHalf < (int)ResPacketSize,
+    HasQuarter = (int)ResPacketSizeQuarter < (int)ResPacketSizeHalf
+  };
+
+  using UnsignedIndex = std::make_unsigned_t<Index>;
+  const Index fullColBlockEnd = LhsPacketSize * (UnsignedIndex(cols) / LhsPacketSize);
+  const Index halfColBlockEnd = LhsPacketSizeHalf * (UnsignedIndex(cols) / LhsPacketSizeHalf);
+  const Index quarterColBlockEnd = LhsPacketSizeQuarter * (UnsignedIndex(cols) / LhsPacketSizeQuarter);
+
+  Index i = 0;
+  for (; i < n8; i += 8) {
+    ResPacket c0 = pzero(ResPacket{}), c1 = pzero(ResPacket{}), c2 = pzero(ResPacket{}), c3 = pzero(ResPacket{}),
+              c4 = pzero(ResPacket{}), c5 = pzero(ResPacket{}), c6 = pzero(ResPacket{}), c7 = pzero(ResPacket{});
+
+    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize) {
+      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j, 0);
+
+      c0 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 0, j), b0, c0);
+      c1 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 1, j), b0, c1);
+      c2 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 2, j), b0, c2);
+      c3 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 3, j), b0, c3);
+      c4 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 4, j), b0, c4);
+      c5 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 5, j), b0, c5);
+      c6 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 6, j), b0, c6);
+      c7 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 7, j), b0, c7);
+    }
+    ResScalar cc0 = predux(c0);
+    ResScalar cc1 = predux(c1);
+    ResScalar cc2 = predux(c2);
+    ResScalar cc3 = predux(c3);
+    ResScalar cc4 = predux(c4);
+    ResScalar cc5 = predux(c5);
+    ResScalar cc6 = predux(c6);
+    ResScalar cc7 = predux(c7);
+
+    for (Index j = fullColBlockEnd; j < cols; ++j) {
+      RhsScalar b0 = rhs(j, 0);
+
+      cc0 += cj.pmul(lhs(i + 0, j), b0);
+      cc1 += cj.pmul(lhs(i + 1, j), b0);
+      cc2 += cj.pmul(lhs(i + 2, j), b0);
+      cc3 += cj.pmul(lhs(i + 3, j), b0);
+      cc4 += cj.pmul(lhs(i + 4, j), b0);
+      cc5 += cj.pmul(lhs(i + 5, j), b0);
+      cc6 += cj.pmul(lhs(i + 6, j), b0);
+      cc7 += cj.pmul(lhs(i + 7, j), b0);
+    }
+    res[(i + 0) * resIncr] += alpha * cc0;
+    res[(i + 1) * resIncr] += alpha * cc1;
+    res[(i + 2) * resIncr] += alpha * cc2;
+    res[(i + 3) * resIncr] += alpha * cc3;
+    res[(i + 4) * resIncr] += alpha * cc4;
+    res[(i + 5) * resIncr] += alpha * cc5;
+    res[(i + 6) * resIncr] += alpha * cc6;
+    res[(i + 7) * resIncr] += alpha * cc7;
+  }
+  if (i < n4) {
+    ResPacket c0 = pzero(ResPacket{}), c1 = pzero(ResPacket{}), c2 = pzero(ResPacket{}), c3 = pzero(ResPacket{});
+
+    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize) {
+      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j, 0);
+
+      c0 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 0, j), b0, c0);
+      c1 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 1, j), b0, c1);
+      c2 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 2, j), b0, c2);
+      c3 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 3, j), b0, c3);
+    }
+    ResScalar cc0 = predux(c0);
+    ResScalar cc1 = predux(c1);
+    ResScalar cc2 = predux(c2);
+    ResScalar cc3 = predux(c3);
+
+    for (Index j = fullColBlockEnd; j < cols; ++j) {
+      RhsScalar b0 = rhs(j, 0);
+
+      cc0 += cj.pmul(lhs(i + 0, j), b0);
+      cc1 += cj.pmul(lhs(i + 1, j), b0);
+      cc2 += cj.pmul(lhs(i + 2, j), b0);
+      cc3 += cj.pmul(lhs(i + 3, j), b0);
+    }
+    res[(i + 0) * resIncr] += alpha * cc0;
+    res[(i + 1) * resIncr] += alpha * cc1;
+    res[(i + 2) * resIncr] += alpha * cc2;
+    res[(i + 3) * resIncr] += alpha * cc3;
+    i += 4;
+  }
+  if (i < n2) {
+    ResPacket c0 = pzero(ResPacket{}), c1 = pzero(ResPacket{});
+
+    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize) {
+      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j, 0);
+
+      c0 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 0, j), b0, c0);
+      c1 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 1, j), b0, c1);
+    }
+    ResScalar cc0 = predux(c0);
+    ResScalar cc1 = predux(c1);
+
+    for (Index j = fullColBlockEnd; j < cols; ++j) {
+      RhsScalar b0 = rhs(j, 0);
+
+      cc0 += cj.pmul(lhs(i + 0, j), b0);
+      cc1 += cj.pmul(lhs(i + 1, j), b0);
+    }
+    res[(i + 0) * resIncr] += alpha * cc0;
+    res[(i + 1) * resIncr] += alpha * cc1;
+    i += 2;
+  }
+  if (i < rows) {
+    ResPacket c0 = pzero(ResPacket{});
+    ResPacketHalf c0_h = pzero(ResPacketHalf{});
+    ResPacketQuarter c0_q = pzero(ResPacketQuarter{});
+
+    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize) {
+      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j, 0);
+      c0 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i, j), b0, c0);
+    }
+    ResScalar cc0 = predux(c0);
+    if (HasHalf) {
+      for (Index j = fullColBlockEnd; j < halfColBlockEnd; j += LhsPacketSizeHalf) {
+        RhsPacketHalf b0 = rhs.template load<RhsPacketHalf, Unaligned>(j, 0);
+        c0_h = pcj_half.pmadd(lhs.template load<LhsPacketHalf, LhsAlignment>(i, j), b0, c0_h);
+      }
+      cc0 += predux(c0_h);
+    }
+    if (HasQuarter) {
+      for (Index j = halfColBlockEnd; j < quarterColBlockEnd; j += LhsPacketSizeQuarter) {
+        RhsPacketQuarter b0 = rhs.template load<RhsPacketQuarter, Unaligned>(j, 0);
+        c0_q = pcj_quarter.pmadd(lhs.template load<LhsPacketQuarter, LhsAlignment>(i, j), b0, c0_q);
+      }
+      cc0 += predux(c0_q);
+    }
+    for (Index j = quarterColBlockEnd; j < cols; ++j) {
+      cc0 += cj.pmul(lhs(i, j), rhs(j, 0));
+    }
+    res[i * resIncr] += alpha * cc0;
+  }
+}
+
+// Recursive template unroller for process_rows_small_cols.
+// Unrolls the row dimension (K = 0..N-1) at compile time, guaranteeing
+// that each accumulator lives in its own register variable regardless
+// of compiler unrolling heuristics.
+template <int K, int N>
+struct gemv_small_cols_unroller {
+  template <typename LhsPacket, typename AccPacket, int Alignment, typename RhsType, typename ConjHelper,
+            typename LhsMapper, typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void madd(AccPacket* acc, const LhsMapper& lhs, Index i, Index j,
+                                                         const RhsType& b0, ConjHelper& pcj) {
+    gemv_small_cols_unroller<K - 1, N>::template madd<LhsPacket, AccPacket, Alignment>(acc, lhs, i, j, b0, pcj);
+    acc[K] = pcj.pmadd(lhs.template load<LhsPacket, Alignment>(i + K, j), b0, acc[K]);
+  }
+
+  template <typename ResScalar, typename RhsScalar, typename ConjHelper, typename LhsMapper, typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void scalar_madd(ResScalar* cc, const LhsMapper& lhs, Index i, Index j,
+                                                                const RhsScalar& b0, ConjHelper& cj) {
+    gemv_small_cols_unroller<K - 1, N>::scalar_madd(cc, lhs, i, j, b0, cj);
+    cc[K] += cj.pmul(lhs(i + K, j), b0);
+  }
+
+  template <typename Scalar, typename Packet>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void predux_accum(Scalar* cc, const Packet* acc) {
+    gemv_small_cols_unroller<K - 1, N>::predux_accum(cc, acc);
+    cc[K] += predux(acc[K]);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void init_zero(Packet* acc) {
+    gemv_small_cols_unroller<K - 1, N>::init_zero(acc);
+    acc[K] = pzero(Packet{});
+  }
+
+  template <typename Scalar, typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void write_result(Scalar* res, Index resIncr, Index i, Scalar alpha,
+                                                                 const Scalar* cc) {
+    gemv_small_cols_unroller<K - 1, N>::write_result(res, resIncr, i, alpha, cc);
+    res[(i + K) * resIncr] += alpha * cc[K];
+  }
+};
+
+template <int N>
+struct gemv_small_cols_unroller<0, N> {
+  template <typename LhsPacket, typename AccPacket, int Alignment, typename RhsType, typename ConjHelper,
+            typename LhsMapper, typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void madd(AccPacket* acc, const LhsMapper& lhs, Index i, Index j,
+                                                         const RhsType& b0, ConjHelper& pcj) {
+    acc[0] = pcj.pmadd(lhs.template load<LhsPacket, Alignment>(i, j), b0, acc[0]);
+  }
+
+  template <typename ResScalar, typename RhsScalar, typename ConjHelper, typename LhsMapper, typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void scalar_madd(ResScalar* cc, const LhsMapper& lhs, Index i, Index j,
+                                                                const RhsScalar& b0, ConjHelper& cj) {
+    cc[0] += cj.pmul(lhs(i, j), b0);
+  }
+
+  template <typename Scalar, typename Packet>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void predux_accum(Scalar* cc, const Packet* acc) {
+    cc[0] += predux(acc[0]);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void init_zero(Packet* acc) {
+    acc[0] = pzero(Packet{});
+  }
+
+  template <typename Scalar, typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void write_result(Scalar* res, Index resIncr, Index i, Scalar alpha,
+                                                                 const Scalar* cc) {
+    res[i * resIncr] += alpha * cc[0];
+  }
+};
+
+template <typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar,
+          typename RhsMapper, bool ConjugateRhs, int Version>
+template <int N>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void
+general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, ConjugateLhs, RhsScalar, RhsMapper, ConjugateRhs,
+                              Version>::process_rows_small_cols(Index i, Index cols, const LhsMapper& lhs,
+                                                                const RhsMapper& rhs, ResScalar* res, Index resIncr,
+                                                                ResScalar alpha, Index halfColBlockEnd,
+                                                                Index quarterColBlockEnd) {
+  conj_helper<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs> cj;
+  conj_helper<LhsPacketHalf, RhsPacketHalf, ConjugateLhs, ConjugateRhs> pcj_half;
+  conj_helper<LhsPacketQuarter, RhsPacketQuarter, ConjugateLhs, ConjugateRhs> pcj_quarter;
+
+  enum {
+    LhsAlignment = Unaligned,
+    ResPacketSizeHalf = HalfTraits::ResPacketSize,
+    ResPacketSizeQuarter = QuarterTraits::ResPacketSize,
+    LhsPacketSizeHalf = HalfTraits::LhsPacketSize,
+    LhsPacketSizeQuarter = QuarterTraits::LhsPacketSize,
+    HasHalf = (int)ResPacketSizeHalf < (int)Traits::ResPacketSize,
+    HasQuarter = (int)ResPacketSizeQuarter < (int)ResPacketSizeHalf
+  };
+
+  using Unroll = gemv_small_cols_unroller<N - 1, N>;
+
+  ResScalar cc[N] = {};
+  if (HasHalf) {
+    ResPacketHalf h[N];
+    Unroll::init_zero(h);
+    for (Index j = 0; j < halfColBlockEnd; j += LhsPacketSizeHalf) {
+      RhsPacketHalf b0 = rhs.template load<RhsPacketHalf, Unaligned>(j, 0);
+      Unroll::template madd<LhsPacketHalf, ResPacketHalf, LhsAlignment>(h, lhs, i, j, b0, pcj_half);
+    }
+    Unroll::predux_accum(cc, h);
+  }
+  if (HasQuarter) {
+    ResPacketQuarter q[N];
+    Unroll::init_zero(q);
+    for (Index j = halfColBlockEnd; j < quarterColBlockEnd; j += LhsPacketSizeQuarter) {
+      RhsPacketQuarter b0 = rhs.template load<RhsPacketQuarter, Unaligned>(j, 0);
+      Unroll::template madd<LhsPacketQuarter, ResPacketQuarter, LhsAlignment>(q, lhs, i, j, b0, pcj_quarter);
+    }
+    Unroll::predux_accum(cc, q);
+  }
+  for (Index j = quarterColBlockEnd; j < cols; ++j) {
+    RhsScalar b0 = rhs(j, 0);
+    Unroll::scalar_madd(cc, lhs, i, j, b0, cj);
+  }
+  Unroll::write_result(res, resIncr, i, alpha, cc);
+}
+
+template <typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar,
+          typename RhsMapper, bool ConjugateRhs, int Version>
+EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void
+general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, ConjugateLhs, RhsScalar, RhsMapper, ConjugateRhs,
+                              Version>::run_small_cols(Index rows, Index cols, const LhsMapper& alhs,
+                                                       const RhsMapper& rhs, ResScalar* res, Index resIncr,
+                                                       ResScalar alpha) {
+  LhsMapper lhs(alhs);
+  eigen_internal_assert(rhs.stride() == 1);
+
+  enum {
+    LhsPacketSizeHalf = HalfTraits::LhsPacketSize,
+    LhsPacketSizeQuarter = QuarterTraits::LhsPacketSize,
+  };
+
+  using UnsignedIndex = std::make_unsigned_t<Index>;
+  const Index halfColBlockEnd = LhsPacketSizeHalf * (UnsignedIndex(cols) / LhsPacketSizeHalf);
+  const Index quarterColBlockEnd = LhsPacketSizeQuarter * (UnsignedIndex(cols) / LhsPacketSizeQuarter);
+
+  const Index n8 = lhs.stride() * sizeof(LhsScalar) > 32000 ? 0 : rows - 7;
+  const Index n4 = rows - 3;
+  const Index n2 = rows - 1;
+
+  Index i = 0;
+  for (; i < n8; i += 8)
+    process_rows_small_cols<8>(i, cols, lhs, rhs, res, resIncr, alpha, halfColBlockEnd, quarterColBlockEnd);
+  if (i < n4) {
+    process_rows_small_cols<4>(i, cols, lhs, rhs, res, resIncr, alpha, halfColBlockEnd, quarterColBlockEnd);
+    i += 4;
+  }
+  if (i < n2) {
+    process_rows_small_cols<2>(i, cols, lhs, rhs, res, resIncr, alpha, halfColBlockEnd, quarterColBlockEnd);
+    i += 2;
+  }
+  if (i < rows) process_rows_small_cols<1>(i, cols, lhs, rhs, res, resIncr, alpha, halfColBlockEnd, quarterColBlockEnd);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GENERAL_MATRIX_VECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
new file mode 100644
index 0000000..18adfd1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
@@ -0,0 +1,141 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   General matrix-vector product functionality based on ?GEMV.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
+#define EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**********************************************************************
+ * This file implements general matrix-vector multiplication using BLAS
+ * gemv function via partial specialization of
+ * general_matrix_vector_product::run(..) method for float, double,
+ * std::complex<float> and std::complex<double> types
+ **********************************************************************/
+
+// gemv specialization
+
+template <typename Index, typename LhsScalar, int StorageOrder, bool ConjugateLhs, typename RhsScalar,
+          bool ConjugateRhs>
+struct general_matrix_vector_product_gemv;
+
+#define EIGEN_BLAS_GEMV_SPECIALIZE(Scalar)                                                                       \
+  template <typename Index, bool ConjugateLhs, bool ConjugateRhs>                                                \
+  struct general_matrix_vector_product<Index, Scalar, const_blas_data_mapper<Scalar, Index, ColMajor>, ColMajor, \
+                                       ConjugateLhs, Scalar, const_blas_data_mapper<Scalar, Index, RowMajor>,    \
+                                       ConjugateRhs, Specialized> {                                              \
+    static void run(Index rows, Index cols, const const_blas_data_mapper<Scalar, Index, ColMajor>& lhs,          \
+                    const const_blas_data_mapper<Scalar, Index, RowMajor>& rhs, Scalar* res, Index resIncr,      \
+                    Scalar alpha) {                                                                              \
+      if (ConjugateLhs) {                                                                                        \
+        general_matrix_vector_product<Index, Scalar, const_blas_data_mapper<Scalar, Index, ColMajor>, ColMajor,  \
+                                      ConjugateLhs, Scalar, const_blas_data_mapper<Scalar, Index, RowMajor>,     \
+                                      ConjugateRhs, BuiltIn>::run(rows, cols, lhs, rhs, res, resIncr, alpha);    \
+      } else {                                                                                                   \
+        general_matrix_vector_product_gemv<Index, Scalar, ColMajor, ConjugateLhs, Scalar, ConjugateRhs>::run(    \
+            rows, cols, lhs.data(), lhs.stride(), rhs.data(), rhs.stride(), res, resIncr, alpha);                \
+      }                                                                                                          \
+    }                                                                                                            \
+  };                                                                                                             \
+  template <typename Index, bool ConjugateLhs, bool ConjugateRhs>                                                \
+  struct general_matrix_vector_product<Index, Scalar, const_blas_data_mapper<Scalar, Index, RowMajor>, RowMajor, \
+                                       ConjugateLhs, Scalar, const_blas_data_mapper<Scalar, Index, ColMajor>,    \
+                                       ConjugateRhs, Specialized> {                                              \
+    static void run(Index rows, Index cols, const const_blas_data_mapper<Scalar, Index, RowMajor>& lhs,          \
+                    const const_blas_data_mapper<Scalar, Index, ColMajor>& rhs, Scalar* res, Index resIncr,      \
+                    Scalar alpha) {                                                                              \
+      general_matrix_vector_product_gemv<Index, Scalar, RowMajor, ConjugateLhs, Scalar, ConjugateRhs>::run(      \
+          rows, cols, lhs.data(), lhs.stride(), rhs.data(), rhs.stride(), res, resIncr, alpha);                  \
+    }                                                                                                            \
+  };
+
+EIGEN_BLAS_GEMV_SPECIALIZE(double)
+EIGEN_BLAS_GEMV_SPECIALIZE(float)
+EIGEN_BLAS_GEMV_SPECIALIZE(dcomplex)
+EIGEN_BLAS_GEMV_SPECIALIZE(scomplex)
+
+#define EIGEN_BLAS_GEMV_SPECIALIZATION(EIGTYPE, BLASTYPE, BLASFUNC)                                                 \
+  template <typename Index, int LhsStorageOrder, bool ConjugateLhs, bool ConjugateRhs>                              \
+  struct general_matrix_vector_product_gemv<Index, EIGTYPE, LhsStorageOrder, ConjugateLhs, EIGTYPE, ConjugateRhs> { \
+    typedef Matrix<EIGTYPE, Dynamic, 1, ColMajor> GEMVVector;                                                       \
+                                                                                                                    \
+    static void run(Index rows, Index cols, const EIGTYPE* lhs, Index lhsStride, const EIGTYPE* rhs, Index rhsIncr, \
+                    EIGTYPE* res, Index resIncr, EIGTYPE alpha) {                                                   \
+      if (rows == 0 || cols == 0) return;                                                                           \
+      BlasIndex m = convert_index<BlasIndex>(rows), n = convert_index<BlasIndex>(cols),                             \
+                lda = convert_index<BlasIndex>(lhsStride), incx = convert_index<BlasIndex>(rhsIncr),                \
+                incy = convert_index<BlasIndex>(resIncr);                                                           \
+      const EIGTYPE beta(1);                                                                                        \
+      const EIGTYPE* x_ptr;                                                                                         \
+      char trans = (LhsStorageOrder == ColMajor) ? 'N' : (ConjugateLhs) ? 'C' : 'T';                                \
+      if (LhsStorageOrder == RowMajor) {                                                                            \
+        m = convert_index<BlasIndex>(cols);                                                                         \
+        n = convert_index<BlasIndex>(rows);                                                                         \
+      }                                                                                                             \
+      GEMVVector x_tmp;                                                                                             \
+      if (ConjugateRhs) {                                                                                           \
+        Map<const GEMVVector, 0, InnerStride<> > map_x(rhs, cols, 1, InnerStride<>(incx));                          \
+        x_tmp = map_x.conjugate();                                                                                  \
+        x_ptr = x_tmp.data();                                                                                       \
+        incx = 1;                                                                                                   \
+      } else {                                                                                                      \
+        x_ptr = rhs;                                                                                                \
+      }                                                                                                             \
+      BLASFUNC(&trans, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda,               \
+               (const BLASTYPE*)x_ptr, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &incy);     \
+    }                                                                                                               \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_GEMV_SPECIALIZATION(double, double, dgemv)
+EIGEN_BLAS_GEMV_SPECIALIZATION(float, float, sgemv)
+EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, MKL_Complex16, zgemv)
+EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, MKL_Complex8, cgemv)
+#else
+EIGEN_BLAS_GEMV_SPECIALIZATION(double, double, dgemv_)
+EIGEN_BLAS_GEMV_SPECIALIZATION(float, float, sgemv_)
+EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, double, zgemv_)
+EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, float, cgemv_)
+#endif
+
+#undef EIGEN_BLAS_GEMV_SPECIALIZE
+#undef EIGEN_BLAS_GEMV_SPECIALIZATION
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/Parallelizer.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/Parallelizer.h
new file mode 100644
index 0000000..f4625e2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/Parallelizer.h
@@ -0,0 +1,282 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARALLELIZER_H
+#define EIGEN_PARALLELIZER_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+// Note that in the following, there are 3 different uses of the concept
+// "number of threads":
+//  1. Max number of threads used by OpenMP or ThreadPool.
+//     * For OpenMP this is typically the value set by the OMP_NUM_THREADS
+//       environment variable, or by a call to omp_set_num_threads() prior to
+//       calling Eigen.
+//     * For ThreadPool, this is the number of threads in the ThreadPool.
+//  2. Max number of threads currently allowed to be used by parallel Eigen
+//     operations. This is set by setNbThreads(), and cannot exceed the value
+//     in 1.
+//  3. The actual number of threads used for a given parallel Eigen operation.
+//     This is typically computed on the fly using a cost model and cannot exceed
+//     the value in 2.
+//     * For OpenMP, this is typically the number of threads specified in individual
+//       "omp parallel" pragmas associated with an Eigen operation.
+//     * For ThreadPool, it is the number of concurrent tasks scheduled in the
+//       threadpool for a given Eigen operation. Notice that since the threadpool
+//       uses task stealing, there is no way to limit the number of concurrently
+//       executing tasks to below the number in 1. except by limiting the total
+//       number of tasks in flight.
+
+#if defined(EIGEN_HAS_OPENMP) && defined(EIGEN_GEMM_THREADPOOL)
+#error "EIGEN_HAS_OPENMP and EIGEN_GEMM_THREADPOOL may not both be defined."
+#endif
+
+namespace Eigen {
+
+namespace internal {
+inline void manage_multi_threading(Action action, int* v);
+}
+
+// Public APIs.
+
+/** Must be call first when calling Eigen from multiple threads */
+EIGEN_DEPRECATED_WITH_REASON("Initialization is no longer needed.") inline void initParallel() {}
+
+/** \returns the max number of threads reserved for Eigen
+ * \sa setNbThreads */
+inline int nbThreads() {
+  int ret;
+  internal::manage_multi_threading(GetAction, &ret);
+  return ret;
+}
+
+/** Sets the max number of threads reserved for Eigen
+ * \sa nbThreads */
+inline void setNbThreads(int v) { internal::manage_multi_threading(SetAction, &v); }
+
+#ifdef EIGEN_GEMM_THREADPOOL
+// Sets the ThreadPool used by Eigen parallel Gemm.
+//
+// NOTICE: This function has a known race condition with
+// parallelize_gemm below, and should not be called while
+// an instance of that function is running.
+//
+// TODO(rmlarsen): Make the device API available instead of
+// storing a local static pointer variable to avoid this issue.
+inline ThreadPool* setGemmThreadPool(ThreadPool* new_pool) {
+  static ThreadPool* pool = nullptr;
+  if (new_pool != nullptr) {
+    // This will wait for work in all threads in *pool to finish,
+    // then destroy the old ThreadPool, and then replace it with new_pool.
+    pool = new_pool;
+    // Reset the number of threads to the number of threads on the new pool.
+    setNbThreads(pool->NumThreads());
+  }
+  return pool;
+}
+
+// Gets the ThreadPool used by Eigen parallel Gemm.
+inline ThreadPool* getGemmThreadPool() { return setGemmThreadPool(nullptr); }
+#endif
+
+namespace internal {
+
+// Implementation.
+
+#if defined(EIGEN_USE_BLAS) || (!defined(EIGEN_HAS_OPENMP) && !defined(EIGEN_GEMM_THREADPOOL))
+
+inline void manage_multi_threading(Action action, int* v) {
+  if (action == SetAction) {
+    eigen_internal_assert(v != nullptr);
+  } else if (action == GetAction) {
+    eigen_internal_assert(v != nullptr);
+    *v = 1;
+  } else {
+    eigen_internal_assert(false);
+  }
+}
+template <typename Index>
+struct GemmParallelInfo {};
+template <bool Condition, typename Functor, typename Index>
+EIGEN_STRONG_INLINE void parallelize_gemm(const Functor& func, Index rows, Index cols, Index /*unused*/,
+                                          bool /*unused*/) {
+  func(0, rows, 0, cols);
+}
+
+#else
+
+template <typename Index>
+struct GemmParallelTaskInfo {
+  GemmParallelTaskInfo() : sync(-1), users(0), lhs_start(0), lhs_length(0) {}
+  std::atomic<Index> sync;
+  std::atomic<int> users;
+  Index lhs_start;
+  Index lhs_length;
+};
+
+template <typename Index>
+struct GemmParallelInfo {
+  const int logical_thread_id;
+  const int num_threads;
+  GemmParallelTaskInfo<Index>* task_info;
+
+  GemmParallelInfo(int logical_thread_id_, int num_threads_, GemmParallelTaskInfo<Index>* task_info_)
+      : logical_thread_id(logical_thread_id_), num_threads(num_threads_), task_info(task_info_) {}
+};
+
+inline void manage_multi_threading(Action action, int* v) {
+  static int m_maxThreads = -1;
+  if (action == SetAction) {
+    eigen_internal_assert(v != nullptr);
+#if defined(EIGEN_HAS_OPENMP)
+    // Calling action == SetAction and *v = 0 means
+    // restoring m_maxThreads to the maximum number of threads specified
+    // for OpenMP.
+    eigen_internal_assert(*v >= 0);
+    int omp_threads = omp_get_max_threads();
+    m_maxThreads = (*v == 0 ? omp_threads : std::min(*v, omp_threads));
+#elif defined(EIGEN_GEMM_THREADPOOL)
+    // Calling action == SetAction and *v = 0 means
+    // restoring m_maxThreads to the number of threads in the ThreadPool,
+    // which defaults to 1 if no pool was provided.
+    eigen_internal_assert(*v >= 0);
+    ThreadPool* pool = getGemmThreadPool();
+    int pool_threads = pool != nullptr ? pool->NumThreads() : 1;
+    m_maxThreads = (*v == 0 ? pool_threads : numext::mini(pool_threads, *v));
+#endif
+  } else if (action == GetAction) {
+    eigen_internal_assert(v != nullptr);
+#if defined(EIGEN_HAS_OPENMP)
+    if (m_maxThreads > 0)
+      *v = m_maxThreads;
+    else
+      *v = omp_get_max_threads();
+#else
+    *v = m_maxThreads;
+#endif
+  } else {
+    eigen_internal_assert(false);
+  }
+}
+
+template <bool Condition, typename Functor, typename Index>
+EIGEN_STRONG_INLINE void parallelize_gemm(const Functor& func, Index rows, Index cols, Index depth, bool transpose) {
+  // Dynamically check whether we should even try to execute in parallel.
+  // The conditions are:
+  // - the max number of threads we can create is greater than 1
+  // - we are not already in a parallel code
+  // - the sizes are large enough
+
+  // compute the maximal number of threads from the size of the product:
+  // This first heuristic takes into account that the product kernel is fully optimized when working with nr columns at
+  // once.
+  Index size = transpose ? rows : cols;
+  Index pb_max_threads = std::max<Index>(1, size / Functor::Traits::nr);
+
+  // compute the maximal number of threads from the total amount of work:
+  double work = static_cast<double>(rows) * static_cast<double>(cols) * static_cast<double>(depth);
+  double kMinTaskSize = 50000;  // FIXME: tune this minimum task-size heuristic based on architecture and scalar type.
+  pb_max_threads = std::max<Index>(1, std::min<Index>(pb_max_threads, static_cast<Index>(work / kMinTaskSize)));
+
+  // compute the number of threads we are going to use
+  int threads = std::min<int>(nbThreads(), static_cast<int>(pb_max_threads));
+
+  // if multi-threading is explicitly disabled, not useful, or if we already are
+  // inside a parallel session, then abort multi-threading
+  bool dont_parallelize = (!Condition) || (threads <= 1);
+#if defined(EIGEN_HAS_OPENMP)
+  // don't parallelize if we are executing in a parallel context already.
+  dont_parallelize |= omp_get_num_threads() > 1;
+#elif defined(EIGEN_GEMM_THREADPOOL)
+  // don't parallelize if we have a trivial threadpool or the current thread id
+  // is != -1, indicating that we are already executing on a thread inside the pool.
+  // In other words, we do not allow nested parallelism, since this would lead to
+  // deadlocks due to the workstealing nature of the threadpool.
+  ThreadPool* pool = getGemmThreadPool();
+  dont_parallelize |= (pool == nullptr || pool->CurrentThreadId() != -1);
+#endif
+  if (dont_parallelize) return func(0, rows, 0, cols);
+
+  func.initParallelSession(threads);
+
+  if (transpose) std::swap(rows, cols);
+
+  ei_declare_aligned_stack_constructed_variable(GemmParallelTaskInfo<Index>, task_info, threads, 0);
+
+#if defined(EIGEN_HAS_OPENMP)
+#pragma omp parallel num_threads(threads)
+  {
+    Index i = omp_get_thread_num();
+    // Note that the actual number of threads might be lower than the number of
+    // requested ones
+    Index actual_threads = omp_get_num_threads();
+    GemmParallelInfo<Index> info(static_cast<int>(i), static_cast<int>(actual_threads), task_info);
+
+    Index blockCols = (cols / actual_threads) & ~Index(0x3);
+    Index blockRows = (rows / actual_threads);
+    blockRows = (blockRows / Functor::Traits::mr) * Functor::Traits::mr;
+
+    Index r0 = i * blockRows;
+    Index actualBlockRows = (i + 1 == actual_threads) ? rows - r0 : blockRows;
+
+    Index c0 = i * blockCols;
+    Index actualBlockCols = (i + 1 == actual_threads) ? cols - c0 : blockCols;
+
+    info.task_info[i].lhs_start = r0;
+    info.task_info[i].lhs_length = actualBlockRows;
+
+    if (transpose)
+      func(c0, actualBlockCols, 0, rows, &info);
+    else
+      func(0, rows, c0, actualBlockCols, &info);
+  }
+
+#elif defined(EIGEN_GEMM_THREADPOOL)
+  Barrier barrier(threads);
+  auto task = [=, &func, &barrier, &task_info](int i) {
+    Index actual_threads = threads;
+    GemmParallelInfo<Index> info(i, static_cast<int>(actual_threads), task_info);
+    Index blockCols = (cols / actual_threads) & ~Index(0x3);
+    Index blockRows = (rows / actual_threads);
+    blockRows = (blockRows / Functor::Traits::mr) * Functor::Traits::mr;
+
+    Index r0 = i * blockRows;
+    Index actualBlockRows = (i + 1 == actual_threads) ? rows - r0 : blockRows;
+
+    Index c0 = i * blockCols;
+    Index actualBlockCols = (i + 1 == actual_threads) ? cols - c0 : blockCols;
+
+    info.task_info[i].lhs_start = r0;
+    info.task_info[i].lhs_length = actualBlockRows;
+
+    if (transpose)
+      func(c0, actualBlockCols, 0, rows, &info);
+    else
+      func(0, rows, c0, actualBlockCols, &info);
+
+    barrier.Notify();
+  };
+  // Notice that we do not schedule more than "threads" tasks, which allows us to
+  // limit number of running threads, even if the threadpool itself was constructed
+  // with a larger number of threads.
+  for (int i = 0; i < threads - 1; ++i) {
+    pool->Schedule([=, task = std::move(task)] { task(i); });
+  }
+  task(threads - 1);
+  barrier.Wait();
+#endif
+}
+
+#endif
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_PARALLELIZER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
new file mode 100644
index 0000000..899283d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
@@ -0,0 +1,483 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_H
+#define EIGEN_SELFADJOINT_MATRIX_MATRIX_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// pack a selfadjoint block diagonal for use with the gebp_kernel
+template <typename Scalar, typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
+struct symm_pack_lhs {
+  template <int BlockRows>
+  inline void pack(Scalar* blockA, const const_blas_data_mapper<Scalar, Index, StorageOrder>& lhs, Index cols, Index i,
+                   Index& count) {
+    // normal copy
+    for (Index k = 0; k < i; k++)
+      for (Index w = 0; w < BlockRows; w++) blockA[count++] = lhs(i + w, k);  // normal
+    // symmetric copy
+    Index h = 0;
+    for (Index k = i; k < i + BlockRows; k++) {
+      for (Index w = 0; w < h; w++) blockA[count++] = numext::conj(lhs(k, i + w));  // transposed
+
+      blockA[count++] = numext::real(lhs(k, k));  // real (diagonal)
+
+      for (Index w = h + 1; w < BlockRows; w++) blockA[count++] = lhs(i + w, k);  // normal
+      ++h;
+    }
+    // transposed copy
+    for (Index k = i + BlockRows; k < cols; k++)
+      for (Index w = 0; w < BlockRows; w++) blockA[count++] = numext::conj(lhs(k, i + w));  // transposed
+  }
+  void operator()(Scalar* blockA, const Scalar* lhs_, Index lhsStride, Index cols, Index rows) {
+    typedef typename unpacket_traits<typename packet_traits<Scalar>::type>::half HalfPacket;
+    typedef typename unpacket_traits<typename unpacket_traits<typename packet_traits<Scalar>::type>::half>::half
+        QuarterPacket;
+    enum {
+      PacketSize = packet_traits<Scalar>::size,
+      HalfPacketSize = unpacket_traits<HalfPacket>::size,
+      QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
+      HasHalf = (int)HalfPacketSize < (int)PacketSize,
+      HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize
+    };
+
+    const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(lhs_, lhsStride);
+    Index count = 0;
+    // Index peeled_mc3 = (rows/Pack1)*Pack1;
+
+    const Index peeled_mc3 = Pack1 >= 3 * PacketSize ? (rows / (3 * PacketSize)) * (3 * PacketSize) : 0;
+    const Index peeled_mc2 =
+        Pack1 >= 2 * PacketSize ? peeled_mc3 + ((rows - peeled_mc3) / (2 * PacketSize)) * (2 * PacketSize) : 0;
+    const Index peeled_mc1 =
+        Pack1 >= 1 * PacketSize ? peeled_mc2 + ((rows - peeled_mc2) / (1 * PacketSize)) * (1 * PacketSize) : 0;
+    const Index peeled_mc_half =
+        Pack1 >= HalfPacketSize ? peeled_mc1 + ((rows - peeled_mc1) / (HalfPacketSize)) * (HalfPacketSize) : 0;
+    const Index peeled_mc_quarter =
+        Pack1 >= QuarterPacketSize
+            ? peeled_mc_half + ((rows - peeled_mc_half) / (QuarterPacketSize)) * (QuarterPacketSize)
+            : 0;
+
+    if (Pack1 >= 3 * PacketSize)
+      for (Index i = 0; i < peeled_mc3; i += 3 * PacketSize) pack<3 * PacketSize>(blockA, lhs, cols, i, count);
+
+    if (Pack1 >= 2 * PacketSize)
+      for (Index i = peeled_mc3; i < peeled_mc2; i += 2 * PacketSize) pack<2 * PacketSize>(blockA, lhs, cols, i, count);
+
+    if (Pack1 >= 1 * PacketSize)
+      for (Index i = peeled_mc2; i < peeled_mc1; i += 1 * PacketSize) pack<1 * PacketSize>(blockA, lhs, cols, i, count);
+
+    if (HasHalf && Pack1 >= HalfPacketSize)
+      for (Index i = peeled_mc1; i < peeled_mc_half; i += HalfPacketSize)
+        pack<HalfPacketSize>(blockA, lhs, cols, i, count);
+
+    if (HasQuarter && Pack1 >= QuarterPacketSize)
+      for (Index i = peeled_mc_half; i < peeled_mc_quarter; i += QuarterPacketSize)
+        pack<QuarterPacketSize>(blockA, lhs, cols, i, count);
+
+    // do the same with mr==1
+    for (Index i = peeled_mc_quarter; i < rows; i++) {
+      for (Index k = 0; k < i; k++) blockA[count++] = lhs(i, k);  // normal
+
+      blockA[count++] = numext::real(lhs(i, i));  // real (diagonal)
+
+      for (Index k = i + 1; k < cols; k++) blockA[count++] = numext::conj(lhs(k, i));  // transposed
+    }
+  }
+};
+
+template <typename Scalar, typename Index, int nr, int StorageOrder>
+struct symm_pack_rhs {
+  enum { PacketSize = packet_traits<Scalar>::size };
+  void operator()(Scalar* blockB, const Scalar* rhs_, Index rhsStride, Index rows, Index cols, Index k2) {
+    Index end_k = k2 + rows;
+    Index count = 0;
+    const_blas_data_mapper<Scalar, Index, StorageOrder> rhs(rhs_, rhsStride);
+    Index packet_cols8 = nr >= 8 ? (cols / 8) * 8 : 0;
+    Index packet_cols4 = nr >= 4 ? (cols / 4) * 4 : 0;
+
+    // first part: normal case
+    for (Index j2 = 0; j2 < k2; j2 += nr) {
+      for (Index k = k2; k < end_k; k++) {
+        blockB[count + 0] = rhs(k, j2 + 0);
+        blockB[count + 1] = rhs(k, j2 + 1);
+        if (nr >= 4) {
+          blockB[count + 2] = rhs(k, j2 + 2);
+          blockB[count + 3] = rhs(k, j2 + 3);
+        }
+        if (nr >= 8) {
+          blockB[count + 4] = rhs(k, j2 + 4);
+          blockB[count + 5] = rhs(k, j2 + 5);
+          blockB[count + 6] = rhs(k, j2 + 6);
+          blockB[count + 7] = rhs(k, j2 + 7);
+        }
+        count += nr;
+      }
+    }
+
+    // second part: diagonal block
+    Index end8 = nr >= 8 ? (std::min)(k2 + rows, packet_cols8) : k2;
+    if (nr >= 8) {
+      for (Index j2 = k2; j2 < end8; j2 += 8) {
+        // again we can split vertically in three different parts (transpose, symmetric, normal)
+        // transpose
+        for (Index k = k2; k < j2; k++) {
+          blockB[count + 0] = numext::conj(rhs(j2 + 0, k));
+          blockB[count + 1] = numext::conj(rhs(j2 + 1, k));
+          blockB[count + 2] = numext::conj(rhs(j2 + 2, k));
+          blockB[count + 3] = numext::conj(rhs(j2 + 3, k));
+          blockB[count + 4] = numext::conj(rhs(j2 + 4, k));
+          blockB[count + 5] = numext::conj(rhs(j2 + 5, k));
+          blockB[count + 6] = numext::conj(rhs(j2 + 6, k));
+          blockB[count + 7] = numext::conj(rhs(j2 + 7, k));
+          count += 8;
+        }
+        // symmetric
+        Index h = 0;
+        for (Index k = j2; k < j2 + 8; k++) {
+          // normal
+          for (Index w = 0; w < h; ++w) blockB[count + w] = rhs(k, j2 + w);
+
+          blockB[count + h] = numext::real(rhs(k, k));
+
+          // transpose
+          for (Index w = h + 1; w < 8; ++w) blockB[count + w] = numext::conj(rhs(j2 + w, k));
+          count += 8;
+          ++h;
+        }
+        // normal
+        for (Index k = j2 + 8; k < end_k; k++) {
+          blockB[count + 0] = rhs(k, j2 + 0);
+          blockB[count + 1] = rhs(k, j2 + 1);
+          blockB[count + 2] = rhs(k, j2 + 2);
+          blockB[count + 3] = rhs(k, j2 + 3);
+          blockB[count + 4] = rhs(k, j2 + 4);
+          blockB[count + 5] = rhs(k, j2 + 5);
+          blockB[count + 6] = rhs(k, j2 + 6);
+          blockB[count + 7] = rhs(k, j2 + 7);
+          count += 8;
+        }
+      }
+    }
+    if (nr >= 4) {
+      for (Index j2 = end8; j2 < (std::min)(k2 + rows, packet_cols4); j2 += 4) {
+        // again we can split vertically in three different parts (transpose, symmetric, normal)
+        // transpose
+        for (Index k = k2; k < j2; k++) {
+          blockB[count + 0] = numext::conj(rhs(j2 + 0, k));
+          blockB[count + 1] = numext::conj(rhs(j2 + 1, k));
+          blockB[count + 2] = numext::conj(rhs(j2 + 2, k));
+          blockB[count + 3] = numext::conj(rhs(j2 + 3, k));
+          count += 4;
+        }
+        // symmetric
+        Index h = 0;
+        for (Index k = j2; k < j2 + 4; k++) {
+          // normal
+          for (Index w = 0; w < h; ++w) blockB[count + w] = rhs(k, j2 + w);
+
+          blockB[count + h] = numext::real(rhs(k, k));
+
+          // transpose
+          for (Index w = h + 1; w < 4; ++w) blockB[count + w] = numext::conj(rhs(j2 + w, k));
+          count += 4;
+          ++h;
+        }
+        // normal
+        for (Index k = j2 + 4; k < end_k; k++) {
+          blockB[count + 0] = rhs(k, j2 + 0);
+          blockB[count + 1] = rhs(k, j2 + 1);
+          blockB[count + 2] = rhs(k, j2 + 2);
+          blockB[count + 3] = rhs(k, j2 + 3);
+          count += 4;
+        }
+      }
+    }
+
+    // third part: transposed
+    if (nr >= 8) {
+      for (Index j2 = k2 + rows; j2 < packet_cols8; j2 += 8) {
+        for (Index k = k2; k < end_k; k++) {
+          blockB[count + 0] = numext::conj(rhs(j2 + 0, k));
+          blockB[count + 1] = numext::conj(rhs(j2 + 1, k));
+          blockB[count + 2] = numext::conj(rhs(j2 + 2, k));
+          blockB[count + 3] = numext::conj(rhs(j2 + 3, k));
+          blockB[count + 4] = numext::conj(rhs(j2 + 4, k));
+          blockB[count + 5] = numext::conj(rhs(j2 + 5, k));
+          blockB[count + 6] = numext::conj(rhs(j2 + 6, k));
+          blockB[count + 7] = numext::conj(rhs(j2 + 7, k));
+          count += 8;
+        }
+      }
+    }
+    if (nr >= 4) {
+      for (Index j2 = (std::max)(packet_cols8, k2 + rows); j2 < packet_cols4; j2 += 4) {
+        for (Index k = k2; k < end_k; k++) {
+          blockB[count + 0] = numext::conj(rhs(j2 + 0, k));
+          blockB[count + 1] = numext::conj(rhs(j2 + 1, k));
+          blockB[count + 2] = numext::conj(rhs(j2 + 2, k));
+          blockB[count + 3] = numext::conj(rhs(j2 + 3, k));
+          count += 4;
+        }
+      }
+    }
+
+    // copy the remaining columns one at a time (=> the same with nr==1)
+    for (Index j2 = packet_cols4; j2 < cols; ++j2) {
+      // transpose
+      Index half = (std::min)(end_k, j2);
+      for (Index k = k2; k < half; k++) {
+        blockB[count] = numext::conj(rhs(j2, k));
+        count += 1;
+      }
+
+      if (half == j2 && half < k2 + rows) {
+        blockB[count] = numext::real(rhs(j2, j2));
+        count += 1;
+      } else
+        half--;
+
+      // normal
+      for (Index k = half + 1; k < k2 + rows; k++) {
+        blockB[count] = rhs(k, j2);
+        count += 1;
+      }
+    }
+  }
+};
+
+/* Optimized selfadjoint matrix * matrix (_SYMM) product built on top of
+ * the general matrix matrix product.
+ */
+template <typename Scalar, typename Index, int LhsStorageOrder, bool LhsSelfAdjoint, bool ConjugateLhs,
+          int RhsStorageOrder, bool RhsSelfAdjoint, bool ConjugateRhs, int ResStorageOrder, int ResInnerStride>
+struct product_selfadjoint_matrix;
+
+template <typename Scalar, typename Index, int LhsStorageOrder, bool LhsSelfAdjoint, bool ConjugateLhs,
+          int RhsStorageOrder, bool RhsSelfAdjoint, bool ConjugateRhs, int ResInnerStride>
+struct product_selfadjoint_matrix<Scalar, Index, LhsStorageOrder, LhsSelfAdjoint, ConjugateLhs, RhsStorageOrder,
+                                  RhsSelfAdjoint, ConjugateRhs, RowMajor, ResInnerStride> {
+  static EIGEN_STRONG_INLINE void run(Index rows, Index cols, const Scalar* lhs, Index lhsStride, const Scalar* rhs,
+                                      Index rhsStride, Scalar* res, Index resIncr, Index resStride, const Scalar& alpha,
+                                      level3_blocking<Scalar, Scalar>& blocking) {
+    product_selfadjoint_matrix<
+        Scalar, Index, logical_xor(RhsSelfAdjoint, RhsStorageOrder == RowMajor) ? ColMajor : RowMajor, RhsSelfAdjoint,
+        NumTraits<Scalar>::IsComplex && logical_xor(RhsSelfAdjoint, ConjugateRhs),
+        logical_xor(LhsSelfAdjoint, LhsStorageOrder == RowMajor) ? ColMajor : RowMajor, LhsSelfAdjoint,
+        NumTraits<Scalar>::IsComplex && logical_xor(LhsSelfAdjoint, ConjugateLhs), ColMajor,
+        ResInnerStride>::run(cols, rows, rhs, rhsStride, lhs, lhsStride, res, resIncr, resStride, alpha, blocking);
+  }
+};
+
+template <typename Scalar, typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder,
+          bool ConjugateRhs, int ResInnerStride>
+struct product_selfadjoint_matrix<Scalar, Index, LhsStorageOrder, true, ConjugateLhs, RhsStorageOrder, false,
+                                  ConjugateRhs, ColMajor, ResInnerStride> {
+  static EIGEN_DONT_INLINE void run(Index rows, Index cols, const Scalar* lhs_, Index lhsStride, const Scalar* rhs_,
+                                    Index rhsStride, Scalar* res, Index resIncr, Index resStride, const Scalar& alpha,
+                                    level3_blocking<Scalar, Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder,
+          bool ConjugateRhs, int ResInnerStride>
+EIGEN_DONT_INLINE void
+product_selfadjoint_matrix<Scalar, Index, LhsStorageOrder, true, ConjugateLhs, RhsStorageOrder, false, ConjugateRhs,
+                           ColMajor, ResInnerStride>::run(Index rows, Index cols, const Scalar* lhs_, Index lhsStride,
+                                                          const Scalar* rhs_, Index rhsStride, Scalar* res_,
+                                                          Index resIncr, Index resStride, const Scalar& alpha,
+                                                          level3_blocking<Scalar, Scalar>& blocking) {
+  Index size = rows;
+
+  typedef gebp_traits<Scalar, Scalar> Traits;
+
+  typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
+  typedef const_blas_data_mapper<Scalar, Index, (LhsStorageOrder == RowMajor) ? ColMajor : RowMajor> LhsTransposeMapper;
+  typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
+  typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+  LhsMapper lhs(lhs_, lhsStride);
+  LhsTransposeMapper lhs_transpose(lhs_, lhsStride);
+  RhsMapper rhs(rhs_, rhsStride);
+  ResMapper res(res_, resStride, resIncr);
+
+  Index kc = blocking.kc();                    // cache block size along the K direction
+  Index mc = (std::min)(rows, blocking.mc());  // cache block size along the M direction
+  // kc must be smaller than mc
+  kc = (std::min)(kc, mc);
+  std::size_t sizeA = kc * mc;
+  std::size_t sizeB = kc * cols;
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+  gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
+  symm_pack_lhs<Scalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs;
+  gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
+  gemm_pack_lhs<Scalar, Index, LhsTransposeMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
+                LhsStorageOrder == RowMajor ? ColMajor : RowMajor, true>
+      pack_lhs_transposed;
+
+  for (Index k2 = 0; k2 < size; k2 += kc) {
+    const Index actual_kc = (std::min)(k2 + kc, size) - k2;
+
+    // we have selected one row panel of rhs and one column panel of lhs
+    // pack rhs's panel into a sequential chunk of memory
+    // and expand each coeff to a constant packet for further reuse
+    pack_rhs(blockB, rhs.getSubMapper(k2, 0), actual_kc, cols);
+
+    // the select lhs's panel has to be split in three different parts:
+    //  1 - the transposed panel above the diagonal block => transposed packed copy
+    //  2 - the diagonal block => special packed copy
+    //  3 - the panel below the diagonal block => generic packed copy
+    for (Index i2 = 0; i2 < k2; i2 += mc) {
+      const Index actual_mc = (std::min)(i2 + mc, k2) - i2;
+      // transposed packed copy
+      pack_lhs_transposed(blockA, lhs_transpose.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+      gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
+    }
+    // the block diagonal
+    {
+      const Index actual_mc = (std::min)(k2 + kc, size) - k2;
+      // symmetric packed copy
+      pack_lhs(blockA, &lhs(k2, k2), lhsStride, actual_kc, actual_mc);
+
+      gebp_kernel(res.getSubMapper(k2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
+    }
+
+    for (Index i2 = k2 + kc; i2 < size; i2 += mc) {
+      const Index actual_mc = (std::min)(i2 + mc, size) - i2;
+      gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
+                    LhsStorageOrder, false>()(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+      gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
+    }
+  }
+}
+
+// matrix * selfadjoint product
+template <typename Scalar, typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder,
+          bool ConjugateRhs, int ResInnerStride>
+struct product_selfadjoint_matrix<Scalar, Index, LhsStorageOrder, false, ConjugateLhs, RhsStorageOrder, true,
+                                  ConjugateRhs, ColMajor, ResInnerStride> {
+  static EIGEN_DONT_INLINE void run(Index rows, Index cols, const Scalar* lhs_, Index lhsStride, const Scalar* rhs_,
+                                    Index rhsStride, Scalar* res, Index resIncr, Index resStride, const Scalar& alpha,
+                                    level3_blocking<Scalar, Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder,
+          bool ConjugateRhs, int ResInnerStride>
+EIGEN_DONT_INLINE void
+product_selfadjoint_matrix<Scalar, Index, LhsStorageOrder, false, ConjugateLhs, RhsStorageOrder, true, ConjugateRhs,
+                           ColMajor, ResInnerStride>::run(Index rows, Index cols, const Scalar* lhs_, Index lhsStride,
+                                                          const Scalar* rhs_, Index rhsStride, Scalar* res_,
+                                                          Index resIncr, Index resStride, const Scalar& alpha,
+                                                          level3_blocking<Scalar, Scalar>& blocking) {
+  Index size = cols;
+
+  typedef gebp_traits<Scalar, Scalar> Traits;
+
+  typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
+  typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+  LhsMapper lhs(lhs_, lhsStride);
+  ResMapper res(res_, resStride, resIncr);
+
+  Index kc = blocking.kc();                    // cache block size along the K direction
+  Index mc = (std::min)(rows, blocking.mc());  // cache block size along the M direction
+  std::size_t sizeA = kc * mc;
+  std::size_t sizeB = kc * cols;
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+  gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
+  gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
+                LhsStorageOrder>
+      pack_lhs;
+  symm_pack_rhs<Scalar, Index, Traits::nr, RhsStorageOrder> pack_rhs;
+
+  for (Index k2 = 0; k2 < size; k2 += kc) {
+    const Index actual_kc = (std::min)(k2 + kc, size) - k2;
+
+    pack_rhs(blockB, rhs_, rhsStride, actual_kc, cols, k2);
+
+    // => GEPP
+    for (Index i2 = 0; i2 < rows; i2 += mc) {
+      const Index actual_mc = (std::min)(i2 + mc, rows) - i2;
+      pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+      gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
+    }
+  }
+}
+
+}  // end namespace internal
+
+/***************************************************************************
+ * Wrapper to product_selfadjoint_matrix
+ ***************************************************************************/
+
+namespace internal {
+
+template <typename Lhs, int LhsMode, typename Rhs, int RhsMode>
+struct selfadjoint_product_impl<Lhs, LhsMode, false, Rhs, RhsMode, false> {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  typedef internal::blas_traits<Lhs> LhsBlasTraits;
+  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+  typedef internal::blas_traits<Rhs> RhsBlasTraits;
+  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+
+  enum {
+    LhsIsUpper = (LhsMode & (Upper | Lower)) == Upper,
+    LhsIsSelfAdjoint = (LhsMode & SelfAdjoint) == SelfAdjoint,
+    RhsIsUpper = (RhsMode & (Upper | Lower)) == Upper,
+    RhsIsSelfAdjoint = (RhsMode & SelfAdjoint) == SelfAdjoint
+  };
+
+  template <typename Dest>
+  static void run(Dest& dst, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha) {
+    eigen_assert(dst.rows() == a_lhs.rows() && dst.cols() == a_rhs.cols());
+
+    add_const_on_value_type_t<ActualLhsType> lhs = LhsBlasTraits::extract(a_lhs);
+    add_const_on_value_type_t<ActualRhsType> rhs = RhsBlasTraits::extract(a_rhs);
+
+    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs) * RhsBlasTraits::extractScalarFactor(a_rhs);
+
+    typedef internal::gemm_blocking_space<(Dest::Flags & RowMajorBit) ? RowMajor : ColMajor, Scalar, Scalar,
+                                          Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime,
+                                          Lhs::MaxColsAtCompileTime, 1>
+        BlockingType;
+
+    BlockingType blocking(lhs.rows(), rhs.cols(), lhs.cols(), 1, false);
+
+    internal::product_selfadjoint_matrix<
+        Scalar, Index,
+        internal::logical_xor(LhsIsUpper, internal::traits<Lhs>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+        LhsIsSelfAdjoint,
+        NumTraits<Scalar>::IsComplex && internal::logical_xor(LhsIsUpper, bool(LhsBlasTraits::NeedToConjugate)),
+        internal::logical_xor(RhsIsUpper, internal::traits<Rhs>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+        RhsIsSelfAdjoint,
+        NumTraits<Scalar>::IsComplex && internal::logical_xor(RhsIsUpper, bool(RhsBlasTraits::NeedToConjugate)),
+        internal::traits<Dest>::Flags & RowMajorBit ? RowMajor : ColMajor,
+        Dest::InnerStrideAtCompileTime>::run(lhs.rows(), rhs.cols(),                                     // sizes
+                                             &lhs.coeffRef(0, 0), lhs.outerStride(),                     // lhs info
+                                             &rhs.coeffRef(0, 0), rhs.outerStride(),                     // rhs info
+                                             &dst.coeffRef(0, 0), dst.innerStride(), dst.outerStride(),  // result info
+                                             actualAlpha, blocking                                       // alpha
+    );
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SELFADJOINT_MATRIX_MATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
new file mode 100644
index 0000000..684a90f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
@@ -0,0 +1,281 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+//
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Self adjoint matrix * matrix product functionality based on ?SYMM/?HEMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
+#define EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/* Optimized selfadjoint matrix * matrix (?SYMM/?HEMM) product */
+
+#define EIGEN_BLAS_SYMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC)                                                \
+  template <typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder, bool ConjugateRhs>      \
+  struct product_selfadjoint_matrix<EIGTYPE, Index, LhsStorageOrder, true, ConjugateLhs, RhsStorageOrder, false, \
+                                    ConjugateRhs, ColMajor, 1> {                                                 \
+    static void run(Index rows, Index cols, const EIGTYPE* _lhs, Index lhsStride, const EIGTYPE* _rhs,           \
+                    Index rhsStride, EIGTYPE* res, Index resIncr, Index resStride, EIGTYPE alpha,                \
+                    level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) {                                           \
+      if (rows == 0 || cols == 0) return;                                                                        \
+      EIGEN_ONLY_USED_FOR_DEBUG(resIncr);                                                                        \
+      eigen_assert(resIncr == 1);                                                                                \
+      char side = 'L', uplo = 'L';                                                                               \
+      BlasIndex m, n, lda, ldb, ldc;                                                                             \
+      const EIGTYPE *a, *b;                                                                                      \
+      EIGTYPE beta(1);                                                                                           \
+      MatrixX##EIGPREFIX b_tmp;                                                                                  \
+                                                                                                                 \
+      /* Set transpose options */                                                                                \
+      /* Set m, n, k */                                                                                          \
+      m = convert_index<BlasIndex>(rows);                                                                        \
+      n = convert_index<BlasIndex>(cols);                                                                        \
+                                                                                                                 \
+      /* Set lda, ldb, ldc */                                                                                    \
+      lda = convert_index<BlasIndex>(lhsStride);                                                                 \
+      ldb = convert_index<BlasIndex>(rhsStride);                                                                 \
+      ldc = convert_index<BlasIndex>(resStride);                                                                 \
+                                                                                                                 \
+      /* Set a, b, c */                                                                                          \
+      if (LhsStorageOrder == RowMajor) uplo = 'U';                                                               \
+      a = _lhs;                                                                                                  \
+                                                                                                                 \
+      if (RhsStorageOrder == RowMajor) {                                                                         \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs, n, m, OuterStride<>(rhsStride));              \
+        b_tmp = rhs.adjoint();                                                                                   \
+        b = b_tmp.data();                                                                                        \
+        ldb = convert_index<BlasIndex>(b_tmp.outerStride());                                                     \
+      } else                                                                                                     \
+        b = _rhs;                                                                                                \
+                                                                                                                 \
+      BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda,        \
+               (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc);        \
+    }                                                                                                            \
+  };
+
+#define EIGEN_BLAS_HEMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC)                                                  \
+  template <typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder, bool ConjugateRhs>        \
+  struct product_selfadjoint_matrix<EIGTYPE, Index, LhsStorageOrder, true, ConjugateLhs, RhsStorageOrder, false,   \
+                                    ConjugateRhs, ColMajor, 1> {                                                   \
+    static void run(Index rows, Index cols, const EIGTYPE* _lhs, Index lhsStride, const EIGTYPE* _rhs,             \
+                    Index rhsStride, EIGTYPE* res, Index resIncr, Index resStride, EIGTYPE alpha,                  \
+                    level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) {                                             \
+      if (rows == 0 || cols == 0) return;                                                                          \
+      EIGEN_ONLY_USED_FOR_DEBUG(resIncr);                                                                          \
+      eigen_assert(resIncr == 1);                                                                                  \
+      char side = 'L', uplo = 'L';                                                                                 \
+      BlasIndex m, n, lda, ldb, ldc;                                                                               \
+      const EIGTYPE *a, *b;                                                                                        \
+      EIGTYPE beta(1);                                                                                             \
+      MatrixX##EIGPREFIX b_tmp;                                                                                    \
+      Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> a_tmp;                                                    \
+                                                                                                                   \
+      /* Set transpose options */                                                                                  \
+      /* Set m, n, k */                                                                                            \
+      m = convert_index<BlasIndex>(rows);                                                                          \
+      n = convert_index<BlasIndex>(cols);                                                                          \
+                                                                                                                   \
+      /* Set lda, ldb, ldc */                                                                                      \
+      lda = convert_index<BlasIndex>(lhsStride);                                                                   \
+      ldb = convert_index<BlasIndex>(rhsStride);                                                                   \
+      ldc = convert_index<BlasIndex>(resStride);                                                                   \
+                                                                                                                   \
+      /* Set a, b, c */                                                                                            \
+      if (((LhsStorageOrder == ColMajor) && ConjugateLhs) || ((LhsStorageOrder == RowMajor) && (!ConjugateLhs))) { \
+        Map<const Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder>, 0, OuterStride<> > lhs(                      \
+            _lhs, m, m, OuterStride<>(lhsStride));                                                                 \
+        a_tmp = lhs.conjugate();                                                                                   \
+        a = a_tmp.data();                                                                                          \
+        lda = convert_index<BlasIndex>(a_tmp.outerStride());                                                       \
+      } else                                                                                                       \
+        a = _lhs;                                                                                                  \
+      if (LhsStorageOrder == RowMajor) uplo = 'U';                                                                 \
+                                                                                                                   \
+      if (RhsStorageOrder == ColMajor && (!ConjugateRhs)) {                                                        \
+        b = _rhs;                                                                                                  \
+      } else {                                                                                                     \
+        if (RhsStorageOrder == ColMajor && ConjugateRhs) {                                                         \
+          Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs, m, n, OuterStride<>(rhsStride));              \
+          b_tmp = rhs.conjugate();                                                                                 \
+        } else if (ConjugateRhs) {                                                                                 \
+          Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs, n, m, OuterStride<>(rhsStride));              \
+          b_tmp = rhs.adjoint();                                                                                   \
+        } else {                                                                                                   \
+          Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs, n, m, OuterStride<>(rhsStride));              \
+          b_tmp = rhs.transpose();                                                                                 \
+        }                                                                                                          \
+        b = b_tmp.data();                                                                                          \
+        ldb = convert_index<BlasIndex>(b_tmp.outerStride());                                                       \
+      }                                                                                                            \
+                                                                                                                   \
+      BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda,          \
+               (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc);          \
+    }                                                                                                              \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_SYMM_L(double, double, d, dsymm)
+EIGEN_BLAS_SYMM_L(float, float, f, ssymm)
+EIGEN_BLAS_HEMM_L(dcomplex, MKL_Complex16, cd, zhemm)
+EIGEN_BLAS_HEMM_L(scomplex, MKL_Complex8, cf, chemm)
+#else
+EIGEN_BLAS_SYMM_L(double, double, d, dsymm_)
+EIGEN_BLAS_SYMM_L(float, float, f, ssymm_)
+EIGEN_BLAS_HEMM_L(dcomplex, double, cd, zhemm_)
+EIGEN_BLAS_HEMM_L(scomplex, float, cf, chemm_)
+#endif
+
+/* Optimized matrix * selfadjoint matrix (?SYMM/?HEMM) product */
+
+#define EIGEN_BLAS_SYMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC)                                                \
+  template <typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder, bool ConjugateRhs>      \
+  struct product_selfadjoint_matrix<EIGTYPE, Index, LhsStorageOrder, false, ConjugateLhs, RhsStorageOrder, true, \
+                                    ConjugateRhs, ColMajor, 1> {                                                 \
+    static void run(Index rows, Index cols, const EIGTYPE* _lhs, Index lhsStride, const EIGTYPE* _rhs,           \
+                    Index rhsStride, EIGTYPE* res, Index resIncr, Index resStride, EIGTYPE alpha,                \
+                    level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) {                                           \
+      if (rows == 0 || cols == 0) return;                                                                        \
+      EIGEN_ONLY_USED_FOR_DEBUG(resIncr);                                                                        \
+      eigen_assert(resIncr == 1);                                                                                \
+      char side = 'R', uplo = 'L';                                                                               \
+      BlasIndex m, n, lda, ldb, ldc;                                                                             \
+      const EIGTYPE *a, *b;                                                                                      \
+      EIGTYPE beta(1);                                                                                           \
+      MatrixX##EIGPREFIX b_tmp;                                                                                  \
+                                                                                                                 \
+      /* Set m, n, k */                                                                                          \
+      m = convert_index<BlasIndex>(rows);                                                                        \
+      n = convert_index<BlasIndex>(cols);                                                                        \
+                                                                                                                 \
+      /* Set lda, ldb, ldc */                                                                                    \
+      lda = convert_index<BlasIndex>(rhsStride);                                                                 \
+      ldb = convert_index<BlasIndex>(lhsStride);                                                                 \
+      ldc = convert_index<BlasIndex>(resStride);                                                                 \
+                                                                                                                 \
+      /* Set a, b, c */                                                                                          \
+      if (RhsStorageOrder == RowMajor) uplo = 'U';                                                               \
+      a = _rhs;                                                                                                  \
+                                                                                                                 \
+      if (LhsStorageOrder == RowMajor) {                                                                         \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs, n, m, OuterStride<>(rhsStride));              \
+        b_tmp = lhs.adjoint();                                                                                   \
+        b = b_tmp.data();                                                                                        \
+        ldb = convert_index<BlasIndex>(b_tmp.outerStride());                                                     \
+      } else                                                                                                     \
+        b = _lhs;                                                                                                \
+                                                                                                                 \
+      BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda,        \
+               (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc);        \
+    }                                                                                                            \
+  };
+
+#define EIGEN_BLAS_HEMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC)                                                  \
+  template <typename Index, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder, bool ConjugateRhs>        \
+  struct product_selfadjoint_matrix<EIGTYPE, Index, LhsStorageOrder, false, ConjugateLhs, RhsStorageOrder, true,   \
+                                    ConjugateRhs, ColMajor, 1> {                                                   \
+    static void run(Index rows, Index cols, const EIGTYPE* _lhs, Index lhsStride, const EIGTYPE* _rhs,             \
+                    Index rhsStride, EIGTYPE* res, Index resIncr, Index resStride, EIGTYPE alpha,                  \
+                    level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) {                                             \
+      EIGEN_ONLY_USED_FOR_DEBUG(resIncr);                                                                          \
+      eigen_assert(resIncr == 1);                                                                                  \
+      char side = 'R', uplo = 'L';                                                                                 \
+      BlasIndex m, n, lda, ldb, ldc;                                                                               \
+      const EIGTYPE *a, *b;                                                                                        \
+      EIGTYPE beta(1);                                                                                             \
+      MatrixX##EIGPREFIX b_tmp;                                                                                    \
+      Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> a_tmp;                                                    \
+                                                                                                                   \
+      /* Set m, n, k */                                                                                            \
+      m = convert_index<BlasIndex>(rows);                                                                          \
+      n = convert_index<BlasIndex>(cols);                                                                          \
+                                                                                                                   \
+      /* Set lda, ldb, ldc */                                                                                      \
+      lda = convert_index<BlasIndex>(rhsStride);                                                                   \
+      ldb = convert_index<BlasIndex>(lhsStride);                                                                   \
+      ldc = convert_index<BlasIndex>(resStride);                                                                   \
+                                                                                                                   \
+      /* Set a, b, c */                                                                                            \
+      if (((RhsStorageOrder == ColMajor) && ConjugateRhs) || ((RhsStorageOrder == RowMajor) && (!ConjugateRhs))) { \
+        Map<const Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder>, 0, OuterStride<> > rhs(                      \
+            _rhs, n, n, OuterStride<>(rhsStride));                                                                 \
+        a_tmp = rhs.conjugate();                                                                                   \
+        a = a_tmp.data();                                                                                          \
+        lda = convert_index<BlasIndex>(a_tmp.outerStride());                                                       \
+      } else                                                                                                       \
+        a = _rhs;                                                                                                  \
+      if (RhsStorageOrder == RowMajor) uplo = 'U';                                                                 \
+                                                                                                                   \
+      if (LhsStorageOrder == ColMajor && (!ConjugateLhs)) {                                                        \
+        b = _lhs;                                                                                                  \
+      } else {                                                                                                     \
+        if (LhsStorageOrder == ColMajor && ConjugateLhs) {                                                         \
+          Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs, m, n, OuterStride<>(lhsStride));              \
+          b_tmp = lhs.conjugate();                                                                                 \
+        } else if (ConjugateLhs) {                                                                                 \
+          Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs, n, m, OuterStride<>(lhsStride));              \
+          b_tmp = lhs.adjoint();                                                                                   \
+        } else {                                                                                                   \
+          Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs, n, m, OuterStride<>(lhsStride));              \
+          b_tmp = lhs.transpose();                                                                                 \
+        }                                                                                                          \
+        b = b_tmp.data();                                                                                          \
+        ldb = convert_index<BlasIndex>(b_tmp.outerStride());                                                       \
+      }                                                                                                            \
+                                                                                                                   \
+      BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda,          \
+               (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc);          \
+    }                                                                                                              \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_SYMM_R(double, double, d, dsymm)
+EIGEN_BLAS_SYMM_R(float, float, f, ssymm)
+EIGEN_BLAS_HEMM_R(dcomplex, MKL_Complex16, cd, zhemm)
+EIGEN_BLAS_HEMM_R(scomplex, MKL_Complex8, cf, chemm)
+#else
+EIGEN_BLAS_SYMM_R(double, double, d, dsymm_)
+EIGEN_BLAS_SYMM_R(float, float, f, ssymm_)
+EIGEN_BLAS_HEMM_R(dcomplex, double, cd, zhemm_)
+EIGEN_BLAS_HEMM_R(scomplex, float, cf, chemm_)
+#endif
+
+#undef EIGEN_BLAS_SYMM_L
+#undef EIGEN_BLAS_SYMM_R
+#undef EIGEN_BLAS_HEMM_R
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h
new file mode 100644
index 0000000..8a84aa4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h
@@ -0,0 +1,251 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_H
+#define EIGEN_SELFADJOINT_MATRIX_VECTOR_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/* Optimized selfadjoint matrix * vector product:
+ * This algorithm processes 2 columns at once that allows to both reduce
+ * the number of load/stores of the result by a factor 2 and to reduce
+ * the instruction dependency.
+ */
+
+template <typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs,
+          int Version = Specialized>
+struct selfadjoint_matrix_vector_product;
+
+template <typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs,
+          int Version>
+struct selfadjoint_matrix_vector_product
+
+{
+  static EIGEN_DONT_INLINE EIGEN_DEVICE_FUNC void run(Index size, const Scalar* lhs, Index lhsStride, const Scalar* rhs,
+                                                      Scalar* res, Scalar alpha);
+};
+
+template <typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs,
+          int Version>
+EIGEN_DONT_INLINE EIGEN_DEVICE_FUNC void
+selfadjoint_matrix_vector_product<Scalar, Index, StorageOrder, UpLo, ConjugateLhs, ConjugateRhs, Version>::run(
+    Index size, const Scalar* lhs, Index lhsStride, const Scalar* rhs, Scalar* res, Scalar alpha) {
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  const Index PacketSize = sizeof(Packet) / sizeof(Scalar);
+
+  enum {
+    IsRowMajor = StorageOrder == RowMajor ? 1 : 0,
+    IsLower = UpLo == Lower ? 1 : 0,
+    FirstTriangular = IsRowMajor == IsLower
+  };
+
+  conj_helper<Scalar, Scalar, NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs, IsRowMajor), ConjugateRhs> cj0;
+  conj_helper<Scalar, Scalar, NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs, !IsRowMajor), ConjugateRhs> cj1;
+  conj_helper<RealScalar, Scalar, false, ConjugateRhs> cjd;
+
+  conj_helper<Packet, Packet, NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs, IsRowMajor), ConjugateRhs> pcj0;
+  conj_helper<Packet, Packet, NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs, !IsRowMajor), ConjugateRhs>
+      pcj1;
+
+  Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha;
+
+  Index bound = numext::maxi(Index(0), size - 8) & 0xfffffffe;
+  if (FirstTriangular) bound = size - bound;
+
+  for (Index j = FirstTriangular ? bound : 0; j < (FirstTriangular ? size : bound); j += 2) {
+    const Scalar* EIGEN_RESTRICT A0 = lhs + j * lhsStride;
+    const Scalar* EIGEN_RESTRICT A1 = lhs + (j + 1) * lhsStride;
+
+    Scalar t0 = cjAlpha * rhs[j];
+    Packet ptmp0 = pset1<Packet>(t0);
+    Scalar t1 = cjAlpha * rhs[j + 1];
+    Packet ptmp1 = pset1<Packet>(t1);
+
+    Scalar t2(0);
+    Packet ptmp2 = pset1<Packet>(t2);
+    Scalar t3(0);
+    Packet ptmp3 = pset1<Packet>(t3);
+
+    Index starti = FirstTriangular ? 0 : j + 2;
+    Index endi = FirstTriangular ? j : size;
+    Index alignedStart = (starti) + internal::first_default_aligned(&res[starti], endi - starti);
+    Index alignedEnd = alignedStart + ((endi - alignedStart) / (PacketSize)) * (PacketSize);
+
+    res[j] += cjd.pmul(numext::real(A0[j]), t0);
+    res[j + 1] += cjd.pmul(numext::real(A1[j + 1]), t1);
+    if (FirstTriangular) {
+      res[j] += cj0.pmul(A1[j], t1);
+      t3 += cj1.pmul(A1[j], rhs[j]);
+    } else {
+      res[j + 1] += cj0.pmul(A0[j + 1], t0);
+      t2 += cj1.pmul(A0[j + 1], rhs[j + 1]);
+    }
+
+    for (Index i = starti; i < alignedStart; ++i) {
+      res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i], t1);
+      t2 += cj1.pmul(A0[i], rhs[i]);
+      t3 += cj1.pmul(A1[i], rhs[i]);
+    }
+    const Scalar* EIGEN_RESTRICT a0It = A0 + alignedStart;
+    const Scalar* EIGEN_RESTRICT a1It = A1 + alignedStart;
+    const Scalar* EIGEN_RESTRICT rhsIt = rhs + alignedStart;
+    Scalar* EIGEN_RESTRICT resIt = res + alignedStart;
+    for (Index i = alignedStart; i < alignedEnd; i += PacketSize) {
+      Packet A0i = ploadu<Packet>(a0It);
+      a0It += PacketSize;
+      Packet A1i = ploadu<Packet>(a1It);
+      a1It += PacketSize;
+      Packet Bi = ploadu<Packet>(rhsIt);
+      rhsIt += PacketSize;  // FIXME: should be aligned in most cases.
+      Packet Xi = pload<Packet>(resIt);
+
+      Xi = pcj0.pmadd(A0i, ptmp0, pcj0.pmadd(A1i, ptmp1, Xi));
+      ptmp2 = pcj1.pmadd(A0i, Bi, ptmp2);
+      ptmp3 = pcj1.pmadd(A1i, Bi, ptmp3);
+      pstore(resIt, Xi);
+      resIt += PacketSize;
+    }
+    for (Index i = alignedEnd; i < endi; i++) {
+      res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i], t1);
+      t2 += cj1.pmul(A0[i], rhs[i]);
+      t3 += cj1.pmul(A1[i], rhs[i]);
+    }
+
+    res[j] += alpha * (t2 + predux(ptmp2));
+    res[j + 1] += alpha * (t3 + predux(ptmp3));
+  }
+  for (Index j = FirstTriangular ? 0 : bound; j < (FirstTriangular ? bound : size); j++) {
+    const Scalar* EIGEN_RESTRICT A0 = lhs + j * lhsStride;
+
+    Scalar t1 = cjAlpha * rhs[j];
+    Scalar t2(0);
+    res[j] += cjd.pmul(numext::real(A0[j]), t1);
+    for (Index i = FirstTriangular ? 0 : j + 1; i < (FirstTriangular ? j : size); i++) {
+      res[i] += cj0.pmul(A0[i], t1);
+      t2 += cj1.pmul(A0[i], rhs[i]);
+    }
+    res[j] += alpha * t2;
+  }
+}
+
+}  // end namespace internal
+
+/***************************************************************************
+ * Wrapper to product_selfadjoint_vector
+ ***************************************************************************/
+
+namespace internal {
+
+template <typename Lhs, int LhsMode, typename Rhs>
+struct selfadjoint_product_impl<Lhs, LhsMode, false, Rhs, 0, true> {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  typedef internal::blas_traits<Lhs> LhsBlasTraits;
+  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+  typedef internal::remove_all_t<ActualLhsType> ActualLhsTypeCleaned;
+
+  typedef internal::blas_traits<Rhs> RhsBlasTraits;
+  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+  typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
+
+  enum { LhsUpLo = LhsMode & (Upper | Lower) };
+
+  // Verify that the Rhs is a vector in the correct orientation.
+  // Otherwise, we break the assumption that we are multiplying
+  // MxN * Nx1.
+  static_assert(Rhs::ColsAtCompileTime == 1, "The RHS must be a column vector.");
+
+  template <typename Dest>
+  static EIGEN_DEVICE_FUNC void run(Dest& dest, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha) {
+    typedef typename Dest::Scalar ResScalar;
+    typedef typename Rhs::Scalar RhsScalar;
+    typedef Map<Matrix<ResScalar, Dynamic, 1>, plain_enum_min(AlignedMax, internal::packet_traits<ResScalar>::size)>
+        MappedDest;
+
+    eigen_assert(dest.rows() == a_lhs.rows() && dest.cols() == a_rhs.cols());
+
+    add_const_on_value_type_t<ActualLhsType> lhs = LhsBlasTraits::extract(a_lhs);
+    add_const_on_value_type_t<ActualRhsType> rhs = RhsBlasTraits::extract(a_rhs);
+
+    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs) * RhsBlasTraits::extractScalarFactor(a_rhs);
+
+    enum {
+      EvalToDest = (Dest::InnerStrideAtCompileTime == 1),
+      UseRhs = (ActualRhsTypeCleaned::InnerStrideAtCompileTime == 1)
+    };
+
+    internal::gemv_static_vector_if<ResScalar, Dest::SizeAtCompileTime, Dest::MaxSizeAtCompileTime, !EvalToDest>
+        static_dest;
+    internal::gemv_static_vector_if<RhsScalar, ActualRhsTypeCleaned::SizeAtCompileTime,
+                                    ActualRhsTypeCleaned::MaxSizeAtCompileTime, !UseRhs>
+        static_rhs;
+
+    ei_declare_aligned_stack_constructed_variable(ResScalar, actualDestPtr, dest.size(),
+                                                  EvalToDest ? dest.data() : static_dest.data());
+
+    ei_declare_aligned_stack_constructed_variable(RhsScalar, actualRhsPtr, rhs.size(),
+                                                  UseRhs ? const_cast<RhsScalar*>(rhs.data()) : static_rhs.data());
+
+    if (!EvalToDest) {
+#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      constexpr int Size = Dest::SizeAtCompileTime;
+      Index size = dest.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+#endif
+      MappedDest(actualDestPtr, dest.size()) = dest;
+    }
+
+    if (!UseRhs) {
+#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      constexpr int Size = ActualRhsTypeCleaned::SizeAtCompileTime;
+      Index size = rhs.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+#endif
+      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, rhs.size()) = rhs;
+    }
+
+    internal::selfadjoint_matrix_vector_product<
+        Scalar, Index, (internal::traits<ActualLhsTypeCleaned>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+        int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate),
+        bool(RhsBlasTraits::NeedToConjugate)>::run(lhs.rows(),                              // size
+                                                   &lhs.coeffRef(0, 0), lhs.outerStride(),  // lhs info
+                                                   actualRhsPtr,                            // rhs info
+                                                   actualDestPtr,                           // result info
+                                                   actualAlpha                              // scale factor
+    );
+
+    if (!EvalToDest) dest = MappedDest(actualDestPtr, dest.size());
+  }
+};
+
+template <typename Lhs, typename Rhs, int RhsMode>
+struct selfadjoint_product_impl<Lhs, 0, true, Rhs, RhsMode, false> {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+  enum { RhsUpLo = RhsMode & (Upper | Lower) };
+
+  template <typename Dest>
+  static void run(Dest& dest, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha) {
+    // let's simply transpose the product
+    Transpose<Dest> destT(dest);
+    selfadjoint_product_impl<Transpose<const Rhs>, int(RhsUpLo) == Upper ? Lower : Upper, false, Transpose<const Lhs>,
+                             0, true>::run(destT, a_rhs.transpose(), a_lhs.transpose(), alpha);
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SELFADJOINT_MATRIX_VECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
new file mode 100644
index 0000000..413d949
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
@@ -0,0 +1,117 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Selfadjoint matrix-vector product functionality based on ?SYMV/HEMV.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
+#define EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**********************************************************************
+ * This file implements selfadjoint matrix-vector multiplication using BLAS
+ **********************************************************************/
+
+// symv/hemv specialization
+
+template <typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs>
+struct selfadjoint_matrix_vector_product_symv
+    : selfadjoint_matrix_vector_product<Scalar, Index, StorageOrder, UpLo, ConjugateLhs, ConjugateRhs, BuiltIn> {};
+
+#define EIGEN_BLAS_SYMV_SPECIALIZE(Scalar)                                                                           \
+  template <typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs>                        \
+  struct selfadjoint_matrix_vector_product<Scalar, Index, StorageOrder, UpLo, ConjugateLhs, ConjugateRhs,            \
+                                           Specialized> {                                                            \
+    static void run(Index size, const Scalar* lhs, Index lhsStride, const Scalar* _rhs, Scalar* res, Scalar alpha) { \
+      enum { IsColMajor = StorageOrder == ColMajor };                                                                \
+      if (IsColMajor == ConjugateLhs) {                                                                              \
+        selfadjoint_matrix_vector_product<Scalar, Index, StorageOrder, UpLo, ConjugateLhs, ConjugateRhs,             \
+                                          BuiltIn>::run(size, lhs, lhsStride, _rhs, res, alpha);                     \
+      } else {                                                                                                       \
+        selfadjoint_matrix_vector_product_symv<Scalar, Index, StorageOrder, UpLo, ConjugateLhs, ConjugateRhs>::run(  \
+            size, lhs, lhsStride, _rhs, res, alpha);                                                                 \
+      }                                                                                                              \
+    }                                                                                                                \
+  };
+
+EIGEN_BLAS_SYMV_SPECIALIZE(double)
+EIGEN_BLAS_SYMV_SPECIALIZE(float)
+EIGEN_BLAS_SYMV_SPECIALIZE(dcomplex)
+EIGEN_BLAS_SYMV_SPECIALIZE(scomplex)
+
+#define EIGEN_BLAS_SYMV_SPECIALIZATION(EIGTYPE, BLASTYPE, BLASFUNC)                                                \
+  template <typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs>                      \
+  struct selfadjoint_matrix_vector_product_symv<EIGTYPE, Index, StorageOrder, UpLo, ConjugateLhs, ConjugateRhs> {  \
+    typedef Matrix<EIGTYPE, Dynamic, 1, ColMajor> SYMVVector;                                                      \
+                                                                                                                   \
+    static void run(Index size, const EIGTYPE* lhs, Index lhsStride, const EIGTYPE* _rhs, EIGTYPE* res,            \
+                    EIGTYPE alpha) {                                                                               \
+      if (size == 0) return;                                                                                       \
+      enum { IsRowMajor = StorageOrder == RowMajor ? 1 : 0, IsLower = UpLo == Lower ? 1 : 0 };                     \
+      BlasIndex n = convert_index<BlasIndex>(size), lda = convert_index<BlasIndex>(lhsStride), incx = 1, incy = 1; \
+      EIGTYPE beta(1);                                                                                             \
+      const EIGTYPE* x_ptr;                                                                                        \
+      char uplo = (IsRowMajor) ? (IsLower ? 'U' : 'L') : (IsLower ? 'L' : 'U');                                    \
+      SYMVVector x_tmp;                                                                                            \
+      if (ConjugateRhs) {                                                                                          \
+        Map<const SYMVVector, 0> map_x(_rhs, size, 1);                                                             \
+        x_tmp = map_x.conjugate();                                                                                 \
+        x_ptr = x_tmp.data();                                                                                      \
+      } else                                                                                                       \
+        x_ptr = _rhs;                                                                                              \
+      BLASFUNC(&uplo, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda,                   \
+               (const BLASTYPE*)x_ptr, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &incy);    \
+    }                                                                                                              \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_SYMV_SPECIALIZATION(double, double, dsymv)
+EIGEN_BLAS_SYMV_SPECIALIZATION(float, float, ssymv)
+EIGEN_BLAS_SYMV_SPECIALIZATION(dcomplex, MKL_Complex16, zhemv)
+EIGEN_BLAS_SYMV_SPECIALIZATION(scomplex, MKL_Complex8, chemv)
+#else
+EIGEN_BLAS_SYMV_SPECIALIZATION(double, double, dsymv_)
+EIGEN_BLAS_SYMV_SPECIALIZATION(float, float, ssymv_)
+EIGEN_BLAS_SYMV_SPECIALIZATION(dcomplex, double, zhemv_)
+EIGEN_BLAS_SYMV_SPECIALIZATION(scomplex, float, chemv_)
+#endif
+
+#undef EIGEN_BLAS_SYMV_SPECIALIZATION
+#undef EIGEN_BLAS_SYMV_SPECIALIZE
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointProduct.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointProduct.h
new file mode 100644
index 0000000..f103465
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointProduct.h
@@ -0,0 +1,133 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINT_PRODUCT_H
+#define EIGEN_SELFADJOINT_PRODUCT_H
+
+/**********************************************************************
+ * This file implements a self adjoint product: C += A A^T updating only
+ * half of the selfadjoint matrix C.
+ * It corresponds to the level 3 SYRK and level 2 SYR Blas routines.
+ **********************************************************************/
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs>
+struct selfadjoint_rank1_update<Scalar, Index, ColMajor, UpLo, ConjLhs, ConjRhs> {
+  static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha) {
+    internal::conj_if<ConjRhs> cj;
+    typedef Map<const Matrix<Scalar, Dynamic, 1> > OtherMap;
+    typedef std::conditional_t<ConjLhs, typename OtherMap::ConjugateReturnType, const OtherMap&> ConjLhsType;
+    for (Index i = 0; i < size; ++i) {
+      Map<Matrix<Scalar, Dynamic, 1> >(mat + stride * i + (UpLo == Lower ? i : 0),
+                                       (UpLo == Lower ? size - i : (i + 1))) +=
+          (alpha * cj(vecY[i])) *
+          ConjLhsType(OtherMap(vecX + (UpLo == Lower ? i : 0), UpLo == Lower ? size - i : (i + 1)));
+    }
+  }
+};
+
+template <typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs>
+struct selfadjoint_rank1_update<Scalar, Index, RowMajor, UpLo, ConjLhs, ConjRhs> {
+  static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha) {
+    selfadjoint_rank1_update<Scalar, Index, ColMajor, UpLo == Lower ? Upper : Lower, ConjRhs, ConjLhs>::run(
+        size, mat, stride, vecY, vecX, alpha);
+  }
+};
+
+template <typename MatrixType, typename OtherType, int UpLo, bool OtherIsVector = OtherType::IsVectorAtCompileTime>
+struct selfadjoint_product_selector;
+
+template <typename MatrixType, typename OtherType, int UpLo>
+struct selfadjoint_product_selector<MatrixType, OtherType, UpLo, true> {
+  static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha) {
+    typedef typename MatrixType::Scalar Scalar;
+    typedef internal::blas_traits<OtherType> OtherBlasTraits;
+    typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType;
+    typedef internal::remove_all_t<ActualOtherType> ActualOtherType_;
+    internal::add_const_on_value_type_t<ActualOtherType> actualOther = OtherBlasTraits::extract(other.derived());
+
+    Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived());
+
+    enum {
+      StorageOrder = (internal::traits<MatrixType>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+      UseOtherDirectly = ActualOtherType_::InnerStrideAtCompileTime == 1
+    };
+    internal::gemv_static_vector_if<Scalar, OtherType::SizeAtCompileTime, OtherType::MaxSizeAtCompileTime,
+                                    !UseOtherDirectly>
+        static_other;
+
+    ei_declare_aligned_stack_constructed_variable(
+        Scalar, actualOtherPtr, other.size(),
+        (UseOtherDirectly ? const_cast<Scalar*>(actualOther.data()) : static_other.data()));
+
+    if (!UseOtherDirectly)
+      Map<typename ActualOtherType_::PlainObject>(actualOtherPtr, actualOther.size()) = actualOther;
+
+    selfadjoint_rank1_update<
+        Scalar, Index, StorageOrder, UpLo, OtherBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex,
+        (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex>::run(other.size(), mat.data(),
+                                                                                  mat.outerStride(), actualOtherPtr,
+                                                                                  actualOtherPtr, actualAlpha);
+  }
+};
+
+template <typename MatrixType, typename OtherType, int UpLo>
+struct selfadjoint_product_selector<MatrixType, OtherType, UpLo, false> {
+  static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha) {
+    typedef typename MatrixType::Scalar Scalar;
+    typedef internal::blas_traits<OtherType> OtherBlasTraits;
+    typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType;
+    typedef internal::remove_all_t<ActualOtherType> ActualOtherType_;
+    internal::add_const_on_value_type_t<ActualOtherType> actualOther = OtherBlasTraits::extract(other.derived());
+
+    Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived());
+
+    enum {
+      IsRowMajor = (internal::traits<MatrixType>::Flags & RowMajorBit) ? 1 : 0,
+      OtherIsRowMajor = ActualOtherType_::Flags & RowMajorBit ? 1 : 0
+    };
+
+    Index size = mat.cols();
+    Index depth = actualOther.cols();
+
+    typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor, Scalar, Scalar,
+                                          MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime,
+                                          ActualOtherType_::MaxColsAtCompileTime>
+        BlockingType;
+
+    BlockingType blocking(size, size, depth, 1, false);
+
+    internal::general_matrix_matrix_triangular_product<
+        Index, Scalar, OtherIsRowMajor ? RowMajor : ColMajor,
+        OtherBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex, Scalar, OtherIsRowMajor ? ColMajor : RowMajor,
+        (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex, IsRowMajor ? RowMajor : ColMajor,
+        MatrixType::InnerStrideAtCompileTime, UpLo>::run(size, depth, actualOther.data(), actualOther.outerStride(),
+                                                         actualOther.data(), actualOther.outerStride(), mat.data(),
+                                                         mat.innerStride(), mat.outerStride(), actualAlpha, blocking);
+  }
+};
+
+// high level API
+
+template <typename MatrixType, unsigned int UpLo>
+template <typename DerivedU>
+EIGEN_DEVICE_FUNC SelfAdjointView<MatrixType, UpLo>& SelfAdjointView<MatrixType, UpLo>::rankUpdate(
+    const MatrixBase<DerivedU>& u, const Scalar& alpha) {
+  selfadjoint_product_selector<MatrixType, DerivedU, UpLo>::run(_expression().const_cast_derived(), u.derived(), alpha);
+
+  return *this;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SELFADJOINT_PRODUCT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointRank2Update.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointRank2Update.h
new file mode 100644
index 0000000..9c234ec
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/SelfadjointRank2Update.h
@@ -0,0 +1,95 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINTRANK2UPTADE_H
+#define EIGEN_SELFADJOINTRANK2UPTADE_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/* Optimized selfadjoint matrix += alpha * uv' + conj(alpha)*vu'
+ * It corresponds to the Level2 syr2 BLAS routine
+ */
+
+template <typename Scalar, typename Index, typename UType, typename VType, int UpLo>
+struct selfadjoint_rank2_update_selector;
+
+template <typename Scalar, typename Index, typename UType, typename VType>
+struct selfadjoint_rank2_update_selector<Scalar, Index, UType, VType, Lower> {
+  static EIGEN_DEVICE_FUNC void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha) {
+    const Index size = u.size();
+    for (Index i = 0; i < size; ++i) {
+      Map<Matrix<Scalar, Dynamic, 1>>(mat + stride * i + i, size - i) +=
+          (numext::conj(alpha) * numext::conj(u.coeff(i))) * v.tail(size - i) +
+          (alpha * numext::conj(v.coeff(i))) * u.tail(size - i);
+    }
+  }
+};
+
+template <typename Scalar, typename Index, typename UType, typename VType>
+struct selfadjoint_rank2_update_selector<Scalar, Index, UType, VType, Upper> {
+  static void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha) {
+    const Index size = u.size();
+    for (Index i = 0; i < size; ++i)
+      Map<Matrix<Scalar, Dynamic, 1>>(mat + stride * i, i + 1) +=
+          (numext::conj(alpha) * numext::conj(u.coeff(i))) * v.head(i + 1) +
+          (alpha * numext::conj(v.coeff(i))) * u.head(i + 1);
+  }
+};
+
+template <bool Cond, typename T>
+using conj_expr_if =
+    std::conditional<!Cond, const T&, CwiseUnaryOp<scalar_conjugate_op<typename traits<T>::Scalar>, T>>;
+
+}  // end namespace internal
+
+template <typename MatrixType, unsigned int UpLo>
+template <typename DerivedU, typename DerivedV>
+EIGEN_DEVICE_FUNC SelfAdjointView<MatrixType, UpLo>& SelfAdjointView<MatrixType, UpLo>::rankUpdate(
+    const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha) {
+  typedef internal::blas_traits<DerivedU> UBlasTraits;
+  typedef typename UBlasTraits::DirectLinearAccessType ActualUType;
+  typedef internal::remove_all_t<ActualUType> ActualUType_;
+  internal::add_const_on_value_type_t<ActualUType> actualU = UBlasTraits::extract(u.derived());
+
+  typedef internal::blas_traits<DerivedV> VBlasTraits;
+  typedef typename VBlasTraits::DirectLinearAccessType ActualVType;
+  typedef internal::remove_all_t<ActualVType> ActualVType_;
+  internal::add_const_on_value_type_t<ActualVType> actualV = VBlasTraits::extract(v.derived());
+
+  // If MatrixType is row major, then we use the routine for lower triangular in the upper triangular case and
+  // vice versa, and take the complex conjugate of all coefficients and vector entries.
+
+  enum { IsRowMajor = (internal::traits<MatrixType>::Flags & RowMajorBit) ? 1 : 0 };
+  Scalar actualAlpha = alpha * UBlasTraits::extractScalarFactor(u.derived()) *
+                       numext::conj(VBlasTraits::extractScalarFactor(v.derived()));
+  if (IsRowMajor) actualAlpha = numext::conj(actualAlpha);
+
+  typedef internal::remove_all_t<
+      typename internal::conj_expr_if<int(IsRowMajor) ^ int(UBlasTraits::NeedToConjugate), ActualUType_>::type>
+      UType;
+  typedef internal::remove_all_t<
+      typename internal::conj_expr_if<int(IsRowMajor) ^ int(VBlasTraits::NeedToConjugate), ActualVType_>::type>
+      VType;
+  internal::selfadjoint_rank2_update_selector<Scalar, Index, UType, VType,
+                                              (IsRowMajor ? int(UpLo == Upper ? Lower : Upper)
+                                                          : UpLo)>::run(_expression().const_cast_derived().data(),
+                                                                        _expression().outerStride(), UType(actualU),
+                                                                        VType(actualV), actualAlpha);
+
+  return *this;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SELFADJOINTRANK2UPTADE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixMatrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixMatrix.h
new file mode 100644
index 0000000..e4e446e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixMatrix.h
@@ -0,0 +1,373 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_H
+#define EIGEN_TRIANGULAR_MATRIX_MATRIX_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/* Optimized triangular matrix * matrix (_TRMM++) product built on top of
+ * the general matrix matrix product.
+ */
+template <typename Scalar, typename Index, int Mode, bool LhsIsTriangular, int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs, int ResStorageOrder, int ResInnerStride, int Version = Specialized>
+struct product_triangular_matrix_matrix;
+
+template <typename Scalar, typename Index, int Mode, bool LhsIsTriangular, int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs, int ResInnerStride, int Version>
+struct product_triangular_matrix_matrix<Scalar, Index, Mode, LhsIsTriangular, LhsStorageOrder, ConjugateLhs,
+                                        RhsStorageOrder, ConjugateRhs, RowMajor, ResInnerStride, Version> {
+  static EIGEN_STRONG_INLINE void run(Index rows, Index cols, Index depth, const Scalar* lhs, Index lhsStride,
+                                      const Scalar* rhs, Index rhsStride, Scalar* res, Index resIncr, Index resStride,
+                                      const Scalar& alpha, level3_blocking<Scalar, Scalar>& blocking) {
+    product_triangular_matrix_matrix<Scalar, Index, (Mode & (UnitDiag | ZeroDiag)) | ((Mode & Upper) ? Lower : Upper),
+                                     (!LhsIsTriangular), RhsStorageOrder == RowMajor ? ColMajor : RowMajor,
+                                     ConjugateRhs, LhsStorageOrder == RowMajor ? ColMajor : RowMajor, ConjugateLhs,
+                                     ColMajor, ResInnerStride>::run(cols, rows, depth, rhs, rhsStride, lhs, lhsStride,
+                                                                    res, resIncr, resStride, alpha, blocking);
+  }
+};
+
+// implements col-major += alpha * op(triangular) * op(general)
+template <typename Scalar, typename Index, int Mode, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder,
+          bool ConjugateRhs, int ResInnerStride, int Version>
+struct product_triangular_matrix_matrix<Scalar, Index, Mode, true, LhsStorageOrder, ConjugateLhs, RhsStorageOrder,
+                                        ConjugateRhs, ColMajor, ResInnerStride, Version> {
+  typedef gebp_traits<Scalar, Scalar> Traits;
+  enum {
+    SmallPanelWidth = 2 * plain_enum_max(Traits::mr, Traits::nr),
+    IsLower = (Mode & Lower) == Lower,
+    SetDiag = (Mode & (ZeroDiag | UnitDiag)) ? 0 : 1
+  };
+
+  static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, Index _depth, const Scalar* lhs_, Index lhsStride,
+                                    const Scalar* rhs_, Index rhsStride, Scalar* res, Index resIncr, Index resStride,
+                                    const Scalar& alpha, level3_blocking<Scalar, Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index, int Mode, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder,
+          bool ConjugateRhs, int ResInnerStride, int Version>
+EIGEN_DONT_INLINE void product_triangular_matrix_matrix<
+    Scalar, Index, Mode, true, LhsStorageOrder, ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, ResInnerStride,
+    Version>::run(Index _rows, Index _cols, Index _depth, const Scalar* lhs_, Index lhsStride, const Scalar* rhs_,
+                  Index rhsStride, Scalar* res_, Index resIncr, Index resStride, const Scalar& alpha,
+                  level3_blocking<Scalar, Scalar>& blocking) {
+  // strip zeros
+  Index diagSize = (std::min)(_rows, _depth);
+  Index rows = IsLower ? _rows : diagSize;
+  Index depth = IsLower ? diagSize : _depth;
+  Index cols = _cols;
+
+  typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
+  typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
+  typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+  LhsMapper lhs(lhs_, lhsStride);
+  RhsMapper rhs(rhs_, rhsStride);
+  ResMapper res(res_, resStride, resIncr);
+
+  Index kc = blocking.kc();                    // cache block size along the K direction
+  Index mc = (std::min)(rows, blocking.mc());  // cache block size along the M direction
+  // The small panel size must not be larger than blocking size.
+  // Usually this should never be the case because SmallPanelWidth^2 is very small
+  // compared to L2 cache size, but let's be safe:
+  Index panelWidth = (std::min)(Index(SmallPanelWidth), (std::min)(kc, mc));
+
+  std::size_t sizeA = kc * mc;
+  std::size_t sizeB = kc * cols;
+
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+  Matrix<Scalar, SmallPanelWidth, SmallPanelWidth, LhsStorageOrder> triangularBuffer;
+  triangularBuffer.setZero();
+  if ((Mode & ZeroDiag) == ZeroDiag)
+    triangularBuffer.diagonal().setZero();
+  else
+    triangularBuffer.diagonal().setOnes();
+
+  gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
+  gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
+                LhsStorageOrder>
+      pack_lhs;
+  gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
+
+  for (Index k2 = IsLower ? depth : 0; IsLower ? k2 > 0 : k2 < depth; IsLower ? k2 -= kc : k2 += kc) {
+    Index actual_kc = (std::min)(IsLower ? k2 : depth - k2, kc);
+    Index actual_k2 = IsLower ? k2 - actual_kc : k2;
+
+    // align blocks with the end of the triangular part for trapezoidal lhs
+    if ((!IsLower) && (k2 < rows) && (k2 + actual_kc > rows)) {
+      actual_kc = rows - k2;
+      k2 = k2 + actual_kc - kc;
+    }
+
+    pack_rhs(blockB, rhs.getSubMapper(actual_k2, 0), actual_kc, cols);
+
+    // the selected lhs's panel has to be split in three different parts:
+    //  1 - the part which is zero => skip it
+    //  2 - the diagonal block => special kernel
+    //  3 - the dense panel below (lower case) or above (upper case) the diagonal block => GEPP
+
+    // the block diagonal, if any:
+    if (IsLower || actual_k2 < rows) {
+      // for each small vertical panels of lhs
+      for (Index k1 = 0; k1 < actual_kc; k1 += panelWidth) {
+        Index actualPanelWidth = std::min<Index>(actual_kc - k1, panelWidth);
+        Index lengthTarget = IsLower ? actual_kc - k1 - actualPanelWidth : k1;
+        Index startBlock = actual_k2 + k1;
+        Index blockBOffset = k1;
+
+        // => GEBP with the micro triangular block
+        // The trick is to pack this micro block while filling the opposite triangular part with zeros.
+        // To this end we do an extra triangular copy to a small temporary buffer
+        for (Index k = 0; k < actualPanelWidth; ++k) {
+          if (SetDiag) triangularBuffer.coeffRef(k, k) = lhs(startBlock + k, startBlock + k);
+          for (Index i = IsLower ? k + 1 : 0; IsLower ? i < actualPanelWidth : i < k; ++i)
+            triangularBuffer.coeffRef(i, k) = lhs(startBlock + i, startBlock + k);
+        }
+        pack_lhs(blockA, LhsMapper(triangularBuffer.data(), triangularBuffer.outerStride()), actualPanelWidth,
+                 actualPanelWidth);
+
+        gebp_kernel(res.getSubMapper(startBlock, 0), blockA, blockB, actualPanelWidth, actualPanelWidth, cols, alpha,
+                    actualPanelWidth, actual_kc, 0, blockBOffset);
+
+        // GEBP with remaining micro panel
+        if (lengthTarget > 0) {
+          Index startTarget = IsLower ? actual_k2 + k1 + actualPanelWidth : actual_k2;
+
+          pack_lhs(blockA, lhs.getSubMapper(startTarget, startBlock), actualPanelWidth, lengthTarget);
+
+          gebp_kernel(res.getSubMapper(startTarget, 0), blockA, blockB, lengthTarget, actualPanelWidth, cols, alpha,
+                      actualPanelWidth, actual_kc, 0, blockBOffset);
+        }
+      }
+    }
+    // the part below (lower case) or above (upper case) the diagonal => GEPP
+    {
+      Index start = IsLower ? k2 : 0;
+      Index end = IsLower ? rows : (std::min)(actual_k2, rows);
+      for (Index i2 = start; i2 < end; i2 += mc) {
+        const Index actual_mc = (std::min)(i2 + mc, end) - i2;
+        gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
+                      LhsStorageOrder, false>()(blockA, lhs.getSubMapper(i2, actual_k2), actual_kc, actual_mc);
+
+        gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha, -1, -1, 0, 0);
+      }
+    }
+  }
+}
+
+// implements col-major += alpha * op(general) * op(triangular)
+template <typename Scalar, typename Index, int Mode, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder,
+          bool ConjugateRhs, int ResInnerStride, int Version>
+struct product_triangular_matrix_matrix<Scalar, Index, Mode, false, LhsStorageOrder, ConjugateLhs, RhsStorageOrder,
+                                        ConjugateRhs, ColMajor, ResInnerStride, Version> {
+  typedef gebp_traits<Scalar, Scalar> Traits;
+  enum {
+    SmallPanelWidth = plain_enum_max(Traits::mr, Traits::nr),
+    IsLower = (Mode & Lower) == Lower,
+    SetDiag = (Mode & (ZeroDiag | UnitDiag)) ? 0 : 1
+  };
+
+  static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, Index _depth, const Scalar* lhs_, Index lhsStride,
+                                    const Scalar* rhs_, Index rhsStride, Scalar* res, Index resIncr, Index resStride,
+                                    const Scalar& alpha, level3_blocking<Scalar, Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index, int Mode, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder,
+          bool ConjugateRhs, int ResInnerStride, int Version>
+EIGEN_DONT_INLINE void product_triangular_matrix_matrix<
+    Scalar, Index, Mode, false, LhsStorageOrder, ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, ResInnerStride,
+    Version>::run(Index _rows, Index _cols, Index _depth, const Scalar* lhs_, Index lhsStride, const Scalar* rhs_,
+                  Index rhsStride, Scalar* res_, Index resIncr, Index resStride, const Scalar& alpha,
+                  level3_blocking<Scalar, Scalar>& blocking) {
+  const Index PacketBytes = packet_traits<Scalar>::size * sizeof(Scalar);
+  // strip zeros
+  Index diagSize = (std::min)(_cols, _depth);
+  Index rows = _rows;
+  Index depth = IsLower ? _depth : diagSize;
+  Index cols = IsLower ? diagSize : _cols;
+
+  typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
+  typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
+  typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+  LhsMapper lhs(lhs_, lhsStride);
+  RhsMapper rhs(rhs_, rhsStride);
+  ResMapper res(res_, resStride, resIncr);
+
+  Index kc = blocking.kc();                    // cache block size along the K direction
+  Index mc = (std::min)(rows, blocking.mc());  // cache block size along the M direction
+
+  std::size_t sizeA = kc * mc;
+  std::size_t sizeB = kc * cols + EIGEN_MAX_ALIGN_BYTES / sizeof(Scalar);
+
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+  Matrix<Scalar, SmallPanelWidth, SmallPanelWidth, RhsStorageOrder> triangularBuffer;
+  triangularBuffer.setZero();
+  if ((Mode & ZeroDiag) == ZeroDiag)
+    triangularBuffer.diagonal().setZero();
+  else
+    triangularBuffer.diagonal().setOnes();
+
+  gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
+  gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
+                LhsStorageOrder>
+      pack_lhs;
+  gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
+  gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder, false, true> pack_rhs_panel;
+
+  for (Index k2 = IsLower ? 0 : depth; IsLower ? k2 < depth : k2 > 0; IsLower ? k2 += kc : k2 -= kc) {
+    Index actual_kc = (std::min)(IsLower ? depth - k2 : k2, kc);
+    Index actual_k2 = IsLower ? k2 : k2 - actual_kc;
+
+    // align blocks with the end of the triangular part for trapezoidal rhs
+    if (IsLower && (k2 < cols) && (actual_k2 + actual_kc > cols)) {
+      actual_kc = cols - k2;
+      k2 = actual_k2 + actual_kc - kc;
+    }
+
+    // remaining size
+    Index rs = IsLower ? (std::min)(cols, actual_k2) : cols - k2;
+    // size of the triangular part
+    Index ts = (IsLower && actual_k2 >= cols) ? 0 : actual_kc;
+
+    Scalar* geb = blockB + ts * ts;
+    geb = geb + internal::first_aligned<PacketBytes>(geb, PacketBytes / sizeof(Scalar));
+
+    pack_rhs(geb, rhs.getSubMapper(actual_k2, IsLower ? 0 : k2), actual_kc, rs);
+
+    // pack the triangular part of the rhs padding the unrolled blocks with zeros
+    if (ts > 0) {
+      for (Index j2 = 0; j2 < actual_kc; j2 += SmallPanelWidth) {
+        Index actualPanelWidth = std::min<Index>(actual_kc - j2, SmallPanelWidth);
+        Index actual_j2 = actual_k2 + j2;
+        Index panelOffset = IsLower ? j2 + actualPanelWidth : 0;
+        Index panelLength = IsLower ? actual_kc - j2 - actualPanelWidth : j2;
+        // general part
+        pack_rhs_panel(blockB + j2 * actual_kc, rhs.getSubMapper(actual_k2 + panelOffset, actual_j2), panelLength,
+                       actualPanelWidth, actual_kc, panelOffset);
+
+        // append the triangular part via a temporary buffer
+        for (Index j = 0; j < actualPanelWidth; ++j) {
+          if (SetDiag) triangularBuffer.coeffRef(j, j) = rhs(actual_j2 + j, actual_j2 + j);
+          for (Index k = IsLower ? j + 1 : 0; IsLower ? k < actualPanelWidth : k < j; ++k)
+            triangularBuffer.coeffRef(k, j) = rhs(actual_j2 + k, actual_j2 + j);
+        }
+
+        pack_rhs_panel(blockB + j2 * actual_kc, RhsMapper(triangularBuffer.data(), triangularBuffer.outerStride()),
+                       actualPanelWidth, actualPanelWidth, actual_kc, j2);
+      }
+    }
+
+    for (Index i2 = 0; i2 < rows; i2 += mc) {
+      const Index actual_mc = (std::min)(mc, rows - i2);
+      pack_lhs(blockA, lhs.getSubMapper(i2, actual_k2), actual_kc, actual_mc);
+
+      // triangular kernel
+      if (ts > 0) {
+        for (Index j2 = 0; j2 < actual_kc; j2 += SmallPanelWidth) {
+          Index actualPanelWidth = std::min<Index>(actual_kc - j2, SmallPanelWidth);
+          Index panelLength = IsLower ? actual_kc - j2 : j2 + actualPanelWidth;
+          Index blockOffset = IsLower ? j2 : 0;
+
+          gebp_kernel(res.getSubMapper(i2, actual_k2 + j2), blockA, blockB + j2 * actual_kc, actual_mc, panelLength,
+                      actualPanelWidth, alpha, actual_kc, actual_kc,  // strides
+                      blockOffset, blockOffset);                      // offsets
+        }
+      }
+      gebp_kernel(res.getSubMapper(i2, IsLower ? 0 : k2), blockA, geb, actual_mc, actual_kc, rs, alpha, -1, -1, 0, 0);
+    }
+  }
+}
+
+/***************************************************************************
+ * Wrapper to product_triangular_matrix_matrix
+ ***************************************************************************/
+
+}  // end namespace internal
+
+namespace internal {
+template <int Mode, bool LhsIsTriangular, typename Lhs, typename Rhs>
+struct triangular_product_impl<Mode, LhsIsTriangular, Lhs, false, Rhs, false> {
+  template <typename Dest>
+  static void run(Dest& dst, const Lhs& a_lhs, const Rhs& a_rhs, const typename Dest::Scalar& alpha) {
+    typedef typename Lhs::Scalar LhsScalar;
+    typedef typename Rhs::Scalar RhsScalar;
+    typedef typename Dest::Scalar Scalar;
+
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+    typedef internal::remove_all_t<ActualLhsType> ActualLhsTypeCleaned;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+    typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
+
+    internal::add_const_on_value_type_t<ActualLhsType> lhs = LhsBlasTraits::extract(a_lhs);
+    internal::add_const_on_value_type_t<ActualRhsType> rhs = RhsBlasTraits::extract(a_rhs);
+
+    // Empty product, return early.  Otherwise, we get `nullptr` use errors below when we try to access
+    // coeffRef(0,0).
+    if (lhs.size() == 0 || rhs.size() == 0) {
+      return;
+    }
+
+    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(a_lhs);
+    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(a_rhs);
+    Scalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
+
+    typedef internal::gemm_blocking_space<(Dest::Flags & RowMajorBit) ? RowMajor : ColMajor, Scalar, Scalar,
+                                          Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime,
+                                          Lhs::MaxColsAtCompileTime, 4>
+        BlockingType;
+
+    enum { IsLower = (Mode & Lower) == Lower };
+    Index stripedRows = ((!LhsIsTriangular) || (IsLower)) ? lhs.rows() : (std::min)(lhs.rows(), lhs.cols());
+    Index stripedCols = ((LhsIsTriangular) || (!IsLower)) ? rhs.cols() : (std::min)(rhs.cols(), rhs.rows());
+    Index stripedDepth = LhsIsTriangular ? ((!IsLower) ? lhs.cols() : (std::min)(lhs.cols(), lhs.rows()))
+                                         : ((IsLower) ? rhs.rows() : (std::min)(rhs.rows(), rhs.cols()));
+
+    BlockingType blocking(stripedRows, stripedCols, stripedDepth, 1, false);
+
+    internal::product_triangular_matrix_matrix<
+        Scalar, Index, Mode, LhsIsTriangular,
+        (internal::traits<ActualLhsTypeCleaned>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+        LhsBlasTraits::NeedToConjugate,
+        (internal::traits<ActualRhsTypeCleaned>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+        RhsBlasTraits::NeedToConjugate, (internal::traits<Dest>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+        Dest::InnerStrideAtCompileTime>::run(stripedRows, stripedCols, stripedDepth,                     // sizes
+                                             &lhs.coeffRef(0, 0), lhs.outerStride(),                     // lhs info
+                                             &rhs.coeffRef(0, 0), rhs.outerStride(),                     // rhs info
+                                             &dst.coeffRef(0, 0), dst.innerStride(), dst.outerStride(),  // result info
+                                             actualAlpha, blocking);
+
+    // Apply correction if the diagonal is unit and a scalar factor was nested:
+    if ((Mode & UnitDiag) == UnitDiag) {
+      if (LhsIsTriangular && !numext::is_exactly_one(lhs_alpha)) {
+        Index diagSize = (std::min)(lhs.rows(), lhs.cols());
+        dst.topRows(diagSize) -= ((lhs_alpha - LhsScalar(1)) * a_rhs).topRows(diagSize);
+      } else if ((!LhsIsTriangular) && !numext::is_exactly_one(rhs_alpha)) {
+        Index diagSize = (std::min)(rhs.rows(), rhs.cols());
+        dst.leftCols(diagSize) -= (rhs_alpha - RhsScalar(1)) * a_lhs.leftCols(diagSize);
+      }
+    }
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRIANGULAR_MATRIX_MATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
new file mode 100644
index 0000000..043011e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
@@ -0,0 +1,329 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Triangular matrix * matrix product functionality based on ?TRMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
+#define EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar, typename Index, int Mode, bool LhsIsTriangular, int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs, int ResStorageOrder>
+struct product_triangular_matrix_matrix_trmm
+    : product_triangular_matrix_matrix<Scalar, Index, Mode, LhsIsTriangular, LhsStorageOrder, ConjugateLhs,
+                                       RhsStorageOrder, ConjugateRhs, ResStorageOrder, 1, BuiltIn> {};
+
+// try to go to BLAS specialization
+#define EIGEN_BLAS_TRMM_SPECIALIZE(Scalar, LhsIsTriangular)                                                           \
+  template <typename Index, int Mode, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder, bool ConjugateRhs> \
+  struct product_triangular_matrix_matrix<Scalar, Index, Mode, LhsIsTriangular, LhsStorageOrder, ConjugateLhs,        \
+                                          RhsStorageOrder, ConjugateRhs, ColMajor, 1, Specialized> {                  \
+    static inline void run(Index _rows, Index _cols, Index _depth, const Scalar* _lhs, Index lhsStride,               \
+                           const Scalar* _rhs, Index rhsStride, Scalar* res, Index resIncr, Index resStride,          \
+                           Scalar alpha, level3_blocking<Scalar, Scalar>& blocking) {                                 \
+      EIGEN_ONLY_USED_FOR_DEBUG(resIncr);                                                                             \
+      eigen_assert(resIncr == 1);                                                                                     \
+      product_triangular_matrix_matrix_trmm<Scalar, Index, Mode, LhsIsTriangular, LhsStorageOrder, ConjugateLhs,      \
+                                            RhsStorageOrder, ConjugateRhs, ColMajor>::run(_rows, _cols, _depth, _lhs, \
+                                                                                          lhsStride, _rhs, rhsStride, \
+                                                                                          res, resStride, alpha,      \
+                                                                                          blocking);                  \
+    }                                                                                                                 \
+  };
+
+EIGEN_BLAS_TRMM_SPECIALIZE(double, true)
+EIGEN_BLAS_TRMM_SPECIALIZE(double, false)
+EIGEN_BLAS_TRMM_SPECIALIZE(dcomplex, true)
+EIGEN_BLAS_TRMM_SPECIALIZE(dcomplex, false)
+EIGEN_BLAS_TRMM_SPECIALIZE(float, true)
+EIGEN_BLAS_TRMM_SPECIALIZE(float, false)
+EIGEN_BLAS_TRMM_SPECIALIZE(scomplex, true)
+EIGEN_BLAS_TRMM_SPECIALIZE(scomplex, false)
+
+// implements col-major += alpha * op(triangular) * op(general)
+#define EIGEN_BLAS_TRMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC)                                                      \
+  template <typename Index, int Mode, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder, bool ConjugateRhs>  \
+  struct product_triangular_matrix_matrix_trmm<EIGTYPE, Index, Mode, true, LhsStorageOrder, ConjugateLhs,              \
+                                               RhsStorageOrder, ConjugateRhs, ColMajor> {                              \
+    enum {                                                                                                             \
+      IsLower = (Mode & Lower) == Lower,                                                                               \
+      SetDiag = (Mode & (ZeroDiag | UnitDiag)) ? 0 : 1,                                                                \
+      IsUnitDiag = (Mode & UnitDiag) ? 1 : 0,                                                                          \
+      IsZeroDiag = (Mode & ZeroDiag) ? 1 : 0,                                                                          \
+      LowUp = IsLower ? Lower : Upper,                                                                                 \
+      conjA = ((LhsStorageOrder == ColMajor) && ConjugateLhs) ? 1 : 0                                                  \
+    };                                                                                                                 \
+                                                                                                                       \
+    static void run(Index _rows, Index _cols, Index _depth, const EIGTYPE* _lhs, Index lhsStride, const EIGTYPE* _rhs, \
+                    Index rhsStride, EIGTYPE* res, Index resStride, EIGTYPE alpha,                                     \
+                    level3_blocking<EIGTYPE, EIGTYPE>& blocking) {                                                     \
+      if (_rows == 0 || _cols == 0 || _depth == 0) return;                                                             \
+      Index diagSize = (std::min)(_rows, _depth);                                                                      \
+      Index rows = IsLower ? _rows : diagSize;                                                                         \
+      Index depth = IsLower ? diagSize : _depth;                                                                       \
+      Index cols = _cols;                                                                                              \
+                                                                                                                       \
+      typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs;                                            \
+      typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs;                                            \
+                                                                                                                       \
+      /* Non-square case - doesn't fit to BLAS ?TRMM. Fall to default triangular product or call BLAS ?GEMM*/          \
+      if (rows != depth) {                                                                                             \
+        /* FIXME handle mkl_domain_get_max_threads */                                                                  \
+        /*int nthr = mkl_domain_get_max_threads(EIGEN_BLAS_DOMAIN_BLAS);*/ int nthr = 1;                               \
+                                                                                                                       \
+        if (((nthr == 1) && (((std::max)(rows, depth) - diagSize) / (double)diagSize < 0.5))) {                        \
+          /* Most likely no benefit to call TRMM or GEMM from BLAS */                                                  \
+          product_triangular_matrix_matrix<EIGTYPE, Index, Mode, true, LhsStorageOrder, ConjugateLhs, RhsStorageOrder, \
+                                           ConjugateRhs, ColMajor, 1, BuiltIn>::run(_rows, _cols, _depth, _lhs,        \
+                                                                                    lhsStride, _rhs, rhsStride, res,   \
+                                                                                    1, resStride, alpha, blocking);    \
+          /*std::cout << "TRMM_L: A is not square! Go to Eigen TRMM implementation!\n";*/                              \
+        } else {                                                                                                       \
+          /* Make sense to call GEMM */                                                                                \
+          Map<const MatrixLhs, 0, OuterStride<> > lhsMap(_lhs, rows, depth, OuterStride<>(lhsStride));                 \
+          MatrixLhs aa_tmp = lhsMap.template triangularView<Mode>();                                                   \
+          BlasIndex aStride = convert_index<BlasIndex>(aa_tmp.outerStride());                                          \
+          gemm_blocking_space<ColMajor, EIGTYPE, EIGTYPE, Dynamic, Dynamic, Dynamic> gemm_blocking(_rows, _cols,       \
+                                                                                                   _depth, 1, true);   \
+          general_matrix_matrix_product<Index, EIGTYPE, LhsStorageOrder, ConjugateLhs, EIGTYPE, RhsStorageOrder,       \
+                                        ConjugateRhs, ColMajor, 1>::run(rows, cols, depth, aa_tmp.data(), aStride,     \
+                                                                        _rhs, rhsStride, res, 1, resStride, alpha,     \
+                                                                        gemm_blocking, 0);                             \
+                                                                                                                       \
+          /*std::cout << "TRMM_L: A is not square! Go to BLAS GEMM implementation! " << nthr<<" \n";*/                 \
+        }                                                                                                              \
+        return;                                                                                                        \
+      }                                                                                                                \
+      char side = 'L', transa, uplo, diag = 'N';                                                                       \
+      EIGTYPE* b;                                                                                                      \
+      const EIGTYPE* a;                                                                                                \
+      BlasIndex m, n, lda, ldb;                                                                                        \
+                                                                                                                       \
+      /* Set m, n */                                                                                                   \
+      m = convert_index<BlasIndex>(diagSize);                                                                          \
+      n = convert_index<BlasIndex>(cols);                                                                              \
+                                                                                                                       \
+      /* Set trans */                                                                                                  \
+      transa = (LhsStorageOrder == RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N';                                     \
+                                                                                                                       \
+      /* Set b, ldb */                                                                                                 \
+      Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs, depth, cols, OuterStride<>(rhsStride));                        \
+      MatrixX##EIGPREFIX b_tmp;                                                                                        \
+                                                                                                                       \
+      if (ConjugateRhs)                                                                                                \
+        b_tmp = rhs.conjugate();                                                                                       \
+      else                                                                                                             \
+        b_tmp = rhs;                                                                                                   \
+      b = b_tmp.data();                                                                                                \
+      ldb = convert_index<BlasIndex>(b_tmp.outerStride());                                                             \
+                                                                                                                       \
+      /* Set uplo */                                                                                                   \
+      uplo = IsLower ? 'L' : 'U';                                                                                      \
+      if (LhsStorageOrder == RowMajor) uplo = (uplo == 'L') ? 'U' : 'L';                                               \
+      /* Set a, lda */                                                                                                 \
+      Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs, rows, depth, OuterStride<>(lhsStride));                        \
+      MatrixLhs a_tmp;                                                                                                 \
+                                                                                                                       \
+      if ((conjA != 0) || (SetDiag == 0)) {                                                                            \
+        if (conjA)                                                                                                     \
+          a_tmp = lhs.conjugate();                                                                                     \
+        else                                                                                                           \
+          a_tmp = lhs;                                                                                                 \
+        if (IsZeroDiag)                                                                                                \
+          a_tmp.diagonal().setZero();                                                                                  \
+        else if (IsUnitDiag)                                                                                           \
+          a_tmp.diagonal().setOnes();                                                                                  \
+        a = a_tmp.data();                                                                                              \
+        lda = convert_index<BlasIndex>(a_tmp.outerStride());                                                           \
+      } else {                                                                                                         \
+        a = _lhs;                                                                                                      \
+        lda = convert_index<BlasIndex>(lhsStride);                                                                     \
+      }                                                                                                                \
+      /*std::cout << "TRMM_L: A is square! Go to BLAS TRMM implementation! \n";*/                                      \
+      /* call ?trmm*/                                                                                                  \
+      BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a,    \
+               &lda, (BLASTYPE*)b, &ldb);                                                                              \
+                                                                                                                       \
+      /* Add op(a_triangular)*b into res*/                                                                             \
+      Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res, rows, cols, OuterStride<>(resStride));                   \
+      res_tmp = res_tmp + b_tmp;                                                                                       \
+    }                                                                                                                  \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRMM_L(double, double, d, dtrmm)
+EIGEN_BLAS_TRMM_L(dcomplex, MKL_Complex16, cd, ztrmm)
+EIGEN_BLAS_TRMM_L(float, float, f, strmm)
+EIGEN_BLAS_TRMM_L(scomplex, MKL_Complex8, cf, ctrmm)
+#else
+EIGEN_BLAS_TRMM_L(double, double, d, dtrmm_)
+EIGEN_BLAS_TRMM_L(dcomplex, double, cd, ztrmm_)
+EIGEN_BLAS_TRMM_L(float, float, f, strmm_)
+EIGEN_BLAS_TRMM_L(scomplex, float, cf, ctrmm_)
+#endif
+
+// implements col-major += alpha * op(general) * op(triangular)
+#define EIGEN_BLAS_TRMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC)                                                      \
+  template <typename Index, int Mode, int LhsStorageOrder, bool ConjugateLhs, int RhsStorageOrder, bool ConjugateRhs>  \
+  struct product_triangular_matrix_matrix_trmm<EIGTYPE, Index, Mode, false, LhsStorageOrder, ConjugateLhs,             \
+                                               RhsStorageOrder, ConjugateRhs, ColMajor> {                              \
+    enum {                                                                                                             \
+      IsLower = (Mode & Lower) == Lower,                                                                               \
+      SetDiag = (Mode & (ZeroDiag | UnitDiag)) ? 0 : 1,                                                                \
+      IsUnitDiag = (Mode & UnitDiag) ? 1 : 0,                                                                          \
+      IsZeroDiag = (Mode & ZeroDiag) ? 1 : 0,                                                                          \
+      LowUp = IsLower ? Lower : Upper,                                                                                 \
+      conjA = ((RhsStorageOrder == ColMajor) && ConjugateRhs) ? 1 : 0                                                  \
+    };                                                                                                                 \
+                                                                                                                       \
+    static void run(Index _rows, Index _cols, Index _depth, const EIGTYPE* _lhs, Index lhsStride, const EIGTYPE* _rhs, \
+                    Index rhsStride, EIGTYPE* res, Index resStride, EIGTYPE alpha,                                     \
+                    level3_blocking<EIGTYPE, EIGTYPE>& blocking) {                                                     \
+      if (_rows == 0 || _cols == 0 || _depth == 0) return;                                                             \
+      Index diagSize = (std::min)(_cols, _depth);                                                                      \
+      Index rows = _rows;                                                                                              \
+      Index depth = IsLower ? _depth : diagSize;                                                                       \
+      Index cols = IsLower ? diagSize : _cols;                                                                         \
+                                                                                                                       \
+      typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs;                                            \
+      typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs;                                            \
+                                                                                                                       \
+      /* Non-square case - doesn't fit to BLAS ?TRMM. Fall to default triangular product or call BLAS ?GEMM*/          \
+      if (cols != depth) {                                                                                             \
+        int nthr = 1 /*mkl_domain_get_max_threads(EIGEN_BLAS_DOMAIN_BLAS)*/;                                           \
+                                                                                                                       \
+        if ((nthr == 1) && (((std::max)(cols, depth) - diagSize) / (double)diagSize < 0.5)) {                          \
+          /* Most likely no benefit to call TRMM or GEMM from BLAS*/                                                   \
+          product_triangular_matrix_matrix<EIGTYPE, Index, Mode, false, LhsStorageOrder, ConjugateLhs,                 \
+                                           RhsStorageOrder, ConjugateRhs, ColMajor, 1, BuiltIn>::run(_rows, _cols,     \
+                                                                                                     _depth, _lhs,     \
+                                                                                                     lhsStride, _rhs,  \
+                                                                                                     rhsStride, res,   \
+                                                                                                     1, resStride,     \
+                                                                                                     alpha, blocking); \
+          /*std::cout << "TRMM_R: A is not square! Go to Eigen TRMM implementation!\n";*/                              \
+        } else {                                                                                                       \
+          /* Make sense to call GEMM */                                                                                \
+          Map<const MatrixRhs, 0, OuterStride<> > rhsMap(_rhs, depth, cols, OuterStride<>(rhsStride));                 \
+          MatrixRhs aa_tmp = rhsMap.template triangularView<Mode>();                                                   \
+          BlasIndex aStride = convert_index<BlasIndex>(aa_tmp.outerStride());                                          \
+          gemm_blocking_space<ColMajor, EIGTYPE, EIGTYPE, Dynamic, Dynamic, Dynamic> gemm_blocking(_rows, _cols,       \
+                                                                                                   _depth, 1, true);   \
+          general_matrix_matrix_product<Index, EIGTYPE, LhsStorageOrder, ConjugateLhs, EIGTYPE, RhsStorageOrder,       \
+                                        ConjugateRhs, ColMajor, 1>::run(rows, cols, depth, _lhs, lhsStride,            \
+                                                                        aa_tmp.data(), aStride, res, 1, resStride,     \
+                                                                        alpha, gemm_blocking, 0);                      \
+                                                                                                                       \
+          /*std::cout << "TRMM_R: A is not square! Go to BLAS GEMM implementation! " << nthr<<" \n";*/                 \
+        }                                                                                                              \
+        return;                                                                                                        \
+      }                                                                                                                \
+      char side = 'R', transa, uplo, diag = 'N';                                                                       \
+      EIGTYPE* b;                                                                                                      \
+      const EIGTYPE* a;                                                                                                \
+      BlasIndex m, n, lda, ldb;                                                                                        \
+                                                                                                                       \
+      /* Set m, n */                                                                                                   \
+      m = convert_index<BlasIndex>(rows);                                                                              \
+      n = convert_index<BlasIndex>(diagSize);                                                                          \
+                                                                                                                       \
+      /* Set trans */                                                                                                  \
+      transa = (RhsStorageOrder == RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N';                                     \
+                                                                                                                       \
+      /* Set b, ldb */                                                                                                 \
+      Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs, rows, depth, OuterStride<>(lhsStride));                        \
+      MatrixX##EIGPREFIX b_tmp;                                                                                        \
+                                                                                                                       \
+      if (ConjugateLhs)                                                                                                \
+        b_tmp = lhs.conjugate();                                                                                       \
+      else                                                                                                             \
+        b_tmp = lhs;                                                                                                   \
+      b = b_tmp.data();                                                                                                \
+      ldb = convert_index<BlasIndex>(b_tmp.outerStride());                                                             \
+                                                                                                                       \
+      /* Set uplo */                                                                                                   \
+      uplo = IsLower ? 'L' : 'U';                                                                                      \
+      if (RhsStorageOrder == RowMajor) uplo = (uplo == 'L') ? 'U' : 'L';                                               \
+      /* Set a, lda */                                                                                                 \
+      Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs, depth, cols, OuterStride<>(rhsStride));                        \
+      MatrixRhs a_tmp;                                                                                                 \
+                                                                                                                       \
+      if ((conjA != 0) || (SetDiag == 0)) {                                                                            \
+        if (conjA)                                                                                                     \
+          a_tmp = rhs.conjugate();                                                                                     \
+        else                                                                                                           \
+          a_tmp = rhs;                                                                                                 \
+        if (IsZeroDiag)                                                                                                \
+          a_tmp.diagonal().setZero();                                                                                  \
+        else if (IsUnitDiag)                                                                                           \
+          a_tmp.diagonal().setOnes();                                                                                  \
+        a = a_tmp.data();                                                                                              \
+        lda = convert_index<BlasIndex>(a_tmp.outerStride());                                                           \
+      } else {                                                                                                         \
+        a = _rhs;                                                                                                      \
+        lda = convert_index<BlasIndex>(rhsStride);                                                                     \
+      }                                                                                                                \
+      /*std::cout << "TRMM_R: A is square! Go to BLAS TRMM implementation! \n";*/                                      \
+      /* call ?trmm*/                                                                                                  \
+      BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a,    \
+               &lda, (BLASTYPE*)b, &ldb);                                                                              \
+                                                                                                                       \
+      /* Add op(a_triangular)*b into res*/                                                                             \
+      Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res, rows, cols, OuterStride<>(resStride));                   \
+      res_tmp = res_tmp + b_tmp;                                                                                       \
+    }                                                                                                                  \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRMM_R(double, double, d, dtrmm)
+EIGEN_BLAS_TRMM_R(dcomplex, MKL_Complex16, cd, ztrmm)
+EIGEN_BLAS_TRMM_R(float, float, f, strmm)
+EIGEN_BLAS_TRMM_R(scomplex, MKL_Complex8, cf, ctrmm)
+#else
+EIGEN_BLAS_TRMM_R(double, double, d, dtrmm_)
+EIGEN_BLAS_TRMM_R(dcomplex, double, cd, ztrmm_)
+EIGEN_BLAS_TRMM_R(float, float, f, strmm_)
+EIGEN_BLAS_TRMM_R(scomplex, float, cf, ctrmm_)
+#endif
+
+#undef EIGEN_BLAS_TRMM_SPECIALIZE
+#undef EIGEN_BLAS_TRMM_L
+#undef EIGEN_BLAS_TRMM_R
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixVector.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixVector.h
new file mode 100644
index 0000000..535f481
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixVector.h
@@ -0,0 +1,347 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIANGULARMATRIXVECTOR_H
+#define EIGEN_TRIANGULARMATRIXVECTOR_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,
+          int StorageOrder, int Version = Specialized>
+struct triangular_matrix_vector_product;
+
+template <typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
+struct triangular_matrix_vector_product<Index, Mode, LhsScalar, ConjLhs, RhsScalar, ConjRhs, ColMajor, Version> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static constexpr bool IsLower = ((Mode & Lower) == Lower);
+  static constexpr bool HasUnitDiag = (Mode & UnitDiag) == UnitDiag;
+  static constexpr bool HasZeroDiag = (Mode & ZeroDiag) == ZeroDiag;
+  static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* lhs_, Index lhsStride,
+                                    const RhsScalar* rhs_, Index rhsIncr, ResScalar* res_, Index resIncr,
+                                    const RhsScalar& alpha);
+};
+
+template <typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
+EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index, Mode, LhsScalar, ConjLhs, RhsScalar, ConjRhs, ColMajor,
+                                                        Version>::run(Index _rows, Index _cols, const LhsScalar* lhs_,
+                                                                      Index lhsStride, const RhsScalar* rhs_,
+                                                                      Index rhsIncr, ResScalar* res_, Index resIncr,
+                                                                      const RhsScalar& alpha) {
+  static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
+  Index size = (std::min)(_rows, _cols);
+  Index rows = IsLower ? _rows : (std::min)(_rows, _cols);
+  Index cols = IsLower ? (std::min)(_rows, _cols) : _cols;
+
+  typedef Map<const Matrix<LhsScalar, Dynamic, Dynamic, ColMajor>, 0, OuterStride<> > LhsMap;
+  const LhsMap lhs(lhs_, rows, cols, OuterStride<>(lhsStride));
+  typename conj_expr_if<ConjLhs, LhsMap>::type cjLhs(lhs);
+
+  typedef Map<const Matrix<RhsScalar, Dynamic, 1>, 0, InnerStride<> > RhsMap;
+  const RhsMap rhs(rhs_, cols, InnerStride<>(rhsIncr));
+  typename conj_expr_if<ConjRhs, RhsMap>::type cjRhs(rhs);
+
+  typedef Map<Matrix<ResScalar, Dynamic, 1> > ResMap;
+  ResMap res(res_, rows);
+
+  typedef const_blas_data_mapper<LhsScalar, Index, ColMajor> LhsMapper;
+  typedef const_blas_data_mapper<RhsScalar, Index, RowMajor> RhsMapper;
+
+  for (Index pi = 0; pi < size; pi += PanelWidth) {
+    Index actualPanelWidth = (std::min)(PanelWidth, size - pi);
+    for (Index k = 0; k < actualPanelWidth; ++k) {
+      Index i = pi + k;
+      Index s = IsLower ? ((HasUnitDiag || HasZeroDiag) ? i + 1 : i) : pi;
+      Index r = IsLower ? actualPanelWidth - k : k + 1;
+      if ((!(HasUnitDiag || HasZeroDiag)) || (--r) > 0)
+        res.segment(s, r) += (alpha * cjRhs.coeff(i)) * cjLhs.col(i).segment(s, r);
+      if (HasUnitDiag) res.coeffRef(i) += alpha * cjRhs.coeff(i);
+    }
+    Index r = IsLower ? rows - pi - actualPanelWidth : pi;
+    if (r > 0) {
+      Index s = IsLower ? pi + actualPanelWidth : 0;
+      general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, ConjLhs, RhsScalar, RhsMapper, ConjRhs,
+                                    BuiltIn>::run(r, actualPanelWidth, LhsMapper(&lhs.coeffRef(s, pi), lhsStride),
+                                                  RhsMapper(&rhs.coeffRef(pi), rhsIncr), &res.coeffRef(s), resIncr,
+                                                  alpha);
+    }
+  }
+  if ((!IsLower) && cols > size) {
+    general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, ConjLhs, RhsScalar, RhsMapper, ConjRhs>::run(
+        rows, cols - size, LhsMapper(&lhs.coeffRef(0, size), lhsStride), RhsMapper(&rhs.coeffRef(size), rhsIncr), res_,
+        resIncr, alpha);
+  }
+}
+
+template <typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
+struct triangular_matrix_vector_product<Index, Mode, LhsScalar, ConjLhs, RhsScalar, ConjRhs, RowMajor, Version> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static constexpr bool IsLower = ((Mode & Lower) == Lower);
+  static constexpr bool HasUnitDiag = (Mode & UnitDiag) == UnitDiag;
+  static constexpr bool HasZeroDiag = (Mode & ZeroDiag) == ZeroDiag;
+  static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* lhs_, Index lhsStride,
+                                    const RhsScalar* rhs_, Index rhsIncr, ResScalar* res_, Index resIncr,
+                                    const ResScalar& alpha);
+};
+
+template <typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
+EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index, Mode, LhsScalar, ConjLhs, RhsScalar, ConjRhs, RowMajor,
+                                                        Version>::run(Index _rows, Index _cols, const LhsScalar* lhs_,
+                                                                      Index lhsStride, const RhsScalar* rhs_,
+                                                                      Index rhsIncr, ResScalar* res_, Index resIncr,
+                                                                      const ResScalar& alpha) {
+  static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
+  Index diagSize = (std::min)(_rows, _cols);
+  Index rows = IsLower ? _rows : diagSize;
+  Index cols = IsLower ? diagSize : _cols;
+
+  typedef Map<const Matrix<LhsScalar, Dynamic, Dynamic, RowMajor>, 0, OuterStride<> > LhsMap;
+  const LhsMap lhs(lhs_, rows, cols, OuterStride<>(lhsStride));
+  typename conj_expr_if<ConjLhs, LhsMap>::type cjLhs(lhs);
+
+  typedef Map<const Matrix<RhsScalar, Dynamic, 1> > RhsMap;
+  const RhsMap rhs(rhs_, cols);
+  typename conj_expr_if<ConjRhs, RhsMap>::type cjRhs(rhs);
+
+  typedef Map<Matrix<ResScalar, Dynamic, 1>, 0, InnerStride<> > ResMap;
+  ResMap res(res_, rows, InnerStride<>(resIncr));
+
+  typedef const_blas_data_mapper<LhsScalar, Index, RowMajor> LhsMapper;
+  typedef const_blas_data_mapper<RhsScalar, Index, RowMajor> RhsMapper;
+
+  for (Index pi = 0; pi < diagSize; pi += PanelWidth) {
+    Index actualPanelWidth = (std::min)(PanelWidth, diagSize - pi);
+    for (Index k = 0; k < actualPanelWidth; ++k) {
+      Index i = pi + k;
+      Index s = IsLower ? pi : ((HasUnitDiag || HasZeroDiag) ? i + 1 : i);
+      Index r = IsLower ? k + 1 : actualPanelWidth - k;
+      if ((!(HasUnitDiag || HasZeroDiag)) || (--r) > 0)
+        res.coeffRef(i) += alpha * (cjLhs.row(i).segment(s, r).cwiseProduct(cjRhs.segment(s, r).transpose())).sum();
+      if (HasUnitDiag) res.coeffRef(i) += alpha * cjRhs.coeff(i);
+    }
+    Index r = IsLower ? pi : cols - pi - actualPanelWidth;
+    if (r > 0) {
+      Index s = IsLower ? 0 : pi + actualPanelWidth;
+      general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, ConjLhs, RhsScalar, RhsMapper, ConjRhs,
+                                    BuiltIn>::run(actualPanelWidth, r, LhsMapper(&lhs.coeffRef(pi, s), lhsStride),
+                                                  RhsMapper(&rhs.coeffRef(s), rhsIncr), &res.coeffRef(pi), resIncr,
+                                                  alpha);
+    }
+  }
+  if (IsLower && rows > diagSize) {
+    general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, ConjLhs, RhsScalar, RhsMapper, ConjRhs>::run(
+        rows - diagSize, cols, LhsMapper(&lhs.coeffRef(diagSize, 0), lhsStride), RhsMapper(&rhs.coeffRef(0), rhsIncr),
+        &res.coeffRef(diagSize), resIncr, alpha);
+  }
+}
+
+/***************************************************************************
+ * Wrapper to product_triangular_vector
+ ***************************************************************************/
+
+template <int Mode, int StorageOrder>
+struct trmv_selector;
+
+}  // end namespace internal
+
+namespace internal {
+
+template <int Mode, typename Lhs, typename Rhs>
+struct triangular_product_impl<Mode, true, Lhs, false, Rhs, true> {
+  template <typename Dest>
+  static void run(Dest& dst, const Lhs& lhs, const Rhs& rhs, const typename Dest::Scalar& alpha) {
+    eigen_assert(dst.rows() == lhs.rows() && dst.cols() == rhs.cols());
+
+    internal::trmv_selector<Mode, (int(internal::traits<Lhs>::Flags) & RowMajorBit) ? RowMajor : ColMajor>::run(
+        lhs, rhs, dst, alpha);
+  }
+};
+
+template <int Mode, typename Lhs, typename Rhs>
+struct triangular_product_impl<Mode, false, Lhs, true, Rhs, false> {
+  template <typename Dest>
+  static void run(Dest& dst, const Lhs& lhs, const Rhs& rhs, const typename Dest::Scalar& alpha) {
+    eigen_assert(dst.rows() == lhs.rows() && dst.cols() == rhs.cols());
+
+    Transpose<Dest> dstT(dst);
+    internal::trmv_selector<(Mode & (UnitDiag | ZeroDiag)) | ((Mode & Lower) ? Upper : Lower),
+                            (int(internal::traits<Rhs>::Flags) & RowMajorBit) ? ColMajor
+                                                                              : RowMajor>::run(rhs.transpose(),
+                                                                                               lhs.transpose(), dstT,
+                                                                                               alpha);
+  }
+};
+
+}  // end namespace internal
+
+namespace internal {
+
+// TODO: find a way to factorize this piece of code with gemv_selector since the logic is exactly the same.
+
+template <int Mode>
+struct trmv_selector<Mode, ColMajor> {
+  template <typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
+    typedef typename Lhs::Scalar LhsScalar;
+    typedef typename Rhs::Scalar RhsScalar;
+    typedef typename Dest::Scalar ResScalar;
+
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+    constexpr int Alignment = (std::min)(int(AlignedMax), int(internal::packet_traits<ResScalar>::size));
+
+    typedef Map<Matrix<ResScalar, Dynamic, 1>, Alignment> MappedDest;
+
+    add_const_on_value_type_t<ActualLhsType> actualLhs = LhsBlasTraits::extract(lhs);
+    add_const_on_value_type_t<ActualRhsType> actualRhs = RhsBlasTraits::extract(rhs);
+
+    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(lhs);
+    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(rhs);
+    ResScalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
+
+    // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
+    // On the other hand, it is good for the cache to pack the vector anyways...
+    constexpr bool EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime == 1;
+    constexpr bool ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex);
+    constexpr bool MightCannotUseDest = (Dest::InnerStrideAtCompileTime != 1) || ComplexByReal;
+
+    gemv_static_vector_if<ResScalar, Dest::SizeAtCompileTime, Dest::MaxSizeAtCompileTime, MightCannotUseDest>
+        static_dest;
+
+    bool alphaIsCompatible = (!ComplexByReal) || numext::is_exactly_zero(numext::imag(actualAlpha));
+    bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
+
+    RhsScalar compatibleAlpha = get_factor<ResScalar, RhsScalar>::run(actualAlpha);
+
+    ei_declare_aligned_stack_constructed_variable(ResScalar, actualDestPtr, dest.size(),
+                                                  evalToDest ? dest.data() : static_dest.data());
+
+    if (!evalToDest) {
+#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      constexpr int Size = Dest::SizeAtCompileTime;
+      Index size = dest.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+#endif
+      if (!alphaIsCompatible) {
+        MappedDest(actualDestPtr, dest.size()).setZero();
+        compatibleAlpha = RhsScalar(1);
+      } else
+        MappedDest(actualDestPtr, dest.size()) = dest;
+    }
+
+    internal::triangular_matrix_vector_product<Index, Mode, LhsScalar, LhsBlasTraits::NeedToConjugate, RhsScalar,
+                                               RhsBlasTraits::NeedToConjugate, ColMajor>::run(actualLhs.rows(),
+                                                                                              actualLhs.cols(),
+                                                                                              actualLhs.data(),
+                                                                                              actualLhs.outerStride(),
+                                                                                              actualRhs.data(),
+                                                                                              actualRhs.innerStride(),
+                                                                                              actualDestPtr, 1,
+                                                                                              compatibleAlpha);
+
+    if (!evalToDest) {
+      if (!alphaIsCompatible)
+        dest += actualAlpha * MappedDest(actualDestPtr, dest.size());
+      else
+        dest = MappedDest(actualDestPtr, dest.size());
+    }
+
+    if (((Mode & UnitDiag) == UnitDiag) && !numext::is_exactly_one(lhs_alpha)) {
+      Index diagSize = (std::min)(lhs.rows(), lhs.cols());
+      dest.head(diagSize) -= (lhs_alpha - LhsScalar(1)) * rhs.head(diagSize);
+    }
+  }
+};
+
+template <int Mode>
+struct trmv_selector<Mode, RowMajor> {
+  template <typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
+    typedef typename Lhs::Scalar LhsScalar;
+    typedef typename Rhs::Scalar RhsScalar;
+    typedef typename Dest::Scalar ResScalar;
+
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+    typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
+
+    std::add_const_t<ActualLhsType> actualLhs = LhsBlasTraits::extract(lhs);
+    std::add_const_t<ActualRhsType> actualRhs = RhsBlasTraits::extract(rhs);
+
+    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(lhs);
+    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(rhs);
+    ResScalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
+
+    constexpr bool DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime == 1;
+
+    const RhsScalar* actualRhsPtr = actualRhs.data();
+
+    // Potentially create a temporary buffer to copy RHS to contiguous memory.
+    gemv_static_vector_if<RhsScalar, ActualRhsTypeCleaned::SizeAtCompileTime,
+                          ActualRhsTypeCleaned::MaxSizeAtCompileTime, !DirectlyUseRhs>
+        static_rhs;  // Fixed-sized array.
+    RhsScalar* buffer = nullptr;
+    if (!DirectlyUseRhs) {
+      // Maybe used fixed-sized buffer, otherwise allocate.
+      if (static_rhs.data() != nullptr) {
+        buffer = static_rhs.data();
+      } else {
+        // Allocate either with alloca or malloc.
+        Eigen::internal::check_size_for_overflow<RhsScalar>(actualRhs.size());
+#ifdef EIGEN_ALLOCA
+        buffer = static_cast<RhsScalar*>((sizeof(RhsScalar) * actualRhs.size() <= EIGEN_STACK_ALLOCATION_LIMIT)
+                                             ? EIGEN_ALIGNED_ALLOCA(sizeof(RhsScalar) * actualRhs.size())
+                                             : Eigen::internal::aligned_malloc(sizeof(RhsScalar) * actualRhs.size()));
+#else
+        buffer = static_cast<RhsScalar*>(Eigen::internal::aligned_malloc(sizeof(RhsScalar) * actualRhs.size()));
+#endif
+      }
+#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      constexpr int Size = ActualRhsTypeCleaned::SizeAtCompileTime;
+      Index size = actualRhs.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+#endif
+      Map<typename ActualRhsTypeCleaned::PlainObject, Eigen::AlignedMax>(buffer, actualRhs.size()) = actualRhs;
+      actualRhsPtr = buffer;
+    }
+    // Deallocate only if malloced.
+    Eigen::internal::aligned_stack_memory_handler<RhsScalar> buffer_stack_memory_destructor(
+        buffer, actualRhs.size(),
+        !DirectlyUseRhs && static_rhs.data() == nullptr && actualRhs.size() > EIGEN_STACK_ALLOCATION_LIMIT);
+
+    internal::triangular_matrix_vector_product<Index, Mode, LhsScalar, LhsBlasTraits::NeedToConjugate, RhsScalar,
+                                               RhsBlasTraits::NeedToConjugate, RowMajor>::run(actualLhs.rows(),
+                                                                                              actualLhs.cols(),
+                                                                                              actualLhs.data(),
+                                                                                              actualLhs.outerStride(),
+                                                                                              actualRhsPtr, 1,
+                                                                                              dest.data(),
+                                                                                              dest.innerStride(),
+                                                                                              actualAlpha);
+
+    if (((Mode & UnitDiag) == UnitDiag) && !numext::is_exactly_one(lhs_alpha)) {
+      Index diagSize = (std::min)(lhs.rows(), lhs.cols());
+      dest.head(diagSize) -= (lhs_alpha - LhsScalar(1)) * rhs.head(diagSize);
+    }
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRIANGULARMATRIXVECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
new file mode 100644
index 0000000..19ed813
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
@@ -0,0 +1,278 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Triangular matrix-vector product functionality based on ?TRMV.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
+#define EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**********************************************************************
+ * This file implements triangular matrix-vector multiplication using BLAS
+ **********************************************************************/
+
+// trmv/hemv specialization
+
+template <typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,
+          int StorageOrder>
+struct triangular_matrix_vector_product_trmv
+    : triangular_matrix_vector_product<Index, Mode, LhsScalar, ConjLhs, RhsScalar, ConjRhs, StorageOrder, BuiltIn> {};
+
+#define EIGEN_BLAS_TRMV_SPECIALIZE(Scalar)                                                                            \
+  template <typename Index, int Mode, bool ConjLhs, bool ConjRhs>                                                     \
+  struct triangular_matrix_vector_product<Index, Mode, Scalar, ConjLhs, Scalar, ConjRhs, ColMajor, Specialized> {     \
+    static void run(Index rows_, Index cols_, const Scalar* lhs_, Index lhsStride, const Scalar* rhs_, Index rhsIncr, \
+                    Scalar* res_, Index resIncr, Scalar alpha) {                                                      \
+      triangular_matrix_vector_product_trmv<Index, Mode, Scalar, ConjLhs, Scalar, ConjRhs, ColMajor>::run(            \
+          rows_, cols_, lhs_, lhsStride, rhs_, rhsIncr, res_, resIncr, alpha);                                        \
+    }                                                                                                                 \
+  };                                                                                                                  \
+  template <typename Index, int Mode, bool ConjLhs, bool ConjRhs>                                                     \
+  struct triangular_matrix_vector_product<Index, Mode, Scalar, ConjLhs, Scalar, ConjRhs, RowMajor, Specialized> {     \
+    static void run(Index rows_, Index cols_, const Scalar* lhs_, Index lhsStride, const Scalar* rhs_, Index rhsIncr, \
+                    Scalar* res_, Index resIncr, Scalar alpha) {                                                      \
+      triangular_matrix_vector_product_trmv<Index, Mode, Scalar, ConjLhs, Scalar, ConjRhs, RowMajor>::run(            \
+          rows_, cols_, lhs_, lhsStride, rhs_, rhsIncr, res_, resIncr, alpha);                                        \
+    }                                                                                                                 \
+  };
+
+EIGEN_BLAS_TRMV_SPECIALIZE(double)
+EIGEN_BLAS_TRMV_SPECIALIZE(float)
+EIGEN_BLAS_TRMV_SPECIALIZE(dcomplex)
+EIGEN_BLAS_TRMV_SPECIALIZE(scomplex)
+
+// implements col-major: res += alpha * op(triangular) * vector
+#define EIGEN_BLAS_TRMV_CM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX, BLASPOSTFIX)                                    \
+  template <typename Index, int Mode, bool ConjLhs, bool ConjRhs>                                                    \
+  struct triangular_matrix_vector_product_trmv<Index, Mode, EIGTYPE, ConjLhs, EIGTYPE, ConjRhs, ColMajor> {          \
+    enum {                                                                                                           \
+      IsLower = (Mode & Lower) == Lower,                                                                             \
+      SetDiag = (Mode & (ZeroDiag | UnitDiag)) ? 0 : 1,                                                              \
+      IsUnitDiag = (Mode & UnitDiag) ? 1 : 0,                                                                        \
+      IsZeroDiag = (Mode & ZeroDiag) ? 1 : 0,                                                                        \
+      LowUp = IsLower ? Lower : Upper                                                                                \
+    };                                                                                                               \
+    static void run(Index rows_, Index cols_, const EIGTYPE* lhs_, Index lhsStride, const EIGTYPE* rhs_,             \
+                    Index rhsIncr, EIGTYPE* res_, Index resIncr, EIGTYPE alpha) {                                    \
+      if (rows_ == 0 || cols_ == 0) return;                                                                          \
+      if (ConjLhs || IsZeroDiag) {                                                                                   \
+        triangular_matrix_vector_product<Index, Mode, EIGTYPE, ConjLhs, EIGTYPE, ConjRhs, ColMajor, BuiltIn>::run(   \
+            rows_, cols_, lhs_, lhsStride, rhs_, rhsIncr, res_, resIncr, alpha);                                     \
+        return;                                                                                                      \
+      }                                                                                                              \
+      Index size = (std::min)(rows_, cols_);                                                                         \
+      Index rows = IsLower ? rows_ : size;                                                                           \
+      Index cols = IsLower ? size : cols_;                                                                           \
+                                                                                                                     \
+      typedef VectorX##EIGPREFIX VectorRhs;                                                                          \
+      EIGTYPE *x, *y;                                                                                                \
+                                                                                                                     \
+      /* Set x*/                                                                                                     \
+      Map<const VectorRhs, 0, InnerStride<> > rhs(rhs_, cols, InnerStride<>(rhsIncr));                               \
+      VectorRhs x_tmp;                                                                                               \
+      if (ConjRhs)                                                                                                   \
+        x_tmp = rhs.conjugate();                                                                                     \
+      else                                                                                                           \
+        x_tmp = rhs;                                                                                                 \
+      x = x_tmp.data();                                                                                              \
+                                                                                                                     \
+      /* Square part handling */                                                                                     \
+                                                                                                                     \
+      char trans, uplo, diag;                                                                                        \
+      BlasIndex m, n, lda, incx, incy;                                                                               \
+      EIGTYPE const* a;                                                                                              \
+      EIGTYPE beta(1);                                                                                               \
+                                                                                                                     \
+      /* Set m, n */                                                                                                 \
+      n = convert_index<BlasIndex>(size);                                                                            \
+      lda = convert_index<BlasIndex>(lhsStride);                                                                     \
+      incx = 1;                                                                                                      \
+      incy = convert_index<BlasIndex>(resIncr);                                                                      \
+                                                                                                                     \
+      /* Set uplo, trans and diag*/                                                                                  \
+      trans = 'N';                                                                                                   \
+      uplo = IsLower ? 'L' : 'U';                                                                                    \
+      diag = IsUnitDiag ? 'U' : 'N';                                                                                 \
+                                                                                                                     \
+      /* call ?TRMV*/                                                                                                \
+      BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)lhs_, &lda, (BLASTYPE*)x, &incx);     \
+                                                                                                                     \
+      /* Add op(a_tr)rhs into res*/                                                                                  \
+      BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)x, &incx,        \
+                                    (BLASTYPE*)res_, &incy);                                                         \
+      /* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/                           \
+      if (size < (std::max)(rows, cols)) {                                                                           \
+        if (ConjRhs)                                                                                                 \
+          x_tmp = rhs.conjugate();                                                                                   \
+        else                                                                                                         \
+          x_tmp = rhs;                                                                                               \
+        x = x_tmp.data();                                                                                            \
+        if (size < rows) {                                                                                           \
+          y = res_ + size * resIncr;                                                                                 \
+          a = lhs_ + size;                                                                                           \
+          m = convert_index<BlasIndex>(rows - size);                                                                 \
+          n = convert_index<BlasIndex>(size);                                                                        \
+        } else {                                                                                                     \
+          x += size;                                                                                                 \
+          y = res_;                                                                                                  \
+          a = lhs_ + size * lda;                                                                                     \
+          m = convert_index<BlasIndex>(size);                                                                        \
+          n = convert_index<BlasIndex>(cols - size);                                                                 \
+        }                                                                                                            \
+        BLASPREFIX##gemv##BLASPOSTFIX(&trans, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, \
+                                      &lda, (const BLASTYPE*)x, &incx, (const BLASTYPE*)&numext::real_ref(beta),     \
+                                      (BLASTYPE*)y, &incy);                                                          \
+      }                                                                                                              \
+    }                                                                                                                \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRMV_CM(double, double, d, d, )
+EIGEN_BLAS_TRMV_CM(dcomplex, MKL_Complex16, cd, z, )
+EIGEN_BLAS_TRMV_CM(float, float, f, s, )
+EIGEN_BLAS_TRMV_CM(scomplex, MKL_Complex8, cf, c, )
+#else
+EIGEN_BLAS_TRMV_CM(double, double, d, d, _)
+EIGEN_BLAS_TRMV_CM(dcomplex, double, cd, z, _)
+EIGEN_BLAS_TRMV_CM(float, float, f, s, _)
+EIGEN_BLAS_TRMV_CM(scomplex, float, cf, c, _)
+#endif
+
+// implements row-major: res += alpha * op(triangular) * vector
+#define EIGEN_BLAS_TRMV_RM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX, BLASPOSTFIX)                                    \
+  template <typename Index, int Mode, bool ConjLhs, bool ConjRhs>                                                    \
+  struct triangular_matrix_vector_product_trmv<Index, Mode, EIGTYPE, ConjLhs, EIGTYPE, ConjRhs, RowMajor> {          \
+    enum {                                                                                                           \
+      IsLower = (Mode & Lower) == Lower,                                                                             \
+      SetDiag = (Mode & (ZeroDiag | UnitDiag)) ? 0 : 1,                                                              \
+      IsUnitDiag = (Mode & UnitDiag) ? 1 : 0,                                                                        \
+      IsZeroDiag = (Mode & ZeroDiag) ? 1 : 0,                                                                        \
+      LowUp = IsLower ? Lower : Upper                                                                                \
+    };                                                                                                               \
+    static void run(Index rows_, Index cols_, const EIGTYPE* lhs_, Index lhsStride, const EIGTYPE* rhs_,             \
+                    Index rhsIncr, EIGTYPE* res_, Index resIncr, EIGTYPE alpha) {                                    \
+      if (rows_ == 0 || cols_ == 0) return;                                                                          \
+      if (IsZeroDiag) {                                                                                              \
+        triangular_matrix_vector_product<Index, Mode, EIGTYPE, ConjLhs, EIGTYPE, ConjRhs, RowMajor, BuiltIn>::run(   \
+            rows_, cols_, lhs_, lhsStride, rhs_, rhsIncr, res_, resIncr, alpha);                                     \
+        return;                                                                                                      \
+      }                                                                                                              \
+      Index size = (std::min)(rows_, cols_);                                                                         \
+      Index rows = IsLower ? rows_ : size;                                                                           \
+      Index cols = IsLower ? size : cols_;                                                                           \
+                                                                                                                     \
+      typedef VectorX##EIGPREFIX VectorRhs;                                                                          \
+      EIGTYPE *x, *y;                                                                                                \
+                                                                                                                     \
+      /* Set x*/                                                                                                     \
+      Map<const VectorRhs, 0, InnerStride<> > rhs(rhs_, cols, InnerStride<>(rhsIncr));                               \
+      VectorRhs x_tmp;                                                                                               \
+      if (ConjRhs)                                                                                                   \
+        x_tmp = rhs.conjugate();                                                                                     \
+      else                                                                                                           \
+        x_tmp = rhs;                                                                                                 \
+      x = x_tmp.data();                                                                                              \
+                                                                                                                     \
+      /* Square part handling */                                                                                     \
+                                                                                                                     \
+      char trans, uplo, diag;                                                                                        \
+      BlasIndex m, n, lda, incx, incy;                                                                               \
+      EIGTYPE const* a;                                                                                              \
+      EIGTYPE beta(1);                                                                                               \
+                                                                                                                     \
+      /* Set m, n */                                                                                                 \
+      n = convert_index<BlasIndex>(size);                                                                            \
+      lda = convert_index<BlasIndex>(lhsStride);                                                                     \
+      incx = 1;                                                                                                      \
+      incy = convert_index<BlasIndex>(resIncr);                                                                      \
+                                                                                                                     \
+      /* Set uplo, trans and diag*/                                                                                  \
+      trans = ConjLhs ? 'C' : 'T';                                                                                   \
+      uplo = IsLower ? 'U' : 'L';                                                                                    \
+      diag = IsUnitDiag ? 'U' : 'N';                                                                                 \
+                                                                                                                     \
+      /* call ?TRMV*/                                                                                                \
+      BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)lhs_, &lda, (BLASTYPE*)x, &incx);     \
+                                                                                                                     \
+      /* Add op(a_tr)rhs into res*/                                                                                  \
+      BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)x, &incx,        \
+                                    (BLASTYPE*)res_, &incy);                                                         \
+      /* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/                           \
+      if (size < (std::max)(rows, cols)) {                                                                           \
+        if (ConjRhs)                                                                                                 \
+          x_tmp = rhs.conjugate();                                                                                   \
+        else                                                                                                         \
+          x_tmp = rhs;                                                                                               \
+        x = x_tmp.data();                                                                                            \
+        if (size < rows) {                                                                                           \
+          y = res_ + size * resIncr;                                                                                 \
+          a = lhs_ + size * lda;                                                                                     \
+          m = convert_index<BlasIndex>(rows - size);                                                                 \
+          n = convert_index<BlasIndex>(size);                                                                        \
+        } else {                                                                                                     \
+          x += size;                                                                                                 \
+          y = res_;                                                                                                  \
+          a = lhs_ + size;                                                                                           \
+          m = convert_index<BlasIndex>(size);                                                                        \
+          n = convert_index<BlasIndex>(cols - size);                                                                 \
+        }                                                                                                            \
+        BLASPREFIX##gemv##BLASPOSTFIX(&trans, &n, &m, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, \
+                                      &lda, (const BLASTYPE*)x, &incx, (const BLASTYPE*)&numext::real_ref(beta),     \
+                                      (BLASTYPE*)y, &incy);                                                          \
+      }                                                                                                              \
+    }                                                                                                                \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRMV_RM(double, double, d, d, )
+EIGEN_BLAS_TRMV_RM(dcomplex, MKL_Complex16, cd, z, )
+EIGEN_BLAS_TRMV_RM(float, float, f, s, )
+EIGEN_BLAS_TRMV_RM(scomplex, MKL_Complex8, cf, c, )
+#else
+EIGEN_BLAS_TRMV_RM(double, double, d, d, _)
+EIGEN_BLAS_TRMV_RM(dcomplex, double, cd, z, _)
+EIGEN_BLAS_TRMV_RM(float, float, f, s, _)
+EIGEN_BLAS_TRMV_RM(scomplex, float, cf, c, _)
+#endif
+
+#undef EIGEN_BLAS_TRMV_RM
+#undef EIGEN_BLAS_TRMV_SPECIALIZE
+#undef EIGEN_BLAS_TRMV_CM
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularSolverMatrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularSolverMatrix.h
new file mode 100644
index 0000000..bee971d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularSolverMatrix.h
@@ -0,0 +1,448 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Modifications Copyright (C) 2022 Intel Corporation
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_H
+#define EIGEN_TRIANGULAR_SOLVER_MATRIX_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride,
+          bool Specialized>
+struct trsmKernelL {
+  // Generic Implementation of triangular solve for triangular matrix on left and multiple rhs.
+  // Handles non-packed matrices.
+  static void kernel(Index size, Index otherSize, const Scalar* _tri, Index triStride, Scalar* _other, Index otherIncr,
+                     Index otherStride);
+};
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride,
+          bool Specialized>
+struct trsmKernelR {
+  // Generic Implementation of triangular solve for triangular matrix on right and multiple lhs.
+  // Handles non-packed matrices.
+  static void kernel(Index size, Index otherSize, const Scalar* _tri, Index triStride, Scalar* _other, Index otherIncr,
+                     Index otherStride);
+};
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride,
+          bool Specialized>
+EIGEN_STRONG_INLINE void trsmKernelL<Scalar, Index, Mode, Conjugate, TriStorageOrder, OtherInnerStride,
+                                     Specialized>::kernel(Index size, Index otherSize, const Scalar* _tri,
+                                                          Index triStride, Scalar* _other, Index otherIncr,
+                                                          Index otherStride) {
+  typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> TriMapper;
+  typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> OtherMapper;
+  TriMapper tri(_tri, triStride);
+  OtherMapper other(_other, otherStride, otherIncr);
+
+  enum { IsLower = (Mode & Lower) == Lower };
+  conj_if<Conjugate> conj;
+
+  // tr solve
+  for (Index k = 0; k < size; ++k) {
+    // TODO: write a small kernel handling this (can be shared with trsv)
+    Index i = IsLower ? k : -k - 1;
+    Index rs = size - k - 1;  // remaining size
+    Index s = TriStorageOrder == RowMajor ? (IsLower ? 0 : i + 1) : IsLower ? i + 1 : i - rs;
+
+    Scalar a = (Mode & UnitDiag) ? Scalar(1) : Scalar(Scalar(1) / conj(tri(i, i)));
+    for (Index j = 0; j < otherSize; ++j) {
+      if (TriStorageOrder == RowMajor) {
+        Scalar b(0);
+        const Scalar* l = &tri(i, s);
+        typename OtherMapper::LinearMapper r = other.getLinearMapper(s, j);
+        for (Index i3 = 0; i3 < k; ++i3) b += conj(l[i3]) * r(i3);
+
+        other(i, j) = (other(i, j) - b) * a;
+      } else {
+        Scalar& otherij = other(i, j);
+        otherij *= a;
+        Scalar b = otherij;
+        typename OtherMapper::LinearMapper r = other.getLinearMapper(s, j);
+        typename TriMapper::LinearMapper l = tri.getLinearMapper(s, i);
+        for (Index i3 = 0; i3 < rs; ++i3) r(i3) -= b * conj(l(i3));
+      }
+    }
+  }
+}
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride,
+          bool Specialized>
+EIGEN_STRONG_INLINE void trsmKernelR<Scalar, Index, Mode, Conjugate, TriStorageOrder, OtherInnerStride,
+                                     Specialized>::kernel(Index size, Index otherSize, const Scalar* _tri,
+                                                          Index triStride, Scalar* _other, Index otherIncr,
+                                                          Index otherStride) {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> LhsMapper;
+  typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> RhsMapper;
+  LhsMapper lhs(_other, otherStride, otherIncr);
+  RhsMapper rhs(_tri, triStride);
+
+  enum { RhsStorageOrder = TriStorageOrder, IsLower = (Mode & Lower) == Lower };
+  conj_if<Conjugate> conj;
+
+  for (Index k = 0; k < size; ++k) {
+    Index j = IsLower ? size - k - 1 : k;
+
+    typename LhsMapper::LinearMapper r = lhs.getLinearMapper(0, j);
+    EIGEN_IF_CONSTEXPR(OtherInnerStride == 1 && packet_traits<Scalar>::Vectorizable) {
+      using Packet = typename packet_traits<Scalar>::type;
+      constexpr Index PS = unpacket_traits<Packet>::size;
+      // Unrolled k3 loop by 4 to reduce r load/store traffic.
+      Index k3 = 0;
+      for (; k3 + 3 < k; k3 += 4) {
+        Index col0 = IsLower ? j + 1 + k3 : k3;
+        Scalar b0 = conj(rhs(col0, j));
+        Scalar b1 = conj(rhs(col0 + 1, j));
+        Scalar b2 = conj(rhs(col0 + 2, j));
+        Scalar b3 = conj(rhs(col0 + 3, j));
+        Packet neg_pb0 = pset1<Packet>(-b0);
+        Packet neg_pb1 = pset1<Packet>(-b1);
+        Packet neg_pb2 = pset1<Packet>(-b2);
+        Packet neg_pb3 = pset1<Packet>(-b3);
+        typename LhsMapper::LinearMapper a0 = lhs.getLinearMapper(0, col0);
+        typename LhsMapper::LinearMapper a1 = lhs.getLinearMapper(0, col0 + 1);
+        typename LhsMapper::LinearMapper a2 = lhs.getLinearMapper(0, col0 + 2);
+        typename LhsMapper::LinearMapper a3 = lhs.getLinearMapper(0, col0 + 3);
+        Index i = 0;
+        for (; i + PS <= otherSize; i += PS) {
+          Packet pr = r.template loadPacket<Packet>(i);
+          pr = pmadd(a0.template loadPacket<Packet>(i), neg_pb0, pr);
+          pr = pmadd(a1.template loadPacket<Packet>(i), neg_pb1, pr);
+          pr = pmadd(a2.template loadPacket<Packet>(i), neg_pb2, pr);
+          pr = pmadd(a3.template loadPacket<Packet>(i), neg_pb3, pr);
+          r.template storePacket<Packet>(i, pr);
+        }
+        for (; i < otherSize; ++i) {
+          r(i) -= a0(i) * b0 + a1(i) * b1 + a2(i) * b2 + a3(i) * b3;
+        }
+      }
+      // Handle remaining k3 iterations with vectorized inner loop.
+      for (; k3 < k; ++k3) {
+        Scalar b = conj(rhs(IsLower ? j + 1 + k3 : k3, j));
+        typename LhsMapper::LinearMapper a = lhs.getLinearMapper(0, IsLower ? j + 1 + k3 : k3);
+        Packet neg_pb = pset1<Packet>(-b);
+        Index i = 0;
+        for (; i + PS <= otherSize; i += PS) {
+          Packet pr = r.template loadPacket<Packet>(i);
+          pr = pmadd(a.template loadPacket<Packet>(i), neg_pb, pr);
+          r.template storePacket<Packet>(i, pr);
+        }
+        for (; i < otherSize; ++i) r(i) -= a(i) * b;
+      }
+      // Vectorized diagonal scaling.
+      EIGEN_IF_CONSTEXPR((Mode & UnitDiag) == 0) {
+        Scalar inv_rjj = RealScalar(1) / conj(rhs(j, j));
+        Packet pinv = pset1<Packet>(inv_rjj);
+        Index i = 0;
+        for (; i + PS <= otherSize; i += PS) {
+          r.template storePacket<Packet>(i, pmul(r.template loadPacket<Packet>(i), pinv));
+        }
+        for (; i < otherSize; ++i) r(i) *= inv_rjj;
+      }
+    }
+    else {
+      for (Index k3 = 0; k3 < k; ++k3) {
+        Scalar b = conj(rhs(IsLower ? j + 1 + k3 : k3, j));
+        typename LhsMapper::LinearMapper a = lhs.getLinearMapper(0, IsLower ? j + 1 + k3 : k3);
+        for (Index i = 0; i < otherSize; ++i) r(i) -= a(i) * b;
+      }
+      EIGEN_IF_CONSTEXPR((Mode & UnitDiag) == 0) {
+        Scalar inv_rjj = RealScalar(1) / conj(rhs(j, j));
+        for (Index i = 0; i < otherSize; ++i) r(i) *= inv_rjj;
+      }
+    }
+  }
+}
+
+// if the rhs is row major, let's transpose the product
+template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder,
+          int OtherInnerStride>
+struct triangular_solve_matrix<Scalar, Index, Side, Mode, Conjugate, TriStorageOrder, RowMajor, OtherInnerStride> {
+  static void run(Index size, Index cols, const Scalar* tri, Index triStride, Scalar* _other, Index otherIncr,
+                  Index otherStride, level3_blocking<Scalar, Scalar>& blocking) {
+    triangular_solve_matrix<
+        Scalar, Index, Side == OnTheLeft ? OnTheRight : OnTheLeft, (Mode & UnitDiag) | ((Mode & Upper) ? Lower : Upper),
+        NumTraits<Scalar>::IsComplex && Conjugate, TriStorageOrder == RowMajor ? ColMajor : RowMajor, ColMajor,
+        OtherInnerStride>::run(size, cols, tri, triStride, _other, otherIncr, otherStride, blocking);
+  }
+};
+
+/* Optimized triangular solver with multiple right hand side and the triangular matrix on the left
+ */
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
+struct triangular_solve_matrix<Scalar, Index, OnTheLeft, Mode, Conjugate, TriStorageOrder, ColMajor, OtherInnerStride> {
+  static EIGEN_DONT_INLINE void run(Index size, Index otherSize, const Scalar* _tri, Index triStride, Scalar* _other,
+                                    Index otherIncr, Index otherStride, level3_blocking<Scalar, Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
+EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar, Index, OnTheLeft, Mode, Conjugate, TriStorageOrder, ColMajor,
+                                               OtherInnerStride>::run(Index size, Index otherSize, const Scalar* _tri,
+                                                                      Index triStride, Scalar* _other, Index otherIncr,
+                                                                      Index otherStride,
+                                                                      level3_blocking<Scalar, Scalar>& blocking) {
+  Index cols = otherSize;
+
+  std::ptrdiff_t l1, l2, l3;
+  manage_caching_sizes(GetAction, &l1, &l2, &l3);
+
+#if defined(EIGEN_VECTORIZE_AVX512) && defined(EIGEN_USE_AVX512_TRSM_L_KERNELS) && EIGEN_USE_AVX512_TRSM_L_KERNELS && \
+    EIGEN_ENABLE_AVX512_NOCOPY_TRSM_L_CUTOFFS
+  EIGEN_IF_CONSTEXPR(
+      (OtherInnerStride == 1 && (std::is_same<Scalar, float>::value || std::is_same<Scalar, double>::value))) {
+    // Very rough cutoffs to determine when to call trsm w/o packing
+    // For small problem sizes trsmKernel compiled with clang is generally faster.
+    // TODO: Investigate better heuristics for cutoffs.
+    double L2Cap = 0.5;  // 50% of L2 size
+    if (size < avx512_trsm_cutoff<Scalar>(l2, cols, L2Cap)) {
+      trsmKernelL<Scalar, Index, Mode, Conjugate, TriStorageOrder, 1, /*Specialized=*/true>::kernel(
+          size, cols, _tri, triStride, _other, 1, otherStride);
+      return;
+    }
+  }
+#endif
+
+  typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> TriMapper;
+  typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> OtherMapper;
+  TriMapper tri(_tri, triStride);
+  OtherMapper other(_other, otherStride, otherIncr);
+
+  typedef gebp_traits<Scalar, Scalar> Traits;
+
+  enum { SmallPanelWidth = plain_enum_max(Traits::mr, Traits::nr), IsLower = (Mode & Lower) == Lower };
+
+  Index kc = blocking.kc();                    // cache block size along the K direction
+  Index mc = (std::min)(size, blocking.mc());  // cache block size along the M direction
+
+  std::size_t sizeA = kc * mc;
+  std::size_t sizeB = kc * cols;
+
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+  gebp_kernel<Scalar, Scalar, Index, OtherMapper, Traits::mr, Traits::nr, Conjugate, false> gebp_kernel;
+  gemm_pack_lhs<Scalar, Index, TriMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
+                TriStorageOrder>
+      pack_lhs;
+  gemm_pack_rhs<Scalar, Index, OtherMapper, Traits::nr, ColMajor, false, true> pack_rhs;
+
+  // the goal here is to subdivise the Rhs panels such that we keep some cache
+  // coherence when accessing the rhs elements
+  Index subcols = cols > 0 ? l2 / (4 * sizeof(Scalar) * std::max<Index>(otherStride, size)) : 0;
+  subcols = std::max<Index>((subcols / Traits::nr) * Traits::nr, Traits::nr);
+
+  for (Index k2 = IsLower ? 0 : size; IsLower ? k2 < size : k2 > 0; IsLower ? k2 += kc : k2 -= kc) {
+    const Index actual_kc = (std::min)(IsLower ? size - k2 : k2, kc);
+
+    // We have selected and packed a big horizontal panel R1 of rhs. Let B be the packed copy of this panel,
+    // and R2 the remaining part of rhs. The corresponding vertical panel of lhs is split into
+    // A11 (the triangular part) and A21 the remaining rectangular part.
+    // Then the high level algorithm is:
+    //  - B = R1                    => general block copy (done during the next step)
+    //  - R1 = A11^-1 B             => tricky part
+    //  - update B from the new R1  => actually this has to be performed continuously during the above step
+    //  - R2 -= A21 * B             => GEPP
+
+    // The tricky part: compute R1 = A11^-1 B while updating B from R1
+    // The idea is to split A11 into multiple small vertical panels.
+    // Each panel can be split into a small triangular part T1k which is processed without optimization,
+    // and the remaining small part T2k which is processed using gebp with appropriate block strides
+    for (Index j2 = 0; j2 < cols; j2 += subcols) {
+      Index actual_cols = (std::min)(cols - j2, subcols);
+      // for each small vertical panels [T1k^T, T2k^T]^T of lhs
+      for (Index k1 = 0; k1 < actual_kc; k1 += SmallPanelWidth) {
+        Index actualPanelWidth = std::min<Index>(actual_kc - k1, SmallPanelWidth);
+        // tr solve
+        {
+          Index i = IsLower ? k2 + k1 : k2 - k1;
+#if defined(EIGEN_VECTORIZE_AVX512) && defined(EIGEN_USE_AVX512_TRSM_L_KERNELS) && EIGEN_USE_AVX512_TRSM_L_KERNELS
+          EIGEN_IF_CONSTEXPR(
+              (OtherInnerStride == 1 && (std::is_same<Scalar, float>::value || std::is_same<Scalar, double>::value))) {
+            i = IsLower ? k2 + k1 : k2 - k1 - actualPanelWidth;
+          }
+#endif
+          trsmKernelL<Scalar, Index, Mode, Conjugate, TriStorageOrder, OtherInnerStride, /*Specialized=*/true>::kernel(
+              actualPanelWidth, actual_cols, _tri + i + (i)*triStride, triStride,
+              _other + i * OtherInnerStride + j2 * otherStride, otherIncr, otherStride);
+        }
+
+        Index lengthTarget = actual_kc - k1 - actualPanelWidth;
+        Index startBlock = IsLower ? k2 + k1 : k2 - k1 - actualPanelWidth;
+        Index blockBOffset = IsLower ? k1 : lengthTarget;
+
+        // update the respective rows of B from other
+        pack_rhs(blockB + actual_kc * j2, other.getSubMapper(startBlock, j2), actualPanelWidth, actual_cols, actual_kc,
+                 blockBOffset);
+
+        // GEBP
+        if (lengthTarget > 0) {
+          Index startTarget = IsLower ? k2 + k1 + actualPanelWidth : k2 - actual_kc;
+
+          pack_lhs(blockA, tri.getSubMapper(startTarget, startBlock), actualPanelWidth, lengthTarget);
+
+          gebp_kernel(other.getSubMapper(startTarget, j2), blockA, blockB + actual_kc * j2, lengthTarget,
+                      actualPanelWidth, actual_cols, Scalar(-1), actualPanelWidth, actual_kc, 0, blockBOffset);
+        }
+      }
+    }
+
+    // R2 -= A21 * B => GEPP
+    {
+      Index start = IsLower ? k2 + kc : 0;
+      Index end = IsLower ? size : k2 - kc;
+      for (Index i2 = start; i2 < end; i2 += mc) {
+        const Index actual_mc = (std::min)(mc, end - i2);
+        if (actual_mc > 0) {
+          pack_lhs(blockA, tri.getSubMapper(i2, IsLower ? k2 : k2 - kc), actual_kc, actual_mc);
+
+          gebp_kernel(other.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, Scalar(-1), -1, -1, 0, 0);
+        }
+      }
+    }
+  }
+}
+
+/* Optimized triangular solver with multiple left hand sides and the triangular matrix on the right
+ */
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
+struct triangular_solve_matrix<Scalar, Index, OnTheRight, Mode, Conjugate, TriStorageOrder, ColMajor,
+                               OtherInnerStride> {
+  static EIGEN_DONT_INLINE void run(Index size, Index otherSize, const Scalar* _tri, Index triStride, Scalar* _other,
+                                    Index otherIncr, Index otherStride, level3_blocking<Scalar, Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
+EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar, Index, OnTheRight, Mode, Conjugate, TriStorageOrder, ColMajor,
+                                               OtherInnerStride>::run(Index size, Index otherSize, const Scalar* _tri,
+                                                                      Index triStride, Scalar* _other, Index otherIncr,
+                                                                      Index otherStride,
+                                                                      level3_blocking<Scalar, Scalar>& blocking) {
+  Index rows = otherSize;
+
+#if defined(EIGEN_VECTORIZE_AVX512) && defined(EIGEN_USE_AVX512_TRSM_R_KERNELS) && EIGEN_USE_AVX512_TRSM_R_KERNELS && \
+    EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS
+  EIGEN_IF_CONSTEXPR(
+      (OtherInnerStride == 1 && (std::is_same<Scalar, float>::value || std::is_same<Scalar, double>::value))) {
+    // TODO: Investigate better heuristics for cutoffs.
+    std::ptrdiff_t l1, l2, l3;
+    manage_caching_sizes(GetAction, &l1, &l2, &l3);
+    double L2Cap = 0.5;  // 50% of L2 size
+    if (size < avx512_trsm_cutoff<Scalar>(l2, rows, L2Cap)) {
+      trsmKernelR<Scalar, Index, Mode, Conjugate, TriStorageOrder, OtherInnerStride, /*Specialized=*/true>::kernel(
+          size, rows, _tri, triStride, _other, 1, otherStride);
+      return;
+    }
+  }
+#endif
+
+  typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> LhsMapper;
+  typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> RhsMapper;
+  LhsMapper lhs(_other, otherStride, otherIncr);
+  RhsMapper rhs(_tri, triStride);
+
+  typedef gebp_traits<Scalar, Scalar> Traits;
+  enum {
+    RhsStorageOrder = TriStorageOrder,
+    SmallPanelWidth = plain_enum_max(Traits::mr, Traits::nr),
+    IsLower = (Mode & Lower) == Lower
+  };
+
+  Index kc = blocking.kc();                    // cache block size along the K direction
+  Index mc = (std::min)(rows, blocking.mc());  // cache block size along the M direction
+
+  std::size_t sizeA = kc * mc;
+  std::size_t sizeB = kc * size;
+
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+  ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+  gebp_kernel<Scalar, Scalar, Index, LhsMapper, Traits::mr, Traits::nr, false, Conjugate> gebp_kernel;
+  gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
+  gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder, false, true> pack_rhs_panel;
+  gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, ColMajor,
+                false, true>
+      pack_lhs_panel;
+
+  for (Index k2 = IsLower ? size : 0; IsLower ? k2 > 0 : k2 < size; IsLower ? k2 -= kc : k2 += kc) {
+    const Index actual_kc = (std::min)(IsLower ? k2 : size - k2, kc);
+    Index actual_k2 = IsLower ? k2 - actual_kc : k2;
+
+    Index startPanel = IsLower ? 0 : k2 + actual_kc;
+    Index rs = IsLower ? actual_k2 : size - actual_k2 - actual_kc;
+    Scalar* geb = blockB + actual_kc * actual_kc;
+
+    if (rs > 0) pack_rhs(geb, rhs.getSubMapper(actual_k2, startPanel), actual_kc, rs);
+
+    // triangular packing (we only pack the panels off the diagonal,
+    // neglecting the blocks overlapping the diagonal
+    {
+      for (Index j2 = 0; j2 < actual_kc; j2 += SmallPanelWidth) {
+        Index actualPanelWidth = std::min<Index>(actual_kc - j2, SmallPanelWidth);
+        Index actual_j2 = actual_k2 + j2;
+        Index panelOffset = IsLower ? j2 + actualPanelWidth : 0;
+        Index panelLength = IsLower ? actual_kc - j2 - actualPanelWidth : j2;
+
+        if (panelLength > 0)
+          pack_rhs_panel(blockB + j2 * actual_kc, rhs.getSubMapper(actual_k2 + panelOffset, actual_j2), panelLength,
+                         actualPanelWidth, actual_kc, panelOffset);
+      }
+    }
+
+    for (Index i2 = 0; i2 < rows; i2 += mc) {
+      const Index actual_mc = (std::min)(mc, rows - i2);
+
+      // triangular solver kernel
+      {
+        // for each small block of the diagonal (=> vertical panels of rhs)
+        for (Index j2 = IsLower ? (actual_kc - ((actual_kc % SmallPanelWidth) ? Index(actual_kc % SmallPanelWidth)
+                                                                              : Index(SmallPanelWidth)))
+                                : 0;
+             IsLower ? j2 >= 0 : j2 < actual_kc; IsLower ? j2 -= SmallPanelWidth : j2 += SmallPanelWidth) {
+          Index actualPanelWidth = std::min<Index>(actual_kc - j2, SmallPanelWidth);
+          Index absolute_j2 = actual_k2 + j2;
+          Index panelOffset = IsLower ? j2 + actualPanelWidth : 0;
+          Index panelLength = IsLower ? actual_kc - j2 - actualPanelWidth : j2;
+
+          // GEBP
+          if (panelLength > 0) {
+            gebp_kernel(lhs.getSubMapper(i2, absolute_j2), blockA, blockB + j2 * actual_kc, actual_mc, panelLength,
+                        actualPanelWidth, Scalar(-1), actual_kc, actual_kc,  // strides
+                        panelOffset, panelOffset);                           // offsets
+          }
+
+          {
+            // unblocked triangular solve
+            trsmKernelR<Scalar, Index, Mode, Conjugate, TriStorageOrder, OtherInnerStride,
+                        /*Specialized=*/true>::kernel(actualPanelWidth, actual_mc,
+                                                      _tri + absolute_j2 + absolute_j2 * triStride, triStride,
+                                                      _other + i2 * OtherInnerStride + absolute_j2 * otherStride,
+                                                      otherIncr, otherStride);
+          }
+          // pack the just computed part of lhs to A
+          pack_lhs_panel(blockA, lhs.getSubMapper(i2, absolute_j2), actualPanelWidth, actual_mc, actual_kc, j2);
+        }
+      }
+
+      if (rs > 0)
+        gebp_kernel(lhs.getSubMapper(i2, startPanel), blockA, geb, actual_mc, actual_kc, rs, Scalar(-1), -1, -1, 0, 0);
+    }
+  }
+}
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRIANGULAR_SOLVER_MATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
new file mode 100644
index 0000000..20c8f20
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
@@ -0,0 +1,168 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Triangular matrix * matrix product functionality based on ?TRMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
+#define EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// implements LeftSide op(triangular)^-1 * general
+#define EIGEN_BLAS_TRSM_L(EIGTYPE, BLASTYPE, BLASFUNC)                                                              \
+  template <typename Index, int Mode, bool Conjugate, int TriStorageOrder>                                          \
+  struct triangular_solve_matrix<EIGTYPE, Index, OnTheLeft, Mode, Conjugate, TriStorageOrder, ColMajor, 1> {        \
+    enum {                                                                                                          \
+      IsLower = (Mode & Lower) == Lower,                                                                            \
+      IsUnitDiag = (Mode & UnitDiag) ? 1 : 0,                                                                       \
+      IsZeroDiag = (Mode & ZeroDiag) ? 1 : 0,                                                                       \
+      conjA = ((TriStorageOrder == ColMajor) && Conjugate) ? 1 : 0                                                  \
+    };                                                                                                              \
+    static void run(Index size, Index otherSize, const EIGTYPE* _tri, Index triStride, EIGTYPE* _other,             \
+                    Index otherIncr, Index otherStride, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) {          \
+      if (size == 0 || otherSize == 0) return;                                                                      \
+      EIGEN_ONLY_USED_FOR_DEBUG(otherIncr);                                                                         \
+      eigen_assert(otherIncr == 1);                                                                                 \
+      BlasIndex m = convert_index<BlasIndex>(size), n = convert_index<BlasIndex>(otherSize), lda, ldb;              \
+      char side = 'L', uplo, diag = 'N', transa;                                                                    \
+      /* Set alpha_ */                                                                                              \
+      EIGTYPE alpha(1);                                                                                             \
+      ldb = convert_index<BlasIndex>(otherStride);                                                                  \
+                                                                                                                    \
+      const EIGTYPE* a;                                                                                             \
+      /* Set trans */                                                                                               \
+      transa = (TriStorageOrder == RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N';                                     \
+      /* Set uplo */                                                                                                \
+      uplo = IsLower ? 'L' : 'U';                                                                                   \
+      if (TriStorageOrder == RowMajor) uplo = (uplo == 'L') ? 'U' : 'L';                                            \
+      /* Set a, lda */                                                                                              \
+      typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri;                                         \
+      Map<const MatrixTri, 0, OuterStride<> > tri(_tri, size, size, OuterStride<>(triStride));                      \
+      MatrixTri a_tmp;                                                                                              \
+                                                                                                                    \
+      if (conjA) {                                                                                                  \
+        a_tmp = tri.conjugate();                                                                                    \
+        a = a_tmp.data();                                                                                           \
+        lda = convert_index<BlasIndex>(a_tmp.outerStride());                                                        \
+      } else {                                                                                                      \
+        a = _tri;                                                                                                   \
+        lda = convert_index<BlasIndex>(triStride);                                                                  \
+      }                                                                                                             \
+      if (IsUnitDiag) diag = 'U';                                                                                   \
+      /* call ?trsm*/                                                                                               \
+      BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, \
+               &lda, (BLASTYPE*)_other, &ldb);                                                                      \
+    }                                                                                                               \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRSM_L(double, double, dtrsm)
+EIGEN_BLAS_TRSM_L(dcomplex, MKL_Complex16, ztrsm)
+EIGEN_BLAS_TRSM_L(float, float, strsm)
+EIGEN_BLAS_TRSM_L(scomplex, MKL_Complex8, ctrsm)
+#else
+EIGEN_BLAS_TRSM_L(double, double, dtrsm_)
+EIGEN_BLAS_TRSM_L(dcomplex, double, ztrsm_)
+EIGEN_BLAS_TRSM_L(float, float, strsm_)
+EIGEN_BLAS_TRSM_L(scomplex, float, ctrsm_)
+#endif
+
+// implements RightSide general * op(triangular)^-1
+#define EIGEN_BLAS_TRSM_R(EIGTYPE, BLASTYPE, BLASFUNC)                                                              \
+  template <typename Index, int Mode, bool Conjugate, int TriStorageOrder>                                          \
+  struct triangular_solve_matrix<EIGTYPE, Index, OnTheRight, Mode, Conjugate, TriStorageOrder, ColMajor, 1> {       \
+    enum {                                                                                                          \
+      IsLower = (Mode & Lower) == Lower,                                                                            \
+      IsUnitDiag = (Mode & UnitDiag) ? 1 : 0,                                                                       \
+      IsZeroDiag = (Mode & ZeroDiag) ? 1 : 0,                                                                       \
+      conjA = ((TriStorageOrder == ColMajor) && Conjugate) ? 1 : 0                                                  \
+    };                                                                                                              \
+    static void run(Index size, Index otherSize, const EIGTYPE* _tri, Index triStride, EIGTYPE* _other,             \
+                    Index otherIncr, Index otherStride, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) {          \
+      if (size == 0 || otherSize == 0) return;                                                                      \
+      EIGEN_ONLY_USED_FOR_DEBUG(otherIncr);                                                                         \
+      eigen_assert(otherIncr == 1);                                                                                 \
+      BlasIndex m = convert_index<BlasIndex>(otherSize), n = convert_index<BlasIndex>(size), lda, ldb;              \
+      char side = 'R', uplo, diag = 'N', transa;                                                                    \
+      /* Set alpha_ */                                                                                              \
+      EIGTYPE alpha(1);                                                                                             \
+      ldb = convert_index<BlasIndex>(otherStride);                                                                  \
+                                                                                                                    \
+      const EIGTYPE* a;                                                                                             \
+      /* Set trans */                                                                                               \
+      transa = (TriStorageOrder == RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N';                                     \
+      /* Set uplo */                                                                                                \
+      uplo = IsLower ? 'L' : 'U';                                                                                   \
+      if (TriStorageOrder == RowMajor) uplo = (uplo == 'L') ? 'U' : 'L';                                            \
+      /* Set a, lda */                                                                                              \
+      typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri;                                         \
+      Map<const MatrixTri, 0, OuterStride<> > tri(_tri, size, size, OuterStride<>(triStride));                      \
+      MatrixTri a_tmp;                                                                                              \
+                                                                                                                    \
+      if (conjA) {                                                                                                  \
+        a_tmp = tri.conjugate();                                                                                    \
+        a = a_tmp.data();                                                                                           \
+        lda = convert_index<BlasIndex>(a_tmp.outerStride());                                                        \
+      } else {                                                                                                      \
+        a = _tri;                                                                                                   \
+        lda = convert_index<BlasIndex>(triStride);                                                                  \
+      }                                                                                                             \
+      if (IsUnitDiag) diag = 'U';                                                                                   \
+      /* call ?trsm*/                                                                                               \
+      BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, \
+               &lda, (BLASTYPE*)_other, &ldb);                                                                      \
+      /*std::cout << "TRMS_L specialization!\n";*/                                                                  \
+    }                                                                                                               \
+  };
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRSM_R(double, double, dtrsm)
+EIGEN_BLAS_TRSM_R(dcomplex, MKL_Complex16, ztrsm)
+EIGEN_BLAS_TRSM_R(float, float, strsm)
+EIGEN_BLAS_TRSM_R(scomplex, MKL_Complex8, ctrsm)
+#else
+EIGEN_BLAS_TRSM_R(double, double, dtrsm_)
+EIGEN_BLAS_TRSM_R(dcomplex, double, ztrsm_)
+EIGEN_BLAS_TRSM_R(float, float, strsm_)
+EIGEN_BLAS_TRSM_R(scomplex, float, ctrsm_)
+#endif
+
+#undef EIGEN_BLAS_TRSM_R
+#undef EIGEN_BLAS_TRSM_L
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularSolverVector.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularSolverVector.h
new file mode 100644
index 0000000..ff7c43f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/products/TriangularSolverVector.h
@@ -0,0 +1,122 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIANGULAR_SOLVER_VECTOR_H
+#define EIGEN_TRIANGULAR_SOLVER_VECTOR_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate, int StorageOrder>
+struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheRight, Mode, Conjugate, StorageOrder> {
+  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs) {
+    triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft,
+                            ((Mode & Upper) == Upper ? Lower : Upper) | (Mode & UnitDiag), Conjugate,
+                            StorageOrder == RowMajor ? ColMajor : RowMajor>::run(size, _lhs, lhsStride, rhs);
+  }
+};
+
+// forward and backward substitution, row-major, rhs is a vector
+template <typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate>
+struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Conjugate, RowMajor> {
+  enum { IsLower = ((Mode & Lower) == Lower) };
+  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs) {
+    typedef Map<const Matrix<LhsScalar, Dynamic, Dynamic, RowMajor>, 0, OuterStride<> > LhsMap;
+    const LhsMap lhs(_lhs, size, size, OuterStride<>(lhsStride));
+
+    typedef const_blas_data_mapper<LhsScalar, Index, RowMajor> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar, Index, ColMajor> RhsMapper;
+
+    std::conditional_t<Conjugate, const CwiseUnaryOp<typename internal::scalar_conjugate_op<LhsScalar>, LhsMap>,
+                       const LhsMap&>
+        cjLhs(lhs);
+    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
+    for (Index pi = IsLower ? 0 : size; IsLower ? pi < size : pi > 0; IsLower ? pi += PanelWidth : pi -= PanelWidth) {
+      Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
+
+      Index r = IsLower ? pi : size - pi;  // remaining size
+      if (r > 0) {
+        // let's directly call the low level product function because:
+        // 1 - it is faster to compile
+        // 2 - it is slightly faster at runtime
+        Index startRow = IsLower ? pi : pi - actualPanelWidth;
+        Index startCol = IsLower ? 0 : pi;
+
+        general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, Conjugate, RhsScalar, RhsMapper,
+                                      false>::run(actualPanelWidth, r,
+                                                  LhsMapper(&lhs.coeffRef(startRow, startCol), lhsStride),
+                                                  RhsMapper(rhs + startCol, 1), rhs + startRow, 1, RhsScalar(-1));
+      }
+
+      for (Index k = 0; k < actualPanelWidth; ++k) {
+        Index i = IsLower ? pi + k : pi - k - 1;
+        Index s = IsLower ? pi : i + 1;
+        if (k > 0)
+          rhs[i] -= (cjLhs.row(i).segment(s, k).transpose().cwiseProduct(
+                         Map<const Matrix<RhsScalar, Dynamic, 1> >(rhs + s, k)))
+                        .sum();
+
+        if ((!(Mode & UnitDiag)) && !is_identically_zero(rhs[i])) rhs[i] /= cjLhs(i, i);
+      }
+    }
+  }
+};
+
+// forward and backward substitution, column-major, rhs is a vector
+template <typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate>
+struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Conjugate, ColMajor> {
+  enum { IsLower = ((Mode & Lower) == Lower) };
+  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs) {
+    typedef Map<const Matrix<LhsScalar, Dynamic, Dynamic, ColMajor>, 0, OuterStride<> > LhsMap;
+    const LhsMap lhs(_lhs, size, size, OuterStride<>(lhsStride));
+    typedef const_blas_data_mapper<LhsScalar, Index, ColMajor> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar, Index, ColMajor> RhsMapper;
+    std::conditional_t<Conjugate, const CwiseUnaryOp<typename internal::scalar_conjugate_op<LhsScalar>, LhsMap>,
+                       const LhsMap&>
+        cjLhs(lhs);
+    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
+
+    for (Index pi = IsLower ? 0 : size; IsLower ? pi < size : pi > 0; IsLower ? pi += PanelWidth : pi -= PanelWidth) {
+      Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
+      Index startBlock = IsLower ? pi : pi - actualPanelWidth;
+      Index endBlock = IsLower ? pi + actualPanelWidth : 0;
+
+      for (Index k = 0; k < actualPanelWidth; ++k) {
+        Index i = IsLower ? pi + k : pi - k - 1;
+        if (!is_identically_zero(rhs[i])) {
+          if (!(Mode & UnitDiag)) rhs[i] /= cjLhs.coeff(i, i);
+
+          Index r = actualPanelWidth - k - 1;  // remaining size
+          Index s = IsLower ? i + 1 : i - r;
+          if (r > 0) Map<Matrix<RhsScalar, Dynamic, 1> >(rhs + s, r) -= rhs[i] * cjLhs.col(i).segment(s, r);
+        }
+      }
+      Index r = IsLower ? size - endBlock : startBlock;  // remaining size
+      if (r > 0) {
+        // let's directly call the low level product function because:
+        // 1 - it is faster to compile
+        // 2 - it is slightly faster at runtime
+        general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, Conjugate, RhsScalar, RhsMapper,
+                                      false>::run(r, actualPanelWidth,
+                                                  LhsMapper(&lhs.coeffRef(endBlock, startBlock), lhsStride),
+                                                  RhsMapper(rhs + startBlock, 1), rhs + endBlock, 1, RhsScalar(-1));
+      }
+    }
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRIANGULAR_SOLVER_VECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/AOCL_Support.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/AOCL_Support.h
new file mode 100644
index 0000000..434ccfd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/AOCL_Support.h
@@ -0,0 +1,175 @@
+/*
+ * This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at https://mozilla.org/MPL/2.0/.
+ *
+ * AOCL_Support.h - AMD Optimizing CPU Libraries Integration Header for Eigen
+ *
+ * Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Description:
+ * ------------
+ * This header file serves as the central configuration and integration point
+ * for AMD Optimizing CPU Libraries (AOCL) with the Eigen C++ template library.
+ * It orchestrates the integration of multiple AOCL components to provide
+ * optimal mathematical computing performance on AMD Zen family processors.
+ *
+ * AOCL Component Integration:
+ * ---------------------------
+ * 1. AOCL Vector Math Library (VML):
+ *    - Provides VRDA (Vector Rapid Double-precision Arithmetic) functions
+ *    - Optimized transcendental functions: exp, sin, cos, sqrt, log, pow, etc.
+ *    - SIMD vectorization for AMD architectures (AVX2, AVX-512)
+ *    - Headers: amdlibm.h, amdlibm_vec.h
+ *
+ * 2. AOCL BLAS (BLIS - BLAS-like Library Instantiation Software):
+ *    - High-performance Basic Linear Algebra Subprograms
+ *    - Supports single-threaded (libblis) and multithreaded (libblis-mt)
+ * variants
+ *    - Optimized matrix operations: GEMM, GEMV, TRSM, etc.
+ *    - Headers: cblas.h, blis.h
+ *
+ * 3. AOCL LAPACK (libFLAME - Formal Linear Algebra Methods Environment):
+ *    - Dense linear algebra operations: factorizations, eigenvalue solvers
+ *    - Matrix decompositions: LU, Cholesky, QR, SVD
+ *    - Eigenvalue/eigenvector computations optimized for AMD hardware
+ *    - Headers: LAPACKE interface
+ *
+ * ------------------------------
+ * EIGEN_AOCL_VML_THRESHOLD (default: 128):
+ *   - Minimum vector size for AOCL VML dispatch
+ *   - Smaller vectors use standard Eigen to avoid function call overhead
+ *   - Optimal values: 64-512 depending on operation and data characteristics
+ *
+ *
+ *
+ * Architecture Support:
+ * ---------------------
+ * Optimized for AMD processor families:
+ * - Zen Architecture (Naples, Rome): AVX2 optimization
+ * - Zen 2 Architecture (Rome, Matisse): Enhanced AVX2
+ * - Zen 3 Architecture (Milan, Vermeer): Improved IPC and cache
+ * - Zen 4 Architecture (Genoa, Raphael): AVX-512 support
+ * - Zen 5 Architecture (Turin, Granite Ridge): Enhanced AVX-512
+ *
+ *
+ * Dependencies:
+ * -------------
+ * Required AOCL components:
+ * - libamdlibm: Core math library with VRDA functions
+ * - libblis or libblis-mt: BLAS implementation
+ * - libflame: LAPACK implementation
+ *
+ * System requirements:
+ * - AMD x86_64 processor (optimal performance)
+ * - Linux, Windows, or compatible POSIX system
+ * - C++11 or later standard
+ * - CMake 3.5+ for build system integration
+ *
+ * Developer:
+ * ----------
+ * Name: Sharad Saurabh Bhaskar
+ * Email: shbhaska@amd.com
+ * Organization: Advanced Micro Devices, Inc.
+ */
+
+#ifndef EIGEN_AOCL_SUPPORT_H
+#define EIGEN_AOCL_SUPPORT_H
+
+#if defined(EIGEN_USE_AOCL_ALL) || defined(EIGEN_USE_AOCL_MT)
+
+#include <complex>
+
+// Define AOCL component flags based on main flags
+#ifdef EIGEN_USE_AOCL_ALL
+#define EIGEN_USE_AOCL_VML  // Enable AOCL Vector Math Library
+#define EIGEN_USE_AOCL_BLAS // Enable AOCL BLAS (BLIS)
+
+// Enable Eigen BLAS backend only if BLIS provides compatible interface
+#if defined(EIGEN_AOCL_BLIS_COMPATIBLE)
+#define EIGEN_USE_BLAS // Enable Eigen BLAS backend
+#endif
+
+#define EIGEN_USE_LAPACKE // Enable LAPACK backend (FLAME)
+#endif
+
+#ifdef EIGEN_USE_AOCL_MT
+#define EIGEN_USE_AOCL_VML  // Enable AOCL Vector Math Library
+#define EIGEN_USE_AOCL_BLAS // Enable AOCL BLAS (BLIS)
+
+// For multithreaded: disable EIGEN_USE_BLAS to avoid signature conflicts
+// Use direct BLIS calls instead through EIGEN_USE_AOCL_BLAS
+// #define EIGEN_USE_BLAS       // Commented out - causes conflicts with BLIS
+// interface
+
+// Note: LAPACKE disabled in MT mode to avoid header conflicts
+#define EIGEN_USE_LAPACKE // Commented out - causes conflicts with BLIS LAPACKE
+#define EIGEN_AOCL_USE_BLIS_MT 1 // Enable multithreaded BLIS
+#endif
+
+// Handle standalone EIGEN_USE_AOCL_VML flag
+#ifndef EIGEN_USE_AOCL_VML
+#ifdef EIGEN_USE_AOCL_ALL
+#define EIGEN_USE_AOCL_VML
+#endif
+#ifdef EIGEN_USE_AOCL_MT
+#define EIGEN_USE_AOCL_VML
+#endif
+#endif
+
+// Configuration constants - define these for any AOCL usage
+#ifndef EIGEN_AOCL_VML_THRESHOLD
+#define EIGEN_AOCL_VML_THRESHOLD 128 // Threshold for VML dispatch
+#endif
+
+#ifndef AOCL_SIMD_WIDTH
+#define AOCL_SIMD_WIDTH 8 // AVX-512: 512 bits / 64 bits per double
+#endif
+
+// Include AOCL Math Library headers for VML
+#if defined(EIGEN_USE_AOCL_VML) || defined(EIGEN_USE_AOCL_ALL) ||              \
+    defined(EIGEN_USE_AOCL_MT)
+#if defined(__has_include)
+#if __has_include("amdlibm.h")
+#include "amdlibm.h"
+#ifndef AMD_LIBM_VEC_EXPERIMENTAL
+#define AMD_LIBM_VEC_EXPERIMENTAL
+#endif
+#if __has_include("amdlibm_vec.h")
+#include "amdlibm_vec.h"
+#endif
+#endif
+#else
+// Fallback for compilers without __has_include
+#include "amdlibm.h"
+#ifndef AMD_LIBM_VEC_EXPERIMENTAL
+#define AMD_LIBM_VEC_EXPERIMENTAL
+#endif
+#include "amdlibm_vec.h"
+#endif
+#endif
+
+// Include CBLAS headers when BLAS is enabled
+#ifdef EIGEN_USE_AOCL_BLAS
+#if defined(__has_include)
+#if __has_include("cblas.h")
+#include "cblas.h"
+#elif __has_include("blis.h")
+#include "blis.h"
+#endif
+#else
+// Fallback
+#include "cblas.h"
+#endif
+#endif
+
+namespace Eigen {
+// AOCL-specific type definitions
+typedef std::complex<double> dcomplex;
+typedef std::complex<float> scomplex;
+typedef int BlasIndex; // Standard BLAS index type
+} // namespace Eigen
+
+#endif // EIGEN_USE_AOCL_ALL || EIGEN_USE_AOCL_MT
+
+#endif // EIGEN_AOCL_SUPPORT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Assert.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Assert.h
new file mode 100644
index 0000000..09a411a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Assert.h
@@ -0,0 +1,158 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022, The Eigen authors.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_UTIL_ASSERT_H
+#define EIGEN_CORE_UTIL_ASSERT_H
+
+// Eigen custom assert function.
+//
+// The combination of Eigen's relative includes and cassert's `assert` function
+// (or any usage of the __FILE__ macro) can lead to ODR issues:
+// a header included using different relative paths in two different TUs will
+// have two different token-for-token definitions, since __FILE__ is expanded
+// as an in-line string with different values.  Normally this would be
+// harmless - the linker would just choose one definition. However, it breaks
+// with C++20 modules when functions in different modules have different
+// definitions.
+//
+// To get around this, we need to use __builtin_FILE() when available, which is
+// considered a single token, and thus satisfies the ODR.
+
+// Only define eigen_plain_assert if we are debugging, and either
+//  - we are not compiling for GPU, or
+//  - gpu debugging is enabled.
+#if !defined(EIGEN_NO_DEBUG) && (!defined(EIGEN_GPU_COMPILE_PHASE) || !defined(EIGEN_NO_DEBUG_GPU))
+
+#include <cassert>
+
+#ifndef EIGEN_USE_CUSTOM_PLAIN_ASSERT
+// Disable new custom asserts by default for now.
+#define EIGEN_USE_CUSTOM_PLAIN_ASSERT 0
+#endif
+
+#if EIGEN_USE_CUSTOM_PLAIN_ASSERT
+
+#ifndef EIGEN_HAS_BUILTIN_FILE
+// Clang can check if __builtin_FILE() is supported.
+// GCC > 5, MSVC 2019 14.26 (1926) all have __builtin_FILE().
+//
+// For NVCC, it's more complicated.  Through trial-and-error:
+//   - nvcc+gcc supports __builtin_FILE() on host, and on device after CUDA 11.
+//   - nvcc+msvc supports __builtin_FILE() only after CUDA 11.
+#if (EIGEN_HAS_BUILTIN(__builtin_FILE) && (EIGEN_COMP_CLANG || !defined(EIGEN_CUDA_ARCH))) ||            \
+    (EIGEN_GNUC_STRICT_AT_LEAST(5, 0, 0) && (EIGEN_COMP_NVCC >= 110000 || !defined(EIGEN_CUDA_ARCH))) || \
+    (EIGEN_COMP_MSVC >= 1926 && (!EIGEN_COMP_NVCC || EIGEN_COMP_NVCC >= 110000))
+#define EIGEN_HAS_BUILTIN_FILE 1
+#else
+#define EIGEN_HAS_BUILTIN_FILE 0
+#endif
+#endif  // EIGEN_HAS_BUILTIN_FILE
+
+#if EIGEN_HAS_BUILTIN_FILE
+#define EIGEN_BUILTIN_FILE __builtin_FILE()
+#define EIGEN_BUILTIN_LINE __builtin_LINE()
+#else
+// Default (potentially unsafe) values.
+#define EIGEN_BUILTIN_FILE __FILE__
+#define EIGEN_BUILTIN_LINE __LINE__
+#endif
+
+// Use __PRETTY_FUNCTION__ when available, since it is more descriptive, as
+// __builtin_FUNCTION() only returns the undecorated function name.
+// This should still be okay ODR-wise since it is a compiler-specific fixed
+// value.  Mixing compilers will likely lead to ODR violations anyways.
+#if EIGEN_COMP_MSVC
+#define EIGEN_BUILTIN_FUNCTION __FUNCSIG__
+#elif EIGEN_COMP_GNUC
+#define EIGEN_BUILTIN_FUNCTION __PRETTY_FUNCTION__
+#else
+#define EIGEN_BUILTIN_FUNCTION __func__
+#endif
+
+namespace Eigen {
+namespace internal {
+
+// Generic default assert handler.
+template <typename EnableIf = void, typename... EmptyArgs>
+struct assert_handler_impl {
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static inline void run(const char* expression, const char* file, unsigned line,
+                                                             const char* function) {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+    // GPU device code doesn't allow stderr or abort, so use printf and raise an
+    // illegal instruction exception to trigger a kernel failure.
+#ifndef EIGEN_NO_IO
+    printf("Assertion failed at %s:%u in %s: %s\n", file == nullptr ? "<file>" : file, line,
+           function == nullptr ? "<function>" : function, expression);
+#endif
+    __trap();
+
+#else  // EIGEN_GPU_COMPILE_PHASE
+
+    // Print to stderr and abort, as specified in <cassert>.
+#ifndef EIGEN_NO_IO
+    fprintf(stderr, "Assertion failed at %s:%u in %s: %s\n", file == nullptr ? "<file>" : file, line,
+            function == nullptr ? "<function>" : function, expression);
+#endif
+    std::abort();
+
+#endif  // EIGEN_GPU_COMPILE_PHASE
+  }
+};
+
+// Use POSIX __assert_fail handler when available.
+//
+// This allows us to integrate with systems that have custom handlers.
+//
+// NOTE: this handler is not always available on all POSIX systems (otherwise
+// we could simply test for __unix__ or similar).  The handler function name
+// seems to depend on the specific toolchain implementation, and differs between
+// compilers, platforms, OSes, etc.  Hence, we detect support via SFINAE.
+template <typename... EmptyArgs>
+struct assert_handler_impl<void_t<decltype(__assert_fail((const char*)nullptr,         // expression
+                                                         (const char*)nullptr,         // file
+                                                         0,                            // line
+                                                         (const char*)nullptr,         // function
+                                                         std::declval<EmptyArgs>()...  // Empty substitution required
+                                                                                       // for SFINAE.
+                                                         ))>,
+                           EmptyArgs...> {
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static inline void run(const char* expression, const char* file, unsigned line,
+                                                             const char* function) {
+    // GCC requires this call to be dependent on the template parameters.
+    __assert_fail(expression, file, line, function, std::declval<EmptyArgs>()...);
+  }
+};
+
+EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE inline void __assert_handler(const char* expression, const char* file,
+                                                                 unsigned line, const char* function) {
+  assert_handler_impl<>::run(expression, file, line, function);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#define eigen_plain_assert(expression)                                                                                \
+  (EIGEN_PREDICT_FALSE(!(expression)) ? Eigen::internal::__assert_handler(#expression, EIGEN_BUILTIN_FILE,            \
+                                                                          EIGEN_BUILTIN_LINE, EIGEN_BUILTIN_FUNCTION) \
+                                      : (void)0)
+
+#else  // EIGEN_USE_CUSTOM_PLAIN_ASSERT
+
+// Use regular assert.
+#define eigen_plain_assert(condition) assert(condition)
+
+#endif  // EIGEN_USE_CUSTOM_PLAIN_ASSERT
+
+#else  // EIGEN_NO_DEBUG
+
+#define eigen_plain_assert(condition) ((void)0)
+
+#endif  // EIGEN_NO_DEBUG
+
+#endif  // EIGEN_CORE_UTIL_ASSERT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/BlasUtil.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/BlasUtil.h
new file mode 100644
index 0000000..2e1ca91
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/BlasUtil.h
@@ -0,0 +1,626 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BLASUTIL_H
+#define EIGEN_BLASUTIL_H
+
+// This file contains many lightweight helper classes used to
+// implement and control fast level 2 and level 3 BLAS-like routines.
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// forward declarations
+template <typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr,
+          bool ConjugateLhs = false, bool ConjugateRhs = false>
+struct gebp_kernel;
+
+template <typename Scalar, typename Index, typename DataMapper, int nr, int StorageOrder, bool Conjugate = false,
+          bool PanelMode = false>
+struct gemm_pack_rhs;
+
+template <typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, int StorageOrder,
+          bool Conjugate = false, bool PanelMode = false>
+struct gemm_pack_lhs;
+
+template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
+          int RhsStorageOrder, bool ConjugateRhs, int ResStorageOrder, int ResInnerStride>
+struct general_matrix_matrix_product;
+
+template <typename Index, typename LhsScalar, typename LhsMapper, int LhsStorageOrder, bool ConjugateLhs,
+          typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version = Specialized>
+struct general_matrix_vector_product;
+
+template <typename From, typename To>
+struct get_factor {
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE To run(const From& x) { return To(x); }
+};
+
+template <typename Scalar>
+struct get_factor<Scalar, typename NumTraits<Scalar>::Real> {
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE typename NumTraits<Scalar>::Real run(const Scalar& x) {
+    return numext::real(x);
+  }
+};
+
+template <typename Scalar, typename Index>
+class BlasVectorMapper {
+ public:
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE BlasVectorMapper(Scalar* data) : m_data(data) {}
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const { return m_data[i]; }
+  template <typename Packet, int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet load(Index i) const {
+    return ploadt<Packet, AlignmentType>(m_data + i);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC bool aligned(Index i) const {
+    return (std::uintptr_t(m_data + i) % sizeof(Packet)) == 0;
+  }
+
+ protected:
+  Scalar* m_data;
+};
+
+template <typename Scalar, typename Index, int AlignmentType, int Incr = 1>
+class BlasLinearMapper;
+
+template <typename Scalar, typename Index, int AlignmentType>
+class BlasLinearMapper<Scalar, Index, AlignmentType> {
+ public:
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE BlasLinearMapper(Scalar* data, Index incr = 1) : m_data(data) {
+    EIGEN_ONLY_USED_FOR_DEBUG(incr);
+    eigen_assert(incr == 1);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(Index i) const { internal::prefetch(&operator()(i)); }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE Scalar& operator()(Index i) const { return m_data[i]; }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i) const {
+    return ploadt<PacketType, AlignmentType>(m_data + i);
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacketPartial(Index i, Index n, Index offset = 0) const {
+    return ploadt_partial<PacketType, AlignmentType>(m_data + i, n, offset);
+  }
+
+  template <typename PacketType, int AlignmentT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType load(Index i) const {
+    return ploadt<PacketType, AlignmentT>(m_data + i);
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketType& p) const {
+    pstoret<Scalar, PacketType, AlignmentType>(m_data + i, p);
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketPartial(Index i, const PacketType& p, Index n,
+                                                                Index offset = 0) const {
+    pstoret_partial<Scalar, PacketType, AlignmentType>(m_data + i, p, n, offset);
+  }
+
+ protected:
+  Scalar* m_data;
+};
+
+// Lightweight helper class to access matrix coefficients.
+template <typename Scalar, typename Index, int StorageOrder, int AlignmentType = Unaligned, int Incr = 1>
+class blas_data_mapper;
+
+// TMP to help PacketBlock store implementation.
+// There's currently no known use case for PacketBlock load.
+// The default implementation assumes ColMajor order.
+// It always store each packet sequentially one `stride` apart.
+template <typename Index, typename Scalar, typename Packet, int n, int idx, int StorageOrder>
+struct PacketBlockManagement {
+  PacketBlockManagement<Index, Scalar, Packet, n, idx - 1, StorageOrder> pbm;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar* to, const Index stride, Index i, Index j,
+                                                   const PacketBlock<Packet, n>& block) const {
+    pbm.store(to, stride, i, j, block);
+    pstoreu<Scalar>(to + i + (j + idx) * stride, block.packet[idx]);
+  }
+};
+
+// PacketBlockManagement specialization to take care of RowMajor order without ifs.
+template <typename Index, typename Scalar, typename Packet, int n, int idx>
+struct PacketBlockManagement<Index, Scalar, Packet, n, idx, RowMajor> {
+  PacketBlockManagement<Index, Scalar, Packet, n, idx - 1, RowMajor> pbm;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar* to, const Index stride, Index i, Index j,
+                                                   const PacketBlock<Packet, n>& block) const {
+    pbm.store(to, stride, i, j, block);
+    pstoreu<Scalar>(to + j + (i + idx) * stride, block.packet[idx]);
+  }
+};
+
+template <typename Index, typename Scalar, typename Packet, int n, int StorageOrder>
+struct PacketBlockManagement<Index, Scalar, Packet, n, -1, StorageOrder> {
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar* to, const Index stride, Index i, Index j,
+                                                   const PacketBlock<Packet, n>& block) const {
+    EIGEN_UNUSED_VARIABLE(to);
+    EIGEN_UNUSED_VARIABLE(stride);
+    EIGEN_UNUSED_VARIABLE(i);
+    EIGEN_UNUSED_VARIABLE(j);
+    EIGEN_UNUSED_VARIABLE(block);
+  }
+};
+
+template <typename Index, typename Scalar, typename Packet, int n>
+struct PacketBlockManagement<Index, Scalar, Packet, n, -1, RowMajor> {
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar* to, const Index stride, Index i, Index j,
+                                                   const PacketBlock<Packet, n>& block) const {
+    EIGEN_UNUSED_VARIABLE(to);
+    EIGEN_UNUSED_VARIABLE(stride);
+    EIGEN_UNUSED_VARIABLE(i);
+    EIGEN_UNUSED_VARIABLE(j);
+    EIGEN_UNUSED_VARIABLE(block);
+  }
+};
+
+template <typename Scalar, typename Index, int StorageOrder, int AlignmentType>
+class blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, 1> {
+ public:
+  typedef BlasLinearMapper<Scalar, Index, AlignmentType> LinearMapper;
+  typedef blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType> SubMapper;
+  typedef BlasVectorMapper<Scalar, Index> VectorMapper;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE blas_data_mapper(Scalar* data, Index stride, Index incr = 1)
+      : m_data(data), m_stride(stride) {
+    EIGEN_ONLY_USED_FOR_DEBUG(incr);
+    eigen_assert(incr == 1);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE SubMapper getSubMapper(Index i, Index j) const {
+    return SubMapper(&operator()(i, j), m_stride);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
+    return LinearMapper(&operator()(i, j));
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const {
+    return VectorMapper(&operator()(i, j));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(Index i, Index j) const { internal::prefetch(&operator()(i, j)); }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE Scalar& operator()(Index i, Index j) const {
+    return m_data[StorageOrder == RowMajor ? j + i * m_stride : i + j * m_stride];
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i, Index j) const {
+    return ploadt<PacketType, AlignmentType>(&operator()(i, j));
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacketPartial(Index i, Index j, Index n,
+                                                                     Index offset = 0) const {
+    return ploadt_partial<PacketType, AlignmentType>(&operator()(i, j), n, offset);
+  }
+
+  template <typename PacketT, int AlignmentT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i, Index j) const {
+    return ploadt<PacketT, AlignmentT>(&operator()(i, j));
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, Index j, const PacketType& p) const {
+    pstoret<Scalar, PacketType, AlignmentType>(&operator()(i, j), p);
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketPartial(Index i, Index j, const PacketType& p, Index n,
+                                                                Index offset = 0) const {
+    pstoret_partial<Scalar, PacketType, AlignmentType>(&operator()(i, j), p, n, offset);
+  }
+
+  template <typename SubPacket>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void scatterPacket(Index i, Index j, const SubPacket& p) const {
+    pscatter<Scalar, SubPacket>(&operator()(i, j), p, m_stride);
+  }
+
+  template <typename SubPacket>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SubPacket gatherPacket(Index i, Index j) const {
+    return pgather<Scalar, SubPacket>(&operator()(i, j), m_stride);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr const Index stride() const { return m_stride; }
+  EIGEN_DEVICE_FUNC constexpr const Index incr() const { return 1; }
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_data; }
+
+  EIGEN_DEVICE_FUNC Index firstAligned(Index size) const {
+    if (std::uintptr_t(m_data) % sizeof(Scalar)) {
+      return -1;
+    }
+    return internal::first_default_aligned(m_data, size);
+  }
+
+  template <typename SubPacket, int n>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketBlock(Index i, Index j,
+                                                              const PacketBlock<SubPacket, n>& block) const {
+    PacketBlockManagement<Index, Scalar, SubPacket, n, n - 1, StorageOrder> pbm;
+    pbm.store(m_data, m_stride, i, j, block);
+  }
+
+ protected:
+  Scalar* EIGEN_RESTRICT m_data;
+  const Index m_stride;
+};
+
+// Implementation of non-natural increment (i.e. inner-stride != 1)
+// The exposed API is not complete yet compared to the Incr==1 case
+// because some features makes less sense in this case.
+template <typename Scalar, typename Index, int AlignmentType, int Incr>
+class BlasLinearMapper {
+ public:
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE BlasLinearMapper(Scalar* data, Index incr)
+      : m_data(data), m_incr(incr) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(int i) const { internal::prefetch(&operator()(i)); }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE Scalar& operator()(Index i) const {
+    return m_data[i * m_incr.value()];
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i) const {
+    return pgather<Scalar, PacketType>(m_data + i * m_incr.value(), m_incr.value());
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacketPartial(Index i, Index n, Index /*offset*/ = 0) const {
+    return pgather_partial<Scalar, PacketType>(m_data + i * m_incr.value(), m_incr.value(), n);
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketType& p) const {
+    pscatter<Scalar, PacketType>(m_data + i * m_incr.value(), p, m_incr.value());
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketPartial(Index i, const PacketType& p, Index n,
+                                                                Index /*offset*/ = 0) const {
+    pscatter_partial<Scalar, PacketType>(m_data + i * m_incr.value(), p, m_incr.value(), n);
+  }
+
+ protected:
+  Scalar* m_data;
+  const internal::variable_if_dynamic<Index, Incr> m_incr;
+};
+
+template <typename Scalar, typename Index, int StorageOrder, int AlignmentType, int Incr>
+class blas_data_mapper {
+ public:
+  typedef BlasLinearMapper<Scalar, Index, AlignmentType, Incr> LinearMapper;
+  typedef blas_data_mapper SubMapper;
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE blas_data_mapper(Scalar* data, Index stride, Index incr)
+      : m_data(data), m_stride(stride), m_incr(incr) {}
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE SubMapper getSubMapper(Index i, Index j) const {
+    return SubMapper(&operator()(i, j), m_stride, m_incr.value());
+  }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
+    return LinearMapper(&operator()(i, j), m_incr.value());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(Index i, Index j) const { internal::prefetch(&operator()(i, j)); }
+
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE Scalar& operator()(Index i, Index j) const {
+    return m_data[StorageOrder == RowMajor ? j * m_incr.value() + i * m_stride : i * m_incr.value() + j * m_stride];
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i, Index j) const {
+    return pgather<Scalar, PacketType>(&operator()(i, j), m_incr.value());
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacketPartial(Index i, Index j, Index n,
+                                                                     Index /*offset*/ = 0) const {
+    return pgather_partial<Scalar, PacketType>(&operator()(i, j), m_incr.value(), n);
+  }
+
+  template <typename PacketT, int AlignmentT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i, Index j) const {
+    return pgather<Scalar, PacketT>(&operator()(i, j), m_incr.value());
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, Index j, const PacketType& p) const {
+    pscatter<Scalar, PacketType>(&operator()(i, j), p, m_incr.value());
+  }
+
+  template <typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketPartial(Index i, Index j, const PacketType& p, Index n,
+                                                                Index /*offset*/ = 0) const {
+    pscatter_partial<Scalar, PacketType>(&operator()(i, j), p, m_incr.value(), n);
+  }
+
+  template <typename SubPacket>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void scatterPacket(Index i, Index j, const SubPacket& p) const {
+    pscatter<Scalar, SubPacket>(&operator()(i, j), p, m_stride);
+  }
+
+  template <typename SubPacket>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SubPacket gatherPacket(Index i, Index j) const {
+    return pgather<Scalar, SubPacket>(&operator()(i, j), m_stride);
+  }
+
+  // storePacketBlock_helper defines a way to access values inside the PacketBlock, this is essentially required by the
+  // Complex types.
+  template <typename SubPacket, typename Scalar_, int n, int idx>
+  struct storePacketBlock_helper {
+    storePacketBlock_helper<SubPacket, Scalar_, n, idx - 1> spbh;
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(
+        const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j,
+        const PacketBlock<SubPacket, n>& block) const {
+      spbh.store(sup, i, j, block);
+      sup->template storePacket<SubPacket>(i, j + idx, block.packet[idx]);
+    }
+  };
+
+  template <typename SubPacket, int n, int idx>
+  struct storePacketBlock_helper<SubPacket, std::complex<float>, n, idx> {
+    storePacketBlock_helper<SubPacket, std::complex<float>, n, idx - 1> spbh;
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(
+        const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j,
+        const PacketBlock<SubPacket, n>& block) const {
+      spbh.store(sup, i, j, block);
+      sup->template storePacket<SubPacket>(i, j + idx, block.packet[idx]);
+    }
+  };
+
+  template <typename SubPacket, int n, int idx>
+  struct storePacketBlock_helper<SubPacket, std::complex<double>, n, idx> {
+    storePacketBlock_helper<SubPacket, std::complex<double>, n, idx - 1> spbh;
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(
+        const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j,
+        const PacketBlock<SubPacket, n>& block) const {
+      spbh.store(sup, i, j, block);
+      for (int l = 0; l < unpacket_traits<SubPacket>::size; l++) {
+        std::complex<double>* v = &sup->operator()(i + l, j + idx);
+        v->real(block.packet[idx].v[2 * l + 0]);
+        v->imag(block.packet[idx].v[2 * l + 1]);
+      }
+    }
+  };
+
+  template <typename SubPacket, typename Scalar_, int n>
+  struct storePacketBlock_helper<SubPacket, Scalar_, n, -1> {
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(
+        const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index,
+        const PacketBlock<SubPacket, n>&) const {}
+  };
+
+  template <typename SubPacket, int n>
+  struct storePacketBlock_helper<SubPacket, std::complex<float>, n, -1> {
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(
+        const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index,
+        const PacketBlock<SubPacket, n>&) const {}
+  };
+
+  template <typename SubPacket, int n>
+  struct storePacketBlock_helper<SubPacket, std::complex<double>, n, -1> {
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(
+        const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index,
+        const PacketBlock<SubPacket, n>&) const {}
+  };
+  // This function stores a PacketBlock on m_data, this approach is really quite slow compare to Incr=1 and should be
+  // avoided when possible.
+  template <typename SubPacket, int n>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketBlock(Index i, Index j,
+                                                              const PacketBlock<SubPacket, n>& block) const {
+    storePacketBlock_helper<SubPacket, Scalar, n, n - 1> spb;
+    spb.store(this, i, j, block);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr const Index stride() const { return m_stride; }
+  EIGEN_DEVICE_FUNC constexpr const Index incr() const { return m_incr.value(); }
+  EIGEN_DEVICE_FUNC constexpr Scalar* data() const { return m_data; }
+
+ protected:
+  Scalar* EIGEN_RESTRICT m_data;
+  const Index m_stride;
+  const internal::variable_if_dynamic<Index, Incr> m_incr;
+};
+
+// lightweight helper class to access matrix coefficients (const version)
+template <typename Scalar, typename Index, int StorageOrder>
+class const_blas_data_mapper : public blas_data_mapper<const Scalar, Index, StorageOrder> {
+ public:
+  typedef const_blas_data_mapper<Scalar, Index, StorageOrder> SubMapper;
+
+  EIGEN_ALWAYS_INLINE const_blas_data_mapper(const Scalar* data, Index stride)
+      : blas_data_mapper<const Scalar, Index, StorageOrder>(data, stride) {}
+
+  EIGEN_ALWAYS_INLINE SubMapper getSubMapper(Index i, Index j) const {
+    return SubMapper(&(this->operator()(i, j)), this->m_stride);
+  }
+};
+
+/* Helper class to analyze the factors of a Product expression.
+ * In particular it allows to pop out operator-, scalar multiples,
+ * and conjugate */
+template <typename XprType>
+struct blas_traits {
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef const XprType& ExtractType;
+  typedef XprType ExtractType_;
+  enum {
+    IsComplex = NumTraits<Scalar>::IsComplex,
+    IsTransposed = false,
+    NeedToConjugate = false,
+    HasUsableDirectAccess =
+        ((int(XprType::Flags) & DirectAccessBit) &&
+         (bool(XprType::IsVectorAtCompileTime) || int(inner_stride_at_compile_time<XprType>::ret) == 1))
+            ? 1
+            : 0,
+    HasScalarFactor = false
+  };
+  typedef std::conditional_t<bool(HasUsableDirectAccess), ExtractType, typename ExtractType_::PlainObject>
+      DirectLinearAccessType;
+  EIGEN_DEVICE_FUNC static inline EIGEN_DEVICE_FUNC ExtractType extract(const XprType& x) { return x; }
+  EIGEN_DEVICE_FUNC static inline EIGEN_DEVICE_FUNC const Scalar extractScalarFactor(const XprType&) {
+    return Scalar(1);
+  }
+};
+
+// pop conjugate
+template <typename Scalar, typename NestedXpr>
+struct blas_traits<CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> > : blas_traits<NestedXpr> {
+  typedef blas_traits<NestedXpr> Base;
+  typedef CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> XprType;
+  typedef typename Base::ExtractType ExtractType;
+
+  enum { IsComplex = NumTraits<Scalar>::IsComplex, NeedToConjugate = Base::NeedToConjugate ? 0 : IsComplex };
+  EIGEN_DEVICE_FUNC static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); }
+  EIGEN_DEVICE_FUNC static inline Scalar extractScalarFactor(const XprType& x) {
+    return conj(Base::extractScalarFactor(x.nestedExpression()));
+  }
+};
+
+// pop scalar multiple
+template <typename Scalar, typename NestedXpr, typename Plain>
+struct blas_traits<
+    CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>, Plain>, NestedXpr> >
+    : blas_traits<NestedXpr> {
+  enum { HasScalarFactor = true };
+  typedef blas_traits<NestedXpr> Base;
+  typedef CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>, Plain>, NestedXpr>
+      XprType;
+  typedef typename Base::ExtractType ExtractType;
+  EIGEN_DEVICE_FUNC static inline EIGEN_DEVICE_FUNC ExtractType extract(const XprType& x) {
+    return Base::extract(x.rhs());
+  }
+  EIGEN_DEVICE_FUNC static inline EIGEN_DEVICE_FUNC Scalar extractScalarFactor(const XprType& x) {
+    return x.lhs().functor().m_other * Base::extractScalarFactor(x.rhs());
+  }
+};
+template <typename Scalar, typename NestedXpr, typename Plain>
+struct blas_traits<
+    CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>, Plain> > >
+    : blas_traits<NestedXpr> {
+  enum { HasScalarFactor = true };
+  typedef blas_traits<NestedXpr> Base;
+  typedef CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>, Plain> >
+      XprType;
+  typedef typename Base::ExtractType ExtractType;
+  EIGEN_DEVICE_FUNC static inline ExtractType extract(const XprType& x) { return Base::extract(x.lhs()); }
+  EIGEN_DEVICE_FUNC static inline Scalar extractScalarFactor(const XprType& x) {
+    return Base::extractScalarFactor(x.lhs()) * x.rhs().functor().m_other;
+  }
+};
+template <typename Scalar, typename Plain1, typename Plain2>
+struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>, Plain1>,
+                                 const CwiseNullaryOp<scalar_constant_op<Scalar>, Plain2> > >
+    : blas_traits<CwiseNullaryOp<scalar_constant_op<Scalar>, Plain1> > {};
+
+// pop opposite
+template <typename Scalar, typename NestedXpr>
+struct blas_traits<CwiseUnaryOp<scalar_opposite_op<Scalar>, NestedXpr> > : blas_traits<NestedXpr> {
+  enum { HasScalarFactor = true };
+  typedef blas_traits<NestedXpr> Base;
+  typedef CwiseUnaryOp<scalar_opposite_op<Scalar>, NestedXpr> XprType;
+  typedef typename Base::ExtractType ExtractType;
+  EIGEN_DEVICE_FUNC static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); }
+  EIGEN_DEVICE_FUNC static inline Scalar extractScalarFactor(const XprType& x) {
+    return -Base::extractScalarFactor(x.nestedExpression());
+  }
+};
+
+// pop/push transpose
+template <typename NestedXpr>
+struct blas_traits<Transpose<NestedXpr> > : blas_traits<NestedXpr> {
+  typedef typename NestedXpr::Scalar Scalar;
+  typedef blas_traits<NestedXpr> Base;
+  typedef Transpose<NestedXpr> XprType;
+  typedef Transpose<const typename Base::ExtractType_>
+      ExtractType;  // const to get rid of a compile error; anyway blas traits are only used on the RHS
+  typedef Transpose<const typename Base::ExtractType_> ExtractType_;
+  typedef std::conditional_t<bool(Base::HasUsableDirectAccess), ExtractType, typename ExtractType::PlainObject>
+      DirectLinearAccessType;
+  enum { IsTransposed = Base::IsTransposed ? 0 : 1 };
+  EIGEN_DEVICE_FUNC static inline ExtractType extract(const XprType& x) {
+    return ExtractType(Base::extract(x.nestedExpression()));
+  }
+  EIGEN_DEVICE_FUNC static inline Scalar extractScalarFactor(const XprType& x) {
+    return Base::extractScalarFactor(x.nestedExpression());
+  }
+};
+
+template <typename T>
+struct blas_traits<const T> : blas_traits<T> {};
+
+template <typename T, bool HasUsableDirectAccess = blas_traits<T>::HasUsableDirectAccess>
+struct extract_data_selector {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE static const typename T::Scalar* run(const T& m) {
+    return blas_traits<T>::extract(m).data();
+  }
+};
+
+template <typename T>
+struct extract_data_selector<T, false> {
+  EIGEN_DEVICE_FUNC constexpr static typename T::Scalar* run(const T&) { return 0; }
+};
+
+template <typename T>
+EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE const typename T::Scalar* extract_data(const T& m) {
+  return extract_data_selector<T>::run(m);
+}
+
+/**
+ * \c combine_scalar_factors extracts and multiplies factors from GEMM and GEMV products.
+ * There is a specialization for booleans
+ */
+template <typename ResScalar, typename Lhs, typename Rhs>
+struct combine_scalar_factors_impl {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE static ResScalar run(const Lhs& lhs, const Rhs& rhs) {
+    return blas_traits<Lhs>::extractScalarFactor(lhs) * blas_traits<Rhs>::extractScalarFactor(rhs);
+  }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE static ResScalar run(const ResScalar& alpha, const Lhs& lhs,
+                                                                       const Rhs& rhs) {
+    return alpha * blas_traits<Lhs>::extractScalarFactor(lhs) * blas_traits<Rhs>::extractScalarFactor(rhs);
+  }
+};
+template <typename Lhs, typename Rhs>
+struct combine_scalar_factors_impl<bool, Lhs, Rhs> {
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE static bool run(const Lhs& lhs, const Rhs& rhs) {
+    return blas_traits<Lhs>::extractScalarFactor(lhs) && blas_traits<Rhs>::extractScalarFactor(rhs);
+  }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE static bool run(const bool& alpha, const Lhs& lhs, const Rhs& rhs) {
+    return alpha && blas_traits<Lhs>::extractScalarFactor(lhs) && blas_traits<Rhs>::extractScalarFactor(rhs);
+  }
+};
+
+template <typename ResScalar, typename Lhs, typename Rhs>
+EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE ResScalar combine_scalar_factors(const ResScalar& alpha, const Lhs& lhs,
+                                                                                 const Rhs& rhs) {
+  return combine_scalar_factors_impl<ResScalar, Lhs, Rhs>::run(alpha, lhs, rhs);
+}
+template <typename ResScalar, typename Lhs, typename Rhs>
+EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE ResScalar combine_scalar_factors(const Lhs& lhs, const Rhs& rhs) {
+  return combine_scalar_factors_impl<ResScalar, Lhs, Rhs>::run(lhs, rhs);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BLASUTIL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ConfigureVectorization.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ConfigureVectorization.h
new file mode 100644
index 0000000..095735d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ConfigureVectorization.h
@@ -0,0 +1,609 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2018 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2020, Arm Limited and Contributors
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CONFIGURE_VECTORIZATION_H
+#define EIGEN_CONFIGURE_VECTORIZATION_H
+
+// Prepare for using the generic clang backend if requested.
+#if defined(EIGEN_VECTORIZE_GENERIC) && !defined(EIGEN_DONT_VECTORIZE) && !defined(EIGEN_DONT_ALIGN)
+#if !EIGEN_ARCH_VECTOR_EXTENSIONS
+#error "The compiler does not support clang vector extensions."
+#endif
+#define EIGEN_VECTORIZE
+#ifndef EIGEN_GENERIC_VECTOR_SIZE_BYTES
+#define EIGEN_GENERIC_VECTOR_SIZE_BYTES 64
+#endif
+#define EIGEN_MAX_ALIGN_BYTES EIGEN_GENERIC_VECTOR_SIZE_BYTES
+#endif
+
+//------------------------------------------------------------------------------------------
+// Static and dynamic alignment control
+//
+// The main purpose of this section is to define EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES
+// as the maximal boundary in bytes on which dynamically and statically allocated data may be alignment respectively.
+// The values of EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES can be specified by the user. If not,
+// a default value is automatically computed based on architecture, compiler, and OS.
+//
+// This section also defines macros EIGEN_ALIGN_TO_BOUNDARY(N) and the shortcuts EIGEN_ALIGN{8,16,32,_MAX}
+// to be used to declare statically aligned buffers.
+//------------------------------------------------------------------------------------------
+
+/* EIGEN_ALIGN_TO_BOUNDARY(n) forces data to be n-byte aligned. This is used to satisfy SIMD requirements.
+ * However, we do that EVEN if vectorization (EIGEN_VECTORIZE) is disabled,
+ * so that vectorization doesn't affect binary compatibility.
+ *
+ * If we made alignment depend on whether or not EIGEN_VECTORIZE is defined, it would be impossible to link
+ * vectorized and non-vectorized code.
+ */
+#if (defined EIGEN_CUDACC)
+#define EIGEN_ALIGN_TO_BOUNDARY(n) __align__(n)
+#define EIGEN_ALIGNOF(x) __alignof(x)
+#else
+#define EIGEN_ALIGN_TO_BOUNDARY(n) alignas(n)
+#define EIGEN_ALIGNOF(x) alignof(x)
+#endif
+
+// Align to the boundary that avoids false sharing.
+//   https://en.cppreference.com/w/cpp/thread/hardware_destructive_interference_size
+// There is a bug in android NDK < r26 where the macro is defined but std::hardware_destructive_interference_size
+// still does not exist.
+#if defined(__cpp_lib_hardware_interference_size) && __cpp_lib_hardware_interference_size >= 201603 && \
+    (!EIGEN_OS_ANDROID || __NDK_MAJOR__ + 0 >= 26)
+#include <new>
+#define EIGEN_ALIGN_TO_AVOID_FALSE_SHARING EIGEN_ALIGN_TO_BOUNDARY(std::hardware_destructive_interference_size)
+#else
+// Overalign for the cache line size of 128 bytes (Apple M1)
+#define EIGEN_ALIGN_TO_AVOID_FALSE_SHARING EIGEN_ALIGN_TO_BOUNDARY(128)
+#endif
+
+// If the user explicitly disable vectorization, then we also disable alignment
+#if defined(EIGEN_DONT_VECTORIZE)
+#if defined(EIGEN_GPUCC)
+// GPU code is always vectorized and requires memory alignment for
+// statically allocated buffers.
+#define EIGEN_IDEAL_MAX_ALIGN_BYTES 16
+#else
+#define EIGEN_IDEAL_MAX_ALIGN_BYTES 0
+#endif
+#elif defined(__AVX512F__)
+// 64 bytes static alignment is preferred only if really required
+#define EIGEN_IDEAL_MAX_ALIGN_BYTES 64
+#elif defined(__AVX__)
+// 32 bytes static alignment is preferred only if really required
+#define EIGEN_IDEAL_MAX_ALIGN_BYTES 32
+#elif defined __HVX__ && (__HVX_LENGTH__ == 128)
+#define EIGEN_IDEAL_MAX_ALIGN_BYTES 128
+#elif defined(EIGEN_RISCV64_USE_RVV10)
+#define EIGEN_IDEAL_MAX_ALIGN_BYTES 64
+#else
+#define EIGEN_IDEAL_MAX_ALIGN_BYTES 16
+#endif
+
+// EIGEN_MIN_ALIGN_BYTES defines the minimal value for which the notion of explicit alignment makes sense
+#define EIGEN_MIN_ALIGN_BYTES 16
+
+// Defined the boundary (in bytes) on which the data needs to be aligned. Note
+// that unless EIGEN_ALIGN is defined and not equal to 0, the data may not be
+// aligned at all regardless of the value of this #define.
+
+#if (defined(EIGEN_DONT_ALIGN_STATICALLY) || defined(EIGEN_DONT_ALIGN)) && defined(EIGEN_MAX_STATIC_ALIGN_BYTES) && \
+    EIGEN_MAX_STATIC_ALIGN_BYTES > 0
+#error EIGEN_MAX_STATIC_ALIGN_BYTES and EIGEN_DONT_ALIGN[_STATICALLY] are both defined with EIGEN_MAX_STATIC_ALIGN_BYTES!=0. Use EIGEN_MAX_STATIC_ALIGN_BYTES=0 as a synonym of EIGEN_DONT_ALIGN_STATICALLY.
+#endif
+
+// EIGEN_DONT_ALIGN_STATICALLY and EIGEN_DONT_ALIGN are deprecated
+// They imply EIGEN_MAX_STATIC_ALIGN_BYTES=0
+#if defined(EIGEN_DONT_ALIGN_STATICALLY) || defined(EIGEN_DONT_ALIGN)
+#ifdef EIGEN_MAX_STATIC_ALIGN_BYTES
+#undef EIGEN_MAX_STATIC_ALIGN_BYTES
+#endif
+#define EIGEN_MAX_STATIC_ALIGN_BYTES 0
+#endif
+
+#ifndef EIGEN_MAX_STATIC_ALIGN_BYTES
+
+// Try to automatically guess what is the best default value for EIGEN_MAX_STATIC_ALIGN_BYTES
+
+// 16 byte alignment is only useful for vectorization. Since it affects the ABI, we need to enable
+// 16 byte alignment on all platforms where vectorization might be enabled. In theory we could always
+// enable alignment, but it can be a cause of problems on some platforms, so we just disable it in
+// certain common platform (compiler+architecture combinations) to avoid these problems.
+// Only static alignment is really problematic (relies on nonstandard compiler extensions),
+// try to keep heap alignment even when we have to disable static alignment.
+#if EIGEN_COMP_GNUC && !(EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM_OR_ARM64 || EIGEN_ARCH_PPC || EIGEN_ARCH_IA64 || \
+                         EIGEN_ARCH_MIPS || EIGEN_ARCH_LOONGARCH64 || EIGEN_ARCH_RISCV)
+#define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 1
+#else
+#define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 0
+#endif
+
+// static alignment is completely disabled with GCC 3, Sun Studio, and QCC/QNX
+#if !EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT && !EIGEN_COMP_SUNCC && !EIGEN_OS_QNX
+#define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 1
+#else
+#define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 0
+#endif
+
+#if EIGEN_ARCH_WANTS_STACK_ALIGNMENT
+#define EIGEN_MAX_STATIC_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
+#else
+#define EIGEN_MAX_STATIC_ALIGN_BYTES 0
+#endif
+
+#endif
+
+// If EIGEN_MAX_ALIGN_BYTES is defined, then it is considered as an upper bound for EIGEN_MAX_STATIC_ALIGN_BYTES
+#if defined(EIGEN_MAX_ALIGN_BYTES) && EIGEN_MAX_ALIGN_BYTES < EIGEN_MAX_STATIC_ALIGN_BYTES
+#undef EIGEN_MAX_STATIC_ALIGN_BYTES
+#define EIGEN_MAX_STATIC_ALIGN_BYTES EIGEN_MAX_ALIGN_BYTES
+#endif
+
+#if EIGEN_MAX_STATIC_ALIGN_BYTES == 0 && !defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
+#define EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
+#endif
+
+// At this stage, EIGEN_MAX_STATIC_ALIGN_BYTES>0 is the true test whether we want to align arrays on the stack or not.
+// It takes into account both the user choice to explicitly enable/disable alignment (by setting
+// EIGEN_MAX_STATIC_ALIGN_BYTES) and the architecture config (EIGEN_ARCH_WANTS_STACK_ALIGNMENT). Henceforth, only
+// EIGEN_MAX_STATIC_ALIGN_BYTES should be used.
+
+// Shortcuts to EIGEN_ALIGN_TO_BOUNDARY
+#define EIGEN_ALIGN8 EIGEN_ALIGN_TO_BOUNDARY(8)
+#define EIGEN_ALIGN16 EIGEN_ALIGN_TO_BOUNDARY(16)
+#define EIGEN_ALIGN32 EIGEN_ALIGN_TO_BOUNDARY(32)
+#define EIGEN_ALIGN64 EIGEN_ALIGN_TO_BOUNDARY(64)
+#if EIGEN_MAX_STATIC_ALIGN_BYTES > 0
+#define EIGEN_ALIGN_MAX EIGEN_ALIGN_TO_BOUNDARY(EIGEN_MAX_STATIC_ALIGN_BYTES)
+#else
+#define EIGEN_ALIGN_MAX
+#endif
+
+// Dynamic alignment control
+
+#if defined(EIGEN_DONT_ALIGN) && defined(EIGEN_MAX_ALIGN_BYTES) && EIGEN_MAX_ALIGN_BYTES > 0
+#error EIGEN_MAX_ALIGN_BYTES and EIGEN_DONT_ALIGN are both defined with EIGEN_MAX_ALIGN_BYTES!=0. Use EIGEN_MAX_ALIGN_BYTES=0 as a synonym of EIGEN_DONT_ALIGN.
+#endif
+
+#ifdef EIGEN_DONT_ALIGN
+#ifdef EIGEN_MAX_ALIGN_BYTES
+#undef EIGEN_MAX_ALIGN_BYTES
+#endif
+#define EIGEN_MAX_ALIGN_BYTES 0
+#elif !defined(EIGEN_MAX_ALIGN_BYTES)
+#define EIGEN_MAX_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
+#endif
+
+#if EIGEN_IDEAL_MAX_ALIGN_BYTES > EIGEN_MAX_ALIGN_BYTES
+#define EIGEN_DEFAULT_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
+#else
+#define EIGEN_DEFAULT_ALIGN_BYTES EIGEN_MAX_ALIGN_BYTES
+#endif
+
+#ifndef EIGEN_UNALIGNED_VECTORIZE
+#define EIGEN_UNALIGNED_VECTORIZE 1
+#endif
+
+//----------------------------------------------------------------------
+
+// if alignment is disabled, then disable vectorization. Note: EIGEN_MAX_ALIGN_BYTES is the proper check, it takes into
+// account both the user's will (EIGEN_MAX_ALIGN_BYTES,EIGEN_DONT_ALIGN) and our own platform checks
+#if EIGEN_MAX_ALIGN_BYTES == 0
+#ifndef EIGEN_DONT_VECTORIZE
+#define EIGEN_DONT_VECTORIZE
+#endif
+#endif
+
+// The following (except #include <malloc.h> and _M_IX86_FP ??) can likely be
+// removed as gcc 4.1 and msvc 2008 are not supported anyways.
+#if EIGEN_COMP_MSVC
+#include <malloc.h>  // for _aligned_malloc -- need it regardless of whether vectorization is enabled
+// a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
+#if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || EIGEN_ARCH_x86_64
+#define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
+#endif
+#else
+#if defined(__SSE2__)
+#define EIGEN_SSE2_ON_NON_MSVC
+#endif
+#endif
+
+#if !(defined(EIGEN_DONT_VECTORIZE) || defined(EIGEN_GPUCC) || defined(EIGEN_VECTORIZE_GENERIC))
+
+#if defined(EIGEN_SSE2_ON_NON_MSVC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)
+
+// Defines symbols for compile-time detection of which instructions are
+// used.
+// EIGEN_VECTORIZE_YY is defined if and only if the instruction set YY is used
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_SSE
+#define EIGEN_VECTORIZE_SSE2
+
+// Detect sse3/ssse3/sse4:
+// gcc and icc defines __SSE3__, ...
+// there is no way to know about this on msvc. You can define EIGEN_VECTORIZE_SSE* if you
+// want to force the use of those instructions with msvc.
+#ifdef __SSE3__
+#define EIGEN_VECTORIZE_SSE3
+#endif
+#ifdef __SSSE3__
+#define EIGEN_VECTORIZE_SSSE3
+#endif
+#ifdef __SSE4_1__
+#define EIGEN_VECTORIZE_SSE4_1
+#endif
+#ifdef __SSE4_2__
+#define EIGEN_VECTORIZE_SSE4_2
+#endif
+#ifdef __AVX__
+#if !defined(EIGEN_USE_SYCL) && !EIGEN_COMP_EMSCRIPTEN
+#define EIGEN_VECTORIZE_AVX
+#endif
+#define EIGEN_VECTORIZE_SSE3
+#define EIGEN_VECTORIZE_SSSE3
+#define EIGEN_VECTORIZE_SSE4_1
+#define EIGEN_VECTORIZE_SSE4_2
+#endif
+#ifdef __AVX2__
+#ifndef EIGEN_USE_SYCL
+#define EIGEN_VECTORIZE_AVX2
+#define EIGEN_VECTORIZE_AVX
+#endif
+#define EIGEN_VECTORIZE_SSE3
+#define EIGEN_VECTORIZE_SSSE3
+#define EIGEN_VECTORIZE_SSE4_1
+#define EIGEN_VECTORIZE_SSE4_2
+#endif
+#if defined(__FMA__) || (EIGEN_COMP_MSVC && defined(__AVX2__))
+// MSVC does not expose a switch dedicated for FMA
+// For MSVC, AVX2 => FMA
+#define EIGEN_VECTORIZE_FMA
+#endif
+#if defined(__AVX512F__)
+#ifndef EIGEN_VECTORIZE_FMA
+#if EIGEN_COMP_GNUC
+#error Please add -mfma to your compiler flags: compiling with -mavx512f alone without SSE/AVX FMA is not supported (bug 1638).
+#else
+#error Please enable FMA in your compiler flags (e.g. -mfma): compiling with AVX512 alone without SSE/AVX FMA is not supported (bug 1638).
+#endif
+#endif
+#ifndef EIGEN_USE_SYCL
+#define EIGEN_VECTORIZE_AVX512
+#define EIGEN_VECTORIZE_AVX2
+#define EIGEN_VECTORIZE_AVX
+#endif
+#define EIGEN_VECTORIZE_FMA
+#define EIGEN_VECTORIZE_SSE3
+#define EIGEN_VECTORIZE_SSSE3
+#define EIGEN_VECTORIZE_SSE4_1
+#define EIGEN_VECTORIZE_SSE4_2
+#ifndef EIGEN_USE_SYCL
+#ifdef __AVX512DQ__
+#define EIGEN_VECTORIZE_AVX512DQ
+#endif
+#ifdef __AVX512ER__
+#define EIGEN_VECTORIZE_AVX512ER
+#endif
+#ifdef __AVX512BF16__
+#define EIGEN_VECTORIZE_AVX512BF16
+#endif
+#ifdef __AVX512VL__
+#define EIGEN_VECTORIZE_AVX512VL
+#endif
+#ifdef __AVX512FP16__
+#ifdef __AVX512VL__
+#define EIGEN_VECTORIZE_AVX512FP16
+// Built-in _Float16.
+#define EIGEN_HAS_BUILTIN_FLOAT16 1
+#else
+#if EIGEN_COMP_GNUC
+#error Please add -mavx512vl to your compiler flags: compiling with -mavx512fp16 alone without AVX512-VL is not supported.
+#else
+#error Please enable AVX512-VL in your compiler flags (e.g. -mavx512vl): compiling with AVX512-FP16 alone without AVX512-VL is not supported.
+#endif
+#endif
+#endif
+#endif
+#endif
+
+// Disable AVX support on broken xcode versions
+#if (EIGEN_COMP_CLANGAPPLE == 11000033) && (__MAC_OS_X_VERSION_MIN_REQUIRED == 101500)
+// A nasty bug in the clang compiler shipped with xcode in a common compilation situation
+// when XCode 11.0 and Mac deployment target macOS 10.15 is https://trac.macports.org/ticket/58776#no1
+#ifdef EIGEN_VECTORIZE_AVX
+#undef EIGEN_VECTORIZE_AVX
+#warning \
+    "Disabling AVX support: clang compiler shipped with XCode 11.[012] generates broken assembly with -macosx-version-min=10.15 and AVX enabled. "
+#ifdef EIGEN_VECTORIZE_AVX2
+#undef EIGEN_VECTORIZE_AVX2
+#endif
+#ifdef EIGEN_VECTORIZE_FMA
+#undef EIGEN_VECTORIZE_FMA
+#endif
+#ifdef EIGEN_VECTORIZE_AVX512
+#undef EIGEN_VECTORIZE_AVX512
+#endif
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+#undef EIGEN_VECTORIZE_AVX512DQ
+#endif
+#ifdef EIGEN_VECTORIZE_AVX512ER
+#undef EIGEN_VECTORIZE_AVX512ER
+#endif
+#endif
+// NOTE: Confirmed test failures in XCode 11.0, and XCode 11.2 with  -macosx-version-min=10.15 and AVX
+// NOTE using -macosx-version-min=10.15 with Xcode 11.0 results in runtime segmentation faults in many tests, 11.2
+// produce core dumps in 3 tests NOTE using -macosx-version-min=10.14 produces functioning and passing tests in all
+// cases NOTE __clang_version__ "11.0.0 (clang-1100.0.33.8)"  XCode 11.0 <- Produces many segfault and core dumping
+// tests
+//                                                                    with  -macosx-version-min=10.15 and AVX
+// NOTE __clang_version__ "11.0.0 (clang-1100.0.33.12)" XCode 11.2 <- Produces 3 core dumping tests with
+//                                                                    -macosx-version-min=10.15 and AVX
+#endif
+
+// include files
+
+// This extern "C" works around a MINGW-w64 compilation issue
+// https://sourceforge.net/tracker/index.php?func=detail&aid=3018394&group_id=202880&atid=983354
+// In essence, intrin.h is included by windows.h and also declares intrinsics (just as emmintrin.h etc. below do).
+// However, intrin.h uses an extern "C" declaration, and g++ thus complains of duplicate declarations
+// with conflicting linkage.  The linkage for intrinsics doesn't matter, but at that stage the compiler doesn't know;
+// so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
+// notice that since these are C headers, the extern "C" is theoretically needed anyways.
+extern "C" {
+// In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.
+// Doing so triggers some issues with ICC. However old gcc versions may not have this file, thus:
+#if EIGEN_COMP_ICC >= 1110 || EIGEN_COMP_EMSCRIPTEN
+#include <immintrin.h>
+#else
+#include <mmintrin.h>
+#include <emmintrin.h>
+#include <xmmintrin.h>
+#ifdef EIGEN_VECTORIZE_SSE3
+#include <pmmintrin.h>
+#endif
+#ifdef EIGEN_VECTORIZE_SSSE3
+#include <tmmintrin.h>
+#endif
+#ifdef EIGEN_VECTORIZE_SSE4_1
+#include <smmintrin.h>
+#endif
+#ifdef EIGEN_VECTORIZE_SSE4_2
+#include <nmmintrin.h>
+#endif
+#if defined(EIGEN_VECTORIZE_AVX) || defined(EIGEN_VECTORIZE_AVX512)
+#include <immintrin.h>
+#endif
+#endif
+}  // end extern "C"
+
+#elif defined(__VSX__) && !defined(__APPLE__)
+
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_VSX 1
+#define EIGEN_VECTORIZE_FMA
+#include <altivec.h>
+// We need to #undef macros defined by <altivec.h> that conflict with standard C++ names.
+// => use __vector instead of vector
+#undef bool
+#undef vector
+#undef pixel
+
+#elif defined __ALTIVEC__
+
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_ALTIVEC
+#define EIGEN_VECTORIZE_FMA
+#include <altivec.h>
+// We need to #undef macros defined by <altivec.h> that conflict with standard C++ names.
+// => use __vector instead of vector
+#undef bool
+#undef vector
+#undef pixel
+
+#elif ((defined __ARM_NEON) || (defined __ARM_NEON__)) && !(defined EIGEN_ARM64_USE_SVE)
+
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_NEON
+#include <arm_neon.h>
+
+// We currently require SVE to be enabled explicitly via EIGEN_ARM64_USE_SVE and
+// will not select the backend automatically
+#elif (defined __ARM_FEATURE_SVE) && (defined EIGEN_ARM64_USE_SVE)
+
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_SVE
+#include <arm_sve.h>
+
+// Since we depend on knowing SVE vector length at compile-time, we need
+// to ensure a fixed length is set
+#if defined __ARM_FEATURE_SVE_BITS
+#define EIGEN_ARM64_SVE_VL __ARM_FEATURE_SVE_BITS
+#else
+#error "Eigen requires a fixed SVE lector length but EIGEN_ARM64_SVE_VL is not set."
+#endif
+
+#elif EIGEN_ARCH_RISCV
+
+#if defined(__riscv_zfh)
+#define EIGEN_HAS_BUILTIN_FLOAT16
+#endif
+
+// We currently require RVV to be enabled explicitly via EIGEN_RISCV64_USE_RVV and
+// will not select the backend automatically
+#if (defined EIGEN_RISCV64_USE_RVV10)
+
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_RVV10
+#include <riscv_vector.h>
+
+// Since we depend on knowing RVV vector length at compile-time, we need
+// to ensure a fixed length is set
+#if defined(__riscv_v_fixed_vlen)
+#define EIGEN_RISCV64_RVV_VL __riscv_v_fixed_vlen
+#if __riscv_v_fixed_vlen >= 256
+#undef EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT
+#define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 1
+#endif
+#else
+#error "Eigen requires a fixed RVV vector length but -mrvv-vector-bits=zvl is not set."
+#endif
+
+#undef EIGEN_STACK_ALLOCATION_LIMIT
+#define EIGEN_STACK_ALLOCATION_LIMIT 196608
+
+#if defined(__riscv_zvfh) && defined(__riscv_zfh)
+#define EIGEN_VECTORIZE_RVV10FP16
+#elif defined(__riscv_zvfh)
+#if defined(__GNUC__) || defined(__clang__)
+#warning "The Eigen::Half vectorization requires Zfh and Zvfh extensions."
+#elif defined(_MSC_VER)
+#pragma message("The Eigen::Half vectorization requires Zfh and Zvfh extensions.")
+#endif
+#endif
+
+#if defined(__riscv_zvfbfwma)
+#define EIGEN_VECTORIZE_RVV10BF16
+#endif
+
+#endif  // defined(EIGEN_ARCH_RISCV)
+
+#elif (defined __s390x__ && defined __VEC__)
+
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_ZVECTOR
+#include <vecintrin.h>
+
+#elif defined __mips_msa
+
+// Limit MSA optimizations to little-endian CPUs for now.
+// TODO: Perhaps, eventually support MSA optimizations on big-endian CPUs?
+#if defined(__BYTE_ORDER__) && (__BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
+#if defined(__LP64__)
+#define EIGEN_MIPS_64
+#else
+#define EIGEN_MIPS_32
+#endif
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_MSA
+#include <msa.h>
+#endif
+
+#elif (defined __loongarch64 && defined __loongarch_sx)
+
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_LSX
+#include <lsxintrin.h>
+
+#elif defined __HVX__ && (__HVX_LENGTH__ == 128)
+
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_HVX
+#include <hexagon_types.h>
+
+#endif
+#endif
+
+// Following the Arm ACLE arm_neon.h should also include arm_fp16.h but not all
+// compilers seem to follow this. We therefore include it explicitly.
+// See also: https://bugs.llvm.org/show_bug.cgi?id=47955
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+#include <arm_fp16.h>
+#endif
+
+// Enable FMA for ARM.
+#if defined(__ARM_FEATURE_FMA)
+#define EIGEN_VECTORIZE_FMA
+#endif
+
+#if defined(__F16C__) && !defined(EIGEN_GPUCC) && (!EIGEN_COMP_CLANG_STRICT || EIGEN_CLANG_STRICT_AT_LEAST(3, 8, 0))
+// We can use the optimized fp16 to float and float to fp16 conversion routines
+#define EIGEN_HAS_FP16_C
+
+#if EIGEN_COMP_GNUC
+// Make sure immintrin.h is included, even if e.g. vectorization is
+// explicitly disabled (see also issue #2395).
+// Note that FP16C intrinsics for gcc and clang are included by immintrin.h,
+// as opposed to emmintrin.h as suggested by Intel:
+// https://software.intel.com/sites/landingpage/IntrinsicsGuide/#othertechs=FP16C&expand=1711
+#include <immintrin.h>
+#endif
+#endif
+
+#if defined EIGEN_CUDACC
+#define EIGEN_VECTORIZE_GPU
+#include <vector_types.h>
+#if EIGEN_CUDA_SDK_VER >= 70500
+#define EIGEN_HAS_CUDA_FP16
+#endif
+#endif
+
+#if defined(EIGEN_HAS_CUDA_FP16)
+#include <cuda_runtime_api.h>
+#include <cuda_fp16.h>
+#endif
+
+#if defined(EIGEN_HIPCC)
+#define EIGEN_VECTORIZE_GPU
+#include <hip/hip_vector_types.h>
+#define EIGEN_HAS_HIP_FP16
+#include <hip/hip_fp16.h>
+#define EIGEN_HAS_HIP_BF16
+#include <hip/hip_bfloat16.h>
+#endif
+
+#if defined(__riscv)
+// Defines the default LMUL for RISC-V
+#ifndef EIGEN_RISCV64_DEFAULT_LMUL
+#define EIGEN_RISCV64_DEFAULT_LMUL 1
+#endif
+#endif
+
+/** \brief Namespace containing all symbols from the %Eigen library. */
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+inline static const char* SimdInstructionSetsInUse(void) {
+#if defined(EIGEN_VECTORIZE_AVX512)
+  return "AVX512, FMA, AVX2, AVX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
+#elif defined(EIGEN_VECTORIZE_AVX)
+  return "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
+#elif defined(EIGEN_VECTORIZE_SSE4_2)
+  return "SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
+#elif defined(EIGEN_VECTORIZE_SSE4_1)
+  return "SSE, SSE2, SSE3, SSSE3, SSE4.1";
+#elif defined(EIGEN_VECTORIZE_SSSE3)
+  return "SSE, SSE2, SSE3, SSSE3";
+#elif defined(EIGEN_VECTORIZE_SSE3)
+  return "SSE, SSE2, SSE3";
+#elif defined(EIGEN_VECTORIZE_SSE2)
+  return "SSE, SSE2";
+#elif defined(EIGEN_VECTORIZE_ALTIVEC)
+  return "AltiVec";
+#elif defined(EIGEN_VECTORIZE_VSX)
+  return "VSX";
+#elif defined(EIGEN_VECTORIZE_NEON)
+  return "ARM NEON";
+#elif defined(EIGEN_VECTORIZE_SVE)
+  return "ARM SVE";
+#elif defined(EIGEN_VECTORIZE_ZVECTOR)
+  return "S390X ZVECTOR";
+#elif defined(EIGEN_VECTORIZE_MSA)
+  return "MIPS MSA";
+#elif defined(EIGEN_VECTORIZE_LSX)
+  return "LOONGARCH64 LSX";
+#else
+  return "None";
+#endif
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CONFIGURE_VECTORIZATION_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Constants.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Constants.h
new file mode 100644
index 0000000..f7e8b70
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Constants.h
@@ -0,0 +1,601 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2020, Arm Limited and Contributors
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CONSTANTS_H
+#define EIGEN_CONSTANTS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** This value means that a positive quantity (e.g., a size) is not known at compile-time, and that instead the value is
+ * stored in some runtime variable.
+ *
+ * Changing the value of Dynamic breaks the ABI, as Dynamic is often used as a template parameter for Matrix.
+ */
+constexpr int Dynamic = -1;
+
+/** This value means that a signed quantity (e.g., a signed index) is not known at compile-time, and that instead its
+ * value has to be specified at runtime.
+ */
+constexpr int DynamicIndex = 0xffffff;
+
+/** This value means that the requested value is not defined.
+ */
+constexpr int Undefined = 0xfffffe;
+
+/** This value means +Infinity; it is currently used only as the p parameter to MatrixBase::lpNorm<int>().
+ * The value Infinity there means the L-infinity norm.
+ */
+constexpr int Infinity = -1;
+
+/** This value means that the cost to evaluate an expression coefficient is either very expensive or
+ * cannot be known at compile time.
+ *
+ * This value has to be positive to (1) simplify cost computation, and (2) allow to distinguish between a very expensive
+ * and very very expensive expressions. It thus must also be large enough to make sure unrolling won't happen and that
+ * sub expressions will be evaluated, but not too large to avoid overflow.
+ */
+constexpr int HugeCost = 10000;
+
+/** \defgroup flags Flags
+ * \ingroup Core_Module
+ *
+ * These are the possible bits which can be OR'ed to constitute the flags of a matrix or
+ * expression.
+ *
+ * It is important to note that these flags are a purely compile-time notion. They are a compile-time property of
+ * an expression type, implemented as enum's. They are not stored in memory at runtime, and they do not incur any
+ * runtime overhead.
+ *
+ * \sa MatrixBase::Flags
+ */
+
+/** \ingroup flags
+ *
+ * for a matrix, this means that the storage order is row-major.
+ * If this bit is not set, the storage order is column-major.
+ * For an expression, this determines the storage order of
+ * the matrix created by evaluation of that expression.
+ * \sa \blank  \ref TopicStorageOrders */
+constexpr unsigned int RowMajorBit = 0x1;
+
+/** \ingroup flags
+ * means the expression should be evaluated by the calling expression */
+constexpr unsigned int EvalBeforeNestingBit = 0x2;
+
+/** \ingroup flags
+ * \deprecated
+ * means the expression should be evaluated before any assignment */
+EIGEN_DEPRECATED constexpr unsigned int EvalBeforeAssigningBit = 0x4;  // FIXME deprecated
+
+/** \ingroup flags
+ *
+ * Short version: means the expression might be vectorized
+ *
+ * Long version: means that the coefficients can be handled by packets
+ * and start at a memory location whose alignment meets the requirements
+ * of the present CPU architecture for optimized packet access. In the fixed-size
+ * case, there is the additional condition that it be possible to access all the
+ * coefficients by packets (this implies the requirement that the size be a multiple of 16 bytes,
+ * and that any nontrivial strides don't break the alignment). In the dynamic-size case,
+ * there is no such condition on the total size and strides, so it might not be possible to access
+ * all coeffs by packets.
+ *
+ * \note This bit can be set regardless of whether vectorization is actually enabled.
+ *       To check for actual vectorizability, see \a ActualPacketAccessBit.
+ */
+constexpr unsigned int PacketAccessBit = 0x8;
+
+#ifdef EIGEN_VECTORIZE
+/** \ingroup flags
+ *
+ * If vectorization is enabled (EIGEN_VECTORIZE is defined) this constant
+ * is set to the value \a PacketAccessBit.
+ *
+ * If vectorization is not enabled (EIGEN_VECTORIZE is not defined) this constant
+ * is set to the value 0.
+ */
+constexpr unsigned int ActualPacketAccessBit = PacketAccessBit;
+#else
+constexpr unsigned int ActualPacketAccessBit = 0x0;
+#endif
+
+/** \ingroup flags
+ *
+ * Short version: means the expression can be seen as 1D vector.
+ *
+ * Long version: means that one can access the coefficients
+ * of this expression by coeff(int), and coeffRef(int) in the case of a lvalue expression. These
+ * index-based access methods are guaranteed
+ * to not have to do any runtime computation of a (row, col)-pair from the index, so that it
+ * is guaranteed that whenever it is available, index-based access is at least as fast as
+ * (row,col)-based access. Expressions for which that isn't possible don't have the LinearAccessBit.
+ *
+ * If both PacketAccessBit and LinearAccessBit are set, then the
+ * packets of this expression can be accessed by packet(int), and writePacket(int) in the case of a
+ * lvalue expression.
+ *
+ * Typically, all vector expressions have the LinearAccessBit, but there is one exception:
+ * Product expressions don't have it, because it would be troublesome for vectorization, even when the
+ * Product is a vector expression. Thus, vector Product expressions allow index-based coefficient access but
+ * not index-based packet access, so they don't have the LinearAccessBit.
+ */
+constexpr unsigned int LinearAccessBit = 0x10;
+
+/** \ingroup flags
+ *
+ * Means the expression has a coeffRef() method, i.e. is writable as its individual coefficients are directly
+ * addressable. This rules out read-only expressions.
+ *
+ * Note that DirectAccessBit and LvalueBit are mutually orthogonal, as there are examples of expression having one but
+ * not the other: \li writable expressions that don't have a very simple memory layout as a strided array, have
+ * LvalueBit but not DirectAccessBit \li Map-to-const expressions, for example Map<const Matrix>, have DirectAccessBit
+ * but not LvalueBit
+ *
+ * Expressions having LvalueBit also have their coeff() method returning a const reference instead of returning a new
+ * value.
+ */
+constexpr unsigned int LvalueBit = 0x20;
+
+/** \ingroup flags
+ *
+ * Means that the underlying array of coefficients can be directly accessed as a plain strided array. The memory layout
+ * of the array of coefficients must be exactly the natural one suggested by rows(), cols(),
+ * outerStride(), innerStride(), and the RowMajorBit. This rules out expressions such as Diagonal, whose coefficients,
+ * though referenceable, do not have such a regular memory layout.
+ *
+ * See the comment on LvalueBit for an explanation of how LvalueBit and DirectAccessBit are mutually orthogonal.
+ */
+constexpr unsigned int DirectAccessBit = 0x40;
+
+/** \deprecated \ingroup flags
+ *
+ * means the first coefficient packet is guaranteed to be aligned.
+ * An expression cannot have the AlignedBit without the PacketAccessBit flag.
+ * In other words, this means we are allow to perform an aligned packet access to the first element regardless
+ * of the expression kind:
+ * \code
+ * expression.packet<Aligned>(0);
+ * \endcode
+ */
+EIGEN_DEPRECATED constexpr unsigned int AlignedBit = 0x80;
+
+constexpr unsigned int NestByRefBit = 0x100;
+
+/** \ingroup flags
+ *
+ * for an expression, this means that the storage order
+ * can be either row-major or column-major.
+ * The precise choice will be decided at evaluation time or when
+ * combined with other expressions.
+ * \sa \blank  \ref RowMajorBit, \ref TopicStorageOrders */
+constexpr unsigned int NoPreferredStorageOrderBit = 0x200;
+
+/** \ingroup flags
+  *
+  * Means that the underlying coefficients can be accessed through pointers to the sparse (un)compressed storage format,
+  * that is, the expression provides:
+  * \code
+    inline const Scalar* valuePtr() const;
+    inline const Index* innerIndexPtr() const;
+    inline const Index* outerIndexPtr() const;
+    inline const Index* innerNonZeroPtr() const;
+    \endcode
+  */
+constexpr unsigned int CompressedAccessBit = 0x400;
+
+// list of flags that are inherited by default
+constexpr unsigned int HereditaryBits = RowMajorBit | EvalBeforeNestingBit;
+
+/** \defgroup enums Enumerations
+ * \ingroup Core_Module
+ *
+ * Various enumerations used in %Eigen. Many of these are used as template parameters.
+ */
+
+/** \ingroup enums
+ * Enum containing possible values for the \c Mode or \c UpLo parameter of
+ * MatrixBase::selfadjointView() and MatrixBase::triangularView(), and selfadjoint solvers. */
+enum UpLoType {
+  /** View matrix as a lower triangular matrix. */
+  Lower = 0x1,
+  /** View matrix as an upper triangular matrix. */
+  Upper = 0x2,
+  /** %Matrix has ones on the diagonal; to be used in combination with #Lower or #Upper. */
+  UnitDiag = 0x4,
+  /** %Matrix has zeros on the diagonal; to be used in combination with #Lower or #Upper. */
+  ZeroDiag = 0x8,
+  /** View matrix as a lower triangular matrix with ones on the diagonal. */
+  UnitLower = UnitDiag | Lower,
+  /** View matrix as an upper triangular matrix with ones on the diagonal. */
+  UnitUpper = UnitDiag | Upper,
+  /** View matrix as a lower triangular matrix with zeros on the diagonal. */
+  StrictlyLower = ZeroDiag | Lower,
+  /** View matrix as an upper triangular matrix with zeros on the diagonal. */
+  StrictlyUpper = ZeroDiag | Upper,
+  /** Used in BandMatrix and SelfAdjointView to indicate that the matrix is self-adjoint. */
+  SelfAdjoint = 0x10,
+  /** Used to support symmetric, non-selfadjoint, complex matrices. */
+  Symmetric = 0x20
+};
+
+/** \ingroup enums
+ * Enum for indicating whether a buffer is aligned or not. */
+enum AlignmentType {
+  Unaligned = 0,    /**< Data pointer has no specific alignment. */
+  Aligned8 = 8,     /**< Data pointer is aligned on a 8 bytes boundary. */
+  Aligned16 = 16,   /**< Data pointer is aligned on a 16 bytes boundary. */
+  Aligned32 = 32,   /**< Data pointer is aligned on a 32 bytes boundary. */
+  Aligned64 = 64,   /**< Data pointer is aligned on a 64 bytes boundary. */
+  Aligned128 = 128, /**< Data pointer is aligned on a 128 bytes boundary. */
+  AlignedMask = 255,
+  Aligned = 16, /**< \deprecated Synonym for Aligned16. */
+#if EIGEN_MAX_ALIGN_BYTES == 128
+  AlignedMax = Aligned128
+#elif EIGEN_MAX_ALIGN_BYTES == 64
+  AlignedMax = Aligned64
+#elif EIGEN_MAX_ALIGN_BYTES == 32
+  AlignedMax = Aligned32
+#elif EIGEN_MAX_ALIGN_BYTES == 16
+  AlignedMax = Aligned16
+#elif EIGEN_MAX_ALIGN_BYTES == 8
+  AlignedMax = Aligned8
+#elif EIGEN_MAX_ALIGN_BYTES == 0
+  AlignedMax = Unaligned
+#else
+#error Invalid value for EIGEN_MAX_ALIGN_BYTES
+#endif
+};
+
+/** \ingroup enums
+ * Enum containing possible values for the \p Direction parameter of
+ * Reverse, PartialReduxExpr and VectorwiseOp. */
+enum DirectionType {
+  /** For Reverse, all columns are reversed;
+   * for PartialReduxExpr and VectorwiseOp, act on columns. */
+  Vertical,
+  /** For Reverse, all rows are reversed;
+   * for PartialReduxExpr and VectorwiseOp, act on rows. */
+  Horizontal,
+  /** For Reverse, both rows and columns are reversed;
+   * not used for PartialReduxExpr and VectorwiseOp. */
+  BothDirections
+};
+
+/** \internal \ingroup enums
+ * Enum to specify how to traverse the entries of a matrix. */
+enum TraversalType {
+  /** \internal Default traversal, no vectorization, no index-based access */
+  DefaultTraversal,
+  /** \internal No vectorization, use index-based access to have only one for loop instead of 2 nested loops */
+  LinearTraversal,
+  /** \internal Equivalent to a slice vectorization for fixed-size matrices having good alignment
+   * and good size */
+  InnerVectorizedTraversal,
+  /** \internal Vectorization path using a single loop plus scalar loops for the
+   * unaligned boundaries */
+  LinearVectorizedTraversal,
+  /** \internal Generic vectorization path using one vectorized loop per row/column with some
+   * scalar loops to handle the unaligned boundaries */
+  SliceVectorizedTraversal,
+  /** \internal Special case to properly handle incompatible scalar types or other defecting cases*/
+  InvalidTraversal,
+  /** \internal Evaluate all entries at once */
+  AllAtOnceTraversal
+};
+
+/** \internal \ingroup enums
+ * Enum to specify whether to unroll loops when traversing over the entries of a matrix. */
+enum UnrollingType {
+  /** \internal Do not unroll loops. */
+  NoUnrolling,
+  /** \internal Unroll only the inner loop, but not the outer loop. */
+  InnerUnrolling,
+  /** \internal Unroll both the inner and the outer loop. If there is only one loop,
+   * because linear traversal is used, then unroll that loop. */
+  CompleteUnrolling
+};
+
+/** \internal \ingroup enums
+ * Enum to specify whether to use the default (built-in) implementation or the specialization. */
+enum SpecializedType { Specialized, BuiltIn };
+
+/** \ingroup enums
+ * Enum containing possible values for the \p Options_ template parameter of
+ * Matrix, Array and BandMatrix. */
+enum StorageOptions {
+  /** Storage order is column major (see \ref TopicStorageOrders). */
+  ColMajor = 0,
+  /** Storage order is row major (see \ref TopicStorageOrders). */
+  RowMajor = 0x1,  // it is only a coincidence that this is equal to RowMajorBit -- don't rely on that
+  /** Align the matrix itself if it is vectorizable fixed-size */
+  AutoAlign = 0,
+  /** Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be requested to be aligned) */ // FIXME --- clarify the situation
+  DontAlign = 0x2
+};
+
+/** \ingroup enums
+ * Enum for specifying whether to apply or solve on the left or right. */
+enum SideType {
+  /** Apply transformation on the left. */
+  OnTheLeft = 1,
+  /** Apply transformation on the right. */
+  OnTheRight = 2
+};
+
+/** \ingroup enums
+ * Enum for specifying NaN-propagation behavior, e.g. for coeff-wise min/max. */
+enum NaNPropagationOptions {
+  /**  Implementation defined behavior if NaNs are present. */
+  PropagateFast = 0,
+  /**  Always propagate NaNs. */
+  PropagateNaN,
+  /**  Always propagate not-NaNs. */
+  PropagateNumbers
+};
+
+/* the following used to be written as:
+ *
+ *   struct NoChange_t {};
+ *   namespace {
+ *     EIGEN_UNUSED NoChange_t NoChange;
+ *   }
+ *
+ * on the ground that it feels dangerous to disambiguate overloaded functions on enum/integer types.
+ * However, this leads to "variable declared but never referenced" warnings on Intel Composer XE,
+ * and we do not know how to get rid of them (bug 450).
+ */
+
+enum NoChange_t { NoChange };
+enum Sequential_t { Sequential };
+enum Default_t { Default };
+
+/** \internal \ingroup enums
+ * Used in AmbiVector. */
+enum AmbiVectorMode { IsDense = 0, IsSparse };
+
+/** \ingroup enums
+ * Used as template parameter in DenseCoeffBase and MapBase to indicate
+ * which accessors should be provided. */
+enum AccessorLevels {
+  /** Read-only access via a member function. */
+  ReadOnlyAccessors,
+  /** Read/write access via member functions. */
+  WriteAccessors,
+  /** Direct read-only access to the coefficients. */
+  DirectAccessors,
+  /** Direct read/write access to the coefficients. */
+  DirectWriteAccessors
+};
+
+/** \ingroup enums
+ * Enum with options to give to various decompositions. */
+enum DecompositionOptions {
+  /** \internal Not used (meant for LDLT?). */
+  Pivoting = 0x01,
+  /** \internal Not used (meant for LDLT?). */
+  NoPivoting = 0x02,
+  /** Used in JacobiSVD to indicate that the square matrix U is to be computed. */
+  ComputeFullU = 0x04,
+  /** Used in JacobiSVD to indicate that the thin matrix U is to be computed. */
+  ComputeThinU = 0x08,
+  /** Used in JacobiSVD to indicate that the square matrix V is to be computed. */
+  ComputeFullV = 0x10,
+  /** Used in JacobiSVD to indicate that the thin matrix V is to be computed. */
+  ComputeThinV = 0x20,
+  /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify
+   * that only the eigenvalues are to be computed and not the eigenvectors. */
+  EigenvaluesOnly = 0x40,
+  /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify
+   * that both the eigenvalues and the eigenvectors are to be computed. */
+  ComputeEigenvectors = 0x80,
+  /** \internal */
+  EigVecMask = EigenvaluesOnly | ComputeEigenvectors,
+  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
+   * solve the generalized eigenproblem \f$ Ax = \lambda B x \f$. */
+  Ax_lBx = 0x100,
+  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
+   * solve the generalized eigenproblem \f$ ABx = \lambda x \f$. */
+  ABx_lx = 0x200,
+  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
+   * solve the generalized eigenproblem \f$ BAx = \lambda x \f$. */
+  BAx_lx = 0x400,
+  /** \internal */
+  GenEigMask = Ax_lBx | ABx_lx | BAx_lx
+};
+
+/** \ingroup enums
+ * Possible values for the \p QRPreconditioner template parameter of JacobiSVD. */
+enum QRPreconditioners {
+  /** Use a QR decomposition with column pivoting as the first step. */
+  ColPivHouseholderQRPreconditioner = 0x0,
+  /** Do not specify what is to be done if the SVD of a non-square matrix is asked for. */
+  NoQRPreconditioner = 0x40,
+  /** Use a QR decomposition without pivoting as the first step. */
+  HouseholderQRPreconditioner = 0x80,
+  /** Use a QR decomposition with full pivoting as the first step. */
+  FullPivHouseholderQRPreconditioner = 0xC0,
+  /** Used to disable the QR Preconditioner in BDCSVD. */
+  DisableQRDecomposition = NoQRPreconditioner
+};
+
+#ifdef Success
+#error The preprocessor symbol 'Success' is defined, possibly by the X11 header file X.h
+#endif
+
+/** \ingroup enums
+ * Enum for reporting the status of a computation. */
+enum ComputationInfo {
+  /** Computation was successful. */
+  Success = 0,
+  /** The provided data did not satisfy the prerequisites. */
+  NumericalIssue = 1,
+  /** Iterative procedure did not converge. */
+  NoConvergence = 2,
+  /** The inputs are invalid, or the algorithm has been improperly called.
+   * When assertions are enabled, such errors trigger an assert. */
+  InvalidInput = 3
+};
+
+/** \ingroup enums
+ * Enum used to specify how a particular transformation is stored in a matrix.
+ * \sa Transform, Hyperplane::transform(). */
+enum TransformTraits {
+  /** Transformation is an isometry. */
+  Isometry = 0x1,
+  /** Transformation is an affine transformation stored as a (Dim+1)^2 matrix whose last row is
+   * assumed to be [0 ... 0 1]. */
+  Affine = 0x2,
+  /** Transformation is an affine transformation stored as a (Dim) x (Dim+1) matrix. */
+  AffineCompact = 0x10 | Affine,
+  /** Transformation is a general projective transformation stored as a (Dim+1)^2 matrix. */
+  Projective = 0x20
+};
+
+/** \internal \ingroup enums
+ * Enum used to choose between implementation depending on the computer architecture. */
+namespace Architecture {
+enum Type {
+  Generic = 0x0,
+  SSE = 0x1,
+  AltiVec = 0x2,
+  VSX = 0x3,
+  NEON = 0x4,
+  MSA = 0x5,
+  SVE = 0x6,
+  HVX = 0x7,
+  LSX = 0x8,
+  RVV10 = 0x9,
+#if defined EIGEN_VECTORIZE_SSE
+  Target = SSE
+#elif defined EIGEN_VECTORIZE_ALTIVEC
+  Target = AltiVec
+#elif defined EIGEN_VECTORIZE_VSX
+  Target = VSX
+#elif defined EIGEN_VECTORIZE_NEON
+  Target = NEON
+#elif defined EIGEN_VECTORIZE_SVE
+  Target = SVE
+#elif defined EIGEN_VECTORIZE_MSA
+  Target = MSA
+#elif defined EIGEN_VECTORIZE_HVX
+  Target = HVX
+#elif defined EIGEN_VECTORIZE_LSX
+  Target = LSX
+#elif defined EIGEN_VECTORIZE_RVV10
+  Target = RVV10
+#else
+  Target = Generic
+#endif
+};
+}  // namespace Architecture
+
+/** \internal \ingroup enums
+ * Enum used as template parameter in Product and product evaluators. */
+enum ProductImplType {
+  DefaultProduct = 0,
+  LazyProduct,
+  AliasFreeProduct,
+  CoeffBasedProductMode,
+  LazyCoeffBasedProductMode,
+  OuterProduct,
+  InnerProduct,
+  GemvProduct,
+  GemmProduct
+};
+
+/** \internal \ingroup enums
+ * Enum used in experimental parallel implementation. */
+enum Action { GetAction, SetAction };
+
+/** The type used to identify a dense storage. */
+struct Dense {};
+
+/** The type used to identify a general sparse storage. */
+struct Sparse {};
+
+/** The type used to identify a general solver (factored) storage. */
+struct SolverStorage {};
+
+/** The type used to identify a permutation storage. */
+struct PermutationStorage {};
+
+/** The type used to identify a permutation storage. */
+struct TranspositionsStorage {};
+
+/** The type used to identify a matrix expression */
+struct MatrixXpr {};
+
+/** The type used to identify an array expression */
+struct ArrayXpr {};
+
+// An evaluator must define its shape. By default, it can be one of the following:
+struct DenseShape {
+  static std::string debugName() { return "DenseShape"; }
+};
+struct SolverShape {
+  static std::string debugName() { return "SolverShape"; }
+};
+struct HomogeneousShape {
+  static std::string debugName() { return "HomogeneousShape"; }
+};
+struct DiagonalShape {
+  static std::string debugName() { return "DiagonalShape"; }
+};
+struct SkewSymmetricShape {
+  static std::string debugName() { return "SkewSymmetricShape"; }
+};
+struct BandShape {
+  static std::string debugName() { return "BandShape"; }
+};
+struct TriangularShape {
+  static std::string debugName() { return "TriangularShape"; }
+};
+struct SelfAdjointShape {
+  static std::string debugName() { return "SelfAdjointShape"; }
+};
+struct PermutationShape {
+  static std::string debugName() { return "PermutationShape"; }
+};
+struct TranspositionsShape {
+  static std::string debugName() { return "TranspositionsShape"; }
+};
+struct SparseShape {
+  static std::string debugName() { return "SparseShape"; }
+};
+
+namespace internal {
+
+// random access iterators based on coeff*() accessors.
+struct IndexBased {};
+
+// evaluator based on iterators to access coefficients.
+struct IteratorBased {};
+
+/** \internal
+ * Constants for comparison functors
+ */
+enum ComparisonName : unsigned int {
+  cmp_EQ = 0,
+  cmp_LT = 1,
+  cmp_LE = 2,
+  cmp_UNORD = 3,
+  cmp_NEQ = 4,
+  cmp_GT = 5,
+  cmp_GE = 6
+};
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CONSTANTS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/DisableStupidWarnings.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/DisableStupidWarnings.h
new file mode 100644
index 0000000..ab0c542
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/DisableStupidWarnings.h
@@ -0,0 +1,146 @@
+#ifndef EIGEN_WARNINGS_DISABLED
+#define EIGEN_WARNINGS_DISABLED
+
+#if defined(_MSC_VER)
+// 4100 - unreferenced formal parameter (occurred e.g. in aligned_allocator::destroy(pointer p))
+// 4101 - unreferenced local variable
+// 4127 - conditional expression is constant
+// 4181 - qualifier applied to reference type ignored
+// 4211 - nonstandard extension used : redefined extern to static
+// 4244 - 'argument' : conversion from 'type1' to 'type2', possible loss of data
+// 4273 - QtAlignedMalloc, inconsistent DLL linkage
+// 4324 - structure was padded due to declspec(align())
+// 4503 - decorated name length exceeded, name was truncated
+// 4512 - assignment operator could not be generated
+// 4522 - 'class' : multiple assignment operators specified
+// 4700 - uninitialized local variable 'xyz' used
+// 4714 - function marked as __forceinline not inlined
+// 4717 - 'function' : recursive on all control paths, function will cause runtime stack overflow
+// 4800 - 'type' : forcing value to bool 'true' or 'false' (performance warning)
+#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
+#pragma warning(push)
+#endif
+#pragma warning(disable : 4100 4101 4127 4181 4211 4244 4273 4324 4503 4512 4522 4700 4714 4717 4800)
+// We currently rely on has_denorm in tests, and need it defined correctly for half/bfloat16.
+#ifndef _SILENCE_CXX23_DENORM_DEPRECATION_WARNING
+#define EIGEN_REENABLE_CXX23_DENORM_DEPRECATION_WARNING 1
+#define _SILENCE_CXX23_DENORM_DEPRECATION_WARNING
+#endif
+
+#elif defined __INTEL_COMPILER
+// 2196 - routine is both "inline" and "noinline" ("noinline" assumed)
+//        ICC 12 generates this warning even without any inline keyword, when defining class methods 'inline' i.e.
+//        inside of class body typedef that may be a reference type.
+// 279  - controlling expression is constant
+//        ICC 12 generates this warning on assert(constant_expression_depending_on_template_params) and frankly this is
+//        a legitimate use case.
+// 1684 - conversion from pointer to same-sized integral type (potential portability problem)
+// 2259 - non-pointer conversion from "Eigen::Index={ptrdiff_t={long}}" to "int" may lose significant bits
+#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
+#pragma warning push
+#endif
+#pragma warning disable 2196 279 1684 2259
+
+#elif defined __clang__
+#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
+#pragma clang diagnostic push
+#endif
+#if defined(__has_warning)
+// -Wconstant-logical-operand - warning: use of logical && with constant operand; switch to bitwise & or remove constant
+//     this is really a stupid warning as it warns on compile-time expressions involving enums
+#if __has_warning("-Wconstant-logical-operand")
+#pragma clang diagnostic ignored "-Wconstant-logical-operand"
+#endif
+#if __has_warning("-Wimplicit-int-float-conversion")
+#pragma clang diagnostic ignored "-Wimplicit-int-float-conversion"
+#endif
+#if (defined(__ALTIVEC__) || defined(__VSX__)) && (!defined(__STDC_VERSION__) || (__STDC_VERSION__ < 201112L))
+// warning: generic selections are a C11-specific feature
+// ignoring warnings thrown at vec_ctf in Altivec/PacketMath.h
+#if __has_warning("-Wc11-extensions")
+#pragma clang diagnostic ignored "-Wc11-extensions"
+#endif
+#endif
+#endif
+
+#elif defined __GNUC__ && !defined(__FUJITSU)
+
+#if (!defined(EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS)) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
+#pragma GCC diagnostic push
+#endif
+// g++ warns about local variables shadowing member functions, which is too strict
+#pragma GCC diagnostic ignored "-Wshadow"
+#if __GNUC__ == 4 && __GNUC_MINOR__ < 8
+// Until g++-4.7 there are warnings when comparing unsigned int vs 0, even in templated functions:
+#pragma GCC diagnostic ignored "-Wtype-limits"
+#endif
+#if __GNUC__ >= 6
+#pragma GCC diagnostic ignored "-Wignored-attributes"
+#endif
+#if __GNUC__ == 7
+// See: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89325
+#pragma GCC diagnostic ignored "-Wattributes"
+#endif
+#endif
+
+#if defined __NVCC__ && defined __CUDACC__
+// MSVC 14.16 (required by CUDA 9.*) does not support the _Pragma keyword, so
+// we instead use Microsoft's __pragma extension.
+#if defined _MSC_VER
+#define EIGEN_MAKE_PRAGMA(X) __pragma(#X)
+#else
+#define EIGEN_MAKE_PRAGMA(X) _Pragma(#X)
+#endif
+#if defined __NVCC_DIAG_PRAGMA_SUPPORT__
+#define EIGEN_NV_DIAG_SUPPRESS(X) EIGEN_MAKE_PRAGMA(nv_diag_suppress X)
+#else
+#define EIGEN_NV_DIAG_SUPPRESS(X) EIGEN_MAKE_PRAGMA(diag_suppress X)
+#endif
+
+EIGEN_NV_DIAG_SUPPRESS(boolean_controlling_expr_is_constant)
+// Disable the "statement is unreachable" message
+EIGEN_NV_DIAG_SUPPRESS(code_is_unreachable)
+// Disable the "dynamic initialization in unreachable code" message
+EIGEN_NV_DIAG_SUPPRESS(initialization_not_reachable)
+// Disable the "invalid error number" message that we get with older versions of nvcc
+EIGEN_NV_DIAG_SUPPRESS(1222)
+// Disable the "calling a __host__ function from a __host__ __device__ function is not allowed" messages (yes, there are
+// many of them and they seem to change with every version of the compiler)
+EIGEN_NV_DIAG_SUPPRESS(2527)
+EIGEN_NV_DIAG_SUPPRESS(2529)
+EIGEN_NV_DIAG_SUPPRESS(2651)
+EIGEN_NV_DIAG_SUPPRESS(2653)
+EIGEN_NV_DIAG_SUPPRESS(2668)
+EIGEN_NV_DIAG_SUPPRESS(2669)
+EIGEN_NV_DIAG_SUPPRESS(2670)
+EIGEN_NV_DIAG_SUPPRESS(2671)
+EIGEN_NV_DIAG_SUPPRESS(2735)
+EIGEN_NV_DIAG_SUPPRESS(2737)
+EIGEN_NV_DIAG_SUPPRESS(2739)
+EIGEN_NV_DIAG_SUPPRESS(2885)
+EIGEN_NV_DIAG_SUPPRESS(2888)
+EIGEN_NV_DIAG_SUPPRESS(2976)
+EIGEN_NV_DIAG_SUPPRESS(2979)
+EIGEN_NV_DIAG_SUPPRESS(20011)
+EIGEN_NV_DIAG_SUPPRESS(20014)
+// Disable the "// __device__ annotation is ignored on a function(...) that is
+//              explicitly defaulted on its first declaration" message.
+// The __device__ annotation seems to actually be needed in some cases,
+// otherwise resulting in kernel runtime errors.
+EIGEN_NV_DIAG_SUPPRESS(2886)
+EIGEN_NV_DIAG_SUPPRESS(2929)
+EIGEN_NV_DIAG_SUPPRESS(2977)
+EIGEN_NV_DIAG_SUPPRESS(20012)
+#undef EIGEN_NV_DIAG_SUPPRESS
+#undef EIGEN_MAKE_PRAGMA
+#endif
+
+#else
+// warnings already disabled:
+#ifndef EIGEN_WARNINGS_DISABLED_2
+#define EIGEN_WARNINGS_DISABLED_2
+#elif defined(EIGEN_INTERNAL_DEBUGGING)
+#error "Do not include \"DisableStupidWarnings.h\" recursively more than twice!"
+#endif
+
+#endif  // not EIGEN_WARNINGS_DISABLED
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/EmulateArray.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/EmulateArray.h
new file mode 100644
index 0000000..6c4c22d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/EmulateArray.h
@@ -0,0 +1,270 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EMULATE_ARRAY_H
+#define EIGEN_EMULATE_ARRAY_H
+
+// CUDA doesn't support the STL containers, so we use our own instead.
+#if defined(EIGEN_GPUCC) || defined(EIGEN_AVOID_STL_ARRAY)
+
+namespace Eigen {
+template <typename T, size_t n>
+class array {
+ public:
+  typedef T value_type;
+  typedef T* iterator;
+  typedef const T* const_iterator;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE iterator begin() { return values; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const_iterator begin() const { return values; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE iterator end() { return values + n; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const_iterator end() const { return values + n; }
+
+  typedef std::reverse_iterator<iterator> reverse_iterator;
+  typedef std::reverse_iterator<const_iterator> const_reverse_iterator;
+
+  EIGEN_STRONG_INLINE reverse_iterator rbegin() { return reverse_iterator(end()); }
+  EIGEN_STRONG_INLINE const_reverse_iterator rbegin() const { return const_reverse_iterator(end()); }
+
+  EIGEN_STRONG_INLINE reverse_iterator rend() { return reverse_iterator(begin()); }
+  EIGEN_STRONG_INLINE const_reverse_iterator rend() const { return const_reverse_iterator(begin()); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& operator[](size_t index) {
+    eigen_internal_assert(index < size());
+    return values[index];
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& operator[](size_t index) const {
+    eigen_internal_assert(index < size());
+    return values[index];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& at(size_t index) {
+    eigen_assert(index < size());
+    return values[index];
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& at(size_t index) const {
+    eigen_assert(index < size());
+    return values[index];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& front() { return values[0]; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& front() const { return values[0]; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& back() { return values[n - 1]; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& back() const { return values[n - 1]; }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static std::size_t size() { return n; }
+
+  T values[n];
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array() {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array(const T& v) {
+    EIGEN_STATIC_ASSERT(n == 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array(const T& v1, const T& v2) {
+    EIGEN_STATIC_ASSERT(n == 2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3) {
+    EIGEN_STATIC_ASSERT(n == 3, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4) {
+    EIGEN_STATIC_ASSERT(n == 4, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4, const T& v5) {
+    EIGEN_STATIC_ASSERT(n == 5, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4, const T& v5,
+                                              const T& v6) {
+    EIGEN_STATIC_ASSERT(n == 6, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+    values[5] = v6;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4, const T& v5,
+                                              const T& v6, const T& v7) {
+    EIGEN_STATIC_ASSERT(n == 7, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+    values[5] = v6;
+    values[6] = v7;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4, const T& v5,
+                                              const T& v6, const T& v7, const T& v8) {
+    EIGEN_STATIC_ASSERT(n == 8, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+    values[5] = v6;
+    values[6] = v7;
+    values[7] = v8;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array(std::initializer_list<T> l) {
+    eigen_assert(l.size() == n);
+    internal::smart_copy(l.begin(), l.end(), values);
+  }
+};
+
+// Specialize array for zero size
+template <typename T>
+class array<T, 0> {
+ public:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& operator[](size_t) {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& operator[](size_t) const {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& front() {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& front() const {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& back() {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& back() const {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::size_t size() { return 0; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array() : dummy() {}
+
+  EIGEN_DEVICE_FUNC array(std::initializer_list<T> l) : dummy() {
+    EIGEN_UNUSED_VARIABLE(l);
+    eigen_assert(l.size() == 0);
+  }
+
+ private:
+  T dummy;
+};
+
+// Comparison operator
+// Todo: implement !=, <, <=, >,  and >=
+template <class T, std::size_t N>
+EIGEN_DEVICE_FUNC bool operator==(const array<T, N>& lhs, const array<T, N>& rhs) {
+  for (std::size_t i = 0; i < N; ++i) {
+    if (lhs[i] != rhs[i]) {
+      return false;
+    }
+  }
+  return true;
+}
+
+namespace internal {
+template <std::size_t I_, class T, std::size_t N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& array_get(array<T, N>& a) {
+  return a[I_];
+}
+template <std::size_t I_, class T, std::size_t N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& array_get(const array<T, N>& a) {
+  return a[I_];
+}
+
+template <class T, std::size_t N>
+struct array_size<array<T, N> > {
+  static constexpr Index value = N;
+};
+template <class T, std::size_t N>
+struct array_size<array<T, N>&> {
+  static constexpr Index value = N;
+};
+template <class T, std::size_t N>
+struct array_size<const array<T, N> > {
+  static constexpr Index value = N;
+};
+template <class T, std::size_t N>
+struct array_size<const array<T, N>&> {
+  static constexpr Index value = N;
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#else
+
+// The compiler supports c++11, and we're not targeting cuda: use std::array as Eigen::array
+#include <array>
+
+namespace Eigen {
+
+template <typename T, std::size_t N>
+using array = std::array<T, N>;
+
+namespace internal {
+/* std::get is only constexpr in C++14, not yet in C++11
+ *     - libstdc++ from version 4.7 onwards has it nevertheless,
+ *                                          so use that
+ *     - libstdc++ older versions: use _M_instance directly
+ *     - libc++ all versions so far: use __elems_ directly
+ *     - all other libs: use std::get to be portable, but
+ *                       this may not be constexpr
+ */
+#if defined(__GLIBCXX__) && __GLIBCXX__ < 20120322
+#define STD_GET_ARR_HACK a._M_instance[I_]
+#elif defined(_LIBCPP_VERSION)
+#define STD_GET_ARR_HACK a.__elems_[I_]
+#else
+#define STD_GET_ARR_HACK std::template get<I_, T, N>(a)
+#endif
+
+template <std::size_t I_, class T, std::size_t N>
+constexpr T& array_get(std::array<T, N>& a) {
+  return (T&)STD_GET_ARR_HACK;
+}
+template <std::size_t I_, class T, std::size_t N>
+constexpr T&& array_get(std::array<T, N>&& a) {
+  return (T&&)STD_GET_ARR_HACK;
+}
+template <std::size_t I_, class T, std::size_t N>
+constexpr T const& array_get(std::array<T, N> const& a) {
+  return (T const&)STD_GET_ARR_HACK;
+}
+
+#undef STD_GET_ARR_HACK
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif
+
+#endif  // EIGEN_EMULATE_ARRAY_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ForwardDeclarations.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ForwardDeclarations.h
new file mode 100644
index 0000000..e2e7155
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ForwardDeclarations.h
@@ -0,0 +1,527 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FORWARDDECLARATIONS_H
+#define EIGEN_FORWARDDECLARATIONS_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <typename T>
+struct traits;
+
+// here we say once and for all that traits<const T> == traits<T>
+// When constness must affect traits, it has to be constness on template parameters on which T itself depends.
+// For example, traits<Map<const T> > != traits<Map<T> >, but
+//              traits<const Map<T> > == traits<Map<T> >
+template <typename T>
+struct traits<const T> : traits<T> {};
+
+template <typename Derived>
+struct has_direct_access {
+  enum { ret = (traits<Derived>::Flags & DirectAccessBit) ? 1 : 0 };
+};
+
+template <typename Derived>
+struct accessors_level {
+  enum {
+    has_direct_access = (traits<Derived>::Flags & DirectAccessBit) ? 1 : 0,
+    has_write_access = (traits<Derived>::Flags & LvalueBit) ? 1 : 0,
+    value = has_direct_access ? (has_write_access ? DirectWriteAccessors : DirectAccessors)
+                              : (has_write_access ? WriteAccessors : ReadOnlyAccessors)
+  };
+};
+
+template <typename T>
+struct evaluator_traits;
+
+template <typename T>
+struct evaluator;
+
+}  // end namespace internal
+
+template <typename T>
+struct NumTraits;
+
+template <typename Derived>
+struct EigenBase;
+template <typename Derived>
+class DenseBase;
+template <typename Derived>
+class PlainObjectBase;
+template <typename Derived, int Level>
+class DenseCoeffsBase;
+
+template <typename Scalar_, int Rows_, int Cols_,
+          int Options_ = AutoAlign | ((Rows_ == 1 && Cols_ != 1)   ? Eigen::RowMajor
+                                      : (Cols_ == 1 && Rows_ != 1) ? Eigen::ColMajor
+                                                                   : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION),
+          int MaxRows_ = Rows_, int MaxCols_ = Cols_>
+class Matrix;
+
+template <typename Derived>
+class MatrixBase;
+template <typename Derived>
+class ArrayBase;
+
+template <typename ExpressionType, unsigned int Added, unsigned int Removed>
+class Flagged;
+template <typename ExpressionType, template <typename> class StorageBase>
+class NoAlias;
+template <typename ExpressionType>
+class NestByValue;
+template <typename ExpressionType>
+class ForceAlignedAccess;
+template <typename ExpressionType>
+class SwapWrapper;
+
+template <typename XprType, int BlockRows = Dynamic, int BlockCols = Dynamic, bool InnerPanel = false>
+class Block;
+template <typename XprType, typename RowIndices, typename ColIndices>
+class IndexedView;
+template <typename XprType, int Rows = Dynamic, int Cols = Dynamic, int Order = 0>
+class Reshaped;
+template <typename FirstType, typename SizeType, typename IncrType>
+class ArithmeticSequence;
+
+template <typename MatrixType, int Size = Dynamic>
+class VectorBlock;
+template <typename MatrixType>
+class Transpose;
+template <typename MatrixType>
+class Conjugate;
+template <typename NullaryOp, typename MatrixType>
+class CwiseNullaryOp;
+template <typename UnaryOp, typename MatrixType>
+class CwiseUnaryOp;
+template <typename BinaryOp, typename Lhs, typename Rhs>
+class CwiseBinaryOp;
+template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
+class CwiseTernaryOp;
+template <typename Decomposition, typename Rhstype>
+class Solve;
+template <typename XprType>
+class Inverse;
+
+template <typename Lhs, typename Rhs, int Option = DefaultProduct>
+class Product;
+
+template <typename Derived>
+class DiagonalBase;
+template <typename DiagonalVectorType_>
+class DiagonalWrapper;
+template <typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime>
+class DiagonalMatrix;
+template <typename MatrixType, typename DiagonalType, int ProductOrder>
+class DiagonalProduct;
+template <typename MatrixType, int Index = 0>
+class Diagonal;
+template <typename Derived>
+class SkewSymmetricBase;
+template <typename VectorType_>
+class SkewSymmetricWrapper;
+template <typename Scalar_>
+class SkewSymmetricMatrix3;
+template <int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime, typename IndexType = int>
+class PermutationMatrix;
+template <int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime, typename IndexType = int>
+class Transpositions;
+template <typename Derived>
+class PermutationBase;
+template <typename Derived>
+class TranspositionsBase;
+template <typename IndicesType_>
+class PermutationWrapper;
+template <typename IndicesType_>
+class TranspositionsWrapper;
+
+template <typename Derived,
+          int Level = internal::accessors_level<Derived>::has_write_access ? WriteAccessors : ReadOnlyAccessors>
+class MapBase;
+template <int OuterStrideAtCompileTime, int InnerStrideAtCompileTime>
+class Stride;
+template <int Value = Dynamic>
+class InnerStride;
+template <int Value = Dynamic>
+class OuterStride;
+template <typename MatrixType, int MapOptions = Unaligned, typename StrideType = Stride<0, 0>>
+class Map;
+template <typename Derived>
+class RefBase;
+template <typename PlainObjectType, int Options = 0,
+          typename StrideType =
+              typename std::conditional_t<PlainObjectType::IsVectorAtCompileTime, InnerStride<1>, OuterStride<>>>
+class Ref;
+template <typename ViewOp, typename MatrixType, typename StrideType = Stride<0, 0>>
+class CwiseUnaryView;
+
+template <typename Derived>
+class TriangularBase;
+template <typename MatrixType, unsigned int Mode>
+class TriangularView;
+template <typename MatrixType, unsigned int Mode>
+class SelfAdjointView;
+template <typename Derived>
+class RealView;
+template <typename MatrixType>
+class SparseView;
+template <typename ExpressionType>
+class WithFormat;
+template <typename MatrixType>
+struct CommaInitializer;
+template <typename Derived>
+class ReturnByValue;
+template <typename ExpressionType>
+class ArrayWrapper;
+template <typename ExpressionType>
+class MatrixWrapper;
+template <typename Derived>
+class SolverBase;
+template <typename XprType>
+class InnerIterator;
+
+namespace internal {
+template <typename XprType>
+class generic_randaccess_stl_iterator;
+template <typename XprType>
+class pointer_based_stl_iterator;
+template <typename XprType, DirectionType Direction>
+class subvector_stl_iterator;
+template <typename XprType, DirectionType Direction>
+class subvector_stl_reverse_iterator;
+template <typename DecompositionType>
+struct kernel_retval_base;
+template <typename DecompositionType>
+struct kernel_retval;
+template <typename DecompositionType>
+struct image_retval_base;
+template <typename DecompositionType>
+struct image_retval;
+}  // end namespace internal
+
+namespace internal {
+template <typename Scalar_, int Rows = Dynamic, int Cols = Dynamic, int Supers = Dynamic, int Subs = Dynamic,
+          int Options = 0>
+class BandMatrix;
+}
+
+namespace internal {
+template <typename Lhs, typename Rhs>
+struct product_type;
+
+template <bool>
+struct EnableIf;
+
+/** \internal
+ * \class product_evaluator
+ * Products need their own evaluator with more template arguments allowing for
+ * easier partial template specializations.
+ */
+template <typename T, int ProductTag = internal::product_type<typename T::Lhs, typename T::Rhs>::ret,
+          typename LhsShape = typename evaluator_traits<typename T::Lhs>::Shape,
+          typename RhsShape = typename evaluator_traits<typename T::Rhs>::Shape,
+          typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
+          typename RhsScalar = typename traits<typename T::Rhs>::Scalar>
+struct product_evaluator;
+}  // namespace internal
+
+template <typename Lhs, typename Rhs, int ProductType = internal::product_type<Lhs, Rhs>::value>
+struct ProductReturnType;
+
+// this is a workaround for sun CC
+template <typename Lhs, typename Rhs>
+struct LazyProductReturnType;
+
+namespace internal {
+
+// Provides scalar/packet-wise product and product with accumulation
+// with optional conjugation of the arguments.
+template <typename LhsScalar, typename RhsScalar, bool ConjLhs = false, bool ConjRhs = false>
+struct conj_helper;
+
+template <typename LhsScalar, typename RhsScalar = LhsScalar>
+struct scalar_sum_op;
+template <typename LhsScalar, typename RhsScalar = LhsScalar>
+struct scalar_difference_op;
+template <typename LhsScalar, typename RhsScalar = LhsScalar>
+struct scalar_conj_product_op;
+template <typename LhsScalar, typename RhsScalar = LhsScalar, int NaNPropagation = PropagateFast>
+struct scalar_min_op;
+template <typename LhsScalar, typename RhsScalar = LhsScalar, int NaNPropagation = PropagateFast>
+struct scalar_max_op;
+template <typename Scalar>
+struct scalar_opposite_op;
+template <typename Scalar>
+struct scalar_conjugate_op;
+template <typename Scalar>
+struct scalar_real_op;
+template <typename Scalar>
+struct scalar_imag_op;
+template <typename Scalar>
+struct scalar_abs_op;
+template <typename Scalar>
+struct scalar_abs2_op;
+template <typename LhsScalar, typename RhsScalar = LhsScalar>
+struct scalar_absolute_difference_op;
+template <typename Scalar>
+struct scalar_sqrt_op;
+template <typename Scalar>
+struct scalar_cbrt_op;
+template <typename Scalar>
+struct scalar_rsqrt_op;
+template <typename Scalar>
+struct scalar_exp_op;
+template <typename Scalar>
+struct scalar_log_op;
+template <typename Scalar>
+struct scalar_cos_op;
+template <typename Scalar>
+struct scalar_sin_op;
+template <typename Scalar>
+struct scalar_acos_op;
+template <typename Scalar>
+struct scalar_asin_op;
+template <typename Scalar>
+struct scalar_tan_op;
+template <typename Scalar>
+struct scalar_atan_op;
+template <typename LhsScalar, typename RhsScalar = LhsScalar>
+struct scalar_atan2_op;
+template <typename Scalar>
+struct scalar_inverse_op;
+template <typename Scalar>
+struct scalar_square_op;
+template <typename Scalar>
+struct scalar_cube_op;
+template <typename Scalar, typename NewType>
+struct scalar_cast_op;
+template <typename Scalar>
+struct scalar_random_op;
+template <typename Scalar>
+struct scalar_constant_op;
+template <typename Scalar>
+struct scalar_identity_op;
+template <typename Scalar>
+struct scalar_sign_op;
+template <typename Scalar, typename ScalarExponent>
+struct scalar_pow_op;
+template <typename Scalar, typename ScalarExponent, bool BaseIsInteger, bool ExponentIsInteger, bool BaseIsComplex,
+          bool ExponentIsComplex>
+struct scalar_unary_pow_op;
+template <typename LhsScalar, typename RhsScalar = LhsScalar>
+struct scalar_hypot_op;
+template <typename LhsScalar, typename RhsScalar = LhsScalar>
+struct scalar_product_op;
+template <typename LhsScalar, typename RhsScalar = LhsScalar>
+struct scalar_quotient_op;
+// logical and bitwise operations
+template <typename Scalar>
+struct scalar_boolean_and_op;
+template <typename Scalar>
+struct scalar_boolean_or_op;
+template <typename Scalar>
+struct scalar_boolean_xor_op;
+template <typename Scalar>
+struct scalar_boolean_not_op;
+template <typename Scalar>
+struct scalar_bitwise_and_op;
+template <typename Scalar>
+struct scalar_bitwise_or_op;
+template <typename Scalar>
+struct scalar_bitwise_xor_op;
+template <typename Scalar>
+struct scalar_bitwise_not_op;
+
+// SpecialFunctions module
+template <typename Scalar>
+struct scalar_lgamma_op;
+template <typename Scalar>
+struct scalar_digamma_op;
+template <typename Scalar>
+struct scalar_erf_op;
+template <typename Scalar>
+struct scalar_erfc_op;
+template <typename Scalar>
+struct scalar_ndtri_op;
+template <typename Scalar>
+struct scalar_igamma_op;
+template <typename Scalar>
+struct scalar_igammac_op;
+template <typename Scalar>
+struct scalar_zeta_op;
+template <typename Scalar>
+struct scalar_betainc_op;
+
+// Bessel functions in SpecialFunctions module
+template <typename Scalar>
+struct scalar_bessel_i0_op;
+template <typename Scalar>
+struct scalar_bessel_i0e_op;
+template <typename Scalar>
+struct scalar_bessel_i1_op;
+template <typename Scalar>
+struct scalar_bessel_i1e_op;
+template <typename Scalar>
+struct scalar_bessel_j0_op;
+template <typename Scalar>
+struct scalar_bessel_y0_op;
+template <typename Scalar>
+struct scalar_bessel_j1_op;
+template <typename Scalar>
+struct scalar_bessel_y1_op;
+template <typename Scalar>
+struct scalar_bessel_k0_op;
+template <typename Scalar>
+struct scalar_bessel_k0e_op;
+template <typename Scalar>
+struct scalar_bessel_k1_op;
+template <typename Scalar>
+struct scalar_bessel_k1e_op;
+
+}  // end namespace internal
+
+struct IOFormat;
+
+// Array module
+template <typename Scalar_, int Rows_, int Cols_,
+          int Options_ = AutoAlign | ((Rows_ == 1 && Cols_ != 1)   ? Eigen::RowMajor
+                                      : (Cols_ == 1 && Rows_ != 1) ? Eigen::ColMajor
+                                                                   : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION),
+          int MaxRows_ = Rows_, int MaxCols_ = Cols_>
+class Array;
+template <typename MatrixType, typename BinaryOp, int Direction>
+class PartialReduxExpr;
+template <typename ExpressionType, int Direction>
+class VectorwiseOp;
+template <typename MatrixType, int RowFactor, int ColFactor>
+class Replicate;
+template <typename MatrixType, int Direction = BothDirections>
+class Reverse;
+
+#if defined(EIGEN_USE_LAPACKE) && defined(lapack_int)
+// Lapacke interface requires StorageIndex to be lapack_int
+typedef lapack_int DefaultPermutationIndex;
+#else
+typedef int DefaultPermutationIndex;
+#endif
+
+template <typename MatrixType, typename PermutationIndex = DefaultPermutationIndex>
+class FullPivLU;
+template <typename MatrixType, typename PermutationIndex = DefaultPermutationIndex>
+class PartialPivLU;
+namespace internal {
+template <typename MatrixType>
+struct inverse_impl;
+}
+template <typename MatrixType>
+class HouseholderQR;
+template <typename MatrixType, typename PermutationIndex = DefaultPermutationIndex>
+class ColPivHouseholderQR;
+template <typename MatrixType, typename PermutationIndex = DefaultPermutationIndex>
+class FullPivHouseholderQR;
+template <typename MatrixType, typename PermutationIndex = DefaultPermutationIndex>
+class CompleteOrthogonalDecomposition;
+template <typename MatrixType>
+class SVDBase;
+template <typename MatrixType, int Options = 0>
+class JacobiSVD;
+template <typename MatrixType, int Options = 0>
+class BDCSVD;
+template <typename MatrixType, int UpLo = Lower>
+class LLT;
+template <typename MatrixType, int UpLo = Lower>
+class LDLT;
+template <typename VectorsType, typename CoeffsType, int Side = OnTheLeft>
+class HouseholderSequence;
+template <typename Scalar>
+class JacobiRotation;
+
+// Geometry module:
+namespace internal {
+template <typename Derived, typename OtherDerived, int Size = MatrixBase<Derived>::SizeAtCompileTime>
+struct cross_impl;
+}
+template <typename Derived, int Dim_>
+class RotationBase;
+template <typename Derived>
+class QuaternionBase;
+template <typename Scalar>
+class Rotation2D;
+template <typename Scalar>
+class AngleAxis;
+template <typename Scalar, int Dim>
+class Translation;
+template <typename Scalar, int Dim>
+class AlignedBox;
+template <typename Scalar, int Options = AutoAlign>
+class Quaternion;
+template <typename Scalar, int Dim, int Mode, int Options_ = AutoAlign>
+class Transform;
+template <typename Scalar_, int AmbientDim_, int Options = AutoAlign>
+class ParametrizedLine;
+template <typename Scalar_, int AmbientDim_, int Options = AutoAlign>
+class Hyperplane;
+template <typename Scalar>
+class UniformScaling;
+template <typename MatrixType, int Direction>
+class Homogeneous;
+
+// Sparse module:
+template <typename Derived>
+class SparseMatrixBase;
+
+// MatrixFunctions module
+template <typename Derived>
+struct MatrixExponentialReturnValue;
+template <typename Derived>
+class MatrixFunctionReturnValue;
+template <typename Derived>
+class MatrixSquareRootReturnValue;
+template <typename Derived>
+class MatrixLogarithmReturnValue;
+template <typename Derived>
+class MatrixPowerReturnValue;
+template <typename Derived>
+class MatrixComplexPowerReturnValue;
+
+namespace internal {
+template <typename Scalar>
+struct stem_function {
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+  typedef ComplexScalar type(ComplexScalar, int);
+};
+}  // namespace internal
+
+template <typename XprType, typename Device>
+struct DeviceWrapper;
+
+namespace internal {
+template <typename Xpr>
+struct eigen_fill_helper;
+template <typename Xpr, bool use_fill = eigen_fill_helper<Xpr>::value>
+struct eigen_fill_impl;
+template <typename Xpr>
+struct eigen_memset_helper;
+template <typename Xpr, bool use_memset = eigen_memset_helper<Xpr>::value>
+struct eigen_zero_impl;
+
+template <typename Packet>
+struct has_packet_segment : std::false_type {};
+
+template <typename T>
+struct complex_array_access;
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_FORWARDDECLARATIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/GpuHipCudaDefines.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/GpuHipCudaDefines.inc
new file mode 100644
index 0000000..4e10500
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/GpuHipCudaDefines.inc
@@ -0,0 +1,101 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2018 Deven Desai <deven.desai.amd@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_USE_GPU) && !defined(EIGEN_CORE_GPU_HIP_CUDA_DEFINES_H)
+#define EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H
+
+// Note that we are using EIGEN_USE_HIP here instead of EIGEN_HIPCC...this is by design
+// There is code in the Tensorflow codebase that will define EIGEN_USE_GPU,  but
+// for some reason gets sent to the gcc/host compiler instead of the gpu/nvcc/hipcc compiler
+// When compiling such files, gcc will end up trying to pick up the CUDA headers by
+// default (see the code within "unsupported/Eigen/CXX11/Tensor" that is guarded by EIGEN_USE_GPU)
+// This will obviously not work when trying to compile tensorflow on a system with no CUDA
+// To work around this issue for HIP systems (and leave the default behaviour intact), the
+// HIP tensorflow build defines EIGEN_USE_HIP when compiling all source files, and
+// "unsupported/Eigen/CXX11/Tensor" has been updated to use HIP header when EIGEN_USE_HIP is
+// defined. In continuation of that requirement, the guard here needs to be EIGEN_USE_HIP as well
+
+#if defined(EIGEN_USE_HIP)
+
+#define gpuStream_t hipStream_t
+#define gpuDeviceProp_t hipDeviceProp_t
+#define gpuError_t hipError_t
+#define gpuSuccess hipSuccess
+#define gpuErrorNotReady hipErrorNotReady
+#define gpuGetDeviceCount hipGetDeviceCount
+#define gpuGetLastError hipGetLastError
+#define gpuPeekAtLastError hipPeekAtLastError
+#define gpuGetErrorName hipGetErrorName
+#define gpuGetErrorString hipGetErrorString
+#define gpuGetDeviceProperties hipGetDeviceProperties
+#define gpuStreamDefault hipStreamDefault
+#define gpuGetDevice hipGetDevice
+#define gpuSetDevice hipSetDevice
+#define gpuMalloc hipMalloc
+#define gpuFree hipFree
+#define gpuMemsetAsync hipMemsetAsync
+#define gpuMemset2DAsync hipMemset2DAsync
+#define gpuMemcpyAsync hipMemcpyAsync
+#define gpuMemcpyDeviceToDevice hipMemcpyDeviceToDevice
+#define gpuMemcpyDeviceToHost hipMemcpyDeviceToHost
+#define gpuMemcpyHostToDevice hipMemcpyHostToDevice
+#define gpuStreamQuery hipStreamQuery
+#define gpuSharedMemConfig hipSharedMemConfig
+#define gpuDeviceSetSharedMemConfig hipDeviceSetSharedMemConfig
+#define gpuStreamSynchronize hipStreamSynchronize
+#define gpuDeviceSynchronize hipDeviceSynchronize
+#define gpuMemcpy hipMemcpy
+
+#else
+
+#define gpuStream_t cudaStream_t
+#define gpuDeviceProp_t cudaDeviceProp
+#define gpuError_t cudaError_t
+#define gpuSuccess cudaSuccess
+#define gpuErrorNotReady cudaErrorNotReady
+#define gpuGetDeviceCount cudaGetDeviceCount
+#define gpuGetLastError cudaGetLastError
+#define gpuPeekAtLastError cudaPeekAtLastError
+#define gpuGetErrorName cudaGetErrorName
+#define gpuGetErrorString cudaGetErrorString
+#define gpuGetDeviceProperties cudaGetDeviceProperties
+#define gpuStreamDefault cudaStreamDefault
+#define gpuGetDevice cudaGetDevice
+#define gpuSetDevice cudaSetDevice
+#define gpuMalloc cudaMalloc
+#define gpuFree cudaFree
+#define gpuMemsetAsync cudaMemsetAsync
+#define gpuMemset2DAsync cudaMemset2DAsync
+#define gpuMemcpyAsync cudaMemcpyAsync
+#define gpuMemcpyDeviceToDevice cudaMemcpyDeviceToDevice
+#define gpuMemcpyDeviceToHost cudaMemcpyDeviceToHost
+#define gpuMemcpyHostToDevice cudaMemcpyHostToDevice
+#define gpuStreamQuery cudaStreamQuery
+#define gpuSharedMemConfig cudaSharedMemConfig
+#define gpuDeviceSetSharedMemConfig cudaDeviceSetSharedMemConfig
+#define gpuStreamSynchronize cudaStreamSynchronize
+#define gpuDeviceSynchronize cudaDeviceSynchronize
+#define gpuMemcpy cudaMemcpy
+
+#endif
+
+// gpu_assert can be overridden
+#ifndef gpu_assert
+
+#if defined(EIGEN_HIP_DEVICE_COMPILE)
+// HIPCC do not support the use of assert on the GPU side.
+#define gpu_assert(COND)
+#else
+#define gpu_assert(COND) eigen_assert(COND)
+#endif
+
+#endif  // gpu_assert
+
+#endif  // EIGEN_CORE_GPU_HIP_CUDA_DEFINES_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/GpuHipCudaUndefines.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/GpuHipCudaUndefines.inc
new file mode 100644
index 0000000..342a323
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/GpuHipCudaUndefines.inc
@@ -0,0 +1,45 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2018 Deven Desai <deven.desai.amd@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_CORE_GPU_HIP_CUDA_DEFINES_H)
+
+#ifndef EIGEN_PERMANENTLY_ENABLE_GPU_HIP_CUDA_DEFINES
+
+#undef gpuStream_t
+#undef gpuDeviceProp_t
+#undef gpuError_t
+#undef gpuSuccess
+#undef gpuErrorNotReady
+#undef gpuGetDeviceCount
+#undef gpuGetErrorString
+#undef gpuGetDeviceProperties
+#undef gpuStreamDefault
+#undef gpuGetDevice
+#undef gpuSetDevice
+#undef gpuMalloc
+#undef gpuFree
+#undef gpuMemsetAsync
+#undef gpuMemset2DAsync
+#undef gpuMemcpyAsync
+#undef gpuMemcpyDeviceToDevice
+#undef gpuMemcpyDeviceToHost
+#undef gpuMemcpyHostToDevice
+#undef gpuStreamQuery
+#undef gpuSharedMemConfig
+#undef gpuDeviceSetSharedMemConfig
+#undef gpuStreamSynchronize
+#undef gpuDeviceSynchronize
+#undef gpuMemcpy
+
+#endif  // EIGEN_PERMANENTLY_ENABLE_GPU_HIP_CUDA_DEFINES
+
+#undef EIGEN_CORE_GPU_HIP_CUDA_DEFINES_H
+
+#endif  // EIGEN_CORE_GPU_HIP_CUDA_DEFINES_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/IndexedViewHelper.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/IndexedViewHelper.h
new file mode 100644
index 0000000..d18b258
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/IndexedViewHelper.h
@@ -0,0 +1,487 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INDEXED_VIEW_HELPER_H
+#define EIGEN_INDEXED_VIEW_HELPER_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+struct symbolic_last_tag {};
+
+struct all_t {};
+
+}  // namespace internal
+
+namespace placeholders {
+
+typedef symbolic::SymbolExpr<internal::symbolic_last_tag> last_t;
+
+/** \var last
+ * \ingroup Core_Module
+ *
+ * Can be used as a parameter to Eigen::seq and Eigen::seqN functions to symbolically reference the last
+ * element/row/columns of the underlying vector or matrix once passed to DenseBase::operator()(const RowIndices&, const
+ * ColIndices&).
+ *
+ * This symbolic placeholder supports standard arithmetic operations.
+ *
+ * A typical usage example would be:
+ * \code
+ * using namespace Eigen;
+ * using Eigen::placeholders::last;
+ * VectorXd v(n);
+ * v(seq(2,last-2)).setOnes();
+ * \endcode
+ *
+ * \sa end
+ */
+static constexpr const last_t last;
+
+typedef symbolic::AddExpr<symbolic::SymbolExpr<internal::symbolic_last_tag>,
+                          symbolic::ValueExpr<Eigen::internal::FixedInt<1>>>
+    lastp1_t;
+typedef Eigen::internal::all_t all_t;
+
+/** \var lastp1
+ * \ingroup Core_Module
+ *
+ * Can be used as a parameter to Eigen::seq and Eigen::seqN functions to symbolically
+ * reference the last+1 element/row/columns of the underlying vector or matrix once
+ * passed to DenseBase::operator()(const RowIndices&, const ColIndices&).
+ *
+ * This symbolic placeholder supports standard arithmetic operations.
+ * It is essentially an alias to last+fix<1>.
+ *
+ * \sa last
+ */
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+static constexpr auto lastp1 = last + fix<1>;
+#else
+// Using a FixedExpr<1> expression is important here to make sure the compiler
+// can fully optimize the computation starting indices with zero overhead.
+static constexpr lastp1_t lastp1 = lastp1_t{};
+#endif
+
+/** \var end
+ * \ingroup Core_Module
+ * \sa lastp1
+ */
+static constexpr lastp1_t end = lastp1;
+
+/** \var all
+ * \ingroup Core_Module
+ * Can be used as a parameter to DenseBase::operator()(const RowIndices&, const ColIndices&) to index all rows or
+ * columns
+ */
+static constexpr Eigen::internal::all_t all;
+
+}  // namespace placeholders
+
+namespace internal {
+
+// Evaluate a symbolic expression or constant given the "size" of an object, allowing
+// any symbols like `last` to be evaluated.  The default here assumes a dynamic constant.
+template <typename Expr, int SizeAtCompileTime, typename EnableIf = void>
+struct SymbolicExpressionEvaluator {
+  static constexpr Index ValueAtCompileTime = Undefined;
+  static Index eval(const Expr& expr, Index /*size*/) { return static_cast<Index>(expr); }
+};
+
+// Symbolic expression with size known at compile-time.
+template <typename Expr, int SizeAtCompileTime>
+struct SymbolicExpressionEvaluator<Expr, SizeAtCompileTime, std::enable_if_t<symbolic::is_symbolic<Expr>::value>> {
+  static constexpr Index ValueAtCompileTime =
+      Expr::Derived::eval_at_compile_time(Eigen::placeholders::last = fix<SizeAtCompileTime - 1>);
+  static Index eval(const Expr& expr, Index /*size*/) {
+    return expr.eval(Eigen::placeholders::last = fix<SizeAtCompileTime - 1>);
+  }
+};
+
+// Symbolic expression with dynamic size.
+template <typename Expr>
+struct SymbolicExpressionEvaluator<Expr, Dynamic, std::enable_if_t<symbolic::is_symbolic<Expr>::value>> {
+  static constexpr Index ValueAtCompileTime = Undefined;
+  static Index eval(const Expr& expr, Index size) { return expr.eval(Eigen::placeholders::last = size - 1); }
+};
+
+// Fixed int.
+template <int N, int SizeAtCompileTime>
+struct SymbolicExpressionEvaluator<FixedInt<N>, SizeAtCompileTime, void> {
+  static constexpr Index ValueAtCompileTime = static_cast<Index>(N);
+  static Index eval(const FixedInt<N>& /*expr*/, Index /*size*/) { return ValueAtCompileTime; }
+};
+
+//--------------------------------------------------------------------------------
+// Handling of generic indices (e.g. array)
+//--------------------------------------------------------------------------------
+
+// Potentially wrap indices in a type that is better-suited for IndexedView evaluation.
+template <typename Indices, int NestedSizeAtCompileTime, typename EnableIf = void>
+struct IndexedViewHelperIndicesWrapper {
+  using type = Indices;
+  static const type& CreateIndexSequence(const Indices& indices, Index /*nested_size*/) { return indices; }
+};
+
+// Extract compile-time and runtime first, size, increments.
+template <typename Indices, typename EnableIf = void>
+struct IndexedViewHelper {
+  static constexpr Index FirstAtCompileTime = Undefined;
+  static constexpr Index SizeAtCompileTime = array_size<Indices>::value;
+  static constexpr Index IncrAtCompileTime = Undefined;
+
+  static constexpr Index first(const Indices& indices) { return static_cast<Index>(indices[0]); }
+  static constexpr Index size(const Indices& indices) { return index_list_size(indices); }
+  static constexpr Index incr(const Indices& /*indices*/) { return Undefined; }
+};
+
+//--------------------------------------------------------------------------------
+// Handling of ArithmeticSequence
+//--------------------------------------------------------------------------------
+
+template <Index FirstAtCompileTime_, Index SizeAtCompileTime_, Index IncrAtCompileTime_>
+class ArithmeticSequenceRange {
+ public:
+  static constexpr Index FirstAtCompileTime = FirstAtCompileTime_;
+  static constexpr Index SizeAtCompileTime = SizeAtCompileTime_;
+  static constexpr Index IncrAtCompileTime = IncrAtCompileTime_;
+
+  constexpr ArithmeticSequenceRange(Index first, Index size, Index incr) : m_first{first}, m_size{size}, m_incr{incr} {}
+  constexpr Index operator[](Index i) const { return first() + i * incr(); }
+  constexpr Index first() const noexcept { return m_first.value(); }
+  constexpr Index size() const noexcept { return m_size.value(); }
+  constexpr Index incr() const noexcept { return m_incr.value(); }
+
+ private:
+  variable_if_dynamicindex<Index, int(FirstAtCompileTime)> m_first;
+  variable_if_dynamic<Index, int(SizeAtCompileTime)> m_size;
+  variable_if_dynamicindex<Index, int(IncrAtCompileTime)> m_incr;
+};
+
+template <typename FirstType, typename SizeType, typename IncrType, int NestedSizeAtCompileTime>
+struct IndexedViewHelperIndicesWrapper<ArithmeticSequence<FirstType, SizeType, IncrType>, NestedSizeAtCompileTime,
+                                       void> {
+  static constexpr Index EvalFirstAtCompileTime =
+      SymbolicExpressionEvaluator<FirstType, NestedSizeAtCompileTime>::ValueAtCompileTime;
+  static constexpr Index EvalSizeAtCompileTime =
+      SymbolicExpressionEvaluator<SizeType, NestedSizeAtCompileTime>::ValueAtCompileTime;
+  static constexpr Index EvalIncrAtCompileTime =
+      SymbolicExpressionEvaluator<IncrType, NestedSizeAtCompileTime>::ValueAtCompileTime;
+
+  static constexpr Index FirstAtCompileTime =
+      (int(EvalFirstAtCompileTime) == Undefined) ? Index(DynamicIndex) : EvalFirstAtCompileTime;
+  static constexpr Index SizeAtCompileTime =
+      (int(EvalSizeAtCompileTime) == Undefined) ? Index(Dynamic) : EvalSizeAtCompileTime;
+  static constexpr Index IncrAtCompileTime =
+      (int(EvalIncrAtCompileTime) == Undefined) ? Index(DynamicIndex) : EvalIncrAtCompileTime;
+
+  using Indices = ArithmeticSequence<FirstType, SizeType, IncrType>;
+  using type = ArithmeticSequenceRange<FirstAtCompileTime, SizeAtCompileTime, IncrAtCompileTime>;
+
+  static type CreateIndexSequence(const Indices& indices, Index nested_size) {
+    Index first =
+        SymbolicExpressionEvaluator<FirstType, NestedSizeAtCompileTime>::eval(indices.firstObject(), nested_size);
+    Index size =
+        SymbolicExpressionEvaluator<SizeType, NestedSizeAtCompileTime>::eval(indices.sizeObject(), nested_size);
+    Index incr =
+        SymbolicExpressionEvaluator<IncrType, NestedSizeAtCompileTime>::eval(indices.incrObject(), nested_size);
+    return type(first, size, incr);
+  }
+};
+
+template <Index FirstAtCompileTime_, Index SizeAtCompileTime_, Index IncrAtCompileTime_>
+struct IndexedViewHelper<ArithmeticSequenceRange<FirstAtCompileTime_, SizeAtCompileTime_, IncrAtCompileTime_>, void> {
+ public:
+  using Indices = ArithmeticSequenceRange<FirstAtCompileTime_, SizeAtCompileTime_, IncrAtCompileTime_>;
+  static constexpr Index FirstAtCompileTime = Indices::FirstAtCompileTime;
+  static constexpr Index SizeAtCompileTime = Indices::SizeAtCompileTime;
+  static constexpr Index IncrAtCompileTime = Indices::IncrAtCompileTime;
+  static Index first(const Indices& indices) { return indices.first(); }
+  static Index size(const Indices& indices) { return indices.size(); }
+  static Index incr(const Indices& indices) { return indices.incr(); }
+};
+
+//--------------------------------------------------------------------------------
+// Handling of a single index.
+//--------------------------------------------------------------------------------
+
+template <Index ValueAtCompileTime>
+class SingleRange {
+ public:
+  static constexpr Index FirstAtCompileTime = ValueAtCompileTime;
+  static constexpr Index SizeAtCompileTime = Index(1);
+  static constexpr Index IncrAtCompileTime = Index(1);  // Needs to be 1 to be treated as block-like.
+
+  constexpr SingleRange(Index v) noexcept : m_value(v) {}
+  constexpr Index operator[](Index) const noexcept { return first(); }
+  constexpr Index first() const noexcept { return m_value.value(); }
+  constexpr Index size() const noexcept { return SizeAtCompileTime; }
+  constexpr Index incr() const noexcept { return IncrAtCompileTime; }
+
+ private:
+  variable_if_dynamicindex<Index, int(ValueAtCompileTime)> m_value;
+};
+
+template <typename T>
+struct is_single_range : public std::false_type {};
+
+template <Index ValueAtCompileTime>
+struct is_single_range<SingleRange<ValueAtCompileTime>> : public std::true_type {};
+
+template <typename SingleIndex, int NestedSizeAtCompileTime>
+struct IndexedViewHelperIndicesWrapper<
+    SingleIndex, NestedSizeAtCompileTime,
+    std::enable_if_t<std::is_integral<SingleIndex>::value || symbolic::is_symbolic<SingleIndex>::value>> {
+  static constexpr Index EvalValueAtCompileTime =
+      SymbolicExpressionEvaluator<SingleIndex, NestedSizeAtCompileTime>::ValueAtCompileTime;
+  static constexpr Index ValueAtCompileTime =
+      (int(EvalValueAtCompileTime) == Undefined) ? Index(DynamicIndex) : EvalValueAtCompileTime;
+  using type = SingleRange<ValueAtCompileTime>;
+  static type CreateIndexSequence(const SingleIndex& index, Index nested_size) {
+    return type(SymbolicExpressionEvaluator<SingleIndex, NestedSizeAtCompileTime>::eval(index, nested_size));
+  }
+};
+
+template <int N, int NestedSizeAtCompileTime>
+struct IndexedViewHelperIndicesWrapper<FixedInt<N>, NestedSizeAtCompileTime, void> {
+  using type = SingleRange<Index(N)>;
+  static type CreateIndexSequence(const FixedInt<N>& /*index*/) { return type(Index(N)); }
+};
+
+template <Index ValueAtCompileTime>
+struct IndexedViewHelper<SingleRange<ValueAtCompileTime>, void> {
+  using Indices = SingleRange<ValueAtCompileTime>;
+  static constexpr Index FirstAtCompileTime = Indices::FirstAtCompileTime;
+  static constexpr Index SizeAtCompileTime = Indices::SizeAtCompileTime;
+  static constexpr Index IncrAtCompileTime = Indices::IncrAtCompileTime;
+
+  static constexpr Index first(const Indices& indices) { return indices.first(); }
+  static constexpr Index size(const Indices& /*indices*/) { return SizeAtCompileTime; }
+  static constexpr Index incr(const Indices& /*indices*/) { return IncrAtCompileTime; }
+};
+
+//--------------------------------------------------------------------------------
+// Handling of all
+//--------------------------------------------------------------------------------
+
+// Convert a symbolic 'all' into a usable range type
+template <Index SizeAtCompileTime_>
+class AllRange {
+ public:
+  static constexpr Index FirstAtCompileTime = Index(0);
+  static constexpr Index SizeAtCompileTime = SizeAtCompileTime_;
+  static constexpr Index IncrAtCompileTime = Index(1);
+  constexpr AllRange(Index size) : m_size(size) {}
+  constexpr Index operator[](Index i) const noexcept { return i; }
+  constexpr Index first() const noexcept { return FirstAtCompileTime; }
+  constexpr Index size() const noexcept { return m_size.value(); }
+  constexpr Index incr() const noexcept { return IncrAtCompileTime; }
+
+ private:
+  variable_if_dynamic<Index, int(SizeAtCompileTime)> m_size;
+};
+
+template <int NestedSizeAtCompileTime>
+struct IndexedViewHelperIndicesWrapper<all_t, NestedSizeAtCompileTime, void> {
+  using type = AllRange<Index(NestedSizeAtCompileTime)>;
+  static type CreateIndexSequence(const all_t& /*indices*/, Index nested_size) { return type(nested_size); }
+};
+
+template <Index SizeAtCompileTime_>
+struct IndexedViewHelper<AllRange<SizeAtCompileTime_>, void> {
+  using Indices = AllRange<SizeAtCompileTime_>;
+  static constexpr Index FirstAtCompileTime = Indices::FirstAtCompileTime;
+  static constexpr Index SizeAtCompileTime = Indices::SizeAtCompileTime;
+  static constexpr Index IncrAtCompileTime = Indices::IncrAtCompileTime;
+
+  static Index first(const Indices& indices) { return indices.first(); }
+  static Index size(const Indices& indices) { return indices.size(); }
+  static Index incr(const Indices& indices) { return indices.incr(); }
+};
+
+// this helper class assumes internal::valid_indexed_view_overload<RowIndices, ColIndices>::value == true
+template <typename Derived, typename RowIndices, typename ColIndices, typename EnableIf = void>
+struct IndexedViewSelector;
+
+template <typename Indices, int SizeAtCompileTime>
+using IvcType = typename internal::IndexedViewHelperIndicesWrapper<Indices, SizeAtCompileTime>::type;
+
+template <int SizeAtCompileTime, typename Indices>
+inline IvcType<Indices, SizeAtCompileTime> CreateIndexSequence(size_t size, const Indices& indices) {
+  return internal::IndexedViewHelperIndicesWrapper<Indices, SizeAtCompileTime>::CreateIndexSequence(indices, size);
+}
+
+// Generic
+template <typename Derived, typename RowIndices, typename ColIndices>
+struct IndexedViewSelector<Derived, RowIndices, ColIndices,
+                           std::enable_if_t<internal::traits<
+                               IndexedView<Derived, IvcType<RowIndices, Derived::RowsAtCompileTime>,
+                                           IvcType<ColIndices, Derived::ColsAtCompileTime>>>::ReturnAsIndexedView>> {
+  using ReturnType = IndexedView<Derived, IvcType<RowIndices, Derived::RowsAtCompileTime>,
+                                 IvcType<ColIndices, Derived::ColsAtCompileTime>>;
+  using ConstReturnType = IndexedView<const Derived, IvcType<RowIndices, Derived::RowsAtCompileTime>,
+                                      IvcType<ColIndices, Derived::ColsAtCompileTime>>;
+
+  static inline ReturnType run(Derived& derived, const RowIndices& rowIndices, const ColIndices& colIndices) {
+    return ReturnType(derived, CreateIndexSequence<Derived::RowsAtCompileTime>(derived.rows(), rowIndices),
+                      CreateIndexSequence<Derived::ColsAtCompileTime>(derived.cols(), colIndices));
+  }
+  static inline ConstReturnType run(const Derived& derived, const RowIndices& rowIndices,
+                                    const ColIndices& colIndices) {
+    return ConstReturnType(derived, CreateIndexSequence<Derived::RowsAtCompileTime>(derived.rows(), rowIndices),
+                           CreateIndexSequence<Derived::ColsAtCompileTime>(derived.cols(), colIndices));
+  }
+};
+
+// Block
+template <typename Derived, typename RowIndices, typename ColIndices>
+struct IndexedViewSelector<
+    Derived, RowIndices, ColIndices,
+    std::enable_if_t<internal::traits<IndexedView<Derived, IvcType<RowIndices, Derived::RowsAtCompileTime>,
+                                                  IvcType<ColIndices, Derived::ColsAtCompileTime>>>::ReturnAsBlock>> {
+  using ActualRowIndices = IvcType<RowIndices, Derived::RowsAtCompileTime>;
+  using ActualColIndices = IvcType<ColIndices, Derived::ColsAtCompileTime>;
+  using IndexedViewType = IndexedView<Derived, ActualRowIndices, ActualColIndices>;
+  using ConstIndexedViewType = IndexedView<const Derived, ActualRowIndices, ActualColIndices>;
+  using ReturnType = typename internal::traits<IndexedViewType>::BlockType;
+  using ConstReturnType = typename internal::traits<ConstIndexedViewType>::BlockType;
+  using RowHelper = internal::IndexedViewHelper<ActualRowIndices>;
+  using ColHelper = internal::IndexedViewHelper<ActualColIndices>;
+
+  static inline ReturnType run(Derived& derived, const RowIndices& rowIndices, const ColIndices& colIndices) {
+    auto actualRowIndices = CreateIndexSequence<Derived::RowsAtCompileTime>(derived.rows(), rowIndices);
+    auto actualColIndices = CreateIndexSequence<Derived::ColsAtCompileTime>(derived.cols(), colIndices);
+    return ReturnType(derived, RowHelper::first(actualRowIndices), ColHelper::first(actualColIndices),
+                      RowHelper::size(actualRowIndices), ColHelper::size(actualColIndices));
+  }
+  static inline ConstReturnType run(const Derived& derived, const RowIndices& rowIndices,
+                                    const ColIndices& colIndices) {
+    auto actualRowIndices = CreateIndexSequence<Derived::RowsAtCompileTime>(derived.rows(), rowIndices);
+    auto actualColIndices = CreateIndexSequence<Derived::ColsAtCompileTime>(derived.cols(), colIndices);
+    return ConstReturnType(derived, RowHelper::first(actualRowIndices), ColHelper::first(actualColIndices),
+                           RowHelper::size(actualRowIndices), ColHelper::size(actualColIndices));
+  }
+};
+
+// Scalar
+template <typename Derived, typename RowIndices, typename ColIndices>
+struct IndexedViewSelector<
+    Derived, RowIndices, ColIndices,
+    std::enable_if_t<internal::traits<IndexedView<Derived, IvcType<RowIndices, Derived::RowsAtCompileTime>,
+                                                  IvcType<ColIndices, Derived::ColsAtCompileTime>>>::ReturnAsScalar>> {
+  using ReturnType = typename DenseBase<Derived>::Scalar&;
+  using ConstReturnType = typename DenseBase<Derived>::CoeffReturnType;
+  using ActualRowIndices = IvcType<RowIndices, Derived::RowsAtCompileTime>;
+  using ActualColIndices = IvcType<ColIndices, Derived::ColsAtCompileTime>;
+  using RowHelper = internal::IndexedViewHelper<ActualRowIndices>;
+  using ColHelper = internal::IndexedViewHelper<ActualColIndices>;
+  static inline ReturnType run(Derived& derived, const RowIndices& rowIndices, const ColIndices& colIndices) {
+    auto actualRowIndices = CreateIndexSequence<Derived::RowsAtCompileTime>(derived.rows(), rowIndices);
+    auto actualColIndices = CreateIndexSequence<Derived::ColsAtCompileTime>(derived.cols(), colIndices);
+    return derived(RowHelper::first(actualRowIndices), ColHelper::first(actualColIndices));
+  }
+  static inline ConstReturnType run(const Derived& derived, const RowIndices& rowIndices,
+                                    const ColIndices& colIndices) {
+    auto actualRowIndices = CreateIndexSequence<Derived::RowsAtCompileTime>(derived.rows(), rowIndices);
+    auto actualColIndices = CreateIndexSequence<Derived::ColsAtCompileTime>(derived.cols(), colIndices);
+    return derived(RowHelper::first(actualRowIndices), ColHelper::first(actualColIndices));
+  }
+};
+
+// this helper class assumes internal::is_valid_index_type<Indices>::value == false
+template <typename Derived, typename Indices, typename EnableIf = void>
+struct VectorIndexedViewSelector;
+
+// Generic
+template <typename Derived, typename Indices>
+struct VectorIndexedViewSelector<
+    Derived, Indices,
+    std::enable_if_t<!internal::is_single_range<IvcType<Indices, Derived::SizeAtCompileTime>>::value &&
+                     internal::IndexedViewHelper<IvcType<Indices, Derived::SizeAtCompileTime>>::IncrAtCompileTime !=
+                         1>> {
+  static constexpr bool IsRowMajor = DenseBase<Derived>::IsRowMajor;
+  using ZeroIndex = internal::SingleRange<Index(0)>;
+  using RowMajorReturnType = IndexedView<Derived, ZeroIndex, IvcType<Indices, Derived::SizeAtCompileTime>>;
+  using ConstRowMajorReturnType = IndexedView<const Derived, ZeroIndex, IvcType<Indices, Derived::SizeAtCompileTime>>;
+
+  using ColMajorReturnType = IndexedView<Derived, IvcType<Indices, Derived::SizeAtCompileTime>, ZeroIndex>;
+  using ConstColMajorReturnType = IndexedView<const Derived, IvcType<Indices, Derived::SizeAtCompileTime>, ZeroIndex>;
+
+  using ReturnType = typename internal::conditional<IsRowMajor, RowMajorReturnType, ColMajorReturnType>::type;
+  using ConstReturnType =
+      typename internal::conditional<IsRowMajor, ConstRowMajorReturnType, ConstColMajorReturnType>::type;
+
+  template <bool UseRowMajor = IsRowMajor, std::enable_if_t<UseRowMajor, bool> = true>
+  static inline RowMajorReturnType run(Derived& derived, const Indices& indices) {
+    return RowMajorReturnType(derived, ZeroIndex(0),
+                              CreateIndexSequence<Derived::ColsAtCompileTime>(derived.cols(), indices));
+  }
+  template <bool UseRowMajor = IsRowMajor, std::enable_if_t<UseRowMajor, bool> = true>
+  static inline ConstRowMajorReturnType run(const Derived& derived, const Indices& indices) {
+    return ConstRowMajorReturnType(derived, ZeroIndex(0),
+                                   CreateIndexSequence<Derived::ColsAtCompileTime>(derived.cols(), indices));
+  }
+  template <bool UseRowMajor = IsRowMajor, std::enable_if_t<!UseRowMajor, bool> = true>
+  static inline ColMajorReturnType run(Derived& derived, const Indices& indices) {
+    return ColMajorReturnType(derived, CreateIndexSequence<Derived::RowsAtCompileTime>(derived.rows(), indices),
+                              ZeroIndex(0));
+  }
+  template <bool UseRowMajor = IsRowMajor, std::enable_if_t<!UseRowMajor, bool> = true>
+  static inline ConstColMajorReturnType run(const Derived& derived, const Indices& indices) {
+    return ConstColMajorReturnType(derived, CreateIndexSequence<Derived::RowsAtCompileTime>(derived.rows(), indices),
+                                   ZeroIndex(0));
+  }
+};
+
+// Block
+template <typename Derived, typename Indices>
+struct VectorIndexedViewSelector<
+    Derived, Indices,
+    std::enable_if_t<!internal::is_single_range<IvcType<Indices, Derived::SizeAtCompileTime>>::value &&
+                     internal::IndexedViewHelper<IvcType<Indices, Derived::SizeAtCompileTime>>::IncrAtCompileTime ==
+                         1>> {
+  using Helper = internal::IndexedViewHelper<IvcType<Indices, Derived::SizeAtCompileTime>>;
+  using ReturnType = VectorBlock<Derived, Helper::SizeAtCompileTime>;
+  using ConstReturnType = VectorBlock<const Derived, Helper::SizeAtCompileTime>;
+  static inline ReturnType run(Derived& derived, const Indices& indices) {
+    auto actualIndices = CreateIndexSequence<Derived::SizeAtCompileTime>(derived.size(), indices);
+    return ReturnType(derived, Helper::first(actualIndices), Helper::size(actualIndices));
+  }
+  static inline ConstReturnType run(const Derived& derived, const Indices& indices) {
+    auto actualIndices = CreateIndexSequence<Derived::SizeAtCompileTime>(derived.size(), indices);
+    return ConstReturnType(derived, Helper::first(actualIndices), Helper::size(actualIndices));
+  }
+};
+
+// Symbolic
+template <typename Derived, typename Indices>
+struct VectorIndexedViewSelector<
+    Derived, Indices,
+    std::enable_if_t<internal::is_single_range<IvcType<Indices, Derived::SizeAtCompileTime>>::value>> {
+  using ReturnType = typename DenseBase<Derived>::Scalar&;
+  using ConstReturnType = typename DenseBase<Derived>::CoeffReturnType;
+  using Helper = internal::IndexedViewHelper<IvcType<Indices, Derived::SizeAtCompileTime>>;
+  static inline ReturnType run(Derived& derived, const Indices& indices) {
+    auto actualIndices = CreateIndexSequence<Derived::SizeAtCompileTime>(derived.size(), indices);
+    return derived(Helper::first(actualIndices));
+  }
+  static inline ConstReturnType run(const Derived& derived, const Indices& indices) {
+    auto actualIndices = CreateIndexSequence<Derived::SizeAtCompileTime>(derived.size(), indices);
+    return derived(Helper::first(actualIndices));
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_INDEXED_VIEW_HELPER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/IntegralConstant.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/IntegralConstant.h
new file mode 100644
index 0000000..f79fea8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/IntegralConstant.h
@@ -0,0 +1,279 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INTEGRAL_CONSTANT_H
+#define EIGEN_INTEGRAL_CONSTANT_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <int N>
+class FixedInt;
+template <int N>
+class VariableAndFixedInt;
+
+/** \internal
+ * \class FixedInt
+ *
+ * This class embeds a compile-time integer \c N.
+ *
+ * It is similar to c++11 std::integral_constant<int,N> but with some additional features
+ * such as:
+ *  - implicit conversion to int
+ *  - arithmetic and some bitwise operators: -, +, *, /, %, &, |
+ *  - c++98/14 compatibility with fix<N> and fix<N>() syntax to define integral constants.
+ *
+ * It is strongly discouraged to directly deal with this class FixedInt. Instances are expected to
+ * be created by the user using Eigen::fix<N> or Eigen::fix<N>().
+ * \code
+ * internal::cleanup_index_type<T>::type
+ * internal::cleanup_index_type<T,DynamicKey>::type
+ * \endcode
+ * where T can a FixedInt<N>, a pointer to function FixedInt<N> (*)(), or numerous other integer-like representations.
+ * \c DynamicKey is either Dynamic (default) or DynamicIndex and used to identify true compile-time values.
+ *
+ * For convenience, you can extract the compile-time value \c N in a generic way using the following helper:
+ * \code
+ * internal::get_fixed_value<T,DefaultVal>::value
+ * \endcode
+ * that will give you \c N if T equals FixedInt<N> or FixedInt<N> (*)(), and \c DefaultVal if T does not embed any
+ * compile-time value (e.g., T==int).
+ *
+ * \sa fix<N>, class VariableAndFixedInt
+ */
+template <int N>
+class FixedInt {
+ public:
+  static constexpr int value = N;
+  constexpr operator int() const { return N; }
+
+  constexpr FixedInt() = default;
+  constexpr FixedInt(std::integral_constant<int, N>) {}
+
+  constexpr FixedInt(VariableAndFixedInt<N> other) {
+#ifndef EIGEN_INTERNAL_DEBUGGING
+    EIGEN_UNUSED_VARIABLE(other);
+#endif
+    eigen_internal_assert(int(other) == N);
+  }
+
+  constexpr FixedInt<-N> operator-() const { return FixedInt<-N>(); }
+
+  template <int M>
+  constexpr FixedInt<N + M> operator+(FixedInt<M>) const {
+    return FixedInt<N + M>();
+  }
+
+  template <int M>
+  constexpr FixedInt<N - M> operator-(FixedInt<M>) const {
+    return FixedInt<N - M>();
+  }
+
+  template <int M>
+  constexpr FixedInt<N * M> operator*(FixedInt<M>) const {
+    return FixedInt<N * M>();
+  }
+
+  template <int M>
+  constexpr FixedInt<N / M> operator/(FixedInt<M>) const {
+    return FixedInt<N / M>();
+  }
+
+  template <int M>
+  constexpr FixedInt<N % M> operator%(FixedInt<M>) const {
+    return FixedInt<N % M>();
+  }
+
+  template <int M>
+  constexpr FixedInt<N | M> operator|(FixedInt<M>) const {
+    return FixedInt<N | M>();
+  }
+
+  template <int M>
+  constexpr FixedInt<N & M> operator&(FixedInt<M>) const {
+    return FixedInt<N & M>();
+  }
+
+  // Needed in C++14 to allow fix<N>():
+  constexpr FixedInt operator()() const { return *this; }
+
+  constexpr VariableAndFixedInt<N> operator()(int val) const { return VariableAndFixedInt<N>(val); }
+};
+
+/** \internal
+ * \class VariableAndFixedInt
+ *
+ * This class embeds both a compile-time integer \c N and a runtime integer.
+ * Both values are supposed to be equal unless the compile-time value \c N has a special
+ * value meaning that the runtime-value should be used. Depending on the context, this special
+ * value can be either Eigen::Dynamic (for positive quantities) or Eigen::DynamicIndex (for
+ * quantities that can be negative).
+ *
+ * It is the return-type of the function Eigen::fix<N>(int), and most of the time this is the only
+ * way it is used. It is strongly discouraged to directly deal with instances of VariableAndFixedInt.
+ * Indeed, in order to write generic code, it is the responsibility of the callee to properly convert
+ * it to either a true compile-time quantity (i.e. a FixedInt<N>), or to a runtime quantity (e.g., an Index)
+ * using the following generic helper:
+ * \code
+ * internal::cleanup_index_type<T>::type
+ * internal::cleanup_index_type<T,DynamicKey>::type
+ * \endcode
+ * where T can be a template instantiation of VariableAndFixedInt or numerous other integer-like representations.
+ * \c DynamicKey is either Dynamic (default) or DynamicIndex and used to identify true compile-time values.
+ *
+ * For convenience, you can also extract the compile-time value \c N using the following helper:
+ * \code
+ * internal::get_fixed_value<T,DefaultVal>::value
+ * \endcode
+ * that will give you \c N if T equals VariableAndFixedInt<N>, and \c DefaultVal if T does not embed any compile-time
+ * value (e.g., T==int).
+ *
+ * \sa fix<N>(int), class FixedInt
+ */
+template <int N>
+class VariableAndFixedInt {
+ public:
+  static const int value = N;
+  constexpr operator int() const { return m_value; }
+  constexpr VariableAndFixedInt(int val) : m_value(val) {}
+
+ protected:
+  int m_value;
+};
+
+template <typename T, int Default = Dynamic>
+struct get_fixed_value {
+  static const int value = Default;
+};
+
+template <int N, int Default>
+struct get_fixed_value<FixedInt<N>, Default> {
+  static const int value = N;
+};
+
+template <int N, int Default>
+struct get_fixed_value<VariableAndFixedInt<N>, Default> {
+  static const int value = N;
+};
+
+template <typename T, int N, int Default>
+struct get_fixed_value<variable_if_dynamic<T, N>, Default> {
+  static const int value = N;
+};
+
+template <typename T>
+EIGEN_DEVICE_FUNC constexpr Index get_runtime_value(const T &x) {
+  return x;
+}
+
+// Cleanup integer/FixedInt/VariableAndFixedInt/etc types:
+
+// By default, no cleanup:
+template <typename T, int DynamicKey = Dynamic, typename EnableIf = void>
+struct cleanup_index_type {
+  typedef T type;
+};
+
+// Convert any integral type (e.g., short, int, unsigned int, etc.) to Eigen::Index
+template <typename T, int DynamicKey>
+struct cleanup_index_type<T, DynamicKey, std::enable_if_t<internal::is_integral<T>::value>> {
+  typedef Index type;
+};
+
+// If VariableAndFixedInt does not match DynamicKey, then we turn it to a pure compile-time value:
+template <int N, int DynamicKey>
+struct cleanup_index_type<VariableAndFixedInt<N>, DynamicKey> {
+  typedef FixedInt<N> type;
+};
+// If VariableAndFixedInt matches DynamicKey, then we turn it to a pure runtime-value (aka Index):
+template <int DynamicKey>
+struct cleanup_index_type<VariableAndFixedInt<DynamicKey>, DynamicKey> {
+  typedef Index type;
+};
+
+template <int N, int DynamicKey>
+struct cleanup_index_type<std::integral_constant<int, N>, DynamicKey> {
+  typedef FixedInt<N> type;
+};
+
+}  // end namespace internal
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+template <int N>
+constexpr internal::FixedInt<N> fix{};
+
+#else  // EIGEN_PARSED_BY_DOXYGEN
+
+/** \var fix<N>()
+ * \ingroup Core_Module
+ *
+ * This \em identifier permits to construct an object embedding a compile-time integer \c N.
+ *
+ * \tparam N the compile-time integer value
+ *
+ * It is typically used in conjunction with the Eigen::seq and Eigen::seqN functions to pass compile-time values to
+ * them: \code seqN(10,fix<4>,fix<-3>)   // <=> [10 7 4 1] \endcode
+ *
+ * See also the function fix(int) to pass both a compile-time and runtime value.
+ *
+ * In c++14, it is implemented as:
+ * \code
+ * template<int N> static const internal::FixedInt<N> fix{};
+ * \endcode
+ * where internal::FixedInt<N> is an internal template class similar to
+ * <a href="http://en.cppreference.com/w/cpp/types/integral_constant">\c std::integral_constant </a><tt> <int,N> </tt>
+ * Here, \c fix<N> is thus an object of type \c internal::FixedInt<N>.
+ *
+ * \sa fix<N>(int), seq, seqN
+ */
+template <int N>
+static const auto fix();
+
+/** \fn fix<N>(int)
+ * \ingroup Core_Module
+ *
+ * This function returns an object embedding both a compile-time integer \c N, and a fallback runtime value \a val.
+ *
+ * \tparam N the compile-time integer value
+ * \param  val the fallback runtime integer value
+ *
+ * This function is a more general version of the \ref fix identifier/function that can be used in template code
+ * where the compile-time value could turn out to actually mean "undefined at compile-time". For positive integers
+ * such as a size or a dimension, this case is identified by Eigen::Dynamic, whereas runtime signed integers
+ * (e.g., an increment/stride) are identified as Eigen::DynamicIndex. In such a case, the runtime value \a val
+ * will be used as a fallback.
+ *
+ * A typical use case would be:
+ * \code
+ * template<typename Derived> void foo(const MatrixBase<Derived> &mat) {
+ *   const int N = Derived::RowsAtCompileTime==Dynamic ? Dynamic : Derived::RowsAtCompileTime/2;
+ *   const int n = mat.rows()/2;
+ *   ... mat( seqN(0,fix<N>(n) ) ...;
+ * }
+ * \endcode
+ * In this example, the function Eigen::seqN knows that the second argument is expected to be a size.
+ * If the passed compile-time value N equals Eigen::Dynamic, then the proxy object returned by fix will be dismissed,
+ * and converted to an Eigen::Index of value \c n. Otherwise, the runtime-value \c n will be dismissed, and the
+ * returned ArithmeticSequence will be of the exact same type as <tt> seqN(0,fix<N>) </tt>.
+ *
+ * \sa fix, seqN, class ArithmeticSequence
+ */
+template <int N>
+static const auto fix(int val);
+
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_INTEGRAL_CONSTANT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/MKL_support.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/MKL_support.h
new file mode 100644
index 0000000..3e44a26
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/MKL_support.h
@@ -0,0 +1,139 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   Include file with common MKL declarations
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_MKL_SUPPORT_H
+#define EIGEN_MKL_SUPPORT_H
+
+#ifdef EIGEN_USE_MKL_ALL
+#ifndef EIGEN_USE_BLAS
+#define EIGEN_USE_BLAS
+#endif
+#ifndef EIGEN_USE_LAPACKE
+#define EIGEN_USE_LAPACKE
+#endif
+#ifndef EIGEN_USE_MKL_VML
+#define EIGEN_USE_MKL_VML
+#endif
+#endif
+
+#ifdef EIGEN_USE_LAPACKE_STRICT
+#define EIGEN_USE_LAPACKE
+#endif
+
+#if defined(EIGEN_USE_MKL_VML) && !defined(EIGEN_USE_MKL)
+#define EIGEN_USE_MKL
+#endif
+
+#if defined EIGEN_USE_MKL
+#if (!defined MKL_DIRECT_CALL) && (!defined EIGEN_MKL_NO_DIRECT_CALL)
+#define MKL_DIRECT_CALL
+#define MKL_DIRECT_CALL_JUST_SET
+#endif
+#include <mkl.h>
+/*Check IMKL version for compatibility: < 10.3 is not usable with Eigen*/
+#ifndef INTEL_MKL_VERSION
+#undef EIGEN_USE_MKL /* INTEL_MKL_VERSION is not even defined on older versions */
+#elif INTEL_MKL_VERSION < \
+    100305 /* the intel-mkl-103-release-notes say this was when the lapacke.h interface was added*/
+#undef EIGEN_USE_MKL
+#endif
+#ifndef EIGEN_USE_MKL
+/*If the MKL version is too old, undef everything*/
+#undef EIGEN_USE_MKL_ALL
+#undef EIGEN_USE_LAPACKE
+#undef EIGEN_USE_MKL_VML
+#undef EIGEN_USE_LAPACKE_STRICT
+#undef EIGEN_USE_LAPACKE
+#ifdef MKL_DIRECT_CALL_JUST_SET
+#undef MKL_DIRECT_CALL
+#endif
+#endif
+#endif
+
+#if defined EIGEN_USE_MKL
+
+#define EIGEN_MKL_VML_THRESHOLD 128
+
+/* MKL_DOMAIN_BLAS, etc are defined only in 10.3 update 7 */
+/* MKL_BLAS, etc are not defined in 11.2 */
+#ifdef MKL_DOMAIN_ALL
+#define EIGEN_MKL_DOMAIN_ALL MKL_DOMAIN_ALL
+#else
+#define EIGEN_MKL_DOMAIN_ALL MKL_ALL
+#endif
+
+#ifdef MKL_DOMAIN_BLAS
+#define EIGEN_MKL_DOMAIN_BLAS MKL_DOMAIN_BLAS
+#else
+#define EIGEN_MKL_DOMAIN_BLAS MKL_BLAS
+#endif
+
+#ifdef MKL_DOMAIN_FFT
+#define EIGEN_MKL_DOMAIN_FFT MKL_DOMAIN_FFT
+#else
+#define EIGEN_MKL_DOMAIN_FFT MKL_FFT
+#endif
+
+#ifdef MKL_DOMAIN_VML
+#define EIGEN_MKL_DOMAIN_VML MKL_DOMAIN_VML
+#else
+#define EIGEN_MKL_DOMAIN_VML MKL_VML
+#endif
+
+#ifdef MKL_DOMAIN_PARDISO
+#define EIGEN_MKL_DOMAIN_PARDISO MKL_DOMAIN_PARDISO
+#else
+#define EIGEN_MKL_DOMAIN_PARDISO MKL_PARDISO
+#endif
+#endif
+
+#if defined(EIGEN_USE_BLAS) && !defined(EIGEN_USE_MKL)
+#include "../../misc/blas.h"
+#endif
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+typedef std::complex<double> dcomplex;
+typedef std::complex<float> scomplex;
+
+#if defined(EIGEN_USE_MKL)
+typedef MKL_INT BlasIndex;
+#else
+typedef int BlasIndex;
+#endif
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MKL_SUPPORT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Macros.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Macros.h
new file mode 100644
index 0000000..8e54430
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Macros.h
@@ -0,0 +1,1392 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MACROS_H
+#define EIGEN_MACROS_H
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+//------------------------------------------------------------------------------------------
+// Eigen version and basic defaults
+//------------------------------------------------------------------------------------------
+
+#define EIGEN_VERSION_AT_LEAST(x, y, z) \
+  (EIGEN_MAJOR_VERSION > x ||           \
+   (EIGEN_MAJOR_VERSION >= x && (EIGEN_MINOR_VERSION > y || (EIGEN_MINOR_VERSION >= y && EIGEN_PATCH_VERSION >= z))))
+
+#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
+#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::RowMajor
+#else
+#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::ColMajor
+#endif
+
+#ifndef EIGEN_DEFAULT_DENSE_INDEX_TYPE
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE std::ptrdiff_t
+#endif
+
+// Upperbound on the C++ version to use.
+// Expected values are 03, 11, 14, 17, etc.
+// By default, let's use an arbitrarily large C++ version.
+#ifndef EIGEN_MAX_CPP_VER
+#define EIGEN_MAX_CPP_VER 99
+#endif
+
+/** Allows to disable some optimizations which might affect the accuracy of the result.
+ * Such optimization are enabled by default, and set EIGEN_FAST_MATH to 0 to disable them.
+ * They currently include:
+ *   - single precision ArrayBase::sin() and ArrayBase::cos() for SSE and AVX vectorization.
+ */
+#ifndef EIGEN_FAST_MATH
+#define EIGEN_FAST_MATH 1
+#endif
+
+#ifndef EIGEN_STACK_ALLOCATION_LIMIT
+// 131072 == 128 KB
+#define EIGEN_STACK_ALLOCATION_LIMIT 131072
+#endif
+
+/* Specify whether to use std::fma for scalar multiply-add instructions.
+ *
+ * On machines that have FMA as a single instruction, this will generally
+ * improve precision without significant performance implications.
+ *
+ * Without a single instruction, performance has been found to be reduced 2-3x
+ * on Intel CPUs, and up to 30x for WASM.
+ *
+ * If unspecified, defaults to using FMA if hardware support is available.
+ * The default should be used in most cases to ensure consistency between
+ * vectorized and non-vectorized paths.
+ */
+#ifndef EIGEN_SCALAR_MADD_USE_FMA
+#ifdef EIGEN_VECTORIZE_FMA
+#define EIGEN_SCALAR_MADD_USE_FMA 1
+#else
+#define EIGEN_SCALAR_MADD_USE_FMA 0
+#endif
+#endif
+
+//------------------------------------------------------------------------------------------
+// Compiler identification, EIGEN_COMP_*
+//------------------------------------------------------------------------------------------
+
+/// \internal EIGEN_COMP_GNUC set to version (e.g., 951 for GCC 9.5.1) for all compilers compatible with GCC
+#ifdef __GNUC__
+#define EIGEN_COMP_GNUC (__GNUC__ * 100 + __GNUC_MINOR__ * 10 + __GNUC_PATCHLEVEL__)
+#else
+#define EIGEN_COMP_GNUC 0
+#endif
+
+/// \internal EIGEN_COMP_CLANG set to version (e.g., 372 for clang 3.7.2) if the compiler is clang
+#if defined(__clang__)
+#define EIGEN_COMP_CLANG (__clang_major__ * 100 + __clang_minor__ * 10 + __clang_patchlevel__)
+#else
+#define EIGEN_COMP_CLANG 0
+#endif
+
+/// \internal EIGEN_COMP_CLANGAPPLE set to the version number (e.g. 9000000 for AppleClang 9.0) if the compiler is
+/// AppleClang
+#if defined(__clang__) && defined(__apple_build_version__)
+#define EIGEN_COMP_CLANGAPPLE __apple_build_version__
+#else
+#define EIGEN_COMP_CLANGAPPLE 0
+#endif
+
+/// \internal EIGEN_COMP_CASTXML set to 1 if being preprocessed by CastXML
+#if defined(__castxml__)
+#define EIGEN_COMP_CASTXML 1
+#else
+#define EIGEN_COMP_CASTXML 0
+#endif
+
+/// \internal EIGEN_COMP_LLVM set to 1 if the compiler backend is llvm
+#if defined(__llvm__)
+#define EIGEN_COMP_LLVM 1
+#else
+#define EIGEN_COMP_LLVM 0
+#endif
+
+/// \internal EIGEN_COMP_ICC set to __INTEL_COMPILER if the compiler is Intel icc compiler, 0 otherwise
+#if defined(__INTEL_COMPILER)
+#define EIGEN_COMP_ICC __INTEL_COMPILER
+#else
+#define EIGEN_COMP_ICC 0
+#endif
+
+/// \internal EIGEN_COMP_CLANGICC set to __INTEL_CLANG_COMPILER if the compiler is Intel icx compiler, 0 otherwise
+#if defined(__INTEL_CLANG_COMPILER)
+#define EIGEN_COMP_CLANGICC __INTEL_CLANG_COMPILER
+#else
+#define EIGEN_COMP_CLANGICC 0
+#endif
+
+/// \internal EIGEN_COMP_MINGW set to 1 if the compiler is mingw
+#if defined(__MINGW32__)
+#define EIGEN_COMP_MINGW 1
+#else
+#define EIGEN_COMP_MINGW 0
+#endif
+
+/// \internal EIGEN_COMP_SUNCC set to 1 if the compiler is Solaris Studio
+#if defined(__SUNPRO_CC)
+#define EIGEN_COMP_SUNCC 1
+#else
+#define EIGEN_COMP_SUNCC 0
+#endif
+
+/// \internal EIGEN_COMP_MSVC set to _MSC_VER if the compiler is Microsoft Visual C++, 0 otherwise.
+#if defined(_MSC_VER)
+#define EIGEN_COMP_MSVC _MSC_VER
+#else
+#define EIGEN_COMP_MSVC 0
+#endif
+
+#if defined(__NVCC__)
+#if defined(__CUDACC_VER_MAJOR__) && (__CUDACC_VER_MAJOR__ >= 9)
+#define EIGEN_COMP_NVCC ((__CUDACC_VER_MAJOR__ * 10000) + (__CUDACC_VER_MINOR__ * 100))
+#elif defined(__CUDACC_VER__)
+#define EIGEN_COMP_NVCC __CUDACC_VER__
+#else
+#error "NVCC did not define compiler version."
+#endif
+#else
+#define EIGEN_COMP_NVCC 0
+#endif
+
+// For the record, here is a table summarizing the possible values for EIGEN_COMP_MSVC:
+//  name        ver   MSC_VER
+//  2015        14      1900
+//  "15"        15      1900
+//  2017-14.1   15.0    1910
+//  2017-14.11  15.3    1911
+//  2017-14.12  15.5    1912
+//  2017-14.13  15.6    1913
+//  2017-14.14  15.7    1914
+//  2017        15.8    1915
+//  2017        15.9    1916
+//  2019 RTW    16.0    1920
+
+/// \internal EIGEN_COMP_MSVC_LANG set to _MSVC_LANG if the compiler is Microsoft Visual C++, 0 otherwise.
+#if defined(_MSVC_LANG)
+#define EIGEN_COMP_MSVC_LANG _MSVC_LANG
+#else
+#define EIGEN_COMP_MSVC_LANG 0
+#endif
+
+// For the record, here is a table summarizing the possible values for EIGEN_COMP_MSVC_LANG:
+// MSVC option                          Standard  MSVC_LANG
+// /std:c++14 (default as of VS 2019)   C++14     201402L
+// /std:c++17                           C++17     201703L
+// /std:c++latest                       >C++17    >201703L
+
+/// \internal EIGEN_COMP_MSVC_STRICT set to 1 if the compiler is really Microsoft Visual C++ and not ,e.g., ICC or
+/// clang-cl
+#if EIGEN_COMP_MSVC && !(EIGEN_COMP_ICC || EIGEN_COMP_LLVM || EIGEN_COMP_CLANG)
+#define EIGEN_COMP_MSVC_STRICT _MSC_VER
+#else
+#define EIGEN_COMP_MSVC_STRICT 0
+#endif
+
+/// \internal EIGEN_COMP_IBM set to xlc version if the compiler is IBM XL C++
+// XLC   version
+// 3.1   0x0301
+// 4.5   0x0405
+// 5.0   0x0500
+// 12.1  0x0C01
+#if defined(__IBMCPP__) || defined(__xlc__) || defined(__ibmxl__)
+#define EIGEN_COMP_IBM __xlC__
+#else
+#define EIGEN_COMP_IBM 0
+#endif
+
+/// \internal EIGEN_COMP_PGI set to PGI version if the compiler is Portland Group Compiler
+#if defined(__PGI)
+#define EIGEN_COMP_PGI (__PGIC__ * 100 + __PGIC_MINOR__)
+#else
+#define EIGEN_COMP_PGI 0
+#endif
+
+/// \internal EIGEN_COMP_NVHPC set to NVHPC version if the compiler is nvc++
+#if defined(__NVCOMPILER)
+#define EIGEN_COMP_NVHPC (__NVCOMPILER_MAJOR__ * 100 + __NVCOMPILER_MINOR__)
+#else
+#define EIGEN_COMP_NVHPC 0
+#endif
+
+/// \internal EIGEN_COMP_ARM set to 1 if the compiler is ARM Compiler
+#if defined(__CC_ARM) || defined(__ARMCC_VERSION)
+#define EIGEN_COMP_ARM 1
+#else
+#define EIGEN_COMP_ARM 0
+#endif
+
+/// \internal EIGEN_COMP_EMSCRIPTEN set to 1 if the compiler is Emscripten Compiler
+#if defined(__EMSCRIPTEN__)
+#define EIGEN_COMP_EMSCRIPTEN 1
+#else
+#define EIGEN_COMP_EMSCRIPTEN 0
+#endif
+
+/// \internal EIGEN_COMP_FCC set to FCC version if the compiler is Fujitsu Compiler (traditional mode)
+/// \note The Fujitsu C/C++ compiler uses the traditional mode based
+/// on EDG g++ 6.1 by default or if invoked with the -Nnoclang flag
+#if defined(__FUJITSU)
+#define EIGEN_COMP_FCC (__FCC_major__ * 100 + __FCC_minor__ * 10 + __FCC_patchlevel__)
+#else
+#define EIGEN_COMP_FCC 0
+#endif
+
+/// \internal EIGEN_COMP_CLANGFCC set to FCC version if the compiler is Fujitsu Compiler (Clang mode)
+/// \note The Fujitsu C/C++ compiler uses the non-traditional mode
+/// based on Clang 7.1.0 if invoked with the -Nclang flag
+#if defined(__CLANG_FUJITSU)
+#define EIGEN_COMP_CLANGFCC (__FCC_major__ * 100 + __FCC_minor__ * 10 + __FCC_patchlevel__)
+#else
+#define EIGEN_COMP_CLANGFCC 0
+#endif
+
+/// \internal EIGEN_COMP_CPE set to CPE version if the compiler is HPE Cray Compiler (GCC based)
+/// \note This is the SVE-enabled C/C++ compiler from the HPE Cray
+/// Programming Environment (CPE) based on Cray GCC 8.1
+#if defined(_CRAYC) && !defined(__clang__)
+#define EIGEN_COMP_CPE (_RELEASE_MAJOR * 100 + _RELEASE_MINOR * 10 + _RELEASE_PATCHLEVEL)
+#else
+#define EIGEN_COMP_CPE 0
+#endif
+
+/// \internal EIGEN_COMP_CLANGCPE set to CPE version if the compiler is HPE Cray Compiler (Clang based)
+/// \note This is the C/C++ compiler from the HPE Cray Programming
+/// Environment (CPE) based on Cray Clang 11.0 without SVE-support
+#if defined(_CRAYC) && defined(__clang__)
+#define EIGEN_COMP_CLANGCPE (_RELEASE_MAJOR * 100 + _RELEASE_MINOR * 10 + _RELEASE_PATCHLEVEL)
+#else
+#define EIGEN_COMP_CLANGCPE 0
+#endif
+
+/// \internal EIGEN_COMP_LCC set to 1 if the compiler is MCST-LCC (MCST eLbrus Compiler Collection)
+#if defined(__LCC__) && defined(__MCST__)
+#define EIGEN_COMP_LCC (__LCC__ * 100 + __LCC_MINOR__)
+#else
+#define EIGEN_COMP_LCC 0
+#endif
+
+/// \internal EIGEN_COMP_GNUC_STRICT set to 1 if the compiler is really GCC and not a compatible compiler (e.g., ICC,
+/// clang, mingw, etc.)
+#if EIGEN_COMP_GNUC &&                                                                                   \
+    !(EIGEN_COMP_CLANG || EIGEN_COMP_ICC || EIGEN_COMP_CLANGICC || EIGEN_COMP_MINGW || EIGEN_COMP_PGI || \
+      EIGEN_COMP_NVHPC || EIGEN_COMP_IBM || EIGEN_COMP_ARM || EIGEN_COMP_EMSCRIPTEN || EIGEN_COMP_FCC || \
+      EIGEN_COMP_CLANGFCC || EIGEN_COMP_CPE || EIGEN_COMP_CLANGCPE || EIGEN_COMP_LCC)
+#define EIGEN_COMP_GNUC_STRICT 1
+#else
+#define EIGEN_COMP_GNUC_STRICT 0
+#endif
+
+// GCC, and compilers that pretend to be it, have different version schemes, so this only makes sense to use with the
+// real GCC.
+#if EIGEN_COMP_GNUC_STRICT
+#define EIGEN_GNUC_STRICT_AT_LEAST(x, y, z)                   \
+  ((__GNUC__ > x) || (__GNUC__ == x && __GNUC_MINOR__ > y) || \
+   (__GNUC__ == x && __GNUC_MINOR__ == y && __GNUC_PATCHLEVEL__ >= z))
+#define EIGEN_GNUC_STRICT_LESS_THAN(x, y, z)                  \
+  ((__GNUC__ < x) || (__GNUC__ == x && __GNUC_MINOR__ < y) || \
+   (__GNUC__ == x && __GNUC_MINOR__ == y && __GNUC_PATCHLEVEL__ < z))
+#else
+#define EIGEN_GNUC_STRICT_AT_LEAST(x, y, z) 0
+#define EIGEN_GNUC_STRICT_LESS_THAN(x, y, z) 0
+#endif
+
+/// \internal EIGEN_COMP_CLANG_STRICT set to 1 if the compiler is really Clang and not a compatible compiler (e.g.,
+/// AppleClang, etc.)
+#if EIGEN_COMP_CLANG && !(EIGEN_COMP_CLANGAPPLE || EIGEN_COMP_CLANGICC || EIGEN_COMP_CLANGFCC || EIGEN_COMP_CLANGCPE)
+#define EIGEN_COMP_CLANG_STRICT 1
+#else
+#define EIGEN_COMP_CLANG_STRICT 0
+#endif
+
+// Clang, and compilers forked from it, have different version schemes, so this only makes sense to use with the real
+// Clang.
+#if EIGEN_COMP_CLANG_STRICT
+#define EIGEN_CLANG_STRICT_AT_LEAST(x, y, z)                                 \
+  ((__clang_major__ > x) || (__clang_major__ == x && __clang_minor__ > y) || \
+   (__clang_major__ == x && __clang_minor__ == y && __clang_patchlevel__ >= z))
+#define EIGEN_CLANG_STRICT_LESS_THAN(x, y, z)                                \
+  ((__clang_major__ < x) || (__clang_major__ == x && __clang_minor__ < y) || \
+   (__clang_major__ == x && __clang_minor__ == y && __clang_patchlevel__ < z))
+#else
+#define EIGEN_CLANG_STRICT_AT_LEAST(x, y, z) 0
+#define EIGEN_CLANG_STRICT_LESS_THAN(x, y, z) 0
+#endif
+
+//------------------------------------------------------------------------------------------
+// Architecture identification, EIGEN_ARCH_*
+//------------------------------------------------------------------------------------------
+
+#if defined(__x86_64__) || (defined(_M_X64) && !defined(_M_ARM64EC)) || defined(__amd64)
+#define EIGEN_ARCH_x86_64 1
+#else
+#define EIGEN_ARCH_x86_64 0
+#endif
+
+#if defined(__i386__) || defined(_M_IX86) || defined(_X86_) || defined(__i386)
+#define EIGEN_ARCH_i386 1
+#else
+#define EIGEN_ARCH_i386 0
+#endif
+
+#if EIGEN_ARCH_x86_64 || EIGEN_ARCH_i386
+#define EIGEN_ARCH_i386_OR_x86_64 1
+#else
+#define EIGEN_ARCH_i386_OR_x86_64 0
+#endif
+
+/// \internal EIGEN_ARCH_ARM set to 1 if the architecture is ARM
+#if defined(__arm__)
+#define EIGEN_ARCH_ARM 1
+#else
+#define EIGEN_ARCH_ARM 0
+#endif
+
+/// \internal EIGEN_ARCH_ARM64 set to 1 if the architecture is ARM64
+#if defined(__aarch64__) || defined(_M_ARM64) || defined(_M_ARM64EC)
+#define EIGEN_ARCH_ARM64 1
+#else
+#define EIGEN_ARCH_ARM64 0
+#endif
+
+/// \internal EIGEN_ARCH_ARM_OR_ARM64 set to 1 if the architecture is ARM or ARM64
+#if EIGEN_ARCH_ARM || EIGEN_ARCH_ARM64
+#define EIGEN_ARCH_ARM_OR_ARM64 1
+#else
+#define EIGEN_ARCH_ARM_OR_ARM64 0
+#endif
+
+/// \internal EIGEN_ARCH_ARMV8 set to 1 if the architecture is armv8 or greater.
+#if EIGEN_ARCH_ARM_OR_ARM64 && defined(__ARM_ARCH) && __ARM_ARCH >= 8
+#define EIGEN_ARCH_ARMV8 1
+#else
+#define EIGEN_ARCH_ARMV8 0
+#endif
+
+/// \internal EIGEN_HAS_ARM64_FP16 set to 1 if the architecture provides an IEEE
+/// compliant Arm fp16 type
+#if EIGEN_ARCH_ARM_OR_ARM64
+#ifndef EIGEN_HAS_ARM64_FP16
+#if defined(__ARM_FP16_FORMAT_IEEE)
+#define EIGEN_HAS_ARM64_FP16 1
+#else
+#define EIGEN_HAS_ARM64_FP16 0
+#endif
+#endif
+#endif
+
+/// \internal EIGEN_ARCH_MIPS set to 1 if the architecture is MIPS
+#if defined(__mips__) || defined(__mips)
+#define EIGEN_ARCH_MIPS 1
+#else
+#define EIGEN_ARCH_MIPS 0
+#endif
+
+/// \internal EIGEN_ARCH_LOONGARCH64 set to 1 if the architecture is LOONGARCH64
+#if defined(__loongarch64)
+#define EIGEN_ARCH_LOONGARCH64 1
+#else
+#define EIGEN_ARCH_LOONGARCH64 0
+#endif
+
+/// \internal EIGEN_ARCH_SPARC set to 1 if the architecture is SPARC
+#if defined(__sparc__) || defined(__sparc)
+#define EIGEN_ARCH_SPARC 1
+#else
+#define EIGEN_ARCH_SPARC 0
+#endif
+
+/// \internal EIGEN_ARCH_IA64 set to 1 if the architecture is Intel Itanium
+#if defined(__ia64__)
+#define EIGEN_ARCH_IA64 1
+#else
+#define EIGEN_ARCH_IA64 0
+#endif
+
+/// \internal EIGEN_ARCH_PPC set to 1 if the architecture is PowerPC
+#if defined(__powerpc__) || defined(__ppc__) || defined(_M_PPC) || defined(__POWERPC__)
+#define EIGEN_ARCH_PPC 1
+#else
+#define EIGEN_ARCH_PPC 0
+#endif
+
+/// \internal EIGEN_ARCH_RISCV set to 1 if the architecture is RISC-V.
+#if defined(__riscv)
+#define EIGEN_ARCH_RISCV 1
+#else
+#define EIGEN_ARCH_RISCV 0
+#endif
+
+//------------------------------------------------------------------------------------------
+// Operating system identification, EIGEN_OS_*
+//------------------------------------------------------------------------------------------
+
+/// \internal EIGEN_OS_UNIX set to 1 if the OS is a unix variant
+#if defined(__unix__) || defined(__unix)
+#define EIGEN_OS_UNIX 1
+#else
+#define EIGEN_OS_UNIX 0
+#endif
+
+/// \internal EIGEN_OS_LINUX set to 1 if the OS is based on Linux kernel
+#if defined(__linux__)
+#define EIGEN_OS_LINUX 1
+#else
+#define EIGEN_OS_LINUX 0
+#endif
+
+/// \internal EIGEN_OS_ANDROID set to 1 if the OS is Android
+// note: ANDROID is defined when using ndk_build, __ANDROID__ is defined when using a standalone toolchain.
+#if defined(__ANDROID__) || defined(ANDROID)
+#define EIGEN_OS_ANDROID 1
+
+// Since NDK r16, `__NDK_MAJOR__` and `__NDK_MINOR__` are defined in
+// <android/ndk-version.h>. For NDK < r16, users should define these macros,
+// e.g. `-D__NDK_MAJOR__=11 -D__NKD_MINOR__=0` for NDK r11.
+#if defined __has_include
+#if __has_include(<android/ndk-version.h>)
+#include <android/ndk-version.h>
+#endif
+#endif
+
+#else
+#define EIGEN_OS_ANDROID 0
+#endif
+
+/// \internal EIGEN_OS_GNULINUX set to 1 if the OS is GNU Linux and not Linux-based OS (e.g., not android)
+#if defined(__gnu_linux__) && !(EIGEN_OS_ANDROID)
+#define EIGEN_OS_GNULINUX 1
+#else
+#define EIGEN_OS_GNULINUX 0
+#endif
+
+/// \internal EIGEN_OS_BSD set to 1 if the OS is a BSD variant
+#if defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__) || defined(__bsdi__) || defined(__DragonFly__)
+#define EIGEN_OS_BSD 1
+#else
+#define EIGEN_OS_BSD 0
+#endif
+
+/// \internal EIGEN_OS_MAC set to 1 if the OS is MacOS
+#if defined(__APPLE__)
+#define EIGEN_OS_MAC 1
+#else
+#define EIGEN_OS_MAC 0
+#endif
+
+/// \internal EIGEN_OS_QNX set to 1 if the OS is QNX
+#if defined(__QNX__)
+#define EIGEN_OS_QNX 1
+#else
+#define EIGEN_OS_QNX 0
+#endif
+
+/// \internal EIGEN_OS_WIN set to 1 if the OS is Windows based
+#if defined(_WIN32)
+#define EIGEN_OS_WIN 1
+#else
+#define EIGEN_OS_WIN 0
+#endif
+
+/// \internal EIGEN_OS_WIN64 set to 1 if the OS is Windows 64bits
+#if defined(_WIN64)
+#define EIGEN_OS_WIN64 1
+#else
+#define EIGEN_OS_WIN64 0
+#endif
+
+/// \internal EIGEN_OS_WINCE set to 1 if the OS is Windows CE
+#if defined(_WIN32_WCE)
+#define EIGEN_OS_WINCE 1
+#else
+#define EIGEN_OS_WINCE 0
+#endif
+
+/// \internal EIGEN_OS_CYGWIN set to 1 if the OS is Windows/Cygwin
+#if defined(__CYGWIN__)
+#define EIGEN_OS_CYGWIN 1
+#else
+#define EIGEN_OS_CYGWIN 0
+#endif
+
+/// \internal EIGEN_OS_WIN_STRICT set to 1 if the OS is really Windows and not some variants
+#if EIGEN_OS_WIN && !(EIGEN_OS_WINCE || EIGEN_OS_CYGWIN)
+#define EIGEN_OS_WIN_STRICT 1
+#else
+#define EIGEN_OS_WIN_STRICT 0
+#endif
+
+/// \internal EIGEN_OS_SUN set to __SUNPRO_C if the OS is SUN
+// compiler  solaris   __SUNPRO_C
+// version   studio
+// 5.7       10        0x570
+// 5.8       11        0x580
+// 5.9       12        0x590
+// 5.10	     12.1      0x5100
+// 5.11	     12.2      0x5110
+// 5.12	     12.3      0x5120
+#if (defined(sun) || defined(__sun)) && !(defined(__SVR4) || defined(__svr4__))
+#define EIGEN_OS_SUN __SUNPRO_C
+#else
+#define EIGEN_OS_SUN 0
+#endif
+
+/// \internal EIGEN_OS_SOLARIS set to 1 if the OS is Solaris
+#if (defined(sun) || defined(__sun)) && (defined(__SVR4) || defined(__svr4__))
+#define EIGEN_OS_SOLARIS 1
+#else
+#define EIGEN_OS_SOLARIS 0
+#endif
+
+//------------------------------------------------------------------------------------------
+// Detect GPU compilers and architectures
+//------------------------------------------------------------------------------------------
+
+// NVCC is not supported as the target platform for HIPCC
+// Note that this also makes EIGEN_CUDACC and EIGEN_HIPCC mutually exclusive
+#if defined(__NVCC__) && defined(__HIPCC__)
+#error "NVCC as the target platform for HIPCC is currently not supported."
+#endif
+
+#if defined(__CUDACC__) && !defined(EIGEN_NO_CUDA) && !defined(__SYCL_DEVICE_ONLY__)
+// Means the compiler is either nvcc or clang with CUDA enabled
+#define EIGEN_CUDACC __CUDACC__
+#endif
+
+#if defined(__CUDA_ARCH__) && !defined(EIGEN_NO_CUDA) && !defined(__SYCL_DEVICE_ONLY__)
+// Means we are generating code for the device
+#define EIGEN_CUDA_ARCH __CUDA_ARCH__
+#endif
+
+#if defined(EIGEN_CUDACC)
+#include <cuda.h>
+#define EIGEN_CUDA_SDK_VER (CUDA_VERSION * 10)
+#else
+#define EIGEN_CUDA_SDK_VER 0
+#endif
+
+#if defined(__HIPCC__) && !defined(EIGEN_NO_HIP) && !defined(__SYCL_DEVICE_ONLY__)
+// Means the compiler is HIPCC (analogous to EIGEN_CUDACC, but for HIP)
+#define EIGEN_HIPCC __HIPCC__
+
+// We need to include hip_runtime.h here because it pulls in
+// ++ hip_common.h which contains the define for  __HIP_DEVICE_COMPILE__
+// ++ host_defines.h which contains the defines for the __host__ and __device__ macros
+#include <hip/hip_runtime.h>
+
+#if defined(__HIP_DEVICE_COMPILE__) && !defined(__SYCL_DEVICE_ONLY__)
+// analogous to EIGEN_CUDA_ARCH, but for HIP
+#define EIGEN_HIP_DEVICE_COMPILE __HIP_DEVICE_COMPILE__
+#endif
+
+// For HIP (ROCm 3.5 and higher), we need to explicitly set the launch_bounds attribute
+// value to 1024. The compiler assigns a default value of 256 when the attribute is not
+// specified. This results in failures on the HIP platform, for cases when a GPU kernel
+// without an explicit launch_bounds attribute is called with a threads_per_block value
+// greater than 256.
+//
+// This is a regression in functionality and is expected to be fixed within the next
+// couple of ROCm releases (compiler will go back to using 1024 value as the default)
+//
+// In the meantime, we will use a "only enabled for HIP" macro to set the launch_bounds
+// attribute.
+
+#define EIGEN_HIP_LAUNCH_BOUNDS_1024 __launch_bounds__(1024)
+
+#endif
+
+#if !defined(EIGEN_HIP_LAUNCH_BOUNDS_1024)
+#define EIGEN_HIP_LAUNCH_BOUNDS_1024
+#endif  // !defined(EIGEN_HIP_LAUNCH_BOUNDS_1024)
+
+// Unify CUDA/HIPCC
+
+#if defined(EIGEN_CUDACC) || defined(EIGEN_HIPCC)
+//
+// If either EIGEN_CUDACC or EIGEN_HIPCC is defined, then define EIGEN_GPUCC
+//
+#define EIGEN_GPUCC
+//
+// EIGEN_HIPCC implies the HIP compiler and is used to tweak Eigen code for use in HIP kernels
+// EIGEN_CUDACC implies the CUDA compiler and is used to tweak Eigen code for use in CUDA kernels
+//
+// In most cases the same tweaks are required to the Eigen code to enable in both the HIP and CUDA kernels.
+// For those cases, the corresponding code should be guarded with
+//      #if defined(EIGEN_GPUCC)
+// instead of
+//      #if defined(EIGEN_CUDACC) || defined(EIGEN_HIPCC)
+//
+// For cases where the tweak is specific to HIP, the code should be guarded with
+//      #if defined(EIGEN_HIPCC)
+//
+// For cases where the tweak is specific to CUDA, the code should be guarded with
+//      #if defined(EIGEN_CUDACC)
+//
+#endif
+
+#if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIP_DEVICE_COMPILE)
+//
+// If either EIGEN_CUDA_ARCH or EIGEN_HIP_DEVICE_COMPILE is defined, then define EIGEN_GPU_COMPILE_PHASE
+//
+#define EIGEN_GPU_COMPILE_PHASE
+//
+// GPU compilers (HIPCC, NVCC) typically do two passes over the source code,
+//   + one to compile the source for the "host" (ie CPU)
+//   + another to compile the source for the "device" (ie. GPU)
+//
+// Code that needs to enabled only during the either the "host" or "device" compilation phase
+// needs to be guarded with a macro that indicates the current compilation phase
+//
+// EIGEN_HIP_DEVICE_COMPILE implies the device compilation phase in HIP
+// EIGEN_CUDA_ARCH implies the device compilation phase in CUDA
+//
+// In most cases, the "host" / "device" specific code is the same for both HIP and CUDA
+// For those cases, the code should be guarded with
+//       #if defined(EIGEN_GPU_COMPILE_PHASE)
+// instead of
+//       #if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIP_DEVICE_COMPILE)
+//
+// For cases where the tweak is specific to HIP, the code should be guarded with
+//      #if defined(EIGEN_HIP_DEVICE_COMPILE)
+//
+// For cases where the tweak is specific to CUDA, the code should be guarded with
+//      #if defined(EIGEN_CUDA_ARCH)
+//
+#endif
+
+/// \internal EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC set to 1 if the architecture
+/// supports Neon vector intrinsics for fp16.
+#if EIGEN_ARCH_ARM_OR_ARM64
+#ifndef EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+// Clang only supports FP16 on aarch64, and not all intrinsics are available
+// on A32 anyways even in GCC (e.g. vdiv_f16, vsqrt_f16).
+#if EIGEN_ARCH_ARM64 && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(EIGEN_GPU_COMPILE_PHASE)
+#define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 1
+#else
+#define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 0
+#endif
+#endif
+#endif
+
+/// \internal EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC set to 1 if the architecture
+/// supports Neon scalar intrinsics for fp16.
+#if EIGEN_ARCH_ARM_OR_ARM64
+#ifndef EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC
+// Clang only supports FP16 on aarch64, and not all intrinsics are available
+// on A32 anyways, even in GCC (e.g. vceqh_f16).
+#if EIGEN_ARCH_ARM64 && defined(__ARM_FEATURE_FP16_SCALAR_ARITHMETIC) && !defined(EIGEN_GPU_COMPILE_PHASE)
+#define EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC 1
+#endif
+#endif
+#endif
+
+#if defined(EIGEN_USE_SYCL) && defined(__SYCL_DEVICE_ONLY__)
+// EIGEN_USE_SYCL is a user-defined macro while __SYCL_DEVICE_ONLY__ is a compiler-defined macro.
+// In most cases we want to check if both macros are defined which can be done using the define below.
+#define SYCL_DEVICE_ONLY
+#endif
+
+//------------------------------------------------------------------------------------------
+// Detect Compiler/Architecture/OS specific features
+//------------------------------------------------------------------------------------------
+
+// Cross compiler wrapper around LLVM's __has_builtin
+#ifdef __has_builtin
+#define EIGEN_HAS_BUILTIN(x) __has_builtin(x)
+#else
+#define EIGEN_HAS_BUILTIN(x) 0
+#endif
+
+// Cross compiler wrapper around LLVM's __has_attribute
+#ifdef __has_attribute
+#define EIGEN_HAS_ATTRIBUTE(x) __has_attribute(x)
+#else
+#define EIGEN_HAS_ATTRIBUTE(x) 0
+#endif
+
+// A Clang feature extension to determine compiler features.
+// We use it to determine 'cxx_rvalue_references'
+#ifndef __has_feature
+#define __has_feature(x) 0
+#endif
+
+// The macro EIGEN_CPLUSPLUS is a replacement for __cplusplus/_MSVC_LANG that
+// works for both platforms, indicating the C++ standard version number.
+//
+// With MSVC, without defining /Zc:__cplusplus, the __cplusplus macro will
+// report 199711L regardless of the language standard specified via /std.
+// We need to rely on _MSVC_LANG instead, which is only available after
+// VS2015.3.
+#if EIGEN_COMP_MSVC_LANG > 0
+#define EIGEN_CPLUSPLUS EIGEN_COMP_MSVC_LANG
+#elif EIGEN_COMP_MSVC >= 1900
+#define EIGEN_CPLUSPLUS 201103L
+#elif defined(__cplusplus)
+#define EIGEN_CPLUSPLUS __cplusplus
+#else
+#define EIGEN_CPLUSPLUS 0
+#endif
+
+// The macro EIGEN_COMP_CXXVER defines the c++ version expected by the compiler.
+// For instance, if compiling with gcc and -std=c++17, then EIGEN_COMP_CXXVER
+// is defined to 17.
+#if EIGEN_CPLUSPLUS >= 202002L
+#define EIGEN_COMP_CXXVER 20
+#elif EIGEN_CPLUSPLUS >= 201703L
+#define EIGEN_COMP_CXXVER 17
+#elif EIGEN_CPLUSPLUS >= 201402L
+#define EIGEN_COMP_CXXVER 14
+#elif EIGEN_CPLUSPLUS >= 201103L
+#define EIGEN_COMP_CXXVER 11
+#else
+#define EIGEN_COMP_CXXVER 03
+#endif
+
+// The macros EIGEN_HAS_CXX?? defines a rough estimate of available c++ features
+// but in practice we should not rely on them but rather on the availability of
+// individual features as defined later.
+// This is why there is no EIGEN_HAS_CXX17.
+#if EIGEN_MAX_CPP_VER < 14 || EIGEN_COMP_CXXVER < 14 || (EIGEN_COMP_MSVC && EIGEN_COMP_MSVC < 1900) || \
+    (EIGEN_COMP_ICC && EIGEN_COMP_ICC < 1500) || (EIGEN_COMP_NVCC && EIGEN_COMP_NVCC < 80000) ||       \
+    (EIGEN_COMP_CLANG_STRICT && EIGEN_COMP_CLANG < 390) ||                                             \
+    (EIGEN_COMP_CLANGAPPLE && EIGEN_COMP_CLANGAPPLE < 9000000) || (EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC < 510)
+#error Eigen requires at least c++14 support.
+#endif
+
+// Does the compiler support C99?
+// Need to include <cmath> to make sure _GLIBCXX_USE_C99 gets defined
+#include <cmath>
+#ifndef EIGEN_HAS_C99_MATH
+#if ((defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901)) ||                                          \
+     (defined(__GNUC__) && defined(_GLIBCXX_USE_C99)) || (defined(_LIBCPP_VERSION) && !defined(_MSC_VER)) || \
+     (EIGEN_COMP_MSVC) || defined(SYCL_DEVICE_ONLY))
+#define EIGEN_HAS_C99_MATH 1
+#else
+#define EIGEN_HAS_C99_MATH 0
+#endif
+#endif
+
+// Does the compiler support std::hash?
+#ifndef EIGEN_HAS_STD_HASH
+// The std::hash struct is defined in C++11 but is not labelled as a __device__
+// function and is not constexpr, so cannot be used on device.
+#if !defined(EIGEN_GPU_COMPILE_PHASE)
+#define EIGEN_HAS_STD_HASH 1
+#else
+#define EIGEN_HAS_STD_HASH 0
+#endif
+#endif  // EIGEN_HAS_STD_HASH
+
+#ifndef EIGEN_HAS_STD_INVOKE_RESULT
+#if EIGEN_MAX_CPP_VER >= 17 && EIGEN_COMP_CXXVER >= 17
+#define EIGEN_HAS_STD_INVOKE_RESULT 1
+#else
+#define EIGEN_HAS_STD_INVOKE_RESULT 0
+#endif
+#endif
+
+#define EIGEN_CONSTEXPR constexpr
+
+// NOTE: the required Apple's clang version is very conservative
+//       and it could be that XCode 9 works just fine.
+// NOTE: the MSVC version is based on https://en.cppreference.com/w/cpp/compiler_support
+//       and not tested.
+// NOTE: Intel C++ Compiler Classic (icc) Version 19.0 and later supports dynamic allocation
+//       for over-aligned data, but not in a manner that is compatible with Eigen.
+//       See https://gitlab.com/libeigen/eigen/-/issues/2575
+// Does the compiler support C++17 if constexpr?
+#ifndef EIGEN_HAS_CXX17_IFCONSTEXPR
+#if EIGEN_MAX_CPP_VER >= 17 && EIGEN_COMP_CXXVER >= 17 &&                                                            \
+    ((EIGEN_COMP_MSVC >= 1911) || (EIGEN_GNUC_STRICT_AT_LEAST(7, 0, 0)) || (EIGEN_CLANG_STRICT_AT_LEAST(3, 9, 0)) || \
+     (EIGEN_COMP_CLANGAPPLE && EIGEN_COMP_CLANGAPPLE >= 10000000))
+#define EIGEN_HAS_CXX17_IFCONSTEXPR 1
+#endif
+#endif
+
+#ifndef EIGEN_HAS_CXX17_OVERALIGN
+#if EIGEN_MAX_CPP_VER >= 17 && EIGEN_COMP_CXXVER >= 17 &&                                                            \
+    ((EIGEN_COMP_MSVC >= 1912) || (EIGEN_GNUC_STRICT_AT_LEAST(7, 0, 0)) || (EIGEN_CLANG_STRICT_AT_LEAST(5, 0, 0)) || \
+     (EIGEN_COMP_CLANGAPPLE && EIGEN_COMP_CLANGAPPLE >= 10000000)) &&                                                \
+    !EIGEN_COMP_ICC
+#define EIGEN_HAS_CXX17_OVERALIGN 1
+#else
+#define EIGEN_HAS_CXX17_OVERALIGN 0
+#endif
+#endif
+
+#if defined(EIGEN_CUDACC)
+// While available already with c++11, this is useful mostly starting with c++14 and relaxed constexpr rules
+#if defined(__NVCC__)
+// nvcc considers constexpr functions as __host__ __device__ with the option --expt-relaxed-constexpr
+#ifdef __CUDACC_RELAXED_CONSTEXPR__
+#define EIGEN_CONSTEXPR_ARE_DEVICE_FUNC
+#endif
+#elif defined(__clang__) && defined(__CUDA__) && __has_feature(cxx_relaxed_constexpr)
+// clang++ always considers constexpr functions as implicitly __host__ __device__
+#define EIGEN_CONSTEXPR_ARE_DEVICE_FUNC
+#endif
+#endif
+
+// Does the compiler support the __int128 and __uint128_t extensions for 128-bit
+// integer arithmetic?
+//
+// Clang and GCC define __SIZEOF_INT128__ when these extensions are supported,
+// but we avoid using them in certain cases:
+//
+// * Building using Clang for Windows, where the Clang runtime library has
+//   128-bit support only on LP64 architectures, but Windows is LLP64.
+#ifndef EIGEN_HAS_BUILTIN_INT128
+#if defined(__SIZEOF_INT128__) && !(EIGEN_OS_WIN && EIGEN_COMP_CLANG)
+#define EIGEN_HAS_BUILTIN_INT128 1
+#else
+#define EIGEN_HAS_BUILTIN_INT128 0
+#endif
+#endif
+
+// Does the compiler support vector types?
+#if EIGEN_HAS_ATTRIBUTE(ext_vector_type) && EIGEN_HAS_BUILTIN(__builtin_vectorelements)
+#define EIGEN_ARCH_VECTOR_EXTENSIONS 1
+#else
+#define EIGEN_ARCH_VECTOR_EXTENSIONS 0
+#endif
+
+// Multidimensional subscript operator feature test
+#if defined(__cpp_multidimensional_subscript) && __cpp_multidimensional_subscript >= 202110L
+#define EIGEN_MULTIDIMENSIONAL_SUBSCRIPT
+#endif
+
+//------------------------------------------------------------------------------------------
+// Preprocessor programming helpers
+//------------------------------------------------------------------------------------------
+
+// This macro can be used to prevent from macro expansion, e.g.:
+//   std::max EIGEN_NOT_A_MACRO(a,b)
+#define EIGEN_NOT_A_MACRO
+
+#define EIGEN_DEBUG_VAR(x) std::cerr << #x << " = " << x << std::endl;
+
+// concatenate two tokens
+#define EIGEN_CAT2(a, b) a##b
+#define EIGEN_CAT(a, b) EIGEN_CAT2(a, b)
+
+#define EIGEN_COMMA ,
+
+// convert a token to a string
+#define EIGEN_MAKESTRING2(a) #a
+#define EIGEN_MAKESTRING(a) EIGEN_MAKESTRING2(a)
+
+// EIGEN_STRONG_INLINE is a stronger version of the inline, using __forceinline on MSVC,
+// but it still doesn't use GCC's always_inline. This is useful in (common) situations where MSVC needs forceinline
+// but GCC is still doing fine with just inline.
+#ifndef EIGEN_STRONG_INLINE
+#if (EIGEN_COMP_MSVC || EIGEN_COMP_ICC) && !defined(EIGEN_GPUCC)
+#define EIGEN_STRONG_INLINE __forceinline
+#else
+#define EIGEN_STRONG_INLINE inline
+#endif
+#endif
+
+// EIGEN_ALWAYS_INLINE is the strongest, it has the effect of making the function inline and adding every possible
+// attribute to maximize inlining. This should only be used when really necessary: in particular,
+// it uses __attribute__((always_inline)) on GCC, which most of the time is useless and can severely harm compile times.
+// FIXME with the always_inline attribute,
+#if EIGEN_COMP_GNUC && !defined(SYCL_DEVICE_ONLY)
+#define EIGEN_ALWAYS_INLINE __attribute__((always_inline)) inline
+#else
+#define EIGEN_ALWAYS_INLINE EIGEN_STRONG_INLINE
+#endif
+
+// EIGEN_LAMBDA_ALWAYS_INLINE forces inlining of lambda functions.
+// On GCC/Clang, __attribute__((always_inline)) works on lambdas.
+// On MSVC, [[msvc::forceinline]] cannot be applied to generic lambdas
+// (those with auto parameters), so we leave it empty and rely on the
+// optimizer to inline small lambda bodies at /O2.
+#if EIGEN_COMP_GNUC && !defined(SYCL_DEVICE_ONLY)
+#define EIGEN_LAMBDA_ALWAYS_INLINE __attribute__((always_inline))
+#else
+#define EIGEN_LAMBDA_ALWAYS_INLINE
+#endif
+
+#if EIGEN_COMP_GNUC
+#define EIGEN_DONT_INLINE __attribute__((noinline))
+#elif EIGEN_COMP_MSVC
+#define EIGEN_DONT_INLINE __declspec(noinline)
+#else
+#define EIGEN_DONT_INLINE
+#endif
+
+#if EIGEN_COMP_GNUC
+#define EIGEN_PERMISSIVE_EXPR __extension__
+#else
+#define EIGEN_PERMISSIVE_EXPR
+#endif
+
+// GPU stuff
+
+// Disable some features when compiling with GPU compilers (SYCL/HIPCC)
+#if defined(SYCL_DEVICE_ONLY) || defined(EIGEN_HIP_DEVICE_COMPILE)
+// Do not try asserts on device code
+#ifndef EIGEN_NO_DEBUG
+#define EIGEN_NO_DEBUG
+#endif
+
+#ifdef EIGEN_INTERNAL_DEBUGGING
+#undef EIGEN_INTERNAL_DEBUGGING
+#endif
+#endif
+
+// No exceptions on device.
+#if defined(SYCL_DEVICE_ONLY) || defined(EIGEN_GPU_COMPILE_PHASE)
+#ifdef EIGEN_EXCEPTIONS
+#undef EIGEN_EXCEPTIONS
+#endif
+#endif
+
+#if defined(SYCL_DEVICE_ONLY)
+#ifndef EIGEN_DONT_VECTORIZE
+#define EIGEN_DONT_VECTORIZE
+#endif
+#define EIGEN_DEVICE_FUNC __attribute__((flatten)) __attribute__((always_inline))
+// All functions callable from CUDA/HIP code must be qualified with __device__
+#elif defined(EIGEN_GPUCC)
+#define EIGEN_DEVICE_FUNC __host__ __device__
+#else
+#define EIGEN_DEVICE_FUNC
+#endif
+
+// this macro allows to get rid of linking errors about multiply defined functions.
+//  - static is not very good because it prevents definitions from different object files to be merged.
+//           So static causes the resulting linked executable to be bloated with multiple copies of the same function.
+//  - inline is not perfect either as it unwantedly hints the compiler toward inlining the function.
+#define EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC
+#define EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC inline
+
+#ifdef NDEBUG
+#ifndef EIGEN_NO_DEBUG
+#define EIGEN_NO_DEBUG
+#endif
+#endif
+
+// eigen_assert can be overridden
+#ifndef eigen_assert
+#define eigen_assert(x) eigen_plain_assert(x)
+#endif
+
+#ifdef EIGEN_INTERNAL_DEBUGGING
+#define eigen_internal_assert(x) eigen_assert(x)
+#else
+#define eigen_internal_assert(x) ((void)0)
+#endif
+
+#if defined(EIGEN_NO_DEBUG) || (defined(EIGEN_GPU_COMPILE_PHASE) && defined(EIGEN_NO_DEBUG_GPU))
+#define EIGEN_ONLY_USED_FOR_DEBUG(x) EIGEN_UNUSED_VARIABLE(x)
+#else
+#define EIGEN_ONLY_USED_FOR_DEBUG(x)
+#endif
+
+#ifndef EIGEN_NO_DEPRECATED_WARNING
+#if EIGEN_COMP_GNUC
+#define EIGEN_DEPRECATED __attribute__((deprecated))
+#elif EIGEN_COMP_MSVC
+#define EIGEN_DEPRECATED __declspec(deprecated)
+#else
+#define EIGEN_DEPRECATED
+#endif
+#else
+#define EIGEN_DEPRECATED
+#endif
+
+#ifndef EIGEN_NO_DEPRECATED_WARNING
+#if EIGEN_COMP_GNUC
+#define EIGEN_DEPRECATED_WITH_REASON(message) __attribute__((deprecated(message)))
+#elif EIGEN_COMP_MSVC
+#define EIGEN_DEPRECATED_WITH_REASON(message) __declspec(deprecated(message))
+#else
+#define EIGEN_DEPRECATED_WITH_REASON(message)
+#endif
+#else
+#define EIGEN_DEPRECATED_WITH_REASON(message)
+#endif
+
+#if EIGEN_COMP_GNUC
+#define EIGEN_UNUSED __attribute__((unused))
+#else
+#define EIGEN_UNUSED
+#endif
+
+#if EIGEN_COMP_GNUC
+#define EIGEN_PRAGMA(tokens) _Pragma(#tokens)
+#define EIGEN_DIAGNOSTICS(tokens) EIGEN_PRAGMA(GCC diagnostic tokens)
+#define EIGEN_DIAGNOSTICS_OFF(msc, gcc) EIGEN_DIAGNOSTICS(gcc)
+#elif EIGEN_COMP_MSVC
+#define EIGEN_PRAGMA(tokens) __pragma(tokens)
+#define EIGEN_DIAGNOSTICS(tokens) EIGEN_PRAGMA(warning(tokens))
+#define EIGEN_DIAGNOSTICS_OFF(msc, gcc) EIGEN_DIAGNOSTICS(msc)
+#else
+#define EIGEN_PRAGMA(tokens)
+#define EIGEN_DIAGNOSTICS(tokens)
+#define EIGEN_DIAGNOSTICS_OFF(msc, gcc)
+#endif
+
+#define EIGEN_DISABLE_DEPRECATED_WARNING EIGEN_DIAGNOSTICS_OFF(disable : 4996, ignored "-Wdeprecated-declarations")
+
+// Suppresses 'unused variable' warnings.
+namespace Eigen {
+namespace internal {
+template <typename T>
+EIGEN_DEVICE_FUNC constexpr void ignore_unused_variable(const T&) {}
+}  // namespace internal
+}  // namespace Eigen
+#define EIGEN_UNUSED_VARIABLE(var) Eigen::internal::ignore_unused_variable(var);
+
+#if !defined(EIGEN_ASM_COMMENT)
+#if EIGEN_COMP_GNUC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM_OR_ARM64 || EIGEN_ARCH_RISCV)
+#define EIGEN_ASM_COMMENT(X) __asm__("#" X)
+#else
+#define EIGEN_ASM_COMMENT(X)
+#endif
+#endif
+
+// Acts as a barrier preventing operations involving `X` from crossing. This
+// occurs, for example, in the fast rounding trick where a magic constant is
+// added then subtracted, which is otherwise compiled away with -ffast-math.
+//
+// See bug 1674
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+#define EIGEN_OPTIMIZATION_BARRIER(X)
+#endif
+
+#if !defined(EIGEN_OPTIMIZATION_BARRIER)
+// Implement the barrier on GNUC compilers or clang-cl.
+#if EIGEN_COMP_GNUC || (defined(__clang__) && defined(_MSC_VER))
+// According to https://gcc.gnu.org/onlinedocs/gcc/Constraints.html:
+//   X: Any operand whatsoever.
+//   r: A register operand is allowed provided that it is in a general
+//      register.
+//   g: Any register, memory or immediate integer operand is allowed, except
+//      for registers that are not general registers.
+//   w: (AArch32/AArch64) Floating point register, Advanced SIMD vector
+//      register or SVE vector register.
+//   x: (SSE) Any SSE register.
+//      (AArch64) Like w, but restricted to registers 0 to 15 inclusive.
+//   v: (PowerPC) An Altivec vector register.
+//   wa:(PowerPC) A VSX register.
+//
+// "X" (uppercase) should work for all cases, though this seems to fail for
+// some versions of GCC for arm/aarch64 with
+//   "error: inconsistent operand constraints in an 'asm'"
+// Clang x86_64/arm/aarch64 seems to require "g" to support both scalars and
+// vectors, otherwise
+//   "error: non-trivial scalar-to-vector conversion, possible invalid
+//    constraint for vector type"
+//
+// GCC for ppc64le generates an internal compiler error with x/X/g.
+// GCC for AVX generates an internal compiler error with X.
+//
+// Tested on icc/gcc/clang for sse, avx, avx2, avx512dq
+//           gcc for arm, aarch64,
+//           gcc for ppc64le,
+// both vectors and scalars.
+//
+// Note that this is restricted to plain types - this will not work
+// directly for std::complex<T>, Eigen::half, Eigen::bfloat16. For these,
+// you will need to apply to the underlying POD type.
+#if EIGEN_ARCH_PPC && EIGEN_COMP_GNUC_STRICT
+// This seems to be broken on clang. Packet4f is loaded into a single
+//   register rather than a vector, zeroing out some entries. Integer
+//   types also generate a compile error.
+#if EIGEN_OS_MAC
+// General, Altivec for Apple (VSX were added in ISA v2.06):
+#define EIGEN_OPTIMIZATION_BARRIER(X) __asm__("" : "+r,v"(X));
+#else
+// General, Altivec, VSX otherwise:
+#define EIGEN_OPTIMIZATION_BARRIER(X) __asm__("" : "+r,v,wa"(X));
+#endif
+#elif EIGEN_ARCH_ARM_OR_ARM64
+#ifdef __ARM_FP
+// General, VFP or NEON.
+// Clang doesn't like "r",
+//    error: non-trivial scalar-to-vector conversion, possible invalid
+//           constraint for vector typ
+#define EIGEN_OPTIMIZATION_BARRIER(X) __asm__("" : "+g,w"(X));
+#else
+// Arm without VFP or NEON.
+// "w" constraint will not compile.
+#define EIGEN_OPTIMIZATION_BARRIER(X) __asm__("" : "+g"(X));
+#endif
+#elif EIGEN_ARCH_i386_OR_x86_64
+// General, SSE.
+#define EIGEN_OPTIMIZATION_BARRIER(X) __asm__("" : "+g,x"(X));
+#else
+// Not implemented for other architectures.
+#define EIGEN_OPTIMIZATION_BARRIER(X)
+#endif
+#else
+// Not implemented for other compilers.
+#define EIGEN_OPTIMIZATION_BARRIER(X)
+#endif
+#endif
+
+#if EIGEN_COMP_MSVC
+// NOTE MSVC often gives C4127 warnings with compiletime if statements. See bug 1362.
+// This workaround suppresses MSVC C4127 warnings for compile-time conditionals.
+#define EIGEN_CONST_CONDITIONAL(cond) (void)0, cond
+#else
+#define EIGEN_CONST_CONDITIONAL(cond) cond
+#endif
+
+#ifdef EIGEN_DONT_USE_RESTRICT_KEYWORD
+#define EIGEN_RESTRICT
+#endif
+#ifndef EIGEN_RESTRICT
+#define EIGEN_RESTRICT __restrict
+#endif
+
+#ifndef EIGEN_DEFAULT_IO_FORMAT
+#ifdef EIGEN_MAKING_DOCS
+// format used in Eigen's documentation
+// needed to define it here as escaping characters in CMake add_definition's argument seems very problematic.
+#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat(3, 0, " ", "\n", "", "")
+#else
+#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat()
+#endif
+#endif
+
+// just an empty macro !
+#define EIGEN_EMPTY
+
+// When compiling CUDA/HIP device code with NVCC or HIPCC
+// pull in math functions from the global namespace.
+// In host mode, and when device code is compiled with clang,
+// use the std versions.
+#if (defined(EIGEN_CUDA_ARCH) && defined(__NVCC__)) || defined(EIGEN_HIP_DEVICE_COMPILE)
+#define EIGEN_USING_STD(FUNC) using ::FUNC;
+#else
+#define EIGEN_USING_STD(FUNC) using std::FUNC;
+#endif
+
+#if EIGEN_COMP_CLANG  // workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653)
+#define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)                                           \
+  using Base::operator=;                                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) {                 \
+    Base::operator=(other);                                                                        \
+    return *this;                                                                                  \
+  }                                                                                                \
+  template <typename OtherDerived>                                                                 \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { \
+    Base::operator=(other.derived());                                                              \
+    return *this;                                                                                  \
+  }
+#else
+#define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)                           \
+  using Base::operator=;                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) { \
+    Base::operator=(other);                                                        \
+    return *this;                                                                  \
+  }
+#endif
+
+/**
+ * \internal
+ * \brief Macro to explicitly define the default copy constructor.
+ * This is necessary, because the implicit definition is deprecated if the copy-assignment is overridden.
+ */
+#define EIGEN_DEFAULT_COPY_CONSTRUCTOR(CLASS) EIGEN_DEVICE_FUNC CLASS(const CLASS&) = default;
+
+/** \internal
+ * \brief Macro to manually inherit assignment operators.
+ * This is necessary, because the implicitly defined assignment operator gets deleted when a custom operator= is
+ * defined. With C++11 or later this also default-implements the copy-constructor
+ */
+#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) \
+  EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)  \
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(Derived)
+
+/** \internal
+ * \brief Macro to manually define default constructors and destructors.
+ * This is necessary when the copy constructor is re-defined.
+ * For empty helper classes this should usually be protected, to avoid accidentally creating empty objects.
+ *
+ * Hiding the default destructor lead to problems in C++03 mode together with boost::multiprecision
+ */
+#define EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(Derived) \
+  EIGEN_DEVICE_FUNC Derived() = default;                        \
+  EIGEN_DEVICE_FUNC ~Derived() = default;
+
+/**
+ * Just a side note. Commenting within defines works only by documenting
+ * behind the object (via '!<'). Comments cannot be multi-line and thus
+ * we have these extra long lines. What is confusing doxygen over here is
+ * that we use '\' and basically have a bunch of typedefs with their
+ * documentation in a single line.
+ **/
+
+#define EIGEN_GENERIC_PUBLIC_INTERFACE(Derived)                                                                        \
+  typedef typename Eigen::internal::traits<Derived>::Scalar                                                            \
+      Scalar; /*!< \brief Numeric type, e.g. float, double, int or std::complex<float>. */                             \
+  typedef typename Eigen::NumTraits<Scalar>::Real                                                                      \
+      RealScalar; /*!< \brief The underlying numeric type for composed scalar types. \details In cases where Scalar is \
+                     e.g. std::complex<T>, T were corresponding to RealScalar. */                                      \
+  typedef typename Base::CoeffReturnType                                                                               \
+      CoeffReturnType; /*!< \brief The return type for coefficient access. \details Depending on whether the object    \
+                          allows direct coefficient access (e.g. for a MatrixXd), this type is either 'const Scalar&'  \
+                          or simply 'Scalar' for objects that do not allow direct coefficient access. */               \
+  typedef typename Eigen::internal::ref_selector<Derived>::type Nested;                                                \
+  typedef typename Eigen::internal::traits<Derived>::StorageKind StorageKind;                                          \
+  typedef typename Eigen::internal::traits<Derived>::StorageIndex StorageIndex;                                        \
+  enum CompileTimeTraits {                                                                                             \
+    RowsAtCompileTime = Eigen::internal::traits<Derived>::RowsAtCompileTime,                                           \
+    ColsAtCompileTime = Eigen::internal::traits<Derived>::ColsAtCompileTime,                                           \
+    Flags = Eigen::internal::traits<Derived>::Flags,                                                                   \
+    SizeAtCompileTime = Base::SizeAtCompileTime,                                                                       \
+    MaxSizeAtCompileTime = Base::MaxSizeAtCompileTime,                                                                 \
+    IsVectorAtCompileTime = Base::IsVectorAtCompileTime                                                                \
+  };                                                                                                                   \
+  using Base::derived;                                                                                                 \
+  using Base::const_cast_derived;
+
+// FIXME Maybe the EIGEN_DENSE_PUBLIC_INTERFACE could be removed as importing PacketScalar is rarely needed
+#define EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \
+  EIGEN_GENERIC_PUBLIC_INTERFACE(Derived)     \
+  typedef typename Base::PacketScalar PacketScalar;
+
+#if EIGEN_HAS_BUILTIN(__builtin_expect) || EIGEN_COMP_GNUC
+#define EIGEN_PREDICT_FALSE(x) (__builtin_expect(x, false))
+#define EIGEN_PREDICT_TRUE(x) (__builtin_expect(false || (x), true))
+#else
+#define EIGEN_PREDICT_FALSE(x) (x)
+#define EIGEN_PREDICT_TRUE(x) (x)
+#endif
+
+// the expression type of a standard coefficient wise binary operation
+#define EIGEN_CWISE_BINARY_RETURN_TYPE(LHS, RHS, OPNAME)                                                       \
+  CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_, OPNAME), _op) < typename internal::traits<LHS>::Scalar, \
+                typename internal::traits<RHS>::Scalar>,                                                       \
+      const LHS, const RHS >
+
+#define EIGEN_MAKE_CWISE_BINARY_OP(METHOD, OPNAME)                                                                \
+  template <typename OtherDerived>                                                                                \
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(                           \
+      Derived, OtherDerived, OPNAME)(METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const { \
+    return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived, OtherDerived, OPNAME)(derived(), other.derived());             \
+  }
+
+#define EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME, TYPEA, TYPEB)     \
+  (Eigen::internal::has_ReturnType<Eigen::ScalarBinaryOpTraits< \
+       TYPEA, TYPEB, EIGEN_CAT(EIGEN_CAT(Eigen::internal::scalar_, OPNAME), _op) < TYPEA, TYPEB> > > ::value)
+
+#define EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(EXPR, SCALAR, OPNAME)                                            \
+  CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_, OPNAME), _op) < typename internal::traits<EXPR>::Scalar, \
+                SCALAR>,                                                                                        \
+      const EXPR, const typename internal::plain_constant_type<EXPR, SCALAR>::type >
+
+#define EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(SCALAR, EXPR, OPNAME)           \
+  CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_, OPNAME), _op) < SCALAR, \
+                typename internal::traits<EXPR>::Scalar>,                      \
+      const typename internal::plain_constant_type<EXPR, SCALAR>::type, const EXPR >
+
+#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD, OPNAME)                                                       \
+  template <typename T>                                                                                              \
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(                      \
+      Derived,                                                                                                       \
+      typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_SCALAR_BINARY_SUPPORTED(          \
+          OPNAME, Scalar, T)>::type,                                                                                 \
+      OPNAME)(METHOD)(const T& scalar) const {                                                                       \
+    typedef typename internal::promote_scalar_arg<Scalar, T, EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME, Scalar, T)>::type \
+        PromotedT;                                                                                                   \
+    return EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived, PromotedT, OPNAME)(                                       \
+        derived(), typename internal::plain_constant_type<Derived, PromotedT>::type(                                 \
+                       derived().rows(), derived().cols(), internal::scalar_constant_op<PromotedT>(scalar)));        \
+  }
+
+#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD, OPNAME)                                                        \
+  template <typename T>                                                                                              \
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE friend const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(               \
+      typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_SCALAR_BINARY_SUPPORTED(          \
+          OPNAME, T, Scalar)>::type,                                                                                 \
+      Derived, OPNAME)(METHOD)(const T& scalar, const StorageBaseType& matrix) {                                     \
+    typedef typename internal::promote_scalar_arg<Scalar, T, EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME, T, Scalar)>::type \
+        PromotedT;                                                                                                   \
+    return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedT, Derived, OPNAME)(                                       \
+        typename internal::plain_constant_type<Derived, PromotedT>::type(                                            \
+            matrix.derived().rows(), matrix.derived().cols(), internal::scalar_constant_op<PromotedT>(scalar)),      \
+        matrix.derived());                                                                                           \
+  }
+
+#define EIGEN_MAKE_SCALAR_BINARY_OP(METHOD, OPNAME)     \
+  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD, OPNAME) \
+  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD, OPNAME)
+
+#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(EIGEN_CUDA_ARCH) && !defined(EIGEN_EXCEPTIONS) && \
+    !defined(EIGEN_USE_SYCL) && !defined(EIGEN_HIP_DEVICE_COMPILE)
+#define EIGEN_EXCEPTIONS
+#endif
+
+#ifdef EIGEN_EXCEPTIONS
+#define EIGEN_THROW_X(X) throw X
+#define EIGEN_THROW throw
+#define EIGEN_TRY try
+#define EIGEN_CATCH(X) catch (X)
+#else
+#if defined(EIGEN_CUDA_ARCH)
+#define EIGEN_THROW_X(X) asm("trap;")
+#define EIGEN_THROW asm("trap;")
+#elif defined(EIGEN_HIP_DEVICE_COMPILE)
+#define EIGEN_THROW_X(X) asm("s_trap 0")
+#define EIGEN_THROW asm("s_trap 0")
+#else
+#define EIGEN_THROW_X(X) std::abort()
+#define EIGEN_THROW std::abort()
+#endif
+#define EIGEN_TRY if (true)
+#define EIGEN_CATCH(X) else
+#endif
+
+// The all function is used to enable a variadic version of eigen_assert which can take a parameter pack as its input.
+namespace Eigen {
+namespace internal {
+
+EIGEN_DEVICE_FUNC constexpr bool all() { return true; }
+
+template <typename T, typename... Ts>
+EIGEN_DEVICE_FUNC constexpr bool all(T t, Ts... ts) {
+  return t && all(ts...);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+// provide override and final specifiers if they are available:
+#define EIGEN_OVERRIDE override
+#define EIGEN_FINAL final
+
+// Wrapping #pragma unroll in a macro since it is required for SYCL
+#if defined(SYCL_DEVICE_ONLY)
+#if defined(_MSC_VER)
+#define EIGEN_UNROLL_LOOP __pragma(unroll)
+#else
+#define EIGEN_UNROLL_LOOP _Pragma("unroll")
+#endif
+#else
+#define EIGEN_UNROLL_LOOP
+#endif
+
+// Notice: Use this macro with caution. The code in the if body should still
+// compile with C++14.
+#if defined(EIGEN_HAS_CXX17_IFCONSTEXPR)
+#define EIGEN_IF_CONSTEXPR(X) if constexpr (X)
+#else
+#define EIGEN_IF_CONSTEXPR(X) if (X)
+#endif
+
+#endif  // EIGEN_MACROS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/MaxSizeVector.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/MaxSizeVector.h
new file mode 100644
index 0000000..3f13b2d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/MaxSizeVector.h
@@ -0,0 +1,138 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FIXEDSIZEVECTOR_H
+#define EIGEN_FIXEDSIZEVECTOR_H
+
+namespace Eigen {
+
+/** \class MaxSizeVector
+ * \ingroup Core_Module
+ *
+ * \brief The MaxSizeVector class.
+ *
+ * The %MaxSizeVector provides a subset of std::vector functionality.
+ *
+ * The goal is to provide basic std::vector operations when using
+ * std::vector is not an option (e.g. on GPU or when compiling using
+ * FMA/AVX, as this can cause either compilation failures or illegal
+ * instruction failures).
+ *
+ * Beware: The constructors are not API compatible with these of
+ * std::vector.
+ */
+template <typename T>
+class MaxSizeVector {
+  static const size_t alignment = internal::plain_enum_max(EIGEN_ALIGNOF(T), sizeof(void*));
+
+ public:
+  // Construct a new MaxSizeVector, reserve n elements.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit MaxSizeVector(size_t n)
+      : m_reserve(n), m_size(0), m_data(static_cast<T*>(internal::handmade_aligned_malloc(n * sizeof(T), alignment))) {}
+
+  // Construct a new MaxSizeVector, reserve and resize to n.
+  // Copy the init value to all elements.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MaxSizeVector(size_t n, const T& init)
+      : m_reserve(n), m_size(n), m_data(static_cast<T*>(internal::handmade_aligned_malloc(n * sizeof(T), alignment))) {
+    size_t i = 0;
+    EIGEN_TRY {
+      for (; i < m_size; ++i) {
+        new (&m_data[i]) T(init);
+      }
+    }
+    EIGEN_CATCH(...) {
+      // Construction failed, destruct in reverse order:
+      for (; (i + 1) > 0; --i) {
+        m_data[i - 1].~T();
+      }
+      internal::handmade_aligned_free(m_data);
+      EIGEN_THROW;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ~MaxSizeVector() {
+    for (size_t i = m_size; i > 0; --i) {
+      m_data[i - 1].~T();
+    }
+    internal::handmade_aligned_free(m_data);
+  }
+
+  void resize(size_t n) {
+    eigen_assert(n <= m_reserve);
+    for (; m_size < n; ++m_size) {
+      new (&m_data[m_size]) T;
+    }
+    for (; m_size > n; --m_size) {
+      m_data[m_size - 1].~T();
+    }
+    eigen_assert(m_size == n);
+  }
+
+  // Append new elements (up to reserved size).
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void push_back(const T& t) {
+    eigen_assert(m_size < m_reserve);
+    new (&m_data[m_size++]) T(t);
+  }
+
+  template <class X>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void emplace_back(const X& x) {
+    eigen_assert(m_size < m_reserve);
+    new (&m_data[m_size++]) T(x);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& operator[](size_t i) const {
+    eigen_assert(i < m_size);
+    return m_data[i];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& operator[](size_t i) {
+    eigen_assert(i < m_size);
+    return m_data[i];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& back() {
+    eigen_assert(m_size > 0);
+    return m_data[m_size - 1];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& back() const {
+    eigen_assert(m_size > 0);
+    return m_data[m_size - 1];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pop_back() {
+    eigen_assert(m_size > 0);
+    m_data[--m_size].~T();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t size() const { return m_size; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool empty() const { return m_size == 0; }
+
+  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data; }
+
+  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data; }
+
+  EIGEN_DEVICE_FUNC constexpr T* begin() { return m_data; }
+
+  EIGEN_DEVICE_FUNC constexpr T* end() { return m_data + m_size; }
+
+  EIGEN_DEVICE_FUNC constexpr const T* begin() const { return m_data; }
+
+  EIGEN_DEVICE_FUNC constexpr const T* end() const { return m_data + m_size; }
+
+ private:
+  size_t m_reserve;
+  size_t m_size;
+  T* m_data;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_FIXEDSIZEVECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Memory.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Memory.h
new file mode 100644
index 0000000..95712ff
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Memory.h
@@ -0,0 +1,1383 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Kenneth Riddile <kfriddile@yahoo.com>
+// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
+// Copyright (C) 2010 Thomas Capricelli <orzel@freehackers.org>
+// Copyright (C) 2013 Pavel Holoborodko <pavel@holoborodko.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************************
+*** Platform checks for aligned malloc functions                           ***
+*****************************************************************************/
+
+#ifndef EIGEN_MEMORY_H
+#define EIGEN_MEMORY_H
+
+#ifndef EIGEN_MALLOC_ALREADY_ALIGNED
+
+// Try to determine automatically if malloc is already aligned.
+
+// On 64-bit systems, glibc's malloc returns 16-byte-aligned pointers, see:
+//   http://www.gnu.org/s/libc/manual/html_node/Aligned-Memory-Blocks.html
+// This is true at least since glibc 2.8.
+// This leaves the question how to detect 64-bit. According to this document,
+//   http://gcc.fyxm.net/summit/2003/Porting%20to%2064%20bit.pdf
+// page 114, "[The] LP64 model [...] is used by all 64-bit UNIX ports" so it's indeed
+// quite safe, at least within the context of glibc, to equate 64-bit with LP64.
+#if defined(__GLIBC__) && ((__GLIBC__ >= 2 && __GLIBC_MINOR__ >= 8) || __GLIBC__ > 2) && defined(__LP64__) && \
+    !defined(__SANITIZE_ADDRESS__) && (EIGEN_DEFAULT_ALIGN_BYTES == 16)
+#define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 1
+#else
+#define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 0
+#endif
+
+// FreeBSD 6 seems to have 16-byte aligned malloc
+//   See http://svn.freebsd.org/viewvc/base/stable/6/lib/libc/stdlib/malloc.c?view=markup
+// FreeBSD 7 seems to have 16-byte aligned malloc except on ARM and MIPS architectures
+//   See http://svn.freebsd.org/viewvc/base/stable/7/lib/libc/stdlib/malloc.c?view=markup
+#if defined(__FreeBSD__) && !(EIGEN_ARCH_ARM || EIGEN_ARCH_MIPS) && (EIGEN_DEFAULT_ALIGN_BYTES == 16)
+#define EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED 1
+#else
+#define EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED 0
+#endif
+
+#if (EIGEN_OS_MAC && (EIGEN_DEFAULT_ALIGN_BYTES == 16)) || (EIGEN_OS_WIN64 && (EIGEN_DEFAULT_ALIGN_BYTES == 16)) || \
+    EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED || EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED
+#define EIGEN_MALLOC_ALREADY_ALIGNED 1
+#else
+#define EIGEN_MALLOC_ALREADY_ALIGNED 0
+#endif
+
+#endif
+
+#ifndef EIGEN_MALLOC_CHECK_THREAD_LOCAL
+
+// Check whether we can use the thread_local keyword to allow or disallow
+// allocating memory with per-thread granularity, by means of the
+// set_is_malloc_allowed() function.
+#ifndef EIGEN_AVOID_THREAD_LOCAL
+
+#if ((EIGEN_COMP_GNUC) || __has_feature(cxx_thread_local) || EIGEN_COMP_MSVC >= 1900) && \
+    !defined(EIGEN_GPU_COMPILE_PHASE)
+#define EIGEN_MALLOC_CHECK_THREAD_LOCAL thread_local
+#else
+#define EIGEN_MALLOC_CHECK_THREAD_LOCAL
+#endif
+
+#else  // EIGEN_AVOID_THREAD_LOCAL
+#define EIGEN_MALLOC_CHECK_THREAD_LOCAL
+#endif  // EIGEN_AVOID_THREAD_LOCAL
+
+#endif
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/*****************************************************************************
+*** Implementation of portable aligned versions of malloc/free/realloc     ***
+*****************************************************************************/
+
+#ifdef EIGEN_NO_MALLOC
+EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed() {
+  eigen_assert(false && "heap allocation is forbidden (EIGEN_NO_MALLOC is defined)");
+}
+EIGEN_DEVICE_FUNC inline void check_that_free_is_allowed() {
+  eigen_assert(false && "heap deallocation is forbidden (EIGEN_NO_MALLOC is defined)");
+}
+#elif defined EIGEN_RUNTIME_NO_MALLOC
+EIGEN_DEVICE_FUNC inline bool is_malloc_allowed_impl(bool update, bool new_value = false) {
+  EIGEN_MALLOC_CHECK_THREAD_LOCAL static bool value = true;
+  if (update == 1) value = new_value;
+  return value;
+}
+EIGEN_DEVICE_FUNC inline bool is_malloc_allowed() { return is_malloc_allowed_impl(false); }
+EIGEN_DEVICE_FUNC inline bool set_is_malloc_allowed(bool new_value) { return is_malloc_allowed_impl(true, new_value); }
+EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed() {
+  eigen_assert(is_malloc_allowed() &&
+               "heap allocation is forbidden (EIGEN_RUNTIME_NO_MALLOC is defined and set_is_malloc_allowed is false)");
+}
+EIGEN_DEVICE_FUNC inline bool is_free_allowed_impl(bool update, bool new_value = false) {
+  EIGEN_MALLOC_CHECK_THREAD_LOCAL static bool value = true;
+  if (update == 1) value = new_value;
+  return value;
+}
+EIGEN_DEVICE_FUNC inline bool is_free_allowed() { return is_free_allowed_impl(false); }
+EIGEN_DEVICE_FUNC inline bool set_is_free_allowed(bool new_value) { return is_free_allowed_impl(true, new_value); }
+EIGEN_DEVICE_FUNC inline void check_that_free_is_allowed() {
+  eigen_assert(is_free_allowed() &&
+               "heap deallocation is forbidden (EIGEN_RUNTIME_NO_MALLOC is defined and set_is_free_allowed is false)");
+}
+#else
+EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed() {}
+EIGEN_DEVICE_FUNC inline void check_that_free_is_allowed() {}
+#endif
+
+EIGEN_DEVICE_FUNC inline void throw_std_bad_alloc() {
+#ifdef EIGEN_EXCEPTIONS
+  throw std::bad_alloc();
+#else
+  std::size_t huge = static_cast<std::size_t>(-1);
+#if defined(EIGEN_HIPCC)
+  //
+  // calls to "::operator new" are to be treated as opaque function calls (i.e no inlining),
+  // and as a consequence the code in the #else block triggers the hipcc warning :
+  // "no overloaded function has restriction specifiers that are compatible with the ambient context"
+  //
+  // "throw_std_bad_alloc" has the EIGEN_DEVICE_FUNC attribute, so it seems that hipcc expects
+  // the same on "operator new"
+  // Reverting code back to the old version in this #if block for the hipcc compiler
+  //
+  new int[huge];
+#else
+  void* unused = ::operator new(huge);
+  EIGEN_UNUSED_VARIABLE(unused);
+#endif
+#endif
+}
+
+/*****************************************************************************
+*** Implementation of handmade aligned functions                           ***
+*****************************************************************************/
+
+/* ----- Hand made implementations of aligned malloc/free and realloc ----- */
+
+/** \internal Like malloc, but the returned pointer is guaranteed to be aligned to `alignment`.
+ * Fast, but wastes `alignment` additional bytes of memory. Does not throw any exception.
+ */
+EIGEN_DEVICE_FUNC inline void* handmade_aligned_malloc(std::size_t size,
+                                                       std::size_t alignment = EIGEN_DEFAULT_ALIGN_BYTES) {
+  eigen_assert(alignment >= sizeof(void*) && alignment <= 256 && (alignment & (alignment - 1)) == 0 &&
+               "Alignment must be at least sizeof(void*), less than or equal to 256, and a power of 2");
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(malloc)
+  void* original = malloc(size + alignment);
+  if (original == nullptr) return nullptr;
+  std::size_t offset = alignment - (reinterpret_cast<std::size_t>(original) & (alignment - 1));
+  void* aligned = static_cast<void*>(static_cast<uint8_t*>(original) + offset);
+  // Store offset - 1, since it is guaranteed to be at least 1.
+  *(static_cast<uint8_t*>(aligned) - 1) = static_cast<uint8_t>(offset - 1);
+  return aligned;
+}
+
+/** \internal Frees memory allocated with handmade_aligned_malloc */
+EIGEN_DEVICE_FUNC inline void handmade_aligned_free(void* ptr) {
+  if (ptr != nullptr) {
+    std::size_t offset = static_cast<std::size_t>(*(static_cast<uint8_t*>(ptr) - 1)) + 1;
+    void* original = static_cast<void*>(static_cast<uint8_t*>(ptr) - offset);
+
+    check_that_free_is_allowed();
+    EIGEN_USING_STD(free)
+    free(original);
+  }
+}
+
+/** \internal
+ * \brief Reallocates aligned memory.
+ * Since we know that our handmade version is based on std::malloc
+ * we can use std::realloc to implement efficient reallocation.
+ */
+EIGEN_DEVICE_FUNC inline void* handmade_aligned_realloc(void* ptr, std::size_t new_size, std::size_t old_size,
+                                                        std::size_t alignment = EIGEN_DEFAULT_ALIGN_BYTES) {
+  if (ptr == nullptr) return handmade_aligned_malloc(new_size, alignment);
+  std::size_t old_offset = static_cast<std::size_t>(*(static_cast<uint8_t*>(ptr) - 1)) + 1;
+  void* old_original = static_cast<uint8_t*>(ptr) - old_offset;
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(realloc)
+  void* original = realloc(old_original, new_size + alignment);
+  if (original == nullptr) return nullptr;
+  if (original == old_original) return ptr;
+  std::size_t offset = alignment - (reinterpret_cast<std::size_t>(original) & (alignment - 1));
+  void* aligned = static_cast<void*>(static_cast<uint8_t*>(original) + offset);
+  if (offset != old_offset) {
+    const void* src = static_cast<const void*>(static_cast<uint8_t*>(original) + old_offset);
+    std::size_t count = (std::min)(new_size, old_size);
+    std::memmove(aligned, src, count);
+  }
+  // Store offset - 1, since it is guaranteed to be at least 1.
+  *(static_cast<uint8_t*>(aligned) - 1) = static_cast<uint8_t>(offset - 1);
+  return aligned;
+}
+
+/** \internal Allocates \a size bytes. The returned pointer is guaranteed to have 16 or 32 bytes alignment depending on
+ * the requirements. On allocation error, the returned pointer is null, and std::bad_alloc is thrown.
+ */
+EIGEN_DEVICE_FUNC inline void* aligned_malloc(std::size_t size) {
+  if (size == 0) return nullptr;
+
+  void* result;
+#if (EIGEN_DEFAULT_ALIGN_BYTES == 0) || EIGEN_MALLOC_ALREADY_ALIGNED
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(malloc)
+  result = malloc(size);
+
+#if EIGEN_DEFAULT_ALIGN_BYTES == 16
+  eigen_assert((size < 16 || (std::size_t(result) % 16) == 0) &&
+               "System's malloc returned an unaligned pointer. Compile with EIGEN_MALLOC_ALREADY_ALIGNED=0 to fallback "
+               "to handmade aligned memory allocator.");
+#endif
+#else
+  result = handmade_aligned_malloc(size);
+#endif
+
+  if (!result && size) throw_std_bad_alloc();
+
+  return result;
+}
+
+/** \internal Frees memory allocated with aligned_malloc. */
+EIGEN_DEVICE_FUNC inline void aligned_free(void* ptr) {
+#if (EIGEN_DEFAULT_ALIGN_BYTES == 0) || EIGEN_MALLOC_ALREADY_ALIGNED
+
+  if (ptr != nullptr) {
+    check_that_free_is_allowed();
+    EIGEN_USING_STD(free)
+    free(ptr);
+  }
+
+#else
+  handmade_aligned_free(ptr);
+#endif
+}
+
+/**
+ * \internal
+ * \brief Reallocates an aligned block of memory.
+ * \throws std::bad_alloc on allocation failure
+ */
+EIGEN_DEVICE_FUNC inline void* aligned_realloc(void* ptr, std::size_t new_size, std::size_t old_size) {
+  if (ptr == nullptr) return aligned_malloc(new_size);
+  if (old_size == new_size) return ptr;
+  if (new_size == 0) {
+    aligned_free(ptr);
+    return nullptr;
+  }
+
+  void* result;
+#if (EIGEN_DEFAULT_ALIGN_BYTES == 0) || EIGEN_MALLOC_ALREADY_ALIGNED
+  EIGEN_UNUSED_VARIABLE(old_size)
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(realloc)
+  result = realloc(ptr, new_size);
+#else
+  result = handmade_aligned_realloc(ptr, new_size, old_size);
+#endif
+
+  if (!result && new_size) throw_std_bad_alloc();
+
+  return result;
+}
+
+/*****************************************************************************
+*** Implementation of conditionally aligned functions                      ***
+*****************************************************************************/
+
+/** \internal Allocates \a size bytes. If Align is true, then the returned ptr is 16-byte-aligned.
+ * On allocation error, the returned pointer is null, and a std::bad_alloc is thrown.
+ */
+template <bool Align>
+EIGEN_DEVICE_FUNC inline void* conditional_aligned_malloc(std::size_t size) {
+  return aligned_malloc(size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void* conditional_aligned_malloc<false>(std::size_t size) {
+  if (size == 0) return nullptr;
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(malloc)
+  void* result = malloc(size);
+
+  if (!result && size) throw_std_bad_alloc();
+  return result;
+}
+
+/** \internal Frees memory allocated with conditional_aligned_malloc */
+template <bool Align>
+EIGEN_DEVICE_FUNC inline void conditional_aligned_free(void* ptr) {
+  aligned_free(ptr);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void conditional_aligned_free<false>(void* ptr) {
+  if (ptr != nullptr) {
+    check_that_free_is_allowed();
+    EIGEN_USING_STD(free)
+    free(ptr);
+  }
+}
+
+template <bool Align>
+EIGEN_DEVICE_FUNC inline void* conditional_aligned_realloc(void* ptr, std::size_t new_size, std::size_t old_size) {
+  return aligned_realloc(ptr, new_size, old_size);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void* conditional_aligned_realloc<false>(void* ptr, std::size_t new_size,
+                                                                  std::size_t old_size) {
+  if (ptr == nullptr) return conditional_aligned_malloc<false>(new_size);
+  if (old_size == new_size) return ptr;
+  if (new_size == 0) {
+    conditional_aligned_free<false>(ptr);
+    return nullptr;
+  }
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(realloc)
+  return realloc(ptr, new_size);
+}
+
+/*****************************************************************************
+*** Construction/destruction of array elements                             ***
+*****************************************************************************/
+
+/** \internal Destructs the elements of an array.
+ * The \a size parameters tells on how many objects to call the destructor of T.
+ */
+template <typename T>
+EIGEN_DEVICE_FUNC inline void destruct_elements_of_array(T* ptr, std::size_t size) {
+  // always destruct an array starting from the end.
+  if (ptr)
+    while (size) ptr[--size].~T();
+}
+
+/** \internal Constructs the elements of an array.
+ * The \a size parameter tells on how many objects to call the constructor of T.
+ */
+template <typename T>
+EIGEN_DEVICE_FUNC inline T* default_construct_elements_of_array(T* ptr, std::size_t size) {
+  std::size_t i = 0;
+  EIGEN_TRY {
+    for (i = 0; i < size; ++i) ::new (ptr + i) T;
+  }
+  EIGEN_CATCH(...) {
+    destruct_elements_of_array(ptr, i);
+    EIGEN_THROW;
+  }
+  return ptr;
+}
+
+/** \internal Copy-constructs the elements of an array.
+ * The \a size parameter tells on how many objects to copy.
+ */
+template <typename T>
+EIGEN_DEVICE_FUNC inline T* copy_construct_elements_of_array(T* ptr, const T* src, std::size_t size) {
+  std::size_t i = 0;
+  EIGEN_TRY {
+    for (i = 0; i < size; ++i) ::new (ptr + i) T(*(src + i));
+  }
+  EIGEN_CATCH(...) {
+    destruct_elements_of_array(ptr, i);
+    EIGEN_THROW;
+  }
+  return ptr;
+}
+
+/** \internal Move-constructs the elements of an array.
+ * The \a size parameter tells on how many objects to move.
+ */
+template <typename T>
+EIGEN_DEVICE_FUNC inline T* move_construct_elements_of_array(T* ptr, T* src, std::size_t size) {
+  std::size_t i = 0;
+  EIGEN_TRY {
+    for (i = 0; i < size; ++i) ::new (ptr + i) T(std::move(*(src + i)));
+  }
+  EIGEN_CATCH(...) {
+    destruct_elements_of_array(ptr, i);
+    EIGEN_THROW;
+  }
+  return ptr;
+}
+
+/*****************************************************************************
+*** Implementation of aligned new/delete-like functions                    ***
+*****************************************************************************/
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void check_size_for_overflow(std::size_t size) {
+  constexpr std::size_t max_elements = (std::numeric_limits<std::ptrdiff_t>::max)() / sizeof(T);
+  if (size > max_elements) throw_std_bad_alloc();
+}
+
+/** \internal Allocates \a size objects of type T. The returned pointer is guaranteed to have 16 bytes alignment.
+ * On allocation error, the returned pointer is undefined, but a std::bad_alloc is thrown.
+ * The default constructor of T is called.
+ */
+template <typename T>
+EIGEN_DEVICE_FUNC inline T* aligned_new(std::size_t size) {
+  check_size_for_overflow<T>(size);
+  T* result = static_cast<T*>(aligned_malloc(sizeof(T) * size));
+  EIGEN_TRY { return default_construct_elements_of_array(result, size); }
+  EIGEN_CATCH(...) {
+    aligned_free(result);
+    EIGEN_THROW;
+  }
+  return result;
+}
+
+template <typename T, bool Align>
+EIGEN_DEVICE_FUNC inline T* conditional_aligned_new(std::size_t size) {
+  check_size_for_overflow<T>(size);
+  T* result = static_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T) * size));
+  EIGEN_TRY { return default_construct_elements_of_array(result, size); }
+  EIGEN_CATCH(...) {
+    conditional_aligned_free<Align>(result);
+    EIGEN_THROW;
+  }
+  return result;
+}
+
+/** \internal Deletes objects constructed with aligned_new
+ * The \a size parameters tells on how many objects to call the destructor of T.
+ */
+template <typename T>
+EIGEN_DEVICE_FUNC inline void aligned_delete(T* ptr, std::size_t size) {
+  destruct_elements_of_array<T>(ptr, size);
+  aligned_free(ptr);
+}
+
+/** \internal Deletes objects constructed with conditional_aligned_new
+ * The \a size parameters tells on how many objects to call the destructor of T.
+ */
+template <typename T, bool Align>
+EIGEN_DEVICE_FUNC inline void conditional_aligned_delete(T* ptr, std::size_t size) {
+  destruct_elements_of_array<T>(ptr, size);
+  conditional_aligned_free<Align>(ptr);
+}
+
+template <typename T, bool Align>
+EIGEN_DEVICE_FUNC inline T* conditional_aligned_realloc_new(T* pts, std::size_t new_size, std::size_t old_size) {
+  check_size_for_overflow<T>(new_size);
+  check_size_for_overflow<T>(old_size);
+
+  // If elements need to be explicitly initialized, we cannot simply realloc
+  // (or memcpy) the memory block - each element needs to be reconstructed.
+  // Otherwise, objects that contain internal pointers like mpfr or
+  // AnnoyingScalar can be pointing to the wrong thing.
+  T* result = static_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T) * new_size));
+  EIGEN_TRY {
+    // Move-construct initial elements.
+    std::size_t copy_size = (std::min)(old_size, new_size);
+    move_construct_elements_of_array(result, pts, copy_size);
+
+    // Default-construct remaining elements.
+    if (new_size > old_size) {
+      default_construct_elements_of_array(result + copy_size, new_size - old_size);
+    }
+
+    // Delete old elements.
+    conditional_aligned_delete<T, Align>(pts, old_size);
+  }
+  EIGEN_CATCH(...) {
+    conditional_aligned_free<Align>(result);
+    EIGEN_THROW;
+  }
+
+  return result;
+}
+
+template <typename T, bool Align>
+EIGEN_DEVICE_FUNC inline T* conditional_aligned_new_auto(std::size_t size) {
+  if (size == 0) return nullptr;  // short-cut. Also fixes Bug 884
+  check_size_for_overflow<T>(size);
+  T* result = static_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T) * size));
+  if (NumTraits<T>::RequireInitialization) {
+    EIGEN_TRY { default_construct_elements_of_array(result, size); }
+    EIGEN_CATCH(...) {
+      conditional_aligned_free<Align>(result);
+      EIGEN_THROW;
+    }
+  }
+  return result;
+}
+
+template <typename T, bool Align>
+EIGEN_DEVICE_FUNC inline T* conditional_aligned_realloc_new_auto(T* pts, std::size_t new_size, std::size_t old_size) {
+  if (NumTraits<T>::RequireInitialization) {
+    return conditional_aligned_realloc_new<T, Align>(pts, new_size, old_size);
+  }
+
+  check_size_for_overflow<T>(new_size);
+  check_size_for_overflow<T>(old_size);
+  return static_cast<T*>(
+      conditional_aligned_realloc<Align>(static_cast<void*>(pts), sizeof(T) * new_size, sizeof(T) * old_size));
+}
+
+template <typename T, bool Align>
+EIGEN_DEVICE_FUNC inline void conditional_aligned_delete_auto(T* ptr, std::size_t size) {
+  if (NumTraits<T>::RequireInitialization) destruct_elements_of_array<T>(ptr, size);
+  conditional_aligned_free<Align>(ptr);
+}
+
+/****************************************************************************/
+
+/** \internal Returns the index of the first element of the array that is well aligned with respect to the requested \a
+ * Alignment.
+ *
+ * \tparam Alignment requested alignment in Bytes.
+ * \param array the address of the start of the array
+ * \param size the size of the array
+ *
+ * \note If no element of the array is well aligned or the requested alignment is not a multiple of a scalar,
+ * the size of the array is returned. For example with SSE, the requested alignment is typically 16-bytes. If
+ * packet size for the given scalar type is 1, then everything is considered well-aligned.
+ *
+ * \note Otherwise, if the Alignment is larger that the scalar size, we rely on the assumptions that sizeof(Scalar) is a
+ * power of 2. On the other hand, we do not assume that the array address is a multiple of sizeof(Scalar), as that fails
+ * for example with Scalar=double on certain 32-bit platforms, see bug #79.
+ *
+ * There is also the variant first_aligned(const MatrixBase&) defined in DenseCoeffsBase.h.
+ * \sa first_default_aligned()
+ */
+template <int Alignment, typename Scalar, typename Index>
+EIGEN_DEVICE_FUNC inline Index first_aligned(const Scalar* array, Index size) {
+  const Index ScalarSize = sizeof(Scalar);
+  const Index AlignmentSize = Alignment / ScalarSize;
+  const Index AlignmentMask = AlignmentSize - 1;
+
+  if (AlignmentSize <= 1) {
+    // Either the requested alignment if smaller than a scalar, or it exactly match a 1 scalar
+    // so that all elements of the array have the same alignment.
+    return 0;
+  } else if ((std::uintptr_t(array) & (sizeof(Scalar) - 1)) || (Alignment % ScalarSize) != 0) {
+    // The array is not aligned to the size of a single scalar, or the requested alignment is not a multiple of the
+    // scalar size. Consequently, no element of the array is well aligned.
+    return size;
+  } else {
+    Index first = (AlignmentSize - (Index((std::uintptr_t(array) / sizeof(Scalar))) & AlignmentMask)) & AlignmentMask;
+    return (first < size) ? first : size;
+  }
+}
+
+/** \internal Returns the index of the first element of the array that is well aligned with respect the largest packet
+ * requirement. \sa first_aligned(Scalar*,Index) and first_default_aligned(DenseBase<Derived>) */
+template <typename Scalar, typename Index>
+EIGEN_DEVICE_FUNC inline Index first_default_aligned(const Scalar* array, Index size) {
+  typedef typename packet_traits<Scalar>::type DefaultPacketType;
+  return first_aligned<unpacket_traits<DefaultPacketType>::alignment>(array, size);
+}
+
+/** \internal Returns the smallest integer multiple of \a base and greater or equal to \a size
+ */
+template <typename Index>
+inline Index first_multiple(Index size, Index base) {
+  return ((size + base - 1) / base) * base;
+}
+
+// std::copy is much slower than memcpy, so let's introduce a smart_copy which
+// use memcpy on trivial types, i.e., on types that does not require an initialization ctor.
+template <typename T, bool UseMemcpy>
+struct smart_copy_helper;
+
+template <typename T>
+EIGEN_DEVICE_FUNC void smart_copy(const T* start, const T* end, T* target) {
+  smart_copy_helper<T, !NumTraits<T>::RequireInitialization>::run(start, end, target);
+}
+
+template <typename T>
+struct smart_copy_helper<T, true> {
+  EIGEN_DEVICE_FUNC static inline void run(const T* start, const T* end, T* target) {
+    std::intptr_t size = std::intptr_t(end) - std::intptr_t(start);
+    if (size == 0) return;
+    eigen_internal_assert(start != 0 && end != 0 && target != 0);
+    EIGEN_USING_STD(memcpy)
+    memcpy(target, start, size);
+  }
+};
+
+template <typename T>
+struct smart_copy_helper<T, false> {
+  EIGEN_DEVICE_FUNC static inline void run(const T* start, const T* end, T* target) { std::copy(start, end, target); }
+};
+
+// intelligent memmove. falls back to std::memmove for POD types, uses std::copy otherwise.
+template <typename T, bool UseMemmove>
+struct smart_memmove_helper;
+
+template <typename T>
+void smart_memmove(const T* start, const T* end, T* target) {
+  smart_memmove_helper<T, !NumTraits<T>::RequireInitialization>::run(start, end, target);
+}
+
+template <typename T>
+struct smart_memmove_helper<T, true> {
+  static inline void run(const T* start, const T* end, T* target) {
+    std::intptr_t size = std::intptr_t(end) - std::intptr_t(start);
+    if (size == 0) return;
+    eigen_internal_assert(start != 0 && end != 0 && target != 0);
+    std::memmove(target, start, size);
+  }
+};
+
+template <typename T>
+struct smart_memmove_helper<T, false> {
+  static inline void run(const T* start, const T* end, T* target) {
+    if (std::uintptr_t(target) < std::uintptr_t(start)) {
+      std::copy(start, end, target);
+    } else {
+      std::ptrdiff_t count = (std::ptrdiff_t(end) - std::ptrdiff_t(start)) / sizeof(T);
+      std::copy_backward(start, end, target + count);
+    }
+  }
+};
+
+template <typename T>
+EIGEN_DEVICE_FUNC T* smart_move(T* start, T* end, T* target) {
+  return std::move(start, end, target);
+}
+
+/*****************************************************************************
+*** Implementation of runtime stack allocation (falling back to malloc)    ***
+*****************************************************************************/
+
+// you can overwrite Eigen's default behavior regarding alloca by defining EIGEN_ALLOCA
+// to the appropriate stack allocation function
+#if !defined EIGEN_ALLOCA && !defined EIGEN_GPU_COMPILE_PHASE
+#if EIGEN_OS_LINUX || EIGEN_OS_MAC || (defined alloca)
+#define EIGEN_ALLOCA alloca
+#elif EIGEN_COMP_MSVC
+#define EIGEN_ALLOCA _alloca
+#endif
+#endif
+
+// With clang -Oz -mthumb, alloca changes the stack pointer in a way that is
+// not allowed in Thumb2. -DEIGEN_STACK_ALLOCATION_LIMIT=0 doesn't work because
+// the compiler still emits bad code because stack allocation checks use "<=".
+// TODO: Eliminate after https://bugs.llvm.org/show_bug.cgi?id=23772
+// is fixed.
+#if defined(__clang__) && defined(__thumb__)
+#undef EIGEN_ALLOCA
+#endif
+
+// This helper class construct the allocated memory, and takes care of destructing and freeing the handled data
+// at destruction time. In practice this helper class is mainly useful to avoid memory leak in case of exceptions.
+template <typename T>
+class aligned_stack_memory_handler : noncopyable {
+ public:
+  /* Creates a stack_memory_handler responsible for the buffer \a ptr of size \a size.
+   * Note that \a ptr can be 0 regardless of the other parameters.
+   * This constructor takes care of constructing/initializing the elements of the buffer if required by the scalar type
+   *T (see NumTraits<T>::RequireInitialization). In this case, the buffer elements will also be destructed when this
+   *handler will be destructed. Finally, if \a dealloc is true, then the pointer \a ptr is freed.
+   **/
+  EIGEN_DEVICE_FUNC aligned_stack_memory_handler(T* ptr, std::size_t size, bool dealloc)
+      : m_ptr(ptr), m_size(size), m_deallocate(dealloc) {
+    if (NumTraits<T>::RequireInitialization && m_ptr) Eigen::internal::default_construct_elements_of_array(m_ptr, size);
+  }
+  EIGEN_DEVICE_FUNC ~aligned_stack_memory_handler() {
+    if (NumTraits<T>::RequireInitialization && m_ptr) Eigen::internal::destruct_elements_of_array<T>(m_ptr, m_size);
+    if (m_deallocate) Eigen::internal::aligned_free(m_ptr);
+  }
+
+ protected:
+  T* m_ptr;
+  std::size_t m_size;
+  bool m_deallocate;
+};
+
+#ifdef EIGEN_ALLOCA
+
+template <typename Xpr, int NbEvaluations,
+          bool MapExternalBuffer = nested_eval<Xpr, NbEvaluations>::Evaluate && Xpr::MaxSizeAtCompileTime == Dynamic>
+struct local_nested_eval_wrapper {
+  static constexpr bool NeedExternalBuffer = false;
+  typedef typename Xpr::Scalar Scalar;
+  typedef typename nested_eval<Xpr, NbEvaluations>::type ObjectType;
+  ObjectType object;
+
+  EIGEN_DEVICE_FUNC local_nested_eval_wrapper(const Xpr& xpr, Scalar* ptr) : object(xpr) {
+    EIGEN_UNUSED_VARIABLE(ptr);
+    eigen_internal_assert(ptr == 0);
+  }
+};
+
+template <typename Xpr, int NbEvaluations>
+struct local_nested_eval_wrapper<Xpr, NbEvaluations, true> {
+  static constexpr bool NeedExternalBuffer = true;
+  typedef typename Xpr::Scalar Scalar;
+  typedef typename plain_object_eval<Xpr>::type PlainObject;
+  typedef Map<PlainObject, EIGEN_DEFAULT_ALIGN_BYTES> ObjectType;
+  ObjectType object;
+
+  EIGEN_DEVICE_FUNC local_nested_eval_wrapper(const Xpr& xpr, Scalar* ptr)
+      : object(ptr == 0 ? reinterpret_cast<Scalar*>(Eigen::internal::aligned_malloc(sizeof(Scalar) * xpr.size())) : ptr,
+               xpr.rows(), xpr.cols()),
+        m_deallocate(ptr == 0) {
+    if (NumTraits<Scalar>::RequireInitialization && object.data())
+      Eigen::internal::default_construct_elements_of_array(object.data(), object.size());
+    object = xpr;
+  }
+
+  EIGEN_DEVICE_FUNC ~local_nested_eval_wrapper() {
+    if (NumTraits<Scalar>::RequireInitialization && object.data())
+      Eigen::internal::destruct_elements_of_array(object.data(), object.size());
+    if (m_deallocate) Eigen::internal::aligned_free(object.data());
+  }
+
+ private:
+  bool m_deallocate;
+};
+
+#endif  // EIGEN_ALLOCA
+
+template <typename T>
+class scoped_array : noncopyable {
+  T* m_ptr;
+
+ public:
+  explicit scoped_array(std::ptrdiff_t size) { m_ptr = new T[size]; }
+  ~scoped_array() { delete[] m_ptr; }
+  T& operator[](std::ptrdiff_t i) { return m_ptr[i]; }
+  const T& operator[](std::ptrdiff_t i) const { return m_ptr[i]; }
+  T*& ptr() { return m_ptr; }
+  const T* ptr() const { return m_ptr; }
+  operator const T*() const { return m_ptr; }
+};
+
+template <typename T>
+void swap(scoped_array<T>& a, scoped_array<T>& b) {
+  std::swap(a.ptr(), b.ptr());
+}
+
+}  // end namespace internal
+
+/** \internal
+ *
+ * The macro ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) declares, allocates,
+ * and construct an aligned buffer named NAME of SIZE elements of type TYPE on the stack
+ * if the size in bytes is smaller than EIGEN_STACK_ALLOCATION_LIMIT, and if stack allocation is supported by the
+ * platform (currently, this is Linux, OSX and Visual Studio only). Otherwise the memory is allocated on the heap. The
+ * allocated buffer is automatically deleted when exiting the scope of this declaration. If BUFFER is non null, then the
+ * declared variable is simply an alias for BUFFER, and no allocation/deletion occurs. Here is an example: \code
+ * {
+ *   ei_declare_aligned_stack_constructed_variable(float,data,size,0);
+ *   // use data[0] to data[size-1]
+ * }
+ * \endcode
+ * The underlying stack allocation function can controlled with the EIGEN_ALLOCA preprocessor token.
+ *
+ * The macro ei_declare_local_nested_eval(XPR_T,XPR,N,NAME) is analogue to
+ * \code
+ *   typename internal::nested_eval<XPRT_T,N>::type NAME(XPR);
+ * \endcode
+ * with the advantage of using aligned stack allocation even if the maximal size of XPR at compile time is unknown.
+ * This is accomplished through alloca if this later is supported and if the required number of bytes
+ * is below EIGEN_STACK_ALLOCATION_LIMIT.
+ */
+#if defined(EIGEN_ALLOCA) && !defined(EIGEN_NO_ALLOCA)
+
+#if EIGEN_DEFAULT_ALIGN_BYTES > 0
+// We always manually re-align the result of EIGEN_ALLOCA.
+// If alloca is already aligned, the compiler should be smart enough to optimize away the re-alignment.
+
+#if ((EIGEN_COMP_GNUC || EIGEN_COMP_CLANG) && !EIGEN_COMP_NVHPC)
+#define EIGEN_ALIGNED_ALLOCA(SIZE) __builtin_alloca_with_align(SIZE, CHAR_BIT* EIGEN_DEFAULT_ALIGN_BYTES)
+#else
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* eigen_aligned_alloca_helper(void* ptr) {
+  constexpr std::uintptr_t mask = EIGEN_DEFAULT_ALIGN_BYTES - 1;
+  std::uintptr_t ptr_int = std::uintptr_t(ptr);
+  std::uintptr_t aligned_ptr_int = (ptr_int + mask) & ~mask;
+  std::uintptr_t offset = aligned_ptr_int - ptr_int;
+  return static_cast<void*>(static_cast<uint8_t*>(ptr) + offset);
+}
+#define EIGEN_ALIGNED_ALLOCA(SIZE) eigen_aligned_alloca_helper(EIGEN_ALLOCA(SIZE + EIGEN_DEFAULT_ALIGN_BYTES - 1))
+#endif
+
+#else
+#define EIGEN_ALIGNED_ALLOCA(SIZE) EIGEN_ALLOCA(SIZE)
+#endif
+
+#define ei_declare_aligned_stack_constructed_variable(TYPE, NAME, SIZE, BUFFER)                                       \
+  Eigen::internal::check_size_for_overflow<TYPE>(SIZE);                                                               \
+  TYPE* NAME = (BUFFER) != 0 ? (BUFFER)                                                                               \
+                             : reinterpret_cast<TYPE*>((sizeof(TYPE) * (SIZE) <= EIGEN_STACK_ALLOCATION_LIMIT)        \
+                                                           ? EIGEN_ALIGNED_ALLOCA(sizeof(TYPE) * (SIZE))              \
+                                                           : Eigen::internal::aligned_malloc(sizeof(TYPE) * (SIZE))); \
+  Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME, _stack_memory_destructor)(                      \
+      (BUFFER) == 0 ? NAME : 0, SIZE, sizeof(TYPE) * (SIZE) > EIGEN_STACK_ALLOCATION_LIMIT)
+
+#define ei_declare_local_nested_eval(XPR_T, XPR, N, NAME)                                        \
+  Eigen::internal::local_nested_eval_wrapper<XPR_T, N> EIGEN_CAT(NAME, _wrapper)(                \
+      XPR, reinterpret_cast<typename XPR_T::Scalar*>(                                            \
+               ((Eigen::internal::local_nested_eval_wrapper<XPR_T, N>::NeedExternalBuffer) &&    \
+                ((sizeof(typename XPR_T::Scalar) * XPR.size()) <= EIGEN_STACK_ALLOCATION_LIMIT)) \
+                   ? EIGEN_ALIGNED_ALLOCA(sizeof(typename XPR_T::Scalar) * XPR.size())           \
+                   : 0));                                                                        \
+  typename Eigen::internal::local_nested_eval_wrapper<XPR_T, N>::ObjectType NAME(EIGEN_CAT(NAME, _wrapper).object)
+
+#else
+
+#define ei_declare_aligned_stack_constructed_variable(TYPE, NAME, SIZE, BUFFER)                                 \
+  Eigen::internal::check_size_for_overflow<TYPE>(SIZE);                                                         \
+  TYPE* NAME =                                                                                                  \
+      (BUFFER) != 0 ? BUFFER : reinterpret_cast<TYPE*>(Eigen::internal::aligned_malloc(sizeof(TYPE) * (SIZE))); \
+  Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME, _stack_memory_destructor)(                \
+      (BUFFER) == 0 ? NAME : 0, SIZE, true)
+
+#define ei_declare_local_nested_eval(XPR_T, XPR, N, NAME) \
+  typename Eigen::internal::nested_eval<XPR_T, N>::type NAME(XPR)
+
+#endif
+
+/*****************************************************************************
+*** Implementation of EIGEN_MAKE_ALIGNED_OPERATOR_NEW [_IF]                ***
+*****************************************************************************/
+
+#if EIGEN_HAS_CXX17_OVERALIGN
+
+// C++17 -> no need to bother about alignment anymore :)
+
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign)
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar, Size)
+
+#else
+
+// HIP does not support new/delete on device.
+#if EIGEN_MAX_ALIGN_BYTES != 0 && !defined(EIGEN_HIP_DEVICE_COMPILE)
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign)                              \
+  EIGEN_DEVICE_FUNC void* operator new(std::size_t size, const std::nothrow_t&) noexcept { \
+    EIGEN_TRY { return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); }  \
+    EIGEN_CATCH(...) { return 0; }                                                         \
+  }
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)                                                              \
+  EIGEN_DEVICE_FUNC void* operator new(std::size_t size) {                                                            \
+    return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size);                                           \
+  }                                                                                                                   \
+  EIGEN_DEVICE_FUNC void* operator new[](std::size_t size) {                                                          \
+    return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size);                                           \
+  }                                                                                                                   \
+  EIGEN_DEVICE_FUNC void operator delete(void* ptr) noexcept {                                                        \
+    Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr);                                                     \
+  }                                                                                                                   \
+  EIGEN_DEVICE_FUNC void operator delete[](void* ptr) noexcept {                                                      \
+    Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr);                                                     \
+  }                                                                                                                   \
+  EIGEN_DEVICE_FUNC void operator delete(void* ptr, std::size_t /* sz */) noexcept {                                  \
+    Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr);                                                     \
+  }                                                                                                                   \
+  EIGEN_DEVICE_FUNC void operator delete[](void* ptr, std::size_t /* sz */) noexcept {                                \
+    Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr);                                                     \
+  }                                                                                                                   \
+  /* in-place new and delete. since (at least afaik) there is no actual   */                                          \
+  /* memory allocated we can safely let the default implementation handle */                                          \
+  /* this particular case. */                                                                                         \
+  EIGEN_DEVICE_FUNC static void* operator new(std::size_t size, void* ptr) { return ::operator new(size, ptr); }      \
+  EIGEN_DEVICE_FUNC static void* operator new[](std::size_t size, void* ptr) { return ::operator new[](size, ptr); }  \
+  EIGEN_DEVICE_FUNC void operator delete(void* memory, void* ptr) noexcept { return ::operator delete(memory, ptr); } \
+  EIGEN_DEVICE_FUNC void operator delete[](void* memory, void* ptr) noexcept {                                        \
+    return ::operator delete[](memory, ptr);                                                                          \
+  }                                                                                                                   \
+  /* nothrow-new (returns zero instead of std::bad_alloc) */                                                          \
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign)                                                               \
+  EIGEN_DEVICE_FUNC void operator delete(void* ptr, const std::nothrow_t&) noexcept {                                 \
+    Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr);                                                     \
+  }                                                                                                                   \
+  typedef void eigen_aligned_operator_new_marker_type;
+#else
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
+#endif
+
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(true)
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar, Size)                                 \
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(                                                                            \
+      bool(((Size) != Eigen::Dynamic) &&                                                                         \
+           (((EIGEN_MAX_ALIGN_BYTES >= 16) && ((sizeof(Scalar) * (Size)) % (EIGEN_MAX_ALIGN_BYTES) == 0)) ||     \
+            ((EIGEN_MAX_ALIGN_BYTES >= 32) && ((sizeof(Scalar) * (Size)) % (EIGEN_MAX_ALIGN_BYTES / 2) == 0)) || \
+            ((EIGEN_MAX_ALIGN_BYTES >= 64) && ((sizeof(Scalar) * (Size)) % (EIGEN_MAX_ALIGN_BYTES / 4) == 0)))))
+
+#endif
+
+/****************************************************************************/
+
+/** \class aligned_allocator
+ * \ingroup Core_Module
+ *
+ * \brief STL compatible allocator to use with types requiring a non-standard alignment.
+ *
+ * The memory is aligned as for dynamically aligned matrix/array types such as MatrixXd.
+ * By default, it will thus provide at least 16 bytes alignment and more in following cases:
+ *  - 32 bytes alignment if AVX is enabled.
+ *  - 64 bytes alignment if AVX512 is enabled.
+ *
+ * This can be controlled using the \c EIGEN_MAX_ALIGN_BYTES macro as documented
+ * \link TopicPreprocessorDirectivesPerformance there \endlink.
+ *
+ * Example:
+ * \code
+ * // Matrix4f requires 16 bytes alignment:
+ * std::map< int, Matrix4f, std::less<int>,
+ *           aligned_allocator<std::pair<const int, Matrix4f> > > my_map_mat4;
+ * // Vector3f does not require 16 bytes alignment, no need to use Eigen's allocator:
+ * std::map< int, Vector3f > my_map_vec3;
+ * \endcode
+ *
+ * \sa \blank \ref TopicStlContainers.
+ */
+template <class T>
+class aligned_allocator {
+ public:
+  typedef std::size_t size_type;
+  typedef std::ptrdiff_t difference_type;
+  typedef T* pointer;
+  typedef const T* const_pointer;
+  typedef T& reference;
+  typedef const T& const_reference;
+  typedef T value_type;
+
+  template <class U>
+  struct rebind {
+    typedef aligned_allocator<U> other;
+  };
+
+  aligned_allocator() = default;
+
+  aligned_allocator(const aligned_allocator&) = default;
+
+  template <class U>
+  aligned_allocator(const aligned_allocator<U>&) {}
+
+  template <class U>
+  constexpr bool operator==(const aligned_allocator<U>&) const noexcept {
+    return true;
+  }
+  template <class U>
+  constexpr bool operator!=(const aligned_allocator<U>&) const noexcept {
+    return false;
+  }
+
+#if EIGEN_COMP_GNUC_STRICT && EIGEN_GNUC_STRICT_AT_LEAST(7, 0, 0)
+  // In gcc std::allocator::max_size() is bugged making gcc triggers a warning:
+  // eigen/Eigen/src/Core/util/Memory.h:189:12: warning: argument 1 value '18446744073709551612' exceeds maximum object
+  // size 9223372036854775807 See https://gcc.gnu.org/bugzilla/show_bug.cgi?id=87544
+  size_type max_size() const { return (std::numeric_limits<std::ptrdiff_t>::max)() / sizeof(T); }
+#endif
+
+  pointer allocate(size_type num, const void* /*hint*/ = 0) {
+    internal::check_size_for_overflow<T>(num);
+    return static_cast<pointer>(internal::aligned_malloc(num * sizeof(T)));
+  }
+
+  void deallocate(pointer p, size_type /*num*/) { internal::aligned_free(p); }
+};
+
+//---------- Cache sizes ----------
+
+#if !defined(EIGEN_NO_CPUID)
+#if EIGEN_COMP_GNUC && EIGEN_ARCH_i386_OR_x86_64
+#if defined(__PIC__) && EIGEN_ARCH_i386
+// Case for x86 with PIC
+#define EIGEN_CPUID(abcd, func, id)                                                  \
+  __asm__ __volatile__("xchgl %%ebx, %k1;cpuid; xchgl %%ebx,%k1"                     \
+                       : "=a"(abcd[0]), "=&r"(abcd[1]), "=c"(abcd[2]), "=d"(abcd[3]) \
+                       : "a"(func), "c"(id));
+#elif defined(__PIC__) && EIGEN_ARCH_x86_64
+// Case for x64 with PIC. In theory this is only a problem with recent gcc and with medium or large code model, not with
+// the default small code model. However, we cannot detect which code model is used, and the xchg overhead is negligible
+// anyway.
+#define EIGEN_CPUID(abcd, func, id)                                                  \
+  __asm__ __volatile__("xchg{q}\t{%%}rbx, %q1; cpuid; xchg{q}\t{%%}rbx, %q1"         \
+                       : "=a"(abcd[0]), "=&r"(abcd[1]), "=c"(abcd[2]), "=d"(abcd[3]) \
+                       : "0"(func), "2"(id));
+#else
+// Case for x86_64 or x86 w/o PIC
+#define EIGEN_CPUID(abcd, func, id) \
+  __asm__ __volatile__("cpuid" : "=a"(abcd[0]), "=b"(abcd[1]), "=c"(abcd[2]), "=d"(abcd[3]) : "0"(func), "2"(id));
+#endif
+#elif EIGEN_COMP_MSVC
+#if EIGEN_ARCH_i386_OR_x86_64
+#define EIGEN_CPUID(abcd, func, id) __cpuidex((int*)abcd, func, id)
+#endif
+#endif
+#endif
+
+namespace internal {
+
+#ifdef EIGEN_CPUID
+
+inline bool cpuid_is_vendor(int abcd[4], const int vendor[3]) {
+  return abcd[1] == vendor[0] && abcd[3] == vendor[1] && abcd[2] == vendor[2];
+}
+
+inline void queryCacheSizes_intel_direct(int& l1, int& l2, int& l3) {
+  int abcd[4];
+  l1 = l2 = l3 = 0;
+  int cache_id = 0;
+  int cache_type = 0;
+  do {
+    abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
+    EIGEN_CPUID(abcd, 0x4, cache_id);
+    cache_type = (abcd[0] & 0x0F) >> 0;
+    if (cache_type == 1 || cache_type == 3)  // data or unified cache
+    {
+      int cache_level = (abcd[0] & 0xE0) >> 5;        // A[7:5]
+      int ways = (abcd[1] & 0xFFC00000) >> 22;        // B[31:22]
+      int partitions = (abcd[1] & 0x003FF000) >> 12;  // B[21:12]
+      int line_size = (abcd[1] & 0x00000FFF) >> 0;    // B[11:0]
+      int sets = (abcd[2]);                           // C[31:0]
+
+      int cache_size = (ways + 1) * (partitions + 1) * (line_size + 1) * (sets + 1);
+
+      switch (cache_level) {
+        case 1:
+          l1 = cache_size;
+          break;
+        case 2:
+          l2 = cache_size;
+          break;
+        case 3:
+          l3 = cache_size;
+          break;
+        default:
+          break;
+      }
+    }
+    cache_id++;
+  } while (cache_type > 0 && cache_id < 16);
+}
+
+inline void queryCacheSizes_intel_codes(int& l1, int& l2, int& l3) {
+  int abcd[4];
+  abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
+  l1 = l2 = l3 = 0;
+  EIGEN_CPUID(abcd, 0x00000002, 0);
+  unsigned char* bytes = reinterpret_cast<unsigned char*>(abcd) + 2;
+  bool check_for_p2_core2 = false;
+  for (int i = 0; i < 14; ++i) {
+    switch (bytes[i]) {
+      case 0x0A:
+        l1 = 8;
+        break;  // 0Ah   data L1 cache, 8 KB, 2 ways, 32 byte lines
+      case 0x0C:
+        l1 = 16;
+        break;  // 0Ch   data L1 cache, 16 KB, 4 ways, 32 byte lines
+      case 0x0E:
+        l1 = 24;
+        break;  // 0Eh   data L1 cache, 24 KB, 6 ways, 64 byte lines
+      case 0x10:
+        l1 = 16;
+        break;  // 10h   data L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64)
+      case 0x15:
+        l1 = 16;
+        break;  // 15h   code L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64)
+      case 0x2C:
+        l1 = 32;
+        break;  // 2Ch   data L1 cache, 32 KB, 8 ways, 64 byte lines
+      case 0x30:
+        l1 = 32;
+        break;  // 30h   code L1 cache, 32 KB, 8 ways, 64 byte lines
+      case 0x60:
+        l1 = 16;
+        break;  // 60h   data L1 cache, 16 KB, 8 ways, 64 byte lines, sectored
+      case 0x66:
+        l1 = 8;
+        break;  // 66h   data L1 cache, 8 KB, 4 ways, 64 byte lines, sectored
+      case 0x67:
+        l1 = 16;
+        break;  // 67h   data L1 cache, 16 KB, 4 ways, 64 byte lines, sectored
+      case 0x68:
+        l1 = 32;
+        break;  // 68h   data L1 cache, 32 KB, 4 ways, 64 byte lines, sectored
+      case 0x1A:
+        l2 = 96;
+        break;  // code and data L2 cache, 96 KB, 6 ways, 64 byte lines (IA-64)
+      case 0x22:
+        l3 = 512;
+        break;  // code and data L3 cache, 512 KB, 4 ways (!), 64 byte lines, dual-sectored
+      case 0x23:
+        l3 = 1024;
+        break;  // code and data L3 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x25:
+        l3 = 2048;
+        break;  // code and data L3 cache, 2048 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x29:
+        l3 = 4096;
+        break;  // code and data L3 cache, 4096 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x39:
+        l2 = 128;
+        break;  // code and data L2 cache, 128 KB, 4 ways, 64 byte lines, sectored
+      case 0x3A:
+        l2 = 192;
+        break;  // code and data L2 cache, 192 KB, 6 ways, 64 byte lines, sectored
+      case 0x3B:
+        l2 = 128;
+        break;  // code and data L2 cache, 128 KB, 2 ways, 64 byte lines, sectored
+      case 0x3C:
+        l2 = 256;
+        break;  // code and data L2 cache, 256 KB, 4 ways, 64 byte lines, sectored
+      case 0x3D:
+        l2 = 384;
+        break;  // code and data L2 cache, 384 KB, 6 ways, 64 byte lines, sectored
+      case 0x3E:
+        l2 = 512;
+        break;  // code and data L2 cache, 512 KB, 4 ways, 64 byte lines, sectored
+      case 0x40:
+        l2 = 0;
+        break;  // no integrated L2 cache (P6 core) or L3 cache (P4 core)
+      case 0x41:
+        l2 = 128;
+        break;  // code and data L2 cache, 128 KB, 4 ways, 32 byte lines
+      case 0x42:
+        l2 = 256;
+        break;  // code and data L2 cache, 256 KB, 4 ways, 32 byte lines
+      case 0x43:
+        l2 = 512;
+        break;  // code and data L2 cache, 512 KB, 4 ways, 32 byte lines
+      case 0x44:
+        l2 = 1024;
+        break;  // code and data L2 cache, 1024 KB, 4 ways, 32 byte lines
+      case 0x45:
+        l2 = 2048;
+        break;  // code and data L2 cache, 2048 KB, 4 ways, 32 byte lines
+      case 0x46:
+        l3 = 4096;
+        break;  // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines
+      case 0x47:
+        l3 = 8192;
+        break;  // code and data L3 cache, 8192 KB, 8 ways, 64 byte lines
+      case 0x48:
+        l2 = 3072;
+        break;  // code and data L2 cache, 3072 KB, 12 ways, 64 byte lines
+      case 0x49:
+        if (l2 != 0)
+          l3 = 4096;
+        else {
+          check_for_p2_core2 = true;
+          l3 = l2 = 4096;
+        }
+        break;  // code and data L3 cache, 4096 KB, 16 ways, 64 byte lines (P4) or L2 for core2
+      case 0x4A:
+        l3 = 6144;
+        break;  // code and data L3 cache, 6144 KB, 12 ways, 64 byte lines
+      case 0x4B:
+        l3 = 8192;
+        break;  // code and data L3 cache, 8192 KB, 16 ways, 64 byte lines
+      case 0x4C:
+        l3 = 12288;
+        break;  // code and data L3 cache, 12288 KB, 12 ways, 64 byte lines
+      case 0x4D:
+        l3 = 16384;
+        break;  // code and data L3 cache, 16384 KB, 16 ways, 64 byte lines
+      case 0x4E:
+        l2 = 6144;
+        break;  // code and data L2 cache, 6144 KB, 24 ways, 64 byte lines
+      case 0x78:
+        l2 = 1024;
+        break;  // code and data L2 cache, 1024 KB, 4 ways, 64 byte lines
+      case 0x79:
+        l2 = 128;
+        break;  // code and data L2 cache, 128 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x7A:
+        l2 = 256;
+        break;  // code and data L2 cache, 256 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x7B:
+        l2 = 512;
+        break;  // code and data L2 cache, 512 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x7C:
+        l2 = 1024;
+        break;  // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x7D:
+        l2 = 2048;
+        break;  // code and data L2 cache, 2048 KB, 8 ways, 64 byte lines
+      case 0x7E:
+        l2 = 256;
+        break;  // code and data L2 cache, 256 KB, 8 ways, 128 byte lines, sect. (IA-64)
+      case 0x7F:
+        l2 = 512;
+        break;  // code and data L2 cache, 512 KB, 2 ways, 64 byte lines
+      case 0x80:
+        l2 = 512;
+        break;  // code and data L2 cache, 512 KB, 8 ways, 64 byte lines
+      case 0x81:
+        l2 = 128;
+        break;  // code and data L2 cache, 128 KB, 8 ways, 32 byte lines
+      case 0x82:
+        l2 = 256;
+        break;  // code and data L2 cache, 256 KB, 8 ways, 32 byte lines
+      case 0x83:
+        l2 = 512;
+        break;  // code and data L2 cache, 512 KB, 8 ways, 32 byte lines
+      case 0x84:
+        l2 = 1024;
+        break;  // code and data L2 cache, 1024 KB, 8 ways, 32 byte lines
+      case 0x85:
+        l2 = 2048;
+        break;  // code and data L2 cache, 2048 KB, 8 ways, 32 byte lines
+      case 0x86:
+        l2 = 512;
+        break;  // code and data L2 cache, 512 KB, 4 ways, 64 byte lines
+      case 0x87:
+        l2 = 1024;
+        break;  // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines
+      case 0x88:
+        l3 = 2048;
+        break;  // code and data L3 cache, 2048 KB, 4 ways, 64 byte lines (IA-64)
+      case 0x89:
+        l3 = 4096;
+        break;  // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines (IA-64)
+      case 0x8A:
+        l3 = 8192;
+        break;  // code and data L3 cache, 8192 KB, 4 ways, 64 byte lines (IA-64)
+      case 0x8D:
+        l3 = 3072;
+        break;  // code and data L3 cache, 3072 KB, 12 ways, 128 byte lines (IA-64)
+
+      default:
+        break;
+    }
+  }
+  if (check_for_p2_core2 && l2 == l3) l3 = 0;
+  l1 *= 1024;
+  l2 *= 1024;
+  l3 *= 1024;
+}
+
+inline void queryCacheSizes_intel(int& l1, int& l2, int& l3, int max_std_funcs) {
+  if (max_std_funcs >= 4)
+    queryCacheSizes_intel_direct(l1, l2, l3);
+  else if (max_std_funcs >= 2)
+    queryCacheSizes_intel_codes(l1, l2, l3);
+  else
+    l1 = l2 = l3 = 0;
+}
+
+inline void queryCacheSizes_amd(int& l1, int& l2, int& l3) {
+  int abcd[4];
+  abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
+
+  // First query the max supported function.
+  EIGEN_CPUID(abcd, 0x80000000, 0);
+  if (static_cast<numext::uint32_t>(abcd[0]) >= static_cast<numext::uint32_t>(0x80000006)) {
+    EIGEN_CPUID(abcd, 0x80000005, 0);
+    l1 = (abcd[2] >> 24) * 1024;  // C[31:24] = L1 size in KB
+    abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
+    EIGEN_CPUID(abcd, 0x80000006, 0);
+    l2 = (abcd[2] >> 16) * 1024;                      // C[31;16] = l2 cache size in KB
+    l3 = ((abcd[3] & 0xFFFC000) >> 18) * 512 * 1024;  // D[31;18] = l3 cache size in 512KB
+  } else {
+    l1 = l2 = l3 = 0;
+  }
+}
+#endif
+
+/** \internal
+ * Queries and returns the cache sizes in Bytes of the L1, L2, and L3 data caches respectively */
+inline void queryCacheSizes(int& l1, int& l2, int& l3) {
+#ifdef EIGEN_CPUID
+  int abcd[4];
+  const int GenuineIntel[] = {0x756e6547, 0x49656e69, 0x6c65746e};
+  const int AuthenticAMD[] = {0x68747541, 0x69746e65, 0x444d4163};
+  const int AMDisbetter_[] = {0x69444d41, 0x74656273, 0x21726574};  // "AMDisbetter!"
+
+  // identify the CPU vendor
+  EIGEN_CPUID(abcd, 0x0, 0);
+  int max_std_funcs = abcd[0];
+  if (cpuid_is_vendor(abcd, GenuineIntel))
+    queryCacheSizes_intel(l1, l2, l3, max_std_funcs);
+  else if (cpuid_is_vendor(abcd, AuthenticAMD) || cpuid_is_vendor(abcd, AMDisbetter_))
+    queryCacheSizes_amd(l1, l2, l3);
+  else
+    // by default let's use Intel's API
+    queryCacheSizes_intel(l1, l2, l3, max_std_funcs);
+
+    // here is the list of other vendors:
+    //   ||cpuid_is_vendor(abcd,"VIA VIA VIA ")
+    //   ||cpuid_is_vendor(abcd,"CyrixInstead")
+    //   ||cpuid_is_vendor(abcd,"CentaurHauls")
+    //   ||cpuid_is_vendor(abcd,"GenuineTMx86")
+    //   ||cpuid_is_vendor(abcd,"TransmetaCPU")
+    //   ||cpuid_is_vendor(abcd,"RiseRiseRise")
+    //   ||cpuid_is_vendor(abcd,"Geode by NSC")
+    //   ||cpuid_is_vendor(abcd,"SiS SiS SiS ")
+    //   ||cpuid_is_vendor(abcd,"UMC UMC UMC ")
+    //   ||cpuid_is_vendor(abcd,"NexGenDriven")
+#else
+  l1 = l2 = l3 = -1;
+#endif
+}
+
+/** \internal
+ * \returns the size in Bytes of the L1 data cache */
+inline int queryL1CacheSize() {
+  int l1(-1), l2, l3;
+  queryCacheSizes(l1, l2, l3);
+  return l1;
+}
+
+/** \internal
+ * \returns the size in Bytes of the L2 or L3 cache if this later is present */
+inline int queryTopLevelCacheSize() {
+  int l1, l2(-1), l3(-1);
+  queryCacheSizes(l1, l2, l3);
+  return (std::max)(l2, l3);
+}
+
+/** \internal
+ * This wraps C++20's std::construct_at, using placement new instead if it is not available.
+ */
+
+#if EIGEN_COMP_CXXVER >= 20 && defined(__cpp_lib_constexpr_dynamic_alloc) && \
+    __cpp_lib_constexpr_dynamic_alloc >= 201907L
+using std::construct_at;
+#else
+template <class T, class... Args>
+EIGEN_DEVICE_FUNC T* construct_at(T* p, Args&&... args) {
+  return ::new (const_cast<void*>(static_cast<const volatile void*>(p))) T(std::forward<Args>(args)...);
+}
+#endif
+
+/** \internal
+ * This wraps C++17's std::destroy_at.  If it's not available it calls the destructor.
+ * The wrapper is not a full replacement for C++20's std::destroy_at as it cannot
+ * be applied to std::array.
+ */
+#if EIGEN_COMP_CXXVER >= 17
+using std::destroy_at;
+#else
+template <class T>
+EIGEN_DEVICE_FUNC void destroy_at(T* p) {
+  p->~T();
+}
+#endif
+
+// FIXME(rmlarsen): Work around missing linker symbol with msan on ARM.
+#if !defined(EIGEN_DONT_ASSUME_ALIGNED) && __has_feature(memory_sanitizer) && (EIGEN_ARCH_ARM || EIGEN_ARCH_ARM64)
+#define EIGEN_DONT_ASSUME_ALIGNED
+#endif
+
+#if !defined(EIGEN_DONT_ASSUME_ALIGNED) && defined(__cpp_lib_assume_aligned) && (__cpp_lib_assume_aligned >= 201811L)
+template <std::size_t N, typename T>
+EIGEN_DEVICE_FUNC constexpr T* assume_aligned(T* ptr) {
+  return std::assume_aligned<N, T>(ptr);
+}
+#elif !defined(EIGEN_DONT_ASSUME_ALIGNED) && EIGEN_HAS_BUILTIN(__builtin_assume_aligned)
+template <std::size_t N, typename T>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC T* assume_aligned(T* ptr) {
+  return static_cast<T*>(__builtin_assume_aligned(ptr, N));
+}
+#else
+template <std::size_t N, typename T>
+EIGEN_DEVICE_FUNC constexpr T* assume_aligned(T* ptr) {
+  return ptr;
+}
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MEMORY_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Meta.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Meta.h
new file mode 100644
index 0000000..ececab8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Meta.h
@@ -0,0 +1,680 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_META_H
+#define EIGEN_META_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+
+#include <cfloat>
+
+#if defined(EIGEN_CUDA_ARCH)
+#include <math_constants.h>
+#endif
+
+#if defined(EIGEN_HIP_DEVICE_COMPILE)
+#include "Eigen/src/Core/arch/HIP/hcc/math_constants.h"
+#endif
+
+#endif
+
+// Define portable (u)int{32,64} types
+#include <cstdint>
+
+namespace Eigen {
+namespace numext {
+typedef std::uint8_t uint8_t;
+typedef std::int8_t int8_t;
+typedef std::uint16_t uint16_t;
+typedef std::int16_t int16_t;
+typedef std::uint32_t uint32_t;
+typedef std::int32_t int32_t;
+typedef std::uint64_t uint64_t;
+typedef std::int64_t int64_t;
+
+template <size_t Size>
+struct get_integer_by_size {
+  typedef void signed_type;
+  typedef void unsigned_type;
+};
+template <>
+struct get_integer_by_size<1> {
+  typedef int8_t signed_type;
+  typedef uint8_t unsigned_type;
+};
+template <>
+struct get_integer_by_size<2> {
+  typedef int16_t signed_type;
+  typedef uint16_t unsigned_type;
+};
+template <>
+struct get_integer_by_size<4> {
+  typedef int32_t signed_type;
+  typedef uint32_t unsigned_type;
+};
+template <>
+struct get_integer_by_size<8> {
+  typedef int64_t signed_type;
+  typedef uint64_t unsigned_type;
+};
+}  // namespace numext
+}  // namespace Eigen
+
+namespace Eigen {
+
+typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE DenseIndex;
+
+/**
+ * \brief The Index type as used for the API.
+ * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
+ * \sa \blank \ref TopicPreprocessorDirectives, StorageIndex.
+ */
+typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE Index;
+
+namespace internal {
+
+/** \internal
+ * \file Meta.h
+ * This file contains generic metaprogramming classes which are not specifically related to Eigen.
+ * \note In case you wonder, yes we're aware that Boost already provides all these features,
+ * we however don't want to add a dependency to Boost.
+ */
+
+using std::false_type;
+using std::true_type;
+
+template <bool Condition>
+using bool_constant = std::integral_constant<bool, Condition>;
+
+// Third-party libraries rely on these.
+using std::conditional;
+using std::remove_const;
+using std::remove_pointer;
+using std::remove_reference;
+
+template <typename T>
+struct remove_all {
+  typedef T type;
+};
+template <typename T>
+struct remove_all<const T> {
+  typedef typename remove_all<T>::type type;
+};
+template <typename T>
+struct remove_all<T const&> {
+  typedef typename remove_all<T>::type type;
+};
+template <typename T>
+struct remove_all<T&> {
+  typedef typename remove_all<T>::type type;
+};
+template <typename T>
+struct remove_all<T const*> {
+  typedef typename remove_all<T>::type type;
+};
+template <typename T>
+struct remove_all<T*> {
+  typedef typename remove_all<T>::type type;
+};
+
+template <typename T>
+using remove_all_t = typename remove_all<T>::type;
+
+// Eigen's is_arithmetic is similar to std::is_arithmetic but can be specialized
+// for SIMD packet types and other Eigen-specific types. The primary template
+// delegates to std::is_arithmetic for fundamental types.
+template <typename T>
+struct is_arithmetic {
+  enum { value = std::is_arithmetic<T>::value };
+};
+// GPU devices treat `long double` as `double`.
+#ifdef EIGEN_GPU_COMPILE_PHASE
+template <>
+struct is_arithmetic<long double> {
+  enum { value = false };
+};
+#endif
+
+using std::is_same;
+
+using std::is_void;
+
+/** \internal
+ * Implementation of std::void_t for SFINAE.
+ *
+ * Pre C++17:
+ * Custom implementation.
+ *
+ * Post C++17: Uses std::void_t
+ */
+#if EIGEN_COMP_CXXVER >= 17 && defined(__cpp_lib_void_t) && __cpp_lib_void_t >= 201411L
+using std::void_t;
+#else
+template <typename...>
+using void_t = void;
+#endif
+
+using std::is_integral;
+
+using std::make_unsigned;
+
+using std::is_const;
+
+template <typename T>
+struct add_const_on_value_type {
+  typedef const T type;
+};
+template <typename T>
+struct add_const_on_value_type<T&> {
+  typedef T const& type;
+};
+template <typename T>
+struct add_const_on_value_type<T*> {
+  typedef T const* type;
+};
+template <typename T>
+struct add_const_on_value_type<T* const> {
+  typedef T const* const type;
+};
+template <typename T>
+struct add_const_on_value_type<T const* const> {
+  typedef T const* const type;
+};
+
+template <typename T>
+using add_const_on_value_type_t = typename add_const_on_value_type<T>::type;
+
+using std::is_convertible;
+
+/** \internal
+ * A base class do disable default copy ctor and copy assignment operator.
+ */
+class noncopyable {
+  EIGEN_DEVICE_FUNC noncopyable(const noncopyable&);
+  EIGEN_DEVICE_FUNC const noncopyable& operator=(const noncopyable&);
+
+ protected:
+  EIGEN_DEVICE_FUNC noncopyable() {}
+  EIGEN_DEVICE_FUNC ~noncopyable() {}
+};
+
+/** \internal
+ * Provides access to the number of elements in the object of as a compile-time constant expression.
+ * It "returns" Eigen::Dynamic if the size cannot be resolved at compile-time (default).
+ *
+ * Similar to std::tuple_size, but more general.
+ *
+ * It currently supports:
+ *  - any types T defining T::SizeAtCompileTime
+ *  - plain C arrays as T[N]
+ *  - std::array (c++11)
+ *  - some internal types such as SingleRange and AllRange
+ *
+ * The second template parameter eases SFINAE-based specializations.
+ */
+template <typename T, typename EnableIf = void>
+struct array_size {
+  static constexpr Index value = Dynamic;
+};
+
+template <typename T>
+struct array_size<T, std::enable_if_t<((T::SizeAtCompileTime & 0) == 0)>> {
+  static constexpr Index value = T::SizeAtCompileTime;
+};
+
+template <typename T, int N>
+struct array_size<const T (&)[N]> {
+  static constexpr Index value = N;
+};
+template <typename T, int N>
+struct array_size<T (&)[N]> {
+  static constexpr Index value = N;
+};
+
+template <typename T, std::size_t N>
+struct array_size<const std::array<T, N>> {
+  static constexpr Index value = N;
+};
+template <typename T, std::size_t N>
+struct array_size<std::array<T, N>> {
+  static constexpr Index value = N;
+};
+
+/** \internal
+ * Analogue of the std::ssize free function.
+ * It returns the signed size of the container or view \a x of type \c T
+ *
+ * It currently supports:
+ *  - any types T defining a member T::size() const
+ *  - plain C arrays as T[N]
+ *
+ * For C++20, this function just forwards to `std::ssize`, or any ADL discoverable `ssize` function.
+ */
+#if EIGEN_COMP_CXXVER >= 20 && defined(__cpp_lib_ssize) && __cpp_lib_ssize >= 201902L
+
+template <typename T>
+constexpr auto index_list_size(T&& x) {
+  using std::ssize;
+  return ssize(std::forward<T>(x));
+}
+
+#else
+
+template <typename T>
+constexpr auto index_list_size(const T& x) {
+  using R = std::common_type_t<std::ptrdiff_t, std::make_signed_t<decltype(x.size())>>;
+  return static_cast<R>(x.size());
+}
+
+template <typename T, std::ptrdiff_t N>
+constexpr std::ptrdiff_t index_list_size(const T (&)[N]) {
+  return N;
+}
+#endif
+
+/** \internal
+ * Convenient struct to get the result type of a nullary, unary, binary, or
+ * ternary functor.
+ *
+ * Pre C++17:
+ * This uses std::result_of. However, note the `type` member removes
+ * const and converts references/pointers to their corresponding value type.
+ *
+ * Post C++17: Uses std::invoke_result
+ */
+#if EIGEN_HAS_STD_INVOKE_RESULT
+template <typename T>
+struct result_of;
+
+template <typename F, typename... ArgTypes>
+struct result_of<F(ArgTypes...)> {
+  typedef typename std::invoke_result<F, ArgTypes...>::type type1;
+  typedef remove_all_t<type1> type;
+};
+
+template <typename F, typename... ArgTypes>
+struct invoke_result {
+  typedef typename std::invoke_result<F, ArgTypes...>::type type1;
+  typedef remove_all_t<type1> type;
+};
+#else
+template <typename T>
+struct result_of {
+  typedef typename std::result_of<T>::type type1;
+  typedef remove_all_t<type1> type;
+};
+
+template <typename F, typename... ArgTypes>
+struct invoke_result {
+  typedef typename result_of<F(ArgTypes...)>::type type1;
+  typedef remove_all_t<type1> type;
+};
+#endif
+
+// Reduces a sequence of bools to true if all are true, false otherwise.
+template <bool... values>
+using reduce_all =
+    std::is_same<std::integer_sequence<bool, values..., true>, std::integer_sequence<bool, true, values...>>;
+
+// Reduces a sequence of bools to true if any are true, false if all false.
+template <bool... values>
+using reduce_any = std::integral_constant<bool, !std::is_same<std::integer_sequence<bool, values..., false>,
+                                                              std::integer_sequence<bool, false, values...>>::value>;
+
+struct meta_yes {
+  char a[1];
+};
+struct meta_no {
+  char a[2];
+};
+
+// Check whether T::ReturnType does exist
+template <typename T>
+struct has_ReturnType {
+  template <typename C>
+  static meta_yes testFunctor(C const*, typename C::ReturnType const* = 0);
+  template <typename C>
+  static meta_no testFunctor(...);
+
+  enum { value = sizeof(testFunctor<T>(static_cast<T*>(0))) == sizeof(meta_yes) };
+};
+
+template <typename T>
+const T* return_ptr();
+
+template <typename T, typename IndexType = Index>
+struct has_nullary_operator {
+  template <typename C>
+  static meta_yes testFunctor(C const*, std::enable_if_t<(sizeof(return_ptr<C>()->operator()()) > 0)>* = 0);
+  static meta_no testFunctor(...);
+
+  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
+};
+
+template <typename T, typename IndexType = Index>
+struct has_unary_operator {
+  template <typename C>
+  static meta_yes testFunctor(C const*, std::enable_if_t<(sizeof(return_ptr<C>()->operator()(IndexType(0))) > 0)>* = 0);
+  static meta_no testFunctor(...);
+
+  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
+};
+
+template <typename T, typename IndexType = Index>
+struct has_binary_operator {
+  template <typename C>
+  static meta_yes testFunctor(
+      C const*, std::enable_if_t<(sizeof(return_ptr<C>()->operator()(IndexType(0), IndexType(0))) > 0)>* = 0);
+  static meta_no testFunctor(...);
+
+  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
+};
+
+/** \internal In short, it computes int(sqrt(\a Y)) with \a Y an integer.
+ * Usage example: \code meta_sqrt<1023>::ret \endcode
+ */
+template <int Y, int InfX = 0, int SupX = ((Y == 1) ? 1 : Y / 2),
+          bool Done = ((SupX - InfX) <= 1 || ((SupX * SupX <= Y) && ((SupX + 1) * (SupX + 1) > Y)))>
+class meta_sqrt {
+  enum {
+    MidX = (InfX + SupX) / 2,
+    TakeInf = MidX * MidX > Y ? 1 : 0,
+    NewInf = int(TakeInf) ? InfX : int(MidX),
+    NewSup = int(TakeInf) ? int(MidX) : SupX
+  };
+
+ public:
+  enum { ret = meta_sqrt<Y, NewInf, NewSup>::ret };
+};
+
+template <int Y, int InfX, int SupX>
+class meta_sqrt<Y, InfX, SupX, true> {
+ public:
+  enum { ret = (SupX * SupX <= Y) ? SupX : InfX };
+};
+
+/** \internal Computes the least common multiple of two positive integer A and B
+ * at compile-time.
+ */
+template <int A, int B, int K = 1, bool Done = ((A * K) % B) == 0, bool Big = (A >= B)>
+struct meta_least_common_multiple {
+  enum { ret = meta_least_common_multiple<A, B, K + 1>::ret };
+};
+template <int A, int B, int K, bool Done>
+struct meta_least_common_multiple<A, B, K, Done, false> {
+  enum { ret = meta_least_common_multiple<B, A, K>::ret };
+};
+template <int A, int B, int K>
+struct meta_least_common_multiple<A, B, K, true, true> {
+  enum { ret = A * K };
+};
+
+/** \internal determines whether the product of two numeric types is allowed and what the return type is */
+template <typename T, typename U>
+struct scalar_product_traits {
+  enum { Defined = 0 };
+};
+
+/** \internal Obtains a POD type suitable to use as storage for an object of a size
+ * of at most Len bytes, aligned as specified by \c Align.
+ */
+template <unsigned Len, unsigned Align>
+struct aligned_storage {
+  struct type {
+    EIGEN_ALIGN_TO_BOUNDARY(Align) unsigned char data[Len];
+  };
+};
+
+}  // end namespace internal
+
+template <typename T>
+struct NumTraits;
+
+namespace numext {
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+template <typename T>
+EIGEN_DEVICE_FUNC constexpr void swap(T& a, T& b) {
+  T tmp = b;
+  b = a;
+  a = tmp;
+}
+#else
+template <typename T>
+constexpr EIGEN_STRONG_INLINE void swap(T& a, T& b) {
+  std::swap(a, b);
+}
+#endif
+
+using std::numeric_limits;
+
+// Handle integer comparisons of different signedness.
+template <typename X, typename Y, bool XIsInteger = NumTraits<X>::IsInteger, bool XIsSigned = NumTraits<X>::IsSigned,
+          bool YIsInteger = NumTraits<Y>::IsInteger, bool YIsSigned = NumTraits<Y>::IsSigned>
+struct equal_strict_impl {
+  static constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool run(const X& x, const Y& y) { return x == y; }
+};
+template <typename X, typename Y>
+struct equal_strict_impl<X, Y, true, false, true, true> {
+  // X is an unsigned integer
+  // Y is a signed integer
+  // if Y is non-negative, it may be represented exactly as its unsigned counterpart.
+  using UnsignedY = typename internal::make_unsigned<Y>::type;
+  static constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool run(const X& x, const Y& y) {
+    return y < Y(0) ? false : (x == static_cast<UnsignedY>(y));
+  }
+};
+template <typename X, typename Y>
+struct equal_strict_impl<X, Y, true, true, true, false> {
+  // X is a signed integer
+  // Y is an unsigned integer
+  static constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool run(const X& x, const Y& y) {
+    return equal_strict_impl<Y, X>::run(y, x);
+  }
+};
+
+// The aim of the following functions is to bypass -Wfloat-equal warnings
+// when we really want a strict equality comparison on floating points.
+template <typename X, typename Y>
+constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool equal_strict(const X& x, const Y& y) {
+  return equal_strict_impl<X, Y>::run(x, y);
+}
+
+#if !defined(EIGEN_GPU_COMPILE_PHASE) || (!defined(EIGEN_CUDA_ARCH) && defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
+template <>
+constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool equal_strict(const float& x, const float& y) {
+  return std::equal_to<float>()(x, y);
+}
+
+template <>
+constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool equal_strict(const double& x, const double& y) {
+  return std::equal_to<double>()(x, y);
+}
+#endif
+
+/**
+ * \internal Performs an exact comparison of x to zero, e.g. to decide whether a term can be ignored.
+ * Use this to to bypass -Wfloat-equal warnings when exact zero is what needs to be tested.
+ */
+template <typename X>
+constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool is_exactly_zero(const X& x) {
+  return equal_strict(x, typename NumTraits<X>::Literal{0});
+}
+
+/**
+ * \internal Performs an exact comparison of x to one, e.g. to decide whether a factor needs to be multiplied.
+ * Use this to to bypass -Wfloat-equal warnings when exact one is what needs to be tested.
+ */
+template <typename X>
+constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool is_exactly_one(const X& x) {
+  return equal_strict(x, typename NumTraits<X>::Literal{1});
+}
+
+template <typename X, typename Y>
+constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool not_equal_strict(const X& x, const Y& y) {
+  return !equal_strict_impl<X, Y>::run(x, y);
+}
+
+#if !defined(EIGEN_GPU_COMPILE_PHASE) || (!defined(EIGEN_CUDA_ARCH) && defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
+template <>
+constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool not_equal_strict(const float& x, const float& y) {
+  return std::not_equal_to<float>()(x, y);
+}
+
+template <>
+constexpr EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool not_equal_strict(const double& x, const double& y) {
+  return std::not_equal_to<double>()(x, y);
+}
+#endif
+
+}  // end namespace numext
+
+namespace internal {
+
+template <typename Scalar>
+struct is_identically_zero_impl {
+  static constexpr bool run(const Scalar& s) { return numext::is_exactly_zero(s); }
+};
+
+template <typename Scalar>
+constexpr EIGEN_STRONG_INLINE bool is_identically_zero(const Scalar& s) {
+  return is_identically_zero_impl<Scalar>::run(s);
+}
+
+/// \internal Returns true if its argument is of integer or enum type.
+/// FIXME this has the same purpose as `is_valid_index_type` in XprHelper.h
+template <typename A>
+constexpr bool is_int_or_enum_v = std::is_enum<A>::value || std::is_integral<A>::value;
+
+template <typename A, typename B>
+constexpr void plain_enum_asserts(A, B) {
+  static_assert(is_int_or_enum_v<A>, "Argument a must be an integer or enum");
+  static_assert(is_int_or_enum_v<B>, "Argument b must be an integer or enum");
+}
+
+/// \internal Gets the minimum of two values which may be integers or enums
+template <typename A, typename B>
+constexpr int plain_enum_min(A a, B b) {
+  plain_enum_asserts(a, b);
+  return ((int)a <= (int)b) ? (int)a : (int)b;
+}
+
+/// \internal Gets the maximum of two values which may be integers or enums
+template <typename A, typename B>
+constexpr int plain_enum_max(A a, B b) {
+  plain_enum_asserts(a, b);
+  return ((int)a >= (int)b) ? (int)a : (int)b;
+}
+
+/**
+ * \internal
+ *  `min_size_prefer_dynamic` gives the min between compile-time sizes. 0 has absolute priority, followed by 1,
+ *  followed by Dynamic, followed by other finite values. The reason for giving Dynamic the priority over
+ *  finite values is that min(3, Dynamic) should be Dynamic, since that could be anything between 0 and 3.
+ */
+template <typename A, typename B>
+constexpr int min_size_prefer_dynamic(A a, B b) {
+  plain_enum_asserts(a, b);
+  if ((int)a == 0 || (int)b == 0) return 0;
+  if ((int)a == 1 || (int)b == 1) return 1;
+  if ((int)a == Dynamic || (int)b == Dynamic) return Dynamic;
+  return plain_enum_min(a, b);
+}
+
+/**
+ * \internal
+ *  min_size_prefer_fixed is a variant of `min_size_prefer_dynamic` comparing MaxSizes. The difference is that finite
+ * values now have priority over Dynamic, so that min(3, Dynamic) gives 3. Indeed, whatever the actual value is (between
+ * 0 and 3), it is not more than 3.
+ */
+template <typename A, typename B>
+constexpr int min_size_prefer_fixed(A a, B b) {
+  plain_enum_asserts(a, b);
+  if ((int)a == 0 || (int)b == 0) return 0;
+  if ((int)a == 1 || (int)b == 1) return 1;
+  if ((int)a == Dynamic && (int)b == Dynamic) return Dynamic;
+  if ((int)a == Dynamic) return (int)b;
+  if ((int)b == Dynamic) return (int)a;
+  return plain_enum_min(a, b);
+}
+
+/// \internal see `min_size_prefer_fixed`. No need for a separate variant for MaxSizes here.
+template <typename A, typename B>
+constexpr int max_size_prefer_dynamic(A a, B b) {
+  plain_enum_asserts(a, b);
+  if ((int)a == Dynamic || (int)b == Dynamic) return Dynamic;
+  return plain_enum_max(a, b);
+}
+
+template <typename A, typename B>
+inline constexpr int size_prefer_fixed(A a, B b) {
+  plain_enum_asserts(a, b);
+  return int(a) == Dynamic ? int(b) : int(a);
+}
+
+template <typename A, typename B>
+inline constexpr bool enum_eq_not_dynamic(A a, B b) {
+  plain_enum_asserts(a, b);
+  if ((int)a == Dynamic || (int)b == Dynamic) return false;
+  return (int)a == (int)b;
+}
+
+template <typename A, typename B>
+constexpr bool enum_lt_not_dynamic(A a, B b) {
+  plain_enum_asserts(a, b);
+  if ((int)a == Dynamic || (int)b == Dynamic) return false;
+  return (int)a < (int)b;
+}
+
+template <typename A, typename B>
+constexpr bool enum_le_not_dynamic(A a, B b) {
+  plain_enum_asserts(a, b);
+  if ((int)a == Dynamic || (int)b == Dynamic) return false;
+  return (int)a <= (int)b;
+}
+
+template <typename A, typename B>
+constexpr bool enum_gt_not_dynamic(A a, B b) {
+  plain_enum_asserts(a, b);
+  if ((int)a == Dynamic || (int)b == Dynamic) return false;
+  return (int)a > (int)b;
+}
+
+template <typename A, typename B>
+constexpr bool enum_ge_not_dynamic(A a, B b) {
+  plain_enum_asserts(a, b);
+  if ((int)a == Dynamic || (int)b == Dynamic) return false;
+  return (int)a >= (int)b;
+}
+
+/// \internal Calculate logical XOR at compile time
+constexpr bool logical_xor(bool a, bool b) { return a != b; }
+
+/// \internal Calculate logical IMPLIES at compile time
+constexpr bool check_implication(bool a, bool b) { return !a || b; }
+
+/// \internal Provide fallback for std::is_constant_evaluated for pre-C++20.
+#if EIGEN_COMP_CXXVER >= 20 && defined(__cpp_lib_is_constant_evaluated) && __cpp_lib_is_constant_evaluated >= 201811L
+using std::is_constant_evaluated;
+#else
+constexpr bool is_constant_evaluated() { return false; }
+#endif
+
+template <typename Scalar>
+using make_complex_t = std::conditional_t<NumTraits<Scalar>::IsComplex, Scalar, std::complex<Scalar>>;
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_META_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/MoreMeta.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/MoreMeta.h
new file mode 100644
index 0000000..a20506e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/MoreMeta.h
@@ -0,0 +1,634 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MOREMETA_H
+#define EIGEN_MOREMETA_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename... tt>
+struct type_list {
+  constexpr static int count = sizeof...(tt);
+};
+
+template <typename t, typename... tt>
+struct type_list<t, tt...> {
+  constexpr static int count = sizeof...(tt) + 1;
+  typedef t first_type;
+};
+
+template <typename T, T... nn>
+struct numeric_list {
+  constexpr static std::size_t count = sizeof...(nn);
+};
+
+template <typename T, T n, T... nn>
+struct numeric_list<T, n, nn...> {
+  static constexpr std::size_t count = sizeof...(nn) + 1;
+  static constexpr T first_value = n;
+};
+
+// Ddoxygen doesn't like the recursive definition of gen_numeric_list.
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+/* numeric list constructors
+ *
+ * equivalencies:
+ *     constructor                                              result
+ *     typename gen_numeric_list<int, 5>::type                  numeric_list<int, 0,1,2,3,4>
+ *     typename gen_numeric_list_reversed<int, 5>::type         numeric_list<int, 4,3,2,1,0>
+ *     typename gen_numeric_list_swapped_pair<int, 5,1,2>::type numeric_list<int, 0,2,1,3,4>
+ *     typename gen_numeric_list_repeated<int, 0, 5>::type      numeric_list<int, 0,0,0,0,0>
+ */
+
+template <typename T, std::size_t n, T start = 0, T... ii>
+struct gen_numeric_list : gen_numeric_list<T, n - 1, start, start + n - 1, ii...> {};
+
+template <typename T, T start, T... ii>
+struct gen_numeric_list<T, 0, start, ii...> {
+  typedef numeric_list<T, ii...> type;
+};
+
+template <typename T, std::size_t n, T start = 0, T... ii>
+struct gen_numeric_list_reversed : gen_numeric_list_reversed<T, n - 1, start, ii..., start + n - 1> {};
+template <typename T, T start, T... ii>
+struct gen_numeric_list_reversed<T, 0, start, ii...> {
+  typedef numeric_list<T, ii...> type;
+};
+
+template <typename T, std::size_t n, T a, T b, T start = 0, T... ii>
+struct gen_numeric_list_swapped_pair
+    : gen_numeric_list_swapped_pair<T, n - 1, a, b, start,
+                                    (start + n - 1) == a ? b : ((start + n - 1) == b ? a : (start + n - 1)), ii...> {};
+template <typename T, T a, T b, T start, T... ii>
+struct gen_numeric_list_swapped_pair<T, 0, a, b, start, ii...> {
+  typedef numeric_list<T, ii...> type;
+};
+
+template <typename T, std::size_t n, T V, T... nn>
+struct gen_numeric_list_repeated : gen_numeric_list_repeated<T, n - 1, V, V, nn...> {};
+template <typename T, T V, T... nn>
+struct gen_numeric_list_repeated<T, 0, V, nn...> {
+  typedef numeric_list<T, nn...> type;
+};
+#else
+template <typename T, std::size_t n, T start = 0, T... ii>
+struct gen_numeric_list;
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+/* list manipulation: concatenate */
+
+template <class a, class b>
+struct concat;
+
+template <typename... as, typename... bs>
+struct concat<type_list<as...>, type_list<bs...>> {
+  typedef type_list<as..., bs...> type;
+};
+template <typename T, T... as, T... bs>
+struct concat<numeric_list<T, as...>, numeric_list<T, bs...>> {
+  typedef numeric_list<T, as..., bs...> type;
+};
+
+template <typename... p>
+struct mconcat;
+template <typename a>
+struct mconcat<a> {
+  typedef a type;
+};
+template <typename a, typename b>
+struct mconcat<a, b> : concat<a, b> {};
+template <typename a, typename b, typename... cs>
+struct mconcat<a, b, cs...> : concat<a, typename mconcat<b, cs...>::type> {};
+
+/* list manipulation: extract slices */
+
+template <int n, typename x>
+struct take;
+
+template <int n, typename a, typename... as>
+struct take<n, type_list<a, as...>> : concat<type_list<a>, typename take<n - 1, type_list<as...>>::type> {};
+
+template <int n>
+struct take<n, type_list<>> {
+  typedef type_list<> type;
+};
+
+template <typename a, typename... as>
+struct take<0, type_list<a, as...>> {
+  typedef type_list<> type;
+};
+
+template <>
+struct take<0, type_list<>> {
+  typedef type_list<> type;
+};
+
+template <typename T, int n, T a, T... as>
+struct take<n, numeric_list<T, a, as...>>
+    : concat<numeric_list<T, a>, typename take<n - 1, numeric_list<T, as...>>::type> {};
+
+template <typename T, T a, T... as>
+struct take<0, numeric_list<T, a, as...>> {
+  typedef numeric_list<T> type;
+};
+
+template <typename T>
+struct take<0, numeric_list<T>> {
+  typedef numeric_list<T> type;
+};
+
+template <typename T, int n, T... ii>
+struct h_skip_helper_numeric;
+template <typename T, int n, T i, T... ii>
+struct h_skip_helper_numeric<T, n, i, ii...> : h_skip_helper_numeric<T, n - 1, ii...> {};
+template <typename T, T i, T... ii>
+struct h_skip_helper_numeric<T, 0, i, ii...> {
+  typedef numeric_list<T, i, ii...> type;
+};
+template <typename T, int n>
+struct h_skip_helper_numeric<T, n> {
+  typedef numeric_list<T> type;
+};
+template <typename T>
+struct h_skip_helper_numeric<T, 0> {
+  typedef numeric_list<T> type;
+};
+
+template <int n, typename... tt>
+struct h_skip_helper_type;
+template <int n, typename t, typename... tt>
+struct h_skip_helper_type<n, t, tt...> : h_skip_helper_type<n - 1, tt...> {};
+template <typename t, typename... tt>
+struct h_skip_helper_type<0, t, tt...> {
+  typedef type_list<t, tt...> type;
+};
+template <int n>
+struct h_skip_helper_type<n> {
+  typedef type_list<> type;
+};
+template <>
+struct h_skip_helper_type<0> {
+  typedef type_list<> type;
+};
+
+template <int n>
+struct h_skip {
+  template <typename T, T... ii>
+  constexpr static typename h_skip_helper_numeric<T, n, ii...>::type helper(numeric_list<T, ii...>) {
+    return typename h_skip_helper_numeric<T, n, ii...>::type();
+  }
+  template <typename... tt>
+  constexpr static typename h_skip_helper_type<n, tt...>::type helper(type_list<tt...>) {
+    return typename h_skip_helper_type<n, tt...>::type();
+  }
+};
+
+template <int n, typename a>
+struct skip {
+  typedef decltype(h_skip<n>::helper(a())) type;
+};
+
+template <int start, int count, typename a>
+struct slice : take<count, typename skip<start, a>::type> {};
+
+/* list manipulation: retrieve single element from list */
+
+template <int n, typename x>
+struct get;
+
+template <int n, typename a, typename... as>
+struct get<n, type_list<a, as...>> : get<n - 1, type_list<as...>> {};
+template <typename a, typename... as>
+struct get<0, type_list<a, as...>> {
+  typedef a type;
+};
+
+template <typename T, int n, T a, T... as>
+struct get<n, numeric_list<T, a, as...>> : get<n - 1, numeric_list<T, as...>> {};
+template <typename T, T a, T... as>
+struct get<0, numeric_list<T, a, as...>> {
+  constexpr static T value = a;
+};
+
+template <std::size_t n, typename T, T a, T... as>
+constexpr T array_get(const numeric_list<T, a, as...>&) {
+  return get<(int)n, numeric_list<T, a, as...>>::value;
+}
+
+/* always get type, regardless of dummy; good for parameter pack expansion */
+
+template <typename T, T dummy, typename t>
+struct id_numeric {
+  typedef t type;
+};
+template <typename dummy, typename t>
+struct id_type {
+  typedef t type;
+};
+
+/* equality checking, flagged version */
+
+template <typename a, typename b>
+struct is_same_gf : is_same<a, b> {
+  constexpr static int global_flags = 0;
+};
+
+/* apply_op to list */
+
+template <bool from_left,  // false
+          template <typename, typename> class op, typename additional_param, typename... values>
+struct h_apply_op_helper {
+  typedef type_list<typename op<values, additional_param>::type...> type;
+};
+template <template <typename, typename> class op, typename additional_param, typename... values>
+struct h_apply_op_helper<true, op, additional_param, values...> {
+  typedef type_list<typename op<additional_param, values>::type...> type;
+};
+
+template <bool from_left, template <typename, typename> class op, typename additional_param>
+struct h_apply_op {
+  template <typename... values>
+  constexpr static typename h_apply_op_helper<from_left, op, additional_param, values...>::type helper(
+      type_list<values...>) {
+    return typename h_apply_op_helper<from_left, op, additional_param, values...>::type();
+  }
+};
+
+template <template <typename, typename> class op, typename additional_param, typename a>
+struct apply_op_from_left {
+  typedef decltype(h_apply_op<true, op, additional_param>::helper(a())) type;
+};
+
+template <template <typename, typename> class op, typename additional_param, typename a>
+struct apply_op_from_right {
+  typedef decltype(h_apply_op<false, op, additional_param>::helper(a())) type;
+};
+
+/* see if an element is in a list */
+
+template <template <typename, typename> class test, typename check_against, typename h_list,
+          bool last_check_positive = false>
+struct contained_in_list;
+
+template <template <typename, typename> class test, typename check_against, typename h_list>
+struct contained_in_list<test, check_against, h_list, true> {
+  constexpr static bool value = true;
+};
+
+template <template <typename, typename> class test, typename check_against, typename a, typename... as>
+struct contained_in_list<test, check_against, type_list<a, as...>, false>
+    : contained_in_list<test, check_against, type_list<as...>, test<check_against, a>::value> {};
+
+template <template <typename, typename> class test, typename check_against, typename... empty>
+struct contained_in_list<test, check_against, type_list<empty...>, false> {
+  constexpr static bool value = false;
+};
+
+/* see if an element is in a list and check for global flags */
+
+template <template <typename, typename> class test, typename check_against, typename h_list, int default_flags = 0,
+          bool last_check_positive = false, int last_check_flags = default_flags>
+struct contained_in_list_gf;
+
+template <template <typename, typename> class test, typename check_against, typename h_list, int default_flags,
+          int last_check_flags>
+struct contained_in_list_gf<test, check_against, h_list, default_flags, true, last_check_flags> {
+  constexpr static bool value = true;
+  constexpr static int global_flags = last_check_flags;
+};
+
+template <template <typename, typename> class test, typename check_against, typename a, typename... as,
+          int default_flags, int last_check_flags>
+struct contained_in_list_gf<test, check_against, type_list<a, as...>, default_flags, false, last_check_flags>
+    : contained_in_list_gf<test, check_against, type_list<as...>, default_flags, test<check_against, a>::value,
+                           test<check_against, a>::global_flags> {};
+
+template <template <typename, typename> class test, typename check_against, typename... empty, int default_flags,
+          int last_check_flags>
+struct contained_in_list_gf<test, check_against, type_list<empty...>, default_flags, false, last_check_flags> {
+  constexpr static bool value = false;
+  constexpr static int global_flags = default_flags;
+};
+
+/* generic reductions */
+
+template <typename Reducer, typename... Ts>
+struct reduce;
+
+template <typename Reducer>
+struct reduce<Reducer> {
+  EIGEN_DEVICE_FUNC constexpr static int run() { return Reducer::Identity; }
+};
+
+template <typename Reducer, typename A>
+struct reduce<Reducer, A> {
+  EIGEN_DEVICE_FUNC constexpr static A run(A a) { return a; }
+};
+
+template <typename Reducer, typename A, typename... Ts>
+struct reduce<Reducer, A, Ts...> {
+  EIGEN_DEVICE_FUNC constexpr static auto run(A a, Ts... ts)
+      -> decltype(Reducer::run(a, reduce<Reducer, Ts...>::run(ts...))) {
+    return Reducer::run(a, reduce<Reducer, Ts...>::run(ts...));
+  }
+};
+
+/* generic binary operations */
+
+struct sum_op {
+  template <typename A, typename B>
+  EIGEN_DEVICE_FUNC constexpr static auto run(A a, B b) -> decltype(a + b) {
+    return a + b;
+  }
+  static constexpr int Identity = 0;
+};
+struct product_op {
+  template <typename A, typename B>
+  EIGEN_DEVICE_FUNC constexpr static auto run(A a, B b) -> decltype(a * b) {
+    return a * b;
+  }
+  static constexpr int Identity = 1;
+};
+
+struct logical_and_op {
+  template <typename A, typename B>
+  constexpr static auto run(A a, B b) -> decltype(a && b) {
+    return a && b;
+  }
+};
+struct logical_or_op {
+  template <typename A, typename B>
+  constexpr static auto run(A a, B b) -> decltype(a || b) {
+    return a || b;
+  }
+};
+
+struct equal_op {
+  template <typename A, typename B>
+  constexpr static auto run(A a, B b) -> decltype(a == b) {
+    return a == b;
+  }
+};
+struct not_equal_op {
+  template <typename A, typename B>
+  constexpr static auto run(A a, B b) -> decltype(a != b) {
+    return a != b;
+  }
+};
+struct lesser_op {
+  template <typename A, typename B>
+  constexpr static auto run(A a, B b) -> decltype(a < b) {
+    return a < b;
+  }
+};
+struct lesser_equal_op {
+  template <typename A, typename B>
+  constexpr static auto run(A a, B b) -> decltype(a <= b) {
+    return a <= b;
+  }
+};
+struct greater_op {
+  template <typename A, typename B>
+  constexpr static auto run(A a, B b) -> decltype(a > b) {
+    return a > b;
+  }
+};
+struct greater_equal_op {
+  template <typename A, typename B>
+  constexpr static auto run(A a, B b) -> decltype(a >= b) {
+    return a >= b;
+  }
+};
+
+/* generic unary operations */
+
+struct not_op {
+  template <typename A>
+  constexpr static auto run(A a) -> decltype(!a) {
+    return !a;
+  }
+};
+struct negation_op {
+  template <typename A>
+  constexpr static auto run(A a) -> decltype(-a) {
+    return -a;
+  }
+};
+struct greater_equal_zero_op {
+  template <typename A>
+  constexpr static auto run(A a) -> decltype(a >= 0) {
+    return a >= 0;
+  }
+};
+
+/* reductions for lists */
+
+// Using auto -> return value spec makes ICC 13.0 and 13.1 crash here,
+// so the return type is specified explicitly using decltype.
+template <typename... Ts>
+EIGEN_DEVICE_FUNC constexpr decltype(reduce<product_op, Ts...>::run((*((Ts*)0))...)) arg_prod(Ts... ts) {
+  return reduce<product_op, Ts...>::run(ts...);
+}
+
+template <typename... Ts>
+constexpr decltype(reduce<sum_op, Ts...>::run((*((Ts*)0))...)) arg_sum(Ts... ts) {
+  return reduce<sum_op, Ts...>::run(ts...);
+}
+
+/* reverse arrays */
+
+template <typename Array, int... n>
+constexpr Array h_array_reverse(Array arr, numeric_list<int, n...>) {
+  return {{array_get<sizeof...(n) - n - 1>(arr)...}};
+}
+
+template <typename T, std::size_t N>
+constexpr array<T, N> array_reverse(array<T, N> arr) {
+  return h_array_reverse(arr, typename gen_numeric_list<int, N>::type());
+}
+
+/* generic array reductions */
+
+// can't reuse standard reduce() interface above because Intel's Compiler
+// *really* doesn't like it, so we just reimplement the stuff
+// (start from N - 1 and work down to 0 because specialization for
+// n == N - 1 also doesn't work in Intel's compiler, so it goes into
+// an infinite loop)
+template <typename Reducer, typename T, std::size_t N, std::size_t n = N - 1>
+struct h_array_reduce {
+  EIGEN_DEVICE_FUNC constexpr static auto run(array<T, N> arr, T identity)
+      -> decltype(Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr, identity), array_get<n>(arr))) {
+    return Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr, identity), array_get<n>(arr));
+  }
+};
+
+template <typename Reducer, typename T, std::size_t N>
+struct h_array_reduce<Reducer, T, N, 0> {
+  EIGEN_DEVICE_FUNC constexpr static T run(const array<T, N>& arr, T) { return array_get<0>(arr); }
+};
+
+template <typename Reducer, typename T>
+struct h_array_reduce<Reducer, T, 0> {
+  EIGEN_DEVICE_FUNC constexpr static T run(const array<T, 0>&, T identity) { return identity; }
+};
+
+template <typename Reducer, typename T, std::size_t N>
+EIGEN_DEVICE_FUNC constexpr auto array_reduce(const array<T, N>& arr, T identity)
+    -> decltype(h_array_reduce<Reducer, T, N>::run(arr, identity)) {
+  return h_array_reduce<Reducer, T, N>::run(arr, identity);
+}
+
+/* standard array reductions */
+
+template <typename T, std::size_t N>
+EIGEN_DEVICE_FUNC constexpr auto array_sum(const array<T, N>& arr)
+    -> decltype(array_reduce<sum_op, T, N>(arr, static_cast<T>(0))) {
+  return array_reduce<sum_op, T, N>(arr, static_cast<T>(0));
+}
+
+template <typename T, std::size_t N>
+EIGEN_DEVICE_FUNC constexpr auto array_prod(const array<T, N>& arr)
+    -> decltype(array_reduce<product_op, T, N>(arr, static_cast<T>(1))) {
+  return array_reduce<product_op, T, N>(arr, static_cast<T>(1));
+}
+
+template <typename t>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE t array_prod(const std::vector<t>& a) {
+  eigen_assert(a.size() > 0);
+  t prod = 1;
+  for (size_t i = 0; i < a.size(); ++i) {
+    prod *= a[i];
+  }
+  return prod;
+}
+
+/* zip an array */
+
+template <typename Op, typename A, typename B, std::size_t N, int... n>
+constexpr array<decltype(Op::run(A(), B())), N> h_array_zip(array<A, N> a, array<B, N> b, numeric_list<int, n...>) {
+  return array<decltype(Op::run(A(), B())), N>{{Op::run(array_get<n>(a), array_get<n>(b))...}};
+}
+
+template <typename Op, typename A, typename B, std::size_t N>
+constexpr array<decltype(Op::run(A(), B())), N> array_zip(array<A, N> a, array<B, N> b) {
+  return h_array_zip<Op>(a, b, typename gen_numeric_list<int, N>::type());
+}
+
+/* zip an array and reduce the result */
+
+template <typename Reducer, typename Op, typename A, typename B, std::size_t N, int... n>
+constexpr auto h_array_zip_and_reduce(array<A, N> a, array<B, N> b, numeric_list<int, n...>)
+    -> decltype(reduce<Reducer, typename id_numeric<int, n, decltype(Op::run(A(), B()))>::type...>::run(
+        Op::run(array_get<n>(a), array_get<n>(b))...)) {
+  return reduce<Reducer, typename id_numeric<int, n, decltype(Op::run(A(), B()))>::type...>::run(
+      Op::run(array_get<n>(a), array_get<n>(b))...);
+}
+
+template <typename Reducer, typename Op, typename A, typename B, std::size_t N>
+constexpr auto array_zip_and_reduce(array<A, N> a, array<B, N> b)
+    -> decltype(h_array_zip_and_reduce<Reducer, Op, A, B, N>(a, b, typename gen_numeric_list<int, N>::type())) {
+  return h_array_zip_and_reduce<Reducer, Op, A, B, N>(a, b, typename gen_numeric_list<int, N>::type());
+}
+
+/* apply stuff to an array */
+
+template <typename Op, typename A, std::size_t N, int... n>
+constexpr array<decltype(Op::run(A())), N> h_array_apply(array<A, N> a, numeric_list<int, n...>) {
+  return array<decltype(Op::run(A())), N>{{Op::run(array_get<n>(a))...}};
+}
+
+template <typename Op, typename A, std::size_t N>
+constexpr array<decltype(Op::run(A())), N> array_apply(array<A, N> a) {
+  return h_array_apply<Op>(a, typename gen_numeric_list<int, N>::type());
+}
+
+/* apply stuff to an array and reduce */
+
+template <typename Reducer, typename Op, typename A, std::size_t N, int... n>
+constexpr auto h_array_apply_and_reduce(array<A, N> arr, numeric_list<int, n...>)
+    -> decltype(reduce<Reducer, typename id_numeric<int, n, decltype(Op::run(A()))>::type...>::run(
+        Op::run(array_get<n>(arr))...)) {
+  return reduce<Reducer, typename id_numeric<int, n, decltype(Op::run(A()))>::type...>::run(
+      Op::run(array_get<n>(arr))...);
+}
+
+template <typename Reducer, typename Op, typename A, std::size_t N>
+constexpr auto array_apply_and_reduce(array<A, N> a)
+    -> decltype(h_array_apply_and_reduce<Reducer, Op, A, N>(a, typename gen_numeric_list<int, N>::type())) {
+  return h_array_apply_and_reduce<Reducer, Op, A, N>(a, typename gen_numeric_list<int, N>::type());
+}
+
+/* repeat a value n times (and make an array out of it
+ * usage:
+ *   array<int, 16> = repeat<16>(42);
+ */
+
+template <int n>
+struct h_repeat {
+  template <typename t, int... ii>
+  constexpr static array<t, n> run(t v, numeric_list<int, ii...>) {
+    return {{typename id_numeric<int, ii, t>::type(v)...}};
+  }
+};
+
+template <int n, typename t>
+constexpr array<t, n> repeat(t v) {
+  return h_repeat<n>::run(v, typename gen_numeric_list<int, n>::type());
+}
+
+/* instantiate a class by a C-style array */
+template <class InstType, typename ArrType, std::size_t N, bool Reverse, typename... Ps>
+struct h_instantiate_by_c_array;
+
+template <class InstType, typename ArrType, std::size_t N, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, N, false, Ps...> {
+  static InstType run(ArrType* arr, Ps... args) {
+    return h_instantiate_by_c_array<InstType, ArrType, N - 1, false, Ps..., ArrType>::run(arr + 1, args..., arr[0]);
+  }
+};
+
+template <class InstType, typename ArrType, std::size_t N, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, N, true, Ps...> {
+  static InstType run(ArrType* arr, Ps... args) {
+    return h_instantiate_by_c_array<InstType, ArrType, N - 1, false, ArrType, Ps...>::run(arr + 1, arr[0], args...);
+  }
+};
+
+template <class InstType, typename ArrType, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, 0, false, Ps...> {
+  static InstType run(ArrType* arr, Ps... args) {
+    (void)arr;
+    return InstType(args...);
+  }
+};
+
+template <class InstType, typename ArrType, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, 0, true, Ps...> {
+  static InstType run(ArrType* arr, Ps... args) {
+    (void)arr;
+    return InstType(args...);
+  }
+};
+
+template <class InstType, typename ArrType, std::size_t N, bool Reverse = false>
+InstType instantiate_by_c_array(ArrType* arr) {
+  return h_instantiate_by_c_array<InstType, ArrType, N, Reverse>::run(arr);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MOREMETA_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ReenableStupidWarnings.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ReenableStupidWarnings.h
new file mode 100644
index 0000000..0af5a43
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ReenableStupidWarnings.h
@@ -0,0 +1,44 @@
+#ifdef EIGEN_WARNINGS_DISABLED_2
+// "DisableStupidWarnings.h" was included twice recursively: Do not re-enable warnings yet!
+#undef EIGEN_WARNINGS_DISABLED_2
+
+#elif defined(EIGEN_WARNINGS_DISABLED)
+#undef EIGEN_WARNINGS_DISABLED
+
+#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
+#ifdef _MSC_VER
+#pragma warning(pop)
+#ifdef EIGEN_REENABLE_CXX23_DENORM_DEPRECATION_WARNING
+#undef EIGEN_REENABLE_CXX23_DENORM_DEPRECATION_WARNING
+#undef _SILENCE_CXX23_DENORM_DEPRECATION_WARNING
+#endif
+
+#elif defined __INTEL_COMPILER
+#pragma warning pop
+#elif defined __clang__
+#pragma clang diagnostic pop
+#elif defined __GNUC__ && !defined(__FUJITSU) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
+#pragma GCC diagnostic pop
+#endif
+
+#if defined __NVCC__
+//    Don't re-enable the diagnostic messages, as it turns out these messages need
+//    to be disabled at the point of the template instantiation (i.e the user code)
+//    otherwise they'll be triggered by nvcc.
+//    #define EIGEN_MAKE_PRAGMA(X) _Pragma(#X)
+//    #if __NVCC_DIAG_PRAGMA_SUPPORT__
+//      #define EIGEN_NV_DIAG_DEFAULT(X) EIGEN_MAKE_PRAGMA(nv_diag_default X)
+//    #else
+//      #define EIGEN_NV_DIAG_DEFAULT(X) EIGEN_MAKE_PRAGMA(diag_default X)
+//    #endif
+//    EIGEN_NV_DIAG_DEFAULT(code_is_unreachable)
+//    EIGEN_NV_DIAG_DEFAULT(initialization_not_reachable)
+//    EIGEN_NV_DIAG_DEFAULT(2651)
+//    EIGEN_NV_DIAG_DEFAULT(2653)
+//    #undef EIGEN_NV_DIAG_DEFAULT
+//    #undef EIGEN_MAKE_PRAGMA
+#endif
+
+#endif
+
+#endif  // EIGEN_WARNINGS_DISABLED
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ReshapedHelper.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ReshapedHelper.h
new file mode 100644
index 0000000..1747950
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/ReshapedHelper.h
@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RESHAPED_HELPER_H
+#define EIGEN_RESHAPED_HELPER_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum AutoSize_t { AutoSize };
+const int AutoOrder = 2;
+
+namespace internal {
+
+template <typename SizeType, typename OtherSize, int TotalSize>
+struct get_compiletime_reshape_size {
+  enum { value = get_fixed_value<SizeType>::value };
+};
+
+template <typename SizeType>
+Index get_runtime_reshape_size(SizeType size, Index /*other*/, Index /*total*/) {
+  return internal::get_runtime_value(size);
+}
+
+template <typename OtherSize, int TotalSize>
+struct get_compiletime_reshape_size<AutoSize_t, OtherSize, TotalSize> {
+  enum {
+    other_size = get_fixed_value<OtherSize>::value,
+    value = (TotalSize == Dynamic || other_size == Dynamic) ? Dynamic : TotalSize / other_size
+  };
+};
+
+inline Index get_runtime_reshape_size(AutoSize_t /*size*/, Index other, Index total) { return total / other; }
+
+constexpr int get_compiletime_reshape_order(int flags, int order) {
+  return order == AutoOrder ? flags & RowMajorBit : order;
+}
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_RESHAPED_HELPER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Serializer.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Serializer.h
new file mode 100644
index 0000000..72ce6cb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/Serializer.h
@@ -0,0 +1,209 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2021 The Eigen Team
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SERIALIZER_H
+#define EIGEN_SERIALIZER_H
+
+#include <type_traits>
+
+// The Serializer class encodes data into a memory buffer so it can be later
+// reconstructed. This is mainly used to send objects back-and-forth between
+// the CPU and GPU.
+
+namespace Eigen {
+
+/**
+ * Serializes an object to a memory buffer.
+ *
+ * Useful for transferring data (e.g. back-and-forth to a device).
+ */
+template <typename T, typename EnableIf = void>
+class Serializer;
+
+// Specialization for POD types.
+template <typename T>
+class Serializer<T,
+                 typename std::enable_if_t<std::is_trivially_copyable<T>::value && std::is_standard_layout<T>::value>> {
+ public:
+  /**
+   * Determines the required size of the serialization buffer for a value.
+   *
+   * \param value the value to serialize.
+   * \return the required size.
+   */
+  EIGEN_DEVICE_FUNC size_t size(const T& value) const { return sizeof(value); }
+
+  /**
+   * Serializes a value to a byte buffer.
+   * \param dest the destination buffer; if this is nullptr, does nothing.
+   * \param end the end of the destination buffer.
+   * \param value the value to serialize.
+   * \return the next memory address past the end of the serialized data.
+   */
+  EIGEN_DEVICE_FUNC uint8_t* serialize(uint8_t* dest, uint8_t* end, const T& value) {
+    if (EIGEN_PREDICT_FALSE(dest == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(dest + sizeof(value) > end)) return nullptr;
+    EIGEN_USING_STD(memcpy)
+    memcpy(dest, &value, sizeof(value));
+    return dest + sizeof(value);
+  }
+
+  /**
+   * Deserializes a value from a byte buffer.
+   * \param src the source buffer; if this is nullptr, does nothing.
+   * \param end the end of the source buffer.
+   * \param value the value to populate.
+   * \return the next unprocessed memory address; nullptr if parsing errors are detected.
+   */
+  EIGEN_DEVICE_FUNC const uint8_t* deserialize(const uint8_t* src, const uint8_t* end, T& value) const {
+    if (EIGEN_PREDICT_FALSE(src == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(src + sizeof(value) > end)) return nullptr;
+    EIGEN_USING_STD(memcpy)
+    memcpy(&value, src, sizeof(value));
+    return src + sizeof(value);
+  }
+};
+
+// Specialization for DenseBase.
+// Serializes [rows, cols, data...].
+template <typename Derived>
+class Serializer<DenseBase<Derived>, void> {
+ public:
+  typedef typename Derived::Scalar Scalar;
+
+  struct Header {
+    typename Derived::Index rows;
+    typename Derived::Index cols;
+  };
+
+  EIGEN_DEVICE_FUNC size_t size(const Derived& value) const { return sizeof(Header) + sizeof(Scalar) * value.size(); }
+
+  EIGEN_DEVICE_FUNC uint8_t* serialize(uint8_t* dest, uint8_t* end, const Derived& value) {
+    if (EIGEN_PREDICT_FALSE(dest == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(dest + size(value) > end)) return nullptr;
+    const size_t header_bytes = sizeof(Header);
+    const size_t data_bytes = sizeof(Scalar) * value.size();
+    Header header = {value.rows(), value.cols()};
+    EIGEN_USING_STD(memcpy)
+    memcpy(dest, &header, header_bytes);
+    dest += header_bytes;
+    memcpy(dest, value.data(), data_bytes);
+    return dest + data_bytes;
+  }
+
+  EIGEN_DEVICE_FUNC const uint8_t* deserialize(const uint8_t* src, const uint8_t* end, Derived& value) const {
+    if (EIGEN_PREDICT_FALSE(src == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(src + sizeof(Header) > end)) return nullptr;
+    const size_t header_bytes = sizeof(Header);
+    Header header;
+    EIGEN_USING_STD(memcpy)
+    memcpy(&header, src, header_bytes);
+    src += header_bytes;
+    const size_t data_bytes = sizeof(Scalar) * header.rows * header.cols;
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    value.resize(header.rows, header.cols);
+    memcpy(value.data(), src, data_bytes);
+    return src + data_bytes;
+  }
+};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+class Serializer<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols>>
+    : public Serializer<DenseBase<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols>>> {};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+class Serializer<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols>>
+    : public Serializer<DenseBase<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols>>> {};
+
+namespace internal {
+
+// Recursive serialization implementation helper.
+template <size_t N, typename... Types>
+struct serialize_impl;
+
+template <size_t N, typename T1, typename... Ts>
+struct serialize_impl<N, T1, Ts...> {
+  using Serializer = Eigen::Serializer<std::decay_t<T1>>;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t serialize_size(const T1& value, const Ts&... args) {
+    Serializer serializer;
+    size_t size = serializer.size(value);
+    return size + serialize_impl<N - 1, Ts...>::serialize_size(args...);
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE uint8_t* serialize(uint8_t* dest, uint8_t* end, const T1& value,
+                                                                  const Ts&... args) {
+    Serializer serializer;
+    dest = serializer.serialize(dest, end, value);
+    return serialize_impl<N - 1, Ts...>::serialize(dest, end, args...);
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const uint8_t* deserialize(const uint8_t* src, const uint8_t* end,
+                                                                          T1& value, Ts&... args) {
+    Serializer serializer;
+    src = serializer.deserialize(src, end, value);
+    return serialize_impl<N - 1, Ts...>::deserialize(src, end, args...);
+  }
+};
+
+// Base case.
+template <>
+struct serialize_impl<0> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t serialize_size() { return 0; }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE uint8_t* serialize(uint8_t* dest, uint8_t* /*end*/) { return dest; }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const uint8_t* deserialize(const uint8_t* src, const uint8_t* /*end*/) {
+    return src;
+  }
+};
+
+}  // namespace internal
+
+/**
+ * Determine the buffer size required to serialize a set of values.
+ *
+ * \param args ... arguments to serialize in sequence.
+ * \return the total size of the required buffer.
+ */
+template <typename... Args>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t serialize_size(const Args&... args) {
+  return internal::serialize_impl<sizeof...(args), Args...>::serialize_size(args...);
+}
+
+/**
+ * Serialize a set of values to the byte buffer.
+ *
+ * \param dest output byte buffer; if this is nullptr, does nothing.
+ * \param end the end of the output byte buffer.
+ * \param args ... arguments to serialize in sequence.
+ * \return the next address after all serialized values.
+ */
+template <typename... Args>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE uint8_t* serialize(uint8_t* dest, uint8_t* end, const Args&... args) {
+  return internal::serialize_impl<sizeof...(args), Args...>::serialize(dest, end, args...);
+}
+
+/**
+ * Deserialize a set of values from the byte buffer.
+ *
+ * \param src input byte buffer; if this is nullptr, does nothing.
+ * \param end the end of input byte buffer.
+ * \param args ... arguments to deserialize in sequence.
+ * \return the next address after all parsed values; nullptr if parsing errors are detected.
+ */
+template <typename... Args>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const uint8_t* deserialize(const uint8_t* src, const uint8_t* end,
+                                                                 Args&... args) {
+  return internal::serialize_impl<sizeof...(args), Args...>::deserialize(src, end, args...);
+}
+
+}  // namespace Eigen
+
+#endif  // EIGEN_SERIALIZER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/StaticAssert.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/StaticAssert.h
new file mode 100644
index 0000000..f062354
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/StaticAssert.h
@@ -0,0 +1,105 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STATIC_ASSERT_H
+#define EIGEN_STATIC_ASSERT_H
+
+/* Some notes on Eigen's static assertion mechanism:
+ *
+ *  - in EIGEN_STATIC_ASSERT(CONDITION,MSG) the parameter CONDITION must be a compile time boolean
+ *    expression, and MSG an enum listed in struct internal::static_assertion<true>
+ *
+ *  - currently EIGEN_STATIC_ASSERT can only be used in function scope
+ *
+ */
+
+#ifndef EIGEN_STATIC_ASSERT
+#ifndef EIGEN_NO_STATIC_ASSERT
+
+#define EIGEN_STATIC_ASSERT(X, MSG) static_assert(X, #MSG);
+
+#else  // EIGEN_NO_STATIC_ASSERT
+
+#define EIGEN_STATIC_ASSERT(CONDITION, MSG)
+
+#endif  // EIGEN_NO_STATIC_ASSERT
+#endif  // EIGEN_STATIC_ASSERT
+
+// static assertion failing if the type \a TYPE is not a vector type
+#define EIGEN_STATIC_ASSERT_VECTOR_ONLY(TYPE) \
+  EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime, YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX)
+
+// static assertion failing if the type \a TYPE is not fixed-size
+#define EIGEN_STATIC_ASSERT_FIXED_SIZE(TYPE)                     \
+  EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime != Eigen::Dynamic, \
+                      YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR)
+
+// static assertion failing if the type \a TYPE is not dynamic-size
+#define EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(TYPE)                   \
+  EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime == Eigen::Dynamic, \
+                      YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR)
+
+// static assertion failing if the type \a TYPE is not a vector type of the given size
+#define EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(TYPE, SIZE)                         \
+  EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime&& TYPE::SizeAtCompileTime == SIZE, \
+                      THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE)
+
+// static assertion failing if the type \a TYPE is not a vector type of the given size
+#define EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(TYPE, ROWS, COLS)                        \
+  EIGEN_STATIC_ASSERT(TYPE::RowsAtCompileTime == ROWS && TYPE::ColsAtCompileTime == COLS, \
+                      THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE)
+
+// static assertion failing if the two vector expression types are not compatible (same fixed-size or dynamic size)
+#define EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(TYPE0, TYPE1)                                                   \
+  EIGEN_STATIC_ASSERT(                                                                                       \
+      (int(TYPE0::SizeAtCompileTime) == Eigen::Dynamic || int(TYPE1::SizeAtCompileTime) == Eigen::Dynamic || \
+       int(TYPE0::SizeAtCompileTime) == int(TYPE1::SizeAtCompileTime)),                                      \
+      YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES)
+
+#define EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0, TYPE1)                                                     \
+  ((int(Eigen::internal::size_of_xpr_at_compile_time<TYPE0>::ret) == 0 &&                                  \
+    int(Eigen::internal::size_of_xpr_at_compile_time<TYPE1>::ret) == 0) ||                                 \
+   ((int(TYPE0::RowsAtCompileTime) == Eigen::Dynamic || int(TYPE1::RowsAtCompileTime) == Eigen::Dynamic || \
+     int(TYPE0::RowsAtCompileTime) == int(TYPE1::RowsAtCompileTime)) &&                                    \
+    (int(TYPE0::ColsAtCompileTime) == Eigen::Dynamic || int(TYPE1::ColsAtCompileTime) == Eigen::Dynamic || \
+     int(TYPE0::ColsAtCompileTime) == int(TYPE1::ColsAtCompileTime))))
+
+#define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \
+  EIGEN_STATIC_ASSERT(!Eigen::NumTraits<TYPE>::IsInteger, THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
+
+// static assertion failing if it is guaranteed at compile-time that the two matrix expression types have different
+// sizes
+#define EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(TYPE0, TYPE1) \
+  EIGEN_STATIC_ASSERT(EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0, TYPE1), YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES)
+
+#define EIGEN_STATIC_ASSERT_SIZE_1x1(TYPE)                                                             \
+  EIGEN_STATIC_ASSERT((TYPE::RowsAtCompileTime == 1 || TYPE::RowsAtCompileTime == Eigen::Dynamic) &&   \
+                          (TYPE::ColsAtCompileTime == 1 || TYPE::ColsAtCompileTime == Eigen::Dynamic), \
+                      THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS)
+
+#define EIGEN_STATIC_ASSERT_LVALUE(Derived) \
+  EIGEN_STATIC_ASSERT(Eigen::internal::is_lvalue<Derived>::value, THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY)
+
+#define EIGEN_STATIC_ASSERT_ARRAYXPR(Derived)                                                                          \
+  EIGEN_STATIC_ASSERT((Eigen::internal::is_same<typename Eigen::internal::traits<Derived>::XprKind, ArrayXpr>::value), \
+                      THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES)
+
+#define EIGEN_STATIC_ASSERT_SAME_XPR_KIND(Derived1, Derived2)                                                 \
+  EIGEN_STATIC_ASSERT((Eigen::internal::is_same<typename Eigen::internal::traits<Derived1>::XprKind,          \
+                                                typename Eigen::internal::traits<Derived2>::XprKind>::value), \
+                      YOU_CANNOT_MIX_ARRAYS_AND_MATRICES)
+
+// Check that a cost value is positive, and that is stay within a reasonable range
+// TODO this check could be enabled for internal debugging only
+#define EIGEN_INTERNAL_CHECK_COST_VALUE(C)                    \
+  EIGEN_STATIC_ASSERT((C) >= 0 && (C) <= HugeCost * HugeCost, \
+                      EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT__INVALID_COST_VALUE);
+
+#endif  // EIGEN_STATIC_ASSERT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/SymbolicIndex.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/SymbolicIndex.h
new file mode 100644
index 0000000..a90e2bb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/SymbolicIndex.h
@@ -0,0 +1,445 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SYMBOLIC_INDEX_H
+#define EIGEN_SYMBOLIC_INDEX_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \namespace Eigen::symbolic
+ * \ingroup Core_Module
+ *
+ * This namespace defines a set of classes and functions to build and evaluate symbolic expressions of scalar type
+ * Index. Here is a simple example:
+ *
+ * \code
+ * // First step, defines symbols:
+ * struct x_tag {};  static const symbolic::SymbolExpr<x_tag> x;
+ * struct y_tag {};  static const symbolic::SymbolExpr<y_tag> y;
+ * struct z_tag {};  static const symbolic::SymbolExpr<z_tag> z;
+ *
+ * // Defines an expression:
+ * auto expr = (x+3)/y+z;
+ *
+ * // And evaluate it: (c++14)
+ * std::cout << expr.eval(x=6,y=3,z=-13) << "\n";
+ *
+ * \endcode
+ *
+ * It is currently only used internally to define and manipulate the
+ * Eigen::placeholders::last and Eigen::placeholders::lastp1 symbols in
+ * Eigen::seq and Eigen::seqN.
+ *
+ */
+namespace symbolic {
+
+template <typename Tag>
+class Symbol;
+template <typename Tag, typename Type>
+class SymbolValue;
+template <typename Arg0>
+class NegateExpr;
+template <typename Arg1, typename Arg2>
+class AddExpr;
+template <typename Arg1, typename Arg2>
+class ProductExpr;
+template <typename Arg1, typename Arg2>
+class QuotientExpr;
+template <typename IndexType = Index>
+class ValueExpr;
+
+/** \class BaseExpr
+ * \ingroup Core_Module
+ * Common base class of any symbolic expressions
+ */
+template <typename Derived_>
+class BaseExpr {
+ public:
+  using Derived = Derived_;
+  constexpr const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  /** Evaluate the expression given the \a values of the symbols.
+   *
+   * \param values defines the values of the symbols, as constructed by SymbolExpr::operator= operator.
+   *
+   */
+  template <typename... Tags, typename... Types>
+  constexpr Index eval(const SymbolValue<Tags, Types>&... values) const {
+    return derived().eval_impl(values...);
+  }
+
+  /** Evaluate the expression at compile time given the \a values of the symbols.
+   *
+   * If a value is not known at compile-time, returns Eigen::Undefined.
+   *
+   */
+  template <typename... Tags, typename... Types>
+  static constexpr Index eval_at_compile_time(const SymbolValue<Tags, Types>&...) {
+    return Derived::eval_at_compile_time_impl(SymbolValue<Tags, Types>{}...);
+  }
+
+  constexpr NegateExpr<Derived> operator-() const { return NegateExpr<Derived>(derived()); }
+
+  constexpr AddExpr<Derived, ValueExpr<>> operator+(Index b) const {
+    return AddExpr<Derived, ValueExpr<>>(derived(), b);
+  }
+  constexpr AddExpr<Derived, ValueExpr<>> operator-(Index a) const {
+    return AddExpr<Derived, ValueExpr<>>(derived(), -a);
+  }
+  constexpr ProductExpr<Derived, ValueExpr<>> operator*(Index a) const {
+    return ProductExpr<Derived, ValueExpr<>>(derived(), a);
+  }
+  constexpr QuotientExpr<Derived, ValueExpr<>> operator/(Index a) const {
+    return QuotientExpr<Derived, ValueExpr<>>(derived(), a);
+  }
+
+  friend constexpr AddExpr<Derived, ValueExpr<>> operator+(Index a, const BaseExpr& b) {
+    return AddExpr<Derived, ValueExpr<>>(b.derived(), a);
+  }
+  friend constexpr AddExpr<NegateExpr<Derived>, ValueExpr<>> operator-(Index a, const BaseExpr& b) {
+    return AddExpr<NegateExpr<Derived>, ValueExpr<>>(-b.derived(), a);
+  }
+  friend constexpr ProductExpr<ValueExpr<>, Derived> operator*(Index a, const BaseExpr& b) {
+    return ProductExpr<ValueExpr<>, Derived>(a, b.derived());
+  }
+  friend constexpr QuotientExpr<ValueExpr<>, Derived> operator/(Index a, const BaseExpr& b) {
+    return QuotientExpr<ValueExpr<>, Derived>(a, b.derived());
+  }
+
+  template <int N>
+  constexpr AddExpr<Derived, ValueExpr<internal::FixedInt<N>>> operator+(internal::FixedInt<N>) const {
+    return AddExpr<Derived, ValueExpr<internal::FixedInt<N>>>(derived(), ValueExpr<internal::FixedInt<N>>());
+  }
+  template <int N>
+  constexpr AddExpr<Derived, ValueExpr<internal::FixedInt<-N>>> operator-(internal::FixedInt<N>) const {
+    return AddExpr<Derived, ValueExpr<internal::FixedInt<-N>>>(derived(), ValueExpr<internal::FixedInt<-N>>());
+  }
+  template <int N>
+  constexpr ProductExpr<Derived, ValueExpr<internal::FixedInt<N>>> operator*(internal::FixedInt<N>) const {
+    return ProductExpr<Derived, ValueExpr<internal::FixedInt<N>>>(derived(), ValueExpr<internal::FixedInt<N>>());
+  }
+  template <int N>
+  constexpr QuotientExpr<Derived, ValueExpr<internal::FixedInt<N>>> operator/(internal::FixedInt<N>) const {
+    return QuotientExpr<Derived, ValueExpr<internal::FixedInt<N>>>(derived(), ValueExpr<internal::FixedInt<N>>());
+  }
+
+  template <int N>
+  friend constexpr AddExpr<Derived, ValueExpr<internal::FixedInt<N>>> operator+(internal::FixedInt<N>,
+                                                                                const BaseExpr& b) {
+    return AddExpr<Derived, ValueExpr<internal::FixedInt<N>>>(b.derived(), ValueExpr<internal::FixedInt<N>>());
+  }
+  template <int N>
+  friend constexpr AddExpr<NegateExpr<Derived>, ValueExpr<internal::FixedInt<N>>> operator-(internal::FixedInt<N>,
+                                                                                            const BaseExpr& b) {
+    return AddExpr<NegateExpr<Derived>, ValueExpr<internal::FixedInt<N>>>(-b.derived(),
+                                                                          ValueExpr<internal::FixedInt<N>>());
+  }
+  template <int N>
+  friend constexpr ProductExpr<ValueExpr<internal::FixedInt<N>>, Derived> operator*(internal::FixedInt<N>,
+                                                                                    const BaseExpr& b) {
+    return ProductExpr<ValueExpr<internal::FixedInt<N>>, Derived>(ValueExpr<internal::FixedInt<N>>(), b.derived());
+  }
+  template <int N>
+  friend constexpr QuotientExpr<ValueExpr<internal::FixedInt<N>>, Derived> operator/(internal::FixedInt<N>,
+                                                                                     const BaseExpr& b) {
+    return QuotientExpr<ValueExpr<internal::FixedInt<N>>, Derived>(ValueExpr<internal::FixedInt<N>>(), b.derived());
+  }
+
+  template <typename OtherDerived>
+  constexpr AddExpr<Derived, OtherDerived> operator+(const BaseExpr<OtherDerived>& b) const {
+    return AddExpr<Derived, OtherDerived>(derived(), b.derived());
+  }
+
+  template <typename OtherDerived>
+  constexpr AddExpr<Derived, NegateExpr<OtherDerived>> operator-(const BaseExpr<OtherDerived>& b) const {
+    return AddExpr<Derived, NegateExpr<OtherDerived>>(derived(), -b.derived());
+  }
+
+  template <typename OtherDerived>
+  constexpr ProductExpr<Derived, OtherDerived> operator*(const BaseExpr<OtherDerived>& b) const {
+    return ProductExpr<Derived, OtherDerived>(derived(), b.derived());
+  }
+
+  template <typename OtherDerived>
+  constexpr QuotientExpr<Derived, OtherDerived> operator/(const BaseExpr<OtherDerived>& b) const {
+    return QuotientExpr<Derived, OtherDerived>(derived(), b.derived());
+  }
+};
+
+template <typename T>
+struct is_symbolic {
+  // BaseExpr has no conversion ctor, so we only have to check whether T can be statically cast to its base class
+  // BaseExpr<T>.
+  enum { value = internal::is_convertible<T, BaseExpr<T>>::value };
+};
+
+// A simple wrapper around an integral value to provide the eval method.
+// We could also use a free-function symbolic_eval...
+template <typename IndexType>
+class ValueExpr : BaseExpr<ValueExpr<IndexType>> {
+ public:
+  constexpr ValueExpr() = default;
+  constexpr ValueExpr(IndexType val) : m_value(val) {}
+  template <typename... Tags, typename... Types>
+  constexpr IndexType eval_impl(const SymbolValue<Tags, Types>&...) const {
+    return m_value;
+  }
+  template <typename... Tags, typename... Types>
+  static constexpr IndexType eval_at_compile_time_impl(const SymbolValue<Tags, Types>&...) {
+    return IndexType(Undefined);
+  }
+
+ protected:
+  IndexType m_value;
+};
+
+// Specialization for compile-time value,
+// It is similar to ValueExpr(N) but this version helps the compiler to generate better code.
+template <int N>
+class ValueExpr<internal::FixedInt<N>> : public BaseExpr<ValueExpr<internal::FixedInt<N>>> {
+ public:
+  constexpr ValueExpr() = default;
+  constexpr ValueExpr(internal::FixedInt<N>) {}
+  template <typename... Tags, typename... Types>
+  constexpr Index eval_impl(const SymbolValue<Tags, Types>&...) const {
+    return Index(N);
+  }
+  template <typename... Tags, typename... Types>
+  static constexpr Index eval_at_compile_time_impl(const SymbolValue<Tags, Types>&...) {
+    return Index(N);
+  }
+};
+
+/** Represents the actual value of a symbol identified by its tag
+ *
+ * It is the return type of SymbolValue::operator=, and most of the time this is only way it is used.
+ */
+template <typename Tag, typename Type>
+class SymbolValue : public BaseExpr<SymbolValue<Tag, Type>> {};
+
+template <typename Tag>
+class SymbolValue<Tag, Index> : public BaseExpr<SymbolValue<Tag, Index>> {
+ public:
+  constexpr SymbolValue() = default;
+
+  /** Default constructor from the value \a val */
+  constexpr SymbolValue(Index val) : m_value(val) {}
+
+  /** \returns the stored value of the symbol */
+  constexpr Index value() const { return m_value; }
+
+  /** \returns the stored value of the symbol at compile time, or Undefined if not known. */
+  static constexpr Index value_at_compile_time() { return Index(Undefined); }
+
+  template <typename... Tags, typename... Types>
+  constexpr Index eval_impl(const SymbolValue<Tags, Types>&...) const {
+    return value();
+  }
+
+  template <typename... Tags, typename... Types>
+  static constexpr Index eval_at_compile_time_impl(const SymbolValue<Tags, Types>&...) {
+    return value_at_compile_time();
+  }
+
+ protected:
+  Index m_value;
+};
+
+template <typename Tag, int N>
+class SymbolValue<Tag, internal::FixedInt<N>> : public BaseExpr<SymbolValue<Tag, internal::FixedInt<N>>> {
+ public:
+  constexpr SymbolValue() = default;
+
+  /** Default constructor from the value \a val */
+  constexpr SymbolValue(internal::FixedInt<N>) {}
+
+  /** \returns the stored value of the symbol */
+  constexpr Index value() const { return static_cast<Index>(N); }
+
+  /** \returns the stored value of the symbol at compile time, or Undefined if not known. */
+  static constexpr Index value_at_compile_time() { return static_cast<Index>(N); }
+
+  template <typename... Tags, typename... Types>
+  constexpr Index eval_impl(const SymbolValue<Tags, Types>&...) const {
+    return value();
+  }
+
+  template <typename... Tags, typename... Types>
+  static constexpr Index eval_at_compile_time_impl(const SymbolValue<Tags, Types>&...) {
+    return value_at_compile_time();
+  }
+};
+
+// Find and return a symbol value based on the tag.
+template <typename Tag, typename... Types>
+struct EvalSymbolValueHelper;
+
+// Empty base case, symbol not found.
+template <typename Tag>
+struct EvalSymbolValueHelper<Tag> {
+  static constexpr Index eval_impl() {
+    eigen_assert(false && "Symbol not found.");
+    return Index(Undefined);
+  }
+  static constexpr Index eval_at_compile_time_impl() { return Index(Undefined); }
+};
+
+// We found a symbol value matching the provided Tag!
+template <typename Tag, typename Type, typename... OtherTypes>
+struct EvalSymbolValueHelper<Tag, SymbolValue<Tag, Type>, OtherTypes...> {
+  static constexpr Index eval_impl(const SymbolValue<Tag, Type>& symbol, const OtherTypes&...) {
+    return symbol.value();
+  }
+  static constexpr Index eval_at_compile_time_impl(const SymbolValue<Tag, Type>& symbol, const OtherTypes&...) {
+    return symbol.value_at_compile_time();
+  }
+};
+
+// No symbol value in first value, recursive search starting with next.
+template <typename Tag, typename T1, typename... OtherTypes>
+struct EvalSymbolValueHelper<Tag, T1, OtherTypes...> {
+  static constexpr Index eval_impl(const T1&, const OtherTypes&... values) {
+    return EvalSymbolValueHelper<Tag, OtherTypes...>::eval_impl(values...);
+  }
+  static constexpr Index eval_at_compile_time_impl(const T1&, const OtherTypes&...) {
+    return EvalSymbolValueHelper<Tag, OtherTypes...>::eval_at_compile_time_impl(OtherTypes{}...);
+  }
+};
+
+/** Expression of a symbol uniquely identified by the template parameter type \c tag */
+template <typename tag>
+class SymbolExpr : public BaseExpr<SymbolExpr<tag>> {
+ public:
+  /** Alias to the template parameter \c tag */
+  typedef tag Tag;
+
+  constexpr SymbolExpr() = default;
+
+  /** Associate the value \a val to the given symbol \c *this, uniquely identified by its \c Tag.
+   *
+   * The returned object should be passed to ExprBase::eval() to evaluate a given expression with this specified
+   * runtime-time value.
+   */
+  constexpr SymbolValue<Tag, Index> operator=(Index val) const { return SymbolValue<Tag, Index>(val); }
+
+  template <int N>
+  constexpr SymbolValue<Tag, internal::FixedInt<N>> operator=(internal::FixedInt<N>) const {
+    return SymbolValue<Tag, internal::FixedInt<N>>{internal::FixedInt<N>{}};
+  }
+
+  template <typename... Tags, typename... Types>
+  constexpr Index eval_impl(const SymbolValue<Tags, Types>&... values) const {
+    return EvalSymbolValueHelper<Tag, SymbolValue<Tags, Types>...>::eval_impl(values...);
+  }
+
+  template <typename... Tags, typename... Types>
+  static constexpr Index eval_at_compile_time_impl(const SymbolValue<Tags, Types>&...) {
+    return EvalSymbolValueHelper<Tag, SymbolValue<Tags, Types>...>::eval_at_compile_time_impl(
+        SymbolValue<Tags, Types>{}...);
+  }
+};
+
+template <typename Arg0>
+class NegateExpr : public BaseExpr<NegateExpr<Arg0>> {
+ public:
+  constexpr NegateExpr() = default;
+  constexpr NegateExpr(const Arg0& arg0) : m_arg0(arg0) {}
+
+  template <typename... Tags, typename... Types>
+  constexpr Index eval_impl(const SymbolValue<Tags, Types>&... values) const {
+    return -m_arg0.eval_impl(values...);
+  }
+
+  template <typename... Tags, typename... Types>
+  static constexpr Index eval_at_compile_time_impl(const SymbolValue<Tags, Types>&...) {
+    constexpr Index v = Arg0::eval_at_compile_time_impl(SymbolValue<Tags, Types>{}...);
+    return (v == Undefined) ? Undefined : -v;
+  }
+
+ protected:
+  Arg0 m_arg0;
+};
+
+template <typename Arg0, typename Arg1>
+class AddExpr : public BaseExpr<AddExpr<Arg0, Arg1>> {
+ public:
+  constexpr AddExpr() = default;
+  constexpr AddExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
+
+  template <typename... Tags, typename... Types>
+  constexpr Index eval_impl(const SymbolValue<Tags, Types>&... values) const {
+    return m_arg0.eval_impl(values...) + m_arg1.eval_impl(values...);
+  }
+
+  template <typename... Tags, typename... Types>
+  static constexpr Index eval_at_compile_time_impl(const SymbolValue<Tags, Types>&...) {
+    constexpr Index v0 = Arg0::eval_at_compile_time_impl(SymbolValue<Tags, Types>{}...);
+    constexpr Index v1 = Arg1::eval_at_compile_time_impl(SymbolValue<Tags, Types>{}...);
+    return (v0 == Undefined || v1 == Undefined) ? Undefined : v0 + v1;
+  }
+
+ protected:
+  Arg0 m_arg0;
+  Arg1 m_arg1;
+};
+
+template <typename Arg0, typename Arg1>
+class ProductExpr : public BaseExpr<ProductExpr<Arg0, Arg1>> {
+ public:
+  constexpr ProductExpr() = default;
+  constexpr ProductExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
+
+  template <typename... Tags, typename... Types>
+  constexpr Index eval_impl(const SymbolValue<Tags, Types>&... values) const {
+    return m_arg0.eval_impl(values...) * m_arg1.eval_impl(values...);
+  }
+
+  template <typename... Tags, typename... Types>
+  static constexpr Index eval_at_compile_time_impl(const SymbolValue<Tags, Types>&...) {
+    constexpr Index v0 = Arg0::eval_at_compile_time_impl(SymbolValue<Tags, Types>{}...);
+    constexpr Index v1 = Arg1::eval_at_compile_time_impl(SymbolValue<Tags, Types>{}...);
+    return (v0 == Undefined || v1 == Undefined) ? Undefined : v0 * v1;
+  }
+
+ protected:
+  Arg0 m_arg0;
+  Arg1 m_arg1;
+};
+
+template <typename Arg0, typename Arg1>
+class QuotientExpr : public BaseExpr<QuotientExpr<Arg0, Arg1>> {
+ public:
+  constexpr QuotientExpr() = default;
+  constexpr QuotientExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
+
+  template <typename... Tags, typename... Types>
+  constexpr Index eval_impl(const SymbolValue<Tags, Types>&... values) const {
+    return m_arg0.eval_impl(values...) / m_arg1.eval_impl(values...);
+  }
+
+  template <typename... Tags, typename... Types>
+  static constexpr Index eval_at_compile_time_impl(const SymbolValue<Tags, Types>&...) {
+    constexpr Index v0 = Arg0::eval_at_compile_time_impl(SymbolValue<Tags, Types>{}...);
+    constexpr Index v1 = Arg1::eval_at_compile_time_impl(SymbolValue<Tags, Types>{}...);
+    return (v0 == Undefined || v1 == Undefined) ? Undefined : v0 / v1;
+  }
+
+ protected:
+  Arg0 m_arg0;
+  Arg1 m_arg1;
+};
+
+}  // end namespace symbolic
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SYMBOLIC_INDEX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/XprHelper.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/XprHelper.h
new file mode 100644
index 0000000..df3d724
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Core/util/XprHelper.h
@@ -0,0 +1,1091 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_XPRHELPER_H
+#define EIGEN_XPRHELPER_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// useful for unsigned / signed integer comparisons when idx is intended to be non-negative
+template <typename IndexType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::make_unsigned_t<IndexType> returnUnsignedIndexValue(const IndexType& idx) {
+  EIGEN_STATIC_ASSERT((NumTraits<IndexType>::IsInteger), THIS FUNCTION IS FOR INTEGER TYPES)
+  eigen_internal_assert(idx >= 0 && "Index value is negative and target type is unsigned");
+  using UnsignedType = std::make_unsigned_t<IndexType>;
+  return static_cast<UnsignedType>(idx);
+}
+
+template <typename IndexDest, typename IndexSrc, bool IndexDestIsInteger = NumTraits<IndexDest>::IsInteger,
+          bool IndexDestIsSigned = NumTraits<IndexDest>::IsSigned,
+          bool IndexSrcIsInteger = NumTraits<IndexSrc>::IsInteger,
+          bool IndexSrcIsSigned = NumTraits<IndexSrc>::IsSigned>
+struct convert_index_impl {
+  static inline EIGEN_DEVICE_FUNC IndexDest run(const IndexSrc& idx) {
+    eigen_internal_assert(idx <= NumTraits<IndexDest>::highest() && "Index value is too big for target type");
+    return static_cast<IndexDest>(idx);
+  }
+};
+template <typename IndexDest, typename IndexSrc>
+struct convert_index_impl<IndexDest, IndexSrc, true, true, true, false> {
+  // IndexDest is a signed integer
+  // IndexSrc is an unsigned integer
+  static inline EIGEN_DEVICE_FUNC IndexDest run(const IndexSrc& idx) {
+    eigen_internal_assert(idx <= returnUnsignedIndexValue(NumTraits<IndexDest>::highest()) &&
+                          "Index value is too big for target type");
+    return static_cast<IndexDest>(idx);
+  }
+};
+template <typename IndexDest, typename IndexSrc>
+struct convert_index_impl<IndexDest, IndexSrc, true, false, true, true> {
+  // IndexDest is an unsigned integer
+  // IndexSrc is a signed integer
+  static inline EIGEN_DEVICE_FUNC IndexDest run(const IndexSrc& idx) {
+    eigen_internal_assert(returnUnsignedIndexValue(idx) <= NumTraits<IndexDest>::highest() &&
+                          "Index value is too big for target type");
+    return static_cast<IndexDest>(idx);
+  }
+};
+
+template <typename IndexDest, typename IndexSrc>
+EIGEN_DEVICE_FUNC inline IndexDest convert_index(const IndexSrc& idx) {
+  return convert_index_impl<IndexDest, IndexSrc>::run(idx);
+}
+
+// true if T can be considered as an integral index (i.e., and integral type or enum)
+template <typename T>
+struct is_valid_index_type {
+  enum { value = internal::is_integral<T>::value || std::is_enum<T>::value };
+};
+
+// true if both types are not valid index types
+template <typename RowIndices, typename ColIndices>
+struct valid_indexed_view_overload {
+  enum {
+    value = !(internal::is_valid_index_type<RowIndices>::value && internal::is_valid_index_type<ColIndices>::value)
+  };
+};
+
+// promote_scalar_arg is an helper used in operation between an expression and a scalar, like:
+//    expression * scalar
+// Its role is to determine how the type T of the scalar operand should be promoted given the scalar type ExprScalar of
+// the given expression. The IsSupported template parameter must be provided by the caller as:
+// internal::has_ReturnType<ScalarBinaryOpTraits<ExprScalar,T,op> >::value using the proper order for ExprScalar and T.
+// Then the logic is as follows:
+//  - if the operation is natively supported as defined by IsSupported, then the scalar type is not promoted, and T is
+//  returned.
+//  - otherwise, NumTraits<ExprScalar>::Literal is returned if T is implicitly convertible to
+//  NumTraits<ExprScalar>::Literal AND that this does not imply a float to integer conversion.
+//  - otherwise, ExprScalar is returned if T is implicitly convertible to ExprScalar AND that this does not imply a
+//  float to integer conversion.
+//  - In all other cases, the promoted type is not defined, and the respective operation is thus invalid and not
+//  available (SFINAE).
+template <typename ExprScalar, typename T, bool IsSupported>
+struct promote_scalar_arg;
+
+template <typename S, typename T>
+struct promote_scalar_arg<S, T, true> {
+  typedef T type;
+};
+
+// Recursively check safe conversion to PromotedType, and then ExprScalar if they are different.
+template <typename ExprScalar, typename T, typename PromotedType,
+          bool ConvertibleToLiteral = internal::is_convertible<T, PromotedType>::value,
+          bool IsSafe = NumTraits<T>::IsInteger || !NumTraits<PromotedType>::IsInteger>
+struct promote_scalar_arg_unsupported;
+
+// Start recursion with NumTraits<ExprScalar>::Literal
+template <typename S, typename T>
+struct promote_scalar_arg<S, T, false> : promote_scalar_arg_unsupported<S, T, typename NumTraits<S>::Literal> {};
+
+// We found a match!
+template <typename S, typename T, typename PromotedType>
+struct promote_scalar_arg_unsupported<S, T, PromotedType, true, true> {
+  typedef PromotedType type;
+};
+
+// No match, but no real-to-integer issues, and ExprScalar and current PromotedType are different,
+// so let's try to promote to ExprScalar
+template <typename ExprScalar, typename T, typename PromotedType>
+struct promote_scalar_arg_unsupported<ExprScalar, T, PromotedType, false, true>
+    : promote_scalar_arg_unsupported<ExprScalar, T, ExprScalar> {};
+
+// Unsafe real-to-integer, let's stop.
+template <typename S, typename T, typename PromotedType, bool ConvertibleToLiteral>
+struct promote_scalar_arg_unsupported<S, T, PromotedType, ConvertibleToLiteral, false> {};
+
+// T is not even convertible to ExprScalar, let's stop.
+template <typename S, typename T>
+struct promote_scalar_arg_unsupported<S, T, S, false, true> {};
+
+// classes inheriting no_assignment_operator don't generate a default operator=.
+class no_assignment_operator {
+ private:
+  no_assignment_operator& operator=(const no_assignment_operator&);
+
+ protected:
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(no_assignment_operator)
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(no_assignment_operator)
+};
+
+/** \internal return the index type with the largest number of bits */
+template <typename I1, typename I2>
+struct promote_index_type {
+  typedef std::conditional_t<(sizeof(I1) < sizeof(I2)), I2, I1> type;
+};
+
+/** \internal If the template parameter Value is Dynamic, this class is just a wrapper around a T variable that
+ * can be accessed using value() and setValue().
+ * Otherwise, this class is an empty structure and value() just returns the template parameter Value.
+ */
+template <typename T, int Value>
+class variable_if_dynamic {
+ public:
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit variable_if_dynamic(T v) {
+    EIGEN_ONLY_USED_FOR_DEBUG(v);
+    eigen_assert(v == T(Value));
+  }
+  EIGEN_DEVICE_FUNC static constexpr T value() { return T(Value); }
+  EIGEN_DEVICE_FUNC constexpr operator T() const { return T(Value); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T v) const {
+    EIGEN_ONLY_USED_FOR_DEBUG(v);
+    eigen_assert(v == T(Value));
+  }
+};
+
+template <typename T>
+class variable_if_dynamic<T, Dynamic> {
+  T m_value;
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit variable_if_dynamic(T value = 0) noexcept : m_value(value) {}
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE T value() const { return m_value; }
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE operator T() const { return m_value; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T value) { m_value = value; }
+};
+
+/** \internal like variable_if_dynamic but for DynamicIndex
+ */
+template <typename T, int Value>
+class variable_if_dynamicindex {
+ public:
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit variable_if_dynamicindex(T v) {
+    EIGEN_ONLY_USED_FOR_DEBUG(v);
+    eigen_assert(v == T(Value));
+  }
+  EIGEN_DEVICE_FUNC static constexpr T value() { return T(Value); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void setValue(T) {}
+};
+
+template <typename T>
+class variable_if_dynamicindex<T, DynamicIndex> {
+  T m_value;
+  EIGEN_DEVICE_FUNC variable_if_dynamicindex() { eigen_assert(false); }
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE explicit variable_if_dynamicindex(T value) : m_value(value) {}
+  EIGEN_DEVICE_FUNC constexpr T EIGEN_STRONG_INLINE value() const { return m_value; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T value) { m_value = value; }
+};
+
+template <typename T>
+struct functor_traits {
+  enum { Cost = 10, PacketAccess = false, IsRepeatable = false };
+};
+
+// estimates the cost of lazily evaluating a generic functor by unwinding the expression
+template <typename Xpr>
+struct nested_functor_cost {
+  static constexpr Index Cost = static_cast<Index>(functor_traits<Xpr>::Cost);
+};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct nested_functor_cost<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols>> {
+  static constexpr Index Cost = 1;
+};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct nested_functor_cost<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols>> {
+  static constexpr Index Cost = 1;
+};
+
+// TODO: assign a cost to the stride type?
+template <typename PlainObjectType, int MapOptions, typename StrideType>
+struct nested_functor_cost<Map<PlainObjectType, MapOptions, StrideType>> : nested_functor_cost<PlainObjectType> {};
+
+template <typename Func, typename Xpr>
+struct nested_functor_cost<CwiseUnaryOp<Func, Xpr>> {
+  using XprCleaned = remove_all_t<Xpr>;
+  using FuncCleaned = remove_all_t<Func>;
+  static constexpr Index Cost = nested_functor_cost<FuncCleaned>::Cost + nested_functor_cost<XprCleaned>::Cost;
+};
+
+template <typename Func, typename Xpr>
+struct nested_functor_cost<CwiseNullaryOp<Func, Xpr>> {
+  using XprCleaned = remove_all_t<Xpr>;
+  using FuncCleaned = remove_all_t<Func>;
+  static constexpr Index Cost = nested_functor_cost<FuncCleaned>::Cost + nested_functor_cost<XprCleaned>::Cost;
+};
+
+template <typename Func, typename LhsXpr, typename RhsXpr>
+struct nested_functor_cost<CwiseBinaryOp<Func, LhsXpr, RhsXpr>> {
+  using LhsXprCleaned = remove_all_t<LhsXpr>;
+  using RhsXprCleaned = remove_all_t<RhsXpr>;
+  using FuncCleaned = remove_all_t<Func>;
+  static constexpr Index Cost = nested_functor_cost<FuncCleaned>::Cost + nested_functor_cost<LhsXprCleaned>::Cost +
+                                nested_functor_cost<RhsXprCleaned>::Cost;
+};
+
+template <typename Func, typename LhsXpr, typename MidXpr, typename RhsXpr>
+struct nested_functor_cost<CwiseTernaryOp<Func, LhsXpr, MidXpr, RhsXpr>> {
+  using LhsXprCleaned = remove_all_t<LhsXpr>;
+  using MidXprCleaned = remove_all_t<MidXpr>;
+  using RhsXprCleaned = remove_all_t<RhsXpr>;
+  using FuncCleaned = remove_all_t<Func>;
+  static constexpr Index Cost = nested_functor_cost<FuncCleaned>::Cost + nested_functor_cost<LhsXprCleaned>::Cost +
+                                nested_functor_cost<MidXprCleaned>::Cost + nested_functor_cost<RhsXprCleaned>::Cost;
+};
+
+template <typename Xpr>
+struct functor_cost {
+  static constexpr Index Cost = plain_enum_max(nested_functor_cost<Xpr>::Cost, 1);
+};
+
+template <typename T>
+struct packet_traits;
+
+template <typename T>
+struct unpacket_traits;
+
+template <int Size, typename PacketType,
+          bool Stop = Size == Dynamic || (Size % unpacket_traits<PacketType>::size) == 0 ||
+                      is_same<PacketType, typename unpacket_traits<PacketType>::half>::value>
+struct find_best_packet_helper;
+
+template <int Size, typename PacketType>
+struct find_best_packet_helper<Size, PacketType, true> {
+  typedef PacketType type;
+};
+
+template <int Size, typename PacketType>
+struct find_best_packet_helper<Size, PacketType, false> {
+  typedef typename find_best_packet_helper<Size, typename unpacket_traits<PacketType>::half>::type type;
+};
+
+template <typename T, int Size>
+struct find_best_packet {
+  typedef typename find_best_packet_helper<Size, typename packet_traits<T>::type>::type type;
+};
+
+template <int Size, typename PacketType,
+          bool Stop = (Size == unpacket_traits<PacketType>::size) ||
+                      is_same<PacketType, typename unpacket_traits<PacketType>::half>::value>
+struct find_packet_by_size_helper;
+template <int Size, typename PacketType>
+struct find_packet_by_size_helper<Size, PacketType, true> {
+  using type = PacketType;
+};
+template <int Size, typename PacketType>
+struct find_packet_by_size_helper<Size, PacketType, false> {
+  using type = typename find_packet_by_size_helper<Size, typename unpacket_traits<PacketType>::half>::type;
+};
+
+template <typename T, int Size>
+struct find_packet_by_size {
+  using type = typename find_packet_by_size_helper<Size, typename packet_traits<T>::type>::type;
+  static constexpr bool value = (Size == unpacket_traits<type>::size);
+};
+template <typename T>
+struct find_packet_by_size<T, 1> {
+  using type = typename unpacket_traits<T>::type;
+  static constexpr bool value = (unpacket_traits<type>::size == 1);
+};
+
+#if EIGEN_MAX_STATIC_ALIGN_BYTES > 0
+constexpr int compute_default_alignment_helper(int ArrayBytes, int AlignmentBytes) {
+  if ((ArrayBytes % AlignmentBytes) == 0) {
+    return AlignmentBytes;
+  } else if (EIGEN_MIN_ALIGN_BYTES < AlignmentBytes) {
+    return compute_default_alignment_helper(ArrayBytes, AlignmentBytes / 2);
+  } else {
+    return 0;
+  }
+}
+#else
+// If static alignment is disabled, no need to bother.
+// This also avoids a division by zero
+constexpr int compute_default_alignment_helper(int ArrayBytes, int AlignmentBytes) {
+  EIGEN_UNUSED_VARIABLE(ArrayBytes);
+  EIGEN_UNUSED_VARIABLE(AlignmentBytes);
+  return 0;
+}
+#endif
+
+template <typename T, int Size>
+struct compute_default_alignment {
+  enum { value = compute_default_alignment_helper(Size * sizeof(T), EIGEN_MAX_STATIC_ALIGN_BYTES) };
+};
+
+template <typename T>
+struct compute_default_alignment<T, Dynamic> {
+  enum { value = EIGEN_MAX_ALIGN_BYTES };
+};
+
+template <typename Scalar_, int Rows_, int Cols_,
+          int Options_ = AutoAlign | ((Rows_ == 1 && Cols_ != 1)   ? RowMajor
+                                      : (Cols_ == 1 && Rows_ != 1) ? ColMajor
+                                                                   : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION),
+          int MaxRows_ = Rows_, int MaxCols_ = Cols_>
+class make_proper_matrix_type {
+  enum {
+    IsColVector = Cols_ == 1 && Rows_ != 1,
+    IsRowVector = Rows_ == 1 && Cols_ != 1,
+    Options = IsColVector   ? (Options_ | ColMajor) & ~RowMajor
+              : IsRowVector ? (Options_ | RowMajor) & ~ColMajor
+                            : Options_
+  };
+
+ public:
+  typedef Matrix<Scalar_, Rows_, Cols_, Options, MaxRows_, MaxCols_> type;
+};
+
+constexpr unsigned compute_matrix_flags(int Options) {
+  unsigned row_major_bit = Options & RowMajor ? RowMajorBit : 0;
+  // FIXME currently we still have to handle DirectAccessBit at the expression level to handle DenseCoeffsBase<>
+  // and then propagate this information to the evaluator's flags.
+  // However, I (Gael) think that DirectAccessBit should only matter at the evaluation stage.
+  return DirectAccessBit | LvalueBit | NestByRefBit | row_major_bit;
+}
+
+constexpr int size_at_compile_time(int rows, int cols) {
+  if (rows == 0 || cols == 0) return 0;
+  if (rows == Dynamic || cols == Dynamic) return Dynamic;
+  return rows * cols;
+}
+
+template <typename XprType>
+struct size_of_xpr_at_compile_time {
+  enum { ret = size_at_compile_time(traits<XprType>::RowsAtCompileTime, traits<XprType>::ColsAtCompileTime) };
+};
+
+/* plain_matrix_type : the difference from eval is that plain_matrix_type is always a plain matrix type,
+ * whereas eval is a const reference in the case of a matrix
+ */
+
+template <typename T, typename StorageKind = typename traits<T>::StorageKind>
+struct plain_matrix_type;
+template <typename T, typename BaseClassType, int Flags>
+struct plain_matrix_type_dense;
+template <typename T>
+struct plain_matrix_type<T, Dense> {
+  typedef typename plain_matrix_type_dense<T, typename traits<T>::XprKind, traits<T>::Flags>::type type;
+};
+template <typename T>
+struct plain_matrix_type<T, DiagonalShape> {
+  typedef typename T::PlainObject type;
+};
+
+template <typename T>
+struct plain_matrix_type<T, SkewSymmetricShape> {
+  typedef typename T::PlainObject type;
+};
+
+template <typename T, int Flags>
+struct plain_matrix_type_dense<T, MatrixXpr, Flags> {
+  typedef Matrix<typename traits<T>::Scalar, traits<T>::RowsAtCompileTime, traits<T>::ColsAtCompileTime,
+                 AutoAlign | (Flags & RowMajorBit ? RowMajor : ColMajor), traits<T>::MaxRowsAtCompileTime,
+                 traits<T>::MaxColsAtCompileTime>
+      type;
+};
+
+template <typename T, int Flags>
+struct plain_matrix_type_dense<T, ArrayXpr, Flags> {
+  typedef Array<typename traits<T>::Scalar, traits<T>::RowsAtCompileTime, traits<T>::ColsAtCompileTime,
+                AutoAlign | (Flags & RowMajorBit ? RowMajor : ColMajor), traits<T>::MaxRowsAtCompileTime,
+                traits<T>::MaxColsAtCompileTime>
+      type;
+};
+
+/* eval : the return type of eval(). For matrices, this is just a const reference
+ * in order to avoid a useless copy
+ */
+
+template <typename T, typename StorageKind = typename traits<T>::StorageKind>
+struct eval;
+
+template <typename T>
+struct eval<T, Dense> {
+  typedef typename plain_matrix_type<T>::type type;
+  //   typedef typename T::PlainObject type;
+  //   typedef T::Matrix<typename traits<T>::Scalar,
+  //                 traits<T>::RowsAtCompileTime,
+  //                 traits<T>::ColsAtCompileTime,
+  //                 AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor),
+  //                 traits<T>::MaxRowsAtCompileTime,
+  //                 traits<T>::MaxColsAtCompileTime
+  //           > type;
+};
+
+template <typename T>
+struct eval<T, DiagonalShape> {
+  typedef typename plain_matrix_type<T>::type type;
+};
+
+template <typename T>
+struct eval<T, SkewSymmetricShape> {
+  typedef typename plain_matrix_type<T>::type type;
+};
+
+// for matrices, no need to evaluate, just use a const reference to avoid a useless copy
+template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct eval<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>, Dense> {
+  typedef const Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>& type;
+};
+
+template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct eval<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>, Dense> {
+  typedef const Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>& type;
+};
+
+/* similar to plain_matrix_type, but using the evaluator's Flags */
+template <typename T, typename StorageKind = typename traits<T>::StorageKind>
+struct plain_object_eval;
+
+template <typename T>
+struct plain_object_eval<T, Dense> {
+  typedef typename plain_matrix_type_dense<T, typename traits<T>::XprKind, evaluator<T>::Flags>::type type;
+};
+
+/* plain_matrix_type_column_major : same as plain_matrix_type but guaranteed to be column-major
+ */
+template <typename T>
+struct plain_matrix_type_column_major {
+  enum {
+    Rows = traits<T>::RowsAtCompileTime,
+    Cols = traits<T>::ColsAtCompileTime,
+    MaxRows = traits<T>::MaxRowsAtCompileTime,
+    MaxCols = traits<T>::MaxColsAtCompileTime
+  };
+  typedef Matrix<typename traits<T>::Scalar, Rows, Cols, (MaxRows == 1 && MaxCols != 1) ? RowMajor : ColMajor, MaxRows,
+                 MaxCols>
+      type;
+};
+
+/* plain_matrix_type_row_major : same as plain_matrix_type but guaranteed to be row-major
+ */
+template <typename T>
+struct plain_matrix_type_row_major {
+  enum {
+    Rows = traits<T>::RowsAtCompileTime,
+    Cols = traits<T>::ColsAtCompileTime,
+    MaxRows = traits<T>::MaxRowsAtCompileTime,
+    MaxCols = traits<T>::MaxColsAtCompileTime
+  };
+  typedef Matrix<typename traits<T>::Scalar, Rows, Cols, (MaxCols == 1 && MaxRows != 1) ? ColMajor : RowMajor, MaxRows,
+                 MaxCols>
+      type;
+};
+
+/** \internal The reference selector for template expressions. The idea is that we don't
+ * need to use references for expressions since they are light weight proxy
+ * objects which should generate no copying overhead. */
+template <typename T>
+struct ref_selector {
+  typedef std::conditional_t<bool(traits<T>::Flags& NestByRefBit), T const&, const T> type;
+
+  typedef std::conditional_t<bool(traits<T>::Flags& NestByRefBit), T&, T> non_const_type;
+};
+
+/** \internal Adds the const qualifier on the value-type of T2 if and only if T1 is a const type */
+template <typename T1, typename T2>
+struct transfer_constness {
+  typedef std::conditional_t<bool(internal::is_const<T1>::value), add_const_on_value_type_t<T2>, T2> type;
+};
+
+// However, we still need a mechanism to detect whether an expression which is evaluated multiple time
+// has to be evaluated into a temporary.
+// That's the purpose of this new nested_eval helper:
+/** \internal Determines how a given expression should be nested when evaluated multiple times.
+ * For example, when you do a * (b+c), Eigen will determine how the expression b+c should be
+ * evaluated into the bigger product expression. The choice is between nesting the expression b+c as-is, or
+ * evaluating that expression b+c into a temporary variable d, and nest d so that the resulting expression is
+ * a*d. Evaluating can be beneficial for example if every coefficient access in the resulting expression causes
+ * many coefficient accesses in the nested expressions -- as is the case with matrix product for example.
+ *
+ * \tparam T the type of the expression being nested.
+ * \tparam n the number of coefficient accesses in the nested expression for each coefficient access in the bigger
+ * expression. \tparam PlainObject the type of the temporary if needed.
+ */
+template <typename T, int n, typename PlainObject = typename plain_object_eval<T>::type>
+struct nested_eval {
+  enum {
+    ScalarReadCost = NumTraits<typename traits<T>::Scalar>::ReadCost,
+    CoeffReadCost =
+        evaluator<T>::CoeffReadCost,  // NOTE What if an evaluator evaluate itself into a temporary?
+                                      //      Then CoeffReadCost will be small (e.g., 1) but we still have to evaluate,
+                                      //      especially if n>1. This situation is already taken care by the
+                                      //      EvalBeforeNestingBit flag, which is turned ON for all evaluator creating a
+                                      //      temporary. This flag is then propagated by the parent evaluators. Another
+                                      //      solution could be to count the number of temps?
+    NAsInteger = n == Dynamic ? HugeCost : n,
+    CostEval = (NAsInteger + 1) * ScalarReadCost + CoeffReadCost,
+    CostNoEval = int(NAsInteger) * int(CoeffReadCost),
+    Evaluate = (int(evaluator<T>::Flags) & EvalBeforeNestingBit) || (int(CostEval) < int(CostNoEval))
+  };
+
+  typedef std::conditional_t<Evaluate, PlainObject, typename ref_selector<T>::type> type;
+};
+
+template <typename T>
+EIGEN_DEVICE_FUNC inline T* const_cast_ptr(const T* ptr) {
+  return const_cast<T*>(ptr);
+}
+
+template <typename Derived, typename XprKind = typename traits<Derived>::XprKind>
+struct dense_xpr_base {
+  /* dense_xpr_base should only ever be used on dense expressions, thus falling either into the MatrixXpr or into the
+   * ArrayXpr cases */
+};
+
+template <typename Derived>
+struct dense_xpr_base<Derived, MatrixXpr> {
+  typedef MatrixBase<Derived> type;
+};
+
+template <typename Derived>
+struct dense_xpr_base<Derived, ArrayXpr> {
+  typedef ArrayBase<Derived> type;
+};
+
+template <typename Derived, typename XprKind = typename traits<Derived>::XprKind,
+          typename StorageKind = typename traits<Derived>::StorageKind>
+struct generic_xpr_base;
+
+template <typename Derived, typename XprKind>
+struct generic_xpr_base<Derived, XprKind, Dense> {
+  typedef typename dense_xpr_base<Derived, XprKind>::type type;
+};
+
+template <typename XprType, typename CastType>
+struct cast_return_type {
+  typedef typename XprType::Scalar CurrentScalarType;
+  typedef remove_all_t<CastType> CastType_;
+  typedef typename CastType_::Scalar NewScalarType;
+  typedef std::conditional_t<is_same<CurrentScalarType, NewScalarType>::value, const XprType&, CastType> type;
+};
+
+template <typename A, typename B>
+struct promote_storage_type;
+
+template <typename A>
+struct promote_storage_type<A, A> {
+  typedef A ret;
+};
+template <typename A>
+struct promote_storage_type<A, const A> {
+  typedef A ret;
+};
+template <typename A>
+struct promote_storage_type<const A, A> {
+  typedef A ret;
+};
+
+/** \internal Specify the "storage kind" of applying a coefficient-wise
+ * binary operations between two expressions of kinds A and B respectively.
+ * The template parameter Functor permits to specialize the resulting storage kind wrt to
+ * the functor.
+ * The default rules are as follows:
+ * \code
+ * A      op A      -> A
+ * A      op dense  -> dense
+ * dense  op B      -> dense
+ * sparse op dense  -> sparse
+ * dense  op sparse -> sparse
+ * \endcode
+ */
+template <typename A, typename B, typename Functor>
+struct cwise_promote_storage_type;
+
+template <typename A, typename Functor>
+struct cwise_promote_storage_type<A, A, Functor> {
+  typedef A ret;
+};
+template <typename Functor>
+struct cwise_promote_storage_type<Dense, Dense, Functor> {
+  typedef Dense ret;
+};
+template <typename A, typename Functor>
+struct cwise_promote_storage_type<A, Dense, Functor> {
+  typedef Dense ret;
+};
+template <typename B, typename Functor>
+struct cwise_promote_storage_type<Dense, B, Functor> {
+  typedef Dense ret;
+};
+template <typename Functor>
+struct cwise_promote_storage_type<Sparse, Dense, Functor> {
+  typedef Sparse ret;
+};
+template <typename Functor>
+struct cwise_promote_storage_type<Dense, Sparse, Functor> {
+  typedef Sparse ret;
+};
+
+template <typename LhsKind, typename RhsKind, int LhsOrder, int RhsOrder>
+struct cwise_promote_storage_order {
+  enum { value = LhsOrder };
+};
+
+template <typename LhsKind, int LhsOrder, int RhsOrder>
+struct cwise_promote_storage_order<LhsKind, Sparse, LhsOrder, RhsOrder> {
+  enum { value = RhsOrder };
+};
+template <typename RhsKind, int LhsOrder, int RhsOrder>
+struct cwise_promote_storage_order<Sparse, RhsKind, LhsOrder, RhsOrder> {
+  enum { value = LhsOrder };
+};
+template <int Order>
+struct cwise_promote_storage_order<Sparse, Sparse, Order, Order> {
+  enum { value = Order };
+};
+
+/** \internal Specify the "storage kind" of multiplying an expression of kind A with kind B.
+ * The template parameter ProductTag permits to specialize the resulting storage kind wrt to
+ * some compile-time properties of the product: GemmProduct, GemvProduct, OuterProduct, InnerProduct.
+ * The default rules are as follows:
+ * \code
+ *  K * K            -> K
+ *  dense * K        -> dense
+ *  K * dense        -> dense
+ *  diag * K         -> K
+ *  K * diag         -> K
+ *  Perm * K         -> K
+ * K * Perm          -> K
+ * \endcode
+ */
+template <typename A, typename B, int ProductTag>
+struct product_promote_storage_type;
+
+template <typename A, int ProductTag>
+struct product_promote_storage_type<A, A, ProductTag> {
+  typedef A ret;
+};
+template <int ProductTag>
+struct product_promote_storage_type<Dense, Dense, ProductTag> {
+  typedef Dense ret;
+};
+template <typename A, int ProductTag>
+struct product_promote_storage_type<A, Dense, ProductTag> {
+  typedef Dense ret;
+};
+template <typename B, int ProductTag>
+struct product_promote_storage_type<Dense, B, ProductTag> {
+  typedef Dense ret;
+};
+
+template <typename A, int ProductTag>
+struct product_promote_storage_type<A, DiagonalShape, ProductTag> {
+  typedef A ret;
+};
+template <typename B, int ProductTag>
+struct product_promote_storage_type<DiagonalShape, B, ProductTag> {
+  typedef B ret;
+};
+template <int ProductTag>
+struct product_promote_storage_type<Dense, DiagonalShape, ProductTag> {
+  typedef Dense ret;
+};
+template <int ProductTag>
+struct product_promote_storage_type<DiagonalShape, Dense, ProductTag> {
+  typedef Dense ret;
+};
+
+template <typename A, int ProductTag>
+struct product_promote_storage_type<A, SkewSymmetricShape, ProductTag> {
+  typedef A ret;
+};
+template <typename B, int ProductTag>
+struct product_promote_storage_type<SkewSymmetricShape, B, ProductTag> {
+  typedef B ret;
+};
+template <int ProductTag>
+struct product_promote_storage_type<Dense, SkewSymmetricShape, ProductTag> {
+  typedef Dense ret;
+};
+template <int ProductTag>
+struct product_promote_storage_type<SkewSymmetricShape, Dense, ProductTag> {
+  typedef Dense ret;
+};
+template <int ProductTag>
+struct product_promote_storage_type<SkewSymmetricShape, SkewSymmetricShape, ProductTag> {
+  typedef Dense ret;
+};
+
+template <typename A, int ProductTag>
+struct product_promote_storage_type<A, PermutationStorage, ProductTag> {
+  typedef A ret;
+};
+template <typename B, int ProductTag>
+struct product_promote_storage_type<PermutationStorage, B, ProductTag> {
+  typedef B ret;
+};
+template <int ProductTag>
+struct product_promote_storage_type<Dense, PermutationStorage, ProductTag> {
+  typedef Dense ret;
+};
+template <int ProductTag>
+struct product_promote_storage_type<PermutationStorage, Dense, ProductTag> {
+  typedef Dense ret;
+};
+
+/** \internal gives the plain matrix or array type to store a row/column/diagonal of a matrix type.
+ * \tparam Scalar optional parameter allowing to pass a different scalar type than the one of the MatrixType.
+ */
+template <typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
+struct plain_row_type {
+  typedef Matrix<Scalar, 1, ExpressionType::ColsAtCompileTime,
+                 int(ExpressionType::PlainObject::Options) | int(RowMajor), 1, ExpressionType::MaxColsAtCompileTime>
+      MatrixRowType;
+  typedef Array<Scalar, 1, ExpressionType::ColsAtCompileTime, int(ExpressionType::PlainObject::Options) | int(RowMajor),
+                1, ExpressionType::MaxColsAtCompileTime>
+      ArrayRowType;
+
+  typedef std::conditional_t<is_same<typename traits<ExpressionType>::XprKind, MatrixXpr>::value, MatrixRowType,
+                             ArrayRowType>
+      type;
+};
+
+template <typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
+struct plain_col_type {
+  typedef Matrix<Scalar, ExpressionType::RowsAtCompileTime, 1, ExpressionType::PlainObject::Options & ~RowMajor,
+                 ExpressionType::MaxRowsAtCompileTime, 1>
+      MatrixColType;
+  typedef Array<Scalar, ExpressionType::RowsAtCompileTime, 1, ExpressionType::PlainObject::Options & ~RowMajor,
+                ExpressionType::MaxRowsAtCompileTime, 1>
+      ArrayColType;
+
+  typedef std::conditional_t<is_same<typename traits<ExpressionType>::XprKind, MatrixXpr>::value, MatrixColType,
+                             ArrayColType>
+      type;
+};
+
+template <typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
+struct plain_diag_type {
+  enum {
+    diag_size = internal::min_size_prefer_dynamic(ExpressionType::RowsAtCompileTime, ExpressionType::ColsAtCompileTime),
+    max_diag_size = min_size_prefer_fixed(ExpressionType::MaxRowsAtCompileTime, ExpressionType::MaxColsAtCompileTime)
+  };
+  typedef Matrix<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1>
+      MatrixDiagType;
+  typedef Array<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> ArrayDiagType;
+
+  typedef std::conditional_t<is_same<typename traits<ExpressionType>::XprKind, MatrixXpr>::value, MatrixDiagType,
+                             ArrayDiagType>
+      type;
+};
+
+template <typename Expr, typename Scalar = typename Expr::Scalar>
+struct plain_constant_type {
+  enum { Options = (traits<Expr>::Flags & RowMajorBit) ? RowMajor : 0 };
+
+  typedef Array<Scalar, traits<Expr>::RowsAtCompileTime, traits<Expr>::ColsAtCompileTime, Options,
+                traits<Expr>::MaxRowsAtCompileTime, traits<Expr>::MaxColsAtCompileTime>
+      array_type;
+
+  typedef Matrix<Scalar, traits<Expr>::RowsAtCompileTime, traits<Expr>::ColsAtCompileTime, Options,
+                 traits<Expr>::MaxRowsAtCompileTime, traits<Expr>::MaxColsAtCompileTime>
+      matrix_type;
+
+  typedef CwiseNullaryOp<
+      scalar_constant_op<Scalar>,
+      const std::conditional_t<is_same<typename traits<Expr>::XprKind, MatrixXpr>::value, matrix_type, array_type>>
+      type;
+};
+
+template <typename ExpressionType>
+struct is_lvalue {
+  enum { value = (!bool(is_const<ExpressionType>::value)) && bool(traits<ExpressionType>::Flags & LvalueBit) };
+};
+
+template <typename T>
+struct is_diagonal {
+  enum { ret = false };
+};
+
+template <typename T>
+struct is_diagonal<DiagonalBase<T>> {
+  enum { ret = true };
+};
+
+template <typename T>
+struct is_diagonal<DiagonalWrapper<T>> {
+  enum { ret = true };
+};
+
+template <typename T, int S>
+struct is_diagonal<DiagonalMatrix<T, S>> {
+  enum { ret = true };
+};
+
+template <typename T>
+struct is_identity {
+  enum { value = false };
+};
+
+template <typename T>
+struct is_identity<CwiseNullaryOp<internal::scalar_identity_op<typename T::Scalar>, T>> {
+  enum { value = true };
+};
+
+template <typename S1, typename S2>
+struct glue_shapes;
+template <>
+struct glue_shapes<DenseShape, TriangularShape> {
+  typedef TriangularShape type;
+};
+
+template <typename T1, typename T2>
+struct possibly_same_dense {
+  enum {
+    value = has_direct_access<T1>::ret && has_direct_access<T2>::ret &&
+            is_same<typename T1::Scalar, typename T2::Scalar>::value
+  };
+};
+
+template <typename T1, typename T2>
+EIGEN_DEVICE_FUNC bool is_same_dense(const T1& mat1, const T2& mat2,
+                                     std::enable_if_t<possibly_same_dense<T1, T2>::value>* = 0) {
+  return (mat1.data() == mat2.data()) && (mat1.innerStride() == mat2.innerStride()) &&
+         (mat1.outerStride() == mat2.outerStride());
+}
+
+template <typename T1, typename T2>
+EIGEN_DEVICE_FUNC bool is_same_dense(const T1&, const T2&, std::enable_if_t<!possibly_same_dense<T1, T2>::value>* = 0) {
+  return false;
+}
+
+// Internal helper defining the cost of a scalar division for the type T.
+// The default heuristic can be specialized for each scalar type and architecture.
+template <typename T, bool Vectorized = false, typename EnableIf = void>
+struct scalar_div_cost {
+  enum { value = 8 * NumTraits<T>::MulCost };
+};
+
+template <typename T, bool Vectorized>
+struct scalar_div_cost<T, Vectorized, std::enable_if_t<NumTraits<T>::IsComplex>> {
+  using RealScalar = typename NumTraits<T>::Real;
+  enum {
+    value =
+        2 * scalar_div_cost<RealScalar>::value + 6 * NumTraits<RealScalar>::MulCost + 3 * NumTraits<RealScalar>::AddCost
+  };
+};
+
+template <bool Vectorized>
+struct scalar_div_cost<signed long, Vectorized, std::conditional_t<sizeof(long) == 8, void, false_type>> {
+  enum { value = 24 };
+};
+template <bool Vectorized>
+struct scalar_div_cost<unsigned long, Vectorized, std::conditional_t<sizeof(long) == 8, void, false_type>> {
+  enum { value = 21 };
+};
+
+#ifdef EIGEN_DEBUG_ASSIGN
+std::string demangle_traversal(int t) {
+  if (t == DefaultTraversal) return "DefaultTraversal";
+  if (t == LinearTraversal) return "LinearTraversal";
+  if (t == InnerVectorizedTraversal) return "InnerVectorizedTraversal";
+  if (t == LinearVectorizedTraversal) return "LinearVectorizedTraversal";
+  if (t == SliceVectorizedTraversal) return "SliceVectorizedTraversal";
+  return "?";
+}
+std::string demangle_unrolling(int t) {
+  if (t == NoUnrolling) return "NoUnrolling";
+  if (t == InnerUnrolling) return "InnerUnrolling";
+  if (t == CompleteUnrolling) return "CompleteUnrolling";
+  return "?";
+}
+std::string demangle_flags(int f) {
+  std::string res;
+  if (f & RowMajorBit) res += " | RowMajor";
+  if (f & PacketAccessBit) res += " | Packet";
+  if (f & LinearAccessBit) res += " | Linear";
+  if (f & LvalueBit) res += " | Lvalue";
+  if (f & DirectAccessBit) res += " | Direct";
+  if (f & NestByRefBit) res += " | NestByRef";
+  if (f & NoPreferredStorageOrderBit) res += " | NoPreferredStorageOrderBit";
+
+  return res;
+}
+#endif
+
+template <typename XprType>
+struct is_block_xpr : std::false_type {};
+
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+struct is_block_xpr<Block<XprType, BlockRows, BlockCols, InnerPanel>> : std::true_type {};
+
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+struct is_block_xpr<const Block<XprType, BlockRows, BlockCols, InnerPanel>> : std::true_type {};
+
+// Helper utility for constructing non-recursive block expressions.
+template <typename XprType>
+struct block_xpr_helper {
+  using BaseType = XprType;
+
+  // For regular block expressions, simply forward along the InnerPanel argument,
+  // which is set when calling row/column expressions.
+  static constexpr bool is_inner_panel(bool inner_panel) { return inner_panel; }
+
+  // Only enable non-const base function if XprType is not const (otherwise we get a duplicate definition).
+  template <typename T = XprType, typename EnableIf = std::enable_if_t<!std::is_const<T>::value>>
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BaseType& base(XprType& xpr) {
+    return xpr;
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const BaseType& base(const XprType& xpr) { return xpr; }
+  static constexpr EIGEN_ALWAYS_INLINE Index row(const XprType& /*xpr*/, Index r) { return r; }
+  static constexpr EIGEN_ALWAYS_INLINE Index col(const XprType& /*xpr*/, Index c) { return c; }
+};
+
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+struct block_xpr_helper<Block<XprType, BlockRows, BlockCols, InnerPanel>> {
+  using BlockXprType = Block<XprType, BlockRows, BlockCols, InnerPanel>;
+  // Recursive helper in case of explicit block-of-block expression.
+  using NestedXprHelper = block_xpr_helper<XprType>;
+  using BaseType = typename NestedXprHelper::BaseType;
+
+  // For block-of-block expressions, we need to combine the InnerPannel trait
+  // with that of the block subexpression.
+  static constexpr bool is_inner_panel(bool inner_panel) { return InnerPanel && inner_panel; }
+
+  // Only enable non-const base function if XprType is not const (otherwise we get a duplicates definition).
+  template <typename T = XprType, typename EnableIf = std::enable_if_t<!std::is_const<T>::value>>
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BaseType& base(BlockXprType& xpr) {
+    return NestedXprHelper::base(xpr.nestedExpression());
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const BaseType& base(const BlockXprType& xpr) {
+    return NestedXprHelper::base(xpr.nestedExpression());
+  }
+  static constexpr EIGEN_ALWAYS_INLINE Index row(const BlockXprType& xpr, Index r) {
+    return xpr.startRow() + NestedXprHelper::row(xpr.nestedExpression(), r);
+  }
+  static constexpr EIGEN_ALWAYS_INLINE Index col(const BlockXprType& xpr, Index c) {
+    return xpr.startCol() + NestedXprHelper::col(xpr.nestedExpression(), c);
+  }
+};
+
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+struct block_xpr_helper<const Block<XprType, BlockRows, BlockCols, InnerPanel>>
+    : block_xpr_helper<Block<XprType, BlockRows, BlockCols, InnerPanel>> {};
+
+template <typename XprType>
+struct is_matrix_base_xpr : std::is_base_of<MatrixBase<remove_all_t<XprType>>, remove_all_t<XprType>> {};
+
+template <typename XprType>
+struct is_permutation_base_xpr : std::is_base_of<PermutationBase<remove_all_t<XprType>>, remove_all_t<XprType>> {};
+
+}  // end namespace internal
+
+/** \class ScalarBinaryOpTraits
+  * \ingroup Core_Module
+  *
+  * \brief Determines whether the given binary operation of two numeric types is allowed and what the scalar return type
+  is.
+  *
+  * This class permits to control the scalar return type of any binary operation performed on two different scalar types
+  through (partial) template specializations.
+  *
+  * For instance, let \c U1, \c U2 and \c U3 be three user defined scalar types for which most operations between
+  instances of \c U1 and \c U2 returns an \c U3.
+  * You can let %Eigen knows that by defining:
+    \code
+    template<typename BinaryOp>
+    struct ScalarBinaryOpTraits<U1,U2,BinaryOp> { typedef U3 ReturnType;  };
+    template<typename BinaryOp>
+    struct ScalarBinaryOpTraits<U2,U1,BinaryOp> { typedef U3 ReturnType;  };
+    \endcode
+  * You can then explicitly disable some particular operations to get more explicit error messages:
+    \code
+    template<>
+    struct ScalarBinaryOpTraits<U1,U2,internal::scalar_max_op<U1,U2> > {};
+    \endcode
+  * Or customize the return type for individual operation:
+    \code
+    template<>
+    struct ScalarBinaryOpTraits<U1,U2,internal::scalar_sum_op<U1,U2> > { typedef U1 ReturnType; };
+    \endcode
+  *
+  * By default, the following generic combinations are supported:
+  <table class="manual">
+  <tr><th>ScalarA</th><th>ScalarB</th><th>BinaryOp</th><th>ReturnType</th><th>Note</th></tr>
+  <tr            ><td>\c T </td><td>\c T </td><td>\c * </td><td>\c T </td><td></td></tr>
+  <tr class="alt"><td>\c NumTraits<T>::Real </td><td>\c T </td><td>\c * </td><td>\c T </td><td>Only if \c
+  NumTraits<T>::IsComplex </td></tr> <tr            ><td>\c T </td><td>\c NumTraits<T>::Real </td><td>\c * </td><td>\c T
+  </td><td>Only if \c NumTraits<T>::IsComplex </td></tr>
+  </table>
+  *
+  * \sa CwiseBinaryOp
+  */
+template <typename ScalarA, typename ScalarB, typename BinaryOp = internal::scalar_product_op<ScalarA, ScalarB>>
+struct ScalarBinaryOpTraits
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    // for backward compatibility, use the hints given by the (deprecated) internal::scalar_product_traits class.
+    : internal::scalar_product_traits<ScalarA, ScalarB>
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+{
+};
+
+template <typename T, typename BinaryOp>
+struct ScalarBinaryOpTraits<T, T, BinaryOp> {
+  typedef T ReturnType;
+};
+
+template <typename T, typename BinaryOp>
+struct ScalarBinaryOpTraits<T, typename NumTraits<std::enable_if_t<NumTraits<T>::IsComplex, T>>::Real, BinaryOp> {
+  typedef T ReturnType;
+};
+template <typename T, typename BinaryOp>
+struct ScalarBinaryOpTraits<typename NumTraits<std::enable_if_t<NumTraits<T>::IsComplex, T>>::Real, T, BinaryOp> {
+  typedef T ReturnType;
+};
+
+// For Matrix * Permutation
+template <typename T, typename BinaryOp>
+struct ScalarBinaryOpTraits<T, void, BinaryOp> {
+  typedef T ReturnType;
+};
+
+// For Permutation * Matrix
+template <typename T, typename BinaryOp>
+struct ScalarBinaryOpTraits<void, T, BinaryOp> {
+  typedef T ReturnType;
+};
+
+// for Permutation*Permutation
+template <typename BinaryOp>
+struct ScalarBinaryOpTraits<void, void, BinaryOp> {
+  typedef void ReturnType;
+};
+
+// We require Lhs and Rhs to have "compatible" scalar types.
+// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized
+// paths. So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user
+// tries to add together a float matrix and a double matrix.
+#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP, LHS, RHS)                               \
+  EIGEN_STATIC_ASSERT(                                                                 \
+      (Eigen::internal::has_ReturnType<ScalarBinaryOpTraits<LHS, RHS, BINOP>>::value), \
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_XPRHELPER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexEigenSolver.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexEigenSolver.h
new file mode 100644
index 0000000..c0aae46
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexEigenSolver.h
@@ -0,0 +1,314 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Claire Maurice
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_EIGEN_SOLVER_H
+#define EIGEN_COMPLEX_EIGEN_SOLVER_H
+
+#include "./ComplexSchur.h"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class ComplexEigenSolver
+ *
+ * \brief Computes eigenvalues and eigenvectors of general complex matrices
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are
+ * computing the eigendecomposition; this is expected to be an
+ * instantiation of the Matrix class template.
+ *
+ * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars
+ * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v
+ * \f$.  If \f$ D \f$ is a diagonal matrix with the eigenvalues on
+ * the diagonal, and \f$ V \f$ is a matrix with the eigenvectors as
+ * its columns, then \f$ A V = V D \f$. The matrix \f$ V \f$ is
+ * almost always invertible, in which case we have \f$ A = V D V^{-1}
+ * \f$. This is called the eigendecomposition.
+ *
+ * The main function in this class is compute(), which computes the
+ * eigenvalues and eigenvectors of a given function. The
+ * documentation for that function contains an example showing the
+ * main features of the class.
+ *
+ * \sa class EigenSolver, class SelfAdjointEigenSolver
+ */
+template <typename MatrixType_>
+class ComplexEigenSolver {
+ public:
+  /** \brief Synonym for the template parameter \p MatrixType_. */
+  typedef MatrixType_ MatrixType;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    Options = internal::traits<MatrixType>::Options,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+
+  /** \brief Scalar type for matrices of type #MatrixType. */
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+
+  /** \brief Complex scalar type for #MatrixType.
+   *
+   * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
+   * \c float or \c double) and just \c Scalar if #Scalar is
+   * complex.
+   */
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+
+  /** \brief Type for vector of eigenvalues as returned by eigenvalues().
+   *
+   * This is a column vector with entries of type #ComplexScalar.
+   * The length of the vector is the size of #MatrixType.
+   */
+  typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & (~RowMajor), MaxColsAtCompileTime, 1> EigenvalueType;
+
+  /** \brief Type for matrix of eigenvectors as returned by eigenvectors().
+   *
+   * This is a square matrix with entries of type #ComplexScalar.
+   * The size is the same as the size of #MatrixType.
+   */
+  typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime,
+                 MaxColsAtCompileTime>
+      EigenvectorType;
+
+  /** \brief Default constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via compute().
+   */
+  ComplexEigenSolver()
+      : m_eivec(), m_eivalues(), m_schur(), m_isInitialized(false), m_eigenvectorsOk(false), m_matX() {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa ComplexEigenSolver()
+   */
+  explicit ComplexEigenSolver(Index size)
+      : m_eivec(size, size),
+        m_eivalues(size),
+        m_schur(size),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false),
+        m_matX(size, size) {}
+
+  /** \brief Constructor; computes eigendecomposition of given matrix.
+   *
+   * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
+   * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+   *    eigenvalues are computed; if false, only the eigenvalues are
+   *    computed.
+   *
+   * This constructor calls compute() to compute the eigendecomposition.
+   */
+  template <typename InputType>
+  explicit ComplexEigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
+      : m_eivec(matrix.rows(), matrix.cols()),
+        m_eivalues(matrix.cols()),
+        m_schur(matrix.rows()),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false),
+        m_matX(matrix.rows(), matrix.cols()) {
+    compute(matrix.derived(), computeEigenvectors);
+  }
+
+  /** \brief Returns the eigenvectors of given matrix.
+   *
+   * \returns  A const reference to the matrix whose columns are the eigenvectors.
+   *
+   * \pre Either the constructor
+   * ComplexEigenSolver(const MatrixType& matrix, bool) or the member
+   * function compute(const MatrixType& matrix, bool) has been called before
+   * to compute the eigendecomposition of a matrix, and
+   * \p computeEigenvectors was set to true (the default).
+   *
+   * This function returns a matrix whose columns are the eigenvectors. Column
+   * \f$ k \f$ is an eigenvector corresponding to eigenvalue number \f$ k
+   * \f$ as returned by eigenvalues().  The eigenvectors are normalized to
+   * have (Euclidean) norm equal to one. The matrix returned by this
+   * function is the matrix \f$ V \f$ in the eigendecomposition \f$ A = V D
+   * V^{-1} \f$, if it exists.
+   *
+   * Example: \include ComplexEigenSolver_eigenvectors.cpp
+   * Output: \verbinclude ComplexEigenSolver_eigenvectors.out
+   */
+  const EigenvectorType& eigenvectors() const {
+    eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec;
+  }
+
+  /** \brief Returns the eigenvalues of given matrix.
+   *
+   * \returns A const reference to the column vector containing the eigenvalues.
+   *
+   * \pre Either the constructor
+   * ComplexEigenSolver(const MatrixType& matrix, bool) or the member
+   * function compute(const MatrixType& matrix, bool) has been called before
+   * to compute the eigendecomposition of a matrix.
+   *
+   * This function returns a column vector containing the
+   * eigenvalues. Eigenvalues are repeated according to their
+   * algebraic multiplicity, so there are as many eigenvalues as
+   * rows in the matrix. The eigenvalues are not sorted in any particular
+   * order.
+   *
+   * Example: \include ComplexEigenSolver_eigenvalues.cpp
+   * Output: \verbinclude ComplexEigenSolver_eigenvalues.out
+   */
+  const EigenvalueType& eigenvalues() const {
+    eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
+    return m_eivalues;
+  }
+
+  /** \brief Computes eigendecomposition of given matrix.
+   *
+   * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
+   * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+   *    eigenvalues are computed; if false, only the eigenvalues are
+   *    computed.
+   * \returns    Reference to \c *this
+   *
+   * This function computes the eigenvalues of the complex matrix \p matrix.
+   * The eigenvalues() function can be used to retrieve them.  If
+   * \p computeEigenvectors is true, then the eigenvectors are also computed
+   * and can be retrieved by calling eigenvectors().
+   *
+   * The matrix is first reduced to Schur form using the
+   * ComplexSchur class. The Schur decomposition is then used to
+   * compute the eigenvalues and eigenvectors.
+   *
+   * The cost of the computation is dominated by the cost of the
+   * Schur decomposition, which is \f$ O(n^3) \f$ where \f$ n \f$
+   * is the size of the matrix.
+   *
+   * Example: \include ComplexEigenSolver_compute.cpp
+   * Output: \verbinclude ComplexEigenSolver_compute.out
+   */
+  template <typename InputType>
+  ComplexEigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
+    return m_schur.info();
+  }
+
+  /** \brief Sets the maximum number of iterations allowed. */
+  ComplexEigenSolver& setMaxIterations(Index maxIters) {
+    m_schur.setMaxIterations(maxIters);
+    return *this;
+  }
+
+  /** \brief Returns the maximum number of iterations. */
+  Index getMaxIterations() { return m_schur.getMaxIterations(); }
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  EigenvectorType m_eivec;
+  EigenvalueType m_eivalues;
+  ComplexSchur<MatrixType> m_schur;
+  bool m_isInitialized;
+  bool m_eigenvectorsOk;
+  EigenvectorType m_matX;
+
+ private:
+  void doComputeEigenvectors(RealScalar matrixnorm);
+  void sortEigenvalues(bool computeEigenvectors);
+};
+
+template <typename MatrixType>
+template <typename InputType>
+ComplexEigenSolver<MatrixType>& ComplexEigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix,
+                                                                        bool computeEigenvectors) {
+  // this code is inspired from Jampack
+  eigen_assert(matrix.cols() == matrix.rows());
+
+  // Do a complex Schur decomposition, A = U T U^*
+  // The eigenvalues are on the diagonal of T.
+  m_schur.compute(matrix.derived(), computeEigenvectors);
+
+  if (m_schur.info() == Success) {
+    m_eivalues = m_schur.matrixT().diagonal();
+    if (computeEigenvectors) doComputeEigenvectors(m_schur.matrixT().norm());
+    sortEigenvalues(computeEigenvectors);
+  }
+
+  m_isInitialized = true;
+  m_eigenvectorsOk = computeEigenvectors;
+  return *this;
+}
+
+template <typename MatrixType>
+void ComplexEigenSolver<MatrixType>::doComputeEigenvectors(RealScalar matrixnorm) {
+  const Index n = m_eivalues.size();
+
+  // Compute X such that T = X D X^(-1), where D is the diagonal of T.
+  // The matrix X is unit triangular.
+  m_matX = EigenvectorType::Zero(n, n);
+  for (Index k = n - 1; k >= 0; k--) {
+    m_matX.coeffRef(k, k) = ComplexScalar(1.0, 0.0);
+    // Compute X(i,k) using the (i,k) entry of the equation X T = D X
+    for (Index i = k - 1; i >= 0; i--) {
+      m_matX.coeffRef(i, k) = -m_schur.matrixT().coeff(i, k);
+      if (k - i - 1 > 0)
+        m_matX.coeffRef(i, k) -=
+            (m_schur.matrixT().row(i).segment(i + 1, k - i - 1) * m_matX.col(k).segment(i + 1, k - i - 1)).value();
+      ComplexScalar z = m_schur.matrixT().coeff(i, i) - m_schur.matrixT().coeff(k, k);
+      if (z == ComplexScalar(0)) {
+        // If the i-th and k-th eigenvalue are equal, then z equals 0.
+        // Use a small value instead, to prevent division by zero.
+        numext::real_ref(z) = numext::maxi(std::numeric_limits<RealScalar>::epsilon() * matrixnorm,
+                                           (std::numeric_limits<RealScalar>::min)());
+      }
+      m_matX.coeffRef(i, k) = m_matX.coeff(i, k) / z;
+    }
+  }
+
+  // Compute V as V = U X; now A = U T U^* = U X D X^(-1) U^* = V D V^(-1)
+  m_eivec.noalias() = m_schur.matrixU() * m_matX;
+  // .. and normalize the eigenvectors
+  for (Index k = 0; k < n; k++) {
+    m_eivec.col(k).stableNormalize();
+  }
+}
+
+template <typename MatrixType>
+void ComplexEigenSolver<MatrixType>::sortEigenvalues(bool computeEigenvectors) {
+  const Index n = m_eivalues.size();
+  for (Index i = 0; i < n; i++) {
+    Index k;
+    m_eivalues.cwiseAbs().tail(n - i).minCoeff(&k);
+    if (k != 0) {
+      k += i;
+      std::swap(m_eivalues[k], m_eivalues[i]);
+      if (computeEigenvectors) m_eivec.col(i).swap(m_eivec.col(k));
+    }
+  }
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_EIGEN_SOLVER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexQZ.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexQZ.h
new file mode 100644
index 0000000..1d5f8d0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexQZ.h
@@ -0,0 +1,651 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Alexey Korepanov
+// Copyright (C) 2025 Ludwig Striet <ludwig.striet@mathematik.uni-freiburg.de>
+//
+// This Source Code Form is subject to the terms of the
+// Mozilla Public License v. 2.0. If a copy of the MPL
+// was not distributed with this file, You can obtain one at
+// https://mozilla.org/MPL/2.0/.
+//
+// Derived from: Eigen/src/Eigenvalues/RealQZ.h
+
+#ifndef EIGEN_COMPLEX_QZ_H_
+#define EIGEN_COMPLEX_QZ_H_
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class ComplexQZ
+ *
+ * \brief Performs a QZ decomposition of a pair of matrices A, B
+ *
+ * \tparam MatrixType_ the type input type of the matrix.
+ *
+ * Given to complex square matrices A and B, this class computes the QZ decomposition
+ * \f$ A = Q S Z \f$, \f$ B = Q T Z\f$ where Q and Z are unitary matrices and
+ * S and T a re upper-triangular matrices. More precisely, Q and Z fulfill
+ * \f$ Q Q* = Id\f$ and \f$ Z Z* = Id\f$. The generalized Eigenvalues are then
+ * obtained as ratios of corresponding diagonal entries, lambda(i) = S(i,i) / T(i, i).
+ *
+ * The QZ algorithm was introduced in the seminal work "An Algorithm for
+ * Generalized Matrix Eigenvalue Problems" by Moler & Stewart in 1973. The matrix
+ * pair S = A, T = B is first transformed to Hessenberg-Triangular form where S is an
+ * upper Hessenberg matrix and T is an upper Triangular matrix.
+ *
+ * This pair is subsequently reduced to the desired form using implicit QZ shifts as
+ * described in the original paper. The algorithms to find small entries on the
+ * diagonals and subdiagonals are based on the variants in the implementation
+ * for Real matrices in the RealQZ class.
+ *
+ * \sa class RealQZ
+ */
+
+namespace Eigen {
+
+template <typename MatrixType_>
+class ComplexQZ {
+ public:
+  using MatrixType = MatrixType_;
+  using Scalar = typename MatrixType_::Scalar;
+  using RealScalar = typename MatrixType_::RealScalar;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    Options = internal::traits<MatrixType>::Options,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+
+  using Vec = Matrix<Scalar, Dynamic, 1>;
+  using Vec2 = Matrix<Scalar, 2, 1>;
+  using Vec3 = Matrix<Scalar, 3, 1>;
+  using Row2 = Matrix<Scalar, 1, 2>;
+  using Mat2 = Matrix<Scalar, 2, 2>;
+
+  /** \brief Returns matrix Q in the QZ decomposition.
+   *
+   * \returns A const reference to the matrix Q.
+   */
+  const MatrixType& matrixQ() const {
+    eigen_assert(m_isInitialized && "ComplexQZ is not initialized.");
+    eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition.");
+    return m_Q;
+  }
+
+  /** \brief Returns matrix Z in the QZ decomposition.
+   *
+   * \returns A const reference to the matrix Z.
+   */
+  const MatrixType& matrixZ() const {
+    eigen_assert(m_isInitialized && "ComplexQZ is not initialized.");
+    eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition.");
+    return m_Z;
+  }
+
+  /** \brief Returns matrix S in the QZ decomposition.
+   *
+   * \returns A const reference to the matrix S.
+   */
+  const MatrixType& matrixS() const {
+    eigen_assert(m_isInitialized && "ComplexQZ is not initialized.");
+    return m_S;
+  }
+
+  /** \brief Returns matrix S in the QZ decomposition.
+   *
+   * \returns A const reference to the matrix S.
+   */
+  const MatrixType& matrixT() const {
+    eigen_assert(m_isInitialized && "ComplexQZ is not initialized.");
+    return m_T;
+  }
+
+  /** \brief Constructor
+   *
+   * \param[in] n size of the matrices whose QZ decomposition we compute
+   *
+   * This constructor is used when we use the compute(...) method later,
+   * especially when we aim to compute the decomposition of two sparse
+   * matrices.
+   */
+  ComplexQZ(Index n, bool computeQZ = true, unsigned int maxIters = 400)
+      : m_n(n),
+        m_S(n, n),
+        m_T(n, n),
+        m_Q(computeQZ ? n : (MatrixType::RowsAtCompileTime == Eigen::Dynamic ? 0 : MatrixType::RowsAtCompileTime),
+            computeQZ ? n : (MatrixType::ColsAtCompileTime == Eigen::Dynamic ? 0 : MatrixType::ColsAtCompileTime)),
+        m_Z(computeQZ ? n : (MatrixType::RowsAtCompileTime == Eigen::Dynamic ? 0 : MatrixType::RowsAtCompileTime),
+            computeQZ ? n : (MatrixType::ColsAtCompileTime == Eigen::Dynamic ? 0 : MatrixType::ColsAtCompileTime)),
+        m_ws(2 * n),
+        m_computeQZ(computeQZ),
+        m_maxIters(maxIters) {}
+
+  /** \brief Constructor. computes the QZ decomposition of given matrices
+   * upon creation
+   *
+   * \param[in] A         input matrix A
+   * \param[in] B         input matrix B
+   * \param[in] computeQZ If false, the matrices Q and Z are not computed
+   *
+   * This constructor calls the compute() method to compute the QZ decomposition.
+   * If input matrices are sparse, call the constructor that uses only the
+   * size as input the computeSparse(...) method.
+   */
+  ComplexQZ(const MatrixType& A, const MatrixType& B, bool computeQZ = true, unsigned int maxIters = 400)
+      : m_n(A.rows()),
+        m_maxIters(maxIters),
+        m_computeQZ(computeQZ),
+        m_S(A.rows(), A.cols()),
+        m_T(A.rows(), A.cols()),
+        m_Q(computeQZ ? m_n : (MatrixType::RowsAtCompileTime == Eigen::Dynamic ? 0 : MatrixType::RowsAtCompileTime),
+            computeQZ ? m_n : (MatrixType::ColsAtCompileTime == Eigen::Dynamic ? 0 : MatrixType::ColsAtCompileTime)),
+        m_Z(computeQZ ? m_n : (MatrixType::RowsAtCompileTime == Eigen::Dynamic ? 0 : MatrixType::RowsAtCompileTime),
+            computeQZ ? m_n : (MatrixType::ColsAtCompileTime == Eigen::Dynamic ? 0 : MatrixType::ColsAtCompileTime)),
+        m_ws(2 * m_n) {
+    compute(A, B, computeQZ);
+  }
+
+  /** \brief Compute the QZ decomposition of complex input matrices
+   *
+   * \param[in] A         Matrix A.
+   * \param[in] B         Matrix B.
+   * \param[in] computeQZ If false, the matrices Q and Z are not computed.
+   */
+  void compute(const MatrixType& A, const MatrixType& B, bool computeQZ = true);
+
+  /** \brief Compute the decomposition of sparse complex input matrices.
+   * Main difference to the compute(...) method is that it computes a
+   * SparseQR decomposition of B
+   *
+   * \param[in] A         Matrix A.
+   * \param[in] B         Matrix B.
+   * \param[in] computeQZ If false, the matrices Q and Z are not computed.
+   */
+  template <typename SparseMatrixType_>
+  void computeSparse(const SparseMatrixType_& A, const SparseMatrixType_& B, bool computeQZ = true);
+
+  /** \brief Reports whether the last computation was successful.
+   *
+   * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+   */
+  ComputationInfo info() const { return m_info; }
+
+  /** \brief number of performed QZ steps
+   */
+  unsigned int iterations() const {
+    eigen_assert(m_isInitialized && "ComplexQZ is not initialized.");
+    return m_global_iter;
+  }
+
+ private:
+  Index m_n;
+  const unsigned int m_maxIters;
+  unsigned int m_global_iter;
+  bool m_isInitialized;
+  bool m_computeQZ;
+  ComputationInfo m_info;
+  MatrixType m_S, m_T, m_Q, m_Z;
+  RealScalar m_normOfT, m_normOfS;
+  Vec m_ws;
+
+  // Test if a Scalar is 0 up to a certain tolerance
+  static bool is_negligible(const Scalar x, const RealScalar tol = NumTraits<RealScalar>::epsilon()) {
+    return numext::abs(x) <= tol;
+  }
+
+  void do_QZ_step(Index p, Index q);
+
+  inline Mat2 computeZk2(const Row2& b);
+
+  // This is basically taken from Eigen3::RealQZ
+  void hessenbergTriangular(const MatrixType& A, const MatrixType& B);
+
+  // This function can be called when m_Q and m_Z are initialized and m_S, m_T
+  // are in hessenberg-triangular form
+  void reduceHessenbergTriangular();
+
+  // Sparse variant of the above method.
+  template <typename SparseMatrixType_>
+  void hessenbergTriangularSparse(const SparseMatrixType_& A, const SparseMatrixType_& B);
+
+  void computeNorms();
+
+  Index findSmallSubdiagEntry(Index l);
+  Index findSmallDiagEntry(Index f, Index l);
+
+  void push_down_zero_ST(Index k, Index l);
+
+  void reduceDiagonal2x2block(Index i);
+};
+
+template <typename MatrixType_>
+void ComplexQZ<MatrixType_>::compute(const MatrixType& A, const MatrixType& B, bool computeQZ) {
+  m_computeQZ = computeQZ;
+  m_n = A.rows();
+
+  eigen_assert(m_n == A.cols() && "A is not a square matrix");
+  eigen_assert(m_n == B.rows() && m_n == B.cols() && "B is not a square matrix or B is not of the same size as A");
+
+  m_isInitialized = true;
+  m_global_iter = 0;
+
+  // This will initialize m_Q and m_Z and bring m_S, m_T to hessenberg-triangular form
+  hessenbergTriangular(A, B);
+
+  // We assume that we already have that S is upper-Hessenberg and T is
+  // upper-triangular. This is what the hessenbergTriangular(...) method does
+  reduceHessenbergTriangular();
+}
+
+// This is basically taken from Eigen3::RealQZ
+template <typename MatrixType_>
+void ComplexQZ<MatrixType_>::hessenbergTriangular(const MatrixType& A, const MatrixType& B) {
+  // Copy A and B, these will be the matrices on which we operate later
+  m_S = A;
+  m_T = B;
+
+  // Perform QR decomposition of the matrix Q
+  HouseholderQR<MatrixType> qr(m_T);
+  m_T = qr.matrixQR();
+  m_T.template triangularView<StrictlyLower>().setZero();
+
+  if (m_computeQZ) m_Q = qr.householderQ();
+
+  // overwrite S with Q* x S
+  m_S.applyOnTheLeft(qr.householderQ().adjoint());
+
+  if (m_computeQZ) m_Z = MatrixType::Identity(m_n, m_n);
+
+  // reduce S to upper Hessenberg with Givens rotations
+  for (Index j = 0; j <= m_n - 3; j++) {
+    for (Index i = m_n - 1; i >= j + 2; i--) {
+      JacobiRotation<Scalar> G;
+      // delete S(i,j)
+      if (!numext::is_exactly_zero(m_S.coeff(i, j))) {
+        G.makeGivens(m_S.coeff(i - 1, j), m_S.coeff(i, j), &m_S.coeffRef(i - 1, j));
+        m_S.coeffRef(i, j) = Scalar(0);
+        m_T.rightCols(m_n - i + 1).applyOnTheLeft(i - 1, i, G.adjoint());
+        m_S.rightCols(m_n - j - 1).applyOnTheLeft(i - 1, i, G.adjoint());
+        // This is what we want to achieve
+        if (!is_negligible(m_S(i, j)))
+          m_info = ComputationInfo::NumericalIssue;
+        else
+          m_S(i, j) = Scalar(0);
+        // update Q
+        if (m_computeQZ) m_Q.applyOnTheRight(i - 1, i, G);
+      }
+
+      if (!numext::is_exactly_zero(m_T.coeff(i, i - 1))) {
+        // Compute rotation and update matrix T
+        G.makeGivens(m_T.coeff(i, i), m_T.coeff(i, i - 1), &m_T.coeffRef(i, i));
+        m_T.topRows(i).applyOnTheRight(i - 1, i, G.adjoint());
+        m_T.coeffRef(i, i - 1) = Scalar(0);
+        // Update matrix S
+        m_S.applyOnTheRight(i - 1, i, G.adjoint());
+        // update Z
+        if (m_computeQZ) m_Z.applyOnTheLeft(i - 1, i, G);
+      }
+    }
+  }
+}
+
+template <typename MatrixType>
+template <typename SparseMatrixType_>
+void ComplexQZ<MatrixType>::hessenbergTriangularSparse(const SparseMatrixType_& A, const SparseMatrixType_& B) {
+  m_S = A.toDense();
+
+  SparseQR<SparseMatrix<Scalar, ColMajor>, NaturalOrdering<Index>> sparseQR;
+
+  eigen_assert(B.isCompressed() &&
+               "SparseQR requires a sparse matrix in compressed mode."
+               "Call .makeCompressed() before passing it to SparseQR");
+
+  // Computing QR decomposition of T...
+  sparseQR.setPivotThreshold(RealScalar(0));  // This prevends algorithm from doing pivoting
+  sparseQR.compute(B);
+  // perform QR decomposition of T, overwrite T with R, save Q
+  // HouseholderQR<Mat> qrT(m_T);
+  m_T = sparseQR.matrixR();
+  m_T.template triangularView<StrictlyLower>().setZero();
+
+  if (m_computeQZ) m_Q = sparseQR.matrixQ();
+
+  // overwrite S with Q* S
+  m_S = sparseQR.matrixQ().adjoint() * m_S;
+
+  if (m_computeQZ) m_Z = MatrixType::Identity(m_n, m_n);
+
+  // reduce S to upper Hessenberg with Givens rotations
+  for (Index j = 0; j <= m_n - 3; j++) {
+    for (Index i = m_n - 1; i >= j + 2; i--) {
+      JacobiRotation<Scalar> G;
+      // kill S(i,j)
+      // if(!numext::is_exactly_zero(_S.coeff(i, j)))
+      if (m_S.coeff(i, j) != Scalar(0)) {
+        // This is the adapted code
+        G.makeGivens(m_S.coeff(i - 1, j), m_S.coeff(i, j), &m_S.coeffRef(i - 1, j));
+        m_S.coeffRef(i, j) = Scalar(0);
+        m_T.rightCols(m_n - i + 1).applyOnTheLeft(i - 1, i, G.adjoint());
+        m_S.rightCols(m_n - j - 1).applyOnTheLeft(i - 1, i, G.adjoint());
+        // This is what we want to achieve
+        if (!is_negligible(m_S(i, j))) {
+          m_info = ComputationInfo::NumericalIssue;
+        }
+        m_S(i, j) = Scalar(0);
+        // update Q
+        if (m_computeQZ) m_Q.applyOnTheRight(i - 1, i, G);
+      }
+
+      if (!numext::is_exactly_zero(m_T.coeff(i, i - 1))) {
+        // Compute rotation and update matrix T
+        G.makeGivens(m_T.coeff(i, i), m_T.coeff(i, i - 1), &m_T.coeffRef(i, i));
+        m_T.topRows(i).applyOnTheRight(i - 1, i, G.adjoint());
+        m_T.coeffRef(i, i - 1) = Scalar(0);
+        // Update matrix S
+        m_S.applyOnTheRight(i - 1, i, G.adjoint());
+        // update Z
+        if (m_computeQZ) m_Z.applyOnTheLeft(i - 1, i, G);
+      }
+    }
+  }
+}
+
+template <typename MatrixType>
+template <typename SparseMatrixType_>
+void ComplexQZ<MatrixType>::computeSparse(const SparseMatrixType_& A, const SparseMatrixType_& B, bool computeQZ) {
+  m_computeQZ = computeQZ;
+  m_n = A.rows();
+  eigen_assert(m_n == A.cols() && "A is not a square matrix");
+  eigen_assert(m_n == B.rows() && m_n == B.cols() && "B is not a square matrix or B is not of the same size as A");
+  m_isInitialized = true;
+  m_global_iter = 0;
+  hessenbergTriangularSparse(A, B);
+
+  // We assume that we already have that A is upper-Hessenberg and B is
+  // upper-triangular. This is what the hessenbergTriangular(...) method does
+  reduceHessenbergTriangular();
+}
+
+template <typename MatrixType_>
+void ComplexQZ<MatrixType_>::reduceHessenbergTriangular() {
+  Index l = m_n - 1, f;
+  unsigned int local_iter = 0;
+  computeNorms();
+
+  while (l > 0 && local_iter < m_maxIters) {
+    f = findSmallSubdiagEntry(l);
+
+    // Subdiag entry is small -> can be safely set to 0
+    if (f > 0) {
+      m_S.coeffRef(f, f - 1) = Scalar(0);
+    }
+    if (f == l) {  // One root found
+      l--;
+      local_iter = 0;
+    } else if (f == l - 1) {  // Two roots found
+      // We found an undesired non-zero at (f+1,f) in S and eliminate it immediately
+      reduceDiagonal2x2block(f);
+      l -= 2;
+      local_iter = 0;
+    } else {
+      Index z = findSmallDiagEntry(f, l);
+      if (z >= f) {
+        push_down_zero_ST(z, l);
+      } else {
+        do_QZ_step(f, m_n - l - 1);
+        local_iter++;
+        m_global_iter++;
+      }
+    }
+  }
+
+  m_info = (local_iter < m_maxIters) ? Success : NoConvergence;
+}
+
+template <typename MatrixType_>
+inline typename ComplexQZ<MatrixType_>::Mat2 ComplexQZ<MatrixType_>::computeZk2(const Row2& b) {
+  Mat2 S;
+  S << Scalar(0), Scalar(1), Scalar(1), Scalar(0);
+  Vec2 bprime = S * b.adjoint();
+  JacobiRotation<Scalar> J;
+  J.makeGivens(bprime(0), bprime(1));
+  Mat2 Z = S;
+  Z.applyOnTheLeft(0, 1, J);
+  Z = S * Z;
+  return Z;
+}
+
+template <typename MatrixType_>
+void ComplexQZ<MatrixType_>::do_QZ_step(Index p, Index q) {
+  // This is certainly not the most efficient way of doing this,
+  // but a readable one.
+  const auto a = [p, this](Index i, Index j) { return m_S(p + i - 1, p + j - 1); };
+  const auto b = [p, this](Index i, Index j) { return m_T(p + i - 1, p + j - 1); };
+  const Index m = m_n - p - q;  // Size of the inner block
+  Scalar x, y, z;
+  // We could introduce doing exceptional shifts from time to time.
+  Scalar W1 = a(m - 1, m - 1) / b(m - 1, m - 1) - a(1, 1) / b(1, 1), W2 = a(m, m) / b(m, m) - a(1, 1) / b(1, 1),
+         W3 = a(m, m - 1) / b(m - 1, m - 1);
+
+  x = (W1 * W2 - a(m - 1, m) / b(m, m) * W3 + W3 * b(m - 1, m) / b(m, m) * a(1, 1) / b(1, 1)) * b(1, 1) / a(2, 1) +
+      a(1, 2) / b(2, 2) - a(1, 1) / b(1, 1) * b(1, 2) / b(2, 2);
+  y = (a(2, 2) / b(2, 2) - a(1, 1) / b(1, 1)) - a(2, 1) / b(1, 1) * b(1, 2) / b(2, 2) - W1 - W2 +
+      W3 * (b(m - 1, m) / b(m, m));
+  z = a(3, 2) / b(2, 2);
+  Vec3 X;
+  const PermutationMatrix<3, 3, int> S3(Vector3i(2, 0, 1));
+  for (Index k = p; k < p + m - 2; k++) {
+    X << x, y, z;
+    Vec2 ess;
+    Scalar tau;
+    RealScalar beta;
+    X.makeHouseholder(ess, tau, beta);
+    // The permutations are needed because the makeHouseHolder-method computes
+    // the householder transformation in a way that the vector is reflected to
+    // (1 0 ... 0) instead of (0 ... 0 1)
+    m_S.template middleRows<3>(k)
+        .rightCols((std::min)(m_n, m_n - k + 1))
+        .applyHouseholderOnTheLeft(ess, tau, m_ws.data());
+    m_T.template middleRows<3>(k).rightCols(m_n - k).applyHouseholderOnTheLeft(ess, tau, m_ws.data());
+    if (m_computeQZ) m_Q.template middleCols<3>(k).applyHouseholderOnTheRight(ess, std::conj(tau), m_ws.data());
+
+    // Compute Matrix Zk1 s.t. (b(k+2,k) ... b(k+2, k+2)) Zk1 = (0,0,*)
+    Vec3 bprime = (m_T.template block<1, 3>(k + 2, k) * S3).adjoint();
+    bprime.makeHouseholder(ess, tau, beta);
+    m_S.template middleCols<3>(k).topRows((std::min)(k + 4, m_n)).applyOnTheRight(S3);
+    m_S.template middleCols<3>(k)
+        .topRows((std::min)(k + 4, m_n))
+        .applyHouseholderOnTheRight(ess, std::conj(tau), m_ws.data());
+    m_S.template middleCols<3>(k).topRows((std::min)(k + 4, m_n)).applyOnTheRight(S3.transpose());
+    m_T.template middleCols<3>(k).topRows((std::min)(k + 3, m_n)).applyOnTheRight(S3);
+    m_T.template middleCols<3>(k)
+        .topRows((std::min)(k + 3, m_n))
+        .applyHouseholderOnTheRight(ess, std::conj(tau), m_ws.data());
+    m_T.template middleCols<3>(k).topRows((std::min)(k + 3, m_n)).applyOnTheRight(S3.transpose());
+    if (m_computeQZ) {
+      m_Z.template middleRows<3>(k).applyOnTheLeft(S3.transpose());
+      m_Z.template middleRows<3>(k).applyHouseholderOnTheLeft(ess, tau, m_ws.data());
+      m_Z.template middleRows<3>(k).applyOnTheLeft(S3);
+    }
+    Mat2 Zk2 = computeZk2(m_T.template block<1, 2>(k + 1, k));
+    m_S.template middleCols<2>(k).topRows((std::min)(k + 4, m_n)).applyOnTheRight(Zk2);
+    m_T.template middleCols<2>(k).topRows((std::min)(k + 3, m_n)).applyOnTheRight(Zk2);
+
+    if (m_computeQZ) m_Z.template middleRows<2>(k).applyOnTheLeft(Zk2.adjoint());
+
+    x = m_S(k + 1, k);
+    y = m_S(k + 2, k);
+    if (k < p + m - 3) {
+      z = m_S(k + 3, k);
+    }
+  };
+
+  // Find a Householdermartirx Qn1 s.t. Qn1 (x y)^T = (* 0)
+  JacobiRotation<Scalar> J;
+  J.makeGivens(x, y);
+  m_S.template middleRows<2>(p + m - 2).applyOnTheLeft(0, 1, J.adjoint());
+  m_T.template middleRows<2>(p + m - 2).applyOnTheLeft(0, 1, J.adjoint());
+
+  if (m_computeQZ) m_Q.template middleCols<2>(p + m - 2).applyOnTheRight(0, 1, J);
+
+  // Find a Householdermatrix Zn1 s.t. (b(n,n-1) b(n,n)) * Zn1 = (0 *)
+  Mat2 Zn1 = computeZk2(m_T.template block<1, 2>(p + m - 1, p + m - 2));
+  m_S.template middleCols<2>(p + m - 2).applyOnTheRight(Zn1);
+  m_T.template middleCols<2>(p + m - 2).applyOnTheRight(Zn1);
+
+  if (m_computeQZ) m_Z.template middleRows<2>(p + m - 2).applyOnTheLeft(Zn1.adjoint());
+}
+
+/** \internal we found an undesired non-zero at (i+1,i) on the subdiagonal of S and reduce the block */
+template <typename MatrixType_>
+void ComplexQZ<MatrixType_>::reduceDiagonal2x2block(Index i) {
+  // We have found a non-zero on the subdiagonal and want to eliminate it
+  Mat2 Si = m_S.template block<2, 2>(i, i), Ti = m_T.template block<2, 2>(i, i);
+  if (is_negligible(Ti(0, 0)) && !is_negligible(Ti(1, 1))) {
+    Eigen::JacobiRotation<Scalar> G;
+    G.makeGivens(m_S(i, i), m_S(i + 1, i));
+    m_S.applyOnTheLeft(i, i + 1, G.adjoint());
+    m_T.applyOnTheLeft(i, i + 1, G.adjoint());
+
+    if (m_computeQZ) m_Q.applyOnTheRight(i, i + 1, G);
+
+  } else if (!is_negligible(Ti(0, 0)) && is_negligible(Ti(1, 1))) {
+    Eigen::JacobiRotation<Scalar> G;
+    G.makeGivens(m_S(i + 1, i + 1), m_S(i + 1, i));
+    m_S.applyOnTheRight(i, i + 1, G.adjoint());
+    m_T.applyOnTheRight(i, i + 1, G.adjoint());
+    if (m_computeQZ) m_Z.applyOnTheLeft(i, i + 1, G);
+  } else if (!is_negligible(Ti(0, 0)) && !is_negligible((Ti(1, 1)))) {
+    Scalar mu = Si(0, 0) / Ti(0, 0);
+    Scalar a12_bar = Si(0, 1) - mu * Ti(0, 1);
+    Scalar a22_bar = Si(1, 1) - mu * Ti(1, 1);
+    Scalar p = Scalar(0.5) * (a22_bar / Ti(1, 1) - Ti(0, 1) * Si(1, 0) / (Ti(0, 0) * Ti(1, 1)));
+    RealScalar sgn_p = p.real() >= RealScalar(0) ? RealScalar(1) : RealScalar(-1);
+    Scalar q = Si(1, 0) * a12_bar / (Ti(0, 0) * Ti(1, 1));
+    Scalar r = p * p + q;
+    Scalar lambda = mu + p + sgn_p * numext::sqrt(r);
+    Mat2 E = Si - lambda * Ti;
+    Index l;
+    E.rowwise().norm().maxCoeff(&l);
+    JacobiRotation<Scalar> G;
+    G.makeGivens(E(l, 1), E(l, 0));
+    m_S.applyOnTheRight(i, i + 1, G.adjoint());
+    m_T.applyOnTheRight(i, i + 1, G.adjoint());
+
+    if (m_computeQZ) m_Z.applyOnTheLeft(i, i + 1, G);
+
+    Mat2 tildeSi = m_S.template block<2, 2>(i, i), tildeTi = m_T.template block<2, 2>(i, i);
+    Mat2 C = tildeSi.norm() < (lambda * tildeTi).norm() ? tildeSi : lambda * tildeTi;
+    G.makeGivens(C(0, 0), C(1, 0));
+    m_S.applyOnTheLeft(i, i + 1, G.adjoint());
+    m_T.applyOnTheLeft(i, i + 1, G.adjoint());
+
+    if (m_computeQZ) m_Q.applyOnTheRight(i, i + 1, G);
+  }
+
+  if (!is_negligible(m_S(i + 1, i), m_normOfS * NumTraits<RealScalar>::epsilon())) {
+    m_info = ComputationInfo::NumericalIssue;
+  } else {
+    m_S(i + 1, i) = Scalar(0);
+  }
+}
+
+/** \internal We found a zero at T(k,k) and want to "push it down" to T(l,l) */
+template <typename MatrixType_>
+void ComplexQZ<MatrixType_>::push_down_zero_ST(Index k, Index l) {
+  // Test Preconditions
+
+  JacobiRotation<Scalar> J;
+  for (Index j = k + 1; j <= l; j++) {
+    // Create a 0 at _T(j, j)
+    J.makeGivens(m_T(j - 1, j), m_T(j, j), &m_T.coeffRef(j - 1, j));
+    if (m_n - j - 1 > 0) {
+      m_T.rightCols(m_n - j - 1).applyOnTheLeft(j - 1, j, J.adjoint());
+    }
+    m_T.coeffRef(j, j) = Scalar(0);
+
+    m_S.applyOnTheLeft(j - 1, j, J.adjoint());
+
+    if (m_computeQZ) m_Q.applyOnTheRight(j - 1, j, J);
+
+    // Delete the non-desired non-zero at _S(j, j-2)
+    if (j > 1) {
+      J.makeGivens(std::conj(m_S(j, j - 1)), std::conj(m_S(j, j - 2)));
+      m_S.applyOnTheRight(j - 1, j - 2, J);
+      m_S(j, j - 2) = Scalar(0);
+      m_T.applyOnTheRight(j - 1, j - 2, J);
+      if (m_computeQZ) m_Z.applyOnTheLeft(j - 1, j - 2, J.adjoint());
+    }
+  }
+
+  // Assume we have the desired structure now, up to the non-zero entry at
+  // _S(l, l-1) which we will delete through a last right-jacobi-rotation
+  J.makeGivens(std::conj(m_S(l, l)), std::conj(m_S(l, l - 1)));
+  m_S.topRows(l + 1).applyOnTheRight(l, l - 1, J);
+
+  if (!is_negligible(m_S(l, l - 1), m_normOfS * NumTraits<Scalar>::epsilon())) {
+    m_info = ComputationInfo::NumericalIssue;
+  } else {
+    m_S(l, l - 1) = Scalar(0);
+  }
+  m_T.topRows(l + 1).applyOnTheRight(l, l - 1, J);
+
+  if (m_computeQZ) m_Z.applyOnTheLeft(l, l - 1, J.adjoint());
+
+  // Ensure postconditions
+  if (!is_negligible(m_T(l, l)) || !is_negligible(m_S(l, l - 1))) {
+    m_info = ComputationInfo::NumericalIssue;
+  } else {
+    m_T(l, l) = Scalar(0);
+    m_S(l, l - 1) = Scalar(0);
+  }
+}
+
+/** \internal Computes vector L1 norms of S and T when in Hessenberg-Triangular form already */
+template <typename MatrixType_>
+void ComplexQZ<MatrixType_>::computeNorms() {
+  const Index size = m_S.cols();
+  m_normOfS = RealScalar(0);
+  m_normOfT = RealScalar(0);
+  for (Index j = 0; j < size; ++j) {
+    m_normOfS += m_S.col(j).segment(0, (std::min)(size, j + 2)).cwiseAbs().sum();
+    m_normOfT += m_T.row(j).segment(j, size - j).cwiseAbs().sum();
+  }
+}
+
+/** \internal Look for single small sub-diagonal element S(res, res-1) and return res (or 0). Copied from Eigen3 RealQZ
+ * implementation */
+template <typename MatrixType_>
+inline Index ComplexQZ<MatrixType_>::findSmallSubdiagEntry(Index iu) {
+  Index res = iu;
+  while (res > 0) {
+    RealScalar s = numext::abs(m_S.coeff(res - 1, res - 1)) + numext::abs(m_S.coeff(res, res));
+    if (s == Scalar(0)) s = m_normOfS;
+    if (numext::abs(m_S.coeff(res, res - 1)) < NumTraits<RealScalar>::epsilon() * s) break;
+    res--;
+  }
+  return res;
+}
+
+//
+/** \internal Look for single small diagonal element T(res, res) for res between f and l, and return res (or f-1).
+ * Copied from Eigen3 RealQZ implementation. */
+template <typename MatrixType_>
+inline Index ComplexQZ<MatrixType_>::findSmallDiagEntry(Index f, Index l) {
+  Index res = l;
+  while (res >= f) {
+    if (numext::abs(m_T.coeff(res, res)) <= NumTraits<RealScalar>::epsilon() * m_normOfT) break;
+    res--;
+  }
+  return res;
+}
+
+}  // namespace Eigen
+
+#endif  // _COMPLEX_QZ_H_
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexSchur.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexSchur.h
new file mode 100644
index 0000000..b9193a2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexSchur.h
@@ -0,0 +1,443 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Claire Maurice
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_SCHUR_H
+#define EIGEN_COMPLEX_SCHUR_H
+
+#include "./HessenbergDecomposition.h"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType, bool IsComplex>
+struct complex_schur_reduce_to_hessenberg;
+}
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class ComplexSchur
+ *
+ * \brief Performs a complex Schur decomposition of a real or complex square matrix
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are
+ * computing the Schur decomposition; this is expected to be an
+ * instantiation of the Matrix class template.
+ *
+ * Given a real or complex square matrix A, this class computes the
+ * Schur decomposition: \f$ A = U T U^*\f$ where U is a unitary
+ * complex matrix, and T is a complex upper triangular matrix.  The
+ * diagonal of the matrix T corresponds to the eigenvalues of the
+ * matrix A.
+ *
+ * Call the function compute() to compute the Schur decomposition of
+ * a given matrix. Alternatively, you can use the
+ * ComplexSchur(const MatrixType&, bool) constructor which computes
+ * the Schur decomposition at construction time. Once the
+ * decomposition is computed, you can use the matrixU() and matrixT()
+ * functions to retrieve the matrices U and V in the decomposition.
+ *
+ * \note This code is inspired from Jampack
+ *
+ * \sa class RealSchur, class EigenSolver, class ComplexEigenSolver
+ */
+template <typename MatrixType_>
+class ComplexSchur {
+ public:
+  typedef MatrixType_ MatrixType;
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    Options = internal::traits<MatrixType>::Options,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+
+  /** \brief Scalar type for matrices of type \p MatrixType_. */
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+
+  /** \brief Complex scalar type for \p MatrixType_.
+   *
+   * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
+   * \c float or \c double) and just \c Scalar if #Scalar is
+   * complex.
+   */
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+
+  /** \brief Type for the matrices in the Schur decomposition.
+   *
+   * This is a square matrix with entries of type #ComplexScalar.
+   * The size is the same as the size of \p MatrixType_.
+   */
+  typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime,
+                 MaxColsAtCompileTime>
+      ComplexMatrixType;
+
+  /** \brief Default constructor.
+   *
+   * \param [in] size  Positive integer, size of the matrix whose Schur decomposition will be computed.
+   *
+   * The default constructor is useful in cases in which the user
+   * intends to perform decompositions via compute().  The \p size
+   * parameter is only used as a hint. It is not an error to give a
+   * wrong \p size, but it may impair performance.
+   *
+   * \sa compute() for an example.
+   */
+  explicit ComplexSchur(Index size = RowsAtCompileTime == Dynamic ? 1 : RowsAtCompileTime)
+      : m_matT(size, size),
+        m_matU(size, size),
+        m_hess(size),
+        m_isInitialized(false),
+        m_matUisUptodate(false),
+        m_maxIters(-1) {}
+
+  /** \brief Constructor; computes Schur decomposition of given matrix.
+   *
+   * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
+   * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
+   *
+   * This constructor calls compute() to compute the Schur decomposition.
+   *
+   * \sa matrixT() and matrixU() for examples.
+   */
+  template <typename InputType>
+  explicit ComplexSchur(const EigenBase<InputType>& matrix, bool computeU = true)
+      : m_matT(matrix.rows(), matrix.cols()),
+        m_matU(matrix.rows(), matrix.cols()),
+        m_hess(matrix.rows()),
+        m_isInitialized(false),
+        m_matUisUptodate(false),
+        m_maxIters(-1) {
+    compute(matrix.derived(), computeU);
+  }
+
+  /** \brief Returns the unitary matrix in the Schur decomposition.
+   *
+   * \returns A const reference to the matrix U.
+   *
+   * It is assumed that either the constructor
+   * ComplexSchur(const MatrixType& matrix, bool computeU) or the
+   * member function compute(const MatrixType& matrix, bool computeU)
+   * has been called before to compute the Schur decomposition of a
+   * matrix, and that \p computeU was set to true (the default
+   * value).
+   *
+   * Example: \include ComplexSchur_matrixU.cpp
+   * Output: \verbinclude ComplexSchur_matrixU.out
+   */
+  const ComplexMatrixType& matrixU() const {
+    eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
+    eigen_assert(m_matUisUptodate && "The matrix U has not been computed during the ComplexSchur decomposition.");
+    return m_matU;
+  }
+
+  /** \brief Returns the triangular matrix in the Schur decomposition.
+   *
+   * \returns A const reference to the matrix T.
+   *
+   * It is assumed that either the constructor
+   * ComplexSchur(const MatrixType& matrix, bool computeU) or the
+   * member function compute(const MatrixType& matrix, bool computeU)
+   * has been called before to compute the Schur decomposition of a
+   * matrix.
+   *
+   * Note that this function returns a plain square matrix. If you want to reference
+   * only the upper triangular part, use:
+   * \code schur.matrixT().triangularView<Upper>() \endcode
+   *
+   * Example: \include ComplexSchur_matrixT.cpp
+   * Output: \verbinclude ComplexSchur_matrixT.out
+   */
+  const ComplexMatrixType& matrixT() const {
+    eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
+    return m_matT;
+  }
+
+  /** \brief Computes Schur decomposition of given matrix.
+    *
+    * \param[in]  matrix  Square matrix whose Schur decomposition is to be computed.
+    * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
+
+    * \returns    Reference to \c *this
+    *
+    * The Schur decomposition is computed by first reducing the
+    * matrix to Hessenberg form using the class
+    * HessenbergDecomposition. The Hessenberg matrix is then reduced
+    * to triangular form by performing QR iterations with a single
+    * shift. The cost of computing the Schur decomposition depends
+    * on the number of iterations; as a rough guide, it may be taken
+    * on the number of iterations; as a rough guide, it may be taken
+    * to be \f$25n^3\f$ complex flops, or \f$10n^3\f$ complex flops
+    * if \a computeU is false.
+    *
+    * Example: \include ComplexSchur_compute.cpp
+    * Output: \verbinclude ComplexSchur_compute.out
+    *
+    * \sa compute(const MatrixType&, bool, Index)
+    */
+  template <typename InputType>
+  ComplexSchur& compute(const EigenBase<InputType>& matrix, bool computeU = true);
+
+  /** \brief Compute Schur decomposition from a given Hessenberg matrix
+   *  \param[in] matrixH Matrix in Hessenberg form H
+   *  \param[in] matrixQ orthogonal matrix Q that transform a matrix A to H : A = Q H Q^T
+   *  \param computeU Computes the matriX U of the Schur vectors
+   * \return Reference to \c *this
+   *
+   *  This routine assumes that the matrix is already reduced in Hessenberg form matrixH
+   *  using either the class HessenbergDecomposition or another mean.
+   *  It computes the upper quasi-triangular matrix T of the Schur decomposition of H
+   *  When computeU is true, this routine computes the matrix U such that
+   *  A = U T U^T =  (QZ) T (QZ)^T = Q H Q^T where A is the initial matrix
+   *
+   * NOTE Q is referenced if computeU is true; so, if the initial orthogonal matrix
+   * is not available, the user should give an identity matrix (Q.setIdentity())
+   *
+   * \sa compute(const MatrixType&, bool)
+   */
+  template <typename HessMatrixType, typename OrthMatrixType>
+  ComplexSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ,
+                                      bool computeU = true);
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
+    return m_info;
+  }
+
+  /** \brief Sets the maximum number of iterations allowed.
+   *
+   * If not specified by the user, the maximum number of iterations is m_maxIterationsPerRow times the size
+   * of the matrix.
+   */
+  ComplexSchur& setMaxIterations(Index maxIters) {
+    m_maxIters = maxIters;
+    return *this;
+  }
+
+  /** \brief Returns the maximum number of iterations. */
+  Index getMaxIterations() { return m_maxIters; }
+
+  /** \brief Maximum number of iterations per row.
+   *
+   * If not otherwise specified, the maximum number of iterations is this number times the size of the
+   * matrix. It is currently set to 30.
+   */
+  static const int m_maxIterationsPerRow = 30;
+
+ protected:
+  ComplexMatrixType m_matT, m_matU;
+  HessenbergDecomposition<MatrixType> m_hess;
+  ComputationInfo m_info;
+  bool m_isInitialized;
+  bool m_matUisUptodate;
+  Index m_maxIters;
+
+ private:
+  bool subdiagonalEntryIsNeglegible(Index i);
+  ComplexScalar computeShift(Index iu, Index iter);
+  void reduceToTriangularForm(bool computeU);
+  friend struct internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>;
+};
+
+/** If m_matT(i+1,i) is negligible in floating point arithmetic
+ * compared to m_matT(i,i) and m_matT(j,j), then set it to zero and
+ * return true, else return false. */
+template <typename MatrixType>
+inline bool ComplexSchur<MatrixType>::subdiagonalEntryIsNeglegible(Index i) {
+  RealScalar d = numext::norm1(m_matT.coeff(i, i)) + numext::norm1(m_matT.coeff(i + 1, i + 1));
+  RealScalar sd = numext::norm1(m_matT.coeff(i + 1, i));
+  if (internal::isMuchSmallerThan(sd, d, NumTraits<RealScalar>::epsilon())) {
+    m_matT.coeffRef(i + 1, i) = ComplexScalar(0);
+    return true;
+  }
+  return false;
+}
+
+/** Compute the shift in the current QR iteration. */
+template <typename MatrixType>
+typename ComplexSchur<MatrixType>::ComplexScalar ComplexSchur<MatrixType>::computeShift(Index iu, Index iter) {
+  using std::abs;
+  if ((iter == 10 || iter == 20) && iu > 1) {
+    // exceptional shift, taken from http://www.netlib.org/eispack/comqr.f
+    return ComplexSchur<MatrixType>::ComplexScalar(abs(numext::real(m_matT.coeff(iu, iu - 1))) +
+                                                   abs(numext::real(m_matT.coeff(iu - 1, iu - 2))));
+  }
+
+  // compute the shift as one of the eigenvalues of t, the 2x2
+  // diagonal block on the bottom of the active submatrix
+  Matrix<ComplexScalar, 2, 2> t = m_matT.template block<2, 2>(iu - 1, iu - 1);
+  RealScalar normt = t.cwiseAbs().sum();
+  t /= normt;  // the normalization by sf is to avoid under/overflow
+
+  ComplexScalar b = t.coeff(0, 1) * t.coeff(1, 0);
+  ComplexScalar c = t.coeff(0, 0) - t.coeff(1, 1);
+  ComplexScalar disc = sqrt(c * c + RealScalar(4) * b);
+  ComplexScalar det = t.coeff(0, 0) * t.coeff(1, 1) - b;
+  ComplexScalar trace = t.coeff(0, 0) + t.coeff(1, 1);
+  ComplexScalar eival1 = (trace + disc) / RealScalar(2);
+  ComplexScalar eival2 = (trace - disc) / RealScalar(2);
+  RealScalar eival1_norm = numext::norm1(eival1);
+  RealScalar eival2_norm = numext::norm1(eival2);
+  // A division by zero can only occur if eival1==eival2==0.
+  // In this case, det==0, and all we have to do is checking that eival2_norm!=0
+  if (eival1_norm > eival2_norm)
+    eival2 = det / eival1;
+  else if (!numext::is_exactly_zero(eival2_norm))
+    eival1 = det / eival2;
+
+  // choose the eigenvalue closest to the bottom entry of the diagonal
+  if (numext::norm1(eival1 - t.coeff(1, 1)) < numext::norm1(eival2 - t.coeff(1, 1)))
+    return normt * eival1;
+  else
+    return normt * eival2;
+}
+
+template <typename MatrixType>
+template <typename InputType>
+ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeU) {
+  m_matUisUptodate = false;
+  eigen_assert(matrix.cols() == matrix.rows());
+
+  if (matrix.cols() == 1) {
+    m_matT = matrix.derived().template cast<ComplexScalar>();
+    if (computeU) m_matU = ComplexMatrixType::Identity(1, 1);
+    m_info = Success;
+    m_isInitialized = true;
+    m_matUisUptodate = computeU;
+    return *this;
+  }
+
+  internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>::run(*this, matrix.derived(),
+                                                                                              computeU);
+  computeFromHessenberg(m_matT, m_matU, computeU);
+  return *this;
+}
+
+template <typename MatrixType>
+template <typename HessMatrixType, typename OrthMatrixType>
+ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::computeFromHessenberg(const HessMatrixType& matrixH,
+                                                                          const OrthMatrixType& matrixQ,
+                                                                          bool computeU) {
+  m_matT = matrixH;
+  if (computeU) m_matU = matrixQ;
+  reduceToTriangularForm(computeU);
+  return *this;
+}
+namespace internal {
+
+/* Reduce given matrix to Hessenberg form */
+template <typename MatrixType, bool IsComplex>
+struct complex_schur_reduce_to_hessenberg {
+  // this is the implementation for the case IsComplex = true
+  static void run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU) {
+    _this.m_hess.compute(matrix);
+    _this.m_matT = _this.m_hess.matrixH();
+    if (computeU) _this.m_matU = _this.m_hess.matrixQ();
+  }
+};
+
+template <typename MatrixType>
+struct complex_schur_reduce_to_hessenberg<MatrixType, false> {
+  static void run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU) {
+    typedef typename ComplexSchur<MatrixType>::ComplexScalar ComplexScalar;
+
+    // Note: m_hess is over RealScalar; m_matT and m_matU is over ComplexScalar
+    _this.m_hess.compute(matrix);
+    _this.m_matT = _this.m_hess.matrixH().template cast<ComplexScalar>();
+    if (computeU) {
+      // TODO: this temporary allocation could potentially be avoided.
+      MatrixType Q = _this.m_hess.matrixQ();
+      _this.m_matU = Q.template cast<ComplexScalar>();
+    }
+  }
+};
+
+}  // end namespace internal
+
+// Reduce the Hessenberg matrix m_matT to triangular form by QR iteration.
+template <typename MatrixType>
+void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU) {
+  Index maxIters = m_maxIters;
+  if (maxIters == -1) maxIters = m_maxIterationsPerRow * m_matT.rows();
+
+  // The matrix m_matT is divided in three parts.
+  // Rows 0,...,il-1 are decoupled from the rest because m_matT(il,il-1) is zero.
+  // Rows il,...,iu is the part we are working on (the active submatrix).
+  // Rows iu+1,...,end are already brought in triangular form.
+  Index iu = m_matT.cols() - 1;
+  Index il;
+  Index iter = 0;       // number of iterations we are working on the (iu,iu) element
+  Index totalIter = 0;  // number of iterations for whole matrix
+
+  while (true) {
+    // find iu, the bottom row of the active submatrix
+    while (iu > 0) {
+      if (!subdiagonalEntryIsNeglegible(iu - 1)) break;
+      iter = 0;
+      --iu;
+    }
+
+    // if iu is zero then we are done; the whole matrix is triangularized
+    if (iu == 0) break;
+
+    // if we spent too many iterations, we give up
+    iter++;
+    totalIter++;
+    if (totalIter > maxIters) break;
+
+    // find il, the top row of the active submatrix
+    il = iu - 1;
+    while (il > 0 && !subdiagonalEntryIsNeglegible(il - 1)) {
+      --il;
+    }
+
+    /* perform the QR step using Givens rotations. The first rotation
+       creates a bulge; the (il+2,il) element becomes nonzero. This
+       bulge is chased down to the bottom of the active submatrix. */
+
+    ComplexScalar shift = computeShift(iu, iter);
+    JacobiRotation<ComplexScalar> rot;
+    rot.makeGivens(m_matT.coeff(il, il) - shift, m_matT.coeff(il + 1, il));
+    m_matT.rightCols(m_matT.cols() - il).applyOnTheLeft(il, il + 1, rot.adjoint());
+    m_matT.topRows((std::min)(il + 2, iu) + 1).applyOnTheRight(il, il + 1, rot);
+    if (computeU) m_matU.applyOnTheRight(il, il + 1, rot);
+
+    for (Index i = il + 1; i < iu; i++) {
+      rot.makeGivens(m_matT.coeffRef(i, i - 1), m_matT.coeffRef(i + 1, i - 1), &m_matT.coeffRef(i, i - 1));
+      m_matT.coeffRef(i + 1, i - 1) = ComplexScalar(0);
+      m_matT.rightCols(m_matT.cols() - i).applyOnTheLeft(i, i + 1, rot.adjoint());
+      m_matT.topRows((std::min)(i + 2, iu) + 1).applyOnTheRight(i, i + 1, rot);
+      if (computeU) m_matU.applyOnTheRight(i, i + 1, rot);
+    }
+  }
+
+  if (totalIter <= maxIters)
+    m_info = Success;
+  else
+    m_info = NoConvergence;
+
+  m_isInitialized = true;
+  m_matUisUptodate = computeU;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_SCHUR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
new file mode 100644
index 0000000..5f66fd9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
@@ -0,0 +1,95 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Complex Schur needed to complex unsymmetrical eigenvalues/eigenvectors.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_COMPLEX_SCHUR_LAPACKE_H
+#define EIGEN_COMPLEX_SCHUR_LAPACKE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \internal Specialization for the data types supported by LAPACKe */
+
+#define EIGEN_LAPACKE_SCHUR_COMPLEX(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, LAPACKE_PREFIX_U, EIGCOLROW,            \
+                                    LAPACKE_COLROW)                                                                \
+  template <>                                                                                                      \
+  template <typename InputType>                                                                                    \
+  inline ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >&                                              \
+  ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix,         \
+                                                                       bool computeU) {                            \
+    typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> MatrixType;                                               \
+    typedef MatrixType::RealScalar RealScalar;                                                                     \
+    typedef std::complex<RealScalar> ComplexScalar;                                                                \
+                                                                                                                   \
+    eigen_assert(matrix.cols() == matrix.rows());                                                                  \
+                                                                                                                   \
+    m_matUisUptodate = false;                                                                                      \
+    if (matrix.cols() == 1) {                                                                                      \
+      m_matT = matrix.derived().template cast<ComplexScalar>();                                                    \
+      if (computeU) m_matU = ComplexMatrixType::Identity(1, 1);                                                    \
+      m_info = Success;                                                                                            \
+      m_isInitialized = true;                                                                                      \
+      m_matUisUptodate = computeU;                                                                                 \
+      return *this;                                                                                                \
+    }                                                                                                              \
+    lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), sdim, info;                                 \
+    lapack_int matrix_order = LAPACKE_COLROW;                                                                      \
+    char jobvs, sort = 'N';                                                                                        \
+    LAPACK_##LAPACKE_PREFIX_U##_SELECT1 select = 0;                                                                \
+    jobvs = (computeU) ? 'V' : 'N';                                                                                \
+    m_matU.resize(n, n);                                                                                           \
+    lapack_int ldvs = internal::convert_index<lapack_int>(m_matU.outerStride());                                   \
+    m_matT = matrix;                                                                                               \
+    lapack_int lda = internal::convert_index<lapack_int>(m_matT.outerStride());                                    \
+    Matrix<EIGTYPE, Dynamic, Dynamic> w;                                                                           \
+    w.resize(n, 1);                                                                                                \
+    info = LAPACKE_##LAPACKE_PREFIX##gees(matrix_order, jobvs, sort, select, n, (LAPACKE_TYPE*)m_matT.data(), lda, \
+                                          &sdim, (LAPACKE_TYPE*)w.data(), (LAPACKE_TYPE*)m_matU.data(), ldvs);     \
+    if (info == 0)                                                                                                 \
+      m_info = Success;                                                                                            \
+    else                                                                                                           \
+      m_info = NoConvergence;                                                                                      \
+                                                                                                                   \
+    m_isInitialized = true;                                                                                        \
+    m_matUisUptodate = computeU;                                                                                   \
+    return *this;                                                                                                  \
+  }
+
+EIGEN_LAPACKE_SCHUR_COMPLEX(dcomplex, lapack_complex_double, z, Z, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_LAPACKE_SCHUR_COMPLEX(scomplex, lapack_complex_float, c, C, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_LAPACKE_SCHUR_COMPLEX(dcomplex, lapack_complex_double, z, Z, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_LAPACKE_SCHUR_COMPLEX(scomplex, lapack_complex_float, c, C, RowMajor, LAPACK_ROW_MAJOR)
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_SCHUR_LAPACKE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/EigenSolver.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/EigenSolver.h
new file mode 100644
index 0000000..9dba7bd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/EigenSolver.h
@@ -0,0 +1,579 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EIGENSOLVER_H
+#define EIGEN_EIGENSOLVER_H
+
+#include "./RealSchur.h"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class EigenSolver
+ *
+ * \brief Computes eigenvalues and eigenvectors of general matrices
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the
+ * eigendecomposition; this is expected to be an instantiation of the Matrix
+ * class template. Currently, only real matrices are supported.
+ *
+ * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars
+ * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v \f$.  If
+ * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and
+ * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V =
+ * V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we
+ * have \f$ A = V D V^{-1} \f$. This is called the eigendecomposition.
+ *
+ * The eigenvalues and eigenvectors of a matrix may be complex, even when the
+ * matrix is real. However, we can choose real matrices \f$ V \f$ and \f$ D
+ * \f$ satisfying \f$ A V = V D \f$, just like the eigendecomposition, if the
+ * matrix \f$ D \f$ is not required to be diagonal, but if it is allowed to
+ * have blocks of the form
+ * \f[ \begin{bmatrix} u & v \\ -v & u \end{bmatrix} \f]
+ * (where \f$ u \f$ and \f$ v \f$ are real numbers) on the diagonal.  These
+ * blocks correspond to complex eigenvalue pairs \f$ u \pm iv \f$. We call
+ * this variant of the eigendecomposition the pseudo-eigendecomposition.
+ *
+ * Call the function compute() to compute the eigenvalues and eigenvectors of
+ * a given matrix. Alternatively, you can use the
+ * EigenSolver(const MatrixType&, bool) constructor which computes the
+ * eigenvalues and eigenvectors at construction time. Once the eigenvalue and
+ * eigenvectors are computed, they can be retrieved with the eigenvalues() and
+ * eigenvectors() functions. The pseudoEigenvalueMatrix() and
+ * pseudoEigenvectors() methods allow the construction of the
+ * pseudo-eigendecomposition.
+ *
+ * The documentation for EigenSolver(const MatrixType&, bool) contains an
+ * example of the typical use of this class.
+ *
+ * \note The implementation is adapted from
+ * <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a> (public domain).
+ * Their code is based on EISPACK.
+ *
+ * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver
+ */
+template <typename MatrixType_>
+class EigenSolver {
+ public:
+  /** \brief Synonym for the template parameter \p MatrixType_. */
+  typedef MatrixType_ MatrixType;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    Options = internal::traits<MatrixType>::Options,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+
+  /** \brief Scalar type for matrices of type #MatrixType. */
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+
+  /** \brief Complex scalar type for #MatrixType.
+   *
+   * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
+   * \c float or \c double) and just \c Scalar if #Scalar is
+   * complex.
+   */
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+
+  /** \brief Type for vector of eigenvalues as returned by eigenvalues().
+   *
+   * This is a column vector with entries of type #ComplexScalar.
+   * The length of the vector is the size of #MatrixType.
+   */
+  typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
+
+  /** \brief Type for matrix of eigenvectors as returned by eigenvectors().
+   *
+   * This is a square matrix with entries of type #ComplexScalar.
+   * The size is the same as the size of #MatrixType.
+   */
+  typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime,
+                 MaxColsAtCompileTime>
+      EigenvectorsType;
+
+  /** \brief Default constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via EigenSolver::compute(const MatrixType&, bool).
+   *
+   * \sa compute() for an example.
+   */
+  EigenSolver()
+      : m_eivec(), m_eivalues(), m_isInitialized(false), m_eigenvectorsOk(false), m_realSchur(), m_matT(), m_tmp() {}
+
+  /** \brief Default constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa EigenSolver()
+   */
+  explicit EigenSolver(Index size)
+      : m_eivec(size, size),
+        m_eivalues(size),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false),
+        m_realSchur(size),
+        m_matT(size, size),
+        m_tmp(size) {}
+
+  /** \brief Constructor; computes eigendecomposition of given matrix.
+   *
+   * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
+   * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+   *    eigenvalues are computed; if false, only the eigenvalues are
+   *    computed.
+   *
+   * This constructor calls compute() to compute the eigenvalues
+   * and eigenvectors.
+   *
+   * Example: \include EigenSolver_EigenSolver_MatrixType.cpp
+   * Output: \verbinclude EigenSolver_EigenSolver_MatrixType.out
+   *
+   * \sa compute()
+   */
+  template <typename InputType>
+  explicit EigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
+      : m_eivec(matrix.rows(), matrix.cols()),
+        m_eivalues(matrix.cols()),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false),
+        m_realSchur(matrix.cols()),
+        m_matT(matrix.rows(), matrix.cols()),
+        m_tmp(matrix.cols()) {
+    compute(matrix.derived(), computeEigenvectors);
+  }
+
+  /** \brief Returns the eigenvectors of given matrix.
+   *
+   * \returns  %Matrix whose columns are the (possibly complex) eigenvectors.
+   *
+   * \pre Either the constructor
+   * EigenSolver(const MatrixType&,bool) or the member function
+   * compute(const MatrixType&, bool) has been called before, and
+   * \p computeEigenvectors was set to true (the default).
+   *
+   * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
+   * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
+   * eigenvectors are normalized to have (Euclidean) norm equal to one. The
+   * matrix returned by this function is the matrix \f$ V \f$ in the
+   * eigendecomposition \f$ A = V D V^{-1} \f$, if it exists.
+   *
+   * Example: \include EigenSolver_eigenvectors.cpp
+   * Output: \verbinclude EigenSolver_eigenvectors.out
+   *
+   * \sa eigenvalues(), pseudoEigenvectors()
+   */
+  EigenvectorsType eigenvectors() const;
+
+  /** \brief Returns the pseudo-eigenvectors of given matrix.
+   *
+   * \returns  Const reference to matrix whose columns are the pseudo-eigenvectors.
+   *
+   * \pre Either the constructor
+   * EigenSolver(const MatrixType&,bool) or the member function
+   * compute(const MatrixType&, bool) has been called before, and
+   * \p computeEigenvectors was set to true (the default).
+   *
+   * The real matrix \f$ V \f$ returned by this function and the
+   * block-diagonal matrix \f$ D \f$ returned by pseudoEigenvalueMatrix()
+   * satisfy \f$ AV = VD \f$.
+   *
+   * Example: \include EigenSolver_pseudoEigenvectors.cpp
+   * Output: \verbinclude EigenSolver_pseudoEigenvectors.out
+   *
+   * \sa pseudoEigenvalueMatrix(), eigenvectors()
+   */
+  const MatrixType& pseudoEigenvectors() const {
+    eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec;
+  }
+
+  /** \brief Returns the block-diagonal matrix in the pseudo-eigendecomposition.
+   *
+   * \returns  A block-diagonal matrix.
+   *
+   * \pre Either the constructor
+   * EigenSolver(const MatrixType&,bool) or the member function
+   * compute(const MatrixType&, bool) has been called before.
+   *
+   * The matrix \f$ D \f$ returned by this function is real and
+   * block-diagonal. The blocks on the diagonal are either 1-by-1 or 2-by-2
+   * blocks of the form
+   * \f$ \begin{bmatrix} u & v \\ -v & u \end{bmatrix} \f$.
+   * These blocks are not sorted in any particular order.
+   * The matrix \f$ D \f$ and the matrix \f$ V \f$ returned by
+   * pseudoEigenvectors() satisfy \f$ AV = VD \f$.
+   *
+   * \sa pseudoEigenvectors() for an example, eigenvalues()
+   */
+  MatrixType pseudoEigenvalueMatrix() const;
+
+  /** \brief Returns the eigenvalues of given matrix.
+   *
+   * \returns A const reference to the column vector containing the eigenvalues.
+   *
+   * \pre Either the constructor
+   * EigenSolver(const MatrixType&,bool) or the member function
+   * compute(const MatrixType&, bool) has been called before.
+   *
+   * The eigenvalues are repeated according to their algebraic multiplicity,
+   * so there are as many eigenvalues as rows in the matrix. The eigenvalues
+   * are not sorted in any particular order.
+   *
+   * Example: \include EigenSolver_eigenvalues.cpp
+   * Output: \verbinclude EigenSolver_eigenvalues.out
+   *
+   * \sa eigenvectors(), pseudoEigenvalueMatrix(),
+   *     MatrixBase::eigenvalues()
+   */
+  const EigenvalueType& eigenvalues() const {
+    eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+    return m_eivalues;
+  }
+
+  /** \brief Computes eigendecomposition of given matrix.
+   *
+   * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
+   * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+   *    eigenvalues are computed; if false, only the eigenvalues are
+   *    computed.
+   * \returns    Reference to \c *this
+   *
+   * This function computes the eigenvalues of the real matrix \p matrix.
+   * The eigenvalues() function can be used to retrieve them.  If
+   * \p computeEigenvectors is true, then the eigenvectors are also computed
+   * and can be retrieved by calling eigenvectors().
+   *
+   * The matrix is first reduced to real Schur form using the RealSchur
+   * class. The Schur decomposition is then used to compute the eigenvalues
+   * and eigenvectors.
+   *
+   * The cost of the computation is dominated by the cost of the
+   * Schur decomposition, which is very approximately \f$ 25n^3 \f$
+   * (where \f$ n \f$ is the size of the matrix) if \p computeEigenvectors
+   * is true, and \f$ 10n^3 \f$ if \p computeEigenvectors is false.
+   *
+   * This method reuses of the allocated data in the EigenSolver object.
+   *
+   * Example: \include EigenSolver_compute.cpp
+   * Output: \verbinclude EigenSolver_compute.out
+   */
+  template <typename InputType>
+  EigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
+
+  /** \returns NumericalIssue if the input contains INF or NaN values or overflow occurred. Returns Success otherwise.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+    return m_info;
+  }
+
+  /** \brief Sets the maximum number of iterations allowed. */
+  EigenSolver& setMaxIterations(Index maxIters) {
+    m_realSchur.setMaxIterations(maxIters);
+    return *this;
+  }
+
+  /** \brief Returns the maximum number of iterations. */
+  Index getMaxIterations() { return m_realSchur.getMaxIterations(); }
+
+ private:
+  void doComputeEigenvectors();
+
+ protected:
+  static void check_template_parameters() {
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
+    EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL);
+  }
+
+  MatrixType m_eivec;
+  EigenvalueType m_eivalues;
+  bool m_isInitialized;
+  bool m_eigenvectorsOk;
+  ComputationInfo m_info;
+  RealSchur<MatrixType> m_realSchur;
+  MatrixType m_matT;
+
+  typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
+  ColumnVectorType m_tmp;
+};
+
+template <typename MatrixType>
+MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const {
+  eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+  const RealScalar precision = RealScalar(2) * NumTraits<RealScalar>::epsilon();
+  const Index n = m_eivalues.rows();
+  MatrixType matD = MatrixType::Zero(n, n);
+  Index i = 0;
+  for (; i < n - 1; ++i) {
+    RealScalar real = numext::real(m_eivalues.coeff(i));
+    RealScalar imag = numext::imag(m_eivalues.coeff(i));
+    matD.coeffRef(i, i) = real;
+    if (!internal::isMuchSmallerThan(imag, real, precision)) {
+      matD.coeffRef(i, i + 1) = imag;
+      matD.coeffRef(i + 1, i) = -imag;
+      matD.coeffRef(i + 1, i + 1) = real;
+      ++i;
+    }
+  }
+  if (i == n - 1) {
+    matD.coeffRef(i, i) = numext::real(m_eivalues.coeff(i));
+  }
+
+  return matD;
+}
+
+template <typename MatrixType>
+typename EigenSolver<MatrixType>::EigenvectorsType EigenSolver<MatrixType>::eigenvectors() const {
+  eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+  eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+  const RealScalar precision = RealScalar(2) * NumTraits<RealScalar>::epsilon();
+  Index n = m_eivec.cols();
+  EigenvectorsType matV(n, n);
+  for (Index j = 0; j < n; ++j) {
+    if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(j)), numext::real(m_eivalues.coeff(j)), precision) ||
+        j + 1 == n) {
+      // we have a real eigen value
+      matV.col(j) = m_eivec.col(j).template cast<ComplexScalar>();
+      matV.col(j).normalize();
+    } else {
+      // we have a pair of complex eigen values
+      for (Index i = 0; i < n; ++i) {
+        matV.coeffRef(i, j) = ComplexScalar(m_eivec.coeff(i, j), m_eivec.coeff(i, j + 1));
+        matV.coeffRef(i, j + 1) = ComplexScalar(m_eivec.coeff(i, j), -m_eivec.coeff(i, j + 1));
+      }
+      matV.col(j).normalize();
+      matV.col(j + 1).normalize();
+      ++j;
+    }
+  }
+  return matV;
+}
+
+template <typename MatrixType>
+template <typename InputType>
+EigenSolver<MatrixType>& EigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix,
+                                                          bool computeEigenvectors) {
+  check_template_parameters();
+
+  using numext::isfinite;
+  using std::abs;
+  using std::sqrt;
+  eigen_assert(matrix.cols() == matrix.rows());
+
+  // Reduce to real Schur form.
+  m_realSchur.compute(matrix.derived(), computeEigenvectors);
+
+  m_info = m_realSchur.info();
+
+  if (m_info == Success) {
+    m_matT = m_realSchur.matrixT();
+    if (computeEigenvectors) m_eivec = m_realSchur.matrixU();
+
+    // Compute eigenvalues from matT
+    m_eivalues.resize(matrix.cols());
+    Index i = 0;
+    while (i < matrix.cols()) {
+      if (i == matrix.cols() - 1 || m_matT.coeff(i + 1, i) == Scalar(0)) {
+        m_eivalues.coeffRef(i) = m_matT.coeff(i, i);
+        if (!(isfinite)(m_eivalues.coeffRef(i))) {
+          m_isInitialized = true;
+          m_eigenvectorsOk = false;
+          m_info = NumericalIssue;
+          return *this;
+        }
+        ++i;
+      } else {
+        Scalar p = Scalar(0.5) * (m_matT.coeff(i, i) - m_matT.coeff(i + 1, i + 1));
+        Scalar z;
+        // Compute z = sqrt(abs(p * p + m_matT.coeff(i+1, i) * m_matT.coeff(i, i+1)));
+        // without overflow
+        {
+          Scalar t0 = m_matT.coeff(i + 1, i);
+          Scalar t1 = m_matT.coeff(i, i + 1);
+          Scalar maxval = numext::maxi<Scalar>(abs(p), numext::maxi<Scalar>(abs(t0), abs(t1)));
+          t0 /= maxval;
+          t1 /= maxval;
+          Scalar p0 = p / maxval;
+          z = maxval * sqrt(abs(p0 * p0 + t0 * t1));
+        }
+
+        m_eivalues.coeffRef(i) = ComplexScalar(m_matT.coeff(i + 1, i + 1) + p, z);
+        m_eivalues.coeffRef(i + 1) = ComplexScalar(m_matT.coeff(i + 1, i + 1) + p, -z);
+        if (!((isfinite)(m_eivalues.coeffRef(i)) && (isfinite)(m_eivalues.coeffRef(i + 1)))) {
+          m_isInitialized = true;
+          m_eigenvectorsOk = false;
+          m_info = NumericalIssue;
+          return *this;
+        }
+        i += 2;
+      }
+    }
+
+    // Compute eigenvectors.
+    if (computeEigenvectors) doComputeEigenvectors();
+  }
+
+  m_isInitialized = true;
+  m_eigenvectorsOk = computeEigenvectors;
+
+  return *this;
+}
+
+template <typename MatrixType>
+void EigenSolver<MatrixType>::doComputeEigenvectors() {
+  using std::abs;
+  const Index size = m_eivec.cols();
+  const Scalar eps = NumTraits<Scalar>::epsilon();
+
+  // inefficient! this is already computed in RealSchur
+  Scalar norm(0);
+  for (Index j = 0; j < size; ++j) {
+    norm += m_matT.row(j).segment((std::max)(j - 1, Index(0)), size - (std::max)(j - 1, Index(0))).cwiseAbs().sum();
+  }
+
+  // Backsubstitute to find vectors of upper triangular form
+  if (norm == Scalar(0)) {
+    return;
+  }
+
+  for (Index n = size - 1; n >= 0; n--) {
+    Scalar p = m_eivalues.coeff(n).real();
+    Scalar q = m_eivalues.coeff(n).imag();
+
+    // Scalar vector
+    if (q == Scalar(0)) {
+      Scalar lastr(0), lastw(0);
+      Index l = n;
+
+      m_matT.coeffRef(n, n) = Scalar(1);
+      for (Index i = n - 1; i >= 0; i--) {
+        Scalar w = m_matT.coeff(i, i) - p;
+        Scalar r = m_matT.row(i).segment(l, n - l + 1).dot(m_matT.col(n).segment(l, n - l + 1));
+
+        if (m_eivalues.coeff(i).imag() < Scalar(0)) {
+          lastw = w;
+          lastr = r;
+        } else {
+          l = i;
+          if (m_eivalues.coeff(i).imag() == Scalar(0)) {
+            if (w != Scalar(0))
+              m_matT.coeffRef(i, n) = -r / w;
+            else
+              m_matT.coeffRef(i, n) = -r / (eps * norm);
+          } else  // Solve real equations
+          {
+            Scalar x = m_matT.coeff(i, i + 1);
+            Scalar y = m_matT.coeff(i + 1, i);
+            Scalar denom = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) +
+                           m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag();
+            Scalar t = (x * lastr - lastw * r) / denom;
+            m_matT.coeffRef(i, n) = t;
+            if (abs(x) > abs(lastw))
+              m_matT.coeffRef(i + 1, n) = (-r - w * t) / x;
+            else
+              m_matT.coeffRef(i + 1, n) = (-lastr - y * t) / lastw;
+          }
+
+          // Overflow control
+          Scalar t = abs(m_matT.coeff(i, n));
+          if ((eps * t) * t > Scalar(1)) m_matT.col(n).tail(size - i) /= t;
+        }
+      }
+    } else if (q < Scalar(0) && n > 0)  // Complex vector
+    {
+      Scalar lastra(0), lastsa(0), lastw(0);
+      Index l = n - 1;
+
+      // Last vector component imaginary so matrix is triangular
+      if (abs(m_matT.coeff(n, n - 1)) > abs(m_matT.coeff(n - 1, n))) {
+        m_matT.coeffRef(n - 1, n - 1) = q / m_matT.coeff(n, n - 1);
+        m_matT.coeffRef(n - 1, n) = -(m_matT.coeff(n, n) - p) / m_matT.coeff(n, n - 1);
+      } else {
+        ComplexScalar cc =
+            ComplexScalar(Scalar(0), -m_matT.coeff(n - 1, n)) / ComplexScalar(m_matT.coeff(n - 1, n - 1) - p, q);
+        m_matT.coeffRef(n - 1, n - 1) = numext::real(cc);
+        m_matT.coeffRef(n - 1, n) = numext::imag(cc);
+      }
+      m_matT.coeffRef(n, n - 1) = Scalar(0);
+      m_matT.coeffRef(n, n) = Scalar(1);
+      for (Index i = n - 2; i >= 0; i--) {
+        Scalar ra = m_matT.row(i).segment(l, n - l + 1).dot(m_matT.col(n - 1).segment(l, n - l + 1));
+        Scalar sa = m_matT.row(i).segment(l, n - l + 1).dot(m_matT.col(n).segment(l, n - l + 1));
+        Scalar w = m_matT.coeff(i, i) - p;
+
+        if (m_eivalues.coeff(i).imag() < Scalar(0)) {
+          lastw = w;
+          lastra = ra;
+          lastsa = sa;
+        } else {
+          l = i;
+          if (m_eivalues.coeff(i).imag() == RealScalar(0)) {
+            ComplexScalar cc = ComplexScalar(-ra, -sa) / ComplexScalar(w, q);
+            m_matT.coeffRef(i, n - 1) = numext::real(cc);
+            m_matT.coeffRef(i, n) = numext::imag(cc);
+          } else {
+            // Solve complex equations
+            Scalar x = m_matT.coeff(i, i + 1);
+            Scalar y = m_matT.coeff(i + 1, i);
+            Scalar vr = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) +
+                        m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag() - q * q;
+            Scalar vi = (m_eivalues.coeff(i).real() - p) * Scalar(2) * q;
+            if ((vr == Scalar(0)) && (vi == Scalar(0)))
+              vr = eps * norm * (abs(w) + abs(q) + abs(x) + abs(y) + abs(lastw));
+
+            ComplexScalar cc = ComplexScalar(x * lastra - lastw * ra + q * sa, x * lastsa - lastw * sa - q * ra) /
+                               ComplexScalar(vr, vi);
+            m_matT.coeffRef(i, n - 1) = numext::real(cc);
+            m_matT.coeffRef(i, n) = numext::imag(cc);
+            if (abs(x) > (abs(lastw) + abs(q))) {
+              m_matT.coeffRef(i + 1, n - 1) = (-ra - w * m_matT.coeff(i, n - 1) + q * m_matT.coeff(i, n)) / x;
+              m_matT.coeffRef(i + 1, n) = (-sa - w * m_matT.coeff(i, n) - q * m_matT.coeff(i, n - 1)) / x;
+            } else {
+              cc = ComplexScalar(-lastra - y * m_matT.coeff(i, n - 1), -lastsa - y * m_matT.coeff(i, n)) /
+                   ComplexScalar(lastw, q);
+              m_matT.coeffRef(i + 1, n - 1) = numext::real(cc);
+              m_matT.coeffRef(i + 1, n) = numext::imag(cc);
+            }
+          }
+
+          // Overflow control
+          Scalar t = numext::maxi<Scalar>(abs(m_matT.coeff(i, n - 1)), abs(m_matT.coeff(i, n)));
+          if ((eps * t) * t > Scalar(1)) m_matT.block(i, n - 1, size - i, 2) /= t;
+        }
+      }
+
+      // We handled a pair of complex conjugate eigenvalues, so need to skip them both
+      n--;
+    } else {
+      eigen_assert(0 && "Internal bug in EigenSolver (INF or NaN has not been detected)");  // this should not happen
+    }
+  }
+
+  // Back transformation to get eigenvectors of original matrix
+  for (Index j = size - 1; j >= 0; j--) {
+    m_tmp.noalias() = m_eivec.leftCols(j + 1) * m_matT.col(j).segment(0, j + 1);
+    m_eivec.col(j) = m_tmp;
+  }
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_EIGENSOLVER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
new file mode 100644
index 0000000..c0a61dc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
@@ -0,0 +1,402 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2016 Tobias Wood <tobias@spinicist.org.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERALIZEDEIGENSOLVER_H
+#define EIGEN_GENERALIZEDEIGENSOLVER_H
+
+#include "./RealQZ.h"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class GeneralizedEigenSolver
+ *
+ * \brief Computes the generalized eigenvalues and eigenvectors of a pair of general matrices
+ *
+ * \tparam MatrixType_ the type of the matrices of which we are computing the
+ * eigen-decomposition; this is expected to be an instantiation of the Matrix
+ * class template. Currently, only real matrices are supported.
+ *
+ * The generalized eigenvalues and eigenvectors of a matrix pair \f$ A \f$ and \f$ B \f$ are scalars
+ * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda Bv \f$.  If
+ * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and
+ * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V =
+ * B V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we
+ * have \f$ A = B V D V^{-1} \f$. This is called the generalized eigen-decomposition.
+ *
+ * The generalized eigenvalues and eigenvectors of a matrix pair may be complex, even when the
+ * matrices are real. Moreover, the generalized eigenvalue might be infinite if the matrix B is
+ * singular. To workaround this difficulty, the eigenvalues are provided as a pair of complex \f$ \alpha \f$
+ * and real \f$ \beta \f$ such that: \f$ \lambda_i = \alpha_i / \beta_i \f$. If \f$ \beta_i \f$ is (nearly) zero,
+ * then one can consider the well defined left eigenvalue \f$ \mu = \beta_i / \alpha_i\f$ such that:
+ * \f$ \mu_i A v_i = B v_i \f$, or even \f$ \mu_i u_i^T A  = u_i^T B \f$ where \f$ u_i \f$ is
+ * called the left eigenvector.
+ *
+ * Call the function compute() to compute the generalized eigenvalues and eigenvectors of
+ * a given matrix pair. Alternatively, you can use the
+ * GeneralizedEigenSolver(const MatrixType&, const MatrixType&, bool) constructor which computes the
+ * eigenvalues and eigenvectors at construction time. Once the eigenvalue and
+ * eigenvectors are computed, they can be retrieved with the eigenvalues() and
+ * eigenvectors() functions.
+ *
+ * Here is an usage example of this class:
+ * Example: \include GeneralizedEigenSolver.cpp
+ * Output: \verbinclude GeneralizedEigenSolver.out
+ *
+ * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver
+ */
+template <typename MatrixType_>
+class GeneralizedEigenSolver {
+ public:
+  /** \brief Synonym for the template parameter \p MatrixType_. */
+  typedef MatrixType_ MatrixType;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    Options = internal::traits<MatrixType>::Options,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+
+  /** \brief Scalar type for matrices of type #MatrixType. */
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+
+  /** \brief Complex scalar type for #MatrixType.
+   *
+   * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
+   * \c float or \c double) and just \c Scalar if #Scalar is
+   * complex.
+   */
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+
+  /** \brief Type for vector of real scalar values eigenvalues as returned by betas().
+   *
+   * This is a column vector with entries of type #Scalar.
+   * The length of the vector is the size of #MatrixType.
+   */
+  typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> VectorType;
+
+  /** \brief Type for vector of complex scalar values eigenvalues as returned by alphas().
+   *
+   * This is a column vector with entries of type #ComplexScalar.
+   * The length of the vector is the size of #MatrixType.
+   */
+  typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ComplexVectorType;
+
+  /** \brief Expression type for the eigenvalues as returned by eigenvalues().
+   */
+  typedef CwiseBinaryOp<internal::scalar_quotient_op<ComplexScalar, Scalar>, ComplexVectorType, VectorType>
+      EigenvalueType;
+
+  /** \brief Type for matrix of eigenvectors as returned by eigenvectors().
+   *
+   * This is a square matrix with entries of type #ComplexScalar.
+   * The size is the same as the size of #MatrixType.
+   */
+  typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime,
+                 MaxColsAtCompileTime>
+      EigenvectorsType;
+
+  /** \brief Default constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via EigenSolver::compute(const MatrixType&, bool).
+   *
+   * \sa compute() for an example.
+   */
+  GeneralizedEigenSolver()
+      : m_eivec(), m_alphas(), m_betas(), m_computeEigenvectors(false), m_isInitialized(false), m_realQZ() {}
+
+  /** \brief Default constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa GeneralizedEigenSolver()
+   */
+  explicit GeneralizedEigenSolver(Index size)
+      : m_eivec(size, size),
+        m_alphas(size),
+        m_betas(size),
+        m_computeEigenvectors(false),
+        m_isInitialized(false),
+        m_realQZ(size),
+        m_tmp(size) {}
+
+  /** \brief Constructor; computes the generalized eigendecomposition of given matrix pair.
+   *
+   * \param[in]  A  Square matrix whose eigendecomposition is to be computed.
+   * \param[in]  B  Square matrix whose eigendecomposition is to be computed.
+   * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+   *    eigenvalues are computed; if false, only the eigenvalues are computed.
+   *
+   * This constructor calls compute() to compute the generalized eigenvalues
+   * and eigenvectors.
+   *
+   * \sa compute()
+   */
+  GeneralizedEigenSolver(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true)
+      : m_eivec(A.rows(), A.cols()),
+        m_alphas(A.cols()),
+        m_betas(A.cols()),
+        m_computeEigenvectors(false),
+        m_isInitialized(false),
+        m_realQZ(A.cols()),
+        m_tmp(A.cols()) {
+    compute(A, B, computeEigenvectors);
+  }
+
+  /** \brief Returns the computed generalized eigenvectors.
+   *
+   * \returns  %Matrix whose columns are the (possibly complex) right eigenvectors.
+   * i.e. the eigenvectors that solve (A - l*B)x = 0. The ordering matches the eigenvalues.
+   *
+   * \pre Either the constructor
+   * GeneralizedEigenSolver(const MatrixType&,const MatrixType&, bool) or the member function
+   * compute(const MatrixType&, const MatrixType& bool) has been called before, and
+   * \p computeEigenvectors was set to true (the default).
+   *
+   * \sa eigenvalues()
+   */
+  EigenvectorsType eigenvectors() const {
+    eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute eigenvectors");
+    eigen_assert(m_computeEigenvectors && "Eigenvectors for GeneralizedEigenSolver were not calculated");
+    return m_eivec;
+  }
+
+  /** \brief Returns an expression of the computed generalized eigenvalues.
+   *
+   * \returns An expression of the column vector containing the eigenvalues.
+   *
+   * It is a shortcut for \code this->alphas().cwiseQuotient(this->betas()); \endcode
+   * Not that betas might contain zeros. It is therefore not recommended to use this function,
+   * but rather directly deal with the alphas and betas vectors.
+   *
+   * \pre Either the constructor
+   * GeneralizedEigenSolver(const MatrixType&,const MatrixType&,bool) or the member function
+   * compute(const MatrixType&,const MatrixType&,bool) has been called before.
+   *
+   * The eigenvalues are repeated according to their algebraic multiplicity,
+   * so there are as many eigenvalues as rows in the matrix. The eigenvalues
+   * are not sorted in any particular order.
+   *
+   * \sa alphas(), betas(), eigenvectors()
+   */
+  EigenvalueType eigenvalues() const {
+    eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute eigenvalues.");
+    return EigenvalueType(m_alphas, m_betas);
+  }
+
+  /** \returns A const reference to the vectors containing the alpha values
+   *
+   * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j).
+   *
+   * \sa betas(), eigenvalues() */
+  const ComplexVectorType& alphas() const {
+    eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute alphas.");
+    return m_alphas;
+  }
+
+  /** \returns A const reference to the vectors containing the beta values
+   *
+   * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j).
+   *
+   * \sa alphas(), eigenvalues() */
+  const VectorType& betas() const {
+    eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute betas.");
+    return m_betas;
+  }
+
+  /** \brief Computes generalized eigendecomposition of given matrix.
+   *
+   * \param[in]  A  Square matrix whose eigendecomposition is to be computed.
+   * \param[in]  B  Square matrix whose eigendecomposition is to be computed.
+   * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+   *    eigenvalues are computed; if false, only the eigenvalues are
+   *    computed.
+   * \returns    Reference to \c *this
+   *
+   * This function computes the eigenvalues of the real matrix \p matrix.
+   * The eigenvalues() function can be used to retrieve them.  If
+   * \p computeEigenvectors is true, then the eigenvectors are also computed
+   * and can be retrieved by calling eigenvectors().
+   *
+   * The matrix is first reduced to real generalized Schur form using the RealQZ
+   * class. The generalized Schur decomposition is then used to compute the eigenvalues
+   * and eigenvectors.
+   *
+   * The cost of the computation is dominated by the cost of the
+   * generalized Schur decomposition.
+   *
+   * This method reuses of the allocated data in the GeneralizedEigenSolver object.
+   */
+  GeneralizedEigenSolver& compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true);
+
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+    return m_realQZ.info();
+  }
+
+  /** Sets the maximal number of iterations allowed.
+   */
+  GeneralizedEigenSolver& setMaxIterations(Index maxIters) {
+    m_realQZ.setMaxIterations(maxIters);
+    return *this;
+  }
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
+
+  EigenvectorsType m_eivec;
+  ComplexVectorType m_alphas;
+  VectorType m_betas;
+  bool m_computeEigenvectors;
+  bool m_isInitialized;
+  RealQZ<MatrixType> m_realQZ;
+  ComplexVectorType m_tmp;
+};
+
+template <typename MatrixType>
+GeneralizedEigenSolver<MatrixType>& GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A,
+                                                                                const MatrixType& B,
+                                                                                bool computeEigenvectors) {
+  using std::abs;
+  using std::sqrt;
+  eigen_assert(A.cols() == A.rows() && B.cols() == A.rows() && B.cols() == B.rows());
+  Index size = A.cols();
+  // Reduce to generalized real Schur form:
+  // A = Q S Z and B = Q T Z
+  m_realQZ.compute(A, B, computeEigenvectors);
+  if (m_realQZ.info() == Success) {
+    // Resize storage
+    m_alphas.resize(size);
+    m_betas.resize(size);
+    if (computeEigenvectors) {
+      m_eivec.resize(size, size);
+      m_tmp.resize(size);
+    }
+
+    // Aliases:
+    Map<VectorType> v(reinterpret_cast<Scalar*>(m_tmp.data()), size);
+    ComplexVectorType& cv = m_tmp;
+    const MatrixType& mS = m_realQZ.matrixS();
+    const MatrixType& mT = m_realQZ.matrixT();
+
+    Index i = 0;
+    while (i < size) {
+      if (i == size - 1 || mS.coeff(i + 1, i) == Scalar(0)) {
+        // Real eigenvalue
+        m_alphas.coeffRef(i) = mS.diagonal().coeff(i);
+        m_betas.coeffRef(i) = mT.diagonal().coeff(i);
+        if (computeEigenvectors) {
+          v.setConstant(Scalar(0.0));
+          v.coeffRef(i) = Scalar(1.0);
+          // For singular eigenvalues do nothing more
+          if (abs(m_betas.coeffRef(i)) >= (std::numeric_limits<RealScalar>::min)()) {
+            // Non-singular eigenvalue
+            const Scalar alpha = real(m_alphas.coeffRef(i));
+            const Scalar beta = m_betas.coeffRef(i);
+            for (Index j = i - 1; j >= 0; j--) {
+              const Index st = j + 1;
+              const Index sz = i - j;
+              if (j > 0 && mS.coeff(j, j - 1) != Scalar(0)) {
+                // 2x2 block
+                Matrix<Scalar, 2, 1> rhs = (alpha * mT.template block<2, Dynamic>(j - 1, st, 2, sz) -
+                                            beta * mS.template block<2, Dynamic>(j - 1, st, 2, sz))
+                                               .lazyProduct(v.segment(st, sz));
+                Matrix<Scalar, 2, 2> lhs =
+                    beta * mS.template block<2, 2>(j - 1, j - 1) - alpha * mT.template block<2, 2>(j - 1, j - 1);
+                v.template segment<2>(j - 1) = lhs.partialPivLu().solve(rhs);
+                j--;
+              } else {
+                v.coeffRef(j) = -v.segment(st, sz)
+                                     .transpose()
+                                     .cwiseProduct(beta * mS.block(j, st, 1, sz) - alpha * mT.block(j, st, 1, sz))
+                                     .sum() /
+                                (beta * mS.coeffRef(j, j) - alpha * mT.coeffRef(j, j));
+              }
+            }
+          }
+          m_eivec.col(i).real().noalias() = m_realQZ.matrixZ().transpose() * v;
+          m_eivec.col(i).real().normalize();
+          m_eivec.col(i).imag().setConstant(0);
+        }
+        ++i;
+      } else {
+        // We need to extract the generalized eigenvalues of the pair of a general 2x2 block S and a positive diagonal
+        // 2x2 block T Then taking beta=T_00*T_11, we can avoid any division, and alpha is the eigenvalues of A = (U^-1
+        // * S * U) * diag(T_11,T_00):
+
+        // T =  [a 0]
+        //      [0 b]
+        RealScalar a = mT.diagonal().coeff(i), b = mT.diagonal().coeff(i + 1);
+        const RealScalar beta = m_betas.coeffRef(i) = m_betas.coeffRef(i + 1) = a * b;
+
+        // ^^ NOTE: using diagonal()(i) instead of coeff(i,i) workarounds a MSVC bug.
+        Matrix<RealScalar, 2, 2> S2 = mS.template block<2, 2>(i, i) * Matrix<Scalar, 2, 1>(b, a).asDiagonal();
+
+        Scalar p = Scalar(0.5) * (S2.coeff(0, 0) - S2.coeff(1, 1));
+        Scalar z = sqrt(abs(p * p + S2.coeff(1, 0) * S2.coeff(0, 1)));
+        const ComplexScalar alpha = ComplexScalar(S2.coeff(1, 1) + p, (beta > 0) ? z : -z);
+        m_alphas.coeffRef(i) = conj(alpha);
+        m_alphas.coeffRef(i + 1) = alpha;
+
+        if (computeEigenvectors) {
+          // Compute eigenvector in position (i+1) and then position (i) is just the conjugate
+          cv.setZero();
+          cv.coeffRef(i + 1) = Scalar(1.0);
+          // here, the "static_cast" workaround expression template issues.
+          cv.coeffRef(i) = -(static_cast<Scalar>(beta * mS.coeffRef(i, i + 1)) - alpha * mT.coeffRef(i, i + 1)) /
+                           (static_cast<Scalar>(beta * mS.coeffRef(i, i)) - alpha * mT.coeffRef(i, i));
+          for (Index j = i - 1; j >= 0; j--) {
+            const Index st = j + 1;
+            const Index sz = i + 1 - j;
+            if (j > 0 && mS.coeff(j, j - 1) != Scalar(0)) {
+              // 2x2 block
+              Matrix<ComplexScalar, 2, 1> rhs = (alpha * mT.template block<2, Dynamic>(j - 1, st, 2, sz) -
+                                                 beta * mS.template block<2, Dynamic>(j - 1, st, 2, sz))
+                                                    .lazyProduct(cv.segment(st, sz));
+              Matrix<ComplexScalar, 2, 2> lhs =
+                  beta * mS.template block<2, 2>(j - 1, j - 1) - alpha * mT.template block<2, 2>(j - 1, j - 1);
+              cv.template segment<2>(j - 1) = lhs.partialPivLu().solve(rhs);
+              j--;
+            } else {
+              cv.coeffRef(j) = cv.segment(st, sz)
+                                   .transpose()
+                                   .cwiseProduct(beta * mS.block(j, st, 1, sz) - alpha * mT.block(j, st, 1, sz))
+                                   .sum() /
+                               (alpha * mT.coeffRef(j, j) - static_cast<Scalar>(beta * mS.coeffRef(j, j)));
+            }
+          }
+          m_eivec.col(i + 1).noalias() = (m_realQZ.matrixZ().transpose() * cv);
+          m_eivec.col(i + 1).normalize();
+          m_eivec.col(i) = m_eivec.col(i + 1).conjugate();
+        }
+        i += 2;
+      }
+    }
+  }
+  m_computeEigenvectors = computeEigenvectors;
+  m_isInitialized = true;
+  return *this;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GENERALIZEDEIGENSOLVER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
new file mode 100644
index 0000000..adff3a3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
@@ -0,0 +1,212 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
+#define EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
+
+#include "./Tridiagonalization.h"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class GeneralizedSelfAdjointEigenSolver
+ *
+ * \brief Computes eigenvalues and eigenvectors of the generalized selfadjoint eigen problem
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the
+ * eigendecomposition; this is expected to be an instantiation of the Matrix
+ * class template.
+ *
+ * This class solves the generalized eigenvalue problem
+ * \f$ Av = \lambda Bv \f$. In this case, the matrix \f$ A \f$ should be
+ * selfadjoint and the matrix \f$ B \f$ should be positive definite.
+ *
+ * Only the \b lower \b triangular \b part of the input matrix is referenced.
+ *
+ * Call the function compute() to compute the eigenvalues and eigenvectors of
+ * a given matrix. Alternatively, you can use the
+ * GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
+ * constructor which computes the eigenvalues and eigenvectors at construction time.
+ * Once the eigenvalue and eigenvectors are computed, they can be retrieved with the eigenvalues()
+ * and eigenvectors() functions.
+ *
+ * The documentation for GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
+ * contains an example of the typical use of this class.
+ *
+ * \sa class SelfAdjointEigenSolver, class EigenSolver, class ComplexEigenSolver
+ */
+template <typename MatrixType_>
+class GeneralizedSelfAdjointEigenSolver : public SelfAdjointEigenSolver<MatrixType_> {
+  typedef SelfAdjointEigenSolver<MatrixType_> Base;
+
+ public:
+  typedef MatrixType_ MatrixType;
+
+  /** \brief Default constructor for fixed-size matrices.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via compute(). This constructor
+   * can only be used if \p MatrixType_ is a fixed-size matrix; use
+   * GeneralizedSelfAdjointEigenSolver(Index) for dynamic-size matrices.
+   */
+  GeneralizedSelfAdjointEigenSolver() : Base() {}
+
+  /** \brief Constructor, pre-allocates memory for dynamic-size matrices.
+   *
+   * \param [in]  size  Positive integer, size of the matrix whose
+   * eigenvalues and eigenvectors will be computed.
+   *
+   * This constructor is useful for dynamic-size matrices, when the user
+   * intends to perform decompositions via compute(). The \p size
+   * parameter is only used as a hint. It is not an error to give a wrong
+   * \p size, but it may impair performance.
+   *
+   * \sa compute() for an example
+   */
+  explicit GeneralizedSelfAdjointEigenSolver(Index size) : Base(size) {}
+
+  /** \brief Constructor; computes generalized eigendecomposition of given matrix pencil.
+   *
+   * \param[in]  matA  Selfadjoint matrix in matrix pencil.
+   *                   Only the lower triangular part of the matrix is referenced.
+   * \param[in]  matB  Positive-definite matrix in matrix pencil.
+   *                   Only the lower triangular part of the matrix is referenced.
+   * \param[in]  options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}.
+   *                     Default is #ComputeEigenvectors|#Ax_lBx.
+   *
+   * This constructor calls compute(const MatrixType&, const MatrixType&, int)
+   * to compute the eigenvalues and (if requested) the eigenvectors of the
+   * generalized eigenproblem \f$ Ax = \lambda B x \f$ with \a matA the
+   * selfadjoint matrix \f$ A \f$ and \a matB the positive definite matrix
+   * \f$ B \f$. Each eigenvector \f$ x \f$ satisfies the property
+   * \f$ x^* B x = 1 \f$. The eigenvectors are computed if
+   * \a options contains ComputeEigenvectors.
+   *
+   * In addition, the two following variants can be solved via \p options:
+   * - \c ABx_lx: \f$ ABx = \lambda x \f$
+   * - \c BAx_lx: \f$ BAx = \lambda x \f$
+   *
+   * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.out
+   *
+   * \sa compute(const MatrixType&, const MatrixType&, int)
+   */
+  GeneralizedSelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB,
+                                    int options = ComputeEigenvectors | Ax_lBx)
+      : Base(matA.cols()) {
+    compute(matA, matB, options);
+  }
+
+  /** \brief Computes generalized eigendecomposition of given matrix pencil.
+   *
+   * \param[in]  matA  Selfadjoint matrix in matrix pencil.
+   *                   Only the lower triangular part of the matrix is referenced.
+   * \param[in]  matB  Positive-definite matrix in matrix pencil.
+   *                   Only the lower triangular part of the matrix is referenced.
+   * \param[in]  options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}.
+   *                     Default is #ComputeEigenvectors|#Ax_lBx.
+   *
+   * \returns    Reference to \c *this
+   *
+   * According to \p options, this function computes eigenvalues and (if requested)
+   * the eigenvectors of one of the following three generalized eigenproblems:
+   * - \c Ax_lBx: \f$ Ax = \lambda B x \f$
+   * - \c ABx_lx: \f$ ABx = \lambda x \f$
+   * - \c BAx_lx: \f$ BAx = \lambda x \f$
+   * with \a matA the selfadjoint matrix \f$ A \f$ and \a matB the positive definite
+   * matrix \f$ B \f$.
+   * In addition, each eigenvector \f$ x \f$ satisfies the property \f$ x^* B x = 1 \f$.
+   *
+   * The eigenvalues() function can be used to retrieve
+   * the eigenvalues. If \p options contains ComputeEigenvectors, then the
+   * eigenvectors are also computed and can be retrieved by calling
+   * eigenvectors().
+   *
+   * The implementation uses LLT to compute the Cholesky decomposition
+   * \f$ B = LL^* \f$ and computes the classical eigendecomposition
+   * of the selfadjoint matrix \f$ L^{-1} A (L^*)^{-1} \f$ if \p options contains Ax_lBx
+   * and of \f$ L^{*} A L \f$ otherwise. This solves the
+   * generalized eigenproblem, because any solution of the generalized
+   * eigenproblem \f$ Ax = \lambda B x \f$ corresponds to a solution
+   * \f$ L^{-1} A (L^*)^{-1} (L^* x) = \lambda (L^* x) \f$ of the
+   * eigenproblem for \f$ L^{-1} A (L^*)^{-1} \f$. Similar statements
+   * can be made for the two other variants.
+   *
+   * Example: \include SelfAdjointEigenSolver_compute_MatrixType2.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType2.out
+   *
+   * \sa GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
+   */
+  GeneralizedSelfAdjointEigenSolver& compute(const MatrixType& matA, const MatrixType& matB,
+                                             int options = ComputeEigenvectors | Ax_lBx);
+
+ protected:
+};
+
+template <typename MatrixType>
+GeneralizedSelfAdjointEigenSolver<MatrixType>& GeneralizedSelfAdjointEigenSolver<MatrixType>::compute(
+    const MatrixType& matA, const MatrixType& matB, int options) {
+  eigen_assert(matA.cols() == matA.rows() && matB.rows() == matA.rows() && matB.cols() == matB.rows());
+  eigen_assert((options & ~(EigVecMask | GenEigMask)) == 0 && (options & EigVecMask) != EigVecMask &&
+               ((options & GenEigMask) == 0 || (options & GenEigMask) == Ax_lBx || (options & GenEigMask) == ABx_lx ||
+                (options & GenEigMask) == BAx_lx) &&
+               "invalid option parameter");
+
+  bool computeEigVecs = ((options & EigVecMask) == 0) || ((options & EigVecMask) == ComputeEigenvectors);
+
+  // Compute the cholesky decomposition of matB = L L' = U'U
+  LLT<MatrixType> cholB(matB);
+
+  int type = (options & GenEigMask);
+  if (type == 0) type = Ax_lBx;
+
+  if (type == Ax_lBx) {
+    // compute C = inv(L) A inv(L')
+    MatrixType matC = matA.template selfadjointView<Lower>();
+    cholB.matrixL().template solveInPlace<OnTheLeft>(matC);
+    cholB.matrixU().template solveInPlace<OnTheRight>(matC);
+
+    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly);
+
+    // transform back the eigen vectors: evecs = inv(U) * evecs
+    if (computeEigVecs) cholB.matrixU().solveInPlace(Base::m_eivec);
+  } else if (type == ABx_lx) {
+    // compute C = L' A L
+    MatrixType matC = matA.template selfadjointView<Lower>();
+    matC = matC * cholB.matrixL();
+    matC = cholB.matrixU() * matC;
+
+    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly);
+
+    // transform back the eigen vectors: evecs = inv(U) * evecs
+    if (computeEigVecs) cholB.matrixU().solveInPlace(Base::m_eivec);
+  } else if (type == BAx_lx) {
+    // compute C = L' A L
+    MatrixType matC = matA.template selfadjointView<Lower>();
+    matC = matC * cholB.matrixL();
+    matC = cholB.matrixU() * matC;
+
+    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly);
+
+    // transform back the eigen vectors: evecs = L * evecs
+    if (computeEigVecs) Base::m_eivec = cholB.matrixL() * Base::m_eivec;
+  }
+
+  return *this;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/HessenbergDecomposition.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/HessenbergDecomposition.h
new file mode 100644
index 0000000..42fe3fd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/HessenbergDecomposition.h
@@ -0,0 +1,356 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HESSENBERGDECOMPOSITION_H
+#define EIGEN_HESSENBERGDECOMPOSITION_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename MatrixType>
+struct HessenbergDecompositionMatrixHReturnType;
+template <typename MatrixType>
+struct traits<HessenbergDecompositionMatrixHReturnType<MatrixType>> {
+  typedef MatrixType ReturnType;
+};
+
+}  // namespace internal
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class HessenbergDecomposition
+ *
+ * \brief Reduces a square matrix to Hessenberg form by an orthogonal similarity transformation
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the Hessenberg decomposition
+ *
+ * This class performs an Hessenberg decomposition of a matrix \f$ A \f$. In
+ * the real case, the Hessenberg decomposition consists of an orthogonal
+ * matrix \f$ Q \f$ and a Hessenberg matrix \f$ H \f$ such that \f$ A = Q H
+ * Q^T \f$. An orthogonal matrix is a matrix whose inverse equals its
+ * transpose (\f$ Q^{-1} = Q^T \f$). A Hessenberg matrix has zeros below the
+ * subdiagonal, so it is almost upper triangular. The Hessenberg decomposition
+ * of a complex matrix is \f$ A = Q H Q^* \f$ with \f$ Q \f$ unitary (that is,
+ * \f$ Q^{-1} = Q^* \f$).
+ *
+ * Call the function compute() to compute the Hessenberg decomposition of a
+ * given matrix. Alternatively, you can use the
+ * HessenbergDecomposition(const MatrixType&) constructor which computes the
+ * Hessenberg decomposition at construction time. Once the decomposition is
+ * computed, you can use the matrixH() and matrixQ() functions to construct
+ * the matrices H and Q in the decomposition.
+ *
+ * The documentation for matrixH() contains an example of the typical use of
+ * this class.
+ *
+ * \sa class ComplexSchur, class Tridiagonalization, \ref QR_Module "QR Module"
+ */
+template <typename MatrixType_>
+class HessenbergDecomposition {
+ public:
+  /** \brief Synonym for the template parameter \p MatrixType_. */
+  typedef MatrixType_ MatrixType;
+
+  enum {
+    Size = MatrixType::RowsAtCompileTime,
+    SizeMinusOne = Size == Dynamic ? Dynamic : Size - 1,
+    Options = internal::traits<MatrixType>::Options,
+    MaxSize = MatrixType::MaxRowsAtCompileTime,
+    MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : MaxSize - 1
+  };
+
+  /** \brief Scalar type for matrices of type #MatrixType. */
+  typedef typename MatrixType::Scalar Scalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+
+  /** \brief Type for vector of Householder coefficients.
+   *
+   * This is column vector with entries of type #Scalar. The length of the
+   * vector is one less than the size of #MatrixType, if it is a fixed-side
+   * type.
+   */
+  typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
+
+  /** \brief Return type of matrixQ() */
+  typedef HouseholderSequence<MatrixType, internal::remove_all_t<typename CoeffVectorType::ConjugateReturnType>>
+      HouseholderSequenceType;
+
+  typedef internal::HessenbergDecompositionMatrixHReturnType<MatrixType> MatrixHReturnType;
+
+  /** \brief Default constructor; the decomposition will be computed later.
+   *
+   * \param [in] size  The size of the matrix whose Hessenberg decomposition will be computed.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via compute().  The \p size parameter is only
+   * used as a hint. It is not an error to give a wrong \p size, but it may
+   * impair performance.
+   *
+   * \sa compute() for an example.
+   */
+  explicit HessenbergDecomposition(Index size = Size == Dynamic ? 2 : Size)
+      : m_matrix(size, size), m_temp(size), m_isInitialized(false) {
+    if (size > 1) m_hCoeffs.resize(size - 1);
+  }
+
+  /** \brief Constructor; computes Hessenberg decomposition of given matrix.
+   *
+   * \param[in]  matrix  Square matrix whose Hessenberg decomposition is to be computed.
+   *
+   * This constructor calls compute() to compute the Hessenberg
+   * decomposition.
+   *
+   * \sa matrixH() for an example.
+   */
+  template <typename InputType>
+  explicit HessenbergDecomposition(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()), m_temp(matrix.rows()), m_isInitialized(false) {
+    if (matrix.rows() < 2) {
+      m_isInitialized = true;
+      return;
+    }
+    m_hCoeffs.resize(matrix.rows() - 1, 1);
+    _compute(m_matrix, m_hCoeffs, m_temp);
+    m_isInitialized = true;
+  }
+
+  /** \brief Computes Hessenberg decomposition of given matrix.
+   *
+   * \param[in]  matrix  Square matrix whose Hessenberg decomposition is to be computed.
+   * \returns    Reference to \c *this
+   *
+   * The Hessenberg decomposition is computed by bringing the columns of the
+   * matrix successively in the required form using Householder reflections
+   * (see, e.g., Algorithm 7.4.2 in Golub \& Van Loan, <i>%Matrix
+   * Computations</i>). The cost is \f$ 10n^3/3 \f$ flops, where \f$ n \f$
+   * denotes the size of the given matrix.
+   *
+   * This method reuses of the allocated data in the HessenbergDecomposition
+   * object.
+   *
+   * Example: \include HessenbergDecomposition_compute.cpp
+   * Output: \verbinclude HessenbergDecomposition_compute.out
+   */
+  template <typename InputType>
+  HessenbergDecomposition& compute(const EigenBase<InputType>& matrix) {
+    m_matrix = matrix.derived();
+    if (matrix.rows() < 2) {
+      m_isInitialized = true;
+      return *this;
+    }
+    m_hCoeffs.resize(matrix.rows() - 1, 1);
+    _compute(m_matrix, m_hCoeffs, m_temp);
+    m_isInitialized = true;
+    return *this;
+  }
+
+  /** \brief Returns the Householder coefficients.
+   *
+   * \returns a const reference to the vector of Householder coefficients
+   *
+   * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
+   * or the member function compute(const MatrixType&) has been called
+   * before to compute the Hessenberg decomposition of a matrix.
+   *
+   * The Householder coefficients allow the reconstruction of the matrix
+   * \f$ Q \f$ in the Hessenberg decomposition from the packed data.
+   *
+   * \sa packedMatrix(), \ref Householder_Module "Householder module"
+   */
+  const CoeffVectorType& householderCoefficients() const {
+    eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
+    return m_hCoeffs;
+  }
+
+  /** \brief Returns the internal representation of the decomposition
+   *
+   *	\returns a const reference to a matrix with the internal representation
+   *	         of the decomposition.
+   *
+   * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
+   * or the member function compute(const MatrixType&) has been called
+   * before to compute the Hessenberg decomposition of a matrix.
+   *
+   * The returned matrix contains the following information:
+   *  - the upper part and lower sub-diagonal represent the Hessenberg matrix H
+   *  - the rest of the lower part contains the Householder vectors that, combined with
+   *    Householder coefficients returned by householderCoefficients(),
+   *    allows to reconstruct the matrix Q as
+   *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
+   *    Here, the matrices \f$ H_i \f$ are the Householder transformations
+   *       \f$ H_i = (I - h_i v_i v_i^T) \f$
+   *    where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and
+   *    \f$ v_i \f$ is the Householder vector defined by
+   *       \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$
+   *    with M the matrix returned by this function.
+   *
+   * See LAPACK for further details on this packed storage.
+   *
+   * Example: \include HessenbergDecomposition_packedMatrix.cpp
+   * Output: \verbinclude HessenbergDecomposition_packedMatrix.out
+   *
+   * \sa householderCoefficients()
+   */
+  const MatrixType& packedMatrix() const {
+    eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
+    return m_matrix;
+  }
+
+  /** \brief Reconstructs the orthogonal matrix Q in the decomposition
+   *
+   * \returns object representing the matrix Q
+   *
+   * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
+   * or the member function compute(const MatrixType&) has been called
+   * before to compute the Hessenberg decomposition of a matrix.
+   *
+   * This function returns a light-weight object of template class
+   * HouseholderSequence. You can either apply it directly to a matrix or
+   * you can convert it to a matrix of type #MatrixType.
+   *
+   * \sa matrixH() for an example, class HouseholderSequence
+   */
+  HouseholderSequenceType matrixQ() const {
+    eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
+    return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate()).setLength(m_matrix.rows() - 1).setShift(1);
+  }
+
+  /** \brief Constructs the Hessenberg matrix H in the decomposition
+   *
+   * \returns expression object representing the matrix H
+   *
+   * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
+   * or the member function compute(const MatrixType&) has been called
+   * before to compute the Hessenberg decomposition of a matrix.
+   *
+   * The object returned by this function constructs the Hessenberg matrix H
+   * when it is assigned to a matrix or otherwise evaluated. The matrix H is
+   * constructed from the packed matrix as returned by packedMatrix(): The
+   * upper part (including the subdiagonal) of the packed matrix contains
+   * the matrix H. It may sometimes be better to directly use the packed
+   * matrix instead of constructing the matrix H.
+   *
+   * Example: \include HessenbergDecomposition_matrixH.cpp
+   * Output: \verbinclude HessenbergDecomposition_matrixH.out
+   *
+   * \sa matrixQ(), packedMatrix()
+   */
+  MatrixHReturnType matrixH() const {
+    eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
+    return MatrixHReturnType(*this);
+  }
+
+ private:
+  typedef Matrix<Scalar, 1, Size, int(Options) | int(RowMajor), 1, MaxSize> VectorType;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  static void _compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp);
+
+ protected:
+  MatrixType m_matrix;
+  CoeffVectorType m_hCoeffs;
+  VectorType m_temp;
+  bool m_isInitialized;
+};
+
+/** \internal
+ * Performs a tridiagonal decomposition of \a matA in place.
+ *
+ * \param matA the input selfadjoint matrix
+ * \param hCoeffs returned Householder coefficients
+ *
+ * The result is written in the lower triangular part of \a matA.
+ *
+ * Implemented from Golub's "%Matrix Computations", algorithm 8.3.1.
+ *
+ * \sa packedMatrix()
+ */
+template <typename MatrixType>
+void HessenbergDecomposition<MatrixType>::_compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp) {
+  eigen_assert(matA.rows() == matA.cols());
+  Index n = matA.rows();
+  temp.resize(n);
+  for (Index i = 0; i < n - 1; ++i) {
+    // let's consider the vector v = i-th column starting at position i+1
+    Index remainingSize = n - i - 1;
+    RealScalar beta;
+    Scalar h;
+    matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta);
+    matA.col(i).coeffRef(i + 1) = beta;
+    hCoeffs.coeffRef(i) = h;
+
+    // Apply similarity transformation to remaining columns,
+    // i.e., compute A = H A H'
+
+    // A = H A
+    matA.bottomRightCorner(remainingSize, remainingSize)
+        .applyHouseholderOnTheLeft(matA.col(i).tail(remainingSize - 1), h, &temp.coeffRef(0));
+
+    // A = A H'
+    matA.rightCols(remainingSize)
+        .applyHouseholderOnTheRight(matA.col(i).tail(remainingSize - 1), numext::conj(h), &temp.coeffRef(0));
+  }
+}
+
+namespace internal {
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \brief Expression type for return value of HessenbergDecomposition::matrixH()
+ *
+ * \tparam MatrixType type of matrix in the Hessenberg decomposition
+ *
+ * Objects of this type represent the Hessenberg matrix in the Hessenberg
+ * decomposition of some matrix. The object holds a reference to the
+ * HessenbergDecomposition class until the it is assigned or evaluated for
+ * some other reason (the reference should remain valid during the life time
+ * of this object). This class is the return type of
+ * HessenbergDecomposition::matrixH(); there is no other intended use for this
+ * class.
+ */
+template <typename MatrixType>
+struct HessenbergDecompositionMatrixHReturnType
+    : public ReturnByValue<HessenbergDecompositionMatrixHReturnType<MatrixType>> {
+ public:
+  /** \brief Constructor.
+   *
+   * \param[in] hess  Hessenberg decomposition
+   */
+  HessenbergDecompositionMatrixHReturnType(const HessenbergDecomposition<MatrixType>& hess) : m_hess(hess) {}
+
+  /** \brief Hessenberg matrix in decomposition.
+   *
+   * \param[out] result  Hessenberg matrix in decomposition \p hess which
+   *                     was passed to the constructor
+   */
+  template <typename ResultType>
+  inline void evalTo(ResultType& result) const {
+    result = m_hess.packedMatrix();
+    Index n = result.rows();
+    if (n > 2) result.bottomLeftCorner(n - 2, n - 2).template triangularView<Lower>().setZero();
+  }
+
+  Index rows() const { return m_hess.packedMatrix().rows(); }
+  Index cols() const { return m_hess.packedMatrix().cols(); }
+
+ protected:
+  const HessenbergDecomposition<MatrixType>& m_hess;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_HESSENBERGDECOMPOSITION_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/InternalHeaderCheck.h
new file mode 100644
index 0000000..374cbd4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_EIGENVALUES_MODULE_H
+#error "Please include Eigen/Eigenvalues instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
new file mode 100644
index 0000000..38f46f8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
@@ -0,0 +1,141 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIXBASEEIGENVALUES_H
+#define EIGEN_MATRIXBASEEIGENVALUES_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Derived, bool IsComplex>
+struct eigenvalues_selector {
+  // this is the implementation for the case IsComplex = true
+  static inline typename MatrixBase<Derived>::EigenvaluesReturnType const run(const MatrixBase<Derived>& m) {
+    typedef typename Derived::PlainObject PlainObject;
+    PlainObject m_eval(m);
+    return ComplexEigenSolver<PlainObject>(m_eval, false).eigenvalues();
+  }
+};
+
+template <typename Derived>
+struct eigenvalues_selector<Derived, false> {
+  static inline typename MatrixBase<Derived>::EigenvaluesReturnType const run(const MatrixBase<Derived>& m) {
+    typedef typename Derived::PlainObject PlainObject;
+    PlainObject m_eval(m);
+    return EigenSolver<PlainObject>(m_eval, false).eigenvalues();
+  }
+};
+
+}  // end namespace internal
+
+/** \brief Computes the eigenvalues of a matrix
+ * \returns Column vector containing the eigenvalues.
+ *
+ * \eigenvalues_module
+ * This function computes the eigenvalues with the help of the EigenSolver
+ * class (for real matrices) or the ComplexEigenSolver class (for complex
+ * matrices).
+ *
+ * The eigenvalues are repeated according to their algebraic multiplicity,
+ * so there are as many eigenvalues as rows in the matrix.
+ *
+ * The SelfAdjointView class provides a better algorithm for selfadjoint
+ * matrices.
+ *
+ * Example: \include MatrixBase_eigenvalues.cpp
+ * Output: \verbinclude MatrixBase_eigenvalues.out
+ *
+ * \sa EigenSolver::eigenvalues(), ComplexEigenSolver::eigenvalues(),
+ *     SelfAdjointView::eigenvalues()
+ */
+template <typename Derived>
+inline typename MatrixBase<Derived>::EigenvaluesReturnType MatrixBase<Derived>::eigenvalues() const {
+  return internal::eigenvalues_selector<Derived, NumTraits<Scalar>::IsComplex>::run(derived());
+}
+
+/** \brief Computes the eigenvalues of a matrix
+ * \returns Column vector containing the eigenvalues.
+ *
+ * \eigenvalues_module
+ * This function computes the eigenvalues with the help of the
+ * SelfAdjointEigenSolver class.  The eigenvalues are repeated according to
+ * their algebraic multiplicity, so there are as many eigenvalues as rows in
+ * the matrix.
+ *
+ * Example: \include SelfAdjointView_eigenvalues.cpp
+ * Output: \verbinclude SelfAdjointView_eigenvalues.out
+ *
+ * \sa SelfAdjointEigenSolver::eigenvalues(), MatrixBase::eigenvalues()
+ */
+template <typename MatrixType, unsigned int UpLo>
+EIGEN_DEVICE_FUNC inline typename SelfAdjointView<MatrixType, UpLo>::EigenvaluesReturnType
+SelfAdjointView<MatrixType, UpLo>::eigenvalues() const {
+  PlainObject thisAsMatrix(*this);
+  return SelfAdjointEigenSolver<PlainObject>(thisAsMatrix, false).eigenvalues();
+}
+
+/** \brief Computes the L2 operator norm
+ * \returns Operator norm of the matrix.
+ *
+ * \eigenvalues_module
+ * This function computes the L2 operator norm of a matrix, which is also
+ * known as the spectral norm. The norm of a matrix \f$ A \f$ is defined to be
+ * \f[ \|A\|_2 = \max_x \frac{\|Ax\|_2}{\|x\|_2} \f]
+ * where the maximum is over all vectors and the norm on the right is the
+ * Euclidean vector norm. The norm equals the largest singular value, which is
+ * the square root of the largest eigenvalue of the positive semi-definite
+ * matrix \f$ A^*A \f$.
+ *
+ * The current implementation uses the eigenvalues of \f$ A^*A \f$, as computed
+ * by SelfAdjointView::eigenvalues(), to compute the operator norm of a
+ * matrix.  The SelfAdjointView class provides a better algorithm for
+ * selfadjoint matrices.
+ *
+ * Example: \include MatrixBase_operatorNorm.cpp
+ * Output: \verbinclude MatrixBase_operatorNorm.out
+ *
+ * \sa SelfAdjointView::eigenvalues(), SelfAdjointView::operatorNorm()
+ */
+template <typename Derived>
+inline typename MatrixBase<Derived>::RealScalar MatrixBase<Derived>::operatorNorm() const {
+  using std::sqrt;
+  typename Derived::PlainObject m_eval(derived());
+  // FIXME: if eigenvalues are guaranteed to be sorted, comparing the first and last is sufficient.
+  return sqrt((m_eval * m_eval.adjoint()).eval().template selfadjointView<Lower>().eigenvalues().maxCoeff());
+}
+
+/** \brief Computes the L2 operator norm
+ * \returns Operator norm of the matrix.
+ *
+ * \eigenvalues_module
+ * This function computes the L2 operator norm of a self-adjoint matrix. For a
+ * self-adjoint matrix, the operator norm is the largest eigenvalue.
+ *
+ * The current implementation uses the eigenvalues of the matrix, as computed
+ * by eigenvalues(), to compute the operator norm of the matrix.
+ *
+ * Example: \include SelfAdjointView_operatorNorm.cpp
+ * Output: \verbinclude SelfAdjointView_operatorNorm.out
+ *
+ * \sa eigenvalues(), MatrixBase::operatorNorm()
+ */
+template <typename MatrixType, unsigned int UpLo>
+EIGEN_DEVICE_FUNC inline typename SelfAdjointView<MatrixType, UpLo>::RealScalar
+SelfAdjointView<MatrixType, UpLo>::operatorNorm() const {
+  return eigenvalues().cwiseAbs().maxCoeff();
+}
+
+}  // end namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/RealQZ.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/RealQZ.h
new file mode 100644
index 0000000..a54d82d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/RealQZ.h
@@ -0,0 +1,587 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Alexey Korepanov <kaikaikai@yandex.ru>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REAL_QZ_H
+#define EIGEN_REAL_QZ_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class RealQZ
+ *
+ * \brief Performs a real QZ decomposition of a pair of square matrices
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the
+ * real QZ decomposition; this is expected to be an instantiation of the
+ * Matrix class template.
+ *
+ * Given a real square matrices A and B, this class computes the real QZ
+ * decomposition: \f$ A = Q S Z \f$, \f$ B = Q T Z \f$ where Q and Z are
+ * real orthogonal matrixes, T is upper-triangular matrix, and S is upper
+ * quasi-triangular matrix. An orthogonal matrix is a matrix whose
+ * inverse is equal to its transpose, \f$ U^{-1} = U^T \f$. A quasi-triangular
+ * matrix is a block-triangular matrix whose diagonal consists of 1-by-1
+ * blocks and 2-by-2 blocks where further reduction is impossible due to
+ * complex eigenvalues.
+ *
+ * The eigenvalues of the pencil \f$ A - z B \f$ can be obtained from
+ * 1x1 and 2x2 blocks on the diagonals of S and T.
+ *
+ * Call the function compute() to compute the real QZ decomposition of a
+ * given pair of matrices. Alternatively, you can use the
+ * RealQZ(const MatrixType& B, const MatrixType& B, bool computeQZ)
+ * constructor which computes the real QZ decomposition at construction
+ * time. Once the decomposition is computed, you can use the matrixS(),
+ * matrixT(), matrixQ() and matrixZ() functions to retrieve the matrices
+ * S, T, Q and Z in the decomposition. If computeQZ==false, some time
+ * is saved by not computing matrices Q and Z.
+ *
+ * Example: \include RealQZ_compute.cpp
+ * Output: \include RealQZ_compute.out
+ *
+ * \note The implementation is based on the algorithm in "Matrix Computations"
+ * by Gene H. Golub and Charles F. Van Loan, and a paper "An algorithm for
+ * generalized eigenvalue problems" by C.B.Moler and G.W.Stewart.
+ *
+ * \sa class RealSchur, class ComplexSchur, class EigenSolver, class ComplexEigenSolver
+ */
+
+template <typename MatrixType_>
+class RealQZ {
+ public:
+  typedef MatrixType_ MatrixType;
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    Options = internal::traits<MatrixType>::Options,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+
+  typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
+  typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
+
+  /** \brief Default constructor.
+   *
+   * \param [in] size  Positive integer, size of the matrix whose QZ decomposition will be computed.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via compute().  The \p size parameter is only
+   * used as a hint. It is not an error to give a wrong \p size, but it may
+   * impair performance.
+   *
+   * \sa compute() for an example.
+   */
+  explicit RealQZ(Index size = RowsAtCompileTime == Dynamic ? 1 : RowsAtCompileTime)
+      : m_S(size, size),
+        m_T(size, size),
+        m_Q(size, size),
+        m_Z(size, size),
+        m_workspace(size * 2),
+        m_maxIters(400),
+        m_isInitialized(false),
+        m_computeQZ(true) {}
+
+  /** \brief Constructor; computes real QZ decomposition of given matrices
+   *
+   * \param[in]  A          Matrix A.
+   * \param[in]  B          Matrix B.
+   * \param[in]  computeQZ  If false, A and Z are not computed.
+   *
+   * This constructor calls compute() to compute the QZ decomposition.
+   */
+  RealQZ(const MatrixType& A, const MatrixType& B, bool computeQZ = true)
+      : m_S(A.rows(), A.cols()),
+        m_T(A.rows(), A.cols()),
+        m_Q(A.rows(), A.cols()),
+        m_Z(A.rows(), A.cols()),
+        m_workspace(A.rows() * 2),
+        m_maxIters(400),
+        m_isInitialized(false),
+        m_computeQZ(true) {
+    compute(A, B, computeQZ);
+  }
+
+  /** \brief Returns matrix Q in the QZ decomposition.
+   *
+   * \returns A const reference to the matrix Q.
+   */
+  const MatrixType& matrixQ() const {
+    eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+    eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition.");
+    return m_Q;
+  }
+
+  /** \brief Returns matrix Z in the QZ decomposition.
+   *
+   * \returns A const reference to the matrix Z.
+   */
+  const MatrixType& matrixZ() const {
+    eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+    eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition.");
+    return m_Z;
+  }
+
+  /** \brief Returns matrix S in the QZ decomposition.
+   *
+   * \returns A const reference to the matrix S.
+   */
+  const MatrixType& matrixS() const {
+    eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+    return m_S;
+  }
+
+  /** \brief Returns matrix S in the QZ decomposition.
+   *
+   * \returns A const reference to the matrix S.
+   */
+  const MatrixType& matrixT() const {
+    eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+    return m_T;
+  }
+
+  /** \brief Computes QZ decomposition of given matrix.
+   *
+   * \param[in]  A          Matrix A.
+   * \param[in]  B          Matrix B.
+   * \param[in]  computeQZ  If false, A and Z are not computed.
+   * \returns    Reference to \c *this
+   */
+  RealQZ& compute(const MatrixType& A, const MatrixType& B, bool computeQZ = true);
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+    return m_info;
+  }
+
+  /** \brief Returns number of performed QR-like iterations.
+   */
+  Index iterations() const {
+    eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+    return m_global_iter;
+  }
+
+  /** Sets the maximal number of iterations allowed to converge to one eigenvalue
+   * or decouple the problem.
+   */
+  RealQZ& setMaxIterations(Index maxIters) {
+    m_maxIters = maxIters;
+    return *this;
+  }
+
+ private:
+  MatrixType m_S, m_T, m_Q, m_Z;
+  Matrix<Scalar, Dynamic, 1> m_workspace;
+  ComputationInfo m_info;
+  Index m_maxIters;
+  bool m_isInitialized;
+  bool m_computeQZ;
+  Scalar m_normOfT, m_normOfS;
+  Index m_global_iter;
+
+  typedef Matrix<Scalar, 3, 1> Vector3s;
+  typedef Matrix<Scalar, 2, 1> Vector2s;
+  typedef Matrix<Scalar, 2, 2> Matrix2s;
+  typedef JacobiRotation<Scalar> JRs;
+
+  void hessenbergTriangular();
+  void computeNorms();
+  Index findSmallSubdiagEntry(Index iu);
+  Index findSmallDiagEntry(Index f, Index l);
+  void splitOffTwoRows(Index i);
+  void pushDownZero(Index z, Index f, Index l);
+  void step(Index f, Index l, Index iter);
+
+};  // RealQZ
+
+/** \internal Reduces S and T to upper Hessenberg - triangular form */
+template <typename MatrixType>
+void RealQZ<MatrixType>::hessenbergTriangular() {
+  const Index dim = m_S.cols();
+
+  // perform QR decomposition of T, overwrite T with R, save Q
+  HouseholderQR<MatrixType> qrT(m_T);
+  m_T = qrT.matrixQR();
+  m_T.template triangularView<StrictlyLower>().setZero();
+  m_Q = qrT.householderQ();
+  // overwrite S with Q* S
+  m_S.applyOnTheLeft(m_Q.adjoint());
+  // init Z as Identity
+  if (m_computeQZ) m_Z = MatrixType::Identity(dim, dim);
+  // reduce S to upper Hessenberg with Givens rotations
+  for (Index j = 0; j <= dim - 3; j++) {
+    for (Index i = dim - 1; i >= j + 2; i--) {
+      JRs G;
+      // kill S(i,j)
+      if (!numext::is_exactly_zero(m_S.coeff(i, j))) {
+        G.makeGivens(m_S.coeff(i - 1, j), m_S.coeff(i, j), &m_S.coeffRef(i - 1, j));
+        m_S.coeffRef(i, j) = Scalar(0.0);
+        m_S.rightCols(dim - j - 1).applyOnTheLeft(i - 1, i, G.adjoint());
+        m_T.rightCols(dim - i + 1).applyOnTheLeft(i - 1, i, G.adjoint());
+        // update Q
+        if (m_computeQZ) m_Q.applyOnTheRight(i - 1, i, G);
+      }
+      // kill T(i,i-1)
+      if (!numext::is_exactly_zero(m_T.coeff(i, i - 1))) {
+        G.makeGivens(m_T.coeff(i, i), m_T.coeff(i, i - 1), &m_T.coeffRef(i, i));
+        m_T.coeffRef(i, i - 1) = Scalar(0.0);
+        m_S.applyOnTheRight(i, i - 1, G);
+        m_T.topRows(i).applyOnTheRight(i, i - 1, G);
+        // update Z
+        if (m_computeQZ) m_Z.applyOnTheLeft(i, i - 1, G.adjoint());
+      }
+    }
+  }
+}
+
+/** \internal Computes vector L1 norms of S and T when in Hessenberg-Triangular form already */
+template <typename MatrixType>
+inline void RealQZ<MatrixType>::computeNorms() {
+  const Index size = m_S.cols();
+  m_normOfS = Scalar(0.0);
+  m_normOfT = Scalar(0.0);
+  for (Index j = 0; j < size; ++j) {
+    m_normOfS += m_S.col(j).segment(0, (std::min)(size, j + 2)).cwiseAbs().sum();
+    m_normOfT += m_T.row(j).segment(j, size - j).cwiseAbs().sum();
+  }
+}
+
+/** \internal Look for single small sub-diagonal element S(res, res-1) and return res (or 0) */
+template <typename MatrixType>
+inline Index RealQZ<MatrixType>::findSmallSubdiagEntry(Index iu) {
+  using std::abs;
+  Index res = iu;
+  while (res > 0) {
+    Scalar s = abs(m_S.coeff(res - 1, res - 1)) + abs(m_S.coeff(res, res));
+    if (numext::is_exactly_zero(s)) s = m_normOfS;
+    if (abs(m_S.coeff(res, res - 1)) < NumTraits<Scalar>::epsilon() * s) break;
+    res--;
+  }
+  return res;
+}
+
+/** \internal Look for single small diagonal element T(res, res) for res between f and l, and return res (or f-1)  */
+template <typename MatrixType>
+inline Index RealQZ<MatrixType>::findSmallDiagEntry(Index f, Index l) {
+  using std::abs;
+  Index res = l;
+  while (res >= f) {
+    if (abs(m_T.coeff(res, res)) <= NumTraits<Scalar>::epsilon() * m_normOfT) break;
+    res--;
+  }
+  return res;
+}
+
+/** \internal decouple 2x2 diagonal block in rows i, i+1 if eigenvalues are real */
+template <typename MatrixType>
+inline void RealQZ<MatrixType>::splitOffTwoRows(Index i) {
+  using std::abs;
+  using std::sqrt;
+  const Index dim = m_S.cols();
+  if (numext::is_exactly_zero(abs(m_S.coeff(i + 1, i)))) return;
+  Index j = findSmallDiagEntry(i, i + 1);
+  if (j == i - 1) {
+    // block of (S T^{-1})
+    Matrix2s STi = m_T.template block<2, 2>(i, i).template triangularView<Upper>().template solve<OnTheRight>(
+        m_S.template block<2, 2>(i, i));
+    Scalar p = Scalar(0.5) * (STi(0, 0) - STi(1, 1));
+    Scalar q = p * p + STi(1, 0) * STi(0, 1);
+    if (q >= 0) {
+      Scalar z = sqrt(q);
+      // one QR-like iteration for ABi - lambda I
+      // is enough - when we know exact eigenvalue in advance,
+      // convergence is immediate
+      JRs G;
+      if (p >= 0)
+        G.makeGivens(p + z, STi(1, 0));
+      else
+        G.makeGivens(p - z, STi(1, 0));
+      m_S.rightCols(dim - i).applyOnTheLeft(i, i + 1, G.adjoint());
+      m_T.rightCols(dim - i).applyOnTheLeft(i, i + 1, G.adjoint());
+      // update Q
+      if (m_computeQZ) m_Q.applyOnTheRight(i, i + 1, G);
+
+      G.makeGivens(m_T.coeff(i + 1, i + 1), m_T.coeff(i + 1, i));
+      m_S.topRows(i + 2).applyOnTheRight(i + 1, i, G);
+      m_T.topRows(i + 2).applyOnTheRight(i + 1, i, G);
+      // update Z
+      if (m_computeQZ) m_Z.applyOnTheLeft(i + 1, i, G.adjoint());
+
+      m_S.coeffRef(i + 1, i) = Scalar(0.0);
+      m_T.coeffRef(i + 1, i) = Scalar(0.0);
+    }
+  } else {
+    pushDownZero(j, i, i + 1);
+  }
+}
+
+/** \internal use zero in T(z,z) to zero S(l,l-1), working in block f..l */
+template <typename MatrixType>
+inline void RealQZ<MatrixType>::pushDownZero(Index z, Index f, Index l) {
+  JRs G;
+  const Index dim = m_S.cols();
+  for (Index zz = z; zz < l; zz++) {
+    // push 0 down
+    Index firstColS = zz > f ? (zz - 1) : zz;
+    G.makeGivens(m_T.coeff(zz, zz + 1), m_T.coeff(zz + 1, zz + 1));
+    m_S.rightCols(dim - firstColS).applyOnTheLeft(zz, zz + 1, G.adjoint());
+    m_T.rightCols(dim - zz).applyOnTheLeft(zz, zz + 1, G.adjoint());
+    m_T.coeffRef(zz + 1, zz + 1) = Scalar(0.0);
+    // update Q
+    if (m_computeQZ) m_Q.applyOnTheRight(zz, zz + 1, G);
+    // kill S(zz+1, zz-1)
+    if (zz > f) {
+      G.makeGivens(m_S.coeff(zz + 1, zz), m_S.coeff(zz + 1, zz - 1));
+      m_S.topRows(zz + 2).applyOnTheRight(zz, zz - 1, G);
+      m_T.topRows(zz + 1).applyOnTheRight(zz, zz - 1, G);
+      m_S.coeffRef(zz + 1, zz - 1) = Scalar(0.0);
+      // update Z
+      if (m_computeQZ) m_Z.applyOnTheLeft(zz, zz - 1, G.adjoint());
+    }
+  }
+  // finally kill S(l,l-1)
+  G.makeGivens(m_S.coeff(l, l), m_S.coeff(l, l - 1));
+  m_S.applyOnTheRight(l, l - 1, G);
+  m_T.applyOnTheRight(l, l - 1, G);
+  m_S.coeffRef(l, l - 1) = Scalar(0.0);
+  // update Z
+  if (m_computeQZ) m_Z.applyOnTheLeft(l, l - 1, G.adjoint());
+}
+
+/** \internal QR-like iterative step for block f..l */
+template <typename MatrixType>
+inline void RealQZ<MatrixType>::step(Index f, Index l, Index iter) {
+  using std::abs;
+  const Index dim = m_S.cols();
+
+  // x, y, z
+  Scalar x, y, z;
+  if (iter == 10) {
+    // Wilkinson ad hoc shift
+    const Scalar a11 = m_S.coeff(f + 0, f + 0), a12 = m_S.coeff(f + 0, f + 1), a21 = m_S.coeff(f + 1, f + 0),
+                 a22 = m_S.coeff(f + 1, f + 1), a32 = m_S.coeff(f + 2, f + 1), b12 = m_T.coeff(f + 0, f + 1),
+                 b11i = Scalar(1.0) / m_T.coeff(f + 0, f + 0), b22i = Scalar(1.0) / m_T.coeff(f + 1, f + 1),
+                 a87 = m_S.coeff(l - 1, l - 2), a98 = m_S.coeff(l - 0, l - 1),
+                 b77i = Scalar(1.0) / m_T.coeff(l - 2, l - 2), b88i = Scalar(1.0) / m_T.coeff(l - 1, l - 1);
+    Scalar ss = abs(a87 * b77i) + abs(a98 * b88i), lpl = Scalar(1.5) * ss, ll = ss * ss;
+    x = ll + a11 * a11 * b11i * b11i - lpl * a11 * b11i + a12 * a21 * b11i * b22i -
+        a11 * a21 * b12 * b11i * b11i * b22i;
+    y = a11 * a21 * b11i * b11i - lpl * a21 * b11i + a21 * a22 * b11i * b22i - a21 * a21 * b12 * b11i * b11i * b22i;
+    z = a21 * a32 * b11i * b22i;
+  } else if (iter == 16) {
+    // another exceptional shift
+    x = m_S.coeff(f, f) / m_T.coeff(f, f) - m_S.coeff(l, l) / m_T.coeff(l, l) +
+        m_S.coeff(l, l - 1) * m_T.coeff(l - 1, l) / (m_T.coeff(l - 1, l - 1) * m_T.coeff(l, l));
+    y = m_S.coeff(f + 1, f) / m_T.coeff(f, f);
+    z = 0;
+  } else if (iter > 23 && !(iter % 8)) {
+    // extremely exceptional shift
+    x = internal::random<Scalar>(-1.0, 1.0);
+    y = internal::random<Scalar>(-1.0, 1.0);
+    z = internal::random<Scalar>(-1.0, 1.0);
+  } else {
+    // Compute the shifts: (x,y,z,0...) = (AB^-1 - l1 I) (AB^-1 - l2 I) e1
+    // where l1 and l2 are the eigenvalues of the 2x2 matrix C = U V^-1 where
+    // U and V are 2x2 bottom right sub matrices of A and B. Thus:
+    //  = AB^-1AB^-1 + l1 l2 I - (l1+l2)(AB^-1)
+    //  = AB^-1AB^-1 + det(M) - tr(M)(AB^-1)
+    // Since we are only interested in having x, y, z with a correct ratio, we have:
+    const Scalar a11 = m_S.coeff(f, f), a12 = m_S.coeff(f, f + 1), a21 = m_S.coeff(f + 1, f),
+                 a22 = m_S.coeff(f + 1, f + 1), a32 = m_S.coeff(f + 2, f + 1),
+
+                 a88 = m_S.coeff(l - 1, l - 1), a89 = m_S.coeff(l - 1, l), a98 = m_S.coeff(l, l - 1),
+                 a99 = m_S.coeff(l, l),
+
+                 b11 = m_T.coeff(f, f), b12 = m_T.coeff(f, f + 1), b22 = m_T.coeff(f + 1, f + 1),
+
+                 b88 = m_T.coeff(l - 1, l - 1), b89 = m_T.coeff(l - 1, l), b99 = m_T.coeff(l, l);
+
+    x = ((a88 / b88 - a11 / b11) * (a99 / b99 - a11 / b11) - (a89 / b99) * (a98 / b88) +
+         (a98 / b88) * (b89 / b99) * (a11 / b11)) *
+            (b11 / a21) +
+        a12 / b22 - (a11 / b11) * (b12 / b22);
+    y = (a22 / b22 - a11 / b11) - (a21 / b11) * (b12 / b22) - (a88 / b88 - a11 / b11) - (a99 / b99 - a11 / b11) +
+        (a98 / b88) * (b89 / b99);
+    z = a32 / b22;
+  }
+
+  JRs G;
+
+  for (Index k = f; k <= l - 2; k++) {
+    // variables for Householder reflections
+    Vector2s essential2;
+    Scalar tau, beta;
+
+    Vector3s hr(x, y, z);
+
+    // Q_k to annihilate S(k+1,k-1) and S(k+2,k-1)
+    hr.makeHouseholderInPlace(tau, beta);
+    essential2 = hr.template bottomRows<2>();
+    Index fc = (std::max)(k - 1, Index(0));  // first col to update
+    m_S.template middleRows<3>(k).rightCols(dim - fc).applyHouseholderOnTheLeft(essential2, tau, m_workspace.data());
+    m_T.template middleRows<3>(k).rightCols(dim - fc).applyHouseholderOnTheLeft(essential2, tau, m_workspace.data());
+    if (m_computeQZ) m_Q.template middleCols<3>(k).applyHouseholderOnTheRight(essential2, tau, m_workspace.data());
+    if (k > f) m_S.coeffRef(k + 2, k - 1) = m_S.coeffRef(k + 1, k - 1) = Scalar(0.0);
+
+    // Z_{k1} to annihilate T(k+2,k+1) and T(k+2,k)
+    hr << m_T.coeff(k + 2, k + 2), m_T.coeff(k + 2, k), m_T.coeff(k + 2, k + 1);
+    hr.makeHouseholderInPlace(tau, beta);
+    essential2 = hr.template bottomRows<2>();
+    {
+      Index lr = (std::min)(k + 4, dim);  // last row to update
+      Map<Matrix<Scalar, Dynamic, 1> > tmp(m_workspace.data(), lr);
+      // S
+      tmp.noalias() = m_S.template middleCols<2>(k).topRows(lr) * essential2;
+      tmp += m_S.col(k + 2).head(lr);
+      m_S.col(k + 2).head(lr) -= tau * tmp;
+      m_S.template middleCols<2>(k).topRows(lr).noalias() -= (tau * tmp) * essential2.adjoint();
+      // T
+      tmp = m_T.template middleCols<2>(k).topRows(lr) * essential2;
+      tmp += m_T.col(k + 2).head(lr);
+      m_T.col(k + 2).head(lr) -= tau * tmp;
+      m_T.template middleCols<2>(k).topRows(lr).noalias() -= (tau * tmp) * essential2.adjoint();
+    }
+    if (m_computeQZ) {
+      // Z
+      Map<Matrix<Scalar, 1, Dynamic> > tmp(m_workspace.data(), dim);
+      tmp.noalias() = essential2.adjoint() * (m_Z.template middleRows<2>(k));
+      tmp += m_Z.row(k + 2);
+      m_Z.row(k + 2) -= tau * tmp;
+      m_Z.template middleRows<2>(k).noalias() -= essential2 * (tau * tmp);
+    }
+    m_T.coeffRef(k + 2, k) = m_T.coeffRef(k + 2, k + 1) = Scalar(0.0);
+
+    // Z_{k2} to annihilate T(k+1,k)
+    G.makeGivens(m_T.coeff(k + 1, k + 1), m_T.coeff(k + 1, k));
+    m_S.applyOnTheRight(k + 1, k, G);
+    m_T.applyOnTheRight(k + 1, k, G);
+    // update Z
+    if (m_computeQZ) m_Z.applyOnTheLeft(k + 1, k, G.adjoint());
+    m_T.coeffRef(k + 1, k) = Scalar(0.0);
+
+    // update x,y,z
+    x = m_S.coeff(k + 1, k);
+    y = m_S.coeff(k + 2, k);
+    if (k < l - 2) z = m_S.coeff(k + 3, k);
+  }  // loop over k
+
+  // Q_{n-1} to annihilate y = S(l,l-2)
+  G.makeGivens(x, y);
+  m_S.applyOnTheLeft(l - 1, l, G.adjoint());
+  m_T.applyOnTheLeft(l - 1, l, G.adjoint());
+  if (m_computeQZ) m_Q.applyOnTheRight(l - 1, l, G);
+  m_S.coeffRef(l, l - 2) = Scalar(0.0);
+
+  // Z_{n-1} to annihilate T(l,l-1)
+  G.makeGivens(m_T.coeff(l, l), m_T.coeff(l, l - 1));
+  m_S.applyOnTheRight(l, l - 1, G);
+  m_T.applyOnTheRight(l, l - 1, G);
+  if (m_computeQZ) m_Z.applyOnTheLeft(l, l - 1, G.adjoint());
+  m_T.coeffRef(l, l - 1) = Scalar(0.0);
+}
+
+template <typename MatrixType>
+RealQZ<MatrixType>& RealQZ<MatrixType>::compute(const MatrixType& A_in, const MatrixType& B_in, bool computeQZ) {
+  const Index dim = A_in.cols();
+
+  eigen_assert(A_in.rows() == dim && A_in.cols() == dim && B_in.rows() == dim && B_in.cols() == dim &&
+               "Need square matrices of the same dimension");
+
+  m_isInitialized = true;
+  m_computeQZ = computeQZ;
+  m_S = A_in;
+  m_T = B_in;
+  m_workspace.resize(dim * 2);
+  m_global_iter = 0;
+
+  // entrance point: hessenberg triangular decomposition
+  hessenbergTriangular();
+  // compute L1 vector norms of T, S into m_normOfS, m_normOfT
+  computeNorms();
+
+  Index l = dim - 1, f, local_iter = 0;
+
+  while (l > 0 && local_iter < m_maxIters) {
+    f = findSmallSubdiagEntry(l);
+    // now rows and columns f..l (including) decouple from the rest of the problem
+    if (f > 0) m_S.coeffRef(f, f - 1) = Scalar(0.0);
+    if (f == l)  // One root found
+    {
+      l--;
+      local_iter = 0;
+    } else if (f == l - 1)  // Two roots found
+    {
+      splitOffTwoRows(f);
+      l -= 2;
+      local_iter = 0;
+    } else  // No convergence yet
+    {
+      // if there's zero on diagonal of T, we can isolate an eigenvalue with Givens rotations
+      Index z = findSmallDiagEntry(f, l);
+      if (z >= f) {
+        // zero found
+        pushDownZero(z, f, l);
+      } else {
+        // We are sure now that S.block(f,f, l-f+1,l-f+1) is underuced upper-Hessenberg
+        // and T.block(f,f, l-f+1,l-f+1) is invertible uper-triangular, which allows to
+        // apply a QR-like iteration to rows and columns f..l.
+        step(f, l, local_iter);
+        local_iter++;
+        m_global_iter++;
+      }
+    }
+  }
+  // check if we converged before reaching iterations limit
+  m_info = (local_iter < m_maxIters) ? Success : NoConvergence;
+
+  // For each non triangular 2x2 diagonal block of S,
+  //    reduce the respective 2x2 diagonal block of T to positive diagonal form using 2x2 SVD.
+  // This step is not mandatory for QZ, but it does help further extraction of eigenvalues/eigenvectors,
+  // and is in par with Lapack/Matlab QZ.
+  if (m_info == Success) {
+    for (Index i = 0; i < dim - 1; ++i) {
+      if (!numext::is_exactly_zero(m_S.coeff(i + 1, i))) {
+        JacobiRotation<Scalar> j_left, j_right;
+        internal::real_2x2_jacobi_svd(m_T, i, i + 1, &j_left, &j_right);
+
+        // Apply resulting Jacobi rotations
+        m_S.applyOnTheLeft(i, i + 1, j_left);
+        m_S.applyOnTheRight(i, i + 1, j_right);
+        m_T.applyOnTheLeft(i, i + 1, j_left);
+        m_T.applyOnTheRight(i, i + 1, j_right);
+        m_T(i + 1, i) = m_T(i, i + 1) = Scalar(0);
+
+        if (m_computeQZ) {
+          m_Q.applyOnTheRight(i, i + 1, j_left.transpose());
+          m_Z.applyOnTheLeft(i, i + 1, j_right.transpose());
+        }
+
+        i++;
+      }
+    }
+  }
+
+  return *this;
+}  // end compute
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_REAL_QZ
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/RealSchur.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/RealSchur.h
new file mode 100644
index 0000000..fda3b2e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/RealSchur.h
@@ -0,0 +1,519 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REAL_SCHUR_H
+#define EIGEN_REAL_SCHUR_H
+
+#include "./HessenbergDecomposition.h"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class RealSchur
+ *
+ * \brief Performs a real Schur decomposition of a square matrix
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the
+ * real Schur decomposition; this is expected to be an instantiation of the
+ * Matrix class template.
+ *
+ * Given a real square matrix A, this class computes the real Schur
+ * decomposition: \f$ A = U T U^T \f$ where U is a real orthogonal matrix and
+ * T is a real quasi-triangular matrix. An orthogonal matrix is a matrix whose
+ * inverse is equal to its transpose, \f$ U^{-1} = U^T \f$. A quasi-triangular
+ * matrix is a block-triangular matrix whose diagonal consists of 1-by-1
+ * blocks and 2-by-2 blocks with complex eigenvalues. The eigenvalues of the
+ * blocks on the diagonal of T are the same as the eigenvalues of the matrix
+ * A, and thus the real Schur decomposition is used in EigenSolver to compute
+ * the eigendecomposition of a matrix.
+ *
+ * Call the function compute() to compute the real Schur decomposition of a
+ * given matrix. Alternatively, you can use the RealSchur(const MatrixType&, bool)
+ * constructor which computes the real Schur decomposition at construction
+ * time. Once the decomposition is computed, you can use the matrixU() and
+ * matrixT() functions to retrieve the matrices U and T in the decomposition.
+ *
+ * The documentation of RealSchur(const MatrixType&, bool) contains an example
+ * of the typical use of this class.
+ *
+ * \note The implementation is adapted from
+ * <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a> (public domain).
+ * Their code is based on EISPACK.
+ *
+ * \sa class ComplexSchur, class EigenSolver, class ComplexEigenSolver
+ */
+template <typename MatrixType_>
+class RealSchur {
+ public:
+  typedef MatrixType_ MatrixType;
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    Options = internal::traits<MatrixType>::Options,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+
+  typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
+  typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
+
+  /** \brief Default constructor.
+   *
+   * \param [in] size  Positive integer, size of the matrix whose Schur decomposition will be computed.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via compute().  The \p size parameter is only
+   * used as a hint. It is not an error to give a wrong \p size, but it may
+   * impair performance.
+   *
+   * \sa compute() for an example.
+   */
+  explicit RealSchur(Index size = RowsAtCompileTime == Dynamic ? 1 : RowsAtCompileTime)
+      : m_matT(size, size),
+        m_matU(size, size),
+        m_workspaceVector(size),
+        m_hess(size),
+        m_isInitialized(false),
+        m_matUisUptodate(false),
+        m_maxIters(-1) {}
+
+  /** \brief Constructor; computes real Schur decomposition of given matrix.
+   *
+   * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
+   * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
+   *
+   * This constructor calls compute() to compute the Schur decomposition.
+   *
+   * Example: \include RealSchur_RealSchur_MatrixType.cpp
+   * Output: \verbinclude RealSchur_RealSchur_MatrixType.out
+   */
+  template <typename InputType>
+  explicit RealSchur(const EigenBase<InputType>& matrix, bool computeU = true)
+      : m_matT(matrix.rows(), matrix.cols()),
+        m_matU(matrix.rows(), matrix.cols()),
+        m_workspaceVector(matrix.rows()),
+        m_hess(matrix.rows()),
+        m_isInitialized(false),
+        m_matUisUptodate(false),
+        m_maxIters(-1) {
+    compute(matrix.derived(), computeU);
+  }
+
+  /** \brief Returns the orthogonal matrix in the Schur decomposition.
+   *
+   * \returns A const reference to the matrix U.
+   *
+   * \pre Either the constructor RealSchur(const MatrixType&, bool) or the
+   * member function compute(const MatrixType&, bool) has been called before
+   * to compute the Schur decomposition of a matrix, and \p computeU was set
+   * to true (the default value).
+   *
+   * \sa RealSchur(const MatrixType&, bool) for an example
+   */
+  const MatrixType& matrixU() const {
+    eigen_assert(m_isInitialized && "RealSchur is not initialized.");
+    eigen_assert(m_matUisUptodate && "The matrix U has not been computed during the RealSchur decomposition.");
+    return m_matU;
+  }
+
+  /** \brief Returns the quasi-triangular matrix in the Schur decomposition.
+   *
+   * \returns A const reference to the matrix T.
+   *
+   * \pre Either the constructor RealSchur(const MatrixType&, bool) or the
+   * member function compute(const MatrixType&, bool) has been called before
+   * to compute the Schur decomposition of a matrix.
+   *
+   * \sa RealSchur(const MatrixType&, bool) for an example
+   */
+  const MatrixType& matrixT() const {
+    eigen_assert(m_isInitialized && "RealSchur is not initialized.");
+    return m_matT;
+  }
+
+  /** \brief Computes Schur decomposition of given matrix.
+   *
+   * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
+   * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
+   * \returns    Reference to \c *this
+   *
+   * The Schur decomposition is computed by first reducing the matrix to
+   * Hessenberg form using the class HessenbergDecomposition. The Hessenberg
+   * matrix is then reduced to triangular form by performing Francis QR
+   * iterations with implicit double shift. The cost of computing the Schur
+   * decomposition depends on the number of iterations; as a rough guide, it
+   * may be taken to be \f$25n^3\f$ flops if \a computeU is true and
+   * \f$10n^3\f$ flops if \a computeU is false.
+   *
+   * Example: \include RealSchur_compute.cpp
+   * Output: \verbinclude RealSchur_compute.out
+   *
+   * \sa compute(const MatrixType&, bool, Index)
+   */
+  template <typename InputType>
+  RealSchur& compute(const EigenBase<InputType>& matrix, bool computeU = true);
+
+  /** \brief Computes Schur decomposition of a Hessenberg matrix H = Z T Z^T
+   *  \param[in] matrixH Matrix in Hessenberg form H
+   *  \param[in] matrixQ orthogonal matrix Q that transform a matrix A to H : A = Q H Q^T
+   *  \param computeU Computes the matriX U of the Schur vectors
+   * \return Reference to \c *this
+   *
+   *  This routine assumes that the matrix is already reduced in Hessenberg form matrixH
+   *  using either the class HessenbergDecomposition or another mean.
+   *  It computes the upper quasi-triangular matrix T of the Schur decomposition of H
+   *  When computeU is true, this routine computes the matrix U such that
+   *  A = U T U^T =  (QZ) T (QZ)^T = Q H Q^T where A is the initial matrix
+   *
+   * NOTE Q is referenced if computeU is true; so, if the initial orthogonal matrix
+   * is not available, the user should give an identity matrix (Q.setIdentity())
+   *
+   * \sa compute(const MatrixType&, bool)
+   */
+  template <typename HessMatrixType, typename OrthMatrixType>
+  RealSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ, bool computeU);
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "RealSchur is not initialized.");
+    return m_info;
+  }
+
+  /** \brief Sets the maximum number of iterations allowed.
+   *
+   * If not specified by the user, the maximum number of iterations is m_maxIterationsPerRow times the size
+   * of the matrix.
+   */
+  RealSchur& setMaxIterations(Index maxIters) {
+    m_maxIters = maxIters;
+    return *this;
+  }
+
+  /** \brief Returns the maximum number of iterations. */
+  Index getMaxIterations() { return m_maxIters; }
+
+  /** \brief Maximum number of iterations per row.
+   *
+   * If not otherwise specified, the maximum number of iterations is this number times the size of the
+   * matrix. It is currently set to 40.
+   */
+  static const int m_maxIterationsPerRow = 40;
+
+ private:
+  MatrixType m_matT;
+  MatrixType m_matU;
+  ColumnVectorType m_workspaceVector;
+  HessenbergDecomposition<MatrixType> m_hess;
+  ComputationInfo m_info;
+  bool m_isInitialized;
+  bool m_matUisUptodate;
+  Index m_maxIters;
+
+  typedef Matrix<Scalar, 3, 1> Vector3s;
+
+  Scalar computeNormOfT();
+  Index findSmallSubdiagEntry(Index iu, const Scalar& considerAsZero);
+  void splitOffTwoRows(Index iu, bool computeU, const Scalar& exshift);
+  void computeShift(Index iu, Index iter, Scalar& exshift, Vector3s& shiftInfo);
+  void initFrancisQRStep(Index il, Index iu, const Vector3s& shiftInfo, Index& im, Vector3s& firstHouseholderVector);
+  void performFrancisQRStep(Index il, Index im, Index iu, bool computeU, const Vector3s& firstHouseholderVector,
+                            Scalar* workspace);
+};
+
+template <typename MatrixType>
+template <typename InputType>
+RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeU) {
+  const Scalar considerAsZero = (std::numeric_limits<Scalar>::min)();
+
+  eigen_assert(matrix.cols() == matrix.rows());
+  Index maxIters = m_maxIters;
+  if (maxIters == -1) maxIters = m_maxIterationsPerRow * matrix.rows();
+
+  Scalar scale = matrix.derived().cwiseAbs().maxCoeff();
+  if (scale < considerAsZero) {
+    m_matT.setZero(matrix.rows(), matrix.cols());
+    if (computeU) m_matU.setIdentity(matrix.rows(), matrix.cols());
+    m_info = Success;
+    m_isInitialized = true;
+    m_matUisUptodate = computeU;
+    return *this;
+  }
+
+  // Step 1. Reduce to Hessenberg form
+  m_hess.compute(matrix.derived() / scale);
+
+  // Step 2. Reduce to real Schur form
+  // Note: we copy m_hess.matrixQ() into m_matU here and not in computeFromHessenberg
+  //       to be able to pass our working-space buffer for the Householder to Dense evaluation.
+  m_workspaceVector.resize(matrix.cols());
+  if (computeU) m_hess.matrixQ().evalTo(m_matU, m_workspaceVector);
+  computeFromHessenberg(m_hess.matrixH(), m_matU, computeU);
+
+  m_matT *= scale;
+
+  return *this;
+}
+template <typename MatrixType>
+template <typename HessMatrixType, typename OrthMatrixType>
+RealSchur<MatrixType>& RealSchur<MatrixType>::computeFromHessenberg(const HessMatrixType& matrixH,
+                                                                    const OrthMatrixType& matrixQ, bool computeU) {
+  using std::abs;
+
+  m_matT = matrixH;
+  m_workspaceVector.resize(m_matT.cols());
+  if (computeU && !internal::is_same_dense(m_matU, matrixQ)) m_matU = matrixQ;
+
+  Index maxIters = m_maxIters;
+  if (maxIters == -1) maxIters = m_maxIterationsPerRow * matrixH.rows();
+  Scalar* workspace = &m_workspaceVector.coeffRef(0);
+
+  // The matrix m_matT is divided in three parts.
+  // Rows 0,...,il-1 are decoupled from the rest because m_matT(il,il-1) is zero.
+  // Rows il,...,iu is the part we are working on (the active window).
+  // Rows iu+1,...,end are already brought in triangular form.
+  Index iu = m_matT.cols() - 1;
+  Index iter = 0;       // iteration count for current eigenvalue
+  Index totalIter = 0;  // iteration count for whole matrix
+  Scalar exshift(0);    // sum of exceptional shifts
+  Scalar norm = computeNormOfT();
+  // sub-diagonal entries smaller than considerAsZero will be treated as zero.
+  // We use eps^2 to enable more precision in small eigenvalues.
+  Scalar considerAsZero =
+      numext::maxi<Scalar>(norm * numext::abs2(NumTraits<Scalar>::epsilon()), (std::numeric_limits<Scalar>::min)());
+
+  if (!numext::is_exactly_zero(norm)) {
+    while (iu >= 0) {
+      Index il = findSmallSubdiagEntry(iu, considerAsZero);
+
+      // Check for convergence
+      if (il == iu)  // One root found
+      {
+        m_matT.coeffRef(iu, iu) = m_matT.coeff(iu, iu) + exshift;
+        if (iu > 0) m_matT.coeffRef(iu, iu - 1) = Scalar(0);
+        iu--;
+        iter = 0;
+      } else if (il == iu - 1)  // Two roots found
+      {
+        splitOffTwoRows(iu, computeU, exshift);
+        iu -= 2;
+        iter = 0;
+      } else  // No convergence yet
+      {
+        // The firstHouseholderVector vector has to be initialized to something to get rid of a silly GCC warning (-O1
+        // -Wall -DNDEBUG )
+        Vector3s firstHouseholderVector = Vector3s::Zero(), shiftInfo;
+        computeShift(iu, iter, exshift, shiftInfo);
+        iter = iter + 1;
+        totalIter = totalIter + 1;
+        if (totalIter > maxIters) break;
+        Index im;
+        initFrancisQRStep(il, iu, shiftInfo, im, firstHouseholderVector);
+        performFrancisQRStep(il, im, iu, computeU, firstHouseholderVector, workspace);
+      }
+    }
+  }
+  if (totalIter <= maxIters)
+    m_info = Success;
+  else
+    m_info = NoConvergence;
+
+  m_isInitialized = true;
+  m_matUisUptodate = computeU;
+  return *this;
+}
+
+/** \internal Computes and returns vector L1 norm of T */
+template <typename MatrixType>
+inline typename MatrixType::Scalar RealSchur<MatrixType>::computeNormOfT() {
+  const Index size = m_matT.cols();
+  // FIXME: a triangular reduction would be more efficient here.
+  // Scalar norm = m_matT.upper().cwiseAbs().sum()
+  //               + m_matT.bottomLeftCorner(size-1,size-1).diagonal().cwiseAbs().sum();
+  Scalar norm(0);
+  for (Index j = 0; j < size; ++j) norm += m_matT.col(j).segment(0, (std::min)(size, j + 2)).cwiseAbs().sum();
+  return norm;
+}
+
+/** \internal Look for single small sub-diagonal element and returns its index */
+template <typename MatrixType>
+inline Index RealSchur<MatrixType>::findSmallSubdiagEntry(Index iu, const Scalar& considerAsZero) {
+  using std::abs;
+  Index res = iu;
+  while (res > 0) {
+    Scalar s = abs(m_matT.coeff(res - 1, res - 1)) + abs(m_matT.coeff(res, res));
+
+    s = numext::maxi<Scalar>(s * NumTraits<Scalar>::epsilon(), considerAsZero);
+
+    if (abs(m_matT.coeff(res, res - 1)) <= s) break;
+    res--;
+  }
+  return res;
+}
+
+/** \internal Update T given that rows iu-1 and iu decouple from the rest. */
+template <typename MatrixType>
+inline void RealSchur<MatrixType>::splitOffTwoRows(Index iu, bool computeU, const Scalar& exshift) {
+  using std::abs;
+  using std::sqrt;
+  const Index size = m_matT.cols();
+
+  // The eigenvalues of the 2x2 matrix [a b; c d] are
+  // trace +/- sqrt(discr/4) where discr = tr^2 - 4*det, tr = a + d, det = ad - bc
+  Scalar p = Scalar(0.5) * (m_matT.coeff(iu - 1, iu - 1) - m_matT.coeff(iu, iu));
+  Scalar q = p * p + m_matT.coeff(iu, iu - 1) * m_matT.coeff(iu - 1, iu);  // q = tr^2 / 4 - det = discr/4
+  m_matT.coeffRef(iu, iu) += exshift;
+  m_matT.coeffRef(iu - 1, iu - 1) += exshift;
+
+  if (q >= Scalar(0))  // Two real eigenvalues
+  {
+    Scalar z = sqrt(abs(q));
+    JacobiRotation<Scalar> rot;
+    if (p >= Scalar(0))
+      rot.makeGivens(p + z, m_matT.coeff(iu, iu - 1));
+    else
+      rot.makeGivens(p - z, m_matT.coeff(iu, iu - 1));
+
+    m_matT.rightCols(size - iu + 1).applyOnTheLeft(iu - 1, iu, rot.adjoint());
+    m_matT.topRows(iu + 1).applyOnTheRight(iu - 1, iu, rot);
+    m_matT.coeffRef(iu, iu - 1) = Scalar(0);
+    if (computeU) m_matU.applyOnTheRight(iu - 1, iu, rot);
+  }
+
+  if (iu > 1) m_matT.coeffRef(iu - 1, iu - 2) = Scalar(0);
+}
+
+/** \internal Form shift in shiftInfo, and update exshift if an exceptional shift is performed. */
+template <typename MatrixType>
+inline void RealSchur<MatrixType>::computeShift(Index iu, Index iter, Scalar& exshift, Vector3s& shiftInfo) {
+  using std::abs;
+  using std::sqrt;
+  shiftInfo.coeffRef(0) = m_matT.coeff(iu, iu);
+  shiftInfo.coeffRef(1) = m_matT.coeff(iu - 1, iu - 1);
+  shiftInfo.coeffRef(2) = m_matT.coeff(iu, iu - 1) * m_matT.coeff(iu - 1, iu);
+
+  // Alternate exceptional shifting strategy every 16 iterations.
+  if (iter > 0 && iter % 16 == 0) {
+    // Wilkinson's original ad hoc shift
+    if (iter % 32 != 0) {
+      exshift += shiftInfo.coeff(0);
+      for (Index i = 0; i <= iu; ++i) m_matT.coeffRef(i, i) -= shiftInfo.coeff(0);
+      Scalar s = abs(m_matT.coeff(iu, iu - 1)) + abs(m_matT.coeff(iu - 1, iu - 2));
+      shiftInfo.coeffRef(0) = Scalar(0.75) * s;
+      shiftInfo.coeffRef(1) = Scalar(0.75) * s;
+      shiftInfo.coeffRef(2) = Scalar(-0.4375) * s * s;
+    } else {
+      // MATLAB's new ad hoc shift
+      Scalar s = (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
+      s = s * s + shiftInfo.coeff(2);
+      if (s > Scalar(0)) {
+        s = sqrt(s);
+        if (shiftInfo.coeff(1) < shiftInfo.coeff(0)) s = -s;
+        s = s + (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
+        s = shiftInfo.coeff(0) - shiftInfo.coeff(2) / s;
+        exshift += s;
+        for (Index i = 0; i <= iu; ++i) m_matT.coeffRef(i, i) -= s;
+        shiftInfo.setConstant(Scalar(0.964));
+      }
+    }
+  }
+}
+
+/** \internal Compute index im at which Francis QR step starts and the first Householder vector. */
+template <typename MatrixType>
+inline void RealSchur<MatrixType>::initFrancisQRStep(Index il, Index iu, const Vector3s& shiftInfo, Index& im,
+                                                     Vector3s& firstHouseholderVector) {
+  using std::abs;
+  Vector3s& v = firstHouseholderVector;  // alias to save typing
+
+  for (im = iu - 2; im >= il; --im) {
+    const Scalar Tmm = m_matT.coeff(im, im);
+    const Scalar r = shiftInfo.coeff(0) - Tmm;
+    const Scalar s = shiftInfo.coeff(1) - Tmm;
+    v.coeffRef(0) = (r * s - shiftInfo.coeff(2)) / m_matT.coeff(im + 1, im) + m_matT.coeff(im, im + 1);
+    v.coeffRef(1) = m_matT.coeff(im + 1, im + 1) - Tmm - r - s;
+    v.coeffRef(2) = m_matT.coeff(im + 2, im + 1);
+    if (im == il) {
+      break;
+    }
+    const Scalar lhs = m_matT.coeff(im, im - 1) * (abs(v.coeff(1)) + abs(v.coeff(2)));
+    const Scalar rhs = v.coeff(0) * (abs(m_matT.coeff(im - 1, im - 1)) + abs(Tmm) + abs(m_matT.coeff(im + 1, im + 1)));
+    if (abs(lhs) < NumTraits<Scalar>::epsilon() * rhs) break;
+  }
+}
+
+/** \internal Perform a Francis QR step involving rows il:iu and columns im:iu. */
+template <typename MatrixType>
+inline void RealSchur<MatrixType>::performFrancisQRStep(Index il, Index im, Index iu, bool computeU,
+                                                        const Vector3s& firstHouseholderVector, Scalar* workspace) {
+  eigen_assert(im >= il);
+  eigen_assert(im <= iu - 2);
+
+  const Index size = m_matT.cols();
+
+  for (Index k = im; k <= iu - 2; ++k) {
+    bool firstIteration = (k == im);
+
+    Vector3s v;
+    if (firstIteration)
+      v = firstHouseholderVector;
+    else
+      v = m_matT.template block<3, 1>(k, k - 1);
+
+    Scalar tau, beta;
+    Matrix<Scalar, 2, 1> ess;
+    v.makeHouseholder(ess, tau, beta);
+
+    if (!numext::is_exactly_zero(beta))  // if v is not zero
+    {
+      if (firstIteration && k > il)
+        m_matT.coeffRef(k, k - 1) = -m_matT.coeff(k, k - 1);
+      else if (!firstIteration)
+        m_matT.coeffRef(k, k - 1) = beta;
+
+      // These Householder transformations form the O(n^3) part of the algorithm
+      m_matT.block(k, k, 3, size - k).applyHouseholderOnTheLeft(ess, tau, workspace);
+      m_matT.block(0, k, (std::min)(iu, k + 3) + 1, 3).applyHouseholderOnTheRight(ess, tau, workspace);
+      if (computeU) m_matU.block(0, k, size, 3).applyHouseholderOnTheRight(ess, tau, workspace);
+    }
+  }
+
+  Matrix<Scalar, 2, 1> v = m_matT.template block<2, 1>(iu - 1, iu - 2);
+  Scalar tau, beta;
+  Matrix<Scalar, 1, 1> ess;
+  v.makeHouseholder(ess, tau, beta);
+
+  if (!numext::is_exactly_zero(beta))  // if v is not zero
+  {
+    m_matT.coeffRef(iu - 1, iu - 2) = beta;
+    m_matT.block(iu - 1, iu - 1, 2, size - iu + 1).applyHouseholderOnTheLeft(ess, tau, workspace);
+    m_matT.block(0, iu - 1, iu + 1, 2).applyHouseholderOnTheRight(ess, tau, workspace);
+    if (computeU) m_matU.block(0, iu - 1, size, 2).applyHouseholderOnTheRight(ess, tau, workspace);
+  }
+
+  // clean up pollution due to round-off errors
+  for (Index i = im + 2; i <= iu; ++i) {
+    m_matT.coeffRef(i, i - 2) = Scalar(0);
+    if (i > im + 2) m_matT.coeffRef(i, i - 3) = Scalar(0);
+  }
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_REAL_SCHUR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
new file mode 100644
index 0000000..05a516d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
@@ -0,0 +1,83 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Real Schur needed to real unsymmetrical eigenvalues/eigenvectors.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_REAL_SCHUR_LAPACKE_H
+#define EIGEN_REAL_SCHUR_LAPACKE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \internal Specialization for the data types supported by LAPACKe */
+
+#define EIGEN_LAPACKE_SCHUR_REAL(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, LAPACKE_PREFIX_U, EIGCOLROW, LAPACKE_COLROW) \
+  template <>                                                                                                        \
+  template <typename InputType>                                                                                      \
+  inline RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >&                                                   \
+  RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix,              \
+                                                                    bool computeU) {                                 \
+    eigen_assert(matrix.cols() == matrix.rows());                                                                    \
+                                                                                                                     \
+    lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), sdim, info;                                   \
+    lapack_int matrix_order = LAPACKE_COLROW;                                                                        \
+    char jobvs, sort = 'N';                                                                                          \
+    LAPACK_##LAPACKE_PREFIX_U##_SELECT2 select = 0;                                                                  \
+    jobvs = (computeU) ? 'V' : 'N';                                                                                  \
+    m_matU.resize(n, n);                                                                                             \
+    lapack_int ldvs = internal::convert_index<lapack_int>(m_matU.outerStride());                                     \
+    m_matT = matrix;                                                                                                 \
+    lapack_int lda = internal::convert_index<lapack_int>(m_matT.outerStride());                                      \
+    Matrix<EIGTYPE, Dynamic, Dynamic> wr, wi;                                                                        \
+    wr.resize(n, 1);                                                                                                 \
+    wi.resize(n, 1);                                                                                                 \
+    info = LAPACKE_##LAPACKE_PREFIX##gees(matrix_order, jobvs, sort, select, n, (LAPACKE_TYPE*)m_matT.data(), lda,   \
+                                          &sdim, (LAPACKE_TYPE*)wr.data(), (LAPACKE_TYPE*)wi.data(),                 \
+                                          (LAPACKE_TYPE*)m_matU.data(), ldvs);                                       \
+    if (info == 0)                                                                                                   \
+      m_info = Success;                                                                                              \
+    else                                                                                                             \
+      m_info = NoConvergence;                                                                                        \
+                                                                                                                     \
+    m_isInitialized = true;                                                                                          \
+    m_matUisUptodate = computeU;                                                                                     \
+    return *this;                                                                                                    \
+  }
+
+EIGEN_LAPACKE_SCHUR_REAL(double, double, d, D, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_LAPACKE_SCHUR_REAL(float, float, s, S, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_LAPACKE_SCHUR_REAL(double, double, d, D, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_LAPACKE_SCHUR_REAL(float, float, s, S, RowMajor, LAPACK_ROW_MAJOR)
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_REAL_SCHUR_LAPACKE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
new file mode 100644
index 0000000..7edac1f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
@@ -0,0 +1,865 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINTEIGENSOLVER_H
+#define EIGEN_SELFADJOINTEIGENSOLVER_H
+
+#include "./Tridiagonalization.h"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename MatrixType_>
+class GeneralizedSelfAdjointEigenSolver;
+
+namespace internal {
+template <typename SolverType, int Size, bool IsComplex>
+struct direct_selfadjoint_eigenvalues;
+
+template <typename MatrixType, typename DiagType, typename SubDiagType>
+EIGEN_DEVICE_FUNC ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag,
+                                                              const Index maxIterations, bool computeEigenvectors,
+                                                              MatrixType& eivec);
+}  // namespace internal
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class SelfAdjointEigenSolver
+ *
+ * \brief Computes eigenvalues and eigenvectors of selfadjoint matrices
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the
+ * eigendecomposition; this is expected to be an instantiation of the Matrix
+ * class template.
+ *
+ * A matrix \f$ A \f$ is selfadjoint if it equals its adjoint. For real
+ * matrices, this means that the matrix is symmetric: it equals its
+ * transpose. This class computes the eigenvalues and eigenvectors of a
+ * selfadjoint matrix. These are the scalars \f$ \lambda \f$ and vectors
+ * \f$ v \f$ such that \f$ Av = \lambda v \f$.  The eigenvalues of a
+ * selfadjoint matrix are always real. If \f$ D \f$ is a diagonal matrix with
+ * the eigenvalues on the diagonal, and \f$ V \f$ is a matrix with the
+ * eigenvectors as its columns, then \f$ A = V D V^{-1} \f$. This is called the
+ * eigendecomposition.
+ *
+ * For a selfadjoint matrix, \f$ V \f$ is unitary, meaning its inverse is equal
+ * to its adjoint, \f$ V^{-1} = V^{\dagger} \f$. If \f$ A \f$ is real, then
+ * \f$ V \f$ is also real and therefore orthogonal, meaning its inverse is
+ * equal to its transpose, \f$ V^{-1} = V^T \f$.
+ *
+ * The algorithm exploits the fact that the matrix is selfadjoint, making it
+ * faster and more accurate than the general purpose eigenvalue algorithms
+ * implemented in EigenSolver and ComplexEigenSolver.
+ *
+ * Only the \b lower \b triangular \b part of the input matrix is referenced.
+ *
+ * Call the function compute() to compute the eigenvalues and eigenvectors of
+ * a given matrix. Alternatively, you can use the
+ * SelfAdjointEigenSolver(const MatrixType&, int) constructor which computes
+ * the eigenvalues and eigenvectors at construction time. Once the eigenvalue
+ * and eigenvectors are computed, they can be retrieved with the eigenvalues()
+ * and eigenvectors() functions.
+ *
+ * The documentation for SelfAdjointEigenSolver(const MatrixType&, int)
+ * contains an example of the typical use of this class.
+ *
+ * To solve the \em generalized eigenvalue problem \f$ Av = \lambda Bv \f$ and
+ * the likes, see the class GeneralizedSelfAdjointEigenSolver.
+ *
+ * \sa MatrixBase::eigenvalues(), class EigenSolver, class ComplexEigenSolver
+ */
+template <typename MatrixType_>
+class SelfAdjointEigenSolver {
+ public:
+  typedef MatrixType_ MatrixType;
+  enum {
+    Size = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    Options = internal::traits<MatrixType>::Options,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+
+  /** \brief Scalar type for matrices of type \p MatrixType_. */
+  typedef typename MatrixType::Scalar Scalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+
+  typedef Matrix<Scalar, Size, Size, ColMajor, MaxColsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType;
+
+  /** \brief Real scalar type for \p MatrixType_.
+   *
+   * This is just \c Scalar if #Scalar is real (e.g., \c float or
+   * \c double), and the type of the real part of \c Scalar if #Scalar is
+   * complex.
+   */
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  friend struct internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver, Size, NumTraits<Scalar>::IsComplex>;
+
+  /** \brief Type for vector of eigenvalues as returned by eigenvalues().
+   *
+   * This is a column vector with entries of type #RealScalar.
+   * The length of the vector is the size of \p MatrixType_.
+   */
+  typedef typename internal::plain_col_type<MatrixType, Scalar>::type VectorType;
+  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVectorType;
+  typedef Tridiagonalization<MatrixType> TridiagonalizationType;
+  typedef typename TridiagonalizationType::SubDiagonalType SubDiagonalType;
+
+  /** \brief Default constructor for fixed-size matrices.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via compute(). This constructor
+   * can only be used if \p MatrixType_ is a fixed-size matrix; use
+   * SelfAdjointEigenSolver(Index) for dynamic-size matrices.
+   *
+   * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver.out
+   */
+  EIGEN_DEVICE_FUNC SelfAdjointEigenSolver()
+      : m_eivec(),
+        m_workspace(),
+        m_eivalues(),
+        m_subdiag(),
+        m_hcoeffs(),
+        m_info(InvalidInput),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false) {}
+
+  /** \brief Constructor, pre-allocates memory for dynamic-size matrices.
+   *
+   * \param [in]  size  Positive integer, size of the matrix whose
+   * eigenvalues and eigenvectors will be computed.
+   *
+   * This constructor is useful for dynamic-size matrices, when the user
+   * intends to perform decompositions via compute(). The \p size
+   * parameter is only used as a hint. It is not an error to give a wrong
+   * \p size, but it may impair performance.
+   *
+   * \sa compute() for an example
+   */
+  EIGEN_DEVICE_FUNC explicit SelfAdjointEigenSolver(Index size)
+      : m_eivec(size, size),
+        m_workspace(size),
+        m_eivalues(size),
+        m_subdiag(size > 1 ? size - 1 : 1),
+        m_hcoeffs(size > 1 ? size - 1 : 1),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false) {}
+
+  /** \brief Constructor; computes eigendecomposition of given matrix.
+   *
+   * \param[in]  matrix  Selfadjoint matrix whose eigendecomposition is to
+   *    be computed. Only the lower triangular part of the matrix is referenced.
+   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   *
+   * This constructor calls compute(const MatrixType&, int) to compute the
+   * eigenvalues of the matrix \p matrix. The eigenvectors are computed if
+   * \p options equals #ComputeEigenvectors.
+   *
+   * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType.out
+   *
+   * \sa compute(const MatrixType&, int)
+   */
+  template <typename InputType>
+  EIGEN_DEVICE_FUNC explicit SelfAdjointEigenSolver(const EigenBase<InputType>& matrix,
+                                                    int options = ComputeEigenvectors)
+      : m_eivec(matrix.rows(), matrix.cols()),
+        m_workspace(matrix.cols()),
+        m_eivalues(matrix.cols()),
+        m_subdiag(matrix.rows() > 1 ? matrix.rows() - 1 : 1),
+        m_hcoeffs(matrix.cols() > 1 ? matrix.cols() - 1 : 1),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false) {
+    compute(matrix.derived(), options);
+  }
+
+  /** \brief Computes eigendecomposition of given matrix.
+   *
+   * \param[in]  matrix  Selfadjoint matrix whose eigendecomposition is to
+   *    be computed. Only the lower triangular part of the matrix is referenced.
+   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   * \returns    Reference to \c *this
+   *
+   * This function computes the eigenvalues of \p matrix.  The eigenvalues()
+   * function can be used to retrieve them.  If \p options equals #ComputeEigenvectors,
+   * then the eigenvectors are also computed and can be retrieved by
+   * calling eigenvectors().
+   *
+   * This implementation uses a symmetric QR algorithm. The matrix is first
+   * reduced to tridiagonal form using the Tridiagonalization class. The
+   * tridiagonal matrix is then brought to diagonal form with implicit
+   * symmetric QR steps with Wilkinson shift. Details can be found in
+   * Section 8.3 of Golub \& Van Loan, <i>%Matrix Computations</i>.
+   *
+   * The cost of the computation is about \f$ 9n^3 \f$ if the eigenvectors
+   * are required and \f$ 4n^3/3 \f$ if they are not required.
+   *
+   * This method reuses the memory in the SelfAdjointEigenSolver object that
+   * was allocated when the object was constructed, if the size of the
+   * matrix does not change.
+   *
+   * Example: \include SelfAdjointEigenSolver_compute_MatrixType.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType.out
+   *
+   * \sa SelfAdjointEigenSolver(const MatrixType&, int)
+   */
+  template <typename InputType>
+  EIGEN_DEVICE_FUNC SelfAdjointEigenSolver& compute(const EigenBase<InputType>& matrix,
+                                                    int options = ComputeEigenvectors);
+
+  /** \brief Computes eigendecomposition of given matrix using a closed-form algorithm
+   *
+   * This is a variant of compute(const MatrixType&, int options) which
+   * directly solves the underlying polynomial equation.
+   *
+   * Currently only 2x2 and 3x3 matrices for which the sizes are known at compile time are supported (e.g., Matrix3d).
+   *
+   * This method is usually significantly faster than the QR iterative algorithm
+   * but it might also be less accurate. It is also worth noting that
+   * for 3x3 matrices it involves trigonometric operations which are
+   * not necessarily available for all scalar types.
+   *
+   * For the 3x3 case, we observed the following worst case relative error regarding the eigenvalues:
+   *   - double: 1e-8
+   *   - float:  1e-3
+   *
+   * \sa compute(const MatrixType&, int options)
+   */
+  EIGEN_DEVICE_FUNC SelfAdjointEigenSolver& computeDirect(const MatrixType& matrix, int options = ComputeEigenvectors);
+
+  /**
+   *\brief Computes the eigen decomposition from a tridiagonal symmetric matrix
+   *
+   * \param[in] diag The vector containing the diagonal of the matrix.
+   * \param[in] subdiag The subdiagonal of the matrix.
+   * \param[in] options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   * \returns Reference to \c *this
+   *
+   * This function assumes that the matrix has been reduced to tridiagonal form.
+   *
+   * \sa compute(const MatrixType&, int) for more information
+   */
+  SelfAdjointEigenSolver& computeFromTridiagonal(const RealVectorType& diag, const SubDiagonalType& subdiag,
+                                                 int options = ComputeEigenvectors);
+
+  /** \brief Returns the eigenvectors of given matrix.
+   *
+   * \returns  A const reference to the matrix whose columns are the eigenvectors.
+   *
+   * \pre The eigenvectors have been computed before.
+   *
+   * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
+   * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
+   * eigenvectors are normalized to have (Euclidean) norm equal to one. If
+   * this object was used to solve the eigenproblem for the selfadjoint
+   * matrix \f$ A \f$, then the matrix returned by this function is the
+   * matrix \f$ V \f$ in the eigendecomposition \f$ A = V D V^{-1} \f$.
+   *
+   * For a selfadjoint matrix, \f$ V \f$ is unitary, meaning its inverse is equal
+   * to its adjoint, \f$ V^{-1} = V^{\dagger} \f$. If \f$ A \f$ is real, then
+   * \f$ V \f$ is also real and therefore orthogonal, meaning its inverse is
+   * equal to its transpose, \f$ V^{-1} = V^T \f$.
+   *
+   * Example: \include SelfAdjointEigenSolver_eigenvectors.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_eigenvectors.out
+   *
+   * \sa eigenvalues()
+   */
+  EIGEN_DEVICE_FUNC const EigenvectorsType& eigenvectors() const {
+    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec;
+  }
+
+  /** \brief Returns the eigenvalues of given matrix.
+   *
+   * \returns A const reference to the column vector containing the eigenvalues.
+   *
+   * \pre The eigenvalues have been computed before.
+   *
+   * The eigenvalues are repeated according to their algebraic multiplicity,
+   * so there are as many eigenvalues as rows in the matrix. The eigenvalues
+   * are sorted in increasing order.
+   *
+   * Example: \include SelfAdjointEigenSolver_eigenvalues.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_eigenvalues.out
+   *
+   * \sa eigenvectors(), MatrixBase::eigenvalues()
+   */
+  EIGEN_DEVICE_FUNC const RealVectorType& eigenvalues() const {
+    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+    return m_eivalues;
+  }
+
+  /** \brief Computes the positive-definite square root of the matrix.
+   *
+   * \returns the positive-definite square root of the matrix
+   *
+   * \pre The eigenvalues and eigenvectors of a positive-definite matrix
+   * have been computed before.
+   *
+   * The square root of a positive-definite matrix \f$ A \f$ is the
+   * positive-definite matrix whose square equals \f$ A \f$. This function
+   * uses the eigendecomposition \f$ A = V D V^{-1} \f$ to compute the
+   * square root as \f$ A^{1/2} = V D^{1/2} V^{-1} \f$.
+   *
+   * Example: \include SelfAdjointEigenSolver_operatorSqrt.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_operatorSqrt.out
+   *
+   * \sa operatorInverseSqrt(), <a href="unsupported/group__MatrixFunctions__Module.html">MatrixFunctions Module</a>
+   */
+  EIGEN_DEVICE_FUNC MatrixType operatorSqrt() const {
+    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint();
+  }
+
+  /** \brief Computes the matrix exponential the matrix.
+   *
+   * \returns the matrix exponential the matrix.
+   *
+   * \pre The eigenvalues and eigenvectors of a positive-definite matrix
+   * have been computed before.
+   *
+   * \sa operatorInverseSqrt(), operatorSqrt(),
+   * <a href="unsupported/group__MatrixFunctions__Module.html">MatrixFunctions Module</a>
+   */
+  EIGEN_DEVICE_FUNC MatrixType operatorExp() const {
+    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec * m_eivalues.array().exp().matrix().asDiagonal() * m_eivec.adjoint();
+  }
+
+  /** \brief Computes the inverse square root of the matrix.
+   *
+   * \returns the inverse positive-definite square root of the matrix
+   *
+   * \pre The eigenvalues and eigenvectors of a positive-definite matrix
+   * have been computed before.
+   *
+   * This function uses the eigendecomposition \f$ A = V D V^{-1} \f$ to
+   * compute the inverse square root as \f$ V D^{-1/2} V^{-1} \f$. This is
+   * cheaper than first computing the square root with operatorSqrt() and
+   * then its inverse with MatrixBase::inverse().
+   *
+   * Example: \include SelfAdjointEigenSolver_operatorInverseSqrt.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_operatorInverseSqrt.out
+   *
+   * \sa operatorSqrt(), MatrixBase::inverse(), <a
+   * href="unsupported/group__MatrixFunctions__Module.html">MatrixFunctions Module</a>
+   */
+  EIGEN_DEVICE_FUNC MatrixType operatorInverseSqrt() const {
+    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();
+  }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+   */
+  EIGEN_DEVICE_FUNC ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+    return m_info;
+  }
+
+  /** \brief Maximum number of iterations.
+   *
+   * The algorithm terminates if it does not converge within m_maxIterations * n iterations, where n
+   * denotes the size of the matrix. This value is currently set to 30 (copied from LAPACK).
+   */
+  static const int m_maxIterations = 30;
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  EigenvectorsType m_eivec;
+  VectorType m_workspace;
+  RealVectorType m_eivalues;
+  typename TridiagonalizationType::SubDiagonalType m_subdiag;
+  typename TridiagonalizationType::CoeffVectorType m_hcoeffs;
+  ComputationInfo m_info;
+  bool m_isInitialized;
+  bool m_eigenvectorsOk;
+};
+
+namespace internal {
+/** \internal
+ *
+ * \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ * Performs a QR step on a tridiagonal symmetric matrix represented as a
+ * pair of two vectors \a diag and \a subdiag.
+ *
+ * \param diag the diagonal part of the input selfadjoint tridiagonal matrix
+ * \param subdiag the sub-diagonal part of the input selfadjoint tridiagonal matrix
+ * \param start starting index of the submatrix to work on
+ * \param end last+1 index of the submatrix to work on
+ * \param matrixQ pointer to the column-major matrix holding the eigenvectors, can be 0
+ * \param n size of the input matrix
+ *
+ * For compilation efficiency reasons, this procedure does not use eigen expression
+ * for its arguments.
+ *
+ * Implemented from Golub's "Matrix Computations", algorithm 8.3.2:
+ * "implicit symmetric QR step with Wilkinson shift"
+ */
+template <int StorageOrder, typename RealScalar, typename Scalar, typename Index>
+EIGEN_DEVICE_FUNC static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end,
+                                                  Scalar* matrixQ, Index n);
+}  // namespace internal
+
+template <typename MatrixType>
+template <typename InputType>
+EIGEN_DEVICE_FUNC SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::compute(
+    const EigenBase<InputType>& a_matrix, int options) {
+  const InputType& matrix(a_matrix.derived());
+
+  EIGEN_USING_STD(abs);
+  eigen_assert(matrix.cols() == matrix.rows());
+  eigen_assert((options & ~(EigVecMask | GenEigMask)) == 0 && (options & EigVecMask) != EigVecMask &&
+               "invalid option parameter");
+  bool computeEigenvectors = (options & ComputeEigenvectors) == ComputeEigenvectors;
+  Index n = matrix.cols();
+  m_eivalues.resize(n, 1);
+
+  if (n == 1) {
+    m_eivec = matrix;
+    m_eivalues.coeffRef(0, 0) = numext::real(m_eivec.coeff(0, 0));
+    if (computeEigenvectors) m_eivec.setOnes(n, n);
+    m_info = Success;
+    m_isInitialized = true;
+    m_eigenvectorsOk = computeEigenvectors;
+    return *this;
+  }
+
+  // declare some aliases
+  RealVectorType& diag = m_eivalues;
+  EigenvectorsType& mat = m_eivec;
+
+  // map the matrix coefficients to [-1:1] to avoid over- and underflow.
+  mat = matrix.template triangularView<Lower>();
+  RealScalar scale = mat.cwiseAbs().maxCoeff();
+  if (numext::is_exactly_zero(scale)) scale = RealScalar(1);
+  mat.template triangularView<Lower>() /= scale;
+  m_subdiag.resize(n - 1);
+  m_hcoeffs.resize(n - 1);
+  internal::tridiagonalization_inplace(mat, diag, m_subdiag, m_hcoeffs, m_workspace, computeEigenvectors);
+
+  m_info = internal::computeFromTridiagonal_impl(diag, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);
+
+  // scale back the eigen values
+  m_eivalues *= scale;
+
+  m_isInitialized = true;
+  m_eigenvectorsOk = computeEigenvectors;
+  return *this;
+}
+
+template <typename MatrixType>
+SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::computeFromTridiagonal(
+    const RealVectorType& diag, const SubDiagonalType& subdiag, int options) {
+  // TODO : Add an option to scale the values beforehand
+  bool computeEigenvectors = (options & ComputeEigenvectors) == ComputeEigenvectors;
+
+  m_eivalues = diag;
+  m_subdiag = subdiag;
+  if (computeEigenvectors) {
+    m_eivec.setIdentity(diag.size(), diag.size());
+  }
+  m_info = internal::computeFromTridiagonal_impl(m_eivalues, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);
+
+  m_isInitialized = true;
+  m_eigenvectorsOk = computeEigenvectors;
+  return *this;
+}
+
+namespace internal {
+/**
+ * \internal
+ * \brief Compute the eigendecomposition from a tridiagonal matrix
+ *
+ * \param[in,out] diag : On input, the diagonal of the matrix, on output the eigenvalues
+ * \param[in,out] subdiag : The subdiagonal part of the matrix (entries are modified during the decomposition)
+ * \param[in] maxIterations : the maximum number of iterations
+ * \param[in] computeEigenvectors : whether the eigenvectors have to be computed or not
+ * \param[out] eivec : The matrix to store the eigenvectors if computeEigenvectors==true. Must be allocated on input.
+ * \returns \c Success or \c NoConvergence
+ */
+template <typename MatrixType, typename DiagType, typename SubDiagType>
+EIGEN_DEVICE_FUNC ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag,
+                                                              const Index maxIterations, bool computeEigenvectors,
+                                                              MatrixType& eivec) {
+  ComputationInfo info;
+  typedef typename MatrixType::Scalar Scalar;
+
+  Index n = diag.size();
+  Index end = n - 1;
+  Index start = 0;
+  Index iter = 0;  // total number of iterations
+
+  typedef typename DiagType::RealScalar RealScalar;
+  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+  const RealScalar precision_inv = RealScalar(1) / NumTraits<RealScalar>::epsilon();
+  while (end > 0) {
+    for (Index i = start; i < end; ++i) {
+      if (numext::abs(subdiag[i]) < considerAsZero) {
+        subdiag[i] = RealScalar(0);
+      } else {
+        // abs(subdiag[i]) <= epsilon * sqrt(abs(diag[i]) + abs(diag[i+1]))
+        // Scaled to prevent underflows.
+        const RealScalar scaled_subdiag = precision_inv * subdiag[i];
+        if (scaled_subdiag * scaled_subdiag <= (numext::abs(diag[i]) + numext::abs(diag[i + 1]))) {
+          subdiag[i] = RealScalar(0);
+        }
+      }
+    }
+
+    // find the largest unreduced block at the end of the matrix.
+    while (end > 0 && numext::is_exactly_zero(subdiag[end - 1])) {
+      end--;
+    }
+    if (end <= 0) break;
+
+    // if we spent too many iterations, we give up
+    iter++;
+    if (iter > maxIterations * n) break;
+
+    start = end - 1;
+    while (start > 0 && !numext::is_exactly_zero(subdiag[start - 1])) start--;
+
+    internal::tridiagonal_qr_step<MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor>(
+        diag.data(), subdiag.data(), start, end, computeEigenvectors ? eivec.data() : (Scalar*)0, n);
+  }
+  if (iter <= maxIterations * n)
+    info = Success;
+  else
+    info = NoConvergence;
+
+  // Sort eigenvalues and corresponding vectors.
+  // TODO: make the sort optional and use a more efficient sorting algorithm.
+  if (info == Success) {
+    for (Index i = 0; i < n - 1; ++i) {
+      Index k;
+      diag.segment(i, n - i).minCoeff(&k);
+      if (k > 0) {
+        numext::swap(diag[i], diag[k + i]);
+        if (computeEigenvectors) eivec.col(i).swap(eivec.col(k + i));
+      }
+    }
+  }
+  return info;
+}
+
+template <typename SolverType, int Size, bool IsComplex>
+struct direct_selfadjoint_eigenvalues {
+  EIGEN_DEVICE_FUNC static inline void run(SolverType& eig, const typename SolverType::MatrixType& A, int options) {
+    eig.compute(A, options);
+  }
+};
+
+template <typename SolverType>
+struct direct_selfadjoint_eigenvalues<SolverType, 3, false> {
+  typedef typename SolverType::MatrixType MatrixType;
+  typedef typename SolverType::RealVectorType VectorType;
+  typedef typename SolverType::Scalar Scalar;
+  typedef typename SolverType::EigenvectorsType EigenvectorsType;
+
+  /** \internal
+   * Computes the roots of the characteristic polynomial of \a m.
+   * For numerical stability m.trace() should be near zero and to avoid over- or underflow m should be normalized.
+   */
+  EIGEN_DEVICE_FUNC static inline void computeRoots(const MatrixType& m, VectorType& roots) {
+    EIGEN_USING_STD(sqrt)
+    EIGEN_USING_STD(atan2)
+    EIGEN_USING_STD(cos)
+    EIGEN_USING_STD(sin)
+    const Scalar s_inv3 = Scalar(1) / Scalar(3);
+    const Scalar s_sqrt3 = sqrt(Scalar(3));
+
+    // The characteristic equation is x^3 - c2*x^2 + c1*x - c0 = 0.  The
+    // eigenvalues are the roots to this equation, all guaranteed to be
+    // real-valued, because the matrix is symmetric.
+    Scalar c0 = m(0, 0) * m(1, 1) * m(2, 2) + Scalar(2) * m(1, 0) * m(2, 0) * m(2, 1) - m(0, 0) * m(2, 1) * m(2, 1) -
+                m(1, 1) * m(2, 0) * m(2, 0) - m(2, 2) * m(1, 0) * m(1, 0);
+    Scalar c1 = m(0, 0) * m(1, 1) - m(1, 0) * m(1, 0) + m(0, 0) * m(2, 2) - m(2, 0) * m(2, 0) + m(1, 1) * m(2, 2) -
+                m(2, 1) * m(2, 1);
+    Scalar c2 = m(0, 0) + m(1, 1) + m(2, 2);
+
+    // Construct the parameters used in classifying the roots of the equation
+    // and in solving the equation for the roots in closed form.
+    Scalar c2_over_3 = c2 * s_inv3;
+    Scalar a_over_3 = (c2 * c2_over_3 - c1) * s_inv3;
+    a_over_3 = numext::maxi(a_over_3, Scalar(0));
+
+    Scalar half_b = Scalar(0.5) * (c0 + c2_over_3 * (Scalar(2) * c2_over_3 * c2_over_3 - c1));
+
+    Scalar q = a_over_3 * a_over_3 * a_over_3 - half_b * half_b;
+    q = numext::maxi(q, Scalar(0));
+
+    // Compute the eigenvalues by solving for the roots of the polynomial.
+    Scalar rho = sqrt(a_over_3);
+    Scalar theta = atan2(sqrt(q), half_b) * s_inv3;  // since sqrt(q) > 0, atan2 is in [0, pi] and theta is in [0, pi/3]
+    Scalar cos_theta = cos(theta);
+    Scalar sin_theta = sin(theta);
+    // roots are already sorted, since cos is monotonically decreasing on [0, pi]
+    roots(0) = c2_over_3 - rho * (cos_theta + s_sqrt3 * sin_theta);  // == 2*rho*cos(theta+2pi/3)
+    roots(1) = c2_over_3 - rho * (cos_theta - s_sqrt3 * sin_theta);  // == 2*rho*cos(theta+ pi/3)
+    roots(2) = c2_over_3 + Scalar(2) * rho * cos_theta;
+  }
+
+  EIGEN_DEVICE_FUNC static inline bool extract_kernel(MatrixType& mat, Ref<VectorType> res,
+                                                      Ref<VectorType> representative) {
+    EIGEN_USING_STD(abs);
+    EIGEN_USING_STD(sqrt);
+    Index i0;
+    // Find non-zero column i0 (by construction, there must exist a non zero coefficient on the diagonal):
+    mat.diagonal().cwiseAbs().maxCoeff(&i0);
+    // mat.col(i0) is a good candidate for an orthogonal vector to the current eigenvector,
+    // so let's save it:
+    representative = mat.col(i0);
+    Scalar n0, n1;
+    VectorType c0, c1;
+    n0 = (c0 = representative.cross(mat.col((i0 + 1) % 3))).squaredNorm();
+    n1 = (c1 = representative.cross(mat.col((i0 + 2) % 3))).squaredNorm();
+    if (n0 > n1)
+      res = c0 / sqrt(n0);
+    else
+      res = c1 / sqrt(n1);
+
+    return true;
+  }
+
+  EIGEN_DEVICE_FUNC static inline void run(SolverType& solver, const MatrixType& mat, int options) {
+    eigen_assert(mat.cols() == 3 && mat.cols() == mat.rows());
+    eigen_assert((options & ~(EigVecMask | GenEigMask)) == 0 && (options & EigVecMask) != EigVecMask &&
+                 "invalid option parameter");
+    bool computeEigenvectors = (options & ComputeEigenvectors) == ComputeEigenvectors;
+
+    EigenvectorsType& eivecs = solver.m_eivec;
+    VectorType& eivals = solver.m_eivalues;
+
+    // Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
+    Scalar shift = mat.trace() / Scalar(3);
+    // TODO: avoid this copy. Currently necessary to suppress bogus values when determining maxCoeff and for
+    // computing the eigenvectors later.
+    MatrixType scaledMat = mat.template selfadjointView<Lower>();
+    scaledMat.diagonal().array() -= shift;
+    Scalar scale = scaledMat.cwiseAbs().maxCoeff();
+    if (scale > 0) scaledMat /= scale;  // TODO: skip remaining operations when scale==0.
+
+    // compute the eigenvalues
+    computeRoots(scaledMat, eivals);
+
+    // compute the eigenvectors
+    if (computeEigenvectors) {
+      if ((eivals(2) - eivals(0)) <= Eigen::NumTraits<Scalar>::epsilon()) {
+        // All three eigenvalues are numerically the same
+        eivecs.setIdentity();
+      } else {
+        MatrixType tmp;
+        tmp = scaledMat;
+
+        // Compute the eigenvector of the most distinct eigenvalue
+        Scalar d0 = eivals(2) - eivals(1);
+        Scalar d1 = eivals(1) - eivals(0);
+        Index k(0), l(2);
+        if (d0 > d1) {
+          numext::swap(k, l);
+          d0 = d1;
+        }
+
+        // Compute the eigenvector of index k
+        {
+          tmp.diagonal().array() -= eivals(k);
+          // By construction, 'tmp' is of rank 2, and its kernel corresponds to the respective eigenvector.
+          extract_kernel(tmp, eivecs.col(k), eivecs.col(l));
+        }
+
+        // Compute eigenvector of index l
+        if (d0 <= 2 * Eigen::NumTraits<Scalar>::epsilon() * d1) {
+          // If d0 is too small, then the two other eigenvalues are numerically the same,
+          // and thus we only have to ortho-normalize the near orthogonal vector we saved above.
+          eivecs.col(l) -= eivecs.col(k).dot(eivecs.col(l)) * eivecs.col(l);
+          eivecs.col(l).normalize();
+        } else {
+          tmp = scaledMat;
+          tmp.diagonal().array() -= eivals(l);
+
+          VectorType dummy;
+          extract_kernel(tmp, eivecs.col(l), dummy);
+        }
+
+        // Compute last eigenvector from the other two
+        eivecs.col(1) = eivecs.col(2).cross(eivecs.col(0)).normalized();
+      }
+    }
+
+    // Rescale back to the original size.
+    eivals *= scale;
+    eivals.array() += shift;
+
+    solver.m_info = Success;
+    solver.m_isInitialized = true;
+    solver.m_eigenvectorsOk = computeEigenvectors;
+  }
+};
+
+// 2x2 direct eigenvalues decomposition, code from Hauke Heibel
+template <typename SolverType>
+struct direct_selfadjoint_eigenvalues<SolverType, 2, false> {
+  typedef typename SolverType::MatrixType MatrixType;
+  typedef typename SolverType::RealVectorType VectorType;
+  typedef typename SolverType::Scalar Scalar;
+  typedef typename SolverType::EigenvectorsType EigenvectorsType;
+
+  EIGEN_DEVICE_FUNC static inline void computeRoots(const MatrixType& m, VectorType& roots) {
+    EIGEN_USING_STD(sqrt);
+    const Scalar t0 = Scalar(0.5) * sqrt(numext::abs2(m(0, 0) - m(1, 1)) + Scalar(4) * numext::abs2(m(1, 0)));
+    const Scalar t1 = Scalar(0.5) * (m(0, 0) + m(1, 1));
+    roots(0) = t1 - t0;
+    roots(1) = t1 + t0;
+  }
+
+  EIGEN_DEVICE_FUNC static inline void run(SolverType& solver, const MatrixType& mat, int options) {
+    EIGEN_USING_STD(sqrt);
+    EIGEN_USING_STD(abs);
+
+    eigen_assert(mat.cols() == 2 && mat.cols() == mat.rows());
+    eigen_assert((options & ~(EigVecMask | GenEigMask)) == 0 && (options & EigVecMask) != EigVecMask &&
+                 "invalid option parameter");
+    bool computeEigenvectors = (options & ComputeEigenvectors) == ComputeEigenvectors;
+
+    EigenvectorsType& eivecs = solver.m_eivec;
+    VectorType& eivals = solver.m_eivalues;
+
+    // Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
+    Scalar shift = mat.trace() / Scalar(2);
+    MatrixType scaledMat = mat;
+    scaledMat.coeffRef(0, 1) = mat.coeff(1, 0);
+    scaledMat.diagonal().array() -= shift;
+    Scalar scale = scaledMat.cwiseAbs().maxCoeff();
+    if (scale > Scalar(0)) scaledMat /= scale;
+
+    // Compute the eigenvalues
+    computeRoots(scaledMat, eivals);
+
+    // compute the eigen vectors
+    if (computeEigenvectors) {
+      if ((eivals(1) - eivals(0)) <= abs(eivals(1)) * Eigen::NumTraits<Scalar>::epsilon()) {
+        eivecs.setIdentity();
+      } else {
+        scaledMat.diagonal().array() -= eivals(1);
+        Scalar a2 = numext::abs2(scaledMat(0, 0));
+        Scalar c2 = numext::abs2(scaledMat(1, 1));
+        Scalar b2 = numext::abs2(scaledMat(1, 0));
+        if (a2 > c2) {
+          eivecs.col(1) << -scaledMat(1, 0), scaledMat(0, 0);
+          eivecs.col(1) /= sqrt(a2 + b2);
+        } else {
+          eivecs.col(1) << -scaledMat(1, 1), scaledMat(1, 0);
+          eivecs.col(1) /= sqrt(c2 + b2);
+        }
+
+        eivecs.col(0) << eivecs.col(1).unitOrthogonal();
+      }
+    }
+
+    // Rescale back to the original size.
+    eivals *= scale;
+    eivals.array() += shift;
+
+    solver.m_info = Success;
+    solver.m_isInitialized = true;
+    solver.m_eigenvectorsOk = computeEigenvectors;
+  }
+};
+
+}  // namespace internal
+
+template <typename MatrixType>
+EIGEN_DEVICE_FUNC SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::computeDirect(
+    const MatrixType& matrix, int options) {
+  internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver, Size, NumTraits<Scalar>::IsComplex>::run(
+      *this, matrix, options);
+  return *this;
+}
+
+namespace internal {
+
+// Francis implicit QR step.
+template <int StorageOrder, typename RealScalar, typename Scalar, typename Index>
+EIGEN_DEVICE_FUNC static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end,
+                                                  Scalar* matrixQ, Index n) {
+  // Wilkinson Shift.
+  RealScalar td = (diag[end - 1] - diag[end]) * RealScalar(0.5);
+  RealScalar e = subdiag[end - 1];
+  // Note that thanks to scaling, e^2 or td^2 cannot overflow, however they can still
+  // underflow thus leading to inf/NaN values when using the following commented code:
+  //   RealScalar e2 = numext::abs2(subdiag[end-1]);
+  //   RealScalar mu = diag[end] - e2 / (td + (td>0 ? 1 : -1) * sqrt(td*td + e2));
+  // This explain the following, somewhat more complicated, version:
+  RealScalar mu = diag[end];
+  if (numext::is_exactly_zero(td)) {
+    mu -= numext::abs(e);
+  } else if (!numext::is_exactly_zero(e)) {
+    const RealScalar e2 = numext::abs2(e);
+    const RealScalar h = numext::hypot(td, e);
+    if (numext::is_exactly_zero(e2)) {
+      mu -= e / ((td + (td > RealScalar(0) ? h : -h)) / e);
+    } else {
+      mu -= e2 / (td + (td > RealScalar(0) ? h : -h));
+    }
+  }
+
+  RealScalar x = diag[start] - mu;
+  RealScalar z = subdiag[start];
+  // If z ever becomes zero, the Givens rotation will be the identity and
+  // z will stay zero for all future iterations.
+  for (Index k = start; k < end && !numext::is_exactly_zero(z); ++k) {
+    JacobiRotation<RealScalar> rot;
+    rot.makeGivens(x, z);
+
+    // do T = G' T G
+    RealScalar sdk = rot.s() * diag[k] + rot.c() * subdiag[k];
+    RealScalar dkp1 = rot.s() * subdiag[k] + rot.c() * diag[k + 1];
+
+    diag[k] =
+        rot.c() * (rot.c() * diag[k] - rot.s() * subdiag[k]) - rot.s() * (rot.c() * subdiag[k] - rot.s() * diag[k + 1]);
+    diag[k + 1] = rot.s() * sdk + rot.c() * dkp1;
+    subdiag[k] = rot.c() * sdk - rot.s() * dkp1;
+
+    if (k > start) subdiag[k - 1] = rot.c() * subdiag[k - 1] - rot.s() * z;
+
+    // "Chasing the bulge" to return to triangular form.
+    x = subdiag[k];
+    if (k < end - 1) {
+      z = -rot.s() * subdiag[k + 1];
+      subdiag[k + 1] = rot.c() * subdiag[k + 1];
+    }
+
+    // apply the givens rotation to the unit matrix Q = Q * G
+    if (matrixQ) {
+      // FIXME: this operation is inefficient for RowMajor storage order.
+      Map<Matrix<Scalar, Dynamic, Dynamic, StorageOrder> > q(matrixQ, n, n);
+      q.applyOnTheRight(k, k + 1, rot);
+    }
+  }
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SELFADJOINTEIGENSOLVER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
new file mode 100644
index 0000000..fabc30d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
@@ -0,0 +1,90 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Self-adjoint eigenvalues/eigenvectors.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_SAEIGENSOLVER_LAPACKE_H
+#define EIGEN_SAEIGENSOLVER_LAPACKE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \internal Specialization for the data types supported by LAPACKe */
+
+#define EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, EIGCOLROW)                   \
+  template <>                                                                                                        \
+  template <typename InputType>                                                                                      \
+  inline SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >&                                      \
+  SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, \
+                                                                                 int options) {                      \
+    eigen_assert(matrix.cols() == matrix.rows());                                                                    \
+    eigen_assert((options & ~(EigVecMask | GenEigMask)) == 0 && (options & EigVecMask) != EigVecMask &&              \
+                 "invalid option parameter");                                                                        \
+    bool computeEigenvectors = (options & ComputeEigenvectors) == ComputeEigenvectors;                               \
+    lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), lda, info;                                    \
+    m_eivalues.resize(n, 1);                                                                                         \
+    m_subdiag.resize(n - 1);                                                                                         \
+    m_eivec = matrix;                                                                                                \
+                                                                                                                     \
+    if (n == 1) {                                                                                                    \
+      m_eivalues.coeffRef(0, 0) = numext::real(m_eivec.coeff(0, 0));                                                 \
+      if (computeEigenvectors) m_eivec.setOnes(n, n);                                                                \
+      m_info = Success;                                                                                              \
+      m_isInitialized = true;                                                                                        \
+      m_eigenvectorsOk = computeEigenvectors;                                                                        \
+      return *this;                                                                                                  \
+    }                                                                                                                \
+                                                                                                                     \
+    lda = internal::convert_index<lapack_int>(m_eivec.outerStride());                                                \
+    char jobz, uplo = 'L' /*, range='A'*/;                                                                           \
+    jobz = computeEigenvectors ? 'V' : 'N';                                                                          \
+                                                                                                                     \
+    info = LAPACKE_##LAPACKE_NAME(LAPACK_COL_MAJOR, jobz, uplo, n, (LAPACKE_TYPE*)m_eivec.data(), lda,               \
+                                  (LAPACKE_RTYPE*)m_eivalues.data());                                                \
+    m_info = (info == 0) ? Success : NoConvergence;                                                                  \
+    m_isInitialized = true;                                                                                          \
+    m_eigenvectorsOk = computeEigenvectors;                                                                          \
+    return *this;                                                                                                    \
+  }
+
+#define EIGEN_LAPACKE_EIG_SELFADJ(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME)       \
+  EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, ColMajor) \
+  EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, RowMajor)
+
+EIGEN_LAPACKE_EIG_SELFADJ(double, double, double, dsyev)
+EIGEN_LAPACKE_EIG_SELFADJ(float, float, float, ssyev)
+EIGEN_LAPACKE_EIG_SELFADJ(dcomplex, lapack_complex_double, double, zheev)
+EIGEN_LAPACKE_EIG_SELFADJ(scomplex, lapack_complex_float, float, cheev)
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SAEIGENSOLVER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/Tridiagonalization.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/Tridiagonalization.h
new file mode 100644
index 0000000..aef8739
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Eigenvalues/Tridiagonalization.h
@@ -0,0 +1,529 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIDIAGONALIZATION_H
+#define EIGEN_TRIDIAGONALIZATION_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename MatrixType>
+struct TridiagonalizationMatrixTReturnType;
+template <typename MatrixType>
+struct traits<TridiagonalizationMatrixTReturnType<MatrixType>> : public traits<typename MatrixType::PlainObject> {
+  typedef typename MatrixType::PlainObject ReturnType;  // FIXME: consider using BandMatrix as ReturnType.
+  enum { Flags = 0 };
+};
+
+template <typename MatrixType, typename CoeffVectorType>
+EIGEN_DEVICE_FUNC void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs);
+}  // namespace internal
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ *
+ * \class Tridiagonalization
+ *
+ * \brief Tridiagonal decomposition of a selfadjoint matrix
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the
+ * tridiagonal decomposition; this is expected to be an instantiation of the
+ * Matrix class template.
+ *
+ * This class performs a tridiagonal decomposition of a selfadjoint matrix \f$ A \f$ such that:
+ * \f$ A = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real symmetric tridiagonal matrix.
+ *
+ * A tridiagonal matrix is a matrix which has nonzero elements only on the
+ * main diagonal and the first diagonal below and above it. The Hessenberg
+ * decomposition of a selfadjoint matrix is in fact a tridiagonal
+ * decomposition. This class is used in SelfAdjointEigenSolver to compute the
+ * eigenvalues and eigenvectors of a selfadjoint matrix.
+ *
+ * Call the function compute() to compute the tridiagonal decomposition of a
+ * given matrix. Alternatively, you can use the Tridiagonalization(const MatrixType&)
+ * constructor which computes the tridiagonal Schur decomposition at
+ * construction time. Once the decomposition is computed, you can use the
+ * matrixQ() and matrixT() functions to retrieve the matrices Q and T in the
+ * decomposition.
+ *
+ * The documentation of Tridiagonalization(const MatrixType&) contains an
+ * example of the typical use of this class.
+ *
+ * \sa class HessenbergDecomposition, class SelfAdjointEigenSolver
+ */
+template <typename MatrixType_>
+class Tridiagonalization {
+ public:
+  /** \brief Synonym for the template parameter \p MatrixType_. */
+  typedef MatrixType_ MatrixType;
+
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+
+  enum {
+    Size = MatrixType::RowsAtCompileTime,
+    SizeMinusOne = Size == Dynamic ? Dynamic : (Size > 1 ? Size - 1 : 1),
+    Options = internal::traits<MatrixType>::Options,
+    MaxSize = MatrixType::MaxRowsAtCompileTime,
+    MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : (MaxSize > 1 ? MaxSize - 1 : 1)
+  };
+
+  typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
+  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type DiagonalType;
+  typedef Matrix<RealScalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> SubDiagonalType;
+  typedef internal::remove_all_t<typename MatrixType::RealReturnType> MatrixTypeRealView;
+  typedef internal::TridiagonalizationMatrixTReturnType<MatrixTypeRealView> MatrixTReturnType;
+
+  typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+                             internal::add_const_on_value_type_t<typename Diagonal<const MatrixType>::RealReturnType>,
+                             const Diagonal<const MatrixType>>
+      DiagonalReturnType;
+
+  typedef std::conditional_t<
+      NumTraits<Scalar>::IsComplex,
+      internal::add_const_on_value_type_t<typename Diagonal<const MatrixType, -1>::RealReturnType>,
+      const Diagonal<const MatrixType, -1>>
+      SubDiagonalReturnType;
+
+  /** \brief Return type of matrixQ() */
+  typedef HouseholderSequence<MatrixType, internal::remove_all_t<typename CoeffVectorType::ConjugateReturnType>>
+      HouseholderSequenceType;
+
+  /** \brief Default constructor.
+   *
+   * \param [in]  size  Positive integer, size of the matrix whose tridiagonal
+   * decomposition will be computed.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via compute().  The \p size parameter is only
+   * used as a hint. It is not an error to give a wrong \p size, but it may
+   * impair performance.
+   *
+   * \sa compute() for an example.
+   */
+  explicit Tridiagonalization(Index size = Size == Dynamic ? 2 : Size)
+      : m_matrix(size, size), m_hCoeffs(size > 1 ? size - 1 : 1), m_isInitialized(false) {}
+
+  /** \brief Constructor; computes tridiagonal decomposition of given matrix.
+   *
+   * \param[in]  matrix  Selfadjoint matrix whose tridiagonal decomposition
+   * is to be computed.
+   *
+   * This constructor calls compute() to compute the tridiagonal decomposition.
+   *
+   * Example: \include Tridiagonalization_Tridiagonalization_MatrixType.cpp
+   * Output: \verbinclude Tridiagonalization_Tridiagonalization_MatrixType.out
+   */
+  template <typename InputType>
+  explicit Tridiagonalization(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()), m_hCoeffs(matrix.cols() > 1 ? matrix.cols() - 1 : 1), m_isInitialized(false) {
+    internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
+    m_isInitialized = true;
+  }
+
+  /** \brief Computes tridiagonal decomposition of given matrix.
+   *
+   * \param[in]  matrix  Selfadjoint matrix whose tridiagonal decomposition
+   * is to be computed.
+   * \returns    Reference to \c *this
+   *
+   * The tridiagonal decomposition is computed by bringing the columns of
+   * the matrix successively in the required form using Householder
+   * reflections. The cost is \f$ 4n^3/3 \f$ flops, where \f$ n \f$ denotes
+   * the size of the given matrix.
+   *
+   * This method reuses of the allocated data in the Tridiagonalization
+   * object, if the size of the matrix does not change.
+   *
+   * Example: \include Tridiagonalization_compute.cpp
+   * Output: \verbinclude Tridiagonalization_compute.out
+   */
+  template <typename InputType>
+  Tridiagonalization& compute(const EigenBase<InputType>& matrix) {
+    m_matrix = matrix.derived();
+    m_hCoeffs.resize(matrix.rows() - 1, 1);
+    internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
+    m_isInitialized = true;
+    return *this;
+  }
+
+  /** \brief Returns the Householder coefficients.
+   *
+   * \returns a const reference to the vector of Householder coefficients
+   *
+   * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+   * the member function compute(const MatrixType&) has been called before
+   * to compute the tridiagonal decomposition of a matrix.
+   *
+   * The Householder coefficients allow the reconstruction of the matrix
+   * \f$ Q \f$ in the tridiagonal decomposition from the packed data.
+   *
+   * Example: \include Tridiagonalization_householderCoefficients.cpp
+   * Output: \verbinclude Tridiagonalization_householderCoefficients.out
+   *
+   * \sa packedMatrix(), \ref Householder_Module "Householder module"
+   */
+  inline CoeffVectorType householderCoefficients() const {
+    eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+    return m_hCoeffs;
+  }
+
+  /** \brief Returns the internal representation of the decomposition
+   *
+   *	\returns a const reference to a matrix with the internal representation
+   *	         of the decomposition.
+   *
+   * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+   * the member function compute(const MatrixType&) has been called before
+   * to compute the tridiagonal decomposition of a matrix.
+   *
+   * The returned matrix contains the following information:
+   *  - the strict upper triangular part is equal to the input matrix A.
+   *  - the diagonal and lower sub-diagonal represent the real tridiagonal
+   *    symmetric matrix T.
+   *  - the rest of the lower part contains the Householder vectors that,
+   *    combined with Householder coefficients returned by
+   *    householderCoefficients(), allows to reconstruct the matrix Q as
+   *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
+   *    Here, the matrices \f$ H_i \f$ are the Householder transformations
+   *       \f$ H_i = (I - h_i v_i v_i^T) \f$
+   *    where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and
+   *    \f$ v_i \f$ is the Householder vector defined by
+   *       \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$
+   *    with M the matrix returned by this function.
+   *
+   * See LAPACK for further details on this packed storage.
+   *
+   * Example: \include Tridiagonalization_packedMatrix.cpp
+   * Output: \verbinclude Tridiagonalization_packedMatrix.out
+   *
+   * \sa householderCoefficients()
+   */
+  inline const MatrixType& packedMatrix() const {
+    eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+    return m_matrix;
+  }
+
+  /** \brief Returns the unitary matrix Q in the decomposition
+   *
+   * \returns object representing the matrix Q
+   *
+   * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+   * the member function compute(const MatrixType&) has been called before
+   * to compute the tridiagonal decomposition of a matrix.
+   *
+   * This function returns a light-weight object of template class
+   * HouseholderSequence. You can either apply it directly to a matrix or
+   * you can convert it to a matrix of type #MatrixType.
+   *
+   * \sa Tridiagonalization(const MatrixType&) for an example,
+   *     matrixT(), class HouseholderSequence
+   */
+  HouseholderSequenceType matrixQ() const {
+    eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+    return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate()).setLength(m_matrix.rows() - 1).setShift(1);
+  }
+
+  /** \brief Returns an expression of the tridiagonal matrix T in the decomposition
+   *
+   * \returns expression object representing the matrix T
+   *
+   * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+   * the member function compute(const MatrixType&) has been called before
+   * to compute the tridiagonal decomposition of a matrix.
+   *
+   * Currently, this function can be used to extract the matrix T from internal
+   * data and copy it to a dense matrix object. In most cases, it may be
+   * sufficient to directly use the packed matrix or the vector expressions
+   * returned by diagonal() and subDiagonal() instead of creating a new
+   * dense copy matrix with this function.
+   *
+   * \sa Tridiagonalization(const MatrixType&) for an example,
+   * matrixQ(), packedMatrix(), diagonal(), subDiagonal()
+   */
+  MatrixTReturnType matrixT() const {
+    eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+    return MatrixTReturnType(m_matrix.real());
+  }
+
+  /** \brief Returns the diagonal of the tridiagonal matrix T in the decomposition.
+   *
+   * \returns expression representing the diagonal of T
+   *
+   * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+   * the member function compute(const MatrixType&) has been called before
+   * to compute the tridiagonal decomposition of a matrix.
+   *
+   * Example: \include Tridiagonalization_diagonal.cpp
+   * Output: \verbinclude Tridiagonalization_diagonal.out
+   *
+   * \sa matrixT(), subDiagonal()
+   */
+  DiagonalReturnType diagonal() const;
+
+  /** \brief Returns the subdiagonal of the tridiagonal matrix T in the decomposition.
+   *
+   * \returns expression representing the subdiagonal of T
+   *
+   * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+   * the member function compute(const MatrixType&) has been called before
+   * to compute the tridiagonal decomposition of a matrix.
+   *
+   * \sa diagonal() for an example, matrixT()
+   */
+  SubDiagonalReturnType subDiagonal() const;
+
+ protected:
+  MatrixType m_matrix;
+  CoeffVectorType m_hCoeffs;
+  bool m_isInitialized;
+};
+
+template <typename MatrixType>
+typename Tridiagonalization<MatrixType>::DiagonalReturnType Tridiagonalization<MatrixType>::diagonal() const {
+  eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+  return m_matrix.diagonal().real();
+}
+
+template <typename MatrixType>
+typename Tridiagonalization<MatrixType>::SubDiagonalReturnType Tridiagonalization<MatrixType>::subDiagonal() const {
+  eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+  return m_matrix.template diagonal<-1>().real();
+}
+
+namespace internal {
+
+/** \internal
+ * Performs a tridiagonal decomposition of the selfadjoint matrix \a matA in-place.
+ *
+ * \param[in,out] matA On input the selfadjoint matrix. Only the \b lower triangular part is referenced.
+ *                     On output, the strict upper part is left unchanged, and the lower triangular part
+ *                     represents the T and Q matrices in packed format has detailed below.
+ * \param[out]    hCoeffs returned Householder coefficients (see below)
+ *
+ * On output, the tridiagonal selfadjoint matrix T is stored in the diagonal
+ * and lower sub-diagonal of the matrix \a matA.
+ * The unitary matrix Q is represented in a compact way as a product of
+ * Householder reflectors \f$ H_i \f$ such that:
+ *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
+ * The Householder reflectors are defined as
+ *       \f$ H_i = (I - h_i v_i v_i^T) \f$
+ * where \f$ h_i = hCoeffs[i]\f$ is the \f$ i \f$th Householder coefficient and
+ * \f$ v_i \f$ is the Householder vector defined by
+ *       \f$ v_i = [ 0, \ldots, 0, 1, matA(i+2,i), \ldots, matA(N-1,i) ]^T \f$.
+ *
+ * Implemented from Golub's "Matrix Computations", algorithm 8.3.1.
+ *
+ * \sa Tridiagonalization::packedMatrix()
+ */
+template <typename MatrixType, typename CoeffVectorType>
+EIGEN_DEVICE_FUNC void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs) {
+  using numext::conj;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  Index n = matA.rows();
+  eigen_assert(n == matA.cols());
+  eigen_assert(n == hCoeffs.size() + 1 || n == 1);
+
+  for (Index i = 0; i < n - 1; ++i) {
+    Index remainingSize = n - i - 1;
+    RealScalar beta;
+    Scalar h;
+    matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta);
+
+    // Apply similarity transformation to remaining columns,
+    // i.e., A = H A H' where H = I - h v v' and v = matA.col(i).tail(n-i-1)
+    matA.col(i).coeffRef(i + 1) = Scalar(1);
+
+    hCoeffs.tail(n - i - 1).noalias() =
+        (matA.bottomRightCorner(remainingSize, remainingSize).template selfadjointView<Lower>() *
+         (conj(h) * matA.col(i).tail(remainingSize)));
+
+    hCoeffs.tail(n - i - 1) +=
+        (conj(h) * RealScalar(-0.5) * (hCoeffs.tail(remainingSize).dot(matA.col(i).tail(remainingSize)))) *
+        matA.col(i).tail(n - i - 1);
+
+    matA.bottomRightCorner(remainingSize, remainingSize)
+        .template selfadjointView<Lower>()
+        .rankUpdate(matA.col(i).tail(remainingSize), hCoeffs.tail(remainingSize), Scalar(-1));
+
+    matA.col(i).coeffRef(i + 1) = beta;
+    hCoeffs.coeffRef(i) = h;
+  }
+}
+
+// forward declaration, implementation at the end of this file
+template <typename MatrixType, int Size = MatrixType::ColsAtCompileTime,
+          bool IsComplex = NumTraits<typename MatrixType::Scalar>::IsComplex>
+struct tridiagonalization_inplace_selector;
+
+/** \brief Performs a full tridiagonalization in place
+ *
+ * \param[in,out]  mat  On input, the selfadjoint matrix whose tridiagonal
+ *    decomposition is to be computed. Only the lower triangular part referenced.
+ *    The rest is left unchanged. On output, the orthogonal matrix Q
+ *    in the decomposition if \p extractQ is true.
+ * \param[out]  diag  The diagonal of the tridiagonal matrix T in the
+ *    decomposition.
+ * \param[out]  subdiag  The subdiagonal of the tridiagonal matrix T in
+ *    the decomposition.
+ * \param[out]  hcoeffs
+ * \param[out]  workspace
+ * \param[in]  extractQ  If true, the orthogonal matrix Q in the
+ *    decomposition is computed and stored in \p mat.
+ *
+ * Computes the tridiagonal decomposition of the selfadjoint matrix \p mat in place
+ * such that \f$ mat = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real
+ * symmetric tridiagonal matrix.
+ *
+ * The tridiagonal matrix T is passed to the output parameters \p diag and \p subdiag. If
+ * \p extractQ is true, then the orthogonal matrix Q is passed to \p mat. Otherwise the lower
+ * part of the matrix \p mat is destroyed.
+ *
+ * The vectors \p diag and \p subdiag are not resized. The function
+ * assumes that they are already of the correct size. The length of the
+ * vector \p diag should equal the number of rows in \p mat, and the
+ * length of the vector \p subdiag should be one left.
+ *
+ * This implementation contains an optimized path for 3-by-3 matrices
+ * which is especially useful for plane fitting.
+ *
+ * \note Currently, it requires two temporary vectors to hold the intermediate
+ * Householder coefficients, and to reconstruct the matrix Q from the Householder
+ * reflectors.
+ *
+ * Example (this uses the same matrix as the example in
+ *    Tridiagonalization::Tridiagonalization(const MatrixType&)):
+ *    \include Tridiagonalization_decomposeInPlace.cpp
+ * Output: \verbinclude Tridiagonalization_decomposeInPlace.out
+ *
+ * \sa class Tridiagonalization
+ */
+template <typename MatrixType, typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType,
+          typename WorkSpaceType>
+EIGEN_DEVICE_FUNC void tridiagonalization_inplace(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag,
+                                                  CoeffVectorType& hcoeffs, WorkSpaceType& workspace, bool extractQ) {
+  eigen_assert(mat.cols() == mat.rows() && diag.size() == mat.rows() && subdiag.size() == mat.rows() - 1);
+  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, hcoeffs, workspace, extractQ);
+}
+
+/** \internal
+ * General full tridiagonalization
+ */
+template <typename MatrixType, int Size, bool IsComplex>
+struct tridiagonalization_inplace_selector {
+  typedef typename Tridiagonalization<MatrixType>::HouseholderSequenceType HouseholderSequenceType;
+  template <typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType, typename WorkSpaceType>
+  static EIGEN_DEVICE_FUNC void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag,
+                                    CoeffVectorType& hCoeffs, WorkSpaceType& workspace, bool extractQ) {
+    tridiagonalization_inplace(mat, hCoeffs);
+    diag = mat.diagonal().real();
+    subdiag = mat.template diagonal<-1>().real();
+    if (extractQ) {
+      HouseholderSequenceType(mat, hCoeffs.conjugate()).setLength(mat.rows() - 1).setShift(1).evalTo(mat, workspace);
+    }
+  }
+};
+
+/** \internal
+ * Specialization for 3x3 real matrices.
+ * Especially useful for plane fitting.
+ */
+template <typename MatrixType>
+struct tridiagonalization_inplace_selector<MatrixType, 3, false> {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  template <typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType, typename WorkSpaceType>
+  static EIGEN_DEVICE_FUNC void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, CoeffVectorType&,
+                                    WorkSpaceType&, bool extractQ) {
+    using std::sqrt;
+    const RealScalar tol = (std::numeric_limits<RealScalar>::min)();
+    diag[0] = mat(0, 0);
+    RealScalar v1norm2 = numext::abs2(mat(2, 0));
+    if (v1norm2 <= tol) {
+      diag[1] = mat(1, 1);
+      diag[2] = mat(2, 2);
+      subdiag[0] = mat(1, 0);
+      subdiag[1] = mat(2, 1);
+      if (extractQ) mat.setIdentity();
+    } else {
+      RealScalar beta = sqrt(numext::abs2(mat(1, 0)) + v1norm2);
+      RealScalar invBeta = RealScalar(1) / beta;
+      Scalar m01 = mat(1, 0) * invBeta;
+      Scalar m02 = mat(2, 0) * invBeta;
+      Scalar q = RealScalar(2) * m01 * mat(2, 1) + m02 * (mat(2, 2) - mat(1, 1));
+      diag[1] = mat(1, 1) + m02 * q;
+      diag[2] = mat(2, 2) - m02 * q;
+      subdiag[0] = beta;
+      subdiag[1] = mat(2, 1) - m01 * q;
+      if (extractQ) {
+        mat << 1, 0, 0, 0, m01, m02, 0, m02, -m01;
+      }
+    }
+  }
+};
+
+/** \internal
+ * Trivial specialization for 1x1 matrices
+ */
+template <typename MatrixType, bool IsComplex>
+struct tridiagonalization_inplace_selector<MatrixType, 1, IsComplex> {
+  typedef typename MatrixType::Scalar Scalar;
+
+  template <typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType, typename WorkSpaceType>
+  static EIGEN_DEVICE_FUNC void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType&, CoeffVectorType&,
+                                    WorkSpaceType&, bool extractQ) {
+    diag(0, 0) = numext::real(mat(0, 0));
+    if (extractQ) mat(0, 0) = Scalar(1);
+  }
+};
+
+/** \internal
+ * \eigenvalues_module \ingroup Eigenvalues_Module
+ *
+ * \brief Expression type for return value of Tridiagonalization::matrixT()
+ *
+ * \tparam MatrixType type of underlying dense matrix
+ */
+template <typename MatrixType>
+struct TridiagonalizationMatrixTReturnType : public ReturnByValue<TridiagonalizationMatrixTReturnType<MatrixType>> {
+ public:
+  /** \brief Constructor.
+   *
+   * \param[in] mat The underlying dense matrix
+   */
+  TridiagonalizationMatrixTReturnType(const MatrixType& mat) : m_matrix(mat) {}
+
+  template <typename ResultType>
+  inline void evalTo(ResultType& result) const {
+    result.setZero();
+    result.template diagonal<1>() = m_matrix.template diagonal<-1>().conjugate();
+    result.diagonal() = m_matrix.diagonal();
+    result.template diagonal<-1>() = m_matrix.template diagonal<-1>();
+  }
+
+  constexpr Index rows() const noexcept { return m_matrix.rows(); }
+  constexpr Index cols() const noexcept { return m_matrix.cols(); }
+
+ protected:
+  typename MatrixType::Nested m_matrix;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRIDIAGONALIZATION_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/AlignedBox.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/AlignedBox.h
new file mode 100644
index 0000000..e97a8f2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/AlignedBox.h
@@ -0,0 +1,485 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// Function void Eigen::AlignedBox::transform(const Transform& transform)
+// is provided under the following license agreement:
+//
+// Software License Agreement (BSD License)
+//
+// Copyright (c) 2011-2014, Willow Garage, Inc.
+// Copyright (c) 2014-2015, Open Source Robotics Foundation
+// All rights reserved.
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions
+// are met:
+//
+//  * Redistributions of source code must retain the above copyright
+//    notice, this list of conditions and the following disclaimer.
+//  * Redistributions in binary form must reproduce the above
+//    copyright notice, this list of conditions and the following
+//    disclaimer in the documentation and/or other materials provided
+//    with the distribution.
+//  * Neither the name of Open Source Robotics Foundation nor the names of its
+//    contributors may be used to endorse or promote products derived
+//    from this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+// FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+// COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+// BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+// LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+// LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+
+#ifndef EIGEN_ALIGNEDBOX_H
+#define EIGEN_ALIGNEDBOX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ *
+ * \class AlignedBox
+ *
+ * \brief An axis aligned box
+ *
+ * \tparam Scalar_ the type of the scalar coefficients
+ * \tparam AmbientDim_ the dimension of the ambient space, can be a compile time value or Dynamic.
+ *
+ * This class represents an axis aligned box as a pair of the minimal and maximal corners.
+ * \warning The result of most methods is undefined when applied to an empty box. You can check for empty boxes using
+ * isEmpty(). \sa alignedboxtypedefs
+ */
+template <typename Scalar_, int AmbientDim_>
+class AlignedBox {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_, AmbientDim_)
+  enum { AmbientDimAtCompileTime = AmbientDim_ };
+  typedef Scalar_ Scalar;
+  typedef NumTraits<Scalar> ScalarTraits;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+  typedef typename ScalarTraits::Real RealScalar;
+  typedef typename ScalarTraits::NonInteger NonInteger;
+  typedef Matrix<Scalar, AmbientDimAtCompileTime, 1> VectorType;
+  typedef CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const VectorType, const VectorType> VectorTypeSum;
+
+  /** Define constants to name the corners of a 1D, 2D or 3D axis aligned bounding box */
+  enum CornerType {
+    /** 1D names @{ */
+    Min = 0,
+    Max = 1,
+    /** @} */
+
+    /** Identifier for 2D corner @{ */
+    BottomLeft = 0,
+    BottomRight = 1,
+    TopLeft = 2,
+    TopRight = 3,
+    /** @} */
+
+    /** Identifier for 3D corner  @{ */
+    BottomLeftFloor = 0,
+    BottomRightFloor = 1,
+    TopLeftFloor = 2,
+    TopRightFloor = 3,
+    BottomLeftCeil = 4,
+    BottomRightCeil = 5,
+    TopLeftCeil = 6,
+    TopRightCeil = 7
+    /** @} */
+  };
+
+  /** Default constructor initializing a null box. */
+  EIGEN_DEVICE_FUNC inline AlignedBox() {
+    if (EIGEN_CONST_CONDITIONAL(AmbientDimAtCompileTime != Dynamic)) setEmpty();
+  }
+
+  /** Constructs a null box with \a _dim the dimension of the ambient space. */
+  EIGEN_DEVICE_FUNC inline explicit AlignedBox(Index _dim) : m_min(_dim), m_max(_dim) { setEmpty(); }
+
+  /** Constructs a box with extremities \a _min and \a _max.
+   * \warning If either component of \a _min is larger than the same component of \a _max, the constructed box is empty.
+   */
+  template <typename OtherVectorType1, typename OtherVectorType2>
+  EIGEN_DEVICE_FUNC inline AlignedBox(const OtherVectorType1& _min, const OtherVectorType2& _max)
+      : m_min(_min), m_max(_max) {}
+
+  /** Constructs a box containing a single point \a p. */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline explicit AlignedBox(const MatrixBase<Derived>& p) : m_min(p), m_max(m_min) {}
+
+  EIGEN_DEVICE_FUNC ~AlignedBox() {}
+
+  /** \returns the dimension in which the box holds */
+  EIGEN_DEVICE_FUNC inline Index dim() const {
+    return AmbientDimAtCompileTime == Dynamic ? m_min.size() : Index(AmbientDimAtCompileTime);
+  }
+
+  /** \deprecated use isEmpty() */
+  EIGEN_DEVICE_FUNC inline bool isNull() const { return isEmpty(); }
+
+  /** \deprecated use setEmpty() */
+  EIGEN_DEVICE_FUNC inline void setNull() { setEmpty(); }
+
+  /** \returns true if the box is empty.
+   * \sa setEmpty */
+  EIGEN_DEVICE_FUNC inline bool isEmpty() const { return (m_min.array() > m_max.array()).any(); }
+
+  /** Makes \c *this an empty box.
+   * \sa isEmpty */
+  EIGEN_DEVICE_FUNC inline void setEmpty() {
+    m_min.setConstant(ScalarTraits::highest());
+    m_max.setConstant(ScalarTraits::lowest());
+  }
+
+  /** \returns the minimal corner */
+  EIGEN_DEVICE_FUNC inline const VectorType&(min)() const { return m_min; }
+  /** \returns a non const reference to the minimal corner */
+  EIGEN_DEVICE_FUNC inline VectorType&(min)() { return m_min; }
+  /** \returns the maximal corner */
+  EIGEN_DEVICE_FUNC inline const VectorType&(max)() const { return m_max; }
+  /** \returns a non const reference to the maximal corner */
+  EIGEN_DEVICE_FUNC inline VectorType&(max)() { return m_max; }
+
+  /** \returns the center of the box */
+  EIGEN_DEVICE_FUNC inline const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(VectorTypeSum, RealScalar, quotient)
+      center() const {
+    return (m_min + m_max) / RealScalar(2);
+  }
+
+  /** \returns the lengths of the sides of the bounding box.
+   * Note that this function does not get the same
+   * result for integral or floating scalar types: see
+   */
+  EIGEN_DEVICE_FUNC inline const CwiseBinaryOp<internal::scalar_difference_op<Scalar, Scalar>, const VectorType,
+                                               const VectorType>
+  sizes() const {
+    return m_max - m_min;
+  }
+
+  /** \returns the volume of the bounding box */
+  EIGEN_DEVICE_FUNC inline Scalar volume() const { return isEmpty() ? Scalar(0) : sizes().prod(); }
+
+  /** \returns an expression for the bounding box diagonal vector
+   * if the length of the diagonal is needed: diagonal().norm()
+   * will provide it.
+   */
+  EIGEN_DEVICE_FUNC inline CwiseBinaryOp<internal::scalar_difference_op<Scalar, Scalar>, const VectorType,
+                                         const VectorType>
+  diagonal() const {
+    return sizes();
+  }
+
+  /** \returns the vertex of the bounding box at the corner defined by
+   * the corner-id corner. It works only for a 1D, 2D or 3D bounding box.
+   * For 1D bounding boxes corners are named by 2 enum constants:
+   * BottomLeft and BottomRight.
+   * For 2D bounding boxes, corners are named by 4 enum constants:
+   * BottomLeft, BottomRight, TopLeft, TopRight.
+   * For 3D bounding boxes, the following names are added:
+   * BottomLeftCeil, BottomRightCeil, TopLeftCeil, TopRightCeil.
+   */
+  EIGEN_DEVICE_FUNC inline VectorType corner(CornerType corner) const {
+    EIGEN_STATIC_ASSERT(AmbientDim_ <= 3, THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE);
+
+    VectorType res;
+
+    Index mult = 1;
+    for (Index d = 0; d < dim(); ++d) {
+      if (mult & corner)
+        res[d] = m_max[d];
+      else
+        res[d] = m_min[d];
+      mult *= 2;
+    }
+    return res;
+  }
+
+  /** \returns a random point inside the bounding box sampled with
+   * a uniform distribution */
+  EIGEN_DEVICE_FUNC inline VectorType sample() const {
+    VectorType r(dim());
+    for (Index d = 0; d < dim(); ++d) {
+      if (!ScalarTraits::IsInteger) {
+        r[d] = m_min[d] + (m_max[d] - m_min[d]) * internal::random<Scalar>(Scalar(0), Scalar(1));
+      } else
+        r[d] = internal::random(m_min[d], m_max[d]);
+    }
+    return r;
+  }
+
+  /** \returns true if the point \a p is inside the box \c *this. */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline bool contains(const MatrixBase<Derived>& p) const {
+    typename internal::nested_eval<Derived, 2>::type p_n(p.derived());
+    return (m_min.array() <= p_n.array()).all() && (p_n.array() <= m_max.array()).all();
+  }
+
+  /** \returns true if the box \a b is entirely inside the box \c *this. */
+  EIGEN_DEVICE_FUNC inline bool contains(const AlignedBox& b) const {
+    return (m_min.array() <= (b.min)().array()).all() && ((b.max)().array() <= m_max.array()).all();
+  }
+
+  /** \returns true if the box \a b is intersecting the box \c *this.
+   * \sa intersection, clamp */
+  EIGEN_DEVICE_FUNC inline bool intersects(const AlignedBox& b) const {
+    return (m_min.array() <= (b.max)().array()).all() && ((b.min)().array() <= m_max.array()).all();
+  }
+
+  /** Extends \c *this such that it contains the point \a p and returns a reference to \c *this.
+   * \sa extend(const AlignedBox&) */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline AlignedBox& extend(const MatrixBase<Derived>& p) {
+    typename internal::nested_eval<Derived, 2>::type p_n(p.derived());
+    m_min = m_min.cwiseMin(p_n);
+    m_max = m_max.cwiseMax(p_n);
+    return *this;
+  }
+
+  /** Extends \c *this such that it contains the box \a b and returns a reference to \c *this.
+   * \sa merged, extend(const MatrixBase&) */
+  EIGEN_DEVICE_FUNC inline AlignedBox& extend(const AlignedBox& b) {
+    m_min = m_min.cwiseMin(b.m_min);
+    m_max = m_max.cwiseMax(b.m_max);
+    return *this;
+  }
+
+  /** Clamps \c *this by the box \a b and returns a reference to \c *this.
+   * \note If the boxes don't intersect, the resulting box is empty.
+   * \sa intersection(), intersects() */
+  EIGEN_DEVICE_FUNC inline AlignedBox& clamp(const AlignedBox& b) {
+    m_min = m_min.cwiseMax(b.m_min);
+    m_max = m_max.cwiseMin(b.m_max);
+    return *this;
+  }
+
+  /** Returns an AlignedBox that is the intersection of \a b and \c *this
+   * \note If the boxes don't intersect, the resulting box is empty.
+   * \sa intersects(), clamp, contains()  */
+  EIGEN_DEVICE_FUNC inline AlignedBox intersection(const AlignedBox& b) const {
+    return AlignedBox(m_min.cwiseMax(b.m_min), m_max.cwiseMin(b.m_max));
+  }
+
+  /** Returns an AlignedBox that is the union of \a b and \c *this.
+   * \note Merging with an empty box may result in a box bigger than \c *this.
+   * \sa extend(const AlignedBox&) */
+  EIGEN_DEVICE_FUNC inline AlignedBox merged(const AlignedBox& b) const {
+    return AlignedBox(m_min.cwiseMin(b.m_min), m_max.cwiseMax(b.m_max));
+  }
+
+  /** Translate \c *this by the vector \a t and returns a reference to \c *this. */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline AlignedBox& translate(const MatrixBase<Derived>& a_t) {
+    const typename internal::nested_eval<Derived, 2>::type t(a_t.derived());
+    m_min += t;
+    m_max += t;
+    return *this;
+  }
+
+  /** \returns a copy of \c *this translated by the vector \a t. */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline AlignedBox translated(const MatrixBase<Derived>& a_t) const {
+    AlignedBox result(m_min, m_max);
+    result.translate(a_t);
+    return result;
+  }
+
+  /** \returns the squared distance between the point \a p and the box \c *this,
+   * and zero if \a p is inside the box.
+   * \sa exteriorDistance(const MatrixBase&), squaredExteriorDistance(const AlignedBox&)
+   */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline Scalar squaredExteriorDistance(const MatrixBase<Derived>& p) const;
+
+  /** \returns the squared distance between the boxes \a b and \c *this,
+   * and zero if the boxes intersect.
+   * \sa exteriorDistance(const AlignedBox&), squaredExteriorDistance(const MatrixBase&)
+   */
+  EIGEN_DEVICE_FUNC inline Scalar squaredExteriorDistance(const AlignedBox& b) const;
+
+  /** \returns the distance between the point \a p and the box \c *this,
+   * and zero if \a p is inside the box.
+   * \sa squaredExteriorDistance(const MatrixBase&), exteriorDistance(const AlignedBox&)
+   */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline NonInteger exteriorDistance(const MatrixBase<Derived>& p) const {
+    EIGEN_USING_STD(sqrt) return sqrt(NonInteger(squaredExteriorDistance(p)));
+  }
+
+  /** \returns the distance between the boxes \a b and \c *this,
+   * and zero if the boxes intersect.
+   * \sa squaredExteriorDistance(const AlignedBox&), exteriorDistance(const MatrixBase&)
+   */
+  EIGEN_DEVICE_FUNC inline NonInteger exteriorDistance(const AlignedBox& b) const {
+    EIGEN_USING_STD(sqrt) return sqrt(NonInteger(squaredExteriorDistance(b)));
+  }
+
+  /**
+   * Specialization of transform for pure translation.
+   */
+  template <int Mode, int Options>
+  EIGEN_DEVICE_FUNC inline void transform(
+      const typename Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>::TranslationType& translation) {
+    this->translate(translation);
+  }
+
+  /**
+   * Transforms this box by \a transform and recomputes it to
+   * still be an axis-aligned box.
+   *
+   * \note This method is provided under BSD license (see the top of this file).
+   */
+  template <int Mode, int Options>
+  EIGEN_DEVICE_FUNC inline void transform(const Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>& transform) {
+    // Only Affine and Isometry transforms are currently supported.
+    EIGEN_STATIC_ASSERT(Mode == Affine || Mode == AffineCompact || Mode == Isometry,
+                        THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS);
+
+    // Method adapted from FCL src/shape/geometric_shapes_utility.cpp#computeBV<AABB, Box>(...)
+    // https://github.com/flexible-collision-library/fcl/blob/fcl-0.4/src/shape/geometric_shapes_utility.cpp#L292
+    //
+    // Here's a nice explanation why it works: https://zeuxcg.org/2010/10/17/aabb-from-obb-with-component-wise-abs/
+
+    // two times rotated extent
+    const VectorType rotated_extent_2 = transform.linear().cwiseAbs() * sizes();
+    // two times new center
+    const VectorType rotated_center_2 =
+        transform.linear() * (this->m_max + this->m_min) + Scalar(2) * transform.translation();
+
+    this->m_max = (rotated_center_2 + rotated_extent_2) / Scalar(2);
+    this->m_min = (rotated_center_2 - rotated_extent_2) / Scalar(2);
+  }
+
+  /**
+   * \returns a copy of \c *this transformed by \a transform and recomputed to
+   * still be an axis-aligned box.
+   */
+  template <int Mode, int Options>
+  EIGEN_DEVICE_FUNC AlignedBox
+  transformed(const Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>& transform) const {
+    AlignedBox result(m_min, m_max);
+    result.transform(transform);
+    return result;
+  }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+   *
+   * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+   * then this function smartly returns a const reference to \c *this.
+   */
+  template <typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline
+      typename internal::cast_return_type<AlignedBox, AlignedBox<NewScalarType, AmbientDimAtCompileTime> >::type
+      cast() const {
+    return typename internal::cast_return_type<AlignedBox, AlignedBox<NewScalarType, AmbientDimAtCompileTime> >::type(
+        *this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template <typename OtherScalarType>
+  EIGEN_DEVICE_FUNC inline explicit AlignedBox(const AlignedBox<OtherScalarType, AmbientDimAtCompileTime>& other) {
+    m_min = (other.min)().template cast<Scalar>();
+    m_max = (other.max)().template cast<Scalar>();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+   * determined by \a prec.
+   *
+   * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const AlignedBox& other,
+                                  const RealScalar& prec = ScalarTraits::dummy_precision()) const {
+    return m_min.isApprox(other.m_min, prec) && m_max.isApprox(other.m_max, prec);
+  }
+
+ protected:
+  VectorType m_min, m_max;
+};
+
+template <typename Scalar, int AmbientDim>
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline Scalar AlignedBox<Scalar, AmbientDim>::squaredExteriorDistance(
+    const MatrixBase<Derived>& a_p) const {
+  typename internal::nested_eval<Derived, 2 * AmbientDim>::type p(a_p.derived());
+  Scalar dist2(0);
+  Scalar aux;
+  for (Index k = 0; k < dim(); ++k) {
+    if (m_min[k] > p[k]) {
+      aux = m_min[k] - p[k];
+      dist2 += aux * aux;
+    } else if (p[k] > m_max[k]) {
+      aux = p[k] - m_max[k];
+      dist2 += aux * aux;
+    }
+  }
+  return dist2;
+}
+
+template <typename Scalar, int AmbientDim>
+EIGEN_DEVICE_FUNC inline Scalar AlignedBox<Scalar, AmbientDim>::squaredExteriorDistance(const AlignedBox& b) const {
+  Scalar dist2(0);
+  Scalar aux;
+  for (Index k = 0; k < dim(); ++k) {
+    if (m_min[k] > b.m_max[k]) {
+      aux = m_min[k] - b.m_max[k];
+      dist2 += aux * aux;
+    } else if (b.m_min[k] > m_max[k]) {
+      aux = b.m_min[k] - m_max[k];
+      dist2 += aux * aux;
+    }
+  }
+  return dist2;
+}
+
+/** \defgroup alignedboxtypedefs Global aligned box typedefs
+ *
+ * \ingroup Geometry_Module
+ *
+ * Eigen defines several typedef shortcuts for most common aligned box types.
+ *
+ * The general patterns are the following:
+ *
+ * \c AlignedBoxSizeType where \c Size can be \c 1, \c 2,\c 3,\c 4 for fixed size boxes or \c X for dynamic size,
+ * and where \c Type can be \c i for integer, \c f for float, \c d for double.
+ *
+ * For example, \c AlignedBox3d is a fixed-size 3x3 aligned box type of doubles, and \c AlignedBoxXf is a dynamic-size
+ * aligned box of floats.
+ *
+ * \sa class AlignedBox
+ */
+
+#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \
+  /** \ingroup alignedboxtypedefs */                            \
+  typedef AlignedBox<Type, Size> AlignedBox##SizeSuffix##TypeSuffix;
+
+#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
+  EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 1, 1)           \
+  EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2)           \
+  EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3)           \
+  EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4)           \
+  EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X)
+
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int, i)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float, f)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double, d)
+
+#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES
+#undef EIGEN_MAKE_TYPEDEFS
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ALIGNEDBOX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/AngleAxis.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/AngleAxis.h
new file mode 100644
index 0000000..a00ed17
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/AngleAxis.h
@@ -0,0 +1,245 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ANGLEAXIS_H
+#define EIGEN_ANGLEAXIS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \class AngleAxis
+ *
+ * \brief Represents a 3D rotation as a rotation angle around an arbitrary 3D axis
+ *
+ * \param Scalar_ the scalar type, i.e., the type of the coefficients.
+ *
+ * \warning When setting up an AngleAxis object, the axis vector \b must \b be \b normalized.
+ *
+ * The following two typedefs are provided for convenience:
+ * \li \c AngleAxisf for \c float
+ * \li \c AngleAxisd for \c double
+ *
+ * Combined with MatrixBase::Unit{X,Y,Z}, AngleAxis can be used to easily
+ * mimic Euler-angles. Here is an example:
+ * \include AngleAxis_mimic_euler.cpp
+ * Output: \verbinclude AngleAxis_mimic_euler.out
+ *
+ * \note This class is not aimed to be used to store a rotation transformation,
+ * but rather to make easier the creation of other rotation (Quaternion, rotation Matrix)
+ * and transformation objects.
+ *
+ * \sa class Quaternion, class Transform, MatrixBase::UnitX()
+ */
+
+namespace internal {
+template <typename Scalar_>
+struct traits<AngleAxis<Scalar_> > {
+  typedef Scalar_ Scalar;
+};
+}  // namespace internal
+
+template <typename Scalar_>
+class AngleAxis : public RotationBase<AngleAxis<Scalar_>, 3> {
+  typedef RotationBase<AngleAxis<Scalar_>, 3> Base;
+
+ public:
+  using Base::operator*;
+
+  enum { Dim = 3 };
+  /** the scalar type of the coefficients */
+  typedef Scalar_ Scalar;
+  typedef Matrix<Scalar, 3, 3> Matrix3;
+  typedef Matrix<Scalar, 3, 1> Vector3;
+  typedef Quaternion<Scalar> QuaternionType;
+
+ protected:
+  Vector3 m_axis;
+  Scalar m_angle;
+
+ public:
+  /** Default constructor without initialization. */
+  EIGEN_DEVICE_FUNC AngleAxis() {}
+  /** Constructs and initialize the angle-axis rotation from an \a angle in radian
+   * and an \a axis which \b must \b be \b normalized.
+   *
+   * \warning If the \a axis vector is not normalized, then the angle-axis object
+   *          represents an invalid rotation. */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline AngleAxis(const Scalar& angle, const MatrixBase<Derived>& axis)
+      : m_axis(axis), m_angle(angle) {}
+  /** Constructs and initialize the angle-axis rotation from a quaternion \a q.
+   * This function implicitly normalizes the quaternion \a q.
+   */
+  template <typename QuatDerived>
+  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const QuaternionBase<QuatDerived>& q) {
+    *this = q;
+  }
+  /** Constructs and initialize the angle-axis rotation from a 3x3 rotation matrix. */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const MatrixBase<Derived>& m) {
+    *this = m;
+  }
+
+  /** \returns the value of the rotation angle in radian */
+  EIGEN_DEVICE_FUNC Scalar angle() const { return m_angle; }
+  /** \returns a read-write reference to the stored angle in radian */
+  EIGEN_DEVICE_FUNC Scalar& angle() { return m_angle; }
+
+  /** \returns the rotation axis */
+  EIGEN_DEVICE_FUNC const Vector3& axis() const { return m_axis; }
+  /** \returns a read-write reference to the stored rotation axis.
+   *
+   * \warning The rotation axis must remain a \b unit vector.
+   */
+  EIGEN_DEVICE_FUNC Vector3& axis() { return m_axis; }
+
+  /** Concatenates two rotations */
+  EIGEN_DEVICE_FUNC inline QuaternionType operator*(const AngleAxis& other) const {
+    return QuaternionType(*this) * QuaternionType(other);
+  }
+
+  /** Concatenates two rotations */
+  EIGEN_DEVICE_FUNC inline QuaternionType operator*(const QuaternionType& other) const {
+    return QuaternionType(*this) * other;
+  }
+
+  /** Concatenates two rotations */
+  friend EIGEN_DEVICE_FUNC inline QuaternionType operator*(const QuaternionType& a, const AngleAxis& b) {
+    return a * QuaternionType(b);
+  }
+
+  /** \returns the inverse rotation, i.e., an angle-axis with opposite rotation angle */
+  EIGEN_DEVICE_FUNC AngleAxis inverse() const { return AngleAxis(-m_angle, m_axis); }
+
+  template <class QuatDerived>
+  EIGEN_DEVICE_FUNC AngleAxis& operator=(const QuaternionBase<QuatDerived>& q);
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC AngleAxis& operator=(const MatrixBase<Derived>& m);
+
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC AngleAxis& fromRotationMatrix(const MatrixBase<Derived>& m);
+  EIGEN_DEVICE_FUNC Matrix3 toRotationMatrix(void) const;
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+   *
+   * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+   * then this function smartly returns a const reference to \c *this.
+   */
+  template <typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<AngleAxis, AngleAxis<NewScalarType> >::type cast()
+      const {
+    return typename internal::cast_return_type<AngleAxis, AngleAxis<NewScalarType> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template <typename OtherScalarType>
+  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const AngleAxis<OtherScalarType>& other) {
+    m_axis = other.axis().template cast<Scalar>();
+    m_angle = Scalar(other.angle());
+  }
+
+  EIGEN_DEVICE_FUNC static inline const AngleAxis Identity() { return AngleAxis(Scalar(0), Vector3::UnitX()); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+   * determined by \a prec.
+   *
+   * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const AngleAxis& other, const typename NumTraits<Scalar>::Real& prec =
+                                                              NumTraits<Scalar>::dummy_precision()) const {
+    return m_axis.isApprox(other.m_axis, prec) && internal::isApprox(m_angle, other.m_angle, prec);
+  }
+};
+
+/** \ingroup Geometry_Module
+ * single precision angle-axis type */
+typedef AngleAxis<float> AngleAxisf;
+/** \ingroup Geometry_Module
+ * double precision angle-axis type */
+typedef AngleAxis<double> AngleAxisd;
+
+/** Set \c *this from a \b unit quaternion.
+ *
+ * The resulting axis is normalized, and the computed angle is in the [0,pi] range.
+ *
+ * This function implicitly normalizes the quaternion \a q.
+ */
+template <typename Scalar>
+template <typename QuatDerived>
+EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const QuaternionBase<QuatDerived>& q) {
+  EIGEN_USING_STD(atan2)
+  EIGEN_USING_STD(abs)
+  Scalar n = q.vec().norm();
+  if (n < NumTraits<Scalar>::epsilon()) n = q.vec().stableNorm();
+
+  if (n != Scalar(0)) {
+    m_angle = Scalar(2) * atan2(n, abs(q.w()));
+    if (q.w() < Scalar(0)) n = -n;
+    m_axis = q.vec() / n;
+  } else {
+    m_angle = Scalar(0);
+    m_axis << Scalar(1), Scalar(0), Scalar(0);
+  }
+  return *this;
+}
+
+/** Set \c *this from a 3x3 rotation matrix \a mat.
+ */
+template <typename Scalar>
+template <typename Derived>
+EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const MatrixBase<Derived>& mat) {
+  // Since a direct conversion would not be really faster,
+  // let's use the robust Quaternion implementation:
+  return *this = QuaternionType(mat);
+}
+
+/**
+ * \brief Sets \c *this from a 3x3 rotation matrix.
+ **/
+template <typename Scalar>
+template <typename Derived>
+EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat) {
+  return *this = QuaternionType(mat);
+}
+
+/** Constructs and \returns an equivalent 3x3 rotation matrix.
+ */
+template <typename Scalar>
+typename AngleAxis<Scalar>::Matrix3 EIGEN_DEVICE_FUNC AngleAxis<Scalar>::toRotationMatrix(void) const {
+  EIGEN_USING_STD(sin)
+  EIGEN_USING_STD(cos)
+  Matrix3 res;
+  Vector3 sin_axis = sin(m_angle) * m_axis;
+  Scalar c = cos(m_angle);
+  Vector3 cos1_axis = (Scalar(1) - c) * m_axis;
+
+  Scalar tmp;
+  tmp = cos1_axis.x() * m_axis.y();
+  res.coeffRef(0, 1) = tmp - sin_axis.z();
+  res.coeffRef(1, 0) = tmp + sin_axis.z();
+
+  tmp = cos1_axis.x() * m_axis.z();
+  res.coeffRef(0, 2) = tmp + sin_axis.y();
+  res.coeffRef(2, 0) = tmp - sin_axis.y();
+
+  tmp = cos1_axis.y() * m_axis.z();
+  res.coeffRef(1, 2) = tmp - sin_axis.x();
+  res.coeffRef(2, 1) = tmp + sin_axis.x();
+
+  res.diagonal() = (cos1_axis.cwiseProduct(m_axis)).array() + c;
+
+  return res;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ANGLEAXIS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/EulerAngles.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/EulerAngles.h
new file mode 100644
index 0000000..366a32c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/EulerAngles.h
@@ -0,0 +1,203 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2023 Juraj Oršulić, University of Zagreb <juraj.orsulic@fer.hr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EULERANGLES_H
+#define EIGEN_EULERANGLES_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ *
+ * \returns the canonical Euler-angles of the rotation matrix \c *this using the convention defined by the triplet (\a
+ * a0,\a a1,\a a2)
+ *
+ * Each of the three parameters \a a0,\a a1,\a a2 represents the respective rotation axis as an integer in {0,1,2}.
+ * For instance, in:
+ * \code Vector3f ea = mat.eulerAngles(2, 0, 2); \endcode
+ * "2" represents the z axis and "0" the x axis, etc. The returned angles are such that
+ * we have the following equality:
+ * \code
+ * mat == AngleAxisf(ea[0], Vector3f::UnitZ())
+ *      * AngleAxisf(ea[1], Vector3f::UnitX())
+ *      * AngleAxisf(ea[2], Vector3f::UnitZ()); \endcode
+ * This corresponds to the right-multiply conventions (with right hand side frames).
+ *
+ * For Tait-Bryan angle configurations (a0 != a2), the returned angles are in the ranges [-pi:pi]x[-pi/2:pi/2]x[-pi:pi].
+ * For proper Euler angle configurations (a0 == a2), the returned angles are in the ranges [-pi:pi]x[0:pi]x[-pi:pi].
+ *
+ * The approach used is also described here:
+ * https://d3cw3dd2w32x2b.cloudfront.net/wp-content/uploads/2012/07/euler-angles.pdf
+ *
+ * \sa class AngleAxis
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline Matrix<typename MatrixBase<Derived>::Scalar, 3, 1> MatrixBase<Derived>::canonicalEulerAngles(
+    Index a0, Index a1, Index a2) const {
+  /* Implemented from Graphics Gems IV */
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Derived, 3, 3)
+
+  Matrix<Scalar, 3, 1> res;
+
+  const Index odd = ((a0 + 1) % 3 == a1) ? 0 : 1;
+  const Index i = a0;
+  const Index j = (a0 + 1 + odd) % 3;
+  const Index k = (a0 + 2 - odd) % 3;
+
+  if (a0 == a2) {
+    // Proper Euler angles (same first and last axis).
+    // The i, j, k indices enable addressing the input matrix as the XYX archetype matrix (see Graphics Gems IV),
+    // where e.g. coeff(k, i) means third column, first row in the XYX archetype matrix:
+    //  c2      s2s1              s2c1
+    //  s2s3   -c2s1s3 + c1c3    -c2c1s3 - s1c3
+    // -s2c3    c2s1c3 + c1s3     c2c1c3 - s1s3
+
+    // Note: s2 is always positive.
+    Scalar s2 = numext::hypot(coeff(j, i), coeff(k, i));
+    if (odd) {
+      res[0] = numext::atan2(coeff(j, i), coeff(k, i));
+      // s2 is always positive, so res[1] will be within the canonical [0, pi] range
+      res[1] = numext::atan2(s2, coeff(i, i));
+    } else {
+      // In the !odd case, signs of all three angles are flipped at the very end. To keep the solution within the
+      // canonical range, we flip the solution and make res[1] always negative here (since s2 is always positive,
+      // -atan2(s2, c2) will always be negative). The final flip at the end due to !odd will thus make res[1] positive
+      // and canonical. NB: in the general case, there are two correct solutions, but only one is canonical. For proper
+      // Euler angles, flipping from one solution to the other involves flipping the sign of the second angle res[1] and
+      // adding/subtracting pi to the first and third angles. The addition/subtraction of pi to the first angle res[0]
+      // is handled here by flipping the signs of arguments to atan2, while the calculation of the third angle does not
+      // need special adjustment since it uses the adjusted res[0] as the input and produces a correct result.
+      res[0] = numext::atan2(-coeff(j, i), -coeff(k, i));
+      res[1] = -numext::atan2(s2, coeff(i, i));
+    }
+
+    // With a=(0,1,0), we have i=0; j=1; k=2, and after computing the first two angles,
+    // we can compute their respective rotation, and apply its inverse to M. Since the result must
+    // be a rotation around x, we have:
+    //
+    //  c2  s1.s2 c1.s2                   1  0   0
+    //  0   c1    -s1       *    M    =   0  c3  s3
+    //  -s2 s1.c2 c1.c2                   0 -s3  c3
+    //
+    //  Thus:  m11.c1 - m21.s1 = c3  &   m12.c1 - m22.s1 = s3
+
+    Scalar s1 = numext::sin(res[0]);
+    Scalar c1 = numext::cos(res[0]);
+    res[2] = numext::atan2(c1 * coeff(j, k) - s1 * coeff(k, k), c1 * coeff(j, j) - s1 * coeff(k, j));
+  } else {
+    // Tait-Bryan angles (all three axes are different; typically used for yaw-pitch-roll calculations).
+    // The i, j, k indices enable addressing the input matrix as the XYZ archetype matrix (see Graphics Gems IV),
+    // where e.g. coeff(k, i) means third column, first row in the XYZ archetype matrix:
+    //  c2c3    s2s1c3 - c1s3     s2c1c3 + s1s3
+    //  c2s3    s2s1s3 + c1c3     s2c1s3 - s1c3
+    // -s2      c2s1              c2c1
+
+    res[0] = numext::atan2(coeff(j, k), coeff(k, k));
+
+    Scalar c2 = numext::hypot(coeff(i, i), coeff(i, j));
+    // c2 is always positive, so the following atan2 will always return a result in the correct canonical middle angle
+    // range [-pi/2, pi/2]
+    res[1] = numext::atan2(-coeff(i, k), c2);
+
+    Scalar s1 = numext::sin(res[0]);
+    Scalar c1 = numext::cos(res[0]);
+    res[2] = numext::atan2(s1 * coeff(k, i) - c1 * coeff(j, i), c1 * coeff(j, j) - s1 * coeff(k, j));
+  }
+  if (!odd) {
+    res = -res;
+  }
+
+  return res;
+}
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ *
+ * \returns the Euler-angles of the rotation matrix \c *this using the convention defined by the triplet (\a a0,\a a1,\a
+ * a2)
+ *
+ * NB: The returned angles are in non-canonical ranges [0:pi]x[-pi:pi]x[-pi:pi]. For canonical Tait-Bryan/proper Euler
+ * ranges, use canonicalEulerAngles.
+ *
+ * \sa MatrixBase::canonicalEulerAngles
+ * \sa class AngleAxis
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline Matrix<typename MatrixBase<Derived>::Scalar, 3, 1> MatrixBase<Derived>::eulerAngles(
+    Index a0, Index a1, Index a2) const {
+  /* Implemented from Graphics Gems IV */
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Derived, 3, 3)
+
+  Matrix<Scalar, 3, 1> res;
+
+  const Index odd = ((a0 + 1) % 3 == a1) ? 0 : 1;
+  const Index i = a0;
+  const Index j = (a0 + 1 + odd) % 3;
+  const Index k = (a0 + 2 - odd) % 3;
+
+  if (a0 == a2) {
+    res[0] = numext::atan2(coeff(j, i), coeff(k, i));
+    if ((odd && res[0] < Scalar(0)) || ((!odd) && res[0] > Scalar(0))) {
+      if (res[0] > Scalar(0)) {
+        res[0] -= Scalar(EIGEN_PI);
+      } else {
+        res[0] += Scalar(EIGEN_PI);
+      }
+
+      Scalar s2 = numext::hypot(coeff(j, i), coeff(k, i));
+      res[1] = -numext::atan2(s2, coeff(i, i));
+    } else {
+      Scalar s2 = numext::hypot(coeff(j, i), coeff(k, i));
+      res[1] = numext::atan2(s2, coeff(i, i));
+    }
+
+    // With a=(0,1,0), we have i=0; j=1; k=2, and after computing the first two angles,
+    // we can compute their respective rotation, and apply its inverse to M. Since the result must
+    // be a rotation around x, we have:
+    //
+    //  c2  s1.s2 c1.s2                   1  0   0
+    //  0   c1    -s1       *    M    =   0  c3  s3
+    //  -s2 s1.c2 c1.c2                   0 -s3  c3
+    //
+    //  Thus:  m11.c1 - m21.s1 = c3  &   m12.c1 - m22.s1 = s3
+
+    Scalar s1 = numext::sin(res[0]);
+    Scalar c1 = numext::cos(res[0]);
+    res[2] = numext::atan2(c1 * coeff(j, k) - s1 * coeff(k, k), c1 * coeff(j, j) - s1 * coeff(k, j));
+  } else {
+    res[0] = numext::atan2(coeff(j, k), coeff(k, k));
+    Scalar c2 = numext::hypot(coeff(i, i), coeff(i, j));
+    if ((odd && res[0] < Scalar(0)) || ((!odd) && res[0] > Scalar(0))) {
+      if (res[0] > Scalar(0)) {
+        res[0] -= Scalar(EIGEN_PI);
+      } else {
+        res[0] += Scalar(EIGEN_PI);
+      }
+      res[1] = numext::atan2(-coeff(i, k), -c2);
+    } else {
+      res[1] = numext::atan2(-coeff(i, k), c2);
+    }
+    Scalar s1 = numext::sin(res[0]);
+    Scalar c1 = numext::cos(res[0]);
+    res[2] = numext::atan2(s1 * coeff(k, i) - c1 * coeff(j, i), c1 * coeff(j, j) - s1 * coeff(k, j));
+  }
+  if (!odd) {
+    res = -res;
+  }
+
+  return res;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_EULERANGLES_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Homogeneous.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Homogeneous.h
new file mode 100644
index 0000000..b304b67
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Homogeneous.h
@@ -0,0 +1,452 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HOMOGENEOUS_H
+#define EIGEN_HOMOGENEOUS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \class Homogeneous
+ *
+ * \brief Expression of one (or a set of) homogeneous vector(s)
+ *
+ * \param MatrixType the type of the object in which we are making homogeneous
+ *
+ * This class represents an expression of one (or a set of) homogeneous vector(s).
+ * It is the return type of MatrixBase::homogeneous() and most of the time
+ * this is the only way it is used.
+ *
+ * \sa MatrixBase::homogeneous()
+ */
+
+namespace internal {
+
+template <typename MatrixType, int Direction>
+struct traits<Homogeneous<MatrixType, Direction> > : traits<MatrixType> {
+  typedef typename traits<MatrixType>::StorageKind StorageKind;
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+  enum {
+    RowsPlusOne = (MatrixType::RowsAtCompileTime != Dynamic) ? int(MatrixType::RowsAtCompileTime) + 1 : Dynamic,
+    ColsPlusOne = (MatrixType::ColsAtCompileTime != Dynamic) ? int(MatrixType::ColsAtCompileTime) + 1 : Dynamic,
+    RowsAtCompileTime = Direction == Vertical ? RowsPlusOne : MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = Direction == Horizontal ? ColsPlusOne : MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    TmpFlags = MatrixTypeNested_::Flags & HereditaryBits,
+    Flags = ColsAtCompileTime == 1   ? (TmpFlags & ~RowMajorBit)
+            : RowsAtCompileTime == 1 ? (TmpFlags | RowMajorBit)
+                                     : TmpFlags
+  };
+};
+
+template <typename MatrixType, typename Lhs>
+struct homogeneous_left_product_impl;
+template <typename MatrixType, typename Rhs>
+struct homogeneous_right_product_impl;
+
+}  // end namespace internal
+
+template <typename MatrixType, int Direction_>
+class Homogeneous : public MatrixBase<Homogeneous<MatrixType, Direction_> >, internal::no_assignment_operator {
+ public:
+  typedef MatrixType NestedExpression;
+  enum { Direction = Direction_ };
+
+  typedef MatrixBase<Homogeneous> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Homogeneous)
+
+  EIGEN_DEVICE_FUNC explicit inline Homogeneous(const MatrixType& matrix) : m_matrix(matrix) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept {
+    return m_matrix.rows() + (int(Direction) == Vertical ? 1 : 0);
+  }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept {
+    return m_matrix.cols() + (int(Direction) == Horizontal ? 1 : 0);
+  }
+
+  EIGEN_DEVICE_FUNC const NestedExpression& nestedExpression() const { return m_matrix; }
+
+  template <typename Rhs>
+  EIGEN_DEVICE_FUNC inline const Product<Homogeneous, Rhs> operator*(const MatrixBase<Rhs>& rhs) const {
+    return Product<Homogeneous, Rhs>(*this, rhs.derived());
+  }
+
+  template <typename Lhs>
+  friend EIGEN_DEVICE_FUNC inline const Product<Lhs, Homogeneous> operator*(const MatrixBase<Lhs>& lhs,
+                                                                            const Homogeneous& rhs) {
+    return Product<Lhs, Homogeneous>(lhs.derived(), rhs);
+  }
+
+  template <typename Scalar, int Dim, int Mode, int Options>
+  friend EIGEN_DEVICE_FUNC inline const Product<Transform<Scalar, Dim, Mode, Options>, Homogeneous> operator*(
+      const Transform<Scalar, Dim, Mode, Options>& lhs, const Homogeneous& rhs) {
+    eigen_assert(int(Direction) == Vertical);
+    return Product<Transform<Scalar, Dim, Mode, Options>, Homogeneous>(lhs, rhs);
+  }
+
+  template <typename Func>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::result_of<Func(Scalar, Scalar)>::type redux(
+      const Func& func) const {
+    return func(m_matrix.redux(func), Scalar(1));
+  }
+
+ protected:
+  typename MatrixType::Nested m_matrix;
+};
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \returns a vector expression that is one longer than the vector argument, with the value 1 symbolically appended as
+ * the last coefficient.
+ *
+ * This can be used to convert affine coordinates to homogeneous coordinates.
+ *
+ * \only_for_vectors
+ *
+ * Example: \include MatrixBase_homogeneous.cpp
+ * Output: \verbinclude MatrixBase_homogeneous.out
+ *
+ * \sa VectorwiseOp::homogeneous(), class Homogeneous
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::HomogeneousReturnType MatrixBase<Derived>::homogeneous() const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return HomogeneousReturnType(derived());
+}
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \returns an expression where the value 1 is symbolically appended as the final coefficient to each column (or row) of
+ * the matrix.
+ *
+ * This can be used to convert affine coordinates to homogeneous coordinates.
+ *
+ * Example: \include VectorwiseOp_homogeneous.cpp
+ * Output: \verbinclude VectorwiseOp_homogeneous.out
+ *
+ * \sa MatrixBase::homogeneous(), class Homogeneous */
+template <typename ExpressionType, int Direction>
+EIGEN_DEVICE_FUNC inline Homogeneous<ExpressionType, Direction> VectorwiseOp<ExpressionType, Direction>::homogeneous()
+    const {
+  return HomogeneousReturnType(_expression());
+}
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \brief homogeneous normalization
+  *
+  * \returns a vector expression of the N-1 first coefficients of \c *this divided by that last coefficient.
+  *
+  * This can be used to convert homogeneous coordinates to affine coordinates.
+  *
+  * It is essentially a shortcut for:
+  * \code
+    this->head(this->size()-1)/this->coeff(this->size()-1);
+    \endcode
+  *
+  * Example: \include MatrixBase_hnormalized.cpp
+  * Output: \verbinclude MatrixBase_hnormalized.out
+  *
+  * \sa VectorwiseOp::hnormalized() */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline const typename MatrixBase<Derived>::HNormalizedReturnType MatrixBase<Derived>::hnormalized()
+    const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return ConstStartMinusOne(derived(), 0, 0, ColsAtCompileTime == 1 ? size() - 1 : 1,
+                            ColsAtCompileTime == 1 ? 1 : size() - 1) /
+         coeff(size() - 1);
+}
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \brief column or row-wise homogeneous normalization
+ *
+ * \returns an expression of the first N-1 coefficients of each column (or row) of \c *this divided by the last
+ * coefficient of each column (or row).
+ *
+ * This can be used to convert homogeneous coordinates to affine coordinates.
+ *
+ * It is conceptually equivalent to calling MatrixBase::hnormalized() to each column (or row) of \c *this.
+ *
+ * Example: \include DirectionWise_hnormalized.cpp
+ * Output: \verbinclude DirectionWise_hnormalized.out
+ *
+ * \sa MatrixBase::hnormalized() */
+template <typename ExpressionType, int Direction>
+EIGEN_DEVICE_FUNC inline const typename VectorwiseOp<ExpressionType, Direction>::HNormalizedReturnType
+VectorwiseOp<ExpressionType, Direction>::hnormalized() const {
+  return HNormalized_Block(_expression(), 0, 0, Direction == Vertical ? _expression().rows() - 1 : _expression().rows(),
+                           Direction == Horizontal ? _expression().cols() - 1 : _expression().cols())
+      .cwiseQuotient(Replicate < HNormalized_Factors, Direction == Vertical ? HNormalized_SizeMinusOne : 1,
+                     Direction == Horizontal
+                         ? HNormalized_SizeMinusOne
+                         : 1 > (HNormalized_Factors(_expression(), Direction == Vertical ? _expression().rows() - 1 : 0,
+                                                    Direction == Horizontal ? _expression().cols() - 1 : 0,
+                                                    Direction == Vertical ? 1 : _expression().rows(),
+                                                    Direction == Horizontal ? 1 : _expression().cols()),
+                                Direction == Vertical ? _expression().rows() - 1 : 1,
+                                Direction == Horizontal ? _expression().cols() - 1 : 1));
+}
+
+namespace internal {
+
+template <typename MatrixOrTransformType>
+struct take_matrix_for_product {
+  typedef MatrixOrTransformType type;
+  EIGEN_DEVICE_FUNC static const type& run(const type& x) { return x; }
+};
+
+template <typename Scalar, int Dim, int Mode, int Options>
+struct take_matrix_for_product<Transform<Scalar, Dim, Mode, Options> > {
+  typedef Transform<Scalar, Dim, Mode, Options> TransformType;
+  typedef std::add_const_t<typename TransformType::ConstAffinePart> type;
+  EIGEN_DEVICE_FUNC static type run(const TransformType& x) { return x.affine(); }
+};
+
+template <typename Scalar, int Dim, int Options>
+struct take_matrix_for_product<Transform<Scalar, Dim, Projective, Options> > {
+  typedef Transform<Scalar, Dim, Projective, Options> TransformType;
+  typedef typename TransformType::MatrixType type;
+  EIGEN_DEVICE_FUNC static const type& run(const TransformType& x) { return x.matrix(); }
+};
+
+template <typename MatrixType, typename Lhs>
+struct traits<homogeneous_left_product_impl<Homogeneous<MatrixType, Vertical>, Lhs> > {
+  typedef typename take_matrix_for_product<Lhs>::type LhsMatrixType;
+  typedef remove_all_t<MatrixType> MatrixTypeCleaned;
+  typedef remove_all_t<LhsMatrixType> LhsMatrixTypeCleaned;
+  typedef typename make_proper_matrix_type<
+      typename traits<MatrixTypeCleaned>::Scalar, LhsMatrixTypeCleaned::RowsAtCompileTime,
+      MatrixTypeCleaned::ColsAtCompileTime, MatrixTypeCleaned::PlainObject::Options,
+      LhsMatrixTypeCleaned::MaxRowsAtCompileTime, MatrixTypeCleaned::MaxColsAtCompileTime>::type ReturnType;
+};
+
+template <typename MatrixType, typename Lhs>
+struct homogeneous_left_product_impl<Homogeneous<MatrixType, Vertical>, Lhs>
+    : public ReturnByValue<homogeneous_left_product_impl<Homogeneous<MatrixType, Vertical>, Lhs> > {
+  typedef typename traits<homogeneous_left_product_impl>::LhsMatrixType LhsMatrixType;
+  typedef remove_all_t<LhsMatrixType> LhsMatrixTypeCleaned;
+  typedef remove_all_t<typename LhsMatrixTypeCleaned::Nested> LhsMatrixTypeNested;
+  EIGEN_DEVICE_FUNC homogeneous_left_product_impl(const Lhs& lhs, const MatrixType& rhs)
+      : m_lhs(take_matrix_for_product<Lhs>::run(lhs)), m_rhs(rhs) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_lhs.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_rhs.cols(); }
+
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC void evalTo(Dest& dst) const {
+    // FIXME: investigate how to allow lazy evaluation of this product when possible.
+    dst = Block < const LhsMatrixTypeNested, LhsMatrixTypeNested::RowsAtCompileTime,
+    LhsMatrixTypeNested::ColsAtCompileTime == Dynamic
+        ? Dynamic
+        : LhsMatrixTypeNested::ColsAtCompileTime - 1 > (m_lhs, 0, 0, m_lhs.rows(), m_lhs.cols() - 1) * m_rhs;
+    dst += m_lhs.col(m_lhs.cols() - 1).rowwise().template replicate<MatrixType::ColsAtCompileTime>(m_rhs.cols());
+  }
+
+  typename LhsMatrixTypeCleaned::Nested m_lhs;
+  typename MatrixType::Nested m_rhs;
+};
+
+template <typename MatrixType, typename Rhs>
+struct traits<homogeneous_right_product_impl<Homogeneous<MatrixType, Horizontal>, Rhs> > {
+  typedef
+      typename make_proper_matrix_type<typename traits<MatrixType>::Scalar, MatrixType::RowsAtCompileTime,
+                                       Rhs::ColsAtCompileTime, MatrixType::PlainObject::Options,
+                                       MatrixType::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime>::type ReturnType;
+};
+
+template <typename MatrixType, typename Rhs>
+struct homogeneous_right_product_impl<Homogeneous<MatrixType, Horizontal>, Rhs>
+    : public ReturnByValue<homogeneous_right_product_impl<Homogeneous<MatrixType, Horizontal>, Rhs> > {
+  typedef remove_all_t<typename Rhs::Nested> RhsNested;
+  EIGEN_DEVICE_FUNC homogeneous_right_product_impl(const MatrixType& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_lhs.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_rhs.cols(); }
+
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC void evalTo(Dest& dst) const {
+    // FIXME: investigate how to allow lazy evaluation of this product when possible.
+    dst = m_lhs * Block < const RhsNested,
+    RhsNested::RowsAtCompileTime == Dynamic ? Dynamic : RhsNested::RowsAtCompileTime - 1,
+    RhsNested::ColsAtCompileTime > (m_rhs, 0, 0, m_rhs.rows() - 1, m_rhs.cols());
+    dst += m_rhs.row(m_rhs.rows() - 1).colwise().template replicate<MatrixType::RowsAtCompileTime>(m_lhs.rows());
+  }
+
+  typename MatrixType::Nested m_lhs;
+  typename Rhs::Nested m_rhs;
+};
+
+template <typename ArgType, int Direction>
+struct evaluator_traits<Homogeneous<ArgType, Direction> > {
+  typedef typename storage_kind_to_evaluator_kind<typename ArgType::StorageKind>::Kind Kind;
+  typedef HomogeneousShape Shape;
+};
+
+template <>
+struct AssignmentKind<DenseShape, HomogeneousShape> {
+  typedef Dense2Dense Kind;
+};
+
+template <typename ArgType, int Direction>
+struct unary_evaluator<Homogeneous<ArgType, Direction>, IndexBased>
+    : evaluator<typename Homogeneous<ArgType, Direction>::PlainObject> {
+  typedef Homogeneous<ArgType, Direction> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& op) : Base(), m_temp(op) {
+    internal::construct_at<Base>(this, m_temp);
+  }
+
+ protected:
+  PlainObject m_temp;
+};
+
+// dense = homogeneous
+template <typename DstXprType, typename ArgType, typename Scalar>
+struct Assignment<DstXprType, Homogeneous<ArgType, Vertical>, internal::assign_op<Scalar, typename ArgType::Scalar>,
+                  Dense2Dense> {
+  typedef Homogeneous<ArgType, Vertical> SrcXprType;
+  EIGEN_DEVICE_FUNC static void run(DstXprType& dst, const SrcXprType& src,
+                                    const internal::assign_op<Scalar, typename ArgType::Scalar>&) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    dst.template topRows<ArgType::RowsAtCompileTime>(src.nestedExpression().rows()) = src.nestedExpression();
+    dst.row(dst.rows() - 1).setOnes();
+  }
+};
+
+// dense = homogeneous
+template <typename DstXprType, typename ArgType, typename Scalar>
+struct Assignment<DstXprType, Homogeneous<ArgType, Horizontal>, internal::assign_op<Scalar, typename ArgType::Scalar>,
+                  Dense2Dense> {
+  typedef Homogeneous<ArgType, Horizontal> SrcXprType;
+  EIGEN_DEVICE_FUNC static void run(DstXprType& dst, const SrcXprType& src,
+                                    const internal::assign_op<Scalar, typename ArgType::Scalar>&) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    dst.template leftCols<ArgType::ColsAtCompileTime>(src.nestedExpression().cols()) = src.nestedExpression();
+    dst.col(dst.cols() - 1).setOnes();
+  }
+};
+
+template <typename LhsArg, typename Rhs, int ProductTag>
+struct generic_product_impl<Homogeneous<LhsArg, Horizontal>, Rhs, HomogeneousShape, DenseShape, ProductTag> {
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const Homogeneous<LhsArg, Horizontal>& lhs, const Rhs& rhs) {
+    homogeneous_right_product_impl<Homogeneous<LhsArg, Horizontal>, Rhs>(lhs.nestedExpression(), rhs).evalTo(dst);
+  }
+};
+
+template <typename Lhs, typename Rhs>
+struct homogeneous_right_product_refactoring_helper {
+  enum { Dim = Lhs::ColsAtCompileTime, Rows = Lhs::RowsAtCompileTime };
+  typedef typename Rhs::template ConstNRowsBlockXpr<Dim>::Type LinearBlockConst;
+  typedef std::remove_const_t<LinearBlockConst> LinearBlock;
+  typedef typename Rhs::ConstRowXpr ConstantColumn;
+  typedef Replicate<const ConstantColumn, Rows, 1> ConstantBlock;
+  typedef Product<Lhs, LinearBlock, LazyProduct> LinearProduct;
+  typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar, typename Rhs::Scalar>, const LinearProduct,
+                        const ConstantBlock>
+      Xpr;
+};
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, HomogeneousShape, DenseShape>
+    : public evaluator<
+          typename homogeneous_right_product_refactoring_helper<typename Lhs::NestedExpression, Rhs>::Xpr> {
+  typedef Product<Lhs, Rhs, LazyProduct> XprType;
+  typedef homogeneous_right_product_refactoring_helper<typename Lhs::NestedExpression, Rhs> helper;
+  typedef typename helper::ConstantBlock ConstantBlock;
+  typedef typename helper::Xpr RefactoredXpr;
+  typedef evaluator<RefactoredXpr> Base;
+
+  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
+      : Base(xpr.lhs().nestedExpression().lazyProduct(
+                 xpr.rhs().template topRows<helper::Dim>(xpr.lhs().nestedExpression().cols())) +
+             ConstantBlock(xpr.rhs().row(xpr.rhs().rows() - 1), xpr.lhs().rows(), 1)) {}
+};
+
+template <typename Lhs, typename RhsArg, int ProductTag>
+struct generic_product_impl<Lhs, Homogeneous<RhsArg, Vertical>, DenseShape, HomogeneousShape, ProductTag> {
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const Lhs& lhs, const Homogeneous<RhsArg, Vertical>& rhs) {
+    homogeneous_left_product_impl<Homogeneous<RhsArg, Vertical>, Lhs>(lhs, rhs.nestedExpression()).evalTo(dst);
+  }
+};
+
+// TODO: this specialization addresses a performance regression from 3.2 to 3.3; optimize this path.
+template <typename Lhs, typename RhsArg, int ProductTag>
+struct generic_product_impl<Lhs, Homogeneous<RhsArg, Vertical>, TriangularShape, HomogeneousShape, ProductTag> {
+  template <typename Dest>
+  static void evalTo(Dest& dst, const Lhs& lhs, const Homogeneous<RhsArg, Vertical>& rhs) {
+    dst.noalias() = lhs * rhs.eval();
+  }
+};
+
+template <typename Lhs, typename Rhs>
+struct homogeneous_left_product_refactoring_helper {
+  enum { Dim = Rhs::RowsAtCompileTime, Cols = Rhs::ColsAtCompileTime };
+  typedef typename Lhs::template ConstNColsBlockXpr<Dim>::Type LinearBlockConst;
+  typedef std::remove_const_t<LinearBlockConst> LinearBlock;
+  typedef typename Lhs::ConstColXpr ConstantColumn;
+  typedef Replicate<const ConstantColumn, 1, Cols> ConstantBlock;
+  typedef Product<LinearBlock, Rhs, LazyProduct> LinearProduct;
+  typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar, typename Rhs::Scalar>, const LinearProduct,
+                        const ConstantBlock>
+      Xpr;
+};
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape, HomogeneousShape>
+    : public evaluator<typename homogeneous_left_product_refactoring_helper<Lhs, typename Rhs::NestedExpression>::Xpr> {
+  typedef Product<Lhs, Rhs, LazyProduct> XprType;
+  typedef homogeneous_left_product_refactoring_helper<Lhs, typename Rhs::NestedExpression> helper;
+  typedef typename helper::ConstantBlock ConstantBlock;
+  typedef typename helper::Xpr RefactoredXpr;
+  typedef evaluator<RefactoredXpr> Base;
+
+  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
+      : Base(xpr.lhs()
+                 .template leftCols<helper::Dim>(xpr.rhs().nestedExpression().rows())
+                 .lazyProduct(xpr.rhs().nestedExpression()) +
+             ConstantBlock(xpr.lhs().col(xpr.lhs().cols() - 1), 1, xpr.rhs().cols())) {}
+};
+
+template <typename Scalar, int Dim, int Mode, int Options, typename RhsArg, int ProductTag>
+struct generic_product_impl<Transform<Scalar, Dim, Mode, Options>, Homogeneous<RhsArg, Vertical>, DenseShape,
+                            HomogeneousShape, ProductTag> {
+  typedef Transform<Scalar, Dim, Mode, Options> TransformType;
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const TransformType& lhs, const Homogeneous<RhsArg, Vertical>& rhs) {
+    homogeneous_left_product_impl<Homogeneous<RhsArg, Vertical>, TransformType>(lhs, rhs.nestedExpression())
+        .evalTo(dst);
+  }
+};
+
+template <typename ExpressionType, int Side, bool Transposed>
+struct permutation_matrix_product<ExpressionType, Side, Transposed, HomogeneousShape>
+    : public permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape> {};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_HOMOGENEOUS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Hyperplane.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Hyperplane.h
new file mode 100644
index 0000000..1bdb811
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Hyperplane.h
@@ -0,0 +1,273 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HYPERPLANE_H
+#define EIGEN_HYPERPLANE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \class Hyperplane
+ *
+ * \brief A hyperplane
+ *
+ * A hyperplane is an affine subspace of dimension n-1 in a space of dimension n.
+ * For example, a hyperplane in a plane is a line; a hyperplane in 3-space is a plane.
+ *
+ * \tparam Scalar_ the scalar type, i.e., the type of the coefficients
+ * \tparam AmbientDim_ the dimension of the ambient space, can be a compile time value or Dynamic.
+ *             Notice that the dimension of the hyperplane is AmbientDim_-1.
+ *
+ * This class represents an hyperplane as the zero set of the implicit equation
+ * \f$ n \cdot x + d = 0 \f$ where \f$ n \f$ is a unit normal vector of the plane (linear part)
+ * and \f$ d \f$ is the distance (offset) to the origin.
+ */
+template <typename Scalar_, int AmbientDim_, int Options_>
+class Hyperplane {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_,
+                                                             AmbientDim_ == Dynamic ? Dynamic : AmbientDim_ + 1)
+  enum { AmbientDimAtCompileTime = AmbientDim_, Options = Options_ };
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+  typedef Matrix<Scalar, AmbientDimAtCompileTime, 1> VectorType;
+  typedef Matrix<Scalar, Index(AmbientDimAtCompileTime) == Dynamic ? Dynamic : Index(AmbientDimAtCompileTime) + 1, 1,
+                 Options>
+      Coefficients;
+  typedef Block<Coefficients, AmbientDimAtCompileTime, 1> NormalReturnType;
+  typedef const Block<const Coefficients, AmbientDimAtCompileTime, 1> ConstNormalReturnType;
+
+  /** Default constructor without initialization */
+  EIGEN_DEVICE_FUNC inline Hyperplane() {}
+
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC Hyperplane(const Hyperplane<Scalar, AmbientDimAtCompileTime, OtherOptions>& other)
+      : m_coeffs(other.coeffs()) {}
+
+  /** Constructs a dynamic-size hyperplane with \a _dim the dimension
+   * of the ambient space */
+  EIGEN_DEVICE_FUNC inline explicit Hyperplane(Index _dim) : m_coeffs(_dim + 1) {}
+
+  /** Construct a plane from its normal \a n and a point \a e onto the plane.
+   * \warning the vector normal is assumed to be normalized.
+   */
+  EIGEN_DEVICE_FUNC inline Hyperplane(const VectorType& n, const VectorType& e) : m_coeffs(n.size() + 1) {
+    normal() = n;
+    offset() = -n.dot(e);
+  }
+
+  /** Constructs a plane from its normal \a n and distance to the origin \a d
+   * such that the algebraic equation of the plane is \f$ n \cdot x + d = 0 \f$.
+   * \warning the vector normal is assumed to be normalized.
+   */
+  EIGEN_DEVICE_FUNC inline Hyperplane(const VectorType& n, const Scalar& d) : m_coeffs(n.size() + 1) {
+    normal() = n;
+    offset() = d;
+  }
+
+  /** Constructs a hyperplane passing through the two points. If the dimension of the ambient space
+   * is greater than 2, then there isn't uniqueness, so an arbitrary choice is made.
+   */
+  EIGEN_DEVICE_FUNC static inline Hyperplane Through(const VectorType& p0, const VectorType& p1) {
+    Hyperplane result(p0.size());
+    result.normal() = (p1 - p0).unitOrthogonal();
+    result.offset() = -p0.dot(result.normal());
+    return result;
+  }
+
+  /** Constructs a hyperplane passing through the three points. The dimension of the ambient space
+   * is required to be exactly 3.
+   */
+  EIGEN_DEVICE_FUNC static inline Hyperplane Through(const VectorType& p0, const VectorType& p1, const VectorType& p2) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 3)
+    Hyperplane result(p0.size());
+    VectorType v0(p2 - p0), v1(p1 - p0);
+    result.normal() = v0.cross(v1);
+    RealScalar norm = result.normal().norm();
+    if (norm <= v0.norm() * v1.norm() * NumTraits<RealScalar>::epsilon()) {
+      Matrix<Scalar, 2, 3> m;
+      m << v0.transpose(), v1.transpose();
+      JacobiSVD<Matrix<Scalar, 2, 3>, ComputeFullV> svd(m);
+      result.normal() = svd.matrixV().col(2);
+    } else
+      result.normal() /= norm;
+    result.offset() = -p0.dot(result.normal());
+    return result;
+  }
+
+  /** Constructs a hyperplane passing through the parametrized line \a parametrized.
+   * If the dimension of the ambient space is greater than 2, then there isn't uniqueness,
+   * so an arbitrary choice is made.
+   */
+  // FIXME: for consistency, consider implementing as a static Through function.
+  EIGEN_DEVICE_FUNC explicit Hyperplane(const ParametrizedLine<Scalar, AmbientDimAtCompileTime>& parametrized) {
+    normal() = parametrized.direction().unitOrthogonal();
+    offset() = -parametrized.origin().dot(normal());
+  }
+
+  EIGEN_DEVICE_FUNC ~Hyperplane() {}
+
+  /** \returns the dimension in which the plane holds */
+  EIGEN_DEVICE_FUNC inline Index dim() const {
+    return AmbientDimAtCompileTime == Dynamic ? m_coeffs.size() - 1 : Index(AmbientDimAtCompileTime);
+  }
+
+  /** normalizes \c *this */
+  EIGEN_DEVICE_FUNC void normalize(void) { m_coeffs /= normal().norm(); }
+
+  /** \returns the signed distance between the plane \c *this and a point \a p.
+   * \sa absDistance()
+   */
+  EIGEN_DEVICE_FUNC inline Scalar signedDistance(const VectorType& p) const { return normal().dot(p) + offset(); }
+
+  /** \returns the absolute distance between the plane \c *this and a point \a p.
+   * \sa signedDistance()
+   */
+  EIGEN_DEVICE_FUNC inline Scalar absDistance(const VectorType& p) const { return numext::abs(signedDistance(p)); }
+
+  /** \returns the projection of a point \a p onto the plane \c *this.
+   */
+  EIGEN_DEVICE_FUNC inline VectorType projection(const VectorType& p) const { return p - signedDistance(p) * normal(); }
+
+  /** \returns a constant reference to the unit normal vector of the plane, which corresponds
+   * to the linear part of the implicit equation.
+   */
+  EIGEN_DEVICE_FUNC inline ConstNormalReturnType normal() const {
+    return ConstNormalReturnType(m_coeffs, 0, 0, dim(), 1);
+  }
+
+  /** \returns a non-constant reference to the unit normal vector of the plane, which corresponds
+   * to the linear part of the implicit equation.
+   */
+  EIGEN_DEVICE_FUNC inline NormalReturnType normal() { return NormalReturnType(m_coeffs, 0, 0, dim(), 1); }
+
+  /** \returns the distance to the origin, which is also the "constant term" of the implicit equation
+   * \warning the vector normal is assumed to be normalized.
+   */
+  EIGEN_DEVICE_FUNC inline const Scalar& offset() const { return m_coeffs.coeff(dim()); }
+
+  /** \returns a non-constant reference to the distance to the origin, which is also the constant part
+   * of the implicit equation */
+  EIGEN_DEVICE_FUNC inline Scalar& offset() { return m_coeffs(dim()); }
+
+  /** \returns a constant reference to the coefficients c_i of the plane equation:
+   * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
+   */
+  EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs; }
+
+  /** \returns a non-constant reference to the coefficients c_i of the plane equation:
+   * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
+   */
+  EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs; }
+
+  /** \returns the intersection of *this with \a other.
+   *
+   * \warning The ambient space must be a plane, i.e. have dimension 2, so that \c *this and \a other are lines.
+   *
+   * \note If \a other is approximately parallel to *this, this method will return any point on *this.
+   */
+  EIGEN_DEVICE_FUNC VectorType intersection(const Hyperplane& other) const {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
+    Scalar det = coeffs().coeff(0) * other.coeffs().coeff(1) - coeffs().coeff(1) * other.coeffs().coeff(0);
+    // since the line equations ax+by=c are normalized with a^2+b^2=1, the following tests
+    // whether the two lines are approximately parallel.
+    if (internal::isMuchSmallerThan(det, Scalar(1))) {  // special case where the two lines are approximately parallel.
+                                                        // Pick any point on the first line.
+      if (numext::abs(coeffs().coeff(1)) > numext::abs(coeffs().coeff(0)))
+        return VectorType(coeffs().coeff(1), -coeffs().coeff(2) / coeffs().coeff(1) - coeffs().coeff(0));
+      else
+        return VectorType(-coeffs().coeff(2) / coeffs().coeff(0) - coeffs().coeff(1), coeffs().coeff(0));
+    } else {  // general case
+      Scalar invdet = Scalar(1) / det;
+      return VectorType(
+          invdet * (coeffs().coeff(1) * other.coeffs().coeff(2) - other.coeffs().coeff(1) * coeffs().coeff(2)),
+          invdet * (other.coeffs().coeff(0) * coeffs().coeff(2) - coeffs().coeff(0) * other.coeffs().coeff(2)));
+    }
+  }
+
+  /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this.
+   *
+   * \param mat the Dim x Dim transformation matrix
+   * \param traits specifies whether the matrix \a mat represents an #Isometry
+   *               or a more generic #Affine transformation. The default is #Affine.
+   */
+  template <typename XprType>
+  EIGEN_DEVICE_FUNC inline Hyperplane& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine) {
+    if (traits == Affine) {
+      normal() = mat.inverse().transpose() * normal();
+      m_coeffs /= normal().norm();
+    } else if (traits == Isometry)
+      normal() = mat * normal();
+    else {
+      eigen_assert(0 && "invalid traits value in Hyperplane::transform()");
+    }
+    return *this;
+  }
+
+  /** Applies the transformation \a t to \c *this and returns a reference to \c *this.
+   *
+   * \param t the transformation of dimension Dim
+   * \param traits specifies whether the transformation \a t represents an #Isometry
+   *               or a more generic #Affine transformation. The default is #Affine.
+   *               Other kind of transformations are not supported.
+   */
+  template <int TrOptions>
+  EIGEN_DEVICE_FUNC inline Hyperplane& transform(const Transform<Scalar, AmbientDimAtCompileTime, Affine, TrOptions>& t,
+                                                 TransformTraits traits = Affine) {
+    transform(t.linear(), traits);
+    offset() -= normal().dot(t.translation());
+    return *this;
+  }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+   *
+   * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+   * then this function smartly returns a const reference to \c *this.
+   */
+  template <typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline
+      typename internal::cast_return_type<Hyperplane,
+                                          Hyperplane<NewScalarType, AmbientDimAtCompileTime, Options> >::type
+      cast() const {
+    return
+        typename internal::cast_return_type<Hyperplane,
+                                            Hyperplane<NewScalarType, AmbientDimAtCompileTime, Options> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template <typename OtherScalarType, int OtherOptions>
+  EIGEN_DEVICE_FUNC inline explicit Hyperplane(
+      const Hyperplane<OtherScalarType, AmbientDimAtCompileTime, OtherOptions>& other) {
+    m_coeffs = other.coeffs().template cast<Scalar>();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+   * determined by \a prec.
+   *
+   * \sa MatrixBase::isApprox() */
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC bool isApprox(
+      const Hyperplane<Scalar, AmbientDimAtCompileTime, OtherOptions>& other,
+      const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const {
+    return m_coeffs.isApprox(other.m_coeffs, prec);
+  }
+
+ protected:
+  Coefficients m_coeffs;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_HYPERPLANE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/InternalHeaderCheck.h
new file mode 100644
index 0000000..a1159a3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_GEOMETRY_MODULE_H
+#error "Please include Eigen/Geometry instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/OrthoMethods.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/OrthoMethods.h
new file mode 100644
index 0000000..758c82e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/OrthoMethods.h
@@ -0,0 +1,257 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ORTHOMETHODS_H
+#define EIGEN_ORTHOMETHODS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Vector3 version (default)
+template <typename Derived, typename OtherDerived, int Size>
+struct cross_impl {
+  typedef typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,
+                                        typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
+  typedef Matrix<Scalar, MatrixBase<Derived>::RowsAtCompileTime, MatrixBase<Derived>::ColsAtCompileTime> return_type;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE return_type run(const MatrixBase<Derived>& first,
+                                                               const MatrixBase<OtherDerived>& second) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived, 3)
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived, 3)
+
+    // Note that there is no need for an expression here since the compiler
+    // optimize such a small temporary very well (even within a complex expression)
+    typename internal::nested_eval<Derived, 2>::type lhs(first.derived());
+    typename internal::nested_eval<OtherDerived, 2>::type rhs(second.derived());
+    return return_type(numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
+                       numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
+                       numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)));
+  }
+};
+
+// Vector2 version
+template <typename Derived, typename OtherDerived>
+struct cross_impl<Derived, OtherDerived, 2> {
+  typedef typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,
+                                        typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
+  typedef Scalar return_type;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE return_type run(const MatrixBase<Derived>& first,
+                                                               const MatrixBase<OtherDerived>& second) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived, 2);
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived, 2);
+    typename internal::nested_eval<Derived, 2>::type lhs(first.derived());
+    typename internal::nested_eval<OtherDerived, 2>::type rhs(second.derived());
+    return numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0));
+  }
+};
+
+}  // end namespace internal
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \returns the cross product of \c *this and \a other. This is either a scalar for size-2 vectors or a size-3 vector
+ * for size-3 vectors.
+ *
+ * This method is implemented for two different cases: between vectors of fixed size 2 and between vectors of fixed
+ * size 3.
+ *
+ * For vectors of size 3, the output is simply the traditional cross product.
+ *
+ * For vectors of size 2, the output is a scalar.
+ * Given vectors \f$ v = \begin{bmatrix} v_1 & v_2 \end{bmatrix} \f$ and \f$ w = \begin{bmatrix} w_1 & w_2 \end{bmatrix}
+ * \f$, the result is simply \f$ v\times w = \overline{v_1 w_2 - v_2 w_1} = \text{conj}\left|\begin{smallmatrix} v_1 &
+ * w_1 \\ v_2 & w_2 \end{smallmatrix}\right| \f$; or, to put it differently, it is the third coordinate of the cross
+ * product of \f$ \begin{bmatrix} v_1 & v_2 & v_3 \end{bmatrix} \f$ and \f$ \begin{bmatrix} w_1 & w_2 & w_3
+ * \end{bmatrix} \f$. For real-valued inputs, the result can be interpreted as the signed area of a parallelogram
+ * spanned by the two vectors.
+ *
+ * \note With complex numbers, the cross product is implemented as
+ * \f[ (\mathbf{a}+i\mathbf{b}) \times (\mathbf{c}+i\mathbf{d}) = (\mathbf{a} \times \mathbf{c} - \mathbf{b} \times
+ * \mathbf{d}) - i(\mathbf{a} \times \mathbf{d} + \mathbf{b} \times \mathbf{c}).\f]
+ * This definition preserves the orthogonality condition that \f$\mathbf{u} \cdot (\mathbf{u} \times \mathbf{v}) =
+ * \mathbf{v} \cdot (\mathbf{u} \times \mathbf{v}) = 0\f$.
+ *
+ * \sa MatrixBase::cross3()
+ */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::cross_impl<Derived, OtherDerived>::return_type
+MatrixBase<Derived>::cross(const MatrixBase<OtherDerived>& other) const {
+  return internal::cross_impl<Derived, OtherDerived>::run(*this, other);
+}
+
+namespace internal {
+
+template <int Arch, typename VectorLhs, typename VectorRhs, typename Scalar = typename VectorLhs::Scalar,
+          bool Vectorizable =
+              bool((int(evaluator<VectorLhs>::Flags) & int(evaluator<VectorRhs>::Flags)) & PacketAccessBit)>
+struct cross3_impl {
+  EIGEN_DEVICE_FUNC static inline typename internal::plain_matrix_type<VectorLhs>::type run(const VectorLhs& lhs,
+                                                                                            const VectorRhs& rhs) {
+    return typename internal::plain_matrix_type<VectorLhs>::type(
+        numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
+        numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
+        numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)), 0);
+  }
+};
+
+}  // namespace internal
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \returns the cross product of \c *this and \a other using only the x, y, and z coefficients
+ *
+ * The size of \c *this and \a other must be four. This function is especially useful
+ * when using 4D vectors instead of 3D ones to get advantage of SSE/AltiVec vectorization.
+ *
+ * \sa MatrixBase::cross()
+ */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::PlainObject MatrixBase<Derived>::cross3(
+    const MatrixBase<OtherDerived>& other) const {
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived, 4)
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived, 4)
+
+  typedef typename internal::nested_eval<Derived, 2>::type DerivedNested;
+  typedef typename internal::nested_eval<OtherDerived, 2>::type OtherDerivedNested;
+  DerivedNested lhs(derived());
+  OtherDerivedNested rhs(other.derived());
+
+  return internal::cross3_impl<Architecture::Target, internal::remove_all_t<DerivedNested>,
+                               internal::remove_all_t<OtherDerivedNested>>::run(lhs, rhs);
+}
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \returns a matrix expression of the cross product of each column or row
+ * of the referenced expression with the \a other vector.
+ *
+ * The referenced matrix must have one dimension equal to 3.
+ * The result matrix has the same dimensions than the referenced one.
+ *
+ * \sa MatrixBase::cross() */
+template <typename ExpressionType, int Direction>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC const typename VectorwiseOp<ExpressionType, Direction>::CrossReturnType
+VectorwiseOp<ExpressionType, Direction>::cross(const MatrixBase<OtherDerived>& other) const {
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived, 3)
+  EIGEN_STATIC_ASSERT(
+      (internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  typename internal::nested_eval<ExpressionType, 2>::type mat(_expression());
+  typename internal::nested_eval<OtherDerived, 2>::type vec(other.derived());
+
+  CrossReturnType res(_expression().rows(), _expression().cols());
+  if (Direction == Vertical) {
+    eigen_assert(CrossReturnType::RowsAtCompileTime == 3 && "the matrix must have exactly 3 rows");
+    res.row(0) = (mat.row(1) * vec.coeff(2) - mat.row(2) * vec.coeff(1)).conjugate();
+    res.row(1) = (mat.row(2) * vec.coeff(0) - mat.row(0) * vec.coeff(2)).conjugate();
+    res.row(2) = (mat.row(0) * vec.coeff(1) - mat.row(1) * vec.coeff(0)).conjugate();
+  } else {
+    eigen_assert(CrossReturnType::ColsAtCompileTime == 3 && "the matrix must have exactly 3 columns");
+    res.col(0) = (mat.col(1) * vec.coeff(2) - mat.col(2) * vec.coeff(1)).conjugate();
+    res.col(1) = (mat.col(2) * vec.coeff(0) - mat.col(0) * vec.coeff(2)).conjugate();
+    res.col(2) = (mat.col(0) * vec.coeff(1) - mat.col(1) * vec.coeff(0)).conjugate();
+  }
+  return res;
+}
+
+namespace internal {
+
+template <typename Derived, int Size = Derived::SizeAtCompileTime>
+struct unitOrthogonal_selector {
+  typedef typename plain_matrix_type<Derived>::type VectorType;
+  typedef typename traits<Derived>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar, 2, 1> Vector2;
+  EIGEN_DEVICE_FUNC static inline VectorType run(const Derived& src) {
+    VectorType perp = VectorType::Zero(src.size());
+    Index maxi = 0;
+    Index sndi = 0;
+    src.cwiseAbs().maxCoeff(&maxi);
+    if (maxi == 0) sndi = 1;
+    RealScalar invnm = RealScalar(1) / (Vector2() << src.coeff(sndi), src.coeff(maxi)).finished().norm();
+    perp.coeffRef(maxi) = -numext::conj(src.coeff(sndi)) * invnm;
+    perp.coeffRef(sndi) = numext::conj(src.coeff(maxi)) * invnm;
+
+    return perp;
+  }
+};
+
+template <typename Derived>
+struct unitOrthogonal_selector<Derived, 3> {
+  typedef typename plain_matrix_type<Derived>::type VectorType;
+  typedef typename traits<Derived>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC static inline VectorType run(const Derived& src) {
+    VectorType perp;
+    /* Let us compute the crossed product of *this with a vector
+     * that is not too close to being collinear to *this.
+     */
+
+    /* unless the x and y coords are both close to zero, we can
+     * simply take ( -y, x, 0 ) and normalize it.
+     */
+    if ((!isMuchSmallerThan(src.x(), src.z())) || (!isMuchSmallerThan(src.y(), src.z()))) {
+      RealScalar invnm = RealScalar(1) / src.template head<2>().norm();
+      perp.coeffRef(0) = -numext::conj(src.y()) * invnm;
+      perp.coeffRef(1) = numext::conj(src.x()) * invnm;
+      perp.coeffRef(2) = 0;
+    }
+    /* if both x and y are close to zero, then the vector is close
+     * to the z-axis, so it's far from collinear to the x-axis for instance.
+     * So we take the crossed product with (1,0,0) and normalize it.
+     */
+    else {
+      RealScalar invnm = RealScalar(1) / src.template tail<2>().norm();
+      perp.coeffRef(0) = 0;
+      perp.coeffRef(1) = -numext::conj(src.z()) * invnm;
+      perp.coeffRef(2) = numext::conj(src.y()) * invnm;
+    }
+
+    return perp;
+  }
+};
+
+template <typename Derived>
+struct unitOrthogonal_selector<Derived, 2> {
+  typedef typename plain_matrix_type<Derived>::type VectorType;
+  EIGEN_DEVICE_FUNC static inline VectorType run(const Derived& src) {
+    return VectorType(-numext::conj(src.y()), numext::conj(src.x())).normalized();
+  }
+};
+
+}  // end namespace internal
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \returns a unit vector which is orthogonal to \c *this
+ *
+ * The size of \c *this must be at least 2. If the size is exactly 2,
+ * then the returned vector is a counter-clockwise rotation of \c *this, i.e., (-y,x).normalized().
+ *
+ * \sa cross()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC typename MatrixBase<Derived>::PlainObject MatrixBase<Derived>::unitOrthogonal() const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return internal::unitOrthogonal_selector<Derived>::run(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ORTHOMETHODS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/ParametrizedLine.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/ParametrizedLine.h
new file mode 100644
index 0000000..5bbd874
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/ParametrizedLine.h
@@ -0,0 +1,232 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARAMETRIZEDLINE_H
+#define EIGEN_PARAMETRIZEDLINE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \class ParametrizedLine
+ *
+ * \brief A parametrized line
+ *
+ * A parametrized line is defined by an origin point \f$ \mathbf{o} \f$ and a unit
+ * direction vector \f$ \mathbf{d} \f$ such that the line corresponds to
+ * the set \f$ l(t) = \mathbf{o} + t \mathbf{d} \f$, \f$ t \in \mathbf{R} \f$.
+ *
+ * \tparam Scalar_ the scalar type, i.e., the type of the coefficients
+ * \tparam AmbientDim_ the dimension of the ambient space, can be a compile time value or Dynamic.
+ */
+template <typename Scalar_, int AmbientDim_, int Options_>
+class ParametrizedLine {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_, AmbientDim_)
+  enum { AmbientDimAtCompileTime = AmbientDim_, Options = Options_ };
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+  typedef Matrix<Scalar, AmbientDimAtCompileTime, 1, Options> VectorType;
+
+  /** Default constructor without initialization */
+  EIGEN_DEVICE_FUNC inline ParametrizedLine() {}
+
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC ParametrizedLine(const ParametrizedLine<Scalar, AmbientDimAtCompileTime, OtherOptions>& other)
+      : m_origin(other.origin()), m_direction(other.direction()) {}
+
+  /** Constructs a dynamic-size line with \a _dim the dimension
+   * of the ambient space */
+  EIGEN_DEVICE_FUNC inline explicit ParametrizedLine(Index _dim) : m_origin(_dim), m_direction(_dim) {}
+
+  /** Initializes a parametrized line of direction \a direction and origin \a origin.
+   * \warning the vector direction is assumed to be normalized.
+   */
+  EIGEN_DEVICE_FUNC ParametrizedLine(const VectorType& origin, const VectorType& direction)
+      : m_origin(origin), m_direction(direction) {}
+
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC explicit ParametrizedLine(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane);
+
+  /** Constructs a parametrized line going from \a p0 to \a p1. */
+  EIGEN_DEVICE_FUNC static inline ParametrizedLine Through(const VectorType& p0, const VectorType& p1) {
+    return ParametrizedLine(p0, (p1 - p0).normalized());
+  }
+
+  EIGEN_DEVICE_FUNC ~ParametrizedLine() {}
+
+  /** \returns the dimension in which the line holds */
+  EIGEN_DEVICE_FUNC inline Index dim() const { return m_direction.size(); }
+
+  EIGEN_DEVICE_FUNC const VectorType& origin() const { return m_origin; }
+  EIGEN_DEVICE_FUNC VectorType& origin() { return m_origin; }
+
+  EIGEN_DEVICE_FUNC const VectorType& direction() const { return m_direction; }
+  EIGEN_DEVICE_FUNC VectorType& direction() { return m_direction; }
+
+  /** \returns the squared distance of a point \a p to its projection onto the line \c *this.
+   * \sa distance()
+   */
+  EIGEN_DEVICE_FUNC RealScalar squaredDistance(const VectorType& p) const {
+    VectorType diff = p - origin();
+    return (diff - direction().dot(diff) * direction()).squaredNorm();
+  }
+  /** \returns the distance of a point \a p to its projection onto the line \c *this.
+   * \sa squaredDistance()
+   */
+  EIGEN_DEVICE_FUNC RealScalar distance(const VectorType& p) const {
+    EIGEN_USING_STD(sqrt) return sqrt(squaredDistance(p));
+  }
+
+  /** \returns the projection of a point \a p onto the line \c *this. */
+  EIGEN_DEVICE_FUNC VectorType projection(const VectorType& p) const {
+    return origin() + direction().dot(p - origin()) * direction();
+  }
+
+  EIGEN_DEVICE_FUNC VectorType pointAt(const Scalar& t) const;
+
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC Scalar
+  intersectionParameter(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const;
+
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC Scalar intersection(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const;
+
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC VectorType
+  intersectionPoint(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const;
+
+  /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this.
+   *
+   * \param mat the Dim x Dim transformation matrix
+   * \param traits specifies whether the matrix \a mat represents an #Isometry
+   *               or a more generic #Affine transformation. The default is #Affine.
+   */
+  template <typename XprType>
+  EIGEN_DEVICE_FUNC inline ParametrizedLine& transform(const MatrixBase<XprType>& mat,
+                                                       TransformTraits traits = Affine) {
+    if (traits == Affine)
+      direction() = (mat * direction()).normalized();
+    else if (traits == Isometry)
+      direction() = mat * direction();
+    else {
+      eigen_assert(0 && "invalid traits value in ParametrizedLine::transform()");
+    }
+    origin() = mat * origin();
+    return *this;
+  }
+
+  /** Applies the transformation \a t to \c *this and returns a reference to \c *this.
+   *
+   * \param t the transformation of dimension Dim
+   * \param traits specifies whether the transformation \a t represents an #Isometry
+   *               or a more generic #Affine transformation. The default is #Affine.
+   *               Other kind of transformations are not supported.
+   */
+  template <int TrOptions>
+  EIGEN_DEVICE_FUNC inline ParametrizedLine& transform(
+      const Transform<Scalar, AmbientDimAtCompileTime, Affine, TrOptions>& t, TransformTraits traits = Affine) {
+    transform(t.linear(), traits);
+    origin() += t.translation();
+    return *this;
+  }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+   *
+   * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+   * then this function smartly returns a const reference to \c *this.
+   */
+  template <typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline
+      typename internal::cast_return_type<ParametrizedLine,
+                                          ParametrizedLine<NewScalarType, AmbientDimAtCompileTime, Options> >::type
+      cast() const {
+    return typename internal::cast_return_type<
+        ParametrizedLine, ParametrizedLine<NewScalarType, AmbientDimAtCompileTime, Options> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template <typename OtherScalarType, int OtherOptions>
+  EIGEN_DEVICE_FUNC inline explicit ParametrizedLine(
+      const ParametrizedLine<OtherScalarType, AmbientDimAtCompileTime, OtherOptions>& other) {
+    m_origin = other.origin().template cast<Scalar>();
+    m_direction = other.direction().template cast<Scalar>();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+   * determined by \a prec.
+   *
+   * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const ParametrizedLine& other, const typename NumTraits<Scalar>::Real& prec =
+                                                                     NumTraits<Scalar>::dummy_precision()) const {
+    return m_origin.isApprox(other.m_origin, prec) && m_direction.isApprox(other.m_direction, prec);
+  }
+
+ protected:
+  VectorType m_origin, m_direction;
+};
+
+/** Constructs a parametrized line from a 2D hyperplane
+ *
+ * \warning the ambient space must have dimension 2 such that the hyperplane actually describes a line
+ */
+template <typename Scalar_, int AmbientDim_, int Options_>
+template <int OtherOptions>
+EIGEN_DEVICE_FUNC inline ParametrizedLine<Scalar_, AmbientDim_, Options_>::ParametrizedLine(
+    const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) {
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
+  direction() = hyperplane.normal().unitOrthogonal();
+  origin() = -hyperplane.normal() * hyperplane.offset();
+}
+
+/** \returns the point at \a t along this line
+ */
+template <typename Scalar_, int AmbientDim_, int Options_>
+EIGEN_DEVICE_FUNC inline typename ParametrizedLine<Scalar_, AmbientDim_, Options_>::VectorType
+ParametrizedLine<Scalar_, AmbientDim_, Options_>::pointAt(const Scalar_& t) const {
+  return origin() + (direction() * t);
+}
+
+/** \returns the parameter value of the intersection between \c *this and the given \a hyperplane
+ */
+template <typename Scalar_, int AmbientDim_, int Options_>
+template <int OtherOptions>
+EIGEN_DEVICE_FUNC inline Scalar_ ParametrizedLine<Scalar_, AmbientDim_, Options_>::intersectionParameter(
+    const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const {
+  return -(hyperplane.offset() + hyperplane.normal().dot(origin())) / hyperplane.normal().dot(direction());
+}
+
+/** \deprecated use intersectionParameter()
+ * \returns the parameter value of the intersection between \c *this and the given \a hyperplane
+ */
+template <typename Scalar_, int AmbientDim_, int Options_>
+template <int OtherOptions>
+EIGEN_DEVICE_FUNC inline Scalar_ ParametrizedLine<Scalar_, AmbientDim_, Options_>::intersection(
+    const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const {
+  return intersectionParameter(hyperplane);
+}
+
+/** \returns the point of the intersection between \c *this and the given hyperplane
+ */
+template <typename Scalar_, int AmbientDim_, int Options_>
+template <int OtherOptions>
+EIGEN_DEVICE_FUNC inline typename ParametrizedLine<Scalar_, AmbientDim_, Options_>::VectorType
+ParametrizedLine<Scalar_, AmbientDim_, Options_>::intersectionPoint(
+    const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const {
+  return pointAt(intersectionParameter(hyperplane));
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PARAMETRIZEDLINE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Quaternion.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Quaternion.h
new file mode 100644
index 0000000..8b71cd8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Quaternion.h
@@ -0,0 +1,923 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Mathieu Gautier <mathieu.gautier@cea.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_QUATERNION_H
+#define EIGEN_QUATERNION_H
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/***************************************************************************
+ * Definition of QuaternionBase<Derived>
+ * The implementation is at the end of the file
+ ***************************************************************************/
+
+namespace internal {
+template <typename Other, int OtherRows = Other::RowsAtCompileTime, int OtherCols = Other::ColsAtCompileTime>
+struct quaternionbase_assign_impl;
+}
+
+/** \geometry_module \ingroup Geometry_Module
+ * \class QuaternionBase
+ * \brief Base class for quaternion expressions
+ * \tparam Derived derived type (CRTP)
+ * \sa class Quaternion
+ */
+template <class Derived>
+class QuaternionBase : public RotationBase<Derived, 3> {
+ public:
+  typedef RotationBase<Derived, 3> Base;
+
+  using Base::operator*;
+  using Base::derived;
+
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef typename internal::traits<Derived>::Coefficients Coefficients;
+  typedef typename Coefficients::CoeffReturnType CoeffReturnType;
+  typedef std::conditional_t<bool(internal::traits<Derived>::Flags& LvalueBit), Scalar&, CoeffReturnType>
+      NonConstCoeffReturnType;
+
+  enum { Flags = Eigen::internal::traits<Derived>::Flags };
+
+  // typedef typename Matrix<Scalar,4,1> Coefficients;
+  /** the type of a 3D vector */
+  typedef Matrix<Scalar, 3, 1> Vector3;
+  /** the equivalent rotation matrix type */
+  typedef Matrix<Scalar, 3, 3> Matrix3;
+  /** the equivalent angle-axis type */
+  typedef AngleAxis<Scalar> AngleAxisType;
+
+  /** \returns the \c x coefficient */
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType x() const { return this->derived().coeffs().coeff(0); }
+  /** \returns the \c y coefficient */
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType y() const { return this->derived().coeffs().coeff(1); }
+  /** \returns the \c z coefficient */
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType z() const { return this->derived().coeffs().coeff(2); }
+  /** \returns the \c w coefficient */
+  EIGEN_DEVICE_FUNC constexpr CoeffReturnType w() const { return this->derived().coeffs().coeff(3); }
+
+  /** \returns a reference to the \c x coefficient (if Derived is a non-const lvalue) */
+  EIGEN_DEVICE_FUNC constexpr NonConstCoeffReturnType x() { return this->derived().coeffs().x(); }
+  /** \returns a reference to the \c y coefficient (if Derived is a non-const lvalue) */
+  EIGEN_DEVICE_FUNC constexpr NonConstCoeffReturnType y() { return this->derived().coeffs().y(); }
+  /** \returns a reference to the \c z coefficient (if Derived is a non-const lvalue) */
+  EIGEN_DEVICE_FUNC constexpr NonConstCoeffReturnType z() { return this->derived().coeffs().z(); }
+  /** \returns a reference to the \c w coefficient (if Derived is a non-const lvalue) */
+  EIGEN_DEVICE_FUNC constexpr NonConstCoeffReturnType w() { return this->derived().coeffs().w(); }
+
+  /** \returns a read-only vector expression of the imaginary part (x,y,z) */
+  EIGEN_DEVICE_FUNC inline const VectorBlock<const Coefficients, 3> vec() const { return coeffs().template head<3>(); }
+
+  /** \returns a vector expression of the imaginary part (x,y,z) */
+  EIGEN_DEVICE_FUNC inline VectorBlock<Coefficients, 3> vec() { return coeffs().template head<3>(); }
+
+  /** \returns a read-only vector expression of the coefficients (x,y,z,w) */
+  EIGEN_DEVICE_FUNC inline const typename internal::traits<Derived>::Coefficients& coeffs() const {
+    return derived().coeffs();
+  }
+
+  /** \returns a vector containing the coefficients, rearranged into the order [\c w, \c x, \c y, \c z].
+   *
+   * This is the order expected by the \code Quaternion(const Scalar& w, const Scalar& x, const Scalar& y, const Scalar&
+   * z) \endcode constructor, but not the order of the internal vector representation. Therefore, it returns a newly
+   * constructed vector.
+   *
+   * \sa QuaternionBase::coeffsScalarLast()
+   * */
+  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Coefficients coeffsScalarFirst() const {
+    return derived().coeffsScalarFirst();
+  }
+
+  /** \returns a vector containing the coefficients in their original order [\c x, \c y, \c z, \c w].
+   *
+   * This is equivalent to \code coeffs() \endcode, but returns a newly constructed vector for uniformity with \code
+   * coeffsScalarFirst() \endcode.
+   *
+   * \sa QuaternionBase::coeffsScalarFirst()
+   * */
+  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Coefficients coeffsScalarLast() const {
+    return derived().coeffsScalarLast();
+  }
+
+  /** \returns a vector expression of the coefficients (x,y,z,w) */
+  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Coefficients& coeffs() { return derived().coeffs(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE QuaternionBase<Derived>& operator=(const QuaternionBase<Derived>& other);
+  template <class OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const QuaternionBase<OtherDerived>& other);
+
+  EIGEN_DEVICE_FUNC Derived& operator=(const AngleAxisType& aa);
+  template <class OtherDerived>
+  EIGEN_DEVICE_FUNC Derived& operator=(const MatrixBase<OtherDerived>& m);
+
+  /** \returns a quaternion representing an identity rotation
+   * \sa MatrixBase::Identity()
+   */
+  EIGEN_DEVICE_FUNC static inline Quaternion<Scalar> Identity() {
+    return Quaternion<Scalar>(Scalar(1), Scalar(0), Scalar(0), Scalar(0));
+  }
+
+  /** \sa QuaternionBase::Identity(), MatrixBase::setIdentity()
+   */
+  EIGEN_DEVICE_FUNC inline QuaternionBase& setIdentity() {
+    coeffs() << Scalar(0), Scalar(0), Scalar(0), Scalar(1);
+    return *this;
+  }
+
+  /** \returns the squared norm of the quaternion's coefficients
+   * \sa QuaternionBase::norm(), MatrixBase::squaredNorm()
+   */
+  EIGEN_DEVICE_FUNC inline Scalar squaredNorm() const { return coeffs().squaredNorm(); }
+
+  /** \returns the norm of the quaternion's coefficients
+   * \sa QuaternionBase::squaredNorm(), MatrixBase::norm()
+   */
+  EIGEN_DEVICE_FUNC inline Scalar norm() const { return coeffs().norm(); }
+
+  /** Normalizes the quaternion \c *this
+   * \sa normalized(), MatrixBase::normalize() */
+  EIGEN_DEVICE_FUNC inline void normalize() { coeffs().normalize(); }
+  /** \returns a normalized copy of \c *this
+   * \sa normalize(), MatrixBase::normalized() */
+  EIGEN_DEVICE_FUNC inline Quaternion<Scalar> normalized() const { return Quaternion<Scalar>(coeffs().normalized()); }
+
+  /** \returns the dot product of \c *this and \a other
+   * Geometrically speaking, the dot product of two unit quaternions
+   * corresponds to the cosine of half the angle between the two rotations.
+   * \sa angularDistance()
+   */
+  template <class OtherDerived>
+  EIGEN_DEVICE_FUNC inline Scalar dot(const QuaternionBase<OtherDerived>& other) const {
+    return coeffs().dot(other.coeffs());
+  }
+
+  template <class OtherDerived>
+  EIGEN_DEVICE_FUNC Scalar angularDistance(const QuaternionBase<OtherDerived>& other) const;
+
+  /** \returns an equivalent 3x3 rotation matrix */
+  EIGEN_DEVICE_FUNC inline Matrix3 toRotationMatrix() const;
+
+  /** \returns the quaternion which transform \a a into \a b through a rotation */
+  template <typename Derived1, typename Derived2>
+  EIGEN_DEVICE_FUNC Derived& setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b);
+
+  template <class OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion<Scalar> operator*(const QuaternionBase<OtherDerived>& q) const;
+  template <class OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator*=(const QuaternionBase<OtherDerived>& q);
+
+  /** \returns the quaternion describing the inverse rotation */
+  EIGEN_DEVICE_FUNC Quaternion<Scalar> inverse() const;
+
+  /** \returns the conjugated quaternion */
+  EIGEN_DEVICE_FUNC Quaternion<Scalar> conjugate() const;
+
+  template <class OtherDerived>
+  EIGEN_DEVICE_FUNC Quaternion<Scalar> slerp(const Scalar& t, const QuaternionBase<OtherDerived>& other) const;
+
+  /** \returns true if each coefficients of \c *this and \a other are all exactly equal.
+   * \warning When using floating point scalar values you probably should rather use a
+   *          fuzzy comparison such as isApprox()
+   * \sa isApprox(), operator!= */
+  template <class OtherDerived>
+  EIGEN_DEVICE_FUNC inline bool operator==(const QuaternionBase<OtherDerived>& other) const {
+    return coeffs() == other.coeffs();
+  }
+
+  /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
+   * \warning When using floating point scalar values you probably should rather use a
+   *          fuzzy comparison such as isApprox()
+   * \sa isApprox(), operator== */
+  template <class OtherDerived>
+  EIGEN_DEVICE_FUNC inline bool operator!=(const QuaternionBase<OtherDerived>& other) const {
+    return coeffs() != other.coeffs();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+   * determined by \a prec.
+   *
+   * \sa MatrixBase::isApprox() */
+  template <class OtherDerived>
+  EIGEN_DEVICE_FUNC bool isApprox(const QuaternionBase<OtherDerived>& other,
+                                  const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const {
+    return coeffs().isApprox(other.coeffs(), prec);
+  }
+
+  /** return the result vector of \a v through the rotation*/
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Vector3 _transformVector(const Vector3& v) const;
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+   *
+   * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+   * then this function smartly returns a const reference to \c *this.
+   */
+  template <typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Derived, Quaternion<NewScalarType> >::type cast() const;
+
+#else
+
+  template <typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline std::enable_if_t<internal::is_same<Scalar, NewScalarType>::value, const Derived&> cast()
+      const {
+    return derived();
+  }
+
+  template <typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline std::enable_if_t<!internal::is_same<Scalar, NewScalarType>::value,
+                                            Quaternion<NewScalarType> >
+  cast() const {
+    return Quaternion<NewScalarType>(coeffs().template cast<NewScalarType>());
+  }
+#endif
+
+#ifndef EIGEN_NO_IO
+  friend std::ostream& operator<<(std::ostream& s, const QuaternionBase<Derived>& q) {
+    s << q.x() << "i + " << q.y() << "j + " << q.z() << "k"
+      << " + " << q.w();
+    return s;
+  }
+#endif
+
+#ifdef EIGEN_QUATERNIONBASE_PLUGIN
+#include EIGEN_QUATERNIONBASE_PLUGIN
+#endif
+ protected:
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(QuaternionBase)
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(QuaternionBase)
+};
+
+/***************************************************************************
+ * Definition/implementation of Quaternion<Scalar>
+ ***************************************************************************/
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \class Quaternion
+ *
+ * \brief The quaternion class used to represent 3D orientations and rotations
+ *
+ * \tparam Scalar_ the scalar type, i.e., the type of the coefficients
+ * \tparam Options_ controls the memory alignment of the coefficients. Can be \# AutoAlign or \# DontAlign. Default is
+ * AutoAlign.
+ *
+ * This class represents a quaternion \f$ w+xi+yj+zk \f$ that is a convenient representation of
+ * orientations and rotations of objects in three dimensions. Compared to other representations
+ * like Euler angles or 3x3 matrices, quaternions offer the following advantages:
+ * \li \b compact storage (4 scalars)
+ * \li \b efficient to compose (28 flops),
+ * \li \b stable spherical interpolation
+ *
+ * The following two typedefs are provided for convenience:
+ * \li \c Quaternionf for \c float
+ * \li \c Quaterniond for \c double
+ *
+ * \warning Operations interpreting the quaternion as rotation have undefined behavior if the quaternion is not
+ * normalized.
+ *
+ * \sa  class AngleAxis, class Transform
+ */
+
+namespace internal {
+template <typename Scalar_, int Options_>
+struct traits<Quaternion<Scalar_, Options_> > {
+  typedef Quaternion<Scalar_, Options_> PlainObject;
+  typedef Scalar_ Scalar;
+  typedef Matrix<Scalar_, 4, 1, Options_> Coefficients;
+  enum { Alignment = internal::traits<Coefficients>::Alignment, Flags = LvalueBit };
+};
+}  // namespace internal
+
+template <typename Scalar_, int Options_>
+class Quaternion : public QuaternionBase<Quaternion<Scalar_, Options_> > {
+ public:
+  typedef QuaternionBase<Quaternion<Scalar_, Options_> > Base;
+  enum { NeedsAlignment = internal::traits<Quaternion>::Alignment > 0 };
+
+  typedef Scalar_ Scalar;
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Quaternion)
+  using Base::operator*=;
+
+  typedef typename internal::traits<Quaternion>::Coefficients Coefficients;
+  typedef typename Base::AngleAxisType AngleAxisType;
+
+  /** Default constructor leaving the quaternion uninitialized. */
+  EIGEN_DEVICE_FUNC inline Quaternion() {}
+
+  /** Constructs and initializes the quaternion \f$ w+xi+yj+zk \f$ from
+   * its four coefficients \a w, \a x, \a y and \a z.
+   *
+   * \warning Note the order of the arguments: the real \a w coefficient first,
+   * while internally the coefficients are stored in the following order:
+   * [\c x, \c y, \c z, \c w]
+   */
+  EIGEN_DEVICE_FUNC inline Quaternion(const Scalar& w, const Scalar& x, const Scalar& y, const Scalar& z)
+      : m_coeffs(x, y, z, w) {}
+
+  /** Constructs and initializes a quaternion from its real part as a scalar,
+   *  and its imaginary part as a 3-vector [\c x, \c y, \c z]
+   */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline Quaternion(const Scalar& w, const Eigen::MatrixBase<Derived>& vec)
+      : m_coeffs(vec.x(), vec.y(), vec.z(), w) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived, 3);
+  }
+
+  /** Constructs and initialize a quaternion from the array data */
+  EIGEN_DEVICE_FUNC explicit inline Quaternion(const Scalar* data) : m_coeffs(data) {}
+
+  /** Copy constructor */
+  template <class Derived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion(const QuaternionBase<Derived>& other) {
+    this->Base::operator=(other);
+  }
+
+  /** Constructs and initializes a quaternion from the angle-axis \a aa */
+  EIGEN_DEVICE_FUNC explicit inline Quaternion(const AngleAxisType& aa) { *this = aa; }
+
+  /** Constructs and initializes a quaternion from either:
+   *  - a rotation matrix expression,
+   *  - a 4D vector expression representing quaternion coefficients in the order [\c x, \c y, \c z, \c w].
+   */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC explicit inline Quaternion(const MatrixBase<Derived>& other) {
+    *this = other;
+  }
+
+  /** Explicit copy constructor with scalar conversion */
+  template <typename OtherScalar, int OtherOptions>
+  EIGEN_DEVICE_FUNC explicit inline Quaternion(const Quaternion<OtherScalar, OtherOptions>& other) {
+    m_coeffs = other.coeffs().template cast<Scalar>();
+  }
+
+  // We define a copy constructor, which means we don't get an implicit move constructor or assignment operator.
+  /** Default move constructor */
+  EIGEN_DEVICE_FUNC inline Quaternion(Quaternion&& other) noexcept(std::is_nothrow_move_constructible<Scalar>::value)
+      : m_coeffs(std::move(other.coeffs())) {}
+
+  /** Default move assignment operator */
+  EIGEN_DEVICE_FUNC Quaternion& operator=(Quaternion&& other) noexcept(std::is_nothrow_move_assignable<Scalar>::value) {
+    m_coeffs = std::move(other.coeffs());
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC static Quaternion UnitRandom();
+
+  EIGEN_DEVICE_FUNC static Quaternion FromCoeffsScalarLast(const Scalar& x, const Scalar& y, const Scalar& z,
+                                                           const Scalar& w);
+
+  EIGEN_DEVICE_FUNC static Quaternion FromCoeffsScalarFirst(const Scalar& w, const Scalar& x, const Scalar& y,
+                                                            const Scalar& z);
+
+  template <typename Derived1, typename Derived2>
+  EIGEN_DEVICE_FUNC static Quaternion FromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b);
+
+  EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs; }
+  EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs; }
+
+  EIGEN_DEVICE_FUNC inline Coefficients coeffsScalarLast() const { return m_coeffs; }
+
+  EIGEN_DEVICE_FUNC inline Coefficients coeffsScalarFirst() const {
+    return {m_coeffs.w(), m_coeffs.x(), m_coeffs.y(), m_coeffs.z()};
+  }
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(bool(NeedsAlignment))
+
+#ifdef EIGEN_QUATERNION_PLUGIN
+#include EIGEN_QUATERNION_PLUGIN
+#endif
+
+ protected:
+  Coefficients m_coeffs;
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  EIGEN_STATIC_ASSERT((Options_ & DontAlign) == Options_, INVALID_MATRIX_TEMPLATE_PARAMETERS)
+#endif
+};
+
+/** \ingroup Geometry_Module
+ * single precision quaternion type */
+typedef Quaternion<float> Quaternionf;
+/** \ingroup Geometry_Module
+ * double precision quaternion type */
+typedef Quaternion<double> Quaterniond;
+
+/***************************************************************************
+ * Specialization of Map<Quaternion<Scalar>>
+ ***************************************************************************/
+
+namespace internal {
+template <typename Scalar_, int Options_>
+struct traits<Map<Quaternion<Scalar_>, Options_> >
+    : traits<Quaternion<Scalar_, (int(Options_) & Aligned) == Aligned ? AutoAlign : DontAlign> > {
+  typedef Map<Matrix<Scalar_, 4, 1>, Options_> Coefficients;
+};
+}  // namespace internal
+
+namespace internal {
+template <typename Scalar_, int Options_>
+struct traits<Map<const Quaternion<Scalar_>, Options_> >
+    : traits<Quaternion<Scalar_, (int(Options_) & Aligned) == Aligned ? AutoAlign : DontAlign> > {
+  typedef Map<const Matrix<Scalar_, 4, 1>, Options_> Coefficients;
+  typedef traits<Quaternion<Scalar_, (int(Options_) & Aligned) == Aligned ? AutoAlign : DontAlign> > TraitsBase;
+  enum { Flags = TraitsBase::Flags & ~LvalueBit };
+};
+}  // namespace internal
+
+/** \ingroup Geometry_Module
+ * \brief Quaternion expression mapping a constant memory buffer
+ *
+ * \tparam Scalar_ the type of the Quaternion coefficients
+ * \tparam Options_ see class Map
+ *
+ * This is a specialization of class Map for Quaternion. This class allows to view
+ * a 4 scalar memory buffer as an Eigen's Quaternion object.
+ *
+ * \sa class Map, class Quaternion, class QuaternionBase
+ */
+template <typename Scalar_, int Options_>
+class Map<const Quaternion<Scalar_>, Options_> : public QuaternionBase<Map<const Quaternion<Scalar_>, Options_> > {
+ public:
+  typedef QuaternionBase<Map<const Quaternion<Scalar_>, Options_> > Base;
+
+  typedef Scalar_ Scalar;
+  typedef typename internal::traits<Map>::Coefficients Coefficients;
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
+  using Base::operator*=;
+
+  /** Constructs a Mapped Quaternion object from the pointer \a coeffs
+   *
+   * The pointer \a coeffs must reference the four coefficients of Quaternion in the following order:
+   * \code *coeffs == {x, y, z, w} \endcode
+   *
+   * If the template parameter Options_ is set to #Aligned, then the pointer coeffs must be aligned. */
+  EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE Map(const Scalar* coeffs) : m_coeffs(coeffs) {}
+
+  EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs; }
+
+  EIGEN_DEVICE_FUNC inline Coefficients coeffsScalarLast() const { return m_coeffs; }
+
+  EIGEN_DEVICE_FUNC inline Coefficients coeffsScalarFirst() const {
+    return {m_coeffs.w(), m_coeffs.x(), m_coeffs.y(), m_coeffs.z()};
+  }
+
+ protected:
+  const Coefficients m_coeffs;
+};
+
+/** \ingroup Geometry_Module
+ * \brief Expression of a quaternion from a memory buffer
+ *
+ * \tparam Scalar_ the type of the Quaternion coefficients
+ * \tparam Options_ see class Map
+ *
+ * This is a specialization of class Map for Quaternion. This class allows to view
+ * a 4 scalar memory buffer as an Eigen's  Quaternion object.
+ *
+ * \sa class Map, class Quaternion, class QuaternionBase
+ */
+template <typename Scalar_, int Options_>
+class Map<Quaternion<Scalar_>, Options_> : public QuaternionBase<Map<Quaternion<Scalar_>, Options_> > {
+ public:
+  typedef QuaternionBase<Map<Quaternion<Scalar_>, Options_> > Base;
+
+  typedef Scalar_ Scalar;
+  typedef typename internal::traits<Map>::Coefficients Coefficients;
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
+  using Base::operator*=;
+
+  /** Constructs a Mapped Quaternion object from the pointer \a coeffs
+   *
+   * The pointer \a coeffs must reference the four coefficients of Quaternion in the following order:
+   * \code *coeffs == {x, y, z, w} \endcode
+   *
+   * If the template parameter Options_ is set to #Aligned, then the pointer coeffs must be aligned. */
+  EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE Map(Scalar* coeffs) : m_coeffs(coeffs) {}
+
+  EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs; }
+  EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs; }
+
+  EIGEN_DEVICE_FUNC inline Coefficients coeffsScalarLast() const { return m_coeffs; }
+
+  EIGEN_DEVICE_FUNC inline Coefficients coeffsScalarFirst() const {
+    return {m_coeffs.w(), m_coeffs.x(), m_coeffs.y(), m_coeffs.z()};
+  }
+
+ protected:
+  Coefficients m_coeffs;
+};
+
+/** \ingroup Geometry_Module
+ * Map an unaligned array of single precision scalars as a quaternion */
+typedef Map<Quaternion<float>, 0> QuaternionMapf;
+/** \ingroup Geometry_Module
+ * Map an unaligned array of double precision scalars as a quaternion */
+typedef Map<Quaternion<double>, 0> QuaternionMapd;
+/** \ingroup Geometry_Module
+ * Map a 16-byte aligned array of single precision scalars as a quaternion */
+typedef Map<Quaternion<float>, Aligned> QuaternionMapAlignedf;
+/** \ingroup Geometry_Module
+ * Map a 16-byte aligned array of double precision scalars as a quaternion */
+typedef Map<Quaternion<double>, Aligned> QuaternionMapAlignedd;
+
+/***************************************************************************
+ * Implementation of QuaternionBase methods
+ ***************************************************************************/
+
+// Generic Quaternion * Quaternion product
+// This product can be specialized for a given architecture via the Arch template argument.
+namespace internal {
+template <int Arch, class Derived1, class Derived2, typename Scalar>
+struct quat_product {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived1>& a,
+                                                                      const QuaternionBase<Derived2>& b) {
+    return Quaternion<Scalar>(a.w() * b.w() - a.x() * b.x() - a.y() * b.y() - a.z() * b.z(),
+                              a.w() * b.x() + a.x() * b.w() + a.y() * b.z() - a.z() * b.y(),
+                              a.w() * b.y() + a.y() * b.w() + a.z() * b.x() - a.x() * b.z(),
+                              a.w() * b.z() + a.z() * b.w() + a.x() * b.y() - a.y() * b.x());
+  }
+};
+}  // namespace internal
+
+/** \returns the concatenation of two rotations as a quaternion-quaternion product */
+template <class Derived>
+template <class OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion<typename internal::traits<Derived>::Scalar>
+QuaternionBase<Derived>::operator*(const QuaternionBase<OtherDerived>& other) const {
+  EIGEN_STATIC_ASSERT(
+      (internal::is_same<typename Derived::Scalar, typename OtherDerived::Scalar>::value),
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+  return internal::quat_product<Architecture::Target, Derived, OtherDerived,
+                                typename internal::traits<Derived>::Scalar>::run(*this, other);
+}
+
+/** \sa operator*(Quaternion) */
+template <class Derived>
+template <class OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator*=(
+    const QuaternionBase<OtherDerived>& other) {
+  derived() = derived() * other.derived();
+  return derived();
+}
+
+/** Rotation of a vector by a quaternion.
+ * \remarks If the quaternion is used to rotate several points (>1)
+ * then it is much more efficient to first convert it to a 3x3 Matrix.
+ * Comparison of the operation cost for n transformations:
+ *   - Quaternion2:    30n
+ *   - Via a Matrix3: 24 + 15n
+ */
+template <class Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename QuaternionBase<Derived>::Vector3
+QuaternionBase<Derived>::_transformVector(const Vector3& v) const {
+  // Note that this algorithm comes from the optimization by hand
+  // of the conversion to a Matrix followed by a Matrix/Vector product.
+  // It appears to be much faster than the common algorithm found
+  // in the literature (30 versus 39 flops). It also requires two
+  // Vector3 as temporaries.
+  Vector3 uv = this->vec().cross(v);
+  uv += uv;
+  return v + this->w() * uv + this->vec().cross(uv);
+}
+
+template <class Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE QuaternionBase<Derived>& QuaternionBase<Derived>::operator=(
+    const QuaternionBase<Derived>& other) {
+  coeffs() = other.coeffs();
+  return derived();
+}
+
+template <class Derived>
+template <class OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator=(
+    const QuaternionBase<OtherDerived>& other) {
+  coeffs() = other.coeffs();
+  return derived();
+}
+
+/** Set \c *this from an angle-axis \a aa and returns a reference to \c *this
+ */
+template <class Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator=(const AngleAxisType& aa) {
+  EIGEN_USING_STD(cos)
+  EIGEN_USING_STD(sin)
+  Scalar ha = Scalar(0.5) * aa.angle();  // Scalar(0.5) to suppress precision loss warnings
+  this->w() = cos(ha);
+  this->vec() = sin(ha) * aa.axis();
+  return derived();
+}
+
+/** Set \c *this from the expression \a xpr:
+ *   - if \a xpr is a 4x1 vector, then \a xpr is assumed to be a quaternion
+ *   - if \a xpr is a 3x3 matrix, then \a xpr is assumed to be rotation matrix
+ *     and \a xpr is converted to a quaternion
+ */
+
+template <class Derived>
+template <class MatrixDerived>
+EIGEN_DEVICE_FUNC inline Derived& QuaternionBase<Derived>::operator=(const MatrixBase<MatrixDerived>& xpr) {
+  EIGEN_STATIC_ASSERT(
+      (internal::is_same<typename Derived::Scalar, typename MatrixDerived::Scalar>::value),
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+  internal::quaternionbase_assign_impl<MatrixDerived>::run(*this, xpr.derived());
+  return derived();
+}
+
+/** Convert the quaternion to a 3x3 rotation matrix. The quaternion is required to
+ * be normalized, otherwise the result is undefined.
+ */
+template <class Derived>
+EIGEN_DEVICE_FUNC inline typename QuaternionBase<Derived>::Matrix3 QuaternionBase<Derived>::toRotationMatrix(
+    void) const {
+  // NOTE if inlined, then gcc 4.2 and 4.4 get rid of the temporary (not gcc 4.3 !!)
+  // if not inlined then the cost of the return by value is huge ~ +35%,
+  // however, not inlining this function is an order of magnitude slower, so
+  // it has to be inlined, and so the return by value is not an issue
+  Matrix3 res;
+
+  const Scalar tx = Scalar(2) * this->x();
+  const Scalar ty = Scalar(2) * this->y();
+  const Scalar tz = Scalar(2) * this->z();
+  const Scalar twx = tx * this->w();
+  const Scalar twy = ty * this->w();
+  const Scalar twz = tz * this->w();
+  const Scalar txx = tx * this->x();
+  const Scalar txy = ty * this->x();
+  const Scalar txz = tz * this->x();
+  const Scalar tyy = ty * this->y();
+  const Scalar tyz = tz * this->y();
+  const Scalar tzz = tz * this->z();
+
+  res.coeffRef(0, 0) = Scalar(1) - (tyy + tzz);
+  res.coeffRef(0, 1) = txy - twz;
+  res.coeffRef(0, 2) = txz + twy;
+  res.coeffRef(1, 0) = txy + twz;
+  res.coeffRef(1, 1) = Scalar(1) - (txx + tzz);
+  res.coeffRef(1, 2) = tyz - twx;
+  res.coeffRef(2, 0) = txz - twy;
+  res.coeffRef(2, 1) = tyz + twx;
+  res.coeffRef(2, 2) = Scalar(1) - (txx + tyy);
+
+  return res;
+}
+
+/** Sets \c *this to be a quaternion representing a rotation between
+ * the two arbitrary vectors \a a and \a b. In other words, the built
+ * rotation represent a rotation sending the line of direction \a a
+ * to the line of direction \a b, both lines passing through the origin.
+ *
+ * \returns a reference to \c *this.
+ *
+ * Note that the two input vectors do \b not have to be normalized, and
+ * do not need to have the same norm.
+ */
+template <class Derived>
+template <typename Derived1, typename Derived2>
+EIGEN_DEVICE_FUNC inline Derived& QuaternionBase<Derived>::setFromTwoVectors(const MatrixBase<Derived1>& a,
+                                                                             const MatrixBase<Derived2>& b) {
+  EIGEN_USING_STD(sqrt)
+  Vector3 v0 = a.normalized();
+  Vector3 v1 = b.normalized();
+  Scalar c = v1.dot(v0);
+
+  // if dot == -1, vectors are nearly opposites
+  // => accurately compute the rotation axis by computing the
+  //    intersection of the two planes. This is done by solving:
+  //       x^T v0 = 0
+  //       x^T v1 = 0
+  //    under the constraint:
+  //       ||x|| = 1
+  //    which yields a singular value problem
+  if (c < Scalar(-1) + NumTraits<Scalar>::dummy_precision()) {
+    c = numext::maxi(c, Scalar(-1));
+    Matrix<Scalar, 2, 3> m;
+    m << v0.transpose(), v1.transpose();
+    JacobiSVD<Matrix<Scalar, 2, 3>, ComputeFullV> svd(m);
+    Vector3 axis = svd.matrixV().col(2);
+
+    Scalar w2 = (Scalar(1) + c) * Scalar(0.5);
+    this->w() = sqrt(w2);
+    this->vec() = axis * sqrt(Scalar(1) - w2);
+    return derived();
+  }
+  Vector3 axis = v0.cross(v1);
+  Scalar s = sqrt((Scalar(1) + c) * Scalar(2));
+  Scalar invs = Scalar(1) / s;
+  this->vec() = axis * invs;
+  this->w() = s * Scalar(0.5);
+
+  return derived();
+}
+
+/** \returns a random unit quaternion following a uniform distribution law on SO(3)
+ *
+ * \note The implementation is based on http://planning.cs.uiuc.edu/node198.html
+ */
+template <typename Scalar, int Options>
+EIGEN_DEVICE_FUNC Quaternion<Scalar, Options> Quaternion<Scalar, Options>::UnitRandom() {
+  EIGEN_USING_STD(sqrt)
+  EIGEN_USING_STD(sin)
+  EIGEN_USING_STD(cos)
+  const Scalar u1 = internal::random<Scalar>(0, 1), u2 = internal::random<Scalar>(0, 2 * EIGEN_PI),
+               u3 = internal::random<Scalar>(0, 2 * EIGEN_PI);
+  const Scalar a = sqrt(Scalar(1) - u1), b = sqrt(u1);
+  return Quaternion(a * sin(u2), a * cos(u2), b * sin(u3), b * cos(u3));
+}
+
+/** Constructs a quaternion from its coefficients in the order [\c x, \c y, \c z, \c w], i.e. vector part [\c x, \c y,
+ * \c z] first, scalar part \a w LAST.
+ *
+ * This factory accepts the parameters in the same order as the underlying coefficient vector. Consider using this
+ * factory function to make the parameter ordering explicit.
+ */
+template <typename Scalar, int Options>
+EIGEN_DEVICE_FUNC Quaternion<Scalar, Options> Quaternion<Scalar, Options>::FromCoeffsScalarLast(const Scalar& x,
+                                                                                                const Scalar& y,
+                                                                                                const Scalar& z,
+                                                                                                const Scalar& w) {
+  return Quaternion(w, x, y, z);
+}
+
+/** Constructs a quaternion from its coefficients in the order [\c w, \c x, \c y, \c z], i.e. scalar part \a w FIRST,
+ * vector part [\c x, \c y, \c z] last.
+ *
+ * This factory accepts the parameters in the same order as the constructor \code Quaternion(const Scalar& w, const
+ * Scalar& x, const Scalar& y, const Scalar& z) \endcode. Consider using this factory function to make the parameter
+ * ordering explicit.
+ */
+template <typename Scalar, int Options>
+EIGEN_DEVICE_FUNC Quaternion<Scalar, Options> Quaternion<Scalar, Options>::FromCoeffsScalarFirst(const Scalar& w,
+                                                                                                 const Scalar& x,
+                                                                                                 const Scalar& y,
+                                                                                                 const Scalar& z) {
+  return Quaternion(w, x, y, z);
+}
+
+/** Returns a quaternion representing a rotation between
+ * the two arbitrary vectors \a a and \a b. In other words, the built
+ * rotation represent a rotation sending the line of direction \a a
+ * to the line of direction \a b, both lines passing through the origin.
+ *
+ * \returns resulting quaternion
+ *
+ * Note that the two input vectors do \b not have to be normalized, and
+ * do not need to have the same norm.
+ */
+template <typename Scalar, int Options>
+template <typename Derived1, typename Derived2>
+EIGEN_DEVICE_FUNC Quaternion<Scalar, Options> Quaternion<Scalar, Options>::FromTwoVectors(
+    const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b) {
+  Quaternion quat;
+  quat.setFromTwoVectors(a, b);
+  return quat;
+}
+
+/** \returns the multiplicative inverse of \c *this
+ * Note that in most cases, i.e., if you simply want the opposite rotation,
+ * and/or the quaternion is normalized, then it is enough to use the conjugate.
+ *
+ * \sa QuaternionBase::conjugate()
+ */
+template <class Derived>
+EIGEN_DEVICE_FUNC inline Quaternion<typename internal::traits<Derived>::Scalar> QuaternionBase<Derived>::inverse()
+    const {
+  // FIXME: consider renaming to multiplicativeInverse() and renaming conjugate() to inverse() or opposite().
+  Scalar n2 = this->squaredNorm();
+  if (n2 > Scalar(0))
+    return Quaternion<Scalar>(conjugate().coeffs() / n2);
+  else {
+    // return an invalid result to flag the error
+    return Quaternion<Scalar>(Coefficients::Zero());
+  }
+}
+
+// Generic conjugate of a Quaternion
+namespace internal {
+template <int Arch, class Derived, typename Scalar>
+struct quat_conj {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived>& q) {
+    return Quaternion<Scalar>(q.w(), -q.x(), -q.y(), -q.z());
+  }
+};
+}  // namespace internal
+
+/** \returns the conjugate of the \c *this which is equal to the multiplicative inverse
+ * if the quaternion is normalized.
+ * The conjugate of a quaternion represents the opposite rotation.
+ *
+ * \sa Quaternion2::inverse()
+ */
+template <class Derived>
+EIGEN_DEVICE_FUNC inline Quaternion<typename internal::traits<Derived>::Scalar> QuaternionBase<Derived>::conjugate()
+    const {
+  return internal::quat_conj<Architecture::Target, Derived, typename internal::traits<Derived>::Scalar>::run(*this);
+}
+
+/** \returns the angle (in radian) between two rotations
+ * \sa dot()
+ */
+template <class Derived>
+template <class OtherDerived>
+EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar QuaternionBase<Derived>::angularDistance(
+    const QuaternionBase<OtherDerived>& other) const {
+  EIGEN_USING_STD(atan2)
+  Quaternion<Scalar> d = (*this) * other.conjugate();
+  return Scalar(2) * atan2(d.vec().norm(), numext::abs(d.w()));
+}
+
+/** \returns the spherical linear interpolation between the two quaternions
+ * \c *this and \a other at the parameter \a t in [0;1].
+ *
+ * This represents an interpolation for a constant motion between \c *this and \a other,
+ * see also http://en.wikipedia.org/wiki/Slerp.
+ */
+template <class Derived>
+template <class OtherDerived>
+EIGEN_DEVICE_FUNC Quaternion<typename internal::traits<Derived>::Scalar> QuaternionBase<Derived>::slerp(
+    const Scalar& t, const QuaternionBase<OtherDerived>& other) const {
+  EIGEN_USING_STD(acos)
+  EIGEN_USING_STD(sin)
+  const Scalar one = Scalar(1) - NumTraits<Scalar>::epsilon();
+  Scalar d = this->dot(other);
+  Scalar absD = numext::abs(d);
+
+  Scalar scale0;
+  Scalar scale1;
+
+  if (absD >= one) {
+    scale0 = Scalar(1) - t;
+    scale1 = t;
+  } else {
+    // theta is the angle between the 2 quaternions
+    Scalar theta = acos(absD);
+    Scalar sinTheta = numext::sqrt(Scalar(1) - absD * absD);
+
+    scale0 = sin((Scalar(1) - t) * theta) / sinTheta;
+    scale1 = sin((t * theta)) / sinTheta;
+  }
+  if (d < Scalar(0)) scale1 = -scale1;
+
+  return Quaternion<Scalar>(scale0 * coeffs() + scale1 * other.coeffs());
+}
+
+namespace internal {
+
+// set from a rotation matrix
+template <typename Other>
+struct quaternionbase_assign_impl<Other, 3, 3> {
+  typedef typename Other::Scalar Scalar;
+  template <class Derived>
+  EIGEN_DEVICE_FUNC static inline void run(QuaternionBase<Derived>& q, const Other& a_mat) {
+    const typename internal::nested_eval<Other, 2>::type mat(a_mat);
+    EIGEN_USING_STD(sqrt)
+    // This algorithm comes from  "Quaternion Calculus and Fast Animation",
+    // Ken Shoemake, 1987 SIGGRAPH course notes
+    Scalar t = mat.trace();
+    if (t > Scalar(0)) {
+      t = sqrt(t + Scalar(1.0));
+      q.w() = Scalar(0.5) * t;
+      t = Scalar(0.5) / t;
+      q.x() = (mat.coeff(2, 1) - mat.coeff(1, 2)) * t;
+      q.y() = (mat.coeff(0, 2) - mat.coeff(2, 0)) * t;
+      q.z() = (mat.coeff(1, 0) - mat.coeff(0, 1)) * t;
+    } else {
+      Index i = 0;
+      if (mat.coeff(1, 1) > mat.coeff(0, 0)) i = 1;
+      if (mat.coeff(2, 2) > mat.coeff(i, i)) i = 2;
+      Index j = (i + 1) % 3;
+      Index k = (j + 1) % 3;
+
+      t = sqrt(mat.coeff(i, i) - mat.coeff(j, j) - mat.coeff(k, k) + Scalar(1.0));
+      q.coeffs().coeffRef(i) = Scalar(0.5) * t;
+      t = Scalar(0.5) / t;
+      q.w() = (mat.coeff(k, j) - mat.coeff(j, k)) * t;
+      q.coeffs().coeffRef(j) = (mat.coeff(j, i) + mat.coeff(i, j)) * t;
+      q.coeffs().coeffRef(k) = (mat.coeff(k, i) + mat.coeff(i, k)) * t;
+    }
+  }
+};
+
+// set from a vector of coefficients assumed to be a quaternion
+template <typename Other>
+struct quaternionbase_assign_impl<Other, 4, 1> {
+  typedef typename Other::Scalar Scalar;
+  template <class Derived>
+  EIGEN_DEVICE_FUNC static inline void run(QuaternionBase<Derived>& q, const Other& vec) {
+    q.coeffs() = vec;
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_QUATERNION_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Rotation2D.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Rotation2D.h
new file mode 100644
index 0000000..515bd27
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Rotation2D.h
@@ -0,0 +1,201 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ROTATION2D_H
+#define EIGEN_ROTATION2D_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \class Rotation2D
+ *
+ * \brief Represents a rotation/orientation in a 2 dimensional space.
+ *
+ * \tparam Scalar_ the scalar type, i.e., the type of the coefficients
+ *
+ * This class is equivalent to a single scalar representing a counter-clockwise rotation
+ * as a single angle in radian. It provides some additional features such as the automatic
+ * conversion from/to a 2x2 rotation matrix. Moreover this class aims to provide a similar
+ * interface to Quaternion in order to facilitate the writing of generic algorithms
+ * dealing with rotations.
+ *
+ * \sa class Quaternion, class Transform
+ */
+
+namespace internal {
+
+template <typename Scalar_>
+struct traits<Rotation2D<Scalar_> > {
+  typedef Scalar_ Scalar;
+};
+}  // end namespace internal
+
+template <typename Scalar_>
+class Rotation2D : public RotationBase<Rotation2D<Scalar_>, 2> {
+  typedef RotationBase<Rotation2D<Scalar_>, 2> Base;
+
+ public:
+  using Base::operator*;
+
+  enum { Dim = 2 };
+  /** the scalar type of the coefficients */
+  typedef Scalar_ Scalar;
+  typedef Matrix<Scalar, 2, 1> Vector2;
+  typedef Matrix<Scalar, 2, 2> Matrix2;
+
+ protected:
+  Scalar m_angle;
+
+ public:
+  /** Construct a 2D counter-clockwise rotation from the angle \a a in radian. */
+  EIGEN_DEVICE_FUNC explicit inline Rotation2D(const Scalar& a) : m_angle(a) {}
+
+  /** Default constructor without initialization. The represented rotation is undefined. */
+  EIGEN_DEVICE_FUNC Rotation2D() {}
+
+  /** Construct a 2D rotation from a 2x2 rotation matrix \a mat.
+   *
+   * \sa fromRotationMatrix()
+   */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC explicit Rotation2D(const MatrixBase<Derived>& m) {
+    fromRotationMatrix(m.derived());
+  }
+
+  /** \returns the rotation angle */
+  EIGEN_DEVICE_FUNC inline Scalar angle() const { return m_angle; }
+
+  /** \returns a read-write reference to the rotation angle */
+  EIGEN_DEVICE_FUNC inline Scalar& angle() { return m_angle; }
+
+  /** \returns the rotation angle in [0,2pi] */
+  EIGEN_DEVICE_FUNC inline Scalar smallestPositiveAngle() const {
+    Scalar tmp = numext::fmod(m_angle, Scalar(2 * EIGEN_PI));
+    return tmp < Scalar(0) ? tmp + Scalar(2 * EIGEN_PI) : tmp;
+  }
+
+  /** \returns the rotation angle in [-pi,pi] */
+  EIGEN_DEVICE_FUNC inline Scalar smallestAngle() const {
+    Scalar tmp = numext::fmod(m_angle, Scalar(2 * EIGEN_PI));
+    if (tmp > Scalar(EIGEN_PI))
+      tmp -= Scalar(2 * EIGEN_PI);
+    else if (tmp < -Scalar(EIGEN_PI))
+      tmp += Scalar(2 * EIGEN_PI);
+    return tmp;
+  }
+
+  /** \returns the inverse rotation */
+  EIGEN_DEVICE_FUNC inline Rotation2D inverse() const { return Rotation2D(-m_angle); }
+
+  /** Concatenates two rotations */
+  EIGEN_DEVICE_FUNC inline Rotation2D operator*(const Rotation2D& other) const {
+    return Rotation2D(m_angle + other.m_angle);
+  }
+
+  /** Concatenates two rotations */
+  EIGEN_DEVICE_FUNC inline Rotation2D& operator*=(const Rotation2D& other) {
+    m_angle += other.m_angle;
+    return *this;
+  }
+
+  /** Applies the rotation to a 2D vector */
+  EIGEN_DEVICE_FUNC Vector2 operator*(const Vector2& vec) const { return toRotationMatrix() * vec; }
+
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC Rotation2D& fromRotationMatrix(const MatrixBase<Derived>& m);
+  EIGEN_DEVICE_FUNC Matrix2 toRotationMatrix() const;
+
+  /** Set \c *this from a 2x2 rotation matrix \a mat.
+   * In other words, this function extract the rotation angle from the rotation matrix.
+   *
+   * This method is an alias for fromRotationMatrix()
+   *
+   * \sa fromRotationMatrix()
+   */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC Rotation2D& operator=(const MatrixBase<Derived>& m) {
+    return fromRotationMatrix(m.derived());
+  }
+
+  /** \returns the spherical interpolation between \c *this and \a other using
+   * parameter \a t. It is in fact equivalent to a linear interpolation.
+   */
+  EIGEN_DEVICE_FUNC inline Rotation2D slerp(const Scalar& t, const Rotation2D& other) const {
+    Scalar dist = Rotation2D(other.m_angle - m_angle).smallestAngle();
+    return Rotation2D(m_angle + dist * t);
+  }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+   *
+   * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+   * then this function smartly returns a const reference to \c *this.
+   */
+  template <typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Rotation2D, Rotation2D<NewScalarType> >::type cast()
+      const {
+    return typename internal::cast_return_type<Rotation2D, Rotation2D<NewScalarType> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template <typename OtherScalarType>
+  EIGEN_DEVICE_FUNC inline explicit Rotation2D(const Rotation2D<OtherScalarType>& other) {
+    m_angle = Scalar(other.angle());
+  }
+
+  EIGEN_DEVICE_FUNC static inline Rotation2D Identity() { return Rotation2D(0); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+   * determined by \a prec.
+   *
+   * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const Rotation2D& other, const typename NumTraits<Scalar>::Real& prec =
+                                                               NumTraits<Scalar>::dummy_precision()) const {
+    return internal::isApprox(m_angle, other.m_angle, prec);
+  }
+};
+
+/** \ingroup Geometry_Module
+ * single precision 2D rotation type */
+typedef Rotation2D<float> Rotation2Df;
+/** \ingroup Geometry_Module
+ * double precision 2D rotation type */
+typedef Rotation2D<double> Rotation2Dd;
+
+/** Set \c *this from a 2x2 rotation matrix \a mat.
+ * In other words, this function extract the rotation angle
+ * from the rotation matrix.
+ */
+template <typename Scalar>
+template <typename Derived>
+EIGEN_DEVICE_FUNC Rotation2D<Scalar>& Rotation2D<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat) {
+  EIGEN_USING_STD(atan2)
+  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime == 2 && Derived::ColsAtCompileTime == 2,
+                      YOU_MADE_A_PROGRAMMING_MISTAKE)
+  m_angle = atan2(mat.coeff(1, 0), mat.coeff(0, 0));
+  return *this;
+}
+
+/** Constructs and \returns an equivalent 2x2 rotation matrix.
+ */
+template <typename Scalar>
+typename Rotation2D<Scalar>::Matrix2 EIGEN_DEVICE_FUNC Rotation2D<Scalar>::toRotationMatrix(void) const {
+  EIGEN_USING_STD(sin)
+  EIGEN_USING_STD(cos)
+  Scalar sinA = sin(m_angle);
+  Scalar cosA = cos(m_angle);
+  return (Matrix2() << cosA, -sinA, sinA, cosA).finished();
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ROTATION2D_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/RotationBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/RotationBase.h
new file mode 100644
index 0000000..3a3a3e3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/RotationBase.h
@@ -0,0 +1,209 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ROTATIONBASE_H
+#define EIGEN_ROTATIONBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// forward declaration
+namespace internal {
+template <typename RotationDerived, typename MatrixType, bool IsVector = MatrixType::IsVectorAtCompileTime>
+struct rotation_base_generic_product_selector;
+}
+
+/** \class RotationBase
+ *
+ * \brief Common base class for compact rotation representations
+ *
+ * \tparam Derived is the derived type, i.e., a rotation type
+ * \tparam Dim_ the dimension of the space
+ */
+template <typename Derived, int Dim_>
+class RotationBase {
+ public:
+  enum { Dim = Dim_ };
+  /** the scalar type of the coefficients */
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+
+  /** corresponding linear transformation matrix type */
+  typedef Matrix<Scalar, Dim, Dim> RotationMatrixType;
+  typedef Matrix<Scalar, Dim, 1> VectorType;
+
+ public:
+  EIGEN_DEVICE_FUNC inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+  EIGEN_DEVICE_FUNC inline Derived& derived() { return *static_cast<Derived*>(this); }
+
+  /** \returns an equivalent rotation matrix */
+  EIGEN_DEVICE_FUNC inline RotationMatrixType toRotationMatrix() const { return derived().toRotationMatrix(); }
+
+  /** \returns an equivalent rotation matrix
+   * This function is added to be conform with the Transform class' naming scheme.
+   */
+  EIGEN_DEVICE_FUNC inline RotationMatrixType matrix() const { return derived().toRotationMatrix(); }
+
+  /** \returns the inverse rotation */
+  EIGEN_DEVICE_FUNC inline Derived inverse() const { return derived().inverse(); }
+
+  /** \returns the concatenation of the rotation \c *this with a translation \a t */
+  EIGEN_DEVICE_FUNC inline Transform<Scalar, Dim, Isometry> operator*(const Translation<Scalar, Dim>& t) const {
+    return Transform<Scalar, Dim, Isometry>(*this) * t;
+  }
+
+  /** \returns the concatenation of the rotation \c *this with a uniform scaling \a s */
+  EIGEN_DEVICE_FUNC inline RotationMatrixType operator*(const UniformScaling<Scalar>& s) const {
+    return toRotationMatrix() * s.factor();
+  }
+
+  /** \returns the concatenation of the rotation \c *this with a generic expression \a e
+   * \a e can be:
+   *  - a DimxDim linear transformation matrix
+   *  - a DimxDim diagonal matrix (axis aligned scaling)
+   *  - a vector of size Dim
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      typename internal::rotation_base_generic_product_selector<Derived, OtherDerived,
+                                                                OtherDerived::IsVectorAtCompileTime>::ReturnType
+      operator*(const EigenBase<OtherDerived>& e) const {
+    return internal::rotation_base_generic_product_selector<Derived, OtherDerived>::run(derived(), e.derived());
+  }
+
+  /** \returns the concatenation of a linear transformation \a l with the rotation \a r */
+  template <typename OtherDerived>
+  friend EIGEN_DEVICE_FUNC inline RotationMatrixType operator*(const EigenBase<OtherDerived>& l, const Derived& r) {
+    return l.derived() * r.toRotationMatrix();
+  }
+
+  /** \returns the concatenation of a scaling \a l with the rotation \a r */
+  EIGEN_DEVICE_FUNC friend inline Transform<Scalar, Dim, Affine> operator*(const DiagonalMatrix<Scalar, Dim>& l,
+                                                                           const Derived& r) {
+    Transform<Scalar, Dim, Affine> res(r);
+    res.linear().applyOnTheLeft(l);
+    return res;
+  }
+
+  /** \returns the concatenation of the rotation \c *this with a transformation \a t */
+  template <int Mode, int Options>
+  EIGEN_DEVICE_FUNC inline Transform<Scalar, Dim, Mode> operator*(
+      const Transform<Scalar, Dim, Mode, Options>& t) const {
+    return toRotationMatrix() * t;
+  }
+
+  template <typename OtherVectorType>
+  EIGEN_DEVICE_FUNC inline VectorType _transformVector(const OtherVectorType& v) const {
+    return toRotationMatrix() * v;
+  }
+};
+
+namespace internal {
+
+// implementation of the generic product rotation * matrix
+template <typename RotationDerived, typename MatrixType>
+struct rotation_base_generic_product_selector<RotationDerived, MatrixType, false> {
+  enum { Dim = RotationDerived::Dim };
+  typedef Matrix<typename RotationDerived::Scalar, Dim, Dim> ReturnType;
+  EIGEN_DEVICE_FUNC static inline ReturnType run(const RotationDerived& r, const MatrixType& m) {
+    return r.toRotationMatrix() * m;
+  }
+};
+
+template <typename RotationDerived, typename Scalar, int Dim, int MaxDim>
+struct rotation_base_generic_product_selector<RotationDerived, DiagonalMatrix<Scalar, Dim, MaxDim>, false> {
+  typedef Transform<Scalar, Dim, Affine> ReturnType;
+  EIGEN_DEVICE_FUNC static inline ReturnType run(const RotationDerived& r,
+                                                 const DiagonalMatrix<Scalar, Dim, MaxDim>& m) {
+    ReturnType res(r);
+    res.linear() *= m;
+    return res;
+  }
+};
+
+template <typename RotationDerived, typename OtherVectorType>
+struct rotation_base_generic_product_selector<RotationDerived, OtherVectorType, true> {
+  enum { Dim = RotationDerived::Dim };
+  typedef Matrix<typename RotationDerived::Scalar, Dim, 1> ReturnType;
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE ReturnType run(const RotationDerived& r, const OtherVectorType& v) {
+    return r._transformVector(v);
+  }
+};
+
+}  // end namespace internal
+
+/** \geometry_module
+ *
+ * \brief Constructs a Dim x Dim rotation matrix from the rotation \a r
+ */
+template <typename Scalar_, int Rows_, int Cols_, int Storage_, int MaxRows_, int MaxCols_>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC Matrix<Scalar_, Rows_, Cols_, Storage_, MaxRows_, MaxCols_>::Matrix(
+    const RotationBase<OtherDerived, ColsAtCompileTime>& r) {
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix, int(OtherDerived::Dim), int(OtherDerived::Dim))
+  *this = r.toRotationMatrix();
+}
+
+/** \geometry_module
+ *
+ * \brief Set a Dim x Dim rotation matrix from the rotation \a r
+ */
+template <typename Scalar_, int Rows_, int Cols_, int Storage_, int MaxRows_, int MaxCols_>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC Matrix<Scalar_, Rows_, Cols_, Storage_, MaxRows_, MaxCols_>&
+Matrix<Scalar_, Rows_, Cols_, Storage_, MaxRows_, MaxCols_>::operator=(
+    const RotationBase<OtherDerived, ColsAtCompileTime>& r) {
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix, int(OtherDerived::Dim), int(OtherDerived::Dim))
+  return *this = r.toRotationMatrix();
+}
+
+namespace internal {
+
+/** \internal
+ *
+ * Helper function to return an arbitrary rotation object to a rotation matrix.
+ *
+ * \tparam Scalar the numeric type of the matrix coefficients
+ * \tparam Dim the dimension of the current space
+ *
+ * It returns a Dim x Dim fixed size matrix.
+ *
+ * Default specializations are provided for:
+ *   - any scalar type (2D),
+ *   - any matrix expression,
+ *   - any type based on RotationBase (e.g., Quaternion, AngleAxis, Rotation2D)
+ *
+ * Currently toRotationMatrix is only used by Transform.
+ *
+ * \sa class Transform, class Rotation2D, class Quaternion, class AngleAxis
+ */
+template <typename Scalar, int Dim>
+EIGEN_DEVICE_FUNC static inline Matrix<Scalar, 2, 2> toRotationMatrix(const Scalar& s) {
+  EIGEN_STATIC_ASSERT(Dim == 2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  return Rotation2D<Scalar>(s).toRotationMatrix();
+}
+
+template <typename Scalar, int Dim, typename OtherDerived>
+EIGEN_DEVICE_FUNC static inline Matrix<Scalar, Dim, Dim> toRotationMatrix(const RotationBase<OtherDerived, Dim>& r) {
+  return r.toRotationMatrix();
+}
+
+template <typename Scalar, int Dim, typename OtherDerived>
+EIGEN_DEVICE_FUNC static inline const MatrixBase<OtherDerived>& toRotationMatrix(const MatrixBase<OtherDerived>& mat) {
+  EIGEN_STATIC_ASSERT(OtherDerived::RowsAtCompileTime == Dim && OtherDerived::ColsAtCompileTime == Dim,
+                      YOU_MADE_A_PROGRAMMING_MISTAKE)
+  return mat;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ROTATIONBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Scaling.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Scaling.h
new file mode 100644
index 0000000..5bb7c45
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Scaling.h
@@ -0,0 +1,195 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SCALING_H
+#define EIGEN_SCALING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \class UniformScaling
+ *
+ * \brief Represents a generic uniform scaling transformation
+ *
+ * \tparam Scalar_ the scalar type, i.e., the type of the coefficients.
+ *
+ * This class represent a uniform scaling transformation. It is the return
+ * type of Scaling(Scalar), and most of the time this is the only way it
+ * is used. In particular, this class is not aimed to be used to store a scaling transformation,
+ * but rather to make easier the constructions and updates of Transform objects.
+ *
+ * To represent an axis aligned scaling, use the DiagonalMatrix class.
+ *
+ * \sa Scaling(), class DiagonalMatrix, MatrixBase::asDiagonal(), class Translation, class Transform
+ */
+
+namespace internal {
+// This helper helps nvcc+MSVC to properly parse this file.
+// See bug 1412.
+template <typename Scalar, int Dim, int Mode>
+struct uniformscaling_times_affine_returntype {
+  enum { NewMode = int(Mode) == int(Isometry) ? Affine : Mode };
+  typedef Transform<Scalar, Dim, NewMode> type;
+};
+}  // namespace internal
+
+template <typename Scalar_>
+class UniformScaling {
+ public:
+  /** the scalar type of the coefficients */
+  typedef Scalar_ Scalar;
+
+ protected:
+  Scalar m_factor;
+
+ public:
+  /** Default constructor without initialization. */
+  UniformScaling() {}
+  /** Constructs and initialize a uniform scaling transformation */
+  explicit inline UniformScaling(const Scalar& s) : m_factor(s) {}
+
+  inline const Scalar& factor() const { return m_factor; }
+  inline Scalar& factor() { return m_factor; }
+
+  /** Concatenates two uniform scaling */
+  inline UniformScaling operator*(const UniformScaling& other) const {
+    return UniformScaling(m_factor * other.factor());
+  }
+
+  /** Concatenates a uniform scaling and a translation */
+  template <int Dim>
+  inline Transform<Scalar, Dim, Affine> operator*(const Translation<Scalar, Dim>& t) const;
+
+  /** Concatenates a uniform scaling and an affine transformation */
+  template <int Dim, int Mode, int Options>
+  inline typename internal::uniformscaling_times_affine_returntype<Scalar, Dim, Mode>::type operator*(
+      const Transform<Scalar, Dim, Mode, Options>& t) const {
+    typename internal::uniformscaling_times_affine_returntype<Scalar, Dim, Mode>::type res = t;
+    res.prescale(factor());
+    return res;
+  }
+
+  /** Concatenates a uniform scaling and a linear transformation matrix */
+  // TODO: return an expression instead of a dense matrix.
+  template <typename Derived>
+  inline typename Eigen::internal::plain_matrix_type<Derived>::type operator*(const MatrixBase<Derived>& other) const {
+    return other * m_factor;
+  }
+
+  template <typename Derived, int Dim>
+  inline Matrix<Scalar, Dim, Dim> operator*(const RotationBase<Derived, Dim>& r) const {
+    return r.toRotationMatrix() * m_factor;
+  }
+
+  /** \returns the inverse scaling */
+  inline UniformScaling inverse() const { return UniformScaling(Scalar(1) / m_factor); }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+   *
+   * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+   * then this function smartly returns a const reference to \c *this.
+   */
+  template <typename NewScalarType>
+  inline UniformScaling<NewScalarType> cast() const {
+    return UniformScaling<NewScalarType>(NewScalarType(m_factor));
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template <typename OtherScalarType>
+  inline explicit UniformScaling(const UniformScaling<OtherScalarType>& other) {
+    m_factor = Scalar(other.factor());
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+   * determined by \a prec.
+   *
+   * \sa MatrixBase::isApprox() */
+  bool isApprox(const UniformScaling& other,
+                const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const {
+    return internal::isApprox(m_factor, other.factor(), prec);
+  }
+};
+
+/** \addtogroup Geometry_Module */
+//@{
+
+/** Concatenates a linear transformation matrix and a uniform scaling
+ * \relates UniformScaling
+ */
+// NOTE this operator is defined in MatrixBase and not as a friend function
+// of UniformScaling to fix an internal crash of Intel's ICC
+template <typename Derived, typename Scalar>
+EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived, Scalar, product)
+operator*(const MatrixBase<Derived>& matrix, const UniformScaling<Scalar>& s) {
+  return matrix.derived() * s.factor();
+}
+
+/** Constructs a uniform scaling from scale factor \a s */
+inline UniformScaling<float> Scaling(float s) { return UniformScaling<float>(s); }
+/** Constructs a uniform scaling from scale factor \a s */
+inline UniformScaling<double> Scaling(double s) { return UniformScaling<double>(s); }
+/** Constructs a uniform scaling from scale factor \a s */
+template <typename RealScalar>
+inline UniformScaling<std::complex<RealScalar> > Scaling(const std::complex<RealScalar>& s) {
+  return UniformScaling<std::complex<RealScalar> >(s);
+}
+
+/** Constructs a 2D axis aligned scaling */
+template <typename Scalar>
+inline DiagonalMatrix<Scalar, 2> Scaling(const Scalar& sx, const Scalar& sy) {
+  return DiagonalMatrix<Scalar, 2>(sx, sy);
+}
+/** Constructs a 3D axis aligned scaling */
+template <typename Scalar>
+inline DiagonalMatrix<Scalar, 3> Scaling(const Scalar& sx, const Scalar& sy, const Scalar& sz) {
+  return DiagonalMatrix<Scalar, 3>(sx, sy, sz);
+}
+
+/** Constructs an axis aligned scaling expression from vector expression \a coeffs
+ * This is an alias for coeffs.asDiagonal()
+ */
+template <typename Derived>
+inline const DiagonalWrapper<const Derived> Scaling(const MatrixBase<Derived>& coeffs) {
+  return coeffs.asDiagonal();
+}
+
+/** Constructs an axis aligned scaling expression from vector \a coeffs when passed as an rvalue reference */
+template <typename Derived>
+inline typename DiagonalWrapper<const Derived>::PlainObject Scaling(MatrixBase<Derived>&& coeffs) {
+  return typename DiagonalWrapper<const Derived>::PlainObject(std::move(coeffs.derived()));
+}
+
+/** \deprecated */
+typedef DiagonalMatrix<float, 2> AlignedScaling2f;
+/** \deprecated */
+typedef DiagonalMatrix<double, 2> AlignedScaling2d;
+/** \deprecated */
+typedef DiagonalMatrix<float, 3> AlignedScaling3f;
+/** \deprecated */
+typedef DiagonalMatrix<double, 3> AlignedScaling3d;
+//@}
+
+template <typename Scalar>
+template <int Dim>
+inline Transform<Scalar, Dim, Affine> UniformScaling<Scalar>::operator*(const Translation<Scalar, Dim>& t) const {
+  Transform<Scalar, Dim, Affine> res;
+  res.matrix().setZero();
+  res.linear().diagonal().fill(factor());
+  res.translation() = factor() * t.vector();
+  res(Dim, Dim) = Scalar(1);
+  return res;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SCALING_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Transform.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Transform.h
new file mode 100644
index 0000000..88b6b30
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Transform.h
@@ -0,0 +1,1494 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRANSFORM_H
+#define EIGEN_TRANSFORM_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Transform>
+struct transform_traits {
+  enum {
+    Dim = Transform::Dim,
+    HDim = Transform::HDim,
+    Mode = Transform::Mode,
+    IsProjective = (int(Mode) == int(Projective))
+  };
+};
+
+template <typename TransformType, typename MatrixType,
+          int Case = transform_traits<TransformType>::IsProjective                                      ? 0
+                     : int(MatrixType::RowsAtCompileTime) == int(transform_traits<TransformType>::HDim) ? 1
+                                                                                                        : 2,
+          int RhsCols = MatrixType::ColsAtCompileTime>
+struct transform_right_product_impl;
+
+template <typename Other, int Mode, int Options, int Dim, int HDim, int OtherRows = Other::RowsAtCompileTime,
+          int OtherCols = Other::ColsAtCompileTime>
+struct transform_left_product_impl;
+
+template <typename Lhs, typename Rhs,
+          bool AnyProjective = transform_traits<Lhs>::IsProjective || transform_traits<Rhs>::IsProjective>
+struct transform_transform_product_impl;
+
+template <typename Other, int Mode, int Options, int Dim, int HDim, int OtherRows = Other::RowsAtCompileTime,
+          int OtherCols = Other::ColsAtCompileTime>
+struct transform_construct_from_matrix;
+
+template <typename TransformType>
+struct transform_take_affine_part;
+
+template <typename Scalar_, int Dim_, int Mode_, int Options_>
+struct traits<Transform<Scalar_, Dim_, Mode_, Options_> > {
+  typedef Scalar_ Scalar;
+  typedef Eigen::Index StorageIndex;
+  typedef Dense StorageKind;
+  enum {
+    Dim1 = Dim_ == Dynamic ? Dim_ : Dim_ + 1,
+    RowsAtCompileTime = Mode_ == Projective ? Dim1 : Dim_,
+    ColsAtCompileTime = Dim1,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    Flags = 0
+  };
+};
+
+template <int Mode>
+struct transform_make_affine;
+
+}  // end namespace internal
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \class Transform
+ *
+ * \brief Represents an homogeneous transformation in a N dimensional space
+ *
+ * \tparam Scalar_ the scalar type, i.e., the type of the coefficients
+ * \tparam Dim_ the dimension of the space
+ * \tparam Mode_ the type of the transformation. Can be:
+ *              - #Affine: the transformation is stored as a (Dim+1)^2 matrix,
+ *                         where the last row is assumed to be [0 ... 0 1].
+ *              - #AffineCompact: the transformation is stored as a (Dim)x(Dim+1) matrix.
+ *              - #Projective: the transformation is stored as a (Dim+1)^2 matrix
+ *                             without any assumption.
+ *              - #Isometry: same as #Affine with the additional assumption that
+ *                           the linear part represents a rotation. This assumption is exploited
+ *                           to speed up some functions such as inverse() and rotation().
+ * \tparam Options_ has the same meaning as in class Matrix. It allows to specify DontAlign and/or RowMajor.
+ *                  These Options are passed directly to the underlying matrix type.
+ *
+ * The homography is internally represented and stored by a matrix which
+ * is available through the matrix() method. To understand the behavior of
+ * this class you have to think a Transform object as its internal
+ * matrix representation. The chosen convention is right multiply:
+ *
+ * \code v' = T * v \endcode
+ *
+ * Therefore, an affine transformation matrix M is shaped like this:
+ *
+ * \f$ \left( \begin{array}{cc}
+ * linear & translation\\
+ * 0 ... 0 & 1
+ * \end{array} \right) \f$
+ *
+ * Note that for a projective transformation the last row can be anything,
+ * and then the interpretation of different parts might be slightly different.
+ *
+ * However, unlike a plain matrix, the Transform class provides many features
+ * simplifying both its assembly and usage. In particular, it can be composed
+ * with any other transformations (Transform,Translation,RotationBase,DiagonalMatrix)
+ * and can be directly used to transform implicit homogeneous vectors. All these
+ * operations are handled via the operator*. For the composition of transformations,
+ * its principle consists to first convert the right/left hand sides of the product
+ * to a compatible (Dim+1)^2 matrix and then perform a pure matrix product.
+ * Of course, internally, operator* tries to perform the minimal number of operations
+ * according to the nature of each terms. Likewise, when applying the transform
+ * to points, the latters are automatically promoted to homogeneous vectors
+ * before doing the matrix product. The conventions to homogeneous representations
+ * are performed as follow:
+ *
+ * \b Translation t (Dim)x(1):
+ * \f$ \left( \begin{array}{cc}
+ * I & t \\
+ * 0\,...\,0 & 1
+ * \end{array} \right) \f$
+ *
+ * \b Rotation R (Dim)x(Dim):
+ * \f$ \left( \begin{array}{cc}
+ * R & 0\\
+ * 0\,...\,0 & 1
+ * \end{array} \right) \f$
+ *<!--
+ * \b Linear \b Matrix L (Dim)x(Dim):
+ * \f$ \left( \begin{array}{cc}
+ * L & 0\\
+ * 0\,...\,0 & 1
+ * \end{array} \right) \f$
+ *
+ * \b Affine \b Matrix A (Dim)x(Dim+1):
+ * \f$ \left( \begin{array}{c}
+ * A\\
+ * 0\,...\,0\,1
+ * \end{array} \right) \f$
+ *-->
+ * \b Scaling \b DiagonalMatrix S (Dim)x(Dim):
+ * \f$ \left( \begin{array}{cc}
+ * S & 0\\
+ * 0\,...\,0 & 1
+ * \end{array} \right) \f$
+ *
+ * \b Column \b point v (Dim)x(1):
+ * \f$ \left( \begin{array}{c}
+ * v\\
+ * 1
+ * \end{array} \right) \f$
+ *
+ * \b Set \b of \b column \b points V1...Vn (Dim)x(n):
+ * \f$ \left( \begin{array}{ccc}
+ * v_1 & ... & v_n\\
+ * 1 & ... & 1
+ * \end{array} \right) \f$
+ *
+ * The concatenation of a Transform object with any kind of other transformation
+ * always returns a Transform object.
+ *
+ * A little exception to the "as pure matrix product" rule is the case of the
+ * transformation of non homogeneous vectors by an affine transformation. In
+ * that case the last matrix row can be ignored, and the product returns non
+ * homogeneous vectors.
+ *
+ * Since, for instance, a Dim x Dim matrix is interpreted as a linear transformation,
+ * it is not possible to directly transform Dim vectors stored in a Dim x Dim matrix.
+ * The solution is either to use a Dim x Dynamic matrix or explicitly request a
+ * vector transformation by making the vector homogeneous:
+ * \code
+ * m' = T * m.colwise().homogeneous();
+ * \endcode
+ * Note that there is zero overhead.
+ *
+ * Conversion methods from/to Qt's QMatrix and QTransform are available if the
+ * preprocessor token EIGEN_QT_SUPPORT is defined.
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_TRANSFORM_PLUGIN.
+ *
+ * \sa class Matrix, class Quaternion
+ */
+template <typename Scalar_, int Dim_, int Mode_, int Options_>
+class Transform {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_,
+                                                             Dim_ == Dynamic ? Dynamic : (Dim_ + 1) * (Dim_ + 1))
+  enum {
+    Mode = Mode_,
+    Options = Options_,
+    Dim = Dim_,       ///< space dimension in which the transformation holds
+    HDim = Dim_ + 1,  ///< size of a respective homogeneous vector
+    Rows = int(Mode) == (AffineCompact) ? Dim : HDim
+  };
+  /** the scalar type of the coefficients */
+  typedef Scalar_ Scalar;
+  typedef Eigen::Index StorageIndex;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+  /** type of the matrix used to represent the transformation */
+  typedef typename internal::make_proper_matrix_type<Scalar, Rows, HDim, Options>::type MatrixType;
+  /** constified MatrixType */
+  typedef const MatrixType ConstMatrixType;
+  /** type of the matrix used to represent the linear part of the transformation */
+  typedef Matrix<Scalar, Dim, Dim, Options> LinearMatrixType;
+  /** type of read/write reference to the linear part of the transformation */
+  typedef Block<MatrixType, Dim, Dim, int(Mode) == (AffineCompact) && (int(Options) & RowMajor) == 0> LinearPart;
+  /** type of read reference to the linear part of the transformation */
+  typedef const Block<ConstMatrixType, Dim, Dim, int(Mode) == (AffineCompact) && (int(Options) & RowMajor) == 0>
+      ConstLinearPart;
+  /** type of read/write reference to the affine part of the transformation */
+  typedef std::conditional_t<int(Mode) == int(AffineCompact), MatrixType&, Block<MatrixType, Dim, HDim> > AffinePart;
+  /** type of read reference to the affine part of the transformation */
+  typedef std::conditional_t<int(Mode) == int(AffineCompact), const MatrixType&,
+                             const Block<const MatrixType, Dim, HDim> >
+      ConstAffinePart;
+  /** type of a vector */
+  typedef Matrix<Scalar, Dim, 1> VectorType;
+  /** type of a read/write reference to the translation part of the rotation */
+  typedef Block<MatrixType, Dim, 1, !(internal::traits<MatrixType>::Flags & RowMajorBit)> TranslationPart;
+  /** type of a read reference to the translation part of the rotation */
+  typedef const Block<ConstMatrixType, Dim, 1, !(internal::traits<MatrixType>::Flags & RowMajorBit)>
+      ConstTranslationPart;
+  /** corresponding translation type */
+  typedef Translation<Scalar, Dim> TranslationType;
+
+  // this intermediate enum is needed to avoid an ICE with gcc 3.4 and 4.0
+  enum { TransformTimeDiagonalMode = ((Mode == int(Isometry)) ? Affine : int(Mode)) };
+  /** The return type of the product between a diagonal matrix and a transform */
+  typedef Transform<Scalar, Dim, TransformTimeDiagonalMode> TransformTimeDiagonalReturnType;
+
+ protected:
+  MatrixType m_matrix;
+
+ public:
+  /** Default constructor without initialization of the meaningful coefficients.
+   * If Mode==Affine or Mode==Isometry, then the last row is set to [0 ... 0 1] */
+  EIGEN_DEVICE_FUNC inline Transform() {
+    check_template_params();
+    internal::transform_make_affine<(int(Mode) == Affine || int(Mode) == Isometry) ? Affine : AffineCompact>::run(
+        m_matrix);
+  }
+
+  EIGEN_DEVICE_FUNC inline explicit Transform(const TranslationType& t) {
+    check_template_params();
+    *this = t;
+  }
+  EIGEN_DEVICE_FUNC inline explicit Transform(const UniformScaling<Scalar>& s) {
+    check_template_params();
+    *this = s;
+  }
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline explicit Transform(const RotationBase<Derived, Dim>& r) {
+    check_template_params();
+    *this = r;
+  }
+
+  typedef internal::transform_take_affine_part<Transform> take_affine_part;
+
+  /** Constructs and initializes a transformation from a Dim^2 or a (Dim+1)^2 matrix. */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline explicit Transform(const EigenBase<OtherDerived>& other) {
+    EIGEN_STATIC_ASSERT(
+        (internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY);
+
+    check_template_params();
+    internal::transform_construct_from_matrix<OtherDerived, Mode, Options, Dim, HDim>::run(this, other.derived());
+  }
+
+  /** Set \c *this from a Dim^2 or (Dim+1)^2 matrix. */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline Transform& operator=(const EigenBase<OtherDerived>& other) {
+    EIGEN_STATIC_ASSERT(
+        (internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY);
+
+    internal::transform_construct_from_matrix<OtherDerived, Mode, Options, Dim, HDim>::run(this, other.derived());
+    return *this;
+  }
+
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC inline Transform(const Transform<Scalar, Dim, Mode, OtherOptions>& other) {
+    check_template_params();
+    // only the options change, we can directly copy the matrices
+    m_matrix = other.matrix();
+  }
+
+  template <int OtherMode, int OtherOptions>
+  EIGEN_DEVICE_FUNC inline Transform(const Transform<Scalar, Dim, OtherMode, OtherOptions>& other) {
+    check_template_params();
+    // prevent conversions as:
+    // Affine | AffineCompact | Isometry = Projective
+    EIGEN_STATIC_ASSERT(internal::check_implication(OtherMode == int(Projective), Mode == int(Projective)),
+                        YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION)
+
+    // prevent conversions as:
+    // Isometry = Affine | AffineCompact
+    EIGEN_STATIC_ASSERT(
+        internal::check_implication(OtherMode == int(Affine) || OtherMode == int(AffineCompact), Mode != int(Isometry)),
+        YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION)
+
+    enum {
+      ModeIsAffineCompact = Mode == int(AffineCompact),
+      OtherModeIsAffineCompact = OtherMode == int(AffineCompact)
+    };
+
+    if (EIGEN_CONST_CONDITIONAL(ModeIsAffineCompact == OtherModeIsAffineCompact)) {
+      // We need the block expression because the code is compiled for all
+      // combinations of transformations and will trigger a compile time error
+      // if one tries to assign the matrices directly
+      m_matrix.template block<Dim, Dim + 1>(0, 0) = other.matrix().template block<Dim, Dim + 1>(0, 0);
+      makeAffine();
+    } else if (EIGEN_CONST_CONDITIONAL(OtherModeIsAffineCompact)) {
+      typedef typename Transform<Scalar, Dim, OtherMode, OtherOptions>::MatrixType OtherMatrixType;
+      internal::transform_construct_from_matrix<OtherMatrixType, Mode, Options, Dim, HDim>::run(this, other.matrix());
+    } else {
+      // here we know that Mode == AffineCompact and OtherMode != AffineCompact.
+      // if OtherMode were Projective, the static assert above would already have caught it.
+      // So the only possibility is that OtherMode == Affine
+      linear() = other.linear();
+      translation() = other.translation();
+    }
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC Transform(const ReturnByValue<OtherDerived>& other) {
+    check_template_params();
+    other.evalTo(*this);
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC Transform& operator=(const ReturnByValue<OtherDerived>& other) {
+    other.evalTo(*this);
+    return *this;
+  }
+
+#ifdef EIGEN_QT_SUPPORT
+#if (QT_VERSION < QT_VERSION_CHECK(6, 0, 0))
+  inline Transform(const QMatrix& other);
+  inline Transform& operator=(const QMatrix& other);
+  inline QMatrix toQMatrix(void) const;
+#endif
+  inline Transform(const QTransform& other);
+  inline Transform& operator=(const QTransform& other);
+  inline QTransform toQTransform(void) const;
+#endif
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept {
+    return int(Mode) == int(Projective) ? m_matrix.cols() : (m_matrix.cols() - 1);
+  }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_matrix.cols(); }
+
+  /** shortcut for m_matrix(row,col);
+   * \sa MatrixBase::operator(Index,Index) const */
+  EIGEN_DEVICE_FUNC inline Scalar operator()(Index row, Index col) const { return m_matrix(row, col); }
+  /** shortcut for m_matrix(row,col);
+   * \sa MatrixBase::operator(Index,Index) */
+  EIGEN_DEVICE_FUNC inline Scalar& operator()(Index row, Index col) { return m_matrix(row, col); }
+
+#ifdef EIGEN_MULTIDIMENSIONAL_SUBSCRIPT
+  /** shortcut for m_matrix(row,col);
+   * \sa MatrixBase::operator(Index,Index) const */
+  EIGEN_DEVICE_FUNC inline Scalar operator[](Index row, Index col) const { return m_matrix[row, col]; }
+  /** shortcut for m_matrix(row,col);
+   * \sa MatrixBase::operator(Index,Index) */
+  EIGEN_DEVICE_FUNC inline Scalar& operator[](Index row, Index col) { return m_matrix[row, col]; }
+#endif
+
+  /** \returns a read-only expression of the transformation matrix */
+  EIGEN_DEVICE_FUNC inline const MatrixType& matrix() const { return m_matrix; }
+  /** \returns a writable expression of the transformation matrix */
+  EIGEN_DEVICE_FUNC inline MatrixType& matrix() { return m_matrix; }
+
+  /** \returns a read-only expression of the linear part of the transformation */
+  EIGEN_DEVICE_FUNC inline ConstLinearPart linear() const { return ConstLinearPart(m_matrix, 0, 0); }
+  /** \returns a writable expression of the linear part of the transformation */
+  EIGEN_DEVICE_FUNC inline LinearPart linear() { return LinearPart(m_matrix, 0, 0); }
+
+  /** \returns a read-only expression of the Dim x HDim affine part of the transformation */
+  EIGEN_DEVICE_FUNC inline ConstAffinePart affine() const { return take_affine_part::run(m_matrix); }
+  /** \returns a writable expression of the Dim x HDim affine part of the transformation */
+  EIGEN_DEVICE_FUNC inline AffinePart affine() { return take_affine_part::run(m_matrix); }
+
+  /** \returns a read-only expression of the translation vector of the transformation */
+  EIGEN_DEVICE_FUNC inline ConstTranslationPart translation() const { return ConstTranslationPart(m_matrix, 0, Dim); }
+  /** \returns a writable expression of the translation vector of the transformation */
+  EIGEN_DEVICE_FUNC inline TranslationPart translation() { return TranslationPart(m_matrix, 0, Dim); }
+
+  /** \returns an expression of the product between the transform \c *this and a matrix expression \a other.
+   *
+   * The right-hand-side \a other can be either:
+   * \li an homogeneous vector of size Dim+1,
+   * \li a set of homogeneous vectors of size Dim+1 x N,
+   * \li a transformation matrix of size Dim+1 x Dim+1.
+   *
+   * Moreover, if \c *this represents an affine transformation (i.e., Mode!=Projective), then \a other can also be:
+   * \li a point of size Dim (computes: \code this->linear() * other + this->translation()\endcode),
+   * \li a set of N points as a Dim x N matrix (computes: \code (this->linear() * other).colwise() +
+   * this->translation()\endcode),
+   *
+   * In all cases, the return type is a matrix or vector of same sizes as the right-hand-side \a other.
+   *
+   * If you want to interpret \a other as a linear or affine transformation, then first convert it to a Transform<>
+   * type, or do your own cooking.
+   *
+   * Finally, if you want to apply Affine transformations to vectors, then explicitly apply the linear part only:
+   * \code
+   * Affine3f A;
+   * Vector3f v1, v2;
+   * v2 = A.linear() * v1;
+   * \endcode
+   *
+   */
+  // note: this function is defined here because some compilers cannot find the respective declaration
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename internal::transform_right_product_impl<Transform,
+                                                                                              OtherDerived>::ResultType
+  operator*(const EigenBase<OtherDerived>& other) const {
+    return internal::transform_right_product_impl<Transform, OtherDerived>::run(*this, other.derived());
+  }
+
+  /** \returns the product expression of a transformation matrix \a a times a transform \a b
+   *
+   * The left hand side \a other can be either:
+   * \li a linear transformation matrix of size Dim x Dim,
+   * \li an affine transformation matrix of size Dim x Dim+1,
+   * \li a general transformation matrix of size Dim+1 x Dim+1.
+   */
+  template <typename OtherDerived>
+  friend EIGEN_DEVICE_FUNC inline const typename internal::transform_left_product_impl<OtherDerived, Mode, Options,
+                                                                                       Dim_, Dim_ + 1>::ResultType
+  operator*(const EigenBase<OtherDerived>& a, const Transform& b) {
+    return internal::transform_left_product_impl<OtherDerived, Mode, Options, Dim, HDim>::run(a.derived(), b);
+  }
+
+  /** \returns The product expression of a transform \a a times a diagonal matrix \a b
+   *
+   * The rhs diagonal matrix is interpreted as an affine scaling transformation. The
+   * product results in a Transform of the same type (mode) as the lhs only if the lhs
+   * mode is no isometry. In that case, the returned transform is an affinity.
+   */
+  template <typename DiagonalDerived>
+  EIGEN_DEVICE_FUNC inline const TransformTimeDiagonalReturnType operator*(
+      const DiagonalBase<DiagonalDerived>& b) const {
+    TransformTimeDiagonalReturnType res(*this);
+    res.linearExt() *= b;
+    return res;
+  }
+
+  /** \returns The product expression of a diagonal matrix \a a times a transform \a b
+   *
+   * The lhs diagonal matrix is interpreted as an affine scaling transformation. The
+   * product results in a Transform of the same type (mode) as the lhs only if the lhs
+   * mode is no isometry. In that case, the returned transform is an affinity.
+   */
+  template <typename DiagonalDerived>
+  EIGEN_DEVICE_FUNC friend inline TransformTimeDiagonalReturnType operator*(const DiagonalBase<DiagonalDerived>& a,
+                                                                            const Transform& b) {
+    TransformTimeDiagonalReturnType res;
+    res.linear().noalias() = a * b.linear();
+    res.translation().noalias() = a * b.translation();
+    if (EIGEN_CONST_CONDITIONAL(Mode != int(AffineCompact))) res.matrix().row(Dim) = b.matrix().row(Dim);
+    return res;
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline Transform& operator*=(const EigenBase<OtherDerived>& other) {
+    return *this = *this * other;
+  }
+
+  /** Concatenates two transformations */
+  EIGEN_DEVICE_FUNC inline const Transform operator*(const Transform& other) const {
+    return internal::transform_transform_product_impl<Transform, Transform>::run(*this, other);
+  }
+
+#if EIGEN_COMP_ICC
+ private:
+  // this intermediate structure permits to workaround a bug in ICC 11:
+  //   error: template instantiation resulted in unexpected function type of "Eigen::Transform<double, 3, 32, 0>
+  //             (const Eigen::Transform<double, 3, 2, 0> &) const"
+  //  (the meaning of a name may have changed since the template declaration -- the type of the template is:
+  // "Eigen::internal::transform_transform_product_impl<Eigen::Transform<double, 3, 32, 0>,
+  //     Eigen::Transform<double, 3, Mode, Options>, <expression>>::ResultType (const Eigen::Transform<double, 3, Mode,
+  //     Options> &) const")
+  //
+  template <int OtherMode, int OtherOptions>
+  struct icc_11_workaround {
+    typedef internal::transform_transform_product_impl<Transform, Transform<Scalar, Dim, OtherMode, OtherOptions> >
+        ProductType;
+    typedef typename ProductType::ResultType ResultType;
+  };
+
+ public:
+  /** Concatenates two different transformations */
+  template <int OtherMode, int OtherOptions>
+  inline typename icc_11_workaround<OtherMode, OtherOptions>::ResultType operator*(
+      const Transform<Scalar, Dim, OtherMode, OtherOptions>& other) const {
+    typedef typename icc_11_workaround<OtherMode, OtherOptions>::ProductType ProductType;
+    return ProductType::run(*this, other);
+  }
+#else
+  /** Concatenates two different transformations */
+  template <int OtherMode, int OtherOptions>
+  EIGEN_DEVICE_FUNC inline
+      typename internal::transform_transform_product_impl<Transform,
+                                                          Transform<Scalar, Dim, OtherMode, OtherOptions> >::ResultType
+      operator*(const Transform<Scalar, Dim, OtherMode, OtherOptions>& other) const {
+    return internal::transform_transform_product_impl<Transform, Transform<Scalar, Dim, OtherMode, OtherOptions> >::run(
+        *this, other);
+  }
+#endif
+
+  /** \sa MatrixBase::setIdentity() */
+  EIGEN_DEVICE_FUNC void setIdentity() { m_matrix.setIdentity(); }
+
+  /**
+   * \brief Returns an identity transformation.
+   * \todo In the future this function should be returning a Transform expression.
+   */
+  EIGEN_DEVICE_FUNC static const Transform Identity() { return Transform(MatrixType::Identity()); }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline Transform& scale(const MatrixBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline Transform& prescale(const MatrixBase<OtherDerived>& other);
+
+  EIGEN_DEVICE_FUNC inline Transform& scale(const Scalar& s);
+  EIGEN_DEVICE_FUNC inline Transform& prescale(const Scalar& s);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline Transform& translate(const MatrixBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline Transform& pretranslate(const MatrixBase<OtherDerived>& other);
+
+  template <typename RotationType>
+  EIGEN_DEVICE_FUNC inline Transform& rotate(const RotationType& rotation);
+
+  template <typename RotationType>
+  EIGEN_DEVICE_FUNC inline Transform& prerotate(const RotationType& rotation);
+
+  EIGEN_DEVICE_FUNC Transform& shear(const Scalar& sx, const Scalar& sy);
+  EIGEN_DEVICE_FUNC Transform& preshear(const Scalar& sx, const Scalar& sy);
+
+  EIGEN_DEVICE_FUNC inline Transform& operator=(const TranslationType& t);
+
+  EIGEN_DEVICE_FUNC inline Transform& operator*=(const TranslationType& t) { return translate(t.vector()); }
+
+  EIGEN_DEVICE_FUNC inline Transform operator*(const TranslationType& t) const;
+
+  EIGEN_DEVICE_FUNC inline Transform& operator=(const UniformScaling<Scalar>& t);
+
+  EIGEN_DEVICE_FUNC inline Transform& operator*=(const UniformScaling<Scalar>& s) { return scale(s.factor()); }
+
+  EIGEN_DEVICE_FUNC inline TransformTimeDiagonalReturnType operator*(const UniformScaling<Scalar>& s) const {
+    TransformTimeDiagonalReturnType res = *this;
+    res.scale(s.factor());
+    return res;
+  }
+
+  EIGEN_DEVICE_FUNC inline Transform& operator*=(const DiagonalMatrix<Scalar, Dim>& s) {
+    linearExt() *= s;
+    return *this;
+  }
+
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline Transform& operator=(const RotationBase<Derived, Dim>& r);
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline Transform& operator*=(const RotationBase<Derived, Dim>& r) {
+    return rotate(r.toRotationMatrix());
+  }
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline Transform operator*(const RotationBase<Derived, Dim>& r) const;
+
+  typedef std::conditional_t<int(Mode) == Isometry, ConstLinearPart, const LinearMatrixType> RotationReturnType;
+  EIGEN_DEVICE_FUNC RotationReturnType rotation() const;
+
+  template <typename RotationMatrixType, typename ScalingMatrixType>
+  EIGEN_DEVICE_FUNC void computeRotationScaling(RotationMatrixType* rotation, ScalingMatrixType* scaling) const;
+  template <typename ScalingMatrixType, typename RotationMatrixType>
+  EIGEN_DEVICE_FUNC void computeScalingRotation(ScalingMatrixType* scaling, RotationMatrixType* rotation) const;
+
+  template <typename PositionDerived, typename OrientationType, typename ScaleDerived>
+  EIGEN_DEVICE_FUNC Transform& fromPositionOrientationScale(const MatrixBase<PositionDerived>& position,
+                                                            const OrientationType& orientation,
+                                                            const MatrixBase<ScaleDerived>& scale);
+
+  EIGEN_DEVICE_FUNC inline Transform inverse(TransformTraits traits = (TransformTraits)Mode) const;
+
+  /** \returns a const pointer to the column major internal matrix */
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_matrix.data(); }
+  /** \returns a non-const pointer to the column major internal matrix */
+  EIGEN_DEVICE_FUNC constexpr Scalar* data() { return m_matrix.data(); }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+   *
+   * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+   * then this function smartly returns a const reference to \c *this.
+   */
+  template <typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline
+      typename internal::cast_return_type<Transform, Transform<NewScalarType, Dim, Mode, Options> >::type
+      cast() const {
+    return typename internal::cast_return_type<Transform, Transform<NewScalarType, Dim, Mode, Options> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template <typename OtherScalarType>
+  EIGEN_DEVICE_FUNC inline explicit Transform(const Transform<OtherScalarType, Dim, Mode, Options>& other) {
+    check_template_params();
+    m_matrix = other.matrix().template cast<Scalar>();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+   * determined by \a prec.
+   *
+   * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const Transform& other, const typename NumTraits<Scalar>::Real& prec =
+                                                              NumTraits<Scalar>::dummy_precision()) const {
+    return m_matrix.isApprox(other.m_matrix, prec);
+  }
+
+  /** Sets the last row to [0 ... 0 1]
+   */
+  EIGEN_DEVICE_FUNC void makeAffine() { internal::transform_make_affine<int(Mode)>::run(m_matrix); }
+
+  /** \internal
+   * \returns the Dim x Dim linear part if the transformation is affine,
+   *          and the HDim x Dim part for projective transformations.
+   */
+  EIGEN_DEVICE_FUNC inline Block<MatrixType, int(Mode) == int(Projective) ? HDim : Dim, Dim> linearExt() {
+    return m_matrix.template block < int(Mode) == int(Projective) ? HDim : Dim, Dim > (0, 0);
+  }
+  /** \internal
+   * \returns the Dim x Dim linear part if the transformation is affine,
+   *          and the HDim x Dim part for projective transformations.
+   */
+  EIGEN_DEVICE_FUNC inline const Block<MatrixType, int(Mode) == int(Projective) ? HDim : Dim, Dim> linearExt() const {
+    return m_matrix.template block < int(Mode) == int(Projective) ? HDim : Dim, Dim > (0, 0);
+  }
+
+  /** \internal
+   * \returns the translation part if the transformation is affine,
+   *          and the last column for projective transformations.
+   */
+  EIGEN_DEVICE_FUNC inline Block<MatrixType, int(Mode) == int(Projective) ? HDim : Dim, 1> translationExt() {
+    return m_matrix.template block < int(Mode) == int(Projective) ? HDim : Dim, 1 > (0, Dim);
+  }
+  /** \internal
+   * \returns the translation part if the transformation is affine,
+   *          and the last column for projective transformations.
+   */
+  EIGEN_DEVICE_FUNC inline const Block<MatrixType, int(Mode) == int(Projective) ? HDim : Dim, 1> translationExt()
+      const {
+    return m_matrix.template block < int(Mode) == int(Projective) ? HDim : Dim, 1 > (0, Dim);
+  }
+
+#ifdef EIGEN_TRANSFORM_PLUGIN
+#include EIGEN_TRANSFORM_PLUGIN
+#endif
+
+ protected:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void check_template_params() {
+    EIGEN_STATIC_ASSERT((Options & (DontAlign | RowMajor)) == Options, INVALID_MATRIX_TEMPLATE_PARAMETERS)
+  }
+#endif
+};
+
+/** \ingroup Geometry_Module */
+typedef Transform<float, 2, Isometry> Isometry2f;
+/** \ingroup Geometry_Module */
+typedef Transform<float, 3, Isometry> Isometry3f;
+/** \ingroup Geometry_Module */
+typedef Transform<double, 2, Isometry> Isometry2d;
+/** \ingroup Geometry_Module */
+typedef Transform<double, 3, Isometry> Isometry3d;
+
+/** \ingroup Geometry_Module */
+typedef Transform<float, 2, Affine> Affine2f;
+/** \ingroup Geometry_Module */
+typedef Transform<float, 3, Affine> Affine3f;
+/** \ingroup Geometry_Module */
+typedef Transform<double, 2, Affine> Affine2d;
+/** \ingroup Geometry_Module */
+typedef Transform<double, 3, Affine> Affine3d;
+
+/** \ingroup Geometry_Module */
+typedef Transform<float, 2, AffineCompact> AffineCompact2f;
+/** \ingroup Geometry_Module */
+typedef Transform<float, 3, AffineCompact> AffineCompact3f;
+/** \ingroup Geometry_Module */
+typedef Transform<double, 2, AffineCompact> AffineCompact2d;
+/** \ingroup Geometry_Module */
+typedef Transform<double, 3, AffineCompact> AffineCompact3d;
+
+/** \ingroup Geometry_Module */
+typedef Transform<float, 2, Projective> Projective2f;
+/** \ingroup Geometry_Module */
+typedef Transform<float, 3, Projective> Projective3f;
+/** \ingroup Geometry_Module */
+typedef Transform<double, 2, Projective> Projective2d;
+/** \ingroup Geometry_Module */
+typedef Transform<double, 3, Projective> Projective3d;
+
+/**************************
+*** Optional QT support ***
+**************************/
+
+#ifdef EIGEN_QT_SUPPORT
+
+#if (QT_VERSION < QT_VERSION_CHECK(6, 0, 0))
+/** Initializes \c *this from a QMatrix assuming the dimension is 2.
+ *
+ * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+Transform<Scalar, Dim, Mode, Options>::Transform(const QMatrix& other) {
+  check_template_params();
+  *this = other;
+}
+
+/** Set \c *this from a QMatrix assuming the dimension is 2.
+ *
+ * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::operator=(const QMatrix& other) {
+  EIGEN_STATIC_ASSERT(Dim == 2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
+    m_matrix << other.m11(), other.m21(), other.dx(), other.m12(), other.m22(), other.dy();
+  else
+    m_matrix << other.m11(), other.m21(), other.dx(), other.m12(), other.m22(), other.dy(), 0, 0, 1;
+  return *this;
+}
+
+/** \returns a QMatrix from \c *this assuming the dimension is 2.
+ *
+ * \warning this conversion might lose data if \c *this is not affine
+ *
+ * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+QMatrix Transform<Scalar, Dim, Mode, Options>::toQMatrix(void) const {
+  check_template_params();
+  EIGEN_STATIC_ASSERT(Dim == 2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  return QMatrix(m_matrix.coeff(0, 0), m_matrix.coeff(1, 0), m_matrix.coeff(0, 1), m_matrix.coeff(1, 1),
+                 m_matrix.coeff(0, 2), m_matrix.coeff(1, 2));
+}
+#endif
+
+/** Initializes \c *this from a QTransform assuming the dimension is 2.
+ *
+ * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+Transform<Scalar, Dim, Mode, Options>::Transform(const QTransform& other) {
+  check_template_params();
+  *this = other;
+}
+
+/** Set \c *this from a QTransform assuming the dimension is 2.
+ *
+ * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::operator=(const QTransform& other) {
+  check_template_params();
+  EIGEN_STATIC_ASSERT(Dim == 2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
+    m_matrix << other.m11(), other.m21(), other.dx(), other.m12(), other.m22(), other.dy();
+  else
+    m_matrix << other.m11(), other.m21(), other.dx(), other.m12(), other.m22(), other.dy(), other.m13(), other.m23(),
+        other.m33();
+  return *this;
+}
+
+/** \returns a QTransform from \c *this assuming the dimension is 2.
+ *
+ * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+QTransform Transform<Scalar, Dim, Mode, Options>::toQTransform(void) const {
+  EIGEN_STATIC_ASSERT(Dim == 2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
+    return QTransform(m_matrix.coeff(0, 0), m_matrix.coeff(1, 0), m_matrix.coeff(0, 1), m_matrix.coeff(1, 1),
+                      m_matrix.coeff(0, 2), m_matrix.coeff(1, 2));
+  else
+    return QTransform(m_matrix.coeff(0, 0), m_matrix.coeff(1, 0), m_matrix.coeff(2, 0), m_matrix.coeff(0, 1),
+                      m_matrix.coeff(1, 1), m_matrix.coeff(2, 1), m_matrix.coeff(0, 2), m_matrix.coeff(1, 2),
+                      m_matrix.coeff(2, 2));
+}
+#endif
+
+/*********************
+*** Procedural API ***
+*********************/
+
+/** Applies on the right the non uniform scale transformation represented
+ * by the vector \a other to \c *this and returns a reference to \c *this.
+ * \sa prescale()
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::scale(
+    const MatrixBase<OtherDerived>& other) {
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived, int(Dim))
+  EIGEN_STATIC_ASSERT(Mode != int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  linearExt().noalias() = (linearExt() * other.asDiagonal());
+  return *this;
+}
+
+/** Applies on the right a uniform scale of a factor \a c to \c *this
+ * and returns a reference to \c *this.
+ * \sa prescale(Scalar)
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC inline Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::scale(
+    const Scalar& s) {
+  EIGEN_STATIC_ASSERT(Mode != int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  linearExt() *= s;
+  return *this;
+}
+
+/** Applies on the left the non uniform scale transformation represented
+ * by the vector \a other to \c *this and returns a reference to \c *this.
+ * \sa scale()
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::prescale(
+    const MatrixBase<OtherDerived>& other) {
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived, int(Dim))
+  EIGEN_STATIC_ASSERT(Mode != int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  affine().noalias() = (other.asDiagonal() * affine());
+  return *this;
+}
+
+/** Applies on the left a uniform scale of a factor \a c to \c *this
+ * and returns a reference to \c *this.
+ * \sa scale(Scalar)
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC inline Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::prescale(
+    const Scalar& s) {
+  EIGEN_STATIC_ASSERT(Mode != int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  m_matrix.template topRows<Dim>() *= s;
+  return *this;
+}
+
+/** Applies on the right the translation matrix represented by the vector \a other
+ * to \c *this and returns a reference to \c *this.
+ * \sa pretranslate()
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::translate(
+    const MatrixBase<OtherDerived>& other) {
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived, int(Dim))
+  translationExt() += linearExt() * other;
+  return *this;
+}
+
+/** Applies on the left the translation matrix represented by the vector \a other
+ * to \c *this and returns a reference to \c *this.
+ * \sa translate()
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::pretranslate(
+    const MatrixBase<OtherDerived>& other) {
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived, int(Dim))
+  if (EIGEN_CONST_CONDITIONAL(int(Mode) == int(Projective)))
+    affine() += other * m_matrix.row(Dim);
+  else
+    translation() += other;
+  return *this;
+}
+
+/** Applies on the right the rotation represented by the rotation \a rotation
+ * to \c *this and returns a reference to \c *this.
+ *
+ * The template parameter \a RotationType is the type of the rotation which
+ * must be known by internal::toRotationMatrix<>.
+ *
+ * Natively supported types includes:
+ *   - any scalar (2D),
+ *   - a Dim x Dim matrix expression,
+ *   - a Quaternion (3D),
+ *   - a AngleAxis (3D)
+ *
+ * This mechanism is easily extendable to support user types such as Euler angles,
+ * or a pair of Quaternion for 4D rotations.
+ *
+ * \sa rotate(Scalar), class Quaternion, class AngleAxis, prerotate(RotationType)
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename RotationType>
+EIGEN_DEVICE_FUNC Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::rotate(
+    const RotationType& rotation) {
+  linearExt() *= internal::toRotationMatrix<Scalar, Dim>(rotation);
+  return *this;
+}
+
+/** Applies on the left the rotation represented by the rotation \a rotation
+ * to \c *this and returns a reference to \c *this.
+ *
+ * See rotate() for further details.
+ *
+ * \sa rotate()
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename RotationType>
+EIGEN_DEVICE_FUNC Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::prerotate(
+    const RotationType& rotation) {
+  m_matrix.template block<Dim, HDim>(0, 0) =
+      internal::toRotationMatrix<Scalar, Dim>(rotation) * m_matrix.template block<Dim, HDim>(0, 0);
+  return *this;
+}
+
+/** Applies on the right the shear transformation represented
+ * by the vector \a other to \c *this and returns a reference to \c *this.
+ * \warning 2D only.
+ * \sa preshear()
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::shear(
+    const Scalar& sx, const Scalar& sy) {
+  EIGEN_STATIC_ASSERT(int(Dim) == 2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  EIGEN_STATIC_ASSERT(Mode != int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  VectorType tmp = linear().col(0) * sy + linear().col(1);
+  linear() << linear().col(0) + linear().col(1) * sx, tmp;
+  return *this;
+}
+
+/** Applies on the left the shear transformation represented
+ * by the vector \a other to \c *this and returns a reference to \c *this.
+ * \warning 2D only.
+ * \sa shear()
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::preshear(
+    const Scalar& sx, const Scalar& sy) {
+  EIGEN_STATIC_ASSERT(int(Dim) == 2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  EIGEN_STATIC_ASSERT(Mode != int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  m_matrix.template block<Dim, HDim>(0, 0) =
+      LinearMatrixType({{1, sy}, {sx, 1}}) * m_matrix.template block<Dim, HDim>(0, 0);
+  return *this;
+}
+
+/******************************************************
+*** Scaling, Translation and Rotation compatibility ***
+******************************************************/
+
+template <typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC inline Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::operator=(
+    const TranslationType& t) {
+  linear().setIdentity();
+  translation() = t.vector();
+  makeAffine();
+  return *this;
+}
+
+template <typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC inline Transform<Scalar, Dim, Mode, Options> Transform<Scalar, Dim, Mode, Options>::operator*(
+    const TranslationType& t) const {
+  Transform res = *this;
+  res.translate(t.vector());
+  return res;
+}
+
+template <typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC inline Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::operator=(
+    const UniformScaling<Scalar>& s) {
+  m_matrix.setZero();
+  linear().diagonal().fill(s.factor());
+  makeAffine();
+  return *this;
+}
+
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::operator=(
+    const RotationBase<Derived, Dim>& r) {
+  linear() = internal::toRotationMatrix<Scalar, Dim>(r);
+  translation().setZero();
+  makeAffine();
+  return *this;
+}
+
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline Transform<Scalar, Dim, Mode, Options> Transform<Scalar, Dim, Mode, Options>::operator*(
+    const RotationBase<Derived, Dim>& r) const {
+  Transform res = *this;
+  res.rotate(r.derived());
+  return res;
+}
+
+/************************
+*** Special functions ***
+************************/
+
+namespace internal {
+template <int Mode>
+struct transform_rotation_impl {
+  template <typename TransformType>
+  EIGEN_DEVICE_FUNC static inline const typename TransformType::LinearMatrixType run(const TransformType& t) {
+    typedef typename TransformType::LinearMatrixType LinearMatrixType;
+    LinearMatrixType result;
+    t.computeRotationScaling(&result, (LinearMatrixType*)0);
+    return result;
+  }
+};
+template <>
+struct transform_rotation_impl<Isometry> {
+  template <typename TransformType>
+  EIGEN_DEVICE_FUNC static inline typename TransformType::ConstLinearPart run(const TransformType& t) {
+    return t.linear();
+  }
+};
+}  // namespace internal
+/** \returns the rotation part of the transformation
+ *
+ * If Mode==Isometry, then this method is an alias for linear(),
+ * otherwise it calls computeRotationScaling() to extract the rotation
+ * through a SVD decomposition.
+ *
+ * \svd_module
+ *
+ * \sa computeRotationScaling(), computeScalingRotation(), class SVD
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC typename Transform<Scalar, Dim, Mode, Options>::RotationReturnType
+Transform<Scalar, Dim, Mode, Options>::rotation() const {
+  return internal::transform_rotation_impl<Mode>::run(*this);
+}
+
+/** decomposes the linear part of the transformation as a product rotation x scaling, the scaling being
+ * not necessarily positive.
+ *
+ * If either pointer is zero, the corresponding computation is skipped.
+ *
+ *
+ *
+ * \svd_module
+ *
+ * \sa computeScalingRotation(), rotation(), class SVD
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename RotationMatrixType, typename ScalingMatrixType>
+EIGEN_DEVICE_FUNC void Transform<Scalar, Dim, Mode, Options>::computeRotationScaling(RotationMatrixType* rotation,
+                                                                                     ScalingMatrixType* scaling) const {
+  // Note that JacobiSVD is faster than BDCSVD for small matrices.
+  JacobiSVD<LinearMatrixType, ComputeFullU | ComputeFullV> svd(linear());
+
+  Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0)
+                 ? Scalar(-1)
+                 : Scalar(1);  // so x has absolute value 1
+  VectorType sv(svd.singularValues());
+  sv.coeffRef(Dim - 1) *= x;
+  if (scaling) (*scaling).noalias() = svd.matrixV() * sv.asDiagonal() * svd.matrixV().adjoint();
+  if (rotation) {
+    LinearMatrixType m(svd.matrixU());
+    m.col(Dim - 1) *= x;
+    (*rotation).noalias() = m * svd.matrixV().adjoint();
+  }
+}
+
+/** decomposes the linear part of the transformation as a product scaling x rotation, the scaling being
+ * not necessarily positive.
+ *
+ * If either pointer is zero, the corresponding computation is skipped.
+ *
+ *
+ *
+ * \svd_module
+ *
+ * \sa computeRotationScaling(), rotation(), class SVD
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename ScalingMatrixType, typename RotationMatrixType>
+EIGEN_DEVICE_FUNC void Transform<Scalar, Dim, Mode, Options>::computeScalingRotation(
+    ScalingMatrixType* scaling, RotationMatrixType* rotation) const {
+  // Note that JacobiSVD is faster than BDCSVD for small matrices.
+  JacobiSVD<LinearMatrixType, ComputeFullU | ComputeFullV> svd(linear());
+
+  Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0)
+                 ? Scalar(-1)
+                 : Scalar(1);  // so x has absolute value 1
+  VectorType sv(svd.singularValues());
+  sv.coeffRef(Dim - 1) *= x;
+  if (scaling) *scaling = svd.matrixU() * sv.asDiagonal() * svd.matrixU().adjoint();
+  if (rotation) {
+    LinearMatrixType m(svd.matrixU());
+    m.col(Dim - 1) *= x;
+    *rotation = m * svd.matrixV().adjoint();
+  }
+}
+
+/** Convenient method to set \c *this from a position, orientation and scale
+ * of a 3D object.
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+template <typename PositionDerived, typename OrientationType, typename ScaleDerived>
+EIGEN_DEVICE_FUNC Transform<Scalar, Dim, Mode, Options>&
+Transform<Scalar, Dim, Mode, Options>::fromPositionOrientationScale(const MatrixBase<PositionDerived>& position,
+                                                                    const OrientationType& orientation,
+                                                                    const MatrixBase<ScaleDerived>& scale) {
+  linear() = internal::toRotationMatrix<Scalar, Dim>(orientation);
+  linear() *= scale.asDiagonal();
+  translation() = position;
+  makeAffine();
+  return *this;
+}
+
+namespace internal {
+
+template <int Mode>
+struct transform_make_affine {
+  template <typename MatrixType>
+  EIGEN_DEVICE_FUNC static void run(MatrixType& mat) {
+    static const int Dim = MatrixType::ColsAtCompileTime - 1;
+    mat.template block<1, Dim>(Dim, 0).setZero();
+    mat.coeffRef(Dim, Dim) = typename MatrixType::Scalar(1);
+  }
+};
+
+template <>
+struct transform_make_affine<AffineCompact> {
+  template <typename MatrixType>
+  EIGEN_DEVICE_FUNC static void run(MatrixType&) {}
+};
+
+// selector needed to avoid taking the inverse of a 3x4 matrix
+template <typename TransformType, int Mode = TransformType::Mode>
+struct projective_transform_inverse {
+  EIGEN_DEVICE_FUNC static inline void run(const TransformType&, TransformType&) {}
+};
+
+template <typename TransformType>
+struct projective_transform_inverse<TransformType, Projective> {
+  EIGEN_DEVICE_FUNC static inline void run(const TransformType& m, TransformType& res) {
+    res.matrix() = m.matrix().inverse();
+  }
+};
+
+}  // end namespace internal
+
+/**
+ *
+ * \returns the inverse transformation according to some given knowledge
+ * on \c *this.
+ *
+ * \param hint allows to optimize the inversion process when the transformation
+ * is known to be not a general transformation (optional). The possible values are:
+ *  - #Projective if the transformation is not necessarily affine, i.e., if the
+ *    last row is not guaranteed to be [0 ... 0 1]
+ *  - #Affine if the last row can be assumed to be [0 ... 0 1]
+ *  - #Isometry if the transformation is only a concatenations of translations
+ *    and rotations.
+ *  The default is the template class parameter \c Mode.
+ *
+ * \warning unless \a traits is always set to NoShear or NoScaling, this function
+ * requires the generic inverse method of MatrixBase defined in the LU module. If
+ * you forget to include this module, then you will get hard to debug linking errors.
+ *
+ * \sa MatrixBase::inverse()
+ */
+template <typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC Transform<Scalar, Dim, Mode, Options> Transform<Scalar, Dim, Mode, Options>::inverse(
+    TransformTraits hint) const {
+  Transform res;
+  if (hint == Projective) {
+    internal::projective_transform_inverse<Transform>::run(*this, res);
+  } else {
+    if (hint == Isometry) {
+      res.matrix().template topLeftCorner<Dim, Dim>() = linear().transpose();
+    } else if (hint & Affine) {
+      res.matrix().template topLeftCorner<Dim, Dim>() = linear().inverse();
+    } else {
+      eigen_assert(false && "Invalid transform traits in Transform::Inverse");
+    }
+    // translation and remaining parts
+    res.matrix().template topRightCorner<Dim, 1>().noalias() =
+        -res.matrix().template topLeftCorner<Dim, Dim>() * translation();
+    res.makeAffine();  // we do need this, because in the beginning res is uninitialized
+  }
+  return res;
+}
+
+namespace internal {
+
+/*****************************************************
+*** Specializations of take affine part            ***
+*****************************************************/
+
+template <typename TransformType>
+struct transform_take_affine_part {
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef typename TransformType::AffinePart AffinePart;
+  typedef typename TransformType::ConstAffinePart ConstAffinePart;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AffinePart run(MatrixType& m) {
+    return m.template block<TransformType::Dim, TransformType::HDim>(0, 0);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ConstAffinePart run(const MatrixType& m) {
+    return m.template block<TransformType::Dim, TransformType::HDim>(0, 0);
+  }
+};
+
+template <typename Scalar, int Dim, int Options>
+struct transform_take_affine_part<Transform<Scalar, Dim, AffineCompact, Options> > {
+  typedef typename Transform<Scalar, Dim, AffineCompact, Options>::MatrixType MatrixType;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MatrixType& run(MatrixType& m) { return m; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const MatrixType& run(const MatrixType& m) { return m; }
+};
+
+/*****************************************************
+*** Specializations of construct from matrix       ***
+*****************************************************/
+
+template <typename Other, int Mode, int Options, int Dim, int HDim>
+struct transform_construct_from_matrix<Other, Mode, Options, Dim, HDim, Dim, Dim> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(
+      Transform<typename Other::Scalar, Dim, Mode, Options>* transform, const Other& other) {
+    transform->linear() = other;
+    transform->translation().setZero();
+    transform->makeAffine();
+  }
+};
+
+template <typename Other, int Mode, int Options, int Dim, int HDim>
+struct transform_construct_from_matrix<Other, Mode, Options, Dim, HDim, Dim, HDim> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(
+      Transform<typename Other::Scalar, Dim, Mode, Options>* transform, const Other& other) {
+    transform->affine() = other;
+    transform->makeAffine();
+  }
+};
+
+template <typename Other, int Mode, int Options, int Dim, int HDim>
+struct transform_construct_from_matrix<Other, Mode, Options, Dim, HDim, HDim, HDim> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(
+      Transform<typename Other::Scalar, Dim, Mode, Options>* transform, const Other& other) {
+    transform->matrix() = other;
+  }
+};
+
+template <typename Other, int Options, int Dim, int HDim>
+struct transform_construct_from_matrix<Other, AffineCompact, Options, Dim, HDim, HDim, HDim> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(
+      Transform<typename Other::Scalar, Dim, AffineCompact, Options>* transform, const Other& other) {
+    transform->matrix() = other.template block<Dim, HDim>(0, 0);
+  }
+};
+
+/**********************************************************
+***   Specializations of operator* with rhs EigenBase   ***
+**********************************************************/
+
+template <int LhsMode, int RhsMode>
+struct transform_product_result {
+  enum {
+    Mode = (LhsMode == (int)Projective || RhsMode == (int)Projective)         ? Projective
+           : (LhsMode == (int)Affine || RhsMode == (int)Affine)               ? Affine
+           : (LhsMode == (int)AffineCompact || RhsMode == (int)AffineCompact) ? AffineCompact
+           : (LhsMode == (int)Isometry || RhsMode == (int)Isometry)           ? Isometry
+                                                                              : Projective
+  };
+};
+
+template <typename TransformType, typename MatrixType, int RhsCols>
+struct transform_right_product_impl<TransformType, MatrixType, 0, RhsCols> {
+  typedef typename MatrixType::PlainObject ResultType;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other) {
+    return T.matrix() * other;
+  }
+};
+
+template <typename TransformType, typename MatrixType, int RhsCols>
+struct transform_right_product_impl<TransformType, MatrixType, 1, RhsCols> {
+  enum {
+    Dim = TransformType::Dim,
+    HDim = TransformType::HDim,
+    OtherRows = MatrixType::RowsAtCompileTime,
+    OtherCols = MatrixType::ColsAtCompileTime
+  };
+
+  typedef typename MatrixType::PlainObject ResultType;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other) {
+    EIGEN_STATIC_ASSERT(OtherRows == HDim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
+
+    typedef Block<ResultType, Dim, OtherCols, int(MatrixType::RowsAtCompileTime) == Dim> TopLeftLhs;
+
+    ResultType res(other.rows(), other.cols());
+    TopLeftLhs(res, 0, 0, Dim, other.cols()).noalias() = T.affine() * other;
+    res.row(OtherRows - 1) = other.row(OtherRows - 1);
+
+    return res;
+  }
+};
+
+template <typename TransformType, typename MatrixType, int RhsCols>
+struct transform_right_product_impl<TransformType, MatrixType, 2, RhsCols> {
+  enum {
+    Dim = TransformType::Dim,
+    HDim = TransformType::HDim,
+    OtherRows = MatrixType::RowsAtCompileTime,
+    OtherCols = MatrixType::ColsAtCompileTime
+  };
+
+  typedef typename MatrixType::PlainObject ResultType;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other) {
+    EIGEN_STATIC_ASSERT(OtherRows == Dim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
+
+    typedef Block<ResultType, Dim, OtherCols, true> TopLeftLhs;
+    ResultType res(
+        Replicate<typename TransformType::ConstTranslationPart, 1, OtherCols>(T.translation(), 1, other.cols()));
+    TopLeftLhs(res, 0, 0, Dim, other.cols()).noalias() += T.linear() * other;
+
+    return res;
+  }
+};
+
+template <typename TransformType, typename MatrixType>
+struct transform_right_product_impl<TransformType, MatrixType, 2, 1>  // rhs is a vector of size Dim
+{
+  typedef typename TransformType::MatrixType TransformMatrix;
+  enum {
+    Dim = TransformType::Dim,
+    HDim = TransformType::HDim,
+    OtherRows = MatrixType::RowsAtCompileTime,
+    WorkingRows = plain_enum_min(TransformMatrix::RowsAtCompileTime, HDim)
+  };
+
+  typedef typename MatrixType::PlainObject ResultType;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other) {
+    EIGEN_STATIC_ASSERT(OtherRows == Dim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
+
+    Matrix<typename ResultType::Scalar, Dim + 1, 1> rhs;
+    rhs.template head<Dim>() = other;
+    rhs[Dim] = typename ResultType::Scalar(1);
+    Matrix<typename ResultType::Scalar, WorkingRows, 1> res(T.matrix() * rhs);
+    return res.template head<Dim>();
+  }
+};
+
+/**********************************************************
+***   Specializations of operator* with lhs EigenBase   ***
+**********************************************************/
+
+// generic HDim x HDim matrix * T => Projective
+template <typename Other, int Mode, int Options, int Dim, int HDim>
+struct transform_left_product_impl<Other, Mode, Options, Dim, HDim, HDim, HDim> {
+  typedef Transform<typename Other::Scalar, Dim, Mode, Options> TransformType;
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef Transform<typename Other::Scalar, Dim, Projective, Options> ResultType;
+  static EIGEN_DEVICE_FUNC ResultType run(const Other& other, const TransformType& tr) {
+    return ResultType(other * tr.matrix());
+  }
+};
+
+// generic HDim x HDim matrix * AffineCompact => Projective
+template <typename Other, int Options, int Dim, int HDim>
+struct transform_left_product_impl<Other, AffineCompact, Options, Dim, HDim, HDim, HDim> {
+  typedef Transform<typename Other::Scalar, Dim, AffineCompact, Options> TransformType;
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef Transform<typename Other::Scalar, Dim, Projective, Options> ResultType;
+  static EIGEN_DEVICE_FUNC ResultType run(const Other& other, const TransformType& tr) {
+    ResultType res;
+    res.matrix().noalias() = other.template block<HDim, Dim>(0, 0) * tr.matrix();
+    res.matrix().col(Dim) += other.col(Dim);
+    return res;
+  }
+};
+
+// affine matrix * T
+template <typename Other, int Mode, int Options, int Dim, int HDim>
+struct transform_left_product_impl<Other, Mode, Options, Dim, HDim, Dim, HDim> {
+  typedef Transform<typename Other::Scalar, Dim, Mode, Options> TransformType;
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef TransformType ResultType;
+  static EIGEN_DEVICE_FUNC ResultType run(const Other& other, const TransformType& tr) {
+    ResultType res;
+    res.affine().noalias() = other * tr.matrix();
+    res.matrix().row(Dim) = tr.matrix().row(Dim);
+    return res;
+  }
+};
+
+// affine matrix * AffineCompact
+template <typename Other, int Options, int Dim, int HDim>
+struct transform_left_product_impl<Other, AffineCompact, Options, Dim, HDim, Dim, HDim> {
+  typedef Transform<typename Other::Scalar, Dim, AffineCompact, Options> TransformType;
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef TransformType ResultType;
+  static EIGEN_DEVICE_FUNC ResultType run(const Other& other, const TransformType& tr) {
+    ResultType res;
+    res.matrix().noalias() = other.template block<Dim, Dim>(0, 0) * tr.matrix();
+    res.translation() += other.col(Dim);
+    return res;
+  }
+};
+
+// linear matrix * T
+template <typename Other, int Mode, int Options, int Dim, int HDim>
+struct transform_left_product_impl<Other, Mode, Options, Dim, HDim, Dim, Dim> {
+  typedef Transform<typename Other::Scalar, Dim, Mode, Options> TransformType;
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef TransformType ResultType;
+  static EIGEN_DEVICE_FUNC ResultType run(const Other& other, const TransformType& tr) {
+    TransformType res;
+    if (Mode != int(AffineCompact)) res.matrix().row(Dim) = tr.matrix().row(Dim);
+    res.matrix().template topRows<Dim>().noalias() = other * tr.matrix().template topRows<Dim>();
+    return res;
+  }
+};
+
+/**********************************************************
+*** Specializations of operator* with another Transform ***
+**********************************************************/
+
+template <typename Scalar, int Dim, int LhsMode, int LhsOptions, int RhsMode, int RhsOptions>
+struct transform_transform_product_impl<Transform<Scalar, Dim, LhsMode, LhsOptions>,
+                                        Transform<Scalar, Dim, RhsMode, RhsOptions>, false> {
+  enum { ResultMode = transform_product_result<LhsMode, RhsMode>::Mode };
+  typedef Transform<Scalar, Dim, LhsMode, LhsOptions> Lhs;
+  typedef Transform<Scalar, Dim, RhsMode, RhsOptions> Rhs;
+  typedef Transform<Scalar, Dim, ResultMode, LhsOptions> ResultType;
+  static EIGEN_DEVICE_FUNC ResultType run(const Lhs& lhs, const Rhs& rhs) {
+    ResultType res;
+    res.linear().noalias() = lhs.linear() * rhs.linear();
+    res.translation() = lhs.linear() * rhs.translation() + lhs.translation();
+    res.makeAffine();
+    return res;
+  }
+};
+
+template <typename Scalar, int Dim, int LhsMode, int LhsOptions, int RhsMode, int RhsOptions>
+struct transform_transform_product_impl<Transform<Scalar, Dim, LhsMode, LhsOptions>,
+                                        Transform<Scalar, Dim, RhsMode, RhsOptions>, true> {
+  typedef Transform<Scalar, Dim, LhsMode, LhsOptions> Lhs;
+  typedef Transform<Scalar, Dim, RhsMode, RhsOptions> Rhs;
+  typedef Transform<Scalar, Dim, Projective> ResultType;
+  static EIGEN_DEVICE_FUNC ResultType run(const Lhs& lhs, const Rhs& rhs) {
+    return ResultType(lhs.matrix() * rhs.matrix());
+  }
+};
+
+template <typename Scalar, int Dim, int LhsOptions, int RhsOptions>
+struct transform_transform_product_impl<Transform<Scalar, Dim, AffineCompact, LhsOptions>,
+                                        Transform<Scalar, Dim, Projective, RhsOptions>, true> {
+  typedef Transform<Scalar, Dim, AffineCompact, LhsOptions> Lhs;
+  typedef Transform<Scalar, Dim, Projective, RhsOptions> Rhs;
+  typedef Transform<Scalar, Dim, Projective> ResultType;
+  static EIGEN_DEVICE_FUNC ResultType run(const Lhs& lhs, const Rhs& rhs) {
+    ResultType res;
+    res.matrix().template topRows<Dim>() = lhs.matrix() * rhs.matrix();
+    res.matrix().row(Dim) = rhs.matrix().row(Dim);
+    return res;
+  }
+};
+
+template <typename Scalar, int Dim, int LhsOptions, int RhsOptions>
+struct transform_transform_product_impl<Transform<Scalar, Dim, Projective, LhsOptions>,
+                                        Transform<Scalar, Dim, AffineCompact, RhsOptions>, true> {
+  typedef Transform<Scalar, Dim, Projective, LhsOptions> Lhs;
+  typedef Transform<Scalar, Dim, AffineCompact, RhsOptions> Rhs;
+  typedef Transform<Scalar, Dim, Projective> ResultType;
+  static EIGEN_DEVICE_FUNC ResultType run(const Lhs& lhs, const Rhs& rhs) {
+    ResultType res(lhs.matrix().template leftCols<Dim>() * rhs.matrix());
+    res.matrix().col(Dim) += lhs.matrix().col(Dim);
+    return res;
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRANSFORM_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Translation.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Translation.h
new file mode 100644
index 0000000..d942ac8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Translation.h
@@ -0,0 +1,204 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRANSLATION_H
+#define EIGEN_TRANSLATION_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+ *
+ * \class Translation
+ *
+ * \brief Represents a translation transformation
+ *
+ * \tparam Scalar_ the scalar type, i.e., the type of the coefficients.
+ * \tparam Dim_ the  dimension of the space, can be a compile time value or Dynamic
+ *
+ * \note This class is not aimed to be used to store a translation transformation,
+ * but rather to make easier the constructions and updates of Transform objects.
+ *
+ * \sa class Scaling, class Transform
+ */
+template <typename Scalar_, int Dim_>
+class Translation {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_, Dim_)
+  /** dimension of the space */
+  enum { Dim = Dim_ };
+  /** the scalar type of the coefficients */
+  typedef Scalar_ Scalar;
+  /** corresponding vector type */
+  typedef Matrix<Scalar, Dim, 1> VectorType;
+  /** corresponding linear transformation matrix type */
+  typedef Matrix<Scalar, Dim, Dim> LinearMatrixType;
+  /** corresponding affine transformation type */
+  typedef Transform<Scalar, Dim, Affine> AffineTransformType;
+  /** corresponding isometric transformation type */
+  typedef Transform<Scalar, Dim, Isometry> IsometryTransformType;
+
+ protected:
+  VectorType m_coeffs;
+
+ public:
+  /** Default constructor without initialization. */
+  EIGEN_DEVICE_FUNC Translation() {}
+  /**  */
+  EIGEN_DEVICE_FUNC inline Translation(const Scalar& sx, const Scalar& sy) {
+    eigen_assert(Dim == 2);
+    m_coeffs.x() = sx;
+    m_coeffs.y() = sy;
+  }
+  /**  */
+  EIGEN_DEVICE_FUNC inline Translation(const Scalar& sx, const Scalar& sy, const Scalar& sz) {
+    eigen_assert(Dim == 3);
+    m_coeffs.x() = sx;
+    m_coeffs.y() = sy;
+    m_coeffs.z() = sz;
+  }
+  /** Constructs and initialize the translation transformation from a vector of translation coefficients */
+  EIGEN_DEVICE_FUNC explicit inline Translation(const VectorType& vector) : m_coeffs(vector) {}
+
+  /** \brief Returns the x-translation by value. **/
+  EIGEN_DEVICE_FUNC constexpr Scalar x() const { return m_coeffs.x(); }
+  /** \brief Returns the y-translation by value. **/
+  EIGEN_DEVICE_FUNC constexpr Scalar y() const { return m_coeffs.y(); }
+  /** \brief Returns the z-translation by value. **/
+  EIGEN_DEVICE_FUNC constexpr Scalar z() const { return m_coeffs.z(); }
+
+  /** \brief Returns the x-translation as a reference. **/
+  EIGEN_DEVICE_FUNC constexpr Scalar& x() { return m_coeffs.x(); }
+  /** \brief Returns the y-translation as a reference. **/
+  EIGEN_DEVICE_FUNC constexpr Scalar& y() { return m_coeffs.y(); }
+  /** \brief Returns the z-translation as a reference. **/
+  EIGEN_DEVICE_FUNC constexpr Scalar& z() { return m_coeffs.z(); }
+
+  EIGEN_DEVICE_FUNC const VectorType& vector() const { return m_coeffs; }
+  EIGEN_DEVICE_FUNC VectorType& vector() { return m_coeffs; }
+
+  EIGEN_DEVICE_FUNC const VectorType& translation() const { return m_coeffs; }
+  EIGEN_DEVICE_FUNC VectorType& translation() { return m_coeffs; }
+
+  /** Concatenates two translation */
+  EIGEN_DEVICE_FUNC inline Translation operator*(const Translation& other) const {
+    return Translation(m_coeffs + other.m_coeffs);
+  }
+
+  /** Concatenates a translation and a uniform scaling */
+  EIGEN_DEVICE_FUNC inline AffineTransformType operator*(const UniformScaling<Scalar>& other) const;
+
+  /** Concatenates a translation and a linear transformation */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline AffineTransformType operator*(const EigenBase<OtherDerived>& linear) const;
+
+  /** Concatenates a translation and a rotation */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline IsometryTransformType operator*(const RotationBase<Derived, Dim>& r) const {
+    return *this * IsometryTransformType(r);
+  }
+
+  /** \returns the concatenation of a linear transformation \a l with the translation \a t */
+  // its a nightmare to define a templated friend function outside its declaration
+  template <typename OtherDerived>
+  friend EIGEN_DEVICE_FUNC inline AffineTransformType operator*(const EigenBase<OtherDerived>& linear,
+                                                                const Translation& t) {
+    AffineTransformType res;
+    res.matrix().setZero();
+    res.linear() = linear.derived();
+    res.translation() = linear.derived() * t.m_coeffs;
+    res.matrix().row(Dim).setZero();
+    res(Dim, Dim) = Scalar(1);
+    return res;
+  }
+
+  /** Concatenates a translation and a transformation */
+  template <int Mode, int Options>
+  EIGEN_DEVICE_FUNC inline Transform<Scalar, Dim, Mode> operator*(
+      const Transform<Scalar, Dim, Mode, Options>& t) const {
+    Transform<Scalar, Dim, Mode> res = t;
+    res.pretranslate(m_coeffs);
+    return res;
+  }
+
+  /** Applies translation to vector */
+  template <typename Derived>
+  inline std::enable_if_t<Derived::IsVectorAtCompileTime, VectorType> operator*(const MatrixBase<Derived>& vec) const {
+    return m_coeffs + vec.derived();
+  }
+
+  /** \returns the inverse translation (opposite) */
+  Translation inverse() const { return Translation(-m_coeffs); }
+
+  static const Translation Identity() { return Translation(VectorType::Zero()); }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+   *
+   * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+   * then this function smartly returns a const reference to \c *this.
+   */
+  template <typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Translation, Translation<NewScalarType, Dim> >::type
+  cast() const {
+    return typename internal::cast_return_type<Translation, Translation<NewScalarType, Dim> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template <typename OtherScalarType>
+  EIGEN_DEVICE_FUNC inline explicit Translation(const Translation<OtherScalarType, Dim>& other) {
+    m_coeffs = other.vector().template cast<Scalar>();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+   * determined by \a prec.
+   *
+   * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const Translation& other, const typename NumTraits<Scalar>::Real& prec =
+                                                                NumTraits<Scalar>::dummy_precision()) const {
+    return m_coeffs.isApprox(other.m_coeffs, prec);
+  }
+};
+
+/** \addtogroup Geometry_Module */
+//@{
+typedef Translation<float, 2> Translation2f;
+typedef Translation<double, 2> Translation2d;
+typedef Translation<float, 3> Translation3f;
+typedef Translation<double, 3> Translation3d;
+//@}
+
+template <typename Scalar, int Dim>
+EIGEN_DEVICE_FUNC inline typename Translation<Scalar, Dim>::AffineTransformType Translation<Scalar, Dim>::operator*(
+    const UniformScaling<Scalar>& other) const {
+  AffineTransformType res;
+  res.matrix().setZero();
+  res.linear().diagonal().fill(other.factor());
+  res.translation() = m_coeffs;
+  res(Dim, Dim) = Scalar(1);
+  return res;
+}
+
+template <typename Scalar, int Dim>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline typename Translation<Scalar, Dim>::AffineTransformType Translation<Scalar, Dim>::operator*(
+    const EigenBase<OtherDerived>& linear) const {
+  AffineTransformType res;
+  res.matrix().setZero();
+  res.linear() = linear.derived();
+  res.translation() = m_coeffs;
+  res.matrix().row(Dim).setZero();
+  res(Dim, Dim) = Scalar(1);
+  return res;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TRANSLATION_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Umeyama.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Umeyama.h
new file mode 100644
index 0000000..8ed6344
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/Umeyama.h
@@ -0,0 +1,161 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_UMEYAMA_H
+#define EIGEN_UMEYAMA_H
+
+// This file requires the user to include
+// * Eigen/Core
+// * Eigen/LU
+// * Eigen/SVD
+// * Eigen/Array
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// These helpers are required since it allows to use mixed types as parameters
+// for the Umeyama. The problem with mixed parameters is that the return type
+// cannot trivially be deduced when float and double types are mixed.
+namespace internal {
+
+// Compile time return type deduction for different MatrixBase types.
+// Different means here different alignment and parameters but the same underlying
+// real scalar type.
+template <typename MatrixType, typename OtherMatrixType>
+struct umeyama_transform_matrix_type {
+  enum {
+    MinRowsAtCompileTime =
+        internal::min_size_prefer_dynamic(MatrixType::RowsAtCompileTime, OtherMatrixType::RowsAtCompileTime),
+
+    // When possible we want to choose some small fixed size value since the result
+    // is likely to fit on the stack. So here, min_size_prefer_dynamic is not what we want.
+    HomogeneousDimension = int(MinRowsAtCompileTime) == Dynamic ? Dynamic : int(MinRowsAtCompileTime) + 1
+  };
+
+  typedef Matrix<typename traits<MatrixType>::Scalar, HomogeneousDimension, HomogeneousDimension,
+                 AutoAlign | (traits<MatrixType>::Flags & RowMajorBit ? RowMajor : ColMajor), HomogeneousDimension,
+                 HomogeneousDimension>
+      type;
+};
+
+}  // namespace internal
+
+/**
+ * \geometry_module \ingroup Geometry_Module
+ *
+ * \brief Returns the transformation between two point sets.
+ *
+ * The algorithm is based on:
+ * "Least-squares estimation of transformation parameters between two point patterns",
+ * Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573
+ *
+ * It estimates parameters \f$ c, \mathbf{R}, \f$ and \f$ \mathbf{t} \f$ such that
+ * \f{align*}
+ *   \frac{1}{n} \sum_{i=1}^n \vert\vert y_i - (c\mathbf{R}x_i + \mathbf{t}) \vert\vert_2^2
+ * \f}
+ * is minimized.
+ *
+ * The algorithm is based on the analysis of the covariance matrix
+ * \f$ \Sigma_{\mathbf{x}\mathbf{y}} \in \mathbb{R}^{d \times d} \f$
+ * of the input point sets \f$ \mathbf{x} \f$ and \f$ \mathbf{y} \f$ where
+ * \f$d\f$ is corresponding to the dimension (which is typically small).
+ * The analysis is involving the SVD having a complexity of \f$O(d^3)\f$
+ * though the actual computational effort lies in the covariance
+ * matrix computation which has an asymptotic lower bound of \f$O(dm)\f$ when
+ * the input point sets have dimension \f$d \times m\f$.
+ *
+ * Currently the method is working only for floating point matrices.
+ *
+ * \todo Should the return type of umeyama() become a Transform?
+ *
+ * \param src Source points \f$ \mathbf{x} = \left( x_1, \hdots, x_n \right) \f$.
+ * \param dst Destination points \f$ \mathbf{y} = \left( y_1, \hdots, y_n \right) \f$.
+ * \param with_scaling Sets \f$ c=1 \f$ when <code>false</code> is passed.
+ * \return The homogeneous transformation
+ * \f{align*}
+ *   T = \begin{bmatrix} c\mathbf{R} & \mathbf{t} \\ \mathbf{0} & 1 \end{bmatrix}
+ * \f}
+ * minimizing the residual above. This transformation is always returned as an
+ * Eigen::Matrix.
+ */
+template <typename Derived, typename OtherDerived>
+typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type umeyama(
+    const MatrixBase<Derived>& src, const MatrixBase<OtherDerived>& dst, bool with_scaling = true) {
+  typedef typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type TransformationMatrixType;
+  typedef typename internal::traits<TransformationMatrixType>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
+  EIGEN_STATIC_ASSERT(
+      (internal::is_same<Scalar, typename internal::traits<OtherDerived>::Scalar>::value),
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  enum { Dimension = internal::min_size_prefer_dynamic(Derived::RowsAtCompileTime, OtherDerived::RowsAtCompileTime) };
+
+  typedef Matrix<Scalar, Dimension, 1> VectorType;
+  typedef Matrix<Scalar, Dimension, Dimension> MatrixType;
+  typedef typename internal::plain_matrix_type_row_major<Derived>::type RowMajorMatrixType;
+
+  const Index m = src.rows();  // dimension
+  const Index n = src.cols();  // number of measurements
+
+  // required for demeaning ...
+  const RealScalar one_over_n = RealScalar(1) / static_cast<RealScalar>(n);
+
+  // computation of mean
+  const VectorType src_mean = src.rowwise().sum() * one_over_n;
+  const VectorType dst_mean = dst.rowwise().sum() * one_over_n;
+
+  // demeaning of src and dst points
+  const RowMajorMatrixType src_demean = src.colwise() - src_mean;
+  const RowMajorMatrixType dst_demean = dst.colwise() - dst_mean;
+
+  // Eq. (38)
+  const MatrixType sigma = one_over_n * dst_demean * src_demean.transpose();
+
+  JacobiSVD<MatrixType, ComputeFullU | ComputeFullV> svd(sigma);
+
+  // Initialize the resulting transformation with an identity matrix...
+  TransformationMatrixType Rt = TransformationMatrixType::Identity(m + 1, m + 1);
+
+  // Eq. (39)
+  VectorType S = VectorType::Ones(m);
+
+  if (svd.matrixU().determinant() * svd.matrixV().determinant() < 0) {
+    Index tmp = m - 1;
+    S(tmp) = -1;
+  }
+
+  // Eq. (40) and (43)
+  Rt.block(0, 0, m, m).noalias() = svd.matrixU() * S.asDiagonal() * svd.matrixV().transpose();
+
+  if (with_scaling) {
+    // Eq. (36)-(37)
+    const Scalar src_var = src_demean.rowwise().squaredNorm().sum() * one_over_n;
+
+    // Eq. (42)
+    const Scalar c = Scalar(1) / src_var * svd.singularValues().dot(S);
+
+    // Eq. (41)
+    Rt.col(m).head(m) = dst_mean;
+    Rt.col(m).head(m).noalias() -= c * Rt.topLeftCorner(m, m) * src_mean;
+    Rt.block(0, 0, m, m) *= c;
+  } else {
+    Rt.col(m).head(m) = dst_mean;
+    Rt.col(m).head(m).noalias() -= Rt.topLeftCorner(m, m) * src_mean;
+  }
+
+  return Rt;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_UMEYAMA_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/arch/Geometry_SIMD.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/arch/Geometry_SIMD.h
new file mode 100644
index 0000000..5601a47
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Geometry/arch/Geometry_SIMD.h
@@ -0,0 +1,154 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Rohit Garg <rpg.314@gmail.com>
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GEOMETRY_SIMD_H
+#define EIGEN_GEOMETRY_SIMD_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <class Derived, class OtherDerived>
+struct quat_product<Architecture::Target, Derived, OtherDerived, float> {
+  enum {
+    AAlignment = traits<Derived>::Alignment,
+    BAlignment = traits<OtherDerived>::Alignment,
+    ResAlignment = traits<Quaternion<float> >::Alignment
+  };
+  static inline Quaternion<float> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b) {
+    evaluator<typename Derived::Coefficients> ae(_a.coeffs());
+    evaluator<typename OtherDerived::Coefficients> be(_b.coeffs());
+    Quaternion<float> res;
+    const float neg_zero = numext::bit_cast<float>(0x80000000u);
+    const float arr[4] = {0.f, 0.f, 0.f, neg_zero};
+    const Packet4f mask = ploadu<Packet4f>(arr);
+    Packet4f a = ae.template packet<AAlignment, Packet4f>(0);
+    Packet4f b = be.template packet<BAlignment, Packet4f>(0);
+    Packet4f s1 = pmul(vec4f_swizzle1(a, 1, 2, 0, 2), vec4f_swizzle1(b, 2, 0, 1, 2));
+    Packet4f s2 = pmul(vec4f_swizzle1(a, 3, 3, 3, 1), vec4f_swizzle1(b, 0, 1, 2, 1));
+    pstoret<float, Packet4f, ResAlignment>(
+        &res.x(), padd(psub(pmul(a, vec4f_swizzle1(b, 3, 3, 3, 3)),
+                            pmul(vec4f_swizzle1(a, 2, 0, 1, 0), vec4f_swizzle1(b, 1, 2, 0, 0))),
+                       pxor(mask, padd(s1, s2))));
+
+    return res;
+  }
+};
+
+template <class Derived>
+struct quat_conj<Architecture::Target, Derived, float> {
+  enum { ResAlignment = traits<Quaternion<float> >::Alignment };
+  static inline Quaternion<float> run(const QuaternionBase<Derived>& q) {
+    evaluator<typename Derived::Coefficients> qe(q.coeffs());
+    Quaternion<float> res;
+    const float neg_zero = numext::bit_cast<float>(0x80000000u);
+    const float arr[4] = {neg_zero, neg_zero, neg_zero, 0.f};
+    const Packet4f mask = ploadu<Packet4f>(arr);
+    pstoret<float, Packet4f, ResAlignment>(&res.x(),
+                                           pxor(mask, qe.template packet<traits<Derived>::Alignment, Packet4f>(0)));
+    return res;
+  }
+};
+
+template <typename VectorLhs, typename VectorRhs>
+struct cross3_impl<Architecture::Target, VectorLhs, VectorRhs, float, true> {
+  using DstPlainType = typename plain_matrix_type<VectorLhs>::type;
+  static constexpr int DstAlignment = evaluator<DstPlainType>::Alignment;
+  static constexpr int LhsAlignment = evaluator<VectorLhs>::Alignment;
+  static constexpr int RhsAlignment = evaluator<VectorRhs>::Alignment;
+  static inline DstPlainType run(const VectorLhs& lhs, const VectorRhs& rhs) {
+    evaluator<VectorLhs> lhs_eval(lhs);
+    evaluator<VectorRhs> rhs_eval(rhs);
+    Packet4f a = lhs_eval.template packet<LhsAlignment, Packet4f>(0);
+    Packet4f b = rhs_eval.template packet<RhsAlignment, Packet4f>(0);
+    Packet4f mul1 = pmul(vec4f_swizzle1(a, 1, 2, 0, 3), vec4f_swizzle1(b, 2, 0, 1, 3));
+    Packet4f mul2 = pmul(vec4f_swizzle1(a, 2, 0, 1, 3), vec4f_swizzle1(b, 1, 2, 0, 3));
+    DstPlainType res;
+    pstoret<float, Packet4f, DstAlignment>(res.data(), psub(mul1, mul2));
+    // Ensure last component is 0 in case original a or b contain inf/nan.
+    res[3] = 0.0f;
+    return res;
+  }
+};
+
+#if (defined EIGEN_VECTORIZE_SSE) || (EIGEN_ARCH_ARM64)
+
+template <class Derived, class OtherDerived>
+struct quat_product<Architecture::Target, Derived, OtherDerived, double> {
+  enum { BAlignment = traits<OtherDerived>::Alignment, ResAlignment = traits<Quaternion<double> >::Alignment };
+
+  static inline Quaternion<double> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b) {
+    Quaternion<double> res;
+
+    evaluator<typename Derived::Coefficients> ae(_a.coeffs());
+    evaluator<typename OtherDerived::Coefficients> be(_b.coeffs());
+
+    const double* a = _a.coeffs().data();
+    Packet2d b_xy = be.template packet<BAlignment, Packet2d>(0);
+    Packet2d b_zw = be.template packet<BAlignment, Packet2d>(2);
+    Packet2d a_xx = pset1<Packet2d>(a[0]);
+    Packet2d a_yy = pset1<Packet2d>(a[1]);
+    Packet2d a_zz = pset1<Packet2d>(a[2]);
+    Packet2d a_ww = pset1<Packet2d>(a[3]);
+
+    // two temporaries:
+    Packet2d t1, t2;
+
+    /*
+     * t1 = ww*xy + yy*zw
+     * t2 = zz*xy - xx*zw
+     * res.xy = t1 +/- swap(t2)
+     */
+    t1 = padd(pmul(a_ww, b_xy), pmul(a_yy, b_zw));
+    t2 = psub(pmul(a_zz, b_xy), pmul(a_xx, b_zw));
+    pstoret<double, Packet2d, ResAlignment>(&res.x(), paddsub(t1, preverse(t2)));
+
+    /*
+     * t1 = ww*zw - yy*xy
+     * t2 = zz*zw + xx*xy
+     * res.zw = t1 -/+ swap(t2) = swap( swap(t1) +/- t2)
+     */
+    t1 = psub(pmul(a_ww, b_zw), pmul(a_yy, b_xy));
+    t2 = padd(pmul(a_zz, b_zw), pmul(a_xx, b_xy));
+    pstoret<double, Packet2d, ResAlignment>(&res.z(), preverse(paddsub(preverse(t1), t2)));
+
+    return res;
+  }
+};
+
+template <class Derived>
+struct quat_conj<Architecture::Target, Derived, double> {
+  enum { ResAlignment = traits<Quaternion<double> >::Alignment };
+  static inline Quaternion<double> run(const QuaternionBase<Derived>& q) {
+    evaluator<typename Derived::Coefficients> qe(q.coeffs());
+    Quaternion<double> res;
+    const double neg_zero = numext::bit_cast<double>(0x8000000000000000ull);
+    const double arr1[2] = {neg_zero, neg_zero};
+    const double arr2[2] = {neg_zero, 0.0};
+    const Packet2d mask0 = ploadu<Packet2d>(arr1);
+    const Packet2d mask2 = ploadu<Packet2d>(arr2);
+    pstoret<double, Packet2d, ResAlignment>(&res.x(),
+                                            pxor(mask0, qe.template packet<traits<Derived>::Alignment, Packet2d>(0)));
+    pstoret<double, Packet2d, ResAlignment>(&res.z(),
+                                            pxor(mask2, qe.template packet<traits<Derived>::Alignment, Packet2d>(2)));
+    return res;
+  }
+};
+
+#endif  // end EIGEN_VECTORIZE_SSE_OR_EIGEN_ARCH_ARM64
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GEOMETRY_SIMD_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/BlockHouseholder.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/BlockHouseholder.h
new file mode 100644
index 0000000..6a4bc67
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/BlockHouseholder.h
@@ -0,0 +1,86 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Vincent Lejeune
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BLOCK_HOUSEHOLDER_H
+#define EIGEN_BLOCK_HOUSEHOLDER_H
+
+// This file contains some helper function to deal with block householder reflectors
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal */
+// This variant avoid modifications in vectors
+template <typename TriangularFactorType, typename VectorsType, typename CoeffsType>
+void make_block_householder_triangular_factor(TriangularFactorType& triFactor, const VectorsType& vectors,
+                                              const CoeffsType& hCoeffs) {
+  const Index nbVecs = vectors.cols();
+  eigen_assert(triFactor.rows() == nbVecs && triFactor.cols() == nbVecs && vectors.rows() >= nbVecs);
+
+  for (Index i = nbVecs - 1; i >= 0; --i) {
+    Index rs = vectors.rows() - i - 1;
+    Index rt = nbVecs - i - 1;
+
+    if (rt > 0) {
+      triFactor.row(i).tail(rt).noalias() = -hCoeffs(i) * vectors.col(i).tail(rs).adjoint() *
+                                            vectors.bottomRightCorner(rs, rt).template triangularView<UnitLower>();
+
+      // FIXME: use the following line with .noalias() once triangular product supports in-place operation.
+      // triFactor.row(i).tail(rt) = triFactor.row(i).tail(rt) * triFactor.bottomRightCorner(rt,rt).template
+      // triangularView<Upper>();
+      for (Index j = nbVecs - 1; j > i; --j) {
+        typename TriangularFactorType::Scalar z = triFactor(i, j);
+        triFactor(i, j) = z * triFactor(j, j);
+        if (nbVecs - j - 1 > 0) triFactor.row(i).tail(nbVecs - j - 1) += z * triFactor.row(j).tail(nbVecs - j - 1);
+      }
+    }
+    triFactor(i, i) = hCoeffs(i);
+  }
+}
+
+/** \internal
+ * if forward then perform   mat = H0 * H1 * H2 * mat
+ * otherwise perform         mat = H2 * H1 * H0 * mat
+ */
+template <typename MatrixType, typename VectorsType, typename CoeffsType>
+void apply_block_householder_on_the_left(MatrixType& mat, const VectorsType& vectors, const CoeffsType& hCoeffs,
+                                         bool forward) {
+  enum { TFactorSize = VectorsType::ColsAtCompileTime };
+  Index nbVecs = vectors.cols();
+  Matrix<typename MatrixType::Scalar, TFactorSize, TFactorSize, RowMajor> T(nbVecs, nbVecs);
+
+  if (forward)
+    make_block_householder_triangular_factor(T, vectors, hCoeffs);
+  else
+    make_block_householder_triangular_factor(T, vectors, hCoeffs.conjugate());
+  const TriangularView<const VectorsType, UnitLower> V(vectors);
+
+  // A -= V T V^* A
+  Matrix<typename MatrixType::Scalar, VectorsType::ColsAtCompileTime, MatrixType::ColsAtCompileTime,
+         (VectorsType::MaxColsAtCompileTime == 1 && MatrixType::MaxColsAtCompileTime != 1) ? RowMajor : ColMajor,
+         VectorsType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime>
+      tmp = V.adjoint() * mat;
+  // FIXME: add .noalias() once triangular product supports in-place operation.
+  if (forward)
+    tmp = T.template triangularView<Upper>() * tmp;
+  else
+    tmp = T.template triangularView<Upper>().adjoint() * tmp;
+  mat.noalias() -= V * tmp;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BLOCK_HOUSEHOLDER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/Householder.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/Householder.h
new file mode 100644
index 0000000..f77e54f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/Householder.h
@@ -0,0 +1,162 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HOUSEHOLDER_H
+#define EIGEN_HOUSEHOLDER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <int N>
+struct decrement_size {
+  static constexpr int ret = N - 1;
+};
+template <>
+struct decrement_size<0> {
+  static constexpr int ret = 0;
+};
+template <>
+struct decrement_size<Dynamic> {
+  static constexpr int ret = Dynamic;
+};
+}  // namespace internal
+
+/** Computes the elementary reflector H such that:
+ * \f$ H *this = [ beta 0 ... 0]^T \f$
+ * where the transformation H is:
+ * \f$ H = I - tau v v^*\f$
+ * and the vector v is:
+ * \f$ v^T = [1 essential^T] \f$
+ *
+ * The essential part of the vector \c v is stored in *this.
+ *
+ * On output:
+ * \param tau the scaling factor of the Householder transformation
+ * \param beta the result of H * \c *this
+ *
+ * \sa MatrixBase::makeHouseholder(), MatrixBase::applyHouseholderOnTheLeft(),
+ *     MatrixBase::applyHouseholderOnTheRight()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC void MatrixBase<Derived>::makeHouseholderInPlace(Scalar& tau, RealScalar& beta) {
+  VectorBlock<Derived, internal::decrement_size<Base::SizeAtCompileTime>::ret> essentialPart(derived(), 1, size() - 1);
+  makeHouseholder(essentialPart, tau, beta);
+}
+
+/** Computes the elementary reflector H such that:
+ * \f$ H *this = [ beta 0 ... 0]^T \f$
+ * where the transformation H is:
+ * \f$ H = I - tau v v^*\f$
+ * and the vector v is:
+ * \f$ v^T = [1 essential^T] \f$
+ *
+ * On output:
+ * \param essential the essential part of the vector \c v
+ * \param tau the scaling factor of the Householder transformation
+ * \param beta the result of H * \c *this
+ *
+ * \sa MatrixBase::makeHouseholderInPlace(), MatrixBase::applyHouseholderOnTheLeft(),
+ *     MatrixBase::applyHouseholderOnTheRight()
+ */
+template <typename Derived>
+template <typename EssentialPart>
+EIGEN_DEVICE_FUNC void MatrixBase<Derived>::makeHouseholder(EssentialPart& essential, Scalar& tau,
+                                                            RealScalar& beta) const {
+  using numext::conj;
+
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(EssentialPart)
+  const VectorBlock<const Derived, EssentialPart::SizeAtCompileTime> tail(derived(), 1, size() - 1);
+
+  RealScalar tailSqNorm = size() == 1 ? RealScalar(0) : tail.unwind().squaredNorm();
+  Scalar c0 = coeff(0);
+  const RealScalar tol = (std::numeric_limits<RealScalar>::min)();
+
+  if (tailSqNorm <= tol && numext::abs2(numext::imag(c0)) <= tol) {
+    tau = RealScalar(0);
+    beta = numext::real(c0);
+    essential.setZero();
+  } else {
+    beta = numext::sqrt(numext::abs2(c0) + tailSqNorm);
+    if (numext::real(c0) >= RealScalar(0)) beta = -beta;
+    essential = tail.unwind() / (c0 - beta);
+    tau = conj((beta - c0) / beta);
+  }
+}
+
+/** Apply the elementary reflector H given by
+ * \f$ H = I - tau v v^*\f$
+ * with
+ * \f$ v^T = [1 essential^T] \f$
+ * from the left to a vector or matrix.
+ *
+ * On input:
+ * \param essential the essential part of the vector \c v
+ * \param tau the scaling factor of the Householder transformation
+ * \param workspace a pointer to working space with at least
+ *                  this->cols() entries
+ *
+ * \sa MatrixBase::makeHouseholder(), MatrixBase::makeHouseholderInPlace(),
+ *     MatrixBase::applyHouseholderOnTheRight()
+ */
+template <typename Derived>
+template <typename EssentialPart>
+EIGEN_DEVICE_FUNC void MatrixBase<Derived>::applyHouseholderOnTheLeft(const EssentialPart& essential, const Scalar& tau,
+                                                                      Scalar* workspace) {
+  if (rows() == 1) {
+    *this *= Scalar(1) - tau;
+  } else if (!numext::is_exactly_zero(tau)) {
+    Map<typename internal::plain_row_type<PlainObject>::type> tmp(workspace, cols());
+    Block<Derived, EssentialPart::SizeAtCompileTime, Derived::ColsAtCompileTime> bottom(derived(), 1, 0, rows() - 1,
+                                                                                        cols());
+    tmp.noalias() = essential.adjoint() * bottom.unwind();
+    tmp = tau * (tmp + this->row(0));
+    this->row(0) = this->row(0) - tmp;
+    bottom.unwind().noalias() -= essential * tmp;
+  }
+}
+
+/** Apply the elementary reflector H given by
+ * \f$ H = I - tau v v^*\f$
+ * with
+ * \f$ v^T = [1 essential^T] \f$
+ * from the right to a vector or matrix.
+ *
+ * On input:
+ * \param essential the essential part of the vector \c v
+ * \param tau the scaling factor of the Householder transformation
+ * \param workspace a pointer to working space with at least
+ *                  this->rows() entries
+ *
+ * \sa MatrixBase::makeHouseholder(), MatrixBase::makeHouseholderInPlace(),
+ *     MatrixBase::applyHouseholderOnTheLeft()
+ */
+template <typename Derived>
+template <typename EssentialPart>
+EIGEN_DEVICE_FUNC void MatrixBase<Derived>::applyHouseholderOnTheRight(const EssentialPart& essential,
+                                                                       const Scalar& tau, Scalar* workspace) {
+  if (cols() == 1) {
+    *this *= Scalar(1) - tau;
+  } else if (!numext::is_exactly_zero(tau)) {
+    Map<typename internal::plain_col_type<PlainObject>::type> tmp(workspace, rows());
+    Block<Derived, Derived::RowsAtCompileTime, EssentialPart::SizeAtCompileTime> right(derived(), 0, 1, rows(),
+                                                                                       cols() - 1);
+    tmp.noalias() = right.unwind() * essential;
+    tmp = tau * (tmp + this->col(0));
+    this->col(0) = this->col(0) - tmp;
+    right.unwind().noalias() -= tmp * essential.adjoint();
+  }
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_HOUSEHOLDER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/HouseholderSequence.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/HouseholderSequence.h
new file mode 100644
index 0000000..d49c961
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/HouseholderSequence.h
@@ -0,0 +1,504 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HOUSEHOLDER_SEQUENCE_H
+#define EIGEN_HOUSEHOLDER_SEQUENCE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup Householder_Module
+ * \householder_module
+ * \class HouseholderSequence
+ * \brief Sequence of Householder reflections acting on subspaces with decreasing size
+ * \tparam VectorsType type of matrix containing the Householder vectors
+ * \tparam CoeffsType  type of vector containing the Householder coefficients
+ * \tparam Side        either OnTheLeft (the default) or OnTheRight
+ *
+ * This class represents a product sequence of Householder reflections where the first Householder reflection
+ * acts on the whole space, the second Householder reflection leaves the one-dimensional subspace spanned by
+ * the first unit vector invariant, the third Householder reflection leaves the two-dimensional subspace
+ * spanned by the first two unit vectors invariant, and so on up to the last reflection which leaves all but
+ * one dimensions invariant and acts only on the last dimension. Such sequences of Householder reflections
+ * are used in several algorithms to zero out certain parts of a matrix. Indeed, the methods
+ * HessenbergDecomposition::matrixQ(), Tridiagonalization::matrixQ(), HouseholderQR::householderQ(),
+ * and ColPivHouseholderQR::householderQ() all return a %HouseholderSequence.
+ *
+ * More precisely, the class %HouseholderSequence represents an \f$ n \times n \f$ matrix \f$ H \f$ of the
+ * form \f$ H = \prod_{i=0}^{n-1} H_i \f$ where the i-th Householder reflection is \f$ H_i = I - h_i v_i
+ * v_i^* \f$. The i-th Householder coefficient \f$ h_i \f$ is a scalar and the i-th Householder vector \f$
+ * v_i \f$ is a vector of the form
+ * \f[
+ * v_i = [\underbrace{0, \ldots, 0}_{i-1\mbox{ zeros}}, 1, \underbrace{*, \ldots,*}_{n-i\mbox{ arbitrary entries}} ].
+ * \f]
+ * The last \f$ n-i \f$ entries of \f$ v_i \f$ are called the essential part of the Householder vector.
+ *
+ * Typical usages are listed below, where H is a HouseholderSequence:
+ * \code
+ * A.applyOnTheRight(H);             // A = A * H
+ * A.applyOnTheLeft(H);              // A = H * A
+ * A.applyOnTheRight(H.adjoint());   // A = A * H^*
+ * A.applyOnTheLeft(H.adjoint());    // A = H^* * A
+ * MatrixXd Q = H;                   // conversion to a dense matrix
+ * \endcode
+ * In addition to the adjoint, you can also apply the inverse (=adjoint), the transpose, and the conjugate operators.
+ *
+ * See the documentation for HouseholderSequence(const VectorsType&, const CoeffsType&) for an example.
+ *
+ * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
+ */
+
+namespace internal {
+
+template <typename VectorsType, typename CoeffsType, int Side>
+struct traits<HouseholderSequence<VectorsType, CoeffsType, Side> > {
+  typedef typename VectorsType::Scalar Scalar;
+  typedef typename VectorsType::StorageIndex StorageIndex;
+  typedef typename VectorsType::StorageKind StorageKind;
+  enum {
+    RowsAtCompileTime =
+        Side == OnTheLeft ? traits<VectorsType>::RowsAtCompileTime : traits<VectorsType>::ColsAtCompileTime,
+    ColsAtCompileTime = RowsAtCompileTime,
+    MaxRowsAtCompileTime =
+        Side == OnTheLeft ? traits<VectorsType>::MaxRowsAtCompileTime : traits<VectorsType>::MaxColsAtCompileTime,
+    MaxColsAtCompileTime = MaxRowsAtCompileTime,
+    Flags = 0
+  };
+};
+
+struct HouseholderSequenceShape {};
+
+template <typename VectorsType, typename CoeffsType, int Side>
+struct evaluator_traits<HouseholderSequence<VectorsType, CoeffsType, Side> >
+    : public evaluator_traits_base<HouseholderSequence<VectorsType, CoeffsType, Side> > {
+  typedef HouseholderSequenceShape Shape;
+};
+
+template <typename VectorsType, typename CoeffsType, int Side>
+struct hseq_side_dependent_impl {
+  typedef Block<const VectorsType, Dynamic, 1> EssentialVectorType;
+  typedef HouseholderSequence<VectorsType, CoeffsType, OnTheLeft> HouseholderSequenceType;
+  static EIGEN_DEVICE_FUNC inline const EssentialVectorType essentialVector(const HouseholderSequenceType& h, Index k) {
+    Index start = k + 1 + h.m_shift;
+    return Block<const VectorsType, Dynamic, 1>(h.m_vectors, start, k, h.rows() - start, 1);
+  }
+};
+
+template <typename VectorsType, typename CoeffsType>
+struct hseq_side_dependent_impl<VectorsType, CoeffsType, OnTheRight> {
+  typedef Transpose<Block<const VectorsType, 1, Dynamic> > EssentialVectorType;
+  typedef HouseholderSequence<VectorsType, CoeffsType, OnTheRight> HouseholderSequenceType;
+  static inline const EssentialVectorType essentialVector(const HouseholderSequenceType& h, Index k) {
+    Index start = k + 1 + h.m_shift;
+    return Block<const VectorsType, 1, Dynamic>(h.m_vectors, k, start, 1, h.rows() - start).transpose();
+  }
+};
+
+template <typename OtherScalarType, typename MatrixType>
+struct matrix_type_times_scalar_type {
+  typedef typename ScalarBinaryOpTraits<OtherScalarType, typename MatrixType::Scalar>::ReturnType ResultScalar;
+  typedef Matrix<ResultScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime, 0,
+                 MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime>
+      Type;
+};
+
+}  // end namespace internal
+
+template <typename VectorsType, typename CoeffsType, int Side>
+class HouseholderSequence : public EigenBase<HouseholderSequence<VectorsType, CoeffsType, Side> > {
+  typedef typename internal::hseq_side_dependent_impl<VectorsType, CoeffsType, Side>::EssentialVectorType
+      EssentialVectorType;
+
+ public:
+  enum {
+    RowsAtCompileTime = internal::traits<HouseholderSequence>::RowsAtCompileTime,
+    ColsAtCompileTime = internal::traits<HouseholderSequence>::ColsAtCompileTime,
+    MaxRowsAtCompileTime = internal::traits<HouseholderSequence>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = internal::traits<HouseholderSequence>::MaxColsAtCompileTime
+  };
+  typedef typename internal::traits<HouseholderSequence>::Scalar Scalar;
+
+  typedef HouseholderSequence<
+      std::conditional_t<NumTraits<Scalar>::IsComplex,
+                         internal::remove_all_t<typename VectorsType::ConjugateReturnType>, VectorsType>,
+      std::conditional_t<NumTraits<Scalar>::IsComplex, internal::remove_all_t<typename CoeffsType::ConjugateReturnType>,
+                         CoeffsType>,
+      Side>
+      ConjugateReturnType;
+
+  typedef HouseholderSequence<
+      VectorsType,
+      std::conditional_t<NumTraits<Scalar>::IsComplex, internal::remove_all_t<typename CoeffsType::ConjugateReturnType>,
+                         CoeffsType>,
+      Side>
+      AdjointReturnType;
+
+  typedef HouseholderSequence<
+      std::conditional_t<NumTraits<Scalar>::IsComplex,
+                         internal::remove_all_t<typename VectorsType::ConjugateReturnType>, VectorsType>,
+      CoeffsType, Side>
+      TransposeReturnType;
+
+  typedef HouseholderSequence<std::add_const_t<VectorsType>, std::add_const_t<CoeffsType>, Side>
+      ConstHouseholderSequence;
+
+  /** \brief Constructor.
+   * \param[in]  v      %Matrix containing the essential parts of the Householder vectors
+   * \param[in]  h      Vector containing the Householder coefficients
+   *
+   * Constructs the Householder sequence with coefficients given by \p h and vectors given by \p v. The
+   * i-th Householder coefficient \f$ h_i \f$ is given by \p h(i) and the essential part of the i-th
+   * Householder vector \f$ v_i \f$ is given by \p v(k,i) with \p k > \p i (the subdiagonal part of the
+   * i-th column). If \p v has fewer columns than rows, then the Householder sequence contains as many
+   * Householder reflections as there are columns.
+   *
+   * \note The %HouseholderSequence object stores \p v and \p h by reference.
+   *
+   * Example: \include HouseholderSequence_HouseholderSequence.cpp
+   * Output: \verbinclude HouseholderSequence_HouseholderSequence.out
+   *
+   * \sa setLength(), setShift()
+   */
+  EIGEN_DEVICE_FUNC HouseholderSequence(const VectorsType& v, const CoeffsType& h)
+      : m_vectors(v), m_coeffs(h), m_reverse(false), m_length(v.diagonalSize()), m_shift(0) {}
+
+  /** \brief Copy constructor. */
+  EIGEN_DEVICE_FUNC HouseholderSequence(const HouseholderSequence& other)
+      : m_vectors(other.m_vectors),
+        m_coeffs(other.m_coeffs),
+        m_reverse(other.m_reverse),
+        m_length(other.m_length),
+        m_shift(other.m_shift) {}
+
+  /** \brief Number of rows of transformation viewed as a matrix.
+   * \returns Number of rows
+   * \details This equals the dimension of the space that the transformation acts on.
+   */
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept {
+    return Side == OnTheLeft ? m_vectors.rows() : m_vectors.cols();
+  }
+
+  /** \brief Number of columns of transformation viewed as a matrix.
+   * \returns Number of columns
+   * \details This equals the dimension of the space that the transformation acts on.
+   */
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return rows(); }
+
+  /** \brief Essential part of a Householder vector.
+   * \param[in]  k  Index of Householder reflection
+   * \returns    Vector containing non-trivial entries of k-th Householder vector
+   *
+   * This function returns the essential part of the Householder vector \f$ v_i \f$. This is a vector of
+   * length \f$ n-i \f$ containing the last \f$ n-i \f$ entries of the vector
+   * \f[
+   * v_i = [\underbrace{0, \ldots, 0}_{i-1\mbox{ zeros}}, 1, \underbrace{*, \ldots,*}_{n-i\mbox{ arbitrary entries}} ].
+   * \f]
+   * The index \f$ i \f$ equals \p k + shift(), corresponding to the k-th column of the matrix \p v
+   * passed to the constructor.
+   *
+   * \sa setShift(), shift()
+   */
+  EIGEN_DEVICE_FUNC const EssentialVectorType essentialVector(Index k) const {
+    eigen_assert(k >= 0 && k < m_length);
+    return internal::hseq_side_dependent_impl<VectorsType, CoeffsType, Side>::essentialVector(*this, k);
+  }
+
+  /** \brief %Transpose of the Householder sequence. */
+  TransposeReturnType transpose() const {
+    return TransposeReturnType(m_vectors.conjugate(), m_coeffs)
+        .setReverseFlag(!m_reverse)
+        .setLength(m_length)
+        .setShift(m_shift);
+  }
+
+  /** \brief Complex conjugate of the Householder sequence. */
+  ConjugateReturnType conjugate() const {
+    return ConjugateReturnType(m_vectors.conjugate(), m_coeffs.conjugate())
+        .setReverseFlag(m_reverse)
+        .setLength(m_length)
+        .setShift(m_shift);
+  }
+
+  /** \returns an expression of the complex conjugate of \c *this if Cond==true,
+   *           returns \c *this otherwise.
+   */
+  template <bool Cond>
+  EIGEN_DEVICE_FUNC inline std::conditional_t<Cond, ConjugateReturnType, ConstHouseholderSequence> conjugateIf() const {
+    typedef std::conditional_t<Cond, ConjugateReturnType, ConstHouseholderSequence> ReturnType;
+    return ReturnType(m_vectors.template conjugateIf<Cond>(), m_coeffs.template conjugateIf<Cond>());
+  }
+
+  /** \brief Adjoint (conjugate transpose) of the Householder sequence. */
+  AdjointReturnType adjoint() const {
+    return AdjointReturnType(m_vectors, m_coeffs.conjugate())
+        .setReverseFlag(!m_reverse)
+        .setLength(m_length)
+        .setShift(m_shift);
+  }
+
+  /** \brief Inverse of the Householder sequence (equals the adjoint). */
+  AdjointReturnType inverse() const { return adjoint(); }
+
+  /** \internal */
+  template <typename DestType>
+  inline EIGEN_DEVICE_FUNC void evalTo(DestType& dst) const {
+    Matrix<Scalar, DestType::RowsAtCompileTime, 1, AutoAlign | ColMajor, DestType::MaxRowsAtCompileTime, 1> workspace(
+        rows());
+    evalTo(dst, workspace);
+  }
+
+  /** \internal */
+  template <typename Dest, typename Workspace>
+  EIGEN_DEVICE_FUNC void evalTo(Dest& dst, Workspace& workspace) const {
+    workspace.resize(rows());
+    Index vecs = m_length;
+    if (internal::is_same_dense(dst, m_vectors)) {
+      // in-place
+      dst.diagonal().setOnes();
+      dst.template triangularView<StrictlyUpper>().setZero();
+      for (Index k = vecs - 1; k >= 0; --k) {
+        Index cornerSize = rows() - k - m_shift;
+        if (m_reverse)
+          dst.bottomRightCorner(cornerSize, cornerSize)
+              .applyHouseholderOnTheRight(essentialVector(k), m_coeffs.coeff(k), workspace.data());
+        else
+          dst.bottomRightCorner(cornerSize, cornerSize)
+              .applyHouseholderOnTheLeft(essentialVector(k), m_coeffs.coeff(k), workspace.data());
+
+        // clear the off diagonal vector
+        dst.col(k).tail(rows() - k - 1).setZero();
+      }
+      // clear the remaining columns if needed
+      for (Index k = 0; k < cols() - vecs; ++k) dst.col(k).tail(rows() - k - 1).setZero();
+    } else if (m_length > BlockSize) {
+      dst.setIdentity(rows(), rows());
+      if (m_reverse)
+        applyThisOnTheLeft(dst, workspace, true);
+      else
+        applyThisOnTheLeft(dst, workspace, true);
+    } else {
+      dst.setIdentity(rows(), rows());
+      for (Index k = vecs - 1; k >= 0; --k) {
+        Index cornerSize = rows() - k - m_shift;
+        if (m_reverse)
+          dst.bottomRightCorner(cornerSize, cornerSize)
+              .applyHouseholderOnTheRight(essentialVector(k), m_coeffs.coeff(k), workspace.data());
+        else
+          dst.bottomRightCorner(cornerSize, cornerSize)
+              .applyHouseholderOnTheLeft(essentialVector(k), m_coeffs.coeff(k), workspace.data());
+      }
+    }
+  }
+
+  /** \internal */
+  template <typename Dest>
+  inline void applyThisOnTheRight(Dest& dst) const {
+    Matrix<Scalar, 1, Dest::RowsAtCompileTime, RowMajor, 1, Dest::MaxRowsAtCompileTime> workspace(dst.rows());
+    applyThisOnTheRight(dst, workspace);
+  }
+
+  /** \internal */
+  template <typename Dest, typename Workspace>
+  inline void applyThisOnTheRight(Dest& dst, Workspace& workspace) const {
+    workspace.resize(dst.rows());
+    for (Index k = 0; k < m_length; ++k) {
+      Index actual_k = m_reverse ? m_length - k - 1 : k;
+      dst.rightCols(rows() - m_shift - actual_k)
+          .applyHouseholderOnTheRight(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data());
+    }
+  }
+
+  /** \internal */
+  template <typename Dest>
+  inline void applyThisOnTheLeft(Dest& dst, bool inputIsIdentity = false) const {
+    Matrix<Scalar, 1, Dest::ColsAtCompileTime, RowMajor, 1, Dest::MaxColsAtCompileTime> workspace;
+    applyThisOnTheLeft(dst, workspace, inputIsIdentity);
+  }
+
+  /** \internal */
+  template <typename Dest, typename Workspace>
+  inline void applyThisOnTheLeft(Dest& dst, Workspace& workspace, bool inputIsIdentity = false) const {
+    if (inputIsIdentity && m_reverse) inputIsIdentity = false;
+    // if the entries are large enough, then apply the reflectors by block
+    if (m_length >= BlockSize && dst.cols() > 1) {
+      // Make sure we have at least 2 useful blocks, otherwise it is point-less:
+      Index blockSize = m_length < Index(2 * BlockSize) ? (m_length + 1) / 2 : Index(BlockSize);
+      for (Index i = 0; i < m_length; i += blockSize) {
+        Index end = m_reverse ? (std::min)(m_length, i + blockSize) : m_length - i;
+        Index k = m_reverse ? i : (std::max)(Index(0), end - blockSize);
+        Index bs = end - k;
+        Index start = k + m_shift;
+
+        typedef Block<internal::remove_all_t<VectorsType>, Dynamic, Dynamic> SubVectorsType;
+        SubVectorsType sub_vecs1(m_vectors.const_cast_derived(), Side == OnTheRight ? k : start,
+                                 Side == OnTheRight ? start : k, Side == OnTheRight ? bs : m_vectors.rows() - start,
+                                 Side == OnTheRight ? m_vectors.cols() - start : bs);
+        std::conditional_t<Side == OnTheRight, Transpose<SubVectorsType>, SubVectorsType&> sub_vecs(sub_vecs1);
+
+        Index dstRows = rows() - m_shift - k;
+
+        if (inputIsIdentity) {
+          Block<Dest, Dynamic, Dynamic> sub_dst = dst.bottomRightCorner(dstRows, dstRows);
+          apply_block_householder_on_the_left(sub_dst, sub_vecs, m_coeffs.segment(k, bs), !m_reverse);
+        } else {
+          auto sub_dst = dst.bottomRows(dstRows);
+          apply_block_householder_on_the_left(sub_dst, sub_vecs, m_coeffs.segment(k, bs), !m_reverse);
+        }
+      }
+    } else {
+      workspace.resize(dst.cols());
+      for (Index k = 0; k < m_length; ++k) {
+        Index actual_k = m_reverse ? k : m_length - k - 1;
+        Index dstRows = rows() - m_shift - actual_k;
+
+        if (inputIsIdentity) {
+          Block<Dest, Dynamic, Dynamic> sub_dst = dst.bottomRightCorner(dstRows, dstRows);
+          sub_dst.applyHouseholderOnTheLeft(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data());
+        } else {
+          auto sub_dst = dst.bottomRows(dstRows);
+          sub_dst.applyHouseholderOnTheLeft(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data());
+        }
+      }
+    }
+  }
+
+  /** \brief Computes the product of a Householder sequence with a matrix.
+   * \param[in]  other  %Matrix being multiplied.
+   * \returns    Expression object representing the product.
+   *
+   * This function computes \f$ HM \f$ where \f$ H \f$ is the Householder sequence represented by \p *this
+   * and \f$ M \f$ is the matrix \p other.
+   */
+  template <typename OtherDerived>
+  typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::Type operator*(
+      const MatrixBase<OtherDerived>& other) const {
+    typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::Type res(
+        other.template cast<typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::ResultScalar>());
+    applyThisOnTheLeft(res, internal::is_identity<OtherDerived>::value && res.rows() == res.cols());
+    return res;
+  }
+
+  template <typename VectorsType_, typename CoeffsType_, int Side_>
+  friend struct internal::hseq_side_dependent_impl;
+
+  /** \brief Sets the length of the Householder sequence.
+   * \param [in]  length  New value for the length.
+   *
+   * By default, the length \f$ n \f$ of the Householder sequence \f$ H = H_0 H_1 \ldots H_{n-1} \f$ is set
+   * to the number of columns of the matrix \p v passed to the constructor, or the number of rows if that
+   * is smaller. After this function is called, the length equals \p length.
+   *
+   * \sa length()
+   */
+  EIGEN_DEVICE_FUNC HouseholderSequence& setLength(Index length) {
+    m_length = length;
+    return *this;
+  }
+
+  /** \brief Sets the shift of the Householder sequence.
+   * \param [in]  shift  New value for the shift.
+   *
+   * By default, a %HouseholderSequence object represents \f$ H = H_0 H_1 \ldots H_{n-1} \f$ and the i-th
+   * column of the matrix \p v passed to the constructor corresponds to the i-th Householder
+   * reflection. After this function is called, the object represents \f$ H = H_{\mathrm{shift}}
+   * H_{\mathrm{shift}+1} \ldots H_{n-1} \f$ and the i-th column of \p v corresponds to the (shift+i)-th
+   * Householder reflection.
+   *
+   * \sa shift()
+   */
+  EIGEN_DEVICE_FUNC HouseholderSequence& setShift(Index shift) {
+    m_shift = shift;
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC Index length() const {
+    return m_length;
+  } /**< \brief Returns the length of the Householder sequence. */
+
+  EIGEN_DEVICE_FUNC Index shift() const {
+    return m_shift;
+  } /**< \brief Returns the shift of the Householder sequence. */
+
+  /* Necessary for .adjoint() and .conjugate() */
+  template <typename VectorsType2, typename CoeffsType2, int Side2>
+  friend class HouseholderSequence;
+
+ protected:
+  /** \internal
+   * \brief Sets the reverse flag.
+   * \param [in]  reverse  New value of the reverse flag.
+   *
+   * By default, the reverse flag is not set. If the reverse flag is set, then this object represents
+   * \f$ H^r = H_{n-1} \ldots H_1 H_0 \f$ instead of \f$ H = H_0 H_1 \ldots H_{n-1} \f$.
+   * \note For real valued HouseholderSequence this is equivalent to transposing \f$ H \f$.
+   *
+   * \sa reverseFlag(), transpose(), adjoint()
+   */
+  HouseholderSequence& setReverseFlag(bool reverse) {
+    m_reverse = reverse;
+    return *this;
+  }
+
+  bool reverseFlag() const { return m_reverse; } /**< \internal \brief Returns the reverse flag. */
+
+  typename VectorsType::Nested m_vectors;
+  typename CoeffsType::Nested m_coeffs;
+  bool m_reverse;
+  Index m_length;
+  Index m_shift;
+  enum { BlockSize = 48 };
+};
+
+/** \brief Computes the product of a matrix with a Householder sequence.
+ * \param[in]  other  %Matrix being multiplied.
+ * \param[in]  h      %HouseholderSequence being multiplied.
+ * \returns    Expression object representing the product.
+ *
+ * This function computes \f$ MH \f$ where \f$ M \f$ is the matrix \p other and \f$ H \f$ is the
+ * Householder sequence represented by \p h.
+ */
+template <typename OtherDerived, typename VectorsType, typename CoeffsType, int Side>
+typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar, OtherDerived>::Type operator*(
+    const MatrixBase<OtherDerived>& other, const HouseholderSequence<VectorsType, CoeffsType, Side>& h) {
+  typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar, OtherDerived>::Type res(
+      other.template cast<typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,
+                                                                           OtherDerived>::ResultScalar>());
+  h.applyThisOnTheRight(res);
+  return res;
+}
+
+/** \ingroup Householder_Module
+ * \householder_module
+ * \brief Convenience function for constructing a Householder sequence.
+ * \returns A HouseholderSequence constructed from the specified arguments.
+ */
+template <typename VectorsType, typename CoeffsType>
+HouseholderSequence<VectorsType, CoeffsType> householderSequence(const VectorsType& v, const CoeffsType& h) {
+  return HouseholderSequence<VectorsType, CoeffsType, OnTheLeft>(v, h);
+}
+
+/** \ingroup Householder_Module
+ * \householder_module
+ * \brief Convenience function for constructing a Householder sequence.
+ * \returns A HouseholderSequence constructed from the specified arguments.
+ * \details This function differs from householderSequence() in that the template argument \p OnTheSide of
+ * the constructed HouseholderSequence is set to OnTheRight, instead of the default OnTheLeft.
+ */
+template <typename VectorsType, typename CoeffsType>
+HouseholderSequence<VectorsType, CoeffsType, OnTheRight> rightHouseholderSequence(const VectorsType& v,
+                                                                                  const CoeffsType& h) {
+  return HouseholderSequence<VectorsType, CoeffsType, OnTheRight>(v, h);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_HOUSEHOLDER_SEQUENCE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/InternalHeaderCheck.h
new file mode 100644
index 0000000..70de89b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Householder/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_HOUSEHOLDER_MODULE_H
+#error "Please include Eigen/Householder instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
new file mode 100644
index 0000000..2be9d43
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
@@ -0,0 +1,213 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BASIC_PRECONDITIONERS_H
+#define EIGEN_BASIC_PRECONDITIONERS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A preconditioner based on the digonal entries
+  *
+  * This class allows to approximately solve for A.x = b problems assuming A is a diagonal matrix.
+  * In other words, this preconditioner neglects all off diagonal entries and, in Eigen's language, solves for:
+    \code
+    A.diagonal().asDiagonal() . x = b
+    \endcode
+  *
+  * \tparam Scalar_ the type of the scalar.
+  *
+  * \implsparsesolverconcept
+  *
+  * This preconditioner is suitable for both selfadjoint and general problems.
+  * The diagonal entries are pre-inverted and stored into a dense vector.
+  *
+  * \note A variant that has yet to be implemented would attempt to preserve the norm of each column.
+  *
+  * \sa class LeastSquareDiagonalPreconditioner, class ConjugateGradient
+  */
+template <typename Scalar_>
+class DiagonalPreconditioner {
+  typedef Scalar_ Scalar;
+  typedef Matrix<Scalar, Dynamic, 1> Vector;
+
+ public:
+  typedef typename Vector::StorageIndex StorageIndex;
+  enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
+
+  DiagonalPreconditioner() : m_isInitialized(false) {}
+
+  template <typename MatType>
+  explicit DiagonalPreconditioner(const MatType& mat) : m_invdiag(mat.cols()) {
+    compute(mat);
+  }
+
+  constexpr Index rows() const noexcept { return m_invdiag.size(); }
+  constexpr Index cols() const noexcept { return m_invdiag.size(); }
+
+  template <typename MatType>
+  DiagonalPreconditioner& analyzePattern(const MatType&) {
+    return *this;
+  }
+
+  template <typename MatType>
+  DiagonalPreconditioner& factorize(const MatType& mat) {
+    m_invdiag.resize(mat.cols());
+    for (int j = 0; j < mat.outerSize(); ++j) {
+      typename MatType::InnerIterator it(mat, j);
+      while (it && it.index() != j) ++it;
+      if (it && it.index() == j && it.value() != Scalar(0))
+        m_invdiag(j) = Scalar(1) / it.value();
+      else
+        m_invdiag(j) = Scalar(1);
+    }
+    m_isInitialized = true;
+    return *this;
+  }
+
+  template <typename MatType>
+  DiagonalPreconditioner& compute(const MatType& mat) {
+    return factorize(mat);
+  }
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const Rhs& b, Dest& x) const {
+    x = m_invdiag.array() * b.array();
+  }
+
+  template <typename Rhs>
+  inline Solve<DiagonalPreconditioner, Rhs> solve(const MatrixBase<Rhs>& b) const {
+    eigen_assert(m_isInitialized && "DiagonalPreconditioner is not initialized.");
+    eigen_assert(m_invdiag.size() == b.rows() &&
+                 "DiagonalPreconditioner::solve(): invalid number of rows of the right hand side matrix b");
+    return Solve<DiagonalPreconditioner, Rhs>(*this, b.derived());
+  }
+
+  ComputationInfo info() { return Success; }
+
+ protected:
+  Vector m_invdiag;
+  bool m_isInitialized;
+};
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief Jacobi preconditioner for LeastSquaresConjugateGradient
+  *
+  * This class allows to approximately solve for A' A x  = A' b problems assuming A' A is a diagonal matrix.
+  * In other words, this preconditioner neglects all off diagonal entries and, in Eigen's language, solves for:
+    \code
+    (A.adjoint() * A).diagonal().asDiagonal() * x = b
+    \endcode
+  *
+  * \tparam Scalar_ the type of the scalar.
+  *
+  * \implsparsesolverconcept
+  *
+  * The diagonal entries are pre-inverted and stored into a dense vector.
+  *
+  * \sa class LeastSquaresConjugateGradient, class DiagonalPreconditioner
+  */
+template <typename Scalar_>
+class LeastSquareDiagonalPreconditioner : public DiagonalPreconditioner<Scalar_> {
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef DiagonalPreconditioner<Scalar_> Base;
+  using Base::m_invdiag;
+
+ public:
+  LeastSquareDiagonalPreconditioner() : Base() {}
+
+  template <typename MatType>
+  explicit LeastSquareDiagonalPreconditioner(const MatType& mat) : Base() {
+    compute(mat);
+  }
+
+  template <typename MatType>
+  LeastSquareDiagonalPreconditioner& analyzePattern(const MatType&) {
+    return *this;
+  }
+
+  template <typename MatType>
+  LeastSquareDiagonalPreconditioner& factorize(const MatType& mat) {
+    // Compute the inverse squared-norm of each column of mat
+    m_invdiag.resize(mat.cols());
+    if (MatType::IsRowMajor) {
+      m_invdiag.setZero();
+      for (Index j = 0; j < mat.outerSize(); ++j) {
+        for (typename MatType::InnerIterator it(mat, j); it; ++it) m_invdiag(it.index()) += numext::abs2(it.value());
+      }
+      for (Index j = 0; j < mat.cols(); ++j)
+        if (numext::real(m_invdiag(j)) > RealScalar(0)) m_invdiag(j) = RealScalar(1) / numext::real(m_invdiag(j));
+    } else {
+      for (Index j = 0; j < mat.outerSize(); ++j) {
+        RealScalar sum = mat.col(j).squaredNorm();
+        if (sum > RealScalar(0))
+          m_invdiag(j) = RealScalar(1) / sum;
+        else
+          m_invdiag(j) = RealScalar(1);
+      }
+    }
+    Base::m_isInitialized = true;
+    return *this;
+  }
+
+  template <typename MatType>
+  LeastSquareDiagonalPreconditioner& compute(const MatType& mat) {
+    return factorize(mat);
+  }
+
+  ComputationInfo info() { return Success; }
+
+ protected:
+};
+
+/** \ingroup IterativeLinearSolvers_Module
+ * \brief A naive preconditioner which approximates any matrix as the identity matrix
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa class DiagonalPreconditioner
+ */
+class IdentityPreconditioner {
+ public:
+  IdentityPreconditioner() {}
+
+  template <typename MatrixType>
+  explicit IdentityPreconditioner(const MatrixType&) {}
+
+  template <typename MatrixType>
+  IdentityPreconditioner& analyzePattern(const MatrixType&) {
+    return *this;
+  }
+
+  template <typename MatrixType>
+  IdentityPreconditioner& factorize(const MatrixType&) {
+    return *this;
+  }
+
+  template <typename MatrixType>
+  IdentityPreconditioner& compute(const MatrixType&) {
+    return *this;
+  }
+
+  template <typename Rhs>
+  inline const Rhs& solve(const Rhs& b) const {
+    return b;
+  }
+
+  ComputationInfo info() { return Success; }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BASIC_PRECONDITIONERS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
new file mode 100644
index 0000000..2d461fe
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
@@ -0,0 +1,219 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BICGSTAB_H
+#define EIGEN_BICGSTAB_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal Low-level bi conjugate gradient stabilized algorithm
+ * \param mat The matrix A
+ * \param rhs The right hand side vector b
+ * \param x On input and initial solution, on output the computed solution.
+ * \param precond A preconditioner being able to efficiently solve for an
+ *                approximation of Ax=b (regardless of b)
+ * \param iters On input the max number of iteration, on output the number of performed iterations.
+ * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+ * \return false in the case of numerical issue, for example a break down of BiCGSTAB.
+ */
+template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x, const Preconditioner& precond, Index& iters,
+              typename Dest::RealScalar& tol_error) {
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  Index maxIters = iters;
+
+  Index n = mat.cols();
+  VectorType r = rhs - mat * x;
+  VectorType r0 = r;
+
+  RealScalar r0_norm = r0.stableNorm();
+  RealScalar r_norm = r0_norm;
+  RealScalar rhs_norm = rhs.stableNorm();
+  if (rhs_norm == 0) {
+    x.setZero();
+    return true;
+  }
+
+  RealScalar tol = tol_error * rhs_norm;
+
+  Scalar rho(1);
+  Scalar alpha(0);
+  Scalar w(1);
+
+  VectorType v = VectorType::Zero(n), p = VectorType::Zero(n);
+  VectorType y(n), z(n);
+  VectorType kt(n), ks(n);
+
+  VectorType s(n), t(n);
+
+  RealScalar eps = NumTraits<Scalar>::epsilon();
+  Index i = 0;
+  Index restarts = 0;
+
+  while (r_norm > tol && i < maxIters) {
+    Scalar rho_old = rho;
+    rho = r0.dot(r);
+    if (Eigen::numext::abs(rho) / Eigen::numext::maxi(r0_norm, r_norm) < eps * Eigen::numext::mini(r0_norm, r_norm)) {
+      // The new residual vector became too orthogonal to the arbitrarily chosen direction r0
+      // Let's restart with a new r0:
+      r = rhs - mat * x;
+      r0 = r;
+      rho = r.squaredNorm();
+      r0_norm = r.stableNorm();
+      alpha = Scalar(0);
+      w = Scalar(1);
+      if (restarts++ == 0) i = 0;
+    }
+    Scalar beta = (rho / rho_old) * (alpha / w);
+    p = r + beta * (p - w * v);
+
+    y = precond.solve(p);
+
+    v.noalias() = mat * y;
+    Scalar theta = r0.dot(v);
+    // For small angles ∠(r0, v) < eps, random restart.
+    RealScalar v_norm = v.stableNorm();
+    if (Eigen::numext::abs(theta) / Eigen::numext::maxi(r0_norm, v_norm) < eps * Eigen::numext::mini(r0_norm, v_norm)) {
+      r = rhs - mat * x;
+      r0.setRandom();
+      r0_norm = r0.stableNorm();
+      rho = Scalar(1);
+      alpha = Scalar(0);
+      w = Scalar(1);
+      if (restarts++ == 0) i = 0;
+      continue;
+    }
+    alpha = rho / theta;
+    s = r - alpha * v;
+
+    z = precond.solve(s);
+    t.noalias() = mat * z;
+
+    RealScalar tmp = t.squaredNorm();
+    if (tmp > RealScalar(0)) {
+      w = t.dot(s) / tmp;
+    } else {
+      w = Scalar(0);
+    }
+    x += alpha * y + w * z;
+    r = s - w * t;
+    r_norm = r.stableNorm();
+    ++i;
+  }
+
+  tol_error = r_norm / rhs_norm;
+  iters = i;
+  return true;
+}
+
+}  // namespace internal
+
+template <typename MatrixType_, typename Preconditioner_ = DiagonalPreconditioner<typename MatrixType_::Scalar> >
+class BiCGSTAB;
+
+namespace internal {
+
+template <typename MatrixType_, typename Preconditioner_>
+struct traits<BiCGSTAB<MatrixType_, Preconditioner_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}  // namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+ * \brief A bi conjugate gradient stabilized solver for sparse square problems
+ *
+ * This class allows to solve for A.x = b sparse linear problems using a bi conjugate gradient
+ * stabilized algorithm. The vectors x and b can be either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, can be a dense or a sparse matrix.
+ * \tparam Preconditioner_ the type of the preconditioner. Default is DiagonalPreconditioner
+ *
+ * \implsparsesolverconcept
+ *
+ * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+ * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+ * and NumTraits<Scalar>::epsilon() for the tolerance.
+ *
+ * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
+ *
+ * \b Performance: when using sparse matrices, best performance is achied for a row-major sparse matrix format.
+ * Moreover, in this case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
+ * See \ref TopicMultiThreading for details.
+ *
+ * This class can be used as the direct solver classes. Here is a typical usage example:
+ * \include BiCGSTAB_simple.cpp
+ *
+ * By default the iterations start with x=0 as an initial guess of the solution.
+ * One can control the start using the solveWithGuess() method.
+ *
+ * BiCGSTAB can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+ *
+ * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+ */
+template <typename MatrixType_, typename Preconditioner_>
+class BiCGSTAB : public IterativeSolverBase<BiCGSTAB<MatrixType_, Preconditioner_> > {
+  typedef IterativeSolverBase<BiCGSTAB> Base;
+  using Base::m_error;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_iterations;
+  using Base::matrix;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+ public:
+  /** Default constructor. */
+  BiCGSTAB() : Base() {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+   *
+   * This constructor is a shortcut for the default constructor followed
+   * by a call to compute().
+   *
+   * \warning this class stores a reference to the matrix A as well as some
+   * precomputed values that depend on it. Therefore, if \a A is changed
+   * this class becomes invalid. Call compute() to update it with the new
+   * matrix A, or modify a copy of A.
+   */
+  template <typename MatrixDerived>
+  explicit BiCGSTAB(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
+
+  ~BiCGSTAB() {}
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const {
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+
+    bool ret = internal::bicgstab(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error);
+
+    m_info = (!ret) ? NumericalIssue : m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+
+ protected:
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BICGSTAB_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
new file mode 100644
index 0000000..5bb0efe
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
@@ -0,0 +1,217 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CONJUGATE_GRADIENT_H
+#define EIGEN_CONJUGATE_GRADIENT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal Low-level conjugate gradient algorithm
+ * \param mat The matrix A
+ * \param rhs The right hand side vector b
+ * \param x On input and initial solution, on output the computed solution.
+ * \param precond A preconditioner being able to efficiently solve for an
+ *                approximation of Ax=b (regardless of b)
+ * \param iters On input the max number of iteration, on output the number of performed iterations.
+ * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+ */
+template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+EIGEN_DONT_INLINE void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x, const Preconditioner& precond,
+                                          Index& iters, typename Dest::RealScalar& tol_error) {
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+
+  RealScalar tol = tol_error;
+  Index maxIters = iters;
+
+  Index n = mat.cols();
+
+  VectorType residual = rhs - mat * x;  // initial residual
+
+  RealScalar rhsNorm2 = rhs.squaredNorm();
+  if (rhsNorm2 == 0) {
+    x.setZero();
+    iters = 0;
+    tol_error = 0;
+    return;
+  }
+  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+  RealScalar threshold = numext::maxi(RealScalar(tol * tol * rhsNorm2), considerAsZero);
+  RealScalar residualNorm2 = residual.squaredNorm();
+  if (residualNorm2 < threshold) {
+    iters = 0;
+    tol_error = numext::sqrt(residualNorm2 / rhsNorm2);
+    return;
+  }
+
+  VectorType p(n);
+  p = precond.solve(residual);  // initial search direction
+
+  VectorType z(n), tmp(n);
+  RealScalar absNew = numext::real(residual.dot(p));  // the square of the absolute value of r scaled by invM
+  Index i = 0;
+  while (i < maxIters) {
+    tmp.noalias() = mat * p;  // the bottleneck of the algorithm
+
+    Scalar alpha = absNew / p.dot(tmp);  // the amount we travel on dir
+    x += alpha * p;                      // update solution
+    residual -= alpha * tmp;             // update residual
+
+    residualNorm2 = residual.squaredNorm();
+    if (residualNorm2 < threshold) break;
+
+    z = precond.solve(residual);  // approximately solve for "A z = residual"
+
+    RealScalar absOld = absNew;
+    absNew = numext::real(residual.dot(z));  // update the absolute value of r
+    RealScalar beta = absNew / absOld;       // calculate the Gram-Schmidt value used to create the new search direction
+    p = z + beta * p;                        // update search direction
+    i++;
+  }
+  tol_error = numext::sqrt(residualNorm2 / rhsNorm2);
+  iters = i;
+}
+
+}  // namespace internal
+
+template <typename MatrixType_, int UpLo_ = Lower,
+          typename Preconditioner_ = DiagonalPreconditioner<typename MatrixType_::Scalar> >
+class ConjugateGradient;
+
+namespace internal {
+
+template <typename MatrixType_, int UpLo_, typename Preconditioner_>
+struct traits<ConjugateGradient<MatrixType_, UpLo_, Preconditioner_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}  // namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A conjugate gradient solver for sparse (or dense) self-adjoint problems
+  *
+  * This class allows to solve for A.x = b linear problems using an iterative conjugate gradient algorithm.
+  * The matrix A must be selfadjoint. The matrix A and the vectors x and b can be either dense or sparse.
+  *
+  * \tparam MatrixType_ the type of the matrix A, can be a dense or a sparse matrix.
+  * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower,
+  *               \c Upper, or \c Lower|Upper in which the full matrix entries will be considered.
+  *               Default is \c Lower, best performance is \c Lower|Upper.
+  * \tparam Preconditioner_ the type of the preconditioner. Default is DiagonalPreconditioner
+  *
+  * \implsparsesolverconcept
+  *
+  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+  * and NumTraits<Scalar>::epsilon() for the tolerance.
+  *
+  * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
+  *
+  * \b Performance: Even though the default value of \c UpLo_ is \c Lower, significantly higher performance is
+  * achieved when using a complete matrix and \b Lower|Upper as the \a UpLo_ template parameter. Moreover, in this
+  * case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
+  * See \ref TopicMultiThreading for details.
+  *
+  * This class can be used as the direct solver classes. Here is a typical usage example:
+    \code
+    int n = 10000;
+    VectorXd x(n), b(n);
+    SparseMatrix<double> A(n,n);
+    // fill A and b
+    ConjugateGradient<SparseMatrix<double>, Lower|Upper> cg;
+    cg.compute(A);
+    x = cg.solve(b);
+    std::cout << "#iterations:     " << cg.iterations() << std::endl;
+    std::cout << "estimated error: " << cg.error()      << std::endl;
+    // update b, and solve again
+    x = cg.solve(b);
+    \endcode
+  *
+  * By default the iterations start with x=0 as an initial guess of the solution.
+  * One can control the start using the solveWithGuess() method.
+  *
+  * ConjugateGradient can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example
+  \endlink.
+  *
+  * \sa class LeastSquaresConjugateGradient, class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+  */
+template <typename MatrixType_, int UpLo_, typename Preconditioner_>
+class ConjugateGradient : public IterativeSolverBase<ConjugateGradient<MatrixType_, UpLo_, Preconditioner_> > {
+  typedef IterativeSolverBase<ConjugateGradient> Base;
+  using Base::m_error;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_iterations;
+  using Base::matrix;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+  enum { UpLo = UpLo_ };
+
+ public:
+  /** Default constructor. */
+  ConjugateGradient() : Base() {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+   *
+   * This constructor is a shortcut for the default constructor followed
+   * by a call to compute().
+   *
+   * \warning this class stores a reference to the matrix A as well as some
+   * precomputed values that depend on it. Therefore, if \a A is changed
+   * this class becomes invalid. Call compute() to update it with the new
+   * matrix A, or modify a copy of A.
+   */
+  template <typename MatrixDerived>
+  explicit ConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
+
+  ~ConjugateGradient() {}
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const {
+    typedef typename Base::MatrixWrapper MatrixWrapper;
+    typedef typename Base::ActualMatrixType ActualMatrixType;
+    enum {
+      TransposeInput = (!MatrixWrapper::MatrixFree) && (UpLo == (Lower | Upper)) && (!MatrixType::IsRowMajor) &&
+                       (!NumTraits<Scalar>::IsComplex)
+    };
+    typedef std::conditional_t<TransposeInput, Transpose<const ActualMatrixType>, ActualMatrixType const&>
+        RowMajorWrapper;
+    EIGEN_STATIC_ASSERT(internal::check_implication(MatrixWrapper::MatrixFree, UpLo == (Lower | Upper)),
+                        MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY);
+    typedef std::conditional_t<UpLo == (Lower | Upper), RowMajorWrapper,
+                               typename MatrixWrapper::template ConstSelfAdjointViewReturnType<UpLo>::Type>
+        SelfAdjointWrapper;
+
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+
+    RowMajorWrapper row_mat(matrix());
+    internal::conjugate_gradient(SelfAdjointWrapper(row_mat), b, x, Base::m_preconditioner, m_iterations, m_error);
+    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+
+ protected:
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CONJUGATE_GRADIENT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
new file mode 100644
index 0000000..a426381
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
@@ -0,0 +1,402 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INCOMPLETE_CHOlESKY_H
+#define EIGEN_INCOMPLETE_CHOlESKY_H
+
+#include <vector>
+#include <list>
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+/**
+ * \brief Modified Incomplete Cholesky with dual threshold
+ *
+ * References : C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
+ *              Limited memory, SIAM J. Sci. Comput.  21(1), pp. 24-45, 1999
+ *
+ * \tparam Scalar the scalar type of the input matrices
+ * \tparam UpLo_ The triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ * \tparam OrderingType_ The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is
+ * AMDOrdering<int>.
+ *
+ * \implsparsesolverconcept
+ *
+ * It performs the following incomplete factorization: \f$ S P A P' S + \sigma I \approx L L' \f$
+ * where L is a lower triangular factor, S is a diagonal scaling matrix, P is a
+ * fill-in reducing permutation as computed by the ordering method, and \f$ \sigma \f$ is a shift
+ * for ensuring the decomposed matrix is positive definite.
+ *
+ * \b Shifting \b strategy: Let \f$ B = S P A P' S \f$  be the scaled matrix on which the factorization is carried out,
+ * and \f$ \beta \f$ be the minimum value of the diagonal. If \f$ \beta > 0 \f$ then, the factorization is directly
+ * performed on the matrix B, and \f$ \sigma = 0 \f$. Otherwise, the factorization is performed on the shifted matrix
+ * \f$ B + \sigma I \f$ for a shifting factor  \f$ \sigma \f$.  We start with \f$ \sigma = \sigma_0 - \beta \f$, where
+ * \f$ \sigma_0 \f$ is the initial shift value as returned and set by setInitialShift() method. The default value is \f$
+ * \sigma_0 = 10^{-3} \f$. If the factorization fails, then the shift in doubled until it succeed or a maximum of ten
+ * attempts. If it still fails, as returned by the info() method, then you can either increase the initial shift, or
+ * better use another preconditioning technique.
+ *
+ */
+template <typename Scalar, int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int> >
+class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar, UpLo_, OrderingType_> > {
+ protected:
+  typedef SparseSolverBase<IncompleteCholesky<Scalar, UpLo_, OrderingType_> > Base;
+  using Base::m_isInitialized;
+
+ public:
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef OrderingType_ OrderingType;
+  typedef typename OrderingType::PermutationType PermutationType;
+  typedef typename PermutationType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> FactorType;
+  typedef Matrix<Scalar, Dynamic, 1> VectorSx;
+  typedef Matrix<RealScalar, Dynamic, 1> VectorRx;
+  typedef Matrix<StorageIndex, Dynamic, 1> VectorIx;
+  typedef std::vector<std::list<StorageIndex> > VectorList;
+  enum { UpLo = UpLo_ };
+  enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
+
+ public:
+  /** Default constructor leaving the object in a partly non-initialized stage.
+   *
+   * You must call compute() or the pair analyzePattern()/factorize() to make it valid.
+   *
+   * \sa IncompleteCholesky(const MatrixType&)
+   */
+  IncompleteCholesky() : m_initialShift(1e-3), m_analysisIsOk(false), m_factorizationIsOk(false) {}
+
+  /** Constructor computing the incomplete factorization for the given matrix \a matrix.
+   */
+  template <typename MatrixType>
+  IncompleteCholesky(const MatrixType& matrix)
+      : m_initialShift(1e-3), m_analysisIsOk(false), m_factorizationIsOk(false) {
+    compute(matrix);
+  }
+
+  /** \returns number of rows of the factored matrix */
+  constexpr Index rows() const noexcept { return m_L.rows(); }
+
+  /** \returns number of columns of the factored matrix */
+  constexpr Index cols() const noexcept { return m_L.cols(); }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * It triggers an assertion if \c *this has not been initialized through the respective constructor,
+   * or a call to compute() or analyzePattern().
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix appears to be negative.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "IncompleteCholesky is not initialized.");
+    return m_info;
+  }
+
+  /** \brief Set the initial shift parameter \f$ \sigma \f$.
+   */
+  void setInitialShift(RealScalar shift) { m_initialShift = shift; }
+
+  /** \brief Computes the fill reducing permutation vector using the sparsity pattern of \a mat
+   */
+  template <typename MatrixType>
+  void analyzePattern(const MatrixType& mat) {
+    OrderingType ord;
+    PermutationType pinv;
+    ord(mat.template selfadjointView<UpLo>(), pinv);
+    if (pinv.size() > 0)
+      m_perm = pinv.inverse();
+    else
+      m_perm.resize(0);
+    m_L.resize(mat.rows(), mat.cols());
+    m_analysisIsOk = true;
+    m_isInitialized = true;
+    m_info = Success;
+  }
+
+  /** \brief Performs the numerical factorization of the input matrix \a mat
+   *
+   * The method analyzePattern() or compute() must have been called beforehand
+   * with a matrix having the same pattern.
+   *
+   * \sa compute(), analyzePattern()
+   */
+  template <typename MatrixType>
+  void factorize(const MatrixType& mat);
+
+  /** Computes or re-computes the incomplete Cholesky factorization of the input matrix \a mat
+   *
+   * It is a shortcut for a sequential call to the analyzePattern() and factorize() methods.
+   *
+   * \sa analyzePattern(), factorize()
+   */
+  template <typename MatrixType>
+  void compute(const MatrixType& mat) {
+    analyzePattern(mat);
+    factorize(mat);
+  }
+
+  // internal
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const Rhs& b, Dest& x) const {
+    eigen_assert(m_factorizationIsOk && "factorize() should be called first");
+    if (m_perm.rows() == b.rows())
+      x = m_perm * b;
+    else
+      x = b;
+    x = m_scale.asDiagonal() * x;
+    x = m_L.template triangularView<Lower>().solve(x);
+    x = m_L.adjoint().template triangularView<Upper>().solve(x);
+    x = m_scale.asDiagonal() * x;
+    if (m_perm.rows() == b.rows()) x = m_perm.inverse() * x;
+  }
+
+  /** \returns the sparse lower triangular factor L */
+  const FactorType& matrixL() const {
+    eigen_assert(m_factorizationIsOk && "factorize() should be called first");
+    return m_L;
+  }
+
+  /** \returns a vector representing the scaling factor S */
+  const VectorRx& scalingS() const {
+    eigen_assert(m_factorizationIsOk && "factorize() should be called first");
+    return m_scale;
+  }
+
+  /** \returns the fill-in reducing permutation P (can be empty for a natural ordering) */
+  const PermutationType& permutationP() const {
+    eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
+    return m_perm;
+  }
+
+  /** \returns the final shift parameter from the computation */
+  RealScalar shift() const { return m_shift; }
+
+ protected:
+  FactorType m_L;             // The lower part stored in CSC
+  VectorRx m_scale;           // The vector for scaling the matrix
+  RealScalar m_initialShift;  // The initial shift parameter
+  bool m_analysisIsOk;
+  bool m_factorizationIsOk;
+  ComputationInfo m_info;
+  PermutationType m_perm;
+  RealScalar m_shift;  // The final shift parameter.
+
+ private:
+  inline void updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col,
+                         const Index& jk, VectorIx& firstElt, VectorList& listCol);
+};
+
+// Based on the following paper:
+//   C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
+//   Limited memory, SIAM J. Sci. Comput.  21(1), pp. 24-45, 1999
+//   http://ftp.mcs.anl.gov/pub/tech_reports/reports/P682.pdf
+template <typename Scalar, int UpLo_, typename OrderingType>
+template <typename MatrixType_>
+void IncompleteCholesky<Scalar, UpLo_, OrderingType>::factorize(const MatrixType_& mat) {
+  using std::sqrt;
+  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
+
+  // Dropping strategy : Keep only the p largest elements per column, where p is the number of elements in the column of
+  // the original matrix. Other strategies will be added
+
+  // Apply the fill-reducing permutation computed in analyzePattern()
+  if (m_perm.rows() == mat.rows())  // To detect the null permutation
+  {
+    // The temporary is needed to make sure that the diagonal entry is properly sorted
+    FactorType tmp(mat.rows(), mat.cols());
+    tmp = mat.template selfadjointView<UpLo_>().twistedBy(m_perm);
+    m_L.template selfadjointView<Lower>() = tmp.template selfadjointView<Lower>();
+  } else {
+    m_L.template selfadjointView<Lower>() = mat.template selfadjointView<UpLo_>();
+  }
+
+  // The algorithm will insert increasingly large shifts on the diagonal until
+  // factorization succeeds. Therefore we have to make sure that there is a
+  // space in the datastructure to store such values, even if the original
+  // matrix has a zero on the diagonal.
+  bool modified = false;
+  for (Index i = 0; i < mat.cols(); ++i) {
+    bool inserted = false;
+    m_L.findOrInsertCoeff(i, i, &inserted);
+    if (inserted) {
+      modified = true;
+    }
+  }
+  if (modified) m_L.makeCompressed();
+
+  Index n = m_L.cols();
+  Index nnz = m_L.nonZeros();
+  Map<VectorSx> vals(m_L.valuePtr(), nnz);           // values
+  Map<VectorIx> rowIdx(m_L.innerIndexPtr(), nnz);    // Row indices
+  Map<VectorIx> colPtr(m_L.outerIndexPtr(), n + 1);  // Pointer to the beginning of each row
+  VectorIx firstElt(n - 1);  // for each j, points to the next entry in vals that will be used in the factorization
+  VectorList listCol(n);     // listCol(j) is a linked list of columns to update column j
+  VectorSx col_vals(n);      // Store a  nonzero values in each column
+  VectorIx col_irow(n);      // Row indices of nonzero elements in each column
+  VectorIx col_pattern(n);
+  col_pattern.fill(-1);
+  StorageIndex col_nnz;
+
+  // Computes the scaling factors
+  m_scale.resize(n);
+  m_scale.setZero();
+  for (Index j = 0; j < n; j++)
+    for (Index k = colPtr[j]; k < colPtr[j + 1]; k++) {
+      m_scale(j) += numext::abs2(vals(k));
+      if (rowIdx[k] != j) m_scale(rowIdx[k]) += numext::abs2(vals(k));
+    }
+
+  m_scale = m_scale.cwiseSqrt().cwiseSqrt();
+
+  for (Index j = 0; j < n; ++j)
+    if (m_scale(j) > (std::numeric_limits<RealScalar>::min)())
+      m_scale(j) = RealScalar(1) / m_scale(j);
+    else
+      m_scale(j) = 1;
+
+  // TODO: disable scaling when roughly uniform to speed up solve().
+
+  // Scale and compute the shift for the matrix
+  RealScalar mindiag = NumTraits<RealScalar>::highest();
+  for (Index j = 0; j < n; j++) {
+    for (Index k = colPtr[j]; k < colPtr[j + 1]; k++) vals[k] *= (m_scale(j) * m_scale(rowIdx[k]));
+    eigen_internal_assert(rowIdx[colPtr[j]] == j &&
+                          "IncompleteCholesky: only the lower triangular part must be stored");
+    mindiag = numext::mini(numext::real(vals[colPtr[j]]), mindiag);
+  }
+
+  FactorType L_save = m_L;
+
+  m_shift = RealScalar(0);
+  if (mindiag <= RealScalar(0.)) m_shift = m_initialShift - mindiag;
+
+  m_info = NumericalIssue;
+
+  // Try to perform the incomplete factorization using the current shift
+  int iter = 0;
+  do {
+    // Apply the shift to the diagonal elements of the matrix
+    for (Index j = 0; j < n; j++) vals[colPtr[j]] += m_shift;
+
+    // jki version of the Cholesky factorization
+    Index j = 0;
+    for (; j < n; ++j) {
+      // Left-looking factorization of the j-th column
+      // First, load the j-th column into col_vals
+      Scalar diag = vals[colPtr[j]];  // It is assumed that only the lower part is stored
+      col_nnz = 0;
+      for (Index i = colPtr[j] + 1; i < colPtr[j + 1]; i++) {
+        StorageIndex l = rowIdx[i];
+        col_vals(col_nnz) = vals[i];
+        col_irow(col_nnz) = l;
+        col_pattern(l) = col_nnz;
+        col_nnz++;
+      }
+      {
+        typename std::list<StorageIndex>::iterator k;
+        // Browse all previous columns that will update column j
+        for (k = listCol[j].begin(); k != listCol[j].end(); k++) {
+          Index jk = firstElt(*k);  // First element to use in the column
+          eigen_internal_assert(rowIdx[jk] == j);
+          Scalar v_j_jk = numext::conj(vals[jk]);
+
+          jk += 1;
+          for (Index i = jk; i < colPtr[*k + 1]; i++) {
+            StorageIndex l = rowIdx[i];
+            if (col_pattern[l] < 0) {
+              col_vals(col_nnz) = vals[i] * v_j_jk;
+              col_irow[col_nnz] = l;
+              col_pattern(l) = col_nnz;
+              col_nnz++;
+            } else
+              col_vals(col_pattern[l]) -= vals[i] * v_j_jk;
+          }
+          updateList(colPtr, rowIdx, vals, *k, jk, firstElt, listCol);
+        }
+      }
+
+      // Scale the current column
+      if (numext::real(diag) <= 0) {
+        if (++iter >= 10) return;
+
+        // increase shift
+        m_shift = numext::maxi(m_initialShift, RealScalar(2) * m_shift);
+        // restore m_L, col_pattern, and listCol
+        vals = Map<const VectorSx>(L_save.valuePtr(), nnz);
+        rowIdx = Map<const VectorIx>(L_save.innerIndexPtr(), nnz);
+        colPtr = Map<const VectorIx>(L_save.outerIndexPtr(), n + 1);
+        col_pattern.fill(-1);
+        for (Index i = 0; i < n; ++i) listCol[i].clear();
+
+        break;
+      }
+
+      RealScalar rdiag = sqrt(numext::real(diag));
+      vals[colPtr[j]] = rdiag;
+      for (Index k = 0; k < col_nnz; ++k) {
+        Index i = col_irow[k];
+        // Scale
+        col_vals(k) /= rdiag;
+        // Update the remaining diagonals with col_vals
+        vals[colPtr[i]] -= numext::abs2(col_vals(k));
+      }
+      // Select the largest p elements
+      // p is the original number of elements in the column (without the diagonal)
+      Index p = colPtr[j + 1] - colPtr[j] - 1;
+      Ref<VectorSx> cvals = col_vals.head(col_nnz);
+      Ref<VectorIx> cirow = col_irow.head(col_nnz);
+      internal::QuickSplit(cvals, cirow, p);
+      // Insert the largest p elements in the matrix
+      Index cpt = 0;
+      for (Index i = colPtr[j] + 1; i < colPtr[j + 1]; i++) {
+        vals[i] = col_vals(cpt);
+        rowIdx[i] = col_irow(cpt);
+        // restore col_pattern:
+        col_pattern(col_irow(cpt)) = -1;
+        cpt++;
+      }
+      // Get the first smallest row index and put it after the diagonal element
+      Index jk = colPtr(j) + 1;
+      updateList(colPtr, rowIdx, vals, j, jk, firstElt, listCol);
+    }
+
+    if (j == n) {
+      m_factorizationIsOk = true;
+      m_info = Success;
+    }
+  } while (m_info != Success);
+}
+
+template <typename Scalar, int UpLo_, typename OrderingType>
+inline void IncompleteCholesky<Scalar, UpLo_, OrderingType>::updateList(Ref<const VectorIx> colPtr,
+                                                                        Ref<VectorIx> rowIdx, Ref<VectorSx> vals,
+                                                                        const Index& col, const Index& jk,
+                                                                        VectorIx& firstElt, VectorList& listCol) {
+  if (jk < colPtr(col + 1)) {
+    Index p = colPtr(col + 1) - jk;
+    Index minpos;
+    rowIdx.segment(jk, p).minCoeff(&minpos);
+    minpos += jk;
+    if (rowIdx(minpos) != rowIdx(jk)) {
+      // Swap
+      std::swap(rowIdx(jk), rowIdx(minpos));
+      std::swap(vals(jk), vals(minpos));
+    }
+    firstElt(col) = internal::convert_index<StorageIndex, Index>(jk);
+    listCol[rowIdx(jk)].push_back(internal::convert_index<StorageIndex, Index>(col));
+  }
+}
+
+}  // end namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
new file mode 100644
index 0000000..3a84e55
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
@@ -0,0 +1,449 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INCOMPLETE_LUT_H
+#define EIGEN_INCOMPLETE_LUT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * Compute a quick-sort split of a vector
+ * On output, the vector row is permuted such that its elements satisfy
+ * abs(row(i)) >= abs(row(ncut)) if i<ncut
+ * abs(row(i)) <= abs(row(ncut)) if i>ncut
+ * \param row The vector of values
+ * \param ind The array of index for the elements in @p row
+ * \param ncut  The number of largest elements to keep
+ **/
+template <typename VectorV, typename VectorI>
+Index QuickSplit(VectorV& row, VectorI& ind, Index ncut) {
+  typedef typename VectorV::RealScalar RealScalar;
+  using std::abs;
+  using std::swap;
+  Index mid;
+  Index n = row.size(); /* length of the vector */
+  Index first, last;
+
+  ncut--; /* to fit the zero-based indices */
+  first = 0;
+  last = n - 1;
+  if (ncut < first || ncut > last) return 0;
+
+  do {
+    mid = first;
+    RealScalar abskey = abs(row(mid));
+    for (Index j = first + 1; j <= last; j++) {
+      if (abs(row(j)) > abskey) {
+        ++mid;
+        swap(row(mid), row(j));
+        swap(ind(mid), ind(j));
+      }
+    }
+    /* Interchange for the pivot element */
+    swap(row(mid), row(first));
+    swap(ind(mid), ind(first));
+
+    if (mid > ncut)
+      last = mid - 1;
+    else if (mid < ncut)
+      first = mid + 1;
+  } while (mid != ncut);
+
+  return 0; /* mid is equal to ncut */
+}
+
+}  // end namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+ * \class IncompleteLUT
+ * \brief Incomplete LU factorization with dual-threshold strategy
+ *
+ * \implsparsesolverconcept
+ *
+ * During the numerical factorization, two dropping rules are used :
+ *  1) any element whose magnitude is less than some tolerance is dropped.
+ *    This tolerance is obtained by multiplying the input tolerance @p droptol
+ *    by the average magnitude of all the original elements in the current row.
+ *  2) After the elimination of the row, only the @p fill largest elements in
+ *    the L part and the @p fill largest elements in the U part are kept
+ *    (in addition to the diagonal element ). Note that @p fill is computed from
+ *    the input parameter @p fillfactor which is used the ratio to control the fill_in
+ *    relatively to the initial number of nonzero elements.
+ *
+ * The two extreme cases are when @p droptol=0 (to keep all the @p fill*2 largest elements)
+ * and when @p fill=n/2 with @p droptol being different to zero.
+ *
+ * References : Yousef Saad, ILUT: A dual threshold incomplete LU factorization,
+ *              Numerical Linear Algebra with Applications, 1(4), pp 387-402, 1994.
+ *
+ * NOTE : The following implementation is derived from the ILUT implementation
+ * in the SPARSKIT package, Copyright (C) 2005, the Regents of the University of Minnesota
+ *  released under the terms of the GNU LGPL:
+ *    http://www-users.cs.umn.edu/~saad/software/SPARSKIT/README
+ * However, Yousef Saad gave us permission to relicense his ILUT code to MPL2.
+ * See the Eigen mailing list archive, thread: ILUT, date: July 8, 2012:
+ *   http://listengine.tuxfamily.org/lists.tuxfamily.org/eigen/2012/07/msg00064.html
+ * alternatively, on GMANE:
+ *   http://comments.gmane.org/gmane.comp.lib.eigen/3302
+ */
+template <typename Scalar_, typename StorageIndex_ = int>
+class IncompleteLUT : public SparseSolverBase<IncompleteLUT<Scalar_, StorageIndex_> > {
+ protected:
+  typedef SparseSolverBase<IncompleteLUT> Base;
+  using Base::m_isInitialized;
+
+ public:
+  typedef Scalar_ Scalar;
+  typedef StorageIndex_ StorageIndex;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar, Dynamic, 1> Vector;
+  typedef Matrix<StorageIndex, Dynamic, 1> VectorI;
+  typedef SparseMatrix<Scalar, RowMajor, StorageIndex> FactorType;
+
+  enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
+
+ public:
+  IncompleteLUT()
+      : m_droptol(NumTraits<Scalar>::dummy_precision()),
+        m_fillfactor(10),
+        m_analysisIsOk(false),
+        m_factorizationIsOk(false) {}
+
+  template <typename MatrixType>
+  explicit IncompleteLUT(const MatrixType& mat, const RealScalar& droptol = NumTraits<Scalar>::dummy_precision(),
+                         int fillfactor = 10)
+      : m_droptol(droptol), m_fillfactor(fillfactor), m_analysisIsOk(false), m_factorizationIsOk(false) {
+    eigen_assert(fillfactor != 0);
+    compute(mat);
+  }
+
+  /** \brief Extraction Method for L-Factor */
+  const FactorType matrixL() const;
+
+  /** \brief Extraction Method for U-Factor */
+  const FactorType matrixU() const;
+
+  constexpr Index rows() const noexcept { return m_lu.rows(); }
+
+  constexpr Index cols() const noexcept { return m_lu.cols(); }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix.appears to be negative.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "IncompleteLUT is not initialized.");
+    return m_info;
+  }
+
+  template <typename MatrixType>
+  void analyzePattern(const MatrixType& amat);
+
+  template <typename MatrixType>
+  void factorize(const MatrixType& amat);
+
+  /**
+   * Compute an incomplete LU factorization with dual threshold on the matrix mat
+   * No pivoting is done in this version
+   *
+   **/
+  template <typename MatrixType>
+  IncompleteLUT& compute(const MatrixType& amat) {
+    analyzePattern(amat);
+    factorize(amat);
+    return *this;
+  }
+
+  void setDroptol(const RealScalar& droptol);
+  void setFillfactor(int fillfactor);
+
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const Rhs& b, Dest& x) const {
+    x = m_Pinv * b;
+    x = m_lu.template triangularView<UnitLower>().solve(x);
+    x = m_lu.template triangularView<Upper>().solve(x);
+    x = m_P * x;
+  }
+
+ protected:
+  /** keeps off-diagonal entries; drops diagonal entries */
+  struct keep_diag {
+    inline bool operator()(const Index& row, const Index& col, const Scalar&) const { return row != col; }
+  };
+
+ protected:
+  FactorType m_lu;
+  RealScalar m_droptol;
+  int m_fillfactor;
+  bool m_analysisIsOk;
+  bool m_factorizationIsOk;
+  ComputationInfo m_info;
+  PermutationMatrix<Dynamic, Dynamic, StorageIndex> m_P;     // Fill-reducing permutation
+  PermutationMatrix<Dynamic, Dynamic, StorageIndex> m_Pinv;  // Inverse permutation
+};
+
+/**
+ * Set control parameter droptol
+ *  \param droptol   Drop any element whose magnitude is less than this tolerance
+ **/
+template <typename Scalar, typename StorageIndex>
+void IncompleteLUT<Scalar, StorageIndex>::setDroptol(const RealScalar& droptol) {
+  this->m_droptol = droptol;
+}
+
+/**
+ * Set control parameter fillfactor
+ * \param fillfactor  This is used to compute the  number @p fill_in of largest elements to keep on each row.
+ **/
+template <typename Scalar, typename StorageIndex>
+void IncompleteLUT<Scalar, StorageIndex>::setFillfactor(int fillfactor) {
+  this->m_fillfactor = fillfactor;
+}
+
+/**
+ * get L-Factor
+ * \return L-Factor is a matrix containing the lower triangular part of the sparse matrix. All elements of the matrix
+ * above the main diagonal are zero.
+ **/
+template <typename Scalar, typename StorageIndex>
+const typename IncompleteLUT<Scalar, StorageIndex>::FactorType IncompleteLUT<Scalar, StorageIndex>::matrixL() const {
+  eigen_assert(m_factorizationIsOk && "factorize() should be called first");
+  return m_lu.template triangularView<UnitLower>();
+}
+
+/**
+ * get U-Factor
+ * \return L-Factor is a matrix containing the upper triangular part of the sparse matrix. All elements of the matrix
+ * below the main diagonal are zero.
+ **/
+template <typename Scalar, typename StorageIndex>
+const typename IncompleteLUT<Scalar, StorageIndex>::FactorType IncompleteLUT<Scalar, StorageIndex>::matrixU() const {
+  eigen_assert(m_factorizationIsOk && "Factorization must be computed first.");
+  return m_lu.template triangularView<Upper>();
+}
+
+template <typename Scalar, typename StorageIndex>
+template <typename MatrixType_>
+void IncompleteLUT<Scalar, StorageIndex>::analyzePattern(const MatrixType_& amat) {
+  // Compute the Fill-reducing permutation
+  // Since ILUT does not perform any numerical pivoting,
+  // it is highly preferable to keep the diagonal through symmetric permutations.
+  // To this end, let's symmetrize the pattern and perform AMD on it.
+  SparseMatrix<Scalar, ColMajor, StorageIndex> mat1 = amat;
+  SparseMatrix<Scalar, ColMajor, StorageIndex> mat2 = amat.transpose();
+  // FIXME: for a nearly symmetric pattern, mat2+mat1 is appropriate;
+  //        for a highly non-symmetric pattern, mat2*mat1 should be preferred.
+  SparseMatrix<Scalar, ColMajor, StorageIndex> AtA = mat2 + mat1;
+  AMDOrdering<StorageIndex> ordering;
+  ordering(AtA, m_P);
+  m_Pinv = m_P.inverse();  // cache the inverse permutation
+  m_analysisIsOk = true;
+  m_factorizationIsOk = false;
+  m_isInitialized = true;
+}
+
+template <typename Scalar, typename StorageIndex>
+template <typename MatrixType_>
+void IncompleteLUT<Scalar, StorageIndex>::factorize(const MatrixType_& amat) {
+  using internal::convert_index;
+  using std::abs;
+  using std::sqrt;
+  using std::swap;
+
+  eigen_assert((amat.rows() == amat.cols()) && "The factorization should be done on a square matrix");
+  Index n = amat.cols();  // Size of the matrix
+  m_lu.resize(n, n);
+  // Declare Working vectors and variables
+  Vector u(n);    // real values of the row -- maximum size is n --
+  VectorI ju(n);  // column position of the values in u -- maximum size  is n
+  VectorI jr(n);  // Indicate the position of the nonzero elements in the vector u -- A zero location is indicated by -1
+
+  // Apply the fill-reducing permutation
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  SparseMatrix<Scalar, RowMajor, StorageIndex> mat;
+  mat = amat.twistedBy(m_Pinv);
+
+  // Initialization
+  jr.fill(-1);
+  ju.fill(0);
+  u.fill(0);
+
+  // number of largest elements to keep in each row:
+  Index fill_in = (amat.nonZeros() * m_fillfactor) / n + 1;
+  if (fill_in > n) fill_in = n;
+
+  // number of largest nonzero elements to keep in the L and the U part of the current row:
+  Index nnzL = fill_in / 2;
+  Index nnzU = nnzL;
+  m_lu.reserve(n * (nnzL + nnzU + 1));
+
+  // global loop over the rows of the sparse matrix
+  for (Index ii = 0; ii < n; ii++) {
+    // 1 - copy the lower and the upper part of the row i of mat in the working vector u
+
+    Index sizeu = 1;  // number of nonzero elements in the upper part of the current row
+    Index sizel = 0;  // number of nonzero elements in the lower part of the current row
+    ju(ii) = convert_index<StorageIndex>(ii);
+    u(ii) = 0;
+    jr(ii) = convert_index<StorageIndex>(ii);
+    RealScalar rownorm = 0;
+
+    typename FactorType::InnerIterator j_it(mat, ii);  // Iterate through the current row ii
+    for (; j_it; ++j_it) {
+      Index k = j_it.index();
+      if (k < ii) {
+        // copy the lower part
+        ju(sizel) = convert_index<StorageIndex>(k);
+        u(sizel) = j_it.value();
+        jr(k) = convert_index<StorageIndex>(sizel);
+        ++sizel;
+      } else if (k == ii) {
+        u(ii) = j_it.value();
+      } else {
+        // copy the upper part
+        Index jpos = ii + sizeu;
+        ju(jpos) = convert_index<StorageIndex>(k);
+        u(jpos) = j_it.value();
+        jr(k) = convert_index<StorageIndex>(jpos);
+        ++sizeu;
+      }
+      rownorm += numext::abs2(j_it.value());
+    }
+
+    // 2 - detect possible zero row
+    if (rownorm == 0) {
+      m_info = NumericalIssue;
+      return;
+    }
+    // Take the 2-norm of the current row as a relative tolerance
+    rownorm = sqrt(rownorm);
+
+    // 3 - eliminate the previous nonzero rows
+    Index jj = 0;
+    Index len = 0;
+    while (jj < sizel) {
+      // In order to eliminate in the correct order,
+      // we must select first the smallest column index among  ju(jj:sizel)
+      Index k;
+      Index minrow = ju.segment(jj, sizel - jj).minCoeff(&k);  // k is relative to the segment
+      k += jj;
+      if (minrow != ju(jj)) {
+        // swap the two locations
+        Index j = ju(jj);
+        swap(ju(jj), ju(k));
+        jr(minrow) = convert_index<StorageIndex>(jj);
+        jr(j) = convert_index<StorageIndex>(k);
+        swap(u(jj), u(k));
+      }
+      // Reset this location
+      jr(minrow) = -1;
+
+      // Start elimination
+      typename FactorType::InnerIterator ki_it(m_lu, minrow);
+      while (ki_it && ki_it.index() < minrow) ++ki_it;
+      eigen_internal_assert(ki_it && ki_it.col() == minrow);
+      Scalar fact = u(jj) / ki_it.value();
+
+      // drop too small elements
+      if (abs(fact) <= m_droptol) {
+        jj++;
+        continue;
+      }
+
+      // linear combination of the current row ii and the row minrow
+      ++ki_it;
+      for (; ki_it; ++ki_it) {
+        Scalar prod = fact * ki_it.value();
+        Index j = ki_it.index();
+        Index jpos = jr(j);
+        if (jpos == -1)  // fill-in element
+        {
+          Index newpos;
+          if (j >= ii)  // dealing with the upper part
+          {
+            newpos = ii + sizeu;
+            sizeu++;
+            eigen_internal_assert(sizeu <= n);
+          } else  // dealing with the lower part
+          {
+            newpos = sizel;
+            sizel++;
+            eigen_internal_assert(sizel <= ii);
+          }
+          ju(newpos) = convert_index<StorageIndex>(j);
+          u(newpos) = -prod;
+          jr(j) = convert_index<StorageIndex>(newpos);
+        } else
+          u(jpos) -= prod;
+      }
+      // store the pivot element
+      u(len) = fact;
+      ju(len) = convert_index<StorageIndex>(minrow);
+      ++len;
+
+      jj++;
+    }  // end of the elimination on the row ii
+
+    // reset the upper part of the pointer jr to zero
+    for (Index k = 0; k < sizeu; k++) jr(ju(ii + k)) = -1;
+
+    // 4 - partially sort and insert the elements in the m_lu matrix
+
+    // sort the L-part of the row
+    sizel = len;
+    len = (std::min)(sizel, nnzL);
+    typename Vector::SegmentReturnType ul(u.segment(0, sizel));
+    typename VectorI::SegmentReturnType jul(ju.segment(0, sizel));
+    internal::QuickSplit(ul, jul, len);
+
+    // store the largest m_fill elements of the L part
+    m_lu.startVec(ii);
+    for (Index k = 0; k < len; k++) m_lu.insertBackByOuterInnerUnordered(ii, ju(k)) = u(k);
+
+    // store the diagonal element
+    // apply a shifting rule to avoid zero pivots (we are doing an incomplete factorization)
+    if (u(ii) == Scalar(0)) u(ii) = sqrt(m_droptol) * rownorm;
+    m_lu.insertBackByOuterInnerUnordered(ii, ii) = u(ii);
+
+    // sort the U-part of the row
+    // apply the dropping rule first
+    len = 0;
+    for (Index k = 1; k < sizeu; k++) {
+      if (abs(u(ii + k)) > m_droptol * rownorm) {
+        ++len;
+        u(ii + len) = u(ii + k);
+        ju(ii + len) = ju(ii + k);
+      }
+    }
+    sizeu = len + 1;  // +1 to take into account the diagonal element
+    len = (std::min)(sizeu, nnzU);
+    typename Vector::SegmentReturnType uu(u.segment(ii + 1, sizeu - 1));
+    typename VectorI::SegmentReturnType juu(ju.segment(ii + 1, sizeu - 1));
+    internal::QuickSplit(uu, juu, len);
+
+    // store the largest elements of the U part
+    for (Index k = ii + 1; k < ii + len; k++) m_lu.insertBackByOuterInnerUnordered(ii, ju(k)) = u(k);
+  }
+  m_lu.finalize();
+  m_lu.makeCompressed();
+
+  m_factorizationIsOk = true;
+  m_info = Success;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_INCOMPLETE_LUT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/InternalHeaderCheck.h
new file mode 100644
index 0000000..b657e84
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
+#error "Please include Eigen/IterativeLinearSolvers instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
new file mode 100644
index 0000000..5caa396
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
@@ -0,0 +1,395 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ITERATIVE_SOLVER_BASE_H
+#define EIGEN_ITERATIVE_SOLVER_BASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename MatrixType>
+struct is_ref_compatible_impl {
+ private:
+  template <typename T0>
+  struct any_conversion {
+    template <typename T>
+    any_conversion(const volatile T&);
+    template <typename T>
+    any_conversion(T&);
+  };
+  struct yes {
+    int a[1];
+  };
+  struct no {
+    int a[2];
+  };
+
+  template <typename T>
+  static yes test(const Ref<const T>&, int);
+  template <typename T>
+  static no test(any_conversion<T>, ...);
+
+ public:
+  static MatrixType ms_from;
+  enum { value = sizeof(test<MatrixType>(ms_from, 0)) == sizeof(yes) };
+};
+
+template <typename MatrixType>
+struct is_ref_compatible {
+  enum { value = is_ref_compatible_impl<remove_all_t<MatrixType>>::value };
+};
+
+template <typename MatrixType, bool MatrixFree = !internal::is_ref_compatible<MatrixType>::value>
+class generic_matrix_wrapper;
+
+// We have an explicit matrix at hand, compatible with Ref<>
+template <typename MatrixType>
+class generic_matrix_wrapper<MatrixType, false> {
+ public:
+  typedef Ref<const MatrixType> ActualMatrixType;
+  template <int UpLo>
+  struct ConstSelfAdjointViewReturnType {
+    typedef typename ActualMatrixType::template ConstSelfAdjointViewReturnType<UpLo>::Type Type;
+  };
+
+  enum { MatrixFree = false };
+
+  generic_matrix_wrapper() : m_dummy(0, 0), m_matrix(m_dummy) {}
+
+  template <typename InputType>
+  generic_matrix_wrapper(const InputType& mat) : m_matrix(mat) {}
+
+  const ActualMatrixType& matrix() const { return m_matrix; }
+
+  template <typename MatrixDerived>
+  void grab(const EigenBase<MatrixDerived>& mat) {
+    internal::destroy_at(&m_matrix);
+    internal::construct_at(&m_matrix, mat.derived());
+  }
+
+  void grab(const Ref<const MatrixType>& mat) {
+    if (&(mat.derived()) != &m_matrix) {
+      internal::destroy_at(&m_matrix);
+      internal::construct_at(&m_matrix, mat);
+    }
+  }
+
+ protected:
+  MatrixType m_dummy;  // used to default initialize the Ref<> object
+  ActualMatrixType m_matrix;
+};
+
+// MatrixType is not compatible with Ref<> -> matrix-free wrapper
+template <typename MatrixType>
+class generic_matrix_wrapper<MatrixType, true> {
+ public:
+  typedef MatrixType ActualMatrixType;
+  template <int UpLo>
+  struct ConstSelfAdjointViewReturnType {
+    typedef ActualMatrixType Type;
+  };
+
+  enum { MatrixFree = true };
+
+  generic_matrix_wrapper() : mp_matrix(0) {}
+
+  generic_matrix_wrapper(const MatrixType& mat) : mp_matrix(&mat) {}
+
+  const ActualMatrixType& matrix() const { return *mp_matrix; }
+
+  void grab(const MatrixType& mat) { mp_matrix = &mat; }
+
+ protected:
+  const ActualMatrixType* mp_matrix;
+};
+
+}  // namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+ * \brief Base class for linear iterative solvers
+ *
+ * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+ */
+template <typename Derived>
+class IterativeSolverBase : public SparseSolverBase<Derived> {
+ protected:
+  typedef SparseSolverBase<Derived> Base;
+  using Base::m_isInitialized;
+
+ public:
+  typedef typename internal::traits<Derived>::MatrixType MatrixType;
+  typedef typename internal::traits<Derived>::Preconditioner Preconditioner;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+ public:
+  using Base::derived;
+
+  /** Default constructor. */
+  IterativeSolverBase() { init(); }
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+   *
+   * This constructor is a shortcut for the default constructor followed
+   * by a call to compute().
+   *
+   * \warning this class stores a reference to the matrix A as well as some
+   * precomputed values that depend on it. Therefore, if \a A is changed
+   * this class becomes invalid. Call compute() to update it with the new
+   * matrix A, or modify a copy of A.
+   */
+  template <typename MatrixDerived>
+  explicit IterativeSolverBase(const EigenBase<MatrixDerived>& A) : m_matrixWrapper(A.derived()) {
+    init();
+    compute(matrix());
+  }
+
+  IterativeSolverBase(IterativeSolverBase&&) = default;
+
+  ~IterativeSolverBase() {}
+
+  /** Initializes the iterative solver for the sparsity pattern of the matrix \a A for further solving \c Ax=b problems.
+   *
+   * Currently, this function mostly calls analyzePattern on the preconditioner. In the future
+   * we might, for instance, implement column reordering for faster matrix vector products.
+   */
+  template <typename MatrixDerived>
+  Derived& analyzePattern(const EigenBase<MatrixDerived>& A) {
+    grab(A.derived());
+    m_preconditioner.analyzePattern(matrix());
+    m_isInitialized = true;
+    m_analysisIsOk = true;
+    m_info = m_preconditioner.info();
+    return derived();
+  }
+
+  /** Initializes the iterative solver with the numerical values of the matrix \a A for further solving \c Ax=b
+   * problems.
+   *
+   * Currently, this function mostly calls factorize on the preconditioner.
+   *
+   * \warning this class stores a reference to the matrix A as well as some
+   * precomputed values that depend on it. Therefore, if \a A is changed
+   * this class becomes invalid. Call compute() to update it with the new
+   * matrix A, or modify a copy of A.
+   */
+  template <typename MatrixDerived>
+  Derived& factorize(const EigenBase<MatrixDerived>& A) {
+    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+    grab(A.derived());
+    m_preconditioner.factorize(matrix());
+    m_factorizationIsOk = true;
+    m_info = m_preconditioner.info();
+    return derived();
+  }
+
+  /** Initializes the iterative solver with the matrix \a A for further solving \c Ax=b problems.
+   *
+   * Currently, this function mostly initializes/computes the preconditioner. In the future
+   * we might, for instance, implement column reordering for faster matrix vector products.
+   *
+   * \warning this class stores a reference to the matrix A as well as some
+   * precomputed values that depend on it. Therefore, if \a A is changed
+   * this class becomes invalid. Call compute() to update it with the new
+   * matrix A, or modify a copy of A.
+   */
+  template <typename MatrixDerived>
+  Derived& compute(const EigenBase<MatrixDerived>& A) {
+    grab(A.derived());
+    m_preconditioner.compute(matrix());
+    m_isInitialized = true;
+    m_analysisIsOk = true;
+    m_factorizationIsOk = true;
+    m_info = m_preconditioner.info();
+    return derived();
+  }
+
+  /** \internal */
+  constexpr Index rows() const noexcept { return matrix().rows(); }
+
+  /** \internal */
+  constexpr Index cols() const noexcept { return matrix().cols(); }
+
+  /** \returns the tolerance threshold used by the stopping criteria.
+   * \sa setTolerance()
+   */
+  RealScalar tolerance() const { return m_tolerance; }
+
+  /** Sets the tolerance threshold used by the stopping criteria.
+   *
+   * This value is used as an upper bound to the relative residual error: |Ax-b|/|b|.
+   * The default value is the machine precision given by NumTraits<Scalar>::epsilon()
+   */
+  Derived& setTolerance(const RealScalar& tolerance) {
+    m_tolerance = tolerance;
+    return derived();
+  }
+
+  /** \returns a read-write reference to the preconditioner for custom configuration. */
+  Preconditioner& preconditioner() { return m_preconditioner; }
+
+  /** \returns a read-only reference to the preconditioner. */
+  const Preconditioner& preconditioner() const { return m_preconditioner; }
+
+  /** \returns the max number of iterations.
+   * It is either the value set by setMaxIterations or, by default,
+   * twice the number of columns of the matrix.
+   */
+  Index maxIterations() const { return (m_maxIterations < 0) ? 2 * matrix().cols() : m_maxIterations; }
+
+  /** Sets the max number of iterations.
+   * Default is twice the number of columns of the matrix.
+   */
+  Derived& setMaxIterations(Index maxIters) {
+    m_maxIterations = maxIters;
+    return derived();
+  }
+
+  /** \returns the number of iterations performed during the last solve */
+  Index iterations() const {
+    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
+    return m_iterations;
+  }
+
+  /** \returns the tolerance error reached during the last solve.
+   * It is a close approximation of the true relative residual error |Ax-b|/|b|.
+   */
+  RealScalar error() const {
+    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
+    return m_error;
+  }
+
+  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A
+   * and \a x0 as an initial solution.
+   *
+   * \sa solve(), compute()
+   */
+  template <typename Rhs, typename Guess>
+  inline const SolveWithGuess<Derived, Rhs, Guess> solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const {
+    eigen_assert(m_isInitialized && "Solver is not initialized.");
+    eigen_assert(derived().rows() == b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
+    return SolveWithGuess<Derived, Rhs, Guess>(derived(), b.derived(), x0);
+  }
+
+  /** \returns Success if the iterations converged, and NoConvergence otherwise. */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
+    return m_info;
+  }
+
+  /** \internal */
+  template <typename Rhs, typename DestDerived>
+  void _solve_with_guess_impl(const Rhs& b, SparseMatrixBase<DestDerived>& aDest) const {
+    eigen_assert(rows() == b.rows());
+
+    Index rhsCols = b.cols();
+    Index size = b.rows();
+    DestDerived& dest(aDest.derived());
+    typedef typename DestDerived::Scalar DestScalar;
+    Eigen::Matrix<DestScalar, Dynamic, 1> tb(size);
+    Eigen::Matrix<DestScalar, Dynamic, 1> tx(cols());
+    // We do not directly fill dest because sparse expressions have to be free of aliasing issue.
+    // For non square least-square problems, b and dest might not have the same size whereas they might alias
+    // each-other.
+    typename DestDerived::PlainObject tmp(cols(), rhsCols);
+    ComputationInfo global_info = Success;
+    for (Index k = 0; k < rhsCols; ++k) {
+      tb = b.col(k);
+      tx = dest.col(k);
+      derived()._solve_vector_with_guess_impl(tb, tx);
+      tmp.col(k) = tx.sparseView(0);
+
+      // The call to _solve_vector_with_guess_impl updates m_info, so if it failed for a previous column
+      // we need to restore it to the worst value.
+      if (m_info == NumericalIssue)
+        global_info = NumericalIssue;
+      else if (m_info == NoConvergence)
+        global_info = NoConvergence;
+    }
+    m_info = global_info;
+    dest.swap(tmp);
+  }
+
+  template <typename Rhs, typename DestDerived>
+  std::enable_if_t<Rhs::ColsAtCompileTime != 1 && DestDerived::ColsAtCompileTime != 1> _solve_with_guess_impl(
+      const Rhs& b, MatrixBase<DestDerived>& aDest) const {
+    eigen_assert(rows() == b.rows());
+
+    Index rhsCols = b.cols();
+    DestDerived& dest(aDest.derived());
+    ComputationInfo global_info = Success;
+    for (Index k = 0; k < rhsCols; ++k) {
+      typename DestDerived::ColXpr xk(dest, k);
+      typename Rhs::ConstColXpr bk(b, k);
+      derived()._solve_vector_with_guess_impl(bk, xk);
+
+      // The call to _solve_vector_with_guess updates m_info, so if it failed for a previous column
+      // we need to restore it to the worst value.
+      if (m_info == NumericalIssue)
+        global_info = NumericalIssue;
+      else if (m_info == NoConvergence)
+        global_info = NoConvergence;
+    }
+    m_info = global_info;
+  }
+
+  template <typename Rhs, typename DestDerived>
+  std::enable_if_t<Rhs::ColsAtCompileTime == 1 || DestDerived::ColsAtCompileTime == 1> _solve_with_guess_impl(
+      const Rhs& b, MatrixBase<DestDerived>& dest) const {
+    derived()._solve_vector_with_guess_impl(b, dest.derived());
+  }
+
+  /** \internal default initial guess = 0 */
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const Rhs& b, Dest& x) const {
+    x.setZero();
+    derived()._solve_with_guess_impl(b, x);
+  }
+
+ protected:
+  void init() {
+    m_isInitialized = false;
+    m_analysisIsOk = false;
+    m_factorizationIsOk = false;
+    m_maxIterations = -1;
+    m_tolerance = NumTraits<Scalar>::epsilon();
+  }
+
+  typedef internal::generic_matrix_wrapper<MatrixType> MatrixWrapper;
+  typedef typename MatrixWrapper::ActualMatrixType ActualMatrixType;
+
+  const ActualMatrixType& matrix() const { return m_matrixWrapper.matrix(); }
+
+  template <typename InputType>
+  void grab(const InputType& A) {
+    m_matrixWrapper.grab(A);
+  }
+
+  MatrixWrapper m_matrixWrapper;
+  Preconditioner m_preconditioner;
+
+  Index m_maxIterations;
+  RealScalar m_tolerance;
+
+  mutable RealScalar m_error;
+  mutable Index m_iterations;
+  mutable ComputationInfo m_info;
+  mutable bool m_analysisIsOk, m_factorizationIsOk;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ITERATIVE_SOLVER_BASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
new file mode 100644
index 0000000..182f319
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
@@ -0,0 +1,193 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
+#define EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal Low-level conjugate gradient algorithm for least-square problems
+ * \param mat The matrix A
+ * \param rhs The right hand side vector b
+ * \param x On input and initial solution, on output the computed solution.
+ * \param precond A preconditioner being able to efficiently solve for an
+ *                approximation of A'Ax=b (regardless of b)
+ * \param iters On input the max number of iteration, on output the number of performed iterations.
+ * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+ */
+template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+EIGEN_DONT_INLINE void least_square_conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
+                                                       const Preconditioner& precond, Index& iters,
+                                                       typename Dest::RealScalar& tol_error) {
+  using std::abs;
+  using std::sqrt;
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+
+  RealScalar tol = tol_error;
+  Index maxIters = iters;
+
+  Index m = mat.rows(), n = mat.cols();
+
+  VectorType residual = rhs - mat * x;
+  VectorType normal_residual = mat.adjoint() * residual;
+
+  RealScalar rhsNorm2 = (mat.adjoint() * rhs).squaredNorm();
+  if (rhsNorm2 == 0) {
+    x.setZero();
+    iters = 0;
+    tol_error = 0;
+    return;
+  }
+  RealScalar threshold = tol * tol * rhsNorm2;
+  RealScalar residualNorm2 = normal_residual.squaredNorm();
+  if (residualNorm2 < threshold) {
+    iters = 0;
+    tol_error = sqrt(residualNorm2 / rhsNorm2);
+    return;
+  }
+
+  VectorType p(n);
+  p = precond.solve(normal_residual);  // initial search direction
+
+  VectorType z(n), tmp(m);
+  RealScalar absNew = numext::real(normal_residual.dot(p));  // the square of the absolute value of r scaled by invM
+  Index i = 0;
+  while (i < maxIters) {
+    tmp.noalias() = mat * p;
+
+    Scalar alpha = absNew / tmp.squaredNorm();             // the amount we travel on dir
+    x += alpha * p;                                        // update solution
+    residual -= alpha * tmp;                               // update residual
+    normal_residual.noalias() = mat.adjoint() * residual;  // update residual of the normal equation
+
+    residualNorm2 = normal_residual.squaredNorm();
+    if (residualNorm2 < threshold) break;
+
+    z = precond.solve(normal_residual);  // approximately solve for "A'A z = normal_residual"
+
+    RealScalar absOld = absNew;
+    absNew = numext::real(normal_residual.dot(z));  // update the absolute value of r
+    RealScalar beta = absNew / absOld;  // calculate the Gram-Schmidt value used to create the new search direction
+    p = z + beta * p;                   // update search direction
+    i++;
+  }
+  tol_error = sqrt(residualNorm2 / rhsNorm2);
+  iters = i;
+}
+
+}  // namespace internal
+
+template <typename MatrixType_,
+          typename Preconditioner_ = LeastSquareDiagonalPreconditioner<typename MatrixType_::Scalar> >
+class LeastSquaresConjugateGradient;
+
+namespace internal {
+
+template <typename MatrixType_, typename Preconditioner_>
+struct traits<LeastSquaresConjugateGradient<MatrixType_, Preconditioner_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}  // namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A conjugate gradient solver for sparse (or dense) least-square problems
+  *
+  * This class solves for the least-squares solution to A x = b using an iterative conjugate gradient algorithm.
+  * The matrix A can be non symmetric and rectangular, but the matrix A' A should be positive-definite to guaranty
+  stability.
+  * Otherwise, the SparseLU or SparseQR classes might be preferable.
+  * The matrix A and the vectors x and b can be either dense or sparse.
+  *
+  * \tparam MatrixType_ the type of the matrix A, can be a dense or a sparse matrix.
+  * \tparam Preconditioner_ the type of the preconditioner. Default is LeastSquareDiagonalPreconditioner
+  *
+  * \implsparsesolverconcept
+  *
+  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+  * and NumTraits<Scalar>::epsilon() for the tolerance.
+  *
+  * This class can be used as the direct solver classes. Here is a typical usage example:
+    \code
+    int m=1000000, n = 10000;
+    VectorXd x(n), b(m);
+    SparseMatrix<double> A(m,n);
+    // fill A and b
+    LeastSquaresConjugateGradient<SparseMatrix<double> > lscg;
+    lscg.compute(A);
+    x = lscg.solve(b);
+    std::cout << "#iterations:     " << lscg.iterations() << std::endl;
+    std::cout << "estimated error: " << lscg.error()      << std::endl;
+    // update b, and solve again
+    x = lscg.solve(b);
+    \endcode
+  *
+  * By default the iterations start with x=0 as an initial guess of the solution.
+  * One can control the start using the solveWithGuess() method.
+  *
+  * \sa class ConjugateGradient, SparseLU, SparseQR
+  */
+template <typename MatrixType_, typename Preconditioner_>
+class LeastSquaresConjugateGradient
+    : public IterativeSolverBase<LeastSquaresConjugateGradient<MatrixType_, Preconditioner_> > {
+  typedef IterativeSolverBase<LeastSquaresConjugateGradient> Base;
+  using Base::m_error;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_iterations;
+  using Base::matrix;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+ public:
+  /** Default constructor. */
+  LeastSquaresConjugateGradient() : Base() {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+   *
+   * This constructor is a shortcut for the default constructor followed
+   * by a call to compute().
+   *
+   * \warning this class stores a reference to the matrix A as well as some
+   * precomputed values that depend on it. Therefore, if \a A is changed
+   * this class becomes invalid. Call compute() to update it with the new
+   * matrix A, or modify a copy of A.
+   */
+  template <typename MatrixDerived>
+  explicit LeastSquaresConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
+
+  ~LeastSquaresConjugateGradient() {}
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const {
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+
+    internal::least_square_conjugate_gradient(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error);
+    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
new file mode 100644
index 0000000..271679f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
@@ -0,0 +1,111 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SOLVEWITHGUESS_H
+#define EIGEN_SOLVEWITHGUESS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Decomposition, typename RhsType, typename GuessType>
+class SolveWithGuess;
+
+/** \class SolveWithGuess
+ * \ingroup IterativeLinearSolvers_Module
+ *
+ * \brief Pseudo expression representing a solving operation
+ *
+ * \tparam Decomposition the type of the matrix or decomposition object
+ * \tparam Rhstype the type of the right-hand side
+ *
+ * This class represents an expression of A.solve(B)
+ * and most of the time this is the only way it is used.
+ *
+ */
+namespace internal {
+
+template <typename Decomposition, typename RhsType, typename GuessType>
+struct traits<SolveWithGuess<Decomposition, RhsType, GuessType> > : traits<Solve<Decomposition, RhsType> > {};
+
+}  // namespace internal
+
+template <typename Decomposition, typename RhsType, typename GuessType>
+class SolveWithGuess : public internal::generic_xpr_base<SolveWithGuess<Decomposition, RhsType, GuessType>, MatrixXpr,
+                                                         typename internal::traits<RhsType>::StorageKind>::type {
+ public:
+  typedef typename internal::traits<SolveWithGuess>::Scalar Scalar;
+  typedef typename internal::traits<SolveWithGuess>::PlainObject PlainObject;
+  typedef typename internal::generic_xpr_base<SolveWithGuess<Decomposition, RhsType, GuessType>, MatrixXpr,
+                                              typename internal::traits<RhsType>::StorageKind>::type Base;
+  typedef typename internal::ref_selector<SolveWithGuess>::type Nested;
+
+  SolveWithGuess(const Decomposition &dec, const RhsType &rhs, const GuessType &guess)
+      : m_dec(dec), m_rhs(rhs), m_guess(guess) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_dec.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_rhs.cols(); }
+
+  EIGEN_DEVICE_FUNC const Decomposition &dec() const { return m_dec; }
+  EIGEN_DEVICE_FUNC const RhsType &rhs() const { return m_rhs; }
+  EIGEN_DEVICE_FUNC const GuessType &guess() const { return m_guess; }
+
+ protected:
+  const Decomposition &m_dec;
+  const RhsType &m_rhs;
+  const GuessType &m_guess;
+
+ private:
+  Scalar coeff(Index row, Index col) const;
+  Scalar coeff(Index i) const;
+};
+
+namespace internal {
+
+// Evaluator of SolveWithGuess -> eval into a temporary
+template <typename Decomposition, typename RhsType, typename GuessType>
+struct evaluator<SolveWithGuess<Decomposition, RhsType, GuessType> >
+    : public evaluator<typename SolveWithGuess<Decomposition, RhsType, GuessType>::PlainObject> {
+  typedef SolveWithGuess<Decomposition, RhsType, GuessType> SolveType;
+  typedef typename SolveType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  evaluator(const SolveType &solve) : m_result(solve.rows(), solve.cols()) {
+    internal::construct_at<Base>(this, m_result);
+    m_result = solve.guess();
+    solve.dec()._solve_with_guess_impl(solve.rhs(), m_result);
+  }
+
+ protected:
+  PlainObject m_result;
+};
+
+// Specialization for "dst = dec.solveWithGuess(rhs)"
+// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse
+// specialization must exist somewhere
+template <typename DstXprType, typename DecType, typename RhsType, typename GuessType, typename Scalar>
+struct Assignment<DstXprType, SolveWithGuess<DecType, RhsType, GuessType>, internal::assign_op<Scalar, Scalar>,
+                  Dense2Dense> {
+  typedef SolveWithGuess<DecType, RhsType, GuessType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar, Scalar> &) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    dst = src.guess();
+    src.dec()._solve_with_guess_impl(src.rhs(), dst /*, src.guess()*/);
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SOLVEWITHGUESS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Jacobi/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Jacobi/InternalHeaderCheck.h
new file mode 100644
index 0000000..b17b1f2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Jacobi/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_JACOBI_MODULE_H
+#error "Please include Eigen/Jacobi instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/Jacobi/Jacobi.h b/data_acq_and_processing/third_party/eigen/Eigen/src/Jacobi/Jacobi.h
new file mode 100644
index 0000000..f24a851
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/Jacobi/Jacobi.h
@@ -0,0 +1,468 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_JACOBI_H
+#define EIGEN_JACOBI_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup Jacobi_Module
+ * \jacobi_module
+ * \class JacobiRotation
+ * \brief Rotation given by a cosine-sine pair.
+ *
+ * This class represents a Jacobi or Givens rotation.
+ * This is a 2D rotation in the plane \c J of angle \f$ \theta \f$ defined by
+ * its cosine \c c and sine \c s as follow:
+ * \f$ J = \left ( \begin{array}{cc} c & \overline s \\ -s  & \overline c \end{array} \right ) \f$
+ *
+ * You can apply the respective counter-clockwise rotation to a column vector \c v by
+ * applying its adjoint on the left: \f$ v = J^* v \f$ that translates to the following Eigen code:
+ * \code
+ * v.applyOnTheLeft(J.adjoint());
+ * \endcode
+ *
+ * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
+ */
+template <typename Scalar>
+class JacobiRotation {
+ public:
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  /** Default constructor without any initialization. */
+  EIGEN_DEVICE_FUNC JacobiRotation() {}
+
+  /** Construct a planar rotation from a cosine-sine pair (\a c, \c s). */
+  EIGEN_DEVICE_FUNC JacobiRotation(const Scalar& c, const Scalar& s) : m_c(c), m_s(s) {}
+
+  EIGEN_DEVICE_FUNC Scalar& c() { return m_c; }
+  EIGEN_DEVICE_FUNC Scalar c() const { return m_c; }
+  EIGEN_DEVICE_FUNC Scalar& s() { return m_s; }
+  EIGEN_DEVICE_FUNC Scalar s() const { return m_s; }
+
+  /** Concatenates two planar rotation */
+  EIGEN_DEVICE_FUNC JacobiRotation operator*(const JacobiRotation& other) {
+    using numext::conj;
+    return JacobiRotation(m_c * other.m_c - conj(m_s) * other.m_s,
+                          conj(m_c * conj(other.m_s) + conj(m_s) * conj(other.m_c)));
+  }
+
+  /** Returns the transposed transformation */
+  EIGEN_DEVICE_FUNC JacobiRotation transpose() const {
+    using numext::conj;
+    return JacobiRotation(m_c, -conj(m_s));
+  }
+
+  /** Returns the adjoint transformation */
+  EIGEN_DEVICE_FUNC JacobiRotation adjoint() const {
+    using numext::conj;
+    return JacobiRotation(conj(m_c), -m_s);
+  }
+
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC bool makeJacobi(const MatrixBase<Derived>&, Index p, Index q);
+  EIGEN_DEVICE_FUNC bool makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z);
+
+  EIGEN_DEVICE_FUNC void makeGivens(const Scalar& p, const Scalar& q, Scalar* r = 0);
+
+ protected:
+  EIGEN_DEVICE_FUNC void makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::true_type);
+  EIGEN_DEVICE_FUNC void makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::false_type);
+
+  Scalar m_c, m_s;
+};
+
+/** Makes \c *this as a Jacobi rotation \a J such that applying \a J on both the right and left sides of the selfadjoint
+ * 2x2 matrix \f$ B = \left ( \begin{array}{cc} x & y \\ \overline y & z \end{array} \right )\f$ yields a diagonal
+ * matrix \f$ A = J^* B J \f$
+ *
+ * \sa MatrixBase::makeJacobi(const MatrixBase<Derived>&, Index, Index), MatrixBase::applyOnTheLeft(),
+ * MatrixBase::applyOnTheRight()
+ */
+template <typename Scalar>
+EIGEN_DEVICE_FUNC bool JacobiRotation<Scalar>::makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z) {
+  using std::abs;
+  using std::sqrt;
+
+  RealScalar deno = RealScalar(2) * abs(y);
+  if (deno < (std::numeric_limits<RealScalar>::min)()) {
+    m_c = Scalar(1);
+    m_s = Scalar(0);
+    return false;
+  } else {
+    RealScalar tau = (x - z) / deno;
+    RealScalar w = sqrt(numext::abs2(tau) + RealScalar(1));
+    RealScalar t;
+    if (tau > RealScalar(0)) {
+      t = RealScalar(1) / (tau + w);
+    } else {
+      t = RealScalar(1) / (tau - w);
+    }
+    RealScalar sign_t = t > RealScalar(0) ? RealScalar(1) : RealScalar(-1);
+    RealScalar n = RealScalar(1) / sqrt(numext::abs2(t) + RealScalar(1));
+    m_s = -sign_t * (numext::conj(y) / abs(y)) * abs(t) * n;
+    m_c = n;
+    return true;
+  }
+}
+
+/** Makes \c *this as a Jacobi rotation \c J such that applying \a J on both the right and left sides of the 2x2
+ * selfadjoint matrix \f$ B = \left ( \begin{array}{cc} \text{this}_{pp} & \text{this}_{pq} \\ (\text{this}_{pq})^* &
+ * \text{this}_{qq} \end{array} \right )\f$ yields a diagonal matrix \f$ A = J^* B J \f$
+ *
+ * Example: \include Jacobi_makeJacobi.cpp
+ * Output: \verbinclude Jacobi_makeJacobi.out
+ *
+ * \sa JacobiRotation::makeJacobi(RealScalar, Scalar, RealScalar), MatrixBase::applyOnTheLeft(),
+ * MatrixBase::applyOnTheRight()
+ */
+template <typename Scalar>
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline bool JacobiRotation<Scalar>::makeJacobi(const MatrixBase<Derived>& m, Index p, Index q) {
+  return makeJacobi(numext::real(m.coeff(p, p)), m.coeff(p, q), numext::real(m.coeff(q, q)));
+}
+
+/** Makes \c *this as a Givens rotation \c G such that applying \f$ G^* \f$ to the left of the vector
+ * \f$ V = \left ( \begin{array}{c} p \\ q \end{array} \right )\f$ yields:
+ * \f$ G^* V = \left ( \begin{array}{c} r \\ 0 \end{array} \right )\f$.
+ *
+ * The value of \a r is returned if \a r is not null (the default is null).
+ * Also note that G is built such that the cosine is always real.
+ *
+ * Example: \include Jacobi_makeGivens.cpp
+ * Output: \verbinclude Jacobi_makeGivens.out
+ *
+ * This function implements the continuous Givens rotation generation algorithm
+ * found in Anderson (2000), Discontinuous Plane Rotations and the Symmetric Eigenvalue Problem.
+ * LAPACK Working Note 150, University of Tennessee, UT-CS-00-454, December 4, 2000.
+ *
+ * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
+ */
+template <typename Scalar>
+EIGEN_DEVICE_FUNC void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r) {
+  makeGivens(p, q, r, std::conditional_t<NumTraits<Scalar>::IsComplex, internal::true_type, internal::false_type>());
+}
+
+// specialization for complexes
+template <typename Scalar>
+EIGEN_DEVICE_FUNC void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r,
+                                                          internal::true_type) {
+  using numext::conj;
+  using std::abs;
+  using std::sqrt;
+
+  if (q == Scalar(0)) {
+    m_c = numext::real(p) < 0 ? Scalar(-1) : Scalar(1);
+    m_s = 0;
+    if (r) *r = m_c * p;
+  } else if (p == Scalar(0)) {
+    m_c = 0;
+    m_s = -q / abs(q);
+    if (r) *r = abs(q);
+  } else {
+    RealScalar p1 = numext::norm1(p);
+    RealScalar q1 = numext::norm1(q);
+    if (p1 >= q1) {
+      Scalar ps = p / p1;
+      RealScalar p2 = numext::abs2(ps);
+      Scalar qs = q / p1;
+      RealScalar q2 = numext::abs2(qs);
+
+      RealScalar u = sqrt(RealScalar(1) + q2 / p2);
+      if (numext::real(p) < RealScalar(0)) u = -u;
+
+      m_c = Scalar(1) / u;
+      m_s = -qs * conj(ps) * (m_c / p2);
+      if (r) *r = p * u;
+    } else {
+      Scalar ps = p / q1;
+      RealScalar p2 = numext::abs2(ps);
+      Scalar qs = q / q1;
+      RealScalar q2 = numext::abs2(qs);
+
+      RealScalar u = q1 * sqrt(p2 + q2);
+      if (numext::real(p) < RealScalar(0)) u = -u;
+
+      p1 = abs(p);
+      ps = p / p1;
+      m_c = p1 / u;
+      m_s = -conj(ps) * (q / u);
+      if (r) *r = ps * u;
+    }
+  }
+}
+
+// specialization for reals
+template <typename Scalar>
+EIGEN_DEVICE_FUNC void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r,
+                                                          internal::false_type) {
+  using std::abs;
+  using std::sqrt;
+  if (numext::is_exactly_zero(q)) {
+    m_c = p < Scalar(0) ? Scalar(-1) : Scalar(1);
+    m_s = Scalar(0);
+    if (r) *r = abs(p);
+  } else if (numext::is_exactly_zero(p)) {
+    m_c = Scalar(0);
+    m_s = q < Scalar(0) ? Scalar(1) : Scalar(-1);
+    if (r) *r = abs(q);
+  } else if (abs(p) > abs(q)) {
+    Scalar t = q / p;
+    Scalar u = sqrt(Scalar(1) + numext::abs2(t));
+    if (p < Scalar(0)) u = -u;
+    m_c = Scalar(1) / u;
+    m_s = -t * m_c;
+    if (r) *r = p * u;
+  } else {
+    Scalar t = p / q;
+    Scalar u = sqrt(Scalar(1) + numext::abs2(t));
+    if (q < Scalar(0)) u = -u;
+    m_s = -Scalar(1) / u;
+    m_c = -t * m_s;
+    if (r) *r = q * u;
+  }
+}
+
+/****************************************************************************************
+ *   Implementation of MatrixBase methods
+ ****************************************************************************************/
+
+namespace internal {
+/** \jacobi_module
+ * Applies the clockwise 2D rotation \a j to the set of 2D vectors of coordinates \a x and \a y:
+ * \f$ \left ( \begin{array}{cc} x \\ y \end{array} \right )  =  J \left ( \begin{array}{cc} x \\ y \end{array} \right )
+ * \f$
+ *
+ * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
+ */
+template <typename VectorX, typename VectorY, typename OtherScalar>
+EIGEN_DEVICE_FUNC void apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x, DenseBase<VectorY>& xpr_y,
+                                                   const JacobiRotation<OtherScalar>& j);
+}  // namespace internal
+
+/** \jacobi_module
+ * Applies the rotation in the plane \a j to the rows \a p and \a q of \c *this, i.e., it computes B = J * B,
+ * with \f$ B = \left ( \begin{array}{cc} \text{*this.row}(p) \\ \text{*this.row}(q) \end{array} \right ) \f$.
+ *
+ * \sa class JacobiRotation, MatrixBase::applyOnTheRight(), internal::apply_rotation_in_the_plane()
+ */
+template <typename Derived>
+template <typename OtherScalar>
+EIGEN_DEVICE_FUNC inline void MatrixBase<Derived>::applyOnTheLeft(Index p, Index q,
+                                                                  const JacobiRotation<OtherScalar>& j) {
+  RowXpr x(this->row(p));
+  RowXpr y(this->row(q));
+  internal::apply_rotation_in_the_plane(x, y, j);
+}
+
+/** \jacobi_module
+ * Applies the rotation in the plane \a j to the columns \a p and \a q of \c *this, i.e., it computes B = B * J
+ * with \f$ B = \left ( \begin{array}{cc} \text{*this.col}(p) & \text{*this.col}(q) \end{array} \right ) \f$.
+ *
+ * \sa class JacobiRotation, MatrixBase::applyOnTheLeft(), internal::apply_rotation_in_the_plane()
+ */
+template <typename Derived>
+template <typename OtherScalar>
+EIGEN_DEVICE_FUNC inline void MatrixBase<Derived>::applyOnTheRight(Index p, Index q,
+                                                                   const JacobiRotation<OtherScalar>& j) {
+  ColXpr x(this->col(p));
+  ColXpr y(this->col(q));
+  internal::apply_rotation_in_the_plane(x, y, j.transpose());
+}
+
+namespace internal {
+
+template <typename Scalar, typename OtherScalar, int SizeAtCompileTime, int MinAlignment, bool Vectorizable>
+struct apply_rotation_in_the_plane_selector {
+  static EIGEN_DEVICE_FUNC inline void run(Scalar* x, Index incrx, Scalar* y, Index incry, Index size, OtherScalar c,
+                                           OtherScalar s) {
+    for (Index i = 0; i < size; ++i) {
+      Scalar xi = *x;
+      Scalar yi = *y;
+      *x = c * xi + numext::conj(s) * yi;
+      *y = -s * xi + numext::conj(c) * yi;
+      x += incrx;
+      y += incry;
+    }
+  }
+};
+
+template <typename Scalar, typename OtherScalar, int SizeAtCompileTime, int MinAlignment>
+struct apply_rotation_in_the_plane_selector<Scalar, OtherScalar, SizeAtCompileTime, MinAlignment,
+                                            true /* vectorizable */> {
+  static inline void run(Scalar* x, Index incrx, Scalar* y, Index incry, Index size, OtherScalar c, OtherScalar s) {
+    typedef typename packet_traits<Scalar>::type Packet;
+    typedef typename packet_traits<OtherScalar>::type OtherPacket;
+
+    constexpr int RequiredAlignment =
+        (std::max<int>)(unpacket_traits<Packet>::alignment, unpacket_traits<OtherPacket>::alignment);
+    constexpr Index PacketSize = packet_traits<Scalar>::size;
+
+    /*** dynamic-size vectorized paths ***/
+    if (size >= 2 * PacketSize && SizeAtCompileTime == Dynamic && ((incrx == 1 && incry == 1) || PacketSize == 1)) {
+      // both vectors are sequentially stored in memory => vectorization
+      constexpr Index Peeling = 2;
+
+      Index alignedStart = internal::first_default_aligned(y, size);
+      Index alignedEnd = alignedStart + ((size - alignedStart) / PacketSize) * PacketSize;
+
+      const OtherPacket pc = pset1<OtherPacket>(c);
+      const OtherPacket ps = pset1<OtherPacket>(s);
+      conj_helper<OtherPacket, Packet, NumTraits<OtherScalar>::IsComplex, false> pcj;
+      conj_helper<OtherPacket, Packet, false, false> pm;
+
+      for (Index i = 0; i < alignedStart; ++i) {
+        Scalar xi = x[i];
+        Scalar yi = y[i];
+        x[i] = c * xi + numext::conj(s) * yi;
+        y[i] = -s * xi + numext::conj(c) * yi;
+      }
+
+      Scalar* EIGEN_RESTRICT px = x + alignedStart;
+      Scalar* EIGEN_RESTRICT py = y + alignedStart;
+
+      if (internal::first_default_aligned(x, size) == alignedStart) {
+        for (Index i = alignedStart; i < alignedEnd; i += PacketSize) {
+          Packet xi = pload<Packet>(px);
+          Packet yi = pload<Packet>(py);
+          pstore(px, pm.pmadd(pc, xi, pcj.pmul(ps, yi)));
+          pstore(py, pcj.pmsub(pc, yi, pm.pmul(ps, xi)));
+          px += PacketSize;
+          py += PacketSize;
+        }
+      } else {
+        Index peelingEnd = alignedStart + ((size - alignedStart) / (Peeling * PacketSize)) * (Peeling * PacketSize);
+        for (Index i = alignedStart; i < peelingEnd; i += Peeling * PacketSize) {
+          Packet xi = ploadu<Packet>(px);
+          Packet xi1 = ploadu<Packet>(px + PacketSize);
+          Packet yi = pload<Packet>(py);
+          Packet yi1 = pload<Packet>(py + PacketSize);
+          pstoreu(px, pm.pmadd(pc, xi, pcj.pmul(ps, yi)));
+          pstoreu(px + PacketSize, pm.pmadd(pc, xi1, pcj.pmul(ps, yi1)));
+          pstore(py, pcj.pmsub(pc, yi, pm.pmul(ps, xi)));
+          pstore(py + PacketSize, pcj.pmsub(pc, yi1, pm.pmul(ps, xi1)));
+          px += Peeling * PacketSize;
+          py += Peeling * PacketSize;
+        }
+        if (alignedEnd != peelingEnd) {
+          Packet xi = ploadu<Packet>(x + peelingEnd);
+          Packet yi = pload<Packet>(y + peelingEnd);
+          pstoreu(x + peelingEnd, pm.pmadd(pc, xi, pcj.pmul(ps, yi)));
+          pstore(y + peelingEnd, pcj.pmsub(pc, yi, pm.pmul(ps, xi)));
+        }
+      }
+
+      for (Index i = alignedEnd; i < size; ++i) {
+        Scalar xi = x[i];
+        Scalar yi = y[i];
+        x[i] = c * xi + numext::conj(s) * yi;
+        y[i] = -s * xi + numext::conj(c) * yi;
+      }
+    }
+
+    /*** fixed-size vectorized path ***/
+    else if (SizeAtCompileTime != Dynamic && MinAlignment >= RequiredAlignment) {
+      const OtherPacket pc = pset1<OtherPacket>(c);
+      const OtherPacket ps = pset1<OtherPacket>(s);
+      conj_helper<OtherPacket, Packet, NumTraits<OtherScalar>::IsComplex, false> pcj;
+      conj_helper<OtherPacket, Packet, false, false> pm;
+      Scalar* EIGEN_RESTRICT px = x;
+      Scalar* EIGEN_RESTRICT py = y;
+      for (Index i = 0; i < size; i += PacketSize) {
+        Packet xi = pload<Packet>(px);
+        Packet yi = pload<Packet>(py);
+        pstore(px, pm.pmadd(pc, xi, pcj.pmul(ps, yi)));
+        pstore(py, pcj.pmsub(pc, yi, pm.pmul(ps, xi)));
+        px += PacketSize;
+        py += PacketSize;
+      }
+    }
+
+    /*** non-vectorized path ***/
+    else {
+      apply_rotation_in_the_plane_selector<Scalar, OtherScalar, SizeAtCompileTime, MinAlignment, false>::run(
+          x, incrx, y, incry, size, c, s);
+    }
+  }
+};
+
+template <typename VectorX, typename VectorY, typename OtherScalar>
+EIGEN_DEVICE_FUNC void inline apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x, DenseBase<VectorY>& xpr_y,
+                                                          const JacobiRotation<OtherScalar>& j) {
+  typedef typename VectorX::Scalar Scalar;
+  constexpr bool Vectorizable = (int(evaluator<VectorX>::Flags) & int(evaluator<VectorY>::Flags) & PacketAccessBit) &&
+                                (int(packet_traits<Scalar>::size) == int(packet_traits<OtherScalar>::size));
+
+  eigen_assert(xpr_x.size() == xpr_y.size());
+  Index size = xpr_x.size();
+  Index incrx = xpr_x.derived().innerStride();
+  Index incry = xpr_y.derived().innerStride();
+
+  Scalar* EIGEN_RESTRICT x = &xpr_x.derived().coeffRef(0);
+  Scalar* EIGEN_RESTRICT y = &xpr_y.derived().coeffRef(0);
+
+  OtherScalar c = j.c();
+  OtherScalar s = j.s();
+  if (numext::is_exactly_one(c) && numext::is_exactly_zero(s)) return;
+
+  constexpr int Alignment = (std::min)(int(evaluator<VectorX>::Alignment), int(evaluator<VectorY>::Alignment));
+  apply_rotation_in_the_plane_selector<Scalar, OtherScalar, VectorX::SizeAtCompileTime, Alignment, Vectorizable>::run(
+      x, incrx, y, incry, size, c, s);
+}
+
+template <typename MatrixType, typename RealScalar, typename Index>
+EIGEN_DONT_INLINE void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
+                                           JacobiRotation<RealScalar>* j_left, JacobiRotation<RealScalar>* j_right) {
+  // Extract 2x2 submatrix into scalars (avoids Matrix construction on stack).
+  const RealScalar m00 = numext::real(matrix.coeff(p, p));
+  const RealScalar m01 = numext::real(matrix.coeff(p, q));
+  const RealScalar m10 = numext::real(matrix.coeff(q, p));
+  const RealScalar m11 = numext::real(matrix.coeff(q, q));
+
+  // Compute the symmetrizing rotation rot1 such that rot1 * [m] is symmetric.
+  const RealScalar t = m00 + m11;
+  const RealScalar d = m10 - m01;
+
+  RealScalar c1, s1;
+  if (numext::abs(d) < (std::numeric_limits<RealScalar>::min)()) {
+    c1 = RealScalar(1);
+    s1 = RealScalar(0);
+  } else {
+    // If d!=0, then t/d cannot overflow because the magnitude of the
+    // entries forming d are not too small compared to the ones forming t.
+    RealScalar u = t / d;
+    s1 = RealScalar(1) / numext::sqrt(RealScalar(1) + numext::abs2(u));
+    c1 = u * s1;
+  }
+
+  // Apply rot1 to the 2x2 submatrix inline (avoids rotation dispatch overhead).
+  // Result is symmetric, so we only need 3 values: a00, a01 (== a10), a11.
+  const RealScalar a00 = c1 * m00 + s1 * m10;
+  const RealScalar a01 = c1 * m01 + s1 * m11;
+  const RealScalar a11 = -s1 * m01 + c1 * m11;
+
+  // Compute the diagonalizing rotation j_right from the symmetrized matrix.
+  j_right->makeJacobi(a00, a01, a11);
+
+  // Compose j_left = rot1 * j_right^T inline (avoids template machinery overhead).
+  const RealScalar jr_c = j_right->c();
+  const RealScalar jr_s = j_right->s();
+  j_left->c() = c1 * jr_c + s1 * jr_s;
+  j_left->s() = s1 * jr_c - c1 * jr_s;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_JACOBI_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/KLUSupport/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/KLUSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000..eb1d671
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/KLUSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_KLUSUPPORT_MODULE_H
+#error "Please include Eigen/KLUSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/KLUSupport/KLUSupport.h b/data_acq_and_processing/third_party/eigen/Eigen/src/KLUSupport/KLUSupport.h
new file mode 100644
index 0000000..f07ad8d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/KLUSupport/KLUSupport.h
@@ -0,0 +1,261 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Kyle Macfarlan <kyle.macfarlan@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_KLUSUPPORT_H
+#define EIGEN_KLUSUPPORT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/* TODO extract L, extract U, compute det, etc... */
+
+/** \ingroup KLUSupport_Module
+ * \brief A sparse LU factorization and solver based on KLU
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a LU factorization
+ * using the KLU library. The sparse matrix A must be squared and full rank.
+ * The vectors or matrices X and B can be either dense or sparse.
+ *
+ * \warning The input matrix A should be in a \b compressed and \b column-major form.
+ * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept, class UmfPackLU, class SparseLU
+ */
+
+inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B[],
+                     klu_common *Common, double) {
+  return klu_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B,
+                   Common);
+}
+
+inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double> B[],
+                     klu_common *Common, std::complex<double>) {
+  return klu_z_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs),
+                     &numext::real_ref(B[0]), Common);
+}
+
+inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B[],
+                      klu_common *Common, double) {
+  return klu_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B,
+                    Common);
+}
+
+inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double> B[],
+                      klu_common *Common, std::complex<double>) {
+  return klu_z_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs),
+                      &numext::real_ref(B[0]), 0, Common);
+}
+
+inline klu_numeric *klu_factor(int Ap[], int Ai[], double Ax[], klu_symbolic *Symbolic, klu_common *Common, double) {
+  return klu_factor(Ap, Ai, Ax, Symbolic, Common);
+}
+
+inline klu_numeric *klu_factor(int Ap[], int Ai[], std::complex<double> Ax[], klu_symbolic *Symbolic,
+                               klu_common *Common, std::complex<double>) {
+  return klu_z_factor(Ap, Ai, &numext::real_ref(Ax[0]), Symbolic, Common);
+}
+
+template <typename MatrixType_>
+class KLU : public SparseSolverBase<KLU<MatrixType_> > {
+ protected:
+  typedef SparseSolverBase<KLU<MatrixType_> > Base;
+  using Base::m_isInitialized;
+
+ public:
+  using Base::_solve_impl;
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef Matrix<Scalar, Dynamic, 1> Vector;
+  typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+  typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+  typedef SparseMatrix<Scalar> LUMatrixType;
+  typedef SparseMatrix<Scalar, ColMajor, int> KLUMatrixType;
+  typedef Ref<const KLUMatrixType, StandardCompressedFormat> KLUMatrixRef;
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+ public:
+  KLU() : m_dummy(0, 0), mp_matrix(m_dummy) { init(); }
+
+  template <typename InputMatrixType>
+  explicit KLU(const InputMatrixType &matrix) : mp_matrix(matrix) {
+    init();
+    compute(matrix);
+  }
+
+  ~KLU() {
+    if (m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
+    if (m_numeric) klu_free_numeric(&m_numeric, &m_common);
+  }
+
+  constexpr Index rows() const noexcept { return mp_matrix.rows(); }
+  constexpr Index cols() const noexcept { return mp_matrix.cols(); }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix.appears to be negative.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+  /** Computes the sparse LU factorization of \a matrix
+   *  Note that the matrix should be column-major, and in compressed format for best performance.
+   *  \sa SparseMatrix::makeCompressed().
+   */
+  template <typename InputMatrixType>
+  void compute(const InputMatrixType &matrix) {
+    if (m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
+    if (m_numeric) klu_free_numeric(&m_numeric, &m_common);
+    grab(matrix.derived());
+    analyzePattern_impl();
+    factorize_impl();
+  }
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize(), compute()
+   */
+  template <typename InputMatrixType>
+  void analyzePattern(const InputMatrixType &matrix) {
+    if (m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
+    if (m_numeric) klu_free_numeric(&m_numeric, &m_common);
+
+    grab(matrix.derived());
+
+    analyzePattern_impl();
+  }
+
+  /** Provides access to the control settings array used by KLU.
+   *
+   * See KLU documentation for details.
+   */
+  inline const klu_common &kluCommon() const { return m_common; }
+
+  /** Provides access to the control settings array used by KLU.
+   *
+   * If this array contains NaN's, the default values are used.
+   *
+   * See KLU documentation for details.
+   */
+  inline klu_common &kluCommon() { return m_common; }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity as the matrix on which the pattern analysis has been performed.
+   *
+   * \sa analyzePattern(), compute()
+   */
+  template <typename InputMatrixType>
+  void factorize(const InputMatrixType &matrix) {
+    eigen_assert(m_analysisIsOk && "KLU: you must first call analyzePattern()");
+    if (m_numeric) klu_free_numeric(&m_numeric, &m_common);
+
+    grab(matrix.derived());
+
+    factorize_impl();
+  }
+
+  /** \internal */
+  template <typename BDerived, typename XDerived>
+  bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
+
+ protected:
+  void init() {
+    m_info = InvalidInput;
+    m_isInitialized = false;
+    m_numeric = 0;
+    m_symbolic = 0;
+    m_extractedDataAreDirty = true;
+
+    klu_defaults(&m_common);
+  }
+
+  void analyzePattern_impl() {
+    m_info = InvalidInput;
+    m_analysisIsOk = false;
+    m_factorizationIsOk = false;
+    m_symbolic = klu_analyze(internal::convert_index<int>(mp_matrix.rows()),
+                             const_cast<StorageIndex *>(mp_matrix.outerIndexPtr()),
+                             const_cast<StorageIndex *>(mp_matrix.innerIndexPtr()), &m_common);
+    if (m_symbolic) {
+      m_isInitialized = true;
+      m_info = Success;
+      m_analysisIsOk = true;
+      m_extractedDataAreDirty = true;
+    }
+  }
+
+  void factorize_impl() {
+    m_numeric = klu_factor(const_cast<StorageIndex *>(mp_matrix.outerIndexPtr()),
+                           const_cast<StorageIndex *>(mp_matrix.innerIndexPtr()),
+                           const_cast<Scalar *>(mp_matrix.valuePtr()), m_symbolic, &m_common, Scalar());
+
+    m_info = m_numeric ? Success : NumericalIssue;
+    m_factorizationIsOk = m_numeric ? 1 : 0;
+    m_extractedDataAreDirty = true;
+  }
+
+  template <typename MatrixDerived>
+  void grab(const EigenBase<MatrixDerived> &A) {
+    internal::destroy_at(&mp_matrix);
+    internal::construct_at(&mp_matrix, A.derived());
+  }
+
+  void grab(const KLUMatrixRef &A) {
+    if (&(A.derived()) != &mp_matrix) {
+      internal::destroy_at(&mp_matrix);
+      internal::construct_at(&mp_matrix, A);
+    }
+  }
+
+  KLUMatrixType m_dummy;
+  KLUMatrixRef mp_matrix;
+
+  klu_numeric *m_numeric;
+  klu_symbolic *m_symbolic;
+  klu_common m_common;
+  mutable ComputationInfo m_info;
+  int m_factorizationIsOk;
+  int m_analysisIsOk;
+  mutable bool m_extractedDataAreDirty;
+
+ private:
+  KLU(const KLU &) {}
+};
+
+template <typename MatrixType>
+template <typename BDerived, typename XDerived>
+bool KLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const {
+  Index rhsCols = b.cols();
+  EIGEN_STATIC_ASSERT((XDerived::Flags & RowMajorBit) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  eigen_assert(m_factorizationIsOk &&
+               "The decomposition is not in a valid state for solving, you must first call either compute() or "
+               "analyzePattern()/factorize()");
+
+  x = b;
+  int info = klu_solve(m_symbolic, m_numeric, b.rows(), rhsCols, x.const_cast_derived().data(),
+                       const_cast<klu_common *>(&m_common), Scalar());
+
+  m_info = info != 0 ? Success : NumericalIssue;
+  return true;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_KLUSUPPORT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/LU/Determinant.h b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/Determinant.h
new file mode 100644
index 0000000..ae4fee3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/Determinant.h
@@ -0,0 +1,98 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DETERMINANT_H
+#define EIGEN_DETERMINANT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline const typename Derived::Scalar bruteforce_det3_helper(const MatrixBase<Derived>& matrix, int a,
+                                                                               int b, int c) {
+  return matrix.coeff(0, a) * (matrix.coeff(1, b) * matrix.coeff(2, c) - matrix.coeff(1, c) * matrix.coeff(2, b));
+}
+
+template <typename Derived, int DeterminantType = Derived::RowsAtCompileTime>
+struct determinant_impl {
+  static inline typename traits<Derived>::Scalar run(const Derived& m) {
+    if (Derived::ColsAtCompileTime == Dynamic && m.rows() == 0) return typename traits<Derived>::Scalar(1);
+    return m.partialPivLu().determinant();
+  }
+};
+
+template <typename Derived>
+struct determinant_impl<Derived, 1> {
+  static inline EIGEN_DEVICE_FUNC typename traits<Derived>::Scalar run(const Derived& m) { return m.coeff(0, 0); }
+};
+
+template <typename Derived>
+struct determinant_impl<Derived, 2> {
+  static inline EIGEN_DEVICE_FUNC typename traits<Derived>::Scalar run(const Derived& m) {
+    return m.coeff(0, 0) * m.coeff(1, 1) - m.coeff(1, 0) * m.coeff(0, 1);
+  }
+};
+
+template <typename Derived>
+struct determinant_impl<Derived, 3> {
+  static inline EIGEN_DEVICE_FUNC typename traits<Derived>::Scalar run(const Derived& m) {
+    return bruteforce_det3_helper(m, 0, 1, 2) - bruteforce_det3_helper(m, 1, 0, 2) + bruteforce_det3_helper(m, 2, 0, 1);
+  }
+};
+
+template <typename Derived>
+struct determinant_impl<Derived, 4> {
+  typedef typename traits<Derived>::Scalar Scalar;
+  static EIGEN_DEVICE_FUNC Scalar run(const Derived& m) {
+    Scalar d2_01 = det2(m, 0, 1);
+    Scalar d2_02 = det2(m, 0, 2);
+    Scalar d2_03 = det2(m, 0, 3);
+    Scalar d2_12 = det2(m, 1, 2);
+    Scalar d2_13 = det2(m, 1, 3);
+    Scalar d2_23 = det2(m, 2, 3);
+    Scalar d3_0 = det3(m, 1, d2_23, 2, d2_13, 3, d2_12);
+    Scalar d3_1 = det3(m, 0, d2_23, 2, d2_03, 3, d2_02);
+    Scalar d3_2 = det3(m, 0, d2_13, 1, d2_03, 3, d2_01);
+    Scalar d3_3 = det3(m, 0, d2_12, 1, d2_02, 2, d2_01);
+    return internal::pmadd(static_cast<Scalar>(-m(0, 3)), d3_0, static_cast<Scalar>(m(1, 3) * d3_1)) +
+           internal::pmadd(static_cast<Scalar>(-m(2, 3)), d3_2, static_cast<Scalar>(m(3, 3) * d3_3));
+  }
+
+ protected:
+  static EIGEN_DEVICE_FUNC Scalar det2(const Derived& m, Index i0, Index i1) {
+    return m(i0, 0) * m(i1, 1) - m(i1, 0) * m(i0, 1);
+  }
+
+  static EIGEN_DEVICE_FUNC Scalar det3(const Derived& m, Index i0, const Scalar& d0, Index i1, const Scalar& d1,
+                                       Index i2, const Scalar& d2) {
+    return internal::pmadd(m(i0, 2), d0,
+                           internal::pmadd(static_cast<Scalar>(-m(i1, 2)), d1, static_cast<Scalar>(m(i2, 2) * d2)));
+  }
+};
+
+}  // end namespace internal
+
+/** \lu_module
+ *
+ * \returns the determinant of this matrix
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar MatrixBase<Derived>::determinant() const {
+  eigen_assert(rows() == cols());
+  typedef typename internal::nested_eval<Derived, Base::RowsAtCompileTime>::type Nested;
+  return internal::determinant_impl<internal::remove_all_t<Nested>>::run(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_DETERMINANT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/LU/FullPivLU.h b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/FullPivLU.h
new file mode 100644
index 0000000..50a0e2b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/FullPivLU.h
@@ -0,0 +1,825 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LU_H
+#define EIGEN_LU_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType_, typename PermutationIndex_>
+struct traits<FullPivLU<MatrixType_, PermutationIndex_> > : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef PermutationIndex_ StorageIndex;
+  enum { Flags = 0 };
+};
+
+}  // end namespace internal
+
+/** \ingroup LU_Module
+ *
+ * \class FullPivLU
+ *
+ * \brief LU decomposition of a matrix with complete pivoting, and related features
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the LU decomposition
+ *
+ * This class represents a LU decomposition of any matrix, with complete pivoting: the matrix A is
+ * decomposed as \f$ A = P^{-1} L U Q^{-1} \f$ where L is unit-lower-triangular, U is
+ * upper-triangular, and P and Q are permutation matrices. This is a rank-revealing LU
+ * decomposition. The eigenvalues (diagonal coefficients) of U are sorted in such a way that any
+ * zeros are at the end.
+ *
+ * This decomposition provides the generic approach to solving systems of linear equations, computing
+ * the rank, invertibility, inverse, kernel, and determinant.
+ *
+ * This LU decomposition is very stable and well tested with large matrices. However there are use cases where the SVD
+ * decomposition is inherently more stable and/or flexible. For example, when computing the kernel of a matrix,
+ * working with the SVD allows to select the smallest singular values of the matrix, something that
+ * the LU decomposition doesn't see.
+ *
+ * The data of the LU decomposition can be directly accessed through the methods matrixLU(),
+ * permutationP(), permutationQ().
+ *
+ * As an example, here is how the original matrix can be retrieved:
+ * \include class_FullPivLU.cpp
+ * Output: \verbinclude class_FullPivLU.out
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * \sa MatrixBase::fullPivLu(), MatrixBase::determinant(), MatrixBase::inverse()
+ */
+template <typename MatrixType_, typename PermutationIndex_>
+class FullPivLU : public SolverBase<FullPivLU<MatrixType_, PermutationIndex_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<FullPivLU> Base;
+  friend class SolverBase<FullPivLU>;
+
+  EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivLU)
+  enum {
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+  using PermutationIndex = PermutationIndex_;
+  typedef typename internal::plain_row_type<MatrixType, PermutationIndex>::type IntRowVectorType;
+  typedef typename internal::plain_col_type<MatrixType, PermutationIndex>::type IntColVectorType;
+  typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime, PermutationIndex> PermutationQType;
+  typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime, PermutationIndex> PermutationPType;
+  typedef typename MatrixType::PlainObject PlainObject;
+
+  /** \brief Reports whether the LU factorization was successful.
+   *
+   * \note This function always returns \c Success. It is provided for compatibility
+   * with other factorization routines.
+   * \returns \c Success
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "FullPivLU is not initialized.");
+    return Success;
+  }
+
+  /**
+   * \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via LU::compute(const MatrixType&).
+   */
+  FullPivLU();
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa FullPivLU()
+   */
+  FullPivLU(Index rows, Index cols);
+
+  /** Constructor.
+   *
+   * \param matrix the matrix of which to compute the LU decomposition.
+   *               It is required to be nonzero.
+   */
+  template <typename InputType>
+  explicit FullPivLU(const EigenBase<InputType>& matrix);
+
+  /** \brief Constructs a LU factorization from a given matrix
+   *
+   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c
+   * MatrixType is a Eigen::Ref.
+   *
+   * \sa FullPivLU(const EigenBase&)
+   */
+  template <typename InputType>
+  explicit FullPivLU(EigenBase<InputType>& matrix);
+
+  /** Computes the LU decomposition of the given matrix.
+   *
+   * \param matrix the matrix of which to compute the LU decomposition.
+   *               It is required to be nonzero.
+   *
+   * \returns a reference to *this
+   */
+  template <typename InputType>
+  FullPivLU& compute(const EigenBase<InputType>& matrix) {
+    m_lu = matrix.derived();
+    computeInPlace();
+    return *this;
+  }
+
+  /** \returns the LU decomposition matrix: the upper-triangular part is U, the
+   * unit-lower-triangular part is L (at least for square matrices; in the non-square
+   * case, special care is needed, see the documentation of class FullPivLU).
+   *
+   * \sa matrixL(), matrixU()
+   */
+  inline const MatrixType& matrixLU() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return m_lu;
+  }
+
+  /** \returns the number of nonzero pivots in the LU decomposition.
+   * Here nonzero is meant in the exact sense, not in a fuzzy sense.
+   * So that notion isn't really intrinsically interesting, but it is
+   * still useful when implementing algorithms.
+   *
+   * \sa rank()
+   */
+  inline Index nonzeroPivots() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return m_nonzero_pivots;
+  }
+
+  /** \returns the absolute value of the biggest pivot, i.e. the biggest
+   *          diagonal coefficient of U.
+   */
+  RealScalar maxPivot() const { return m_maxpivot; }
+
+  /** \returns the permutation matrix P
+   *
+   * \sa permutationQ()
+   */
+  EIGEN_DEVICE_FUNC inline const PermutationPType& permutationP() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return m_p;
+  }
+
+  /** \returns the permutation matrix Q
+   *
+   * \sa permutationP()
+   */
+  inline const PermutationQType& permutationQ() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return m_q;
+  }
+
+  /** \returns the kernel of the matrix, also called its null-space. The columns of the returned matrix
+   * will form a basis of the kernel.
+   *
+   * \note If the kernel has dimension zero, then the returned matrix is a column-vector filled with zeros.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   *
+   * Example: \include FullPivLU_kernel.cpp
+   * Output: \verbinclude FullPivLU_kernel.out
+   *
+   * \sa image()
+   */
+  inline const internal::kernel_retval<FullPivLU> kernel() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return internal::kernel_retval<FullPivLU>(*this);
+  }
+
+  /** \returns the image of the matrix, also called its column-space. The columns of the returned matrix
+   * will form a basis of the image (column-space).
+   *
+   * \param originalMatrix the original matrix, of which *this is the LU decomposition.
+   *                       The reason why it is needed to pass it here, is that this allows
+   *                       a large optimization, as otherwise this method would need to reconstruct it
+   *                       from the LU decomposition.
+   *
+   * \note If the image has dimension zero, then the returned matrix is a column-vector filled with zeros.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   *
+   * Example: \include FullPivLU_image.cpp
+   * Output: \verbinclude FullPivLU_image.out
+   *
+   * \sa kernel()
+   */
+  inline const internal::image_retval<FullPivLU> image(const MatrixType& originalMatrix) const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return internal::image_retval<FullPivLU>(*this, originalMatrix);
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \return a solution x to the equation Ax=b, where A is the matrix of which
+   * *this is the LU decomposition.
+   *
+   * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix,
+   *          the only requirement in order for the equation to make sense is that
+   *          b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition.
+   *
+   * \returns a solution.
+   *
+   * \note_about_checking_solutions
+   *
+   * \note_about_arbitrary_choice_of_solution
+   * \note_about_using_kernel_to_study_multiple_solutions
+   *
+   * Example: \include FullPivLU_solve.cpp
+   * Output: \verbinclude FullPivLU_solve.out
+   *
+   * \sa TriangularView::solve(), kernel(), inverse()
+   */
+  template <typename Rhs>
+  inline Solve<FullPivLU, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  /** \returns an estimate of the reciprocal condition number of the matrix of which \c *this is
+      the LU decomposition.
+    */
+  inline RealScalar rcond() const {
+    eigen_assert(m_isInitialized && "FullPivLU is not initialized.");
+    if (!isInvertible()) {
+      return RealScalar(0);
+    }
+    return internal::rcond_estimate_helper(m_l1_norm, *this);
+  }
+
+  /** \returns the determinant of the matrix of which
+   * *this is the LU decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the LU decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers
+   *       optimized paths.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   *
+   * \sa MatrixBase::determinant()
+   */
+  typename internal::traits<MatrixType>::Scalar determinant() const;
+
+  /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
+   * who need to determine when pivots are to be considered nonzero. This is not used for the
+   * LU decomposition itself.
+   *
+   * When it needs to get the threshold value, Eigen calls threshold(). By default, this
+   * uses a formula to automatically determine a reasonable threshold.
+   * Once you have called the present method setThreshold(const RealScalar&),
+   * your value is used instead.
+   *
+   * \param threshold The new value to use as the threshold.
+   *
+   * A pivot will be considered nonzero if its absolute value is strictly greater than
+   *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
+   * where maxpivot is the biggest pivot.
+   *
+   * If you want to come back to the default behavior, call setThreshold(Default_t)
+   */
+  FullPivLU& setThreshold(const RealScalar& threshold) {
+    m_usePrescribedThreshold = true;
+    m_prescribedThreshold = threshold;
+    return *this;
+  }
+
+  /** Allows to come back to the default behavior, letting Eigen use its default formula for
+   * determining the threshold.
+   *
+   * You should pass the special object Eigen::Default as parameter here.
+   * \code lu.setThreshold(Eigen::Default); \endcode
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  FullPivLU& setThreshold(Default_t) {
+    m_usePrescribedThreshold = false;
+    return *this;
+  }
+
+  /** Returns the threshold that will be used by certain methods such as rank().
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  RealScalar threshold() const {
+    eigen_assert(m_isInitialized || m_usePrescribedThreshold);
+    return m_usePrescribedThreshold ? m_prescribedThreshold
+                                    // this formula comes from experimenting (see "LU precision tuning" thread on the
+                                    // list) and turns out to be identical to Higham's formula used already in LDLt.
+                                    : NumTraits<Scalar>::epsilon() * RealScalar(m_lu.diagonalSize());
+  }
+
+  /** \returns the rank of the matrix of which *this is the LU decomposition.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline Index rank() const {
+    using std::abs;
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
+    Index result = 0;
+    for (Index i = 0; i < m_nonzero_pivots; ++i) result += (abs(m_lu.coeff(i, i)) > premultiplied_threshold);
+    return result;
+  }
+
+  /** \returns the dimension of the kernel of the matrix of which *this is the LU decomposition.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline Index dimensionOfKernel() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return cols() - rank();
+  }
+
+  /** \returns true if the matrix of which *this is the LU decomposition represents an injective
+   *          linear map, i.e. has trivial kernel; false otherwise.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline bool isInjective() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return rank() == cols();
+  }
+
+  /** \returns true if the matrix of which *this is the LU decomposition represents a surjective
+   *          linear map; false otherwise.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline bool isSurjective() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return rank() == rows();
+  }
+
+  /** \returns true if the matrix of which *this is the LU decomposition is invertible.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline bool isInvertible() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return isInjective() && (m_lu.rows() == m_lu.cols());
+  }
+
+  /** \returns the inverse of the matrix of which *this is the LU decomposition.
+   *
+   * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
+   *       Use isInvertible() to first determine whether this matrix is invertible.
+   *
+   * \sa MatrixBase::inverse()
+   */
+  inline Inverse<FullPivLU> inverse() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!");
+    return Inverse<FullPivLU>(*this);
+  }
+
+  MatrixType reconstructedMatrix() const;
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_lu.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_lu.cols(); }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  void computeInPlace();
+
+  MatrixType m_lu;
+  PermutationPType m_p;
+  PermutationQType m_q;
+  IntColVectorType m_rowsTranspositions;
+  IntRowVectorType m_colsTranspositions;
+  Index m_nonzero_pivots;
+  RealScalar m_l1_norm;
+  RealScalar m_maxpivot, m_prescribedThreshold;
+  signed char m_det_pq;
+  bool m_isInitialized, m_usePrescribedThreshold;
+};
+
+template <typename MatrixType, typename PermutationIndex>
+FullPivLU<MatrixType, PermutationIndex>::FullPivLU() : m_isInitialized(false), m_usePrescribedThreshold(false) {}
+
+template <typename MatrixType, typename PermutationIndex>
+FullPivLU<MatrixType, PermutationIndex>::FullPivLU(Index rows, Index cols)
+    : m_lu(rows, cols),
+      m_p(rows),
+      m_q(cols),
+      m_rowsTranspositions(rows),
+      m_colsTranspositions(cols),
+      m_isInitialized(false),
+      m_usePrescribedThreshold(false) {}
+
+template <typename MatrixType, typename PermutationIndex>
+template <typename InputType>
+FullPivLU<MatrixType, PermutationIndex>::FullPivLU(const EigenBase<InputType>& matrix)
+    : m_lu(matrix.rows(), matrix.cols()),
+      m_p(matrix.rows()),
+      m_q(matrix.cols()),
+      m_rowsTranspositions(matrix.rows()),
+      m_colsTranspositions(matrix.cols()),
+      m_isInitialized(false),
+      m_usePrescribedThreshold(false) {
+  compute(matrix.derived());
+}
+
+template <typename MatrixType, typename PermutationIndex>
+template <typename InputType>
+FullPivLU<MatrixType, PermutationIndex>::FullPivLU(EigenBase<InputType>& matrix)
+    : m_lu(matrix.derived()),
+      m_p(matrix.rows()),
+      m_q(matrix.cols()),
+      m_rowsTranspositions(matrix.rows()),
+      m_colsTranspositions(matrix.cols()),
+      m_isInitialized(false),
+      m_usePrescribedThreshold(false) {
+  computeInPlace();
+}
+
+template <typename MatrixType, typename PermutationIndex>
+void FullPivLU<MatrixType, PermutationIndex>::computeInPlace() {
+  eigen_assert(m_lu.rows() <= NumTraits<PermutationIndex>::highest() &&
+               m_lu.cols() <= NumTraits<PermutationIndex>::highest());
+
+  m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
+
+  const Index size = m_lu.diagonalSize();
+  const Index rows = m_lu.rows();
+  const Index cols = m_lu.cols();
+
+  // will store the transpositions, before we accumulate them at the end.
+  // can't accumulate on-the-fly because that will be done in reverse order for the rows.
+  m_rowsTranspositions.resize(m_lu.rows());
+  m_colsTranspositions.resize(m_lu.cols());
+  Index number_of_transpositions = 0;  // number of NONTRIVIAL transpositions, i.e. m_rowsTranspositions[i]!=i
+
+  m_nonzero_pivots = size;  // the generic case is that in which all pivots are nonzero (invertible case)
+  m_maxpivot = RealScalar(0);
+
+  for (Index k = 0; k < size; ++k) {
+    // First, we need to find the pivot.
+
+    // biggest coefficient in the remaining bottom-right corner (starting at row k, col k)
+    Index row_of_biggest_in_corner, col_of_biggest_in_corner;
+    typedef internal::scalar_score_coeff_op<Scalar> Scoring;
+    typedef typename Scoring::result_type Score;
+    Score biggest_in_corner;
+    biggest_in_corner = m_lu.bottomRightCorner(rows - k, cols - k)
+                            .unaryExpr(Scoring())
+                            .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
+    row_of_biggest_in_corner += k;  // correct the values! since they were computed in the corner,
+    col_of_biggest_in_corner += k;  // need to add k to them.
+
+    if (numext::is_exactly_zero(biggest_in_corner)) {
+      // before exiting, make sure to initialize the still uninitialized transpositions
+      // in a sane state without destroying what we already have.
+      m_nonzero_pivots = k;
+      for (Index i = k; i < size; ++i) {
+        m_rowsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
+        m_colsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
+      }
+      break;
+    }
+
+    RealScalar abs_pivot = internal::abs_knowing_score<Scalar>()(
+        m_lu(row_of_biggest_in_corner, col_of_biggest_in_corner), biggest_in_corner);
+    if (abs_pivot > m_maxpivot) m_maxpivot = abs_pivot;
+
+    // Now that we've found the pivot, we need to apply the row/col swaps to
+    // bring it to the location (k,k).
+
+    m_rowsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(row_of_biggest_in_corner);
+    m_colsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(col_of_biggest_in_corner);
+    if (k != row_of_biggest_in_corner) {
+      m_lu.row(k).swap(m_lu.row(row_of_biggest_in_corner));
+      ++number_of_transpositions;
+    }
+    if (k != col_of_biggest_in_corner) {
+      m_lu.col(k).swap(m_lu.col(col_of_biggest_in_corner));
+      ++number_of_transpositions;
+    }
+
+    // Now that the pivot is at the right location, we update the remaining
+    // bottom-right corner by Gaussian elimination.
+
+    if (k < rows - 1) m_lu.col(k).tail(rows - k - 1) /= m_lu.coeff(k, k);
+    if (k < size - 1)
+      m_lu.block(k + 1, k + 1, rows - k - 1, cols - k - 1).noalias() -=
+          m_lu.col(k).tail(rows - k - 1) * m_lu.row(k).tail(cols - k - 1);
+  }
+
+  // the main loop is over, we still have to accumulate the transpositions to find the
+  // permutations P and Q
+
+  m_p.setIdentity(rows);
+  for (Index k = size - 1; k >= 0; --k) m_p.applyTranspositionOnTheRight(k, m_rowsTranspositions.coeff(k));
+
+  m_q.setIdentity(cols);
+  for (Index k = 0; k < size; ++k) m_q.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k));
+
+  m_det_pq = (number_of_transpositions % 2) ? -1 : 1;
+
+  m_isInitialized = true;
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename internal::traits<MatrixType>::Scalar FullPivLU<MatrixType, PermutationIndex>::determinant() const {
+  eigen_assert(m_isInitialized && "LU is not initialized.");
+  eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the determinant of a non-square matrix!");
+  return Scalar(m_det_pq) * Scalar(m_lu.diagonal().prod());
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: \f$ P^{-1} L U Q^{-1} \f$.
+ * This function is provided for debug purposes. */
+template <typename MatrixType, typename PermutationIndex>
+MatrixType FullPivLU<MatrixType, PermutationIndex>::reconstructedMatrix() const {
+  eigen_assert(m_isInitialized && "LU is not initialized.");
+  const Index smalldim = (std::min)(m_lu.rows(), m_lu.cols());
+  // LU
+  MatrixType res(m_lu.rows(), m_lu.cols());
+  // FIXME: the .toDenseMatrix() calls should not be needed.
+  res = m_lu.leftCols(smalldim).template triangularView<UnitLower>().toDenseMatrix() *
+        m_lu.topRows(smalldim).template triangularView<Upper>().toDenseMatrix();
+
+  // P^{-1}(LU)
+  res = m_p.inverse() * res;
+
+  // (P^{-1}LU)Q^{-1}
+  res = res * m_q.inverse();
+
+  return res;
+}
+
+/********* Implementation of kernel() **************************************************/
+
+namespace internal {
+template <typename MatrixType_, typename PermutationIndex_>
+struct kernel_retval<FullPivLU<MatrixType_, PermutationIndex_> >
+    : kernel_retval_base<FullPivLU<MatrixType_, PermutationIndex_> > {
+  using DecompositionType = FullPivLU<MatrixType_, PermutationIndex_>;
+  EIGEN_MAKE_KERNEL_HELPERS(DecompositionType)
+
+  enum {
+    MaxSmallDimAtCompileTime = min_size_prefer_fixed(MatrixType::MaxColsAtCompileTime, MatrixType::MaxRowsAtCompileTime)
+  };
+
+  template <typename Dest>
+  void evalTo(Dest& dst) const {
+    using std::abs;
+    const Index cols = dec().matrixLU().cols(), dimker = cols - rank();
+    if (dimker == 0) {
+      // The Kernel is just {0}, so it doesn't have a basis properly speaking, but let's
+      // avoid crashing/asserting as that depends on floating point calculations. Let's
+      // just return a single column vector filled with zeros.
+      dst.setZero();
+      return;
+    }
+
+    /* Let us use the following lemma:
+     *
+     * Lemma: If the matrix A has the LU decomposition PAQ = LU,
+     * then Ker A = Q(Ker U).
+     *
+     * Proof: trivial: just keep in mind that P, Q, L are invertible.
+     */
+
+    /* Thus, all we need to do is to compute Ker U, and then apply Q.
+     *
+     * U is upper triangular, with eigenvalues sorted so that any zeros appear at the end.
+     * Thus, the diagonal of U ends with exactly
+     * dimKer zero's. Let us use that to construct dimKer linearly
+     * independent vectors in Ker U.
+     */
+
+    Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
+    RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
+    Index p = 0;
+    for (Index i = 0; i < dec().nonzeroPivots(); ++i)
+      if (abs(dec().matrixLU().coeff(i, i)) > premultiplied_threshold) pivots.coeffRef(p++) = i;
+    eigen_internal_assert(p == rank());
+
+    // Construct a temporary trapezoid matrix m by taking the U matrix and permuting
+    // the rows and cols to bring the nonnegligible pivots to the top of the main diagonal.
+    // This is needed to apply our triangular solvers.
+    // FIXME: simplify once triangularView supports rectangular matrices.
+    Matrix<typename MatrixType::Scalar, Dynamic, Dynamic, traits<MatrixType>::Options, MaxSmallDimAtCompileTime,
+           MatrixType::MaxColsAtCompileTime>
+        m(dec().matrixLU().block(0, 0, rank(), cols));
+    for (Index i = 0; i < rank(); ++i) {
+      if (i) m.row(i).head(i).setZero();
+      m.row(i).tail(cols - i) = dec().matrixLU().row(pivots.coeff(i)).tail(cols - i);
+    }
+    m.block(0, 0, rank(), rank());
+    m.block(0, 0, rank(), rank()).template triangularView<StrictlyLower>().setZero();
+    for (Index i = 0; i < rank(); ++i) m.col(i).swap(m.col(pivots.coeff(i)));
+
+    // ok, we have our trapezoid matrix, we can apply the triangular solver.
+    // notice that the math behind this suggests that we should apply this to the
+    // negative of the RHS, but for performance we just put the negative sign elsewhere, see below.
+    m.topLeftCorner(rank(), rank()).template triangularView<Upper>().solveInPlace(m.topRightCorner(rank(), dimker));
+
+    // now we must undo the column permutation that we had applied!
+    for (Index i = rank() - 1; i >= 0; --i) m.col(i).swap(m.col(pivots.coeff(i)));
+
+    // see the negative sign in the next line, that's what we were talking about above.
+    for (Index i = 0; i < rank(); ++i) dst.row(dec().permutationQ().indices().coeff(i)) = -m.row(i).tail(dimker);
+    for (Index i = rank(); i < cols; ++i) dst.row(dec().permutationQ().indices().coeff(i)).setZero();
+    for (Index k = 0; k < dimker; ++k) dst.coeffRef(dec().permutationQ().indices().coeff(rank() + k), k) = Scalar(1);
+  }
+};
+
+/***** Implementation of image() *****************************************************/
+
+template <typename MatrixType_, typename PermutationIndex_>
+struct image_retval<FullPivLU<MatrixType_, PermutationIndex_> >
+    : image_retval_base<FullPivLU<MatrixType_, PermutationIndex_> > {
+  using DecompositionType = FullPivLU<MatrixType_, PermutationIndex_>;
+  EIGEN_MAKE_IMAGE_HELPERS(DecompositionType)
+
+  enum {
+    MaxSmallDimAtCompileTime = min_size_prefer_fixed(MatrixType::MaxColsAtCompileTime, MatrixType::MaxRowsAtCompileTime)
+  };
+
+  template <typename Dest>
+  void evalTo(Dest& dst) const {
+    using std::abs;
+    if (rank() == 0) {
+      // The Image is just {0}, so it doesn't have a basis properly speaking, but let's
+      // avoid crashing/asserting as that depends on floating point calculations. Let's
+      // just return a single column vector filled with zeros.
+      dst.setZero();
+      return;
+    }
+
+    Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
+    RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
+    Index p = 0;
+    for (Index i = 0; i < dec().nonzeroPivots(); ++i)
+      if (abs(dec().matrixLU().coeff(i, i)) > premultiplied_threshold) pivots.coeffRef(p++) = i;
+    eigen_internal_assert(p == rank());
+
+    for (Index i = 0; i < rank(); ++i)
+      dst.col(i) = originalMatrix().col(dec().permutationQ().indices().coeff(pivots.coeff(i)));
+  }
+};
+
+/***** Implementation of solve() *****************************************************/
+
+}  // end namespace internal
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType_, typename PermutationIndex_>
+template <typename RhsType, typename DstType>
+void FullPivLU<MatrixType_, PermutationIndex_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
+  /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1}.
+   * So we proceed as follows:
+   * Step 1: compute c = P * rhs.
+   * Step 2: replace c by the solution x to Lx = c. Exists because L is invertible.
+   * Step 3: replace c by the solution x to Ux = c. May or may not exist.
+   * Step 4: result = Q * c;
+   */
+
+  const Index rows = this->rows(), cols = this->cols(), nonzero_pivots = this->rank();
+  const Index smalldim = (std::min)(rows, cols);
+
+  if (nonzero_pivots == 0) {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(rhs.rows(), rhs.cols());
+
+  // Step 1
+  c = permutationP() * rhs;
+
+  // Step 2
+  m_lu.topLeftCorner(smalldim, smalldim).template triangularView<UnitLower>().solveInPlace(c.topRows(smalldim));
+  if (rows > cols) c.bottomRows(rows - cols).noalias() -= m_lu.bottomRows(rows - cols) * c.topRows(cols);
+
+  // Step 3
+  m_lu.topLeftCorner(nonzero_pivots, nonzero_pivots)
+      .template triangularView<Upper>()
+      .solveInPlace(c.topRows(nonzero_pivots));
+
+  // Step 4
+  for (Index i = 0; i < nonzero_pivots; ++i) dst.row(permutationQ().indices().coeff(i)) = c.row(i);
+  for (Index i = nonzero_pivots; i < m_lu.cols(); ++i) dst.row(permutationQ().indices().coeff(i)).setZero();
+}
+
+template <typename MatrixType_, typename PermutationIndex_>
+template <bool Conjugate, typename RhsType, typename DstType>
+void FullPivLU<MatrixType_, PermutationIndex_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
+  /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1},
+   * and since permutations are real and unitary, we can write this
+   * as   A^T = Q U^T L^T P,
+   * So we proceed as follows:
+   * Step 1: compute c = Q^T rhs.
+   * Step 2: replace c by the solution x to U^T x = c. May or may not exist.
+   * Step 3: replace c by the solution x to L^T x = c.
+   * Step 4: result = P^T c.
+   * If Conjugate is true, replace "^T" by "^*" above.
+   */
+
+  const Index rows = this->rows(), cols = this->cols(), nonzero_pivots = this->rank();
+  const Index smalldim = (std::min)(rows, cols);
+
+  if (nonzero_pivots == 0) {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(rhs.rows(), rhs.cols());
+
+  // Step 1
+  c = permutationQ().inverse() * rhs;
+
+  // Step 2
+  m_lu.topLeftCorner(nonzero_pivots, nonzero_pivots)
+      .template triangularView<Upper>()
+      .transpose()
+      .template conjugateIf<Conjugate>()
+      .solveInPlace(c.topRows(nonzero_pivots));
+
+  // Step 3
+  m_lu.topLeftCorner(smalldim, smalldim)
+      .template triangularView<UnitLower>()
+      .transpose()
+      .template conjugateIf<Conjugate>()
+      .solveInPlace(c.topRows(smalldim));
+
+  // Step 4
+  PermutationPType invp = permutationP().inverse().eval();
+  for (Index i = 0; i < smalldim; ++i) dst.row(invp.indices().coeff(i)) = c.row(i);
+  for (Index i = smalldim; i < rows; ++i) dst.row(invp.indices().coeff(i)).setZero();
+}
+
+#endif
+
+namespace internal {
+
+/***** Implementation of inverse() *****************************************************/
+template <typename DstXprType, typename MatrixType, typename PermutationIndex>
+struct Assignment<
+    DstXprType, Inverse<FullPivLU<MatrixType, PermutationIndex> >,
+    internal::assign_op<typename DstXprType::Scalar, typename FullPivLU<MatrixType, PermutationIndex>::Scalar>,
+    Dense2Dense> {
+  typedef FullPivLU<MatrixType, PermutationIndex> LuType;
+  typedef Inverse<LuType> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::assign_op<typename DstXprType::Scalar, typename MatrixType::Scalar>&) {
+    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
+  }
+};
+}  // end namespace internal
+
+/******* MatrixBase methods *****************************************************************/
+
+/** \lu_module
+ *
+ * \return the full-pivoting LU decomposition of \c *this.
+ *
+ * \sa class FullPivLU
+ */
+template <typename Derived>
+template <typename PermutationIndex>
+inline FullPivLU<typename MatrixBase<Derived>::PlainObject, PermutationIndex> MatrixBase<Derived>::fullPivLu() const {
+  return FullPivLU<PlainObject, PermutationIndex>(eval());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LU_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/LU/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/InternalHeaderCheck.h
new file mode 100644
index 0000000..f346b17
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_LU_MODULE_H
+#error "Please include Eigen/LU instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/LU/InverseImpl.h b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/InverseImpl.h
new file mode 100644
index 0000000..5c24dfe
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/InverseImpl.h
@@ -0,0 +1,353 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INVERSE_IMPL_H
+#define EIGEN_INVERSE_IMPL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**********************************
+*** General case implementation ***
+**********************************/
+
+template <typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime>
+struct compute_inverse {
+  EIGEN_DEVICE_FUNC static inline void run(const MatrixType& matrix, ResultType& result) {
+    result = matrix.partialPivLu().inverse();
+  }
+};
+
+template <typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime>
+struct compute_inverse_and_det_with_check { /* nothing! general case not supported. */
+};
+
+/****************************
+*** Size 1 implementation ***
+****************************/
+
+template <typename MatrixType, typename ResultType>
+struct compute_inverse<MatrixType, ResultType, 1> {
+  EIGEN_DEVICE_FUNC static inline void run(const MatrixType& matrix, ResultType& result) {
+    typedef typename MatrixType::Scalar Scalar;
+    internal::evaluator<MatrixType> matrixEval(matrix);
+    result.coeffRef(0, 0) = Scalar(1) / matrixEval.coeff(0, 0);
+  }
+};
+
+template <typename MatrixType, typename ResultType>
+struct compute_inverse_and_det_with_check<MatrixType, ResultType, 1> {
+  EIGEN_DEVICE_FUNC static inline void run(const MatrixType& matrix,
+                                           const typename MatrixType::RealScalar& absDeterminantThreshold,
+                                           ResultType& result, typename ResultType::Scalar& determinant,
+                                           bool& invertible) {
+    using std::abs;
+    determinant = matrix.coeff(0, 0);
+    invertible = abs(determinant) > absDeterminantThreshold;
+    if (invertible) result.coeffRef(0, 0) = typename ResultType::Scalar(1) / determinant;
+  }
+};
+
+/****************************
+*** Size 2 implementation ***
+****************************/
+
+template <typename MatrixType, typename ResultType>
+EIGEN_DEVICE_FUNC inline void compute_inverse_size2_helper(const MatrixType& matrix,
+                                                           const typename ResultType::Scalar& invdet,
+                                                           ResultType& result) {
+  typename ResultType::Scalar temp = matrix.coeff(0, 0);
+  result.coeffRef(0, 0) = matrix.coeff(1, 1) * invdet;
+  result.coeffRef(1, 0) = -matrix.coeff(1, 0) * invdet;
+  result.coeffRef(0, 1) = -matrix.coeff(0, 1) * invdet;
+  result.coeffRef(1, 1) = temp * invdet;
+}
+
+template <typename MatrixType, typename ResultType>
+struct compute_inverse<MatrixType, ResultType, 2> {
+  EIGEN_DEVICE_FUNC static inline void run(const MatrixType& matrix, ResultType& result) {
+    typedef typename ResultType::Scalar Scalar;
+    const Scalar invdet = typename MatrixType::Scalar(1) / matrix.determinant();
+    compute_inverse_size2_helper(matrix, invdet, result);
+  }
+};
+
+template <typename MatrixType, typename ResultType>
+struct compute_inverse_and_det_with_check<MatrixType, ResultType, 2> {
+  EIGEN_DEVICE_FUNC static inline void run(const MatrixType& matrix,
+                                           const typename MatrixType::RealScalar& absDeterminantThreshold,
+                                           ResultType& inverse, typename ResultType::Scalar& determinant,
+                                           bool& invertible) {
+    using std::abs;
+    typedef typename ResultType::Scalar Scalar;
+    determinant = matrix.determinant();
+    invertible = abs(determinant) > absDeterminantThreshold;
+    if (!invertible) return;
+    const Scalar invdet = Scalar(1) / determinant;
+    compute_inverse_size2_helper(matrix, invdet, inverse);
+  }
+};
+
+/****************************
+*** Size 3 implementation ***
+****************************/
+
+template <typename MatrixType, int i, int j>
+EIGEN_DEVICE_FUNC inline typename MatrixType::Scalar cofactor_3x3(const MatrixType& m) {
+  enum { i1 = (i + 1) % 3, i2 = (i + 2) % 3, j1 = (j + 1) % 3, j2 = (j + 2) % 3 };
+  return m.coeff(i1, j1) * m.coeff(i2, j2) - m.coeff(i1, j2) * m.coeff(i2, j1);
+}
+
+template <typename MatrixType, typename ResultType>
+EIGEN_DEVICE_FUNC inline void compute_inverse_size3_helper(
+    const MatrixType& matrix, const typename ResultType::Scalar& invdet,
+    const Matrix<typename ResultType::Scalar, 3, 1>& cofactors_col0, ResultType& result) {
+  // Compute cofactors in a way that avoids aliasing issues.
+  typedef typename ResultType::Scalar Scalar;
+  const Scalar c01 = cofactor_3x3<MatrixType, 0, 1>(matrix) * invdet;
+  const Scalar c11 = cofactor_3x3<MatrixType, 1, 1>(matrix) * invdet;
+  const Scalar c02 = cofactor_3x3<MatrixType, 0, 2>(matrix) * invdet;
+  result.coeffRef(1, 2) = cofactor_3x3<MatrixType, 2, 1>(matrix) * invdet;
+  result.coeffRef(2, 1) = cofactor_3x3<MatrixType, 1, 2>(matrix) * invdet;
+  result.coeffRef(2, 2) = cofactor_3x3<MatrixType, 2, 2>(matrix) * invdet;
+  result.coeffRef(1, 0) = c01;
+  result.coeffRef(1, 1) = c11;
+  result.coeffRef(2, 0) = c02;
+  result.row(0) = cofactors_col0 * invdet;
+}
+
+template <typename MatrixType, typename ResultType>
+struct compute_inverse<MatrixType, ResultType, 3> {
+  EIGEN_DEVICE_FUNC static inline void run(const MatrixType& matrix, ResultType& result) {
+    typedef typename ResultType::Scalar Scalar;
+    Matrix<typename MatrixType::Scalar, 3, 1> cofactors_col0;
+    cofactors_col0.coeffRef(0) = cofactor_3x3<MatrixType, 0, 0>(matrix);
+    cofactors_col0.coeffRef(1) = cofactor_3x3<MatrixType, 1, 0>(matrix);
+    cofactors_col0.coeffRef(2) = cofactor_3x3<MatrixType, 2, 0>(matrix);
+    const Scalar det = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
+    const Scalar invdet = Scalar(1) / det;
+    compute_inverse_size3_helper(matrix, invdet, cofactors_col0, result);
+  }
+};
+
+template <typename MatrixType, typename ResultType>
+struct compute_inverse_and_det_with_check<MatrixType, ResultType, 3> {
+  EIGEN_DEVICE_FUNC static inline void run(const MatrixType& matrix,
+                                           const typename MatrixType::RealScalar& absDeterminantThreshold,
+                                           ResultType& inverse, typename ResultType::Scalar& determinant,
+                                           bool& invertible) {
+    typedef typename ResultType::Scalar Scalar;
+    Matrix<Scalar, 3, 1> cofactors_col0;
+    cofactors_col0.coeffRef(0) = cofactor_3x3<MatrixType, 0, 0>(matrix);
+    cofactors_col0.coeffRef(1) = cofactor_3x3<MatrixType, 1, 0>(matrix);
+    cofactors_col0.coeffRef(2) = cofactor_3x3<MatrixType, 2, 0>(matrix);
+    determinant = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
+    invertible = Eigen::numext::abs(determinant) > absDeterminantThreshold;
+    if (!invertible) return;
+    const Scalar invdet = Scalar(1) / determinant;
+    compute_inverse_size3_helper(matrix, invdet, cofactors_col0, inverse);
+  }
+};
+
+/****************************
+*** Size 4 implementation ***
+****************************/
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline const typename Derived::Scalar general_det3_helper(const MatrixBase<Derived>& matrix, int i1,
+                                                                            int i2, int i3, int j1, int j2, int j3) {
+  return matrix.coeff(i1, j1) *
+         (matrix.coeff(i2, j2) * matrix.coeff(i3, j3) - matrix.coeff(i2, j3) * matrix.coeff(i3, j2));
+}
+
+template <typename MatrixType, int i, int j>
+EIGEN_DEVICE_FUNC inline typename MatrixType::Scalar cofactor_4x4(const MatrixType& matrix) {
+  enum { i1 = (i + 1) % 4, i2 = (i + 2) % 4, i3 = (i + 3) % 4, j1 = (j + 1) % 4, j2 = (j + 2) % 4, j3 = (j + 3) % 4 };
+  return general_det3_helper(matrix, i1, i2, i3, j1, j2, j3) + general_det3_helper(matrix, i2, i3, i1, j1, j2, j3) +
+         general_det3_helper(matrix, i3, i1, i2, j1, j2, j3);
+}
+
+template <int Arch, typename Scalar, typename MatrixType, typename ResultType>
+struct compute_inverse_size4 {
+  EIGEN_DEVICE_FUNC static void run(const MatrixType& matrix, ResultType& result) {
+    result.coeffRef(0, 0) = cofactor_4x4<MatrixType, 0, 0>(matrix);
+    result.coeffRef(1, 0) = -cofactor_4x4<MatrixType, 0, 1>(matrix);
+    result.coeffRef(2, 0) = cofactor_4x4<MatrixType, 0, 2>(matrix);
+    result.coeffRef(3, 0) = -cofactor_4x4<MatrixType, 0, 3>(matrix);
+    result.coeffRef(0, 2) = cofactor_4x4<MatrixType, 2, 0>(matrix);
+    result.coeffRef(1, 2) = -cofactor_4x4<MatrixType, 2, 1>(matrix);
+    result.coeffRef(2, 2) = cofactor_4x4<MatrixType, 2, 2>(matrix);
+    result.coeffRef(3, 2) = -cofactor_4x4<MatrixType, 2, 3>(matrix);
+    result.coeffRef(0, 1) = -cofactor_4x4<MatrixType, 1, 0>(matrix);
+    result.coeffRef(1, 1) = cofactor_4x4<MatrixType, 1, 1>(matrix);
+    result.coeffRef(2, 1) = -cofactor_4x4<MatrixType, 1, 2>(matrix);
+    result.coeffRef(3, 1) = cofactor_4x4<MatrixType, 1, 3>(matrix);
+    result.coeffRef(0, 3) = -cofactor_4x4<MatrixType, 3, 0>(matrix);
+    result.coeffRef(1, 3) = cofactor_4x4<MatrixType, 3, 1>(matrix);
+    result.coeffRef(2, 3) = -cofactor_4x4<MatrixType, 3, 2>(matrix);
+    result.coeffRef(3, 3) = cofactor_4x4<MatrixType, 3, 3>(matrix);
+    result /= (matrix.col(0).cwiseProduct(result.row(0).transpose())).sum();
+  }
+};
+
+template <typename MatrixType, typename ResultType>
+struct compute_inverse<MatrixType, ResultType, 4>
+    : compute_inverse_size4<Architecture::Target, typename MatrixType::Scalar, MatrixType, ResultType> {};
+
+template <typename MatrixType, typename ResultType>
+struct compute_inverse_and_det_with_check<MatrixType, ResultType, 4> {
+  EIGEN_DEVICE_FUNC static inline void run(const MatrixType& matrix,
+                                           const typename MatrixType::RealScalar& absDeterminantThreshold,
+                                           ResultType& inverse, typename ResultType::Scalar& determinant,
+                                           bool& invertible) {
+    using std::abs;
+    determinant = matrix.determinant();
+    invertible = abs(determinant) > absDeterminantThreshold;
+    if (invertible && extract_data(matrix) != extract_data(inverse)) {
+      compute_inverse<MatrixType, ResultType>::run(matrix, inverse);
+    } else if (invertible) {
+      MatrixType matrix_t = matrix;
+      compute_inverse<MatrixType, ResultType>::run(matrix_t, inverse);
+    }
+  }
+};
+
+/*************************
+*** MatrixBase methods ***
+*************************/
+
+}  // end namespace internal
+
+namespace internal {
+
+// Specialization for "dense = dense_xpr.inverse()"
+template <typename DstXprType, typename XprType>
+struct Assignment<DstXprType, Inverse<XprType>,
+                  internal::assign_op<typename DstXprType::Scalar, typename XprType::Scalar>, Dense2Dense> {
+  typedef Inverse<XprType> SrcXprType;
+  EIGEN_DEVICE_FUNC static void run(DstXprType& dst, const SrcXprType& src,
+                                    const internal::assign_op<typename DstXprType::Scalar, typename XprType::Scalar>&) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    const int Size = plain_enum_min(XprType::ColsAtCompileTime, DstXprType::ColsAtCompileTime);
+    EIGEN_ONLY_USED_FOR_DEBUG(Size);
+    eigen_assert(((Size <= 1) || (Size > 4) || (extract_data(src.nestedExpression()) != extract_data(dst))) &&
+                 "Aliasing problem detected in inverse(), you need to do inverse().eval() here.");
+
+    typedef typename internal::nested_eval<XprType, XprType::ColsAtCompileTime>::type ActualXprType;
+    typedef internal::remove_all_t<ActualXprType> ActualXprTypeCleanded;
+
+    ActualXprType actual_xpr(src.nestedExpression());
+
+    compute_inverse<ActualXprTypeCleanded, DstXprType>::run(actual_xpr, dst);
+  }
+};
+
+}  // end namespace internal
+
+/** \lu_module
+ *
+ * \returns the matrix inverse of this matrix.
+ *
+ * For small fixed sizes up to 4x4, this method uses cofactors.
+ * In the general case, this method uses class PartialPivLU.
+ *
+ * \note This matrix must be invertible, otherwise the result is undefined. If you need an
+ * invertibility check, do the following:
+ * \li for fixed sizes up to 4x4, use computeInverseAndDetWithCheck().
+ * \li for the general case, use class PartialPivLU.
+ *
+ * Example: \include MatrixBase_inverse.cpp
+ * Output: \verbinclude MatrixBase_inverse.out
+ *
+ * \sa computeInverseAndDetWithCheck()
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline Inverse<Derived> MatrixBase<Derived>::inverse() const {
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsInteger, THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
+  eigen_assert(rows() == cols());
+  return Inverse<Derived>(derived());
+}
+
+/** \lu_module
+ *
+ * Computation of matrix inverse and determinant, with invertibility check.
+ *
+ * This is only for fixed-size square matrices of size up to 4x4.
+ *
+ * Notice that it will trigger a copy of input matrix when trying to do the inverse in place.
+ *
+ * \param inverse Reference to the matrix in which to store the inverse.
+ * \param determinant Reference to the variable in which to store the determinant.
+ * \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
+ * \param absDeterminantThreshold Optional parameter controlling the invertibility check.
+ *                                The matrix will be declared invertible if the absolute value of its
+ *                                determinant is greater than this threshold.
+ *
+ * Example: \include MatrixBase_computeInverseAndDetWithCheck.cpp
+ * Output: \verbinclude MatrixBase_computeInverseAndDetWithCheck.out
+ *
+ * \sa inverse(), computeInverseWithCheck()
+ */
+template <typename Derived>
+template <typename ResultType>
+inline void MatrixBase<Derived>::computeInverseAndDetWithCheck(ResultType& inverse,
+                                                               typename ResultType::Scalar& determinant,
+                                                               bool& invertible,
+                                                               const RealScalar& absDeterminantThreshold) const {
+  // i'd love to put some static assertions there, but SFINAE means that they have no effect...
+  eigen_assert(rows() == cols());
+  // for 2x2, it's worth giving a chance to avoid evaluating.
+  // for larger sizes, evaluating has negligible cost and limits code size.
+  typedef std::conditional_t<RowsAtCompileTime == 2,
+                             internal::remove_all_t<typename internal::nested_eval<Derived, 2>::type>, PlainObject>
+      MatrixType;
+  internal::compute_inverse_and_det_with_check<MatrixType, ResultType>::run(derived(), absDeterminantThreshold, inverse,
+                                                                            determinant, invertible);
+}
+
+/** \lu_module
+ *
+ * Computation of matrix inverse, with invertibility check.
+ *
+ * This is only for fixed-size square matrices of size up to 4x4.
+ *
+ * Notice that it will trigger a copy of input matrix when trying to do the inverse in place.
+ *
+ * \param inverse Reference to the matrix in which to store the inverse.
+ * \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
+ * \param absDeterminantThreshold Optional parameter controlling the invertibility check.
+ *                                The matrix will be declared invertible if the absolute value of its
+ *                                determinant is greater than this threshold.
+ *
+ * Example: \include MatrixBase_computeInverseWithCheck.cpp
+ * Output: \verbinclude MatrixBase_computeInverseWithCheck.out
+ *
+ * \sa inverse(), computeInverseAndDetWithCheck()
+ */
+template <typename Derived>
+template <typename ResultType>
+inline void MatrixBase<Derived>::computeInverseWithCheck(ResultType& inverse, bool& invertible,
+                                                         const RealScalar& absDeterminantThreshold) const {
+  Scalar determinant;
+  // i'd love to put some static assertions there, but SFINAE means that they have no effect...
+  eigen_assert(rows() == cols());
+  computeInverseAndDetWithCheck(inverse, determinant, invertible, absDeterminantThreshold);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_INVERSE_IMPL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/LU/PartialPivLU.h b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/PartialPivLU.h
new file mode 100644
index 0000000..4f4227e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/PartialPivLU.h
@@ -0,0 +1,583 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARTIALLU_H
+#define EIGEN_PARTIALLU_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType_, typename PermutationIndex_>
+struct traits<PartialPivLU<MatrixType_, PermutationIndex_> > : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef PermutationIndex_ StorageIndex;
+  typedef traits<MatrixType_> BaseTraits;
+  enum { Flags = BaseTraits::Flags & RowMajorBit, CoeffReadCost = Dynamic };
+};
+
+template <typename T, typename Derived>
+struct enable_if_ref;
+
+template <typename T, typename Derived>
+struct enable_if_ref<Ref<T>, Derived> {
+  typedef Derived type;
+};
+
+}  // end namespace internal
+
+/** \ingroup LU_Module
+ *
+ * \class PartialPivLU
+ *
+ * \brief LU decomposition of a matrix with partial pivoting, and related features
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the LU decomposition
+ *
+ * This class represents a LU decomposition of a \b square \b invertible matrix, with partial pivoting: the matrix A
+ * is decomposed as A = PLU where L is unit-lower-triangular, U is upper-triangular, and P
+ * is a permutation matrix.
+ *
+ * Typically, partial pivoting LU decomposition is only considered numerically stable for square invertible
+ * matrices. Thus LAPACK's dgesv and dgesvx require the matrix to be square and invertible. The present class
+ * does the same. It will assert that the matrix is square, but it won't (actually it can't) check that the
+ * matrix is invertible: it is your task to check that you only use this decomposition on invertible matrices.
+ *
+ * The guaranteed safe alternative, working for all matrices, is the full pivoting LU decomposition, provided
+ * by class FullPivLU.
+ *
+ * This is \b not a rank-revealing LU decomposition. Many features are intentionally absent from this class,
+ * such as rank computation. If you need these features, use class FullPivLU.
+ *
+ * This LU decomposition is suitable to invert invertible matrices. It is what MatrixBase::inverse() uses
+ * in the general case.
+ * On the other hand, it is \b not suitable to determine whether a given matrix is invertible.
+ *
+ * The data of the LU decomposition can be directly accessed through the methods matrixLU(), permutationP().
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * \sa MatrixBase::partialPivLu(), MatrixBase::determinant(), MatrixBase::inverse(), MatrixBase::computeInverse(), class
+ * FullPivLU
+ */
+template <typename MatrixType_, typename PermutationIndex_>
+class PartialPivLU : public SolverBase<PartialPivLU<MatrixType_, PermutationIndex_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<PartialPivLU> Base;
+  friend class SolverBase<PartialPivLU>;
+
+  EIGEN_GENERIC_PUBLIC_INTERFACE(PartialPivLU)
+  enum {
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+  using PermutationIndex = PermutationIndex_;
+  typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime, PermutationIndex> PermutationType;
+  typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime, PermutationIndex> TranspositionType;
+  typedef typename MatrixType::PlainObject PlainObject;
+
+  /** \brief Reports whether the LU factorization was successful.
+   *
+   * \note This function always returns \c Success. It is provided for compatibility
+   * with other factorization routines.
+   * \returns \c Success
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+    return Success;
+  }
+
+  /**
+   * \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via PartialPivLU::compute(const MatrixType&).
+   */
+  PartialPivLU();
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa PartialPivLU()
+   */
+  explicit PartialPivLU(Index size);
+
+  /** Constructor.
+   *
+   * \param matrix the matrix of which to compute the LU decomposition.
+   *
+   * \warning The matrix should have full rank (e.g. if it's square, it should be invertible).
+   * If you need to deal with non-full rank, use class FullPivLU instead.
+   */
+  template <typename InputType>
+  explicit PartialPivLU(const EigenBase<InputType>& matrix);
+
+  /** Constructor for \link InplaceDecomposition inplace decomposition \endlink
+   *
+   * \param matrix the matrix of which to compute the LU decomposition.
+   *
+   * \warning The matrix should have full rank (e.g. if it's square, it should be invertible).
+   * If you need to deal with non-full rank, use class FullPivLU instead.
+   */
+  template <typename InputType>
+  explicit PartialPivLU(EigenBase<InputType>& matrix);
+
+  template <typename InputType>
+  PartialPivLU& compute(const EigenBase<InputType>& matrix) {
+    m_lu = matrix.derived();
+    compute();
+    return *this;
+  }
+
+  /** \returns the LU decomposition matrix: the upper-triangular part is U, the
+   * unit-lower-triangular part is L (at least for square matrices; in the non-square
+   * case, special care is needed, see the documentation of class FullPivLU).
+   *
+   * \sa matrixL(), matrixU()
+   */
+  inline const MatrixType& matrixLU() const {
+    eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+    return m_lu;
+  }
+
+  /** \returns the permutation matrix P.
+   */
+  inline const PermutationType& permutationP() const {
+    eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+    return m_p;
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** This method returns the solution x to the equation Ax=b, where A is the matrix of which
+   * *this is the LU decomposition.
+   *
+   * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix,
+   *          the only requirement in order for the equation to make sense is that
+   *          b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition.
+   *
+   * \returns the solution.
+   *
+   * Example: \include PartialPivLU_solve.cpp
+   * Output: \verbinclude PartialPivLU_solve.out
+   *
+   * Since this PartialPivLU class assumes anyway that the matrix A is invertible, the solution
+   * theoretically exists and is unique regardless of b.
+   *
+   * \sa TriangularView::solve(), inverse(), computeInverse()
+   */
+  template <typename Rhs>
+  inline Solve<PartialPivLU, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  /** \returns an estimate of the reciprocal condition number of the matrix of which \c *this is
+      the LU decomposition.
+    */
+  inline RealScalar rcond() const {
+    eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+    return internal::rcond_estimate_helper(m_l1_norm, *this);
+  }
+
+  /** \returns the inverse of the matrix of which *this is the LU decomposition.
+   *
+   * \warning The matrix being decomposed here is assumed to be invertible. If you need to check for
+   *          invertibility, use class FullPivLU instead.
+   *
+   * \sa MatrixBase::inverse(), LU::inverse()
+   */
+  inline Inverse<PartialPivLU> inverse() const {
+    eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+    return Inverse<PartialPivLU>(*this);
+  }
+
+  /** \returns the determinant of the matrix of which
+   * *this is the LU decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the LU decomposition has already been computed.
+   *
+   * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers
+   *       optimized paths.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   *
+   * \sa MatrixBase::determinant()
+   */
+  Scalar determinant() const;
+
+  MatrixType reconstructedMatrix() const;
+
+  constexpr Index rows() const noexcept { return m_lu.rows(); }
+  constexpr Index cols() const noexcept { return m_lu.cols(); }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  EIGEN_DEVICE_FUNC void _solve_impl(const RhsType& rhs, DstType& dst) const {
+    /* The decomposition PA = LU can be rewritten as A = P^{-1} L U.
+     * So we proceed as follows:
+     * Step 1: compute c = Pb.
+     * Step 2: replace c by the solution x to Lx = c.
+     * Step 3: replace c by the solution x to Ux = c.
+     */
+
+    // Step 1
+    dst = permutationP() * rhs;
+
+    // Step 2
+    m_lu.template triangularView<UnitLower>().solveInPlace(dst);
+
+    // Step 3
+    m_lu.template triangularView<Upper>().solveInPlace(dst);
+  }
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  EIGEN_DEVICE_FUNC void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
+    /* The decomposition PA = LU can be rewritten as A^T = U^T L^T P.
+     * So we proceed as follows:
+     * Step 1: compute c as the solution to L^T c = b
+     * Step 2: replace c by the solution x to U^T x = c.
+     * Step 3: update  c = P^-1 c.
+     */
+
+    eigen_assert(rhs.rows() == m_lu.cols());
+
+    // Step 1
+    dst = m_lu.template triangularView<Upper>().transpose().template conjugateIf<Conjugate>().solve(rhs);
+    // Step 2
+    m_lu.template triangularView<UnitLower>().transpose().template conjugateIf<Conjugate>().solveInPlace(dst);
+    // Step 3
+    dst = permutationP().transpose() * dst;
+  }
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  void compute();
+
+  MatrixType m_lu;
+  PermutationType m_p;
+  TranspositionType m_rowsTranspositions;
+  RealScalar m_l1_norm;
+  signed char m_det_p;
+  bool m_isInitialized;
+};
+
+template <typename MatrixType, typename PermutationIndex>
+PartialPivLU<MatrixType, PermutationIndex>::PartialPivLU()
+    : m_lu(), m_p(), m_rowsTranspositions(), m_l1_norm(0), m_det_p(0), m_isInitialized(false) {}
+
+template <typename MatrixType, typename PermutationIndex>
+PartialPivLU<MatrixType, PermutationIndex>::PartialPivLU(Index size)
+    : m_lu(size, size), m_p(size), m_rowsTranspositions(size), m_l1_norm(0), m_det_p(0), m_isInitialized(false) {}
+
+template <typename MatrixType, typename PermutationIndex>
+template <typename InputType>
+PartialPivLU<MatrixType, PermutationIndex>::PartialPivLU(const EigenBase<InputType>& matrix)
+    : m_lu(matrix.rows(), matrix.cols()),
+      m_p(matrix.rows()),
+      m_rowsTranspositions(matrix.rows()),
+      m_l1_norm(0),
+      m_det_p(0),
+      m_isInitialized(false) {
+  compute(matrix.derived());
+}
+
+template <typename MatrixType, typename PermutationIndex>
+template <typename InputType>
+PartialPivLU<MatrixType, PermutationIndex>::PartialPivLU(EigenBase<InputType>& matrix)
+    : m_lu(matrix.derived()),
+      m_p(matrix.rows()),
+      m_rowsTranspositions(matrix.rows()),
+      m_l1_norm(0),
+      m_det_p(0),
+      m_isInitialized(false) {
+  compute();
+}
+
+namespace internal {
+
+/** \internal This is the blocked version of fullpivlu_unblocked() */
+template <typename Scalar, int StorageOrder, typename PivIndex, int SizeAtCompileTime = Dynamic>
+struct partial_lu_impl {
+  static constexpr int UnBlockedBound = 16;
+  static constexpr bool UnBlockedAtCompileTime = SizeAtCompileTime != Dynamic && SizeAtCompileTime <= UnBlockedBound;
+  static constexpr int ActualSizeAtCompileTime = UnBlockedAtCompileTime ? SizeAtCompileTime : Dynamic;
+  // Remaining rows and columns at compile-time:
+  static constexpr int RRows = SizeAtCompileTime == 2 ? 1 : Dynamic;
+  static constexpr int RCols = SizeAtCompileTime == 2 ? 1 : Dynamic;
+  typedef Matrix<Scalar, ActualSizeAtCompileTime, ActualSizeAtCompileTime, StorageOrder> MatrixType;
+  typedef Ref<MatrixType> MatrixTypeRef;
+  typedef Ref<Matrix<Scalar, Dynamic, Dynamic, StorageOrder> > BlockType;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  /** \internal performs the LU decomposition in-place of the matrix \a lu
+   * using an unblocked algorithm.
+   *
+   * In addition, this function returns the row transpositions in the
+   * vector \a row_transpositions which must have a size equal to the number
+   * of columns of the matrix \a lu, and an integer \a nb_transpositions
+   * which returns the actual number of transpositions.
+   *
+   * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise.
+   */
+  static Index unblocked_lu(MatrixTypeRef& lu, PivIndex* row_transpositions, PivIndex& nb_transpositions) {
+    typedef scalar_score_coeff_op<Scalar> Scoring;
+    typedef typename Scoring::result_type Score;
+    const Index rows = lu.rows();
+    const Index cols = lu.cols();
+    const Index size = (std::min)(rows, cols);
+    // For small compile-time matrices it is worth processing the last row separately:
+    //  speedup: +100% for 2x2, +10% for others.
+    const Index endk = UnBlockedAtCompileTime ? size - 1 : size;
+    nb_transpositions = 0;
+    Index first_zero_pivot = -1;
+    for (Index k = 0; k < endk; ++k) {
+      int rrows = internal::convert_index<int>(rows - k - 1);
+      int rcols = internal::convert_index<int>(cols - k - 1);
+
+      Index row_of_biggest_in_col;
+      Score biggest_in_corner = lu.col(k).tail(rows - k).unaryExpr(Scoring()).maxCoeff(&row_of_biggest_in_col);
+      row_of_biggest_in_col += k;
+
+      row_transpositions[k] = PivIndex(row_of_biggest_in_col);
+
+      if (!numext::is_exactly_zero(biggest_in_corner)) {
+        if (k != row_of_biggest_in_col) {
+          lu.row(k).swap(lu.row(row_of_biggest_in_col));
+          ++nb_transpositions;
+        }
+
+        lu.col(k).tail(fix<RRows>(rrows)) /= lu.coeff(k, k);
+      } else if (first_zero_pivot == -1) {
+        // the pivot is exactly zero, we record the index of the first pivot which is exactly 0,
+        // and continue the factorization such we still have A = PLU
+        first_zero_pivot = k;
+      }
+
+      if (k < rows - 1)
+        lu.bottomRightCorner(fix<RRows>(rrows), fix<RCols>(rcols)).noalias() -=
+            lu.col(k).tail(fix<RRows>(rrows)) * lu.row(k).tail(fix<RCols>(rcols));
+    }
+
+    // special handling of the last entry
+    if (UnBlockedAtCompileTime) {
+      Index k = endk;
+      row_transpositions[k] = PivIndex(k);
+      if (numext::is_exactly_zero(Scoring()(lu(k, k))) && first_zero_pivot == -1) first_zero_pivot = k;
+    }
+
+    return first_zero_pivot;
+  }
+
+  /** \internal performs the LU decomposition in-place of the matrix represented
+   * by the variables \a rows, \a cols, \a lu_data, and \a lu_stride using a
+   * recursive, blocked algorithm.
+   *
+   * In addition, this function returns the row transpositions in the
+   * vector \a row_transpositions which must have a size equal to the number
+   * of columns of the matrix \a lu, and an integer \a nb_transpositions
+   * which returns the actual number of transpositions.
+   *
+   * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise.
+   *
+   * \note This very low level interface using pointers, etc. is to:
+   *   1 - reduce the number of instantiations to the strict minimum
+   *   2 - avoid infinite recursion of the instantiations with Block<Block<Block<...> > >
+   */
+  static Index blocked_lu(Index rows, Index cols, Scalar* lu_data, Index luStride, PivIndex* row_transpositions,
+                          PivIndex& nb_transpositions, Index maxBlockSize = 256) {
+    MatrixTypeRef lu = MatrixType::Map(lu_data, rows, cols, OuterStride<>(luStride));
+
+    const Index size = (std::min)(rows, cols);
+
+    // if the matrix is too small, no blocking:
+    if (UnBlockedAtCompileTime || size <= UnBlockedBound) {
+      return unblocked_lu(lu, row_transpositions, nb_transpositions);
+    }
+
+    // automatically adjust the number of subdivisions to the size
+    // of the matrix so that there is enough sub blocks:
+    Index blockSize;
+    {
+      blockSize = size / 8;
+      blockSize = (blockSize / 16) * 16;
+      blockSize = (std::min)((std::max)(blockSize, Index(8)), maxBlockSize);
+    }
+
+    nb_transpositions = 0;
+    Index first_zero_pivot = -1;
+    for (Index k = 0; k < size; k += blockSize) {
+      Index bs = (std::min)(size - k, blockSize);  // actual size of the block
+      Index trows = rows - k - bs;                 // trailing rows
+      Index tsize = size - k - bs;                 // trailing size
+
+      // partition the matrix:
+      //                          A00 | A01 | A02
+      // lu  = A_0 | A_1 | A_2 =  A10 | A11 | A12
+      //                          A20 | A21 | A22
+      BlockType A_0 = lu.block(0, 0, rows, k);
+      BlockType A_2 = lu.block(0, k + bs, rows, tsize);
+      BlockType A11 = lu.block(k, k, bs, bs);
+      BlockType A12 = lu.block(k, k + bs, bs, tsize);
+      BlockType A21 = lu.block(k + bs, k, trows, bs);
+      BlockType A22 = lu.block(k + bs, k + bs, trows, tsize);
+
+      PivIndex nb_transpositions_in_panel;
+      // recursively call the blocked LU algorithm on [A11^T A21^T]^T
+      // with a very small blocking size:
+      Index ret = blocked_lu(trows + bs, bs, &lu.coeffRef(k, k), luStride, row_transpositions + k,
+                             nb_transpositions_in_panel, 16);
+      if (ret >= 0 && first_zero_pivot == -1) first_zero_pivot = k + ret;
+
+      nb_transpositions += nb_transpositions_in_panel;
+      // update permutations and apply them to A_0
+      for (Index i = k; i < k + bs; ++i) {
+        Index piv = (row_transpositions[i] += internal::convert_index<PivIndex>(k));
+        A_0.row(i).swap(A_0.row(piv));
+      }
+
+      if (trows) {
+        // apply permutations to A_2
+        for (Index i = k; i < k + bs; ++i) A_2.row(i).swap(A_2.row(row_transpositions[i]));
+
+        // A12 = A11^-1 A12
+        A11.template triangularView<UnitLower>().solveInPlace(A12);
+
+        A22.noalias() -= A21 * A12;
+      }
+    }
+    return first_zero_pivot;
+  }
+};
+
+/** \internal performs the LU decomposition with partial pivoting in-place.
+ */
+template <typename MatrixType, typename TranspositionType>
+void partial_lu_inplace(MatrixType& lu, TranspositionType& row_transpositions,
+                        typename TranspositionType::StorageIndex& nb_transpositions) {
+  // Special-case of zero matrix.
+  if (lu.rows() == 0 || lu.cols() == 0) {
+    nb_transpositions = 0;
+    return;
+  }
+  eigen_assert(lu.cols() == row_transpositions.size());
+  eigen_assert(row_transpositions.size() < 2 ||
+               (&row_transpositions.coeffRef(1) - &row_transpositions.coeffRef(0)) == 1);
+
+  partial_lu_impl<typename MatrixType::Scalar, MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor,
+                  typename TranspositionType::StorageIndex,
+                  internal::min_size_prefer_fixed(MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime)>::
+      blocked_lu(lu.rows(), lu.cols(), &lu.coeffRef(0, 0), lu.outerStride(), &row_transpositions.coeffRef(0),
+                 nb_transpositions);
+}
+
+}  // end namespace internal
+
+template <typename MatrixType, typename PermutationIndex>
+void PartialPivLU<MatrixType, PermutationIndex>::compute() {
+  eigen_assert(m_lu.rows() < NumTraits<PermutationIndex>::highest());
+
+  if (m_lu.cols() > 0)
+    m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
+  else
+    m_l1_norm = RealScalar(0);
+
+  eigen_assert(m_lu.rows() == m_lu.cols() && "PartialPivLU is only for square (and moreover invertible) matrices");
+  const Index size = m_lu.rows();
+
+  m_rowsTranspositions.resize(size);
+
+  typename TranspositionType::StorageIndex nb_transpositions;
+  internal::partial_lu_inplace(m_lu, m_rowsTranspositions, nb_transpositions);
+  m_det_p = (nb_transpositions % 2) ? -1 : 1;
+
+  m_p = m_rowsTranspositions;
+
+  m_isInitialized = true;
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename PartialPivLU<MatrixType, PermutationIndex>::Scalar PartialPivLU<MatrixType, PermutationIndex>::determinant()
+    const {
+  eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+  return Scalar(m_det_p) * m_lu.diagonal().prod();
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: P^{-1} L U.
+ * This function is provided for debug purpose. */
+template <typename MatrixType, typename PermutationIndex>
+MatrixType PartialPivLU<MatrixType, PermutationIndex>::reconstructedMatrix() const {
+  eigen_assert(m_isInitialized && "LU is not initialized.");
+  // LU
+  MatrixType res = m_lu.template triangularView<UnitLower>().toDenseMatrix() * m_lu.template triangularView<Upper>();
+
+  // P^{-1}(LU)
+  res = m_p.inverse() * res;
+
+  return res;
+}
+
+/***** Implementation details *****************************************************/
+
+namespace internal {
+
+/***** Implementation of inverse() *****************************************************/
+template <typename DstXprType, typename MatrixType, typename PermutationIndex>
+struct Assignment<
+    DstXprType, Inverse<PartialPivLU<MatrixType, PermutationIndex> >,
+    internal::assign_op<typename DstXprType::Scalar, typename PartialPivLU<MatrixType, PermutationIndex>::Scalar>,
+    Dense2Dense> {
+  typedef PartialPivLU<MatrixType, PermutationIndex> LuType;
+  typedef Inverse<LuType> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::assign_op<typename DstXprType::Scalar, typename LuType::Scalar>&) {
+    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
+  }
+};
+}  // end namespace internal
+
+/******** MatrixBase methods *******/
+
+/** \lu_module
+ *
+ * \return the partial-pivoting LU decomposition of \c *this.
+ *
+ * \sa class PartialPivLU
+ */
+template <typename Derived>
+template <typename PermutationIndex>
+inline PartialPivLU<typename MatrixBase<Derived>::PlainObject, PermutationIndex> MatrixBase<Derived>::partialPivLu()
+    const {
+  return PartialPivLU<PlainObject, PermutationIndex>(eval());
+}
+
+/** \lu_module
+ *
+ * Synonym of partialPivLu().
+ *
+ * \return the partial-pivoting LU decomposition of \c *this.
+ *
+ * \sa class PartialPivLU
+ */
+template <typename Derived>
+template <typename PermutationIndex>
+inline PartialPivLU<typename MatrixBase<Derived>::PlainObject, PermutationIndex> MatrixBase<Derived>::lu() const {
+  return PartialPivLU<PlainObject, PermutationIndex>(eval());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PARTIALLU_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/LU/PartialPivLU_LAPACKE.h b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/PartialPivLU_LAPACKE.h
new file mode 100644
index 0000000..086c3c3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/PartialPivLU_LAPACKE.h
@@ -0,0 +1,97 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *     LU decomposition with partial pivoting based on LAPACKE_?getrf function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_PARTIALLU_LAPACK_H
+#define EIGEN_PARTIALLU_LAPACK_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+namespace lapacke_helpers {
+// -------------------------------------------------------------------------------------------------------------------
+//        Generic lapacke partial lu implementation that converts arguments and dispatches to the function above
+// -------------------------------------------------------------------------------------------------------------------
+
+template <typename Scalar, int StorageOrder>
+struct lapacke_partial_lu {
+  /** \internal performs the LU decomposition in-place of the matrix represented */
+  static lapack_int blocked_lu(Index rows, Index cols, Scalar* lu_data, Index luStride, lapack_int* row_transpositions,
+                               lapack_int& nb_transpositions, lapack_int maxBlockSize = 256) {
+    EIGEN_UNUSED_VARIABLE(maxBlockSize);
+    // Set up parameters for getrf
+    lapack_int matrix_order = StorageOrder == RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR;
+    lapack_int lda = to_lapack(luStride);
+    Scalar* a = lu_data;
+    lapack_int* ipiv = row_transpositions;
+    lapack_int m = to_lapack(rows);
+    lapack_int n = to_lapack(cols);
+    nb_transpositions = 0;
+
+    lapack_int info = getrf(matrix_order, m, n, to_lapack(a), lda, ipiv);
+    eigen_assert(info >= 0);
+
+    for (int i = 0; i < m; i++) {
+      ipiv[i]--;
+      if (ipiv[i] != i) nb_transpositions++;
+    }
+    lapack_int first_zero_pivot = info;
+    return first_zero_pivot;
+  }
+};
+}  // end namespace lapacke_helpers
+
+/*
+ * Here, we just put the generic implementation from lapacke_partial_lu into a partial specialization of the
+ * partial_lu_impl type. This specialization is more specialized than the generic implementations that Eigen implements,
+ * so if the Scalar type matches they will be chosen.
+ */
+#define EIGEN_LAPACKE_PARTIAL_LU(EIGTYPE)                            \
+  template <int StorageOrder>                                        \
+  struct partial_lu_impl<EIGTYPE, StorageOrder, lapack_int, Dynamic> \
+      : public lapacke_helpers::lapacke_partial_lu<EIGTYPE, StorageOrder> {};
+
+EIGEN_LAPACKE_PARTIAL_LU(double)
+EIGEN_LAPACKE_PARTIAL_LU(float)
+EIGEN_LAPACKE_PARTIAL_LU(std::complex<double>)
+EIGEN_LAPACKE_PARTIAL_LU(std::complex<float>)
+
+#undef EIGEN_LAPACKE_PARTIAL_LU
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PARTIALLU_LAPACK_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/LU/arch/InverseSize4.h b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/arch/InverseSize4.h
new file mode 100644
index 0000000..29c9b03
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/LU/arch/InverseSize4.h
@@ -0,0 +1,353 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2001 Intel Corporation
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+//
+// The algorithm below is a reimplementation of former \src\LU\Inverse_SSE.h using PacketMath.
+// inv(M) = M#/|M|, where inv(M), M# and |M| denote the inverse of M,
+// adjugate of M and determinant of M respectively. M# is computed block-wise
+// using specific formulae. For proof, see:
+// https://lxjk.github.io/2017/09/03/Fast-4x4-Matrix-Inverse-with-SSE-SIMD-Explained.html
+// Variable names are adopted from \src\LU\Inverse_SSE.h.
+//
+// The SSE code for the 4x4 float and double matrix inverse in former (deprecated) \src\LU\Inverse_SSE.h
+// comes from the following Intel's library:
+// http://software.intel.com/en-us/articles/optimized-matrix-library-for-use-with-the-intel-pentiumr-4-processors-sse2-instructions/
+//
+// Here is the respective copyright and license statement:
+//
+//   Copyright (c) 2001 Intel Corporation.
+//
+// Permission is granted to use, copy, distribute and prepare derivative works
+// of this library for any purpose and without fee, provided, that the above
+// copyright notice and this statement appear in all copies.
+// Intel makes no representations about the suitability of this software for
+// any purpose, and specifically disclaims all warranties.
+// See LEGAL.TXT for all the legal information.
+//
+// TODO: Unify implementations of different data types (i.e. float and double).
+#ifndef EIGEN_INVERSE_SIZE_4_H
+#define EIGEN_INVERSE_SIZE_4_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+#if EIGEN_COMP_GNUC_STRICT
+// These routines requires bit manipulation of the sign, which is not compatible
+// with fastmath.
+#pragma GCC push_options
+#pragma GCC optimize("no-fast-math")
+#endif
+
+namespace Eigen {
+namespace internal {
+template <typename MatrixType, typename ResultType>
+struct compute_inverse_size4<Architecture::Target, float, MatrixType, ResultType> {
+  enum {
+    MatrixAlignment = traits<MatrixType>::Alignment,
+    ResultAlignment = traits<ResultType>::Alignment,
+    StorageOrdersMatch = (MatrixType::Flags & RowMajorBit) == (ResultType::Flags & RowMajorBit)
+  };
+  typedef std::conditional_t<(MatrixType::Flags & LinearAccessBit), MatrixType const &,
+                             typename MatrixType::PlainObject>
+      ActualMatrixType;
+
+  static void run(const MatrixType &mat, ResultType &result) {
+    ActualMatrixType matrix(mat);
+
+    const float *data = matrix.data();
+    const Index stride = matrix.innerStride();
+    Packet4f L1 = ploadt<Packet4f, MatrixAlignment>(data);
+    Packet4f L2 = ploadt<Packet4f, MatrixAlignment>(data + stride * 4);
+    Packet4f L3 = ploadt<Packet4f, MatrixAlignment>(data + stride * 8);
+    Packet4f L4 = ploadt<Packet4f, MatrixAlignment>(data + stride * 12);
+
+    // Four 2x2 sub-matrices of the input matrix
+    // input = [[A, B],
+    //          [C, D]]
+    Packet4f A, B, C, D;
+
+    if (!StorageOrdersMatch) {
+      A = vec4f_unpacklo(L1, L2);
+      B = vec4f_unpacklo(L3, L4);
+      C = vec4f_unpackhi(L1, L2);
+      D = vec4f_unpackhi(L3, L4);
+    } else {
+      A = vec4f_movelh(L1, L2);
+      B = vec4f_movehl(L2, L1);
+      C = vec4f_movelh(L3, L4);
+      D = vec4f_movehl(L4, L3);
+    }
+
+    Packet4f AB, DC;
+
+    // AB = A# * B, where A# denotes the adjugate of A, and * denotes matrix product.
+    AB = pmul(vec4f_swizzle2(A, A, 3, 3, 0, 0), B);
+    AB = psub(AB, pmul(vec4f_swizzle2(A, A, 1, 1, 2, 2), vec4f_swizzle2(B, B, 2, 3, 0, 1)));
+
+    // DC = D#*C
+    DC = pmul(vec4f_swizzle2(D, D, 3, 3, 0, 0), C);
+    DC = psub(DC, pmul(vec4f_swizzle2(D, D, 1, 1, 2, 2), vec4f_swizzle2(C, C, 2, 3, 0, 1)));
+
+    // determinants of the sub-matrices
+    Packet4f dA, dB, dC, dD;
+
+    dA = pmul(vec4f_swizzle2(A, A, 3, 3, 1, 1), A);
+    dA = psub(dA, vec4f_movehl(dA, dA));
+
+    dB = pmul(vec4f_swizzle2(B, B, 3, 3, 1, 1), B);
+    dB = psub(dB, vec4f_movehl(dB, dB));
+
+    dC = pmul(vec4f_swizzle2(C, C, 3, 3, 1, 1), C);
+    dC = psub(dC, vec4f_movehl(dC, dC));
+
+    dD = pmul(vec4f_swizzle2(D, D, 3, 3, 1, 1), D);
+    dD = psub(dD, vec4f_movehl(dD, dD));
+
+    Packet4f d, d1, d2;
+
+    d = pmul(vec4f_swizzle2(DC, DC, 0, 2, 1, 3), AB);
+    d = padd(d, vec4f_movehl(d, d));
+    d = padd(d, vec4f_swizzle2(d, d, 1, 0, 0, 0));
+    d1 = pmul(dA, dD);
+    d2 = pmul(dB, dC);
+
+    // determinant of the input matrix, det = |A||D| + |B||C| - trace(A#*B*D#*C)
+    Packet4f det = vec4f_duplane(psub(padd(d1, d2), d), 0);
+
+    // reciprocal of the determinant of the input matrix, rd = 1/det
+    Packet4f rd = preciprocal(det);
+
+    // Four sub-matrices of the inverse
+    Packet4f iA, iB, iC, iD;
+
+    // iD = D*|A| - C*A#*B
+    iD = pmul(vec4f_swizzle2(C, C, 0, 0, 2, 2), vec4f_movelh(AB, AB));
+    iD = padd(iD, pmul(vec4f_swizzle2(C, C, 1, 1, 3, 3), vec4f_movehl(AB, AB)));
+    iD = psub(pmul(D, vec4f_duplane(dA, 0)), iD);
+
+    // iA = A*|D| - B*D#*C
+    iA = pmul(vec4f_swizzle2(B, B, 0, 0, 2, 2), vec4f_movelh(DC, DC));
+    iA = padd(iA, pmul(vec4f_swizzle2(B, B, 1, 1, 3, 3), vec4f_movehl(DC, DC)));
+    iA = psub(pmul(A, vec4f_duplane(dD, 0)), iA);
+
+    // iB = C*|B| - D * (A#B)# = C*|B| - D*B#*A
+    iB = pmul(D, vec4f_swizzle2(AB, AB, 3, 0, 3, 0));
+    iB = psub(iB, pmul(vec4f_swizzle2(D, D, 1, 0, 3, 2), vec4f_swizzle2(AB, AB, 2, 1, 2, 1)));
+    iB = psub(pmul(C, vec4f_duplane(dB, 0)), iB);
+
+    // iC = B*|C| - A * (D#C)# = B*|C| - A*C#*D
+    iC = pmul(A, vec4f_swizzle2(DC, DC, 3, 0, 3, 0));
+    iC = psub(iC, pmul(vec4f_swizzle2(A, A, 1, 0, 3, 2), vec4f_swizzle2(DC, DC, 2, 1, 2, 1)));
+    iC = psub(pmul(B, vec4f_duplane(dC, 0)), iC);
+
+    EIGEN_ALIGN_MAX const float sign_mask[4] = {0.0f, -0.0f, -0.0f, 0.0f};
+    const Packet4f p4f_sign_PNNP = pload<Packet4f>(sign_mask);
+    rd = pxor(rd, p4f_sign_PNNP);
+    iA = pmul(iA, rd);
+    iB = pmul(iB, rd);
+    iC = pmul(iC, rd);
+    iD = pmul(iD, rd);
+
+    Index res_stride = result.outerStride();
+    float *res = result.data();
+
+    pstoret<float, Packet4f, ResultAlignment>(res + 0, vec4f_swizzle2(iA, iB, 3, 1, 3, 1));
+    pstoret<float, Packet4f, ResultAlignment>(res + res_stride, vec4f_swizzle2(iA, iB, 2, 0, 2, 0));
+    pstoret<float, Packet4f, ResultAlignment>(res + 2 * res_stride, vec4f_swizzle2(iC, iD, 3, 1, 3, 1));
+    pstoret<float, Packet4f, ResultAlignment>(res + 3 * res_stride, vec4f_swizzle2(iC, iD, 2, 0, 2, 0));
+  }
+};
+
+#if !(defined EIGEN_VECTORIZE_NEON && !(EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG))
+// same algorithm as above, except that each operand is split into
+// halves for two registers to hold.
+template <typename MatrixType, typename ResultType>
+struct compute_inverse_size4<Architecture::Target, double, MatrixType, ResultType> {
+  enum {
+    MatrixAlignment = traits<MatrixType>::Alignment,
+    ResultAlignment = traits<ResultType>::Alignment,
+    StorageOrdersMatch = (MatrixType::Flags & RowMajorBit) == (ResultType::Flags & RowMajorBit)
+  };
+  typedef std::conditional_t<(MatrixType::Flags & LinearAccessBit), MatrixType const &,
+                             typename MatrixType::PlainObject>
+      ActualMatrixType;
+
+  static void run(const MatrixType &mat, ResultType &result) {
+    ActualMatrixType matrix(mat);
+
+    // Four 2x2 sub-matrices of the input matrix, each is further divided into upper and lower
+    // row e.g. A1, upper row of A, A2, lower row of A
+    // input = [[A, B],  =  [[[A1, [B1,
+    //          [C, D]]        A2], B2]],
+    //                       [[C1, [D1,
+    //                         C2], D2]]]
+
+    Packet2d A1, A2, B1, B2, C1, C2, D1, D2;
+
+    const double *data = matrix.data();
+    const Index stride = matrix.innerStride();
+    if (StorageOrdersMatch) {
+      A1 = ploadt<Packet2d, MatrixAlignment>(data + stride * 0);
+      B1 = ploadt<Packet2d, MatrixAlignment>(data + stride * 2);
+      A2 = ploadt<Packet2d, MatrixAlignment>(data + stride * 4);
+      B2 = ploadt<Packet2d, MatrixAlignment>(data + stride * 6);
+      C1 = ploadt<Packet2d, MatrixAlignment>(data + stride * 8);
+      D1 = ploadt<Packet2d, MatrixAlignment>(data + stride * 10);
+      C2 = ploadt<Packet2d, MatrixAlignment>(data + stride * 12);
+      D2 = ploadt<Packet2d, MatrixAlignment>(data + stride * 14);
+    } else {
+      Packet2d temp;
+      A1 = ploadt<Packet2d, MatrixAlignment>(data + stride * 0);
+      C1 = ploadt<Packet2d, MatrixAlignment>(data + stride * 2);
+      A2 = ploadt<Packet2d, MatrixAlignment>(data + stride * 4);
+      C2 = ploadt<Packet2d, MatrixAlignment>(data + stride * 6);
+      temp = A1;
+      A1 = vec2d_unpacklo(A1, A2);
+      A2 = vec2d_unpackhi(temp, A2);
+
+      temp = C1;
+      C1 = vec2d_unpacklo(C1, C2);
+      C2 = vec2d_unpackhi(temp, C2);
+
+      B1 = ploadt<Packet2d, MatrixAlignment>(data + stride * 8);
+      D1 = ploadt<Packet2d, MatrixAlignment>(data + stride * 10);
+      B2 = ploadt<Packet2d, MatrixAlignment>(data + stride * 12);
+      D2 = ploadt<Packet2d, MatrixAlignment>(data + stride * 14);
+
+      temp = B1;
+      B1 = vec2d_unpacklo(B1, B2);
+      B2 = vec2d_unpackhi(temp, B2);
+
+      temp = D1;
+      D1 = vec2d_unpacklo(D1, D2);
+      D2 = vec2d_unpackhi(temp, D2);
+    }
+
+    // determinants of the sub-matrices
+    Packet2d dA, dB, dC, dD;
+
+    dA = vec2d_swizzle2(A2, A2, 1);
+    dA = pmul(A1, dA);
+    dA = psub(dA, vec2d_duplane(dA, 1));
+
+    dB = vec2d_swizzle2(B2, B2, 1);
+    dB = pmul(B1, dB);
+    dB = psub(dB, vec2d_duplane(dB, 1));
+
+    dC = vec2d_swizzle2(C2, C2, 1);
+    dC = pmul(C1, dC);
+    dC = psub(dC, vec2d_duplane(dC, 1));
+
+    dD = vec2d_swizzle2(D2, D2, 1);
+    dD = pmul(D1, dD);
+    dD = psub(dD, vec2d_duplane(dD, 1));
+
+    Packet2d DC1, DC2, AB1, AB2;
+
+    // AB = A# * B, where A# denotes the adjugate of A, and * denotes matrix product.
+    AB1 = pmul(B1, vec2d_duplane(A2, 1));
+    AB2 = pmul(B2, vec2d_duplane(A1, 0));
+    AB1 = psub(AB1, pmul(B2, vec2d_duplane(A1, 1)));
+    AB2 = psub(AB2, pmul(B1, vec2d_duplane(A2, 0)));
+
+    // DC = D#*C
+    DC1 = pmul(C1, vec2d_duplane(D2, 1));
+    DC2 = pmul(C2, vec2d_duplane(D1, 0));
+    DC1 = psub(DC1, pmul(C2, vec2d_duplane(D1, 1)));
+    DC2 = psub(DC2, pmul(C1, vec2d_duplane(D2, 0)));
+
+    Packet2d d1, d2;
+
+    // determinant of the input matrix, det = |A||D| + |B||C| - trace(A#*B*D#*C)
+    Packet2d det;
+
+    // reciprocal of the determinant of the input matrix, rd = 1/det
+    Packet2d rd;
+
+    d1 = pmul(AB1, vec2d_swizzle2(DC1, DC2, 0));
+    d2 = pmul(AB2, vec2d_swizzle2(DC1, DC2, 3));
+    rd = padd(d1, d2);
+    rd = padd(rd, vec2d_duplane(rd, 1));
+
+    d1 = pmul(dA, dD);
+    d2 = pmul(dB, dC);
+
+    det = padd(d1, d2);
+    det = psub(det, rd);
+    det = vec2d_duplane(det, 0);
+    rd = pdiv(pset1<Packet2d>(1.0), det);
+
+    // rows of four sub-matrices of the inverse
+    Packet2d iA1, iA2, iB1, iB2, iC1, iC2, iD1, iD2;
+
+    // iD = D*|A| - C*A#*B
+    iD1 = pmul(AB1, vec2d_duplane(C1, 0));
+    iD2 = pmul(AB1, vec2d_duplane(C2, 0));
+    iD1 = padd(iD1, pmul(AB2, vec2d_duplane(C1, 1)));
+    iD2 = padd(iD2, pmul(AB2, vec2d_duplane(C2, 1)));
+    dA = vec2d_duplane(dA, 0);
+    iD1 = psub(pmul(D1, dA), iD1);
+    iD2 = psub(pmul(D2, dA), iD2);
+
+    // iA = A*|D| - B*D#*C
+    iA1 = pmul(DC1, vec2d_duplane(B1, 0));
+    iA2 = pmul(DC1, vec2d_duplane(B2, 0));
+    iA1 = padd(iA1, pmul(DC2, vec2d_duplane(B1, 1)));
+    iA2 = padd(iA2, pmul(DC2, vec2d_duplane(B2, 1)));
+    dD = vec2d_duplane(dD, 0);
+    iA1 = psub(pmul(A1, dD), iA1);
+    iA2 = psub(pmul(A2, dD), iA2);
+
+    // iB = C*|B| - D * (A#B)# = C*|B| - D*B#*A
+    iB1 = pmul(D1, vec2d_swizzle2(AB2, AB1, 1));
+    iB2 = pmul(D2, vec2d_swizzle2(AB2, AB1, 1));
+    iB1 = psub(iB1, pmul(vec2d_swizzle2(D1, D1, 1), vec2d_swizzle2(AB2, AB1, 2)));
+    iB2 = psub(iB2, pmul(vec2d_swizzle2(D2, D2, 1), vec2d_swizzle2(AB2, AB1, 2)));
+    dB = vec2d_duplane(dB, 0);
+    iB1 = psub(pmul(C1, dB), iB1);
+    iB2 = psub(pmul(C2, dB), iB2);
+
+    // iC = B*|C| - A * (D#C)# = B*|C| - A*C#*D
+    iC1 = pmul(A1, vec2d_swizzle2(DC2, DC1, 1));
+    iC2 = pmul(A2, vec2d_swizzle2(DC2, DC1, 1));
+    iC1 = psub(iC1, pmul(vec2d_swizzle2(A1, A1, 1), vec2d_swizzle2(DC2, DC1, 2)));
+    iC2 = psub(iC2, pmul(vec2d_swizzle2(A2, A2, 1), vec2d_swizzle2(DC2, DC1, 2)));
+    dC = vec2d_duplane(dC, 0);
+    iC1 = psub(pmul(B1, dC), iC1);
+    iC2 = psub(pmul(B2, dC), iC2);
+
+    EIGEN_ALIGN_MAX const double sign_mask1[2] = {0.0, -0.0};
+    EIGEN_ALIGN_MAX const double sign_mask2[2] = {-0.0, 0.0};
+    const Packet2d sign_PN = pload<Packet2d>(sign_mask1);
+    const Packet2d sign_NP = pload<Packet2d>(sign_mask2);
+    d1 = pxor(rd, sign_PN);
+    d2 = pxor(rd, sign_NP);
+
+    Index res_stride = result.outerStride();
+    double *res = result.data();
+    pstoret<double, Packet2d, ResultAlignment>(res + 0, pmul(vec2d_swizzle2(iA2, iA1, 3), d1));
+    pstoret<double, Packet2d, ResultAlignment>(res + res_stride, pmul(vec2d_swizzle2(iA2, iA1, 0), d2));
+    pstoret<double, Packet2d, ResultAlignment>(res + 2, pmul(vec2d_swizzle2(iB2, iB1, 3), d1));
+    pstoret<double, Packet2d, ResultAlignment>(res + res_stride + 2, pmul(vec2d_swizzle2(iB2, iB1, 0), d2));
+    pstoret<double, Packet2d, ResultAlignment>(res + 2 * res_stride, pmul(vec2d_swizzle2(iC2, iC1, 3), d1));
+    pstoret<double, Packet2d, ResultAlignment>(res + 3 * res_stride, pmul(vec2d_swizzle2(iC2, iC1, 0), d2));
+    pstoret<double, Packet2d, ResultAlignment>(res + 2 * res_stride + 2, pmul(vec2d_swizzle2(iD2, iD1, 3), d1));
+    pstoret<double, Packet2d, ResultAlignment>(res + 3 * res_stride + 2, pmul(vec2d_swizzle2(iD2, iD1, 0), d2));
+  }
+};
+#endif
+}  // namespace internal
+}  // namespace Eigen
+
+#if EIGEN_COMP_GNUC_STRICT
+#pragma GCC pop_options
+#endif
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/MetisSupport/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/MetisSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000..9d34825
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/MetisSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_METISSUPPORT_MODULE_H
+#error "Please include Eigen/MetisSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/MetisSupport/MetisSupport.h b/data_acq_and_processing/third_party/eigen/Eigen/src/MetisSupport/MetisSupport.h
new file mode 100644
index 0000000..961c840
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/MetisSupport/MetisSupport.h
@@ -0,0 +1,125 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#ifndef METIS_SUPPORT_H
+#define METIS_SUPPORT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+/**
+ * Get the fill-reducing ordering from the METIS package
+ *
+ * If A is the original matrix and Ap is the permuted matrix,
+ * the fill-reducing permutation is defined as follows :
+ * Row (column) i of A is the matperm(i) row (column) of Ap.
+ * WARNING: As computed by METIS, this corresponds to the vector iperm (instead of perm)
+ */
+template <typename StorageIndex>
+class MetisOrdering {
+ public:
+  typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+  typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
+
+  template <typename MatrixType>
+  void get_symmetrized_graph(const MatrixType& A) {
+    Index m = A.cols();
+    eigen_assert((A.rows() == A.cols()) && "ONLY FOR SQUARED MATRICES");
+    // Get the transpose of the input matrix
+    MatrixType At = A.transpose();
+    // Get the number of nonzeros elements in each row/col of At+A
+    Index TotNz = 0;
+    IndexVector visited(m);
+    visited.setConstant(-1);
+    for (StorageIndex j = 0; j < m; j++) {
+      // Compute the union structure of A(j,:) and At(j,:)
+      visited(j) = j;  // Do not include the diagonal element
+      // Get the nonzeros in row/column j of A
+      for (typename MatrixType::InnerIterator it(A, j); it; ++it) {
+        Index idx = it.index();  // Get the row index (for column major) or column index (for row major)
+        if (visited(idx) != j) {
+          visited(idx) = j;
+          ++TotNz;
+        }
+      }
+      // Get the nonzeros in row/column j of At
+      for (typename MatrixType::InnerIterator it(At, j); it; ++it) {
+        Index idx = it.index();
+        if (visited(idx) != j) {
+          visited(idx) = j;
+          ++TotNz;
+        }
+      }
+    }
+    // Reserve place for A + At
+    m_indexPtr.resize(m + 1);
+    m_innerIndices.resize(TotNz);
+
+    // Now compute the real adjacency list of each column/row
+    visited.setConstant(-1);
+    StorageIndex CurNz = 0;
+    for (StorageIndex j = 0; j < m; j++) {
+      m_indexPtr(j) = CurNz;
+
+      visited(j) = j;  // Do not include the diagonal element
+      // Add the pattern of row/column j of A to A+At
+      for (typename MatrixType::InnerIterator it(A, j); it; ++it) {
+        StorageIndex idx = it.index();  // Get the row index (for column major) or column index (for row major)
+        if (visited(idx) != j) {
+          visited(idx) = j;
+          m_innerIndices(CurNz) = idx;
+          CurNz++;
+        }
+      }
+      // Add the pattern of row/column j of At to A+At
+      for (typename MatrixType::InnerIterator it(At, j); it; ++it) {
+        StorageIndex idx = it.index();
+        if (visited(idx) != j) {
+          visited(idx) = j;
+          m_innerIndices(CurNz) = idx;
+          ++CurNz;
+        }
+      }
+    }
+    m_indexPtr(m) = CurNz;
+  }
+
+  template <typename MatrixType>
+  void operator()(const MatrixType& A, PermutationType& matperm) {
+    StorageIndex m = internal::convert_index<StorageIndex>(
+        A.cols());  // must be StorageIndex, because it is passed by address to METIS
+    IndexVector perm(m), iperm(m);
+    // First, symmetrize the matrix graph.
+    get_symmetrized_graph(A);
+    int output_error;
+
+    // Call the fill-reducing routine from METIS
+    output_error = METIS_NodeND(&m, m_indexPtr.data(), m_innerIndices.data(), NULL, NULL, perm.data(), iperm.data());
+
+    if (output_error != METIS_OK) {
+      // FIXME The ordering interface should define a class of possible errors
+      std::cerr << "ERROR WHILE CALLING THE METIS PACKAGE \n";
+      return;
+    }
+
+    // Get the fill-reducing permutation
+    // NOTE:  If Ap is the permuted matrix then perm and iperm vectors are defined as follows
+    // Row (column) i of Ap is the perm(i) row(column) of A, and row (column) i of A is the iperm(i) row(column) of Ap
+
+    matperm.resize(m);
+    for (int j = 0; j < m; j++) matperm.indices()(iperm(j)) = j;
+  }
+
+ protected:
+  IndexVector m_indexPtr;      // Pointer to the adjacenccy list of each row/column
+  IndexVector m_innerIndices;  // Adjacency list
+};
+
+}  // namespace Eigen
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/Amd.h b/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/Amd.h
new file mode 100644
index 0000000..0b0bf02
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/Amd.h
@@ -0,0 +1,413 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+NOTE: this routine has been adapted from the CSparse library:
+
+Copyright (c) 2006, Timothy A. Davis.
+http://www.suitesparse.com
+
+The author of CSparse, Timothy A. Davis., has executed a license with Google LLC
+to permit distribution of this code and derivative works as part of Eigen under
+the Mozilla Public License v. 2.0, as stated at the top of this file.
+*/
+
+#ifndef EIGEN_SPARSE_AMD_H
+#define EIGEN_SPARSE_AMD_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename T>
+inline T amd_flip(const T& i) {
+  return -i - 2;
+}
+template <typename T>
+inline T amd_unflip(const T& i) {
+  return i < 0 ? amd_flip(i) : i;
+}
+template <typename T0, typename T1>
+inline bool amd_marked(const T0* w, const T1& j) {
+  return w[j] < 0;
+}
+template <typename T0, typename T1>
+inline void amd_mark(const T0* w, const T1& j) {
+  return w[j] = amd_flip(w[j]);
+}
+
+/* clear w */
+template <typename StorageIndex>
+static StorageIndex cs_wclear(StorageIndex mark, StorageIndex lemax, StorageIndex* w, StorageIndex n) {
+  StorageIndex k;
+  if (mark < 2 || (mark + lemax < 0)) {
+    for (k = 0; k < n; k++)
+      if (w[k] != 0) w[k] = 1;
+    mark = 2;
+  }
+  return (mark); /* at this point, w[0..n-1] < mark holds */
+}
+
+/* depth-first search and postorder of a tree rooted at node j */
+template <typename StorageIndex>
+StorageIndex cs_tdfs(StorageIndex j, StorageIndex k, StorageIndex* head, const StorageIndex* next, StorageIndex* post,
+                     StorageIndex* stack) {
+  StorageIndex i, p, top = 0;
+  if (!head || !next || !post || !stack) return (-1); /* check inputs */
+  stack[0] = j;                                       /* place j on the stack */
+  while (top >= 0)                                    /* while (stack is not empty) */
+  {
+    p = stack[top]; /* p = top of stack */
+    i = head[p];    /* i = youngest child of p */
+    if (i == -1) {
+      top--;         /* p has no unordered children left */
+      post[k++] = p; /* node p is the kth postordered node */
+    } else {
+      head[p] = next[i]; /* remove i from children of p */
+      stack[++top] = i;  /* start dfs on child node i */
+    }
+  }
+  return k;
+}
+
+/** \internal
+ * \ingroup OrderingMethods_Module
+ * Approximate minimum degree ordering algorithm.
+ *
+ * \param[in] C the input selfadjoint matrix stored in compressed column major format.
+ * \param[out] perm the permutation P reducing the fill-in of the input matrix \a C
+ *
+ * Note that the input matrix \a C must be complete, that is both the upper and lower parts have to be stored, as well
+ * as the diagonal entries. On exit the values of C are destroyed */
+template <typename Scalar, typename StorageIndex>
+void minimum_degree_ordering(SparseMatrix<Scalar, ColMajor, StorageIndex>& C,
+                             PermutationMatrix<Dynamic, Dynamic, StorageIndex>& perm) {
+  using std::sqrt;
+
+  StorageIndex d, dk, dext, lemax = 0, e, elenk, eln, i, j, k, k1, k2, k3, jlast, ln, dense, nzmax, mindeg = 0, nvi,
+                            nvj, nvk, mark, wnvi, ok, nel = 0, p, p1, p2, p3, p4, pj, pk, pk1, pk2, pn, q, t, h;
+
+  StorageIndex n = StorageIndex(C.cols());
+  dense = std::max<StorageIndex>(16, StorageIndex(10 * sqrt(double(n)))); /* find dense threshold */
+  dense = (std::min)(n - 2, dense);
+
+  StorageIndex cnz = StorageIndex(C.nonZeros());
+  perm.resize(n + 1);
+  t = cnz + cnz / 5 + 2 * n; /* add elbow room to C */
+  C.resizeNonZeros(t);
+
+  // get workspace
+  ei_declare_aligned_stack_constructed_variable(StorageIndex, W, 8 * (n + 1), 0);
+  StorageIndex* len = W;
+  StorageIndex* nv = W + (n + 1);
+  StorageIndex* next = W + 2 * (n + 1);
+  StorageIndex* head = W + 3 * (n + 1);
+  StorageIndex* elen = W + 4 * (n + 1);
+  StorageIndex* degree = W + 5 * (n + 1);
+  StorageIndex* w = W + 6 * (n + 1);
+  StorageIndex* hhead = W + 7 * (n + 1);
+  StorageIndex* last = perm.indices().data(); /* use P as workspace for last */
+
+  /* --- Initialize quotient graph ---------------------------------------- */
+  StorageIndex* Cp = C.outerIndexPtr();
+  StorageIndex* Ci = C.innerIndexPtr();
+  for (k = 0; k < n; k++) len[k] = Cp[k + 1] - Cp[k];
+  len[n] = 0;
+  nzmax = t;
+
+  for (i = 0; i <= n; i++) {
+    head[i] = -1;  // degree list i is empty
+    last[i] = -1;
+    next[i] = -1;
+    hhead[i] = -1;       // hash list i is empty
+    nv[i] = 1;           // node i is just one node
+    w[i] = 1;            // node i is alive
+    elen[i] = 0;         // Ek of node i is empty
+    degree[i] = len[i];  // degree of node i
+  }
+  mark = internal::cs_wclear<StorageIndex>(0, 0, w, n); /* clear w */
+
+  /* --- Initialize degree lists ------------------------------------------ */
+  for (i = 0; i < n; i++) {
+    bool has_diag = false;
+    for (p = Cp[i]; p < Cp[i + 1]; ++p)
+      if (Ci[p] == i) {
+        has_diag = true;
+        break;
+      }
+
+    d = degree[i];
+    if (d == 1 && has_diag) /* node i is empty */
+    {
+      elen[i] = -2; /* element i is dead */
+      nel++;
+      Cp[i] = -1; /* i is a root of assembly tree */
+      w[i] = 0;
+    } else if (d > dense || !has_diag) /* node i is dense or has no structural diagonal element */
+    {
+      nv[i] = 0;    /* absorb i into element n */
+      elen[i] = -1; /* node i is dead */
+      nel++;
+      Cp[i] = amd_flip(n);
+      nv[n]++;
+    } else {
+      if (head[d] != -1) last[head[d]] = i;
+      next[i] = head[d]; /* put node i in degree list d */
+      head[d] = i;
+    }
+  }
+
+  elen[n] = -2; /* n is a dead element */
+  Cp[n] = -1;   /* n is a root of assembly tree */
+  w[n] = 0;     /* n is a dead element */
+
+  while (nel < n) /* while (selecting pivots) do */
+  {
+    /* --- Select node of minimum approximate degree -------------------- */
+    for (k = -1; mindeg < n && (k = head[mindeg]) == -1; mindeg++) {
+    }
+    if (next[k] != -1) last[next[k]] = -1;
+    head[mindeg] = next[k]; /* remove k from degree list */
+    elenk = elen[k];        /* elenk = |Ek| */
+    nvk = nv[k];            /* # of nodes k represents */
+    nel += nvk;             /* nv[k] nodes of A eliminated */
+
+    /* --- Garbage collection ------------------------------------------- */
+    if (elenk > 0 && cnz + mindeg >= nzmax) {
+      for (j = 0; j < n; j++) {
+        if ((p = Cp[j]) >= 0) /* j is a live node or element */
+        {
+          Cp[j] = Ci[p];       /* save first entry of object */
+          Ci[p] = amd_flip(j); /* first entry is now amd_flip(j) */
+        }
+      }
+      for (q = 0, p = 0; p < cnz;) /* scan all of memory */
+      {
+        if ((j = amd_flip(Ci[p++])) >= 0) /* found object j */
+        {
+          Ci[q] = Cp[j]; /* restore first entry of object */
+          Cp[j] = q++;   /* new pointer to object j */
+          for (k3 = 0; k3 < len[j] - 1; k3++) Ci[q++] = Ci[p++];
+        }
+      }
+      cnz = q; /* Ci[cnz...nzmax-1] now free */
+    }
+
+    /* --- Construct new element ---------------------------------------- */
+    dk = 0;
+    nv[k] = -nvk; /* flag k as in Lk */
+    p = Cp[k];
+    pk1 = (elenk == 0) ? p : cnz; /* do in place if elen[k] == 0 */
+    pk2 = pk1;
+    for (k1 = 1; k1 <= elenk + 1; k1++) {
+      if (k1 > elenk) {
+        e = k;               /* search the nodes in k */
+        pj = p;              /* list of nodes starts at Ci[pj]*/
+        ln = len[k] - elenk; /* length of list of nodes in k */
+      } else {
+        e = Ci[p++]; /* search the nodes in e */
+        pj = Cp[e];
+        ln = len[e]; /* length of list of nodes in e */
+      }
+      for (k2 = 1; k2 <= ln; k2++) {
+        i = Ci[pj++];
+        if ((nvi = nv[i]) <= 0) continue; /* node i dead, or seen */
+        dk += nvi;                        /* degree[Lk] += size of node i */
+        nv[i] = -nvi;                     /* negate nv[i] to denote i in Lk*/
+        Ci[pk2++] = i;                    /* place i in Lk */
+        if (next[i] != -1) last[next[i]] = last[i];
+        if (last[i] != -1) /* remove i from degree list */
+        {
+          next[last[i]] = next[i];
+        } else {
+          head[degree[i]] = next[i];
+        }
+      }
+      if (e != k) {
+        Cp[e] = amd_flip(k); /* absorb e into k */
+        w[e] = 0;            /* e is now a dead element */
+      }
+    }
+    if (elenk != 0) cnz = pk2; /* Ci[cnz...nzmax] is free */
+    degree[k] = dk;            /* external degree of k - |Lk\i| */
+    Cp[k] = pk1;               /* element k is in Ci[pk1..pk2-1] */
+    len[k] = pk2 - pk1;
+    elen[k] = -2; /* k is now an element */
+
+    /* --- Find set differences ----------------------------------------- */
+    mark = internal::cs_wclear<StorageIndex>(mark, lemax, w, n); /* clear w if necessary */
+    for (pk = pk1; pk < pk2; pk++)                               /* scan 1: find |Le\Lk| */
+    {
+      i = Ci[pk];
+      if ((eln = elen[i]) <= 0) continue; /* skip if elen[i] empty */
+      nvi = -nv[i];                       /* nv[i] was negated */
+      wnvi = mark - nvi;
+      for (p = Cp[i]; p <= Cp[i] + eln - 1; p++) /* scan Ei */
+      {
+        e = Ci[p];
+        if (w[e] >= mark) {
+          w[e] -= nvi;        /* decrement |Le\Lk| */
+        } else if (w[e] != 0) /* ensure e is a live element */
+        {
+          w[e] = degree[e] + wnvi; /* 1st time e seen in scan 1 */
+        }
+      }
+    }
+
+    /* --- Degree update ------------------------------------------------ */
+    for (pk = pk1; pk < pk2; pk++) /* scan2: degree update */
+    {
+      i = Ci[pk]; /* consider node i in Lk */
+      p1 = Cp[i];
+      p2 = p1 + elen[i] - 1;
+      pn = p1;
+      for (h = 0, d = 0, p = p1; p <= p2; p++) /* scan Ei */
+      {
+        e = Ci[p];
+        if (w[e] != 0) /* e is an unabsorbed element */
+        {
+          dext = w[e] - mark; /* dext = |Le\Lk| */
+          if (dext > 0) {
+            d += dext;    /* sum up the set differences */
+            Ci[pn++] = e; /* keep e in Ei */
+            h += e;       /* compute the hash of node i */
+          } else {
+            Cp[e] = amd_flip(k); /* aggressive absorb. e->k */
+            w[e] = 0;            /* e is a dead element */
+          }
+        }
+      }
+      elen[i] = pn - p1 + 1; /* elen[i] = |Ei| */
+      p3 = pn;
+      p4 = p1 + len[i];
+      for (p = p2 + 1; p < p4; p++) /* prune edges in Ai */
+      {
+        j = Ci[p];
+        if ((nvj = nv[j]) <= 0) continue; /* node j dead or in Lk */
+        d += nvj;                         /* degree(i) += |j| */
+        Ci[pn++] = j;                     /* place j in node list of i */
+        h += j;                           /* compute hash for node i */
+      }
+      if (d == 0) /* check for mass elimination */
+      {
+        Cp[i] = amd_flip(k); /* absorb i into k */
+        nvi = -nv[i];
+        dk -= nvi;  /* |Lk| -= |i| */
+        nvk += nvi; /* |k| += nv[i] */
+        nel += nvi;
+        nv[i] = 0;
+        elen[i] = -1; /* node i is dead */
+      } else {
+        degree[i] = std::min<StorageIndex>(degree[i], d); /* update degree(i) */
+        Ci[pn] = Ci[p3];                                  /* move first node to end */
+        Ci[p3] = Ci[p1];                                  /* move 1st el. to end of Ei */
+        Ci[p1] = k;                                       /* add k as 1st element in of Ei */
+        len[i] = pn - p1 + 1;                             /* new len of adj. list of node i */
+        h %= n;                                           /* finalize hash of i */
+        next[i] = hhead[h];                               /* place i in hash bucket */
+        hhead[h] = i;
+        last[i] = h; /* save hash of i in last[i] */
+      }
+    }               /* scan2 is done */
+    degree[k] = dk; /* finalize |Lk| */
+    lemax = std::max<StorageIndex>(lemax, dk);
+    mark = internal::cs_wclear<StorageIndex>(mark + lemax, lemax, w, n); /* clear w */
+
+    /* --- Supernode detection ------------------------------------------ */
+    for (pk = pk1; pk < pk2; pk++) {
+      i = Ci[pk];
+      if (nv[i] >= 0) continue; /* skip if i is dead */
+      h = last[i];              /* scan hash bucket of node i */
+      i = hhead[h];
+      hhead[h] = -1; /* hash bucket will be empty */
+      for (; i != -1 && next[i] != -1; i = next[i], mark++) {
+        ln = len[i];
+        eln = elen[i];
+        for (p = Cp[i] + 1; p <= Cp[i] + ln - 1; p++) w[Ci[p]] = mark;
+        jlast = i;
+        for (j = next[i]; j != -1;) /* compare i with all j */
+        {
+          ok = (len[j] == ln) && (elen[j] == eln);
+          for (p = Cp[j] + 1; ok && p <= Cp[j] + ln - 1; p++) {
+            if (w[Ci[p]] != mark) ok = 0; /* compare i and j*/
+          }
+          if (ok) /* i and j are identical */
+          {
+            Cp[j] = amd_flip(i); /* absorb j into i */
+            nv[i] += nv[j];
+            nv[j] = 0;
+            elen[j] = -1; /* node j is dead */
+            j = next[j];  /* delete j from hash bucket */
+            next[jlast] = j;
+          } else {
+            jlast = j; /* j and i are different */
+            j = next[j];
+          }
+        }
+      }
+    }
+
+    /* --- Finalize new element------------------------------------------ */
+    for (p = pk1, pk = pk1; pk < pk2; pk++) /* finalize Lk */
+    {
+      i = Ci[pk];
+      if ((nvi = -nv[i]) <= 0) continue; /* skip if i is dead */
+      nv[i] = nvi;                       /* restore nv[i] */
+      d = degree[i] + dk - nvi;          /* compute external degree(i) */
+      d = std::min<StorageIndex>(d, n - nel - nvi);
+      if (head[d] != -1) last[head[d]] = i;
+      next[i] = head[d]; /* put i back in degree list */
+      last[i] = -1;
+      head[d] = i;
+      mindeg = std::min<StorageIndex>(mindeg, d); /* find new minimum degree */
+      degree[i] = d;
+      Ci[p++] = i; /* place i in Lk */
+    }
+    nv[k] = nvk;                 /* # nodes absorbed into k */
+    if ((len[k] = p - pk1) == 0) /* length of adj list of element k*/
+    {
+      Cp[k] = -1; /* k is a root of the tree */
+      w[k] = 0;   /* k is now a dead element */
+    }
+    if (elenk != 0) cnz = p; /* free unused space in Lk */
+  }
+
+  /* --- Postordering ----------------------------------------------------- */
+  for (i = 0; i < n; i++) Cp[i] = amd_flip(Cp[i]); /* fix assembly tree */
+  for (j = 0; j <= n; j++) head[j] = -1;
+  for (j = n; j >= 0; j--) /* place unordered nodes in lists */
+  {
+    if (nv[j] > 0) continue; /* skip if j is an element */
+    next[j] = head[Cp[j]];   /* place j in list of its parent */
+    head[Cp[j]] = j;
+  }
+  for (e = n; e >= 0; e--) /* place elements in lists */
+  {
+    if (nv[e] <= 0) continue; /* skip unless e is an element */
+    if (Cp[e] != -1) {
+      next[e] = head[Cp[e]]; /* place e in list of its parent */
+      head[Cp[e]] = e;
+    }
+  }
+  for (k = 0, i = 0; i <= n; i++) /* postorder the assembly tree */
+  {
+    if (Cp[i] == -1) k = internal::cs_tdfs<StorageIndex>(i, k, head, next, perm.indices().data(), w);
+  }
+
+  perm.indices().conservativeResize(n);
+}
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_AMD_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/Eigen_Colamd.h b/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/Eigen_Colamd.h
new file mode 100644
index 0000000..02be282
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/Eigen_Colamd.h
@@ -0,0 +1,1690 @@
+// // This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire Nuentsa Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// This file is modified from the colamd/symamd library. The copyright is below
+
+//   The authors of the code itself are Stefan I. Larimore and Timothy A.
+//   Davis (davis@cise.ufl.edu), University of Florida.  The algorithm was
+//   developed in collaboration with John Gilbert, Xerox PARC, and Esmond
+//   Ng, Oak Ridge National Laboratory.
+//
+//     Date:
+//
+//   September 8, 2003.  Version 2.3.
+//
+//     Acknowledgements:
+//
+//   This work was supported by the National Science Foundation, under
+//   grants DMS-9504974 and DMS-9803599.
+//
+//     Notice:
+//
+//   Copyright (c) 1998-2003 by the University of Florida.
+//   All Rights Reserved.
+//
+//   THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+//   EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+//
+//   Permission is hereby granted to use, copy, modify, and/or distribute
+//   this program, provided that the Copyright, this License, and the
+//   Availability of the original version is retained on all copies and made
+//   accessible to the end-user of any code or package that includes COLAMD
+//   or any modified version of COLAMD.
+//
+//     Availability:
+//
+//   The colamd/symamd library is available at
+//
+//       http://www.suitesparse.com
+
+#ifndef EIGEN_COLAMD_H
+#define EIGEN_COLAMD_H
+
+namespace Eigen {
+namespace internal {
+namespace Colamd {
+
+/* Ensure that debugging is turned off: */
+#ifndef COLAMD_NDEBUG
+#define COLAMD_NDEBUG
+#endif /* NDEBUG */
+
+/* ========================================================================== */
+/* === Knob and statistics definitions ====================================== */
+/* ========================================================================== */
+
+/* size of the knobs [ ] array.  Only knobs [0..1] are currently used. */
+const int NKnobs = 20;
+
+/* number of output statistics.  Only stats [0..6] are currently used. */
+const int NStats = 20;
+
+/* Indices into knobs and stats array. */
+enum KnobsStatsIndex {
+  /* knobs [0] and stats [0]: dense row knob and output statistic. */
+  DenseRow = 0,
+
+  /* knobs [1] and stats [1]: dense column knob and output statistic. */
+  DenseCol = 1,
+
+  /* stats [2]: memory defragmentation count output statistic */
+  DefragCount = 2,
+
+  /* stats [3]: colamd status:  zero OK, > 0 warning or notice, < 0 error */
+  Status = 3,
+
+  /* stats [4..6]: error info, or info on jumbled columns */
+  Info1 = 4,
+  Info2 = 5,
+  Info3 = 6
+};
+
+/* error codes returned in stats [3]: */
+enum Status {
+  Ok = 0,
+  OkButJumbled = 1,
+  ErrorANotPresent = -1,
+  ErrorPNotPresent = -2,
+  ErrorNrowNegative = -3,
+  ErrorNcolNegative = -4,
+  ErrorNnzNegative = -5,
+  ErrorP0Nonzero = -6,
+  ErrorATooSmall = -7,
+  ErrorColLengthNegative = -8,
+  ErrorRowIndexOutOfBounds = -9,
+  ErrorOutOfMemory = -10,
+  ErrorInternalError = -999
+};
+/* ========================================================================== */
+/* === Definitions ========================================================== */
+/* ========================================================================== */
+
+template <typename IndexType>
+IndexType ones_complement(const IndexType r) {
+  return (-(r)-1);
+}
+
+/* -------------------------------------------------------------------------- */
+const int Empty = -1;
+
+/* Row and column status */
+enum RowColumnStatus { Alive = 0, Dead = -1 };
+
+/* Column status */
+enum ColumnStatus { DeadPrincipal = -1, DeadNonPrincipal = -2 };
+
+/* ========================================================================== */
+/* === Colamd reporting mechanism =========================================== */
+/* ========================================================================== */
+
+// == Row and Column structures ==
+template <typename IndexType>
+struct ColStructure {
+  IndexType start; /* index for A of first row in this column, or Dead */
+  /* if column is dead */
+  IndexType length; /* number of rows in this column */
+  union {
+    IndexType thickness; /* number of original columns represented by this */
+    /* col, if the column is alive */
+    IndexType parent; /* parent in parent tree super-column structure, if */
+    /* the column is dead */
+  } shared1;
+  union {
+    IndexType score; /* the score used to maintain heap, if col is alive */
+    IndexType order; /* pivot ordering of this column, if col is dead */
+  } shared2;
+  union {
+    IndexType headhash; /* head of a hash bucket, if col is at the head of */
+    /* a degree list */
+    IndexType hash; /* hash value, if col is not in a degree list */
+    IndexType prev; /* previous column in degree list, if col is in a */
+    /* degree list (but not at the head of a degree list) */
+  } shared3;
+  union {
+    IndexType degree_next; /* next column, if col is in a degree list */
+    IndexType hash_next;   /* next column, if col is in a hash list */
+  } shared4;
+
+  inline bool is_dead() const { return start < Alive; }
+
+  inline bool is_alive() const { return start >= Alive; }
+
+  inline bool is_dead_principal() const { return start == DeadPrincipal; }
+
+  inline void kill_principal() { start = DeadPrincipal; }
+
+  inline void kill_non_principal() { start = DeadNonPrincipal; }
+};
+
+template <typename IndexType>
+struct RowStructure {
+  IndexType start;  /* index for A of first col in this row */
+  IndexType length; /* number of principal columns in this row */
+  union {
+    IndexType degree; /* number of principal & non-principal columns in row */
+    IndexType p;      /* used as a row pointer in init_rows_cols () */
+  } shared1;
+  union {
+    IndexType mark;         /* for computing set differences and marking dead rows*/
+    IndexType first_column; /* first column in row (used in garbage collection) */
+  } shared2;
+
+  inline bool is_dead() const { return shared2.mark < Alive; }
+
+  inline bool is_alive() const { return shared2.mark >= Alive; }
+
+  inline void kill() { shared2.mark = Dead; }
+};
+
+/* ========================================================================== */
+/* === Colamd recommended memory size ======================================= */
+/* ========================================================================== */
+
+/*
+  The recommended length Alen of the array A passed to colamd is given by
+  the COLAMD_RECOMMENDED (nnz, n_row, n_col) macro.  It returns -1 if any
+  argument is negative.  2*nnz space is required for the row and column
+  indices of the matrix. colamd_c (n_col) + colamd_r (n_row) space is
+  required for the Col and Row arrays, respectively, which are internal to
+  colamd.  An additional n_col space is the minimal amount of "elbow room",
+  and nnz/5 more space is recommended for run time efficiency.
+
+  This macro is not needed when using symamd.
+
+  Explicit typecast to IndexType added Sept. 23, 2002, COLAMD version 2.2, to avoid
+  gcc -pedantic warning messages.
+*/
+template <typename IndexType>
+inline IndexType colamd_c(IndexType n_col) {
+  return IndexType(((n_col) + 1) * sizeof(ColStructure<IndexType>) / sizeof(IndexType));
+}
+
+template <typename IndexType>
+inline IndexType colamd_r(IndexType n_row) {
+  return IndexType(((n_row) + 1) * sizeof(RowStructure<IndexType>) / sizeof(IndexType));
+}
+
+// Prototypes of non-user callable routines
+template <typename IndexType>
+static IndexType init_rows_cols(IndexType n_row, IndexType n_col, RowStructure<IndexType> Row[],
+                                ColStructure<IndexType> col[], IndexType A[], IndexType p[], IndexType stats[NStats]);
+
+template <typename IndexType>
+static void init_scoring(IndexType n_row, IndexType n_col, RowStructure<IndexType> Row[], ColStructure<IndexType> Col[],
+                         IndexType A[], IndexType head[], double knobs[NKnobs], IndexType *p_n_row2,
+                         IndexType *p_n_col2, IndexType *p_max_deg);
+
+template <typename IndexType>
+static IndexType find_ordering(IndexType n_row, IndexType n_col, IndexType Alen, RowStructure<IndexType> Row[],
+                               ColStructure<IndexType> Col[], IndexType A[], IndexType head[], IndexType n_col2,
+                               IndexType max_deg, IndexType pfree);
+
+template <typename IndexType>
+static void order_children(IndexType n_col, ColStructure<IndexType> Col[], IndexType p[]);
+
+template <typename IndexType>
+static void detect_super_cols(ColStructure<IndexType> Col[], IndexType A[], IndexType head[], IndexType row_start,
+                              IndexType row_length);
+
+template <typename IndexType>
+static IndexType garbage_collection(IndexType n_row, IndexType n_col, RowStructure<IndexType> Row[],
+                                    ColStructure<IndexType> Col[], IndexType A[], IndexType *pfree);
+
+template <typename IndexType>
+static inline IndexType clear_mark(IndexType n_row, RowStructure<IndexType> Row[]);
+
+/* === No debugging ========================================================= */
+
+#define COLAMD_DEBUG0(params) ;
+#define COLAMD_DEBUG1(params) ;
+#define COLAMD_DEBUG2(params) ;
+#define COLAMD_DEBUG3(params) ;
+#define COLAMD_DEBUG4(params) ;
+
+#define COLAMD_ASSERT(expression) ((void)0)
+
+/**
+ * \brief Returns the recommended value of Alen
+ *
+ * Returns recommended value of Alen for use by colamd.
+ * Returns -1 if any input argument is negative.
+ * The use of this routine or macro is optional.
+ * Note that the macro uses its arguments   more than once,
+ * so be careful for side effects, if you pass expressions as arguments to COLAMD_RECOMMENDED.
+ *
+ * \param nnz nonzeros in A
+ * \param n_row number of rows in A
+ * \param n_col number of columns in A
+ * \return recommended value of Alen for use by colamd
+ */
+template <typename IndexType>
+inline IndexType recommended(IndexType nnz, IndexType n_row, IndexType n_col) {
+  if ((nnz) < 0 || (n_row) < 0 || (n_col) < 0)
+    return (-1);
+  else
+    return (2 * (nnz) + colamd_c(n_col) + colamd_r(n_row) + (n_col) + ((nnz) / 5));
+}
+
+/**
+ * \brief set default parameters  The use of this routine is optional.
+ *
+ * Colamd: rows with more than (knobs [DenseRow] * n_col)
+ * entries are removed prior to ordering.  Columns with more than
+ * (knobs [DenseCol] * n_row) entries are removed prior to
+ * ordering, and placed last in the output column ordering.
+ *
+ * DenseRow and DenseCol are defined as 0 and 1,
+ * respectively, in colamd.h.  Default values of these two knobs
+ * are both 0.5.  Currently, only knobs [0] and knobs [1] are
+ * used, but future versions may use more knobs.  If so, they will
+ * be properly set to their defaults by the future version of
+ * colamd_set_defaults, so that the code that calls colamd will
+ * not need to change, assuming that you either use
+ * colamd_set_defaults, or pass a (double *) NULL pointer as the
+ * knobs array to colamd or symamd.
+ *
+ * \param knobs parameter settings for colamd
+ */
+
+static inline void set_defaults(double knobs[NKnobs]) {
+  /* === Local variables ================================================== */
+
+  int i;
+
+  if (!knobs) {
+    return; /* no knobs to initialize */
+  }
+  for (i = 0; i < NKnobs; i++) {
+    knobs[i] = 0;
+  }
+  knobs[Colamd::DenseRow] = 0.5; /* ignore rows over 50% dense */
+  knobs[Colamd::DenseCol] = 0.5; /* ignore columns over 50% dense */
+}
+
+/**
+ * \brief  Computes a column ordering using the column approximate minimum degree ordering
+ *
+ * Computes a column ordering (Q) of A such that P(AQ)=LU or
+ * (AQ)'AQ=LL' have less fill-in and require fewer floating point
+ * operations than factorizing the unpermuted matrix A or A'A,
+ * respectively.
+ *
+ *
+ * \param n_row number of rows in A
+ * \param n_col number of columns in A
+ * \param Alen size of the array A
+ * \param A row indices of the matrix, of size ALen
+ * \param p column pointers of A, of size n_col+1
+ * \param knobs parameter settings for colamd
+ * \param stats colamd output statistics and error codes
+ */
+template <typename IndexType>
+static bool compute_ordering(IndexType n_row, IndexType n_col, IndexType Alen, IndexType *A, IndexType *p,
+                             double knobs[NKnobs], IndexType stats[NStats]) {
+  /* === Local variables ================================================== */
+
+  IndexType i;                          /* loop index */
+  IndexType nnz;                        /* nonzeros in A */
+  IndexType Row_size;                   /* size of Row [], in integers */
+  IndexType Col_size;                   /* size of Col [], in integers */
+  IndexType need;                       /* minimum required length of A */
+  Colamd::RowStructure<IndexType> *Row; /* pointer into A of Row [0..n_row] array */
+  Colamd::ColStructure<IndexType> *Col; /* pointer into A of Col [0..n_col] array */
+  IndexType n_col2;                     /* number of non-dense, non-empty columns */
+  IndexType n_row2;                     /* number of non-dense, non-empty rows */
+  IndexType ngarbage;                   /* number of garbage collections performed */
+  IndexType max_deg;                    /* maximum row degree */
+  double default_knobs[NKnobs];         /* default knobs array */
+
+  /* === Check the input arguments ======================================== */
+
+  if (!stats) {
+    COLAMD_DEBUG0(("colamd: stats not present\n"));
+    return (false);
+  }
+  for (i = 0; i < NStats; i++) {
+    stats[i] = 0;
+  }
+  stats[Colamd::Status] = Colamd::Ok;
+  stats[Colamd::Info1] = -1;
+  stats[Colamd::Info2] = -1;
+
+  if (!A) /* A is not present */
+  {
+    stats[Colamd::Status] = Colamd::ErrorANotPresent;
+    COLAMD_DEBUG0(("colamd: A not present\n"));
+    return (false);
+  }
+
+  if (!p) /* p is not present */
+  {
+    stats[Colamd::Status] = Colamd::ErrorPNotPresent;
+    COLAMD_DEBUG0(("colamd: p not present\n"));
+    return (false);
+  }
+
+  if (n_row < 0) /* n_row must be >= 0 */
+  {
+    stats[Colamd::Status] = Colamd::ErrorNrowNegative;
+    stats[Colamd::Info1] = n_row;
+    COLAMD_DEBUG0(("colamd: nrow negative %d\n", n_row));
+    return (false);
+  }
+
+  if (n_col < 0) /* n_col must be >= 0 */
+  {
+    stats[Colamd::Status] = Colamd::ErrorNcolNegative;
+    stats[Colamd::Info1] = n_col;
+    COLAMD_DEBUG0(("colamd: ncol negative %d\n", n_col));
+    return (false);
+  }
+
+  nnz = p[n_col];
+  if (nnz < 0) /* nnz must be >= 0 */
+  {
+    stats[Colamd::Status] = Colamd::ErrorNnzNegative;
+    stats[Colamd::Info1] = nnz;
+    COLAMD_DEBUG0(("colamd: number of entries negative %d\n", nnz));
+    return (false);
+  }
+
+  if (p[0] != 0) {
+    stats[Colamd::Status] = Colamd::ErrorP0Nonzero;
+    stats[Colamd::Info1] = p[0];
+    COLAMD_DEBUG0(("colamd: p[0] not zero %d\n", p[0]));
+    return (false);
+  }
+
+  /* === If no knobs, set default knobs =================================== */
+
+  if (!knobs) {
+    set_defaults(default_knobs);
+    knobs = default_knobs;
+  }
+
+  /* === Allocate the Row and Col arrays from array A ===================== */
+
+  Col_size = colamd_c(n_col);
+  Row_size = colamd_r(n_row);
+  need = 2 * nnz + n_col + Col_size + Row_size;
+
+  if (need > Alen) {
+    /* not enough space in array A to perform the ordering */
+    stats[Colamd::Status] = Colamd::ErrorATooSmall;
+    stats[Colamd::Info1] = need;
+    stats[Colamd::Info2] = Alen;
+    COLAMD_DEBUG0(("colamd: Need Alen >= %d, given only Alen = %d\n", need, Alen));
+    return (false);
+  }
+
+  Alen -= Col_size + Row_size;
+  Col = (ColStructure<IndexType> *)&A[Alen];
+  Row = (RowStructure<IndexType> *)&A[Alen + Col_size];
+
+  /* === Construct the row and column data structures ===================== */
+
+  if (!Colamd::init_rows_cols(n_row, n_col, Row, Col, A, p, stats)) {
+    /* input matrix is invalid */
+    COLAMD_DEBUG0(("colamd: Matrix invalid\n"));
+    return (false);
+  }
+
+  /* === Initialize scores, kill dense rows/columns ======================= */
+
+  Colamd::init_scoring(n_row, n_col, Row, Col, A, p, knobs, &n_row2, &n_col2, &max_deg);
+
+  /* === Order the supercolumns =========================================== */
+
+  ngarbage = Colamd::find_ordering(n_row, n_col, Alen, Row, Col, A, p, n_col2, max_deg, 2 * nnz);
+
+  /* === Order the non-principal columns ================================== */
+
+  Colamd::order_children(n_col, Col, p);
+
+  /* === Return statistics in stats ======================================= */
+
+  stats[Colamd::DenseRow] = n_row - n_row2;
+  stats[Colamd::DenseCol] = n_col - n_col2;
+  stats[Colamd::DefragCount] = ngarbage;
+  COLAMD_DEBUG0(("colamd: done.\n"));
+  return (true);
+}
+
+/* ========================================================================== */
+/* === NON-USER-CALLABLE ROUTINES: ========================================== */
+/* ========================================================================== */
+
+/* There are no user-callable routines beyond this point in the file */
+
+/* ========================================================================== */
+/* === init_rows_cols ======================================================= */
+/* ========================================================================== */
+
+/*
+  Takes the column form of the matrix in A and creates the row form of the
+  matrix.  Also, row and column attributes are stored in the Col and Row
+  structs.  If the columns are un-sorted or contain duplicate row indices,
+  this routine will also sort and remove duplicate row indices from the
+  column form of the matrix.  Returns false if the matrix is invalid,
+  true otherwise.  Not user-callable.
+*/
+template <typename IndexType>
+static IndexType init_rows_cols /* returns true if OK, or false otherwise */
+    (
+        /* === Parameters ======================================================= */
+
+        IndexType n_row,               /* number of rows of A */
+        IndexType n_col,               /* number of columns of A */
+        RowStructure<IndexType> Row[], /* of size n_row+1 */
+        ColStructure<IndexType> Col[], /* of size n_col+1 */
+        IndexType A[],                 /* row indices of A, of size Alen */
+        IndexType p[],                 /* pointers to columns in A, of size n_col+1 */
+        IndexType stats[NStats]        /* colamd statistics */
+    ) {
+  /* === Local variables ================================================== */
+
+  IndexType col;      /* a column index */
+  IndexType row;      /* a row index */
+  IndexType *cp;      /* a column pointer */
+  IndexType *cp_end;  /* a pointer to the end of a column */
+  IndexType *rp;      /* a row pointer */
+  IndexType *rp_end;  /* a pointer to the end of a row */
+  IndexType last_row; /* previous row */
+
+  /* === Initialize columns, and check column pointers ==================== */
+
+  for (col = 0; col < n_col; col++) {
+    Col[col].start = p[col];
+    Col[col].length = p[col + 1] - p[col];
+
+    if ((Col[col].length) < 0)  // extra parentheses to work-around gcc bug 10200
+    {
+      /* column pointers must be non-decreasing */
+      stats[Colamd::Status] = Colamd::ErrorColLengthNegative;
+      stats[Colamd::Info1] = col;
+      stats[Colamd::Info2] = Col[col].length;
+      COLAMD_DEBUG0(("colamd: col %d length %d < 0\n", col, Col[col].length));
+      return (false);
+    }
+
+    Col[col].shared1.thickness = 1;
+    Col[col].shared2.score = 0;
+    Col[col].shared3.prev = Empty;
+    Col[col].shared4.degree_next = Empty;
+  }
+
+  /* p [0..n_col] no longer needed, used as "head" in subsequent routines */
+
+  /* === Scan columns, compute row degrees, and check row indices ========= */
+
+  stats[Info3] = 0; /* number of duplicate or unsorted row indices*/
+
+  for (row = 0; row < n_row; row++) {
+    Row[row].length = 0;
+    Row[row].shared2.mark = -1;
+  }
+
+  for (col = 0; col < n_col; col++) {
+    last_row = -1;
+
+    cp = &A[p[col]];
+    cp_end = &A[p[col + 1]];
+
+    while (cp < cp_end) {
+      row = *cp++;
+
+      /* make sure row indices within range */
+      if (row < 0 || row >= n_row) {
+        stats[Colamd::Status] = Colamd::ErrorRowIndexOutOfBounds;
+        stats[Colamd::Info1] = col;
+        stats[Colamd::Info2] = row;
+        stats[Colamd::Info3] = n_row;
+        COLAMD_DEBUG0(("colamd: row %d col %d out of bounds\n", row, col));
+        return (false);
+      }
+
+      if (row <= last_row || Row[row].shared2.mark == col) {
+        /* row index are unsorted or repeated (or both), thus col */
+        /* is jumbled.  This is a notice, not an error condition. */
+        stats[Colamd::Status] = Colamd::OkButJumbled;
+        stats[Colamd::Info1] = col;
+        stats[Colamd::Info2] = row;
+        (stats[Colamd::Info3])++;
+        COLAMD_DEBUG1(("colamd: row %d col %d unsorted/duplicate\n", row, col));
+      }
+
+      if (Row[row].shared2.mark != col) {
+        Row[row].length++;
+      } else {
+        /* this is a repeated entry in the column, */
+        /* it will be removed */
+        Col[col].length--;
+      }
+
+      /* mark the row as having been seen in this column */
+      Row[row].shared2.mark = col;
+
+      last_row = row;
+    }
+  }
+
+  /* === Compute row pointers ============================================= */
+
+  /* row form of the matrix starts directly after the column */
+  /* form of matrix in A */
+  Row[0].start = p[n_col];
+  Row[0].shared1.p = Row[0].start;
+  Row[0].shared2.mark = -1;
+  for (row = 1; row < n_row; row++) {
+    Row[row].start = Row[row - 1].start + Row[row - 1].length;
+    Row[row].shared1.p = Row[row].start;
+    Row[row].shared2.mark = -1;
+  }
+
+  /* === Create row form ================================================== */
+
+  if (stats[Status] == OkButJumbled) {
+    /* if cols jumbled, watch for repeated row indices */
+    for (col = 0; col < n_col; col++) {
+      cp = &A[p[col]];
+      cp_end = &A[p[col + 1]];
+      while (cp < cp_end) {
+        row = *cp++;
+        if (Row[row].shared2.mark != col) {
+          A[(Row[row].shared1.p)++] = col;
+          Row[row].shared2.mark = col;
+        }
+      }
+    }
+  } else {
+    /* if cols not jumbled, we don't need the mark (this is faster) */
+    for (col = 0; col < n_col; col++) {
+      cp = &A[p[col]];
+      cp_end = &A[p[col + 1]];
+      while (cp < cp_end) {
+        A[(Row[*cp++].shared1.p)++] = col;
+      }
+    }
+  }
+
+  /* === Clear the row marks and set row degrees ========================== */
+
+  for (row = 0; row < n_row; row++) {
+    Row[row].shared2.mark = 0;
+    Row[row].shared1.degree = Row[row].length;
+  }
+
+  /* === See if we need to re-create columns ============================== */
+
+  if (stats[Status] == OkButJumbled) {
+    COLAMD_DEBUG0(("colamd: reconstructing column form, matrix jumbled\n"));
+
+    /* === Compute col pointers ========================================= */
+
+    /* col form of the matrix starts at A [0]. */
+    /* Note, we may have a gap between the col form and the row */
+    /* form if there were duplicate entries, if so, it will be */
+    /* removed upon the first garbage collection */
+    Col[0].start = 0;
+    p[0] = Col[0].start;
+    for (col = 1; col < n_col; col++) {
+      /* note that the lengths here are for pruned columns, i.e. */
+      /* no duplicate row indices will exist for these columns */
+      Col[col].start = Col[col - 1].start + Col[col - 1].length;
+      p[col] = Col[col].start;
+    }
+
+    /* === Re-create col form =========================================== */
+
+    for (row = 0; row < n_row; row++) {
+      rp = &A[Row[row].start];
+      rp_end = rp + Row[row].length;
+      while (rp < rp_end) {
+        A[(p[*rp++])++] = row;
+      }
+    }
+  }
+
+  /* === Done.  Matrix is not (or no longer) jumbled ====================== */
+
+  return (true);
+}
+
+/* ========================================================================== */
+/* === init_scoring ========================================================= */
+/* ========================================================================== */
+
+/*
+  Kills dense or empty columns and rows, calculates an initial score for
+  each column, and places all columns in the degree lists.  Not user-callable.
+*/
+template <typename IndexType>
+static void init_scoring(
+    /* === Parameters ======================================================= */
+
+    IndexType n_row,               /* number of rows of A */
+    IndexType n_col,               /* number of columns of A */
+    RowStructure<IndexType> Row[], /* of size n_row+1 */
+    ColStructure<IndexType> Col[], /* of size n_col+1 */
+    IndexType A[],                 /* column form and row form of A */
+    IndexType head[],              /* of size n_col+1 */
+    double knobs[NKnobs],          /* parameters */
+    IndexType *p_n_row2,           /* number of non-dense, non-empty rows */
+    IndexType *p_n_col2,           /* number of non-dense, non-empty columns */
+    IndexType *p_max_deg           /* maximum row degree */
+) {
+  /* === Local variables ================================================== */
+
+  IndexType c;               /* a column index */
+  IndexType r, row;          /* a row index */
+  IndexType *cp;             /* a column pointer */
+  IndexType deg;             /* degree of a row or column */
+  IndexType *cp_end;         /* a pointer to the end of a column */
+  IndexType *new_cp;         /* new column pointer */
+  IndexType col_length;      /* length of pruned column */
+  IndexType score;           /* current column score */
+  IndexType n_col2;          /* number of non-dense, non-empty columns */
+  IndexType n_row2;          /* number of non-dense, non-empty rows */
+  IndexType dense_row_count; /* remove rows with more entries than this */
+  IndexType dense_col_count; /* remove cols with more entries than this */
+  IndexType min_score;       /* smallest column score */
+  IndexType max_deg;         /* maximum row degree */
+  IndexType next_col;        /* Used to add to degree list.*/
+
+  /* === Extract knobs ==================================================== */
+
+  dense_row_count = numext::maxi(IndexType(0), numext::mini(IndexType(knobs[Colamd::DenseRow] * n_col), n_col));
+  dense_col_count = numext::maxi(IndexType(0), numext::mini(IndexType(knobs[Colamd::DenseCol] * n_row), n_row));
+  COLAMD_DEBUG1(("colamd: densecount: %d %d\n", dense_row_count, dense_col_count));
+  max_deg = 0;
+  n_col2 = n_col;
+  n_row2 = n_row;
+
+  /* === Kill empty columns =============================================== */
+
+  /* Put the empty columns at the end in their natural order, so that LU */
+  /* factorization can proceed as far as possible. */
+  for (c = n_col - 1; c >= 0; c--) {
+    deg = Col[c].length;
+    if (deg == 0) {
+      /* this is an empty column, kill and order it last */
+      Col[c].shared2.order = --n_col2;
+      Col[c].kill_principal();
+    }
+  }
+  COLAMD_DEBUG1(("colamd: null columns killed: %d\n", n_col - n_col2));
+
+  /* === Kill dense columns =============================================== */
+
+  /* Put the dense columns at the end, in their natural order */
+  for (c = n_col - 1; c >= 0; c--) {
+    /* skip any dead columns */
+    if (Col[c].is_dead()) {
+      continue;
+    }
+    deg = Col[c].length;
+    if (deg > dense_col_count) {
+      /* this is a dense column, kill and order it last */
+      Col[c].shared2.order = --n_col2;
+      /* decrement the row degrees */
+      cp = &A[Col[c].start];
+      cp_end = cp + Col[c].length;
+      while (cp < cp_end) {
+        Row[*cp++].shared1.degree--;
+      }
+      Col[c].kill_principal();
+    }
+  }
+  COLAMD_DEBUG1(("colamd: Dense and null columns killed: %d\n", n_col - n_col2));
+
+  /* === Kill dense and empty rows ======================================== */
+
+  for (r = 0; r < n_row; r++) {
+    deg = Row[r].shared1.degree;
+    COLAMD_ASSERT(deg >= 0 && deg <= n_col);
+    if (deg > dense_row_count || deg == 0) {
+      /* kill a dense or empty row */
+      Row[r].kill();
+      --n_row2;
+    } else {
+      /* keep track of max degree of remaining rows */
+      max_deg = numext::maxi(max_deg, deg);
+    }
+  }
+  COLAMD_DEBUG1(("colamd: Dense and null rows killed: %d\n", n_row - n_row2));
+
+  /* === Compute initial column scores ==================================== */
+
+  /* At this point the row degrees are accurate.  They reflect the number */
+  /* of "live" (non-dense) columns in each row.  No empty rows exist. */
+  /* Some "live" columns may contain only dead rows, however.  These are */
+  /* pruned in the code below. */
+
+  /* now find the initial matlab score for each column */
+  for (c = n_col - 1; c >= 0; c--) {
+    /* skip dead column */
+    if (Col[c].is_dead()) {
+      continue;
+    }
+    score = 0;
+    cp = &A[Col[c].start];
+    new_cp = cp;
+    cp_end = cp + Col[c].length;
+    while (cp < cp_end) {
+      /* get a row */
+      row = *cp++;
+      /* skip if dead */
+      if (Row[row].is_dead()) {
+        continue;
+      }
+      /* compact the column */
+      *new_cp++ = row;
+      /* add row's external degree */
+      score += Row[row].shared1.degree - 1;
+      /* guard against integer overflow */
+      score = numext::mini(score, n_col);
+    }
+    /* determine pruned column length */
+    col_length = (IndexType)(new_cp - &A[Col[c].start]);
+    if (col_length == 0) {
+      /* a newly-made null column (all rows in this col are "dense" */
+      /* and have already been killed) */
+      COLAMD_DEBUG2(("Newly null killed: %d\n", c));
+      Col[c].shared2.order = --n_col2;
+      Col[c].kill_principal();
+    } else {
+      /* set column length and set score */
+      COLAMD_ASSERT(score >= 0);
+      COLAMD_ASSERT(score <= n_col);
+      Col[c].length = col_length;
+      Col[c].shared2.score = score;
+    }
+  }
+  COLAMD_DEBUG1(("colamd: Dense, null, and newly-null columns killed: %d\n", n_col - n_col2));
+
+  /* At this point, all empty rows and columns are dead.  All live columns */
+  /* are "clean" (containing no dead rows) and simplicial (no supercolumns */
+  /* yet).  Rows may contain dead columns, but all live rows contain at */
+  /* least one live column. */
+
+  /* === Initialize degree lists ========================================== */
+
+  /* clear the hash buckets */
+  for (c = 0; c <= n_col; c++) {
+    head[c] = Empty;
+  }
+  min_score = n_col;
+  /* place in reverse order, so low column indices are at the front */
+  /* of the lists.  This is to encourage natural tie-breaking */
+  for (c = n_col - 1; c >= 0; c--) {
+    /* only add principal columns to degree lists */
+    if (Col[c].is_alive()) {
+      COLAMD_DEBUG4(("place %d score %d minscore %d ncol %d\n", c, Col[c].shared2.score, min_score, n_col));
+
+      /* === Add columns score to DList =============================== */
+
+      score = Col[c].shared2.score;
+
+      COLAMD_ASSERT(min_score >= 0);
+      COLAMD_ASSERT(min_score <= n_col);
+      COLAMD_ASSERT(score >= 0);
+      COLAMD_ASSERT(score <= n_col);
+      COLAMD_ASSERT(head[score] >= Empty);
+
+      /* now add this column to dList at proper score location */
+      next_col = head[score];
+      Col[c].shared3.prev = Empty;
+      Col[c].shared4.degree_next = next_col;
+
+      /* if there already was a column with the same score, set its */
+      /* previous pointer to this new column */
+      if (next_col != Empty) {
+        Col[next_col].shared3.prev = c;
+      }
+      head[score] = c;
+
+      /* see if this score is less than current min */
+      min_score = numext::mini(min_score, score);
+    }
+  }
+
+  /* === Return number of remaining columns, and max row degree =========== */
+
+  *p_n_col2 = n_col2;
+  *p_n_row2 = n_row2;
+  *p_max_deg = max_deg;
+}
+
+/* ========================================================================== */
+/* === find_ordering ======================================================== */
+/* ========================================================================== */
+
+/*
+  Order the principal columns of the supercolumn form of the matrix
+  (no supercolumns on input).  Uses a minimum approximate column minimum
+  degree ordering method.  Not user-callable.
+*/
+template <typename IndexType>
+static IndexType find_ordering /* return the number of garbage collections */
+    (
+        /* === Parameters ======================================================= */
+
+        IndexType n_row,               /* number of rows of A */
+        IndexType n_col,               /* number of columns of A */
+        IndexType Alen,                /* size of A, 2*nnz + n_col or larger */
+        RowStructure<IndexType> Row[], /* of size n_row+1 */
+        ColStructure<IndexType> Col[], /* of size n_col+1 */
+        IndexType A[],                 /* column form and row form of A */
+        IndexType head[],              /* of size n_col+1 */
+        IndexType n_col2,              /* Remaining columns to order */
+        IndexType max_deg,             /* Maximum row degree */
+        IndexType pfree                /* index of first free slot (2*nnz on entry) */
+    ) {
+  /* === Local variables ================================================== */
+
+  IndexType k;                   /* current pivot ordering step */
+  IndexType pivot_col;           /* current pivot column */
+  IndexType *cp;                 /* a column pointer */
+  IndexType *rp;                 /* a row pointer */
+  IndexType pivot_row;           /* current pivot row */
+  IndexType *new_cp;             /* modified column pointer */
+  IndexType *new_rp;             /* modified row pointer */
+  IndexType pivot_row_start;     /* pointer to start of pivot row */
+  IndexType pivot_row_degree;    /* number of columns in pivot row */
+  IndexType pivot_row_length;    /* number of supercolumns in pivot row */
+  IndexType pivot_col_score;     /* score of pivot column */
+  IndexType needed_memory;       /* free space needed for pivot row */
+  IndexType *cp_end;             /* pointer to the end of a column */
+  IndexType *rp_end;             /* pointer to the end of a row */
+  IndexType row;                 /* a row index */
+  IndexType col;                 /* a column index */
+  IndexType max_score;           /* maximum possible score */
+  IndexType cur_score;           /* score of current column */
+  unsigned int hash;             /* hash value for supernode detection */
+  IndexType head_column;         /* head of hash bucket */
+  IndexType first_col;           /* first column in hash bucket */
+  IndexType tag_mark;            /* marker value for mark array */
+  IndexType row_mark;            /* Row [row].shared2.mark */
+  IndexType set_difference;      /* set difference size of row with pivot row */
+  IndexType min_score;           /* smallest column score */
+  IndexType col_thickness;       /* "thickness" (no. of columns in a supercol) */
+  IndexType max_mark;            /* maximum value of tag_mark */
+  IndexType pivot_col_thickness; /* number of columns represented by pivot col */
+  IndexType prev_col;            /* Used by Dlist operations. */
+  IndexType next_col;            /* Used by Dlist operations. */
+  IndexType ngarbage;            /* number of garbage collections performed */
+
+  /* === Initialization and clear mark ==================================== */
+
+  max_mark = INT_MAX - n_col; /* INT_MAX defined in <limits.h> */
+  tag_mark = Colamd::clear_mark(n_row, Row);
+  min_score = 0;
+  ngarbage = 0;
+  COLAMD_DEBUG1(("colamd: Ordering, n_col2=%d\n", n_col2));
+
+  /* === Order the columns ================================================ */
+
+  for (k = 0; k < n_col2; /* 'k' is incremented below */) {
+    /* === Select pivot column, and order it ============================ */
+
+    /* make sure degree list isn't empty */
+    COLAMD_ASSERT(min_score >= 0);
+    COLAMD_ASSERT(min_score <= n_col);
+    COLAMD_ASSERT(head[min_score] >= Empty);
+
+    /* get pivot column from head of minimum degree list */
+    while (min_score < n_col && head[min_score] == Empty) {
+      min_score++;
+    }
+    pivot_col = head[min_score];
+    COLAMD_ASSERT(pivot_col >= 0 && pivot_col <= n_col);
+    next_col = Col[pivot_col].shared4.degree_next;
+    head[min_score] = next_col;
+    if (next_col != Empty) {
+      Col[next_col].shared3.prev = Empty;
+    }
+
+    COLAMD_ASSERT(Col[pivot_col].is_alive());
+    COLAMD_DEBUG3(("Pivot col: %d\n", pivot_col));
+
+    /* remember score for defrag check */
+    pivot_col_score = Col[pivot_col].shared2.score;
+
+    /* the pivot column is the kth column in the pivot order */
+    Col[pivot_col].shared2.order = k;
+
+    /* increment order count by column thickness */
+    pivot_col_thickness = Col[pivot_col].shared1.thickness;
+    k += pivot_col_thickness;
+    COLAMD_ASSERT(pivot_col_thickness > 0);
+
+    /* === Garbage_collection, if necessary ============================= */
+
+    needed_memory = numext::mini(pivot_col_score, n_col - k);
+    if (pfree + needed_memory >= Alen) {
+      pfree = Colamd::garbage_collection(n_row, n_col, Row, Col, A, &A[pfree]);
+      ngarbage++;
+      /* after garbage collection we will have enough */
+      COLAMD_ASSERT(pfree + needed_memory < Alen);
+      /* garbage collection has wiped out the Row[].shared2.mark array */
+      tag_mark = Colamd::clear_mark(n_row, Row);
+    }
+
+    /* === Compute pivot row pattern ==================================== */
+
+    /* get starting location for this new merged row */
+    pivot_row_start = pfree;
+
+    /* initialize new row counts to zero */
+    pivot_row_degree = 0;
+
+    /* tag pivot column as having been visited so it isn't included */
+    /* in merged pivot row */
+    Col[pivot_col].shared1.thickness = -pivot_col_thickness;
+
+    /* pivot row is the union of all rows in the pivot column pattern */
+    cp = &A[Col[pivot_col].start];
+    cp_end = cp + Col[pivot_col].length;
+    while (cp < cp_end) {
+      /* get a row */
+      row = *cp++;
+      COLAMD_DEBUG4(("Pivot col pattern %d %d\n", Row[row].is_alive(), row));
+      /* skip if row is dead */
+      if (Row[row].is_dead()) {
+        continue;
+      }
+      rp = &A[Row[row].start];
+      rp_end = rp + Row[row].length;
+      while (rp < rp_end) {
+        /* get a column */
+        col = *rp++;
+        /* add the column, if alive and untagged */
+        col_thickness = Col[col].shared1.thickness;
+        if (col_thickness > 0 && Col[col].is_alive()) {
+          /* tag column in pivot row */
+          Col[col].shared1.thickness = -col_thickness;
+          COLAMD_ASSERT(pfree < Alen);
+          /* place column in pivot row */
+          A[pfree++] = col;
+          pivot_row_degree += col_thickness;
+        }
+      }
+    }
+
+    /* clear tag on pivot column */
+    Col[pivot_col].shared1.thickness = pivot_col_thickness;
+    max_deg = numext::maxi(max_deg, pivot_row_degree);
+
+    /* === Kill all rows used to construct pivot row ==================== */
+
+    /* also kill pivot row, temporarily */
+    cp = &A[Col[pivot_col].start];
+    cp_end = cp + Col[pivot_col].length;
+    while (cp < cp_end) {
+      /* may be killing an already dead row */
+      row = *cp++;
+      COLAMD_DEBUG3(("Kill row in pivot col: %d\n", row));
+      Row[row].kill();
+    }
+
+    /* === Select a row index to use as the new pivot row =============== */
+
+    pivot_row_length = pfree - pivot_row_start;
+    if (pivot_row_length > 0) {
+      /* pick the "pivot" row arbitrarily (first row in col) */
+      pivot_row = A[Col[pivot_col].start];
+      COLAMD_DEBUG3(("Pivotal row is %d\n", pivot_row));
+    } else {
+      /* there is no pivot row, since it is of zero length */
+      pivot_row = Empty;
+      COLAMD_ASSERT(pivot_row_length == 0);
+    }
+    COLAMD_ASSERT(Col[pivot_col].length > 0 || pivot_row_length == 0);
+
+    /* === Approximate degree computation =============================== */
+
+    /* Here begins the computation of the approximate degree.  The column */
+    /* score is the sum of the pivot row "length", plus the size of the */
+    /* set differences of each row in the column minus the pattern of the */
+    /* pivot row itself.  The column ("thickness") itself is also */
+    /* excluded from the column score (we thus use an approximate */
+    /* external degree). */
+
+    /* The time taken by the following code (compute set differences, and */
+    /* add them up) is proportional to the size of the data structure */
+    /* being scanned - that is, the sum of the sizes of each column in */
+    /* the pivot row.  Thus, the amortized time to compute a column score */
+    /* is proportional to the size of that column (where size, in this */
+    /* context, is the column "length", or the number of row indices */
+    /* in that column).  The number of row indices in a column is */
+    /* monotonically non-decreasing, from the length of the original */
+    /* column on input to colamd. */
+
+    /* === Compute set differences ====================================== */
+
+    COLAMD_DEBUG3(("** Computing set differences phase. **\n"));
+
+    /* pivot row is currently dead - it will be revived later. */
+
+    COLAMD_DEBUG3(("Pivot row: "));
+    /* for each column in pivot row */
+    rp = &A[pivot_row_start];
+    rp_end = rp + pivot_row_length;
+    while (rp < rp_end) {
+      col = *rp++;
+      COLAMD_ASSERT(Col[col].is_alive() && col != pivot_col);
+      COLAMD_DEBUG3(("Col: %d\n", col));
+
+      /* clear tags used to construct pivot row pattern */
+      col_thickness = -Col[col].shared1.thickness;
+      COLAMD_ASSERT(col_thickness > 0);
+      Col[col].shared1.thickness = col_thickness;
+
+      /* === Remove column from degree list =========================== */
+
+      cur_score = Col[col].shared2.score;
+      prev_col = Col[col].shared3.prev;
+      next_col = Col[col].shared4.degree_next;
+      COLAMD_ASSERT(cur_score >= 0);
+      COLAMD_ASSERT(cur_score <= n_col);
+      COLAMD_ASSERT(cur_score >= Empty);
+      if (prev_col == Empty) {
+        head[cur_score] = next_col;
+      } else {
+        Col[prev_col].shared4.degree_next = next_col;
+      }
+      if (next_col != Empty) {
+        Col[next_col].shared3.prev = prev_col;
+      }
+
+      /* === Scan the column ========================================== */
+
+      cp = &A[Col[col].start];
+      cp_end = cp + Col[col].length;
+      while (cp < cp_end) {
+        /* get a row */
+        row = *cp++;
+        /* skip if dead */
+        if (Row[row].is_dead()) {
+          continue;
+        }
+        row_mark = Row[row].shared2.mark;
+        COLAMD_ASSERT(row != pivot_row);
+        set_difference = row_mark - tag_mark;
+        /* check if the row has been seen yet */
+        if (set_difference < 0) {
+          COLAMD_ASSERT(Row[row].shared1.degree <= max_deg);
+          set_difference = Row[row].shared1.degree;
+        }
+        /* subtract column thickness from this row's set difference */
+        set_difference -= col_thickness;
+        COLAMD_ASSERT(set_difference >= 0);
+        /* absorb this row if the set difference becomes zero */
+        if (set_difference == 0) {
+          COLAMD_DEBUG3(("aggressive absorption. Row: %d\n", row));
+          Row[row].kill();
+        } else {
+          /* save the new mark */
+          Row[row].shared2.mark = set_difference + tag_mark;
+        }
+      }
+    }
+
+    /* === Add up set differences for each column ======================= */
+
+    COLAMD_DEBUG3(("** Adding set differences phase. **\n"));
+
+    /* for each column in pivot row */
+    rp = &A[pivot_row_start];
+    rp_end = rp + pivot_row_length;
+    while (rp < rp_end) {
+      /* get a column */
+      col = *rp++;
+      COLAMD_ASSERT(Col[col].is_alive() && col != pivot_col);
+      hash = 0;
+      cur_score = 0;
+      cp = &A[Col[col].start];
+      /* compact the column */
+      new_cp = cp;
+      cp_end = cp + Col[col].length;
+
+      COLAMD_DEBUG4(("Adding set diffs for Col: %d.\n", col));
+
+      while (cp < cp_end) {
+        /* get a row */
+        row = *cp++;
+        COLAMD_ASSERT(row >= 0 && row < n_row);
+        /* skip if dead */
+        if (Row[row].is_dead()) {
+          continue;
+        }
+        row_mark = Row[row].shared2.mark;
+        COLAMD_ASSERT(row_mark > tag_mark);
+        /* compact the column */
+        *new_cp++ = row;
+        /* compute hash function */
+        hash += row;
+        /* add set difference */
+        cur_score += row_mark - tag_mark;
+        /* integer overflow... */
+        cur_score = numext::mini(cur_score, n_col);
+      }
+
+      /* recompute the column's length */
+      Col[col].length = (IndexType)(new_cp - &A[Col[col].start]);
+
+      /* === Further mass elimination ================================= */
+
+      if (Col[col].length == 0) {
+        COLAMD_DEBUG4(("further mass elimination. Col: %d\n", col));
+        /* nothing left but the pivot row in this column */
+        Col[col].kill_principal();
+        pivot_row_degree -= Col[col].shared1.thickness;
+        COLAMD_ASSERT(pivot_row_degree >= 0);
+        /* order it */
+        Col[col].shared2.order = k;
+        /* increment order count by column thickness */
+        k += Col[col].shared1.thickness;
+      } else {
+        /* === Prepare for supercolumn detection ==================== */
+
+        COLAMD_DEBUG4(("Preparing supercol detection for Col: %d.\n", col));
+
+        /* save score so far */
+        Col[col].shared2.score = cur_score;
+
+        /* add column to hash table, for supercolumn detection */
+        hash %= n_col + 1;
+
+        COLAMD_DEBUG4((" Hash = %d, n_col = %d.\n", hash, n_col));
+        COLAMD_ASSERT(hash <= n_col);
+
+        head_column = head[hash];
+        if (head_column > Empty) {
+          /* degree list "hash" is non-empty, use prev (shared3) of */
+          /* first column in degree list as head of hash bucket */
+          first_col = Col[head_column].shared3.headhash;
+          Col[head_column].shared3.headhash = col;
+        } else {
+          /* degree list "hash" is empty, use head as hash bucket */
+          first_col = -(head_column + 2);
+          head[hash] = -(col + 2);
+        }
+        Col[col].shared4.hash_next = first_col;
+
+        /* save hash function in Col [col].shared3.hash */
+        Col[col].shared3.hash = (IndexType)hash;
+        COLAMD_ASSERT(Col[col].is_alive());
+      }
+    }
+
+    /* The approximate external column degree is now computed.  */
+
+    /* === Supercolumn detection ======================================== */
+
+    COLAMD_DEBUG3(("** Supercolumn detection phase. **\n"));
+
+    Colamd::detect_super_cols(Col, A, head, pivot_row_start, pivot_row_length);
+
+    /* === Kill the pivotal column ====================================== */
+
+    Col[pivot_col].kill_principal();
+
+    /* === Clear mark =================================================== */
+
+    tag_mark += (max_deg + 1);
+    if (tag_mark >= max_mark) {
+      COLAMD_DEBUG2(("clearing tag_mark\n"));
+      tag_mark = Colamd::clear_mark(n_row, Row);
+    }
+
+    /* === Finalize the new pivot row, and column scores ================ */
+
+    COLAMD_DEBUG3(("** Finalize scores phase. **\n"));
+
+    /* for each column in pivot row */
+    rp = &A[pivot_row_start];
+    /* compact the pivot row */
+    new_rp = rp;
+    rp_end = rp + pivot_row_length;
+    while (rp < rp_end) {
+      col = *rp++;
+      /* skip dead columns */
+      if (Col[col].is_dead()) {
+        continue;
+      }
+      *new_rp++ = col;
+      /* add new pivot row to column */
+      A[Col[col].start + (Col[col].length++)] = pivot_row;
+
+      /* retrieve score so far and add on pivot row's degree. */
+      /* (we wait until here for this in case the pivot */
+      /* row's degree was reduced due to mass elimination). */
+      cur_score = Col[col].shared2.score + pivot_row_degree;
+
+      /* calculate the max possible score as the number of */
+      /* external columns minus the 'k' value minus the */
+      /* columns thickness */
+      max_score = n_col - k - Col[col].shared1.thickness;
+
+      /* make the score the external degree of the union-of-rows */
+      cur_score -= Col[col].shared1.thickness;
+
+      /* make sure score is less or equal than the max score */
+      cur_score = numext::mini(cur_score, max_score);
+      COLAMD_ASSERT(cur_score >= 0);
+
+      /* store updated score */
+      Col[col].shared2.score = cur_score;
+
+      /* === Place column back in degree list ========================= */
+
+      COLAMD_ASSERT(min_score >= 0);
+      COLAMD_ASSERT(min_score <= n_col);
+      COLAMD_ASSERT(cur_score >= 0);
+      COLAMD_ASSERT(cur_score <= n_col);
+      COLAMD_ASSERT(head[cur_score] >= Empty);
+      next_col = head[cur_score];
+      Col[col].shared4.degree_next = next_col;
+      Col[col].shared3.prev = Empty;
+      if (next_col != Empty) {
+        Col[next_col].shared3.prev = col;
+      }
+      head[cur_score] = col;
+
+      /* see if this score is less than current min */
+      min_score = numext::mini(min_score, cur_score);
+    }
+
+    /* === Resurrect the new pivot row ================================== */
+
+    if (pivot_row_degree > 0) {
+      /* update pivot row length to reflect any cols that were killed */
+      /* during super-col detection and mass elimination */
+      Row[pivot_row].start = pivot_row_start;
+      Row[pivot_row].length = (IndexType)(new_rp - &A[pivot_row_start]);
+      Row[pivot_row].shared1.degree = pivot_row_degree;
+      Row[pivot_row].shared2.mark = 0;
+      /* pivot row is no longer dead */
+    }
+  }
+
+  /* === All principal columns have now been ordered ====================== */
+
+  return (ngarbage);
+}
+
+/* ========================================================================== */
+/* === order_children ======================================================= */
+/* ========================================================================== */
+
+/*
+  The find_ordering routine has ordered all of the principal columns (the
+  representatives of the supercolumns).  The non-principal columns have not
+  yet been ordered.  This routine orders those columns by walking up the
+  parent tree (a column is a child of the column which absorbed it).  The
+  final permutation vector is then placed in p [0 ... n_col-1], with p [0]
+  being the first column, and p [n_col-1] being the last.  It doesn't look
+  like it at first glance, but be assured that this routine takes time linear
+  in the number of columns.  Although not immediately obvious, the time
+  taken by this routine is O (n_col), that is, linear in the number of
+  columns.  Not user-callable.
+*/
+template <typename IndexType>
+static inline void order_children(
+    /* === Parameters ======================================================= */
+
+    IndexType n_col,               /* number of columns of A */
+    ColStructure<IndexType> Col[], /* of size n_col+1 */
+    IndexType p[]                  /* p [0 ... n_col-1] is the column permutation*/
+) {
+  /* === Local variables ================================================== */
+
+  IndexType i;      /* loop counter for all columns */
+  IndexType c;      /* column index */
+  IndexType parent; /* index of column's parent */
+  IndexType order;  /* column's order */
+
+  /* === Order each non-principal column ================================== */
+
+  for (i = 0; i < n_col; i++) {
+    /* find an un-ordered non-principal column */
+    COLAMD_ASSERT(col_is_dead(Col, i));
+    if (!Col[i].is_dead_principal() && Col[i].shared2.order == Empty) {
+      parent = i;
+      /* once found, find its principal parent */
+      do {
+        parent = Col[parent].shared1.parent;
+      } while (!Col[parent].is_dead_principal());
+
+      /* now, order all un-ordered non-principal columns along path */
+      /* to this parent.  collapse tree at the same time */
+      c = i;
+      /* get order of parent */
+      order = Col[parent].shared2.order;
+
+      do {
+        COLAMD_ASSERT(Col[c].shared2.order == Empty);
+
+        /* order this column */
+        Col[c].shared2.order = order++;
+        /* collapse tree */
+        Col[c].shared1.parent = parent;
+
+        /* get immediate parent of this column */
+        c = Col[c].shared1.parent;
+
+        /* continue until we hit an ordered column.  There are */
+        /* guaranteed not to be anymore unordered columns */
+        /* above an ordered column */
+      } while (Col[c].shared2.order == Empty);
+
+      /* re-order the super_col parent to largest order for this group */
+      Col[parent].shared2.order = order;
+    }
+  }
+
+  /* === Generate the permutation ========================================= */
+
+  for (c = 0; c < n_col; c++) {
+    p[Col[c].shared2.order] = c;
+  }
+}
+
+/* ========================================================================== */
+/* === detect_super_cols ==================================================== */
+/* ========================================================================== */
+
+/*
+  Detects supercolumns by finding matches between columns in the hash buckets.
+  Check amongst columns in the set A [row_start ... row_start + row_length-1].
+  The columns under consideration are currently *not* in the degree lists,
+  and have already been placed in the hash buckets.
+
+  The hash bucket for columns whose hash function is equal to h is stored
+  as follows:
+
+  if head [h] is >= 0, then head [h] contains a degree list, so:
+
+  head [h] is the first column in degree bucket h.
+  Col [head [h]].headhash gives the first column in hash bucket h.
+
+  otherwise, the degree list is empty, and:
+
+  -(head [h] + 2) is the first column in hash bucket h.
+
+  For a column c in a hash bucket, Col [c].shared3.prev is NOT a "previous
+  column" pointer.  Col [c].shared3.hash is used instead as the hash number
+  for that column.  The value of Col [c].shared4.hash_next is the next column
+  in the same hash bucket.
+
+  Assuming no, or "few" hash collisions, the time taken by this routine is
+  linear in the sum of the sizes (lengths) of each column whose score has
+  just been computed in the approximate degree computation.
+  Not user-callable.
+*/
+template <typename IndexType>
+static void detect_super_cols(
+    /* === Parameters ======================================================= */
+
+    ColStructure<IndexType> Col[], /* of size n_col+1 */
+    IndexType A[],                 /* row indices of A */
+    IndexType head[],              /* head of degree lists and hash buckets */
+    IndexType row_start,           /* pointer to set of columns to check */
+    IndexType row_length           /* number of columns to check */
+) {
+  /* === Local variables ================================================== */
+
+  IndexType hash;        /* hash value for a column */
+  IndexType *rp;         /* pointer to a row */
+  IndexType c;           /* a column index */
+  IndexType super_c;     /* column index of the column to absorb into */
+  IndexType *cp1;        /* column pointer for column super_c */
+  IndexType *cp2;        /* column pointer for column c */
+  IndexType length;      /* length of column super_c */
+  IndexType prev_c;      /* column preceding c in hash bucket */
+  IndexType i;           /* loop counter */
+  IndexType *rp_end;     /* pointer to the end of the row */
+  IndexType col;         /* a column index in the row to check */
+  IndexType head_column; /* first column in hash bucket or degree list */
+  IndexType first_col;   /* first column in hash bucket */
+
+  /* === Consider each column in the row ================================== */
+
+  rp = &A[row_start];
+  rp_end = rp + row_length;
+  while (rp < rp_end) {
+    col = *rp++;
+    if (Col[col].is_dead()) {
+      continue;
+    }
+
+    /* get hash number for this column */
+    hash = Col[col].shared3.hash;
+    COLAMD_ASSERT(hash <= n_col);
+
+    /* === Get the first column in this hash bucket ===================== */
+
+    head_column = head[hash];
+    if (head_column > Empty) {
+      first_col = Col[head_column].shared3.headhash;
+    } else {
+      first_col = -(head_column + 2);
+    }
+
+    /* === Consider each column in the hash bucket ====================== */
+
+    for (super_c = first_col; super_c != Empty; super_c = Col[super_c].shared4.hash_next) {
+      COLAMD_ASSERT(Col[super_c].is_alive());
+      COLAMD_ASSERT(Col[super_c].shared3.hash == hash);
+      length = Col[super_c].length;
+
+      /* prev_c is the column preceding column c in the hash bucket */
+      prev_c = super_c;
+
+      /* === Compare super_c with all columns after it ================ */
+
+      for (c = Col[super_c].shared4.hash_next; c != Empty; c = Col[c].shared4.hash_next) {
+        COLAMD_ASSERT(c != super_c);
+        COLAMD_ASSERT(Col[c].is_alive());
+        COLAMD_ASSERT(Col[c].shared3.hash == hash);
+
+        /* not identical if lengths or scores are different */
+        if (Col[c].length != length || Col[c].shared2.score != Col[super_c].shared2.score) {
+          prev_c = c;
+          continue;
+        }
+
+        /* compare the two columns */
+        cp1 = &A[Col[super_c].start];
+        cp2 = &A[Col[c].start];
+
+        for (i = 0; i < length; i++) {
+          /* the columns are "clean" (no dead rows) */
+          COLAMD_ASSERT(cp1->is_alive());
+          COLAMD_ASSERT(cp2->is_alive());
+          /* row indices will same order for both supercols, */
+          /* no gather scatter necessary */
+          if (*cp1++ != *cp2++) {
+            break;
+          }
+        }
+
+        /* the two columns are different if the for-loop "broke" */
+        if (i != length) {
+          prev_c = c;
+          continue;
+        }
+
+        /* === Got it!  two columns are identical =================== */
+
+        COLAMD_ASSERT(Col[c].shared2.score == Col[super_c].shared2.score);
+
+        Col[super_c].shared1.thickness += Col[c].shared1.thickness;
+        Col[c].shared1.parent = super_c;
+        Col[c].kill_non_principal();
+        /* order c later, in order_children() */
+        Col[c].shared2.order = Empty;
+        /* remove c from hash bucket */
+        Col[prev_c].shared4.hash_next = Col[c].shared4.hash_next;
+      }
+    }
+
+    /* === Empty this hash bucket ======================================= */
+
+    if (head_column > Empty) {
+      /* corresponding degree list "hash" is not empty */
+      Col[head_column].shared3.headhash = Empty;
+    } else {
+      /* corresponding degree list "hash" is empty */
+      head[hash] = Empty;
+    }
+  }
+}
+
+/* ========================================================================== */
+/* === garbage_collection =================================================== */
+/* ========================================================================== */
+
+/*
+  Defragments and compacts columns and rows in the workspace A.  Used when
+  all available memory has been used while performing row merging.  Returns
+  the index of the first free position in A, after garbage collection.  The
+  time taken by this routine is linear is the size of the array A, which is
+  itself linear in the number of nonzeros in the input matrix.
+  Not user-callable.
+*/
+template <typename IndexType>
+static IndexType garbage_collection /* returns the new value of pfree */
+    (
+        /* === Parameters ======================================================= */
+
+        IndexType n_row,               /* number of rows */
+        IndexType n_col,               /* number of columns */
+        RowStructure<IndexType> Row[], /* row info */
+        ColStructure<IndexType> Col[], /* column info */
+        IndexType A[],                 /* A [0 ... Alen-1] holds the matrix */
+        IndexType *pfree               /* &A [0] ... pfree is in use */
+    ) {
+  /* === Local variables ================================================== */
+
+  IndexType *psrc;  /* source pointer */
+  IndexType *pdest; /* destination pointer */
+  IndexType j;      /* counter */
+  IndexType r;      /* a row index */
+  IndexType c;      /* a column index */
+  IndexType length; /* length of a row or column */
+
+  /* === Defragment the columns =========================================== */
+
+  pdest = &A[0];
+  for (c = 0; c < n_col; c++) {
+    if (Col[c].is_alive()) {
+      psrc = &A[Col[c].start];
+
+      /* move and compact the column */
+      COLAMD_ASSERT(pdest <= psrc);
+      Col[c].start = (IndexType)(pdest - &A[0]);
+      length = Col[c].length;
+      for (j = 0; j < length; j++) {
+        r = *psrc++;
+        if (Row[r].is_alive()) {
+          *pdest++ = r;
+        }
+      }
+      Col[c].length = (IndexType)(pdest - &A[Col[c].start]);
+    }
+  }
+
+  /* === Prepare to defragment the rows =================================== */
+
+  for (r = 0; r < n_row; r++) {
+    if (Row[r].is_alive()) {
+      if (Row[r].length == 0) {
+        /* this row is of zero length.  cannot compact it, so kill it */
+        COLAMD_DEBUG3(("Defrag row kill\n"));
+        Row[r].kill();
+      } else {
+        /* save first column index in Row [r].shared2.first_column */
+        psrc = &A[Row[r].start];
+        Row[r].shared2.first_column = *psrc;
+        COLAMD_ASSERT(Row[r].is_alive());
+        /* flag the start of the row with the one's complement of row */
+        *psrc = ones_complement(r);
+      }
+    }
+  }
+
+  /* === Defragment the rows ============================================== */
+
+  psrc = pdest;
+  while (psrc < pfree) {
+    /* find a negative number ... the start of a row */
+    if (*psrc++ < 0) {
+      psrc--;
+      /* get the row index */
+      r = ones_complement(*psrc);
+      COLAMD_ASSERT(r >= 0 && r < n_row);
+      /* restore first column index */
+      *psrc = Row[r].shared2.first_column;
+      COLAMD_ASSERT(Row[r].is_alive());
+
+      /* move and compact the row */
+      COLAMD_ASSERT(pdest <= psrc);
+      Row[r].start = (IndexType)(pdest - &A[0]);
+      length = Row[r].length;
+      for (j = 0; j < length; j++) {
+        c = *psrc++;
+        if (Col[c].is_alive()) {
+          *pdest++ = c;
+        }
+      }
+      Row[r].length = (IndexType)(pdest - &A[Row[r].start]);
+    }
+  }
+  /* ensure we found all the rows */
+  COLAMD_ASSERT(debug_rows == 0);
+
+  /* === Return the new value of pfree ==================================== */
+
+  return ((IndexType)(pdest - &A[0]));
+}
+
+/* ========================================================================== */
+/* === clear_mark =========================================================== */
+/* ========================================================================== */
+
+/*
+  Clears the Row [].shared2.mark array, and returns the new tag_mark.
+  Return value is the new tag_mark.  Not user-callable.
+*/
+template <typename IndexType>
+static inline IndexType clear_mark /* return the new value for tag_mark */
+    (
+        /* === Parameters ======================================================= */
+
+        IndexType n_row,              /* number of rows in A */
+        RowStructure<IndexType> Row[] /* Row [0 ... n_row-1].shared2.mark is set to zero */
+    ) {
+  /* === Local variables ================================================== */
+
+  IndexType r;
+
+  for (r = 0; r < n_row; r++) {
+    if (Row[r].is_alive()) {
+      Row[r].shared2.mark = 0;
+    }
+  }
+  return (1);
+}
+
+}  // namespace Colamd
+}  // namespace internal
+}  // namespace Eigen
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/InternalHeaderCheck.h
new file mode 100644
index 0000000..713c447
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
+#error "Please include Eigen/OrderingMethods instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/Ordering.h b/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/Ordering.h
new file mode 100644
index 0000000..282d09f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/OrderingMethods/Ordering.h
@@ -0,0 +1,146 @@
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012  Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ORDERING_H
+#define EIGEN_ORDERING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+#include "Eigen_Colamd.h"
+
+namespace Eigen {
+namespace internal {
+
+/** \internal
+ * \ingroup OrderingMethods_Module
+ * \param[in] A the input non-symmetric matrix
+ * \param[out] symmat the symmetric pattern A^T+A from the input matrix \a A.
+ * FIXME: only the sparsity pattern should be used here; values should be ignored.
+ */
+template <typename MatrixType>
+void ordering_helper_at_plus_a(const MatrixType& A, MatrixType& symmat) {
+  MatrixType C;
+  C = A.transpose();  // NOTE: Could be  costly
+  for (int i = 0; i < C.rows(); i++) {
+    for (typename MatrixType::InnerIterator it(C, i); it; ++it) it.valueRef() = typename MatrixType::Scalar(0);
+  }
+  symmat = C + A;
+}
+
+}  // namespace internal
+
+/** \ingroup OrderingMethods_Module
+ * \class AMDOrdering
+ *
+ * Functor computing the \em approximate \em minimum \em degree ordering
+ * If the matrix is not structurally symmetric, an ordering of A^T+A is computed
+ * \tparam  StorageIndex The type of indices of the matrix
+ * \sa COLAMDOrdering
+ */
+template <typename StorageIndex>
+class AMDOrdering {
+ public:
+  typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+
+  /** Compute the permutation vector from a sparse matrix
+   * This routine is much faster if the input matrix is column-major
+   */
+  template <typename MatrixType>
+  void operator()(const MatrixType& mat, PermutationType& perm) {
+    // Compute the symmetric pattern
+    SparseMatrix<typename MatrixType::Scalar, ColMajor, StorageIndex> symm;
+    internal::ordering_helper_at_plus_a(mat, symm);
+
+    // Call the AMD routine
+    // m_mat.prune(keep_diag());
+    internal::minimum_degree_ordering(symm, perm);
+  }
+
+  /** Compute the permutation with a selfadjoint matrix */
+  template <typename SrcType, unsigned int SrcUpLo>
+  void operator()(const SparseSelfAdjointView<SrcType, SrcUpLo>& mat, PermutationType& perm) {
+    SparseMatrix<typename SrcType::Scalar, ColMajor, StorageIndex> C;
+    C = mat;
+
+    // Call the AMD routine
+    // m_mat.prune(keep_diag()); //Remove the diagonal elements
+    internal::minimum_degree_ordering(C, perm);
+  }
+};
+
+/** \ingroup OrderingMethods_Module
+ * \class NaturalOrdering
+ *
+ * Functor computing the natural ordering (identity)
+ *
+ * \note Returns an empty permutation matrix
+ * \tparam  StorageIndex The type of indices of the matrix
+ */
+template <typename StorageIndex>
+class NaturalOrdering {
+ public:
+  typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+
+  /** Compute the permutation vector from a column-major sparse matrix */
+  template <typename MatrixType>
+  void operator()(const MatrixType& /*mat*/, PermutationType& perm) {
+    perm.resize(0);
+  }
+};
+
+/** \ingroup OrderingMethods_Module
+ * \class COLAMDOrdering
+ *
+ * \tparam  StorageIndex The type of indices of the matrix
+ *
+ * Functor computing the \em column \em approximate \em minimum \em degree ordering
+ * The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()).
+ */
+template <typename StorageIndex>
+class COLAMDOrdering {
+ public:
+  typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+  typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
+
+  /** Compute the permutation vector \a perm form the sparse matrix \a mat
+   * \warning The input sparse matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
+   */
+  template <typename MatrixType>
+  void operator()(const MatrixType& mat, PermutationType& perm) {
+    eigen_assert(mat.isCompressed() &&
+                 "COLAMDOrdering requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it "
+                 "to COLAMDOrdering");
+
+    StorageIndex m = StorageIndex(mat.rows());
+    StorageIndex n = StorageIndex(mat.cols());
+    StorageIndex nnz = StorageIndex(mat.nonZeros());
+    // Get the recommended value of Alen to be used by colamd
+    StorageIndex Alen = internal::Colamd::recommended(nnz, m, n);
+    // Set the default parameters
+    double knobs[internal::Colamd::NKnobs];
+    StorageIndex stats[internal::Colamd::NStats];
+    internal::Colamd::set_defaults(knobs);
+
+    IndexVector p(n + 1), A(Alen);
+    for (StorageIndex i = 0; i <= n; i++) p(i) = mat.outerIndexPtr()[i];
+    for (StorageIndex i = 0; i < nnz; i++) A(i) = mat.innerIndexPtr()[i];
+    // Call Colamd routine to compute the ordering
+    StorageIndex info = internal::Colamd::compute_ordering(m, n, Alen, A.data(), p.data(), knobs, stats);
+    EIGEN_UNUSED_VARIABLE(info);
+    eigen_assert(info && "COLAMD failed ");
+
+    perm.resize(n);
+    for (StorageIndex i = 0; i < n; i++) perm.indices()(p(i)) = i;
+  }
+};
+
+}  // end namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/PaStiXSupport/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/PaStiXSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000..f588e50
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/PaStiXSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
+#error "Please include Eigen/PaStiXSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/PaStiXSupport/PaStiXSupport.h b/data_acq_and_processing/third_party/eigen/Eigen/src/PaStiXSupport/PaStiXSupport.h
new file mode 100644
index 0000000..fb75133
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/PaStiXSupport/PaStiXSupport.h
@@ -0,0 +1,632 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PASTIXSUPPORT_H
+#define EIGEN_PASTIXSUPPORT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+#if defined(DCOMPLEX)
+#define PASTIX_COMPLEX COMPLEX
+#define PASTIX_DCOMPLEX DCOMPLEX
+#else
+#define PASTIX_COMPLEX std::complex<float>
+#define PASTIX_DCOMPLEX std::complex<double>
+#endif
+
+/** \ingroup PaStiXSupport_Module
+ * \brief Interface to the PaStix solver
+ *
+ * This class is used to solve the linear systems A.X = B via the PaStix library.
+ * The matrix can be either real or complex, symmetric or not.
+ *
+ * \sa TutorialSparseDirectSolvers
+ */
+template <typename MatrixType_, bool IsStrSym = false>
+class PastixLU;
+template <typename MatrixType_, int Options>
+class PastixLLT;
+template <typename MatrixType_, int Options>
+class PastixLDLT;
+
+namespace internal {
+
+template <class Pastix>
+struct pastix_traits;
+
+template <typename MatrixType_>
+struct pastix_traits<PastixLU<MatrixType_> > {
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType_::Scalar Scalar;
+  typedef typename MatrixType_::RealScalar RealScalar;
+  typedef typename MatrixType_::StorageIndex StorageIndex;
+};
+
+template <typename MatrixType_, int Options>
+struct pastix_traits<PastixLLT<MatrixType_, Options> > {
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType_::Scalar Scalar;
+  typedef typename MatrixType_::RealScalar RealScalar;
+  typedef typename MatrixType_::StorageIndex StorageIndex;
+};
+
+template <typename MatrixType_, int Options>
+struct pastix_traits<PastixLDLT<MatrixType_, Options> > {
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType_::Scalar Scalar;
+  typedef typename MatrixType_::RealScalar RealScalar;
+  typedef typename MatrixType_::StorageIndex StorageIndex;
+};
+
+inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, float *vals,
+                         int *perm, int *invp, float *x, int nbrhs, int *iparm, double *dparm) {
+  if (n == 0) {
+    ptr = NULL;
+    idx = NULL;
+    vals = NULL;
+  }
+  if (nbrhs == 0) {
+    x = NULL;
+    nbrhs = 1;
+  }
+  s_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm);
+}
+
+inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, double *vals,
+                         int *perm, int *invp, double *x, int nbrhs, int *iparm, double *dparm) {
+  if (n == 0) {
+    ptr = NULL;
+    idx = NULL;
+    vals = NULL;
+  }
+  if (nbrhs == 0) {
+    x = NULL;
+    nbrhs = 1;
+  }
+  d_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm);
+}
+
+inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx,
+                         std::complex<float> *vals, int *perm, int *invp, std::complex<float> *x, int nbrhs, int *iparm,
+                         double *dparm) {
+  if (n == 0) {
+    ptr = NULL;
+    idx = NULL;
+    vals = NULL;
+  }
+  if (nbrhs == 0) {
+    x = NULL;
+    nbrhs = 1;
+  }
+  c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_COMPLEX *>(vals), perm, invp,
+           reinterpret_cast<PASTIX_COMPLEX *>(x), nbrhs, iparm, dparm);
+}
+
+inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx,
+                         std::complex<double> *vals, int *perm, int *invp, std::complex<double> *x, int nbrhs,
+                         int *iparm, double *dparm) {
+  if (n == 0) {
+    ptr = NULL;
+    idx = NULL;
+    vals = NULL;
+  }
+  if (nbrhs == 0) {
+    x = NULL;
+    nbrhs = 1;
+  }
+  z_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_DCOMPLEX *>(vals), perm, invp,
+           reinterpret_cast<PASTIX_DCOMPLEX *>(x), nbrhs, iparm, dparm);
+}
+
+// Convert the matrix  to Fortran-style Numbering
+template <typename MatrixType>
+void c_to_fortran_numbering(MatrixType &mat) {
+  if (!(mat.outerIndexPtr()[0])) {
+    int i;
+    for (i = 0; i <= mat.rows(); ++i) ++mat.outerIndexPtr()[i];
+    for (i = 0; i < mat.nonZeros(); ++i) ++mat.innerIndexPtr()[i];
+  }
+}
+
+// Convert to C-style Numbering
+template <typename MatrixType>
+void fortran_to_c_numbering(MatrixType &mat) {
+  // Check the Numbering
+  if (mat.outerIndexPtr()[0] == 1) {  // Convert to C-style numbering
+    int i;
+    for (i = 0; i <= mat.rows(); ++i) --mat.outerIndexPtr()[i];
+    for (i = 0; i < mat.nonZeros(); ++i) --mat.innerIndexPtr()[i];
+  }
+}
+}  // namespace internal
+
+// This is the base class to interface with PaStiX functions.
+// Users should not used this class directly.
+template <class Derived>
+class PastixBase : public SparseSolverBase<Derived> {
+ protected:
+  typedef SparseSolverBase<Derived> Base;
+  using Base::derived;
+  using Base::m_isInitialized;
+
+ public:
+  using Base::_solve_impl;
+
+  typedef typename internal::pastix_traits<Derived>::MatrixType MatrixType_;
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef Matrix<Scalar, Dynamic, 1> Vector;
+  typedef SparseMatrix<Scalar, ColMajor> ColSpMatrix;
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+ public:
+  PastixBase() : m_initisOk(false), m_analysisIsOk(false), m_factorizationIsOk(false), m_pastixdata(0), m_size(0) {
+    init();
+  }
+
+  ~PastixBase() { clean(); }
+
+  template <typename Rhs, typename Dest>
+  bool _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const;
+
+  /** Returns a reference to the integer vector IPARM of PaStiX parameters
+   * to modify the default parameters.
+   * The statistics related to the different phases of factorization and solve are saved here as well
+   * \sa analyzePattern() factorize()
+   */
+  Array<StorageIndex, IPARM_SIZE, 1> &iparm() { return m_iparm; }
+
+  /** Return a reference to a particular index parameter of the IPARM vector
+   * \sa iparm()
+   */
+
+  int &iparm(int idxparam) { return m_iparm(idxparam); }
+
+  /** Returns a reference to the double vector DPARM of PaStiX parameters
+   * The statistics related to the different phases of factorization and solve are saved here as well
+   * \sa analyzePattern() factorize()
+   */
+  Array<double, DPARM_SIZE, 1> &dparm() { return m_dparm; }
+
+  /** Return a reference to a particular index parameter of the DPARM vector
+   * \sa dparm()
+   */
+  double &dparm(int idxparam) { return m_dparm(idxparam); }
+
+  inline Index cols() const { return m_size; }
+  inline Index rows() const { return m_size; }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the PaStiX reports a problem
+   *          \c InvalidInput if the input matrix is invalid
+   *
+   * \sa iparm()
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+ protected:
+  // Initialize the Pastix data structure, check the matrix
+  void init();
+
+  // Compute the ordering and the symbolic factorization
+  void analyzePattern(ColSpMatrix &mat);
+
+  // Compute the numerical factorization
+  void factorize(ColSpMatrix &mat);
+
+  // Free all the data allocated by Pastix
+  void clean() {
+    eigen_assert(m_initisOk && "The Pastix structure should be allocated first");
+    m_iparm(IPARM_START_TASK) = API_TASK_CLEAN;
+    m_iparm(IPARM_END_TASK) = API_TASK_CLEAN;
+    internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar *)0, m_perm.data(), m_invp.data(), 0, 0,
+                           m_iparm.data(), m_dparm.data());
+  }
+
+  void compute(ColSpMatrix &mat);
+
+  int m_initisOk;
+  int m_analysisIsOk;
+  int m_factorizationIsOk;
+  mutable ComputationInfo m_info;
+  mutable pastix_data_t *m_pastixdata;              // Data structure for pastix
+  mutable int m_comm;                               // The MPI communicator identifier
+  mutable Array<int, IPARM_SIZE, 1> m_iparm;        // integer vector for the input parameters
+  mutable Array<double, DPARM_SIZE, 1> m_dparm;     // Scalar vector for the input parameters
+  mutable Matrix<StorageIndex, Dynamic, 1> m_perm;  // Permutation vector
+  mutable Matrix<StorageIndex, Dynamic, 1> m_invp;  // Inverse permutation vector
+  mutable int m_size;                               // Size of the matrix
+};
+
+/** Initialize the PaStiX data structure.
+ *A first call to this function fills iparm and dparm with the default PaStiX parameters
+ * \sa iparm() dparm()
+ */
+template <class Derived>
+void PastixBase<Derived>::init() {
+  m_size = 0;
+  m_iparm.setZero(IPARM_SIZE);
+  m_dparm.setZero(DPARM_SIZE);
+
+  m_iparm(IPARM_MODIFY_PARAMETER) = API_NO;
+  pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, 0, 0, 0, 0, 1, m_iparm.data(), m_dparm.data());
+
+  m_iparm[IPARM_MATRIX_VERIFICATION] = API_NO;
+  m_iparm[IPARM_VERBOSE] = API_VERBOSE_NOT;
+  m_iparm[IPARM_ORDERING] = API_ORDER_SCOTCH;
+  m_iparm[IPARM_INCOMPLETE] = API_NO;
+  m_iparm[IPARM_OOC_LIMIT] = 2000;
+  m_iparm[IPARM_RHS_MAKING] = API_RHS_B;
+  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
+
+  m_iparm(IPARM_START_TASK) = API_TASK_INIT;
+  m_iparm(IPARM_END_TASK) = API_TASK_INIT;
+  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar *)0, 0, 0, 0, 0, m_iparm.data(),
+                         m_dparm.data());
+
+  // Check the returned error
+  if (m_iparm(IPARM_ERROR_NUMBER)) {
+    m_info = InvalidInput;
+    m_initisOk = false;
+  } else {
+    m_info = Success;
+    m_initisOk = true;
+  }
+}
+
+template <class Derived>
+void PastixBase<Derived>::compute(ColSpMatrix &mat) {
+  eigen_assert(mat.rows() == mat.cols() && "The input matrix should be squared");
+
+  analyzePattern(mat);
+  factorize(mat);
+
+  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
+}
+
+template <class Derived>
+void PastixBase<Derived>::analyzePattern(ColSpMatrix &mat) {
+  eigen_assert(m_initisOk && "The initialization of PaSTiX failed");
+
+  // clean previous calls
+  if (m_size > 0) clean();
+
+  m_size = internal::convert_index<int>(mat.rows());
+  m_perm.resize(m_size);
+  m_invp.resize(m_size);
+
+  m_iparm(IPARM_START_TASK) = API_TASK_ORDERING;
+  m_iparm(IPARM_END_TASK) = API_TASK_ANALYSE;
+  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
+                         mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
+
+  // Check the returned error
+  if (m_iparm(IPARM_ERROR_NUMBER)) {
+    m_info = NumericalIssue;
+    m_analysisIsOk = false;
+  } else {
+    m_info = Success;
+    m_analysisIsOk = true;
+  }
+}
+
+template <class Derived>
+void PastixBase<Derived>::factorize(ColSpMatrix &mat) {
+  //   if(&m_cpyMat != &mat) m_cpyMat = mat;
+  eigen_assert(m_analysisIsOk && "The analysis phase should be called before the factorization phase");
+  m_iparm(IPARM_START_TASK) = API_TASK_NUMFACT;
+  m_iparm(IPARM_END_TASK) = API_TASK_NUMFACT;
+  m_size = internal::convert_index<int>(mat.rows());
+
+  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
+                         mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
+
+  // Check the returned error
+  if (m_iparm(IPARM_ERROR_NUMBER)) {
+    m_info = NumericalIssue;
+    m_factorizationIsOk = false;
+    m_isInitialized = false;
+  } else {
+    m_info = Success;
+    m_factorizationIsOk = true;
+    m_isInitialized = true;
+  }
+}
+
+/* Solve the system */
+template <typename Base>
+template <typename Rhs, typename Dest>
+bool PastixBase<Base>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const {
+  eigen_assert(m_isInitialized && "The matrix should be factorized first");
+  EIGEN_STATIC_ASSERT((Dest::Flags & RowMajorBit) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  int rhs = 1;
+
+  x = b; /* on return, x is overwritten by the computed solution */
+
+  for (int i = 0; i < b.cols(); i++) {
+    m_iparm[IPARM_START_TASK] = API_TASK_SOLVE;
+    m_iparm[IPARM_END_TASK] = API_TASK_REFINE;
+
+    internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, internal::convert_index<int>(x.rows()), 0, 0, 0,
+                           m_perm.data(), m_invp.data(), &x(0, i), rhs, m_iparm.data(), m_dparm.data());
+  }
+
+  // Check the returned error
+  m_info = m_iparm(IPARM_ERROR_NUMBER) == 0 ? Success : NumericalIssue;
+
+  return m_iparm(IPARM_ERROR_NUMBER) == 0;
+}
+
+/** \ingroup PaStiXSupport_Module
+ * \class PastixLU
+ * \brief Sparse direct LU solver based on PaStiX library
+ *
+ * This class is used to solve the linear systems A.X = B with a supernodal LU
+ * factorization in the PaStiX library. The matrix A should be squared and nonsingular
+ * PaStiX requires that the matrix A has a symmetric structural pattern.
+ * This interface can symmetrize the input matrix otherwise.
+ * The vectors or matrices X and B can be either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam IsStrSym Indicates if the input matrix has a symmetric pattern, default is false
+ * NOTE : Note that if the analysis and factorization phase are called separately,
+ * the input matrix will be symmetrized at each call, hence it is advised to
+ * symmetrize the matrix in a end-user program and set \p IsStrSym to true
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept, class SparseLU
+ *
+ */
+template <typename MatrixType_, bool IsStrSym>
+class PastixLU : public PastixBase<PastixLU<MatrixType_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef PastixBase<PastixLU<MatrixType> > Base;
+  typedef typename Base::ColSpMatrix ColSpMatrix;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+
+ public:
+  PastixLU() : Base() { init(); }
+
+  explicit PastixLU(const MatrixType &matrix) : Base() {
+    init();
+    compute(matrix);
+  }
+  /** Compute the LU supernodal factorization of \p matrix.
+   * iparm and dparm can be used to tune the PaStiX parameters.
+   * see the PaStiX user's manual
+   * \sa analyzePattern() factorize()
+   */
+  void compute(const MatrixType &matrix) {
+    m_structureIsUptodate = false;
+    ColSpMatrix temp;
+    grabMatrix(matrix, temp);
+    Base::compute(temp);
+  }
+  /** Compute the LU symbolic factorization of \p matrix using its sparsity pattern.
+   * Several ordering methods can be used at this step. See the PaStiX user's manual.
+   * The result of this operation can be used with successive matrices having the same pattern as \p matrix
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType &matrix) {
+    m_structureIsUptodate = false;
+    ColSpMatrix temp;
+    grabMatrix(matrix, temp);
+    Base::analyzePattern(temp);
+  }
+
+  /** Compute the LU supernodal factorization of \p matrix
+   * WARNING The matrix \p matrix should have the same structural pattern
+   * as the same used in the analysis phase.
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType &matrix) {
+    ColSpMatrix temp;
+    grabMatrix(matrix, temp);
+    Base::factorize(temp);
+  }
+
+ protected:
+  void init() {
+    m_structureIsUptodate = false;
+    m_iparm(IPARM_SYM) = API_SYM_NO;
+    m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
+  }
+
+  void grabMatrix(const MatrixType &matrix, ColSpMatrix &out) {
+    if (IsStrSym)
+      out = matrix;
+    else {
+      if (!m_structureIsUptodate) {
+        // update the transposed structure
+        m_transposedStructure = matrix.transpose();
+
+        // Set the elements of the matrix to zero
+        for (Index j = 0; j < m_transposedStructure.outerSize(); ++j)
+          for (typename ColSpMatrix::InnerIterator it(m_transposedStructure, j); it; ++it) it.valueRef() = 0.0;
+
+        m_structureIsUptodate = true;
+      }
+
+      out = m_transposedStructure + matrix;
+    }
+    internal::c_to_fortran_numbering(out);
+  }
+
+  using Base::m_dparm;
+  using Base::m_iparm;
+
+  ColSpMatrix m_transposedStructure;
+  bool m_structureIsUptodate;
+};
+
+/** \ingroup PaStiXSupport_Module
+ * \class PastixLLT
+ * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
+ *
+ * This class is used to solve the linear systems A.X = B via a LL^T supernodal Cholesky factorization
+ * available in the PaStiX library. The matrix A should be symmetric and positive definite
+ * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
+ * The vectors or matrices X and B can be either dense or sparse
+ *
+ * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept, class SimplicialLLT
+ */
+template <typename MatrixType_, int UpLo_>
+class PastixLLT : public PastixBase<PastixLLT<MatrixType_, UpLo_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef PastixBase<PastixLLT<MatrixType, UpLo_> > Base;
+  typedef typename Base::ColSpMatrix ColSpMatrix;
+
+ public:
+  enum { UpLo = UpLo_ };
+  PastixLLT() : Base() { init(); }
+
+  explicit PastixLLT(const MatrixType &matrix) : Base() {
+    init();
+    compute(matrix);
+  }
+
+  /** Compute the L factor of the LL^T supernodal factorization of \p matrix
+   * \sa analyzePattern() factorize()
+   */
+  void compute(const MatrixType &matrix) {
+    ColSpMatrix temp;
+    grabMatrix(matrix, temp);
+    Base::compute(temp);
+  }
+
+  /** Compute the LL^T symbolic factorization of \p matrix using its sparsity pattern
+   * The result of this operation can be used with successive matrices having the same pattern as \p matrix
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType &matrix) {
+    ColSpMatrix temp;
+    grabMatrix(matrix, temp);
+    Base::analyzePattern(temp);
+  }
+  /** Compute the LL^T supernodal numerical factorization of \p matrix
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType &matrix) {
+    ColSpMatrix temp;
+    grabMatrix(matrix, temp);
+    Base::factorize(temp);
+  }
+
+ protected:
+  using Base::m_iparm;
+
+  void init() {
+    m_iparm(IPARM_SYM) = API_SYM_YES;
+    m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT;
+  }
+
+  void grabMatrix(const MatrixType &matrix, ColSpMatrix &out) {
+    out.resize(matrix.rows(), matrix.cols());
+    // Pastix supports only lower, column-major matrices
+    out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
+    internal::c_to_fortran_numbering(out);
+  }
+};
+
+/** \ingroup PaStiXSupport_Module
+ * \class PastixLDLT
+ * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
+ *
+ * This class is used to solve the linear systems A.X = B via a LDL^T supernodal Cholesky factorization
+ * available in the PaStiX library. The matrix A should be symmetric and positive definite
+ * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
+ * The vectors or matrices X and B can be either dense or sparse
+ *
+ * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept, class SimplicialLDLT
+ */
+template <typename MatrixType_, int UpLo_>
+class PastixLDLT : public PastixBase<PastixLDLT<MatrixType_, UpLo_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef PastixBase<PastixLDLT<MatrixType, UpLo_> > Base;
+  typedef typename Base::ColSpMatrix ColSpMatrix;
+
+ public:
+  enum { UpLo = UpLo_ };
+  PastixLDLT() : Base() { init(); }
+
+  explicit PastixLDLT(const MatrixType &matrix) : Base() {
+    init();
+    compute(matrix);
+  }
+
+  /** Compute the L and D factors of the LDL^T factorization of \p matrix
+   * \sa analyzePattern() factorize()
+   */
+  void compute(const MatrixType &matrix) {
+    ColSpMatrix temp;
+    grabMatrix(matrix, temp);
+    Base::compute(temp);
+  }
+
+  /** Compute the LDL^T symbolic factorization of \p matrix using its sparsity pattern
+   * The result of this operation can be used with successive matrices having the same pattern as \p matrix
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType &matrix) {
+    ColSpMatrix temp;
+    grabMatrix(matrix, temp);
+    Base::analyzePattern(temp);
+  }
+  /** Compute the LDL^T supernodal numerical factorization of \p matrix
+   *
+   */
+  void factorize(const MatrixType &matrix) {
+    ColSpMatrix temp;
+    grabMatrix(matrix, temp);
+    Base::factorize(temp);
+  }
+
+ protected:
+  using Base::m_iparm;
+
+  void init() {
+    m_iparm(IPARM_SYM) = API_SYM_YES;
+    m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT;
+  }
+
+  void grabMatrix(const MatrixType &matrix, ColSpMatrix &out) {
+    // Pastix supports only lower, column-major matrices
+    out.resize(matrix.rows(), matrix.cols());
+    out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
+    internal::c_to_fortran_numbering(out);
+  }
+};
+
+}  // end namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/PardisoSupport/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/PardisoSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000..8ef33f0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/PardisoSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
+#error "Please include Eigen/PardisoSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/PardisoSupport/PardisoSupport.h b/data_acq_and_processing/third_party/eigen/Eigen/src/PardisoSupport/PardisoSupport.h
new file mode 100644
index 0000000..90df3cd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/PardisoSupport/PardisoSupport.h
@@ -0,0 +1,500 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL PARDISO
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_PARDISOSUPPORT_H
+#define EIGEN_PARDISOSUPPORT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename MatrixType_>
+class PardisoLU;
+template <typename MatrixType_, int Options = Upper>
+class PardisoLLT;
+template <typename MatrixType_, int Options = Upper>
+class PardisoLDLT;
+
+namespace internal {
+template <typename IndexType>
+struct pardiso_run_selector {
+  static IndexType run(_MKL_DSS_HANDLE_t pt, IndexType maxfct, IndexType mnum, IndexType type, IndexType phase,
+                       IndexType n, void* a, IndexType* ia, IndexType* ja, IndexType* perm, IndexType nrhs,
+                       IndexType* iparm, IndexType msglvl, void* b, void* x) {
+    IndexType error = 0;
+    ::pardiso(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
+    return error;
+  }
+};
+template <>
+struct pardiso_run_selector<long long int> {
+  typedef long long int IndexType;
+  static IndexType run(_MKL_DSS_HANDLE_t pt, IndexType maxfct, IndexType mnum, IndexType type, IndexType phase,
+                       IndexType n, void* a, IndexType* ia, IndexType* ja, IndexType* perm, IndexType nrhs,
+                       IndexType* iparm, IndexType msglvl, void* b, void* x) {
+    IndexType error = 0;
+    ::pardiso_64(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
+    return error;
+  }
+};
+
+template <class Pardiso>
+struct pardiso_traits;
+
+template <typename MatrixType_>
+struct pardiso_traits<PardisoLU<MatrixType_> > {
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType_::Scalar Scalar;
+  typedef typename MatrixType_::RealScalar RealScalar;
+  typedef typename MatrixType_::StorageIndex StorageIndex;
+};
+
+template <typename MatrixType_, int Options>
+struct pardiso_traits<PardisoLLT<MatrixType_, Options> > {
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType_::Scalar Scalar;
+  typedef typename MatrixType_::RealScalar RealScalar;
+  typedef typename MatrixType_::StorageIndex StorageIndex;
+};
+
+template <typename MatrixType_, int Options>
+struct pardiso_traits<PardisoLDLT<MatrixType_, Options> > {
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType_::Scalar Scalar;
+  typedef typename MatrixType_::RealScalar RealScalar;
+  typedef typename MatrixType_::StorageIndex StorageIndex;
+};
+
+}  // end namespace internal
+
+template <class Derived>
+class PardisoImpl : public SparseSolverBase<Derived> {
+ protected:
+  typedef SparseSolverBase<Derived> Base;
+  using Base::derived;
+  using Base::m_isInitialized;
+
+  typedef internal::pardiso_traits<Derived> Traits;
+
+ public:
+  using Base::_solve_impl;
+
+  typedef typename Traits::MatrixType MatrixType;
+  typedef typename Traits::Scalar Scalar;
+  typedef typename Traits::RealScalar RealScalar;
+  typedef typename Traits::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, RowMajor, StorageIndex> SparseMatrixType;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef Matrix<StorageIndex, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+  typedef Matrix<StorageIndex, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+  typedef Array<StorageIndex, 64, 1, DontAlign> ParameterType;
+  enum { ScalarIsComplex = NumTraits<Scalar>::IsComplex, ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
+
+  PardisoImpl() : m_analysisIsOk(false), m_factorizationIsOk(false) {
+    eigen_assert((sizeof(StorageIndex) >= sizeof(_INTEGER_t) && sizeof(StorageIndex) <= 8) &&
+                 "Non-supported index type");
+    m_iparm.setZero();
+    m_msglvl = 0;  // No output
+    m_isInitialized = false;
+  }
+
+  ~PardisoImpl() { pardisoRelease(); }
+
+  inline Index cols() const { return m_size; }
+  inline Index rows() const { return m_size; }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix appears to be negative.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  /** \warning for advanced usage only.
+   * \returns a reference to the parameter array controlling PARDISO.
+   * See the PARDISO manual to know how to use it. */
+  ParameterType& pardisoParameterArray() { return m_iparm; }
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  Derived& analyzePattern(const MatrixType& matrix);
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
+   * performed.
+   *
+   * \sa analyzePattern()
+   */
+  Derived& factorize(const MatrixType& matrix);
+
+  Derived& compute(const MatrixType& matrix);
+
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const;
+
+ protected:
+  void pardisoRelease() {
+    if (m_isInitialized)  // Factorization ran at least once
+    {
+      internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, -1,
+                                                        internal::convert_index<StorageIndex>(m_size), 0, 0, 0,
+                                                        m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
+      m_isInitialized = false;
+    }
+  }
+
+  void pardisoInit(int type) {
+    m_type = type;
+    bool symmetric = std::abs(m_type) < 10;
+    m_iparm[0] = 1;                   // No solver default
+    m_iparm[1] = 2;                   // use Metis for the ordering
+    m_iparm[2] = 0;                   // Reserved. Set to zero. (Was number of processors / OMP_NUM_THREADS.)
+    m_iparm[3] = 0;                   // No iterative-direct algorithm
+    m_iparm[4] = 0;                   // No user fill-in reducing permutation
+    m_iparm[5] = 0;                   // Write solution into x, b is left unchanged
+    m_iparm[6] = 0;                   // Not in use
+    m_iparm[7] = 2;                   // Max numbers of iterative refinement steps
+    m_iparm[8] = 0;                   // Not in use
+    m_iparm[9] = 13;                  // Perturb the pivot elements with 1E-13
+    m_iparm[10] = symmetric ? 0 : 1;  // Use nonsymmetric permutation and scaling MPS
+    m_iparm[11] = 0;                  // Not in use
+    m_iparm[12] = symmetric ? 0 : 1;  // Maximum weighted matching algorithm is switched-off (default for symmetric).
+                                      // Try m_iparm[12] = 1 in case of inappropriate accuracy
+    m_iparm[13] = 0;                  // Output: Number of perturbed pivots
+    m_iparm[14] = 0;                  // Not in use
+    m_iparm[15] = 0;                  // Not in use
+    m_iparm[16] = 0;                  // Not in use
+    m_iparm[17] = -1;                 // Output: Number of nonzeros in the factor LU
+    m_iparm[18] = -1;                 // Output: Mflops for LU factorization
+    m_iparm[19] = 0;                  // Output: Numbers of CG Iterations
+
+    m_iparm[20] = 0;  // 1x1 pivoting
+    m_iparm[26] = 0;  // No matrix checker
+    m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0;
+    m_iparm[34] = 1;  // C indexing
+    m_iparm[36] = 0;  // CSR
+    m_iparm[59] = 0;  // 0 - In-Core ; 1 - Automatic switch between In-Core and Out-of-Core modes ; 2 - Out-of-Core
+
+    memset(m_pt, 0, sizeof(m_pt));
+  }
+
+ protected:
+  // cached data to reduce reallocation, etc.
+
+  void manageErrorCode(Index error) const {
+    switch (error) {
+      case 0:
+        m_info = Success;
+        break;
+      case -4:
+      case -7:
+        m_info = NumericalIssue;
+        break;
+      default:
+        m_info = InvalidInput;
+    }
+  }
+
+  mutable SparseMatrixType m_matrix;
+  mutable ComputationInfo m_info;
+  bool m_analysisIsOk, m_factorizationIsOk;
+  StorageIndex m_type, m_msglvl;
+  mutable void* m_pt[64];
+  mutable ParameterType m_iparm;
+  mutable IntColVectorType m_perm;
+  Index m_size;
+};
+
+template <class Derived>
+Derived& PardisoImpl<Derived>::compute(const MatrixType& a) {
+  m_size = a.rows();
+  eigen_assert(a.rows() == a.cols());
+
+  pardisoRelease();
+  m_perm.setZero(m_size);
+  derived().getMatrix(a);
+
+  Index error;
+  error = internal::pardiso_run_selector<StorageIndex>::run(
+      m_pt, 1, 1, m_type, 12, internal::convert_index<StorageIndex>(m_size), m_matrix.valuePtr(),
+      m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
+  manageErrorCode(error);
+  m_analysisIsOk = m_info == Eigen::Success;
+  m_factorizationIsOk = m_info == Eigen::Success;
+  m_isInitialized = true;
+  return derived();
+}
+
+template <class Derived>
+Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a) {
+  m_size = a.rows();
+  eigen_assert(m_size == a.cols());
+
+  pardisoRelease();
+  m_perm.setZero(m_size);
+  derived().getMatrix(a);
+
+  Index error;
+  error = internal::pardiso_run_selector<StorageIndex>::run(
+      m_pt, 1, 1, m_type, 11, internal::convert_index<StorageIndex>(m_size), m_matrix.valuePtr(),
+      m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
+
+  manageErrorCode(error);
+  m_analysisIsOk = m_info == Eigen::Success;
+  m_factorizationIsOk = false;
+  m_isInitialized = true;
+  return derived();
+}
+
+template <class Derived>
+Derived& PardisoImpl<Derived>::factorize(const MatrixType& a) {
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  eigen_assert(m_size == a.rows() && m_size == a.cols());
+
+  derived().getMatrix(a);
+
+  Index error;
+  error = internal::pardiso_run_selector<StorageIndex>::run(
+      m_pt, 1, 1, m_type, 22, internal::convert_index<StorageIndex>(m_size), m_matrix.valuePtr(),
+      m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
+
+  manageErrorCode(error);
+  m_factorizationIsOk = m_info == Eigen::Success;
+  return derived();
+}
+
+template <class Derived>
+template <typename BDerived, typename XDerived>
+void PardisoImpl<Derived>::_solve_impl(const MatrixBase<BDerived>& b, MatrixBase<XDerived>& x) const {
+  if (m_iparm[0] == 0)  // Factorization was not computed
+  {
+    m_info = InvalidInput;
+    return;
+  }
+
+  // Index n = m_matrix.rows();
+  Index nrhs = Index(b.cols());
+  eigen_assert(m_size == b.rows());
+  eigen_assert(((MatrixBase<BDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) &&
+               "Row-major right hand sides are not supported");
+  eigen_assert(((MatrixBase<XDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) &&
+               "Row-major matrices of unknowns are not supported");
+  eigen_assert(((nrhs == 1) || b.outerStride() == b.rows()));
+
+  //  switch (transposed) {
+  //    case SvNoTrans    : m_iparm[11] = 0 ; break;
+  //    case SvTranspose  : m_iparm[11] = 2 ; break;
+  //    case SvAdjoint    : m_iparm[11] = 1 ; break;
+  //    default:
+  //      //std::cerr << "Eigen: transposition  option \"" << transposed << "\" not supported by the PARDISO backend\n";
+  //      m_iparm[11] = 0;
+  //  }
+
+  Scalar* rhs_ptr = const_cast<Scalar*>(b.derived().data());
+  Matrix<Scalar, Dynamic, Dynamic, ColMajor> tmp;
+
+  // Pardiso cannot solve in-place
+  if (rhs_ptr == x.derived().data()) {
+    tmp = b;
+    rhs_ptr = tmp.data();
+  }
+
+  Index error;
+  error = internal::pardiso_run_selector<StorageIndex>::run(
+      m_pt, 1, 1, m_type, 33, internal::convert_index<StorageIndex>(m_size), m_matrix.valuePtr(),
+      m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), internal::convert_index<StorageIndex>(nrhs),
+      m_iparm.data(), m_msglvl, rhs_ptr, x.derived().data());
+
+  manageErrorCode(error);
+}
+
+/** \ingroup PardisoSupport_Module
+ * \class PardisoLU
+ * \brief A sparse direct LU factorization and solver based on the PARDISO library
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
+ * using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
+ * The vectors or matrices X and B can be either dense or sparse.
+ *
+ * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
+ * \code solver.pardisoParameterArray()[59] = 1; \endcode
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept, class SparseLU
+ */
+template <typename MatrixType>
+class PardisoLU : public PardisoImpl<PardisoLU<MatrixType> > {
+ protected:
+  typedef PardisoImpl<PardisoLU> Base;
+  using Base::m_matrix;
+  using Base::pardisoInit;
+  friend class PardisoImpl<PardisoLU<MatrixType> >;
+
+ public:
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::RealScalar RealScalar;
+
+  using Base::compute;
+  using Base::solve;
+
+  PardisoLU() : Base() { pardisoInit(Base::ScalarIsComplex ? 13 : 11); }
+
+  explicit PardisoLU(const MatrixType& matrix) : Base() {
+    pardisoInit(Base::ScalarIsComplex ? 13 : 11);
+    compute(matrix);
+  }
+
+ protected:
+  void getMatrix(const MatrixType& matrix) {
+    m_matrix = matrix;
+    m_matrix.makeCompressed();
+  }
+};
+
+/** \ingroup PardisoSupport_Module
+ * \class PardisoLLT
+ * \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization
+ * using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
+ * The vectors or matrices X and B can be either dense or sparse.
+ *
+ * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
+ * \code solver.pardisoParameterArray()[59] = 1; \endcode
+ *
+ * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular
+ * part has to be used. Upper|Lower can be used to tell both triangular parts can be used as input.
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept, class SimplicialLLT
+ */
+template <typename MatrixType, int UpLo_>
+class PardisoLLT : public PardisoImpl<PardisoLLT<MatrixType, UpLo_> > {
+ protected:
+  typedef PardisoImpl<PardisoLLT<MatrixType, UpLo_> > Base;
+  using Base::m_matrix;
+  using Base::pardisoInit;
+  friend class PardisoImpl<PardisoLLT<MatrixType, UpLo_> >;
+
+ public:
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::RealScalar RealScalar;
+  typedef typename Base::StorageIndex StorageIndex;
+  enum { UpLo = UpLo_ };
+  using Base::compute;
+
+  PardisoLLT() : Base() { pardisoInit(Base::ScalarIsComplex ? 4 : 2); }
+
+  explicit PardisoLLT(const MatrixType& matrix) : Base() {
+    pardisoInit(Base::ScalarIsComplex ? 4 : 2);
+    compute(matrix);
+  }
+
+ protected:
+  void getMatrix(const MatrixType& matrix) {
+    // PARDISO supports only upper, row-major matrices
+    PermutationMatrix<Dynamic, Dynamic, StorageIndex> p_null;
+    m_matrix.resize(matrix.rows(), matrix.cols());
+    m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
+    m_matrix.makeCompressed();
+  }
+};
+
+/** \ingroup PardisoSupport_Module
+ * \class PardisoLDLT
+ * \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
+ * using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite.
+ * For complex matrices, A can also be symmetric only, see the \a Options template parameter.
+ * The vectors or matrices X and B can be either dense or sparse.
+ *
+ * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
+ * \code solver.pardisoParameterArray()[59] = 1; \endcode
+ *
+ * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the
+ * upper triangular part has to be used. Symmetric can be used for symmetric, non-selfadjoint complex matrices, the
+ * default being to assume a selfadjoint matrix. Upper|Lower can be used to tell both triangular parts can be used as
+ * input.
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept, class SimplicialLDLT
+ */
+template <typename MatrixType, int Options>
+class PardisoLDLT : public PardisoImpl<PardisoLDLT<MatrixType, Options> > {
+ protected:
+  typedef PardisoImpl<PardisoLDLT<MatrixType, Options> > Base;
+  using Base::m_matrix;
+  using Base::pardisoInit;
+  friend class PardisoImpl<PardisoLDLT<MatrixType, Options> >;
+
+ public:
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::RealScalar RealScalar;
+  typedef typename Base::StorageIndex StorageIndex;
+  using Base::compute;
+  enum { UpLo = Options & (Upper | Lower) };
+
+  PardisoLDLT() : Base() { pardisoInit(Base::ScalarIsComplex ? (bool(Options & Symmetric) ? 6 : -4) : -2); }
+
+  explicit PardisoLDLT(const MatrixType& matrix) : Base() {
+    pardisoInit(Base::ScalarIsComplex ? (bool(Options & Symmetric) ? 6 : -4) : -2);
+    compute(matrix);
+  }
+
+  void getMatrix(const MatrixType& matrix) {
+    // PARDISO supports only upper, row-major matrices
+    PermutationMatrix<Dynamic, Dynamic, StorageIndex> p_null;
+    m_matrix.resize(matrix.rows(), matrix.cols());
+    m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
+    m_matrix.makeCompressed();
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PARDISOSUPPORT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/QR/ColPivHouseholderQR.h b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/ColPivHouseholderQR.h
new file mode 100644
index 0000000..f1659e8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/ColPivHouseholderQR.h
@@ -0,0 +1,674 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
+#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType_, typename PermutationIndex_>
+struct traits<ColPivHouseholderQR<MatrixType_, PermutationIndex_>> : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef PermutationIndex_ PermutationIndex;
+  enum { Flags = 0 };
+};
+
+}  // end namespace internal
+
+/** \ingroup QR_Module
+ *
+ * \class ColPivHouseholderQR
+ *
+ * \brief Householder rank-revealing QR decomposition of a matrix with column-pivoting
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the QR decomposition
+ *
+ * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b Q and \b R
+ * such that
+ * \f[
+ *  \mathbf{A} \, \mathbf{P} = \mathbf{Q} \, \mathbf{R}
+ * \f]
+ * by using Householder transformations. Here, \b P is a permutation matrix, \b Q a unitary matrix and \b R an
+ * upper triangular matrix.
+ *
+ * This decomposition performs column pivoting in order to be rank-revealing and improve
+ * numerical stability. It is slower than HouseholderQR, and faster than FullPivHouseholderQR.
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * \sa MatrixBase::colPivHouseholderQr()
+ */
+template <typename MatrixType_, typename PermutationIndex_>
+class ColPivHouseholderQR : public SolverBase<ColPivHouseholderQR<MatrixType_, PermutationIndex_>> {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<ColPivHouseholderQR> Base;
+  friend class SolverBase<ColPivHouseholderQR>;
+  typedef PermutationIndex_ PermutationIndex;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(ColPivHouseholderQR)
+
+  enum {
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+  typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime, PermutationIndex> PermutationType;
+  typedef typename internal::plain_row_type<MatrixType, PermutationIndex>::type IntRowVectorType;
+  typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
+  typedef typename internal::plain_row_type<MatrixType, RealScalar>::type RealRowVectorType;
+  typedef HouseholderSequence<MatrixType, internal::remove_all_t<typename HCoeffsType::ConjugateReturnType>>
+      HouseholderSequenceType;
+  typedef typename MatrixType::PlainObject PlainObject;
+
+ private:
+  void init(Index rows, Index cols) {
+    Index diag = numext::mini(rows, cols);
+    m_hCoeffs.resize(diag);
+    m_colsPermutation.resize(cols);
+    m_colsTranspositions.resize(cols);
+    m_temp.resize(cols);
+    m_colNormsUpdated.resize(cols);
+    m_colNormsDirect.resize(cols);
+    m_isInitialized = false;
+    m_usePrescribedThreshold = false;
+  }
+
+ public:
+  /**
+   * \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via ColPivHouseholderQR::compute(const MatrixType&).
+   */
+  ColPivHouseholderQR()
+      : m_qr(),
+        m_hCoeffs(),
+        m_colsPermutation(),
+        m_colsTranspositions(),
+        m_temp(),
+        m_colNormsUpdated(),
+        m_colNormsDirect(),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false) {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa ColPivHouseholderQR()
+   */
+  ColPivHouseholderQR(Index rows, Index cols) : m_qr(rows, cols) { init(rows, cols); }
+
+  /** \brief Constructs a QR factorization from a given matrix
+   *
+   * This constructor computes the QR factorization of the matrix \a matrix by calling
+   * the method compute(). It is a short cut for:
+   *
+   * \code
+   * ColPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
+   * qr.compute(matrix);
+   * \endcode
+   *
+   * \sa compute()
+   */
+  template <typename InputType>
+  explicit ColPivHouseholderQR(const EigenBase<InputType>& matrix) : m_qr(matrix.rows(), matrix.cols()) {
+    init(matrix.rows(), matrix.cols());
+    compute(matrix.derived());
+  }
+
+  /** \brief Constructs a QR factorization from a given matrix
+   *
+   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c
+   * MatrixType is a Eigen::Ref.
+   *
+   * \sa ColPivHouseholderQR(const EigenBase&)
+   */
+  template <typename InputType>
+  explicit ColPivHouseholderQR(EigenBase<InputType>& matrix) : m_qr(matrix.derived()) {
+    init(matrix.rows(), matrix.cols());
+    computeInPlace();
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
+   * *this is the QR decomposition, if any exists.
+   *
+   * \param b the right-hand-side of the equation to solve.
+   *
+   * \returns a solution.
+   *
+   * \note_about_checking_solutions
+   *
+   * \note_about_arbitrary_choice_of_solution
+   *
+   * Example: \include ColPivHouseholderQR_solve.cpp
+   * Output: \verbinclude ColPivHouseholderQR_solve.out
+   */
+  template <typename Rhs>
+  inline Solve<ColPivHouseholderQR, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  HouseholderSequenceType householderQ() const;
+  HouseholderSequenceType matrixQ() const { return householderQ(); }
+
+  /** \returns a reference to the matrix where the Householder QR decomposition is stored
+   */
+  const MatrixType& matrixQR() const {
+    eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+    return m_qr;
+  }
+
+  /** \returns a reference to the matrix where the result Householder QR is stored
+   * \warning The strict lower part of this matrix contains internal values.
+   * Only the upper triangular part should be referenced. To get it, use
+   * \code matrixR().template triangularView<Upper>() \endcode
+   * For rank-deficient matrices, use
+   * \code
+   * matrixR().topLeftCorner(rank(), rank()).template triangularView<Upper>()
+   * \endcode
+   */
+  const MatrixType& matrixR() const {
+    eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+    return m_qr;
+  }
+
+  template <typename InputType>
+  ColPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
+
+  /** \returns a const reference to the column permutation matrix */
+  const PermutationType& colsPermutation() const {
+    eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+    return m_colsPermutation;
+  }
+
+  /** \returns the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   *
+   * \sa absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::Scalar determinant() const;
+
+  /** \returns the absolute value of the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   *
+   * \sa determinant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::RealScalar absDeterminant() const;
+
+  /** \returns the natural log of the absolute value of the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \note This method is useful to work around the risk of overflow/underflow that's inherent
+   * to determinant computation.
+   *
+   * \sa determinant(), absDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::RealScalar logAbsDeterminant() const;
+
+  /** \returns the sign of the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \note This method is useful to work around the risk of overflow/underflow that's inherent
+   * to determinant computation.
+   *
+   * \sa determinant(), absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::Scalar signDeterminant() const;
+
+  /** \returns the rank of the matrix of which *this is the QR decomposition.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline Index rank() const {
+    using std::abs;
+    eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+    RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
+    Index result = 0;
+    for (Index i = 0; i < m_nonzero_pivots; ++i) result += (abs(m_qr.coeff(i, i)) > premultiplied_threshold);
+    return result;
+  }
+
+  /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline Index dimensionOfKernel() const {
+    eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+    return cols() - rank();
+  }
+
+  /** \returns true if the matrix of which *this is the QR decomposition represents an injective
+   *          linear map, i.e. has trivial kernel; false otherwise.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline bool isInjective() const {
+    eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+    return rank() == cols();
+  }
+
+  /** \returns true if the matrix of which *this is the QR decomposition represents a surjective
+   *          linear map; false otherwise.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline bool isSurjective() const {
+    eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+    return rank() == rows();
+  }
+
+  /** \returns true if the matrix of which *this is the QR decomposition is invertible.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline bool isInvertible() const {
+    eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+    return isInjective() && isSurjective();
+  }
+
+  /** \returns the inverse of the matrix of which *this is the QR decomposition.
+   *
+   * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
+   *       Use isInvertible() to first determine whether this matrix is invertible.
+   */
+  inline Inverse<ColPivHouseholderQR> inverse() const {
+    eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+    return Inverse<ColPivHouseholderQR>(*this);
+  }
+
+  inline Index rows() const { return m_qr.rows(); }
+  inline Index cols() const { return m_qr.cols(); }
+
+  /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
+   *
+   * For advanced uses only.
+   */
+  const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
+
+  /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
+   * who need to determine when pivots are to be considered nonzero. This is not used for the
+   * QR decomposition itself.
+   *
+   * When it needs to get the threshold value, Eigen calls threshold(). By default, this
+   * uses a formula to automatically determine a reasonable threshold.
+   * Once you have called the present method setThreshold(const RealScalar&),
+   * your value is used instead.
+   *
+   * \param threshold The new value to use as the threshold.
+   *
+   * A pivot will be considered nonzero if its absolute value is strictly greater than
+   *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
+   * where maxpivot is the biggest pivot.
+   *
+   * If you want to come back to the default behavior, call setThreshold(Default_t)
+   */
+  ColPivHouseholderQR& setThreshold(const RealScalar& threshold) {
+    m_usePrescribedThreshold = true;
+    m_prescribedThreshold = threshold;
+    return *this;
+  }
+
+  /** Allows to come back to the default behavior, letting Eigen use its default formula for
+   * determining the threshold.
+   *
+   * You should pass the special object Eigen::Default as parameter here.
+   * \code qr.setThreshold(Eigen::Default); \endcode
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  ColPivHouseholderQR& setThreshold(Default_t) {
+    m_usePrescribedThreshold = false;
+    return *this;
+  }
+
+  /** Returns the threshold that will be used by certain methods such as rank().
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  RealScalar threshold() const {
+    eigen_assert(m_isInitialized || m_usePrescribedThreshold);
+    return m_usePrescribedThreshold ? m_prescribedThreshold
+                                    // this formula comes from experimenting (see "LU precision tuning" thread on the
+                                    // list) and turns out to be identical to Higham's formula used already in LDLt.
+                                    : NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize());
+  }
+
+  /** \returns the number of nonzero pivots in the QR decomposition.
+   * Here nonzero is meant in the exact sense, not in a fuzzy sense.
+   * So that notion isn't really intrinsically interesting, but it is
+   * still useful when implementing algorithms.
+   *
+   * \sa rank()
+   */
+  inline Index nonzeroPivots() const {
+    eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+    return m_nonzero_pivots;
+  }
+
+  /** \returns the absolute value of the biggest pivot, i.e. the biggest
+   *          diagonal coefficient of R.
+   */
+  RealScalar maxPivot() const { return m_maxpivot; }
+
+  /** \brief Reports whether the QR factorization was successful.
+   *
+   * \note This function always returns \c Success. It is provided for compatibility
+   * with other factorization routines.
+   * \returns \c Success
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return Success;
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
+#endif
+
+ protected:
+  friend class CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>;
+
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  void computeInPlace();
+
+  MatrixType m_qr;
+  HCoeffsType m_hCoeffs;
+  PermutationType m_colsPermutation;
+  IntRowVectorType m_colsTranspositions;
+  RowVectorType m_temp;
+  RealRowVectorType m_colNormsUpdated;
+  RealRowVectorType m_colNormsDirect;
+  bool m_isInitialized, m_usePrescribedThreshold;
+  RealScalar m_prescribedThreshold, m_maxpivot;
+  Index m_nonzero_pivots;
+  Index m_det_p;
+};
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::Scalar ColPivHouseholderQR<MatrixType, PermutationIndex>::determinant() const {
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  Scalar detQ;
+  internal::householder_determinant<HCoeffsType, Scalar, NumTraits<Scalar>::IsComplex>::run(m_hCoeffs, detQ);
+  return isInjective() ? (detQ * Scalar(m_det_p)) * m_qr.diagonal().prod() : Scalar(0);
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType, PermutationIndex>::absDeterminant() const {
+  using std::abs;
+  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return isInjective() ? abs(m_qr.diagonal().prod()) : RealScalar(0);
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType, PermutationIndex>::logAbsDeterminant() const {
+  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return isInjective() ? m_qr.diagonal().cwiseAbs().array().log().sum() : -NumTraits<RealScalar>::infinity();
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::Scalar ColPivHouseholderQR<MatrixType, PermutationIndex>::signDeterminant() const {
+  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  Scalar detQ;
+  internal::householder_determinant<HCoeffsType, Scalar, NumTraits<Scalar>::IsComplex>::run(m_hCoeffs, detQ);
+  return isInjective() ? (detQ * Scalar(m_det_p)) * m_qr.diagonal().array().sign().prod() : Scalar(0);
+}
+
+/** Performs the QR factorization of the given matrix \a matrix. The result of
+ * the factorization is stored into \c *this, and a reference to \c *this
+ * is returned.
+ *
+ * \sa class ColPivHouseholderQR, ColPivHouseholderQR(const MatrixType&)
+ */
+template <typename MatrixType, typename PermutationIndex>
+template <typename InputType>
+ColPivHouseholderQR<MatrixType, PermutationIndex>& ColPivHouseholderQR<MatrixType, PermutationIndex>::compute(
+    const EigenBase<InputType>& matrix) {
+  m_qr = matrix.derived();
+  computeInPlace();
+  return *this;
+}
+
+template <typename MatrixType, typename PermutationIndex>
+void ColPivHouseholderQR<MatrixType, PermutationIndex>::computeInPlace() {
+  eigen_assert(m_qr.cols() <= NumTraits<PermutationIndex>::highest());
+
+  using std::abs;
+
+  Index rows = m_qr.rows();
+  Index cols = m_qr.cols();
+  Index size = m_qr.diagonalSize();
+
+  m_hCoeffs.resize(size);
+
+  m_temp.resize(cols);
+
+  m_colsTranspositions.resize(m_qr.cols());
+  Index number_of_transpositions = 0;
+
+  m_colNormsUpdated.resize(cols);
+  m_colNormsDirect.resize(cols);
+  for (Index k = 0; k < cols; ++k) {
+    // colNormsDirect(k) caches the most recent directly computed norm of
+    // column k.
+    m_colNormsDirect.coeffRef(k) = m_qr.col(k).norm();
+    m_colNormsUpdated.coeffRef(k) = m_colNormsDirect.coeffRef(k);
+  }
+
+  RealScalar threshold_helper =
+      numext::abs2<RealScalar>(m_colNormsUpdated.maxCoeff() * NumTraits<RealScalar>::epsilon()) / RealScalar(rows);
+  RealScalar norm_downdate_threshold = numext::sqrt(NumTraits<RealScalar>::epsilon());
+
+  m_nonzero_pivots = size;  // the generic case is that in which all pivots are nonzero (invertible case)
+  m_maxpivot = RealScalar(0);
+
+  for (Index k = 0; k < size; ++k) {
+    // first, we look up in our table m_colNormsUpdated which column has the biggest norm
+    Index biggest_col_index;
+    RealScalar biggest_col_sq_norm = numext::abs2(m_colNormsUpdated.tail(cols - k).maxCoeff(&biggest_col_index));
+    biggest_col_index += k;
+
+    // Track the number of meaningful pivots but do not stop the decomposition to make
+    // sure that the initial matrix is properly reproduced. See bug 941.
+    if (m_nonzero_pivots == size && biggest_col_sq_norm < threshold_helper * RealScalar(rows - k)) m_nonzero_pivots = k;
+
+    // apply the transposition to the columns
+    m_colsTranspositions.coeffRef(k) = static_cast<PermutationIndex>(biggest_col_index);
+    if (k != biggest_col_index) {
+      m_qr.col(k).swap(m_qr.col(biggest_col_index));
+      std::swap(m_colNormsUpdated.coeffRef(k), m_colNormsUpdated.coeffRef(biggest_col_index));
+      std::swap(m_colNormsDirect.coeffRef(k), m_colNormsDirect.coeffRef(biggest_col_index));
+      ++number_of_transpositions;
+    }
+
+    // generate the householder vector, store it below the diagonal
+    RealScalar beta;
+    m_qr.col(k).tail(rows - k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
+
+    // apply the householder transformation to the diagonal coefficient
+    m_qr.coeffRef(k, k) = beta;
+
+    // remember the maximum absolute value of diagonal coefficients
+    if (abs(beta) > m_maxpivot) m_maxpivot = abs(beta);
+
+    // apply the householder transformation
+    m_qr.bottomRightCorner(rows - k, cols - k - 1)
+        .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows - k - 1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k + 1));
+
+    // update our table of norms of the columns
+    for (Index j = k + 1; j < cols; ++j) {
+      // The following implements the stable norm downgrade step discussed in
+      // http://www.netlib.org/lapack/lawnspdf/lawn176.pdf
+      // and used in LAPACK routines xGEQPF and xGEQP3.
+      // See lines 278-297 in http://www.netlib.org/lapack/explore-html/dc/df4/sgeqpf_8f_source.html
+      if (!numext::is_exactly_zero(m_colNormsUpdated.coeffRef(j))) {
+        RealScalar temp = abs(m_qr.coeffRef(k, j)) / m_colNormsUpdated.coeffRef(j);
+        temp = (RealScalar(1) + temp) * (RealScalar(1) - temp);
+        temp = temp < RealScalar(0) ? RealScalar(0) : temp;
+        RealScalar temp2 =
+            temp * numext::abs2<RealScalar>(m_colNormsUpdated.coeffRef(j) / m_colNormsDirect.coeffRef(j));
+        if (temp2 <= norm_downdate_threshold) {
+          // The updated norm has become too inaccurate so re-compute the column
+          // norm directly.
+          m_colNormsDirect.coeffRef(j) = m_qr.col(j).tail(rows - k - 1).norm();
+          m_colNormsUpdated.coeffRef(j) = m_colNormsDirect.coeffRef(j);
+        } else {
+          m_colNormsUpdated.coeffRef(j) *= numext::sqrt(temp);
+        }
+      }
+    }
+  }
+
+  m_colsPermutation.setIdentity(cols);
+  for (Index k = 0; k < size /*m_nonzero_pivots*/; ++k)
+    m_colsPermutation.applyTranspositionOnTheRight(k, static_cast<Index>(m_colsTranspositions.coeff(k)));
+
+  m_det_p = (number_of_transpositions % 2) ? -1 : 1;
+  m_isInitialized = true;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType_, typename PermutationIndex_>
+template <typename RhsType, typename DstType>
+void ColPivHouseholderQR<MatrixType_, PermutationIndex_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
+  const Index nonzero_pivots = nonzeroPivots();
+
+  if (nonzero_pivots == 0) {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(rhs);
+
+  c.applyOnTheLeft(householderQ().setLength(nonzero_pivots).adjoint());
+
+  m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots)
+      .template triangularView<Upper>()
+      .solveInPlace(c.topRows(nonzero_pivots));
+
+  for (Index i = 0; i < nonzero_pivots; ++i) dst.row(m_colsPermutation.indices().coeff(i)) = c.row(i);
+  for (Index i = nonzero_pivots; i < cols(); ++i) dst.row(m_colsPermutation.indices().coeff(i)).setZero();
+}
+
+template <typename MatrixType_, typename PermutationIndex_>
+template <bool Conjugate, typename RhsType, typename DstType>
+void ColPivHouseholderQR<MatrixType_, PermutationIndex_>::_solve_impl_transposed(const RhsType& rhs,
+                                                                                 DstType& dst) const {
+  const Index nonzero_pivots = nonzeroPivots();
+
+  if (nonzero_pivots == 0) {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(m_colsPermutation.transpose() * rhs);
+
+  m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots)
+      .template triangularView<Upper>()
+      .transpose()
+      .template conjugateIf<Conjugate>()
+      .solveInPlace(c.topRows(nonzero_pivots));
+
+  dst.topRows(nonzero_pivots) = c.topRows(nonzero_pivots);
+  dst.bottomRows(rows() - nonzero_pivots).setZero();
+
+  dst.applyOnTheLeft(householderQ().setLength(nonzero_pivots).template conjugateIf<!Conjugate>());
+}
+#endif
+
+namespace internal {
+
+template <typename DstXprType, typename MatrixType, typename PermutationIndex>
+struct Assignment<DstXprType, Inverse<ColPivHouseholderQR<MatrixType, PermutationIndex>>,
+                  internal::assign_op<typename DstXprType::Scalar,
+                                      typename ColPivHouseholderQR<MatrixType, PermutationIndex>::Scalar>,
+                  Dense2Dense> {
+  typedef ColPivHouseholderQR<MatrixType, PermutationIndex> QrType;
+  typedef Inverse<QrType> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::assign_op<typename DstXprType::Scalar, typename QrType::Scalar>&) {
+    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
+  }
+};
+
+}  // end namespace internal
+
+/** \returns the matrix Q as a sequence of householder transformations.
+ * You can extract the meaningful part only by using:
+ * \code qr.householderQ().setLength(qr.nonzeroPivots()) \endcode*/
+template <typename MatrixType, typename PermutationIndex>
+typename ColPivHouseholderQR<MatrixType, PermutationIndex>::HouseholderSequenceType
+ColPivHouseholderQR<MatrixType, PermutationIndex>::householderQ() const {
+  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+  return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate());
+}
+
+/** \return the column-pivoting Householder QR decomposition of \c *this.
+ *
+ * \sa class ColPivHouseholderQR
+ */
+template <typename Derived>
+template <typename PermutationIndexType>
+ColPivHouseholderQR<typename MatrixBase<Derived>::PlainObject, PermutationIndexType>
+MatrixBase<Derived>::colPivHouseholderQr() const {
+  return ColPivHouseholderQR<PlainObject, PermutationIndexType>(eval());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
new file mode 100644
index 0000000..37ac55f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
@@ -0,0 +1,161 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Householder QR decomposition of a matrix with column pivoting based on
+ *    LAPACKE_?geqp3 function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
+#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+#if defined(EIGEN_USE_LAPACKE)
+
+template <typename Scalar>
+inline lapack_int call_geqp3(int matrix_layout, lapack_int m, lapack_int n, Scalar* a, lapack_int lda, lapack_int* jpvt,
+                             Scalar* tau);
+template <>
+inline lapack_int call_geqp3(int matrix_layout, lapack_int m, lapack_int n, float* a, lapack_int lda, lapack_int* jpvt,
+                             float* tau) {
+  return LAPACKE_sgeqp3(matrix_layout, m, n, a, lda, jpvt, tau);
+}
+template <>
+inline lapack_int call_geqp3(int matrix_layout, lapack_int m, lapack_int n, double* a, lapack_int lda, lapack_int* jpvt,
+                             double* tau) {
+  return LAPACKE_dgeqp3(matrix_layout, m, n, a, lda, jpvt, tau);
+}
+template <>
+inline lapack_int call_geqp3(int matrix_layout, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                             lapack_int* jpvt, lapack_complex_float* tau) {
+  return LAPACKE_cgeqp3(matrix_layout, m, n, a, lda, jpvt, tau);
+}
+template <>
+inline lapack_int call_geqp3(int matrix_layout, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                             lapack_int* jpvt, lapack_complex_double* tau) {
+  return LAPACKE_zgeqp3(matrix_layout, m, n, a, lda, jpvt, tau);
+}
+
+template <typename MatrixType>
+struct ColPivHouseholderQR_LAPACKE_impl {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename internal::lapacke_helpers::translate_type_imp<Scalar>::type LapackeType;
+  static constexpr int LapackeStorage = MatrixType::IsRowMajor ? (LAPACK_ROW_MAJOR) : (LAPACK_COL_MAJOR);
+
+  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+  typedef PermutationMatrix<Dynamic, Dynamic, lapack_int> PermutationType;
+
+  static void run(MatrixType& qr, HCoeffsType& hCoeffs, PermutationType& colsPermutation, Index& nonzero_pivots,
+                  RealScalar& maxpivot, bool usePrescribedThreshold, RealScalar prescribedThreshold, Index& det_p,
+                  bool& isInitialized) {
+    isInitialized = false;
+    hCoeffs.resize(qr.diagonalSize());
+    nonzero_pivots = 0;
+    maxpivot = RealScalar(0);
+    colsPermutation.resize(qr.cols());
+    colsPermutation.indices().setZero();
+
+    lapack_int rows = internal::lapacke_helpers::to_lapack(qr.rows());
+    lapack_int cols = internal::lapacke_helpers::to_lapack(qr.cols());
+    LapackeType* qr_data = (LapackeType*)(qr.data());
+    lapack_int lda = internal::lapacke_helpers::to_lapack(qr.outerStride());
+    lapack_int* perm_data = colsPermutation.indices().data();
+    LapackeType* hCoeffs_data = (LapackeType*)(hCoeffs.data());
+
+    lapack_int info = call_geqp3(LapackeStorage, rows, cols, qr_data, lda, perm_data, hCoeffs_data);
+    if (info != 0) return;
+
+    maxpivot = qr.diagonal().cwiseAbs().maxCoeff();
+    hCoeffs.adjointInPlace();
+    RealScalar defaultThreshold = NumTraits<RealScalar>::epsilon() * RealScalar(qr.diagonalSize());
+    RealScalar threshold = usePrescribedThreshold ? prescribedThreshold : defaultThreshold;
+    RealScalar premultiplied_threshold = maxpivot * threshold;
+    nonzero_pivots = (qr.diagonal().cwiseAbs().array() > premultiplied_threshold).count();
+    colsPermutation.indices().array() -= 1;
+    det_p = colsPermutation.determinant();
+    isInitialized = true;
+  };
+
+  static void init(Index rows, Index cols, HCoeffsType& hCoeffs, PermutationType& colsPermutation,
+                   bool& usePrescribedThreshold, bool& isInitialized) {
+    Index diag = numext::mini(rows, cols);
+    hCoeffs.resize(diag);
+    colsPermutation.resize(cols);
+    usePrescribedThreshold = false;
+    isInitialized = false;
+  }
+};
+
+#define COLPIVQR_LAPACKE_COMPUTEINPLACE(EIGTYPE)                                                                   \
+  template <>                                                                                                      \
+  inline void ColPivHouseholderQR<EIGTYPE, lapack_int>::computeInPlace() {                                         \
+    ColPivHouseholderQR_LAPACKE_impl<MatrixType>::run(m_qr, m_hCoeffs, m_colsPermutation, m_nonzero_pivots,        \
+                                                      m_maxpivot, m_usePrescribedThreshold, m_prescribedThreshold, \
+                                                      m_det_p, m_isInitialized);                                   \
+  }
+
+#define COLPIVQR_LAPACKE_INIT(EIGTYPE)                                                                            \
+  template <>                                                                                                     \
+  inline void ColPivHouseholderQR<EIGTYPE, lapack_int>::init(Index rows, Index cols) {                            \
+    ColPivHouseholderQR_LAPACKE_impl<MatrixType>::init(rows, cols, m_hCoeffs, m_colsPermutation, m_isInitialized, \
+                                                       m_usePrescribedThreshold);                                 \
+  }
+
+#define COLPIVQR_LAPACKE(EIGTYPE)               \
+  COLPIVQR_LAPACKE_COMPUTEINPLACE(EIGTYPE)      \
+  COLPIVQR_LAPACKE_INIT(EIGTYPE)                \
+  COLPIVQR_LAPACKE_COMPUTEINPLACE(Ref<EIGTYPE>) \
+  COLPIVQR_LAPACKE_INIT(Ref<EIGTYPE>)
+
+typedef Matrix<float, Dynamic, Dynamic, ColMajor> MatrixXfC;
+typedef Matrix<double, Dynamic, Dynamic, ColMajor> MatrixXdC;
+typedef Matrix<std::complex<float>, Dynamic, Dynamic, ColMajor> MatrixXcfC;
+typedef Matrix<std::complex<double>, Dynamic, Dynamic, ColMajor> MatrixXcdC;
+typedef Matrix<float, Dynamic, Dynamic, RowMajor> MatrixXfR;
+typedef Matrix<double, Dynamic, Dynamic, RowMajor> MatrixXdR;
+typedef Matrix<std::complex<float>, Dynamic, Dynamic, RowMajor> MatrixXcfR;
+typedef Matrix<std::complex<double>, Dynamic, Dynamic, RowMajor> MatrixXcdR;
+
+COLPIVQR_LAPACKE(MatrixXfC)
+COLPIVQR_LAPACKE(MatrixXdC)
+COLPIVQR_LAPACKE(MatrixXcfC)
+COLPIVQR_LAPACKE(MatrixXcdC)
+COLPIVQR_LAPACKE(MatrixXfR)
+COLPIVQR_LAPACKE(MatrixXdR)
+COLPIVQR_LAPACKE(MatrixXcfR)
+COLPIVQR_LAPACKE(MatrixXcdR)
+
+#endif
+}  // end namespace Eigen
+
+#endif  // EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h
new file mode 100644
index 0000000..1e5cf54
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h
@@ -0,0 +1,648 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Rasmus Munk Larsen <rmlarsen@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
+#define EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType_, typename PermutationIndex_>
+struct traits<CompleteOrthogonalDecomposition<MatrixType_, PermutationIndex_>> : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef PermutationIndex_ PermutationIndex;
+  enum { Flags = 0 };
+};
+
+}  // end namespace internal
+
+/** \ingroup QR_Module
+ *
+ * \class CompleteOrthogonalDecomposition
+ *
+ * \brief Complete orthogonal decomposition (COD) of a matrix.
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the COD.
+ *
+ * This class performs a rank-revealing complete orthogonal decomposition of a
+ * matrix  \b A into matrices \b P, \b Q, \b T, and \b Z such that
+ * \f[
+ *  \mathbf{A} \, \mathbf{P} = \mathbf{Q} \,
+ *                     \begin{bmatrix} \mathbf{T} &  \mathbf{0} \\
+ *                                     \mathbf{0} & \mathbf{0} \end{bmatrix} \, \mathbf{Z}
+ * \f]
+ * by using Householder transformations. Here, \b P is a permutation matrix,
+ * \b Q and \b Z are unitary matrices and \b T an upper triangular matrix of
+ * size rank-by-rank. \b A may be rank deficient.
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * \sa MatrixBase::completeOrthogonalDecomposition()
+ */
+template <typename MatrixType_, typename PermutationIndex_>
+class CompleteOrthogonalDecomposition
+    : public SolverBase<CompleteOrthogonalDecomposition<MatrixType_, PermutationIndex_>> {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<CompleteOrthogonalDecomposition> Base;
+
+  template <typename Derived>
+  friend struct internal::solve_assertion;
+  typedef PermutationIndex_ PermutationIndex;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(CompleteOrthogonalDecomposition)
+  enum {
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+  typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime, PermutationIndex> PermutationType;
+  typedef typename internal::plain_row_type<MatrixType, Index>::type IntRowVectorType;
+  typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
+  typedef typename internal::plain_row_type<MatrixType, RealScalar>::type RealRowVectorType;
+  typedef HouseholderSequence<MatrixType, internal::remove_all_t<typename HCoeffsType::ConjugateReturnType>>
+      HouseholderSequenceType;
+  typedef typename MatrixType::PlainObject PlainObject;
+
+ public:
+  /**
+   * \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via
+   * \c CompleteOrthogonalDecomposition::compute(const* MatrixType&).
+   */
+  CompleteOrthogonalDecomposition() : m_cpqr(), m_zCoeffs(), m_temp() {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa CompleteOrthogonalDecomposition()
+   */
+  CompleteOrthogonalDecomposition(Index rows, Index cols)
+      : m_cpqr(rows, cols), m_zCoeffs((std::min)(rows, cols)), m_temp(cols) {}
+
+  /** \brief Constructs a complete orthogonal decomposition from a given
+   * matrix.
+   *
+   * This constructor computes the complete orthogonal decomposition of the
+   * matrix \a matrix by calling the method compute(). The default
+   * threshold for rank determination will be used. It is a short cut for:
+   *
+   * \code
+   * CompleteOrthogonalDecomposition<MatrixType> cod(matrix.rows(),
+   *                                                 matrix.cols());
+   * cod.setThreshold(Default);
+   * cod.compute(matrix);
+   * \endcode
+   *
+   * \sa compute()
+   */
+  template <typename InputType>
+  explicit CompleteOrthogonalDecomposition(const EigenBase<InputType>& matrix)
+      : m_cpqr(matrix.rows(), matrix.cols()),
+        m_zCoeffs((std::min)(matrix.rows(), matrix.cols())),
+        m_temp(matrix.cols()) {
+    compute(matrix.derived());
+  }
+
+  /** \brief Constructs a complete orthogonal decomposition from a given matrix
+   *
+   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c
+   * MatrixType is a Eigen::Ref.
+   *
+   * \sa CompleteOrthogonalDecomposition(const EigenBase&)
+   */
+  template <typename InputType>
+  explicit CompleteOrthogonalDecomposition(EigenBase<InputType>& matrix)
+      : m_cpqr(matrix.derived()), m_zCoeffs((std::min)(matrix.rows(), matrix.cols())), m_temp(matrix.cols()) {
+    computeInPlace();
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** This method computes the minimum-norm solution X to a least squares
+   * problem \f[\mathrm{minimize} \|A X - B\|, \f] where \b A is the matrix of
+   * which \c *this is the complete orthogonal decomposition.
+   *
+   * \param b the right-hand sides of the problem to solve.
+   *
+   * \returns a solution.
+   *
+   */
+  template <typename Rhs>
+  inline Solve<CompleteOrthogonalDecomposition, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  HouseholderSequenceType householderQ(void) const;
+  HouseholderSequenceType matrixQ(void) const { return m_cpqr.householderQ(); }
+
+  /** \returns the matrix \b Z.
+   */
+  MatrixType matrixZ() const {
+    MatrixType Z = MatrixType::Identity(m_cpqr.cols(), m_cpqr.cols());
+    applyZOnTheLeftInPlace<false>(Z);
+    return Z;
+  }
+
+  /** \returns a reference to the matrix where the complete orthogonal
+   * decomposition is stored
+   */
+  const MatrixType& matrixQTZ() const { return m_cpqr.matrixQR(); }
+
+  /** \returns a reference to the matrix where the complete orthogonal
+   * decomposition is stored.
+   * \warning The strict lower part and \code cols() - rank() \endcode right
+   * columns of this matrix contains internal values.
+   * Only the upper triangular part should be referenced. To get it, use
+   * \code matrixT().template triangularView<Upper>() \endcode
+   * For rank-deficient matrices, use
+   * \code
+   * matrixT().topLeftCorner(rank(), rank()).template triangularView<Upper>()
+   * \endcode
+   */
+  const MatrixType& matrixT() const { return m_cpqr.matrixQR(); }
+
+  template <typename InputType>
+  CompleteOrthogonalDecomposition& compute(const EigenBase<InputType>& matrix) {
+    // Compute the column pivoted QR factorization A P = Q R.
+    m_cpqr.compute(matrix);
+    computeInPlace();
+    return *this;
+  }
+
+  /** \returns a const reference to the column permutation matrix */
+  const PermutationType& colsPermutation() const { return m_cpqr.colsPermutation(); }
+
+  /** \returns the determinant of the matrix of which
+   * *this is the complete orthogonal decomposition. It has only linear
+   * complexity (that is, O(n) where n is the dimension of the square matrix)
+   * as the complete orthogonal decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   *
+   * \sa absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::Scalar determinant() const;
+
+  /** \returns the absolute value of the determinant of the matrix of which
+   * *this is the complete orthogonal decomposition. It has only linear
+   * complexity (that is, O(n) where n is the dimension of the square matrix)
+   * as the complete orthogonal decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   *
+   * \sa determinant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::RealScalar absDeterminant() const;
+
+  /** \returns the natural log of the absolute value of the determinant of the
+   * matrix of which *this is the complete orthogonal decomposition. It has
+   * only linear complexity (that is, O(n) where n is the dimension of the
+   * square matrix) as the complete orthogonal decomposition has already been
+   * computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \note This method is useful to work around the risk of overflow/underflow
+   * that's inherent to determinant computation.
+   *
+   * \sa determinant(), absDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::RealScalar logAbsDeterminant() const;
+
+  /** \returns the sign of the determinant of the
+   * matrix of which *this is the complete orthogonal decomposition. It has
+   * only linear complexity (that is, O(n) where n is the dimension of the
+   * square matrix) as the complete orthogonal decomposition has already been
+   * computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \note This method is useful to work around the risk of overflow/underflow
+   * that's inherent to determinant computation.
+   *
+   * \sa determinant(), absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::Scalar signDeterminant() const;
+
+  /** \returns the rank of the matrix of which *this is the complete orthogonal
+   * decomposition.
+   *
+   * \note This method has to determine which pivots should be considered
+   * nonzero. For that, it uses the threshold value that you can control by
+   * calling setThreshold(const RealScalar&).
+   */
+  inline Index rank() const { return m_cpqr.rank(); }
+
+  /** \returns the dimension of the kernel of the matrix of which *this is the
+   * complete orthogonal decomposition.
+   *
+   * \note This method has to determine which pivots should be considered
+   * nonzero. For that, it uses the threshold value that you can control by
+   * calling setThreshold(const RealScalar&).
+   */
+  inline Index dimensionOfKernel() const { return m_cpqr.dimensionOfKernel(); }
+
+  /** \returns true if the matrix of which *this is the decomposition represents
+   * an injective linear map, i.e. has trivial kernel; false otherwise.
+   *
+   * \note This method has to determine which pivots should be considered
+   * nonzero. For that, it uses the threshold value that you can control by
+   * calling setThreshold(const RealScalar&).
+   */
+  inline bool isInjective() const { return m_cpqr.isInjective(); }
+
+  /** \returns true if the matrix of which *this is the decomposition represents
+   * a surjective linear map; false otherwise.
+   *
+   * \note This method has to determine which pivots should be considered
+   * nonzero. For that, it uses the threshold value that you can control by
+   * calling setThreshold(const RealScalar&).
+   */
+  inline bool isSurjective() const { return m_cpqr.isSurjective(); }
+
+  /** \returns true if the matrix of which *this is the complete orthogonal
+   * decomposition is invertible.
+   *
+   * \note This method has to determine which pivots should be considered
+   * nonzero. For that, it uses the threshold value that you can control by
+   * calling setThreshold(const RealScalar&).
+   */
+  inline bool isInvertible() const { return m_cpqr.isInvertible(); }
+
+  /** \returns the pseudo-inverse of the matrix of which *this is the complete
+   * orthogonal decomposition.
+   * \warning: Do not compute \c this->pseudoInverse()*rhs to solve a linear systems.
+   * It is more efficient and numerically stable to call \c this->solve(rhs).
+   */
+  inline Inverse<CompleteOrthogonalDecomposition> pseudoInverse() const {
+    eigen_assert(m_cpqr.m_isInitialized && "CompleteOrthogonalDecomposition is not initialized.");
+    return Inverse<CompleteOrthogonalDecomposition>(*this);
+  }
+
+  inline Index rows() const { return m_cpqr.rows(); }
+  inline Index cols() const { return m_cpqr.cols(); }
+
+  /** \returns a const reference to the vector of Householder coefficients used
+   * to represent the factor \c Q.
+   *
+   * For advanced uses only.
+   */
+  inline const HCoeffsType& hCoeffs() const { return m_cpqr.hCoeffs(); }
+
+  /** \returns a const reference to the vector of Householder coefficients
+   * used to represent the factor \c Z.
+   *
+   * For advanced uses only.
+   */
+  const HCoeffsType& zCoeffs() const { return m_zCoeffs; }
+
+  /** Allows to prescribe a threshold to be used by certain methods, such as
+   * rank(), who need to determine when pivots are to be considered nonzero.
+   * Most be called before calling compute().
+   *
+   * When it needs to get the threshold value, Eigen calls threshold(). By
+   * default, this uses a formula to automatically determine a reasonable
+   * threshold. Once you have called the present method
+   * setThreshold(const RealScalar&), your value is used instead.
+   *
+   * \param threshold The new value to use as the threshold.
+   *
+   * A pivot will be considered nonzero if its absolute value is strictly
+   * greater than
+   *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
+   * where maxpivot is the biggest pivot.
+   *
+   * If you want to come back to the default behavior, call
+   * setThreshold(Default_t)
+   */
+  CompleteOrthogonalDecomposition& setThreshold(const RealScalar& threshold) {
+    m_cpqr.setThreshold(threshold);
+    return *this;
+  }
+
+  /** Allows to come back to the default behavior, letting Eigen use its default
+   * formula for determining the threshold.
+   *
+   * You should pass the special object Eigen::Default as parameter here.
+   * \code qr.setThreshold(Eigen::Default); \endcode
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  CompleteOrthogonalDecomposition& setThreshold(Default_t) {
+    m_cpqr.setThreshold(Default);
+    return *this;
+  }
+
+  /** Returns the threshold that will be used by certain methods such as rank().
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  RealScalar threshold() const { return m_cpqr.threshold(); }
+
+  /** \returns the number of nonzero pivots in the complete orthogonal
+   * decomposition. Here nonzero is meant in the exact sense, not in a
+   * fuzzy sense. So that notion isn't really intrinsically interesting,
+   * but it is still useful when implementing algorithms.
+   *
+   * \sa rank()
+   */
+  inline Index nonzeroPivots() const { return m_cpqr.nonzeroPivots(); }
+
+  /** \returns the absolute value of the biggest pivot, i.e. the biggest
+   *          diagonal coefficient of R.
+   */
+  inline RealScalar maxPivot() const { return m_cpqr.maxPivot(); }
+
+  /** \brief Reports whether the complete orthogonal decomposition was
+   * successful.
+   *
+   * \note This function always returns \c Success. It is provided for
+   * compatibility
+   * with other factorization routines.
+   * \returns \c Success
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_cpqr.m_isInitialized && "Decomposition is not initialized.");
+    return Success;
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  template <bool Transpose_, typename Rhs>
+  void _check_solve_assertion(const Rhs& b) const {
+    EIGEN_ONLY_USED_FOR_DEBUG(b);
+    eigen_assert(m_cpqr.m_isInitialized && "CompleteOrthogonalDecomposition is not initialized.");
+    eigen_assert((Transpose_ ? derived().cols() : derived().rows()) == b.rows() &&
+                 "CompleteOrthogonalDecomposition::solve(): invalid number of rows of the right hand side matrix b");
+  }
+
+  void computeInPlace();
+
+  /** Overwrites \b rhs with \f$ \mathbf{Z} * \mathbf{rhs} \f$ or
+   *  \f$ \mathbf{\overline Z} * \mathbf{rhs} \f$ if \c Conjugate
+   *  is set to \c true.
+   */
+  template <bool Conjugate, typename Rhs>
+  void applyZOnTheLeftInPlace(Rhs& rhs) const;
+
+  /** Overwrites \b rhs with \f$ \mathbf{Z}^* * \mathbf{rhs} \f$.
+   */
+  template <typename Rhs>
+  void applyZAdjointOnTheLeftInPlace(Rhs& rhs) const;
+
+  ColPivHouseholderQR<MatrixType, PermutationIndex> m_cpqr;
+  HCoeffsType m_zCoeffs;
+  RowVectorType m_temp;
+};
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::Scalar CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::determinant() const {
+  return m_cpqr.determinant();
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::absDeterminant() const {
+  return m_cpqr.absDeterminant();
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::logAbsDeterminant()
+    const {
+  return m_cpqr.logAbsDeterminant();
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::Scalar CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::signDeterminant() const {
+  return m_cpqr.signDeterminant();
+}
+
+/** Performs the complete orthogonal decomposition of the given matrix \a
+ * matrix. The result of the factorization is stored into \c *this, and a
+ * reference to \c *this is returned.
+ *
+ * \sa class CompleteOrthogonalDecomposition,
+ * CompleteOrthogonalDecomposition(const MatrixType&)
+ */
+template <typename MatrixType, typename PermutationIndex>
+void CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::computeInPlace() {
+  eigen_assert(m_cpqr.cols() <= NumTraits<PermutationIndex>::highest());
+
+  const Index rank = m_cpqr.rank();
+  const Index cols = m_cpqr.cols();
+  const Index rows = m_cpqr.rows();
+  m_zCoeffs.resize((std::min)(rows, cols));
+  m_temp.resize(cols);
+
+  if (rank < cols) {
+    // We have reduced the (permuted) matrix to the form
+    //   [R11 R12]
+    //   [ 0  R22]
+    // where R11 is r-by-r (r = rank) upper triangular, R12 is
+    // r-by-(n-r), and R22 is empty or the norm of R22 is negligible.
+    // We now compute the complete orthogonal decomposition by applying
+    // Householder transformations from the right to the upper trapezoidal
+    // matrix X = [R11 R12] to zero out R12 and obtain the factorization
+    // [R11 R12] = [T11 0] * Z, where T11 is r-by-r upper triangular and
+    // Z = Z(0) * Z(1) ... Z(r-1) is an n-by-n orthogonal matrix.
+    // We store the data representing Z in R12 and m_zCoeffs.
+    for (Index k = rank - 1; k >= 0; --k) {
+      if (k != rank - 1) {
+        // Given the API for Householder reflectors, it is more convenient if
+        // we swap the leading parts of columns k and r-1 (zero-based) to form
+        // the matrix X_k = [X(0:k, k), X(0:k, r:n)]
+        m_cpqr.m_qr.col(k).head(k + 1).swap(m_cpqr.m_qr.col(rank - 1).head(k + 1));
+      }
+      // Construct Householder reflector Z(k) to zero out the last row of X_k,
+      // i.e. choose Z(k) such that
+      // [X(k, k), X(k, r:n)] * Z(k) = [beta, 0, .., 0].
+      RealScalar beta;
+      m_cpqr.m_qr.row(k).tail(cols - rank + 1).makeHouseholderInPlace(m_zCoeffs(k), beta);
+      m_cpqr.m_qr(k, rank - 1) = beta;
+      if (k > 0) {
+        // Apply Z(k) to the first k rows of X_k
+        m_cpqr.m_qr.topRightCorner(k, cols - rank + 1)
+            .applyHouseholderOnTheRight(m_cpqr.m_qr.row(k).tail(cols - rank).adjoint(), m_zCoeffs(k), &m_temp(0));
+      }
+      if (k != rank - 1) {
+        // Swap X(0:k,k) back to its proper location.
+        m_cpqr.m_qr.col(k).head(k + 1).swap(m_cpqr.m_qr.col(rank - 1).head(k + 1));
+      }
+    }
+  }
+}
+
+template <typename MatrixType, typename PermutationIndex>
+template <bool Conjugate, typename Rhs>
+void CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::applyZOnTheLeftInPlace(Rhs& rhs) const {
+  const Index cols = this->cols();
+  const Index nrhs = rhs.cols();
+  const Index rank = this->rank();
+  Matrix<typename Rhs::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
+  for (Index k = rank - 1; k >= 0; --k) {
+    if (k != rank - 1) {
+      rhs.row(k).swap(rhs.row(rank - 1));
+    }
+    rhs.middleRows(rank - 1, cols - rank + 1)
+        .applyHouseholderOnTheLeft(matrixQTZ().row(k).tail(cols - rank).transpose().template conjugateIf<!Conjugate>(),
+                                   zCoeffs().template conjugateIf<Conjugate>()(k), &temp(0));
+    if (k != rank - 1) {
+      rhs.row(k).swap(rhs.row(rank - 1));
+    }
+  }
+}
+
+template <typename MatrixType, typename PermutationIndex>
+template <typename Rhs>
+void CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::applyZAdjointOnTheLeftInPlace(Rhs& rhs) const {
+  const Index cols = this->cols();
+  const Index nrhs = rhs.cols();
+  const Index rank = this->rank();
+  Matrix<typename Rhs::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
+  for (Index k = 0; k < rank; ++k) {
+    if (k != rank - 1) {
+      rhs.row(k).swap(rhs.row(rank - 1));
+    }
+    rhs.middleRows(rank - 1, cols - rank + 1)
+        .applyHouseholderOnTheLeft(matrixQTZ().row(k).tail(cols - rank).adjoint(), zCoeffs()(k), &temp(0));
+    if (k != rank - 1) {
+      rhs.row(k).swap(rhs.row(rank - 1));
+    }
+  }
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType_, typename PermutationIndex_>
+template <typename RhsType, typename DstType>
+void CompleteOrthogonalDecomposition<MatrixType_, PermutationIndex_>::_solve_impl(const RhsType& rhs,
+                                                                                  DstType& dst) const {
+  const Index rank = this->rank();
+  if (rank == 0) {
+    dst.setZero();
+    return;
+  }
+
+  // Compute c = Q^* * rhs
+  typename RhsType::PlainObject c(rhs);
+  c.applyOnTheLeft(matrixQ().setLength(rank).adjoint());
+
+  // Solve T z = c(1:rank, :)
+  dst.topRows(rank) = matrixT().topLeftCorner(rank, rank).template triangularView<Upper>().solve(c.topRows(rank));
+
+  const Index cols = this->cols();
+  if (rank < cols) {
+    // Compute y = Z^* * [ z ]
+    //                   [ 0 ]
+    dst.bottomRows(cols - rank).setZero();
+    applyZAdjointOnTheLeftInPlace(dst);
+  }
+
+  // Undo permutation to get x = P^{-1} * y.
+  dst = colsPermutation() * dst;
+}
+
+template <typename MatrixType_, typename PermutationIndex_>
+template <bool Conjugate, typename RhsType, typename DstType>
+void CompleteOrthogonalDecomposition<MatrixType_, PermutationIndex_>::_solve_impl_transposed(const RhsType& rhs,
+                                                                                             DstType& dst) const {
+  const Index rank = this->rank();
+
+  if (rank == 0) {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(colsPermutation().transpose() * rhs);
+
+  if (rank < cols()) {
+    applyZOnTheLeftInPlace<!Conjugate>(c);
+  }
+
+  matrixT()
+      .topLeftCorner(rank, rank)
+      .template triangularView<Upper>()
+      .transpose()
+      .template conjugateIf<Conjugate>()
+      .solveInPlace(c.topRows(rank));
+
+  dst.topRows(rank) = c.topRows(rank);
+  dst.bottomRows(rows() - rank).setZero();
+
+  dst.applyOnTheLeft(householderQ().setLength(rank).template conjugateIf<!Conjugate>());
+}
+#endif
+
+namespace internal {
+
+template <typename MatrixType, typename PermutationIndex>
+struct traits<Inverse<CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>>>
+    : traits<typename Transpose<typename MatrixType::PlainObject>::PlainObject> {
+  enum { Flags = 0 };
+};
+
+template <typename DstXprType, typename MatrixType, typename PermutationIndex>
+struct Assignment<DstXprType, Inverse<CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>>,
+                  internal::assign_op<typename DstXprType::Scalar,
+                                      typename CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::Scalar>,
+                  Dense2Dense> {
+  typedef CompleteOrthogonalDecomposition<MatrixType, PermutationIndex> CodType;
+  typedef Inverse<CodType> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::assign_op<typename DstXprType::Scalar, typename CodType::Scalar>&) {
+    typedef Matrix<typename CodType::Scalar, CodType::RowsAtCompileTime, CodType::RowsAtCompileTime, 0,
+                   CodType::MaxRowsAtCompileTime, CodType::MaxRowsAtCompileTime>
+        IdentityMatrixType;
+    dst = src.nestedExpression().solve(IdentityMatrixType::Identity(src.cols(), src.cols()));
+  }
+};
+
+}  // end namespace internal
+
+/** \returns the matrix Q as a sequence of householder transformations */
+template <typename MatrixType, typename PermutationIndex>
+typename CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::HouseholderSequenceType
+CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::householderQ() const {
+  return m_cpqr.householderQ();
+}
+
+/** \return the complete orthogonal decomposition of \c *this.
+ *
+ * \sa class CompleteOrthogonalDecomposition
+ */
+template <typename Derived>
+template <typename PermutationIndex>
+CompleteOrthogonalDecomposition<typename MatrixBase<Derived>::PlainObject, PermutationIndex>
+MatrixBase<Derived>::completeOrthogonalDecomposition() const {
+  return CompleteOrthogonalDecomposition<PlainObject>(eval());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/QR/FullPivHouseholderQR.h b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/FullPivHouseholderQR.h
new file mode 100644
index 0000000..b91792d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/FullPivHouseholderQR.h
@@ -0,0 +1,727 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
+#define EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename MatrixType_, typename PermutationIndex_>
+struct traits<FullPivHouseholderQR<MatrixType_, PermutationIndex_> > : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef PermutationIndex_ PermutationIndex;
+  enum { Flags = 0 };
+};
+
+template <typename MatrixType, typename PermutationIndex>
+struct FullPivHouseholderQRMatrixQReturnType;
+
+template <typename MatrixType, typename PermutationIndex>
+struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType, PermutationIndex> > {
+  typedef typename MatrixType::PlainObject ReturnType;
+};
+
+}  // end namespace internal
+
+/** \ingroup QR_Module
+ *
+ * \class FullPivHouseholderQR
+ *
+ * \brief Householder rank-revealing QR decomposition of a matrix with full pivoting
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the QR decomposition
+ *
+ * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b P', \b Q and \b R
+ * such that
+ * \f[
+ *  \mathbf{P} \, \mathbf{A} \, \mathbf{P}' = \mathbf{Q} \, \mathbf{R}
+ * \f]
+ * by using Householder transformations. Here, \b P and \b P' are permutation matrices, \b Q a unitary matrix
+ * and \b R an upper triangular matrix.
+ *
+ * This decomposition performs a very prudent full pivoting in order to be rank-revealing and achieve optimal
+ * numerical stability. The trade-off is that it is slower than HouseholderQR and ColPivHouseholderQR.
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * \sa MatrixBase::fullPivHouseholderQr()
+ */
+template <typename MatrixType_, typename PermutationIndex_>
+class FullPivHouseholderQR : public SolverBase<FullPivHouseholderQR<MatrixType_, PermutationIndex_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<FullPivHouseholderQR> Base;
+  friend class SolverBase<FullPivHouseholderQR>;
+  typedef PermutationIndex_ PermutationIndex;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivHouseholderQR)
+
+  enum {
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+  typedef internal::FullPivHouseholderQRMatrixQReturnType<MatrixType, PermutationIndex> MatrixQReturnType;
+  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+  typedef Matrix<PermutationIndex, 1, internal::min_size_prefer_dynamic(ColsAtCompileTime, RowsAtCompileTime), RowMajor,
+                 1, internal::min_size_prefer_fixed(MaxColsAtCompileTime, MaxRowsAtCompileTime)>
+      IntDiagSizeVectorType;
+  typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime, PermutationIndex> PermutationType;
+  typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
+  typedef typename internal::plain_col_type<MatrixType>::type ColVectorType;
+  typedef typename MatrixType::PlainObject PlainObject;
+
+  /** \brief Reports whether the QR factorization was successful.
+   *
+   * \note This function always returns \c Success. It is provided for compatibility
+   * with other factorization routines.
+   * \returns \c Success
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+    return Success;
+  }
+
+  /** \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via FullPivHouseholderQR::compute(const MatrixType&).
+   */
+  FullPivHouseholderQR()
+      : m_qr(),
+        m_hCoeffs(),
+        m_rows_transpositions(),
+        m_cols_transpositions(),
+        m_cols_permutation(),
+        m_temp(),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false) {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa FullPivHouseholderQR()
+   */
+  FullPivHouseholderQR(Index rows, Index cols)
+      : m_qr(rows, cols),
+        m_hCoeffs((std::min)(rows, cols)),
+        m_rows_transpositions((std::min)(rows, cols)),
+        m_cols_transpositions((std::min)(rows, cols)),
+        m_cols_permutation(cols),
+        m_temp(cols),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false) {}
+
+  /** \brief Constructs a QR factorization from a given matrix
+   *
+   * This constructor computes the QR factorization of the matrix \a matrix by calling
+   * the method compute(). It is a short cut for:
+   *
+   * \code
+   * FullPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
+   * qr.compute(matrix);
+   * \endcode
+   *
+   * \sa compute()
+   */
+  template <typename InputType>
+  explicit FullPivHouseholderQR(const EigenBase<InputType>& matrix)
+      : m_qr(matrix.rows(), matrix.cols()),
+        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
+        m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
+        m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())),
+        m_cols_permutation(matrix.cols()),
+        m_temp(matrix.cols()),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false) {
+    compute(matrix.derived());
+  }
+
+  /** \brief Constructs a QR factorization from a given matrix
+   *
+   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c
+   * MatrixType is a Eigen::Ref.
+   *
+   * \sa FullPivHouseholderQR(const EigenBase&)
+   */
+  template <typename InputType>
+  explicit FullPivHouseholderQR(EigenBase<InputType>& matrix)
+      : m_qr(matrix.derived()),
+        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
+        m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
+        m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())),
+        m_cols_permutation(matrix.cols()),
+        m_temp(matrix.cols()),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false) {
+    computeInPlace();
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
+   * \c *this is the QR decomposition.
+   *
+   * \param b the right-hand-side of the equation to solve.
+   *
+   * \returns the exact or least-square solution if the rank is greater or equal to the number of columns of A,
+   * and an arbitrary solution otherwise.
+   *
+   * \note_about_checking_solutions
+   *
+   * \note_about_arbitrary_choice_of_solution
+   *
+   * Example: \include FullPivHouseholderQR_solve.cpp
+   * Output: \verbinclude FullPivHouseholderQR_solve.out
+   */
+  template <typename Rhs>
+  inline Solve<FullPivHouseholderQR, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  /** \returns Expression object representing the matrix Q
+   */
+  MatrixQReturnType matrixQ(void) const;
+
+  /** \returns a reference to the matrix where the Householder QR decomposition is stored
+   */
+  const MatrixType& matrixQR() const {
+    eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+    return m_qr;
+  }
+
+  template <typename InputType>
+  FullPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
+
+  /** \returns a const reference to the column permutation matrix */
+  const PermutationType& colsPermutation() const {
+    eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+    return m_cols_permutation;
+  }
+
+  /** \returns a const reference to the vector of indices representing the rows transpositions */
+  const IntDiagSizeVectorType& rowsTranspositions() const {
+    eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+    return m_rows_transpositions;
+  }
+
+  /** \returns the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   *
+   * \sa absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::Scalar determinant() const;
+
+  /** \returns the absolute value of the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   *
+   * \sa determinant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::RealScalar absDeterminant() const;
+
+  /** \returns the natural log of the absolute value of the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \note This method is useful to work around the risk of overflow/underflow that's inherent
+   * to determinant computation.
+   *
+   * \sa determinant(), absDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::RealScalar logAbsDeterminant() const;
+
+  /** \returns the sign of the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \note This method is useful to work around the risk of overflow/underflow that's inherent
+   * to determinant computation.
+   *
+   * \sa determinant(), absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::Scalar signDeterminant() const;
+
+  /** \returns the rank of the matrix of which *this is the QR decomposition.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline Index rank() const {
+    using std::abs;
+    eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+    RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
+    Index result = 0;
+    for (Index i = 0; i < m_nonzero_pivots; ++i) result += (abs(m_qr.coeff(i, i)) > premultiplied_threshold);
+    return result;
+  }
+
+  /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline Index dimensionOfKernel() const {
+    eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+    return cols() - rank();
+  }
+
+  /** \returns true if the matrix of which *this is the QR decomposition represents an injective
+   *          linear map, i.e. has trivial kernel; false otherwise.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline bool isInjective() const {
+    eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+    return rank() == cols();
+  }
+
+  /** \returns true if the matrix of which *this is the QR decomposition represents a surjective
+   *          linear map; false otherwise.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline bool isSurjective() const {
+    eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+    return rank() == rows();
+  }
+
+  /** \returns true if the matrix of which *this is the QR decomposition is invertible.
+   *
+   * \note This method has to determine which pivots should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline bool isInvertible() const {
+    eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+    return isInjective() && isSurjective();
+  }
+
+  /** \returns the inverse of the matrix of which *this is the QR decomposition.
+   *
+   * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
+   *       Use isInvertible() to first determine whether this matrix is invertible.
+   */
+  inline Inverse<FullPivHouseholderQR> inverse() const {
+    eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+    return Inverse<FullPivHouseholderQR>(*this);
+  }
+
+  inline Index rows() const { return m_qr.rows(); }
+  inline Index cols() const { return m_qr.cols(); }
+
+  /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
+   *
+   * For advanced uses only.
+   */
+  const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
+
+  /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
+   * who need to determine when pivots are to be considered nonzero. This is not used for the
+   * QR decomposition itself.
+   *
+   * When it needs to get the threshold value, Eigen calls threshold(). By default, this
+   * uses a formula to automatically determine a reasonable threshold.
+   * Once you have called the present method setThreshold(const RealScalar&),
+   * your value is used instead.
+   *
+   * \param threshold The new value to use as the threshold.
+   *
+   * A pivot will be considered nonzero if its absolute value is strictly greater than
+   *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
+   * where maxpivot is the biggest pivot.
+   *
+   * If you want to come back to the default behavior, call setThreshold(Default_t)
+   */
+  FullPivHouseholderQR& setThreshold(const RealScalar& threshold) {
+    m_usePrescribedThreshold = true;
+    m_prescribedThreshold = threshold;
+    return *this;
+  }
+
+  /** Allows to come back to the default behavior, letting Eigen use its default formula for
+   * determining the threshold.
+   *
+   * You should pass the special object Eigen::Default as parameter here.
+   * \code qr.setThreshold(Eigen::Default); \endcode
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  FullPivHouseholderQR& setThreshold(Default_t) {
+    m_usePrescribedThreshold = false;
+    return *this;
+  }
+
+  /** Returns the threshold that will be used by certain methods such as rank().
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  RealScalar threshold() const {
+    eigen_assert(m_isInitialized || m_usePrescribedThreshold);
+    return m_usePrescribedThreshold ? m_prescribedThreshold
+                                    // this formula comes from experimenting (see "LU precision tuning" thread on the
+                                    // list) and turns out to be identical to Higham's formula used already in LDLt.
+                                    : NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize());
+  }
+
+  /** \returns the number of nonzero pivots in the QR decomposition.
+   * Here nonzero is meant in the exact sense, not in a fuzzy sense.
+   * So that notion isn't really intrinsically interesting, but it is
+   * still useful when implementing algorithms.
+   *
+   * \sa rank()
+   */
+  inline Index nonzeroPivots() const {
+    eigen_assert(m_isInitialized && "LU is not initialized.");
+    return m_nonzero_pivots;
+  }
+
+  /** \returns the absolute value of the biggest pivot, i.e. the biggest
+   *          diagonal coefficient of U.
+   */
+  RealScalar maxPivot() const { return m_maxpivot; }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  void computeInPlace();
+
+  MatrixType m_qr;
+  HCoeffsType m_hCoeffs;
+  IntDiagSizeVectorType m_rows_transpositions;
+  IntDiagSizeVectorType m_cols_transpositions;
+  PermutationType m_cols_permutation;
+  RowVectorType m_temp;
+  bool m_isInitialized, m_usePrescribedThreshold;
+  RealScalar m_prescribedThreshold, m_maxpivot;
+  Index m_nonzero_pivots;
+  RealScalar m_precision;
+  Index m_det_p;
+};
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::Scalar FullPivHouseholderQR<MatrixType, PermutationIndex>::determinant() const {
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  Scalar detQ;
+  internal::householder_determinant<HCoeffsType, Scalar, NumTraits<Scalar>::IsComplex>::run(m_hCoeffs, detQ);
+  return isInjective() ? (detQ * Scalar(m_det_p)) * m_qr.diagonal().prod() : Scalar(0);
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType, PermutationIndex>::absDeterminant() const {
+  using std::abs;
+  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return isInjective() ? abs(m_qr.diagonal().prod()) : RealScalar(0);
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType, PermutationIndex>::logAbsDeterminant() const {
+  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return isInjective() ? m_qr.diagonal().cwiseAbs().array().log().sum() : -NumTraits<RealScalar>::infinity();
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::Scalar FullPivHouseholderQR<MatrixType, PermutationIndex>::signDeterminant() const {
+  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  Scalar detQ;
+  internal::householder_determinant<HCoeffsType, Scalar, NumTraits<Scalar>::IsComplex>::run(m_hCoeffs, detQ);
+  return isInjective() ? (detQ * Scalar(m_det_p)) * m_qr.diagonal().array().sign().prod() : Scalar(0);
+}
+
+/** Performs the QR factorization of the given matrix \a matrix. The result of
+ * the factorization is stored into \c *this, and a reference to \c *this
+ * is returned.
+ *
+ * \sa class FullPivHouseholderQR, FullPivHouseholderQR(const MatrixType&)
+ */
+template <typename MatrixType, typename PermutationIndex>
+template <typename InputType>
+FullPivHouseholderQR<MatrixType, PermutationIndex>& FullPivHouseholderQR<MatrixType, PermutationIndex>::compute(
+    const EigenBase<InputType>& matrix) {
+  m_qr = matrix.derived();
+  computeInPlace();
+  return *this;
+}
+
+template <typename MatrixType, typename PermutationIndex>
+void FullPivHouseholderQR<MatrixType, PermutationIndex>::computeInPlace() {
+  eigen_assert(m_qr.cols() <= NumTraits<PermutationIndex>::highest());
+  using std::abs;
+  Index rows = m_qr.rows();
+  Index cols = m_qr.cols();
+  Index size = (std::min)(rows, cols);
+
+  m_hCoeffs.resize(size);
+
+  m_temp.resize(cols);
+
+  m_precision = NumTraits<Scalar>::epsilon() * RealScalar(size);
+
+  m_rows_transpositions.resize(size);
+  m_cols_transpositions.resize(size);
+  Index number_of_transpositions = 0;
+
+  RealScalar biggest(0);
+
+  m_nonzero_pivots = size;  // the generic case is that in which all pivots are nonzero (invertible case)
+  m_maxpivot = RealScalar(0);
+
+  for (Index k = 0; k < size; ++k) {
+    Index row_of_biggest_in_corner, col_of_biggest_in_corner;
+    typedef internal::scalar_score_coeff_op<Scalar> Scoring;
+    typedef typename Scoring::result_type Score;
+
+    Score score = m_qr.bottomRightCorner(rows - k, cols - k)
+                      .unaryExpr(Scoring())
+                      .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
+    row_of_biggest_in_corner += k;
+    col_of_biggest_in_corner += k;
+    RealScalar biggest_in_corner =
+        internal::abs_knowing_score<Scalar>()(m_qr(row_of_biggest_in_corner, col_of_biggest_in_corner), score);
+    if (k == 0) biggest = biggest_in_corner;
+
+    // if the corner is negligible, then we have less than full rank, and we can finish early
+    if (internal::isMuchSmallerThan(biggest_in_corner, biggest, m_precision)) {
+      m_nonzero_pivots = k;
+      for (Index i = k; i < size; i++) {
+        m_rows_transpositions.coeffRef(i) = internal::convert_index<PermutationIndex>(i);
+        m_cols_transpositions.coeffRef(i) = internal::convert_index<PermutationIndex>(i);
+        m_hCoeffs.coeffRef(i) = Scalar(0);
+      }
+      break;
+    }
+
+    m_rows_transpositions.coeffRef(k) = internal::convert_index<PermutationIndex>(row_of_biggest_in_corner);
+    m_cols_transpositions.coeffRef(k) = internal::convert_index<PermutationIndex>(col_of_biggest_in_corner);
+    if (k != row_of_biggest_in_corner) {
+      m_qr.row(k).tail(cols - k).swap(m_qr.row(row_of_biggest_in_corner).tail(cols - k));
+      ++number_of_transpositions;
+    }
+    if (k != col_of_biggest_in_corner) {
+      m_qr.col(k).swap(m_qr.col(col_of_biggest_in_corner));
+      ++number_of_transpositions;
+    }
+
+    RealScalar beta;
+    m_qr.col(k).tail(rows - k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
+    m_qr.coeffRef(k, k) = beta;
+
+    // remember the maximum absolute value of diagonal coefficients
+    if (abs(beta) > m_maxpivot) m_maxpivot = abs(beta);
+
+    m_qr.bottomRightCorner(rows - k, cols - k - 1)
+        .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows - k - 1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k + 1));
+  }
+
+  m_cols_permutation.setIdentity(cols);
+  for (Index k = 0; k < size; ++k) m_cols_permutation.applyTranspositionOnTheRight(k, m_cols_transpositions.coeff(k));
+
+  m_det_p = (number_of_transpositions % 2) ? -1 : 1;
+  m_isInitialized = true;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType_, typename PermutationIndex_>
+template <typename RhsType, typename DstType>
+void FullPivHouseholderQR<MatrixType_, PermutationIndex_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
+  const Index l_rank = rank();
+
+  // FIXME: introduce nonzeroPivots() and apply the same improvements as in FullPivLU.
+  if (l_rank == 0) {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(rhs);
+
+  Matrix<typename RhsType::Scalar, 1, RhsType::ColsAtCompileTime> temp(rhs.cols());
+  for (Index k = 0; k < l_rank; ++k) {
+    Index remainingSize = rows() - k;
+    c.row(k).swap(c.row(m_rows_transpositions.coeff(k)));
+    c.bottomRightCorner(remainingSize, rhs.cols())
+        .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize - 1), m_hCoeffs.coeff(k), &temp.coeffRef(0));
+  }
+
+  m_qr.topLeftCorner(l_rank, l_rank).template triangularView<Upper>().solveInPlace(c.topRows(l_rank));
+
+  for (Index i = 0; i < l_rank; ++i) dst.row(m_cols_permutation.indices().coeff(i)) = c.row(i);
+  for (Index i = l_rank; i < cols(); ++i) dst.row(m_cols_permutation.indices().coeff(i)).setZero();
+}
+
+template <typename MatrixType_, typename PermutationIndex_>
+template <bool Conjugate, typename RhsType, typename DstType>
+void FullPivHouseholderQR<MatrixType_, PermutationIndex_>::_solve_impl_transposed(const RhsType& rhs,
+                                                                                  DstType& dst) const {
+  const Index l_rank = rank();
+
+  if (l_rank == 0) {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(m_cols_permutation.transpose() * rhs);
+
+  m_qr.topLeftCorner(l_rank, l_rank)
+      .template triangularView<Upper>()
+      .transpose()
+      .template conjugateIf<Conjugate>()
+      .solveInPlace(c.topRows(l_rank));
+
+  dst.topRows(l_rank) = c.topRows(l_rank);
+  dst.bottomRows(rows() - l_rank).setZero();
+
+  Matrix<Scalar, 1, DstType::ColsAtCompileTime> temp(dst.cols());
+  const Index size = (std::min)(rows(), cols());
+  for (Index k = size - 1; k >= 0; --k) {
+    Index remainingSize = rows() - k;
+
+    dst.bottomRightCorner(remainingSize, dst.cols())
+        .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize - 1).template conjugateIf<!Conjugate>(),
+                                   m_hCoeffs.template conjugateIf<Conjugate>().coeff(k), &temp.coeffRef(0));
+
+    dst.row(k).swap(dst.row(m_rows_transpositions.coeff(k)));
+  }
+}
+#endif
+
+namespace internal {
+
+template <typename DstXprType, typename MatrixType, typename PermutationIndex>
+struct Assignment<DstXprType, Inverse<FullPivHouseholderQR<MatrixType, PermutationIndex> >,
+                  internal::assign_op<typename DstXprType::Scalar,
+                                      typename FullPivHouseholderQR<MatrixType, PermutationIndex>::Scalar>,
+                  Dense2Dense> {
+  typedef FullPivHouseholderQR<MatrixType, PermutationIndex> QrType;
+  typedef Inverse<QrType> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::assign_op<typename DstXprType::Scalar, typename QrType::Scalar>&) {
+    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
+  }
+};
+
+/** \ingroup QR_Module
+ *
+ * \brief Expression type for return value of FullPivHouseholderQR::matrixQ()
+ *
+ * \tparam MatrixType type of underlying dense matrix
+ */
+template <typename MatrixType, typename PermutationIndex>
+struct FullPivHouseholderQRMatrixQReturnType
+    : public ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType, PermutationIndex> > {
+ public:
+  typedef typename FullPivHouseholderQR<MatrixType, PermutationIndex>::IntDiagSizeVectorType IntDiagSizeVectorType;
+  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+  typedef Matrix<typename MatrixType::Scalar, 1, MatrixType::RowsAtCompileTime, RowMajor, 1,
+                 MatrixType::MaxRowsAtCompileTime>
+      WorkVectorType;
+
+  FullPivHouseholderQRMatrixQReturnType(const MatrixType& qr, const HCoeffsType& hCoeffs,
+                                        const IntDiagSizeVectorType& rowsTranspositions)
+      : m_qr(qr), m_hCoeffs(hCoeffs), m_rowsTranspositions(rowsTranspositions) {}
+
+  template <typename ResultType>
+  void evalTo(ResultType& result) const {
+    const Index rows = m_qr.rows();
+    WorkVectorType workspace(rows);
+    evalTo(result, workspace);
+  }
+
+  template <typename ResultType>
+  void evalTo(ResultType& result, WorkVectorType& workspace) const {
+    using numext::conj;
+    // compute the product H'_0 H'_1 ... H'_n-1,
+    // where H_k is the k-th Householder transformation I - h_k v_k v_k'
+    // and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...]
+    const Index rows = m_qr.rows();
+    const Index cols = m_qr.cols();
+    const Index size = (std::min)(rows, cols);
+    workspace.resize(rows);
+    result.setIdentity(rows, rows);
+    for (Index k = size - 1; k >= 0; k--) {
+      result.block(k, k, rows - k, rows - k)
+          .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows - k - 1), conj(m_hCoeffs.coeff(k)), &workspace.coeffRef(k));
+      result.row(k).swap(result.row(m_rowsTranspositions.coeff(k)));
+    }
+  }
+
+  Index rows() const { return m_qr.rows(); }
+  Index cols() const { return m_qr.rows(); }
+
+ protected:
+  typename MatrixType::Nested m_qr;
+  typename HCoeffsType::Nested m_hCoeffs;
+  typename IntDiagSizeVectorType::Nested m_rowsTranspositions;
+};
+
+}  // end namespace internal
+
+template <typename MatrixType, typename PermutationIndex>
+inline typename FullPivHouseholderQR<MatrixType, PermutationIndex>::MatrixQReturnType
+FullPivHouseholderQR<MatrixType, PermutationIndex>::matrixQ() const {
+  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+  return MatrixQReturnType(m_qr, m_hCoeffs, m_rows_transpositions);
+}
+
+/** \return the full-pivoting Householder QR decomposition of \c *this.
+ *
+ * \sa class FullPivHouseholderQR
+ */
+template <typename Derived>
+template <typename PermutationIndex>
+FullPivHouseholderQR<typename MatrixBase<Derived>::PlainObject, PermutationIndex>
+MatrixBase<Derived>::fullPivHouseholderQr() const {
+  return FullPivHouseholderQR<PlainObject, PermutationIndex>(eval());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/QR/HouseholderQR.h b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/HouseholderQR.h
new file mode 100644
index 0000000..e7173a5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/HouseholderQR.h
@@ -0,0 +1,543 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2010 Vincent Lejeune
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_QR_H
+#define EIGEN_QR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType_>
+struct traits<HouseholderQR<MatrixType_>> : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
+}  // end namespace internal
+
+/** \ingroup QR_Module
+ *
+ *
+ * \class HouseholderQR
+ *
+ * \brief Householder QR decomposition of a matrix
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the QR decomposition
+ *
+ * This class performs a QR decomposition of a matrix \b A into matrices \b Q and \b R
+ * such that
+ * \f[
+ *  \mathbf{A} = \mathbf{Q} \, \mathbf{R}
+ * \f]
+ * by using Householder transformations. Here, \b Q a unitary matrix and \b R an upper triangular matrix.
+ * The result is stored in a compact way compatible with LAPACK.
+ *
+ * Note that no pivoting is performed. This is \b not a rank-revealing decomposition.
+ * If you want that feature, use FullPivHouseholderQR or ColPivHouseholderQR instead.
+ *
+ * This Householder QR decomposition is faster, but less numerically stable and less feature-full than
+ * FullPivHouseholderQR or ColPivHouseholderQR.
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * \sa MatrixBase::householderQr()
+ */
+template <typename MatrixType_>
+class HouseholderQR : public SolverBase<HouseholderQR<MatrixType_>> {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<HouseholderQR> Base;
+  friend class SolverBase<HouseholderQR>;
+
+  EIGEN_GENERIC_PUBLIC_INTERFACE(HouseholderQR)
+  enum {
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+  typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, (MatrixType::Flags & RowMajorBit) ? RowMajor : ColMajor,
+                 MaxRowsAtCompileTime, MaxRowsAtCompileTime>
+      MatrixQType;
+  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+  typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
+  typedef HouseholderSequence<MatrixType, internal::remove_all_t<typename HCoeffsType::ConjugateReturnType>>
+      HouseholderSequenceType;
+
+  /** \brief Reports whether the QR factorization was successful.
+   *
+   * \note This function always returns \c Success. It is provided for compatibility
+   * with other factorization routines.
+   * \returns \c Success
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "HouseHolderQR is not initialized.");
+    return Success;
+  }
+
+  /**
+   * \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via HouseholderQR::compute(const MatrixType&).
+   */
+  HouseholderQR() : m_qr(), m_hCoeffs(), m_temp(), m_isInitialized(false) {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa HouseholderQR()
+   */
+  HouseholderQR(Index rows, Index cols)
+      : m_qr(rows, cols), m_hCoeffs((std::min)(rows, cols)), m_temp(cols), m_isInitialized(false) {}
+
+  /** \brief Constructs a QR factorization from a given matrix
+   *
+   * This constructor computes the QR factorization of the matrix \a matrix by calling
+   * the method compute(). It is a short cut for:
+   *
+   * \code
+   * HouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
+   * qr.compute(matrix);
+   * \endcode
+   *
+   * \sa compute()
+   */
+  template <typename InputType>
+  explicit HouseholderQR(const EigenBase<InputType>& matrix)
+      : m_qr(matrix.rows(), matrix.cols()),
+        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
+        m_temp(matrix.cols()),
+        m_isInitialized(false) {
+    compute(matrix.derived());
+  }
+
+  /** \brief Constructs a QR factorization from a given matrix
+   *
+   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
+   * \c MatrixType is a Eigen::Ref.
+   *
+   * \sa HouseholderQR(const EigenBase&)
+   */
+  template <typename InputType>
+  explicit HouseholderQR(EigenBase<InputType>& matrix)
+      : m_qr(matrix.derived()),
+        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
+        m_temp(matrix.cols()),
+        m_isInitialized(false) {
+    computeInPlace();
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
+   * *this is the QR decomposition, if any exists.
+   *
+   * \param b the right-hand-side of the equation to solve.
+   *
+   * \returns a solution.
+   *
+   * \note_about_checking_solutions
+   *
+   * \note_about_arbitrary_choice_of_solution
+   *
+   * Example: \include HouseholderQR_solve.cpp
+   * Output: \verbinclude HouseholderQR_solve.out
+   */
+  template <typename Rhs>
+  inline Solve<HouseholderQR, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  /** This method returns an expression of the unitary matrix Q as a sequence of Householder transformations.
+   *
+   * The returned expression can directly be used to perform matrix products. It can also be assigned to a dense Matrix
+   * object. Here is an example showing how to recover the full or thin matrix Q, as well as how to perform matrix
+   * products using operator*:
+   *
+   * Example: \include HouseholderQR_householderQ.cpp
+   * Output: \verbinclude HouseholderQR_householderQ.out
+   */
+  HouseholderSequenceType householderQ() const {
+    eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+    return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate());
+  }
+
+  /** \returns a reference to the matrix where the Householder QR decomposition is stored
+   * in a LAPACK-compatible way.
+   */
+  const MatrixType& matrixQR() const {
+    eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+    return m_qr;
+  }
+
+  template <typename InputType>
+  HouseholderQR& compute(const EigenBase<InputType>& matrix) {
+    m_qr = matrix.derived();
+    computeInPlace();
+    return *this;
+  }
+
+  /** \returns the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   * Also, do not rely on the determinant being exactly zero for testing
+   * singularity or rank-deficiency.
+   *
+   * \sa absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::Scalar determinant() const;
+
+  /** \returns the absolute value of the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   * Also, do not rely on the determinant being exactly zero for testing
+   * singularity or rank-deficiency.
+   *
+   * \sa determinant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::RealScalar absDeterminant() const;
+
+  /** \returns the natural log of the absolute value of the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \note This method is useful to work around the risk of overflow/underflow that's inherent
+   * to determinant computation.
+   *
+   * \warning Do not rely on the determinant being exactly zero for testing
+   * singularity or rank-deficiency.
+   *
+   * \sa determinant(), absDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::RealScalar logAbsDeterminant() const;
+
+  /** \returns the sign of the determinant of the matrix of which
+   * *this is the QR decomposition. It has only linear complexity
+   * (that is, O(n) where n is the dimension of the square matrix)
+   * as the QR decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \note This method is useful to work around the risk of overflow/underflow that's inherent
+   * to determinant computation.
+   *
+   * \warning Do not rely on the determinant being exactly zero for testing
+   * singularity or rank-deficiency.
+   *
+   * \sa determinant(), absDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::Scalar signDeterminant() const;
+
+  inline Index rows() const { return m_qr.rows(); }
+  inline Index cols() const { return m_qr.cols(); }
+
+  /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
+   *
+   * For advanced uses only.
+   */
+  const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  void computeInPlace();
+
+  MatrixType m_qr;
+  HCoeffsType m_hCoeffs;
+  RowVectorType m_temp;
+  bool m_isInitialized;
+};
+
+namespace internal {
+
+/** \internal */
+template <typename HCoeffs, typename Scalar, bool IsComplex>
+struct householder_determinant {
+  static void run(const HCoeffs& hCoeffs, Scalar& out_det) {
+    out_det = Scalar(1);
+    Index size = hCoeffs.rows();
+    for (Index i = 0; i < size; i++) {
+      // For each valid reflection Q_n,
+      // det(Q_n) = - conj(h_n) / h_n
+      // where h_n is the Householder coefficient.
+      if (hCoeffs(i) != Scalar(0)) out_det *= -numext::conj(hCoeffs(i)) / hCoeffs(i);
+    }
+  }
+};
+
+/** \internal */
+template <typename HCoeffs, typename Scalar>
+struct householder_determinant<HCoeffs, Scalar, false> {
+  static void run(const HCoeffs& hCoeffs, Scalar& out_det) {
+    bool negated = false;
+    Index size = hCoeffs.rows();
+    for (Index i = 0; i < size; i++) {
+      // Each valid reflection negates the determinant.
+      if (hCoeffs(i) != Scalar(0)) negated ^= true;
+    }
+    out_det = negated ? Scalar(-1) : Scalar(1);
+  }
+};
+
+}  // end namespace internal
+
+template <typename MatrixType>
+typename MatrixType::Scalar HouseholderQR<MatrixType>::determinant() const {
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  Scalar detQ;
+  internal::householder_determinant<HCoeffsType, Scalar, NumTraits<Scalar>::IsComplex>::run(m_hCoeffs, detQ);
+  return m_qr.diagonal().prod() * detQ;
+}
+
+template <typename MatrixType>
+typename MatrixType::RealScalar HouseholderQR<MatrixType>::absDeterminant() const {
+  using std::abs;
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return abs(m_qr.diagonal().prod());
+}
+
+template <typename MatrixType>
+typename MatrixType::RealScalar HouseholderQR<MatrixType>::logAbsDeterminant() const {
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return m_qr.diagonal().cwiseAbs().array().log().sum();
+}
+
+template <typename MatrixType>
+typename MatrixType::Scalar HouseholderQR<MatrixType>::signDeterminant() const {
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  Scalar detQ;
+  internal::householder_determinant<HCoeffsType, Scalar, NumTraits<Scalar>::IsComplex>::run(m_hCoeffs, detQ);
+  return detQ * m_qr.diagonal().array().sign().prod();
+}
+
+namespace internal {
+
+/** \internal */
+template <typename MatrixQR, typename HCoeffs>
+void householder_qr_inplace_unblocked(MatrixQR& mat, HCoeffs& hCoeffs, typename MatrixQR::Scalar* tempData = 0) {
+  typedef typename MatrixQR::Scalar Scalar;
+  typedef typename MatrixQR::RealScalar RealScalar;
+  Index rows = mat.rows();
+  Index cols = mat.cols();
+  Index size = (std::min)(rows, cols);
+
+  eigen_assert(hCoeffs.size() == size);
+
+  typedef Matrix<Scalar, MatrixQR::ColsAtCompileTime, 1> TempType;
+  TempType tempVector;
+  if (tempData == 0) {
+    tempVector.resize(cols);
+    tempData = tempVector.data();
+  }
+
+  for (Index k = 0; k < size; ++k) {
+    Index remainingRows = rows - k;
+    Index remainingCols = cols - k - 1;
+
+    RealScalar beta;
+    mat.col(k).tail(remainingRows).makeHouseholderInPlace(hCoeffs.coeffRef(k), beta);
+    mat.coeffRef(k, k) = beta;
+
+    // apply H to remaining part of m_qr from the left
+    mat.bottomRightCorner(remainingRows, remainingCols)
+        .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows - 1), hCoeffs.coeffRef(k), tempData + k + 1);
+  }
+}
+
+// TODO: expose a public API for rank-1 QR update.
+/** \internal
+ * Basically a modified copy of @c Eigen::internal::householder_qr_inplace_unblocked that
+ * performs a rank-1 update of the QR matrix in compact storage. This function assumes, that
+ * the first @c k-1 columns of the matrix @c mat contain the QR decomposition of \f$A^N\f$ up to
+ * column k-1. Then the QR decomposition of the k-th column (given by @c newColumn) is computed by
+ * applying the k-1 Householder projectors on it and finally compute the projector \f$H_k\f$ of
+ * it. On exit the matrix @c mat and the vector @c hCoeffs contain the QR decomposition of the
+ * first k columns of \f$A^N\f$. The \a tempData argument must point to at least mat.cols() scalars.  */
+template <typename MatrixQR, typename HCoeffs, typename VectorQR>
+void householder_qr_inplace_update(MatrixQR& mat, HCoeffs& hCoeffs, const VectorQR& newColumn,
+                                   typename MatrixQR::Index k, typename MatrixQR::Scalar* tempData) {
+  typedef typename MatrixQR::Index Index;
+  typedef typename MatrixQR::RealScalar RealScalar;
+  Index rows = mat.rows();
+
+  eigen_assert(k < mat.cols());
+  eigen_assert(k < rows);
+  eigen_assert(hCoeffs.size() == mat.cols());
+  eigen_assert(newColumn.size() == rows);
+  eigen_assert(tempData);
+
+  // Store new column in mat at column k
+  mat.col(k) = newColumn;
+  // Apply H = H_1...H_{k-1} on newColumn (skip if k=0)
+  for (Index i = 0; i < k; ++i) {
+    Index remainingRows = rows - i;
+    mat.col(k)
+        .tail(remainingRows)
+        .applyHouseholderOnTheLeft(mat.col(i).tail(remainingRows - 1), hCoeffs.coeffRef(i), tempData + i + 1);
+  }
+  // Construct Householder projector in-place in column k
+  RealScalar beta;
+  mat.col(k).tail(rows - k).makeHouseholderInPlace(hCoeffs.coeffRef(k), beta);
+  mat.coeffRef(k, k) = beta;
+}
+
+/** \internal */
+template <typename MatrixQR, typename HCoeffs, typename MatrixQRScalar = typename MatrixQR::Scalar,
+          bool InnerStrideIsOne = (MatrixQR::InnerStrideAtCompileTime == 1 && HCoeffs::InnerStrideAtCompileTime == 1)>
+struct householder_qr_inplace_blocked {
+  // This is specialized for LAPACK-supported Scalar types in HouseholderQR_LAPACKE.h
+  static void run(MatrixQR& mat, HCoeffs& hCoeffs, Index maxBlockSize = 32, typename MatrixQR::Scalar* tempData = 0) {
+    typedef typename MatrixQR::Scalar Scalar;
+    typedef Block<MatrixQR, Dynamic, Dynamic> BlockType;
+
+    Index rows = mat.rows();
+    Index cols = mat.cols();
+    Index size = (std::min)(rows, cols);
+
+    typedef Matrix<Scalar, Dynamic, 1, ColMajor, MatrixQR::MaxColsAtCompileTime, 1> TempType;
+    TempType tempVector;
+    if (tempData == 0) {
+      tempVector.resize(cols);
+      tempData = tempVector.data();
+    }
+
+    Index blockSize = (std::min)(maxBlockSize, size);
+
+    Index k = 0;
+    for (k = 0; k < size; k += blockSize) {
+      Index bs = (std::min)(size - k, blockSize);  // actual size of the block
+      Index tcols = cols - k - bs;                 // trailing columns
+      Index brows = rows - k;                      // rows of the block
+
+      // partition the matrix:
+      //        A00 | A01 | A02
+      // mat  = A10 | A11 | A12
+      //        A20 | A21 | A22
+      // and performs the qr dec of [A11^T A12^T]^T
+      // and update [A21^T A22^T]^T using level 3 operations.
+      // Finally, the algorithm continue on A22
+
+      BlockType A11_21 = mat.block(k, k, brows, bs);
+      Block<HCoeffs, Dynamic, 1> hCoeffsSegment = hCoeffs.segment(k, bs);
+
+      householder_qr_inplace_unblocked(A11_21, hCoeffsSegment, tempData);
+
+      if (tcols) {
+        BlockType A21_22 = mat.block(k, k + bs, brows, tcols);
+        apply_block_householder_on_the_left(A21_22, A11_21, hCoeffsSegment, false);  // false == backward
+      }
+    }
+  }
+};
+
+}  // end namespace internal
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType_>
+template <typename RhsType, typename DstType>
+void HouseholderQR<MatrixType_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
+  const Index rank = (std::min)(rows(), cols());
+
+  typename RhsType::PlainObject c(rhs);
+
+  c.applyOnTheLeft(householderQ().setLength(rank).adjoint());
+
+  m_qr.topLeftCorner(rank, rank).template triangularView<Upper>().solveInPlace(c.topRows(rank));
+
+  dst.topRows(rank) = c.topRows(rank);
+  dst.bottomRows(cols() - rank).setZero();
+}
+
+template <typename MatrixType_>
+template <bool Conjugate, typename RhsType, typename DstType>
+void HouseholderQR<MatrixType_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
+  const Index rank = (std::min)(rows(), cols());
+
+  typename RhsType::PlainObject c(rhs);
+
+  m_qr.topLeftCorner(rank, rank)
+      .template triangularView<Upper>()
+      .transpose()
+      .template conjugateIf<Conjugate>()
+      .solveInPlace(c.topRows(rank));
+
+  dst.topRows(rank) = c.topRows(rank);
+  dst.bottomRows(rows() - rank).setZero();
+
+  dst.applyOnTheLeft(householderQ().setLength(rank).template conjugateIf<!Conjugate>());
+}
+#endif
+
+/** Performs the QR factorization of the given matrix \a matrix. The result of
+ * the factorization is stored into \c *this, and a reference to \c *this
+ * is returned.
+ *
+ * \sa class HouseholderQR, HouseholderQR(const MatrixType&)
+ */
+template <typename MatrixType>
+void HouseholderQR<MatrixType>::computeInPlace() {
+  Index rows = m_qr.rows();
+  Index cols = m_qr.cols();
+  Index size = (std::min)(rows, cols);
+
+  m_hCoeffs.resize(size);
+
+  m_temp.resize(cols);
+
+  internal::householder_qr_inplace_blocked<MatrixType, HCoeffsType>::run(m_qr, m_hCoeffs, 48, m_temp.data());
+
+  m_isInitialized = true;
+}
+
+/** \return the Householder QR decomposition of \c *this.
+ *
+ * \sa class HouseholderQR
+ */
+template <typename Derived>
+HouseholderQR<typename MatrixBase<Derived>::PlainObject> MatrixBase<Derived>::householderQr() const {
+  return HouseholderQR<PlainObject>(eval());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_QR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/QR/HouseholderQR_LAPACKE.h b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/HouseholderQR_LAPACKE.h
new file mode 100644
index 0000000..3b62175
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/HouseholderQR_LAPACKE.h
@@ -0,0 +1,77 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Householder QR decomposition of a matrix w/o pivoting based on
+ *    LAPACKE_?geqrf function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_QR_LAPACKE_H
+#define EIGEN_QR_LAPACKE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+namespace lapacke_helpers {
+
+template <typename MatrixQR, typename HCoeffs>
+struct lapacke_hqr {
+  static void run(MatrixQR& mat, HCoeffs& hCoeffs, Index = 32, typename MatrixQR::Scalar* = 0) {
+    lapack_int m = to_lapack(mat.rows());
+    lapack_int n = to_lapack(mat.cols());
+    lapack_int lda = to_lapack(mat.outerStride());
+    lapack_int matrix_order = lapack_storage_of(mat);
+    geqrf(matrix_order, m, n, to_lapack(mat.data()), lda, to_lapack(hCoeffs.data()));
+    hCoeffs.adjointInPlace();
+  }
+};
+
+}  // namespace lapacke_helpers
+
+/** \internal Specialization for the data types supported by LAPACKe */
+#define EIGEN_LAPACKE_HH_QR(EIGTYPE)                                      \
+  template <typename MatrixQR, typename HCoeffs>                          \
+  struct householder_qr_inplace_blocked<MatrixQR, HCoeffs, EIGTYPE, true> \
+      : public lapacke_helpers::lapacke_hqr<MatrixQR, HCoeffs> {};
+
+EIGEN_LAPACKE_HH_QR(double)
+EIGEN_LAPACKE_HH_QR(float)
+EIGEN_LAPACKE_HH_QR(std::complex<double>)
+EIGEN_LAPACKE_HH_QR(std::complex<float>)
+
+#undef EIGEN_LAPACKE_HH_QR
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_QR_LAPACKE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/QR/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/InternalHeaderCheck.h
new file mode 100644
index 0000000..bf8df01
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/QR/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_QR_MODULE_H
+#error "Please include Eigen/QR instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SPQRSupport/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SPQRSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000..8d94ba4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SPQRSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPQRSUPPORT_MODULE_H
+#error "Please include Eigen/SPQRSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
new file mode 100644
index 0000000..3132794
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
@@ -0,0 +1,315 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SUITESPARSEQRSUPPORT_H
+#define EIGEN_SUITESPARSEQRSUPPORT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename MatrixType>
+class SPQR;
+template <typename SPQRType>
+struct SPQRMatrixQReturnType;
+template <typename SPQRType>
+struct SPQRMatrixQTransposeReturnType;
+template <typename SPQRType, typename Derived>
+struct SPQR_QProduct;
+namespace internal {
+template <typename SPQRType>
+struct traits<SPQRMatrixQReturnType<SPQRType> > {
+  typedef typename SPQRType::MatrixType ReturnType;
+};
+template <typename SPQRType>
+struct traits<SPQRMatrixQTransposeReturnType<SPQRType> > {
+  typedef typename SPQRType::MatrixType ReturnType;
+};
+template <typename SPQRType, typename Derived>
+struct traits<SPQR_QProduct<SPQRType, Derived> > {
+  typedef typename Derived::PlainObject ReturnType;
+};
+}  // End namespace internal
+
+/**
+ * \ingroup SPQRSupport_Module
+ * \class SPQR
+ * \brief Sparse QR factorization based on SuiteSparseQR library
+ *
+ * This class is used to perform a multithreaded and multifrontal rank-revealing QR decomposition
+ * of sparse matrices. The result is then used to solve linear leasts_square systems.
+ * Clearly, a QR factorization is returned such that A*P = Q*R where :
+ *
+ * P is the column permutation. Use colsPermutation() to get it.
+ *
+ * Q is the orthogonal matrix represented as Householder reflectors.
+ * Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose.
+ * You can then apply it to a vector.
+ *
+ * R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix.
+ * NOTE : The Index type of R is always SuiteSparse_long. You can get it with SPQR::Index
+ *
+ * \tparam MatrixType_ The type of the sparse matrix A, must be a column-major SparseMatrix<>
+ *
+ * \implsparsesolverconcept
+ *
+ *
+ */
+template <typename MatrixType_>
+class SPQR : public SparseSolverBase<SPQR<MatrixType_> > {
+ protected:
+  typedef SparseSolverBase<SPQR<MatrixType_> > Base;
+  using Base::m_isInitialized;
+
+ public:
+  typedef typename MatrixType_::Scalar Scalar;
+  typedef typename MatrixType_::RealScalar RealScalar;
+  typedef SuiteSparse_long StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> MatrixType;
+  typedef Map<PermutationMatrix<Dynamic, Dynamic, StorageIndex> > PermutationType;
+  enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
+
+ public:
+  SPQR()
+      : m_analysisIsOk(false),
+        m_factorizationIsOk(false),
+        m_isRUpToDate(false),
+        m_ordering(SPQR_ORDERING_DEFAULT),
+        m_allow_tol(SPQR_DEFAULT_TOL),
+        m_tolerance(NumTraits<Scalar>::epsilon()),
+        m_cR(0),
+        m_E(0),
+        m_H(0),
+        m_HPinv(0),
+        m_HTau(0),
+        m_useDefaultThreshold(true) {
+    cholmod_l_start(&m_cc);
+  }
+
+  explicit SPQR(const MatrixType_& matrix)
+      : m_analysisIsOk(false),
+        m_factorizationIsOk(false),
+        m_isRUpToDate(false),
+        m_ordering(SPQR_ORDERING_DEFAULT),
+        m_allow_tol(SPQR_DEFAULT_TOL),
+        m_tolerance(NumTraits<Scalar>::epsilon()),
+        m_cR(0),
+        m_E(0),
+        m_H(0),
+        m_HPinv(0),
+        m_HTau(0),
+        m_useDefaultThreshold(true) {
+    cholmod_l_start(&m_cc);
+    compute(matrix);
+  }
+
+  ~SPQR() {
+    SPQR_free();
+    cholmod_l_finish(&m_cc);
+  }
+  void SPQR_free() {
+    cholmod_l_free_sparse(&m_H, &m_cc);
+    cholmod_l_free_sparse(&m_cR, &m_cc);
+    cholmod_l_free_dense(&m_HTau, &m_cc);
+    std::free(m_E);
+    std::free(m_HPinv);
+  }
+
+  void compute(const MatrixType_& matrix) {
+    if (m_isInitialized) SPQR_free();
+
+    MatrixType mat(matrix);
+
+    /* Compute the default threshold as in MatLab, see:
+     * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
+     * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011, Page 8:3
+     */
+    RealScalar pivotThreshold = m_tolerance;
+    if (m_useDefaultThreshold) {
+      RealScalar max2Norm = 0.0;
+      for (int j = 0; j < mat.cols(); j++) max2Norm = numext::maxi(max2Norm, mat.col(j).norm());
+      if (numext::is_exactly_zero(max2Norm)) max2Norm = RealScalar(1);
+      pivotThreshold = 20 * (mat.rows() + mat.cols()) * max2Norm * NumTraits<RealScalar>::epsilon();
+    }
+    cholmod_sparse A;
+    A = viewAsCholmod(mat);
+    m_rows = matrix.rows();
+    m_rank = SuiteSparseQR<Scalar>(m_ordering, pivotThreshold, internal::convert_index<StorageIndex>(matrix.cols()), &A,
+                                   &m_cR, &m_E, &m_H, &m_HPinv, &m_HTau, &m_cc);
+
+    if (!m_cR) {
+      m_info = NumericalIssue;
+      m_isInitialized = false;
+      return;
+    }
+    m_info = Success;
+    m_isInitialized = true;
+    m_isRUpToDate = false;
+  }
+  /**
+   * Get the number of rows of the input matrix and the Q matrix
+   */
+  inline Index rows() const { return m_rows; }
+
+  /**
+   * Get the number of columns of the input matrix.
+   */
+  inline Index cols() const { return m_cR->ncol; }
+
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const {
+    eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
+    eigen_assert(b.cols() == 1 && "This method is for vectors only");
+
+    // Compute Q^T * b
+    typename Dest::PlainObject y, y2;
+    y = matrixQ().transpose() * b;
+
+    // Solves with the triangular matrix R
+    Index rk = this->rank();
+    y2 = y;
+    y.resize((std::max)(cols(), Index(y.rows())), y.cols());
+    y.topRows(rk) = this->matrixR().topLeftCorner(rk, rk).template triangularView<Upper>().solve(y2.topRows(rk));
+
+    // Apply the column permutation
+    // colsPermutation() performs a copy of the permutation,
+    // so let's apply it manually:
+    for (Index i = 0; i < rk; ++i) dest.row(m_E[i]) = y.row(i);
+    for (Index i = rk; i < cols(); ++i) dest.row(m_E[i]).setZero();
+
+    //       y.bottomRows(y.rows()-rk).setZero();
+    //       dest = colsPermutation() * y.topRows(cols());
+
+    m_info = Success;
+  }
+
+  /** \returns the sparse triangular factor R. It is a sparse matrix
+   */
+  const MatrixType matrixR() const {
+    eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
+    if (!m_isRUpToDate) {
+      m_R = viewAsEigen<Scalar, StorageIndex>(*m_cR);
+      m_isRUpToDate = true;
+    }
+    return m_R;
+  }
+  /// Get an expression of the matrix Q
+  SPQRMatrixQReturnType<SPQR> matrixQ() const { return SPQRMatrixQReturnType<SPQR>(*this); }
+  /// Get the permutation that was applied to columns of A
+  PermutationType colsPermutation() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return PermutationType(m_E, m_cR->ncol);
+  }
+  /**
+   * Gets the rank of the matrix.
+   * It should be equal to matrixQR().cols if the matrix is full-rank
+   */
+  Index rank() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_cc.SPQR_istat[4];
+  }
+  /// Set the fill-reducing ordering method to be used
+  void setSPQROrdering(int ord) { m_ordering = ord; }
+  /// Set the tolerance tol to treat columns with 2-norm < =tol as zero
+  void setPivotThreshold(const RealScalar& tol) {
+    m_useDefaultThreshold = false;
+    m_tolerance = tol;
+  }
+
+  /** \returns a pointer to the SPQR workspace */
+  cholmod_common* cholmodCommon() const { return &m_cc; }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the sparse QR can not be computed
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+ protected:
+  bool m_analysisIsOk;
+  bool m_factorizationIsOk;
+  mutable bool m_isRUpToDate;
+  mutable ComputationInfo m_info;
+  int m_ordering;                           // Ordering method to use, see SPQR's manual
+  int m_allow_tol;                          // Allow to use some tolerance during numerical factorization.
+  RealScalar m_tolerance;                   // treat columns with 2-norm below this tolerance as zero
+  mutable cholmod_sparse* m_cR = nullptr;   // The sparse R factor in cholmod format
+  mutable MatrixType m_R;                   // The sparse matrix R in Eigen format
+  mutable StorageIndex* m_E = nullptr;      // The permutation applied to columns
+  mutable cholmod_sparse* m_H = nullptr;    // The householder vectors
+  mutable StorageIndex* m_HPinv = nullptr;  // The row permutation of H
+  mutable cholmod_dense* m_HTau = nullptr;  // The Householder coefficients
+  mutable Index m_rank;                     // The rank of the matrix
+  mutable cholmod_common m_cc;              // Workspace and parameters
+  bool m_useDefaultThreshold;               // Use default threshold
+  Index m_rows;
+  template <typename, typename>
+  friend struct SPQR_QProduct;
+};
+
+template <typename SPQRType, typename Derived>
+struct SPQR_QProduct : ReturnByValue<SPQR_QProduct<SPQRType, Derived> > {
+  typedef typename SPQRType::Scalar Scalar;
+  typedef typename SPQRType::StorageIndex StorageIndex;
+  // Define the constructor to get reference to argument types
+  SPQR_QProduct(const SPQRType& spqr, const Derived& other, bool transpose)
+      : m_spqr(spqr), m_other(other), m_transpose(transpose) {}
+
+  inline Index rows() const { return m_transpose ? m_spqr.rows() : m_spqr.cols(); }
+  inline Index cols() const { return m_other.cols(); }
+  // Assign to a vector
+  template <typename ResType>
+  void evalTo(ResType& res) const {
+    cholmod_dense y_cd;
+    cholmod_dense* x_cd;
+    int method = m_transpose ? SPQR_QTX : SPQR_QX;
+    cholmod_common* cc = m_spqr.cholmodCommon();
+    y_cd = viewAsCholmod(m_other.const_cast_derived());
+    x_cd = SuiteSparseQR_qmult<Scalar>(method, m_spqr.m_H, m_spqr.m_HTau, m_spqr.m_HPinv, &y_cd, cc);
+    res = Matrix<Scalar, ResType::RowsAtCompileTime, ResType::ColsAtCompileTime>::Map(
+        reinterpret_cast<Scalar*>(x_cd->x), x_cd->nrow, x_cd->ncol);
+    cholmod_l_free_dense(&x_cd, cc);
+  }
+  const SPQRType& m_spqr;
+  const Derived& m_other;
+  bool m_transpose;
+};
+template <typename SPQRType>
+struct SPQRMatrixQReturnType {
+  SPQRMatrixQReturnType(const SPQRType& spqr) : m_spqr(spqr) {}
+  template <typename Derived>
+  SPQR_QProduct<SPQRType, Derived> operator*(const MatrixBase<Derived>& other) {
+    return SPQR_QProduct<SPQRType, Derived>(m_spqr, other.derived(), false);
+  }
+  SPQRMatrixQTransposeReturnType<SPQRType> adjoint() const { return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr); }
+  // To use for operations with the transpose of Q
+  SPQRMatrixQTransposeReturnType<SPQRType> transpose() const {
+    return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr);
+  }
+  const SPQRType& m_spqr;
+};
+
+template <typename SPQRType>
+struct SPQRMatrixQTransposeReturnType {
+  SPQRMatrixQTransposeReturnType(const SPQRType& spqr) : m_spqr(spqr) {}
+  template <typename Derived>
+  SPQR_QProduct<SPQRType, Derived> operator*(const MatrixBase<Derived>& other) {
+    return SPQR_QProduct<SPQRType, Derived>(m_spqr, other.derived(), true);
+  }
+  const SPQRType& m_spqr;
+};
+
+}  // End namespace Eigen
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/BDCSVD.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/BDCSVD.h
new file mode 100644
index 0000000..04bf515
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/BDCSVD.h
@@ -0,0 +1,1470 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// We used the "A Divide-And-Conquer Algorithm for the Bidiagonal SVD"
+// research report written by Ming Gu and Stanley C.Eisenstat
+// The code variable names correspond to the names they used in their
+// report
+//
+// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
+// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
+// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
+// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
+// Copyright (C) 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2014-2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BDCSVD_H
+#define EIGEN_BDCSVD_H
+// #define EIGEN_BDCSVD_DEBUG_VERBOSE
+// #define EIGEN_BDCSVD_SANITY_CHECKS
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+#undef eigen_internal_assert
+#define eigen_internal_assert(X) assert(X);
+#endif
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+#include <iostream>
+#endif
+
+namespace Eigen {
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+IOFormat bdcsvdfmt(8, 0, ", ", "\n", "  [", "]");
+#endif
+
+template <typename MatrixType_, int Options>
+class BDCSVD;
+
+namespace internal {
+
+template <typename MatrixType_, int Options>
+struct traits<BDCSVD<MatrixType_, Options> > : svd_traits<MatrixType_, Options> {
+  typedef MatrixType_ MatrixType;
+};
+
+}  // end namespace internal
+
+/** \ingroup SVD_Module
+ *
+ *
+ * \class BDCSVD
+ *
+ * \brief class Bidiagonal Divide and Conquer SVD
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the SVD decomposition
+ *
+ * \tparam Options_ this optional parameter allows one to specify options for computing unitaries \a U and \a V.
+ *                  Possible values are #ComputeThinU, #ComputeThinV, #ComputeFullU, #ComputeFullV, and
+ *                  #DisableQRDecomposition. It is not possible to request both the thin and full version of \a U or
+ *                  \a V. By default, unitaries are not computed. BDCSVD uses R-Bidiagonalization to improve
+ *                  performance on tall and wide matrices. For backwards compatility, the option
+ *                  #DisableQRDecomposition can be used to disable this optimization.
+ *
+ * This class first reduces the input matrix to bi-diagonal form using class UpperBidiagonalization,
+ * and then performs a divide-and-conquer diagonalization. Small blocks are diagonalized using class JacobiSVD.
+ * You can control the switching size with the setSwitchSize() method, default is 16.
+ * For small matrice (<16), it is thus preferable to directly use JacobiSVD. For larger ones, BDCSVD is highly
+ * recommended and can several order of magnitude faster.
+ *
+ * \warning this algorithm is unlikely to provide accurate result when compiled with unsafe math optimizations.
+ * For instance, this concerns Intel's compiler (ICC), which performs such optimization by default unless
+ * you compile with the \c -fp-model \c precise option. Likewise, the \c -ffast-math option of GCC or clang will
+ * significantly degrade the accuracy.
+ *
+ * \sa class JacobiSVD
+ */
+template <typename MatrixType_, int Options_>
+class BDCSVD : public SVDBase<BDCSVD<MatrixType_, Options_> > {
+  typedef SVDBase<BDCSVD> Base;
+
+ public:
+  using Base::cols;
+  using Base::computeU;
+  using Base::computeV;
+  using Base::diagSize;
+  using Base::rows;
+
+  typedef MatrixType_ MatrixType;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::RealScalar RealScalar;
+  typedef typename NumTraits<RealScalar>::Literal Literal;
+  typedef typename Base::Index Index;
+  enum {
+    Options = Options_,
+    QRDecomposition = Options & internal::QRPreconditionerBits,
+    ComputationOptions = Options & internal::ComputationOptionsBits,
+    RowsAtCompileTime = Base::RowsAtCompileTime,
+    ColsAtCompileTime = Base::ColsAtCompileTime,
+    DiagSizeAtCompileTime = Base::DiagSizeAtCompileTime,
+    MaxRowsAtCompileTime = Base::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = Base::MaxColsAtCompileTime,
+    MaxDiagSizeAtCompileTime = Base::MaxDiagSizeAtCompileTime,
+    MatrixOptions = Base::MatrixOptions
+  };
+
+  typedef typename Base::MatrixUType MatrixUType;
+  typedef typename Base::MatrixVType MatrixVType;
+  typedef typename Base::SingularValuesType SingularValuesType;
+
+  typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> MatrixX;
+  typedef Matrix<RealScalar, Dynamic, Dynamic, ColMajor> MatrixXr;
+  typedef Matrix<RealScalar, Dynamic, 1> VectorType;
+  typedef Array<RealScalar, Dynamic, 1> ArrayXr;
+  typedef Array<Index, 1, Dynamic> ArrayXi;
+  typedef Ref<ArrayXr> ArrayRef;
+  typedef Ref<ArrayXi> IndicesRef;
+
+  /** \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via BDCSVD::compute(const MatrixType&).
+   */
+  BDCSVD() : m_algoswap(16), m_isTranspose(false), m_compU(false), m_compV(false), m_numIters(0) {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem size and \a Options template parameter.
+   * \sa BDCSVD()
+   */
+  BDCSVD(Index rows, Index cols) : m_algoswap(16), m_numIters(0) {
+    allocate(rows, cols, internal::get_computation_options(Options));
+  }
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem size and the \a computationOptions.
+   *
+   * One \b cannot request unitaries using both the \a Options template parameter
+   * and the constructor. If possible, prefer using the \a Options template parameter.
+   *
+   * \param rows number of rows for the input matrix
+   * \param cols number of columns for the input matrix
+   * \param computationOptions specification for computing Thin/Full unitaries U/V
+   * \sa BDCSVD()
+   *
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  EIGEN_DEPRECATED_WITH_REASON("Options should be specified using the class template parameter.")
+  BDCSVD(Index rows, Index cols, unsigned int computationOptions) : m_algoswap(16), m_numIters(0) {
+    internal::check_svd_options_assertions<MatrixType, Options>(computationOptions, rows, cols);
+    allocate(rows, cols, computationOptions);
+  }
+
+  /** \brief Constructor performing the decomposition of given matrix, using the custom options specified
+   *         with the \a Options template parameter.
+   *
+   * \param matrix the matrix to decompose
+   */
+  template <typename Derived>
+  BDCSVD(const MatrixBase<Derived>& matrix) : m_algoswap(16), m_numIters(0) {
+    compute_impl(matrix, internal::get_computation_options(Options));
+  }
+
+  /** \brief Constructor performing the decomposition of given matrix using specified options
+   *         for computing unitaries.
+   *
+   *  One \b cannot request unitaries using both the \a Options template parameter
+   *  and the constructor. If possible, prefer using the \a Options template parameter.
+   *
+   * \param matrix the matrix to decompose
+   * \param computationOptions specification for computing Thin/Full unitaries U/V
+   *
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  template <typename Derived>
+  EIGEN_DEPRECATED_WITH_REASON("Options should be specified using the class template parameter.")
+  BDCSVD(const MatrixBase<Derived>& matrix, unsigned int computationOptions) : m_algoswap(16), m_numIters(0) {
+    internal::check_svd_options_assertions<MatrixType, Options>(computationOptions, matrix.rows(), matrix.cols());
+    compute_impl(matrix, computationOptions);
+  }
+
+  ~BDCSVD() {}
+
+  /** \brief Method performing the decomposition of given matrix. Computes Thin/Full unitaries U/V if specified
+   *         using the \a Options template parameter or the class constructor.
+   *
+   * \param matrix the matrix to decompose
+   */
+  template <typename Derived>
+  BDCSVD& compute(const MatrixBase<Derived>& matrix) {
+    return compute_impl(matrix, m_computationOptions);
+  }
+
+  /** \brief Method performing the decomposition of given matrix, as specified by
+   *         the `computationOptions` parameter.
+   *
+   * \param matrix the matrix to decompose
+   * \param computationOptions specify whether to compute Thin/Full unitaries U/V
+   *
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  template <typename Derived>
+  EIGEN_DEPRECATED_WITH_REASON("Options should be specified using the class template parameter.")
+  BDCSVD& compute(const MatrixBase<Derived>& matrix, unsigned int computationOptions) {
+    internal::check_svd_options_assertions<MatrixType, Options>(computationOptions, matrix.rows(), matrix.cols());
+    return compute_impl(matrix, computationOptions);
+  }
+
+  void setSwitchSize(int s) {
+    eigen_assert(s >= 3 && "BDCSVD the size of the algo switch has to be at least 3.");
+    m_algoswap = s;
+  }
+
+ private:
+  template <typename Derived>
+  BDCSVD& compute_impl(const MatrixBase<Derived>& matrix, unsigned int computationOptions);
+  void divide(Index firstCol, Index lastCol, Index firstRowW, Index firstColW, Index shift);
+  void computeSVDofM(Index firstCol, Index n, MatrixXr& U, VectorType& singVals, MatrixXr& V);
+  void computeSingVals(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm, VectorType& singVals,
+                       ArrayRef shifts, ArrayRef mus);
+  void perturbCol0(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm, const VectorType& singVals,
+                   const ArrayRef& shifts, const ArrayRef& mus, ArrayRef zhat);
+  void computeSingVecs(const ArrayRef& zhat, const ArrayRef& diag, const IndicesRef& perm, const VectorType& singVals,
+                       const ArrayRef& shifts, const ArrayRef& mus, MatrixXr& U, MatrixXr& V);
+  void deflation43(Index firstCol, Index shift, Index i, Index size);
+  void deflation44(Index firstColu, Index firstColm, Index firstRowW, Index firstColW, Index i, Index j, Index size);
+  void deflation(Index firstCol, Index lastCol, Index k, Index firstRowW, Index firstColW, Index shift);
+  template <typename HouseholderU, typename HouseholderV, typename NaiveU, typename NaiveV>
+  void copyUV(const HouseholderU& householderU, const HouseholderV& householderV, const NaiveU& naiveU,
+              const NaiveV& naivev);
+  void structured_update(Block<MatrixXr, Dynamic, Dynamic> A, const MatrixXr& B, Index n1);
+  static RealScalar secularEq(RealScalar x, const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm,
+                              const ArrayRef& diagShifted, RealScalar shift);
+  template <typename SVDType>
+  void computeBaseCase(SVDType& svd, Index n, Index firstCol, Index firstRowW, Index firstColW, Index shift);
+
+ protected:
+  void allocate(Index rows, Index cols, unsigned int computationOptions);
+  MatrixXr m_naiveU, m_naiveV;
+  MatrixXr m_computed;
+  Index m_nRec;
+  ArrayXr m_workspace;
+  ArrayXi m_workspaceI;
+  int m_algoswap;
+  bool m_isTranspose, m_compU, m_compV, m_useQrDecomp;
+  JacobiSVD<MatrixType, ComputationOptions> smallSvd;
+  HouseholderQR<MatrixX> qrDecomp;
+  internal::UpperBidiagonalization<MatrixX> bid;
+  MatrixX copyWorkspace;
+  MatrixX reducedTriangle;
+
+  using Base::m_computationOptions;
+  using Base::m_computeThinU;
+  using Base::m_computeThinV;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_matrixU;
+  using Base::m_matrixV;
+  using Base::m_nonzeroSingularValues;
+  using Base::m_singularValues;
+
+ public:
+  int m_numIters;
+};  // end class BDCSVD
+
+// Method to allocate and initialize matrix and attributes
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::allocate(Index rows, Index cols, unsigned int computationOptions) {
+  if (Base::allocate(rows, cols, computationOptions)) return;
+
+  if (cols < m_algoswap)
+    smallSvd.allocate(rows, cols, Options == 0 ? computationOptions : internal::get_computation_options(Options));
+
+  m_computed = MatrixXr::Zero(diagSize() + 1, diagSize());
+  m_compU = computeV();
+  m_compV = computeU();
+  m_isTranspose = (cols > rows);
+  if (m_isTranspose) std::swap(m_compU, m_compV);
+
+  // kMinAspectRatio is the crossover point that determines if we perform R-Bidiagonalization
+  // or bidiagonalize the input matrix directly.
+  // It is based off of LAPACK's dgesdd routine, which uses 11.0/6.0
+  // we use a larger scalar to prevent a regression for relatively square matrices.
+  constexpr Index kMinAspectRatio = 4;
+  constexpr bool disableQrDecomp = static_cast<int>(QRDecomposition) == static_cast<int>(DisableQRDecomposition);
+  m_useQrDecomp = !disableQrDecomp && ((rows / kMinAspectRatio > cols) || (cols / kMinAspectRatio > rows));
+  if (m_useQrDecomp) {
+    qrDecomp = HouseholderQR<MatrixX>((std::max)(rows, cols), (std::min)(rows, cols));
+    reducedTriangle = MatrixX(diagSize(), diagSize());
+  }
+
+  copyWorkspace = MatrixX(m_isTranspose ? cols : rows, m_isTranspose ? rows : cols);
+  bid = internal::UpperBidiagonalization<MatrixX>(m_useQrDecomp ? diagSize() : copyWorkspace.rows(),
+                                                  m_useQrDecomp ? diagSize() : copyWorkspace.cols());
+
+  if (m_compU)
+    m_naiveU = MatrixXr::Zero(diagSize() + 1, diagSize() + 1);
+  else
+    m_naiveU = MatrixXr::Zero(2, diagSize() + 1);
+
+  if (m_compV) m_naiveV = MatrixXr::Zero(diagSize(), diagSize());
+
+  m_workspace.resize((diagSize() + 1) * (diagSize() + 1) * 3);
+  m_workspaceI.resize(3 * diagSize());
+}  // end allocate
+
+template <typename MatrixType, int Options>
+template <typename Derived>
+BDCSVD<MatrixType, Options>& BDCSVD<MatrixType, Options>::compute_impl(const MatrixBase<Derived>& matrix,
+                                                                       unsigned int computationOptions) {
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived, MatrixType);
+  EIGEN_STATIC_ASSERT((std::is_same<typename Derived::Scalar, typename MatrixType::Scalar>::value),
+                      Input matrix must have the same Scalar type as the BDCSVD object.);
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "\n\n\n================================================================================================="
+               "=====================\n\n\n";
+#endif
+  using std::abs;
+
+  allocate(matrix.rows(), matrix.cols(), computationOptions);
+
+  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
+
+  //**** step -1 - If the problem is too small, directly falls back to JacobiSVD and return
+  if (matrix.cols() < m_algoswap) {
+    smallSvd.compute(matrix);
+    m_isInitialized = true;
+    m_info = smallSvd.info();
+    if (m_info == Success || m_info == NoConvergence) {
+      if (computeU()) m_matrixU = smallSvd.matrixU();
+      if (computeV()) m_matrixV = smallSvd.matrixV();
+      m_singularValues = smallSvd.singularValues();
+      m_nonzeroSingularValues = smallSvd.nonzeroSingularValues();
+    }
+    return *this;
+  }
+
+  //**** step 0 - Copy the input matrix and apply scaling to reduce over/under-flows
+  RealScalar scale = matrix.cwiseAbs().template maxCoeff<PropagateNaN>();
+  if (!(numext::isfinite)(scale)) {
+    m_isInitialized = true;
+    m_info = InvalidInput;
+    return *this;
+  }
+
+  if (numext::is_exactly_zero(scale)) scale = Literal(1);
+
+  if (m_isTranspose)
+    copyWorkspace = matrix.adjoint() / scale;
+  else
+    copyWorkspace = matrix / scale;
+
+  //**** step 1 - Bidiagonalization.
+  // If the problem is sufficiently rectangular, we perform R-Bidiagonalization: compute A = Q(R/0)
+  // and then bidiagonalize R. Otherwise, if the problem is relatively square, we
+  // bidiagonalize the input matrix directly.
+  if (m_useQrDecomp) {
+    qrDecomp.compute(copyWorkspace);
+    reducedTriangle = qrDecomp.matrixQR().topRows(diagSize());
+    reducedTriangle.template triangularView<StrictlyLower>().setZero();
+    bid.compute(reducedTriangle);
+  } else {
+    bid.compute(copyWorkspace);
+  }
+
+  //**** step 2 - Divide & Conquer
+  m_naiveU.setZero();
+  m_naiveV.setZero();
+  // FIXME: this line involves a temporary matrix.
+  m_computed.topRows(diagSize()) = bid.bidiagonal().toDenseMatrix().transpose();
+  m_computed.template bottomRows<1>().setZero();
+  divide(0, diagSize() - 1, 0, 0, 0);
+  if (m_info != Success && m_info != NoConvergence) {
+    m_isInitialized = true;
+    return *this;
+  }
+
+  //**** step 3 - Copy singular values and vectors
+  for (int i = 0; i < diagSize(); i++) {
+    RealScalar a = abs(m_computed.coeff(i, i));
+    m_singularValues.coeffRef(i) = a * scale;
+    if (a < considerZero) {
+      m_nonzeroSingularValues = i;
+      m_singularValues.tail(diagSize() - i - 1).setZero();
+      break;
+    } else if (i == diagSize() - 1) {
+      m_nonzeroSingularValues = i + 1;
+      break;
+    }
+  }
+
+  //**** step 4 - Finalize unitaries U and V
+  if (m_isTranspose)
+    copyUV(bid.householderV(), bid.householderU(), m_naiveV, m_naiveU);
+  else
+    copyUV(bid.householderU(), bid.householderV(), m_naiveU, m_naiveV);
+
+  if (m_useQrDecomp) {
+    if (m_isTranspose && computeV())
+      m_matrixV.applyOnTheLeft(qrDecomp.householderQ());
+    else if (!m_isTranspose && computeU())
+      m_matrixU.applyOnTheLeft(qrDecomp.householderQ());
+  }
+
+  m_isInitialized = true;
+  return *this;
+}  // end compute
+
+template <typename MatrixType, int Options>
+template <typename HouseholderU, typename HouseholderV, typename NaiveU, typename NaiveV>
+void BDCSVD<MatrixType, Options>::copyUV(const HouseholderU& householderU, const HouseholderV& householderV,
+                                         const NaiveU& naiveU, const NaiveV& naiveV) {
+  // Note exchange of U and V: m_matrixU is set from m_naiveV and vice versa
+  if (computeU()) {
+    Index Ucols = m_computeThinU ? diagSize() : rows();
+    m_matrixU = MatrixX::Identity(rows(), Ucols);
+    m_matrixU.topLeftCorner(diagSize(), diagSize()) =
+        naiveV.template cast<Scalar>().topLeftCorner(diagSize(), diagSize());
+    // FIXME: the following conditionals involve temporary buffers.
+    if (m_useQrDecomp)
+      m_matrixU.topLeftCorner(householderU.cols(), diagSize()).applyOnTheLeft(householderU);
+    else
+      m_matrixU.applyOnTheLeft(householderU);
+  }
+  if (computeV()) {
+    Index Vcols = m_computeThinV ? diagSize() : cols();
+    m_matrixV = MatrixX::Identity(cols(), Vcols);
+    m_matrixV.topLeftCorner(diagSize(), diagSize()) =
+        naiveU.template cast<Scalar>().topLeftCorner(diagSize(), diagSize());
+    // FIXME: the following conditionals involve temporary buffers.
+    if (m_useQrDecomp)
+      m_matrixV.topLeftCorner(householderV.cols(), diagSize()).applyOnTheLeft(householderV);
+    else
+      m_matrixV.applyOnTheLeft(householderV);
+  }
+}
+
+/** \internal
+ * Performs A = A * B exploiting the special structure of the matrix A. Splitting A as:
+ *  A = [A1]
+ *      [A2]
+ * such that A1.rows()==n1, then we assume that at least half of the columns of A1 and A2 are zeros.
+ * We can thus pack them prior to the matrix product. However, this is only worth the effort if the matrix is large
+ * enough.
+ */
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::structured_update(Block<MatrixXr, Dynamic, Dynamic> A, const MatrixXr& B, Index n1) {
+  Index n = A.rows();
+  if (n > 100) {
+    // If the matrices are large enough, let's exploit the sparse structure of A by
+    // splitting it in half (wrt n1), and packing the non-zero columns.
+    Index n2 = n - n1;
+    Map<MatrixXr> A1(m_workspace.data(), n1, n);
+    Map<MatrixXr> A2(m_workspace.data() + n1 * n, n2, n);
+    Map<MatrixXr> B1(m_workspace.data() + n * n, n, n);
+    Map<MatrixXr> B2(m_workspace.data() + 2 * n * n, n, n);
+    Index k1 = 0, k2 = 0;
+    for (Index j = 0; j < n; ++j) {
+      if ((A.col(j).head(n1).array() != Literal(0)).any()) {
+        A1.col(k1) = A.col(j).head(n1);
+        B1.row(k1) = B.row(j);
+        ++k1;
+      }
+      if ((A.col(j).tail(n2).array() != Literal(0)).any()) {
+        A2.col(k2) = A.col(j).tail(n2);
+        B2.row(k2) = B.row(j);
+        ++k2;
+      }
+    }
+
+    A.topRows(n1).noalias() = A1.leftCols(k1) * B1.topRows(k1);
+    A.bottomRows(n2).noalias() = A2.leftCols(k2) * B2.topRows(k2);
+  } else {
+    Map<MatrixXr, Aligned> tmp(m_workspace.data(), n, n);
+    tmp.noalias() = A * B;
+    A = tmp;
+  }
+}
+
+template <typename MatrixType, int Options>
+template <typename SVDType>
+void BDCSVD<MatrixType, Options>::computeBaseCase(SVDType& svd, Index n, Index firstCol, Index firstRowW,
+                                                  Index firstColW, Index shift) {
+  svd.compute(m_computed.block(firstCol, firstCol, n + 1, n));
+  m_info = svd.info();
+  if (m_info != Success && m_info != NoConvergence) return;
+  if (m_compU)
+    m_naiveU.block(firstCol, firstCol, n + 1, n + 1).real() = svd.matrixU();
+  else {
+    m_naiveU.row(0).segment(firstCol, n + 1).real() = svd.matrixU().row(0);
+    m_naiveU.row(1).segment(firstCol, n + 1).real() = svd.matrixU().row(n);
+  }
+  if (m_compV) m_naiveV.block(firstRowW, firstColW, n, n).real() = svd.matrixV();
+  m_computed.block(firstCol + shift, firstCol + shift, n + 1, n).setZero();
+  m_computed.diagonal().segment(firstCol + shift, n) = svd.singularValues().head(n);
+}
+
+// The divide algorithm is done "in place", we are always working on subsets of the same matrix. The divide methods
+// takes as argument the place of the submatrix we are currently working on.
+
+//@param firstCol : The Index of the first column of the submatrix of m_computed and for m_naiveU;
+//@param lastCol : The Index of the last column of the submatrix of m_computed and for m_naiveU;
+// lastCol + 1 - firstCol is the size of the submatrix.
+//@param firstRowW : The Index of the first row of the matrix W that we are to change. (see the reference paper section
+// 1 for more information on W)
+//@param firstColW : Same as firstRowW with the column.
+//@param shift : Each time one takes the left submatrix, one must add 1 to the shift. Why? Because! We actually want the
+// last column of the U submatrix
+// to become the first column (*coeff) and to shift all the other columns to the right. There are more details on the
+// reference paper.
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::divide(Index firstCol, Index lastCol, Index firstRowW, Index firstColW, Index shift) {
+  // requires rows = cols + 1;
+  using std::abs;
+  using std::pow;
+  using std::sqrt;
+  const Index n = lastCol - firstCol + 1;
+  const Index k = n / 2;
+  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
+  RealScalar alphaK;
+  RealScalar betaK;
+  RealScalar r0;
+  RealScalar lambda, phi, c0, s0;
+  VectorType l, f;
+  // We use the other algorithm which is more efficient for small
+  // matrices.
+  if (n < m_algoswap) {
+    // FIXME: this block involves temporaries.
+    if (m_compV) {
+      JacobiSVD<MatrixXr, ComputeFullU | ComputeFullV> baseSvd;
+      computeBaseCase(baseSvd, n, firstCol, firstRowW, firstColW, shift);
+    } else {
+      JacobiSVD<MatrixXr, ComputeFullU> baseSvd;
+      computeBaseCase(baseSvd, n, firstCol, firstRowW, firstColW, shift);
+    }
+    return;
+  }
+  // We use the divide and conquer algorithm
+  alphaK = m_computed(firstCol + k, firstCol + k);
+  betaK = m_computed(firstCol + k + 1, firstCol + k);
+  // The divide must be done in that order in order to have good results. Divide change the data inside the submatrices
+  // and the divide of the right submatrice reads one column of the left submatrice. That's why we need to treat the
+  // right submatrix before the left one.
+  divide(k + 1 + firstCol, lastCol, k + 1 + firstRowW, k + 1 + firstColW, shift);
+  if (m_info != Success && m_info != NoConvergence) return;
+  divide(firstCol, k - 1 + firstCol, firstRowW, firstColW + 1, shift + 1);
+  if (m_info != Success && m_info != NoConvergence) return;
+
+  if (m_compU) {
+    lambda = m_naiveU(firstCol + k, firstCol + k);
+    phi = m_naiveU(firstCol + k + 1, lastCol + 1);
+  } else {
+    lambda = m_naiveU(1, firstCol + k);
+    phi = m_naiveU(0, lastCol + 1);
+  }
+  r0 = sqrt((abs(alphaK * lambda) * abs(alphaK * lambda)) + abs(betaK * phi) * abs(betaK * phi));
+  if (m_compU) {
+    l = m_naiveU.row(firstCol + k).segment(firstCol, k);
+    f = m_naiveU.row(firstCol + k + 1).segment(firstCol + k + 1, n - k - 1);
+  } else {
+    l = m_naiveU.row(1).segment(firstCol, k);
+    f = m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1);
+  }
+  if (m_compV) m_naiveV(firstRowW + k, firstColW) = Literal(1);
+  if (r0 < considerZero) {
+    c0 = Literal(1);
+    s0 = Literal(0);
+  } else {
+    c0 = alphaK * lambda / r0;
+    s0 = betaK * phi / r0;
+  }
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+
+  if (m_compU) {
+    MatrixXr q1(m_naiveU.col(firstCol + k).segment(firstCol, k + 1));
+    // we shiftW Q1 to the right
+    for (Index i = firstCol + k - 1; i >= firstCol; i--)
+      m_naiveU.col(i + 1).segment(firstCol, k + 1) = m_naiveU.col(i).segment(firstCol, k + 1);
+    // we shift q1 at the left with a factor c0
+    m_naiveU.col(firstCol).segment(firstCol, k + 1) = (q1 * c0);
+    // last column = q1 * - s0
+    m_naiveU.col(lastCol + 1).segment(firstCol, k + 1) = (q1 * (-s0));
+    // first column = q2 * s0
+    m_naiveU.col(firstCol).segment(firstCol + k + 1, n - k) =
+        m_naiveU.col(lastCol + 1).segment(firstCol + k + 1, n - k) * s0;
+    // q2 *= c0
+    m_naiveU.col(lastCol + 1).segment(firstCol + k + 1, n - k) *= c0;
+  } else {
+    RealScalar q1 = m_naiveU(0, firstCol + k);
+    // we shift Q1 to the right
+    for (Index i = firstCol + k - 1; i >= firstCol; i--) m_naiveU(0, i + 1) = m_naiveU(0, i);
+    // we shift q1 at the left with a factor c0
+    m_naiveU(0, firstCol) = (q1 * c0);
+    // last column = q1 * - s0
+    m_naiveU(0, lastCol + 1) = (q1 * (-s0));
+    // first column = q2 * s0
+    m_naiveU(1, firstCol) = m_naiveU(1, lastCol + 1) * s0;
+    // q2 *= c0
+    m_naiveU(1, lastCol + 1) *= c0;
+    m_naiveU.row(1).segment(firstCol + 1, k).setZero();
+    m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1).setZero();
+  }
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+
+  m_computed(firstCol + shift, firstCol + shift) = r0;
+  m_computed.col(firstCol + shift).segment(firstCol + shift + 1, k) = alphaK * l.transpose().real();
+  m_computed.col(firstCol + shift).segment(firstCol + shift + k + 1, n - k - 1) = betaK * f.transpose().real();
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  ArrayXr tmp1 = (m_computed.block(firstCol + shift, firstCol + shift, n, n)).jacobiSvd().singularValues();
+#endif
+  // Second part: try to deflate singular values in combined matrix
+  deflation(firstCol, lastCol, k, firstRowW, firstColW, shift);
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  ArrayXr tmp2 = (m_computed.block(firstCol + shift, firstCol + shift, n, n)).jacobiSvd().singularValues();
+  std::cout << "\n\nj1 = " << tmp1.transpose().format(bdcsvdfmt) << "\n";
+  std::cout << "j2 = " << tmp2.transpose().format(bdcsvdfmt) << "\n\n";
+  std::cout << "err:      " << ((tmp1 - tmp2).abs() > 1e-12 * tmp2.abs()).transpose() << "\n";
+  static int count = 0;
+  std::cout << "# " << ++count << "\n\n";
+  eigen_internal_assert((tmp1 - tmp2).matrix().norm() < 1e-14 * tmp2.matrix().norm());
+//   eigen_internal_assert(count<681);
+//   eigen_internal_assert(((tmp1-tmp2).abs()<1e-13*tmp2.abs()).all());
+#endif
+
+  // Third part: compute SVD of combined matrix
+  MatrixXr UofSVD, VofSVD;
+  VectorType singVals;
+  computeSVDofM(firstCol + shift, n, UofSVD, singVals, VofSVD);
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(UofSVD.allFinite());
+  eigen_internal_assert(VofSVD.allFinite());
+#endif
+
+  if (m_compU)
+    structured_update(m_naiveU.block(firstCol, firstCol, n + 1, n + 1), UofSVD, (n + 2) / 2);
+  else {
+    Map<Matrix<RealScalar, 2, Dynamic>, Aligned> tmp(m_workspace.data(), 2, n + 1);
+    tmp.noalias() = m_naiveU.middleCols(firstCol, n + 1) * UofSVD;
+    m_naiveU.middleCols(firstCol, n + 1) = tmp;
+  }
+
+  if (m_compV) structured_update(m_naiveV.block(firstRowW, firstColW, n, n), VofSVD, (n + 1) / 2);
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+
+  m_computed.block(firstCol + shift, firstCol + shift, n, n).setZero();
+  m_computed.block(firstCol + shift, firstCol + shift, n, n).diagonal() = singVals;
+}  // end divide
+
+// Compute SVD of m_computed.block(firstCol, firstCol, n + 1, n); this block only has non-zeros in
+// the first column and on the diagonal and has undergone deflation, so diagonal is in increasing
+// order except for possibly the (0,0) entry. The computed SVD is stored U, singVals and V, except
+// that if m_compV is false, then V is not computed. Singular values are sorted in decreasing order.
+//
+// TODO: opportunities for optimization: better root-finding algorithm, better stopping criterion,
+// better handling of round-off errors, and consistent ordering.
+// For instance, to solve the secular equation using FMM, see
+// http://www.stat.uchicago.edu/~lekheng/courses/302/classics/greengard-rokhlin.pdf
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::computeSVDofM(Index firstCol, Index n, MatrixXr& U, VectorType& singVals,
+                                                MatrixXr& V) {
+  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
+  using std::abs;
+  ArrayRef col0 = m_computed.col(firstCol).segment(firstCol, n);
+  m_workspace.head(n) = m_computed.block(firstCol, firstCol, n, n).diagonal();
+  ArrayRef diag = m_workspace.head(n);
+  diag(0) = Literal(0);
+
+  // Allocate space for singular values and vectors
+  singVals.resize(n);
+  U.resize(n + 1, n + 1);
+  if (m_compV) V.resize(n, n);
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  if (col0.hasNaN() || diag.hasNaN()) std::cout << "\n\nHAS NAN\n\n";
+#endif
+
+  // Many singular values might have been deflated, the zero ones have been moved to the end,
+  // but others are interleaved and we must ignore them at this stage.
+  // To this end, let's compute a permutation skipping them:
+  Index actual_n = n;
+  while (actual_n > 1 && numext::is_exactly_zero(diag(actual_n - 1))) {
+    --actual_n;
+    eigen_internal_assert(numext::is_exactly_zero(col0(actual_n)));
+  }
+  Index m = 0;  // size of the deflated problem
+  for (Index k = 0; k < actual_n; ++k)
+    if (abs(col0(k)) > considerZero) m_workspaceI(m++) = k;
+  Map<ArrayXi> perm(m_workspaceI.data(), m);
+
+  Map<ArrayXr> shifts(m_workspace.data() + 1 * n, n);
+  Map<ArrayXr> mus(m_workspace.data() + 2 * n, n);
+  Map<ArrayXr> zhat(m_workspace.data() + 3 * n, n);
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "computeSVDofM using:\n";
+  std::cout << "  z: " << col0.transpose() << "\n";
+  std::cout << "  d: " << diag.transpose() << "\n";
+#endif
+
+  // Compute singVals, shifts, and mus
+  computeSingVals(col0, diag, perm, singVals, shifts, mus);
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "  j:        "
+            << (m_computed.block(firstCol, firstCol, n, n)).jacobiSvd().singularValues().transpose().reverse()
+            << "\n\n";
+  std::cout << "  sing-val: " << singVals.transpose() << "\n";
+  std::cout << "  mu:       " << mus.transpose() << "\n";
+  std::cout << "  shift:    " << shifts.transpose() << "\n";
+
+  {
+    std::cout << "\n\n    mus:    " << mus.head(actual_n).transpose() << "\n\n";
+    std::cout << "    check1 (expect0) : "
+              << ((singVals.array() - (shifts + mus)) / singVals.array()).head(actual_n).transpose() << "\n\n";
+    eigen_internal_assert((((singVals.array() - (shifts + mus)) / singVals.array()).head(actual_n) >= 0).all());
+    std::cout << "    check2 (>0)      : " << ((singVals.array() - diag) / singVals.array()).head(actual_n).transpose()
+              << "\n\n";
+    eigen_internal_assert((((singVals.array() - diag) / singVals.array()).head(actual_n) >= 0).all());
+  }
+#endif
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(singVals.allFinite());
+  eigen_internal_assert(mus.allFinite());
+  eigen_internal_assert(shifts.allFinite());
+#endif
+
+  // Compute zhat
+  perturbCol0(col0, diag, perm, singVals, shifts, mus, zhat);
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "  zhat: " << zhat.transpose() << "\n";
+#endif
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(zhat.allFinite());
+#endif
+
+  computeSingVecs(zhat, diag, perm, singVals, shifts, mus, U, V);
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "U^T U: " << (U.transpose() * U - MatrixXr(MatrixXr::Identity(U.cols(), U.cols()))).norm() << "\n";
+  std::cout << "V^T V: " << (V.transpose() * V - MatrixXr(MatrixXr::Identity(V.cols(), V.cols()))).norm() << "\n";
+#endif
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+  eigen_internal_assert(U.allFinite());
+  eigen_internal_assert(V.allFinite());
+//   eigen_internal_assert((U.transpose() * U - MatrixXr(MatrixXr::Identity(U.cols(),U.cols()))).norm() <
+//   100*NumTraits<RealScalar>::epsilon() * n); eigen_internal_assert((V.transpose() * V -
+//   MatrixXr(MatrixXr::Identity(V.cols(),V.cols()))).norm() < 100*NumTraits<RealScalar>::epsilon() * n);
+#endif
+
+  // Because of deflation, the singular values might not be completely sorted.
+  // Fortunately, reordering them is a O(n) problem
+  for (Index i = 0; i < actual_n - 1; ++i) {
+    if (singVals(i) > singVals(i + 1)) {
+      using std::swap;
+      swap(singVals(i), singVals(i + 1));
+      U.col(i).swap(U.col(i + 1));
+      if (m_compV) V.col(i).swap(V.col(i + 1));
+    }
+  }
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  {
+    bool singular_values_sorted =
+        (((singVals.segment(1, actual_n - 1) - singVals.head(actual_n - 1))).array() >= 0).all();
+    if (!singular_values_sorted)
+      std::cout << "Singular values are not sorted: " << singVals.segment(1, actual_n).transpose() << "\n";
+    eigen_internal_assert(singular_values_sorted);
+  }
+#endif
+
+  // Reverse order so that singular values in increased order
+  // Because of deflation, the zeros singular-values are already at the end
+  singVals.head(actual_n).reverseInPlace();
+  U.leftCols(actual_n).rowwise().reverseInPlace();
+  if (m_compV) V.leftCols(actual_n).rowwise().reverseInPlace();
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  JacobiSVD<MatrixXr> jsvd(m_computed.block(firstCol, firstCol, n, n));
+  std::cout << "  * j:        " << jsvd.singularValues().transpose() << "\n\n";
+  std::cout << "  * sing-val: " << singVals.transpose() << "\n";
+//   std::cout << "  * err:      " << ((jsvd.singularValues()-singVals)>1e-13*singVals.norm()).transpose() << "\n";
+#endif
+}
+
+template <typename MatrixType, int Options>
+typename BDCSVD<MatrixType, Options>::RealScalar BDCSVD<MatrixType, Options>::secularEq(
+    RealScalar mu, const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm, const ArrayRef& diagShifted,
+    RealScalar shift) {
+  Index m = perm.size();
+  RealScalar res = Literal(1);
+  for (Index i = 0; i < m; ++i) {
+    Index j = perm(i);
+    // The following expression could be rewritten to involve only a single division,
+    // but this would make the expression more sensitive to overflow.
+    res += (col0(j) / (diagShifted(j) - mu)) * (col0(j) / (diag(j) + shift + mu));
+  }
+  return res;
+}
+
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::computeSingVals(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm,
+                                                  VectorType& singVals, ArrayRef shifts, ArrayRef mus) {
+  using std::abs;
+  using std::sqrt;
+  using std::swap;
+
+  Index n = col0.size();
+  Index actual_n = n;
+  // Note that here actual_n is computed based on col0(i)==0 instead of diag(i)==0 as above
+  // because 1) we have diag(i)==0 => col0(i)==0 and 2) if col0(i)==0, then diag(i) is already a singular value.
+  while (actual_n > 1 && numext::is_exactly_zero(col0(actual_n - 1))) --actual_n;
+
+  for (Index k = 0; k < n; ++k) {
+    if (numext::is_exactly_zero(col0(k)) || actual_n == 1) {
+      // if col0(k) == 0, then entry is deflated, so singular value is on diagonal
+      // if actual_n==1, then the deflated problem is already diagonalized
+      singVals(k) = k == 0 ? col0(0) : diag(k);
+      mus(k) = Literal(0);
+      shifts(k) = k == 0 ? col0(0) : diag(k);
+      continue;
+    }
+
+    // otherwise, use secular equation to find singular value
+    RealScalar left = diag(k);
+    RealScalar right;  // was: = (k != actual_n-1) ? diag(k+1) : (diag(actual_n-1) + col0.matrix().norm());
+    if (k == actual_n - 1)
+      right = (diag(actual_n - 1) + col0.matrix().norm());
+    else {
+      // Skip deflated singular values,
+      // recall that at this stage we assume that z[j]!=0 and all entries for which z[j]==0 have been put aside.
+      // This should be equivalent to using perm[]
+      Index l = k + 1;
+      while (numext::is_exactly_zero(col0(l))) {
+        ++l;
+        eigen_internal_assert(l < actual_n);
+      }
+      right = diag(l);
+    }
+
+    // first decide whether it's closer to the left end or the right end
+    RealScalar mid = left + (right - left) / Literal(2);
+    RealScalar fMid = secularEq(mid, col0, diag, perm, diag, Literal(0));
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+    std::cout << "right-left = " << right - left << "\n";
+    //     std::cout << "fMid = " << fMid << " " << secularEq(mid-left, col0, diag, perm, ArrayXr(diag-left), left)
+    //                            << " " << secularEq(mid-right, col0, diag, perm, ArrayXr(diag-right), right)   <<
+    //                            "\n";
+    std::cout << "     = " << secularEq(left + RealScalar(0.000001) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.1) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.2) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.3) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.4) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.49) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.5) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.51) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.6) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.7) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.8) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.9) * (right - left), col0, diag, perm, diag, 0) << " "
+              << secularEq(left + RealScalar(0.999999) * (right - left), col0, diag, perm, diag, 0) << "\n";
+#endif
+    RealScalar shift = (k == actual_n - 1 || fMid > Literal(0)) ? left : right;
+
+    // measure everything relative to shift
+    Map<ArrayXr> diagShifted(m_workspace.data() + 4 * n, n);
+    diagShifted = diag - shift;
+
+    if (k != actual_n - 1) {
+      // check that after the shift, f(mid) is still negative:
+      RealScalar midShifted = (right - left) / RealScalar(2);
+      // we can test exact equality here, because shift comes from `... ? left : right`
+      if (numext::equal_strict(shift, right)) midShifted = -midShifted;
+      RealScalar fMidShifted = secularEq(midShifted, col0, diag, perm, diagShifted, shift);
+      if (fMidShifted > 0) {
+        // fMid was erroneous, fix it:
+        shift = fMidShifted > Literal(0) ? left : right;
+        diagShifted = diag - shift;
+      }
+    }
+
+    // initial guess
+    RealScalar muPrev, muCur;
+    // we can test exact equality here, because shift comes from `... ? left : right`
+    if (numext::equal_strict(shift, left)) {
+      muPrev = (right - left) * RealScalar(0.1);
+      if (k == actual_n - 1)
+        muCur = right - left;
+      else
+        muCur = (right - left) * RealScalar(0.5);
+    } else {
+      muPrev = -(right - left) * RealScalar(0.1);
+      muCur = -(right - left) * RealScalar(0.5);
+    }
+
+    RealScalar fPrev = secularEq(muPrev, col0, diag, perm, diagShifted, shift);
+    RealScalar fCur = secularEq(muCur, col0, diag, perm, diagShifted, shift);
+    if (abs(fPrev) < abs(fCur)) {
+      swap(fPrev, fCur);
+      swap(muPrev, muCur);
+    }
+
+    // rational interpolation: fit a function of the form a / mu + b through the two previous
+    // iterates and use its zero to compute the next iterate
+    bool useBisection = fPrev * fCur > Literal(0);
+    while (!numext::is_exactly_zero(fCur) &&
+           abs(muCur - muPrev) >
+               Literal(8) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(muCur), abs(muPrev)) &&
+           abs(fCur - fPrev) > NumTraits<RealScalar>::epsilon() && !useBisection) {
+      ++m_numIters;
+
+      // Find a and b such that the function f(mu) = a / mu + b matches the current and previous samples.
+      RealScalar a = (fCur - fPrev) / (Literal(1) / muCur - Literal(1) / muPrev);
+      RealScalar b = fCur - a / muCur;
+      // And find mu such that f(mu)==0:
+      RealScalar muZero = -a / b;
+      RealScalar fZero = secularEq(muZero, col0, diag, perm, diagShifted, shift);
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+      eigen_internal_assert((numext::isfinite)(fZero));
+#endif
+
+      muPrev = muCur;
+      fPrev = fCur;
+      muCur = muZero;
+      fCur = fZero;
+
+      // we can test exact equality here, because shift comes from `... ? left : right`
+      if (numext::equal_strict(shift, left) && (muCur < Literal(0) || muCur > right - left)) useBisection = true;
+      if (numext::equal_strict(shift, right) && (muCur < -(right - left) || muCur > Literal(0))) useBisection = true;
+      if (abs(fCur) > abs(fPrev)) useBisection = true;
+    }
+
+    // fall back on bisection method if rational interpolation did not work
+    if (useBisection) {
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+      std::cout << "useBisection for k = " << k << ", actual_n = " << actual_n << "\n";
+#endif
+      RealScalar leftShifted, rightShifted;
+      // we can test exact equality here, because shift comes from `... ? left : right`
+      if (numext::equal_strict(shift, left)) {
+        // to avoid overflow, we must have mu > max(real_min, |z(k)|/sqrt(real_max)),
+        // the factor 2 is to be more conservative
+        leftShifted =
+            numext::maxi<RealScalar>((std::numeric_limits<RealScalar>::min)(),
+                                     Literal(2) * abs(col0(k)) / sqrt((std::numeric_limits<RealScalar>::max)()));
+
+        // check that we did it right:
+        eigen_internal_assert(
+            (numext::isfinite)((col0(k) / leftShifted) * (col0(k) / (diag(k) + shift + leftShifted))));
+        // It is unclear why k==0 would need special handling here:
+        // if (k == 0) rightShifted = right - left; else
+        rightShifted = (k == actual_n - 1)
+                           ? right
+                           : ((right - left) * RealScalar(0.51));  // theoretically we can take 0.5, but let's be safe
+      } else {
+        leftShifted = -(right - left) * RealScalar(0.51);
+        if (k + 1 < n)
+          rightShifted = -numext::maxi<RealScalar>((std::numeric_limits<RealScalar>::min)(),
+                                                   abs(col0(k + 1)) / sqrt((std::numeric_limits<RealScalar>::max)()));
+        else
+          rightShifted = -(std::numeric_limits<RealScalar>::min)();
+      }
+
+      RealScalar fLeft = secularEq(leftShifted, col0, diag, perm, diagShifted, shift);
+      eigen_internal_assert(fLeft < Literal(0));
+
+#if defined EIGEN_BDCSVD_DEBUG_VERBOSE || defined EIGEN_BDCSVD_SANITY_CHECKS || defined EIGEN_INTERNAL_DEBUGGING
+      RealScalar fRight = secularEq(rightShifted, col0, diag, perm, diagShifted, shift);
+#endif
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+      if (!(numext::isfinite)(fLeft))
+        std::cout << "f(" << leftShifted << ") =" << fLeft << " ; " << left << " " << shift << " " << right << "\n";
+      eigen_internal_assert((numext::isfinite)(fLeft));
+
+      if (!(numext::isfinite)(fRight))
+        std::cout << "f(" << rightShifted << ") =" << fRight << " ; " << left << " " << shift << " " << right << "\n";
+        // eigen_internal_assert((numext::isfinite)(fRight));
+#endif
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+      if (!(fLeft * fRight < 0)) {
+        std::cout << "f(leftShifted) using  leftShifted=" << leftShifted
+                  << " ;  diagShifted(1:10):" << diagShifted.head(10).transpose() << "\n ; "
+                  << "left==shift=" << bool(left == shift) << " ; left-shift = " << (left - shift) << "\n";
+        std::cout << "k=" << k << ", " << fLeft << " * " << fRight << " == " << fLeft * fRight << "  ;  "
+                  << "[" << left << " .. " << right << "] -> [" << leftShifted << " " << rightShifted
+                  << "], shift=" << shift << " ,  f(right)=" << secularEq(0, col0, diag, perm, diagShifted, shift)
+                  << " == " << secularEq(right, col0, diag, perm, diag, 0) << " == " << fRight << "\n";
+      }
+#endif
+      eigen_internal_assert(fLeft * fRight < Literal(0));
+
+      if (fLeft < Literal(0)) {
+        while (rightShifted - leftShifted > Literal(2) * NumTraits<RealScalar>::epsilon() *
+                                                numext::maxi<RealScalar>(abs(leftShifted), abs(rightShifted))) {
+          RealScalar midShifted = (leftShifted + rightShifted) / Literal(2);
+          fMid = secularEq(midShifted, col0, diag, perm, diagShifted, shift);
+          eigen_internal_assert((numext::isfinite)(fMid));
+
+          if (fLeft * fMid < Literal(0)) {
+            rightShifted = midShifted;
+          } else {
+            leftShifted = midShifted;
+            fLeft = fMid;
+          }
+        }
+        muCur = (leftShifted + rightShifted) / Literal(2);
+      } else {
+        // We have a problem as shifting on the left or right give either a positive or negative value
+        // at the middle of [left,right]...
+        // Instead of abbording or entering an infinite loop,
+        // let's just use the middle as the estimated zero-crossing:
+        muCur = (right - left) * RealScalar(0.5);
+        // we can test exact equality here, because shift comes from `... ? left : right`
+        if (numext::equal_strict(shift, right)) muCur = -muCur;
+      }
+    }
+
+    singVals[k] = shift + muCur;
+    shifts[k] = shift;
+    mus[k] = muCur;
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+    if (k + 1 < n)
+      std::cout << "found " << singVals[k] << " == " << shift << " + " << muCur << " from " << diag(k) << " .. "
+                << diag(k + 1) << "\n";
+#endif
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+    eigen_internal_assert(k == 0 || singVals[k] >= singVals[k - 1]);
+    eigen_internal_assert(singVals[k] >= diag(k));
+#endif
+
+    // perturb singular value slightly if it equals diagonal entry to avoid division by zero later
+    // (deflation is supposed to avoid this from happening)
+    // - this does no seem to be necessary anymore -
+    // if (singVals[k] == left) singVals[k] *= 1 + NumTraits<RealScalar>::epsilon();
+    // if (singVals[k] == right) singVals[k] *= 1 - NumTraits<RealScalar>::epsilon();
+  }
+}
+
+// zhat is perturbation of col0 for which singular vectors can be computed stably (see Section 3.1)
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::perturbCol0(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm,
+                                              const VectorType& singVals, const ArrayRef& shifts, const ArrayRef& mus,
+                                              ArrayRef zhat) {
+  using std::sqrt;
+  Index n = col0.size();
+  Index m = perm.size();
+  if (m == 0) {
+    zhat.setZero();
+    return;
+  }
+  Index lastIdx = perm(m - 1);
+  // The offset permits to skip deflated entries while computing zhat
+  for (Index k = 0; k < n; ++k) {
+    if (numext::is_exactly_zero(col0(k)))  // deflated
+      zhat(k) = Literal(0);
+    else {
+      // see equation (3.6)
+      RealScalar dk = diag(k);
+      RealScalar prod = (singVals(lastIdx) + dk) * (mus(lastIdx) + (shifts(lastIdx) - dk));
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+      if (prod < 0) {
+        std::cout << "k = " << k << " ;  z(k)=" << col0(k) << ", diag(k)=" << dk << "\n";
+        std::cout << "prod = "
+                  << "(" << singVals(lastIdx) << " + " << dk << ") * (" << mus(lastIdx) << " + (" << shifts(lastIdx)
+                  << " - " << dk << "))"
+                  << "\n";
+        std::cout << "     = " << singVals(lastIdx) + dk << " * " << mus(lastIdx) + (shifts(lastIdx) - dk) << "\n";
+      }
+      eigen_internal_assert(prod >= 0);
+#endif
+
+      for (Index l = 0; l < m; ++l) {
+        Index i = perm(l);
+        if (i != k) {
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+          if (i >= k && (l == 0 || l - 1 >= m)) {
+            std::cout << "Error in perturbCol0\n";
+            std::cout << "  " << k << "/" << n << " " << l << "/" << m << " " << i << "/" << n << " ; " << col0(k)
+                      << " " << diag(k) << " "
+                      << "\n";
+            std::cout << "  " << diag(i) << "\n";
+            Index j = (i < k /*|| l==0*/) ? i : perm(l - 1);
+            std::cout << "  "
+                      << "j=" << j << "\n";
+          }
+#endif
+          Index j = i < k ? i : l > 0 ? perm(l - 1) : i;
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+          if (!(dk != Literal(0) || diag(i) != Literal(0))) {
+            std::cout << "k=" << k << ", i=" << i << ", l=" << l << ", perm.size()=" << perm.size() << "\n";
+          }
+          eigen_internal_assert(dk != Literal(0) || diag(i) != Literal(0));
+#endif
+          prod *= ((singVals(j) + dk) / ((diag(i) + dk))) * ((mus(j) + (shifts(j) - dk)) / ((diag(i) - dk)));
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+          eigen_internal_assert(prod >= 0);
+#endif
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+          if (i != k &&
+              numext::abs(((singVals(j) + dk) * (mus(j) + (shifts(j) - dk))) / ((diag(i) + dk) * (diag(i) - dk)) - 1) >
+                  0.9)
+            std::cout << "     "
+                      << ((singVals(j) + dk) * (mus(j) + (shifts(j) - dk))) / ((diag(i) + dk) * (diag(i) - dk))
+                      << " == (" << (singVals(j) + dk) << " * " << (mus(j) + (shifts(j) - dk)) << ") / ("
+                      << (diag(i) + dk) << " * " << (diag(i) - dk) << ")\n";
+#endif
+        }
+      }
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+      std::cout << "zhat(" << k << ") =  sqrt( " << prod << ")  ;  " << (singVals(lastIdx) + dk) << " * "
+                << mus(lastIdx) + shifts(lastIdx) << " - " << dk << "\n";
+#endif
+      RealScalar tmp = sqrt(prod);
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+      eigen_internal_assert((numext::isfinite)(tmp));
+#endif
+      zhat(k) = col0(k) > Literal(0) ? RealScalar(tmp) : RealScalar(-tmp);
+    }
+  }
+}
+
+// compute singular vectors
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::computeSingVecs(const ArrayRef& zhat, const ArrayRef& diag, const IndicesRef& perm,
+                                                  const VectorType& singVals, const ArrayRef& shifts,
+                                                  const ArrayRef& mus, MatrixXr& U, MatrixXr& V) {
+  Index n = zhat.size();
+  Index m = perm.size();
+
+  for (Index k = 0; k < n; ++k) {
+    if (numext::is_exactly_zero(zhat(k))) {
+      U.col(k) = VectorType::Unit(n + 1, k);
+      if (m_compV) V.col(k) = VectorType::Unit(n, k);
+    } else {
+      U.col(k).setZero();
+      for (Index l = 0; l < m; ++l) {
+        Index i = perm(l);
+        U(i, k) = zhat(i) / (((diag(i) - shifts(k)) - mus(k))) / ((diag(i) + singVals[k]));
+      }
+      U(n, k) = Literal(0);
+      U.col(k).normalize();
+
+      if (m_compV) {
+        V.col(k).setZero();
+        for (Index l = 1; l < m; ++l) {
+          Index i = perm(l);
+          V(i, k) = diag(i) * zhat(i) / (((diag(i) - shifts(k)) - mus(k))) / ((diag(i) + singVals[k]));
+        }
+        V(0, k) = Literal(-1);
+        V.col(k).normalize();
+      }
+    }
+  }
+  U.col(n) = VectorType::Unit(n + 1, n);
+}
+
+// page 12_13
+// i >= 1, di almost null and zi non null.
+// We use a rotation to zero out zi applied to the left of M, and set di = 0.
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::deflation43(Index firstCol, Index shift, Index i, Index size) {
+  using std::abs;
+  using std::pow;
+  using std::sqrt;
+  Index start = firstCol + shift;
+  RealScalar c = m_computed(start, start);
+  RealScalar s = m_computed(start + i, start);
+  RealScalar r = numext::hypot(c, s);
+  if (numext::is_exactly_zero(r)) {
+    m_computed(start + i, start + i) = Literal(0);
+    return;
+  }
+  m_computed(start, start) = r;
+  m_computed(start + i, start) = Literal(0);
+  m_computed(start + i, start + i) = Literal(0);
+
+  JacobiRotation<RealScalar> J(c / r, -s / r);
+  if (m_compU)
+    m_naiveU.middleRows(firstCol, size + 1).applyOnTheRight(firstCol, firstCol + i, J);
+  else
+    m_naiveU.applyOnTheRight(firstCol, firstCol + i, J);
+}  // end deflation 43
+
+// page 13
+// i,j >= 1, i > j, and |di - dj| < epsilon * norm2(M)
+// We apply two rotations to have zi = 0, and dj = di.
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::deflation44(Index firstColu, Index firstColm, Index firstRowW, Index firstColW,
+                                              Index i, Index j, Index size) {
+  using std::abs;
+  using std::conj;
+  using std::pow;
+  using std::sqrt;
+
+  RealScalar s = m_computed(firstColm + i, firstColm);
+  RealScalar c = m_computed(firstColm + j, firstColm);
+  RealScalar r = numext::hypot(c, s);
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "deflation 4.4: " << i << "," << j << " -> " << c << " " << s << " " << r << " ; "
+            << m_computed(firstColm + i - 1, firstColm) << " " << m_computed(firstColm + i, firstColm) << " "
+            << m_computed(firstColm + i + 1, firstColm) << " " << m_computed(firstColm + i + 2, firstColm) << "\n";
+  std::cout << m_computed(firstColm + i - 1, firstColm + i - 1) << " " << m_computed(firstColm + i, firstColm + i)
+            << " " << m_computed(firstColm + i + 1, firstColm + i + 1) << " "
+            << m_computed(firstColm + i + 2, firstColm + i + 2) << "\n";
+#endif
+  if (numext::is_exactly_zero(r)) {
+    m_computed(firstColm + j, firstColm + j) = m_computed(firstColm + i, firstColm + i);
+    return;
+  }
+  c /= r;
+  s /= r;
+  m_computed(firstColm + j, firstColm) = r;
+  m_computed(firstColm + j, firstColm + j) = m_computed(firstColm + i, firstColm + i);
+  m_computed(firstColm + i, firstColm) = Literal(0);
+
+  JacobiRotation<RealScalar> J(c, -s);
+  if (m_compU)
+    m_naiveU.middleRows(firstColu, size + 1).applyOnTheRight(firstColu + j, firstColu + i, J);
+  else
+    m_naiveU.applyOnTheRight(firstColu + j, firstColu + i, J);
+  if (m_compV) m_naiveV.middleRows(firstRowW, size).applyOnTheRight(firstColW + j, firstColW + i, J);
+}  // end deflation 44
+
+// acts on block from (firstCol+shift, firstCol+shift) to (lastCol+shift, lastCol+shift) [inclusive]
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::deflation(Index firstCol, Index lastCol, Index k, Index firstRowW, Index firstColW,
+                                            Index shift) {
+  using std::abs;
+  using std::sqrt;
+  const Index length = lastCol + 1 - firstCol;
+
+  Block<MatrixXr, Dynamic, 1> col0(m_computed, firstCol + shift, firstCol + shift, length, 1);
+  Diagonal<MatrixXr> fulldiag(m_computed);
+  VectorBlock<Diagonal<MatrixXr>, Dynamic> diag(fulldiag, firstCol + shift, length);
+
+  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
+  RealScalar maxDiag = diag.tail((std::max)(Index(1), length - 1)).cwiseAbs().maxCoeff();
+  RealScalar epsilon_strict = numext::maxi<RealScalar>(considerZero, NumTraits<RealScalar>::epsilon() * maxDiag);
+  RealScalar epsilon_coarse =
+      Literal(8) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(col0.cwiseAbs().maxCoeff(), maxDiag);
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "\ndeflate:" << diag.head(k + 1).transpose() << "  |  "
+            << diag.segment(k + 1, length - k - 1).transpose() << "\n";
+#endif
+
+  // condition 4.1
+  if (diag(0) < epsilon_coarse) {
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+    std::cout << "deflation 4.1, because " << diag(0) << " < " << epsilon_coarse << "\n";
+#endif
+    diag(0) = epsilon_coarse;
+  }
+
+  // condition 4.2
+  for (Index i = 1; i < length; ++i)
+    if (abs(col0(i)) < epsilon_strict) {
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+      std::cout << "deflation 4.2, set z(" << i << ") to zero because " << abs(col0(i)) << " < " << epsilon_strict
+                << "  (diag(" << i << ")=" << diag(i) << ")\n";
+#endif
+      col0(i) = Literal(0);
+    }
+
+  // condition 4.3
+  for (Index i = 1; i < length; i++)
+    if (diag(i) < epsilon_coarse) {
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+      std::cout << "deflation 4.3, cancel z(" << i << ")=" << col0(i) << " because diag(" << i << ")=" << diag(i)
+                << " < " << epsilon_coarse << "\n";
+#endif
+      deflation43(firstCol, shift, i, length);
+    }
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "to be sorted: " << diag.transpose() << "\n\n";
+  std::cout << "            : " << col0.transpose() << "\n\n";
+#endif
+  {
+    // Check for total deflation:
+    // If we have a total deflation, then we have to consider col0(0)==diag(0) as a singular value during sorting.
+    const bool total_deflation = (col0.tail(length - 1).array().abs() < considerZero).all();
+
+    // Sort the diagonal entries, since diag(1:k-1) and diag(k:length) are already sorted, let's do a sorted merge.
+    // First, compute the respective permutation.
+    Index* permutation = m_workspaceI.data();
+    {
+      permutation[0] = 0;
+      Index p = 1;
+
+      // Move deflated diagonal entries at the end.
+      for (Index i = 1; i < length; ++i)
+        if (diag(i) < considerZero) permutation[p++] = i;
+
+      Index i = 1, j = k + 1;
+      for (; p < length; ++p) {
+        if (i > k)
+          permutation[p] = j++;
+        else if (j >= length)
+          permutation[p] = i++;
+        else if (diag(i) < diag(j))
+          permutation[p] = j++;
+        else
+          permutation[p] = i++;
+      }
+    }
+
+    // If we have a total deflation, then we have to insert diag(0) at the right place
+    if (total_deflation) {
+      for (Index i = 1; i < length; ++i) {
+        Index pi = permutation[i];
+        if (diag(pi) < considerZero || diag(0) < diag(pi))
+          permutation[i - 1] = permutation[i];
+        else {
+          permutation[i - 1] = 0;
+          break;
+        }
+      }
+    }
+
+    // Current index of each col, and current column of each index
+    Index* realInd = m_workspaceI.data() + length;
+    Index* realCol = m_workspaceI.data() + 2 * length;
+
+    for (int pos = 0; pos < length; pos++) {
+      realCol[pos] = pos;
+      realInd[pos] = pos;
+    }
+
+    for (Index i = total_deflation ? 0 : 1; i < length; i++) {
+      const Index pi = permutation[length - (total_deflation ? i + 1 : i)];
+      const Index J = realCol[pi];
+
+      using std::swap;
+      // swap diagonal and first column entries:
+      swap(diag(i), diag(J));
+      if (i != 0 && J != 0) swap(col0(i), col0(J));
+
+      // change columns
+      if (m_compU)
+        m_naiveU.col(firstCol + i)
+            .segment(firstCol, length + 1)
+            .swap(m_naiveU.col(firstCol + J).segment(firstCol, length + 1));
+      else
+        m_naiveU.col(firstCol + i).segment(0, 2).swap(m_naiveU.col(firstCol + J).segment(0, 2));
+      if (m_compV)
+        m_naiveV.col(firstColW + i)
+            .segment(firstRowW, length)
+            .swap(m_naiveV.col(firstColW + J).segment(firstRowW, length));
+
+      // update real pos
+      const Index realI = realInd[i];
+      realCol[realI] = J;
+      realCol[pi] = i;
+      realInd[J] = realI;
+      realInd[i] = pi;
+    }
+  }
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "sorted: " << diag.transpose().format(bdcsvdfmt) << "\n";
+  std::cout << "      : " << col0.transpose() << "\n\n";
+#endif
+
+  // condition 4.4
+  {
+    Index i = length - 1;
+    // Find last non-deflated entry.
+    while (i > 0 && (diag(i) < considerZero || abs(col0(i)) < considerZero)) --i;
+
+    for (; i > 1; --i)
+      if ((diag(i) - diag(i - 1)) < epsilon_strict) {
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+        std::cout << "deflation 4.4 with i = " << i << " because " << diag(i) << " - " << diag(i - 1)
+                  << " == " << (diag(i) - diag(i - 1)) << " < " << epsilon_strict << "\n";
+#endif
+        eigen_internal_assert(abs(diag(i) - diag(i - 1)) < epsilon_coarse &&
+                              " diagonal entries are not properly sorted");
+        deflation44(firstCol, firstCol + shift, firstRowW, firstColW, i, i - 1, length);
+      }
+  }
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  for (Index j = 2; j < length; ++j) eigen_internal_assert(diag(j - 1) <= diag(j) || abs(diag(j)) < considerZero);
+#endif
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+}  // end deflation
+
+/** \svd_module
+ *
+ * \return the singular value decomposition of \c *this computed by Divide & Conquer algorithm
+ *
+ * \sa class BDCSVD
+ */
+template <typename Derived>
+template <int Options>
+BDCSVD<typename MatrixBase<Derived>::PlainObject, Options> MatrixBase<Derived>::bdcSvd() const {
+  return BDCSVD<PlainObject, Options>(*this);
+}
+
+/** \svd_module
+ *
+ * \return the singular value decomposition of \c *this computed by Divide & Conquer algorithm
+ *
+ * \sa class BDCSVD
+ */
+template <typename Derived>
+template <int Options>
+BDCSVD<typename MatrixBase<Derived>::PlainObject, Options> MatrixBase<Derived>::bdcSvd(
+    unsigned int computationOptions) const {
+  return BDCSVD<PlainObject, Options>(*this, computationOptions);
+}
+
+}  // end namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/BDCSVD_LAPACKE.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/BDCSVD_LAPACKE.h
new file mode 100644
index 0000000..5d2b8c7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/BDCSVD_LAPACKE.h
@@ -0,0 +1,174 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022 Melven Roehrig-Zoellner <Melven.Roehrig-Zoellner@DLR.de>
+// Copyright (c) 2011, Intel Corporation. All rights reserved.
+//
+// This file is based on the JacobiSVD_LAPACKE.h originally from Intel -
+// see license notice below:
+/*
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Singular Value Decomposition - SVD (divide and conquer variant)
+ ********************************************************************************
+*/
+#ifndef EIGEN_BDCSVD_LAPACKE_H
+#define EIGEN_BDCSVD_LAPACKE_H
+
+namespace Eigen {
+
+namespace internal {
+
+namespace lapacke_helpers {
+
+/** \internal Specialization for the data types supported by LAPACKe */
+
+// defining a derived class to allow access to protected members
+template <typename MatrixType_, int Options>
+class BDCSVD_LAPACKE : public BDCSVD<MatrixType_, Options> {
+  typedef BDCSVD<MatrixType_, Options> SVD;
+  typedef typename SVD::MatrixType MatrixType;
+  typedef typename SVD::Scalar Scalar;
+  typedef typename SVD::RealScalar RealScalar;
+
+ public:
+  // construct this by moving from a parent object
+  BDCSVD_LAPACKE(SVD&& svd) : SVD(std::move(svd)) {}
+
+  template <typename Derived>
+  void compute_impl_lapacke(const MatrixBase<Derived>& matrix, unsigned int computationOptions) {
+    SVD::allocate(matrix.rows(), matrix.cols(), computationOptions);
+
+    SVD::m_nonzeroSingularValues = SVD::m_diagSize;
+
+    // prepare arguments to ?gesdd
+    const lapack_int matrix_order = lapack_storage_of(matrix);
+    const char jobz = (SVD::m_computeFullU || SVD::m_computeFullV)   ? 'A'
+                      : (SVD::m_computeThinU || SVD::m_computeThinV) ? 'S'
+                                                                     : 'N';
+    const lapack_int u_cols = (jobz == 'A') ? to_lapack(SVD::rows()) : (jobz == 'S') ? to_lapack(SVD::diagSize()) : 1;
+    const lapack_int vt_rows = (jobz == 'A') ? to_lapack(SVD::cols()) : (jobz == 'S') ? to_lapack(SVD::diagSize()) : 1;
+    lapack_int ldu, ldvt;
+    Scalar *u, *vt, dummy;
+    MatrixType localU;
+    if (SVD::computeU() && !(SVD::m_computeThinU && SVD::m_computeFullV)) {
+      ldu = to_lapack(SVD::m_matrixU.outerStride());
+      u = SVD::m_matrixU.data();
+    } else if (SVD::computeV()) {
+      localU.resize(SVD::rows(), u_cols);
+      ldu = to_lapack(localU.outerStride());
+      u = localU.data();
+    } else {
+      ldu = 1;
+      u = &dummy;
+    }
+    MatrixType localV;
+    if (SVD::computeU() || SVD::computeV()) {
+      localV.resize(vt_rows, SVD::cols());
+      ldvt = to_lapack(localV.outerStride());
+      vt = localV.data();
+    } else {
+      ldvt = 1;
+      vt = &dummy;
+    }
+    MatrixType temp;
+    temp = matrix;
+
+    // actual call to ?gesdd
+    lapack_int info = gesdd(matrix_order, jobz, to_lapack(SVD::rows()), to_lapack(SVD::cols()), to_lapack(temp.data()),
+                            to_lapack(temp.outerStride()), (RealScalar*)SVD::m_singularValues.data(), to_lapack(u), ldu,
+                            to_lapack(vt), ldvt);
+
+    // Check the result of the LAPACK call
+    if (info < 0 || !SVD::m_singularValues.allFinite()) {
+      // this includes info == -4 => NaN entry in A
+      SVD::m_info = InvalidInput;
+    } else if (info > 0) {
+      SVD::m_info = NoConvergence;
+    } else {
+      SVD::m_info = Success;
+      if (SVD::m_computeThinU && SVD::m_computeFullV) {
+        SVD::m_matrixU = localU.leftCols(SVD::m_matrixU.cols());
+      }
+      if (SVD::computeV()) {
+        SVD::m_matrixV = localV.adjoint().leftCols(SVD::m_matrixV.cols());
+      }
+    }
+    SVD::m_isInitialized = true;
+  }
+};
+
+template <typename MatrixType_, int Options, typename Derived>
+BDCSVD<MatrixType_, Options>& BDCSVD_wrapper(BDCSVD<MatrixType_, Options>& svd, const MatrixBase<Derived>& matrix,
+                                             int computationOptions) {
+  // we need to move to the wrapper type and back
+  BDCSVD_LAPACKE<MatrixType_, Options> tmpSvd(std::move(svd));
+  tmpSvd.compute_impl_lapacke(matrix, computationOptions);
+  svd = std::move(tmpSvd);
+  return svd;
+}
+
+}  // end namespace lapacke_helpers
+
+}  // end namespace internal
+
+#define EIGEN_LAPACKE_SDD(EIGTYPE, EIGCOLROW, OPTIONS)                                           \
+  template <>                                                                                    \
+  template <typename Derived>                                                                    \
+  inline BDCSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>&        \
+  BDCSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>::compute_impl( \
+      const MatrixBase<Derived>& matrix, unsigned int computationOptions) {                      \
+    return internal::lapacke_helpers::BDCSVD_wrapper(*this, matrix, computationOptions);         \
+  }
+
+#define EIGEN_LAPACK_SDD_OPTIONS(OPTIONS)        \
+  EIGEN_LAPACKE_SDD(double, ColMajor, OPTIONS)   \
+  EIGEN_LAPACKE_SDD(float, ColMajor, OPTIONS)    \
+  EIGEN_LAPACKE_SDD(dcomplex, ColMajor, OPTIONS) \
+  EIGEN_LAPACKE_SDD(scomplex, ColMajor, OPTIONS) \
+                                                 \
+  EIGEN_LAPACKE_SDD(double, RowMajor, OPTIONS)   \
+  EIGEN_LAPACKE_SDD(float, RowMajor, OPTIONS)    \
+  EIGEN_LAPACKE_SDD(dcomplex, RowMajor, OPTIONS) \
+  EIGEN_LAPACKE_SDD(scomplex, RowMajor, OPTIONS)
+
+EIGEN_LAPACK_SDD_OPTIONS(0)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeThinU)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeThinV)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeFullU)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeFullV)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeThinU | ComputeThinV)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeFullU | ComputeFullV)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeThinU | ComputeFullV)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeFullU | ComputeThinV)
+
+#undef EIGEN_LAPACK_SDD_OPTIONS
+
+#undef EIGEN_LAPACKE_SDD
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BDCSVD_LAPACKE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/InternalHeaderCheck.h
new file mode 100644
index 0000000..fa67b96
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SVD_MODULE_H
+#error "Please include Eigen/SVD instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/JacobiSVD.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/JacobiSVD.h
new file mode 100644
index 0000000..1b08bb3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/JacobiSVD.h
@@ -0,0 +1,851 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2013-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_JACOBISVD_H
+#define EIGEN_JACOBISVD_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// forward declaration (needed by ICC)
+// the empty body is required by MSVC
+template <typename MatrixType, int Options, bool IsComplex = NumTraits<typename MatrixType::Scalar>::IsComplex>
+struct svd_precondition_2x2_block_to_be_real {};
+
+/*** QR preconditioners (R-SVD)
+ ***
+ *** Their role is to reduce the problem of computing the SVD to the case of a square matrix.
+ *** This approach, known as R-SVD, is an optimization for rectangular-enough matrices, and is a requirement for
+ *** JacobiSVD which by itself is only able to work on square matrices.
+ ***/
+
+enum { PreconditionIfMoreColsThanRows, PreconditionIfMoreRowsThanCols };
+
+template <typename MatrixType, int QRPreconditioner, int Case>
+struct qr_preconditioner_should_do_anything {
+  enum {
+    a = MatrixType::RowsAtCompileTime != Dynamic && MatrixType::ColsAtCompileTime != Dynamic &&
+        MatrixType::ColsAtCompileTime <= MatrixType::RowsAtCompileTime,
+    b = MatrixType::RowsAtCompileTime != Dynamic && MatrixType::ColsAtCompileTime != Dynamic &&
+        MatrixType::RowsAtCompileTime <= MatrixType::ColsAtCompileTime,
+    ret = !((QRPreconditioner == NoQRPreconditioner) || (Case == PreconditionIfMoreColsThanRows && bool(a)) ||
+            (Case == PreconditionIfMoreRowsThanCols && bool(b)))
+  };
+};
+
+template <typename MatrixType, int Options, int QRPreconditioner, int Case,
+          bool DoAnything = qr_preconditioner_should_do_anything<MatrixType, QRPreconditioner, Case>::ret>
+struct qr_preconditioner_impl {};
+
+template <typename MatrixType, int Options, int QRPreconditioner, int Case>
+class qr_preconditioner_impl<MatrixType, Options, QRPreconditioner, Case, false> {
+ public:
+  void allocate(const JacobiSVD<MatrixType, Options>&) {}
+  template <typename Xpr>
+  bool run(JacobiSVD<MatrixType, Options>&, const Xpr&) {
+    return false;
+  }
+};
+
+/*** preconditioner using FullPivHouseholderQR ***/
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, FullPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols,
+                             true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum { WorkspaceSize = MatrixType::RowsAtCompileTime, MaxWorkspaceSize = MatrixType::MaxRowsAtCompileTime };
+
+  typedef Matrix<Scalar, 1, WorkspaceSize, RowMajor, 1, MaxWorkspaceSize> WorkspaceType;
+
+  void allocate(const SVDType& svd) {
+    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols()) {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.rows(), svd.cols());
+    }
+    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
+  }
+  template <typename Xpr>
+  bool run(SVDType& svd, const Xpr& matrix) {
+    if (matrix.rows() > matrix.cols()) {
+      m_qr.compute(matrix);
+      svd.m_workMatrix = m_qr.matrixQR().block(0, 0, matrix.cols(), matrix.cols()).template triangularView<Upper>();
+      if (svd.m_computeFullU) m_qr.matrixQ().evalTo(svd.m_matrixU, m_workspace);
+      if (svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
+      return true;
+    }
+    return false;
+  }
+
+ private:
+  typedef FullPivHouseholderQR<MatrixType> QRType;
+  QRType m_qr;
+  WorkspaceType m_workspace;
+};
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, FullPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows,
+                             true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    MatrixOptions = traits<MatrixType>::Options
+  };
+
+  typedef typename internal::make_proper_matrix_type<Scalar, ColsAtCompileTime, RowsAtCompileTime, MatrixOptions,
+                                                     MaxColsAtCompileTime, MaxRowsAtCompileTime>::type
+      TransposeTypeWithSameStorageOrder;
+
+  void allocate(const SVDType& svd) {
+    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols()) {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.cols(), svd.rows());
+    }
+    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
+  }
+  template <typename Xpr>
+  bool run(SVDType& svd, const Xpr& matrix) {
+    if (matrix.cols() > matrix.rows()) {
+      m_qr.compute(matrix.adjoint());
+      svd.m_workMatrix =
+          m_qr.matrixQR().block(0, 0, matrix.rows(), matrix.rows()).template triangularView<Upper>().adjoint();
+      if (svd.m_computeFullV) m_qr.matrixQ().evalTo(svd.m_matrixV, m_workspace);
+      if (svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
+      return true;
+    } else
+      return false;
+  }
+
+ private:
+  typedef FullPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
+  QRType m_qr;
+  typename plain_row_type<MatrixType>::type m_workspace;
+};
+
+/*** preconditioner using ColPivHouseholderQR ***/
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, ColPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols,
+                             true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum {
+    WorkspaceSize = internal::traits<SVDType>::MatrixUColsAtCompileTime,
+    MaxWorkspaceSize = internal::traits<SVDType>::MatrixUMaxColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, 1, WorkspaceSize, RowMajor, 1, MaxWorkspaceSize> WorkspaceType;
+
+  void allocate(const SVDType& svd) {
+    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols()) {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.rows(), svd.cols());
+    }
+    if (svd.m_computeFullU)
+      m_workspace.resize(svd.rows());
+    else if (svd.m_computeThinU)
+      m_workspace.resize(svd.cols());
+  }
+  template <typename Xpr>
+  bool run(SVDType& svd, const Xpr& matrix) {
+    if (matrix.rows() > matrix.cols()) {
+      m_qr.compute(matrix);
+      svd.m_workMatrix = m_qr.matrixQR().block(0, 0, matrix.cols(), matrix.cols()).template triangularView<Upper>();
+      if (svd.m_computeFullU)
+        m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
+      else if (svd.m_computeThinU) {
+        svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
+        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
+      }
+      if (svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
+      return true;
+    }
+    return false;
+  }
+
+ private:
+  typedef ColPivHouseholderQR<MatrixType> QRType;
+  QRType m_qr;
+  WorkspaceType m_workspace;
+};
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, ColPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows,
+                             true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    MatrixOptions = internal::traits<MatrixType>::Options,
+    WorkspaceSize = internal::traits<SVDType>::MatrixVColsAtCompileTime,
+    MaxWorkspaceSize = internal::traits<SVDType>::MatrixVMaxColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, WorkspaceSize, 1, ColMajor, MaxWorkspaceSize, 1> WorkspaceType;
+
+  typedef typename internal::make_proper_matrix_type<Scalar, ColsAtCompileTime, RowsAtCompileTime, MatrixOptions,
+                                                     MaxColsAtCompileTime, MaxRowsAtCompileTime>::type
+      TransposeTypeWithSameStorageOrder;
+
+  void allocate(const SVDType& svd) {
+    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols()) {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.cols(), svd.rows());
+    }
+    if (svd.m_computeFullV)
+      m_workspace.resize(svd.cols());
+    else if (svd.m_computeThinV)
+      m_workspace.resize(svd.rows());
+  }
+  template <typename Xpr>
+  bool run(SVDType& svd, const Xpr& matrix) {
+    if (matrix.cols() > matrix.rows()) {
+      m_qr.compute(matrix.adjoint());
+
+      svd.m_workMatrix =
+          m_qr.matrixQR().block(0, 0, matrix.rows(), matrix.rows()).template triangularView<Upper>().adjoint();
+      if (svd.m_computeFullV)
+        m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
+      else if (svd.m_computeThinV) {
+        svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
+        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
+      }
+      if (svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
+      return true;
+    } else
+      return false;
+  }
+
+ private:
+  typedef ColPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
+  QRType m_qr;
+  WorkspaceType m_workspace;
+};
+
+/*** preconditioner using HouseholderQR ***/
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, HouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum {
+    WorkspaceSize = internal::traits<SVDType>::MatrixUColsAtCompileTime,
+    MaxWorkspaceSize = internal::traits<SVDType>::MatrixUMaxColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, 1, WorkspaceSize, RowMajor, 1, MaxWorkspaceSize> WorkspaceType;
+
+  void allocate(const SVDType& svd) {
+    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols()) {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.rows(), svd.cols());
+    }
+    if (svd.m_computeFullU)
+      m_workspace.resize(svd.rows());
+    else if (svd.m_computeThinU)
+      m_workspace.resize(svd.cols());
+  }
+  template <typename Xpr>
+  bool run(SVDType& svd, const Xpr& matrix) {
+    if (matrix.rows() > matrix.cols()) {
+      m_qr.compute(matrix);
+      svd.m_workMatrix = m_qr.matrixQR().block(0, 0, matrix.cols(), matrix.cols()).template triangularView<Upper>();
+      if (svd.m_computeFullU)
+        m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
+      else if (svd.m_computeThinU) {
+        svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
+        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
+      }
+      if (svd.computeV()) svd.m_matrixV.setIdentity(matrix.cols(), matrix.cols());
+      return true;
+    }
+    return false;
+  }
+
+ private:
+  typedef HouseholderQR<MatrixType> QRType;
+  QRType m_qr;
+  WorkspaceType m_workspace;
+};
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, HouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    MatrixOptions = internal::traits<MatrixType>::Options,
+    WorkspaceSize = internal::traits<SVDType>::MatrixVColsAtCompileTime,
+    MaxWorkspaceSize = internal::traits<SVDType>::MatrixVMaxColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, WorkspaceSize, 1, ColMajor, MaxWorkspaceSize, 1> WorkspaceType;
+
+  typedef typename internal::make_proper_matrix_type<Scalar, ColsAtCompileTime, RowsAtCompileTime, MatrixOptions,
+                                                     MaxColsAtCompileTime, MaxRowsAtCompileTime>::type
+      TransposeTypeWithSameStorageOrder;
+
+  void allocate(const SVDType& svd) {
+    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols()) {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.cols(), svd.rows());
+    }
+    if (svd.m_computeFullV)
+      m_workspace.resize(svd.cols());
+    else if (svd.m_computeThinV)
+      m_workspace.resize(svd.rows());
+  }
+
+  template <typename Xpr>
+  bool run(SVDType& svd, const Xpr& matrix) {
+    if (matrix.cols() > matrix.rows()) {
+      m_qr.compute(matrix.adjoint());
+
+      svd.m_workMatrix =
+          m_qr.matrixQR().block(0, 0, matrix.rows(), matrix.rows()).template triangularView<Upper>().adjoint();
+      if (svd.m_computeFullV)
+        m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
+      else if (svd.m_computeThinV) {
+        svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
+        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
+      }
+      if (svd.computeU()) svd.m_matrixU.setIdentity(matrix.rows(), matrix.rows());
+      return true;
+    } else
+      return false;
+  }
+
+ private:
+  typedef HouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
+  QRType m_qr;
+  WorkspaceType m_workspace;
+};
+
+/*** 2x2 SVD implementation
+ ***
+ *** JacobiSVD consists in performing a series of 2x2 SVD subproblems
+ ***/
+
+template <typename MatrixType, int Options>
+struct svd_precondition_2x2_block_to_be_real<MatrixType, Options, false> {
+  typedef JacobiSVD<MatrixType, Options> SVD;
+  typedef typename MatrixType::RealScalar RealScalar;
+  static bool run(typename SVD::WorkMatrixType&, SVD&, Index, Index, RealScalar&) { return true; }
+};
+
+template <typename MatrixType, int Options>
+struct svd_precondition_2x2_block_to_be_real<MatrixType, Options, true> {
+  typedef JacobiSVD<MatrixType, Options> SVD;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  static bool run(typename SVD::WorkMatrixType& work_matrix, SVD& svd, Index p, Index q, RealScalar& maxDiagEntry) {
+    using std::abs;
+    using std::sqrt;
+    Scalar z;
+    JacobiRotation<Scalar> rot;
+    RealScalar n = sqrt(numext::abs2(work_matrix.coeff(p, p)) + numext::abs2(work_matrix.coeff(q, p)));
+
+    const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+    const RealScalar precision = NumTraits<Scalar>::epsilon();
+
+    if (numext::is_exactly_zero(n)) {
+      // make sure first column is zero
+      work_matrix.coeffRef(p, p) = work_matrix.coeffRef(q, p) = Scalar(0);
+
+      if (abs(numext::imag(work_matrix.coeff(p, q))) > considerAsZero) {
+        // work_matrix.coeff(p,q) can be zero if work_matrix.coeff(q,p) is not zero but small enough to underflow when
+        // computing n
+        z = abs(work_matrix.coeff(p, q)) / work_matrix.coeff(p, q);
+        work_matrix.row(p) *= z;
+        if (svd.computeU()) svd.m_matrixU.col(p) *= conj(z);
+      }
+      if (abs(numext::imag(work_matrix.coeff(q, q))) > considerAsZero) {
+        z = abs(work_matrix.coeff(q, q)) / work_matrix.coeff(q, q);
+        work_matrix.row(q) *= z;
+        if (svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
+      }
+      // otherwise the second row is already zero, so we have nothing to do.
+    } else {
+      rot.c() = conj(work_matrix.coeff(p, p)) / n;
+      rot.s() = work_matrix.coeff(q, p) / n;
+      work_matrix.applyOnTheLeft(p, q, rot);
+      if (svd.computeU()) svd.m_matrixU.applyOnTheRight(p, q, rot.adjoint());
+      if (abs(numext::imag(work_matrix.coeff(p, q))) > considerAsZero) {
+        z = abs(work_matrix.coeff(p, q)) / work_matrix.coeff(p, q);
+        work_matrix.col(q) *= z;
+        if (svd.computeV()) svd.m_matrixV.col(q) *= z;
+      }
+      if (abs(numext::imag(work_matrix.coeff(q, q))) > considerAsZero) {
+        z = abs(work_matrix.coeff(q, q)) / work_matrix.coeff(q, q);
+        work_matrix.row(q) *= z;
+        if (svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
+      }
+    }
+
+    // update largest diagonal entry
+    maxDiagEntry = numext::maxi<RealScalar>(
+        maxDiagEntry, numext::maxi<RealScalar>(abs(work_matrix.coeff(p, p)), abs(work_matrix.coeff(q, q))));
+    // and check whether the 2x2 block is already diagonal
+    RealScalar threshold = numext::maxi<RealScalar>(considerAsZero, precision * maxDiagEntry);
+    return abs(work_matrix.coeff(p, q)) > threshold || abs(work_matrix.coeff(q, p)) > threshold;
+  }
+};
+
+template <typename MatrixType_, int Options>
+struct traits<JacobiSVD<MatrixType_, Options> > : svd_traits<MatrixType_, Options> {
+  typedef MatrixType_ MatrixType;
+};
+
+}  // end namespace internal
+
+/** \ingroup SVD_Module
+ *
+ *
+ * \class JacobiSVD
+ *
+ * \brief Two-sided Jacobi SVD decomposition of a rectangular matrix
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the SVD decomposition
+ * \tparam Options this optional parameter allows one to specify the type of QR decomposition that will be used
+ * internally for the R-SVD step for non-square matrices. Additionally, it allows one to specify whether to compute thin
+ * or full unitaries \a U and \a V. See discussion of possible values below.
+ *
+ * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
+ *   \f[ A = U S V^* \f]
+ * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero
+ * outside of its main diagonal; the diagonal entries of S are known as the \em singular \em values of \a A and the
+ * columns of \a U and \a V are known as the left and right \em singular \em vectors of \a A respectively.
+ *
+ * Singular values are always sorted in decreasing order.
+ *
+ * This JacobiSVD decomposition computes only the singular values by default. If you want \a U or \a V, you need to ask
+ * for them explicitly.
+ *
+ * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p
+ * matrix, letting \a m be the smaller value among \a n and \a p, there are only \a m singular vectors; the remaining
+ * columns of \a U and \a V do not correspond to actual singular vectors. Asking for \em thin \a U or \a V means asking
+ * for only their \a m first columns to be formed. So \a U is then a n-by-m matrix, and \a V is then a p-by-m matrix.
+ * Notice that thin \a U and \a V are all you need for (least squares) solving.
+ *
+ * Here's an example demonstrating basic usage:
+ * \include JacobiSVD_basic.cpp
+ * Output: \verbinclude JacobiSVD_basic.out
+ *
+ * This JacobiSVD class is a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The
+ * downside is that it's slower than bidiagonalizing SVD algorithms for large square matrices; however its complexity is
+ * still \f$ O(n^2p) \f$ where \a n is the smaller dimension and \a p is the greater dimension, meaning that it is still
+ * of the same order of complexity as the faster bidiagonalizing R-SVD algorithms. In particular, like any R-SVD, it
+ * takes advantage of non-squareness in that its complexity is only linear in the greater dimension.
+ *
+ * If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is
+ * guaranteed to terminate in finite (and reasonable) time.
+ *
+ * The possible QR preconditioners that can be set with Options template parameter are:
+ * \li ColPivHouseholderQRPreconditioner is the default. In practice it's very safe. It uses column-pivoting QR.
+ * \li FullPivHouseholderQRPreconditioner, is the safest and slowest. It uses full-pivoting QR.
+ *     Contrary to other QRs, it doesn't allow computing thin unitaries.
+ * \li HouseholderQRPreconditioner is the fastest, and less safe and accurate than the pivoting variants. It uses
+ * non-pivoting QR. This is very similar in safety and accuracy to the bidiagonalization process used by bidiagonalizing
+ * SVD algorithms (since bidiagonalization is inherently non-pivoting). However the resulting SVD is still more reliable
+ * than bidiagonalizing SVDs because the Jacobi-based iterarive process is more reliable than the optimized bidiagonal
+ * SVD iterations. \li NoQRPreconditioner allows not to use a QR preconditioner at all. This is useful if you know that
+ * you will only be computing JacobiSVD decompositions of square matrices. Non-square matrices require a QR
+ * preconditioner. Using this option will result in faster compilation and smaller executable code. It won't
+ * significantly speed up computation, since JacobiSVD is always checking if QR preconditioning is needed before
+ * applying it anyway.
+ *
+ * One may also use the Options template parameter to specify how the unitaries should be computed. The options are
+ * #ComputeThinU, #ComputeThinV, #ComputeFullU, #ComputeFullV. It is not possible to request both the thin and full
+ * versions of a unitary. By default, unitaries will not be computed.
+ *
+ * You can set the QRPreconditioner and unitary options together: JacobiSVD<MatrixType,
+ * ColPivHouseholderQRPreconditioner | ComputeThinU | ComputeFullV>
+ *
+ * \sa MatrixBase::jacobiSvd()
+ */
+template <typename MatrixType_, int Options_>
+class JacobiSVD : public SVDBase<JacobiSVD<MatrixType_, Options_> > {
+  typedef SVDBase<JacobiSVD> Base;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::RealScalar RealScalar;
+  enum : int {
+    Options = Options_,
+    QRPreconditioner = internal::get_qr_preconditioner(Options),
+    RowsAtCompileTime = Base::RowsAtCompileTime,
+    ColsAtCompileTime = Base::ColsAtCompileTime,
+    DiagSizeAtCompileTime = Base::DiagSizeAtCompileTime,
+    MaxRowsAtCompileTime = Base::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = Base::MaxColsAtCompileTime,
+    MaxDiagSizeAtCompileTime = Base::MaxDiagSizeAtCompileTime,
+    MatrixOptions = Base::MatrixOptions
+  };
+
+  typedef typename Base::MatrixUType MatrixUType;
+  typedef typename Base::MatrixVType MatrixVType;
+  typedef typename Base::SingularValuesType SingularValuesType;
+  typedef Matrix<Scalar, DiagSizeAtCompileTime, DiagSizeAtCompileTime, MatrixOptions, MaxDiagSizeAtCompileTime,
+                 MaxDiagSizeAtCompileTime>
+      WorkMatrixType;
+
+  /** \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via JacobiSVD::compute(const MatrixType&).
+   */
+  JacobiSVD() {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem size and \a Options template parameter.
+   *
+   * \sa JacobiSVD()
+   */
+  JacobiSVD(Index rows, Index cols) { allocate(rows, cols, internal::get_computation_options(Options)); }
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem size.
+   *
+   * One \b cannot request unitaries using both the \a Options template parameter
+   * and the constructor. If possible, prefer using the \a Options template parameter.
+   *
+   * \param rows number of rows for the input matrix
+   * \param cols number of columns for the input matrix
+   * \param computationOptions specify whether to compute Thin/Full unitaries U/V
+   * \sa JacobiSVD()
+   *
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  EIGEN_DEPRECATED_WITH_REASON("Options should be specified using the class template parameter.")
+  JacobiSVD(Index rows, Index cols, unsigned int computationOptions) {
+    internal::check_svd_options_assertions<MatrixType, Options>(computationOptions, rows, cols);
+    allocate(rows, cols, computationOptions);
+  }
+
+  /** \brief Constructor performing the decomposition of given matrix, using the custom options specified
+   *         with the \a Options template parameter.
+   *
+   * \param matrix the matrix to decompose
+   */
+  template <typename Derived>
+  explicit JacobiSVD(const MatrixBase<Derived>& matrix) {
+    compute_impl(matrix, internal::get_computation_options(Options));
+  }
+
+  template <typename Derived>
+  explicit JacobiSVD(const TriangularBase<Derived>& matrix) {
+    compute_impl(matrix, internal::get_computation_options(Options));
+  }
+
+  /** \brief Constructor performing the decomposition of given matrix using specified options
+   *         for computing unitaries.
+   *
+   *  One \b cannot request unitiaries using both the \a Options template parameter
+   *  and the constructor. If possible, prefer using the \a Options template parameter.
+   *
+   * \param matrix the matrix to decompose
+   * \param computationOptions specify whether to compute Thin/Full unitaries U/V
+   *
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  // EIGEN_DEPRECATED // TODO(cantonios): re-enable after fixing a few 3p libraries that error on deprecation warnings.
+  template <typename Derived>
+  JacobiSVD(const MatrixBase<Derived>& matrix, unsigned int computationOptions) {
+    internal::check_svd_options_assertions<MatrixBase<Derived>, Options>(computationOptions, matrix.rows(),
+                                                                         matrix.cols());
+    compute_impl(matrix, computationOptions);
+  }
+
+  /** \brief Method performing the decomposition of given matrix. Computes Thin/Full unitaries U/V if specified
+   *         using the \a Options template parameter or the class constructor.
+   *
+   * \param matrix the matrix to decompose
+   */
+  template <typename Derived>
+  JacobiSVD& compute(const MatrixBase<Derived>& matrix) {
+    return compute_impl(matrix, m_computationOptions);
+  }
+
+  template <typename Derived>
+  JacobiSVD& compute(const TriangularBase<Derived>& matrix) {
+    return compute_impl(matrix, m_computationOptions);
+  }
+
+  /** \brief Method performing the decomposition of given matrix, as specified by
+   *         the `computationOptions` parameter.
+   *
+   * \param matrix the matrix to decompose
+   * \param computationOptions specify whether to compute Thin/Full unitaries U/V
+   *
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  template <typename Derived>
+  EIGEN_DEPRECATED_WITH_REASON("Options should be specified using the class template parameter.")
+  JacobiSVD& compute(const MatrixBase<Derived>& matrix, unsigned int computationOptions) {
+    internal::check_svd_options_assertions<MatrixBase<Derived>, Options>(m_computationOptions, matrix.rows(),
+                                                                         matrix.cols());
+    return compute_impl(matrix, computationOptions);
+  }
+
+  using Base::cols;
+  using Base::computeU;
+  using Base::computeV;
+  using Base::diagSize;
+  using Base::rank;
+  using Base::rows;
+
+  void allocate(Index rows_, Index cols_, unsigned int computationOptions) {
+    if (Base::allocate(rows_, cols_, computationOptions)) return;
+    eigen_assert(!(ShouldComputeThinU && int(QRPreconditioner) == int(FullPivHouseholderQRPreconditioner)) &&
+                 !(ShouldComputeThinU && int(QRPreconditioner) == int(FullPivHouseholderQRPreconditioner)) &&
+                 "JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
+                 "Use the ColPivHouseholderQR preconditioner instead.");
+
+    m_workMatrix.resize(diagSize(), diagSize());
+    if (cols() > rows()) m_qr_precond_morecols.allocate(*this);
+    if (rows() > cols()) m_qr_precond_morerows.allocate(*this);
+  }
+
+ private:
+  template <typename Derived>
+  JacobiSVD& compute_impl(const TriangularBase<Derived>& matrix, unsigned int computationOptions);
+  template <typename Derived>
+  JacobiSVD& compute_impl(const MatrixBase<Derived>& matrix, unsigned int computationOptions);
+
+ protected:
+  using Base::m_computationOptions;
+  using Base::m_computeFullU;
+  using Base::m_computeFullV;
+  using Base::m_computeThinU;
+  using Base::m_computeThinV;
+  using Base::m_info;
+  using Base::m_isAllocated;
+  using Base::m_isInitialized;
+  using Base::m_matrixU;
+  using Base::m_matrixV;
+  using Base::m_nonzeroSingularValues;
+  using Base::m_prescribedThreshold;
+  using Base::m_singularValues;
+  using Base::m_usePrescribedThreshold;
+  using Base::ShouldComputeThinU;
+  using Base::ShouldComputeThinV;
+
+  EIGEN_STATIC_ASSERT(!(ShouldComputeThinU && int(QRPreconditioner) == int(FullPivHouseholderQRPreconditioner)) &&
+                          !(ShouldComputeThinU && int(QRPreconditioner) == int(FullPivHouseholderQRPreconditioner)),
+                      "JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
+                      "Use the ColPivHouseholderQR preconditioner instead.")
+
+  template <typename MatrixType__, int Options__, bool IsComplex_>
+  friend struct internal::svd_precondition_2x2_block_to_be_real;
+  template <typename MatrixType__, int Options__, int QRPreconditioner_, int Case_, bool DoAnything_>
+  friend struct internal::qr_preconditioner_impl;
+
+  internal::qr_preconditioner_impl<MatrixType, Options, QRPreconditioner, internal::PreconditionIfMoreColsThanRows>
+      m_qr_precond_morecols;
+  internal::qr_preconditioner_impl<MatrixType, Options, QRPreconditioner, internal::PreconditionIfMoreRowsThanCols>
+      m_qr_precond_morerows;
+  WorkMatrixType m_workMatrix;
+};
+
+template <typename MatrixType, int Options>
+template <typename Derived>
+JacobiSVD<MatrixType, Options>& JacobiSVD<MatrixType, Options>::compute_impl(const TriangularBase<Derived>& matrix,
+                                                                             unsigned int computationOptions) {
+  return compute_impl(matrix.toDenseMatrix(), computationOptions);
+}
+
+template <typename MatrixType, int Options>
+template <typename Derived>
+JacobiSVD<MatrixType, Options>& JacobiSVD<MatrixType, Options>::compute_impl(const MatrixBase<Derived>& matrix,
+                                                                             unsigned int computationOptions) {
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived, MatrixType);
+  EIGEN_STATIC_ASSERT((std::is_same<typename Derived::Scalar, typename MatrixType::Scalar>::value),
+                      Input matrix must have the same Scalar type as the BDCSVD object.);
+
+  using std::abs;
+
+  allocate(matrix.rows(), matrix.cols(), computationOptions);
+
+  // currently we stop when we reach precision 2*epsilon as the last bit of precision can require an unreasonable number
+  // of iterations, only worsening the precision of U and V as we accumulate more rotations
+  const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();
+
+  // limit for denormal numbers to be considered zero in order to avoid infinite loops (see bug 286)
+  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+
+  // Scaling factor to reduce over/under-flows
+  RealScalar scale = matrix.cwiseAbs().template maxCoeff<PropagateNaN>();
+  if (!(numext::isfinite)(scale)) {
+    m_isInitialized = true;
+    m_info = InvalidInput;
+    m_nonzeroSingularValues = 0;
+    m_singularValues.setZero();
+    return *this;
+  }
+  if (numext::is_exactly_zero(scale)) scale = RealScalar(1);
+
+  /*** step 1. The R-SVD step: we use a QR decomposition to reduce to the case of a square matrix */
+
+  if (rows() != cols()) {
+    m_qr_precond_morecols.run(*this, matrix / scale);
+    m_qr_precond_morerows.run(*this, matrix / scale);
+  } else {
+    m_workMatrix =
+        matrix.template topLeftCorner<DiagSizeAtCompileTime, DiagSizeAtCompileTime>(diagSize(), diagSize()) / scale;
+    if (m_computeFullU) m_matrixU.setIdentity(rows(), rows());
+    if (m_computeThinU) m_matrixU.setIdentity(rows(), diagSize());
+    if (m_computeFullV) m_matrixV.setIdentity(cols(), cols());
+    if (m_computeThinV) m_matrixV.setIdentity(cols(), diagSize());
+  }
+
+  /*** step 2. The main Jacobi SVD iteration. ***/
+  RealScalar maxDiagEntry = m_workMatrix.cwiseAbs().diagonal().maxCoeff();
+
+  bool finished = false;
+  while (!finished) {
+    finished = true;
+
+    // do a sweep: for all index pairs (p,q), perform SVD of the corresponding 2x2 sub-matrix
+    // Threshold is hoisted before the double loop; it only needs updating when maxDiagEntry
+    // increases (which only happens inside the rotation block). Since maxDiagEntry is
+    // monotonically non-decreasing, a slightly stale threshold is conservative.
+    RealScalar threshold = numext::maxi<RealScalar>(considerAsZero, precision * maxDiagEntry);
+
+    for (Index p = 1; p < diagSize(); ++p) {
+      for (Index q = 0; q < p; ++q) {
+        // if this 2x2 sub-matrix is not diagonal already...
+        // notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't
+        // keep us iterating forever. Similarly, small denormal numbers are considered zero.
+        if (abs(m_workMatrix.coeff(p, q)) > threshold || abs(m_workMatrix.coeff(q, p)) > threshold) {
+          finished = false;
+          // perform SVD decomposition of 2x2 sub-matrix corresponding to indices p,q to make it diagonal
+          // the complex to real operation returns true if the updated 2x2 block is not already diagonal
+          if (internal::svd_precondition_2x2_block_to_be_real<MatrixType, Options>::run(m_workMatrix, *this, p, q,
+                                                                                        maxDiagEntry)) {
+            JacobiRotation<RealScalar> j_left, j_right;
+            internal::real_2x2_jacobi_svd(m_workMatrix, p, q, &j_left, &j_right);
+
+            // accumulate resulting Jacobi rotations
+            m_workMatrix.applyOnTheLeft(p, q, j_left);
+            if (computeU()) m_matrixU.applyOnTheRight(p, q, j_left.transpose());
+
+            m_workMatrix.applyOnTheRight(p, q, j_right);
+            if (computeV()) m_matrixV.applyOnTheRight(p, q, j_right);
+
+            // keep track of the largest diagonal coefficient
+            maxDiagEntry = numext::maxi<RealScalar>(
+                maxDiagEntry, numext::maxi<RealScalar>(abs(m_workMatrix.coeff(p, p)), abs(m_workMatrix.coeff(q, q))));
+            threshold = numext::maxi<RealScalar>(considerAsZero, precision * maxDiagEntry);
+          }
+        }
+      }
+    }
+  }
+
+  /*** step 3. The work matrix is now diagonal, so ensure it's positive so its diagonal entries are the singular values
+   * ***/
+
+  for (Index i = 0; i < diagSize(); ++i) {
+    // For a complex matrix, some diagonal coefficients might note have been
+    // treated by svd_precondition_2x2_block_to_be_real, and the imaginary part
+    // of some diagonal entry might not be null.
+    if (NumTraits<Scalar>::IsComplex && abs(numext::imag(m_workMatrix.coeff(i, i))) > considerAsZero) {
+      RealScalar a = abs(m_workMatrix.coeff(i, i));
+      m_singularValues.coeffRef(i) = abs(a);
+      if (computeU()) m_matrixU.col(i) *= m_workMatrix.coeff(i, i) / a;
+    } else {
+      // m_workMatrix.coeff(i,i) is already real, no difficulty:
+      RealScalar a = numext::real(m_workMatrix.coeff(i, i));
+      m_singularValues.coeffRef(i) = abs(a);
+      if (computeU() && (a < RealScalar(0))) m_matrixU.col(i) = -m_matrixU.col(i);
+    }
+  }
+
+  m_singularValues *= scale;
+
+  /*** step 4. Sort singular values in descending order and compute the number of nonzero singular values ***/
+
+  m_nonzeroSingularValues = diagSize();
+  for (Index i = 0; i < diagSize(); i++) {
+    Index pos;
+    RealScalar maxRemainingSingularValue = m_singularValues.tail(diagSize() - i).maxCoeff(&pos);
+    if (numext::is_exactly_zero(maxRemainingSingularValue)) {
+      m_nonzeroSingularValues = i;
+      break;
+    }
+    if (pos) {
+      pos += i;
+      std::swap(m_singularValues.coeffRef(i), m_singularValues.coeffRef(pos));
+      if (computeU()) m_matrixU.col(pos).swap(m_matrixU.col(i));
+      if (computeV()) m_matrixV.col(pos).swap(m_matrixV.col(i));
+    }
+  }
+
+  m_isInitialized = true;
+  return *this;
+}
+
+/** \svd_module
+ *
+ * \return the singular value decomposition of \c *this computed by two-sided
+ * Jacobi transformations.
+ *
+ * \sa class JacobiSVD
+ */
+template <typename Derived>
+template <int Options>
+JacobiSVD<typename MatrixBase<Derived>::PlainObject, Options> MatrixBase<Derived>::jacobiSvd() const {
+  return JacobiSVD<PlainObject, Options>(*this);
+}
+
+template <typename Derived>
+template <int Options>
+JacobiSVD<typename MatrixBase<Derived>::PlainObject, Options> MatrixBase<Derived>::jacobiSvd(
+    unsigned int computationOptions) const {
+  return JacobiSVD<PlainObject, Options>(*this, computationOptions);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_JACOBISVD_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/JacobiSVD_LAPACKE.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/JacobiSVD_LAPACKE.h
new file mode 100644
index 0000000..db26366
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/JacobiSVD_LAPACKE.h
@@ -0,0 +1,127 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Singular Value Decomposition - SVD.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_JACOBISVD_LAPACKE_H
+#define EIGEN_JACOBISVD_LAPACKE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \internal Specialization for the data types supported by LAPACKe */
+
+#define EIGEN_LAPACKE_SVD(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_PREFIX, EIGCOLROW, LAPACKE_COLROW, OPTIONS) \
+  template <>                                                                                                       \
+  template <typename Derived>                                                                                       \
+  inline JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>&                        \
+  JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>::compute_impl(                 \
+      const MatrixBase<Derived>& matrix, unsigned int computationOptions) {                                         \
+    /*typedef MatrixType::Scalar Scalar;*/                                                                          \
+    /*typedef MatrixType::RealScalar RealScalar;*/                                                                  \
+    allocate(matrix.rows(), matrix.cols(), computationOptions);                                                     \
+                                                                                                                    \
+    /*const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();*/                                  \
+    m_nonzeroSingularValues = diagSize();                                                                           \
+                                                                                                                    \
+    lapack_int lda = internal::convert_index<lapack_int>(matrix.outerStride()), ldu, ldvt;                          \
+    lapack_int matrix_order = LAPACKE_COLROW;                                                                       \
+    char jobu, jobvt;                                                                                               \
+    LAPACKE_TYPE *u, *vt, dummy;                                                                                    \
+    jobu = (m_computeFullU) ? 'A' : (m_computeThinU) ? 'S' : 'N';                                                   \
+    jobvt = (m_computeFullV) ? 'A' : (m_computeThinV) ? 'S' : 'N';                                                  \
+    if (computeU()) {                                                                                               \
+      ldu = internal::convert_index<lapack_int>(m_matrixU.outerStride());                                           \
+      u = (LAPACKE_TYPE*)m_matrixU.data();                                                                          \
+    } else {                                                                                                        \
+      ldu = 1;                                                                                                      \
+      u = &dummy;                                                                                                   \
+    }                                                                                                               \
+    MatrixType localV;                                                                                              \
+    lapack_int vt_rows = (m_computeFullV)   ? internal::convert_index<lapack_int>(cols())                           \
+                         : (m_computeThinV) ? internal::convert_index<lapack_int>(diagSize())                       \
+                                            : 1;                                                                    \
+    if (computeV()) {                                                                                               \
+      localV.resize(vt_rows, cols());                                                                               \
+      ldvt = internal::convert_index<lapack_int>(localV.outerStride());                                             \
+      vt = (LAPACKE_TYPE*)localV.data();                                                                            \
+    } else {                                                                                                        \
+      ldvt = 1;                                                                                                     \
+      vt = &dummy;                                                                                                  \
+    }                                                                                                               \
+    Matrix<LAPACKE_RTYPE, Dynamic, Dynamic> superb;                                                                 \
+    superb.resize(diagSize(), 1);                                                                                   \
+    MatrixType m_temp;                                                                                              \
+    m_temp = matrix;                                                                                                \
+    lapack_int info = LAPACKE_##LAPACKE_PREFIX##gesvd(                                                              \
+        matrix_order, jobu, jobvt, internal::convert_index<lapack_int>(rows()),                                     \
+        internal::convert_index<lapack_int>(cols()), (LAPACKE_TYPE*)m_temp.data(), lda,                             \
+        (LAPACKE_RTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data());                                  \
+    /* Check the result of the LAPACK call */                                                                       \
+    if (info < 0 || !m_singularValues.allFinite()) {                                                                \
+      m_info = InvalidInput;                                                                                        \
+    } else if (info > 0) {                                                                                          \
+      m_info = NoConvergence;                                                                                       \
+    } else {                                                                                                        \
+      m_info = Success;                                                                                             \
+      if (computeV()) m_matrixV = localV.adjoint();                                                                 \
+    }                                                                                                               \
+    /* for(int i=0;i<diagSize();i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--;     \
+     * m_singularValues.coeffRef(i)=RealScalar(0);}*/                                                               \
+    m_isInitialized = true;                                                                                         \
+    return *this;                                                                                                   \
+  }
+
+#define EIGEN_LAPACK_SVD_OPTIONS(OPTIONS)                                                            \
+  EIGEN_LAPACKE_SVD(double, double, double, d, ColMajor, LAPACK_COL_MAJOR, OPTIONS)                  \
+  EIGEN_LAPACKE_SVD(float, float, float, s, ColMajor, LAPACK_COL_MAJOR, OPTIONS)                     \
+  EIGEN_LAPACKE_SVD(dcomplex, lapack_complex_double, double, z, ColMajor, LAPACK_COL_MAJOR, OPTIONS) \
+  EIGEN_LAPACKE_SVD(scomplex, lapack_complex_float, float, c, ColMajor, LAPACK_COL_MAJOR, OPTIONS)   \
+                                                                                                     \
+  EIGEN_LAPACKE_SVD(double, double, double, d, RowMajor, LAPACK_ROW_MAJOR, OPTIONS)                  \
+  EIGEN_LAPACKE_SVD(float, float, float, s, RowMajor, LAPACK_ROW_MAJOR, OPTIONS)                     \
+  EIGEN_LAPACKE_SVD(dcomplex, lapack_complex_double, double, z, RowMajor, LAPACK_ROW_MAJOR, OPTIONS) \
+  EIGEN_LAPACKE_SVD(scomplex, lapack_complex_float, float, c, RowMajor, LAPACK_ROW_MAJOR, OPTIONS)
+
+EIGEN_LAPACK_SVD_OPTIONS(0)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeThinU)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeThinV)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeFullU)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeFullV)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeThinU | ComputeThinV)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeFullU | ComputeFullV)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeThinU | ComputeFullV)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeFullU | ComputeThinV)
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_JACOBISVD_LAPACKE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/SVDBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/SVDBase.h
new file mode 100644
index 0000000..c34bf0d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/SVDBase.h
@@ -0,0 +1,436 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
+// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
+// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
+// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SVDBASE_H
+#define EIGEN_SVDBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+enum OptionsMasks {
+  QRPreconditionerBits = NoQRPreconditioner | HouseholderQRPreconditioner | ColPivHouseholderQRPreconditioner |
+                         FullPivHouseholderQRPreconditioner,
+  ComputationOptionsBits = ComputeThinU | ComputeFullU | ComputeThinV | ComputeFullV
+};
+
+constexpr int get_qr_preconditioner(int options) { return options & QRPreconditionerBits; }
+
+constexpr int get_computation_options(int options) { return options & ComputationOptionsBits; }
+
+constexpr bool should_svd_compute_thin_u(int options) { return (options & ComputeThinU) != 0; }
+constexpr bool should_svd_compute_full_u(int options) { return (options & ComputeFullU) != 0; }
+constexpr bool should_svd_compute_thin_v(int options) { return (options & ComputeThinV) != 0; }
+constexpr bool should_svd_compute_full_v(int options) { return (options & ComputeFullV) != 0; }
+
+template <typename MatrixType, int Options>
+void check_svd_options_assertions(unsigned int computationOptions, Index rows, Index cols) {
+  EIGEN_STATIC_ASSERT((Options & ComputationOptionsBits) == 0,
+                      "SVDBase: Cannot request U or V using both static and runtime options, even if they match. "
+                      "Requesting unitaries at runtime is DEPRECATED: "
+                      "Prefer requesting unitaries statically, using the Options template parameter.");
+  eigen_assert(
+      !(should_svd_compute_thin_u(computationOptions) && cols < rows && MatrixType::RowsAtCompileTime != Dynamic) &&
+      !(should_svd_compute_thin_v(computationOptions) && rows < cols && MatrixType::ColsAtCompileTime != Dynamic) &&
+      "SVDBase: If thin U is requested at runtime, your matrix must have more rows than columns or a dynamic number of "
+      "rows."
+      "Similarly, if thin V is requested at runtime, you matrix must have more columns than rows or a dynamic number "
+      "of columns.");
+  (void)computationOptions;
+  (void)rows;
+  (void)cols;
+}
+
+template <typename Derived>
+struct traits<SVDBase<Derived> > : traits<Derived> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
+template <typename MatrixType, int Options_>
+struct svd_traits : traits<MatrixType> {
+  static constexpr int Options = Options_;
+  static constexpr bool ShouldComputeFullU = internal::should_svd_compute_full_u(Options);
+  static constexpr bool ShouldComputeThinU = internal::should_svd_compute_thin_u(Options);
+  static constexpr bool ShouldComputeFullV = internal::should_svd_compute_full_v(Options);
+  static constexpr bool ShouldComputeThinV = internal::should_svd_compute_thin_v(Options);
+  enum {
+    DiagSizeAtCompileTime =
+        internal::min_size_prefer_dynamic(MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime),
+    MaxDiagSizeAtCompileTime =
+        internal::min_size_prefer_dynamic(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime),
+    MatrixUColsAtCompileTime = ShouldComputeThinU ? DiagSizeAtCompileTime : MatrixType::RowsAtCompileTime,
+    MatrixVColsAtCompileTime = ShouldComputeThinV ? DiagSizeAtCompileTime : MatrixType::ColsAtCompileTime,
+    MatrixUMaxColsAtCompileTime = ShouldComputeThinU ? MaxDiagSizeAtCompileTime : MatrixType::MaxRowsAtCompileTime,
+    MatrixVMaxColsAtCompileTime = ShouldComputeThinV ? MaxDiagSizeAtCompileTime : MatrixType::MaxColsAtCompileTime
+  };
+};
+}  // namespace internal
+
+/** \ingroup SVD_Module
+ *
+ *
+ * \class SVDBase
+ *
+ * \brief Base class of SVD algorithms
+ *
+ * \tparam Derived the type of the actual SVD decomposition
+ *
+ * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
+ *   \f[ A = U S V^* \f]
+ * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero
+ * outside of its main diagonal; the diagonal entries of S are known as the \em singular \em values of \a A and the
+ * columns of \a U and \a V are known as the left and right \em singular \em vectors of \a A respectively.
+ *
+ * Singular values are always sorted in decreasing order.
+ *
+ *
+ * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p
+ * matrix, letting \a m be the smaller value among \a n and \a p, there are only \a m singular vectors; the remaining
+ * columns of \a U and \a V do not correspond to actual singular vectors. Asking for \em thin \a U or \a V means asking
+ * for only their \a m first columns to be formed. So \a U is then a n-by-m matrix, and \a V is then a p-by-m matrix.
+ * Notice that thin \a U and \a V are all you need for (least squares) solving.
+ *
+ * The status of the computation can be retrieved using the \a info() method. Unless \a info() returns \a Success, the
+ * results should be not considered well defined.
+ *
+ * If the input matrix has inf or nan coefficients, the result of the computation is undefined, and \a info() will
+ * return \a InvalidInput, but the computation is guaranteed to terminate in finite (and reasonable) time. \sa class
+ * BDCSVD, class JacobiSVD
+ */
+template <typename Derived>
+class SVDBase : public SolverBase<SVDBase<Derived> > {
+ public:
+  template <typename Derived_>
+  friend struct internal::solve_assertion;
+
+  typedef typename internal::traits<Derived>::MatrixType MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+  typedef typename Eigen::internal::traits<SVDBase>::StorageIndex StorageIndex;
+
+  static constexpr bool ShouldComputeFullU = internal::traits<Derived>::ShouldComputeFullU;
+  static constexpr bool ShouldComputeThinU = internal::traits<Derived>::ShouldComputeThinU;
+  static constexpr bool ShouldComputeFullV = internal::traits<Derived>::ShouldComputeFullV;
+  static constexpr bool ShouldComputeThinV = internal::traits<Derived>::ShouldComputeThinV;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    DiagSizeAtCompileTime = internal::min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime),
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    MaxDiagSizeAtCompileTime = internal::min_size_prefer_fixed(MaxRowsAtCompileTime, MaxColsAtCompileTime),
+    MatrixOptions = internal::traits<MatrixType>::Options,
+    MatrixUColsAtCompileTime = internal::traits<Derived>::MatrixUColsAtCompileTime,
+    MatrixVColsAtCompileTime = internal::traits<Derived>::MatrixVColsAtCompileTime,
+    MatrixUMaxColsAtCompileTime = internal::traits<Derived>::MatrixUMaxColsAtCompileTime,
+    MatrixVMaxColsAtCompileTime = internal::traits<Derived>::MatrixVMaxColsAtCompileTime
+  };
+
+  EIGEN_STATIC_ASSERT(!(ShouldComputeFullU && ShouldComputeThinU), "SVDBase: Cannot request both full and thin U")
+  EIGEN_STATIC_ASSERT(!(ShouldComputeFullV && ShouldComputeThinV), "SVDBase: Cannot request both full and thin V")
+
+  typedef
+      typename internal::make_proper_matrix_type<Scalar, RowsAtCompileTime, MatrixUColsAtCompileTime, MatrixOptions,
+                                                 MaxRowsAtCompileTime, MatrixUMaxColsAtCompileTime>::type MatrixUType;
+  typedef
+      typename internal::make_proper_matrix_type<Scalar, ColsAtCompileTime, MatrixVColsAtCompileTime, MatrixOptions,
+                                                 MaxColsAtCompileTime, MatrixVMaxColsAtCompileTime>::type MatrixVType;
+
+  typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType;
+
+  Derived& derived() { return *static_cast<Derived*>(this); }
+  const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  /** \returns the \a U matrix.
+   *
+   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
+   * the U matrix is n-by-n if you asked for \link Eigen::ComputeFullU ComputeFullU \endlink, and is n-by-m if you asked
+   * for \link Eigen::ComputeThinU ComputeThinU \endlink.
+   *
+   * The \a m first columns of \a U are the left singular vectors of the matrix being decomposed.
+   *
+   * This method asserts that you asked for \a U to be computed.
+   */
+  const MatrixUType& matrixU() const {
+    _check_compute_assertions();
+    eigen_assert(computeU() && "This SVD decomposition didn't compute U. Did you ask for it?");
+    return m_matrixU;
+  }
+
+  /** \returns the \a V matrix.
+   *
+   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
+   * the V matrix is p-by-p if you asked for \link Eigen::ComputeFullV ComputeFullV \endlink, and is p-by-m if you asked
+   * for \link Eigen::ComputeThinV ComputeThinV \endlink.
+   *
+   * The \a m first columns of \a V are the right singular vectors of the matrix being decomposed.
+   *
+   * This method asserts that you asked for \a V to be computed.
+   */
+  const MatrixVType& matrixV() const {
+    _check_compute_assertions();
+    eigen_assert(computeV() && "This SVD decomposition didn't compute V. Did you ask for it?");
+    return m_matrixV;
+  }
+
+  /** \returns the vector of singular values.
+   *
+   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p, the
+   * returned vector has size \a m.  Singular values are always sorted in decreasing order.
+   */
+  const SingularValuesType& singularValues() const {
+    _check_compute_assertions();
+    return m_singularValues;
+  }
+
+  /** \returns the number of singular values that are not exactly 0 */
+  Index nonzeroSingularValues() const {
+    _check_compute_assertions();
+    return m_nonzeroSingularValues;
+  }
+
+  /** \returns the rank of the matrix of which \c *this is the SVD.
+   *
+   * \note This method has to determine which singular values should be considered nonzero.
+   *       For that, it uses the threshold value that you can control by calling
+   *       setThreshold(const RealScalar&).
+   */
+  inline Index rank() const {
+    using std::abs;
+    _check_compute_assertions();
+    if (m_singularValues.size() == 0) return 0;
+    RealScalar premultiplied_threshold =
+        numext::maxi<RealScalar>(m_singularValues.coeff(0) * threshold(), (std::numeric_limits<RealScalar>::min)());
+    Index i = m_nonzeroSingularValues - 1;
+    while (i >= 0 && m_singularValues.coeff(i) < premultiplied_threshold) --i;
+    return i + 1;
+  }
+
+  /** Allows to prescribe a threshold to be used by certain methods, such as rank() and solve(),
+   * which need to determine when singular values are to be considered nonzero.
+   * This is not used for the SVD decomposition itself.
+   *
+   * When it needs to get the threshold value, Eigen calls threshold().
+   * The default is \c NumTraits<Scalar>::epsilon()
+   *
+   * \param threshold The new value to use as the threshold.
+   *
+   * A singular value will be considered nonzero if its value is strictly greater than
+   *  \f$ \vert singular value \vert \leqslant threshold \times \vert max singular value \vert \f$.
+   *
+   * If you want to come back to the default behavior, call setThreshold(Default_t)
+   */
+  Derived& setThreshold(const RealScalar& threshold) {
+    m_usePrescribedThreshold = true;
+    m_prescribedThreshold = threshold;
+    return derived();
+  }
+
+  /** Allows to come back to the default behavior, letting Eigen use its default formula for
+   * determining the threshold.
+   *
+   * You should pass the special object Eigen::Default as parameter here.
+   * \code svd.setThreshold(Eigen::Default); \endcode
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  Derived& setThreshold(Default_t) {
+    m_usePrescribedThreshold = false;
+    return derived();
+  }
+
+  /** Returns the threshold that will be used by certain methods such as rank().
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  RealScalar threshold() const {
+    eigen_assert(m_isInitialized || m_usePrescribedThreshold);
+    // this temporary is needed to workaround a MSVC issue
+    Index diagSize = (std::max<Index>)(1, m_diagSize);
+    return m_usePrescribedThreshold ? m_prescribedThreshold : RealScalar(diagSize) * NumTraits<Scalar>::epsilon();
+  }
+
+  /** \returns true if \a U (full or thin) is asked for in this SVD decomposition */
+  inline bool computeU() const { return m_computeFullU || m_computeThinU; }
+  /** \returns true if \a V (full or thin) is asked for in this SVD decomposition */
+  inline bool computeV() const { return m_computeFullV || m_computeThinV; }
+
+  inline Index rows() const { return m_rows.value(); }
+  inline Index cols() const { return m_cols.value(); }
+  inline Index diagSize() const { return m_diagSize.value(); }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \returns a (least squares) solution of \f$ A x = b \f$ using the current SVD decomposition of A.
+   *
+   * \param b the right-hand-side of the equation to solve.
+   *
+   * \note Solving requires both U and V to be computed. Thin U and V are enough, there is no need for full U or V.
+   *
+   * \note SVD solving is implicitly least-squares. Thus, this method serves both purposes of exact solving and
+   * least-squares solving. In other words, the returned solution is guaranteed to minimize the Euclidean norm \f$ \Vert
+   * A x - b \Vert \f$.
+   */
+  template <typename Rhs>
+  inline Solve<Derived, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful.
+   */
+  EIGEN_DEVICE_FUNC ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "SVD is not initialized.");
+    return m_info;
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  void _check_compute_assertions() const { eigen_assert(m_isInitialized && "SVD is not initialized."); }
+
+  template <bool Transpose_, typename Rhs>
+  void _check_solve_assertion(const Rhs& b) const {
+    EIGEN_ONLY_USED_FOR_DEBUG(b);
+    _check_compute_assertions();
+    eigen_assert(computeU() && computeV() &&
+                 "SVDBase::solve(): Both unitaries U and V are required to be computed (thin unitaries suffice).");
+    eigen_assert((Transpose_ ? cols() : rows()) == b.rows() &&
+                 "SVDBase::solve(): invalid number of rows of the right hand side matrix b");
+  }
+
+  // return true if already allocated
+  bool allocate(Index rows, Index cols, unsigned int computationOptions);
+
+  MatrixUType m_matrixU;
+  MatrixVType m_matrixV;
+  SingularValuesType m_singularValues;
+  ComputationInfo m_info;
+  bool m_isInitialized, m_isAllocated, m_usePrescribedThreshold;
+  bool m_computeFullU, m_computeThinU;
+  bool m_computeFullV, m_computeThinV;
+  unsigned int m_computationOptions;
+  Index m_nonzeroSingularValues;
+  internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
+  internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
+  internal::variable_if_dynamic<Index, DiagSizeAtCompileTime> m_diagSize;
+  RealScalar m_prescribedThreshold;
+
+  /** \brief Default Constructor.
+   *
+   * Default constructor of SVDBase
+   */
+  SVDBase()
+      : m_matrixU(MatrixUType()),
+        m_matrixV(MatrixVType()),
+        m_singularValues(SingularValuesType()),
+        m_info(Success),
+        m_isInitialized(false),
+        m_isAllocated(false),
+        m_usePrescribedThreshold(false),
+        m_computeFullU(ShouldComputeFullU),
+        m_computeThinU(ShouldComputeThinU),
+        m_computeFullV(ShouldComputeFullV),
+        m_computeThinV(ShouldComputeThinV),
+        m_computationOptions(internal::traits<Derived>::Options),
+        m_nonzeroSingularValues(0),
+        m_rows(RowsAtCompileTime),
+        m_cols(ColsAtCompileTime),
+        m_diagSize(DiagSizeAtCompileTime),
+        m_prescribedThreshold(0) {}
+};
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename Derived>
+template <typename RhsType, typename DstType>
+void SVDBase<Derived>::_solve_impl(const RhsType& rhs, DstType& dst) const {
+  // A = U S V^*
+  // So A^{-1} = V S^{-1} U^*
+
+  Matrix<typename RhsType::Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime,
+         RhsType::MaxColsAtCompileTime>
+      tmp;
+  Index l_rank = rank();
+  tmp.noalias() = m_matrixU.leftCols(l_rank).adjoint() * rhs;
+  tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
+  dst.noalias() = m_matrixV.leftCols(l_rank) * tmp;
+}
+
+template <typename Derived>
+template <bool Conjugate, typename RhsType, typename DstType>
+void SVDBase<Derived>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
+  // A = U S V^*
+  // So  A^{-*} = U S^{-1} V^*
+  // And A^{-T} = U_conj S^{-1} V^T
+  Matrix<typename RhsType::Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime,
+         RhsType::MaxColsAtCompileTime>
+      tmp;
+  Index l_rank = rank();
+
+  tmp.noalias() = m_matrixV.leftCols(l_rank).transpose().template conjugateIf<Conjugate>() * rhs;
+  tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
+  dst = m_matrixU.template conjugateIf<!Conjugate>().leftCols(l_rank) * tmp;
+}
+#endif
+
+template <typename Derived>
+bool SVDBase<Derived>::allocate(Index rows, Index cols, unsigned int computationOptions) {
+  eigen_assert(rows >= 0 && cols >= 0);
+
+  if (m_isAllocated && rows == m_rows.value() && cols == m_cols.value() && computationOptions == m_computationOptions) {
+    return true;
+  }
+
+  m_rows.setValue(rows);
+  m_cols.setValue(cols);
+  m_info = Success;
+  m_isInitialized = false;
+  m_isAllocated = true;
+  m_computationOptions = computationOptions;
+  m_computeFullU = ShouldComputeFullU || internal::should_svd_compute_full_u(computationOptions);
+  m_computeThinU = ShouldComputeThinU || internal::should_svd_compute_thin_u(computationOptions);
+  m_computeFullV = ShouldComputeFullV || internal::should_svd_compute_full_v(computationOptions);
+  m_computeThinV = ShouldComputeThinV || internal::should_svd_compute_thin_v(computationOptions);
+
+  eigen_assert(!(m_computeFullU && m_computeThinU) && "SVDBase: you can't ask for both full and thin U");
+  eigen_assert(!(m_computeFullV && m_computeThinV) && "SVDBase: you can't ask for both full and thin V");
+
+  m_diagSize.setValue(numext::mini(m_rows.value(), m_cols.value()));
+  m_singularValues.resize(m_diagSize.value());
+  if (RowsAtCompileTime == Dynamic)
+    m_matrixU.resize(m_rows.value(), m_computeFullU ? m_rows.value() : m_computeThinU ? m_diagSize.value() : 0);
+  if (ColsAtCompileTime == Dynamic)
+    m_matrixV.resize(m_cols.value(), m_computeFullV ? m_cols.value() : m_computeThinV ? m_diagSize.value() : 0);
+
+  return false;
+}
+
+}  // namespace Eigen
+
+#endif  // EIGEN_SVDBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/UpperBidiagonalization.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/UpperBidiagonalization.h
new file mode 100644
index 0000000..2cf020e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SVD/UpperBidiagonalization.h
@@ -0,0 +1,366 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2013-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BIDIAGONALIZATION_H
+#define EIGEN_BIDIAGONALIZATION_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+// UpperBidiagonalization may be replaced by a Bidiagonalization class; not part of stable API.
+// Kept for now as it is one of the few tests exercising the BandMatrix class.
+
+template <typename MatrixType_>
+class UpperBidiagonalization {
+ public:
+  typedef MatrixType_ MatrixType;
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    ColsAtCompileTimeMinusOne = internal::decrement_size<ColsAtCompileTime>::ret
+  };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Eigen::Index Index;  ///< \deprecated since Eigen 3.3
+  typedef Matrix<Scalar, 1, ColsAtCompileTime> RowVectorType;
+  typedef Matrix<Scalar, RowsAtCompileTime, 1> ColVectorType;
+  typedef BandMatrix<RealScalar, ColsAtCompileTime, ColsAtCompileTime, 1, 0, RowMajor> BidiagonalType;
+  typedef Matrix<Scalar, ColsAtCompileTime, 1> DiagVectorType;
+  typedef Matrix<Scalar, ColsAtCompileTimeMinusOne, 1> SuperDiagVectorType;
+  typedef HouseholderSequence<
+      const MatrixType, const internal::remove_all_t<typename Diagonal<const MatrixType, 0>::ConjugateReturnType> >
+      HouseholderUSequenceType;
+  typedef HouseholderSequence<const internal::remove_all_t<typename MatrixType::ConjugateReturnType>,
+                              Diagonal<const MatrixType, 1>, OnTheRight>
+      HouseholderVSequenceType;
+
+  /**
+   * \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via Bidiagonalization::compute(const MatrixType&).
+   */
+  UpperBidiagonalization() : m_householder(), m_bidiagonal(0, 0), m_isInitialized(false) {}
+
+  explicit UpperBidiagonalization(const MatrixType& matrix)
+      : m_householder(matrix.rows(), matrix.cols()),
+        m_bidiagonal(matrix.cols(), matrix.cols()),
+        m_isInitialized(false) {
+    compute(matrix);
+  }
+
+  UpperBidiagonalization(Index rows, Index cols)
+      : m_householder(rows, cols), m_bidiagonal(cols, cols), m_isInitialized(false) {}
+
+  UpperBidiagonalization& compute(const MatrixType& matrix);
+  UpperBidiagonalization& computeUnblocked(const MatrixType& matrix);
+
+  const MatrixType& householder() const { return m_householder; }
+  const BidiagonalType& bidiagonal() const { return m_bidiagonal; }
+
+  const HouseholderUSequenceType householderU() const {
+    eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized.");
+    return HouseholderUSequenceType(m_householder, m_householder.diagonal().conjugate());
+  }
+
+  const HouseholderVSequenceType householderV()  // const here gives nasty errors and i'm lazy
+  {
+    eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized.");
+    return HouseholderVSequenceType(m_householder.conjugate(), m_householder.const_derived().template diagonal<1>())
+        .setLength(m_householder.cols() - 1)
+        .setShift(1);
+  }
+
+ protected:
+  MatrixType m_householder;
+  BidiagonalType m_bidiagonal;
+  bool m_isInitialized;
+};
+
+// Standard upper bidiagonalization without fancy optimizations
+// This version should be faster for small matrix size
+template <typename MatrixType>
+void upperbidiagonalization_inplace_unblocked(MatrixType& mat, typename MatrixType::RealScalar* diagonal,
+                                              typename MatrixType::RealScalar* upper_diagonal,
+                                              typename MatrixType::Scalar* tempData = 0) {
+  typedef typename MatrixType::Scalar Scalar;
+
+  Index rows = mat.rows();
+  Index cols = mat.cols();
+
+  typedef Matrix<Scalar, Dynamic, 1, ColMajor, MatrixType::MaxRowsAtCompileTime, 1> TempType;
+  TempType tempVector;
+  if (tempData == 0) {
+    tempVector.resize(rows);
+    tempData = tempVector.data();
+  }
+
+  for (Index k = 0; /* breaks at k==cols-1 below */; ++k) {
+    Index remainingRows = rows - k;
+    Index remainingCols = cols - k - 1;
+
+    // construct left householder transform in-place in A
+    mat.col(k).tail(remainingRows).makeHouseholderInPlace(mat.coeffRef(k, k), diagonal[k]);
+    // apply householder transform to remaining part of A on the left
+    mat.bottomRightCorner(remainingRows, remainingCols)
+        .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows - 1), mat.coeff(k, k), tempData);
+
+    if (k == cols - 1) break;
+
+    // construct right householder transform in-place in mat
+    mat.row(k).tail(remainingCols).makeHouseholderInPlace(mat.coeffRef(k, k + 1), upper_diagonal[k]);
+    // apply householder transform to remaining part of mat on the left
+    mat.bottomRightCorner(remainingRows - 1, remainingCols)
+        .applyHouseholderOnTheRight(mat.row(k).tail(remainingCols - 1).adjoint(), mat.coeff(k, k + 1), tempData);
+  }
+}
+
+/** \internal
+ * Helper routine for the block reduction to upper bidiagonal form.
+ *
+ * Let's partition the matrix A:
+ *
+ *      | A00 A01 |
+ *  A = |         |
+ *      | A10 A11 |
+ *
+ * This function reduces to bidiagonal form the left \c rows x \a blockSize vertical panel [A00/A10]
+ * and the \a blockSize x \c cols horizontal panel [A00 A01] of the matrix \a A. The bottom-right block A11
+ * is updated using matrix-matrix products:
+ *   A22 -= V * Y^T - X * U^T
+ * where V and U contains the left and right Householder vectors. U and V are stored in A10, and A01
+ * respectively, and the update matrices X and Y are computed during the reduction.
+ *
+ */
+template <typename MatrixType>
+void upperbidiagonalization_blocked_helper(
+    MatrixType& A, typename MatrixType::RealScalar* diagonal, typename MatrixType::RealScalar* upper_diagonal, Index bs,
+    Ref<Matrix<typename MatrixType::Scalar, Dynamic, Dynamic, traits<MatrixType>::Flags & RowMajorBit> > X,
+    Ref<Matrix<typename MatrixType::Scalar, Dynamic, Dynamic, traits<MatrixType>::Flags & RowMajorBit> > Y) {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename NumTraits<RealScalar>::Literal Literal;
+  static constexpr int StorageOrder = (traits<MatrixType>::Flags & RowMajorBit) ? RowMajor : ColMajor;
+  typedef InnerStride<StorageOrder == ColMajor ? 1 : Dynamic> ColInnerStride;
+  typedef InnerStride<StorageOrder == ColMajor ? Dynamic : 1> RowInnerStride;
+  typedef Ref<Matrix<Scalar, Dynamic, 1>, 0, ColInnerStride> SubColumnType;
+  typedef Ref<Matrix<Scalar, 1, Dynamic>, 0, RowInnerStride> SubRowType;
+  typedef Ref<Matrix<Scalar, Dynamic, Dynamic, StorageOrder> > SubMatType;
+
+  Index brows = A.rows();
+  Index bcols = A.cols();
+
+  Scalar tau_u, tau_u_prev(0), tau_v;
+
+  for (Index k = 0; k < bs; ++k) {
+    Index remainingRows = brows - k;
+    Index remainingCols = bcols - k - 1;
+
+    SubMatType X_k1(X.block(k, 0, remainingRows, k));
+    SubMatType V_k1(A.block(k, 0, remainingRows, k));
+
+    // 1 - update the k-th column of A
+    SubColumnType v_k = A.col(k).tail(remainingRows);
+    v_k -= V_k1 * Y.row(k).head(k).adjoint();
+    if (k) v_k.noalias() -= X_k1 * A.col(k).head(k);
+
+    // 2 - construct left Householder transform in-place
+    v_k.makeHouseholderInPlace(tau_v, diagonal[k]);
+
+    if (k + 1 < bcols) {
+      SubMatType Y_k(Y.block(k + 1, 0, remainingCols, k + 1));
+      SubMatType U_k1(A.block(0, k + 1, k, remainingCols));
+
+      // this eases the application of Householder transforAions
+      // A(k,k) will store tau_v later
+      A(k, k) = Scalar(1);
+
+      // 3 - Compute y_k^T = tau_v * ( A^T*v_k - Y_k-1*V_k-1^T*v_k - U_k-1*X_k-1^T*v_k )
+      {
+        SubColumnType y_k(Y.col(k).tail(remainingCols));
+
+        // let's use the beginning of column k of Y as a temporary vector
+        SubColumnType tmp(Y.col(k).head(k));
+        y_k.noalias() = A.block(k, k + 1, remainingRows, remainingCols).adjoint() * v_k;  // bottleneck
+        tmp.noalias() = V_k1.adjoint() * v_k;
+        y_k.noalias() -= Y_k.leftCols(k) * tmp;
+        tmp.noalias() = X_k1.adjoint() * v_k;
+        y_k.noalias() -= U_k1.adjoint() * tmp;
+        y_k *= numext::conj(tau_v);
+      }
+
+      // 4 - update k-th row of A (it will become u_k)
+      SubRowType u_k(A.row(k).tail(remainingCols));
+      u_k = u_k.conjugate();
+      {
+        u_k.noalias() -= Y_k * A.row(k).head(k + 1).adjoint();
+        if (k) u_k -= U_k1.adjoint() * X.row(k).head(k).adjoint();
+      }
+
+      // 5 - construct right Householder transform in-place
+      u_k.makeHouseholderInPlace(tau_u, upper_diagonal[k]);
+
+      // this eases the application of Householder transformations
+      // A(k,k+1) will store tau_u later
+      A(k, k + 1) = Scalar(1);
+
+      // 6 - Compute x_k = tau_u * ( A*u_k - X_k-1*U_k-1^T*u_k - V_k*Y_k^T*u_k )
+      {
+        SubColumnType x_k(X.col(k).tail(remainingRows - 1));
+
+        // let's use the beginning of column k of X as a temporary vectors
+        // note that tmp0 and tmp1 overlaps
+        SubColumnType tmp0(X.col(k).head(k)), tmp1(X.col(k).head(k + 1));
+
+        x_k.noalias() = A.block(k + 1, k + 1, remainingRows - 1, remainingCols) * u_k.transpose();  // bottleneck
+        tmp0.noalias() = U_k1 * u_k.transpose();
+        x_k.noalias() -= X_k1.bottomRows(remainingRows - 1) * tmp0;
+        tmp1.noalias() = Y_k.adjoint() * u_k.transpose();
+        x_k.noalias() -= A.block(k + 1, 0, remainingRows - 1, k + 1) * tmp1;
+        x_k *= numext::conj(tau_u);
+        tau_u = numext::conj(tau_u);
+        u_k = u_k.conjugate();
+      }
+
+      if (k > 0) A.coeffRef(k - 1, k) = tau_u_prev;
+      tau_u_prev = tau_u;
+    } else
+      A.coeffRef(k - 1, k) = tau_u_prev;
+
+    A.coeffRef(k, k) = tau_v;
+  }
+
+  if (bs < bcols) A.coeffRef(bs - 1, bs) = tau_u_prev;
+
+  // update A22
+  if (bcols > bs && brows > bs) {
+    SubMatType A11(A.bottomRightCorner(brows - bs, bcols - bs));
+    SubMatType A10(A.block(bs, 0, brows - bs, bs));
+    SubMatType A01(A.block(0, bs, bs, bcols - bs));
+    Scalar tmp = A01(bs - 1, 0);
+    A01(bs - 1, 0) = Literal(1);
+    A11.noalias() -= A10 * Y.topLeftCorner(bcols, bs).bottomRows(bcols - bs).adjoint();
+    A11.noalias() -= X.topLeftCorner(brows, bs).bottomRows(brows - bs) * A01;
+    A01(bs - 1, 0) = tmp;
+  }
+}
+
+/** \internal
+ *
+ * Implementation of a block-bidiagonal reduction.
+ * It is based on the following paper:
+ *   The Design of a Parallel Dense Linear Algebra Software Library: Reduction to Hessenberg, Tridiagonal, and
+ * Bidiagonal Form. by Jaeyoung Choi, Jack J. Dongarra, David W. Walker. (1995) section 3.3
+ */
+template <typename MatrixType, typename BidiagType>
+void upperbidiagonalization_inplace_blocked(MatrixType& A, BidiagType& bidiagonal, Index maxBlockSize = 32,
+                                            typename MatrixType::Scalar* /*tempData*/ = 0) {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef Block<MatrixType, Dynamic, Dynamic> BlockType;
+
+  Index rows = A.rows();
+  Index cols = A.cols();
+  Index size = (std::min)(rows, cols);
+
+  // X and Y are work space
+  static constexpr int StorageOrder = (traits<MatrixType>::Flags & RowMajorBit) ? RowMajor : ColMajor;
+  Matrix<Scalar, MatrixType::RowsAtCompileTime, Dynamic, StorageOrder, MatrixType::MaxRowsAtCompileTime> X(
+      rows, maxBlockSize);
+  Matrix<Scalar, MatrixType::ColsAtCompileTime, Dynamic, StorageOrder, MatrixType::MaxColsAtCompileTime> Y(
+      cols, maxBlockSize);
+  Index blockSize = (std::min)(maxBlockSize, size);
+
+  Index k = 0;
+  for (k = 0; k < size; k += blockSize) {
+    Index bs = (std::min)(size - k, blockSize);  // actual size of the block
+    Index brows = rows - k;                      // rows of the block
+    Index bcols = cols - k;                      // columns of the block
+
+    // partition the matrix A:
+    //
+    //      | A00 A01 A02 |
+    //      |             |
+    // A  = | A10 A11 A12 |
+    //      |             |
+    //      | A20 A21 A22 |
+    //
+    // where A11 is a bs x bs diagonal block,
+    // and let:
+    //      | A11 A12 |
+    //  B = |         |
+    //      | A21 A22 |
+
+    BlockType B = A.block(k, k, brows, bcols);
+
+    // This stage performs the bidiagonalization of A11, A21, A12, and updating of A22.
+    // Finally, the algorithm continue on the updated A22.
+    //
+    // However, if B is too small, or A22 empty, then let's use an unblocked strategy
+
+    auto upper_diagonal = bidiagonal.template diagonal<1>();
+    typename MatrixType::RealScalar* upper_diagonal_ptr =
+        upper_diagonal.size() > 0 ? &upper_diagonal.coeffRef(k) : nullptr;
+
+    if (k + bs == cols || bcols < 48)  // somewhat arbitrary threshold
+    {
+      upperbidiagonalization_inplace_unblocked(B, &(bidiagonal.template diagonal<0>().coeffRef(k)), upper_diagonal_ptr,
+                                               X.data());
+      break;  // We're done
+    } else {
+      upperbidiagonalization_blocked_helper<BlockType>(B, &(bidiagonal.template diagonal<0>().coeffRef(k)),
+                                                       upper_diagonal_ptr, bs, X.topLeftCorner(brows, bs),
+                                                       Y.topLeftCorner(bcols, bs));
+    }
+  }
+}
+
+template <typename MatrixType_>
+UpperBidiagonalization<MatrixType_>& UpperBidiagonalization<MatrixType_>::computeUnblocked(const MatrixType_& matrix) {
+  Index rows = matrix.rows();
+  Index cols = matrix.cols();
+  EIGEN_ONLY_USED_FOR_DEBUG(cols);
+
+  eigen_assert(rows >= cols && "UpperBidiagonalization is only for matrices satisfying rows>=cols.");
+
+  m_householder = matrix;
+
+  ColVectorType temp(rows);
+
+  upperbidiagonalization_inplace_unblocked(m_householder, &(m_bidiagonal.template diagonal<0>().coeffRef(0)),
+                                           &(m_bidiagonal.template diagonal<1>().coeffRef(0)), temp.data());
+
+  m_isInitialized = true;
+  return *this;
+}
+
+template <typename MatrixType_>
+UpperBidiagonalization<MatrixType_>& UpperBidiagonalization<MatrixType_>::compute(const MatrixType_& matrix) {
+  Index rows = matrix.rows();
+  Index cols = matrix.cols();
+  EIGEN_ONLY_USED_FOR_DEBUG(rows);
+  EIGEN_ONLY_USED_FOR_DEBUG(cols);
+
+  eigen_assert(rows >= cols && "UpperBidiagonalization is only for matrices satisfying rows>=cols.");
+
+  m_householder = matrix;
+  upperbidiagonalization_inplace_blocked(m_householder, m_bidiagonal);
+
+  m_isInitialized = true;
+  return *this;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BIDIAGONALIZATION_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCholesky/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCholesky/InternalHeaderCheck.h
new file mode 100644
index 0000000..f8d8762
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCholesky/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
+#error "Please include Eigen/SparseCholesky instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCholesky/SimplicialCholesky.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCholesky/SimplicialCholesky.h
new file mode 100644
index 0000000..997bc22
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCholesky/SimplicialCholesky.h
@@ -0,0 +1,868 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SIMPLICIAL_CHOLESKY_H
+#define EIGEN_SIMPLICIAL_CHOLESKY_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum SimplicialCholeskyMode { SimplicialCholeskyLLT, SimplicialCholeskyLDLT };
+
+namespace internal {
+template <typename CholMatrixType, typename InputMatrixType>
+struct simplicial_cholesky_grab_input {
+  typedef CholMatrixType const* ConstCholMatrixPtr;
+  static void run(const InputMatrixType& input, ConstCholMatrixPtr& pmat, CholMatrixType& tmp) {
+    tmp = input;
+    pmat = &tmp;
+  }
+};
+
+template <typename MatrixType>
+struct simplicial_cholesky_grab_input<MatrixType, MatrixType> {
+  typedef MatrixType const* ConstMatrixPtr;
+  static void run(const MatrixType& input, ConstMatrixPtr& pmat, MatrixType& /*tmp*/) { pmat = &input; }
+};
+}  // end namespace internal
+
+/** \ingroup SparseCholesky_Module
+ * \brief A base class for direct sparse Cholesky factorizations
+ *
+ * This is a base class for LL^T and LDL^T Cholesky factorizations of sparse matrices that are
+ * selfadjoint and positive definite. These factorizations allow for solving A.X = B where
+ * X and B can be either dense or sparse.
+ *
+ * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
+ * such that the factorized matrix is P A P^-1.
+ *
+ * \tparam Derived the type of the derived class, that is the actual factorization type.
+ *
+ */
+template <typename Derived>
+class SimplicialCholeskyBase : public SparseSolverBase<Derived> {
+  typedef SparseSolverBase<Derived> Base;
+  using Base::m_isInitialized;
+
+ public:
+  typedef typename internal::traits<Derived>::MatrixType MatrixType;
+  typedef typename internal::traits<Derived>::OrderingType OrderingType;
+  enum { UpLo = internal::traits<Derived>::UpLo };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename internal::traits<Derived>::DiagonalScalar DiagonalScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> CholMatrixType;
+  typedef CholMatrixType const* ConstCholMatrixPtr;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef Matrix<StorageIndex, Dynamic, 1> VectorI;
+
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+ public:
+  using Base::derived;
+
+  /** Default constructor */
+  SimplicialCholeskyBase()
+      : m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false), m_shiftOffset(0), m_shiftScale(1) {}
+
+  explicit SimplicialCholeskyBase(const MatrixType& matrix)
+      : m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false), m_shiftOffset(0), m_shiftScale(1) {
+    derived().compute(matrix);
+  }
+
+  ~SimplicialCholeskyBase() {}
+
+  Derived& derived() { return *static_cast<Derived*>(this); }
+  const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  inline Index cols() const { return m_matrix.cols(); }
+  inline Index rows() const { return m_matrix.rows(); }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix.appears to be negative.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  /** \returns the permutation P
+   * \sa permutationPinv() */
+  const PermutationMatrix<Dynamic, Dynamic, StorageIndex>& permutationP() const { return m_P; }
+
+  /** \returns the inverse P^-1 of the permutation P
+   * \sa permutationP() */
+  const PermutationMatrix<Dynamic, Dynamic, StorageIndex>& permutationPinv() const { return m_Pinv; }
+
+  /** Sets the shift parameters that will be used to adjust the diagonal coefficients during the numerical
+   * factorization.
+   *
+   * During the numerical factorization, the diagonal coefficients are transformed by the following linear model:\n
+   * \c d_ii = \a offset + \a scale * \c d_ii
+   *
+   * The default is the identity transformation with \a offset=0, and \a scale=1.
+   *
+   * \returns a reference to \c *this.
+   */
+  Derived& setShift(const DiagonalScalar& offset, const DiagonalScalar& scale = 1) {
+    m_shiftOffset = offset;
+    m_shiftScale = scale;
+    return derived();
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal */
+  template <typename Stream>
+  void dumpMemory(Stream& s) {
+    int total = 0;
+    s << "  L:        "
+      << ((total += (m_matrix.cols() + 1) * sizeof(int) + m_matrix.nonZeros() * (sizeof(int) + sizeof(Scalar))) >> 20)
+      << "Mb"
+      << "\n";
+    s << "  diag:     " << ((total += m_diag.size() * sizeof(Scalar)) >> 20) << "Mb"
+      << "\n";
+    s << "  tree:     " << ((total += m_parent.size() * sizeof(int)) >> 20) << "Mb"
+      << "\n";
+    s << "  nonzeros: " << ((total += m_workSpace.size() * sizeof(int)) >> 20) << "Mb"
+      << "\n";
+    s << "  perm:     " << ((total += m_P.size() * sizeof(int)) >> 20) << "Mb"
+      << "\n";
+    s << "  perm^-1:  " << ((total += m_Pinv.size() * sizeof(int)) >> 20) << "Mb"
+      << "\n";
+    s << "  TOTAL:    " << (total >> 20) << "Mb"
+      << "\n";
+  }
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const {
+    eigen_assert(m_factorizationIsOk &&
+                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
+                 "symbolic()/numeric()");
+    eigen_assert(m_matrix.rows() == b.rows());
+
+    if (m_info != Success) return;
+
+    if (m_P.size() > 0)
+      dest = m_P * b;
+    else
+      dest = b;
+
+    if (m_matrix.nonZeros() > 0)  // otherwise L==I
+      derived().matrixL().solveInPlace(dest);
+
+    if (m_diag.size() > 0) dest = m_diag.asDiagonal().inverse() * dest;
+
+    if (m_matrix.nonZeros() > 0)  // otherwise U==I
+      derived().matrixU().solveInPlace(dest);
+
+    if (m_P.size() > 0) dest = m_Pinv * dest;
+  }
+
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const SparseMatrixBase<Rhs>& b, SparseMatrixBase<Dest>& dest) const {
+    internal::solve_sparse_through_dense_panels(derived(), b, dest);
+  }
+
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+
+ protected:
+  /** Computes the sparse Cholesky decomposition of \a matrix */
+  template <bool DoLDLT, bool NonHermitian>
+  void compute(const MatrixType& matrix) {
+    eigen_assert(matrix.rows() == matrix.cols());
+    Index size = matrix.cols();
+    CholMatrixType tmp(size, size);
+    ConstCholMatrixPtr pmat;
+    ordering<NonHermitian>(matrix, pmat, tmp);
+    analyzePattern_preordered(*pmat, DoLDLT);
+    factorize_preordered<DoLDLT, NonHermitian>(*pmat);
+  }
+
+  template <bool DoLDLT, bool NonHermitian>
+  void factorize(const MatrixType& a) {
+    eigen_assert(a.rows() == a.cols());
+    Index size = a.cols();
+    CholMatrixType tmp(size, size);
+    ConstCholMatrixPtr pmat;
+
+    if (m_P.size() == 0 && (int(UpLo) & int(Upper)) == Upper) {
+      // If there is no ordering, try to directly use the input matrix without any copy
+      internal::simplicial_cholesky_grab_input<CholMatrixType, MatrixType>::run(a, pmat, tmp);
+    } else {
+      internal::permute_symm_to_symm<UpLo, Upper, NonHermitian>(a, tmp, m_P.indices().data());
+      pmat = &tmp;
+    }
+
+    factorize_preordered<DoLDLT, NonHermitian>(*pmat);
+  }
+
+  template <bool DoLDLT, bool NonHermitian>
+  void factorize_preordered(const CholMatrixType& a);
+
+  template <bool DoLDLT, bool NonHermitian>
+  void analyzePattern(const MatrixType& a) {
+    eigen_assert(a.rows() == a.cols());
+    Index size = a.cols();
+    CholMatrixType tmp(size, size);
+    ConstCholMatrixPtr pmat;
+    ordering<NonHermitian>(a, pmat, tmp);
+    analyzePattern_preordered(*pmat, DoLDLT);
+  }
+  void analyzePattern_preordered(const CholMatrixType& a, bool doLDLT);
+
+  template <bool NonHermitian>
+  void ordering(const MatrixType& a, ConstCholMatrixPtr& pmat, CholMatrixType& ap);
+
+  inline DiagonalScalar getDiag(Scalar x) { return internal::traits<Derived>::getDiag(x); }
+  inline Scalar getSymm(Scalar x) { return internal::traits<Derived>::getSymm(x); }
+
+  /** keeps off-diagonal entries; drops diagonal entries */
+  struct keep_diag {
+    inline bool operator()(const Index& row, const Index& col, const Scalar&) const { return row != col; }
+  };
+
+  mutable ComputationInfo m_info;
+  bool m_factorizationIsOk;
+  bool m_analysisIsOk;
+
+  CholMatrixType m_matrix;
+  VectorType m_diag;  // the diagonal coefficients (LDLT mode)
+  VectorI m_parent;   // elimination tree
+  VectorI m_workSpace;
+  PermutationMatrix<Dynamic, Dynamic, StorageIndex> m_P;     // the permutation
+  PermutationMatrix<Dynamic, Dynamic, StorageIndex> m_Pinv;  // the inverse permutation
+
+  DiagonalScalar m_shiftOffset;
+  DiagonalScalar m_shiftScale;
+};
+
+template <typename MatrixType_, int UpLo_ = Lower,
+          typename Ordering_ = AMDOrdering<typename MatrixType_::StorageIndex> >
+class SimplicialLLT;
+template <typename MatrixType_, int UpLo_ = Lower,
+          typename Ordering_ = AMDOrdering<typename MatrixType_::StorageIndex> >
+class SimplicialLDLT;
+template <typename MatrixType_, int UpLo_ = Lower,
+          typename Ordering_ = AMDOrdering<typename MatrixType_::StorageIndex> >
+class SimplicialNonHermitianLLT;
+template <typename MatrixType_, int UpLo_ = Lower,
+          typename Ordering_ = AMDOrdering<typename MatrixType_::StorageIndex> >
+class SimplicialNonHermitianLDLT;
+template <typename MatrixType_, int UpLo_ = Lower,
+          typename Ordering_ = AMDOrdering<typename MatrixType_::StorageIndex> >
+class SimplicialCholesky;
+
+namespace internal {
+
+template <typename MatrixType_, int UpLo_, typename Ordering_>
+struct traits<SimplicialLLT<MatrixType_, UpLo_, Ordering_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Ordering_ OrderingType;
+  enum { UpLo = UpLo_ };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar DiagonalScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> CholMatrixType;
+  typedef TriangularView<const CholMatrixType, Eigen::Lower> MatrixL;
+  typedef TriangularView<const typename CholMatrixType::AdjointReturnType, Eigen::Upper> MatrixU;
+  static inline MatrixL getL(const CholMatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const CholMatrixType& m) { return MatrixU(m.adjoint()); }
+  static inline DiagonalScalar getDiag(Scalar x) { return numext::real(x); }
+  static inline Scalar getSymm(Scalar x) { return numext::conj(x); }
+};
+
+template <typename MatrixType_, int UpLo_, typename Ordering_>
+struct traits<SimplicialLDLT<MatrixType_, UpLo_, Ordering_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Ordering_ OrderingType;
+  enum { UpLo = UpLo_ };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar DiagonalScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> CholMatrixType;
+  typedef TriangularView<const CholMatrixType, Eigen::UnitLower> MatrixL;
+  typedef TriangularView<const typename CholMatrixType::AdjointReturnType, Eigen::UnitUpper> MatrixU;
+  static inline MatrixL getL(const CholMatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const CholMatrixType& m) { return MatrixU(m.adjoint()); }
+  static inline DiagonalScalar getDiag(Scalar x) { return numext::real(x); }
+  static inline Scalar getSymm(Scalar x) { return numext::conj(x); }
+};
+
+template <typename MatrixType_, int UpLo_, typename Ordering_>
+struct traits<SimplicialNonHermitianLLT<MatrixType_, UpLo_, Ordering_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Ordering_ OrderingType;
+  enum { UpLo = UpLo_ };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Scalar DiagonalScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> CholMatrixType;
+  typedef TriangularView<const CholMatrixType, Eigen::Lower> MatrixL;
+  typedef TriangularView<const typename CholMatrixType::ConstTransposeReturnType, Eigen::Upper> MatrixU;
+  static inline MatrixL getL(const CholMatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const CholMatrixType& m) { return MatrixU(m.transpose()); }
+  static inline DiagonalScalar getDiag(Scalar x) { return x; }
+  static inline Scalar getSymm(Scalar x) { return x; }
+};
+
+template <typename MatrixType_, int UpLo_, typename Ordering_>
+struct traits<SimplicialNonHermitianLDLT<MatrixType_, UpLo_, Ordering_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Ordering_ OrderingType;
+  enum { UpLo = UpLo_ };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Scalar DiagonalScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> CholMatrixType;
+  typedef TriangularView<const CholMatrixType, Eigen::UnitLower> MatrixL;
+  typedef TriangularView<const typename CholMatrixType::ConstTransposeReturnType, Eigen::UnitUpper> MatrixU;
+  static inline MatrixL getL(const CholMatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const CholMatrixType& m) { return MatrixU(m.transpose()); }
+  static inline DiagonalScalar getDiag(Scalar x) { return x; }
+  static inline Scalar getSymm(Scalar x) { return x; }
+};
+
+template <typename MatrixType_, int UpLo_, typename Ordering_>
+struct traits<SimplicialCholesky<MatrixType_, UpLo_, Ordering_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Ordering_ OrderingType;
+  enum { UpLo = UpLo_ };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar DiagonalScalar;
+  static inline DiagonalScalar getDiag(Scalar x) { return numext::real(x); }
+  static inline Scalar getSymm(Scalar x) { return numext::conj(x); }
+};
+
+}  // namespace internal
+
+/** \ingroup SparseCholesky_Module
+ * \class SimplicialLLT
+ * \brief A direct sparse LLT Cholesky factorizations
+ *
+ * This class provides a LL^T Cholesky factorizations of sparse matrices that are
+ * selfadjoint and positive definite. The factorization allows for solving A.X = B where
+ * X and B can be either dense or sparse.
+ *
+ * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
+ * such that the factorized matrix is P A P^-1.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ * \tparam Ordering_ The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<>
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa class SimplicialLDLT, class AMDOrdering, class NaturalOrdering
+ */
+template <typename MatrixType_, int UpLo_, typename Ordering_>
+class SimplicialLLT : public SimplicialCholeskyBase<SimplicialLLT<MatrixType_, UpLo_, Ordering_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  enum { UpLo = UpLo_ };
+  typedef SimplicialCholeskyBase<SimplicialLLT> Base;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, Index> CholMatrixType;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef internal::traits<SimplicialLLT> Traits;
+  typedef typename Traits::MatrixL MatrixL;
+  typedef typename Traits::MatrixU MatrixU;
+
+ public:
+  /** Default constructor */
+  SimplicialLLT() : Base() {}
+  /** Constructs and performs the LLT factorization of \a matrix */
+  explicit SimplicialLLT(const MatrixType& matrix) : Base(matrix) {}
+
+  /** \returns an expression of the factor L */
+  inline const MatrixL matrixL() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
+    return Traits::getL(Base::m_matrix);
+  }
+
+  /** \returns an expression of the factor U (= L^*) */
+  inline const MatrixU matrixU() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
+    return Traits::getU(Base::m_matrix);
+  }
+
+  /** Computes the sparse Cholesky decomposition of \a matrix */
+  SimplicialLLT& compute(const MatrixType& matrix) {
+    Base::template compute<false, false>(matrix);
+    return *this;
+  }
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType& a) { Base::template analyzePattern<false, false>(a); }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
+   * performed.
+   *
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType& a) { Base::template factorize<false, false>(a); }
+
+  /** \returns the determinant of the underlying matrix from the current factorization */
+  Scalar determinant() const {
+    Scalar detL = Base::m_matrix.diagonal().prod();
+    return numext::abs2(detL);
+  }
+};
+
+/** \ingroup SparseCholesky_Module
+ * \class SimplicialLDLT
+ * \brief A direct sparse LDLT Cholesky factorizations without square root.
+ *
+ * This class provides a LDL^T Cholesky factorizations without square root of sparse matrices that are
+ * selfadjoint and positive definite. The factorization allows for solving A.X = B where
+ * X and B can be either dense or sparse.
+ *
+ * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
+ * such that the factorized matrix is P A P^-1.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ * \tparam Ordering_ The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<>
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa class SimplicialLLT, class AMDOrdering, class NaturalOrdering
+ */
+template <typename MatrixType_, int UpLo_, typename Ordering_>
+class SimplicialLDLT : public SimplicialCholeskyBase<SimplicialLDLT<MatrixType_, UpLo_, Ordering_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  enum { UpLo = UpLo_ };
+  typedef SimplicialCholeskyBase<SimplicialLDLT> Base;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> CholMatrixType;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef internal::traits<SimplicialLDLT> Traits;
+  typedef typename Traits::MatrixL MatrixL;
+  typedef typename Traits::MatrixU MatrixU;
+
+ public:
+  /** Default constructor */
+  SimplicialLDLT() : Base() {}
+
+  /** Constructs and performs the LLT factorization of \a matrix */
+  explicit SimplicialLDLT(const MatrixType& matrix) : Base(matrix) {}
+
+  /** \returns a vector expression of the diagonal D */
+  inline const VectorType vectorD() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+    return Base::m_diag;
+  }
+  /** \returns an expression of the factor L */
+  inline const MatrixL matrixL() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+    return Traits::getL(Base::m_matrix);
+  }
+
+  /** \returns an expression of the factor U (= L^*) */
+  inline const MatrixU matrixU() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+    return Traits::getU(Base::m_matrix);
+  }
+
+  /** Computes the sparse Cholesky decomposition of \a matrix */
+  SimplicialLDLT& compute(const MatrixType& matrix) {
+    Base::template compute<true, false>(matrix);
+    return *this;
+  }
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType& a) { Base::template analyzePattern<true, false>(a); }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
+   * performed.
+   *
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType& a) { Base::template factorize<true, false>(a); }
+
+  /** \returns the determinant of the underlying matrix from the current factorization */
+  Scalar determinant() const { return Base::m_diag.prod(); }
+};
+
+/** \ingroup SparseCholesky_Module
+ * \class SimplicialNonHermitianLLT
+ * \brief A direct sparse LLT Cholesky factorizations, for symmetric non-hermitian matrices.
+ *
+ * This class provides a LL^T Cholesky factorizations of sparse matrices that are
+ * symmetric but not hermitian. For real matrices, this is equivalent to the regular LLT factorization.
+ * The factorization allows for solving A.X = B where X and B can be either dense or sparse.
+ *
+ * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
+ * such that the factorized matrix is P A P^-1.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ * \tparam Ordering_ The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<>
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa class SimplicialNonHermitianLDLT, SimplicialLLT, class AMDOrdering, class NaturalOrdering
+ */
+template <typename MatrixType_, int UpLo_, typename Ordering_>
+class SimplicialNonHermitianLLT
+    : public SimplicialCholeskyBase<SimplicialNonHermitianLLT<MatrixType_, UpLo_, Ordering_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  enum { UpLo = UpLo_ };
+  typedef SimplicialCholeskyBase<SimplicialNonHermitianLLT> Base;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> CholMatrixType;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef internal::traits<SimplicialNonHermitianLLT> Traits;
+  typedef typename Traits::MatrixL MatrixL;
+  typedef typename Traits::MatrixU MatrixU;
+
+ public:
+  /** Default constructor */
+  SimplicialNonHermitianLLT() : Base() {}
+
+  /** Constructs and performs the LLT factorization of \a matrix */
+  explicit SimplicialNonHermitianLLT(const MatrixType& matrix) : Base(matrix) {}
+
+  /** \returns an expression of the factor L */
+  inline const MatrixL matrixL() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
+    return Traits::getL(Base::m_matrix);
+  }
+
+  /** \returns an expression of the factor U (= L^*) */
+  inline const MatrixU matrixU() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
+    return Traits::getU(Base::m_matrix);
+  }
+
+  /** Computes the sparse Cholesky decomposition of \a matrix */
+  SimplicialNonHermitianLLT& compute(const MatrixType& matrix) {
+    Base::template compute<false, true>(matrix);
+    return *this;
+  }
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType& a) { Base::template analyzePattern<false, true>(a); }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
+   * performed.
+   *
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType& a) { Base::template factorize<false, true>(a); }
+
+  /** \returns the determinant of the underlying matrix from the current factorization */
+  Scalar determinant() const {
+    Scalar detL = Base::m_matrix.diagonal().prod();
+    return detL * detL;
+  }
+};
+
+/** \ingroup SparseCholesky_Module
+ * \class SimplicialNonHermitianLDLT
+ * \brief A direct sparse LDLT Cholesky factorizations without square root, for symmetric non-hermitian matrices.
+ *
+ * This class provides a LDL^T Cholesky factorizations without square root of sparse matrices that are
+ * symmetric but not hermitian. For real matrices, this is equivalent to the regular LDLT factorization.
+ * The factorization allows for solving A.X = B where X and B can be either dense or sparse.
+ *
+ * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
+ * such that the factorized matrix is P A P^-1.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ * \tparam Ordering_ The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<>
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa class SimplicialNonHermitianLLT, SimplicialLDLT, class AMDOrdering, class NaturalOrdering
+ */
+template <typename MatrixType_, int UpLo_, typename Ordering_>
+class SimplicialNonHermitianLDLT
+    : public SimplicialCholeskyBase<SimplicialNonHermitianLDLT<MatrixType_, UpLo_, Ordering_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  enum { UpLo = UpLo_ };
+  typedef SimplicialCholeskyBase<SimplicialNonHermitianLDLT> Base;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> CholMatrixType;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef internal::traits<SimplicialNonHermitianLDLT> Traits;
+  typedef typename Traits::MatrixL MatrixL;
+  typedef typename Traits::MatrixU MatrixU;
+
+ public:
+  /** Default constructor */
+  SimplicialNonHermitianLDLT() : Base() {}
+
+  /** Constructs and performs the LLT factorization of \a matrix */
+  explicit SimplicialNonHermitianLDLT(const MatrixType& matrix) : Base(matrix) {}
+
+  /** \returns a vector expression of the diagonal D */
+  inline const VectorType vectorD() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+    return Base::m_diag;
+  }
+  /** \returns an expression of the factor L */
+  inline const MatrixL matrixL() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+    return Traits::getL(Base::m_matrix);
+  }
+
+  /** \returns an expression of the factor U (= L^*) */
+  inline const MatrixU matrixU() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+    return Traits::getU(Base::m_matrix);
+  }
+
+  /** Computes the sparse Cholesky decomposition of \a matrix */
+  SimplicialNonHermitianLDLT& compute(const MatrixType& matrix) {
+    Base::template compute<true, true>(matrix);
+    return *this;
+  }
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType& a) { Base::template analyzePattern<true, true>(a); }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
+   * performed.
+   *
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType& a) { Base::template factorize<true, true>(a); }
+
+  /** \returns the determinant of the underlying matrix from the current factorization */
+  Scalar determinant() const { return Base::m_diag.prod(); }
+};
+
+/** \deprecated use SimplicialLDLT or class SimplicialLLT
+ * \ingroup SparseCholesky_Module
+ * \class SimplicialCholesky
+ *
+ * \sa class SimplicialLDLT, class SimplicialLLT
+ */
+template <typename MatrixType_, int UpLo_, typename Ordering_>
+class SimplicialCholesky : public SimplicialCholeskyBase<SimplicialCholesky<MatrixType_, UpLo_, Ordering_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  enum { UpLo = UpLo_ };
+  typedef SimplicialCholeskyBase<SimplicialCholesky> Base;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> CholMatrixType;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef internal::traits<SimplicialLDLT<MatrixType, UpLo> > LDLTTraits;
+  typedef internal::traits<SimplicialLLT<MatrixType, UpLo> > LLTTraits;
+
+ public:
+  SimplicialCholesky() : Base(), m_LDLT(true) {}
+
+  explicit SimplicialCholesky(const MatrixType& matrix) : Base(), m_LDLT(true) { compute(matrix); }
+
+  SimplicialCholesky& setMode(SimplicialCholeskyMode mode) {
+    switch (mode) {
+      case SimplicialCholeskyLLT:
+        m_LDLT = false;
+        break;
+      case SimplicialCholeskyLDLT:
+        m_LDLT = true;
+        break;
+      default:
+        break;
+    }
+
+    return *this;
+  }
+
+  inline const VectorType vectorD() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial Cholesky not factorized");
+    return Base::m_diag;
+  }
+  inline const CholMatrixType rawMatrix() const {
+    eigen_assert(Base::m_factorizationIsOk && "Simplicial Cholesky not factorized");
+    return Base::m_matrix;
+  }
+
+  /** Computes the sparse Cholesky decomposition of \a matrix */
+  SimplicialCholesky& compute(const MatrixType& matrix) {
+    if (m_LDLT)
+      Base::template compute<true, false>(matrix);
+    else
+      Base::template compute<false, false>(matrix);
+    return *this;
+  }
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType& a) {
+    if (m_LDLT)
+      Base::template analyzePattern<true, false>(a);
+    else
+      Base::template analyzePattern<false, false>(a);
+  }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
+   * performed.
+   *
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType& a) {
+    if (m_LDLT)
+      Base::template factorize<true, false>(a);
+    else
+      Base::template factorize<false, false>(a);
+  }
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const {
+    eigen_assert(Base::m_factorizationIsOk &&
+                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
+                 "symbolic()/numeric()");
+    eigen_assert(Base::m_matrix.rows() == b.rows());
+
+    if (Base::m_info != Success) return;
+
+    if (Base::m_P.size() > 0)
+      dest = Base::m_P * b;
+    else
+      dest = b;
+
+    if (Base::m_matrix.nonZeros() > 0)  // otherwise L==I
+    {
+      if (m_LDLT)
+        LDLTTraits::getL(Base::m_matrix).solveInPlace(dest);
+      else
+        LLTTraits::getL(Base::m_matrix).solveInPlace(dest);
+    }
+
+    if (Base::m_diag.size() > 0) dest = Base::m_diag.real().asDiagonal().inverse() * dest;
+
+    if (Base::m_matrix.nonZeros() > 0)  // otherwise I==I
+    {
+      if (m_LDLT)
+        LDLTTraits::getU(Base::m_matrix).solveInPlace(dest);
+      else
+        LLTTraits::getU(Base::m_matrix).solveInPlace(dest);
+    }
+
+    if (Base::m_P.size() > 0) dest = Base::m_Pinv * dest;
+  }
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const SparseMatrixBase<Rhs>& b, SparseMatrixBase<Dest>& dest) const {
+    internal::solve_sparse_through_dense_panels(*this, b, dest);
+  }
+
+  Scalar determinant() const {
+    if (m_LDLT) {
+      return Base::m_diag.prod();
+    } else {
+      Scalar detL = Diagonal<const CholMatrixType>(Base::m_matrix).prod();
+      return numext::abs2(detL);
+    }
+  }
+
+ protected:
+  bool m_LDLT;
+};
+
+template <typename Derived>
+template <bool NonHermitian>
+void SimplicialCholeskyBase<Derived>::ordering(const MatrixType& a, ConstCholMatrixPtr& pmat, CholMatrixType& ap) {
+  eigen_assert(a.rows() == a.cols());
+  const Index size = a.rows();
+  pmat = &ap;
+  // Note that ordering methods compute the inverse permutation
+  if (!internal::is_same<OrderingType, NaturalOrdering<StorageIndex> >::value) {
+    {
+      CholMatrixType C;
+      internal::permute_symm_to_fullsymm<UpLo, NonHermitian>(a, C, NULL);
+
+      OrderingType ordering;
+      ordering(C, m_Pinv);
+    }
+
+    if (m_Pinv.size() > 0)
+      m_P = m_Pinv.inverse();
+    else
+      m_P.resize(0);
+
+    ap.resize(size, size);
+    internal::permute_symm_to_symm<UpLo, Upper, NonHermitian>(a, ap, m_P.indices().data());
+  } else {
+    m_Pinv.resize(0);
+    m_P.resize(0);
+    if (int(UpLo) == int(Lower) || MatrixType::IsRowMajor) {
+      // we have to transpose the lower part to to the upper one
+      ap.resize(size, size);
+      internal::permute_symm_to_symm<UpLo, Upper, NonHermitian>(a, ap, NULL);
+    } else
+      internal::simplicial_cholesky_grab_input<CholMatrixType, MatrixType>::run(a, pmat, ap);
+  }
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SIMPLICIAL_CHOLESKY_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
new file mode 100644
index 0000000..97f2142
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
@@ -0,0 +1,388 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+NOTE: these functions have been adapted from the LDL library:
+
+LDL Copyright (c) 2005 by Timothy A. Davis.  All Rights Reserved.
+
+The author of LDL, Timothy A. Davis., has executed a license with Google LLC
+to permit distribution of this code and derivative works as part of Eigen under
+the Mozilla Public License v. 2.0, as stated at the top of this file.
+ */
+
+#ifndef EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
+#define EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar, typename StorageIndex>
+struct simpl_chol_helper {
+  using CholMatrixType = SparseMatrix<Scalar, ColMajor, StorageIndex>;
+  using InnerIterator = typename CholMatrixType::InnerIterator;
+  using VectorI = Matrix<StorageIndex, Dynamic, 1>;
+  static constexpr StorageIndex kEmpty = -1;
+
+  // Implementation of a stack or last-in first-out structure with some debugging machinery.
+  struct Stack {
+    StorageIndex* m_data;
+    Index m_size;
+#ifndef EIGEN_NO_DEBUG
+    const Index m_maxSize;
+    Stack(StorageIndex* data, StorageIndex size, StorageIndex maxSize)
+        : m_data(data), m_size(size), m_maxSize(maxSize) {
+      eigen_assert(size >= 0);
+      eigen_assert(maxSize >= size);
+    }
+#else
+    Stack(StorageIndex* data, StorageIndex size, StorageIndex /*maxSize*/) : m_data(data), m_size(size) {}
+#endif
+    bool empty() const { return m_size == 0; }
+    Index size() const { return m_size; }
+    StorageIndex back() const {
+      eigen_assert(m_size > 0);
+      return m_data[m_size - 1];
+    }
+    void push(const StorageIndex& value) {
+#ifndef EIGEN_NO_DEBUG
+      eigen_assert(m_size < m_maxSize);
+#endif
+      m_data[m_size] = value;
+      m_size++;
+    }
+    void pop() {
+      eigen_assert(m_size > 0);
+      m_size--;
+    }
+  };
+
+  // Implementation of a disjoint-set or union-find structure with path compression.
+  struct DisjointSet {
+    StorageIndex* m_set;
+    DisjointSet(StorageIndex* set, StorageIndex size) : m_set(set) { std::iota(set, set + size, 0); }
+    // Find the set representative or root of `u`.
+    StorageIndex find(StorageIndex u) const {
+      eigen_assert(u != kEmpty);
+      while (m_set[u] != u) {
+        // manually unroll the loop by a factor of 2 to improve performance
+        u = m_set[m_set[u]];
+      }
+      return u;
+    }
+    // Perform full path compression such that each node from `u` to `v` points to `v`.
+    void compress(StorageIndex u, StorageIndex v) {
+      eigen_assert(u != kEmpty);
+      eigen_assert(v != kEmpty);
+      while (m_set[u] != v) {
+        StorageIndex next = m_set[u];
+        m_set[u] = v;
+        u = next;
+      }
+    }
+  };
+
+  // Computes the higher adjacency pattern by transposing the input lower adjacency matrix.
+  // Only the index arrays are calculated, as the values are not needed for the symbolic factorization.
+  // The outer index array provides the size requirements of the inner index array.
+
+  // Computes the outer index array of the higher adjacency matrix.
+  static void calc_hadj_outer(const StorageIndex size, const CholMatrixType& ap, StorageIndex* outerIndex) {
+    for (StorageIndex j = 1; j < size; j++) {
+      for (InnerIterator it(ap, j); it; ++it) {
+        StorageIndex i = it.index();
+        if (i < j) outerIndex[i + 1]++;
+      }
+    }
+    std::partial_sum(outerIndex, outerIndex + size + 1, outerIndex);
+  }
+
+  // inner index array
+  static void calc_hadj_inner(const StorageIndex size, const CholMatrixType& ap, const StorageIndex* outerIndex,
+                              StorageIndex* innerIndex, StorageIndex* tmp) {
+    std::fill_n(tmp, size, 0);
+
+    for (StorageIndex j = 1; j < size; j++) {
+      for (InnerIterator it(ap, j); it; ++it) {
+        StorageIndex i = it.index();
+        if (i < j) {
+          StorageIndex b = outerIndex[i] + tmp[i];
+          innerIndex[b] = j;
+          tmp[i]++;
+        }
+      }
+    }
+  }
+
+  // Adapted from:
+  // Joseph W. Liu. (1986).
+  // A compact row storage scheme for Cholesky factors using elimination trees.
+  // ACM Trans. Math. Softw. 12, 2 (June 1986), 127-148. https://doi.org/10.1145/6497.6499
+
+  // Computes the elimination forest of the lower adjacency matrix, a compact representation of the sparse L factor.
+  // The L factor may contain multiple elimination trees if a column contains only its diagonal element.
+  // Each elimination tree is an n-ary tree in which each node points to its parent.
+  static void calc_etree(const StorageIndex size, const CholMatrixType& ap, StorageIndex* parent, StorageIndex* tmp) {
+    std::fill_n(parent, size, kEmpty);
+
+    DisjointSet ancestor(tmp, size);
+
+    for (StorageIndex j = 1; j < size; j++) {
+      for (InnerIterator it(ap, j); it; ++it) {
+        StorageIndex i = it.index();
+        if (i < j) {
+          StorageIndex r = ancestor.find(i);
+          if (r != j) parent[r] = j;
+          ancestor.compress(i, j);
+        }
+      }
+    }
+  }
+
+  // Computes the child pointers of the parent tree to facilitate a depth-first search traversal.
+  static void calc_lineage(const StorageIndex size, const StorageIndex* parent, StorageIndex* firstChild,
+                           StorageIndex* firstSibling) {
+    std::fill_n(firstChild, size, kEmpty);
+    std::fill_n(firstSibling, size, kEmpty);
+
+    for (StorageIndex j = 0; j < size; j++) {
+      StorageIndex p = parent[j];
+      if (p == kEmpty) continue;
+      StorageIndex c = firstChild[p];
+      if (c == kEmpty)
+        firstChild[p] = j;
+      else {
+        while (firstSibling[c] != kEmpty) c = firstSibling[c];
+        firstSibling[c] = j;
+      }
+    }
+  }
+
+  // Computes a post-ordered traversal of the elimination tree.
+  static void calc_post(const StorageIndex size, const StorageIndex* parent, StorageIndex* firstChild,
+                        const StorageIndex* firstSibling, StorageIndex* post, StorageIndex* dfs) {
+    Stack post_stack(post, 0, size);
+    for (StorageIndex j = 0; j < size; j++) {
+      if (parent[j] != kEmpty) continue;
+      // Begin at a root
+      Stack dfs_stack(dfs, 0, size);
+      dfs_stack.push(j);
+      while (!dfs_stack.empty()) {
+        StorageIndex i = dfs_stack.back();
+        StorageIndex c = firstChild[i];
+        if (c == kEmpty) {
+          post_stack.push(i);
+          dfs_stack.pop();
+        } else {
+          dfs_stack.push(c);
+          // Remove the path from `i` to `c` for future traversals.
+          firstChild[i] = firstSibling[c];
+        }
+      }
+    }
+    eigen_assert(post_stack.size() == size);
+    eigen_assert(std::all_of(firstChild, firstChild + size, [](StorageIndex a) { return a == kEmpty; }));
+  }
+
+  // Adapted from:
+  // Gilbert, J. R., Ng, E., & Peyton, B. W. (1994).
+  // An efficient algorithm to compute row and column counts for sparse Cholesky factorization.
+  // SIAM Journal on Matrix Analysis and Applications, 15(4), 1075-1091.
+
+  // Computes the non-zero pattern of the L factor.
+  static void calc_colcount(const StorageIndex size, const StorageIndex* hadjOuter, const StorageIndex* hadjInner,
+                            const StorageIndex* parent, StorageIndex* prevLeaf, StorageIndex* tmp,
+                            const StorageIndex* post, StorageIndex* nonZerosPerCol, bool doLDLT) {
+    // initialize nonZerosPerCol with 1 for leaves, 0 for non-leaves
+    std::fill_n(nonZerosPerCol, size, 1);
+    for (StorageIndex j = 0; j < size; j++) {
+      StorageIndex p = parent[j];
+      // p is not a leaf
+      if (p != kEmpty) nonZerosPerCol[p] = 0;
+    }
+
+    DisjointSet parentSet(tmp, size);
+    // prevLeaf is already initialized
+    eigen_assert(std::all_of(prevLeaf, prevLeaf + size, [](StorageIndex a) { return a == kEmpty; }));
+
+    for (StorageIndex j_ = 0; j_ < size; j_++) {
+      StorageIndex j = post[j_];
+      nonZerosPerCol[j] += hadjOuter[j + 1] - hadjOuter[j];
+      for (StorageIndex k = hadjOuter[j]; k < hadjOuter[j + 1]; k++) {
+        StorageIndex i = hadjInner[k];
+        eigen_assert(i > j);
+        StorageIndex prev = prevLeaf[i];
+        if (prev != kEmpty) {
+          StorageIndex q = parentSet.find(prev);
+          parentSet.compress(prev, q);
+          nonZerosPerCol[q]--;
+        }
+        prevLeaf[i] = j;
+      }
+      StorageIndex p = parent[j];
+      if (p != kEmpty) parentSet.compress(j, p);
+    }
+
+    for (StorageIndex j = 0; j < size; j++) {
+      StorageIndex p = parent[j];
+      if (p != kEmpty) nonZerosPerCol[p] += nonZerosPerCol[j] - 1;
+      if (doLDLT) nonZerosPerCol[j]--;
+    }
+  }
+
+  // Finalizes the non zero pattern of the L factor and allocates the memory for the factorization.
+  static void init_matrix(const StorageIndex size, const StorageIndex* nonZerosPerCol, CholMatrixType& L) {
+    eigen_assert(L.outerIndexPtr()[0] == 0);
+    std::partial_sum(nonZerosPerCol, nonZerosPerCol + size, L.outerIndexPtr() + 1);
+    L.resizeNonZeros(L.outerIndexPtr()[size]);
+  }
+
+  // Driver routine for the symbolic sparse Cholesky factorization.
+  static void run(const StorageIndex size, const CholMatrixType& ap, CholMatrixType& L, VectorI& parent,
+                  VectorI& workSpace, bool doLDLT) {
+    parent.resize(size);
+    workSpace.resize(4 * size);
+    L.resize(size, size);
+
+    StorageIndex* tmp1 = workSpace.data();
+    StorageIndex* tmp2 = workSpace.data() + size;
+    StorageIndex* tmp3 = workSpace.data() + 2 * size;
+    StorageIndex* tmp4 = workSpace.data() + 3 * size;
+
+    // Borrow L's outer index array for the higher adjacency pattern.
+    StorageIndex* hadj_outer = L.outerIndexPtr();
+    calc_hadj_outer(size, ap, hadj_outer);
+    // Request additional temporary storage for the inner indices of the higher adjacency pattern.
+    ei_declare_aligned_stack_constructed_variable(StorageIndex, hadj_inner, hadj_outer[size], nullptr);
+    calc_hadj_inner(size, ap, hadj_outer, hadj_inner, tmp1);
+
+    calc_etree(size, ap, parent.data(), tmp1);
+    calc_lineage(size, parent.data(), tmp1, tmp2);
+    calc_post(size, parent.data(), tmp1, tmp2, tmp3, tmp4);
+    calc_colcount(size, hadj_outer, hadj_inner, parent.data(), tmp1, tmp2, tmp3, tmp4, doLDLT);
+    init_matrix(size, tmp4, L);
+  }
+};
+
+// Symbol is ODR-used, so we need a definition.
+template <typename Scalar, typename StorageIndex>
+constexpr StorageIndex simpl_chol_helper<Scalar, StorageIndex>::kEmpty;
+
+}  // namespace internal
+
+template <typename Derived>
+void SimplicialCholeskyBase<Derived>::analyzePattern_preordered(const CholMatrixType& ap, bool doLDLT) {
+  using Helper = internal::simpl_chol_helper<Scalar, StorageIndex>;
+
+  eigen_assert(ap.innerSize() == ap.outerSize());
+  const StorageIndex size = internal::convert_index<StorageIndex>(ap.outerSize());
+
+  Helper::run(size, ap, m_matrix, m_parent, m_workSpace, doLDLT);
+
+  m_isInitialized = true;
+  m_info = Success;
+  m_analysisIsOk = true;
+  m_factorizationIsOk = false;
+}
+
+template <typename Derived>
+template <bool DoLDLT, bool NonHermitian>
+void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType& ap) {
+  using std::sqrt;
+  const StorageIndex size = StorageIndex(ap.rows());
+
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  eigen_assert(ap.rows() == ap.cols());
+  eigen_assert(m_parent.size() == size);
+  eigen_assert(m_workSpace.size() >= 3 * size);
+
+  const StorageIndex* Lp = m_matrix.outerIndexPtr();
+  StorageIndex* Li = m_matrix.innerIndexPtr();
+  Scalar* Lx = m_matrix.valuePtr();
+
+  ei_declare_aligned_stack_constructed_variable(Scalar, y, size, 0);
+  StorageIndex* nonZerosPerCol = m_workSpace.data();
+  StorageIndex* pattern = m_workSpace.data() + size;
+  StorageIndex* tags = m_workSpace.data() + 2 * size;
+
+  bool ok = true;
+  m_diag.resize(DoLDLT ? size : 0);
+
+  for (StorageIndex k = 0; k < size; ++k) {
+    // compute nonzero pattern of kth row of L, in topological order
+    y[k] = Scalar(0);         // Y(0:k) is now all zero
+    StorageIndex top = size;  // stack for pattern is empty
+    tags[k] = k;              // mark node k as visited
+    nonZerosPerCol[k] = 0;    // count of nonzeros in column k of L
+    for (typename CholMatrixType::InnerIterator it(ap, k); it; ++it) {
+      StorageIndex i = it.index();
+      if (i <= k) {
+        y[i] += getSymm(it.value()); /* scatter A(i,k) into Y (sum duplicates) */
+        Index len;
+        for (len = 0; tags[i] != k; i = m_parent[i]) {
+          pattern[len++] = i; /* L(k,i) is nonzero */
+          tags[i] = k;        /* mark i as visited */
+        }
+        while (len > 0) pattern[--top] = pattern[--len];
+      }
+    }
+
+    /* compute numerical values kth row of L (a sparse triangular solve) */
+
+    DiagonalScalar d =
+        getDiag(y[k]) * m_shiftScale + m_shiftOffset;  // get D(k,k), apply the shift function, and clear Y(k)
+    y[k] = Scalar(0);
+    for (; top < size; ++top) {
+      Index i = pattern[top]; /* pattern[top:n-1] is pattern of L(:,k) */
+      Scalar yi = y[i];       /* get and clear Y(i) */
+      y[i] = Scalar(0);
+
+      /* the nonzero entry L(k,i) */
+      Scalar l_ki;
+      if (DoLDLT)
+        l_ki = yi / getDiag(m_diag[i]);
+      else
+        yi = l_ki = yi / Lx[Lp[i]];
+
+      Index p2 = Lp[i] + nonZerosPerCol[i];
+      Index p;
+      for (p = Lp[i] + (DoLDLT ? 0 : 1); p < p2; ++p) y[Li[p]] -= getSymm(Lx[p]) * yi;
+      d -= getDiag(l_ki * getSymm(yi));
+      Li[p] = k; /* store L(k,i) in column form of L */
+      Lx[p] = l_ki;
+      ++nonZerosPerCol[i]; /* increment count of nonzeros in col i */
+    }
+    if (DoLDLT) {
+      m_diag[k] = d;
+      if (d == RealScalar(0)) {
+        ok = false; /* failure, D(k,k) is zero */
+        break;
+      }
+    } else {
+      Index p = Lp[k] + nonZerosPerCol[k]++;
+      Li[p] = k; /* store L(k,k) = sqrt (d) in column k */
+      if (NonHermitian ? d == RealScalar(0) : numext::real(d) <= RealScalar(0)) {
+        ok = false; /* failure, matrix is not positive definite */
+        break;
+      }
+      Lx[p] = sqrt(d);
+    }
+  }
+
+  m_info = ok ? Success : NumericalIssue;
+  m_factorizationIsOk = true;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/AmbiVector.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/AmbiVector.h
new file mode 100644
index 0000000..4af6f88
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/AmbiVector.h
@@ -0,0 +1,329 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AMBIVECTOR_H
+#define EIGEN_AMBIVECTOR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * Hybrid sparse/dense vector class designed for intensive read-write operations.
+ *
+ * See BasicSparseLLT and SparseProduct for usage examples.
+ */
+template <typename Scalar_, typename StorageIndex_>
+class AmbiVector {
+ public:
+  typedef Scalar_ Scalar;
+  typedef StorageIndex_ StorageIndex;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  explicit AmbiVector(Index size)
+      : m_buffer(0), m_zero(0), m_size(0), m_end(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1) {
+    resize(size);
+  }
+
+  void init(double estimatedDensity);
+  void init(int mode);
+
+  Index nonZeros() const;
+
+  /** Specifies a sub-vector to work on */
+  void setBounds(Index start, Index end) {
+    m_start = convert_index(start);
+    m_end = convert_index(end);
+  }
+
+  void setZero();
+
+  void restart();
+  Scalar& coeffRef(Index i);
+  Scalar& coeff(Index i);
+
+  class Iterator;
+
+  ~AmbiVector() { delete[] m_buffer; }
+
+  void resize(Index size) {
+    if (m_allocatedSize < size) reallocate(size);
+    m_size = convert_index(size);
+  }
+
+  StorageIndex size() const { return m_size; }
+
+ protected:
+  StorageIndex convert_index(Index idx) { return internal::convert_index<StorageIndex>(idx); }
+
+  void reallocate(Index size) {
+    // if the size of the matrix is not too large, let's allocate a bit more than needed such
+    // that we can handle dense vector even in sparse mode.
+    delete[] m_buffer;
+    if (size < 1000) {
+      Index allocSize = (size * sizeof(ListEl) + sizeof(Scalar) - 1) / sizeof(Scalar);
+      m_allocatedElements = convert_index((allocSize * sizeof(Scalar)) / sizeof(ListEl));
+      m_buffer = new Scalar[allocSize];
+    } else {
+      m_allocatedElements = convert_index((size * sizeof(Scalar)) / sizeof(ListEl));
+      m_buffer = new Scalar[size];
+    }
+    m_size = convert_index(size);
+    m_start = 0;
+    m_end = m_size;
+  }
+
+  void reallocateSparse() {
+    Index copyElements = m_allocatedElements;
+    m_allocatedElements = (std::min)(StorageIndex(m_allocatedElements * 1.5), m_size);
+    Index allocSize = m_allocatedElements * sizeof(ListEl);
+    allocSize = (allocSize + sizeof(Scalar) - 1) / sizeof(Scalar);
+    Scalar* newBuffer = new Scalar[allocSize];
+    std::memcpy(newBuffer, m_buffer, copyElements * sizeof(ListEl));
+    delete[] m_buffer;
+    m_buffer = newBuffer;
+  }
+
+ protected:
+  // element type of the linked list
+  struct ListEl {
+    StorageIndex next;
+    StorageIndex index;
+    Scalar value;
+  };
+
+  // used to store data in both mode
+  Scalar* m_buffer;
+  Scalar m_zero;
+  StorageIndex m_size;
+  StorageIndex m_start;
+  StorageIndex m_end;
+  StorageIndex m_allocatedSize;
+  StorageIndex m_allocatedElements;
+  StorageIndex m_mode;
+
+  // linked list mode
+  StorageIndex m_llStart;
+  StorageIndex m_llCurrent;
+  StorageIndex m_llSize;
+};
+
+/** \returns the number of non zeros in the current sub vector */
+template <typename Scalar_, typename StorageIndex_>
+Index AmbiVector<Scalar_, StorageIndex_>::nonZeros() const {
+  if (m_mode == IsSparse)
+    return m_llSize;
+  else
+    return m_end - m_start;
+}
+
+template <typename Scalar_, typename StorageIndex_>
+void AmbiVector<Scalar_, StorageIndex_>::init(double estimatedDensity) {
+  if (estimatedDensity > 0.1)
+    init(IsDense);
+  else
+    init(IsSparse);
+}
+
+template <typename Scalar_, typename StorageIndex_>
+void AmbiVector<Scalar_, StorageIndex_>::init(int mode) {
+  m_mode = mode;
+  // This is only necessary in sparse mode, but we set these unconditionally to avoid some maybe-uninitialized warnings
+  // if (m_mode==IsSparse)
+  {
+    m_llSize = 0;
+    m_llStart = -1;
+  }
+}
+
+/** Must be called whenever we might perform a write access
+ * with an index smaller than the previous one.
+ *
+ * Don't worry, this function is extremely cheap.
+ */
+template <typename Scalar_, typename StorageIndex_>
+void AmbiVector<Scalar_, StorageIndex_>::restart() {
+  m_llCurrent = m_llStart;
+}
+
+/** Set all coefficients of current subvector to zero */
+template <typename Scalar_, typename StorageIndex_>
+void AmbiVector<Scalar_, StorageIndex_>::setZero() {
+  if (m_mode == IsDense) {
+    for (Index i = m_start; i < m_end; ++i) m_buffer[i] = Scalar(0);
+  } else {
+    eigen_assert(m_mode == IsSparse);
+    m_llSize = 0;
+    m_llStart = -1;
+  }
+}
+
+template <typename Scalar_, typename StorageIndex_>
+Scalar_& AmbiVector<Scalar_, StorageIndex_>::coeffRef(Index i) {
+  if (m_mode == IsDense)
+    return m_buffer[i];
+  else {
+    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+    // TODO: factor out the following code to reduce code generation
+    eigen_assert(m_mode == IsSparse);
+    if (m_llSize == 0) {
+      // this is the first element
+      m_llStart = 0;
+      m_llCurrent = 0;
+      ++m_llSize;
+      llElements[0].value = Scalar(0);
+      llElements[0].index = convert_index(i);
+      llElements[0].next = -1;
+      return llElements[0].value;
+    } else if (i < llElements[m_llStart].index) {
+      // this is going to be the new first element of the list
+      ListEl& el = llElements[m_llSize];
+      el.value = Scalar(0);
+      el.index = convert_index(i);
+      el.next = m_llStart;
+      m_llStart = m_llSize;
+      ++m_llSize;
+      m_llCurrent = m_llStart;
+      return el.value;
+    } else {
+      StorageIndex nextel = llElements[m_llCurrent].next;
+      eigen_assert(i >= llElements[m_llCurrent].index &&
+                   "you must call restart() before inserting an element with lower or equal index");
+      while (nextel >= 0 && llElements[nextel].index <= i) {
+        m_llCurrent = nextel;
+        nextel = llElements[nextel].next;
+      }
+
+      if (llElements[m_llCurrent].index == i) {
+        // the coefficient already exists and we found it !
+        return llElements[m_llCurrent].value;
+      } else {
+        if (m_llSize >= m_allocatedElements) {
+          reallocateSparse();
+          llElements = reinterpret_cast<ListEl*>(m_buffer);
+        }
+        eigen_internal_assert(m_llSize < m_allocatedElements && "internal error: overflow in sparse mode");
+        // let's insert a new coefficient
+        ListEl& el = llElements[m_llSize];
+        el.value = Scalar(0);
+        el.index = convert_index(i);
+        el.next = llElements[m_llCurrent].next;
+        llElements[m_llCurrent].next = m_llSize;
+        ++m_llSize;
+        return el.value;
+      }
+    }
+  }
+}
+
+template <typename Scalar_, typename StorageIndex_>
+Scalar_& AmbiVector<Scalar_, StorageIndex_>::coeff(Index i) {
+  if (m_mode == IsDense)
+    return m_buffer[i];
+  else {
+    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+    eigen_assert(m_mode == IsSparse);
+    if ((m_llSize == 0) || (i < llElements[m_llStart].index)) {
+      return m_zero;
+    } else {
+      Index elid = m_llStart;
+      while (elid >= 0 && llElements[elid].index < i) elid = llElements[elid].next;
+
+      if (llElements[elid].index == i)
+        return llElements[m_llCurrent].value;
+      else
+        return m_zero;
+    }
+  }
+}
+
+/** Iterator over the nonzero coefficients */
+template <typename Scalar_, typename StorageIndex_>
+class AmbiVector<Scalar_, StorageIndex_>::Iterator {
+ public:
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  /** Default constructor
+   * \param vec the vector on which we iterate
+   * \param epsilon the minimal value used to prune zero coefficients.
+   * In practice, all coefficients having a magnitude smaller than \a epsilon
+   * are skipped.
+   */
+  explicit Iterator(const AmbiVector& vec, const RealScalar& epsilon = 0) : m_vector(vec) {
+    using std::abs;
+    m_epsilon = epsilon;
+    m_isDense = m_vector.m_mode == IsDense;
+    if (m_isDense) {
+      m_currentEl = 0;    // this is to avoid a compilation warning
+      m_cachedValue = 0;  // this is to avoid a compilation warning
+      m_cachedIndex = m_vector.m_start - 1;
+      ++(*this);
+    } else {
+      ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+      m_currentEl = m_vector.m_llStart;
+      while (m_currentEl >= 0 && abs(llElements[m_currentEl].value) <= m_epsilon)
+        m_currentEl = llElements[m_currentEl].next;
+      if (m_currentEl < 0) {
+        m_cachedValue = 0;  // this is to avoid a compilation warning
+        m_cachedIndex = -1;
+      } else {
+        m_cachedIndex = llElements[m_currentEl].index;
+        m_cachedValue = llElements[m_currentEl].value;
+      }
+    }
+  }
+
+  StorageIndex index() const { return m_cachedIndex; }
+  Scalar value() const { return m_cachedValue; }
+
+  operator bool() const { return m_cachedIndex >= 0; }
+
+  Iterator& operator++() {
+    using std::abs;
+    if (m_isDense) {
+      do {
+        ++m_cachedIndex;
+      } while (m_cachedIndex < m_vector.m_end && abs(m_vector.m_buffer[m_cachedIndex]) <= m_epsilon);
+      if (m_cachedIndex < m_vector.m_end)
+        m_cachedValue = m_vector.m_buffer[m_cachedIndex];
+      else
+        m_cachedIndex = -1;
+    } else {
+      ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+      do {
+        m_currentEl = llElements[m_currentEl].next;
+      } while (m_currentEl >= 0 && abs(llElements[m_currentEl].value) <= m_epsilon);
+      if (m_currentEl < 0) {
+        m_cachedIndex = -1;
+      } else {
+        m_cachedIndex = llElements[m_currentEl].index;
+        m_cachedValue = llElements[m_currentEl].value;
+      }
+    }
+    return *this;
+  }
+
+ protected:
+  const AmbiVector& m_vector;  // the target vector
+  StorageIndex m_currentEl;    // the current element in sparse/linked-list mode
+  RealScalar m_epsilon;        // epsilon used to prune zero coefficients
+  StorageIndex m_cachedIndex;  // current coordinate
+  Scalar m_cachedValue;        // current value
+  bool m_isDense;              // mode of the vector
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_AMBIVECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/CompressedStorage.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/CompressedStorage.h
new file mode 100644
index 0000000..8f8a696
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/CompressedStorage.h
@@ -0,0 +1,206 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPRESSED_STORAGE_H
+#define EIGEN_COMPRESSED_STORAGE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * Stores a sparse set of values as a list of values and a list of indices.
+ *
+ */
+template <typename Scalar_, typename StorageIndex_>
+class CompressedStorage {
+ public:
+  typedef Scalar_ Scalar;
+  typedef StorageIndex_ StorageIndex;
+
+ protected:
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+ public:
+  CompressedStorage() : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0) {}
+
+  explicit CompressedStorage(Index size) : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0) { resize(size); }
+
+  CompressedStorage(const CompressedStorage& other) : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0) {
+    *this = other;
+  }
+
+  CompressedStorage& operator=(const CompressedStorage& other) {
+    resize(other.size());
+    if (other.size() > 0) {
+      internal::smart_copy(other.m_values, other.m_values + m_size, m_values);
+      internal::smart_copy(other.m_indices, other.m_indices + m_size, m_indices);
+    }
+    return *this;
+  }
+
+  void swap(CompressedStorage& other) {
+    std::swap(m_values, other.m_values);
+    std::swap(m_indices, other.m_indices);
+    std::swap(m_size, other.m_size);
+    std::swap(m_allocatedSize, other.m_allocatedSize);
+  }
+
+  ~CompressedStorage() {
+    conditional_aligned_delete_auto<Scalar, true>(m_values, m_allocatedSize);
+    conditional_aligned_delete_auto<StorageIndex, true>(m_indices, m_allocatedSize);
+  }
+
+  void reserve(Index size) {
+    Index newAllocatedSize = m_size + size;
+    if (newAllocatedSize > m_allocatedSize) reallocate(newAllocatedSize);
+  }
+
+  void squeeze() {
+    if (m_allocatedSize > m_size) reallocate(m_size);
+  }
+
+  void resize(Index size, double reserveSizeFactor = 0) {
+    if (m_allocatedSize < size) {
+      // Avoid underflow on the std::min<Index> call by choosing the smaller index type.
+      using SmallerIndexType =
+          typename std::conditional<static_cast<size_t>((std::numeric_limits<Index>::max)()) <
+                                        static_cast<size_t>((std::numeric_limits<StorageIndex>::max)()),
+                                    Index, StorageIndex>::type;
+      Index realloc_size =
+          (std::min<Index>)(NumTraits<SmallerIndexType>::highest(), size + Index(reserveSizeFactor * double(size)));
+      if (realloc_size < size) internal::throw_std_bad_alloc();
+      reallocate(realloc_size);
+    }
+    m_size = size;
+  }
+
+  void append(const Scalar& v, Index i) {
+    Index id = m_size;
+    resize(m_size + 1, 1);
+    m_values[id] = v;
+    m_indices[id] = internal::convert_index<StorageIndex>(i);
+  }
+
+  inline Index size() const { return m_size; }
+  inline Index allocatedSize() const { return m_allocatedSize; }
+  inline void clear() { m_size = 0; }
+
+  const Scalar* valuePtr() const { return m_values; }
+  Scalar* valuePtr() { return m_values; }
+  const StorageIndex* indexPtr() const { return m_indices; }
+  StorageIndex* indexPtr() { return m_indices; }
+
+  inline Scalar& value(Index i) {
+    eigen_internal_assert(m_values != 0);
+    return m_values[i];
+  }
+  inline const Scalar& value(Index i) const {
+    eigen_internal_assert(m_values != 0);
+    return m_values[i];
+  }
+
+  inline StorageIndex& index(Index i) {
+    eigen_internal_assert(m_indices != 0);
+    return m_indices[i];
+  }
+  inline const StorageIndex& index(Index i) const {
+    eigen_internal_assert(m_indices != 0);
+    return m_indices[i];
+  }
+
+  /** \returns the largest \c k such that for all \c j in [0,k) index[\c j]\<\a key */
+  inline Index searchLowerIndex(Index key) const { return searchLowerIndex(0, m_size, key); }
+
+  /** \returns the largest \c k in [start,end) such that for all \c j in [start,k) index[\c j]\<\a key */
+  inline Index searchLowerIndex(Index start, Index end, Index key) const {
+    return static_cast<Index>(std::distance(m_indices, std::lower_bound(m_indices + start, m_indices + end, key)));
+  }
+
+  /** \returns the stored value at index \a key
+   * If the value does not exist, then the value \a defaultValue is returned without any insertion. */
+  inline Scalar at(Index key, const Scalar& defaultValue = Scalar(0)) const {
+    if (m_size == 0)
+      return defaultValue;
+    else if (key == m_indices[m_size - 1])
+      return m_values[m_size - 1];
+    // ^^  optimization: let's first check if it is the last coefficient
+    // (very common in high level algorithms)
+    const Index id = searchLowerIndex(0, m_size - 1, key);
+    return ((id < m_size) && (m_indices[id] == key)) ? m_values[id] : defaultValue;
+  }
+
+  /** Like at(), but the search is performed in the range [start,end) */
+  inline Scalar atInRange(Index start, Index end, Index key, const Scalar& defaultValue = Scalar(0)) const {
+    if (start >= end)
+      return defaultValue;
+    else if (end > start && key == m_indices[end - 1])
+      return m_values[end - 1];
+    // ^^  optimization: let's first check if it is the last coefficient
+    // (very common in high level algorithms)
+    const Index id = searchLowerIndex(start, end - 1, key);
+    return ((id < end) && (m_indices[id] == key)) ? m_values[id] : defaultValue;
+  }
+
+  /** \returns a reference to the value at index \a key
+   * If the value does not exist, then the value \a defaultValue is inserted
+   * such that the keys are sorted. */
+  inline Scalar& atWithInsertion(Index key, const Scalar& defaultValue = Scalar(0)) {
+    Index id = searchLowerIndex(0, m_size, key);
+    if (id >= m_size || m_indices[id] != key) {
+      if (m_allocatedSize < m_size + 1) {
+        Index newAllocatedSize = 2 * (m_size + 1);
+        m_values = conditional_aligned_realloc_new_auto<Scalar, true>(m_values, newAllocatedSize, m_allocatedSize);
+        m_indices =
+            conditional_aligned_realloc_new_auto<StorageIndex, true>(m_indices, newAllocatedSize, m_allocatedSize);
+        m_allocatedSize = newAllocatedSize;
+      }
+      if (m_size > id) {
+        internal::smart_memmove(m_values + id, m_values + m_size, m_values + id + 1);
+        internal::smart_memmove(m_indices + id, m_indices + m_size, m_indices + id + 1);
+      }
+      m_size++;
+      m_indices[id] = internal::convert_index<StorageIndex>(key);
+      m_values[id] = defaultValue;
+    }
+    return m_values[id];
+  }
+
+  inline void moveChunk(Index from, Index to, Index chunkSize) {
+    eigen_internal_assert(chunkSize >= 0 && to + chunkSize <= m_size);
+    internal::smart_memmove(m_values + from, m_values + from + chunkSize, m_values + to);
+    internal::smart_memmove(m_indices + from, m_indices + from + chunkSize, m_indices + to);
+  }
+
+ protected:
+  inline void reallocate(Index size) {
+#ifdef EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN
+    EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN
+#endif
+    eigen_internal_assert(size != m_allocatedSize);
+    m_values = conditional_aligned_realloc_new_auto<Scalar, true>(m_values, size, m_allocatedSize);
+    m_indices = conditional_aligned_realloc_new_auto<StorageIndex, true>(m_indices, size, m_allocatedSize);
+    m_allocatedSize = size;
+  }
+
+ protected:
+  Scalar* m_values;
+  StorageIndex* m_indices;
+  Index m_size;
+  Index m_allocatedSize;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPRESSED_STORAGE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
new file mode 100644
index 0000000..2beadab
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
@@ -0,0 +1,308 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
+#define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Lhs, typename Rhs, typename ResultType>
+static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res,
+                                                    bool sortedInsertion = false) {
+  typedef typename remove_all_t<Lhs>::Scalar LhsScalar;
+  typedef typename remove_all_t<Rhs>::Scalar RhsScalar;
+  typedef typename remove_all_t<ResultType>::Scalar ResScalar;
+
+  // make sure to call innerSize/outerSize since we fake the storage order.
+  Index rows = lhs.innerSize();
+  Index cols = rhs.outerSize();
+  eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+  ei_declare_aligned_stack_constructed_variable(bool, mask, rows, 0);
+  ei_declare_aligned_stack_constructed_variable(ResScalar, values, rows, 0);
+  ei_declare_aligned_stack_constructed_variable(Index, indices, rows, 0);
+
+  std::memset(mask, 0, sizeof(bool) * rows);
+
+  evaluator<Lhs> lhsEval(lhs);
+  evaluator<Rhs> rhsEval(rhs);
+
+  // estimate the number of non zero entries
+  // given a rhs column containing Y non zeros, we assume that the respective Y columns
+  // of the lhs differs in average of one non zeros, thus the number of non zeros for
+  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
+  // per column of the lhs.
+  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
+  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
+
+  res.setZero();
+  res.reserve(Index(estimated_nnz_prod));
+  // we compute each column of the result, one after the other
+  for (Index j = 0; j < cols; ++j) {
+    res.startVec(j);
+    Index nnz = 0;
+    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt) {
+      RhsScalar y = rhsIt.value();
+      Index k = rhsIt.index();
+      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt) {
+        Index i = lhsIt.index();
+        LhsScalar x = lhsIt.value();
+        if (!mask[i]) {
+          mask[i] = true;
+          values[i] = x * y;
+          indices[nnz] = i;
+          ++nnz;
+        } else
+          values[i] += x * y;
+      }
+    }
+    if (!sortedInsertion) {
+      // unordered insertion
+      for (Index k = 0; k < nnz; ++k) {
+        Index i = indices[k];
+        res.insertBackByOuterInnerUnordered(j, i) = values[i];
+        mask[i] = false;
+      }
+    } else {
+      // alternative ordered insertion code:
+      const Index t200 = rows / 11;  // 11 == (log2(200)*1.39)
+      const Index t = (rows * 100) / 139;
+
+      // FIXME: reserve space for the expected number of non-zeros.
+      // FIXME: implement faster sorting for very small nnz counts.
+      // if the result is sparse enough => use a quick sort
+      // otherwise => loop through the entire vector
+      // In order to avoid to perform an expensive log2 when the
+      // result is clearly very sparse we use a linear bound up to 200.
+      if ((nnz < 200 && nnz < t200) || nnz * numext::log2(int(nnz)) < t) {
+        if (nnz > 1) std::sort(indices, indices + nnz);
+        for (Index k = 0; k < nnz; ++k) {
+          Index i = indices[k];
+          res.insertBackByOuterInner(j, i) = values[i];
+          mask[i] = false;
+        }
+      } else {
+        // dense path
+        for (Index i = 0; i < rows; ++i) {
+          if (mask[i]) {
+            mask[i] = false;
+            res.insertBackByOuterInner(j, i) = values[i];
+          }
+        }
+      }
+    }
+  }
+  res.finalize();
+}
+
+}  // end namespace internal
+
+namespace internal {
+
+// Helper template to generate new sparse matrix types
+template <class Source, int Order>
+using WithStorageOrder = SparseMatrix<typename Source::Scalar, Order, typename Source::StorageIndex>;
+
+template <typename Lhs, typename Rhs, typename ResultType,
+          int LhsStorageOrder = (traits<Lhs>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+          int RhsStorageOrder = (traits<Rhs>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+          int ResStorageOrder = (traits<ResultType>::Flags & RowMajorBit) ? RowMajor : ColMajor>
+struct conservative_sparse_sparse_product_selector;
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs, Rhs, ResultType, ColMajor, ColMajor, ColMajor> {
+  typedef remove_all_t<Lhs> LhsCleaned;
+  typedef typename LhsCleaned::Scalar Scalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    using RowMajorMatrix = WithStorageOrder<ResultType, RowMajor>;
+    using ColMajorMatrixAux = WithStorageOrder<ResultType, ColMajor>;
+
+    // If the result is tall and thin (in the extreme case a column vector)
+    // then it is faster to sort the coefficients inplace instead of transposing twice.
+    // FIXME: this heuristic has known limitations and should be improved.
+    if (lhs.rows() > rhs.cols()) {
+      using ColMajorMatrix = typename sparse_eval<ColMajorMatrixAux, ResultType::RowsAtCompileTime,
+                                                  ResultType::ColsAtCompileTime, ColMajorMatrixAux::Flags>::type;
+      ColMajorMatrix resCol(lhs.rows(), rhs.cols());
+      // perform sorted insertion
+      internal::conservative_sparse_sparse_product_impl<Lhs, Rhs, ColMajorMatrix>(lhs, rhs, resCol, true);
+      res = resCol.markAsRValue();
+    } else {
+      ColMajorMatrixAux resCol(lhs.rows(), rhs.cols());
+      // resort to transpose to sort the entries
+      internal::conservative_sparse_sparse_product_impl<Lhs, Rhs, ColMajorMatrixAux>(lhs, rhs, resCol, false);
+      RowMajorMatrix resRow(resCol);
+      res = resRow.markAsRValue();
+    }
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs, Rhs, ResultType, RowMajor, ColMajor, ColMajor> {
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    using RowMajorRhs = WithStorageOrder<Rhs, RowMajor>;
+    using RowMajorRes = WithStorageOrder<ResultType, RowMajor>;
+    RowMajorRhs rhsRow = rhs;
+    RowMajorRes resRow(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<RowMajorRhs, Lhs, RowMajorRes>(rhsRow, lhs, resRow);
+    res = resRow;
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs, Rhs, ResultType, ColMajor, RowMajor, ColMajor> {
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    using RowMajorLhs = WithStorageOrder<Lhs, RowMajor>;
+    using RowMajorRes = WithStorageOrder<ResultType, RowMajor>;
+    RowMajorLhs lhsRow = lhs;
+    RowMajorRes resRow(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Rhs, RowMajorLhs, RowMajorRes>(rhs, lhsRow, resRow);
+    res = resRow;
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs, Rhs, ResultType, RowMajor, RowMajor, ColMajor> {
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    using RowMajorRes = WithStorageOrder<ResultType, RowMajor>;
+    RowMajorRes resRow(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Rhs, Lhs, RowMajorRes>(rhs, lhs, resRow);
+    res = resRow;
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs, Rhs, ResultType, ColMajor, ColMajor, RowMajor> {
+  typedef typename traits<remove_all_t<Lhs>>::Scalar Scalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    using ColMajorRes = WithStorageOrder<ResultType, ColMajor>;
+    ColMajorRes resCol(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Lhs, Rhs, ColMajorRes>(lhs, rhs, resCol);
+    res = resCol;
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs, Rhs, ResultType, RowMajor, ColMajor, RowMajor> {
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    using ColMajorLhs = WithStorageOrder<Lhs, ColMajor>;
+    using ColMajorRes = WithStorageOrder<ResultType, ColMajor>;
+    ColMajorLhs lhsCol = lhs;
+    ColMajorRes resCol(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<ColMajorLhs, Rhs, ColMajorRes>(lhsCol, rhs, resCol);
+    res = resCol;
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs, Rhs, ResultType, ColMajor, RowMajor, RowMajor> {
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    using ColMajorRhs = WithStorageOrder<Rhs, ColMajor>;
+    using ColMajorRes = WithStorageOrder<ResultType, ColMajor>;
+    ColMajorRhs rhsCol = rhs;
+    ColMajorRes resCol(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Lhs, ColMajorRhs, ColMajorRes>(lhs, rhsCol, resCol);
+    res = resCol;
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs, Rhs, ResultType, RowMajor, RowMajor, RowMajor> {
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    using ColMajorRes = WithStorageOrder<ResultType, ColMajor>;
+    using RowMajorRes = WithStorageOrder<ResultType, RowMajor>;
+    RowMajorRes resRow(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Rhs, Lhs, RowMajorRes>(rhs, lhs, resRow);
+    // sort the non zeros:
+    ColMajorRes resCol(resRow);
+    res = resCol;
+  }
+};
+
+}  // end namespace internal
+
+namespace internal {
+
+template <typename Lhs, typename Rhs, typename ResultType>
+static void sparse_sparse_to_dense_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+  typedef typename remove_all_t<Lhs>::Scalar LhsScalar;
+  typedef typename remove_all_t<Rhs>::Scalar RhsScalar;
+  Index cols = rhs.outerSize();
+  eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+  evaluator<Lhs> lhsEval(lhs);
+  evaluator<Rhs> rhsEval(rhs);
+
+  for (Index j = 0; j < cols; ++j) {
+    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt) {
+      RhsScalar y = rhsIt.value();
+      Index k = rhsIt.index();
+      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt) {
+        Index i = lhsIt.index();
+        LhsScalar x = lhsIt.value();
+        res.coeffRef(i, j) += x * y;
+      }
+    }
+  }
+}
+
+}  // end namespace internal
+
+namespace internal {
+
+template <typename Lhs, typename Rhs, typename ResultType,
+          int LhsStorageOrder = (traits<Lhs>::Flags & RowMajorBit) ? RowMajor : ColMajor,
+          int RhsStorageOrder = (traits<Rhs>::Flags & RowMajorBit) ? RowMajor : ColMajor>
+struct sparse_sparse_to_dense_product_selector;
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_to_dense_product_selector<Lhs, Rhs, ResultType, ColMajor, ColMajor> {
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    internal::sparse_sparse_to_dense_product_impl<Lhs, Rhs, ResultType>(lhs, rhs, res);
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_to_dense_product_selector<Lhs, Rhs, ResultType, RowMajor, ColMajor> {
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    using ColMajorLhs = WithStorageOrder<Lhs, ColMajor>;
+    ColMajorLhs lhsCol(lhs);
+    internal::sparse_sparse_to_dense_product_impl<ColMajorLhs, Rhs, ResultType>(lhsCol, rhs, res);
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_to_dense_product_selector<Lhs, Rhs, ResultType, ColMajor, RowMajor> {
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    using ColMajorRhs = WithStorageOrder<Rhs, ColMajor>;
+    ColMajorRhs rhsCol(rhs);
+    internal::sparse_sparse_to_dense_product_impl<Lhs, ColMajorRhs, ResultType>(lhs, rhsCol, res);
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_to_dense_product_selector<Lhs, Rhs, ResultType, RowMajor, RowMajor> {
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
+    Transpose<ResultType> trRes(res);
+    internal::sparse_sparse_to_dense_product_impl<Rhs, Lhs, Transpose<ResultType>>(rhs, lhs, trRes);
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/InternalHeaderCheck.h
new file mode 100644
index 0000000..9de5936
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPARSECORE_MODULE_H
+#error "Please include Eigen/SparseCore instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseAssign.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseAssign.h
new file mode 100644
index 0000000..b794263
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseAssign.h
@@ -0,0 +1,279 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEASSIGN_H
+#define EIGEN_SPARSEASSIGN_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Derived>
+template <typename OtherDerived>
+Derived &SparseMatrixBase<Derived>::operator=(const EigenBase<OtherDerived> &other) {
+  internal::call_assignment_no_alias(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+Derived &SparseMatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived> &other) {
+  // TODO: use the evaluator mechanism
+  other.evalTo(derived());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+inline Derived &SparseMatrixBase<Derived>::operator=(const SparseMatrixBase<OtherDerived> &other) {
+  // by default sparse evaluation do not alias, so we can safely bypass the generic call_assignment routine
+  internal::Assignment<Derived, OtherDerived, internal::assign_op<Scalar, typename OtherDerived::Scalar>>::run(
+      derived(), other.derived(), internal::assign_op<Scalar, typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template <typename Derived>
+inline Derived &SparseMatrixBase<Derived>::operator=(const Derived &other) {
+  internal::call_assignment_no_alias(derived(), other.derived());
+  return derived();
+}
+
+namespace internal {
+
+template <>
+struct storage_kind_to_evaluator_kind<Sparse> {
+  typedef IteratorBased Kind;
+};
+
+template <>
+struct storage_kind_to_shape<Sparse> {
+  typedef SparseShape Shape;
+};
+
+struct Sparse2Sparse {};
+struct Sparse2Dense {};
+
+template <>
+struct AssignmentKind<SparseShape, SparseShape> {
+  typedef Sparse2Sparse Kind;
+};
+template <>
+struct AssignmentKind<SparseShape, SparseTriangularShape> {
+  typedef Sparse2Sparse Kind;
+};
+template <>
+struct AssignmentKind<DenseShape, SparseShape> {
+  typedef Sparse2Dense Kind;
+};
+template <>
+struct AssignmentKind<DenseShape, SparseTriangularShape> {
+  typedef Sparse2Dense Kind;
+};
+
+template <typename DstXprType, typename SrcXprType>
+void assign_sparse_to_sparse(DstXprType &dst, const SrcXprType &src) {
+  typedef typename DstXprType::Scalar Scalar;
+  typedef internal::evaluator<DstXprType> DstEvaluatorType;
+  typedef internal::evaluator<SrcXprType> SrcEvaluatorType;
+
+  SrcEvaluatorType srcEvaluator(src);
+
+  constexpr bool transpose = (DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit);
+  const Index outerEvaluationSize = (SrcEvaluatorType::Flags & RowMajorBit) ? src.rows() : src.cols();
+
+  Index reserveSize = 0;
+  for (Index j = 0; j < outerEvaluationSize; ++j)
+    for (typename SrcEvaluatorType::InnerIterator it(srcEvaluator, j); it; ++it) reserveSize++;
+
+  if ((!transpose) && src.isRValue()) {
+    // eval without temporary
+    dst.resize(src.rows(), src.cols());
+    dst.setZero();
+    dst.reserve(reserveSize);
+    for (Index j = 0; j < outerEvaluationSize; ++j) {
+      dst.startVec(j);
+      for (typename SrcEvaluatorType::InnerIterator it(srcEvaluator, j); it; ++it) {
+        Scalar v = it.value();
+        dst.insertBackByOuterInner(j, it.index()) = v;
+      }
+    }
+    dst.finalize();
+  } else {
+    // eval through a temporary
+    eigen_assert((((internal::traits<DstXprType>::SupportedAccessPatterns & OuterRandomAccessPattern) ==
+                   OuterRandomAccessPattern) ||
+                  (!((DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit)))) &&
+                 "the transpose operation is supposed to be handled in SparseMatrix::operator=");
+
+    enum { Flip = (DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit) };
+
+    DstXprType temp(src.rows(), src.cols());
+
+    temp.reserve(reserveSize);
+    for (Index j = 0; j < outerEvaluationSize; ++j) {
+      temp.startVec(j);
+      for (typename SrcEvaluatorType::InnerIterator it(srcEvaluator, j); it; ++it) {
+        Scalar v = it.value();
+        temp.insertBackByOuterInner(Flip ? it.index() : j, Flip ? j : it.index()) = v;
+      }
+    }
+    temp.finalize();
+
+    dst = temp.markAsRValue();
+  }
+}
+
+// Generic Sparse to Sparse assignment
+template <typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Sparse> {
+  static void run(DstXprType &dst, const SrcXprType &src,
+                  const internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar> & /*func*/) {
+    assign_sparse_to_sparse(dst.derived(), src.derived());
+  }
+};
+
+// Generic Sparse to Dense assignment
+template <typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
+struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Dense, Weak> {
+  static void run(DstXprType &dst, const SrcXprType &src, const Functor &func) {
+    if (internal::is_same<Functor,
+                          internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>>::value)
+      dst.setZero();
+
+    internal::evaluator<SrcXprType> srcEval(src);
+    resize_if_allowed(dst, src, func);
+    internal::evaluator<DstXprType> dstEval(dst);
+
+    const Index outerEvaluationSize = (internal::evaluator<SrcXprType>::Flags & RowMajorBit) ? src.rows() : src.cols();
+    for (Index j = 0; j < outerEvaluationSize; ++j)
+      for (typename internal::evaluator<SrcXprType>::InnerIterator i(srcEval, j); i; ++i)
+        func.assignCoeff(dstEval.coeffRef(i.row(), i.col()), i.value());
+  }
+};
+
+// Specialization for dense ?= dense +/- sparse and dense ?= sparse +/- dense
+template <typename DstXprType, typename Func1, typename Func2>
+struct assignment_from_dense_op_sparse {
+  template <typename SrcXprType, typename InitialFunc>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src,
+                                                        const InitialFunc & /*func*/) {
+#ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN
+    EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN
+#endif
+
+    call_assignment_no_alias(dst, src.lhs(), Func1());
+    call_assignment_no_alias(dst, src.rhs(), Func2());
+  }
+
+  // Specialization for dense1 = sparse + dense2; -> dense1 = dense2; dense1 += sparse;
+  template <typename Lhs, typename Rhs, typename Scalar>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      std::enable_if_t<internal::is_same<typename internal::evaluator_traits<Rhs>::Shape, DenseShape>::value>
+      run(DstXprType &dst, const CwiseBinaryOp<internal::scalar_sum_op<Scalar, Scalar>, const Lhs, const Rhs> &src,
+          const internal::assign_op<typename DstXprType::Scalar, Scalar> & /*func*/) {
+#ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN
+    EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN
+#endif
+
+    // Apply the dense matrix first, then the sparse one.
+    call_assignment_no_alias(dst, src.rhs(), Func1());
+    call_assignment_no_alias(dst, src.lhs(), Func2());
+  }
+
+  // Specialization for dense1 = sparse - dense2; -> dense1 = -dense2; dense1 += sparse;
+  template <typename Lhs, typename Rhs, typename Scalar>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      std::enable_if_t<internal::is_same<typename internal::evaluator_traits<Rhs>::Shape, DenseShape>::value>
+      run(DstXprType &dst,
+          const CwiseBinaryOp<internal::scalar_difference_op<Scalar, Scalar>, const Lhs, const Rhs> &src,
+          const internal::assign_op<typename DstXprType::Scalar, Scalar> & /*func*/) {
+#ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN
+    EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN
+#endif
+
+    // Apply the dense matrix first, then the sparse one.
+    call_assignment_no_alias(dst, -src.rhs(), Func1());
+    call_assignment_no_alias(dst, src.lhs(), add_assign_op<typename DstXprType::Scalar, typename Lhs::Scalar>());
+  }
+};
+
+#define EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(ASSIGN_OP, BINOP, ASSIGN_OP2)                                        \
+  template <typename DstXprType, typename Lhs, typename Rhs, typename Scalar>                                   \
+  struct Assignment<                                                                                            \
+      DstXprType, CwiseBinaryOp<internal::BINOP<Scalar, Scalar>, const Lhs, const Rhs>,                         \
+      internal::ASSIGN_OP<typename DstXprType::Scalar, Scalar>, Sparse2Dense,                                   \
+      std::enable_if_t<internal::is_same<typename internal::evaluator_traits<Lhs>::Shape, DenseShape>::value || \
+                       internal::is_same<typename internal::evaluator_traits<Rhs>::Shape, DenseShape>::value>>  \
+      : assignment_from_dense_op_sparse<DstXprType,                                                             \
+                                        internal::ASSIGN_OP<typename DstXprType::Scalar, typename Lhs::Scalar>, \
+                                        internal::ASSIGN_OP2<typename DstXprType::Scalar, typename Rhs::Scalar>> {}
+
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(assign_op, scalar_sum_op, add_assign_op);
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(add_assign_op, scalar_sum_op, add_assign_op);
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(sub_assign_op, scalar_sum_op, sub_assign_op);
+
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(assign_op, scalar_difference_op, sub_assign_op);
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(add_assign_op, scalar_difference_op, sub_assign_op);
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(sub_assign_op, scalar_difference_op, add_assign_op);
+
+// Specialization for "dst = dec.solve(rhs)"
+// NOTE we need to specialize it for Sparse2Sparse to avoid ambiguous specialization error
+template <typename DstXprType, typename DecType, typename RhsType, typename Scalar>
+struct Assignment<DstXprType, Solve<DecType, RhsType>, internal::assign_op<Scalar, Scalar>, Sparse2Sparse> {
+  typedef Solve<DecType, RhsType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar, Scalar> &) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    src.dec()._solve_impl(src.rhs(), dst);
+  }
+};
+
+struct Diagonal2Sparse {};
+
+template <>
+struct AssignmentKind<SparseShape, DiagonalShape> {
+  typedef Diagonal2Sparse Kind;
+};
+
+template <typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Sparse> {
+  typedef typename DstXprType::StorageIndex StorageIndex;
+  typedef typename DstXprType::Scalar Scalar;
+
+  template <int Options, typename AssignFunc>
+  static void run(SparseMatrix<Scalar, Options, StorageIndex> &dst, const SrcXprType &src, const AssignFunc &func) {
+    dst.assignDiagonal(src.diagonal(), func);
+  }
+
+  template <typename DstDerived>
+  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src,
+                  const internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar> & /*func*/) {
+    dst.derived().diagonal() = src.diagonal();
+  }
+
+  template <typename DstDerived>
+  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src,
+                  const internal::add_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar> & /*func*/) {
+    dst.derived().diagonal() += src.diagonal();
+  }
+
+  template <typename DstDerived>
+  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src,
+                  const internal::sub_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar> & /*func*/) {
+    dst.derived().diagonal() -= src.diagonal();
+  }
+};
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSEASSIGN_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseBlock.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseBlock.h
new file mode 100644
index 0000000..3affff6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseBlock.h
@@ -0,0 +1,560 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_BLOCK_H
+#define EIGEN_SPARSE_BLOCK_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// Subset of columns or rows
+template <typename XprType, int BlockRows, int BlockCols>
+class BlockImpl<XprType, BlockRows, BlockCols, true, Sparse>
+    : public SparseMatrixBase<Block<XprType, BlockRows, BlockCols, true> > {
+  typedef internal::remove_all_t<typename XprType::Nested> MatrixTypeNested_;
+  typedef Block<XprType, BlockRows, BlockCols, true> BlockType;
+
+ public:
+  enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
+
+ protected:
+  enum { OuterSize = IsRowMajor ? BlockRows : BlockCols };
+  typedef SparseMatrixBase<BlockType> Base;
+  using Base::convert_index;
+
+ public:
+  EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
+
+  inline BlockImpl(XprType& xpr, Index i) : m_matrix(xpr), m_outerStart(convert_index(i)), m_outerSize(OuterSize) {}
+
+  inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
+      : m_matrix(xpr),
+        m_outerStart(convert_index(IsRowMajor ? startRow : startCol)),
+        m_outerSize(convert_index(IsRowMajor ? blockRows : blockCols)) {}
+
+  EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+  EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  Index nonZeros() const {
+    typedef internal::evaluator<XprType> EvaluatorType;
+    EvaluatorType matEval(m_matrix);
+    Index nnz = 0;
+    Index end = m_outerStart + m_outerSize.value();
+    for (Index j = m_outerStart; j < end; ++j)
+      for (typename EvaluatorType::InnerIterator it(matEval, j); it; ++it) ++nnz;
+    return nnz;
+  }
+
+  inline const Scalar coeff(Index row, Index col) const {
+    return m_matrix.coeff(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 : m_outerStart));
+  }
+
+  inline const Scalar coeff(Index index) const {
+    return m_matrix.coeff(IsRowMajor ? m_outerStart : index, IsRowMajor ? index : m_outerStart);
+  }
+
+  inline const XprType& nestedExpression() const { return m_matrix; }
+  inline XprType& nestedExpression() { return m_matrix; }
+  Index startRow() const { return IsRowMajor ? m_outerStart : 0; }
+  Index startCol() const { return IsRowMajor ? 0 : m_outerStart; }
+  Index blockRows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+  Index blockCols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  inline const Scalar* valuePtr() const { return m_matrix.valuePtr(); }
+  inline Scalar* valuePtr() { return m_matrix.valuePtr(); }
+
+  inline const StorageIndex* innerIndexPtr() const { return m_matrix.innerIndexPtr(); }
+  inline StorageIndex* innerIndexPtr() { return m_matrix.innerIndexPtr(); }
+
+  inline const StorageIndex* outerIndexPtr() const {
+    const StorageIndex* p = m_matrix.outerIndexPtr();
+    return p ? p + m_outerStart : 0;
+  }
+  inline StorageIndex* outerIndexPtr() {
+    StorageIndex* p = m_matrix.outerIndexPtr();
+    return p ? p + m_outerStart : 0;
+  }
+
+  inline const StorageIndex* innerNonZeroPtr() const {
+    const StorageIndex* p = m_matrix.innerNonZeroPtr();
+    return p ? p + m_outerStart : 0;
+  }
+  inline StorageIndex* innerNonZeroPtr() {
+    StorageIndex* p = m_matrix.innerNonZeroPtr();
+    return p ? p + m_outerStart : 0;
+  }
+
+  bool isCompressed() const { return m_matrix.innerNonZeroPtr() == 0; }
+
+ protected:
+  typename internal::ref_selector<XprType>::non_const_type m_matrix;
+  Index m_outerStart;
+  const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
+
+ protected:
+  // Disable assignment with clear error message.
+  // Note that simply removing operator= yields compilation errors with ICC+MSVC
+  template <typename T>
+  BlockImpl& operator=(const T&) {
+    EIGEN_STATIC_ASSERT(sizeof(T) == 0, THIS_SPARSE_BLOCK_SUBEXPRESSION_IS_READ_ONLY);
+    return *this;
+  }
+};
+
+/***************************************************************************
+ * specialization for SparseMatrix
+ ***************************************************************************/
+
+namespace internal {
+
+template <typename SparseMatrixType, int BlockRows, int BlockCols>
+class sparse_matrix_block_impl : public SparseCompressedBase<Block<SparseMatrixType, BlockRows, BlockCols, true> > {
+  typedef internal::remove_all_t<typename SparseMatrixType::Nested> MatrixTypeNested_;
+  typedef Block<SparseMatrixType, BlockRows, BlockCols, true> BlockType;
+  typedef SparseCompressedBase<Block<SparseMatrixType, BlockRows, BlockCols, true> > Base;
+  using Base::convert_index;
+
+ public:
+  enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
+  EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
+ protected:
+  typedef typename Base::IndexVector IndexVector;
+  enum { OuterSize = IsRowMajor ? BlockRows : BlockCols };
+
+ public:
+  inline sparse_matrix_block_impl(SparseMatrixType& xpr, Index i)
+      : m_matrix(xpr), m_outerStart(convert_index(i)), m_outerSize(OuterSize) {}
+
+  inline sparse_matrix_block_impl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows,
+                                  Index blockCols)
+      : m_matrix(xpr),
+        m_outerStart(convert_index(IsRowMajor ? startRow : startCol)),
+        m_outerSize(convert_index(IsRowMajor ? blockRows : blockCols)) {}
+
+  template <typename OtherDerived>
+  inline BlockType& operator=(const SparseMatrixBase<OtherDerived>& other) {
+    typedef internal::remove_all_t<typename SparseMatrixType::Nested> NestedMatrixType_;
+    NestedMatrixType_& matrix = m_matrix;
+    // This assignment is slow if this vector set is not empty
+    // and/or it is not at the end of the nonzeros of the underlying matrix.
+
+    // 1 - eval to a temporary to avoid transposition and/or aliasing issues
+    Ref<const SparseMatrix<Scalar, IsRowMajor ? RowMajor : ColMajor, StorageIndex> > tmp(other.derived());
+    eigen_internal_assert(tmp.outerSize() == m_outerSize.value());
+
+    // 2 - let's check whether there is enough allocated memory
+    Index nnz = tmp.nonZeros();
+    Index start =
+        m_outerStart == 0 ? 0 : m_matrix.outerIndexPtr()[m_outerStart];        // starting position of the current block
+    Index end = m_matrix.outerIndexPtr()[m_outerStart + m_outerSize.value()];  // ending position of the current block
+    Index block_size = end - start;                                            // available room in the current block
+    Index tail_size = m_matrix.outerIndexPtr()[m_matrix.outerSize()] - end;
+
+    Index free_size = m_matrix.isCompressed() ? Index(matrix.data().allocatedSize()) + block_size : block_size;
+
+    Index tmp_start = tmp.outerIndexPtr()[0];
+
+    bool update_trailing_pointers = false;
+    if (nnz > free_size) {
+      // realloc manually to reduce copies
+      typename SparseMatrixType::Storage newdata(m_matrix.data().allocatedSize() - block_size + nnz);
+
+      internal::smart_copy(m_matrix.valuePtr(), m_matrix.valuePtr() + start, newdata.valuePtr());
+      internal::smart_copy(m_matrix.innerIndexPtr(), m_matrix.innerIndexPtr() + start, newdata.indexPtr());
+
+      internal::smart_copy(tmp.valuePtr() + tmp_start, tmp.valuePtr() + tmp_start + nnz, newdata.valuePtr() + start);
+      internal::smart_copy(tmp.innerIndexPtr() + tmp_start, tmp.innerIndexPtr() + tmp_start + nnz,
+                           newdata.indexPtr() + start);
+
+      internal::smart_copy(matrix.valuePtr() + end, matrix.valuePtr() + end + tail_size,
+                           newdata.valuePtr() + start + nnz);
+      internal::smart_copy(matrix.innerIndexPtr() + end, matrix.innerIndexPtr() + end + tail_size,
+                           newdata.indexPtr() + start + nnz);
+
+      newdata.resize(m_matrix.outerIndexPtr()[m_matrix.outerSize()] - block_size + nnz);
+
+      matrix.data().swap(newdata);
+
+      update_trailing_pointers = true;
+    } else {
+      if (m_matrix.isCompressed() && nnz != block_size) {
+        // no need to realloc, simply copy the tail at its respective position and insert tmp
+        matrix.data().resize(start + nnz + tail_size);
+
+        internal::smart_memmove(matrix.valuePtr() + end, matrix.valuePtr() + end + tail_size,
+                                matrix.valuePtr() + start + nnz);
+        internal::smart_memmove(matrix.innerIndexPtr() + end, matrix.innerIndexPtr() + end + tail_size,
+                                matrix.innerIndexPtr() + start + nnz);
+
+        update_trailing_pointers = true;
+      }
+
+      internal::smart_copy(tmp.valuePtr() + tmp_start, tmp.valuePtr() + tmp_start + nnz, matrix.valuePtr() + start);
+      internal::smart_copy(tmp.innerIndexPtr() + tmp_start, tmp.innerIndexPtr() + tmp_start + nnz,
+                           matrix.innerIndexPtr() + start);
+    }
+
+    // update outer index pointers and innerNonZeros
+    if (IsVectorAtCompileTime) {
+      if (!m_matrix.isCompressed()) matrix.innerNonZeroPtr()[m_outerStart] = StorageIndex(nnz);
+      matrix.outerIndexPtr()[m_outerStart] = StorageIndex(start);
+    } else {
+      StorageIndex p = StorageIndex(start);
+      for (Index k = 0; k < m_outerSize.value(); ++k) {
+        StorageIndex nnz_k = internal::convert_index<StorageIndex>(tmp.innerVector(k).nonZeros());
+        if (!m_matrix.isCompressed()) matrix.innerNonZeroPtr()[m_outerStart + k] = nnz_k;
+        matrix.outerIndexPtr()[m_outerStart + k] = p;
+        p += nnz_k;
+      }
+    }
+
+    if (update_trailing_pointers) {
+      StorageIndex offset = internal::convert_index<StorageIndex>(nnz - block_size);
+      for (Index k = m_outerStart + m_outerSize.value(); k <= matrix.outerSize(); ++k) {
+        matrix.outerIndexPtr()[k] += offset;
+      }
+    }
+
+    return derived();
+  }
+
+  inline BlockType& operator=(const BlockType& other) { return operator= <BlockType>(other); }
+
+  inline const Scalar* valuePtr() const { return m_matrix.valuePtr(); }
+  inline Scalar* valuePtr() { return m_matrix.valuePtr(); }
+
+  inline const StorageIndex* innerIndexPtr() const { return m_matrix.innerIndexPtr(); }
+  inline StorageIndex* innerIndexPtr() { return m_matrix.innerIndexPtr(); }
+
+  inline const StorageIndex* outerIndexPtr() const { return m_matrix.outerIndexPtr() + m_outerStart; }
+  inline StorageIndex* outerIndexPtr() { return m_matrix.outerIndexPtr() + m_outerStart; }
+
+  inline const StorageIndex* innerNonZeroPtr() const {
+    return isCompressed() ? 0 : (m_matrix.innerNonZeroPtr() + m_outerStart);
+  }
+  inline StorageIndex* innerNonZeroPtr() { return isCompressed() ? 0 : (m_matrix.innerNonZeroPtr() + m_outerStart); }
+
+  bool isCompressed() const { return m_matrix.innerNonZeroPtr() == 0; }
+
+  inline Scalar& coeffRef(Index row, Index col) {
+    return m_matrix.coeffRef(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 : m_outerStart));
+  }
+
+  inline const Scalar coeff(Index row, Index col) const {
+    return m_matrix.coeff(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 : m_outerStart));
+  }
+
+  inline const Scalar coeff(Index index) const {
+    return m_matrix.coeff(IsRowMajor ? m_outerStart : index, IsRowMajor ? index : m_outerStart);
+  }
+
+  const Scalar& lastCoeff() const {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(sparse_matrix_block_impl);
+    eigen_assert(Base::nonZeros() > 0);
+    if (m_matrix.isCompressed())
+      return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart + 1] - 1];
+    else
+      return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart] + m_matrix.innerNonZeroPtr()[m_outerStart] - 1];
+  }
+
+  EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+  EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  inline const SparseMatrixType& nestedExpression() const { return m_matrix; }
+  inline SparseMatrixType& nestedExpression() { return m_matrix; }
+  Index startRow() const { return IsRowMajor ? m_outerStart : 0; }
+  Index startCol() const { return IsRowMajor ? 0 : m_outerStart; }
+  Index blockRows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+  Index blockCols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+ protected:
+  typename internal::ref_selector<SparseMatrixType>::non_const_type m_matrix;
+  Index m_outerStart;
+  const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
+};
+
+}  // namespace internal
+
+template <typename Scalar_, int Options_, typename StorageIndex_, int BlockRows, int BlockCols>
+class BlockImpl<SparseMatrix<Scalar_, Options_, StorageIndex_>, BlockRows, BlockCols, true, Sparse>
+    : public internal::sparse_matrix_block_impl<SparseMatrix<Scalar_, Options_, StorageIndex_>, BlockRows, BlockCols> {
+ public:
+  typedef StorageIndex_ StorageIndex;
+  typedef SparseMatrix<Scalar_, Options_, StorageIndex_> SparseMatrixType;
+  typedef internal::sparse_matrix_block_impl<SparseMatrixType, BlockRows, BlockCols> Base;
+  inline BlockImpl(SparseMatrixType& xpr, Index i) : Base(xpr, i) {}
+
+  inline BlockImpl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
+      : Base(xpr, startRow, startCol, blockRows, blockCols) {}
+
+  using Base::operator=;
+};
+
+template <typename Scalar_, int Options_, typename StorageIndex_, int BlockRows, int BlockCols>
+class BlockImpl<const SparseMatrix<Scalar_, Options_, StorageIndex_>, BlockRows, BlockCols, true, Sparse>
+    : public internal::sparse_matrix_block_impl<const SparseMatrix<Scalar_, Options_, StorageIndex_>, BlockRows,
+                                                BlockCols> {
+ public:
+  typedef StorageIndex_ StorageIndex;
+  typedef const SparseMatrix<Scalar_, Options_, StorageIndex_> SparseMatrixType;
+  typedef internal::sparse_matrix_block_impl<SparseMatrixType, BlockRows, BlockCols> Base;
+  inline BlockImpl(SparseMatrixType& xpr, Index i) : Base(xpr, i) {}
+
+  inline BlockImpl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
+      : Base(xpr, startRow, startCol, blockRows, blockCols) {}
+
+  using Base::operator=;
+
+ private:
+  template <typename Derived>
+  BlockImpl(const SparseMatrixBase<Derived>& xpr, Index i);
+  template <typename Derived>
+  BlockImpl(const SparseMatrixBase<Derived>& xpr);
+};
+
+//----------
+
+/** Generic implementation of sparse Block expression.
+ * Real-only.
+ */
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Sparse>
+    : public SparseMatrixBase<Block<XprType, BlockRows, BlockCols, InnerPanel> >, internal::no_assignment_operator {
+  typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
+  typedef SparseMatrixBase<BlockType> Base;
+  using Base::convert_index;
+
+ public:
+  enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
+  EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
+
+  typedef internal::remove_all_t<typename XprType::Nested> MatrixTypeNested_;
+
+  /** Column or Row constructor
+   */
+  inline BlockImpl(XprType& xpr, Index i)
+      : m_matrix(xpr),
+        m_startRow((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) ? convert_index(i) : 0),
+        m_startCol((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) ? convert_index(i) : 0),
+        m_blockRows(BlockRows == 1 ? 1 : xpr.rows()),
+        m_blockCols(BlockCols == 1 ? 1 : xpr.cols()) {}
+
+  /** Dynamic-size constructor
+   */
+  inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
+      : m_matrix(xpr),
+        m_startRow(convert_index(startRow)),
+        m_startCol(convert_index(startCol)),
+        m_blockRows(convert_index(blockRows)),
+        m_blockCols(convert_index(blockCols)) {}
+
+  inline Index rows() const { return m_blockRows.value(); }
+  inline Index cols() const { return m_blockCols.value(); }
+
+  inline Scalar& coeffRef(Index row, Index col) {
+    return m_matrix.coeffRef(row + m_startRow.value(), col + m_startCol.value());
+  }
+
+  inline const Scalar coeff(Index row, Index col) const {
+    return m_matrix.coeff(row + m_startRow.value(), col + m_startCol.value());
+  }
+
+  inline Scalar& coeffRef(Index index) {
+    return m_matrix.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                             m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+  }
+
+  inline const Scalar coeff(Index index) const {
+    return m_matrix.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+  }
+
+  inline const XprType& nestedExpression() const { return m_matrix; }
+  inline XprType& nestedExpression() { return m_matrix; }
+  Index startRow() const { return m_startRow.value(); }
+  Index startCol() const { return m_startCol.value(); }
+  Index blockRows() const { return m_blockRows.value(); }
+  Index blockCols() const { return m_blockCols.value(); }
+
+ protected:
+  //     friend class internal::GenericSparseBlockInnerIteratorImpl<XprType,BlockRows,BlockCols,InnerPanel>;
+  friend struct internal::unary_evaluator<Block<XprType, BlockRows, BlockCols, InnerPanel>, internal::IteratorBased,
+                                          Scalar>;
+
+  Index nonZeros() const { return Dynamic; }
+
+  typename internal::ref_selector<XprType>::non_const_type m_matrix;
+  const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
+  const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
+  const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows;
+  const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols;
+
+ protected:
+  // Disable assignment with clear error message.
+  // Note that simply removing operator= yields compilation errors with ICC+MSVC
+  template <typename T>
+  BlockImpl& operator=(const T&) {
+    EIGEN_STATIC_ASSERT(sizeof(T) == 0, THIS_SPARSE_BLOCK_SUBEXPRESSION_IS_READ_ONLY);
+    return *this;
+  }
+};
+
+namespace internal {
+
+template <typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IteratorBased>
+    : public evaluator_base<Block<ArgType, BlockRows, BlockCols, InnerPanel> > {
+  class InnerVectorInnerIterator;
+  class OuterVectorInnerIterator;
+
+ public:
+  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename XprType::Scalar Scalar;
+
+  enum {
+    IsRowMajor = XprType::IsRowMajor,
+    OuterVector = (BlockCols == 1 && ArgType::IsRowMajor) || (BlockRows == 1 && !ArgType::IsRowMajor),
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+    Flags = XprType::Flags
+  };
+
+  typedef std::conditional_t<OuterVector, OuterVectorInnerIterator, InnerVectorInnerIterator> InnerIterator;
+
+  explicit unary_evaluator(const XprType& op) : m_argImpl(op.nestedExpression()), m_block(op) {}
+
+  inline Index nonZerosEstimate() const {
+    const Index nnz = m_block.nonZeros();
+    if (nnz < 0) {
+      // Scale the non-zero estimate for the underlying expression linearly with block size.
+      // Return zero if the underlying block is empty.
+      const Index nested_sz = m_block.nestedExpression().size();
+      return nested_sz == 0 ? 0 : m_argImpl.nonZerosEstimate() * m_block.size() / nested_sz;
+    }
+    return nnz;
+  }
+
+ protected:
+  typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
+
+  evaluator<ArgType> m_argImpl;
+  const XprType& m_block;
+};
+
+template <typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+class unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IteratorBased>::InnerVectorInnerIterator
+    : public EvalIterator {
+  // NOTE MSVC fails to compile if we don't explicitly "import" IsRowMajor from unary_evaluator
+  //      because the base class EvalIterator has a private IsRowMajor enum too. (bug #1786)
+  // NOTE We cannot call it IsRowMajor because it would shadow unary_evaluator::IsRowMajor
+  enum { XprIsRowMajor = unary_evaluator::IsRowMajor };
+  const XprType& m_block;
+  Index m_end;
+
+ public:
+  EIGEN_STRONG_INLINE InnerVectorInnerIterator(const unary_evaluator& aEval, Index outer)
+      : EvalIterator(aEval.m_argImpl, outer + (XprIsRowMajor ? aEval.m_block.startRow() : aEval.m_block.startCol())),
+        m_block(aEval.m_block),
+        m_end(XprIsRowMajor ? aEval.m_block.startCol() + aEval.m_block.blockCols()
+                            : aEval.m_block.startRow() + aEval.m_block.blockRows()) {
+    while ((EvalIterator::operator bool()) &&
+           (EvalIterator::index() < (XprIsRowMajor ? m_block.startCol() : m_block.startRow())))
+      EvalIterator::operator++();
+  }
+
+  inline StorageIndex index() const {
+    return EvalIterator::index() - convert_index<StorageIndex>(XprIsRowMajor ? m_block.startCol() : m_block.startRow());
+  }
+  inline Index outer() const {
+    return EvalIterator::outer() - (XprIsRowMajor ? m_block.startRow() : m_block.startCol());
+  }
+  inline Index row() const { return EvalIterator::row() - m_block.startRow(); }
+  inline Index col() const { return EvalIterator::col() - m_block.startCol(); }
+
+  inline operator bool() const { return EvalIterator::operator bool() && EvalIterator::index() < m_end; }
+};
+
+template <typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+class unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IteratorBased>::OuterVectorInnerIterator {
+  // NOTE see above
+  enum { XprIsRowMajor = unary_evaluator::IsRowMajor };
+  const unary_evaluator& m_eval;
+  Index m_outerPos;
+  const Index m_innerIndex;
+  Index m_end;
+  EvalIterator m_it;
+
+ public:
+  EIGEN_STRONG_INLINE OuterVectorInnerIterator(const unary_evaluator& aEval, Index outer)
+      : m_eval(aEval),
+        m_outerPos((XprIsRowMajor ? aEval.m_block.startCol() : aEval.m_block.startRow())),
+        m_innerIndex(XprIsRowMajor ? aEval.m_block.startRow() : aEval.m_block.startCol()),
+        m_end(XprIsRowMajor ? aEval.m_block.startCol() + aEval.m_block.blockCols()
+                            : aEval.m_block.startRow() + aEval.m_block.blockRows()),
+        m_it(m_eval.m_argImpl, m_outerPos) {
+    EIGEN_UNUSED_VARIABLE(outer);
+    eigen_assert(outer == 0);
+
+    while (m_it && m_it.index() < m_innerIndex) ++m_it;
+    if ((!m_it) || (m_it.index() != m_innerIndex)) ++(*this);
+  }
+
+  inline StorageIndex index() const {
+    return convert_index<StorageIndex>(m_outerPos -
+                                       (XprIsRowMajor ? m_eval.m_block.startCol() : m_eval.m_block.startRow()));
+  }
+  inline Index outer() const { return 0; }
+  inline Index row() const { return XprIsRowMajor ? 0 : index(); }
+  inline Index col() const { return XprIsRowMajor ? index() : 0; }
+
+  inline Scalar value() const { return m_it.value(); }
+  inline Scalar& valueRef() { return m_it.valueRef(); }
+
+  inline OuterVectorInnerIterator& operator++() {
+    // search next non-zero entry
+    while (++m_outerPos < m_end) {
+      // Restart iterator at the next inner-vector:
+      internal::destroy_at(&m_it);
+      internal::construct_at(&m_it, m_eval.m_argImpl, m_outerPos);
+      // search for the key m_innerIndex in the current outer-vector
+      while (m_it && m_it.index() < m_innerIndex) ++m_it;
+      if (m_it && m_it.index() == m_innerIndex) break;
+    }
+    return *this;
+  }
+
+  inline operator bool() const { return m_outerPos < m_end; }
+};
+
+template <typename Scalar_, int Options_, typename StorageIndex_, int BlockRows, int BlockCols>
+struct unary_evaluator<Block<SparseMatrix<Scalar_, Options_, StorageIndex_>, BlockRows, BlockCols, true>, IteratorBased>
+    : evaluator<
+          SparseCompressedBase<Block<SparseMatrix<Scalar_, Options_, StorageIndex_>, BlockRows, BlockCols, true> > > {
+  typedef Block<SparseMatrix<Scalar_, Options_, StorageIndex_>, BlockRows, BlockCols, true> XprType;
+  typedef evaluator<SparseCompressedBase<XprType> > Base;
+  explicit unary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+template <typename Scalar_, int Options_, typename StorageIndex_, int BlockRows, int BlockCols>
+struct unary_evaluator<Block<const SparseMatrix<Scalar_, Options_, StorageIndex_>, BlockRows, BlockCols, true>,
+                       IteratorBased>
+    : evaluator<SparseCompressedBase<
+          Block<const SparseMatrix<Scalar_, Options_, StorageIndex_>, BlockRows, BlockCols, true> > > {
+  typedef Block<const SparseMatrix<Scalar_, Options_, StorageIndex_>, BlockRows, BlockCols, true> XprType;
+  typedef evaluator<SparseCompressedBase<XprType> > Base;
+  explicit unary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_BLOCK_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseColEtree.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseColEtree.h
new file mode 100644
index 0000000..76575c9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseColEtree.h
@@ -0,0 +1,194 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+
+ * NOTE: This file is the modified version of sp_coletree.c file in SuperLU
+
+ * -- SuperLU routine (version 3.1) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * August 1, 2008
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSE_COLETREE_H
+#define SPARSE_COLETREE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** Find the root of the tree/set containing the vertex i : Use Path halving */
+template <typename Index, typename IndexVector>
+Index etree_find(Index i, IndexVector& pp) {
+  Index p = pp(i);   // Parent
+  Index gp = pp(p);  // Grand parent
+  while (gp != p) {
+    pp(i) = gp;  // Parent pointer on find path is changed to former grand parent
+    i = gp;
+    p = pp(i);
+    gp = pp(p);
+  }
+  return p;
+}
+
+/** Compute the column elimination tree of a sparse matrix
+ * \param mat The matrix in column-major format.
+ * \param parent The elimination tree
+ * \param firstRowElt The column index of the first element in each row
+ * \param perm The permutation to apply to the column of \b mat
+ */
+template <typename MatrixType, typename IndexVector>
+int coletree(const MatrixType& mat, IndexVector& parent, IndexVector& firstRowElt,
+             typename MatrixType::StorageIndex* perm = 0) {
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  StorageIndex nc = convert_index<StorageIndex>(mat.cols());  // Number of columns
+  StorageIndex m = convert_index<StorageIndex>(mat.rows());
+  StorageIndex diagSize = (std::min)(nc, m);
+  IndexVector root(nc);  // root of subtree of etree
+  root.setZero();
+  IndexVector pp(nc);  // disjoint sets
+  pp.setZero();        // Initialize disjoint sets
+  parent.resize(mat.cols());
+  // Compute first nonzero column in each row
+  firstRowElt.resize(m);
+  firstRowElt.setConstant(nc);
+  firstRowElt.segment(0, diagSize).setLinSpaced(diagSize, 0, diagSize - 1);
+  bool found_diag;
+  for (StorageIndex col = 0; col < nc; col++) {
+    StorageIndex pcol = col;
+    if (perm) pcol = perm[col];
+    for (typename MatrixType::InnerIterator it(mat, pcol); it; ++it) {
+      Index row = it.row();
+      firstRowElt(row) = (std::min)(firstRowElt(row), col);
+    }
+  }
+  /* Compute etree by Liu's algorithm for symmetric matrices,
+          except use (firstRowElt[r],c) in place of an edge (r,c) of A.
+    Thus each row clique in A'*A is replaced by a star
+    centered at its first vertex, which has the same fill. */
+  StorageIndex rset, cset, rroot;
+  for (StorageIndex col = 0; col < nc; col++) {
+    found_diag = col >= m;
+    pp(col) = col;
+    cset = col;
+    root(cset) = col;
+    parent(col) = nc;
+    /* The diagonal element is treated here even if it does not exist in the matrix
+     * hence the loop is executed once more */
+    StorageIndex pcol = col;
+    if (perm) pcol = perm[col];
+    for (typename MatrixType::InnerIterator it(mat, pcol); it || !found_diag;
+         ++it) {  //  A sequence of interleaved find and union is performed
+      Index i = col;
+      if (it) i = it.index();
+      if (i == col) found_diag = true;
+
+      StorageIndex row = firstRowElt(i);
+      if (row >= col) continue;
+      rset = internal::etree_find(row, pp);  // Find the name of the set containing row
+      rroot = root(rset);
+      if (rroot != col) {
+        parent(rroot) = col;
+        pp(cset) = rset;
+        cset = rset;
+        root(cset) = col;
+      }
+    }
+  }
+  return 0;
+}
+
+/**
+ * Depth-first search from vertex n.  No recursion.
+ * This routine was contributed by Cédric Doucet, CEDRAT Group, Meylan, France.
+ */
+template <typename IndexVector>
+void nr_etdfs(typename IndexVector::Scalar n, IndexVector& parent, IndexVector& first_kid, IndexVector& next_kid,
+              IndexVector& post, typename IndexVector::Scalar postnum) {
+  typedef typename IndexVector::Scalar StorageIndex;
+  StorageIndex current = n, first, next;
+  while (postnum != n) {
+    // No kid for the current node
+    first = first_kid(current);
+
+    // no kid for the current node
+    if (first == -1) {
+      // Numbering this node because it has no kid
+      post(current) = postnum++;
+
+      // looking for the next kid
+      next = next_kid(current);
+      while (next == -1) {
+        // No more kids : back to the parent node
+        current = parent(current);
+        // numbering the parent node
+        post(current) = postnum++;
+
+        // Get the next kid
+        next = next_kid(current);
+      }
+      // stopping criterion
+      if (postnum == n + 1) return;
+
+      // Updating current node
+      current = next;
+    } else {
+      current = first;
+    }
+  }
+}
+
+/**
+ * \brief Post order a tree
+ * \param n the number of nodes
+ * \param parent Input tree
+ * \param post postordered tree
+ */
+template <typename IndexVector>
+void treePostorder(typename IndexVector::Scalar n, IndexVector& parent, IndexVector& post) {
+  typedef typename IndexVector::Scalar StorageIndex;
+  IndexVector first_kid, next_kid;  // Linked list of children
+  StorageIndex postnum;
+  // Allocate storage for working arrays and results
+  first_kid.resize(n + 1);
+  next_kid.setZero(n + 1);
+  post.setZero(n + 1);
+
+  // Set up structure describing children
+  first_kid.setConstant(-1);
+  for (StorageIndex v = n - 1; v >= 0; v--) {
+    StorageIndex dad = parent(v);
+    next_kid(v) = first_kid(dad);
+    first_kid(dad) = v;
+  }
+
+  // Depth-first search from dummy root vertex #n
+  postnum = 0;
+  internal::nr_etdfs(n, parent, first_kid, next_kid, post, postnum);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // SPARSE_COLETREE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseCompressedBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseCompressedBase.h
new file mode 100644
index 0000000..420e9fa
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseCompressedBase.h
@@ -0,0 +1,592 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_COMPRESSED_BASE_H
+#define EIGEN_SPARSE_COMPRESSED_BASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Derived>
+class SparseCompressedBase;
+
+namespace internal {
+
+template <typename Derived>
+struct traits<SparseCompressedBase<Derived>> : traits<Derived> {};
+
+template <typename Derived, class Comp, bool IsVector>
+struct inner_sort_impl;
+
+}  // end namespace internal
+
+/** \ingroup SparseCore_Module
+ * \class SparseCompressedBase
+ * \brief Common base class for sparse [compressed]-{row|column}-storage format.
+ *
+ * This class defines the common interface for all derived classes implementing the compressed sparse storage format,
+ * such as:
+ *  - SparseMatrix
+ *  - Ref<SparseMatrixType,Options>
+ *  - Map<SparseMatrixType>
+ *
+ */
+template <typename Derived>
+class SparseCompressedBase : public SparseMatrixBase<Derived> {
+ public:
+  typedef SparseMatrixBase<Derived> Base;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseCompressedBase)
+  using Base::operator=;
+  using Base::IsRowMajor;
+
+  class InnerIterator;
+  class ReverseInnerIterator;
+
+ protected:
+  typedef typename Base::IndexVector IndexVector;
+  Eigen::Map<IndexVector> innerNonZeros() {
+    return Eigen::Map<IndexVector>(innerNonZeroPtr(), isCompressed() ? 0 : derived().outerSize());
+  }
+  const Eigen::Map<const IndexVector> innerNonZeros() const {
+    return Eigen::Map<const IndexVector>(innerNonZeroPtr(), isCompressed() ? 0 : derived().outerSize());
+  }
+
+ public:
+  /** \returns the number of non zero coefficients */
+  inline Index nonZeros() const {
+    if (Derived::IsVectorAtCompileTime && outerIndexPtr() == 0)
+      return derived().nonZeros();
+    else if (derived().outerSize() == 0)
+      return 0;
+    else if (isCompressed())
+      return outerIndexPtr()[derived().outerSize()] - outerIndexPtr()[0];
+    else
+      return innerNonZeros().sum();
+  }
+
+  /** \returns a const pointer to the array of values.
+   * This function is aimed at interoperability with other libraries.
+   * \sa innerIndexPtr(), outerIndexPtr() */
+  inline const Scalar* valuePtr() const { return derived().valuePtr(); }
+  /** \returns a non-const pointer to the array of values.
+   * This function is aimed at interoperability with other libraries.
+   * \sa innerIndexPtr(), outerIndexPtr() */
+  inline Scalar* valuePtr() { return derived().valuePtr(); }
+
+  /** \returns a const pointer to the array of inner indices.
+   * This function is aimed at interoperability with other libraries.
+   * \sa valuePtr(), outerIndexPtr() */
+  inline const StorageIndex* innerIndexPtr() const { return derived().innerIndexPtr(); }
+  /** \returns a non-const pointer to the array of inner indices.
+   * This function is aimed at interoperability with other libraries.
+   * \sa valuePtr(), outerIndexPtr() */
+  inline StorageIndex* innerIndexPtr() { return derived().innerIndexPtr(); }
+
+  /** \returns a const pointer to the array of the starting positions of the inner vectors.
+   * This function is aimed at interoperability with other libraries.
+   * \warning it returns the null pointer 0 for SparseVector
+   * \sa valuePtr(), innerIndexPtr() */
+  inline const StorageIndex* outerIndexPtr() const { return derived().outerIndexPtr(); }
+  /** \returns a non-const pointer to the array of the starting positions of the inner vectors.
+   * This function is aimed at interoperability with other libraries.
+   * \warning it returns the null pointer 0 for SparseVector
+   * \sa valuePtr(), innerIndexPtr() */
+  inline StorageIndex* outerIndexPtr() { return derived().outerIndexPtr(); }
+
+  /** \returns a const pointer to the array of the number of non zeros of the inner vectors.
+   * This function is aimed at interoperability with other libraries.
+   * \warning it returns the null pointer 0 in compressed mode */
+  inline const StorageIndex* innerNonZeroPtr() const { return derived().innerNonZeroPtr(); }
+  /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors.
+   * This function is aimed at interoperability with other libraries.
+   * \warning it returns the null pointer 0 in compressed mode */
+  inline StorageIndex* innerNonZeroPtr() { return derived().innerNonZeroPtr(); }
+
+  /** \returns whether \c *this is in compressed form. */
+  inline bool isCompressed() const { return innerNonZeroPtr() == 0; }
+
+  /** \returns a read-only view of the stored coefficients as a 1D array expression.
+   *
+   * \warning this method is for \b compressed \b storage \b only, and it will trigger an assertion otherwise.
+   *
+   * \sa valuePtr(), isCompressed() */
+  const Map<const Array<Scalar, Dynamic, 1>> coeffs() const {
+    eigen_assert(isCompressed());
+    return Array<Scalar, Dynamic, 1>::Map(valuePtr(), nonZeros());
+  }
+
+  /** \returns a read-write view of the stored coefficients as a 1D array expression
+   *
+   * \warning this method is for \b compressed \b storage \b only, and it will trigger an assertion otherwise.
+   *
+   * Here is an example:
+   * \include SparseMatrix_coeffs.cpp
+   * and the output is:
+   * \include SparseMatrix_coeffs.out
+   *
+   * \sa valuePtr(), isCompressed() */
+  Map<Array<Scalar, Dynamic, 1>> coeffs() {
+    eigen_assert(isCompressed());
+    return Array<Scalar, Dynamic, 1>::Map(valuePtr(), nonZeros());
+  }
+
+  /** sorts the inner vectors in the range [begin,end) with respect to `Comp`
+   * \sa innerIndicesAreSorted() */
+  template <class Comp = std::less<>>
+  inline void sortInnerIndices(Index begin, Index end) {
+    eigen_assert(begin >= 0 && end <= derived().outerSize() && end >= begin);
+    internal::inner_sort_impl<Derived, Comp, IsVectorAtCompileTime>::run(*this, begin, end);
+  }
+
+  /** \returns the index of the first inner vector in the range [begin,end) that is not sorted with respect to `Comp`,
+   * or `end` if the range is fully sorted \sa sortInnerIndices() */
+  template <class Comp = std::less<>>
+  inline Index innerIndicesAreSorted(Index begin, Index end) const {
+    eigen_assert(begin >= 0 && end <= derived().outerSize() && end >= begin);
+    return internal::inner_sort_impl<Derived, Comp, IsVectorAtCompileTime>::check(*this, begin, end);
+  }
+
+  /** sorts the inner vectors in the range [0,outerSize) with respect to `Comp`
+   * \sa innerIndicesAreSorted() */
+  template <class Comp = std::less<>>
+  inline void sortInnerIndices() {
+    Index begin = 0;
+    Index end = derived().outerSize();
+    internal::inner_sort_impl<Derived, Comp, IsVectorAtCompileTime>::run(*this, begin, end);
+  }
+
+  /** \returns the index of the first inner vector in the range [0,outerSize) that is not sorted with respect to `Comp`,
+   * or `outerSize` if the range is fully sorted \sa sortInnerIndices() */
+  template <class Comp = std::less<>>
+  inline Index innerIndicesAreSorted() const {
+    Index begin = 0;
+    Index end = derived().outerSize();
+    return internal::inner_sort_impl<Derived, Comp, IsVectorAtCompileTime>::check(*this, begin, end);
+  }
+
+ protected:
+  /** Default constructor. Do nothing. */
+  SparseCompressedBase() {}
+
+  /** \internal return the index of the coeff at (row,col) or just before if it does not exist.
+   * This is an analogue of std::lower_bound.
+   */
+  internal::LowerBoundIndex lower_bound(Index row, Index col) const {
+    eigen_internal_assert(row >= 0 && row < this->rows() && col >= 0 && col < this->cols());
+
+    const Index outer = Derived::IsRowMajor ? row : col;
+    const Index inner = Derived::IsRowMajor ? col : row;
+
+    Index start = this->outerIndexPtr()[outer];
+    Index end = this->isCompressed() ? this->outerIndexPtr()[outer + 1]
+                                     : this->outerIndexPtr()[outer] + this->innerNonZeroPtr()[outer];
+    eigen_assert(end >= start && "you are using a non finalized sparse matrix or written coefficient does not exist");
+    internal::LowerBoundIndex p;
+    p.value =
+        std::lower_bound(this->innerIndexPtr() + start, this->innerIndexPtr() + end, inner) - this->innerIndexPtr();
+    p.found = (p.value < end) && (this->innerIndexPtr()[p.value] == inner);
+    return p;
+  }
+
+  friend struct internal::evaluator<SparseCompressedBase<Derived>>;
+
+ private:
+  template <typename OtherDerived>
+  explicit SparseCompressedBase(const SparseCompressedBase<OtherDerived>&);
+};
+
+template <typename Derived>
+class SparseCompressedBase<Derived>::InnerIterator {
+ public:
+  InnerIterator() : m_values(0), m_indices(0), m_outer(0), m_id(0), m_end(0) {}
+
+  InnerIterator(const InnerIterator& other)
+      : m_values(other.m_values),
+        m_indices(other.m_indices),
+        m_outer(other.m_outer),
+        m_id(other.m_id),
+        m_end(other.m_end) {}
+
+  InnerIterator& operator=(const InnerIterator& other) {
+    m_values = other.m_values;
+    m_indices = other.m_indices;
+    const_cast<OuterType&>(m_outer).setValue(other.m_outer.value());
+    m_id = other.m_id;
+    m_end = other.m_end;
+    return *this;
+  }
+
+  InnerIterator(const SparseCompressedBase& mat, Index outer)
+      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer) {
+    if (Derived::IsVectorAtCompileTime && mat.outerIndexPtr() == 0) {
+      m_id = 0;
+      m_end = mat.nonZeros();
+    } else {
+      m_id = mat.outerIndexPtr()[outer];
+      if (mat.isCompressed())
+        m_end = mat.outerIndexPtr()[outer + 1];
+      else
+        m_end = m_id + mat.innerNonZeroPtr()[outer];
+    }
+  }
+
+  explicit InnerIterator(const SparseCompressedBase& mat) : InnerIterator(mat, Index(0)) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  }
+
+  explicit InnerIterator(const internal::CompressedStorage<Scalar, StorageIndex>& data)
+      : m_values(data.valuePtr()), m_indices(data.indexPtr()), m_outer(0), m_id(0), m_end(data.size()) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  }
+
+  inline InnerIterator& operator++() {
+    m_id++;
+    return *this;
+  }
+  inline InnerIterator& operator+=(Index i) {
+    m_id += i;
+    return *this;
+  }
+
+  inline InnerIterator operator+(Index i) {
+    InnerIterator result = *this;
+    result += i;
+    return result;
+  }
+
+  inline const Scalar& value() const { return m_values[m_id]; }
+  inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id]); }
+
+  inline StorageIndex index() const { return m_indices[m_id]; }
+  inline Index outer() const { return m_outer.value(); }
+  inline Index row() const { return IsRowMajor ? m_outer.value() : index(); }
+  inline Index col() const { return IsRowMajor ? index() : m_outer.value(); }
+
+  inline operator bool() const { return (m_id < m_end); }
+
+ protected:
+  const Scalar* m_values;
+  const StorageIndex* m_indices;
+  typedef internal::variable_if_dynamic<Index, Derived::IsVectorAtCompileTime ? 0 : Dynamic> OuterType;
+  const OuterType m_outer;
+  Index m_id;
+  Index m_end;
+
+ private:
+  // If you get here, then you're not using the right InnerIterator type, e.g.:
+  //   SparseMatrix<double,RowMajor> A;
+  //   SparseMatrix<double>::InnerIterator it(A,0);
+  template <typename T>
+  InnerIterator(const SparseMatrixBase<T>&, Index outer);
+};
+
+template <typename Derived>
+class SparseCompressedBase<Derived>::ReverseInnerIterator {
+ public:
+  ReverseInnerIterator(const SparseCompressedBase& mat, Index outer)
+      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer) {
+    if (Derived::IsVectorAtCompileTime && mat.outerIndexPtr() == 0) {
+      m_start = 0;
+      m_id = mat.nonZeros();
+    } else {
+      m_start = mat.outerIndexPtr()[outer];
+      if (mat.isCompressed())
+        m_id = mat.outerIndexPtr()[outer + 1];
+      else
+        m_id = m_start + mat.innerNonZeroPtr()[outer];
+    }
+  }
+
+  explicit ReverseInnerIterator(const SparseCompressedBase& mat)
+      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(0), m_start(0), m_id(mat.nonZeros()) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  }
+
+  explicit ReverseInnerIterator(const internal::CompressedStorage<Scalar, StorageIndex>& data)
+      : m_values(data.valuePtr()), m_indices(data.indexPtr()), m_outer(0), m_start(0), m_id(data.size()) {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  }
+
+  inline ReverseInnerIterator& operator--() {
+    --m_id;
+    return *this;
+  }
+  inline ReverseInnerIterator& operator-=(Index i) {
+    m_id -= i;
+    return *this;
+  }
+
+  inline ReverseInnerIterator operator-(Index i) {
+    ReverseInnerIterator result = *this;
+    result -= i;
+    return result;
+  }
+
+  inline const Scalar& value() const { return m_values[m_id - 1]; }
+  inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id - 1]); }
+
+  inline StorageIndex index() const { return m_indices[m_id - 1]; }
+  inline Index outer() const { return m_outer.value(); }
+  inline Index row() const { return IsRowMajor ? m_outer.value() : index(); }
+  inline Index col() const { return IsRowMajor ? index() : m_outer.value(); }
+
+  inline operator bool() const { return (m_id > m_start); }
+
+ protected:
+  const Scalar* m_values;
+  const StorageIndex* m_indices;
+  typedef internal::variable_if_dynamic<Index, Derived::IsVectorAtCompileTime ? 0 : Dynamic> OuterType;
+  const OuterType m_outer;
+  Index m_start;
+  Index m_id;
+};
+
+namespace internal {
+
+// modified from https://artificial-mind.net/blog/2020/11/28/std-sort-multiple-ranges
+
+template <typename Scalar, typename StorageIndex>
+class StorageVal;
+template <typename Scalar, typename StorageIndex>
+class StorageRef;
+template <typename Scalar, typename StorageIndex>
+class CompressedStorageIterator;
+
+// class to hold an index/value pair
+template <typename Scalar, typename StorageIndex>
+class StorageVal {
+ public:
+  StorageVal(const StorageIndex& innerIndex, const Scalar& value) : m_innerIndex(innerIndex), m_value(value) {}
+  StorageVal(const StorageVal& other) : m_innerIndex(other.m_innerIndex), m_value(other.m_value) {}
+  StorageVal(StorageVal&& other) = default;
+
+  inline const StorageIndex& key() const { return m_innerIndex; }
+  inline StorageIndex& key() { return m_innerIndex; }
+  inline const Scalar& value() const { return m_value; }
+  inline Scalar& value() { return m_value; }
+
+  // enables StorageVal to be compared with respect to any type that is convertible to StorageIndex
+  inline operator StorageIndex() const { return m_innerIndex; }
+
+ protected:
+  StorageIndex m_innerIndex;
+  Scalar m_value;
+
+ private:
+  StorageVal() = delete;
+};
+// class to hold an index/value iterator pair
+// used to define assignment, swap, and comparison operators for CompressedStorageIterator
+template <typename Scalar, typename StorageIndex>
+class StorageRef {
+ public:
+  using value_type = StorageVal<Scalar, StorageIndex>;
+
+  // StorageRef Needs to be move-able for sort on macos.
+  StorageRef(StorageRef&& other) = default;
+
+  inline StorageRef& operator=(const StorageRef& other) {
+    key() = other.key();
+    value() = other.value();
+    return *this;
+  }
+  inline StorageRef& operator=(const value_type& other) {
+    key() = other.key();
+    value() = other.value();
+    return *this;
+  }
+  inline operator value_type() const { return value_type(key(), value()); }
+  inline friend void swap(const StorageRef& a, const StorageRef& b) {
+    std::iter_swap(a.keyPtr(), b.keyPtr());
+    std::iter_swap(a.valuePtr(), b.valuePtr());
+  }
+
+  inline const StorageIndex& key() const { return *m_innerIndexIterator; }
+  inline StorageIndex& key() { return *m_innerIndexIterator; }
+  inline const Scalar& value() const { return *m_valueIterator; }
+  inline Scalar& value() { return *m_valueIterator; }
+  inline StorageIndex* keyPtr() const { return m_innerIndexIterator; }
+  inline Scalar* valuePtr() const { return m_valueIterator; }
+
+  // enables StorageRef to be compared with respect to any type that is convertible to StorageIndex
+  inline operator StorageIndex() const { return *m_innerIndexIterator; }
+
+ protected:
+  StorageIndex* m_innerIndexIterator;
+  Scalar* m_valueIterator;
+
+ private:
+  StorageRef() = delete;
+  // these constructors are called by the CompressedStorageIterator constructors for convenience only
+  StorageRef(StorageIndex* innerIndexIterator, Scalar* valueIterator)
+      : m_innerIndexIterator(innerIndexIterator), m_valueIterator(valueIterator) {}
+  StorageRef(const StorageRef& other)
+      : m_innerIndexIterator(other.m_innerIndexIterator), m_valueIterator(other.m_valueIterator) {}
+
+  friend class CompressedStorageIterator<Scalar, StorageIndex>;
+};
+
+// STL-compatible iterator class that operates on inner indices and values
+template <typename Scalar, typename StorageIndex>
+class CompressedStorageIterator {
+ public:
+  using iterator_category = std::random_access_iterator_tag;
+  using reference = StorageRef<Scalar, StorageIndex>;
+  using difference_type = Index;
+  using value_type = typename reference::value_type;
+  using pointer = value_type*;
+
+  CompressedStorageIterator() = delete;
+  CompressedStorageIterator(difference_type index, StorageIndex* innerIndexPtr, Scalar* valuePtr)
+      : m_index(index), m_data(innerIndexPtr, valuePtr) {}
+  CompressedStorageIterator(difference_type index, reference data) : m_index(index), m_data(data) {}
+  CompressedStorageIterator(const CompressedStorageIterator& other) : m_index(other.m_index), m_data(other.m_data) {}
+  CompressedStorageIterator(CompressedStorageIterator&& other) = default;
+  inline CompressedStorageIterator& operator=(const CompressedStorageIterator& other) {
+    m_index = other.m_index;
+    m_data = other.m_data;
+    return *this;
+  }
+
+  inline CompressedStorageIterator operator+(difference_type offset) const {
+    return CompressedStorageIterator(m_index + offset, m_data);
+  }
+  inline CompressedStorageIterator operator-(difference_type offset) const {
+    return CompressedStorageIterator(m_index - offset, m_data);
+  }
+  inline difference_type operator-(const CompressedStorageIterator& other) const { return m_index - other.m_index; }
+  inline CompressedStorageIterator& operator++() {
+    ++m_index;
+    return *this;
+  }
+  inline CompressedStorageIterator& operator--() {
+    --m_index;
+    return *this;
+  }
+  inline CompressedStorageIterator& operator+=(difference_type offset) {
+    m_index += offset;
+    return *this;
+  }
+  inline CompressedStorageIterator& operator-=(difference_type offset) {
+    m_index -= offset;
+    return *this;
+  }
+  inline reference operator*() const { return reference(m_data.keyPtr() + m_index, m_data.valuePtr() + m_index); }
+  inline reference operator[](int index) { return *(*this + index); }
+
+#define MAKE_COMP(OP) \
+  inline bool operator OP(const CompressedStorageIterator& other) const { return m_index OP other.m_index; }
+  MAKE_COMP(<)
+  MAKE_COMP(>)
+  MAKE_COMP(>=)
+  MAKE_COMP(<=)
+  MAKE_COMP(!=)
+  MAKE_COMP(==)
+#undef MAKE_COMP
+
+ protected:
+  difference_type m_index;
+  reference m_data;
+};
+
+template <typename Derived, class Comp, bool IsVector>
+struct inner_sort_impl {
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::StorageIndex StorageIndex;
+  static inline void run(SparseCompressedBase<Derived>& obj, Index begin, Index end) {
+    const bool is_compressed = obj.isCompressed();
+    for (Index outer = begin; outer < end; outer++) {
+      Index begin_offset = obj.outerIndexPtr()[outer];
+      Index end_offset = is_compressed ? obj.outerIndexPtr()[outer + 1] : (begin_offset + obj.innerNonZeroPtr()[outer]);
+      CompressedStorageIterator<Scalar, StorageIndex> begin_it(begin_offset, obj.innerIndexPtr(), obj.valuePtr());
+      CompressedStorageIterator<Scalar, StorageIndex> end_it(end_offset, obj.innerIndexPtr(), obj.valuePtr());
+      std::sort(begin_it, end_it, Comp());
+    }
+  }
+  static inline Index check(const SparseCompressedBase<Derived>& obj, Index begin, Index end) {
+    const bool is_compressed = obj.isCompressed();
+    for (Index outer = begin; outer < end; outer++) {
+      Index begin_offset = obj.outerIndexPtr()[outer];
+      Index end_offset = is_compressed ? obj.outerIndexPtr()[outer + 1] : (begin_offset + obj.innerNonZeroPtr()[outer]);
+      const StorageIndex* begin_it = obj.innerIndexPtr() + begin_offset;
+      const StorageIndex* end_it = obj.innerIndexPtr() + end_offset;
+      bool is_sorted = std::is_sorted(begin_it, end_it, Comp());
+      if (!is_sorted) return outer;
+    }
+    return end;
+  }
+};
+template <typename Derived, class Comp>
+struct inner_sort_impl<Derived, Comp, true> {
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::StorageIndex StorageIndex;
+  static inline void run(SparseCompressedBase<Derived>& obj, Index, Index) {
+    Index begin_offset = 0;
+    Index end_offset = obj.nonZeros();
+    CompressedStorageIterator<Scalar, StorageIndex> begin_it(begin_offset, obj.innerIndexPtr(), obj.valuePtr());
+    CompressedStorageIterator<Scalar, StorageIndex> end_it(end_offset, obj.innerIndexPtr(), obj.valuePtr());
+    std::sort(begin_it, end_it, Comp());
+  }
+  static inline Index check(const SparseCompressedBase<Derived>& obj, Index, Index) {
+    Index begin_offset = 0;
+    Index end_offset = obj.nonZeros();
+    const StorageIndex* begin_it = obj.innerIndexPtr() + begin_offset;
+    const StorageIndex* end_it = obj.innerIndexPtr() + end_offset;
+    return std::is_sorted(begin_it, end_it, Comp()) ? 1 : 0;
+  }
+};
+
+template <typename Derived>
+struct evaluator<SparseCompressedBase<Derived>> : evaluator_base<Derived> {
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::InnerIterator InnerIterator;
+
+  enum { CoeffReadCost = NumTraits<Scalar>::ReadCost, Flags = Derived::Flags };
+
+  evaluator() : m_matrix(0), m_zero(0) { EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); }
+  explicit evaluator(const Derived& mat) : m_matrix(&mat), m_zero(0) { EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); }
+
+  inline Index nonZerosEstimate() const { return m_matrix->nonZeros(); }
+
+  operator Derived&() { return m_matrix->const_cast_derived(); }
+  operator const Derived&() const { return *m_matrix; }
+
+  typedef typename DenseCoeffsBase<Derived, ReadOnlyAccessors>::CoeffReturnType CoeffReturnType;
+  const Scalar& coeff(Index row, Index col) const {
+    Index p = find(row, col);
+
+    if (p == Dynamic)
+      return m_zero;
+    else
+      return m_matrix->const_cast_derived().valuePtr()[p];
+  }
+
+  Scalar& coeffRef(Index row, Index col) {
+    Index p = find(row, col);
+    eigen_assert(p != Dynamic && "written coefficient does not exist");
+    return m_matrix->const_cast_derived().valuePtr()[p];
+  }
+
+ protected:
+  Index find(Index row, Index col) const {
+    internal::LowerBoundIndex p = m_matrix->lower_bound(row, col);
+    return p.found ? p.value : Dynamic;
+  }
+
+  const Derived* m_matrix;
+  const Scalar m_zero;
+};
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_COMPRESSED_BASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseCwiseBinaryOp.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
new file mode 100644
index 0000000..e3dcd57
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
@@ -0,0 +1,938 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_CWISE_BINARY_OP_H
+#define EIGEN_SPARSE_CWISE_BINARY_OP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// Here we have to handle 3 cases:
+//  1 - sparse op dense
+//  2 - dense op sparse
+//  3 - sparse op sparse
+// We also need to implement a 4th iterator for:
+//  4 - dense op dense
+// Finally, we also need to distinguish between the product and other operations :
+//                configuration      returned mode
+//  1 - sparse op dense    product      sparse
+//                         generic      dense
+//  2 - dense op sparse    product      sparse
+//                         generic      dense
+//  3 - sparse op sparse   product      sparse
+//                         generic      sparse
+//  4 - dense op dense     product      dense
+//                         generic      dense
+//
+// TODO: to ease compiler job, we could specialize product/quotient with a scalar
+//      and fallback to cwise-unary evaluator using bind1st_op and bind2nd_op.
+
+template <typename BinaryOp, typename Lhs, typename Rhs>
+class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Sparse> : public SparseMatrixBase<CwiseBinaryOp<BinaryOp, Lhs, Rhs> > {
+ public:
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
+  typedef SparseMatrixBase<Derived> Base;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(Derived)
+  EIGEN_STATIC_ASSERT(((!internal::is_same<typename internal::traits<Lhs>::StorageKind,
+                                           typename internal::traits<Rhs>::StorageKind>::value) ||
+                       ((internal::evaluator<Lhs>::Flags & RowMajorBit) ==
+                        (internal::evaluator<Rhs>::Flags & RowMajorBit))),
+                      THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH)
+};
+
+namespace internal {
+
+// The default evaluator performs an "arithmetic" operation on two input arrays.
+// Given input arrays 'lhs' and 'rhs' and binary functor 'func',
+// the sparse destination array 'dst' is evaluated as follows:
+//   if lhs(i,j) and rhs(i,j) are present, dst(i,j) = func(lhs(i,j), rhs(i,j))
+//   if lhs(i,j) is present and rhs(i,j) is null, dst(i,j) = func(lhs(i,j), 0)
+//   if lhs(i,j) is null and rhs(i,j) is present, dst(i,j) = func(0, rhs(i,j))
+
+// Generic "sparse OP sparse"
+template <typename XprType>
+struct binary_sparse_evaluator;
+
+template <typename BinaryOp, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IteratorBased, IteratorBased>
+    : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> > {
+ protected:
+  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
+  typedef typename evaluator<Rhs>::InnerIterator RhsIterator;
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef typename XprType::StorageIndex StorageIndex;
+
+ public:
+  class InnerIterator {
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
+        : m_lhsIter(aEval.m_lhsImpl, outer),
+          m_rhsIter(aEval.m_rhsImpl, outer),
+          m_functor(aEval.m_functor),
+          m_value(Scalar(0)) {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      if (m_lhsIter && m_rhsIter && (m_lhsIter.index() == m_rhsIter.index())) {
+        m_id = m_lhsIter.index();
+        m_value = m_functor(m_lhsIter.value(), m_rhsIter.value());
+        ++m_lhsIter;
+        ++m_rhsIter;
+      } else if (m_lhsIter && (!m_rhsIter || (m_lhsIter.index() < m_rhsIter.index()))) {
+        m_id = m_lhsIter.index();
+        m_value = m_functor(m_lhsIter.value(), Scalar(0));
+        ++m_lhsIter;
+      } else if (m_rhsIter && (!m_lhsIter || (m_lhsIter.index() > m_rhsIter.index()))) {
+        m_id = m_rhsIter.index();
+        m_value = m_functor(Scalar(0), m_rhsIter.value());
+        ++m_rhsIter;
+      } else {
+        m_id = -1;
+      }
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return Lhs::IsRowMajor ? m_lhsIter.row() : index(); }
+    EIGEN_STRONG_INLINE Index col() const { return Lhs::IsRowMajor ? index() : m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id >= 0; }
+
+   protected:
+    LhsIterator m_lhsIter;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+  };
+
+  enum {
+    CoeffReadCost =
+        int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit binary_evaluator(const XprType& xpr) : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_lhsImpl.nonZerosEstimate() + m_rhsImpl.nonZerosEstimate(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<Lhs> m_lhsImpl;
+  evaluator<Rhs> m_rhsImpl;
+};
+
+// dense op sparse
+template <typename BinaryOp, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IteratorBased>
+    : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> > {
+ protected:
+  typedef typename evaluator<Rhs>::InnerIterator RhsIterator;
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef typename XprType::StorageIndex StorageIndex;
+
+ public:
+  class InnerIterator {
+    enum { IsRowMajor = (int(Rhs::Flags) & RowMajorBit) == RowMajorBit };
+
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
+        : m_lhsEval(aEval.m_lhsImpl),
+          m_rhsIter(aEval.m_rhsImpl, outer),
+          m_functor(aEval.m_functor),
+          m_value(0),
+          m_id(-1),
+          m_innerSize(aEval.m_expr.rhs().innerSize()) {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      ++m_id;
+      if (m_id < m_innerSize) {
+        Scalar lhsVal = m_lhsEval.coeff(IsRowMajor ? m_rhsIter.outer() : m_id, IsRowMajor ? m_id : m_rhsIter.outer());
+        if (m_rhsIter && m_rhsIter.index() == m_id) {
+          m_value = m_functor(lhsVal, m_rhsIter.value());
+          ++m_rhsIter;
+        } else
+          m_value = m_functor(lhsVal, Scalar(0));
+      }
+
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const {
+      eigen_internal_assert(m_id < m_innerSize);
+      return m_value;
+    }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_rhsIter.outer() : m_id; }
+    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_rhsIter.outer(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id < m_innerSize; }
+
+   protected:
+    const evaluator<Lhs>& m_lhsEval;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+    StorageIndex m_innerSize;
+  };
+
+  enum {
+    CoeffReadCost =
+        int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit binary_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()), m_expr(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_expr.size(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<Lhs> m_lhsImpl;
+  evaluator<Rhs> m_rhsImpl;
+  const XprType& m_expr;
+};
+
+// sparse op dense
+template <typename BinaryOp, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IteratorBased, IndexBased>
+    : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> > {
+ protected:
+  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef typename XprType::StorageIndex StorageIndex;
+
+ public:
+  class InnerIterator {
+    enum { IsRowMajor = (int(Lhs::Flags) & RowMajorBit) == RowMajorBit };
+
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
+        : m_lhsIter(aEval.m_lhsImpl, outer),
+          m_rhsEval(aEval.m_rhsImpl),
+          m_functor(aEval.m_functor),
+          m_value(0),
+          m_id(-1),
+          m_innerSize(aEval.m_expr.lhs().innerSize()) {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      ++m_id;
+      if (m_id < m_innerSize) {
+        Scalar rhsVal = m_rhsEval.coeff(IsRowMajor ? m_lhsIter.outer() : m_id, IsRowMajor ? m_id : m_lhsIter.outer());
+        if (m_lhsIter && m_lhsIter.index() == m_id) {
+          m_value = m_functor(m_lhsIter.value(), rhsVal);
+          ++m_lhsIter;
+        } else
+          m_value = m_functor(Scalar(0), rhsVal);
+      }
+
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const {
+      eigen_internal_assert(m_id < m_innerSize);
+      return m_value;
+    }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_lhsIter.outer() : m_id; }
+    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_lhsIter.outer(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id < m_innerSize; }
+
+   protected:
+    LhsIterator m_lhsIter;
+    const evaluator<Rhs>& m_rhsEval;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+    StorageIndex m_innerSize;
+  };
+
+  enum {
+    CoeffReadCost =
+        int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit binary_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()), m_expr(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_expr.size(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<Lhs> m_lhsImpl;
+  evaluator<Rhs> m_rhsImpl;
+  const XprType& m_expr;
+};
+
+template <typename T, typename LhsKind = typename evaluator_traits<typename T::Lhs>::Kind,
+          typename RhsKind = typename evaluator_traits<typename T::Rhs>::Kind,
+          typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
+          typename RhsScalar = typename traits<typename T::Rhs>::Scalar>
+struct sparse_conjunction_evaluator;
+
+// "sparse .* sparse"
+template <typename T1, typename T2, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1, T2>, Lhs, Rhs>, IteratorBased, IteratorBased>
+    : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1, T2>, Lhs, Rhs> > {
+  typedef CwiseBinaryOp<scalar_product_op<T1, T2>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+// "dense .* sparse"
+template <typename T1, typename T2, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1, T2>, Lhs, Rhs>, IndexBased, IteratorBased>
+    : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1, T2>, Lhs, Rhs> > {
+  typedef CwiseBinaryOp<scalar_product_op<T1, T2>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+// "sparse .* dense"
+template <typename T1, typename T2, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1, T2>, Lhs, Rhs>, IteratorBased, IndexBased>
+    : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1, T2>, Lhs, Rhs> > {
+  typedef CwiseBinaryOp<scalar_product_op<T1, T2>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+// "sparse ./ dense"
+template <typename T1, typename T2, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_quotient_op<T1, T2>, Lhs, Rhs>, IteratorBased, IndexBased>
+    : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_quotient_op<T1, T2>, Lhs, Rhs> > {
+  typedef CwiseBinaryOp<scalar_quotient_op<T1, T2>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+// "sparse && sparse"
+template <typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs>, IteratorBased, IteratorBased>
+    : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> > {
+  typedef CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+// "dense && sparse"
+template <typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs>, IndexBased, IteratorBased>
+    : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> > {
+  typedef CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+// "sparse && dense"
+template <typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs>, IteratorBased, IndexBased>
+    : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> > {
+  typedef CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+// The conjunction "^" evaluator performs a logical "and" or set "intersection" operation on two input arrays.
+// Given input arrays 'lhs' and 'rhs' and binary functor 'func',
+// the sparse destination array 'dst' is evaluated as follows:
+//   if lhs(i,j) and rhs(i,j) are present, dst(i,j) = func(lhs(i,j), rhs(i,j))
+//   if lhs(i,j) is present and rhs(i,j) is null, dst(i,j) is null
+//   if lhs(i,j) is null and rhs(i,j) is present, dst(i,j) is null
+
+// "sparse ^ sparse"
+template <typename XprType>
+struct sparse_conjunction_evaluator<XprType, IteratorBased, IteratorBased> : evaluator_base<XprType> {
+ protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef typename evaluator<LhsArg>::InnerIterator LhsIterator;
+  typedef typename evaluator<RhsArg>::InnerIterator RhsIterator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+
+ public:
+  class InnerIterator {
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
+        : m_lhsIter(aEval.m_lhsImpl, outer), m_rhsIter(aEval.m_rhsImpl, outer), m_functor(aEval.m_functor) {
+      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index())) {
+        if (m_lhsIter.index() < m_rhsIter.index())
+          ++m_lhsIter;
+        else
+          ++m_rhsIter;
+      }
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      ++m_lhsIter;
+      ++m_rhsIter;
+      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index())) {
+        if (m_lhsIter.index() < m_rhsIter.index())
+          ++m_lhsIter;
+        else
+          ++m_rhsIter;
+      }
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(m_lhsIter.value(), m_rhsIter.value()); }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_lhsIter.index(); }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return (m_lhsIter && m_rhsIter); }
+
+   protected:
+    LhsIterator m_lhsIter;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+  };
+
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) +
+                    int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit sparse_conjunction_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const {
+    return (std::min)(m_lhsImpl.nonZerosEstimate(), m_rhsImpl.nonZerosEstimate());
+  }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+};
+
+// "dense ^ sparse"
+template <typename XprType>
+struct sparse_conjunction_evaluator<XprType, IndexBased, IteratorBased> : evaluator_base<XprType> {
+ protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef evaluator<LhsArg> LhsEvaluator;
+  typedef typename evaluator<RhsArg>::InnerIterator RhsIterator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+
+ public:
+  class InnerIterator {
+    enum { IsRowMajor = (int(RhsArg::Flags) & RowMajorBit) == RowMajorBit };
+
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
+        : m_lhsEval(aEval.m_lhsImpl), m_rhsIter(aEval.m_rhsImpl, outer), m_functor(aEval.m_functor), m_outer(outer) {}
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      ++m_rhsIter;
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const {
+      return m_functor(
+          m_lhsEval.coeff(IsRowMajor ? m_outer : m_rhsIter.index(), IsRowMajor ? m_rhsIter.index() : m_outer),
+          m_rhsIter.value());
+    }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_rhsIter.index(); }
+    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_rhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_rhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_rhsIter; }
+
+   protected:
+    const LhsEvaluator& m_lhsEval;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    const Index m_outer;
+  };
+
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) +
+                    int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit sparse_conjunction_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_rhsImpl.nonZerosEstimate(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+};
+
+// "sparse ^ dense"
+template <typename XprType>
+struct sparse_conjunction_evaluator<XprType, IteratorBased, IndexBased> : evaluator_base<XprType> {
+ protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef typename evaluator<LhsArg>::InnerIterator LhsIterator;
+  typedef evaluator<RhsArg> RhsEvaluator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+
+ public:
+  class InnerIterator {
+    enum { IsRowMajor = (int(LhsArg::Flags) & RowMajorBit) == RowMajorBit };
+
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
+        : m_lhsIter(aEval.m_lhsImpl, outer), m_rhsEval(aEval.m_rhsImpl), m_functor(aEval.m_functor), m_outer(outer) {}
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      ++m_lhsIter;
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const {
+      return m_functor(m_lhsIter.value(), m_rhsEval.coeff(IsRowMajor ? m_outer : m_lhsIter.index(),
+                                                          IsRowMajor ? m_lhsIter.index() : m_outer));
+    }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_lhsIter.index(); }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_lhsIter; }
+
+   protected:
+    LhsIterator m_lhsIter;
+    const evaluator<RhsArg>& m_rhsEval;
+    const BinaryOp& m_functor;
+    const Index m_outer;
+  };
+
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) +
+                    int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit sparse_conjunction_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_lhsImpl.nonZerosEstimate(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+};
+
+template <typename T, typename LhsKind = typename evaluator_traits<typename T::Lhs>::Kind,
+          typename RhsKind = typename evaluator_traits<typename T::Rhs>::Kind,
+          typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
+          typename RhsScalar = typename traits<typename T::Rhs>::Scalar>
+struct sparse_disjunction_evaluator;
+
+// The disjunction "v" evaluator performs a logical "or" or set "union" operation on two input arrays.
+// Given input arrays 'lhs' and 'rhs' and binary functor 'func',
+// the sparse destination array 'dst' is evaluated as follows:
+//   if lhs(i,j) and rhs(i,j) are present, dst(i,j) = func(lhs(i,j), rhs(i,j))
+//   if lhs(i,j) is present and rhs(i,j) is null, dst(i,j) = lhs(i,j)
+//   if lhs(i,j) is null and rhs(i,j) is present, dst(i,j) = rhs(i,j)
+
+// "sparse v sparse"
+template <typename XprType>
+struct sparse_disjunction_evaluator<XprType, IteratorBased, IteratorBased> : evaluator_base<XprType> {
+ protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef typename evaluator<LhsArg>::InnerIterator LhsIterator;
+  typedef typename evaluator<RhsArg>::InnerIterator RhsIterator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+
+ public:
+  class InnerIterator {
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_disjunction_evaluator& aEval, Index outer)
+        : m_lhsIter(aEval.m_lhsImpl, outer),
+          m_rhsIter(aEval.m_rhsImpl, outer),
+          m_functor(aEval.m_functor),
+          m_value(Scalar(0)) {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      if (m_lhsIter && m_rhsIter && (m_lhsIter.index() == m_rhsIter.index())) {
+        m_id = m_lhsIter.index();
+        m_value = m_functor(m_lhsIter.value(), m_rhsIter.value());
+        ++m_lhsIter;
+        ++m_rhsIter;
+      } else if (m_lhsIter && (!m_rhsIter || (m_lhsIter.index() < m_rhsIter.index()))) {
+        m_id = m_lhsIter.index();
+        m_value = m_lhsIter.value();
+        ++m_lhsIter;
+      } else if (m_rhsIter && (!m_lhsIter || (m_lhsIter.index() > m_rhsIter.index()))) {
+        m_id = m_rhsIter.index();
+        m_value = m_rhsIter.value();
+        ++m_rhsIter;
+      } else {
+        m_id = -1;
+      }
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return LhsArg::IsRowMajor ? m_lhsIter.row() : index(); }
+    EIGEN_STRONG_INLINE Index col() const { return LhsArg::IsRowMajor ? index() : m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id >= 0; }
+
+   protected:
+    LhsIterator m_lhsIter;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+  };
+
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) +
+                    int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit sparse_disjunction_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_lhsImpl.nonZerosEstimate() + m_rhsImpl.nonZerosEstimate(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+};
+
+// "dense v sparse"
+template <typename XprType>
+struct sparse_disjunction_evaluator<XprType, IndexBased, IteratorBased> : evaluator_base<XprType> {
+ protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef evaluator<LhsArg> LhsEvaluator;
+  typedef typename evaluator<RhsArg>::InnerIterator RhsIterator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+
+ public:
+  class InnerIterator {
+    enum { IsRowMajor = (int(RhsArg::Flags) & RowMajorBit) == RowMajorBit };
+
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_disjunction_evaluator& aEval, Index outer)
+        : m_lhsEval(aEval.m_lhsImpl),
+          m_rhsIter(aEval.m_rhsImpl, outer),
+          m_functor(aEval.m_functor),
+          m_value(0),
+          m_id(-1),
+          m_innerSize(aEval.m_expr.rhs().innerSize()) {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      ++m_id;
+      if (m_id < m_innerSize) {
+        Scalar lhsVal = m_lhsEval.coeff(IsRowMajor ? m_rhsIter.outer() : m_id, IsRowMajor ? m_id : m_rhsIter.outer());
+        if (m_rhsIter && m_rhsIter.index() == m_id) {
+          m_value = m_functor(lhsVal, m_rhsIter.value());
+          ++m_rhsIter;
+        } else
+          m_value = lhsVal;
+      }
+
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const {
+      eigen_internal_assert(m_id < m_innerSize);
+      return m_value;
+    }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_rhsIter.outer() : m_id; }
+    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_rhsIter.outer(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id < m_innerSize; }
+
+   protected:
+    const evaluator<LhsArg>& m_lhsEval;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+    StorageIndex m_innerSize;
+  };
+
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) +
+                    int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit sparse_disjunction_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()), m_expr(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_expr.size(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+  const XprType& m_expr;
+};
+
+// "sparse v dense"
+template <typename XprType>
+struct sparse_disjunction_evaluator<XprType, IteratorBased, IndexBased> : evaluator_base<XprType> {
+ protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef typename evaluator<LhsArg>::InnerIterator LhsIterator;
+  typedef evaluator<RhsArg> RhsEvaluator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+
+ public:
+  class InnerIterator {
+    enum { IsRowMajor = (int(LhsArg::Flags) & RowMajorBit) == RowMajorBit };
+
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_disjunction_evaluator& aEval, Index outer)
+        : m_lhsIter(aEval.m_lhsImpl, outer),
+          m_rhsEval(aEval.m_rhsImpl),
+          m_functor(aEval.m_functor),
+          m_value(0),
+          m_id(-1),
+          m_innerSize(aEval.m_expr.lhs().innerSize()) {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      ++m_id;
+      if (m_id < m_innerSize) {
+        Scalar rhsVal = m_rhsEval.coeff(IsRowMajor ? m_lhsIter.outer() : m_id, IsRowMajor ? m_id : m_lhsIter.outer());
+        if (m_lhsIter && m_lhsIter.index() == m_id) {
+          m_value = m_functor(m_lhsIter.value(), rhsVal);
+          ++m_lhsIter;
+        } else
+          m_value = rhsVal;
+      }
+
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const {
+      eigen_internal_assert(m_id < m_innerSize);
+      return m_value;
+    }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_lhsIter.outer() : m_id; }
+    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_lhsIter.outer(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id < m_innerSize; }
+
+   protected:
+    LhsIterator m_lhsIter;
+    const evaluator<RhsArg>& m_rhsEval;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+    StorageIndex m_innerSize;
+  };
+
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) +
+                    int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit sparse_disjunction_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()), m_expr(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_expr.size(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+  const XprType& m_expr;
+};
+
+// when DupFunc is wrapped with scalar_dup_op, use disjunction evaluator
+template <typename T1, typename T2, typename DupFunc, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_disjunction_op<DupFunc, T1, T2>, Lhs, Rhs>, IteratorBased, IteratorBased>
+    : sparse_disjunction_evaluator<CwiseBinaryOp<scalar_disjunction_op<DupFunc, T1, T2>, Lhs, Rhs> > {
+  typedef CwiseBinaryOp<scalar_disjunction_op<DupFunc, T1, T2>, Lhs, Rhs> XprType;
+  typedef sparse_disjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+}  // namespace internal
+
+/***************************************************************************
+ * Implementation of SparseMatrixBase and SparseCwise functions/operators
+ ***************************************************************************/
+
+template <typename Derived>
+template <typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator+=(const EigenBase<OtherDerived>& other) {
+  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator-=(const EigenBase<OtherDerived>& other) {
+  call_assignment(derived(), other.derived(), internal::assign_op<Scalar, typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_STRONG_INLINE Derived& SparseMatrixBase<Derived>::operator-=(const SparseMatrixBase<OtherDerived>& other) {
+  return derived() = derived() - other.derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_STRONG_INLINE Derived& SparseMatrixBase<Derived>::operator+=(const SparseMatrixBase<OtherDerived>& other) {
+  return derived() = derived() + other.derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator+=(const DiagonalBase<OtherDerived>& other) {
+  call_assignment_no_alias(derived(), other.derived(),
+                           internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator-=(const DiagonalBase<OtherDerived>& other) {
+  call_assignment_no_alias(derived(), other.derived(),
+                           internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_STRONG_INLINE const typename SparseMatrixBase<Derived>::template CwiseProductDenseReturnType<OtherDerived>::Type
+SparseMatrixBase<Derived>::cwiseProduct(const MatrixBase<OtherDerived>& other) const {
+  return typename CwiseProductDenseReturnType<OtherDerived>::Type(derived(), other.derived());
+}
+
+template <typename DenseDerived, typename SparseDerived>
+EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar, typename SparseDerived::Scalar>,
+                  const DenseDerived, const SparseDerived>
+    operator+(const MatrixBase<DenseDerived>& a, const SparseMatrixBase<SparseDerived>& b) {
+  return CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar, typename SparseDerived::Scalar>,
+                       const DenseDerived, const SparseDerived>(a.derived(), b.derived());
+}
+
+template <typename SparseDerived, typename DenseDerived>
+EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar, typename DenseDerived::Scalar>,
+                  const SparseDerived, const DenseDerived>
+    operator+(const SparseMatrixBase<SparseDerived>& a, const MatrixBase<DenseDerived>& b) {
+  return CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar, typename DenseDerived::Scalar>,
+                       const SparseDerived, const DenseDerived>(a.derived(), b.derived());
+}
+
+template <typename DenseDerived, typename SparseDerived>
+EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar, typename SparseDerived::Scalar>,
+                  const DenseDerived, const SparseDerived>
+    operator-(const MatrixBase<DenseDerived>& a, const SparseMatrixBase<SparseDerived>& b) {
+  return CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar, typename SparseDerived::Scalar>,
+                       const DenseDerived, const SparseDerived>(a.derived(), b.derived());
+}
+
+template <typename SparseDerived, typename DenseDerived>
+EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar, typename DenseDerived::Scalar>,
+                  const SparseDerived, const DenseDerived>
+    operator-(const SparseMatrixBase<SparseDerived>& a, const MatrixBase<DenseDerived>& b) {
+  return CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar, typename DenseDerived::Scalar>,
+                       const SparseDerived, const DenseDerived>(a.derived(), b.derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_CWISE_BINARY_OP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseCwiseUnaryOp.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
new file mode 100644
index 0000000..9fc1e66
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
@@ -0,0 +1,142 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_CWISE_UNARY_OP_H
+#define EIGEN_SPARSE_CWISE_UNARY_OP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename UnaryOp, typename ArgType>
+struct unary_evaluator<CwiseUnaryOp<UnaryOp, ArgType>, IteratorBased>
+    : public evaluator_base<CwiseUnaryOp<UnaryOp, ArgType> > {
+ public:
+  typedef CwiseUnaryOp<UnaryOp, ArgType> XprType;
+
+  class InnerIterator;
+
+  enum {
+    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit unary_evaluator(const XprType& op) : m_functor(op.functor()), m_argImpl(op.nestedExpression()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_argImpl.nonZerosEstimate(); }
+
+ protected:
+  typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
+
+  const UnaryOp m_functor;
+  evaluator<ArgType> m_argImpl;
+};
+
+template <typename UnaryOp, typename ArgType>
+class unary_evaluator<CwiseUnaryOp<UnaryOp, ArgType>, IteratorBased>::InnerIterator
+    : public unary_evaluator<CwiseUnaryOp<UnaryOp, ArgType>, IteratorBased>::EvalIterator {
+ protected:
+  typedef typename XprType::Scalar Scalar;
+  typedef typename unary_evaluator<CwiseUnaryOp<UnaryOp, ArgType>, IteratorBased>::EvalIterator Base;
+
+ public:
+  EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
+      : Base(unaryOp.m_argImpl, outer), m_functor(unaryOp.m_functor) {}
+
+  EIGEN_STRONG_INLINE InnerIterator& operator++() {
+    Base::operator++();
+    return *this;
+  }
+
+  EIGEN_STRONG_INLINE Scalar value() const { return m_functor(Base::value()); }
+
+ protected:
+  const UnaryOp m_functor;
+
+ private:
+  Scalar& valueRef();
+};
+
+template <typename ViewOp, typename ArgType>
+struct unary_evaluator<CwiseUnaryView<ViewOp, ArgType>, IteratorBased>
+    : public evaluator_base<CwiseUnaryView<ViewOp, ArgType> > {
+ public:
+  typedef CwiseUnaryView<ViewOp, ArgType> XprType;
+
+  class InnerIterator;
+
+  enum {
+    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<ViewOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit unary_evaluator(const XprType& op) : m_functor(op.functor()), m_argImpl(op.nestedExpression()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<ViewOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+ protected:
+  typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
+
+  const ViewOp m_functor;
+  evaluator<ArgType> m_argImpl;
+};
+
+template <typename ViewOp, typename ArgType>
+class unary_evaluator<CwiseUnaryView<ViewOp, ArgType>, IteratorBased>::InnerIterator
+    : public unary_evaluator<CwiseUnaryView<ViewOp, ArgType>, IteratorBased>::EvalIterator {
+ protected:
+  typedef typename XprType::Scalar Scalar;
+  typedef typename unary_evaluator<CwiseUnaryView<ViewOp, ArgType>, IteratorBased>::EvalIterator Base;
+
+ public:
+  EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
+      : Base(unaryOp.m_argImpl, outer), m_functor(unaryOp.m_functor) {}
+
+  EIGEN_STRONG_INLINE InnerIterator& operator++() {
+    Base::operator++();
+    return *this;
+  }
+
+  EIGEN_STRONG_INLINE Scalar value() const { return m_functor(Base::value()); }
+  EIGEN_STRONG_INLINE Scalar& valueRef() { return m_functor(Base::valueRef()); }
+
+ protected:
+  const ViewOp m_functor;
+};
+
+}  // end namespace internal
+
+template <typename Derived>
+EIGEN_STRONG_INLINE Derived& SparseMatrixBase<Derived>::operator*=(const Scalar& other) {
+  typedef typename internal::evaluator<Derived>::InnerIterator EvalIterator;
+  internal::evaluator<Derived> thisEval(derived());
+  for (Index j = 0; j < outerSize(); ++j)
+    for (EvalIterator i(thisEval, j); i; ++i) i.valueRef() *= other;
+  return derived();
+}
+
+template <typename Derived>
+EIGEN_STRONG_INLINE Derived& SparseMatrixBase<Derived>::operator/=(const Scalar& other) {
+  typedef typename internal::evaluator<Derived>::InnerIterator EvalIterator;
+  internal::evaluator<Derived> thisEval(derived());
+  for (Index j = 0; j < outerSize(); ++j)
+    for (EvalIterator i(thisEval, j); i; ++i) i.valueRef() /= other;
+  return derived();
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_CWISE_UNARY_OP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseDenseProduct.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseDenseProduct.h
new file mode 100644
index 0000000..98f81dd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseDenseProduct.h
@@ -0,0 +1,489 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEDENSEPRODUCT_H
+#define EIGEN_SPARSEDENSEPRODUCT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <>
+struct product_promote_storage_type<Sparse, Dense, OuterProduct> {
+  typedef Sparse ret;
+};
+template <>
+struct product_promote_storage_type<Dense, Sparse, OuterProduct> {
+  typedef Sparse ret;
+};
+
+// Type trait to detect if a sparse type supports direct compressed storage access
+// (i.e., has valuePtr(), innerIndexPtr(), outerIndexPtr(), isCompressed()).
+// All types deriving from SparseCompressedBase provide these methods.
+template <typename T>
+struct has_compressed_storage : std::is_base_of<SparseCompressedBase<T>, T> {};
+
+template <typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType,
+          int LhsStorageOrder = ((SparseLhsType::Flags & RowMajorBit) == RowMajorBit) ? RowMajor : ColMajor,
+          bool ColPerCol = ((DenseRhsType::Flags & RowMajorBit) == 0) || DenseRhsType::ColsAtCompileTime == 1>
+struct sparse_time_dense_product_impl;
+
+// RowMajor, single column (ColPerCol=true): CSR SpMV
+template <typename SparseLhsType, typename DenseRhsType, typename DenseResType>
+struct sparse_time_dense_product_impl<SparseLhsType, DenseRhsType, DenseResType, typename DenseResType::Scalar,
+                                      RowMajor, true> {
+  typedef internal::remove_all_t<SparseLhsType> Lhs;
+  typedef internal::remove_all_t<DenseRhsType> Rhs;
+  typedef internal::remove_all_t<DenseResType> Res;
+  typedef typename evaluator<Lhs>::InnerIterator LhsInnerIterator;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename Res::Scalar ResScalar;
+
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res,
+                  const typename Res::Scalar& alpha) {
+    LhsEval lhsEval(lhs);
+    Index n = lhs.outerSize();
+
+    for (Index c = 0; c < rhs.cols(); ++c) {
+      runCol(lhsEval, lhs, rhs, res, alpha, n, c, std::integral_constant<bool, has_compressed_storage<Lhs>::value>());
+    }
+  }
+
+  // Direct pointer path: works for both compressed and non-compressed storage.
+  static void runCol(const LhsEval& /*lhsEval*/, const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res,
+                     const ResScalar& alpha, Index n, Index c, std::true_type /* has_compressed_storage */) {
+    runColImpl(lhs, rhs, res, alpha, n, c, std::integral_constant<bool, bool(DenseRhsType::Flags & DirectAccessBit)>());
+  }
+
+  template <typename RhsT>
+  static void runColImpl(const SparseLhsType& lhs, const RhsT& rhs, DenseResType& res, const ResScalar& alpha, Index n,
+                         Index c, std::true_type) {
+    const Lhs& mat = lhs;
+    const auto* vals = mat.valuePtr();
+    const auto* inds = mat.innerIndexPtr();
+    const auto* outer = mat.outerIndexPtr();
+    const auto* innerNnz = mat.innerNonZeroPtr();
+    // The fast rhs pointer path requires unit inner stride (common case: VectorXd, contiguous matrix column).
+    if (rhs.innerStride() == 1) {
+      const auto* x = rhs.data() + c * rhs.outerStride();
+#ifdef EIGEN_HAS_OPENMP
+      Index threads = Eigen::nbThreads();
+      if (threads > 1 && mat.nonZeros() > 20000) {
+#pragma omp parallel for schedule(dynamic, (n + threads * 4 - 1) / (threads * 4)) num_threads(threads)
+        for (Index i = 0; i < n; ++i) {
+          Index k = outer[i];
+          const Index end = innerNnz ? outer[i] + innerNnz[i] : outer[i + 1];
+          ResScalar sum0(0), sum1(0);
+          for (; k < end; ++k) {
+            sum0 += vals[k] * x[inds[k]];
+            ++k;
+            if (k < end) {
+              sum1 += vals[k] * x[inds[k]];
+            }
+          }
+          res.coeffRef(i, c) += alpha * (sum0 + sum1);
+        }
+      } else
+#endif
+      {
+        for (Index i = 0; i < n; ++i) {
+          Index k = outer[i];
+          const Index end = innerNnz ? outer[i] + innerNnz[i] : outer[i + 1];
+          // Two independent accumulators to break the dependency chain
+          ResScalar sum0(0), sum1(0);
+          for (; k < end; ++k) {
+            sum0 += vals[k] * x[inds[k]];
+            ++k;
+            if (k < end) {
+              sum1 += vals[k] * x[inds[k]];
+            }
+          }
+          res.coeffRef(i, c) += alpha * (sum0 + sum1);
+        }
+      }
+    } else {
+      runColImpl(lhs, rhs, res, alpha, n, c, std::false_type());
+    }
+  }
+
+  // Use fall-back path without direct access to rhs.
+  template <typename RhsT>
+  static void runColImpl(const SparseLhsType& lhs, const RhsT& rhs, DenseResType& res, const ResScalar& alpha, Index n,
+                         Index c, std::false_type) {
+    const Lhs& mat = lhs;
+    const auto* vals = mat.valuePtr();
+    const auto* inds = mat.innerIndexPtr();
+    const auto* outer = mat.outerIndexPtr();
+    const auto* innerNnz = mat.innerNonZeroPtr();
+    // Non-unit rhs stride (or no direct access): use direct pointers for sparse side, coeff() for rhs
+    for (Index i = 0; i < n; ++i) {
+      Index k = outer[i];
+      const Index end = innerNnz ? outer[i] + innerNnz[i] : outer[i + 1];
+      ResScalar sum0(0), sum1(0);
+      for (; k < end; ++k) {
+        sum0 += vals[k] * rhs.coeff(inds[k], c);
+        ++k;
+        if (k < end) {
+          sum1 += vals[k] * rhs.coeff(inds[k], c);
+        }
+      }
+      res.coeffRef(i, c) += alpha * (sum0 + sum1);
+    }
+  }
+
+  // Iterator fallback path
+  static void runCol(const LhsEval& lhsEval, const SparseLhsType& /*lhs*/, const DenseRhsType& rhs, DenseResType& res,
+                     const ResScalar& alpha, Index n, Index c, std::false_type /* has_compressed_storage */) {
+#ifdef EIGEN_HAS_OPENMP
+    Index threads = Eigen::nbThreads();
+    if (threads > 1 && lhsEval.nonZerosEstimate() > 20000) {
+#pragma omp parallel for schedule(dynamic, (n + threads * 4 - 1) / (threads * 4)) num_threads(threads)
+      for (Index i = 0; i < n; ++i) processRow(lhsEval, rhs, res, alpha, i, c);
+    } else
+#endif
+    {
+      for (Index i = 0; i < n; ++i) processRow(lhsEval, rhs, res, alpha, i, c);
+    }
+  }
+
+  static void processRow(const LhsEval& lhsEval, const DenseRhsType& rhs, DenseResType& res, const ResScalar& alpha,
+                         Index i, Index col) {
+    ResScalar tmp_a(0);
+    ResScalar tmp_b(0);
+    for (LhsInnerIterator it(lhsEval, i); it; ++it) {
+      tmp_a += it.value() * rhs.coeff(it.index(), col);
+      ++it;
+      if (it) {
+        tmp_b += it.value() * rhs.coeff(it.index(), col);
+      }
+    }
+    res.coeffRef(i, col) += alpha * (tmp_a + tmp_b);
+  }
+};
+
+// ColMajor, single column (ColPerCol=true): CSC SpMV
+template <typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType>
+struct sparse_time_dense_product_impl<SparseLhsType, DenseRhsType, DenseResType, AlphaType, ColMajor, true> {
+  typedef internal::remove_all_t<SparseLhsType> Lhs;
+  typedef internal::remove_all_t<DenseRhsType> Rhs;
+  typedef internal::remove_all_t<DenseResType> Res;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename LhsEval::InnerIterator LhsInnerIterator;
+
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha) {
+    runImpl(lhs, rhs, res, alpha, std::integral_constant<bool, has_compressed_storage<Lhs>::value>());
+  }
+
+  // Direct pointer path: works for both compressed and non-compressed storage.
+  static void runImpl(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha,
+                      std::true_type /* has_compressed_storage */) {
+    typedef typename Lhs::Scalar LhsScalar;
+    typedef typename Lhs::StorageIndex StorageIndex;
+    const Lhs& mat = lhs;
+    const LhsScalar* vals = mat.valuePtr();
+    const StorageIndex* inds = mat.innerIndexPtr();
+    const auto* outer = mat.outerIndexPtr();
+    const auto* innerNnz = mat.innerNonZeroPtr();
+    // The fast result pointer path requires contiguous ColMajor result layout.
+    // Transpose<ColMajor> reports innerStride()==1 but is actually RowMajor, so check both.
+    if (!(Res::Flags & RowMajorBit) && res.innerStride() == 1) {
+      for (Index c = 0; c < rhs.cols(); ++c) {
+        typename Res::Scalar* y = res.data() + c * res.outerStride();
+        for (Index j = 0; j < lhs.outerSize(); ++j) {
+          typename ScalarBinaryOpTraits<AlphaType, typename Rhs::Scalar>::ReturnType rhs_j(alpha * rhs.coeff(j, c));
+          const Index start = outer[j];
+          const Index end = innerNnz ? outer[j] + innerNnz[j] : outer[j + 1];
+          Index k = start;
+          // 4-way unrolled scatter-add (no SIMD: writes are scattered)
+          for (; k + 3 < end; k += 4) {
+            y[inds[k]] += vals[k] * rhs_j;
+            y[inds[k + 1]] += vals[k + 1] * rhs_j;
+            y[inds[k + 2]] += vals[k + 2] * rhs_j;
+            y[inds[k + 3]] += vals[k + 3] * rhs_j;
+          }
+          for (; k < end; ++k) y[inds[k]] += vals[k] * rhs_j;
+        }
+      }
+    } else {
+      // Non-unit result stride: use coeffRef() for result access
+      for (Index c = 0; c < rhs.cols(); ++c) {
+        for (Index j = 0; j < lhs.outerSize(); ++j) {
+          typename ScalarBinaryOpTraits<AlphaType, typename Rhs::Scalar>::ReturnType rhs_j(alpha * rhs.coeff(j, c));
+          const Index start = outer[j];
+          const Index end = innerNnz ? outer[j] + innerNnz[j] : outer[j + 1];
+          for (Index k = start; k < end; ++k) res.coeffRef(inds[k], c) += vals[k] * rhs_j;
+        }
+      }
+    }
+  }
+
+  // Iterator-based fallback
+  static void runImpl(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha,
+                      std::false_type /* has_compressed_storage */) {
+    LhsEval lhsEval(lhs);
+    for (Index c = 0; c < rhs.cols(); ++c) {
+      for (Index j = 0; j < lhs.outerSize(); ++j) {
+        typename ScalarBinaryOpTraits<AlphaType, typename Rhs::Scalar>::ReturnType rhs_j(alpha * rhs.coeff(j, c));
+        for (LhsInnerIterator it(lhsEval, j); it; ++it) res.coeffRef(it.index(), c) += it.value() * rhs_j;
+      }
+    }
+  }
+};
+
+// RowMajor, multiple columns (ColPerCol=false): sparse * dense_matrix
+template <typename SparseLhsType, typename DenseRhsType, typename DenseResType>
+struct sparse_time_dense_product_impl<SparseLhsType, DenseRhsType, DenseResType, typename DenseResType::Scalar,
+                                      RowMajor, false> {
+  typedef internal::remove_all_t<SparseLhsType> Lhs;
+  typedef internal::remove_all_t<DenseRhsType> Rhs;
+  typedef internal::remove_all_t<DenseResType> Res;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename LhsEval::InnerIterator LhsInnerIterator;
+
+  static constexpr bool IsCompressedLhs = has_compressed_storage<Lhs>::value;
+
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res,
+                  const typename Res::Scalar& alpha) {
+    Index n = lhs.rows();
+    LhsEval lhsEval(lhs);
+
+#ifdef EIGEN_HAS_OPENMP
+    Index threads = Eigen::nbThreads();
+    // This 20000 threshold has been found experimentally on 2D and 3D Poisson problems.
+    // It basically represents the minimal amount of work to be done to be worth it.
+    if (threads > 1 && lhsEval.nonZerosEstimate() * rhs.cols() > 20000) {
+#pragma omp parallel for schedule(dynamic, (n + threads * 4 - 1) / (threads * 4)) num_threads(threads)
+      for (Index i = 0; i < n; ++i)
+        processRow(lhsEval, lhs, rhs, res, alpha, i, std::integral_constant<bool, IsCompressedLhs>());
+    } else
+#endif
+    {
+      for (Index i = 0; i < n; ++i)
+        processRow(lhsEval, lhs, rhs, res, alpha, i, std::integral_constant<bool, IsCompressedLhs>());
+    }
+  }
+
+  // Direct pointer path: works for both compressed and non-compressed storage.
+  static void processRow(const LhsEval& /*lhsEval*/, const SparseLhsType& lhs, const DenseRhsType& rhs, Res& res,
+                         const typename Res::Scalar& alpha, Index i, std::true_type /* has_compressed_storage */) {
+    typedef typename Lhs::Scalar LhsScalar;
+    typedef typename Lhs::StorageIndex StorageIndex;
+    const Lhs& mat = lhs;
+    const LhsScalar* vals = mat.valuePtr();
+    const StorageIndex* inds = mat.innerIndexPtr();
+    const Index start = mat.outerIndexPtr()[i];
+    const auto* innerNnz = mat.innerNonZeroPtr();
+    const Index end = innerNnz ? start + innerNnz[i] : mat.outerIndexPtr()[i + 1];
+    typename Res::RowXpr res_i(res.row(i));
+    for (Index k = start; k < end; ++k) res_i += (alpha * vals[k]) * rhs.row(inds[k]);
+  }
+
+  static void processRow(const LhsEval& lhsEval, const SparseLhsType& /*lhs*/, const DenseRhsType& rhs, Res& res,
+                         const typename Res::Scalar& alpha, Index i, std::false_type /* has_compressed_storage */) {
+    typename Res::RowXpr res_i(res.row(i));
+    for (LhsInnerIterator it(lhsEval, i); it; ++it) res_i += (alpha * it.value()) * rhs.row(it.index());
+  }
+};
+
+// ColMajor, multiple columns (ColPerCol=false): sparse * dense_matrix
+template <typename SparseLhsType, typename DenseRhsType, typename DenseResType>
+struct sparse_time_dense_product_impl<SparseLhsType, DenseRhsType, DenseResType, typename DenseResType::Scalar,
+                                      ColMajor, false> {
+  typedef internal::remove_all_t<SparseLhsType> Lhs;
+  typedef internal::remove_all_t<DenseRhsType> Rhs;
+  typedef internal::remove_all_t<DenseResType> Res;
+  typedef typename evaluator<Lhs>::InnerIterator LhsInnerIterator;
+
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res,
+                  const typename Res::Scalar& alpha) {
+    runImpl(lhs, rhs, res, alpha, std::integral_constant<bool, has_compressed_storage<Lhs>::value>());
+  }
+
+  // Direct pointer path: works for both compressed and non-compressed storage.
+  static void runImpl(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res,
+                      const typename Res::Scalar& alpha, std::true_type /* has_compressed_storage */) {
+    typedef typename Lhs::Scalar LhsScalar;
+    typedef typename Lhs::StorageIndex StorageIndex;
+    const Lhs& mat = lhs;
+    const LhsScalar* vals = mat.valuePtr();
+    const StorageIndex* inds = mat.innerIndexPtr();
+    const auto* outer = mat.outerIndexPtr();
+    const auto* innerNnz = mat.innerNonZeroPtr();
+    for (Index j = 0; j < lhs.outerSize(); ++j) {
+      typename Rhs::ConstRowXpr rhs_j(rhs.row(j));
+      const Index start = outer[j];
+      const Index end = innerNnz ? outer[j] + innerNnz[j] : outer[j + 1];
+      for (Index k = start; k < end; ++k) res.row(inds[k]) += (alpha * vals[k]) * rhs_j;
+    }
+  }
+
+  static void runImpl(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res,
+                      const typename Res::Scalar& alpha, std::false_type /* has_compressed_storage */) {
+    evaluator<Lhs> lhsEval(lhs);
+    for (Index j = 0; j < lhs.outerSize(); ++j) {
+      typename Rhs::ConstRowXpr rhs_j(rhs.row(j));
+      for (LhsInnerIterator it(lhsEval, j); it; ++it) res.row(it.index()) += (alpha * it.value()) * rhs_j;
+    }
+  }
+};
+
+template <typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType>
+inline void sparse_time_dense_product(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res,
+                                      const AlphaType& alpha) {
+  sparse_time_dense_product_impl<SparseLhsType, DenseRhsType, DenseResType, AlphaType>::run(lhs, rhs, res, alpha);
+}
+
+}  // end namespace internal
+
+namespace internal {
+
+template <typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, SparseShape, DenseShape, ProductType>
+    : generic_product_impl_base<Lhs, Rhs, generic_product_impl<Lhs, Rhs, SparseShape, DenseShape, ProductType> > {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  template <typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) {
+    typedef typename nested_eval<Lhs, ((Rhs::Flags & RowMajorBit) == 0) ? 1 : Rhs::ColsAtCompileTime>::type LhsNested;
+    typedef typename nested_eval<Rhs, ((Lhs::Flags & RowMajorBit) == 0) ? 1 : Dynamic>::type RhsNested;
+    LhsNested lhsNested(lhs);
+    RhsNested rhsNested(rhs);
+    internal::sparse_time_dense_product(lhsNested, rhsNested, dst, alpha);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, SparseTriangularShape, DenseShape, ProductType>
+    : generic_product_impl<Lhs, Rhs, SparseShape, DenseShape, ProductType> {};
+
+template <typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, DenseShape, SparseShape, ProductType>
+    : generic_product_impl_base<Lhs, Rhs, generic_product_impl<Lhs, Rhs, DenseShape, SparseShape, ProductType> > {
+  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
+
+  template <typename Dst>
+  static void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha) {
+    typedef typename nested_eval<Lhs, ((Rhs::Flags & RowMajorBit) == 0) ? Dynamic : 1>::type LhsNested;
+    typedef typename nested_eval<Rhs, ((Lhs::Flags & RowMajorBit) == RowMajorBit) ? 1 : Lhs::RowsAtCompileTime>::type
+        RhsNested;
+    LhsNested lhsNested(lhs);
+    RhsNested rhsNested(rhs);
+
+    // transpose everything
+    Transpose<Dst> dstT(dst);
+    internal::sparse_time_dense_product(rhsNested.transpose(), lhsNested.transpose(), dstT, alpha);
+  }
+};
+
+template <typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, DenseShape, SparseTriangularShape, ProductType>
+    : generic_product_impl<Lhs, Rhs, DenseShape, SparseShape, ProductType> {};
+
+template <typename LhsT, typename RhsT, bool NeedToTranspose>
+struct sparse_dense_outer_product_evaluator {
+ protected:
+  typedef std::conditional_t<NeedToTranspose, RhsT, LhsT> Lhs1;
+  typedef std::conditional_t<NeedToTranspose, LhsT, RhsT> ActualRhs;
+  typedef Product<LhsT, RhsT, DefaultProduct> ProdXprType;
+
+  // if the actual left-hand side is a dense vector,
+  // then build a sparse-view so that we can seamlessly iterate over it.
+  typedef std::conditional_t<is_same<typename internal::traits<Lhs1>::StorageKind, Sparse>::value, Lhs1,
+                             SparseView<Lhs1> >
+      ActualLhs;
+  typedef std::conditional_t<is_same<typename internal::traits<Lhs1>::StorageKind, Sparse>::value, Lhs1 const&,
+                             SparseView<Lhs1> >
+      LhsArg;
+
+  typedef evaluator<ActualLhs> LhsEval;
+  typedef evaluator<ActualRhs> RhsEval;
+  typedef typename evaluator<ActualLhs>::InnerIterator LhsIterator;
+  typedef typename ProdXprType::Scalar Scalar;
+
+ public:
+  enum { Flags = NeedToTranspose ? RowMajorBit : 0, CoeffReadCost = HugeCost };
+
+  class InnerIterator : public LhsIterator {
+   public:
+    InnerIterator(const sparse_dense_outer_product_evaluator& xprEval, Index outer)
+        : LhsIterator(xprEval.m_lhsXprImpl, 0),
+          m_outer(outer),
+          m_empty(false),
+          m_factor(get(xprEval.m_rhsXprImpl, outer, typename internal::traits<ActualRhs>::StorageKind())) {}
+
+    EIGEN_STRONG_INLINE Index outer() const { return m_outer; }
+    EIGEN_STRONG_INLINE Index row() const { return NeedToTranspose ? m_outer : LhsIterator::index(); }
+    EIGEN_STRONG_INLINE Index col() const { return NeedToTranspose ? LhsIterator::index() : m_outer; }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return LhsIterator::value() * m_factor; }
+    EIGEN_STRONG_INLINE operator bool() const { return LhsIterator::operator bool() && (!m_empty); }
+
+   protected:
+    Scalar get(const RhsEval& rhs, Index outer, Dense = Dense()) const { return rhs.coeff(outer); }
+
+    Scalar get(const RhsEval& rhs, Index outer, Sparse = Sparse()) {
+      typename RhsEval::InnerIterator it(rhs, outer);
+      if (it && it.index() == 0 && it.value() != Scalar(0)) return it.value();
+      m_empty = true;
+      return Scalar(0);
+    }
+
+    Index m_outer;
+    bool m_empty;
+    Scalar m_factor;
+  };
+
+  sparse_dense_outer_product_evaluator(const Lhs1& lhs, const ActualRhs& rhs)
+      : m_lhs(lhs), m_lhsXprImpl(m_lhs), m_rhsXprImpl(rhs) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  // transpose case
+  sparse_dense_outer_product_evaluator(const ActualRhs& rhs, const Lhs1& lhs)
+      : m_lhs(lhs), m_lhsXprImpl(m_lhs), m_rhsXprImpl(rhs) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+ protected:
+  const LhsArg m_lhs;
+  evaluator<ActualLhs> m_lhsXprImpl;
+  evaluator<ActualRhs> m_rhsXprImpl;
+};
+
+// sparse * dense outer product
+template <typename Lhs, typename Rhs>
+struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, OuterProduct, SparseShape, DenseShape>
+    : sparse_dense_outer_product_evaluator<Lhs, Rhs, Lhs::IsRowMajor> {
+  typedef sparse_dense_outer_product_evaluator<Lhs, Rhs, Lhs::IsRowMajor> Base;
+
+  typedef Product<Lhs, Rhs> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+
+  explicit product_evaluator(const XprType& xpr) : Base(xpr.lhs(), xpr.rhs()) {}
+};
+
+template <typename Lhs, typename Rhs>
+struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, OuterProduct, DenseShape, SparseShape>
+    : sparse_dense_outer_product_evaluator<Lhs, Rhs, Rhs::IsRowMajor> {
+  typedef sparse_dense_outer_product_evaluator<Lhs, Rhs, Rhs::IsRowMajor> Base;
+
+  typedef Product<Lhs, Rhs> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+
+  explicit product_evaluator(const XprType& xpr) : Base(xpr.lhs(), xpr.rhs()) {}
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSEDENSEPRODUCT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseDiagonalProduct.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseDiagonalProduct.h
new file mode 100644
index 0000000..e02fdaa
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseDiagonalProduct.h
@@ -0,0 +1,146 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_DIAGONAL_PRODUCT_H
+#define EIGEN_SPARSE_DIAGONAL_PRODUCT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// The product of a diagonal matrix with a sparse matrix can be easily
+// implemented using expression template.
+// We have two consider very different cases:
+// 1 - diag * row-major sparse
+//     => each inner vector <=> scalar * sparse vector product
+//     => so we can reuse CwiseUnaryOp::InnerIterator
+// 2 - diag * col-major sparse
+//     => each inner vector <=> densevector * sparse vector cwise product
+//     => again, we can reuse specialization of CwiseBinaryOp::InnerIterator
+//        for that particular case
+// The two other cases are symmetric.
+
+namespace internal {
+
+enum { SDP_AsScalarProduct, SDP_AsCwiseProduct };
+
+template <typename SparseXprType, typename DiagonalCoeffType, int SDP_Tag>
+struct sparse_diagonal_product_evaluator;
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, ProductTag, DiagonalShape, SparseShape>
+    : public sparse_diagonal_product_evaluator<Rhs, typename Lhs::DiagonalVectorType,
+                                               Rhs::Flags & RowMajorBit ? SDP_AsScalarProduct : SDP_AsCwiseProduct> {
+  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
+  enum {
+    CoeffReadCost = HugeCost,
+    Flags = Rhs::Flags & RowMajorBit,
+    Alignment = 0
+  };  // FIXME: compute proper CoeffReadCost and propagate Flags.
+
+  typedef sparse_diagonal_product_evaluator<Rhs, typename Lhs::DiagonalVectorType,
+                                            Rhs::Flags & RowMajorBit ? SDP_AsScalarProduct : SDP_AsCwiseProduct>
+      Base;
+  explicit product_evaluator(const XprType &xpr) : Base(xpr.rhs(), xpr.lhs().diagonal()) {}
+};
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, ProductTag, SparseShape, DiagonalShape>
+    : public sparse_diagonal_product_evaluator<Lhs, Transpose<const typename Rhs::DiagonalVectorType>,
+                                               Lhs::Flags & RowMajorBit ? SDP_AsCwiseProduct : SDP_AsScalarProduct> {
+  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
+  enum {
+    CoeffReadCost = HugeCost,
+    Flags = Lhs::Flags & RowMajorBit,
+    Alignment = 0
+  };  // FIXME: compute proper CoeffReadCost and propagate Flags.
+
+  typedef sparse_diagonal_product_evaluator<Lhs, Transpose<const typename Rhs::DiagonalVectorType>,
+                                            Lhs::Flags & RowMajorBit ? SDP_AsCwiseProduct : SDP_AsScalarProduct>
+      Base;
+  explicit product_evaluator(const XprType &xpr) : Base(xpr.lhs(), xpr.rhs().diagonal().transpose()) {}
+};
+
+template <typename SparseXprType, typename DiagonalCoeffType>
+struct sparse_diagonal_product_evaluator<SparseXprType, DiagonalCoeffType, SDP_AsScalarProduct> {
+ protected:
+  typedef typename evaluator<SparseXprType>::InnerIterator SparseXprInnerIterator;
+  typedef typename SparseXprType::Scalar Scalar;
+
+ public:
+  class InnerIterator : public SparseXprInnerIterator {
+   public:
+    InnerIterator(const sparse_diagonal_product_evaluator &xprEval, Index outer)
+        : SparseXprInnerIterator(xprEval.m_sparseXprImpl, outer), m_coeff(xprEval.m_diagCoeffImpl.coeff(outer)) {}
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_coeff * SparseXprInnerIterator::value(); }
+
+   protected:
+    typename DiagonalCoeffType::Scalar m_coeff;
+  };
+
+  sparse_diagonal_product_evaluator(const SparseXprType &sparseXpr, const DiagonalCoeffType &diagCoeff)
+      : m_sparseXprImpl(sparseXpr), m_diagCoeffImpl(diagCoeff) {}
+
+  Index nonZerosEstimate() const { return m_sparseXprImpl.nonZerosEstimate(); }
+
+ protected:
+  evaluator<SparseXprType> m_sparseXprImpl;
+  evaluator<DiagonalCoeffType> m_diagCoeffImpl;
+};
+
+template <typename SparseXprType, typename DiagCoeffType>
+struct sparse_diagonal_product_evaluator<SparseXprType, DiagCoeffType, SDP_AsCwiseProduct> {
+  typedef typename SparseXprType::Scalar Scalar;
+  typedef typename SparseXprType::StorageIndex StorageIndex;
+
+  typedef typename nested_eval<DiagCoeffType, SparseXprType::IsRowMajor ? SparseXprType::RowsAtCompileTime
+                                                                        : SparseXprType::ColsAtCompileTime>::type
+      DiagCoeffNested;
+
+  class InnerIterator {
+    typedef typename evaluator<SparseXprType>::InnerIterator SparseXprIter;
+
+   public:
+    InnerIterator(const sparse_diagonal_product_evaluator &xprEval, Index outer)
+        : m_sparseIter(xprEval.m_sparseXprEval, outer), m_diagCoeffNested(xprEval.m_diagCoeffNested) {}
+
+    inline Scalar value() const { return m_sparseIter.value() * m_diagCoeffNested.coeff(index()); }
+    inline StorageIndex index() const { return m_sparseIter.index(); }
+    inline Index outer() const { return m_sparseIter.outer(); }
+    inline Index col() const { return SparseXprType::IsRowMajor ? m_sparseIter.index() : m_sparseIter.outer(); }
+    inline Index row() const { return SparseXprType::IsRowMajor ? m_sparseIter.outer() : m_sparseIter.index(); }
+
+    EIGEN_STRONG_INLINE InnerIterator &operator++() {
+      ++m_sparseIter;
+      return *this;
+    }
+    inline operator bool() const { return m_sparseIter; }
+
+   protected:
+    SparseXprIter m_sparseIter;
+    DiagCoeffNested m_diagCoeffNested;
+  };
+
+  sparse_diagonal_product_evaluator(const SparseXprType &sparseXpr, const DiagCoeffType &diagCoeff)
+      : m_sparseXprEval(sparseXpr), m_diagCoeffNested(diagCoeff) {}
+
+  Index nonZerosEstimate() const { return m_sparseXprEval.nonZerosEstimate(); }
+
+ protected:
+  evaluator<SparseXprType> m_sparseXprEval;
+  DiagCoeffNested m_diagCoeffNested;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_DIAGONAL_PRODUCT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseDot.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseDot.h
new file mode 100644
index 0000000..485605f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseDot.h
@@ -0,0 +1,100 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_DOT_H
+#define EIGEN_SPARSE_DOT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Derived>
+template <typename OtherDerived>
+inline typename internal::traits<Derived>::Scalar SparseMatrixBase<Derived>::dot(
+    const MatrixBase<OtherDerived>& other) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived, OtherDerived)
+  EIGEN_STATIC_ASSERT(
+      (internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(size() == other.size());
+  eigen_assert(other.size() > 0 && "you are using a non initialized vector");
+
+  internal::evaluator<Derived> thisEval(derived());
+  typename internal::evaluator<Derived>::InnerIterator i(thisEval, 0);
+  // Two accumulators, which breaks the dependency chain on the accumulator
+  // and allows more instruction-level parallelism in the following loop.
+  Scalar res1(0);
+  Scalar res2(0);
+  for (; i; ++i) {
+    res1 = numext::madd<Scalar>(numext::conj(i.value()), other.coeff(i.index()), res1);
+    ++i;
+    if (i) {
+      res2 = numext::madd<Scalar>(numext::conj(i.value()), other.coeff(i.index()), res2);
+    }
+  }
+  return res1 + res2;
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+inline typename internal::traits<Derived>::Scalar SparseMatrixBase<Derived>::dot(
+    const SparseMatrixBase<OtherDerived>& other) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived, OtherDerived)
+  EIGEN_STATIC_ASSERT(
+      (internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(size() == other.size());
+
+  internal::evaluator<Derived> thisEval(derived());
+  typename internal::evaluator<Derived>::InnerIterator i(thisEval, 0);
+
+  internal::evaluator<OtherDerived> otherEval(other.derived());
+  typename internal::evaluator<OtherDerived>::InnerIterator j(otherEval, 0);
+
+  Scalar res(0);
+  while (i && j) {
+    if (i.index() == j.index()) {
+      res = numext::madd<Scalar>(numext::conj(i.value()), j.value(), res);
+      ++i;
+      ++j;
+    } else if (i.index() < j.index())
+      ++i;
+    else
+      ++j;
+  }
+  return res;
+}
+
+template <typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real SparseMatrixBase<Derived>::squaredNorm()
+    const {
+  return numext::real((*this).cwiseAbs2().sum());
+}
+
+template <typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real SparseMatrixBase<Derived>::norm() const {
+  using std::sqrt;
+  return sqrt(squaredNorm());
+}
+
+template <typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real SparseMatrixBase<Derived>::blueNorm()
+    const {
+  return internal::blueNorm_impl(*this);
+}
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_DOT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseFuzzy.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseFuzzy.h
new file mode 100644
index 0000000..2285845
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseFuzzy.h
@@ -0,0 +1,31 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_FUZZY_H
+#define EIGEN_SPARSE_FUZZY_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Derived>
+template <typename OtherDerived>
+bool SparseMatrixBase<Derived>::isApprox(const SparseMatrixBase<OtherDerived>& other, const RealScalar& prec) const {
+  const typename internal::nested_eval<Derived, 2, PlainObject>::type actualA(derived());
+  std::conditional_t<bool(IsRowMajor) == bool(OtherDerived::IsRowMajor),
+                     const typename internal::nested_eval<OtherDerived, 2, PlainObject>::type, const PlainObject>
+      actualB(other.derived());
+
+  return (actualA - actualB).squaredNorm() <= prec * prec * numext::mini(actualA.squaredNorm(), actualB.squaredNorm());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_FUZZY_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseMap.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseMap.h
new file mode 100644
index 0000000..73e29c7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseMap.h
@@ -0,0 +1,295 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_MAP_H
+#define EIGEN_SPARSE_MAP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct traits<Map<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> >
+    : public traits<SparseMatrix<MatScalar, MatOptions, MatIndex> > {
+  typedef SparseMatrix<MatScalar, MatOptions, MatIndex> PlainObjectType;
+  typedef traits<PlainObjectType> TraitsBase;
+  enum { Flags = TraitsBase::Flags & (~NestByRefBit) };
+};
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct traits<Map<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> >
+    : public traits<SparseMatrix<MatScalar, MatOptions, MatIndex> > {
+  typedef SparseMatrix<MatScalar, MatOptions, MatIndex> PlainObjectType;
+  typedef traits<PlainObjectType> TraitsBase;
+  enum { Flags = TraitsBase::Flags & (~(NestByRefBit | LvalueBit)) };
+};
+
+}  // end namespace internal
+
+template <typename Derived,
+          int Level = internal::accessors_level<Derived>::has_write_access ? WriteAccessors : ReadOnlyAccessors>
+class SparseMapBase;
+
+/** \ingroup SparseCore_Module
+ * class SparseMapBase
+ * \brief Common base class for Map and Ref instance of sparse matrix and vector.
+ */
+template <typename Derived>
+class SparseMapBase<Derived, ReadOnlyAccessors> : public SparseCompressedBase<Derived> {
+ public:
+  typedef SparseCompressedBase<Derived> Base;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::StorageIndex StorageIndex;
+  enum { IsRowMajor = Base::IsRowMajor };
+  using Base::operator=;
+
+ protected:
+  typedef std::conditional_t<bool(internal::is_lvalue<Derived>::value), Scalar*, const Scalar*> ScalarPointer;
+  typedef std::conditional_t<bool(internal::is_lvalue<Derived>::value), StorageIndex*, const StorageIndex*>
+      IndexPointer;
+
+  Index m_outerSize;
+  Index m_innerSize;
+  Array<StorageIndex, 2, 1> m_zero_nnz;
+  IndexPointer m_outerIndex;
+  IndexPointer m_innerIndices;
+  ScalarPointer m_values;
+  IndexPointer m_innerNonZeros;
+
+ public:
+  /** \copydoc SparseMatrixBase::rows() */
+  inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
+  /** \copydoc SparseMatrixBase::cols() */
+  inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
+  /** \copydoc SparseMatrixBase::innerSize() */
+  inline Index innerSize() const { return m_innerSize; }
+  /** \copydoc SparseMatrixBase::outerSize() */
+  inline Index outerSize() const { return m_outerSize; }
+  /** \copydoc SparseCompressedBase::nonZeros */
+  inline Index nonZeros() const { return m_zero_nnz[1]; }
+
+  /** \copydoc SparseCompressedBase::isCompressed */
+  bool isCompressed() const { return m_innerNonZeros == 0; }
+
+  //----------------------------------------
+  // direct access interface
+  /** \copydoc SparseMatrix::valuePtr */
+  inline const Scalar* valuePtr() const { return m_values; }
+  /** \copydoc SparseMatrix::innerIndexPtr */
+  inline const StorageIndex* innerIndexPtr() const { return m_innerIndices; }
+  /** \copydoc SparseMatrix::outerIndexPtr */
+  inline const StorageIndex* outerIndexPtr() const { return m_outerIndex; }
+  /** \copydoc SparseMatrix::innerNonZeroPtr */
+  inline const StorageIndex* innerNonZeroPtr() const { return m_innerNonZeros; }
+  //----------------------------------------
+
+  /** \copydoc SparseMatrix::coeff */
+  inline Scalar coeff(Index row, Index col) const {
+    const Index outer = IsRowMajor ? row : col;
+    const Index inner = IsRowMajor ? col : row;
+
+    Index start = m_outerIndex[outer];
+    Index end = isCompressed() ? m_outerIndex[outer + 1] : start + m_innerNonZeros[outer];
+    if (start == end)
+      return Scalar(0);
+    else if (end > 0 && inner == m_innerIndices[end - 1])
+      return m_values[end - 1];
+    // ^^  optimization: let's first check if it is the last coefficient
+    // (very common in high level algorithms)
+
+    const StorageIndex* r = std::lower_bound(&m_innerIndices[start], &m_innerIndices[end - 1], inner);
+    const Index id = r - &m_innerIndices[0];
+    return ((*r == inner) && (id < end)) ? m_values[id] : Scalar(0);
+  }
+
+  inline SparseMapBase(Index rows, Index cols, Index nnz, IndexPointer outerIndexPtr, IndexPointer innerIndexPtr,
+                       ScalarPointer valuePtr, IndexPointer innerNonZerosPtr = 0)
+      : m_outerSize(IsRowMajor ? rows : cols),
+        m_innerSize(IsRowMajor ? cols : rows),
+        m_zero_nnz(0, internal::convert_index<StorageIndex>(nnz)),
+        m_outerIndex(outerIndexPtr),
+        m_innerIndices(innerIndexPtr),
+        m_values(valuePtr),
+        m_innerNonZeros(innerNonZerosPtr) {}
+
+  // for vectors
+  inline SparseMapBase(Index size, Index nnz, IndexPointer innerIndexPtr, ScalarPointer valuePtr)
+      : m_outerSize(1),
+        m_innerSize(size),
+        m_zero_nnz(0, internal::convert_index<StorageIndex>(nnz)),
+        m_outerIndex(m_zero_nnz.data()),
+        m_innerIndices(innerIndexPtr),
+        m_values(valuePtr),
+        m_innerNonZeros(0) {}
+
+  /** Empty destructor */
+  inline ~SparseMapBase() {}
+
+ protected:
+  inline SparseMapBase() {}
+};
+
+/** \ingroup SparseCore_Module
+ * class SparseMapBase
+ * \brief Common base class for writable Map and Ref instance of sparse matrix and vector.
+ */
+template <typename Derived>
+class SparseMapBase<Derived, WriteAccessors> : public SparseMapBase<Derived, ReadOnlyAccessors> {
+  typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
+
+ public:
+  typedef SparseMapBase<Derived, ReadOnlyAccessors> Base;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::StorageIndex StorageIndex;
+  enum { IsRowMajor = Base::IsRowMajor };
+
+  using Base::operator=;
+
+ public:
+  //----------------------------------------
+  // direct access interface
+  using Base::innerIndexPtr;
+  using Base::innerNonZeroPtr;
+  using Base::outerIndexPtr;
+  using Base::valuePtr;
+  /** \copydoc SparseMatrix::valuePtr */
+  inline Scalar* valuePtr() { return Base::m_values; }
+  /** \copydoc SparseMatrix::innerIndexPtr */
+  inline StorageIndex* innerIndexPtr() { return Base::m_innerIndices; }
+  /** \copydoc SparseMatrix::outerIndexPtr */
+  inline StorageIndex* outerIndexPtr() { return Base::m_outerIndex; }
+  /** \copydoc SparseMatrix::innerNonZeroPtr */
+  inline StorageIndex* innerNonZeroPtr() { return Base::m_innerNonZeros; }
+  //----------------------------------------
+
+  /** \copydoc SparseMatrix::coeffRef */
+  inline Scalar& coeffRef(Index row, Index col) {
+    const Index outer = IsRowMajor ? row : col;
+    const Index inner = IsRowMajor ? col : row;
+
+    Index start = Base::m_outerIndex[outer];
+    Index end = Base::isCompressed() ? Base::m_outerIndex[outer + 1] : start + Base::m_innerNonZeros[outer];
+    eigen_assert(end >= start && "you probably called coeffRef on a non finalized matrix");
+    eigen_assert(end > start && "coeffRef cannot be called on a zero coefficient");
+    StorageIndex* r = std::lower_bound(&Base::m_innerIndices[start], &Base::m_innerIndices[end], inner);
+    const Index id = r - &Base::m_innerIndices[0];
+    eigen_assert((*r == inner) && (id < end) && "coeffRef cannot be called on a zero coefficient");
+    return const_cast<Scalar*>(Base::m_values)[id];
+  }
+
+  inline SparseMapBase(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr, StorageIndex* innerIndexPtr,
+                       Scalar* valuePtr, StorageIndex* innerNonZerosPtr = 0)
+      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr) {}
+
+  // for vectors
+  inline SparseMapBase(Index size, Index nnz, StorageIndex* innerIndexPtr, Scalar* valuePtr)
+      : Base(size, nnz, innerIndexPtr, valuePtr) {}
+
+  /** Empty destructor */
+  inline ~SparseMapBase() {}
+
+ protected:
+  inline SparseMapBase() {}
+};
+
+/** \ingroup SparseCore_Module
+ *
+ * \brief Specialization of class Map for SparseMatrix-like storage.
+ *
+ * \tparam SparseMatrixType the equivalent sparse matrix type of the referenced data, it must be a template instance of
+ * class SparseMatrix.
+ *
+ * \sa class Map, class SparseMatrix, class Ref<SparseMatrixType,Options>
+ */
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Map<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>
+    : public SparseMapBase<Map<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> >
+#else
+template <typename SparseMatrixType>
+class Map<SparseMatrixType> : public SparseMapBase<Derived, WriteAccessors>
+#endif
+{
+ public:
+  typedef SparseMapBase<Map> Base;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(Map)
+  enum { IsRowMajor = Base::IsRowMajor };
+
+ public:
+  /** Constructs a read-write Map to a sparse matrix of size \a rows x \a cols, containing \a nnz non-zero coefficients,
+   * stored as a sparse format as defined by the pointers \a outerIndexPtr, \a innerIndexPtr, and \a valuePtr.
+   * If the optional parameter \a innerNonZerosPtr is the null pointer, then a standard compressed format is assumed.
+   * The inner indices must be sorted appropriately.
+   *
+   * This constructor is available only if \c SparseMatrixType is non-const.
+   *
+   * More details on the expected storage schemes are given in the \ref TutorialSparse "manual pages".
+   */
+  inline Map(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr, StorageIndex* innerIndexPtr,
+             Scalar* valuePtr, StorageIndex* innerNonZerosPtr = 0)
+      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr) {}
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** Empty destructor */
+  inline ~Map() {}
+};
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Map<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>
+    : public SparseMapBase<Map<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> > {
+ public:
+  typedef SparseMapBase<Map> Base;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(Map)
+  enum { IsRowMajor = Base::IsRowMajor };
+
+ public:
+#endif
+  /** This is the const version of the above constructor.
+   *
+   * This constructor is available only if \c SparseMatrixType is const, e.g.:
+   * \code Map<const SparseMatrix<double> >  \endcode
+   */
+  inline Map(Index rows, Index cols, Index nnz, const StorageIndex* outerIndexPtr, const StorageIndex* innerIndexPtr,
+             const Scalar* valuePtr, const StorageIndex* innerNonZerosPtr = 0)
+      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr) {}
+
+  /** Empty destructor */
+  inline ~Map() {}
+};
+
+namespace internal {
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Map<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> >
+    : evaluator<SparseCompressedBase<Map<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> > > {
+  typedef evaluator<SparseCompressedBase<Map<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> > >
+      Base;
+  typedef Map<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> XprType;
+  evaluator() : Base() {}
+  explicit evaluator(const XprType& mat) : Base(mat) {}
+};
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Map<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> >
+    : evaluator<SparseCompressedBase<Map<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> > > {
+  typedef evaluator<
+      SparseCompressedBase<Map<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> > >
+      Base;
+  typedef Map<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> XprType;
+  evaluator() : Base() {}
+  explicit evaluator(const XprType& mat) : Base(mat) {}
+};
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_MAP_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseMatrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseMatrix.h
new file mode 100644
index 0000000..782e595
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseMatrix.h
@@ -0,0 +1,1876 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEMATRIX_H
+#define EIGEN_SPARSEMATRIX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup SparseCore_Module
+ *
+ * \class SparseMatrix
+ *
+ * \brief A versatible sparse matrix representation
+ *
+ * This class implements a more versatile variants of the common \em compressed row/column storage format.
+ * Each colmun's (resp. row) non zeros are stored as a pair of value with associated row (resp. colmiun) index.
+ * All the non zeros are stored in a single large buffer. Unlike the \em compressed format, there might be extra
+ * space in between the nonzeros of two successive colmuns (resp. rows) such that insertion of new non-zero
+ * can be done with limited memory reallocation and copies.
+ *
+ * A call to the function makeCompressed() turns the matrix into the standard \em compressed format
+ * compatible with many library.
+ *
+ * More details on this storage sceheme are given in the \ref TutorialSparse "manual pages".
+ *
+ * \tparam Scalar_ the scalar type, i.e. the type of the coefficients
+ * \tparam Options_ Union of bit flags controlling the storage scheme. Currently the only possibility
+ *                 is ColMajor or RowMajor. The default is 0 which means column-major.
+ * \tparam StorageIndex_ the type of the indices. It has to be a \b signed type (e.g., short, int, std::ptrdiff_t).
+ * Default is \c int.
+ *
+ * \warning In %Eigen 3.2, the undocumented type \c SparseMatrix::Index was improperly defined as the storage index type
+ * (e.g., int), whereas it is now (starting from %Eigen 3.3) deprecated and always defined as Eigen::Index. Codes making
+ * use of \c SparseMatrix::Index, might thus likely have to be changed to use \c SparseMatrix::StorageIndex instead.
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEMATRIX_PLUGIN.
+ */
+
+namespace internal {
+template <typename Scalar_, int Options_, typename StorageIndex_>
+struct traits<SparseMatrix<Scalar_, Options_, StorageIndex_>> {
+  typedef Scalar_ Scalar;
+  typedef StorageIndex_ StorageIndex;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    Options = Options_,
+    Flags = Options_ | NestByRefBit | LvalueBit | CompressedAccessBit,
+    SupportedAccessPatterns = InnerRandomAccessPattern
+  };
+};
+
+template <typename Scalar_, int Options_, typename StorageIndex_, int DiagIndex>
+struct traits<Diagonal<SparseMatrix<Scalar_, Options_, StorageIndex_>, DiagIndex>> {
+  typedef SparseMatrix<Scalar_, Options_, StorageIndex_> MatrixType;
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef StorageIndex_ StorageIndex;
+  typedef MatrixXpr XprKind;
+
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = 1,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = 1,
+    Flags = LvalueBit
+  };
+};
+
+template <typename Scalar_, int Options_, typename StorageIndex_, int DiagIndex>
+struct traits<Diagonal<const SparseMatrix<Scalar_, Options_, StorageIndex_>, DiagIndex>>
+    : public traits<Diagonal<SparseMatrix<Scalar_, Options_, StorageIndex_>, DiagIndex>> {
+  enum { Flags = 0 };
+};
+
+template <typename StorageIndex>
+struct sparse_reserve_op {
+  EIGEN_DEVICE_FUNC sparse_reserve_op(Index begin, Index end, Index size) {
+    Index range = numext::mini(end - begin, size);
+    m_begin = begin;
+    m_end = begin + range;
+    m_val = StorageIndex(size / range);
+    m_remainder = StorageIndex(size % range);
+  }
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex operator()(IndexType i) const {
+    if ((i >= m_begin) && (i < m_end))
+      return m_val + ((i - m_begin) < m_remainder ? 1 : 0);
+    else
+      return 0;
+  }
+  StorageIndex m_val, m_remainder;
+  Index m_begin, m_end;
+};
+
+template <typename Scalar>
+struct functor_traits<sparse_reserve_op<Scalar>> {
+  enum { Cost = 1, PacketAccess = false, IsRepeatable = true };
+};
+
+}  // end namespace internal
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+class SparseMatrix : public SparseCompressedBase<SparseMatrix<Scalar_, Options_, StorageIndex_>> {
+  typedef SparseCompressedBase<SparseMatrix> Base;
+  using Base::convert_index;
+  friend class SparseVector<Scalar_, 0, StorageIndex_>;
+  template <typename, typename, typename, typename, typename>
+  friend struct internal::Assignment;
+
+ public:
+  using Base::isCompressed;
+  using Base::nonZeros;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseMatrix)
+  using Base::operator+=;
+  using Base::operator-=;
+
+  typedef Eigen::Map<SparseMatrix<Scalar, Options_, StorageIndex>> Map;
+  typedef Diagonal<SparseMatrix> DiagonalReturnType;
+  typedef Diagonal<const SparseMatrix> ConstDiagonalReturnType;
+  typedef typename Base::InnerIterator InnerIterator;
+  typedef typename Base::ReverseInnerIterator ReverseInnerIterator;
+
+  using Base::IsRowMajor;
+  typedef internal::CompressedStorage<Scalar, StorageIndex> Storage;
+  enum { Options = Options_ };
+
+  typedef typename Base::IndexVector IndexVector;
+  typedef typename Base::ScalarVector ScalarVector;
+
+ protected:
+  typedef SparseMatrix<Scalar, IsRowMajor ? ColMajor : RowMajor, StorageIndex> TransposedSparseMatrix;
+
+  Index m_outerSize;
+  Index m_innerSize;
+  StorageIndex* m_outerIndex;
+  StorageIndex* m_innerNonZeros;  // optional, if null then the data is compressed
+  Storage m_data;
+
+ public:
+  /** \returns the number of rows of the matrix */
+  inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
+  /** \returns the number of columns of the matrix */
+  inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
+
+  /** \returns the number of rows (resp. columns) of the matrix if the storage order column major (resp. row major) */
+  inline Index innerSize() const { return m_innerSize; }
+  /** \returns the number of columns (resp. rows) of the matrix if the storage order column major (resp. row major) */
+  inline Index outerSize() const { return m_outerSize; }
+
+  /** \returns a const pointer to the array of values.
+   * This function is aimed at interoperability with other libraries.
+   * \sa innerIndexPtr(), outerIndexPtr() */
+  inline const Scalar* valuePtr() const { return m_data.valuePtr(); }
+  /** \returns a non-const pointer to the array of values.
+   * This function is aimed at interoperability with other libraries.
+   * \sa innerIndexPtr(), outerIndexPtr() */
+  inline Scalar* valuePtr() { return m_data.valuePtr(); }
+
+  /** \returns a const pointer to the array of inner indices.
+   * This function is aimed at interoperability with other libraries.
+   * \sa valuePtr(), outerIndexPtr() */
+  inline const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); }
+  /** \returns a non-const pointer to the array of inner indices.
+   * This function is aimed at interoperability with other libraries.
+   * \sa valuePtr(), outerIndexPtr() */
+  inline StorageIndex* innerIndexPtr() { return m_data.indexPtr(); }
+
+  /** \returns a const pointer to the array of the starting positions of the inner vectors.
+   * This function is aimed at interoperability with other libraries.
+   * \sa valuePtr(), innerIndexPtr() */
+  inline const StorageIndex* outerIndexPtr() const { return m_outerIndex; }
+  /** \returns a non-const pointer to the array of the starting positions of the inner vectors.
+   * This function is aimed at interoperability with other libraries.
+   * \sa valuePtr(), innerIndexPtr() */
+  inline StorageIndex* outerIndexPtr() { return m_outerIndex; }
+
+  /** \returns a const pointer to the array of the number of non zeros of the inner vectors.
+   * This function is aimed at interoperability with other libraries.
+   * \warning it returns the null pointer 0 in compressed mode */
+  inline const StorageIndex* innerNonZeroPtr() const { return m_innerNonZeros; }
+  /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors.
+   * This function is aimed at interoperability with other libraries.
+   * \warning it returns the null pointer 0 in compressed mode */
+  inline StorageIndex* innerNonZeroPtr() { return m_innerNonZeros; }
+
+  /** \internal */
+  constexpr Storage& data() { return m_data; }
+  /** \internal */
+  constexpr const Storage& data() const { return m_data; }
+
+  /** \returns the value of the matrix at position \a i, \a j
+   * This function returns Scalar(0) if the element is an explicit \em zero */
+  inline Scalar coeff(Index row, Index col) const {
+    eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+
+    const Index outer = IsRowMajor ? row : col;
+    const Index inner = IsRowMajor ? col : row;
+    Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer + 1];
+    return m_data.atInRange(m_outerIndex[outer], end, inner);
+  }
+
+  /** \returns a non-const reference to the value of the matrix at position \a i, \a j.
+   *
+   * If the element does not exist then it is inserted via the insert(Index,Index) function
+   * which itself turns the matrix into a non compressed form if that was not the case.
+   * The output parameter `inserted` is set to true.
+   *
+   * Otherwise, if the element does exist, `inserted` will be set to false.
+   *
+   * This is a O(log(nnz_j)) operation (binary search) plus the cost of insert(Index,Index)
+   * function if the element does not already exist.
+   */
+  inline Scalar& findOrInsertCoeff(Index row, Index col, bool* inserted) {
+    eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+    const Index outer = IsRowMajor ? row : col;
+    const Index inner = IsRowMajor ? col : row;
+    Index start = m_outerIndex[outer];
+    Index end = isCompressed() ? m_outerIndex[outer + 1] : m_outerIndex[outer] + m_innerNonZeros[outer];
+    eigen_assert(end >= start && "you probably called coeffRef on a non finalized matrix");
+    Index dst = start == end ? end : m_data.searchLowerIndex(start, end, inner);
+    if (dst == end) {
+      Index capacity = m_outerIndex[outer + 1] - end;
+      if (capacity > 0) {
+        // implies uncompressed: push to back of vector
+        m_innerNonZeros[outer]++;
+        m_data.index(end) = StorageIndex(inner);
+        m_data.value(end) = Scalar(0);
+        if (inserted != nullptr) {
+          *inserted = true;
+        }
+        return m_data.value(end);
+      }
+    }
+    if ((dst < end) && (m_data.index(dst) == inner)) {
+      // this coefficient exists, return a reference to it
+      if (inserted != nullptr) {
+        *inserted = false;
+      }
+      return m_data.value(dst);
+    } else {
+      if (inserted != nullptr) {
+        *inserted = true;
+      }
+      // insertion will require reconfiguring the buffer
+      return insertAtByOuterInner(outer, inner, dst);
+    }
+  }
+
+  /** \returns a non-const reference to the value of the matrix at position \a i, \a j
+   *
+   * If the element does not exist then it is inserted via the insert(Index,Index) function
+   * which itself turns the matrix into a non compressed form if that was not the case.
+   *
+   * This is a O(log(nnz_j)) operation (binary search) plus the cost of insert(Index,Index)
+   * function if the element does not already exist.
+   */
+  inline Scalar& coeffRef(Index row, Index col) { return findOrInsertCoeff(row, col, nullptr); }
+
+  /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
+   * The non zero coefficient must \b not already exist.
+   *
+   * If the matrix \c *this is in compressed mode, then \c *this is turned into uncompressed
+   * mode while reserving room for 2 x this->innerSize() non zeros if reserve(Index) has not been called earlier.
+   * In this case, the insertion procedure is optimized for a \e sequential insertion mode where elements are assumed to
+   * be inserted by increasing outer-indices.
+   *
+   * If that's not the case, then it is strongly recommended to either use a triplet-list to assemble the matrix, or to
+   * first call reserve(const SizesType &) to reserve the appropriate number of non-zero elements per inner vector.
+   *
+   * Assuming memory has been appropriately reserved, this function performs a sorted insertion in O(1)
+   * if the elements of each inner vector are inserted in increasing inner index order, and in O(nnz_j) for a random
+   * insertion.
+   *
+   */
+  inline Scalar& insert(Index row, Index col);
+
+ public:
+  /** Removes all non zeros but keep allocated memory
+   *
+   * This function does not free the currently allocated memory. To release as much as memory as possible,
+   * call \code mat.data().squeeze(); \endcode after resizing it.
+   *
+   * \sa resize(Index,Index), data()
+   */
+  inline void setZero() {
+    m_data.clear();
+    using std::fill_n;
+    fill_n(m_outerIndex, m_outerSize + 1, StorageIndex(0));
+    if (m_innerNonZeros) {
+      fill_n(m_innerNonZeros, m_outerSize, StorageIndex(0));
+    }
+  }
+
+  /** Preallocates \a reserveSize non zeros.
+   *
+   * Precondition: the matrix must be in compressed mode. */
+  inline void reserve(Index reserveSize) {
+    eigen_assert(isCompressed() && "This function does not make sense in non compressed mode.");
+    m_data.reserve(reserveSize);
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** Preallocates \a reserveSize[\c j] non zeros for each column (resp. row) \c j.
+    *
+    * This function turns the matrix in non-compressed mode.
+    *
+    * The type \c SizesType must expose the following interface:
+      \code
+      typedef value_type;
+      const value_type& operator[](i) const;
+      \endcode
+    * for \c i in the [0,this->outerSize()[ range.
+    * Typical choices include std::vector<int>, Eigen::VectorXi, Eigen::VectorXi::Constant, etc.
+    */
+  template <class SizesType>
+  inline void reserve(const SizesType& reserveSizes);
+#else
+  template <class SizesType>
+  inline void reserve(const SizesType& reserveSizes,
+                      const typename SizesType::value_type& enableif = typename SizesType::value_type()) {
+    EIGEN_UNUSED_VARIABLE(enableif);
+    reserveInnerVectors(reserveSizes);
+  }
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+ protected:
+  template <class SizesType>
+  inline void reserveInnerVectors(const SizesType& reserveSizes) {
+    if (isCompressed()) {
+      Index totalReserveSize = 0;
+      for (Index j = 0; j < m_outerSize; ++j) totalReserveSize += internal::convert_index<Index>(reserveSizes[j]);
+
+      // if reserveSizes is empty, don't do anything!
+      if (totalReserveSize == 0) return;
+
+      // turn the matrix into non-compressed mode
+      m_innerNonZeros = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize);
+
+      // temporarily use m_innerSizes to hold the new starting points.
+      StorageIndex* newOuterIndex = m_innerNonZeros;
+
+      Index count = 0;
+      for (Index j = 0; j < m_outerSize; ++j) {
+        newOuterIndex[j] = internal::convert_index<StorageIndex>(count);
+        Index reserveSize = internal::convert_index<Index>(reserveSizes[j]);
+        count += reserveSize + internal::convert_index<Index>(m_outerIndex[j + 1] - m_outerIndex[j]);
+      }
+
+      m_data.reserve(totalReserveSize);
+      StorageIndex previousOuterIndex = m_outerIndex[m_outerSize];
+      for (Index j = m_outerSize - 1; j >= 0; --j) {
+        StorageIndex innerNNZ = previousOuterIndex - m_outerIndex[j];
+        StorageIndex begin = m_outerIndex[j];
+        StorageIndex end = begin + innerNNZ;
+        StorageIndex target = newOuterIndex[j];
+        internal::smart_memmove(innerIndexPtr() + begin, innerIndexPtr() + end, innerIndexPtr() + target);
+        internal::smart_memmove(valuePtr() + begin, valuePtr() + end, valuePtr() + target);
+        previousOuterIndex = m_outerIndex[j];
+        m_outerIndex[j] = newOuterIndex[j];
+        m_innerNonZeros[j] = innerNNZ;
+      }
+      if (m_outerSize > 0)
+        m_outerIndex[m_outerSize] = m_outerIndex[m_outerSize - 1] + m_innerNonZeros[m_outerSize - 1] +
+                                    internal::convert_index<StorageIndex>(reserveSizes[m_outerSize - 1]);
+
+      m_data.resize(m_outerIndex[m_outerSize]);
+    } else {
+      StorageIndex* newOuterIndex = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize + 1);
+
+      Index count = 0;
+      for (Index j = 0; j < m_outerSize; ++j) {
+        newOuterIndex[j] = internal::convert_index<StorageIndex>(count);
+        Index alreadyReserved =
+            internal::convert_index<Index>(m_outerIndex[j + 1] - m_outerIndex[j] - m_innerNonZeros[j]);
+        Index reserveSize = internal::convert_index<Index>(reserveSizes[j]);
+        Index toReserve = numext::maxi(reserveSize, alreadyReserved);
+        count += toReserve + internal::convert_index<Index>(m_innerNonZeros[j]);
+      }
+      newOuterIndex[m_outerSize] = internal::convert_index<StorageIndex>(count);
+
+      m_data.resize(count);
+      for (Index j = m_outerSize - 1; j >= 0; --j) {
+        StorageIndex innerNNZ = m_innerNonZeros[j];
+        StorageIndex begin = m_outerIndex[j];
+        StorageIndex target = newOuterIndex[j];
+        m_data.moveChunk(begin, target, innerNNZ);
+      }
+
+      std::swap(m_outerIndex, newOuterIndex);
+      internal::conditional_aligned_delete_auto<StorageIndex, true>(newOuterIndex, m_outerSize + 1);
+    }
+  }
+
+ public:
+  //--- low level purely coherent filling ---
+
+  /** \internal
+   * \returns a reference to the non zero coefficient at position \a row, \a col assuming that:
+   * - the nonzero does not already exist
+   * - the new coefficient is the last one according to the storage order
+   *
+   * Before filling a given inner vector you must call the statVec(Index) function.
+   *
+   * After an insertion session, you should call the finalize() function.
+   *
+   * \sa insert, insertBackByOuterInner, startVec */
+  inline Scalar& insertBack(Index row, Index col) {
+    return insertBackByOuterInner(IsRowMajor ? row : col, IsRowMajor ? col : row);
+  }
+
+  /** \internal
+   * \sa insertBack, startVec */
+  inline Scalar& insertBackByOuterInner(Index outer, Index inner) {
+    eigen_assert(Index(m_outerIndex[outer + 1]) == m_data.size() && "Invalid ordered insertion (invalid outer index)");
+    eigen_assert((m_outerIndex[outer + 1] - m_outerIndex[outer] == 0 || m_data.index(m_data.size() - 1) < inner) &&
+                 "Invalid ordered insertion (invalid inner index)");
+    StorageIndex p = m_outerIndex[outer + 1];
+    ++m_outerIndex[outer + 1];
+    m_data.append(Scalar(0), inner);
+    return m_data.value(p);
+  }
+
+  /** \internal
+   * \warning use it only if you know what you are doing */
+  inline Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner) {
+    StorageIndex p = m_outerIndex[outer + 1];
+    ++m_outerIndex[outer + 1];
+    m_data.append(Scalar(0), inner);
+    return m_data.value(p);
+  }
+
+  /** \internal
+   * \sa insertBack, insertBackByOuterInner */
+  inline void startVec(Index outer) {
+    eigen_assert(m_outerIndex[outer] == Index(m_data.size()) &&
+                 "You must call startVec for each inner vector sequentially");
+    eigen_assert(m_outerIndex[outer + 1] == 0 && "You must call startVec for each inner vector sequentially");
+    m_outerIndex[outer + 1] = m_outerIndex[outer];
+  }
+
+  /** \internal
+   * Must be called after inserting a set of non zero entries using the low level compressed API.
+   */
+  inline void finalize() {
+    if (isCompressed()) {
+      StorageIndex size = internal::convert_index<StorageIndex>(m_data.size());
+      Index i = m_outerSize;
+      // find the last filled column
+      while (i >= 0 && m_outerIndex[i] == 0) --i;
+      ++i;
+      while (i <= m_outerSize) {
+        m_outerIndex[i] = size;
+        ++i;
+      }
+    }
+  }
+
+  // remove outer vectors j, j+1 ... j+num-1 and resize the matrix
+  void removeOuterVectors(Index j, Index num = 1) {
+    eigen_assert(num >= 0 && j >= 0 && j + num <= m_outerSize && "Invalid parameters");
+
+    const Index newRows = IsRowMajor ? m_outerSize - num : rows();
+    const Index newCols = IsRowMajor ? cols() : m_outerSize - num;
+
+    const Index begin = j + num;
+    const Index end = m_outerSize;
+    const Index target = j;
+
+    // if the removed vectors are not empty, uncompress the matrix
+    if (m_outerIndex[j + num] > m_outerIndex[j]) uncompress();
+
+    // shift m_outerIndex and m_innerNonZeros [num] to the left
+    internal::smart_memmove(m_outerIndex + begin, m_outerIndex + end + 1, m_outerIndex + target);
+    if (!isCompressed())
+      internal::smart_memmove(m_innerNonZeros + begin, m_innerNonZeros + end, m_innerNonZeros + target);
+
+    // if m_outerIndex[0] > 0, shift the data within the first vector while it is easy to do so
+    if (m_outerIndex[0] > StorageIndex(0)) {
+      uncompress();
+      const Index from = internal::convert_index<Index>(m_outerIndex[0]);
+      const Index to = Index(0);
+      const Index chunkSize = internal::convert_index<Index>(m_innerNonZeros[0]);
+      m_data.moveChunk(from, to, chunkSize);
+      m_outerIndex[0] = StorageIndex(0);
+    }
+
+    // truncate the matrix to the smaller size
+    conservativeResize(newRows, newCols);
+  }
+
+  // insert empty outer vectors at indices j, j+1 ... j+num-1 and resize the matrix
+  void insertEmptyOuterVectors(Index j, Index num = 1) {
+    using std::fill_n;
+    eigen_assert(num >= 0 && j >= 0 && j < m_outerSize && "Invalid parameters");
+
+    const Index newRows = IsRowMajor ? m_outerSize + num : rows();
+    const Index newCols = IsRowMajor ? cols() : m_outerSize + num;
+
+    const Index begin = j;
+    const Index end = m_outerSize;
+    const Index target = j + num;
+
+    // expand the matrix to the larger size
+    conservativeResize(newRows, newCols);
+
+    // shift m_outerIndex and m_innerNonZeros [num] to the right
+    internal::smart_memmove(m_outerIndex + begin, m_outerIndex + end + 1, m_outerIndex + target);
+    // m_outerIndex[begin] == m_outerIndex[target], set all indices in this range to same value
+    fill_n(m_outerIndex + begin, num, m_outerIndex[begin]);
+
+    if (!isCompressed()) {
+      internal::smart_memmove(m_innerNonZeros + begin, m_innerNonZeros + end, m_innerNonZeros + target);
+      // set the nonzeros of the newly inserted vectors to 0
+      fill_n(m_innerNonZeros + begin, num, StorageIndex(0));
+    }
+  }
+
+  template <typename InputIterators>
+  void setFromTriplets(const InputIterators& begin, const InputIterators& end);
+
+  template <typename InputIterators, typename DupFunctor>
+  void setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
+
+  template <typename Derived, typename DupFunctor>
+  void collapseDuplicates(DenseBase<Derived>& wi, DupFunctor dup_func = DupFunctor());
+
+  template <typename InputIterators>
+  void setFromSortedTriplets(const InputIterators& begin, const InputIterators& end);
+
+  template <typename InputIterators, typename DupFunctor>
+  void setFromSortedTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
+
+  template <typename InputIterators>
+  void insertFromTriplets(const InputIterators& begin, const InputIterators& end);
+
+  template <typename InputIterators, typename DupFunctor>
+  void insertFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
+
+  template <typename InputIterators>
+  void insertFromSortedTriplets(const InputIterators& begin, const InputIterators& end);
+
+  template <typename InputIterators, typename DupFunctor>
+  void insertFromSortedTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
+
+  //---
+
+  /** \internal
+   * same as insert(Index,Index) except that the indices are given relative to the storage order */
+  Scalar& insertByOuterInner(Index j, Index i) {
+    eigen_assert(j >= 0 && j < m_outerSize && "invalid outer index");
+    eigen_assert(i >= 0 && i < m_innerSize && "invalid inner index");
+    Index start = m_outerIndex[j];
+    Index end = isCompressed() ? m_outerIndex[j + 1] : start + m_innerNonZeros[j];
+    Index dst = start == end ? end : m_data.searchLowerIndex(start, end, i);
+    if (dst == end) {
+      Index capacity = m_outerIndex[j + 1] - end;
+      if (capacity > 0) {
+        // implies uncompressed: push to back of vector
+        m_innerNonZeros[j]++;
+        m_data.index(end) = StorageIndex(i);
+        m_data.value(end) = Scalar(0);
+        return m_data.value(end);
+      }
+    }
+    eigen_assert((dst == end || m_data.index(dst) != i) &&
+                 "you cannot insert an element that already exists, you must call coeffRef to this end");
+    return insertAtByOuterInner(j, i, dst);
+  }
+
+  /** Turns the matrix into the \em compressed format.
+   */
+  void makeCompressed() {
+    if (isCompressed()) return;
+
+    eigen_internal_assert(m_outerIndex != 0 && m_outerSize > 0);
+
+    StorageIndex start = m_outerIndex[1];
+    m_outerIndex[1] = m_innerNonZeros[0];
+    // try to move fewer, larger contiguous chunks
+    Index copyStart = start;
+    Index copyTarget = m_innerNonZeros[0];
+    for (Index j = 1; j < m_outerSize; j++) {
+      StorageIndex end = start + m_innerNonZeros[j];
+      StorageIndex nextStart = m_outerIndex[j + 1];
+      // dont forget to move the last chunk!
+      bool breakUpCopy = (end != nextStart) || (j == m_outerSize - 1);
+      if (breakUpCopy) {
+        Index chunkSize = end - copyStart;
+        if (chunkSize > 0) m_data.moveChunk(copyStart, copyTarget, chunkSize);
+        copyStart = nextStart;
+        copyTarget += chunkSize;
+      }
+      start = nextStart;
+      m_outerIndex[j + 1] = m_outerIndex[j] + m_innerNonZeros[j];
+    }
+    m_data.resize(m_outerIndex[m_outerSize]);
+
+    // release as much memory as possible
+    internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+    m_innerNonZeros = 0;
+    m_data.squeeze();
+  }
+
+  /** Turns the matrix into the uncompressed mode */
+  void uncompress() {
+    if (!isCompressed()) return;
+    m_innerNonZeros = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize);
+    if (m_outerIndex[m_outerSize] == 0) {
+      using std::fill_n;
+      fill_n(m_innerNonZeros, m_outerSize, StorageIndex(0));
+    } else {
+      for (Index j = 0; j < m_outerSize; j++) m_innerNonZeros[j] = m_outerIndex[j + 1] - m_outerIndex[j];
+    }
+  }
+
+  /** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerance \a epsilon */
+  void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision()) {
+    prune(default_prunning_func(reference, epsilon));
+  }
+
+  /** Turns the matrix into compressed format, and suppresses all nonzeros which do not satisfy the predicate \a keep.
+   * The functor type \a KeepFunc must implement the following function:
+   * \code
+   * bool operator() (const Index& row, const Index& col, const Scalar& value) const;
+   * \endcode
+   * \sa prune(Scalar,RealScalar)
+   */
+  template <typename KeepFunc>
+  void prune(const KeepFunc& keep = KeepFunc()) {
+    StorageIndex k = 0;
+    for (Index j = 0; j < m_outerSize; ++j) {
+      StorageIndex previousStart = m_outerIndex[j];
+      if (isCompressed())
+        m_outerIndex[j] = k;
+      else
+        k = m_outerIndex[j];
+      StorageIndex end = isCompressed() ? m_outerIndex[j + 1] : previousStart + m_innerNonZeros[j];
+      for (StorageIndex i = previousStart; i < end; ++i) {
+        StorageIndex row = IsRowMajor ? StorageIndex(j) : m_data.index(i);
+        StorageIndex col = IsRowMajor ? m_data.index(i) : StorageIndex(j);
+        bool keepEntry = keep(row, col, m_data.value(i));
+        if (keepEntry) {
+          m_data.value(k) = m_data.value(i);
+          m_data.index(k) = m_data.index(i);
+          ++k;
+        } else if (!isCompressed())
+          m_innerNonZeros[j]--;
+      }
+    }
+    if (isCompressed()) {
+      m_outerIndex[m_outerSize] = k;
+      m_data.resize(k, 0);
+    }
+  }
+
+  /** Resizes the matrix to a \a rows x \a cols matrix leaving old values untouched.
+   *
+   * If the sizes of the matrix are decreased, then the matrix is turned to \b uncompressed-mode
+   * and the storage of the out of bounds coefficients is kept and reserved.
+   * Call makeCompressed() to pack the entries and squeeze extra memory.
+   *
+   * \sa reserve(), setZero(), makeCompressed()
+   */
+  void conservativeResize(Index rows, Index cols) {
+    // If one dimension is null, then there is nothing to be preserved
+    if (rows == 0 || cols == 0) return resize(rows, cols);
+
+    Index newOuterSize = IsRowMajor ? rows : cols;
+    Index newInnerSize = IsRowMajor ? cols : rows;
+
+    Index innerChange = newInnerSize - m_innerSize;
+    Index outerChange = newOuterSize - m_outerSize;
+
+    if (outerChange != 0) {
+      m_outerIndex = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>(m_outerIndex, newOuterSize + 1,
+                                                                                        m_outerSize + 1);
+
+      if (!isCompressed())
+        m_innerNonZeros = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>(m_innerNonZeros,
+                                                                                             newOuterSize, m_outerSize);
+
+      if (outerChange > 0) {
+        StorageIndex lastIdx = m_outerSize == 0 ? StorageIndex(0) : m_outerIndex[m_outerSize];
+        using std::fill_n;
+        fill_n(m_outerIndex + m_outerSize, outerChange + 1, lastIdx);
+
+        if (!isCompressed()) fill_n(m_innerNonZeros + m_outerSize, outerChange, StorageIndex(0));
+      }
+    }
+    m_outerSize = newOuterSize;
+
+    if (innerChange < 0) {
+      for (Index j = 0; j < m_outerSize; j++) {
+        Index start = m_outerIndex[j];
+        Index end = isCompressed() ? m_outerIndex[j + 1] : start + m_innerNonZeros[j];
+        Index lb = m_data.searchLowerIndex(start, end, newInnerSize);
+        if (lb != end) {
+          uncompress();
+          m_innerNonZeros[j] = StorageIndex(lb - start);
+        }
+      }
+    }
+    m_innerSize = newInnerSize;
+
+    Index newSize = m_outerIndex[m_outerSize];
+    eigen_assert(newSize <= m_data.size());
+    m_data.resize(newSize);
+  }
+
+  /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero.
+   *
+   * This function does not free the currently allocated memory. To release as much as memory as possible,
+   * call \code mat.data().squeeze(); \endcode after resizing it.
+   *
+   * \sa reserve(), setZero()
+   */
+  void resize(Index rows, Index cols) {
+    const Index outerSize = IsRowMajor ? rows : cols;
+    m_innerSize = IsRowMajor ? cols : rows;
+    m_data.clear();
+
+    if ((m_outerIndex == 0) || (m_outerSize != outerSize)) {
+      m_outerIndex = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>(m_outerIndex, outerSize + 1,
+                                                                                        m_outerSize + 1);
+      m_outerSize = outerSize;
+    }
+
+    internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+    m_innerNonZeros = 0;
+
+    using std::fill_n;
+    fill_n(m_outerIndex, m_outerSize + 1, StorageIndex(0));
+  }
+
+  /** \internal
+   * Resize the nonzero vector to \a size */
+  void resizeNonZeros(Index size) { m_data.resize(size); }
+
+  /** \returns a const expression of the diagonal coefficients. */
+  const ConstDiagonalReturnType diagonal() const { return ConstDiagonalReturnType(*this); }
+
+  /** \returns a read-write expression of the diagonal coefficients.
+   * \warning If the diagonal entries are written, then all diagonal
+   * entries \b must already exist, otherwise an assertion will be raised.
+   */
+  DiagonalReturnType diagonal() { return DiagonalReturnType(*this); }
+
+  /** Default constructor yielding an empty \c 0 \c x \c 0 matrix */
+  inline SparseMatrix() : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) { resize(0, 0); }
+
+  /** Constructs a \a rows \c x \a cols empty matrix */
+  inline SparseMatrix(Index rows, Index cols) : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) {
+    resize(rows, cols);
+  }
+
+  /** Constructs a sparse matrix from the sparse expression \a other */
+  template <typename OtherDerived>
+  inline SparseMatrix(const SparseMatrixBase<OtherDerived>& other)
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) {
+    EIGEN_STATIC_ASSERT(
+        (internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+    const bool needToTranspose = (Flags & RowMajorBit) != (internal::evaluator<OtherDerived>::Flags & RowMajorBit);
+    if (needToTranspose)
+      *this = other.derived();
+    else {
+#ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+      EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+#endif
+      internal::call_assignment_no_alias(*this, other.derived());
+    }
+  }
+
+  /** Constructs a sparse matrix from the sparse selfadjoint view \a other */
+  template <typename OtherDerived, unsigned int UpLo>
+  inline SparseMatrix(const SparseSelfAdjointView<OtherDerived, UpLo>& other)
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) {
+    Base::operator=(other);
+  }
+
+  /** Move constructor */
+  inline SparseMatrix(SparseMatrix&& other) : SparseMatrix() { this->swap(other); }
+
+  template <typename OtherDerived>
+  inline SparseMatrix(SparseCompressedBase<OtherDerived>&& other) : SparseMatrix() {
+    *this = other.derived().markAsRValue();
+  }
+
+  /** Copy constructor (it performs a deep copy) */
+  inline SparseMatrix(const SparseMatrix& other)
+      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) {
+    *this = other.derived();
+  }
+
+  /** \brief Copy constructor with in-place evaluation */
+  template <typename OtherDerived>
+  SparseMatrix(const ReturnByValue<OtherDerived>& other)
+      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) {
+    initAssignment(other);
+    other.evalTo(*this);
+  }
+
+  /** \brief Copy constructor with in-place evaluation */
+  template <typename OtherDerived>
+  explicit SparseMatrix(const DiagonalBase<OtherDerived>& other)
+      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) {
+    *this = other.derived();
+  }
+
+  /** Swaps the content of two sparse matrices of the same type.
+   * This is a fast operation that simply swaps the underlying pointers and parameters. */
+  inline void swap(SparseMatrix& other) {
+    // EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
+    std::swap(m_outerIndex, other.m_outerIndex);
+    std::swap(m_innerSize, other.m_innerSize);
+    std::swap(m_outerSize, other.m_outerSize);
+    std::swap(m_innerNonZeros, other.m_innerNonZeros);
+    m_data.swap(other.m_data);
+  }
+  /** Free-function swap. */
+  friend EIGEN_DEVICE_FUNC void swap(SparseMatrix& a, SparseMatrix& b) { a.swap(b); }
+
+  /** Sets *this to the identity matrix.
+   * This function also turns the matrix into compressed mode, and drop any reserved memory. */
+  inline void setIdentity() {
+    eigen_assert(m_outerSize == m_innerSize && "ONLY FOR SQUARED MATRICES");
+    internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+    m_innerNonZeros = 0;
+    m_data.resize(m_outerSize);
+    // is it necessary to squeeze?
+    m_data.squeeze();
+    std::iota(m_outerIndex, m_outerIndex + m_outerSize + 1, StorageIndex(0));
+    std::iota(innerIndexPtr(), innerIndexPtr() + m_outerSize, StorageIndex(0));
+    using std::fill_n;
+    fill_n(valuePtr(), m_outerSize, Scalar(1));
+  }
+
+  inline SparseMatrix& operator=(const SparseMatrix& other) {
+    if (other.isRValue()) {
+      swap(other.const_cast_derived());
+    } else if (this != &other) {
+#ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+      EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+#endif
+      initAssignment(other);
+      if (other.isCompressed()) {
+        internal::smart_copy(other.m_outerIndex, other.m_outerIndex + m_outerSize + 1, m_outerIndex);
+        m_data = other.m_data;
+      } else {
+        Base::operator=(other);
+      }
+    }
+    return *this;
+  }
+
+  inline SparseMatrix& operator=(SparseMatrix&& other) {
+    this->swap(other);
+    return *this;
+  }
+
+  template <typename OtherDerived>
+  inline SparseMatrix& operator=(const EigenBase<OtherDerived>& other) {
+    return Base::operator=(other.derived());
+  }
+
+  template <typename Lhs, typename Rhs>
+  inline SparseMatrix& operator=(const Product<Lhs, Rhs, AliasFreeProduct>& other);
+
+  template <typename OtherDerived>
+  EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  inline SparseMatrix& operator=(SparseCompressedBase<OtherDerived>&& other) {
+    *this = other.derived().markAsRValue();
+    return *this;
+  }
+
+#ifndef EIGEN_NO_IO
+  friend std::ostream& operator<<(std::ostream& s, const SparseMatrix& m) {
+    EIGEN_DBG_SPARSE(
+        s << "Nonzero entries:\n"; if (m.isCompressed()) {
+          for (Index i = 0; i < m.nonZeros(); ++i) s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
+        } else {
+          for (Index i = 0; i < m.outerSize(); ++i) {
+            Index p = m.m_outerIndex[i];
+            Index pe = m.m_outerIndex[i] + m.m_innerNonZeros[i];
+            Index k = p;
+            for (; k < pe; ++k) {
+              s << "(" << m.m_data.value(k) << "," << m.m_data.index(k) << ") ";
+            }
+            for (; k < m.m_outerIndex[i + 1]; ++k) {
+              s << "(_,_) ";
+            }
+          }
+        } s << std::endl;
+        s << std::endl; s << "Outer pointers:\n";
+        for (Index i = 0; i < m.outerSize(); ++i) { s << m.m_outerIndex[i] << " "; } s << " $" << std::endl;
+        if (!m.isCompressed()) {
+          s << "Inner non zeros:\n";
+          for (Index i = 0; i < m.outerSize(); ++i) {
+            s << m.m_innerNonZeros[i] << " ";
+          }
+          s << " $" << std::endl;
+        } s
+        << std::endl;);
+    s << static_cast<const SparseMatrixBase<SparseMatrix>&>(m);
+    return s;
+  }
+#endif
+
+  /** Destructor */
+  inline ~SparseMatrix() {
+    internal::conditional_aligned_delete_auto<StorageIndex, true>(m_outerIndex, m_outerSize + 1);
+    internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+  }
+
+  /** Overloaded for performance */
+  Scalar sum() const;
+
+#ifdef EIGEN_SPARSEMATRIX_PLUGIN
+#include EIGEN_SPARSEMATRIX_PLUGIN
+#endif
+
+ protected:
+  template <typename Other>
+  void initAssignment(const Other& other) {
+    resize(other.rows(), other.cols());
+    internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+    m_innerNonZeros = 0;
+  }
+
+  /** \internal
+   * \sa insert(Index,Index) */
+  EIGEN_DEPRECATED EIGEN_DONT_INLINE Scalar& insertCompressed(Index row, Index col);
+
+  /** \internal
+   * A vector object that is equal to 0 everywhere but v at the position i */
+  class SingletonVector {
+    StorageIndex m_index;
+    StorageIndex m_value;
+
+   public:
+    typedef StorageIndex value_type;
+    SingletonVector(Index i, Index v) : m_index(convert_index(i)), m_value(convert_index(v)) {}
+
+    StorageIndex operator[](Index i) const { return i == m_index ? m_value : 0; }
+  };
+
+  /** \internal
+   * \sa insert(Index,Index) */
+  EIGEN_DEPRECATED EIGEN_DONT_INLINE Scalar& insertUncompressed(Index row, Index col);
+
+ public:
+  /** \internal
+   * \sa insert(Index,Index) */
+  EIGEN_STRONG_INLINE Scalar& insertBackUncompressed(Index row, Index col) {
+    const Index outer = IsRowMajor ? row : col;
+    const Index inner = IsRowMajor ? col : row;
+
+    eigen_assert(!isCompressed());
+    eigen_assert(m_innerNonZeros[outer] <= (m_outerIndex[outer + 1] - m_outerIndex[outer]));
+
+    Index p = m_outerIndex[outer] + m_innerNonZeros[outer]++;
+    m_data.index(p) = StorageIndex(inner);
+    m_data.value(p) = Scalar(0);
+    return m_data.value(p);
+  }
+
+ protected:
+  struct IndexPosPair {
+    IndexPosPair(Index a_i, Index a_p) : i(a_i), p(a_p) {}
+    Index i;
+    Index p;
+  };
+
+  /** \internal assign \a diagXpr to the diagonal of \c *this
+   * There are different strategies:
+   *   1 - if *this is overwritten (Func==assign_op) or *this is empty, then we can work treat *this as a dense vector
+   * expression. 2 - otherwise, for each diagonal coeff, 2.a - if it already exists, then we update it, 2.b - if the
+   * correct position is at the end of the vector, and there is capacity, push to back 2.b - otherwise, the insertion
+   * requires a data move, record insertion locations and handle in a second pass 3 - at the end, if some entries failed
+   * to be updated in-place, then we alloc a new buffer, copy each chunk at the right position, and insert the new
+   * elements.
+   */
+  template <typename DiagXpr, typename Func>
+  void assignDiagonal(const DiagXpr diagXpr, const Func& assignFunc) {
+    constexpr StorageIndex kEmptyIndexVal(-1);
+    typedef typename ScalarVector::AlignedMapType ValueMap;
+
+    Index n = diagXpr.size();
+
+    const bool overwrite = internal::is_same<Func, internal::assign_op<Scalar, Scalar>>::value;
+    if (overwrite) {
+      if ((m_outerSize != n) || (m_innerSize != n)) resize(n, n);
+    }
+
+    if (m_data.size() == 0 || overwrite) {
+      internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+      m_innerNonZeros = 0;
+      resizeNonZeros(n);
+      ValueMap valueMap(valuePtr(), n);
+      std::iota(m_outerIndex, m_outerIndex + n + 1, StorageIndex(0));
+      std::iota(innerIndexPtr(), innerIndexPtr() + n, StorageIndex(0));
+      valueMap.setZero();
+      internal::call_assignment_no_alias(valueMap, diagXpr, assignFunc);
+    } else {
+      internal::evaluator<DiagXpr> diaEval(diagXpr);
+
+      ei_declare_aligned_stack_constructed_variable(StorageIndex, tmp, n, 0);
+      typename IndexVector::AlignedMapType insertionLocations(tmp, n);
+      insertionLocations.setConstant(kEmptyIndexVal);
+
+      Index deferredInsertions = 0;
+      Index shift = 0;
+
+      for (Index j = 0; j < n; j++) {
+        Index begin = m_outerIndex[j];
+        Index end = isCompressed() ? m_outerIndex[j + 1] : begin + m_innerNonZeros[j];
+        Index capacity = m_outerIndex[j + 1] - end;
+        Index dst = m_data.searchLowerIndex(begin, end, j);
+        // the entry exists: update it now
+        if (dst != end && m_data.index(dst) == StorageIndex(j))
+          assignFunc.assignCoeff(m_data.value(dst), diaEval.coeff(j));
+        // the entry belongs at the back of the vector: push to back
+        else if (dst == end && capacity > 0)
+          assignFunc.assignCoeff(insertBackUncompressed(j, j), diaEval.coeff(j));
+        // the insertion requires a data move, record insertion location and handle in second pass
+        else {
+          insertionLocations.coeffRef(j) = StorageIndex(dst);
+          deferredInsertions++;
+          // if there is no capacity, all vectors to the right of this are shifted
+          if (capacity == 0) shift++;
+        }
+      }
+
+      if (deferredInsertions > 0) {
+        m_data.resize(m_data.size() + shift);
+        Index copyEnd = isCompressed() ? m_outerIndex[m_outerSize]
+                                       : m_outerIndex[m_outerSize - 1] + m_innerNonZeros[m_outerSize - 1];
+        for (Index j = m_outerSize - 1; deferredInsertions > 0; j--) {
+          Index begin = m_outerIndex[j];
+          Index end = isCompressed() ? m_outerIndex[j + 1] : begin + m_innerNonZeros[j];
+          Index capacity = m_outerIndex[j + 1] - end;
+
+          bool doInsertion = insertionLocations(j) >= 0;
+          bool breakUpCopy = doInsertion && (capacity > 0);
+          // break up copy for sorted insertion into inactive nonzeros
+          // optionally, add another criterium, i.e. 'breakUpCopy || (capacity > threhsold)'
+          // where `threshold >= 0` to skip inactive nonzeros in each vector
+          // this reduces the total number of copied elements, but requires more moveChunk calls
+          if (breakUpCopy) {
+            Index copyBegin = m_outerIndex[j + 1];
+            Index to = copyBegin + shift;
+            Index chunkSize = copyEnd - copyBegin;
+            m_data.moveChunk(copyBegin, to, chunkSize);
+            copyEnd = end;
+          }
+
+          m_outerIndex[j + 1] += shift;
+
+          if (doInsertion) {
+            // if there is capacity, shift into the inactive nonzeros
+            if (capacity > 0) shift++;
+            Index copyBegin = insertionLocations(j);
+            Index to = copyBegin + shift;
+            Index chunkSize = copyEnd - copyBegin;
+            m_data.moveChunk(copyBegin, to, chunkSize);
+            Index dst = to - 1;
+            m_data.index(dst) = StorageIndex(j);
+            m_data.value(dst) = Scalar(0);
+            assignFunc.assignCoeff(m_data.value(dst), diaEval.coeff(j));
+            if (!isCompressed()) m_innerNonZeros[j]++;
+            shift--;
+            deferredInsertions--;
+            copyEnd = copyBegin;
+          }
+        }
+      }
+      eigen_assert((shift == 0) && (deferredInsertions == 0));
+    }
+  }
+
+  /* These functions are used to avoid a redundant binary search operation in functions such as coeffRef() and assume
+   * `dst` is the appropriate sorted insertion point */
+  EIGEN_STRONG_INLINE Scalar& insertAtByOuterInner(Index outer, Index inner, Index dst);
+  Scalar& insertCompressedAtByOuterInner(Index outer, Index inner, Index dst);
+  Scalar& insertUncompressedAtByOuterInner(Index outer, Index inner, Index dst);
+
+ private:
+  EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned, THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
+  EIGEN_STATIC_ASSERT((Options & (ColMajor | RowMajor)) == Options, INVALID_MATRIX_TEMPLATE_PARAMETERS)
+
+  struct default_prunning_func {
+    default_prunning_func(const Scalar& ref, const RealScalar& eps) : reference(ref), epsilon(eps) {}
+    inline bool operator()(const Index&, const Index&, const Scalar& value) const {
+      return !internal::isMuchSmallerThan(value, reference, epsilon);
+    }
+    Scalar reference;
+    RealScalar epsilon;
+  };
+};
+
+namespace internal {
+
+// Creates a compressed sparse matrix from a range of unsorted triplets
+// Requires temporary storage to handle duplicate entries
+template <typename InputIterator, typename SparseMatrixType, typename DupFunctor>
+void set_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat,
+                       DupFunctor dup_func) {
+  constexpr bool IsRowMajor = SparseMatrixType::IsRowMajor;
+  using StorageIndex = typename SparseMatrixType::StorageIndex;
+  using IndexMap = typename VectorX<StorageIndex>::AlignedMapType;
+  using TransposedSparseMatrix =
+      SparseMatrix<typename SparseMatrixType::Scalar, IsRowMajor ? ColMajor : RowMajor, StorageIndex>;
+
+  if (begin == end) {
+    // Clear out existing data (if any).
+    mat.setZero();
+    return;
+  }
+
+  // There are two strategies to consider for constructing a matrix from unordered triplets:
+  // A) construct the 'mat' in its native storage order and sort in-place (less memory); or,
+  // B) construct the transposed matrix and use an implicit sort upon assignment to `mat` (less time).
+  // This routine uses B) for faster execution time.
+  TransposedSparseMatrix trmat(mat.rows(), mat.cols());
+
+  // scan triplets to determine allocation size before constructing matrix
+  Index nonZeros = 0;
+  for (InputIterator it(begin); it != end; ++it) {
+    eigen_assert(it->row() >= 0 && it->row() < mat.rows() && it->col() >= 0 && it->col() < mat.cols());
+    StorageIndex j = convert_index<StorageIndex>(IsRowMajor ? it->col() : it->row());
+    if (nonZeros == NumTraits<StorageIndex>::highest()) internal::throw_std_bad_alloc();
+    trmat.outerIndexPtr()[j + 1]++;
+    nonZeros++;
+  }
+
+  std::partial_sum(trmat.outerIndexPtr(), trmat.outerIndexPtr() + trmat.outerSize() + 1, trmat.outerIndexPtr());
+  eigen_assert(nonZeros == trmat.outerIndexPtr()[trmat.outerSize()]);
+  trmat.resizeNonZeros(nonZeros);
+
+  // construct temporary array to track insertions (outersize) and collapse duplicates (innersize)
+  ei_declare_aligned_stack_constructed_variable(StorageIndex, tmp, numext::maxi(mat.innerSize(), mat.outerSize()), 0);
+  smart_copy(trmat.outerIndexPtr(), trmat.outerIndexPtr() + trmat.outerSize(), tmp);
+
+  // push triplets to back of each vector
+  for (InputIterator it(begin); it != end; ++it) {
+    StorageIndex j = convert_index<StorageIndex>(IsRowMajor ? it->col() : it->row());
+    StorageIndex i = convert_index<StorageIndex>(IsRowMajor ? it->row() : it->col());
+    StorageIndex k = tmp[j];
+    trmat.data().index(k) = i;
+    trmat.data().value(k) = it->value();
+    tmp[j]++;
+  }
+
+  IndexMap wi(tmp, trmat.innerSize());
+  trmat.collapseDuplicates(wi, dup_func);
+  // implicit sorting
+  mat = trmat;
+}
+
+// Creates a compressed sparse matrix from a sorted range of triplets
+template <typename InputIterator, typename SparseMatrixType, typename DupFunctor>
+void set_from_triplets_sorted(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat,
+                              DupFunctor dup_func) {
+  constexpr bool IsRowMajor = SparseMatrixType::IsRowMajor;
+  using StorageIndex = typename SparseMatrixType::StorageIndex;
+
+  if (begin == end) return;
+
+  constexpr StorageIndex kEmptyIndexValue(-1);
+  // deallocate inner nonzeros if present and zero outerIndexPtr
+  mat.resize(mat.rows(), mat.cols());
+  // use outer indices to count non zero entries (excluding duplicate entries)
+  StorageIndex previous_j = kEmptyIndexValue;
+  StorageIndex previous_i = kEmptyIndexValue;
+  // scan triplets to determine allocation size before constructing matrix
+  Index nonZeros = 0;
+  for (InputIterator it(begin); it != end; ++it) {
+    eigen_assert(it->row() >= 0 && it->row() < mat.rows() && it->col() >= 0 && it->col() < mat.cols());
+    StorageIndex j = convert_index<StorageIndex>(IsRowMajor ? it->row() : it->col());
+    StorageIndex i = convert_index<StorageIndex>(IsRowMajor ? it->col() : it->row());
+    eigen_assert(j > previous_j || (j == previous_j && i >= previous_i));
+    // identify duplicates by examining previous location
+    bool duplicate = (previous_j == j) && (previous_i == i);
+    if (!duplicate) {
+      if (nonZeros == NumTraits<StorageIndex>::highest()) internal::throw_std_bad_alloc();
+      nonZeros++;
+      mat.outerIndexPtr()[j + 1]++;
+      previous_j = j;
+      previous_i = i;
+    }
+  }
+
+  // finalize outer indices and allocate memory
+  std::partial_sum(mat.outerIndexPtr(), mat.outerIndexPtr() + mat.outerSize() + 1, mat.outerIndexPtr());
+  eigen_assert(nonZeros == mat.outerIndexPtr()[mat.outerSize()]);
+  mat.resizeNonZeros(nonZeros);
+
+  previous_i = kEmptyIndexValue;
+  previous_j = kEmptyIndexValue;
+  Index back = 0;
+  for (InputIterator it(begin); it != end; ++it) {
+    StorageIndex j = convert_index<StorageIndex>(IsRowMajor ? it->row() : it->col());
+    StorageIndex i = convert_index<StorageIndex>(IsRowMajor ? it->col() : it->row());
+    bool duplicate = (previous_j == j) && (previous_i == i);
+    if (duplicate) {
+      mat.data().value(back - 1) = dup_func(mat.data().value(back - 1), it->value());
+    } else {
+      // push triplets to back
+      mat.data().index(back) = i;
+      mat.data().value(back) = it->value();
+      previous_j = j;
+      previous_i = i;
+      back++;
+    }
+  }
+  eigen_assert(back == nonZeros);
+  // matrix is finalized
+}
+
+// thin wrapper around a generic binary functor to use the sparse disjunction evaluator instead of the default
+// "arithmetic" evaluator
+template <typename DupFunctor, typename LhsScalar, typename RhsScalar = LhsScalar>
+struct scalar_disjunction_op {
+  using result_type = typename result_of<DupFunctor(LhsScalar, RhsScalar)>::type;
+  scalar_disjunction_op(const DupFunctor& op) : m_functor(op) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return m_functor(a, b);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const DupFunctor& functor() const { return m_functor; }
+  const DupFunctor& m_functor;
+};
+
+template <typename DupFunctor, typename LhsScalar, typename RhsScalar>
+struct functor_traits<scalar_disjunction_op<DupFunctor, LhsScalar, RhsScalar>> : public functor_traits<DupFunctor> {};
+
+// Creates a compressed sparse matrix from its existing entries and those from an unsorted range of triplets
+template <typename InputIterator, typename SparseMatrixType, typename DupFunctor>
+void insert_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat,
+                          DupFunctor dup_func) {
+  using Scalar = typename SparseMatrixType::Scalar;
+  using SrcXprType =
+      CwiseBinaryOp<scalar_disjunction_op<DupFunctor, Scalar>, const SparseMatrixType, const SparseMatrixType>;
+
+  // set_from_triplets is necessary to sort the inner indices and remove the duplicate entries
+  SparseMatrixType trips(mat.rows(), mat.cols());
+  set_from_triplets(begin, end, trips, dup_func);
+
+  SrcXprType src = mat.binaryExpr(trips, scalar_disjunction_op<DupFunctor, Scalar>(dup_func));
+  // the sparse assignment procedure creates a temporary matrix and swaps the final result
+  assign_sparse_to_sparse<SparseMatrixType, SrcXprType>(mat, src);
+}
+
+// Creates a compressed sparse matrix from its existing entries and those from an sorted range of triplets
+template <typename InputIterator, typename SparseMatrixType, typename DupFunctor>
+void insert_from_triplets_sorted(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat,
+                                 DupFunctor dup_func) {
+  using Scalar = typename SparseMatrixType::Scalar;
+  using SrcXprType =
+      CwiseBinaryOp<scalar_disjunction_op<DupFunctor, Scalar>, const SparseMatrixType, const SparseMatrixType>;
+
+  // TODO: process triplets without making a copy
+  SparseMatrixType trips(mat.rows(), mat.cols());
+  set_from_triplets_sorted(begin, end, trips, dup_func);
+
+  SrcXprType src = mat.binaryExpr(trips, scalar_disjunction_op<DupFunctor, Scalar>(dup_func));
+  // the sparse assignment procedure creates a temporary matrix and swaps the final result
+  assign_sparse_to_sparse<SparseMatrixType, SrcXprType>(mat, src);
+}
+
+}  // namespace internal
+
+/** Fill the matrix \c *this with the list of \em triplets defined in the half-open range from \a begin to \a end.
+  *
+  * A \em triplet is a tuple (i,j,value) defining a non-zero element.
+  * The input list of triplets does not have to be sorted, and may contain duplicated elements.
+  * In any case, the result is a \b sorted and \b compressed sparse matrix where the duplicates have been summed up.
+  * This is a \em O(n) operation, with \em n the number of triplet elements.
+  * The initial contents of \c *this are destroyed.
+  * The matrix \c *this must be properly resized beforehand using the SparseMatrix(Index,Index) constructor,
+  * or the resize(Index,Index) method. The sizes are not extracted from the triplet list.
+  *
+  * The \a InputIterators value_type must provide the following interface:
+  * \code
+  * Scalar value() const; // the value
+  * IndexType row() const;   // the row index i
+  * IndexType col() const;   // the column index j
+  * \endcode
+  * See for instance the Eigen::Triplet template class.
+  *
+  * Here is a typical usage example:
+  * \code
+    typedef Triplet<double> T;
+    std::vector<T> tripletList;
+    tripletList.reserve(estimation_of_entries);
+    for(...)
+    {
+      // ...
+      tripletList.push_back(T(i,j,v_ij));
+    }
+    SparseMatrixType m(rows,cols);
+    m.setFromTriplets(tripletList.begin(), tripletList.end());
+    // m is ready to go!
+  * \endcode
+  *
+  * \warning The list of triplets is read multiple times (at least twice). Therefore, it is not recommended to define
+  * an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather
+  * be explicitly stored into a std::vector for instance.
+  */
+template <typename Scalar, int Options_, typename StorageIndex_>
+template <typename InputIterators>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::setFromTriplets(const InputIterators& begin,
+                                                                    const InputIterators& end) {
+  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>>(
+      begin, end, *this, internal::scalar_sum_op<Scalar, Scalar>());
+}
+
+/** The same as setFromTriplets but when duplicates are met the functor \a dup_func is applied:
+ * \code
+ * value = dup_func(OldValue, NewValue)
+ * \endcode
+ * Here is a C++11 example keeping the latest entry only:
+ * \code
+ * mat.setFromTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
+ * \endcode
+ */
+template <typename Scalar, int Options_, typename StorageIndex_>
+template <typename InputIterators, typename DupFunctor>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::setFromTriplets(const InputIterators& begin,
+                                                                    const InputIterators& end, DupFunctor dup_func) {
+  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(
+      begin, end, *this, dup_func);
+}
+
+/** The same as setFromTriplets but triplets are assumed to be pre-sorted. This is faster and requires less temporary
+ * storage. Two triplets `a` and `b` are appropriately ordered if: \code ColMajor: ((a.col() != b.col()) ? (a.col() <
+ * b.col()) : (a.row() < b.row()) RowMajor: ((a.row() != b.row()) ? (a.row() < b.row()) : (a.col() < b.col()) \endcode
+ */
+template <typename Scalar, int Options_, typename StorageIndex_>
+template <typename InputIterators>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::setFromSortedTriplets(const InputIterators& begin,
+                                                                          const InputIterators& end) {
+  internal::set_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>>(
+      begin, end, *this, internal::scalar_sum_op<Scalar, Scalar>());
+}
+
+/** The same as setFromSortedTriplets but when duplicates are met the functor \a dup_func is applied:
+ * \code
+ * value = dup_func(OldValue, NewValue)
+ * \endcode
+ * Here is a C++11 example keeping the latest entry only:
+ * \code
+ * mat.setFromSortedTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
+ * \endcode
+ */
+template <typename Scalar, int Options_, typename StorageIndex_>
+template <typename InputIterators, typename DupFunctor>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::setFromSortedTriplets(const InputIterators& begin,
+                                                                          const InputIterators& end,
+                                                                          DupFunctor dup_func) {
+  internal::set_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(
+      begin, end, *this, dup_func);
+}
+
+/** Insert a batch of elements into the matrix \c *this with the list of \em triplets defined in the half-open range
+  from \a begin to \a end.
+  *
+  * A \em triplet is a tuple (i,j,value) defining a non-zero element.
+  * The input list of triplets does not have to be sorted, and may contain duplicated elements.
+  * In any case, the result is a \b sorted and \b compressed sparse matrix where the duplicates have been summed up.
+  * This is a \em O(n) operation, with \em n the number of triplet elements.
+  * The initial contents of \c *this are preserved (except for the summation of duplicate elements).
+  * The matrix \c *this must be properly sized beforehand. The sizes are not extracted from the triplet list.
+  *
+  * The \a InputIterators value_type must provide the following interface:
+  * \code
+  * Scalar value() const; // the value
+  * IndexType row() const;   // the row index i
+  * IndexType col() const;   // the column index j
+  * \endcode
+  * See for instance the Eigen::Triplet template class.
+  *
+  * Here is a typical usage example:
+  * \code
+    SparseMatrixType m(rows,cols); // m contains nonzero entries
+    typedef Triplet<double> T;
+    std::vector<T> tripletList;
+    tripletList.reserve(estimation_of_entries);
+    for(...)
+    {
+      // ...
+      tripletList.push_back(T(i,j,v_ij));
+    }
+
+    m.insertFromTriplets(tripletList.begin(), tripletList.end());
+    // m is ready to go!
+  * \endcode
+  *
+  * \warning The list of triplets is read multiple times (at least twice). Therefore, it is not recommended to define
+  * an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather
+  * be explicitly stored into a std::vector for instance.
+  */
+template <typename Scalar, int Options_, typename StorageIndex_>
+template <typename InputIterators>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::insertFromTriplets(const InputIterators& begin,
+                                                                       const InputIterators& end) {
+  internal::insert_from_triplets<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>>(
+      begin, end, *this, internal::scalar_sum_op<Scalar, Scalar>());
+}
+
+/** The same as insertFromTriplets but when duplicates are met the functor \a dup_func is applied:
+ * \code
+ * value = dup_func(OldValue, NewValue)
+ * \endcode
+ * Here is a C++11 example keeping the latest entry only:
+ * \code
+ * mat.insertFromTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
+ * \endcode
+ */
+template <typename Scalar, int Options_, typename StorageIndex_>
+template <typename InputIterators, typename DupFunctor>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::insertFromTriplets(const InputIterators& begin,
+                                                                       const InputIterators& end, DupFunctor dup_func) {
+  internal::insert_from_triplets<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(
+      begin, end, *this, dup_func);
+}
+
+/** The same as insertFromTriplets but triplets are assumed to be pre-sorted. This is faster and requires less temporary
+ * storage. Two triplets `a` and `b` are appropriately ordered if: \code ColMajor: ((a.col() != b.col()) ? (a.col() <
+ * b.col()) : (a.row() < b.row()) RowMajor: ((a.row() != b.row()) ? (a.row() < b.row()) : (a.col() < b.col()) \endcode
+ */
+template <typename Scalar, int Options_, typename StorageIndex_>
+template <typename InputIterators>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::insertFromSortedTriplets(const InputIterators& begin,
+                                                                             const InputIterators& end) {
+  internal::insert_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>>(
+      begin, end, *this, internal::scalar_sum_op<Scalar, Scalar>());
+}
+
+/** The same as insertFromSortedTriplets but when duplicates are met the functor \a dup_func is applied:
+ * \code
+ * value = dup_func(OldValue, NewValue)
+ * \endcode
+ * Here is a C++11 example keeping the latest entry only:
+ * \code
+ * mat.insertFromSortedTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
+ * \endcode
+ */
+template <typename Scalar, int Options_, typename StorageIndex_>
+template <typename InputIterators, typename DupFunctor>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::insertFromSortedTriplets(const InputIterators& begin,
+                                                                             const InputIterators& end,
+                                                                             DupFunctor dup_func) {
+  internal::insert_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(
+      begin, end, *this, dup_func);
+}
+
+/** \internal */
+template <typename Scalar_, int Options_, typename StorageIndex_>
+template <typename Derived, typename DupFunctor>
+void SparseMatrix<Scalar_, Options_, StorageIndex_>::collapseDuplicates(DenseBase<Derived>& wi, DupFunctor dup_func) {
+  // removes duplicate entries and compresses the matrix
+  // the excess allocated memory is not released
+  // the inner indices do not need to be sorted, nor is the matrix returned in a sorted state
+  eigen_assert(wi.size() == m_innerSize);
+  constexpr StorageIndex kEmptyIndexValue(-1);
+  wi.setConstant(kEmptyIndexValue);
+  StorageIndex count = 0;
+  const bool is_compressed = isCompressed();
+  // for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers
+  for (Index j = 0; j < m_outerSize; ++j) {
+    const StorageIndex newBegin = count;
+    const StorageIndex end = is_compressed ? m_outerIndex[j + 1] : m_outerIndex[j] + m_innerNonZeros[j];
+    for (StorageIndex k = m_outerIndex[j]; k < end; ++k) {
+      StorageIndex i = m_data.index(k);
+      if (wi(i) >= newBegin) {
+        // entry at k is a duplicate
+        // accumulate it into the primary entry located at wi(i)
+        m_data.value(wi(i)) = dup_func(m_data.value(wi(i)), m_data.value(k));
+      } else {
+        // k is the primary entry in j with inner index i
+        // shift it to the left and record its location at wi(i)
+        m_data.index(count) = i;
+        m_data.value(count) = m_data.value(k);
+        wi(i) = count;
+        ++count;
+      }
+    }
+    m_outerIndex[j] = newBegin;
+  }
+  m_outerIndex[m_outerSize] = count;
+  m_data.resize(count);
+
+  // turn the matrix into compressed form (if it is not already)
+  internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+  m_innerNonZeros = 0;
+}
+
+/** \internal */
+template <typename Scalar, int Options_, typename StorageIndex_>
+template <typename OtherDerived>
+EIGEN_DONT_INLINE SparseMatrix<Scalar, Options_, StorageIndex_>&
+SparseMatrix<Scalar, Options_, StorageIndex_>::operator=(const SparseMatrixBase<OtherDerived>& other) {
+  EIGEN_STATIC_ASSERT(
+      (internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+#ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+  EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+#endif
+
+  const bool needToTranspose = (Flags & RowMajorBit) != (internal::evaluator<OtherDerived>::Flags & RowMajorBit);
+  if (needToTranspose) {
+#ifdef EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN
+    EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN
+#endif
+    // two passes algorithm:
+    //  1 - compute the number of coeffs per dest inner vector
+    //  2 - do the actual copy/eval
+    // Since each coeff of the rhs has to be evaluated twice, let's evaluate it if needed
+    typedef
+        typename internal::nested_eval<OtherDerived, 2, typename internal::plain_matrix_type<OtherDerived>::type>::type
+            OtherCopy;
+    typedef internal::remove_all_t<OtherCopy> OtherCopy_;
+    typedef internal::evaluator<OtherCopy_> OtherCopyEval;
+    OtherCopy otherCopy(other.derived());
+    OtherCopyEval otherCopyEval(otherCopy);
+
+    SparseMatrix dest(other.rows(), other.cols());
+    Eigen::Map<IndexVector>(dest.m_outerIndex, dest.outerSize()).setZero();
+
+    // pass 1
+    // FIXME: merge the above copy into this pass to avoid iterating twice.
+    for (Index j = 0; j < otherCopy.outerSize(); ++j)
+      for (typename OtherCopyEval::InnerIterator it(otherCopyEval, j); it; ++it) ++dest.m_outerIndex[it.index()];
+
+    // prefix sum
+    StorageIndex count = 0;
+    IndexVector positions(dest.outerSize());
+    for (Index j = 0; j < dest.outerSize(); ++j) {
+      StorageIndex tmp = dest.m_outerIndex[j];
+      dest.m_outerIndex[j] = count;
+      positions[j] = count;
+      count += tmp;
+    }
+    dest.m_outerIndex[dest.outerSize()] = count;
+    // alloc
+    dest.m_data.resize(count);
+    // pass 2
+    for (StorageIndex j = 0; j < otherCopy.outerSize(); ++j) {
+      for (typename OtherCopyEval::InnerIterator it(otherCopyEval, j); it; ++it) {
+        Index pos = positions[it.index()]++;
+        dest.m_data.index(pos) = j;
+        dest.m_data.value(pos) = it.value();
+      }
+    }
+    this->swap(dest);
+    return *this;
+  } else {
+    if (other.isRValue()) {
+      initAssignment(other.derived());
+    }
+    // there is no special optimization
+    return Base::operator=(other.derived());
+  }
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+inline typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insert(Index row, Index col) {
+  return insertByOuterInner(IsRowMajor ? row : col, IsRowMajor ? col : row);
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+EIGEN_STRONG_INLINE typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insertAtByOuterInner(Index outer, Index inner, Index dst) {
+  // random insertion into compressed matrix is very slow
+  uncompress();
+  return insertUncompressedAtByOuterInner(outer, inner, dst);
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+EIGEN_DEPRECATED EIGEN_DONT_INLINE typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insertUncompressed(Index row, Index col) {
+  eigen_assert(!isCompressed());
+  Index outer = IsRowMajor ? row : col;
+  Index inner = IsRowMajor ? col : row;
+  Index start = m_outerIndex[outer];
+  Index end = start + m_innerNonZeros[outer];
+  Index dst = start == end ? end : m_data.searchLowerIndex(start, end, inner);
+  if (dst == end) {
+    Index capacity = m_outerIndex[outer + 1] - end;
+    if (capacity > 0) {
+      // implies uncompressed: push to back of vector
+      m_innerNonZeros[outer]++;
+      m_data.index(end) = StorageIndex(inner);
+      m_data.value(end) = Scalar(0);
+      return m_data.value(end);
+    }
+  }
+  eigen_assert((dst == end || m_data.index(dst) != inner) &&
+               "you cannot insert an element that already exists, you must call coeffRef to this end");
+  return insertUncompressedAtByOuterInner(outer, inner, dst);
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+EIGEN_DEPRECATED EIGEN_DONT_INLINE typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insertCompressed(Index row, Index col) {
+  eigen_assert(isCompressed());
+  Index outer = IsRowMajor ? row : col;
+  Index inner = IsRowMajor ? col : row;
+  Index start = m_outerIndex[outer];
+  Index end = m_outerIndex[outer + 1];
+  Index dst = start == end ? end : m_data.searchLowerIndex(start, end, inner);
+  eigen_assert((dst == end || m_data.index(dst) != inner) &&
+               "you cannot insert an element that already exists, you must call coeffRef to this end");
+  return insertCompressedAtByOuterInner(outer, inner, dst);
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insertCompressedAtByOuterInner(Index outer, Index inner, Index dst) {
+  eigen_assert(isCompressed());
+  // compressed insertion always requires expanding the buffer
+  // first, check if there is adequate allocated memory
+  if (m_data.allocatedSize() <= m_data.size()) {
+    // if there is no capacity for a single insertion, double the capacity
+    // increase capacity by a minimum of 32
+    Index minReserve = 32;
+    Index reserveSize = numext::maxi(minReserve, m_data.allocatedSize());
+    m_data.reserve(reserveSize);
+  }
+  m_data.resize(m_data.size() + 1);
+  Index chunkSize = m_outerIndex[m_outerSize] - dst;
+  // shift the existing data to the right if necessary
+  m_data.moveChunk(dst, dst + 1, chunkSize);
+  // update nonzero counts
+  // potentially O(outerSize) bottleneck!
+  for (Index j = outer; j < m_outerSize; j++) m_outerIndex[j + 1]++;
+  // initialize the coefficient
+  m_data.index(dst) = StorageIndex(inner);
+  m_data.value(dst) = Scalar(0);
+  // return a reference to the coefficient
+  return m_data.value(dst);
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insertUncompressedAtByOuterInner(Index outer, Index inner, Index dst) {
+  eigen_assert(!isCompressed());
+  // find a vector with capacity, starting at `outer` and searching to the left and right
+  for (Index leftTarget = outer - 1, rightTarget = outer; (leftTarget >= 0) || (rightTarget < m_outerSize);) {
+    if (rightTarget < m_outerSize) {
+      Index start = m_outerIndex[rightTarget];
+      Index end = start + m_innerNonZeros[rightTarget];
+      Index nextStart = m_outerIndex[rightTarget + 1];
+      Index capacity = nextStart - end;
+      if (capacity > 0) {
+        // move [dst, end) to dst+1 and insert at dst
+        Index chunkSize = end - dst;
+        if (chunkSize > 0) m_data.moveChunk(dst, dst + 1, chunkSize);
+        m_innerNonZeros[outer]++;
+        for (Index j = outer; j < rightTarget; j++) m_outerIndex[j + 1]++;
+        m_data.index(dst) = StorageIndex(inner);
+        m_data.value(dst) = Scalar(0);
+        return m_data.value(dst);
+      }
+      rightTarget++;
+    }
+    if (leftTarget >= 0) {
+      Index start = m_outerIndex[leftTarget];
+      Index end = start + m_innerNonZeros[leftTarget];
+      Index nextStart = m_outerIndex[leftTarget + 1];
+      Index capacity = nextStart - end;
+      if (capacity > 0) {
+        // tricky: dst is a lower bound, so we must insert at dst-1 when shifting left
+        // move [nextStart, dst) to nextStart-1 and insert at dst-1
+        Index chunkSize = dst - nextStart;
+        if (chunkSize > 0) m_data.moveChunk(nextStart, nextStart - 1, chunkSize);
+        m_innerNonZeros[outer]++;
+        for (Index j = leftTarget; j < outer; j++) m_outerIndex[j + 1]--;
+        m_data.index(dst - 1) = StorageIndex(inner);
+        m_data.value(dst - 1) = Scalar(0);
+        return m_data.value(dst - 1);
+      }
+      leftTarget--;
+    }
+  }
+
+  // no room for interior insertion
+  // nonZeros() == m_data.size()
+  // record offset as outerIndxPtr will change
+  Index dst_offset = dst - m_outerIndex[outer];
+  // allocate space for random insertion
+  if (m_data.allocatedSize() == 0) {
+    // fast method to allocate space for one element per vector in empty matrix
+    m_data.resize(m_outerSize);
+    std::iota(m_outerIndex, m_outerIndex + m_outerSize + 1, StorageIndex(0));
+  } else {
+    // check for integer overflow: if maxReserveSize == 0, insertion is not possible
+    Index maxReserveSize = static_cast<Index>(NumTraits<StorageIndex>::highest()) - m_data.allocatedSize();
+    eigen_assert(maxReserveSize > 0);
+    if (m_outerSize <= maxReserveSize) {
+      // allocate space for one additional element per vector
+      reserveInnerVectors(IndexVector::Constant(m_outerSize, 1));
+    } else {
+      // handle the edge case where StorageIndex is insufficient to reserve outerSize additional elements
+      // allocate space for one additional element in the interval [outer,maxReserveSize)
+      typedef internal::sparse_reserve_op<StorageIndex> ReserveSizesOp;
+      typedef CwiseNullaryOp<ReserveSizesOp, IndexVector> ReserveSizesXpr;
+      ReserveSizesXpr reserveSizesXpr(m_outerSize, 1, ReserveSizesOp(outer, m_outerSize, maxReserveSize));
+      reserveInnerVectors(reserveSizesXpr);
+    }
+  }
+  // insert element at `dst` with new outer indices
+  Index start = m_outerIndex[outer];
+  Index end = start + m_innerNonZeros[outer];
+  Index new_dst = start + dst_offset;
+  Index chunkSize = end - new_dst;
+  if (chunkSize > 0) m_data.moveChunk(new_dst, new_dst + 1, chunkSize);
+  m_innerNonZeros[outer]++;
+  m_data.index(new_dst) = StorageIndex(inner);
+  m_data.value(new_dst) = Scalar(0);
+  return m_data.value(new_dst);
+}
+
+namespace internal {
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+struct evaluator<SparseMatrix<Scalar_, Options_, StorageIndex_>>
+    : evaluator<SparseCompressedBase<SparseMatrix<Scalar_, Options_, StorageIndex_>>> {
+  typedef evaluator<SparseCompressedBase<SparseMatrix<Scalar_, Options_, StorageIndex_>>> Base;
+  typedef SparseMatrix<Scalar_, Options_, StorageIndex_> SparseMatrixType;
+  evaluator() : Base() {}
+  explicit evaluator(const SparseMatrixType& mat) : Base(mat) {}
+};
+
+}  // namespace internal
+
+// Specialization for SparseMatrix.
+// Serializes [rows, cols, isCompressed, outerSize, innerBufferSize,
+// innerNonZeros, outerIndices, innerIndices, values].
+template <typename Scalar, int Options, typename StorageIndex>
+class Serializer<SparseMatrix<Scalar, Options, StorageIndex>, void> {
+ public:
+  typedef SparseMatrix<Scalar, Options, StorageIndex> SparseMat;
+
+  struct Header {
+    typename SparseMat::Index rows;
+    typename SparseMat::Index cols;
+    bool compressed;
+    Index outer_size;
+    Index inner_buffer_size;
+  };
+
+  EIGEN_DEVICE_FUNC size_t size(const SparseMat& value) const {
+    // innerNonZeros.
+    std::size_t num_storage_indices = value.isCompressed() ? 0 : value.outerSize();
+    // Outer indices.
+    num_storage_indices += value.outerSize() + 1;
+    // Inner indices.
+    const StorageIndex inner_buffer_size = value.outerIndexPtr()[value.outerSize()];
+    num_storage_indices += inner_buffer_size;
+    // Values.
+    std::size_t num_values = inner_buffer_size;
+    return sizeof(Header) + sizeof(Scalar) * num_values + sizeof(StorageIndex) * num_storage_indices;
+  }
+
+  EIGEN_DEVICE_FUNC uint8_t* serialize(uint8_t* dest, uint8_t* end, const SparseMat& value) {
+    if (EIGEN_PREDICT_FALSE(dest == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(dest + size(value) > end)) return nullptr;
+
+    const size_t header_bytes = sizeof(Header);
+    Header header = {value.rows(), value.cols(), value.isCompressed(), value.outerSize(),
+                     value.outerIndexPtr()[value.outerSize()]};
+    EIGEN_USING_STD(memcpy)
+    memcpy(dest, &header, header_bytes);
+    dest += header_bytes;
+
+    // innerNonZeros.
+    if (!header.compressed) {
+      std::size_t data_bytes = sizeof(StorageIndex) * header.outer_size;
+      memcpy(dest, value.innerNonZeroPtr(), data_bytes);
+      dest += data_bytes;
+    }
+
+    // Outer indices.
+    std::size_t data_bytes = sizeof(StorageIndex) * (header.outer_size + 1);
+    memcpy(dest, value.outerIndexPtr(), data_bytes);
+    dest += data_bytes;
+
+    // Inner indices.
+    data_bytes = sizeof(StorageIndex) * header.inner_buffer_size;
+    memcpy(dest, value.innerIndexPtr(), data_bytes);
+    dest += data_bytes;
+
+    // Values.
+    data_bytes = sizeof(Scalar) * header.inner_buffer_size;
+    memcpy(dest, value.valuePtr(), data_bytes);
+    dest += data_bytes;
+
+    return dest;
+  }
+
+  EIGEN_DEVICE_FUNC const uint8_t* deserialize(const uint8_t* src, const uint8_t* end, SparseMat& value) const {
+    if (EIGEN_PREDICT_FALSE(src == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(src + sizeof(Header) > end)) return nullptr;
+
+    const size_t header_bytes = sizeof(Header);
+    Header header;
+    EIGEN_USING_STD(memcpy)
+    memcpy(&header, src, header_bytes);
+    src += header_bytes;
+
+    value.setZero();
+    value.resize(header.rows, header.cols);
+    if (header.compressed) {
+      value.makeCompressed();
+    } else {
+      value.uncompress();
+    }
+
+    // Adjust value ptr size.
+    value.data().resize(header.inner_buffer_size);
+
+    // Initialize compressed state and inner non-zeros.
+    if (!header.compressed) {
+      // Inner non-zero counts.
+      std::size_t data_bytes = sizeof(StorageIndex) * header.outer_size;
+      if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+      memcpy(value.innerNonZeroPtr(), src, data_bytes);
+      src += data_bytes;
+    }
+
+    // Outer indices.
+    std::size_t data_bytes = sizeof(StorageIndex) * (header.outer_size + 1);
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    memcpy(value.outerIndexPtr(), src, data_bytes);
+    src += data_bytes;
+
+    // Inner indices.
+    data_bytes = sizeof(StorageIndex) * header.inner_buffer_size;
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    memcpy(value.innerIndexPtr(), src, data_bytes);
+    src += data_bytes;
+
+    // Values.
+    data_bytes = sizeof(Scalar) * header.inner_buffer_size;
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    memcpy(value.valuePtr(), src, data_bytes);
+    src += data_bytes;
+    return src;
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSEMATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseMatrixBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseMatrixBase.h
new file mode 100644
index 0000000..acdaf93
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseMatrixBase.h
@@ -0,0 +1,451 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEMATRIXBASE_H
+#define EIGEN_SPARSEMATRIXBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup SparseCore_Module
+ *
+ * \class SparseMatrixBase
+ *
+ * \brief Base class of any sparse matrices or sparse expressions
+ *
+ * \tparam Derived is the derived type, e.g. a sparse matrix type, or an expression, etc.
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEMATRIXBASE_PLUGIN.
+ */
+template <typename Derived>
+class SparseMatrixBase : public EigenBase<Derived> {
+ public:
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+
+  /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
+   *
+   * It is an alias for the Scalar type */
+  typedef Scalar value_type;
+
+  typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+
+  /** The integer type used to \b store indices within a SparseMatrix.
+   * For a \c SparseMatrix<Scalar,Options,IndexType> it an alias of the third template parameter \c IndexType. */
+  typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+
+  typedef typename internal::add_const_on_value_type_if_arithmetic<typename internal::packet_traits<Scalar>::type>::type
+      PacketReturnType;
+
+  typedef SparseMatrixBase StorageBaseType;
+
+  typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
+  typedef Matrix<Scalar, Dynamic, 1> ScalarVector;
+
+  template <typename OtherDerived>
+  Derived& operator=(const EigenBase<OtherDerived>& other);
+
+  enum {
+
+    RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+    /**< The number of rows at compile-time. This is just a copy of the value provided
+     * by the \a Derived type. If a value is not known at compile-time,
+     * it is set to the \a Dynamic constant.
+     * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
+
+    ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+    /**< The number of columns at compile-time. This is just a copy of the value provided
+     * by the \a Derived type. If a value is not known at compile-time,
+     * it is set to the \a Dynamic constant.
+     * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
+
+    SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
+    /**< This is equal to the number of coefficients, i.e. the number of
+     * rows times the number of columns, or to \a Dynamic if this is not
+     * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
+
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+
+    MaxSizeAtCompileTime = internal::size_at_compile_time(MaxRowsAtCompileTime, MaxColsAtCompileTime),
+
+    IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
+    /**< This is set to true if either the number of rows or the number of
+     * columns is known at compile-time to be equal to 1. Indeed, in that case,
+     * we are dealing with a column-vector (if there is only one column) or with
+     * a row-vector (if there is only one row). */
+
+    NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0
+                    : bool(IsVectorAtCompileTime)  ? 1
+                                                   : 2,
+    /**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors,
+     * and 2 for matrices.
+     */
+
+    Flags = internal::traits<Derived>::Flags,
+    /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
+     * constructed from this one. See the \ref flags "list of flags".
+     */
+
+    IsRowMajor = Flags & RowMajorBit ? 1 : 0,
+
+    InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
+                             : int(IsRowMajor)          ? int(ColsAtCompileTime)
+                                                        : int(RowsAtCompileTime),
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    HasDirectAccess_ = (int(Flags) & DirectAccessBit) ? 1 : 0  // workaround sunCC
+#endif
+  };
+
+  /** \internal the return type of MatrixBase::adjoint() */
+  typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+                             CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, Eigen::Transpose<const Derived> >,
+                             Transpose<const Derived> >
+      AdjointReturnType;
+  typedef Transpose<Derived> TransposeReturnType;
+  typedef Transpose<const Derived> ConstTransposeReturnType;
+
+  // FIXME: storage order may not match evaluator storage order.
+  typedef SparseMatrix<Scalar, Flags & RowMajorBit ? RowMajor : ColMajor, StorageIndex> PlainObject;
+
+  /** This is the "real scalar" type; if the \a Scalar type is already real numbers
+   * (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
+   * \a Scalar is \a std::complex<T> then RealScalar is \a T.
+   *
+   * \sa class NumTraits
+   */
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal the return type of coeff()
+   */
+  typedef std::conditional_t<HasDirectAccess_, const Scalar&, Scalar> CoeffReturnType;
+
+  /** \internal Represents a matrix with all coefficients equal to one another*/
+  typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>, Matrix<Scalar, Dynamic, Dynamic> > ConstantReturnType;
+
+  /** type of the equivalent dense matrix */
+  typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime> DenseMatrixType;
+  /** type of the equivalent square matrix */
+  typedef Matrix<Scalar, internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime),
+                 internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)>
+      SquareMatrixType;
+
+  inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+  inline Derived& derived() { return *static_cast<Derived*>(this); }
+  inline Derived& const_cast_derived() const { return *static_cast<Derived*>(const_cast<SparseMatrixBase*>(this)); }
+
+  typedef EigenBase<Derived> Base;
+
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::SparseMatrixBase
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+#define EIGEN_DOC_UNARY_ADDONS(METHOD,                                                                               \
+                               OP) /** <p>This method does not change the sparsity of \c *this: the OP is applied to \
+                                      explicitly stored coefficients only. \sa SparseCompressedBase::coeffs() </p> */
+#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL /** <p> \warning This method returns a read-only expression for any   \
+                                                  sparse matrices. \sa \ref TutorialSparse_SubMatrices "Sparse block \
+                                                  operations" </p> */
+#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(                                                                       \
+    COND) /** <p> \warning This method returns a read-write expression for COND sparse matrices only. Otherwise, the \
+             returned expression is read-only. \sa \ref TutorialSparse_SubMatrices "Sparse block operations" </p> */
+#else
+#define EIGEN_DOC_UNARY_ADDONS(X, Y)
+#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
+#endif
+#include "../plugins/CommonCwiseUnaryOps.inc"
+#include "../plugins/CommonCwiseBinaryOps.inc"
+#include "../plugins/MatrixCwiseUnaryOps.inc"
+#include "../plugins/MatrixCwiseBinaryOps.inc"
+#include "../plugins/BlockMethods.inc"
+#ifdef EIGEN_SPARSEMATRIXBASE_PLUGIN
+#include EIGEN_SPARSEMATRIXBASE_PLUGIN
+#endif
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_DOC_UNARY_ADDONS
+#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
+
+  /** \returns the number of rows. \sa cols() */
+  inline Index rows() const { return derived().rows(); }
+  /** \returns the number of columns. \sa rows() */
+  inline Index cols() const { return derived().cols(); }
+  /** \returns the number of coefficients, which is \a rows()*cols().
+   * \sa rows(), cols(). */
+  inline Index size() const { return rows() * cols(); }
+  /** \returns true if either the number of rows or the number of columns is equal to 1.
+   * In other words, this function returns
+   * \code rows()==1 || cols()==1 \endcode
+   * \sa rows(), cols(), IsVectorAtCompileTime. */
+  inline bool isVector() const { return rows() == 1 || cols() == 1; }
+  /** \returns the size of the storage major dimension,
+   * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
+  Index outerSize() const { return (int(Flags) & RowMajorBit) ? this->rows() : this->cols(); }
+  /** \returns the size of the inner dimension according to the storage order,
+   * i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
+  Index innerSize() const { return (int(Flags) & RowMajorBit) ? this->cols() : this->rows(); }
+
+  bool isRValue() const { return m_isRValue; }
+  Derived& markAsRValue() {
+    m_isRValue = true;
+    return derived();
+  }
+
+  SparseMatrixBase() : m_isRValue(false) { /* TODO: validate traits flags. */
+  }
+
+  template <typename OtherDerived>
+  Derived& operator=(const ReturnByValue<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  inline Derived& operator=(const SparseMatrixBase<OtherDerived>& other);
+
+  inline Derived& operator=(const Derived& other);
+
+ protected:
+  template <typename OtherDerived>
+  inline Derived& assign(const OtherDerived& other);
+
+  template <typename OtherDerived>
+  inline void assignGeneric(const OtherDerived& other);
+
+ public:
+#ifndef EIGEN_NO_IO
+  friend std::ostream& operator<<(std::ostream& s, const SparseMatrixBase& m) {
+    using Nested = typename Derived::Nested;
+    using NestedCleaned = typename internal::remove_all<Nested>::type;
+
+    if (Flags & RowMajorBit) {
+      Nested nm(m.derived());
+      internal::evaluator<NestedCleaned> thisEval(nm);
+
+      // compute global width
+      std::size_t width = 0;
+      {
+        std::ostringstream ss0;
+        ss0.copyfmt(s);
+        ss0 << Scalar(0);
+        width = ss0.str().size();
+        for (Index row = 0; row < nm.outerSize(); ++row) {
+          for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, row); it; ++it) {
+            std::ostringstream ss;
+            ss.copyfmt(s);
+            ss << it.value();
+
+            const std::size_t potential_width = ss.str().size();
+            if (potential_width > width) width = potential_width;
+          }
+        }
+      }
+
+      for (Index row = 0; row < nm.outerSize(); ++row) {
+        Index col = 0;
+        for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, row); it; ++it) {
+          for (; col < it.index(); ++col) {
+            s.width(width);
+            s << Scalar(0) << " ";
+          }
+          s.width(width);
+          s << it.value() << " ";
+          ++col;
+        }
+        for (; col < m.cols(); ++col) {
+          s.width(width);
+          s << Scalar(0) << " ";
+        }
+        s << std::endl;
+      }
+    } else {
+      Nested nm(m.derived());
+      internal::evaluator<NestedCleaned> thisEval(nm);
+      if (m.cols() == 1) {
+        // compute local width (single col)
+        std::size_t width = 0;
+        {
+          std::ostringstream ss0;
+          ss0.copyfmt(s);
+          ss0 << Scalar(0);
+          width = ss0.str().size();
+          for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, 0); it; ++it) {
+            std::ostringstream ss;
+            ss.copyfmt(s);
+            ss << it.value();
+
+            const std::size_t potential_width = ss.str().size();
+            if (potential_width > width) width = potential_width;
+          }
+        }
+
+        Index row = 0;
+        for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, 0); it; ++it) {
+          for (; row < it.index(); ++row) {
+            s.width(width);
+            s << Scalar(0) << std::endl;
+          }
+          s.width(width);
+          s << it.value() << std::endl;
+          ++row;
+        }
+        for (; row < m.rows(); ++row) {
+          s.width(width);
+          s << Scalar(0) << std::endl;
+        }
+      } else {
+        SparseMatrix<Scalar, RowMajorBit, StorageIndex> trans = m;
+        s << static_cast<const SparseMatrixBase<SparseMatrix<Scalar, RowMajorBit, StorageIndex> >&>(trans);
+      }
+    }
+    return s;
+  }
+#endif
+
+  template <typename OtherDerived>
+  Derived& operator+=(const SparseMatrixBase<OtherDerived>& other);
+  template <typename OtherDerived>
+  Derived& operator-=(const SparseMatrixBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  Derived& operator+=(const DiagonalBase<OtherDerived>& other);
+  template <typename OtherDerived>
+  Derived& operator-=(const DiagonalBase<OtherDerived>& other);
+
+  template <typename OtherDerived>
+  Derived& operator+=(const EigenBase<OtherDerived>& other);
+  template <typename OtherDerived>
+  Derived& operator-=(const EigenBase<OtherDerived>& other);
+
+  Derived& operator*=(const Scalar& other);
+  Derived& operator/=(const Scalar& other);
+
+  template <typename OtherDerived>
+  struct CwiseProductDenseReturnType {
+    typedef CwiseBinaryOp<
+        internal::scalar_product_op<typename ScalarBinaryOpTraits<
+            typename internal::traits<Derived>::Scalar, typename internal::traits<OtherDerived>::Scalar>::ReturnType>,
+        const Derived, const OtherDerived>
+        Type;
+  };
+
+  template <typename OtherDerived>
+  EIGEN_STRONG_INLINE const typename CwiseProductDenseReturnType<OtherDerived>::Type cwiseProduct(
+      const MatrixBase<OtherDerived>& other) const;
+
+  // sparse * diagonal
+  template <typename OtherDerived>
+  const Product<Derived, OtherDerived> operator*(const DiagonalBase<OtherDerived>& other) const {
+    return Product<Derived, OtherDerived>(derived(), other.derived());
+  }
+
+  // diagonal * sparse
+  template <typename OtherDerived>
+  friend const Product<OtherDerived, Derived> operator*(const DiagonalBase<OtherDerived>& lhs,
+                                                        const SparseMatrixBase& rhs) {
+    return Product<OtherDerived, Derived>(lhs.derived(), rhs.derived());
+  }
+
+  // sparse * sparse
+  template <typename OtherDerived>
+  const Product<Derived, OtherDerived, AliasFreeProduct> operator*(const SparseMatrixBase<OtherDerived>& other) const;
+
+  // sparse * dense
+  template <typename OtherDerived>
+  const Product<Derived, OtherDerived> operator*(const MatrixBase<OtherDerived>& other) const {
+    return Product<Derived, OtherDerived>(derived(), other.derived());
+  }
+
+  // dense * sparse
+  template <typename OtherDerived>
+  friend const Product<OtherDerived, Derived> operator*(const MatrixBase<OtherDerived>& lhs,
+                                                        const SparseMatrixBase& rhs) {
+    return Product<OtherDerived, Derived>(lhs.derived(), rhs.derived());
+  }
+
+  /** \returns an expression of P H P^-1 where H is the matrix represented by \c *this */
+  SparseSymmetricPermutationProduct<Derived, Upper | Lower> twistedBy(
+      const PermutationMatrix<Dynamic, Dynamic, StorageIndex>& perm) const {
+    return SparseSymmetricPermutationProduct<Derived, Upper | Lower>(derived(), perm);
+  }
+
+  template <typename OtherDerived>
+  Derived& operator*=(const SparseMatrixBase<OtherDerived>& other);
+
+  template <int Mode>
+  inline const TriangularView<const Derived, Mode> triangularView() const;
+
+  template <unsigned int UpLo>
+  struct SelfAdjointViewReturnType {
+    typedef SparseSelfAdjointView<Derived, UpLo> Type;
+  };
+  template <unsigned int UpLo>
+  struct ConstSelfAdjointViewReturnType {
+    typedef const SparseSelfAdjointView<const Derived, UpLo> Type;
+  };
+
+  template <unsigned int UpLo>
+  inline typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
+  template <unsigned int UpLo>
+  inline typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
+
+  template <typename OtherDerived>
+  Scalar dot(const MatrixBase<OtherDerived>& other) const;
+  template <typename OtherDerived>
+  Scalar dot(const SparseMatrixBase<OtherDerived>& other) const;
+  RealScalar squaredNorm() const;
+  RealScalar norm() const;
+  RealScalar blueNorm() const;
+
+  TransposeReturnType transpose() { return TransposeReturnType(derived()); }
+  const ConstTransposeReturnType transpose() const { return ConstTransposeReturnType(derived()); }
+  const AdjointReturnType adjoint() const { return AdjointReturnType(transpose()); }
+
+  DenseMatrixType toDense() const { return DenseMatrixType(derived()); }
+
+  template <typename OtherDerived>
+  bool isApprox(const SparseMatrixBase<OtherDerived>& other,
+                const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+  template <typename OtherDerived>
+  bool isApprox(const MatrixBase<OtherDerived>& other,
+                const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const {
+    return toDense().isApprox(other, prec);
+  }
+
+  /** \returns the matrix or vector obtained by evaluating this expression.
+   *
+   * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
+   * a const reference, in order to avoid a useless copy.
+   */
+  inline const typename internal::eval<Derived>::type eval() const {
+    return typename internal::eval<Derived>::type(derived());
+  }
+
+  Scalar sum() const;
+
+  inline const SparseView<Derived> pruned(const Scalar& reference = Scalar(0),
+                                          const RealScalar& epsilon = NumTraits<Scalar>::dummy_precision()) const;
+
+ protected:
+  bool m_isRValue;
+
+  static inline StorageIndex convert_index(const Index idx) { return internal::convert_index<StorageIndex>(idx); }
+
+ private:
+  template <typename Dest>
+  void evalTo(Dest&) const;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSEMATRIXBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparsePermutation.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparsePermutation.h
new file mode 100644
index 0000000..56f572d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparsePermutation.h
@@ -0,0 +1,249 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_PERMUTATION_H
+#define EIGEN_SPARSE_PERMUTATION_H
+
+// This file implements sparse * permutation products
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename ExpressionType, typename PlainObjectType,
+          bool NeedEval = !is_same<ExpressionType, PlainObjectType>::value>
+struct XprHelper {
+  XprHelper(const ExpressionType& xpr) : m_xpr(xpr) {}
+  inline const PlainObjectType& xpr() const { return m_xpr; }
+  // this is a new PlainObjectType initialized by xpr
+  const PlainObjectType m_xpr;
+};
+template <typename ExpressionType, typename PlainObjectType>
+struct XprHelper<ExpressionType, PlainObjectType, false> {
+  XprHelper(const ExpressionType& xpr) : m_xpr(xpr) {}
+  inline const PlainObjectType& xpr() const { return m_xpr; }
+  // this is a reference to xpr
+  const PlainObjectType& m_xpr;
+};
+
+template <typename PermDerived, bool NeedInverseEval>
+struct PermHelper {
+  using IndicesType = typename PermDerived::IndicesType;
+  using PermutationIndex = typename IndicesType::Scalar;
+  using type = PermutationMatrix<IndicesType::SizeAtCompileTime, IndicesType::MaxSizeAtCompileTime, PermutationIndex>;
+  PermHelper(const PermDerived& perm) : m_perm(perm.inverse()) {}
+  inline const type& perm() const { return m_perm; }
+  // this is a new PermutationMatrix initialized by perm.inverse()
+  const type m_perm;
+};
+template <typename PermDerived>
+struct PermHelper<PermDerived, false> {
+  using type = PermDerived;
+  PermHelper(const PermDerived& perm) : m_perm(perm) {}
+  inline const type& perm() const { return m_perm; }
+  // this is a reference to perm
+  const type& m_perm;
+};
+
+template <typename ExpressionType, int Side, bool Transposed>
+struct permutation_matrix_product<ExpressionType, Side, Transposed, SparseShape> {
+  using MatrixType = typename nested_eval<ExpressionType, 1>::type;
+  using MatrixTypeCleaned = remove_all_t<MatrixType>;
+
+  using Scalar = typename MatrixTypeCleaned::Scalar;
+  using StorageIndex = typename MatrixTypeCleaned::StorageIndex;
+
+  // the actual "return type" is `Dest`. this is a temporary type
+  using ReturnType = SparseMatrix<Scalar, MatrixTypeCleaned::IsRowMajor ? RowMajor : ColMajor, StorageIndex>;
+  using TmpHelper = XprHelper<ExpressionType, ReturnType>;
+
+  static constexpr bool NeedOuterPermutation = ExpressionType::IsRowMajor ? Side == OnTheLeft : Side == OnTheRight;
+  static constexpr bool NeedInversePermutation = Transposed ? Side == OnTheLeft : Side == OnTheRight;
+
+  template <typename Dest, typename PermutationType>
+  static inline void permute_outer(Dest& dst, const PermutationType& perm, const ExpressionType& xpr) {
+    // if ExpressionType is not ReturnType, evaluate `xpr` (allocation)
+    // otherwise, just reference `xpr`
+    // TODO: handle trivial expressions such as CwiseBinaryOp without temporary
+    const TmpHelper tmpHelper(xpr);
+    const ReturnType& tmp = tmpHelper.xpr();
+
+    ReturnType result(tmp.rows(), tmp.cols());
+
+    for (Index j = 0; j < tmp.outerSize(); j++) {
+      Index jp = perm.indices().coeff(j);
+      Index jsrc = NeedInversePermutation ? jp : j;
+      Index jdst = NeedInversePermutation ? j : jp;
+      Index begin = tmp.outerIndexPtr()[jsrc];
+      Index end = tmp.isCompressed() ? tmp.outerIndexPtr()[jsrc + 1] : begin + tmp.innerNonZeroPtr()[jsrc];
+      result.outerIndexPtr()[jdst + 1] += end - begin;
+    }
+
+    std::partial_sum(result.outerIndexPtr(), result.outerIndexPtr() + result.outerSize() + 1, result.outerIndexPtr());
+    result.resizeNonZeros(result.nonZeros());
+
+    for (Index j = 0; j < tmp.outerSize(); j++) {
+      Index jp = perm.indices().coeff(j);
+      Index jsrc = NeedInversePermutation ? jp : j;
+      Index jdst = NeedInversePermutation ? j : jp;
+      Index begin = tmp.outerIndexPtr()[jsrc];
+      Index end = tmp.isCompressed() ? tmp.outerIndexPtr()[jsrc + 1] : begin + tmp.innerNonZeroPtr()[jsrc];
+      Index target = result.outerIndexPtr()[jdst];
+      smart_copy(tmp.innerIndexPtr() + begin, tmp.innerIndexPtr() + end, result.innerIndexPtr() + target);
+      smart_copy(tmp.valuePtr() + begin, tmp.valuePtr() + end, result.valuePtr() + target);
+    }
+    dst = std::move(result);
+  }
+
+  template <typename Dest, typename PermutationType>
+  static inline void permute_inner(Dest& dst, const PermutationType& perm, const ExpressionType& xpr) {
+    using InnerPermHelper = PermHelper<PermutationType, NeedInversePermutation>;
+    using InnerPermType = typename InnerPermHelper::type;
+
+    // if ExpressionType is not ReturnType, evaluate `xpr` (allocation)
+    // otherwise, just reference `xpr`
+    // TODO: handle trivial expressions such as CwiseBinaryOp without temporary
+    const TmpHelper tmpHelper(xpr);
+    const ReturnType& tmp = tmpHelper.xpr();
+
+    // if inverse permutation of inner indices is requested, calculate perm.inverse() (allocation)
+    // otherwise, just reference `perm`
+    const InnerPermHelper permHelper(perm);
+    const InnerPermType& innerPerm = permHelper.perm();
+
+    ReturnType result(tmp.rows(), tmp.cols());
+
+    for (Index j = 0; j < tmp.outerSize(); j++) {
+      Index begin = tmp.outerIndexPtr()[j];
+      Index end = tmp.isCompressed() ? tmp.outerIndexPtr()[j + 1] : begin + tmp.innerNonZeroPtr()[j];
+      result.outerIndexPtr()[j + 1] += end - begin;
+    }
+
+    std::partial_sum(result.outerIndexPtr(), result.outerIndexPtr() + result.outerSize() + 1, result.outerIndexPtr());
+    result.resizeNonZeros(result.nonZeros());
+
+    for (Index j = 0; j < tmp.outerSize(); j++) {
+      Index begin = tmp.outerIndexPtr()[j];
+      Index end = tmp.isCompressed() ? tmp.outerIndexPtr()[j + 1] : begin + tmp.innerNonZeroPtr()[j];
+      Index target = result.outerIndexPtr()[j];
+      std::transform(tmp.innerIndexPtr() + begin, tmp.innerIndexPtr() + end, result.innerIndexPtr() + target,
+                     [&innerPerm](StorageIndex i) { return innerPerm.indices().coeff(i); });
+      smart_copy(tmp.valuePtr() + begin, tmp.valuePtr() + end, result.valuePtr() + target);
+    }
+    // the inner indices were permuted, and must be sorted
+    result.sortInnerIndices();
+    dst = std::move(result);
+  }
+
+  template <typename Dest, typename PermutationType, bool DoOuter = NeedOuterPermutation,
+            std::enable_if_t<DoOuter, int> = 0>
+  static inline void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr) {
+    permute_outer(dst, perm, xpr);
+  }
+
+  template <typename Dest, typename PermutationType, bool DoOuter = NeedOuterPermutation,
+            std::enable_if_t<!DoOuter, int> = 0>
+  static inline void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr) {
+    permute_inner(dst, perm, xpr);
+  }
+};
+
+}  // namespace internal
+
+namespace internal {
+
+template <int ProductTag>
+struct product_promote_storage_type<Sparse, PermutationStorage, ProductTag> {
+  typedef Sparse ret;
+};
+template <int ProductTag>
+struct product_promote_storage_type<PermutationStorage, Sparse, ProductTag> {
+  typedef Sparse ret;
+};
+
+// TODO, the following two overloads are only needed to define the right temporary type through
+// typename traits<permutation_sparse_matrix_product<Rhs,Lhs,OnTheRight,false> >::ReturnType
+// whereas it should be correctly handled by traits<Product<> >::PlainObject
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, AliasFreeProduct>, ProductTag, PermutationShape, SparseShape>
+    : public evaluator<typename permutation_matrix_product<Rhs, OnTheLeft, false, SparseShape>::ReturnType> {
+  typedef Product<Lhs, Rhs, AliasFreeProduct> XprType;
+  typedef typename permutation_matrix_product<Rhs, OnTheLeft, false, SparseShape>::ReturnType PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  enum { Flags = Base::Flags | EvalBeforeNestingBit };
+
+  explicit product_evaluator(const XprType& xpr) : m_result(xpr.rows(), xpr.cols()) {
+    internal::construct_at<Base>(this, m_result);
+    generic_product_impl<Lhs, Rhs, PermutationShape, SparseShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
+  }
+
+ protected:
+  PlainObject m_result;
+};
+
+template <typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, AliasFreeProduct>, ProductTag, SparseShape, PermutationShape>
+    : public evaluator<typename permutation_matrix_product<Lhs, OnTheRight, false, SparseShape>::ReturnType> {
+  typedef Product<Lhs, Rhs, AliasFreeProduct> XprType;
+  typedef typename permutation_matrix_product<Lhs, OnTheRight, false, SparseShape>::ReturnType PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  enum { Flags = Base::Flags | EvalBeforeNestingBit };
+
+  explicit product_evaluator(const XprType& xpr) : m_result(xpr.rows(), xpr.cols()) {
+    ::new (static_cast<Base*>(this)) Base(m_result);
+    generic_product_impl<Lhs, Rhs, SparseShape, PermutationShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
+  }
+
+ protected:
+  PlainObject m_result;
+};
+
+}  // end namespace internal
+
+/** \returns the matrix with the permutation applied to the columns
+ */
+template <typename SparseDerived, typename PermDerived>
+inline const Product<SparseDerived, PermDerived, AliasFreeProduct> operator*(
+    const SparseMatrixBase<SparseDerived>& matrix, const PermutationBase<PermDerived>& perm) {
+  return Product<SparseDerived, PermDerived, AliasFreeProduct>(matrix.derived(), perm.derived());
+}
+
+/** \returns the matrix with the permutation applied to the rows
+ */
+template <typename SparseDerived, typename PermDerived>
+inline const Product<PermDerived, SparseDerived, AliasFreeProduct> operator*(
+    const PermutationBase<PermDerived>& perm, const SparseMatrixBase<SparseDerived>& matrix) {
+  return Product<PermDerived, SparseDerived, AliasFreeProduct>(perm.derived(), matrix.derived());
+}
+
+/** \returns the matrix with the inverse permutation applied to the columns.
+ */
+template <typename SparseDerived, typename PermutationType>
+inline const Product<SparseDerived, Inverse<PermutationType>, AliasFreeProduct> operator*(
+    const SparseMatrixBase<SparseDerived>& matrix, const InverseImpl<PermutationType, PermutationStorage>& tperm) {
+  return Product<SparseDerived, Inverse<PermutationType>, AliasFreeProduct>(matrix.derived(), tperm.derived());
+}
+
+/** \returns the matrix with the inverse permutation applied to the rows.
+ */
+template <typename SparseDerived, typename PermutationType>
+inline const Product<Inverse<PermutationType>, SparseDerived, AliasFreeProduct> operator*(
+    const InverseImpl<PermutationType, PermutationStorage>& tperm, const SparseMatrixBase<SparseDerived>& matrix) {
+  return Product<Inverse<PermutationType>, SparseDerived, AliasFreeProduct>(tperm.derived(), matrix.derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseProduct.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseProduct.h
new file mode 100644
index 0000000..249dabc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseProduct.h
@@ -0,0 +1,178 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEPRODUCT_H
+#define EIGEN_SPARSEPRODUCT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \returns an expression of the product of two sparse matrices.
+ * By default a conservative product preserving the symbolic non zeros is performed.
+ * The automatic pruning of the small values can be achieved by calling the pruned() function
+ * in which case a totally different product algorithm is employed:
+ * \code
+ * C = (A*B).pruned();             // suppress numerical zeros (exact)
+ * C = (A*B).pruned(ref);
+ * C = (A*B).pruned(ref,epsilon);
+ * \endcode
+ * where \c ref is a meaningful non zero reference value.
+ * */
+template <typename Derived>
+template <typename OtherDerived>
+inline const Product<Derived, OtherDerived, AliasFreeProduct> SparseMatrixBase<Derived>::operator*(
+    const SparseMatrixBase<OtherDerived>& other) const {
+  return Product<Derived, OtherDerived, AliasFreeProduct>(derived(), other.derived());
+}
+
+namespace internal {
+
+// sparse * sparse
+template <typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType> {
+  template <typename Dest>
+  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs) {
+    evalTo(dst, lhs, rhs, typename evaluator_traits<Dest>::Shape());
+  }
+
+  // dense += sparse * sparse
+  template <typename Dest, typename ActualLhs>
+  static void addTo(Dest& dst, const ActualLhs& lhs, const Rhs& rhs,
+                    std::enable_if_t<is_same<typename evaluator_traits<Dest>::Shape, DenseShape>::value, int*>* = 0) {
+    typedef typename nested_eval<ActualLhs, Dynamic>::type LhsNested;
+    typedef typename nested_eval<Rhs, Dynamic>::type RhsNested;
+    LhsNested lhsNested(lhs);
+    RhsNested rhsNested(rhs);
+    internal::sparse_sparse_to_dense_product_selector<remove_all_t<LhsNested>, remove_all_t<RhsNested>, Dest>::run(
+        lhsNested, rhsNested, dst);
+  }
+
+  // dense -= sparse * sparse
+  template <typename Dest>
+  static void subTo(Dest& dst, const Lhs& lhs, const Rhs& rhs,
+                    std::enable_if_t<is_same<typename evaluator_traits<Dest>::Shape, DenseShape>::value, int*>* = 0) {
+    addTo(dst, -lhs, rhs);
+  }
+
+ protected:
+  // sparse = sparse * sparse
+  template <typename Dest>
+  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, SparseShape) {
+    typedef typename nested_eval<Lhs, Dynamic>::type LhsNested;
+    typedef typename nested_eval<Rhs, Dynamic>::type RhsNested;
+    LhsNested lhsNested(lhs);
+    RhsNested rhsNested(rhs);
+    internal::conservative_sparse_sparse_product_selector<remove_all_t<LhsNested>, remove_all_t<RhsNested>, Dest>::run(
+        lhsNested, rhsNested, dst);
+  }
+
+  // dense = sparse * sparse
+  template <typename Dest>
+  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, DenseShape) {
+    dst.setZero();
+    addTo(dst, lhs, rhs);
+  }
+};
+
+// sparse * sparse-triangular
+template <typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, SparseShape, SparseTriangularShape, ProductType>
+    : public generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType> {};
+
+// sparse-triangular * sparse
+template <typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, SparseTriangularShape, SparseShape, ProductType>
+    : public generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType> {};
+
+// dense = sparse-product (can be sparse*sparse, sparse*perm, etc.)
+template <typename DstXprType, typename Lhs, typename Rhs>
+struct Assignment<
+    DstXprType, Product<Lhs, Rhs, AliasFreeProduct>,
+    internal::assign_op<typename DstXprType::Scalar, typename Product<Lhs, Rhs, AliasFreeProduct>::Scalar>,
+    Sparse2Dense> {
+  typedef Product<Lhs, Rhs, AliasFreeProduct> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>&) {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
+
+    generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());
+  }
+};
+
+// dense += sparse-product (can be sparse*sparse, sparse*perm, etc.)
+template <typename DstXprType, typename Lhs, typename Rhs>
+struct Assignment<
+    DstXprType, Product<Lhs, Rhs, AliasFreeProduct>,
+    internal::add_assign_op<typename DstXprType::Scalar, typename Product<Lhs, Rhs, AliasFreeProduct>::Scalar>,
+    Sparse2Dense> {
+  typedef Product<Lhs, Rhs, AliasFreeProduct> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::add_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>&) {
+    generic_product_impl<Lhs, Rhs>::addTo(dst, src.lhs(), src.rhs());
+  }
+};
+
+// dense -= sparse-product (can be sparse*sparse, sparse*perm, etc.)
+template <typename DstXprType, typename Lhs, typename Rhs>
+struct Assignment<
+    DstXprType, Product<Lhs, Rhs, AliasFreeProduct>,
+    internal::sub_assign_op<typename DstXprType::Scalar, typename Product<Lhs, Rhs, AliasFreeProduct>::Scalar>,
+    Sparse2Dense> {
+  typedef Product<Lhs, Rhs, AliasFreeProduct> SrcXprType;
+  static void run(DstXprType& dst, const SrcXprType& src,
+                  const internal::sub_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>&) {
+    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());
+  }
+};
+
+template <typename Lhs, typename Rhs, int Options>
+struct unary_evaluator<SparseView<Product<Lhs, Rhs, Options> >, IteratorBased>
+    : public evaluator<typename Product<Lhs, Rhs, DefaultProduct>::PlainObject> {
+  typedef SparseView<Product<Lhs, Rhs, Options> > XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  explicit unary_evaluator(const XprType& xpr) : m_result(xpr.rows(), xpr.cols()) {
+    using std::abs;
+    internal::construct_at<Base>(this, m_result);
+    typedef typename nested_eval<Lhs, Dynamic>::type LhsNested;
+    typedef typename nested_eval<Rhs, Dynamic>::type RhsNested;
+    LhsNested lhsNested(xpr.nestedExpression().lhs());
+    RhsNested rhsNested(xpr.nestedExpression().rhs());
+
+    internal::sparse_sparse_product_with_pruning_selector<remove_all_t<LhsNested>, remove_all_t<RhsNested>,
+                                                          PlainObject>::run(lhsNested, rhsNested, m_result,
+                                                                            abs(xpr.reference()) * xpr.epsilon());
+  }
+
+ protected:
+  PlainObject m_result;
+};
+
+}  // end namespace internal
+
+// sparse matrix = sparse-product (can be sparse*sparse, sparse*perm, etc.)
+template <typename Scalar, int Options_, typename StorageIndex_>
+template <typename Lhs, typename Rhs>
+SparseMatrix<Scalar, Options_, StorageIndex_>& SparseMatrix<Scalar, Options_, StorageIndex_>::operator=(
+    const Product<Lhs, Rhs, AliasFreeProduct>& src) {
+  // std::cout << "in Assignment : " << DstOptions << "\n";
+  SparseMatrix dst(src.rows(), src.cols());
+  internal::generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());
+  this->swap(dst);
+  return *this;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSEPRODUCT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseRedux.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseRedux.h
new file mode 100644
index 0000000..732e4f7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseRedux.h
@@ -0,0 +1,47 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEREDUX_H
+#define EIGEN_SPARSEREDUX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Derived>
+typename internal::traits<Derived>::Scalar SparseMatrixBase<Derived>::sum() const {
+  eigen_assert(rows() > 0 && cols() > 0 && "you are using a non initialized matrix");
+  Scalar res(0);
+  internal::evaluator<Derived> thisEval(derived());
+  for (Index j = 0; j < outerSize(); ++j)
+    for (typename internal::evaluator<Derived>::InnerIterator iter(thisEval, j); iter; ++iter) res += iter.value();
+  return res;
+}
+
+template <typename Scalar_, int Options_, typename Index_>
+typename internal::traits<SparseMatrix<Scalar_, Options_, Index_> >::Scalar
+SparseMatrix<Scalar_, Options_, Index_>::sum() const {
+  eigen_assert(rows() > 0 && cols() > 0 && "you are using a non initialized matrix");
+  if (this->isCompressed())
+    return Matrix<Scalar, 1, Dynamic>::Map(m_data.valuePtr(), m_data.size()).sum();
+  else
+    return Base::sum();
+}
+
+template <typename Scalar_, int Options_, typename Index_>
+typename internal::traits<SparseVector<Scalar_, Options_, Index_> >::Scalar
+SparseVector<Scalar_, Options_, Index_>::sum() const {
+  eigen_assert(rows() > 0 && cols() > 0 && "you are using a non initialized matrix");
+  return Matrix<Scalar, 1, Dynamic>::Map(m_data.valuePtr(), m_data.size()).sum();
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSEREDUX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseRef.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseRef.h
new file mode 100644
index 0000000..aa8ae34
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseRef.h
@@ -0,0 +1,371 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_REF_H
+#define EIGEN_SPARSE_REF_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum {
+  StandardCompressedFormat =
+      2 /**< used by Ref<SparseMatrix> to specify whether the input storage must be in standard compressed form */
+};
+
+namespace internal {
+
+template <typename Derived>
+class SparseRefBase;
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options_, typename StrideType_>
+struct traits<Ref<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options_, StrideType_>>
+    : public traits<SparseMatrix<MatScalar, MatOptions, MatIndex>> {
+  typedef SparseMatrix<MatScalar, MatOptions, MatIndex> PlainObjectType;
+  enum { Options = Options_, Flags = traits<PlainObjectType>::Flags | CompressedAccessBit | NestByRefBit };
+
+  template <typename Derived>
+  struct match {
+    enum {
+      StorageOrderMatch = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime ||
+                          ((PlainObjectType::Flags & RowMajorBit) == (Derived::Flags & RowMajorBit)),
+      MatchAtCompileTime = (Derived::Flags & CompressedAccessBit) && StorageOrderMatch
+    };
+    typedef std::conditional_t<MatchAtCompileTime, internal::true_type, internal::false_type> type;
+  };
+};
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options_, typename StrideType_>
+struct traits<Ref<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options_, StrideType_>>
+    : public traits<Ref<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options_, StrideType_>> {
+  enum {
+    Flags =
+        (traits<SparseMatrix<MatScalar, MatOptions, MatIndex>>::Flags | CompressedAccessBit | NestByRefBit) & ~LvalueBit
+  };
+};
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options_, typename StrideType_>
+struct traits<Ref<SparseVector<MatScalar, MatOptions, MatIndex>, Options_, StrideType_>>
+    : public traits<SparseVector<MatScalar, MatOptions, MatIndex>> {
+  typedef SparseVector<MatScalar, MatOptions, MatIndex> PlainObjectType;
+  enum { Options = Options_, Flags = traits<PlainObjectType>::Flags | CompressedAccessBit | NestByRefBit };
+
+  template <typename Derived>
+  struct match {
+    enum { MatchAtCompileTime = (Derived::Flags & CompressedAccessBit) && Derived::IsVectorAtCompileTime };
+    typedef std::conditional_t<MatchAtCompileTime, internal::true_type, internal::false_type> type;
+  };
+};
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options_, typename StrideType_>
+struct traits<Ref<const SparseVector<MatScalar, MatOptions, MatIndex>, Options_, StrideType_>>
+    : public traits<Ref<SparseVector<MatScalar, MatOptions, MatIndex>, Options_, StrideType_>> {
+  enum {
+    Flags =
+        (traits<SparseVector<MatScalar, MatOptions, MatIndex>>::Flags | CompressedAccessBit | NestByRefBit) & ~LvalueBit
+  };
+};
+
+template <typename Derived>
+struct traits<SparseRefBase<Derived>> : public traits<Derived> {};
+
+template <typename Derived>
+class SparseRefBase : public SparseMapBase<Derived> {
+ public:
+  typedef SparseMapBase<Derived> Base;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseRefBase)
+
+  SparseRefBase()
+      : Base(RowsAtCompileTime == Dynamic ? 0 : RowsAtCompileTime, ColsAtCompileTime == Dynamic ? 0 : ColsAtCompileTime,
+             0, 0, 0, 0, 0) {}
+
+ protected:
+  template <typename Expression>
+  void construct(Expression& expr) {
+    if (expr.outerIndexPtr() == 0)
+      internal::construct_at<Base>(this, expr.size(), expr.nonZeros(), expr.innerIndexPtr(), expr.valuePtr());
+    else
+      internal::construct_at<Base>(this, expr.rows(), expr.cols(), expr.nonZeros(), expr.outerIndexPtr(),
+                                   expr.innerIndexPtr(), expr.valuePtr(), expr.innerNonZeroPtr());
+  }
+};
+
+}  // namespace internal
+
+/**
+ * \ingroup SparseCore_Module
+ *
+ * \brief A sparse matrix expression referencing an existing sparse expression
+ *
+ * \tparam SparseMatrixType the equivalent sparse matrix type of the referenced data, it must be a template instance of
+ * class SparseMatrix. \tparam Options specifies whether the a standard compressed format is required \c Options is  \c
+ * #StandardCompressedFormat, or \c 0. The default is \c 0.
+ *
+ * \sa class Ref
+ */
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Ref<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>
+    : public internal::SparseRefBase<Ref<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>>
+#else
+template <typename SparseMatrixType, int Options>
+class Ref<SparseMatrixType, Options>
+    : public SparseMapBase<Derived, WriteAccessors>  // Note: 'Derived' is used here intentionally; it resolves
+                                                     // correctly via CRTP.
+#endif
+{
+  typedef SparseMatrix<MatScalar, MatOptions, MatIndex> PlainObjectType;
+  typedef internal::traits<Ref> Traits;
+  template <int OtherOptions>
+  inline Ref(const SparseMatrix<MatScalar, OtherOptions, MatIndex>& expr);
+  template <int OtherOptions>
+  inline Ref(const Map<SparseMatrix<MatScalar, OtherOptions, MatIndex>>& expr);
+
+ public:
+  typedef internal::SparseRefBase<Ref> Base;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <int OtherOptions>
+  inline Ref(SparseMatrix<MatScalar, OtherOptions, MatIndex>& expr) {
+    EIGEN_STATIC_ASSERT(
+        bool(Traits::template match<SparseMatrix<MatScalar, OtherOptions, MatIndex>>::MatchAtCompileTime),
+        STORAGE_LAYOUT_DOES_NOT_MATCH);
+    eigen_assert(((Options & int(StandardCompressedFormat)) == 0) || (expr.isCompressed()));
+    Base::construct(expr.derived());
+  }
+
+  template <int OtherOptions>
+  inline Ref(Map<SparseMatrix<MatScalar, OtherOptions, MatIndex>>& expr) {
+    EIGEN_STATIC_ASSERT(
+        bool(Traits::template match<SparseMatrix<MatScalar, OtherOptions, MatIndex>>::MatchAtCompileTime),
+        STORAGE_LAYOUT_DOES_NOT_MATCH);
+    eigen_assert(((Options & int(StandardCompressedFormat)) == 0) || (expr.isCompressed()));
+    Base::construct(expr.derived());
+  }
+
+  template <typename Derived>
+  inline Ref(const SparseCompressedBase<Derived>& expr)
+#else
+  /** Implicit constructor from any sparse expression (2D matrix or 1D vector) */
+  template <typename Derived>
+  inline Ref(SparseCompressedBase<Derived>& expr)
+#endif
+  {
+    EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+    EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+    eigen_assert(((Options & int(StandardCompressedFormat)) == 0) || (expr.isCompressed()));
+    Base::construct(expr.const_cast_derived());
+  }
+};
+
+// this is the const ref version
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Ref<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>
+    : public internal::SparseRefBase<Ref<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>> {
+  typedef SparseMatrix<MatScalar, MatOptions, MatIndex> TPlainObjectType;
+  typedef internal::traits<Ref> Traits;
+
+ public:
+  typedef internal::SparseRefBase<Ref> Base;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
+
+  template <typename Derived>
+  inline Ref(const SparseMatrixBase<Derived>& expr) : m_hasCopy(false) {
+    construct(expr.derived(), typename Traits::template match<Derived>::type());
+  }
+
+  inline Ref(const Ref& other) : Base(other), m_hasCopy(false) {
+    // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
+  }
+
+  template <typename OtherRef>
+  inline Ref(const RefBase<OtherRef>& other) : m_hasCopy(false) {
+    construct(other.derived(), typename Traits::template match<OtherRef>::type());
+  }
+
+  ~Ref() {
+    if (m_hasCopy) {
+      internal::destroy_at(reinterpret_cast<TPlainObjectType*>(&m_storage));
+    }
+  }
+
+ protected:
+  template <typename Expression>
+  void construct(const Expression& expr, internal::true_type) {
+    if ((Options & int(StandardCompressedFormat)) && (!expr.isCompressed())) {
+      TPlainObjectType* obj = internal::construct_at(reinterpret_cast<TPlainObjectType*>(&m_storage), expr);
+      m_hasCopy = true;
+      Base::construct(*obj);
+    } else {
+      Base::construct(expr);
+    }
+  }
+
+  template <typename Expression>
+  void construct(const Expression& expr, internal::false_type) {
+    TPlainObjectType* obj = internal::construct_at(reinterpret_cast<TPlainObjectType*>(&m_storage), expr);
+    m_hasCopy = true;
+    Base::construct(*obj);
+  }
+
+ protected:
+  typename internal::aligned_storage<sizeof(TPlainObjectType), EIGEN_ALIGNOF(TPlainObjectType)>::type m_storage;
+  bool m_hasCopy;
+};
+
+/**
+ * \ingroup SparseCore_Module
+ *
+ * \brief A sparse vector expression referencing an existing sparse vector expression
+ *
+ * \tparam SparseVectorType the equivalent sparse vector type of the referenced data, it must be a template instance of
+ * class SparseVector.
+ *
+ * \sa class Ref
+ */
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Ref<SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType>
+    : public internal::SparseRefBase<Ref<SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType>>
+#else
+template <typename SparseVectorType>
+class Ref<SparseVectorType> : public SparseMapBase<Derived, WriteAccessors>
+#endif
+{
+  typedef SparseVector<MatScalar, MatOptions, MatIndex> PlainObjectType;
+  typedef internal::traits<Ref> Traits;
+  template <int OtherOptions>
+  inline Ref(const SparseVector<MatScalar, OtherOptions, MatIndex>& expr);
+
+ public:
+  typedef internal::SparseRefBase<Ref> Base;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <int OtherOptions>
+  inline Ref(SparseVector<MatScalar, OtherOptions, MatIndex>& expr) {
+    EIGEN_STATIC_ASSERT(
+        bool(Traits::template match<SparseVector<MatScalar, OtherOptions, MatIndex>>::MatchAtCompileTime),
+        STORAGE_LAYOUT_DOES_NOT_MATCH);
+    Base::construct(expr.derived());
+  }
+
+  template <typename Derived>
+  inline Ref(const SparseCompressedBase<Derived>& expr)
+#else
+  /** Implicit constructor from any 1D sparse vector expression */
+  template <typename Derived>
+  inline Ref(SparseCompressedBase<Derived>& expr)
+#endif
+  {
+    EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+    EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+    Base::construct(expr.const_cast_derived());
+  }
+};
+
+// this is the const ref version
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Ref<const SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType>
+    : public internal::SparseRefBase<Ref<const SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType>> {
+  typedef SparseVector<MatScalar, MatOptions, MatIndex> TPlainObjectType;
+  typedef internal::traits<Ref> Traits;
+
+ public:
+  typedef internal::SparseRefBase<Ref> Base;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
+
+  template <typename Derived>
+  inline Ref(const SparseMatrixBase<Derived>& expr) : m_hasCopy(false) {
+    construct(expr.derived(), typename Traits::template match<Derived>::type());
+  }
+
+  inline Ref(const Ref& other) : Base(other), m_hasCopy(false) {
+    // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
+  }
+
+  template <typename OtherRef>
+  inline Ref(const RefBase<OtherRef>& other) : m_hasCopy(false) {
+    construct(other.derived(), typename Traits::template match<OtherRef>::type());
+  }
+
+  ~Ref() {
+    if (m_hasCopy) {
+      internal::destroy_at(reinterpret_cast<TPlainObjectType*>(&m_storage));
+    }
+  }
+
+ protected:
+  template <typename Expression>
+  void construct(const Expression& expr, internal::true_type) {
+    Base::construct(expr);
+  }
+
+  template <typename Expression>
+  void construct(const Expression& expr, internal::false_type) {
+    TPlainObjectType* obj = internal::construct_at(reinterpret_cast<TPlainObjectType*>(&m_storage), expr);
+    m_hasCopy = true;
+    Base::construct(*obj);
+  }
+
+ protected:
+  typename internal::aligned_storage<sizeof(TPlainObjectType), EIGEN_ALIGNOF(TPlainObjectType)>::type m_storage;
+  bool m_hasCopy;
+};
+
+namespace internal {
+
+// FIXME: consider introducing a general evaluator_ref that we can specialize for any sparse object once, and thus
+// remove this copy-pasta thing...
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Ref<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>>
+    : evaluator<SparseCompressedBase<Ref<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>>> {
+  typedef evaluator<SparseCompressedBase<Ref<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>>> Base;
+  typedef Ref<SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> XprType;
+  evaluator() : Base() {}
+  explicit evaluator(const XprType& mat) : Base(mat) {}
+};
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Ref<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>>
+    : evaluator<SparseCompressedBase<Ref<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>>> {
+  typedef evaluator<SparseCompressedBase<Ref<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType>>>
+      Base;
+  typedef Ref<const SparseMatrix<MatScalar, MatOptions, MatIndex>, Options, StrideType> XprType;
+  evaluator() : Base() {}
+  explicit evaluator(const XprType& mat) : Base(mat) {}
+};
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Ref<SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType>>
+    : evaluator<SparseCompressedBase<Ref<SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType>>> {
+  typedef evaluator<SparseCompressedBase<Ref<SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType>>> Base;
+  typedef Ref<SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType> XprType;
+  evaluator() : Base() {}
+  explicit evaluator(const XprType& mat) : Base(mat) {}
+};
+
+template <typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Ref<const SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType>>
+    : evaluator<SparseCompressedBase<Ref<const SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType>>> {
+  typedef evaluator<SparseCompressedBase<Ref<const SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType>>>
+      Base;
+  typedef Ref<const SparseVector<MatScalar, MatOptions, MatIndex>, Options, StrideType> XprType;
+  evaluator() : Base() {}
+  explicit evaluator(const XprType& mat) : Base(mat) {}
+};
+
+}  // namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_REF_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseSelfAdjointView.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseSelfAdjointView.h
new file mode 100644
index 0000000..210462f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseSelfAdjointView.h
@@ -0,0 +1,613 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_SELFADJOINTVIEW_H
+#define EIGEN_SPARSE_SELFADJOINTVIEW_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup SparseCore_Module
+ * \class SparseSelfAdjointView
+ *
+ * \brief Pseudo expression to manipulate a triangular sparse matrix as a selfadjoint matrix.
+ *
+ * \param MatrixType the type of the dense matrix storing the coefficients
+ * \param Mode can be either \c #Lower or \c #Upper
+ *
+ * This class is an expression of a sefladjoint matrix from a triangular part of a matrix
+ * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
+ * and most of the time this is the only way that it is used.
+ *
+ * \sa SparseMatrixBase::selfadjointView()
+ */
+namespace internal {
+
+template <typename MatrixType, unsigned int Mode>
+struct traits<SparseSelfAdjointView<MatrixType, Mode> > : traits<MatrixType> {};
+
+template <int SrcMode, int DstMode, bool NonHermitian, typename MatrixType, int DestOrder>
+void permute_symm_to_symm(
+    const MatrixType& mat,
+    SparseMatrix<typename MatrixType::Scalar, DestOrder, typename MatrixType::StorageIndex>& _dest,
+    const typename MatrixType::StorageIndex* perm = 0);
+
+template <int Mode, bool NonHermitian, typename MatrixType, int DestOrder>
+void permute_symm_to_fullsymm(
+    const MatrixType& mat,
+    SparseMatrix<typename MatrixType::Scalar, DestOrder, typename MatrixType::StorageIndex>& _dest,
+    const typename MatrixType::StorageIndex* perm = 0);
+
+}  // namespace internal
+
+template <typename MatrixType, unsigned int Mode_>
+class SparseSelfAdjointView : public EigenBase<SparseSelfAdjointView<MatrixType, Mode_> > {
+ public:
+  enum {
+    Mode = Mode_,
+    TransposeMode = ((int(Mode) & int(Upper)) ? Lower : 0) | ((int(Mode) & int(Lower)) ? Upper : 0),
+    RowsAtCompileTime = internal::traits<SparseSelfAdjointView>::RowsAtCompileTime,
+    ColsAtCompileTime = internal::traits<SparseSelfAdjointView>::ColsAtCompileTime
+  };
+
+  typedef EigenBase<SparseSelfAdjointView> Base;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef Matrix<StorageIndex, Dynamic, 1> VectorI;
+  typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
+  typedef internal::remove_all_t<MatrixTypeNested> MatrixTypeNested_;
+
+  explicit inline SparseSelfAdjointView(MatrixType& matrix) : m_matrix(matrix) {
+    eigen_assert(rows() == cols() && "SelfAdjointView is only for squared matrices");
+  }
+
+  inline Index rows() const { return m_matrix.rows(); }
+  inline Index cols() const { return m_matrix.cols(); }
+
+  /** \internal \returns a reference to the nested matrix */
+  const MatrixTypeNested_& matrix() const { return m_matrix; }
+  std::remove_reference_t<MatrixTypeNested>& matrix() { return m_matrix; }
+
+  /** \returns an expression of the matrix product between a sparse self-adjoint matrix \c *this and a sparse matrix \a
+   * rhs.
+   *
+   * Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix
+   * product. Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing
+   * the product.
+   */
+  template <typename OtherDerived>
+  Product<SparseSelfAdjointView, OtherDerived> operator*(const SparseMatrixBase<OtherDerived>& rhs) const {
+    return Product<SparseSelfAdjointView, OtherDerived>(*this, rhs.derived());
+  }
+
+  /** \returns an expression of the matrix product between a sparse matrix \a lhs and a sparse self-adjoint matrix \a
+   * rhs.
+   *
+   * Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix
+   * product. Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing
+   * the product.
+   */
+  template <typename OtherDerived>
+  friend Product<OtherDerived, SparseSelfAdjointView> operator*(const SparseMatrixBase<OtherDerived>& lhs,
+                                                                const SparseSelfAdjointView& rhs) {
+    return Product<OtherDerived, SparseSelfAdjointView>(lhs.derived(), rhs);
+  }
+
+  /** Efficient sparse self-adjoint matrix times dense vector/matrix product */
+  template <typename OtherDerived>
+  Product<SparseSelfAdjointView, OtherDerived> operator*(const MatrixBase<OtherDerived>& rhs) const {
+    return Product<SparseSelfAdjointView, OtherDerived>(*this, rhs.derived());
+  }
+
+  /** Efficient dense vector/matrix times sparse self-adjoint matrix product */
+  template <typename OtherDerived>
+  friend Product<OtherDerived, SparseSelfAdjointView> operator*(const MatrixBase<OtherDerived>& lhs,
+                                                                const SparseSelfAdjointView& rhs) {
+    return Product<OtherDerived, SparseSelfAdjointView>(lhs.derived(), rhs);
+  }
+
+  /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
+   * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
+   *
+   * \returns a reference to \c *this
+   *
+   * To perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
+   * call this function with u.adjoint().
+   */
+  template <typename DerivedU>
+  SparseSelfAdjointView& rankUpdate(const SparseMatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
+
+  /** \returns an expression of P H P^-1 */
+  // TODO: implement twists in a more evaluator friendly fashion
+  SparseSymmetricPermutationProduct<MatrixTypeNested_, Mode> twistedBy(
+      const PermutationMatrix<Dynamic, Dynamic, StorageIndex>& perm) const {
+    return SparseSymmetricPermutationProduct<MatrixTypeNested_, Mode>(m_matrix, perm);
+  }
+
+  template <typename SrcMatrixType, int SrcMode>
+  SparseSelfAdjointView& operator=(const SparseSymmetricPermutationProduct<SrcMatrixType, SrcMode>& permutedMatrix) {
+    internal::call_assignment_no_alias_no_transpose(*this, permutedMatrix);
+    return *this;
+  }
+
+  SparseSelfAdjointView& operator=(const SparseSelfAdjointView& src) {
+    PermutationMatrix<Dynamic, Dynamic, StorageIndex> pnull;
+    return *this = src.twistedBy(pnull);
+  }
+
+  // Since we override the copy-assignment operator, we need to explicitly redeclare the copy-constructor
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(SparseSelfAdjointView)
+
+  template <typename SrcMatrixType, unsigned int SrcMode>
+  SparseSelfAdjointView& operator=(const SparseSelfAdjointView<SrcMatrixType, SrcMode>& src) {
+    PermutationMatrix<Dynamic, Dynamic, StorageIndex> pnull;
+    return *this = src.twistedBy(pnull);
+  }
+
+  void resize(Index rows, Index cols) {
+    EIGEN_ONLY_USED_FOR_DEBUG(rows);
+    EIGEN_ONLY_USED_FOR_DEBUG(cols);
+    eigen_assert(rows == this->rows() && cols == this->cols() &&
+                 "SparseSelfadjointView::resize() does not actually allow to resize.");
+  }
+
+ protected:
+  MatrixTypeNested m_matrix;
+  // mutable VectorI m_countPerRow;
+  // mutable VectorI m_countPerCol;
+ private:
+  template <typename Dest>
+  void evalTo(Dest&) const;
+};
+
+/***************************************************************************
+ * Implementation of SparseMatrixBase methods
+ ***************************************************************************/
+
+template <typename Derived>
+template <unsigned int UpLo>
+typename SparseMatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type
+SparseMatrixBase<Derived>::selfadjointView() const {
+  return SparseSelfAdjointView<const Derived, UpLo>(derived());
+}
+
+template <typename Derived>
+template <unsigned int UpLo>
+typename SparseMatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type
+SparseMatrixBase<Derived>::selfadjointView() {
+  return SparseSelfAdjointView<Derived, UpLo>(derived());
+}
+
+/***************************************************************************
+ * Implementation of SparseSelfAdjointView methods
+ ***************************************************************************/
+
+template <typename MatrixType, unsigned int Mode>
+template <typename DerivedU>
+SparseSelfAdjointView<MatrixType, Mode>& SparseSelfAdjointView<MatrixType, Mode>::rankUpdate(
+    const SparseMatrixBase<DerivedU>& u, const Scalar& alpha) {
+  SparseMatrix<Scalar, (MatrixType::Flags & RowMajorBit) ? RowMajor : ColMajor> tmp = u * u.adjoint();
+  if (alpha == Scalar(0))
+    m_matrix = tmp.template triangularView<Mode>();
+  else
+    m_matrix += alpha * tmp.template triangularView<Mode>();
+
+  return *this;
+}
+
+namespace internal {
+
+// TODO: currently a selfadjoint expression has the form SelfAdjointView<.,.>
+//      in the future selfadjoint-ness should be defined by the expression traits
+//      such that Transpose<SelfAdjointView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to
+//      make it work)
+template <typename MatrixType, unsigned int Mode>
+struct evaluator_traits<SparseSelfAdjointView<MatrixType, Mode> > {
+  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
+  typedef SparseSelfAdjointShape Shape;
+};
+
+struct SparseSelfAdjoint2Sparse {};
+
+template <>
+struct AssignmentKind<SparseShape, SparseSelfAdjointShape> {
+  typedef SparseSelfAdjoint2Sparse Kind;
+};
+template <>
+struct AssignmentKind<SparseSelfAdjointShape, SparseShape> {
+  typedef Sparse2Sparse Kind;
+};
+
+template <typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, SparseSelfAdjoint2Sparse> {
+  typedef typename DstXprType::StorageIndex StorageIndex;
+  typedef internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar> AssignOpType;
+
+  template <typename DestScalar, int StorageOrder>
+  static void run(SparseMatrix<DestScalar, StorageOrder, StorageIndex>& dst, const SrcXprType& src,
+                  const AssignOpType& /*func*/) {
+    internal::permute_symm_to_fullsymm<SrcXprType::Mode, false>(src.matrix(), dst);
+  }
+
+  // FIXME: the handling of += and -= in sparse matrices should be cleanup so that next two overloads could be reduced
+  // to:
+  template <typename DestScalar, int StorageOrder, typename AssignFunc>
+  static void run(SparseMatrix<DestScalar, StorageOrder, StorageIndex>& dst, const SrcXprType& src,
+                  const AssignFunc& func) {
+    SparseMatrix<DestScalar, StorageOrder, StorageIndex> tmp(src.rows(), src.cols());
+    run(tmp, src, AssignOpType());
+    call_assignment_no_alias_no_transpose(dst, tmp, func);
+  }
+
+  template <typename DestScalar, int StorageOrder>
+  static void run(SparseMatrix<DestScalar, StorageOrder, StorageIndex>& dst, const SrcXprType& src,
+                  const internal::add_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /* func */) {
+    SparseMatrix<DestScalar, StorageOrder, StorageIndex> tmp(src.rows(), src.cols());
+    run(tmp, src, AssignOpType());
+    dst += tmp;
+  }
+
+  template <typename DestScalar, int StorageOrder>
+  static void run(SparseMatrix<DestScalar, StorageOrder, StorageIndex>& dst, const SrcXprType& src,
+                  const internal::sub_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /* func */) {
+    SparseMatrix<DestScalar, StorageOrder, StorageIndex> tmp(src.rows(), src.cols());
+    run(tmp, src, AssignOpType());
+    dst -= tmp;
+  }
+};
+
+}  // end namespace internal
+
+/***************************************************************************
+ * Implementation of sparse self-adjoint time dense matrix
+ ***************************************************************************/
+
+namespace internal {
+
+template <int Mode, typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType>
+inline void sparse_selfadjoint_time_dense_product(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res,
+                                                  const AlphaType& alpha) {
+  EIGEN_ONLY_USED_FOR_DEBUG(alpha);
+
+  typedef typename internal::nested_eval<SparseLhsType, DenseRhsType::MaxColsAtCompileTime>::type SparseLhsTypeNested;
+  typedef internal::remove_all_t<SparseLhsTypeNested> SparseLhsTypeNestedCleaned;
+  typedef evaluator<SparseLhsTypeNestedCleaned> LhsEval;
+  typedef typename LhsEval::InnerIterator LhsIterator;
+  typedef typename SparseLhsType::Scalar LhsScalar;
+
+  enum {
+    LhsIsRowMajor = (LhsEval::Flags & RowMajorBit) == RowMajorBit,
+    ProcessFirstHalf = ((Mode & (Upper | Lower)) == (Upper | Lower)) || ((Mode & Upper) && !LhsIsRowMajor) ||
+                       ((Mode & Lower) && LhsIsRowMajor),
+    ProcessSecondHalf = !ProcessFirstHalf
+  };
+
+  SparseLhsTypeNested lhs_nested(lhs);
+  LhsEval lhsEval(lhs_nested);
+
+  // work on one column at once
+  for (Index k = 0; k < rhs.cols(); ++k) {
+    for (Index j = 0; j < lhs.outerSize(); ++j) {
+      LhsIterator i(lhsEval, j);
+      // handle diagonal coeff
+      if (ProcessSecondHalf) {
+        while (i && i.index() < j) ++i;
+        if (i && i.index() == j) {
+          res.coeffRef(j, k) += alpha * i.value() * rhs.coeff(j, k);
+          ++i;
+        }
+      }
+
+      // premultiplied rhs for scatters
+      typename ScalarBinaryOpTraits<AlphaType, typename DenseRhsType::Scalar>::ReturnType rhs_j(alpha * rhs(j, k));
+      // accumulator for partial scalar product
+      typename DenseResType::Scalar res_j(0);
+      for (; (ProcessFirstHalf ? i && i.index() < j : i); ++i) {
+        LhsScalar lhs_ij = i.value();
+        if (!LhsIsRowMajor) lhs_ij = numext::conj(lhs_ij);
+        res_j += lhs_ij * rhs.coeff(i.index(), k);
+        res(i.index(), k) += numext::conj(lhs_ij) * rhs_j;
+      }
+      res.coeffRef(j, k) += alpha * res_j;
+
+      // handle diagonal coeff
+      if (ProcessFirstHalf && i && (i.index() == j)) res.coeffRef(j, k) += alpha * i.value() * rhs.coeff(j, k);
+    }
+  }
+}
+
+template <typename LhsView, typename Rhs, int ProductType>
+struct generic_product_impl<LhsView, Rhs, SparseSelfAdjointShape, DenseShape, ProductType>
+    : generic_product_impl_base<LhsView, Rhs,
+                                generic_product_impl<LhsView, Rhs, SparseSelfAdjointShape, DenseShape, ProductType> > {
+  template <typename Dest>
+  static void scaleAndAddTo(Dest& dst, const LhsView& lhsView, const Rhs& rhs, const typename Dest::Scalar& alpha) {
+    typedef typename LhsView::MatrixTypeNested_ Lhs;
+    typedef typename nested_eval<Lhs, Dynamic>::type LhsNested;
+    typedef typename nested_eval<Rhs, Dynamic>::type RhsNested;
+    LhsNested lhsNested(lhsView.matrix());
+    RhsNested rhsNested(rhs);
+
+    internal::sparse_selfadjoint_time_dense_product<LhsView::Mode>(lhsNested, rhsNested, dst, alpha);
+  }
+};
+
+template <typename Lhs, typename RhsView, int ProductType>
+struct generic_product_impl<Lhs, RhsView, DenseShape, SparseSelfAdjointShape, ProductType>
+    : generic_product_impl_base<Lhs, RhsView,
+                                generic_product_impl<Lhs, RhsView, DenseShape, SparseSelfAdjointShape, ProductType> > {
+  template <typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const RhsView& rhsView, const typename Dest::Scalar& alpha) {
+    typedef typename RhsView::MatrixTypeNested_ Rhs;
+    typedef typename nested_eval<Lhs, Dynamic>::type LhsNested;
+    typedef typename nested_eval<Rhs, Dynamic>::type RhsNested;
+    LhsNested lhsNested(lhs);
+    RhsNested rhsNested(rhsView.matrix());
+
+    // transpose everything
+    Transpose<Dest> dstT(dst);
+    internal::sparse_selfadjoint_time_dense_product<RhsView::TransposeMode>(rhsNested.transpose(),
+                                                                            lhsNested.transpose(), dstT, alpha);
+  }
+};
+
+// NOTE: these two overloads are needed to evaluate the sparse selfadjoint view into a full sparse matrix
+// TODO: maybe the copy could be handled by generic_product_impl so that these overloads would not be needed anymore
+
+template <typename LhsView, typename Rhs, int ProductTag>
+struct product_evaluator<Product<LhsView, Rhs, DefaultProduct>, ProductTag, SparseSelfAdjointShape, SparseShape>
+    : public evaluator<typename Product<typename Rhs::PlainObject, Rhs, DefaultProduct>::PlainObject> {
+  typedef Product<LhsView, Rhs, DefaultProduct> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  product_evaluator(const XprType& xpr) : m_lhs(xpr.lhs()), m_result(xpr.rows(), xpr.cols()) {
+    internal::construct_at<Base>(this, m_result);
+    generic_product_impl<typename Rhs::PlainObject, Rhs, SparseShape, SparseShape, ProductTag>::evalTo(m_result, m_lhs,
+                                                                                                       xpr.rhs());
+  }
+
+ protected:
+  typename Rhs::PlainObject m_lhs;
+  PlainObject m_result;
+};
+
+template <typename Lhs, typename RhsView, int ProductTag>
+struct product_evaluator<Product<Lhs, RhsView, DefaultProduct>, ProductTag, SparseShape, SparseSelfAdjointShape>
+    : public evaluator<typename Product<Lhs, typename Lhs::PlainObject, DefaultProduct>::PlainObject> {
+  typedef Product<Lhs, RhsView, DefaultProduct> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  product_evaluator(const XprType& xpr) : m_rhs(xpr.rhs()), m_result(xpr.rows(), xpr.cols()) {
+    ::new (static_cast<Base*>(this)) Base(m_result);
+    generic_product_impl<Lhs, typename Lhs::PlainObject, SparseShape, SparseShape, ProductTag>::evalTo(
+        m_result, xpr.lhs(), m_rhs);
+  }
+
+ protected:
+  typename Lhs::PlainObject m_rhs;
+  PlainObject m_result;
+};
+
+}  // namespace internal
+
+/***************************************************************************
+ * Implementation of symmetric copies and permutations
+ ***************************************************************************/
+namespace internal {
+
+template <int Mode, bool NonHermitian, typename MatrixType, int DestOrder>
+void permute_symm_to_fullsymm(
+    const MatrixType& mat,
+    SparseMatrix<typename MatrixType::Scalar, DestOrder, typename MatrixType::StorageIndex>& _dest,
+    const typename MatrixType::StorageIndex* perm) {
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef SparseMatrix<Scalar, DestOrder, StorageIndex> Dest;
+  typedef Matrix<StorageIndex, Dynamic, 1> VectorI;
+  typedef evaluator<MatrixType> MatEval;
+  typedef typename evaluator<MatrixType>::InnerIterator MatIterator;
+
+  MatEval matEval(mat);
+  Dest& dest(_dest.derived());
+  enum { StorageOrderMatch = int(Dest::IsRowMajor) == int(MatrixType::IsRowMajor) };
+
+  Index size = mat.rows();
+  VectorI count;
+  count.resize(size);
+  count.setZero();
+  dest.resize(size, size);
+  for (Index j = 0; j < size; ++j) {
+    Index jp = perm ? perm[j] : j;
+    for (MatIterator it(matEval, j); it; ++it) {
+      Index i = it.index();
+      Index r = it.row();
+      Index c = it.col();
+      Index ip = perm ? perm[i] : i;
+      if (Mode == int(Upper | Lower))
+        count[StorageOrderMatch ? jp : ip]++;
+      else if (r == c)
+        count[ip]++;
+      else if ((Mode == Lower && r > c) || (Mode == Upper && r < c)) {
+        count[ip]++;
+        count[jp]++;
+      }
+    }
+  }
+  Index nnz = count.sum();
+
+  // reserve space
+  dest.resizeNonZeros(nnz);
+  dest.outerIndexPtr()[0] = 0;
+  for (Index j = 0; j < size; ++j) dest.outerIndexPtr()[j + 1] = dest.outerIndexPtr()[j] + count[j];
+  for (Index j = 0; j < size; ++j) count[j] = dest.outerIndexPtr()[j];
+
+  // copy data
+  for (StorageIndex j = 0; j < size; ++j) {
+    for (MatIterator it(matEval, j); it; ++it) {
+      StorageIndex i = internal::convert_index<StorageIndex>(it.index());
+      Index r = it.row();
+      Index c = it.col();
+
+      StorageIndex jp = perm ? perm[j] : j;
+      StorageIndex ip = perm ? perm[i] : i;
+
+      if (Mode == int(Upper | Lower)) {
+        Index k = count[StorageOrderMatch ? jp : ip]++;
+        dest.innerIndexPtr()[k] = StorageOrderMatch ? ip : jp;
+        dest.valuePtr()[k] = it.value();
+      } else if (r == c) {
+        Index k = count[ip]++;
+        dest.innerIndexPtr()[k] = ip;
+        dest.valuePtr()[k] = it.value();
+      } else if (((Mode & Lower) == Lower && r > c) || ((Mode & Upper) == Upper && r < c)) {
+        if (!StorageOrderMatch) std::swap(ip, jp);
+        Index k = count[jp]++;
+        dest.innerIndexPtr()[k] = ip;
+        dest.valuePtr()[k] = it.value();
+        k = count[ip]++;
+        dest.innerIndexPtr()[k] = jp;
+        dest.valuePtr()[k] = (NonHermitian ? it.value() : numext::conj(it.value()));
+      }
+    }
+  }
+}
+
+template <int SrcMode_, int DstMode_, bool NonHermitian, typename MatrixType, int DstOrder>
+void permute_symm_to_symm(const MatrixType& mat,
+                          SparseMatrix<typename MatrixType::Scalar, DstOrder, typename MatrixType::StorageIndex>& _dest,
+                          const typename MatrixType::StorageIndex* perm) {
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef typename MatrixType::Scalar Scalar;
+  SparseMatrix<Scalar, DstOrder, StorageIndex>& dest(_dest.derived());
+  typedef Matrix<StorageIndex, Dynamic, 1> VectorI;
+  typedef evaluator<MatrixType> MatEval;
+  typedef typename evaluator<MatrixType>::InnerIterator MatIterator;
+
+  enum {
+    SrcOrder = MatrixType::IsRowMajor ? RowMajor : ColMajor,
+    StorageOrderMatch = int(SrcOrder) == int(DstOrder),
+    DstMode = DstOrder == RowMajor ? (DstMode_ == Upper ? Lower : Upper) : DstMode_,
+    SrcMode = SrcOrder == RowMajor ? (SrcMode_ == Upper ? Lower : Upper) : SrcMode_
+  };
+
+  MatEval matEval(mat);
+
+  Index size = mat.rows();
+  VectorI count(size);
+  count.setZero();
+  dest.resize(size, size);
+  for (StorageIndex j = 0; j < size; ++j) {
+    StorageIndex jp = perm ? perm[j] : j;
+    for (MatIterator it(matEval, j); it; ++it) {
+      StorageIndex i = it.index();
+      if ((int(SrcMode) == int(Lower) && i < j) || (int(SrcMode) == int(Upper) && i > j)) continue;
+
+      StorageIndex ip = perm ? perm[i] : i;
+      count[int(DstMode) == int(Lower) ? (std::min)(ip, jp) : (std::max)(ip, jp)]++;
+    }
+  }
+  dest.outerIndexPtr()[0] = 0;
+  for (Index j = 0; j < size; ++j) dest.outerIndexPtr()[j + 1] = dest.outerIndexPtr()[j] + count[j];
+  dest.resizeNonZeros(dest.outerIndexPtr()[size]);
+  for (Index j = 0; j < size; ++j) count[j] = dest.outerIndexPtr()[j];
+
+  for (StorageIndex j = 0; j < size; ++j) {
+    for (MatIterator it(matEval, j); it; ++it) {
+      StorageIndex i = it.index();
+      if ((int(SrcMode) == int(Lower) && i < j) || (int(SrcMode) == int(Upper) && i > j)) continue;
+
+      StorageIndex jp = perm ? perm[j] : j;
+      StorageIndex ip = perm ? perm[i] : i;
+
+      Index k = count[int(DstMode) == int(Lower) ? (std::min)(ip, jp) : (std::max)(ip, jp)]++;
+      dest.innerIndexPtr()[k] = int(DstMode) == int(Lower) ? (std::max)(ip, jp) : (std::min)(ip, jp);
+
+      if (!StorageOrderMatch) std::swap(ip, jp);
+      if (((int(DstMode) == int(Lower) && ip < jp) || (int(DstMode) == int(Upper) && ip > jp)))
+        dest.valuePtr()[k] = (NonHermitian ? it.value() : numext::conj(it.value()));
+      else
+        dest.valuePtr()[k] = it.value();
+    }
+  }
+}
+
+}  // namespace internal
+
+// TODO implement twists in a more evaluator friendly fashion
+
+namespace internal {
+
+template <typename MatrixType, int Mode>
+struct traits<SparseSymmetricPermutationProduct<MatrixType, Mode> > : traits<MatrixType> {};
+
+}  // namespace internal
+
+template <typename MatrixType, int Mode>
+class SparseSymmetricPermutationProduct : public EigenBase<SparseSymmetricPermutationProduct<MatrixType, Mode> > {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  enum {
+    RowsAtCompileTime = internal::traits<SparseSymmetricPermutationProduct>::RowsAtCompileTime,
+    ColsAtCompileTime = internal::traits<SparseSymmetricPermutationProduct>::ColsAtCompileTime
+  };
+
+ protected:
+  typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> Perm;
+
+ public:
+  typedef Matrix<StorageIndex, Dynamic, 1> VectorI;
+  typedef typename MatrixType::Nested MatrixTypeNested;
+  typedef internal::remove_all_t<MatrixTypeNested> NestedExpression;
+
+  SparseSymmetricPermutationProduct(const MatrixType& mat, const Perm& perm) : m_matrix(mat), m_perm(perm) {}
+
+  inline Index rows() const { return m_matrix.rows(); }
+  inline Index cols() const { return m_matrix.cols(); }
+
+  const NestedExpression& matrix() const { return m_matrix; }
+  const Perm& perm() const { return m_perm; }
+
+ protected:
+  MatrixTypeNested m_matrix;
+  const Perm& m_perm;
+};
+
+namespace internal {
+
+template <typename DstXprType, typename MatrixType, int Mode, typename Scalar>
+struct Assignment<DstXprType, SparseSymmetricPermutationProduct<MatrixType, Mode>,
+                  internal::assign_op<Scalar, typename MatrixType::Scalar>, Sparse2Sparse> {
+  typedef SparseSymmetricPermutationProduct<MatrixType, Mode> SrcXprType;
+  typedef typename DstXprType::StorageIndex DstIndex;
+  template <int Options>
+  static void run(SparseMatrix<Scalar, Options, DstIndex>& dst, const SrcXprType& src,
+                  const internal::assign_op<Scalar, typename MatrixType::Scalar>&) {
+    // internal::permute_symm_to_fullsymm<Mode>(m_matrix,_dest,m_perm.indices().data());
+    SparseMatrix<Scalar, (Options & RowMajor) == RowMajor ? ColMajor : RowMajor, DstIndex> tmp;
+    internal::permute_symm_to_fullsymm<Mode, false>(src.matrix(), tmp, src.perm().indices().data());
+    dst = tmp;
+  }
+
+  template <typename DestType, unsigned int DestMode>
+  static void run(SparseSelfAdjointView<DestType, DestMode>& dst, const SrcXprType& src,
+                  const internal::assign_op<Scalar, typename MatrixType::Scalar>&) {
+    internal::permute_symm_to_symm<Mode, DestMode, false>(src.matrix(), dst.matrix(), src.perm().indices().data());
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseSolverBase.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseSolverBase.h
new file mode 100644
index 0000000..acf1564
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseSolverBase.h
@@ -0,0 +1,115 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSESOLVERBASE_H
+#define EIGEN_SPARSESOLVERBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * Helper functions to solve with a sparse right-hand-side and result.
+ * The rhs is decomposed into small vertical panels which are solved through dense temporaries.
+ */
+template <typename Decomposition, typename Rhs, typename Dest>
+std::enable_if_t<Rhs::ColsAtCompileTime != 1 && Dest::ColsAtCompileTime != 1> solve_sparse_through_dense_panels(
+    const Decomposition& dec, const Rhs& rhs, Dest& dest) {
+  EIGEN_STATIC_ASSERT((Dest::Flags & RowMajorBit) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  typedef typename Dest::Scalar DestScalar;
+  // we process the sparse rhs per block of NbColsAtOnce columns temporarily stored into a dense matrix.
+  static const Index NbColsAtOnce = 4;
+  Index rhsCols = rhs.cols();
+  Index size = rhs.rows();
+  // the temporary matrices do not need more columns than NbColsAtOnce:
+  Index tmpCols = (std::min)(rhsCols, NbColsAtOnce);
+  Eigen::Matrix<DestScalar, Dynamic, Dynamic> tmp(size, tmpCols);
+  Eigen::Matrix<DestScalar, Dynamic, Dynamic> tmpX(size, tmpCols);
+  for (Index k = 0; k < rhsCols; k += NbColsAtOnce) {
+    Index actualCols = std::min<Index>(rhsCols - k, NbColsAtOnce);
+    tmp.leftCols(actualCols) = rhs.middleCols(k, actualCols);
+    tmpX.leftCols(actualCols) = dec.solve(tmp.leftCols(actualCols));
+    dest.middleCols(k, actualCols) = tmpX.leftCols(actualCols).sparseView();
+  }
+}
+
+// Overload for vector as rhs
+template <typename Decomposition, typename Rhs, typename Dest>
+std::enable_if_t<Rhs::ColsAtCompileTime == 1 || Dest::ColsAtCompileTime == 1> solve_sparse_through_dense_panels(
+    const Decomposition& dec, const Rhs& rhs, Dest& dest) {
+  typedef typename Dest::Scalar DestScalar;
+  Index size = rhs.rows();
+  Eigen::Matrix<DestScalar, Dynamic, 1> rhs_dense(rhs);
+  Eigen::Matrix<DestScalar, Dynamic, 1> dest_dense(size);
+  dest_dense = dec.solve(rhs_dense);
+  dest = dest_dense.sparseView();
+}
+
+}  // end namespace internal
+
+/** \class SparseSolverBase
+ * \ingroup SparseCore_Module
+ * \brief A base class for sparse solvers
+ *
+ * \tparam Derived the actual type of the solver.
+ *
+ */
+template <typename Derived>
+class SparseSolverBase : internal::noncopyable {
+ public:
+  /** Default constructor */
+  SparseSolverBase() : m_isInitialized(false) {}
+
+  SparseSolverBase(SparseSolverBase&& other) : internal::noncopyable{}, m_isInitialized{other.m_isInitialized} {}
+
+  ~SparseSolverBase() {}
+
+  Derived& derived() { return *static_cast<Derived*>(this); }
+  const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
+   *
+   * \sa compute()
+   */
+  template <typename Rhs>
+  inline Solve<Derived, Rhs> solve(const MatrixBase<Rhs>& b) const {
+    eigen_assert(m_isInitialized && "Solver is not initialized.");
+    eigen_assert(derived().rows() == b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
+    return Solve<Derived, Rhs>(derived(), b.derived());
+  }
+
+  /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
+   *
+   * \sa compute()
+   */
+  template <typename Rhs>
+  inline Solve<Derived, Rhs> solve(const SparseMatrixBase<Rhs>& b) const {
+    eigen_assert(m_isInitialized && "Solver is not initialized.");
+    eigen_assert(derived().rows() == b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
+    return Solve<Derived, Rhs>(derived(), b.derived());
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal default implementation of solving with a sparse rhs */
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const SparseMatrixBase<Rhs>& b, SparseMatrixBase<Dest>& dest) const {
+    internal::solve_sparse_through_dense_panels(derived(), b.derived(), dest.derived());
+  }
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+
+ protected:
+  mutable bool m_isInitialized;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSESOLVERBASE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseSparseProductWithPruning.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
new file mode 100644
index 0000000..84816ae
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
@@ -0,0 +1,181 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
+#define EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// perform a pseudo in-place sparse * sparse product assuming all matrices are col major
+template <typename Lhs, typename Rhs, typename ResultType>
+static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res,
+                                                    const typename ResultType::RealScalar& tolerance) {
+  // return sparse_sparse_product_with_pruning_impl2(lhs,rhs,res);
+
+  typedef typename remove_all_t<Rhs>::Scalar RhsScalar;
+  typedef typename remove_all_t<ResultType>::Scalar ResScalar;
+  typedef typename remove_all_t<Lhs>::StorageIndex StorageIndex;
+
+  // make sure to call innerSize/outerSize since we fake the storage order.
+  Index rows = lhs.innerSize();
+  Index cols = rhs.outerSize();
+  // Index size = lhs.outerSize();
+  eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+  // allocate a temporary buffer
+  AmbiVector<ResScalar, StorageIndex> tempVector(rows);
+
+  // mimics a resizeByInnerOuter:
+  if (ResultType::IsRowMajor)
+    res.resize(cols, rows);
+  else
+    res.resize(rows, cols);
+
+  evaluator<Lhs> lhsEval(lhs);
+  evaluator<Rhs> rhsEval(rhs);
+
+  // estimate the number of non zero entries
+  // given a rhs column containing Y non zeros, we assume that the respective Y columns
+  // of the lhs differs in average of one non zeros, thus the number of non zeros for
+  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
+  // per column of the lhs.
+  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
+  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
+
+  res.reserve(estimated_nnz_prod);
+  double ratioColRes = double(estimated_nnz_prod) / (double(lhs.rows()) * double(rhs.cols()));
+  for (Index j = 0; j < cols; ++j) {
+    // FIXME: compute a more accurate per-column nnz ratio for res.
+    tempVector.init(ratioColRes);
+    tempVector.setZero();
+    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt) {
+      // FIXME: rewrite as tmp += rhsIt.value() * lhs.col(rhsIt.index()).
+      tempVector.restart();
+      RhsScalar x = rhsIt.value();
+      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, rhsIt.index()); lhsIt; ++lhsIt) {
+        tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
+      }
+    }
+    res.startVec(j);
+    for (typename AmbiVector<ResScalar, StorageIndex>::Iterator it(tempVector, tolerance); it; ++it)
+      res.insertBackByOuterInner(j, it.index()) = it.value();
+  }
+  res.finalize();
+}
+
+template <typename Lhs, typename Rhs, typename ResultType, int LhsStorageOrder = traits<Lhs>::Flags & RowMajorBit,
+          int RhsStorageOrder = traits<Rhs>::Flags & RowMajorBit,
+          int ResStorageOrder = traits<ResultType>::Flags & RowMajorBit>
+struct sparse_sparse_product_with_pruning_selector;
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs, Rhs, ResultType, ColMajor, ColMajor, ColMajor> {
+  typedef typename ResultType::RealScalar RealScalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) {
+    remove_all_t<ResultType> res_(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Lhs, Rhs, ResultType>(lhs, rhs, res_, tolerance);
+    res.swap(res_);
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs, Rhs, ResultType, ColMajor, ColMajor, RowMajor> {
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) {
+    // we need a col-major matrix to hold the result
+    typedef SparseMatrix<typename ResultType::Scalar, ColMajor, typename ResultType::StorageIndex> SparseTemporaryType;
+    SparseTemporaryType res_(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Lhs, Rhs, SparseTemporaryType>(lhs, rhs, res_, tolerance);
+    res = res_;
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs, Rhs, ResultType, RowMajor, RowMajor, RowMajor> {
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) {
+    // let's transpose the product to get a column x column product
+    remove_all_t<ResultType> res_(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Rhs, Lhs, ResultType>(rhs, lhs, res_, tolerance);
+    res.swap(res_);
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs, Rhs, ResultType, RowMajor, RowMajor, ColMajor> {
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) {
+    typedef SparseMatrix<typename Lhs::Scalar, ColMajor, typename Lhs::StorageIndex> ColMajorMatrixLhs;
+    typedef SparseMatrix<typename Rhs::Scalar, ColMajor, typename Lhs::StorageIndex> ColMajorMatrixRhs;
+    ColMajorMatrixLhs colLhs(lhs);
+    ColMajorMatrixRhs colRhs(rhs);
+    internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs, ColMajorMatrixRhs, ResultType>(colLhs, colRhs,
+                                                                                                        res, tolerance);
+
+    // let's transpose the product to get a column x column product
+    //     typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
+    //     SparseTemporaryType res_(res.cols(), res.rows());
+    //     sparse_sparse_product_with_pruning_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, res_);
+    //     res = res_.transpose();
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs, Rhs, ResultType, ColMajor, RowMajor, RowMajor> {
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) {
+    typedef SparseMatrix<typename Lhs::Scalar, RowMajor, typename Lhs::StorageIndex> RowMajorMatrixLhs;
+    RowMajorMatrixLhs rowLhs(lhs);
+    sparse_sparse_product_with_pruning_selector<RowMajorMatrixLhs, Rhs, ResultType, RowMajor, RowMajor>(rowLhs, rhs,
+                                                                                                        res, tolerance);
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs, Rhs, ResultType, RowMajor, ColMajor, RowMajor> {
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) {
+    typedef SparseMatrix<typename Rhs::Scalar, RowMajor, typename Lhs::StorageIndex> RowMajorMatrixRhs;
+    RowMajorMatrixRhs rowRhs(rhs);
+    sparse_sparse_product_with_pruning_selector<Lhs, RowMajorMatrixRhs, ResultType, RowMajor, RowMajor, RowMajor>(
+        lhs, rowRhs, res, tolerance);
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs, Rhs, ResultType, ColMajor, RowMajor, ColMajor> {
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) {
+    typedef SparseMatrix<typename Rhs::Scalar, ColMajor, typename Lhs::StorageIndex> ColMajorMatrixRhs;
+    ColMajorMatrixRhs colRhs(rhs);
+    internal::sparse_sparse_product_with_pruning_impl<Lhs, ColMajorMatrixRhs, ResultType>(lhs, colRhs, res, tolerance);
+  }
+};
+
+template <typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs, Rhs, ResultType, RowMajor, ColMajor, ColMajor> {
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) {
+    typedef SparseMatrix<typename Lhs::Scalar, ColMajor, typename Lhs::StorageIndex> ColMajorMatrixLhs;
+    ColMajorMatrixLhs colLhs(lhs);
+    internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs, Rhs, ResultType>(colLhs, rhs, res, tolerance);
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseTranspose.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseTranspose.h
new file mode 100644
index 0000000..158f778
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseTranspose.h
@@ -0,0 +1,83 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSETRANSPOSE_H
+#define EIGEN_SPARSETRANSPOSE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType, int CompressedAccess = int(MatrixType::Flags & CompressedAccessBit)>
+class SparseTransposeImpl : public SparseMatrixBase<Transpose<MatrixType> > {};
+
+template <typename MatrixType>
+class SparseTransposeImpl<MatrixType, CompressedAccessBit> : public SparseCompressedBase<Transpose<MatrixType> > {
+  typedef SparseCompressedBase<Transpose<MatrixType> > Base;
+
+ public:
+  using Base::derived;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::StorageIndex StorageIndex;
+
+  inline Index nonZeros() const { return derived().nestedExpression().nonZeros(); }
+
+  inline const Scalar* valuePtr() const { return derived().nestedExpression().valuePtr(); }
+  inline const StorageIndex* innerIndexPtr() const { return derived().nestedExpression().innerIndexPtr(); }
+  inline const StorageIndex* outerIndexPtr() const { return derived().nestedExpression().outerIndexPtr(); }
+  inline const StorageIndex* innerNonZeroPtr() const { return derived().nestedExpression().innerNonZeroPtr(); }
+
+  inline Scalar* valuePtr() { return derived().nestedExpression().valuePtr(); }
+  inline StorageIndex* innerIndexPtr() { return derived().nestedExpression().innerIndexPtr(); }
+  inline StorageIndex* outerIndexPtr() { return derived().nestedExpression().outerIndexPtr(); }
+  inline StorageIndex* innerNonZeroPtr() { return derived().nestedExpression().innerNonZeroPtr(); }
+};
+}  // namespace internal
+
+template <typename MatrixType>
+class TransposeImpl<MatrixType, Sparse> : public internal::SparseTransposeImpl<MatrixType> {
+ protected:
+  typedef internal::SparseTransposeImpl<MatrixType> Base;
+};
+
+namespace internal {
+
+template <typename ArgType>
+struct unary_evaluator<Transpose<ArgType>, IteratorBased> : public evaluator_base<Transpose<ArgType> > {
+  typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
+
+ public:
+  typedef Transpose<ArgType> XprType;
+
+  inline Index nonZerosEstimate() const { return m_argImpl.nonZerosEstimate(); }
+
+  class InnerIterator : public EvalIterator {
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
+        : EvalIterator(unaryOp.m_argImpl, outer) {}
+
+    Index row() const { return EvalIterator::col(); }
+    Index col() const { return EvalIterator::row(); }
+  };
+
+  enum { CoeffReadCost = evaluator<ArgType>::CoeffReadCost, Flags = XprType::Flags };
+
+  explicit unary_evaluator(const XprType& op) : m_argImpl(op.nestedExpression()) {}
+
+ protected:
+  evaluator<ArgType> m_argImpl;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSETRANSPOSE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseTriangularView.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseTriangularView.h
new file mode 100644
index 0000000..a6c3eaa
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseTriangularView.h
@@ -0,0 +1,177 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_TRIANGULARVIEW_H
+#define EIGEN_SPARSE_TRIANGULARVIEW_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup SparseCore_Module
+ *
+ * \brief Base class for a triangular part in a \b sparse matrix
+ *
+ * This class is an abstract base class of class TriangularView, and objects of type TriangularViewImpl cannot be
+ * instantiated. It extends class TriangularView with additional methods which are available for sparse expressions
+ * only.
+ *
+ * \sa class TriangularView, SparseMatrixBase::triangularView()
+ */
+template <typename MatrixType, unsigned int Mode>
+class TriangularViewImpl<MatrixType, Mode, Sparse> : public SparseMatrixBase<TriangularView<MatrixType, Mode> > {
+  enum {
+    SkipFirst =
+        ((Mode & Lower) && !(MatrixType::Flags & RowMajorBit)) || ((Mode & Upper) && (MatrixType::Flags & RowMajorBit)),
+    SkipLast = !SkipFirst,
+    SkipDiag = (Mode & ZeroDiag) ? 1 : 0,
+    HasUnitDiag = (Mode & UnitDiag) ? 1 : 0
+  };
+
+  typedef TriangularView<MatrixType, Mode> TriangularViewType;
+
+ protected:
+  // dummy solve function to make TriangularView happy.
+  void solve() const;
+
+  typedef SparseMatrixBase<TriangularViewType> Base;
+
+ public:
+  EIGEN_SPARSE_PUBLIC_INTERFACE(TriangularViewType)
+
+  typedef typename MatrixType::Nested MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNestedNonRef;
+  typedef internal::remove_all_t<MatrixTypeNested> MatrixTypeNestedCleaned;
+
+  template <typename RhsType, typename DstType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void _solve_impl(const RhsType& rhs, DstType& dst) const {
+    if (!(internal::is_same<RhsType, DstType>::value && internal::extract_data(dst) == internal::extract_data(rhs)))
+      dst = rhs;
+    this->solveInPlace(dst);
+  }
+
+  /** Applies the inverse of \c *this to the dense vector or matrix \a other, "in-place" */
+  template <typename OtherDerived>
+  void solveInPlace(MatrixBase<OtherDerived>& other) const;
+
+  /** Applies the inverse of \c *this to the sparse vector or matrix \a other, "in-place" */
+  template <typename OtherDerived>
+  void solveInPlace(SparseMatrixBase<OtherDerived>& other) const;
+};
+
+namespace internal {
+
+template <typename ArgType, unsigned int Mode>
+struct unary_evaluator<TriangularView<ArgType, Mode>, IteratorBased> : evaluator_base<TriangularView<ArgType, Mode> > {
+  typedef TriangularView<ArgType, Mode> XprType;
+
+ protected:
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
+
+  enum {
+    SkipFirst =
+        ((Mode & Lower) && !(ArgType::Flags & RowMajorBit)) || ((Mode & Upper) && (ArgType::Flags & RowMajorBit)),
+    SkipLast = !SkipFirst,
+    SkipDiag = (Mode & ZeroDiag) ? 1 : 0,
+    HasUnitDiag = (Mode & UnitDiag) ? 1 : 0
+  };
+
+ public:
+  enum { CoeffReadCost = evaluator<ArgType>::CoeffReadCost, Flags = XprType::Flags };
+
+  explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_arg(xpr.nestedExpression()) {}
+
+  inline Index nonZerosEstimate() const { return m_argImpl.nonZerosEstimate(); }
+
+  class InnerIterator : public EvalIterator {
+    typedef EvalIterator Base;
+
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& xprEval, Index outer)
+        : Base(xprEval.m_argImpl, outer),
+          m_returnOne(false),
+          m_containsDiag(Base::outer() < xprEval.m_arg.innerSize()) {
+      if (SkipFirst) {
+        while ((*this) && ((HasUnitDiag || SkipDiag) ? this->index() <= outer : this->index() < outer))
+          Base::operator++();
+        if (HasUnitDiag) m_returnOne = m_containsDiag;
+      } else if (HasUnitDiag && ((!Base::operator bool()) || Base::index() >= Base::outer())) {
+        if ((!SkipFirst) && Base::operator bool()) Base::operator++();
+        m_returnOne = m_containsDiag;
+      }
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      if (HasUnitDiag && m_returnOne)
+        m_returnOne = false;
+      else {
+        Base::operator++();
+        if (HasUnitDiag && (!SkipFirst) && ((!Base::operator bool()) || Base::index() >= Base::outer())) {
+          if ((!SkipFirst) && Base::operator bool()) Base::operator++();
+          m_returnOne = m_containsDiag;
+        }
+      }
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE operator bool() const {
+      if (HasUnitDiag && m_returnOne) return true;
+      if (SkipFirst)
+        return Base::operator bool();
+      else {
+        if (SkipDiag)
+          return (Base::operator bool() && this->index() < this->outer());
+        else
+          return (Base::operator bool() && this->index() <= this->outer());
+      }
+    }
+
+    inline Index row() const { return (ArgType::Flags & RowMajorBit ? Base::outer() : this->index()); }
+    inline Index col() const { return (ArgType::Flags & RowMajorBit ? this->index() : Base::outer()); }
+    inline StorageIndex index() const {
+      if (HasUnitDiag && m_returnOne)
+        return internal::convert_index<StorageIndex>(Base::outer());
+      else
+        return Base::index();
+    }
+    inline Scalar value() const {
+      if (HasUnitDiag && m_returnOne)
+        return Scalar(1);
+      else
+        return Base::value();
+    }
+
+   protected:
+    bool m_returnOne;
+    bool m_containsDiag;
+
+   private:
+    Scalar& valueRef();
+  };
+
+ protected:
+  evaluator<ArgType> m_argImpl;
+  const ArgType& m_arg;
+};
+
+}  // end namespace internal
+
+template <typename Derived>
+template <int Mode>
+inline const TriangularView<const Derived, Mode> SparseMatrixBase<Derived>::triangularView() const {
+  return TriangularView<const Derived, Mode>(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_TRIANGULARVIEW_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseUtil.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseUtil.h
new file mode 100644
index 0000000..8eedd54
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseUtil.h
@@ -0,0 +1,209 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEUTIL_H
+#define EIGEN_SPARSEUTIL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+#ifdef NDEBUG
+#define EIGEN_DBG_SPARSE(X)
+#else
+#define EIGEN_DBG_SPARSE(X) X
+#endif
+
+#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op)                                    \
+  template <typename OtherDerived>                                                               \
+  EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::SparseMatrixBase<OtherDerived>& other) { \
+    return Base::operator Op(other.derived());                                                   \
+  }                                                                                              \
+  EIGEN_STRONG_INLINE Derived& operator Op(const Derived & other) { return Base::operator Op(other); }
+
+#define EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \
+  template <typename Other>                                          \
+  EIGEN_STRONG_INLINE Derived& operator Op(const Other & scalar) {   \
+    return Base::operator Op(scalar);                                \
+  }
+
+#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =)
+
+#define EIGEN_SPARSE_PUBLIC_INTERFACE(Derived) EIGEN_GENERIC_PUBLIC_INTERFACE(Derived)
+
+const int CoherentAccessPattern = 0x1;
+const int InnerRandomAccessPattern = 0x2 | CoherentAccessPattern;
+const int OuterRandomAccessPattern = 0x4 | CoherentAccessPattern;
+const int RandomAccessPattern = 0x8 | OuterRandomAccessPattern | InnerRandomAccessPattern;
+
+template <typename Scalar_, int Flags_ = 0, typename StorageIndex_ = int>
+class SparseMatrix;
+template <typename Scalar_, int Flags_ = 0, typename StorageIndex_ = int>
+class SparseVector;
+
+template <typename MatrixType, unsigned int UpLo>
+class SparseSelfAdjointView;
+template <typename Lhs, typename Rhs>
+class SparseDiagonalProduct;
+template <typename MatrixType>
+class SparseView;
+
+template <typename Lhs, typename Rhs>
+class SparseSparseProduct;
+template <typename Lhs, typename Rhs>
+class SparseTimeDenseProduct;
+template <typename Lhs, typename Rhs>
+class DenseTimeSparseProduct;
+template <typename Lhs, typename Rhs, bool Transpose>
+class SparseDenseOuterProduct;
+
+template <typename Lhs, typename Rhs>
+struct SparseSparseProductReturnType;
+template <typename Lhs, typename Rhs,
+          int InnerSize = internal::min_size_prefer_fixed(internal::traits<Lhs>::ColsAtCompileTime,
+                                                          internal::traits<Rhs>::RowsAtCompileTime)>
+struct DenseSparseProductReturnType;
+
+template <typename Lhs, typename Rhs,
+          int InnerSize = internal::min_size_prefer_fixed(internal::traits<Lhs>::ColsAtCompileTime,
+                                                          internal::traits<Rhs>::RowsAtCompileTime)>
+struct SparseDenseProductReturnType;
+template <typename MatrixType, int UpLo>
+class SparseSymmetricPermutationProduct;
+
+namespace internal {
+
+template <typename T, int Rows, int Cols, int Flags>
+struct sparse_eval;
+
+template <typename T>
+struct eval<T, Sparse> : sparse_eval<T, traits<T>::RowsAtCompileTime, traits<T>::ColsAtCompileTime, traits<T>::Flags> {
+};
+
+template <typename T, int Cols, int Flags>
+struct sparse_eval<T, 1, Cols, Flags> {
+  typedef typename traits<T>::Scalar Scalar_;
+  typedef typename traits<T>::StorageIndex StorageIndex_;
+
+ public:
+  typedef SparseVector<Scalar_, RowMajor, StorageIndex_> type;
+};
+
+template <typename T, int Rows, int Flags>
+struct sparse_eval<T, Rows, 1, Flags> {
+  typedef typename traits<T>::Scalar Scalar_;
+  typedef typename traits<T>::StorageIndex StorageIndex_;
+
+ public:
+  typedef SparseVector<Scalar_, ColMajor, StorageIndex_> type;
+};
+
+// TODO: consider unifying with plain_matrix_type<T, Sparse>.
+template <typename T, int Rows, int Cols, int Flags>
+struct sparse_eval {
+  typedef typename traits<T>::Scalar Scalar_;
+  typedef typename traits<T>::StorageIndex StorageIndex_;
+  enum { Options_ = ((Flags & RowMajorBit) == RowMajorBit) ? RowMajor : ColMajor };
+
+ public:
+  typedef SparseMatrix<Scalar_, Options_, StorageIndex_> type;
+};
+
+template <typename T, int Flags>
+struct sparse_eval<T, 1, 1, Flags> {
+  typedef typename traits<T>::Scalar Scalar_;
+
+ public:
+  typedef Matrix<Scalar_, 1, 1> type;
+};
+
+template <typename T>
+struct plain_matrix_type<T, Sparse> {
+  typedef typename traits<T>::Scalar Scalar_;
+  typedef typename traits<T>::StorageIndex StorageIndex_;
+  enum { Options_ = ((evaluator<T>::Flags & RowMajorBit) == RowMajorBit) ? RowMajor : ColMajor };
+
+ public:
+  typedef SparseMatrix<Scalar_, Options_, StorageIndex_> type;
+};
+
+template <typename T>
+struct plain_object_eval<T, Sparse>
+    : sparse_eval<T, traits<T>::RowsAtCompileTime, traits<T>::ColsAtCompileTime, evaluator<T>::Flags> {};
+
+template <typename Decomposition, typename RhsType>
+struct solve_traits<Decomposition, RhsType, Sparse> {
+  typedef typename sparse_eval<RhsType, RhsType::RowsAtCompileTime, RhsType::ColsAtCompileTime,
+                               traits<RhsType>::Flags>::type PlainObject;
+};
+
+template <typename Derived>
+struct generic_xpr_base<Derived, MatrixXpr, Sparse> {
+  typedef SparseMatrixBase<Derived> type;
+};
+
+struct SparseTriangularShape {
+  static std::string debugName() { return "SparseTriangularShape"; }
+};
+struct SparseSelfAdjointShape {
+  static std::string debugName() { return "SparseSelfAdjointShape"; }
+};
+
+template <>
+struct glue_shapes<SparseShape, SelfAdjointShape> {
+  typedef SparseSelfAdjointShape type;
+};
+template <>
+struct glue_shapes<SparseShape, TriangularShape> {
+  typedef SparseTriangularShape type;
+};
+
+// return type of SparseCompressedBase::lower_bound;
+struct LowerBoundIndex {
+  LowerBoundIndex() : value(-1), found(false) {}
+  LowerBoundIndex(Index val, bool ok) : value(val), found(ok) {}
+  Index value;
+  bool found;
+};
+
+}  // end namespace internal
+
+/** \ingroup SparseCore_Module
+ *
+ * \class Triplet
+ *
+ * \brief A small structure to hold a non zero as a triplet (i,j,value).
+ *
+ * \sa SparseMatrix::setFromTriplets()
+ */
+template <typename Scalar, typename StorageIndex = typename SparseMatrix<Scalar>::StorageIndex>
+class Triplet {
+ public:
+  Triplet() : m_row(0), m_col(0), m_value(0) {}
+
+  Triplet(const StorageIndex& i, const StorageIndex& j, const Scalar& v = Scalar(0)) : m_row(i), m_col(j), m_value(v) {}
+
+  /** \returns the row index of the element */
+  const StorageIndex& row() const { return m_row; }
+
+  /** \returns the column index of the element */
+  const StorageIndex& col() const { return m_col; }
+
+  /** \returns the value of the element */
+  const Scalar& value() const { return m_value; }
+
+ protected:
+  StorageIndex m_row, m_col;
+  Scalar m_value;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSEUTIL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseVector.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseVector.h
new file mode 100644
index 0000000..66fbb07
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseVector.h
@@ -0,0 +1,532 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEVECTOR_H
+#define EIGEN_SPARSEVECTOR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup SparseCore_Module
+ * \class SparseVector
+ *
+ * \brief a sparse vector class
+ *
+ * \tparam Scalar_ the scalar type, i.e. the type of the coefficients
+ *
+ * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEVECTOR_PLUGIN.
+ */
+
+namespace internal {
+template <typename Scalar_, int Options_, typename StorageIndex_>
+struct traits<SparseVector<Scalar_, Options_, StorageIndex_> > {
+  typedef Scalar_ Scalar;
+  typedef StorageIndex_ StorageIndex;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    IsColVector = (Options_ & RowMajorBit) ? 0 : 1,
+
+    RowsAtCompileTime = IsColVector ? Dynamic : 1,
+    ColsAtCompileTime = IsColVector ? 1 : Dynamic,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    Flags = Options_ | NestByRefBit | LvalueBit | (IsColVector ? 0 : RowMajorBit) | CompressedAccessBit,
+    SupportedAccessPatterns = InnerRandomAccessPattern
+  };
+};
+
+// Sparse-Vector-Assignment kinds:
+enum { SVA_RuntimeSwitch, SVA_Inner, SVA_Outer };
+
+template <typename Dest, typename Src,
+          int AssignmentKind = !bool(Src::IsVectorAtCompileTime)  ? SVA_RuntimeSwitch
+                               : Src::InnerSizeAtCompileTime == 1 ? SVA_Outer
+                                                                  : SVA_Inner>
+struct sparse_vector_assign_selector;
+
+}  // namespace internal
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+class SparseVector : public SparseCompressedBase<SparseVector<Scalar_, Options_, StorageIndex_> > {
+  typedef SparseCompressedBase<SparseVector> Base;
+  using Base::convert_index;
+
+ public:
+  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseVector)
+  EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=)
+  EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=)
+
+  typedef internal::CompressedStorage<Scalar, StorageIndex> Storage;
+  enum { IsColVector = internal::traits<SparseVector>::IsColVector };
+
+  enum { Options = Options_ };
+
+  EIGEN_STRONG_INLINE Index rows() const { return IsColVector ? m_size : 1; }
+  EIGEN_STRONG_INLINE Index cols() const { return IsColVector ? 1 : m_size; }
+  EIGEN_STRONG_INLINE Index innerSize() const { return m_size; }
+  EIGEN_STRONG_INLINE Index outerSize() const { return 1; }
+
+  EIGEN_STRONG_INLINE const Scalar* valuePtr() const { return m_data.valuePtr(); }
+  EIGEN_STRONG_INLINE Scalar* valuePtr() { return m_data.valuePtr(); }
+
+  EIGEN_STRONG_INLINE const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); }
+  EIGEN_STRONG_INLINE StorageIndex* innerIndexPtr() { return m_data.indexPtr(); }
+
+  inline const StorageIndex* outerIndexPtr() const { return 0; }
+  inline StorageIndex* outerIndexPtr() { return 0; }
+  inline const StorageIndex* innerNonZeroPtr() const { return 0; }
+  inline StorageIndex* innerNonZeroPtr() { return 0; }
+
+  /** \internal */
+  constexpr Storage& data() { return m_data; }
+  /** \internal */
+  constexpr const Storage& data() const { return m_data; }
+
+  inline Scalar coeff(Index row, Index col) const {
+    eigen_assert(IsColVector ? (col == 0 && row >= 0 && row < m_size) : (row == 0 && col >= 0 && col < m_size));
+    return coeff(IsColVector ? row : col);
+  }
+  inline Scalar coeff(Index i) const {
+    eigen_assert(i >= 0 && i < m_size);
+    return m_data.at(StorageIndex(i));
+  }
+
+  inline Scalar& coeffRef(Index row, Index col) {
+    eigen_assert(IsColVector ? (col == 0 && row >= 0 && row < m_size) : (row == 0 && col >= 0 && col < m_size));
+    return coeffRef(IsColVector ? row : col);
+  }
+
+  /** \returns a reference to the coefficient value at given index \a i
+   * This operation involves a log(rho*size) binary search. If the coefficient does not
+   * exist yet, then a sorted insertion into a sequential buffer is performed.
+   *
+   * This insertion might be very costly if the number of nonzeros above \a i is large.
+   */
+  inline Scalar& coeffRef(Index i) {
+    eigen_assert(i >= 0 && i < m_size);
+
+    return m_data.atWithInsertion(StorageIndex(i));
+  }
+
+ public:
+  typedef typename Base::InnerIterator InnerIterator;
+  typedef typename Base::ReverseInnerIterator ReverseInnerIterator;
+
+  inline void setZero() { m_data.clear(); }
+
+  /** \returns the number of non zero coefficients */
+  inline Index nonZeros() const { return m_data.size(); }
+
+  inline void startVec(Index outer) {
+    EIGEN_UNUSED_VARIABLE(outer);
+    eigen_assert(outer == 0);
+  }
+
+  inline Scalar& insertBackByOuterInner(Index outer, Index inner) {
+    EIGEN_UNUSED_VARIABLE(outer);
+    eigen_assert(outer == 0);
+    return insertBack(inner);
+  }
+  inline Scalar& insertBack(Index i) {
+    m_data.append(Scalar(0), i);
+    return m_data.value(m_data.size() - 1);
+  }
+
+  Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner) {
+    EIGEN_UNUSED_VARIABLE(outer);
+    eigen_assert(outer == 0);
+    return insertBackUnordered(inner);
+  }
+  inline Scalar& insertBackUnordered(Index i) {
+    m_data.append(Scalar(0), i);
+    return m_data.value(m_data.size() - 1);
+  }
+
+  inline Scalar& insert(Index row, Index col) {
+    eigen_assert(IsColVector ? (col == 0 && row >= 0 && row < m_size) : (row == 0 && col >= 0 && col < m_size));
+
+    Index inner = IsColVector ? row : col;
+    Index outer = IsColVector ? col : row;
+    EIGEN_ONLY_USED_FOR_DEBUG(outer);
+    eigen_assert(outer == 0);
+    return insert(inner);
+  }
+  Scalar& insert(Index i) {
+    eigen_assert(i >= 0 && i < m_size);
+
+    Index startId = 0;
+    Index p = Index(m_data.size()) - 1;
+    // TODO: implement smart reallocation.
+    m_data.resize(p + 2, 1);
+
+    while ((p >= startId) && (m_data.index(p) > i)) {
+      m_data.index(p + 1) = m_data.index(p);
+      m_data.value(p + 1) = m_data.value(p);
+      --p;
+    }
+    m_data.index(p + 1) = convert_index(i);
+    m_data.value(p + 1) = Scalar(0);
+    return m_data.value(p + 1);
+  }
+
+  /**
+   */
+  inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); }
+
+  inline void finalize() {}
+
+  /** \copydoc SparseMatrix::prune(const Scalar&,const RealScalar&) */
+  Index prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision()) {
+    return prune([&](const Scalar& val) { return !internal::isMuchSmallerThan(val, reference, epsilon); });
+  }
+
+  /**
+   * \brief Prunes the entries of the vector based on a `predicate`
+   * \tparam F Type of the predicate.
+   * \param keep_predicate The predicate that is used to test whether a value should be kept. A callable that
+   * gets passed om a `Scalar` value and returns a boolean. If the predicate returns true, the value is kept.
+   * \return The new number of structural non-zeros.
+   */
+  template <class F>
+  Index prune(F&& keep_predicate) {
+    Index k = 0;
+    Index n = m_data.size();
+    for (Index i = 0; i < n; ++i) {
+      if (keep_predicate(m_data.value(i))) {
+        m_data.value(k) = std::move(m_data.value(i));
+        m_data.index(k) = m_data.index(i);
+        ++k;
+      }
+    }
+    m_data.resize(k);
+    return k;
+  }
+
+  /** Resizes the sparse vector to \a rows x \a cols
+   *
+   * This method is provided for compatibility with matrices.
+   * For a column vector, \a cols must be equal to 1.
+   * For a row vector, \a rows must be equal to 1.
+   *
+   * \sa resize(Index)
+   */
+  void resize(Index rows, Index cols) {
+    eigen_assert((IsColVector ? cols : rows) == 1 && "Outer dimension must equal 1");
+    resize(IsColVector ? rows : cols);
+  }
+
+  /** Resizes the sparse vector to \a newSize
+   * This method deletes all entries, thus leaving an empty sparse vector
+   *
+   * \sa  conservativeResize(), setZero() */
+  void resize(Index newSize) {
+    m_size = newSize;
+    m_data.clear();
+  }
+
+  /** Resizes the sparse vector to \a newSize, while leaving old values untouched.
+   *
+   * If the size of the vector is decreased, then the storage of the out-of bounds coefficients is kept and reserved.
+   * Call .data().squeeze() to free extra memory.
+   *
+   * \sa reserve(), setZero()
+   */
+  void conservativeResize(Index newSize) {
+    if (newSize < m_size) {
+      Index i = 0;
+      while (i < m_data.size() && m_data.index(i) < newSize) ++i;
+      m_data.resize(i);
+    }
+    m_size = newSize;
+  }
+
+  void resizeNonZeros(Index size) { m_data.resize(size); }
+
+  inline SparseVector() : m_size(0) { resize(0); }
+
+  explicit inline SparseVector(Index size) : m_size(0) { resize(size); }
+
+  inline SparseVector(Index rows, Index cols) : m_size(0) { resize(rows, cols); }
+
+  template <typename OtherDerived>
+  inline SparseVector(const SparseMatrixBase<OtherDerived>& other) : m_size(0) {
+#ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+    EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+#endif
+    *this = other.derived();
+  }
+
+  inline SparseVector(const SparseVector& other) : Base(other), m_size(0) { *this = other.derived(); }
+
+  /** Swaps the values of \c *this and \a other.
+   * Overloaded for performance: this version performs a \em shallow swap by swapping pointers and attributes only.
+   * \sa SparseMatrixBase::swap()
+   */
+  inline void swap(SparseVector& other) {
+    std::swap(m_size, other.m_size);
+    m_data.swap(other.m_data);
+  }
+  friend EIGEN_DEVICE_FUNC void swap(SparseVector& a, SparseVector& b) { a.swap(b); }
+
+  template <int OtherOptions>
+  inline void swap(SparseMatrix<Scalar, OtherOptions, StorageIndex>& other) {
+    eigen_assert(other.outerSize() == 1);
+    std::swap(m_size, other.m_innerSize);
+    m_data.swap(other.m_data);
+  }
+  template <int OtherOptions>
+  friend EIGEN_DEVICE_FUNC void swap(SparseVector& a, SparseMatrix<Scalar, OtherOptions, StorageIndex>& b) {
+    a.swap(b);
+  }
+  template <int OtherOptions>
+  friend EIGEN_DEVICE_FUNC void swap(SparseMatrix<Scalar, OtherOptions, StorageIndex>& a, SparseVector& b) {
+    b.swap(a);
+  }
+
+  inline SparseVector& operator=(const SparseVector& other) {
+    if (other.isRValue()) {
+      swap(other.const_cast_derived());
+    } else {
+      resize(other.size());
+      m_data = other.m_data;
+    }
+    return *this;
+  }
+
+  template <typename OtherDerived>
+  inline SparseVector& operator=(const SparseMatrixBase<OtherDerived>& other) {
+    SparseVector tmp(other.size());
+    internal::sparse_vector_assign_selector<SparseVector, OtherDerived>::run(tmp, other.derived());
+    this->swap(tmp);
+    return *this;
+  }
+
+  inline SparseVector(SparseVector&& other) : SparseVector() { this->swap(other); }
+
+  template <typename OtherDerived>
+  inline SparseVector(SparseCompressedBase<OtherDerived>&& other) : SparseVector() {
+    *this = other.derived().markAsRValue();
+  }
+
+  inline SparseVector& operator=(SparseVector&& other) {
+    this->swap(other);
+    return *this;
+  }
+
+  template <typename OtherDerived>
+  inline SparseVector& operator=(SparseCompressedBase<OtherDerived>&& other) {
+    *this = other.derived().markAsRValue();
+    return *this;
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename Lhs, typename Rhs>
+  inline SparseVector& operator=(const SparseSparseProduct<Lhs, Rhs>& product) {
+    return Base::operator=(product);
+  }
+#endif
+
+#ifndef EIGEN_NO_IO
+  friend std::ostream& operator<<(std::ostream& s, const SparseVector& m) {
+    for (Index i = 0; i < m.nonZeros(); ++i) s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
+    s << std::endl;
+    return s;
+  }
+#endif
+
+  /** Destructor */
+  inline ~SparseVector() {}
+
+  /** Overloaded for performance */
+  Scalar sum() const;
+
+ public:
+  /** \internal \deprecated use setZero() and reserve() */
+  EIGEN_DEPRECATED_WITH_REASON("Use .setZero() and .reserve() instead.") void startFill(Index reserve) {
+    setZero();
+    m_data.reserve(reserve);
+  }
+
+  /** \internal \deprecated use insertBack(Index,Index) */
+  EIGEN_DEPRECATED_WITH_REASON("Use .insertBack() instead.") Scalar& fill(Index r, Index c) {
+    eigen_assert(r == 0 || c == 0);
+    return fill(IsColVector ? r : c);
+  }
+
+  /** \internal \deprecated use insertBack(Index) */
+  EIGEN_DEPRECATED_WITH_REASON("Use .insertBack() instead.") Scalar& fill(Index i) {
+    m_data.append(Scalar(0), i);
+    return m_data.value(m_data.size() - 1);
+  }
+
+  /** \internal \deprecated use insert(Index,Index) */
+  EIGEN_DEPRECATED_WITH_REASON("Use .insert() instead.") Scalar& fillrand(Index r, Index c) {
+    eigen_assert(r == 0 || c == 0);
+    return fillrand(IsColVector ? r : c);
+  }
+
+  /** \internal \deprecated use insert(Index) */
+  EIGEN_DEPRECATED_WITH_REASON("Use .insert() instead.") Scalar& fillrand(Index i) { return insert(i); }
+
+  /** \internal \deprecated use finalize() */
+  EIGEN_DEPRECATED_WITH_REASON("Use .finalize() instead.") void endFill() {}
+
+  // These two functions were here in the 3.1 release, so let's keep them in case some code rely on them.
+  /** \internal \deprecated use data() */
+  EIGEN_DEPRECATED_WITH_REASON("Use .data() instead.") Storage& _data() { return m_data; }
+  /** \internal \deprecated use data() */
+  EIGEN_DEPRECATED_WITH_REASON("Use .data() instead.") const Storage& _data() const { return m_data; }
+
+#ifdef EIGEN_SPARSEVECTOR_PLUGIN
+#include EIGEN_SPARSEVECTOR_PLUGIN
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned, THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
+  EIGEN_STATIC_ASSERT((Options_ & (ColMajor | RowMajor)) == Options, INVALID_MATRIX_TEMPLATE_PARAMETERS)
+
+  Storage m_data;
+  Index m_size;
+};
+
+namespace internal {
+
+template <typename Scalar_, int Options_, typename Index_>
+struct evaluator<SparseVector<Scalar_, Options_, Index_> > : evaluator_base<SparseVector<Scalar_, Options_, Index_> > {
+  typedef SparseVector<Scalar_, Options_, Index_> SparseVectorType;
+  typedef evaluator_base<SparseVectorType> Base;
+  typedef typename SparseVectorType::InnerIterator InnerIterator;
+  typedef typename SparseVectorType::ReverseInnerIterator ReverseInnerIterator;
+
+  enum { CoeffReadCost = NumTraits<Scalar_>::ReadCost, Flags = SparseVectorType::Flags };
+
+  evaluator() : Base() {}
+
+  explicit evaluator(const SparseVectorType& mat) : m_matrix(&mat) { EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); }
+
+  inline Index nonZerosEstimate() const { return m_matrix->nonZeros(); }
+
+  operator SparseVectorType&() { return m_matrix->const_cast_derived(); }
+  operator const SparseVectorType&() const { return *m_matrix; }
+
+  const SparseVectorType* m_matrix;
+};
+
+template <typename Dest, typename Src>
+struct sparse_vector_assign_selector<Dest, Src, SVA_Inner> {
+  static void run(Dest& dst, const Src& src) {
+    eigen_internal_assert(src.innerSize() == src.size());
+    typedef internal::evaluator<Src> SrcEvaluatorType;
+    SrcEvaluatorType srcEval(src);
+    for (typename SrcEvaluatorType::InnerIterator it(srcEval, 0); it; ++it) dst.insert(it.index()) = it.value();
+  }
+};
+
+template <typename Dest, typename Src>
+struct sparse_vector_assign_selector<Dest, Src, SVA_Outer> {
+  static void run(Dest& dst, const Src& src) {
+    eigen_internal_assert(src.outerSize() == src.size());
+    typedef internal::evaluator<Src> SrcEvaluatorType;
+    SrcEvaluatorType srcEval(src);
+    for (Index i = 0; i < src.size(); ++i) {
+      typename SrcEvaluatorType::InnerIterator it(srcEval, i);
+      if (it) dst.insert(i) = it.value();
+    }
+  }
+};
+
+template <typename Dest, typename Src>
+struct sparse_vector_assign_selector<Dest, Src, SVA_RuntimeSwitch> {
+  static void run(Dest& dst, const Src& src) {
+    if (src.outerSize() == 1)
+      sparse_vector_assign_selector<Dest, Src, SVA_Inner>::run(dst, src);
+    else
+      sparse_vector_assign_selector<Dest, Src, SVA_Outer>::run(dst, src);
+  }
+};
+
+}  // namespace internal
+
+// Specialization for SparseVector.
+// Serializes [size, numNonZeros, innerIndices, values].
+template <typename Scalar, int Options, typename StorageIndex>
+class Serializer<SparseVector<Scalar, Options, StorageIndex>, void> {
+ public:
+  typedef SparseVector<Scalar, Options, StorageIndex> SparseMat;
+
+  struct Header {
+    typename SparseMat::Index size;
+    Index num_non_zeros;
+  };
+
+  EIGEN_DEVICE_FUNC size_t size(const SparseMat& value) const {
+    return sizeof(Header) + (sizeof(Scalar) + sizeof(StorageIndex)) * value.nonZeros();
+  }
+
+  EIGEN_DEVICE_FUNC uint8_t* serialize(uint8_t* dest, uint8_t* end, const SparseMat& value) {
+    if (EIGEN_PREDICT_FALSE(dest == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(dest + size(value) > end)) return nullptr;
+
+    const size_t header_bytes = sizeof(Header);
+    Header header = {value.innerSize(), value.nonZeros()};
+    EIGEN_USING_STD(memcpy)
+    memcpy(dest, &header, header_bytes);
+    dest += header_bytes;
+
+    // Inner indices.
+    std::size_t data_bytes = sizeof(StorageIndex) * header.num_non_zeros;
+    memcpy(dest, value.innerIndexPtr(), data_bytes);
+    dest += data_bytes;
+
+    // Values.
+    data_bytes = sizeof(Scalar) * header.num_non_zeros;
+    memcpy(dest, value.valuePtr(), data_bytes);
+    dest += data_bytes;
+
+    return dest;
+  }
+
+  EIGEN_DEVICE_FUNC const uint8_t* deserialize(const uint8_t* src, const uint8_t* end, SparseMat& value) const {
+    if (EIGEN_PREDICT_FALSE(src == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(src + sizeof(Header) > end)) return nullptr;
+
+    const size_t header_bytes = sizeof(Header);
+    Header header;
+    EIGEN_USING_STD(memcpy)
+    memcpy(&header, src, header_bytes);
+    src += header_bytes;
+
+    value.setZero();
+    value.resize(header.size);
+    value.resizeNonZeros(header.num_non_zeros);
+
+    // Inner indices.
+    std::size_t data_bytes = sizeof(StorageIndex) * header.num_non_zeros;
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    memcpy(value.innerIndexPtr(), src, data_bytes);
+    src += data_bytes;
+
+    // Values.
+    data_bytes = sizeof(Scalar) * header.num_non_zeros;
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    memcpy(value.valuePtr(), src, data_bytes);
+    src += data_bytes;
+    return src;
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSEVECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseView.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseView.h
new file mode 100644
index 0000000..d566898
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/SparseView.h
@@ -0,0 +1,225 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Daniel Lowengrub <lowdanie@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEVIEW_H
+#define EIGEN_SPARSEVIEW_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename MatrixType>
+struct traits<SparseView<MatrixType> > : traits<MatrixType> {
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef Sparse StorageKind;
+  enum { Flags = int(traits<MatrixType>::Flags) & (RowMajorBit) };
+};
+
+}  // end namespace internal
+
+/** \ingroup SparseCore_Module
+ * \class SparseView
+ *
+ * \brief Expression of a dense or sparse matrix with zero or too small values removed
+ *
+ * \tparam MatrixType the type of the object of which we are removing the small entries
+ *
+ * This class represents an expression of a given dense or sparse matrix with
+ * entries smaller than \c reference * \c epsilon are removed.
+ * It is the return type of MatrixBase::sparseView() and SparseMatrixBase::pruned()
+ * and most of the time this is the only way it is used.
+ *
+ * \sa MatrixBase::sparseView(), SparseMatrixBase::pruned()
+ */
+template <typename MatrixType>
+class SparseView : public SparseMatrixBase<SparseView<MatrixType> > {
+  typedef typename MatrixType::Nested MatrixTypeNested;
+  typedef internal::remove_all_t<MatrixTypeNested> MatrixTypeNested_;
+  typedef SparseMatrixBase<SparseView> Base;
+
+ public:
+  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseView)
+  typedef internal::remove_all_t<MatrixType> NestedExpression;
+
+  explicit SparseView(const MatrixType& mat, const Scalar& reference = Scalar(0),
+                      const RealScalar& epsilon = NumTraits<Scalar>::dummy_precision())
+      : m_matrix(mat), m_reference(reference), m_epsilon(epsilon) {}
+
+  inline Index rows() const { return m_matrix.rows(); }
+  inline Index cols() const { return m_matrix.cols(); }
+
+  inline Index innerSize() const { return m_matrix.innerSize(); }
+  inline Index outerSize() const { return m_matrix.outerSize(); }
+
+  /** \returns the nested expression */
+  const internal::remove_all_t<MatrixTypeNested>& nestedExpression() const { return m_matrix; }
+
+  Scalar reference() const { return m_reference; }
+  RealScalar epsilon() const { return m_epsilon; }
+
+ protected:
+  MatrixTypeNested m_matrix;
+  Scalar m_reference;
+  RealScalar m_epsilon;
+};
+
+namespace internal {
+
+// TODO: find a way to unify the two following variants
+// This is tricky because implementing an inner iterator on top of an IndexBased evaluator is
+// not easy because the evaluators do not expose the sizes of the underlying expression.
+
+template <typename ArgType>
+struct unary_evaluator<SparseView<ArgType>, IteratorBased> : public evaluator_base<SparseView<ArgType> > {
+  typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
+
+ public:
+  typedef SparseView<ArgType> XprType;
+
+  class InnerIterator : public EvalIterator {
+   protected:
+    typedef typename XprType::Scalar Scalar;
+
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& sve, Index outer)
+        : EvalIterator(sve.m_argImpl, outer), m_view(sve.m_view) {
+      incrementToNonZero();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      EvalIterator::operator++();
+      incrementToNonZero();
+      return *this;
+    }
+
+    using EvalIterator::value;
+
+   protected:
+    const XprType& m_view;
+
+   private:
+    void incrementToNonZero() {
+      while ((bool(*this)) && internal::isMuchSmallerThan(value(), m_view.reference(), m_view.epsilon())) {
+        EvalIterator::operator++();
+      }
+    }
+  };
+
+  enum { CoeffReadCost = evaluator<ArgType>::CoeffReadCost, Flags = XprType::Flags };
+
+  explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_view(xpr) {}
+
+ protected:
+  evaluator<ArgType> m_argImpl;
+  const XprType& m_view;
+};
+
+template <typename ArgType>
+struct unary_evaluator<SparseView<ArgType>, IndexBased> : public evaluator_base<SparseView<ArgType> > {
+ public:
+  typedef SparseView<ArgType> XprType;
+
+ protected:
+  enum { IsRowMajor = (XprType::Flags & RowMajorBit) == RowMajorBit };
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::StorageIndex StorageIndex;
+
+ public:
+  class InnerIterator {
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& sve, Index outer)
+        : m_sve(sve), m_inner(0), m_outer(outer), m_end(sve.m_view.innerSize()) {
+      incrementToNonZero();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      m_inner++;
+      incrementToNonZero();
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const {
+      return (IsRowMajor) ? m_sve.m_argImpl.coeff(m_outer, m_inner) : m_sve.m_argImpl.coeff(m_inner, m_outer);
+    }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_inner; }
+    inline Index row() const { return IsRowMajor ? m_outer : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner >= 0; }
+
+   protected:
+    const unary_evaluator& m_sve;
+    Index m_inner;
+    const Index m_outer;
+    const Index m_end;
+
+   private:
+    void incrementToNonZero() {
+      while ((bool(*this)) && internal::isMuchSmallerThan(value(), m_sve.m_view.reference(), m_sve.m_view.epsilon())) {
+        m_inner++;
+      }
+    }
+  };
+
+  enum { CoeffReadCost = evaluator<ArgType>::CoeffReadCost, Flags = XprType::Flags };
+
+  explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_view(xpr) {}
+
+ protected:
+  evaluator<ArgType> m_argImpl;
+  const XprType& m_view;
+};
+
+}  // end namespace internal
+
+/** \ingroup SparseCore_Module
+ *
+ * \returns a sparse expression of the dense expression \c *this with values smaller than
+ * \a reference * \a epsilon removed.
+ *
+ * This method is typically used when prototyping to convert a quickly assembled dense Matrix \c D to a SparseMatrix \c
+ * S: \code MatrixXd D(n,m); SparseMatrix<double> S; S = D.sparseView();             // suppress numerical zeros (exact)
+ * S = D.sparseView(reference);
+ * S = D.sparseView(reference,epsilon);
+ * \endcode
+ * where \a reference is a meaningful non zero reference value,
+ * and \a epsilon is a tolerance factor defaulting to NumTraits<Scalar>::dummy_precision().
+ *
+ * \sa SparseMatrixBase::pruned(), class SparseView */
+template <typename Derived>
+const SparseView<Derived> MatrixBase<Derived>::sparseView(const Scalar& reference,
+                                                          const typename NumTraits<Scalar>::Real& epsilon) const {
+  return SparseView<Derived>(derived(), reference, epsilon);
+}
+
+/** \returns an expression of \c *this with values smaller than
+ * \a reference * \a epsilon removed.
+ *
+ * This method is typically used in conjunction with the product of two sparse matrices
+ * to automatically prune the smallest values as follows:
+ * \code
+ * C = (A*B).pruned();             // suppress numerical zeros (exact)
+ * C = (A*B).pruned(ref);
+ * C = (A*B).pruned(ref,epsilon);
+ * \endcode
+ * where \c ref is a meaningful non zero reference value.
+ * */
+template <typename Derived>
+const SparseView<Derived> SparseMatrixBase<Derived>::pruned(const Scalar& reference, const RealScalar& epsilon) const {
+  return SparseView<Derived>(derived(), reference, epsilon);
+}
+
+}  // end namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/TriangularSolver.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/TriangularSolver.h
new file mode 100644
index 0000000..b005487
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseCore/TriangularSolver.h
@@ -0,0 +1,270 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSETRIANGULARSOLVER_H
+#define EIGEN_SPARSETRIANGULARSOLVER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Lhs, typename Rhs, int Mode,
+          int UpLo = (Mode & Lower)   ? Lower
+                     : (Mode & Upper) ? Upper
+                                      : -1,
+          int StorageOrder = int(traits<Lhs>::Flags) & RowMajorBit>
+struct sparse_solve_triangular_selector;
+
+// forward substitution, row-major
+template <typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs, Rhs, Mode, Lower, RowMajor> {
+  typedef typename Rhs::Scalar Scalar;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
+  static void run(const Lhs& lhs, Rhs& other) {
+    LhsEval lhsEval(lhs);
+    for (Index col = 0; col < other.cols(); ++col) {
+      for (Index i = 0; i < lhs.rows(); ++i) {
+        Scalar tmp = other.coeff(i, col);
+        Scalar lastVal(0);
+        Index lastIndex = 0;
+        for (LhsIterator it(lhsEval, i); it; ++it) {
+          lastVal = it.value();
+          lastIndex = it.index();
+          if (lastIndex == i) break;
+          tmp = numext::madd<Scalar>(-lastVal, other.coeff(lastIndex, col), tmp);
+        }
+        if (Mode & UnitDiag)
+          other.coeffRef(i, col) = tmp;
+        else {
+          eigen_assert(lastIndex == i);
+          other.coeffRef(i, col) = tmp / lastVal;
+        }
+      }
+    }
+  }
+};
+
+// backward substitution, row-major
+template <typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs, Rhs, Mode, Upper, RowMajor> {
+  typedef typename Rhs::Scalar Scalar;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
+  static void run(const Lhs& lhs, Rhs& other) {
+    LhsEval lhsEval(lhs);
+    for (Index col = 0; col < other.cols(); ++col) {
+      for (Index i = lhs.rows() - 1; i >= 0; --i) {
+        Scalar tmp = other.coeff(i, col);
+        Scalar l_ii(0);
+        LhsIterator it(lhsEval, i);
+        while (it && it.index() < i) ++it;
+        if (!(Mode & UnitDiag)) {
+          eigen_assert(it && it.index() == i);
+          l_ii = it.value();
+          ++it;
+        } else if (it && it.index() == i)
+          ++it;
+        for (; it; ++it) {
+          tmp = numext::madd<Scalar>(-it.value(), other.coeff(it.index(), col), tmp);
+        }
+
+        if (Mode & UnitDiag)
+          other.coeffRef(i, col) = tmp;
+        else
+          other.coeffRef(i, col) = tmp / l_ii;
+      }
+    }
+  }
+};
+
+// forward substitution, col-major
+template <typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs, Rhs, Mode, Lower, ColMajor> {
+  typedef typename Rhs::Scalar Scalar;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
+  static void run(const Lhs& lhs, Rhs& other) {
+    LhsEval lhsEval(lhs);
+    for (Index col = 0; col < other.cols(); ++col) {
+      for (Index i = 0; i < lhs.cols(); ++i) {
+        Scalar& tmp = other.coeffRef(i, col);
+        if (!numext::is_exactly_zero(tmp))  // optimization when other is actually sparse
+        {
+          LhsIterator it(lhsEval, i);
+          while (it && it.index() < i) ++it;
+          if (!(Mode & UnitDiag)) {
+            eigen_assert(it && it.index() == i);
+            tmp /= it.value();
+          }
+          if (it && it.index() == i) ++it;
+          for (; it; ++it) {
+            other.coeffRef(it.index(), col) = numext::madd<Scalar>(-tmp, it.value(), other.coeffRef(it.index(), col));
+          }
+        }
+      }
+    }
+  }
+};
+
+// backward substitution, col-major
+template <typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs, Rhs, Mode, Upper, ColMajor> {
+  typedef typename Rhs::Scalar Scalar;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
+  static void run(const Lhs& lhs, Rhs& other) {
+    LhsEval lhsEval(lhs);
+    for (Index col = 0; col < other.cols(); ++col) {
+      for (Index i = lhs.cols() - 1; i >= 0; --i) {
+        Scalar& tmp = other.coeffRef(i, col);
+        if (!numext::is_exactly_zero(tmp))  // optimization when other is actually sparse
+        {
+          if (!(Mode & UnitDiag)) {
+            // TODO: replace this with a binary search. make sure the binary search is safe for partially sorted
+            // elements
+            LhsIterator it(lhsEval, i);
+            while (it && it.index() != i) ++it;
+            eigen_assert(it && it.index() == i);
+            other.coeffRef(i, col) /= it.value();
+          }
+          LhsIterator it(lhsEval, i);
+          for (; it && it.index() < i; ++it) {
+            other.coeffRef(it.index(), col) = numext::madd<Scalar>(-tmp, it.value(), other.coeffRef(it.index(), col));
+          }
+        }
+      }
+    }
+  }
+};
+
+}  // end namespace internal
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+template <typename ExpressionType, unsigned int Mode>
+template <typename OtherDerived>
+void TriangularViewImpl<ExpressionType, Mode, Sparse>::solveInPlace(MatrixBase<OtherDerived>& other) const {
+  eigen_assert(derived().cols() == derived().rows() && derived().cols() == other.rows());
+  eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper | Lower)));
+
+  enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
+
+  typedef std::conditional_t<copy, typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>
+      OtherCopy;
+  OtherCopy otherCopy(other.derived());
+
+  internal::sparse_solve_triangular_selector<ExpressionType, std::remove_reference_t<OtherCopy>, Mode>::run(
+      derived().nestedExpression(), otherCopy);
+
+  if (copy) other = otherCopy;
+}
+#endif
+
+// pure sparse path
+
+namespace internal {
+
+template <typename Lhs, typename Rhs, int Mode,
+          int UpLo = (Mode & Lower)   ? Lower
+                     : (Mode & Upper) ? Upper
+                                      : -1,
+          int StorageOrder = int(Lhs::Flags) & (RowMajorBit)>
+struct sparse_solve_triangular_sparse_selector;
+
+// forward substitution, col-major
+template <typename Lhs, typename Rhs, int Mode, int UpLo>
+struct sparse_solve_triangular_sparse_selector<Lhs, Rhs, Mode, UpLo, ColMajor> {
+  typedef typename Rhs::Scalar Scalar;
+  typedef typename promote_index_type<typename traits<Lhs>::StorageIndex, typename traits<Rhs>::StorageIndex>::type
+      StorageIndex;
+  static void run(const Lhs& lhs, Rhs& other) {
+    const bool IsLower = (UpLo == Lower);
+    AmbiVector<Scalar, StorageIndex> tempVector(other.rows() * 2);
+    tempVector.setBounds(0, other.rows());
+
+    Rhs res(other.rows(), other.cols());
+    res.reserve(other.nonZeros());
+
+    for (Index col = 0; col < other.cols(); ++col) {
+      // FIXME: estimate the number of non-zeros per column for better allocation.
+      tempVector.init(.99 /*float(other.col(col).nonZeros())/float(other.rows())*/);
+      tempVector.setZero();
+      tempVector.restart();
+      for (typename Rhs::InnerIterator rhsIt(other, col); rhsIt; ++rhsIt) {
+        tempVector.coeffRef(rhsIt.index()) = rhsIt.value();
+      }
+
+      for (Index i = IsLower ? 0 : lhs.cols() - 1; IsLower ? i < lhs.cols() : i >= 0; i += IsLower ? 1 : -1) {
+        tempVector.restart();
+        Scalar& ci = tempVector.coeffRef(i);
+        if (!numext::is_exactly_zero(ci)) {
+          // find
+          typename Lhs::InnerIterator it(lhs, i);
+          if (!(Mode & UnitDiag)) {
+            if (IsLower) {
+              eigen_assert(it.index() == i);
+              ci /= it.value();
+            } else
+              ci /= lhs.coeff(i, i);
+          }
+          tempVector.restart();
+          if (IsLower) {
+            if (it.index() == i) ++it;
+            for (; it; ++it) {
+              tempVector.coeffRef(it.index()) = numext::madd<Scalar>(-ci, it.value(), tempVector.coeffRef(it.index()));
+            }
+          } else {
+            for (; it && it.index() < i; ++it) {
+              tempVector.coeffRef(it.index()) = numext::madd<Scalar>(-ci, it.value(), tempVector.coeffRef(it.index()));
+            }
+          }
+        }
+      }
+
+      // FIXME: compute a reference value to filter zeros.
+      for (typename AmbiVector<Scalar, StorageIndex>::Iterator it(tempVector /*,1e-12*/); it; ++it) {
+        // FIXME: use insertBack for better performance.
+        res.insert(it.index(), col) = it.value();
+      }
+    }
+    res.finalize();
+    other = res.markAsRValue();
+  }
+};
+
+}  // end namespace internal
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename ExpressionType, unsigned int Mode>
+template <typename OtherDerived>
+void TriangularViewImpl<ExpressionType, Mode, Sparse>::solveInPlace(SparseMatrixBase<OtherDerived>& other) const {
+  eigen_assert(derived().cols() == derived().rows() && derived().cols() == other.rows());
+  eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper | Lower)));
+
+  //   enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
+
+  //   typedef std::conditional_t<copy,
+  //     typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&> OtherCopy;
+  //   OtherCopy otherCopy(other.derived());
+
+  internal::sparse_solve_triangular_sparse_selector<ExpressionType, OtherDerived, Mode>::run(
+      derived().nestedExpression(), other.derived());
+
+  //   if (copy)
+  //     other = otherCopy;
+}
+#endif
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSETRIANGULARSOLVER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/InternalHeaderCheck.h
new file mode 100644
index 0000000..78ebfcc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPARSELU_MODULE_H
+#error "Please include Eigen/SparseLU instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU.h
new file mode 100644
index 0000000..f1662c5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU.h
@@ -0,0 +1,968 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_LU_H
+#define EIGEN_SPARSE_LU_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename MatrixType_, typename OrderingType_ = COLAMDOrdering<typename MatrixType_::StorageIndex>>
+class SparseLU;
+template <typename MappedSparseMatrixType>
+struct SparseLUMatrixLReturnType;
+template <typename MatrixLType, typename MatrixUType>
+struct SparseLUMatrixUReturnType;
+
+template <bool Conjugate, class SparseLUType>
+class SparseLUTransposeView : public SparseSolverBase<SparseLUTransposeView<Conjugate, SparseLUType>> {
+ protected:
+  typedef SparseSolverBase<SparseLUTransposeView<Conjugate, SparseLUType>> APIBase;
+  using APIBase::m_isInitialized;
+
+ public:
+  typedef typename SparseLUType::Scalar Scalar;
+  typedef typename SparseLUType::StorageIndex StorageIndex;
+  typedef typename SparseLUType::MatrixType MatrixType;
+  typedef typename SparseLUType::OrderingType OrderingType;
+
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+  SparseLUTransposeView() : APIBase(), m_sparseLU(NULL) {}
+  SparseLUTransposeView(const SparseLUTransposeView& view) : APIBase() {
+    this->m_sparseLU = view.m_sparseLU;
+    this->m_isInitialized = view.m_isInitialized;
+  }
+  void setIsInitialized(const bool isInitialized) { this->m_isInitialized = isInitialized; }
+  void setSparseLU(SparseLUType* sparseLU) { m_sparseLU = sparseLU; }
+  using APIBase::_solve_impl;
+  template <typename Rhs, typename Dest>
+  bool _solve_impl(const MatrixBase<Rhs>& B, MatrixBase<Dest>& X_base) const {
+    Dest& X(X_base.derived());
+    eigen_assert(m_sparseLU->info() == Success && "The matrix should be factorized first");
+    EIGEN_STATIC_ASSERT((Dest::Flags & RowMajorBit) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+
+    // const_cast_derived() is needed to enable aliasing detection when applying the permutations.
+    for (Index j = 0; j < B.cols(); ++j) {
+      X.col(j) = m_sparseLU->colsPermutation() * B.const_cast_derived().col(j);
+    }
+    // Forward substitution with transposed or adjoint of U
+    m_sparseLU->matrixU().template solveTransposedInPlace<Conjugate>(X);
+
+    // Backward substitution with transposed or adjoint of L
+    m_sparseLU->matrixL().template solveTransposedInPlace<Conjugate>(X);
+
+    // Permute back the solution
+    for (Index j = 0; j < B.cols(); ++j) X.col(j) = m_sparseLU->rowsPermutation().transpose() * X.col(j);
+    return true;
+  }
+  inline Index rows() const { return m_sparseLU->rows(); }
+  inline Index cols() const { return m_sparseLU->cols(); }
+
+ private:
+  SparseLUType* m_sparseLU;
+  SparseLUTransposeView& operator=(const SparseLUTransposeView&);
+};
+
+/** \ingroup SparseLU_Module
+ * \class SparseLU
+ *
+ * \brief Sparse supernodal LU factorization for general matrices
+ *
+ * This class implements the supernodal LU factorization for general matrices.
+ * It uses the main techniques from the sequential SuperLU package
+ * (http://crd-legacy.lbl.gov/~xiaoye/SuperLU/). It handles transparently real
+ * and complex arithmetic with single and double precision, depending on the
+ * scalar type of your input matrix.
+ * The code has been optimized to provide BLAS-3 operations during supernode-panel updates.
+ * It benefits directly from the built-in high-performant Eigen BLAS routines.
+ * Moreover, when the size of a supernode is very small, the BLAS calls are avoided to
+ * enable a better optimization from the compiler. For best performance,
+ * you should compile it with NDEBUG flag to avoid the numerous bounds checking on vectors.
+ *
+ * An important parameter of this class is the ordering method. It is used to reorder the columns
+ * (and eventually the rows) of the matrix to reduce the number of new elements that are created during
+ * numerical factorization. The cheapest method available is COLAMD.
+ * See  \link OrderingMethods_Module the OrderingMethods module \endlink for the list of
+ * built-in and external ordering methods.
+ *
+ * Simple example with key steps
+ * \code
+ * VectorXd x(n), b(n);
+ * SparseMatrix<double> A;
+ * SparseLU<SparseMatrix<double>, COLAMDOrdering<int> > solver;
+ * // Fill A and b.
+ * // Compute the ordering permutation vector from the structural pattern of A.
+ * solver.analyzePattern(A);
+ * // Compute the numerical factorization.
+ * solver.factorize(A);
+ * // Use the factors to solve the linear system.
+ * x = solver.solve(b);
+ * \endcode
+ *
+ * We can directly call compute() instead of analyzePattern() and factorize()
+ * \code
+ * VectorXd x(n), b(n);
+ * SparseMatrix<double> A;
+ * SparseLU<SparseMatrix<double>, COLAMDOrdering<int> > solver;
+ * // Fill A and b.
+ * solver.compute(A);
+ * // Use the factors to solve the linear system.
+ * x = solver.solve(b);
+ * \endcode
+ *
+ * Or give the matrix to the constructor SparseLU(const MatrixType& matrix)
+ * \code
+ * VectorXd x(n), b(n);
+ * SparseMatrix<double> A;
+ * // Fill A and b.
+ * SparseLU<SparseMatrix<double>, COLAMDOrdering<int> > solver(A);
+ * // Use the factors to solve the linear system.
+ * x = solver.solve(b);
+ * \endcode
+ *
+ * \warning The input matrix A should be in a \b compressed and \b column-major form.
+ * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
+ *
+ * \note Unlike the initial SuperLU implementation, there is no step to equilibrate the matrix.
+ * For badly scaled matrices, this step can be useful to reduce the pivoting during factorization.
+ * If this is the case for your matrices, you can try the basic scaling method at
+ *  "unsupported/Eigen/src/IterativeSolvers/Scaling.h"
+ *
+ * \tparam MatrixType_ The type of the sparse matrix. It must be a column-major SparseMatrix<>
+ * \tparam OrderingType_ The ordering method to use, either AMD, COLAMD or METIS. Default is COLMAD
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept
+ * \sa \ref OrderingMethods_Module
+ */
+template <typename MatrixType_, typename OrderingType_>
+class SparseLU : public SparseSolverBase<SparseLU<MatrixType_, OrderingType_>>,
+                 public internal::SparseLUImpl<typename MatrixType_::Scalar, typename MatrixType_::StorageIndex> {
+ protected:
+  typedef SparseSolverBase<SparseLU<MatrixType_, OrderingType_>> APIBase;
+  using APIBase::m_isInitialized;
+
+ public:
+  using APIBase::_solve_impl;
+
+  typedef MatrixType_ MatrixType;
+  typedef OrderingType_ OrderingType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> NCMatrix;
+  typedef internal::MappedSuperNodalMatrix<Scalar, StorageIndex> SCMatrix;
+  typedef Matrix<Scalar, Dynamic, 1> ScalarVector;
+  typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
+  typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+  typedef internal::SparseLUImpl<Scalar, StorageIndex> Base;
+
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+ public:
+  /** \brief Basic constructor of the solver.
+   *
+   * Construct a SparseLU. As no matrix is given as argument, compute() should be called afterward with a matrix.
+   */
+  SparseLU()
+      : m_lastError(""), m_Ustore(0, 0, 0, 0, 0, 0), m_symmetricmode(false), m_diagpivotthresh(1.0), m_detPermR(1) {
+    initperfvalues();
+  }
+  /** \brief Constructor of the solver already based on a specific matrix.
+   *
+   * Construct a SparseLU. compute() is already called with the given matrix.
+   */
+  explicit SparseLU(const MatrixType& matrix)
+      : m_lastError(""), m_Ustore(0, 0, 0, 0, 0, 0), m_symmetricmode(false), m_diagpivotthresh(1.0), m_detPermR(1) {
+    initperfvalues();
+    compute(matrix);
+  }
+
+  ~SparseLU() {
+    // Free all explicit dynamic pointers
+  }
+
+  void analyzePattern(const MatrixType& matrix);
+  void factorize(const MatrixType& matrix);
+  void simplicialfactorize(const MatrixType& matrix);
+
+  /** \brief Analyze and factorize the matrix so the solver is ready to solve.
+   *
+   * Compute the symbolic and numeric factorization of the input sparse matrix.
+   * The input matrix should be in column-major storage, otherwise analyzePattern()
+   * will do a heavy copy.
+   *
+   * Call analyzePattern() followed by factorize()
+   *
+   * \sa analyzePattern(), factorize()
+   */
+  void compute(const MatrixType& matrix) {
+    // Analyze
+    analyzePattern(matrix);
+    // Factorize
+    factorize(matrix);
+  }
+
+  /** \brief Return a solver for the transposed matrix.
+   *
+   * \returns an expression of the transposed of the factored matrix.
+   *
+   * A typical usage is to solve for the transposed problem A^T x = b:
+   * \code
+   * solver.compute(A);
+   * x = solver.transpose().solve(b);
+   * \endcode
+   *
+   * \sa adjoint(), solve()
+   */
+  const SparseLUTransposeView<false, SparseLU<MatrixType_, OrderingType_>> transpose() {
+    SparseLUTransposeView<false, SparseLU<MatrixType_, OrderingType_>> transposeView;
+    transposeView.setSparseLU(this);
+    transposeView.setIsInitialized(this->m_isInitialized);
+    return transposeView;
+  }
+
+  /** \brief Return a solver for the adjointed matrix.
+   *
+   * \returns an expression of the adjoint of the factored matrix
+   *
+   * A typical usage is to solve for the adjoint problem A' x = b:
+   * \code
+   * solver.compute(A);
+   * x = solver.adjoint().solve(b);
+   * \endcode
+   *
+   * For real scalar types, this function is equivalent to transpose().
+   *
+   * \sa transpose(), solve()
+   */
+  const SparseLUTransposeView<true, SparseLU<MatrixType_, OrderingType_>> adjoint() {
+    SparseLUTransposeView<true, SparseLU<MatrixType_, OrderingType_>> adjointView;
+    adjointView.setSparseLU(this);
+    adjointView.setIsInitialized(this->m_isInitialized);
+    return adjointView;
+  }
+
+  /** \brief Give the number of rows.
+   */
+  inline Index rows() const { return m_mat.rows(); }
+  /** \brief Give the number of columns.
+   */
+  inline Index cols() const { return m_mat.cols(); }
+  /** \brief Let you set that the pattern of the input matrix is symmetric
+   */
+  void isSymmetric(bool sym) { m_symmetricmode = sym; }
+
+  /** \brief Give the matrixL
+   *
+   * \returns an expression of the matrix L, internally stored as supernodes
+   * The only operation available with this expression is the triangular solve
+   * \code
+   * y = b; matrixL().solveInPlace(y);
+   * \endcode
+   */
+  SparseLUMatrixLReturnType<SCMatrix> matrixL() const { return SparseLUMatrixLReturnType<SCMatrix>(m_Lstore); }
+  /** \brief Give the MatrixU
+   *
+   * \returns an expression of the matrix U,
+   * The only operation available with this expression is the triangular solve
+   * \code
+   * y = b; matrixU().solveInPlace(y);
+   * \endcode
+   */
+  SparseLUMatrixUReturnType<SCMatrix, Map<SparseMatrix<Scalar, ColMajor, StorageIndex>>> matrixU() const {
+    return SparseLUMatrixUReturnType<SCMatrix, Map<SparseMatrix<Scalar, ColMajor, StorageIndex>>>(m_Lstore, m_Ustore);
+  }
+
+  /** \brief Give the row matrix permutation.
+   *
+   * \returns a reference to the row matrix permutation \f$ P_r \f$ such that \f$P_r A P_c^T = L U\f$
+   * \sa colsPermutation()
+   */
+  inline const PermutationType& rowsPermutation() const { return m_perm_r; }
+  /** \brief Give the column matrix permutation.
+   *
+   * \returns a reference to the column matrix permutation\f$ P_c^T \f$ such that \f$P_r A P_c^T = L U\f$
+   * \sa rowsPermutation()
+   */
+  inline const PermutationType& colsPermutation() const { return m_perm_c; }
+  /** Set the threshold used for a diagonal entry to be an acceptable pivot. */
+  void setPivotThreshold(const RealScalar& thresh) { m_diagpivotthresh = thresh; }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \brief Solve a system \f$ A X = B \f$
+   *
+   * \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
+   *
+   * \warning the destination matrix X in X = this->solve(B) must be colmun-major.
+   *
+   * \sa compute()
+   */
+  template <typename Rhs>
+  inline Solve<SparseLU, Rhs> solve(const MatrixBase<Rhs>& B) const;
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the LU factorization reports a problem, zero diagonal for instance
+   *          \c InvalidInput if the input matrix is invalid
+   *
+   * You can get a readable error message with lastErrorMessage().
+   *
+   * \sa lastErrorMessage()
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  /** \brief Give a human readable error
+   *
+   * \returns A string describing the type of error
+   */
+  std::string lastErrorMessage() const { return m_lastError; }
+
+  template <typename Rhs, typename Dest>
+  bool _solve_impl(const MatrixBase<Rhs>& B, MatrixBase<Dest>& X_base) const {
+    Dest& X(X_base.derived());
+    eigen_assert(m_factorizationIsOk && "The matrix should be factorized first");
+    EIGEN_STATIC_ASSERT((Dest::Flags & RowMajorBit) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+
+    // Permute the right hand side to form X = Pr*B
+    // on return, X is overwritten by the computed solution
+    X.resize(B.rows(), B.cols());
+
+    // const_cast_derived() is needed to enable aliasing detection when applying the permutations.
+    for (Index j = 0; j < B.cols(); ++j) X.col(j) = rowsPermutation() * B.const_cast_derived().col(j);
+
+    // Forward substitution with L
+    this->matrixL().solveInPlace(X);
+    this->matrixU().solveInPlace(X);
+
+    // Permute back the solution
+    for (Index j = 0; j < B.cols(); ++j) X.col(j) = colsPermutation().inverse() * X.col(j);
+
+    return true;
+  }
+
+  /** \brief Give the absolute value of the determinant.
+   *
+   * \returns the absolute value of the determinant of the matrix of which
+   * *this is the LU factorization.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   *
+   * \sa logAbsDeterminant(), signDeterminant()
+   */
+  Scalar absDeterminant() {
+    using std::abs;
+    eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
+    // Initialize with the determinant of the row matrix
+    Scalar det = Scalar(1.);
+    // Note that the diagonal blocks of U are stored in supernodes,
+    // which are available in the  L part :)
+    for (Index j = 0; j < this->cols(); ++j) {
+      for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it) {
+        if (it.index() == j) {
+          det *= abs(it.value());
+          break;
+        }
+      }
+    }
+    return det;
+  }
+
+  /** \brief Give the natural log of the absolute determinant.
+   *
+   * \returns the natural log of the absolute value of the determinant of the matrix
+   * of which *this is the LU factorization
+   *
+   * \note This method is useful to work around the risk of overflow/underflow that's
+   * inherent to the determinant computation.
+   *
+   * \sa absDeterminant(), signDeterminant()
+   */
+  Scalar logAbsDeterminant() const {
+    using std::abs;
+    using std::log;
+
+    eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
+    Scalar det = Scalar(0.);
+    for (Index j = 0; j < this->cols(); ++j) {
+      for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it) {
+        if (it.row() < j) continue;
+        if (it.row() == j) {
+          det += log(abs(it.value()));
+          break;
+        }
+      }
+    }
+    return det;
+  }
+
+  /** \brief Give the sign of the determinant.
+   *
+   * \returns A number representing the sign of the determinant
+   *
+   * \sa absDeterminant(), logAbsDeterminant()
+   */
+  Scalar signDeterminant() {
+    eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
+    // Initialize with the determinant of the row matrix
+    Index det = 1;
+    // Note that the diagonal blocks of U are stored in supernodes,
+    // which are available in the  L part :)
+    for (Index j = 0; j < this->cols(); ++j) {
+      for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it) {
+        if (it.index() == j) {
+          if (it.value() < 0)
+            det = -det;
+          else if (it.value() == 0)
+            return 0;
+          break;
+        }
+      }
+    }
+    return det * m_detPermR * m_detPermC;
+  }
+
+  /** \brief Give the determinant.
+   *
+   * \returns The determinant of the matrix.
+   *
+   * \sa absDeterminant(), logAbsDeterminant()
+   */
+  Scalar determinant() {
+    eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
+    // Initialize with the determinant of the row matrix
+    Scalar det = Scalar(1.);
+    // Note that the diagonal blocks of U are stored in supernodes,
+    // which are available in the  L part :)
+    for (Index j = 0; j < this->cols(); ++j) {
+      for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it) {
+        if (it.index() == j) {
+          det *= it.value();
+          break;
+        }
+      }
+    }
+    return (m_detPermR * m_detPermC) > 0 ? det : -det;
+  }
+
+  /** \brief Give the number of non zero in matrix L.
+   */
+  Index nnzL() const { return m_nnzL; }
+  /** \brief Give the number of non zero in matrix U.
+   */
+  Index nnzU() const { return m_nnzU; }
+
+ protected:
+  // Functions
+  void initperfvalues() {
+    m_perfv.panel_size = 16;
+    m_perfv.relax = 1;
+    m_perfv.maxsuper = 128;
+    m_perfv.rowblk = 16;
+    m_perfv.colblk = 8;
+    m_perfv.fillfactor = 20;
+  }
+
+  // Variables
+  mutable ComputationInfo m_info;
+  bool m_factorizationIsOk;
+  bool m_analysisIsOk;
+  std::string m_lastError;
+  NCMatrix m_mat;                                              // The input (permuted ) matrix
+  SCMatrix m_Lstore;                                           // The lower triangular matrix (supernodal)
+  Map<SparseMatrix<Scalar, ColMajor, StorageIndex>> m_Ustore;  // The upper triangular matrix
+  PermutationType m_perm_c;                                    // Column permutation
+  PermutationType m_perm_r;                                    // Row permutation
+  IndexVector m_etree;                                         // Column elimination tree
+
+  typename Base::GlobalLU_t m_glu;
+
+  // SparseLU options
+  bool m_symmetricmode;
+  // values for performance
+  internal::perfvalues m_perfv;
+  RealScalar m_diagpivotthresh;  // Specifies the threshold used for a diagonal entry to be an acceptable pivot
+  Index m_nnzL, m_nnzU;          // Nonzeros in L and U factors
+  Index m_detPermR, m_detPermC;  // Determinants of the permutation matrices
+ private:
+  // Disable copy constructor
+  SparseLU(const SparseLU&);
+};  // End class SparseLU
+
+// Functions needed by the analysis phase
+/** \brief Compute the column permutation.
+ *
+ * Compute the column permutation to minimize the fill-in
+ *
+ *  - Apply this permutation to the input matrix -
+ *
+ *  - Compute the column elimination tree on the permuted matrix
+ *
+ *  - Postorder the elimination tree and the column permutation
+ *
+ * It is possible to call compute() instead of analyzePattern() + factorize().
+ *
+ * If the matrix is row-major this function will do an heavy copy.
+ *
+ * \sa factorize(), compute()
+ */
+template <typename MatrixType, typename OrderingType>
+void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat) {
+  // TODO  It is possible as in SuperLU to compute row and columns scaling vectors to equilibrate the matrix mat.
+
+  // Firstly, copy the whole input matrix.
+  m_mat = mat;
+
+  // Compute fill-in ordering
+  OrderingType ord;
+  ord(m_mat, m_perm_c);
+
+  // Apply the permutation to the column of the input  matrix
+  if (m_perm_c.size()) {
+    m_mat.uncompress();  // NOTE: The effect of this command is only to create the InnerNonzeros pointers. FIXME : This
+                         // vector is filled but not subsequently used.
+    // Then, permute only the column pointers
+    ei_declare_aligned_stack_constructed_variable(
+        StorageIndex, outerIndexPtr, mat.cols() + 1,
+        mat.isCompressed() ? const_cast<StorageIndex*>(mat.outerIndexPtr()) : 0);
+
+    // If the input matrix 'mat' is uncompressed, then the outer-indices do not match the ones of m_mat, and a copy is
+    // thus needed.
+    if (!mat.isCompressed())
+      IndexVector::Map(outerIndexPtr, mat.cols() + 1) = IndexVector::Map(m_mat.outerIndexPtr(), mat.cols() + 1);
+
+    // Apply the permutation and compute the nnz per column.
+    for (Index i = 0; i < mat.cols(); i++) {
+      m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i];
+      m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i + 1] - outerIndexPtr[i];
+    }
+  }
+
+  // Compute the column elimination tree of the permuted matrix
+  IndexVector firstRowElt;
+  internal::coletree(m_mat, m_etree, firstRowElt);
+
+  // In symmetric mode, do not do postorder here
+  if (!m_symmetricmode) {
+    IndexVector post, iwork;
+    // Post order etree
+    internal::treePostorder(StorageIndex(m_mat.cols()), m_etree, post);
+
+    // Renumber etree in postorder
+    Index m = m_mat.cols();
+    iwork.resize(m + 1);
+    for (Index i = 0; i < m; ++i) iwork(post(i)) = post(m_etree(i));
+    m_etree = iwork;
+
+    // Postmultiply A*Pc by post, i.e reorder the matrix according to the postorder of the etree
+    PermutationType post_perm(m);
+    for (Index i = 0; i < m; i++) post_perm.indices()(i) = post(i);
+
+    // Combine the two permutations : postorder the permutation for future use
+    if (m_perm_c.size()) {
+      m_perm_c = post_perm * m_perm_c;
+    }
+
+  }  // end postordering
+
+  m_analysisIsOk = true;
+}
+
+// Functions needed by the numerical factorization phase
+
+/** \brief Factorize the matrix to get the solver ready.
+ *
+ *  - Numerical factorization
+ *  - Interleaved with the symbolic factorization
+ *
+ * To get error of this function you should check info(), you can get more info of
+ * errors with lastErrorMessage().
+ *
+ * In the past (before 2012 (git history is not older)), this function was returning an integer.
+ * This exit was 0 if successful factorization.
+ * > 0 if info = i, and i is been completed, but the factor U is exactly singular,
+ * and division by zero will occur if it is used to solve a system of equation.
+ * > A->ncol: number of bytes allocated when memory allocation failure occurred, plus A->ncol.
+ * If lwork = -1, it is the estimated amount of space needed, plus A->ncol.
+ *
+ * Note: 'A' in the above description refers to the factored matrix (historical naming from SuperLU).
+ *
+ * \sa analyzePattern(), compute(), SparseLU(), info(), lastErrorMessage()
+ */
+template <typename MatrixType, typename OrderingType>
+void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix) {
+  using internal::emptyIdxLU;
+  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
+  eigen_assert((matrix.rows() == matrix.cols()) && "Only for squared matrices");
+
+  m_isInitialized = true;
+
+  // Apply the column permutation computed in analyzepattern()
+  m_mat = matrix;
+  if (m_perm_c.size()) {
+    m_mat.uncompress();  // NOTE: The effect of this command is only to create the InnerNonzeros pointers.
+    // Then, permute only the column pointers
+    const StorageIndex* outerIndexPtr;
+    if (matrix.isCompressed())
+      outerIndexPtr = matrix.outerIndexPtr();
+    else {
+      StorageIndex* outerIndexPtr_t = new StorageIndex[matrix.cols() + 1];
+      for (Index i = 0; i <= matrix.cols(); i++) outerIndexPtr_t[i] = m_mat.outerIndexPtr()[i];
+      outerIndexPtr = outerIndexPtr_t;
+    }
+    for (Index i = 0; i < matrix.cols(); i++) {
+      m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i];
+      m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i + 1] - outerIndexPtr[i];
+    }
+    if (!matrix.isCompressed()) delete[] outerIndexPtr;
+  } else {  // FIXME This should not be needed if the empty permutation is handled transparently
+    m_perm_c.resize(matrix.cols());
+    for (StorageIndex i = 0; i < matrix.cols(); ++i) m_perm_c.indices()(i) = i;
+  }
+
+  Index m = m_mat.rows();
+  Index n = m_mat.cols();
+  Index nnz = m_mat.nonZeros();
+  Index maxpanel = m_perfv.panel_size * m;
+  // Allocate working storage common to the factor routines
+  Index lwork = 0;
+  // Return the size of actually allocated memory when allocation failed,
+  // and 0 on success.
+  Index info = Base::memInit(m, n, nnz, lwork, m_perfv.fillfactor, m_perfv.panel_size, m_glu);
+  if (info) {
+    m_lastError = "UNABLE TO ALLOCATE WORKING MEMORY\n\n";
+    m_factorizationIsOk = false;
+    return;
+  }
+
+  // Set up pointers for integer working arrays
+  IndexVector segrep(m);
+  segrep.setZero();
+  IndexVector parent(m);
+  parent.setZero();
+  IndexVector xplore(m);
+  xplore.setZero();
+  IndexVector repfnz(maxpanel);
+  IndexVector panel_lsub(maxpanel);
+  IndexVector xprune(n);
+  xprune.setZero();
+  IndexVector marker(m * internal::LUNoMarker);
+  marker.setZero();
+
+  repfnz.setConstant(-1);
+  panel_lsub.setConstant(-1);
+
+  // Set up pointers for scalar working arrays
+  ScalarVector dense;
+  dense.setZero(maxpanel);
+  ScalarVector tempv;
+  tempv.setZero(internal::LUnumTempV(m, m_perfv.panel_size, m_perfv.maxsuper, /*m_perfv.rowblk*/ m));
+
+  // Compute the inverse of perm_c
+  PermutationType iperm_c(m_perm_c.inverse());
+
+  // Identify initial relaxed snodes
+  IndexVector relax_end(n);
+  if (m_symmetricmode == true)
+    Base::heap_relax_snode(n, m_etree, m_perfv.relax, marker, relax_end);
+  else
+    Base::relax_snode(n, m_etree, m_perfv.relax, marker, relax_end);
+
+  m_perm_r.resize(m);
+  m_perm_r.indices().setConstant(-1);
+  marker.setConstant(-1);
+  m_detPermR = 1;  // Record the determinant of the row permutation
+
+  m_glu.supno(0) = emptyIdxLU;
+  m_glu.xsup.setConstant(0);
+  m_glu.xsup(0) = m_glu.xlsub(0) = m_glu.xusub(0) = m_glu.xlusup(0) = Index(0);
+
+  // Work on one 'panel' at a time. A panel is one of the following :
+  //  (a) a relaxed supernode at the bottom of the etree, or
+  //  (b) panel_size contiguous columns, <panel_size> defined by the user
+  Index jcol;
+  Index pivrow;  // Pivotal row number in the original row matrix
+  Index nseg1;   // Number of segments in U-column above panel row jcol
+  Index nseg;    // Number of segments in each U-column
+  Index irep;
+  Index i, k, jj;
+  for (jcol = 0; jcol < n;) {
+    // Adjust panel size so that a panel won't overlap with the next relaxed snode.
+    Index panel_size = m_perfv.panel_size;  // upper bound on panel width
+    for (k = jcol + 1; k < (std::min)(jcol + panel_size, n); k++) {
+      if (relax_end(k) != emptyIdxLU) {
+        panel_size = k - jcol;
+        break;
+      }
+    }
+    if (k == n) panel_size = n - jcol;
+
+    // Symbolic outer factorization on a panel of columns
+    Base::panel_dfs(m, panel_size, jcol, m_mat, m_perm_r.indices(), nseg1, dense, panel_lsub, segrep, repfnz, xprune,
+                    marker, parent, xplore, m_glu);
+
+    // Numeric sup-panel updates in topological order
+    Base::panel_bmod(m, panel_size, jcol, nseg1, dense, tempv, segrep, repfnz, m_glu);
+
+    // Sparse LU within the panel, and below the panel diagonal
+    for (jj = jcol; jj < jcol + panel_size; jj++) {
+      k = (jj - jcol) * m;  // Column index for w-wide arrays
+
+      nseg = nseg1;  // begin after all the panel segments
+      // Depth-first-search for the current column
+      VectorBlock<IndexVector> panel_lsubk(panel_lsub, k, m);
+      VectorBlock<IndexVector> repfnz_k(repfnz, k, m);
+      // Return 0 on success and > 0 number of bytes allocated when run out of space.
+      info = Base::column_dfs(m, jj, m_perm_r.indices(), m_perfv.maxsuper, nseg, panel_lsubk, segrep, repfnz_k, xprune,
+                              marker, parent, xplore, m_glu);
+      if (info) {
+        m_lastError = "UNABLE TO EXPAND MEMORY IN COLUMN_DFS() ";
+        m_info = NumericalIssue;
+        m_factorizationIsOk = false;
+        return;
+      }
+      // Numeric updates to this column
+      VectorBlock<ScalarVector> dense_k(dense, k, m);
+      VectorBlock<IndexVector> segrep_k(segrep, nseg1, m - nseg1);
+      // Return 0 on success and > 0 number of bytes allocated when run out of space.
+      info = Base::column_bmod(jj, (nseg - nseg1), dense_k, tempv, segrep_k, repfnz_k, jcol, m_glu);
+      if (info) {
+        m_lastError = "UNABLE TO EXPAND MEMORY IN COLUMN_BMOD() ";
+        m_info = NumericalIssue;
+        m_factorizationIsOk = false;
+        return;
+      }
+
+      // Copy the U-segments to ucol(*)
+      // Return 0 on success and > 0 number of bytes allocated when run out of space.
+      info = Base::copy_to_ucol(jj, nseg, segrep, repfnz_k, m_perm_r.indices(), dense_k, m_glu);
+      if (info) {
+        m_lastError = "UNABLE TO EXPAND MEMORY IN COPY_TO_UCOL() ";
+        m_info = NumericalIssue;
+        m_factorizationIsOk = false;
+        return;
+      }
+
+      // Form the L-segment
+      // Return O if success, i > 0 if U(i, i) is exactly zero.
+      info = Base::pivotL(jj, m_diagpivotthresh, m_perm_r.indices(), iperm_c.indices(), pivrow, m_glu);
+      if (info) {
+        m_lastError = "THE MATRIX IS STRUCTURALLY SINGULAR";
+#ifndef EIGEN_NO_IO
+        std::ostringstream returnInfo;
+        returnInfo << " ... ZERO COLUMN AT ";
+        returnInfo << info;
+        m_lastError += returnInfo.str();
+#endif
+        m_info = NumericalIssue;
+        m_factorizationIsOk = false;
+        return;
+      }
+
+      // Update the determinant of the row permutation matrix
+      // FIXME: the following test is not correct; it should account for iperm_c, and pivrow is not
+      // directly the row pivot.
+      if (pivrow != jj) m_detPermR = -m_detPermR;
+
+      // Prune columns (0:jj-1) using column jj
+      Base::pruneL(jj, m_perm_r.indices(), pivrow, nseg, segrep, repfnz_k, xprune, m_glu);
+
+      // Reset repfnz for this column
+      for (i = 0; i < nseg; i++) {
+        irep = segrep(i);
+        repfnz_k(irep) = emptyIdxLU;
+      }
+    }                    // end SparseLU within the panel
+    jcol += panel_size;  // Move to the next panel
+  }                      // end for -- end elimination
+
+  m_detPermR = m_perm_r.determinant();
+  m_detPermC = m_perm_c.determinant();
+
+  // Count the number of nonzeros in factors
+  Base::countnz(n, m_nnzL, m_nnzU, m_glu);
+  // Apply permutation  to the L subscripts
+  Base::fixupL(n, m_perm_r.indices(), m_glu);
+
+  // Create supernode matrix L
+  m_Lstore.setInfos(m, n, m_glu.lusup, m_glu.xlusup, m_glu.lsub, m_glu.xlsub, m_glu.supno, m_glu.xsup);
+  // Create the column major upper sparse matrix  U;
+  new (&m_Ustore) Map<SparseMatrix<Scalar, ColMajor, StorageIndex>>(m, n, m_nnzU, m_glu.xusub.data(), m_glu.usub.data(),
+                                                                    m_glu.ucol.data());
+
+  m_info = Success;
+  m_factorizationIsOk = true;
+}
+
+template <typename MappedSupernodalType>
+struct SparseLUMatrixLReturnType : internal::no_assignment_operator {
+  typedef typename MappedSupernodalType::Scalar Scalar;
+  explicit SparseLUMatrixLReturnType(const MappedSupernodalType& mapL) : m_mapL(mapL) {}
+  Index rows() const { return m_mapL.rows(); }
+  Index cols() const { return m_mapL.cols(); }
+  template <typename Dest>
+  void solveInPlace(MatrixBase<Dest>& X) const {
+    m_mapL.solveInPlace(X);
+  }
+  template <bool Conjugate, typename Dest>
+  void solveTransposedInPlace(MatrixBase<Dest>& X) const {
+    m_mapL.template solveTransposedInPlace<Conjugate>(X);
+  }
+
+  SparseMatrix<Scalar, ColMajor, Index> toSparse() const {
+    ArrayXi colCount = ArrayXi::Ones(cols());
+    for (Index i = 0; i < cols(); i++) {
+      typename MappedSupernodalType::InnerIterator iter(m_mapL, i);
+      for (; iter; ++iter) {
+        if (iter.row() > iter.col()) {
+          colCount(iter.col())++;
+        }
+      }
+    }
+    SparseMatrix<Scalar, ColMajor, Index> sL(rows(), cols());
+    sL.reserve(colCount);
+    for (Index i = 0; i < cols(); i++) {
+      sL.insert(i, i) = 1.0;
+      typename MappedSupernodalType::InnerIterator iter(m_mapL, i);
+      for (; iter; ++iter) {
+        if (iter.row() > iter.col()) {
+          sL.insert(iter.row(), iter.col()) = iter.value();
+        }
+      }
+    }
+    sL.makeCompressed();
+    return sL;
+  }
+
+  const MappedSupernodalType& m_mapL;
+};
+
+template <typename MatrixLType, typename MatrixUType>
+struct SparseLUMatrixUReturnType : internal::no_assignment_operator {
+  typedef typename MatrixLType::Scalar Scalar;
+  SparseLUMatrixUReturnType(const MatrixLType& mapL, const MatrixUType& mapU) : m_mapL(mapL), m_mapU(mapU) {}
+  Index rows() const { return m_mapL.rows(); }
+  Index cols() const { return m_mapL.cols(); }
+
+  template <typename Dest>
+  void solveInPlace(MatrixBase<Dest>& X) const {
+    Index nrhs = X.cols();
+    // Backward solve with U
+    for (Index k = m_mapL.nsuper(); k >= 0; k--) {
+      Index fsupc = m_mapL.supToCol()[k];
+      Index lda = m_mapL.colIndexPtr()[fsupc + 1] - m_mapL.colIndexPtr()[fsupc];  // leading dimension
+      Index nsupc = m_mapL.supToCol()[k + 1] - fsupc;
+      Index luptr = m_mapL.colIndexPtr()[fsupc];
+
+      if (nsupc == 1) {
+        for (Index j = 0; j < nrhs; j++) {
+          X(fsupc, j) /= m_mapL.valuePtr()[luptr];
+        }
+      } else {
+        // FIXME: the following lines should use Block expressions and not Map!
+        Map<const Matrix<Scalar, Dynamic, Dynamic, ColMajor>, 0, OuterStride<>> A(&(m_mapL.valuePtr()[luptr]), nsupc,
+                                                                                  nsupc, OuterStride<>(lda));
+        typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+        U = A.template triangularView<Upper>().solve(U);
+      }
+
+      for (Index j = 0; j < nrhs; ++j) {
+        for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++) {
+          typename MatrixUType::InnerIterator it(m_mapU, jcol);
+          for (; it; ++it) {
+            Index irow = it.index();
+            X(irow, j) -= X(jcol, j) * it.value();
+          }
+        }
+      }
+    }  // End For U-solve
+  }
+
+  template <bool Conjugate, typename Dest>
+  void solveTransposedInPlace(MatrixBase<Dest>& X) const {
+    using numext::conj;
+    Index nrhs = X.cols();
+    // Forward solve with U
+    for (Index k = 0; k <= m_mapL.nsuper(); k++) {
+      Index fsupc = m_mapL.supToCol()[k];
+      Index lda = m_mapL.colIndexPtr()[fsupc + 1] - m_mapL.colIndexPtr()[fsupc];  // leading dimension
+      Index nsupc = m_mapL.supToCol()[k + 1] - fsupc;
+      Index luptr = m_mapL.colIndexPtr()[fsupc];
+
+      for (Index j = 0; j < nrhs; ++j) {
+        for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++) {
+          typename MatrixUType::InnerIterator it(m_mapU, jcol);
+          for (; it; ++it) {
+            Index irow = it.index();
+            X(jcol, j) -= X(irow, j) * (Conjugate ? conj(it.value()) : it.value());
+          }
+        }
+      }
+      if (nsupc == 1) {
+        for (Index j = 0; j < nrhs; j++) {
+          X(fsupc, j) /= (Conjugate ? conj(m_mapL.valuePtr()[luptr]) : m_mapL.valuePtr()[luptr]);
+        }
+      } else {
+        Map<const Matrix<Scalar, Dynamic, Dynamic, ColMajor>, 0, OuterStride<>> A(&(m_mapL.valuePtr()[luptr]), nsupc,
+                                                                                  nsupc, OuterStride<>(lda));
+        typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+        if (Conjugate)
+          U = A.adjoint().template triangularView<Lower>().solve(U);
+        else
+          U = A.transpose().template triangularView<Lower>().solve(U);
+      }
+    }  // End For U-solve
+  }
+
+  SparseMatrix<Scalar, RowMajor, Index> toSparse() {
+    ArrayXi rowCount = ArrayXi::Zero(rows());
+    for (Index i = 0; i < cols(); i++) {
+      typename MatrixLType::InnerIterator iter(m_mapL, i);
+      for (; iter; ++iter) {
+        if (iter.row() <= iter.col()) {
+          rowCount(iter.row())++;
+        }
+      }
+    }
+
+    SparseMatrix<Scalar, RowMajor, Index> sU(rows(), cols());
+    sU.reserve(rowCount);
+    for (Index i = 0; i < cols(); i++) {
+      typename MatrixLType::InnerIterator iter(m_mapL, i);
+      for (; iter; ++iter) {
+        if (iter.row() <= iter.col()) {
+          sU.insert(iter.row(), iter.col()) = iter.value();
+        }
+      }
+    }
+    sU.makeCompressed();
+    const SparseMatrix<Scalar, RowMajor, Index> u = m_mapU;  // convert to RowMajor
+    sU += u;
+    return sU;
+  }
+
+  const MatrixLType& m_mapL;
+  const MatrixUType& m_mapU;
+};
+
+}  // End namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLUImpl.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLUImpl.h
new file mode 100644
index 0000000..96b9c65
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLUImpl.h
@@ -0,0 +1,79 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#ifndef SPARSELU_IMPL_H
+#define SPARSELU_IMPL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/** \ingroup SparseLU_Module
+ * \class SparseLUImpl
+ * Base class for sparseLU
+ */
+template <typename Scalar, typename StorageIndex>
+class SparseLUImpl {
+ public:
+  typedef Matrix<Scalar, Dynamic, 1> ScalarVector;
+  typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
+  typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> ScalarMatrix;
+  typedef Map<ScalarMatrix, 0, OuterStride<> > MappedMatrixBlock;
+  typedef typename ScalarVector::RealScalar RealScalar;
+  typedef Ref<Matrix<Scalar, Dynamic, 1> > BlockScalarVector;
+  typedef Ref<Matrix<StorageIndex, Dynamic, 1> > BlockIndexVector;
+  typedef LU_GlobalLU_t<IndexVector, ScalarVector> GlobalLU_t;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> MatrixType;
+
+ protected:
+  template <typename VectorType>
+  Index expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev, Index& num_expansions);
+  Index memInit(Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size, GlobalLU_t& glu);
+  template <typename VectorType>
+  Index memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype, Index& num_expansions);
+  void heap_relax_snode(const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants,
+                        IndexVector& relax_end);
+  void relax_snode(const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants,
+                   IndexVector& relax_end);
+  Index snode_dfs(const Index jcol, const Index kcol, const MatrixType& mat, IndexVector& xprune, IndexVector& marker,
+                  GlobalLU_t& glu);
+  Index snode_bmod(const Index jcol, const Index fsupc, ScalarVector& dense, GlobalLU_t& glu);
+  Index pivotL(const Index jcol, const RealScalar& diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c,
+               Index& pivrow, GlobalLU_t& glu);
+  template <typename Traits>
+  void dfs_kernel(const StorageIndex jj, IndexVector& perm_r, Index& nseg, IndexVector& panel_lsub, IndexVector& segrep,
+                  Ref<IndexVector> repfnz_col, IndexVector& xprune, Ref<IndexVector> marker, IndexVector& parent,
+                  IndexVector& xplore, GlobalLU_t& glu, Index& nextl_col, Index krow, Traits& traits);
+  void panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A, IndexVector& perm_r, Index& nseg,
+                 ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz,
+                 IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu);
+
+  void panel_bmod(const Index m, const Index w, const Index jcol, const Index nseg, ScalarVector& dense,
+                  ScalarVector& tempv, IndexVector& segrep, IndexVector& repfnz, GlobalLU_t& glu);
+  Index column_dfs(const Index m, const Index jcol, IndexVector& perm_r, Index maxsuper, Index& nseg,
+                   BlockIndexVector lsub_col, IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune,
+                   IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu);
+  Index column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector& tempv,
+                    BlockIndexVector segrep, BlockIndexVector repfnz, Index fpanelc, GlobalLU_t& glu);
+  Index copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep, BlockIndexVector repfnz,
+                     IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu);
+  void pruneL(const Index jcol, const IndexVector& perm_r, const Index pivrow, const Index nseg,
+              const IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, GlobalLU_t& glu);
+  void countnz(const Index n, Index& nnzL, Index& nnzU, GlobalLU_t& glu);
+  void fixupL(const Index n, const IndexVector& perm_r, GlobalLU_t& glu);
+
+  template <typename, typename>
+  friend struct column_dfs_traits;
+};
+
+}  // end namespace internal
+}  // namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_Memory.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_Memory.h
new file mode 100644
index 0000000..7acfa5c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_Memory.h
@@ -0,0 +1,210 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+
+ * NOTE: This file is the modified version of [s,d,c,z]memory.c files in SuperLU
+
+ * -- SuperLU routine (version 3.1) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * August 1, 2008
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+
+#ifndef EIGEN_SPARSELU_MEMORY
+#define EIGEN_SPARSELU_MEMORY
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+enum { LUNoMarker = 3 };
+enum { emptyIdxLU = -1 };
+inline Index LUnumTempV(Index& m, Index& w, Index& t, Index& b) { return (std::max)(m, (t + b) * w); }
+
+template <typename Scalar>
+inline Index LUTempSpace(Index& m, Index& w) {
+  return (2 * w + 4 + LUNoMarker) * m * sizeof(Index) + (w + 1) * m * sizeof(Scalar);
+}
+
+/**
+ * Expand the existing storage to accommodate more fill-ins
+ * \param vec Valid pointer to the vector to allocate or expand
+ * \param[in,out] length  At input, contain the current length of the vector that is to be increased. At output, length
+ * of the newly allocated vector \param[in] nbElts Current number of elements in the factors \param keep_prev  1: use
+ * length  and do not expand the vector; 0: compute new_len and expand \param[in,out] num_expansions Number of times the
+ * memory has been expanded
+ */
+template <typename Scalar, typename StorageIndex>
+template <typename VectorType>
+Index SparseLUImpl<Scalar, StorageIndex>::expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev,
+                                                 Index& num_expansions) {
+  float alpha = 1.5;  // Ratio of the memory increase
+  Index new_len;      // New size of the allocated memory
+
+  if (num_expansions == 0 || keep_prev)
+    new_len = length;  // First time allocate requested
+  else
+    new_len = (std::max)(length + 1, Index(alpha * length));
+
+  VectorType old_vec;  // Temporary vector to hold the previous values
+  if (nbElts > 0) old_vec = vec.segment(0, nbElts);
+
+    // Allocate or expand the current vector
+#ifdef EIGEN_EXCEPTIONS
+  try
+#endif
+  {
+    vec.resize(new_len);
+  }
+#ifdef EIGEN_EXCEPTIONS
+  catch (std::bad_alloc&)
+#else
+  if (!vec.size())
+#endif
+  {
+    if (!num_expansions) {
+      // First time to allocate from LUMemInit()
+      // Let LUMemInit() deals with it.
+      return -1;
+    }
+    if (keep_prev) {
+      // In this case, the memory length should not not be reduced
+      return new_len;
+    } else {
+      // Reduce the size and increase again
+      Index tries = 0;  // Number of attempts
+      do {
+        alpha = (alpha + 1) / 2;
+        new_len = (std::max)(length + 1, Index(alpha * length));
+#ifdef EIGEN_EXCEPTIONS
+        try
+#endif
+        {
+          vec.resize(new_len);
+        }
+#ifdef EIGEN_EXCEPTIONS
+        catch (std::bad_alloc&)
+#else
+        if (!vec.size())
+#endif
+        {
+          tries += 1;
+          if (tries > 10) return new_len;
+        }
+      } while (!vec.size());
+    }
+  }
+  // Copy the previous values to the newly allocated space
+  if (nbElts > 0) vec.segment(0, nbElts) = old_vec;
+
+  length = new_len;
+  if (num_expansions) ++num_expansions;
+  return 0;
+}
+
+/**
+ * \brief  Allocate various working space for the numerical factorization phase.
+ * \param m number of rows of the input matrix
+ * \param n number of columns
+ * \param annz number of initial nonzeros in the matrix
+ * \param lwork  if lwork=-1, this routine returns an estimated size of the required memory
+ * \param glu persistent data to facilitate multiple factors (may be deleted later).
+ * \param fillratio estimated ratio of fill in the factors
+ * \param panel_size Size of a panel
+ * \return an estimated size of the required memory if lwork = -1; otherwise, return the size of actually allocated
+ * memory when allocation failed, and 0 on success \note Unlike SuperLU, this routine does not support successive
+ * factorization with the same pattern and the same row permutation
+ */
+template <typename Scalar, typename StorageIndex>
+Index SparseLUImpl<Scalar, StorageIndex>::memInit(Index m, Index n, Index annz, Index lwork, Index fillratio,
+                                                  Index panel_size, GlobalLU_t& glu) {
+  Index& num_expansions = glu.num_expansions;  // No memory expansions so far
+  num_expansions = 0;
+  glu.nzumax = glu.nzlumax = (std::min)(fillratio * (annz + 1) / n, m) * n;  // estimated number of nonzeros in U
+  glu.nzlmax = (std::max)(Index(4), fillratio) * (annz + 1) / 4;             // estimated  nnz in L factor
+  // Return the estimated size to the user if necessary
+  Index tempSpace;
+  tempSpace = (2 * panel_size + 4 + LUNoMarker) * m * sizeof(Index) + (panel_size + 1) * m * sizeof(Scalar);
+  if (lwork == emptyIdxLU) {
+    Index estimated_size;
+    estimated_size = (5 * n + 5) * sizeof(Index) + tempSpace + (glu.nzlmax + glu.nzumax) * sizeof(Index) +
+                     (glu.nzlumax + glu.nzumax) * sizeof(Scalar) + n;
+    return estimated_size;
+  }
+
+  // Setup the required space
+
+  // First allocate Integer pointers for L\U factors
+  glu.xsup.resize(n + 1);
+  glu.supno.resize(n + 1);
+  glu.xlsub.resize(n + 1);
+  glu.xlusup.resize(n + 1);
+  glu.xusub.resize(n + 1);
+
+  // Reserve memory for L/U factors
+  do {
+    if ((expand<ScalarVector>(glu.lusup, glu.nzlumax, 0, 0, num_expansions) < 0) ||
+        (expand<ScalarVector>(glu.ucol, glu.nzumax, 0, 0, num_expansions) < 0) ||
+        (expand<IndexVector>(glu.lsub, glu.nzlmax, 0, 0, num_expansions) < 0) ||
+        (expand<IndexVector>(glu.usub, glu.nzumax, 0, 1, num_expansions) < 0)) {
+      // Reduce the estimated size and retry
+      glu.nzlumax /= 2;
+      glu.nzumax /= 2;
+      glu.nzlmax /= 2;
+      if (glu.nzlumax < annz) return glu.nzlumax;
+    }
+  } while (!glu.lusup.size() || !glu.ucol.size() || !glu.lsub.size() || !glu.usub.size());
+
+  ++num_expansions;
+  return 0;
+
+}  // end LuMemInit
+
+/**
+ * \brief Expand the existing storage
+ * \param vec vector to expand
+ * \param[in,out] maxlen On input, previous size of vec (Number of elements to copy ). on output, new size
+ * \param nbElts current number of elements in the vector.
+ * \param memtype Type of the element to expand
+ * \param num_expansions Number of expansions
+ * \return 0 on success, > 0 size of the memory allocated so far
+ */
+template <typename Scalar, typename StorageIndex>
+template <typename VectorType>
+Index SparseLUImpl<Scalar, StorageIndex>::memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype,
+                                                   Index& num_expansions) {
+  Index failed_size;
+  if (memtype == USUB)
+    failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 1, num_expansions);
+  else
+    failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 0, num_expansions);
+
+  if (failed_size) return failed_size;
+
+  return 0;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+#endif  // EIGEN_SPARSELU_MEMORY
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_Structs.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_Structs.h
new file mode 100644
index 0000000..85ba884
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_Structs.h
@@ -0,0 +1,113 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+ * NOTE: This file comes from a partly modified version of files slu_[s,d,c,z]defs.h
+ * -- SuperLU routine (version 4.1) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * November, 2010
+ *
+ * Global data structures used in LU factorization -
+ *
+ *   nsuper: #supernodes = nsuper + 1, numbered [0, nsuper].
+ *   (xsup,supno): supno[i] is the supernode no to which i belongs;
+ *  xsup(s) points to the beginning of the s-th supernode.
+ *  e.g.   supno 0 1 2 2 3 3 3 4 4 4 4 4   (n=12)
+ *          xsup 0 1 2 4 7 12
+ *  Note: dfs will be performed on supernode rep. relative to the new
+ *        row pivoting ordering
+ *
+ *   (xlsub,lsub): lsub[*] contains the compressed subscript of
+ *  rectangular supernodes; xlsub[j] points to the starting
+ *  location of the j-th column in lsub[*]. Note that xlsub
+ *  is indexed by column.
+ *  Storage: original row subscripts
+ *
+ *      During the course of sparse LU factorization, we also use
+ *  (xlsub,lsub) for the purpose of symmetric pruning. For each
+ *  supernode {s,s+1,...,t=s+r} with first column s and last
+ *  column t, the subscript set
+ *    lsub[j], j=xlsub[s], .., xlsub[s+1]-1
+ *  is the structure of column s (i.e. structure of this supernode).
+ *  It is used for the storage of numerical values.
+ *  Furthermore,
+ *    lsub[j], j=xlsub[t], .., xlsub[t+1]-1
+ *  is the structure of the last column t of this supernode.
+ *  It is for the purpose of symmetric pruning. Therefore, the
+ *  structural subscripts can be rearranged without making physical
+ *  interchanges among the numerical values.
+ *
+ *  However, if the supernode has only one column, then we
+ *  only keep one set of subscripts. For any subscript interchange
+ *  performed, similar interchange must be done on the numerical
+ *  values.
+ *
+ *  The last column structures (for pruning) will be removed
+ *  after the numerical LU factorization phase.
+ *
+ *   (xlusup,lusup): lusup[*] contains the numerical values of the
+ *  rectangular supernodes; xlusup[j] points to the starting
+ *  location of the j-th column in storage vector lusup[*]
+ *  Note: xlusup is indexed by column.
+ *  Each rectangular supernode is stored by column-major
+ *  scheme, consistent with Fortran 2-dim array storage.
+ *
+ *   (xusub,ucol,usub): ucol[*] stores the numerical values of
+ *  U-columns outside the rectangular supernodes. The row
+ *  subscript of nonzero ucol[k] is stored in usub[k].
+ *  xusub[i] points to the starting location of column i in ucol.
+ *  Storage: new row subscripts; that is subscripts of PA.
+ */
+
+#ifndef EIGEN_LU_STRUCTS
+#define EIGEN_LU_STRUCTS
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+enum MemType { LUSUP, UCOL, LSUB, USUB, LLVL, ULVL };
+
+template <typename IndexVector, typename ScalarVector>
+struct LU_GlobalLU_t {
+  typedef typename IndexVector::Scalar StorageIndex;
+  IndexVector xsup;    // First supernode column ... xsup(s) points to the beginning of the s-th supernode
+  IndexVector supno;   // Supernode number corresponding to this column (column to supernode mapping)
+  ScalarVector lusup;  // nonzero values of L ordered by columns
+  IndexVector lsub;    // Compressed row indices of L rectangular supernodes.
+  IndexVector xlusup;  // pointers to the beginning of each column in lusup
+  IndexVector xlsub;   // pointers to the beginning of each column in lsub
+  Index nzlmax;        // Current max size of lsub
+  Index nzlumax;       // Current max size of lusup
+  ScalarVector ucol;   // nonzero values of U ordered by columns
+  IndexVector usub;    // row indices of U columns in ucol
+  IndexVector xusub;   // Pointers to the beginning of each column of U in ucol
+  Index nzumax;        // Current max size of ucol
+  Index n;             // Number of columns in the matrix
+  Index num_expansions;
+};
+
+// Values to set for performance
+struct perfvalues {
+  Index panel_size;  // a panel consists of at most <panel_size> consecutive columns
+  Index relax;       // To control degree of relaxing supernodes. If the number of nodes (columns)
+                     // in a subtree of the elimination tree is less than relax, this subtree is considered
+                     // as one supernode regardless of the row structures of those columns
+  Index maxsuper;    // The maximum size for a supernode in complete LU
+  Index rowblk;      // The minimum row dimension for 2-D blocking to be used;
+  Index colblk;      // The minimum column dimension for 2-D blocking to be used;
+  Index fillfactor;  // The estimated fills factors for L and U, compared with A
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+#endif  // EIGEN_LU_STRUCTS
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
new file mode 100644
index 0000000..98b7348
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
@@ -0,0 +1,315 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSELU_SUPERNODAL_MATRIX_H
+#define EIGEN_SPARSELU_SUPERNODAL_MATRIX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/** \ingroup SparseLU_Module
+ * \brief a class to manipulate the L supernodal factor from the SparseLU factorization
+ *
+ * This class  contain the data to easily store
+ * and manipulate the supernodes during the factorization and solution phase of Sparse LU.
+ * Only the lower triangular matrix has supernodes.
+ *
+ * NOTE : This class corresponds to the SCformat structure in SuperLU
+ *
+ */
+// TODO: add InnerIterator, SuperInnerIterator, and triangular solve support.
+template <typename Scalar_, typename StorageIndex_>
+class MappedSuperNodalMatrix {
+ public:
+  typedef Scalar_ Scalar;
+  typedef StorageIndex_ StorageIndex;
+  typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
+  typedef Matrix<Scalar, Dynamic, 1> ScalarVector;
+
+ public:
+  MappedSuperNodalMatrix() {}
+  MappedSuperNodalMatrix(Index m, Index n, ScalarVector& nzval, IndexVector& nzval_colptr, IndexVector& rowind,
+                         IndexVector& rowind_colptr, IndexVector& col_to_sup, IndexVector& sup_to_col) {
+    setInfos(m, n, nzval, nzval_colptr, rowind, rowind_colptr, col_to_sup, sup_to_col);
+  }
+
+  ~MappedSuperNodalMatrix() {}
+  /**
+   * Set appropriate pointers for the lower triangular supernodal matrix
+   * These infos are available at the end of the numerical factorization
+   * FIXME This class will be modified such that it can be use in the course
+   * of the factorization.
+   */
+  void setInfos(Index m, Index n, ScalarVector& nzval, IndexVector& nzval_colptr, IndexVector& rowind,
+                IndexVector& rowind_colptr, IndexVector& col_to_sup, IndexVector& sup_to_col) {
+    m_row = m;
+    m_col = n;
+    m_nzval = nzval.data();
+    m_nzval_colptr = nzval_colptr.data();
+    m_rowind = rowind.data();
+    m_rowind_colptr = rowind_colptr.data();
+    m_nsuper = col_to_sup(n);
+    m_col_to_sup = col_to_sup.data();
+    m_sup_to_col = sup_to_col.data();
+  }
+
+  /**
+   * Number of rows
+   */
+  Index rows() const { return m_row; }
+
+  /**
+   * Number of columns
+   */
+  Index cols() const { return m_col; }
+
+  /**
+   * Return the array of nonzero values packed by column
+   *
+   * The size is nnz
+   */
+  Scalar* valuePtr() { return m_nzval; }
+
+  const Scalar* valuePtr() const { return m_nzval; }
+  /**
+   * Return the pointers to the beginning of each column in \ref valuePtr()
+   */
+  StorageIndex* colIndexPtr() { return m_nzval_colptr; }
+
+  const StorageIndex* colIndexPtr() const { return m_nzval_colptr; }
+
+  /**
+   * Return the array of compressed row indices of all supernodes
+   */
+  StorageIndex* rowIndex() { return m_rowind; }
+
+  const StorageIndex* rowIndex() const { return m_rowind; }
+
+  /**
+   * Return the location in \em rowvaluePtr() which starts each column
+   */
+  StorageIndex* rowIndexPtr() { return m_rowind_colptr; }
+
+  const StorageIndex* rowIndexPtr() const { return m_rowind_colptr; }
+
+  /**
+   * Return the array of column-to-supernode mapping
+   */
+  StorageIndex* colToSup() { return m_col_to_sup; }
+
+  const StorageIndex* colToSup() const { return m_col_to_sup; }
+  /**
+   * Return the array of supernode-to-column mapping
+   */
+  StorageIndex* supToCol() { return m_sup_to_col; }
+
+  const StorageIndex* supToCol() const { return m_sup_to_col; }
+
+  /**
+   * Return the number of supernodes
+   */
+  Index nsuper() const { return m_nsuper; }
+
+  class InnerIterator;
+  template <typename Dest>
+  void solveInPlace(MatrixBase<Dest>& X) const;
+  template <bool Conjugate, typename Dest>
+  void solveTransposedInPlace(MatrixBase<Dest>& X) const;
+
+ protected:
+  Index m_row;                    // Number of rows
+  Index m_col;                    // Number of columns
+  Index m_nsuper;                 // Number of supernodes
+  Scalar* m_nzval;                // array of nonzero values packed by column
+  StorageIndex* m_nzval_colptr;   // nzval_colptr[j] Stores the location in nzval[] which starts column j
+  StorageIndex* m_rowind;         // Array of compressed row indices of rectangular supernodes
+  StorageIndex* m_rowind_colptr;  // rowind_colptr[j] stores the location in rowind[] which starts column j
+  StorageIndex* m_col_to_sup;     // col_to_sup[j] is the supernode number to which column j belongs
+  StorageIndex* m_sup_to_col;     // sup_to_col[s] points to the starting column of the s-th supernode
+
+ private:
+};
+
+/**
+ * \brief InnerIterator class to iterate over nonzero values of the current column in the supernodal matrix L
+ *
+ */
+template <typename Scalar, typename StorageIndex>
+class MappedSuperNodalMatrix<Scalar, StorageIndex>::InnerIterator {
+ public:
+  InnerIterator(const MappedSuperNodalMatrix& mat, Index outer)
+      : m_matrix(mat),
+        m_outer(outer),
+        m_supno(mat.colToSup()[outer]),
+        m_idval(mat.colIndexPtr()[outer]),
+        m_startidval(m_idval),
+        m_endidval(mat.colIndexPtr()[outer + 1]),
+        m_idrow(mat.rowIndexPtr()[mat.supToCol()[mat.colToSup()[outer]]]),
+        m_endidrow(mat.rowIndexPtr()[mat.supToCol()[mat.colToSup()[outer]] + 1]) {}
+  inline InnerIterator& operator++() {
+    m_idval++;
+    m_idrow++;
+    return *this;
+  }
+  inline Scalar value() const { return m_matrix.valuePtr()[m_idval]; }
+
+  inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_idval]); }
+
+  inline Index index() const { return m_matrix.rowIndex()[m_idrow]; }
+  inline Index row() const { return index(); }
+  inline Index col() const { return m_outer; }
+
+  inline Index supIndex() const { return m_supno; }
+
+  inline operator bool() const {
+    return ((m_idval < m_endidval) && (m_idval >= m_startidval) && (m_idrow < m_endidrow));
+  }
+
+ protected:
+  const MappedSuperNodalMatrix& m_matrix;  // Supernodal lower triangular matrix
+  const Index m_outer;                     // Current column
+  const Index m_supno;                     // Current SuperNode number
+  Index m_idval;                           // Index to browse the values in the current column
+  const Index m_startidval;                // Start of the column value
+  const Index m_endidval;                  // End of the column value
+  Index m_idrow;                           // Index to browse the row indices
+  Index m_endidrow;                        // End index of row indices of the current column
+};
+
+/**
+ * \brief Solve with the supernode triangular matrix
+ *
+ */
+template <typename Scalar, typename Index_>
+template <typename Dest>
+void MappedSuperNodalMatrix<Scalar, Index_>::solveInPlace(MatrixBase<Dest>& X) const {
+  /* Explicit type conversion as the Index type of MatrixBase<Dest> may be wider than Index */
+  //    eigen_assert(X.rows() <= NumTraits<Index>::highest());
+  //    eigen_assert(X.cols() <= NumTraits<Index>::highest());
+  Index n = int(X.rows());
+  Index nrhs = Index(X.cols());
+  const Scalar* Lval = valuePtr();                                           // Nonzero values
+  Matrix<Scalar, Dynamic, Dest::ColsAtCompileTime, ColMajor> work(n, nrhs);  // working vector
+  work.setZero();
+  for (Index k = 0; k <= nsuper(); k++) {
+    Index fsupc = supToCol()[k];                      // First column of the current supernode
+    Index istart = rowIndexPtr()[fsupc];              // Pointer index to the subscript of the current column
+    Index nsupr = rowIndexPtr()[fsupc + 1] - istart;  // Number of rows in the current supernode
+    Index nsupc = supToCol()[k + 1] - fsupc;          // Number of columns in the current supernode
+    Index nrow = nsupr - nsupc;                       // Number of rows in the non-diagonal part of the supernode
+    Index irow;                                       // Current index row
+
+    if (nsupc == 1) {
+      for (Index j = 0; j < nrhs; j++) {
+        InnerIterator it(*this, fsupc);
+        ++it;  // Skip the diagonal element
+        for (; it; ++it) {
+          irow = it.row();
+          X(irow, j) -= X(fsupc, j) * it.value();
+        }
+      }
+    } else {
+      // The supernode has more than one column
+      Index luptr = colIndexPtr()[fsupc];
+      Index lda = colIndexPtr()[fsupc + 1] - luptr;
+
+      // Triangular solve
+      Map<const Matrix<Scalar, Dynamic, Dynamic, ColMajor>, 0, OuterStride<> > A(&(Lval[luptr]), nsupc, nsupc,
+                                                                                 OuterStride<>(lda));
+      typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+      U = A.template triangularView<UnitLower>().solve(U);
+      // Matrix-vector product
+      new (&A) Map<const Matrix<Scalar, Dynamic, Dynamic, ColMajor>, 0, OuterStride<> >(&(Lval[luptr + nsupc]), nrow,
+                                                                                        nsupc, OuterStride<>(lda));
+      work.topRows(nrow).noalias() = A * U;
+
+      // Begin Scatter
+      for (Index j = 0; j < nrhs; j++) {
+        Index iptr = istart + nsupc;
+        for (Index i = 0; i < nrow; i++) {
+          irow = rowIndex()[iptr];
+          X(irow, j) -= work(i, j);  // Scatter operation
+          work(i, j) = Scalar(0);
+          iptr++;
+        }
+      }
+    }
+  }
+}
+
+template <typename Scalar, typename Index_>
+template <bool Conjugate, typename Dest>
+void MappedSuperNodalMatrix<Scalar, Index_>::solveTransposedInPlace(MatrixBase<Dest>& X) const {
+  using numext::conj;
+  Index n = int(X.rows());
+  Index nrhs = Index(X.cols());
+  const Scalar* Lval = valuePtr();                                           // Nonzero values
+  Matrix<Scalar, Dynamic, Dest::ColsAtCompileTime, ColMajor> work(n, nrhs);  // working vector
+  work.setZero();
+  for (Index k = nsuper(); k >= 0; k--) {
+    Index fsupc = supToCol()[k];                      // First column of the current supernode
+    Index istart = rowIndexPtr()[fsupc];              // Pointer index to the subscript of the current column
+    Index nsupr = rowIndexPtr()[fsupc + 1] - istart;  // Number of rows in the current supernode
+    Index nsupc = supToCol()[k + 1] - fsupc;          // Number of columns in the current supernode
+    Index nrow = nsupr - nsupc;                       // Number of rows in the non-diagonal part of the supernode
+    Index irow;                                       // Current index row
+
+    if (nsupc == 1) {
+      for (Index j = 0; j < nrhs; j++) {
+        InnerIterator it(*this, fsupc);
+        ++it;  // Skip the diagonal element
+        for (; it; ++it) {
+          irow = it.row();
+          X(fsupc, j) -= X(irow, j) * (Conjugate ? conj(it.value()) : it.value());
+        }
+      }
+    } else {
+      // The supernode has more than one column
+      Index luptr = colIndexPtr()[fsupc];
+      Index lda = colIndexPtr()[fsupc + 1] - luptr;
+
+      // Begin Gather
+      for (Index j = 0; j < nrhs; j++) {
+        Index iptr = istart + nsupc;
+        for (Index i = 0; i < nrow; i++) {
+          irow = rowIndex()[iptr];
+          work.topRows(nrow)(i, j) = X(irow, j);  // Gather operation
+          iptr++;
+        }
+      }
+
+      // Matrix-vector product with transposed submatrix
+      Map<const Matrix<Scalar, Dynamic, Dynamic, ColMajor>, 0, OuterStride<> > A(&(Lval[luptr + nsupc]), nrow, nsupc,
+                                                                                 OuterStride<>(lda));
+      typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+      if (Conjugate)
+        U = U - A.adjoint() * work.topRows(nrow);
+      else
+        U = U - A.transpose() * work.topRows(nrow);
+
+      // Triangular solve (of transposed diagonal block)
+      new (&A) Map<const Matrix<Scalar, Dynamic, Dynamic, ColMajor>, 0, OuterStride<> >(&(Lval[luptr]), nsupc, nsupc,
+                                                                                        OuterStride<>(lda));
+      if (Conjugate)
+        U = A.adjoint().template triangularView<UnitUpper>().solve(U);
+      else
+        U = A.transpose().template triangularView<UnitUpper>().solve(U);
+    }
+  }
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSELU_MATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_Utils.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_Utils.h
new file mode 100644
index 0000000..ef087cd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_Utils.h
@@ -0,0 +1,75 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSELU_UTILS_H
+#define EIGEN_SPARSELU_UTILS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \brief Count Nonzero elements in the factors
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar, StorageIndex>::countnz(const Index n, Index& nnzL, Index& nnzU, GlobalLU_t& glu) {
+  nnzL = 0;
+  nnzU = (glu.xusub)(n);
+  Index nsuper = (glu.supno)(n);
+  Index jlen;
+  Index i, j, fsupc;
+  if (n <= 0) return;
+  // For each supernode
+  for (i = 0; i <= nsuper; i++) {
+    fsupc = glu.xsup(i);
+    jlen = glu.xlsub(fsupc + 1) - glu.xlsub(fsupc);
+
+    for (j = fsupc; j < glu.xsup(i + 1); j++) {
+      nnzL += jlen;
+      nnzU += j - fsupc + 1;
+      jlen--;
+    }
+  }
+}
+
+/**
+ * \brief Fix up the data storage lsub for L-subscripts.
+ *
+ * It removes the subscripts sets for structural pruning,
+ * and applies permutation to the remaining subscripts
+ *
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar, StorageIndex>::fixupL(const Index n, const IndexVector& perm_r, GlobalLU_t& glu) {
+  Index fsupc, i, j, k, jstart;
+
+  StorageIndex nextl = 0;
+  Index nsuper = (glu.supno)(n);
+
+  // For each supernode
+  for (i = 0; i <= nsuper; i++) {
+    fsupc = glu.xsup(i);
+    jstart = glu.xlsub(fsupc);
+    glu.xlsub(fsupc) = nextl;
+    for (j = jstart; j < glu.xlsub(fsupc + 1); j++) {
+      glu.lsub(nextl) = perm_r(glu.lsub(j));  // Now indexed into P*A
+      nextl++;
+    }
+    for (k = fsupc + 1; k < glu.xsup(i + 1); k++) glu.xlsub(k) = nextl;  // other columns in supernode i
+  }
+
+  glu.xlsub(n) = nextl;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+#endif  // EIGEN_SPARSELU_UTILS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_column_bmod.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_column_bmod.h
new file mode 100644
index 0000000..014ffc2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_column_bmod.h
@@ -0,0 +1,177 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+
+ * NOTE: This file is the modified version of xcolumn_bmod.c file in SuperLU
+
+ * -- SuperLU routine (version 3.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * October 15, 2003
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_COLUMN_BMOD_H
+#define SPARSELU_COLUMN_BMOD_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+/**
+ * \brief Performs numeric block updates (sup-col) in topological order
+ *
+ * \param jcol current column to update
+ * \param nseg Number of segments in the U part
+ * \param dense Store the full representation of the column
+ * \param tempv working array
+ * \param segrep segment representative ...
+ * \param repfnz first nonzero column in each row  ...
+ * \param fpanelc First column in the current panel
+ * \param glu Global LU data.
+ * \return 0 - successful return
+ *         > 0 - number of bytes allocated when run out of space
+ *
+ */
+template <typename Scalar, typename StorageIndex>
+Index SparseLUImpl<Scalar, StorageIndex>::column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense,
+                                                      ScalarVector& tempv, BlockIndexVector segrep,
+                                                      BlockIndexVector repfnz, Index fpanelc, GlobalLU_t& glu) {
+  Index jsupno, k, ksub, krep, ksupno;
+  Index lptr, nrow, isub, irow, nextlu, new_next, ufirst;
+  Index fsupc, nsupc, nsupr, luptr, kfnz, no_zeros;
+  /* krep = representative of current k-th supernode
+   * fsupc =  first supernodal column
+   * nsupc = number of columns in a supernode
+   * nsupr = number of rows in a supernode
+   * luptr = location of supernodal LU-block in storage
+   * kfnz = first nonz in the k-th supernodal segment
+   * no_zeros = no lf leading zeros in a supernodal U-segment
+   */
+
+  jsupno = glu.supno(jcol);
+  // For each nonzero supernode segment of U[*,j] in topological order
+  k = nseg - 1;
+  Index d_fsupc;  // distance between the first column of the current panel and the
+                  // first column of the current snode
+  Index fst_col;  // First column within small LU update
+  Index segsize;
+  for (ksub = 0; ksub < nseg; ksub++) {
+    krep = segrep(k);
+    k--;
+    ksupno = glu.supno(krep);
+    if (jsupno != ksupno) {
+      // outside the rectangular supernode
+      fsupc = glu.xsup(ksupno);
+      fst_col = (std::max)(fsupc, fpanelc);
+
+      // Distance from the current supernode to the current panel;
+      // d_fsupc = 0 if fsupc > fpanelc
+      d_fsupc = fst_col - fsupc;
+
+      luptr = glu.xlusup(fst_col) + d_fsupc;
+      lptr = glu.xlsub(fsupc) + d_fsupc;
+
+      kfnz = repfnz(krep);
+      kfnz = (std::max)(kfnz, fpanelc);
+
+      segsize = krep - kfnz + 1;
+      nsupc = krep - fst_col + 1;
+      nsupr = glu.xlsub(fsupc + 1) - glu.xlsub(fsupc);
+      nrow = nsupr - d_fsupc - nsupc;
+      Index lda = glu.xlusup(fst_col + 1) - glu.xlusup(fst_col);
+
+      // Perform a triangular solver and block update,
+      // then scatter the result of sup-col update to dense
+      no_zeros = kfnz - fst_col;
+      if (segsize == 1)
+        LU_kernel_bmod<1>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
+      else
+        LU_kernel_bmod<Dynamic>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
+    }  // end if jsupno
+  }    // end for each segment
+
+  // Process the supernodal portion of  L\U[*,j]
+  nextlu = glu.xlusup(jcol);
+  fsupc = glu.xsup(jsupno);
+
+  // copy the SPA dense into L\U[*,j]
+  Index mem;
+  new_next = nextlu + glu.xlsub(fsupc + 1) - glu.xlsub(fsupc);
+  Index offset = internal::first_multiple<Index>(new_next, internal::packet_traits<Scalar>::size) - new_next;
+  if (offset) new_next += offset;
+  while (new_next > glu.nzlumax) {
+    mem = memXpand<ScalarVector>(glu.lusup, glu.nzlumax, nextlu, LUSUP, glu.num_expansions);
+    if (mem) return mem;
+  }
+
+  for (isub = glu.xlsub(fsupc); isub < glu.xlsub(fsupc + 1); isub++) {
+    irow = glu.lsub(isub);
+    glu.lusup(nextlu) = dense(irow);
+    dense(irow) = Scalar(0.0);
+    ++nextlu;
+  }
+
+  if (offset) {
+    glu.lusup.segment(nextlu, offset).setZero();
+    nextlu += offset;
+  }
+  glu.xlusup(jcol + 1) = StorageIndex(nextlu);  // close L\U(*,jcol);
+
+  /* For more updates within the panel (also within the current supernode),
+   * should start from the first column of the panel, or the first column
+   * of the supernode, whichever is bigger. There are two cases:
+   *  1) fsupc < fpanelc, then fst_col <-- fpanelc
+   *  2) fsupc >= fpanelc, then fst_col <-- fsupc
+   */
+  fst_col = (std::max)(fsupc, fpanelc);
+
+  if (fst_col < jcol) {
+    // Distance between the current supernode and the current panel
+    // d_fsupc = 0 if fsupc >= fpanelc
+    d_fsupc = fst_col - fsupc;
+
+    lptr = glu.xlsub(fsupc) + d_fsupc;
+    luptr = glu.xlusup(fst_col) + d_fsupc;
+    nsupr = glu.xlsub(fsupc + 1) - glu.xlsub(fsupc);  // leading dimension
+    nsupc = jcol - fst_col;                           // excluding jcol
+    nrow = nsupr - d_fsupc - nsupc;
+
+    // points to the beginning of jcol in snode L\U(jsupno)
+    ufirst = glu.xlusup(jcol) + d_fsupc;
+    Index lda = glu.xlusup(jcol + 1) - glu.xlusup(jcol);
+    MappedMatrixBlock A(&(glu.lusup.data()[luptr]), nsupc, nsupc, OuterStride<>(lda));
+    VectorBlock<ScalarVector> u(glu.lusup, ufirst, nsupc);
+    u = A.template triangularView<UnitLower>().solve(u);
+
+    new (&A) MappedMatrixBlock(&(glu.lusup.data()[luptr + nsupc]), nrow, nsupc, OuterStride<>(lda));
+    VectorBlock<ScalarVector> l(glu.lusup, ufirst + nsupc, nrow);
+    l.noalias() -= A * u;
+
+  }  // End if fst_col
+  return 0;
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // SPARSELU_COLUMN_BMOD_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_column_dfs.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_column_dfs.h
new file mode 100644
index 0000000..71a9ff4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_column_dfs.h
@@ -0,0 +1,168 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+
+ * NOTE: This file is the modified version of [s,d,c,z]column_dfs.c file in SuperLU
+
+ * -- SuperLU routine (version 2.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * November 15, 1997
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_COLUMN_DFS_H
+#define SPARSELU_COLUMN_DFS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <typename Scalar, typename StorageIndex>
+class SparseLUImpl;
+
+template <typename IndexVector, typename ScalarVector>
+struct column_dfs_traits : no_assignment_operator {
+  typedef typename ScalarVector::Scalar Scalar;
+  typedef typename IndexVector::Scalar StorageIndex;
+  column_dfs_traits(Index jcol, Index& jsuper, typename SparseLUImpl<Scalar, StorageIndex>::GlobalLU_t& glu,
+                    SparseLUImpl<Scalar, StorageIndex>& luImpl)
+      : m_jcol(jcol), m_jsuper_ref(jsuper), m_glu(glu), m_luImpl(luImpl) {}
+  bool update_segrep(Index /*krep*/, Index /*jj*/) { return true; }
+  void mem_expand(IndexVector& lsub, Index& nextl, Index chmark) {
+    if (nextl >= m_glu.nzlmax) m_luImpl.memXpand(lsub, m_glu.nzlmax, nextl, LSUB, m_glu.num_expansions);
+    if (chmark != (m_jcol - 1)) m_jsuper_ref = emptyIdxLU;
+  }
+  enum { ExpandMem = true };
+
+  Index m_jcol;
+  Index& m_jsuper_ref;
+  typename SparseLUImpl<Scalar, StorageIndex>::GlobalLU_t& m_glu;
+  SparseLUImpl<Scalar, StorageIndex>& m_luImpl;
+};
+
+/**
+ * \brief Performs a symbolic factorization on column jcol and decide the supernode boundary
+ *
+ * A supernode representative is the last column of a supernode.
+ * The nonzeros in U[*,j] are segments that end at supernodes representatives.
+ * The routine returns a list of the supernodal representatives
+ * in topological order of the dfs that generates them.
+ * The location of the first nonzero in each supernodal segment
+ * (supernodal entry location) is also returned.
+ *
+ * \param m number of rows in the matrix
+ * \param jcol Current column
+ * \param perm_r Row permutation
+ * \param maxsuper  Maximum number of column allowed in a supernode
+ * \param [in,out] nseg Number of segments in current U[*,j] - new segments appended
+ * \param lsub_col defines the rhs vector to start the dfs
+ * \param [in,out] segrep Segment representatives - new segments appended
+ * \param repfnz  First nonzero location in each row
+ * \param xprune
+ * \param marker  marker[i] == jj, if i was visited during dfs of current column jj;
+ * \param parent
+ * \param xplore working array
+ * \param glu global LU data
+ * \return 0 success
+ *         > 0 number of bytes allocated when run out of space
+ *
+ */
+template <typename Scalar, typename StorageIndex>
+Index SparseLUImpl<Scalar, StorageIndex>::column_dfs(const Index m, const Index jcol, IndexVector& perm_r,
+                                                     Index maxsuper, Index& nseg, BlockIndexVector lsub_col,
+                                                     IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune,
+                                                     IndexVector& marker, IndexVector& parent, IndexVector& xplore,
+                                                     GlobalLU_t& glu) {
+  Index jsuper = glu.supno(jcol);
+  Index nextl = glu.xlsub(jcol);
+  VectorBlock<IndexVector> marker2(marker, 2 * m, m);
+
+  column_dfs_traits<IndexVector, ScalarVector> traits(jcol, jsuper, glu, *this);
+
+  // For each nonzero in A(*,jcol) do dfs
+  for (Index k = 0; ((k < m) ? lsub_col[k] != emptyIdxLU : false); k++) {
+    Index krow = lsub_col(k);
+    lsub_col(k) = emptyIdxLU;
+    Index kmark = marker2(krow);
+
+    // krow was visited before, go to the next nonz;
+    if (kmark == jcol) continue;
+
+    dfs_kernel(StorageIndex(jcol), perm_r, nseg, glu.lsub, segrep, repfnz, xprune, marker2, parent, xplore, glu, nextl,
+               krow, traits);
+  }  // for each nonzero ...
+
+  Index fsupc;
+  StorageIndex nsuper = glu.supno(jcol);
+  StorageIndex jcolp1 = StorageIndex(jcol) + 1;
+  Index jcolm1 = jcol - 1;
+
+  // check to see if j belongs in the same supernode as j-1
+  if (jcol == 0) {  // Do nothing for column 0
+    nsuper = glu.supno(0) = 0;
+  } else {
+    fsupc = glu.xsup(nsuper);
+    StorageIndex jptr = glu.xlsub(jcol);  // Not yet compressed
+    StorageIndex jm1ptr = glu.xlsub(jcolm1);
+
+    // Use supernodes of type T2 : see SuperLU paper
+    if ((nextl - jptr != jptr - jm1ptr - 1)) jsuper = emptyIdxLU;
+
+    // Make sure the number of columns in a supernode doesn't
+    // exceed threshold
+    if ((jcol - fsupc) >= maxsuper) jsuper = emptyIdxLU;
+
+    /* If jcol starts a new supernode, reclaim storage space in
+     * glu.lsub from previous supernode. Note we only store
+     * the subscript set of the first and last columns of
+     * a supernode. (first for num values, last for pruning)
+     */
+    if (jsuper == emptyIdxLU) {    // starts a new supernode
+      if ((fsupc < jcolm1 - 1)) {  // >= 3 columns in nsuper
+        StorageIndex ito = glu.xlsub(fsupc + 1);
+        glu.xlsub(jcolm1) = ito;
+        StorageIndex istop = ito + jptr - jm1ptr;
+        xprune(jcolm1) = istop;  // initialize xprune(jcol-1)
+        glu.xlsub(jcol) = istop;
+
+        for (StorageIndex ifrom = jm1ptr; ifrom < nextl; ++ifrom, ++ito) glu.lsub(ito) = glu.lsub(ifrom);
+        nextl = ito;  // = istop + length(jcol)
+      }
+      nsuper++;
+      glu.supno(jcol) = nsuper;
+    }  // if a new supernode
+  }    // end else:  jcol > 0
+
+  // Tidy up the pointers before exit
+  glu.xsup(nsuper + 1) = jcolp1;
+  glu.supno(jcolp1) = nsuper;
+  xprune(jcol) = StorageIndex(nextl);  // Initialize upper bound for pruning
+  glu.xlsub(jcolp1) = StorageIndex(nextl);
+
+  return 0;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
new file mode 100644
index 0000000..12e7650
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
@@ -0,0 +1,106 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+/*
+
+ * NOTE: This file is the modified version of [s,d,c,z]copy_to_ucol.c file in SuperLU
+
+ * -- SuperLU routine (version 2.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * November 15, 1997
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_COPY_TO_UCOL_H
+#define SPARSELU_COPY_TO_UCOL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \brief Performs numeric block updates (sup-col) in topological order
+ *
+ * \param jcol current column to update
+ * \param nseg Number of segments in the U part
+ * \param segrep segment representative ...
+ * \param repfnz First nonzero column in each row  ...
+ * \param perm_r Row permutation
+ * \param dense Store the full representation of the column
+ * \param glu Global LU data.
+ * \return 0 - successful return
+ *         > 0 - number of bytes allocated when run out of space
+ *
+ */
+template <typename Scalar, typename StorageIndex>
+Index SparseLUImpl<Scalar, StorageIndex>::copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep,
+                                                       BlockIndexVector repfnz, IndexVector& perm_r,
+                                                       BlockScalarVector dense, GlobalLU_t& glu) {
+  Index ksub, krep, ksupno;
+
+  Index jsupno = glu.supno(jcol);
+
+  // For each nonzero supernode segment of U[*,j] in topological order
+  Index k = nseg - 1, i;
+  StorageIndex nextu = glu.xusub(jcol);
+  Index kfnz, isub, segsize;
+  Index new_next, irow;
+  Index fsupc, mem;
+  for (ksub = 0; ksub < nseg; ksub++) {
+    krep = segrep(k);
+    k--;
+    ksupno = glu.supno(krep);
+    if (jsupno != ksupno)  // should go into ucol();
+    {
+      kfnz = repfnz(krep);
+      if (kfnz != emptyIdxLU) {  // Nonzero U-segment
+        fsupc = glu.xsup(ksupno);
+        isub = glu.xlsub(fsupc) + kfnz - fsupc;
+        segsize = krep - kfnz + 1;
+        new_next = nextu + segsize;
+        while (new_next > glu.nzumax) {
+          mem = memXpand<ScalarVector>(glu.ucol, glu.nzumax, nextu, UCOL, glu.num_expansions);
+          if (mem) return mem;
+          mem = memXpand<IndexVector>(glu.usub, glu.nzumax, nextu, USUB, glu.num_expansions);
+          if (mem) return mem;
+        }
+
+        for (i = 0; i < segsize; i++) {
+          irow = glu.lsub(isub);
+          glu.usub(nextu) = perm_r(irow);  // Unlike the L part, the U part is stored in its final order
+          glu.ucol(nextu) = dense(irow);
+          dense(irow) = Scalar(0.0);
+          nextu++;
+          isub++;
+        }
+
+      }  // end nonzero U-segment
+
+    }  // end if jsupno
+
+  }                             // end for each segment
+  glu.xusub(jcol + 1) = nextu;  // close U(*,jcol)
+  return 0;
+}
+
+}  // namespace internal
+}  // end namespace Eigen
+
+#endif  // SPARSELU_COPY_TO_UCOL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
new file mode 100644
index 0000000..86080e4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
@@ -0,0 +1,114 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* This file is a modified version of heap_relax_snode.c file in SuperLU
+ * -- SuperLU routine (version 3.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * October 15, 2003
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+
+#ifndef SPARSELU_HEAP_RELAX_SNODE_H
+#define SPARSELU_HEAP_RELAX_SNODE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \brief Identify the initial relaxed supernodes
+ *
+ * This routine applied to a symmetric elimination tree.
+ * It assumes that the matrix has been reordered according to the postorder of the etree
+ * \param n The number of columns
+ * \param et elimination tree
+ * \param relax_columns Maximum number of columns allowed in a relaxed snode
+ * \param descendants Number of descendants of each node in the etree
+ * \param relax_end last column in a supernode
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar, StorageIndex>::heap_relax_snode(const Index n, IndexVector& et, const Index relax_columns,
+                                                          IndexVector& descendants, IndexVector& relax_end) {
+  // The etree may not be postordered, but its heap ordered
+  IndexVector post;
+  internal::treePostorder(StorageIndex(n), et, post);  // Post order etree
+  IndexVector inv_post(n + 1);
+  for (StorageIndex i = 0; i < n + 1; ++i) inv_post(post(i)) = i;  // Compute the inverse postorder permutation.
+
+  // Renumber etree in postorder
+  IndexVector iwork(n);
+  IndexVector et_save(n + 1);
+  for (Index i = 0; i < n; ++i) {
+    iwork(post(i)) = post(et(i));
+  }
+  et_save = et;  // Save the original etree
+  et = iwork;
+
+  // compute the number of descendants of each node in the etree
+  relax_end.setConstant(emptyIdxLU);
+  Index j, parent;
+  descendants.setZero();
+  for (j = 0; j < n; j++) {
+    parent = et(j);
+    if (parent != n)  // not the dummy root
+      descendants(parent) += descendants(j) + 1;
+  }
+  // Identify the relaxed supernodes by postorder traversal of the etree
+  Index snode_start;  // beginning of a snode
+  StorageIndex k;
+  StorageIndex l;
+  for (j = 0; j < n;) {
+    parent = et(j);
+    snode_start = j;
+    while (parent != n && descendants(parent) < relax_columns) {
+      j = parent;
+      parent = et(j);
+    }
+    // Found a supernode in postordered etree, j is the last column
+    k = StorageIndex(n);
+    for (Index i = snode_start; i <= j; ++i) k = (std::min)(k, inv_post(i));
+    l = inv_post(j);
+    if ((l - k) == (j - snode_start))  // Same number of columns in the snode
+    {
+      // This is also a supernode in the original etree
+      relax_end(k) = l;  // Record last column
+    } else {
+      for (Index i = snode_start; i <= j; ++i) {
+        l = inv_post(i);
+        if (descendants(i) == 0) {
+          relax_end(l) = l;
+        }
+      }
+    }
+    j++;
+    // Search for a new leaf
+    while (descendants(j) != 0 && j < n) j++;
+  }  // End postorder traversal of the etree
+
+  // Recover the original etree
+  et = et_save;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+#endif  // SPARSELU_HEAP_RELAX_SNODE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_kernel_bmod.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
new file mode 100644
index 0000000..54bda0c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
@@ -0,0 +1,133 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef SPARSELU_KERNEL_BMOD_H
+#define SPARSELU_KERNEL_BMOD_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <int SegSizeAtCompileTime>
+struct LU_kernel_bmod {
+  /** \internal
+   * \brief Performs numeric block updates from a given supernode to a single column
+   *
+   * \param segsize Size of the segment (and blocks ) to use for updates
+   * \param[in,out] dense Packed values of the original matrix
+   * \param tempv temporary vector to use for updates
+   * \param lusup array containing the supernodes
+   * \param lda Leading dimension in the supernode
+   * \param nrow Number of rows in the rectangular part of the supernode
+   * \param lsub compressed row subscripts of supernodes
+   * \param lptr pointer to the first column of the current supernode in lsub
+   * \param no_zeros Number of nonzeros elements before the diagonal part of the supernode
+   */
+  template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
+  static EIGEN_DONT_INLINE void run(const Index segsize, BlockScalarVector& dense, ScalarVector& tempv,
+                                    ScalarVector& lusup, Index& luptr, const Index lda, const Index nrow,
+                                    IndexVector& lsub, const Index lptr, const Index no_zeros);
+};
+
+template <int SegSizeAtCompileTime>
+template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
+EIGEN_DONT_INLINE void LU_kernel_bmod<SegSizeAtCompileTime>::run(const Index segsize, BlockScalarVector& dense,
+                                                                 ScalarVector& tempv, ScalarVector& lusup, Index& luptr,
+                                                                 const Index lda, const Index nrow, IndexVector& lsub,
+                                                                 const Index lptr, const Index no_zeros) {
+  typedef typename ScalarVector::Scalar Scalar;
+  // First, copy U[*,j] segment from dense(*) to tempv(*)
+  // The result of triangular solve is in tempv[*];
+  // The result of matric-vector update is in dense[*]
+  Index isub = lptr + no_zeros;
+  Index i;
+  Index irow;
+  for (i = 0; i < ((SegSizeAtCompileTime == Dynamic) ? segsize : SegSizeAtCompileTime); i++) {
+    irow = lsub(isub);
+    tempv(i) = dense(irow);
+    ++isub;
+  }
+  // Dense triangular solve -- start effective triangle
+  luptr += lda * no_zeros + no_zeros;
+  // Form Eigen matrix and vector
+  Map<Matrix<Scalar, SegSizeAtCompileTime, SegSizeAtCompileTime, ColMajor>, 0, OuterStride<> > A(
+      &(lusup.data()[luptr]), segsize, segsize, OuterStride<>(lda));
+  Map<Matrix<Scalar, SegSizeAtCompileTime, 1> > u(tempv.data(), segsize);
+
+  u = A.template triangularView<UnitLower>().solve(u);
+
+  // Dense matrix-vector product y <-- B*x
+  luptr += segsize;
+  const Index PacketSize = internal::packet_traits<Scalar>::size;
+  Index ldl = internal::first_multiple(nrow, PacketSize);
+  Map<Matrix<Scalar, Dynamic, SegSizeAtCompileTime, ColMajor>, 0, OuterStride<> > B(&(lusup.data()[luptr]), nrow,
+                                                                                    segsize, OuterStride<>(lda));
+  Index aligned_offset = internal::first_default_aligned(tempv.data() + segsize, PacketSize);
+  Index aligned_with_B_offset = (PacketSize - internal::first_default_aligned(B.data(), PacketSize)) % PacketSize;
+  Map<Matrix<Scalar, Dynamic, 1>, 0, OuterStride<> > l(tempv.data() + segsize + aligned_offset + aligned_with_B_offset,
+                                                       nrow, OuterStride<>(ldl));
+
+  l.noalias() = B * u;
+
+  // Scatter tempv[] into SPA dense[] as a temporary storage
+  isub = lptr + no_zeros;
+  for (i = 0; i < ((SegSizeAtCompileTime == Dynamic) ? segsize : SegSizeAtCompileTime); i++) {
+    irow = lsub(isub++);
+    dense(irow) = tempv(i);
+  }
+
+  // Scatter l into SPA dense[]
+  for (i = 0; i < nrow; i++) {
+    irow = lsub(isub++);
+    dense(irow) -= l(i);
+  }
+}
+
+template <>
+struct LU_kernel_bmod<1> {
+  template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
+  static EIGEN_DONT_INLINE void run(const Index /*segsize*/, BlockScalarVector& dense, ScalarVector& /*tempv*/,
+                                    ScalarVector& lusup, Index& luptr, const Index lda, const Index nrow,
+                                    IndexVector& lsub, const Index lptr, const Index no_zeros);
+};
+
+template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
+EIGEN_DONT_INLINE void LU_kernel_bmod<1>::run(const Index /*segsize*/, BlockScalarVector& dense,
+                                              ScalarVector& /*tempv*/, ScalarVector& lusup, Index& luptr,
+                                              const Index lda, const Index nrow, IndexVector& lsub, const Index lptr,
+                                              const Index no_zeros) {
+  typedef typename ScalarVector::Scalar Scalar;
+  typedef typename IndexVector::Scalar StorageIndex;
+  Scalar f = dense(lsub(lptr + no_zeros));
+  luptr += lda * no_zeros + no_zeros + 1;
+  const Scalar* a(lusup.data() + luptr);
+  const StorageIndex* irow(lsub.data() + lptr + no_zeros + 1);
+  Index i = 0;
+  for (; i + 1 < nrow; i += 2) {
+    Index i0 = *(irow++);
+    Index i1 = *(irow++);
+    Scalar a0 = *(a++);
+    Scalar a1 = *(a++);
+    Scalar d0 = dense.coeff(i0);
+    Scalar d1 = dense.coeff(i1);
+    d0 -= f * a0;
+    d1 -= f * a1;
+    dense.coeffRef(i0) = d0;
+    dense.coeffRef(i1) = d1;
+  }
+  if (i < nrow) dense.coeffRef(*(irow++)) -= f * *(a++);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+#endif  // SPARSELU_KERNEL_BMOD_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_panel_bmod.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_panel_bmod.h
new file mode 100644
index 0000000..505d982
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_panel_bmod.h
@@ -0,0 +1,215 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+
+ * NOTE: This file is the modified version of [s,d,c,z]panel_bmod.c file in SuperLU
+
+ * -- SuperLU routine (version 3.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * October 15, 2003
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_PANEL_BMOD_H
+#define SPARSELU_PANEL_BMOD_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \brief Performs numeric block updates (sup-panel) in topological order.
+ *
+ * Before entering this routine, the original nonzeros in the panel
+ * were already copied into the spa[m,w]
+ *
+ * \param m number of rows in the matrix
+ * \param w Panel size
+ * \param jcol Starting  column of the panel
+ * \param nseg Number of segments in the U part
+ * \param dense Store the full representation of the panel
+ * \param tempv working array
+ * \param segrep segment representative... first row in the segment
+ * \param repfnz First nonzero rows
+ * \param glu Global LU data.
+ *
+ *
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar, StorageIndex>::panel_bmod(const Index m, const Index w, const Index jcol, const Index nseg,
+                                                    ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep,
+                                                    IndexVector& repfnz, GlobalLU_t& glu) {
+  Index ksub, jj, nextl_col;
+  Index fsupc, nsupc, nsupr, nrow;
+  Index krep, kfnz;
+  Index lptr;   // points to the row subscripts of a supernode
+  Index luptr;  // ...
+  Index segsize, no_zeros;
+  // For each nonz supernode segment of U[*,j] in topological order
+  Index k = nseg - 1;
+  const Index PacketSize = internal::packet_traits<Scalar>::size;
+
+  for (ksub = 0; ksub < nseg; ksub++) {  // For each updating supernode
+    /* krep = representative of current k-th supernode
+     * fsupc =  first supernodal column
+     * nsupc = number of columns in a supernode
+     * nsupr = number of rows in a supernode
+     */
+    krep = segrep(k);
+    k--;
+    fsupc = glu.xsup(glu.supno(krep));
+    nsupc = krep - fsupc + 1;
+    nsupr = glu.xlsub(fsupc + 1) - glu.xlsub(fsupc);
+    nrow = nsupr - nsupc;
+    lptr = glu.xlsub(fsupc);
+
+    // loop over the panel columns to detect the actual number of columns and rows
+    Index u_rows = 0;
+    Index u_cols = 0;
+    for (jj = jcol; jj < jcol + w; jj++) {
+      nextl_col = (jj - jcol) * m;
+      VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m);  // First nonzero column index for each row
+
+      kfnz = repfnz_col(krep);
+      if (kfnz == emptyIdxLU) continue;  // skip any zero segment
+
+      segsize = krep - kfnz + 1;
+      u_cols++;
+      u_rows = (std::max)(segsize, u_rows);
+    }
+
+    if (nsupc >= 2) {
+      Index ldu = internal::first_multiple<Index>(u_rows, PacketSize);
+      Map<ScalarMatrix, Aligned, OuterStride<> > U(tempv.data(), u_rows, u_cols, OuterStride<>(ldu));
+
+      // gather U
+      Index u_col = 0;
+      for (jj = jcol; jj < jcol + w; jj++) {
+        nextl_col = (jj - jcol) * m;
+        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m);  // First nonzero column index for each row
+        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m);   // Scatter/gather entire matrix column from/to here
+
+        kfnz = repfnz_col(krep);
+        if (kfnz == emptyIdxLU) continue;  // skip any zero segment
+
+        segsize = krep - kfnz + 1;
+        luptr = glu.xlusup(fsupc);
+        no_zeros = kfnz - fsupc;
+
+        Index isub = lptr + no_zeros;
+        Index off = u_rows - segsize;
+        for (Index i = 0; i < off; i++) U(i, u_col) = 0;
+        for (Index i = 0; i < segsize; i++) {
+          Index irow = glu.lsub(isub);
+          U(i + off, u_col) = dense_col(irow);
+          ++isub;
+        }
+        u_col++;
+      }
+      // solve U = A^-1 U
+      luptr = glu.xlusup(fsupc);
+      Index lda = glu.xlusup(fsupc + 1) - glu.xlusup(fsupc);
+      no_zeros = (krep - u_rows + 1) - fsupc;
+      luptr += lda * no_zeros + no_zeros;
+      MappedMatrixBlock A(glu.lusup.data() + luptr, u_rows, u_rows, OuterStride<>(lda));
+      U = A.template triangularView<UnitLower>().solve(U);
+
+      // update
+      luptr += u_rows;
+      MappedMatrixBlock B(glu.lusup.data() + luptr, nrow, u_rows, OuterStride<>(lda));
+      eigen_assert(tempv.size() > w * ldu + nrow * w + 1);
+
+      Index ldl = internal::first_multiple<Index>(nrow, PacketSize);
+      Index offset = (PacketSize - internal::first_default_aligned(B.data(), PacketSize)) % PacketSize;
+      MappedMatrixBlock L(tempv.data() + w * ldu + offset, nrow, u_cols, OuterStride<>(ldl));
+
+      L.noalias() = B * U;
+
+      // scatter U and L
+      u_col = 0;
+      for (jj = jcol; jj < jcol + w; jj++) {
+        nextl_col = (jj - jcol) * m;
+        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m);  // First nonzero column index for each row
+        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m);   // Scatter/gather entire matrix column from/to here
+
+        kfnz = repfnz_col(krep);
+        if (kfnz == emptyIdxLU) continue;  // skip any zero segment
+
+        segsize = krep - kfnz + 1;
+        no_zeros = kfnz - fsupc;
+        Index isub = lptr + no_zeros;
+
+        Index off = u_rows - segsize;
+        for (Index i = 0; i < segsize; i++) {
+          Index irow = glu.lsub(isub++);
+          dense_col(irow) = U.coeff(i + off, u_col);
+          U.coeffRef(i + off, u_col) = 0;
+        }
+
+        // Scatter l into SPA dense[]
+        for (Index i = 0; i < nrow; i++) {
+          Index irow = glu.lsub(isub++);
+          dense_col(irow) -= L.coeff(i, u_col);
+          L.coeffRef(i, u_col) = 0;
+        }
+        u_col++;
+      }
+    } else  // level 2 only
+    {
+      // Sequence through each column in the panel
+      for (jj = jcol; jj < jcol + w; jj++) {
+        nextl_col = (jj - jcol) * m;
+        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m);  // First nonzero column index for each row
+        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m);   // Scatter/gather entire matrix column from/to here
+
+        kfnz = repfnz_col(krep);
+        if (kfnz == emptyIdxLU) continue;  // skip any zero segment
+
+        segsize = krep - kfnz + 1;
+        luptr = glu.xlusup(fsupc);
+
+        Index lda = glu.xlusup(fsupc + 1) - glu.xlusup(fsupc);  // nsupr
+
+        // Perform a trianglar solve and block update,
+        // then scatter the result of sup-col update to dense[]
+        no_zeros = kfnz - fsupc;
+        if (segsize == 1)
+          LU_kernel_bmod<1>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
+        else if (segsize == 2)
+          LU_kernel_bmod<2>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
+        else if (segsize == 3)
+          LU_kernel_bmod<3>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
+        else
+          LU_kernel_bmod<Dynamic>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr,
+                                       no_zeros);
+      }  // End for each column in the panel
+    }
+
+  }  // End for each updating supernode
+}  // end panel bmod
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // SPARSELU_PANEL_BMOD_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_panel_dfs.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_panel_dfs.h
new file mode 100644
index 0000000..4412396
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_panel_dfs.h
@@ -0,0 +1,232 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+
+ * NOTE: This file is the modified version of [s,d,c,z]panel_dfs.c file in SuperLU
+
+ * -- SuperLU routine (version 2.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * November 15, 1997
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_PANEL_DFS_H
+#define SPARSELU_PANEL_DFS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename IndexVector>
+struct panel_dfs_traits {
+  typedef typename IndexVector::Scalar StorageIndex;
+  panel_dfs_traits(Index jcol, StorageIndex* marker) : m_jcol(jcol), m_marker(marker) {}
+  bool update_segrep(Index krep, StorageIndex jj) {
+    if (m_marker[krep] < m_jcol) {
+      m_marker[krep] = jj;
+      return true;
+    }
+    return false;
+  }
+  void mem_expand(IndexVector& /*glu.lsub*/, Index /*nextl*/, Index /*chmark*/) {}
+  enum { ExpandMem = false };
+  Index m_jcol;
+  StorageIndex* m_marker;
+};
+
+template <typename Scalar, typename StorageIndex>
+template <typename Traits>
+void SparseLUImpl<Scalar, StorageIndex>::dfs_kernel(const StorageIndex jj, IndexVector& perm_r, Index& nseg,
+                                                    IndexVector& panel_lsub, IndexVector& segrep,
+                                                    Ref<IndexVector> repfnz_col, IndexVector& xprune,
+                                                    Ref<IndexVector> marker, IndexVector& parent, IndexVector& xplore,
+                                                    GlobalLU_t& glu, Index& nextl_col, Index krow, Traits& traits) {
+  StorageIndex kmark = marker(krow);
+
+  // For each unmarked krow of jj
+  marker(krow) = jj;
+  StorageIndex kperm = perm_r(krow);
+  if (kperm == emptyIdxLU) {
+    // krow is in L : place it in structure of L(*, jj)
+    panel_lsub(nextl_col++) = StorageIndex(krow);  // krow is indexed into A
+
+    traits.mem_expand(panel_lsub, nextl_col, kmark);
+  } else {
+    // krow is in U : if its supernode-representative krep
+    // has been explored, update repfnz(*)
+    // krep = supernode representative of the current row
+    StorageIndex krep = glu.xsup(glu.supno(kperm) + 1) - 1;
+    // First nonzero element in the current column:
+    StorageIndex myfnz = repfnz_col(krep);
+
+    if (myfnz != emptyIdxLU) {
+      // Representative visited before
+      if (myfnz > kperm) repfnz_col(krep) = kperm;
+
+    } else {
+      // Otherwise, perform dfs starting at krep
+      StorageIndex oldrep = emptyIdxLU;
+      parent(krep) = oldrep;
+      repfnz_col(krep) = kperm;
+      StorageIndex xdfs = glu.xlsub(krep);
+      Index maxdfs = xprune(krep);
+
+      StorageIndex kpar;
+      do {
+        // For each unmarked kchild of krep
+        while (xdfs < maxdfs) {
+          StorageIndex kchild = glu.lsub(xdfs);
+          xdfs++;
+          StorageIndex chmark = marker(kchild);
+
+          if (chmark != jj) {
+            marker(kchild) = jj;
+            StorageIndex chperm = perm_r(kchild);
+
+            if (chperm == emptyIdxLU) {
+              // case kchild is in L: place it in L(*, j)
+              panel_lsub(nextl_col++) = kchild;
+              traits.mem_expand(panel_lsub, nextl_col, chmark);
+            } else {
+              // case kchild is in U :
+              // chrep = its supernode-rep. If its rep has been explored,
+              // update its repfnz(*)
+              StorageIndex chrep = glu.xsup(glu.supno(chperm) + 1) - 1;
+              myfnz = repfnz_col(chrep);
+
+              if (myfnz != emptyIdxLU) {  // Visited before
+                if (myfnz > chperm) repfnz_col(chrep) = chperm;
+              } else {  // Cont. dfs at snode-rep of kchild
+                xplore(krep) = xdfs;
+                oldrep = krep;
+                krep = chrep;  // Go deeper down G(L)
+                parent(krep) = oldrep;
+                repfnz_col(krep) = chperm;
+                xdfs = glu.xlsub(krep);
+                maxdfs = xprune(krep);
+
+              }  // end if myfnz != -1
+            }    // end if chperm == -1
+
+          }  // end if chmark !=jj
+        }    // end while xdfs < maxdfs
+
+        // krow has no more unexplored nbrs :
+        //    Place snode-rep krep in postorder DFS, if this
+        //    segment is seen for the first time. (Note that
+        //    "repfnz(krep)" may change later.)
+        //    Baktrack dfs to its parent
+        if (traits.update_segrep(krep, jj)) {
+          segrep(nseg) = krep;
+          ++nseg;
+        }
+
+        kpar = parent(krep);            // Pop recursion, mimic recursion
+        if (kpar == emptyIdxLU) break;  // dfs done
+        krep = kpar;
+        xdfs = xplore(krep);
+        maxdfs = xprune(krep);
+
+      } while (kpar != emptyIdxLU);  // Do until empty stack
+
+    }  // end if (myfnz = -1)
+
+  }  // end if (kperm == -1)
+}
+
+/**
+ * \brief Performs a symbolic factorization on a panel of columns [jcol, jcol+w)
+ *
+ * A supernode representative is the last column of a supernode.
+ * The nonzeros in U[*,j] are segments that end at supernodes representatives
+ *
+ * The routine returns a list of the supernodal representatives
+ * in topological order of the dfs that generates them. This list is
+ * a superset of the topological order of each individual column within
+ * the panel.
+ * The location of the first nonzero in each supernodal segment
+ * (supernodal entry location) is also returned. Each column has
+ * a separate list for this purpose.
+ *
+ * Two markers arrays are used for dfs :
+ *    marker[i] == jj, if i was visited during dfs of current column jj;
+ *    marker1[i] >= jcol, if i was visited by earlier columns in this panel;
+ *
+ * \param[in] m number of rows in the matrix
+ * \param[in] w Panel size
+ * \param[in] jcol Starting  column of the panel
+ * \param[in] A Input matrix in column-major storage
+ * \param[in] perm_r Row permutation
+ * \param[out] nseg Number of U segments
+ * \param[out] dense Accumulate the column vectors of the panel
+ * \param[out] panel_lsub Subscripts of the row in the panel
+ * \param[out] segrep Segment representative i.e first nonzero row of each segment
+ * \param[out] repfnz First nonzero location in each row
+ * \param[out] xprune The pruned elimination tree
+ * \param[out] marker work vector
+ * \param  parent The elimination tree
+ * \param xplore work vector
+ * \param glu The global data structure
+ *
+ */
+
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar, StorageIndex>::panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A,
+                                                   IndexVector& perm_r, Index& nseg, ScalarVector& dense,
+                                                   IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz,
+                                                   IndexVector& xprune, IndexVector& marker, IndexVector& parent,
+                                                   IndexVector& xplore, GlobalLU_t& glu) {
+  Index nextl_col;  // Next available position in panel_lsub[*,jj]
+
+  // Initialize pointers
+  VectorBlock<IndexVector> marker1(marker, m, m);
+  nseg = 0;
+
+  panel_dfs_traits<IndexVector> traits(jcol, marker1.data());
+
+  // For each column in the panel
+  for (StorageIndex jj = StorageIndex(jcol); jj < jcol + w; jj++) {
+    nextl_col = (jj - jcol) * m;
+
+    VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m);  // First nonzero location in each row
+    VectorBlock<ScalarVector> dense_col(dense, nextl_col, m);   // Accumulate a column vector here
+
+    // For each nnz in A[*, jj] do depth first search
+    for (typename MatrixType::InnerIterator it(A, jj); it; ++it) {
+      Index krow = it.row();
+      dense_col(krow) = it.value();
+
+      StorageIndex kmark = marker(krow);
+      if (kmark == jj) continue;  // krow visited before, go to the next nonzero
+
+      dfs_kernel(jj, perm_r, nseg, panel_lsub, segrep, repfnz_col, xprune, marker, parent, xplore, glu, nextl_col, krow,
+                 traits);
+    }  // end for nonzeros in column jj
+
+  }  // end for column jj
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // SPARSELU_PANEL_DFS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_pivotL.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_pivotL.h
new file mode 100644
index 0000000..10a090b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_pivotL.h
@@ -0,0 +1,136 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+
+ * NOTE: This file is the modified version of xpivotL.c file in SuperLU
+
+ * -- SuperLU routine (version 3.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * October 15, 2003
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_PIVOTL_H
+#define SPARSELU_PIVOTL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \brief Performs the numerical pivoting on the current column of L, and the CDIV operation.
+ *
+ * Pivot policy :
+ * (1) Compute thresh = u * max_(i>=j) abs(A_ij);
+ * (2) IF user specifies pivot row k and abs(A_kj) >= thresh THEN
+ *           pivot row = k;
+ *       ELSE IF abs(A_jj) >= thresh THEN
+ *           pivot row = j;
+ *       ELSE
+ *           pivot row = m;
+ *
+ *   Note: If you absolutely want to use a given pivot order, then set u=0.0.
+ *
+ * \param jcol The current column of L
+ * \param diagpivotthresh diagonal pivoting threshold
+ * \param[in,out] perm_r Row permutation (threshold pivoting)
+ * \param[in] iperm_c column permutation - used to finf diagonal of Pc*A*Pc'
+ * \param[out] pivrow  The pivot row
+ * \param glu Global LU data
+ * \return 0 if success, i > 0 if U(i,i) is exactly zero
+ *
+ */
+template <typename Scalar, typename StorageIndex>
+Index SparseLUImpl<Scalar, StorageIndex>::pivotL(const Index jcol, const RealScalar& diagpivotthresh,
+                                                 IndexVector& perm_r, IndexVector& iperm_c, Index& pivrow,
+                                                 GlobalLU_t& glu) {
+  Index fsupc = (glu.xsup)((glu.supno)(jcol));  // First column in the supernode containing the column jcol
+  Index nsupc = jcol - fsupc;                   // Number of columns in the supernode portion, excluding jcol; nsupc >=0
+  Index lptr = glu.xlsub(fsupc);  // pointer to the starting location of the row subscripts for this supernode portion
+  Index nsupr = glu.xlsub(fsupc + 1) - lptr;                    // Number of rows in the supernode
+  Index lda = glu.xlusup(fsupc + 1) - glu.xlusup(fsupc);        // leading dimension
+  Scalar* lu_sup_ptr = &(glu.lusup.data()[glu.xlusup(fsupc)]);  // Start of the current supernode
+  Scalar* lu_col_ptr = &(glu.lusup.data()[glu.xlusup(jcol)]);   // Start of jcol in the supernode
+  StorageIndex* lsub_ptr = &(glu.lsub.data()[lptr]);            // Start of row indices of the supernode
+
+  // Determine the largest abs numerical value for partial pivoting
+  Index diagind = iperm_c(jcol);  // diagonal index
+  RealScalar pivmax(-1.0);
+  Index pivptr = nsupc;
+  Index diag = emptyIdxLU;
+  RealScalar rtemp;
+  Index isub, icol, itemp, k;
+  for (isub = nsupc; isub < nsupr; ++isub) {
+    using std::abs;
+    rtemp = abs(lu_col_ptr[isub]);
+    if (rtemp > pivmax) {
+      pivmax = rtemp;
+      pivptr = isub;
+    }
+    if (lsub_ptr[isub] == diagind) diag = isub;
+  }
+
+  // Test for singularity
+  if (pivmax <= RealScalar(0.0)) {
+    // if pivmax == -1, the column is structurally empty, otherwise it is only numerically zero
+    pivrow = pivmax < RealScalar(0.0) ? diagind : lsub_ptr[pivptr];
+    perm_r(pivrow) = StorageIndex(jcol);
+    return (jcol + 1);
+  }
+
+  RealScalar thresh = diagpivotthresh * pivmax;
+
+  // Choose appropriate pivotal element
+
+  {
+    // Test if the diagonal element can be used as a pivot (given the threshold value)
+    if (diag >= 0) {
+      // Diagonal element exists
+      using std::abs;
+      rtemp = abs(lu_col_ptr[diag]);
+      if (rtemp != RealScalar(0.0) && rtemp >= thresh) pivptr = diag;
+    }
+    pivrow = lsub_ptr[pivptr];
+  }
+
+  // Record pivot row
+  perm_r(pivrow) = StorageIndex(jcol);
+  // Interchange row subscripts
+  if (pivptr != nsupc) {
+    std::swap(lsub_ptr[pivptr], lsub_ptr[nsupc]);
+    // Interchange numerical values as well, for the two rows in the whole snode
+    // such that L is indexed the same way as A
+    for (icol = 0; icol <= nsupc; icol++) {
+      itemp = pivptr + icol * lda;
+      std::swap(lu_sup_ptr[itemp], lu_sup_ptr[nsupc + icol * lda]);
+    }
+  }
+  // cdiv operations
+  Scalar temp = Scalar(1.0) / lu_col_ptr[nsupc];
+  for (k = nsupc + 1; k < nsupr; k++) lu_col_ptr[k] *= temp;
+  return 0;
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // SPARSELU_PIVOTL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_pruneL.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_pruneL.h
new file mode 100644
index 0000000..620f285
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_pruneL.h
@@ -0,0 +1,130 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+
+ * NOTE: This file is the modified version of [s,d,c,z]pruneL.c file in SuperLU
+
+ * -- SuperLU routine (version 2.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * November 15, 1997
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_PRUNEL_H
+#define SPARSELU_PRUNEL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \brief Prunes the L-structure.
+ *
+ * It prunes the L-structure  of supernodes whose L-structure contains the current pivot row "pivrow"
+ *
+ *
+ * \param jcol The current column of L
+ * \param[in] perm_r Row permutation
+ * \param[out] pivrow  The pivot row
+ * \param nseg Number of segments
+ * \param segrep
+ * \param repfnz
+ * \param[out] xprune
+ * \param glu Global LU data
+ *
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar, StorageIndex>::pruneL(const Index jcol, const IndexVector& perm_r, const Index pivrow,
+                                                const Index nseg, const IndexVector& segrep, BlockIndexVector repfnz,
+                                                IndexVector& xprune, GlobalLU_t& glu) {
+  // For each supernode-rep irep in U(*,j]
+  Index jsupno = glu.supno(jcol);
+  Index i, irep, irep1;
+  bool movnum, do_prune = false;
+  Index kmin = 0, kmax = 0, minloc, maxloc, krow;
+  for (i = 0; i < nseg; i++) {
+    irep = segrep(i);
+    irep1 = irep + 1;
+    do_prune = false;
+
+    // Don't prune with a zero U-segment
+    if (repfnz(irep) == emptyIdxLU) continue;
+
+    // If a snode overlaps with the next panel, then the U-segment
+    // is fragmented into two parts -- irep and irep1. We should let
+    // pruning occur at the rep-column in irep1s snode.
+    if (glu.supno(irep) == glu.supno(irep1)) continue;  // don't prune
+
+    // If it has not been pruned & it has a nonz in row L(pivrow,i)
+    if (glu.supno(irep) != jsupno) {
+      if (xprune(irep) >= glu.xlsub(irep1)) {
+        kmin = glu.xlsub(irep);
+        kmax = glu.xlsub(irep1) - 1;
+        for (krow = kmin; krow <= kmax; krow++) {
+          if (glu.lsub(krow) == pivrow) {
+            do_prune = true;
+            break;
+          }
+        }
+      }
+
+      if (do_prune) {
+        // do a quicksort-type partition
+        // movnum=true means that the num values have to be exchanged
+        movnum = false;
+        if (irep == glu.xsup(glu.supno(irep)))  // Snode of size 1
+          movnum = true;
+
+        while (kmin <= kmax) {
+          if (perm_r(glu.lsub(kmax)) == emptyIdxLU)
+            kmax--;
+          else if (perm_r(glu.lsub(kmin)) != emptyIdxLU)
+            kmin++;
+          else {
+            // kmin below pivrow (not yet pivoted), and kmax
+            // above pivrow: interchange the two subscripts
+            std::swap(glu.lsub(kmin), glu.lsub(kmax));
+
+            // If the supernode has only one column, then we
+            // only keep one set of subscripts. For any subscript
+            // intercnahge performed, similar interchange must be
+            // done on the numerical values.
+            if (movnum) {
+              minloc = glu.xlusup(irep) + (kmin - glu.xlsub(irep));
+              maxloc = glu.xlusup(irep) + (kmax - glu.xlsub(irep));
+              std::swap(glu.lusup(minloc), glu.lusup(maxloc));
+            }
+            kmin++;
+            kmax--;
+          }
+        }  // end while
+
+        xprune(irep) = StorageIndex(kmin);  // Pruning
+      }                                     // end if do_prune
+    }                                       // end pruning
+  }                                         // End for each U-segment
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // SPARSELU_PRUNEL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_relax_snode.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_relax_snode.h
new file mode 100644
index 0000000..df3869e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseLU/SparseLU_relax_snode.h
@@ -0,0 +1,81 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* This file is a modified version of heap_relax_snode.c file in SuperLU
+ * -- SuperLU routine (version 3.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * October 15, 2003
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+
+#ifndef SPARSELU_RELAX_SNODE_H
+#define SPARSELU_RELAX_SNODE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**
+ * \brief Identify the initial relaxed supernodes
+ *
+ * This routine is applied to a column elimination tree.
+ * It assumes that the matrix has been reordered according to the postorder of the etree
+ * \param n  the number of columns
+ * \param et elimination tree
+ * \param relax_columns Maximum number of columns allowed in a relaxed snode
+ * \param descendants Number of descendants of each node in the etree
+ * \param relax_end last column in a supernode
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar, StorageIndex>::relax_snode(const Index n, IndexVector& et, const Index relax_columns,
+                                                     IndexVector& descendants, IndexVector& relax_end) {
+  // compute the number of descendants of each node in the etree
+  Index parent;
+  relax_end.setConstant(emptyIdxLU);
+  descendants.setZero();
+  for (Index j = 0; j < n; j++) {
+    parent = et(j);
+    if (parent != n)  // not the dummy root
+      descendants(parent) += descendants(j) + 1;
+  }
+  // Identify the relaxed supernodes by postorder traversal of the etree
+  Index snode_start;  // beginning of a snode
+  for (Index j = 0; j < n;) {
+    parent = et(j);
+    snode_start = j;
+    while (parent != n && descendants(parent) < relax_columns) {
+      j = parent;
+      parent = et(j);
+    }
+    // Found a supernode in postordered etree, j is the last column
+    relax_end(snode_start) = StorageIndex(j);  // Record last column
+    j++;
+    // Search for a new leaf
+    while (descendants(j) != 0 && j < n) j++;
+  }  // End postorder traversal of the etree
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseQR/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseQR/InternalHeaderCheck.h
new file mode 100644
index 0000000..0564e93
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseQR/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPARSEQR_MODULE_H
+#error "Please include Eigen/SparseQR instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SparseQR/SparseQR.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseQR/SparseQR.h
new file mode 100644
index 0000000..d11e664
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SparseQR/SparseQR.h
@@ -0,0 +1,705 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012-2013 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_QR_H
+#define EIGEN_SPARSE_QR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename MatrixType, typename OrderingType>
+class SparseQR;
+template <typename SparseQRType>
+struct SparseQRMatrixQReturnType;
+template <typename SparseQRType>
+struct SparseQRMatrixQTransposeReturnType;
+template <typename SparseQRType, typename Derived>
+struct SparseQR_QProduct;
+namespace internal {
+template <typename SparseQRType>
+struct traits<SparseQRMatrixQReturnType<SparseQRType> > {
+  typedef typename SparseQRType::MatrixType ReturnType;
+  typedef typename ReturnType::StorageIndex StorageIndex;
+  typedef typename ReturnType::StorageKind StorageKind;
+  enum { RowsAtCompileTime = Dynamic, ColsAtCompileTime = Dynamic };
+};
+template <typename SparseQRType>
+struct traits<SparseQRMatrixQTransposeReturnType<SparseQRType> > {
+  typedef typename SparseQRType::MatrixType ReturnType;
+};
+template <typename SparseQRType, typename Derived>
+struct traits<SparseQR_QProduct<SparseQRType, Derived> > {
+  typedef typename Derived::PlainObject ReturnType;
+};
+}  // End namespace internal
+
+/**
+ * \ingroup SparseQR_Module
+ * \class SparseQR
+ * \brief Sparse left-looking QR factorization with numerical column pivoting
+ *
+ * This class implements a left-looking QR decomposition of sparse matrices
+ * with numerical column pivoting.
+ * When a column has a norm less than a given tolerance
+ * it is implicitly permuted to the end. The QR factorization thus obtained is
+ * given by A*P = Q*R where R is upper triangular or trapezoidal.
+ *
+ * P is the column permutation which is the product of the fill-reducing and the
+ * numerical permutations. Use colsPermutation() to get it.
+ *
+ * Q is the orthogonal matrix represented as products of Householder reflectors.
+ * Use matrixQ() to get an expression and matrixQ().adjoint() to get the adjoint.
+ * You can then apply it to a vector.
+ *
+ * R is the sparse triangular or trapezoidal matrix. The later occurs when A is rank-deficient.
+ * matrixR().topLeftCorner(rank(), rank()) always returns a triangular factor of full rank.
+ *
+ * \tparam MatrixType_ The type of the sparse matrix A, must be a column-major SparseMatrix<>
+ * \tparam OrderingType_ The fill-reducing ordering method. See the \link OrderingMethods_Module
+ *  OrderingMethods \endlink module for the list of built-in and external ordering methods.
+ *
+ * \implsparsesolverconcept
+ *
+ * The numerical pivoting strategy and default threshold are the same as in SuiteSparse QR, and
+ * detailed in the following paper:
+ * <i>
+ * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
+ * Sparse QR Factorization", ACM Trans. on Math. Soft. 38(1), 2011.
+ * </i>
+ * Even though it is qualified as "rank-revealing", this strategy might fail for some
+ * rank deficient problems. When this class is used to solve linear or least-square problems
+ * it is thus strongly recommended to check the accuracy of the computed solution. If it
+ * failed, it usually helps to increase the threshold with setPivotThreshold.
+ *
+ * \warning The input sparse matrix A must be in compressed mode (see SparseMatrix::makeCompressed()).
+ * \warning For complex matrices matrixQ().transpose() will actually return the adjoint matrix.
+ *
+ */
+template <typename MatrixType_, typename OrderingType_>
+class SparseQR : public SparseSolverBase<SparseQR<MatrixType_, OrderingType_> > {
+ protected:
+  typedef SparseSolverBase<SparseQR<MatrixType_, OrderingType_> > Base;
+  using Base::m_isInitialized;
+
+ public:
+  using Base::_solve_impl;
+  typedef MatrixType_ MatrixType;
+  typedef OrderingType_ OrderingType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> QRMatrixType;
+  typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
+  typedef Matrix<Scalar, Dynamic, 1> ScalarVector;
+  typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+ public:
+  SparseQR()
+      : m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true), m_isQSorted(false), m_isEtreeOk(false) {}
+
+  /** Construct a QR factorization of the matrix \a mat.
+   *
+   * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
+   *
+   * \sa compute()
+   */
+  explicit SparseQR(const MatrixType& mat)
+      : m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true), m_isQSorted(false), m_isEtreeOk(false) {
+    compute(mat);
+  }
+
+  /** Computes the QR factorization of the sparse matrix \a mat.
+   *
+   * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
+   *
+   * \sa analyzePattern(), factorize()
+   */
+  void compute(const MatrixType& mat) {
+    analyzePattern(mat);
+    factorize(mat);
+  }
+  void analyzePattern(const MatrixType& mat);
+  void factorize(const MatrixType& mat);
+
+  /** \returns the number of rows of the represented matrix.
+   */
+  inline Index rows() const { return m_pmat.rows(); }
+
+  /** \returns the number of columns of the represented matrix.
+   */
+  inline Index cols() const { return m_pmat.cols(); }
+
+  /** \returns a const reference to the \b sparse upper triangular matrix R of the QR factorization.
+   * \warning The entries of the returned matrix are not sorted. This means that using it in algorithms
+   *          expecting sorted entries will fail. This include random coefficient accesses (SpaseMatrix::coeff()),
+   *          and coefficient-wise operations. Matrix products and triangular solves are fine though.
+   *
+   * To sort the entries, you can assign it to a row-major matrix, and if a column-major matrix
+   * is required, you can copy it again:
+   * \code
+   * SparseMatrix<double>          R  = qr.matrixR();  // column-major, not sorted!
+   * SparseMatrix<double,RowMajor> Rr = qr.matrixR();  // row-major, sorted
+   * SparseMatrix<double>          Rc = Rr;            // column-major, sorted
+   * \endcode
+   */
+  const QRMatrixType& matrixR() const { return m_R; }
+
+  /** \returns the number of non linearly dependent columns as determined by the pivoting threshold.
+   *
+   * \sa setPivotThreshold()
+   */
+  Index rank() const {
+    eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
+    return m_nonzeropivots;
+  }
+
+  /** \returns an expression of the matrix Q as products of sparse Householder reflectors.
+   * The common usage of this function is to apply it to a dense matrix or vector
+   * \code
+   * VectorXd B1, B2;
+   * // Initialize B1
+   * B2 = matrixQ() * B1;
+   * \endcode
+   *
+   * To get a plain SparseMatrix representation of Q:
+   * \code
+   * SparseMatrix<double> Q;
+   * Q = SparseQR<SparseMatrix<double> >(A).matrixQ();
+   * \endcode
+   * Internally, this call simply performs a sparse product between the matrix Q
+   * and a sparse identity matrix. However, due to the fact that the sparse
+   * reflectors are stored unsorted, two transpositions are needed to sort
+   * them before performing the product.
+   */
+  SparseQRMatrixQReturnType<SparseQR> matrixQ() const { return SparseQRMatrixQReturnType<SparseQR>(*this); }
+
+  /** \returns a const reference to the column permutation P that was applied to A such that A*P = Q*R
+   * It is the combination of the fill-in reducing permutation and numerical column pivoting.
+   */
+  const PermutationType& colsPermutation() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_outputPerm_c;
+  }
+
+  /** \returns A string describing the type of error.
+   * This method is provided to ease debugging, not to handle errors.
+   */
+  std::string lastErrorMessage() const { return m_lastError; }
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  bool _solve_impl(const MatrixBase<Rhs>& B, MatrixBase<Dest>& dest) const {
+    eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
+    eigen_assert(this->rows() == B.rows() &&
+                 "SparseQR::solve() : invalid number of rows in the right hand side matrix");
+
+    Index rank = this->rank();
+
+    // Compute Q^* * b;
+    typename Dest::PlainObject y, b;
+    y = this->matrixQ().adjoint() * B;
+    b = y;
+
+    // Solve with the triangular matrix R
+    y.resize((std::max<Index>)(cols(), y.rows()), y.cols());
+    y.topRows(rank) = this->matrixR().topLeftCorner(rank, rank).template triangularView<Upper>().solve(b.topRows(rank));
+    y.bottomRows(y.rows() - rank).setZero();
+
+    // Apply the column permutation
+    if (m_perm_c.size())
+      dest = colsPermutation() * y.topRows(cols());
+    else
+      dest = y.topRows(cols());
+
+    m_info = Success;
+    return true;
+  }
+
+  /** Sets the threshold that is used to determine linearly dependent columns during the factorization.
+   *
+   * In practice, if during the factorization the norm of the column that has to be eliminated is below
+   * this threshold, then the entire column is treated as zero, and it is moved at the end.
+   */
+  void setPivotThreshold(const RealScalar& threshold) {
+    m_useDefaultThreshold = false;
+    m_threshold = threshold;
+  }
+
+  /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
+   *
+   * \sa compute()
+   */
+  template <typename Rhs>
+  inline Solve<SparseQR, Rhs> solve(const MatrixBase<Rhs>& B) const {
+    eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
+    eigen_assert(this->rows() == B.rows() &&
+                 "SparseQR::solve() : invalid number of rows in the right hand side matrix");
+    return Solve<SparseQR, Rhs>(*this, B.derived());
+  }
+  template <typename Rhs>
+  inline Solve<SparseQR, Rhs> solve(const SparseMatrixBase<Rhs>& B) const {
+    eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
+    eigen_assert(this->rows() == B.rows() &&
+                 "SparseQR::solve() : invalid number of rows in the right hand side matrix");
+    return Solve<SparseQR, Rhs>(*this, B.derived());
+  }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the QR factorization reports a numerical problem
+   *          \c InvalidInput if the input matrix is invalid
+   *
+   * \sa iparm()
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  /** \internal */
+  inline void _sort_matrix_Q() {
+    if (this->m_isQSorted) return;
+    // The matrix Q is sorted during the transposition
+    SparseMatrix<Scalar, RowMajor, Index> mQrm(this->m_Q);
+    this->m_Q = mQrm;
+    this->m_isQSorted = true;
+  }
+
+ protected:
+  bool m_analysisIsok;
+  bool m_factorizationIsok;
+  mutable ComputationInfo m_info;
+  std::string m_lastError;
+  QRMatrixType m_pmat;             // Temporary matrix
+  QRMatrixType m_R;                // The triangular factor matrix
+  QRMatrixType m_Q;                // The orthogonal reflectors
+  ScalarVector m_hcoeffs;          // The Householder coefficients
+  PermutationType m_perm_c;        // Fill-reducing  Column  permutation
+  PermutationType m_pivotperm;     // The permutation for rank revealing
+  PermutationType m_outputPerm_c;  // The final column permutation
+  RealScalar m_threshold;          // Threshold to determine null Householder reflections
+  bool m_useDefaultThreshold;      // Use default threshold
+  Index m_nonzeropivots;           // Number of non zero pivots found
+  IndexVector m_etree;             // Column elimination tree
+  IndexVector m_firstRowElt;       // First element in each row
+  bool m_isQSorted;                // whether Q is sorted or not
+  bool m_isEtreeOk;                // whether the elimination tree match the initial input matrix
+
+  template <typename, typename>
+  friend struct SparseQR_QProduct;
+};
+
+/** \brief Preprocessing step of a QR factorization
+ *
+ * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
+ *
+ * In this step, the fill-reducing permutation is computed and applied to the columns of A
+ * and the column elimination tree is computed as well. Only the sparsity pattern of \a mat is exploited.
+ *
+ * \note In this step it is assumed that there is no empty row in the matrix \a mat.
+ */
+template <typename MatrixType, typename OrderingType>
+void SparseQR<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat) {
+  eigen_assert(
+      mat.isCompressed() &&
+      "SparseQR requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to SparseQR");
+  // Copy to a column major matrix if the input is rowmajor
+  std::conditional_t<MatrixType::IsRowMajor, QRMatrixType, const MatrixType&> matCpy(mat);
+  // Compute the column fill reducing ordering
+  OrderingType ord;
+  ord(matCpy, m_perm_c);
+  Index n = mat.cols();
+  Index m = mat.rows();
+  Index diagSize = (std::min)(m, n);
+
+  if (!m_perm_c.size()) {
+    m_perm_c.resize(n);
+    m_perm_c.indices().setLinSpaced(n, 0, StorageIndex(n - 1));
+  }
+
+  // Compute the column elimination tree of the permuted matrix
+  m_outputPerm_c = m_perm_c.inverse();
+  internal::coletree(matCpy, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
+  m_isEtreeOk = true;
+
+  m_R.resize(m, n);
+  m_Q.resize(m, diagSize);
+
+  // Allocate space for nonzero elements: rough estimation
+  m_R.reserve(2 * mat.nonZeros());  // FIXME: get a tighter bound via symbolic factorization using the etree.
+  m_Q.reserve(2 * mat.nonZeros());
+  m_hcoeffs.resize(diagSize);
+  m_analysisIsok = true;
+}
+
+/** \brief Performs the numerical QR factorization of the input matrix
+ *
+ * The function SparseQR::analyzePattern(const MatrixType&) must have been called beforehand with
+ * a matrix having the same sparsity pattern than \a mat.
+ *
+ * \param mat The sparse column-major matrix
+ */
+template <typename MatrixType, typename OrderingType>
+void SparseQR<MatrixType, OrderingType>::factorize(const MatrixType& mat) {
+  using std::abs;
+
+  eigen_assert(m_analysisIsok && "analyzePattern() should be called before this step");
+  StorageIndex m = StorageIndex(mat.rows());
+  StorageIndex n = StorageIndex(mat.cols());
+  StorageIndex diagSize = (std::min)(m, n);
+  IndexVector mark((std::max)(m, n));
+  mark.setConstant(-1);          // Record the visited nodes
+  IndexVector Ridx(n), Qidx(m);  // Store temporarily the row indexes for the current column of R and Q
+  Index nzcolR, nzcolQ;          // Number of nonzero for the current column of R and Q
+  ScalarVector tval(m);          // The dense vector used to compute the current column
+
+  m_R.setZero();
+  m_Q.setZero();
+  m_pmat = mat;
+  if (!m_isEtreeOk) {
+    m_outputPerm_c = m_perm_c.inverse();
+    internal::coletree(m_pmat, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
+    m_isEtreeOk = true;
+  }
+
+  m_pmat.uncompress();  // To have the innerNonZeroPtr allocated
+
+  // Apply the fill-in reducing permutation lazily:
+  {
+    // If the input is row major, copy the original column indices,
+    // otherwise directly use the input matrix
+    //
+    IndexVector originalOuterIndicesCpy;
+    const StorageIndex* originalOuterIndices = mat.outerIndexPtr();
+    if (MatrixType::IsRowMajor) {
+      originalOuterIndicesCpy = IndexVector::Map(m_pmat.outerIndexPtr(), n + 1);
+      originalOuterIndices = originalOuterIndicesCpy.data();
+    }
+
+    for (int i = 0; i < n; i++) {
+      Index p = m_perm_c.size() ? m_perm_c.indices()(i) : i;
+      m_pmat.outerIndexPtr()[p] = originalOuterIndices[i];
+      m_pmat.innerNonZeroPtr()[p] = originalOuterIndices[i + 1] - originalOuterIndices[i];
+    }
+  }
+
+  /* Compute the default threshold as in MatLab, see:
+   * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
+   * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011, Page 8:3
+   */
+  RealScalar pivotThreshold;
+  if (m_useDefaultThreshold) {
+    RealScalar max2Norm = 0.0;
+    for (int j = 0; j < n; j++) max2Norm = numext::maxi(max2Norm, m_pmat.col(j).norm());
+    if (max2Norm == RealScalar(0)) max2Norm = RealScalar(1);
+    pivotThreshold = 20 * (m + n) * max2Norm * NumTraits<RealScalar>::epsilon();
+  } else {
+    pivotThreshold = m_threshold;
+  }
+
+  // Initialize the numerical permutation
+  m_pivotperm.setIdentity(n);
+
+  StorageIndex nonzeroCol = 0;  // Record the number of valid pivots
+  m_Q.startVec(0);
+
+  // Left looking rank-revealing QR factorization: compute a column of R and Q at a time
+  for (StorageIndex col = 0; col < n; ++col) {
+    mark.setConstant(-1);
+    m_R.startVec(col);
+    mark(nonzeroCol) = col;
+    Qidx(0) = nonzeroCol;
+    nzcolR = 0;
+    nzcolQ = 1;
+    bool found_diag = nonzeroCol >= m;
+    tval.setZero();
+
+    // Symbolic factorization: find the nonzero locations of the column k of the factors R and Q, i.e.,
+    // all the nodes (with indexes lower than rank) reachable through the column elimination tree (etree) rooted at node
+    // k. Note: if the diagonal entry does not exist, then its contribution must be explicitly added, thus the trick
+    // with found_diag that permits to do one more iteration on the diagonal element if this one has not been found.
+    for (typename QRMatrixType::InnerIterator itp(m_pmat, col); itp || !found_diag; ++itp) {
+      StorageIndex curIdx = nonzeroCol;
+      if (itp) curIdx = StorageIndex(itp.row());
+      if (curIdx == nonzeroCol) found_diag = true;
+
+      // Get the nonzeros indexes of the current column of R
+      StorageIndex st = m_firstRowElt(curIdx);  // The traversal of the etree starts here
+      if (st < 0) {
+        m_lastError = "Empty row found during numerical factorization";
+        m_info = InvalidInput;
+        return;
+      }
+
+      // Traverse the etree
+      Index bi = nzcolR;
+      for (; mark(st) != col; st = m_etree(st)) {
+        Ridx(nzcolR) = st;  // Add this row to the list,
+        mark(st) = col;     // and mark this row as visited
+        nzcolR++;
+      }
+
+      // Reverse the list to get the topological ordering
+      Index nt = nzcolR - bi;
+      for (Index i = 0; i < nt / 2; i++) std::swap(Ridx(bi + i), Ridx(nzcolR - i - 1));
+
+      // Copy the current (curIdx,pcol) value of the input matrix
+      if (itp)
+        tval(curIdx) = itp.value();
+      else
+        tval(curIdx) = Scalar(0);
+
+      // Compute the pattern of Q(:,k)
+      if (curIdx > nonzeroCol && mark(curIdx) != col) {
+        Qidx(nzcolQ) = curIdx;  // Add this row to the pattern of Q,
+        mark(curIdx) = col;     // and mark it as visited
+        nzcolQ++;
+      }
+    }
+
+    // Browse all the indexes of R(:,col) in reverse order
+    for (Index i = nzcolR - 1; i >= 0; i--) {
+      Index curIdx = Ridx(i);
+
+      // Apply the curIdx-th householder vector to the current column (temporarily stored into tval)
+      Scalar tdot(0);
+
+      // First compute q' * tval
+      tdot = m_Q.col(curIdx).dot(tval);
+
+      tdot *= m_hcoeffs(curIdx);
+
+      // Then update tval = tval - q * tau
+      tval -= tdot * m_Q.col(curIdx);
+
+      // Detect fill-in for the current column of Q
+      if (m_etree(Ridx(i)) == nonzeroCol) {
+        for (typename QRMatrixType::InnerIterator itq(m_Q, curIdx); itq; ++itq) {
+          StorageIndex iQ = StorageIndex(itq.row());
+          if (mark(iQ) != col) {
+            Qidx(nzcolQ++) = iQ;  // Add this row to the pattern of Q,
+            mark(iQ) = col;       // and mark it as visited
+          }
+        }
+      }
+    }  // End update current column
+
+    Scalar tau = RealScalar(0);
+    RealScalar beta = 0;
+
+    if (nonzeroCol < diagSize) {
+      // Compute the Householder reflection that eliminate the current column
+      // FIXME: refactor to use the Householder module's reflector computation.
+      Scalar c0 = nzcolQ ? tval(Qidx(0)) : Scalar(0);
+
+      // First, the squared norm of Q((col+1):m, col)
+      RealScalar sqrNorm = 0.;
+      for (Index itq = 1; itq < nzcolQ; ++itq) sqrNorm += numext::abs2(tval(Qidx(itq)));
+      if (sqrNorm == RealScalar(0) && numext::imag(c0) == RealScalar(0)) {
+        beta = numext::real(c0);
+        tval(Qidx(0)) = 1;
+      } else {
+        using std::sqrt;
+        beta = sqrt(numext::abs2(c0) + sqrNorm);
+        if (numext::real(c0) >= RealScalar(0)) beta = -beta;
+        tval(Qidx(0)) = 1;
+        for (Index itq = 1; itq < nzcolQ; ++itq) tval(Qidx(itq)) /= (c0 - beta);
+        tau = numext::conj((beta - c0) / beta);
+      }
+    }
+
+    // Insert values in R
+    for (Index i = nzcolR - 1; i >= 0; i--) {
+      Index curIdx = Ridx(i);
+      if (curIdx < nonzeroCol) {
+        m_R.insertBackByOuterInnerUnordered(col, curIdx) = tval(curIdx);
+        tval(curIdx) = Scalar(0.);
+      }
+    }
+
+    if (nonzeroCol < diagSize && abs(beta) >= pivotThreshold) {
+      m_R.insertBackByOuterInner(col, nonzeroCol) = beta;
+      // The householder coefficient
+      m_hcoeffs(nonzeroCol) = tau;
+      // Record the householder reflections
+      for (Index itq = 0; itq < nzcolQ; ++itq) {
+        Index iQ = Qidx(itq);
+        m_Q.insertBackByOuterInnerUnordered(nonzeroCol, iQ) = tval(iQ);
+        tval(iQ) = Scalar(0.);
+      }
+      nonzeroCol++;
+      if (nonzeroCol < diagSize) m_Q.startVec(nonzeroCol);
+    } else {
+      // Zero pivot found: move implicitly this column to the end
+      for (Index j = nonzeroCol; j < n - 1; j++) std::swap(m_pivotperm.indices()(j), m_pivotperm.indices()[j + 1]);
+
+      // Recompute the column elimination tree
+      internal::coletree(m_pmat, m_etree, m_firstRowElt, m_pivotperm.indices().data());
+      m_isEtreeOk = false;
+    }
+  }
+
+  m_hcoeffs.tail(diagSize - nonzeroCol).setZero();
+
+  // Finalize the column pointers of the sparse matrices R and Q
+  m_Q.finalize();
+  m_Q.makeCompressed();
+  m_R.finalize();
+  m_R.makeCompressed();
+  m_isQSorted = false;
+
+  m_nonzeropivots = nonzeroCol;
+
+  if (nonzeroCol < n) {
+    // Permute the triangular factor to put the 'dead' columns to the end
+    QRMatrixType tempR(m_R);
+    m_R = tempR * m_pivotperm;
+
+    // Update the column permutation
+    m_outputPerm_c = m_outputPerm_c * m_pivotperm;
+  }
+
+  m_isInitialized = true;
+  m_factorizationIsok = true;
+  m_info = Success;
+}
+
+template <typename SparseQRType, typename Derived>
+struct SparseQR_QProduct : ReturnByValue<SparseQR_QProduct<SparseQRType, Derived> > {
+  typedef typename SparseQRType::QRMatrixType MatrixType;
+  typedef typename SparseQRType::Scalar Scalar;
+  // Get the references
+  SparseQR_QProduct(const SparseQRType& qr, const Derived& other, bool transpose)
+      : m_qr(qr), m_other(other), m_transpose(transpose) {}
+  inline Index rows() const { return m_qr.matrixQ().rows(); }
+  inline Index cols() const { return m_other.cols(); }
+
+  // Assign to a vector
+  template <typename DesType>
+  void evalTo(DesType& res) const {
+    Index m = m_qr.rows();
+    Index n = m_qr.cols();
+    Index diagSize = (std::min)(m, n);
+    res = m_other;
+    if (m_transpose) {
+      eigen_assert(m_qr.m_Q.rows() == m_other.rows() && "Non conforming object sizes");
+      // Compute res = Q' * other column by column
+      for (Index j = 0; j < res.cols(); j++) {
+        for (Index k = 0; k < diagSize; k++) {
+          Scalar tau = Scalar(0);
+          tau = m_qr.m_Q.col(k).dot(res.col(j));
+          if (tau == Scalar(0)) continue;
+          tau = tau * m_qr.m_hcoeffs(k);
+          res.col(j) -= tau * m_qr.m_Q.col(k);
+        }
+      }
+    } else {
+      eigen_assert(m_qr.matrixQ().cols() == m_other.rows() && "Non conforming object sizes");
+
+      res.conservativeResize(rows(), cols());
+
+      // Compute res = Q * other column by column
+      for (Index j = 0; j < res.cols(); j++) {
+        Index start_k = internal::is_identity<Derived>::value ? numext::mini(j, diagSize - 1) : diagSize - 1;
+        for (Index k = start_k; k >= 0; k--) {
+          Scalar tau = Scalar(0);
+          tau = m_qr.m_Q.col(k).dot(res.col(j));
+          if (tau == Scalar(0)) continue;
+          tau = tau * numext::conj(m_qr.m_hcoeffs(k));
+          res.col(j) -= tau * m_qr.m_Q.col(k);
+        }
+      }
+    }
+  }
+
+  const SparseQRType& m_qr;
+  const Derived& m_other;
+  bool m_transpose;  // TODO: rename to m_adjoint; this flag controls adjoint application.
+};
+
+template <typename SparseQRType>
+struct SparseQRMatrixQReturnType : public EigenBase<SparseQRMatrixQReturnType<SparseQRType> > {
+  typedef typename SparseQRType::Scalar Scalar;
+  typedef Matrix<Scalar, Dynamic, Dynamic> DenseMatrix;
+  enum { RowsAtCompileTime = Dynamic, ColsAtCompileTime = Dynamic };
+  explicit SparseQRMatrixQReturnType(const SparseQRType& qr) : m_qr(qr) {}
+  template <typename Derived>
+  SparseQR_QProduct<SparseQRType, Derived> operator*(const MatrixBase<Derived>& other) {
+    return SparseQR_QProduct<SparseQRType, Derived>(m_qr, other.derived(), false);
+  }
+  // To use for operations with the adjoint of Q
+  SparseQRMatrixQTransposeReturnType<SparseQRType> adjoint() const {
+    return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr);
+  }
+  inline Index rows() const { return m_qr.rows(); }
+  inline Index cols() const { return m_qr.rows(); }
+  // To use for operations with the transpose of Q. FIXME: currently identical to adjoint(); specialize for complex.
+  SparseQRMatrixQTransposeReturnType<SparseQRType> transpose() const {
+    return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr);
+  }
+  const SparseQRType& m_qr;
+};
+
+// TODO: rename to SparseQRMatrixQAdjointReturnType; this represents the adjoint of Q.
+template <typename SparseQRType>
+struct SparseQRMatrixQTransposeReturnType {
+  explicit SparseQRMatrixQTransposeReturnType(const SparseQRType& qr) : m_qr(qr) {}
+  template <typename Derived>
+  SparseQR_QProduct<SparseQRType, Derived> operator*(const MatrixBase<Derived>& other) {
+    return SparseQR_QProduct<SparseQRType, Derived>(m_qr, other.derived(), true);
+  }
+  const SparseQRType& m_qr;
+};
+
+namespace internal {
+
+template <typename SparseQRType>
+struct evaluator_traits<SparseQRMatrixQReturnType<SparseQRType> > {
+  typedef typename SparseQRType::MatrixType MatrixType;
+  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
+  typedef SparseShape Shape;
+};
+
+template <typename DstXprType, typename SparseQRType>
+struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>,
+                  internal::assign_op<typename DstXprType::Scalar, typename DstXprType::Scalar>, Sparse2Sparse> {
+  typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType;
+  typedef typename DstXprType::Scalar Scalar;
+  typedef typename DstXprType::StorageIndex StorageIndex;
+  static void run(DstXprType& dst, const SrcXprType& src, const internal::assign_op<Scalar, Scalar>& /*func*/) {
+    typename DstXprType::PlainObject idMat(src.rows(), src.cols());
+    idMat.setIdentity();
+    // Sort the sparse householder reflectors if needed
+    const_cast<SparseQRType*>(&src.m_qr)->_sort_matrix_Q();
+    dst = SparseQR_QProduct<SparseQRType, DstXprType>(src.m_qr, idMat, false);
+  }
+};
+
+template <typename DstXprType, typename SparseQRType>
+struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>,
+                  internal::assign_op<typename DstXprType::Scalar, typename DstXprType::Scalar>, Sparse2Dense> {
+  typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType;
+  typedef typename DstXprType::Scalar Scalar;
+  typedef typename DstXprType::StorageIndex StorageIndex;
+  static void run(DstXprType& dst, const SrcXprType& src, const internal::assign_op<Scalar, Scalar>& /*func*/) {
+    dst = src.m_qr.matrixQ() * DstXprType::Identity(src.m_qr.rows(), src.m_qr.rows());
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/StdDeque.h b/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/StdDeque.h
new file mode 100644
index 0000000..692281b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/StdDeque.h
@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDDEQUE_H
+#define EIGEN_STDDEQUE_H
+
+#ifndef EIGEN_STDDEQUE_MODULE_H
+#error "Please include Eigen/StdDeque instead of including this file directly."
+#endif
+
+#include "details.h"
+
+/**
+ * This section contains a convenience MACRO which allows an easy specialization of
+ * std::deque such that for data types with alignment issues the correct allocator
+ * is used automatically.
+ */
+#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)                                                \
+  namespace std {                                                                                 \
+  template <>                                                                                     \
+  class deque<__VA_ARGS__, std::allocator<__VA_ARGS__> >                                          \
+      : public deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > {                        \
+    typedef deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > deque_base;                 \
+                                                                                                  \
+   public:                                                                                        \
+    typedef __VA_ARGS__ value_type;                                                               \
+    typedef deque_base::allocator_type allocator_type;                                            \
+    typedef deque_base::size_type size_type;                                                      \
+    typedef deque_base::iterator iterator;                                                        \
+    explicit deque(const allocator_type& a = allocator_type()) : deque_base(a) {}                 \
+    template <typename InputIterator>                                                             \
+    deque(InputIterator first, InputIterator last, const allocator_type& a = allocator_type())    \
+        : deque_base(first, last, a) {}                                                           \
+    deque(const deque& c) : deque_base(c) {}                                                      \
+    explicit deque(size_type num, const value_type& val = value_type()) : deque_base(num, val) {} \
+    deque(iterator start_, iterator end_) : deque_base(start_, end_) {}                           \
+    deque& operator=(const deque& x) {                                                            \
+      deque_base::operator=(x);                                                                   \
+      return *this;                                                                               \
+    }                                                                                             \
+  };                                                                                              \
+  }
+
+#endif  // EIGEN_STDDEQUE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/StdList.h b/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/StdList.h
new file mode 100644
index 0000000..26395df
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/StdList.h
@@ -0,0 +1,50 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDLIST_H
+#define EIGEN_STDLIST_H
+
+#ifndef EIGEN_STDLIST_MODULE_H
+#error "Please include Eigen/StdList instead of including this file directly."
+#endif
+
+#include "details.h"
+
+/**
+ * This section contains a convenience MACRO which allows an easy specialization of
+ * std::list such that for data types with alignment issues the correct allocator
+ * is used automatically.
+ */
+#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)                                               \
+  namespace std {                                                                               \
+  template <>                                                                                   \
+  class list<__VA_ARGS__, std::allocator<__VA_ARGS__> >                                         \
+      : public list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > {                       \
+    typedef list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > list_base;                 \
+                                                                                                \
+   public:                                                                                      \
+    typedef __VA_ARGS__ value_type;                                                             \
+    typedef list_base::allocator_type allocator_type;                                           \
+    typedef list_base::size_type size_type;                                                     \
+    typedef list_base::iterator iterator;                                                       \
+    explicit list(const allocator_type& a = allocator_type()) : list_base(a) {}                 \
+    template <typename InputIterator>                                                           \
+    list(InputIterator first, InputIterator last, const allocator_type& a = allocator_type())   \
+        : list_base(first, last, a) {}                                                          \
+    list(const list& c) : list_base(c) {}                                                       \
+    explicit list(size_type num, const value_type& val = value_type()) : list_base(num, val) {} \
+    list(iterator start_, iterator end_) : list_base(start_, end_) {}                           \
+    list& operator=(const list& x) {                                                            \
+      list_base::operator=(x);                                                                  \
+      return *this;                                                                             \
+    }                                                                                           \
+  };                                                                                            \
+  }
+
+#endif  // EIGEN_STDLIST_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/StdVector.h b/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/StdVector.h
new file mode 100644
index 0000000..dd538e9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/StdVector.h
@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDVECTOR_H
+#define EIGEN_STDVECTOR_H
+
+#ifndef EIGEN_STDVECTOR_MODULE_H
+#error "Please include Eigen/StdVector instead of including this file directly."
+#endif
+
+#include "details.h"
+
+/**
+ * This section contains a convenience MACRO which allows an easy specialization of
+ * std::vector such that for data types with alignment issues the correct allocator
+ * is used automatically.
+ */
+#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)                                                 \
+  namespace std {                                                                                   \
+  template <>                                                                                       \
+  class vector<__VA_ARGS__, std::allocator<__VA_ARGS__> >                                           \
+      : public vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > {                         \
+    typedef vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > vector_base;                 \
+                                                                                                    \
+   public:                                                                                          \
+    typedef __VA_ARGS__ value_type;                                                                 \
+    typedef vector_base::allocator_type allocator_type;                                             \
+    typedef vector_base::size_type size_type;                                                       \
+    typedef vector_base::iterator iterator;                                                         \
+    explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {}                 \
+    template <typename InputIterator>                                                               \
+    vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type())     \
+        : vector_base(first, last, a) {}                                                            \
+    vector(const vector& c) : vector_base(c) {}                                                     \
+    explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \
+    vector(iterator start_, iterator end_) : vector_base(start_, end_) {}                           \
+    vector& operator=(const vector& x) {                                                            \
+      vector_base::operator=(x);                                                                    \
+      return *this;                                                                                 \
+    }                                                                                               \
+  };                                                                                                \
+  }
+
+#endif  // EIGEN_STDVECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/details.h b/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/details.h
new file mode 100644
index 0000000..9bc9d21
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/StlSupport/details.h
@@ -0,0 +1,82 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STL_DETAILS_H
+#define EIGEN_STL_DETAILS_H
+
+#ifndef EIGEN_ALIGNED_ALLOCATOR
+#define EIGEN_ALIGNED_ALLOCATOR Eigen::aligned_allocator
+#endif
+
+namespace Eigen {
+
+// This one is needed to prevent reimplementing the whole std::vector.
+template <class T>
+class aligned_allocator_indirection : public EIGEN_ALIGNED_ALLOCATOR<T> {
+ public:
+  typedef std::size_t size_type;
+  typedef std::ptrdiff_t difference_type;
+  typedef T* pointer;
+  typedef const T* const_pointer;
+  typedef T& reference;
+  typedef const T& const_reference;
+  typedef T value_type;
+
+  template <class U>
+  struct rebind {
+    typedef aligned_allocator_indirection<U> other;
+  };
+
+  aligned_allocator_indirection() {}
+  aligned_allocator_indirection(const aligned_allocator_indirection&) : EIGEN_ALIGNED_ALLOCATOR<T>() {}
+  aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR<T>&) {}
+  template <class U>
+  aligned_allocator_indirection(const aligned_allocator_indirection<U>&) {}
+  template <class U>
+  aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR<U>&) {}
+  ~aligned_allocator_indirection() {}
+};
+
+#if EIGEN_COMP_MSVC
+
+// sometimes, MSVC detects, at compile time, that the argument x
+// in std::vector::resize(size_t s,T x) won't be aligned and generate an error
+// even if this function is never called. Whence this little wrapper.
+#define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) \
+  std::conditional_t<Eigen::internal::is_arithmetic<T>::value, T, Eigen::internal::workaround_msvc_stl_support<T> >
+
+namespace internal {
+template <typename T>
+struct workaround_msvc_stl_support : public T {
+  inline workaround_msvc_stl_support() : T() {}
+  inline workaround_msvc_stl_support(const T& other) : T(other) {}
+  inline operator T&() { return *static_cast<T*>(this); }
+  inline operator const T&() const { return *static_cast<const T*>(this); }
+  template <typename OtherT>
+  inline T& operator=(const OtherT& other) {
+    T::operator=(other);
+    return *this;
+  }
+  inline workaround_msvc_stl_support& operator=(const workaround_msvc_stl_support& other) {
+    T::operator=(other);
+    return *this;
+  }
+};
+}  // namespace internal
+
+#else
+
+#define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) T
+
+#endif
+
+}  // namespace Eigen
+
+#endif  // EIGEN_STL_DETAILS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SuperLUSupport/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SuperLUSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000..94a62b5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SuperLUSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
+#error "Please include Eigen/SuperLUSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/SuperLUSupport/SuperLUSupport.h b/data_acq_and_processing/third_party/eigen/Eigen/src/SuperLUSupport/SuperLUSupport.h
new file mode 100644
index 0000000..bed414b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/SuperLUSupport/SuperLUSupport.h
@@ -0,0 +1,929 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SUPERLUSUPPORT_H
+#define EIGEN_SUPERLUSUPPORT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+#if defined(SUPERLU_MAJOR_VERSION) && (SUPERLU_MAJOR_VERSION >= 5)
+#define DECL_GSSVX(PREFIX, FLOATTYPE, KEYTYPE)                                                                         \
+  extern "C" {                                                                                                         \
+  extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *, char *, FLOATTYPE *, FLOATTYPE *, \
+                            SuperMatrix *, SuperMatrix *, void *, int, SuperMatrix *, SuperMatrix *, FLOATTYPE *,      \
+                            FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, GlobalLU_t *, mem_usage_t *, SuperLUStat_t *,       \
+                            int *);                                                                                    \
+  }                                                                                                                    \
+  inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A, int *perm_c, int *perm_r, int *etree,         \
+                             char *equed, FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L, SuperMatrix *U, void *work,      \
+                             int lwork, SuperMatrix *B, SuperMatrix *X, FLOATTYPE *recip_pivot_growth,                 \
+                             FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr, SuperLUStat_t *stats, int *info,      \
+                             KEYTYPE) {                                                                                \
+    mem_usage_t mem_usage;                                                                                             \
+    GlobalLU_t gLU;                                                                                                    \
+    PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L, U, work, lwork, B, X, recip_pivot_growth, rcond,  \
+                  ferr, berr, &gLU, &mem_usage, stats, info);                                                          \
+    return mem_usage.for_lu; /* bytes used by the factor storage */                                                    \
+  }
+#else  // version < 5.0
+#define DECL_GSSVX(PREFIX, FLOATTYPE, KEYTYPE)                                                                         \
+  extern "C" {                                                                                                         \
+  extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *, char *, FLOATTYPE *, FLOATTYPE *, \
+                            SuperMatrix *, SuperMatrix *, void *, int, SuperMatrix *, SuperMatrix *, FLOATTYPE *,      \
+                            FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, mem_usage_t *, SuperLUStat_t *, int *);             \
+  }                                                                                                                    \
+  inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A, int *perm_c, int *perm_r, int *etree,         \
+                             char *equed, FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L, SuperMatrix *U, void *work,      \
+                             int lwork, SuperMatrix *B, SuperMatrix *X, FLOATTYPE *recip_pivot_growth,                 \
+                             FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr, SuperLUStat_t *stats, int *info,      \
+                             KEYTYPE) {                                                                                \
+    mem_usage_t mem_usage;                                                                                             \
+    PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L, U, work, lwork, B, X, recip_pivot_growth, rcond,  \
+                  ferr, berr, &mem_usage, stats, info);                                                                \
+    return mem_usage.for_lu; /* bytes used by the factor storage */                                                    \
+  }
+#endif
+
+DECL_GSSVX(s, float, float)
+DECL_GSSVX(c, float, std::complex<float>)
+DECL_GSSVX(d, double, double)
+DECL_GSSVX(z, double, std::complex<double>)
+
+#ifdef MILU_ALPHA
+#define EIGEN_SUPERLU_HAS_ILU
+#endif
+
+#ifdef EIGEN_SUPERLU_HAS_ILU
+
+// similarly for the incomplete factorization using gsisx
+#if defined(SUPERLU_MAJOR_VERSION) && (SUPERLU_MAJOR_VERSION >= 5)
+#define DECL_GSISX(PREFIX, FLOATTYPE, KEYTYPE)                                                                         \
+  extern "C" {                                                                                                         \
+  extern void PREFIX##gsisx(superlu_options_t *, SuperMatrix *, int *, int *, int *, char *, FLOATTYPE *, FLOATTYPE *, \
+                            SuperMatrix *, SuperMatrix *, void *, int, SuperMatrix *, SuperMatrix *, FLOATTYPE *,      \
+                            FLOATTYPE *, GlobalLU_t *, mem_usage_t *, SuperLUStat_t *, int *);                         \
+  }                                                                                                                    \
+  inline float SuperLU_gsisx(superlu_options_t *options, SuperMatrix *A, int *perm_c, int *perm_r, int *etree,         \
+                             char *equed, FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L, SuperMatrix *U, void *work,      \
+                             int lwork, SuperMatrix *B, SuperMatrix *X, FLOATTYPE *recip_pivot_growth,                 \
+                             FLOATTYPE *rcond, SuperLUStat_t *stats, int *info, KEYTYPE) {                             \
+    mem_usage_t mem_usage;                                                                                             \
+    GlobalLU_t gLU;                                                                                                    \
+    PREFIX##gsisx(options, A, perm_c, perm_r, etree, equed, R, C, L, U, work, lwork, B, X, recip_pivot_growth, rcond,  \
+                  &gLU, &mem_usage, stats, info);                                                                      \
+    return mem_usage.for_lu; /* bytes used by the factor storage */                                                    \
+  }
+#else  // version < 5.0
+#define DECL_GSISX(PREFIX, FLOATTYPE, KEYTYPE)                                                                         \
+  extern "C" {                                                                                                         \
+  extern void PREFIX##gsisx(superlu_options_t *, SuperMatrix *, int *, int *, int *, char *, FLOATTYPE *, FLOATTYPE *, \
+                            SuperMatrix *, SuperMatrix *, void *, int, SuperMatrix *, SuperMatrix *, FLOATTYPE *,      \
+                            FLOATTYPE *, mem_usage_t *, SuperLUStat_t *, int *);                                       \
+  }                                                                                                                    \
+  inline float SuperLU_gsisx(superlu_options_t *options, SuperMatrix *A, int *perm_c, int *perm_r, int *etree,         \
+                             char *equed, FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L, SuperMatrix *U, void *work,      \
+                             int lwork, SuperMatrix *B, SuperMatrix *X, FLOATTYPE *recip_pivot_growth,                 \
+                             FLOATTYPE *rcond, SuperLUStat_t *stats, int *info, KEYTYPE) {                             \
+    mem_usage_t mem_usage;                                                                                             \
+    PREFIX##gsisx(options, A, perm_c, perm_r, etree, equed, R, C, L, U, work, lwork, B, X, recip_pivot_growth, rcond,  \
+                  &mem_usage, stats, info);                                                                            \
+    return mem_usage.for_lu; /* bytes used by the factor storage */                                                    \
+  }
+#endif
+
+DECL_GSISX(s, float, float)
+DECL_GSISX(c, float, std::complex<float>)
+DECL_GSISX(d, double, double)
+DECL_GSISX(z, double, std::complex<double>)
+
+#endif
+
+template <typename MatrixType>
+struct SluMatrixMapHelper;
+
+/** \internal
+ *
+ * A wrapper class for SuperLU matrices. It supports only compressed sparse matrices
+ * and dense matrices. Supernodal and other fancy format are not supported by this wrapper.
+ *
+ * This wrapper class mainly aims to avoids the need of dynamic allocation of the storage structure.
+ */
+struct SluMatrix : SuperMatrix {
+  SluMatrix() { Store = &storage; }
+
+  SluMatrix(const SluMatrix &other) : SuperMatrix(other) {
+    Store = &storage;
+    storage = other.storage;
+  }
+
+  SluMatrix &operator=(const SluMatrix &other) {
+    SuperMatrix::operator=(static_cast<const SuperMatrix &>(other));
+    Store = &storage;
+    storage = other.storage;
+    return *this;
+  }
+
+  struct {
+    union {
+      int nnz;
+      int lda;
+    };
+    void *values;
+    int *innerInd;
+    int *outerInd;
+  } storage;
+
+  void setStorageType(Stype_t t) {
+    Stype = t;
+    if (t == SLU_NC || t == SLU_NR || t == SLU_DN)
+      Store = &storage;
+    else {
+      eigen_assert(false && "storage type not supported");
+      Store = 0;
+    }
+  }
+
+  template <typename Scalar>
+  void setScalarType() {
+    if (internal::is_same<Scalar, float>::value)
+      Dtype = SLU_S;
+    else if (internal::is_same<Scalar, double>::value)
+      Dtype = SLU_D;
+    else if (internal::is_same<Scalar, std::complex<float> >::value)
+      Dtype = SLU_C;
+    else if (internal::is_same<Scalar, std::complex<double> >::value)
+      Dtype = SLU_Z;
+    else {
+      eigen_assert(false && "Scalar type not supported by SuperLU");
+    }
+  }
+
+  template <typename MatrixType>
+  static SluMatrix Map(MatrixBase<MatrixType> &_mat) {
+    MatrixType &mat(_mat.derived());
+    eigen_assert(((MatrixType::Flags & RowMajorBit) != RowMajorBit) &&
+                 "row-major dense matrices are not supported by SuperLU");
+    SluMatrix res;
+    res.setStorageType(SLU_DN);
+    res.setScalarType<typename MatrixType::Scalar>();
+    res.Mtype = SLU_GE;
+
+    res.nrow = internal::convert_index<int>(mat.rows());
+    res.ncol = internal::convert_index<int>(mat.cols());
+
+    res.storage.lda = internal::convert_index<int>(MatrixType::IsVectorAtCompileTime ? mat.size() : mat.outerStride());
+    res.storage.values = (void *)(mat.data());
+    return res;
+  }
+
+  template <typename MatrixType>
+  static SluMatrix Map(SparseMatrixBase<MatrixType> &a_mat) {
+    MatrixType &mat(a_mat.derived());
+    SluMatrix res;
+    if ((MatrixType::Flags & RowMajorBit) == RowMajorBit) {
+      res.setStorageType(SLU_NR);
+      res.nrow = internal::convert_index<int>(mat.cols());
+      res.ncol = internal::convert_index<int>(mat.rows());
+    } else {
+      res.setStorageType(SLU_NC);
+      res.nrow = internal::convert_index<int>(mat.rows());
+      res.ncol = internal::convert_index<int>(mat.cols());
+    }
+
+    res.Mtype = SLU_GE;
+
+    res.storage.nnz = internal::convert_index<int>(mat.nonZeros());
+    res.storage.values = mat.valuePtr();
+    res.storage.innerInd = mat.innerIndexPtr();
+    res.storage.outerInd = mat.outerIndexPtr();
+
+    res.setScalarType<typename MatrixType::Scalar>();
+
+    // FIXME: the following type mapping is approximate.
+    if (int(MatrixType::Flags) & int(Upper)) res.Mtype = SLU_TRU;
+    if (int(MatrixType::Flags) & int(Lower)) res.Mtype = SLU_TRL;
+
+    eigen_assert(((int(MatrixType::Flags) & int(SelfAdjoint)) == 0) &&
+                 "SelfAdjoint matrix shape not supported by SuperLU");
+
+    return res;
+  }
+};
+
+template <typename Scalar, int Rows, int Cols, int Options, int MRows, int MCols>
+struct SluMatrixMapHelper<Matrix<Scalar, Rows, Cols, Options, MRows, MCols> > {
+  typedef Matrix<Scalar, Rows, Cols, Options, MRows, MCols> MatrixType;
+  static void run(MatrixType &mat, SluMatrix &res) {
+    eigen_assert(((Options & RowMajor) != RowMajor) && "row-major dense matrices is not supported by SuperLU");
+    res.setStorageType(SLU_DN);
+    res.setScalarType<Scalar>();
+    res.Mtype = SLU_GE;
+
+    res.nrow = mat.rows();
+    res.ncol = mat.cols();
+
+    res.storage.lda = mat.outerStride();
+    res.storage.values = mat.data();
+  }
+};
+
+template <typename Derived>
+struct SluMatrixMapHelper<SparseMatrixBase<Derived> > {
+  typedef Derived MatrixType;
+  static void run(MatrixType &mat, SluMatrix &res) {
+    if ((MatrixType::Flags & RowMajorBit) == RowMajorBit) {
+      res.setStorageType(SLU_NR);
+      res.nrow = mat.cols();
+      res.ncol = mat.rows();
+    } else {
+      res.setStorageType(SLU_NC);
+      res.nrow = mat.rows();
+      res.ncol = mat.cols();
+    }
+
+    res.Mtype = SLU_GE;
+
+    res.storage.nnz = mat.nonZeros();
+    res.storage.values = mat.valuePtr();
+    res.storage.innerInd = mat.innerIndexPtr();
+    res.storage.outerInd = mat.outerIndexPtr();
+
+    res.setScalarType<typename MatrixType::Scalar>();
+
+    // FIXME: the following type mapping is approximate.
+    if (MatrixType::Flags & Upper) res.Mtype = SLU_TRU;
+    if (MatrixType::Flags & Lower) res.Mtype = SLU_TRL;
+
+    eigen_assert(((MatrixType::Flags & SelfAdjoint) == 0) && "SelfAdjoint matrix shape not supported by SuperLU");
+  }
+};
+
+namespace internal {
+
+template <typename MatrixType>
+SluMatrix asSluMatrix(MatrixType &mat) {
+  return SluMatrix::Map(mat);
+}
+
+/** View a Super LU matrix as an Eigen expression */
+template <typename Scalar, int Flags, typename Index>
+Map<SparseMatrix<Scalar, Flags, Index> > map_superlu(SluMatrix &sluMat) {
+  eigen_assert(((Flags & RowMajor) == RowMajor && sluMat.Stype == SLU_NR) ||
+               ((Flags & ColMajor) == ColMajor && sluMat.Stype == SLU_NC));
+
+  Index outerSize = (Flags & RowMajor) == RowMajor ? sluMat.ncol : sluMat.nrow;
+
+  return Map<SparseMatrix<Scalar, Flags, Index> >(sluMat.nrow, sluMat.ncol, sluMat.storage.outerInd[outerSize],
+                                                  sluMat.storage.outerInd, sluMat.storage.innerInd,
+                                                  reinterpret_cast<Scalar *>(sluMat.storage.values));
+}
+
+}  // end namespace internal
+
+/** \ingroup SuperLUSupport_Module
+ * \class SuperLUBase
+ * \brief The base class for the direct and incomplete LU factorization of SuperLU
+ */
+template <typename MatrixType_, typename Derived>
+class SuperLUBase : public SparseSolverBase<Derived> {
+ protected:
+  typedef SparseSolverBase<Derived> Base;
+  using Base::derived;
+  using Base::m_isInitialized;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef Matrix<Scalar, Dynamic, 1> Vector;
+  typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+  typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+  typedef Map<PermutationMatrix<Dynamic, Dynamic, int> > PermutationMap;
+  typedef SparseMatrix<Scalar> LUMatrixType;
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+ public:
+  SuperLUBase() {}
+
+  ~SuperLUBase() { clearFactors(); }
+
+  inline Index rows() const { return m_matrix.rows(); }
+  inline Index cols() const { return m_matrix.cols(); }
+
+  /** \returns a reference to the Super LU option object to configure the  Super LU algorithms. */
+  inline superlu_options_t &options() { return m_sluOptions; }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix.appears to be negative.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  /** Computes the sparse Cholesky decomposition of \a matrix */
+  void compute(const MatrixType &matrix) {
+    derived().analyzePattern(matrix);
+    derived().factorize(matrix);
+  }
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType & /*matrix*/) {
+    m_isInitialized = true;
+    m_info = Success;
+    m_analysisIsOk = true;
+    m_factorizationIsOk = false;
+  }
+
+  template <typename Stream>
+  void dumpMemory(Stream & /*s*/) {}
+
+ protected:
+  void initFactorization(const MatrixType &a) {
+    set_default_options(&this->m_sluOptions);
+
+    const Index size = a.rows();
+    m_matrix = a;
+
+    m_sluA = internal::asSluMatrix(m_matrix);
+    clearFactors();
+
+    m_p.resize(size);
+    m_q.resize(size);
+    m_sluRscale.resize(size);
+    m_sluCscale.resize(size);
+    m_sluEtree.resize(size);
+
+    // set empty B and X
+    m_sluB.setStorageType(SLU_DN);
+    m_sluB.setScalarType<Scalar>();
+    m_sluB.Mtype = SLU_GE;
+    m_sluB.storage.values = 0;
+    m_sluB.nrow = 0;
+    m_sluB.ncol = 0;
+    m_sluB.storage.lda = internal::convert_index<int>(size);
+    m_sluX = m_sluB;
+
+    m_extractedDataAreDirty = true;
+  }
+
+  void init() {
+    m_info = InvalidInput;
+    m_isInitialized = false;
+    m_sluL.Store = 0;
+    m_sluU.Store = 0;
+  }
+
+  void extractData() const;
+
+  void clearFactors() {
+    if (m_sluL.Store) Destroy_SuperNode_Matrix(&m_sluL);
+    if (m_sluU.Store) Destroy_CompCol_Matrix(&m_sluU);
+
+    m_sluL.Store = 0;
+    m_sluU.Store = 0;
+
+    memset(&m_sluL, 0, sizeof m_sluL);
+    memset(&m_sluU, 0, sizeof m_sluU);
+  }
+
+  // cached data to reduce reallocation, etc.
+  mutable LUMatrixType m_l;
+  mutable LUMatrixType m_u;
+  mutable IntColVectorType m_p;
+  mutable IntRowVectorType m_q;
+
+  mutable LUMatrixType m_matrix;  // copy of the factorized matrix
+  mutable SluMatrix m_sluA;
+  mutable SuperMatrix m_sluL, m_sluU;
+  mutable SluMatrix m_sluB, m_sluX;
+  mutable SuperLUStat_t m_sluStat;
+  mutable superlu_options_t m_sluOptions;
+  mutable std::vector<int> m_sluEtree;
+  mutable Matrix<RealScalar, Dynamic, 1> m_sluRscale, m_sluCscale;
+  mutable Matrix<RealScalar, Dynamic, 1> m_sluFerr, m_sluBerr;
+  mutable char m_sluEqued;
+
+  mutable ComputationInfo m_info;
+  int m_factorizationIsOk;
+  int m_analysisIsOk;
+  mutable bool m_extractedDataAreDirty;
+
+ private:
+  SuperLUBase(SuperLUBase &) {}
+};
+
+/** \ingroup SuperLUSupport_Module
+ * \class SuperLU
+ * \brief A sparse direct LU factorization and solver based on the SuperLU library
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
+ * using the SuperLU library. The sparse matrix A must be squared and invertible. The vectors or matrices
+ * X and B can be either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ *
+ * \warning This class is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept, class SparseLU
+ */
+template <typename MatrixType_>
+class SuperLU : public SuperLUBase<MatrixType_, SuperLU<MatrixType_> > {
+ public:
+  typedef SuperLUBase<MatrixType_, SuperLU> Base;
+  typedef MatrixType_ MatrixType;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::RealScalar RealScalar;
+  typedef typename Base::StorageIndex StorageIndex;
+  typedef typename Base::IntRowVectorType IntRowVectorType;
+  typedef typename Base::IntColVectorType IntColVectorType;
+  typedef typename Base::PermutationMap PermutationMap;
+  typedef typename Base::LUMatrixType LUMatrixType;
+  typedef TriangularView<LUMatrixType, Lower | UnitDiag> LMatrixType;
+  typedef TriangularView<LUMatrixType, Upper> UMatrixType;
+
+ public:
+  using Base::_solve_impl;
+
+  SuperLU() : Base() { init(); }
+
+  explicit SuperLU(const MatrixType &matrix) : Base() {
+    init();
+    Base::compute(matrix);
+  }
+
+  ~SuperLU() {}
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType &matrix) {
+    m_info = InvalidInput;
+    m_isInitialized = false;
+    Base::analyzePattern(matrix);
+  }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
+   * performed.
+   *
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType &matrix);
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
+
+  inline const LMatrixType &matrixL() const {
+    if (m_extractedDataAreDirty) this->extractData();
+    return m_l;
+  }
+
+  inline const UMatrixType &matrixU() const {
+    if (m_extractedDataAreDirty) this->extractData();
+    return m_u;
+  }
+
+  inline const IntColVectorType &permutationP() const {
+    if (m_extractedDataAreDirty) this->extractData();
+    return m_p;
+  }
+
+  inline const IntRowVectorType &permutationQ() const {
+    if (m_extractedDataAreDirty) this->extractData();
+    return m_q;
+  }
+
+  Scalar determinant() const;
+
+ protected:
+  using Base::m_l;
+  using Base::m_matrix;
+  using Base::m_p;
+  using Base::m_q;
+  using Base::m_sluA;
+  using Base::m_sluB;
+  using Base::m_sluBerr;
+  using Base::m_sluCscale;
+  using Base::m_sluEqued;
+  using Base::m_sluEtree;
+  using Base::m_sluFerr;
+  using Base::m_sluL;
+  using Base::m_sluOptions;
+  using Base::m_sluRscale;
+  using Base::m_sluStat;
+  using Base::m_sluU;
+  using Base::m_sluX;
+  using Base::m_u;
+
+  using Base::m_analysisIsOk;
+  using Base::m_extractedDataAreDirty;
+  using Base::m_factorizationIsOk;
+  using Base::m_info;
+  using Base::m_isInitialized;
+
+  void init() {
+    Base::init();
+
+    set_default_options(&this->m_sluOptions);
+    m_sluOptions.PrintStat = NO;
+    m_sluOptions.ConditionNumber = NO;
+    m_sluOptions.Trans = NOTRANS;
+    m_sluOptions.ColPerm = COLAMD;
+  }
+
+ private:
+  SuperLU(SuperLU &) {}
+};
+
+template <typename MatrixType>
+void SuperLU<MatrixType>::factorize(const MatrixType &a) {
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  if (!m_analysisIsOk) {
+    m_info = InvalidInput;
+    return;
+  }
+
+  this->initFactorization(a);
+
+  m_sluOptions.ColPerm = COLAMD;
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+  RealScalar ferr, berr;
+
+  StatInit(&m_sluStat);
+  SuperLU_gssvx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0], &m_sluEqued, &m_sluRscale[0],
+                &m_sluCscale[0], &m_sluL, &m_sluU, NULL, 0, &m_sluB, &m_sluX, &recip_pivot_growth, &rcond, &ferr, &berr,
+                &m_sluStat, &info, Scalar());
+  StatFree(&m_sluStat);
+
+  m_extractedDataAreDirty = true;
+
+  // FIXME: implement more detailed error checking based on SuperLU info codes.
+  m_info = info == 0 ? Success : NumericalIssue;
+  m_factorizationIsOk = true;
+}
+
+template <typename MatrixType>
+template <typename Rhs, typename Dest>
+void SuperLU<MatrixType>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const {
+  eigen_assert(m_factorizationIsOk &&
+               "The decomposition is not in a valid state for solving, you must first call either compute() or "
+               "analyzePattern()/factorize()");
+
+  const Index rhsCols = b.cols();
+  eigen_assert(m_matrix.rows() == b.rows());
+
+  m_sluOptions.Trans = NOTRANS;
+  m_sluOptions.Fact = FACTORED;
+  m_sluOptions.IterRefine = NOREFINE;
+
+  m_sluFerr.resize(rhsCols);
+  m_sluBerr.resize(rhsCols);
+
+  Ref<const Matrix<typename Rhs::Scalar, Dynamic, Dynamic, ColMajor> > b_ref(b);
+  Ref<const Matrix<typename Dest::Scalar, Dynamic, Dynamic, ColMajor> > x_ref(x);
+
+  m_sluB = SluMatrix::Map(b_ref.const_cast_derived());
+  m_sluX = SluMatrix::Map(x_ref.const_cast_derived());
+
+  typename Rhs::PlainObject b_cpy;
+  if (m_sluEqued != 'N') {
+    b_cpy = b;
+    m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());
+  }
+
+  StatInit(&m_sluStat);
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+  SuperLU_gssvx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0], &m_sluEqued, &m_sluRscale[0],
+                &m_sluCscale[0], &m_sluL, &m_sluU, NULL, 0, &m_sluB, &m_sluX, &recip_pivot_growth, &rcond,
+                &m_sluFerr[0], &m_sluBerr[0], &m_sluStat, &info, Scalar());
+  StatFree(&m_sluStat);
+
+  if (x.derived().data() != x_ref.data()) x = x_ref;
+
+  m_info = info == 0 ? Success : NumericalIssue;
+}
+
+// the code of this extractData() function has been adapted from the SuperLU's Matlab support code,
+//
+//  Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+//
+//  THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+//  EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+//
+template <typename MatrixType, typename Derived>
+void SuperLUBase<MatrixType, Derived>::extractData() const {
+  eigen_assert(m_factorizationIsOk &&
+               "The decomposition is not in a valid state for extracting factors, you must first call either compute() "
+               "or analyzePattern()/factorize()");
+  if (m_extractedDataAreDirty) {
+    int upper;
+    int fsupc, istart, nsupr;
+    int lastl = 0, lastu = 0;
+    SCformat *Lstore = static_cast<SCformat *>(m_sluL.Store);
+    NCformat *Ustore = static_cast<NCformat *>(m_sluU.Store);
+    Scalar *SNptr;
+
+    const Index size = m_matrix.rows();
+    m_l.resize(size, size);
+    m_l.resizeNonZeros(Lstore->nnz);
+    m_u.resize(size, size);
+    m_u.resizeNonZeros(Ustore->nnz);
+
+    int *Lcol = m_l.outerIndexPtr();
+    int *Lrow = m_l.innerIndexPtr();
+    Scalar *Lval = m_l.valuePtr();
+
+    int *Ucol = m_u.outerIndexPtr();
+    int *Urow = m_u.innerIndexPtr();
+    Scalar *Uval = m_u.valuePtr();
+
+    Ucol[0] = 0;
+    Ucol[0] = 0;
+
+    /* for each supernode */
+    for (int k = 0; k <= Lstore->nsuper; ++k) {
+      fsupc = L_FST_SUPC(k);
+      istart = L_SUB_START(fsupc);
+      nsupr = L_SUB_START(fsupc + 1) - istart;
+      upper = 1;
+
+      /* for each column in the supernode */
+      for (int j = fsupc; j < L_FST_SUPC(k + 1); ++j) {
+        SNptr = &((Scalar *)Lstore->nzval)[L_NZ_START(j)];
+
+        /* Extract U */
+        for (int i = U_NZ_START(j); i < U_NZ_START(j + 1); ++i) {
+          Uval[lastu] = ((Scalar *)Ustore->nzval)[i];
+          /* Matlab doesn't like explicit zero. */
+          if (Uval[lastu] != 0.0) Urow[lastu++] = U_SUB(i);
+        }
+        for (int i = 0; i < upper; ++i) {
+          /* upper triangle in the supernode */
+          Uval[lastu] = SNptr[i];
+          /* Matlab doesn't like explicit zero. */
+          if (Uval[lastu] != 0.0) Urow[lastu++] = L_SUB(istart + i);
+        }
+        Ucol[j + 1] = lastu;
+
+        /* Extract L */
+        Lval[lastl] = 1.0; /* unit diagonal */
+        Lrow[lastl++] = L_SUB(istart + upper - 1);
+        for (int i = upper; i < nsupr; ++i) {
+          Lval[lastl] = SNptr[i];
+          /* Matlab doesn't like explicit zero. */
+          if (Lval[lastl] != 0.0) Lrow[lastl++] = L_SUB(istart + i);
+        }
+        Lcol[j + 1] = lastl;
+
+        ++upper;
+      } /* for j ... */
+
+    } /* for k ... */
+
+    // squeeze the matrices :
+    m_l.resizeNonZeros(lastl);
+    m_u.resizeNonZeros(lastu);
+
+    m_extractedDataAreDirty = false;
+  }
+}
+
+template <typename MatrixType>
+typename SuperLU<MatrixType>::Scalar SuperLU<MatrixType>::determinant() const {
+  eigen_assert(m_factorizationIsOk &&
+               "The decomposition is not in a valid state for computing the determinant, you must first call either "
+               "compute() or analyzePattern()/factorize()");
+
+  if (m_extractedDataAreDirty) this->extractData();
+
+  Scalar det = Scalar(1);
+  for (int j = 0; j < m_u.cols(); ++j) {
+    if (m_u.outerIndexPtr()[j + 1] - m_u.outerIndexPtr()[j] > 0) {
+      int lastId = m_u.outerIndexPtr()[j + 1] - 1;
+      eigen_assert(m_u.innerIndexPtr()[lastId] <= j);
+      if (m_u.innerIndexPtr()[lastId] == j) det *= m_u.valuePtr()[lastId];
+    }
+  }
+  if (PermutationMap(m_p.data(), m_p.size()).determinant() * PermutationMap(m_q.data(), m_q.size()).determinant() < 0)
+    det = -det;
+  if (m_sluEqued != 'N')
+    return det / m_sluRscale.prod() / m_sluCscale.prod();
+  else
+    return det;
+}
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+#define EIGEN_SUPERLU_HAS_ILU
+#endif
+
+#ifdef EIGEN_SUPERLU_HAS_ILU
+
+/** \ingroup SuperLUSupport_Module
+ * \class SuperILU
+ * \brief A sparse direct \b incomplete LU factorization and solver based on the SuperLU library
+ *
+ * This class allows to solve for an approximate solution of A.X = B sparse linear problems via an incomplete LU
+ * factorization using the SuperLU library. This class is aimed to be used as a preconditioner of the iterative linear
+ * solvers.
+ *
+ * \warning This class is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept, class IncompleteLUT, class ConjugateGradient, class BiCGSTAB
+ */
+
+template <typename MatrixType_>
+class SuperILU : public SuperLUBase<MatrixType_, SuperILU<MatrixType_> > {
+ public:
+  typedef SuperLUBase<MatrixType_, SuperILU> Base;
+  typedef MatrixType_ MatrixType;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::RealScalar RealScalar;
+
+ public:
+  using Base::_solve_impl;
+
+  SuperILU() : Base() { init(); }
+
+  SuperILU(const MatrixType &matrix) : Base() {
+    init();
+    Base::compute(matrix);
+  }
+
+  ~SuperILU() {}
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType &matrix) { Base::analyzePattern(matrix); }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
+   * performed.
+   *
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType &matrix);
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+
+ protected:
+  using Base::m_l;
+  using Base::m_matrix;
+  using Base::m_p;
+  using Base::m_q;
+  using Base::m_sluA;
+  using Base::m_sluB;
+  using Base::m_sluBerr;
+  using Base::m_sluCscale;
+  using Base::m_sluEqued;
+  using Base::m_sluEtree;
+  using Base::m_sluFerr;
+  using Base::m_sluL;
+  using Base::m_sluOptions;
+  using Base::m_sluRscale;
+  using Base::m_sluStat;
+  using Base::m_sluU;
+  using Base::m_sluX;
+  using Base::m_u;
+
+  using Base::m_analysisIsOk;
+  using Base::m_extractedDataAreDirty;
+  using Base::m_factorizationIsOk;
+  using Base::m_info;
+  using Base::m_isInitialized;
+
+  void init() {
+    Base::init();
+
+    ilu_set_default_options(&m_sluOptions);
+    m_sluOptions.PrintStat = NO;
+    m_sluOptions.ConditionNumber = NO;
+    m_sluOptions.Trans = NOTRANS;
+    m_sluOptions.ColPerm = MMD_AT_PLUS_A;
+
+    // no attempt to preserve column sum
+    m_sluOptions.ILU_MILU = SILU;
+    // only basic ILU(k) support -- no direct control over memory consumption
+    // better to use ILU_DropRule = DROP_BASIC | DROP_AREA
+    // and set ILU_FillFactor to max memory growth
+    m_sluOptions.ILU_DropRule = DROP_BASIC;
+    m_sluOptions.ILU_DropTol = NumTraits<Scalar>::dummy_precision() * 10;
+  }
+
+ private:
+  SuperILU(SuperILU &) {}
+};
+
+template <typename MatrixType>
+void SuperILU<MatrixType>::factorize(const MatrixType &a) {
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  if (!m_analysisIsOk) {
+    m_info = InvalidInput;
+    return;
+  }
+
+  this->initFactorization(a);
+
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+
+  StatInit(&m_sluStat);
+  SuperLU_gsisx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0], &m_sluEqued, &m_sluRscale[0],
+                &m_sluCscale[0], &m_sluL, &m_sluU, NULL, 0, &m_sluB, &m_sluX, &recip_pivot_growth, &rcond, &m_sluStat,
+                &info, Scalar());
+  StatFree(&m_sluStat);
+
+  // FIXME: implement more detailed error checking based on SuperLU info codes.
+  m_info = info == 0 ? Success : NumericalIssue;
+  m_factorizationIsOk = true;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType>
+template <typename Rhs, typename Dest>
+void SuperILU<MatrixType>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const {
+  eigen_assert(m_factorizationIsOk &&
+               "The decomposition is not in a valid state for solving, you must first call either compute() or "
+               "analyzePattern()/factorize()");
+
+  const int rhsCols = b.cols();
+  eigen_assert(m_matrix.rows() == b.rows());
+
+  m_sluOptions.Trans = NOTRANS;
+  m_sluOptions.Fact = FACTORED;
+  m_sluOptions.IterRefine = NOREFINE;
+
+  m_sluFerr.resize(rhsCols);
+  m_sluBerr.resize(rhsCols);
+
+  Ref<const Matrix<typename Rhs::Scalar, Dynamic, Dynamic, ColMajor> > b_ref(b);
+  Ref<const Matrix<typename Dest::Scalar, Dynamic, Dynamic, ColMajor> > x_ref(x);
+
+  m_sluB = SluMatrix::Map(b_ref.const_cast_derived());
+  m_sluX = SluMatrix::Map(x_ref.const_cast_derived());
+
+  typename Rhs::PlainObject b_cpy;
+  if (m_sluEqued != 'N') {
+    b_cpy = b;
+    m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());
+  }
+
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+
+  StatInit(&m_sluStat);
+  SuperLU_gsisx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0], &m_sluEqued, &m_sluRscale[0],
+                &m_sluCscale[0], &m_sluL, &m_sluU, NULL, 0, &m_sluB, &m_sluX, &recip_pivot_growth, &rcond, &m_sluStat,
+                &info, Scalar());
+  StatFree(&m_sluStat);
+
+  if (x.derived().data() != x_ref.data()) x = x_ref;
+
+  m_info = info == 0 ? Success : NumericalIssue;
+}
+#endif
+
+#endif
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SUPERLUSUPPORT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/Barrier.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/Barrier.h
new file mode 100644
index 0000000..8b2f8da
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/Barrier.h
@@ -0,0 +1,70 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Rasmus Munk Larsen <rmlarsen@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// Barrier is an object that allows one or more threads to wait until
+// Notify has been called a specified number of times.
+
+#ifndef EIGEN_CXX11_THREADPOOL_BARRIER_H
+#define EIGEN_CXX11_THREADPOOL_BARRIER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+class Barrier {
+ public:
+  Barrier(unsigned int count) : state_(count << 1), notified_(false) {
+    eigen_plain_assert(((count << 1) >> 1) == count);
+  }
+  ~Barrier() { eigen_plain_assert((state_ >> 1) == 0); }
+
+  void Notify() {
+    unsigned int v = state_.fetch_sub(2, std::memory_order_acq_rel) - 2;
+    if (v != 1) {
+      // Clear the lowest bit (waiter flag) and check that the original state
+      // value was not zero. If it was zero, it means that notify was called
+      // more times than the original count.
+      eigen_plain_assert(((v + 2) & ~1) != 0);
+      return;  // either count has not dropped to 0, or waiter is not waiting
+    }
+    EIGEN_MUTEX_LOCK l(mu_);
+    eigen_plain_assert(!notified_);
+    notified_ = true;
+    cv_.notify_all();
+  }
+
+  void Wait() {
+    unsigned int v = state_.fetch_or(1, std::memory_order_acq_rel);
+    if ((v >> 1) == 0) return;
+    EIGEN_MUTEX_LOCK l(mu_);
+    while (!notified_) {
+      cv_.wait(l);
+    }
+  }
+
+ private:
+  EIGEN_MUTEX mu_;
+  EIGEN_CONDVAR cv_;
+  std::atomic<unsigned int> state_;  // low bit is waiter flag
+  bool notified_;
+};
+
+// Notification is an object that allows a user to to wait for another
+// thread to signal a notification that an event has occurred.
+//
+// Multiple threads can wait on the same Notification object,
+// but only one caller must call Notify() on the object.
+struct Notification : Barrier {
+  Notification() : Barrier(1){};
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_BARRIER_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/CoreThreadPoolDevice.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/CoreThreadPoolDevice.h
new file mode 100644
index 0000000..c603a38
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/CoreThreadPoolDevice.h
@@ -0,0 +1,336 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2023 Charlie Schlosser <cs.schlosser@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_THREAD_POOL_DEVICE_H
+#define EIGEN_CORE_THREAD_POOL_DEVICE_H
+
+namespace Eigen {
+
+// CoreThreadPoolDevice provides an easy-to-understand Device for parallelizing Eigen Core expressions with
+// Threadpool. Expressions are recursively split evenly until the evaluation cost is less than the threshold for
+// delegating the task to a thread.
+/*
+                 a
+                / \
+               /   \
+              /     \
+             /       \
+            /         \
+           /           \
+          /             \
+         a               e
+        / \             / \
+       /   \           /   \
+      /     \         /     \
+     a       c       e       g
+    / \     / \     / \     / \
+   /   \   /   \   /   \   /   \
+  a     b c     d e     f g     h
+*/
+// Each task descends the binary tree to the left, delegates the right task to a new thread, and continues to the
+// left. This ensures that work is evenly distributed to the thread pool as quickly as possible and minimizes the number
+// of tasks created during the evaluation. Consider an expression that is divided into 8 chunks. The
+// primary task 'a' creates tasks 'e' 'c' and 'b', and executes its portion of the expression at the bottom of the
+// tree. Likewise, task 'e' creates tasks 'g' and 'f', and executes its portion of the expression.
+
+struct CoreThreadPoolDevice {
+  using Task = std::function<void()>;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoreThreadPoolDevice(ThreadPool& pool, float threadCostThreshold = 3e-5f)
+      : m_pool(pool) {
+    eigen_assert(threadCostThreshold >= 0.0f && "threadCostThreshold must be non-negative");
+    m_costFactor = threadCostThreshold;
+  }
+
+  template <int PacketSize>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int calculateLevels(Index size, float cost) const {
+    eigen_assert(cost >= 0.0f && "cost must be non-negative");
+    Index numOps = size / PacketSize;
+    int actualThreads = numOps < m_pool.NumThreads() ? static_cast<int>(numOps) : m_pool.NumThreads();
+    float totalCost = static_cast<float>(numOps) * cost;
+    float idealThreads = totalCost * m_costFactor;
+    if (idealThreads < static_cast<float>(actualThreads)) {
+      idealThreads = numext::maxi(idealThreads, 1.0f);
+      actualThreads = numext::mini(actualThreads, static_cast<int>(idealThreads));
+    }
+    int maxLevel = internal::log2_ceil(actualThreads);
+    return maxLevel;
+  }
+
+// MSVC does not like inlining parallelForImpl
+#if EIGEN_COMP_MSVC && !EIGEN_COMP_CLANG
+#define EIGEN_PARALLEL_FOR_INLINE
+#else
+#define EIGEN_PARALLEL_FOR_INLINE EIGEN_STRONG_INLINE
+#endif
+
+  template <typename UnaryFunctor, int PacketSize>
+  EIGEN_DEVICE_FUNC EIGEN_PARALLEL_FOR_INLINE void parallelForImpl(Index begin, Index end, UnaryFunctor& f,
+                                                                   Barrier& barrier, int level) {
+    while (level > 0) {
+      level--;
+      Index size = end - begin;
+      eigen_assert(size % PacketSize == 0 && "this function assumes size is a multiple of PacketSize");
+      Index mid = begin + numext::round_down(size >> 1, PacketSize);
+      Task right = [this, mid, end, &f, &barrier, level]() {
+        parallelForImpl<UnaryFunctor, PacketSize>(mid, end, f, barrier, level);
+      };
+      m_pool.Schedule(std::move(right));
+      end = mid;
+    }
+    for (Index i = begin; i < end; i += PacketSize) f(i);
+    barrier.Notify();
+  }
+
+  template <typename BinaryFunctor, int PacketSize>
+  EIGEN_DEVICE_FUNC EIGEN_PARALLEL_FOR_INLINE void parallelForImpl(Index outerBegin, Index outerEnd, Index innerBegin,
+                                                                   Index innerEnd, BinaryFunctor& f, Barrier& barrier,
+                                                                   int level) {
+    while (level > 0) {
+      level--;
+      Index outerSize = outerEnd - outerBegin;
+      if (outerSize > 1) {
+        Index outerMid = outerBegin + (outerSize >> 1);
+        Task right = [this, &f, &barrier, outerMid, outerEnd, innerBegin, innerEnd, level]() {
+          parallelForImpl<BinaryFunctor, PacketSize>(outerMid, outerEnd, innerBegin, innerEnd, f, barrier, level);
+        };
+        m_pool.Schedule(std::move(right));
+        outerEnd = outerMid;
+      } else {
+        Index innerSize = innerEnd - innerBegin;
+        eigen_assert(innerSize % PacketSize == 0 && "this function assumes innerSize is a multiple of PacketSize");
+        Index innerMid = innerBegin + numext::round_down(innerSize >> 1, PacketSize);
+        Task right = [this, &f, &barrier, outerBegin, outerEnd, innerMid, innerEnd, level]() {
+          parallelForImpl<BinaryFunctor, PacketSize>(outerBegin, outerEnd, innerMid, innerEnd, f, barrier, level);
+        };
+        m_pool.Schedule(std::move(right));
+        innerEnd = innerMid;
+      }
+    }
+    for (Index outer = outerBegin; outer < outerEnd; outer++)
+      for (Index inner = innerBegin; inner < innerEnd; inner += PacketSize) f(outer, inner);
+    barrier.Notify();
+  }
+
+#undef EIGEN_PARALLEL_FOR_INLINE
+
+  template <typename UnaryFunctor, int PacketSize>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void parallelFor(Index begin, Index end, UnaryFunctor& f, float cost) {
+    Index size = end - begin;
+    int maxLevel = calculateLevels<PacketSize>(size, cost);
+    Barrier barrier(1 << maxLevel);
+    parallelForImpl<UnaryFunctor, PacketSize>(begin, end, f, barrier, maxLevel);
+    barrier.Wait();
+  }
+
+  template <typename BinaryFunctor, int PacketSize>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void parallelFor(Index outerBegin, Index outerEnd, Index innerBegin,
+                                                         Index innerEnd, BinaryFunctor& f, float cost) {
+    Index outerSize = outerEnd - outerBegin;
+    Index innerSize = innerEnd - innerBegin;
+    Index size = outerSize * innerSize;
+    int maxLevel = calculateLevels<PacketSize>(size, cost);
+    Barrier barrier(1 << maxLevel);
+    parallelForImpl<BinaryFunctor, PacketSize>(outerBegin, outerEnd, innerBegin, innerEnd, f, barrier, maxLevel);
+    barrier.Wait();
+  }
+
+  ThreadPool& m_pool;
+  // costFactor is the cost of delegating a task to a thread
+  // the inverse is used to avoid a floating point division
+  float m_costFactor;
+};
+
+// specialization of coefficient-wise assignment loops for CoreThreadPoolDevice
+
+namespace internal {
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+struct Kernel;
+#endif
+
+template <typename Kernel>
+struct cost_helper {
+  using SrcEvaluatorType = typename Kernel::SrcEvaluatorType;
+  using DstEvaluatorType = typename Kernel::DstEvaluatorType;
+  using SrcXprType = typename SrcEvaluatorType::XprType;
+  using DstXprType = typename DstEvaluatorType::XprType;
+  static constexpr Index Cost = functor_cost<SrcXprType>::Cost + functor_cost<DstXprType>::Cost;
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_with_device<Kernel, CoreThreadPoolDevice, DefaultTraversal, NoUnrolling> {
+  static constexpr Index XprEvaluationCost = cost_helper<Kernel>::Cost;
+  struct AssignmentFunctor : public Kernel {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AssignmentFunctor(Kernel& kernel) : Kernel(kernel) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index outer, Index inner) {
+      this->assignCoeffByOuterInner(outer, inner);
+    }
+  };
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Kernel& kernel, CoreThreadPoolDevice& device) {
+    const Index innerSize = kernel.innerSize();
+    const Index outerSize = kernel.outerSize();
+    constexpr float cost = static_cast<float>(XprEvaluationCost);
+    AssignmentFunctor functor(kernel);
+    device.template parallelFor<AssignmentFunctor, 1>(0, outerSize, 0, innerSize, functor, cost);
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_with_device<Kernel, CoreThreadPoolDevice, DefaultTraversal, InnerUnrolling> {
+  using DstXprType = typename Kernel::DstEvaluatorType::XprType;
+  static constexpr Index XprEvaluationCost = cost_helper<Kernel>::Cost, InnerSize = DstXprType::InnerSizeAtCompileTime;
+  struct AssignmentFunctor : public Kernel {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AssignmentFunctor(Kernel& kernel) : Kernel(kernel) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index outer) {
+      copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, 0, InnerSize>::run(*this, outer);
+    }
+  };
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Kernel& kernel, CoreThreadPoolDevice& device) {
+    const Index outerSize = kernel.outerSize();
+    AssignmentFunctor functor(kernel);
+    constexpr float cost = static_cast<float>(XprEvaluationCost) * static_cast<float>(InnerSize);
+    device.template parallelFor<AssignmentFunctor, 1>(0, outerSize, functor, cost);
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_with_device<Kernel, CoreThreadPoolDevice, InnerVectorizedTraversal, NoUnrolling> {
+  using PacketType = typename Kernel::PacketType;
+  static constexpr Index XprEvaluationCost = cost_helper<Kernel>::Cost, PacketSize = unpacket_traits<PacketType>::size,
+                         SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
+                         DstAlignment = Kernel::AssignmentTraits::DstAlignment;
+  struct AssignmentFunctor : public Kernel {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AssignmentFunctor(Kernel& kernel) : Kernel(kernel) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index outer, Index inner) {
+      this->template assignPacketByOuterInner<Unaligned, Unaligned, PacketType>(outer, inner);
+    }
+  };
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Kernel& kernel, CoreThreadPoolDevice& device) {
+    const Index innerSize = kernel.innerSize();
+    const Index outerSize = kernel.outerSize();
+    const float cost = static_cast<float>(XprEvaluationCost) * static_cast<float>(innerSize);
+    AssignmentFunctor functor(kernel);
+    device.template parallelFor<AssignmentFunctor, PacketSize>(0, outerSize, 0, innerSize, functor, cost);
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_with_device<Kernel, CoreThreadPoolDevice, InnerVectorizedTraversal, InnerUnrolling> {
+  using PacketType = typename Kernel::PacketType;
+  using DstXprType = typename Kernel::DstEvaluatorType::XprType;
+  static constexpr Index XprEvaluationCost = cost_helper<Kernel>::Cost, PacketSize = unpacket_traits<PacketType>::size,
+                         SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
+                         DstAlignment = Kernel::AssignmentTraits::DstAlignment,
+                         InnerSize = DstXprType::InnerSizeAtCompileTime;
+  struct AssignmentFunctor : public Kernel {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AssignmentFunctor(Kernel& kernel) : Kernel(kernel) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index outer) {
+      copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, InnerSize, SrcAlignment, DstAlignment>::run(*this, outer);
+    }
+  };
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Kernel& kernel, CoreThreadPoolDevice& device) {
+    const Index outerSize = kernel.outerSize();
+    constexpr float cost = static_cast<float>(XprEvaluationCost) * static_cast<float>(InnerSize);
+    AssignmentFunctor functor(kernel);
+    device.template parallelFor<AssignmentFunctor, PacketSize>(0, outerSize, functor, cost);
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_with_device<Kernel, CoreThreadPoolDevice, SliceVectorizedTraversal, NoUnrolling> {
+  using Scalar = typename Kernel::Scalar;
+  using PacketType = typename Kernel::PacketType;
+  static constexpr Index XprEvaluationCost = cost_helper<Kernel>::Cost, PacketSize = unpacket_traits<PacketType>::size;
+  struct PacketAssignmentFunctor : public Kernel {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketAssignmentFunctor(Kernel& kernel) : Kernel(kernel) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index outer, Index inner) {
+      this->template assignPacketByOuterInner<Unaligned, Unaligned, PacketType>(outer, inner);
+    }
+  };
+  struct ScalarAssignmentFunctor : public Kernel {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScalarAssignmentFunctor(Kernel& kernel) : Kernel(kernel) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index outer) {
+      const Index innerSize = this->innerSize();
+      const Index packetAccessSize = numext::round_down(innerSize, PacketSize);
+      for (Index inner = packetAccessSize; inner < innerSize; inner++) this->assignCoeffByOuterInner(outer, inner);
+    }
+  };
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Kernel& kernel, CoreThreadPoolDevice& device) {
+    const Index outerSize = kernel.outerSize();
+    const Index innerSize = kernel.innerSize();
+    const Index packetAccessSize = numext::round_down(innerSize, PacketSize);
+    constexpr float packetCost = static_cast<float>(XprEvaluationCost);
+    const float scalarCost = static_cast<float>(XprEvaluationCost) * static_cast<float>(innerSize - packetAccessSize);
+    PacketAssignmentFunctor packetFunctor(kernel);
+    ScalarAssignmentFunctor scalarFunctor(kernel);
+    device.template parallelFor<PacketAssignmentFunctor, PacketSize>(0, outerSize, 0, packetAccessSize, packetFunctor,
+                                                                     packetCost);
+    device.template parallelFor<ScalarAssignmentFunctor, 1>(0, outerSize, scalarFunctor, scalarCost);
+  };
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_with_device<Kernel, CoreThreadPoolDevice, LinearTraversal, NoUnrolling> {
+  static constexpr Index XprEvaluationCost = cost_helper<Kernel>::Cost;
+  struct AssignmentFunctor : public Kernel {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AssignmentFunctor(Kernel& kernel) : Kernel(kernel) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index index) { this->assignCoeff(index); }
+  };
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Kernel& kernel, CoreThreadPoolDevice& device) {
+    const Index size = kernel.size();
+    constexpr float cost = static_cast<float>(XprEvaluationCost);
+    AssignmentFunctor functor(kernel);
+    device.template parallelFor<AssignmentFunctor, 1>(0, size, functor, cost);
+  }
+};
+
+template <typename Kernel>
+struct dense_assignment_loop_with_device<Kernel, CoreThreadPoolDevice, LinearVectorizedTraversal, NoUnrolling> {
+  using Scalar = typename Kernel::Scalar;
+  using PacketType = typename Kernel::PacketType;
+  static constexpr Index XprEvaluationCost = cost_helper<Kernel>::Cost,
+                         RequestedAlignment = Kernel::AssignmentTraits::LinearRequiredAlignment,
+                         PacketSize = unpacket_traits<PacketType>::size,
+                         DstIsAligned = Kernel::AssignmentTraits::DstAlignment >= RequestedAlignment,
+                         DstAlignment = packet_traits<Scalar>::AlignedOnScalar ? RequestedAlignment
+                                                                               : Kernel::AssignmentTraits::DstAlignment,
+                         SrcAlignment = Kernel::AssignmentTraits::JointAlignment;
+  struct AssignmentFunctor : public Kernel {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AssignmentFunctor(Kernel& kernel) : Kernel(kernel) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index index) {
+      this->template assignPacket<DstAlignment, SrcAlignment, PacketType>(index);
+    }
+  };
+  static constexpr bool UsePacketSegment = Kernel::AssignmentTraits::UsePacketSegment;
+  using head_loop =
+      unaligned_dense_assignment_loop<PacketType, DstAlignment, SrcAlignment, UsePacketSegment, DstIsAligned>;
+  using tail_loop = unaligned_dense_assignment_loop<PacketType, DstAlignment, SrcAlignment, UsePacketSegment, false>;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Kernel& kernel, CoreThreadPoolDevice& device) {
+    const Index size = kernel.size();
+    const Index alignedStart =
+        DstIsAligned ? 0 : internal::first_aligned<RequestedAlignment>(kernel.dstDataPtr(), size);
+    const Index alignedEnd = alignedStart + numext::round_down(size - alignedStart, PacketSize);
+
+    head_loop::run(kernel, 0, alignedStart);
+
+    constexpr float cost = static_cast<float>(XprEvaluationCost);
+    AssignmentFunctor functor(kernel);
+    device.template parallelFor<AssignmentFunctor, PacketSize>(alignedStart, alignedEnd, functor, cost);
+
+    tail_loop::run(kernel, alignedEnd, size);
+  }
+};
+
+}  // namespace internal
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CORE_THREAD_POOL_DEVICE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/EventCount.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/EventCount.h
new file mode 100644
index 0000000..6eda6f4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/EventCount.h
@@ -0,0 +1,241 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H
+#define EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// EventCount allows to wait for arbitrary predicates in non-blocking
+// algorithms. Think of condition variable, but wait predicate does not need to
+// be protected by a mutex. Usage:
+// Waiting thread does:
+//
+//   if (predicate)
+//     return act();
+//   EventCount::Waiter& w = waiters[my_index];
+//   ec.Prewait(&w);
+//   if (predicate) {
+//     ec.CancelWait(&w);
+//     return act();
+//   }
+//   ec.CommitWait(&w);
+//
+// Notifying thread does:
+//
+//   predicate = true;
+//   ec.Notify(true);
+//
+// Notify is cheap if there are no waiting threads. Prewait/CommitWait are not
+// cheap, but they are executed only if the preceding predicate check has
+// failed.
+//
+// Algorithm outline:
+// There are two main variables: predicate (managed by user) and state_.
+// Operation closely resembles Dekker mutual algorithm:
+// https://en.wikipedia.org/wiki/Dekker%27s_algorithm
+// Waiting thread sets state_ then checks predicate, Notifying thread sets
+// predicate then checks state_. Due to seq_cst fences in between these
+// operations it is guaranteed than either waiter will see predicate change
+// and won't block, or notifying thread will see state_ change and will unblock
+// the waiter, or both. But it can't happen that both threads don't see each
+// other changes, which would lead to deadlock.
+class EventCount {
+ public:
+  class Waiter;
+
+  EventCount(MaxSizeVector<Waiter>& waiters) : state_(kStackMask), waiters_(waiters) {
+    eigen_plain_assert(waiters.size() < (1 << kWaiterBits) - 1);
+  }
+
+  EventCount(const EventCount&) = delete;
+  void operator=(const EventCount&) = delete;
+
+  ~EventCount() {
+    // Ensure there are no waiters.
+    eigen_plain_assert(state_.load() == kStackMask);
+  }
+
+  // Prewait prepares for waiting.
+  // After calling Prewait, the thread must re-check the wait predicate
+  // and then call either CancelWait or CommitWait.
+  void Prewait() {
+    uint64_t state = state_.load(std::memory_order_relaxed);
+    for (;;) {
+      CheckState(state);
+      uint64_t newstate = state + kWaiterInc;
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate, std::memory_order_seq_cst)) return;
+    }
+  }
+
+  // CommitWait commits waiting after Prewait.
+  void CommitWait(Waiter* w) {
+    eigen_plain_assert((w->epoch & ~kEpochMask) == 0);
+    w->state = Waiter::kNotSignaled;
+    const uint64_t me = (w - &waiters_[0]) | w->epoch;
+    uint64_t state = state_.load(std::memory_order_seq_cst);
+    for (;;) {
+      CheckState(state, true);
+      uint64_t newstate;
+      if ((state & kSignalMask) != 0) {
+        // Consume the signal and return immediately.
+        newstate = state - kWaiterInc - kSignalInc;
+      } else {
+        // Remove this thread from pre-wait counter and add to the waiter stack.
+        newstate = ((state & kWaiterMask) - kWaiterInc) | me;
+        w->next.store(state & (kStackMask | kEpochMask), std::memory_order_relaxed);
+      }
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate, std::memory_order_acq_rel)) {
+        if ((state & kSignalMask) == 0) {
+          w->epoch += kEpochInc;
+          Park(w);
+        }
+        return;
+      }
+    }
+  }
+
+  // CancelWait cancels effects of the previous Prewait call.
+  void CancelWait() {
+    uint64_t state = state_.load(std::memory_order_relaxed);
+    for (;;) {
+      CheckState(state, true);
+      uint64_t newstate = state - kWaiterInc;
+      // We don't know if the thread was also notified or not,
+      // so we should not consume a signal unconditionally.
+      // Only if number of waiters is equal to number of signals,
+      // we know that the thread was notified and we must take away the signal.
+      if (((state & kWaiterMask) >> kWaiterShift) == ((state & kSignalMask) >> kSignalShift)) newstate -= kSignalInc;
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate, std::memory_order_acq_rel)) return;
+    }
+  }
+
+  // Notify wakes one or all waiting threads.
+  // Must be called after changing the associated wait predicate.
+  void Notify(bool notifyAll) {
+    std::atomic_thread_fence(std::memory_order_seq_cst);
+    uint64_t state = state_.load(std::memory_order_acquire);
+    for (;;) {
+      CheckState(state);
+      const uint64_t waiters = (state & kWaiterMask) >> kWaiterShift;
+      const uint64_t signals = (state & kSignalMask) >> kSignalShift;
+      // Easy case: no waiters.
+      if ((state & kStackMask) == kStackMask && waiters == signals) return;
+      uint64_t newstate;
+      if (notifyAll) {
+        // Empty wait stack and set signal to number of pre-wait threads.
+        newstate = (state & kWaiterMask) | (waiters << kSignalShift) | kStackMask;
+      } else if (signals < waiters) {
+        // There is a thread in pre-wait state, unblock it.
+        newstate = state + kSignalInc;
+      } else {
+        // Pop a waiter from list and unpark it.
+        Waiter* w = &waiters_[state & kStackMask];
+        uint64_t next = w->next.load(std::memory_order_relaxed);
+        newstate = (state & (kWaiterMask | kSignalMask)) | next;
+      }
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate, std::memory_order_acq_rel)) {
+        if (!notifyAll && (signals < waiters)) return;  // unblocked pre-wait thread
+        if ((state & kStackMask) == kStackMask) return;
+        Waiter* w = &waiters_[state & kStackMask];
+        if (!notifyAll) w->next.store(kStackMask, std::memory_order_relaxed);
+        Unpark(w);
+        return;
+      }
+    }
+  }
+
+ private:
+  // State_ layout:
+  // - low kWaiterBits is a stack of waiters committed wait
+  //   (indexes in waiters_ array are used as stack elements,
+  //   kStackMask means empty stack).
+  // - next kWaiterBits is count of waiters in prewait state.
+  // - next kWaiterBits is count of pending signals.
+  // - remaining bits are ABA counter for the stack.
+  //   (stored in Waiter node and incremented on push).
+  static const uint64_t kWaiterBits = 14;
+  static const uint64_t kStackMask = (1ull << kWaiterBits) - 1;
+  static const uint64_t kWaiterShift = kWaiterBits;
+  static const uint64_t kWaiterMask = ((1ull << kWaiterBits) - 1) << kWaiterShift;
+  static const uint64_t kWaiterInc = 1ull << kWaiterShift;
+  static const uint64_t kSignalShift = 2 * kWaiterBits;
+  static const uint64_t kSignalMask = ((1ull << kWaiterBits) - 1) << kSignalShift;
+  static const uint64_t kSignalInc = 1ull << kSignalShift;
+  static const uint64_t kEpochShift = 3 * kWaiterBits;
+  static const uint64_t kEpochBits = 64 - kEpochShift;
+  static const uint64_t kEpochMask = ((1ull << kEpochBits) - 1) << kEpochShift;
+  static const uint64_t kEpochInc = 1ull << kEpochShift;
+
+ public:
+  class Waiter {
+    friend class EventCount;
+
+    enum State {
+      kNotSignaled,
+      kWaiting,
+      kSignaled,
+    };
+
+    EIGEN_ALIGN_TO_AVOID_FALSE_SHARING std::atomic<uint64_t> next{kStackMask};
+    EIGEN_MUTEX mu;
+    EIGEN_CONDVAR cv;
+    uint64_t epoch{0};
+    unsigned state{kNotSignaled};
+  };
+
+ private:
+  static void CheckState(uint64_t state, bool waiter = false) {
+    static_assert(kEpochBits >= 20, "not enough bits to prevent ABA problem");
+    const uint64_t waiters = (state & kWaiterMask) >> kWaiterShift;
+    const uint64_t signals = (state & kSignalMask) >> kSignalShift;
+    eigen_plain_assert(waiters >= signals);
+    eigen_plain_assert(waiters < (1 << kWaiterBits) - 1);
+    eigen_plain_assert(!waiter || waiters > 0);
+    (void)waiters;
+    (void)signals;
+  }
+
+  void Park(Waiter* w) {
+    EIGEN_MUTEX_LOCK lock(w->mu);
+    while (w->state != Waiter::kSignaled) {
+      w->state = Waiter::kWaiting;
+      w->cv.wait(lock);
+    }
+  }
+
+  void Unpark(Waiter* w) {
+    for (Waiter* next; w; w = next) {
+      uint64_t wnext = w->next.load(std::memory_order_relaxed) & kStackMask;
+      next = wnext == kStackMask ? nullptr : &waiters_[internal::convert_index<size_t>(wnext)];
+      unsigned state;
+      {
+        EIGEN_MUTEX_LOCK lock(w->mu);
+        state = w->state;
+        w->state = Waiter::kSignaled;
+      }
+      // Avoid notifying if it wasn't waiting.
+      if (state == Waiter::kWaiting) w->cv.notify_one();
+    }
+  }
+
+  std::atomic<uint64_t> state_;
+  MaxSizeVector<Waiter>& waiters_;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ForkJoin.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ForkJoin.h
new file mode 100644
index 0000000..588636a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ForkJoin.h
@@ -0,0 +1,140 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2025 Weiwei Kong <weiweikong@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_THREADPOOL_FORKJOIN_H
+#define EIGEN_THREADPOOL_FORKJOIN_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// ForkJoinScheduler provides implementations of various non-blocking ParallelFor algorithms for unary
+// and binary parallel tasks. More specifically, the implementations follow the binary tree-based
+// algorithm from the following paper:
+//
+//   Lea, D. (2000, June). A java fork/join framework. *In Proceedings of the
+//   ACM 2000 conference on Java Grande* (pp. 36-43).
+//
+// For a given binary task function `f(i,j)` and integers `num_threads`, `granularity`, `start`, and `end`,
+// the implemented parallel for algorithm schedules and executes at most `num_threads` of the functions
+// from the following set in parallel (either synchronously or asynchronously):
+//
+//   f(start,start+s_1), f(start+s_1,start+s_2), ..., f(start+s_n,end)
+//
+// where `s_{j+1} - s_{j}` and `end - s_n` are roughly within a factor of two of `granularity`. For a unary
+// task function `g(k)`, the same operation is applied with
+//
+//   f(i,j) = [&](){ for(Index k = i; k < j; ++k) g(k); };
+//
+// Note that the parameter `granularity` should be tuned by the user based on the trade-off of running the
+// given task function sequentially vs. scheduling individual tasks in parallel. An example of a partially
+// tuned `granularity` is in `Eigen::CoreThreadPoolDevice::parallelFor(...)` where the template
+// parameter `PacketSize` and float input `cost` are used to indirectly compute a granularity level for a
+// given task function.
+//
+// Example usage #1 (synchronous):
+// ```
+// ThreadPool thread_pool(num_threads);
+// ForkJoinScheduler::ParallelFor(0, num_tasks, granularity, std::move(parallel_task), &thread_pool);
+// ```
+//
+// Example usage #2 (executing multiple tasks asynchronously, each one parallelized with ParallelFor):
+// ```
+// ThreadPool thread_pool(num_threads);
+// Barrier barrier(num_async_calls);
+// auto done = [&](){ barrier.Notify(); };
+// for (Index k=0; k<num_async_calls; ++k) {
+//   ForkJoinScheduler::ParallelForAsync(task_start[k], task_end[k], granularity[k], parallel_task[k], done,
+//   &thread_pool);
+// }
+// barrier.Wait();
+// ```
+class ForkJoinScheduler {
+ public:
+  // Runs `do_func` asynchronously for the range [start, end) with a specified
+  // granularity. `do_func` should be of type `std::function<void(Index,
+  // Index)`. `done()` is called exactly once after all tasks have been executed.
+  template <typename DoFnType, typename DoneFnType, typename ThreadPoolEnv>
+  static void ParallelForAsync(Index start, Index end, Index granularity, DoFnType&& do_func, DoneFnType&& done,
+                               ThreadPoolTempl<ThreadPoolEnv>* thread_pool) {
+    if (start >= end) {
+      done();
+      return;
+    }
+    thread_pool->Schedule([start, end, granularity, thread_pool, do_func = std::forward<DoFnType>(do_func),
+                           done = std::forward<DoneFnType>(done)]() {
+      RunParallelFor(start, end, granularity, do_func, thread_pool);
+      done();
+    });
+  }
+
+  // Synchronous variant of ParallelForAsync.
+  // WARNING: Making nested calls to `ParallelFor`, e.g., calling `ParallelFor` inside a task passed into another
+  // `ParallelFor` call, may lead to deadlocks due to how task stealing is implemented.
+  template <typename DoFnType, typename ThreadPoolEnv>
+  static void ParallelFor(Index start, Index end, Index granularity, DoFnType&& do_func,
+                          ThreadPoolTempl<ThreadPoolEnv>* thread_pool) {
+    if (start >= end) return;
+    Barrier barrier(1);
+    auto done = [&barrier]() { barrier.Notify(); };
+    ParallelForAsync(start, end, granularity, do_func, done, thread_pool);
+    barrier.Wait();
+  }
+
+ private:
+  // Schedules `right_thunk`, runs `left_thunk`, and runs other tasks until `right_thunk` has finished.
+  template <typename LeftType, typename RightType, typename ThreadPoolEnv>
+  static void ForkJoin(LeftType&& left_thunk, RightType&& right_thunk, ThreadPoolTempl<ThreadPoolEnv>* thread_pool) {
+    typedef typename ThreadPoolTempl<ThreadPoolEnv>::Task Task;
+    std::atomic<bool> right_done(false);
+    auto execute_right = [&right_thunk, &right_done]() {
+      std::forward<RightType>(right_thunk)();
+      right_done.store(true, std::memory_order_release);
+    };
+    thread_pool->Schedule(execute_right);
+    std::forward<LeftType>(left_thunk)();
+    Task task;
+    while (!right_done.load(std::memory_order_acquire)) {
+      thread_pool->MaybeGetTask(&task);
+      if (task.f) task.f();
+    }
+  }
+
+  static Index ComputeMidpoint(Index start, Index end, Index granularity) {
+    // Typical workloads choose initial values of `{start, end, granularity}` such that `start - end` and
+    // `granularity` are powers of two. Since modern processors usually implement (2^x)-way
+    // set-associative caches, we minimize the number of cache misses by choosing midpoints that are not
+    // powers of two (to avoid having two addresses in the main memory pointing to the same point in the
+    // cache). More specifically, we choose the midpoint at (roughly) the 9/16 mark.
+    const Index size = end - start;
+    const Index offset = numext::round_down(9 * (size + 1) / 16, granularity);
+    return start + offset;
+  }
+
+  template <typename DoFnType, typename ThreadPoolEnv>
+  static void RunParallelFor(Index start, Index end, Index granularity, DoFnType&& do_func,
+                             ThreadPoolTempl<ThreadPoolEnv>* thread_pool) {
+    Index mid = ComputeMidpoint(start, end, granularity);
+    if ((end - start) < granularity || mid == start || mid == end) {
+      do_func(start, end);
+      return;
+    }
+    ForkJoin([start, mid, granularity, &do_func,
+              thread_pool]() { RunParallelFor(start, mid, granularity, do_func, thread_pool); },
+             [mid, end, granularity, &do_func, thread_pool]() {
+               RunParallelFor(mid, end, granularity, do_func, thread_pool);
+             },
+             thread_pool);
+  }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_THREADPOOL_FORKJOIN_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/InternalHeaderCheck.h
new file mode 100644
index 0000000..5b27ef4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/InternalHeaderCheck.h
@@ -0,0 +1,4 @@
+#ifndef EIGEN_THREADPOOL_MODULE_H
+#error \
+    "Please include unsupported/Eigen/CXX11/ThreadPool instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/NonBlockingThreadPool.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/NonBlockingThreadPool.h
new file mode 100644
index 0000000..a9f0beb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/NonBlockingThreadPool.h
@@ -0,0 +1,587 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
+#define EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Environment>
+class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
+ public:
+  typedef typename Environment::EnvThread Thread;
+  typedef typename Environment::Task Task;
+  typedef RunQueue<Task, 1024> Queue;
+
+  struct PerThread {
+    constexpr PerThread() : pool(NULL), rand(0), thread_id(-1) {}
+    ThreadPoolTempl* pool;  // Parent pool, or null for normal threads.
+    uint64_t rand;          // Random generator state.
+    int thread_id;          // Worker thread index in pool.
+  };
+
+  struct ThreadData {
+    constexpr ThreadData() : thread(), steal_partition(0), queue() {}
+    std::unique_ptr<Thread> thread;
+    std::atomic<unsigned> steal_partition;
+    Queue queue;
+  };
+
+  ThreadPoolTempl(int num_threads, Environment env = Environment()) : ThreadPoolTempl(num_threads, true, env) {}
+
+  ThreadPoolTempl(int num_threads, bool allow_spinning, Environment env = Environment())
+      : env_(env),
+        num_threads_(num_threads),
+        allow_spinning_(allow_spinning),
+        spin_count_(
+            // TODO(dvyukov,rmlarsen): The time spent in NonEmptyQueueIndex() is proportional to num_threads_ and
+            // we assume that new work is scheduled at a constant rate, so we divide `kSpintCount` by number of
+            // threads and number of spinning threads. The constant was picked based on a fair dice roll, tune it.
+            allow_spinning && num_threads > 0 ? kSpinCount / kMaxSpinningThreads / num_threads : 0),
+        thread_data_(num_threads),
+        all_coprimes_(num_threads),
+        waiters_(num_threads),
+        global_steal_partition_(EncodePartition(0, num_threads_)),
+        spinning_state_(0),
+        blocked_(0),
+        done_(false),
+        cancelled_(false),
+        ec_(waiters_) {
+    waiters_.resize(num_threads_);
+    // Calculate coprimes of all numbers [1, num_threads].
+    // Coprimes are used for random walks over all threads in Steal
+    // and NonEmptyQueueIndex. Iteration is based on the fact that if we take
+    // a random starting thread index t and calculate num_threads - 1 subsequent
+    // indices as (t + coprime) % num_threads, we will cover all threads without
+    // repetitions (effectively getting a presudo-random permutation of thread
+    // indices).
+    eigen_plain_assert(num_threads_ < kMaxThreads);
+    for (int i = 1; i <= num_threads_; ++i) {
+      all_coprimes_.emplace_back(i);
+      ComputeCoprimes(i, &all_coprimes_.back());
+    }
+#ifndef EIGEN_THREAD_LOCAL
+    init_barrier_.reset(new Barrier(num_threads_));
+#endif
+    thread_data_.resize(num_threads_);
+    for (int i = 0; i < num_threads_; i++) {
+      SetStealPartition(i, EncodePartition(0, num_threads_));
+      thread_data_[i].thread.reset(env_.CreateThread([this, i]() { WorkerLoop(i); }));
+    }
+#ifndef EIGEN_THREAD_LOCAL
+    // Wait for workers to initialize per_thread_map_. Otherwise we might race
+    // with them in Schedule or CurrentThreadId.
+    init_barrier_->Wait();
+#endif
+  }
+
+  ~ThreadPoolTempl() {
+    done_ = true;
+
+    // Now if all threads block without work, they will start exiting.
+    // But note that threads can continue to work arbitrary long,
+    // block, submit new work, unblock and otherwise live full life.
+    if (!cancelled_) {
+      ec_.Notify(true);
+    } else {
+      // Since we were cancelled, there might be entries in the queues.
+      // Empty them to prevent their destructor from asserting.
+      for (size_t i = 0; i < thread_data_.size(); i++) {
+        thread_data_[i].queue.Flush();
+      }
+    }
+    // Join threads explicitly (by destroying) to avoid destruction order within
+    // this class.
+    for (size_t i = 0; i < thread_data_.size(); ++i) thread_data_[i].thread.reset();
+  }
+
+  void SetStealPartitions(const std::vector<std::pair<unsigned, unsigned>>& partitions) {
+    eigen_plain_assert(partitions.size() == static_cast<std::size_t>(num_threads_));
+
+    // Pass this information to each thread queue.
+    for (int i = 0; i < num_threads_; i++) {
+      const auto& pair = partitions[i];
+      unsigned start = pair.first, end = pair.second;
+      AssertBounds(start, end);
+      unsigned val = EncodePartition(start, end);
+      SetStealPartition(i, val);
+    }
+  }
+
+  void Schedule(std::function<void()> fn) EIGEN_OVERRIDE { ScheduleWithHint(std::move(fn), 0, num_threads_); }
+
+  void ScheduleWithHint(std::function<void()> fn, int start, int limit) override {
+    Task t = env_.CreateTask(std::move(fn));
+    PerThread* pt = GetPerThread();
+    if (pt->pool == this) {
+      // Worker thread of this pool, push onto the thread's queue.
+      Queue& q = thread_data_[pt->thread_id].queue;
+      t = q.PushFront(std::move(t));
+    } else {
+      // A free-standing thread (or worker of another pool), push onto a random
+      // queue.
+      eigen_plain_assert(start < limit);
+      eigen_plain_assert(limit <= num_threads_);
+      int num_queues = limit - start;
+      int rnd = Rand(&pt->rand) % num_queues;
+      eigen_plain_assert(start + rnd < limit);
+      Queue& q = thread_data_[start + rnd].queue;
+      t = q.PushBack(std::move(t));
+    }
+    // Note: below we touch this after making w available to worker threads.
+    // Strictly speaking, this can lead to a racy-use-after-free. Consider that
+    // Schedule is called from a thread that is neither main thread nor a worker
+    // thread of this pool. Then, execution of w directly or indirectly
+    // completes overall computations, which in turn leads to destruction of
+    // this. We expect that such scenario is prevented by program, that is,
+    // this is kept alive while any threads can potentially be in Schedule.
+    if (!t.f) {
+      if (IsNotifyParkedThreadRequired()) {
+        ec_.Notify(false);
+      }
+    } else {
+      env_.ExecuteTask(t);  // Push failed, execute directly.
+    }
+  }
+
+  // Tries to assign work to the current task.
+  void MaybeGetTask(Task* t) {
+    PerThread* pt = GetPerThread();
+    const int thread_id = pt->thread_id;
+    // If we are not a worker thread of this pool, we can't get any work.
+    if (thread_id < 0) return;
+    Queue& q = thread_data_[thread_id].queue;
+    *t = q.PopFront();
+    if (t->f) return;
+    if (num_threads_ == 1) {
+      // For num_threads_ == 1 there is no point in going through the expensive
+      // steal loop. Moreover, since NonEmptyQueueIndex() calls PopBack() on the
+      // victim queues it might reverse the order in which ops are executed
+      // compared to the order in which they are scheduled, which tends to be
+      // counter-productive for the types of I/O workloads single thread pools
+      // tend to be used for.
+      for (int i = 0; i < spin_count_ && !t->f; ++i) *t = q.PopFront();
+    } else {
+      if (EIGEN_PREDICT_FALSE(!t->f)) *t = LocalSteal();
+      if (EIGEN_PREDICT_FALSE(!t->f)) *t = GlobalSteal();
+      if (EIGEN_PREDICT_FALSE(!t->f)) {
+        if (allow_spinning_ && StartSpinning()) {
+          for (int i = 0; i < spin_count_ && !t->f; ++i) *t = GlobalSteal();
+          // Notify `spinning_state_` that we are no longer spinning.
+          bool has_no_notify_task = StopSpinning();
+          // If a task was submitted to the queue without a call to
+          // `ec_.Notify()` (if `IsNotifyParkedThreadRequired()` returned
+          // false), and we didn't steal anything above, we must try to
+          // steal one more time, to make sure that this task will be
+          // executed. We will not necessarily find it, because it might
+          // have been already stolen by some other thread.
+          if (has_no_notify_task && !t->f) *t = GlobalSteal();
+        }
+      }
+    }
+  }
+
+  void Cancel() EIGEN_OVERRIDE {
+    cancelled_ = true;
+    done_ = true;
+
+    // Let each thread know it's been cancelled.
+#ifdef EIGEN_THREAD_ENV_SUPPORTS_CANCELLATION
+    for (size_t i = 0; i < thread_data_.size(); i++) {
+      thread_data_[i].thread->OnCancel();
+    }
+#endif
+
+    // Wake up the threads without work to let them exit on their own.
+    ec_.Notify(true);
+  }
+
+  int NumThreads() const EIGEN_FINAL { return num_threads_; }
+
+  int CurrentThreadId() const EIGEN_FINAL {
+    const PerThread* pt = const_cast<ThreadPoolTempl*>(this)->GetPerThread();
+    if (pt->pool == this) {
+      return pt->thread_id;
+    } else {
+      return -1;
+    }
+  }
+
+ private:
+  // Create a single atomic<int> that encodes start and limit information for
+  // each thread.
+  // We expect num_threads_ < 65536, so we can store them in a single
+  // std::atomic<unsigned>.
+  // Exposed publicly as static functions so that external callers can reuse
+  // this encode/decode logic for maintaining their own thread-safe copies of
+  // scheduling and steal domain(s).
+  static constexpr int kMaxPartitionBits = 16;
+  static constexpr int kMaxThreads = 1 << kMaxPartitionBits;
+
+  inline unsigned EncodePartition(unsigned start, unsigned limit) { return (start << kMaxPartitionBits) | limit; }
+
+  inline void DecodePartition(unsigned val, unsigned* start, unsigned* limit) {
+    *limit = val & (kMaxThreads - 1);
+    val >>= kMaxPartitionBits;
+    *start = val;
+  }
+
+  void AssertBounds(int start, int end) {
+    eigen_plain_assert(start >= 0);
+    eigen_plain_assert(start < end);  // non-zero sized partition
+    eigen_plain_assert(end <= num_threads_);
+  }
+
+  inline void SetStealPartition(size_t i, unsigned val) {
+    thread_data_[i].steal_partition.store(val, std::memory_order_relaxed);
+  }
+
+  inline unsigned GetStealPartition(int i) { return thread_data_[i].steal_partition.load(std::memory_order_relaxed); }
+
+  void ComputeCoprimes(int N, MaxSizeVector<unsigned>* coprimes) {
+    for (int i = 1; i <= N; i++) {
+      unsigned a = i;
+      unsigned b = N;
+      // If GCD(a, b) == 1, then a and b are coprimes.
+      while (b != 0) {
+        unsigned tmp = a;
+        a = b;
+        b = tmp % b;
+      }
+      if (a == 1) {
+        coprimes->push_back(i);
+      }
+    }
+  }
+
+  // Maximum number of threads that can spin in steal loop.
+  static constexpr int kMaxSpinningThreads = 1;
+
+  // The number of steal loop spin iterations before parking (this number is
+  // divided by the number of threads, to get spin count for each thread).
+  static constexpr int kSpinCount = 5000;
+
+  // If there are enough active threads with empty pending-task queues, a thread
+  // that runs out of work can just be parked without spinning, because these
+  // active threads will go into a steal loop after finishing their current
+  // tasks.
+  //
+  // In the worst case when all active threads are executing long/expensive
+  // tasks, the next Schedule() will have to wait until one of the parked
+  // threads will be unparked, however this should be very rare in practice.
+  static constexpr int kMinActiveThreadsToStartSpinning = 4;
+
+  struct SpinningState {
+    // Spinning state layout:
+    //
+    // - Low 32 bits encode the number of threads that are spinning in steal
+    //   loop.
+    //
+    // - High 32 bits encode the number of tasks that were submitted to the pool
+    //   without a call to `ec_.Notify()`. This number can't be larger than
+    //   the number of spinning threads. Each spinning thread, when it exits the
+    //   spin loop must check if this number is greater than zero, and maybe
+    //   make another attempt to steal a task and decrement it by one.
+    static constexpr uint64_t kNumSpinningMask = 0x00000000FFFFFFFF;
+    static constexpr uint64_t kNumNoNotifyMask = 0xFFFFFFFF00000000;
+    static constexpr uint64_t kNumNoNotifyShift = 32;
+
+    uint64_t num_spinning;         // number of spinning threads
+    uint64_t num_no_notification;  // number of tasks submitted without
+                                   // notifying waiting threads
+
+    // Decodes `spinning_state_` value.
+    static SpinningState Decode(uint64_t state) {
+      uint64_t num_spinning = (state & kNumSpinningMask);
+      uint64_t num_no_notification = (state & kNumNoNotifyMask) >> kNumNoNotifyShift;
+
+      eigen_plain_assert(num_no_notification <= num_spinning);
+      return {num_spinning, num_no_notification};
+    }
+
+    // Encodes as `spinning_state_` value.
+    uint64_t Encode() const {
+      eigen_plain_assert(num_no_notification <= num_spinning);
+      return (num_no_notification << kNumNoNotifyShift) | num_spinning;
+    }
+  };
+
+  Environment env_;
+  const int num_threads_;
+  const bool allow_spinning_;
+  const int spin_count_;
+  MaxSizeVector<ThreadData> thread_data_;
+  MaxSizeVector<MaxSizeVector<unsigned>> all_coprimes_;
+  MaxSizeVector<EventCount::Waiter> waiters_;
+  unsigned global_steal_partition_;
+  std::atomic<uint64_t> spinning_state_;
+  std::atomic<unsigned> blocked_;
+  std::atomic<bool> done_;
+  std::atomic<bool> cancelled_;
+  EventCount ec_;
+#ifndef EIGEN_THREAD_LOCAL
+  std::unique_ptr<Barrier> init_barrier_;
+  EIGEN_MUTEX per_thread_map_mutex_;  // Protects per_thread_map_.
+  std::unordered_map<uint64_t, std::unique_ptr<PerThread>> per_thread_map_;
+#endif
+
+  unsigned NumBlockedThreads() const { return blocked_.load(); }
+  unsigned NumActiveThreads() const { return num_threads_ - blocked_.load(); }
+
+  // Main worker thread loop.
+  void WorkerLoop(int thread_id) {
+#ifndef EIGEN_THREAD_LOCAL
+    std::unique_ptr<PerThread> new_pt(new PerThread());
+    per_thread_map_mutex_.lock();
+    bool insertOK = per_thread_map_.emplace(GlobalThreadIdHash(), std::move(new_pt)).second;
+    eigen_plain_assert(insertOK);
+    EIGEN_UNUSED_VARIABLE(insertOK);
+    per_thread_map_mutex_.unlock();
+    init_barrier_->Notify();
+    init_barrier_->Wait();
+#endif
+    PerThread* pt = GetPerThread();
+    pt->pool = this;
+    pt->rand = GlobalThreadIdHash();
+    pt->thread_id = thread_id;
+    Task t;
+    while (!cancelled_.load(std::memory_order_relaxed)) {
+      MaybeGetTask(&t);
+      // If we still don't have a task, wait for one. Return if thread pool is
+      // in cancelled state.
+      if (EIGEN_PREDICT_FALSE(!t.f)) {
+        EventCount::Waiter* waiter = &waiters_[pt->thread_id];
+        if (!WaitForWork(waiter, &t)) return;
+      }
+      if (EIGEN_PREDICT_TRUE(t.f)) env_.ExecuteTask(t);
+    }
+  }
+
+  // Steal tries to steal work from other worker threads in the range [start,
+  // limit) in best-effort manner.
+  Task Steal(unsigned start, unsigned limit) {
+    PerThread* pt = GetPerThread();
+    const size_t size = limit - start;
+    unsigned r = Rand(&pt->rand);
+    // Reduce r into [0, size) range, this utilizes trick from
+    // https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
+    eigen_plain_assert(all_coprimes_[size - 1].size() < (1 << 30));
+    unsigned victim = ((uint64_t)r * (uint64_t)size) >> 32;
+    unsigned index = ((uint64_t)all_coprimes_[size - 1].size() * (uint64_t)r) >> 32;
+    unsigned inc = all_coprimes_[size - 1][index];
+
+    for (unsigned i = 0; i < size; i++) {
+      eigen_plain_assert(start + victim < limit);
+      Task t = thread_data_[start + victim].queue.PopBack();
+      if (t.f) {
+        return t;
+      }
+      victim += inc;
+      if (victim >= size) {
+        victim -= static_cast<unsigned int>(size);
+      }
+    }
+    return Task();
+  }
+
+  // Steals work within threads belonging to the partition.
+  Task LocalSteal() {
+    PerThread* pt = GetPerThread();
+    unsigned partition = GetStealPartition(pt->thread_id);
+    // If thread steal partition is the same as global partition, there is no
+    // need to go through the steal loop twice.
+    if (global_steal_partition_ == partition) return Task();
+    unsigned start, limit;
+    DecodePartition(partition, &start, &limit);
+    AssertBounds(start, limit);
+
+    return Steal(start, limit);
+  }
+
+  // Steals work from any other thread in the pool.
+  Task GlobalSteal() { return Steal(0, num_threads_); }
+
+  // WaitForWork blocks until new work is available (returns true), or if it is
+  // time to exit (returns false). Can optionally return a task to execute in t
+  // (in such case t.f != nullptr on return).
+  bool WaitForWork(EventCount::Waiter* waiter, Task* t) {
+    eigen_plain_assert(!t->f);
+    // We already did best-effort emptiness check in Steal, so prepare for
+    // blocking.
+    ec_.Prewait();
+    // Now do a reliable emptiness check.
+    int victim = NonEmptyQueueIndex();
+    if (victim != -1) {
+      ec_.CancelWait();
+      if (cancelled_) {
+        return false;
+      } else {
+        *t = thread_data_[victim].queue.PopBack();
+        return true;
+      }
+    }
+    // Number of blocked threads is used as termination condition.
+    // If we are shutting down and all worker threads blocked without work,
+    // that's we are done.
+    blocked_++;
+    // TODO: is blocked_ required to be unsigned?
+    if (done_ && blocked_ == static_cast<unsigned>(num_threads_)) {
+      ec_.CancelWait();
+      // Almost done, but need to re-check queues.
+      // Consider that all queues are empty and all worker threads are preempted
+      // right after incrementing blocked_ above. Now a free-standing thread
+      // submits work and calls destructor (which sets done_). If we don't
+      // re-check queues, we will exit leaving the work unexecuted.
+      if (NonEmptyQueueIndex() != -1) {
+        // Note: we must not pop from queues before we decrement blocked_,
+        // otherwise the following scenario is possible. Consider that instead
+        // of checking for emptiness we popped the only element from queues.
+        // Now other worker threads can start exiting, which is bad if the
+        // work item submits other work. So we just check emptiness here,
+        // which ensures that all worker threads exit at the same time.
+        blocked_--;
+        return true;
+      }
+      // Reached stable termination state.
+      ec_.Notify(true);
+      return false;
+    }
+    ec_.CommitWait(waiter);
+    blocked_--;
+    return true;
+  }
+
+  int NonEmptyQueueIndex() {
+    PerThread* pt = GetPerThread();
+    // We intentionally design NonEmptyQueueIndex to steal work from
+    // anywhere in the queue so threads don't block in WaitForWork() forever
+    // when all threads in their partition go to sleep. Steal is still local.
+    const size_t size = thread_data_.size();
+    unsigned r = Rand(&pt->rand);
+    unsigned inc = all_coprimes_[size - 1][r % all_coprimes_[size - 1].size()];
+    unsigned victim = r % size;
+    for (unsigned i = 0; i < size; i++) {
+      if (!thread_data_[victim].queue.Empty()) {
+        return victim;
+      }
+      victim += inc;
+      if (victim >= size) {
+        victim -= static_cast<unsigned int>(size);
+      }
+    }
+    return -1;
+  }
+
+  // StartSpinning() checks if the number of threads in the spin loop is less
+  // than the allowed maximum. If so, increments the number of spinning threads
+  // by one and returns true (caller must enter the spin loop). Otherwise
+  // returns false, and the caller must not enter the spin loop.
+  bool StartSpinning() {
+    if (NumActiveThreads() > kMinActiveThreadsToStartSpinning) return false;
+
+    uint64_t spinning = spinning_state_.load(std::memory_order_relaxed);
+    for (;;) {
+      SpinningState state = SpinningState::Decode(spinning);
+
+      if ((state.num_spinning - state.num_no_notification) >= kMaxSpinningThreads) {
+        return false;
+      }
+
+      // Increment the number of spinning threads.
+      ++state.num_spinning;
+
+      if (spinning_state_.compare_exchange_weak(spinning, state.Encode(), std::memory_order_relaxed)) {
+        return true;
+      }
+    }
+  }
+
+  // StopSpinning() decrements the number of spinning threads by one. It also
+  // checks if there were any tasks submitted into the pool without notifying
+  // parked threads, and decrements the count by one. Returns true if the number
+  // of tasks submitted without notification was decremented. In this case,
+  // caller thread might have to call Steal() one more time.
+  bool StopSpinning() {
+    uint64_t spinning = spinning_state_.load(std::memory_order_relaxed);
+    for (;;) {
+      SpinningState state = SpinningState::Decode(spinning);
+
+      // Decrement the number of spinning threads.
+      --state.num_spinning;
+
+      // Maybe decrement the number of tasks submitted without notification.
+      bool has_no_notify_task = state.num_no_notification > 0;
+      if (has_no_notify_task) --state.num_no_notification;
+
+      if (spinning_state_.compare_exchange_weak(spinning, state.Encode(), std::memory_order_relaxed)) {
+        return has_no_notify_task;
+      }
+    }
+  }
+
+  // IsNotifyParkedThreadRequired() returns true if parked thread must be
+  // notified about new added task. If there are threads spinning in the steal
+  // loop, there is no need to unpark any of the waiting threads, the task will
+  // be picked up by one of the spinning threads.
+  bool IsNotifyParkedThreadRequired() {
+    uint64_t spinning = spinning_state_.load(std::memory_order_relaxed);
+    for (;;) {
+      SpinningState state = SpinningState::Decode(spinning);
+
+      // If the number of tasks submitted without notifying parked threads is
+      // equal to the number of spinning threads, we must wake up one of the
+      // parked threads.
+      if (state.num_no_notification == state.num_spinning) return true;
+
+      // Increment the number of tasks submitted without notification.
+      ++state.num_no_notification;
+
+      if (spinning_state_.compare_exchange_weak(spinning, state.Encode(), std::memory_order_relaxed)) {
+        return false;
+      }
+    }
+  }
+
+  static EIGEN_STRONG_INLINE uint64_t GlobalThreadIdHash() {
+    return std::hash<std::thread::id>()(std::this_thread::get_id());
+  }
+
+  EIGEN_STRONG_INLINE PerThread* GetPerThread() {
+#ifndef EIGEN_THREAD_LOCAL
+    static PerThread dummy;
+    auto it = per_thread_map_.find(GlobalThreadIdHash());
+    if (it == per_thread_map_.end()) {
+      return &dummy;
+    } else {
+      return it->second.get();
+    }
+#else
+    EIGEN_THREAD_LOCAL PerThread per_thread_;
+    PerThread* pt = &per_thread_;
+    return pt;
+#endif
+  }
+
+  static EIGEN_STRONG_INLINE unsigned Rand(uint64_t* state) {
+    uint64_t current = *state;
+    // Update the internal state
+    *state = current * 6364136223846793005ULL + 0xda3e39cb94b95bdbULL;
+    // Generate the random output (using the PCG-XSH-RS scheme)
+    return static_cast<unsigned>((current ^ (current >> 22)) >> (22 + (current >> 61)));
+  }
+};
+
+typedef ThreadPoolTempl<StlThreadEnvironment> ThreadPool;
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/RunQueue.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/RunQueue.h
new file mode 100644
index 0000000..e419ae7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/RunQueue.h
@@ -0,0 +1,230 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_RUNQUEUE_H
+#define EIGEN_CXX11_THREADPOOL_RUNQUEUE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// RunQueue is a fixed-size, partially non-blocking deque or Work items.
+// Operations on front of the queue must be done by a single thread (owner),
+// operations on back of the queue can be done by multiple threads concurrently.
+//
+// Algorithm outline:
+// All remote threads operating on the queue back are serialized by a mutex.
+// This ensures that at most two threads access state: owner and one remote
+// thread (Size aside). The algorithm ensures that the occupied region of the
+// underlying array is logically continuous (can wraparound, but no stray
+// occupied elements). Owner operates on one end of this region, remote thread
+// operates on the other end. Synchronization between these threads
+// (potential consumption of the last element and take up of the last empty
+// element) happens by means of state variable in each element. States are:
+// empty, busy (in process of insertion of removal) and ready. Threads claim
+// elements (empty->busy and ready->busy transitions) by means of a CAS
+// operation. The finishing transition (busy->empty and busy->ready) are done
+// with plain store as the element is exclusively owned by the current thread.
+//
+// Note: we could permit only pointers as elements, then we would not need
+// separate state variable as null/non-null pointer value would serve as state,
+// but that would require malloc/free per operation for large, complex values
+// (and this is designed to store std::function<()>).
+template <typename Work, unsigned kSize>
+class RunQueue {
+ public:
+  RunQueue() : front_(0), back_(0) {
+    // require power-of-two for fast masking
+    eigen_plain_assert((kSize & (kSize - 1)) == 0);
+    eigen_plain_assert(kSize > 2);            // why would you do this?
+    eigen_plain_assert(kSize <= (64 << 10));  // leave enough space for counter
+    for (unsigned i = 0; i < kSize; i++) array_[i].state.store(kEmpty, std::memory_order_relaxed);
+  }
+
+  ~RunQueue() { eigen_plain_assert(Size() == 0); }
+
+  // PushFront inserts w at the beginning of the queue.
+  // If queue is full returns w, otherwise returns default-constructed Work.
+  Work PushFront(Work w) {
+    unsigned front = front_.load(std::memory_order_relaxed);
+    Elem* e = &array_[front & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kEmpty || !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire)) return w;
+    front_.store(front + 1 + (kSize << 1), std::memory_order_release);
+    e->w = std::move(w);
+    e->state.store(kReady, std::memory_order_release);
+    return Work();
+  }
+
+  // PopFront removes and returns the first element in the queue.
+  // If the queue was empty returns default-constructed Work.
+  Work PopFront() {
+    unsigned front = front_.load(std::memory_order_relaxed);
+    Elem* e = &array_[(front - 1) & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kReady || !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire)) return Work();
+    Work w = std::move(e->w);
+    e->state.store(kEmpty, std::memory_order_release);
+    front = ((front - 1) & kMask2) | (front & ~kMask2);
+    front_.store(front, std::memory_order_release);
+    return w;
+  }
+
+  // PushBack adds w at the end of the queue.
+  // If queue is full returns w, otherwise returns default-constructed Work.
+  Work PushBack(Work w) {
+    EIGEN_MUTEX_LOCK lock(mutex_);
+    unsigned back = back_.load(std::memory_order_relaxed);
+    Elem* e = &array_[(back - 1) & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kEmpty || !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire)) return w;
+    back = ((back - 1) & kMask2) | (back & ~kMask2);
+    back_.store(back, std::memory_order_release);
+    e->w = std::move(w);
+    e->state.store(kReady, std::memory_order_release);
+    return Work();
+  }
+
+  // PopBack removes and returns the last elements in the queue.
+  Work PopBack() {
+    if (Empty()) return Work();
+    EIGEN_MUTEX_LOCK lock(mutex_);
+    unsigned back = back_.load(std::memory_order_relaxed);
+    Elem* e = &array_[back & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kReady || !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire)) return Work();
+    Work w = std::move(e->w);
+    e->state.store(kEmpty, std::memory_order_release);
+    back_.store(back + 1 + (kSize << 1), std::memory_order_release);
+    return w;
+  }
+
+  // PopBackHalf removes and returns half last elements in the queue.
+  // Returns number of elements removed.
+  unsigned PopBackHalf(std::vector<Work>* result) {
+    if (Empty()) return 0;
+    EIGEN_MUTEX_LOCK lock(mutex_);
+    unsigned back = back_.load(std::memory_order_relaxed);
+    unsigned size = Size();
+    unsigned mid = back;
+    if (size > 1) mid = back + (size - 1) / 2;
+    unsigned n = 0;
+    unsigned start = 0;
+    for (; static_cast<int>(mid - back) >= 0; mid--) {
+      Elem* e = &array_[mid & kMask];
+      uint8_t s = e->state.load(std::memory_order_relaxed);
+      if (n == 0) {
+        if (s != kReady || !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire)) continue;
+        start = mid;
+      } else {
+        // Note: no need to store temporal kBusy, we exclusively own these
+        // elements.
+        eigen_plain_assert(s == kReady);
+      }
+      result->push_back(std::move(e->w));
+      e->state.store(kEmpty, std::memory_order_release);
+      n++;
+    }
+    if (n != 0) back_.store(start + 1 + (kSize << 1), std::memory_order_release);
+    return n;
+  }
+
+  // Size returns current queue size.
+  // Can be called by any thread at any time.
+  unsigned Size() const { return SizeOrNotEmpty<true>(); }
+
+  // Empty tests whether container is empty.
+  // Can be called by any thread at any time.
+  bool Empty() const { return SizeOrNotEmpty<false>() == 0; }
+
+  // Delete all the elements from the queue.
+  void Flush() {
+    while (!Empty()) {
+      PopFront();
+    }
+  }
+
+ private:
+  static const unsigned kMask = kSize - 1;
+  static const unsigned kMask2 = (kSize << 1) - 1;
+
+  enum State {
+    kEmpty,
+    kBusy,
+    kReady,
+  };
+
+  struct Elem {
+    std::atomic<uint8_t> state;
+    Work w;
+  };
+
+  // Low log(kSize) + 1 bits in front_ and back_ contain rolling index of
+  // front/back, respectively. The remaining bits contain modification counters
+  // that are incremented on Push operations. This allows us to (1) distinguish
+  // between empty and full conditions (if we would use log(kSize) bits for
+  // position, these conditions would be indistinguishable); (2) obtain
+  // consistent snapshot of front_/back_ for Size operation using the
+  // modification counters.
+  EIGEN_ALIGN_TO_AVOID_FALSE_SHARING std::atomic<unsigned> front_;
+  EIGEN_ALIGN_TO_AVOID_FALSE_SHARING std::atomic<unsigned> back_;
+  EIGEN_MUTEX mutex_;  // guards `PushBack` and `PopBack` (accesses `back_`)
+
+  EIGEN_ALIGN_TO_AVOID_FALSE_SHARING Elem array_[kSize];
+
+  // SizeOrNotEmpty returns current queue size; if NeedSizeEstimate is false,
+  // only whether the size is 0 is guaranteed to be correct.
+  // Can be called by any thread at any time.
+  template <bool NeedSizeEstimate>
+  unsigned SizeOrNotEmpty() const {
+    // Emptiness plays critical role in thread pool blocking. So we go to great
+    // effort to not produce false positives (claim non-empty queue as empty).
+    unsigned front = front_.load(std::memory_order_acquire);
+    for (;;) {
+      // Capture a consistent snapshot of front/tail.
+      unsigned back = back_.load(std::memory_order_acquire);
+      unsigned front1 = front_.load(std::memory_order_relaxed);
+      if (front != front1) {
+        front = front1;
+        std::atomic_thread_fence(std::memory_order_acquire);
+        continue;
+      }
+      if (NeedSizeEstimate) {
+        return CalculateSize(front, back);
+      } else {
+        // This value will be 0 if the queue is empty, and undefined otherwise.
+        unsigned maybe_zero = ((front ^ back) & kMask2);
+        // Queue size estimate must agree with maybe zero check on the queue
+        // empty/non-empty state.
+        eigen_assert((CalculateSize(front, back) == 0) == (maybe_zero == 0));
+        return maybe_zero;
+      }
+    }
+  }
+
+  EIGEN_ALWAYS_INLINE unsigned CalculateSize(unsigned front, unsigned back) const {
+    int size = (front & kMask2) - (back & kMask2);
+    // Fix overflow.
+    if (EIGEN_PREDICT_FALSE(size < 0)) size += 2 * kSize;
+    // Order of modification in push/pop is crafted to make the queue look
+    // larger than it is during concurrent modifications. E.g. push can
+    // increment size before the corresponding pop has decremented it.
+    // So the computed size can be up to kSize + 1, fix it.
+    if (EIGEN_PREDICT_FALSE(size > static_cast<int>(kSize))) size = kSize;
+    return static_cast<unsigned>(size);
+  }
+
+  RunQueue(const RunQueue&) = delete;
+  void operator=(const RunQueue&) = delete;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_RUNQUEUE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadCancel.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadCancel.h
new file mode 100644
index 0000000..6f4dc67
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadCancel.h
@@ -0,0 +1,21 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
+
+// Try to come up with a portable way to cancel a thread
+#if EIGEN_OS_GNULINUX
+#define EIGEN_THREAD_CANCEL(t) pthread_cancel(t.native_handle());
+#define EIGEN_SUPPORTS_THREAD_CANCELLATION 1
+#else
+#define EIGEN_THREAD_CANCEL(t)
+#endif
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadEnvironment.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadEnvironment.h
new file mode 100644
index 0000000..b4dd05c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadEnvironment.h
@@ -0,0 +1,43 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+struct StlThreadEnvironment {
+  struct Task {
+    std::function<void()> f;
+  };
+
+  // EnvThread constructor must start the thread,
+  // destructor must join the thread.
+  class EnvThread {
+   public:
+    EnvThread(std::function<void()> f) : thr_(std::move(f)) {}
+    ~EnvThread() { thr_.join(); }
+    // This function is called when the threadpool is cancelled.
+    void OnCancel() {}
+
+   private:
+    std::thread thr_;
+  };
+
+  EnvThread* CreateThread(std::function<void()> f) { return new EnvThread(std::move(f)); }
+  Task CreateTask(std::function<void()> f) { return Task{std::move(f)}; }
+  void ExecuteTask(const Task& t) { t.f(); }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadLocal.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadLocal.h
new file mode 100644
index 0000000..f3ad5cc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadLocal.h
@@ -0,0 +1,289 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
+
+#ifdef EIGEN_AVOID_THREAD_LOCAL
+
+#ifdef EIGEN_THREAD_LOCAL
+#undef EIGEN_THREAD_LOCAL
+#endif
+
+#else
+
+#if ((EIGEN_COMP_GNUC) || __has_feature(cxx_thread_local) || EIGEN_COMP_MSVC)
+#define EIGEN_THREAD_LOCAL static thread_local
+#endif
+
+// Disable TLS for Apple and Android builds with older toolchains.
+#if defined(__APPLE__)
+// Included for TARGET_OS_IPHONE, __IPHONE_OS_VERSION_MIN_REQUIRED,
+// __IPHONE_8_0.
+#include <Availability.h>
+#include <TargetConditionals.h>
+#endif
+// Checks whether C++11's `thread_local` storage duration specifier is
+// supported.
+#if EIGEN_COMP_CLANGAPPLE && \
+    ((EIGEN_COMP_CLANGAPPLE < 8000042) || (TARGET_OS_IPHONE && __IPHONE_OS_VERSION_MIN_REQUIRED < __IPHONE_9_0))
+// Notes: Xcode's clang did not support `thread_local` until version
+// 8, and even then not for all iOS < 9.0.
+#undef EIGEN_THREAD_LOCAL
+
+#elif defined(__ANDROID__) && EIGEN_COMP_CLANG
+// There are platforms for which TLS should not be used even though the compiler
+// makes it seem like it's supported (Android NDK < r12b for example).
+// This is primarily because of linker problems and toolchain misconfiguration:
+// TLS isn't supported until NDK r12b per
+// https://developer.android.com/ndk/downloads/revision_history.html
+
+#if defined(__ANDROID__) && defined(__clang__) && defined(__NDK_MAJOR__) && defined(__NDK_MINOR__) && \
+    ((__NDK_MAJOR__ < 12) || ((__NDK_MAJOR__ == 12) && (__NDK_MINOR__ < 1)))
+#undef EIGEN_THREAD_LOCAL
+#endif
+#endif  // defined(__ANDROID__) && defined(__clang__)
+
+#endif  // EIGEN_AVOID_THREAD_LOCAL
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename T>
+struct ThreadLocalNoOpInitialize {
+  void operator()(T&) const {}
+};
+
+template <typename T>
+struct ThreadLocalNoOpRelease {
+  void operator()(T&) const {}
+};
+
+}  // namespace internal
+
+// Thread local container for elements of type T, that does not use thread local
+// storage. As long as the number of unique threads accessing this storage
+// is smaller than `capacity_`, it is lock-free and wait-free. Otherwise it will
+// use a mutex for synchronization.
+//
+// Type `T` has to be default constructible, and by default each thread will get
+// a default constructed value. It is possible to specify custom `initialize`
+// callable, that will be called lazily from each thread accessing this object,
+// and will be passed a default initialized object of type `T`. Also it's
+// possible to pass a custom `release` callable, that will be invoked before
+// calling ~T().
+//
+// Example:
+//
+//   struct Counter {
+//     int value = 0;
+//   }
+//
+//   Eigen::ThreadLocal<Counter> counter(10);
+//
+//   // Each thread will have access to its own counter object.
+//   Counter& cnt = counter.local();
+//   cnt++;
+//
+// WARNING: Eigen::ThreadLocal uses the OS-specific value returned by
+// std::this_thread::get_id() to identify threads. This value is not guaranteed
+// to be unique except for the life of the thread. A newly created thread may
+// get an OS-specific ID equal to that of an already destroyed thread.
+//
+// Somewhat similar to TBB thread local storage, with similar restrictions:
+// https://www.threadingbuildingblocks.org/docs/help/reference/thread_local_storage/enumerable_thread_specific_cls.html
+//
+template <typename T, typename Initialize = internal::ThreadLocalNoOpInitialize<T>,
+          typename Release = internal::ThreadLocalNoOpRelease<T>>
+class ThreadLocal {
+  // We preallocate default constructed elements in MaxSizedVector.
+  static_assert(std::is_default_constructible<T>::value, "ThreadLocal data type must be default constructible");
+
+ public:
+  explicit ThreadLocal(int capacity)
+      : ThreadLocal(capacity, internal::ThreadLocalNoOpInitialize<T>(), internal::ThreadLocalNoOpRelease<T>()) {}
+
+  ThreadLocal(int capacity, Initialize initialize)
+      : ThreadLocal(capacity, std::move(initialize), internal::ThreadLocalNoOpRelease<T>()) {}
+
+  ThreadLocal(int capacity, Initialize initialize, Release release)
+      : initialize_(std::move(initialize)),
+        release_(std::move(release)),
+        capacity_(capacity),
+        data_(capacity_),
+        ptr_(capacity_),
+        filled_records_(0) {
+    eigen_assert(capacity_ >= 0);
+    data_.resize(capacity_);
+    for (int i = 0; i < capacity_; ++i) {
+      ptr_.emplace_back(nullptr);
+    }
+  }
+
+  T& local() {
+    std::thread::id this_thread = std::this_thread::get_id();
+    if (capacity_ == 0) return SpilledLocal(this_thread);
+
+    std::size_t h = std::hash<std::thread::id>()(this_thread);
+    const int start_idx = h % capacity_;
+
+    // NOTE: From the definition of `std::this_thread::get_id()` it is
+    // guaranteed that we never can have concurrent insertions with the same key
+    // to our hash-map like data structure. If we didn't find an element during
+    // the initial traversal, it's guaranteed that no one else could have
+    // inserted it while we are in this function. This allows to massively
+    // simplify out lock-free insert-only hash map.
+
+    // Check if we already have an element for `this_thread`.
+    int idx = start_idx;
+    while (ptr_[idx].load() != nullptr) {
+      ThreadIdAndValue& record = *(ptr_[idx].load());
+      if (record.thread_id == this_thread) return record.value;
+
+      idx += 1;
+      if (idx >= capacity_) idx -= capacity_;
+      if (idx == start_idx) break;
+    }
+
+    // If we are here, it means that we found an insertion point in lookup
+    // table at `idx`, or we did a full traversal and table is full.
+
+    // If lock-free storage is full, fallback on mutex.
+    if (filled_records_.load() >= capacity_) return SpilledLocal(this_thread);
+
+    // We double check that we still have space to insert an element into a lock
+    // free storage. If old value in `filled_records_` is larger than the
+    // records capacity, it means that some other thread added an element while
+    // we were traversing lookup table.
+    int insertion_index = filled_records_.fetch_add(1, std::memory_order_relaxed);
+    if (insertion_index >= capacity_) return SpilledLocal(this_thread);
+
+    // At this point it's guaranteed that we can access to
+    // data_[insertion_index_] without a data race.
+    data_[insertion_index].thread_id = this_thread;
+    initialize_(data_[insertion_index].value);
+
+    // That's the pointer we'll put into the lookup table.
+    ThreadIdAndValue* inserted = &data_[insertion_index];
+
+    // We'll use nullptr pointer to ThreadIdAndValue in a compare-and-swap loop.
+    ThreadIdAndValue* empty = nullptr;
+
+    // Now we have to find an insertion point into the lookup table. We start
+    // from the `idx` that was identified as an insertion point above, it's
+    // guaranteed that we will have an empty record somewhere in a lookup table
+    // (because we created a record in the `data_`).
+    const int insertion_idx = idx;
+
+    do {
+      // Always start search from the original insertion candidate.
+      idx = insertion_idx;
+      while (ptr_[idx].load() != nullptr) {
+        idx += 1;
+        if (idx >= capacity_) idx -= capacity_;
+        // If we did a full loop, it means that we don't have any free entries
+        // in the lookup table, and this means that something is terribly wrong.
+        eigen_assert(idx != insertion_idx);
+      }
+      // Atomic CAS of the pointer guarantees that any other thread, that will
+      // follow this pointer will see all the mutations in the `data_`.
+    } while (!ptr_[idx].compare_exchange_weak(empty, inserted));
+
+    return inserted->value;
+  }
+
+  // WARN: It's not thread safe to call it concurrently with `local()`.
+  void ForEach(std::function<void(std::thread::id, T&)> f) {
+    // Reading directly from `data_` is unsafe, because only CAS to the
+    // record in `ptr_` makes all changes visible to other threads.
+    for (auto& ptr : ptr_) {
+      ThreadIdAndValue* record = ptr.load();
+      if (record == nullptr) continue;
+      f(record->thread_id, record->value);
+    }
+
+    // We did not spill into the map based storage.
+    if (filled_records_.load(std::memory_order_relaxed) < capacity_) return;
+
+    // Adds a happens before edge from the last call to SpilledLocal().
+    EIGEN_MUTEX_LOCK lock(mu_);
+    for (auto& kv : per_thread_map_) {
+      f(kv.first, kv.second);
+    }
+  }
+
+  // WARN: It's not thread safe to call it concurrently with `local()`.
+  ~ThreadLocal() {
+    // Reading directly from `data_` is unsafe, because only CAS to the record
+    // in `ptr_` makes all changes visible to other threads.
+    for (auto& ptr : ptr_) {
+      ThreadIdAndValue* record = ptr.load();
+      if (record == nullptr) continue;
+      release_(record->value);
+    }
+
+    // We did not spill into the map based storage.
+    if (filled_records_.load(std::memory_order_relaxed) < capacity_) return;
+
+    // Adds a happens before edge from the last call to SpilledLocal().
+    EIGEN_MUTEX_LOCK lock(mu_);
+    for (auto& kv : per_thread_map_) {
+      release_(kv.second);
+    }
+  }
+
+ private:
+  struct ThreadIdAndValue {
+    std::thread::id thread_id;
+    T value;
+  };
+
+  // Use unordered map guarded by a mutex when lock free storage is full.
+  T& SpilledLocal(std::thread::id this_thread) {
+    EIGEN_MUTEX_LOCK lock(mu_);
+
+    auto it = per_thread_map_.find(this_thread);
+    if (it == per_thread_map_.end()) {
+      auto result = per_thread_map_.emplace(this_thread, T());
+      eigen_assert(result.second);
+      initialize_((*result.first).second);
+      return (*result.first).second;
+    } else {
+      return it->second;
+    }
+  }
+
+  Initialize initialize_;
+  Release release_;
+  const int capacity_;
+
+  // Storage that backs lock-free lookup table `ptr_`. Records stored in this
+  // storage contiguously starting from index 0.
+  MaxSizeVector<ThreadIdAndValue> data_;
+
+  // Atomic pointers to the data stored in `data_`. Used as a lookup table for
+  // linear probing hash map (https://en.wikipedia.org/wiki/Linear_probing).
+  MaxSizeVector<std::atomic<ThreadIdAndValue*>> ptr_;
+
+  // Number of records stored in the `data_`.
+  std::atomic<int> filled_records_;
+
+  // We fallback on per thread map if lock-free storage is full. In practice
+  // this should never happen, if `capacity_` is a reasonable estimate of the
+  // number of threads running in a system.
+  EIGEN_MUTEX mu_;  // Protects per_thread_map_.
+  std::unordered_map<std::thread::id, T> per_thread_map_;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadPoolInterface.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadPoolInterface.h
new file mode 100644
index 0000000..d07c03e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadPoolInterface.h
@@ -0,0 +1,50 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// This defines an interface that ThreadPoolDevice can take to use
+// custom thread pools underneath.
+class ThreadPoolInterface {
+ public:
+  // Submits a closure to be run by a thread in the pool.
+  virtual void Schedule(std::function<void()> fn) = 0;
+
+  // Submits a closure to be run by threads in the range [start, end) in the
+  // pool.
+  virtual void ScheduleWithHint(std::function<void()> fn, int /*start*/, int /*end*/) {
+    // Just defer to Schedule in case sub-classes aren't interested in
+    // overriding this functionality.
+    Schedule(fn);
+  }
+
+  // If implemented, stop processing the closures that have been enqueued.
+  // Currently running closures may still be processed.
+  // If not implemented, does nothing.
+  virtual void Cancel() {}
+
+  // Returns the number of threads in the pool.
+  virtual int NumThreads() const = 0;
+
+  // Returns a logical thread index between 0 and NumThreads() - 1 if called
+  // from one of the threads in the pool. Returns -1 otherwise.
+  virtual int CurrentThreadId() const = 0;
+
+  virtual ~ThreadPoolInterface() {}
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadYield.h b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadYield.h
new file mode 100644
index 0000000..f556ff6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/ThreadPool/ThreadYield.h
@@ -0,0 +1,16 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
+
+// Try to come up with a portable way to yield
+#define EIGEN_THREAD_YIELD() std::this_thread::yield()
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/UmfPackSupport/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/UmfPackSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000..64112f1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/UmfPackSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
+#error "Please include Eigen/UmfPackSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/UmfPackSupport/UmfPackSupport.h b/data_acq_and_processing/third_party/eigen/Eigen/src/UmfPackSupport/UmfPackSupport.h
new file mode 100644
index 0000000..51e4db1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/UmfPackSupport/UmfPackSupport.h
@@ -0,0 +1,606 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_UMFPACKSUPPORT_H
+#define EIGEN_UMFPACKSUPPORT_H
+
+// for compatibility with super old version of umfpack,
+// This may not be strictly needed, but it is harmless.
+#ifndef SuiteSparse_long
+#ifdef UF_long
+#define SuiteSparse_long UF_long
+#else
+#error neither SuiteSparse_long nor UF_long are defined
+#endif
+#endif
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/* TODO extract L, extract U, compute det, etc... */
+
+// generic double/complex<double> wrapper functions:
+
+// Defaults
+inline void umfpack_defaults(double control[UMFPACK_CONTROL], double, int) { umfpack_di_defaults(control); }
+
+inline void umfpack_defaults(double control[UMFPACK_CONTROL], std::complex<double>, int) {
+  umfpack_zi_defaults(control);
+}
+
+inline void umfpack_defaults(double control[UMFPACK_CONTROL], double, SuiteSparse_long) {
+  umfpack_dl_defaults(control);
+}
+
+inline void umfpack_defaults(double control[UMFPACK_CONTROL], std::complex<double>, SuiteSparse_long) {
+  umfpack_zl_defaults(control);
+}
+
+// Report info
+inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], double, int) {
+  umfpack_di_report_info(control, info);
+}
+
+inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], std::complex<double>, int) {
+  umfpack_zi_report_info(control, info);
+}
+
+inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], double, SuiteSparse_long) {
+  umfpack_dl_report_info(control, info);
+}
+
+inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], std::complex<double>,
+                                SuiteSparse_long) {
+  umfpack_zl_report_info(control, info);
+}
+
+// Report status
+inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, double, int) {
+  umfpack_di_report_status(control, status);
+}
+
+inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, std::complex<double>, int) {
+  umfpack_zi_report_status(control, status);
+}
+
+inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, double, SuiteSparse_long) {
+  umfpack_dl_report_status(control, status);
+}
+
+inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, std::complex<double>, SuiteSparse_long) {
+  umfpack_zl_report_status(control, status);
+}
+
+// report control
+inline void umfpack_report_control(double control[UMFPACK_CONTROL], double, int) { umfpack_di_report_control(control); }
+
+inline void umfpack_report_control(double control[UMFPACK_CONTROL], std::complex<double>, int) {
+  umfpack_zi_report_control(control);
+}
+
+inline void umfpack_report_control(double control[UMFPACK_CONTROL], double, SuiteSparse_long) {
+  umfpack_dl_report_control(control);
+}
+
+inline void umfpack_report_control(double control[UMFPACK_CONTROL], std::complex<double>, SuiteSparse_long) {
+  umfpack_zl_report_control(control);
+}
+
+// Free numeric
+inline void umfpack_free_numeric(void **Numeric, double, int) {
+  umfpack_di_free_numeric(Numeric);
+  *Numeric = 0;
+}
+
+inline void umfpack_free_numeric(void **Numeric, std::complex<double>, int) {
+  umfpack_zi_free_numeric(Numeric);
+  *Numeric = 0;
+}
+
+inline void umfpack_free_numeric(void **Numeric, double, SuiteSparse_long) {
+  umfpack_dl_free_numeric(Numeric);
+  *Numeric = 0;
+}
+
+inline void umfpack_free_numeric(void **Numeric, std::complex<double>, SuiteSparse_long) {
+  umfpack_zl_free_numeric(Numeric);
+  *Numeric = 0;
+}
+
+// Free symbolic
+inline void umfpack_free_symbolic(void **Symbolic, double, int) {
+  umfpack_di_free_symbolic(Symbolic);
+  *Symbolic = 0;
+}
+
+inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>, int) {
+  umfpack_zi_free_symbolic(Symbolic);
+  *Symbolic = 0;
+}
+
+inline void umfpack_free_symbolic(void **Symbolic, double, SuiteSparse_long) {
+  umfpack_dl_free_symbolic(Symbolic);
+  *Symbolic = 0;
+}
+
+inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>, SuiteSparse_long) {
+  umfpack_zl_free_symbolic(Symbolic);
+  *Symbolic = 0;
+}
+
+// Symbolic
+inline int umfpack_symbolic(int n_row, int n_col, const int Ap[], const int Ai[], const double Ax[], void **Symbolic,
+                            const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_di_symbolic(n_row, n_col, Ap, Ai, Ax, Symbolic, Control, Info);
+}
+
+inline int umfpack_symbolic(int n_row, int n_col, const int Ap[], const int Ai[], const std::complex<double> Ax[],
+                            void **Symbolic, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_zi_symbolic(n_row, n_col, Ap, Ai, &numext::real_ref(Ax[0]), 0, Symbolic, Control, Info);
+}
+inline SuiteSparse_long umfpack_symbolic(SuiteSparse_long n_row, SuiteSparse_long n_col, const SuiteSparse_long Ap[],
+                                         const SuiteSparse_long Ai[], const double Ax[], void **Symbolic,
+                                         const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_dl_symbolic(n_row, n_col, Ap, Ai, Ax, Symbolic, Control, Info);
+}
+
+inline SuiteSparse_long umfpack_symbolic(SuiteSparse_long n_row, SuiteSparse_long n_col, const SuiteSparse_long Ap[],
+                                         const SuiteSparse_long Ai[], const std::complex<double> Ax[], void **Symbolic,
+                                         const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_zl_symbolic(n_row, n_col, Ap, Ai, &numext::real_ref(Ax[0]), 0, Symbolic, Control, Info);
+}
+
+// Numeric
+inline int umfpack_numeric(const int Ap[], const int Ai[], const double Ax[], void *Symbolic, void **Numeric,
+                           const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_di_numeric(Ap, Ai, Ax, Symbolic, Numeric, Control, Info);
+}
+
+inline int umfpack_numeric(const int Ap[], const int Ai[], const std::complex<double> Ax[], void *Symbolic,
+                           void **Numeric, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_zi_numeric(Ap, Ai, &numext::real_ref(Ax[0]), 0, Symbolic, Numeric, Control, Info);
+}
+inline SuiteSparse_long umfpack_numeric(const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const double Ax[],
+                                        void *Symbolic, void **Numeric, const double Control[UMFPACK_CONTROL],
+                                        double Info[UMFPACK_INFO]) {
+  return umfpack_dl_numeric(Ap, Ai, Ax, Symbolic, Numeric, Control, Info);
+}
+
+inline SuiteSparse_long umfpack_numeric(const SuiteSparse_long Ap[], const SuiteSparse_long Ai[],
+                                        const std::complex<double> Ax[], void *Symbolic, void **Numeric,
+                                        const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_zl_numeric(Ap, Ai, &numext::real_ref(Ax[0]), 0, Symbolic, Numeric, Control, Info);
+}
+
+// solve
+inline int umfpack_solve(int sys, const int Ap[], const int Ai[], const double Ax[], double X[], const double B[],
+                         void *Numeric, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_di_solve(sys, Ap, Ai, Ax, X, B, Numeric, Control, Info);
+}
+
+inline int umfpack_solve(int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[],
+                         std::complex<double> X[], const std::complex<double> B[], void *Numeric,
+                         const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_zi_solve(sys, Ap, Ai, &numext::real_ref(Ax[0]), 0, &numext::real_ref(X[0]), 0, &numext::real_ref(B[0]),
+                          0, Numeric, Control, Info);
+}
+
+inline SuiteSparse_long umfpack_solve(int sys, const SuiteSparse_long Ap[], const SuiteSparse_long Ai[],
+                                      const double Ax[], double X[], const double B[], void *Numeric,
+                                      const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_dl_solve(sys, Ap, Ai, Ax, X, B, Numeric, Control, Info);
+}
+
+inline SuiteSparse_long umfpack_solve(int sys, const SuiteSparse_long Ap[], const SuiteSparse_long Ai[],
+                                      const std::complex<double> Ax[], std::complex<double> X[],
+                                      const std::complex<double> B[], void *Numeric,
+                                      const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) {
+  return umfpack_zl_solve(sys, Ap, Ai, &numext::real_ref(Ax[0]), 0, &numext::real_ref(X[0]), 0, &numext::real_ref(B[0]),
+                          0, Numeric, Control, Info);
+}
+
+// Get Lunz
+inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double) {
+  return umfpack_di_get_lunz(lnz, unz, n_row, n_col, nz_udiag, Numeric);
+}
+
+inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric,
+                            std::complex<double>) {
+  return umfpack_zi_get_lunz(lnz, unz, n_row, n_col, nz_udiag, Numeric);
+}
+
+inline SuiteSparse_long umfpack_get_lunz(SuiteSparse_long *lnz, SuiteSparse_long *unz, SuiteSparse_long *n_row,
+                                         SuiteSparse_long *n_col, SuiteSparse_long *nz_udiag, void *Numeric, double) {
+  return umfpack_dl_get_lunz(lnz, unz, n_row, n_col, nz_udiag, Numeric);
+}
+
+inline SuiteSparse_long umfpack_get_lunz(SuiteSparse_long *lnz, SuiteSparse_long *unz, SuiteSparse_long *n_row,
+                                         SuiteSparse_long *n_col, SuiteSparse_long *nz_udiag, void *Numeric,
+                                         std::complex<double>) {
+  return umfpack_zl_get_lunz(lnz, unz, n_row, n_col, nz_udiag, Numeric);
+}
+
+// Get Numeric
+inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[], int P[], int Q[],
+                               double Dx[], int *do_recip, double Rs[], void *Numeric) {
+  return umfpack_di_get_numeric(Lp, Lj, Lx, Up, Ui, Ux, P, Q, Dx, do_recip, Rs, Numeric);
+}
+
+inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[],
+                               std::complex<double> Ux[], int P[], int Q[], std::complex<double> Dx[], int *do_recip,
+                               double Rs[], void *Numeric) {
+  double &lx0_real = numext::real_ref(Lx[0]);
+  double &ux0_real = numext::real_ref(Ux[0]);
+  double &dx0_real = numext::real_ref(Dx[0]);
+  return umfpack_zi_get_numeric(Lp, Lj, Lx ? &lx0_real : 0, 0, Up, Ui, Ux ? &ux0_real : 0, 0, P, Q, Dx ? &dx0_real : 0,
+                                0, do_recip, Rs, Numeric);
+}
+inline SuiteSparse_long umfpack_get_numeric(SuiteSparse_long Lp[], SuiteSparse_long Lj[], double Lx[],
+                                            SuiteSparse_long Up[], SuiteSparse_long Ui[], double Ux[],
+                                            SuiteSparse_long P[], SuiteSparse_long Q[], double Dx[],
+                                            SuiteSparse_long *do_recip, double Rs[], void *Numeric) {
+  return umfpack_dl_get_numeric(Lp, Lj, Lx, Up, Ui, Ux, P, Q, Dx, do_recip, Rs, Numeric);
+}
+
+inline SuiteSparse_long umfpack_get_numeric(SuiteSparse_long Lp[], SuiteSparse_long Lj[], std::complex<double> Lx[],
+                                            SuiteSparse_long Up[], SuiteSparse_long Ui[], std::complex<double> Ux[],
+                                            SuiteSparse_long P[], SuiteSparse_long Q[], std::complex<double> Dx[],
+                                            SuiteSparse_long *do_recip, double Rs[], void *Numeric) {
+  double &lx0_real = numext::real_ref(Lx[0]);
+  double &ux0_real = numext::real_ref(Ux[0]);
+  double &dx0_real = numext::real_ref(Dx[0]);
+  return umfpack_zl_get_numeric(Lp, Lj, Lx ? &lx0_real : 0, 0, Up, Ui, Ux ? &ux0_real : 0, 0, P, Q, Dx ? &dx0_real : 0,
+                                0, do_recip, Rs, Numeric);
+}
+
+// Get Determinant
+inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info[UMFPACK_INFO], int) {
+  return umfpack_di_get_determinant(Mx, Ex, NumericHandle, User_Info);
+}
+
+inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle,
+                                   double User_Info[UMFPACK_INFO], int) {
+  double &mx_real = numext::real_ref(*Mx);
+  return umfpack_zi_get_determinant(&mx_real, 0, Ex, NumericHandle, User_Info);
+}
+
+inline SuiteSparse_long umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle,
+                                                double User_Info[UMFPACK_INFO], SuiteSparse_long) {
+  return umfpack_dl_get_determinant(Mx, Ex, NumericHandle, User_Info);
+}
+
+inline SuiteSparse_long umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle,
+                                                double User_Info[UMFPACK_INFO], SuiteSparse_long) {
+  double &mx_real = numext::real_ref(*Mx);
+  return umfpack_zl_get_determinant(&mx_real, 0, Ex, NumericHandle, User_Info);
+}
+
+/** \ingroup UmfPackSupport_Module
+ * \brief A sparse LU factorization and solver based on UmfPack
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a LU factorization
+ * using the UmfPack library. The sparse matrix A must be squared and full rank.
+ * The vectors or matrices X and B can be either dense or sparse.
+ *
+ * \warning The input matrix A should be in a \b compressed and \b column-major form.
+ * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ *
+ * \implsparsesolverconcept
+ *
+ * \sa \ref TutorialSparseSolverConcept, class SparseLU
+ */
+template <typename MatrixType_>
+class UmfPackLU : public SparseSolverBase<UmfPackLU<MatrixType_> > {
+ protected:
+  typedef SparseSolverBase<UmfPackLU<MatrixType_> > Base;
+  using Base::m_isInitialized;
+
+ public:
+  using Base::_solve_impl;
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef Matrix<Scalar, Dynamic, 1> Vector;
+  typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+  typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+  typedef SparseMatrix<Scalar> LUMatrixType;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex> UmfpackMatrixType;
+  typedef Ref<const UmfpackMatrixType, StandardCompressedFormat> UmfpackMatrixRef;
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+ public:
+  typedef Array<double, UMFPACK_CONTROL, 1> UmfpackControl;
+  typedef Array<double, UMFPACK_INFO, 1> UmfpackInfo;
+
+  UmfPackLU() : m_dummy(0, 0), mp_matrix(m_dummy) { init(); }
+
+  template <typename InputMatrixType>
+  explicit UmfPackLU(const InputMatrixType &matrix) : mp_matrix(matrix) {
+    init();
+    compute(matrix);
+  }
+
+  ~UmfPackLU() {
+    if (m_symbolic) umfpack_free_symbolic(&m_symbolic, Scalar(), StorageIndex());
+    if (m_numeric) umfpack_free_numeric(&m_numeric, Scalar(), StorageIndex());
+  }
+
+  inline Index rows() const { return mp_matrix.rows(); }
+  inline Index cols() const { return mp_matrix.cols(); }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix.appears to be negative.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  inline const LUMatrixType &matrixL() const {
+    if (m_extractedDataAreDirty) extractData();
+    return m_l;
+  }
+
+  inline const LUMatrixType &matrixU() const {
+    if (m_extractedDataAreDirty) extractData();
+    return m_u;
+  }
+
+  inline const IntColVectorType &permutationP() const {
+    if (m_extractedDataAreDirty) extractData();
+    return m_p;
+  }
+
+  inline const IntRowVectorType &permutationQ() const {
+    if (m_extractedDataAreDirty) extractData();
+    return m_q;
+  }
+
+  /** Computes the sparse Cholesky decomposition of \a matrix
+   *  Note that the matrix should be column-major, and in compressed format for best performance.
+   *  \sa SparseMatrix::makeCompressed().
+   */
+  template <typename InputMatrixType>
+  void compute(const InputMatrixType &matrix) {
+    if (m_symbolic) umfpack_free_symbolic(&m_symbolic, Scalar(), StorageIndex());
+    if (m_numeric) umfpack_free_numeric(&m_numeric, Scalar(), StorageIndex());
+    grab(matrix.derived());
+    analyzePattern_impl();
+    factorize_impl();
+  }
+
+  /** Performs a symbolic decomposition on the sparsity of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize(), compute()
+   */
+  template <typename InputMatrixType>
+  void analyzePattern(const InputMatrixType &matrix) {
+    if (m_symbolic) umfpack_free_symbolic(&m_symbolic, Scalar(), StorageIndex());
+    if (m_numeric) umfpack_free_numeric(&m_numeric, Scalar(), StorageIndex());
+
+    grab(matrix.derived());
+
+    analyzePattern_impl();
+  }
+
+  /** Provides the return status code returned by UmfPack during the numeric
+   * factorization.
+   *
+   * \sa factorize(), compute()
+   */
+  inline int umfpackFactorizeReturncode() const {
+    eigen_assert(m_numeric && "UmfPackLU: you must first call factorize()");
+    return m_fact_errorCode;
+  }
+
+  /** Provides access to the control settings array used by UmfPack.
+   *
+   * If this array contains NaN's, the default values are used.
+   *
+   * See UMFPACK documentation for details.
+   */
+  inline const UmfpackControl &umfpackControl() const { return m_control; }
+
+  /** Provides access to the control settings array used by UmfPack.
+   *
+   * If this array contains NaN's, the default values are used.
+   *
+   * See UMFPACK documentation for details.
+   */
+  inline UmfpackControl &umfpackControl() { return m_control; }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity as the matrix on which the pattern analysis has been performed.
+   *
+   * \sa analyzePattern(), compute()
+   */
+  template <typename InputMatrixType>
+  void factorize(const InputMatrixType &matrix) {
+    eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
+    if (m_numeric) umfpack_free_numeric(&m_numeric, Scalar(), StorageIndex());
+
+    grab(matrix.derived());
+
+    factorize_impl();
+  }
+
+  /** Prints the current UmfPack control settings.
+   *
+   * \sa umfpackControl()
+   */
+  void printUmfpackControl() { umfpack_report_control(m_control.data(), Scalar(), StorageIndex()); }
+
+  /** Prints statistics collected by UmfPack.
+   *
+   * \sa analyzePattern(), compute()
+   */
+  void printUmfpackInfo() {
+    eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
+    umfpack_report_info(m_control.data(), m_umfpackInfo.data(), Scalar(), StorageIndex());
+  }
+
+  /** Prints the status of the previous factorization operation performed by UmfPack (symbolic or numerical
+   * factorization).
+   *
+   * \sa analyzePattern(), compute()
+   */
+  void printUmfpackStatus() {
+    eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
+    umfpack_report_status(m_control.data(), m_fact_errorCode, Scalar(), StorageIndex());
+  }
+
+  /** \internal */
+  template <typename BDerived, typename XDerived>
+  bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
+
+  Scalar determinant() const;
+
+  void extractData() const;
+
+ protected:
+  void init() {
+    m_info = InvalidInput;
+    m_isInitialized = false;
+    m_numeric = 0;
+    m_symbolic = 0;
+    m_extractedDataAreDirty = true;
+
+    umfpack_defaults(m_control.data(), Scalar(), StorageIndex());
+  }
+
+  void analyzePattern_impl() {
+    m_fact_errorCode = umfpack_symbolic(internal::convert_index<StorageIndex>(mp_matrix.rows()),
+                                        internal::convert_index<StorageIndex>(mp_matrix.cols()),
+                                        mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
+                                        &m_symbolic, m_control.data(), m_umfpackInfo.data());
+
+    m_isInitialized = true;
+    m_info = m_fact_errorCode ? InvalidInput : Success;
+    m_analysisIsOk = true;
+    m_factorizationIsOk = false;
+    m_extractedDataAreDirty = true;
+  }
+
+  void factorize_impl() {
+    m_fact_errorCode = umfpack_numeric(mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
+                                       m_symbolic, &m_numeric, m_control.data(), m_umfpackInfo.data());
+
+    m_info = m_fact_errorCode == UMFPACK_OK ? Success : NumericalIssue;
+    m_factorizationIsOk = true;
+    m_extractedDataAreDirty = true;
+  }
+
+  template <typename MatrixDerived>
+  void grab(const EigenBase<MatrixDerived> &A) {
+    internal::destroy_at(&mp_matrix);
+    internal::construct_at(&mp_matrix, A.derived());
+  }
+
+  void grab(const UmfpackMatrixRef &A) {
+    if (&(A.derived()) != &mp_matrix) {
+      internal::destroy_at(&mp_matrix);
+      internal::construct_at(&mp_matrix, A);
+    }
+  }
+
+  // cached data to reduce reallocation, etc.
+  mutable LUMatrixType m_l;
+  StorageIndex m_fact_errorCode;
+  UmfpackControl m_control;
+  mutable UmfpackInfo m_umfpackInfo;
+
+  mutable LUMatrixType m_u;
+  mutable IntColVectorType m_p;
+  mutable IntRowVectorType m_q;
+
+  UmfpackMatrixType m_dummy;
+  UmfpackMatrixRef mp_matrix;
+
+  void *m_numeric;
+  void *m_symbolic;
+
+  mutable ComputationInfo m_info;
+  int m_factorizationIsOk;
+  int m_analysisIsOk;
+  mutable bool m_extractedDataAreDirty;
+
+ private:
+  UmfPackLU(const UmfPackLU &) {}
+};
+
+template <typename MatrixType>
+void UmfPackLU<MatrixType>::extractData() const {
+  if (m_extractedDataAreDirty) {
+    // get size of the data
+    StorageIndex lnz, unz, rows, cols, nz_udiag;
+    umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
+
+    // allocate data
+    m_l.resize(rows, (std::min)(rows, cols));
+    m_l.resizeNonZeros(lnz);
+
+    m_u.resize((std::min)(rows, cols), cols);
+    m_u.resizeNonZeros(unz);
+
+    m_p.resize(rows);
+    m_q.resize(cols);
+
+    // extract
+    umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(), m_u.outerIndexPtr(),
+                        m_u.innerIndexPtr(), m_u.valuePtr(), m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
+
+    m_extractedDataAreDirty = false;
+  }
+}
+
+template <typename MatrixType>
+typename UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const {
+  Scalar det;
+  umfpack_get_determinant(&det, 0, m_numeric, 0, StorageIndex());
+  return det;
+}
+
+template <typename MatrixType>
+template <typename BDerived, typename XDerived>
+bool UmfPackLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const {
+  Index rhsCols = b.cols();
+  eigen_assert((BDerived::Flags & RowMajorBit) == 0 && "UmfPackLU backend does not support non col-major rhs yet");
+  eigen_assert((XDerived::Flags & RowMajorBit) == 0 && "UmfPackLU backend does not support non col-major result yet");
+  eigen_assert(b.derived().data() != x.derived().data() && " Umfpack does not support inplace solve");
+
+  Scalar *x_ptr = 0;
+  Matrix<Scalar, Dynamic, 1> x_tmp;
+  if (x.innerStride() != 1) {
+    x_tmp.resize(x.rows());
+    x_ptr = x_tmp.data();
+  }
+  for (int j = 0; j < rhsCols; ++j) {
+    if (x.innerStride() == 1) x_ptr = &x.col(j).coeffRef(0);
+    StorageIndex errorCode =
+        umfpack_solve(UMFPACK_A, mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(), x_ptr,
+                      &b.const_cast_derived().col(j).coeffRef(0), m_numeric, m_control.data(), m_umfpackInfo.data());
+    if (x.innerStride() != 1) x.col(j) = x_tmp;
+    if (errorCode != 0) return false;
+  }
+
+  return true;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_UMFPACKSUPPORT_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/misc/Image.h b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/Image.h
new file mode 100644
index 0000000..38d516e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/Image.h
@@ -0,0 +1,80 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MISC_IMAGE_H
+#define EIGEN_MISC_IMAGE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \class image_retval_base
+ *
+ */
+template <typename DecompositionType>
+struct traits<image_retval_base<DecompositionType> > {
+  typedef typename DecompositionType::MatrixType MatrixType;
+  typedef Matrix<typename MatrixType::Scalar,
+                 MatrixType::RowsAtCompileTime,  // the image is a subspace of the destination space, whose
+                                                 // dimension is the number of rows of the original matrix
+                 Dynamic,                        // we don't know at compile time the dimension of the image (the rank)
+                 traits<MatrixType>::Options,
+                 MatrixType::MaxRowsAtCompileTime,  // the image matrix will consist of columns from the original
+                                                    // matrix,
+                 MatrixType::MaxColsAtCompileTime   // so it has the same number of rows and at most as many columns.
+                 >
+      ReturnType;
+};
+
+template <typename DecompositionType_>
+struct image_retval_base : public ReturnByValue<image_retval_base<DecompositionType_> > {
+  typedef DecompositionType_ DecompositionType;
+  typedef typename DecompositionType::MatrixType MatrixType;
+  typedef ReturnByValue<image_retval_base> Base;
+
+  image_retval_base(const DecompositionType& dec, const MatrixType& originalMatrix)
+      : m_dec(dec), m_rank(dec.rank()), m_cols(m_rank == 0 ? 1 : m_rank), m_originalMatrix(originalMatrix) {}
+
+  inline Index rows() const { return m_dec.rows(); }
+  inline Index cols() const { return m_cols; }
+  inline Index rank() const { return m_rank; }
+  inline const DecompositionType& dec() const { return m_dec; }
+  inline const MatrixType& originalMatrix() const { return m_originalMatrix; }
+
+  template <typename Dest>
+  inline void evalTo(Dest& dst) const {
+    static_cast<const image_retval<DecompositionType>*>(this)->evalTo(dst);
+  }
+
+ protected:
+  const DecompositionType& m_dec;
+  Index m_rank, m_cols;
+  const MatrixType& m_originalMatrix;
+};
+
+}  // end namespace internal
+
+#define EIGEN_MAKE_IMAGE_HELPERS(DecompositionType)                   \
+  typedef typename DecompositionType::MatrixType MatrixType;          \
+  typedef typename MatrixType::Scalar Scalar;                         \
+  typedef typename MatrixType::RealScalar RealScalar;                 \
+  typedef Eigen::internal::image_retval_base<DecompositionType> Base; \
+  using Base::dec;                                                    \
+  using Base::originalMatrix;                                         \
+  using Base::rank;                                                   \
+  using Base::rows;                                                   \
+  using Base::cols;                                                   \
+  image_retval(const DecompositionType& dec, const MatrixType& originalMatrix) : Base(dec, originalMatrix) {}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MISC_IMAGE_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/misc/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/InternalHeaderCheck.h
new file mode 100644
index 0000000..1cea572
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CORE_MODULE_H
+#error "Please include Eigen/Core instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/misc/Kernel.h b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/Kernel.h
new file mode 100644
index 0000000..3ed458b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/Kernel.h
@@ -0,0 +1,77 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MISC_KERNEL_H
+#define EIGEN_MISC_KERNEL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \class kernel_retval_base
+ *
+ */
+template <typename DecompositionType>
+struct traits<kernel_retval_base<DecompositionType> > {
+  typedef typename DecompositionType::MatrixType MatrixType;
+  typedef Matrix<typename MatrixType::Scalar,
+                 MatrixType::ColsAtCompileTime,  // the number of rows in the "kernel matrix"
+                                                 // is the number of cols of the original matrix
+                                                 // so that the product "matrix * kernel = zero" makes sense
+                 Dynamic,                        // we don't know at compile-time the dimension of the kernel
+                 traits<MatrixType>::Options,
+                 MatrixType::MaxColsAtCompileTime,  // see explanation for 2nd template parameter
+                 MatrixType::MaxColsAtCompileTime   // the kernel is a subspace of the domain space,
+                                                    // whose dimension is the number of columns of the original matrix
+                 >
+      ReturnType;
+};
+
+template <typename DecompositionType_>
+struct kernel_retval_base : public ReturnByValue<kernel_retval_base<DecompositionType_> > {
+  typedef DecompositionType_ DecompositionType;
+  typedef ReturnByValue<kernel_retval_base> Base;
+
+  explicit kernel_retval_base(const DecompositionType& dec)
+      : m_dec(dec), m_rank(dec.rank()), m_cols(m_rank == dec.cols() ? 1 : dec.cols() - m_rank) {}
+
+  inline Index rows() const { return m_dec.cols(); }
+  inline Index cols() const { return m_cols; }
+  inline Index rank() const { return m_rank; }
+  inline const DecompositionType& dec() const { return m_dec; }
+
+  template <typename Dest>
+  inline void evalTo(Dest& dst) const {
+    static_cast<const kernel_retval<DecompositionType>*>(this)->evalTo(dst);
+  }
+
+ protected:
+  const DecompositionType& m_dec;
+  Index m_rank, m_cols;
+};
+
+}  // end namespace internal
+
+#define EIGEN_MAKE_KERNEL_HELPERS(DecompositionType)                   \
+  typedef typename DecompositionType::MatrixType MatrixType;           \
+  typedef typename MatrixType::Scalar Scalar;                          \
+  typedef typename MatrixType::RealScalar RealScalar;                  \
+  typedef Eigen::internal::kernel_retval_base<DecompositionType> Base; \
+  using Base::dec;                                                     \
+  using Base::rank;                                                    \
+  using Base::rows;                                                    \
+  using Base::cols;                                                    \
+  kernel_retval(const DecompositionType& dec) : Base(dec) {}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MISC_KERNEL_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/misc/blas.h b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/blas.h
new file mode 100644
index 0000000..f12bc7c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/blas.h
@@ -0,0 +1,97 @@
+#ifndef EIGEN_MISC_BLAS_H
+#define EIGEN_MISC_BLAS_H
+
+extern "C" {
+
+#define BLASFUNC(FUNC) FUNC##_
+
+/* Level 1 routines */
+
+void BLASFUNC(saxpy)(const int *, const float *, const float *, const int *, float *, const int *);
+void BLASFUNC(daxpy)(const int *, const double *, const double *, const int *, double *, const int *);
+void BLASFUNC(caxpy)(const int *, const float *, const float *, const int *, float *, const int *);
+void BLASFUNC(zaxpy)(const int *, const double *, const double *, const int *, double *, const int *);
+
+/* Level 2 routines */
+
+void BLASFUNC(sgemv)(const char *, const int *, const int *, const float *, const float *, const int *, const float *,
+                     const int *, const float *, float *, const int *);
+void BLASFUNC(dgemv)(const char *, const int *, const int *, const double *, const double *, const int *,
+                     const double *, const int *, const double *, double *, const int *);
+void BLASFUNC(cgemv)(const char *, const int *, const int *, const float *, const float *, const int *, const float *,
+                     const int *, const float *, float *, const int *);
+void BLASFUNC(zgemv)(const char *, const int *, const int *, const double *, const double *, const int *,
+                     const double *, const int *, const double *, double *, const int *);
+
+void BLASFUNC(strmv)(const char *, const char *, const char *, const int *, const float *, const int *, float *,
+                     const int *);
+void BLASFUNC(dtrmv)(const char *, const char *, const char *, const int *, const double *, const int *, double *,
+                     const int *);
+void BLASFUNC(ctrmv)(const char *, const char *, const char *, const int *, const float *, const int *, float *,
+                     const int *);
+void BLASFUNC(ztrmv)(const char *, const char *, const char *, const int *, const double *, const int *, double *,
+                     const int *);
+
+void BLASFUNC(ssymv)(const char *, const int *, const float *, const float *, const int *, const float *, const int *,
+                     const float *, float *, const int *);
+void BLASFUNC(dsymv)(const char *, const int *, const double *, const double *, const int *, const double *,
+                     const int *, const double *, double *, const int *);
+
+void BLASFUNC(chemv)(const char *, const int *, const float *, const float *, const int *, const float *, const int *,
+                     const float *, float *, const int *);
+void BLASFUNC(zhemv)(const char *, const int *, const double *, const double *, const int *, const double *,
+                     const int *, const double *, double *, const int *);
+
+/* Level 3 routines */
+
+void BLASFUNC(sgemm)(const char *, const char *, const int *, const int *, const int *, const float *, const float *,
+                     const int *, const float *, const int *, const float *, float *, const int *);
+void BLASFUNC(dgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *,
+                     const int *, const double *, const int *, const double *, double *, const int *);
+void BLASFUNC(cgemm)(const char *, const char *, const int *, const int *, const int *, const float *, const float *,
+                     const int *, const float *, const int *, const float *, float *, const int *);
+void BLASFUNC(zgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *,
+                     const int *, const double *, const int *, const double *, double *, const int *);
+
+void BLASFUNC(strsm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,
+                     const float *, const int *, float *, const int *);
+void BLASFUNC(dtrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *,
+                     const double *, const int *, double *, const int *);
+void BLASFUNC(ctrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,
+                     const float *, const int *, float *, const int *);
+void BLASFUNC(ztrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *,
+                     const double *, const int *, double *, const int *);
+
+void BLASFUNC(strmm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,
+                     const float *, const int *, float *, const int *);
+void BLASFUNC(dtrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *,
+                     const double *, const int *, double *, const int *);
+void BLASFUNC(ctrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,
+                     const float *, const int *, float *, const int *);
+void BLASFUNC(ztrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *,
+                     const double *, const int *, double *, const int *);
+
+void BLASFUNC(ssymm)(const char *, const char *, const int *, const int *, const float *, const float *, const int *,
+                     const float *, const int *, const float *, float *, const int *);
+void BLASFUNC(dsymm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *,
+                     const double *, const int *, const double *, double *, const int *);
+
+void BLASFUNC(ssyrk)(const char *, const char *, const int *, const int *, const float *, const float *, const int *,
+                     const float *, float *, const int *);
+void BLASFUNC(dsyrk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *,
+                     const double *, double *, const int *);
+
+void BLASFUNC(chemm)(const char *, const char *, const int *, const int *, const float *, const float *, const int *,
+                     const float *, const int *, const float *, float *, const int *);
+void BLASFUNC(zhemm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *,
+                     const double *, const int *, const double *, double *, const int *);
+
+void BLASFUNC(cherk)(const char *, const char *, const int *, const int *, const float *, const float *, const int *,
+                     const float *, float *, const int *);
+void BLASFUNC(zherk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *,
+                     const double *, double *, const int *);
+
+#undef BLASFUNC
+}
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/misc/lapacke.h b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/lapacke.h
new file mode 100644
index 0000000..94afd50
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/lapacke.h
@@ -0,0 +1,10085 @@
+/*****************************************************************************
+  Copyright (c) 2010, Intel Corp.
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+    * Redistributions of source code must retain the above copyright notice,
+      this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of Intel Corporation nor the names of its contributors
+      may be used to endorse or promote products derived from this software
+      without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+  THE POSSIBILITY OF SUCH DAMAGE.
+******************************************************************************
+* Contents: Native C interface to LAPACK
+* Author: Intel Corporation
+* Generated November, 2011
+*****************************************************************************/
+
+#ifndef _MKL_LAPACKE_H_
+
+#ifndef _LAPACKE_H_
+#define _LAPACKE_H_
+
+/*
+ *  Turn on HAVE_LAPACK_CONFIG_H to redefine C-LAPACK datatypes
+ */
+#ifdef HAVE_LAPACK_CONFIG_H
+#include "lapacke_config.h"
+#endif
+
+#include <stdlib.h>
+
+#ifndef lapack_int
+#ifdef LAPACK_ILP64
+#define lapack_int int64_t
+#else
+#define lapack_int int
+#endif
+#endif
+
+#ifndef lapack_logical
+#define lapack_logical lapack_int
+#endif
+
+/* Complex types are structures equivalent to the
+ * Fortran complex types COMPLEX(4) and COMPLEX(8).
+ *
+ * One can also redefine the types with his own types
+ * for example by including in the code definitions like
+ *
+ * #define lapack_complex_float std::complex<float>
+ * #define lapack_complex_double std::complex<double>
+ *
+ * or define these types in the command line:
+ *
+ * -Dlapack_complex_float="std::complex<float>"
+ * -Dlapack_complex_double="std::complex<double>"
+ */
+
+#ifndef LAPACK_COMPLEX_CUSTOM
+
+/* Complex type (single precision) */
+#ifndef lapack_complex_float
+#define lapack_complex_float std::complex<float>
+#endif
+
+#ifndef lapack_complex_float_real
+#define lapack_complex_float_real(z) (creal(z))
+#endif
+
+#ifndef lapack_complex_float_imag
+#define lapack_complex_float_imag(z) (cimag(z))
+#endif
+
+lapack_complex_float lapack_make_complex_float(float re, float im);
+
+/* Complex type (double precision) */
+#ifndef lapack_complex_double
+#define lapack_complex_double std::complex<double>
+#endif
+
+#ifndef lapack_complex_double_real
+#define lapack_complex_double_real(z) (creal(z))
+#endif
+
+#ifndef lapack_complex_double_imag
+#define lapack_complex_double_imag(z) (cimag(z))
+#endif
+
+lapack_complex_double lapack_make_complex_double(double re, double im);
+
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+#ifndef LAPACKE_malloc
+#define LAPACKE_malloc(size) malloc(size)
+#endif
+#ifndef LAPACKE_free
+#define LAPACKE_free(p) free(p)
+#endif
+
+#define LAPACK_C2INT(x) (lapack_int)(*((float*)&x))
+#define LAPACK_Z2INT(x) (lapack_int)(*((double*)&x))
+
+#define LAPACK_ROW_MAJOR 101
+#define LAPACK_COL_MAJOR 102
+
+#define LAPACK_WORK_MEMORY_ERROR -1010
+#define LAPACK_TRANSPOSE_MEMORY_ERROR -1011
+
+/* Callback logical functions of one, two, or three arguments are used
+ *  to select eigenvalues to sort to the top left of the Schur form.
+ *  The value is selected if function returns TRUE (non-zero). */
+
+typedef lapack_logical (*LAPACK_S_SELECT2)(const float*, const float*);
+typedef lapack_logical (*LAPACK_S_SELECT3)(const float*, const float*, const float*);
+typedef lapack_logical (*LAPACK_D_SELECT2)(const double*, const double*);
+typedef lapack_logical (*LAPACK_D_SELECT3)(const double*, const double*, const double*);
+
+typedef lapack_logical (*LAPACK_C_SELECT1)(const lapack_complex_float*);
+typedef lapack_logical (*LAPACK_C_SELECT2)(const lapack_complex_float*, const lapack_complex_float*);
+typedef lapack_logical (*LAPACK_Z_SELECT1)(const lapack_complex_double*);
+typedef lapack_logical (*LAPACK_Z_SELECT2)(const lapack_complex_double*, const lapack_complex_double*);
+
+#include "lapacke_mangling.h"
+
+#define LAPACK_lsame LAPACK_GLOBAL(lsame, LSAME)
+lapack_logical LAPACK_lsame(char* ca, char* cb, lapack_int lca, lapack_int lcb);
+
+/* C-LAPACK function prototypes */
+
+lapack_int LAPACKE_sbdsdc(int matrix_order, char uplo, char compq, lapack_int n, float* d, float* e, float* u,
+                          lapack_int ldu, float* vt, lapack_int ldvt, float* q, lapack_int* iq);
+lapack_int LAPACKE_dbdsdc(int matrix_order, char uplo, char compq, lapack_int n, double* d, double* e, double* u,
+                          lapack_int ldu, double* vt, lapack_int ldvt, double* q, lapack_int* iq);
+
+lapack_int LAPACKE_sbdsqr(int matrix_order, char uplo, lapack_int n, lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                          float* d, float* e, float* vt, lapack_int ldvt, float* u, lapack_int ldu, float* c,
+                          lapack_int ldc);
+lapack_int LAPACKE_dbdsqr(int matrix_order, char uplo, lapack_int n, lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                          double* d, double* e, double* vt, lapack_int ldvt, double* u, lapack_int ldu, double* c,
+                          lapack_int ldc);
+lapack_int LAPACKE_cbdsqr(int matrix_order, char uplo, lapack_int n, lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                          float* d, float* e, lapack_complex_float* vt, lapack_int ldvt, lapack_complex_float* u,
+                          lapack_int ldu, lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zbdsqr(int matrix_order, char uplo, lapack_int n, lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                          double* d, double* e, lapack_complex_double* vt, lapack_int ldvt, lapack_complex_double* u,
+                          lapack_int ldu, lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_sdisna(char job, lapack_int m, lapack_int n, const float* d, float* sep);
+lapack_int LAPACKE_ddisna(char job, lapack_int m, lapack_int n, const double* d, double* sep);
+
+lapack_int LAPACKE_sgbbrd(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int ncc, lapack_int kl,
+                          lapack_int ku, float* ab, lapack_int ldab, float* d, float* e, float* q, lapack_int ldq,
+                          float* pt, lapack_int ldpt, float* c, lapack_int ldc);
+lapack_int LAPACKE_dgbbrd(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int ncc, lapack_int kl,
+                          lapack_int ku, double* ab, lapack_int ldab, double* d, double* e, double* q, lapack_int ldq,
+                          double* pt, lapack_int ldpt, double* c, lapack_int ldc);
+lapack_int LAPACKE_cgbbrd(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int ncc, lapack_int kl,
+                          lapack_int ku, lapack_complex_float* ab, lapack_int ldab, float* d, float* e,
+                          lapack_complex_float* q, lapack_int ldq, lapack_complex_float* pt, lapack_int ldpt,
+                          lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zgbbrd(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int ncc, lapack_int kl,
+                          lapack_int ku, lapack_complex_double* ab, lapack_int ldab, double* d, double* e,
+                          lapack_complex_double* q, lapack_int ldq, lapack_complex_double* pt, lapack_int ldpt,
+                          lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_sgbcon(int matrix_order, char norm, lapack_int n, lapack_int kl, lapack_int ku, const float* ab,
+                          lapack_int ldab, const lapack_int* ipiv, float anorm, float* rcond);
+lapack_int LAPACKE_dgbcon(int matrix_order, char norm, lapack_int n, lapack_int kl, lapack_int ku, const double* ab,
+                          lapack_int ldab, const lapack_int* ipiv, double anorm, double* rcond);
+lapack_int LAPACKE_cgbcon(int matrix_order, char norm, lapack_int n, lapack_int kl, lapack_int ku,
+                          const lapack_complex_float* ab, lapack_int ldab, const lapack_int* ipiv, float anorm,
+                          float* rcond);
+lapack_int LAPACKE_zgbcon(int matrix_order, char norm, lapack_int n, lapack_int kl, lapack_int ku,
+                          const lapack_complex_double* ab, lapack_int ldab, const lapack_int* ipiv, double anorm,
+                          double* rcond);
+
+lapack_int LAPACKE_sgbequ(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, const float* ab,
+                          lapack_int ldab, float* r, float* c, float* rowcnd, float* colcnd, float* amax);
+lapack_int LAPACKE_dgbequ(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, const double* ab,
+                          lapack_int ldab, double* r, double* c, double* rowcnd, double* colcnd, double* amax);
+lapack_int LAPACKE_cgbequ(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                          const lapack_complex_float* ab, lapack_int ldab, float* r, float* c, float* rowcnd,
+                          float* colcnd, float* amax);
+lapack_int LAPACKE_zgbequ(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                          const lapack_complex_double* ab, lapack_int ldab, double* r, double* c, double* rowcnd,
+                          double* colcnd, double* amax);
+
+lapack_int LAPACKE_sgbequb(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, const float* ab,
+                           lapack_int ldab, float* r, float* c, float* rowcnd, float* colcnd, float* amax);
+lapack_int LAPACKE_dgbequb(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, const double* ab,
+                           lapack_int ldab, double* r, double* c, double* rowcnd, double* colcnd, double* amax);
+lapack_int LAPACKE_cgbequb(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                           const lapack_complex_float* ab, lapack_int ldab, float* r, float* c, float* rowcnd,
+                           float* colcnd, float* amax);
+lapack_int LAPACKE_zgbequb(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                           const lapack_complex_double* ab, lapack_int ldab, double* r, double* c, double* rowcnd,
+                           double* colcnd, double* amax);
+
+lapack_int LAPACKE_sgbrfs(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                          const float* ab, lapack_int ldab, const float* afb, lapack_int ldafb, const lapack_int* ipiv,
+                          const float* b, lapack_int ldb, float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_dgbrfs(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                          const double* ab, lapack_int ldab, const double* afb, lapack_int ldafb,
+                          const lapack_int* ipiv, const double* b, lapack_int ldb, double* x, lapack_int ldx,
+                          double* ferr, double* berr);
+lapack_int LAPACKE_cgbrfs(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                          const lapack_complex_float* ab, lapack_int ldab, const lapack_complex_float* afb,
+                          lapack_int ldafb, const lapack_int* ipiv, const lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_zgbrfs(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                          const lapack_complex_double* ab, lapack_int ldab, const lapack_complex_double* afb,
+                          lapack_int ldafb, const lapack_int* ipiv, const lapack_complex_double* b, lapack_int ldb,
+                          lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr);
+
+lapack_int LAPACKE_sgbrfsx(int matrix_order, char trans, char equed, lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, const float* ab, lapack_int ldab, const float* afb, lapack_int ldafb,
+                           const lapack_int* ipiv, const float* r, const float* c, const float* b, lapack_int ldb,
+                           float* x, lapack_int ldx, float* rcond, float* berr, lapack_int n_err_bnds,
+                           float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams, float* params);
+lapack_int LAPACKE_dgbrfsx(int matrix_order, char trans, char equed, lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, const double* ab, lapack_int ldab, const double* afb, lapack_int ldafb,
+                           const lapack_int* ipiv, const double* r, const double* c, const double* b, lapack_int ldb,
+                           double* x, lapack_int ldx, double* rcond, double* berr, lapack_int n_err_bnds,
+                           double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams, double* params);
+lapack_int LAPACKE_cgbrfsx(int matrix_order, char trans, char equed, lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, const lapack_complex_float* ab, lapack_int ldab,
+                           const lapack_complex_float* afb, lapack_int ldafb, const lapack_int* ipiv, const float* r,
+                           const float* c, const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                           lapack_int ldx, float* rcond, float* berr, lapack_int n_err_bnds, float* err_bnds_norm,
+                           float* err_bnds_comp, lapack_int nparams, float* params);
+lapack_int LAPACKE_zgbrfsx(int matrix_order, char trans, char equed, lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, const lapack_complex_double* ab, lapack_int ldab,
+                           const lapack_complex_double* afb, lapack_int ldafb, const lapack_int* ipiv, const double* r,
+                           const double* c, const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                           lapack_int ldx, double* rcond, double* berr, lapack_int n_err_bnds, double* err_bnds_norm,
+                           double* err_bnds_comp, lapack_int nparams, double* params);
+
+lapack_int LAPACKE_sgbsv(int matrix_order, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs, float* ab,
+                         lapack_int ldab, lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dgbsv(int matrix_order, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs, double* ab,
+                         lapack_int ldab, lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_cgbsv(int matrix_order, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                         lapack_complex_float* ab, lapack_int ldab, lapack_int* ipiv, lapack_complex_float* b,
+                         lapack_int ldb);
+lapack_int LAPACKE_zgbsv(int matrix_order, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                         lapack_complex_double* ab, lapack_int ldab, lapack_int* ipiv, lapack_complex_double* b,
+                         lapack_int ldb);
+
+lapack_int LAPACKE_sgbsvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                          lapack_int nrhs, float* ab, lapack_int ldab, float* afb, lapack_int ldafb, lapack_int* ipiv,
+                          char* equed, float* r, float* c, float* b, lapack_int ldb, float* x, lapack_int ldx,
+                          float* rcond, float* ferr, float* berr, float* rpivot);
+lapack_int LAPACKE_dgbsvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                          lapack_int nrhs, double* ab, lapack_int ldab, double* afb, lapack_int ldafb, lapack_int* ipiv,
+                          char* equed, double* r, double* c, double* b, lapack_int ldb, double* x, lapack_int ldx,
+                          double* rcond, double* ferr, double* berr, double* rpivot);
+lapack_int LAPACKE_cgbsvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                          lapack_int nrhs, lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* afb,
+                          lapack_int ldafb, lapack_int* ipiv, char* equed, float* r, float* c, lapack_complex_float* b,
+                          lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* rcond, float* ferr,
+                          float* berr, float* rpivot);
+lapack_int LAPACKE_zgbsvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                          lapack_int nrhs, lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* afb,
+                          lapack_int ldafb, lapack_int* ipiv, char* equed, double* r, double* c,
+                          lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* rcond, double* ferr, double* berr, double* rpivot);
+
+lapack_int LAPACKE_sgbsvxx(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, float* ab, lapack_int ldab, float* afb, lapack_int ldafb, lapack_int* ipiv,
+                           char* equed, float* r, float* c, float* b, lapack_int ldb, float* x, lapack_int ldx,
+                           float* rcond, float* rpvgrw, float* berr, lapack_int n_err_bnds, float* err_bnds_norm,
+                           float* err_bnds_comp, lapack_int nparams, float* params);
+lapack_int LAPACKE_dgbsvxx(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, double* ab, lapack_int ldab, double* afb, lapack_int ldafb,
+                           lapack_int* ipiv, char* equed, double* r, double* c, double* b, lapack_int ldb, double* x,
+                           lapack_int ldx, double* rcond, double* rpvgrw, double* berr, lapack_int n_err_bnds,
+                           double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams, double* params);
+lapack_int LAPACKE_cgbsvxx(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* afb,
+                           lapack_int ldafb, lapack_int* ipiv, char* equed, float* r, float* c, lapack_complex_float* b,
+                           lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* rcond, float* rpvgrw,
+                           float* berr, lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                           lapack_int nparams, float* params);
+lapack_int LAPACKE_zgbsvxx(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* afb,
+                           lapack_int ldafb, lapack_int* ipiv, char* equed, double* r, double* c,
+                           lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* rpvgrw, double* berr, lapack_int n_err_bnds, double* err_bnds_norm,
+                           double* err_bnds_comp, lapack_int nparams, double* params);
+
+lapack_int LAPACKE_sgbtrf(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, float* ab,
+                          lapack_int ldab, lapack_int* ipiv);
+lapack_int LAPACKE_dgbtrf(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, double* ab,
+                          lapack_int ldab, lapack_int* ipiv);
+lapack_int LAPACKE_cgbtrf(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                          lapack_complex_float* ab, lapack_int ldab, lapack_int* ipiv);
+lapack_int LAPACKE_zgbtrf(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                          lapack_complex_double* ab, lapack_int ldab, lapack_int* ipiv);
+
+lapack_int LAPACKE_sgbtrs(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                          const float* ab, lapack_int ldab, const lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dgbtrs(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                          const double* ab, lapack_int ldab, const lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_cgbtrs(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                          const lapack_complex_float* ab, lapack_int ldab, const lapack_int* ipiv,
+                          lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zgbtrs(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                          const lapack_complex_double* ab, lapack_int ldab, const lapack_int* ipiv,
+                          lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sgebak(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          const float* scale, lapack_int m, float* v, lapack_int ldv);
+lapack_int LAPACKE_dgebak(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          const double* scale, lapack_int m, double* v, lapack_int ldv);
+lapack_int LAPACKE_cgebak(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          const float* scale, lapack_int m, lapack_complex_float* v, lapack_int ldv);
+lapack_int LAPACKE_zgebak(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          const double* scale, lapack_int m, lapack_complex_double* v, lapack_int ldv);
+
+lapack_int LAPACKE_sgebal(int matrix_order, char job, lapack_int n, float* a, lapack_int lda, lapack_int* ilo,
+                          lapack_int* ihi, float* scale);
+lapack_int LAPACKE_dgebal(int matrix_order, char job, lapack_int n, double* a, lapack_int lda, lapack_int* ilo,
+                          lapack_int* ihi, double* scale);
+lapack_int LAPACKE_cgebal(int matrix_order, char job, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_int* ilo, lapack_int* ihi, float* scale);
+lapack_int LAPACKE_zgebal(int matrix_order, char job, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_int* ilo, lapack_int* ihi, double* scale);
+
+lapack_int LAPACKE_sgebrd(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* d, float* e,
+                          float* tauq, float* taup);
+lapack_int LAPACKE_dgebrd(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* d, double* e,
+                          double* tauq, double* taup);
+lapack_int LAPACKE_cgebrd(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          float* d, float* e, lapack_complex_float* tauq, lapack_complex_float* taup);
+lapack_int LAPACKE_zgebrd(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          double* d, double* e, lapack_complex_double* tauq, lapack_complex_double* taup);
+
+lapack_int LAPACKE_sgecon(int matrix_order, char norm, lapack_int n, const float* a, lapack_int lda, float anorm,
+                          float* rcond);
+lapack_int LAPACKE_dgecon(int matrix_order, char norm, lapack_int n, const double* a, lapack_int lda, double anorm,
+                          double* rcond);
+lapack_int LAPACKE_cgecon(int matrix_order, char norm, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                          float anorm, float* rcond);
+lapack_int LAPACKE_zgecon(int matrix_order, char norm, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                          double anorm, double* rcond);
+
+lapack_int LAPACKE_sgeequ(int matrix_order, lapack_int m, lapack_int n, const float* a, lapack_int lda, float* r,
+                          float* c, float* rowcnd, float* colcnd, float* amax);
+lapack_int LAPACKE_dgeequ(int matrix_order, lapack_int m, lapack_int n, const double* a, lapack_int lda, double* r,
+                          double* c, double* rowcnd, double* colcnd, double* amax);
+lapack_int LAPACKE_cgeequ(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                          float* r, float* c, float* rowcnd, float* colcnd, float* amax);
+lapack_int LAPACKE_zgeequ(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                          double* r, double* c, double* rowcnd, double* colcnd, double* amax);
+
+lapack_int LAPACKE_sgeequb(int matrix_order, lapack_int m, lapack_int n, const float* a, lapack_int lda, float* r,
+                           float* c, float* rowcnd, float* colcnd, float* amax);
+lapack_int LAPACKE_dgeequb(int matrix_order, lapack_int m, lapack_int n, const double* a, lapack_int lda, double* r,
+                           double* c, double* rowcnd, double* colcnd, double* amax);
+lapack_int LAPACKE_cgeequb(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                           float* r, float* c, float* rowcnd, float* colcnd, float* amax);
+lapack_int LAPACKE_zgeequb(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                           double* r, double* c, double* rowcnd, double* colcnd, double* amax);
+
+lapack_int LAPACKE_sgees(int matrix_order, char jobvs, char sort, LAPACK_S_SELECT2 select, lapack_int n, float* a,
+                         lapack_int lda, lapack_int* sdim, float* wr, float* wi, float* vs, lapack_int ldvs);
+lapack_int LAPACKE_dgees(int matrix_order, char jobvs, char sort, LAPACK_D_SELECT2 select, lapack_int n, double* a,
+                         lapack_int lda, lapack_int* sdim, double* wr, double* wi, double* vs, lapack_int ldvs);
+lapack_int LAPACKE_cgees(int matrix_order, char jobvs, char sort, LAPACK_C_SELECT1 select, lapack_int n,
+                         lapack_complex_float* a, lapack_int lda, lapack_int* sdim, lapack_complex_float* w,
+                         lapack_complex_float* vs, lapack_int ldvs);
+lapack_int LAPACKE_zgees(int matrix_order, char jobvs, char sort, LAPACK_Z_SELECT1 select, lapack_int n,
+                         lapack_complex_double* a, lapack_int lda, lapack_int* sdim, lapack_complex_double* w,
+                         lapack_complex_double* vs, lapack_int ldvs);
+
+lapack_int LAPACKE_sgeesx(int matrix_order, char jobvs, char sort, LAPACK_S_SELECT2 select, char sense, lapack_int n,
+                          float* a, lapack_int lda, lapack_int* sdim, float* wr, float* wi, float* vs, lapack_int ldvs,
+                          float* rconde, float* rcondv);
+lapack_int LAPACKE_dgeesx(int matrix_order, char jobvs, char sort, LAPACK_D_SELECT2 select, char sense, lapack_int n,
+                          double* a, lapack_int lda, lapack_int* sdim, double* wr, double* wi, double* vs,
+                          lapack_int ldvs, double* rconde, double* rcondv);
+lapack_int LAPACKE_cgeesx(int matrix_order, char jobvs, char sort, LAPACK_C_SELECT1 select, char sense, lapack_int n,
+                          lapack_complex_float* a, lapack_int lda, lapack_int* sdim, lapack_complex_float* w,
+                          lapack_complex_float* vs, lapack_int ldvs, float* rconde, float* rcondv);
+lapack_int LAPACKE_zgeesx(int matrix_order, char jobvs, char sort, LAPACK_Z_SELECT1 select, char sense, lapack_int n,
+                          lapack_complex_double* a, lapack_int lda, lapack_int* sdim, lapack_complex_double* w,
+                          lapack_complex_double* vs, lapack_int ldvs, double* rconde, double* rcondv);
+
+lapack_int LAPACKE_sgeev(int matrix_order, char jobvl, char jobvr, lapack_int n, float* a, lapack_int lda, float* wr,
+                         float* wi, float* vl, lapack_int ldvl, float* vr, lapack_int ldvr);
+lapack_int LAPACKE_dgeev(int matrix_order, char jobvl, char jobvr, lapack_int n, double* a, lapack_int lda, double* wr,
+                         double* wi, double* vl, lapack_int ldvl, double* vr, lapack_int ldvr);
+lapack_int LAPACKE_cgeev(int matrix_order, char jobvl, char jobvr, lapack_int n, lapack_complex_float* a,
+                         lapack_int lda, lapack_complex_float* w, lapack_complex_float* vl, lapack_int ldvl,
+                         lapack_complex_float* vr, lapack_int ldvr);
+lapack_int LAPACKE_zgeev(int matrix_order, char jobvl, char jobvr, lapack_int n, lapack_complex_double* a,
+                         lapack_int lda, lapack_complex_double* w, lapack_complex_double* vl, lapack_int ldvl,
+                         lapack_complex_double* vr, lapack_int ldvr);
+
+lapack_int LAPACKE_sgeevx(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n, float* a,
+                          lapack_int lda, float* wr, float* wi, float* vl, lapack_int ldvl, float* vr, lapack_int ldvr,
+                          lapack_int* ilo, lapack_int* ihi, float* scale, float* abnrm, float* rconde, float* rcondv);
+lapack_int LAPACKE_dgeevx(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n, double* a,
+                          lapack_int lda, double* wr, double* wi, double* vl, lapack_int ldvl, double* vr,
+                          lapack_int ldvr, lapack_int* ilo, lapack_int* ihi, double* scale, double* abnrm,
+                          double* rconde, double* rcondv);
+lapack_int LAPACKE_cgeevx(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                          lapack_complex_float* a, lapack_int lda, lapack_complex_float* w, lapack_complex_float* vl,
+                          lapack_int ldvl, lapack_complex_float* vr, lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
+                          float* scale, float* abnrm, float* rconde, float* rcondv);
+lapack_int LAPACKE_zgeevx(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                          lapack_complex_double* a, lapack_int lda, lapack_complex_double* w, lapack_complex_double* vl,
+                          lapack_int ldvl, lapack_complex_double* vr, lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
+                          double* scale, double* abnrm, double* rconde, double* rcondv);
+
+lapack_int LAPACKE_sgehrd(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, float* a, lapack_int lda,
+                          float* tau);
+lapack_int LAPACKE_dgehrd(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, double* a, lapack_int lda,
+                          double* tau);
+lapack_int LAPACKE_cgehrd(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* tau);
+lapack_int LAPACKE_zgehrd(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* tau);
+
+lapack_int LAPACKE_sgejsv(int matrix_order, char joba, char jobu, char jobv, char jobr, char jobt, char jobp,
+                          lapack_int m, lapack_int n, float* a, lapack_int lda, float* sva, float* u, lapack_int ldu,
+                          float* v, lapack_int ldv, float* stat, lapack_int* istat);
+lapack_int LAPACKE_dgejsv(int matrix_order, char joba, char jobu, char jobv, char jobr, char jobt, char jobp,
+                          lapack_int m, lapack_int n, double* a, lapack_int lda, double* sva, double* u, lapack_int ldu,
+                          double* v, lapack_int ldv, double* stat, lapack_int* istat);
+
+lapack_int LAPACKE_sgelq2(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau);
+lapack_int LAPACKE_dgelq2(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau);
+lapack_int LAPACKE_cgelq2(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* tau);
+lapack_int LAPACKE_zgelq2(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* tau);
+
+lapack_int LAPACKE_sgelqf(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau);
+lapack_int LAPACKE_dgelqf(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau);
+lapack_int LAPACKE_cgelqf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* tau);
+lapack_int LAPACKE_zgelqf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* tau);
+
+lapack_int LAPACKE_sgels(int matrix_order, char trans, lapack_int m, lapack_int n, lapack_int nrhs, float* a,
+                         lapack_int lda, float* b, lapack_int ldb);
+lapack_int LAPACKE_dgels(int matrix_order, char trans, lapack_int m, lapack_int n, lapack_int nrhs, double* a,
+                         lapack_int lda, double* b, lapack_int ldb);
+lapack_int LAPACKE_cgels(int matrix_order, char trans, lapack_int m, lapack_int n, lapack_int nrhs,
+                         lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zgels(int matrix_order, char trans, lapack_int m, lapack_int n, lapack_int nrhs,
+                         lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sgelsd(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, float* a, lapack_int lda,
+                          float* b, lapack_int ldb, float* s, float rcond, lapack_int* rank);
+lapack_int LAPACKE_dgelsd(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                          double* b, lapack_int ldb, double* s, double rcond, lapack_int* rank);
+lapack_int LAPACKE_cgelsd(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* b, lapack_int ldb, float* s, float rcond,
+                          lapack_int* rank);
+lapack_int LAPACKE_zgelsd(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b, lapack_int ldb, double* s, double rcond,
+                          lapack_int* rank);
+
+lapack_int LAPACKE_sgelss(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, float* a, lapack_int lda,
+                          float* b, lapack_int ldb, float* s, float rcond, lapack_int* rank);
+lapack_int LAPACKE_dgelss(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                          double* b, lapack_int ldb, double* s, double rcond, lapack_int* rank);
+lapack_int LAPACKE_cgelss(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* b, lapack_int ldb, float* s, float rcond,
+                          lapack_int* rank);
+lapack_int LAPACKE_zgelss(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b, lapack_int ldb, double* s, double rcond,
+                          lapack_int* rank);
+
+lapack_int LAPACKE_sgelsy(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, float* a, lapack_int lda,
+                          float* b, lapack_int ldb, lapack_int* jpvt, float rcond, lapack_int* rank);
+lapack_int LAPACKE_dgelsy(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                          double* b, lapack_int ldb, lapack_int* jpvt, double rcond, lapack_int* rank);
+lapack_int LAPACKE_cgelsy(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* b, lapack_int ldb, lapack_int* jpvt, float rcond,
+                          lapack_int* rank);
+lapack_int LAPACKE_zgelsy(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_int* jpvt, double rcond,
+                          lapack_int* rank);
+
+lapack_int LAPACKE_sgeqlf(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau);
+lapack_int LAPACKE_dgeqlf(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau);
+lapack_int LAPACKE_cgeqlf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* tau);
+lapack_int LAPACKE_zgeqlf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* tau);
+
+lapack_int LAPACKE_sgeqp3(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, lapack_int* jpvt,
+                          float* tau);
+lapack_int LAPACKE_dgeqp3(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, lapack_int* jpvt,
+                          double* tau);
+lapack_int LAPACKE_cgeqp3(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_int* jpvt, lapack_complex_float* tau);
+lapack_int LAPACKE_zgeqp3(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_int* jpvt, lapack_complex_double* tau);
+
+lapack_int LAPACKE_sgeqpf(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, lapack_int* jpvt,
+                          float* tau);
+lapack_int LAPACKE_dgeqpf(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, lapack_int* jpvt,
+                          double* tau);
+lapack_int LAPACKE_cgeqpf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_int* jpvt, lapack_complex_float* tau);
+lapack_int LAPACKE_zgeqpf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_int* jpvt, lapack_complex_double* tau);
+
+lapack_int LAPACKE_sgeqr2(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau);
+lapack_int LAPACKE_dgeqr2(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau);
+lapack_int LAPACKE_cgeqr2(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* tau);
+lapack_int LAPACKE_zgeqr2(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* tau);
+
+lapack_int LAPACKE_sgeqrf(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau);
+lapack_int LAPACKE_dgeqrf(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau);
+lapack_int LAPACKE_cgeqrf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* tau);
+lapack_int LAPACKE_zgeqrf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* tau);
+
+lapack_int LAPACKE_sgeqrfp(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau);
+lapack_int LAPACKE_dgeqrfp(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau);
+lapack_int LAPACKE_cgeqrfp(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* tau);
+lapack_int LAPACKE_zgeqrfp(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* tau);
+
+lapack_int LAPACKE_sgerfs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const float* a, lapack_int lda,
+                          const float* af, lapack_int ldaf, const lapack_int* ipiv, const float* b, lapack_int ldb,
+                          float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_dgerfs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const double* a, lapack_int lda,
+                          const double* af, lapack_int ldaf, const lapack_int* ipiv, const double* b, lapack_int ldb,
+                          double* x, lapack_int ldx, double* ferr, double* berr);
+lapack_int LAPACKE_cgerfs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const lapack_complex_float* a,
+                          lapack_int lda, const lapack_complex_float* af, lapack_int ldaf, const lapack_int* ipiv,
+                          const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_zgerfs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const lapack_complex_double* a,
+                          lapack_int lda, const lapack_complex_double* af, lapack_int ldaf, const lapack_int* ipiv,
+                          const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* ferr, double* berr);
+
+lapack_int LAPACKE_sgerfsx(int matrix_order, char trans, char equed, lapack_int n, lapack_int nrhs, const float* a,
+                           lapack_int lda, const float* af, lapack_int ldaf, const lapack_int* ipiv, const float* r,
+                           const float* c, const float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond,
+                           float* berr, lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                           lapack_int nparams, float* params);
+lapack_int LAPACKE_dgerfsx(int matrix_order, char trans, char equed, lapack_int n, lapack_int nrhs, const double* a,
+                           lapack_int lda, const double* af, lapack_int ldaf, const lapack_int* ipiv, const double* r,
+                           const double* c, const double* b, lapack_int ldb, double* x, lapack_int ldx, double* rcond,
+                           double* berr, lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                           lapack_int nparams, double* params);
+lapack_int LAPACKE_cgerfsx(int matrix_order, char trans, char equed, lapack_int n, lapack_int nrhs,
+                           const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                           lapack_int ldaf, const lapack_int* ipiv, const float* r, const float* c,
+                           const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                           float* rcond, float* berr, lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                           lapack_int nparams, float* params);
+lapack_int LAPACKE_zgerfsx(int matrix_order, char trans, char equed, lapack_int n, lapack_int nrhs,
+                           const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                           lapack_int ldaf, const lapack_int* ipiv, const double* r, const double* c,
+                           const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* berr, lapack_int n_err_bnds, double* err_bnds_norm,
+                           double* err_bnds_comp, lapack_int nparams, double* params);
+
+lapack_int LAPACKE_sgerqf(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau);
+lapack_int LAPACKE_dgerqf(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau);
+lapack_int LAPACKE_cgerqf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* tau);
+lapack_int LAPACKE_zgerqf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* tau);
+
+lapack_int LAPACKE_sgesdd(int matrix_order, char jobz, lapack_int m, lapack_int n, float* a, lapack_int lda, float* s,
+                          float* u, lapack_int ldu, float* vt, lapack_int ldvt);
+lapack_int LAPACKE_dgesdd(int matrix_order, char jobz, lapack_int m, lapack_int n, double* a, lapack_int lda, double* s,
+                          double* u, lapack_int ldu, double* vt, lapack_int ldvt);
+lapack_int LAPACKE_cgesdd(int matrix_order, char jobz, lapack_int m, lapack_int n, lapack_complex_float* a,
+                          lapack_int lda, float* s, lapack_complex_float* u, lapack_int ldu, lapack_complex_float* vt,
+                          lapack_int ldvt);
+lapack_int LAPACKE_zgesdd(int matrix_order, char jobz, lapack_int m, lapack_int n, lapack_complex_double* a,
+                          lapack_int lda, double* s, lapack_complex_double* u, lapack_int ldu,
+                          lapack_complex_double* vt, lapack_int ldvt);
+
+lapack_int LAPACKE_sgesv(int matrix_order, lapack_int n, lapack_int nrhs, float* a, lapack_int lda, lapack_int* ipiv,
+                         float* b, lapack_int ldb);
+lapack_int LAPACKE_dgesv(int matrix_order, lapack_int n, lapack_int nrhs, double* a, lapack_int lda, lapack_int* ipiv,
+                         double* b, lapack_int ldb);
+lapack_int LAPACKE_cgesv(int matrix_order, lapack_int n, lapack_int nrhs, lapack_complex_float* a, lapack_int lda,
+                         lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zgesv(int matrix_order, lapack_int n, lapack_int nrhs, lapack_complex_double* a, lapack_int lda,
+                         lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+lapack_int LAPACKE_dsgesv(int matrix_order, lapack_int n, lapack_int nrhs, double* a, lapack_int lda, lapack_int* ipiv,
+                          double* b, lapack_int ldb, double* x, lapack_int ldx, lapack_int* iter);
+lapack_int LAPACKE_zcgesv(int matrix_order, lapack_int n, lapack_int nrhs, lapack_complex_double* a, lapack_int lda,
+                          lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                          lapack_int ldx, lapack_int* iter);
+
+lapack_int LAPACKE_sgesvd(int matrix_order, char jobu, char jobvt, lapack_int m, lapack_int n, float* a, lapack_int lda,
+                          float* s, float* u, lapack_int ldu, float* vt, lapack_int ldvt, float* superb);
+lapack_int LAPACKE_dgesvd(int matrix_order, char jobu, char jobvt, lapack_int m, lapack_int n, double* a,
+                          lapack_int lda, double* s, double* u, lapack_int ldu, double* vt, lapack_int ldvt,
+                          double* superb);
+lapack_int LAPACKE_cgesvd(int matrix_order, char jobu, char jobvt, lapack_int m, lapack_int n, lapack_complex_float* a,
+                          lapack_int lda, float* s, lapack_complex_float* u, lapack_int ldu, lapack_complex_float* vt,
+                          lapack_int ldvt, float* superb);
+lapack_int LAPACKE_zgesvd(int matrix_order, char jobu, char jobvt, lapack_int m, lapack_int n, lapack_complex_double* a,
+                          lapack_int lda, double* s, lapack_complex_double* u, lapack_int ldu,
+                          lapack_complex_double* vt, lapack_int ldvt, double* superb);
+
+lapack_int LAPACKE_sgesvj(int matrix_order, char joba, char jobu, char jobv, lapack_int m, lapack_int n, float* a,
+                          lapack_int lda, float* sva, lapack_int mv, float* v, lapack_int ldv, float* stat);
+lapack_int LAPACKE_dgesvj(int matrix_order, char joba, char jobu, char jobv, lapack_int m, lapack_int n, double* a,
+                          lapack_int lda, double* sva, lapack_int mv, double* v, lapack_int ldv, double* stat);
+
+lapack_int LAPACKE_sgesvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, float* a,
+                          lapack_int lda, float* af, lapack_int ldaf, lapack_int* ipiv, char* equed, float* r, float* c,
+                          float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* ferr, float* berr,
+                          float* rpivot);
+lapack_int LAPACKE_dgesvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, double* a,
+                          lapack_int lda, double* af, lapack_int ldaf, lapack_int* ipiv, char* equed, double* r,
+                          double* c, double* b, lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* ferr,
+                          double* berr, double* rpivot);
+lapack_int LAPACKE_cgesvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                          lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                          lapack_int* ipiv, char* equed, float* r, float* c, lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* x, lapack_int ldx, float* rcond, float* ferr, float* berr,
+                          float* rpivot);
+lapack_int LAPACKE_zgesvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                          lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                          lapack_int* ipiv, char* equed, double* r, double* c, lapack_complex_double* b, lapack_int ldb,
+                          lapack_complex_double* x, lapack_int ldx, double* rcond, double* ferr, double* berr,
+                          double* rpivot);
+
+lapack_int LAPACKE_sgesvxx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, float* a,
+                           lapack_int lda, float* af, lapack_int ldaf, lapack_int* ipiv, char* equed, float* r,
+                           float* c, float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* rpvgrw,
+                           float* berr, lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                           lapack_int nparams, float* params);
+lapack_int LAPACKE_dgesvxx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, double* a,
+                           lapack_int lda, double* af, lapack_int ldaf, lapack_int* ipiv, char* equed, double* r,
+                           double* c, double* b, lapack_int ldb, double* x, lapack_int ldx, double* rcond,
+                           double* rpvgrw, double* berr, lapack_int n_err_bnds, double* err_bnds_norm,
+                           double* err_bnds_comp, lapack_int nparams, double* params);
+lapack_int LAPACKE_cgesvxx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                           lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                           lapack_int* ipiv, char* equed, float* r, float* c, lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* rcond, float* rpvgrw, float* berr,
+                           lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                           float* params);
+lapack_int LAPACKE_zgesvxx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                           lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                           lapack_int* ipiv, char* equed, double* r, double* c, lapack_complex_double* b,
+                           lapack_int ldb, lapack_complex_double* x, lapack_int ldx, double* rcond, double* rpvgrw,
+                           double* berr, lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                           lapack_int nparams, double* params);
+
+lapack_int LAPACKE_sgetf2(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, lapack_int* ipiv);
+lapack_int LAPACKE_dgetf2(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, lapack_int* ipiv);
+lapack_int LAPACKE_cgetf2(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_int* ipiv);
+lapack_int LAPACKE_zgetf2(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_int* ipiv);
+
+lapack_int LAPACKE_sgetrf(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, lapack_int* ipiv);
+lapack_int LAPACKE_dgetrf(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, lapack_int* ipiv);
+lapack_int LAPACKE_cgetrf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_int* ipiv);
+lapack_int LAPACKE_zgetrf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_int* ipiv);
+
+lapack_int LAPACKE_sgetri(int matrix_order, lapack_int n, float* a, lapack_int lda, const lapack_int* ipiv);
+lapack_int LAPACKE_dgetri(int matrix_order, lapack_int n, double* a, lapack_int lda, const lapack_int* ipiv);
+lapack_int LAPACKE_cgetri(int matrix_order, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          const lapack_int* ipiv);
+lapack_int LAPACKE_zgetri(int matrix_order, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          const lapack_int* ipiv);
+
+lapack_int LAPACKE_sgetrs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const float* a, lapack_int lda,
+                          const lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dgetrs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const double* a, lapack_int lda,
+                          const lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_cgetrs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const lapack_complex_float* a,
+                          lapack_int lda, const lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zgetrs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const lapack_complex_double* a,
+                          lapack_int lda, const lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sggbak(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          const float* lscale, const float* rscale, lapack_int m, float* v, lapack_int ldv);
+lapack_int LAPACKE_dggbak(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          const double* lscale, const double* rscale, lapack_int m, double* v, lapack_int ldv);
+lapack_int LAPACKE_cggbak(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          const float* lscale, const float* rscale, lapack_int m, lapack_complex_float* v,
+                          lapack_int ldv);
+lapack_int LAPACKE_zggbak(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          const double* lscale, const double* rscale, lapack_int m, lapack_complex_double* v,
+                          lapack_int ldv);
+
+lapack_int LAPACKE_sggbal(int matrix_order, char job, lapack_int n, float* a, lapack_int lda, float* b, lapack_int ldb,
+                          lapack_int* ilo, lapack_int* ihi, float* lscale, float* rscale);
+lapack_int LAPACKE_dggbal(int matrix_order, char job, lapack_int n, double* a, lapack_int lda, double* b,
+                          lapack_int ldb, lapack_int* ilo, lapack_int* ihi, double* lscale, double* rscale);
+lapack_int LAPACKE_cggbal(int matrix_order, char job, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* b, lapack_int ldb, lapack_int* ilo, lapack_int* ihi, float* lscale,
+                          float* rscale);
+lapack_int LAPACKE_zggbal(int matrix_order, char job, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* b, lapack_int ldb, lapack_int* ilo, lapack_int* ihi, double* lscale,
+                          double* rscale);
+
+lapack_int LAPACKE_sgges(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_S_SELECT3 selctg, lapack_int n,
+                         float* a, lapack_int lda, float* b, lapack_int ldb, lapack_int* sdim, float* alphar,
+                         float* alphai, float* beta, float* vsl, lapack_int ldvsl, float* vsr, lapack_int ldvsr);
+lapack_int LAPACKE_dgges(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_D_SELECT3 selctg, lapack_int n,
+                         double* a, lapack_int lda, double* b, lapack_int ldb, lapack_int* sdim, double* alphar,
+                         double* alphai, double* beta, double* vsl, lapack_int ldvsl, double* vsr, lapack_int ldvsr);
+lapack_int LAPACKE_cgges(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_C_SELECT2 selctg, lapack_int n,
+                         lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                         lapack_int* sdim, lapack_complex_float* alpha, lapack_complex_float* beta,
+                         lapack_complex_float* vsl, lapack_int ldvsl, lapack_complex_float* vsr, lapack_int ldvsr);
+lapack_int LAPACKE_zgges(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_Z_SELECT2 selctg, lapack_int n,
+                         lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                         lapack_int* sdim, lapack_complex_double* alpha, lapack_complex_double* beta,
+                         lapack_complex_double* vsl, lapack_int ldvsl, lapack_complex_double* vsr, lapack_int ldvsr);
+
+lapack_int LAPACKE_sggesx(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_S_SELECT3 selctg, char sense,
+                          lapack_int n, float* a, lapack_int lda, float* b, lapack_int ldb, lapack_int* sdim,
+                          float* alphar, float* alphai, float* beta, float* vsl, lapack_int ldvsl, float* vsr,
+                          lapack_int ldvsr, float* rconde, float* rcondv);
+lapack_int LAPACKE_dggesx(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_D_SELECT3 selctg, char sense,
+                          lapack_int n, double* a, lapack_int lda, double* b, lapack_int ldb, lapack_int* sdim,
+                          double* alphar, double* alphai, double* beta, double* vsl, lapack_int ldvsl, double* vsr,
+                          lapack_int ldvsr, double* rconde, double* rcondv);
+lapack_int LAPACKE_cggesx(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_C_SELECT2 selctg, char sense,
+                          lapack_int n, lapack_complex_float* a, lapack_int lda, lapack_complex_float* b,
+                          lapack_int ldb, lapack_int* sdim, lapack_complex_float* alpha, lapack_complex_float* beta,
+                          lapack_complex_float* vsl, lapack_int ldvsl, lapack_complex_float* vsr, lapack_int ldvsr,
+                          float* rconde, float* rcondv);
+lapack_int LAPACKE_zggesx(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_Z_SELECT2 selctg, char sense,
+                          lapack_int n, lapack_complex_double* a, lapack_int lda, lapack_complex_double* b,
+                          lapack_int ldb, lapack_int* sdim, lapack_complex_double* alpha, lapack_complex_double* beta,
+                          lapack_complex_double* vsl, lapack_int ldvsl, lapack_complex_double* vsr, lapack_int ldvsr,
+                          double* rconde, double* rcondv);
+
+lapack_int LAPACKE_sggev(int matrix_order, char jobvl, char jobvr, lapack_int n, float* a, lapack_int lda, float* b,
+                         lapack_int ldb, float* alphar, float* alphai, float* beta, float* vl, lapack_int ldvl,
+                         float* vr, lapack_int ldvr);
+lapack_int LAPACKE_dggev(int matrix_order, char jobvl, char jobvr, lapack_int n, double* a, lapack_int lda, double* b,
+                         lapack_int ldb, double* alphar, double* alphai, double* beta, double* vl, lapack_int ldvl,
+                         double* vr, lapack_int ldvr);
+lapack_int LAPACKE_cggev(int matrix_order, char jobvl, char jobvr, lapack_int n, lapack_complex_float* a,
+                         lapack_int lda, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* alpha,
+                         lapack_complex_float* beta, lapack_complex_float* vl, lapack_int ldvl,
+                         lapack_complex_float* vr, lapack_int ldvr);
+lapack_int LAPACKE_zggev(int matrix_order, char jobvl, char jobvr, lapack_int n, lapack_complex_double* a,
+                         lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* alpha,
+                         lapack_complex_double* beta, lapack_complex_double* vl, lapack_int ldvl,
+                         lapack_complex_double* vr, lapack_int ldvr);
+
+lapack_int LAPACKE_sggevx(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n, float* a,
+                          lapack_int lda, float* b, lapack_int ldb, float* alphar, float* alphai, float* beta,
+                          float* vl, lapack_int ldvl, float* vr, lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
+                          float* lscale, float* rscale, float* abnrm, float* bbnrm, float* rconde, float* rcondv);
+lapack_int LAPACKE_dggevx(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n, double* a,
+                          lapack_int lda, double* b, lapack_int ldb, double* alphar, double* alphai, double* beta,
+                          double* vl, lapack_int ldvl, double* vr, lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
+                          double* lscale, double* rscale, double* abnrm, double* bbnrm, double* rconde, double* rcondv);
+lapack_int LAPACKE_cggevx(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                          lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* alpha, lapack_complex_float* beta, lapack_complex_float* vl,
+                          lapack_int ldvl, lapack_complex_float* vr, lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
+                          float* lscale, float* rscale, float* abnrm, float* bbnrm, float* rconde, float* rcondv);
+lapack_int LAPACKE_zggevx(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                          lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                          lapack_complex_double* alpha, lapack_complex_double* beta, lapack_complex_double* vl,
+                          lapack_int ldvl, lapack_complex_double* vr, lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
+                          double* lscale, double* rscale, double* abnrm, double* bbnrm, double* rconde, double* rcondv);
+
+lapack_int LAPACKE_sggglm(int matrix_order, lapack_int n, lapack_int m, lapack_int p, float* a, lapack_int lda,
+                          float* b, lapack_int ldb, float* d, float* x, float* y);
+lapack_int LAPACKE_dggglm(int matrix_order, lapack_int n, lapack_int m, lapack_int p, double* a, lapack_int lda,
+                          double* b, lapack_int ldb, double* d, double* x, double* y);
+lapack_int LAPACKE_cggglm(int matrix_order, lapack_int n, lapack_int m, lapack_int p, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* d,
+                          lapack_complex_float* x, lapack_complex_float* y);
+lapack_int LAPACKE_zggglm(int matrix_order, lapack_int n, lapack_int m, lapack_int p, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* d,
+                          lapack_complex_double* x, lapack_complex_double* y);
+
+lapack_int LAPACKE_sgghrd(int matrix_order, char compq, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          float* a, lapack_int lda, float* b, lapack_int ldb, float* q, lapack_int ldq, float* z,
+                          lapack_int ldz);
+lapack_int LAPACKE_dgghrd(int matrix_order, char compq, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          double* a, lapack_int lda, double* b, lapack_int ldb, double* q, lapack_int ldq, double* z,
+                          lapack_int ldz);
+lapack_int LAPACKE_cgghrd(int matrix_order, char compq, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* q, lapack_int ldq, lapack_complex_float* z, lapack_int ldz);
+lapack_int LAPACKE_zgghrd(int matrix_order, char compq, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                          lapack_complex_double* q, lapack_int ldq, lapack_complex_double* z, lapack_int ldz);
+
+lapack_int LAPACKE_sgglse(int matrix_order, lapack_int m, lapack_int n, lapack_int p, float* a, lapack_int lda,
+                          float* b, lapack_int ldb, float* c, float* d, float* x);
+lapack_int LAPACKE_dgglse(int matrix_order, lapack_int m, lapack_int n, lapack_int p, double* a, lapack_int lda,
+                          double* b, lapack_int ldb, double* c, double* d, double* x);
+lapack_int LAPACKE_cgglse(int matrix_order, lapack_int m, lapack_int n, lapack_int p, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* c,
+                          lapack_complex_float* d, lapack_complex_float* x);
+lapack_int LAPACKE_zgglse(int matrix_order, lapack_int m, lapack_int n, lapack_int p, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* c,
+                          lapack_complex_double* d, lapack_complex_double* x);
+
+lapack_int LAPACKE_sggqrf(int matrix_order, lapack_int n, lapack_int m, lapack_int p, float* a, lapack_int lda,
+                          float* taua, float* b, lapack_int ldb, float* taub);
+lapack_int LAPACKE_dggqrf(int matrix_order, lapack_int n, lapack_int m, lapack_int p, double* a, lapack_int lda,
+                          double* taua, double* b, lapack_int ldb, double* taub);
+lapack_int LAPACKE_cggqrf(int matrix_order, lapack_int n, lapack_int m, lapack_int p, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* taua, lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* taub);
+lapack_int LAPACKE_zggqrf(int matrix_order, lapack_int n, lapack_int m, lapack_int p, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* taua, lapack_complex_double* b, lapack_int ldb,
+                          lapack_complex_double* taub);
+
+lapack_int LAPACKE_sggrqf(int matrix_order, lapack_int m, lapack_int p, lapack_int n, float* a, lapack_int lda,
+                          float* taua, float* b, lapack_int ldb, float* taub);
+lapack_int LAPACKE_dggrqf(int matrix_order, lapack_int m, lapack_int p, lapack_int n, double* a, lapack_int lda,
+                          double* taua, double* b, lapack_int ldb, double* taub);
+lapack_int LAPACKE_cggrqf(int matrix_order, lapack_int m, lapack_int p, lapack_int n, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* taua, lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* taub);
+lapack_int LAPACKE_zggrqf(int matrix_order, lapack_int m, lapack_int p, lapack_int n, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* taua, lapack_complex_double* b, lapack_int ldb,
+                          lapack_complex_double* taub);
+
+lapack_int LAPACKE_sggsvd(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int n, lapack_int p,
+                          lapack_int* k, lapack_int* l, float* a, lapack_int lda, float* b, lapack_int ldb,
+                          float* alpha, float* beta, float* u, lapack_int ldu, float* v, lapack_int ldv, float* q,
+                          lapack_int ldq, lapack_int* iwork);
+lapack_int LAPACKE_dggsvd(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int n, lapack_int p,
+                          lapack_int* k, lapack_int* l, double* a, lapack_int lda, double* b, lapack_int ldb,
+                          double* alpha, double* beta, double* u, lapack_int ldu, double* v, lapack_int ldv, double* q,
+                          lapack_int ldq, lapack_int* iwork);
+lapack_int LAPACKE_cggsvd(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int n, lapack_int p,
+                          lapack_int* k, lapack_int* l, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* b, lapack_int ldb, float* alpha, float* beta, lapack_complex_float* u,
+                          lapack_int ldu, lapack_complex_float* v, lapack_int ldv, lapack_complex_float* q,
+                          lapack_int ldq, lapack_int* iwork);
+lapack_int LAPACKE_zggsvd(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int n, lapack_int p,
+                          lapack_int* k, lapack_int* l, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* b, lapack_int ldb, double* alpha, double* beta,
+                          lapack_complex_double* u, lapack_int ldu, lapack_complex_double* v, lapack_int ldv,
+                          lapack_complex_double* q, lapack_int ldq, lapack_int* iwork);
+
+lapack_int LAPACKE_sggsvp(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p, lapack_int n,
+                          float* a, lapack_int lda, float* b, lapack_int ldb, float tola, float tolb, lapack_int* k,
+                          lapack_int* l, float* u, lapack_int ldu, float* v, lapack_int ldv, float* q, lapack_int ldq);
+lapack_int LAPACKE_dggsvp(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p, lapack_int n,
+                          double* a, lapack_int lda, double* b, lapack_int ldb, double tola, double tolb, lapack_int* k,
+                          lapack_int* l, double* u, lapack_int ldu, double* v, lapack_int ldv, double* q,
+                          lapack_int ldq);
+lapack_int LAPACKE_cggsvp(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p, lapack_int n,
+                          lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb, float tola,
+                          float tolb, lapack_int* k, lapack_int* l, lapack_complex_float* u, lapack_int ldu,
+                          lapack_complex_float* v, lapack_int ldv, lapack_complex_float* q, lapack_int ldq);
+lapack_int LAPACKE_zggsvp(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p, lapack_int n,
+                          lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                          double tola, double tolb, lapack_int* k, lapack_int* l, lapack_complex_double* u,
+                          lapack_int ldu, lapack_complex_double* v, lapack_int ldv, lapack_complex_double* q,
+                          lapack_int ldq);
+
+lapack_int LAPACKE_sgtcon(char norm, lapack_int n, const float* dl, const float* d, const float* du, const float* du2,
+                          const lapack_int* ipiv, float anorm, float* rcond);
+lapack_int LAPACKE_dgtcon(char norm, lapack_int n, const double* dl, const double* d, const double* du,
+                          const double* du2, const lapack_int* ipiv, double anorm, double* rcond);
+lapack_int LAPACKE_cgtcon(char norm, lapack_int n, const lapack_complex_float* dl, const lapack_complex_float* d,
+                          const lapack_complex_float* du, const lapack_complex_float* du2, const lapack_int* ipiv,
+                          float anorm, float* rcond);
+lapack_int LAPACKE_zgtcon(char norm, lapack_int n, const lapack_complex_double* dl, const lapack_complex_double* d,
+                          const lapack_complex_double* du, const lapack_complex_double* du2, const lapack_int* ipiv,
+                          double anorm, double* rcond);
+
+lapack_int LAPACKE_sgtrfs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const float* dl, const float* d,
+                          const float* du, const float* dlf, const float* df, const float* duf, const float* du2,
+                          const lapack_int* ipiv, const float* b, lapack_int ldb, float* x, lapack_int ldx, float* ferr,
+                          float* berr);
+lapack_int LAPACKE_dgtrfs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const double* dl,
+                          const double* d, const double* du, const double* dlf, const double* df, const double* duf,
+                          const double* du2, const lapack_int* ipiv, const double* b, lapack_int ldb, double* x,
+                          lapack_int ldx, double* ferr, double* berr);
+lapack_int LAPACKE_cgtrfs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const lapack_complex_float* dl,
+                          const lapack_complex_float* d, const lapack_complex_float* du,
+                          const lapack_complex_float* dlf, const lapack_complex_float* df,
+                          const lapack_complex_float* duf, const lapack_complex_float* du2, const lapack_int* ipiv,
+                          const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_zgtrfs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const lapack_complex_double* dl,
+                          const lapack_complex_double* d, const lapack_complex_double* du,
+                          const lapack_complex_double* dlf, const lapack_complex_double* df,
+                          const lapack_complex_double* duf, const lapack_complex_double* du2, const lapack_int* ipiv,
+                          const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* ferr, double* berr);
+
+lapack_int LAPACKE_sgtsv(int matrix_order, lapack_int n, lapack_int nrhs, float* dl, float* d, float* du, float* b,
+                         lapack_int ldb);
+lapack_int LAPACKE_dgtsv(int matrix_order, lapack_int n, lapack_int nrhs, double* dl, double* d, double* du, double* b,
+                         lapack_int ldb);
+lapack_int LAPACKE_cgtsv(int matrix_order, lapack_int n, lapack_int nrhs, lapack_complex_float* dl,
+                         lapack_complex_float* d, lapack_complex_float* du, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zgtsv(int matrix_order, lapack_int n, lapack_int nrhs, lapack_complex_double* dl,
+                         lapack_complex_double* d, lapack_complex_double* du, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sgtsvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, const float* dl,
+                          const float* d, const float* du, float* dlf, float* df, float* duf, float* du2,
+                          lapack_int* ipiv, const float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_dgtsvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, const double* dl,
+                          const double* d, const double* du, double* dlf, double* df, double* duf, double* du2,
+                          lapack_int* ipiv, const double* b, lapack_int ldb, double* x, lapack_int ldx, double* rcond,
+                          double* ferr, double* berr);
+lapack_int LAPACKE_cgtsvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_float* dl, const lapack_complex_float* d, const lapack_complex_float* du,
+                          lapack_complex_float* dlf, lapack_complex_float* df, lapack_complex_float* duf,
+                          lapack_complex_float* du2, lapack_int* ipiv, const lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* x, lapack_int ldx, float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_zgtsvx(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_double* dl, const lapack_complex_double* d,
+                          const lapack_complex_double* du, lapack_complex_double* dlf, lapack_complex_double* df,
+                          lapack_complex_double* duf, lapack_complex_double* du2, lapack_int* ipiv,
+                          const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* rcond, double* ferr, double* berr);
+
+lapack_int LAPACKE_sgttrf(lapack_int n, float* dl, float* d, float* du, float* du2, lapack_int* ipiv);
+lapack_int LAPACKE_dgttrf(lapack_int n, double* dl, double* d, double* du, double* du2, lapack_int* ipiv);
+lapack_int LAPACKE_cgttrf(lapack_int n, lapack_complex_float* dl, lapack_complex_float* d, lapack_complex_float* du,
+                          lapack_complex_float* du2, lapack_int* ipiv);
+lapack_int LAPACKE_zgttrf(lapack_int n, lapack_complex_double* dl, lapack_complex_double* d, lapack_complex_double* du,
+                          lapack_complex_double* du2, lapack_int* ipiv);
+
+lapack_int LAPACKE_sgttrs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const float* dl, const float* d,
+                          const float* du, const float* du2, const lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dgttrs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const double* dl,
+                          const double* d, const double* du, const double* du2, const lapack_int* ipiv, double* b,
+                          lapack_int ldb);
+lapack_int LAPACKE_cgttrs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const lapack_complex_float* dl,
+                          const lapack_complex_float* d, const lapack_complex_float* du,
+                          const lapack_complex_float* du2, const lapack_int* ipiv, lapack_complex_float* b,
+                          lapack_int ldb);
+lapack_int LAPACKE_zgttrs(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const lapack_complex_double* dl,
+                          const lapack_complex_double* d, const lapack_complex_double* du,
+                          const lapack_complex_double* du2, const lapack_int* ipiv, lapack_complex_double* b,
+                          lapack_int ldb);
+
+lapack_int LAPACKE_chbev(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, lapack_complex_float* ab,
+                         lapack_int ldab, float* w, lapack_complex_float* z, lapack_int ldz);
+lapack_int LAPACKE_zhbev(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, lapack_complex_double* ab,
+                         lapack_int ldab, double* w, lapack_complex_double* z, lapack_int ldz);
+
+lapack_int LAPACKE_chbevd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, lapack_complex_float* ab,
+                          lapack_int ldab, float* w, lapack_complex_float* z, lapack_int ldz);
+lapack_int LAPACKE_zhbevd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd,
+                          lapack_complex_double* ab, lapack_int ldab, double* w, lapack_complex_double* z,
+                          lapack_int ldz);
+
+lapack_int LAPACKE_chbevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int kd,
+                          lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* q, lapack_int ldq, float vl,
+                          float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w,
+                          lapack_complex_float* z, lapack_int ldz, lapack_int* ifail);
+lapack_int LAPACKE_zhbevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int kd,
+                          lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* q, lapack_int ldq,
+                          double vl, double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                          lapack_complex_double* z, lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_chbgst(int matrix_order, char vect, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                          lapack_complex_float* ab, lapack_int ldab, const lapack_complex_float* bb, lapack_int ldbb,
+                          lapack_complex_float* x, lapack_int ldx);
+lapack_int LAPACKE_zhbgst(int matrix_order, char vect, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                          lapack_complex_double* ab, lapack_int ldab, const lapack_complex_double* bb, lapack_int ldbb,
+                          lapack_complex_double* x, lapack_int ldx);
+
+lapack_int LAPACKE_chbgv(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                         lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* bb, lapack_int ldbb, float* w,
+                         lapack_complex_float* z, lapack_int ldz);
+lapack_int LAPACKE_zhbgv(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                         lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* bb, lapack_int ldbb,
+                         double* w, lapack_complex_double* z, lapack_int ldz);
+
+lapack_int LAPACKE_chbgvd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                          lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* bb, lapack_int ldbb,
+                          float* w, lapack_complex_float* z, lapack_int ldz);
+lapack_int LAPACKE_zhbgvd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                          lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* bb, lapack_int ldbb,
+                          double* w, lapack_complex_double* z, lapack_int ldz);
+
+lapack_int LAPACKE_chbgvx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int ka,
+                          lapack_int kb, lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* bb,
+                          lapack_int ldbb, lapack_complex_float* q, lapack_int ldq, float vl, float vu, lapack_int il,
+                          lapack_int iu, float abstol, lapack_int* m, float* w, lapack_complex_float* z, lapack_int ldz,
+                          lapack_int* ifail);
+lapack_int LAPACKE_zhbgvx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int ka,
+                          lapack_int kb, lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* bb,
+                          lapack_int ldbb, lapack_complex_double* q, lapack_int ldq, double vl, double vu,
+                          lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                          lapack_complex_double* z, lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_chbtrd(int matrix_order, char vect, char uplo, lapack_int n, lapack_int kd, lapack_complex_float* ab,
+                          lapack_int ldab, float* d, float* e, lapack_complex_float* q, lapack_int ldq);
+lapack_int LAPACKE_zhbtrd(int matrix_order, char vect, char uplo, lapack_int n, lapack_int kd,
+                          lapack_complex_double* ab, lapack_int ldab, double* d, double* e, lapack_complex_double* q,
+                          lapack_int ldq);
+
+lapack_int LAPACKE_checon(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                          const lapack_int* ipiv, float anorm, float* rcond);
+lapack_int LAPACKE_zhecon(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                          const lapack_int* ipiv, double anorm, double* rcond);
+
+lapack_int LAPACKE_cheequb(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                           float* s, float* scond, float* amax);
+lapack_int LAPACKE_zheequb(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                           double* s, double* scond, double* amax);
+
+lapack_int LAPACKE_cheev(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                         float* w);
+lapack_int LAPACKE_zheev(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                         double* w);
+
+lapack_int LAPACKE_cheevd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          float* w);
+lapack_int LAPACKE_zheevd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_double* a,
+                          lapack_int lda, double* w);
+
+lapack_int LAPACKE_cheevr(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_complex_float* a,
+                          lapack_int lda, float vl, float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m,
+                          float* w, lapack_complex_float* z, lapack_int ldz, lapack_int* isuppz);
+lapack_int LAPACKE_zheevr(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_complex_double* a,
+                          lapack_int lda, double vl, double vu, lapack_int il, lapack_int iu, double abstol,
+                          lapack_int* m, double* w, lapack_complex_double* z, lapack_int ldz, lapack_int* isuppz);
+
+lapack_int LAPACKE_cheevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_complex_float* a,
+                          lapack_int lda, float vl, float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m,
+                          float* w, lapack_complex_float* z, lapack_int ldz, lapack_int* ifail);
+lapack_int LAPACKE_zheevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_complex_double* a,
+                          lapack_int lda, double vl, double vu, lapack_int il, lapack_int iu, double abstol,
+                          lapack_int* m, double* w, lapack_complex_double* z, lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_chegst(int matrix_order, lapack_int itype, char uplo, lapack_int n, lapack_complex_float* a,
+                          lapack_int lda, const lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zhegst(int matrix_order, lapack_int itype, char uplo, lapack_int n, lapack_complex_double* a,
+                          lapack_int lda, const lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_chegv(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                         lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb, float* w);
+lapack_int LAPACKE_zhegv(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                         lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb, double* w);
+
+lapack_int LAPACKE_chegvd(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                          lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb, float* w);
+lapack_int LAPACKE_zhegvd(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                          lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                          double* w);
+
+lapack_int LAPACKE_chegvx(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                          lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb, float vl,
+                          float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w,
+                          lapack_complex_float* z, lapack_int ldz, lapack_int* ifail);
+lapack_int LAPACKE_zhegvx(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                          lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb, double vl,
+                          double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                          lapack_complex_double* z, lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_cherfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* a,
+                          lapack_int lda, const lapack_complex_float* af, lapack_int ldaf, const lapack_int* ipiv,
+                          const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_zherfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* a,
+                          lapack_int lda, const lapack_complex_double* af, lapack_int ldaf, const lapack_int* ipiv,
+                          const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* ferr, double* berr);
+
+lapack_int LAPACKE_cherfsx(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                           const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                           lapack_int ldaf, const lapack_int* ipiv, const float* s, const lapack_complex_float* b,
+                           lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* rcond, float* berr,
+                           lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                           float* params);
+lapack_int LAPACKE_zherfsx(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                           const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                           lapack_int ldaf, const lapack_int* ipiv, const double* s, const lapack_complex_double* b,
+                           lapack_int ldb, lapack_complex_double* x, lapack_int ldx, double* rcond, double* berr,
+                           lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                           double* params);
+
+lapack_int LAPACKE_chesv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                         lapack_int lda, lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zhesv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                         lapack_int lda, lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_chesvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                          lapack_int* ipiv, const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                          lapack_int ldx, float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_zhesvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                          lapack_int* ipiv, const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                          lapack_int ldx, double* rcond, double* ferr, double* berr);
+
+lapack_int LAPACKE_chesvxx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                           lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                           lapack_int* ipiv, char* equed, float* s, lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* rcond, float* rpvgrw, float* berr,
+                           lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                           float* params);
+lapack_int LAPACKE_zhesvxx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                           lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                           lapack_int* ipiv, char* equed, double* s, lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx, double* rcond, double* rpvgrw, double* berr,
+                           lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                           double* params);
+
+lapack_int LAPACKE_chetrd(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda, float* d,
+                          float* e, lapack_complex_float* tau);
+lapack_int LAPACKE_zhetrd(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          double* d, double* e, lapack_complex_double* tau);
+
+lapack_int LAPACKE_chetrf(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_int* ipiv);
+lapack_int LAPACKE_zhetrf(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_int* ipiv);
+
+lapack_int LAPACKE_chetri(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          const lapack_int* ipiv);
+lapack_int LAPACKE_zhetri(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          const lapack_int* ipiv);
+
+lapack_int LAPACKE_chetrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* a,
+                          lapack_int lda, const lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zhetrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* a,
+                          lapack_int lda, const lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_chfrk(int matrix_order, char transr, char uplo, char trans, lapack_int n, lapack_int k, float alpha,
+                         const lapack_complex_float* a, lapack_int lda, float beta, lapack_complex_float* c);
+lapack_int LAPACKE_zhfrk(int matrix_order, char transr, char uplo, char trans, lapack_int n, lapack_int k, double alpha,
+                         const lapack_complex_double* a, lapack_int lda, double beta, lapack_complex_double* c);
+
+lapack_int LAPACKE_shgeqz(int matrix_order, char job, char compq, char compz, lapack_int n, lapack_int ilo,
+                          lapack_int ihi, float* h, lapack_int ldh, float* t, lapack_int ldt, float* alphar,
+                          float* alphai, float* beta, float* q, lapack_int ldq, float* z, lapack_int ldz);
+lapack_int LAPACKE_dhgeqz(int matrix_order, char job, char compq, char compz, lapack_int n, lapack_int ilo,
+                          lapack_int ihi, double* h, lapack_int ldh, double* t, lapack_int ldt, double* alphar,
+                          double* alphai, double* beta, double* q, lapack_int ldq, double* z, lapack_int ldz);
+lapack_int LAPACKE_chgeqz(int matrix_order, char job, char compq, char compz, lapack_int n, lapack_int ilo,
+                          lapack_int ihi, lapack_complex_float* h, lapack_int ldh, lapack_complex_float* t,
+                          lapack_int ldt, lapack_complex_float* alpha, lapack_complex_float* beta,
+                          lapack_complex_float* q, lapack_int ldq, lapack_complex_float* z, lapack_int ldz);
+lapack_int LAPACKE_zhgeqz(int matrix_order, char job, char compq, char compz, lapack_int n, lapack_int ilo,
+                          lapack_int ihi, lapack_complex_double* h, lapack_int ldh, lapack_complex_double* t,
+                          lapack_int ldt, lapack_complex_double* alpha, lapack_complex_double* beta,
+                          lapack_complex_double* q, lapack_int ldq, lapack_complex_double* z, lapack_int ldz);
+
+lapack_int LAPACKE_chpcon(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap,
+                          const lapack_int* ipiv, float anorm, float* rcond);
+lapack_int LAPACKE_zhpcon(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap,
+                          const lapack_int* ipiv, double anorm, double* rcond);
+
+lapack_int LAPACKE_chpev(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_float* ap, float* w,
+                         lapack_complex_float* z, lapack_int ldz);
+lapack_int LAPACKE_zhpev(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_double* ap, double* w,
+                         lapack_complex_double* z, lapack_int ldz);
+
+lapack_int LAPACKE_chpevd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_float* ap, float* w,
+                          lapack_complex_float* z, lapack_int ldz);
+lapack_int LAPACKE_zhpevd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_double* ap, double* w,
+                          lapack_complex_double* z, lapack_int ldz);
+
+lapack_int LAPACKE_chpevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_complex_float* ap,
+                          float vl, float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w,
+                          lapack_complex_float* z, lapack_int ldz, lapack_int* ifail);
+lapack_int LAPACKE_zhpevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_complex_double* ap,
+                          double vl, double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                          lapack_complex_double* z, lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_chpgst(int matrix_order, lapack_int itype, char uplo, lapack_int n, lapack_complex_float* ap,
+                          const lapack_complex_float* bp);
+lapack_int LAPACKE_zhpgst(int matrix_order, lapack_int itype, char uplo, lapack_int n, lapack_complex_double* ap,
+                          const lapack_complex_double* bp);
+
+lapack_int LAPACKE_chpgv(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                         lapack_complex_float* ap, lapack_complex_float* bp, float* w, lapack_complex_float* z,
+                         lapack_int ldz);
+lapack_int LAPACKE_zhpgv(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                         lapack_complex_double* ap, lapack_complex_double* bp, double* w, lapack_complex_double* z,
+                         lapack_int ldz);
+
+lapack_int LAPACKE_chpgvd(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                          lapack_complex_float* ap, lapack_complex_float* bp, float* w, lapack_complex_float* z,
+                          lapack_int ldz);
+lapack_int LAPACKE_zhpgvd(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                          lapack_complex_double* ap, lapack_complex_double* bp, double* w, lapack_complex_double* z,
+                          lapack_int ldz);
+
+lapack_int LAPACKE_chpgvx(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                          lapack_complex_float* ap, lapack_complex_float* bp, float vl, float vu, lapack_int il,
+                          lapack_int iu, float abstol, lapack_int* m, float* w, lapack_complex_float* z, lapack_int ldz,
+                          lapack_int* ifail);
+lapack_int LAPACKE_zhpgvx(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                          lapack_complex_double* ap, lapack_complex_double* bp, double vl, double vu, lapack_int il,
+                          lapack_int iu, double abstol, lapack_int* m, double* w, lapack_complex_double* z,
+                          lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_chprfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* ap,
+                          const lapack_complex_float* afp, const lapack_int* ipiv, const lapack_complex_float* b,
+                          lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_zhprfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* ap,
+                          const lapack_complex_double* afp, const lapack_int* ipiv, const lapack_complex_double* b,
+                          lapack_int ldb, lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr);
+
+lapack_int LAPACKE_chpsv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* ap,
+                         lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zhpsv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* ap,
+                         lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_chpsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_float* ap, lapack_complex_float* afp, lapack_int* ipiv,
+                          const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                          float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_zhpsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_double* ap, lapack_complex_double* afp, lapack_int* ipiv,
+                          const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* rcond, double* ferr, double* berr);
+
+lapack_int LAPACKE_chptrd(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap, float* d, float* e,
+                          lapack_complex_float* tau);
+lapack_int LAPACKE_zhptrd(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap, double* d, double* e,
+                          lapack_complex_double* tau);
+
+lapack_int LAPACKE_chptrf(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap, lapack_int* ipiv);
+lapack_int LAPACKE_zhptrf(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap, lapack_int* ipiv);
+
+lapack_int LAPACKE_chptri(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap, const lapack_int* ipiv);
+lapack_int LAPACKE_zhptri(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap, const lapack_int* ipiv);
+
+lapack_int LAPACKE_chptrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* ap,
+                          const lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zhptrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* ap,
+                          const lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_shsein(int matrix_order, char job, char eigsrc, char initv, lapack_logical* select, lapack_int n,
+                          const float* h, lapack_int ldh, float* wr, const float* wi, float* vl, lapack_int ldvl,
+                          float* vr, lapack_int ldvr, lapack_int mm, lapack_int* m, lapack_int* ifaill,
+                          lapack_int* ifailr);
+lapack_int LAPACKE_dhsein(int matrix_order, char job, char eigsrc, char initv, lapack_logical* select, lapack_int n,
+                          const double* h, lapack_int ldh, double* wr, const double* wi, double* vl, lapack_int ldvl,
+                          double* vr, lapack_int ldvr, lapack_int mm, lapack_int* m, lapack_int* ifaill,
+                          lapack_int* ifailr);
+lapack_int LAPACKE_chsein(int matrix_order, char job, char eigsrc, char initv, const lapack_logical* select,
+                          lapack_int n, const lapack_complex_float* h, lapack_int ldh, lapack_complex_float* w,
+                          lapack_complex_float* vl, lapack_int ldvl, lapack_complex_float* vr, lapack_int ldvr,
+                          lapack_int mm, lapack_int* m, lapack_int* ifaill, lapack_int* ifailr);
+lapack_int LAPACKE_zhsein(int matrix_order, char job, char eigsrc, char initv, const lapack_logical* select,
+                          lapack_int n, const lapack_complex_double* h, lapack_int ldh, lapack_complex_double* w,
+                          lapack_complex_double* vl, lapack_int ldvl, lapack_complex_double* vr, lapack_int ldvr,
+                          lapack_int mm, lapack_int* m, lapack_int* ifaill, lapack_int* ifailr);
+
+lapack_int LAPACKE_shseqr(int matrix_order, char job, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          float* h, lapack_int ldh, float* wr, float* wi, float* z, lapack_int ldz);
+lapack_int LAPACKE_dhseqr(int matrix_order, char job, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          double* h, lapack_int ldh, double* wr, double* wi, double* z, lapack_int ldz);
+lapack_int LAPACKE_chseqr(int matrix_order, char job, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          lapack_complex_float* h, lapack_int ldh, lapack_complex_float* w, lapack_complex_float* z,
+                          lapack_int ldz);
+lapack_int LAPACKE_zhseqr(int matrix_order, char job, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                          lapack_complex_double* h, lapack_int ldh, lapack_complex_double* w, lapack_complex_double* z,
+                          lapack_int ldz);
+
+lapack_int LAPACKE_clacgv(lapack_int n, lapack_complex_float* x, lapack_int incx);
+lapack_int LAPACKE_zlacgv(lapack_int n, lapack_complex_double* x, lapack_int incx);
+
+lapack_int LAPACKE_slacpy(int matrix_order, char uplo, lapack_int m, lapack_int n, const float* a, lapack_int lda,
+                          float* b, lapack_int ldb);
+lapack_int LAPACKE_dlacpy(int matrix_order, char uplo, lapack_int m, lapack_int n, const double* a, lapack_int lda,
+                          double* b, lapack_int ldb);
+lapack_int LAPACKE_clacpy(int matrix_order, char uplo, lapack_int m, lapack_int n, const lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zlacpy(int matrix_order, char uplo, lapack_int m, lapack_int n, const lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_zlag2c(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_float* sa, lapack_int ldsa);
+
+lapack_int LAPACKE_slag2d(int matrix_order, lapack_int m, lapack_int n, const float* sa, lapack_int ldsa, double* a,
+                          lapack_int lda);
+
+lapack_int LAPACKE_dlag2s(int matrix_order, lapack_int m, lapack_int n, const double* a, lapack_int lda, float* sa,
+                          lapack_int ldsa);
+
+lapack_int LAPACKE_clag2z(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_float* sa, lapack_int ldsa,
+                          lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_slagge(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, const float* d,
+                          float* a, lapack_int lda, lapack_int* iseed);
+lapack_int LAPACKE_dlagge(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, const double* d,
+                          double* a, lapack_int lda, lapack_int* iseed);
+lapack_int LAPACKE_clagge(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, const float* d,
+                          lapack_complex_float* a, lapack_int lda, lapack_int* iseed);
+lapack_int LAPACKE_zlagge(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, const double* d,
+                          lapack_complex_double* a, lapack_int lda, lapack_int* iseed);
+
+float LAPACKE_slamch(char cmach);
+double LAPACKE_dlamch(char cmach);
+
+float LAPACKE_slange(int matrix_order, char norm, lapack_int m, lapack_int n, const float* a, lapack_int lda);
+double LAPACKE_dlange(int matrix_order, char norm, lapack_int m, lapack_int n, const double* a, lapack_int lda);
+float LAPACKE_clange(int matrix_order, char norm, lapack_int m, lapack_int n, const lapack_complex_float* a,
+                     lapack_int lda);
+double LAPACKE_zlange(int matrix_order, char norm, lapack_int m, lapack_int n, const lapack_complex_double* a,
+                      lapack_int lda);
+
+float LAPACKE_clanhe(int matrix_order, char norm, char uplo, lapack_int n, const lapack_complex_float* a,
+                     lapack_int lda);
+double LAPACKE_zlanhe(int matrix_order, char norm, char uplo, lapack_int n, const lapack_complex_double* a,
+                      lapack_int lda);
+
+float LAPACKE_slansy(int matrix_order, char norm, char uplo, lapack_int n, const float* a, lapack_int lda);
+double LAPACKE_dlansy(int matrix_order, char norm, char uplo, lapack_int n, const double* a, lapack_int lda);
+float LAPACKE_clansy(int matrix_order, char norm, char uplo, lapack_int n, const lapack_complex_float* a,
+                     lapack_int lda);
+double LAPACKE_zlansy(int matrix_order, char norm, char uplo, lapack_int n, const lapack_complex_double* a,
+                      lapack_int lda);
+
+float LAPACKE_slantr(int matrix_order, char norm, char uplo, char diag, lapack_int m, lapack_int n, const float* a,
+                     lapack_int lda);
+double LAPACKE_dlantr(int matrix_order, char norm, char uplo, char diag, lapack_int m, lapack_int n, const double* a,
+                      lapack_int lda);
+float LAPACKE_clantr(int matrix_order, char norm, char uplo, char diag, lapack_int m, lapack_int n,
+                     const lapack_complex_float* a, lapack_int lda);
+double LAPACKE_zlantr(int matrix_order, char norm, char uplo, char diag, lapack_int m, lapack_int n,
+                      const lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_slarfb(int matrix_order, char side, char trans, char direct, char storev, lapack_int m, lapack_int n,
+                          lapack_int k, const float* v, lapack_int ldv, const float* t, lapack_int ldt, float* c,
+                          lapack_int ldc);
+lapack_int LAPACKE_dlarfb(int matrix_order, char side, char trans, char direct, char storev, lapack_int m, lapack_int n,
+                          lapack_int k, const double* v, lapack_int ldv, const double* t, lapack_int ldt, double* c,
+                          lapack_int ldc);
+lapack_int LAPACKE_clarfb(int matrix_order, char side, char trans, char direct, char storev, lapack_int m, lapack_int n,
+                          lapack_int k, const lapack_complex_float* v, lapack_int ldv, const lapack_complex_float* t,
+                          lapack_int ldt, lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zlarfb(int matrix_order, char side, char trans, char direct, char storev, lapack_int m, lapack_int n,
+                          lapack_int k, const lapack_complex_double* v, lapack_int ldv, const lapack_complex_double* t,
+                          lapack_int ldt, lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_slarfg(lapack_int n, float* alpha, float* x, lapack_int incx, float* tau);
+lapack_int LAPACKE_dlarfg(lapack_int n, double* alpha, double* x, lapack_int incx, double* tau);
+lapack_int LAPACKE_clarfg(lapack_int n, lapack_complex_float* alpha, lapack_complex_float* x, lapack_int incx,
+                          lapack_complex_float* tau);
+lapack_int LAPACKE_zlarfg(lapack_int n, lapack_complex_double* alpha, lapack_complex_double* x, lapack_int incx,
+                          lapack_complex_double* tau);
+
+lapack_int LAPACKE_slarft(int matrix_order, char direct, char storev, lapack_int n, lapack_int k, const float* v,
+                          lapack_int ldv, const float* tau, float* t, lapack_int ldt);
+lapack_int LAPACKE_dlarft(int matrix_order, char direct, char storev, lapack_int n, lapack_int k, const double* v,
+                          lapack_int ldv, const double* tau, double* t, lapack_int ldt);
+lapack_int LAPACKE_clarft(int matrix_order, char direct, char storev, lapack_int n, lapack_int k,
+                          const lapack_complex_float* v, lapack_int ldv, const lapack_complex_float* tau,
+                          lapack_complex_float* t, lapack_int ldt);
+lapack_int LAPACKE_zlarft(int matrix_order, char direct, char storev, lapack_int n, lapack_int k,
+                          const lapack_complex_double* v, lapack_int ldv, const lapack_complex_double* tau,
+                          lapack_complex_double* t, lapack_int ldt);
+
+lapack_int LAPACKE_slarfx(int matrix_order, char side, lapack_int m, lapack_int n, const float* v, float tau, float* c,
+                          lapack_int ldc, float* work);
+lapack_int LAPACKE_dlarfx(int matrix_order, char side, lapack_int m, lapack_int n, const double* v, double tau,
+                          double* c, lapack_int ldc, double* work);
+lapack_int LAPACKE_clarfx(int matrix_order, char side, lapack_int m, lapack_int n, const lapack_complex_float* v,
+                          lapack_complex_float tau, lapack_complex_float* c, lapack_int ldc,
+                          lapack_complex_float* work);
+lapack_int LAPACKE_zlarfx(int matrix_order, char side, lapack_int m, lapack_int n, const lapack_complex_double* v,
+                          lapack_complex_double tau, lapack_complex_double* c, lapack_int ldc,
+                          lapack_complex_double* work);
+
+lapack_int LAPACKE_slarnv(lapack_int idist, lapack_int* iseed, lapack_int n, float* x);
+lapack_int LAPACKE_dlarnv(lapack_int idist, lapack_int* iseed, lapack_int n, double* x);
+lapack_int LAPACKE_clarnv(lapack_int idist, lapack_int* iseed, lapack_int n, lapack_complex_float* x);
+lapack_int LAPACKE_zlarnv(lapack_int idist, lapack_int* iseed, lapack_int n, lapack_complex_double* x);
+
+lapack_int LAPACKE_slaset(int matrix_order, char uplo, lapack_int m, lapack_int n, float alpha, float beta, float* a,
+                          lapack_int lda);
+lapack_int LAPACKE_dlaset(int matrix_order, char uplo, lapack_int m, lapack_int n, double alpha, double beta, double* a,
+                          lapack_int lda);
+lapack_int LAPACKE_claset(int matrix_order, char uplo, lapack_int m, lapack_int n, lapack_complex_float alpha,
+                          lapack_complex_float beta, lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zlaset(int matrix_order, char uplo, lapack_int m, lapack_int n, lapack_complex_double alpha,
+                          lapack_complex_double beta, lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_slasrt(char id, lapack_int n, float* d);
+lapack_int LAPACKE_dlasrt(char id, lapack_int n, double* d);
+
+lapack_int LAPACKE_slaswp(int matrix_order, lapack_int n, float* a, lapack_int lda, lapack_int k1, lapack_int k2,
+                          const lapack_int* ipiv, lapack_int incx);
+lapack_int LAPACKE_dlaswp(int matrix_order, lapack_int n, double* a, lapack_int lda, lapack_int k1, lapack_int k2,
+                          const lapack_int* ipiv, lapack_int incx);
+lapack_int LAPACKE_claswp(int matrix_order, lapack_int n, lapack_complex_float* a, lapack_int lda, lapack_int k1,
+                          lapack_int k2, const lapack_int* ipiv, lapack_int incx);
+lapack_int LAPACKE_zlaswp(int matrix_order, lapack_int n, lapack_complex_double* a, lapack_int lda, lapack_int k1,
+                          lapack_int k2, const lapack_int* ipiv, lapack_int incx);
+
+lapack_int LAPACKE_slatms(int matrix_order, lapack_int m, lapack_int n, char dist, lapack_int* iseed, char sym,
+                          float* d, lapack_int mode, float cond, float dmax, lapack_int kl, lapack_int ku, char pack,
+                          float* a, lapack_int lda);
+lapack_int LAPACKE_dlatms(int matrix_order, lapack_int m, lapack_int n, char dist, lapack_int* iseed, char sym,
+                          double* d, lapack_int mode, double cond, double dmax, lapack_int kl, lapack_int ku, char pack,
+                          double* a, lapack_int lda);
+lapack_int LAPACKE_clatms(int matrix_order, lapack_int m, lapack_int n, char dist, lapack_int* iseed, char sym,
+                          float* d, lapack_int mode, float cond, float dmax, lapack_int kl, lapack_int ku, char pack,
+                          lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zlatms(int matrix_order, lapack_int m, lapack_int n, char dist, lapack_int* iseed, char sym,
+                          double* d, lapack_int mode, double cond, double dmax, lapack_int kl, lapack_int ku, char pack,
+                          lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_slauum(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda);
+lapack_int LAPACKE_dlauum(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda);
+lapack_int LAPACKE_clauum(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zlauum(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_sopgtr(int matrix_order, char uplo, lapack_int n, const float* ap, const float* tau, float* q,
+                          lapack_int ldq);
+lapack_int LAPACKE_dopgtr(int matrix_order, char uplo, lapack_int n, const double* ap, const double* tau, double* q,
+                          lapack_int ldq);
+
+lapack_int LAPACKE_sopmtr(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                          const float* ap, const float* tau, float* c, lapack_int ldc);
+lapack_int LAPACKE_dopmtr(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                          const double* ap, const double* tau, double* c, lapack_int ldc);
+
+lapack_int LAPACKE_sorgbr(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int k, float* a,
+                          lapack_int lda, const float* tau);
+lapack_int LAPACKE_dorgbr(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int k, double* a,
+                          lapack_int lda, const double* tau);
+
+lapack_int LAPACKE_sorghr(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, float* a, lapack_int lda,
+                          const float* tau);
+lapack_int LAPACKE_dorghr(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, double* a, lapack_int lda,
+                          const double* tau);
+
+lapack_int LAPACKE_sorglq(int matrix_order, lapack_int m, lapack_int n, lapack_int k, float* a, lapack_int lda,
+                          const float* tau);
+lapack_int LAPACKE_dorglq(int matrix_order, lapack_int m, lapack_int n, lapack_int k, double* a, lapack_int lda,
+                          const double* tau);
+
+lapack_int LAPACKE_sorgql(int matrix_order, lapack_int m, lapack_int n, lapack_int k, float* a, lapack_int lda,
+                          const float* tau);
+lapack_int LAPACKE_dorgql(int matrix_order, lapack_int m, lapack_int n, lapack_int k, double* a, lapack_int lda,
+                          const double* tau);
+
+lapack_int LAPACKE_sorgqr(int matrix_order, lapack_int m, lapack_int n, lapack_int k, float* a, lapack_int lda,
+                          const float* tau);
+lapack_int LAPACKE_dorgqr(int matrix_order, lapack_int m, lapack_int n, lapack_int k, double* a, lapack_int lda,
+                          const double* tau);
+
+lapack_int LAPACKE_sorgrq(int matrix_order, lapack_int m, lapack_int n, lapack_int k, float* a, lapack_int lda,
+                          const float* tau);
+lapack_int LAPACKE_dorgrq(int matrix_order, lapack_int m, lapack_int n, lapack_int k, double* a, lapack_int lda,
+                          const double* tau);
+
+lapack_int LAPACKE_sorgtr(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, const float* tau);
+lapack_int LAPACKE_dorgtr(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda, const double* tau);
+
+lapack_int LAPACKE_sormbr(int matrix_order, char vect, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc);
+lapack_int LAPACKE_dormbr(int matrix_order, char vect, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc);
+
+lapack_int LAPACKE_sormhr(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int ilo,
+                          lapack_int ihi, const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc);
+lapack_int LAPACKE_dormhr(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int ilo,
+                          lapack_int ihi, const double* a, lapack_int lda, const double* tau, double* c,
+                          lapack_int ldc);
+
+lapack_int LAPACKE_sormlq(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc);
+lapack_int LAPACKE_dormlq(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc);
+
+lapack_int LAPACKE_sormql(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc);
+lapack_int LAPACKE_dormql(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc);
+
+lapack_int LAPACKE_sormqr(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc);
+lapack_int LAPACKE_dormqr(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc);
+
+lapack_int LAPACKE_sormrq(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc);
+lapack_int LAPACKE_dormrq(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc);
+
+lapack_int LAPACKE_sormrz(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          lapack_int l, const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc);
+lapack_int LAPACKE_dormrz(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          lapack_int l, const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc);
+
+lapack_int LAPACKE_sormtr(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                          const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc);
+lapack_int LAPACKE_dormtr(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                          const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc);
+
+lapack_int LAPACKE_spbcon(int matrix_order, char uplo, lapack_int n, lapack_int kd, const float* ab, lapack_int ldab,
+                          float anorm, float* rcond);
+lapack_int LAPACKE_dpbcon(int matrix_order, char uplo, lapack_int n, lapack_int kd, const double* ab, lapack_int ldab,
+                          double anorm, double* rcond);
+lapack_int LAPACKE_cpbcon(int matrix_order, char uplo, lapack_int n, lapack_int kd, const lapack_complex_float* ab,
+                          lapack_int ldab, float anorm, float* rcond);
+lapack_int LAPACKE_zpbcon(int matrix_order, char uplo, lapack_int n, lapack_int kd, const lapack_complex_double* ab,
+                          lapack_int ldab, double anorm, double* rcond);
+
+lapack_int LAPACKE_spbequ(int matrix_order, char uplo, lapack_int n, lapack_int kd, const float* ab, lapack_int ldab,
+                          float* s, float* scond, float* amax);
+lapack_int LAPACKE_dpbequ(int matrix_order, char uplo, lapack_int n, lapack_int kd, const double* ab, lapack_int ldab,
+                          double* s, double* scond, double* amax);
+lapack_int LAPACKE_cpbequ(int matrix_order, char uplo, lapack_int n, lapack_int kd, const lapack_complex_float* ab,
+                          lapack_int ldab, float* s, float* scond, float* amax);
+lapack_int LAPACKE_zpbequ(int matrix_order, char uplo, lapack_int n, lapack_int kd, const lapack_complex_double* ab,
+                          lapack_int ldab, double* s, double* scond, double* amax);
+
+lapack_int LAPACKE_spbrfs(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs, const float* ab,
+                          lapack_int ldab, const float* afb, lapack_int ldafb, const float* b, lapack_int ldb, float* x,
+                          lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_dpbrfs(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs, const double* ab,
+                          lapack_int ldab, const double* afb, lapack_int ldafb, const double* b, lapack_int ldb,
+                          double* x, lapack_int ldx, double* ferr, double* berr);
+lapack_int LAPACKE_cpbrfs(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                          const lapack_complex_float* ab, lapack_int ldab, const lapack_complex_float* afb,
+                          lapack_int ldafb, const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                          lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_zpbrfs(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                          const lapack_complex_double* ab, lapack_int ldab, const lapack_complex_double* afb,
+                          lapack_int ldafb, const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                          lapack_int ldx, double* ferr, double* berr);
+
+lapack_int LAPACKE_spbstf(int matrix_order, char uplo, lapack_int n, lapack_int kb, float* bb, lapack_int ldbb);
+lapack_int LAPACKE_dpbstf(int matrix_order, char uplo, lapack_int n, lapack_int kb, double* bb, lapack_int ldbb);
+lapack_int LAPACKE_cpbstf(int matrix_order, char uplo, lapack_int n, lapack_int kb, lapack_complex_float* bb,
+                          lapack_int ldbb);
+lapack_int LAPACKE_zpbstf(int matrix_order, char uplo, lapack_int n, lapack_int kb, lapack_complex_double* bb,
+                          lapack_int ldbb);
+
+lapack_int LAPACKE_spbsv(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs, float* ab,
+                         lapack_int ldab, float* b, lapack_int ldb);
+lapack_int LAPACKE_dpbsv(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs, double* ab,
+                         lapack_int ldab, double* b, lapack_int ldb);
+lapack_int LAPACKE_cpbsv(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                         lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpbsv(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                         lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_spbsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                          float* ab, lapack_int ldab, float* afb, lapack_int ldafb, char* equed, float* s, float* b,
+                          lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_dpbsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                          double* ab, lapack_int ldab, double* afb, lapack_int ldafb, char* equed, double* s, double* b,
+                          lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* ferr, double* berr);
+lapack_int LAPACKE_cpbsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                          lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* afb, lapack_int ldafb,
+                          char* equed, float* s, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                          lapack_int ldx, float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_zpbsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                          lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* afb, lapack_int ldafb,
+                          char* equed, double* s, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                          lapack_int ldx, double* rcond, double* ferr, double* berr);
+
+lapack_int LAPACKE_spbtrf(int matrix_order, char uplo, lapack_int n, lapack_int kd, float* ab, lapack_int ldab);
+lapack_int LAPACKE_dpbtrf(int matrix_order, char uplo, lapack_int n, lapack_int kd, double* ab, lapack_int ldab);
+lapack_int LAPACKE_cpbtrf(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_complex_float* ab,
+                          lapack_int ldab);
+lapack_int LAPACKE_zpbtrf(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_complex_double* ab,
+                          lapack_int ldab);
+
+lapack_int LAPACKE_spbtrs(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs, const float* ab,
+                          lapack_int ldab, float* b, lapack_int ldb);
+lapack_int LAPACKE_dpbtrs(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs, const double* ab,
+                          lapack_int ldab, double* b, lapack_int ldb);
+lapack_int LAPACKE_cpbtrs(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                          const lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpbtrs(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                          const lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_spftrf(int matrix_order, char transr, char uplo, lapack_int n, float* a);
+lapack_int LAPACKE_dpftrf(int matrix_order, char transr, char uplo, lapack_int n, double* a);
+lapack_int LAPACKE_cpftrf(int matrix_order, char transr, char uplo, lapack_int n, lapack_complex_float* a);
+lapack_int LAPACKE_zpftrf(int matrix_order, char transr, char uplo, lapack_int n, lapack_complex_double* a);
+
+lapack_int LAPACKE_spftri(int matrix_order, char transr, char uplo, lapack_int n, float* a);
+lapack_int LAPACKE_dpftri(int matrix_order, char transr, char uplo, lapack_int n, double* a);
+lapack_int LAPACKE_cpftri(int matrix_order, char transr, char uplo, lapack_int n, lapack_complex_float* a);
+lapack_int LAPACKE_zpftri(int matrix_order, char transr, char uplo, lapack_int n, lapack_complex_double* a);
+
+lapack_int LAPACKE_spftrs(int matrix_order, char transr, char uplo, lapack_int n, lapack_int nrhs, const float* a,
+                          float* b, lapack_int ldb);
+lapack_int LAPACKE_dpftrs(int matrix_order, char transr, char uplo, lapack_int n, lapack_int nrhs, const double* a,
+                          double* b, lapack_int ldb);
+lapack_int LAPACKE_cpftrs(int matrix_order, char transr, char uplo, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_float* a, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpftrs(int matrix_order, char transr, char uplo, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_double* a, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_spocon(int matrix_order, char uplo, lapack_int n, const float* a, lapack_int lda, float anorm,
+                          float* rcond);
+lapack_int LAPACKE_dpocon(int matrix_order, char uplo, lapack_int n, const double* a, lapack_int lda, double anorm,
+                          double* rcond);
+lapack_int LAPACKE_cpocon(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                          float anorm, float* rcond);
+lapack_int LAPACKE_zpocon(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                          double anorm, double* rcond);
+
+lapack_int LAPACKE_spoequ(int matrix_order, lapack_int n, const float* a, lapack_int lda, float* s, float* scond,
+                          float* amax);
+lapack_int LAPACKE_dpoequ(int matrix_order, lapack_int n, const double* a, lapack_int lda, double* s, double* scond,
+                          double* amax);
+lapack_int LAPACKE_cpoequ(int matrix_order, lapack_int n, const lapack_complex_float* a, lapack_int lda, float* s,
+                          float* scond, float* amax);
+lapack_int LAPACKE_zpoequ(int matrix_order, lapack_int n, const lapack_complex_double* a, lapack_int lda, double* s,
+                          double* scond, double* amax);
+
+lapack_int LAPACKE_spoequb(int matrix_order, lapack_int n, const float* a, lapack_int lda, float* s, float* scond,
+                           float* amax);
+lapack_int LAPACKE_dpoequb(int matrix_order, lapack_int n, const double* a, lapack_int lda, double* s, double* scond,
+                           double* amax);
+lapack_int LAPACKE_cpoequb(int matrix_order, lapack_int n, const lapack_complex_float* a, lapack_int lda, float* s,
+                           float* scond, float* amax);
+lapack_int LAPACKE_zpoequb(int matrix_order, lapack_int n, const lapack_complex_double* a, lapack_int lda, double* s,
+                           double* scond, double* amax);
+
+lapack_int LAPACKE_sporfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* a, lapack_int lda,
+                          const float* af, lapack_int ldaf, const float* b, lapack_int ldb, float* x, lapack_int ldx,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_dporfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* a, lapack_int lda,
+                          const double* af, lapack_int ldaf, const double* b, lapack_int ldb, double* x, lapack_int ldx,
+                          double* ferr, double* berr);
+lapack_int LAPACKE_cporfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* a,
+                          lapack_int lda, const lapack_complex_float* af, lapack_int ldaf,
+                          const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_zporfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* a,
+                          lapack_int lda, const lapack_complex_double* af, lapack_int ldaf,
+                          const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* ferr, double* berr);
+
+lapack_int LAPACKE_sporfsx(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs, const float* a,
+                           lapack_int lda, const float* af, lapack_int ldaf, const float* s, const float* b,
+                           lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* berr, lapack_int n_err_bnds,
+                           float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams, float* params);
+lapack_int LAPACKE_dporfsx(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs, const double* a,
+                           lapack_int lda, const double* af, lapack_int ldaf, const double* s, const double* b,
+                           lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* berr,
+                           lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                           double* params);
+lapack_int LAPACKE_cporfsx(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                           const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                           lapack_int ldaf, const float* s, const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* rcond, float* berr, lapack_int n_err_bnds,
+                           float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams, float* params);
+lapack_int LAPACKE_zporfsx(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                           const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                           lapack_int ldaf, const double* s, const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx, double* rcond, double* berr, lapack_int n_err_bnds,
+                           double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams, double* params);
+
+lapack_int LAPACKE_sposv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, float* a, lapack_int lda, float* b,
+                         lapack_int ldb);
+lapack_int LAPACKE_dposv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                         double* b, lapack_int ldb);
+lapack_int LAPACKE_cposv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                         lapack_int lda, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zposv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                         lapack_int lda, lapack_complex_double* b, lapack_int ldb);
+lapack_int LAPACKE_dsposv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                          double* b, lapack_int ldb, double* x, lapack_int ldx, lapack_int* iter);
+lapack_int LAPACKE_zcposv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                          lapack_int ldx, lapack_int* iter);
+
+lapack_int LAPACKE_sposvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, float* a,
+                          lapack_int lda, float* af, lapack_int ldaf, char* equed, float* s, float* b, lapack_int ldb,
+                          float* x, lapack_int ldx, float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_dposvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, double* a,
+                          lapack_int lda, double* af, lapack_int ldaf, char* equed, double* s, double* b,
+                          lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* ferr, double* berr);
+lapack_int LAPACKE_cposvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                          char* equed, float* s, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                          lapack_int ldx, float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_zposvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                          char* equed, double* s, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                          lapack_int ldx, double* rcond, double* ferr, double* berr);
+
+lapack_int LAPACKE_sposvxx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, float* a,
+                           lapack_int lda, float* af, lapack_int ldaf, char* equed, float* s, float* b, lapack_int ldb,
+                           float* x, lapack_int ldx, float* rcond, float* rpvgrw, float* berr, lapack_int n_err_bnds,
+                           float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams, float* params);
+lapack_int LAPACKE_dposvxx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, double* a,
+                           lapack_int lda, double* af, lapack_int ldaf, char* equed, double* s, double* b,
+                           lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* rpvgrw, double* berr,
+                           lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                           double* params);
+lapack_int LAPACKE_cposvxx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                           lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                           char* equed, float* s, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                           lapack_int ldx, float* rcond, float* rpvgrw, float* berr, lapack_int n_err_bnds,
+                           float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams, float* params);
+lapack_int LAPACKE_zposvxx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                           lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                           char* equed, double* s, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                           lapack_int ldx, double* rcond, double* rpvgrw, double* berr, lapack_int n_err_bnds,
+                           double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams, double* params);
+
+lapack_int LAPACKE_spotrf(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda);
+lapack_int LAPACKE_dpotrf(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda);
+lapack_int LAPACKE_cpotrf(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zpotrf(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_spotri(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda);
+lapack_int LAPACKE_dpotri(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda);
+lapack_int LAPACKE_cpotri(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zpotri(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_spotrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* a, lapack_int lda,
+                          float* b, lapack_int ldb);
+lapack_int LAPACKE_dpotrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* a, lapack_int lda,
+                          double* b, lapack_int ldb);
+lapack_int LAPACKE_cpotrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpotrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sppcon(int matrix_order, char uplo, lapack_int n, const float* ap, float anorm, float* rcond);
+lapack_int LAPACKE_dppcon(int matrix_order, char uplo, lapack_int n, const double* ap, double anorm, double* rcond);
+lapack_int LAPACKE_cppcon(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap, float anorm,
+                          float* rcond);
+lapack_int LAPACKE_zppcon(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap, double anorm,
+                          double* rcond);
+
+lapack_int LAPACKE_sppequ(int matrix_order, char uplo, lapack_int n, const float* ap, float* s, float* scond,
+                          float* amax);
+lapack_int LAPACKE_dppequ(int matrix_order, char uplo, lapack_int n, const double* ap, double* s, double* scond,
+                          double* amax);
+lapack_int LAPACKE_cppequ(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap, float* s,
+                          float* scond, float* amax);
+lapack_int LAPACKE_zppequ(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap, double* s,
+                          double* scond, double* amax);
+
+lapack_int LAPACKE_spprfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* ap, const float* afp,
+                          const float* b, lapack_int ldb, float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_dpprfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* ap,
+                          const double* afp, const double* b, lapack_int ldb, double* x, lapack_int ldx, double* ferr,
+                          double* berr);
+lapack_int LAPACKE_cpprfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* ap,
+                          const lapack_complex_float* afp, const lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_zpprfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* ap,
+                          const lapack_complex_double* afp, const lapack_complex_double* b, lapack_int ldb,
+                          lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr);
+
+lapack_int LAPACKE_sppsv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, float* ap, float* b,
+                         lapack_int ldb);
+lapack_int LAPACKE_dppsv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, double* ap, double* b,
+                         lapack_int ldb);
+lapack_int LAPACKE_cppsv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* ap,
+                         lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zppsv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* ap,
+                         lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sppsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, float* ap, float* afp,
+                          char* equed, float* s, float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_dppsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, double* ap,
+                          double* afp, char* equed, double* s, double* b, lapack_int ldb, double* x, lapack_int ldx,
+                          double* rcond, double* ferr, double* berr);
+lapack_int LAPACKE_cppsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          lapack_complex_float* ap, lapack_complex_float* afp, char* equed, float* s,
+                          lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                          float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_zppsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          lapack_complex_double* ap, lapack_complex_double* afp, char* equed, double* s,
+                          lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* rcond, double* ferr, double* berr);
+
+lapack_int LAPACKE_spptrf(int matrix_order, char uplo, lapack_int n, float* ap);
+lapack_int LAPACKE_dpptrf(int matrix_order, char uplo, lapack_int n, double* ap);
+lapack_int LAPACKE_cpptrf(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap);
+lapack_int LAPACKE_zpptrf(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap);
+
+lapack_int LAPACKE_spptri(int matrix_order, char uplo, lapack_int n, float* ap);
+lapack_int LAPACKE_dpptri(int matrix_order, char uplo, lapack_int n, double* ap);
+lapack_int LAPACKE_cpptri(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap);
+lapack_int LAPACKE_zpptri(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap);
+
+lapack_int LAPACKE_spptrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* ap, float* b,
+                          lapack_int ldb);
+lapack_int LAPACKE_dpptrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* ap, double* b,
+                          lapack_int ldb);
+lapack_int LAPACKE_cpptrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* ap,
+                          lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpptrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* ap,
+                          lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_spstrf(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, lapack_int* piv,
+                          lapack_int* rank, float tol);
+lapack_int LAPACKE_dpstrf(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda, lapack_int* piv,
+                          lapack_int* rank, double tol);
+lapack_int LAPACKE_cpstrf(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_int* piv, lapack_int* rank, float tol);
+lapack_int LAPACKE_zpstrf(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_int* piv, lapack_int* rank, double tol);
+
+lapack_int LAPACKE_sptcon(lapack_int n, const float* d, const float* e, float anorm, float* rcond);
+lapack_int LAPACKE_dptcon(lapack_int n, const double* d, const double* e, double anorm, double* rcond);
+lapack_int LAPACKE_cptcon(lapack_int n, const float* d, const lapack_complex_float* e, float anorm, float* rcond);
+lapack_int LAPACKE_zptcon(lapack_int n, const double* d, const lapack_complex_double* e, double anorm, double* rcond);
+
+lapack_int LAPACKE_spteqr(int matrix_order, char compz, lapack_int n, float* d, float* e, float* z, lapack_int ldz);
+lapack_int LAPACKE_dpteqr(int matrix_order, char compz, lapack_int n, double* d, double* e, double* z, lapack_int ldz);
+lapack_int LAPACKE_cpteqr(int matrix_order, char compz, lapack_int n, float* d, float* e, lapack_complex_float* z,
+                          lapack_int ldz);
+lapack_int LAPACKE_zpteqr(int matrix_order, char compz, lapack_int n, double* d, double* e, lapack_complex_double* z,
+                          lapack_int ldz);
+
+lapack_int LAPACKE_sptrfs(int matrix_order, lapack_int n, lapack_int nrhs, const float* d, const float* e,
+                          const float* df, const float* ef, const float* b, lapack_int ldb, float* x, lapack_int ldx,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_dptrfs(int matrix_order, lapack_int n, lapack_int nrhs, const double* d, const double* e,
+                          const double* df, const double* ef, const double* b, lapack_int ldb, double* x,
+                          lapack_int ldx, double* ferr, double* berr);
+lapack_int LAPACKE_cptrfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* d,
+                          const lapack_complex_float* e, const float* df, const lapack_complex_float* ef,
+                          const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_zptrfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* d,
+                          const lapack_complex_double* e, const double* df, const lapack_complex_double* ef,
+                          const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* ferr, double* berr);
+
+lapack_int LAPACKE_sptsv(int matrix_order, lapack_int n, lapack_int nrhs, float* d, float* e, float* b, lapack_int ldb);
+lapack_int LAPACKE_dptsv(int matrix_order, lapack_int n, lapack_int nrhs, double* d, double* e, double* b,
+                         lapack_int ldb);
+lapack_int LAPACKE_cptsv(int matrix_order, lapack_int n, lapack_int nrhs, float* d, lapack_complex_float* e,
+                         lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zptsv(int matrix_order, lapack_int n, lapack_int nrhs, double* d, lapack_complex_double* e,
+                         lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sptsvx(int matrix_order, char fact, lapack_int n, lapack_int nrhs, const float* d, const float* e,
+                          float* df, float* ef, const float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_dptsvx(int matrix_order, char fact, lapack_int n, lapack_int nrhs, const double* d, const double* e,
+                          double* df, double* ef, const double* b, lapack_int ldb, double* x, lapack_int ldx,
+                          double* rcond, double* ferr, double* berr);
+lapack_int LAPACKE_cptsvx(int matrix_order, char fact, lapack_int n, lapack_int nrhs, const float* d,
+                          const lapack_complex_float* e, float* df, lapack_complex_float* ef,
+                          const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                          float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_zptsvx(int matrix_order, char fact, lapack_int n, lapack_int nrhs, const double* d,
+                          const lapack_complex_double* e, double* df, lapack_complex_double* ef,
+                          const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* rcond, double* ferr, double* berr);
+
+lapack_int LAPACKE_spttrf(lapack_int n, float* d, float* e);
+lapack_int LAPACKE_dpttrf(lapack_int n, double* d, double* e);
+lapack_int LAPACKE_cpttrf(lapack_int n, float* d, lapack_complex_float* e);
+lapack_int LAPACKE_zpttrf(lapack_int n, double* d, lapack_complex_double* e);
+
+lapack_int LAPACKE_spttrs(int matrix_order, lapack_int n, lapack_int nrhs, const float* d, const float* e, float* b,
+                          lapack_int ldb);
+lapack_int LAPACKE_dpttrs(int matrix_order, lapack_int n, lapack_int nrhs, const double* d, const double* e, double* b,
+                          lapack_int ldb);
+lapack_int LAPACKE_cpttrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* d,
+                          const lapack_complex_float* e, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpttrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* d,
+                          const lapack_complex_double* e, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_ssbev(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, float* ab,
+                         lapack_int ldab, float* w, float* z, lapack_int ldz);
+lapack_int LAPACKE_dsbev(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, double* ab,
+                         lapack_int ldab, double* w, double* z, lapack_int ldz);
+
+lapack_int LAPACKE_ssbevd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, float* ab,
+                          lapack_int ldab, float* w, float* z, lapack_int ldz);
+lapack_int LAPACKE_dsbevd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, double* ab,
+                          lapack_int ldab, double* w, double* z, lapack_int ldz);
+
+lapack_int LAPACKE_ssbevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int kd, float* ab,
+                          lapack_int ldab, float* q, lapack_int ldq, float vl, float vu, lapack_int il, lapack_int iu,
+                          float abstol, lapack_int* m, float* w, float* z, lapack_int ldz, lapack_int* ifail);
+lapack_int LAPACKE_dsbevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int kd, double* ab,
+                          lapack_int ldab, double* q, lapack_int ldq, double vl, double vu, lapack_int il,
+                          lapack_int iu, double abstol, lapack_int* m, double* w, double* z, lapack_int ldz,
+                          lapack_int* ifail);
+
+lapack_int LAPACKE_ssbgst(int matrix_order, char vect, char uplo, lapack_int n, lapack_int ka, lapack_int kb, float* ab,
+                          lapack_int ldab, const float* bb, lapack_int ldbb, float* x, lapack_int ldx);
+lapack_int LAPACKE_dsbgst(int matrix_order, char vect, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                          double* ab, lapack_int ldab, const double* bb, lapack_int ldbb, double* x, lapack_int ldx);
+
+lapack_int LAPACKE_ssbgv(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb, float* ab,
+                         lapack_int ldab, float* bb, lapack_int ldbb, float* w, float* z, lapack_int ldz);
+lapack_int LAPACKE_dsbgv(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb, double* ab,
+                         lapack_int ldab, double* bb, lapack_int ldbb, double* w, double* z, lapack_int ldz);
+
+lapack_int LAPACKE_ssbgvd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb, float* ab,
+                          lapack_int ldab, float* bb, lapack_int ldbb, float* w, float* z, lapack_int ldz);
+lapack_int LAPACKE_dsbgvd(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                          double* ab, lapack_int ldab, double* bb, lapack_int ldbb, double* w, double* z,
+                          lapack_int ldz);
+
+lapack_int LAPACKE_ssbgvx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int ka,
+                          lapack_int kb, float* ab, lapack_int ldab, float* bb, lapack_int ldbb, float* q,
+                          lapack_int ldq, float vl, float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m,
+                          float* w, float* z, lapack_int ldz, lapack_int* ifail);
+lapack_int LAPACKE_dsbgvx(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int ka,
+                          lapack_int kb, double* ab, lapack_int ldab, double* bb, lapack_int ldbb, double* q,
+                          lapack_int ldq, double vl, double vu, lapack_int il, lapack_int iu, double abstol,
+                          lapack_int* m, double* w, double* z, lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_ssbtrd(int matrix_order, char vect, char uplo, lapack_int n, lapack_int kd, float* ab,
+                          lapack_int ldab, float* d, float* e, float* q, lapack_int ldq);
+lapack_int LAPACKE_dsbtrd(int matrix_order, char vect, char uplo, lapack_int n, lapack_int kd, double* ab,
+                          lapack_int ldab, double* d, double* e, double* q, lapack_int ldq);
+
+lapack_int LAPACKE_ssfrk(int matrix_order, char transr, char uplo, char trans, lapack_int n, lapack_int k, float alpha,
+                         const float* a, lapack_int lda, float beta, float* c);
+lapack_int LAPACKE_dsfrk(int matrix_order, char transr, char uplo, char trans, lapack_int n, lapack_int k, double alpha,
+                         const double* a, lapack_int lda, double beta, double* c);
+
+lapack_int LAPACKE_sspcon(int matrix_order, char uplo, lapack_int n, const float* ap, const lapack_int* ipiv,
+                          float anorm, float* rcond);
+lapack_int LAPACKE_dspcon(int matrix_order, char uplo, lapack_int n, const double* ap, const lapack_int* ipiv,
+                          double anorm, double* rcond);
+lapack_int LAPACKE_cspcon(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap,
+                          const lapack_int* ipiv, float anorm, float* rcond);
+lapack_int LAPACKE_zspcon(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap,
+                          const lapack_int* ipiv, double anorm, double* rcond);
+
+lapack_int LAPACKE_sspev(int matrix_order, char jobz, char uplo, lapack_int n, float* ap, float* w, float* z,
+                         lapack_int ldz);
+lapack_int LAPACKE_dspev(int matrix_order, char jobz, char uplo, lapack_int n, double* ap, double* w, double* z,
+                         lapack_int ldz);
+
+lapack_int LAPACKE_sspevd(int matrix_order, char jobz, char uplo, lapack_int n, float* ap, float* w, float* z,
+                          lapack_int ldz);
+lapack_int LAPACKE_dspevd(int matrix_order, char jobz, char uplo, lapack_int n, double* ap, double* w, double* z,
+                          lapack_int ldz);
+
+lapack_int LAPACKE_sspevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, float* ap, float vl,
+                          float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w, float* z,
+                          lapack_int ldz, lapack_int* ifail);
+lapack_int LAPACKE_dspevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, double* ap, double vl,
+                          double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w, double* z,
+                          lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_sspgst(int matrix_order, lapack_int itype, char uplo, lapack_int n, float* ap, const float* bp);
+lapack_int LAPACKE_dspgst(int matrix_order, lapack_int itype, char uplo, lapack_int n, double* ap, const double* bp);
+
+lapack_int LAPACKE_sspgv(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, float* ap, float* bp,
+                         float* w, float* z, lapack_int ldz);
+lapack_int LAPACKE_dspgv(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, double* ap, double* bp,
+                         double* w, double* z, lapack_int ldz);
+
+lapack_int LAPACKE_sspgvd(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, float* ap, float* bp,
+                          float* w, float* z, lapack_int ldz);
+lapack_int LAPACKE_dspgvd(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, double* ap,
+                          double* bp, double* w, double* z, lapack_int ldz);
+
+lapack_int LAPACKE_sspgvx(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n, float* ap,
+                          float* bp, float vl, float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m,
+                          float* w, float* z, lapack_int ldz, lapack_int* ifail);
+lapack_int LAPACKE_dspgvx(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                          double* ap, double* bp, double vl, double vu, lapack_int il, lapack_int iu, double abstol,
+                          lapack_int* m, double* w, double* z, lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_ssprfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* ap, const float* afp,
+                          const lapack_int* ipiv, const float* b, lapack_int ldb, float* x, lapack_int ldx, float* ferr,
+                          float* berr);
+lapack_int LAPACKE_dsprfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* ap,
+                          const double* afp, const lapack_int* ipiv, const double* b, lapack_int ldb, double* x,
+                          lapack_int ldx, double* ferr, double* berr);
+lapack_int LAPACKE_csprfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* ap,
+                          const lapack_complex_float* afp, const lapack_int* ipiv, const lapack_complex_float* b,
+                          lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_zsprfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* ap,
+                          const lapack_complex_double* afp, const lapack_int* ipiv, const lapack_complex_double* b,
+                          lapack_int ldb, lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr);
+
+lapack_int LAPACKE_sspsv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, float* ap, lapack_int* ipiv,
+                         float* b, lapack_int ldb);
+lapack_int LAPACKE_dspsv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, double* ap, lapack_int* ipiv,
+                         double* b, lapack_int ldb);
+lapack_int LAPACKE_cspsv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* ap,
+                         lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zspsv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* ap,
+                         lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sspsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, const float* ap,
+                          float* afp, lapack_int* ipiv, const float* b, lapack_int ldb, float* x, lapack_int ldx,
+                          float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_dspsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, const double* ap,
+                          double* afp, lapack_int* ipiv, const double* b, lapack_int ldb, double* x, lapack_int ldx,
+                          double* rcond, double* ferr, double* berr);
+lapack_int LAPACKE_cspsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_float* ap, lapack_complex_float* afp, lapack_int* ipiv,
+                          const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                          float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_zspsvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_double* ap, lapack_complex_double* afp, lapack_int* ipiv,
+                          const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* rcond, double* ferr, double* berr);
+
+lapack_int LAPACKE_ssptrd(int matrix_order, char uplo, lapack_int n, float* ap, float* d, float* e, float* tau);
+lapack_int LAPACKE_dsptrd(int matrix_order, char uplo, lapack_int n, double* ap, double* d, double* e, double* tau);
+
+lapack_int LAPACKE_ssptrf(int matrix_order, char uplo, lapack_int n, float* ap, lapack_int* ipiv);
+lapack_int LAPACKE_dsptrf(int matrix_order, char uplo, lapack_int n, double* ap, lapack_int* ipiv);
+lapack_int LAPACKE_csptrf(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap, lapack_int* ipiv);
+lapack_int LAPACKE_zsptrf(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap, lapack_int* ipiv);
+
+lapack_int LAPACKE_ssptri(int matrix_order, char uplo, lapack_int n, float* ap, const lapack_int* ipiv);
+lapack_int LAPACKE_dsptri(int matrix_order, char uplo, lapack_int n, double* ap, const lapack_int* ipiv);
+lapack_int LAPACKE_csptri(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap, const lapack_int* ipiv);
+lapack_int LAPACKE_zsptri(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap, const lapack_int* ipiv);
+
+lapack_int LAPACKE_ssptrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* ap,
+                          const lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dsptrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* ap,
+                          const lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_csptrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* ap,
+                          const lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zsptrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* ap,
+                          const lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sstebz(char range, char order, lapack_int n, float vl, float vu, lapack_int il, lapack_int iu,
+                          float abstol, const float* d, const float* e, lapack_int* m, lapack_int* nsplit, float* w,
+                          lapack_int* iblock, lapack_int* isplit);
+lapack_int LAPACKE_dstebz(char range, char order, lapack_int n, double vl, double vu, lapack_int il, lapack_int iu,
+                          double abstol, const double* d, const double* e, lapack_int* m, lapack_int* nsplit, double* w,
+                          lapack_int* iblock, lapack_int* isplit);
+
+lapack_int LAPACKE_sstedc(int matrix_order, char compz, lapack_int n, float* d, float* e, float* z, lapack_int ldz);
+lapack_int LAPACKE_dstedc(int matrix_order, char compz, lapack_int n, double* d, double* e, double* z, lapack_int ldz);
+lapack_int LAPACKE_cstedc(int matrix_order, char compz, lapack_int n, float* d, float* e, lapack_complex_float* z,
+                          lapack_int ldz);
+lapack_int LAPACKE_zstedc(int matrix_order, char compz, lapack_int n, double* d, double* e, lapack_complex_double* z,
+                          lapack_int ldz);
+
+lapack_int LAPACKE_sstegr(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl, float vu,
+                          lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w, float* z, lapack_int ldz,
+                          lapack_int* isuppz);
+lapack_int LAPACKE_dstegr(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                          double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w, double* z,
+                          lapack_int ldz, lapack_int* isuppz);
+lapack_int LAPACKE_cstegr(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl, float vu,
+                          lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w, lapack_complex_float* z,
+                          lapack_int ldz, lapack_int* isuppz);
+lapack_int LAPACKE_zstegr(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                          double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                          lapack_complex_double* z, lapack_int ldz, lapack_int* isuppz);
+
+lapack_int LAPACKE_sstein(int matrix_order, lapack_int n, const float* d, const float* e, lapack_int m, const float* w,
+                          const lapack_int* iblock, const lapack_int* isplit, float* z, lapack_int ldz,
+                          lapack_int* ifailv);
+lapack_int LAPACKE_dstein(int matrix_order, lapack_int n, const double* d, const double* e, lapack_int m,
+                          const double* w, const lapack_int* iblock, const lapack_int* isplit, double* z,
+                          lapack_int ldz, lapack_int* ifailv);
+lapack_int LAPACKE_cstein(int matrix_order, lapack_int n, const float* d, const float* e, lapack_int m, const float* w,
+                          const lapack_int* iblock, const lapack_int* isplit, lapack_complex_float* z, lapack_int ldz,
+                          lapack_int* ifailv);
+lapack_int LAPACKE_zstein(int matrix_order, lapack_int n, const double* d, const double* e, lapack_int m,
+                          const double* w, const lapack_int* iblock, const lapack_int* isplit, lapack_complex_double* z,
+                          lapack_int ldz, lapack_int* ifailv);
+
+lapack_int LAPACKE_sstemr(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl, float vu,
+                          lapack_int il, lapack_int iu, lapack_int* m, float* w, float* z, lapack_int ldz,
+                          lapack_int nzc, lapack_int* isuppz, lapack_logical* tryrac);
+lapack_int LAPACKE_dstemr(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                          double vu, lapack_int il, lapack_int iu, lapack_int* m, double* w, double* z, lapack_int ldz,
+                          lapack_int nzc, lapack_int* isuppz, lapack_logical* tryrac);
+lapack_int LAPACKE_cstemr(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl, float vu,
+                          lapack_int il, lapack_int iu, lapack_int* m, float* w, lapack_complex_float* z,
+                          lapack_int ldz, lapack_int nzc, lapack_int* isuppz, lapack_logical* tryrac);
+lapack_int LAPACKE_zstemr(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                          double vu, lapack_int il, lapack_int iu, lapack_int* m, double* w, lapack_complex_double* z,
+                          lapack_int ldz, lapack_int nzc, lapack_int* isuppz, lapack_logical* tryrac);
+
+lapack_int LAPACKE_ssteqr(int matrix_order, char compz, lapack_int n, float* d, float* e, float* z, lapack_int ldz);
+lapack_int LAPACKE_dsteqr(int matrix_order, char compz, lapack_int n, double* d, double* e, double* z, lapack_int ldz);
+lapack_int LAPACKE_csteqr(int matrix_order, char compz, lapack_int n, float* d, float* e, lapack_complex_float* z,
+                          lapack_int ldz);
+lapack_int LAPACKE_zsteqr(int matrix_order, char compz, lapack_int n, double* d, double* e, lapack_complex_double* z,
+                          lapack_int ldz);
+
+lapack_int LAPACKE_ssterf(lapack_int n, float* d, float* e);
+lapack_int LAPACKE_dsterf(lapack_int n, double* d, double* e);
+
+lapack_int LAPACKE_sstev(int matrix_order, char jobz, lapack_int n, float* d, float* e, float* z, lapack_int ldz);
+lapack_int LAPACKE_dstev(int matrix_order, char jobz, lapack_int n, double* d, double* e, double* z, lapack_int ldz);
+
+lapack_int LAPACKE_sstevd(int matrix_order, char jobz, lapack_int n, float* d, float* e, float* z, lapack_int ldz);
+lapack_int LAPACKE_dstevd(int matrix_order, char jobz, lapack_int n, double* d, double* e, double* z, lapack_int ldz);
+
+lapack_int LAPACKE_sstevr(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl, float vu,
+                          lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w, float* z, lapack_int ldz,
+                          lapack_int* isuppz);
+lapack_int LAPACKE_dstevr(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                          double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w, double* z,
+                          lapack_int ldz, lapack_int* isuppz);
+
+lapack_int LAPACKE_sstevx(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl, float vu,
+                          lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w, float* z, lapack_int ldz,
+                          lapack_int* ifail);
+lapack_int LAPACKE_dstevx(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                          double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w, double* z,
+                          lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_ssycon(int matrix_order, char uplo, lapack_int n, const float* a, lapack_int lda,
+                          const lapack_int* ipiv, float anorm, float* rcond);
+lapack_int LAPACKE_dsycon(int matrix_order, char uplo, lapack_int n, const double* a, lapack_int lda,
+                          const lapack_int* ipiv, double anorm, double* rcond);
+lapack_int LAPACKE_csycon(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                          const lapack_int* ipiv, float anorm, float* rcond);
+lapack_int LAPACKE_zsycon(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                          const lapack_int* ipiv, double anorm, double* rcond);
+
+lapack_int LAPACKE_ssyequb(int matrix_order, char uplo, lapack_int n, const float* a, lapack_int lda, float* s,
+                           float* scond, float* amax);
+lapack_int LAPACKE_dsyequb(int matrix_order, char uplo, lapack_int n, const double* a, lapack_int lda, double* s,
+                           double* scond, double* amax);
+lapack_int LAPACKE_csyequb(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                           float* s, float* scond, float* amax);
+lapack_int LAPACKE_zsyequb(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                           double* s, double* scond, double* amax);
+
+lapack_int LAPACKE_ssyev(int matrix_order, char jobz, char uplo, lapack_int n, float* a, lapack_int lda, float* w);
+lapack_int LAPACKE_dsyev(int matrix_order, char jobz, char uplo, lapack_int n, double* a, lapack_int lda, double* w);
+
+lapack_int LAPACKE_ssyevd(int matrix_order, char jobz, char uplo, lapack_int n, float* a, lapack_int lda, float* w);
+lapack_int LAPACKE_dsyevd(int matrix_order, char jobz, char uplo, lapack_int n, double* a, lapack_int lda, double* w);
+
+lapack_int LAPACKE_ssyevr(int matrix_order, char jobz, char range, char uplo, lapack_int n, float* a, lapack_int lda,
+                          float vl, float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w,
+                          float* z, lapack_int ldz, lapack_int* isuppz);
+lapack_int LAPACKE_dsyevr(int matrix_order, char jobz, char range, char uplo, lapack_int n, double* a, lapack_int lda,
+                          double vl, double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                          double* z, lapack_int ldz, lapack_int* isuppz);
+
+lapack_int LAPACKE_ssyevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, float* a, lapack_int lda,
+                          float vl, float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w,
+                          float* z, lapack_int ldz, lapack_int* ifail);
+lapack_int LAPACKE_dsyevx(int matrix_order, char jobz, char range, char uplo, lapack_int n, double* a, lapack_int lda,
+                          double vl, double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                          double* z, lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_ssygst(int matrix_order, lapack_int itype, char uplo, lapack_int n, float* a, lapack_int lda,
+                          const float* b, lapack_int ldb);
+lapack_int LAPACKE_dsygst(int matrix_order, lapack_int itype, char uplo, lapack_int n, double* a, lapack_int lda,
+                          const double* b, lapack_int ldb);
+
+lapack_int LAPACKE_ssygv(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, float* a,
+                         lapack_int lda, float* b, lapack_int ldb, float* w);
+lapack_int LAPACKE_dsygv(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, double* a,
+                         lapack_int lda, double* b, lapack_int ldb, double* w);
+
+lapack_int LAPACKE_ssygvd(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, float* a,
+                          lapack_int lda, float* b, lapack_int ldb, float* w);
+lapack_int LAPACKE_dsygvd(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, double* a,
+                          lapack_int lda, double* b, lapack_int ldb, double* w);
+
+lapack_int LAPACKE_ssygvx(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n, float* a,
+                          lapack_int lda, float* b, lapack_int ldb, float vl, float vu, lapack_int il, lapack_int iu,
+                          float abstol, lapack_int* m, float* w, float* z, lapack_int ldz, lapack_int* ifail);
+lapack_int LAPACKE_dsygvx(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n, double* a,
+                          lapack_int lda, double* b, lapack_int ldb, double vl, double vu, lapack_int il, lapack_int iu,
+                          double abstol, lapack_int* m, double* w, double* z, lapack_int ldz, lapack_int* ifail);
+
+lapack_int LAPACKE_ssyrfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* a, lapack_int lda,
+                          const float* af, lapack_int ldaf, const lapack_int* ipiv, const float* b, lapack_int ldb,
+                          float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_dsyrfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* a, lapack_int lda,
+                          const double* af, lapack_int ldaf, const lapack_int* ipiv, const double* b, lapack_int ldb,
+                          double* x, lapack_int ldx, double* ferr, double* berr);
+lapack_int LAPACKE_csyrfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* a,
+                          lapack_int lda, const lapack_complex_float* af, lapack_int ldaf, const lapack_int* ipiv,
+                          const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_zsyrfs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* a,
+                          lapack_int lda, const lapack_complex_double* af, lapack_int ldaf, const lapack_int* ipiv,
+                          const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                          double* ferr, double* berr);
+
+lapack_int LAPACKE_ssyrfsx(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs, const float* a,
+                           lapack_int lda, const float* af, lapack_int ldaf, const lapack_int* ipiv, const float* s,
+                           const float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* berr,
+                           lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                           float* params);
+lapack_int LAPACKE_dsyrfsx(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs, const double* a,
+                           lapack_int lda, const double* af, lapack_int ldaf, const lapack_int* ipiv, const double* s,
+                           const double* b, lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* berr,
+                           lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                           double* params);
+lapack_int LAPACKE_csyrfsx(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                           const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                           lapack_int ldaf, const lapack_int* ipiv, const float* s, const lapack_complex_float* b,
+                           lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* rcond, float* berr,
+                           lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                           float* params);
+lapack_int LAPACKE_zsyrfsx(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                           const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                           lapack_int ldaf, const lapack_int* ipiv, const double* s, const lapack_complex_double* b,
+                           lapack_int ldb, lapack_complex_double* x, lapack_int ldx, double* rcond, double* berr,
+                           lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                           double* params);
+
+lapack_int LAPACKE_ssysv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, float* a, lapack_int lda,
+                         lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dsysv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                         lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_csysv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                         lapack_int lda, lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zsysv(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                         lapack_int lda, lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_ssysvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, const float* a,
+                          lapack_int lda, float* af, lapack_int ldaf, lapack_int* ipiv, const float* b, lapack_int ldb,
+                          float* x, lapack_int ldx, float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_dsysvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, const double* a,
+                          lapack_int lda, double* af, lapack_int ldaf, lapack_int* ipiv, const double* b,
+                          lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* ferr, double* berr);
+lapack_int LAPACKE_csysvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                          lapack_int* ipiv, const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                          lapack_int ldx, float* rcond, float* ferr, float* berr);
+lapack_int LAPACKE_zsysvx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                          lapack_int* ipiv, const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                          lapack_int ldx, double* rcond, double* ferr, double* berr);
+
+lapack_int LAPACKE_ssysvxx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, float* a,
+                           lapack_int lda, float* af, lapack_int ldaf, lapack_int* ipiv, char* equed, float* s,
+                           float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* rpvgrw, float* berr,
+                           lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                           float* params);
+lapack_int LAPACKE_dsysvxx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, double* a,
+                           lapack_int lda, double* af, lapack_int ldaf, lapack_int* ipiv, char* equed, double* s,
+                           double* b, lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* rpvgrw,
+                           double* berr, lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                           lapack_int nparams, double* params);
+lapack_int LAPACKE_csysvxx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                           lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                           lapack_int* ipiv, char* equed, float* s, lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* rcond, float* rpvgrw, float* berr,
+                           lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                           float* params);
+lapack_int LAPACKE_zsysvxx(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                           lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                           lapack_int* ipiv, char* equed, double* s, lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx, double* rcond, double* rpvgrw, double* berr,
+                           lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                           double* params);
+
+lapack_int LAPACKE_ssytrd(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, float* d, float* e,
+                          float* tau);
+lapack_int LAPACKE_dsytrd(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda, double* d, double* e,
+                          double* tau);
+
+lapack_int LAPACKE_ssytrf(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, lapack_int* ipiv);
+lapack_int LAPACKE_dsytrf(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda, lapack_int* ipiv);
+lapack_int LAPACKE_csytrf(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_int* ipiv);
+lapack_int LAPACKE_zsytrf(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_int* ipiv);
+
+lapack_int LAPACKE_ssytri(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, const lapack_int* ipiv);
+lapack_int LAPACKE_dsytri(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda, const lapack_int* ipiv);
+lapack_int LAPACKE_csytri(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          const lapack_int* ipiv);
+lapack_int LAPACKE_zsytri(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          const lapack_int* ipiv);
+
+lapack_int LAPACKE_ssytrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* a, lapack_int lda,
+                          const lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dsytrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* a, lapack_int lda,
+                          const lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_csytrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* a,
+                          lapack_int lda, const lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zsytrs(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* a,
+                          lapack_int lda, const lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_stbcon(int matrix_order, char norm, char uplo, char diag, lapack_int n, lapack_int kd,
+                          const float* ab, lapack_int ldab, float* rcond);
+lapack_int LAPACKE_dtbcon(int matrix_order, char norm, char uplo, char diag, lapack_int n, lapack_int kd,
+                          const double* ab, lapack_int ldab, double* rcond);
+lapack_int LAPACKE_ctbcon(int matrix_order, char norm, char uplo, char diag, lapack_int n, lapack_int kd,
+                          const lapack_complex_float* ab, lapack_int ldab, float* rcond);
+lapack_int LAPACKE_ztbcon(int matrix_order, char norm, char uplo, char diag, lapack_int n, lapack_int kd,
+                          const lapack_complex_double* ab, lapack_int ldab, double* rcond);
+
+lapack_int LAPACKE_stbrfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                          lapack_int nrhs, const float* ab, lapack_int ldab, const float* b, lapack_int ldb,
+                          const float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_dtbrfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                          lapack_int nrhs, const double* ab, lapack_int ldab, const double* b, lapack_int ldb,
+                          const double* x, lapack_int ldx, double* ferr, double* berr);
+lapack_int LAPACKE_ctbrfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                          lapack_int nrhs, const lapack_complex_float* ab, lapack_int ldab,
+                          const lapack_complex_float* b, lapack_int ldb, const lapack_complex_float* x, lapack_int ldx,
+                          float* ferr, float* berr);
+lapack_int LAPACKE_ztbrfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                          lapack_int nrhs, const lapack_complex_double* ab, lapack_int ldab,
+                          const lapack_complex_double* b, lapack_int ldb, const lapack_complex_double* x,
+                          lapack_int ldx, double* ferr, double* berr);
+
+lapack_int LAPACKE_stbtrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                          lapack_int nrhs, const float* ab, lapack_int ldab, float* b, lapack_int ldb);
+lapack_int LAPACKE_dtbtrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                          lapack_int nrhs, const double* ab, lapack_int ldab, double* b, lapack_int ldb);
+lapack_int LAPACKE_ctbtrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                          lapack_int nrhs, const lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* b,
+                          lapack_int ldb);
+lapack_int LAPACKE_ztbtrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                          lapack_int nrhs, const lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* b,
+                          lapack_int ldb);
+
+lapack_int LAPACKE_stfsm(int matrix_order, char transr, char side, char uplo, char trans, char diag, lapack_int m,
+                         lapack_int n, float alpha, const float* a, float* b, lapack_int ldb);
+lapack_int LAPACKE_dtfsm(int matrix_order, char transr, char side, char uplo, char trans, char diag, lapack_int m,
+                         lapack_int n, double alpha, const double* a, double* b, lapack_int ldb);
+lapack_int LAPACKE_ctfsm(int matrix_order, char transr, char side, char uplo, char trans, char diag, lapack_int m,
+                         lapack_int n, lapack_complex_float alpha, const lapack_complex_float* a,
+                         lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_ztfsm(int matrix_order, char transr, char side, char uplo, char trans, char diag, lapack_int m,
+                         lapack_int n, lapack_complex_double alpha, const lapack_complex_double* a,
+                         lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_stftri(int matrix_order, char transr, char uplo, char diag, lapack_int n, float* a);
+lapack_int LAPACKE_dtftri(int matrix_order, char transr, char uplo, char diag, lapack_int n, double* a);
+lapack_int LAPACKE_ctftri(int matrix_order, char transr, char uplo, char diag, lapack_int n, lapack_complex_float* a);
+lapack_int LAPACKE_ztftri(int matrix_order, char transr, char uplo, char diag, lapack_int n, lapack_complex_double* a);
+
+lapack_int LAPACKE_stfttp(int matrix_order, char transr, char uplo, lapack_int n, const float* arf, float* ap);
+lapack_int LAPACKE_dtfttp(int matrix_order, char transr, char uplo, lapack_int n, const double* arf, double* ap);
+lapack_int LAPACKE_ctfttp(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_float* arf,
+                          lapack_complex_float* ap);
+lapack_int LAPACKE_ztfttp(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_double* arf,
+                          lapack_complex_double* ap);
+
+lapack_int LAPACKE_stfttr(int matrix_order, char transr, char uplo, lapack_int n, const float* arf, float* a,
+                          lapack_int lda);
+lapack_int LAPACKE_dtfttr(int matrix_order, char transr, char uplo, lapack_int n, const double* arf, double* a,
+                          lapack_int lda);
+lapack_int LAPACKE_ctfttr(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_float* arf,
+                          lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_ztfttr(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_double* arf,
+                          lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_stgevc(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                          const float* s, lapack_int lds, const float* p, lapack_int ldp, float* vl, lapack_int ldvl,
+                          float* vr, lapack_int ldvr, lapack_int mm, lapack_int* m);
+lapack_int LAPACKE_dtgevc(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                          const double* s, lapack_int lds, const double* p, lapack_int ldp, double* vl, lapack_int ldvl,
+                          double* vr, lapack_int ldvr, lapack_int mm, lapack_int* m);
+lapack_int LAPACKE_ctgevc(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                          const lapack_complex_float* s, lapack_int lds, const lapack_complex_float* p, lapack_int ldp,
+                          lapack_complex_float* vl, lapack_int ldvl, lapack_complex_float* vr, lapack_int ldvr,
+                          lapack_int mm, lapack_int* m);
+lapack_int LAPACKE_ztgevc(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                          const lapack_complex_double* s, lapack_int lds, const lapack_complex_double* p,
+                          lapack_int ldp, lapack_complex_double* vl, lapack_int ldvl, lapack_complex_double* vr,
+                          lapack_int ldvr, lapack_int mm, lapack_int* m);
+
+lapack_int LAPACKE_stgexc(int matrix_order, lapack_logical wantq, lapack_logical wantz, lapack_int n, float* a,
+                          lapack_int lda, float* b, lapack_int ldb, float* q, lapack_int ldq, float* z, lapack_int ldz,
+                          lapack_int* ifst, lapack_int* ilst);
+lapack_int LAPACKE_dtgexc(int matrix_order, lapack_logical wantq, lapack_logical wantz, lapack_int n, double* a,
+                          lapack_int lda, double* b, lapack_int ldb, double* q, lapack_int ldq, double* z,
+                          lapack_int ldz, lapack_int* ifst, lapack_int* ilst);
+lapack_int LAPACKE_ctgexc(int matrix_order, lapack_logical wantq, lapack_logical wantz, lapack_int n,
+                          lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* q, lapack_int ldq, lapack_complex_float* z, lapack_int ldz,
+                          lapack_int ifst, lapack_int ilst);
+lapack_int LAPACKE_ztgexc(int matrix_order, lapack_logical wantq, lapack_logical wantz, lapack_int n,
+                          lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                          lapack_complex_double* q, lapack_int ldq, lapack_complex_double* z, lapack_int ldz,
+                          lapack_int ifst, lapack_int ilst);
+
+lapack_int LAPACKE_stgsen(int matrix_order, lapack_int ijob, lapack_logical wantq, lapack_logical wantz,
+                          const lapack_logical* select, lapack_int n, float* a, lapack_int lda, float* b,
+                          lapack_int ldb, float* alphar, float* alphai, float* beta, float* q, lapack_int ldq, float* z,
+                          lapack_int ldz, lapack_int* m, float* pl, float* pr, float* dif);
+lapack_int LAPACKE_dtgsen(int matrix_order, lapack_int ijob, lapack_logical wantq, lapack_logical wantz,
+                          const lapack_logical* select, lapack_int n, double* a, lapack_int lda, double* b,
+                          lapack_int ldb, double* alphar, double* alphai, double* beta, double* q, lapack_int ldq,
+                          double* z, lapack_int ldz, lapack_int* m, double* pl, double* pr, double* dif);
+lapack_int LAPACKE_ctgsen(int matrix_order, lapack_int ijob, lapack_logical wantq, lapack_logical wantz,
+                          const lapack_logical* select, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* b, lapack_int ldb, lapack_complex_float* alpha,
+                          lapack_complex_float* beta, lapack_complex_float* q, lapack_int ldq, lapack_complex_float* z,
+                          lapack_int ldz, lapack_int* m, float* pl, float* pr, float* dif);
+lapack_int LAPACKE_ztgsen(int matrix_order, lapack_int ijob, lapack_logical wantq, lapack_logical wantz,
+                          const lapack_logical* select, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* b, lapack_int ldb, lapack_complex_double* alpha,
+                          lapack_complex_double* beta, lapack_complex_double* q, lapack_int ldq,
+                          lapack_complex_double* z, lapack_int ldz, lapack_int* m, double* pl, double* pr, double* dif);
+
+lapack_int LAPACKE_stgsja(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p, lapack_int n,
+                          lapack_int k, lapack_int l, float* a, lapack_int lda, float* b, lapack_int ldb, float tola,
+                          float tolb, float* alpha, float* beta, float* u, lapack_int ldu, float* v, lapack_int ldv,
+                          float* q, lapack_int ldq, lapack_int* ncycle);
+lapack_int LAPACKE_dtgsja(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p, lapack_int n,
+                          lapack_int k, lapack_int l, double* a, lapack_int lda, double* b, lapack_int ldb, double tola,
+                          double tolb, double* alpha, double* beta, double* u, lapack_int ldu, double* v,
+                          lapack_int ldv, double* q, lapack_int ldq, lapack_int* ncycle);
+lapack_int LAPACKE_ctgsja(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p, lapack_int n,
+                          lapack_int k, lapack_int l, lapack_complex_float* a, lapack_int lda, lapack_complex_float* b,
+                          lapack_int ldb, float tola, float tolb, float* alpha, float* beta, lapack_complex_float* u,
+                          lapack_int ldu, lapack_complex_float* v, lapack_int ldv, lapack_complex_float* q,
+                          lapack_int ldq, lapack_int* ncycle);
+lapack_int LAPACKE_ztgsja(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p, lapack_int n,
+                          lapack_int k, lapack_int l, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* b, lapack_int ldb, double tola, double tolb, double* alpha,
+                          double* beta, lapack_complex_double* u, lapack_int ldu, lapack_complex_double* v,
+                          lapack_int ldv, lapack_complex_double* q, lapack_int ldq, lapack_int* ncycle);
+
+lapack_int LAPACKE_stgsna(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                          const float* a, lapack_int lda, const float* b, lapack_int ldb, const float* vl,
+                          lapack_int ldvl, const float* vr, lapack_int ldvr, float* s, float* dif, lapack_int mm,
+                          lapack_int* m);
+lapack_int LAPACKE_dtgsna(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                          const double* a, lapack_int lda, const double* b, lapack_int ldb, const double* vl,
+                          lapack_int ldvl, const double* vr, lapack_int ldvr, double* s, double* dif, lapack_int mm,
+                          lapack_int* m);
+lapack_int LAPACKE_ctgsna(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                          const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* b, lapack_int ldb,
+                          const lapack_complex_float* vl, lapack_int ldvl, const lapack_complex_float* vr,
+                          lapack_int ldvr, float* s, float* dif, lapack_int mm, lapack_int* m);
+lapack_int LAPACKE_ztgsna(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                          const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* b,
+                          lapack_int ldb, const lapack_complex_double* vl, lapack_int ldvl,
+                          const lapack_complex_double* vr, lapack_int ldvr, double* s, double* dif, lapack_int mm,
+                          lapack_int* m);
+
+lapack_int LAPACKE_stgsyl(int matrix_order, char trans, lapack_int ijob, lapack_int m, lapack_int n, const float* a,
+                          lapack_int lda, const float* b, lapack_int ldb, float* c, lapack_int ldc, const float* d,
+                          lapack_int ldd, const float* e, lapack_int lde, float* f, lapack_int ldf, float* scale,
+                          float* dif);
+lapack_int LAPACKE_dtgsyl(int matrix_order, char trans, lapack_int ijob, lapack_int m, lapack_int n, const double* a,
+                          lapack_int lda, const double* b, lapack_int ldb, double* c, lapack_int ldc, const double* d,
+                          lapack_int ldd, const double* e, lapack_int lde, double* f, lapack_int ldf, double* scale,
+                          double* dif);
+lapack_int LAPACKE_ctgsyl(int matrix_order, char trans, lapack_int ijob, lapack_int m, lapack_int n,
+                          const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* c, lapack_int ldc, const lapack_complex_float* d, lapack_int ldd,
+                          const lapack_complex_float* e, lapack_int lde, lapack_complex_float* f, lapack_int ldf,
+                          float* scale, float* dif);
+lapack_int LAPACKE_ztgsyl(int matrix_order, char trans, lapack_int ijob, lapack_int m, lapack_int n,
+                          const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* b,
+                          lapack_int ldb, lapack_complex_double* c, lapack_int ldc, const lapack_complex_double* d,
+                          lapack_int ldd, const lapack_complex_double* e, lapack_int lde, lapack_complex_double* f,
+                          lapack_int ldf, double* scale, double* dif);
+
+lapack_int LAPACKE_stpcon(int matrix_order, char norm, char uplo, char diag, lapack_int n, const float* ap,
+                          float* rcond);
+lapack_int LAPACKE_dtpcon(int matrix_order, char norm, char uplo, char diag, lapack_int n, const double* ap,
+                          double* rcond);
+lapack_int LAPACKE_ctpcon(int matrix_order, char norm, char uplo, char diag, lapack_int n,
+                          const lapack_complex_float* ap, float* rcond);
+lapack_int LAPACKE_ztpcon(int matrix_order, char norm, char uplo, char diag, lapack_int n,
+                          const lapack_complex_double* ap, double* rcond);
+
+lapack_int LAPACKE_stprfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const float* ap, const float* b, lapack_int ldb, const float* x, lapack_int ldx, float* ferr,
+                          float* berr);
+lapack_int LAPACKE_dtprfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const double* ap, const double* b, lapack_int ldb, const double* x, lapack_int ldx,
+                          double* ferr, double* berr);
+lapack_int LAPACKE_ctprfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_float* ap, const lapack_complex_float* b, lapack_int ldb,
+                          const lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_ztprfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_double* ap, const lapack_complex_double* b, lapack_int ldb,
+                          const lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr);
+
+lapack_int LAPACKE_stptri(int matrix_order, char uplo, char diag, lapack_int n, float* ap);
+lapack_int LAPACKE_dtptri(int matrix_order, char uplo, char diag, lapack_int n, double* ap);
+lapack_int LAPACKE_ctptri(int matrix_order, char uplo, char diag, lapack_int n, lapack_complex_float* ap);
+lapack_int LAPACKE_ztptri(int matrix_order, char uplo, char diag, lapack_int n, lapack_complex_double* ap);
+
+lapack_int LAPACKE_stptrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const float* ap, float* b, lapack_int ldb);
+lapack_int LAPACKE_dtptrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const double* ap, double* b, lapack_int ldb);
+lapack_int LAPACKE_ctptrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_float* ap, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_ztptrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_double* ap, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_stpttf(int matrix_order, char transr, char uplo, lapack_int n, const float* ap, float* arf);
+lapack_int LAPACKE_dtpttf(int matrix_order, char transr, char uplo, lapack_int n, const double* ap, double* arf);
+lapack_int LAPACKE_ctpttf(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_float* ap,
+                          lapack_complex_float* arf);
+lapack_int LAPACKE_ztpttf(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_double* ap,
+                          lapack_complex_double* arf);
+
+lapack_int LAPACKE_stpttr(int matrix_order, char uplo, lapack_int n, const float* ap, float* a, lapack_int lda);
+lapack_int LAPACKE_dtpttr(int matrix_order, char uplo, lapack_int n, const double* ap, double* a, lapack_int lda);
+lapack_int LAPACKE_ctpttr(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap,
+                          lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_ztpttr(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap,
+                          lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_strcon(int matrix_order, char norm, char uplo, char diag, lapack_int n, const float* a,
+                          lapack_int lda, float* rcond);
+lapack_int LAPACKE_dtrcon(int matrix_order, char norm, char uplo, char diag, lapack_int n, const double* a,
+                          lapack_int lda, double* rcond);
+lapack_int LAPACKE_ctrcon(int matrix_order, char norm, char uplo, char diag, lapack_int n,
+                          const lapack_complex_float* a, lapack_int lda, float* rcond);
+lapack_int LAPACKE_ztrcon(int matrix_order, char norm, char uplo, char diag, lapack_int n,
+                          const lapack_complex_double* a, lapack_int lda, double* rcond);
+
+lapack_int LAPACKE_strevc(int matrix_order, char side, char howmny, lapack_logical* select, lapack_int n,
+                          const float* t, lapack_int ldt, float* vl, lapack_int ldvl, float* vr, lapack_int ldvr,
+                          lapack_int mm, lapack_int* m);
+lapack_int LAPACKE_dtrevc(int matrix_order, char side, char howmny, lapack_logical* select, lapack_int n,
+                          const double* t, lapack_int ldt, double* vl, lapack_int ldvl, double* vr, lapack_int ldvr,
+                          lapack_int mm, lapack_int* m);
+lapack_int LAPACKE_ctrevc(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                          lapack_complex_float* t, lapack_int ldt, lapack_complex_float* vl, lapack_int ldvl,
+                          lapack_complex_float* vr, lapack_int ldvr, lapack_int mm, lapack_int* m);
+lapack_int LAPACKE_ztrevc(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                          lapack_complex_double* t, lapack_int ldt, lapack_complex_double* vl, lapack_int ldvl,
+                          lapack_complex_double* vr, lapack_int ldvr, lapack_int mm, lapack_int* m);
+
+lapack_int LAPACKE_strexc(int matrix_order, char compq, lapack_int n, float* t, lapack_int ldt, float* q,
+                          lapack_int ldq, lapack_int* ifst, lapack_int* ilst);
+lapack_int LAPACKE_dtrexc(int matrix_order, char compq, lapack_int n, double* t, lapack_int ldt, double* q,
+                          lapack_int ldq, lapack_int* ifst, lapack_int* ilst);
+lapack_int LAPACKE_ctrexc(int matrix_order, char compq, lapack_int n, lapack_complex_float* t, lapack_int ldt,
+                          lapack_complex_float* q, lapack_int ldq, lapack_int ifst, lapack_int ilst);
+lapack_int LAPACKE_ztrexc(int matrix_order, char compq, lapack_int n, lapack_complex_double* t, lapack_int ldt,
+                          lapack_complex_double* q, lapack_int ldq, lapack_int ifst, lapack_int ilst);
+
+lapack_int LAPACKE_strrfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const float* a, lapack_int lda, const float* b, lapack_int ldb, const float* x,
+                          lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_dtrrfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const double* a, lapack_int lda, const double* b, lapack_int ldb, const double* x,
+                          lapack_int ldx, double* ferr, double* berr);
+lapack_int LAPACKE_ctrrfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* b, lapack_int ldb,
+                          const lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr);
+lapack_int LAPACKE_ztrrfs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* b,
+                          lapack_int ldb, const lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr);
+
+lapack_int LAPACKE_strsen(int matrix_order, char job, char compq, const lapack_logical* select, lapack_int n, float* t,
+                          lapack_int ldt, float* q, lapack_int ldq, float* wr, float* wi, lapack_int* m, float* s,
+                          float* sep);
+lapack_int LAPACKE_dtrsen(int matrix_order, char job, char compq, const lapack_logical* select, lapack_int n, double* t,
+                          lapack_int ldt, double* q, lapack_int ldq, double* wr, double* wi, lapack_int* m, double* s,
+                          double* sep);
+lapack_int LAPACKE_ctrsen(int matrix_order, char job, char compq, const lapack_logical* select, lapack_int n,
+                          lapack_complex_float* t, lapack_int ldt, lapack_complex_float* q, lapack_int ldq,
+                          lapack_complex_float* w, lapack_int* m, float* s, float* sep);
+lapack_int LAPACKE_ztrsen(int matrix_order, char job, char compq, const lapack_logical* select, lapack_int n,
+                          lapack_complex_double* t, lapack_int ldt, lapack_complex_double* q, lapack_int ldq,
+                          lapack_complex_double* w, lapack_int* m, double* s, double* sep);
+
+lapack_int LAPACKE_strsna(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                          const float* t, lapack_int ldt, const float* vl, lapack_int ldvl, const float* vr,
+                          lapack_int ldvr, float* s, float* sep, lapack_int mm, lapack_int* m);
+lapack_int LAPACKE_dtrsna(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                          const double* t, lapack_int ldt, const double* vl, lapack_int ldvl, const double* vr,
+                          lapack_int ldvr, double* s, double* sep, lapack_int mm, lapack_int* m);
+lapack_int LAPACKE_ctrsna(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                          const lapack_complex_float* t, lapack_int ldt, const lapack_complex_float* vl,
+                          lapack_int ldvl, const lapack_complex_float* vr, lapack_int ldvr, float* s, float* sep,
+                          lapack_int mm, lapack_int* m);
+lapack_int LAPACKE_ztrsna(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                          const lapack_complex_double* t, lapack_int ldt, const lapack_complex_double* vl,
+                          lapack_int ldvl, const lapack_complex_double* vr, lapack_int ldvr, double* s, double* sep,
+                          lapack_int mm, lapack_int* m);
+
+lapack_int LAPACKE_strsyl(int matrix_order, char trana, char tranb, lapack_int isgn, lapack_int m, lapack_int n,
+                          const float* a, lapack_int lda, const float* b, lapack_int ldb, float* c, lapack_int ldc,
+                          float* scale);
+lapack_int LAPACKE_dtrsyl(int matrix_order, char trana, char tranb, lapack_int isgn, lapack_int m, lapack_int n,
+                          const double* a, lapack_int lda, const double* b, lapack_int ldb, double* c, lapack_int ldc,
+                          double* scale);
+lapack_int LAPACKE_ctrsyl(int matrix_order, char trana, char tranb, lapack_int isgn, lapack_int m, lapack_int n,
+                          const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* c, lapack_int ldc, float* scale);
+lapack_int LAPACKE_ztrsyl(int matrix_order, char trana, char tranb, lapack_int isgn, lapack_int m, lapack_int n,
+                          const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* b,
+                          lapack_int ldb, lapack_complex_double* c, lapack_int ldc, double* scale);
+
+lapack_int LAPACKE_strtri(int matrix_order, char uplo, char diag, lapack_int n, float* a, lapack_int lda);
+lapack_int LAPACKE_dtrtri(int matrix_order, char uplo, char diag, lapack_int n, double* a, lapack_int lda);
+lapack_int LAPACKE_ctrtri(int matrix_order, char uplo, char diag, lapack_int n, lapack_complex_float* a,
+                          lapack_int lda);
+lapack_int LAPACKE_ztrtri(int matrix_order, char uplo, char diag, lapack_int n, lapack_complex_double* a,
+                          lapack_int lda);
+
+lapack_int LAPACKE_strtrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const float* a, lapack_int lda, float* b, lapack_int ldb);
+lapack_int LAPACKE_dtrtrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const double* a, lapack_int lda, double* b, lapack_int ldb);
+lapack_int LAPACKE_ctrtrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_ztrtrs(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                          const lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_strttf(int matrix_order, char transr, char uplo, lapack_int n, const float* a, lapack_int lda,
+                          float* arf);
+lapack_int LAPACKE_dtrttf(int matrix_order, char transr, char uplo, lapack_int n, const double* a, lapack_int lda,
+                          double* arf);
+lapack_int LAPACKE_ctrttf(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* arf);
+lapack_int LAPACKE_ztrttf(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* arf);
+
+lapack_int LAPACKE_strttp(int matrix_order, char uplo, lapack_int n, const float* a, lapack_int lda, float* ap);
+lapack_int LAPACKE_dtrttp(int matrix_order, char uplo, lapack_int n, const double* a, lapack_int lda, double* ap);
+lapack_int LAPACKE_ctrttp(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* ap);
+lapack_int LAPACKE_ztrttp(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* ap);
+
+lapack_int LAPACKE_stzrzf(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau);
+lapack_int LAPACKE_dtzrzf(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau);
+lapack_int LAPACKE_ctzrzf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* tau);
+lapack_int LAPACKE_ztzrzf(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* tau);
+
+lapack_int LAPACKE_cungbr(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int k,
+                          lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau);
+lapack_int LAPACKE_zungbr(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int k,
+                          lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau);
+
+lapack_int LAPACKE_cunghr(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, lapack_complex_float* a,
+                          lapack_int lda, const lapack_complex_float* tau);
+lapack_int LAPACKE_zunghr(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, lapack_complex_double* a,
+                          lapack_int lda, const lapack_complex_double* tau);
+
+lapack_int LAPACKE_cunglq(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_float* a,
+                          lapack_int lda, const lapack_complex_float* tau);
+lapack_int LAPACKE_zunglq(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_double* a,
+                          lapack_int lda, const lapack_complex_double* tau);
+
+lapack_int LAPACKE_cungql(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_float* a,
+                          lapack_int lda, const lapack_complex_float* tau);
+lapack_int LAPACKE_zungql(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_double* a,
+                          lapack_int lda, const lapack_complex_double* tau);
+
+lapack_int LAPACKE_cungqr(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_float* a,
+                          lapack_int lda, const lapack_complex_float* tau);
+lapack_int LAPACKE_zungqr(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_double* a,
+                          lapack_int lda, const lapack_complex_double* tau);
+
+lapack_int LAPACKE_cungrq(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_float* a,
+                          lapack_int lda, const lapack_complex_float* tau);
+lapack_int LAPACKE_zungrq(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_double* a,
+                          lapack_int lda, const lapack_complex_double* tau);
+
+lapack_int LAPACKE_cungtr(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          const lapack_complex_float* tau);
+lapack_int LAPACKE_zungtr(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                          const lapack_complex_double* tau);
+
+lapack_int LAPACKE_cunmbr(int matrix_order, char vect, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                          lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zunmbr(int matrix_order, char vect, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                          lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_cunmhr(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int ilo,
+                          lapack_int ihi, const lapack_complex_float* a, lapack_int lda,
+                          const lapack_complex_float* tau, lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zunmhr(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int ilo,
+                          lapack_int ihi, const lapack_complex_double* a, lapack_int lda,
+                          const lapack_complex_double* tau, lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_cunmlq(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                          lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zunmlq(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                          lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_cunmql(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                          lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zunmql(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                          lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_cunmqr(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                          lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zunmqr(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                          lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_cunmrq(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                          lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zunmrq(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                          lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_cunmrz(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          lapack_int l, const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                          lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zunmrz(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                          lapack_int l, const lapack_complex_double* a, lapack_int lda,
+                          const lapack_complex_double* tau, lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_cunmtr(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                          const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                          lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zunmtr(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                          const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                          lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_cupgtr(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap,
+                          const lapack_complex_float* tau, lapack_complex_float* q, lapack_int ldq);
+lapack_int LAPACKE_zupgtr(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap,
+                          const lapack_complex_double* tau, lapack_complex_double* q, lapack_int ldq);
+
+lapack_int LAPACKE_cupmtr(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                          const lapack_complex_float* ap, const lapack_complex_float* tau, lapack_complex_float* c,
+                          lapack_int ldc);
+lapack_int LAPACKE_zupmtr(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                          const lapack_complex_double* ap, const lapack_complex_double* tau, lapack_complex_double* c,
+                          lapack_int ldc);
+
+lapack_int LAPACKE_sbdsdc_work(int matrix_order, char uplo, char compq, lapack_int n, float* d, float* e, float* u,
+                               lapack_int ldu, float* vt, lapack_int ldvt, float* q, lapack_int* iq, float* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_dbdsdc_work(int matrix_order, char uplo, char compq, lapack_int n, double* d, double* e, double* u,
+                               lapack_int ldu, double* vt, lapack_int ldvt, double* q, lapack_int* iq, double* work,
+                               lapack_int* iwork);
+
+lapack_int LAPACKE_sbdsqr_work(int matrix_order, char uplo, lapack_int n, lapack_int ncvt, lapack_int nru,
+                               lapack_int ncc, float* d, float* e, float* vt, lapack_int ldvt, float* u, lapack_int ldu,
+                               float* c, lapack_int ldc, float* work);
+lapack_int LAPACKE_dbdsqr_work(int matrix_order, char uplo, lapack_int n, lapack_int ncvt, lapack_int nru,
+                               lapack_int ncc, double* d, double* e, double* vt, lapack_int ldvt, double* u,
+                               lapack_int ldu, double* c, lapack_int ldc, double* work);
+lapack_int LAPACKE_cbdsqr_work(int matrix_order, char uplo, lapack_int n, lapack_int ncvt, lapack_int nru,
+                               lapack_int ncc, float* d, float* e, lapack_complex_float* vt, lapack_int ldvt,
+                               lapack_complex_float* u, lapack_int ldu, lapack_complex_float* c, lapack_int ldc,
+                               float* work);
+lapack_int LAPACKE_zbdsqr_work(int matrix_order, char uplo, lapack_int n, lapack_int ncvt, lapack_int nru,
+                               lapack_int ncc, double* d, double* e, lapack_complex_double* vt, lapack_int ldvt,
+                               lapack_complex_double* u, lapack_int ldu, lapack_complex_double* c, lapack_int ldc,
+                               double* work);
+
+lapack_int LAPACKE_sdisna_work(char job, lapack_int m, lapack_int n, const float* d, float* sep);
+lapack_int LAPACKE_ddisna_work(char job, lapack_int m, lapack_int n, const double* d, double* sep);
+
+lapack_int LAPACKE_sgbbrd_work(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int ncc, lapack_int kl,
+                               lapack_int ku, float* ab, lapack_int ldab, float* d, float* e, float* q, lapack_int ldq,
+                               float* pt, lapack_int ldpt, float* c, lapack_int ldc, float* work);
+lapack_int LAPACKE_dgbbrd_work(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int ncc, lapack_int kl,
+                               lapack_int ku, double* ab, lapack_int ldab, double* d, double* e, double* q,
+                               lapack_int ldq, double* pt, lapack_int ldpt, double* c, lapack_int ldc, double* work);
+lapack_int LAPACKE_cgbbrd_work(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int ncc, lapack_int kl,
+                               lapack_int ku, lapack_complex_float* ab, lapack_int ldab, float* d, float* e,
+                               lapack_complex_float* q, lapack_int ldq, lapack_complex_float* pt, lapack_int ldpt,
+                               lapack_complex_float* c, lapack_int ldc, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgbbrd_work(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int ncc, lapack_int kl,
+                               lapack_int ku, lapack_complex_double* ab, lapack_int ldab, double* d, double* e,
+                               lapack_complex_double* q, lapack_int ldq, lapack_complex_double* pt, lapack_int ldpt,
+                               lapack_complex_double* c, lapack_int ldc, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgbcon_work(int matrix_order, char norm, lapack_int n, lapack_int kl, lapack_int ku, const float* ab,
+                               lapack_int ldab, const lapack_int* ipiv, float anorm, float* rcond, float* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_dgbcon_work(int matrix_order, char norm, lapack_int n, lapack_int kl, lapack_int ku,
+                               const double* ab, lapack_int ldab, const lapack_int* ipiv, double anorm, double* rcond,
+                               double* work, lapack_int* iwork);
+lapack_int LAPACKE_cgbcon_work(int matrix_order, char norm, lapack_int n, lapack_int kl, lapack_int ku,
+                               const lapack_complex_float* ab, lapack_int ldab, const lapack_int* ipiv, float anorm,
+                               float* rcond, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgbcon_work(int matrix_order, char norm, lapack_int n, lapack_int kl, lapack_int ku,
+                               const lapack_complex_double* ab, lapack_int ldab, const lapack_int* ipiv, double anorm,
+                               double* rcond, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgbequ_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                               const float* ab, lapack_int ldab, float* r, float* c, float* rowcnd, float* colcnd,
+                               float* amax);
+lapack_int LAPACKE_dgbequ_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                               const double* ab, lapack_int ldab, double* r, double* c, double* rowcnd, double* colcnd,
+                               double* amax);
+lapack_int LAPACKE_cgbequ_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                               const lapack_complex_float* ab, lapack_int ldab, float* r, float* c, float* rowcnd,
+                               float* colcnd, float* amax);
+lapack_int LAPACKE_zgbequ_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                               const lapack_complex_double* ab, lapack_int ldab, double* r, double* c, double* rowcnd,
+                               double* colcnd, double* amax);
+
+lapack_int LAPACKE_sgbequb_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                                const float* ab, lapack_int ldab, float* r, float* c, float* rowcnd, float* colcnd,
+                                float* amax);
+lapack_int LAPACKE_dgbequb_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                                const double* ab, lapack_int ldab, double* r, double* c, double* rowcnd, double* colcnd,
+                                double* amax);
+lapack_int LAPACKE_cgbequb_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                                const lapack_complex_float* ab, lapack_int ldab, float* r, float* c, float* rowcnd,
+                                float* colcnd, float* amax);
+lapack_int LAPACKE_zgbequb_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                                const lapack_complex_double* ab, lapack_int ldab, double* r, double* c, double* rowcnd,
+                                double* colcnd, double* amax);
+
+lapack_int LAPACKE_sgbrfs_work(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, const float* ab, lapack_int ldab, const float* afb, lapack_int ldafb,
+                               const lapack_int* ipiv, const float* b, lapack_int ldb, float* x, lapack_int ldx,
+                               float* ferr, float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dgbrfs_work(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, const double* ab, lapack_int ldab, const double* afb, lapack_int ldafb,
+                               const lapack_int* ipiv, const double* b, lapack_int ldb, double* x, lapack_int ldx,
+                               double* ferr, double* berr, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cgbrfs_work(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, const lapack_complex_float* ab, lapack_int ldab,
+                               const lapack_complex_float* afb, lapack_int ldafb, const lapack_int* ipiv,
+                               const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgbrfs_work(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, const lapack_complex_double* ab, lapack_int ldab,
+                               const lapack_complex_double* afb, lapack_int ldafb, const lapack_int* ipiv,
+                               const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                               double* ferr, double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgbrfsx_work(int matrix_order, char trans, char equed, lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, const float* ab, lapack_int ldab, const float* afb, lapack_int ldafb,
+                                const lapack_int* ipiv, const float* r, const float* c, const float* b, lapack_int ldb,
+                                float* x, lapack_int ldx, float* rcond, float* berr, lapack_int n_err_bnds,
+                                float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams, float* params,
+                                float* work, lapack_int* iwork);
+lapack_int LAPACKE_dgbrfsx_work(int matrix_order, char trans, char equed, lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, const double* ab, lapack_int ldab, const double* afb, lapack_int ldafb,
+                                const lapack_int* ipiv, const double* r, const double* c, const double* b,
+                                lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* berr,
+                                lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                                double* params, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cgbrfsx_work(int matrix_order, char trans, char equed, lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, const lapack_complex_float* ab, lapack_int ldab,
+                                const lapack_complex_float* afb, lapack_int ldafb, const lapack_int* ipiv,
+                                const float* r, const float* c, const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx, float* rcond, float* berr,
+                                lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                                float* params, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgbrfsx_work(int matrix_order, char trans, char equed, lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, const lapack_complex_double* ab, lapack_int ldab,
+                                const lapack_complex_double* afb, lapack_int ldafb, const lapack_int* ipiv,
+                                const double* r, const double* c, const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx, double* rcond, double* berr,
+                                lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                                double* params, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgbsv_work(int matrix_order, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs, float* ab,
+                              lapack_int ldab, lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dgbsv_work(int matrix_order, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs, double* ab,
+                              lapack_int ldab, lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_cgbsv_work(int matrix_order, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                              lapack_complex_float* ab, lapack_int ldab, lapack_int* ipiv, lapack_complex_float* b,
+                              lapack_int ldb);
+lapack_int LAPACKE_zgbsv_work(int matrix_order, lapack_int n, lapack_int kl, lapack_int ku, lapack_int nrhs,
+                              lapack_complex_double* ab, lapack_int ldab, lapack_int* ipiv, lapack_complex_double* b,
+                              lapack_int ldb);
+
+lapack_int LAPACKE_sgbsvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, float* ab, lapack_int ldab, float* afb, lapack_int ldafb,
+                               lapack_int* ipiv, char* equed, float* r, float* c, float* b, lapack_int ldb, float* x,
+                               lapack_int ldx, float* rcond, float* ferr, float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dgbsvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, double* ab, lapack_int ldab, double* afb, lapack_int ldafb,
+                               lapack_int* ipiv, char* equed, double* r, double* c, double* b, lapack_int ldb,
+                               double* x, lapack_int ldx, double* rcond, double* ferr, double* berr, double* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_cgbsvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* afb,
+                               lapack_int ldafb, lapack_int* ipiv, char* equed, float* r, float* c,
+                               lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* rcond, float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgbsvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* afb,
+                               lapack_int ldafb, lapack_int* ipiv, char* equed, double* r, double* c,
+                               lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                               double* rcond, double* ferr, double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgbsvxx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, float* ab, lapack_int ldab, float* afb, lapack_int ldafb,
+                                lapack_int* ipiv, char* equed, float* r, float* c, float* b, lapack_int ldb, float* x,
+                                lapack_int ldx, float* rcond, float* rpvgrw, float* berr, lapack_int n_err_bnds,
+                                float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams, float* params,
+                                float* work, lapack_int* iwork);
+lapack_int LAPACKE_dgbsvxx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, double* ab, lapack_int ldab, double* afb, lapack_int ldafb,
+                                lapack_int* ipiv, char* equed, double* r, double* c, double* b, lapack_int ldb,
+                                double* x, lapack_int ldx, double* rcond, double* rpvgrw, double* berr,
+                                lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                                double* params, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cgbsvxx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* afb,
+                                lapack_int ldafb, lapack_int* ipiv, char* equed, float* r, float* c,
+                                lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                                float* rcond, float* rpvgrw, float* berr, lapack_int n_err_bnds, float* err_bnds_norm,
+                                float* err_bnds_comp, lapack_int nparams, float* params, lapack_complex_float* work,
+                                float* rwork);
+lapack_int LAPACKE_zgbsvxx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* afb,
+                                lapack_int ldafb, lapack_int* ipiv, char* equed, double* r, double* c,
+                                lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                                double* rcond, double* rpvgrw, double* berr, lapack_int n_err_bnds,
+                                double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams, double* params,
+                                lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgbtrf_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, float* ab,
+                               lapack_int ldab, lapack_int* ipiv);
+lapack_int LAPACKE_dgbtrf_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku, double* ab,
+                               lapack_int ldab, lapack_int* ipiv);
+lapack_int LAPACKE_cgbtrf_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_complex_float* ab, lapack_int ldab, lapack_int* ipiv);
+lapack_int LAPACKE_zgbtrf_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_complex_double* ab, lapack_int ldab, lapack_int* ipiv);
+
+lapack_int LAPACKE_sgbtrs_work(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, const float* ab, lapack_int ldab, const lapack_int* ipiv, float* b,
+                               lapack_int ldb);
+lapack_int LAPACKE_dgbtrs_work(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, const double* ab, lapack_int ldab, const lapack_int* ipiv, double* b,
+                               lapack_int ldb);
+lapack_int LAPACKE_cgbtrs_work(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, const lapack_complex_float* ab, lapack_int ldab, const lapack_int* ipiv,
+                               lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zgbtrs_work(int matrix_order, char trans, lapack_int n, lapack_int kl, lapack_int ku,
+                               lapack_int nrhs, const lapack_complex_double* ab, lapack_int ldab,
+                               const lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sgebak_work(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               const float* scale, lapack_int m, float* v, lapack_int ldv);
+lapack_int LAPACKE_dgebak_work(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               const double* scale, lapack_int m, double* v, lapack_int ldv);
+lapack_int LAPACKE_cgebak_work(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               const float* scale, lapack_int m, lapack_complex_float* v, lapack_int ldv);
+lapack_int LAPACKE_zgebak_work(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               const double* scale, lapack_int m, lapack_complex_double* v, lapack_int ldv);
+
+lapack_int LAPACKE_sgebal_work(int matrix_order, char job, lapack_int n, float* a, lapack_int lda, lapack_int* ilo,
+                               lapack_int* ihi, float* scale);
+lapack_int LAPACKE_dgebal_work(int matrix_order, char job, lapack_int n, double* a, lapack_int lda, lapack_int* ilo,
+                               lapack_int* ihi, double* scale);
+lapack_int LAPACKE_cgebal_work(int matrix_order, char job, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_int* ilo, lapack_int* ihi, float* scale);
+lapack_int LAPACKE_zgebal_work(int matrix_order, char job, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_int* ilo, lapack_int* ihi, double* scale);
+
+lapack_int LAPACKE_sgebrd_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* d,
+                               float* e, float* tauq, float* taup, float* work, lapack_int lwork);
+lapack_int LAPACKE_dgebrd_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* d,
+                               double* e, double* tauq, double* taup, double* work, lapack_int lwork);
+lapack_int LAPACKE_cgebrd_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               float* d, float* e, lapack_complex_float* tauq, lapack_complex_float* taup,
+                               lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zgebrd_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               double* d, double* e, lapack_complex_double* tauq, lapack_complex_double* taup,
+                               lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sgecon_work(int matrix_order, char norm, lapack_int n, const float* a, lapack_int lda, float anorm,
+                               float* rcond, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dgecon_work(int matrix_order, char norm, lapack_int n, const double* a, lapack_int lda, double anorm,
+                               double* rcond, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cgecon_work(int matrix_order, char norm, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                               float anorm, float* rcond, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgecon_work(int matrix_order, char norm, lapack_int n, const lapack_complex_double* a,
+                               lapack_int lda, double anorm, double* rcond, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgeequ_work(int matrix_order, lapack_int m, lapack_int n, const float* a, lapack_int lda, float* r,
+                               float* c, float* rowcnd, float* colcnd, float* amax);
+lapack_int LAPACKE_dgeequ_work(int matrix_order, lapack_int m, lapack_int n, const double* a, lapack_int lda, double* r,
+                               double* c, double* rowcnd, double* colcnd, double* amax);
+lapack_int LAPACKE_cgeequ_work(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_float* a,
+                               lapack_int lda, float* r, float* c, float* rowcnd, float* colcnd, float* amax);
+lapack_int LAPACKE_zgeequ_work(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_double* a,
+                               lapack_int lda, double* r, double* c, double* rowcnd, double* colcnd, double* amax);
+
+lapack_int LAPACKE_sgeequb_work(int matrix_order, lapack_int m, lapack_int n, const float* a, lapack_int lda, float* r,
+                                float* c, float* rowcnd, float* colcnd, float* amax);
+lapack_int LAPACKE_dgeequb_work(int matrix_order, lapack_int m, lapack_int n, const double* a, lapack_int lda,
+                                double* r, double* c, double* rowcnd, double* colcnd, double* amax);
+lapack_int LAPACKE_cgeequb_work(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_float* a,
+                                lapack_int lda, float* r, float* c, float* rowcnd, float* colcnd, float* amax);
+lapack_int LAPACKE_zgeequb_work(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_double* a,
+                                lapack_int lda, double* r, double* c, double* rowcnd, double* colcnd, double* amax);
+
+lapack_int LAPACKE_sgees_work(int matrix_order, char jobvs, char sort, LAPACK_S_SELECT2 select, lapack_int n, float* a,
+                              lapack_int lda, lapack_int* sdim, float* wr, float* wi, float* vs, lapack_int ldvs,
+                              float* work, lapack_int lwork, lapack_logical* bwork);
+lapack_int LAPACKE_dgees_work(int matrix_order, char jobvs, char sort, LAPACK_D_SELECT2 select, lapack_int n, double* a,
+                              lapack_int lda, lapack_int* sdim, double* wr, double* wi, double* vs, lapack_int ldvs,
+                              double* work, lapack_int lwork, lapack_logical* bwork);
+lapack_int LAPACKE_cgees_work(int matrix_order, char jobvs, char sort, LAPACK_C_SELECT1 select, lapack_int n,
+                              lapack_complex_float* a, lapack_int lda, lapack_int* sdim, lapack_complex_float* w,
+                              lapack_complex_float* vs, lapack_int ldvs, lapack_complex_float* work, lapack_int lwork,
+                              float* rwork, lapack_logical* bwork);
+lapack_int LAPACKE_zgees_work(int matrix_order, char jobvs, char sort, LAPACK_Z_SELECT1 select, lapack_int n,
+                              lapack_complex_double* a, lapack_int lda, lapack_int* sdim, lapack_complex_double* w,
+                              lapack_complex_double* vs, lapack_int ldvs, lapack_complex_double* work, lapack_int lwork,
+                              double* rwork, lapack_logical* bwork);
+
+lapack_int LAPACKE_sgeesx_work(int matrix_order, char jobvs, char sort, LAPACK_S_SELECT2 select, char sense,
+                               lapack_int n, float* a, lapack_int lda, lapack_int* sdim, float* wr, float* wi,
+                               float* vs, lapack_int ldvs, float* rconde, float* rcondv, float* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int liwork, lapack_logical* bwork);
+lapack_int LAPACKE_dgeesx_work(int matrix_order, char jobvs, char sort, LAPACK_D_SELECT2 select, char sense,
+                               lapack_int n, double* a, lapack_int lda, lapack_int* sdim, double* wr, double* wi,
+                               double* vs, lapack_int ldvs, double* rconde, double* rcondv, double* work,
+                               lapack_int lwork, lapack_int* iwork, lapack_int liwork, lapack_logical* bwork);
+lapack_int LAPACKE_cgeesx_work(int matrix_order, char jobvs, char sort, LAPACK_C_SELECT1 select, char sense,
+                               lapack_int n, lapack_complex_float* a, lapack_int lda, lapack_int* sdim,
+                               lapack_complex_float* w, lapack_complex_float* vs, lapack_int ldvs, float* rconde,
+                               float* rcondv, lapack_complex_float* work, lapack_int lwork, float* rwork,
+                               lapack_logical* bwork);
+lapack_int LAPACKE_zgeesx_work(int matrix_order, char jobvs, char sort, LAPACK_Z_SELECT1 select, char sense,
+                               lapack_int n, lapack_complex_double* a, lapack_int lda, lapack_int* sdim,
+                               lapack_complex_double* w, lapack_complex_double* vs, lapack_int ldvs, double* rconde,
+                               double* rcondv, lapack_complex_double* work, lapack_int lwork, double* rwork,
+                               lapack_logical* bwork);
+
+lapack_int LAPACKE_sgeev_work(int matrix_order, char jobvl, char jobvr, lapack_int n, float* a, lapack_int lda,
+                              float* wr, float* wi, float* vl, lapack_int ldvl, float* vr, lapack_int ldvr, float* work,
+                              lapack_int lwork);
+lapack_int LAPACKE_dgeev_work(int matrix_order, char jobvl, char jobvr, lapack_int n, double* a, lapack_int lda,
+                              double* wr, double* wi, double* vl, lapack_int ldvl, double* vr, lapack_int ldvr,
+                              double* work, lapack_int lwork);
+lapack_int LAPACKE_cgeev_work(int matrix_order, char jobvl, char jobvr, lapack_int n, lapack_complex_float* a,
+                              lapack_int lda, lapack_complex_float* w, lapack_complex_float* vl, lapack_int ldvl,
+                              lapack_complex_float* vr, lapack_int ldvr, lapack_complex_float* work, lapack_int lwork,
+                              float* rwork);
+lapack_int LAPACKE_zgeev_work(int matrix_order, char jobvl, char jobvr, lapack_int n, lapack_complex_double* a,
+                              lapack_int lda, lapack_complex_double* w, lapack_complex_double* vl, lapack_int ldvl,
+                              lapack_complex_double* vr, lapack_int ldvr, lapack_complex_double* work, lapack_int lwork,
+                              double* rwork);
+
+lapack_int LAPACKE_sgeevx_work(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                               float* a, lapack_int lda, float* wr, float* wi, float* vl, lapack_int ldvl, float* vr,
+                               lapack_int ldvr, lapack_int* ilo, lapack_int* ihi, float* scale, float* abnrm,
+                               float* rconde, float* rcondv, float* work, lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_dgeevx_work(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                               double* a, lapack_int lda, double* wr, double* wi, double* vl, lapack_int ldvl,
+                               double* vr, lapack_int ldvr, lapack_int* ilo, lapack_int* ihi, double* scale,
+                               double* abnrm, double* rconde, double* rcondv, double* work, lapack_int lwork,
+                               lapack_int* iwork);
+lapack_int LAPACKE_cgeevx_work(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                               lapack_complex_float* a, lapack_int lda, lapack_complex_float* w,
+                               lapack_complex_float* vl, lapack_int ldvl, lapack_complex_float* vr, lapack_int ldvr,
+                               lapack_int* ilo, lapack_int* ihi, float* scale, float* abnrm, float* rconde,
+                               float* rcondv, lapack_complex_float* work, lapack_int lwork, float* rwork);
+lapack_int LAPACKE_zgeevx_work(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda, lapack_complex_double* w,
+                               lapack_complex_double* vl, lapack_int ldvl, lapack_complex_double* vr, lapack_int ldvr,
+                               lapack_int* ilo, lapack_int* ihi, double* scale, double* abnrm, double* rconde,
+                               double* rcondv, lapack_complex_double* work, lapack_int lwork, double* rwork);
+
+lapack_int LAPACKE_sgehrd_work(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, float* a, lapack_int lda,
+                               float* tau, float* work, lapack_int lwork);
+lapack_int LAPACKE_dgehrd_work(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, double* a,
+                               lapack_int lda, double* tau, double* work, lapack_int lwork);
+lapack_int LAPACKE_cgehrd_work(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* tau, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zgehrd_work(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* tau, lapack_complex_double* work,
+                               lapack_int lwork);
+
+lapack_int LAPACKE_sgejsv_work(int matrix_order, char joba, char jobu, char jobv, char jobr, char jobt, char jobp,
+                               lapack_int m, lapack_int n, float* a, lapack_int lda, float* sva, float* u,
+                               lapack_int ldu, float* v, lapack_int ldv, float* work, lapack_int lwork,
+                               lapack_int* iwork);
+lapack_int LAPACKE_dgejsv_work(int matrix_order, char joba, char jobu, char jobv, char jobr, char jobt, char jobp,
+                               lapack_int m, lapack_int n, double* a, lapack_int lda, double* sva, double* u,
+                               lapack_int ldu, double* v, lapack_int ldv, double* work, lapack_int lwork,
+                               lapack_int* iwork);
+
+lapack_int LAPACKE_sgelq2_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau,
+                               float* work);
+lapack_int LAPACKE_dgelq2_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau,
+                               double* work);
+lapack_int LAPACKE_cgelq2_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* tau, lapack_complex_float* work);
+lapack_int LAPACKE_zgelq2_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* tau, lapack_complex_double* work);
+
+lapack_int LAPACKE_sgelqf_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dgelqf_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_cgelqf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* tau, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zgelqf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* tau, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sgels_work(int matrix_order, char trans, lapack_int m, lapack_int n, lapack_int nrhs, float* a,
+                              lapack_int lda, float* b, lapack_int ldb, float* work, lapack_int lwork);
+lapack_int LAPACKE_dgels_work(int matrix_order, char trans, lapack_int m, lapack_int n, lapack_int nrhs, double* a,
+                              lapack_int lda, double* b, lapack_int ldb, double* work, lapack_int lwork);
+lapack_int LAPACKE_cgels_work(int matrix_order, char trans, lapack_int m, lapack_int n, lapack_int nrhs,
+                              lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                              lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zgels_work(int matrix_order, char trans, lapack_int m, lapack_int n, lapack_int nrhs,
+                              lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                              lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sgelsd_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, float* a, lapack_int lda,
+                               float* b, lapack_int ldb, float* s, float rcond, lapack_int* rank, float* work,
+                               lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_dgelsd_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                               double* b, lapack_int ldb, double* s, double rcond, lapack_int* rank, double* work,
+                               lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_cgelsd_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* b, lapack_int ldb, float* s, float rcond,
+                               lapack_int* rank, lapack_complex_float* work, lapack_int lwork, float* rwork,
+                               lapack_int* iwork);
+lapack_int LAPACKE_zgelsd_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* b, lapack_int ldb, double* s, double rcond,
+                               lapack_int* rank, lapack_complex_double* work, lapack_int lwork, double* rwork,
+                               lapack_int* iwork);
+
+lapack_int LAPACKE_sgelss_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, float* a, lapack_int lda,
+                               float* b, lapack_int ldb, float* s, float rcond, lapack_int* rank, float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_dgelss_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                               double* b, lapack_int ldb, double* s, double rcond, lapack_int* rank, double* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_cgelss_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* b, lapack_int ldb, float* s, float rcond,
+                               lapack_int* rank, lapack_complex_float* work, lapack_int lwork, float* rwork);
+lapack_int LAPACKE_zgelss_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* b, lapack_int ldb, double* s, double rcond,
+                               lapack_int* rank, lapack_complex_double* work, lapack_int lwork, double* rwork);
+
+lapack_int LAPACKE_sgelsy_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, float* a, lapack_int lda,
+                               float* b, lapack_int ldb, lapack_int* jpvt, float rcond, lapack_int* rank, float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_dgelsy_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                               double* b, lapack_int ldb, lapack_int* jpvt, double rcond, lapack_int* rank,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_cgelsy_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* b, lapack_int ldb, lapack_int* jpvt, float rcond,
+                               lapack_int* rank, lapack_complex_float* work, lapack_int lwork, float* rwork);
+lapack_int LAPACKE_zgelsy_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_int* jpvt, double rcond,
+                               lapack_int* rank, lapack_complex_double* work, lapack_int lwork, double* rwork);
+
+lapack_int LAPACKE_sgeqlf_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dgeqlf_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_cgeqlf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* tau, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zgeqlf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* tau, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sgeqp3_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, lapack_int* jpvt,
+                               float* tau, float* work, lapack_int lwork);
+lapack_int LAPACKE_dgeqp3_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda,
+                               lapack_int* jpvt, double* tau, double* work, lapack_int lwork);
+lapack_int LAPACKE_cgeqp3_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_int* jpvt, lapack_complex_float* tau, lapack_complex_float* work,
+                               lapack_int lwork, float* rwork);
+lapack_int LAPACKE_zgeqp3_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_int* jpvt, lapack_complex_double* tau, lapack_complex_double* work,
+                               lapack_int lwork, double* rwork);
+
+lapack_int LAPACKE_sgeqpf_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, lapack_int* jpvt,
+                               float* tau, float* work);
+lapack_int LAPACKE_dgeqpf_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda,
+                               lapack_int* jpvt, double* tau, double* work);
+lapack_int LAPACKE_cgeqpf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_int* jpvt, lapack_complex_float* tau, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgeqpf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_int* jpvt, lapack_complex_double* tau, lapack_complex_double* work,
+                               double* rwork);
+
+lapack_int LAPACKE_sgeqr2_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau,
+                               float* work);
+lapack_int LAPACKE_dgeqr2_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau,
+                               double* work);
+lapack_int LAPACKE_cgeqr2_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* tau, lapack_complex_float* work);
+lapack_int LAPACKE_zgeqr2_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* tau, lapack_complex_double* work);
+
+lapack_int LAPACKE_sgeqrf_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dgeqrf_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_cgeqrf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* tau, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zgeqrf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* tau, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sgeqrfp_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau,
+                                float* work, lapack_int lwork);
+lapack_int LAPACKE_dgeqrfp_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau,
+                                double* work, lapack_int lwork);
+lapack_int LAPACKE_cgeqrfp_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* tau, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zgeqrfp_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* tau, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sgerfs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const float* a,
+                               lapack_int lda, const float* af, lapack_int ldaf, const lapack_int* ipiv, const float* b,
+                               lapack_int ldb, float* x, lapack_int ldx, float* ferr, float* berr, float* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_dgerfs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const double* a,
+                               lapack_int lda, const double* af, lapack_int ldaf, const lapack_int* ipiv,
+                               const double* b, lapack_int ldb, double* x, lapack_int ldx, double* ferr, double* berr,
+                               double* work, lapack_int* iwork);
+lapack_int LAPACKE_cgerfs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                               lapack_int ldaf, const lapack_int* ipiv, const lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr,
+                               lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgerfs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                               lapack_int ldaf, const lapack_int* ipiv, const lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgerfsx_work(int matrix_order, char trans, char equed, lapack_int n, lapack_int nrhs, const float* a,
+                                lapack_int lda, const float* af, lapack_int ldaf, const lapack_int* ipiv,
+                                const float* r, const float* c, const float* b, lapack_int ldb, float* x,
+                                lapack_int ldx, float* rcond, float* berr, lapack_int n_err_bnds, float* err_bnds_norm,
+                                float* err_bnds_comp, lapack_int nparams, float* params, float* work,
+                                lapack_int* iwork);
+lapack_int LAPACKE_dgerfsx_work(int matrix_order, char trans, char equed, lapack_int n, lapack_int nrhs,
+                                const double* a, lapack_int lda, const double* af, lapack_int ldaf,
+                                const lapack_int* ipiv, const double* r, const double* c, const double* b,
+                                lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* berr,
+                                lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                                double* params, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cgerfsx_work(int matrix_order, char trans, char equed, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                                lapack_int ldaf, const lapack_int* ipiv, const float* r, const float* c,
+                                const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                                float* rcond, float* berr, lapack_int n_err_bnds, float* err_bnds_norm,
+                                float* err_bnds_comp, lapack_int nparams, float* params, lapack_complex_float* work,
+                                float* rwork);
+lapack_int LAPACKE_zgerfsx_work(int matrix_order, char trans, char equed, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                                lapack_int ldaf, const lapack_int* ipiv, const double* r, const double* c,
+                                const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                                lapack_int ldx, double* rcond, double* berr, lapack_int n_err_bnds,
+                                double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams, double* params,
+                                lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgerqf_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dgerqf_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_cgerqf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* tau, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zgerqf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* tau, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sgesdd_work(int matrix_order, char jobz, lapack_int m, lapack_int n, float* a, lapack_int lda,
+                               float* s, float* u, lapack_int ldu, float* vt, lapack_int ldvt, float* work,
+                               lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_dgesdd_work(int matrix_order, char jobz, lapack_int m, lapack_int n, double* a, lapack_int lda,
+                               double* s, double* u, lapack_int ldu, double* vt, lapack_int ldvt, double* work,
+                               lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_cgesdd_work(int matrix_order, char jobz, lapack_int m, lapack_int n, lapack_complex_float* a,
+                               lapack_int lda, float* s, lapack_complex_float* u, lapack_int ldu,
+                               lapack_complex_float* vt, lapack_int ldvt, lapack_complex_float* work, lapack_int lwork,
+                               float* rwork, lapack_int* iwork);
+lapack_int LAPACKE_zgesdd_work(int matrix_order, char jobz, lapack_int m, lapack_int n, lapack_complex_double* a,
+                               lapack_int lda, double* s, lapack_complex_double* u, lapack_int ldu,
+                               lapack_complex_double* vt, lapack_int ldvt, lapack_complex_double* work,
+                               lapack_int lwork, double* rwork, lapack_int* iwork);
+
+lapack_int LAPACKE_sgesv_work(int matrix_order, lapack_int n, lapack_int nrhs, float* a, lapack_int lda,
+                              lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dgesv_work(int matrix_order, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                              lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_cgesv_work(int matrix_order, lapack_int n, lapack_int nrhs, lapack_complex_float* a, lapack_int lda,
+                              lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zgesv_work(int matrix_order, lapack_int n, lapack_int nrhs, lapack_complex_double* a, lapack_int lda,
+                              lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+lapack_int LAPACKE_dsgesv_work(int matrix_order, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                               lapack_int* ipiv, double* b, lapack_int ldb, double* x, lapack_int ldx, double* work,
+                               float* swork, lapack_int* iter);
+lapack_int LAPACKE_zcgesv_work(int matrix_order, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                               lapack_int lda, lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, lapack_complex_double* work,
+                               lapack_complex_float* swork, double* rwork, lapack_int* iter);
+
+lapack_int LAPACKE_sgesvd_work(int matrix_order, char jobu, char jobvt, lapack_int m, lapack_int n, float* a,
+                               lapack_int lda, float* s, float* u, lapack_int ldu, float* vt, lapack_int ldvt,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dgesvd_work(int matrix_order, char jobu, char jobvt, lapack_int m, lapack_int n, double* a,
+                               lapack_int lda, double* s, double* u, lapack_int ldu, double* vt, lapack_int ldvt,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_cgesvd_work(int matrix_order, char jobu, char jobvt, lapack_int m, lapack_int n,
+                               lapack_complex_float* a, lapack_int lda, float* s, lapack_complex_float* u,
+                               lapack_int ldu, lapack_complex_float* vt, lapack_int ldvt, lapack_complex_float* work,
+                               lapack_int lwork, float* rwork);
+lapack_int LAPACKE_zgesvd_work(int matrix_order, char jobu, char jobvt, lapack_int m, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda, double* s, lapack_complex_double* u,
+                               lapack_int ldu, lapack_complex_double* vt, lapack_int ldvt, lapack_complex_double* work,
+                               lapack_int lwork, double* rwork);
+
+lapack_int LAPACKE_sgesvj_work(int matrix_order, char joba, char jobu, char jobv, lapack_int m, lapack_int n, float* a,
+                               lapack_int lda, float* sva, lapack_int mv, float* v, lapack_int ldv, float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_dgesvj_work(int matrix_order, char joba, char jobu, char jobv, lapack_int m, lapack_int n, double* a,
+                               lapack_int lda, double* sva, lapack_int mv, double* v, lapack_int ldv, double* work,
+                               lapack_int lwork);
+
+lapack_int LAPACKE_sgesvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, float* a,
+                               lapack_int lda, float* af, lapack_int ldaf, lapack_int* ipiv, char* equed, float* r,
+                               float* c, float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* ferr,
+                               float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dgesvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, double* a,
+                               lapack_int lda, double* af, lapack_int ldaf, lapack_int* ipiv, char* equed, double* r,
+                               double* c, double* b, lapack_int ldb, double* x, lapack_int ldx, double* rcond,
+                               double* ferr, double* berr, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cgesvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                               lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                               lapack_int* ipiv, char* equed, float* r, float* c, lapack_complex_float* b,
+                               lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* rcond, float* ferr,
+                               float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgesvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                               lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                               lapack_int* ipiv, char* equed, double* r, double* c, lapack_complex_double* b,
+                               lapack_int ldb, lapack_complex_double* x, lapack_int ldx, double* rcond, double* ferr,
+                               double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgesvxx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, float* a,
+                                lapack_int lda, float* af, lapack_int ldaf, lapack_int* ipiv, char* equed, float* r,
+                                float* c, float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond,
+                                float* rpvgrw, float* berr, lapack_int n_err_bnds, float* err_bnds_norm,
+                                float* err_bnds_comp, lapack_int nparams, float* params, float* work,
+                                lapack_int* iwork);
+lapack_int LAPACKE_dgesvxx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, double* a,
+                                lapack_int lda, double* af, lapack_int ldaf, lapack_int* ipiv, char* equed, double* r,
+                                double* c, double* b, lapack_int ldb, double* x, lapack_int ldx, double* rcond,
+                                double* rpvgrw, double* berr, lapack_int n_err_bnds, double* err_bnds_norm,
+                                double* err_bnds_comp, lapack_int nparams, double* params, double* work,
+                                lapack_int* iwork);
+lapack_int LAPACKE_cgesvxx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                                lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                                lapack_int* ipiv, char* equed, float* r, float* c, lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* rcond, float* rpvgrw,
+                                float* berr, lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                                lapack_int nparams, float* params, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgesvxx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                                lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                                lapack_int* ipiv, char* equed, double* r, double* c, lapack_complex_double* b,
+                                lapack_int ldb, lapack_complex_double* x, lapack_int ldx, double* rcond, double* rpvgrw,
+                                double* berr, lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                                lapack_int nparams, double* params, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgetf2_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda,
+                               lapack_int* ipiv);
+lapack_int LAPACKE_dgetf2_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda,
+                               lapack_int* ipiv);
+lapack_int LAPACKE_cgetf2_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_int* ipiv);
+lapack_int LAPACKE_zgetf2_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_int* ipiv);
+
+lapack_int LAPACKE_sgetrf_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda,
+                               lapack_int* ipiv);
+lapack_int LAPACKE_dgetrf_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda,
+                               lapack_int* ipiv);
+lapack_int LAPACKE_cgetrf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_int* ipiv);
+lapack_int LAPACKE_zgetrf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_int* ipiv);
+
+lapack_int LAPACKE_sgetri_work(int matrix_order, lapack_int n, float* a, lapack_int lda, const lapack_int* ipiv,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dgetri_work(int matrix_order, lapack_int n, double* a, lapack_int lda, const lapack_int* ipiv,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_cgetri_work(int matrix_order, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               const lapack_int* ipiv, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zgetri_work(int matrix_order, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               const lapack_int* ipiv, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sgetrs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const float* a,
+                               lapack_int lda, const lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dgetrs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const double* a,
+                               lapack_int lda, const lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_cgetrs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_int* ipiv,
+                               lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zgetrs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_int* ipiv,
+                               lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sggbak_work(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               const float* lscale, const float* rscale, lapack_int m, float* v, lapack_int ldv);
+lapack_int LAPACKE_dggbak_work(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               const double* lscale, const double* rscale, lapack_int m, double* v, lapack_int ldv);
+lapack_int LAPACKE_cggbak_work(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               const float* lscale, const float* rscale, lapack_int m, lapack_complex_float* v,
+                               lapack_int ldv);
+lapack_int LAPACKE_zggbak_work(int matrix_order, char job, char side, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               const double* lscale, const double* rscale, lapack_int m, lapack_complex_double* v,
+                               lapack_int ldv);
+
+lapack_int LAPACKE_sggbal_work(int matrix_order, char job, lapack_int n, float* a, lapack_int lda, float* b,
+                               lapack_int ldb, lapack_int* ilo, lapack_int* ihi, float* lscale, float* rscale,
+                               float* work);
+lapack_int LAPACKE_dggbal_work(int matrix_order, char job, lapack_int n, double* a, lapack_int lda, double* b,
+                               lapack_int ldb, lapack_int* ilo, lapack_int* ihi, double* lscale, double* rscale,
+                               double* work);
+lapack_int LAPACKE_cggbal_work(int matrix_order, char job, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* b, lapack_int ldb, lapack_int* ilo, lapack_int* ihi, float* lscale,
+                               float* rscale, float* work);
+lapack_int LAPACKE_zggbal_work(int matrix_order, char job, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* b, lapack_int ldb, lapack_int* ilo, lapack_int* ihi,
+                               double* lscale, double* rscale, double* work);
+
+lapack_int LAPACKE_sgges_work(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_S_SELECT3 selctg,
+                              lapack_int n, float* a, lapack_int lda, float* b, lapack_int ldb, lapack_int* sdim,
+                              float* alphar, float* alphai, float* beta, float* vsl, lapack_int ldvsl, float* vsr,
+                              lapack_int ldvsr, float* work, lapack_int lwork, lapack_logical* bwork);
+lapack_int LAPACKE_dgges_work(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_D_SELECT3 selctg,
+                              lapack_int n, double* a, lapack_int lda, double* b, lapack_int ldb, lapack_int* sdim,
+                              double* alphar, double* alphai, double* beta, double* vsl, lapack_int ldvsl, double* vsr,
+                              lapack_int ldvsr, double* work, lapack_int lwork, lapack_logical* bwork);
+lapack_int LAPACKE_cgges_work(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_C_SELECT2 selctg,
+                              lapack_int n, lapack_complex_float* a, lapack_int lda, lapack_complex_float* b,
+                              lapack_int ldb, lapack_int* sdim, lapack_complex_float* alpha, lapack_complex_float* beta,
+                              lapack_complex_float* vsl, lapack_int ldvsl, lapack_complex_float* vsr, lapack_int ldvsr,
+                              lapack_complex_float* work, lapack_int lwork, float* rwork, lapack_logical* bwork);
+lapack_int LAPACKE_zgges_work(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_Z_SELECT2 selctg,
+                              lapack_int n, lapack_complex_double* a, lapack_int lda, lapack_complex_double* b,
+                              lapack_int ldb, lapack_int* sdim, lapack_complex_double* alpha,
+                              lapack_complex_double* beta, lapack_complex_double* vsl, lapack_int ldvsl,
+                              lapack_complex_double* vsr, lapack_int ldvsr, lapack_complex_double* work,
+                              lapack_int lwork, double* rwork, lapack_logical* bwork);
+
+lapack_int LAPACKE_sggesx_work(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_S_SELECT3 selctg,
+                               char sense, lapack_int n, float* a, lapack_int lda, float* b, lapack_int ldb,
+                               lapack_int* sdim, float* alphar, float* alphai, float* beta, float* vsl,
+                               lapack_int ldvsl, float* vsr, lapack_int ldvsr, float* rconde, float* rcondv,
+                               float* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork,
+                               lapack_logical* bwork);
+lapack_int LAPACKE_dggesx_work(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_D_SELECT3 selctg,
+                               char sense, lapack_int n, double* a, lapack_int lda, double* b, lapack_int ldb,
+                               lapack_int* sdim, double* alphar, double* alphai, double* beta, double* vsl,
+                               lapack_int ldvsl, double* vsr, lapack_int ldvsr, double* rconde, double* rcondv,
+                               double* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork,
+                               lapack_logical* bwork);
+lapack_int LAPACKE_cggesx_work(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_C_SELECT2 selctg,
+                               char sense, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* b, lapack_int ldb, lapack_int* sdim, lapack_complex_float* alpha,
+                               lapack_complex_float* beta, lapack_complex_float* vsl, lapack_int ldvsl,
+                               lapack_complex_float* vsr, lapack_int ldvsr, float* rconde, float* rcondv,
+                               lapack_complex_float* work, lapack_int lwork, float* rwork, lapack_int* iwork,
+                               lapack_int liwork, lapack_logical* bwork);
+lapack_int LAPACKE_zggesx_work(int matrix_order, char jobvsl, char jobvsr, char sort, LAPACK_Z_SELECT2 selctg,
+                               char sense, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* b, lapack_int ldb, lapack_int* sdim, lapack_complex_double* alpha,
+                               lapack_complex_double* beta, lapack_complex_double* vsl, lapack_int ldvsl,
+                               lapack_complex_double* vsr, lapack_int ldvsr, double* rconde, double* rcondv,
+                               lapack_complex_double* work, lapack_int lwork, double* rwork, lapack_int* iwork,
+                               lapack_int liwork, lapack_logical* bwork);
+
+lapack_int LAPACKE_sggev_work(int matrix_order, char jobvl, char jobvr, lapack_int n, float* a, lapack_int lda,
+                              float* b, lapack_int ldb, float* alphar, float* alphai, float* beta, float* vl,
+                              lapack_int ldvl, float* vr, lapack_int ldvr, float* work, lapack_int lwork);
+lapack_int LAPACKE_dggev_work(int matrix_order, char jobvl, char jobvr, lapack_int n, double* a, lapack_int lda,
+                              double* b, lapack_int ldb, double* alphar, double* alphai, double* beta, double* vl,
+                              lapack_int ldvl, double* vr, lapack_int ldvr, double* work, lapack_int lwork);
+lapack_int LAPACKE_cggev_work(int matrix_order, char jobvl, char jobvr, lapack_int n, lapack_complex_float* a,
+                              lapack_int lda, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* alpha,
+                              lapack_complex_float* beta, lapack_complex_float* vl, lapack_int ldvl,
+                              lapack_complex_float* vr, lapack_int ldvr, lapack_complex_float* work, lapack_int lwork,
+                              float* rwork);
+lapack_int LAPACKE_zggev_work(int matrix_order, char jobvl, char jobvr, lapack_int n, lapack_complex_double* a,
+                              lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* alpha,
+                              lapack_complex_double* beta, lapack_complex_double* vl, lapack_int ldvl,
+                              lapack_complex_double* vr, lapack_int ldvr, lapack_complex_double* work, lapack_int lwork,
+                              double* rwork);
+
+lapack_int LAPACKE_sggevx_work(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                               float* a, lapack_int lda, float* b, lapack_int ldb, float* alphar, float* alphai,
+                               float* beta, float* vl, lapack_int ldvl, float* vr, lapack_int ldvr, lapack_int* ilo,
+                               lapack_int* ihi, float* lscale, float* rscale, float* abnrm, float* bbnrm, float* rconde,
+                               float* rcondv, float* work, lapack_int lwork, lapack_int* iwork, lapack_logical* bwork);
+lapack_int LAPACKE_dggevx_work(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                               double* a, lapack_int lda, double* b, lapack_int ldb, double* alphar, double* alphai,
+                               double* beta, double* vl, lapack_int ldvl, double* vr, lapack_int ldvr, lapack_int* ilo,
+                               lapack_int* ihi, double* lscale, double* rscale, double* abnrm, double* bbnrm,
+                               double* rconde, double* rcondv, double* work, lapack_int lwork, lapack_int* iwork,
+                               lapack_logical* bwork);
+lapack_int LAPACKE_cggevx_work(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                               lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* alpha, lapack_complex_float* beta, lapack_complex_float* vl,
+                               lapack_int ldvl, lapack_complex_float* vr, lapack_int ldvr, lapack_int* ilo,
+                               lapack_int* ihi, float* lscale, float* rscale, float* abnrm, float* bbnrm, float* rconde,
+                               float* rcondv, lapack_complex_float* work, lapack_int lwork, float* rwork,
+                               lapack_int* iwork, lapack_logical* bwork);
+lapack_int LAPACKE_zggevx_work(int matrix_order, char balanc, char jobvl, char jobvr, char sense, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* alpha, lapack_complex_double* beta, lapack_complex_double* vl,
+                               lapack_int ldvl, lapack_complex_double* vr, lapack_int ldvr, lapack_int* ilo,
+                               lapack_int* ihi, double* lscale, double* rscale, double* abnrm, double* bbnrm,
+                               double* rconde, double* rcondv, lapack_complex_double* work, lapack_int lwork,
+                               double* rwork, lapack_int* iwork, lapack_logical* bwork);
+
+lapack_int LAPACKE_sggglm_work(int matrix_order, lapack_int n, lapack_int m, lapack_int p, float* a, lapack_int lda,
+                               float* b, lapack_int ldb, float* d, float* x, float* y, float* work, lapack_int lwork);
+lapack_int LAPACKE_dggglm_work(int matrix_order, lapack_int n, lapack_int m, lapack_int p, double* a, lapack_int lda,
+                               double* b, lapack_int ldb, double* d, double* x, double* y, double* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_cggglm_work(int matrix_order, lapack_int n, lapack_int m, lapack_int p, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* d,
+                               lapack_complex_float* x, lapack_complex_float* y, lapack_complex_float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_zggglm_work(int matrix_order, lapack_int n, lapack_int m, lapack_int p, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* d,
+                               lapack_complex_double* x, lapack_complex_double* y, lapack_complex_double* work,
+                               lapack_int lwork);
+
+lapack_int LAPACKE_sgghrd_work(int matrix_order, char compq, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               float* a, lapack_int lda, float* b, lapack_int ldb, float* q, lapack_int ldq, float* z,
+                               lapack_int ldz);
+lapack_int LAPACKE_dgghrd_work(int matrix_order, char compq, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               double* a, lapack_int lda, double* b, lapack_int ldb, double* q, lapack_int ldq,
+                               double* z, lapack_int ldz);
+lapack_int LAPACKE_cgghrd_work(int matrix_order, char compq, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* q, lapack_int ldq, lapack_complex_float* z, lapack_int ldz);
+lapack_int LAPACKE_zgghrd_work(int matrix_order, char compq, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* q, lapack_int ldq, lapack_complex_double* z, lapack_int ldz);
+
+lapack_int LAPACKE_sgglse_work(int matrix_order, lapack_int m, lapack_int n, lapack_int p, float* a, lapack_int lda,
+                               float* b, lapack_int ldb, float* c, float* d, float* x, float* work, lapack_int lwork);
+lapack_int LAPACKE_dgglse_work(int matrix_order, lapack_int m, lapack_int n, lapack_int p, double* a, lapack_int lda,
+                               double* b, lapack_int ldb, double* c, double* d, double* x, double* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_cgglse_work(int matrix_order, lapack_int m, lapack_int n, lapack_int p, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* c,
+                               lapack_complex_float* d, lapack_complex_float* x, lapack_complex_float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_zgglse_work(int matrix_order, lapack_int m, lapack_int n, lapack_int p, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* c,
+                               lapack_complex_double* d, lapack_complex_double* x, lapack_complex_double* work,
+                               lapack_int lwork);
+
+lapack_int LAPACKE_sggqrf_work(int matrix_order, lapack_int n, lapack_int m, lapack_int p, float* a, lapack_int lda,
+                               float* taua, float* b, lapack_int ldb, float* taub, float* work, lapack_int lwork);
+lapack_int LAPACKE_dggqrf_work(int matrix_order, lapack_int n, lapack_int m, lapack_int p, double* a, lapack_int lda,
+                               double* taua, double* b, lapack_int ldb, double* taub, double* work, lapack_int lwork);
+lapack_int LAPACKE_cggqrf_work(int matrix_order, lapack_int n, lapack_int m, lapack_int p, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* taua, lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* taub, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zggqrf_work(int matrix_order, lapack_int n, lapack_int m, lapack_int p, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* taua, lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* taub, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sggrqf_work(int matrix_order, lapack_int m, lapack_int p, lapack_int n, float* a, lapack_int lda,
+                               float* taua, float* b, lapack_int ldb, float* taub, float* work, lapack_int lwork);
+lapack_int LAPACKE_dggrqf_work(int matrix_order, lapack_int m, lapack_int p, lapack_int n, double* a, lapack_int lda,
+                               double* taua, double* b, lapack_int ldb, double* taub, double* work, lapack_int lwork);
+lapack_int LAPACKE_cggrqf_work(int matrix_order, lapack_int m, lapack_int p, lapack_int n, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* taua, lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* taub, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zggrqf_work(int matrix_order, lapack_int m, lapack_int p, lapack_int n, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* taua, lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* taub, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sggsvd_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int n,
+                               lapack_int p, lapack_int* k, lapack_int* l, float* a, lapack_int lda, float* b,
+                               lapack_int ldb, float* alpha, float* beta, float* u, lapack_int ldu, float* v,
+                               lapack_int ldv, float* q, lapack_int ldq, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dggsvd_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int n,
+                               lapack_int p, lapack_int* k, lapack_int* l, double* a, lapack_int lda, double* b,
+                               lapack_int ldb, double* alpha, double* beta, double* u, lapack_int ldu, double* v,
+                               lapack_int ldv, double* q, lapack_int ldq, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cggsvd_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int n,
+                               lapack_int p, lapack_int* k, lapack_int* l, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* b, lapack_int ldb, float* alpha, float* beta,
+                               lapack_complex_float* u, lapack_int ldu, lapack_complex_float* v, lapack_int ldv,
+                               lapack_complex_float* q, lapack_int ldq, lapack_complex_float* work, float* rwork,
+                               lapack_int* iwork);
+lapack_int LAPACKE_zggsvd_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int n,
+                               lapack_int p, lapack_int* k, lapack_int* l, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* b, lapack_int ldb, double* alpha, double* beta,
+                               lapack_complex_double* u, lapack_int ldu, lapack_complex_double* v, lapack_int ldv,
+                               lapack_complex_double* q, lapack_int ldq, lapack_complex_double* work, double* rwork,
+                               lapack_int* iwork);
+
+lapack_int LAPACKE_sggsvp_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p,
+                               lapack_int n, float* a, lapack_int lda, float* b, lapack_int ldb, float tola, float tolb,
+                               lapack_int* k, lapack_int* l, float* u, lapack_int ldu, float* v, lapack_int ldv,
+                               float* q, lapack_int ldq, lapack_int* iwork, float* tau, float* work);
+lapack_int LAPACKE_dggsvp_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p,
+                               lapack_int n, double* a, lapack_int lda, double* b, lapack_int ldb, double tola,
+                               double tolb, lapack_int* k, lapack_int* l, double* u, lapack_int ldu, double* v,
+                               lapack_int ldv, double* q, lapack_int ldq, lapack_int* iwork, double* tau, double* work);
+lapack_int LAPACKE_cggsvp_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p,
+                               lapack_int n, lapack_complex_float* a, lapack_int lda, lapack_complex_float* b,
+                               lapack_int ldb, float tola, float tolb, lapack_int* k, lapack_int* l,
+                               lapack_complex_float* u, lapack_int ldu, lapack_complex_float* v, lapack_int ldv,
+                               lapack_complex_float* q, lapack_int ldq, lapack_int* iwork, float* rwork,
+                               lapack_complex_float* tau, lapack_complex_float* work);
+lapack_int LAPACKE_zggsvp_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p,
+                               lapack_int n, lapack_complex_double* a, lapack_int lda, lapack_complex_double* b,
+                               lapack_int ldb, double tola, double tolb, lapack_int* k, lapack_int* l,
+                               lapack_complex_double* u, lapack_int ldu, lapack_complex_double* v, lapack_int ldv,
+                               lapack_complex_double* q, lapack_int ldq, lapack_int* iwork, double* rwork,
+                               lapack_complex_double* tau, lapack_complex_double* work);
+
+lapack_int LAPACKE_sgtcon_work(char norm, lapack_int n, const float* dl, const float* d, const float* du,
+                               const float* du2, const lapack_int* ipiv, float anorm, float* rcond, float* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_dgtcon_work(char norm, lapack_int n, const double* dl, const double* d, const double* du,
+                               const double* du2, const lapack_int* ipiv, double anorm, double* rcond, double* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_cgtcon_work(char norm, lapack_int n, const lapack_complex_float* dl, const lapack_complex_float* d,
+                               const lapack_complex_float* du, const lapack_complex_float* du2, const lapack_int* ipiv,
+                               float anorm, float* rcond, lapack_complex_float* work);
+lapack_int LAPACKE_zgtcon_work(char norm, lapack_int n, const lapack_complex_double* dl, const lapack_complex_double* d,
+                               const lapack_complex_double* du, const lapack_complex_double* du2,
+                               const lapack_int* ipiv, double anorm, double* rcond, lapack_complex_double* work);
+
+lapack_int LAPACKE_sgtrfs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const float* dl,
+                               const float* d, const float* du, const float* dlf, const float* df, const float* duf,
+                               const float* du2, const lapack_int* ipiv, const float* b, lapack_int ldb, float* x,
+                               lapack_int ldx, float* ferr, float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dgtrfs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const double* dl,
+                               const double* d, const double* du, const double* dlf, const double* df,
+                               const double* duf, const double* du2, const lapack_int* ipiv, const double* b,
+                               lapack_int ldb, double* x, lapack_int ldx, double* ferr, double* berr, double* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_cgtrfs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* dl, const lapack_complex_float* d,
+                               const lapack_complex_float* du, const lapack_complex_float* dlf,
+                               const lapack_complex_float* df, const lapack_complex_float* duf,
+                               const lapack_complex_float* du2, const lapack_int* ipiv, const lapack_complex_float* b,
+                               lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr,
+                               lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgtrfs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* dl, const lapack_complex_double* d,
+                               const lapack_complex_double* du, const lapack_complex_double* dlf,
+                               const lapack_complex_double* df, const lapack_complex_double* duf,
+                               const lapack_complex_double* du2, const lapack_int* ipiv, const lapack_complex_double* b,
+                               lapack_int ldb, lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgtsv_work(int matrix_order, lapack_int n, lapack_int nrhs, float* dl, float* d, float* du, float* b,
+                              lapack_int ldb);
+lapack_int LAPACKE_dgtsv_work(int matrix_order, lapack_int n, lapack_int nrhs, double* dl, double* d, double* du,
+                              double* b, lapack_int ldb);
+lapack_int LAPACKE_cgtsv_work(int matrix_order, lapack_int n, lapack_int nrhs, lapack_complex_float* dl,
+                              lapack_complex_float* d, lapack_complex_float* du, lapack_complex_float* b,
+                              lapack_int ldb);
+lapack_int LAPACKE_zgtsv_work(int matrix_order, lapack_int n, lapack_int nrhs, lapack_complex_double* dl,
+                              lapack_complex_double* d, lapack_complex_double* du, lapack_complex_double* b,
+                              lapack_int ldb);
+
+lapack_int LAPACKE_sgtsvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, const float* dl,
+                               const float* d, const float* du, float* dlf, float* df, float* duf, float* du2,
+                               lapack_int* ipiv, const float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond,
+                               float* ferr, float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dgtsvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs, const double* dl,
+                               const double* d, const double* du, double* dlf, double* df, double* duf, double* du2,
+                               lapack_int* ipiv, const double* b, lapack_int ldb, double* x, lapack_int ldx,
+                               double* rcond, double* ferr, double* berr, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cgtsvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* dl, const lapack_complex_float* d,
+                               const lapack_complex_float* du, lapack_complex_float* dlf, lapack_complex_float* df,
+                               lapack_complex_float* duf, lapack_complex_float* du2, lapack_int* ipiv,
+                               const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* rcond, float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zgtsvx_work(int matrix_order, char fact, char trans, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* dl, const lapack_complex_double* d,
+                               const lapack_complex_double* du, lapack_complex_double* dlf, lapack_complex_double* df,
+                               lapack_complex_double* duf, lapack_complex_double* du2, lapack_int* ipiv,
+                               const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                               double* rcond, double* ferr, double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sgttrf_work(lapack_int n, float* dl, float* d, float* du, float* du2, lapack_int* ipiv);
+lapack_int LAPACKE_dgttrf_work(lapack_int n, double* dl, double* d, double* du, double* du2, lapack_int* ipiv);
+lapack_int LAPACKE_cgttrf_work(lapack_int n, lapack_complex_float* dl, lapack_complex_float* d,
+                               lapack_complex_float* du, lapack_complex_float* du2, lapack_int* ipiv);
+lapack_int LAPACKE_zgttrf_work(lapack_int n, lapack_complex_double* dl, lapack_complex_double* d,
+                               lapack_complex_double* du, lapack_complex_double* du2, lapack_int* ipiv);
+
+lapack_int LAPACKE_sgttrs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const float* dl,
+                               const float* d, const float* du, const float* du2, const lapack_int* ipiv, float* b,
+                               lapack_int ldb);
+lapack_int LAPACKE_dgttrs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs, const double* dl,
+                               const double* d, const double* du, const double* du2, const lapack_int* ipiv, double* b,
+                               lapack_int ldb);
+lapack_int LAPACKE_cgttrs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* dl, const lapack_complex_float* d,
+                               const lapack_complex_float* du, const lapack_complex_float* du2, const lapack_int* ipiv,
+                               lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zgttrs_work(int matrix_order, char trans, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* dl, const lapack_complex_double* d,
+                               const lapack_complex_double* du, const lapack_complex_double* du2,
+                               const lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_chbev_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd,
+                              lapack_complex_float* ab, lapack_int ldab, float* w, lapack_complex_float* z,
+                              lapack_int ldz, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zhbev_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd,
+                              lapack_complex_double* ab, lapack_int ldab, double* w, lapack_complex_double* z,
+                              lapack_int ldz, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_chbevd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd,
+                               lapack_complex_float* ab, lapack_int ldab, float* w, lapack_complex_float* z,
+                               lapack_int ldz, lapack_complex_float* work, lapack_int lwork, float* rwork,
+                               lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_zhbevd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd,
+                               lapack_complex_double* ab, lapack_int ldab, double* w, lapack_complex_double* z,
+                               lapack_int ldz, lapack_complex_double* work, lapack_int lwork, double* rwork,
+                               lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_chbevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int kd,
+                               lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* q, lapack_int ldq,
+                               float vl, float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w,
+                               lapack_complex_float* z, lapack_int ldz, lapack_complex_float* work, float* rwork,
+                               lapack_int* iwork, lapack_int* ifail);
+lapack_int LAPACKE_zhbevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int kd,
+                               lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* q, lapack_int ldq,
+                               double vl, double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m,
+                               double* w, lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work,
+                               double* rwork, lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_chbgst_work(int matrix_order, char vect, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                               lapack_complex_float* ab, lapack_int ldab, const lapack_complex_float* bb,
+                               lapack_int ldbb, lapack_complex_float* x, lapack_int ldx, lapack_complex_float* work,
+                               float* rwork);
+lapack_int LAPACKE_zhbgst_work(int matrix_order, char vect, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                               lapack_complex_double* ab, lapack_int ldab, const lapack_complex_double* bb,
+                               lapack_int ldbb, lapack_complex_double* x, lapack_int ldx, lapack_complex_double* work,
+                               double* rwork);
+
+lapack_int LAPACKE_chbgv_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                              lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* bb, lapack_int ldbb,
+                              float* w, lapack_complex_float* z, lapack_int ldz, lapack_complex_float* work,
+                              float* rwork);
+lapack_int LAPACKE_zhbgv_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                              lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* bb, lapack_int ldbb,
+                              double* w, lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work,
+                              double* rwork);
+
+lapack_int LAPACKE_chbgvd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                               lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* bb, lapack_int ldbb,
+                               float* w, lapack_complex_float* z, lapack_int ldz, lapack_complex_float* work,
+                               lapack_int lwork, float* rwork, lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_zhbgvd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                               lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* bb, lapack_int ldbb,
+                               double* w, lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work,
+                               lapack_int lwork, double* rwork, lapack_int lrwork, lapack_int* iwork,
+                               lapack_int liwork);
+
+lapack_int LAPACKE_chbgvx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int ka,
+                               lapack_int kb, lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* bb,
+                               lapack_int ldbb, lapack_complex_float* q, lapack_int ldq, float vl, float vu,
+                               lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w,
+                               lapack_complex_float* z, lapack_int ldz, lapack_complex_float* work, float* rwork,
+                               lapack_int* iwork, lapack_int* ifail);
+lapack_int LAPACKE_zhbgvx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int ka,
+                               lapack_int kb, lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* bb,
+                               lapack_int ldbb, lapack_complex_double* q, lapack_int ldq, double vl, double vu,
+                               lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                               lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work, double* rwork,
+                               lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_chbtrd_work(int matrix_order, char vect, char uplo, lapack_int n, lapack_int kd,
+                               lapack_complex_float* ab, lapack_int ldab, float* d, float* e, lapack_complex_float* q,
+                               lapack_int ldq, lapack_complex_float* work);
+lapack_int LAPACKE_zhbtrd_work(int matrix_order, char vect, char uplo, lapack_int n, lapack_int kd,
+                               lapack_complex_double* ab, lapack_int ldab, double* d, double* e,
+                               lapack_complex_double* q, lapack_int ldq, lapack_complex_double* work);
+
+lapack_int LAPACKE_checon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                               const lapack_int* ipiv, float anorm, float* rcond, lapack_complex_float* work);
+lapack_int LAPACKE_zhecon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a,
+                               lapack_int lda, const lapack_int* ipiv, double anorm, double* rcond,
+                               lapack_complex_double* work);
+
+lapack_int LAPACKE_cheequb_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a,
+                                lapack_int lda, float* s, float* scond, float* amax, lapack_complex_float* work);
+lapack_int LAPACKE_zheequb_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a,
+                                lapack_int lda, double* s, double* scond, double* amax, lapack_complex_double* work);
+
+lapack_int LAPACKE_cheev_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_float* a,
+                              lapack_int lda, float* w, lapack_complex_float* work, lapack_int lwork, float* rwork);
+lapack_int LAPACKE_zheev_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_double* a,
+                              lapack_int lda, double* w, lapack_complex_double* work, lapack_int lwork, double* rwork);
+
+lapack_int LAPACKE_cheevd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_float* a,
+                               lapack_int lda, float* w, lapack_complex_float* work, lapack_int lwork, float* rwork,
+                               lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_zheevd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_double* a,
+                               lapack_int lda, double* w, lapack_complex_double* work, lapack_int lwork, double* rwork,
+                               lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_cheevr_work(int matrix_order, char jobz, char range, char uplo, lapack_int n,
+                               lapack_complex_float* a, lapack_int lda, float vl, float vu, lapack_int il,
+                               lapack_int iu, float abstol, lapack_int* m, float* w, lapack_complex_float* z,
+                               lapack_int ldz, lapack_int* isuppz, lapack_complex_float* work, lapack_int lwork,
+                               float* rwork, lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_zheevr_work(int matrix_order, char jobz, char range, char uplo, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda, double vl, double vu, lapack_int il,
+                               lapack_int iu, double abstol, lapack_int* m, double* w, lapack_complex_double* z,
+                               lapack_int ldz, lapack_int* isuppz, lapack_complex_double* work, lapack_int lwork,
+                               double* rwork, lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_cheevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n,
+                               lapack_complex_float* a, lapack_int lda, float vl, float vu, lapack_int il,
+                               lapack_int iu, float abstol, lapack_int* m, float* w, lapack_complex_float* z,
+                               lapack_int ldz, lapack_complex_float* work, lapack_int lwork, float* rwork,
+                               lapack_int* iwork, lapack_int* ifail);
+lapack_int LAPACKE_zheevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda, double vl, double vu, lapack_int il,
+                               lapack_int iu, double abstol, lapack_int* m, double* w, lapack_complex_double* z,
+                               lapack_int ldz, lapack_complex_double* work, lapack_int lwork, double* rwork,
+                               lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_chegst_work(int matrix_order, lapack_int itype, char uplo, lapack_int n, lapack_complex_float* a,
+                               lapack_int lda, const lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zhegst_work(int matrix_order, lapack_int itype, char uplo, lapack_int n, lapack_complex_double* a,
+                               lapack_int lda, const lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_chegv_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                              lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                              float* w, lapack_complex_float* work, lapack_int lwork, float* rwork);
+lapack_int LAPACKE_zhegv_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                              lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                              double* w, lapack_complex_double* work, lapack_int lwork, double* rwork);
+
+lapack_int LAPACKE_chegvd_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                               lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                               float* w, lapack_complex_float* work, lapack_int lwork, float* rwork, lapack_int lrwork,
+                               lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_zhegvd_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                               double* w, lapack_complex_double* work, lapack_int lwork, double* rwork,
+                               lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_chegvx_work(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                               lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                               float vl, float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w,
+                               lapack_complex_float* z, lapack_int ldz, lapack_complex_float* work, lapack_int lwork,
+                               float* rwork, lapack_int* iwork, lapack_int* ifail);
+lapack_int LAPACKE_zhegvx_work(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                               double vl, double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m,
+                               double* w, lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work,
+                               lapack_int lwork, double* rwork, lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_cherfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                               lapack_int ldaf, const lapack_int* ipiv, const lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr,
+                               lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zherfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                               lapack_int ldaf, const lapack_int* ipiv, const lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_cherfsx_work(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                                lapack_int ldaf, const lapack_int* ipiv, const float* s, const lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* rcond, float* berr,
+                                lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                                float* params, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zherfsx_work(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                                lapack_int ldaf, const lapack_int* ipiv, const double* s,
+                                const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                                lapack_int ldx, double* rcond, double* berr, lapack_int n_err_bnds,
+                                double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams, double* params,
+                                lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_chesv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                              lapack_int lda, lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb,
+                              lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zhesv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                              lapack_int lda, lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb,
+                              lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_chesvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                               lapack_int* ipiv, const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                               lapack_int ldx, float* rcond, float* ferr, float* berr, lapack_complex_float* work,
+                               lapack_int lwork, float* rwork);
+lapack_int LAPACKE_zhesvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, lapack_complex_double* af,
+                               lapack_int ldaf, lapack_int* ipiv, const lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* rcond, double* ferr, double* berr,
+                               lapack_complex_double* work, lapack_int lwork, double* rwork);
+
+lapack_int LAPACKE_chesvxx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                                lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                                lapack_int* ipiv, char* equed, float* s, lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx, float* rcond, float* rpvgrw, float* berr,
+                                lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                                float* params, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zhesvxx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                                lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                                lapack_int* ipiv, char* equed, double* s, lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx, double* rcond, double* rpvgrw, double* berr,
+                                lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                                double* params, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_chetrd_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               float* d, float* e, lapack_complex_float* tau, lapack_complex_float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_zhetrd_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               double* d, double* e, lapack_complex_double* tau, lapack_complex_double* work,
+                               lapack_int lwork);
+
+lapack_int LAPACKE_chetrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_int* ipiv, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zhetrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_int* ipiv, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_chetri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               const lapack_int* ipiv, lapack_complex_float* work);
+lapack_int LAPACKE_zhetri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               const lapack_int* ipiv, lapack_complex_double* work);
+
+lapack_int LAPACKE_chetrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_int* ipiv,
+                               lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zhetrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_int* ipiv,
+                               lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_chfrk_work(int matrix_order, char transr, char uplo, char trans, lapack_int n, lapack_int k,
+                              float alpha, const lapack_complex_float* a, lapack_int lda, float beta,
+                              lapack_complex_float* c);
+lapack_int LAPACKE_zhfrk_work(int matrix_order, char transr, char uplo, char trans, lapack_int n, lapack_int k,
+                              double alpha, const lapack_complex_double* a, lapack_int lda, double beta,
+                              lapack_complex_double* c);
+
+lapack_int LAPACKE_shgeqz_work(int matrix_order, char job, char compq, char compz, lapack_int n, lapack_int ilo,
+                               lapack_int ihi, float* h, lapack_int ldh, float* t, lapack_int ldt, float* alphar,
+                               float* alphai, float* beta, float* q, lapack_int ldq, float* z, lapack_int ldz,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dhgeqz_work(int matrix_order, char job, char compq, char compz, lapack_int n, lapack_int ilo,
+                               lapack_int ihi, double* h, lapack_int ldh, double* t, lapack_int ldt, double* alphar,
+                               double* alphai, double* beta, double* q, lapack_int ldq, double* z, lapack_int ldz,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_chgeqz_work(int matrix_order, char job, char compq, char compz, lapack_int n, lapack_int ilo,
+                               lapack_int ihi, lapack_complex_float* h, lapack_int ldh, lapack_complex_float* t,
+                               lapack_int ldt, lapack_complex_float* alpha, lapack_complex_float* beta,
+                               lapack_complex_float* q, lapack_int ldq, lapack_complex_float* z, lapack_int ldz,
+                               lapack_complex_float* work, lapack_int lwork, float* rwork);
+lapack_int LAPACKE_zhgeqz_work(int matrix_order, char job, char compq, char compz, lapack_int n, lapack_int ilo,
+                               lapack_int ihi, lapack_complex_double* h, lapack_int ldh, lapack_complex_double* t,
+                               lapack_int ldt, lapack_complex_double* alpha, lapack_complex_double* beta,
+                               lapack_complex_double* q, lapack_int ldq, lapack_complex_double* z, lapack_int ldz,
+                               lapack_complex_double* work, lapack_int lwork, double* rwork);
+
+lapack_int LAPACKE_chpcon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap,
+                               const lapack_int* ipiv, float anorm, float* rcond, lapack_complex_float* work);
+lapack_int LAPACKE_zhpcon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap,
+                               const lapack_int* ipiv, double anorm, double* rcond, lapack_complex_double* work);
+
+lapack_int LAPACKE_chpev_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_float* ap, float* w,
+                              lapack_complex_float* z, lapack_int ldz, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zhpev_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_double* ap,
+                              double* w, lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work,
+                              double* rwork);
+
+lapack_int LAPACKE_chpevd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_float* ap, float* w,
+                               lapack_complex_float* z, lapack_int ldz, lapack_complex_float* work, lapack_int lwork,
+                               float* rwork, lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_zhpevd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_complex_double* ap,
+                               double* w, lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work,
+                               lapack_int lwork, double* rwork, lapack_int lrwork, lapack_int* iwork,
+                               lapack_int liwork);
+
+lapack_int LAPACKE_chpevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n,
+                               lapack_complex_float* ap, float vl, float vu, lapack_int il, lapack_int iu, float abstol,
+                               lapack_int* m, float* w, lapack_complex_float* z, lapack_int ldz,
+                               lapack_complex_float* work, float* rwork, lapack_int* iwork, lapack_int* ifail);
+lapack_int LAPACKE_zhpevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n,
+                               lapack_complex_double* ap, double vl, double vu, lapack_int il, lapack_int iu,
+                               double abstol, lapack_int* m, double* w, lapack_complex_double* z, lapack_int ldz,
+                               lapack_complex_double* work, double* rwork, lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_chpgst_work(int matrix_order, lapack_int itype, char uplo, lapack_int n, lapack_complex_float* ap,
+                               const lapack_complex_float* bp);
+lapack_int LAPACKE_zhpgst_work(int matrix_order, lapack_int itype, char uplo, lapack_int n, lapack_complex_double* ap,
+                               const lapack_complex_double* bp);
+
+lapack_int LAPACKE_chpgv_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                              lapack_complex_float* ap, lapack_complex_float* bp, float* w, lapack_complex_float* z,
+                              lapack_int ldz, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zhpgv_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                              lapack_complex_double* ap, lapack_complex_double* bp, double* w, lapack_complex_double* z,
+                              lapack_int ldz, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_chpgvd_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                               lapack_complex_float* ap, lapack_complex_float* bp, float* w, lapack_complex_float* z,
+                               lapack_int ldz, lapack_complex_float* work, lapack_int lwork, float* rwork,
+                               lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_zhpgvd_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n,
+                               lapack_complex_double* ap, lapack_complex_double* bp, double* w,
+                               lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work, lapack_int lwork,
+                               double* rwork, lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_chpgvx_work(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                               lapack_complex_float* ap, lapack_complex_float* bp, float vl, float vu, lapack_int il,
+                               lapack_int iu, float abstol, lapack_int* m, float* w, lapack_complex_float* z,
+                               lapack_int ldz, lapack_complex_float* work, float* rwork, lapack_int* iwork,
+                               lapack_int* ifail);
+lapack_int LAPACKE_zhpgvx_work(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                               lapack_complex_double* ap, lapack_complex_double* bp, double vl, double vu,
+                               lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                               lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work, double* rwork,
+                               lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_chprfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* ap, const lapack_complex_float* afp, const lapack_int* ipiv,
+                               const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zhprfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* ap, const lapack_complex_double* afp,
+                               const lapack_int* ipiv, const lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_chpsv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* ap,
+                              lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zhpsv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* ap,
+                              lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_chpsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* ap, lapack_complex_float* afp, lapack_int* ipiv,
+                               const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* rcond, float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zhpsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* ap, lapack_complex_double* afp, lapack_int* ipiv,
+                               const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                               double* rcond, double* ferr, double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_chptrd_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap, float* d, float* e,
+                               lapack_complex_float* tau);
+lapack_int LAPACKE_zhptrd_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap, double* d,
+                               double* e, lapack_complex_double* tau);
+
+lapack_int LAPACKE_chptrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap, lapack_int* ipiv);
+lapack_int LAPACKE_zhptrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap, lapack_int* ipiv);
+
+lapack_int LAPACKE_chptri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap,
+                               const lapack_int* ipiv, lapack_complex_float* work);
+lapack_int LAPACKE_zhptri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap,
+                               const lapack_int* ipiv, lapack_complex_double* work);
+
+lapack_int LAPACKE_chptrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* ap, const lapack_int* ipiv, lapack_complex_float* b,
+                               lapack_int ldb);
+lapack_int LAPACKE_zhptrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* ap, const lapack_int* ipiv, lapack_complex_double* b,
+                               lapack_int ldb);
+
+lapack_int LAPACKE_shsein_work(int matrix_order, char job, char eigsrc, char initv, lapack_logical* select,
+                               lapack_int n, const float* h, lapack_int ldh, float* wr, const float* wi, float* vl,
+                               lapack_int ldvl, float* vr, lapack_int ldvr, lapack_int mm, lapack_int* m, float* work,
+                               lapack_int* ifaill, lapack_int* ifailr);
+lapack_int LAPACKE_dhsein_work(int matrix_order, char job, char eigsrc, char initv, lapack_logical* select,
+                               lapack_int n, const double* h, lapack_int ldh, double* wr, const double* wi, double* vl,
+                               lapack_int ldvl, double* vr, lapack_int ldvr, lapack_int mm, lapack_int* m, double* work,
+                               lapack_int* ifaill, lapack_int* ifailr);
+lapack_int LAPACKE_chsein_work(int matrix_order, char job, char eigsrc, char initv, const lapack_logical* select,
+                               lapack_int n, const lapack_complex_float* h, lapack_int ldh, lapack_complex_float* w,
+                               lapack_complex_float* vl, lapack_int ldvl, lapack_complex_float* vr, lapack_int ldvr,
+                               lapack_int mm, lapack_int* m, lapack_complex_float* work, float* rwork,
+                               lapack_int* ifaill, lapack_int* ifailr);
+lapack_int LAPACKE_zhsein_work(int matrix_order, char job, char eigsrc, char initv, const lapack_logical* select,
+                               lapack_int n, const lapack_complex_double* h, lapack_int ldh, lapack_complex_double* w,
+                               lapack_complex_double* vl, lapack_int ldvl, lapack_complex_double* vr, lapack_int ldvr,
+                               lapack_int mm, lapack_int* m, lapack_complex_double* work, double* rwork,
+                               lapack_int* ifaill, lapack_int* ifailr);
+
+lapack_int LAPACKE_shseqr_work(int matrix_order, char job, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               float* h, lapack_int ldh, float* wr, float* wi, float* z, lapack_int ldz, float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_dhseqr_work(int matrix_order, char job, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               double* h, lapack_int ldh, double* wr, double* wi, double* z, lapack_int ldz,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_chseqr_work(int matrix_order, char job, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               lapack_complex_float* h, lapack_int ldh, lapack_complex_float* w,
+                               lapack_complex_float* z, lapack_int ldz, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zhseqr_work(int matrix_order, char job, char compz, lapack_int n, lapack_int ilo, lapack_int ihi,
+                               lapack_complex_double* h, lapack_int ldh, lapack_complex_double* w,
+                               lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_clacgv_work(lapack_int n, lapack_complex_float* x, lapack_int incx);
+lapack_int LAPACKE_zlacgv_work(lapack_int n, lapack_complex_double* x, lapack_int incx);
+
+lapack_int LAPACKE_slacpy_work(int matrix_order, char uplo, lapack_int m, lapack_int n, const float* a, lapack_int lda,
+                               float* b, lapack_int ldb);
+lapack_int LAPACKE_dlacpy_work(int matrix_order, char uplo, lapack_int m, lapack_int n, const double* a, lapack_int lda,
+                               double* b, lapack_int ldb);
+lapack_int LAPACKE_clacpy_work(int matrix_order, char uplo, lapack_int m, lapack_int n, const lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zlacpy_work(int matrix_order, char uplo, lapack_int m, lapack_int n, const lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_zlag2c_work(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_float* sa, lapack_int ldsa);
+
+lapack_int LAPACKE_slag2d_work(int matrix_order, lapack_int m, lapack_int n, const float* sa, lapack_int ldsa,
+                               double* a, lapack_int lda);
+
+lapack_int LAPACKE_dlag2s_work(int matrix_order, lapack_int m, lapack_int n, const double* a, lapack_int lda, float* sa,
+                               lapack_int ldsa);
+
+lapack_int LAPACKE_clag2z_work(int matrix_order, lapack_int m, lapack_int n, const lapack_complex_float* sa,
+                               lapack_int ldsa, lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_slagge_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                               const float* d, float* a, lapack_int lda, lapack_int* iseed, float* work);
+lapack_int LAPACKE_dlagge_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                               const double* d, double* a, lapack_int lda, lapack_int* iseed, double* work);
+lapack_int LAPACKE_clagge_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                               const float* d, lapack_complex_float* a, lapack_int lda, lapack_int* iseed,
+                               lapack_complex_float* work);
+lapack_int LAPACKE_zlagge_work(int matrix_order, lapack_int m, lapack_int n, lapack_int kl, lapack_int ku,
+                               const double* d, lapack_complex_double* a, lapack_int lda, lapack_int* iseed,
+                               lapack_complex_double* work);
+
+lapack_int LAPACKE_claghe_work(int matrix_order, lapack_int n, lapack_int k, const float* d, lapack_complex_float* a,
+                               lapack_int lda, lapack_int* iseed, lapack_complex_float* work);
+lapack_int LAPACKE_zlaghe_work(int matrix_order, lapack_int n, lapack_int k, const double* d, lapack_complex_double* a,
+                               lapack_int lda, lapack_int* iseed, lapack_complex_double* work);
+
+lapack_int LAPACKE_slagsy_work(int matrix_order, lapack_int n, lapack_int k, const float* d, float* a, lapack_int lda,
+                               lapack_int* iseed, float* work);
+lapack_int LAPACKE_dlagsy_work(int matrix_order, lapack_int n, lapack_int k, const double* d, double* a, lapack_int lda,
+                               lapack_int* iseed, double* work);
+lapack_int LAPACKE_clagsy_work(int matrix_order, lapack_int n, lapack_int k, const float* d, lapack_complex_float* a,
+                               lapack_int lda, lapack_int* iseed, lapack_complex_float* work);
+lapack_int LAPACKE_zlagsy_work(int matrix_order, lapack_int n, lapack_int k, const double* d, lapack_complex_double* a,
+                               lapack_int lda, lapack_int* iseed, lapack_complex_double* work);
+
+lapack_int LAPACKE_slapmr_work(int matrix_order, lapack_logical forwrd, lapack_int m, lapack_int n, float* x,
+                               lapack_int ldx, lapack_int* k);
+lapack_int LAPACKE_dlapmr_work(int matrix_order, lapack_logical forwrd, lapack_int m, lapack_int n, double* x,
+                               lapack_int ldx, lapack_int* k);
+lapack_int LAPACKE_clapmr_work(int matrix_order, lapack_logical forwrd, lapack_int m, lapack_int n,
+                               lapack_complex_float* x, lapack_int ldx, lapack_int* k);
+lapack_int LAPACKE_zlapmr_work(int matrix_order, lapack_logical forwrd, lapack_int m, lapack_int n,
+                               lapack_complex_double* x, lapack_int ldx, lapack_int* k);
+
+lapack_int LAPACKE_slartgp_work(float f, float g, float* cs, float* sn, float* r);
+lapack_int LAPACKE_dlartgp_work(double f, double g, double* cs, double* sn, double* r);
+
+lapack_int LAPACKE_slartgs_work(float x, float y, float sigma, float* cs, float* sn);
+lapack_int LAPACKE_dlartgs_work(double x, double y, double sigma, double* cs, double* sn);
+
+float LAPACKE_slapy2_work(float x, float y);
+double LAPACKE_dlapy2_work(double x, double y);
+
+float LAPACKE_slapy3_work(float x, float y, float z);
+double LAPACKE_dlapy3_work(double x, double y, double z);
+
+float LAPACKE_slamch_work(char cmach);
+double LAPACKE_dlamch_work(char cmach);
+
+float LAPACKE_slange_work(int matrix_order, char norm, lapack_int m, lapack_int n, const float* a, lapack_int lda,
+                          float* work);
+double LAPACKE_dlange_work(int matrix_order, char norm, lapack_int m, lapack_int n, const double* a, lapack_int lda,
+                           double* work);
+float LAPACKE_clange_work(int matrix_order, char norm, lapack_int m, lapack_int n, const lapack_complex_float* a,
+                          lapack_int lda, float* work);
+double LAPACKE_zlange_work(int matrix_order, char norm, lapack_int m, lapack_int n, const lapack_complex_double* a,
+                           lapack_int lda, double* work);
+
+float LAPACKE_clanhe_work(int matrix_order, char norm, char uplo, lapack_int n, const lapack_complex_float* a,
+                          lapack_int lda, float* work);
+double LAPACKE_zlanhe_work(int matrix_order, char norm, char uplo, lapack_int n, const lapack_complex_double* a,
+                           lapack_int lda, double* work);
+
+float LAPACKE_slansy_work(int matrix_order, char norm, char uplo, lapack_int n, const float* a, lapack_int lda,
+                          float* work);
+double LAPACKE_dlansy_work(int matrix_order, char norm, char uplo, lapack_int n, const double* a, lapack_int lda,
+                           double* work);
+float LAPACKE_clansy_work(int matrix_order, char norm, char uplo, lapack_int n, const lapack_complex_float* a,
+                          lapack_int lda, float* work);
+double LAPACKE_zlansy_work(int matrix_order, char norm, char uplo, lapack_int n, const lapack_complex_double* a,
+                           lapack_int lda, double* work);
+
+float LAPACKE_slantr_work(int matrix_order, char norm, char uplo, char diag, lapack_int m, lapack_int n, const float* a,
+                          lapack_int lda, float* work);
+double LAPACKE_dlantr_work(int matrix_order, char norm, char uplo, char diag, lapack_int m, lapack_int n,
+                           const double* a, lapack_int lda, double* work);
+float LAPACKE_clantr_work(int matrix_order, char norm, char uplo, char diag, lapack_int m, lapack_int n,
+                          const lapack_complex_float* a, lapack_int lda, float* work);
+double LAPACKE_zlantr_work(int matrix_order, char norm, char uplo, char diag, lapack_int m, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda, double* work);
+
+lapack_int LAPACKE_slarfb_work(int matrix_order, char side, char trans, char direct, char storev, lapack_int m,
+                               lapack_int n, lapack_int k, const float* v, lapack_int ldv, const float* t,
+                               lapack_int ldt, float* c, lapack_int ldc, float* work, lapack_int ldwork);
+lapack_int LAPACKE_dlarfb_work(int matrix_order, char side, char trans, char direct, char storev, lapack_int m,
+                               lapack_int n, lapack_int k, const double* v, lapack_int ldv, const double* t,
+                               lapack_int ldt, double* c, lapack_int ldc, double* work, lapack_int ldwork);
+lapack_int LAPACKE_clarfb_work(int matrix_order, char side, char trans, char direct, char storev, lapack_int m,
+                               lapack_int n, lapack_int k, const lapack_complex_float* v, lapack_int ldv,
+                               const lapack_complex_float* t, lapack_int ldt, lapack_complex_float* c, lapack_int ldc,
+                               lapack_complex_float* work, lapack_int ldwork);
+lapack_int LAPACKE_zlarfb_work(int matrix_order, char side, char trans, char direct, char storev, lapack_int m,
+                               lapack_int n, lapack_int k, const lapack_complex_double* v, lapack_int ldv,
+                               const lapack_complex_double* t, lapack_int ldt, lapack_complex_double* c, lapack_int ldc,
+                               lapack_complex_double* work, lapack_int ldwork);
+
+lapack_int LAPACKE_slarfg_work(lapack_int n, float* alpha, float* x, lapack_int incx, float* tau);
+lapack_int LAPACKE_dlarfg_work(lapack_int n, double* alpha, double* x, lapack_int incx, double* tau);
+lapack_int LAPACKE_clarfg_work(lapack_int n, lapack_complex_float* alpha, lapack_complex_float* x, lapack_int incx,
+                               lapack_complex_float* tau);
+lapack_int LAPACKE_zlarfg_work(lapack_int n, lapack_complex_double* alpha, lapack_complex_double* x, lapack_int incx,
+                               lapack_complex_double* tau);
+
+lapack_int LAPACKE_slarft_work(int matrix_order, char direct, char storev, lapack_int n, lapack_int k, const float* v,
+                               lapack_int ldv, const float* tau, float* t, lapack_int ldt);
+lapack_int LAPACKE_dlarft_work(int matrix_order, char direct, char storev, lapack_int n, lapack_int k, const double* v,
+                               lapack_int ldv, const double* tau, double* t, lapack_int ldt);
+lapack_int LAPACKE_clarft_work(int matrix_order, char direct, char storev, lapack_int n, lapack_int k,
+                               const lapack_complex_float* v, lapack_int ldv, const lapack_complex_float* tau,
+                               lapack_complex_float* t, lapack_int ldt);
+lapack_int LAPACKE_zlarft_work(int matrix_order, char direct, char storev, lapack_int n, lapack_int k,
+                               const lapack_complex_double* v, lapack_int ldv, const lapack_complex_double* tau,
+                               lapack_complex_double* t, lapack_int ldt);
+
+lapack_int LAPACKE_slarfx_work(int matrix_order, char side, lapack_int m, lapack_int n, const float* v, float tau,
+                               float* c, lapack_int ldc, float* work);
+lapack_int LAPACKE_dlarfx_work(int matrix_order, char side, lapack_int m, lapack_int n, const double* v, double tau,
+                               double* c, lapack_int ldc, double* work);
+lapack_int LAPACKE_clarfx_work(int matrix_order, char side, lapack_int m, lapack_int n, const lapack_complex_float* v,
+                               lapack_complex_float tau, lapack_complex_float* c, lapack_int ldc,
+                               lapack_complex_float* work);
+lapack_int LAPACKE_zlarfx_work(int matrix_order, char side, lapack_int m, lapack_int n, const lapack_complex_double* v,
+                               lapack_complex_double tau, lapack_complex_double* c, lapack_int ldc,
+                               lapack_complex_double* work);
+
+lapack_int LAPACKE_slarnv_work(lapack_int idist, lapack_int* iseed, lapack_int n, float* x);
+lapack_int LAPACKE_dlarnv_work(lapack_int idist, lapack_int* iseed, lapack_int n, double* x);
+lapack_int LAPACKE_clarnv_work(lapack_int idist, lapack_int* iseed, lapack_int n, lapack_complex_float* x);
+lapack_int LAPACKE_zlarnv_work(lapack_int idist, lapack_int* iseed, lapack_int n, lapack_complex_double* x);
+
+lapack_int LAPACKE_slaset_work(int matrix_order, char uplo, lapack_int m, lapack_int n, float alpha, float beta,
+                               float* a, lapack_int lda);
+lapack_int LAPACKE_dlaset_work(int matrix_order, char uplo, lapack_int m, lapack_int n, double alpha, double beta,
+                               double* a, lapack_int lda);
+lapack_int LAPACKE_claset_work(int matrix_order, char uplo, lapack_int m, lapack_int n, lapack_complex_float alpha,
+                               lapack_complex_float beta, lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zlaset_work(int matrix_order, char uplo, lapack_int m, lapack_int n, lapack_complex_double alpha,
+                               lapack_complex_double beta, lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_slasrt_work(char id, lapack_int n, float* d);
+lapack_int LAPACKE_dlasrt_work(char id, lapack_int n, double* d);
+
+lapack_int LAPACKE_slaswp_work(int matrix_order, lapack_int n, float* a, lapack_int lda, lapack_int k1, lapack_int k2,
+                               const lapack_int* ipiv, lapack_int incx);
+lapack_int LAPACKE_dlaswp_work(int matrix_order, lapack_int n, double* a, lapack_int lda, lapack_int k1, lapack_int k2,
+                               const lapack_int* ipiv, lapack_int incx);
+lapack_int LAPACKE_claswp_work(int matrix_order, lapack_int n, lapack_complex_float* a, lapack_int lda, lapack_int k1,
+                               lapack_int k2, const lapack_int* ipiv, lapack_int incx);
+lapack_int LAPACKE_zlaswp_work(int matrix_order, lapack_int n, lapack_complex_double* a, lapack_int lda, lapack_int k1,
+                               lapack_int k2, const lapack_int* ipiv, lapack_int incx);
+
+lapack_int LAPACKE_slatms_work(int matrix_order, lapack_int m, lapack_int n, char dist, lapack_int* iseed, char sym,
+                               float* d, lapack_int mode, float cond, float dmax, lapack_int kl, lapack_int ku,
+                               char pack, float* a, lapack_int lda, float* work);
+lapack_int LAPACKE_dlatms_work(int matrix_order, lapack_int m, lapack_int n, char dist, lapack_int* iseed, char sym,
+                               double* d, lapack_int mode, double cond, double dmax, lapack_int kl, lapack_int ku,
+                               char pack, double* a, lapack_int lda, double* work);
+lapack_int LAPACKE_clatms_work(int matrix_order, lapack_int m, lapack_int n, char dist, lapack_int* iseed, char sym,
+                               float* d, lapack_int mode, float cond, float dmax, lapack_int kl, lapack_int ku,
+                               char pack, lapack_complex_float* a, lapack_int lda, lapack_complex_float* work);
+lapack_int LAPACKE_zlatms_work(int matrix_order, lapack_int m, lapack_int n, char dist, lapack_int* iseed, char sym,
+                               double* d, lapack_int mode, double cond, double dmax, lapack_int kl, lapack_int ku,
+                               char pack, lapack_complex_double* a, lapack_int lda, lapack_complex_double* work);
+
+lapack_int LAPACKE_slauum_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda);
+lapack_int LAPACKE_dlauum_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda);
+lapack_int LAPACKE_clauum_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zlauum_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_sopgtr_work(int matrix_order, char uplo, lapack_int n, const float* ap, const float* tau, float* q,
+                               lapack_int ldq, float* work);
+lapack_int LAPACKE_dopgtr_work(int matrix_order, char uplo, lapack_int n, const double* ap, const double* tau,
+                               double* q, lapack_int ldq, double* work);
+
+lapack_int LAPACKE_sopmtr_work(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                               const float* ap, const float* tau, float* c, lapack_int ldc, float* work);
+lapack_int LAPACKE_dopmtr_work(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                               const double* ap, const double* tau, double* c, lapack_int ldc, double* work);
+
+lapack_int LAPACKE_sorgbr_work(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int k, float* a,
+                               lapack_int lda, const float* tau, float* work, lapack_int lwork);
+lapack_int LAPACKE_dorgbr_work(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int k, double* a,
+                               lapack_int lda, const double* tau, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sorghr_work(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, float* a, lapack_int lda,
+                               const float* tau, float* work, lapack_int lwork);
+lapack_int LAPACKE_dorghr_work(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, double* a,
+                               lapack_int lda, const double* tau, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sorglq_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, float* a, lapack_int lda,
+                               const float* tau, float* work, lapack_int lwork);
+lapack_int LAPACKE_dorglq_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, double* a, lapack_int lda,
+                               const double* tau, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sorgql_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, float* a, lapack_int lda,
+                               const float* tau, float* work, lapack_int lwork);
+lapack_int LAPACKE_dorgql_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, double* a, lapack_int lda,
+                               const double* tau, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sorgqr_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, float* a, lapack_int lda,
+                               const float* tau, float* work, lapack_int lwork);
+lapack_int LAPACKE_dorgqr_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, double* a, lapack_int lda,
+                               const double* tau, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sorgrq_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, float* a, lapack_int lda,
+                               const float* tau, float* work, lapack_int lwork);
+lapack_int LAPACKE_dorgrq_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, double* a, lapack_int lda,
+                               const double* tau, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sorgtr_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, const float* tau,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dorgtr_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda, const double* tau,
+                               double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sormbr_work(int matrix_order, char vect, char side, char trans, lapack_int m, lapack_int n,
+                               lapack_int k, const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dormbr_work(int matrix_order, char vect, char side, char trans, lapack_int m, lapack_int n,
+                               lapack_int k, const double* a, lapack_int lda, const double* tau, double* c,
+                               lapack_int ldc, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sormhr_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int ilo,
+                               lapack_int ihi, const float* a, lapack_int lda, const float* tau, float* c,
+                               lapack_int ldc, float* work, lapack_int lwork);
+lapack_int LAPACKE_dormhr_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int ilo,
+                               lapack_int ihi, const double* a, lapack_int lda, const double* tau, double* c,
+                               lapack_int ldc, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sormlq_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc, float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_dormlq_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc,
+                               double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sormql_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc, float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_dormql_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc,
+                               double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sormqr_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc, float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_dormqr_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc,
+                               double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sormrq_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc, float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_dormrq_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc,
+                               double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sormrz_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               lapack_int l, const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dormrz_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               lapack_int l, const double* a, lapack_int lda, const double* tau, double* c,
+                               lapack_int ldc, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_sormtr_work(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                               const float* a, lapack_int lda, const float* tau, float* c, lapack_int ldc, float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_dormtr_work(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                               const double* a, lapack_int lda, const double* tau, double* c, lapack_int ldc,
+                               double* work, lapack_int lwork);
+
+lapack_int LAPACKE_spbcon_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, const float* ab,
+                               lapack_int ldab, float anorm, float* rcond, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dpbcon_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, const double* ab,
+                               lapack_int ldab, double anorm, double* rcond, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cpbcon_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, const lapack_complex_float* ab,
+                               lapack_int ldab, float anorm, float* rcond, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zpbcon_work(int matrix_order, char uplo, lapack_int n, lapack_int kd,
+                               const lapack_complex_double* ab, lapack_int ldab, double anorm, double* rcond,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_spbequ_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, const float* ab,
+                               lapack_int ldab, float* s, float* scond, float* amax);
+lapack_int LAPACKE_dpbequ_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, const double* ab,
+                               lapack_int ldab, double* s, double* scond, double* amax);
+lapack_int LAPACKE_cpbequ_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, const lapack_complex_float* ab,
+                               lapack_int ldab, float* s, float* scond, float* amax);
+lapack_int LAPACKE_zpbequ_work(int matrix_order, char uplo, lapack_int n, lapack_int kd,
+                               const lapack_complex_double* ab, lapack_int ldab, double* s, double* scond,
+                               double* amax);
+
+lapack_int LAPACKE_spbrfs_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               const float* ab, lapack_int ldab, const float* afb, lapack_int ldafb, const float* b,
+                               lapack_int ldb, float* x, lapack_int ldx, float* ferr, float* berr, float* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_dpbrfs_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               const double* ab, lapack_int ldab, const double* afb, lapack_int ldafb, const double* b,
+                               lapack_int ldb, double* x, lapack_int ldx, double* ferr, double* berr, double* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_cpbrfs_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               const lapack_complex_float* ab, lapack_int ldab, const lapack_complex_float* afb,
+                               lapack_int ldafb, const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                               lapack_int ldx, float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zpbrfs_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               const lapack_complex_double* ab, lapack_int ldab, const lapack_complex_double* afb,
+                               lapack_int ldafb, const lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_spbstf_work(int matrix_order, char uplo, lapack_int n, lapack_int kb, float* bb, lapack_int ldbb);
+lapack_int LAPACKE_dpbstf_work(int matrix_order, char uplo, lapack_int n, lapack_int kb, double* bb, lapack_int ldbb);
+lapack_int LAPACKE_cpbstf_work(int matrix_order, char uplo, lapack_int n, lapack_int kb, lapack_complex_float* bb,
+                               lapack_int ldbb);
+lapack_int LAPACKE_zpbstf_work(int matrix_order, char uplo, lapack_int n, lapack_int kb, lapack_complex_double* bb,
+                               lapack_int ldbb);
+
+lapack_int LAPACKE_spbsv_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs, float* ab,
+                              lapack_int ldab, float* b, lapack_int ldb);
+lapack_int LAPACKE_dpbsv_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs, double* ab,
+                              lapack_int ldab, double* b, lapack_int ldb);
+lapack_int LAPACKE_cpbsv_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                              lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpbsv_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                              lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_spbsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               float* ab, lapack_int ldab, float* afb, lapack_int ldafb, char* equed, float* s,
+                               float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* ferr,
+                               float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dpbsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               double* ab, lapack_int ldab, double* afb, lapack_int ldafb, char* equed, double* s,
+                               double* b, lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* ferr,
+                               double* berr, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cpbsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* afb, lapack_int ldafb,
+                               char* equed, float* s, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                               lapack_int ldx, float* rcond, float* ferr, float* berr, lapack_complex_float* work,
+                               float* rwork);
+lapack_int LAPACKE_zpbsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* afb, lapack_int ldafb,
+                               char* equed, double* s, lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* rcond, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_spbtrf_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, float* ab, lapack_int ldab);
+lapack_int LAPACKE_dpbtrf_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, double* ab, lapack_int ldab);
+lapack_int LAPACKE_cpbtrf_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_complex_float* ab,
+                               lapack_int ldab);
+lapack_int LAPACKE_zpbtrf_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_complex_double* ab,
+                               lapack_int ldab);
+
+lapack_int LAPACKE_spbtrs_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               const float* ab, lapack_int ldab, float* b, lapack_int ldb);
+lapack_int LAPACKE_dpbtrs_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               const double* ab, lapack_int ldab, double* b, lapack_int ldb);
+lapack_int LAPACKE_cpbtrs_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               const lapack_complex_float* ab, lapack_int ldab, lapack_complex_float* b,
+                               lapack_int ldb);
+lapack_int LAPACKE_zpbtrs_work(int matrix_order, char uplo, lapack_int n, lapack_int kd, lapack_int nrhs,
+                               const lapack_complex_double* ab, lapack_int ldab, lapack_complex_double* b,
+                               lapack_int ldb);
+
+lapack_int LAPACKE_spftrf_work(int matrix_order, char transr, char uplo, lapack_int n, float* a);
+lapack_int LAPACKE_dpftrf_work(int matrix_order, char transr, char uplo, lapack_int n, double* a);
+lapack_int LAPACKE_cpftrf_work(int matrix_order, char transr, char uplo, lapack_int n, lapack_complex_float* a);
+lapack_int LAPACKE_zpftrf_work(int matrix_order, char transr, char uplo, lapack_int n, lapack_complex_double* a);
+
+lapack_int LAPACKE_spftri_work(int matrix_order, char transr, char uplo, lapack_int n, float* a);
+lapack_int LAPACKE_dpftri_work(int matrix_order, char transr, char uplo, lapack_int n, double* a);
+lapack_int LAPACKE_cpftri_work(int matrix_order, char transr, char uplo, lapack_int n, lapack_complex_float* a);
+lapack_int LAPACKE_zpftri_work(int matrix_order, char transr, char uplo, lapack_int n, lapack_complex_double* a);
+
+lapack_int LAPACKE_spftrs_work(int matrix_order, char transr, char uplo, lapack_int n, lapack_int nrhs, const float* a,
+                               float* b, lapack_int ldb);
+lapack_int LAPACKE_dpftrs_work(int matrix_order, char transr, char uplo, lapack_int n, lapack_int nrhs, const double* a,
+                               double* b, lapack_int ldb);
+lapack_int LAPACKE_cpftrs_work(int matrix_order, char transr, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpftrs_work(int matrix_order, char transr, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_spocon_work(int matrix_order, char uplo, lapack_int n, const float* a, lapack_int lda, float anorm,
+                               float* rcond, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dpocon_work(int matrix_order, char uplo, lapack_int n, const double* a, lapack_int lda, double anorm,
+                               double* rcond, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cpocon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                               float anorm, float* rcond, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zpocon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a,
+                               lapack_int lda, double anorm, double* rcond, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_spoequ_work(int matrix_order, lapack_int n, const float* a, lapack_int lda, float* s, float* scond,
+                               float* amax);
+lapack_int LAPACKE_dpoequ_work(int matrix_order, lapack_int n, const double* a, lapack_int lda, double* s,
+                               double* scond, double* amax);
+lapack_int LAPACKE_cpoequ_work(int matrix_order, lapack_int n, const lapack_complex_float* a, lapack_int lda, float* s,
+                               float* scond, float* amax);
+lapack_int LAPACKE_zpoequ_work(int matrix_order, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                               double* s, double* scond, double* amax);
+
+lapack_int LAPACKE_spoequb_work(int matrix_order, lapack_int n, const float* a, lapack_int lda, float* s, float* scond,
+                                float* amax);
+lapack_int LAPACKE_dpoequb_work(int matrix_order, lapack_int n, const double* a, lapack_int lda, double* s,
+                                double* scond, double* amax);
+lapack_int LAPACKE_cpoequb_work(int matrix_order, lapack_int n, const lapack_complex_float* a, lapack_int lda, float* s,
+                                float* scond, float* amax);
+lapack_int LAPACKE_zpoequb_work(int matrix_order, lapack_int n, const lapack_complex_double* a, lapack_int lda,
+                                double* s, double* scond, double* amax);
+
+lapack_int LAPACKE_sporfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* a,
+                               lapack_int lda, const float* af, lapack_int ldaf, const float* b, lapack_int ldb,
+                               float* x, lapack_int ldx, float* ferr, float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dporfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* a,
+                               lapack_int lda, const double* af, lapack_int ldaf, const double* b, lapack_int ldb,
+                               double* x, lapack_int ldx, double* ferr, double* berr, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cporfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                               lapack_int ldaf, const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                               lapack_int ldx, float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zporfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                               lapack_int ldaf, const lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sporfsx_work(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs, const float* a,
+                                lapack_int lda, const float* af, lapack_int ldaf, const float* s, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* berr,
+                                lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                                float* params, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dporfsx_work(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs, const double* a,
+                                lapack_int lda, const double* af, lapack_int ldaf, const double* s, const double* b,
+                                lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* berr,
+                                lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                                double* params, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cporfsx_work(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                                lapack_int ldaf, const float* s, const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx, float* rcond, float* berr,
+                                lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                                float* params, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zporfsx_work(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                                lapack_int ldaf, const double* s, const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx, double* rcond, double* berr,
+                                lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                                double* params, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sposv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, float* a, lapack_int lda,
+                              float* b, lapack_int ldb);
+lapack_int LAPACKE_dposv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                              double* b, lapack_int ldb);
+lapack_int LAPACKE_cposv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                              lapack_int lda, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zposv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                              lapack_int lda, lapack_complex_double* b, lapack_int ldb);
+lapack_int LAPACKE_dsposv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                               double* b, lapack_int ldb, double* x, lapack_int ldx, double* work, float* swork,
+                               lapack_int* iter);
+lapack_int LAPACKE_zcposv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                               lapack_int ldx, lapack_complex_double* work, lapack_complex_float* swork, double* rwork,
+                               lapack_int* iter);
+
+lapack_int LAPACKE_sposvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, float* a,
+                               lapack_int lda, float* af, lapack_int ldaf, char* equed, float* s, float* b,
+                               lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* ferr, float* berr,
+                               float* work, lapack_int* iwork);
+lapack_int LAPACKE_dposvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, double* a,
+                               lapack_int lda, double* af, lapack_int ldaf, char* equed, double* s, double* b,
+                               lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* ferr, double* berr,
+                               double* work, lapack_int* iwork);
+lapack_int LAPACKE_cposvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                               char* equed, float* s, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                               lapack_int ldx, float* rcond, float* ferr, float* berr, lapack_complex_float* work,
+                               float* rwork);
+lapack_int LAPACKE_zposvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                               char* equed, double* s, lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* rcond, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sposvxx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, float* a,
+                                lapack_int lda, float* af, lapack_int ldaf, char* equed, float* s, float* b,
+                                lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* rpvgrw, float* berr,
+                                lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                                float* params, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dposvxx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, double* a,
+                                lapack_int lda, double* af, lapack_int ldaf, char* equed, double* s, double* b,
+                                lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* rpvgrw, double* berr,
+                                lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                                double* params, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cposvxx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                                lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                                char* equed, float* s, lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                                lapack_int ldx, float* rcond, float* rpvgrw, float* berr, lapack_int n_err_bnds,
+                                float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams, float* params,
+                                lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zposvxx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                                lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                                char* equed, double* s, lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx, double* rcond, double* rpvgrw, double* berr,
+                                lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                                double* params, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_spotrf_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda);
+lapack_int LAPACKE_dpotrf_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda);
+lapack_int LAPACKE_cpotrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zpotrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_spotri_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda);
+lapack_int LAPACKE_dpotri_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda);
+lapack_int LAPACKE_cpotri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zpotri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_spotrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* a,
+                               lapack_int lda, float* b, lapack_int ldb);
+lapack_int LAPACKE_dpotrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* a,
+                               lapack_int lda, double* b, lapack_int ldb);
+lapack_int LAPACKE_cpotrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpotrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, lapack_complex_double* b,
+                               lapack_int ldb);
+
+lapack_int LAPACKE_sppcon_work(int matrix_order, char uplo, lapack_int n, const float* ap, float anorm, float* rcond,
+                               float* work, lapack_int* iwork);
+lapack_int LAPACKE_dppcon_work(int matrix_order, char uplo, lapack_int n, const double* ap, double anorm, double* rcond,
+                               double* work, lapack_int* iwork);
+lapack_int LAPACKE_cppcon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap, float anorm,
+                               float* rcond, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zppcon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap, double anorm,
+                               double* rcond, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sppequ_work(int matrix_order, char uplo, lapack_int n, const float* ap, float* s, float* scond,
+                               float* amax);
+lapack_int LAPACKE_dppequ_work(int matrix_order, char uplo, lapack_int n, const double* ap, double* s, double* scond,
+                               double* amax);
+lapack_int LAPACKE_cppequ_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap, float* s,
+                               float* scond, float* amax);
+lapack_int LAPACKE_zppequ_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap, double* s,
+                               double* scond, double* amax);
+
+lapack_int LAPACKE_spprfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* ap,
+                               const float* afp, const float* b, lapack_int ldb, float* x, lapack_int ldx, float* ferr,
+                               float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dpprfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* ap,
+                               const double* afp, const double* b, lapack_int ldb, double* x, lapack_int ldx,
+                               double* ferr, double* berr, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cpprfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* ap, const lapack_complex_float* afp,
+                               const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zpprfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* ap, const lapack_complex_double* afp,
+                               const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                               double* ferr, double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sppsv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, float* ap, float* b,
+                              lapack_int ldb);
+lapack_int LAPACKE_dppsv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, double* ap, double* b,
+                              lapack_int ldb);
+lapack_int LAPACKE_cppsv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* ap,
+                              lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zppsv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* ap,
+                              lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sppsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, float* ap,
+                               float* afp, char* equed, float* s, float* b, lapack_int ldb, float* x, lapack_int ldx,
+                               float* rcond, float* ferr, float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dppsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, double* ap,
+                               double* afp, char* equed, double* s, double* b, lapack_int ldb, double* x,
+                               lapack_int ldx, double* rcond, double* ferr, double* berr, double* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_cppsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               lapack_complex_float* ap, lapack_complex_float* afp, char* equed, float* s,
+                               lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* rcond, float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zppsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               lapack_complex_double* ap, lapack_complex_double* afp, char* equed, double* s,
+                               lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                               double* rcond, double* ferr, double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_spptrf_work(int matrix_order, char uplo, lapack_int n, float* ap);
+lapack_int LAPACKE_dpptrf_work(int matrix_order, char uplo, lapack_int n, double* ap);
+lapack_int LAPACKE_cpptrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap);
+lapack_int LAPACKE_zpptrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap);
+
+lapack_int LAPACKE_spptri_work(int matrix_order, char uplo, lapack_int n, float* ap);
+lapack_int LAPACKE_dpptri_work(int matrix_order, char uplo, lapack_int n, double* ap);
+lapack_int LAPACKE_cpptri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap);
+lapack_int LAPACKE_zpptri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap);
+
+lapack_int LAPACKE_spptrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* ap, float* b,
+                               lapack_int ldb);
+lapack_int LAPACKE_dpptrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* ap, double* b,
+                               lapack_int ldb);
+lapack_int LAPACKE_cpptrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* ap, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpptrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* ap, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_spstrf_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, lapack_int* piv,
+                               lapack_int* rank, float tol, float* work);
+lapack_int LAPACKE_dpstrf_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda, lapack_int* piv,
+                               lapack_int* rank, double tol, double* work);
+lapack_int LAPACKE_cpstrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_int* piv, lapack_int* rank, float tol, float* work);
+lapack_int LAPACKE_zpstrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_int* piv, lapack_int* rank, double tol, double* work);
+
+lapack_int LAPACKE_sptcon_work(lapack_int n, const float* d, const float* e, float anorm, float* rcond, float* work);
+lapack_int LAPACKE_dptcon_work(lapack_int n, const double* d, const double* e, double anorm, double* rcond,
+                               double* work);
+lapack_int LAPACKE_cptcon_work(lapack_int n, const float* d, const lapack_complex_float* e, float anorm, float* rcond,
+                               float* work);
+lapack_int LAPACKE_zptcon_work(lapack_int n, const double* d, const lapack_complex_double* e, double anorm,
+                               double* rcond, double* work);
+
+lapack_int LAPACKE_spteqr_work(int matrix_order, char compz, lapack_int n, float* d, float* e, float* z, lapack_int ldz,
+                               float* work);
+lapack_int LAPACKE_dpteqr_work(int matrix_order, char compz, lapack_int n, double* d, double* e, double* z,
+                               lapack_int ldz, double* work);
+lapack_int LAPACKE_cpteqr_work(int matrix_order, char compz, lapack_int n, float* d, float* e, lapack_complex_float* z,
+                               lapack_int ldz, float* work);
+lapack_int LAPACKE_zpteqr_work(int matrix_order, char compz, lapack_int n, double* d, double* e,
+                               lapack_complex_double* z, lapack_int ldz, double* work);
+
+lapack_int LAPACKE_sptrfs_work(int matrix_order, lapack_int n, lapack_int nrhs, const float* d, const float* e,
+                               const float* df, const float* ef, const float* b, lapack_int ldb, float* x,
+                               lapack_int ldx, float* ferr, float* berr, float* work);
+lapack_int LAPACKE_dptrfs_work(int matrix_order, lapack_int n, lapack_int nrhs, const double* d, const double* e,
+                               const double* df, const double* ef, const double* b, lapack_int ldb, double* x,
+                               lapack_int ldx, double* ferr, double* berr, double* work);
+lapack_int LAPACKE_cptrfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* d,
+                               const lapack_complex_float* e, const float* df, const lapack_complex_float* ef,
+                               const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zptrfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* d,
+                               const lapack_complex_double* e, const double* df, const lapack_complex_double* ef,
+                               const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                               double* ferr, double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sptsv_work(int matrix_order, lapack_int n, lapack_int nrhs, float* d, float* e, float* b,
+                              lapack_int ldb);
+lapack_int LAPACKE_dptsv_work(int matrix_order, lapack_int n, lapack_int nrhs, double* d, double* e, double* b,
+                              lapack_int ldb);
+lapack_int LAPACKE_cptsv_work(int matrix_order, lapack_int n, lapack_int nrhs, float* d, lapack_complex_float* e,
+                              lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zptsv_work(int matrix_order, lapack_int n, lapack_int nrhs, double* d, lapack_complex_double* e,
+                              lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sptsvx_work(int matrix_order, char fact, lapack_int n, lapack_int nrhs, const float* d,
+                               const float* e, float* df, float* ef, const float* b, lapack_int ldb, float* x,
+                               lapack_int ldx, float* rcond, float* ferr, float* berr, float* work);
+lapack_int LAPACKE_dptsvx_work(int matrix_order, char fact, lapack_int n, lapack_int nrhs, const double* d,
+                               const double* e, double* df, double* ef, const double* b, lapack_int ldb, double* x,
+                               lapack_int ldx, double* rcond, double* ferr, double* berr, double* work);
+lapack_int LAPACKE_cptsvx_work(int matrix_order, char fact, lapack_int n, lapack_int nrhs, const float* d,
+                               const lapack_complex_float* e, float* df, lapack_complex_float* ef,
+                               const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* rcond, float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zptsvx_work(int matrix_order, char fact, lapack_int n, lapack_int nrhs, const double* d,
+                               const lapack_complex_double* e, double* df, lapack_complex_double* ef,
+                               const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                               double* rcond, double* ferr, double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_spttrf_work(lapack_int n, float* d, float* e);
+lapack_int LAPACKE_dpttrf_work(lapack_int n, double* d, double* e);
+lapack_int LAPACKE_cpttrf_work(lapack_int n, float* d, lapack_complex_float* e);
+lapack_int LAPACKE_zpttrf_work(lapack_int n, double* d, lapack_complex_double* e);
+
+lapack_int LAPACKE_spttrs_work(int matrix_order, lapack_int n, lapack_int nrhs, const float* d, const float* e,
+                               float* b, lapack_int ldb);
+lapack_int LAPACKE_dpttrs_work(int matrix_order, lapack_int n, lapack_int nrhs, const double* d, const double* e,
+                               double* b, lapack_int ldb);
+lapack_int LAPACKE_cpttrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* d,
+                               const lapack_complex_float* e, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zpttrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* d,
+                               const lapack_complex_double* e, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_ssbev_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, float* ab,
+                              lapack_int ldab, float* w, float* z, lapack_int ldz, float* work);
+lapack_int LAPACKE_dsbev_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, double* ab,
+                              lapack_int ldab, double* w, double* z, lapack_int ldz, double* work);
+
+lapack_int LAPACKE_ssbevd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, float* ab,
+                               lapack_int ldab, float* w, float* z, lapack_int ldz, float* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dsbevd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int kd, double* ab,
+                               lapack_int ldab, double* w, double* z, lapack_int ldz, double* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_ssbevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int kd,
+                               float* ab, lapack_int ldab, float* q, lapack_int ldq, float vl, float vu, lapack_int il,
+                               lapack_int iu, float abstol, lapack_int* m, float* w, float* z, lapack_int ldz,
+                               float* work, lapack_int* iwork, lapack_int* ifail);
+lapack_int LAPACKE_dsbevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int kd,
+                               double* ab, lapack_int ldab, double* q, lapack_int ldq, double vl, double vu,
+                               lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w, double* z,
+                               lapack_int ldz, double* work, lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_ssbgst_work(int matrix_order, char vect, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                               float* ab, lapack_int ldab, const float* bb, lapack_int ldbb, float* x, lapack_int ldx,
+                               float* work);
+lapack_int LAPACKE_dsbgst_work(int matrix_order, char vect, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                               double* ab, lapack_int ldab, const double* bb, lapack_int ldbb, double* x,
+                               lapack_int ldx, double* work);
+
+lapack_int LAPACKE_ssbgv_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                              float* ab, lapack_int ldab, float* bb, lapack_int ldbb, float* w, float* z,
+                              lapack_int ldz, float* work);
+lapack_int LAPACKE_dsbgv_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                              double* ab, lapack_int ldab, double* bb, lapack_int ldbb, double* w, double* z,
+                              lapack_int ldz, double* work);
+
+lapack_int LAPACKE_ssbgvd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                               float* ab, lapack_int ldab, float* bb, lapack_int ldbb, float* w, float* z,
+                               lapack_int ldz, float* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dsbgvd_work(int matrix_order, char jobz, char uplo, lapack_int n, lapack_int ka, lapack_int kb,
+                               double* ab, lapack_int ldab, double* bb, lapack_int ldbb, double* w, double* z,
+                               lapack_int ldz, double* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_ssbgvx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int ka,
+                               lapack_int kb, float* ab, lapack_int ldab, float* bb, lapack_int ldbb, float* q,
+                               lapack_int ldq, float vl, float vu, lapack_int il, lapack_int iu, float abstol,
+                               lapack_int* m, float* w, float* z, lapack_int ldz, float* work, lapack_int* iwork,
+                               lapack_int* ifail);
+lapack_int LAPACKE_dsbgvx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, lapack_int ka,
+                               lapack_int kb, double* ab, lapack_int ldab, double* bb, lapack_int ldbb, double* q,
+                               lapack_int ldq, double vl, double vu, lapack_int il, lapack_int iu, double abstol,
+                               lapack_int* m, double* w, double* z, lapack_int ldz, double* work, lapack_int* iwork,
+                               lapack_int* ifail);
+
+lapack_int LAPACKE_ssbtrd_work(int matrix_order, char vect, char uplo, lapack_int n, lapack_int kd, float* ab,
+                               lapack_int ldab, float* d, float* e, float* q, lapack_int ldq, float* work);
+lapack_int LAPACKE_dsbtrd_work(int matrix_order, char vect, char uplo, lapack_int n, lapack_int kd, double* ab,
+                               lapack_int ldab, double* d, double* e, double* q, lapack_int ldq, double* work);
+
+lapack_int LAPACKE_ssfrk_work(int matrix_order, char transr, char uplo, char trans, lapack_int n, lapack_int k,
+                              float alpha, const float* a, lapack_int lda, float beta, float* c);
+lapack_int LAPACKE_dsfrk_work(int matrix_order, char transr, char uplo, char trans, lapack_int n, lapack_int k,
+                              double alpha, const double* a, lapack_int lda, double beta, double* c);
+
+lapack_int LAPACKE_sspcon_work(int matrix_order, char uplo, lapack_int n, const float* ap, const lapack_int* ipiv,
+                               float anorm, float* rcond, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dspcon_work(int matrix_order, char uplo, lapack_int n, const double* ap, const lapack_int* ipiv,
+                               double anorm, double* rcond, double* work, lapack_int* iwork);
+lapack_int LAPACKE_cspcon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap,
+                               const lapack_int* ipiv, float anorm, float* rcond, lapack_complex_float* work);
+lapack_int LAPACKE_zspcon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap,
+                               const lapack_int* ipiv, double anorm, double* rcond, lapack_complex_double* work);
+
+lapack_int LAPACKE_sspev_work(int matrix_order, char jobz, char uplo, lapack_int n, float* ap, float* w, float* z,
+                              lapack_int ldz, float* work);
+lapack_int LAPACKE_dspev_work(int matrix_order, char jobz, char uplo, lapack_int n, double* ap, double* w, double* z,
+                              lapack_int ldz, double* work);
+
+lapack_int LAPACKE_sspevd_work(int matrix_order, char jobz, char uplo, lapack_int n, float* ap, float* w, float* z,
+                               lapack_int ldz, float* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dspevd_work(int matrix_order, char jobz, char uplo, lapack_int n, double* ap, double* w, double* z,
+                               lapack_int ldz, double* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_sspevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, float* ap, float vl,
+                               float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w, float* z,
+                               lapack_int ldz, float* work, lapack_int* iwork, lapack_int* ifail);
+lapack_int LAPACKE_dspevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, double* ap, double vl,
+                               double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                               double* z, lapack_int ldz, double* work, lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_sspgst_work(int matrix_order, lapack_int itype, char uplo, lapack_int n, float* ap, const float* bp);
+lapack_int LAPACKE_dspgst_work(int matrix_order, lapack_int itype, char uplo, lapack_int n, double* ap,
+                               const double* bp);
+
+lapack_int LAPACKE_sspgv_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, float* ap,
+                              float* bp, float* w, float* z, lapack_int ldz, float* work);
+lapack_int LAPACKE_dspgv_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, double* ap,
+                              double* bp, double* w, double* z, lapack_int ldz, double* work);
+
+lapack_int LAPACKE_sspgvd_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, float* ap,
+                               float* bp, float* w, float* z, lapack_int ldz, float* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dspgvd_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, double* ap,
+                               double* bp, double* w, double* z, lapack_int ldz, double* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_sspgvx_work(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                               float* ap, float* bp, float vl, float vu, lapack_int il, lapack_int iu, float abstol,
+                               lapack_int* m, float* w, float* z, lapack_int ldz, float* work, lapack_int* iwork,
+                               lapack_int* ifail);
+lapack_int LAPACKE_dspgvx_work(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                               double* ap, double* bp, double vl, double vu, lapack_int il, lapack_int iu,
+                               double abstol, lapack_int* m, double* w, double* z, lapack_int ldz, double* work,
+                               lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_ssprfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* ap,
+                               const float* afp, const lapack_int* ipiv, const float* b, lapack_int ldb, float* x,
+                               lapack_int ldx, float* ferr, float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dsprfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* ap,
+                               const double* afp, const lapack_int* ipiv, const double* b, lapack_int ldb, double* x,
+                               lapack_int ldx, double* ferr, double* berr, double* work, lapack_int* iwork);
+lapack_int LAPACKE_csprfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* ap, const lapack_complex_float* afp, const lapack_int* ipiv,
+                               const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zsprfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* ap, const lapack_complex_double* afp,
+                               const lapack_int* ipiv, const lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_sspsv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, float* ap, lapack_int* ipiv,
+                              float* b, lapack_int ldb);
+lapack_int LAPACKE_dspsv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, double* ap, lapack_int* ipiv,
+                              double* b, lapack_int ldb);
+lapack_int LAPACKE_cspsv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* ap,
+                              lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zspsv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* ap,
+                              lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_sspsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, const float* ap,
+                               float* afp, lapack_int* ipiv, const float* b, lapack_int ldb, float* x, lapack_int ldx,
+                               float* rcond, float* ferr, float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dspsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, const double* ap,
+                               double* afp, lapack_int* ipiv, const double* b, lapack_int ldb, double* x,
+                               lapack_int ldx, double* rcond, double* ferr, double* berr, double* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_cspsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* ap, lapack_complex_float* afp, lapack_int* ipiv,
+                               const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x, lapack_int ldx,
+                               float* rcond, float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zspsvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* ap, lapack_complex_double* afp, lapack_int* ipiv,
+                               const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x, lapack_int ldx,
+                               double* rcond, double* ferr, double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_ssptrd_work(int matrix_order, char uplo, lapack_int n, float* ap, float* d, float* e, float* tau);
+lapack_int LAPACKE_dsptrd_work(int matrix_order, char uplo, lapack_int n, double* ap, double* d, double* e,
+                               double* tau);
+
+lapack_int LAPACKE_ssptrf_work(int matrix_order, char uplo, lapack_int n, float* ap, lapack_int* ipiv);
+lapack_int LAPACKE_dsptrf_work(int matrix_order, char uplo, lapack_int n, double* ap, lapack_int* ipiv);
+lapack_int LAPACKE_csptrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap, lapack_int* ipiv);
+lapack_int LAPACKE_zsptrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap, lapack_int* ipiv);
+
+lapack_int LAPACKE_ssptri_work(int matrix_order, char uplo, lapack_int n, float* ap, const lapack_int* ipiv,
+                               float* work);
+lapack_int LAPACKE_dsptri_work(int matrix_order, char uplo, lapack_int n, double* ap, const lapack_int* ipiv,
+                               double* work);
+lapack_int LAPACKE_csptri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* ap,
+                               const lapack_int* ipiv, lapack_complex_float* work);
+lapack_int LAPACKE_zsptri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* ap,
+                               const lapack_int* ipiv, lapack_complex_double* work);
+
+lapack_int LAPACKE_ssptrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* ap,
+                               const lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dsptrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* ap,
+                               const lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_csptrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* ap, const lapack_int* ipiv, lapack_complex_float* b,
+                               lapack_int ldb);
+lapack_int LAPACKE_zsptrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* ap, const lapack_int* ipiv, lapack_complex_double* b,
+                               lapack_int ldb);
+
+lapack_int LAPACKE_sstebz_work(char range, char order, lapack_int n, float vl, float vu, lapack_int il, lapack_int iu,
+                               float abstol, const float* d, const float* e, lapack_int* m, lapack_int* nsplit,
+                               float* w, lapack_int* iblock, lapack_int* isplit, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dstebz_work(char range, char order, lapack_int n, double vl, double vu, lapack_int il, lapack_int iu,
+                               double abstol, const double* d, const double* e, lapack_int* m, lapack_int* nsplit,
+                               double* w, lapack_int* iblock, lapack_int* isplit, double* work, lapack_int* iwork);
+
+lapack_int LAPACKE_sstedc_work(int matrix_order, char compz, lapack_int n, float* d, float* e, float* z, lapack_int ldz,
+                               float* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dstedc_work(int matrix_order, char compz, lapack_int n, double* d, double* e, double* z,
+                               lapack_int ldz, double* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_cstedc_work(int matrix_order, char compz, lapack_int n, float* d, float* e, lapack_complex_float* z,
+                               lapack_int ldz, lapack_complex_float* work, lapack_int lwork, float* rwork,
+                               lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_zstedc_work(int matrix_order, char compz, lapack_int n, double* d, double* e,
+                               lapack_complex_double* z, lapack_int ldz, lapack_complex_double* work, lapack_int lwork,
+                               double* rwork, lapack_int lrwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_sstegr_work(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl,
+                               float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w, float* z,
+                               lapack_int ldz, lapack_int* isuppz, float* work, lapack_int lwork, lapack_int* iwork,
+                               lapack_int liwork);
+lapack_int LAPACKE_dstegr_work(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                               double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                               double* z, lapack_int ldz, lapack_int* isuppz, double* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_cstegr_work(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl,
+                               float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w,
+                               lapack_complex_float* z, lapack_int ldz, lapack_int* isuppz, float* work,
+                               lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_zstegr_work(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                               double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                               lapack_complex_double* z, lapack_int ldz, lapack_int* isuppz, double* work,
+                               lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_sstein_work(int matrix_order, lapack_int n, const float* d, const float* e, lapack_int m,
+                               const float* w, const lapack_int* iblock, const lapack_int* isplit, float* z,
+                               lapack_int ldz, float* work, lapack_int* iwork, lapack_int* ifailv);
+lapack_int LAPACKE_dstein_work(int matrix_order, lapack_int n, const double* d, const double* e, lapack_int m,
+                               const double* w, const lapack_int* iblock, const lapack_int* isplit, double* z,
+                               lapack_int ldz, double* work, lapack_int* iwork, lapack_int* ifailv);
+lapack_int LAPACKE_cstein_work(int matrix_order, lapack_int n, const float* d, const float* e, lapack_int m,
+                               const float* w, const lapack_int* iblock, const lapack_int* isplit,
+                               lapack_complex_float* z, lapack_int ldz, float* work, lapack_int* iwork,
+                               lapack_int* ifailv);
+lapack_int LAPACKE_zstein_work(int matrix_order, lapack_int n, const double* d, const double* e, lapack_int m,
+                               const double* w, const lapack_int* iblock, const lapack_int* isplit,
+                               lapack_complex_double* z, lapack_int ldz, double* work, lapack_int* iwork,
+                               lapack_int* ifailv);
+
+lapack_int LAPACKE_sstemr_work(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl,
+                               float vu, lapack_int il, lapack_int iu, lapack_int* m, float* w, float* z,
+                               lapack_int ldz, lapack_int nzc, lapack_int* isuppz, lapack_logical* tryrac, float* work,
+                               lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dstemr_work(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                               double vu, lapack_int il, lapack_int iu, lapack_int* m, double* w, double* z,
+                               lapack_int ldz, lapack_int nzc, lapack_int* isuppz, lapack_logical* tryrac, double* work,
+                               lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_cstemr_work(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl,
+                               float vu, lapack_int il, lapack_int iu, lapack_int* m, float* w, lapack_complex_float* z,
+                               lapack_int ldz, lapack_int nzc, lapack_int* isuppz, lapack_logical* tryrac, float* work,
+                               lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_zstemr_work(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                               double vu, lapack_int il, lapack_int iu, lapack_int* m, double* w,
+                               lapack_complex_double* z, lapack_int ldz, lapack_int nzc, lapack_int* isuppz,
+                               lapack_logical* tryrac, double* work, lapack_int lwork, lapack_int* iwork,
+                               lapack_int liwork);
+
+lapack_int LAPACKE_ssteqr_work(int matrix_order, char compz, lapack_int n, float* d, float* e, float* z, lapack_int ldz,
+                               float* work);
+lapack_int LAPACKE_dsteqr_work(int matrix_order, char compz, lapack_int n, double* d, double* e, double* z,
+                               lapack_int ldz, double* work);
+lapack_int LAPACKE_csteqr_work(int matrix_order, char compz, lapack_int n, float* d, float* e, lapack_complex_float* z,
+                               lapack_int ldz, float* work);
+lapack_int LAPACKE_zsteqr_work(int matrix_order, char compz, lapack_int n, double* d, double* e,
+                               lapack_complex_double* z, lapack_int ldz, double* work);
+
+lapack_int LAPACKE_ssterf_work(lapack_int n, float* d, float* e);
+lapack_int LAPACKE_dsterf_work(lapack_int n, double* d, double* e);
+
+lapack_int LAPACKE_sstev_work(int matrix_order, char jobz, lapack_int n, float* d, float* e, float* z, lapack_int ldz,
+                              float* work);
+lapack_int LAPACKE_dstev_work(int matrix_order, char jobz, lapack_int n, double* d, double* e, double* z,
+                              lapack_int ldz, double* work);
+
+lapack_int LAPACKE_sstevd_work(int matrix_order, char jobz, lapack_int n, float* d, float* e, float* z, lapack_int ldz,
+                               float* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dstevd_work(int matrix_order, char jobz, lapack_int n, double* d, double* e, double* z,
+                               lapack_int ldz, double* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_sstevr_work(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl,
+                               float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w, float* z,
+                               lapack_int ldz, lapack_int* isuppz, float* work, lapack_int lwork, lapack_int* iwork,
+                               lapack_int liwork);
+lapack_int LAPACKE_dstevr_work(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                               double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                               double* z, lapack_int ldz, lapack_int* isuppz, double* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_sstevx_work(int matrix_order, char jobz, char range, lapack_int n, float* d, float* e, float vl,
+                               float vu, lapack_int il, lapack_int iu, float abstol, lapack_int* m, float* w, float* z,
+                               lapack_int ldz, float* work, lapack_int* iwork, lapack_int* ifail);
+lapack_int LAPACKE_dstevx_work(int matrix_order, char jobz, char range, lapack_int n, double* d, double* e, double vl,
+                               double vu, lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w,
+                               double* z, lapack_int ldz, double* work, lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_ssycon_work(int matrix_order, char uplo, lapack_int n, const float* a, lapack_int lda,
+                               const lapack_int* ipiv, float anorm, float* rcond, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dsycon_work(int matrix_order, char uplo, lapack_int n, const double* a, lapack_int lda,
+                               const lapack_int* ipiv, double anorm, double* rcond, double* work, lapack_int* iwork);
+lapack_int LAPACKE_csycon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                               const lapack_int* ipiv, float anorm, float* rcond, lapack_complex_float* work);
+lapack_int LAPACKE_zsycon_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a,
+                               lapack_int lda, const lapack_int* ipiv, double anorm, double* rcond,
+                               lapack_complex_double* work);
+
+lapack_int LAPACKE_ssyequb_work(int matrix_order, char uplo, lapack_int n, const float* a, lapack_int lda, float* s,
+                                float* scond, float* amax, float* work);
+lapack_int LAPACKE_dsyequb_work(int matrix_order, char uplo, lapack_int n, const double* a, lapack_int lda, double* s,
+                                double* scond, double* amax, double* work);
+lapack_int LAPACKE_csyequb_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a,
+                                lapack_int lda, float* s, float* scond, float* amax, lapack_complex_float* work);
+lapack_int LAPACKE_zsyequb_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a,
+                                lapack_int lda, double* s, double* scond, double* amax, lapack_complex_double* work);
+
+lapack_int LAPACKE_ssyev_work(int matrix_order, char jobz, char uplo, lapack_int n, float* a, lapack_int lda, float* w,
+                              float* work, lapack_int lwork);
+lapack_int LAPACKE_dsyev_work(int matrix_order, char jobz, char uplo, lapack_int n, double* a, lapack_int lda,
+                              double* w, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_ssyevd_work(int matrix_order, char jobz, char uplo, lapack_int n, float* a, lapack_int lda, float* w,
+                               float* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dsyevd_work(int matrix_order, char jobz, char uplo, lapack_int n, double* a, lapack_int lda,
+                               double* w, double* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_ssyevr_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, float* a,
+                               lapack_int lda, float vl, float vu, lapack_int il, lapack_int iu, float abstol,
+                               lapack_int* m, float* w, float* z, lapack_int ldz, lapack_int* isuppz, float* work,
+                               lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dsyevr_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, double* a,
+                               lapack_int lda, double vl, double vu, lapack_int il, lapack_int iu, double abstol,
+                               lapack_int* m, double* w, double* z, lapack_int ldz, lapack_int* isuppz, double* work,
+                               lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_ssyevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, float* a,
+                               lapack_int lda, float vl, float vu, lapack_int il, lapack_int iu, float abstol,
+                               lapack_int* m, float* w, float* z, lapack_int ldz, float* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int* ifail);
+lapack_int LAPACKE_dsyevx_work(int matrix_order, char jobz, char range, char uplo, lapack_int n, double* a,
+                               lapack_int lda, double vl, double vu, lapack_int il, lapack_int iu, double abstol,
+                               lapack_int* m, double* w, double* z, lapack_int ldz, double* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_ssygst_work(int matrix_order, lapack_int itype, char uplo, lapack_int n, float* a, lapack_int lda,
+                               const float* b, lapack_int ldb);
+lapack_int LAPACKE_dsygst_work(int matrix_order, lapack_int itype, char uplo, lapack_int n, double* a, lapack_int lda,
+                               const double* b, lapack_int ldb);
+
+lapack_int LAPACKE_ssygv_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, float* a,
+                              lapack_int lda, float* b, lapack_int ldb, float* w, float* work, lapack_int lwork);
+lapack_int LAPACKE_dsygv_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, double* a,
+                              lapack_int lda, double* b, lapack_int ldb, double* w, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_ssygvd_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, float* a,
+                               lapack_int lda, float* b, lapack_int ldb, float* w, float* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dsygvd_work(int matrix_order, lapack_int itype, char jobz, char uplo, lapack_int n, double* a,
+                               lapack_int lda, double* b, lapack_int ldb, double* w, double* work, lapack_int lwork,
+                               lapack_int* iwork, lapack_int liwork);
+
+lapack_int LAPACKE_ssygvx_work(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                               float* a, lapack_int lda, float* b, lapack_int ldb, float vl, float vu, lapack_int il,
+                               lapack_int iu, float abstol, lapack_int* m, float* w, float* z, lapack_int ldz,
+                               float* work, lapack_int lwork, lapack_int* iwork, lapack_int* ifail);
+lapack_int LAPACKE_dsygvx_work(int matrix_order, lapack_int itype, char jobz, char range, char uplo, lapack_int n,
+                               double* a, lapack_int lda, double* b, lapack_int ldb, double vl, double vu,
+                               lapack_int il, lapack_int iu, double abstol, lapack_int* m, double* w, double* z,
+                               lapack_int ldz, double* work, lapack_int lwork, lapack_int* iwork, lapack_int* ifail);
+
+lapack_int LAPACKE_ssyrfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* a,
+                               lapack_int lda, const float* af, lapack_int ldaf, const lapack_int* ipiv, const float* b,
+                               lapack_int ldb, float* x, lapack_int ldx, float* ferr, float* berr, float* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_dsyrfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* a,
+                               lapack_int lda, const double* af, lapack_int ldaf, const lapack_int* ipiv,
+                               const double* b, lapack_int ldb, double* x, lapack_int ldx, double* ferr, double* berr,
+                               double* work, lapack_int* iwork);
+lapack_int LAPACKE_csyrfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                               lapack_int ldaf, const lapack_int* ipiv, const lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr,
+                               lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zsyrfs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                               lapack_int ldaf, const lapack_int* ipiv, const lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_ssyrfsx_work(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs, const float* a,
+                                lapack_int lda, const float* af, lapack_int ldaf, const lapack_int* ipiv,
+                                const float* s, const float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond,
+                                float* berr, lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                                lapack_int nparams, float* params, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dsyrfsx_work(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs, const double* a,
+                                lapack_int lda, const double* af, lapack_int ldaf, const lapack_int* ipiv,
+                                const double* s, const double* b, lapack_int ldb, double* x, lapack_int ldx,
+                                double* rcond, double* berr, lapack_int n_err_bnds, double* err_bnds_norm,
+                                double* err_bnds_comp, lapack_int nparams, double* params, double* work,
+                                lapack_int* iwork);
+lapack_int LAPACKE_csyrfsx_work(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* af,
+                                lapack_int ldaf, const lapack_int* ipiv, const float* s, const lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x, lapack_int ldx, float* rcond, float* berr,
+                                lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                                float* params, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zsyrfsx_work(int matrix_order, char uplo, char equed, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* af,
+                                lapack_int ldaf, const lapack_int* ipiv, const double* s,
+                                const lapack_complex_double* b, lapack_int ldb, lapack_complex_double* x,
+                                lapack_int ldx, double* rcond, double* berr, lapack_int n_err_bnds,
+                                double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams, double* params,
+                                lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_ssysv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, float* a, lapack_int lda,
+                              lapack_int* ipiv, float* b, lapack_int ldb, float* work, lapack_int lwork);
+lapack_int LAPACKE_dsysv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, double* a, lapack_int lda,
+                              lapack_int* ipiv, double* b, lapack_int ldb, double* work, lapack_int lwork);
+lapack_int LAPACKE_csysv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_float* a,
+                              lapack_int lda, lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb,
+                              lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zsysv_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, lapack_complex_double* a,
+                              lapack_int lda, lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb,
+                              lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_ssysvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, const float* a,
+                               lapack_int lda, float* af, lapack_int ldaf, lapack_int* ipiv, const float* b,
+                               lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* ferr, float* berr,
+                               float* work, lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_dsysvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, const double* a,
+                               lapack_int lda, double* af, lapack_int ldaf, lapack_int* ipiv, const double* b,
+                               lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* ferr, double* berr,
+                               double* work, lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_csysvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                               lapack_int* ipiv, const lapack_complex_float* b, lapack_int ldb, lapack_complex_float* x,
+                               lapack_int ldx, float* rcond, float* ferr, float* berr, lapack_complex_float* work,
+                               lapack_int lwork, float* rwork);
+lapack_int LAPACKE_zsysvx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, lapack_complex_double* af,
+                               lapack_int ldaf, lapack_int* ipiv, const lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* x, lapack_int ldx, double* rcond, double* ferr, double* berr,
+                               lapack_complex_double* work, lapack_int lwork, double* rwork);
+
+lapack_int LAPACKE_ssysvxx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, float* a,
+                                lapack_int lda, float* af, lapack_int ldaf, lapack_int* ipiv, char* equed, float* s,
+                                float* b, lapack_int ldb, float* x, lapack_int ldx, float* rcond, float* rpvgrw,
+                                float* berr, lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                                lapack_int nparams, float* params, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dsysvxx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs, double* a,
+                                lapack_int lda, double* af, lapack_int ldaf, lapack_int* ipiv, char* equed, double* s,
+                                double* b, lapack_int ldb, double* x, lapack_int ldx, double* rcond, double* rpvgrw,
+                                double* berr, lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                                lapack_int nparams, double* params, double* work, lapack_int* iwork);
+lapack_int LAPACKE_csysvxx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                                lapack_complex_float* a, lapack_int lda, lapack_complex_float* af, lapack_int ldaf,
+                                lapack_int* ipiv, char* equed, float* s, lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx, float* rcond, float* rpvgrw, float* berr,
+                                lapack_int n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int nparams,
+                                float* params, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_zsysvxx_work(int matrix_order, char fact, char uplo, lapack_int n, lapack_int nrhs,
+                                lapack_complex_double* a, lapack_int lda, lapack_complex_double* af, lapack_int ldaf,
+                                lapack_int* ipiv, char* equed, double* s, lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx, double* rcond, double* rpvgrw, double* berr,
+                                lapack_int n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int nparams,
+                                double* params, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_ssytrd_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, float* d, float* e,
+                               float* tau, float* work, lapack_int lwork);
+lapack_int LAPACKE_dsytrd_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda, double* d,
+                               double* e, double* tau, double* work, lapack_int lwork);
+
+lapack_int LAPACKE_ssytrf_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, lapack_int* ipiv,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dsytrf_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda, lapack_int* ipiv,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_csytrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_int* ipiv, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zsytrf_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_int* ipiv, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_ssytri_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda,
+                               const lapack_int* ipiv, float* work);
+lapack_int LAPACKE_dsytri_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda,
+                               const lapack_int* ipiv, double* work);
+lapack_int LAPACKE_csytri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               const lapack_int* ipiv, lapack_complex_float* work);
+lapack_int LAPACKE_zsytri_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               const lapack_int* ipiv, lapack_complex_double* work);
+
+lapack_int LAPACKE_ssytrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* a,
+                               lapack_int lda, const lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_dsytrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* a,
+                               lapack_int lda, const lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_csytrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_int* ipiv,
+                               lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_zsytrs_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_int* ipiv,
+                               lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_stbcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n, lapack_int kd,
+                               const float* ab, lapack_int ldab, float* rcond, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dtbcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n, lapack_int kd,
+                               const double* ab, lapack_int ldab, double* rcond, double* work, lapack_int* iwork);
+lapack_int LAPACKE_ctbcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n, lapack_int kd,
+                               const lapack_complex_float* ab, lapack_int ldab, float* rcond,
+                               lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_ztbcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n, lapack_int kd,
+                               const lapack_complex_double* ab, lapack_int ldab, double* rcond,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_stbrfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                               lapack_int nrhs, const float* ab, lapack_int ldab, const float* b, lapack_int ldb,
+                               const float* x, lapack_int ldx, float* ferr, float* berr, float* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_dtbrfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                               lapack_int nrhs, const double* ab, lapack_int ldab, const double* b, lapack_int ldb,
+                               const double* x, lapack_int ldx, double* ferr, double* berr, double* work,
+                               lapack_int* iwork);
+lapack_int LAPACKE_ctbrfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                               lapack_int nrhs, const lapack_complex_float* ab, lapack_int ldab,
+                               const lapack_complex_float* b, lapack_int ldb, const lapack_complex_float* x,
+                               lapack_int ldx, float* ferr, float* berr, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_ztbrfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                               lapack_int nrhs, const lapack_complex_double* ab, lapack_int ldab,
+                               const lapack_complex_double* b, lapack_int ldb, const lapack_complex_double* x,
+                               lapack_int ldx, double* ferr, double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_stbtrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                               lapack_int nrhs, const float* ab, lapack_int ldab, float* b, lapack_int ldb);
+lapack_int LAPACKE_dtbtrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                               lapack_int nrhs, const double* ab, lapack_int ldab, double* b, lapack_int ldb);
+lapack_int LAPACKE_ctbtrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                               lapack_int nrhs, const lapack_complex_float* ab, lapack_int ldab,
+                               lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_ztbtrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int kd,
+                               lapack_int nrhs, const lapack_complex_double* ab, lapack_int ldab,
+                               lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_stfsm_work(int matrix_order, char transr, char side, char uplo, char trans, char diag, lapack_int m,
+                              lapack_int n, float alpha, const float* a, float* b, lapack_int ldb);
+lapack_int LAPACKE_dtfsm_work(int matrix_order, char transr, char side, char uplo, char trans, char diag, lapack_int m,
+                              lapack_int n, double alpha, const double* a, double* b, lapack_int ldb);
+lapack_int LAPACKE_ctfsm_work(int matrix_order, char transr, char side, char uplo, char trans, char diag, lapack_int m,
+                              lapack_int n, lapack_complex_float alpha, const lapack_complex_float* a,
+                              lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_ztfsm_work(int matrix_order, char transr, char side, char uplo, char trans, char diag, lapack_int m,
+                              lapack_int n, lapack_complex_double alpha, const lapack_complex_double* a,
+                              lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_stftri_work(int matrix_order, char transr, char uplo, char diag, lapack_int n, float* a);
+lapack_int LAPACKE_dtftri_work(int matrix_order, char transr, char uplo, char diag, lapack_int n, double* a);
+lapack_int LAPACKE_ctftri_work(int matrix_order, char transr, char uplo, char diag, lapack_int n,
+                               lapack_complex_float* a);
+lapack_int LAPACKE_ztftri_work(int matrix_order, char transr, char uplo, char diag, lapack_int n,
+                               lapack_complex_double* a);
+
+lapack_int LAPACKE_stfttp_work(int matrix_order, char transr, char uplo, lapack_int n, const float* arf, float* ap);
+lapack_int LAPACKE_dtfttp_work(int matrix_order, char transr, char uplo, lapack_int n, const double* arf, double* ap);
+lapack_int LAPACKE_ctfttp_work(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_float* arf,
+                               lapack_complex_float* ap);
+lapack_int LAPACKE_ztfttp_work(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_double* arf,
+                               lapack_complex_double* ap);
+
+lapack_int LAPACKE_stfttr_work(int matrix_order, char transr, char uplo, lapack_int n, const float* arf, float* a,
+                               lapack_int lda);
+lapack_int LAPACKE_dtfttr_work(int matrix_order, char transr, char uplo, lapack_int n, const double* arf, double* a,
+                               lapack_int lda);
+lapack_int LAPACKE_ctfttr_work(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_float* arf,
+                               lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_ztfttr_work(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_double* arf,
+                               lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_stgevc_work(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                               const float* s, lapack_int lds, const float* p, lapack_int ldp, float* vl,
+                               lapack_int ldvl, float* vr, lapack_int ldvr, lapack_int mm, lapack_int* m, float* work);
+lapack_int LAPACKE_dtgevc_work(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                               const double* s, lapack_int lds, const double* p, lapack_int ldp, double* vl,
+                               lapack_int ldvl, double* vr, lapack_int ldvr, lapack_int mm, lapack_int* m,
+                               double* work);
+lapack_int LAPACKE_ctgevc_work(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                               const lapack_complex_float* s, lapack_int lds, const lapack_complex_float* p,
+                               lapack_int ldp, lapack_complex_float* vl, lapack_int ldvl, lapack_complex_float* vr,
+                               lapack_int ldvr, lapack_int mm, lapack_int* m, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_ztgevc_work(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                               const lapack_complex_double* s, lapack_int lds, const lapack_complex_double* p,
+                               lapack_int ldp, lapack_complex_double* vl, lapack_int ldvl, lapack_complex_double* vr,
+                               lapack_int ldvr, lapack_int mm, lapack_int* m, lapack_complex_double* work,
+                               double* rwork);
+
+lapack_int LAPACKE_stgexc_work(int matrix_order, lapack_logical wantq, lapack_logical wantz, lapack_int n, float* a,
+                               lapack_int lda, float* b, lapack_int ldb, float* q, lapack_int ldq, float* z,
+                               lapack_int ldz, lapack_int* ifst, lapack_int* ilst, float* work, lapack_int lwork);
+lapack_int LAPACKE_dtgexc_work(int matrix_order, lapack_logical wantq, lapack_logical wantz, lapack_int n, double* a,
+                               lapack_int lda, double* b, lapack_int ldb, double* q, lapack_int ldq, double* z,
+                               lapack_int ldz, lapack_int* ifst, lapack_int* ilst, double* work, lapack_int lwork);
+lapack_int LAPACKE_ctgexc_work(int matrix_order, lapack_logical wantq, lapack_logical wantz, lapack_int n,
+                               lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* q, lapack_int ldq, lapack_complex_float* z, lapack_int ldz,
+                               lapack_int ifst, lapack_int ilst);
+lapack_int LAPACKE_ztgexc_work(int matrix_order, lapack_logical wantq, lapack_logical wantz, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* q, lapack_int ldq, lapack_complex_double* z, lapack_int ldz,
+                               lapack_int ifst, lapack_int ilst);
+
+lapack_int LAPACKE_stgsen_work(int matrix_order, lapack_int ijob, lapack_logical wantq, lapack_logical wantz,
+                               const lapack_logical* select, lapack_int n, float* a, lapack_int lda, float* b,
+                               lapack_int ldb, float* alphar, float* alphai, float* beta, float* q, lapack_int ldq,
+                               float* z, lapack_int ldz, lapack_int* m, float* pl, float* pr, float* dif, float* work,
+                               lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_dtgsen_work(int matrix_order, lapack_int ijob, lapack_logical wantq, lapack_logical wantz,
+                               const lapack_logical* select, lapack_int n, double* a, lapack_int lda, double* b,
+                               lapack_int ldb, double* alphar, double* alphai, double* beta, double* q, lapack_int ldq,
+                               double* z, lapack_int ldz, lapack_int* m, double* pl, double* pr, double* dif,
+                               double* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_ctgsen_work(int matrix_order, lapack_int ijob, lapack_logical wantq, lapack_logical wantz,
+                               const lapack_logical* select, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* b, lapack_int ldb, lapack_complex_float* alpha,
+                               lapack_complex_float* beta, lapack_complex_float* q, lapack_int ldq,
+                               lapack_complex_float* z, lapack_int ldz, lapack_int* m, float* pl, float* pr, float* dif,
+                               lapack_complex_float* work, lapack_int lwork, lapack_int* iwork, lapack_int liwork);
+lapack_int LAPACKE_ztgsen_work(int matrix_order, lapack_int ijob, lapack_logical wantq, lapack_logical wantz,
+                               const lapack_logical* select, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* b, lapack_int ldb, lapack_complex_double* alpha,
+                               lapack_complex_double* beta, lapack_complex_double* q, lapack_int ldq,
+                               lapack_complex_double* z, lapack_int ldz, lapack_int* m, double* pl, double* pr,
+                               double* dif, lapack_complex_double* work, lapack_int lwork, lapack_int* iwork,
+                               lapack_int liwork);
+
+lapack_int LAPACKE_stgsja_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p,
+                               lapack_int n, lapack_int k, lapack_int l, float* a, lapack_int lda, float* b,
+                               lapack_int ldb, float tola, float tolb, float* alpha, float* beta, float* u,
+                               lapack_int ldu, float* v, lapack_int ldv, float* q, lapack_int ldq, float* work,
+                               lapack_int* ncycle);
+lapack_int LAPACKE_dtgsja_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p,
+                               lapack_int n, lapack_int k, lapack_int l, double* a, lapack_int lda, double* b,
+                               lapack_int ldb, double tola, double tolb, double* alpha, double* beta, double* u,
+                               lapack_int ldu, double* v, lapack_int ldv, double* q, lapack_int ldq, double* work,
+                               lapack_int* ncycle);
+lapack_int LAPACKE_ctgsja_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p,
+                               lapack_int n, lapack_int k, lapack_int l, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* b, lapack_int ldb, float tola, float tolb, float* alpha,
+                               float* beta, lapack_complex_float* u, lapack_int ldu, lapack_complex_float* v,
+                               lapack_int ldv, lapack_complex_float* q, lapack_int ldq, lapack_complex_float* work,
+                               lapack_int* ncycle);
+lapack_int LAPACKE_ztgsja_work(int matrix_order, char jobu, char jobv, char jobq, lapack_int m, lapack_int p,
+                               lapack_int n, lapack_int k, lapack_int l, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* b, lapack_int ldb, double tola, double tolb, double* alpha,
+                               double* beta, lapack_complex_double* u, lapack_int ldu, lapack_complex_double* v,
+                               lapack_int ldv, lapack_complex_double* q, lapack_int ldq, lapack_complex_double* work,
+                               lapack_int* ncycle);
+
+lapack_int LAPACKE_stgsna_work(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                               const float* a, lapack_int lda, const float* b, lapack_int ldb, const float* vl,
+                               lapack_int ldvl, const float* vr, lapack_int ldvr, float* s, float* dif, lapack_int mm,
+                               lapack_int* m, float* work, lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_dtgsna_work(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                               const double* a, lapack_int lda, const double* b, lapack_int ldb, const double* vl,
+                               lapack_int ldvl, const double* vr, lapack_int ldvr, double* s, double* dif,
+                               lapack_int mm, lapack_int* m, double* work, lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_ctgsna_work(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* b,
+                               lapack_int ldb, const lapack_complex_float* vl, lapack_int ldvl,
+                               const lapack_complex_float* vr, lapack_int ldvr, float* s, float* dif, lapack_int mm,
+                               lapack_int* m, lapack_complex_float* work, lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_ztgsna_work(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* b,
+                               lapack_int ldb, const lapack_complex_double* vl, lapack_int ldvl,
+                               const lapack_complex_double* vr, lapack_int ldvr, double* s, double* dif, lapack_int mm,
+                               lapack_int* m, lapack_complex_double* work, lapack_int lwork, lapack_int* iwork);
+
+lapack_int LAPACKE_stgsyl_work(int matrix_order, char trans, lapack_int ijob, lapack_int m, lapack_int n,
+                               const float* a, lapack_int lda, const float* b, lapack_int ldb, float* c, lapack_int ldc,
+                               const float* d, lapack_int ldd, const float* e, lapack_int lde, float* f, lapack_int ldf,
+                               float* scale, float* dif, float* work, lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_dtgsyl_work(int matrix_order, char trans, lapack_int ijob, lapack_int m, lapack_int n,
+                               const double* a, lapack_int lda, const double* b, lapack_int ldb, double* c,
+                               lapack_int ldc, const double* d, lapack_int ldd, const double* e, lapack_int lde,
+                               double* f, lapack_int ldf, double* scale, double* dif, double* work, lapack_int lwork,
+                               lapack_int* iwork);
+lapack_int LAPACKE_ctgsyl_work(int matrix_order, char trans, lapack_int ijob, lapack_int m, lapack_int n,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* b,
+                               lapack_int ldb, lapack_complex_float* c, lapack_int ldc, const lapack_complex_float* d,
+                               lapack_int ldd, const lapack_complex_float* e, lapack_int lde, lapack_complex_float* f,
+                               lapack_int ldf, float* scale, float* dif, lapack_complex_float* work, lapack_int lwork,
+                               lapack_int* iwork);
+lapack_int LAPACKE_ztgsyl_work(int matrix_order, char trans, lapack_int ijob, lapack_int m, lapack_int n,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* b,
+                               lapack_int ldb, lapack_complex_double* c, lapack_int ldc, const lapack_complex_double* d,
+                               lapack_int ldd, const lapack_complex_double* e, lapack_int lde, lapack_complex_double* f,
+                               lapack_int ldf, double* scale, double* dif, lapack_complex_double* work,
+                               lapack_int lwork, lapack_int* iwork);
+
+lapack_int LAPACKE_stpcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n, const float* ap,
+                               float* rcond, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dtpcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n, const double* ap,
+                               double* rcond, double* work, lapack_int* iwork);
+lapack_int LAPACKE_ctpcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n,
+                               const lapack_complex_float* ap, float* rcond, lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_ztpcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n,
+                               const lapack_complex_double* ap, double* rcond, lapack_complex_double* work,
+                               double* rwork);
+
+lapack_int LAPACKE_stprfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const float* ap, const float* b, lapack_int ldb, const float* x, lapack_int ldx,
+                               float* ferr, float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dtprfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const double* ap, const double* b, lapack_int ldb, const double* x, lapack_int ldx,
+                               double* ferr, double* berr, double* work, lapack_int* iwork);
+lapack_int LAPACKE_ctprfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* ap, const lapack_complex_float* b, lapack_int ldb,
+                               const lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr,
+                               lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_ztprfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* ap, const lapack_complex_double* b, lapack_int ldb,
+                               const lapack_complex_double* x, lapack_int ldx, double* ferr, double* berr,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_stptri_work(int matrix_order, char uplo, char diag, lapack_int n, float* ap);
+lapack_int LAPACKE_dtptri_work(int matrix_order, char uplo, char diag, lapack_int n, double* ap);
+lapack_int LAPACKE_ctptri_work(int matrix_order, char uplo, char diag, lapack_int n, lapack_complex_float* ap);
+lapack_int LAPACKE_ztptri_work(int matrix_order, char uplo, char diag, lapack_int n, lapack_complex_double* ap);
+
+lapack_int LAPACKE_stptrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const float* ap, float* b, lapack_int ldb);
+lapack_int LAPACKE_dtptrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const double* ap, double* b, lapack_int ldb);
+lapack_int LAPACKE_ctptrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* ap, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_ztptrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* ap, lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_stpttf_work(int matrix_order, char transr, char uplo, lapack_int n, const float* ap, float* arf);
+lapack_int LAPACKE_dtpttf_work(int matrix_order, char transr, char uplo, lapack_int n, const double* ap, double* arf);
+lapack_int LAPACKE_ctpttf_work(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_float* ap,
+                               lapack_complex_float* arf);
+lapack_int LAPACKE_ztpttf_work(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_double* ap,
+                               lapack_complex_double* arf);
+
+lapack_int LAPACKE_stpttr_work(int matrix_order, char uplo, lapack_int n, const float* ap, float* a, lapack_int lda);
+lapack_int LAPACKE_dtpttr_work(int matrix_order, char uplo, lapack_int n, const double* ap, double* a, lapack_int lda);
+lapack_int LAPACKE_ctpttr_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap,
+                               lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_ztpttr_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap,
+                               lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_strcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n, const float* a,
+                               lapack_int lda, float* rcond, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dtrcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n, const double* a,
+                               lapack_int lda, double* rcond, double* work, lapack_int* iwork);
+lapack_int LAPACKE_ctrcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n,
+                               const lapack_complex_float* a, lapack_int lda, float* rcond, lapack_complex_float* work,
+                               float* rwork);
+lapack_int LAPACKE_ztrcon_work(int matrix_order, char norm, char uplo, char diag, lapack_int n,
+                               const lapack_complex_double* a, lapack_int lda, double* rcond,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_strevc_work(int matrix_order, char side, char howmny, lapack_logical* select, lapack_int n,
+                               const float* t, lapack_int ldt, float* vl, lapack_int ldvl, float* vr, lapack_int ldvr,
+                               lapack_int mm, lapack_int* m, float* work);
+lapack_int LAPACKE_dtrevc_work(int matrix_order, char side, char howmny, lapack_logical* select, lapack_int n,
+                               const double* t, lapack_int ldt, double* vl, lapack_int ldvl, double* vr,
+                               lapack_int ldvr, lapack_int mm, lapack_int* m, double* work);
+lapack_int LAPACKE_ctrevc_work(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                               lapack_complex_float* t, lapack_int ldt, lapack_complex_float* vl, lapack_int ldvl,
+                               lapack_complex_float* vr, lapack_int ldvr, lapack_int mm, lapack_int* m,
+                               lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_ztrevc_work(int matrix_order, char side, char howmny, const lapack_logical* select, lapack_int n,
+                               lapack_complex_double* t, lapack_int ldt, lapack_complex_double* vl, lapack_int ldvl,
+                               lapack_complex_double* vr, lapack_int ldvr, lapack_int mm, lapack_int* m,
+                               lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_strexc_work(int matrix_order, char compq, lapack_int n, float* t, lapack_int ldt, float* q,
+                               lapack_int ldq, lapack_int* ifst, lapack_int* ilst, float* work);
+lapack_int LAPACKE_dtrexc_work(int matrix_order, char compq, lapack_int n, double* t, lapack_int ldt, double* q,
+                               lapack_int ldq, lapack_int* ifst, lapack_int* ilst, double* work);
+lapack_int LAPACKE_ctrexc_work(int matrix_order, char compq, lapack_int n, lapack_complex_float* t, lapack_int ldt,
+                               lapack_complex_float* q, lapack_int ldq, lapack_int ifst, lapack_int ilst);
+lapack_int LAPACKE_ztrexc_work(int matrix_order, char compq, lapack_int n, lapack_complex_double* t, lapack_int ldt,
+                               lapack_complex_double* q, lapack_int ldq, lapack_int ifst, lapack_int ilst);
+
+lapack_int LAPACKE_strrfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const float* a, lapack_int lda, const float* b, lapack_int ldb, const float* x,
+                               lapack_int ldx, float* ferr, float* berr, float* work, lapack_int* iwork);
+lapack_int LAPACKE_dtrrfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const double* a, lapack_int lda, const double* b, lapack_int ldb, const double* x,
+                               lapack_int ldx, double* ferr, double* berr, double* work, lapack_int* iwork);
+lapack_int LAPACKE_ctrrfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* b,
+                               lapack_int ldb, const lapack_complex_float* x, lapack_int ldx, float* ferr, float* berr,
+                               lapack_complex_float* work, float* rwork);
+lapack_int LAPACKE_ztrrfs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* b,
+                               lapack_int ldb, const lapack_complex_double* x, lapack_int ldx, double* ferr,
+                               double* berr, lapack_complex_double* work, double* rwork);
+
+lapack_int LAPACKE_strsen_work(int matrix_order, char job, char compq, const lapack_logical* select, lapack_int n,
+                               float* t, lapack_int ldt, float* q, lapack_int ldq, float* wr, float* wi, lapack_int* m,
+                               float* s, float* sep, float* work, lapack_int lwork, lapack_int* iwork,
+                               lapack_int liwork);
+lapack_int LAPACKE_dtrsen_work(int matrix_order, char job, char compq, const lapack_logical* select, lapack_int n,
+                               double* t, lapack_int ldt, double* q, lapack_int ldq, double* wr, double* wi,
+                               lapack_int* m, double* s, double* sep, double* work, lapack_int lwork, lapack_int* iwork,
+                               lapack_int liwork);
+lapack_int LAPACKE_ctrsen_work(int matrix_order, char job, char compq, const lapack_logical* select, lapack_int n,
+                               lapack_complex_float* t, lapack_int ldt, lapack_complex_float* q, lapack_int ldq,
+                               lapack_complex_float* w, lapack_int* m, float* s, float* sep, lapack_complex_float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_ztrsen_work(int matrix_order, char job, char compq, const lapack_logical* select, lapack_int n,
+                               lapack_complex_double* t, lapack_int ldt, lapack_complex_double* q, lapack_int ldq,
+                               lapack_complex_double* w, lapack_int* m, double* s, double* sep,
+                               lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_strsna_work(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                               const float* t, lapack_int ldt, const float* vl, lapack_int ldvl, const float* vr,
+                               lapack_int ldvr, float* s, float* sep, lapack_int mm, lapack_int* m, float* work,
+                               lapack_int ldwork, lapack_int* iwork);
+lapack_int LAPACKE_dtrsna_work(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                               const double* t, lapack_int ldt, const double* vl, lapack_int ldvl, const double* vr,
+                               lapack_int ldvr, double* s, double* sep, lapack_int mm, lapack_int* m, double* work,
+                               lapack_int ldwork, lapack_int* iwork);
+lapack_int LAPACKE_ctrsna_work(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                               const lapack_complex_float* t, lapack_int ldt, const lapack_complex_float* vl,
+                               lapack_int ldvl, const lapack_complex_float* vr, lapack_int ldvr, float* s, float* sep,
+                               lapack_int mm, lapack_int* m, lapack_complex_float* work, lapack_int ldwork,
+                               float* rwork);
+lapack_int LAPACKE_ztrsna_work(int matrix_order, char job, char howmny, const lapack_logical* select, lapack_int n,
+                               const lapack_complex_double* t, lapack_int ldt, const lapack_complex_double* vl,
+                               lapack_int ldvl, const lapack_complex_double* vr, lapack_int ldvr, double* s,
+                               double* sep, lapack_int mm, lapack_int* m, lapack_complex_double* work,
+                               lapack_int ldwork, double* rwork);
+
+lapack_int LAPACKE_strsyl_work(int matrix_order, char trana, char tranb, lapack_int isgn, lapack_int m, lapack_int n,
+                               const float* a, lapack_int lda, const float* b, lapack_int ldb, float* c, lapack_int ldc,
+                               float* scale);
+lapack_int LAPACKE_dtrsyl_work(int matrix_order, char trana, char tranb, lapack_int isgn, lapack_int m, lapack_int n,
+                               const double* a, lapack_int lda, const double* b, lapack_int ldb, double* c,
+                               lapack_int ldc, double* scale);
+lapack_int LAPACKE_ctrsyl_work(int matrix_order, char trana, char tranb, lapack_int isgn, lapack_int m, lapack_int n,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* b,
+                               lapack_int ldb, lapack_complex_float* c, lapack_int ldc, float* scale);
+lapack_int LAPACKE_ztrsyl_work(int matrix_order, char trana, char tranb, lapack_int isgn, lapack_int m, lapack_int n,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* b,
+                               lapack_int ldb, lapack_complex_double* c, lapack_int ldc, double* scale);
+
+lapack_int LAPACKE_strtri_work(int matrix_order, char uplo, char diag, lapack_int n, float* a, lapack_int lda);
+lapack_int LAPACKE_dtrtri_work(int matrix_order, char uplo, char diag, lapack_int n, double* a, lapack_int lda);
+lapack_int LAPACKE_ctrtri_work(int matrix_order, char uplo, char diag, lapack_int n, lapack_complex_float* a,
+                               lapack_int lda);
+lapack_int LAPACKE_ztrtri_work(int matrix_order, char uplo, char diag, lapack_int n, lapack_complex_double* a,
+                               lapack_int lda);
+
+lapack_int LAPACKE_strtrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const float* a, lapack_int lda, float* b, lapack_int ldb);
+lapack_int LAPACKE_dtrtrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const double* a, lapack_int lda, double* b, lapack_int ldb);
+lapack_int LAPACKE_ctrtrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_float* a, lapack_int lda, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_ztrtrs_work(int matrix_order, char uplo, char trans, char diag, lapack_int n, lapack_int nrhs,
+                               const lapack_complex_double* a, lapack_int lda, lapack_complex_double* b,
+                               lapack_int ldb);
+
+lapack_int LAPACKE_strttf_work(int matrix_order, char transr, char uplo, lapack_int n, const float* a, lapack_int lda,
+                               float* arf);
+lapack_int LAPACKE_dtrttf_work(int matrix_order, char transr, char uplo, lapack_int n, const double* a, lapack_int lda,
+                               double* arf);
+lapack_int LAPACKE_ctrttf_work(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* arf);
+lapack_int LAPACKE_ztrttf_work(int matrix_order, char transr, char uplo, lapack_int n, const lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* arf);
+
+lapack_int LAPACKE_strttp_work(int matrix_order, char uplo, lapack_int n, const float* a, lapack_int lda, float* ap);
+lapack_int LAPACKE_dtrttp_work(int matrix_order, char uplo, lapack_int n, const double* a, lapack_int lda, double* ap);
+lapack_int LAPACKE_ctrttp_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* ap);
+lapack_int LAPACKE_ztrttp_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* ap);
+
+lapack_int LAPACKE_stzrzf_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* tau,
+                               float* work, lapack_int lwork);
+lapack_int LAPACKE_dtzrzf_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* tau,
+                               double* work, lapack_int lwork);
+lapack_int LAPACKE_ctzrzf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* tau, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_ztzrzf_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* tau, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cungbr_work(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int k,
+                               lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                               lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zungbr_work(int matrix_order, char vect, lapack_int m, lapack_int n, lapack_int k,
+                               lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                               lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cunghr_work(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, lapack_complex_float* a,
+                               lapack_int lda, const lapack_complex_float* tau, lapack_complex_float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_zunghr_work(int matrix_order, lapack_int n, lapack_int ilo, lapack_int ihi, lapack_complex_double* a,
+                               lapack_int lda, const lapack_complex_double* tau, lapack_complex_double* work,
+                               lapack_int lwork);
+
+lapack_int LAPACKE_cunglq_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_float* a,
+                               lapack_int lda, const lapack_complex_float* tau, lapack_complex_float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_zunglq_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_double* a,
+                               lapack_int lda, const lapack_complex_double* tau, lapack_complex_double* work,
+                               lapack_int lwork);
+
+lapack_int LAPACKE_cungql_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_float* a,
+                               lapack_int lda, const lapack_complex_float* tau, lapack_complex_float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_zungql_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_double* a,
+                               lapack_int lda, const lapack_complex_double* tau, lapack_complex_double* work,
+                               lapack_int lwork);
+
+lapack_int LAPACKE_cungqr_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_float* a,
+                               lapack_int lda, const lapack_complex_float* tau, lapack_complex_float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_zungqr_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_double* a,
+                               lapack_int lda, const lapack_complex_double* tau, lapack_complex_double* work,
+                               lapack_int lwork);
+
+lapack_int LAPACKE_cungrq_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_float* a,
+                               lapack_int lda, const lapack_complex_float* tau, lapack_complex_float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_zungrq_work(int matrix_order, lapack_int m, lapack_int n, lapack_int k, lapack_complex_double* a,
+                               lapack_int lda, const lapack_complex_double* tau, lapack_complex_double* work,
+                               lapack_int lwork);
+
+lapack_int LAPACKE_cungtr_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                               const lapack_complex_float* tau, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zungtr_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                               const lapack_complex_double* tau, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cunmbr_work(int matrix_order, char vect, char side, char trans, lapack_int m, lapack_int n,
+                               lapack_int k, const lapack_complex_float* a, lapack_int lda,
+                               const lapack_complex_float* tau, lapack_complex_float* c, lapack_int ldc,
+                               lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zunmbr_work(int matrix_order, char vect, char side, char trans, lapack_int m, lapack_int n,
+                               lapack_int k, const lapack_complex_double* a, lapack_int lda,
+                               const lapack_complex_double* tau, lapack_complex_double* c, lapack_int ldc,
+                               lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cunmhr_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int ilo,
+                               lapack_int ihi, const lapack_complex_float* a, lapack_int lda,
+                               const lapack_complex_float* tau, lapack_complex_float* c, lapack_int ldc,
+                               lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zunmhr_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int ilo,
+                               lapack_int ihi, const lapack_complex_double* a, lapack_int lda,
+                               const lapack_complex_double* tau, lapack_complex_double* c, lapack_int ldc,
+                               lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cunmlq_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                               lapack_complex_float* c, lapack_int ldc, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zunmlq_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                               lapack_complex_double* c, lapack_int ldc, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cunmql_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                               lapack_complex_float* c, lapack_int ldc, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zunmql_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                               lapack_complex_double* c, lapack_int ldc, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cunmqr_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                               lapack_complex_float* c, lapack_int ldc, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zunmqr_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                               lapack_complex_double* c, lapack_int ldc, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cunmrq_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                               lapack_complex_float* c, lapack_int ldc, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zunmrq_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                               lapack_complex_double* c, lapack_int ldc, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cunmrz_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               lapack_int l, const lapack_complex_float* a, lapack_int lda,
+                               const lapack_complex_float* tau, lapack_complex_float* c, lapack_int ldc,
+                               lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zunmrz_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                               lapack_int l, const lapack_complex_double* a, lapack_int lda,
+                               const lapack_complex_double* tau, lapack_complex_double* c, lapack_int ldc,
+                               lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cunmtr_work(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                               const lapack_complex_float* a, lapack_int lda, const lapack_complex_float* tau,
+                               lapack_complex_float* c, lapack_int ldc, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_zunmtr_work(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                               const lapack_complex_double* a, lapack_int lda, const lapack_complex_double* tau,
+                               lapack_complex_double* c, lapack_int ldc, lapack_complex_double* work, lapack_int lwork);
+
+lapack_int LAPACKE_cupgtr_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_float* ap,
+                               const lapack_complex_float* tau, lapack_complex_float* q, lapack_int ldq,
+                               lapack_complex_float* work);
+lapack_int LAPACKE_zupgtr_work(int matrix_order, char uplo, lapack_int n, const lapack_complex_double* ap,
+                               const lapack_complex_double* tau, lapack_complex_double* q, lapack_int ldq,
+                               lapack_complex_double* work);
+
+lapack_int LAPACKE_cupmtr_work(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                               const lapack_complex_float* ap, const lapack_complex_float* tau, lapack_complex_float* c,
+                               lapack_int ldc, lapack_complex_float* work);
+lapack_int LAPACKE_zupmtr_work(int matrix_order, char side, char uplo, char trans, lapack_int m, lapack_int n,
+                               const lapack_complex_double* ap, const lapack_complex_double* tau,
+                               lapack_complex_double* c, lapack_int ldc, lapack_complex_double* work);
+
+lapack_int LAPACKE_claghe(int matrix_order, lapack_int n, lapack_int k, const float* d, lapack_complex_float* a,
+                          lapack_int lda, lapack_int* iseed);
+lapack_int LAPACKE_zlaghe(int matrix_order, lapack_int n, lapack_int k, const double* d, lapack_complex_double* a,
+                          lapack_int lda, lapack_int* iseed);
+
+lapack_int LAPACKE_slagsy(int matrix_order, lapack_int n, lapack_int k, const float* d, float* a, lapack_int lda,
+                          lapack_int* iseed);
+lapack_int LAPACKE_dlagsy(int matrix_order, lapack_int n, lapack_int k, const double* d, double* a, lapack_int lda,
+                          lapack_int* iseed);
+lapack_int LAPACKE_clagsy(int matrix_order, lapack_int n, lapack_int k, const float* d, lapack_complex_float* a,
+                          lapack_int lda, lapack_int* iseed);
+lapack_int LAPACKE_zlagsy(int matrix_order, lapack_int n, lapack_int k, const double* d, lapack_complex_double* a,
+                          lapack_int lda, lapack_int* iseed);
+
+lapack_int LAPACKE_slapmr(int matrix_order, lapack_logical forwrd, lapack_int m, lapack_int n, float* x, lapack_int ldx,
+                          lapack_int* k);
+lapack_int LAPACKE_dlapmr(int matrix_order, lapack_logical forwrd, lapack_int m, lapack_int n, double* x,
+                          lapack_int ldx, lapack_int* k);
+lapack_int LAPACKE_clapmr(int matrix_order, lapack_logical forwrd, lapack_int m, lapack_int n, lapack_complex_float* x,
+                          lapack_int ldx, lapack_int* k);
+lapack_int LAPACKE_zlapmr(int matrix_order, lapack_logical forwrd, lapack_int m, lapack_int n, lapack_complex_double* x,
+                          lapack_int ldx, lapack_int* k);
+
+float LAPACKE_slapy2(float x, float y);
+double LAPACKE_dlapy2(double x, double y);
+
+float LAPACKE_slapy3(float x, float y, float z);
+double LAPACKE_dlapy3(double x, double y, double z);
+
+lapack_int LAPACKE_slartgp(float f, float g, float* cs, float* sn, float* r);
+lapack_int LAPACKE_dlartgp(double f, double g, double* cs, double* sn, double* r);
+
+lapack_int LAPACKE_slartgs(float x, float y, float sigma, float* cs, float* sn);
+lapack_int LAPACKE_dlartgs(double x, double y, double sigma, double* cs, double* sn);
+
+// LAPACK 3.3.0
+lapack_int LAPACKE_cbbcsd(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans, lapack_int m,
+                          lapack_int p, lapack_int q, float* theta, float* phi, lapack_complex_float* u1,
+                          lapack_int ldu1, lapack_complex_float* u2, lapack_int ldu2, lapack_complex_float* v1t,
+                          lapack_int ldv1t, lapack_complex_float* v2t, lapack_int ldv2t, float* b11d, float* b11e,
+                          float* b12d, float* b12e, float* b21d, float* b21e, float* b22d, float* b22e);
+lapack_int LAPACKE_cbbcsd_work(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans,
+                               lapack_int m, lapack_int p, lapack_int q, float* theta, float* phi,
+                               lapack_complex_float* u1, lapack_int ldu1, lapack_complex_float* u2, lapack_int ldu2,
+                               lapack_complex_float* v1t, lapack_int ldv1t, lapack_complex_float* v2t, lapack_int ldv2t,
+                               float* b11d, float* b11e, float* b12d, float* b12e, float* b21d, float* b21e,
+                               float* b22d, float* b22e, float* rwork, lapack_int lrwork);
+lapack_int LAPACKE_cheswapr(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int i1,
+                            lapack_int i2);
+lapack_int LAPACKE_cheswapr_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int i1,
+                                 lapack_int i2);
+lapack_int LAPACKE_chetri2(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                           const lapack_int* ipiv);
+lapack_int LAPACKE_chetri2_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                                const lapack_int* ipiv, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_chetri2x(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                            const lapack_int* ipiv, lapack_int nb);
+lapack_int LAPACKE_chetri2x_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                                 const lapack_int* ipiv, lapack_complex_float* work, lapack_int nb);
+lapack_int LAPACKE_chetrs2(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_chetrs2_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a, lapack_int lda, const lapack_int* ipiv,
+                                lapack_complex_float* b, lapack_int ldb, lapack_complex_float* work);
+lapack_int LAPACKE_csyconv(int matrix_order, char uplo, char way, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                           const lapack_int* ipiv);
+lapack_int LAPACKE_csyconv_work(int matrix_order, char uplo, char way, lapack_int n, lapack_complex_float* a,
+                                lapack_int lda, const lapack_int* ipiv, lapack_complex_float* work);
+lapack_int LAPACKE_csyswapr(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int i1,
+                            lapack_int i2);
+lapack_int LAPACKE_csyswapr_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int i1,
+                                 lapack_int i2);
+lapack_int LAPACKE_csytri2(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                           const lapack_int* ipiv);
+lapack_int LAPACKE_csytri2_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                                const lapack_int* ipiv, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_csytri2x(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                            const lapack_int* ipiv, lapack_int nb);
+lapack_int LAPACKE_csytri2x_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                                 const lapack_int* ipiv, lapack_complex_float* work, lapack_int nb);
+lapack_int LAPACKE_csytrs2(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_int* ipiv, lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_csytrs2_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a, lapack_int lda, const lapack_int* ipiv,
+                                lapack_complex_float* b, lapack_int ldb, lapack_complex_float* work);
+lapack_int LAPACKE_cunbdb(int matrix_order, char trans, char signs, lapack_int m, lapack_int p, lapack_int q,
+                          lapack_complex_float* x11, lapack_int ldx11, lapack_complex_float* x12, lapack_int ldx12,
+                          lapack_complex_float* x21, lapack_int ldx21, lapack_complex_float* x22, lapack_int ldx22,
+                          float* theta, float* phi, lapack_complex_float* taup1, lapack_complex_float* taup2,
+                          lapack_complex_float* tauq1, lapack_complex_float* tauq2);
+lapack_int LAPACKE_cunbdb_work(int matrix_order, char trans, char signs, lapack_int m, lapack_int p, lapack_int q,
+                               lapack_complex_float* x11, lapack_int ldx11, lapack_complex_float* x12, lapack_int ldx12,
+                               lapack_complex_float* x21, lapack_int ldx21, lapack_complex_float* x22, lapack_int ldx22,
+                               float* theta, float* phi, lapack_complex_float* taup1, lapack_complex_float* taup2,
+                               lapack_complex_float* tauq1, lapack_complex_float* tauq2, lapack_complex_float* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_cuncsd(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans, char signs,
+                          lapack_int m, lapack_int p, lapack_int q, lapack_complex_float* x11, lapack_int ldx11,
+                          lapack_complex_float* x12, lapack_int ldx12, lapack_complex_float* x21, lapack_int ldx21,
+                          lapack_complex_float* x22, lapack_int ldx22, float* theta, lapack_complex_float* u1,
+                          lapack_int ldu1, lapack_complex_float* u2, lapack_int ldu2, lapack_complex_float* v1t,
+                          lapack_int ldv1t, lapack_complex_float* v2t, lapack_int ldv2t);
+lapack_int LAPACKE_cuncsd_work(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans,
+                               char signs, lapack_int m, lapack_int p, lapack_int q, lapack_complex_float* x11,
+                               lapack_int ldx11, lapack_complex_float* x12, lapack_int ldx12, lapack_complex_float* x21,
+                               lapack_int ldx21, lapack_complex_float* x22, lapack_int ldx22, float* theta,
+                               lapack_complex_float* u1, lapack_int ldu1, lapack_complex_float* u2, lapack_int ldu2,
+                               lapack_complex_float* v1t, lapack_int ldv1t, lapack_complex_float* v2t, lapack_int ldv2t,
+                               lapack_complex_float* work, lapack_int lwork, float* rwork, lapack_int lrwork,
+                               lapack_int* iwork);
+lapack_int LAPACKE_dbbcsd(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans, lapack_int m,
+                          lapack_int p, lapack_int q, double* theta, double* phi, double* u1, lapack_int ldu1,
+                          double* u2, lapack_int ldu2, double* v1t, lapack_int ldv1t, double* v2t, lapack_int ldv2t,
+                          double* b11d, double* b11e, double* b12d, double* b12e, double* b21d, double* b21e,
+                          double* b22d, double* b22e);
+lapack_int LAPACKE_dbbcsd_work(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans,
+                               lapack_int m, lapack_int p, lapack_int q, double* theta, double* phi, double* u1,
+                               lapack_int ldu1, double* u2, lapack_int ldu2, double* v1t, lapack_int ldv1t, double* v2t,
+                               lapack_int ldv2t, double* b11d, double* b11e, double* b12d, double* b12e, double* b21d,
+                               double* b21e, double* b22d, double* b22e, double* work, lapack_int lwork);
+lapack_int LAPACKE_dorbdb(int matrix_order, char trans, char signs, lapack_int m, lapack_int p, lapack_int q,
+                          double* x11, lapack_int ldx11, double* x12, lapack_int ldx12, double* x21, lapack_int ldx21,
+                          double* x22, lapack_int ldx22, double* theta, double* phi, double* taup1, double* taup2,
+                          double* tauq1, double* tauq2);
+lapack_int LAPACKE_dorbdb_work(int matrix_order, char trans, char signs, lapack_int m, lapack_int p, lapack_int q,
+                               double* x11, lapack_int ldx11, double* x12, lapack_int ldx12, double* x21,
+                               lapack_int ldx21, double* x22, lapack_int ldx22, double* theta, double* phi,
+                               double* taup1, double* taup2, double* tauq1, double* tauq2, double* work,
+                               lapack_int lwork);
+lapack_int LAPACKE_dorcsd(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans, char signs,
+                          lapack_int m, lapack_int p, lapack_int q, double* x11, lapack_int ldx11, double* x12,
+                          lapack_int ldx12, double* x21, lapack_int ldx21, double* x22, lapack_int ldx22, double* theta,
+                          double* u1, lapack_int ldu1, double* u2, lapack_int ldu2, double* v1t, lapack_int ldv1t,
+                          double* v2t, lapack_int ldv2t);
+lapack_int LAPACKE_dorcsd_work(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans,
+                               char signs, lapack_int m, lapack_int p, lapack_int q, double* x11, lapack_int ldx11,
+                               double* x12, lapack_int ldx12, double* x21, lapack_int ldx21, double* x22,
+                               lapack_int ldx22, double* theta, double* u1, lapack_int ldu1, double* u2,
+                               lapack_int ldu2, double* v1t, lapack_int ldv1t, double* v2t, lapack_int ldv2t,
+                               double* work, lapack_int lwork, lapack_int* iwork);
+lapack_int LAPACKE_dsyconv(int matrix_order, char uplo, char way, lapack_int n, double* a, lapack_int lda,
+                           const lapack_int* ipiv);
+lapack_int LAPACKE_dsyconv_work(int matrix_order, char uplo, char way, lapack_int n, double* a, lapack_int lda,
+                                const lapack_int* ipiv, double* work);
+lapack_int LAPACKE_dsyswapr(int matrix_order, char uplo, lapack_int n, double* a, lapack_int i1, lapack_int i2);
+lapack_int LAPACKE_dsyswapr_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int i1, lapack_int i2);
+lapack_int LAPACKE_dsytri2(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda,
+                           const lapack_int* ipiv);
+lapack_int LAPACKE_dsytri2_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda,
+                                const lapack_int* ipiv, lapack_complex_double* work, lapack_int lwork);
+lapack_int LAPACKE_dsytri2x(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda,
+                            const lapack_int* ipiv, lapack_int nb);
+lapack_int LAPACKE_dsytri2x_work(int matrix_order, char uplo, lapack_int n, double* a, lapack_int lda,
+                                 const lapack_int* ipiv, double* work, lapack_int nb);
+lapack_int LAPACKE_dsytrs2(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* a, lapack_int lda,
+                           const lapack_int* ipiv, double* b, lapack_int ldb);
+lapack_int LAPACKE_dsytrs2_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const double* a,
+                                lapack_int lda, const lapack_int* ipiv, double* b, lapack_int ldb, double* work);
+lapack_int LAPACKE_sbbcsd(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans, lapack_int m,
+                          lapack_int p, lapack_int q, float* theta, float* phi, float* u1, lapack_int ldu1, float* u2,
+                          lapack_int ldu2, float* v1t, lapack_int ldv1t, float* v2t, lapack_int ldv2t, float* b11d,
+                          float* b11e, float* b12d, float* b12e, float* b21d, float* b21e, float* b22d, float* b22e);
+lapack_int LAPACKE_sbbcsd_work(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans,
+                               lapack_int m, lapack_int p, lapack_int q, float* theta, float* phi, float* u1,
+                               lapack_int ldu1, float* u2, lapack_int ldu2, float* v1t, lapack_int ldv1t, float* v2t,
+                               lapack_int ldv2t, float* b11d, float* b11e, float* b12d, float* b12e, float* b21d,
+                               float* b21e, float* b22d, float* b22e, float* work, lapack_int lwork);
+lapack_int LAPACKE_sorbdb(int matrix_order, char trans, char signs, lapack_int m, lapack_int p, lapack_int q,
+                          float* x11, lapack_int ldx11, float* x12, lapack_int ldx12, float* x21, lapack_int ldx21,
+                          float* x22, lapack_int ldx22, float* theta, float* phi, float* taup1, float* taup2,
+                          float* tauq1, float* tauq2);
+lapack_int LAPACKE_sorbdb_work(int matrix_order, char trans, char signs, lapack_int m, lapack_int p, lapack_int q,
+                               float* x11, lapack_int ldx11, float* x12, lapack_int ldx12, float* x21, lapack_int ldx21,
+                               float* x22, lapack_int ldx22, float* theta, float* phi, float* taup1, float* taup2,
+                               float* tauq1, float* tauq2, float* work, lapack_int lwork);
+lapack_int LAPACKE_sorcsd(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans, char signs,
+                          lapack_int m, lapack_int p, lapack_int q, float* x11, lapack_int ldx11, float* x12,
+                          lapack_int ldx12, float* x21, lapack_int ldx21, float* x22, lapack_int ldx22, float* theta,
+                          float* u1, lapack_int ldu1, float* u2, lapack_int ldu2, float* v1t, lapack_int ldv1t,
+                          float* v2t, lapack_int ldv2t);
+lapack_int LAPACKE_sorcsd_work(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans,
+                               char signs, lapack_int m, lapack_int p, lapack_int q, float* x11, lapack_int ldx11,
+                               float* x12, lapack_int ldx12, float* x21, lapack_int ldx21, float* x22, lapack_int ldx22,
+                               float* theta, float* u1, lapack_int ldu1, float* u2, lapack_int ldu2, float* v1t,
+                               lapack_int ldv1t, float* v2t, lapack_int ldv2t, float* work, lapack_int lwork,
+                               lapack_int* iwork);
+lapack_int LAPACKE_ssyconv(int matrix_order, char uplo, char way, lapack_int n, float* a, lapack_int lda,
+                           const lapack_int* ipiv);
+lapack_int LAPACKE_ssyconv_work(int matrix_order, char uplo, char way, lapack_int n, float* a, lapack_int lda,
+                                const lapack_int* ipiv, float* work);
+lapack_int LAPACKE_ssyswapr(int matrix_order, char uplo, lapack_int n, float* a, lapack_int i1, lapack_int i2);
+lapack_int LAPACKE_ssyswapr_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int i1, lapack_int i2);
+lapack_int LAPACKE_ssytri2(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, const lapack_int* ipiv);
+lapack_int LAPACKE_ssytri2_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda,
+                                const lapack_int* ipiv, lapack_complex_float* work, lapack_int lwork);
+lapack_int LAPACKE_ssytri2x(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda, const lapack_int* ipiv,
+                            lapack_int nb);
+lapack_int LAPACKE_ssytri2x_work(int matrix_order, char uplo, lapack_int n, float* a, lapack_int lda,
+                                 const lapack_int* ipiv, float* work, lapack_int nb);
+lapack_int LAPACKE_ssytrs2(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* a, lapack_int lda,
+                           const lapack_int* ipiv, float* b, lapack_int ldb);
+lapack_int LAPACKE_ssytrs2_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const float* a,
+                                lapack_int lda, const lapack_int* ipiv, float* b, lapack_int ldb, float* work);
+lapack_int LAPACKE_zbbcsd(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans, lapack_int m,
+                          lapack_int p, lapack_int q, double* theta, double* phi, lapack_complex_double* u1,
+                          lapack_int ldu1, lapack_complex_double* u2, lapack_int ldu2, lapack_complex_double* v1t,
+                          lapack_int ldv1t, lapack_complex_double* v2t, lapack_int ldv2t, double* b11d, double* b11e,
+                          double* b12d, double* b12e, double* b21d, double* b21e, double* b22d, double* b22e);
+lapack_int LAPACKE_zbbcsd_work(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans,
+                               lapack_int m, lapack_int p, lapack_int q, double* theta, double* phi,
+                               lapack_complex_double* u1, lapack_int ldu1, lapack_complex_double* u2, lapack_int ldu2,
+                               lapack_complex_double* v1t, lapack_int ldv1t, lapack_complex_double* v2t,
+                               lapack_int ldv2t, double* b11d, double* b11e, double* b12d, double* b12e, double* b21d,
+                               double* b21e, double* b22d, double* b22e, double* rwork, lapack_int lrwork);
+lapack_int LAPACKE_zheswapr(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int i1,
+                            lapack_int i2);
+lapack_int LAPACKE_zheswapr_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int i1,
+                                 lapack_int i2);
+lapack_int LAPACKE_zhetri2(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                           const lapack_int* ipiv);
+lapack_int LAPACKE_zhetri2_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                                const lapack_int* ipiv, lapack_complex_double* work, lapack_int lwork);
+lapack_int LAPACKE_zhetri2x(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                            const lapack_int* ipiv, lapack_int nb);
+lapack_int LAPACKE_zhetri2x_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                                 const lapack_int* ipiv, lapack_complex_double* work, lapack_int nb);
+lapack_int LAPACKE_zhetrs2(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+lapack_int LAPACKE_zhetrs2_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a, lapack_int lda, const lapack_int* ipiv,
+                                lapack_complex_double* b, lapack_int ldb, lapack_complex_double* work);
+lapack_int LAPACKE_zsyconv(int matrix_order, char uplo, char way, lapack_int n, lapack_complex_double* a,
+                           lapack_int lda, const lapack_int* ipiv);
+lapack_int LAPACKE_zsyconv_work(int matrix_order, char uplo, char way, lapack_int n, lapack_complex_double* a,
+                                lapack_int lda, const lapack_int* ipiv, lapack_complex_double* work);
+lapack_int LAPACKE_zsyswapr(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int i1,
+                            lapack_int i2);
+lapack_int LAPACKE_zsyswapr_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int i1,
+                                 lapack_int i2);
+lapack_int LAPACKE_zsytri2(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                           const lapack_int* ipiv);
+lapack_int LAPACKE_zsytri2_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                                const lapack_int* ipiv, lapack_complex_double* work, lapack_int lwork);
+lapack_int LAPACKE_zsytri2x(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                            const lapack_int* ipiv, lapack_int nb);
+lapack_int LAPACKE_zsytri2x_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                                 const lapack_int* ipiv, lapack_complex_double* work, lapack_int nb);
+lapack_int LAPACKE_zsytrs2(int matrix_order, char uplo, lapack_int n, lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_int* ipiv, lapack_complex_double* b, lapack_int ldb);
+lapack_int LAPACKE_zsytrs2_work(int matrix_order, char uplo, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a, lapack_int lda, const lapack_int* ipiv,
+                                lapack_complex_double* b, lapack_int ldb, lapack_complex_double* work);
+lapack_int LAPACKE_zunbdb(int matrix_order, char trans, char signs, lapack_int m, lapack_int p, lapack_int q,
+                          lapack_complex_double* x11, lapack_int ldx11, lapack_complex_double* x12, lapack_int ldx12,
+                          lapack_complex_double* x21, lapack_int ldx21, lapack_complex_double* x22, lapack_int ldx22,
+                          double* theta, double* phi, lapack_complex_double* taup1, lapack_complex_double* taup2,
+                          lapack_complex_double* tauq1, lapack_complex_double* tauq2);
+lapack_int LAPACKE_zunbdb_work(int matrix_order, char trans, char signs, lapack_int m, lapack_int p, lapack_int q,
+                               lapack_complex_double* x11, lapack_int ldx11, lapack_complex_double* x12,
+                               lapack_int ldx12, lapack_complex_double* x21, lapack_int ldx21,
+                               lapack_complex_double* x22, lapack_int ldx22, double* theta, double* phi,
+                               lapack_complex_double* taup1, lapack_complex_double* taup2, lapack_complex_double* tauq1,
+                               lapack_complex_double* tauq2, lapack_complex_double* work, lapack_int lwork);
+lapack_int LAPACKE_zuncsd(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans, char signs,
+                          lapack_int m, lapack_int p, lapack_int q, lapack_complex_double* x11, lapack_int ldx11,
+                          lapack_complex_double* x12, lapack_int ldx12, lapack_complex_double* x21, lapack_int ldx21,
+                          lapack_complex_double* x22, lapack_int ldx22, double* theta, lapack_complex_double* u1,
+                          lapack_int ldu1, lapack_complex_double* u2, lapack_int ldu2, lapack_complex_double* v1t,
+                          lapack_int ldv1t, lapack_complex_double* v2t, lapack_int ldv2t);
+lapack_int LAPACKE_zuncsd_work(int matrix_order, char jobu1, char jobu2, char jobv1t, char jobv2t, char trans,
+                               char signs, lapack_int m, lapack_int p, lapack_int q, lapack_complex_double* x11,
+                               lapack_int ldx11, lapack_complex_double* x12, lapack_int ldx12,
+                               lapack_complex_double* x21, lapack_int ldx21, lapack_complex_double* x22,
+                               lapack_int ldx22, double* theta, lapack_complex_double* u1, lapack_int ldu1,
+                               lapack_complex_double* u2, lapack_int ldu2, lapack_complex_double* v1t, lapack_int ldv1t,
+                               lapack_complex_double* v2t, lapack_int ldv2t, lapack_complex_double* work,
+                               lapack_int lwork, double* rwork, lapack_int lrwork, lapack_int* iwork);
+// LAPACK 3.4.0
+lapack_int LAPACKE_sgemqrt(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int nb, const float* v, lapack_int ldv, const float* t, lapack_int ldt, float* c,
+                           lapack_int ldc);
+lapack_int LAPACKE_dgemqrt(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int nb, const double* v, lapack_int ldv, const double* t, lapack_int ldt, double* c,
+                           lapack_int ldc);
+lapack_int LAPACKE_cgemqrt(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int nb, const lapack_complex_float* v, lapack_int ldv, const lapack_complex_float* t,
+                           lapack_int ldt, lapack_complex_float* c, lapack_int ldc);
+lapack_int LAPACKE_zgemqrt(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int nb, const lapack_complex_double* v, lapack_int ldv,
+                           const lapack_complex_double* t, lapack_int ldt, lapack_complex_double* c, lapack_int ldc);
+
+lapack_int LAPACKE_sgeqrt(int matrix_order, lapack_int m, lapack_int n, lapack_int nb, float* a, lapack_int lda,
+                          float* t, lapack_int ldt);
+lapack_int LAPACKE_dgeqrt(int matrix_order, lapack_int m, lapack_int n, lapack_int nb, double* a, lapack_int lda,
+                          double* t, lapack_int ldt);
+lapack_int LAPACKE_cgeqrt(int matrix_order, lapack_int m, lapack_int n, lapack_int nb, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* t, lapack_int ldt);
+lapack_int LAPACKE_zgeqrt(int matrix_order, lapack_int m, lapack_int n, lapack_int nb, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* t, lapack_int ldt);
+
+lapack_int LAPACKE_sgeqrt2(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* t,
+                           lapack_int ldt);
+lapack_int LAPACKE_dgeqrt2(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* t,
+                           lapack_int ldt);
+lapack_int LAPACKE_cgeqrt2(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* t, lapack_int ldt);
+lapack_int LAPACKE_zgeqrt2(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* t, lapack_int ldt);
+
+lapack_int LAPACKE_sgeqrt3(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* t,
+                           lapack_int ldt);
+lapack_int LAPACKE_dgeqrt3(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* t,
+                           lapack_int ldt);
+lapack_int LAPACKE_cgeqrt3(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* t, lapack_int ldt);
+lapack_int LAPACKE_zgeqrt3(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* t, lapack_int ldt);
+
+lapack_int LAPACKE_stpmqrt(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int l, lapack_int nb, const float* v, lapack_int ldv, const float* t, lapack_int ldt,
+                           float* a, lapack_int lda, float* b, lapack_int ldb);
+lapack_int LAPACKE_dtpmqrt(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int l, lapack_int nb, const double* v, lapack_int ldv, const double* t,
+                           lapack_int ldt, double* a, lapack_int lda, double* b, lapack_int ldb);
+lapack_int LAPACKE_ctpmqrt(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int l, lapack_int nb, const lapack_complex_float* v, lapack_int ldv,
+                           const lapack_complex_float* t, lapack_int ldt, lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb);
+lapack_int LAPACKE_ztpmqrt(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int l, lapack_int nb, const lapack_complex_double* v, lapack_int ldv,
+                           const lapack_complex_double* t, lapack_int ldt, lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb);
+
+lapack_int LAPACKE_dtpqrt(int matrix_order, lapack_int m, lapack_int n, lapack_int l, lapack_int nb, double* a,
+                          lapack_int lda, double* b, lapack_int ldb, double* t, lapack_int ldt);
+lapack_int LAPACKE_ctpqrt(int matrix_order, lapack_int m, lapack_int n, lapack_int l, lapack_int nb,
+                          lapack_complex_float* a, lapack_int lda, lapack_complex_float* t, lapack_complex_float* b,
+                          lapack_int ldb, lapack_int ldt);
+lapack_int LAPACKE_ztpqrt(int matrix_order, lapack_int m, lapack_int n, lapack_int l, lapack_int nb,
+                          lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                          lapack_complex_double* t, lapack_int ldt);
+
+lapack_int LAPACKE_stpqrt2(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* b,
+                           lapack_int ldb, float* t, lapack_int ldt);
+lapack_int LAPACKE_dtpqrt2(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* b,
+                           lapack_int ldb, double* t, lapack_int ldt);
+lapack_int LAPACKE_ctpqrt2(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb, lapack_complex_float* t, lapack_int ldt);
+lapack_int LAPACKE_ztpqrt2(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb, lapack_complex_double* t, lapack_int ldt);
+
+lapack_int LAPACKE_stprfb(int matrix_order, char side, char trans, char direct, char storev, lapack_int m, lapack_int n,
+                          lapack_int k, lapack_int l, const float* v, lapack_int ldv, const float* t, lapack_int ldt,
+                          float* a, lapack_int lda, float* b, lapack_int ldb, lapack_int myldwork);
+lapack_int LAPACKE_dtprfb(int matrix_order, char side, char trans, char direct, char storev, lapack_int m, lapack_int n,
+                          lapack_int k, lapack_int l, const double* v, lapack_int ldv, const double* t, lapack_int ldt,
+                          double* a, lapack_int lda, double* b, lapack_int ldb, lapack_int myldwork);
+lapack_int LAPACKE_ctprfb(int matrix_order, char side, char trans, char direct, char storev, lapack_int m, lapack_int n,
+                          lapack_int k, lapack_int l, const lapack_complex_float* v, lapack_int ldv,
+                          const lapack_complex_float* t, lapack_int ldt, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* b, lapack_int ldb, lapack_int myldwork);
+lapack_int LAPACKE_ztprfb(int matrix_order, char side, char trans, char direct, char storev, lapack_int m, lapack_int n,
+                          lapack_int k, lapack_int l, const lapack_complex_double* v, lapack_int ldv,
+                          const lapack_complex_double* t, lapack_int ldt, lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* b, lapack_int ldb, lapack_int myldwork);
+
+lapack_int LAPACKE_sgemqrt_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int nb, const float* v, lapack_int ldv, const float* t, lapack_int ldt, float* c,
+                                lapack_int ldc, float* work);
+lapack_int LAPACKE_dgemqrt_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int nb, const double* v, lapack_int ldv, const double* t, lapack_int ldt,
+                                double* c, lapack_int ldc, double* work);
+lapack_int LAPACKE_cgemqrt_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int nb, const lapack_complex_float* v, lapack_int ldv,
+                                const lapack_complex_float* t, lapack_int ldt, lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work);
+lapack_int LAPACKE_zgemqrt_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int nb, const lapack_complex_double* v, lapack_int ldv,
+                                const lapack_complex_double* t, lapack_int ldt, lapack_complex_double* c,
+                                lapack_int ldc, lapack_complex_double* work);
+
+lapack_int LAPACKE_sgeqrt_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nb, float* a, lapack_int lda,
+                               float* t, lapack_int ldt, float* work);
+lapack_int LAPACKE_dgeqrt_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nb, double* a, lapack_int lda,
+                               double* t, lapack_int ldt, double* work);
+lapack_int LAPACKE_cgeqrt_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nb, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* t, lapack_int ldt, lapack_complex_float* work);
+lapack_int LAPACKE_zgeqrt_work(int matrix_order, lapack_int m, lapack_int n, lapack_int nb, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* t, lapack_int ldt, lapack_complex_double* work);
+
+lapack_int LAPACKE_sgeqrt2_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* t,
+                                lapack_int ldt);
+lapack_int LAPACKE_dgeqrt2_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* t,
+                                lapack_int ldt);
+lapack_int LAPACKE_cgeqrt2_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* t, lapack_int ldt);
+lapack_int LAPACKE_zgeqrt2_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* t, lapack_int ldt);
+
+lapack_int LAPACKE_sgeqrt3_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* t,
+                                lapack_int ldt);
+lapack_int LAPACKE_dgeqrt3_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* t,
+                                lapack_int ldt);
+lapack_int LAPACKE_cgeqrt3_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* t, lapack_int ldt);
+lapack_int LAPACKE_zgeqrt3_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* t, lapack_int ldt);
+
+lapack_int LAPACKE_stpmqrt_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int l, lapack_int nb, const float* v, lapack_int ldv, const float* t,
+                                lapack_int ldt, float* a, lapack_int lda, float* b, lapack_int ldb, float* work);
+lapack_int LAPACKE_dtpmqrt_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int l, lapack_int nb, const double* v, lapack_int ldv, const double* t,
+                                lapack_int ldt, double* a, lapack_int lda, double* b, lapack_int ldb, double* work);
+lapack_int LAPACKE_ctpmqrt_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int l, lapack_int nb, const lapack_complex_float* v, lapack_int ldv,
+                                const lapack_complex_float* t, lapack_int ldt, lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb, lapack_complex_float* work);
+lapack_int LAPACKE_ztpmqrt_work(int matrix_order, char side, char trans, lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int l, lapack_int nb, const lapack_complex_double* v, lapack_int ldv,
+                                const lapack_complex_double* t, lapack_int ldt, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b, lapack_int ldb, lapack_complex_double* work);
+
+lapack_int LAPACKE_dtpqrt_work(int matrix_order, lapack_int m, lapack_int n, lapack_int l, lapack_int nb, double* a,
+                               lapack_int lda, double* b, lapack_int ldb, double* t, lapack_int ldt, double* work);
+lapack_int LAPACKE_ctpqrt_work(int matrix_order, lapack_int m, lapack_int n, lapack_int l, lapack_int nb,
+                               lapack_complex_float* a, lapack_int lda, lapack_complex_float* t,
+                               lapack_complex_float* b, lapack_int ldb, lapack_int ldt, lapack_complex_float* work);
+lapack_int LAPACKE_ztpqrt_work(int matrix_order, lapack_int m, lapack_int n, lapack_int l, lapack_int nb,
+                               lapack_complex_double* a, lapack_int lda, lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* t, lapack_int ldt, lapack_complex_double* work);
+
+lapack_int LAPACKE_stpqrt2_work(int matrix_order, lapack_int m, lapack_int n, float* a, lapack_int lda, float* b,
+                                lapack_int ldb, float* t, lapack_int ldt);
+lapack_int LAPACKE_dtpqrt2_work(int matrix_order, lapack_int m, lapack_int n, double* a, lapack_int lda, double* b,
+                                lapack_int ldb, double* t, lapack_int ldt);
+lapack_int LAPACKE_ctpqrt2_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb, lapack_complex_float* t, lapack_int ldt);
+lapack_int LAPACKE_ztpqrt2_work(int matrix_order, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb, lapack_complex_double* t, lapack_int ldt);
+
+lapack_int LAPACKE_stprfb_work(int matrix_order, char side, char trans, char direct, char storev, lapack_int m,
+                               lapack_int n, lapack_int k, lapack_int l, const float* v, lapack_int ldv, const float* t,
+                               lapack_int ldt, float* a, lapack_int lda, float* b, lapack_int ldb, const float* mywork,
+                               lapack_int myldwork);
+lapack_int LAPACKE_dtprfb_work(int matrix_order, char side, char trans, char direct, char storev, lapack_int m,
+                               lapack_int n, lapack_int k, lapack_int l, const double* v, lapack_int ldv,
+                               const double* t, lapack_int ldt, double* a, lapack_int lda, double* b, lapack_int ldb,
+                               const double* mywork, lapack_int myldwork);
+lapack_int LAPACKE_ctprfb_work(int matrix_order, char side, char trans, char direct, char storev, lapack_int m,
+                               lapack_int n, lapack_int k, lapack_int l, const lapack_complex_float* v, lapack_int ldv,
+                               const lapack_complex_float* t, lapack_int ldt, lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* b, lapack_int ldb, const float* mywork, lapack_int myldwork);
+lapack_int LAPACKE_ztprfb_work(int matrix_order, char side, char trans, char direct, char storev, lapack_int m,
+                               lapack_int n, lapack_int k, lapack_int l, const lapack_complex_double* v, lapack_int ldv,
+                               const lapack_complex_double* t, lapack_int ldt, lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* b, lapack_int ldb, const double* mywork, lapack_int myldwork);
+// LAPACK 3.X.X
+lapack_int LAPACKE_csyr(int matrix_order, char uplo, lapack_int n, lapack_complex_float alpha,
+                        const lapack_complex_float* x, lapack_int incx, lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zsyr(int matrix_order, char uplo, lapack_int n, lapack_complex_double alpha,
+                        const lapack_complex_double* x, lapack_int incx, lapack_complex_double* a, lapack_int lda);
+
+lapack_int LAPACKE_csyr_work(int matrix_order, char uplo, lapack_int n, lapack_complex_float alpha,
+                             const lapack_complex_float* x, lapack_int incx, lapack_complex_float* a, lapack_int lda);
+lapack_int LAPACKE_zsyr_work(int matrix_order, char uplo, lapack_int n, lapack_complex_double alpha,
+                             const lapack_complex_double* x, lapack_int incx, lapack_complex_double* a, lapack_int lda);
+
+#define LAPACK_sgetrf LAPACK_GLOBAL(sgetrf, SGETRF)
+#define LAPACK_dgetrf LAPACK_GLOBAL(dgetrf, DGETRF)
+#define LAPACK_cgetrf LAPACK_GLOBAL(cgetrf, CGETRF)
+#define LAPACK_zgetrf LAPACK_GLOBAL(zgetrf, ZGETRF)
+#define LAPACK_sgbtrf LAPACK_GLOBAL(sgbtrf, SGBTRF)
+#define LAPACK_dgbtrf LAPACK_GLOBAL(dgbtrf, DGBTRF)
+#define LAPACK_cgbtrf LAPACK_GLOBAL(cgbtrf, CGBTRF)
+#define LAPACK_zgbtrf LAPACK_GLOBAL(zgbtrf, ZGBTRF)
+#define LAPACK_sgttrf LAPACK_GLOBAL(sgttrf, SGTTRF)
+#define LAPACK_dgttrf LAPACK_GLOBAL(dgttrf, DGTTRF)
+#define LAPACK_cgttrf LAPACK_GLOBAL(cgttrf, CGTTRF)
+#define LAPACK_zgttrf LAPACK_GLOBAL(zgttrf, ZGTTRF)
+#define LAPACK_spotrf LAPACK_GLOBAL(spotrf, SPOTRF)
+#define LAPACK_dpotrf LAPACK_GLOBAL(dpotrf, DPOTRF)
+#define LAPACK_cpotrf LAPACK_GLOBAL(cpotrf, CPOTRF)
+#define LAPACK_zpotrf LAPACK_GLOBAL(zpotrf, ZPOTRF)
+#define LAPACK_dpstrf LAPACK_GLOBAL(dpstrf, DPSTRF)
+#define LAPACK_spstrf LAPACK_GLOBAL(spstrf, SPSTRF)
+#define LAPACK_zpstrf LAPACK_GLOBAL(zpstrf, ZPSTRF)
+#define LAPACK_cpstrf LAPACK_GLOBAL(cpstrf, CPSTRF)
+#define LAPACK_dpftrf LAPACK_GLOBAL(dpftrf, DPFTRF)
+#define LAPACK_spftrf LAPACK_GLOBAL(spftrf, SPFTRF)
+#define LAPACK_zpftrf LAPACK_GLOBAL(zpftrf, ZPFTRF)
+#define LAPACK_cpftrf LAPACK_GLOBAL(cpftrf, CPFTRF)
+#define LAPACK_spptrf LAPACK_GLOBAL(spptrf, SPPTRF)
+#define LAPACK_dpptrf LAPACK_GLOBAL(dpptrf, DPPTRF)
+#define LAPACK_cpptrf LAPACK_GLOBAL(cpptrf, CPPTRF)
+#define LAPACK_zpptrf LAPACK_GLOBAL(zpptrf, ZPPTRF)
+#define LAPACK_spbtrf LAPACK_GLOBAL(spbtrf, SPBTRF)
+#define LAPACK_dpbtrf LAPACK_GLOBAL(dpbtrf, DPBTRF)
+#define LAPACK_cpbtrf LAPACK_GLOBAL(cpbtrf, CPBTRF)
+#define LAPACK_zpbtrf LAPACK_GLOBAL(zpbtrf, ZPBTRF)
+#define LAPACK_spttrf LAPACK_GLOBAL(spttrf, SPTTRF)
+#define LAPACK_dpttrf LAPACK_GLOBAL(dpttrf, DPTTRF)
+#define LAPACK_cpttrf LAPACK_GLOBAL(cpttrf, CPTTRF)
+#define LAPACK_zpttrf LAPACK_GLOBAL(zpttrf, ZPTTRF)
+#define LAPACK_ssytrf LAPACK_GLOBAL(ssytrf, SSYTRF)
+#define LAPACK_dsytrf LAPACK_GLOBAL(dsytrf, DSYTRF)
+#define LAPACK_csytrf LAPACK_GLOBAL(csytrf, CSYTRF)
+#define LAPACK_zsytrf LAPACK_GLOBAL(zsytrf, ZSYTRF)
+#define LAPACK_chetrf LAPACK_GLOBAL(chetrf, CHETRF)
+#define LAPACK_zhetrf LAPACK_GLOBAL(zhetrf, ZHETRF)
+#define LAPACK_ssptrf LAPACK_GLOBAL(ssptrf, SSPTRF)
+#define LAPACK_dsptrf LAPACK_GLOBAL(dsptrf, DSPTRF)
+#define LAPACK_csptrf LAPACK_GLOBAL(csptrf, CSPTRF)
+#define LAPACK_zsptrf LAPACK_GLOBAL(zsptrf, ZSPTRF)
+#define LAPACK_chptrf LAPACK_GLOBAL(chptrf, CHPTRF)
+#define LAPACK_zhptrf LAPACK_GLOBAL(zhptrf, ZHPTRF)
+#define LAPACK_sgetrs LAPACK_GLOBAL(sgetrs, SGETRS)
+#define LAPACK_dgetrs LAPACK_GLOBAL(dgetrs, DGETRS)
+#define LAPACK_cgetrs LAPACK_GLOBAL(cgetrs, CGETRS)
+#define LAPACK_zgetrs LAPACK_GLOBAL(zgetrs, ZGETRS)
+#define LAPACK_sgbtrs LAPACK_GLOBAL(sgbtrs, SGBTRS)
+#define LAPACK_dgbtrs LAPACK_GLOBAL(dgbtrs, DGBTRS)
+#define LAPACK_cgbtrs LAPACK_GLOBAL(cgbtrs, CGBTRS)
+#define LAPACK_zgbtrs LAPACK_GLOBAL(zgbtrs, ZGBTRS)
+#define LAPACK_sgttrs LAPACK_GLOBAL(sgttrs, SGTTRS)
+#define LAPACK_dgttrs LAPACK_GLOBAL(dgttrs, DGTTRS)
+#define LAPACK_cgttrs LAPACK_GLOBAL(cgttrs, CGTTRS)
+#define LAPACK_zgttrs LAPACK_GLOBAL(zgttrs, ZGTTRS)
+#define LAPACK_spotrs LAPACK_GLOBAL(spotrs, SPOTRS)
+#define LAPACK_dpotrs LAPACK_GLOBAL(dpotrs, DPOTRS)
+#define LAPACK_cpotrs LAPACK_GLOBAL(cpotrs, CPOTRS)
+#define LAPACK_zpotrs LAPACK_GLOBAL(zpotrs, ZPOTRS)
+#define LAPACK_dpftrs LAPACK_GLOBAL(dpftrs, DPFTRS)
+#define LAPACK_spftrs LAPACK_GLOBAL(spftrs, SPFTRS)
+#define LAPACK_zpftrs LAPACK_GLOBAL(zpftrs, ZPFTRS)
+#define LAPACK_cpftrs LAPACK_GLOBAL(cpftrs, CPFTRS)
+#define LAPACK_spptrs LAPACK_GLOBAL(spptrs, SPPTRS)
+#define LAPACK_dpptrs LAPACK_GLOBAL(dpptrs, DPPTRS)
+#define LAPACK_cpptrs LAPACK_GLOBAL(cpptrs, CPPTRS)
+#define LAPACK_zpptrs LAPACK_GLOBAL(zpptrs, ZPPTRS)
+#define LAPACK_spbtrs LAPACK_GLOBAL(spbtrs, SPBTRS)
+#define LAPACK_dpbtrs LAPACK_GLOBAL(dpbtrs, DPBTRS)
+#define LAPACK_cpbtrs LAPACK_GLOBAL(cpbtrs, CPBTRS)
+#define LAPACK_zpbtrs LAPACK_GLOBAL(zpbtrs, ZPBTRS)
+#define LAPACK_spttrs LAPACK_GLOBAL(spttrs, SPTTRS)
+#define LAPACK_dpttrs LAPACK_GLOBAL(dpttrs, DPTTRS)
+#define LAPACK_cpttrs LAPACK_GLOBAL(cpttrs, CPTTRS)
+#define LAPACK_zpttrs LAPACK_GLOBAL(zpttrs, ZPTTRS)
+#define LAPACK_ssytrs LAPACK_GLOBAL(ssytrs, SSYTRS)
+#define LAPACK_dsytrs LAPACK_GLOBAL(dsytrs, DSYTRS)
+#define LAPACK_csytrs LAPACK_GLOBAL(csytrs, CSYTRS)
+#define LAPACK_zsytrs LAPACK_GLOBAL(zsytrs, ZSYTRS)
+#define LAPACK_chetrs LAPACK_GLOBAL(chetrs, CHETRS)
+#define LAPACK_zhetrs LAPACK_GLOBAL(zhetrs, ZHETRS)
+#define LAPACK_ssptrs LAPACK_GLOBAL(ssptrs, SSPTRS)
+#define LAPACK_dsptrs LAPACK_GLOBAL(dsptrs, DSPTRS)
+#define LAPACK_csptrs LAPACK_GLOBAL(csptrs, CSPTRS)
+#define LAPACK_zsptrs LAPACK_GLOBAL(zsptrs, ZSPTRS)
+#define LAPACK_chptrs LAPACK_GLOBAL(chptrs, CHPTRS)
+#define LAPACK_zhptrs LAPACK_GLOBAL(zhptrs, ZHPTRS)
+#define LAPACK_strtrs LAPACK_GLOBAL(strtrs, STRTRS)
+#define LAPACK_dtrtrs LAPACK_GLOBAL(dtrtrs, DTRTRS)
+#define LAPACK_ctrtrs LAPACK_GLOBAL(ctrtrs, CTRTRS)
+#define LAPACK_ztrtrs LAPACK_GLOBAL(ztrtrs, ZTRTRS)
+#define LAPACK_stptrs LAPACK_GLOBAL(stptrs, STPTRS)
+#define LAPACK_dtptrs LAPACK_GLOBAL(dtptrs, DTPTRS)
+#define LAPACK_ctptrs LAPACK_GLOBAL(ctptrs, CTPTRS)
+#define LAPACK_ztptrs LAPACK_GLOBAL(ztptrs, ZTPTRS)
+#define LAPACK_stbtrs LAPACK_GLOBAL(stbtrs, STBTRS)
+#define LAPACK_dtbtrs LAPACK_GLOBAL(dtbtrs, DTBTRS)
+#define LAPACK_ctbtrs LAPACK_GLOBAL(ctbtrs, CTBTRS)
+#define LAPACK_ztbtrs LAPACK_GLOBAL(ztbtrs, ZTBTRS)
+#define LAPACK_sgecon LAPACK_GLOBAL(sgecon, SGECON)
+#define LAPACK_dgecon LAPACK_GLOBAL(dgecon, DGECON)
+#define LAPACK_cgecon LAPACK_GLOBAL(cgecon, CGECON)
+#define LAPACK_zgecon LAPACK_GLOBAL(zgecon, ZGECON)
+#define LAPACK_sgbcon LAPACK_GLOBAL(sgbcon, SGBCON)
+#define LAPACK_dgbcon LAPACK_GLOBAL(dgbcon, DGBCON)
+#define LAPACK_cgbcon LAPACK_GLOBAL(cgbcon, CGBCON)
+#define LAPACK_zgbcon LAPACK_GLOBAL(zgbcon, ZGBCON)
+#define LAPACK_sgtcon LAPACK_GLOBAL(sgtcon, SGTCON)
+#define LAPACK_dgtcon LAPACK_GLOBAL(dgtcon, DGTCON)
+#define LAPACK_cgtcon LAPACK_GLOBAL(cgtcon, CGTCON)
+#define LAPACK_zgtcon LAPACK_GLOBAL(zgtcon, ZGTCON)
+#define LAPACK_spocon LAPACK_GLOBAL(spocon, SPOCON)
+#define LAPACK_dpocon LAPACK_GLOBAL(dpocon, DPOCON)
+#define LAPACK_cpocon LAPACK_GLOBAL(cpocon, CPOCON)
+#define LAPACK_zpocon LAPACK_GLOBAL(zpocon, ZPOCON)
+#define LAPACK_sppcon LAPACK_GLOBAL(sppcon, SPPCON)
+#define LAPACK_dppcon LAPACK_GLOBAL(dppcon, DPPCON)
+#define LAPACK_cppcon LAPACK_GLOBAL(cppcon, CPPCON)
+#define LAPACK_zppcon LAPACK_GLOBAL(zppcon, ZPPCON)
+#define LAPACK_spbcon LAPACK_GLOBAL(spbcon, SPBCON)
+#define LAPACK_dpbcon LAPACK_GLOBAL(dpbcon, DPBCON)
+#define LAPACK_cpbcon LAPACK_GLOBAL(cpbcon, CPBCON)
+#define LAPACK_zpbcon LAPACK_GLOBAL(zpbcon, ZPBCON)
+#define LAPACK_sptcon LAPACK_GLOBAL(sptcon, SPTCON)
+#define LAPACK_dptcon LAPACK_GLOBAL(dptcon, DPTCON)
+#define LAPACK_cptcon LAPACK_GLOBAL(cptcon, CPTCON)
+#define LAPACK_zptcon LAPACK_GLOBAL(zptcon, ZPTCON)
+#define LAPACK_ssycon LAPACK_GLOBAL(ssycon, SSYCON)
+#define LAPACK_dsycon LAPACK_GLOBAL(dsycon, DSYCON)
+#define LAPACK_csycon LAPACK_GLOBAL(csycon, CSYCON)
+#define LAPACK_zsycon LAPACK_GLOBAL(zsycon, ZSYCON)
+#define LAPACK_checon LAPACK_GLOBAL(checon, CHECON)
+#define LAPACK_zhecon LAPACK_GLOBAL(zhecon, ZHECON)
+#define LAPACK_sspcon LAPACK_GLOBAL(sspcon, SSPCON)
+#define LAPACK_dspcon LAPACK_GLOBAL(dspcon, DSPCON)
+#define LAPACK_cspcon LAPACK_GLOBAL(cspcon, CSPCON)
+#define LAPACK_zspcon LAPACK_GLOBAL(zspcon, ZSPCON)
+#define LAPACK_chpcon LAPACK_GLOBAL(chpcon, CHPCON)
+#define LAPACK_zhpcon LAPACK_GLOBAL(zhpcon, ZHPCON)
+#define LAPACK_strcon LAPACK_GLOBAL(strcon, STRCON)
+#define LAPACK_dtrcon LAPACK_GLOBAL(dtrcon, DTRCON)
+#define LAPACK_ctrcon LAPACK_GLOBAL(ctrcon, CTRCON)
+#define LAPACK_ztrcon LAPACK_GLOBAL(ztrcon, ZTRCON)
+#define LAPACK_stpcon LAPACK_GLOBAL(stpcon, STPCON)
+#define LAPACK_dtpcon LAPACK_GLOBAL(dtpcon, DTPCON)
+#define LAPACK_ctpcon LAPACK_GLOBAL(ctpcon, CTPCON)
+#define LAPACK_ztpcon LAPACK_GLOBAL(ztpcon, ZTPCON)
+#define LAPACK_stbcon LAPACK_GLOBAL(stbcon, STBCON)
+#define LAPACK_dtbcon LAPACK_GLOBAL(dtbcon, DTBCON)
+#define LAPACK_ctbcon LAPACK_GLOBAL(ctbcon, CTBCON)
+#define LAPACK_ztbcon LAPACK_GLOBAL(ztbcon, ZTBCON)
+#define LAPACK_sgerfs LAPACK_GLOBAL(sgerfs, SGERFS)
+#define LAPACK_dgerfs LAPACK_GLOBAL(dgerfs, DGERFS)
+#define LAPACK_cgerfs LAPACK_GLOBAL(cgerfs, CGERFS)
+#define LAPACK_zgerfs LAPACK_GLOBAL(zgerfs, ZGERFS)
+#define LAPACK_dgerfsx LAPACK_GLOBAL(dgerfsx, DGERFSX)
+#define LAPACK_sgerfsx LAPACK_GLOBAL(sgerfsx, SGERFSX)
+#define LAPACK_zgerfsx LAPACK_GLOBAL(zgerfsx, ZGERFSX)
+#define LAPACK_cgerfsx LAPACK_GLOBAL(cgerfsx, CGERFSX)
+#define LAPACK_sgbrfs LAPACK_GLOBAL(sgbrfs, SGBRFS)
+#define LAPACK_dgbrfs LAPACK_GLOBAL(dgbrfs, DGBRFS)
+#define LAPACK_cgbrfs LAPACK_GLOBAL(cgbrfs, CGBRFS)
+#define LAPACK_zgbrfs LAPACK_GLOBAL(zgbrfs, ZGBRFS)
+#define LAPACK_dgbrfsx LAPACK_GLOBAL(dgbrfsx, DGBRFSX)
+#define LAPACK_sgbrfsx LAPACK_GLOBAL(sgbrfsx, SGBRFSX)
+#define LAPACK_zgbrfsx LAPACK_GLOBAL(zgbrfsx, ZGBRFSX)
+#define LAPACK_cgbrfsx LAPACK_GLOBAL(cgbrfsx, CGBRFSX)
+#define LAPACK_sgtrfs LAPACK_GLOBAL(sgtrfs, SGTRFS)
+#define LAPACK_dgtrfs LAPACK_GLOBAL(dgtrfs, DGTRFS)
+#define LAPACK_cgtrfs LAPACK_GLOBAL(cgtrfs, CGTRFS)
+#define LAPACK_zgtrfs LAPACK_GLOBAL(zgtrfs, ZGTRFS)
+#define LAPACK_sporfs LAPACK_GLOBAL(sporfs, SPORFS)
+#define LAPACK_dporfs LAPACK_GLOBAL(dporfs, DPORFS)
+#define LAPACK_cporfs LAPACK_GLOBAL(cporfs, CPORFS)
+#define LAPACK_zporfs LAPACK_GLOBAL(zporfs, ZPORFS)
+#define LAPACK_dporfsx LAPACK_GLOBAL(dporfsx, DPORFSX)
+#define LAPACK_sporfsx LAPACK_GLOBAL(sporfsx, SPORFSX)
+#define LAPACK_zporfsx LAPACK_GLOBAL(zporfsx, ZPORFSX)
+#define LAPACK_cporfsx LAPACK_GLOBAL(cporfsx, CPORFSX)
+#define LAPACK_spprfs LAPACK_GLOBAL(spprfs, SPPRFS)
+#define LAPACK_dpprfs LAPACK_GLOBAL(dpprfs, DPPRFS)
+#define LAPACK_cpprfs LAPACK_GLOBAL(cpprfs, CPPRFS)
+#define LAPACK_zpprfs LAPACK_GLOBAL(zpprfs, ZPPRFS)
+#define LAPACK_spbrfs LAPACK_GLOBAL(spbrfs, SPBRFS)
+#define LAPACK_dpbrfs LAPACK_GLOBAL(dpbrfs, DPBRFS)
+#define LAPACK_cpbrfs LAPACK_GLOBAL(cpbrfs, CPBRFS)
+#define LAPACK_zpbrfs LAPACK_GLOBAL(zpbrfs, ZPBRFS)
+#define LAPACK_sptrfs LAPACK_GLOBAL(sptrfs, SPTRFS)
+#define LAPACK_dptrfs LAPACK_GLOBAL(dptrfs, DPTRFS)
+#define LAPACK_cptrfs LAPACK_GLOBAL(cptrfs, CPTRFS)
+#define LAPACK_zptrfs LAPACK_GLOBAL(zptrfs, ZPTRFS)
+#define LAPACK_ssyrfs LAPACK_GLOBAL(ssyrfs, SSYRFS)
+#define LAPACK_dsyrfs LAPACK_GLOBAL(dsyrfs, DSYRFS)
+#define LAPACK_csyrfs LAPACK_GLOBAL(csyrfs, CSYRFS)
+#define LAPACK_zsyrfs LAPACK_GLOBAL(zsyrfs, ZSYRFS)
+#define LAPACK_dsyrfsx LAPACK_GLOBAL(dsyrfsx, DSYRFSX)
+#define LAPACK_ssyrfsx LAPACK_GLOBAL(ssyrfsx, SSYRFSX)
+#define LAPACK_zsyrfsx LAPACK_GLOBAL(zsyrfsx, ZSYRFSX)
+#define LAPACK_csyrfsx LAPACK_GLOBAL(csyrfsx, CSYRFSX)
+#define LAPACK_cherfs LAPACK_GLOBAL(cherfs, CHERFS)
+#define LAPACK_zherfs LAPACK_GLOBAL(zherfs, ZHERFS)
+#define LAPACK_zherfsx LAPACK_GLOBAL(zherfsx, ZHERFSX)
+#define LAPACK_cherfsx LAPACK_GLOBAL(cherfsx, CHERFSX)
+#define LAPACK_ssprfs LAPACK_GLOBAL(ssprfs, SSPRFS)
+#define LAPACK_dsprfs LAPACK_GLOBAL(dsprfs, DSPRFS)
+#define LAPACK_csprfs LAPACK_GLOBAL(csprfs, CSPRFS)
+#define LAPACK_zsprfs LAPACK_GLOBAL(zsprfs, ZSPRFS)
+#define LAPACK_chprfs LAPACK_GLOBAL(chprfs, CHPRFS)
+#define LAPACK_zhprfs LAPACK_GLOBAL(zhprfs, ZHPRFS)
+#define LAPACK_strrfs LAPACK_GLOBAL(strrfs, STRRFS)
+#define LAPACK_dtrrfs LAPACK_GLOBAL(dtrrfs, DTRRFS)
+#define LAPACK_ctrrfs LAPACK_GLOBAL(ctrrfs, CTRRFS)
+#define LAPACK_ztrrfs LAPACK_GLOBAL(ztrrfs, ZTRRFS)
+#define LAPACK_stprfs LAPACK_GLOBAL(stprfs, STPRFS)
+#define LAPACK_dtprfs LAPACK_GLOBAL(dtprfs, DTPRFS)
+#define LAPACK_ctprfs LAPACK_GLOBAL(ctprfs, CTPRFS)
+#define LAPACK_ztprfs LAPACK_GLOBAL(ztprfs, ZTPRFS)
+#define LAPACK_stbrfs LAPACK_GLOBAL(stbrfs, STBRFS)
+#define LAPACK_dtbrfs LAPACK_GLOBAL(dtbrfs, DTBRFS)
+#define LAPACK_ctbrfs LAPACK_GLOBAL(ctbrfs, CTBRFS)
+#define LAPACK_ztbrfs LAPACK_GLOBAL(ztbrfs, ZTBRFS)
+#define LAPACK_sgetri LAPACK_GLOBAL(sgetri, SGETRI)
+#define LAPACK_dgetri LAPACK_GLOBAL(dgetri, DGETRI)
+#define LAPACK_cgetri LAPACK_GLOBAL(cgetri, CGETRI)
+#define LAPACK_zgetri LAPACK_GLOBAL(zgetri, ZGETRI)
+#define LAPACK_spotri LAPACK_GLOBAL(spotri, SPOTRI)
+#define LAPACK_dpotri LAPACK_GLOBAL(dpotri, DPOTRI)
+#define LAPACK_cpotri LAPACK_GLOBAL(cpotri, CPOTRI)
+#define LAPACK_zpotri LAPACK_GLOBAL(zpotri, ZPOTRI)
+#define LAPACK_dpftri LAPACK_GLOBAL(dpftri, DPFTRI)
+#define LAPACK_spftri LAPACK_GLOBAL(spftri, SPFTRI)
+#define LAPACK_zpftri LAPACK_GLOBAL(zpftri, ZPFTRI)
+#define LAPACK_cpftri LAPACK_GLOBAL(cpftri, CPFTRI)
+#define LAPACK_spptri LAPACK_GLOBAL(spptri, SPPTRI)
+#define LAPACK_dpptri LAPACK_GLOBAL(dpptri, DPPTRI)
+#define LAPACK_cpptri LAPACK_GLOBAL(cpptri, CPPTRI)
+#define LAPACK_zpptri LAPACK_GLOBAL(zpptri, ZPPTRI)
+#define LAPACK_ssytri LAPACK_GLOBAL(ssytri, SSYTRI)
+#define LAPACK_dsytri LAPACK_GLOBAL(dsytri, DSYTRI)
+#define LAPACK_csytri LAPACK_GLOBAL(csytri, CSYTRI)
+#define LAPACK_zsytri LAPACK_GLOBAL(zsytri, ZSYTRI)
+#define LAPACK_chetri LAPACK_GLOBAL(chetri, CHETRI)
+#define LAPACK_zhetri LAPACK_GLOBAL(zhetri, ZHETRI)
+#define LAPACK_ssptri LAPACK_GLOBAL(ssptri, SSPTRI)
+#define LAPACK_dsptri LAPACK_GLOBAL(dsptri, DSPTRI)
+#define LAPACK_csptri LAPACK_GLOBAL(csptri, CSPTRI)
+#define LAPACK_zsptri LAPACK_GLOBAL(zsptri, ZSPTRI)
+#define LAPACK_chptri LAPACK_GLOBAL(chptri, CHPTRI)
+#define LAPACK_zhptri LAPACK_GLOBAL(zhptri, ZHPTRI)
+#define LAPACK_strtri LAPACK_GLOBAL(strtri, STRTRI)
+#define LAPACK_dtrtri LAPACK_GLOBAL(dtrtri, DTRTRI)
+#define LAPACK_ctrtri LAPACK_GLOBAL(ctrtri, CTRTRI)
+#define LAPACK_ztrtri LAPACK_GLOBAL(ztrtri, ZTRTRI)
+#define LAPACK_dtftri LAPACK_GLOBAL(dtftri, DTFTRI)
+#define LAPACK_stftri LAPACK_GLOBAL(stftri, STFTRI)
+#define LAPACK_ztftri LAPACK_GLOBAL(ztftri, ZTFTRI)
+#define LAPACK_ctftri LAPACK_GLOBAL(ctftri, CTFTRI)
+#define LAPACK_stptri LAPACK_GLOBAL(stptri, STPTRI)
+#define LAPACK_dtptri LAPACK_GLOBAL(dtptri, DTPTRI)
+#define LAPACK_ctptri LAPACK_GLOBAL(ctptri, CTPTRI)
+#define LAPACK_ztptri LAPACK_GLOBAL(ztptri, ZTPTRI)
+#define LAPACK_sgeequ LAPACK_GLOBAL(sgeequ, SGEEQU)
+#define LAPACK_dgeequ LAPACK_GLOBAL(dgeequ, DGEEQU)
+#define LAPACK_cgeequ LAPACK_GLOBAL(cgeequ, CGEEQU)
+#define LAPACK_zgeequ LAPACK_GLOBAL(zgeequ, ZGEEQU)
+#define LAPACK_dgeequb LAPACK_GLOBAL(dgeequb, DGEEQUB)
+#define LAPACK_sgeequb LAPACK_GLOBAL(sgeequb, SGEEQUB)
+#define LAPACK_zgeequb LAPACK_GLOBAL(zgeequb, ZGEEQUB)
+#define LAPACK_cgeequb LAPACK_GLOBAL(cgeequb, CGEEQUB)
+#define LAPACK_sgbequ LAPACK_GLOBAL(sgbequ, SGBEQU)
+#define LAPACK_dgbequ LAPACK_GLOBAL(dgbequ, DGBEQU)
+#define LAPACK_cgbequ LAPACK_GLOBAL(cgbequ, CGBEQU)
+#define LAPACK_zgbequ LAPACK_GLOBAL(zgbequ, ZGBEQU)
+#define LAPACK_dgbequb LAPACK_GLOBAL(dgbequb, DGBEQUB)
+#define LAPACK_sgbequb LAPACK_GLOBAL(sgbequb, SGBEQUB)
+#define LAPACK_zgbequb LAPACK_GLOBAL(zgbequb, ZGBEQUB)
+#define LAPACK_cgbequb LAPACK_GLOBAL(cgbequb, CGBEQUB)
+#define LAPACK_spoequ LAPACK_GLOBAL(spoequ, SPOEQU)
+#define LAPACK_dpoequ LAPACK_GLOBAL(dpoequ, DPOEQU)
+#define LAPACK_cpoequ LAPACK_GLOBAL(cpoequ, CPOEQU)
+#define LAPACK_zpoequ LAPACK_GLOBAL(zpoequ, ZPOEQU)
+#define LAPACK_dpoequb LAPACK_GLOBAL(dpoequb, DPOEQUB)
+#define LAPACK_spoequb LAPACK_GLOBAL(spoequb, SPOEQUB)
+#define LAPACK_zpoequb LAPACK_GLOBAL(zpoequb, ZPOEQUB)
+#define LAPACK_cpoequb LAPACK_GLOBAL(cpoequb, CPOEQUB)
+#define LAPACK_sppequ LAPACK_GLOBAL(sppequ, SPPEQU)
+#define LAPACK_dppequ LAPACK_GLOBAL(dppequ, DPPEQU)
+#define LAPACK_cppequ LAPACK_GLOBAL(cppequ, CPPEQU)
+#define LAPACK_zppequ LAPACK_GLOBAL(zppequ, ZPPEQU)
+#define LAPACK_spbequ LAPACK_GLOBAL(spbequ, SPBEQU)
+#define LAPACK_dpbequ LAPACK_GLOBAL(dpbequ, DPBEQU)
+#define LAPACK_cpbequ LAPACK_GLOBAL(cpbequ, CPBEQU)
+#define LAPACK_zpbequ LAPACK_GLOBAL(zpbequ, ZPBEQU)
+#define LAPACK_dsyequb LAPACK_GLOBAL(dsyequb, DSYEQUB)
+#define LAPACK_ssyequb LAPACK_GLOBAL(ssyequb, SSYEQUB)
+#define LAPACK_zsyequb LAPACK_GLOBAL(zsyequb, ZSYEQUB)
+#define LAPACK_csyequb LAPACK_GLOBAL(csyequb, CSYEQUB)
+#define LAPACK_zheequb LAPACK_GLOBAL(zheequb, ZHEEQUB)
+#define LAPACK_cheequb LAPACK_GLOBAL(cheequb, CHEEQUB)
+#define LAPACK_sgesv LAPACK_GLOBAL(sgesv, SGESV)
+#define LAPACK_dgesv LAPACK_GLOBAL(dgesv, DGESV)
+#define LAPACK_cgesv LAPACK_GLOBAL(cgesv, CGESV)
+#define LAPACK_zgesv LAPACK_GLOBAL(zgesv, ZGESV)
+#define LAPACK_dsgesv LAPACK_GLOBAL(dsgesv, DSGESV)
+#define LAPACK_zcgesv LAPACK_GLOBAL(zcgesv, ZCGESV)
+#define LAPACK_sgesvx LAPACK_GLOBAL(sgesvx, SGESVX)
+#define LAPACK_dgesvx LAPACK_GLOBAL(dgesvx, DGESVX)
+#define LAPACK_cgesvx LAPACK_GLOBAL(cgesvx, CGESVX)
+#define LAPACK_zgesvx LAPACK_GLOBAL(zgesvx, ZGESVX)
+#define LAPACK_dgesvxx LAPACK_GLOBAL(dgesvxx, DGESVXX)
+#define LAPACK_sgesvxx LAPACK_GLOBAL(sgesvxx, SGESVXX)
+#define LAPACK_zgesvxx LAPACK_GLOBAL(zgesvxx, ZGESVXX)
+#define LAPACK_cgesvxx LAPACK_GLOBAL(cgesvxx, CGESVXX)
+#define LAPACK_sgbsv LAPACK_GLOBAL(sgbsv, SGBSV)
+#define LAPACK_dgbsv LAPACK_GLOBAL(dgbsv, DGBSV)
+#define LAPACK_cgbsv LAPACK_GLOBAL(cgbsv, CGBSV)
+#define LAPACK_zgbsv LAPACK_GLOBAL(zgbsv, ZGBSV)
+#define LAPACK_sgbsvx LAPACK_GLOBAL(sgbsvx, SGBSVX)
+#define LAPACK_dgbsvx LAPACK_GLOBAL(dgbsvx, DGBSVX)
+#define LAPACK_cgbsvx LAPACK_GLOBAL(cgbsvx, CGBSVX)
+#define LAPACK_zgbsvx LAPACK_GLOBAL(zgbsvx, ZGBSVX)
+#define LAPACK_dgbsvxx LAPACK_GLOBAL(dgbsvxx, DGBSVXX)
+#define LAPACK_sgbsvxx LAPACK_GLOBAL(sgbsvxx, SGBSVXX)
+#define LAPACK_zgbsvxx LAPACK_GLOBAL(zgbsvxx, ZGBSVXX)
+#define LAPACK_cgbsvxx LAPACK_GLOBAL(cgbsvxx, CGBSVXX)
+#define LAPACK_sgtsv LAPACK_GLOBAL(sgtsv, SGTSV)
+#define LAPACK_dgtsv LAPACK_GLOBAL(dgtsv, DGTSV)
+#define LAPACK_cgtsv LAPACK_GLOBAL(cgtsv, CGTSV)
+#define LAPACK_zgtsv LAPACK_GLOBAL(zgtsv, ZGTSV)
+#define LAPACK_sgtsvx LAPACK_GLOBAL(sgtsvx, SGTSVX)
+#define LAPACK_dgtsvx LAPACK_GLOBAL(dgtsvx, DGTSVX)
+#define LAPACK_cgtsvx LAPACK_GLOBAL(cgtsvx, CGTSVX)
+#define LAPACK_zgtsvx LAPACK_GLOBAL(zgtsvx, ZGTSVX)
+#define LAPACK_sposv LAPACK_GLOBAL(sposv, SPOSV)
+#define LAPACK_dposv LAPACK_GLOBAL(dposv, DPOSV)
+#define LAPACK_cposv LAPACK_GLOBAL(cposv, CPOSV)
+#define LAPACK_zposv LAPACK_GLOBAL(zposv, ZPOSV)
+#define LAPACK_dsposv LAPACK_GLOBAL(dsposv, DSPOSV)
+#define LAPACK_zcposv LAPACK_GLOBAL(zcposv, ZCPOSV)
+#define LAPACK_sposvx LAPACK_GLOBAL(sposvx, SPOSVX)
+#define LAPACK_dposvx LAPACK_GLOBAL(dposvx, DPOSVX)
+#define LAPACK_cposvx LAPACK_GLOBAL(cposvx, CPOSVX)
+#define LAPACK_zposvx LAPACK_GLOBAL(zposvx, ZPOSVX)
+#define LAPACK_dposvxx LAPACK_GLOBAL(dposvxx, DPOSVXX)
+#define LAPACK_sposvxx LAPACK_GLOBAL(sposvxx, SPOSVXX)
+#define LAPACK_zposvxx LAPACK_GLOBAL(zposvxx, ZPOSVXX)
+#define LAPACK_cposvxx LAPACK_GLOBAL(cposvxx, CPOSVXX)
+#define LAPACK_sppsv LAPACK_GLOBAL(sppsv, SPPSV)
+#define LAPACK_dppsv LAPACK_GLOBAL(dppsv, DPPSV)
+#define LAPACK_cppsv LAPACK_GLOBAL(cppsv, CPPSV)
+#define LAPACK_zppsv LAPACK_GLOBAL(zppsv, ZPPSV)
+#define LAPACK_sppsvx LAPACK_GLOBAL(sppsvx, SPPSVX)
+#define LAPACK_dppsvx LAPACK_GLOBAL(dppsvx, DPPSVX)
+#define LAPACK_cppsvx LAPACK_GLOBAL(cppsvx, CPPSVX)
+#define LAPACK_zppsvx LAPACK_GLOBAL(zppsvx, ZPPSVX)
+#define LAPACK_spbsv LAPACK_GLOBAL(spbsv, SPBSV)
+#define LAPACK_dpbsv LAPACK_GLOBAL(dpbsv, DPBSV)
+#define LAPACK_cpbsv LAPACK_GLOBAL(cpbsv, CPBSV)
+#define LAPACK_zpbsv LAPACK_GLOBAL(zpbsv, ZPBSV)
+#define LAPACK_spbsvx LAPACK_GLOBAL(spbsvx, SPBSVX)
+#define LAPACK_dpbsvx LAPACK_GLOBAL(dpbsvx, DPBSVX)
+#define LAPACK_cpbsvx LAPACK_GLOBAL(cpbsvx, CPBSVX)
+#define LAPACK_zpbsvx LAPACK_GLOBAL(zpbsvx, ZPBSVX)
+#define LAPACK_sptsv LAPACK_GLOBAL(sptsv, SPTSV)
+#define LAPACK_dptsv LAPACK_GLOBAL(dptsv, DPTSV)
+#define LAPACK_cptsv LAPACK_GLOBAL(cptsv, CPTSV)
+#define LAPACK_zptsv LAPACK_GLOBAL(zptsv, ZPTSV)
+#define LAPACK_sptsvx LAPACK_GLOBAL(sptsvx, SPTSVX)
+#define LAPACK_dptsvx LAPACK_GLOBAL(dptsvx, DPTSVX)
+#define LAPACK_cptsvx LAPACK_GLOBAL(cptsvx, CPTSVX)
+#define LAPACK_zptsvx LAPACK_GLOBAL(zptsvx, ZPTSVX)
+#define LAPACK_ssysv LAPACK_GLOBAL(ssysv, SSYSV)
+#define LAPACK_dsysv LAPACK_GLOBAL(dsysv, DSYSV)
+#define LAPACK_csysv LAPACK_GLOBAL(csysv, CSYSV)
+#define LAPACK_zsysv LAPACK_GLOBAL(zsysv, ZSYSV)
+#define LAPACK_ssysvx LAPACK_GLOBAL(ssysvx, SSYSVX)
+#define LAPACK_dsysvx LAPACK_GLOBAL(dsysvx, DSYSVX)
+#define LAPACK_csysvx LAPACK_GLOBAL(csysvx, CSYSVX)
+#define LAPACK_zsysvx LAPACK_GLOBAL(zsysvx, ZSYSVX)
+#define LAPACK_dsysvxx LAPACK_GLOBAL(dsysvxx, DSYSVXX)
+#define LAPACK_ssysvxx LAPACK_GLOBAL(ssysvxx, SSYSVXX)
+#define LAPACK_zsysvxx LAPACK_GLOBAL(zsysvxx, ZSYSVXX)
+#define LAPACK_csysvxx LAPACK_GLOBAL(csysvxx, CSYSVXX)
+#define LAPACK_chesv LAPACK_GLOBAL(chesv, CHESV)
+#define LAPACK_zhesv LAPACK_GLOBAL(zhesv, ZHESV)
+#define LAPACK_chesvx LAPACK_GLOBAL(chesvx, CHESVX)
+#define LAPACK_zhesvx LAPACK_GLOBAL(zhesvx, ZHESVX)
+#define LAPACK_zhesvxx LAPACK_GLOBAL(zhesvxx, ZHESVXX)
+#define LAPACK_chesvxx LAPACK_GLOBAL(chesvxx, CHESVXX)
+#define LAPACK_sspsv LAPACK_GLOBAL(sspsv, SSPSV)
+#define LAPACK_dspsv LAPACK_GLOBAL(dspsv, DSPSV)
+#define LAPACK_cspsv LAPACK_GLOBAL(cspsv, CSPSV)
+#define LAPACK_zspsv LAPACK_GLOBAL(zspsv, ZSPSV)
+#define LAPACK_sspsvx LAPACK_GLOBAL(sspsvx, SSPSVX)
+#define LAPACK_dspsvx LAPACK_GLOBAL(dspsvx, DSPSVX)
+#define LAPACK_cspsvx LAPACK_GLOBAL(cspsvx, CSPSVX)
+#define LAPACK_zspsvx LAPACK_GLOBAL(zspsvx, ZSPSVX)
+#define LAPACK_chpsv LAPACK_GLOBAL(chpsv, CHPSV)
+#define LAPACK_zhpsv LAPACK_GLOBAL(zhpsv, ZHPSV)
+#define LAPACK_chpsvx LAPACK_GLOBAL(chpsvx, CHPSVX)
+#define LAPACK_zhpsvx LAPACK_GLOBAL(zhpsvx, ZHPSVX)
+#define LAPACK_sgeqrf LAPACK_GLOBAL(sgeqrf, SGEQRF)
+#define LAPACK_dgeqrf LAPACK_GLOBAL(dgeqrf, DGEQRF)
+#define LAPACK_cgeqrf LAPACK_GLOBAL(cgeqrf, CGEQRF)
+#define LAPACK_zgeqrf LAPACK_GLOBAL(zgeqrf, ZGEQRF)
+#define LAPACK_sgeqpf LAPACK_GLOBAL(sgeqpf, SGEQPF)
+#define LAPACK_dgeqpf LAPACK_GLOBAL(dgeqpf, DGEQPF)
+#define LAPACK_cgeqpf LAPACK_GLOBAL(cgeqpf, CGEQPF)
+#define LAPACK_zgeqpf LAPACK_GLOBAL(zgeqpf, ZGEQPF)
+#define LAPACK_sgeqp3 LAPACK_GLOBAL(sgeqp3, SGEQP3)
+#define LAPACK_dgeqp3 LAPACK_GLOBAL(dgeqp3, DGEQP3)
+#define LAPACK_cgeqp3 LAPACK_GLOBAL(cgeqp3, CGEQP3)
+#define LAPACK_zgeqp3 LAPACK_GLOBAL(zgeqp3, ZGEQP3)
+#define LAPACK_sorgqr LAPACK_GLOBAL(sorgqr, SORGQR)
+#define LAPACK_dorgqr LAPACK_GLOBAL(dorgqr, DORGQR)
+#define LAPACK_sormqr LAPACK_GLOBAL(sormqr, SORMQR)
+#define LAPACK_dormqr LAPACK_GLOBAL(dormqr, DORMQR)
+#define LAPACK_cungqr LAPACK_GLOBAL(cungqr, CUNGQR)
+#define LAPACK_zungqr LAPACK_GLOBAL(zungqr, ZUNGQR)
+#define LAPACK_cunmqr LAPACK_GLOBAL(cunmqr, CUNMQR)
+#define LAPACK_zunmqr LAPACK_GLOBAL(zunmqr, ZUNMQR)
+#define LAPACK_sgelqf LAPACK_GLOBAL(sgelqf, SGELQF)
+#define LAPACK_dgelqf LAPACK_GLOBAL(dgelqf, DGELQF)
+#define LAPACK_cgelqf LAPACK_GLOBAL(cgelqf, CGELQF)
+#define LAPACK_zgelqf LAPACK_GLOBAL(zgelqf, ZGELQF)
+#define LAPACK_sorglq LAPACK_GLOBAL(sorglq, SORGLQ)
+#define LAPACK_dorglq LAPACK_GLOBAL(dorglq, DORGLQ)
+#define LAPACK_sormlq LAPACK_GLOBAL(sormlq, SORMLQ)
+#define LAPACK_dormlq LAPACK_GLOBAL(dormlq, DORMLQ)
+#define LAPACK_cunglq LAPACK_GLOBAL(cunglq, CUNGLQ)
+#define LAPACK_zunglq LAPACK_GLOBAL(zunglq, ZUNGLQ)
+#define LAPACK_cunmlq LAPACK_GLOBAL(cunmlq, CUNMLQ)
+#define LAPACK_zunmlq LAPACK_GLOBAL(zunmlq, ZUNMLQ)
+#define LAPACK_sgeqlf LAPACK_GLOBAL(sgeqlf, SGEQLF)
+#define LAPACK_dgeqlf LAPACK_GLOBAL(dgeqlf, DGEQLF)
+#define LAPACK_cgeqlf LAPACK_GLOBAL(cgeqlf, CGEQLF)
+#define LAPACK_zgeqlf LAPACK_GLOBAL(zgeqlf, ZGEQLF)
+#define LAPACK_sorgql LAPACK_GLOBAL(sorgql, SORGQL)
+#define LAPACK_dorgql LAPACK_GLOBAL(dorgql, DORGQL)
+#define LAPACK_cungql LAPACK_GLOBAL(cungql, CUNGQL)
+#define LAPACK_zungql LAPACK_GLOBAL(zungql, ZUNGQL)
+#define LAPACK_sormql LAPACK_GLOBAL(sormql, SORMQL)
+#define LAPACK_dormql LAPACK_GLOBAL(dormql, DORMQL)
+#define LAPACK_cunmql LAPACK_GLOBAL(cunmql, CUNMQL)
+#define LAPACK_zunmql LAPACK_GLOBAL(zunmql, ZUNMQL)
+#define LAPACK_sgerqf LAPACK_GLOBAL(sgerqf, SGERQF)
+#define LAPACK_dgerqf LAPACK_GLOBAL(dgerqf, DGERQF)
+#define LAPACK_cgerqf LAPACK_GLOBAL(cgerqf, CGERQF)
+#define LAPACK_zgerqf LAPACK_GLOBAL(zgerqf, ZGERQF)
+#define LAPACK_sorgrq LAPACK_GLOBAL(sorgrq, SORGRQ)
+#define LAPACK_dorgrq LAPACK_GLOBAL(dorgrq, DORGRQ)
+#define LAPACK_cungrq LAPACK_GLOBAL(cungrq, CUNGRQ)
+#define LAPACK_zungrq LAPACK_GLOBAL(zungrq, ZUNGRQ)
+#define LAPACK_sormrq LAPACK_GLOBAL(sormrq, SORMRQ)
+#define LAPACK_dormrq LAPACK_GLOBAL(dormrq, DORMRQ)
+#define LAPACK_cunmrq LAPACK_GLOBAL(cunmrq, CUNMRQ)
+#define LAPACK_zunmrq LAPACK_GLOBAL(zunmrq, ZUNMRQ)
+#define LAPACK_stzrzf LAPACK_GLOBAL(stzrzf, STZRZF)
+#define LAPACK_dtzrzf LAPACK_GLOBAL(dtzrzf, DTZRZF)
+#define LAPACK_ctzrzf LAPACK_GLOBAL(ctzrzf, CTZRZF)
+#define LAPACK_ztzrzf LAPACK_GLOBAL(ztzrzf, ZTZRZF)
+#define LAPACK_sormrz LAPACK_GLOBAL(sormrz, SORMRZ)
+#define LAPACK_dormrz LAPACK_GLOBAL(dormrz, DORMRZ)
+#define LAPACK_cunmrz LAPACK_GLOBAL(cunmrz, CUNMRZ)
+#define LAPACK_zunmrz LAPACK_GLOBAL(zunmrz, ZUNMRZ)
+#define LAPACK_sggqrf LAPACK_GLOBAL(sggqrf, SGGQRF)
+#define LAPACK_dggqrf LAPACK_GLOBAL(dggqrf, DGGQRF)
+#define LAPACK_cggqrf LAPACK_GLOBAL(cggqrf, CGGQRF)
+#define LAPACK_zggqrf LAPACK_GLOBAL(zggqrf, ZGGQRF)
+#define LAPACK_sggrqf LAPACK_GLOBAL(sggrqf, SGGRQF)
+#define LAPACK_dggrqf LAPACK_GLOBAL(dggrqf, DGGRQF)
+#define LAPACK_cggrqf LAPACK_GLOBAL(cggrqf, CGGRQF)
+#define LAPACK_zggrqf LAPACK_GLOBAL(zggrqf, ZGGRQF)
+#define LAPACK_sgebrd LAPACK_GLOBAL(sgebrd, SGEBRD)
+#define LAPACK_dgebrd LAPACK_GLOBAL(dgebrd, DGEBRD)
+#define LAPACK_cgebrd LAPACK_GLOBAL(cgebrd, CGEBRD)
+#define LAPACK_zgebrd LAPACK_GLOBAL(zgebrd, ZGEBRD)
+#define LAPACK_sgbbrd LAPACK_GLOBAL(sgbbrd, SGBBRD)
+#define LAPACK_dgbbrd LAPACK_GLOBAL(dgbbrd, DGBBRD)
+#define LAPACK_cgbbrd LAPACK_GLOBAL(cgbbrd, CGBBRD)
+#define LAPACK_zgbbrd LAPACK_GLOBAL(zgbbrd, ZGBBRD)
+#define LAPACK_sorgbr LAPACK_GLOBAL(sorgbr, SORGBR)
+#define LAPACK_dorgbr LAPACK_GLOBAL(dorgbr, DORGBR)
+#define LAPACK_sormbr LAPACK_GLOBAL(sormbr, SORMBR)
+#define LAPACK_dormbr LAPACK_GLOBAL(dormbr, DORMBR)
+#define LAPACK_cungbr LAPACK_GLOBAL(cungbr, CUNGBR)
+#define LAPACK_zungbr LAPACK_GLOBAL(zungbr, ZUNGBR)
+#define LAPACK_cunmbr LAPACK_GLOBAL(cunmbr, CUNMBR)
+#define LAPACK_zunmbr LAPACK_GLOBAL(zunmbr, ZUNMBR)
+#define LAPACK_sbdsqr LAPACK_GLOBAL(sbdsqr, SBDSQR)
+#define LAPACK_dbdsqr LAPACK_GLOBAL(dbdsqr, DBDSQR)
+#define LAPACK_cbdsqr LAPACK_GLOBAL(cbdsqr, CBDSQR)
+#define LAPACK_zbdsqr LAPACK_GLOBAL(zbdsqr, ZBDSQR)
+#define LAPACK_sbdsdc LAPACK_GLOBAL(sbdsdc, SBDSDC)
+#define LAPACK_dbdsdc LAPACK_GLOBAL(dbdsdc, DBDSDC)
+#define LAPACK_ssytrd LAPACK_GLOBAL(ssytrd, SSYTRD)
+#define LAPACK_dsytrd LAPACK_GLOBAL(dsytrd, DSYTRD)
+#define LAPACK_sorgtr LAPACK_GLOBAL(sorgtr, SORGTR)
+#define LAPACK_dorgtr LAPACK_GLOBAL(dorgtr, DORGTR)
+#define LAPACK_sormtr LAPACK_GLOBAL(sormtr, SORMTR)
+#define LAPACK_dormtr LAPACK_GLOBAL(dormtr, DORMTR)
+#define LAPACK_chetrd LAPACK_GLOBAL(chetrd, CHETRD)
+#define LAPACK_zhetrd LAPACK_GLOBAL(zhetrd, ZHETRD)
+#define LAPACK_cungtr LAPACK_GLOBAL(cungtr, CUNGTR)
+#define LAPACK_zungtr LAPACK_GLOBAL(zungtr, ZUNGTR)
+#define LAPACK_cunmtr LAPACK_GLOBAL(cunmtr, CUNMTR)
+#define LAPACK_zunmtr LAPACK_GLOBAL(zunmtr, ZUNMTR)
+#define LAPACK_ssptrd LAPACK_GLOBAL(ssptrd, SSPTRD)
+#define LAPACK_dsptrd LAPACK_GLOBAL(dsptrd, DSPTRD)
+#define LAPACK_sopgtr LAPACK_GLOBAL(sopgtr, SOPGTR)
+#define LAPACK_dopgtr LAPACK_GLOBAL(dopgtr, DOPGTR)
+#define LAPACK_sopmtr LAPACK_GLOBAL(sopmtr, SOPMTR)
+#define LAPACK_dopmtr LAPACK_GLOBAL(dopmtr, DOPMTR)
+#define LAPACK_chptrd LAPACK_GLOBAL(chptrd, CHPTRD)
+#define LAPACK_zhptrd LAPACK_GLOBAL(zhptrd, ZHPTRD)
+#define LAPACK_cupgtr LAPACK_GLOBAL(cupgtr, CUPGTR)
+#define LAPACK_zupgtr LAPACK_GLOBAL(zupgtr, ZUPGTR)
+#define LAPACK_cupmtr LAPACK_GLOBAL(cupmtr, CUPMTR)
+#define LAPACK_zupmtr LAPACK_GLOBAL(zupmtr, ZUPMTR)
+#define LAPACK_ssbtrd LAPACK_GLOBAL(ssbtrd, SSBTRD)
+#define LAPACK_dsbtrd LAPACK_GLOBAL(dsbtrd, DSBTRD)
+#define LAPACK_chbtrd LAPACK_GLOBAL(chbtrd, CHBTRD)
+#define LAPACK_zhbtrd LAPACK_GLOBAL(zhbtrd, ZHBTRD)
+#define LAPACK_ssterf LAPACK_GLOBAL(ssterf, SSTERF)
+#define LAPACK_dsterf LAPACK_GLOBAL(dsterf, DSTERF)
+#define LAPACK_ssteqr LAPACK_GLOBAL(ssteqr, SSTEQR)
+#define LAPACK_dsteqr LAPACK_GLOBAL(dsteqr, DSTEQR)
+#define LAPACK_csteqr LAPACK_GLOBAL(csteqr, CSTEQR)
+#define LAPACK_zsteqr LAPACK_GLOBAL(zsteqr, ZSTEQR)
+#define LAPACK_sstemr LAPACK_GLOBAL(sstemr, SSTEMR)
+#define LAPACK_dstemr LAPACK_GLOBAL(dstemr, DSTEMR)
+#define LAPACK_cstemr LAPACK_GLOBAL(cstemr, CSTEMR)
+#define LAPACK_zstemr LAPACK_GLOBAL(zstemr, ZSTEMR)
+#define LAPACK_sstedc LAPACK_GLOBAL(sstedc, SSTEDC)
+#define LAPACK_dstedc LAPACK_GLOBAL(dstedc, DSTEDC)
+#define LAPACK_cstedc LAPACK_GLOBAL(cstedc, CSTEDC)
+#define LAPACK_zstedc LAPACK_GLOBAL(zstedc, ZSTEDC)
+#define LAPACK_sstegr LAPACK_GLOBAL(sstegr, SSTEGR)
+#define LAPACK_dstegr LAPACK_GLOBAL(dstegr, DSTEGR)
+#define LAPACK_cstegr LAPACK_GLOBAL(cstegr, CSTEGR)
+#define LAPACK_zstegr LAPACK_GLOBAL(zstegr, ZSTEGR)
+#define LAPACK_spteqr LAPACK_GLOBAL(spteqr, SPTEQR)
+#define LAPACK_dpteqr LAPACK_GLOBAL(dpteqr, DPTEQR)
+#define LAPACK_cpteqr LAPACK_GLOBAL(cpteqr, CPTEQR)
+#define LAPACK_zpteqr LAPACK_GLOBAL(zpteqr, ZPTEQR)
+#define LAPACK_sstebz LAPACK_GLOBAL(sstebz, SSTEBZ)
+#define LAPACK_dstebz LAPACK_GLOBAL(dstebz, DSTEBZ)
+#define LAPACK_sstein LAPACK_GLOBAL(sstein, SSTEIN)
+#define LAPACK_dstein LAPACK_GLOBAL(dstein, DSTEIN)
+#define LAPACK_cstein LAPACK_GLOBAL(cstein, CSTEIN)
+#define LAPACK_zstein LAPACK_GLOBAL(zstein, ZSTEIN)
+#define LAPACK_sdisna LAPACK_GLOBAL(sdisna, SDISNA)
+#define LAPACK_ddisna LAPACK_GLOBAL(ddisna, DDISNA)
+#define LAPACK_ssygst LAPACK_GLOBAL(ssygst, SSYGST)
+#define LAPACK_dsygst LAPACK_GLOBAL(dsygst, DSYGST)
+#define LAPACK_chegst LAPACK_GLOBAL(chegst, CHEGST)
+#define LAPACK_zhegst LAPACK_GLOBAL(zhegst, ZHEGST)
+#define LAPACK_sspgst LAPACK_GLOBAL(sspgst, SSPGST)
+#define LAPACK_dspgst LAPACK_GLOBAL(dspgst, DSPGST)
+#define LAPACK_chpgst LAPACK_GLOBAL(chpgst, CHPGST)
+#define LAPACK_zhpgst LAPACK_GLOBAL(zhpgst, ZHPGST)
+#define LAPACK_ssbgst LAPACK_GLOBAL(ssbgst, SSBGST)
+#define LAPACK_dsbgst LAPACK_GLOBAL(dsbgst, DSBGST)
+#define LAPACK_chbgst LAPACK_GLOBAL(chbgst, CHBGST)
+#define LAPACK_zhbgst LAPACK_GLOBAL(zhbgst, ZHBGST)
+#define LAPACK_spbstf LAPACK_GLOBAL(spbstf, SPBSTF)
+#define LAPACK_dpbstf LAPACK_GLOBAL(dpbstf, DPBSTF)
+#define LAPACK_cpbstf LAPACK_GLOBAL(cpbstf, CPBSTF)
+#define LAPACK_zpbstf LAPACK_GLOBAL(zpbstf, ZPBSTF)
+#define LAPACK_sgehrd LAPACK_GLOBAL(sgehrd, SGEHRD)
+#define LAPACK_dgehrd LAPACK_GLOBAL(dgehrd, DGEHRD)
+#define LAPACK_cgehrd LAPACK_GLOBAL(cgehrd, CGEHRD)
+#define LAPACK_zgehrd LAPACK_GLOBAL(zgehrd, ZGEHRD)
+#define LAPACK_sorghr LAPACK_GLOBAL(sorghr, SORGHR)
+#define LAPACK_dorghr LAPACK_GLOBAL(dorghr, DORGHR)
+#define LAPACK_sormhr LAPACK_GLOBAL(sormhr, SORMHR)
+#define LAPACK_dormhr LAPACK_GLOBAL(dormhr, DORMHR)
+#define LAPACK_cunghr LAPACK_GLOBAL(cunghr, CUNGHR)
+#define LAPACK_zunghr LAPACK_GLOBAL(zunghr, ZUNGHR)
+#define LAPACK_cunmhr LAPACK_GLOBAL(cunmhr, CUNMHR)
+#define LAPACK_zunmhr LAPACK_GLOBAL(zunmhr, ZUNMHR)
+#define LAPACK_sgebal LAPACK_GLOBAL(sgebal, SGEBAL)
+#define LAPACK_dgebal LAPACK_GLOBAL(dgebal, DGEBAL)
+#define LAPACK_cgebal LAPACK_GLOBAL(cgebal, CGEBAL)
+#define LAPACK_zgebal LAPACK_GLOBAL(zgebal, ZGEBAL)
+#define LAPACK_sgebak LAPACK_GLOBAL(sgebak, SGEBAK)
+#define LAPACK_dgebak LAPACK_GLOBAL(dgebak, DGEBAK)
+#define LAPACK_cgebak LAPACK_GLOBAL(cgebak, CGEBAK)
+#define LAPACK_zgebak LAPACK_GLOBAL(zgebak, ZGEBAK)
+#define LAPACK_shseqr LAPACK_GLOBAL(shseqr, SHSEQR)
+#define LAPACK_dhseqr LAPACK_GLOBAL(dhseqr, DHSEQR)
+#define LAPACK_chseqr LAPACK_GLOBAL(chseqr, CHSEQR)
+#define LAPACK_zhseqr LAPACK_GLOBAL(zhseqr, ZHSEQR)
+#define LAPACK_shsein LAPACK_GLOBAL(shsein, SHSEIN)
+#define LAPACK_dhsein LAPACK_GLOBAL(dhsein, DHSEIN)
+#define LAPACK_chsein LAPACK_GLOBAL(chsein, CHSEIN)
+#define LAPACK_zhsein LAPACK_GLOBAL(zhsein, ZHSEIN)
+#define LAPACK_strevc LAPACK_GLOBAL(strevc, STREVC)
+#define LAPACK_dtrevc LAPACK_GLOBAL(dtrevc, DTREVC)
+#define LAPACK_ctrevc LAPACK_GLOBAL(ctrevc, CTREVC)
+#define LAPACK_ztrevc LAPACK_GLOBAL(ztrevc, ZTREVC)
+#define LAPACK_strsna LAPACK_GLOBAL(strsna, STRSNA)
+#define LAPACK_dtrsna LAPACK_GLOBAL(dtrsna, DTRSNA)
+#define LAPACK_ctrsna LAPACK_GLOBAL(ctrsna, CTRSNA)
+#define LAPACK_ztrsna LAPACK_GLOBAL(ztrsna, ZTRSNA)
+#define LAPACK_strexc LAPACK_GLOBAL(strexc, STREXC)
+#define LAPACK_dtrexc LAPACK_GLOBAL(dtrexc, DTREXC)
+#define LAPACK_ctrexc LAPACK_GLOBAL(ctrexc, CTREXC)
+#define LAPACK_ztrexc LAPACK_GLOBAL(ztrexc, ZTREXC)
+#define LAPACK_strsen LAPACK_GLOBAL(strsen, STRSEN)
+#define LAPACK_dtrsen LAPACK_GLOBAL(dtrsen, DTRSEN)
+#define LAPACK_ctrsen LAPACK_GLOBAL(ctrsen, CTRSEN)
+#define LAPACK_ztrsen LAPACK_GLOBAL(ztrsen, ZTRSEN)
+#define LAPACK_strsyl LAPACK_GLOBAL(strsyl, STRSYL)
+#define LAPACK_dtrsyl LAPACK_GLOBAL(dtrsyl, DTRSYL)
+#define LAPACK_ctrsyl LAPACK_GLOBAL(ctrsyl, CTRSYL)
+#define LAPACK_ztrsyl LAPACK_GLOBAL(ztrsyl, ZTRSYL)
+#define LAPACK_sgghrd LAPACK_GLOBAL(sgghrd, SGGHRD)
+#define LAPACK_dgghrd LAPACK_GLOBAL(dgghrd, DGGHRD)
+#define LAPACK_cgghrd LAPACK_GLOBAL(cgghrd, CGGHRD)
+#define LAPACK_zgghrd LAPACK_GLOBAL(zgghrd, ZGGHRD)
+#define LAPACK_sggbal LAPACK_GLOBAL(sggbal, SGGBAL)
+#define LAPACK_dggbal LAPACK_GLOBAL(dggbal, DGGBAL)
+#define LAPACK_cggbal LAPACK_GLOBAL(cggbal, CGGBAL)
+#define LAPACK_zggbal LAPACK_GLOBAL(zggbal, ZGGBAL)
+#define LAPACK_sggbak LAPACK_GLOBAL(sggbak, SGGBAK)
+#define LAPACK_dggbak LAPACK_GLOBAL(dggbak, DGGBAK)
+#define LAPACK_cggbak LAPACK_GLOBAL(cggbak, CGGBAK)
+#define LAPACK_zggbak LAPACK_GLOBAL(zggbak, ZGGBAK)
+#define LAPACK_shgeqz LAPACK_GLOBAL(shgeqz, SHGEQZ)
+#define LAPACK_dhgeqz LAPACK_GLOBAL(dhgeqz, DHGEQZ)
+#define LAPACK_chgeqz LAPACK_GLOBAL(chgeqz, CHGEQZ)
+#define LAPACK_zhgeqz LAPACK_GLOBAL(zhgeqz, ZHGEQZ)
+#define LAPACK_stgevc LAPACK_GLOBAL(stgevc, STGEVC)
+#define LAPACK_dtgevc LAPACK_GLOBAL(dtgevc, DTGEVC)
+#define LAPACK_ctgevc LAPACK_GLOBAL(ctgevc, CTGEVC)
+#define LAPACK_ztgevc LAPACK_GLOBAL(ztgevc, ZTGEVC)
+#define LAPACK_stgexc LAPACK_GLOBAL(stgexc, STGEXC)
+#define LAPACK_dtgexc LAPACK_GLOBAL(dtgexc, DTGEXC)
+#define LAPACK_ctgexc LAPACK_GLOBAL(ctgexc, CTGEXC)
+#define LAPACK_ztgexc LAPACK_GLOBAL(ztgexc, ZTGEXC)
+#define LAPACK_stgsen LAPACK_GLOBAL(stgsen, STGSEN)
+#define LAPACK_dtgsen LAPACK_GLOBAL(dtgsen, DTGSEN)
+#define LAPACK_ctgsen LAPACK_GLOBAL(ctgsen, CTGSEN)
+#define LAPACK_ztgsen LAPACK_GLOBAL(ztgsen, ZTGSEN)
+#define LAPACK_stgsyl LAPACK_GLOBAL(stgsyl, STGSYL)
+#define LAPACK_dtgsyl LAPACK_GLOBAL(dtgsyl, DTGSYL)
+#define LAPACK_ctgsyl LAPACK_GLOBAL(ctgsyl, CTGSYL)
+#define LAPACK_ztgsyl LAPACK_GLOBAL(ztgsyl, ZTGSYL)
+#define LAPACK_stgsna LAPACK_GLOBAL(stgsna, STGSNA)
+#define LAPACK_dtgsna LAPACK_GLOBAL(dtgsna, DTGSNA)
+#define LAPACK_ctgsna LAPACK_GLOBAL(ctgsna, CTGSNA)
+#define LAPACK_ztgsna LAPACK_GLOBAL(ztgsna, ZTGSNA)
+#define LAPACK_sggsvp LAPACK_GLOBAL(sggsvp, SGGSVP)
+#define LAPACK_dggsvp LAPACK_GLOBAL(dggsvp, DGGSVP)
+#define LAPACK_cggsvp LAPACK_GLOBAL(cggsvp, CGGSVP)
+#define LAPACK_zggsvp LAPACK_GLOBAL(zggsvp, ZGGSVP)
+#define LAPACK_stgsja LAPACK_GLOBAL(stgsja, STGSJA)
+#define LAPACK_dtgsja LAPACK_GLOBAL(dtgsja, DTGSJA)
+#define LAPACK_ctgsja LAPACK_GLOBAL(ctgsja, CTGSJA)
+#define LAPACK_ztgsja LAPACK_GLOBAL(ztgsja, ZTGSJA)
+#define LAPACK_sgels LAPACK_GLOBAL(sgels, SGELS)
+#define LAPACK_dgels LAPACK_GLOBAL(dgels, DGELS)
+#define LAPACK_cgels LAPACK_GLOBAL(cgels, CGELS)
+#define LAPACK_zgels LAPACK_GLOBAL(zgels, ZGELS)
+#define LAPACK_sgelsy LAPACK_GLOBAL(sgelsy, SGELSY)
+#define LAPACK_dgelsy LAPACK_GLOBAL(dgelsy, DGELSY)
+#define LAPACK_cgelsy LAPACK_GLOBAL(cgelsy, CGELSY)
+#define LAPACK_zgelsy LAPACK_GLOBAL(zgelsy, ZGELSY)
+#define LAPACK_sgelss LAPACK_GLOBAL(sgelss, SGELSS)
+#define LAPACK_dgelss LAPACK_GLOBAL(dgelss, DGELSS)
+#define LAPACK_cgelss LAPACK_GLOBAL(cgelss, CGELSS)
+#define LAPACK_zgelss LAPACK_GLOBAL(zgelss, ZGELSS)
+#define LAPACK_sgelsd LAPACK_GLOBAL(sgelsd, SGELSD)
+#define LAPACK_dgelsd LAPACK_GLOBAL(dgelsd, DGELSD)
+#define LAPACK_cgelsd LAPACK_GLOBAL(cgelsd, CGELSD)
+#define LAPACK_zgelsd LAPACK_GLOBAL(zgelsd, ZGELSD)
+#define LAPACK_sgglse LAPACK_GLOBAL(sgglse, SGGLSE)
+#define LAPACK_dgglse LAPACK_GLOBAL(dgglse, DGGLSE)
+#define LAPACK_cgglse LAPACK_GLOBAL(cgglse, CGGLSE)
+#define LAPACK_zgglse LAPACK_GLOBAL(zgglse, ZGGLSE)
+#define LAPACK_sggglm LAPACK_GLOBAL(sggglm, SGGGLM)
+#define LAPACK_dggglm LAPACK_GLOBAL(dggglm, DGGGLM)
+#define LAPACK_cggglm LAPACK_GLOBAL(cggglm, CGGGLM)
+#define LAPACK_zggglm LAPACK_GLOBAL(zggglm, ZGGGLM)
+#define LAPACK_ssyev LAPACK_GLOBAL(ssyev, SSYEV)
+#define LAPACK_dsyev LAPACK_GLOBAL(dsyev, DSYEV)
+#define LAPACK_cheev LAPACK_GLOBAL(cheev, CHEEV)
+#define LAPACK_zheev LAPACK_GLOBAL(zheev, ZHEEV)
+#define LAPACK_ssyevd LAPACK_GLOBAL(ssyevd, SSYEVD)
+#define LAPACK_dsyevd LAPACK_GLOBAL(dsyevd, DSYEVD)
+#define LAPACK_cheevd LAPACK_GLOBAL(cheevd, CHEEVD)
+#define LAPACK_zheevd LAPACK_GLOBAL(zheevd, ZHEEVD)
+#define LAPACK_ssyevx LAPACK_GLOBAL(ssyevx, SSYEVX)
+#define LAPACK_dsyevx LAPACK_GLOBAL(dsyevx, DSYEVX)
+#define LAPACK_cheevx LAPACK_GLOBAL(cheevx, CHEEVX)
+#define LAPACK_zheevx LAPACK_GLOBAL(zheevx, ZHEEVX)
+#define LAPACK_ssyevr LAPACK_GLOBAL(ssyevr, SSYEVR)
+#define LAPACK_dsyevr LAPACK_GLOBAL(dsyevr, DSYEVR)
+#define LAPACK_cheevr LAPACK_GLOBAL(cheevr, CHEEVR)
+#define LAPACK_zheevr LAPACK_GLOBAL(zheevr, ZHEEVR)
+#define LAPACK_sspev LAPACK_GLOBAL(sspev, SSPEV)
+#define LAPACK_dspev LAPACK_GLOBAL(dspev, DSPEV)
+#define LAPACK_chpev LAPACK_GLOBAL(chpev, CHPEV)
+#define LAPACK_zhpev LAPACK_GLOBAL(zhpev, ZHPEV)
+#define LAPACK_sspevd LAPACK_GLOBAL(sspevd, SSPEVD)
+#define LAPACK_dspevd LAPACK_GLOBAL(dspevd, DSPEVD)
+#define LAPACK_chpevd LAPACK_GLOBAL(chpevd, CHPEVD)
+#define LAPACK_zhpevd LAPACK_GLOBAL(zhpevd, ZHPEVD)
+#define LAPACK_sspevx LAPACK_GLOBAL(sspevx, SSPEVX)
+#define LAPACK_dspevx LAPACK_GLOBAL(dspevx, DSPEVX)
+#define LAPACK_chpevx LAPACK_GLOBAL(chpevx, CHPEVX)
+#define LAPACK_zhpevx LAPACK_GLOBAL(zhpevx, ZHPEVX)
+#define LAPACK_ssbev LAPACK_GLOBAL(ssbev, SSBEV)
+#define LAPACK_dsbev LAPACK_GLOBAL(dsbev, DSBEV)
+#define LAPACK_chbev LAPACK_GLOBAL(chbev, CHBEV)
+#define LAPACK_zhbev LAPACK_GLOBAL(zhbev, ZHBEV)
+#define LAPACK_ssbevd LAPACK_GLOBAL(ssbevd, SSBEVD)
+#define LAPACK_dsbevd LAPACK_GLOBAL(dsbevd, DSBEVD)
+#define LAPACK_chbevd LAPACK_GLOBAL(chbevd, CHBEVD)
+#define LAPACK_zhbevd LAPACK_GLOBAL(zhbevd, ZHBEVD)
+#define LAPACK_ssbevx LAPACK_GLOBAL(ssbevx, SSBEVX)
+#define LAPACK_dsbevx LAPACK_GLOBAL(dsbevx, DSBEVX)
+#define LAPACK_chbevx LAPACK_GLOBAL(chbevx, CHBEVX)
+#define LAPACK_zhbevx LAPACK_GLOBAL(zhbevx, ZHBEVX)
+#define LAPACK_sstev LAPACK_GLOBAL(sstev, SSTEV)
+#define LAPACK_dstev LAPACK_GLOBAL(dstev, DSTEV)
+#define LAPACK_sstevd LAPACK_GLOBAL(sstevd, SSTEVD)
+#define LAPACK_dstevd LAPACK_GLOBAL(dstevd, DSTEVD)
+#define LAPACK_sstevx LAPACK_GLOBAL(sstevx, SSTEVX)
+#define LAPACK_dstevx LAPACK_GLOBAL(dstevx, DSTEVX)
+#define LAPACK_sstevr LAPACK_GLOBAL(sstevr, SSTEVR)
+#define LAPACK_dstevr LAPACK_GLOBAL(dstevr, DSTEVR)
+#define LAPACK_sgees LAPACK_GLOBAL(sgees, SGEES)
+#define LAPACK_dgees LAPACK_GLOBAL(dgees, DGEES)
+#define LAPACK_cgees LAPACK_GLOBAL(cgees, CGEES)
+#define LAPACK_zgees LAPACK_GLOBAL(zgees, ZGEES)
+#define LAPACK_sgeesx LAPACK_GLOBAL(sgeesx, SGEESX)
+#define LAPACK_dgeesx LAPACK_GLOBAL(dgeesx, DGEESX)
+#define LAPACK_cgeesx LAPACK_GLOBAL(cgeesx, CGEESX)
+#define LAPACK_zgeesx LAPACK_GLOBAL(zgeesx, ZGEESX)
+#define LAPACK_sgeev LAPACK_GLOBAL(sgeev, SGEEV)
+#define LAPACK_dgeev LAPACK_GLOBAL(dgeev, DGEEV)
+#define LAPACK_cgeev LAPACK_GLOBAL(cgeev, CGEEV)
+#define LAPACK_zgeev LAPACK_GLOBAL(zgeev, ZGEEV)
+#define LAPACK_sgeevx LAPACK_GLOBAL(sgeevx, SGEEVX)
+#define LAPACK_dgeevx LAPACK_GLOBAL(dgeevx, DGEEVX)
+#define LAPACK_cgeevx LAPACK_GLOBAL(cgeevx, CGEEVX)
+#define LAPACK_zgeevx LAPACK_GLOBAL(zgeevx, ZGEEVX)
+#define LAPACK_sgesvd LAPACK_GLOBAL(sgesvd, SGESVD)
+#define LAPACK_dgesvd LAPACK_GLOBAL(dgesvd, DGESVD)
+#define LAPACK_cgesvd LAPACK_GLOBAL(cgesvd, CGESVD)
+#define LAPACK_zgesvd LAPACK_GLOBAL(zgesvd, ZGESVD)
+#define LAPACK_sgesdd LAPACK_GLOBAL(sgesdd, SGESDD)
+#define LAPACK_dgesdd LAPACK_GLOBAL(dgesdd, DGESDD)
+#define LAPACK_cgesdd LAPACK_GLOBAL(cgesdd, CGESDD)
+#define LAPACK_zgesdd LAPACK_GLOBAL(zgesdd, ZGESDD)
+#define LAPACK_dgejsv LAPACK_GLOBAL(dgejsv, DGEJSV)
+#define LAPACK_sgejsv LAPACK_GLOBAL(sgejsv, SGEJSV)
+#define LAPACK_dgesvj LAPACK_GLOBAL(dgesvj, DGESVJ)
+#define LAPACK_sgesvj LAPACK_GLOBAL(sgesvj, SGESVJ)
+#define LAPACK_sggsvd LAPACK_GLOBAL(sggsvd, SGGSVD)
+#define LAPACK_dggsvd LAPACK_GLOBAL(dggsvd, DGGSVD)
+#define LAPACK_cggsvd LAPACK_GLOBAL(cggsvd, CGGSVD)
+#define LAPACK_zggsvd LAPACK_GLOBAL(zggsvd, ZGGSVD)
+#define LAPACK_ssygv LAPACK_GLOBAL(ssygv, SSYGV)
+#define LAPACK_dsygv LAPACK_GLOBAL(dsygv, DSYGV)
+#define LAPACK_chegv LAPACK_GLOBAL(chegv, CHEGV)
+#define LAPACK_zhegv LAPACK_GLOBAL(zhegv, ZHEGV)
+#define LAPACK_ssygvd LAPACK_GLOBAL(ssygvd, SSYGVD)
+#define LAPACK_dsygvd LAPACK_GLOBAL(dsygvd, DSYGVD)
+#define LAPACK_chegvd LAPACK_GLOBAL(chegvd, CHEGVD)
+#define LAPACK_zhegvd LAPACK_GLOBAL(zhegvd, ZHEGVD)
+#define LAPACK_ssygvx LAPACK_GLOBAL(ssygvx, SSYGVX)
+#define LAPACK_dsygvx LAPACK_GLOBAL(dsygvx, DSYGVX)
+#define LAPACK_chegvx LAPACK_GLOBAL(chegvx, CHEGVX)
+#define LAPACK_zhegvx LAPACK_GLOBAL(zhegvx, ZHEGVX)
+#define LAPACK_sspgv LAPACK_GLOBAL(sspgv, SSPGV)
+#define LAPACK_dspgv LAPACK_GLOBAL(dspgv, DSPGV)
+#define LAPACK_chpgv LAPACK_GLOBAL(chpgv, CHPGV)
+#define LAPACK_zhpgv LAPACK_GLOBAL(zhpgv, ZHPGV)
+#define LAPACK_sspgvd LAPACK_GLOBAL(sspgvd, SSPGVD)
+#define LAPACK_dspgvd LAPACK_GLOBAL(dspgvd, DSPGVD)
+#define LAPACK_chpgvd LAPACK_GLOBAL(chpgvd, CHPGVD)
+#define LAPACK_zhpgvd LAPACK_GLOBAL(zhpgvd, ZHPGVD)
+#define LAPACK_sspgvx LAPACK_GLOBAL(sspgvx, SSPGVX)
+#define LAPACK_dspgvx LAPACK_GLOBAL(dspgvx, DSPGVX)
+#define LAPACK_chpgvx LAPACK_GLOBAL(chpgvx, CHPGVX)
+#define LAPACK_zhpgvx LAPACK_GLOBAL(zhpgvx, ZHPGVX)
+#define LAPACK_ssbgv LAPACK_GLOBAL(ssbgv, SSBGV)
+#define LAPACK_dsbgv LAPACK_GLOBAL(dsbgv, DSBGV)
+#define LAPACK_chbgv LAPACK_GLOBAL(chbgv, CHBGV)
+#define LAPACK_zhbgv LAPACK_GLOBAL(zhbgv, ZHBGV)
+#define LAPACK_ssbgvd LAPACK_GLOBAL(ssbgvd, SSBGVD)
+#define LAPACK_dsbgvd LAPACK_GLOBAL(dsbgvd, DSBGVD)
+#define LAPACK_chbgvd LAPACK_GLOBAL(chbgvd, CHBGVD)
+#define LAPACK_zhbgvd LAPACK_GLOBAL(zhbgvd, ZHBGVD)
+#define LAPACK_ssbgvx LAPACK_GLOBAL(ssbgvx, SSBGVX)
+#define LAPACK_dsbgvx LAPACK_GLOBAL(dsbgvx, DSBGVX)
+#define LAPACK_chbgvx LAPACK_GLOBAL(chbgvx, CHBGVX)
+#define LAPACK_zhbgvx LAPACK_GLOBAL(zhbgvx, ZHBGVX)
+#define LAPACK_sgges LAPACK_GLOBAL(sgges, SGGES)
+#define LAPACK_dgges LAPACK_GLOBAL(dgges, DGGES)
+#define LAPACK_cgges LAPACK_GLOBAL(cgges, CGGES)
+#define LAPACK_zgges LAPACK_GLOBAL(zgges, ZGGES)
+#define LAPACK_sggesx LAPACK_GLOBAL(sggesx, SGGESX)
+#define LAPACK_dggesx LAPACK_GLOBAL(dggesx, DGGESX)
+#define LAPACK_cggesx LAPACK_GLOBAL(cggesx, CGGESX)
+#define LAPACK_zggesx LAPACK_GLOBAL(zggesx, ZGGESX)
+#define LAPACK_sggev LAPACK_GLOBAL(sggev, SGGEV)
+#define LAPACK_dggev LAPACK_GLOBAL(dggev, DGGEV)
+#define LAPACK_cggev LAPACK_GLOBAL(cggev, CGGEV)
+#define LAPACK_zggev LAPACK_GLOBAL(zggev, ZGGEV)
+#define LAPACK_sggevx LAPACK_GLOBAL(sggevx, SGGEVX)
+#define LAPACK_dggevx LAPACK_GLOBAL(dggevx, DGGEVX)
+#define LAPACK_cggevx LAPACK_GLOBAL(cggevx, CGGEVX)
+#define LAPACK_zggevx LAPACK_GLOBAL(zggevx, ZGGEVX)
+#define LAPACK_dsfrk LAPACK_GLOBAL(dsfrk, DSFRK)
+#define LAPACK_ssfrk LAPACK_GLOBAL(ssfrk, SSFRK)
+#define LAPACK_zhfrk LAPACK_GLOBAL(zhfrk, ZHFRK)
+#define LAPACK_chfrk LAPACK_GLOBAL(chfrk, CHFRK)
+#define LAPACK_dtfsm LAPACK_GLOBAL(dtfsm, DTFSM)
+#define LAPACK_stfsm LAPACK_GLOBAL(stfsm, STFSM)
+#define LAPACK_ztfsm LAPACK_GLOBAL(ztfsm, ZTFSM)
+#define LAPACK_ctfsm LAPACK_GLOBAL(ctfsm, CTFSM)
+#define LAPACK_dtfttp LAPACK_GLOBAL(dtfttp, DTFTTP)
+#define LAPACK_stfttp LAPACK_GLOBAL(stfttp, STFTTP)
+#define LAPACK_ztfttp LAPACK_GLOBAL(ztfttp, ZTFTTP)
+#define LAPACK_ctfttp LAPACK_GLOBAL(ctfttp, CTFTTP)
+#define LAPACK_dtfttr LAPACK_GLOBAL(dtfttr, DTFTTR)
+#define LAPACK_stfttr LAPACK_GLOBAL(stfttr, STFTTR)
+#define LAPACK_ztfttr LAPACK_GLOBAL(ztfttr, ZTFTTR)
+#define LAPACK_ctfttr LAPACK_GLOBAL(ctfttr, CTFTTR)
+#define LAPACK_dtpttf LAPACK_GLOBAL(dtpttf, DTPTTF)
+#define LAPACK_stpttf LAPACK_GLOBAL(stpttf, STPTTF)
+#define LAPACK_ztpttf LAPACK_GLOBAL(ztpttf, ZTPTTF)
+#define LAPACK_ctpttf LAPACK_GLOBAL(ctpttf, CTPTTF)
+#define LAPACK_dtpttr LAPACK_GLOBAL(dtpttr, DTPTTR)
+#define LAPACK_stpttr LAPACK_GLOBAL(stpttr, STPTTR)
+#define LAPACK_ztpttr LAPACK_GLOBAL(ztpttr, ZTPTTR)
+#define LAPACK_ctpttr LAPACK_GLOBAL(ctpttr, CTPTTR)
+#define LAPACK_dtrttf LAPACK_GLOBAL(dtrttf, DTRTTF)
+#define LAPACK_strttf LAPACK_GLOBAL(strttf, STRTTF)
+#define LAPACK_ztrttf LAPACK_GLOBAL(ztrttf, ZTRTTF)
+#define LAPACK_ctrttf LAPACK_GLOBAL(ctrttf, CTRTTF)
+#define LAPACK_dtrttp LAPACK_GLOBAL(dtrttp, DTRTTP)
+#define LAPACK_strttp LAPACK_GLOBAL(strttp, STRTTP)
+#define LAPACK_ztrttp LAPACK_GLOBAL(ztrttp, ZTRTTP)
+#define LAPACK_ctrttp LAPACK_GLOBAL(ctrttp, CTRTTP)
+#define LAPACK_sgeqrfp LAPACK_GLOBAL(sgeqrfp, SGEQRFP)
+#define LAPACK_dgeqrfp LAPACK_GLOBAL(dgeqrfp, DGEQRFP)
+#define LAPACK_cgeqrfp LAPACK_GLOBAL(cgeqrfp, CGEQRFP)
+#define LAPACK_zgeqrfp LAPACK_GLOBAL(zgeqrfp, ZGEQRFP)
+#define LAPACK_clacgv LAPACK_GLOBAL(clacgv, CLACGV)
+#define LAPACK_zlacgv LAPACK_GLOBAL(zlacgv, ZLACGV)
+#define LAPACK_slarnv LAPACK_GLOBAL(slarnv, SLARNV)
+#define LAPACK_dlarnv LAPACK_GLOBAL(dlarnv, DLARNV)
+#define LAPACK_clarnv LAPACK_GLOBAL(clarnv, CLARNV)
+#define LAPACK_zlarnv LAPACK_GLOBAL(zlarnv, ZLARNV)
+#define LAPACK_sgeqr2 LAPACK_GLOBAL(sgeqr2, SGEQR2)
+#define LAPACK_dgeqr2 LAPACK_GLOBAL(dgeqr2, DGEQR2)
+#define LAPACK_cgeqr2 LAPACK_GLOBAL(cgeqr2, CGEQR2)
+#define LAPACK_zgeqr2 LAPACK_GLOBAL(zgeqr2, ZGEQR2)
+#define LAPACK_slacpy LAPACK_GLOBAL(slacpy, SLACPY)
+#define LAPACK_dlacpy LAPACK_GLOBAL(dlacpy, DLACPY)
+#define LAPACK_clacpy LAPACK_GLOBAL(clacpy, CLACPY)
+#define LAPACK_zlacpy LAPACK_GLOBAL(zlacpy, ZLACPY)
+#define LAPACK_sgetf2 LAPACK_GLOBAL(sgetf2, SGETF2)
+#define LAPACK_dgetf2 LAPACK_GLOBAL(dgetf2, DGETF2)
+#define LAPACK_cgetf2 LAPACK_GLOBAL(cgetf2, CGETF2)
+#define LAPACK_zgetf2 LAPACK_GLOBAL(zgetf2, ZGETF2)
+#define LAPACK_slaswp LAPACK_GLOBAL(slaswp, SLASWP)
+#define LAPACK_dlaswp LAPACK_GLOBAL(dlaswp, DLASWP)
+#define LAPACK_claswp LAPACK_GLOBAL(claswp, CLASWP)
+#define LAPACK_zlaswp LAPACK_GLOBAL(zlaswp, ZLASWP)
+#define LAPACK_slange LAPACK_GLOBAL(slange, SLANGE)
+#define LAPACK_dlange LAPACK_GLOBAL(dlange, DLANGE)
+#define LAPACK_clange LAPACK_GLOBAL(clange, CLANGE)
+#define LAPACK_zlange LAPACK_GLOBAL(zlange, ZLANGE)
+#define LAPACK_clanhe LAPACK_GLOBAL(clanhe, CLANHE)
+#define LAPACK_zlanhe LAPACK_GLOBAL(zlanhe, ZLANHE)
+#define LAPACK_slansy LAPACK_GLOBAL(slansy, SLANSY)
+#define LAPACK_dlansy LAPACK_GLOBAL(dlansy, DLANSY)
+#define LAPACK_clansy LAPACK_GLOBAL(clansy, CLANSY)
+#define LAPACK_zlansy LAPACK_GLOBAL(zlansy, ZLANSY)
+#define LAPACK_slantr LAPACK_GLOBAL(slantr, SLANTR)
+#define LAPACK_dlantr LAPACK_GLOBAL(dlantr, DLANTR)
+#define LAPACK_clantr LAPACK_GLOBAL(clantr, CLANTR)
+#define LAPACK_zlantr LAPACK_GLOBAL(zlantr, ZLANTR)
+#define LAPACK_slamch LAPACK_GLOBAL(slamch, SLAMCH)
+#define LAPACK_dlamch LAPACK_GLOBAL(dlamch, DLAMCH)
+#define LAPACK_sgelq2 LAPACK_GLOBAL(sgelq2, SGELQ2)
+#define LAPACK_dgelq2 LAPACK_GLOBAL(dgelq2, DGELQ2)
+#define LAPACK_cgelq2 LAPACK_GLOBAL(cgelq2, CGELQ2)
+#define LAPACK_zgelq2 LAPACK_GLOBAL(zgelq2, ZGELQ2)
+#define LAPACK_slarfb LAPACK_GLOBAL(slarfb, SLARFB)
+#define LAPACK_dlarfb LAPACK_GLOBAL(dlarfb, DLARFB)
+#define LAPACK_clarfb LAPACK_GLOBAL(clarfb, CLARFB)
+#define LAPACK_zlarfb LAPACK_GLOBAL(zlarfb, ZLARFB)
+#define LAPACK_slarfg LAPACK_GLOBAL(slarfg, SLARFG)
+#define LAPACK_dlarfg LAPACK_GLOBAL(dlarfg, DLARFG)
+#define LAPACK_clarfg LAPACK_GLOBAL(clarfg, CLARFG)
+#define LAPACK_zlarfg LAPACK_GLOBAL(zlarfg, ZLARFG)
+#define LAPACK_slarft LAPACK_GLOBAL(slarft, SLARFT)
+#define LAPACK_dlarft LAPACK_GLOBAL(dlarft, DLARFT)
+#define LAPACK_clarft LAPACK_GLOBAL(clarft, CLARFT)
+#define LAPACK_zlarft LAPACK_GLOBAL(zlarft, ZLARFT)
+#define LAPACK_slarfx LAPACK_GLOBAL(slarfx, SLARFX)
+#define LAPACK_dlarfx LAPACK_GLOBAL(dlarfx, DLARFX)
+#define LAPACK_clarfx LAPACK_GLOBAL(clarfx, CLARFX)
+#define LAPACK_zlarfx LAPACK_GLOBAL(zlarfx, ZLARFX)
+#define LAPACK_slatms LAPACK_GLOBAL(slatms, SLATMS)
+#define LAPACK_dlatms LAPACK_GLOBAL(dlatms, DLATMS)
+#define LAPACK_clatms LAPACK_GLOBAL(clatms, CLATMS)
+#define LAPACK_zlatms LAPACK_GLOBAL(zlatms, ZLATMS)
+#define LAPACK_slag2d LAPACK_GLOBAL(slag2d, SLAG2D)
+#define LAPACK_dlag2s LAPACK_GLOBAL(dlag2s, DLAG2S)
+#define LAPACK_clag2z LAPACK_GLOBAL(clag2z, CLAG2Z)
+#define LAPACK_zlag2c LAPACK_GLOBAL(zlag2c, ZLAG2C)
+#define LAPACK_slauum LAPACK_GLOBAL(slauum, SLAUUM)
+#define LAPACK_dlauum LAPACK_GLOBAL(dlauum, DLAUUM)
+#define LAPACK_clauum LAPACK_GLOBAL(clauum, CLAUUM)
+#define LAPACK_zlauum LAPACK_GLOBAL(zlauum, ZLAUUM)
+#define LAPACK_slagge LAPACK_GLOBAL(slagge, SLAGGE)
+#define LAPACK_dlagge LAPACK_GLOBAL(dlagge, DLAGGE)
+#define LAPACK_clagge LAPACK_GLOBAL(clagge, CLAGGE)
+#define LAPACK_zlagge LAPACK_GLOBAL(zlagge, ZLAGGE)
+#define LAPACK_slaset LAPACK_GLOBAL(slaset, SLASET)
+#define LAPACK_dlaset LAPACK_GLOBAL(dlaset, DLASET)
+#define LAPACK_claset LAPACK_GLOBAL(claset, CLASET)
+#define LAPACK_zlaset LAPACK_GLOBAL(zlaset, ZLASET)
+#define LAPACK_slasrt LAPACK_GLOBAL(slasrt, SLASRT)
+#define LAPACK_dlasrt LAPACK_GLOBAL(dlasrt, DLASRT)
+#define LAPACK_slagsy LAPACK_GLOBAL(slagsy, SLAGSY)
+#define LAPACK_dlagsy LAPACK_GLOBAL(dlagsy, DLAGSY)
+#define LAPACK_clagsy LAPACK_GLOBAL(clagsy, CLAGSY)
+#define LAPACK_zlagsy LAPACK_GLOBAL(zlagsy, ZLAGSY)
+#define LAPACK_claghe LAPACK_GLOBAL(claghe, CLAGHE)
+#define LAPACK_zlaghe LAPACK_GLOBAL(zlaghe, ZLAGHE)
+#define LAPACK_slapmr LAPACK_GLOBAL(slapmr, SLAPMR)
+#define LAPACK_dlapmr LAPACK_GLOBAL(dlapmr, DLAPMR)
+#define LAPACK_clapmr LAPACK_GLOBAL(clapmr, CLAPMR)
+#define LAPACK_zlapmr LAPACK_GLOBAL(zlapmr, ZLAPMR)
+#define LAPACK_slapy2 LAPACK_GLOBAL(slapy2, SLAPY2)
+#define LAPACK_dlapy2 LAPACK_GLOBAL(dlapy2, DLAPY2)
+#define LAPACK_slapy3 LAPACK_GLOBAL(slapy3, SLAPY3)
+#define LAPACK_dlapy3 LAPACK_GLOBAL(dlapy3, DLAPY3)
+#define LAPACK_slartgp LAPACK_GLOBAL(slartgp, SLARTGP)
+#define LAPACK_dlartgp LAPACK_GLOBAL(dlartgp, DLARTGP)
+#define LAPACK_slartgs LAPACK_GLOBAL(slartgs, SLARTGS)
+#define LAPACK_dlartgs LAPACK_GLOBAL(dlartgs, DLARTGS)
+// LAPACK 3.3.0
+#define LAPACK_cbbcsd LAPACK_GLOBAL(cbbcsd, CBBCSD)
+#define LAPACK_cheswapr LAPACK_GLOBAL(cheswapr, CHESWAPR)
+#define LAPACK_chetri2 LAPACK_GLOBAL(chetri2, CHETRI2)
+#define LAPACK_chetri2x LAPACK_GLOBAL(chetri2x, CHETRI2X)
+#define LAPACK_chetrs2 LAPACK_GLOBAL(chetrs2, CHETRS2)
+#define LAPACK_csyconv LAPACK_GLOBAL(csyconv, CSYCONV)
+#define LAPACK_csyswapr LAPACK_GLOBAL(csyswapr, CSYSWAPR)
+#define LAPACK_csytri2 LAPACK_GLOBAL(csytri2, CSYTRI2)
+#define LAPACK_csytri2x LAPACK_GLOBAL(csytri2x, CSYTRI2X)
+#define LAPACK_csytrs2 LAPACK_GLOBAL(csytrs2, CSYTRS2)
+#define LAPACK_cunbdb LAPACK_GLOBAL(cunbdb, CUNBDB)
+#define LAPACK_cuncsd LAPACK_GLOBAL(cuncsd, CUNCSD)
+#define LAPACK_dbbcsd LAPACK_GLOBAL(dbbcsd, DBBCSD)
+#define LAPACK_dorbdb LAPACK_GLOBAL(dorbdb, DORBDB)
+#define LAPACK_dorcsd LAPACK_GLOBAL(dorcsd, DORCSD)
+#define LAPACK_dsyconv LAPACK_GLOBAL(dsyconv, DSYCONV)
+#define LAPACK_dsyswapr LAPACK_GLOBAL(dsyswapr, DSYSWAPR)
+#define LAPACK_dsytri2 LAPACK_GLOBAL(dsytri2, DSYTRI2)
+#define LAPACK_dsytri2x LAPACK_GLOBAL(dsytri2x, DSYTRI2X)
+#define LAPACK_dsytrs2 LAPACK_GLOBAL(dsytrs2, DSYTRS2)
+#define LAPACK_sbbcsd LAPACK_GLOBAL(sbbcsd, SBBCSD)
+#define LAPACK_sorbdb LAPACK_GLOBAL(sorbdb, SORBDB)
+#define LAPACK_sorcsd LAPACK_GLOBAL(sorcsd, SORCSD)
+#define LAPACK_ssyconv LAPACK_GLOBAL(ssyconv, SSYCONV)
+#define LAPACK_ssyswapr LAPACK_GLOBAL(ssyswapr, SSYSWAPR)
+#define LAPACK_ssytri2 LAPACK_GLOBAL(ssytri2, SSYTRI2)
+#define LAPACK_ssytri2x LAPACK_GLOBAL(ssytri2x, SSYTRI2X)
+#define LAPACK_ssytrs2 LAPACK_GLOBAL(ssytrs2, SSYTRS2)
+#define LAPACK_zbbcsd LAPACK_GLOBAL(zbbcsd, ZBBCSD)
+#define LAPACK_zheswapr LAPACK_GLOBAL(zheswapr, ZHESWAPR)
+#define LAPACK_zhetri2 LAPACK_GLOBAL(zhetri2, ZHETRI2)
+#define LAPACK_zhetri2x LAPACK_GLOBAL(zhetri2x, ZHETRI2X)
+#define LAPACK_zhetrs2 LAPACK_GLOBAL(zhetrs2, ZHETRS2)
+#define LAPACK_zsyconv LAPACK_GLOBAL(zsyconv, ZSYCONV)
+#define LAPACK_zsyswapr LAPACK_GLOBAL(zsyswapr, ZSYSWAPR)
+#define LAPACK_zsytri2 LAPACK_GLOBAL(zsytri2, ZSYTRI2)
+#define LAPACK_zsytri2x LAPACK_GLOBAL(zsytri2x, ZSYTRI2X)
+#define LAPACK_zsytrs2 LAPACK_GLOBAL(zsytrs2, ZSYTRS2)
+#define LAPACK_zunbdb LAPACK_GLOBAL(zunbdb, ZUNBDB)
+#define LAPACK_zuncsd LAPACK_GLOBAL(zuncsd, ZUNCSD)
+// LAPACK 3.4.0
+#define LAPACK_sgemqrt LAPACK_GLOBAL(sgemqrt, SGEMQRT)
+#define LAPACK_dgemqrt LAPACK_GLOBAL(dgemqrt, DGEMQRT)
+#define LAPACK_cgemqrt LAPACK_GLOBAL(cgemqrt, CGEMQRT)
+#define LAPACK_zgemqrt LAPACK_GLOBAL(zgemqrt, ZGEMQRT)
+#define LAPACK_sgeqrt LAPACK_GLOBAL(sgeqrt, SGEQRT)
+#define LAPACK_dgeqrt LAPACK_GLOBAL(dgeqrt, DGEQRT)
+#define LAPACK_cgeqrt LAPACK_GLOBAL(cgeqrt, CGEQRT)
+#define LAPACK_zgeqrt LAPACK_GLOBAL(zgeqrt, ZGEQRT)
+#define LAPACK_sgeqrt2 LAPACK_GLOBAL(sgeqrt2, SGEQRT2)
+#define LAPACK_dgeqrt2 LAPACK_GLOBAL(dgeqrt2, DGEQRT2)
+#define LAPACK_cgeqrt2 LAPACK_GLOBAL(cgeqrt2, CGEQRT2)
+#define LAPACK_zgeqrt2 LAPACK_GLOBAL(zgeqrt2, ZGEQRT2)
+#define LAPACK_sgeqrt3 LAPACK_GLOBAL(sgeqrt3, SGEQRT3)
+#define LAPACK_dgeqrt3 LAPACK_GLOBAL(dgeqrt3, DGEQRT3)
+#define LAPACK_cgeqrt3 LAPACK_GLOBAL(cgeqrt3, CGEQRT3)
+#define LAPACK_zgeqrt3 LAPACK_GLOBAL(zgeqrt3, ZGEQRT3)
+#define LAPACK_stpmqrt LAPACK_GLOBAL(stpmqrt, STPMQRT)
+#define LAPACK_dtpmqrt LAPACK_GLOBAL(dtpmqrt, DTPMQRT)
+#define LAPACK_ctpmqrt LAPACK_GLOBAL(ctpmqrt, CTPMQRT)
+#define LAPACK_ztpmqrt LAPACK_GLOBAL(ztpmqrt, ZTPMQRT)
+#define LAPACK_dtpqrt LAPACK_GLOBAL(dtpqrt, DTPQRT)
+#define LAPACK_ctpqrt LAPACK_GLOBAL(ctpqrt, CTPQRT)
+#define LAPACK_ztpqrt LAPACK_GLOBAL(ztpqrt, ZTPQRT)
+#define LAPACK_stpqrt2 LAPACK_GLOBAL(stpqrt2, STPQRT2)
+#define LAPACK_dtpqrt2 LAPACK_GLOBAL(dtpqrt2, DTPQRT2)
+#define LAPACK_ctpqrt2 LAPACK_GLOBAL(ctpqrt2, CTPQRT2)
+#define LAPACK_ztpqrt2 LAPACK_GLOBAL(ztpqrt2, ZTPQRT2)
+#define LAPACK_stprfb LAPACK_GLOBAL(stprfb, STPRFB)
+#define LAPACK_dtprfb LAPACK_GLOBAL(dtprfb, DTPRFB)
+#define LAPACK_ctprfb LAPACK_GLOBAL(ctprfb, CTPRFB)
+#define LAPACK_ztprfb LAPACK_GLOBAL(ztprfb, ZTPRFB)
+// LAPACK 3.X.X
+#define LAPACK_csyr LAPACK_GLOBAL(csyr, CSYR)
+#define LAPACK_zsyr LAPACK_GLOBAL(zsyr, ZSYR)
+
+void LAPACK_sgetrf(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, lapack_int* ipiv, lapack_int* info);
+void LAPACK_dgetrf(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, lapack_int* ipiv, lapack_int* info);
+void LAPACK_cgetrf(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_int* info);
+void LAPACK_zgetrf(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_int* info);
+void LAPACK_sgbtrf(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, float* ab, lapack_int* ldab,
+                   lapack_int* ipiv, lapack_int* info);
+void LAPACK_dgbtrf(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, double* ab, lapack_int* ldab,
+                   lapack_int* ipiv, lapack_int* info);
+void LAPACK_cgbtrf(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_complex_float* ab,
+                   lapack_int* ldab, lapack_int* ipiv, lapack_int* info);
+void LAPACK_zgbtrf(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_complex_double* ab,
+                   lapack_int* ldab, lapack_int* ipiv, lapack_int* info);
+void LAPACK_sgttrf(lapack_int* n, float* dl, float* d, float* du, float* du2, lapack_int* ipiv, lapack_int* info);
+void LAPACK_dgttrf(lapack_int* n, double* dl, double* d, double* du, double* du2, lapack_int* ipiv, lapack_int* info);
+void LAPACK_cgttrf(lapack_int* n, lapack_complex_float* dl, lapack_complex_float* d, lapack_complex_float* du,
+                   lapack_complex_float* du2, lapack_int* ipiv, lapack_int* info);
+void LAPACK_zgttrf(lapack_int* n, lapack_complex_double* dl, lapack_complex_double* d, lapack_complex_double* du,
+                   lapack_complex_double* du2, lapack_int* ipiv, lapack_int* info);
+void LAPACK_spotrf(char* uplo, lapack_int* n, float* a, lapack_int* lda, lapack_int* info);
+void LAPACK_dpotrf(char* uplo, lapack_int* n, double* a, lapack_int* lda, lapack_int* info);
+void LAPACK_cpotrf(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* info);
+void LAPACK_zpotrf(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* info);
+void LAPACK_dpstrf(char* uplo, lapack_int* n, double* a, lapack_int* lda, lapack_int* piv, lapack_int* rank,
+                   double* tol, double* work, lapack_int* info);
+void LAPACK_spstrf(char* uplo, lapack_int* n, float* a, lapack_int* lda, lapack_int* piv, lapack_int* rank, float* tol,
+                   float* work, lapack_int* info);
+void LAPACK_zpstrf(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* piv,
+                   lapack_int* rank, double* tol, double* work, lapack_int* info);
+void LAPACK_cpstrf(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* piv,
+                   lapack_int* rank, float* tol, float* work, lapack_int* info);
+void LAPACK_dpftrf(char* transr, char* uplo, lapack_int* n, double* a, lapack_int* info);
+void LAPACK_spftrf(char* transr, char* uplo, lapack_int* n, float* a, lapack_int* info);
+void LAPACK_zpftrf(char* transr, char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* info);
+void LAPACK_cpftrf(char* transr, char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* info);
+void LAPACK_spptrf(char* uplo, lapack_int* n, float* ap, lapack_int* info);
+void LAPACK_dpptrf(char* uplo, lapack_int* n, double* ap, lapack_int* info);
+void LAPACK_cpptrf(char* uplo, lapack_int* n, lapack_complex_float* ap, lapack_int* info);
+void LAPACK_zpptrf(char* uplo, lapack_int* n, lapack_complex_double* ap, lapack_int* info);
+void LAPACK_spbtrf(char* uplo, lapack_int* n, lapack_int* kd, float* ab, lapack_int* ldab, lapack_int* info);
+void LAPACK_dpbtrf(char* uplo, lapack_int* n, lapack_int* kd, double* ab, lapack_int* ldab, lapack_int* info);
+void LAPACK_cpbtrf(char* uplo, lapack_int* n, lapack_int* kd, lapack_complex_float* ab, lapack_int* ldab,
+                   lapack_int* info);
+void LAPACK_zpbtrf(char* uplo, lapack_int* n, lapack_int* kd, lapack_complex_double* ab, lapack_int* ldab,
+                   lapack_int* info);
+void LAPACK_spttrf(lapack_int* n, float* d, float* e, lapack_int* info);
+void LAPACK_dpttrf(lapack_int* n, double* d, double* e, lapack_int* info);
+void LAPACK_cpttrf(lapack_int* n, float* d, lapack_complex_float* e, lapack_int* info);
+void LAPACK_zpttrf(lapack_int* n, double* d, lapack_complex_double* e, lapack_int* info);
+void LAPACK_ssytrf(char* uplo, lapack_int* n, float* a, lapack_int* lda, lapack_int* ipiv, float* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_dsytrf(char* uplo, lapack_int* n, double* a, lapack_int* lda, lapack_int* ipiv, double* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_csytrf(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zsytrf(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_chetrf(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zhetrf(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_ssptrf(char* uplo, lapack_int* n, float* ap, lapack_int* ipiv, lapack_int* info);
+void LAPACK_dsptrf(char* uplo, lapack_int* n, double* ap, lapack_int* ipiv, lapack_int* info);
+void LAPACK_csptrf(char* uplo, lapack_int* n, lapack_complex_float* ap, lapack_int* ipiv, lapack_int* info);
+void LAPACK_zsptrf(char* uplo, lapack_int* n, lapack_complex_double* ap, lapack_int* ipiv, lapack_int* info);
+void LAPACK_chptrf(char* uplo, lapack_int* n, lapack_complex_float* ap, lapack_int* ipiv, lapack_int* info);
+void LAPACK_zhptrf(char* uplo, lapack_int* n, lapack_complex_double* ap, lapack_int* ipiv, lapack_int* info);
+void LAPACK_sgetrs(char* trans, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda,
+                   const lapack_int* ipiv, float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dgetrs(char* trans, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda,
+                   const lapack_int* ipiv, double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_cgetrs(char* trans, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a, lapack_int* lda,
+                   const lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zgetrs(char* trans, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a, lapack_int* lda,
+                   const lapack_int* ipiv, lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_sgbtrs(char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, const float* ab,
+                   lapack_int* ldab, const lapack_int* ipiv, float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dgbtrs(char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, const double* ab,
+                   lapack_int* ldab, const lapack_int* ipiv, double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_cgbtrs(char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                   const lapack_complex_float* ab, lapack_int* ldab, const lapack_int* ipiv, lapack_complex_float* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_zgbtrs(char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                   const lapack_complex_double* ab, lapack_int* ldab, const lapack_int* ipiv, lapack_complex_double* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_sgttrs(char* trans, lapack_int* n, lapack_int* nrhs, const float* dl, const float* d, const float* du,
+                   const float* du2, const lapack_int* ipiv, float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dgttrs(char* trans, lapack_int* n, lapack_int* nrhs, const double* dl, const double* d, const double* du,
+                   const double* du2, const lapack_int* ipiv, double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_cgttrs(char* trans, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* dl,
+                   const lapack_complex_float* d, const lapack_complex_float* du, const lapack_complex_float* du2,
+                   const lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zgttrs(char* trans, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* dl,
+                   const lapack_complex_double* d, const lapack_complex_double* du, const lapack_complex_double* du2,
+                   const lapack_int* ipiv, lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_spotrs(char* uplo, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda, float* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_dpotrs(char* uplo, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda, double* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_cpotrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zpotrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dpftrs(char* transr, char* uplo, lapack_int* n, lapack_int* nrhs, const double* a, double* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_spftrs(char* transr, char* uplo, lapack_int* n, lapack_int* nrhs, const float* a, float* b, lapack_int* ldb,
+                   lapack_int* info);
+void LAPACK_zpftrs(char* transr, char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_cpftrs(char* transr, char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_spptrs(char* uplo, lapack_int* n, lapack_int* nrhs, const float* ap, float* b, lapack_int* ldb,
+                   lapack_int* info);
+void LAPACK_dpptrs(char* uplo, lapack_int* n, lapack_int* nrhs, const double* ap, double* b, lapack_int* ldb,
+                   lapack_int* info);
+void LAPACK_cpptrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* ap, lapack_complex_float* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_zpptrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* ap,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_spbtrs(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, const float* ab, lapack_int* ldab,
+                   float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dpbtrs(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, const double* ab, lapack_int* ldab,
+                   double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_cpbtrs(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, const lapack_complex_float* ab,
+                   lapack_int* ldab, lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zpbtrs(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, const lapack_complex_double* ab,
+                   lapack_int* ldab, lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_spttrs(lapack_int* n, lapack_int* nrhs, const float* d, const float* e, float* b, lapack_int* ldb,
+                   lapack_int* info);
+void LAPACK_dpttrs(lapack_int* n, lapack_int* nrhs, const double* d, const double* e, double* b, lapack_int* ldb,
+                   lapack_int* info);
+void LAPACK_cpttrs(char* uplo, lapack_int* n, lapack_int* nrhs, const float* d, const lapack_complex_float* e,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zpttrs(char* uplo, lapack_int* n, lapack_int* nrhs, const double* d, const lapack_complex_double* e,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_ssytrs(char* uplo, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda, const lapack_int* ipiv,
+                   float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dsytrs(char* uplo, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda,
+                   const lapack_int* ipiv, double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_csytrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a, lapack_int* lda,
+                   const lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zsytrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a, lapack_int* lda,
+                   const lapack_int* ipiv, lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_chetrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a, lapack_int* lda,
+                   const lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zhetrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a, lapack_int* lda,
+                   const lapack_int* ipiv, lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_ssptrs(char* uplo, lapack_int* n, lapack_int* nrhs, const float* ap, const lapack_int* ipiv, float* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_dsptrs(char* uplo, lapack_int* n, lapack_int* nrhs, const double* ap, const lapack_int* ipiv, double* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_csptrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* ap, const lapack_int* ipiv,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zsptrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* ap, const lapack_int* ipiv,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_chptrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* ap, const lapack_int* ipiv,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zhptrs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* ap, const lapack_int* ipiv,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_strtrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const float* a,
+                   lapack_int* lda, float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dtrtrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const double* a,
+                   lapack_int* lda, double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_ctrtrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a,
+                   lapack_int* lda, lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_ztrtrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a,
+                   lapack_int* lda, lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_stptrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const float* ap, float* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_dtptrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const double* ap, double* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_ctptrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* ap,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_ztptrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs,
+                   const lapack_complex_double* ap, lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_stbtrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   const float* ab, lapack_int* ldab, float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dtbtrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   const double* ab, lapack_int* ldab, double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_ctbtrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   const lapack_complex_float* ab, lapack_int* ldab, lapack_complex_float* b, lapack_int* ldb,
+                   lapack_int* info);
+void LAPACK_ztbtrs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   const lapack_complex_double* ab, lapack_int* ldab, lapack_complex_double* b, lapack_int* ldb,
+                   lapack_int* info);
+void LAPACK_sgecon(char* norm, lapack_int* n, const float* a, lapack_int* lda, float* anorm, float* rcond, float* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_dgecon(char* norm, lapack_int* n, const double* a, lapack_int* lda, double* anorm, double* rcond,
+                   double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cgecon(char* norm, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, float* anorm,
+                   float* rcond, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zgecon(char* norm, lapack_int* n, const lapack_complex_double* a, lapack_int* lda, double* anorm,
+                   double* rcond, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sgbcon(char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku, const float* ab, lapack_int* ldab,
+                   const lapack_int* ipiv, float* anorm, float* rcond, float* work, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_dgbcon(char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku, const double* ab, lapack_int* ldab,
+                   const lapack_int* ipiv, double* anorm, double* rcond, double* work, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_cgbcon(char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku, const lapack_complex_float* ab,
+                   lapack_int* ldab, const lapack_int* ipiv, float* anorm, float* rcond, lapack_complex_float* work,
+                   float* rwork, lapack_int* info);
+void LAPACK_zgbcon(char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku, const lapack_complex_double* ab,
+                   lapack_int* ldab, const lapack_int* ipiv, double* anorm, double* rcond, lapack_complex_double* work,
+                   double* rwork, lapack_int* info);
+void LAPACK_sgtcon(char* norm, lapack_int* n, const float* dl, const float* d, const float* du, const float* du2,
+                   const lapack_int* ipiv, float* anorm, float* rcond, float* work, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_dgtcon(char* norm, lapack_int* n, const double* dl, const double* d, const double* du, const double* du2,
+                   const lapack_int* ipiv, double* anorm, double* rcond, double* work, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_cgtcon(char* norm, lapack_int* n, const lapack_complex_float* dl, const lapack_complex_float* d,
+                   const lapack_complex_float* du, const lapack_complex_float* du2, const lapack_int* ipiv,
+                   float* anorm, float* rcond, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zgtcon(char* norm, lapack_int* n, const lapack_complex_double* dl, const lapack_complex_double* d,
+                   const lapack_complex_double* du, const lapack_complex_double* du2, const lapack_int* ipiv,
+                   double* anorm, double* rcond, lapack_complex_double* work, lapack_int* info);
+void LAPACK_spocon(char* uplo, lapack_int* n, const float* a, lapack_int* lda, float* anorm, float* rcond, float* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_dpocon(char* uplo, lapack_int* n, const double* a, lapack_int* lda, double* anorm, double* rcond,
+                   double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cpocon(char* uplo, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, float* anorm,
+                   float* rcond, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zpocon(char* uplo, lapack_int* n, const lapack_complex_double* a, lapack_int* lda, double* anorm,
+                   double* rcond, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sppcon(char* uplo, lapack_int* n, const float* ap, float* anorm, float* rcond, float* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_dppcon(char* uplo, lapack_int* n, const double* ap, double* anorm, double* rcond, double* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_cppcon(char* uplo, lapack_int* n, const lapack_complex_float* ap, float* anorm, float* rcond,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zppcon(char* uplo, lapack_int* n, const lapack_complex_double* ap, double* anorm, double* rcond,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_spbcon(char* uplo, lapack_int* n, lapack_int* kd, const float* ab, lapack_int* ldab, float* anorm,
+                   float* rcond, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dpbcon(char* uplo, lapack_int* n, lapack_int* kd, const double* ab, lapack_int* ldab, double* anorm,
+                   double* rcond, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cpbcon(char* uplo, lapack_int* n, lapack_int* kd, const lapack_complex_float* ab, lapack_int* ldab,
+                   float* anorm, float* rcond, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zpbcon(char* uplo, lapack_int* n, lapack_int* kd, const lapack_complex_double* ab, lapack_int* ldab,
+                   double* anorm, double* rcond, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sptcon(lapack_int* n, const float* d, const float* e, float* anorm, float* rcond, float* work,
+                   lapack_int* info);
+void LAPACK_dptcon(lapack_int* n, const double* d, const double* e, double* anorm, double* rcond, double* work,
+                   lapack_int* info);
+void LAPACK_cptcon(lapack_int* n, const float* d, const lapack_complex_float* e, float* anorm, float* rcond,
+                   float* work, lapack_int* info);
+void LAPACK_zptcon(lapack_int* n, const double* d, const lapack_complex_double* e, double* anorm, double* rcond,
+                   double* work, lapack_int* info);
+void LAPACK_ssycon(char* uplo, lapack_int* n, const float* a, lapack_int* lda, const lapack_int* ipiv, float* anorm,
+                   float* rcond, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dsycon(char* uplo, lapack_int* n, const double* a, lapack_int* lda, const lapack_int* ipiv, double* anorm,
+                   double* rcond, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_csycon(char* uplo, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, const lapack_int* ipiv,
+                   float* anorm, float* rcond, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zsycon(char* uplo, lapack_int* n, const lapack_complex_double* a, lapack_int* lda, const lapack_int* ipiv,
+                   double* anorm, double* rcond, lapack_complex_double* work, lapack_int* info);
+void LAPACK_checon(char* uplo, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, const lapack_int* ipiv,
+                   float* anorm, float* rcond, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zhecon(char* uplo, lapack_int* n, const lapack_complex_double* a, lapack_int* lda, const lapack_int* ipiv,
+                   double* anorm, double* rcond, lapack_complex_double* work, lapack_int* info);
+void LAPACK_sspcon(char* uplo, lapack_int* n, const float* ap, const lapack_int* ipiv, float* anorm, float* rcond,
+                   float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dspcon(char* uplo, lapack_int* n, const double* ap, const lapack_int* ipiv, double* anorm, double* rcond,
+                   double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cspcon(char* uplo, lapack_int* n, const lapack_complex_float* ap, const lapack_int* ipiv, float* anorm,
+                   float* rcond, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zspcon(char* uplo, lapack_int* n, const lapack_complex_double* ap, const lapack_int* ipiv, double* anorm,
+                   double* rcond, lapack_complex_double* work, lapack_int* info);
+void LAPACK_chpcon(char* uplo, lapack_int* n, const lapack_complex_float* ap, const lapack_int* ipiv, float* anorm,
+                   float* rcond, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zhpcon(char* uplo, lapack_int* n, const lapack_complex_double* ap, const lapack_int* ipiv, double* anorm,
+                   double* rcond, lapack_complex_double* work, lapack_int* info);
+void LAPACK_strcon(char* norm, char* uplo, char* diag, lapack_int* n, const float* a, lapack_int* lda, float* rcond,
+                   float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dtrcon(char* norm, char* uplo, char* diag, lapack_int* n, const double* a, lapack_int* lda, double* rcond,
+                   double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_ctrcon(char* norm, char* uplo, char* diag, lapack_int* n, const lapack_complex_float* a, lapack_int* lda,
+                   float* rcond, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_ztrcon(char* norm, char* uplo, char* diag, lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
+                   double* rcond, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_stpcon(char* norm, char* uplo, char* diag, lapack_int* n, const float* ap, float* rcond, float* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_dtpcon(char* norm, char* uplo, char* diag, lapack_int* n, const double* ap, double* rcond, double* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_ctpcon(char* norm, char* uplo, char* diag, lapack_int* n, const lapack_complex_float* ap, float* rcond,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_ztpcon(char* norm, char* uplo, char* diag, lapack_int* n, const lapack_complex_double* ap, double* rcond,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_stbcon(char* norm, char* uplo, char* diag, lapack_int* n, lapack_int* kd, const float* ab, lapack_int* ldab,
+                   float* rcond, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dtbcon(char* norm, char* uplo, char* diag, lapack_int* n, lapack_int* kd, const double* ab,
+                   lapack_int* ldab, double* rcond, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_ctbcon(char* norm, char* uplo, char* diag, lapack_int* n, lapack_int* kd, const lapack_complex_float* ab,
+                   lapack_int* ldab, float* rcond, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_ztbcon(char* norm, char* uplo, char* diag, lapack_int* n, lapack_int* kd, const lapack_complex_double* ab,
+                   lapack_int* ldab, double* rcond, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sgerfs(char* trans, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda, const float* af,
+                   lapack_int* ldaf, const lapack_int* ipiv, const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                   float* ferr, float* berr, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dgerfs(char* trans, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda, const double* af,
+                   lapack_int* ldaf, const lapack_int* ipiv, const double* b, lapack_int* ldb, double* x,
+                   lapack_int* ldx, double* ferr, double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cgerfs(char* trans, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* af, lapack_int* ldaf, const lapack_int* ipiv,
+                   const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                   float* ferr, float* berr, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zgerfs(char* trans, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* af, lapack_int* ldaf, const lapack_int* ipiv,
+                   const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                   double* ferr, double* berr, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_dgerfsx(char* trans, char* equed, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda,
+                    const double* af, lapack_int* ldaf, const lapack_int* ipiv, const double* r, const double* c,
+                    const double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* rcond, double* berr,
+                    lapack_int* n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int* nparams,
+                    double* params, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_sgerfsx(char* trans, char* equed, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda,
+                    const float* af, lapack_int* ldaf, const lapack_int* ipiv, const float* r, const float* c,
+                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx, float* rcond, float* berr,
+                    lapack_int* n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int* nparams,
+                    float* params, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_zgerfsx(char* trans, char* equed, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* af, lapack_int* ldaf, const lapack_int* ipiv,
+                    const double* r, const double* c, const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* rcond, double* berr, lapack_int* n_err_bnds,
+                    double* err_bnds_norm, double* err_bnds_comp, lapack_int* nparams, double* params,
+                    lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_cgerfsx(char* trans, char* equed, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* af, lapack_int* ldaf, const lapack_int* ipiv,
+                    const float* r, const float* c, const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* rcond, float* berr, lapack_int* n_err_bnds,
+                    float* err_bnds_norm, float* err_bnds_comp, lapack_int* nparams, float* params,
+                    lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_sgbrfs(char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, const float* ab,
+                   lapack_int* ldab, const float* afb, lapack_int* ldafb, const lapack_int* ipiv, const float* b,
+                   lapack_int* ldb, float* x, lapack_int* ldx, float* ferr, float* berr, float* work, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_dgbrfs(char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, const double* ab,
+                   lapack_int* ldab, const double* afb, lapack_int* ldafb, const lapack_int* ipiv, const double* b,
+                   lapack_int* ldb, double* x, lapack_int* ldx, double* ferr, double* berr, double* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_cgbrfs(char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                   const lapack_complex_float* ab, lapack_int* ldab, const lapack_complex_float* afb, lapack_int* ldafb,
+                   const lapack_int* ipiv, const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x,
+                   lapack_int* ldx, float* ferr, float* berr, lapack_complex_float* work, float* rwork,
+                   lapack_int* info);
+void LAPACK_zgbrfs(char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                   const lapack_complex_double* ab, lapack_int* ldab, const lapack_complex_double* afb,
+                   lapack_int* ldafb, const lapack_int* ipiv, const lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* x, lapack_int* ldx, double* ferr, double* berr, lapack_complex_double* work,
+                   double* rwork, lapack_int* info);
+void LAPACK_dgbrfsx(char* trans, char* equed, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                    const double* ab, lapack_int* ldab, const double* afb, lapack_int* ldafb, const lapack_int* ipiv,
+                    const double* r, const double* c, const double* b, lapack_int* ldb, double* x, lapack_int* ldx,
+                    double* rcond, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                    lapack_int* nparams, double* params, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_sgbrfsx(char* trans, char* equed, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                    const float* ab, lapack_int* ldab, const float* afb, lapack_int* ldafb, const lapack_int* ipiv,
+                    const float* r, const float* c, const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                    float* rcond, float* berr, lapack_int* n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                    lapack_int* nparams, float* params, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_zgbrfsx(char* trans, char* equed, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                    const lapack_complex_double* ab, lapack_int* ldab, const lapack_complex_double* afb,
+                    lapack_int* ldafb, const lapack_int* ipiv, const double* r, const double* c,
+                    const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* rcond, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                    lapack_int* nparams, double* params, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_cgbrfsx(char* trans, char* equed, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                    const lapack_complex_float* ab, lapack_int* ldab, const lapack_complex_float* afb,
+                    lapack_int* ldafb, const lapack_int* ipiv, const float* r, const float* c,
+                    const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* rcond, float* berr, lapack_int* n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                    lapack_int* nparams, float* params, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_sgtrfs(char* trans, lapack_int* n, lapack_int* nrhs, const float* dl, const float* d, const float* du,
+                   const float* dlf, const float* df, const float* duf, const float* du2, const lapack_int* ipiv,
+                   const float* b, lapack_int* ldb, float* x, lapack_int* ldx, float* ferr, float* berr, float* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_dgtrfs(char* trans, lapack_int* n, lapack_int* nrhs, const double* dl, const double* d, const double* du,
+                   const double* dlf, const double* df, const double* duf, const double* du2, const lapack_int* ipiv,
+                   const double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* ferr, double* berr,
+                   double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cgtrfs(char* trans, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* dl,
+                   const lapack_complex_float* d, const lapack_complex_float* du, const lapack_complex_float* dlf,
+                   const lapack_complex_float* df, const lapack_complex_float* duf, const lapack_complex_float* du2,
+                   const lapack_int* ipiv, const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x,
+                   lapack_int* ldx, float* ferr, float* berr, lapack_complex_float* work, float* rwork,
+                   lapack_int* info);
+void LAPACK_zgtrfs(char* trans, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* dl,
+                   const lapack_complex_double* d, const lapack_complex_double* du, const lapack_complex_double* dlf,
+                   const lapack_complex_double* df, const lapack_complex_double* duf, const lapack_complex_double* du2,
+                   const lapack_int* ipiv, const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x,
+                   lapack_int* ldx, double* ferr, double* berr, lapack_complex_double* work, double* rwork,
+                   lapack_int* info);
+void LAPACK_sporfs(char* uplo, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda, const float* af,
+                   lapack_int* ldaf, const float* b, lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
+                   float* berr, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dporfs(char* uplo, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda, const double* af,
+                   lapack_int* ldaf, const double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
+                   double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cporfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* af, lapack_int* ldaf, const lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* x, lapack_int* ldx, float* ferr, float* berr, lapack_complex_float* work,
+                   float* rwork, lapack_int* info);
+void LAPACK_zporfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* af, lapack_int* ldaf, const lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* x, lapack_int* ldx, double* ferr, double* berr, lapack_complex_double* work,
+                   double* rwork, lapack_int* info);
+void LAPACK_dporfsx(char* uplo, char* equed, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda,
+                    const double* af, lapack_int* ldaf, const double* s, const double* b, lapack_int* ldb, double* x,
+                    lapack_int* ldx, double* rcond, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm,
+                    double* err_bnds_comp, lapack_int* nparams, double* params, double* work, lapack_int* iwork,
+                    lapack_int* info);
+void LAPACK_sporfsx(char* uplo, char* equed, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda,
+                    const float* af, lapack_int* ldaf, const float* s, const float* b, lapack_int* ldb, float* x,
+                    lapack_int* ldx, float* rcond, float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
+                    float* err_bnds_comp, lapack_int* nparams, float* params, float* work, lapack_int* iwork,
+                    lapack_int* info);
+void LAPACK_zporfsx(char* uplo, char* equed, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* af, lapack_int* ldaf, const double* s,
+                    const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* rcond, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                    lapack_int* nparams, double* params, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_cporfsx(char* uplo, char* equed, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* af, lapack_int* ldaf, const float* s,
+                    const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* rcond, float* berr, lapack_int* n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                    lapack_int* nparams, float* params, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_spprfs(char* uplo, lapack_int* n, lapack_int* nrhs, const float* ap, const float* afp, const float* b,
+                   lapack_int* ldb, float* x, lapack_int* ldx, float* ferr, float* berr, float* work, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_dpprfs(char* uplo, lapack_int* n, lapack_int* nrhs, const double* ap, const double* afp, const double* b,
+                   lapack_int* ldb, double* x, lapack_int* ldx, double* ferr, double* berr, double* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_cpprfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* ap,
+                   const lapack_complex_float* afp, const lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* x, lapack_int* ldx, float* ferr, float* berr, lapack_complex_float* work,
+                   float* rwork, lapack_int* info);
+void LAPACK_zpprfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* ap,
+                   const lapack_complex_double* afp, const lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* x, lapack_int* ldx, double* ferr, double* berr, lapack_complex_double* work,
+                   double* rwork, lapack_int* info);
+void LAPACK_spbrfs(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, const float* ab, lapack_int* ldab,
+                   const float* afb, lapack_int* ldafb, const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                   float* ferr, float* berr, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dpbrfs(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, const double* ab, lapack_int* ldab,
+                   const double* afb, lapack_int* ldafb, const double* b, lapack_int* ldb, double* x, lapack_int* ldx,
+                   double* ferr, double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cpbrfs(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, const lapack_complex_float* ab,
+                   lapack_int* ldab, const lapack_complex_float* afb, lapack_int* ldafb, const lapack_complex_float* b,
+                   lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx, float* ferr, float* berr,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zpbrfs(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, const lapack_complex_double* ab,
+                   lapack_int* ldab, const lapack_complex_double* afb, lapack_int* ldafb,
+                   const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                   double* ferr, double* berr, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sptrfs(lapack_int* n, lapack_int* nrhs, const float* d, const float* e, const float* df, const float* ef,
+                   const float* b, lapack_int* ldb, float* x, lapack_int* ldx, float* ferr, float* berr, float* work,
+                   lapack_int* info);
+void LAPACK_dptrfs(lapack_int* n, lapack_int* nrhs, const double* d, const double* e, const double* df,
+                   const double* ef, const double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
+                   double* berr, double* work, lapack_int* info);
+void LAPACK_cptrfs(char* uplo, lapack_int* n, lapack_int* nrhs, const float* d, const lapack_complex_float* e,
+                   const float* df, const lapack_complex_float* ef, const lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* x, lapack_int* ldx, float* ferr, float* berr, lapack_complex_float* work,
+                   float* rwork, lapack_int* info);
+void LAPACK_zptrfs(char* uplo, lapack_int* n, lapack_int* nrhs, const double* d, const lapack_complex_double* e,
+                   const double* df, const lapack_complex_double* ef, const lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* x, lapack_int* ldx, double* ferr, double* berr, lapack_complex_double* work,
+                   double* rwork, lapack_int* info);
+void LAPACK_ssyrfs(char* uplo, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda, const float* af,
+                   lapack_int* ldaf, const lapack_int* ipiv, const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                   float* ferr, float* berr, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dsyrfs(char* uplo, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda, const double* af,
+                   lapack_int* ldaf, const lapack_int* ipiv, const double* b, lapack_int* ldb, double* x,
+                   lapack_int* ldx, double* ferr, double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_csyrfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* af, lapack_int* ldaf, const lapack_int* ipiv,
+                   const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                   float* ferr, float* berr, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zsyrfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* af, lapack_int* ldaf, const lapack_int* ipiv,
+                   const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                   double* ferr, double* berr, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_dsyrfsx(char* uplo, char* equed, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda,
+                    const double* af, lapack_int* ldaf, const lapack_int* ipiv, const double* s, const double* b,
+                    lapack_int* ldb, double* x, lapack_int* ldx, double* rcond, double* berr, lapack_int* n_err_bnds,
+                    double* err_bnds_norm, double* err_bnds_comp, lapack_int* nparams, double* params, double* work,
+                    lapack_int* iwork, lapack_int* info);
+void LAPACK_ssyrfsx(char* uplo, char* equed, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda,
+                    const float* af, lapack_int* ldaf, const lapack_int* ipiv, const float* s, const float* b,
+                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond, float* berr, lapack_int* n_err_bnds,
+                    float* err_bnds_norm, float* err_bnds_comp, lapack_int* nparams, float* params, float* work,
+                    lapack_int* iwork, lapack_int* info);
+void LAPACK_zsyrfsx(char* uplo, char* equed, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* af, lapack_int* ldaf, const lapack_int* ipiv,
+                    const double* s, const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x,
+                    lapack_int* ldx, double* rcond, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm,
+                    double* err_bnds_comp, lapack_int* nparams, double* params, lapack_complex_double* work,
+                    double* rwork, lapack_int* info);
+void LAPACK_csyrfsx(char* uplo, char* equed, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* af, lapack_int* ldaf, const lapack_int* ipiv,
+                    const float* s, const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x,
+                    lapack_int* ldx, float* rcond, float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
+                    float* err_bnds_comp, lapack_int* nparams, float* params, lapack_complex_float* work, float* rwork,
+                    lapack_int* info);
+void LAPACK_cherfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* af, lapack_int* ldaf, const lapack_int* ipiv,
+                   const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                   float* ferr, float* berr, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zherfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* af, lapack_int* ldaf, const lapack_int* ipiv,
+                   const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                   double* ferr, double* berr, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_zherfsx(char* uplo, char* equed, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* af, lapack_int* ldaf, const lapack_int* ipiv,
+                    const double* s, const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x,
+                    lapack_int* ldx, double* rcond, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm,
+                    double* err_bnds_comp, lapack_int* nparams, double* params, lapack_complex_double* work,
+                    double* rwork, lapack_int* info);
+void LAPACK_cherfsx(char* uplo, char* equed, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* af, lapack_int* ldaf, const lapack_int* ipiv,
+                    const float* s, const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x,
+                    lapack_int* ldx, float* rcond, float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
+                    float* err_bnds_comp, lapack_int* nparams, float* params, lapack_complex_float* work, float* rwork,
+                    lapack_int* info);
+void LAPACK_ssprfs(char* uplo, lapack_int* n, lapack_int* nrhs, const float* ap, const float* afp,
+                   const lapack_int* ipiv, const float* b, lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
+                   float* berr, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dsprfs(char* uplo, lapack_int* n, lapack_int* nrhs, const double* ap, const double* afp,
+                   const lapack_int* ipiv, const double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
+                   double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_csprfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* ap,
+                   const lapack_complex_float* afp, const lapack_int* ipiv, const lapack_complex_float* b,
+                   lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx, float* ferr, float* berr,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zsprfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* ap,
+                   const lapack_complex_double* afp, const lapack_int* ipiv, const lapack_complex_double* b,
+                   lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx, double* ferr, double* berr,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_chprfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* ap,
+                   const lapack_complex_float* afp, const lapack_int* ipiv, const lapack_complex_float* b,
+                   lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx, float* ferr, float* berr,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zhprfs(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* ap,
+                   const lapack_complex_double* afp, const lapack_int* ipiv, const lapack_complex_double* b,
+                   lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx, double* ferr, double* berr,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_strrfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const float* a,
+                   lapack_int* lda, const float* b, lapack_int* ldb, const float* x, lapack_int* ldx, float* ferr,
+                   float* berr, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dtrrfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const double* a,
+                   lapack_int* lda, const double* b, lapack_int* ldb, const double* x, lapack_int* ldx, double* ferr,
+                   double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_ctrrfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a,
+                   lapack_int* lda, const lapack_complex_float* b, lapack_int* ldb, const lapack_complex_float* x,
+                   lapack_int* ldx, float* ferr, float* berr, lapack_complex_float* work, float* rwork,
+                   lapack_int* info);
+void LAPACK_ztrrfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a,
+                   lapack_int* lda, const lapack_complex_double* b, lapack_int* ldb, const lapack_complex_double* x,
+                   lapack_int* ldx, double* ferr, double* berr, lapack_complex_double* work, double* rwork,
+                   lapack_int* info);
+void LAPACK_stprfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const float* ap,
+                   const float* b, lapack_int* ldb, const float* x, lapack_int* ldx, float* ferr, float* berr,
+                   float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dtprfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const double* ap,
+                   const double* b, lapack_int* ldb, const double* x, lapack_int* ldx, double* ferr, double* berr,
+                   double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_ctprfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* ap,
+                   const lapack_complex_float* b, lapack_int* ldb, const lapack_complex_float* x, lapack_int* ldx,
+                   float* ferr, float* berr, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_ztprfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* nrhs,
+                   const lapack_complex_double* ap, const lapack_complex_double* b, lapack_int* ldb,
+                   const lapack_complex_double* x, lapack_int* ldx, double* ferr, double* berr,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_stbrfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   const float* ab, lapack_int* ldab, const float* b, lapack_int* ldb, const float* x, lapack_int* ldx,
+                   float* ferr, float* berr, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dtbrfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   const double* ab, lapack_int* ldab, const double* b, lapack_int* ldb, const double* x,
+                   lapack_int* ldx, double* ferr, double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_ctbrfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   const lapack_complex_float* ab, lapack_int* ldab, const lapack_complex_float* b, lapack_int* ldb,
+                   const lapack_complex_float* x, lapack_int* ldx, float* ferr, float* berr, lapack_complex_float* work,
+                   float* rwork, lapack_int* info);
+void LAPACK_ztbrfs(char* uplo, char* trans, char* diag, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   const lapack_complex_double* ab, lapack_int* ldab, const lapack_complex_double* b, lapack_int* ldb,
+                   const lapack_complex_double* x, lapack_int* ldx, double* ferr, double* berr,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sgetri(lapack_int* n, float* a, lapack_int* lda, const lapack_int* ipiv, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dgetri(lapack_int* n, double* a, lapack_int* lda, const lapack_int* ipiv, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_cgetri(lapack_int* n, lapack_complex_float* a, lapack_int* lda, const lapack_int* ipiv,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zgetri(lapack_int* n, lapack_complex_double* a, lapack_int* lda, const lapack_int* ipiv,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_spotri(char* uplo, lapack_int* n, float* a, lapack_int* lda, lapack_int* info);
+void LAPACK_dpotri(char* uplo, lapack_int* n, double* a, lapack_int* lda, lapack_int* info);
+void LAPACK_cpotri(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* info);
+void LAPACK_zpotri(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* info);
+void LAPACK_dpftri(char* transr, char* uplo, lapack_int* n, double* a, lapack_int* info);
+void LAPACK_spftri(char* transr, char* uplo, lapack_int* n, float* a, lapack_int* info);
+void LAPACK_zpftri(char* transr, char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* info);
+void LAPACK_cpftri(char* transr, char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* info);
+void LAPACK_spptri(char* uplo, lapack_int* n, float* ap, lapack_int* info);
+void LAPACK_dpptri(char* uplo, lapack_int* n, double* ap, lapack_int* info);
+void LAPACK_cpptri(char* uplo, lapack_int* n, lapack_complex_float* ap, lapack_int* info);
+void LAPACK_zpptri(char* uplo, lapack_int* n, lapack_complex_double* ap, lapack_int* info);
+void LAPACK_ssytri(char* uplo, lapack_int* n, float* a, lapack_int* lda, const lapack_int* ipiv, float* work,
+                   lapack_int* info);
+void LAPACK_dsytri(char* uplo, lapack_int* n, double* a, lapack_int* lda, const lapack_int* ipiv, double* work,
+                   lapack_int* info);
+void LAPACK_csytri(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, const lapack_int* ipiv,
+                   lapack_complex_float* work, lapack_int* info);
+void LAPACK_zsytri(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, const lapack_int* ipiv,
+                   lapack_complex_double* work, lapack_int* info);
+void LAPACK_chetri(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, const lapack_int* ipiv,
+                   lapack_complex_float* work, lapack_int* info);
+void LAPACK_zhetri(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, const lapack_int* ipiv,
+                   lapack_complex_double* work, lapack_int* info);
+void LAPACK_ssptri(char* uplo, lapack_int* n, float* ap, const lapack_int* ipiv, float* work, lapack_int* info);
+void LAPACK_dsptri(char* uplo, lapack_int* n, double* ap, const lapack_int* ipiv, double* work, lapack_int* info);
+void LAPACK_csptri(char* uplo, lapack_int* n, lapack_complex_float* ap, const lapack_int* ipiv,
+                   lapack_complex_float* work, lapack_int* info);
+void LAPACK_zsptri(char* uplo, lapack_int* n, lapack_complex_double* ap, const lapack_int* ipiv,
+                   lapack_complex_double* work, lapack_int* info);
+void LAPACK_chptri(char* uplo, lapack_int* n, lapack_complex_float* ap, const lapack_int* ipiv,
+                   lapack_complex_float* work, lapack_int* info);
+void LAPACK_zhptri(char* uplo, lapack_int* n, lapack_complex_double* ap, const lapack_int* ipiv,
+                   lapack_complex_double* work, lapack_int* info);
+void LAPACK_strtri(char* uplo, char* diag, lapack_int* n, float* a, lapack_int* lda, lapack_int* info);
+void LAPACK_dtrtri(char* uplo, char* diag, lapack_int* n, double* a, lapack_int* lda, lapack_int* info);
+void LAPACK_ctrtri(char* uplo, char* diag, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* info);
+void LAPACK_ztrtri(char* uplo, char* diag, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* info);
+void LAPACK_dtftri(char* transr, char* uplo, char* diag, lapack_int* n, double* a, lapack_int* info);
+void LAPACK_stftri(char* transr, char* uplo, char* diag, lapack_int* n, float* a, lapack_int* info);
+void LAPACK_ztftri(char* transr, char* uplo, char* diag, lapack_int* n, lapack_complex_double* a, lapack_int* info);
+void LAPACK_ctftri(char* transr, char* uplo, char* diag, lapack_int* n, lapack_complex_float* a, lapack_int* info);
+void LAPACK_stptri(char* uplo, char* diag, lapack_int* n, float* ap, lapack_int* info);
+void LAPACK_dtptri(char* uplo, char* diag, lapack_int* n, double* ap, lapack_int* info);
+void LAPACK_ctptri(char* uplo, char* diag, lapack_int* n, lapack_complex_float* ap, lapack_int* info);
+void LAPACK_ztptri(char* uplo, char* diag, lapack_int* n, lapack_complex_double* ap, lapack_int* info);
+void LAPACK_sgeequ(lapack_int* m, lapack_int* n, const float* a, lapack_int* lda, float* r, float* c, float* rowcnd,
+                   float* colcnd, float* amax, lapack_int* info);
+void LAPACK_dgeequ(lapack_int* m, lapack_int* n, const double* a, lapack_int* lda, double* r, double* c, double* rowcnd,
+                   double* colcnd, double* amax, lapack_int* info);
+void LAPACK_cgeequ(lapack_int* m, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, float* r, float* c,
+                   float* rowcnd, float* colcnd, float* amax, lapack_int* info);
+void LAPACK_zgeequ(lapack_int* m, lapack_int* n, const lapack_complex_double* a, lapack_int* lda, double* r, double* c,
+                   double* rowcnd, double* colcnd, double* amax, lapack_int* info);
+void LAPACK_dgeequb(lapack_int* m, lapack_int* n, const double* a, lapack_int* lda, double* r, double* c,
+                    double* rowcnd, double* colcnd, double* amax, lapack_int* info);
+void LAPACK_sgeequb(lapack_int* m, lapack_int* n, const float* a, lapack_int* lda, float* r, float* c, float* rowcnd,
+                    float* colcnd, float* amax, lapack_int* info);
+void LAPACK_zgeequb(lapack_int* m, lapack_int* n, const lapack_complex_double* a, lapack_int* lda, double* r, double* c,
+                    double* rowcnd, double* colcnd, double* amax, lapack_int* info);
+void LAPACK_cgeequb(lapack_int* m, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, float* r, float* c,
+                    float* rowcnd, float* colcnd, float* amax, lapack_int* info);
+void LAPACK_sgbequ(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const float* ab, lapack_int* ldab,
+                   float* r, float* c, float* rowcnd, float* colcnd, float* amax, lapack_int* info);
+void LAPACK_dgbequ(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const double* ab, lapack_int* ldab,
+                   double* r, double* c, double* rowcnd, double* colcnd, double* amax, lapack_int* info);
+void LAPACK_cgbequ(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const lapack_complex_float* ab,
+                   lapack_int* ldab, float* r, float* c, float* rowcnd, float* colcnd, float* amax, lapack_int* info);
+void LAPACK_zgbequ(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const lapack_complex_double* ab,
+                   lapack_int* ldab, double* r, double* c, double* rowcnd, double* colcnd, double* amax,
+                   lapack_int* info);
+void LAPACK_dgbequb(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const double* ab, lapack_int* ldab,
+                    double* r, double* c, double* rowcnd, double* colcnd, double* amax, lapack_int* info);
+void LAPACK_sgbequb(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const float* ab, lapack_int* ldab,
+                    float* r, float* c, float* rowcnd, float* colcnd, float* amax, lapack_int* info);
+void LAPACK_zgbequb(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const lapack_complex_double* ab,
+                    lapack_int* ldab, double* r, double* c, double* rowcnd, double* colcnd, double* amax,
+                    lapack_int* info);
+void LAPACK_cgbequb(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const lapack_complex_float* ab,
+                    lapack_int* ldab, float* r, float* c, float* rowcnd, float* colcnd, float* amax, lapack_int* info);
+void LAPACK_spoequ(lapack_int* n, const float* a, lapack_int* lda, float* s, float* scond, float* amax,
+                   lapack_int* info);
+void LAPACK_dpoequ(lapack_int* n, const double* a, lapack_int* lda, double* s, double* scond, double* amax,
+                   lapack_int* info);
+void LAPACK_cpoequ(lapack_int* n, const lapack_complex_float* a, lapack_int* lda, float* s, float* scond, float* amax,
+                   lapack_int* info);
+void LAPACK_zpoequ(lapack_int* n, const lapack_complex_double* a, lapack_int* lda, double* s, double* scond,
+                   double* amax, lapack_int* info);
+void LAPACK_dpoequb(lapack_int* n, const double* a, lapack_int* lda, double* s, double* scond, double* amax,
+                    lapack_int* info);
+void LAPACK_spoequb(lapack_int* n, const float* a, lapack_int* lda, float* s, float* scond, float* amax,
+                    lapack_int* info);
+void LAPACK_zpoequb(lapack_int* n, const lapack_complex_double* a, lapack_int* lda, double* s, double* scond,
+                    double* amax, lapack_int* info);
+void LAPACK_cpoequb(lapack_int* n, const lapack_complex_float* a, lapack_int* lda, float* s, float* scond, float* amax,
+                    lapack_int* info);
+void LAPACK_sppequ(char* uplo, lapack_int* n, const float* ap, float* s, float* scond, float* amax, lapack_int* info);
+void LAPACK_dppequ(char* uplo, lapack_int* n, const double* ap, double* s, double* scond, double* amax,
+                   lapack_int* info);
+void LAPACK_cppequ(char* uplo, lapack_int* n, const lapack_complex_float* ap, float* s, float* scond, float* amax,
+                   lapack_int* info);
+void LAPACK_zppequ(char* uplo, lapack_int* n, const lapack_complex_double* ap, double* s, double* scond, double* amax,
+                   lapack_int* info);
+void LAPACK_spbequ(char* uplo, lapack_int* n, lapack_int* kd, const float* ab, lapack_int* ldab, float* s, float* scond,
+                   float* amax, lapack_int* info);
+void LAPACK_dpbequ(char* uplo, lapack_int* n, lapack_int* kd, const double* ab, lapack_int* ldab, double* s,
+                   double* scond, double* amax, lapack_int* info);
+void LAPACK_cpbequ(char* uplo, lapack_int* n, lapack_int* kd, const lapack_complex_float* ab, lapack_int* ldab,
+                   float* s, float* scond, float* amax, lapack_int* info);
+void LAPACK_zpbequ(char* uplo, lapack_int* n, lapack_int* kd, const lapack_complex_double* ab, lapack_int* ldab,
+                   double* s, double* scond, double* amax, lapack_int* info);
+void LAPACK_dsyequb(char* uplo, lapack_int* n, const double* a, lapack_int* lda, double* s, double* scond, double* amax,
+                    double* work, lapack_int* info);
+void LAPACK_ssyequb(char* uplo, lapack_int* n, const float* a, lapack_int* lda, float* s, float* scond, float* amax,
+                    float* work, lapack_int* info);
+void LAPACK_zsyequb(char* uplo, lapack_int* n, const lapack_complex_double* a, lapack_int* lda, double* s,
+                    double* scond, double* amax, lapack_complex_double* work, lapack_int* info);
+void LAPACK_csyequb(char* uplo, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, float* s, float* scond,
+                    float* amax, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zheequb(char* uplo, lapack_int* n, const lapack_complex_double* a, lapack_int* lda, double* s,
+                    double* scond, double* amax, lapack_complex_double* work, lapack_int* info);
+void LAPACK_cheequb(char* uplo, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, float* s, float* scond,
+                    float* amax, lapack_complex_float* work, lapack_int* info);
+void LAPACK_sgesv(lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, lapack_int* ipiv, float* b,
+                  lapack_int* ldb, lapack_int* info);
+void LAPACK_dgesv(lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, lapack_int* ipiv, double* b,
+                  lapack_int* ldb, lapack_int* info);
+void LAPACK_cgesv(lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
+                  lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zgesv(lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
+                  lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dsgesv(lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, lapack_int* ipiv, double* b,
+                   lapack_int* ldb, double* x, lapack_int* ldx, double* work, float* swork, lapack_int* iter,
+                   lapack_int* info);
+void LAPACK_zcgesv(lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                   lapack_complex_double* work, lapack_complex_float* swork, double* rwork, lapack_int* iter,
+                   lapack_int* info);
+void LAPACK_sgesvx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, float* af,
+                   lapack_int* ldaf, lapack_int* ipiv, char* equed, float* r, float* c, float* b, lapack_int* ldb,
+                   float* x, lapack_int* ldx, float* rcond, float* ferr, float* berr, float* work, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_dgesvx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* af,
+                   lapack_int* ldaf, lapack_int* ipiv, char* equed, double* r, double* c, double* b, lapack_int* ldb,
+                   double* x, lapack_int* ldx, double* rcond, double* ferr, double* berr, double* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_cgesvx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* af, lapack_int* ldaf, lapack_int* ipiv, char* equed, float* r, float* c,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                   float* ferr, float* berr, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zgesvx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* af, lapack_int* ldaf, lapack_int* ipiv, char* equed, double* r, double* c,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                   double* ferr, double* berr, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_dgesvxx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* af,
+                    lapack_int* ldaf, lapack_int* ipiv, char* equed, double* r, double* c, double* b, lapack_int* ldb,
+                    double* x, lapack_int* ldx, double* rcond, double* rpvgrw, double* berr, lapack_int* n_err_bnds,
+                    double* err_bnds_norm, double* err_bnds_comp, lapack_int* nparams, double* params, double* work,
+                    lapack_int* iwork, lapack_int* info);
+void LAPACK_sgesvxx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, float* af,
+                    lapack_int* ldaf, lapack_int* ipiv, char* equed, float* r, float* c, float* b, lapack_int* ldb,
+                    float* x, lapack_int* ldx, float* rcond, float* rpvgrw, float* berr, lapack_int* n_err_bnds,
+                    float* err_bnds_norm, float* err_bnds_comp, lapack_int* nparams, float* params, float* work,
+                    lapack_int* iwork, lapack_int* info);
+void LAPACK_zgesvxx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* af, lapack_int* ldaf, lapack_int* ipiv, char* equed, double* r, double* c,
+                    lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                    double* rpvgrw, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                    lapack_int* nparams, double* params, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_cgesvxx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* af, lapack_int* ldaf, lapack_int* ipiv, char* equed, float* r, float* c,
+                    lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                    float* rpvgrw, float* berr, lapack_int* n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                    lapack_int* nparams, float* params, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_sgbsv(lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, float* ab, lapack_int* ldab,
+                  lapack_int* ipiv, float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dgbsv(lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, double* ab, lapack_int* ldab,
+                  lapack_int* ipiv, double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_cgbsv(lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, lapack_complex_float* ab,
+                  lapack_int* ldab, lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zgbsv(lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, lapack_complex_double* ab,
+                  lapack_int* ldab, lapack_int* ipiv, lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_sgbsvx(char* fact, char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, float* ab,
+                   lapack_int* ldab, float* afb, lapack_int* ldafb, lapack_int* ipiv, char* equed, float* r, float* c,
+                   float* b, lapack_int* ldb, float* x, lapack_int* ldx, float* rcond, float* ferr, float* berr,
+                   float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dgbsvx(char* fact, char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, double* ab,
+                   lapack_int* ldab, double* afb, lapack_int* ldafb, lapack_int* ipiv, char* equed, double* r,
+                   double* c, double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* rcond, double* ferr,
+                   double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cgbsvx(char* fact, char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                   lapack_complex_float* ab, lapack_int* ldab, lapack_complex_float* afb, lapack_int* ldafb,
+                   lapack_int* ipiv, char* equed, float* r, float* c, lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* x, lapack_int* ldx, float* rcond, float* ferr, float* berr,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zgbsvx(char* fact, char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                   lapack_complex_double* ab, lapack_int* ldab, lapack_complex_double* afb, lapack_int* ldafb,
+                   lapack_int* ipiv, char* equed, double* r, double* c, lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* x, lapack_int* ldx, double* rcond, double* ferr, double* berr,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_dgbsvxx(char* fact, char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                    double* ab, lapack_int* ldab, double* afb, lapack_int* ldafb, lapack_int* ipiv, char* equed,
+                    double* r, double* c, double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
+                    double* rpvgrw, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                    lapack_int* nparams, double* params, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_sgbsvxx(char* fact, char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs, float* ab,
+                    lapack_int* ldab, float* afb, lapack_int* ldafb, lapack_int* ipiv, char* equed, float* r, float* c,
+                    float* b, lapack_int* ldb, float* x, lapack_int* ldx, float* rcond, float* rpvgrw, float* berr,
+                    lapack_int* n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int* nparams,
+                    float* params, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_zgbsvxx(char* fact, char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                    lapack_complex_double* ab, lapack_int* ldab, lapack_complex_double* afb, lapack_int* ldafb,
+                    lapack_int* ipiv, char* equed, double* r, double* c, lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* rcond, double* rpvgrw, double* berr,
+                    lapack_int* n_err_bnds, double* err_bnds_norm, double* err_bnds_comp, lapack_int* nparams,
+                    double* params, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_cgbsvxx(char* fact, char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku, lapack_int* nrhs,
+                    lapack_complex_float* ab, lapack_int* ldab, lapack_complex_float* afb, lapack_int* ldafb,
+                    lapack_int* ipiv, char* equed, float* r, float* c, lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* rcond, float* rpvgrw, float* berr,
+                    lapack_int* n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int* nparams,
+                    float* params, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_sgtsv(lapack_int* n, lapack_int* nrhs, float* dl, float* d, float* du, float* b, lapack_int* ldb,
+                  lapack_int* info);
+void LAPACK_dgtsv(lapack_int* n, lapack_int* nrhs, double* dl, double* d, double* du, double* b, lapack_int* ldb,
+                  lapack_int* info);
+void LAPACK_cgtsv(lapack_int* n, lapack_int* nrhs, lapack_complex_float* dl, lapack_complex_float* d,
+                  lapack_complex_float* du, lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zgtsv(lapack_int* n, lapack_int* nrhs, lapack_complex_double* dl, lapack_complex_double* d,
+                  lapack_complex_double* du, lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_sgtsvx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, const float* dl, const float* d,
+                   const float* du, float* dlf, float* df, float* duf, float* du2, lapack_int* ipiv, const float* b,
+                   lapack_int* ldb, float* x, lapack_int* ldx, float* rcond, float* ferr, float* berr, float* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_dgtsvx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, const double* dl, const double* d,
+                   const double* du, double* dlf, double* df, double* duf, double* du2, lapack_int* ipiv,
+                   const double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* rcond, double* ferr,
+                   double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cgtsvx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* dl,
+                   const lapack_complex_float* d, const lapack_complex_float* du, lapack_complex_float* dlf,
+                   lapack_complex_float* df, lapack_complex_float* duf, lapack_complex_float* du2, lapack_int* ipiv,
+                   const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                   float* rcond, float* ferr, float* berr, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zgtsvx(char* fact, char* trans, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* dl,
+                   const lapack_complex_double* d, const lapack_complex_double* du, lapack_complex_double* dlf,
+                   lapack_complex_double* df, lapack_complex_double* duf, lapack_complex_double* du2, lapack_int* ipiv,
+                   const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                   double* rcond, double* ferr, double* berr, lapack_complex_double* work, double* rwork,
+                   lapack_int* info);
+void LAPACK_sposv(char* uplo, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                  lapack_int* info);
+void LAPACK_dposv(char* uplo, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                  lapack_int* info);
+void LAPACK_cposv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                  lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zposv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                  lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dsposv(char* uplo, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                   double* x, lapack_int* ldx, double* work, float* swork, lapack_int* iter, lapack_int* info);
+void LAPACK_zcposv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                   lapack_complex_double* work, lapack_complex_float* swork, double* rwork, lapack_int* iter,
+                   lapack_int* info);
+void LAPACK_sposvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, float* af,
+                   lapack_int* ldaf, char* equed, float* s, float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                   float* rcond, float* ferr, float* berr, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dposvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* af,
+                   lapack_int* ldaf, char* equed, double* s, double* b, lapack_int* ldb, double* x, lapack_int* ldx,
+                   double* rcond, double* ferr, double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cposvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* af, lapack_int* ldaf, char* equed, float* s, lapack_complex_float* b,
+                   lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx, float* rcond, float* ferr, float* berr,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zposvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* af, lapack_int* ldaf, char* equed, double* s, lapack_complex_double* b,
+                   lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx, double* rcond, double* ferr,
+                   double* berr, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_dposvxx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* af,
+                    lapack_int* ldaf, char* equed, double* s, double* b, lapack_int* ldb, double* x, lapack_int* ldx,
+                    double* rcond, double* rpvgrw, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm,
+                    double* err_bnds_comp, lapack_int* nparams, double* params, double* work, lapack_int* iwork,
+                    lapack_int* info);
+void LAPACK_sposvxx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, float* af,
+                    lapack_int* ldaf, char* equed, float* s, float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                    float* rcond, float* rpvgrw, float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
+                    float* err_bnds_comp, lapack_int* nparams, float* params, float* work, lapack_int* iwork,
+                    lapack_int* info);
+void LAPACK_zposvxx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* af, lapack_int* ldaf, char* equed, double* s, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx, double* rcond, double* rpvgrw,
+                    double* berr, lapack_int* n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                    lapack_int* nparams, double* params, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_cposvxx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* af, lapack_int* ldaf, char* equed, float* s, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx, float* rcond, float* rpvgrw, float* berr,
+                    lapack_int* n_err_bnds, float* err_bnds_norm, float* err_bnds_comp, lapack_int* nparams,
+                    float* params, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_sppsv(char* uplo, lapack_int* n, lapack_int* nrhs, float* ap, float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dppsv(char* uplo, lapack_int* n, lapack_int* nrhs, double* ap, double* b, lapack_int* ldb,
+                  lapack_int* info);
+void LAPACK_cppsv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* ap, lapack_complex_float* b,
+                  lapack_int* ldb, lapack_int* info);
+void LAPACK_zppsv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* ap, lapack_complex_double* b,
+                  lapack_int* ldb, lapack_int* info);
+void LAPACK_sppsvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, float* ap, float* afp, char* equed,
+                   float* s, float* b, lapack_int* ldb, float* x, lapack_int* ldx, float* rcond, float* ferr,
+                   float* berr, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dppsvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, double* ap, double* afp, char* equed,
+                   double* s, double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* rcond, double* ferr,
+                   double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cppsvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* ap,
+                   lapack_complex_float* afp, char* equed, float* s, lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* x, lapack_int* ldx, float* rcond, float* ferr, float* berr,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zppsvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* ap,
+                   lapack_complex_double* afp, char* equed, double* s, lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* x, lapack_int* ldx, double* rcond, double* ferr, double* berr,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_spbsv(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, float* ab, lapack_int* ldab, float* b,
+                  lapack_int* ldb, lapack_int* info);
+void LAPACK_dpbsv(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, double* ab, lapack_int* ldab, double* b,
+                  lapack_int* ldb, lapack_int* info);
+void LAPACK_cpbsv(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, lapack_complex_float* ab,
+                  lapack_int* ldab, lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zpbsv(char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, lapack_complex_double* ab,
+                  lapack_int* ldab, lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_spbsvx(char* fact, char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, float* ab, lapack_int* ldab,
+                   float* afb, lapack_int* ldafb, char* equed, float* s, float* b, lapack_int* ldb, float* x,
+                   lapack_int* ldx, float* rcond, float* ferr, float* berr, float* work, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_dpbsvx(char* fact, char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, double* ab,
+                   lapack_int* ldab, double* afb, lapack_int* ldafb, char* equed, double* s, double* b, lapack_int* ldb,
+                   double* x, lapack_int* ldx, double* rcond, double* ferr, double* berr, double* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_cpbsvx(char* fact, char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, lapack_complex_float* ab,
+                   lapack_int* ldab, lapack_complex_float* afb, lapack_int* ldafb, char* equed, float* s,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                   float* ferr, float* berr, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zpbsvx(char* fact, char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs, lapack_complex_double* ab,
+                   lapack_int* ldab, lapack_complex_double* afb, lapack_int* ldafb, char* equed, double* s,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                   double* ferr, double* berr, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sptsv(lapack_int* n, lapack_int* nrhs, float* d, float* e, float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_dptsv(lapack_int* n, lapack_int* nrhs, double* d, double* e, double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_cptsv(lapack_int* n, lapack_int* nrhs, float* d, lapack_complex_float* e, lapack_complex_float* b,
+                  lapack_int* ldb, lapack_int* info);
+void LAPACK_zptsv(lapack_int* n, lapack_int* nrhs, double* d, lapack_complex_double* e, lapack_complex_double* b,
+                  lapack_int* ldb, lapack_int* info);
+void LAPACK_sptsvx(char* fact, lapack_int* n, lapack_int* nrhs, const float* d, const float* e, float* df, float* ef,
+                   const float* b, lapack_int* ldb, float* x, lapack_int* ldx, float* rcond, float* ferr, float* berr,
+                   float* work, lapack_int* info);
+void LAPACK_dptsvx(char* fact, lapack_int* n, lapack_int* nrhs, const double* d, const double* e, double* df,
+                   double* ef, const double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
+                   double* ferr, double* berr, double* work, lapack_int* info);
+void LAPACK_cptsvx(char* fact, lapack_int* n, lapack_int* nrhs, const float* d, const lapack_complex_float* e,
+                   float* df, lapack_complex_float* ef, const lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* x, lapack_int* ldx, float* rcond, float* ferr, float* berr,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zptsvx(char* fact, lapack_int* n, lapack_int* nrhs, const double* d, const lapack_complex_double* e,
+                   double* df, lapack_complex_double* ef, const lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* x, lapack_int* ldx, double* rcond, double* ferr, double* berr,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_ssysv(char* uplo, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, lapack_int* ipiv, float* b,
+                  lapack_int* ldb, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dsysv(char* uplo, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, lapack_int* ipiv, double* b,
+                  lapack_int* ldb, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_csysv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                  lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* work,
+                  lapack_int* lwork, lapack_int* info);
+void LAPACK_zsysv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                  lapack_int* ipiv, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* work,
+                  lapack_int* lwork, lapack_int* info);
+void LAPACK_ssysvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda, float* af,
+                   lapack_int* ldaf, lapack_int* ipiv, const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                   float* rcond, float* ferr, float* berr, float* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_dsysvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda,
+                   double* af, lapack_int* ldaf, lapack_int* ipiv, const double* b, lapack_int* ldb, double* x,
+                   lapack_int* ldx, double* rcond, double* ferr, double* berr, double* work, lapack_int* lwork,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_csysvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a,
+                   lapack_int* lda, lapack_complex_float* af, lapack_int* ldaf, lapack_int* ipiv,
+                   const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                   float* rcond, float* ferr, float* berr, lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                   lapack_int* info);
+void LAPACK_zsysvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a,
+                   lapack_int* lda, lapack_complex_double* af, lapack_int* ldaf, lapack_int* ipiv,
+                   const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                   double* rcond, double* ferr, double* berr, lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int* info);
+void LAPACK_dsysvxx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* af,
+                    lapack_int* ldaf, lapack_int* ipiv, char* equed, double* s, double* b, lapack_int* ldb, double* x,
+                    lapack_int* ldx, double* rcond, double* rpvgrw, double* berr, lapack_int* n_err_bnds,
+                    double* err_bnds_norm, double* err_bnds_comp, lapack_int* nparams, double* params, double* work,
+                    lapack_int* iwork, lapack_int* info);
+void LAPACK_ssysvxx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, float* af,
+                    lapack_int* ldaf, lapack_int* ipiv, char* equed, float* s, float* b, lapack_int* ldb, float* x,
+                    lapack_int* ldx, float* rcond, float* rpvgrw, float* berr, lapack_int* n_err_bnds,
+                    float* err_bnds_norm, float* err_bnds_comp, lapack_int* nparams, float* params, float* work,
+                    lapack_int* iwork, lapack_int* info);
+void LAPACK_zsysvxx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* af, lapack_int* ldaf, lapack_int* ipiv, char* equed, double* s,
+                    lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                    double* rpvgrw, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                    lapack_int* nparams, double* params, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_csysvxx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* af, lapack_int* ldaf, lapack_int* ipiv, char* equed, float* s,
+                    lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                    float* rpvgrw, float* berr, lapack_int* n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                    lapack_int* nparams, float* params, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_chesv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                  lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* work,
+                  lapack_int* lwork, lapack_int* info);
+void LAPACK_zhesv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                  lapack_int* ipiv, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* work,
+                  lapack_int* lwork, lapack_int* info);
+void LAPACK_chesvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a,
+                   lapack_int* lda, lapack_complex_float* af, lapack_int* ldaf, lapack_int* ipiv,
+                   const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                   float* rcond, float* ferr, float* berr, lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                   lapack_int* info);
+void LAPACK_zhesvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a,
+                   lapack_int* lda, lapack_complex_double* af, lapack_int* ldaf, lapack_int* ipiv,
+                   const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                   double* rcond, double* ferr, double* berr, lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int* info);
+void LAPACK_zhesvxx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* af, lapack_int* ldaf, lapack_int* ipiv, char* equed, double* s,
+                    lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                    double* rpvgrw, double* berr, lapack_int* n_err_bnds, double* err_bnds_norm, double* err_bnds_comp,
+                    lapack_int* nparams, double* params, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_chesvxx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* af, lapack_int* ldaf, lapack_int* ipiv, char* equed, float* s,
+                    lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                    float* rpvgrw, float* berr, lapack_int* n_err_bnds, float* err_bnds_norm, float* err_bnds_comp,
+                    lapack_int* nparams, float* params, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_sspsv(char* uplo, lapack_int* n, lapack_int* nrhs, float* ap, lapack_int* ipiv, float* b, lapack_int* ldb,
+                  lapack_int* info);
+void LAPACK_dspsv(char* uplo, lapack_int* n, lapack_int* nrhs, double* ap, lapack_int* ipiv, double* b, lapack_int* ldb,
+                  lapack_int* info);
+void LAPACK_cspsv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* ap, lapack_int* ipiv,
+                  lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zspsv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* ap, lapack_int* ipiv,
+                  lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_sspsvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const float* ap, float* afp,
+                   lapack_int* ipiv, const float* b, lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
+                   float* ferr, float* berr, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dspsvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const double* ap, double* afp,
+                   lapack_int* ipiv, const double* b, lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
+                   double* ferr, double* berr, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_cspsvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* ap,
+                   lapack_complex_float* afp, lapack_int* ipiv, const lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* x, lapack_int* ldx, float* rcond, float* ferr, float* berr,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zspsvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* ap,
+                   lapack_complex_double* afp, lapack_int* ipiv, const lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* x, lapack_int* ldx, double* rcond, double* ferr, double* berr,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_chpsv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_float* ap, lapack_int* ipiv,
+                  lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zhpsv(char* uplo, lapack_int* n, lapack_int* nrhs, lapack_complex_double* ap, lapack_int* ipiv,
+                  lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_chpsvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* ap,
+                   lapack_complex_float* afp, lapack_int* ipiv, const lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* x, lapack_int* ldx, float* rcond, float* ferr, float* berr,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zhpsvx(char* fact, char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* ap,
+                   lapack_complex_double* afp, lapack_int* ipiv, const lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* x, lapack_int* ldx, double* rcond, double* ferr, double* berr,
+                   lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sgeqrf(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* tau, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dgeqrf(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* tau, double* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_cgeqrf(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* tau,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zgeqrf(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* tau,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sgeqpf(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, lapack_int* jpvt, float* tau, float* work,
+                   lapack_int* info);
+void LAPACK_dgeqpf(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, lapack_int* jpvt, double* tau,
+                   double* work, lapack_int* info);
+void LAPACK_cgeqpf(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* jpvt,
+                   lapack_complex_float* tau, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zgeqpf(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* jpvt,
+                   lapack_complex_double* tau, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sgeqp3(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, lapack_int* jpvt, float* tau, float* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_dgeqp3(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, lapack_int* jpvt, double* tau,
+                   double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cgeqp3(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* jpvt,
+                   lapack_complex_float* tau, lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                   lapack_int* info);
+void LAPACK_zgeqp3(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* jpvt,
+                   lapack_complex_double* tau, lapack_complex_double* work, lapack_int* lwork, double* rwork,
+                   lapack_int* info);
+void LAPACK_sorgqr(lapack_int* m, lapack_int* n, lapack_int* k, float* a, lapack_int* lda, const float* tau,
+                   float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dorgqr(lapack_int* m, lapack_int* n, lapack_int* k, double* a, lapack_int* lda, const double* tau,
+                   double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sormqr(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const float* a,
+                   lapack_int* lda, const float* tau, float* c, lapack_int* ldc, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dormqr(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const double* a,
+                   lapack_int* lda, const double* tau, double* c, lapack_int* ldc, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_cungqr(lapack_int* m, lapack_int* n, lapack_int* k, lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* tau, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zungqr(lapack_int* m, lapack_int* n, lapack_int* k, lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* tau, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cunmqr(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const lapack_complex_float* a,
+                   lapack_int* lda, const lapack_complex_float* tau, lapack_complex_float* c, lapack_int* ldc,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zunmqr(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const lapack_complex_double* a,
+                   lapack_int* lda, const lapack_complex_double* tau, lapack_complex_double* c, lapack_int* ldc,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sgelqf(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* tau, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dgelqf(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* tau, double* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_cgelqf(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* tau,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zgelqf(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* tau,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sorglq(lapack_int* m, lapack_int* n, lapack_int* k, float* a, lapack_int* lda, const float* tau,
+                   float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dorglq(lapack_int* m, lapack_int* n, lapack_int* k, double* a, lapack_int* lda, const double* tau,
+                   double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sormlq(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const float* a,
+                   lapack_int* lda, const float* tau, float* c, lapack_int* ldc, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dormlq(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const double* a,
+                   lapack_int* lda, const double* tau, double* c, lapack_int* ldc, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_cunglq(lapack_int* m, lapack_int* n, lapack_int* k, lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* tau, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zunglq(lapack_int* m, lapack_int* n, lapack_int* k, lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* tau, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cunmlq(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const lapack_complex_float* a,
+                   lapack_int* lda, const lapack_complex_float* tau, lapack_complex_float* c, lapack_int* ldc,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zunmlq(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const lapack_complex_double* a,
+                   lapack_int* lda, const lapack_complex_double* tau, lapack_complex_double* c, lapack_int* ldc,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sgeqlf(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* tau, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dgeqlf(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* tau, double* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_cgeqlf(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* tau,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zgeqlf(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* tau,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sorgql(lapack_int* m, lapack_int* n, lapack_int* k, float* a, lapack_int* lda, const float* tau,
+                   float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dorgql(lapack_int* m, lapack_int* n, lapack_int* k, double* a, lapack_int* lda, const double* tau,
+                   double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cungql(lapack_int* m, lapack_int* n, lapack_int* k, lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* tau, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zungql(lapack_int* m, lapack_int* n, lapack_int* k, lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* tau, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sormql(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const float* a,
+                   lapack_int* lda, const float* tau, float* c, lapack_int* ldc, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dormql(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const double* a,
+                   lapack_int* lda, const double* tau, double* c, lapack_int* ldc, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_cunmql(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const lapack_complex_float* a,
+                   lapack_int* lda, const lapack_complex_float* tau, lapack_complex_float* c, lapack_int* ldc,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zunmql(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const lapack_complex_double* a,
+                   lapack_int* lda, const lapack_complex_double* tau, lapack_complex_double* c, lapack_int* ldc,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sgerqf(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* tau, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dgerqf(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* tau, double* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_cgerqf(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* tau,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zgerqf(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* tau,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sorgrq(lapack_int* m, lapack_int* n, lapack_int* k, float* a, lapack_int* lda, const float* tau,
+                   float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dorgrq(lapack_int* m, lapack_int* n, lapack_int* k, double* a, lapack_int* lda, const double* tau,
+                   double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cungrq(lapack_int* m, lapack_int* n, lapack_int* k, lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* tau, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zungrq(lapack_int* m, lapack_int* n, lapack_int* k, lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* tau, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sormrq(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const float* a,
+                   lapack_int* lda, const float* tau, float* c, lapack_int* ldc, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dormrq(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const double* a,
+                   lapack_int* lda, const double* tau, double* c, lapack_int* ldc, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_cunmrq(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const lapack_complex_float* a,
+                   lapack_int* lda, const lapack_complex_float* tau, lapack_complex_float* c, lapack_int* ldc,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zunmrq(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const lapack_complex_double* a,
+                   lapack_int* lda, const lapack_complex_double* tau, lapack_complex_double* c, lapack_int* ldc,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_stzrzf(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* tau, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dtzrzf(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* tau, double* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_ctzrzf(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* tau,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_ztzrzf(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* tau,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sormrz(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l, const float* a,
+                   lapack_int* lda, const float* tau, float* c, lapack_int* ldc, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dormrz(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l, const double* a,
+                   lapack_int* lda, const double* tau, double* c, lapack_int* ldc, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_cunmrz(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
+                   const lapack_complex_float* a, lapack_int* lda, const lapack_complex_float* tau,
+                   lapack_complex_float* c, lapack_int* ldc, lapack_complex_float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_zunmrz(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
+                   const lapack_complex_double* a, lapack_int* lda, const lapack_complex_double* tau,
+                   lapack_complex_double* c, lapack_int* ldc, lapack_complex_double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_sggqrf(lapack_int* n, lapack_int* m, lapack_int* p, float* a, lapack_int* lda, float* taua, float* b,
+                   lapack_int* ldb, float* taub, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dggqrf(lapack_int* n, lapack_int* m, lapack_int* p, double* a, lapack_int* lda, double* taua, double* b,
+                   lapack_int* ldb, double* taub, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cggqrf(lapack_int* n, lapack_int* m, lapack_int* p, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* taua, lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* taub,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zggqrf(lapack_int* n, lapack_int* m, lapack_int* p, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* taua, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* taub,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sggrqf(lapack_int* m, lapack_int* p, lapack_int* n, float* a, lapack_int* lda, float* taua, float* b,
+                   lapack_int* ldb, float* taub, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dggrqf(lapack_int* m, lapack_int* p, lapack_int* n, double* a, lapack_int* lda, double* taua, double* b,
+                   lapack_int* ldb, double* taub, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cggrqf(lapack_int* m, lapack_int* p, lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* taua, lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* taub,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zggrqf(lapack_int* m, lapack_int* p, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* taua, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* taub,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sgebrd(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* d, float* e, float* tauq,
+                   float* taup, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dgebrd(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* d, double* e, double* tauq,
+                   double* taup, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cgebrd(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, float* d, float* e,
+                   lapack_complex_float* tauq, lapack_complex_float* taup, lapack_complex_float* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_zgebrd(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, double* d, double* e,
+                   lapack_complex_double* tauq, lapack_complex_double* taup, lapack_complex_double* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_sgbbrd(char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc, lapack_int* kl, lapack_int* ku, float* ab,
+                   lapack_int* ldab, float* d, float* e, float* q, lapack_int* ldq, float* pt, lapack_int* ldpt,
+                   float* c, lapack_int* ldc, float* work, lapack_int* info);
+void LAPACK_dgbbrd(char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc, lapack_int* kl, lapack_int* ku,
+                   double* ab, lapack_int* ldab, double* d, double* e, double* q, lapack_int* ldq, double* pt,
+                   lapack_int* ldpt, double* c, lapack_int* ldc, double* work, lapack_int* info);
+void LAPACK_cgbbrd(char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc, lapack_int* kl, lapack_int* ku,
+                   lapack_complex_float* ab, lapack_int* ldab, float* d, float* e, lapack_complex_float* q,
+                   lapack_int* ldq, lapack_complex_float* pt, lapack_int* ldpt, lapack_complex_float* c,
+                   lapack_int* ldc, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zgbbrd(char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc, lapack_int* kl, lapack_int* ku,
+                   lapack_complex_double* ab, lapack_int* ldab, double* d, double* e, lapack_complex_double* q,
+                   lapack_int* ldq, lapack_complex_double* pt, lapack_int* ldpt, lapack_complex_double* c,
+                   lapack_int* ldc, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sorgbr(char* vect, lapack_int* m, lapack_int* n, lapack_int* k, float* a, lapack_int* lda, const float* tau,
+                   float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dorgbr(char* vect, lapack_int* m, lapack_int* n, lapack_int* k, double* a, lapack_int* lda,
+                   const double* tau, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sormbr(char* vect, char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const float* a,
+                   lapack_int* lda, const float* tau, float* c, lapack_int* ldc, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_dormbr(char* vect, char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, const double* a,
+                   lapack_int* lda, const double* tau, double* c, lapack_int* ldc, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_cungbr(char* vect, lapack_int* m, lapack_int* n, lapack_int* k, lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* tau, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zungbr(char* vect, lapack_int* m, lapack_int* n, lapack_int* k, lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* tau, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cunmbr(char* vect, char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k,
+                   const lapack_complex_float* a, lapack_int* lda, const lapack_complex_float* tau,
+                   lapack_complex_float* c, lapack_int* ldc, lapack_complex_float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_zunmbr(char* vect, char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k,
+                   const lapack_complex_double* a, lapack_int* lda, const lapack_complex_double* tau,
+                   lapack_complex_double* c, lapack_int* ldc, lapack_complex_double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_sbdsqr(char* uplo, lapack_int* n, lapack_int* ncvt, lapack_int* nru, lapack_int* ncc, float* d, float* e,
+                   float* vt, lapack_int* ldvt, float* u, lapack_int* ldu, float* c, lapack_int* ldc, float* work,
+                   lapack_int* info);
+void LAPACK_dbdsqr(char* uplo, lapack_int* n, lapack_int* ncvt, lapack_int* nru, lapack_int* ncc, double* d, double* e,
+                   double* vt, lapack_int* ldvt, double* u, lapack_int* ldu, double* c, lapack_int* ldc, double* work,
+                   lapack_int* info);
+void LAPACK_cbdsqr(char* uplo, lapack_int* n, lapack_int* ncvt, lapack_int* nru, lapack_int* ncc, float* d, float* e,
+                   lapack_complex_float* vt, lapack_int* ldvt, lapack_complex_float* u, lapack_int* ldu,
+                   lapack_complex_float* c, lapack_int* ldc, float* work, lapack_int* info);
+void LAPACK_zbdsqr(char* uplo, lapack_int* n, lapack_int* ncvt, lapack_int* nru, lapack_int* ncc, double* d, double* e,
+                   lapack_complex_double* vt, lapack_int* ldvt, lapack_complex_double* u, lapack_int* ldu,
+                   lapack_complex_double* c, lapack_int* ldc, double* work, lapack_int* info);
+void LAPACK_sbdsdc(char* uplo, char* compq, lapack_int* n, float* d, float* e, float* u, lapack_int* ldu, float* vt,
+                   lapack_int* ldvt, float* q, lapack_int* iq, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dbdsdc(char* uplo, char* compq, lapack_int* n, double* d, double* e, double* u, lapack_int* ldu, double* vt,
+                   lapack_int* ldvt, double* q, lapack_int* iq, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_ssytrd(char* uplo, lapack_int* n, float* a, lapack_int* lda, float* d, float* e, float* tau, float* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_dsytrd(char* uplo, lapack_int* n, double* a, lapack_int* lda, double* d, double* e, double* tau,
+                   double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sorgtr(char* uplo, lapack_int* n, float* a, lapack_int* lda, const float* tau, float* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_dorgtr(char* uplo, lapack_int* n, double* a, lapack_int* lda, const double* tau, double* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_sormtr(char* side, char* uplo, char* trans, lapack_int* m, lapack_int* n, const float* a, lapack_int* lda,
+                   const float* tau, float* c, lapack_int* ldc, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dormtr(char* side, char* uplo, char* trans, lapack_int* m, lapack_int* n, const double* a, lapack_int* lda,
+                   const double* tau, double* c, lapack_int* ldc, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_chetrd(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, float* d, float* e,
+                   lapack_complex_float* tau, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zhetrd(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, double* d, double* e,
+                   lapack_complex_double* tau, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cungtr(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, const lapack_complex_float* tau,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zungtr(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* tau, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cunmtr(char* side, char* uplo, char* trans, lapack_int* m, lapack_int* n, const lapack_complex_float* a,
+                   lapack_int* lda, const lapack_complex_float* tau, lapack_complex_float* c, lapack_int* ldc,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zunmtr(char* side, char* uplo, char* trans, lapack_int* m, lapack_int* n, const lapack_complex_double* a,
+                   lapack_int* lda, const lapack_complex_double* tau, lapack_complex_double* c, lapack_int* ldc,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_ssptrd(char* uplo, lapack_int* n, float* ap, float* d, float* e, float* tau, lapack_int* info);
+void LAPACK_dsptrd(char* uplo, lapack_int* n, double* ap, double* d, double* e, double* tau, lapack_int* info);
+void LAPACK_sopgtr(char* uplo, lapack_int* n, const float* ap, const float* tau, float* q, lapack_int* ldq, float* work,
+                   lapack_int* info);
+void LAPACK_dopgtr(char* uplo, lapack_int* n, const double* ap, const double* tau, double* q, lapack_int* ldq,
+                   double* work, lapack_int* info);
+void LAPACK_sopmtr(char* side, char* uplo, char* trans, lapack_int* m, lapack_int* n, const float* ap, const float* tau,
+                   float* c, lapack_int* ldc, float* work, lapack_int* info);
+void LAPACK_dopmtr(char* side, char* uplo, char* trans, lapack_int* m, lapack_int* n, const double* ap,
+                   const double* tau, double* c, lapack_int* ldc, double* work, lapack_int* info);
+void LAPACK_chptrd(char* uplo, lapack_int* n, lapack_complex_float* ap, float* d, float* e, lapack_complex_float* tau,
+                   lapack_int* info);
+void LAPACK_zhptrd(char* uplo, lapack_int* n, lapack_complex_double* ap, double* d, double* e,
+                   lapack_complex_double* tau, lapack_int* info);
+void LAPACK_cupgtr(char* uplo, lapack_int* n, const lapack_complex_float* ap, const lapack_complex_float* tau,
+                   lapack_complex_float* q, lapack_int* ldq, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zupgtr(char* uplo, lapack_int* n, const lapack_complex_double* ap, const lapack_complex_double* tau,
+                   lapack_complex_double* q, lapack_int* ldq, lapack_complex_double* work, lapack_int* info);
+void LAPACK_cupmtr(char* side, char* uplo, char* trans, lapack_int* m, lapack_int* n, const lapack_complex_float* ap,
+                   const lapack_complex_float* tau, lapack_complex_float* c, lapack_int* ldc,
+                   lapack_complex_float* work, lapack_int* info);
+void LAPACK_zupmtr(char* side, char* uplo, char* trans, lapack_int* m, lapack_int* n, const lapack_complex_double* ap,
+                   const lapack_complex_double* tau, lapack_complex_double* c, lapack_int* ldc,
+                   lapack_complex_double* work, lapack_int* info);
+void LAPACK_ssbtrd(char* vect, char* uplo, lapack_int* n, lapack_int* kd, float* ab, lapack_int* ldab, float* d,
+                   float* e, float* q, lapack_int* ldq, float* work, lapack_int* info);
+void LAPACK_dsbtrd(char* vect, char* uplo, lapack_int* n, lapack_int* kd, double* ab, lapack_int* ldab, double* d,
+                   double* e, double* q, lapack_int* ldq, double* work, lapack_int* info);
+void LAPACK_chbtrd(char* vect, char* uplo, lapack_int* n, lapack_int* kd, lapack_complex_float* ab, lapack_int* ldab,
+                   float* d, float* e, lapack_complex_float* q, lapack_int* ldq, lapack_complex_float* work,
+                   lapack_int* info);
+void LAPACK_zhbtrd(char* vect, char* uplo, lapack_int* n, lapack_int* kd, lapack_complex_double* ab, lapack_int* ldab,
+                   double* d, double* e, lapack_complex_double* q, lapack_int* ldq, lapack_complex_double* work,
+                   lapack_int* info);
+void LAPACK_ssterf(lapack_int* n, float* d, float* e, lapack_int* info);
+void LAPACK_dsterf(lapack_int* n, double* d, double* e, lapack_int* info);
+void LAPACK_ssteqr(char* compz, lapack_int* n, float* d, float* e, float* z, lapack_int* ldz, float* work,
+                   lapack_int* info);
+void LAPACK_dsteqr(char* compz, lapack_int* n, double* d, double* e, double* z, lapack_int* ldz, double* work,
+                   lapack_int* info);
+void LAPACK_csteqr(char* compz, lapack_int* n, float* d, float* e, lapack_complex_float* z, lapack_int* ldz,
+                   float* work, lapack_int* info);
+void LAPACK_zsteqr(char* compz, lapack_int* n, double* d, double* e, lapack_complex_double* z, lapack_int* ldz,
+                   double* work, lapack_int* info);
+void LAPACK_sstemr(char* jobz, char* range, lapack_int* n, float* d, float* e, float* vl, float* vu, lapack_int* il,
+                   lapack_int* iu, lapack_int* m, float* w, float* z, lapack_int* ldz, lapack_int* nzc,
+                   lapack_int* isuppz, lapack_logical* tryrac, float* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_dstemr(char* jobz, char* range, lapack_int* n, double* d, double* e, double* vl, double* vu, lapack_int* il,
+                   lapack_int* iu, lapack_int* m, double* w, double* z, lapack_int* ldz, lapack_int* nzc,
+                   lapack_int* isuppz, lapack_logical* tryrac, double* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_cstemr(char* jobz, char* range, lapack_int* n, float* d, float* e, float* vl, float* vu, lapack_int* il,
+                   lapack_int* iu, lapack_int* m, float* w, lapack_complex_float* z, lapack_int* ldz, lapack_int* nzc,
+                   lapack_int* isuppz, lapack_logical* tryrac, float* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_zstemr(char* jobz, char* range, lapack_int* n, double* d, double* e, double* vl, double* vu, lapack_int* il,
+                   lapack_int* iu, lapack_int* m, double* w, lapack_complex_double* z, lapack_int* ldz, lapack_int* nzc,
+                   lapack_int* isuppz, lapack_logical* tryrac, double* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_sstedc(char* compz, lapack_int* n, float* d, float* e, float* z, lapack_int* ldz, float* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_dstedc(char* compz, lapack_int* n, double* d, double* e, double* z, lapack_int* ldz, double* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_cstedc(char* compz, lapack_int* n, float* d, float* e, lapack_complex_float* z, lapack_int* ldz,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* lrwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_zstedc(char* compz, lapack_int* n, double* d, double* e, lapack_complex_double* z, lapack_int* ldz,
+                   lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_sstegr(char* jobz, char* range, lapack_int* n, float* d, float* e, float* vl, float* vu, lapack_int* il,
+                   lapack_int* iu, float* abstol, lapack_int* m, float* w, float* z, lapack_int* ldz,
+                   lapack_int* isuppz, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_dstegr(char* jobz, char* range, lapack_int* n, double* d, double* e, double* vl, double* vu, lapack_int* il,
+                   lapack_int* iu, double* abstol, lapack_int* m, double* w, double* z, lapack_int* ldz,
+                   lapack_int* isuppz, double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_cstegr(char* jobz, char* range, lapack_int* n, float* d, float* e, float* vl, float* vu, lapack_int* il,
+                   lapack_int* iu, float* abstol, lapack_int* m, float* w, lapack_complex_float* z, lapack_int* ldz,
+                   lapack_int* isuppz, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_zstegr(char* jobz, char* range, lapack_int* n, double* d, double* e, double* vl, double* vu, lapack_int* il,
+                   lapack_int* iu, double* abstol, lapack_int* m, double* w, lapack_complex_double* z, lapack_int* ldz,
+                   lapack_int* isuppz, double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_spteqr(char* compz, lapack_int* n, float* d, float* e, float* z, lapack_int* ldz, float* work,
+                   lapack_int* info);
+void LAPACK_dpteqr(char* compz, lapack_int* n, double* d, double* e, double* z, lapack_int* ldz, double* work,
+                   lapack_int* info);
+void LAPACK_cpteqr(char* compz, lapack_int* n, float* d, float* e, lapack_complex_float* z, lapack_int* ldz,
+                   float* work, lapack_int* info);
+void LAPACK_zpteqr(char* compz, lapack_int* n, double* d, double* e, lapack_complex_double* z, lapack_int* ldz,
+                   double* work, lapack_int* info);
+void LAPACK_sstebz(char* range, char* order, lapack_int* n, float* vl, float* vu, lapack_int* il, lapack_int* iu,
+                   float* abstol, const float* d, const float* e, lapack_int* m, lapack_int* nsplit, float* w,
+                   lapack_int* iblock, lapack_int* isplit, float* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_dstebz(char* range, char* order, lapack_int* n, double* vl, double* vu, lapack_int* il, lapack_int* iu,
+                   double* abstol, const double* d, const double* e, lapack_int* m, lapack_int* nsplit, double* w,
+                   lapack_int* iblock, lapack_int* isplit, double* work, lapack_int* iwork, lapack_int* info);
+void LAPACK_sstein(lapack_int* n, const float* d, const float* e, lapack_int* m, const float* w,
+                   const lapack_int* iblock, const lapack_int* isplit, float* z, lapack_int* ldz, float* work,
+                   lapack_int* iwork, lapack_int* ifailv, lapack_int* info);
+void LAPACK_dstein(lapack_int* n, const double* d, const double* e, lapack_int* m, const double* w,
+                   const lapack_int* iblock, const lapack_int* isplit, double* z, lapack_int* ldz, double* work,
+                   lapack_int* iwork, lapack_int* ifailv, lapack_int* info);
+void LAPACK_cstein(lapack_int* n, const float* d, const float* e, lapack_int* m, const float* w,
+                   const lapack_int* iblock, const lapack_int* isplit, lapack_complex_float* z, lapack_int* ldz,
+                   float* work, lapack_int* iwork, lapack_int* ifailv, lapack_int* info);
+void LAPACK_zstein(lapack_int* n, const double* d, const double* e, lapack_int* m, const double* w,
+                   const lapack_int* iblock, const lapack_int* isplit, lapack_complex_double* z, lapack_int* ldz,
+                   double* work, lapack_int* iwork, lapack_int* ifailv, lapack_int* info);
+void LAPACK_sdisna(char* job, lapack_int* m, lapack_int* n, const float* d, float* sep, lapack_int* info);
+void LAPACK_ddisna(char* job, lapack_int* m, lapack_int* n, const double* d, double* sep, lapack_int* info);
+void LAPACK_ssygst(lapack_int* itype, char* uplo, lapack_int* n, float* a, lapack_int* lda, const float* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_dsygst(lapack_int* itype, char* uplo, lapack_int* n, double* a, lapack_int* lda, const double* b,
+                   lapack_int* ldb, lapack_int* info);
+void LAPACK_chegst(lapack_int* itype, char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_zhegst(lapack_int* itype, char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* b, lapack_int* ldb, lapack_int* info);
+void LAPACK_sspgst(lapack_int* itype, char* uplo, lapack_int* n, float* ap, const float* bp, lapack_int* info);
+void LAPACK_dspgst(lapack_int* itype, char* uplo, lapack_int* n, double* ap, const double* bp, lapack_int* info);
+void LAPACK_chpgst(lapack_int* itype, char* uplo, lapack_int* n, lapack_complex_float* ap,
+                   const lapack_complex_float* bp, lapack_int* info);
+void LAPACK_zhpgst(lapack_int* itype, char* uplo, lapack_int* n, lapack_complex_double* ap,
+                   const lapack_complex_double* bp, lapack_int* info);
+void LAPACK_ssbgst(char* vect, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, float* ab, lapack_int* ldab,
+                   const float* bb, lapack_int* ldbb, float* x, lapack_int* ldx, float* work, lapack_int* info);
+void LAPACK_dsbgst(char* vect, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, double* ab, lapack_int* ldab,
+                   const double* bb, lapack_int* ldbb, double* x, lapack_int* ldx, double* work, lapack_int* info);
+void LAPACK_chbgst(char* vect, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, lapack_complex_float* ab,
+                   lapack_int* ldab, const lapack_complex_float* bb, lapack_int* ldbb, lapack_complex_float* x,
+                   lapack_int* ldx, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zhbgst(char* vect, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, lapack_complex_double* ab,
+                   lapack_int* ldab, const lapack_complex_double* bb, lapack_int* ldbb, lapack_complex_double* x,
+                   lapack_int* ldx, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_spbstf(char* uplo, lapack_int* n, lapack_int* kb, float* bb, lapack_int* ldbb, lapack_int* info);
+void LAPACK_dpbstf(char* uplo, lapack_int* n, lapack_int* kb, double* bb, lapack_int* ldbb, lapack_int* info);
+void LAPACK_cpbstf(char* uplo, lapack_int* n, lapack_int* kb, lapack_complex_float* bb, lapack_int* ldbb,
+                   lapack_int* info);
+void LAPACK_zpbstf(char* uplo, lapack_int* n, lapack_int* kb, lapack_complex_double* bb, lapack_int* ldbb,
+                   lapack_int* info);
+void LAPACK_sgehrd(lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* a, lapack_int* lda, float* tau, float* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_dgehrd(lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* a, lapack_int* lda, double* tau,
+                   double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cgehrd(lapack_int* n, lapack_int* ilo, lapack_int* ihi, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* tau, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zgehrd(lapack_int* n, lapack_int* ilo, lapack_int* ihi, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* tau, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sorghr(lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* a, lapack_int* lda, const float* tau,
+                   float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dorghr(lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* a, lapack_int* lda, const double* tau,
+                   double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sormhr(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* ilo, lapack_int* ihi,
+                   const float* a, lapack_int* lda, const float* tau, float* c, lapack_int* ldc, float* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_dormhr(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* ilo, lapack_int* ihi,
+                   const double* a, lapack_int* lda, const double* tau, double* c, lapack_int* ldc, double* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_cunghr(lapack_int* n, lapack_int* ilo, lapack_int* ihi, lapack_complex_float* a, lapack_int* lda,
+                   const lapack_complex_float* tau, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zunghr(lapack_int* n, lapack_int* ilo, lapack_int* ihi, lapack_complex_double* a, lapack_int* lda,
+                   const lapack_complex_double* tau, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cunmhr(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* ilo, lapack_int* ihi,
+                   const lapack_complex_float* a, lapack_int* lda, const lapack_complex_float* tau,
+                   lapack_complex_float* c, lapack_int* ldc, lapack_complex_float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_zunmhr(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* ilo, lapack_int* ihi,
+                   const lapack_complex_double* a, lapack_int* lda, const lapack_complex_double* tau,
+                   lapack_complex_double* c, lapack_int* ldc, lapack_complex_double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_sgebal(char* job, lapack_int* n, float* a, lapack_int* lda, lapack_int* ilo, lapack_int* ihi, float* scale,
+                   lapack_int* info);
+void LAPACK_dgebal(char* job, lapack_int* n, double* a, lapack_int* lda, lapack_int* ilo, lapack_int* ihi,
+                   double* scale, lapack_int* info);
+void LAPACK_cgebal(char* job, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* ilo, lapack_int* ihi,
+                   float* scale, lapack_int* info);
+void LAPACK_zgebal(char* job, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* ilo,
+                   lapack_int* ihi, double* scale, lapack_int* info);
+void LAPACK_sgebak(char* job, char* side, lapack_int* n, lapack_int* ilo, lapack_int* ihi, const float* scale,
+                   lapack_int* m, float* v, lapack_int* ldv, lapack_int* info);
+void LAPACK_dgebak(char* job, char* side, lapack_int* n, lapack_int* ilo, lapack_int* ihi, const double* scale,
+                   lapack_int* m, double* v, lapack_int* ldv, lapack_int* info);
+void LAPACK_cgebak(char* job, char* side, lapack_int* n, lapack_int* ilo, lapack_int* ihi, const float* scale,
+                   lapack_int* m, lapack_complex_float* v, lapack_int* ldv, lapack_int* info);
+void LAPACK_zgebak(char* job, char* side, lapack_int* n, lapack_int* ilo, lapack_int* ihi, const double* scale,
+                   lapack_int* m, lapack_complex_double* v, lapack_int* ldv, lapack_int* info);
+void LAPACK_shseqr(char* job, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* h, lapack_int* ldh,
+                   float* wr, float* wi, float* z, lapack_int* ldz, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dhseqr(char* job, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* h, lapack_int* ldh,
+                   double* wr, double* wi, double* z, lapack_int* ldz, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_chseqr(char* job, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi, lapack_complex_float* h,
+                   lapack_int* ldh, lapack_complex_float* w, lapack_complex_float* z, lapack_int* ldz,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zhseqr(char* job, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi, lapack_complex_double* h,
+                   lapack_int* ldh, lapack_complex_double* w, lapack_complex_double* z, lapack_int* ldz,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_shsein(char* job, char* eigsrc, char* initv, lapack_logical* select, lapack_int* n, const float* h,
+                   lapack_int* ldh, float* wr, const float* wi, float* vl, lapack_int* ldvl, float* vr,
+                   lapack_int* ldvr, lapack_int* mm, lapack_int* m, float* work, lapack_int* ifaill, lapack_int* ifailr,
+                   lapack_int* info);
+void LAPACK_dhsein(char* job, char* eigsrc, char* initv, lapack_logical* select, lapack_int* n, const double* h,
+                   lapack_int* ldh, double* wr, const double* wi, double* vl, lapack_int* ldvl, double* vr,
+                   lapack_int* ldvr, lapack_int* mm, lapack_int* m, double* work, lapack_int* ifaill,
+                   lapack_int* ifailr, lapack_int* info);
+void LAPACK_chsein(char* job, char* eigsrc, char* initv, const lapack_logical* select, lapack_int* n,
+                   const lapack_complex_float* h, lapack_int* ldh, lapack_complex_float* w, lapack_complex_float* vl,
+                   lapack_int* ldvl, lapack_complex_float* vr, lapack_int* ldvr, lapack_int* mm, lapack_int* m,
+                   lapack_complex_float* work, float* rwork, lapack_int* ifaill, lapack_int* ifailr, lapack_int* info);
+void LAPACK_zhsein(char* job, char* eigsrc, char* initv, const lapack_logical* select, lapack_int* n,
+                   const lapack_complex_double* h, lapack_int* ldh, lapack_complex_double* w, lapack_complex_double* vl,
+                   lapack_int* ldvl, lapack_complex_double* vr, lapack_int* ldvr, lapack_int* mm, lapack_int* m,
+                   lapack_complex_double* work, double* rwork, lapack_int* ifaill, lapack_int* ifailr,
+                   lapack_int* info);
+void LAPACK_strevc(char* side, char* howmny, lapack_logical* select, lapack_int* n, const float* t, lapack_int* ldt,
+                   float* vl, lapack_int* ldvl, float* vr, lapack_int* ldvr, lapack_int* mm, lapack_int* m, float* work,
+                   lapack_int* info);
+void LAPACK_dtrevc(char* side, char* howmny, lapack_logical* select, lapack_int* n, const double* t, lapack_int* ldt,
+                   double* vl, lapack_int* ldvl, double* vr, lapack_int* ldvr, lapack_int* mm, lapack_int* m,
+                   double* work, lapack_int* info);
+void LAPACK_ctrevc(char* side, char* howmny, const lapack_logical* select, lapack_int* n, lapack_complex_float* t,
+                   lapack_int* ldt, lapack_complex_float* vl, lapack_int* ldvl, lapack_complex_float* vr,
+                   lapack_int* ldvr, lapack_int* mm, lapack_int* m, lapack_complex_float* work, float* rwork,
+                   lapack_int* info);
+void LAPACK_ztrevc(char* side, char* howmny, const lapack_logical* select, lapack_int* n, lapack_complex_double* t,
+                   lapack_int* ldt, lapack_complex_double* vl, lapack_int* ldvl, lapack_complex_double* vr,
+                   lapack_int* ldvr, lapack_int* mm, lapack_int* m, lapack_complex_double* work, double* rwork,
+                   lapack_int* info);
+void LAPACK_strsna(char* job, char* howmny, const lapack_logical* select, lapack_int* n, const float* t,
+                   lapack_int* ldt, const float* vl, lapack_int* ldvl, const float* vr, lapack_int* ldvr, float* s,
+                   float* sep, lapack_int* mm, lapack_int* m, float* work, lapack_int* ldwork, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_dtrsna(char* job, char* howmny, const lapack_logical* select, lapack_int* n, const double* t,
+                   lapack_int* ldt, const double* vl, lapack_int* ldvl, const double* vr, lapack_int* ldvr, double* s,
+                   double* sep, lapack_int* mm, lapack_int* m, double* work, lapack_int* ldwork, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_ctrsna(char* job, char* howmny, const lapack_logical* select, lapack_int* n, const lapack_complex_float* t,
+                   lapack_int* ldt, const lapack_complex_float* vl, lapack_int* ldvl, const lapack_complex_float* vr,
+                   lapack_int* ldvr, float* s, float* sep, lapack_int* mm, lapack_int* m, lapack_complex_float* work,
+                   lapack_int* ldwork, float* rwork, lapack_int* info);
+void LAPACK_ztrsna(char* job, char* howmny, const lapack_logical* select, lapack_int* n, const lapack_complex_double* t,
+                   lapack_int* ldt, const lapack_complex_double* vl, lapack_int* ldvl, const lapack_complex_double* vr,
+                   lapack_int* ldvr, double* s, double* sep, lapack_int* mm, lapack_int* m, lapack_complex_double* work,
+                   lapack_int* ldwork, double* rwork, lapack_int* info);
+void LAPACK_strexc(char* compq, lapack_int* n, float* t, lapack_int* ldt, float* q, lapack_int* ldq, lapack_int* ifst,
+                   lapack_int* ilst, float* work, lapack_int* info);
+void LAPACK_dtrexc(char* compq, lapack_int* n, double* t, lapack_int* ldt, double* q, lapack_int* ldq, lapack_int* ifst,
+                   lapack_int* ilst, double* work, lapack_int* info);
+void LAPACK_ctrexc(char* compq, lapack_int* n, lapack_complex_float* t, lapack_int* ldt, lapack_complex_float* q,
+                   lapack_int* ldq, lapack_int* ifst, lapack_int* ilst, lapack_int* info);
+void LAPACK_ztrexc(char* compq, lapack_int* n, lapack_complex_double* t, lapack_int* ldt, lapack_complex_double* q,
+                   lapack_int* ldq, lapack_int* ifst, lapack_int* ilst, lapack_int* info);
+void LAPACK_strsen(char* job, char* compq, const lapack_logical* select, lapack_int* n, float* t, lapack_int* ldt,
+                   float* q, lapack_int* ldq, float* wr, float* wi, lapack_int* m, float* s, float* sep, float* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_dtrsen(char* job, char* compq, const lapack_logical* select, lapack_int* n, double* t, lapack_int* ldt,
+                   double* q, lapack_int* ldq, double* wr, double* wi, lapack_int* m, double* s, double* sep,
+                   double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_ctrsen(char* job, char* compq, const lapack_logical* select, lapack_int* n, lapack_complex_float* t,
+                   lapack_int* ldt, lapack_complex_float* q, lapack_int* ldq, lapack_complex_float* w, lapack_int* m,
+                   float* s, float* sep, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_ztrsen(char* job, char* compq, const lapack_logical* select, lapack_int* n, lapack_complex_double* t,
+                   lapack_int* ldt, lapack_complex_double* q, lapack_int* ldq, lapack_complex_double* w, lapack_int* m,
+                   double* s, double* sep, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_strsyl(char* trana, char* tranb, lapack_int* isgn, lapack_int* m, lapack_int* n, const float* a,
+                   lapack_int* lda, const float* b, lapack_int* ldb, float* c, lapack_int* ldc, float* scale,
+                   lapack_int* info);
+void LAPACK_dtrsyl(char* trana, char* tranb, lapack_int* isgn, lapack_int* m, lapack_int* n, const double* a,
+                   lapack_int* lda, const double* b, lapack_int* ldb, double* c, lapack_int* ldc, double* scale,
+                   lapack_int* info);
+void LAPACK_ctrsyl(char* trana, char* tranb, lapack_int* isgn, lapack_int* m, lapack_int* n,
+                   const lapack_complex_float* a, lapack_int* lda, const lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* c, lapack_int* ldc, float* scale, lapack_int* info);
+void LAPACK_ztrsyl(char* trana, char* tranb, lapack_int* isgn, lapack_int* m, lapack_int* n,
+                   const lapack_complex_double* a, lapack_int* lda, const lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* c, lapack_int* ldc, double* scale, lapack_int* info);
+void LAPACK_sgghrd(char* compq, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* a, lapack_int* lda,
+                   float* b, lapack_int* ldb, float* q, lapack_int* ldq, float* z, lapack_int* ldz, lapack_int* info);
+void LAPACK_dgghrd(char* compq, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* a,
+                   lapack_int* lda, double* b, lapack_int* ldb, double* q, lapack_int* ldq, double* z, lapack_int* ldz,
+                   lapack_int* info);
+void LAPACK_cgghrd(char* compq, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi, lapack_complex_float* a,
+                   lapack_int* lda, lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* q, lapack_int* ldq,
+                   lapack_complex_float* z, lapack_int* ldz, lapack_int* info);
+void LAPACK_zgghrd(char* compq, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi, lapack_complex_double* a,
+                   lapack_int* lda, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* q,
+                   lapack_int* ldq, lapack_complex_double* z, lapack_int* ldz, lapack_int* info);
+void LAPACK_sggbal(char* job, lapack_int* n, float* a, lapack_int* lda, float* b, lapack_int* ldb, lapack_int* ilo,
+                   lapack_int* ihi, float* lscale, float* rscale, float* work, lapack_int* info);
+void LAPACK_dggbal(char* job, lapack_int* n, double* a, lapack_int* lda, double* b, lapack_int* ldb, lapack_int* ilo,
+                   lapack_int* ihi, double* lscale, double* rscale, double* work, lapack_int* info);
+void LAPACK_cggbal(char* job, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* b,
+                   lapack_int* ldb, lapack_int* ilo, lapack_int* ihi, float* lscale, float* rscale, float* work,
+                   lapack_int* info);
+void LAPACK_zggbal(char* job, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* b,
+                   lapack_int* ldb, lapack_int* ilo, lapack_int* ihi, double* lscale, double* rscale, double* work,
+                   lapack_int* info);
+void LAPACK_sggbak(char* job, char* side, lapack_int* n, lapack_int* ilo, lapack_int* ihi, const float* lscale,
+                   const float* rscale, lapack_int* m, float* v, lapack_int* ldv, lapack_int* info);
+void LAPACK_dggbak(char* job, char* side, lapack_int* n, lapack_int* ilo, lapack_int* ihi, const double* lscale,
+                   const double* rscale, lapack_int* m, double* v, lapack_int* ldv, lapack_int* info);
+void LAPACK_cggbak(char* job, char* side, lapack_int* n, lapack_int* ilo, lapack_int* ihi, const float* lscale,
+                   const float* rscale, lapack_int* m, lapack_complex_float* v, lapack_int* ldv, lapack_int* info);
+void LAPACK_zggbak(char* job, char* side, lapack_int* n, lapack_int* ilo, lapack_int* ihi, const double* lscale,
+                   const double* rscale, lapack_int* m, lapack_complex_double* v, lapack_int* ldv, lapack_int* info);
+void LAPACK_shgeqz(char* job, char* compq, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* h,
+                   lapack_int* ldh, float* t, lapack_int* ldt, float* alphar, float* alphai, float* beta, float* q,
+                   lapack_int* ldq, float* z, lapack_int* ldz, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dhgeqz(char* job, char* compq, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* h,
+                   lapack_int* ldh, double* t, lapack_int* ldt, double* alphar, double* alphai, double* beta, double* q,
+                   lapack_int* ldq, double* z, lapack_int* ldz, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_chgeqz(char* job, char* compq, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi,
+                   lapack_complex_float* h, lapack_int* ldh, lapack_complex_float* t, lapack_int* ldt,
+                   lapack_complex_float* alpha, lapack_complex_float* beta, lapack_complex_float* q, lapack_int* ldq,
+                   lapack_complex_float* z, lapack_int* ldz, lapack_complex_float* work, lapack_int* lwork,
+                   float* rwork, lapack_int* info);
+void LAPACK_zhgeqz(char* job, char* compq, char* compz, lapack_int* n, lapack_int* ilo, lapack_int* ihi,
+                   lapack_complex_double* h, lapack_int* ldh, lapack_complex_double* t, lapack_int* ldt,
+                   lapack_complex_double* alpha, lapack_complex_double* beta, lapack_complex_double* q, lapack_int* ldq,
+                   lapack_complex_double* z, lapack_int* ldz, lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int* info);
+void LAPACK_stgevc(char* side, char* howmny, const lapack_logical* select, lapack_int* n, const float* s,
+                   lapack_int* lds, const float* p, lapack_int* ldp, float* vl, lapack_int* ldvl, float* vr,
+                   lapack_int* ldvr, lapack_int* mm, lapack_int* m, float* work, lapack_int* info);
+void LAPACK_dtgevc(char* side, char* howmny, const lapack_logical* select, lapack_int* n, const double* s,
+                   lapack_int* lds, const double* p, lapack_int* ldp, double* vl, lapack_int* ldvl, double* vr,
+                   lapack_int* ldvr, lapack_int* mm, lapack_int* m, double* work, lapack_int* info);
+void LAPACK_ctgevc(char* side, char* howmny, const lapack_logical* select, lapack_int* n, const lapack_complex_float* s,
+                   lapack_int* lds, const lapack_complex_float* p, lapack_int* ldp, lapack_complex_float* vl,
+                   lapack_int* ldvl, lapack_complex_float* vr, lapack_int* ldvr, lapack_int* mm, lapack_int* m,
+                   lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_ztgevc(char* side, char* howmny, const lapack_logical* select, lapack_int* n,
+                   const lapack_complex_double* s, lapack_int* lds, const lapack_complex_double* p, lapack_int* ldp,
+                   lapack_complex_double* vl, lapack_int* ldvl, lapack_complex_double* vr, lapack_int* ldvr,
+                   lapack_int* mm, lapack_int* m, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_stgexc(lapack_logical* wantq, lapack_logical* wantz, lapack_int* n, float* a, lapack_int* lda, float* b,
+                   lapack_int* ldb, float* q, lapack_int* ldq, float* z, lapack_int* ldz, lapack_int* ifst,
+                   lapack_int* ilst, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dtgexc(lapack_logical* wantq, lapack_logical* wantz, lapack_int* n, double* a, lapack_int* lda, double* b,
+                   lapack_int* ldb, double* q, lapack_int* ldq, double* z, lapack_int* ldz, lapack_int* ifst,
+                   lapack_int* ilst, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_ctgexc(lapack_logical* wantq, lapack_logical* wantz, lapack_int* n, lapack_complex_float* a,
+                   lapack_int* lda, lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* q, lapack_int* ldq,
+                   lapack_complex_float* z, lapack_int* ldz, lapack_int* ifst, lapack_int* ilst, lapack_int* info);
+void LAPACK_ztgexc(lapack_logical* wantq, lapack_logical* wantz, lapack_int* n, lapack_complex_double* a,
+                   lapack_int* lda, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* q,
+                   lapack_int* ldq, lapack_complex_double* z, lapack_int* ldz, lapack_int* ifst, lapack_int* ilst,
+                   lapack_int* info);
+void LAPACK_stgsen(lapack_int* ijob, lapack_logical* wantq, lapack_logical* wantz, const lapack_logical* select,
+                   lapack_int* n, float* a, lapack_int* lda, float* b, lapack_int* ldb, float* alphar, float* alphai,
+                   float* beta, float* q, lapack_int* ldq, float* z, lapack_int* ldz, lapack_int* m, float* pl,
+                   float* pr, float* dif, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_dtgsen(lapack_int* ijob, lapack_logical* wantq, lapack_logical* wantz, const lapack_logical* select,
+                   lapack_int* n, double* a, lapack_int* lda, double* b, lapack_int* ldb, double* alphar,
+                   double* alphai, double* beta, double* q, lapack_int* ldq, double* z, lapack_int* ldz, lapack_int* m,
+                   double* pl, double* pr, double* dif, double* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_ctgsen(lapack_int* ijob, lapack_logical* wantq, lapack_logical* wantz, const lapack_logical* select,
+                   lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* alpha, lapack_complex_float* beta, lapack_complex_float* q, lapack_int* ldq,
+                   lapack_complex_float* z, lapack_int* ldz, lapack_int* m, float* pl, float* pr, float* dif,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_ztgsen(lapack_int* ijob, lapack_logical* wantq, lapack_logical* wantz, const lapack_logical* select,
+                   lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* alpha, lapack_complex_double* beta, lapack_complex_double* q, lapack_int* ldq,
+                   lapack_complex_double* z, lapack_int* ldz, lapack_int* m, double* pl, double* pr, double* dif,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_stgsyl(char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n, const float* a, lapack_int* lda,
+                   const float* b, lapack_int* ldb, float* c, lapack_int* ldc, const float* d, lapack_int* ldd,
+                   const float* e, lapack_int* lde, float* f, lapack_int* ldf, float* scale, float* dif, float* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_dtgsyl(char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n, const double* a, lapack_int* lda,
+                   const double* b, lapack_int* ldb, double* c, lapack_int* ldc, const double* d, lapack_int* ldd,
+                   const double* e, lapack_int* lde, double* f, lapack_int* ldf, double* scale, double* dif,
+                   double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_ctgsyl(char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n, const lapack_complex_float* a,
+                   lapack_int* lda, const lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* c,
+                   lapack_int* ldc, const lapack_complex_float* d, lapack_int* ldd, const lapack_complex_float* e,
+                   lapack_int* lde, lapack_complex_float* f, lapack_int* ldf, float* scale, float* dif,
+                   lapack_complex_float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_ztgsyl(char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n, const lapack_complex_double* a,
+                   lapack_int* lda, const lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* c,
+                   lapack_int* ldc, const lapack_complex_double* d, lapack_int* ldd, const lapack_complex_double* e,
+                   lapack_int* lde, lapack_complex_double* f, lapack_int* ldf, double* scale, double* dif,
+                   lapack_complex_double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_stgsna(char* job, char* howmny, const lapack_logical* select, lapack_int* n, const float* a,
+                   lapack_int* lda, const float* b, lapack_int* ldb, const float* vl, lapack_int* ldvl, const float* vr,
+                   lapack_int* ldvr, float* s, float* dif, lapack_int* mm, lapack_int* m, float* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_dtgsna(char* job, char* howmny, const lapack_logical* select, lapack_int* n, const double* a,
+                   lapack_int* lda, const double* b, lapack_int* ldb, const double* vl, lapack_int* ldvl,
+                   const double* vr, lapack_int* ldvr, double* s, double* dif, lapack_int* mm, lapack_int* m,
+                   double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_ctgsna(char* job, char* howmny, const lapack_logical* select, lapack_int* n, const lapack_complex_float* a,
+                   lapack_int* lda, const lapack_complex_float* b, lapack_int* ldb, const lapack_complex_float* vl,
+                   lapack_int* ldvl, const lapack_complex_float* vr, lapack_int* ldvr, float* s, float* dif,
+                   lapack_int* mm, lapack_int* m, lapack_complex_float* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_ztgsna(char* job, char* howmny, const lapack_logical* select, lapack_int* n, const lapack_complex_double* a,
+                   lapack_int* lda, const lapack_complex_double* b, lapack_int* ldb, const lapack_complex_double* vl,
+                   lapack_int* ldvl, const lapack_complex_double* vr, lapack_int* ldvr, double* s, double* dif,
+                   lapack_int* mm, lapack_int* m, lapack_complex_double* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_sggsvp(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* p, lapack_int* n, float* a,
+                   lapack_int* lda, float* b, lapack_int* ldb, float* tola, float* tolb, lapack_int* k, lapack_int* l,
+                   float* u, lapack_int* ldu, float* v, lapack_int* ldv, float* q, lapack_int* ldq, lapack_int* iwork,
+                   float* tau, float* work, lapack_int* info);
+void LAPACK_dggsvp(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* p, lapack_int* n, double* a,
+                   lapack_int* lda, double* b, lapack_int* ldb, double* tola, double* tolb, lapack_int* k,
+                   lapack_int* l, double* u, lapack_int* ldu, double* v, lapack_int* ldv, double* q, lapack_int* ldq,
+                   lapack_int* iwork, double* tau, double* work, lapack_int* info);
+void LAPACK_cggsvp(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* p, lapack_int* n,
+                   lapack_complex_float* a, lapack_int* lda, lapack_complex_float* b, lapack_int* ldb, float* tola,
+                   float* tolb, lapack_int* k, lapack_int* l, lapack_complex_float* u, lapack_int* ldu,
+                   lapack_complex_float* v, lapack_int* ldv, lapack_complex_float* q, lapack_int* ldq,
+                   lapack_int* iwork, float* rwork, lapack_complex_float* tau, lapack_complex_float* work,
+                   lapack_int* info);
+void LAPACK_zggsvp(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* p, lapack_int* n,
+                   lapack_complex_double* a, lapack_int* lda, lapack_complex_double* b, lapack_int* ldb, double* tola,
+                   double* tolb, lapack_int* k, lapack_int* l, lapack_complex_double* u, lapack_int* ldu,
+                   lapack_complex_double* v, lapack_int* ldv, lapack_complex_double* q, lapack_int* ldq,
+                   lapack_int* iwork, double* rwork, lapack_complex_double* tau, lapack_complex_double* work,
+                   lapack_int* info);
+void LAPACK_stgsja(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* p, lapack_int* n, lapack_int* k,
+                   lapack_int* l, float* a, lapack_int* lda, float* b, lapack_int* ldb, float* tola, float* tolb,
+                   float* alpha, float* beta, float* u, lapack_int* ldu, float* v, lapack_int* ldv, float* q,
+                   lapack_int* ldq, float* work, lapack_int* ncycle, lapack_int* info);
+void LAPACK_dtgsja(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* p, lapack_int* n, lapack_int* k,
+                   lapack_int* l, double* a, lapack_int* lda, double* b, lapack_int* ldb, double* tola, double* tolb,
+                   double* alpha, double* beta, double* u, lapack_int* ldu, double* v, lapack_int* ldv, double* q,
+                   lapack_int* ldq, double* work, lapack_int* ncycle, lapack_int* info);
+void LAPACK_ctgsja(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* p, lapack_int* n, lapack_int* k,
+                   lapack_int* l, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
+                   float* tola, float* tolb, float* alpha, float* beta, lapack_complex_float* u, lapack_int* ldu,
+                   lapack_complex_float* v, lapack_int* ldv, lapack_complex_float* q, lapack_int* ldq,
+                   lapack_complex_float* work, lapack_int* ncycle, lapack_int* info);
+void LAPACK_ztgsja(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* p, lapack_int* n, lapack_int* k,
+                   lapack_int* l, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
+                   double* tola, double* tolb, double* alpha, double* beta, lapack_complex_double* u, lapack_int* ldu,
+                   lapack_complex_double* v, lapack_int* ldv, lapack_complex_double* q, lapack_int* ldq,
+                   lapack_complex_double* work, lapack_int* ncycle, lapack_int* info);
+void LAPACK_sgels(char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, float* b,
+                  lapack_int* ldb, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dgels(char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* b,
+                  lapack_int* ldb, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cgels(char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                  lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* work, lapack_int* lwork,
+                  lapack_int* info);
+void LAPACK_zgels(char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a,
+                  lapack_int* lda, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* work,
+                  lapack_int* lwork, lapack_int* info);
+void LAPACK_sgelsy(lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                   lapack_int* jpvt, float* rcond, lapack_int* rank, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dgelsy(lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* b,
+                   lapack_int* ldb, lapack_int* jpvt, double* rcond, lapack_int* rank, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_cgelsy(lapack_int* m, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int* jpvt, float* rcond, lapack_int* rank,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* info);
+void LAPACK_zgelsy(lapack_int* m, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_int* jpvt, double* rcond, lapack_int* rank,
+                   lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* info);
+void LAPACK_sgelss(lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                   float* s, float* rcond, lapack_int* rank, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dgelss(lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* b,
+                   lapack_int* ldb, double* s, double* rcond, lapack_int* rank, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_cgelss(lapack_int* m, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb, float* s, float* rcond, lapack_int* rank,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* info);
+void LAPACK_zgelss(lapack_int* m, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb, double* s, double* rcond, lapack_int* rank,
+                   lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* info);
+void LAPACK_sgelsd(lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                   float* s, float* rcond, lapack_int* rank, float* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_dgelsd(lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda, double* b,
+                   lapack_int* ldb, double* s, double* rcond, lapack_int* rank, double* work, lapack_int* lwork,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_cgelsd(lapack_int* m, lapack_int* n, lapack_int* nrhs, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb, float* s, float* rcond, lapack_int* rank,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_zgelsd(lapack_int* m, lapack_int* n, lapack_int* nrhs, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb, double* s, double* rcond, lapack_int* rank,
+                   lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_sgglse(lapack_int* m, lapack_int* n, lapack_int* p, float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                   float* c, float* d, float* x, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dgglse(lapack_int* m, lapack_int* n, lapack_int* p, double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                   double* c, double* d, double* x, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cgglse(lapack_int* m, lapack_int* n, lapack_int* p, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* c, lapack_complex_float* d,
+                   lapack_complex_float* x, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zgglse(lapack_int* m, lapack_int* n, lapack_int* p, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* c, lapack_complex_double* d,
+                   lapack_complex_double* x, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_sggglm(lapack_int* n, lapack_int* m, lapack_int* p, float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                   float* d, float* x, float* y, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dggglm(lapack_int* n, lapack_int* m, lapack_int* p, double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                   double* d, double* x, double* y, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cggglm(lapack_int* n, lapack_int* m, lapack_int* p, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* d, lapack_complex_float* x,
+                   lapack_complex_float* y, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zggglm(lapack_int* n, lapack_int* m, lapack_int* p, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* d, lapack_complex_double* x,
+                   lapack_complex_double* y, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_ssyev(char* jobz, char* uplo, lapack_int* n, float* a, lapack_int* lda, float* w, float* work,
+                  lapack_int* lwork, lapack_int* info);
+void LAPACK_dsyev(char* jobz, char* uplo, lapack_int* n, double* a, lapack_int* lda, double* w, double* work,
+                  lapack_int* lwork, lapack_int* info);
+void LAPACK_cheev(char* jobz, char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, float* w,
+                  lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* info);
+void LAPACK_zheev(char* jobz, char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, double* w,
+                  lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* info);
+void LAPACK_ssyevd(char* jobz, char* uplo, lapack_int* n, float* a, lapack_int* lda, float* w, float* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_dsyevd(char* jobz, char* uplo, lapack_int* n, double* a, lapack_int* lda, double* w, double* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_cheevd(char* jobz, char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, float* w,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* lrwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_zheevd(char* jobz, char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, double* w,
+                   lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_ssyevx(char* jobz, char* range, char* uplo, lapack_int* n, float* a, lapack_int* lda, float* vl, float* vu,
+                   lapack_int* il, lapack_int* iu, float* abstol, lapack_int* m, float* w, float* z, lapack_int* ldz,
+                   float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_dsyevx(char* jobz, char* range, char* uplo, lapack_int* n, double* a, lapack_int* lda, double* vl,
+                   double* vu, lapack_int* il, lapack_int* iu, double* abstol, lapack_int* m, double* w, double* z,
+                   lapack_int* ldz, double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* ifail,
+                   lapack_int* info);
+void LAPACK_cheevx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                   float* vl, float* vu, lapack_int* il, lapack_int* iu, float* abstol, lapack_int* m, float* w,
+                   lapack_complex_float* z, lapack_int* ldz, lapack_complex_float* work, lapack_int* lwork,
+                   float* rwork, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_zheevx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                   double* vl, double* vu, lapack_int* il, lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                   lapack_complex_double* z, lapack_int* ldz, lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_ssyevr(char* jobz, char* range, char* uplo, lapack_int* n, float* a, lapack_int* lda, float* vl, float* vu,
+                   lapack_int* il, lapack_int* iu, float* abstol, lapack_int* m, float* w, float* z, lapack_int* ldz,
+                   lapack_int* isuppz, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_dsyevr(char* jobz, char* range, char* uplo, lapack_int* n, double* a, lapack_int* lda, double* vl,
+                   double* vu, lapack_int* il, lapack_int* iu, double* abstol, lapack_int* m, double* w, double* z,
+                   lapack_int* ldz, lapack_int* isuppz, double* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_cheevr(char* jobz, char* range, char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                   float* vl, float* vu, lapack_int* il, lapack_int* iu, float* abstol, lapack_int* m, float* w,
+                   lapack_complex_float* z, lapack_int* ldz, lapack_int* isuppz, lapack_complex_float* work,
+                   lapack_int* lwork, float* rwork, lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_zheevr(char* jobz, char* range, char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                   double* vl, double* vu, lapack_int* il, lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                   lapack_complex_double* z, lapack_int* ldz, lapack_int* isuppz, lapack_complex_double* work,
+                   lapack_int* lwork, double* rwork, lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_sspev(char* jobz, char* uplo, lapack_int* n, float* ap, float* w, float* z, lapack_int* ldz, float* work,
+                  lapack_int* info);
+void LAPACK_dspev(char* jobz, char* uplo, lapack_int* n, double* ap, double* w, double* z, lapack_int* ldz,
+                  double* work, lapack_int* info);
+void LAPACK_chpev(char* jobz, char* uplo, lapack_int* n, lapack_complex_float* ap, float* w, lapack_complex_float* z,
+                  lapack_int* ldz, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zhpev(char* jobz, char* uplo, lapack_int* n, lapack_complex_double* ap, double* w, lapack_complex_double* z,
+                  lapack_int* ldz, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sspevd(char* jobz, char* uplo, lapack_int* n, float* ap, float* w, float* z, lapack_int* ldz, float* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_dspevd(char* jobz, char* uplo, lapack_int* n, double* ap, double* w, double* z, lapack_int* ldz,
+                   double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_chpevd(char* jobz, char* uplo, lapack_int* n, lapack_complex_float* ap, float* w, lapack_complex_float* z,
+                   lapack_int* ldz, lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* lrwork,
+                   lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_zhpevd(char* jobz, char* uplo, lapack_int* n, lapack_complex_double* ap, double* w,
+                   lapack_complex_double* z, lapack_int* ldz, lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_sspevx(char* jobz, char* range, char* uplo, lapack_int* n, float* ap, float* vl, float* vu, lapack_int* il,
+                   lapack_int* iu, float* abstol, lapack_int* m, float* w, float* z, lapack_int* ldz, float* work,
+                   lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_dspevx(char* jobz, char* range, char* uplo, lapack_int* n, double* ap, double* vl, double* vu,
+                   lapack_int* il, lapack_int* iu, double* abstol, lapack_int* m, double* w, double* z, lapack_int* ldz,
+                   double* work, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_chpevx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_complex_float* ap, float* vl, float* vu,
+                   lapack_int* il, lapack_int* iu, float* abstol, lapack_int* m, float* w, lapack_complex_float* z,
+                   lapack_int* ldz, lapack_complex_float* work, float* rwork, lapack_int* iwork, lapack_int* ifail,
+                   lapack_int* info);
+void LAPACK_zhpevx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_complex_double* ap, double* vl,
+                   double* vu, lapack_int* il, lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                   lapack_complex_double* z, lapack_int* ldz, lapack_complex_double* work, double* rwork,
+                   lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_ssbev(char* jobz, char* uplo, lapack_int* n, lapack_int* kd, float* ab, lapack_int* ldab, float* w,
+                  float* z, lapack_int* ldz, float* work, lapack_int* info);
+void LAPACK_dsbev(char* jobz, char* uplo, lapack_int* n, lapack_int* kd, double* ab, lapack_int* ldab, double* w,
+                  double* z, lapack_int* ldz, double* work, lapack_int* info);
+void LAPACK_chbev(char* jobz, char* uplo, lapack_int* n, lapack_int* kd, lapack_complex_float* ab, lapack_int* ldab,
+                  float* w, lapack_complex_float* z, lapack_int* ldz, lapack_complex_float* work, float* rwork,
+                  lapack_int* info);
+void LAPACK_zhbev(char* jobz, char* uplo, lapack_int* n, lapack_int* kd, lapack_complex_double* ab, lapack_int* ldab,
+                  double* w, lapack_complex_double* z, lapack_int* ldz, lapack_complex_double* work, double* rwork,
+                  lapack_int* info);
+void LAPACK_ssbevd(char* jobz, char* uplo, lapack_int* n, lapack_int* kd, float* ab, lapack_int* ldab, float* w,
+                   float* z, lapack_int* ldz, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_dsbevd(char* jobz, char* uplo, lapack_int* n, lapack_int* kd, double* ab, lapack_int* ldab, double* w,
+                   double* z, lapack_int* ldz, double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_chbevd(char* jobz, char* uplo, lapack_int* n, lapack_int* kd, lapack_complex_float* ab, lapack_int* ldab,
+                   float* w, lapack_complex_float* z, lapack_int* ldz, lapack_complex_float* work, lapack_int* lwork,
+                   float* rwork, lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_zhbevd(char* jobz, char* uplo, lapack_int* n, lapack_int* kd, lapack_complex_double* ab, lapack_int* ldab,
+                   double* w, lapack_complex_double* z, lapack_int* ldz, lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_ssbevx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_int* kd, float* ab, lapack_int* ldab,
+                   float* q, lapack_int* ldq, float* vl, float* vu, lapack_int* il, lapack_int* iu, float* abstol,
+                   lapack_int* m, float* w, float* z, lapack_int* ldz, float* work, lapack_int* iwork,
+                   lapack_int* ifail, lapack_int* info);
+void LAPACK_dsbevx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_int* kd, double* ab, lapack_int* ldab,
+                   double* q, lapack_int* ldq, double* vl, double* vu, lapack_int* il, lapack_int* iu, double* abstol,
+                   lapack_int* m, double* w, double* z, lapack_int* ldz, double* work, lapack_int* iwork,
+                   lapack_int* ifail, lapack_int* info);
+void LAPACK_chbevx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_int* kd, lapack_complex_float* ab,
+                   lapack_int* ldab, lapack_complex_float* q, lapack_int* ldq, float* vl, float* vu, lapack_int* il,
+                   lapack_int* iu, float* abstol, lapack_int* m, float* w, lapack_complex_float* z, lapack_int* ldz,
+                   lapack_complex_float* work, float* rwork, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_zhbevx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_int* kd, lapack_complex_double* ab,
+                   lapack_int* ldab, lapack_complex_double* q, lapack_int* ldq, double* vl, double* vu, lapack_int* il,
+                   lapack_int* iu, double* abstol, lapack_int* m, double* w, lapack_complex_double* z, lapack_int* ldz,
+                   lapack_complex_double* work, double* rwork, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_sstev(char* jobz, lapack_int* n, float* d, float* e, float* z, lapack_int* ldz, float* work,
+                  lapack_int* info);
+void LAPACK_dstev(char* jobz, lapack_int* n, double* d, double* e, double* z, lapack_int* ldz, double* work,
+                  lapack_int* info);
+void LAPACK_sstevd(char* jobz, lapack_int* n, float* d, float* e, float* z, lapack_int* ldz, float* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_dstevd(char* jobz, lapack_int* n, double* d, double* e, double* z, lapack_int* ldz, double* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_sstevx(char* jobz, char* range, lapack_int* n, float* d, float* e, float* vl, float* vu, lapack_int* il,
+                   lapack_int* iu, float* abstol, lapack_int* m, float* w, float* z, lapack_int* ldz, float* work,
+                   lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_dstevx(char* jobz, char* range, lapack_int* n, double* d, double* e, double* vl, double* vu, lapack_int* il,
+                   lapack_int* iu, double* abstol, lapack_int* m, double* w, double* z, lapack_int* ldz, double* work,
+                   lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_sstevr(char* jobz, char* range, lapack_int* n, float* d, float* e, float* vl, float* vu, lapack_int* il,
+                   lapack_int* iu, float* abstol, lapack_int* m, float* w, float* z, lapack_int* ldz,
+                   lapack_int* isuppz, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_dstevr(char* jobz, char* range, lapack_int* n, double* d, double* e, double* vl, double* vu, lapack_int* il,
+                   lapack_int* iu, double* abstol, lapack_int* m, double* w, double* z, lapack_int* ldz,
+                   lapack_int* isuppz, double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_sgees(char* jobvs, char* sort, LAPACK_S_SELECT2 select, lapack_int* n, float* a, lapack_int* lda,
+                  lapack_int* sdim, float* wr, float* wi, float* vs, lapack_int* ldvs, float* work, lapack_int* lwork,
+                  lapack_logical* bwork, lapack_int* info);
+void LAPACK_dgees(char* jobvs, char* sort, LAPACK_D_SELECT2 select, lapack_int* n, double* a, lapack_int* lda,
+                  lapack_int* sdim, double* wr, double* wi, double* vs, lapack_int* ldvs, double* work,
+                  lapack_int* lwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_cgees(char* jobvs, char* sort, LAPACK_C_SELECT1 select, lapack_int* n, lapack_complex_float* a,
+                  lapack_int* lda, lapack_int* sdim, lapack_complex_float* w, lapack_complex_float* vs,
+                  lapack_int* ldvs, lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_logical* bwork,
+                  lapack_int* info);
+void LAPACK_zgees(char* jobvs, char* sort, LAPACK_Z_SELECT1 select, lapack_int* n, lapack_complex_double* a,
+                  lapack_int* lda, lapack_int* sdim, lapack_complex_double* w, lapack_complex_double* vs,
+                  lapack_int* ldvs, lapack_complex_double* work, lapack_int* lwork, double* rwork,
+                  lapack_logical* bwork, lapack_int* info);
+void LAPACK_sgeesx(char* jobvs, char* sort, LAPACK_S_SELECT2 select, char* sense, lapack_int* n, float* a,
+                   lapack_int* lda, lapack_int* sdim, float* wr, float* wi, float* vs, lapack_int* ldvs, float* rconde,
+                   float* rcondv, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_logical* bwork, lapack_int* info);
+void LAPACK_dgeesx(char* jobvs, char* sort, LAPACK_D_SELECT2 select, char* sense, lapack_int* n, double* a,
+                   lapack_int* lda, lapack_int* sdim, double* wr, double* wi, double* vs, lapack_int* ldvs,
+                   double* rconde, double* rcondv, double* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_cgeesx(char* jobvs, char* sort, LAPACK_C_SELECT1 select, char* sense, lapack_int* n,
+                   lapack_complex_float* a, lapack_int* lda, lapack_int* sdim, lapack_complex_float* w,
+                   lapack_complex_float* vs, lapack_int* ldvs, float* rconde, float* rcondv, lapack_complex_float* work,
+                   lapack_int* lwork, float* rwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_zgeesx(char* jobvs, char* sort, LAPACK_Z_SELECT1 select, char* sense, lapack_int* n,
+                   lapack_complex_double* a, lapack_int* lda, lapack_int* sdim, lapack_complex_double* w,
+                   lapack_complex_double* vs, lapack_int* ldvs, double* rconde, double* rcondv,
+                   lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_logical* bwork,
+                   lapack_int* info);
+void LAPACK_sgeev(char* jobvl, char* jobvr, lapack_int* n, float* a, lapack_int* lda, float* wr, float* wi, float* vl,
+                  lapack_int* ldvl, float* vr, lapack_int* ldvr, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dgeev(char* jobvl, char* jobvr, lapack_int* n, double* a, lapack_int* lda, double* wr, double* wi,
+                  double* vl, lapack_int* ldvl, double* vr, lapack_int* ldvr, double* work, lapack_int* lwork,
+                  lapack_int* info);
+void LAPACK_cgeev(char* jobvl, char* jobvr, lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                  lapack_complex_float* w, lapack_complex_float* vl, lapack_int* ldvl, lapack_complex_float* vr,
+                  lapack_int* ldvr, lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* info);
+void LAPACK_zgeev(char* jobvl, char* jobvr, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                  lapack_complex_double* w, lapack_complex_double* vl, lapack_int* ldvl, lapack_complex_double* vr,
+                  lapack_int* ldvr, lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* info);
+void LAPACK_sgeevx(char* balanc, char* jobvl, char* jobvr, char* sense, lapack_int* n, float* a, lapack_int* lda,
+                   float* wr, float* wi, float* vl, lapack_int* ldvl, float* vr, lapack_int* ldvr, lapack_int* ilo,
+                   lapack_int* ihi, float* scale, float* abnrm, float* rconde, float* rcondv, float* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_dgeevx(char* balanc, char* jobvl, char* jobvr, char* sense, lapack_int* n, double* a, lapack_int* lda,
+                   double* wr, double* wi, double* vl, lapack_int* ldvl, double* vr, lapack_int* ldvr, lapack_int* ilo,
+                   lapack_int* ihi, double* scale, double* abnrm, double* rconde, double* rcondv, double* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_cgeevx(char* balanc, char* jobvl, char* jobvr, char* sense, lapack_int* n, lapack_complex_float* a,
+                   lapack_int* lda, lapack_complex_float* w, lapack_complex_float* vl, lapack_int* ldvl,
+                   lapack_complex_float* vr, lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi, float* scale,
+                   float* abnrm, float* rconde, float* rcondv, lapack_complex_float* work, lapack_int* lwork,
+                   float* rwork, lapack_int* info);
+void LAPACK_zgeevx(char* balanc, char* jobvl, char* jobvr, char* sense, lapack_int* n, lapack_complex_double* a,
+                   lapack_int* lda, lapack_complex_double* w, lapack_complex_double* vl, lapack_int* ldvl,
+                   lapack_complex_double* vr, lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi, double* scale,
+                   double* abnrm, double* rconde, double* rcondv, lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int* info);
+void LAPACK_sgesvd(char* jobu, char* jobvt, lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* s, float* u,
+                   lapack_int* ldu, float* vt, lapack_int* ldvt, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dgesvd(char* jobu, char* jobvt, lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* s,
+                   double* u, lapack_int* ldu, double* vt, lapack_int* ldvt, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_cgesvd(char* jobu, char* jobvt, lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                   float* s, lapack_complex_float* u, lapack_int* ldu, lapack_complex_float* vt, lapack_int* ldvt,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* info);
+void LAPACK_zgesvd(char* jobu, char* jobvt, lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                   double* s, lapack_complex_double* u, lapack_int* ldu, lapack_complex_double* vt, lapack_int* ldvt,
+                   lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* info);
+void LAPACK_sgesdd(char* jobz, lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* s, float* u,
+                   lapack_int* ldu, float* vt, lapack_int* ldvt, float* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_dgesdd(char* jobz, lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* s, double* u,
+                   lapack_int* ldu, double* vt, lapack_int* ldvt, double* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* info);
+void LAPACK_cgesdd(char* jobz, lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, float* s,
+                   lapack_complex_float* u, lapack_int* ldu, lapack_complex_float* vt, lapack_int* ldvt,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_zgesdd(char* jobz, lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, double* s,
+                   lapack_complex_double* u, lapack_int* ldu, lapack_complex_double* vt, lapack_int* ldvt,
+                   lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_dgejsv(char* joba, char* jobu, char* jobv, char* jobr, char* jobt, char* jobp, lapack_int* m, lapack_int* n,
+                   double* a, lapack_int* lda, double* sva, double* u, lapack_int* ldu, double* v, lapack_int* ldv,
+                   double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_sgejsv(char* joba, char* jobu, char* jobv, char* jobr, char* jobt, char* jobp, lapack_int* m, lapack_int* n,
+                   float* a, lapack_int* lda, float* sva, float* u, lapack_int* ldu, float* v, lapack_int* ldv,
+                   float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_dgesvj(char* joba, char* jobu, char* jobv, lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                   double* sva, lapack_int* mv, double* v, lapack_int* ldv, double* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_sgesvj(char* joba, char* jobu, char* jobv, lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                   float* sva, lapack_int* mv, float* v, lapack_int* ldv, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_sggsvd(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* n, lapack_int* p, lapack_int* k,
+                   lapack_int* l, float* a, lapack_int* lda, float* b, lapack_int* ldb, float* alpha, float* beta,
+                   float* u, lapack_int* ldu, float* v, lapack_int* ldv, float* q, lapack_int* ldq, float* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_dggsvd(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* n, lapack_int* p, lapack_int* k,
+                   lapack_int* l, double* a, lapack_int* lda, double* b, lapack_int* ldb, double* alpha, double* beta,
+                   double* u, lapack_int* ldu, double* v, lapack_int* ldv, double* q, lapack_int* ldq, double* work,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_cggsvd(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* n, lapack_int* p, lapack_int* k,
+                   lapack_int* l, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
+                   float* alpha, float* beta, lapack_complex_float* u, lapack_int* ldu, lapack_complex_float* v,
+                   lapack_int* ldv, lapack_complex_float* q, lapack_int* ldq, lapack_complex_float* work, float* rwork,
+                   lapack_int* iwork, lapack_int* info);
+void LAPACK_zggsvd(char* jobu, char* jobv, char* jobq, lapack_int* m, lapack_int* n, lapack_int* p, lapack_int* k,
+                   lapack_int* l, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
+                   double* alpha, double* beta, lapack_complex_double* u, lapack_int* ldu, lapack_complex_double* v,
+                   lapack_int* ldv, lapack_complex_double* q, lapack_int* ldq, lapack_complex_double* work,
+                   double* rwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_ssygv(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, float* a, lapack_int* lda, float* b,
+                  lapack_int* ldb, float* w, float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dsygv(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, double* a, lapack_int* lda, double* b,
+                  lapack_int* ldb, double* w, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_chegv(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                  lapack_complex_float* b, lapack_int* ldb, float* w, lapack_complex_float* work, lapack_int* lwork,
+                  float* rwork, lapack_int* info);
+void LAPACK_zhegv(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                  lapack_complex_double* b, lapack_int* ldb, double* w, lapack_complex_double* work, lapack_int* lwork,
+                  double* rwork, lapack_int* info);
+void LAPACK_ssygvd(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, float* a, lapack_int* lda, float* b,
+                   lapack_int* ldb, float* w, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_dsygvd(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, double* a, lapack_int* lda, double* b,
+                   lapack_int* ldb, double* w, double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_chegvd(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb, float* w, lapack_complex_float* work, lapack_int* lwork,
+                   float* rwork, lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_zhegvd(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb, double* w, lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_ssygvx(lapack_int* itype, char* jobz, char* range, char* uplo, lapack_int* n, float* a, lapack_int* lda,
+                   float* b, lapack_int* ldb, float* vl, float* vu, lapack_int* il, lapack_int* iu, float* abstol,
+                   lapack_int* m, float* w, float* z, lapack_int* ldz, float* work, lapack_int* lwork,
+                   lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_dsygvx(lapack_int* itype, char* jobz, char* range, char* uplo, lapack_int* n, double* a, lapack_int* lda,
+                   double* b, lapack_int* ldb, double* vl, double* vu, lapack_int* il, lapack_int* iu, double* abstol,
+                   lapack_int* m, double* w, double* z, lapack_int* ldz, double* work, lapack_int* lwork,
+                   lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_chegvx(lapack_int* itype, char* jobz, char* range, char* uplo, lapack_int* n, lapack_complex_float* a,
+                   lapack_int* lda, lapack_complex_float* b, lapack_int* ldb, float* vl, float* vu, lapack_int* il,
+                   lapack_int* iu, float* abstol, lapack_int* m, float* w, lapack_complex_float* z, lapack_int* ldz,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* iwork, lapack_int* ifail,
+                   lapack_int* info);
+void LAPACK_zhegvx(lapack_int* itype, char* jobz, char* range, char* uplo, lapack_int* n, lapack_complex_double* a,
+                   lapack_int* lda, lapack_complex_double* b, lapack_int* ldb, double* vl, double* vu, lapack_int* il,
+                   lapack_int* iu, double* abstol, lapack_int* m, double* w, lapack_complex_double* z, lapack_int* ldz,
+                   lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* iwork, lapack_int* ifail,
+                   lapack_int* info);
+void LAPACK_sspgv(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, float* ap, float* bp, float* w, float* z,
+                  lapack_int* ldz, float* work, lapack_int* info);
+void LAPACK_dspgv(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, double* ap, double* bp, double* w,
+                  double* z, lapack_int* ldz, double* work, lapack_int* info);
+void LAPACK_chpgv(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, lapack_complex_float* ap,
+                  lapack_complex_float* bp, float* w, lapack_complex_float* z, lapack_int* ldz,
+                  lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zhpgv(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, lapack_complex_double* ap,
+                  lapack_complex_double* bp, double* w, lapack_complex_double* z, lapack_int* ldz,
+                  lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_sspgvd(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, float* ap, float* bp, float* w, float* z,
+                   lapack_int* ldz, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_dspgvd(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, double* ap, double* bp, double* w,
+                   double* z, lapack_int* ldz, double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_int* info);
+void LAPACK_chpgvd(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, lapack_complex_float* ap,
+                   lapack_complex_float* bp, float* w, lapack_complex_float* z, lapack_int* ldz,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* lrwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_zhpgvd(lapack_int* itype, char* jobz, char* uplo, lapack_int* n, lapack_complex_double* ap,
+                   lapack_complex_double* bp, double* w, lapack_complex_double* z, lapack_int* ldz,
+                   lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_int* info);
+void LAPACK_sspgvx(lapack_int* itype, char* jobz, char* range, char* uplo, lapack_int* n, float* ap, float* bp,
+                   float* vl, float* vu, lapack_int* il, lapack_int* iu, float* abstol, lapack_int* m, float* w,
+                   float* z, lapack_int* ldz, float* work, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_dspgvx(lapack_int* itype, char* jobz, char* range, char* uplo, lapack_int* n, double* ap, double* bp,
+                   double* vl, double* vu, lapack_int* il, lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                   double* z, lapack_int* ldz, double* work, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_chpgvx(lapack_int* itype, char* jobz, char* range, char* uplo, lapack_int* n, lapack_complex_float* ap,
+                   lapack_complex_float* bp, float* vl, float* vu, lapack_int* il, lapack_int* iu, float* abstol,
+                   lapack_int* m, float* w, lapack_complex_float* z, lapack_int* ldz, lapack_complex_float* work,
+                   float* rwork, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_zhpgvx(lapack_int* itype, char* jobz, char* range, char* uplo, lapack_int* n, lapack_complex_double* ap,
+                   lapack_complex_double* bp, double* vl, double* vu, lapack_int* il, lapack_int* iu, double* abstol,
+                   lapack_int* m, double* w, lapack_complex_double* z, lapack_int* ldz, lapack_complex_double* work,
+                   double* rwork, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_ssbgv(char* jobz, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, float* ab, lapack_int* ldab,
+                  float* bb, lapack_int* ldbb, float* w, float* z, lapack_int* ldz, float* work, lapack_int* info);
+void LAPACK_dsbgv(char* jobz, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, double* ab, lapack_int* ldab,
+                  double* bb, lapack_int* ldbb, double* w, double* z, lapack_int* ldz, double* work, lapack_int* info);
+void LAPACK_chbgv(char* jobz, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, lapack_complex_float* ab,
+                  lapack_int* ldab, lapack_complex_float* bb, lapack_int* ldbb, float* w, lapack_complex_float* z,
+                  lapack_int* ldz, lapack_complex_float* work, float* rwork, lapack_int* info);
+void LAPACK_zhbgv(char* jobz, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, lapack_complex_double* ab,
+                  lapack_int* ldab, lapack_complex_double* bb, lapack_int* ldbb, double* w, lapack_complex_double* z,
+                  lapack_int* ldz, lapack_complex_double* work, double* rwork, lapack_int* info);
+void LAPACK_ssbgvd(char* jobz, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, float* ab, lapack_int* ldab,
+                   float* bb, lapack_int* ldbb, float* w, float* z, lapack_int* ldz, float* work, lapack_int* lwork,
+                   lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_dsbgvd(char* jobz, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, double* ab, lapack_int* ldab,
+                   double* bb, lapack_int* ldbb, double* w, double* z, lapack_int* ldz, double* work, lapack_int* lwork,
+                   lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_chbgvd(char* jobz, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, lapack_complex_float* ab,
+                   lapack_int* ldab, lapack_complex_float* bb, lapack_int* ldbb, float* w, lapack_complex_float* z,
+                   lapack_int* ldz, lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* lrwork,
+                   lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_zhbgvd(char* jobz, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, lapack_complex_double* ab,
+                   lapack_int* ldab, lapack_complex_double* bb, lapack_int* ldbb, double* w, lapack_complex_double* z,
+                   lapack_int* ldz, lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* lrwork,
+                   lapack_int* iwork, lapack_int* liwork, lapack_int* info);
+void LAPACK_ssbgvx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, float* ab,
+                   lapack_int* ldab, float* bb, lapack_int* ldbb, float* q, lapack_int* ldq, float* vl, float* vu,
+                   lapack_int* il, lapack_int* iu, float* abstol, lapack_int* m, float* w, float* z, lapack_int* ldz,
+                   float* work, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_dsbgvx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb, double* ab,
+                   lapack_int* ldab, double* bb, lapack_int* ldbb, double* q, lapack_int* ldq, double* vl, double* vu,
+                   lapack_int* il, lapack_int* iu, double* abstol, lapack_int* m, double* w, double* z, lapack_int* ldz,
+                   double* work, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_chbgvx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb,
+                   lapack_complex_float* ab, lapack_int* ldab, lapack_complex_float* bb, lapack_int* ldbb,
+                   lapack_complex_float* q, lapack_int* ldq, float* vl, float* vu, lapack_int* il, lapack_int* iu,
+                   float* abstol, lapack_int* m, float* w, lapack_complex_float* z, lapack_int* ldz,
+                   lapack_complex_float* work, float* rwork, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_zhbgvx(char* jobz, char* range, char* uplo, lapack_int* n, lapack_int* ka, lapack_int* kb,
+                   lapack_complex_double* ab, lapack_int* ldab, lapack_complex_double* bb, lapack_int* ldbb,
+                   lapack_complex_double* q, lapack_int* ldq, double* vl, double* vu, lapack_int* il, lapack_int* iu,
+                   double* abstol, lapack_int* m, double* w, lapack_complex_double* z, lapack_int* ldz,
+                   lapack_complex_double* work, double* rwork, lapack_int* iwork, lapack_int* ifail, lapack_int* info);
+void LAPACK_sgges(char* jobvsl, char* jobvsr, char* sort, LAPACK_S_SELECT3 selctg, lapack_int* n, float* a,
+                  lapack_int* lda, float* b, lapack_int* ldb, lapack_int* sdim, float* alphar, float* alphai,
+                  float* beta, float* vsl, lapack_int* ldvsl, float* vsr, lapack_int* ldvsr, float* work,
+                  lapack_int* lwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_dgges(char* jobvsl, char* jobvsr, char* sort, LAPACK_D_SELECT3 selctg, lapack_int* n, double* a,
+                  lapack_int* lda, double* b, lapack_int* ldb, lapack_int* sdim, double* alphar, double* alphai,
+                  double* beta, double* vsl, lapack_int* ldvsl, double* vsr, lapack_int* ldvsr, double* work,
+                  lapack_int* lwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_cgges(char* jobvsl, char* jobvsr, char* sort, LAPACK_C_SELECT2 selctg, lapack_int* n,
+                  lapack_complex_float* a, lapack_int* lda, lapack_complex_float* b, lapack_int* ldb, lapack_int* sdim,
+                  lapack_complex_float* alpha, lapack_complex_float* beta, lapack_complex_float* vsl, lapack_int* ldvsl,
+                  lapack_complex_float* vsr, lapack_int* ldvsr, lapack_complex_float* work, lapack_int* lwork,
+                  float* rwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_zgges(char* jobvsl, char* jobvsr, char* sort, LAPACK_Z_SELECT2 selctg, lapack_int* n,
+                  lapack_complex_double* a, lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
+                  lapack_int* sdim, lapack_complex_double* alpha, lapack_complex_double* beta,
+                  lapack_complex_double* vsl, lapack_int* ldvsl, lapack_complex_double* vsr, lapack_int* ldvsr,
+                  lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_logical* bwork,
+                  lapack_int* info);
+void LAPACK_sggesx(char* jobvsl, char* jobvsr, char* sort, LAPACK_S_SELECT3 selctg, char* sense, lapack_int* n,
+                   float* a, lapack_int* lda, float* b, lapack_int* ldb, lapack_int* sdim, float* alphar, float* alphai,
+                   float* beta, float* vsl, lapack_int* ldvsl, float* vsr, lapack_int* ldvsr, float* rconde,
+                   float* rcondv, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_logical* bwork, lapack_int* info);
+void LAPACK_dggesx(char* jobvsl, char* jobvsr, char* sort, LAPACK_D_SELECT3 selctg, char* sense, lapack_int* n,
+                   double* a, lapack_int* lda, double* b, lapack_int* ldb, lapack_int* sdim, double* alphar,
+                   double* alphai, double* beta, double* vsl, lapack_int* ldvsl, double* vsr, lapack_int* ldvsr,
+                   double* rconde, double* rcondv, double* work, lapack_int* lwork, lapack_int* iwork,
+                   lapack_int* liwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_cggesx(char* jobvsl, char* jobvsr, char* sort, LAPACK_C_SELECT2 selctg, char* sense, lapack_int* n,
+                   lapack_complex_float* a, lapack_int* lda, lapack_complex_float* b, lapack_int* ldb, lapack_int* sdim,
+                   lapack_complex_float* alpha, lapack_complex_float* beta, lapack_complex_float* vsl,
+                   lapack_int* ldvsl, lapack_complex_float* vsr, lapack_int* ldvsr, float* rconde, float* rcondv,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* iwork, lapack_int* liwork,
+                   lapack_logical* bwork, lapack_int* info);
+void LAPACK_zggesx(char* jobvsl, char* jobvsr, char* sort, LAPACK_Z_SELECT2 selctg, char* sense, lapack_int* n,
+                   lapack_complex_double* a, lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
+                   lapack_int* sdim, lapack_complex_double* alpha, lapack_complex_double* beta,
+                   lapack_complex_double* vsl, lapack_int* ldvsl, lapack_complex_double* vsr, lapack_int* ldvsr,
+                   double* rconde, double* rcondv, lapack_complex_double* work, lapack_int* lwork, double* rwork,
+                   lapack_int* iwork, lapack_int* liwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_sggev(char* jobvl, char* jobvr, lapack_int* n, float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                  float* alphar, float* alphai, float* beta, float* vl, lapack_int* ldvl, float* vr, lapack_int* ldvr,
+                  float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dggev(char* jobvl, char* jobvr, lapack_int* n, double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                  double* alphar, double* alphai, double* beta, double* vl, lapack_int* ldvl, double* vr,
+                  lapack_int* ldvr, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cggev(char* jobvl, char* jobvr, lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                  lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* alpha, lapack_complex_float* beta,
+                  lapack_complex_float* vl, lapack_int* ldvl, lapack_complex_float* vr, lapack_int* ldvr,
+                  lapack_complex_float* work, lapack_int* lwork, float* rwork, lapack_int* info);
+void LAPACK_zggev(char* jobvl, char* jobvr, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                  lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* alpha, lapack_complex_double* beta,
+                  lapack_complex_double* vl, lapack_int* ldvl, lapack_complex_double* vr, lapack_int* ldvr,
+                  lapack_complex_double* work, lapack_int* lwork, double* rwork, lapack_int* info);
+void LAPACK_sggevx(char* balanc, char* jobvl, char* jobvr, char* sense, lapack_int* n, float* a, lapack_int* lda,
+                   float* b, lapack_int* ldb, float* alphar, float* alphai, float* beta, float* vl, lapack_int* ldvl,
+                   float* vr, lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi, float* lscale, float* rscale,
+                   float* abnrm, float* bbnrm, float* rconde, float* rcondv, float* work, lapack_int* lwork,
+                   lapack_int* iwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_dggevx(char* balanc, char* jobvl, char* jobvr, char* sense, lapack_int* n, double* a, lapack_int* lda,
+                   double* b, lapack_int* ldb, double* alphar, double* alphai, double* beta, double* vl,
+                   lapack_int* ldvl, double* vr, lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi, double* lscale,
+                   double* rscale, double* abnrm, double* bbnrm, double* rconde, double* rcondv, double* work,
+                   lapack_int* lwork, lapack_int* iwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_cggevx(char* balanc, char* jobvl, char* jobvr, char* sense, lapack_int* n, lapack_complex_float* a,
+                   lapack_int* lda, lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* alpha,
+                   lapack_complex_float* beta, lapack_complex_float* vl, lapack_int* ldvl, lapack_complex_float* vr,
+                   lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi, float* lscale, float* rscale, float* abnrm,
+                   float* bbnrm, float* rconde, float* rcondv, lapack_complex_float* work, lapack_int* lwork,
+                   float* rwork, lapack_int* iwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_zggevx(char* balanc, char* jobvl, char* jobvr, char* sense, lapack_int* n, lapack_complex_double* a,
+                   lapack_int* lda, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* alpha,
+                   lapack_complex_double* beta, lapack_complex_double* vl, lapack_int* ldvl, lapack_complex_double* vr,
+                   lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi, double* lscale, double* rscale, double* abnrm,
+                   double* bbnrm, double* rconde, double* rcondv, lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int* iwork, lapack_logical* bwork, lapack_int* info);
+void LAPACK_dsfrk(char* transr, char* uplo, char* trans, lapack_int* n, lapack_int* k, double* alpha, const double* a,
+                  lapack_int* lda, double* beta, double* c);
+void LAPACK_ssfrk(char* transr, char* uplo, char* trans, lapack_int* n, lapack_int* k, float* alpha, const float* a,
+                  lapack_int* lda, float* beta, float* c);
+void LAPACK_zhfrk(char* transr, char* uplo, char* trans, lapack_int* n, lapack_int* k, double* alpha,
+                  const lapack_complex_double* a, lapack_int* lda, double* beta, lapack_complex_double* c);
+void LAPACK_chfrk(char* transr, char* uplo, char* trans, lapack_int* n, lapack_int* k, float* alpha,
+                  const lapack_complex_float* a, lapack_int* lda, float* beta, lapack_complex_float* c);
+void LAPACK_dtfsm(char* transr, char* side, char* uplo, char* trans, char* diag, lapack_int* m, lapack_int* n,
+                  double* alpha, const double* a, double* b, lapack_int* ldb);
+void LAPACK_stfsm(char* transr, char* side, char* uplo, char* trans, char* diag, lapack_int* m, lapack_int* n,
+                  float* alpha, const float* a, float* b, lapack_int* ldb);
+void LAPACK_ztfsm(char* transr, char* side, char* uplo, char* trans, char* diag, lapack_int* m, lapack_int* n,
+                  lapack_complex_double* alpha, const lapack_complex_double* a, lapack_complex_double* b,
+                  lapack_int* ldb);
+void LAPACK_ctfsm(char* transr, char* side, char* uplo, char* trans, char* diag, lapack_int* m, lapack_int* n,
+                  lapack_complex_float* alpha, const lapack_complex_float* a, lapack_complex_float* b, lapack_int* ldb);
+void LAPACK_dtfttp(char* transr, char* uplo, lapack_int* n, const double* arf, double* ap, lapack_int* info);
+void LAPACK_stfttp(char* transr, char* uplo, lapack_int* n, const float* arf, float* ap, lapack_int* info);
+void LAPACK_ztfttp(char* transr, char* uplo, lapack_int* n, const lapack_complex_double* arf, lapack_complex_double* ap,
+                   lapack_int* info);
+void LAPACK_ctfttp(char* transr, char* uplo, lapack_int* n, const lapack_complex_float* arf, lapack_complex_float* ap,
+                   lapack_int* info);
+void LAPACK_dtfttr(char* transr, char* uplo, lapack_int* n, const double* arf, double* a, lapack_int* lda,
+                   lapack_int* info);
+void LAPACK_stfttr(char* transr, char* uplo, lapack_int* n, const float* arf, float* a, lapack_int* lda,
+                   lapack_int* info);
+void LAPACK_ztfttr(char* transr, char* uplo, lapack_int* n, const lapack_complex_double* arf, lapack_complex_double* a,
+                   lapack_int* lda, lapack_int* info);
+void LAPACK_ctfttr(char* transr, char* uplo, lapack_int* n, const lapack_complex_float* arf, lapack_complex_float* a,
+                   lapack_int* lda, lapack_int* info);
+void LAPACK_dtpttf(char* transr, char* uplo, lapack_int* n, const double* ap, double* arf, lapack_int* info);
+void LAPACK_stpttf(char* transr, char* uplo, lapack_int* n, const float* ap, float* arf, lapack_int* info);
+void LAPACK_ztpttf(char* transr, char* uplo, lapack_int* n, const lapack_complex_double* ap, lapack_complex_double* arf,
+                   lapack_int* info);
+void LAPACK_ctpttf(char* transr, char* uplo, lapack_int* n, const lapack_complex_float* ap, lapack_complex_float* arf,
+                   lapack_int* info);
+void LAPACK_dtpttr(char* uplo, lapack_int* n, const double* ap, double* a, lapack_int* lda, lapack_int* info);
+void LAPACK_stpttr(char* uplo, lapack_int* n, const float* ap, float* a, lapack_int* lda, lapack_int* info);
+void LAPACK_ztpttr(char* uplo, lapack_int* n, const lapack_complex_double* ap, lapack_complex_double* a,
+                   lapack_int* lda, lapack_int* info);
+void LAPACK_ctpttr(char* uplo, lapack_int* n, const lapack_complex_float* ap, lapack_complex_float* a, lapack_int* lda,
+                   lapack_int* info);
+void LAPACK_dtrttf(char* transr, char* uplo, lapack_int* n, const double* a, lapack_int* lda, double* arf,
+                   lapack_int* info);
+void LAPACK_strttf(char* transr, char* uplo, lapack_int* n, const float* a, lapack_int* lda, float* arf,
+                   lapack_int* info);
+void LAPACK_ztrttf(char* transr, char* uplo, lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* arf, lapack_int* info);
+void LAPACK_ctrttf(char* transr, char* uplo, lapack_int* n, const lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* arf, lapack_int* info);
+void LAPACK_dtrttp(char* uplo, lapack_int* n, const double* a, lapack_int* lda, double* ap, lapack_int* info);
+void LAPACK_strttp(char* uplo, lapack_int* n, const float* a, lapack_int* lda, float* ap, lapack_int* info);
+void LAPACK_ztrttp(char* uplo, lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* ap, lapack_int* info);
+void LAPACK_ctrttp(char* uplo, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, lapack_complex_float* ap,
+                   lapack_int* info);
+void LAPACK_sgeqrfp(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* tau, float* work, lapack_int* lwork,
+                    lapack_int* info);
+void LAPACK_dgeqrfp(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* tau, double* work,
+                    lapack_int* lwork, lapack_int* info);
+void LAPACK_cgeqrfp(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* tau,
+                    lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zgeqrfp(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_clacgv(lapack_int* n, lapack_complex_float* x, lapack_int* incx);
+void LAPACK_zlacgv(lapack_int* n, lapack_complex_double* x, lapack_int* incx);
+void LAPACK_slarnv(lapack_int* idist, lapack_int* iseed, lapack_int* n, float* x);
+void LAPACK_dlarnv(lapack_int* idist, lapack_int* iseed, lapack_int* n, double* x);
+void LAPACK_clarnv(lapack_int* idist, lapack_int* iseed, lapack_int* n, lapack_complex_float* x);
+void LAPACK_zlarnv(lapack_int* idist, lapack_int* iseed, lapack_int* n, lapack_complex_double* x);
+void LAPACK_sgeqr2(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* tau, float* work, lapack_int* info);
+void LAPACK_dgeqr2(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* tau, double* work,
+                   lapack_int* info);
+void LAPACK_cgeqr2(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* tau,
+                   lapack_complex_float* work, lapack_int* info);
+void LAPACK_zgeqr2(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* tau,
+                   lapack_complex_double* work, lapack_int* info);
+void LAPACK_slacpy(char* uplo, lapack_int* m, lapack_int* n, const float* a, lapack_int* lda, float* b,
+                   lapack_int* ldb);
+void LAPACK_dlacpy(char* uplo, lapack_int* m, lapack_int* n, const double* a, lapack_int* lda, double* b,
+                   lapack_int* ldb);
+void LAPACK_clacpy(char* uplo, lapack_int* m, lapack_int* n, const lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb);
+void LAPACK_zlacpy(char* uplo, lapack_int* m, lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb);
+void LAPACK_sgetf2(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, lapack_int* ipiv, lapack_int* info);
+void LAPACK_dgetf2(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, lapack_int* ipiv, lapack_int* info);
+void LAPACK_cgetf2(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_int* info);
+void LAPACK_zgetf2(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_int* info);
+void LAPACK_slaswp(lapack_int* n, float* a, lapack_int* lda, lapack_int* k1, lapack_int* k2, const lapack_int* ipiv,
+                   lapack_int* incx);
+void LAPACK_dlaswp(lapack_int* n, double* a, lapack_int* lda, lapack_int* k1, lapack_int* k2, const lapack_int* ipiv,
+                   lapack_int* incx);
+void LAPACK_claswp(lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* k1, lapack_int* k2,
+                   const lapack_int* ipiv, lapack_int* incx);
+void LAPACK_zlaswp(lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* k1, lapack_int* k2,
+                   const lapack_int* ipiv, lapack_int* incx);
+float LAPACK_slange(char* norm, lapack_int* m, lapack_int* n, const float* a, lapack_int* lda, float* work);
+double LAPACK_dlange(char* norm, lapack_int* m, lapack_int* n, const double* a, lapack_int* lda, double* work);
+float LAPACK_clange(char* norm, lapack_int* m, lapack_int* n, const lapack_complex_float* a, lapack_int* lda,
+                    float* work);
+double LAPACK_zlange(char* norm, lapack_int* m, lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
+                     double* work);
+float LAPACK_clanhe(char* norm, char* uplo, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, float* work);
+double LAPACK_zlanhe(char* norm, char* uplo, lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
+                     double* work);
+float LAPACK_slansy(char* norm, char* uplo, lapack_int* n, const float* a, lapack_int* lda, float* work);
+double LAPACK_dlansy(char* norm, char* uplo, lapack_int* n, const double* a, lapack_int* lda, double* work);
+float LAPACK_clansy(char* norm, char* uplo, lapack_int* n, const lapack_complex_float* a, lapack_int* lda, float* work);
+double LAPACK_zlansy(char* norm, char* uplo, lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
+                     double* work);
+float LAPACK_slantr(char* norm, char* uplo, char* diag, lapack_int* m, lapack_int* n, const float* a, lapack_int* lda,
+                    float* work);
+double LAPACK_dlantr(char* norm, char* uplo, char* diag, lapack_int* m, lapack_int* n, const double* a, lapack_int* lda,
+                     double* work);
+float LAPACK_clantr(char* norm, char* uplo, char* diag, lapack_int* m, lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, float* work);
+double LAPACK_zlantr(char* norm, char* uplo, char* diag, lapack_int* m, lapack_int* n, const lapack_complex_double* a,
+                     lapack_int* lda, double* work);
+float LAPACK_slamch(char* cmach);
+double LAPACK_dlamch(char* cmach);
+void LAPACK_sgelq2(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* tau, float* work, lapack_int* info);
+void LAPACK_dgelq2(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* tau, double* work,
+                   lapack_int* info);
+void LAPACK_cgelq2(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* tau,
+                   lapack_complex_float* work, lapack_int* info);
+void LAPACK_zgelq2(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* tau,
+                   lapack_complex_double* work, lapack_int* info);
+void LAPACK_slarfb(char* side, char* trans, char* direct, char* storev, lapack_int* m, lapack_int* n, lapack_int* k,
+                   const float* v, lapack_int* ldv, const float* t, lapack_int* ldt, float* c, lapack_int* ldc,
+                   float* work, lapack_int* ldwork);
+void LAPACK_dlarfb(char* side, char* trans, char* direct, char* storev, lapack_int* m, lapack_int* n, lapack_int* k,
+                   const double* v, lapack_int* ldv, const double* t, lapack_int* ldt, double* c, lapack_int* ldc,
+                   double* work, lapack_int* ldwork);
+void LAPACK_clarfb(char* side, char* trans, char* direct, char* storev, lapack_int* m, lapack_int* n, lapack_int* k,
+                   const lapack_complex_float* v, lapack_int* ldv, const lapack_complex_float* t, lapack_int* ldt,
+                   lapack_complex_float* c, lapack_int* ldc, lapack_complex_float* work, lapack_int* ldwork);
+void LAPACK_zlarfb(char* side, char* trans, char* direct, char* storev, lapack_int* m, lapack_int* n, lapack_int* k,
+                   const lapack_complex_double* v, lapack_int* ldv, const lapack_complex_double* t, lapack_int* ldt,
+                   lapack_complex_double* c, lapack_int* ldc, lapack_complex_double* work, lapack_int* ldwork);
+void LAPACK_slarfg(lapack_int* n, float* alpha, float* x, lapack_int* incx, float* tau);
+void LAPACK_dlarfg(lapack_int* n, double* alpha, double* x, lapack_int* incx, double* tau);
+void LAPACK_clarfg(lapack_int* n, lapack_complex_float* alpha, lapack_complex_float* x, lapack_int* incx,
+                   lapack_complex_float* tau);
+void LAPACK_zlarfg(lapack_int* n, lapack_complex_double* alpha, lapack_complex_double* x, lapack_int* incx,
+                   lapack_complex_double* tau);
+void LAPACK_slarft(char* direct, char* storev, lapack_int* n, lapack_int* k, const float* v, lapack_int* ldv,
+                   const float* tau, float* t, lapack_int* ldt);
+void LAPACK_dlarft(char* direct, char* storev, lapack_int* n, lapack_int* k, const double* v, lapack_int* ldv,
+                   const double* tau, double* t, lapack_int* ldt);
+void LAPACK_clarft(char* direct, char* storev, lapack_int* n, lapack_int* k, const lapack_complex_float* v,
+                   lapack_int* ldv, const lapack_complex_float* tau, lapack_complex_float* t, lapack_int* ldt);
+void LAPACK_zlarft(char* direct, char* storev, lapack_int* n, lapack_int* k, const lapack_complex_double* v,
+                   lapack_int* ldv, const lapack_complex_double* tau, lapack_complex_double* t, lapack_int* ldt);
+void LAPACK_slarfx(char* side, lapack_int* m, lapack_int* n, const float* v, float* tau, float* c, lapack_int* ldc,
+                   float* work);
+void LAPACK_dlarfx(char* side, lapack_int* m, lapack_int* n, const double* v, double* tau, double* c, lapack_int* ldc,
+                   double* work);
+void LAPACK_clarfx(char* side, lapack_int* m, lapack_int* n, const lapack_complex_float* v, lapack_complex_float* tau,
+                   lapack_complex_float* c, lapack_int* ldc, lapack_complex_float* work);
+void LAPACK_zlarfx(char* side, lapack_int* m, lapack_int* n, const lapack_complex_double* v, lapack_complex_double* tau,
+                   lapack_complex_double* c, lapack_int* ldc, lapack_complex_double* work);
+void LAPACK_slatms(lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed, char* sym, float* d, lapack_int* mode,
+                   float* cond, float* dmax, lapack_int* kl, lapack_int* ku, char* pack, float* a, lapack_int* lda,
+                   float* work, lapack_int* info);
+void LAPACK_dlatms(lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed, char* sym, double* d, lapack_int* mode,
+                   double* cond, double* dmax, lapack_int* kl, lapack_int* ku, char* pack, double* a, lapack_int* lda,
+                   double* work, lapack_int* info);
+void LAPACK_clatms(lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed, char* sym, float* d, lapack_int* mode,
+                   float* cond, float* dmax, lapack_int* kl, lapack_int* ku, char* pack, lapack_complex_float* a,
+                   lapack_int* lda, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zlatms(lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed, char* sym, double* d, lapack_int* mode,
+                   double* cond, double* dmax, lapack_int* kl, lapack_int* ku, char* pack, lapack_complex_double* a,
+                   lapack_int* lda, lapack_complex_double* work, lapack_int* info);
+void LAPACK_slag2d(lapack_int* m, lapack_int* n, const float* sa, lapack_int* ldsa, double* a, lapack_int* lda,
+                   lapack_int* info);
+void LAPACK_dlag2s(lapack_int* m, lapack_int* n, const double* a, lapack_int* lda, float* sa, lapack_int* ldsa,
+                   lapack_int* info);
+void LAPACK_clag2z(lapack_int* m, lapack_int* n, const lapack_complex_float* sa, lapack_int* ldsa,
+                   lapack_complex_double* a, lapack_int* lda, lapack_int* info);
+void LAPACK_zlag2c(lapack_int* m, lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_float* sa, lapack_int* ldsa, lapack_int* info);
+void LAPACK_slauum(char* uplo, lapack_int* n, float* a, lapack_int* lda, lapack_int* info);
+void LAPACK_dlauum(char* uplo, lapack_int* n, double* a, lapack_int* lda, lapack_int* info);
+void LAPACK_clauum(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_int* info);
+void LAPACK_zlauum(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_int* info);
+void LAPACK_slagge(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const float* d, float* a,
+                   lapack_int* lda, lapack_int* iseed, float* work, lapack_int* info);
+void LAPACK_dlagge(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const double* d, double* a,
+                   lapack_int* lda, lapack_int* iseed, double* work, lapack_int* info);
+void LAPACK_clagge(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const float* d,
+                   lapack_complex_float* a, lapack_int* lda, lapack_int* iseed, lapack_complex_float* work,
+                   lapack_int* info);
+void LAPACK_zlagge(lapack_int* m, lapack_int* n, lapack_int* kl, lapack_int* ku, const double* d,
+                   lapack_complex_double* a, lapack_int* lda, lapack_int* iseed, lapack_complex_double* work,
+                   lapack_int* info);
+void LAPACK_slaset(char* uplo, lapack_int* m, lapack_int* n, float* alpha, float* beta, float* a, lapack_int* lda);
+void LAPACK_dlaset(char* uplo, lapack_int* m, lapack_int* n, double* alpha, double* beta, double* a, lapack_int* lda);
+void LAPACK_claset(char* uplo, lapack_int* m, lapack_int* n, lapack_complex_float* alpha, lapack_complex_float* beta,
+                   lapack_complex_float* a, lapack_int* lda);
+void LAPACK_zlaset(char* uplo, lapack_int* m, lapack_int* n, lapack_complex_double* alpha, lapack_complex_double* beta,
+                   lapack_complex_double* a, lapack_int* lda);
+void LAPACK_slasrt(char* id, lapack_int* n, float* d, lapack_int* info);
+void LAPACK_dlasrt(char* id, lapack_int* n, double* d, lapack_int* info);
+void LAPACK_claghe(lapack_int* n, lapack_int* k, const float* d, lapack_complex_float* a, lapack_int* lda,
+                   lapack_int* iseed, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zlaghe(lapack_int* n, lapack_int* k, const double* d, lapack_complex_double* a, lapack_int* lda,
+                   lapack_int* iseed, lapack_complex_double* work, lapack_int* info);
+void LAPACK_slagsy(lapack_int* n, lapack_int* k, const float* d, float* a, lapack_int* lda, lapack_int* iseed,
+                   float* work, lapack_int* info);
+void LAPACK_dlagsy(lapack_int* n, lapack_int* k, const double* d, double* a, lapack_int* lda, lapack_int* iseed,
+                   double* work, lapack_int* info);
+void LAPACK_clagsy(lapack_int* n, lapack_int* k, const float* d, lapack_complex_float* a, lapack_int* lda,
+                   lapack_int* iseed, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zlagsy(lapack_int* n, lapack_int* k, const double* d, lapack_complex_double* a, lapack_int* lda,
+                   lapack_int* iseed, lapack_complex_double* work, lapack_int* info);
+void LAPACK_slapmr(lapack_logical* forwrd, lapack_int* m, lapack_int* n, float* x, lapack_int* ldx, lapack_int* k);
+void LAPACK_dlapmr(lapack_logical* forwrd, lapack_int* m, lapack_int* n, double* x, lapack_int* ldx, lapack_int* k);
+void LAPACK_clapmr(lapack_logical* forwrd, lapack_int* m, lapack_int* n, lapack_complex_float* x, lapack_int* ldx,
+                   lapack_int* k);
+void LAPACK_zlapmr(lapack_logical* forwrd, lapack_int* m, lapack_int* n, lapack_complex_double* x, lapack_int* ldx,
+                   lapack_int* k);
+float LAPACK_slapy2(float* x, float* y);
+double LAPACK_dlapy2(double* x, double* y);
+float LAPACK_slapy3(float* x, float* y, float* z);
+double LAPACK_dlapy3(double* x, double* y, double* z);
+void LAPACK_slartgp(float* f, float* g, float* cs, float* sn, float* r);
+void LAPACK_dlartgp(double* f, double* g, double* cs, double* sn, double* r);
+void LAPACK_slartgs(float* x, float* y, float* sigma, float* cs, float* sn);
+void LAPACK_dlartgs(double* x, double* y, double* sigma, double* cs, double* sn);
+// LAPACK 3.3.0
+void LAPACK_cbbcsd(char* jobu1, char* jobu2, char* jobv1t, char* jobv2t, char* trans, lapack_int* m, lapack_int* p,
+                   lapack_int* q, float* theta, float* phi, lapack_complex_float* u1, lapack_int* ldu1,
+                   lapack_complex_float* u2, lapack_int* ldu2, lapack_complex_float* v1t, lapack_int* ldv1t,
+                   lapack_complex_float* v2t, lapack_int* ldv2t, float* b11d, float* b11e, float* b12d, float* b12e,
+                   float* b21d, float* b21e, float* b22d, float* b22e, float* rwork, lapack_int* lrwork,
+                   lapack_int* info);
+void LAPACK_cheswapr(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* i1, lapack_int* i2);
+void LAPACK_chetri2(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, const lapack_int* ipiv,
+                    lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_chetri2x(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, const lapack_int* ipiv,
+                     lapack_complex_float* work, lapack_int* nb, lapack_int* info);
+void LAPACK_chetrs2(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* work,
+                    lapack_int* info);
+void LAPACK_csyconv(char* uplo, char* way, lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_float* work, lapack_int* info);
+void LAPACK_csyswapr(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* i1, lapack_int* i2);
+void LAPACK_csytri2(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, const lapack_int* ipiv,
+                    lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_csytri2x(char* uplo, lapack_int* n, lapack_complex_float* a, lapack_int* lda, const lapack_int* ipiv,
+                     lapack_complex_float* work, lapack_int* nb, lapack_int* info);
+void LAPACK_csytrs2(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb, lapack_complex_float* work,
+                    lapack_int* info);
+void LAPACK_cunbdb(char* trans, char* signs, lapack_int* m, lapack_int* p, lapack_int* q, lapack_complex_float* x11,
+                   lapack_int* ldx11, lapack_complex_float* x12, lapack_int* ldx12, lapack_complex_float* x21,
+                   lapack_int* ldx21, lapack_complex_float* x22, lapack_int* ldx22, float* theta, float* phi,
+                   lapack_complex_float* taup1, lapack_complex_float* taup2, lapack_complex_float* tauq1,
+                   lapack_complex_float* tauq2, lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_cuncsd(char* jobu1, char* jobu2, char* jobv1t, char* jobv2t, char* trans, char* signs, lapack_int* m,
+                   lapack_int* p, lapack_int* q, lapack_complex_float* x11, lapack_int* ldx11,
+                   lapack_complex_float* x12, lapack_int* ldx12, lapack_complex_float* x21, lapack_int* ldx21,
+                   lapack_complex_float* x22, lapack_int* ldx22, float* theta, lapack_complex_float* u1,
+                   lapack_int* ldu1, lapack_complex_float* u2, lapack_int* ldu2, lapack_complex_float* v1t,
+                   lapack_int* ldv1t, lapack_complex_float* v2t, lapack_int* ldv2t, lapack_complex_float* work,
+                   lapack_int* lwork, float* rwork, lapack_int* lrwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_dbbcsd(char* jobu1, char* jobu2, char* jobv1t, char* jobv2t, char* trans, lapack_int* m, lapack_int* p,
+                   lapack_int* q, double* theta, double* phi, double* u1, lapack_int* ldu1, double* u2,
+                   lapack_int* ldu2, double* v1t, lapack_int* ldv1t, double* v2t, lapack_int* ldv2t, double* b11d,
+                   double* b11e, double* b12d, double* b12e, double* b21d, double* b21e, double* b22d, double* b22e,
+                   double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dorbdb(char* trans, char* signs, lapack_int* m, lapack_int* p, lapack_int* q, double* x11,
+                   lapack_int* ldx11, double* x12, lapack_int* ldx12, double* x21, lapack_int* ldx21, double* x22,
+                   lapack_int* ldx22, double* theta, double* phi, double* taup1, double* taup2, double* tauq1,
+                   double* tauq2, double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dorcsd(char* jobu1, char* jobu2, char* jobv1t, char* jobv2t, char* trans, char* signs, lapack_int* m,
+                   lapack_int* p, lapack_int* q, double* x11, lapack_int* ldx11, double* x12, lapack_int* ldx12,
+                   double* x21, lapack_int* ldx21, double* x22, lapack_int* ldx22, double* theta, double* u1,
+                   lapack_int* ldu1, double* u2, lapack_int* ldu2, double* v1t, lapack_int* ldv1t, double* v2t,
+                   lapack_int* ldv2t, double* work, lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_dsyconv(char* uplo, char* way, lapack_int* n, double* a, lapack_int* lda, const lapack_int* ipiv,
+                    double* work, lapack_int* info);
+void LAPACK_dsyswapr(char* uplo, lapack_int* n, double* a, lapack_int* i1, lapack_int* i2);
+void LAPACK_dsytri2(char* uplo, lapack_int* n, double* a, lapack_int* lda, const lapack_int* ipiv,
+                    lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_dsytri2x(char* uplo, lapack_int* n, double* a, lapack_int* lda, const lapack_int* ipiv, double* work,
+                     lapack_int* nb, lapack_int* info);
+void LAPACK_dsytrs2(char* uplo, lapack_int* n, lapack_int* nrhs, const double* a, lapack_int* lda,
+                    const lapack_int* ipiv, double* b, lapack_int* ldb, double* work, lapack_int* info);
+void LAPACK_sbbcsd(char* jobu1, char* jobu2, char* jobv1t, char* jobv2t, char* trans, lapack_int* m, lapack_int* p,
+                   lapack_int* q, float* theta, float* phi, float* u1, lapack_int* ldu1, float* u2, lapack_int* ldu2,
+                   float* v1t, lapack_int* ldv1t, float* v2t, lapack_int* ldv2t, float* b11d, float* b11e, float* b12d,
+                   float* b12e, float* b21d, float* b21e, float* b22d, float* b22e, float* work, lapack_int* lwork,
+                   lapack_int* info);
+void LAPACK_sorbdb(char* trans, char* signs, lapack_int* m, lapack_int* p, lapack_int* q, float* x11, lapack_int* ldx11,
+                   float* x12, lapack_int* ldx12, float* x21, lapack_int* ldx21, float* x22, lapack_int* ldx22,
+                   float* theta, float* phi, float* taup1, float* taup2, float* tauq1, float* tauq2, float* work,
+                   lapack_int* lwork, lapack_int* info);
+void LAPACK_sorcsd(char* jobu1, char* jobu2, char* jobv1t, char* jobv2t, char* trans, char* signs, lapack_int* m,
+                   lapack_int* p, lapack_int* q, float* x11, lapack_int* ldx11, float* x12, lapack_int* ldx12,
+                   float* x21, lapack_int* ldx21, float* x22, lapack_int* ldx22, float* theta, float* u1,
+                   lapack_int* ldu1, float* u2, lapack_int* ldu2, float* v1t, lapack_int* ldv1t, float* v2t,
+                   lapack_int* ldv2t, float* work, lapack_int* lwork, lapack_int* iwork, lapack_int* info);
+void LAPACK_ssyconv(char* uplo, char* way, lapack_int* n, float* a, lapack_int* lda, const lapack_int* ipiv,
+                    float* work, lapack_int* info);
+void LAPACK_ssyswapr(char* uplo, lapack_int* n, float* a, lapack_int* i1, lapack_int* i2);
+void LAPACK_ssytri2(char* uplo, lapack_int* n, float* a, lapack_int* lda, const lapack_int* ipiv,
+                    lapack_complex_float* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_ssytri2x(char* uplo, lapack_int* n, float* a, lapack_int* lda, const lapack_int* ipiv, float* work,
+                     lapack_int* nb, lapack_int* info);
+void LAPACK_ssytrs2(char* uplo, lapack_int* n, lapack_int* nrhs, const float* a, lapack_int* lda,
+                    const lapack_int* ipiv, float* b, lapack_int* ldb, float* work, lapack_int* info);
+void LAPACK_zbbcsd(char* jobu1, char* jobu2, char* jobv1t, char* jobv2t, char* trans, lapack_int* m, lapack_int* p,
+                   lapack_int* q, double* theta, double* phi, lapack_complex_double* u1, lapack_int* ldu1,
+                   lapack_complex_double* u2, lapack_int* ldu2, lapack_complex_double* v1t, lapack_int* ldv1t,
+                   lapack_complex_double* v2t, lapack_int* ldv2t, double* b11d, double* b11e, double* b12d,
+                   double* b12e, double* b21d, double* b21e, double* b22d, double* b22e, double* rwork,
+                   lapack_int* lrwork, lapack_int* info);
+void LAPACK_zheswapr(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* i1, lapack_int* i2);
+void LAPACK_zhetri2(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, const lapack_int* ipiv,
+                    lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zhetri2x(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, const lapack_int* ipiv,
+                     lapack_complex_double* work, lapack_int* nb, lapack_int* info);
+void LAPACK_zhetrs2(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* work,
+                    lapack_int* info);
+void LAPACK_zsyconv(char* uplo, char* way, lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_double* work, lapack_int* info);
+void LAPACK_zsyswapr(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* i1, lapack_int* i2);
+void LAPACK_zsytri2(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, const lapack_int* ipiv,
+                    lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zsytri2x(char* uplo, lapack_int* n, lapack_complex_double* a, lapack_int* lda, const lapack_int* ipiv,
+                     lapack_complex_double* work, lapack_int* nb, lapack_int* info);
+void LAPACK_zsytrs2(char* uplo, lapack_int* n, lapack_int* nrhs, const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* work,
+                    lapack_int* info);
+void LAPACK_zunbdb(char* trans, char* signs, lapack_int* m, lapack_int* p, lapack_int* q, lapack_complex_double* x11,
+                   lapack_int* ldx11, lapack_complex_double* x12, lapack_int* ldx12, lapack_complex_double* x21,
+                   lapack_int* ldx21, lapack_complex_double* x22, lapack_int* ldx22, double* theta, double* phi,
+                   lapack_complex_double* taup1, lapack_complex_double* taup2, lapack_complex_double* tauq1,
+                   lapack_complex_double* tauq2, lapack_complex_double* work, lapack_int* lwork, lapack_int* info);
+void LAPACK_zuncsd(char* jobu1, char* jobu2, char* jobv1t, char* jobv2t, char* trans, char* signs, lapack_int* m,
+                   lapack_int* p, lapack_int* q, lapack_complex_double* x11, lapack_int* ldx11,
+                   lapack_complex_double* x12, lapack_int* ldx12, lapack_complex_double* x21, lapack_int* ldx21,
+                   lapack_complex_double* x22, lapack_int* ldx22, double* theta, lapack_complex_double* u1,
+                   lapack_int* ldu1, lapack_complex_double* u2, lapack_int* ldu2, lapack_complex_double* v1t,
+                   lapack_int* ldv1t, lapack_complex_double* v2t, lapack_int* ldv2t, lapack_complex_double* work,
+                   lapack_int* lwork, double* rwork, lapack_int* lrwork, lapack_int* iwork, lapack_int* info);
+// LAPACK 3.4.0
+void LAPACK_sgemqrt(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* nb,
+                    const float* v, lapack_int* ldv, const float* t, lapack_int* ldt, float* c, lapack_int* ldc,
+                    float* work, lapack_int* info);
+void LAPACK_dgemqrt(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* nb,
+                    const double* v, lapack_int* ldv, const double* t, lapack_int* ldt, double* c, lapack_int* ldc,
+                    double* work, lapack_int* info);
+void LAPACK_cgemqrt(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* nb,
+                    const lapack_complex_float* v, lapack_int* ldv, const lapack_complex_float* t, lapack_int* ldt,
+                    lapack_complex_float* c, lapack_int* ldc, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zgemqrt(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* nb,
+                    const lapack_complex_double* v, lapack_int* ldv, const lapack_complex_double* t, lapack_int* ldt,
+                    lapack_complex_double* c, lapack_int* ldc, lapack_complex_double* work, lapack_int* info);
+void LAPACK_sgeqrt(lapack_int* m, lapack_int* n, lapack_int* nb, float* a, lapack_int* lda, float* t, lapack_int* ldt,
+                   float* work, lapack_int* info);
+void LAPACK_dgeqrt(lapack_int* m, lapack_int* n, lapack_int* nb, double* a, lapack_int* lda, double* t, lapack_int* ldt,
+                   double* work, lapack_int* info);
+void LAPACK_cgeqrt(lapack_int* m, lapack_int* n, lapack_int* nb, lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* t, lapack_int* ldt, lapack_complex_float* work, lapack_int* info);
+void LAPACK_zgeqrt(lapack_int* m, lapack_int* n, lapack_int* nb, lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* t, lapack_int* ldt, lapack_complex_double* work, lapack_int* info);
+void LAPACK_sgeqrt2(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* t, lapack_int* ldt,
+                    lapack_int* info);
+void LAPACK_dgeqrt2(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* t, lapack_int* ldt,
+                    lapack_int* info);
+void LAPACK_cgeqrt2(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* t,
+                    lapack_int* ldt, lapack_int* info);
+void LAPACK_zgeqrt2(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* t,
+                    lapack_int* ldt, lapack_int* info);
+void LAPACK_sgeqrt3(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* t, lapack_int* ldt,
+                    lapack_int* info);
+void LAPACK_dgeqrt3(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* t, lapack_int* ldt,
+                    lapack_int* info);
+void LAPACK_cgeqrt3(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* t,
+                    lapack_int* ldt, lapack_int* info);
+void LAPACK_zgeqrt3(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* t,
+                    lapack_int* ldt, lapack_int* info);
+void LAPACK_stpmqrt(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l, lapack_int* nb,
+                    const float* v, lapack_int* ldv, const float* t, lapack_int* ldt, float* a, lapack_int* lda,
+                    float* b, lapack_int* ldb, float* work, lapack_int* info);
+void LAPACK_dtpmqrt(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l, lapack_int* nb,
+                    const double* v, lapack_int* ldv, const double* t, lapack_int* ldt, double* a, lapack_int* lda,
+                    double* b, lapack_int* ldb, double* work, lapack_int* info);
+void LAPACK_ctpmqrt(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l, lapack_int* nb,
+                    const lapack_complex_float* v, lapack_int* ldv, const lapack_complex_float* t, lapack_int* ldt,
+                    lapack_complex_float* a, lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* work, lapack_int* info);
+void LAPACK_ztpmqrt(char* side, char* trans, lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l, lapack_int* nb,
+                    const lapack_complex_double* v, lapack_int* ldv, const lapack_complex_double* t, lapack_int* ldt,
+                    lapack_complex_double* a, lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* work, lapack_int* info);
+void LAPACK_dtpqrt(lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb, double* a, lapack_int* lda, double* b,
+                   lapack_int* ldb, double* t, lapack_int* ldt, double* work, lapack_int* info);
+void LAPACK_ctpqrt(lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb, lapack_complex_float* a,
+                   lapack_int* lda, lapack_complex_float* t, lapack_complex_float* b, lapack_int* ldb, lapack_int* ldt,
+                   lapack_complex_float* work, lapack_int* info);
+void LAPACK_ztpqrt(lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb, lapack_complex_double* a,
+                   lapack_int* lda, lapack_complex_double* b, lapack_int* ldb, lapack_complex_double* t,
+                   lapack_int* ldt, lapack_complex_double* work, lapack_int* info);
+void LAPACK_stpqrt2(lapack_int* m, lapack_int* n, float* a, lapack_int* lda, float* b, lapack_int* ldb, float* t,
+                    lapack_int* ldt, lapack_int* info);
+void LAPACK_dtpqrt2(lapack_int* m, lapack_int* n, double* a, lapack_int* lda, double* b, lapack_int* ldb, double* t,
+                    lapack_int* ldt, lapack_int* info);
+void LAPACK_ctpqrt2(lapack_int* m, lapack_int* n, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* t, lapack_int* ldt, lapack_int* info);
+void LAPACK_ztpqrt2(lapack_int* m, lapack_int* n, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* t, lapack_int* ldt, lapack_int* info);
+void LAPACK_stprfb(char* side, char* trans, char* direct, char* storev, lapack_int* m, lapack_int* n, lapack_int* k,
+                   lapack_int* l, const float* v, lapack_int* ldv, const float* t, lapack_int* ldt, float* a,
+                   lapack_int* lda, float* b, lapack_int* ldb, const float* mywork, lapack_int* myldwork);
+void LAPACK_dtprfb(char* side, char* trans, char* direct, char* storev, lapack_int* m, lapack_int* n, lapack_int* k,
+                   lapack_int* l, const double* v, lapack_int* ldv, const double* t, lapack_int* ldt, double* a,
+                   lapack_int* lda, double* b, lapack_int* ldb, const double* mywork, lapack_int* myldwork);
+void LAPACK_ctprfb(char* side, char* trans, char* direct, char* storev, lapack_int* m, lapack_int* n, lapack_int* k,
+                   lapack_int* l, const lapack_complex_float* v, lapack_int* ldv, const lapack_complex_float* t,
+                   lapack_int* ldt, lapack_complex_float* a, lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
+                   const float* mywork, lapack_int* myldwork);
+void LAPACK_ztprfb(char* side, char* trans, char* direct, char* storev, lapack_int* m, lapack_int* n, lapack_int* k,
+                   lapack_int* l, const lapack_complex_double* v, lapack_int* ldv, const lapack_complex_double* t,
+                   lapack_int* ldt, lapack_complex_double* a, lapack_int* lda, lapack_complex_double* b,
+                   lapack_int* ldb, const double* mywork, lapack_int* myldwork);
+// LAPACK 3.X.X
+void LAPACK_csyr(char* uplo, lapack_int* n, lapack_complex_float* alpha, const lapack_complex_float* x,
+                 lapack_int* incx, lapack_complex_float* a, lapack_int* lda);
+void LAPACK_zsyr(char* uplo, lapack_int* n, lapack_complex_double* alpha, const lapack_complex_double* x,
+                 lapack_int* incx, lapack_complex_double* a, lapack_int* lda);
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif /* _LAPACKE_H_ */
+
+#endif /* _MKL_LAPACKE_H_ */
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/misc/lapacke_helpers.h b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/lapacke_helpers.h
new file mode 100644
index 0000000..ff98639
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/lapacke_helpers.h
@@ -0,0 +1,163 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2021 Erik Schultheis <erik.schultheis@aalto.fi>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LAPACKE_HELPERS_H
+#define EIGEN_LAPACKE_HELPERS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
+#include "lapacke.h"
+#endif
+
+namespace Eigen {
+namespace internal {
+/**
+ * \internal
+ * \brief Implementation details and helper functions for the lapacke glue code.
+ */
+namespace lapacke_helpers {
+
+// ---------------------------------------------------------------------------------------------------------------------
+//                  Translation from Eigen to Lapacke for types and constants
+// ---------------------------------------------------------------------------------------------------------------------
+
+// For complex numbers, the types in Eigen and Lapacke are different, but layout compatible.
+template <typename Scalar>
+struct translate_type_imp;
+template <>
+struct translate_type_imp<float> {
+  using type = float;
+};
+template <>
+struct translate_type_imp<double> {
+  using type = double;
+};
+template <>
+struct translate_type_imp<std::complex<double>> {
+  using type = lapack_complex_double;
+};
+template <>
+struct translate_type_imp<std::complex<float>> {
+  using type = lapack_complex_float;
+};
+
+/// Given an Eigen types, this is defined to be the corresponding, layout-compatible lapack type
+template <typename Scalar>
+using translated_type = typename translate_type_imp<Scalar>::type;
+
+/// These functions convert their arguments from Eigen to Lapack types
+/// This function performs conversion for any of the translations defined above.
+template <typename Source, typename Target = translated_type<Source>>
+EIGEN_ALWAYS_INLINE auto to_lapack(Source value) {
+  return static_cast<Target>(value);
+}
+
+/// This function performs conversions for pointer types corresponding to the translations abovce.
+/// This is valid because the translations are between layout-compatible types.
+template <typename Source, typename Target = translated_type<Source>>
+EIGEN_ALWAYS_INLINE auto to_lapack(Source *value) {
+  return reinterpret_cast<Target *>(value);
+}
+
+/// This function converts the Eigen Index to a lapack index, with possible range checks
+/// \sa internal::convert_index
+EIGEN_ALWAYS_INLINE lapack_int to_lapack(Index index) { return convert_index<lapack_int>(index); }
+
+/// translates storage order of the given Eigen object to the corresponding lapack constant
+template <typename Derived>
+EIGEN_ALWAYS_INLINE constexpr lapack_int lapack_storage_of(const EigenBase<Derived> &) {
+  return Derived::IsRowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR;
+}
+
+// ---------------------------------------------------------------------------------------------------------------------
+//              Automatic generation of low-level wrappers
+// ---------------------------------------------------------------------------------------------------------------------
+
+/*!
+ * \internal
+ * \brief Helper type to facilitate the wrapping of raw LAPACKE functions for different types into a single, overloaded
+ * C++ function. This is achieved in combination with \r EIGEN_MAKE_LAPACKE_WRAPPER \details This implementation works
+ * by providing an overloaded call function that just forwards its arguments to the underlying lapack function. Each of
+ * these overloads is enabled only if the call is actually well formed. Because these lapack functions take pointers to
+ * the underlying scalar type as arguments, even though the actual Scalars would be implicitly convertible, the pointers
+ * are not and therefore only a single overload can be valid at the same time. Thus, despite all functions taking fully
+ * generic `Args&&... args` as arguments, there is never any ambiguity.
+ */
+template <typename DoubleFn, typename SingleFn, typename DoubleCpxFn, typename SingleCpxFn>
+struct WrappingHelper {
+  // The naming of double, single, double complex and single complex is purely for readability
+  // and doesn't actually affect the workings of this class. In principle, the arguments can
+  // be supplied in any permuted order.
+  DoubleFn double_;
+  SingleFn single_;
+  DoubleCpxFn double_cpx_;
+  SingleCpxFn single_cpx_;
+
+  template <typename... Args>
+  auto call(Args &&...args) -> decltype(double_(std::forward<Args>(args)...)) {
+    return double_(std::forward<Args>(args)...);
+  }
+
+  template <typename... Args>
+  auto call(Args &&...args) -> decltype(single_(std::forward<Args>(args)...)) {
+    return single_(std::forward<Args>(args)...);
+  }
+
+  template <typename... Args>
+  auto call(Args &&...args) -> decltype(double_cpx_(std::forward<Args>(args)...)) {
+    return double_cpx_(std::forward<Args>(args)...);
+  }
+
+  template <typename... Args>
+  auto call(Args &&...args) -> decltype(single_cpx_(std::forward<Args>(args)...)) {
+    return single_cpx_(std::forward<Args>(args)...);
+  }
+};
+
+/** \internal Helper function that generates a `WrappingHelper` object with the given function pointers and
+ * invokes its `call` method, thus selecting one of the overloads.
+ * \sa EIGEN_MAKE_LAPACKE_WRAPPER
+ */
+template <typename DoubleFn, typename SingleFn, typename DoubleCpxFn, typename SingleCpxFn, typename... Args>
+EIGEN_ALWAYS_INLINE auto call_wrapper(DoubleFn df, SingleFn sf, DoubleCpxFn dcf, SingleCpxFn scf, Args &&...args) {
+  WrappingHelper<DoubleFn, SingleFn, DoubleCpxFn, SingleCpxFn> helper{df, sf, dcf, scf};
+  return helper.call(std::forward<Args>(args)...);
+}
+
+/**
+ * \internal
+ * Generates a new function `Function` that dispatches to the corresponding LAPACKE_? prefixed functions.
+ * \sa WrappingHelper
+ */
+#define EIGEN_MAKE_LAPACKE_WRAPPER(FUNCTION)                                                                \
+  template <typename... Args>                                                                               \
+  EIGEN_ALWAYS_INLINE auto FUNCTION(Args &&...args) {                                                       \
+    return call_wrapper(LAPACKE_d##FUNCTION, LAPACKE_s##FUNCTION, LAPACKE_z##FUNCTION, LAPACKE_c##FUNCTION, \
+                        std::forward<Args>(args)...);                                                       \
+  }
+
+// Now with this macro and the helper wrappers, we can generate the dispatch for all the lapacke functions that are
+// used in Eigen.
+// We define these here instead of in the files where they are used because this allows us to #undef the macro again
+// right here
+EIGEN_MAKE_LAPACKE_WRAPPER(potrf)
+EIGEN_MAKE_LAPACKE_WRAPPER(getrf)
+EIGEN_MAKE_LAPACKE_WRAPPER(geqrf)
+EIGEN_MAKE_LAPACKE_WRAPPER(gesdd)
+
+#undef EIGEN_MAKE_LAPACKE_WRAPPER
+}  // namespace lapacke_helpers
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_LAPACKE_HELPERS_H
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/misc/lapacke_mangling.h b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/lapacke_mangling.h
new file mode 100644
index 0000000..d852de7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/misc/lapacke_mangling.h
@@ -0,0 +1,16 @@
+#ifndef LAPACK_HEADER_INCLUDED
+#define LAPACK_HEADER_INCLUDED
+
+#ifndef LAPACK_GLOBAL
+#if defined(LAPACK_GLOBAL_PATTERN_LC) || defined(ADD_)
+#define LAPACK_GLOBAL(lcname, UCNAME) lcname##_
+#elif defined(LAPACK_GLOBAL_PATTERN_UC) || defined(UPPER)
+#define LAPACK_GLOBAL(lcname, UCNAME) UCNAME
+#elif defined(LAPACK_GLOBAL_PATTERN_MC) || defined(NOCHANGE)
+#define LAPACK_GLOBAL(lcname, UCNAME) lcname
+#else
+#define LAPACK_GLOBAL(lcname, UCNAME) lcname##_
+#endif
+#endif
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/ArrayCwiseBinaryOps.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/ArrayCwiseBinaryOps.inc
new file mode 100644
index 0000000..ae240f1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/ArrayCwiseBinaryOps.inc
@@ -0,0 +1,324 @@
+
+/** \returns an expression of the coefficient wise product of \c *this and \a other
+ *
+ * \sa MatrixBase::cwiseProduct
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived, OtherDerived, product)
+operator*(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const {
+  return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived, OtherDerived, product)(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient wise quotient of \c *this and \a other
+ *
+ * \sa MatrixBase::cwiseQuotient
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseBinaryOp<
+    internal::scalar_quotient_op<Scalar, typename OtherDerived::Scalar>, const Derived, const OtherDerived>
+operator/(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const {
+  return CwiseBinaryOp<internal::scalar_quotient_op<Scalar, typename OtherDerived::Scalar>, const Derived,
+                       const OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise min of \c *this and \a other
+ *
+ * Example: \include Cwise_min.cpp
+ * Output: \verbinclude Cwise_min.out
+ *
+ * \sa max()
+ */
+template <int NaNPropagation = PropagateFast, typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_min_op<Scalar, Scalar, NaNPropagation>, const Derived, const OtherDerived>
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+    min
+#else
+    (min)
+#endif
+    (const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const {
+  return CwiseBinaryOp<internal::scalar_min_op<Scalar, Scalar, NaNPropagation>, const Derived, const OtherDerived>(
+      derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise min of \c *this and scalar \a other
+ *
+ * \sa max()
+ */
+template <int NaNPropagation = PropagateFast>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_min_op<Scalar, Scalar, NaNPropagation>, const Derived,
+                  const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+    min
+#else
+    (min)
+#endif
+    (const Scalar &other) const {
+  return (min<NaNPropagation>)(Derived::PlainObject::Constant(rows(), cols(), other));
+}
+
+/** \returns an expression of the coefficient-wise max of \c *this and \a other
+ *
+ * Example: \include Cwise_max.cpp
+ * Output: \verbinclude Cwise_max.out
+ *
+ * \sa min()
+ */
+template <int NaNPropagation = PropagateFast, typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_max_op<Scalar, Scalar, NaNPropagation>, const Derived, const OtherDerived>
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+    max
+#else
+    (max)
+#endif
+    (const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const {
+  return CwiseBinaryOp<internal::scalar_max_op<Scalar, Scalar, NaNPropagation>, const Derived, const OtherDerived>(
+      derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise max of \c *this and scalar \a other
+ *
+ * \sa min()
+ */
+template <int NaNPropagation = PropagateFast>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_max_op<Scalar, Scalar, NaNPropagation>, const Derived,
+                  const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+    max
+#else
+    (max)
+#endif
+    (const Scalar &other) const {
+  return (max<NaNPropagation>)(Derived::PlainObject::Constant(rows(), cols(), other));
+}
+
+/** \returns an expression of the coefficient-wise absdiff of \c *this and \a other
+ *
+ * \sa absolute_difference()
+ */
+EIGEN_MAKE_CWISE_BINARY_OP(absolute_difference, absolute_difference)
+
+/** \returns an expression of the coefficient-wise absolute_difference of \c *this and scalar \a other
+ *
+ * \sa absolute_difference()
+ */
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_absolute_difference_op<Scalar, Scalar>, const Derived,
+                  const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+    absolute_difference
+#else
+    (absolute_difference)
+#endif
+    (const Scalar &other) const {
+  return (absolute_difference)(Derived::PlainObject::Constant(rows(), cols(), other));
+}
+
+/** \returns an expression of the coefficient-wise power of \c *this to the given array of \a exponents.
+ *
+ * This function computes the coefficient-wise power.
+ *
+ * Example: \include Cwise_array_power_array.cpp
+ * Output: \verbinclude Cwise_array_power_array.out
+ */
+EIGEN_MAKE_CWISE_BINARY_OP(pow, pow)
+
+/** \returns an expression of the coefficient-wise atan2(\c *this, \a y), where \a y is the given array argument.
+ *
+ * This function computes the coefficient-wise atan2.
+ *
+ */
+EIGEN_MAKE_CWISE_BINARY_OP(atan2, atan2)
+
+// TODO: code generating macros could be moved to Macros.h and could include generation of documentation
+#define EIGEN_MAKE_CWISE_COMP_OP(OP, COMPARATOR)                                                                     \
+  template <typename OtherDerived>                                                                                   \
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const                                                              \
+      CwiseBinaryOp<internal::scalar_cmp_op<Scalar, typename OtherDerived::Scalar, internal::cmp_##COMPARATOR>,      \
+                    const Derived, const OtherDerived>                                                               \
+      OP(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const {                                        \
+    return CwiseBinaryOp<internal::scalar_cmp_op<Scalar, typename OtherDerived::Scalar, internal::cmp_##COMPARATOR>, \
+                         const Derived, const OtherDerived>(derived(), other.derived());                             \
+  }                                                                                                                  \
+  typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_##COMPARATOR>, const Derived,          \
+                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >                    \
+      Cmp##COMPARATOR##ReturnType;                                                                                   \
+  typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_##COMPARATOR>,                         \
+                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject>, const Derived>      \
+      RCmp##COMPARATOR##ReturnType;                                                                                  \
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const Cmp##COMPARATOR##ReturnType OP(const Scalar &s) const {      \
+    return this->OP(Derived::PlainObject::Constant(rows(), cols(), s));                                              \
+  }                                                                                                                  \
+  EIGEN_DEVICE_FUNC constexpr friend EIGEN_STRONG_INLINE const RCmp##COMPARATOR##ReturnType OP(                      \
+      const Scalar &s, const EIGEN_CURRENT_STORAGE_BASE_CLASS<Derived> &d) {                                         \
+    return Derived::PlainObject::Constant(d.rows(), d.cols(), s).OP(d);                                              \
+  }
+
+#define EIGEN_MAKE_CWISE_COMP_R_OP(OP, R_OP, RCOMPARATOR)                                                             \
+  template <typename OtherDerived>                                                                                    \
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const                                                               \
+      CwiseBinaryOp<internal::scalar_cmp_op<typename OtherDerived::Scalar, Scalar, internal::cmp_##RCOMPARATOR>,      \
+                    const OtherDerived, const Derived>                                                                \
+      OP(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const {                                         \
+    return CwiseBinaryOp<internal::scalar_cmp_op<typename OtherDerived::Scalar, Scalar, internal::cmp_##RCOMPARATOR>, \
+                         const OtherDerived, const Derived>(other.derived(), derived());                              \
+  }                                                                                                                   \
+  EIGEN_DEVICE_FUNC constexpr inline const RCmp##RCOMPARATOR##ReturnType OP(const Scalar &s) const {                  \
+    return Derived::PlainObject::Constant(rows(), cols(), s).R_OP(*this);                                             \
+  }                                                                                                                   \
+  friend inline const Cmp##RCOMPARATOR##ReturnType OP(const Scalar &s, const Derived &d) {                            \
+    return d.R_OP(Derived::PlainObject::Constant(d.rows(), d.cols(), s));                                             \
+  }
+
+/** \returns an expression of the coefficient-wise \< operator of *this and \a other
+ *
+ * Example: \include Cwise_less.cpp
+ * Output: \verbinclude Cwise_less.out
+ *
+ * \sa all(), any(), operator>(), operator<=()
+ */
+EIGEN_MAKE_CWISE_COMP_OP(operator<, LT)
+
+/** \returns an expression of the coefficient-wise \<= operator of *this and \a other
+ *
+ * Example: \include Cwise_less_equal.cpp
+ * Output: \verbinclude Cwise_less_equal.out
+ *
+ * \sa all(), any(), operator>=(), operator<()
+ */
+EIGEN_MAKE_CWISE_COMP_OP(operator<=, LE)
+
+/** \returns an expression of the coefficient-wise \> operator of *this and \a other
+ *
+ * Example: \include Cwise_greater.cpp
+ * Output: \verbinclude Cwise_greater.out
+ *
+ * \sa all(), any(), operator>=(), operator<()
+ */
+EIGEN_MAKE_CWISE_COMP_R_OP(operator>, operator<, LT)
+
+/** \returns an expression of the coefficient-wise \>= operator of *this and \a other
+ *
+ * Example: \include Cwise_greater_equal.cpp
+ * Output: \verbinclude Cwise_greater_equal.out
+ *
+ * \sa all(), any(), operator>(), operator<=()
+ */
+EIGEN_MAKE_CWISE_COMP_R_OP(operator>=, operator<=, LE)
+
+/** \returns an expression of the coefficient-wise == operator of *this and \a other
+ *
+ * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+ * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+ * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+ * isMuchSmallerThan().
+ *
+ * Example: \include Cwise_equal_equal.cpp
+ * Output: \verbinclude Cwise_equal_equal.out
+ *
+ * \sa all(), any(), isApprox(), isMuchSmallerThan()
+ */
+EIGEN_MAKE_CWISE_COMP_OP(operator==, EQ)
+
+/** \returns an expression of the coefficient-wise != operator of *this and \a other
+ *
+ * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+ * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+ * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+ * isMuchSmallerThan().
+ *
+ * Example: \include Cwise_not_equal.cpp
+ * Output: \verbinclude Cwise_not_equal.out
+ *
+ * \sa all(), any(), isApprox(), isMuchSmallerThan()
+ */
+EIGEN_MAKE_CWISE_COMP_OP(operator!=, NEQ)
+
+#undef EIGEN_MAKE_CWISE_COMP_OP
+#undef EIGEN_MAKE_CWISE_COMP_R_OP
+
+// scalar addition
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+EIGEN_MAKE_SCALAR_BINARY_OP(operator+, sum)
+#else
+/** \returns an expression of \c *this with each coeff incremented by the constant \a scalar
+ *
+ * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+ *
+ * Example: \include Cwise_plus.cpp
+ * Output: \verbinclude Cwise_plus.out
+ *
+ * \sa operator+=(), operator-()
+ */
+template <typename T>
+const CwiseBinaryOp<internal::scalar_sum_op<Scalar, T>, Derived, Constant<T> > operator+(const T &scalar) const;
+/** \returns an expression of \a expr with each coeff incremented by the constant \a scalar
+ *
+ * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+ */
+template <typename T>
+friend const CwiseBinaryOp<internal::scalar_sum_op<T, Scalar>, Constant<T>, Derived> operator+(
+    const T &scalar, const StorageBaseType &expr);
+#endif
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+EIGEN_MAKE_SCALAR_BINARY_OP(operator-, difference)
+#else
+/** \returns an expression of \c *this with each coeff decremented by the constant \a scalar
+ *
+ * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+ *
+ * Example: \include Cwise_minus.cpp
+ * Output: \verbinclude Cwise_minus.out
+ *
+ * \sa operator+=(), operator-()
+ */
+template <typename T>
+const CwiseBinaryOp<internal::scalar_difference_op<Scalar, T>, Derived, Constant<T> > operator-(const T &scalar) const;
+/** \returns an expression of the constant matrix of value \a scalar decremented by the coefficients of \a expr
+ *
+ * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+ */
+template <typename T>
+friend const CwiseBinaryOp<internal::scalar_difference_op<T, Scalar>, Constant<T>, Derived> operator-(
+    const T &scalar, const StorageBaseType &expr);
+#endif
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(operator/, quotient)
+#else
+/**
+ * \brief Component-wise division of the scalar \a s by array elements of \a a.
+ *
+ * \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression
+ * (\c Derived::Scalar).
+ */
+template <typename T>
+friend inline const CwiseBinaryOp<internal::scalar_quotient_op<T, Scalar>, Constant<T>, Derived> operator/(
+    const T &s, const StorageBaseType &a);
+#endif
+
+/** \returns an expression of the coefficient-wise zeta function.
+ *
+ * \specialfunctions_module
+ *
+ * It returns the Riemann zeta function of two arguments \c *this and \a q:
+ *
+ * \param q is the shift, it must be > 0
+ *
+ * \note *this is the exponent, it must be > 1.
+ * \note This function supports only float and double scalar types. To support other scalar types, the user has
+ * to provide implementations of zeta(T,T) for any scalar type T to be supported.
+ *
+ * This method is an alias for zeta(*this,q);
+ *
+ * \sa Eigen::zeta()
+ */
+template <typename DerivedQ>
+inline const CwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const DerivedQ> zeta(
+    const EIGEN_CURRENT_STORAGE_BASE_CLASS<DerivedQ> &q) const {
+  return CwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const DerivedQ>(this->derived(), q.derived());
+}
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/ArrayCwiseUnaryOps.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/ArrayCwiseUnaryOps.inc
new file mode 100644
index 0000000..c06dc41
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/ArrayCwiseUnaryOps.inc
@@ -0,0 +1,548 @@
+typedef CwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> AbsReturnType;
+typedef CwiseUnaryOp<internal::scalar_arg_op<Scalar>, const Derived> ArgReturnType;
+typedef CwiseUnaryOp<internal::scalar_carg_op<Scalar>, const Derived> CArgReturnType;
+typedef CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const Derived> Abs2ReturnType;
+typedef CwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> SqrtReturnType;
+typedef CwiseUnaryOp<internal::scalar_cbrt_op<Scalar>, const Derived> CbrtReturnType;
+typedef CwiseUnaryOp<internal::scalar_rsqrt_op<Scalar>, const Derived> RsqrtReturnType;
+typedef CwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived> SignReturnType;
+typedef CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> InverseReturnType;
+typedef CwiseUnaryOp<internal::scalar_boolean_not_op<Scalar>, const Derived> BooleanNotReturnType;
+typedef CwiseUnaryOp<internal::scalar_bitwise_not_op<Scalar>, const Derived> BitwiseNotReturnType;
+
+typedef CwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived> ExpReturnType;
+typedef CwiseUnaryOp<internal::scalar_exp2_op<Scalar>, const Derived> Exp2ReturnType;
+typedef CwiseUnaryOp<internal::scalar_expm1_op<Scalar>, const Derived> Expm1ReturnType;
+typedef CwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived> LogReturnType;
+typedef CwiseUnaryOp<internal::scalar_log1p_op<Scalar>, const Derived> Log1pReturnType;
+typedef CwiseUnaryOp<internal::scalar_log10_op<Scalar>, const Derived> Log10ReturnType;
+typedef CwiseUnaryOp<internal::scalar_log2_op<Scalar>, const Derived> Log2ReturnType;
+typedef CwiseUnaryOp<internal::scalar_cos_op<Scalar>, const Derived> CosReturnType;
+typedef CwiseUnaryOp<internal::scalar_sin_op<Scalar>, const Derived> SinReturnType;
+typedef CwiseUnaryOp<internal::scalar_tan_op<Scalar>, const Derived> TanReturnType;
+typedef CwiseUnaryOp<internal::scalar_acos_op<Scalar>, const Derived> AcosReturnType;
+typedef CwiseUnaryOp<internal::scalar_asin_op<Scalar>, const Derived> AsinReturnType;
+typedef CwiseUnaryOp<internal::scalar_atan_op<Scalar>, const Derived> AtanReturnType;
+typedef CwiseUnaryOp<internal::scalar_tanh_op<Scalar>, const Derived> TanhReturnType;
+typedef CwiseUnaryOp<internal::scalar_logistic_op<Scalar>, const Derived> LogisticReturnType;
+typedef CwiseUnaryOp<internal::scalar_sinh_op<Scalar>, const Derived> SinhReturnType;
+typedef CwiseUnaryOp<internal::scalar_atanh_op<Scalar>, const Derived> AtanhReturnType;
+typedef CwiseUnaryOp<internal::scalar_asinh_op<Scalar>, const Derived> AsinhReturnType;
+typedef CwiseUnaryOp<internal::scalar_acosh_op<Scalar>, const Derived> AcoshReturnType;
+typedef CwiseUnaryOp<internal::scalar_cosh_op<Scalar>, const Derived> CoshReturnType;
+typedef CwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived> SquareReturnType;
+typedef CwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived> CubeReturnType;
+typedef CwiseUnaryOp<internal::scalar_round_op<Scalar>, const Derived> RoundReturnType;
+typedef CwiseUnaryOp<internal::scalar_rint_op<Scalar>, const Derived> RintReturnType;
+typedef CwiseUnaryOp<internal::scalar_floor_op<Scalar>, const Derived> FloorReturnType;
+typedef CwiseUnaryOp<internal::scalar_ceil_op<Scalar>, const Derived> CeilReturnType;
+typedef CwiseUnaryOp<internal::scalar_trunc_op<Scalar>, const Derived> TruncReturnType;
+typedef CwiseUnaryOp<internal::scalar_isnan_op<Scalar>, const Derived> IsNaNReturnType;
+typedef CwiseUnaryOp<internal::scalar_isinf_op<Scalar>, const Derived> IsInfReturnType;
+typedef CwiseUnaryOp<internal::scalar_isfinite_op<Scalar>, const Derived> IsFiniteReturnType;
+typedef CwiseUnaryOp<internal::scalar_isfinite_op<Scalar, true>, const Derived> IsFiniteTypedReturnType;
+
+/** \returns an expression of the coefficient-wise absolute value of \c *this
+ *
+ * Example: \include Cwise_abs.cpp
+ * Output: \verbinclude Cwise_abs.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_abs">Math functions</a>, abs2()
+ */
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const AbsReturnType abs() const { return AbsReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise phase angle of \c *this
+ *
+ * Example: \include Cwise_arg.cpp
+ * Output: \verbinclude Cwise_arg.out
+ *
+ * \sa abs()
+ */
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const ArgReturnType arg() const { return ArgReturnType(derived()); }
+
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CArgReturnType carg() const { return CArgReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise squared absolute value of \c *this
+ *
+ * Example: \include Cwise_abs2.cpp
+ * Output: \verbinclude Cwise_abs2.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_abs2">Math functions</a>, abs(), square()
+ */
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const Abs2ReturnType abs2() const { return Abs2ReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise exponential of *this.
+ *
+ * This function computes the coefficient-wise exponential. The function MatrixBase::exp() in the
+ * unsupported module MatrixFunctions computes the matrix exponential.
+ *
+ * Example: \include Cwise_exp.cpp
+ * Output: \verbinclude Cwise_exp.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_exp">Math functions</a>, exp2(), pow(), log(), sin(),
+ * cos()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const ExpReturnType exp() const { return ExpReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise exponential of *this.
+ *
+ * This function computes the coefficient-wise base2 exponential, i.e. 2^x.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_exp">Math functions</a>, exp(), pow(), log(), sin(),
+ * cos()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const Exp2ReturnType exp2() const { return Exp2ReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise exponential of *this minus 1.
+ *
+ * In exact arithmetic, \c x.expm1() is equivalent to \c x.exp() - 1,
+ * however, with finite precision, this function is much more accurate when \c x is close to zero.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_expm1">Math functions</a>, exp()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const Expm1ReturnType expm1() const { return Expm1ReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise logarithm of *this.
+ *
+ * This function computes the coefficient-wise logarithm. The function MatrixBase::log() in the
+ * unsupported module MatrixFunctions computes the matrix logarithm.
+ *
+ * Example: \include Cwise_log.cpp
+ * Output: \verbinclude Cwise_log.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log">Math functions</a>, log()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const LogReturnType log() const { return LogReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise logarithm of 1 plus \c *this.
+ *
+ * In exact arithmetic, \c x.log() is equivalent to \c (x+1).log(),
+ * however, with finite precision, this function is much more accurate when \c x is close to zero.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log1p">Math functions</a>, log()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const Log1pReturnType log1p() const { return Log1pReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise base-10 logarithm of *this.
+ *
+ * This function computes the coefficient-wise base-10 logarithm.
+ *
+ * Example: \include Cwise_log10.cpp
+ * Output: \verbinclude Cwise_log10.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log10">Math functions</a>, log()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const Log10ReturnType log10() const { return Log10ReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise base-2 logarithm of *this.
+ *
+ * This function computes the coefficient-wise base-2 logarithm.
+ *
+ */
+EIGEN_DEVICE_FUNC constexpr inline const Log2ReturnType log2() const { return Log2ReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise square root of *this.
+ *
+ * This function computes the coefficient-wise square root. The function MatrixBase::sqrt() in the
+ * unsupported module MatrixFunctions computes the matrix square root.
+ *
+ * Example: \include Cwise_sqrt.cpp
+ * Output: \verbinclude Cwise_sqrt.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sqrt">Math functions</a>, pow(), square(), cbrt()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const SqrtReturnType sqrt() const { return SqrtReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise cube root of *this.
+ *
+ * This function computes the coefficient-wise cube root.
+ *
+ * Example: \include Cwise_cbrt.cpp
+ * Output: \verbinclude Cwise_cbrt.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cbrt">Math functions</a>, sqrt(), pow(), square()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const CbrtReturnType cbrt() const { return CbrtReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise inverse square root of *this.
+ *
+ * This function computes the coefficient-wise inverse square root.
+ *
+ * Example: \include Cwise_sqrt.cpp
+ * Output: \verbinclude Cwise_sqrt.out
+ *
+ * \sa pow(), square()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const RsqrtReturnType rsqrt() const { return RsqrtReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise signum of *this.
+ *
+ * This function computes the coefficient-wise signum.
+ *
+ * Example: \include Cwise_sign.cpp
+ * Output: \verbinclude Cwise_sign.out
+ *
+ * \sa pow(), square()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const SignReturnType sign() const { return SignReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise cosine of *this.
+ *
+ * This function computes the coefficient-wise cosine. The function MatrixBase::cos() in the
+ * unsupported module MatrixFunctions computes the matrix cosine.
+ *
+ * Example: \include Cwise_cos.cpp
+ * Output: \verbinclude Cwise_cos.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cos">Math functions</a>, sin(), acos()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const CosReturnType cos() const { return CosReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise sine of *this.
+ *
+ * This function computes the coefficient-wise sine. The function MatrixBase::sin() in the
+ * unsupported module MatrixFunctions computes the matrix sine.
+ *
+ * Example: \include Cwise_sin.cpp
+ * Output: \verbinclude Cwise_sin.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sin">Math functions</a>, cos(), asin()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const SinReturnType sin() const { return SinReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise tan of *this.
+ *
+ * Example: \include Cwise_tan.cpp
+ * Output: \verbinclude Cwise_tan.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_tan">Math functions</a>, cos(), sin()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const TanReturnType tan() const { return TanReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise arc tan of *this.
+ *
+ * Example: \include Cwise_atan.cpp
+ * Output: \verbinclude Cwise_atan.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_atan">Math functions</a>, tan(), asin(), acos()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const AtanReturnType atan() const { return AtanReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise arc cosine of *this.
+ *
+ * Example: \include Cwise_acos.cpp
+ * Output: \verbinclude Cwise_acos.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_acos">Math functions</a>, cos(), asin()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const AcosReturnType acos() const { return AcosReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise arc sine of *this.
+ *
+ * Example: \include Cwise_asin.cpp
+ * Output: \verbinclude Cwise_asin.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_asin">Math functions</a>, sin(), acos()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const AsinReturnType asin() const { return AsinReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise hyperbolic tan of *this.
+ *
+ * Example: \include Cwise_tanh.cpp
+ * Output: \verbinclude Cwise_tanh.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_tanh">Math functions</a>, tan(), sinh(), cosh()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const TanhReturnType tanh() const { return TanhReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise hyperbolic sin of *this.
+ *
+ * Example: \include Cwise_sinh.cpp
+ * Output: \verbinclude Cwise_sinh.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sinh">Math functions</a>, sin(), tanh(), cosh()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const SinhReturnType sinh() const { return SinhReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise hyperbolic cos of *this.
+ *
+ * Example: \include Cwise_cosh.cpp
+ * Output: \verbinclude Cwise_cosh.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cosh">Math functions</a>, tanh(), sinh(), cosh()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const CoshReturnType cosh() const { return CoshReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise inverse hyperbolic tan of *this.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_atanh">Math functions</a>, atanh(), asinh(), acosh()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const AtanhReturnType atanh() const { return AtanhReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise inverse hyperbolic sin of *this.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_asinh">Math functions</a>, atanh(), asinh(), acosh()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const AsinhReturnType asinh() const { return AsinhReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise inverse hyperbolic cos of *this.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_acosh">Math functions</a>, atanh(), asinh(), acosh()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const AcoshReturnType acosh() const { return AcoshReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise logistic of *this.
+ */
+EIGEN_DEVICE_FUNC constexpr inline const LogisticReturnType logistic() const { return LogisticReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise inverse of *this.
+ *
+ * Example: \include Cwise_inverse.cpp
+ * Output: \verbinclude Cwise_inverse.out
+ *
+ * \sa operator/(), operator*()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const InverseReturnType inverse() const { return InverseReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise square of *this.
+ *
+ * Example: \include Cwise_square.cpp
+ * Output: \verbinclude Cwise_square.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_squareE">Math functions</a>, abs2(), cube(), pow()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const SquareReturnType square() const { return SquareReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise cube of *this.
+ *
+ * Example: \include Cwise_cube.cpp
+ * Output: \verbinclude Cwise_cube.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cube">Math functions</a>, square(), pow()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const CubeReturnType cube() const { return CubeReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise rint of *this.
+ *
+ * Example: \include Cwise_rint.cpp
+ * Output: \verbinclude Cwise_rint.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_rint">Math functions</a>, ceil(), floor()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const RintReturnType rint() const { return RintReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise round of *this.
+ *
+ * Example: \include Cwise_round.cpp
+ * Output: \verbinclude Cwise_round.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_round">Math functions</a>, ceil(), floor()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const RoundReturnType round() const { return RoundReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise floor of *this.
+ *
+ * Example: \include Cwise_floor.cpp
+ * Output: \verbinclude Cwise_floor.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_floor">Math functions</a>, ceil(), round()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const FloorReturnType floor() const { return FloorReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise ceil of *this.
+ *
+ * Example: \include Cwise_ceil.cpp
+ * Output: \verbinclude Cwise_ceil.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_ceil">Math functions</a>, floor(), round()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const CeilReturnType ceil() const { return CeilReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise truncation of *this.
+ *
+ * Example: \include Cwise_trunc.cpp
+ * Output: \verbinclude Cwise_trunc.out
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_trunc">Math functions</a>, floor(), round()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const TruncReturnType trunc() const { return TruncReturnType(derived()); }
+
+template <int N>
+struct ShiftRightXpr {
+  typedef CwiseUnaryOp<internal::scalar_shift_right_op<Scalar, N>, const Derived> Type;
+};
+
+/** \returns an expression of \c *this with the \a Scalar type arithmetically
+ * shifted right by \a N bit positions.
+ *
+ * The template parameter \a N specifies the number of bit positions to shift.
+ *
+ * \sa shiftLeft()
+ */
+template <int N>
+EIGEN_DEVICE_FUNC constexpr typename ShiftRightXpr<N>::Type shiftRight() const {
+  return typename ShiftRightXpr<N>::Type(derived());
+}
+
+template <int N>
+struct ShiftLeftXpr {
+  typedef CwiseUnaryOp<internal::scalar_shift_left_op<Scalar, N>, const Derived> Type;
+};
+
+/** \returns an expression of \c *this with the \a Scalar type logically
+ * shifted left by \a N bit positions.
+ *
+ * The template parameter \a N specifies the number of bit positions to shift.
+ *
+ * \sa shiftRight()
+ */
+template <int N>
+EIGEN_DEVICE_FUNC constexpr typename ShiftLeftXpr<N>::Type shiftLeft() const {
+  return typename ShiftLeftXpr<N>::Type(derived());
+}
+
+/** \returns an expression of the coefficient-wise isnan of *this.
+ *
+ * Example: \include Cwise_isNaN.cpp
+ * Output: \verbinclude Cwise_isNaN.out
+ *
+ * \sa isfinite(), isinf()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const IsNaNReturnType isNaN() const { return IsNaNReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise isinf of *this.
+ *
+ * Example: \include Cwise_isInf.cpp
+ * Output: \verbinclude Cwise_isInf.out
+ *
+ * \sa isnan(), isfinite()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const IsInfReturnType isInf() const { return IsInfReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise isfinite of *this.
+ *
+ * Example: \include Cwise_isFinite.cpp
+ * Output: \verbinclude Cwise_isFinite.out
+ *
+ * \sa isnan(), isinf()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const IsFiniteReturnType isFinite() const { return IsFiniteReturnType(derived()); }
+EIGEN_DEVICE_FUNC constexpr inline const IsFiniteTypedReturnType isFiniteTyped() const {
+  return IsFiniteTypedReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise ! operator of *this
+ *
+ * Example: \include Cwise_boolean_not.cpp
+ * Output: \verbinclude Cwise_boolean_not.out
+ *
+ * \sa operator!=()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const BooleanNotReturnType operator!() const {
+  return BooleanNotReturnType(derived());
+}
+
+/** \returns an expression of the bitwise ~ operator of *this
+ */
+EIGEN_DEVICE_FUNC constexpr inline const BitwiseNotReturnType operator~() const {
+  return BitwiseNotReturnType(derived());
+}
+
+// --- SpecialFunctions module ---
+
+typedef CwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, const Derived> LgammaReturnType;
+typedef CwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived> DigammaReturnType;
+typedef CwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived> ErfReturnType;
+typedef CwiseUnaryOp<internal::scalar_erfc_op<Scalar>, const Derived> ErfcReturnType;
+typedef CwiseUnaryOp<internal::scalar_ndtri_op<Scalar>, const Derived> NdtriReturnType;
+
+/** \cpp11 \returns an expression of the coefficient-wise ln(|gamma(*this)|).
+ *
+ * \specialfunctions_module
+ *
+ * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+ * or float/double in non c++11 mode, the user has to provide implementations of lgamma(T) for any scalar
+ * type T to be supported.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_lgamma">Math functions</a>, digamma()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const LgammaReturnType lgamma() const { return LgammaReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise digamma (psi, derivative of lgamma).
+ *
+ * \specialfunctions_module
+ *
+ * \note This function supports only float and double scalar types. To support other scalar types,
+ * the user has to provide implementations of digamma(T) for any scalar
+ * type T to be supported.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_digamma">Math functions</a>, Eigen::digamma(),
+ * Eigen::polygamma(), lgamma()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const DigammaReturnType digamma() const { return DigammaReturnType(derived()); }
+
+/** \cpp11 \returns an expression of the coefficient-wise Gauss error
+ * function of *this.
+ *
+ * \specialfunctions_module
+ *
+ * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+ * or float/double in non c++11 mode, the user has to provide implementations of erf(T) for any scalar
+ * type T to be supported.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_erf">Math functions</a>, erfc()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const ErfReturnType erf() const { return ErfReturnType(derived()); }
+
+/** \cpp11 \returns an expression of the coefficient-wise Complementary error
+ * function of *this.
+ *
+ * \specialfunctions_module
+ *
+ * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+ * or float/double in non c++11 mode, the user has to provide implementations of erfc(T) for any scalar
+ * type T to be supported.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_erfc">Math functions</a>, erf()
+ */
+EIGEN_DEVICE_FUNC constexpr inline const ErfcReturnType erfc() const { return ErfcReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise inverse of the CDF of the Normal distribution function
+ * function of *this.
+ *
+ * \specialfunctions_module
+ *
+ * In other words, considering `x = ndtri(y)`, it returns the argument, x, for which the area under the
+ * Gaussian probability density function (integrated from minus infinity to x) is equal to y.
+ *
+ * \note This function supports only float and double scalar types. To support other scalar types,
+ * the user has to provide implementations of ndtri(T) for any scalar type T to be supported.
+ *
+ * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_ndtri">Math functions</a>
+ */
+EIGEN_DEVICE_FUNC constexpr inline const NdtriReturnType ndtri() const { return NdtriReturnType(derived()); }
+
+template <typename ScalarExponent>
+using UnaryPowReturnType =
+    std::enable_if_t<internal::is_arithmetic<typename NumTraits<ScalarExponent>::Real>::value,
+                     CwiseUnaryOp<internal::scalar_unary_pow_op<Scalar, ScalarExponent>, const Derived>>;
+
+/** \returns an expression of the coefficients of \c *this raised to the constant power \a exponent
+ *
+ * \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type
+ *         of the given expression.
+ * \param exponent the scalar exponent value.
+ *
+ * This function computes the coefficient-wise power. The function MatrixBase::pow() in the
+ * unsupported module MatrixFunctions computes the matrix power.
+ *
+ * Example: \include Cwise_pow.cpp
+ * Output: \verbinclude Cwise_pow.out
+ *
+ * \sa ArrayBase::pow(ArrayBase), square(), cube(), exp(), log()
+ */
+template <typename ScalarExponent>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const UnaryPowReturnType<ScalarExponent> pow(
+    const ScalarExponent& exponent) const {
+  return UnaryPowReturnType<ScalarExponent>(derived(), internal::scalar_unary_pow_op<Scalar, ScalarExponent>(exponent));
+}
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/BlockMethods.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/BlockMethods.inc
new file mode 100644
index 0000000..57e347e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/BlockMethods.inc
@@ -0,0 +1,1380 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+/// \internal expression type of a column */
+typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, 1, !IsRowMajor> ColXpr;
+typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, 1, !IsRowMajor> ConstColXpr;
+/// \internal expression type of a row */
+typedef Block<Derived, 1, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> RowXpr;
+typedef const Block<const Derived, 1, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> ConstRowXpr;
+/// \internal expression type of a block of whole columns */
+typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, Dynamic, !IsRowMajor> ColsBlockXpr;
+typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, Dynamic, !IsRowMajor>
+    ConstColsBlockXpr;
+/// \internal expression type of a block of whole rows */
+typedef Block<Derived, Dynamic, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> RowsBlockXpr;
+typedef const Block<const Derived, Dynamic, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> ConstRowsBlockXpr;
+/// \internal expression type of a block of whole columns */
+template <int N>
+struct NColsBlockXpr {
+  typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, N, !IsRowMajor> Type;
+};
+template <int N>
+struct ConstNColsBlockXpr {
+  typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, N, !IsRowMajor> Type;
+};
+/// \internal expression type of a block of whole rows */
+template <int N>
+struct NRowsBlockXpr {
+  typedef Block<Derived, N, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> Type;
+};
+template <int N>
+struct ConstNRowsBlockXpr {
+  typedef const Block<const Derived, N, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> Type;
+};
+/// \internal expression of a block */
+typedef Block<Derived> BlockXpr;
+typedef const Block<const Derived> ConstBlockXpr;
+/// \internal expression of a block of fixed sizes */
+template <int Rows, int Cols>
+struct FixedBlockXpr {
+  typedef Block<Derived, Rows, Cols> Type;
+};
+template <int Rows, int Cols>
+struct ConstFixedBlockXpr {
+  typedef Block<const Derived, Rows, Cols> Type;
+};
+
+typedef VectorBlock<Derived> SegmentReturnType;
+typedef const VectorBlock<const Derived> ConstSegmentReturnType;
+template <int Size>
+struct FixedSegmentReturnType {
+  typedef VectorBlock<Derived, Size> Type;
+};
+template <int Size>
+struct ConstFixedSegmentReturnType {
+  typedef const VectorBlock<const Derived, Size> Type;
+};
+
+/// \internal inner-vector
+typedef Block<Derived, IsRowMajor ? 1 : Dynamic, IsRowMajor ? Dynamic : 1, true> InnerVectorReturnType;
+typedef Block<const Derived, IsRowMajor ? 1 : Dynamic, IsRowMajor ? Dynamic : 1, true> ConstInnerVectorReturnType;
+
+/// \internal set of inner-vectors
+typedef Block<Derived, Dynamic, Dynamic, true> InnerVectorsReturnType;
+typedef Block<const Derived, Dynamic, Dynamic, true> ConstInnerVectorsReturnType;
+
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+/// \returns an expression of a block in \c *this with either dynamic or fixed sizes.
+///
+/// \param  startRow  the first row in the block
+/// \param  startCol  the first column in the block
+/// \param  blockRows number of rows in the block, specified at either run-time or compile-time
+/// \param  blockCols number of columns in the block, specified at either run-time or compile-time
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example using runtime (aka dynamic) sizes: \include MatrixBase_block_int_int_int_int.cpp
+/// Output: \verbinclude MatrixBase_block_int_int_int_int.out
+///
+/// \newin{3.4}:
+///
+/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing
+/// Eigen::fix<N>, or Eigen::fix<N>(n) as arguments. In the later case, \c n plays the role of a runtime fallback value
+/// in case \c N equals Eigen::Dynamic. Here is an example with a fixed number of rows \c NRows and dynamic number of
+/// columns \c cols: \code mat.block(i,j,fix<NRows>,cols) \endcode
+///
+/// This function thus fully covers the features offered by the following overloads block<NRows,NCols>(Index, Index),
+/// and block<NRows,NCols>(Index, Index, Index, Index) that are thus obsolete. Indeed, this generic version avoids
+/// redundancy, it preserves the argument order, and prevents the need to rely on the template keyword in templated
+/// code.
+///
+/// but with less redundancy and more consistency as it does not modify the argument order
+/// and seamlessly enable hybrid fixed/dynamic sizes.
+///
+/// \note Even in the case that the returned expression has dynamic size, in the case
+/// when it is applied to a fixed-size matrix, it inherits a fixed maximal size,
+/// which means that evaluating it does not cause a dynamic memory allocation.
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block, fix, fix<N>(int)
+///
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                           internal::get_fixed_value<NColsType>::value>::Type
+#else
+    typename FixedBlockXpr<..., ...>::Type
+#endif
+    block(Index startRow, Index startCol, NRowsType blockRows, NColsType blockCols) {
+  return
+      typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                             internal::get_fixed_value<NColsType>::value>::Type(derived(), startRow, startCol,
+                                                                                internal::get_runtime_value(blockRows),
+                                                                                internal::get_runtime_value(blockCols));
+}
+
+/// This is the const version of block(Index,Index,NRowsType,NColsType)
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                                      internal::get_fixed_value<NColsType>::value>::Type
+#else
+    const typename ConstFixedBlockXpr<..., ...>::Type
+#endif
+    block(Index startRow, Index startCol, NRowsType blockRows, NColsType blockCols) const {
+  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                                     internal::get_fixed_value<NColsType>::value>::Type(derived(), startRow, startCol,
+                                                                                        internal::get_runtime_value(
+                                                                                            blockRows),
+                                                                                        internal::get_runtime_value(
+                                                                                            blockCols));
+}
+
+/// \returns a expression of a top-right corner of \c *this with either dynamic or fixed sizes.
+///
+/// \param cRows the number of rows in the corner
+/// \param cCols the number of columns in the corner
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example with dynamic sizes: \include MatrixBase_topRightCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_topRightCorner_int_int.out
+///
+/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing
+/// Eigen::fix<N>, or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink
+/// for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                           internal::get_fixed_value<NColsType>::value>::Type
+#else
+    typename FixedBlockXpr<..., ...>::Type
+#endif
+    topRightCorner(NRowsType cRows, NColsType cCols) {
+  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                                internal::get_fixed_value<NColsType>::value>::Type(derived(), 0,
+                                                                                   cols() - internal::get_runtime_value(
+                                                                                                cCols),
+                                                                                   internal::get_runtime_value(cRows),
+                                                                                   internal::get_runtime_value(cCols));
+}
+
+/// This is the const version of topRightCorner(NRowsType, NColsType).
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                                      internal::get_fixed_value<NColsType>::value>::Type
+#else
+    const typename ConstFixedBlockXpr<..., ...>::Type
+#endif
+    topRightCorner(NRowsType cRows, NColsType cCols) const {
+  return
+      typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                                  internal::get_fixed_value<NColsType>::value>::Type(derived(), 0,
+                                                                                     cols() -
+                                                                                         internal::get_runtime_value(
+                                                                                             cCols),
+                                                                                     internal::get_runtime_value(cRows),
+                                                                                     internal::get_runtime_value(
+                                                                                         cCols));
+}
+
+/// \returns an expression of a fixed-size top-right corner of \c *this.
+///
+/// \tparam CRows the number of rows in the corner
+/// \tparam CCols the number of columns in the corner
+///
+/// Example: \include MatrixBase_template_int_int_topRightCorner.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_topRightCorner.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block, block<int,int>(Index,Index)
+///
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedBlockXpr<CRows, CCols>::Type topRightCorner() {
+  return typename FixedBlockXpr<CRows, CCols>::Type(derived(), 0, cols() - CCols);
+}
+
+/// This is the const version of topRightCorner<int, int>().
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<CRows, CCols>::Type topRightCorner()
+    const {
+  return typename ConstFixedBlockXpr<CRows, CCols>::Type(derived(), 0, cols() - CCols);
+}
+
+/// \returns an expression of a top-right corner of \c *this.
+///
+/// \tparam CRows number of rows in corner as specified at compile-time
+/// \tparam CCols number of columns in corner as specified at compile-time
+/// \param  cRows number of rows in corner as specified at run-time
+/// \param  cCols number of columns in corner as specified at run-time
+///
+/// This function is mainly useful for corners where the number of rows is specified at compile-time
+/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
+/// information should not contradict. In other words, \a cRows should equal \a CRows unless
+/// \a CRows is \a Dynamic, and the same for the number of columns.
+///
+/// Example: \include MatrixBase_template_int_int_topRightCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_topRightCorner_int_int.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block
+///
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedBlockXpr<CRows, CCols>::Type topRightCorner(Index cRows,
+                                                                                                          Index cCols) {
+  return typename FixedBlockXpr<CRows, CCols>::Type(derived(), 0, cols() - cCols, cRows, cCols);
+}
+
+/// This is the const version of topRightCorner<int, int>(Index, Index).
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<CRows, CCols>::Type topRightCorner(
+    Index cRows, Index cCols) const {
+  return typename ConstFixedBlockXpr<CRows, CCols>::Type(derived(), 0, cols() - cCols, cRows, cCols);
+}
+
+/// \returns an expression of a top-left corner of \c *this  with either dynamic or fixed sizes.
+///
+/// \param cRows the number of rows in the corner
+/// \param cCols the number of columns in the corner
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include MatrixBase_topLeftCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_topLeftCorner_int_int.out
+///
+/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing
+/// Eigen::fix<N>, or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink
+/// for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                           internal::get_fixed_value<NColsType>::value>::Type
+#else
+    typename FixedBlockXpr<..., ...>::Type
+#endif
+    topLeftCorner(NRowsType cRows, NColsType cCols) {
+  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                                internal::get_fixed_value<NColsType>::value>::Type(derived(), 0, 0,
+                                                                                   internal::get_runtime_value(cRows),
+                                                                                   internal::get_runtime_value(cCols));
+}
+
+/// This is the const version of topLeftCorner(Index, Index).
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                                      internal::get_fixed_value<NColsType>::value>::Type
+#else
+    const typename ConstFixedBlockXpr<..., ...>::Type
+#endif
+    topLeftCorner(NRowsType cRows, NColsType cCols) const {
+  return
+      typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                                  internal::get_fixed_value<NColsType>::value>::Type(derived(), 0, 0,
+                                                                                     internal::get_runtime_value(cRows),
+                                                                                     internal::get_runtime_value(
+                                                                                         cCols));
+}
+
+/// \returns an expression of a fixed-size top-left corner of \c *this.
+///
+/// The template parameters CRows and CCols are the number of rows and columns in the corner.
+///
+/// Example: \include MatrixBase_template_int_int_topLeftCorner.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_topLeftCorner.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedBlockXpr<CRows, CCols>::Type topLeftCorner() {
+  return typename FixedBlockXpr<CRows, CCols>::Type(derived(), 0, 0);
+}
+
+/// This is the const version of topLeftCorner<int, int>().
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<CRows, CCols>::Type topLeftCorner()
+    const {
+  return typename ConstFixedBlockXpr<CRows, CCols>::Type(derived(), 0, 0);
+}
+
+/// \returns an expression of a top-left corner of \c *this.
+///
+/// \tparam CRows number of rows in corner as specified at compile-time
+/// \tparam CCols number of columns in corner as specified at compile-time
+/// \param  cRows number of rows in corner as specified at run-time
+/// \param  cCols number of columns in corner as specified at run-time
+///
+/// This function is mainly useful for corners where the number of rows is specified at compile-time
+/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
+/// information should not contradict. In other words, \a cRows should equal \a CRows unless
+/// \a CRows is \a Dynamic, and the same for the number of columns.
+///
+/// Example: \include MatrixBase_template_int_int_topLeftCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_topLeftCorner_int_int.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block
+///
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedBlockXpr<CRows, CCols>::Type topLeftCorner(Index cRows,
+                                                                                                         Index cCols) {
+  return typename FixedBlockXpr<CRows, CCols>::Type(derived(), 0, 0, cRows, cCols);
+}
+
+/// This is the const version of topLeftCorner<int, int>(Index, Index).
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<CRows, CCols>::Type topLeftCorner(
+    Index cRows, Index cCols) const {
+  return typename ConstFixedBlockXpr<CRows, CCols>::Type(derived(), 0, 0, cRows, cCols);
+}
+
+/// \returns an expression of a bottom-right corner of \c *this  with either dynamic or fixed sizes.
+///
+/// \param cRows the number of rows in the corner
+/// \param cCols the number of columns in the corner
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include MatrixBase_bottomRightCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_bottomRightCorner_int_int.out
+///
+/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing
+/// Eigen::fix<N>, or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink
+/// for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                           internal::get_fixed_value<NColsType>::value>::Type
+#else
+    typename FixedBlockXpr<..., ...>::Type
+#endif
+    bottomRightCorner(NRowsType cRows, NColsType cCols) {
+  return
+      typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value, internal::get_fixed_value<NColsType>::value>::
+          Type(derived(), rows() - internal::get_runtime_value(cRows), cols() - internal::get_runtime_value(cCols),
+               internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
+}
+
+/// This is the const version of bottomRightCorner(NRowsType, NColsType).
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                                      internal::get_fixed_value<NColsType>::value>::Type
+#else
+    const typename ConstFixedBlockXpr<..., ...>::Type
+#endif
+    bottomRightCorner(NRowsType cRows, NColsType cCols) const {
+  return typename ConstFixedBlockXpr<
+      internal::get_fixed_value<NRowsType>::value,
+      internal::get_fixed_value<NColsType>::value>::Type(derived(), rows() - internal::get_runtime_value(cRows),
+                                                         cols() - internal::get_runtime_value(cCols),
+                                                         internal::get_runtime_value(cRows),
+                                                         internal::get_runtime_value(cCols));
+}
+
+/// \returns an expression of a fixed-size bottom-right corner of \c *this.
+///
+/// The template parameters CRows and CCols are the number of rows and columns in the corner.
+///
+/// Example: \include MatrixBase_template_int_int_bottomRightCorner.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_bottomRightCorner.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedBlockXpr<CRows, CCols>::Type bottomRightCorner() {
+  return typename FixedBlockXpr<CRows, CCols>::Type(derived(), rows() - CRows, cols() - CCols);
+}
+
+/// This is the const version of bottomRightCorner<int, int>().
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<CRows, CCols>::Type
+bottomRightCorner() const {
+  return typename ConstFixedBlockXpr<CRows, CCols>::Type(derived(), rows() - CRows, cols() - CCols);
+}
+
+/// \returns an expression of a bottom-right corner of \c *this.
+///
+/// \tparam CRows number of rows in corner as specified at compile-time
+/// \tparam CCols number of columns in corner as specified at compile-time
+/// \param  cRows number of rows in corner as specified at run-time
+/// \param  cCols number of columns in corner as specified at run-time
+///
+/// This function is mainly useful for corners where the number of rows is specified at compile-time
+/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
+/// information should not contradict. In other words, \a cRows should equal \a CRows unless
+/// \a CRows is \a Dynamic, and the same for the number of columns.
+///
+/// Example: \include MatrixBase_template_int_int_bottomRightCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_bottomRightCorner_int_int.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block
+///
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedBlockXpr<CRows, CCols>::Type bottomRightCorner(
+    Index cRows, Index cCols) {
+  return typename FixedBlockXpr<CRows, CCols>::Type(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
+}
+
+/// This is the const version of bottomRightCorner<int, int>(Index, Index).
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<CRows, CCols>::Type bottomRightCorner(
+    Index cRows, Index cCols) const {
+  return typename ConstFixedBlockXpr<CRows, CCols>::Type(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
+}
+
+/// \returns an expression of a bottom-left corner of \c *this  with either dynamic or fixed sizes.
+///
+/// \param cRows the number of rows in the corner
+/// \param cCols the number of columns in the corner
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include MatrixBase_bottomLeftCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_bottomLeftCorner_int_int.out
+///
+/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing
+/// Eigen::fix<N>, or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink
+/// for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                           internal::get_fixed_value<NColsType>::value>::Type
+#else
+    typename FixedBlockXpr<..., ...>::Type
+#endif
+    bottomLeftCorner(NRowsType cRows, NColsType cCols) {
+  return
+      typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                             internal::get_fixed_value<NColsType>::value>::Type(derived(),
+                                                                                rows() -
+                                                                                    internal::get_runtime_value(cRows),
+                                                                                0, internal::get_runtime_value(cRows),
+                                                                                internal::get_runtime_value(cCols));
+}
+
+/// This is the const version of bottomLeftCorner(NRowsType, NColsType).
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,
+                                internal::get_fixed_value<NColsType>::value>::Type
+#else
+    typename ConstFixedBlockXpr<..., ...>::Type
+#endif
+    bottomLeftCorner(NRowsType cRows, NColsType cCols) const {
+  return typename ConstFixedBlockXpr<
+      internal::get_fixed_value<NRowsType>::value,
+      internal::get_fixed_value<NColsType>::value>::Type(derived(), rows() - internal::get_runtime_value(cRows), 0,
+                                                         internal::get_runtime_value(cRows),
+                                                         internal::get_runtime_value(cCols));
+}
+
+/// \returns an expression of a fixed-size bottom-left corner of \c *this.
+///
+/// The template parameters CRows and CCols are the number of rows and columns in the corner.
+///
+/// Example: \include MatrixBase_template_int_int_bottomLeftCorner.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_bottomLeftCorner.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedBlockXpr<CRows, CCols>::Type bottomLeftCorner() {
+  return typename FixedBlockXpr<CRows, CCols>::Type(derived(), rows() - CRows, 0);
+}
+
+/// This is the const version of bottomLeftCorner<int, int>().
+template <int CRows, int CCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<CRows, CCols>::Type bottomLeftCorner()
+    const {
+  return typename ConstFixedBlockXpr<CRows, CCols>::Type(derived(), rows() - CRows, 0);
+}
+
+/// \returns an expression of a bottom-left corner of \c *this.
+///
+/// \tparam CRows number of rows in corner as specified at compile-time
+/// \tparam CCols number of columns in corner as specified at compile-time
+/// \param  cRows number of rows in corner as specified at run-time
+/// \param  cCols number of columns in corner as specified at run-time
+///
+/// This function is mainly useful for corners where the number of rows is specified at compile-time
+/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
+/// information should not contradict. In other words, \a cRows should equal \a CRows unless
+/// \a CRows is \a Dynamic, and the same for the number of columns.
+///
+/// Example: \include MatrixBase_template_int_int_bottomLeftCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_bottomLeftCorner_int_int.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block
+///
+template <int CRows, int CCols>
+EIGEN_STRONG_INLINE typename FixedBlockXpr<CRows, CCols>::Type bottomLeftCorner(Index cRows, Index cCols) {
+  return typename FixedBlockXpr<CRows, CCols>::Type(derived(), rows() - cRows, 0, cRows, cCols);
+}
+
+/// This is the const version of bottomLeftCorner<int, int>(Index, Index).
+template <int CRows, int CCols>
+EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<CRows, CCols>::Type bottomLeftCorner(Index cRows,
+                                                                                           Index cCols) const {
+  return typename ConstFixedBlockXpr<CRows, CCols>::Type(derived(), rows() - cRows, 0, cRows, cCols);
+}
+
+/// \returns a block consisting of the top rows of \c *this.
+///
+/// \param n the number of rows in the block
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+///
+/// Example: \include MatrixBase_topRows_int.cpp
+/// Output: \verbinclude MatrixBase_topRows_int.out
+///
+/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <typename NRowsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+    typename NRowsBlockXpr<...>::Type
+#endif
+    topRows(NRowsType n) {
+  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type(
+      derived(), 0, 0, internal::get_runtime_value(n), cols());
+}
+
+/// This is the const version of topRows(NRowsType).
+template <typename NRowsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+    const typename ConstNRowsBlockXpr<...>::Type
+#endif
+    topRows(NRowsType n) const {
+  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type(
+      derived(), 0, 0, internal::get_runtime_value(n), cols());
+}
+
+/// \returns a block consisting of the top rows of \c *this.
+///
+/// \tparam N the number of rows in the block as specified at compile-time
+/// \param n the number of rows in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_topRows.cpp
+/// Output: \verbinclude MatrixBase_template_int_topRows.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename NRowsBlockXpr<N>::Type topRows(Index n = N) {
+  return typename NRowsBlockXpr<N>::Type(derived(), 0, 0, n, cols());
+}
+
+/// This is the const version of topRows<int>().
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename ConstNRowsBlockXpr<N>::Type topRows(Index n = N) const {
+  return typename ConstNRowsBlockXpr<N>::Type(derived(), 0, 0, n, cols());
+}
+
+/// \returns a block consisting of the bottom rows of \c *this.
+///
+/// \param n the number of rows in the block
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+///
+/// Example: \include MatrixBase_bottomRows_int.cpp
+/// Output: \verbinclude MatrixBase_bottomRows_int.out
+///
+/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <typename NRowsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+    typename NRowsBlockXpr<...>::Type
+#endif
+    bottomRows(NRowsType n) {
+  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type(
+      derived(), rows() - internal::get_runtime_value(n), 0, internal::get_runtime_value(n), cols());
+}
+
+/// This is the const version of bottomRows(NRowsType).
+template <typename NRowsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+    const typename ConstNRowsBlockXpr<...>::Type
+#endif
+    bottomRows(NRowsType n) const {
+  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type(
+      derived(), rows() - internal::get_runtime_value(n), 0, internal::get_runtime_value(n), cols());
+}
+
+/// \returns a block consisting of the bottom rows of \c *this.
+///
+/// \tparam N the number of rows in the block as specified at compile-time
+/// \param n the number of rows in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_bottomRows.cpp
+/// Output: \verbinclude MatrixBase_template_int_bottomRows.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename NRowsBlockXpr<N>::Type bottomRows(Index n = N) {
+  return typename NRowsBlockXpr<N>::Type(derived(), rows() - n, 0, n, cols());
+}
+
+/// This is the const version of bottomRows<int>().
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename ConstNRowsBlockXpr<N>::Type bottomRows(Index n = N) const {
+  return typename ConstNRowsBlockXpr<N>::Type(derived(), rows() - n, 0, n, cols());
+}
+
+/// \returns a block consisting of a range of rows of \c *this.
+///
+/// \param startRow the index of the first row in the block
+/// \param n the number of rows in the block
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+///
+/// Example: \include DenseBase_middleRows_int.cpp
+/// Output: \verbinclude DenseBase_middleRows_int.out
+///
+/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <typename NRowsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+    typename NRowsBlockXpr<...>::Type
+#endif
+    middleRows(Index startRow, NRowsType n) {
+  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type(
+      derived(), startRow, 0, internal::get_runtime_value(n), cols());
+}
+
+/// This is the const version of middleRows(Index,NRowsType).
+template <typename NRowsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+    const typename ConstNRowsBlockXpr<...>::Type
+#endif
+    middleRows(Index startRow, NRowsType n) const {
+  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type(
+      derived(), startRow, 0, internal::get_runtime_value(n), cols());
+}
+
+/// \returns a block consisting of a range of rows of \c *this.
+///
+/// \tparam N the number of rows in the block as specified at compile-time
+/// \param startRow the index of the first row in the block
+/// \param n the number of rows in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include DenseBase_template_int_middleRows.cpp
+/// Output: \verbinclude DenseBase_template_int_middleRows.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename NRowsBlockXpr<N>::Type middleRows(Index startRow,
+                                                                                           Index n = N) {
+  return typename NRowsBlockXpr<N>::Type(derived(), startRow, 0, n, cols());
+}
+
+/// This is the const version of middleRows<int>().
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename ConstNRowsBlockXpr<N>::Type middleRows(Index startRow,
+                                                                                                Index n = N) const {
+  return typename ConstNRowsBlockXpr<N>::Type(derived(), startRow, 0, n, cols());
+}
+
+/// \returns a block consisting of the left columns of \c *this.
+///
+/// \param n the number of columns in the block
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include MatrixBase_leftCols_int.cpp
+/// Output: \verbinclude MatrixBase_leftCols_int.out
+///
+/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+    typename NColsBlockXpr<...>::Type
+#endif
+    leftCols(NColsType n) {
+  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type(derived(), 0, 0, rows(),
+                                                                                   internal::get_runtime_value(n));
+}
+
+/// This is the const version of leftCols(NColsType).
+template <typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+    const typename ConstNColsBlockXpr<...>::Type
+#endif
+    leftCols(NColsType n) const {
+  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type(derived(), 0, 0, rows(),
+                                                                                        internal::get_runtime_value(n));
+}
+
+/// \returns a block consisting of the left columns of \c *this.
+///
+/// \tparam N the number of columns in the block as specified at compile-time
+/// \param n the number of columns in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_leftCols.cpp
+/// Output: \verbinclude MatrixBase_template_int_leftCols.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename NColsBlockXpr<N>::Type leftCols(Index n = N) {
+  return typename NColsBlockXpr<N>::Type(derived(), 0, 0, rows(), n);
+}
+
+/// This is the const version of leftCols<int>().
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename ConstNColsBlockXpr<N>::Type leftCols(Index n = N) const {
+  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, 0, rows(), n);
+}
+
+/// \returns a block consisting of the right columns of \c *this.
+///
+/// \param n the number of columns in the block
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include MatrixBase_rightCols_int.cpp
+/// Output: \verbinclude MatrixBase_rightCols_int.out
+///
+/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+    typename NColsBlockXpr<...>::Type
+#endif
+    rightCols(NColsType n) {
+  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type(
+      derived(), 0, cols() - internal::get_runtime_value(n), rows(), internal::get_runtime_value(n));
+}
+
+/// This is the const version of rightCols(NColsType).
+template <typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+    const typename ConstNColsBlockXpr<...>::Type
+#endif
+    rightCols(NColsType n) const {
+  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type(
+      derived(), 0, cols() - internal::get_runtime_value(n), rows(), internal::get_runtime_value(n));
+}
+
+/// \returns a block consisting of the right columns of \c *this.
+///
+/// \tparam N the number of columns in the block as specified at compile-time
+/// \param n the number of columns in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_rightCols.cpp
+/// Output: \verbinclude MatrixBase_template_int_rightCols.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename NColsBlockXpr<N>::Type rightCols(Index n = N) {
+  return typename NColsBlockXpr<N>::Type(derived(), 0, cols() - n, rows(), n);
+}
+
+/// This is the const version of rightCols<int>().
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename ConstNColsBlockXpr<N>::Type rightCols(Index n = N) const {
+  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, cols() - n, rows(), n);
+}
+
+/// \returns a block consisting of a range of columns of \c *this.
+///
+/// \param startCol the index of the first column in the block
+/// \param numCols the number of columns in the block
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include DenseBase_middleCols_int.cpp
+/// Output: \verbinclude DenseBase_middleCols_int.out
+///
+/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+    typename NColsBlockXpr<...>::Type
+#endif
+    middleCols(Index startCol, NColsType numCols) {
+  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type(
+      derived(), 0, startCol, rows(), internal::get_runtime_value(numCols));
+}
+
+/// This is the const version of middleCols(Index,NColsType).
+template <typename NColsType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+    const typename ConstNColsBlockXpr<...>::Type
+#endif
+    middleCols(Index startCol, NColsType numCols) const {
+  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type(
+      derived(), 0, startCol, rows(), internal::get_runtime_value(numCols));
+}
+
+/// \returns a block consisting of a range of columns of \c *this.
+///
+/// \tparam N the number of columns in the block as specified at compile-time
+/// \param startCol the index of the first column in the block
+/// \param n the number of columns in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include DenseBase_template_int_middleCols.cpp
+/// Output: \verbinclude DenseBase_template_int_middleCols.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column - major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename NColsBlockXpr<N>::Type middleCols(Index startCol,
+                                                                                           Index n = N) {
+  return typename NColsBlockXpr<N>::Type(derived(), 0, startCol, rows(), n);
+}
+
+/// This is the const version of middleCols<int>().
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename ConstNColsBlockXpr<N>::Type middleCols(Index startCol,
+                                                                                                Index n = N) const {
+  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, startCol, rows(), n);
+}
+
+/// \returns a fixed-size expression of a block of \c *this.
+///
+/// The template parameters \a NRows and \a NCols are the number of
+/// rows and columns in the block.
+///
+/// \param startRow the first row in the block
+/// \param startCol the first column in the block
+///
+/// Example: \include MatrixBase_block_int_int.cpp
+/// Output: \verbinclude MatrixBase_block_int_int.out
+///
+/// \note The usage of this overload is discouraged from %Eigen 3.4, better use the generic
+/// block(Index,Index,NRowsType,NColsType), here is the one-to-one equivalence:
+/// \code
+/// mat.template block<NRows,NCols>(i,j)  <-->  mat.block(i,j,fix<NRows>,fix<NCols>)
+/// \endcode
+///
+/// \note since block is a templated member, the keyword template has to be used
+/// if the matrix type is also a template parameter: \code m.template block<3,3>(1,1); \endcode
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int NRows, int NCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedBlockXpr<NRows, NCols>::Type block(Index startRow,
+                                                                                                 Index startCol) {
+  return typename FixedBlockXpr<NRows, NCols>::Type(derived(), startRow, startCol);
+}
+
+/// This is the const version of block<>(Index, Index). */
+template <int NRows, int NCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<NRows, NCols>::Type block(
+    Index startRow, Index startCol) const {
+  return typename ConstFixedBlockXpr<NRows, NCols>::Type(derived(), startRow, startCol);
+}
+
+/// \returns an expression of a block of \c *this.
+///
+/// \tparam NRows number of rows in block as specified at compile-time
+/// \tparam NCols number of columns in block as specified at compile-time
+/// \param  startRow  the first row in the block
+/// \param  startCol  the first column in the block
+/// \param  blockRows number of rows in block as specified at run-time
+/// \param  blockCols number of columns in block as specified at run-time
+///
+/// This function is mainly useful for blocks where the number of rows is specified at compile-time
+/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
+/// information should not contradict. In other words, \a blockRows should equal \a NRows unless
+/// \a NRows is \a Dynamic, and the same for the number of columns.
+///
+/// Example: \include MatrixBase_template_int_int_block_int_int_int_int.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_block_int_int_int_int.out
+///
+/// \note The usage of this overload is discouraged from %Eigen 3.4, better use the generic
+/// block(Index,Index,NRowsType,NColsType), here is the one-to-one complete equivalence:
+/// \code
+/// mat.template block<NRows,NCols>(i,j,rows,cols)     <-->  mat.block(i,j,fix<NRows>(rows),fix<NCols>(cols))
+/// \endcode
+/// If we known that, e.g., NRows==Dynamic and NCols!=Dynamic, then the equivalence becomes:
+/// \code
+/// mat.template block<Dynamic,NCols>(i,j,rows,NCols)  <-->  mat.block(i,j,rows,fix<NCols>)
+/// \endcode
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template <int NRows, int NCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedBlockXpr<NRows, NCols>::Type block(Index startRow,
+                                                                                                 Index startCol,
+                                                                                                 Index blockRows,
+                                                                                                 Index blockCols) {
+  return typename FixedBlockXpr<NRows, NCols>::Type(derived(), startRow, startCol, blockRows, blockCols);
+}
+
+/// This is the const version of block<>(Index, Index, Index, Index).
+template <int NRows, int NCols>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<NRows, NCols>::Type block(
+    Index startRow, Index startCol, Index blockRows, Index blockCols) const {
+  return typename ConstFixedBlockXpr<NRows, NCols>::Type(derived(), startRow, startCol, blockRows, blockCols);
+}
+
+/// \returns an expression of the \a i-th column of \c *this. Note that the numbering starts at 0.
+///
+/// Example: \include MatrixBase_col.cpp
+/// Output: \verbinclude MatrixBase_col.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column - major)
+/**
+ * \sa row(), class Block */
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE ColXpr col(Index i) { return ColXpr(derived(), i); }
+
+/// This is the const version of col().
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE ConstColXpr col(Index i) const { return ConstColXpr(derived(), i); }
+
+/// \returns an expression of the \a i-th row of \c *this. Note that the numbering starts at 0.
+///
+/// Example: \include MatrixBase_row.cpp
+/// Output: \verbinclude MatrixBase_row.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row - major)
+/**
+ * \sa col(), class Block */
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE RowXpr row(Index i) { return RowXpr(derived(), i); }
+
+/// This is the const version of row(). */
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE ConstRowXpr row(Index i) const { return ConstRowXpr(derived(), i); }
+
+/// \returns an expression of a segment (i.e. a vector block) in \c *this with either dynamic or fixed sizes.
+///
+/// \only_for_vectors
+///
+/// \param start the first coefficient in the segment
+/// \param n the number of coefficients in the segment
+/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
+///
+/// Example: \include MatrixBase_segment_int_int.cpp
+/// Output: \verbinclude MatrixBase_segment_int_int.out
+///
+/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+/// \note Even in the case that the returned expression has dynamic size, in the case
+/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
+/// which means that evaluating it does not cause a dynamic memory allocation.
+///
+/// \sa block(Index,Index,NRowsType,NColsType), fix<N>, fix<N>(int), class Block
+///
+template <typename NType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+    typename FixedSegmentReturnType<...>::Type
+#endif
+    segment(Index start, NType n) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type(derived(), start,
+                                                                                        internal::get_runtime_value(n));
+}
+
+/// This is the const version of segment(Index,NType).
+template <typename NType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+    const typename ConstFixedSegmentReturnType<...>::Type
+#endif
+    segment(Index start, NType n) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type(
+      derived(), start, internal::get_runtime_value(n));
+}
+
+/// \returns an expression of the first coefficients of \c *this with either dynamic or fixed sizes.
+///
+/// \only_for_vectors
+///
+/// \param n the number of coefficients in the segment
+/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
+///
+/// Example: \include MatrixBase_start_int.cpp
+/// Output: \verbinclude MatrixBase_start_int.out
+///
+/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+/// \note Even in the case that the returned expression has dynamic size, in the case
+/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
+/// which means that evaluating it does not cause a dynamic memory allocation.
+///
+/// \sa class Block, block(Index,Index)
+///
+template <typename NType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+    typename FixedSegmentReturnType<...>::Type
+#endif
+    head(NType n) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type(derived(), 0,
+                                                                                        internal::get_runtime_value(n));
+}
+
+/// This is the const version of head(NType).
+template <typename NType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+    const typename ConstFixedSegmentReturnType<...>::Type
+#endif
+    head(NType n) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type(
+      derived(), 0, internal::get_runtime_value(n));
+}
+
+/// \returns an expression of a last coefficients of \c *this with either dynamic or fixed sizes.
+///
+/// \only_for_vectors
+///
+/// \param n the number of coefficients in the segment
+/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
+///
+/// Example: \include MatrixBase_end_int.cpp
+/// Output: \verbinclude MatrixBase_end_int.out
+///
+/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+/// \note Even in the case that the returned expression has dynamic size, in the case
+/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
+/// which means that evaluating it does not cause a dynamic memory allocation.
+///
+/// \sa class Block, block(Index,Index)
+///
+template <typename NType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+    typename FixedSegmentReturnType<...>::Type
+#endif
+    tail(NType n) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type(
+      derived(), this->size() - internal::get_runtime_value(n), internal::get_runtime_value(n));
+}
+
+/// This is the const version of tail(Index).
+template <typename NType>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+    const typename ConstFixedSegmentReturnType<...>::Type
+#endif
+    tail(NType n) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type(
+      derived(), this->size() - internal::get_runtime_value(n), internal::get_runtime_value(n));
+}
+
+/// \returns a fixed-size expression of a segment (i.e. a vector block) in \c *this
+///
+/// \only_for_vectors
+///
+/// \tparam N the number of coefficients in the segment as specified at compile-time
+/// \param start the index of the first element in the segment
+/// \param n the number of coefficients in the segment as specified at compile-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_segment.cpp
+/// Output: \verbinclude MatrixBase_template_int_segment.out
+///
+/// \sa segment(Index,NType), class Block
+///
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedSegmentReturnType<N>::Type segment(Index start,
+                                                                                                 Index n = N) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<N>::Type(derived(), start, n);
+}
+
+/// This is the const version of segment<int>(Index).
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename ConstFixedSegmentReturnType<N>::Type segment(
+    Index start, Index n = N) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<N>::Type(derived(), start, n);
+}
+
+/// \returns a fixed-size expression of the first coefficients of \c *this.
+///
+/// \only_for_vectors
+///
+/// \tparam N the number of coefficients in the segment as specified at compile-time
+/// \param  n the number of coefficients in the segment as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_start.cpp
+/// Output: \verbinclude MatrixBase_template_int_start.out
+///
+/// \sa head(NType), class Block
+///
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedSegmentReturnType<N>::Type head(Index n = N) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<N>::Type(derived(), 0, n);
+}
+
+/// This is the const version of head<int>().
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename ConstFixedSegmentReturnType<N>::Type head(Index n = N) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<N>::Type(derived(), 0, n);
+}
+
+/// \returns a fixed-size expression of the last coefficients of \c *this.
+///
+/// \only_for_vectors
+///
+/// \tparam N the number of coefficients in the segment as specified at compile-time
+/// \param  n the number of coefficients in the segment as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_end.cpp
+/// Output: \verbinclude MatrixBase_template_int_end.out
+///
+/// \sa tail(NType), class Block
+///
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename FixedSegmentReturnType<N>::Type tail(Index n = N) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<N>::Type(derived(), size() - n, n);
+}
+
+/// This is the const version of tail<int>.
+template <int N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE typename ConstFixedSegmentReturnType<N>::Type tail(Index n = N) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<N>::Type(derived(), size() - n, n);
+}
+
+/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+/// is col-major (resp. row-major).
+///
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE InnerVectorReturnType innerVector(Index outer) {
+  return InnerVectorReturnType(derived(), outer);
+}
+
+/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+/// is col-major (resp. row-major). Read-only.
+///
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const ConstInnerVectorReturnType innerVector(Index outer) const {
+  return ConstInnerVectorReturnType(derived(), outer);
+}
+
+/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+/// is col-major (resp. row-major).
+///
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE InnerVectorsReturnType innerVectors(Index outerStart, Index outerSize) {
+  return Block<Derived, Dynamic, Dynamic, true>(derived(), IsRowMajor ? outerStart : 0, IsRowMajor ? 0 : outerStart,
+                                                IsRowMajor ? outerSize : rows(), IsRowMajor ? cols() : outerSize);
+}
+
+/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+/// is col-major (resp. row-major). Read-only.
+///
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const ConstInnerVectorsReturnType innerVectors(Index outerStart,
+                                                                                               Index outerSize) const {
+  return Block<const Derived, Dynamic, Dynamic, true>(derived(), IsRowMajor ? outerStart : 0,
+                                                      IsRowMajor ? 0 : outerStart, IsRowMajor ? outerSize : rows(),
+                                                      IsRowMajor ? cols() : outerSize);
+}
+
+/** \returns the i-th subvector (column or vector) according to the \c Direction
+ * \sa subVectors()
+ */
+template <DirectionType Direction>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE std::conditional_t<Direction == Vertical, ColXpr, RowXpr> subVector(
+    Index i) {
+  return std::conditional_t<Direction == Vertical, ColXpr, RowXpr>(derived(), i);
+}
+
+/** This is the const version of subVector(Index) */
+template <DirectionType Direction>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE std::conditional_t<Direction == Vertical, ConstColXpr, ConstRowXpr>
+subVector(Index i) const {
+  return std::conditional_t<Direction == Vertical, ConstColXpr, ConstRowXpr>(derived(), i);
+}
+
+/** \returns the number of subvectors (rows or columns) in the direction \c Direction
+ * \sa subVector(Index)
+ */
+template <DirectionType Direction>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Index subVectors() const {
+  return (Direction == Vertical) ? cols() : rows();
+}
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/CommonCwiseBinaryOps.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/CommonCwiseBinaryOps.inc
new file mode 100644
index 0000000..d5127e3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/CommonCwiseBinaryOps.inc
@@ -0,0 +1,122 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// This file is a base class plugin containing common coefficient wise functions.
+
+/** \returns an expression of the difference of \c *this and \a other
+ *
+ * \note If you want to subtract a given scalar from all coefficients, see Cwise::operator-().
+ *
+ * \sa class CwiseBinaryOp, operator-=()
+ */
+EIGEN_MAKE_CWISE_BINARY_OP(operator-, difference)
+
+/** \returns an expression of the sum of \c *this and \a other
+ *
+ * \note If you want to add a given scalar to all coefficients, see Cwise::operator+().
+ *
+ * \sa class CwiseBinaryOp, operator+=()
+ */
+EIGEN_MAKE_CWISE_BINARY_OP(operator+, sum)
+
+/** \returns an expression of a custom coefficient-wise operator \a func of *this and \a other
+ *
+ * The template parameter \a CustomBinaryOp is the type of the functor
+ * of the custom operator (see class CwiseBinaryOp for an example)
+ *
+ * Here is an example illustrating the use of custom functors:
+ * \include class_CwiseBinaryOp.cpp
+ * Output: \verbinclude class_CwiseBinaryOp.out
+ *
+ * \sa class CwiseBinaryOp, operator+(), operator-(), cwiseProduct()
+ */
+template <typename CustomBinaryOp, typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>
+binaryExpr(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other,
+           const CustomBinaryOp& func = CustomBinaryOp()) const {
+  return CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other.derived(), func);
+}
+
+/** \returns an expression of \c *this scaled by the scalar factor \a scalar
+ *
+ * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+ */
+EIGEN_MAKE_SCALAR_BINARY_OP(operator*, product)
+
+/** \returns an expression of \c *this divided by the scalar value \a scalar
+ *
+ * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+ */
+EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(operator/, quotient)
+
+/** \returns an expression of the coefficient-wise boolean \b and operator of \c *this and \a other
+ *
+ * Example: \include Cwise_boolean_and.cpp
+ * Output: \verbinclude Cwise_boolean_and.out
+ *
+ * \sa operator||(), select()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryOp<internal::scalar_boolean_and_op<Scalar>, const Derived,
+                                                       const OtherDerived>
+operator&&(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_boolean_and_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                   other.derived());
+}
+
+/** \returns an expression of the coefficient-wise boolean \b or operator of \c *this and \a other
+ *
+ * Example: \include Cwise_boolean_or.cpp
+ * Output: \verbinclude Cwise_boolean_or.out
+ *
+ * \sa operator&&(), select()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryOp<internal::scalar_boolean_or_op<Scalar>, const Derived,
+                                                       const OtherDerived>
+operator||(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_boolean_or_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                  other.derived());
+}
+
+/** \returns an expression of the bitwise \b and operator of \c *this and \a other
+ *
+ * \sa operator|(), operator^()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryOp<internal::scalar_bitwise_and_op<Scalar>, const Derived,
+                                                       const OtherDerived>
+operator&(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_bitwise_and_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                   other.derived());
+}
+
+/** \returns an expression of the bitwise boolean \b or operator of \c *this and \a other
+ *
+ * \sa operator&(), operator^()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryOp<internal::scalar_bitwise_or_op<Scalar>, const Derived,
+                                                       const OtherDerived>
+operator|(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_bitwise_or_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                  other.derived());
+}
+
+/** \returns an expression of the bitwise xor operator of *this and \a other
+ * \sa operator&(), operator|()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryOp<internal::scalar_bitwise_xor_op<Scalar>, const Derived,
+                                                       const OtherDerived>
+operator^(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_bitwise_xor_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                   other.derived());
+}
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/CommonCwiseUnaryOps.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/CommonCwiseUnaryOps.inc
new file mode 100644
index 0000000..bf8a940
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/CommonCwiseUnaryOps.inc
@@ -0,0 +1,167 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// This file is a base class plugin containing common coefficient wise functions.
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+/** \internal the return type of conjugate() */
+typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+                           const CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Derived>, const Derived&>
+    ConjugateReturnType;
+/** \internal the return type of real() const */
+typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+                           const CwiseUnaryOp<internal::scalar_real_op<Scalar>, const Derived>, const Derived&>
+    RealReturnType;
+/** \internal the return type of real() */
+typedef std::conditional_t<NumTraits<Scalar>::IsComplex, CwiseUnaryView<internal::scalar_real_ref_op<Scalar>, Derived>,
+                           Derived&>
+    NonConstRealReturnType;
+/** \internal the return type of imag() const */
+typedef CwiseUnaryOp<internal::scalar_imag_op<Scalar>, const Derived> ImagReturnType;
+/** \internal the return type of imag() */
+typedef CwiseUnaryView<internal::scalar_imag_ref_op<Scalar>, Derived> NonConstImagReturnType;
+
+typedef CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived> NegativeReturnType;
+
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+/// \returns an expression of the opposite of \c *this
+///
+EIGEN_DOC_UNARY_ADDONS(operator-, opposite)
+///
+EIGEN_DEVICE_FUNC constexpr inline const NegativeReturnType operator-() const { return NegativeReturnType(derived()); }
+
+template <class NewType>
+struct CastXpr {
+  typedef typename internal::cast_return_type<
+      Derived, const CwiseUnaryOp<internal::core_cast_op<Scalar, NewType>, const Derived> >::type Type;
+};
+
+/// \returns an expression of \c *this with the \a Scalar type casted to
+/// \a NewScalar.
+///
+/// The template parameter \a NewScalar is the type we are casting the scalars to.
+///
+EIGEN_DOC_UNARY_ADDONS(cast, conversion function)
+///
+/// \sa class CwiseUnaryOp
+///
+template <typename NewType>
+EIGEN_DEVICE_FUNC constexpr typename CastXpr<NewType>::Type cast() const {
+  return typename CastXpr<NewType>::Type(derived());
+}
+
+/// \returns an expression of the complex conjugate of \c *this.
+///
+EIGEN_DOC_UNARY_ADDONS(conjugate, complex conjugate)
+///
+/// \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_conj">Math functions</a>, MatrixBase::adjoint()
+EIGEN_DEVICE_FUNC constexpr inline ConjugateReturnType conjugate() const { return ConjugateReturnType(derived()); }
+
+/// \returns an expression of the complex conjugate of \c *this if Cond==true, returns derived() otherwise.
+///
+EIGEN_DOC_UNARY_ADDONS(conjugate, complex conjugate)
+///
+/// \sa conjugate()
+template <bool Cond>
+EIGEN_DEVICE_FUNC constexpr inline std::conditional_t<Cond, ConjugateReturnType, const Derived&> conjugateIf() const {
+  typedef std::conditional_t<Cond, ConjugateReturnType, const Derived&> ReturnType;
+  return ReturnType(derived());
+}
+
+/// \returns a read-only expression of the real part of \c *this.
+///
+EIGEN_DOC_UNARY_ADDONS(real, real part function)
+///
+/// \sa imag()
+EIGEN_DEVICE_FUNC constexpr inline RealReturnType real() const { return RealReturnType(derived()); }
+
+/// \returns an read-only expression of the imaginary part of \c *this.
+///
+EIGEN_DOC_UNARY_ADDONS(imag, imaginary part function)
+///
+/// \sa real()
+EIGEN_DEVICE_FUNC constexpr inline const ImagReturnType imag() const { return ImagReturnType(derived()); }
+
+/// \brief Apply a unary operator coefficient-wise
+/// \param[in]  func  Functor implementing the unary operator
+/// \tparam  CustomUnaryOp Type of \a func
+/// \returns An expression of a custom coefficient-wise unary operator \a func of *this
+///
+/// The function \c ptr_fun() from the C++ standard library can be used to make functors out of normal functions.
+///
+/// Example:
+/// \include class_CwiseUnaryOp_ptrfun.cpp
+/// Output: \verbinclude class_CwiseUnaryOp_ptrfun.out
+///
+/// Genuine functors allow for more possibilities, for instance it may contain a state.
+///
+/// Example:
+/// \include class_CwiseUnaryOp.cpp
+/// Output: \verbinclude class_CwiseUnaryOp.out
+///
+EIGEN_DOC_UNARY_ADDONS(unaryExpr, unary function)
+///
+/// \sa unaryViewExpr, binaryExpr, class CwiseUnaryOp
+///
+template <typename CustomUnaryOp>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseUnaryOp<CustomUnaryOp, const Derived> unaryExpr(
+    const CustomUnaryOp& func = CustomUnaryOp()) const {
+  return CwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func);
+}
+
+/// \returns a const expression of a custom coefficient-wise unary operator \a func of *this
+///
+/// The template parameter \a CustomUnaryOp is the type of the functor
+/// of the custom unary operator.
+///
+/// Example:
+/// \include class_CwiseUnaryOp.cpp
+/// Output: \verbinclude class_CwiseUnaryOp.out
+///
+EIGEN_DOC_UNARY_ADDONS(unaryViewExpr, unary function)
+///
+/// \sa unaryExpr, binaryExpr class CwiseUnaryOp
+///
+template <typename CustomViewOp>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseUnaryView<CustomViewOp, const Derived> unaryViewExpr(
+    const CustomViewOp& func = CustomViewOp()) const {
+  return CwiseUnaryView<CustomViewOp, const Derived>(derived(), func);
+}
+
+/// \returns a non-const expression of a custom coefficient-wise unary view \a func of *this
+///
+/// The template parameter \a CustomUnaryOp is the type of the functor
+/// of the custom unary operator.
+///
+EIGEN_DOC_UNARY_ADDONS(unaryViewExpr, unary function)
+///
+/// \sa unaryExpr, binaryExpr class CwiseUnaryOp
+///
+template <typename CustomViewOp>
+EIGEN_DEVICE_FUNC constexpr inline CwiseUnaryView<CustomViewOp, Derived> unaryViewExpr(
+    const CustomViewOp& func = CustomViewOp()) {
+  return CwiseUnaryView<CustomViewOp, Derived>(derived(), func);
+}
+
+/// \returns a non const expression of the real part of \c *this.
+///
+EIGEN_DOC_UNARY_ADDONS(real, real part function)
+///
+/// \sa imag()
+EIGEN_DEVICE_FUNC constexpr inline NonConstRealReturnType real() { return NonConstRealReturnType(derived()); }
+
+/// \returns a non const expression of the imaginary part of \c *this.
+///
+EIGEN_DOC_UNARY_ADDONS(imag, imaginary part function)
+///
+/// \sa real()
+EIGEN_DEVICE_FUNC constexpr inline NonConstImagReturnType imag() { return NonConstImagReturnType(derived()); }
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/IndexedViewMethods.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/IndexedViewMethods.inc
new file mode 100644
index 0000000..a51e349
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/IndexedViewMethods.inc
@@ -0,0 +1,192 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if !defined(EIGEN_PARSED_BY_DOXYGEN)
+
+public:
+// SFINAE dummy types
+
+template <typename RowIndices, typename ColIndices>
+using EnableOverload = std::enable_if_t<
+    internal::valid_indexed_view_overload<RowIndices, ColIndices>::value && internal::is_lvalue<Derived>::value, bool>;
+
+template <typename RowIndices, typename ColIndices>
+using EnableConstOverload =
+    std::enable_if_t<internal::valid_indexed_view_overload<RowIndices, ColIndices>::value, bool>;
+
+template <typename Indices>
+using EnableVectorOverload =
+    std::enable_if_t<!internal::is_valid_index_type<Indices>::value && internal::is_lvalue<Derived>::value, bool>;
+
+template <typename Indices>
+using EnableConstVectorOverload = std::enable_if_t<!internal::is_valid_index_type<Indices>::value, bool>;
+
+public:
+// Public API for 2D matrices/arrays
+
+// non-const versions
+
+ template <typename RowIndices, typename ColIndices>
+ using IndexedViewType = typename internal::IndexedViewSelector<Derived, RowIndices, ColIndices>::ReturnType;
+
+ template <typename RowIndices, typename ColIndices, EnableOverload<RowIndices, ColIndices> = true>
+ IndexedViewType<RowIndices, ColIndices> operator()(const RowIndices& rowIndices, const ColIndices& colIndices) {
+   return internal::IndexedViewSelector<Derived, RowIndices, ColIndices>::run(derived(), rowIndices, colIndices);
+ }
+
+template <typename RowType, size_t RowSize, typename ColIndices, typename RowIndices = Array<RowType, RowSize, 1>,
+          EnableOverload<RowIndices, ColIndices> = true>
+IndexedViewType<RowIndices, ColIndices> operator()(const RowType (&rowIndices)[RowSize], const ColIndices& colIndices) {
+  return internal::IndexedViewSelector<Derived, RowIndices, ColIndices>::run(derived(), RowIndices{rowIndices},
+                                                                             colIndices);
+}
+
+template <typename RowIndices, typename ColType, size_t ColSize, typename ColIndices = Array<ColType, ColSize, 1>,
+          EnableOverload<RowIndices, ColIndices> = true>
+IndexedViewType<RowIndices, ColIndices> operator()(const RowIndices& rowIndices, const ColType (&colIndices)[ColSize]) {
+  return internal::IndexedViewSelector<Derived, RowIndices, ColIndices>::run(derived(), rowIndices,
+                                                                             ColIndices{colIndices});
+}
+
+template <typename RowType, size_t RowSize, typename ColType, size_t ColSize,
+          typename RowIndices = Array<RowType, RowSize, 1>, typename ColIndices = Array<ColType, ColSize, 1>,
+          EnableOverload<RowIndices, ColIndices> = true>
+IndexedViewType<RowIndices, ColIndices> operator()(const RowType (&rowIndices)[RowSize],
+                                                   const ColType (&colIndices)[ColSize]) {
+  return internal::IndexedViewSelector<Derived, RowIndices, ColIndices>::run(derived(), RowIndices{rowIndices},
+                                                                             ColIndices{colIndices});
+}
+
+// const versions
+
+template <typename RowIndices, typename ColIndices>
+using ConstIndexedViewType = typename internal::IndexedViewSelector<Derived, RowIndices, ColIndices>::ConstReturnType;
+
+template <typename RowIndices, typename ColIndices, EnableConstOverload<RowIndices, ColIndices> = true>
+ConstIndexedViewType<RowIndices, ColIndices> operator()(const RowIndices& rowIndices,
+                                                        const ColIndices& colIndices) const {
+  return internal::IndexedViewSelector<Derived, RowIndices, ColIndices>::run(derived(), rowIndices, colIndices);
+}
+
+template <typename RowType, size_t RowSize, typename ColIndices, typename RowIndices = Array<RowType, RowSize, 1>,
+          EnableConstOverload<RowIndices, ColIndices> = true>
+ConstIndexedViewType<RowIndices, ColIndices> operator()(const RowType (&rowIndices)[RowSize],
+                                                        const ColIndices& colIndices) const {
+  return internal::IndexedViewSelector<Derived, RowIndices, ColIndices>::run(derived(), RowIndices{rowIndices},
+                                                                             colIndices);
+}
+
+template <typename RowIndices, typename ColType, size_t ColSize, typename ColIndices = Array<ColType, ColSize, 1>,
+          EnableConstOverload<RowIndices, ColIndices> = true>
+ConstIndexedViewType<RowIndices, ColIndices> operator()(const RowIndices& rowIndices,
+                                                        const ColType (&colIndices)[ColSize]) const {
+  return internal::IndexedViewSelector<Derived, RowIndices, ColIndices>::run(derived(), rowIndices,
+                                                                             ColIndices{colIndices});
+}
+
+template <typename RowType, size_t RowSize, typename ColType, size_t ColSize,
+          typename RowIndices = Array<RowType, RowSize, 1>, typename ColIndices = Array<ColType, ColSize, 1>,
+          EnableConstOverload<RowIndices, ColIndices> = true>
+ConstIndexedViewType<RowIndices, ColIndices> operator()(const RowType (&rowIndices)[RowSize],
+                                                        const ColType (&colIndices)[ColSize]) const {
+  return internal::IndexedViewSelector<Derived, RowIndices, ColIndices>::run(derived(), RowIndices{rowIndices},
+                                                                             ColIndices{colIndices});
+}
+
+// Public API for 1D vectors/arrays
+
+// non-const versions
+
+template <typename Indices>
+using VectorIndexedViewType = typename internal::VectorIndexedViewSelector<Derived, Indices>::ReturnType;
+
+template <typename Indices, EnableVectorOverload<Indices> = true>
+VectorIndexedViewType<Indices> operator()(const Indices& indices) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return internal::VectorIndexedViewSelector<Derived, Indices>::run(derived(), indices);
+}
+
+template <typename IndexType, size_t Size, typename Indices = Array<IndexType, Size, 1>,
+          EnableVectorOverload<Indices> = true>
+VectorIndexedViewType<Indices> operator()(const IndexType (&indices)[Size]) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return internal::VectorIndexedViewSelector<Derived, Indices>::run(derived(), Indices{indices});
+}
+
+// const versions
+
+template <typename Indices>
+using ConstVectorIndexedViewType = typename internal::VectorIndexedViewSelector<Derived, Indices>::ConstReturnType;
+
+template <typename Indices, EnableConstVectorOverload<Indices> = true>
+ConstVectorIndexedViewType<Indices> operator()(const Indices& indices) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return internal::VectorIndexedViewSelector<Derived, Indices>::run(derived(), indices);
+}
+
+template <typename IndexType, size_t Size, typename Indices = Array<IndexType, Size, 1>,
+          EnableConstVectorOverload<Indices> = true>
+ConstVectorIndexedViewType<Indices> operator()(const IndexType (&indices)[Size]) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return internal::VectorIndexedViewSelector<Derived, Indices>::run(derived(), Indices{indices});
+}
+
+#else  // EIGEN_PARSED_BY_DOXYGEN
+
+/**
+ * \returns a generic submatrix view defined by the rows and columns indexed \a rowIndices and \a colIndices
+ * respectively.
+ *
+ * Each parameter must either be:
+ *  - An integer indexing a single row or column
+ *  - Eigen::placeholders::all indexing the full set of respective rows or columns in increasing order
+ *  - An ArithmeticSequence as returned by the Eigen::seq and Eigen::seqN functions
+ *  - Any %Eigen's vector/array of integers or expressions
+ *  - Plain C arrays: \c int[N]
+ *  - And more generally any type exposing the following two member functions:
+ * \code
+ * <integral type> operator[](<integral type>) const;
+ * <integral type> size() const;
+ * \endcode
+ * where \c <integral \c type>  stands for any integer type compatible with Eigen::Index (i.e. \c std::ptrdiff_t).
+ *
+ * The last statement implies compatibility with \c std::vector, \c std::valarray, \c std::array, many of the Range-v3's
+ * ranges, etc.
+ *
+ * If the submatrix can be represented using a starting position \c (i,j) and positive sizes \c (rows,columns), then
+ * this method will returns a Block object after extraction of the relevant information from the passed arguments. This
+ * is the case when all arguments are either:
+ *  - An integer
+ *  - Eigen::placeholders::all
+ *  - An ArithmeticSequence with compile-time increment strictly equal to 1, as returned by Eigen::seq(a,b), and
+ * Eigen::seqN(a,N).
+ *
+ * Otherwise a more general IndexedView<Derived,RowIndices',ColIndices'> object will be returned, after conversion of
+ * the inputs to more suitable types \c RowIndices' and \c ColIndices'.
+ *
+ * For 1D vectors and arrays, you better use the operator()(const Indices&) overload, which behave the same way but
+ * taking a single parameter.
+ *
+ * See also this <a
+ * href="https://stackoverflow.com/questions/46110917/eigen-replicate-items-along-one-dimension-without-useless-allocations">question</a>
+ * and its answer for an example of how to duplicate coefficients.
+ *
+ * \sa operator()(const Indices&), class Block, class IndexedView, DenseBase::block(Index,Index,Index,Index)
+ */
+template <typename RowIndices, typename ColIndices>
+IndexedView_or_Block operator()(const RowIndices& rowIndices, const ColIndices& colIndices);
+
+/** This is an overload of operator()(const RowIndices&, const ColIndices&) for 1D vectors or arrays
+ *
+ * \only_for_vectors
+ */
+template <typename Indices>
+IndexedView_or_VectorBlock operator()(const Indices& indices);
+
+#endif  // EIGEN_PARSED_BY_DOXYGEN
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/InternalHeaderCheck.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/InternalHeaderCheck.inc
new file mode 100644
index 0000000..ac6821d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/InternalHeaderCheck.inc
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CORE_MODULE_H
+#error "Please include Eigen/plugins instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/MatrixCwiseBinaryOps.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/MatrixCwiseBinaryOps.inc
new file mode 100644
index 0000000..8e0da06
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/MatrixCwiseBinaryOps.inc
@@ -0,0 +1,335 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// This file is a base class plugin containing matrix specifics coefficient wise functions.
+
+/** \returns an expression of the Schur product (coefficient wise product) of *this and \a other
+ *
+ * Example: \include MatrixBase_cwiseProduct.cpp
+ * Output: \verbinclude MatrixBase_cwiseProduct.out
+ *
+ * \sa class CwiseBinaryOp, cwiseAbs2
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived, OtherDerived, product)
+    cwiseProduct(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived, OtherDerived, product)(derived(), other.derived());
+}
+
+template <typename OtherDerived>
+using CwiseBinaryEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const OtherDerived>;
+template <typename OtherDerived>
+using CwiseBinaryNotEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const OtherDerived>;
+template <typename OtherDerived>
+using CwiseBinaryLessReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const OtherDerived>;
+template <typename OtherDerived>
+using CwiseBinaryGreaterReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const OtherDerived>;
+template <typename OtherDerived>
+using CwiseBinaryLessOrEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const OtherDerived>;
+template <typename OtherDerived>
+using CwiseBinaryGreaterOrEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const OtherDerived>;
+
+/** \returns an expression of the coefficient-wise == operator of *this and \a other
+ *
+ * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+ * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+ * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+ * isMuchSmallerThan().
+ *
+ * Example: \include MatrixBase_cwiseEqual.cpp
+ * Output: \verbinclude MatrixBase_cwiseEqual.out
+ *
+ * \sa cwiseNotEqual(), isApprox(), isMuchSmallerThan()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryEqualReturnType<OtherDerived> cwiseEqual(
+    const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise != operator of *this and \a other
+ *
+ * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+ * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+ * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+ * isMuchSmallerThan().
+ *
+ * Example: \include MatrixBase_cwiseNotEqual.cpp
+ * Output: \verbinclude MatrixBase_cwiseNotEqual.out
+ *
+ * \sa cwiseEqual(), isApprox(), isMuchSmallerThan()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryNotEqualReturnType<OtherDerived> cwiseNotEqual(
+    const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryNotEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise < operator of *this and \a other */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryLessReturnType<OtherDerived> cwiseLess(
+    const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryLessReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise > operator of *this and \a other */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryGreaterReturnType<OtherDerived> cwiseGreater(
+    const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryGreaterReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise <= operator of *this and \a other */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryLessOrEqualReturnType<OtherDerived> cwiseLessOrEqual(
+    const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryLessOrEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise >= operator of *this and \a other */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr inline const CwiseBinaryGreaterOrEqualReturnType<OtherDerived> cwiseGreaterOrEqual(
+    const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryGreaterOrEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise min of *this and \a other
+ *
+ * Example: \include MatrixBase_cwiseMin.cpp
+ * Output: \verbinclude MatrixBase_cwiseMin.out
+ *
+ * \sa class CwiseBinaryOp, max()
+ */
+template <int NaNPropagation = PropagateFast, typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_min_op<Scalar, Scalar, NaNPropagation>, const Derived, const OtherDerived>
+    cwiseMin(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_min_op<Scalar, Scalar, NaNPropagation>, const Derived, const OtherDerived>(
+      derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise min of *this and scalar \a other
+ *
+ * \sa class CwiseBinaryOp, min()
+ */
+template <int NaNPropagation = PropagateFast>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_min_op<Scalar, Scalar, NaNPropagation>, const Derived, const ConstantReturnType>
+    cwiseMin(const Scalar& other) const {
+  return cwiseMin<NaNPropagation>(Derived::Constant(rows(), cols(), other));
+}
+
+/** \returns an expression of the coefficient-wise max of *this and \a other
+ *
+ * Example: \include MatrixBase_cwiseMax.cpp
+ * Output: \verbinclude MatrixBase_cwiseMax.out
+ *
+ * \sa class CwiseBinaryOp, min()
+ */
+template <int NaNPropagation = PropagateFast, typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_max_op<Scalar, Scalar, NaNPropagation>, const Derived, const OtherDerived>
+    cwiseMax(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_max_op<Scalar, Scalar, NaNPropagation>, const Derived, const OtherDerived>(
+      derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise max of *this and scalar \a other
+ *
+ * \sa class CwiseBinaryOp, min()
+ */
+template <int NaNPropagation = PropagateFast>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_max_op<Scalar, Scalar, NaNPropagation>, const Derived, const ConstantReturnType>
+    cwiseMax(const Scalar& other) const {
+  return cwiseMax<NaNPropagation>(Derived::Constant(rows(), cols(), other));
+}
+
+/** \returns an expression of the coefficient-wise quotient of *this and \a other
+ *
+ * Example: \include MatrixBase_cwiseQuotient.cpp
+ * Output: \verbinclude MatrixBase_cwiseQuotient.out
+ *
+ * \sa class CwiseBinaryOp, cwiseProduct(), cwiseInverse()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const
+    CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>
+    cwiseQuotient(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                other.derived());
+}
+
+using CwiseScalarEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const ConstantReturnType>;
+using CwiseScalarNotEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const ConstantReturnType>;
+using CwiseScalarLessReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const ConstantReturnType>;
+using CwiseScalarGreaterReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const ConstantReturnType>;
+using CwiseScalarLessOrEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const ConstantReturnType>;
+using CwiseScalarGreaterOrEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const ConstantReturnType>;
+
+/** \returns an expression of the coefficient-wise == operator of \c *this and a scalar \a s
+ *
+ * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+ * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+ * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+ * isMuchSmallerThan().
+ *
+ * \sa cwiseEqual(const MatrixBase<OtherDerived> &) const
+ */
+EIGEN_DEVICE_FUNC constexpr inline const CwiseScalarEqualReturnType cwiseEqual(const Scalar& s) const {
+  return CwiseScalarEqualReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+/** \returns an expression of the coefficient-wise == operator of \c *this and a scalar \a s
+ *
+ * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+ * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+ * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+ * isMuchSmallerThan().
+ *
+ * \sa cwiseEqual(const MatrixBase<OtherDerived> &) const
+ */
+EIGEN_DEVICE_FUNC constexpr inline const CwiseScalarNotEqualReturnType cwiseNotEqual(const Scalar& s) const {
+  return CwiseScalarNotEqualReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+/** \returns an expression of the coefficient-wise < operator of \c *this and a scalar \a s */
+EIGEN_DEVICE_FUNC constexpr inline const CwiseScalarLessReturnType cwiseLess(const Scalar& s) const {
+  return CwiseScalarLessReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+/** \returns an expression of the coefficient-wise > operator of \c *this and a scalar \a s */
+EIGEN_DEVICE_FUNC constexpr inline const CwiseScalarGreaterReturnType cwiseGreater(const Scalar& s) const {
+  return CwiseScalarGreaterReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+/** \returns an expression of the coefficient-wise <= operator of \c *this and a scalar \a s */
+EIGEN_DEVICE_FUNC constexpr inline const CwiseScalarLessOrEqualReturnType cwiseLessOrEqual(const Scalar& s) const {
+  return CwiseScalarLessOrEqualReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+/** \returns an expression of the coefficient-wise >= operator of \c *this and a scalar \a s */
+EIGEN_DEVICE_FUNC constexpr inline const CwiseScalarGreaterOrEqualReturnType cwiseGreaterOrEqual(
+    const Scalar& s) const {
+  return CwiseScalarGreaterOrEqualReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+template <typename OtherDerived>
+using CwiseBinaryTypedEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ, true>, const Derived, const OtherDerived>;
+template <typename OtherDerived>
+using CwiseBinaryTypedNotEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ, true>, const Derived, const OtherDerived>;
+template <typename OtherDerived>
+using CwiseBinaryTypedLessReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT, true>, const Derived, const OtherDerived>;
+template <typename OtherDerived>
+using CwiseBinaryTypedGreaterReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT, true>, const Derived, const OtherDerived>;
+template <typename OtherDerived>
+using CwiseBinaryTypedLessOrEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE, true>, const Derived, const OtherDerived>;
+template <typename OtherDerived>
+using CwiseBinaryTypedGreaterOrEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE, true>, const Derived, const OtherDerived>;
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseBinaryTypedEqualReturnType<OtherDerived> cwiseTypedEqual(
+    const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryTypedEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseBinaryTypedNotEqualReturnType<OtherDerived>
+cwiseTypedNotEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryTypedNotEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseBinaryTypedLessReturnType<OtherDerived> cwiseTypedLess(
+    const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryTypedLessReturnType<OtherDerived>(derived(), other.derived());
+}
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseBinaryTypedGreaterReturnType<OtherDerived> cwiseTypedGreater(
+    const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryTypedGreaterReturnType<OtherDerived>(derived(), other.derived());
+}
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseBinaryTypedLessOrEqualReturnType<OtherDerived>
+cwiseTypedLessOrEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryTypedLessOrEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseBinaryTypedGreaterOrEqualReturnType<OtherDerived>
+cwiseTypedGreaterOrEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryTypedGreaterOrEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+using CwiseScalarTypedEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ, true>,
+                                                      const Derived, const ConstantReturnType>;
+using CwiseScalarTypedNotEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ, true>, const Derived,
+                  const ConstantReturnType>;
+using CwiseScalarTypedLessReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT, true>,
+                                                     const Derived, const ConstantReturnType>;
+using CwiseScalarTypedGreaterReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT, true>,
+                                                        const Derived, const ConstantReturnType>;
+using CwiseScalarTypedLessOrEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE, true>, const Derived,
+                  const ConstantReturnType>;
+using CwiseScalarTypedGreaterOrEqualReturnType =
+    CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE, true>, const Derived,
+                  const ConstantReturnType>;
+
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseScalarTypedEqualReturnType
+cwiseTypedEqual(const Scalar& s) const {
+  return CwiseScalarTypedEqualReturnType(derived(), ConstantReturnType(rows(), cols(), s));
+}
+
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseScalarTypedNotEqualReturnType
+cwiseTypedNotEqual(const Scalar& s) const {
+  return CwiseScalarTypedNotEqualReturnType(derived(), ConstantReturnType(rows(), cols(), s));
+}
+
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseScalarTypedLessReturnType
+cwiseTypedLess(const Scalar& s) const {
+  return CwiseScalarTypedLessReturnType(derived(), ConstantReturnType(rows(), cols(), s));
+}
+
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseScalarTypedGreaterReturnType
+cwiseTypedGreater(const Scalar& s) const {
+  return CwiseScalarTypedGreaterReturnType(derived(), ConstantReturnType(rows(), cols(), s));
+}
+
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseScalarTypedLessOrEqualReturnType
+cwiseTypedLessOrEqual(const Scalar& s) const {
+  return CwiseScalarTypedLessOrEqualReturnType(derived(), ConstantReturnType(rows(), cols(), s));
+}
+
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseScalarTypedGreaterOrEqualReturnType
+cwiseTypedGreaterOrEqual(const Scalar& s) const {
+  return CwiseScalarTypedGreaterOrEqualReturnType(derived(), ConstantReturnType(rows(), cols(), s));
+}
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/MatrixCwiseUnaryOps.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/MatrixCwiseUnaryOps.inc
new file mode 100644
index 0000000..a57548c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/MatrixCwiseUnaryOps.inc
@@ -0,0 +1,129 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// This file is included into the body of the base classes supporting matrix specific coefficient-wise functions.
+// This include MatrixBase and SparseMatrixBase.
+
+typedef CwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> CwiseAbsReturnType;
+typedef CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const Derived> CwiseAbs2ReturnType;
+typedef CwiseUnaryOp<internal::scalar_arg_op<Scalar>, const Derived> CwiseArgReturnType;
+typedef CwiseUnaryOp<internal::scalar_carg_op<Scalar>, const Derived> CwiseCArgReturnType;
+typedef CwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> CwiseSqrtReturnType;
+typedef CwiseUnaryOp<internal::scalar_cbrt_op<Scalar>, const Derived> CwiseCbrtReturnType;
+typedef CwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived> CwiseSquareReturnType;
+typedef CwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived> CwiseSignReturnType;
+typedef CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> CwiseInverseReturnType;
+
+/// \returns an expression of the coefficient-wise absolute value of \c *this
+///
+/// Example: \include MatrixBase_cwiseAbs.cpp
+/// Output: \verbinclude MatrixBase_cwiseAbs.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseAbs, absolute value)
+///
+/// \sa cwiseAbs2()
+///
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseAbsReturnType cwiseAbs() const {
+  return CwiseAbsReturnType(derived());
+}
+
+/// \returns an expression of the coefficient-wise squared absolute value of \c *this
+///
+/// Example: \include MatrixBase_cwiseAbs2.cpp
+/// Output: \verbinclude MatrixBase_cwiseAbs2.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseAbs2, squared absolute value)
+///
+/// \sa cwiseAbs()
+///
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseAbs2ReturnType cwiseAbs2() const {
+  return CwiseAbs2ReturnType(derived());
+}
+
+/// \returns an expression of the coefficient-wise square root of *this.
+///
+/// Example: \include MatrixBase_cwiseSqrt.cpp
+/// Output: \verbinclude MatrixBase_cwiseSqrt.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseSqrt, square - root)
+///
+/// \sa cwisePow(), cwiseSquare(), cwiseCbrt()
+///
+EIGEN_DEVICE_FUNC constexpr inline const CwiseSqrtReturnType cwiseSqrt() const {
+  return CwiseSqrtReturnType(derived());
+}
+
+/// \returns an expression of the coefficient-wise cube root of *this.
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseCbrt, cube - root)
+///
+/// \sa cwiseSqrt(), cwiseSquare(), cwisePow()
+///
+EIGEN_DEVICE_FUNC constexpr inline const CwiseCbrtReturnType cwiseCbrt() const {
+  return CwiseCbrtReturnType(derived());
+}
+
+/// \returns an expression of the coefficient-wise square of *this.
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseSquare, square)
+///
+/// \sa cwisePow(), cwiseSqrt(), cwiseCbrt()
+///
+EIGEN_DEVICE_FUNC constexpr inline const CwiseSquareReturnType cwiseSquare() const {
+  return CwiseSquareReturnType(derived());
+}
+
+/// \returns an expression of the coefficient-wise signum of *this.
+///
+/// Example: \include MatrixBase_cwiseSign.cpp
+/// Output: \verbinclude MatrixBase_cwiseSign.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseSign, sign function)
+///
+EIGEN_DEVICE_FUNC constexpr inline const CwiseSignReturnType cwiseSign() const {
+  return CwiseSignReturnType(derived());
+}
+
+/// \returns an expression of the coefficient-wise inverse of *this.
+///
+/// Example: \include MatrixBase_cwiseInverse.cpp
+/// Output: \verbinclude MatrixBase_cwiseInverse.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseInverse, inverse)
+///
+/// \sa cwiseProduct()
+///
+EIGEN_DEVICE_FUNC constexpr inline const CwiseInverseReturnType cwiseInverse() const {
+  return CwiseInverseReturnType(derived());
+}
+
+/// \returns an expression of the coefficient-wise phase angle of \c *this
+///
+/// Example: \include MatrixBase_cwiseArg.cpp
+/// Output: \verbinclude MatrixBase_cwiseArg.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseArg, arg)
+
+EIGEN_DEVICE_FUNC constexpr inline const CwiseArgReturnType cwiseArg() const { return CwiseArgReturnType(derived()); }
+
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE const CwiseCArgReturnType cwiseCArg() const {
+  return CwiseCArgReturnType(derived());
+}
+
+template <typename ScalarExponent>
+using CwisePowReturnType =
+    std::enable_if_t<internal::is_arithmetic<typename NumTraits<ScalarExponent>::Real>::value,
+                     CwiseUnaryOp<internal::scalar_unary_pow_op<Scalar, ScalarExponent>, const Derived>>;
+
+template <typename ScalarExponent>
+EIGEN_DEVICE_FUNC constexpr inline const CwisePowReturnType<ScalarExponent> cwisePow(
+    const ScalarExponent& exponent) const {
+  return CwisePowReturnType<ScalarExponent>(derived(), internal::scalar_unary_pow_op<Scalar, ScalarExponent>(exponent));
+}
diff --git a/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/ReshapedMethods.inc b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/ReshapedMethods.inc
new file mode 100644
index 0000000..1e3a196
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/Eigen/src/plugins/ReshapedMethods.inc
@@ -0,0 +1,133 @@
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+
+/// \returns an expression of \c *this with reshaped sizes.
+///
+/// \param nRows the number of rows in the reshaped expression, specified at either run-time or compile-time, or
+/// AutoSize \param nCols the number of columns in the reshaped expression, specified at either run-time or
+/// compile-time, or AutoSize \tparam Order specifies whether the coefficients should be processed in column-major-order
+/// (ColMajor), in row-major-order (RowMajor),
+///               or follows the \em natural order of the nested expression (AutoOrder). The default is ColMajor.
+/// \tparam NRowsType the type of the value handling the number of rows, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns, typically Index.
+///
+/// Dynamic size example: \include MatrixBase_reshaped_int_int.cpp
+/// Output: \verbinclude MatrixBase_reshaped_int_int.out
+///
+/// The number of rows \a nRows and columns \a nCols can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments. In the later case, \c n plays the role of a runtime fallback value in case \c N
+/// equals Eigen::Dynamic. Here is an example with a fixed number of rows and columns: \include
+/// MatrixBase_reshaped_fixed.cpp Output: \verbinclude MatrixBase_reshaped_fixed.out
+///
+/// Finally, one of the sizes parameter can be automatically deduced from the other one by passing AutoSize as in the
+/// following example: \include MatrixBase_reshaped_auto.cpp Output: \verbinclude MatrixBase_reshaped_auto.out AutoSize
+/// does preserve compile-time sizes when possible, i.e., when the sizes of the input are known at compile time \b and
+/// that the other size is passed at compile-time using Eigen::fix<N> as above.
+///
+/// \sa class Reshaped, fix, fix<N>(int)
+///
+template <int Order = ColMajor, typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr inline Reshaped<Derived, ...> reshaped(NRowsType nRows, NColsType nCols);
+
+/// This is the const version of reshaped(NRowsType,NColsType).
+template <int Order = ColMajor, typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr inline const Reshaped<const Derived, ...> reshaped(NRowsType nRows, NColsType nCols) const;
+
+/// \returns an expression of \c *this with columns (or rows) stacked to a linear column vector
+///
+/// \tparam Order specifies whether the coefficients should be processed in column-major-order (ColMajor), in
+/// row-major-order (RowMajor),
+///               or follows the \em natural order of the nested expression (AutoOrder). The default is ColMajor.
+///
+/// This overloads is essentially a shortcut for `A.reshaped<Order>(AutoSize,fix<1>)`.
+///
+/// - If `Order==ColMajor` (the default), then it returns a column-vector from the stacked columns of \c *this.
+/// - If `Order==RowMajor`, then it returns a column-vector from the stacked rows of \c *this.
+/// - If `Order==AutoOrder`, then it returns a column-vector with elements stacked following the storage order of \c
+/// *this.
+///   This mode is the recommended one when the particular ordering of the element is not relevant.
+///
+/// Example:
+/// \include MatrixBase_reshaped_to_vector.cpp
+/// Output: \verbinclude MatrixBase_reshaped_to_vector.out
+///
+/// If you want more control, you can still fall back to reshaped(NRowsType,NColsType).
+///
+/// \sa reshaped(NRowsType,NColsType), class Reshaped
+///
+template <int Order = ColMajor>
+EIGEN_DEVICE_FUNC constexpr inline Reshaped<Derived, ...> reshaped();
+
+/// This is the const version of reshaped().
+template <int Order = ColMajor>
+EIGEN_DEVICE_FUNC constexpr inline const Reshaped<const Derived, ...> reshaped() const;
+
+#else
+
+// This file is automatically included twice to generate const and non-const versions
+
+#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
+#define EIGEN_RESHAPED_METHOD_CONST const
+#else
+#define EIGEN_RESHAPED_METHOD_CONST
+#endif
+
+#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
+
+// This part is included once
+
+#endif
+
+template <typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr inline Reshaped<
+    EIGEN_RESHAPED_METHOD_CONST Derived,
+    internal::get_compiletime_reshape_size<NRowsType, NColsType, SizeAtCompileTime>::value,
+    internal::get_compiletime_reshape_size<NColsType, NRowsType, SizeAtCompileTime>::value>
+reshaped(NRowsType nRows, NColsType nCols) EIGEN_RESHAPED_METHOD_CONST {
+  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
+                  internal::get_compiletime_reshape_size<NRowsType, NColsType, SizeAtCompileTime>::value,
+                  internal::get_compiletime_reshape_size<NColsType, NRowsType, SizeAtCompileTime>::value>(
+      derived(), internal::get_runtime_reshape_size(nRows, internal::get_runtime_value(nCols), size()),
+      internal::get_runtime_reshape_size(nCols, internal::get_runtime_value(nRows), size()));
+}
+
+template <int Order, typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC constexpr inline Reshaped<
+    EIGEN_RESHAPED_METHOD_CONST Derived,
+    internal::get_compiletime_reshape_size<NRowsType, NColsType, SizeAtCompileTime>::value,
+    internal::get_compiletime_reshape_size<NColsType, NRowsType, SizeAtCompileTime>::value,
+    internal::get_compiletime_reshape_order(Flags, Order)>
+reshaped(NRowsType nRows, NColsType nCols) EIGEN_RESHAPED_METHOD_CONST {
+  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
+                  internal::get_compiletime_reshape_size<NRowsType, NColsType, SizeAtCompileTime>::value,
+                  internal::get_compiletime_reshape_size<NColsType, NRowsType, SizeAtCompileTime>::value,
+                  internal::get_compiletime_reshape_order(Flags, Order)>(
+      derived(), internal::get_runtime_reshape_size(nRows, internal::get_runtime_value(nCols), size()),
+      internal::get_runtime_reshape_size(nCols, internal::get_runtime_value(nRows), size()));
+}
+
+// Views as linear vectors
+
+EIGEN_DEVICE_FUNC constexpr inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived, SizeAtCompileTime, 1> reshaped()
+    EIGEN_RESHAPED_METHOD_CONST {
+  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived, SizeAtCompileTime, 1>(derived(), size(), 1);
+}
+
+template <int Order>
+EIGEN_DEVICE_FUNC constexpr inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived, SizeAtCompileTime, 1,
+                                            internal::get_compiletime_reshape_order(Flags, Order)>
+reshaped() EIGEN_RESHAPED_METHOD_CONST {
+  EIGEN_STATIC_ASSERT(Order == RowMajor || Order == ColMajor || Order == AutoOrder, INVALID_TEMPLATE_PARAMETER);
+  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived, SizeAtCompileTime, 1,
+                  internal::get_compiletime_reshape_order(Flags, Order)>(derived(), size(), 1);
+}
+
+#undef EIGEN_RESHAPED_METHOD_CONST
+
+#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
+#define EIGEN_RESHAPED_METHOD_2ND_PASS
+#include "ReshapedMethods.inc"
+#undef EIGEN_RESHAPED_METHOD_2ND_PASS
+#endif
+
+#endif  // EIGEN_PARSED_BY_DOXYGEN
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/CMakeLists.txt
new file mode 100644
index 0000000..3904601
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/CMakeLists.txt
@@ -0,0 +1,11 @@
+add_subdirectory(Eigen)
+if(EIGEN_BUILD_DOC)
+  add_subdirectory(doc EXCLUDE_FROM_ALL)
+endif()
+if(EIGEN_BUILD_TESTING)
+  if(EIGEN_LEAVE_TEST_IN_ALL_TARGET)
+    add_subdirectory(test) # CTest automatic test building relies on the "all" target.
+  else()
+    add_subdirectory(test EXCLUDE_FROM_ALL)
+  endif()
+endif()
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/AdolcForward b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/AdolcForward
new file mode 100644
index 0000000..7da9997
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/AdolcForward
@@ -0,0 +1,153 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ADLOC_FORWARD_MODULE_H
+#define EIGEN_ADLOC_FORWARD_MODULE_H
+
+//--------------------------------------------------------------------------------
+//
+// This file provides support for adolc's adouble type in forward mode.
+// ADOL-C is a C++ automatic differentiation library,
+// see https://projects.coin-or.org/ADOL-C for more information.
+//
+// Note that the maximal number of directions is controlled by
+// the preprocessor token NUMBER_DIRECTIONS. The default is 2.
+//
+//--------------------------------------------------------------------------------
+
+#define ADOLC_TAPELESS
+#ifndef NUMBER_DIRECTIONS
+#define NUMBER_DIRECTIONS 2
+#endif
+#include <adolc/adtl.h>
+
+// adolc defines some very stupid macros:
+#if defined(malloc)
+#undef malloc
+#endif
+
+#if defined(calloc)
+#undef calloc
+#endif
+
+#if defined(realloc)
+#undef realloc
+#endif
+
+#include "../../Eigen/Core"
+
+namespace Eigen {
+
+/**
+ * \defgroup AdolcForward_Module Adolc forward module
+ * This module provides support for adolc's adouble type in forward mode.
+ * ADOL-C is a C++ automatic differentiation library,
+ * see https://projects.coin-or.org/ADOL-C for more information.
+ * It mainly consists in:
+ *  - a struct Eigen::NumTraits<adtl::adouble> specialization
+ *  - overloads of internal::* math function for adtl::adouble type.
+ *
+ * Note that the maximal number of directions is controlled by
+ * the preprocessor token NUMBER_DIRECTIONS. The default is 2.
+ *
+ * \code
+ * #include <unsupported/Eigen/AdolcSupport>
+ * \endcode
+ */
+//@{
+
+}  // namespace Eigen
+
+// Eigen's require a few additional functions which must be defined in the same namespace
+// than the custom scalar type own namespace
+namespace adtl {
+
+inline const adouble& conj(const adouble& x) { return x; }
+inline const adouble& real(const adouble& x) { return x; }
+inline adouble imag(const adouble&) { return 0.; }
+inline adouble abs(const adouble& x) { return fabs(x); }
+inline adouble abs2(const adouble& x) { return x * x; }
+
+inline bool(isinf)(const adouble& x) { return (Eigen::numext::isinf)(x.getValue()); }
+inline bool(isnan)(const adouble& x) { return (Eigen::numext::isnan)(x.getValue()); }
+
+}  // namespace adtl
+
+namespace Eigen {
+
+template <>
+struct NumTraits<adtl::adouble> : NumTraits<double> {
+  typedef adtl::adouble Real;
+  typedef adtl::adouble NonInteger;
+  typedef adtl::adouble Nested;
+  enum {
+    IsComplex = 0,
+    IsInteger = 0,
+    IsSigned = 1,
+    RequireInitialization = 1,
+    ReadCost = 1,
+    AddCost = 1,
+    MulCost = 1
+  };
+};
+
+template <typename Functor>
+class AdolcForwardJacobian : public Functor {
+  typedef adtl::adouble ActiveScalar;
+
+ public:
+  AdolcForwardJacobian() : Functor() {}
+  AdolcForwardJacobian(const Functor& f) : Functor(f) {}
+
+  // forward constructors
+  template <typename T0>
+  AdolcForwardJacobian(const T0& a0) : Functor(a0) {}
+  template <typename T0, typename T1>
+  AdolcForwardJacobian(const T0& a0, const T1& a1) : Functor(a0, a1) {}
+  template <typename T0, typename T1, typename T2>
+  AdolcForwardJacobian(const T0& a0, const T1& a1, const T1& a2) : Functor(a0, a1, a2) {}
+
+  typedef typename Functor::InputType InputType;
+  typedef typename Functor::ValueType ValueType;
+  typedef typename Functor::JacobianType JacobianType;
+
+  typedef Matrix<ActiveScalar, InputType::SizeAtCompileTime, 1> ActiveInput;
+  typedef Matrix<ActiveScalar, ValueType::SizeAtCompileTime, 1> ActiveValue;
+
+  void operator()(const InputType& x, ValueType* v, JacobianType* _jac) const {
+    eigen_assert(v != 0);
+    if (!_jac) {
+      Functor::operator()(x, v);
+      return;
+    }
+
+    JacobianType& jac = *_jac;
+
+    ActiveInput ax = x.template cast<ActiveScalar>();
+    ActiveValue av(jac.rows());
+
+    for (int j = 0; j < jac.cols(); j++)
+      for (int i = 0; i < jac.cols(); i++) ax[i].setADValue(j, i == j ? 1 : 0);
+
+    Functor::operator()(ax, &av);
+
+    for (int i = 0; i < jac.rows(); i++) {
+      (*v)[i] = av[i].getValue();
+      for (int j = 0; j < jac.cols(); j++) jac.coeffRef(i, j) = av[i].getADValue(j);
+    }
+  }
+
+ protected:
+};
+
+//@}
+
+}  // namespace Eigen
+
+#endif  // EIGEN_ADLOC_FORWARD_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/AlignedVector3 b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/AlignedVector3
new file mode 100644
index 0000000..f2d6416
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/AlignedVector3
@@ -0,0 +1,211 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ALIGNED_VECTOR3_MODULE_H
+#define EIGEN_ALIGNED_VECTOR3_MODULE_H
+
+#include "../../Eigen/Geometry"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+namespace Eigen {
+
+/**
+ * \defgroup AlignedVector3_Module Aligned vector3 module
+ *
+ * \code
+ * #include <unsupported/Eigen/AlignedVector3>
+ * \endcode
+ */
+//@{
+
+/** \class AlignedVector3
+ *
+ * \brief A vectorization friendly 3D vector
+ *
+ * This class represents a 3D vector internally using a 4D vector
+ * such that vectorization can be seamlessly enabled. Of course,
+ * the same result can be achieved by directly using a 4D vector.
+ * This class makes this process simpler.
+ *
+ */
+// TODO: Specialize Cwise.
+template <typename Scalar_>
+class AlignedVector3;
+
+namespace internal {
+template <typename Scalar_>
+struct traits<AlignedVector3<Scalar_> > : traits<Matrix<Scalar_, 3, 1, 0, 4, 1> > {};
+}  // namespace internal
+
+template <typename Scalar_>
+class AlignedVector3 : public MatrixBase<AlignedVector3<Scalar_> > {
+  typedef Matrix<Scalar_, 4, 1> CoeffType;
+  CoeffType m_coeffs;
+
+ public:
+  typedef MatrixBase<AlignedVector3<Scalar_> > Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(AlignedVector3)
+  using Base::operator*;
+
+  inline Index rows() const { return 3; }
+  inline Index cols() const { return 1; }
+
+  Scalar* data() { return m_coeffs.data(); }
+  const Scalar* data() const { return m_coeffs.data(); }
+  Index innerStride() const { return 1; }
+  Index outerStride() const { return 3; }
+
+  inline const Scalar& coeff(Index row, Index col) const { return m_coeffs.coeff(row, col); }
+
+  inline Scalar& coeffRef(Index row, Index col) { return m_coeffs.coeffRef(row, col); }
+
+  inline const Scalar& coeff(Index index) const { return m_coeffs.coeff(index); }
+
+  inline Scalar& coeffRef(Index index) { return m_coeffs.coeffRef(index); }
+
+  inline AlignedVector3() {}
+
+  inline AlignedVector3(const Scalar& x, const Scalar& y, const Scalar& z) : m_coeffs(x, y, z, Scalar(0)) {}
+
+  inline AlignedVector3(const AlignedVector3& other) : Base(), m_coeffs(other.m_coeffs) {}
+
+  template <typename XprType, int Size = XprType::SizeAtCompileTime>
+  struct generic_assign_selector {};
+
+  template <typename XprType>
+  struct generic_assign_selector<XprType, 4> {
+    inline static void run(AlignedVector3& dest, const XprType& src) { dest.m_coeffs = src; }
+  };
+
+  template <typename XprType>
+  struct generic_assign_selector<XprType, 3> {
+    inline static void run(AlignedVector3& dest, const XprType& src) {
+      dest.m_coeffs.template head<3>() = src;
+      dest.m_coeffs.w() = Scalar(0);
+    }
+  };
+
+  template <typename Derived>
+  inline AlignedVector3(const MatrixBase<Derived>& other) {
+    generic_assign_selector<Derived>::run(*this, other.derived());
+  }
+
+  inline AlignedVector3& operator=(const AlignedVector3& other) {
+    m_coeffs = other.m_coeffs;
+    return *this;
+  }
+
+  template <typename Derived>
+  inline AlignedVector3& operator=(const MatrixBase<Derived>& other) {
+    generic_assign_selector<Derived>::run(*this, other.derived());
+    return *this;
+  }
+
+  inline AlignedVector3 operator+(const AlignedVector3& other) const {
+    return AlignedVector3(m_coeffs + other.m_coeffs);
+  }
+
+  inline AlignedVector3& operator+=(const AlignedVector3& other) {
+    m_coeffs += other.m_coeffs;
+    return *this;
+  }
+
+  inline AlignedVector3 operator-(const AlignedVector3& other) const {
+    return AlignedVector3(m_coeffs - other.m_coeffs);
+  }
+
+  inline AlignedVector3 operator-() const { return AlignedVector3(-m_coeffs); }
+
+  inline AlignedVector3 operator-=(const AlignedVector3& other) {
+    m_coeffs -= other.m_coeffs;
+    return *this;
+  }
+
+  inline AlignedVector3 operator*(const Scalar& s) const { return AlignedVector3(m_coeffs * s); }
+
+  inline friend AlignedVector3 operator*(const Scalar& s, const AlignedVector3& vec) {
+    return AlignedVector3(s * vec.m_coeffs);
+  }
+
+  inline AlignedVector3& operator*=(const Scalar& s) {
+    m_coeffs *= s;
+    return *this;
+  }
+
+  inline AlignedVector3 operator/(const Scalar& s) const { return AlignedVector3(m_coeffs / s); }
+
+  inline AlignedVector3& operator/=(const Scalar& s) {
+    m_coeffs /= s;
+    return *this;
+  }
+
+  inline Scalar dot(const AlignedVector3& other) const {
+    eigen_assert(m_coeffs.w() == Scalar(0));
+    eigen_assert(other.m_coeffs.w() == Scalar(0));
+    return m_coeffs.dot(other.m_coeffs);
+  }
+
+  inline void normalize() { m_coeffs /= norm(); }
+
+  inline AlignedVector3 normalized() const { return AlignedVector3(m_coeffs / norm()); }
+
+  inline Scalar sum() const {
+    eigen_assert(m_coeffs.w() == Scalar(0));
+    return m_coeffs.sum();
+  }
+
+  inline Scalar squaredNorm() const {
+    eigen_assert(m_coeffs.w() == Scalar(0));
+    return m_coeffs.squaredNorm();
+  }
+
+  inline Scalar norm() const {
+    using std::sqrt;
+    return sqrt(squaredNorm());
+  }
+
+  inline AlignedVector3 cross(const AlignedVector3& other) const {
+    return AlignedVector3(m_coeffs.cross3(other.m_coeffs));
+  }
+
+  template <typename Derived>
+  inline bool isApprox(const MatrixBase<Derived>& other,
+                       const RealScalar& eps = NumTraits<Scalar>::dummy_precision()) const {
+    return m_coeffs.template head<3>().isApprox(other, eps);
+  }
+
+  CoeffType& coeffs() { return m_coeffs; }
+  const CoeffType& coeffs() const { return m_coeffs; }
+};
+
+namespace internal {
+
+template <typename Scalar_>
+struct eval<AlignedVector3<Scalar_>, Dense> {
+  typedef const AlignedVector3<Scalar_>& type;
+};
+
+template <typename Scalar>
+struct evaluator<AlignedVector3<Scalar> > : evaluator<Matrix<Scalar, 4, 1> > {
+  typedef AlignedVector3<Scalar> XprType;
+  typedef evaluator<Matrix<Scalar, 4, 1> > Base;
+
+  evaluator(const XprType& m) : Base(m.coeffs()) {}
+};
+
+}  // namespace internal
+
+//@}
+
+}  // namespace Eigen
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_ALIGNED_VECTOR3_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/ArpackSupport b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/ArpackSupport
new file mode 100644
index 0000000..3987f7c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/ArpackSupport
@@ -0,0 +1,33 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARPACKSUPPORT_MODULE_H
+#define EIGEN_ARPACKSUPPORT_MODULE_H
+
+#include "../../Eigen/Core"
+
+/** \defgroup ArpackSupport_Module Arpack support module
+ *
+ * This module provides a wrapper to Arpack, a library for sparse eigenvalue decomposition.
+ *
+ * \code
+ * #include <Eigen/ArpackSupport>
+ * \endcode
+ */
+
+#include "../../Eigen/SparseCholesky"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+#include "src/Eigenvalues/ArpackSelfAdjointEigenSolver.h"
+// IWYU pragma: end_exports
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_ARPACKSUPPORT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/AutoDiff b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/AutoDiff
new file mode 100644
index 0000000..0480c69
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/AutoDiff
@@ -0,0 +1,48 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AUTODIFF_MODULE_H
+#define EIGEN_AUTODIFF_MODULE_H
+
+#include "../../Eigen/Core"
+
+namespace Eigen {
+
+/**
+ * \defgroup AutoDiff_Module Auto Diff module
+ *
+ * This module features forward automatic differentation via a simple
+ * templated scalar type wrapper AutoDiffScalar.
+ *
+ * Warning : this should NOT be confused with numerical differentiation, which
+ * is a different method and has its own module in Eigen : \ref NumericalDiff_Module.
+ *
+ * \code
+ * #include <unsupported/Eigen/AutoDiff>
+ * \endcode
+ */
+//@{
+
+}  // namespace Eigen
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+#include "src/AutoDiff/CoherentPadOp.h"
+#include "src/AutoDiff/AutoDiffScalar.h"
+// #include "src/AutoDiff/AutoDiffVector.h"
+#include "src/AutoDiff/AutoDiffJacobian.h"
+// IWYU pragma: end_exports
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+namespace Eigen {
+//@}
+}
+
+#endif  // EIGEN_AUTODIFF_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/BVH b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/BVH
new file mode 100644
index 0000000..58fb72f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/BVH
@@ -0,0 +1,108 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BVH_MODULE_H
+#define EIGEN_BVH_MODULE_H
+
+#include "../../Eigen/Core"
+#include "../../Eigen/Geometry"
+#include "../../Eigen/StdVector"
+#include <algorithm>
+#include <queue>
+
+namespace Eigen {
+
+/**
+  * \defgroup BVH_Module BVH module
+  * \brief This module provides generic bounding volume hierarchy algorithms
+  * and reference tree implementations.
+  *
+  *
+  * \code
+  * #include <unsupported/Eigen/BVH>
+  * \endcode
+  *
+  * A bounding volume hierarchy (BVH) can accelerate many geometric queries.  This module provides a generic
+  implementation
+  * of the two basic algorithms over a BVH: intersection of a query object against all objects in the hierarchy and
+  minimization
+  * of a function over the objects in the hierarchy.  It also provides intersection and minimization over a cartesian
+  product of
+  * two BVH's.  A BVH accelerates intersection by using the fact that if a query object does not intersect a volume,
+  then it cannot
+  * intersect any object contained in that volume.  Similarly, a BVH accelerates minimization because the minimum of a
+  function
+  * over a volume is no greater than the minimum of a function over any object contained in it.
+  *
+  * Some sample queries that can be written in terms of intersection are:
+  *   - Determine all points where a ray intersects a triangle mesh
+  *   - Given a set of points, determine which are contained in a query sphere
+  *   - Given a set of spheres, determine which contain the query point
+  *   - Given a set of disks, determine if any is completely contained in a query rectangle (represent each 2D disk as a
+  point \f$(x,y,r)\f$
+  *     in 3D and represent the rectangle as a pyramid based on the original rectangle and shrinking in the \f$r\f$
+  direction)
+  *   - Given a set of points, count how many pairs are \f$d\pm\epsilon\f$ apart (done by looking at the cartesian
+  product of the set
+  *     of points with itself)
+  *
+  * Some sample queries that can be written in terms of function minimization over a set of objects are:
+  *   - Find the intersection between a ray and a triangle mesh closest to the ray origin (function is infinite off the
+  ray)
+  *   - Given a polyline and a query point, determine the closest point on the polyline to the query
+  *   - Find the diameter of a point cloud (done by looking at the cartesian product and using negative distance as the
+  function)
+  *   - Determine how far two meshes are from colliding (this is also a cartesian product query)
+  *
+  * This implementation decouples the basic algorithms both from the type of hierarchy (and the types of the bounding
+  volumes) and
+  * from the particulars of the query.  To enable abstraction from the BVH, the BVH is required to implement a generic
+  mechanism
+  * for traversal.  To abstract from the query, the query is responsible for keeping track of results.
+  *
+  * To be used in the algorithms, a hierarchy must implement the following traversal mechanism (see KdBVH for a sample
+  implementation): \code typedef Volume  //the type of bounding volume typedef Object  //the type of object in the
+  hierarchy typedef Index   //a reference to a node in the hierarchy--typically an int or a pointer typedef
+  VolumeIterator //an iterator type over node children--returns Index typedef ObjectIterator //an iterator over object
+  (leaf) children--returns const Object & Index getRootIndex() const //returns the index of the hierarchy root const
+  Volume &getVolume(Index index) const //returns the bounding volume of the node at given index void getChildren(Index
+  index, VolumeIterator &outVBegin, VolumeIterator &outVEnd, ObjectIterator &outOBegin, ObjectIterator &outOEnd) const
+      //getChildren takes a node index and makes [outVBegin, outVEnd) range over its node children
+      //and [outOBegin, outOEnd) range over its object children
+    \endcode
+  *
+  * To use the hierarchy, call BVIntersect or BVMinimize, passing it a BVH (or two, for cartesian product) and a
+  minimizer or intersector.
+  * For an intersection query on a single BVH, the intersector encapsulates the query and must provide two functions:
+  * \code
+      bool intersectVolume(const Volume &volume) //returns true if the query intersects the volume
+      bool intersectObject(const Object &object) //returns true if the intersection search should terminate immediately
+    \endcode
+  * The guarantee that BVIntersect provides is that intersectObject will be called on every object whose bounding volume
+  * intersects the query (but possibly on other objects too) unless the search is terminated prematurely.  It is the
+  * responsibility of the intersectObject function to keep track of the results in whatever manner is appropriate.
+  * The cartesian product intersection and the BVMinimize queries are similar--see their individual documentation.
+  *
+  * The following is a simple but complete example for how to use the BVH to accelerate the search for a closest
+  red-blue point pair:
+  * \include BVH_Example.cpp
+  * Output: \verbinclude BVH_Example.out
+  */
+}
+
+//@{
+
+// IWYU pragma: begin_exports
+#include "src/BVH/BVAlgorithms.h"
+#include "src/BVH/KdBVH.h"
+// IWYU pragma: end_exports
+
+//@}
+
+#endif  // EIGEN_BVH_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CMakeLists.txt
new file mode 100644
index 0000000..1517ba9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CMakeLists.txt
@@ -0,0 +1,31 @@
+set(Eigen_HEADERS
+  AdolcForward
+  AlignedVector3
+  ArpackSupport
+  AutoDiff
+  BVH
+  EulerAngles
+  FFT
+  IterativeSolvers
+  KroneckerProduct
+  LevenbergMarquardt
+  MatrixFunctions
+  MPRealSupport
+  NNLS
+  NonLinearOptimization
+  NumericalDiff
+  OpenGLSupport
+  Polynomials
+  SparseExtra
+  SpecialFunctions
+  Splines
+  )
+
+install(FILES
+  ${Eigen_HEADERS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/unsupported/Eigen COMPONENT Devel
+  )
+
+install(DIRECTORY src DESTINATION ${INCLUDE_INSTALL_DIR}/unsupported/Eigen COMPONENT Devel FILES_MATCHING PATTERN "*.h")
+
+add_subdirectory(CXX11)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/CMakeLists.txt
new file mode 100644
index 0000000..385ed24
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/CMakeLists.txt
@@ -0,0 +1,8 @@
+set(Eigen_CXX11_HEADERS Tensor TensorSymmetry ThreadPool)
+
+install(FILES
+  ${Eigen_CXX11_HEADERS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/unsupported/Eigen/CXX11 COMPONENT Devel
+  )
+
+install(DIRECTORY src DESTINATION ${INCLUDE_INSTALL_DIR}/unsupported/Eigen/CXX11 COMPONENT Devel FILES_MATCHING PATTERN "*.h")
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/Tensor b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/Tensor
new file mode 100644
index 0000000..7375a9b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/Tensor
@@ -0,0 +1,142 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// #ifndef EIGEN_CXX11_TENSOR_MODULE_H
+#define EIGEN_CXX11_TENSOR_MODULE_H
+
+#include "../../../Eigen/Core"
+
+#include "../SpecialFunctions"
+
+#include "../../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+#include "../../../Eigen/src/Core/util/Meta.h"
+#include "../../../Eigen/src/Core/util/MaxSizeVector.h"
+// IWYU pragma: end_exports
+
+/** \defgroup CXX11_Tensor_Module Tensor Module
+ *
+ * This module provides a Tensor class for storing arbitrarily indexed
+ * objects.
+ *
+ * \code
+ * #include <Eigen/CXX11/Tensor>
+ * \endcode
+ *
+ * Much of the documentation can be found \ref eigen_tensors "here".
+ */
+
+#include <atomic>
+#include <chrono>
+#include <cmath>
+#include <cstddef>
+#include <cstring>
+#include <iterator>
+#include <numeric>
+#include <random>
+#include <thread>
+
+#if defined(EIGEN_USE_THREADS) || defined(EIGEN_USE_SYCL)
+#include "../../../Eigen/ThreadPool"
+#endif
+
+#ifdef EIGEN_USE_GPU
+#include <iostream>
+#if defined(EIGEN_USE_HIP)
+#include <hip/hip_runtime.h>
+#else
+#include <cuda_runtime.h>
+#endif
+#endif
+
+// IWYU pragma: begin_exports
+#include "src/Tensor/TensorMacros.h"
+#include "src/Tensor/TensorForwardDeclarations.h"
+#include "src/Tensor/TensorMeta.h"
+#include "src/Tensor/TensorFunctors.h"
+#include "src/Tensor/TensorCostModel.h"
+#include "src/Tensor/TensorDeviceDefault.h"
+#include "src/Tensor/TensorDeviceThreadPool.h"
+#include "src/Tensor/TensorDeviceGpu.h"
+#ifndef gpu_assert
+#define gpu_assert(x)
+#endif
+#include "src/Tensor/TensorDeviceSycl.h"
+#include "src/Tensor/TensorIndexList.h"
+#include "src/Tensor/TensorDimensionList.h"
+#include "src/Tensor/TensorDimensions.h"
+#include "src/Tensor/TensorInitializer.h"
+#include "src/Tensor/TensorTraits.h"
+#include "src/Tensor/TensorRandom.h"
+#include "src/Tensor/TensorUInt128.h"
+#include "src/Tensor/TensorIntDiv.h"
+#include "src/Tensor/TensorGlobalFunctions.h"
+
+#include "src/Tensor/TensorIO.h"
+
+#include "src/Tensor/TensorBase.h"
+#include "src/Tensor/TensorBlock.h"
+
+#include "src/Tensor/TensorEvaluator.h"
+#include "src/Tensor/TensorExpr.h"
+#include "src/Tensor/TensorReduction.h"
+#include "src/Tensor/TensorReductionGpu.h"
+#include "src/Tensor/TensorArgMax.h"
+#include "src/Tensor/TensorConcatenation.h"
+#include "src/Tensor/TensorContractionMapper.h"
+#include "src/Tensor/TensorContractionBlocking.h"
+#include "src/Tensor/TensorContraction.h"
+#include "src/Tensor/TensorContractionThreadPool.h"
+#include "src/Tensor/TensorContractionGpu.h"
+#include "src/Tensor/TensorConversion.h"
+#include "src/Tensor/TensorConvolution.h"
+#include "src/Tensor/TensorFFT.h"
+#include "src/Tensor/TensorPatch.h"
+#include "src/Tensor/TensorImagePatch.h"
+#include "src/Tensor/TensorVolumePatch.h"
+#include "src/Tensor/TensorBroadcasting.h"
+#include "src/Tensor/TensorChipping.h"
+#include "src/Tensor/TensorInflation.h"
+#include "src/Tensor/TensorLayoutSwap.h"
+#include "src/Tensor/TensorMorphing.h"
+#include "src/Tensor/TensorPadding.h"
+#include "src/Tensor/TensorReverse.h"
+#include "src/Tensor/TensorRoll.h"
+#include "src/Tensor/TensorShuffling.h"
+#include "src/Tensor/TensorStriding.h"
+#include "src/Tensor/TensorCustomOp.h"
+#include "src/Tensor/TensorEvalTo.h"
+#include "src/Tensor/TensorForcedEval.h"
+#include "src/Tensor/TensorGenerator.h"
+#include "src/Tensor/TensorAssign.h"
+#include "src/Tensor/TensorScan.h"
+#include "src/Tensor/TensorTrace.h"
+
+#ifdef EIGEN_USE_SYCL
+#include "src/Tensor/TensorReductionSycl.h"
+#include "src/Tensor/TensorConvolutionSycl.h"
+#include "src/Tensor/TensorContractionSycl.h"
+#include "src/Tensor/TensorScanSycl.h"
+#endif
+
+#include "src/Tensor/TensorExecutor.h"
+#include "src/Tensor/TensorDevice.h"
+
+#include "src/Tensor/TensorStorage.h"
+#include "src/Tensor/Tensor.h"
+#include "src/Tensor/TensorFixedSize.h"
+#include "src/Tensor/TensorMap.h"
+#include "src/Tensor/TensorRef.h"
+// IWYU pragma: end_exports
+
+#include "../../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+// #endif // EIGEN_CXX11_TENSOR_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/TensorSymmetry b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/TensorSymmetry
new file mode 100644
index 0000000..0bf9a48
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/TensorSymmetry
@@ -0,0 +1,40 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_MODULE_H
+#define EIGEN_CXX11_TENSORSYMMETRY_MODULE_H
+
+#include "Tensor"
+
+#include "../../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#include "src/util/CXX11Meta.h"
+
+/** \defgroup TensorSymmetry_Module Tensor Symmetry Module
+ *
+ * This module provides a classes that allow for the definition of
+ * symmetries w.r.t. tensor indices.
+ *
+ * Including this module will implicitly include the Tensor module.
+ *
+ * \code
+ * #include <Eigen/TensorSymmetry>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/TensorSymmetry/util/TemplateGroupTheory.h"
+#include "src/TensorSymmetry/Symmetry.h"
+#include "src/TensorSymmetry/StaticSymmetry.h"
+#include "src/TensorSymmetry/DynamicSymmetry.h"
+// IWYU pragma: end_exports
+
+#include "../../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_CXX11_TENSORSYMMETRY_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/ThreadPool b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/ThreadPool
new file mode 100644
index 0000000..d487333
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/ThreadPool
@@ -0,0 +1 @@
+#include "../../../Eigen/ThreadPool"  // IWYU pragma: export
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/InternalHeaderCheck.h
new file mode 100644
index 0000000..9e4c1ed
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CXX11_TENSOR_MODULE_H
+#error "Please include unsupported/Eigen/CXX11/Tensor instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/README.md b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/README.md
new file mode 100644
index 0000000..9a39343
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/README.md
@@ -0,0 +1,2352 @@
+# Eigen Tensors {#eigen_tensors}
+
+Tensors are multidimensional arrays of elements. Elements are typically scalars,
+but more complex types such as strings are also supported.
+
+## Tensor Classes
+
+You can manipulate a tensor with one of the following classes.  They all are in
+the namespace `::Eigen.`
+
+### Class Tensor<data_type, rank>
+
+This is the class to use to create a tensor and allocate memory for it.  The
+class is templatized with the tensor datatype, such as float or int, and the
+tensor rank.  The rank is the number of dimensions, for example rank 2 is a
+matrix.
+
+Tensors of this class are resizable.  For example, if you assign a tensor of a
+different size to a Tensor, that tensor is resized to match its new value.
+
+#### Constructor Tensor<data_type, rank>(size0, size1, ...)
+
+Constructor for a Tensor.  The constructor must be passed `rank` integers
+indicating the sizes of the instance along each of the the `rank`
+dimensions.
+
+```cpp
+// Create a tensor of rank 3 of sizes 2, 3, 4.  This tensor owns
+// memory to hold 24 floating point values (24 = 2 x 3 x 4).
+Tensor<float, 3> t_3d(2, 3, 4);
+
+// Resize t_3d by assigning a tensor of different sizes, but same rank.
+t_3d = Tensor<float, 3>(3, 4, 3);
+```
+
+#### Constructor Tensor<data_type, rank>(size_array)
+
+Constructor where the sizes for the constructor are specified as an array of
+values instead of an explicitly list of parameters.  The array type to use is
+`Eigen::array<Eigen::Index>`.  The array can be constructed automatically
+from an initializer list.
+
+```cpp
+// Create a tensor of strings of rank 2 with sizes 5, 7.
+Tensor<string, 2> t_2d({5, 7});
+```
+
+### Class TensorFixedSize<data_type, Sizes<size0, size1, ...>>
+
+Class to use for tensors of fixed size, where the size is known at compile
+time.  Fixed sized tensors can provide very fast computations because all their
+dimensions are known by the compiler.  FixedSize tensors are not resizable.
+
+If the total number of elements in a fixed size tensor is small enough the
+tensor data is held onto the stack and does not cause heap allocation and free.
+
+```cpp
+// Create a 4 x 3 tensor of floats.
+TensorFixedSize<float, Sizes<4, 3>> t_4x3;
+```
+
+### Class TensorMap<Tensor<data_type, rank>>
+
+This is the class to use to create a tensor on top of memory allocated and
+owned by another part of your code.  It allows to view any piece of allocated
+memory as a `Tensor`.  Instances of this class do not own the memory where the
+data are stored.
+
+A `TensorMap` is not resizable because it does not own the memory where its data
+are stored.
+
+#### Constructor TensorMap<Tensor<data_type, rank>>(data, size0, size1, ...)
+
+Constructor for a Tensor.  The constructor must be passed a pointer to the
+storage for the data, and "rank" size attributes.  The storage has to be
+large enough to hold all the data.
+
+```cpp
+// Map a tensor of ints on top of stack-allocated storage.
+int storage[128];  // 2 x 4 x 2 x 8 = 128
+TensorMap<Tensor<int, 4>> t_4d(storage, 2, 4, 2, 8);
+
+// The same storage can be viewed as a different tensor.
+// You can also pass the sizes as an array.
+TensorMap<Tensor<int, 2>> t_2d(storage, 16, 8);
+
+// You can also map fixed-size tensors.  Here we get a 1d view of
+// the 2d fixed-size tensor.
+TensorFixedSize<float, Sizes<4, 3>> t_4x3;
+TensorMap<Tensor<float, 1>> t_12(t_4x3.data(), 12);
+```
+
+#### Class TensorRef
+
+See **Assigning to a `TensorRef`**.
+
+## Accessing Tensor Elements
+
+#### data_type tensor(index0, index1...)
+
+Return the element at position `(index0, index1...)` in tensor
+`tensor`.  You must pass as many parameters as the rank of `tensor`.
+The expression can be used as an l-value to set the value of the element at the
+specified position.  The value returned is of the datatype of the tensor.
+
+```cpp
+// Set the value of the element at position (0, 1, 0);
+Tensor<float, 3> t_3d(2, 3, 4);
+t_3d(0, 1, 0) = 12.0f;
+
+// Initialize all elements to random values.
+for (int i = 0; i < 2; ++i) {
+  for (int j = 0; j < 3; ++j) {
+    for (int k = 0; k < 4; ++k) {
+      t_3d(i, j, k) = ...some random value...;
+    }
+  }
+}
+
+// Print elements of a tensor.
+for (int i = 0; i < 2; ++i) {
+  std::cout << t_3d(i, 0, 0);
+}
+```
+
+## TensorLayout
+
+The tensor library supports 2 layouts: `ColMajor` (the default) and
+`RowMajor`.
+
+The layout of a tensor is optionally specified as part of its type. If not
+specified explicitly column major is assumed.
+
+```cpp
+Tensor<float, 3, ColMajor> col_major;  // equivalent to Tensor<float, 3>
+TensorMap<Tensor<float, 3, RowMajor> > row_major(data, ...);
+```
+
+All the arguments to an expression must use the same layout. Attempting to mix
+different layouts will result in a compilation error.
+
+It is possible to change the layout of a tensor or an expression using the
+`swap_layout()` method.  Note that this will also reverse the order of the
+dimensions.
+
+```cpp
+Tensor<float, 2, ColMajor> col_major(2, 4);
+Tensor<float, 2, RowMajor> row_major(2, 4);
+
+Tensor<float, 2> col_major_result = col_major;  // ok, layouts match
+Tensor<float, 2> col_major_result = row_major;  // will not compile
+
+// Simple layout swap
+col_major_result = row_major.swap_layout();
+eigen_assert(col_major_result.dimension(0) == 4);
+eigen_assert(col_major_result.dimension(1) == 2);
+
+// Swap the layout and preserve the order of the dimensions
+array<int, 2> shuffle(1, 0);
+col_major_result = row_major.swap_layout().shuffle(shuffle);
+eigen_assert(col_major_result.dimension(0) == 2);
+eigen_assert(col_major_result.dimension(1) == 4);
+```
+
+## Tensor Operations
+
+The Eigen Tensor library provides a vast library of operations on Tensors:
+numerical operations such as addition and multiplication, geometry operations
+such as slicing and shuffling, etc.  These operations are available as methods
+of the `Tensor` classes, and in some cases as operator overloads.  For example
+the following code computes the elementwise addition of two tensors:
+
+```cpp
+Tensor<float, 3> t1(2, 3, 4);
+t2.setRandom();
+Tensor<float, 3> t2(2, 3, 4);
+t2.setRandom();
+// Set t3 to the element wise sum of t1 and t2
+Tensor<float, 3> t3 = t1 + t2;
+```
+
+While the code above looks easy enough, it is important to understand that the
+expression `t1 + t2` is not actually adding the values of the tensors.  The
+expression instead constructs a "tensor operator" object of the class
+`TensorCwiseBinaryOp<scalar_sum>`, which has references to the tensors
+`t1` and `t2`.  This is a small C++ object that knows how to add
+`t1` and `t2`.  It is only when the value of the expression is assigned
+to the tensor `t3` that the addition is actually performed.  Technically,
+this happens through the overloading of `operator=` in the Tensor class.
+
+This mechanism for computing tensor expressions allows for lazy evaluation and
+optimizations which are what make the tensor library very fast.
+
+Of course, the tensor operators do nest, and the expression `t1 + t2 * 0.3f`
+is actually represented with the (approximate) tree of operators:
+
+```cpp
+TensorCwiseBinaryOp<scalar_sum>(t1, TensorCwiseUnaryOp<scalar_mul>(t2, 0.3f))
+```
+
+### Tensor Operations and C++ "auto"
+
+Because `Tensor` operations create tensor operators, the C++ `auto` keyword
+does not have its intuitive meaning.  Consider these 2 lines of code:
+
+```cpp
+Tensor<float, 3> t3 = t1 + t2;
+auto t4 = t1 + t2;
+```
+
+In the first line we allocate the tensor `t3` and it will contain the
+result of the addition of `t1` and `t2`.  In the second line, `t4`
+is actually the tree of tensor operators that will compute the addition of
+`t1` and `t2`.  In fact, `t4` is *not* a tensor and you cannot get
+the values of its elements:
+
+```cpp
+Tensor<float, 3> t3 = t1 + t2;
+std::cout << t3(0, 0, 0);  // OK prints the value of t1(0, 0, 0) + t2(0, 0, 0)
+
+auto t4 = t1 + t2;
+std::cout << t4(0, 0, 0);  // Compilation error!
+```
+
+When you use `auto` you do not get a `Tensor` as a result but instead a
+non-evaluated expression.
+So only use `auto` to delay evaluation.
+
+Unfortunately, there is no single underlying concrete type for holding
+non-evaluated expressions, hence you have to use `auto` in the case when you do
+want to hold non-evaluated expressions.
+
+When you need the results of set of tensor computations you have to assign the
+result to a `Tensor` that will be capable of holding onto them.  This can be
+either a normal `Tensor`, a `TensorFixedSize`, or a `TensorMap` on an existing
+piece of memory.  All the following will work:
+
+```cpp
+auto t4 = t1 + t2;
+
+Tensor<float, 3> result = t4;  // Could also be: result(t4);
+std::cout << result(0, 0, 0);
+
+TensorMap<float, 4> result(<a float* with enough space>, <size0>, ...) = t4;
+std::cout << result(0, 0, 0);
+
+TensorFixedSize<float, Sizes<size0, ...>> result = t4;
+std::cout << result(0, 0, 0);
+```
+
+Until you need the results, you can keep the operation around, and even reuse
+it for additional operations.  As long as you keep the expression as an
+operation, no computation is performed.
+
+```cpp
+// One way to compute exp((t1 + t2) * 0.2f);
+auto t3 = t1 + t2;
+auto t4 = t3 * 0.2f;
+auto t5 = t4.exp();
+Tensor<float, 3> result = t5;
+
+// Another way, exactly as efficient as the previous one:
+Tensor<float, 3> result = ((t1 + t2) * 0.2f).exp();
+```
+
+### Controlling When Expression are Evaluated
+
+There are several ways to control when expressions are evaluated:
+
+*   Assignment to a `Tensor`, `TensorFixedSize`, or `TensorMap`.
+*   Use of the `eval()` method.
+*   Assignment to a `TensorRef`.
+
+#### Assigning to a Tensor, TensorFixedSize, or TensorMap.
+
+The most common way to evaluate an expression is to assign it to a `Tensor`.
+In the example below, the `auto` declarations make the intermediate values
+"Operations", not Tensors, and do not cause the expressions to be evaluated.
+The assignment to the Tensor `result` causes the evaluation of all the
+operations.
+
+```cpp
+auto t3 = t1 + t2;             // t3 is an Operation.
+auto t4 = t3 * 0.2f;           // t4 is an Operation.
+auto t5 = t4.exp();            // t5 is an Operation.
+Tensor<float, 3> result = t5;  // The operations are evaluated.
+```
+
+If you know the ranks and sizes of the Operation value you can assign the
+Operation to a `TensorFixedSize` instead of a `Tensor`, which is a bit more efficient.
+
+```cpp
+// We know that the result is a 4x4x2 tensor!
+TensorFixedSize<float, Sizes<4, 4, 2>> result = t5;
+```
+
+Similarly, assigning an expression to a `TensorMap` causes its evaluation.
+Like tensors of type `TensorFixedSize`, a `TensorMap` cannot be resized so they have to
+have the rank and sizes of the expression that are assigned to them.
+
+#### Calling eval().
+
+When you compute large composite expressions, you sometimes want to tell Eigen
+that an intermediate value in the expression tree is worth evaluating ahead of
+time.
+This is done by inserting a call to the `eval()` method of the
+expression Operation.
+
+```cpp
+// The previous example could have been written:
+Tensor<float, 3> result = ((t1 + t2) * 0.2f).exp();
+
+// If you want to compute (t1 + t2) once ahead of time you can write:
+Tensor<float, 3> result = ((t1 + t2).eval() * 0.2f).exp();
+```
+
+Semantically, calling `eval()` is equivalent to materializing the value of
+the expression in a temporary `Tensor` of the right size.
+The code above in effect does:
+
+```cpp
+// .eval() knows the size!
+TensorFixedSize<float, Sizes<4, 4, 2>> tmp = t1 + t2;
+Tensor<float, 3> result = (tmp * 0.2f).exp();
+```
+
+Note that the return value of `eval()` is itself an Operation, so the
+following code does not do what you may think:
+
+```cpp
+// Here t3 is an evaluation Operation.  t3 has not been evaluated yet.
+auto t3 = (t1 + t2).eval();
+
+// You can use t3 in another expression.  Still no evaluation.
+auto t4 = (t3 * 0.2f).exp();
+
+// The value is evaluated when you assign the Operation to a Tensor, using
+// an intermediate tensor to represent t3.x
+Tensor<float, 3> result = t4;
+```
+
+While in the examples above calling `eval()` does not make a difference in
+performance, in other cases it can make a huge difference.  In the expression
+below the `broadcast()` expression causes the `X.maximum()` expression
+to be evaluated many times:
+
+```cpp
+Tensor<...> X ...;
+Tensor<...> Y = ((X - X.maximum(depth_dim).reshape(dims2d).broadcast(bcast))
+                 * beta).exp();
+```
+
+Inserting a call to `eval()` between the `maximum()` and
+`reshape()` calls guarantees that `maximum()` is only computed once and
+greatly speeds-up execution:
+
+```cpp
+Tensor<...> Y =
+  ((X - X.maximum(depth_dim).eval().reshape(dims2d).broadcast(bcast))
+    * beta).exp();
+```
+
+In the other example below, the tensor `Y` is both used in the expression and its assignment.
+This is an aliasing problem and if the evaluation is not done in the right order
+Y will be updated incrementally during the evaluation
+resulting in bogus results:
+
+```cpp
+ Tensor<...> Y ...;
+ Y = Y / (Y.sum(depth_dim).reshape(dims2d).broadcast(bcast));
+```
+
+Inserting a call to `eval()` between the `sum()` and `reshape()`
+expressions ensures that the sum is computed before any updates to `Y` are
+done.
+
+```cpp
+ Y = Y / (Y.sum(depth_dim).eval().reshape(dims2d).broadcast(bcast));
+```
+
+Note that an eval around the full right hand side expression is not needed
+because the generated has to compute the `i`-th value of the right hand side
+before assigning it to the left hand side.
+
+However, if you were assigning the expression value to a shuffle of `Y`
+then you would need to force an eval for correctness by adding an `eval()`
+call for the right hand side:
+
+```cpp
+ Y.shuffle(...) =
+    (Y / (Y.sum(depth_dim).eval().reshape(dims2d).broadcast(bcast))).eval();
+```
+
+#### Assigning to a TensorRef.
+
+If you need to access only a few elements from the value of an expression you
+can avoid materializing the value in a full tensor by using a `TensorRef`.
+
+A `TensorRef` is a small wrapper class for any Eigen Operation.  It provides
+overloads for the `()` operator that let you access individual values in
+the expression.
+`TensorRef` is convenient, because the Operation themselves do
+not provide a way to access individual elements.
+
+```cpp
+// Create a TensorRef for the expression.  The expression is not
+// evaluated yet.
+TensorRef<Tensor<float, 3> > ref = ((t1 + t2) * 0.2f).exp();
+
+// Use "ref" to access individual elements.  The expression is evaluated
+// on the fly.
+float at_0 = ref(0, 0, 0);
+std::cout << ref(0, 1, 0);
+```
+
+Only use `TensorRef` when you need a subset of the values of the expression.
+`TensorRef` only computes the values you access.
+However note that if you are going to access all the values it will be much
+ faster to materialize the results in a `Tensor` first.
+
+In some cases, if the full `Tensor` result would be very large, you may save
+memory by accessing it as a `TensorRef`.
+But not always.
+So don't count on it.
+
+
+### Controlling How Expressions Are Evaluated
+
+The tensor library provides several implementations of the various operations
+such as contractions and convolutions.  The implementations are optimized for
+different environments: single threaded on CPU, multi threaded on CPU, or on a GPU using cuda.
+
+You can choose which implementation to use with the `device()` call.  If
+you do not choose an implementation explicitly the default implementation that
+uses a single thread on the CPU is used.
+
+The default implementation has been optimized for recent Intel CPUs, taking
+advantage of SSE, AVX, and FMA instructions.  Work is ongoing to tune the
+library on ARM CPUs.  Note that you need to pass compiler-dependent flags
+to enable the use of SSE, AVX, and other instructions.
+
+For example, the following code adds two tensors using the default
+single-threaded CPU implementation:
+
+```cpp
+Tensor<float, 2> a(30, 40);
+Tensor<float, 2> b(30, 40);
+Tensor<float, 2> c = a + b;
+```
+
+To choose a different implementation you have to insert a `device()` call
+before the assignment of the result.  For technical C++ reasons this requires
+that the `Tensor` for the result be declared on its own.
+This means that you have to know the size of the result.
+
+```cpp
+Eigen::Tensor<float, 2> c(30, 40);
+c.device(...) = a + b;
+```
+
+The call to `device()` must be the last call on the left of the operator=.
+
+You must pass to the `device()` call an Eigen device object.  There are
+presently three devices you can use: `DefaultDevice`, `ThreadPoolDevice` and
+`GpuDevice`.
+
+
+#### Evaluating With the DefaultDevice
+
+This is exactly the same as not inserting a `device()` call.
+
+```cpp
+DefaultDevice my_device;
+c.device(my_device) = a + b;
+```
+
+#### Evaluating with a Thread Pool
+
+```cpp
+// Create the Eigen ThreadPool
+Eigen::ThreadPool pool(8 /* number of threads in pool */)
+
+// Create the Eigen ThreadPoolDevice.
+Eigen::ThreadPoolDevice my_device(&pool, 4 /* number of threads to use */);
+
+// Now just use the device when evaluating expressions.
+Eigen::Tensor<float, 2> c(30, 50);
+c.device(my_device) = a.contract(b, dot_product_dims);
+```
+
+
+#### Evaluating On GPU
+
+This is presently a bit more complicated than just using a thread pool device.
+You need to create a GPU device but you also need to explicitly allocate the
+memory for tensors with cuda.
+
+
+## API Reference
+
+### Datatypes
+
+In the documentation of the tensor methods and Operation we mention datatypes
+that are tensor-type specific:
+
+#### <Tensor-Type>::Dimensions
+
+Acts like an array of `int`. Has an `int size` attribute, and can be
+indexed like an array to access individual values.  Used to represent the
+dimensions of a tensor.  See `dimensions()`.
+
+#### <Tensor-Type>::Index
+
+Acts like an `int`.  Used for indexing tensors along their dimensions.  See
+`operator()`, `dimension()`, and `size()`.
+
+#### <Tensor-Type>::Scalar
+
+Represents the datatype of individual tensor elements.  For example, for a
+`Tensor<float>`, `Scalar` is the type `float`.  See `setConstant()`.
+
+#### (Operation)
+
+We use this pseudo type to indicate that a tensor Operation is returned by a
+method.  We indicate in the text the type and dimensions of the tensor that the
+Operation returns after evaluation.
+
+The Operation will have to be evaluated, for example by assigning it to a
+`Tensor`, before you can access the values of the resulting tensor.  You can also
+access the values through a `TensorRef`.
+
+
+## Built-in Tensor Methods
+
+These are usual C++ methods that act on tensors immediately.  They are not
+Operations which provide delayed evaluation of their results.  Unless specified
+otherwise, all the methods listed below are available on all tensor classes:
+`Tensor`, `TensorFixedSize`, and `TensorMap`.
+
+## Metadata
+
+### int NumDimensions
+
+Constant value indicating the number of dimensions of a `Tensor`.
+This is also known as the tensor rank.
+
+```cpp
+Eigen::Tensor<float, 2> a(3, 4);
+std::cout << "Dims " << a.NumDimensions;
+// Dims 2
+```
+
+### Dimensions dimensions()
+
+Returns an array-like object representing the dimensions of the tensor.
+The actual type of the `dimensions()` result is `<Tensor-Type>::Dimensions`.
+
+```cpp
+Eigen::Tensor<float, 2> a(3, 4);
+const Eigen::Tensor<float, 2>::Dimensions& d = a.dimensions();
+std::cout << "Dim size: " << d.size << ", dim 0: " << d[0]
+          << ", dim 1: " << d[1];
+//  Dim size: 2, dim 0: 3, dim 1: 4
+```
+
+If you use a C++11 compiler, you can use `auto` to simplify the code:
+
+```cpp
+const auto& d = a.dimensions();
+std::cout << "Dim size: " << d.size << ", dim 0: " << d[0]
+        << ", dim 1: " << d[1];
+// Dim size: 2, dim 0: 3, dim 1: 4
+```
+
+### Index dimension(Index n)
+
+Returns the n-th dimension of the tensor.  The actual type of the
+`dimension()` result is `<Tensor-Type>::Index`, but you can
+always use it like an int.
+
+```cpp
+Eigen::Tensor<float, 2> a(3, 4);
+int dim1 = a.dimension(1);
+std::cout << "Dim 1: " << dim1;
+// Dim 1: 4
+```
+
+### Index size()
+
+Returns the total number of elements in the tensor.  This is the product of all
+the tensor dimensions.  The actual type of the `size()` result is
+`<Tensor-Type>::Index`, but you can always use it like an int.
+
+```cpp
+Eigen::Tensor<float, 2> a(3, 4);
+std::cout << "Size: " << a.size();
+/// Size: 12
+```
+
+### Getting Dimensions From An Operation
+
+A few operations provide `dimensions()` directly,
+e.g. `TensorReslicingOp`.  Most operations defer calculating dimensions
+until the operation is being evaluated.  If you need access to the dimensions
+of a deferred operation, you can wrap it in a `TensorRef` (see
+**Assigning to a TensorRef** above), which provides
+`dimensions()` and `dimension()` as above.
+
+`TensorRef` can also wrap the plain `Tensor` types, so this is a useful idiom in
+templated contexts where the underlying object could be either a raw `Tensor`
+or some deferred operation (e.g. a slice of a `Tensor`).  In this case, the
+template code can wrap the object in a TensorRef and reason about its
+dimensionality while remaining agnostic to the underlying type.
+
+
+## Constructors
+
+### Tensor
+
+Creates a tensor of the specified size. The number of arguments must be equal
+to the rank of the tensor. The content of the tensor is not initialized.
+
+```cpp
+Eigen::Tensor<float, 2> a(3, 4);
+std::cout << "NumRows: " << a.dimension(0) << " NumCols: " << a.dimension(1) << endl;
+// NumRows: 3 NumCols: 4
+```
+### TensorFixedSize
+
+Creates a tensor of the specified size. The number of arguments in the `Sizes<>`
+template parameter determines the rank of the tensor. The content of the tensor
+is not initialized.
+
+```cpp
+Eigen::TensorFixedSize<float, Sizes<3, 4>> a;
+std::cout << "Rank: " << a.rank() << endl;
+// Rank: 2
+std::cout << "NumRows: " << a.dimension(0)
+          << " NumCols: " << a.dimension(1) << endl;
+// NumRows: 3 NumCols: 4
+```
+
+### TensorMap
+
+Creates a tensor mapping an existing array of data. The data must not be freed
+until the `TensorMap` is discarded, and the size of the data must be large enough
+to accommodate the coefficients of the tensor.
+
+```cpp
+float data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+Eigen::TensorMap<Tensor<float, 2>> a(data, 3, 4);
+std::cout << "NumRows: " << a.dimension(0) << " NumCols: " << a.dimension(1) << endl;
+// NumRows: 3 NumCols: 4
+std::cout << "a(1, 2): " << a(1, 2) << endl;
+// a(1, 2): 7
+```
+
+## Contents Initialization
+
+When a new `Tensor` or a new `TensorFixedSize` are created, memory is allocated to
+hold all the tensor elements, but the memory is not initialized.  Similarly,
+when a new `TensorMap` is created on top of non-initialized memory the memory its
+contents are not initialized.
+
+You can use one of the methods below to initialize the tensor memory.  These
+have an immediate effect on the tensor and return the tensor itself as a
+result.  These are not tensor Operations which delay evaluation.
+
+### <Tensor-Type> setConstant(const Scalar& val)
+
+Sets all elements of the tensor to the constant value `val`.  `Scalar`
+is the type of data stored in the tensor.  You can pass any value that is
+convertible to that type.
+
+Returns the tensor itself in case you want to chain another call.
+
+```cpp
+a.setConstant(12.3f);
+std::cout << "Constant: " << endl << a << endl << endl;
+
+// Constant:
+// 12.3 12.3 12.3 12.3
+// 12.3 12.3 12.3 12.3
+// 12.3 12.3 12.3 12.3
+```
+Note that `setConstant()` can be used on any tensor where the element type
+has a copy constructor and an `operator=()`:
+
+```cpp
+Eigen::Tensor<string, 2> a(2, 3);
+a.setConstant("yolo");
+std::cout << "String tensor: " << endl << a << endl << endl;
+
+// String tensor:
+// yolo yolo yolo
+// yolo yolo yolo
+```
+
+### <Tensor-Type> setZero()
+
+Fills the tensor with zeros.  Equivalent to `setConstant(Scalar(0))`.
+Returns the tensor itself in case you want to chain another call.
+
+```cpp
+a.setZero();
+std::cout << "Zeros: " << endl << a << endl << endl;
+
+// Zeros:
+// 0 0 0 0
+// 0 0 0 0
+// 0 0 0 0
+```
+
+### <Tensor-Type> setValues({..initializer_list})
+
+Fills the tensor with explicit values specified in a std::initializer_list.
+The type of the initializer list depends on the type and rank of the tensor.
+
+If the tensor has rank N, the initializer list must be nested N times.  The
+most deeply nested lists must contains P scalars of the `Tensor` type where P is
+the size of the last dimension of the Tensor.
+
+For example, for a `TensorFixedSize<float, 2, 3>` the initializer list must
+contains 2 lists of 3 floats each.
+
+`setValues()` returns the tensor itself in case you want to chain another
+call.
+
+```cpp
+Eigen::Tensor<float, 2> a(2, 3);
+a.setValues({{0.0f, 1.0f, 2.0f}, {3.0f, 4.0f, 5.0f}});
+std::cout << "a" << endl << a << endl << endl;
+
+// a
+// 0 1 2
+// 3 4 5
+```
+
+If a list is too short, the corresponding elements of the tensor will not be
+changed.  This is valid at each level of nesting.  For example the following
+code only sets the values of the first row of the tensor.
+
+```cpp
+Eigen::Tensor<int, 2> a(2, 3);
+a.setConstant(1000);
+a.setValues({{10, 20, 30}});
+std::cout << "a" << endl << a << endl << endl;
+// a
+// 10   20   30
+// 1000 1000 1000
+```
+
+### <Tensor-Type> setRandom()
+
+Fills the tensor with random values.  Returns the tensor itself in case you
+want to chain another call.
+
+```cpp
+a.setRandom();
+std::cout << "Random: " << endl << a << endl << endl;
+// Random:
+//   0.680375    0.59688  -0.329554    0.10794
+//  -0.211234   0.823295   0.536459 -0.0452059
+//   0.566198  -0.604897  -0.444451   0.257742
+```
+
+You can customize `setRandom()` by providing your own random number
+generator as a template argument:
+
+```cpp
+a.setRandom<MyRandomGenerator>();
+```
+
+Here, `MyRandomGenerator` must be a struct with the following member
+functions, where Scalar and Index are the same as `<Tensor-Type>::Scalar`
+and `<Tensor-Type>::Index`.
+
+See `struct UniformRandomGenerator` in TensorFunctors.h for an example.
+
+```cpp
+// Custom number generator for use with setRandom().
+struct MyRandomGenerator {
+  // Default and copy constructors. Both are needed
+  MyRandomGenerator() { }
+  MyRandomGenerator(const MyRandomGenerator& ) { }
+
+  // Return a random value to be used.  "element_location" is the
+  // location of the entry to set in the tensor, it can typically
+  // be ignored.
+  Scalar operator()(Eigen::DenseIndex element_location,
+                    Eigen::DenseIndex /*unused*/ = 0) const {
+    return <randomly generated value of type T>;
+  }
+
+  // Same as above but generates several numbers at a time.
+  typename internal::packet_traits<Scalar>::type packetOp(
+      Eigen::DenseIndex packet_location, Eigen::DenseIndex /*unused*/ = 0) const {
+    return <a packet of randomly generated values>;
+  }
+};
+```
+
+You can also use one of the 2 random number generators that are part of the
+tensor library:
+*   UniformRandomGenerator
+*   NormalRandomGenerator
+
+## Data Access
+
+The Tensor, TensorFixedSize, and TensorRef classes provide the following
+accessors to access the tensor coefficients:
+
+```cpp
+const Scalar& operator()(const array<Index, NumIndices>& indices)
+const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices)
+Scalar& operator()(const array<Index, NumIndices>& indices)
+Scalar& operator()(Index firstIndex, IndexTypes... otherIndices)
+```
+
+The number of indices must be equal to the rank of the tensor. Moreover, these
+accessors are not available on tensor expressions. In order to access the
+values of a tensor expression, the expression must either be evaluated or
+wrapped in a TensorRef.
+
+### Scalar* data() and const Scalar* data() const
+
+Returns a pointer to the storage for the tensor.  The pointer is const if the
+tensor was const.  This allows direct access to the data.  The layout of the
+data depends on the tensor layout: `RowMajor` or `ColMajor`.
+
+This access is usually only needed for special cases, for example when mixing
+Eigen Tensor code with other libraries.
+
+Scalar is the type of data stored in the tensor.
+
+```cpp
+Eigen::Tensor<float, 2> a(3, 4);
+float* a_data = a.data();
+a_data[0] = 123.45f;
+std::cout << "a(0, 0): " << a(0, 0);
+// a(0, 0): 123.45
+```
+
+## Tensor Operations
+
+All the methods documented below return non evaluated tensor `Operations`.
+These can be chained: you can apply another `Tensor` Operation to the value
+returned by the method.
+
+The chain of Operation is evaluated lazily, typically when it is assigned to a
+tensor.  See **Controlling When Expression are Evaluated** for more details about
+their evaluation.
+
+### (Operation) constant(const Scalar& val)
+
+Returns a tensor of the same type and dimensions as the original tensor but
+where all elements have the value `val`.
+
+This is useful, for example, when you want to add or subtract a constant from a
+tensor, or multiply every element of a tensor by a scalar.
+However, such operations can also be performed using operator overloads (see `operator+`).
+
+
+```cpp
+Eigen::Tensor<float, 2> a(2, 3);
+a.setConstant(1.0f);
+Eigen::Tensor<float, 2> b = a + a.constant(2.0f);
+Eigen::Tensor<float, 2> c = b * b.constant(0.2f);
+std::cout << "a" << endl << a << endl << endl;
+std::cout << "b" << endl << b << endl << endl;
+std::cout << "c" << endl << c << endl << endl;
+// a
+// 1 1 1
+// 1 1 1
+
+// b
+// 3 3 3
+// 3 3 3
+
+// c
+// 0.6 0.6 0.6
+// 0.6 0.6 0.6
+```
+
+### (Operation) random()
+
+Returns a tensor of the same type and dimensions as the current tensor
+but where all elements have random values.
+
+This is for example useful to add random values to an existing tensor.
+The generation of random values can be customized in the same manner
+as for `setRandom()`.
+
+```cpp
+Eigen::Tensor<float, 2> a(2, 3);
+a.setConstant(1.0f);
+Eigen::Tensor<float, 2> b = a + a.random();
+std::cout << "a\n" << a << "\n\n";
+std::cout << "b\n" << b << "\n\n";
+
+// a
+// 1 1 1
+// 1 1 1
+// b
+// 1.68038   1.5662  1.82329
+// 0.788766  1.59688
+```
+
+## Unary Element Wise Operations
+
+All these operations take a single input tensor as argument and return a tensor
+of the same type and dimensions as the tensor to which they are applied.  The
+requested operations are applied to each element independently.
+
+### (Operation) operator-()
+
+Returns a tensor of the same type and dimensions as the original tensor
+containing the opposite values of the original tensor.
+
+```cpp
+Eigen::Tensor<float, 2> a(2, 3);
+a.setConstant(1.0f);
+Eigen::Tensor<float, 2> b = -a;
+std::cout << "a\n" << a << "\n\n";
+std::cout << "b\n" << b << "\n\n";
+
+// a
+// 1 1 1
+// 1 1 1
+//
+// b
+// -1 -1 -1
+// -1 -1 -1
+```
+
+### (Operation) sqrt()
+
+Returns a tensor of the same type and dimensions as the original tensor
+containing the square roots of the original tensor.
+
+### (Operation) rsqrt()
+
+Returns a tensor of the same type and dimensions as the original tensor
+containing the inverse square roots of the original tensor.
+
+### (Operation) square()
+
+Returns a tensor of the same type and dimensions as the original tensor
+containing the squares of the original tensor values.
+
+### (Operation) inverse()
+
+Returns a tensor of the same type and dimensions as the original tensor
+containing the inverse of the original tensor values.
+
+### (Operation) exp()
+
+Returns a tensor of the same type and dimensions as the original tensor
+containing the exponential of the original tensor.
+
+### (Operation) log()
+
+Returns a tensor of the same type and dimensions as the original tensor
+containing the natural logarithms of the original tensor.
+
+### (Operation) abs()
+
+Returns a tensor of the same type and dimensions as the original tensor
+containing the absolute values of the original tensor.
+
+### (Operation) arg()
+
+Returns a tensor with the same dimensions as the original tensor
+containing the complex argument (phase angle) of the values of the
+original tensor.
+
+### (Operation) real()
+
+Returns a tensor with the same dimensions as the original tensor
+containing the real part of the complex values of the original tensor.
+The result has a real-valued scalar type.
+
+### (Operation) imag()
+
+Returns a tensor with the same dimensions as the original tensor
+containing the imaginary part of the complex values of the original
+tensor.
+The result has a real-valued scalar type.
+
+### (Operation) pow(Scalar exponent)
+
+Returns a tensor of the same type and dimensions as the original tensor
+containing the coefficients of the original tensor to the power of the
+exponent.
+
+The type of the exponent, Scalar, is always the same as the type of the
+tensor coefficients.  For example, only integer exponents can be used in
+conjunction with tensors of integer values.
+
+You can use `cast()` to lift this restriction.  For example this computes
+cubic roots of an int Tensor:
+
+```cpp
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{0, 1, 8}, {27, 64, 125}});
+Eigen::Tensor<double, 2> b = a.cast<double>().pow(1.0 / 3.0);
+std::cout << "a" << endl << a << endl << endl;
+std::cout << "b" << endl << b << endl << endl;
+
+// a
+// 0   1   8
+// 27  64 125
+//
+// b
+// 0 1 2
+// 3 4 5
+```
+
+### (Operation)  operator* (Scalar s)
+
+Multiplies every element of the input tensor by the scalar `s`:
+```cpp
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{1, 2, 3},
+                {4, 5, 6}});
+Eigen::Tensor<int,2> scaled_a = a * 2;
+
+std::cout << "a\n" << a << "\n";
+std::cout << "scaled_a\n" << scaled_a << "\n";
+
+// a
+// 1 2 3
+// 4 5 6
+//
+// scaled_a
+// 2  4  6
+// 8 10 12
+```
+### (Operation) operator+ (Scalar s)
+Adds `s` to every element in the tensor.
+
+### (Operation) operator- (Scalar s)
+Subtracts `s` from every element in the tensor.
+
+### (Operation) operator/ (Scalar s)
+Divides every element in the tensor by `s`.
+
+### (Operation) operator% (Scalar s)
+Computes the element-wise modulus (remainder) of each tensor element divided by `s`
+
+**Only integer types are supported.**
+For floating-point tensors, implement a `unaryExpr` using `std::fmod`.
+
+### (Operation)  cwiseMax(Scalar threshold)
+Returns the coefficient-wise maximum between two tensors.
+```cpp
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{0, 100, 200}, {300, 400, 500}});
+
+Eigen::Tensor<int, 2> b(2, 3);
+b.setValues({{-1, -2, 300}, {-4, 555, -6}});
+
+Eigen::Tensor<int, 2> c = a.cwiseMax(b);
+
+std::cout << "a\n" << a << "\n"
+            << "b\n" << b << "\n"
+            << "c\n" << c << "\n";
+
+// a
+//   0 100 200
+// 300 400 500
+
+// b
+// -1  -2 300
+// -4 555  -6
+
+// c
+//   0 100 300
+// 300 555 500
+```
+### (Operation)  cwiseMin(Scalar threshold)
+Returns the coefficient-wise minimum between two tensors.
+
+```cpp
+Eigen::Tensor<int, 2> a(2, 2);
+a.setValues({{0, 100}, {300, -900}});
+
+Eigen::Tensor<int, 2> b(2, 2);
+b.setValues({{-1, -2}, {400, 555}});
+
+Eigen::Tensor<int, 2> c = a.cwiseMin(b);
+
+std::cout << "a\n" << a << "\n"
+          << "b\n" << b << "\n"
+          << "c\n" << c << "\n";
+
+// a
+//   0  100
+// 300 -900
+
+// b
+//  -1  -2
+// 400 555
+
+// c
+//  -1   -2
+// 300 -900
+```
+
+### (Operation)  unaryExpr(const CustomUnaryOp& func)
+Applies a user defined function to each element in the tensor.
+Supports lambdas or functor structs with an operator().
+
+Using lambda:
+```cpp
+Eigen::Tensor<float, 2> a(2, 3);
+a.setValues({{0, -.5, -1}, {.5, 1.5, 2.0}});
+auto my_func = [](float el){ return std::abs(el + 0.5f);};
+Eigen::Tensor<float, 2> b = a.unaryExpr(my_func);
+std::cout << "a\n" << a << "\n"
+        << "b\n" << b << "\n";
+=>
+a
+    0  -0.5   -1
+0.5   1.5    2
+b
+0.5     0  0.5
+    1     2  2.5
+```
+
+Using a functor to normalize and clamp values to `[-1.0, 1.0]`:
+
+```cpp
+template<typename Scalar>
+struct NormalizedClamp {
+NormalizedClamp(Scalar lo, Scalar hi) : _lo(lo), _hi(hi) {}
+Scalar operator()(Scalar x) const {
+    if (x < _lo) return Scalar(0);
+    if (x > _hi) return Scalar(1);
+    return (x - _lo) / (_hi - _lo);
+}
+Scalar _lo, _hi;
+};
+
+Eigen::Tensor<float, 2> c = a.unaryExpr(NormalizedClamp<float>(-1.0f, 1.0f));
+std::cout << "c\n" << c << "\n";
+
+// c
+// 0.5    0.25    0
+// 0.75   1       1
+```
+
+
+## Binary Element Wise Operations
+
+These operations take two input tensors as arguments. The 2 input tensors should
+be of the same type and dimensions. The result is a tensor of the same
+dimensions as the tensors to which they are applied, and unless otherwise
+specified it is also of the same type. The requested operations are applied to
+each pair of elements independently.
+
+### (Operation) operator+(const OtherDerived& other)
+
+Returns a tensor of the same type and dimensions as the input tensors
+containing the coefficient wise sums of the inputs.
+
+### (Operation) operator-(const OtherDerived& other)
+
+Returns a tensor of the same type and dimensions as the input tensors
+containing the coefficient wise differences of the inputs.
+
+### (Operation) operator*(const OtherDerived& other)
+
+Returns a tensor of the same type and dimensions as the input tensors
+containing the coefficient wise products of the inputs.
+
+### (Operation) operator/(const OtherDerived& other)
+
+Returns a tensor of the same type and dimensions as the input tensors
+containing the coefficient wise quotients of the inputs.
+
+This operator is not supported for integer types.
+
+### (Operation) cwiseMax(const OtherDerived& other)
+
+Returns a tensor of the same type and dimensions as the input tensors
+containing the coefficient wise maximums of the inputs.
+
+### (Operation) cwiseMin(const OtherDerived& other)
+
+Returns a tensor of the same type and dimensions as the input tensors
+containing the coefficient wise minimums of the inputs.
+
+### (Operation) Logical operators
+
+The following boolean operators are supported:
+
+ * `operator&&(const OtherDerived& other)`
+ * `operator||(const OtherDerived& other)`
+ * `operator<(const OtherDerived& other)`
+ * `operator<=(const OtherDerived& other)`
+ * `operator>(const OtherDerived& other)`
+ * `operator>=(const OtherDerived& other)`
+ * `operator==(const OtherDerived& other)`
+ * `operator!=(const OtherDerived& other)`
+
+ as well as bitwise operators:
+
+ * `operator&(const OtherDerived& other)`
+ * `operator|(const OtherDerived& other)`
+ * `operator^(const OtherDerived& other)`
+
+The resulting tensor retains the input scalar type.
+
+## Selection (select(const ThenDerived& thenTensor, const ElseDerived& elseTensor)
+
+Selection is a coefficient-wise ternary operator that is the tensor equivalent
+to the if-then-else operation.
+
+```cpp
+    Tensor<bool, 3> if = ...;
+    Tensor<float, 3> then = ...;
+    Tensor<float, 3> else = ...;
+    Tensor<float, 3> result = if.select(then, else);
+```
+
+The 3 arguments must be of the same dimensions, which will also be the dimension
+of the result.  The 'if' tensor must be of type boolean, the 'then' and the
+'else' tensor must be of the same type, which will also be the type of the
+result.
+
+Each coefficient in the result is equal to the corresponding coefficient in the
+'then' tensor if the corresponding value in the 'if' tensor is true. If not, the
+resulting coefficient will come from the 'else' tensor.
+
+
+## Contraction
+
+Tensor *contractions* are a generalization of the matrix product to the
+multidimensional case.
+
+```cpp
+// Create 2 matrices using tensors of rank 2
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{1, 2, 3}, {6, 5, 4}});
+Eigen::Tensor<int, 2> b(3, 2);
+b.setValues({{1, 2}, {4, 5}, {5, 6}});
+
+// Compute the traditional matrix product
+Eigen::array<Eigen::IndexPair<int>, 1> product_dims = { Eigen::IndexPair<int>(1, 0) };
+Eigen::Tensor<int, 2> AB = a.contract(b, product_dims);
+
+// Compute the product of the transpose of the matrices
+Eigen::array<Eigen::IndexPair<int>, 1> transposed_product_dims = { Eigen::IndexPair<int>(0, 1) };
+Eigen::Tensor<int, 2> AtBt = a.contract(b, transposed_product_dims);
+
+// Contraction to scalar value using a double contraction.
+// First coordinate of both tensors are contracted as well as both second coordinates, i.e., this computes the sum of the squares of the elements.
+Eigen::array<Eigen::IndexPair<int>, 2> double_contraction_product_dims = { Eigen::IndexPair<int>(0, 0), Eigen::IndexPair<int>(1, 1) };
+Eigen::Tensor<int, 0> AdoubleContractedA = a.contract(a, double_contraction_product_dims);
+
+// Extracting the scalar value of the tensor contraction for further usage
+int value = AdoubleContractedA(0);
+```
+
+## Reduction Operations
+
+A *Reduction* operation returns a tensor with fewer dimensions than the
+original tensor.  The values in the returned tensor are computed by applying a
+*reduction operator* to slices of values from the original tensor.  You specify
+the dimensions along which the slices are made.
+
+The Eigen Tensor library provides a set of predefined reduction operators such
+as `maximum()` and `sum()` and lets you define additional operators by
+implementing a few methods from a reductor template.
+
+### Reduction Dimensions
+
+All reduction operations take a single parameter of type
+`<TensorType>::``Dimensions` which can always be specified as an array of
+ints.  These are called the "reduction dimensions."  The values are the indices
+of the dimensions of the input tensor over which the reduction is done.  The
+parameter can have at most as many element as the rank of the input tensor;
+each element must be less than the tensor rank, as it indicates one of the
+dimensions to reduce.
+
+Each dimension of the input tensor should occur at most once in the reduction
+dimensions as the implementation does not remove duplicates.
+
+The order of the values in the reduction dimensions does not affect the
+results, but the code may execute faster if you list the dimensions in
+increasing order.
+
+Example: Reduction along one dimension.
+```cpp
+// Create a tensor of 2 dimensions
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{1, 2, 3}, {6, 5, 4}});
+// Reduce it along the second dimension (1)...
+Eigen::array<int, 1> dims({1 /* dimension to reduce */});
+// ...using the "maximum" operator.
+// The result is a tensor with one dimension.  The size of
+// that dimension is the same as the first (non-reduced) dimension of a.
+Eigen::Tensor<int, 1> b = a.maximum(dims);
+std::cout << "a" << endl << a << endl << endl;
+std::cout << "b" << endl << b << endl << endl;
+
+// a
+// 1 2 3
+// 6 5 4
+
+// b
+// 3
+// 6
+```
+Example: Reduction along two dimensions.
+```cpp
+Eigen::Tensor<float, 3, Eigen::ColMajor> a(2, 3, 4);
+a.setValues({{{0.0f, 1.0f, 2.0f, 3.0f},
+                {7.0f, 6.0f, 5.0f, 4.0f},
+                {8.0f, 9.0f, 10.0f, 11.0f}},
+                {{12.0f, 13.0f, 14.0f, 15.0f},
+                {19.0f, 18.0f, 17.0f, 16.0f},
+                {20.0f, 21.0f, 22.0f, 23.0f}}});
+// The tensor a has 3 dimensions.  We reduce along the
+// first 2, resulting in a tensor with a single dimension
+// of size 4 (the last dimension of a.)
+// Note that we pass the array of reduction dimensions
+// directly to the maximum() call.
+Eigen::Tensor<float, 1, Eigen::ColMajor> b =
+    a.maximum(Eigen::array<int, 2>({0, 1}));
+std::cout << "b" << endl << b << endl << endl;
+
+// b
+// 20
+// 21
+// 22
+// 23
+```
+#### Reduction along all dimensions
+
+As a special case, if you pass no parameter to a reduction operation the
+original tensor is reduced along *all* its dimensions.  The result is a
+scalar, represented as a zero-dimension tensor.
+
+```cpp
+Eigen::Tensor<float, 3> a(2, 3, 4);
+a.setValues({{{0.0f, 1.0f, 2.0f, 3.0f},
+              {7.0f, 6.0f, 5.0f, 4.0f},
+              {8.0f, 9.0f, 10.0f, 11.0f}},
+              {{12.0f, 13.0f, 14.0f, 15.0f},
+              {19.0f, 18.0f, 17.0f, 16.0f},
+              {20.0f, 21.0f, 22.0f, 23.0f}}});
+// Reduce along all dimensions using the sum() operator.
+Eigen::Tensor<float, 0> b = a.sum();
+std::cout << "b\n" << b;
+
+// b
+// 276
+```
+You can extract the scalar directly by casting the expression and extract the first and only coefficient:
+```cpp
+float sum = static_cast<Eigen::Tensor<float, 0>>(a.sum())();
+```
+
+### (Operation) sum(const Dimensions& reduction_dims)
+### (Operation) sum()
+
+Reduce a tensor using the `sum()` operator.  The resulting values
+are the sum of the reduced values.
+
+### (Operation) mean(const Dimensions& reduction_dims)
+### (Operation) mean()
+
+Reduce a tensor using the `mean()` operator.  The resulting values
+are the mean of the reduced values.
+
+### (Operation) maximum(const Dimensions& reduction_dims)
+### (Operation) maximum()
+
+Reduce a tensor using the `maximum()` operator.  The resulting values are the
+largest of the reduced values.
+
+### (Operation) minimum(const Dimensions& reduction_dims)
+### (Operation) minimum()
+
+Reduce a tensor using the `minimum()` operator.  The resulting values
+are the smallest of the reduced values.
+
+### (Operation) prod(const Dimensions& reduction_dims)
+### (Operation) prod()
+
+Reduce a tensor using the `prod()` operator.  The resulting values
+are the product of the reduced values.
+
+### (Operation) all(const Dimensions& reduction_dims)
+### (Operation) all()
+Reduce a tensor using the `all()` operator.  Casts tensor to bool and then checks
+whether all elements are true.  Runs through all elements rather than
+short-circuiting, so may be significantly inefficient.
+
+### (Operation) any(const Dimensions& reduction_dims)
+### (Operation) any()
+Reduce a tensor using the `any()` operator.  Casts tensor to bool and then checks
+whether any element is true.  Runs through all elements rather than
+short-circuiting, so may be significantly inefficient.
+
+
+### (Operation) argmax(const Dimensions& reduction_dim)
+### (Operation) argmax()
+
+Reduce a tensor using the `argmax()` operator.
+
+The resulting values are the indices of the largest elements along the specified dimension.
+
+Only a single `reduction_dim` is supported.
+
+If multiple elements share the maximum value, the one with the **lowest index** is returned.
+
+```cpp
+Eigen::Tensor<float, 2> a(2, 3);
+a.setValues({{1, 4, 8}, {3, 4, 2}});
+
+Eigen::Tensor<Eigen::Index, 1> argmax_dim0 = a.argmax(0);
+
+std::cout << "a:\n" << a << "\n";
+for (int i = 0; i < argmax_dim0.size(); ++i) {
+    std::cout << "argmax along dim 0 at index " << i << " = " << argmax_dim0(i) << "\n";
+}
+
+// a:
+// 1 4 8
+// 3 4 2
+// argmax along dim 0 at index 0 = 1
+// argmax along dim 0 at index 1 = 0
+// argmax along dim 0 at index 2 = 0
+```
+
+ To compute the index of the global maximum, use the overload without arguments (which flattens the tensor).
+
+
+```cpp
+Eigen::Tensor<Eigen::Index, 0> argmax_flat = a.argmax();
+std::cout << "Flat argmax index: " << argmax_flat();
+
+// Flat argmax index: 4
+```
+
+### (Operation) argmin(const Dimensions& reduction_dim)
+### (Operation) argmin()
+See `argmax`.
+
+### (Operation) reduce(const Dimensions& reduction_dims, const Reducer& reducer)
+
+Reduce a tensor using a user-defined reduction operator.  See `SumReducer`
+in TensorFunctors.h for information on how to implement a reduction operator.
+
+
+## Trace
+
+A *Trace* operation returns a tensor with fewer dimensions than the original
+tensor. It returns a tensor whose elements are the sum of the elements of the
+original tensor along the main diagonal for a list of specified dimensions, the
+"trace dimensions". Similar to the `Reduction Dimensions`, the trace dimensions
+are passed as an input parameter to the operation, are of type `<TensorType>::``Dimensions`
+, and have the same requirements when passed as an input parameter. In addition,
+the trace dimensions must have the same size.
+
+Example: Trace along 2 dimensions.
+
+```cpp
+// Create a tensor of 3 dimensions
+Eigen::Tensor<int, 3> a(2, 2, 3);
+a.setValues({{{1, 2, 3}, {4, 5, 6}}, {{7, 8, 9}, {10, 11, 12}}});
+// Specify the dimensions along which the trace will be computed.
+// In this example, the trace can only be computed along the dimensions
+// with indices 0 and 1
+Eigen::array<int, 2> dims({0, 1});
+// The output tensor contains all but the trace dimensions.
+Tensor<int, 1> a_trace = a.trace(dims);
+std::cout << "a_trace:" << endl;
+std::cout << a_trace << endl;
+
+// a_trace:
+// 11
+// 13
+// 15
+```
+
+### (Operation) trace(const Dimensions& new_dims)
+### (Operation) trace()
+
+As a special case, if no parameter is passed to the operation, trace is computed
+along *all* dimensions of the input tensor.
+
+Example: Trace along all dimensions.
+
+```cpp
+// Create a tensor of 3 dimensions, with all dimensions having the same size.
+Eigen::Tensor<int, 3> a(3, 3, 3);
+a.setValues({{{1, 2, 3}, {4, 5, 6}, {7, 8, 9}},
+            {{10, 11, 12}, {13, 14, 15}, {16, 17, 18}},
+            {{19, 20, 21}, {22, 23, 24}, {25, 26, 27}}});
+// Result is a zero dimension tensor
+Tensor<int, 0> a_trace = a.trace();
+std::cout<<"a_trace:"<<endl;
+std::cout<<a_trace<<endl;
+
+// a_trace:
+// 42
+```
+
+## Scan Operations
+
+A *Scan* operation returns a tensor with the same dimensions as the original
+tensor. The operation performs an inclusive scan along the specified
+axis, which means it computes a running total along the axis for a given
+reduction operation.
+If the reduction operation corresponds to summation, then this computes the
+prefix sum of the tensor along the given axis.
+
+Example:
+Cumulative sum along the second dimension
+
+```cpp
+// Create a tensor of 2 dimensions
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{1, 2, 3}, {4, 5, 6}});
+// Scan it along the second dimension (1) using summation
+Eigen::Tensor<int, 2> b = a.cumsum(1);
+// The result is a tensor with the same size as the input
+std::cout << "a" << endl << a << endl << endl;
+std::cout << "b" << endl << b << endl << endl;
+
+// a
+// 1 2 3
+// 4 5 6
+
+// b
+// 1  3  6
+// 4  9 15
+```
+
+### (Operation) cumsum(const Index& axis)
+
+Perform a scan by summing consecutive entries.
+
+### (Operation) cumprod(const Index& axis)
+
+Perform a scan by multiplying consecutive entries.
+
+## Convolutions
+
+### (Operation) convolve(const Kernel& kernel, const Dimensions& dims)
+
+Returns a tensor that is the output of the convolution of the input tensor with the kernel,
+along the specified dimensions of the input tensor. The dimension size for dimensions of the output tensor
+which were part of the convolution will be reduced by the formula:
+```cpp
+output_dim_size = input_dim_size - kernel_dim_size + 1 // (requires: input_dim_size >= kernel_dim_size).
+```
+The dimension sizes for dimensions that were not part of the convolution will remain the same.
+Performance of the convolution can depend on the length of the stride(s) of the input tensor dimension(s) along which the
+convolution is computed (the first dimension has the shortest stride for `ColMajor`, whereas `RowMajor`'s shortest stride is
+for the last dimension).
+
+```cpp
+// Compute convolution along the second and third dimension.
+Tensor<float, 4, DataLayout> input(3, 3, 7, 11);
+Tensor<float, 2, DataLayout> kernel(2, 2);
+Tensor<float, 4, DataLayout> output(3, 2, 6, 11);
+input.setRandom();
+kernel.setRandom();
+
+Eigen::array<ptrdiff_t, 2> dims({1, 2});  // Specify second and third dimension for convolution.
+output = input.convolve(kernel, dims);
+
+for (int i = 0; i < 3; ++i) {
+  for (int j = 0; j < 2; ++j) {
+    for (int k = 0; k < 6; ++k) {
+      for (int l = 0; l < 11; ++l) {
+        const float result = output(i,j,k,l);
+        const float expected = input(i,j+0,k+0,l) * kernel(0,0) +
+                               input(i,j+1,k+0,l) * kernel(1,0) +
+                               input(i,j+0,k+1,l) * kernel(0,1) +
+                               input(i,j+1,k+1,l) * kernel(1,1);
+        VERIFY_IS_APPROX(result, expected);
+      }
+    }
+  }
+}
+```
+
+## Geometrical Operations
+
+These operations return a `Tensor` with different dimensions than the original
+`Tensor`.  They can be used to access slices of tensors, see them with different
+dimensions, or pad tensors with additional data.
+
+### (Operation) reshape(const Dimensions& new_dims)
+
+Returns a view of the input tensor that has been reshaped to the specified
+new dimensions.
+
+The argument `new_dims` is an array of Index values.
+
+The rank of the resulting tensor is equal to the number of elements in `new_dims`.
+
+The product of all the sizes in the new dimension array must be equal to
+the number of elements in the input tensor.
+
+```cpp
+// Increase the rank of the input tensor by introducing a new dimension
+// of size 1.
+Tensor<float, 2> input(7, 11);
+array<int, 3> three_dims{{7, 11, 1}};
+Tensor<float, 3> result = input.reshape(three_dims);
+
+// Decrease the rank of the input tensor by merging 2 dimensions;
+array<int, 1> one_dim{{7 * 11}};
+Tensor<float, 1> result = input.reshape(one_dim);
+```
+
+This operation does not move any data in the input tensor, so the resulting
+contents of a reshaped `Tensor` depend on the data layout of the original `Tensor`.
+
+For example this is what happens when you `reshape()` a 2D `ColMajor` tensor
+to one dimension:
+
+```cpp
+Eigen::Tensor<float, 2, Eigen::ColMajor> a(2, 3);
+a.setValues({{0.0f, 100.0f, 200.0f}, {300.0f, 400.0f, 500.0f}});
+Eigen::array<Eigen::DenseIndex, 1> one_dim({3 * 2});
+Eigen::Tensor<float, 1, Eigen::ColMajor> b = a.reshape(one_dim);
+std::cout << "b" << endl << b << endl;
+
+// b
+//   0
+// 300
+// 100
+// 400
+// 200
+// 500
+```
+
+This is what happens when the 2D `Tensor` is `RowMajor`:
+
+```cpp
+Eigen::Tensor<float, 2, Eigen::RowMajor> a(2, 3);
+a.setValues({{0.0f, 100.0f, 200.0f}, {300.0f, 400.0f, 500.0f}});
+Eigen::array<Eigen::DenseIndex, 1> one_dim({3 * 2});
+Eigen::Tensor<float, 1, Eigen::RowMajor> b = a.reshape(one_dim);
+std::cout << "b" << endl << b << endl;
+
+// b
+//   0
+// 100
+// 200
+// 300
+// 400
+// 500
+```
+
+The reshape operation is a lvalue. In other words, it can be used on the left
+side of the assignment operator.
+
+The previous example can be rewritten as follow:
+
+```cpp
+Eigen::Tensor<float, 2, Eigen::ColMajor> a(2, 3);
+a.setValues({{0.0f, 100.0f, 200.0f}, {300.0f, 400.0f, 500.0f}});
+Eigen::array<Eigen::DenseIndex, 2> two_dim({2, 3});
+Eigen::Tensor<float, 1, Eigen::ColMajor> b(6);
+b.reshape(two_dim) = a;
+std::cout << "b" << endl << b << endl;
+
+// b
+//   0
+// 300
+// 100
+// 400
+// 200
+// 500
+```
+
+Note that "b" itself was not reshaped but that instead the assignment is done to
+the reshape view of b.
+
+### (Operation) shuffle(const Shuffle& shuffle)
+
+Returns a view of the input tensor whose dimensions have been
+reordered according to the specified permutation.
+
+The argument `shuffle` is an array of `Index` values:
+* Its size is the rank of the input tensor.
+* It must contain a permutation of `[0, 1, ..., rank - 1]`.
+* The `i`-th dimension of the output tensor corresponds to the size of the dimension at position `shuffle[i]` in the input tensor. For example:
+
+```cpp
+// Shuffle all dimensions to the left by 1.
+Tensor<float, 3> input(20, 30, 50);
+// ... set some values in input.
+Tensor<float, 3> output = input.shuffle({1, 2, 0});
+
+eigen_assert(output.dimension(0) == 30);
+eigen_assert(output.dimension(1) == 50);
+eigen_assert(output.dimension(2) == 20);
+
+// Indices into the output tensor are shuffled accordingly to formulate
+// indices into the input tensor.
+eigen_assert(output(3, 7, 11) == input(11, 3, 7));
+
+// In general:
+eigen_assert(output(..., indices[shuffle[i]], ...) ==
+             input(..., indices[i], ...));
+```
+
+The shuffle operation results in a lvalue, which means that it can be assigned
+to. In other words, it can be used on the left side of the assignment operator.
+
+Let's rewrite the previous example to take advantage of this feature:
+
+```cpp
+// Shuffle all dimensions to the left by 1.
+Tensor<float, 3> input(20, 30, 50);
+input.setRandom();
+Tensor<float, 3> output(30, 50, 20);
+output.shuffle({2, 0, 1}) = input;
+```
+
+### (Operation) stride(const Strides& strides)
+
+Returns a view of the input tensor that strides (skips stride-1
+elements) along each of the dimensions.
+
+The argument strides is an array of `Index` values:
+* Its size is the rank of the input tensor.
+* Must be >= 1
+
+ The dimensions of the resulting tensor are `ceil(input_dimensions[i] / strides[i])`.
+
+For example this is what happens when you `stride()` a 2D tensor:
+
+```cpp
+Eigen::Tensor<int, 2> a(4, 3);
+a.setValues({{0, 100, 200},
+             {300, 400, 500},
+             {600, 700, 800},
+             {900, 1000, 1100}});
+Eigen::array<Eigen::DenseIndex, 2> strides({3, 2});
+Eigen::Tensor<int, 2> b = a.stride(strides);
+std::cout << "b" << endl << b << endl;
+// b
+//    0   200
+//  900  1100
+```
+
+It is possible to assign a tensor to a stride:
+```cpp
+Tensor<float, 3> input(20, 30, 50);
+input.setRandom();
+Tensor<float, 3> output(40, 90, 200);
+output.stride({2, 3, 4}) = input;
+```
+
+### (Operation) slice(const StartIndices& offsets, const Sizes& extents)
+
+Returns a sub-tensor of the given tensor. For each dimension i, the slice is
+made of the coefficients stored between `offset[i]` and `offset[i] + extents[i]` in
+the input tensor.
+
+```cpp
+Eigen::Tensor<int, 2> a(4, 3);
+a.setValues({{0, 100, 200}, {300, 400, 500},
+             {600, 700, 800}, {900, 1000, 1100}});
+Eigen::array<Eigen::Index, 2> offsets = {1, 0};
+Eigen::array<Eigen::Index, 2> extents = {2, 2};
+Eigen::Tensor<int, 2> slice = a.slice(offsets, extents);
+std::cout << "a" << endl << a << endl;
+// a
+//    0   100   200
+//  300   400   500
+//  600   700   800
+//  900  1000  1100
+
+std::cout << "slice" << endl << slice << endl;
+// slice
+//  300   400
+//  600   700
+```
+
+### (Operation) stridedSlice(const StartIndices& start, const StopIndices& stop, const Strides& strides)
+
+Returns a sub-tensor by selecting elements using `start`, `stop` (exclusive), and `strides` for each dimension.
+
+This is similar to slicing in Python using [start:stop:step].
+
+``` cpp
+Eigen::Tensor<int, 2> a(4, 6);
+a.setValues({{  0,  10,  20,  30,  40,   50},
+             {100, 110, 120, 130, 140,  150},
+             {200, 210, 220, 230, 240,  250},
+             {300, 310, 320, 330, 340,  350}});
+
+Eigen::array<Eigen::Index, 2> start = {1, 1};
+Eigen::array<Eigen::Index, 2> stop  = {4, 6}; // Stop is exclusive
+Eigen::array<Eigen::Index, 2> strides = {2, 2};
+
+Eigen::Tensor<int, 2> sub = a.stridedSlice(start, stop, strides);
+
+std::cout << "a\n" << a << "\n";
+std::cout << "sub\n" << sub << "\n";
+
+// a
+//   0  10  20  30  40  50
+// 100 110 120 130 140 150
+// 200 210 220 230 240 250
+// 300 310 320 330 340 350
+
+// sub
+// 110 130 150
+// 310 330 350
+```
+It is also possible to assign to a strided slice:
+
+``` cpp
+Eigen::Tensor<int, 2> b(sub.dimensions());
+b.setConstant(-1);
+a.stridedSlice(start, stop, strides) = b;
+std::cout << "modified a\n" << a << "\n";
+
+
+// modified a
+//   0  10  20  30  40  50
+// 100  -1 120  -1 140  -1
+// 200 210 220 230 240 250
+// 300  -1 320  -1 340  -1
+
+```
+### (Operation) chip(const Index offset, const Index dim)
+
+A chip is a special kind of slice.
+It is the subtensor at the given offset in the dimension `dim`.
+
+The returned tensor has one fewer dimension than the input tensor: the dimension dim is removed.
+
+For example, a matrix chip would be either a row or a column of the input matrix:
+
+```cpp
+Eigen::Tensor<int, 2> a(4, 3);
+a.setValues({{0, 100, 200}, {300, 400, 500},
+             {600, 700, 800}, {900, 1000, 1100}});
+Eigen::Tensor<int, 1> row_3 = a.chip(2, 0);
+Eigen::Tensor<int, 1> col_2 = a.chip(1, 1);
+std::cout << "a\n" << a << "\n";
+
+// a
+//    0   100   200
+//  300   400   500
+//  600   700   800
+//  900  1000  1100
+
+std::cout << "row_3\n" << row_3 << "\n";
+// row_3
+//    600   700   800
+
+std::cout << "col_2\n" << col_2 << "\n";
+// col_2
+//   100   400   700    1000
+```
+
+It is possible to assign values to a tensor chip since the chip operation is a
+lvalue. For example:
+
+```cpp
+Eigen::Tensor<int, 1> a(3);
+a.setValues({{100, 200, 300}});
+Eigen::Tensor<int, 2> b(2, 3);
+b.setZero();
+b.chip(0, 0) = a;
+std::cout << "a\n" << a << "\n";
+std::cout << "b\n" << b << "\n";
+
+// a
+// 100
+// 200
+// 300
+
+// b
+//   100   200   300
+//     0     0     0
+```
+
+
+The dimension can also be passed as a template parameter:
+
+```cpp
+b.chip<0>(1) = a;  // Equivalent to b.chip(1,0) = a;
+```
+
+Note that only one dimension can be chipped at a time.
+To chip off multiple dimensions, you can chain calls
+
+```cpp
+Eigen::Tensor<int, 3> a(2, 3, 4);
+Eigen::Tensor<int, 1> b = b.chip<2>(0) // Now has shape [2,3]
+                           .chip<1>(0); // Now has shape [2]
+```
+
+Be careful in which order you chip, as each operation affects the shape of the intermediate result.
+For example:
+
+```cpp
+// AVOID THIS
+Eigen::Tensor<int, 1> c = b.chip<1>(0) // Now has shape [2,4]
+                           .chip<1>(0); // Now has shape [2]
+```
+
+In general, it’s more intuitive to chip from the outermost dimension first.
+
+
+### (Operation) reverse(const ReverseDimensions& reverse)
+
+Returns a view of the input tensor that reverses the order of the coefficients
+along a subset of the dimensions.  The argument reverse is an array of boolean
+values that indicates whether or not the order of the coefficients should be
+reversed along each of the dimensions.  This operation preserves the dimensions
+of the input tensor.
+
+For example this is what happens when you `reverse()` the first dimension
+of a 2D tensor:
+
+```cpp
+Eigen::Tensor<int, 2> a(4, 3);
+a.setValues({{0, 100, 200}, {300, 400, 500},
+            {600, 700, 800}, {900, 1000, 1100}});
+Eigen::array<bool, 2> reverse({true, false});
+Eigen::Tensor<int, 2> b = a.reverse(reverse);
+std::cout << "a\n" << a << "\n";
+std::cout << "b\n" << b << "\n";
+
+// a
+//    0   100   200
+//  300   400   500
+//  600   700   800
+//  900  1000  1100
+// b
+//  900  1000  1100
+//  600   700   800
+//  300   400   500
+//    0   100   200
+```
+
+### (Operation) roll(const Rolls& shifts)
+
+Returns a tensor with the elements **circularly shifted** (like bit rotation) along one or more dimensions.
+
+For each dimension `i`, the content is shifted by `shifts[i]` positions:
+
+- A **positive shift** of `+s` moves each value to a **lower index** by `s`.
+- A **negative shift** of `-s` moves each value to a **higher index** by `s`.
+
+```cpp
+Eigen::Tensor<int, 2> a(3, 4);
+a.setValues({{ 1,  2,  3,  4},
+             { 5,  6,  7,  8},
+             { 9, 10, 11, 12}});
+
+Eigen::array<Eigen::Index, 2> shifts = {1, -2};
+
+Eigen::Tensor<int, 2> rolled = a.roll(shifts);
+
+std::cout << "a\n" << a << "\n";
+std::cout << "rolled\n" << rolled << "\n";
+
+// a
+// 1  2  3  4
+// 5  6  7  8
+// 9 10 11 12
+//
+// rolled
+// 7  8  5  6
+// 11 12  9 10
+// 3  4  1  2
+```
+
+### (Operation) broadcast(const Broadcast& broadcast)
+
+Returns a view of the input tensor in which the input is replicated one to many
+times.
+The broadcast argument specifies how many copies of the input tensor need to be
+made in each of the dimensions.
+
+```cpp
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{0, 100, 200}, {300, 400, 500}});
+Eigen::array<int, 2> bcast({3, 2});
+Eigen::Tensor<int, 2> b = a.broadcast(bcast);
+std::cout << "a" << endl << a << endl << "b" << endl << b << endl;
+// a
+//    0   100   200
+//  300   400   500
+// b
+//    0   100   200    0   100   200
+//  300   400   500  300   400   500
+//    0   100   200    0   100   200
+//  300   400   500  300   400   500
+//    0   100   200    0   100   200
+//  300   400   500  300   400   500
+```
+
+Note: Broadcasting does not increase rank.
+To broadcast into higher dimensions, you must first reshape the tensor with singleton (1) dimensions:
+
+```cpp
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{0, 100, 200}, {300, 400, 500}});
+
+Eigen::array<Eigen::Index, 3> new_shape = {1, 2, 3}; //Reshape to [1, 2, 3]
+Eigen::array<int, 3> bcast = {4, 1, 1}; // Broadcast to [4, 2, 3]
+Eigen::Tensor<int, 3> b = a.reshape(new_shape).broadcast(bcast);
+
+std::cout << "b dimensions: " << b.dimensions() << "\n";
+std::cout << b << "\n";
+```
+
+### (Operation) concatenate(const OtherDerived& other, Axis axis)
+
+Returns a view of two tensors joined along a specified axis.
+The dimensions of the two tensors must match on all axes except the concatenation axis.
+The resulting tensor has the same rank as the inputs.
+
+```cpp
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{0, 100, 200}, {300, 400, 500}});
+
+Eigen::Tensor<int, 2> b(2, 3);
+b.setValues({{-1, -2, -3}, {-4, -5, -6}});
+
+// Concatenate along dimension 0: resulting shape is [4, 3]
+Eigen::Tensor<int, 2> c = a.concatenate(b, 0);
+
+// Concatenate along dimension 1: resulting shape is [2, 6]
+Eigen::Tensor<int, 2> d = a.concatenate(b, 1);
+
+std::cout << "a\n" << a << "\n"
+          << "b\n" << b << "\n"
+          << "c (concatenated along dim 0)\n" << c << "\n"
+          << "d (concatenated along dim 1)\n" << d << "\n";
+// a
+//   0 100 200
+// 300 400 500
+// b
+// -1 -2 -3
+// -4 -5 -6
+// c (concatenated along dim 0)
+//   0 100 200
+// 300 400 500
+//  -1  -2  -3
+//  -4  -5  -6
+// d (concatenated along dim 1)
+//   0 100 200  -1  -2  -3
+// 300 400 500  -4  -5  -6
+```
+
+### (Operation)  pad(const PaddingDimensions& padding)
+
+Returns a view of the input tensor in which the input is padded with zeros.
+
+```cpp
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{0, 100, 200}, {300, 400, 500}});
+Eigen::array<pair<int, int>, 2> paddings;
+paddings[0] = make_pair(0, 1);
+paddings[1] = make_pair(2, 3);
+Eigen::Tensor<int, 2> b = a.pad(paddings);
+std::cout << "a" << endl << a << endl << "b" << endl << b << endl;
+// a
+//    0   100   200
+//  300   400   500
+// b
+//    0     0     0    0
+//    0     0     0    0
+//    0   100   200    0
+//  300   400   500    0
+//    0     0     0    0
+//    0     0     0    0
+//    0     0     0    0
+```
+
+### (Operation)  extract_patches(const PatchDims& patch_dims)
+
+Returns a tensor of coefficient patches extracted from the input tensor, where
+each patch is of dimension specified by `patch_dims`. The returned tensor has
+one greater dimension than the input tensor, which is used to index each patch.
+The patch index in the output tensor depends on the data layout of the input
+tensor: the patch index is the last dimension `ColMajor` layout, and the first
+dimension in `RowMajor` layout.
+
+For example, given the following input tensor:
+
+```cpp
+Eigen::Tensor<float, 2, DataLayout> tensor(3,4);
+tensor.setValues({{0.0f, 1.0f, 2.0f, 3.0f},
+                  {4.0f, 5.0f, 6.0f, 7.0f},
+                  {8.0f, 9.0f, 10.0f, 11.0f}});
+
+std::cout << "tensor: " << endl << tensor << endl;
+
+// tensor:
+//  0   1   2   3
+//  4   5   6   7
+//  8   9  10  11
+```
+
+Six 2x2 patches can be extracted and indexed using the following code:
+
+```cpp
+Eigen::Tensor<float, 3, DataLayout> patch;
+Eigen::array<ptrdiff_t, 2> patch_dims;
+patch_dims[0] = 2;
+patch_dims[1] = 2;
+patch = tensor.extract_patches(patch_dims);
+for (int k = 0; k < 6; ++k) {
+  std::cout << "patch index: " << k << endl;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 2; ++j) {
+      if (DataLayout == ColMajor) {
+        std::cout << patch(i, j, k) << " ";
+      } else {
+        std::cout << patch(k, i, j) << " ";
+      }
+    }
+    std::cout << endl;
+  }
+}
+```
+
+This code results in the following output when the data layout is `ColMajor`:
+
+    patch index: 0
+    0 1
+    4 5
+    patch index: 1
+    4 5
+    8 9
+    patch index: 2
+    1 2
+    5 6
+    patch index: 3
+    5 6
+    9 10
+    patch index: 4
+    2 3
+    6 7
+    patch index: 5
+    6 7
+    10 11
+
+This code results in the following output when the data layout is RowMajor:
+
+**NOTE**: the set of patches is the same as in `ColMajor`, but are indexed differently
+
+    patch index: 0
+    0 1
+    4 5
+    patch index: 1
+    1 2
+    5 6
+    patch index: 2
+    2 3
+    6 7
+    patch index: 3
+    4 5
+    8 9
+    patch index: 4
+    5 6
+    9 10
+    patch index: 5
+    6 7
+    10 11
+
+### (Operation)  extract_image_patches(const Index patch_rows, const Index patch_cols, const Index row_stride, const Index col_stride, const PaddingType padding_type)
+
+Returns a tensor of coefficient image patches extracted from the input tensor,
+which is expected to have dimensions ordered as follows (depending on the data
+layout of the input tensor, and the number of additional dimensions 'N'):
+
+- `ColMajor`
+    - 1st dimension: channels (of size d)
+    - 2nd dimension: rows (of size r)
+    - 3rd dimension: columns (of size c)
+    - 4th-Nth dimension: time (for video) or batch (for bulk processing).
+
+* `RowMajor` (reverse order of `ColMajor`)
+    - 1st-Nth dimension: time (for video) or batch (for bulk processing).
+    - N+1'th dimension: columns (of size c)
+    - N+2'th dimension: rows (of size r)
+    - N+3'th dimension: channels (of size d)
+
+The returned tensor has one greater dimension than the input tensor, which is
+used to index each patch. The patch index in the output tensor depends on the
+data layout of the input tensor: the patch index is the 4'th dimension in
+`ColMajor` layout, and the 4'th from the last dimension in `RowMajor` layout.
+
+For example, given the following input tensor with the following dimension
+sizes:
+- depth:   2
+- rows:    3
+- columns: 5
+- batch:   7
+
+```cpp
+Tensor<float, 4> tensor(2,3,5,7);
+Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout();
+```
+
+2x2 image patches can be extracted and indexed using the following code:
+
+#### 2D patch: `ColMajor` (patch indexed by second-to-last dimension)
+
+```cpp
+Tensor<float, 5> twod_patch;
+twod_patch = tensor.extract_image_patches<2, 2>();
+// twod_patch.dimension(0) == 2
+// twod_patch.dimension(1) == 2
+// twod_patch.dimension(2) == 2
+// twod_patch.dimension(3) == 3*5
+// twod_patch.dimension(4) == 7
+```
+
+#### 2D patch: `RowMajor` (patch indexed by the second dimension)
+
+```cpp
+Tensor<float, 5, RowMajor> twod_patch_row_major;
+twod_patch_row_major = tensor_row_major.extract_image_patches<2, 2>();
+// twod_patch_row_major.dimension(0) == 7
+// twod_patch_row_major.dimension(1) == 3*5
+// twod_patch_row_major.dimension(2) == 2
+// twod_patch_row_major.dimension(3) == 2
+// twod_patch_row_major.dimension(4) == 2
+```
+
+## Special Operations
+
+### (Operation) cast<T>()
+
+Returns a tensor of type `T` with the same dimensions as the original tensor.
+The returned tensor contains the values of the original tensor converted to
+type `T`.
+
+```cpp
+Eigen::Tensor<float, 2> a(2, 3);
+Eigen::Tensor<int, 2> b = a.cast<int>();
+```
+
+This can be useful for example if you need to do element-wise division of
+Tensors of integers.
+This is not currently supported by the Tensor library
+but you can easily cast the tensors to floats to do the division:
+
+```cpp
+Eigen::Tensor<int, 2> a(2, 3);
+a.setValues({{0, 1, 2}, {3, 4, 5}});
+Eigen::Tensor<int, 2> b =
+    (a.cast<float>() / a.constant(2).cast<float>()).cast<int>();
+std::cout << "a\n" << a << "\n";
+std::cout << "b\n" << b << "\n";
+
+// a
+// 0 1 2
+// 3 4 5
+//
+// b
+// 0 0 1
+// 1 2 2
+```
+
+### (Operation)     eval()
+See **Calling eval()**.
+
+
+
+## Tensor Printing
+Tensors can be printed into a stream object (e.g. `std::cout`) using different formatting options.
+
+```cpp
+Eigen::Tensor<float, 3> tensor3d = {4, 3, 2};
+tensor3d.setValues( {{{1, 2},
+                      {3, 4},
+                      {5, 6}},
+                     {{7, 8},
+                      {9, 10},
+                      {11, 12}},
+                     {{13, 14},
+                      {15, 16},
+                      {17, 18}},
+                     {{19, 20},
+                      {21, 22},
+                      {23, 24}}} );
+std::cout << tensor3d.format(Eigen::TensorIOFormat::Plain()) << ;
+//  1  2
+//  3  4
+//  5  6
+//
+//  7  8
+//  9 10
+// 11 12
+//
+// 13 14
+// 15 16
+// 17 18
+//
+// 19 20
+// 21 22
+// 23 24
+```
+
+In the example, we used the predefined format `Eigen::TensorIOFormat::Plain`.
+Here is the list of all predefined formats from which you can choose:
+- `Eigen::TensorIOFormat::Plain()` for a plain output without braces. Different submatrices are separated by a blank line.
+- `Eigen::TensorIOFormat::Numpy()` for numpy-like output.
+- `Eigen::TensorIOFormat::Native()` for a `c++` like output which can be directly copy-pasted to `setValues()`.
+- `Eigen::TensorIOFormat::Legacy()` for a backwards compatible printing of tensors.
+
+If you send the tensor directly to the stream the default format is called which is `Eigen::IOFormats::Plain()`.
+
+You can define your own format by explicitly providing a `Eigen::TensorIOFormat` class instance. Here, you can specify:
+- The overall prefix and suffix with `std::string tenPrefix` and `std::string tenSuffix`
+- The prefix, separator and suffix for each new element, row, matrix, 3d subtensor, ... with `std::vector<std::string> prefix`, `std::vector<std::string> separator` and `std::vector<std::string> suffix`. Note that the first entry in each of the vectors refer to the last dimension of the tensor, e.g. `separator[0]` will be printed between adjacent elements,  `separator[1]` will be printed between adjacent matrices, ...
+- `char fill`: character which will be placed if the elements are aligned.
+- `int precision`
+- `int flags`: an OR-ed combination of flags, the default value is 0, the only currently available flag is `Eigen::DontAlignCols` which allows to disable the alignment of columns, resulting in faster code.
+
+## Representation of scalar values
+
+Scalar values are often represented by tensors of size 1 and rank 0.
+
+For example `Tensor<T, N>::maximum()` returns a `Tensor<T, 0>`.
+
+Similarly, the inner product of 2 1d tensors (through contractions) returns a 0d tensor.
+
+The scalar value can be extracted as explained in **Reduction along all dimensions**.
+
+
+## Limitations
+
+*   The number of tensor dimensions is currently limited to 250 when using a
+    compiler that supports cxx11. It is limited to only 5 for older compilers.
+*   The `IndexList` class requires a cxx11 compliant compiler. You can use an
+    array of indices instead if you don't have access to a modern compiler.
+*   On GPUs only floating point values are properly tested and optimized for.
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/Tensor.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/Tensor.h
new file mode 100644
index 0000000..9dc9591
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/Tensor.h
@@ -0,0 +1,382 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_H
+#define EIGEN_CXX11_TENSOR_TENSOR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class Tensor
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief The tensor class.
+ *
+ * The %Tensor class is the work-horse for all \em dense tensors within Eigen.
+ *
+ * The %Tensor class encompasses only dynamic-size objects so far.
+ *
+ * The first two template parameters are required:
+ * \tparam Scalar_  Numeric type, e.g. float, double, int or `std::complex<float>`.
+ *                 User defined scalar types are supported as well (see \ref user_defined_scalars "here").
+ * \tparam NumIndices_ Number of indices (i.e. rank of the tensor)
+ *
+ * The remaining template parameters are optional -- in most cases you don't have to worry about them.
+ * \tparam Options_  A combination of either \b #RowMajor or \b #ColMajor, and of either
+ *                 \b #AutoAlign or \b #DontAlign.
+ *                 The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter
+ * controls alignment, which is required for vectorization. It defaults to aligning tensors. Note that tensors currently
+ * do not support any operations that profit from vectorization. Support for such operations (i.e. adding two tensors
+ * etc.) is planned.
+ *
+ * You can access elements of tensors using normal subscripting:
+ *
+ * \code
+ * Eigen::Tensor<double, 4> t(10, 10, 10, 10);
+ * t(0, 1, 2, 3) = 42.0;
+ * \endcode
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_TENSOR_PLUGIN,
+ * \c EIGEN_TENSORBASE_PLUGIN, and \c EIGEN_READONLY_TENSORBASE_PLUGIN.
+ *
+ * <i><b>Some notes:</b></i>
+ *
+ * <dl>
+ * <dt><b>Relation to other parts of Eigen:</b></dt>
+ * <dd>The midterm development goal for this class is to have a similar hierarchy as Eigen uses for matrices, so that
+ * taking blocks or using tensors in expressions is easily possible, including an interface with the vector/matrix code
+ * by providing .asMatrix() and .asVector() (or similar) methods for rank 2 and 1 tensors. However, currently, the
+ * %Tensor class does not provide any of these features and is only available as a stand-alone class that just allows
+ * for coefficient access. Also, when fixed-size tensors are implemented, the number of template arguments is likely to
+ * change dramatically.</dd>
+ * </dl>
+ *
+ * \ref TopicStorageOrders
+ */
+
+template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
+class Tensor : public TensorBase<Tensor<Scalar_, NumIndices_, Options_, IndexType_> > {
+ public:
+  typedef Tensor<Scalar_, NumIndices_, Options_, IndexType_> Self;
+  typedef TensorBase<Tensor<Scalar_, NumIndices_, Options_, IndexType_> > Base;
+  typedef typename Eigen::internal::nested<Self>::type Nested;
+  typedef typename internal::traits<Self>::StorageKind StorageKind;
+  typedef typename internal::traits<Self>::Index Index;
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef typename Base::CoeffReturnType CoeffReturnType;
+
+  enum { IsAligned = (EIGEN_MAX_ALIGN_BYTES > 0) && !(Options_ & DontAlign), CoordAccess = true, RawAccess = true };
+
+  static constexpr int Layout = Options_ & RowMajor ? RowMajor : ColMajor;
+  static constexpr int Options = Options_;
+  static constexpr int NumIndices = NumIndices_;
+  typedef DSizes<Index, NumIndices_> Dimensions;
+
+ protected:
+  TensorStorage<Scalar, Dimensions, Options> m_storage;
+
+  template <typename CustomIndices>
+  struct isOfNormalIndex {
+    static const bool is_array = internal::is_base_of<array<Index, NumIndices>, CustomIndices>::value;
+    static const bool is_int = NumTraits<CustomIndices>::IsInteger;
+    static const bool value = is_array | is_int;
+  };
+
+ public:
+  // Metadata
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const { return NumIndices; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_storage.dimensions(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_storage.size(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() { return m_storage.data(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar* data() const { return m_storage.data(); }
+
+  // This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+  // work, because that uses base().coeffRef() - and we don't yet
+  // implement a similar class hierarchy
+  inline Self& base() { return *this; }
+  inline const Self& base() const { return *this; }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index firstIndex, Index secondIndex,
+                                                            IndexTypes... otherIndices) const {
+    // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return coeff(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+  }
+
+  // normal indices
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const {
+    eigen_internal_assert(checkIndexRange(indices));
+    return m_storage.data()[linearizedIndex(indices)];
+  }
+
+  // custom indices
+  template <typename CustomIndices, EIGEN_SFINAE_ENABLE_IF(!(isOfNormalIndex<CustomIndices>::value))>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(CustomIndices& indices) const {
+    return coeff(internal::customIndices2Array<Index, NumIndices>(indices));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff() const {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return m_storage.data()[0];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const {
+    eigen_internal_assert(index >= 0 && index < size());
+    return m_storage.data()[index];
+  }
+
+  template <typename... IndexTypes>
+  inline Scalar& coeffRef(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) {
+    // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return coeffRef(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+  }
+
+  // normal indices
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices) {
+    eigen_internal_assert(checkIndexRange(indices));
+    return m_storage.data()[linearizedIndex(indices)];
+  }
+
+  // custom indices
+  template <typename CustomIndices, EIGEN_SFINAE_ENABLE_IF(!(isOfNormalIndex<CustomIndices>::value))>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(CustomIndices& indices) {
+    return coeffRef(internal::customIndices2Array<Index, NumIndices>(indices));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef() {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return m_storage.data()[0];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    eigen_internal_assert(index >= 0 && index < size());
+    return m_storage.data()[index];
+  }
+
+  template <typename... IndexTypes>
+  inline const Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const {
+    // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return this->operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+  }
+
+  // custom indices
+  template <typename CustomIndices, EIGEN_SFINAE_ENABLE_IF(!(isOfNormalIndex<CustomIndices>::value))>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(CustomIndices& indices) const {
+    return coeff(internal::customIndices2Array<Index, NumIndices>(indices));
+  }
+
+  // normal indices
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const {
+    return coeff(indices);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const {
+    eigen_internal_assert(index >= 0 && index < size());
+    return coeff(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()() const {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return coeff();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const {
+    // The bracket operator is only for vectors, use the parenthesis operator instead.
+    EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return coeff(index);
+  }
+
+  template <typename... IndexTypes>
+  inline Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) {
+    // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+  }
+
+  // normal indices
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices) {
+    return coeffRef(indices);
+  }
+
+  // custom indices
+  template <typename CustomIndices, EIGEN_SFINAE_ENABLE_IF(!(isOfNormalIndex<CustomIndices>::value))>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(CustomIndices& indices) {
+    return coeffRef(internal::customIndices2Array<Index, NumIndices>(indices));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index index) {
+    eigen_assert(index >= 0 && index < size());
+    return coeffRef(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()() {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return coeffRef();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator[](Index index) {
+    // The bracket operator is only for vectors, use the parenthesis operator instead
+    EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return coeffRef(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor() : m_storage() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(const Self& other) : Base(other), m_storage(other.m_storage) {}
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index firstDimension, IndexTypes... otherDimensions)
+      : m_storage(firstDimension, otherDimensions...) {
+    // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  }
+
+  /** Normal Dimension */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Tensor(const array<Index, NumIndices>& dimensions)
+      : m_storage(internal::array_prod(dimensions), dimensions) {
+    EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, ReadOnlyAccessors>& other) {
+    EIGEN_STATIC_ASSERT(OtherDerived::NumDimensions == Base::NumDimensions, Number_of_dimensions_must_match)
+    typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
+    Assign assign(*this, other.derived());
+    resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
+    internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, WriteAccessors>& other) {
+    EIGEN_STATIC_ASSERT(OtherDerived::NumDimensions == Base::NumDimensions, Number_of_dimensions_must_match)
+    typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
+    Assign assign(*this, other.derived());
+    resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
+    internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Self&& other) : m_storage(std::move(other.m_storage)) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor& operator=(Self&& other) {
+    m_storage = std::move(other.m_storage);
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor& operator=(const Tensor& other) {
+    typedef TensorAssignOp<Tensor, const Tensor> Assign;
+    Assign assign(*this, other);
+    resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
+    internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+    return *this;
+  }
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor& operator=(const OtherDerived& other) {
+    typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
+    Assign assign(*this, other);
+    resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
+    internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+    return *this;
+  }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC void resize(Index firstDimension, IndexTypes... otherDimensions) {
+    // The number of dimensions used to resize a tensor must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    resize(array<Index, NumIndices>{{firstDimension, otherDimensions...}});
+  }
+
+  /** Normal Dimension */
+  EIGEN_DEVICE_FUNC void resize(const array<Index, NumIndices>& dimensions) {
+#ifndef EIGEN_NO_DEBUG
+    Index size = Index(1);
+    for (int i = 0; i < NumIndices; i++) {
+      internal::check_rows_cols_for_overflow<Dynamic, Dynamic, Dynamic>::run(size, dimensions[i]);
+      size *= dimensions[i];
+    }
+#else
+    Index size = internal::array_prod(dimensions);
+#endif
+
+#ifdef EIGEN_INITIALIZE_COEFFS
+    bool size_changed = size != this->size();
+    m_storage.resize(size, dimensions);
+    if (size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+#else
+    m_storage.resize(size, dimensions);
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC void resize() {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    // Nothing to do: rank 0 tensors have fixed size
+  }
+
+  template <typename FirstType, typename... OtherTypes>
+  EIGEN_DEVICE_FUNC void resize(const Eigen::IndexList<FirstType, OtherTypes...>& dimensions) {
+    array<Index, NumIndices> dims;
+    for (int i = 0; i < NumIndices; ++i) {
+      dims[i] = static_cast<Index>(dimensions[i]);
+    }
+    resize(dims);
+  }
+
+  /** Custom Dimension */
+  template <typename CustomDimension, EIGEN_SFINAE_ENABLE_IF(!(isOfNormalIndex<CustomDimension>::value))>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(CustomDimension& dimensions) {
+    resize(internal::customIndices2Array<Index, NumIndices>(dimensions));
+  }
+
+  template <typename std::ptrdiff_t... Indices>
+  EIGEN_DEVICE_FUNC void resize(const Sizes<Indices...>& dimensions) {
+    array<Index, NumIndices> dims;
+    for (int i = 0; i < NumIndices; ++i) {
+      dims[i] = static_cast<Index>(dimensions[i]);
+    }
+    resize(dims);
+  }
+
+#ifdef EIGEN_TENSOR_PLUGIN
+#include EIGEN_TENSOR_PLUGIN
+#endif
+
+ protected:
+  bool checkIndexRange(const array<Index, NumIndices>& indices) const {
+    using internal::array_apply_and_reduce;
+    using internal::array_zip_and_reduce;
+    using internal::greater_equal_zero_op;
+    using internal::lesser_op;
+    using internal::logical_and_op;
+
+    return
+        // check whether the indices are all >= 0
+        array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) &&
+        // check whether the indices fit in the dimensions
+        array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const {
+    if (Options & RowMajor) {
+      return m_storage.dimensions().IndexOfRowMajor(indices);
+    } else {
+      return m_storage.dimensions().IndexOfColMajor(indices);
+    }
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
new file mode 100644
index 0000000..5d49fa9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
@@ -0,0 +1,282 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
+//                    Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
+#define EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <typename XprType>
+struct traits<TensorIndexPairOp<XprType>> : public traits<XprType> {
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef Pair<Index, typename XprTraits::Scalar> Scalar;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+};
+
+template <typename XprType>
+struct eval<TensorIndexPairOp<XprType>, Eigen::Dense> {
+  typedef const TensorIndexPairOp<XprType> EIGEN_DEVICE_REF type;
+};
+
+template <typename XprType>
+struct nested<TensorIndexPairOp<XprType>, 1, typename eval<TensorIndexPairOp<XprType>>::type> {
+  typedef TensorIndexPairOp<XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor + Index Pair class.
+ */
+template <typename XprType>
+class TensorIndexPairOp : public TensorBase<TensorIndexPairOp<XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorIndexPairOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename Eigen::internal::nested<TensorIndexPairOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorIndexPairOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorIndexPairOp>::Index Index;
+  typedef Pair<Index, typename XprType::CoeffReturnType> CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIndexPairOp(const XprType& expr) : m_xpr(expr) {}
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+};
+
+// Eval as rvalue
+template <typename ArgType, typename Device>
+struct TensorEvaluator<const TensorIndexPairOp<ArgType>, Device> {
+  typedef TensorIndexPairOp<ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  static constexpr int NumDims = internal::array_size<Dimensions>::value;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_impl(op.expression(), device) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return CoeffReturnType(index, m_impl.coeff(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, 1);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+namespace internal {
+
+/** \class TensorPairIndex
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Converts to Tensor<Pair<Index, Scalar> > and reduces to Tensor<Index>.
+ *
+ */
+template <typename ReduceOp, typename Dims, typename XprType>
+struct traits<TensorPairReducerOp<ReduceOp, Dims, XprType>> : public traits<XprType> {
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef Index Scalar;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
+  static constexpr int Layout = XprTraits::Layout;
+};
+
+template <typename ReduceOp, typename Dims, typename XprType>
+struct eval<TensorPairReducerOp<ReduceOp, Dims, XprType>, Eigen::Dense> {
+  typedef const TensorPairReducerOp<ReduceOp, Dims, XprType> EIGEN_DEVICE_REF type;
+};
+
+template <typename ReduceOp, typename Dims, typename XprType>
+struct nested<TensorPairReducerOp<ReduceOp, Dims, XprType>, 1,
+              typename eval<TensorPairReducerOp<ReduceOp, Dims, XprType>>::type> {
+  typedef TensorPairReducerOp<ReduceOp, Dims, XprType> type;
+};
+
+}  // end namespace internal
+
+template <typename ReduceOp, typename Dims, typename XprType>
+class TensorPairReducerOp : public TensorBase<TensorPairReducerOp<ReduceOp, Dims, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorPairReducerOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename Eigen::internal::nested<TensorPairReducerOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorPairReducerOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorPairReducerOp>::Index Index;
+  typedef Index CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPairReducerOp(const XprType& expr, const ReduceOp& reduce_op,
+                                                            const Index return_dim, const Dims& reduce_dims)
+      : m_xpr(expr), m_reduce_op(reduce_op), m_return_dim(return_dim), m_reduce_dims(reduce_dims) {}
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_DEVICE_FUNC const ReduceOp& reduce_op() const { return m_reduce_op; }
+
+  EIGEN_DEVICE_FUNC const Dims& reduce_dims() const { return m_reduce_dims; }
+
+  EIGEN_DEVICE_FUNC Index return_dim() const { return m_return_dim; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const ReduceOp m_reduce_op;
+  const Index m_return_dim;
+  const Dims m_reduce_dims;
+};
+
+// Eval as rvalue
+template <typename ReduceOp, typename Dims, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorPairReducerOp<ReduceOp, Dims, ArgType>, Device> {
+  typedef TensorPairReducerOp<ReduceOp, Dims, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename TensorIndexPairOp<ArgType>::CoeffReturnType PairType;
+  typedef typename TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexPairOp<ArgType>>,
+                                   Device>::Dimensions Dimensions;
+  typedef typename TensorEvaluator<const TensorIndexPairOp<ArgType>, Device>::Dimensions InputDimensions;
+  static constexpr int NumDims = internal::array_size<InputDimensions>::value;
+  typedef array<Index, NumDims> StrideDims;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  typedef StorageMemory<PairType, Device> PairStorageMem;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+  static constexpr int Layout =
+      TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexPairOp<ArgType>>, Device>::Layout;
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_orig_impl(op.expression(), device),
+        m_impl(op.expression().index_pairs().reduce(op.reduce_dims(), op.reduce_op()), device),
+        m_return_dim(op.return_dim()) {
+    gen_strides(m_orig_impl.dimensions(), m_strides);
+    if (Layout == static_cast<int>(ColMajor)) {
+      const Index total_size = internal::array_prod(m_orig_impl.dimensions());
+      m_stride_mod = (m_return_dim < NumDims - 1) ? m_strides[m_return_dim + 1] : total_size;
+    } else {
+      const Index total_size = internal::array_prod(m_orig_impl.dimensions());
+      m_stride_mod = (m_return_dim > 0) ? m_strides[m_return_dim - 1] : total_size;
+    }
+    // If m_return_dim is not a valid index, returns 1 or this can crash on Windows.
+    m_stride_div =
+        ((m_return_dim >= 0) && (m_return_dim < static_cast<Index>(m_strides.size()))) ? m_strides[m_return_dim] : 1;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    const PairType v = m_impl.coeff(index);
+    return (m_return_dim < 0) ? v.first : (v.first % m_stride_mod) / m_stride_div;
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double compute_cost =
+        1.0 + (m_return_dim < 0 ? 0.0 : (TensorOpCost::ModCost<Index>() + TensorOpCost::DivCost<Index>()));
+    return m_orig_impl.costPerCoeff(vectorized) + m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost);
+  }
+
+ private:
+  EIGEN_DEVICE_FUNC void gen_strides(const InputDimensions& dims, StrideDims& strides) {
+    if (m_return_dim < 0) {
+      return;  // Won't be using the strides.
+    }
+    eigen_assert(m_return_dim < NumDims && "Asking to convert index to a dimension outside of the rank");
+
+    // Calculate m_stride_div and m_stride_mod, which are used to
+    // calculate the value of an index w.r.t. the m_return_dim.
+    if (Layout == static_cast<int>(ColMajor)) {
+      strides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        strides[i] = strides[i - 1] * dims[i - 1];
+      }
+    } else {
+      strides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        strides[i] = strides[i + 1] * dims[i + 1];
+      }
+    }
+  }
+
+ protected:
+  TensorEvaluator<const TensorIndexPairOp<ArgType>, Device> m_orig_impl;
+  TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexPairOp<ArgType>>, Device> m_impl;
+  const Index m_return_dim;
+  StrideDims m_strides;
+  Index m_stride_mod;
+  Index m_stride_div;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
new file mode 100644
index 0000000..330eae7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
@@ -0,0 +1,211 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
+#define EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename LhsXprType, typename RhsXprType>
+struct traits<TensorAssignOp<LhsXprType, RhsXprType> > {
+  typedef typename LhsXprType::Scalar Scalar;
+  typedef typename traits<LhsXprType>::StorageKind StorageKind;
+  typedef
+      typename promote_index_type<typename traits<LhsXprType>::Index, typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef std::remove_reference_t<LhsNested> LhsNested_;
+  typedef std::remove_reference_t<RhsNested> RhsNested_;
+  static constexpr std::size_t NumDimensions = internal::traits<LhsXprType>::NumDimensions;
+  static constexpr int Layout = internal::traits<LhsXprType>::Layout;
+  typedef typename traits<LhsXprType>::PointerType PointerType;
+
+  enum { Flags = 0 };
+};
+
+template <typename LhsXprType, typename RhsXprType>
+struct eval<TensorAssignOp<LhsXprType, RhsXprType>, Eigen::Dense> {
+  typedef const TensorAssignOp<LhsXprType, RhsXprType>& type;
+};
+
+template <typename LhsXprType, typename RhsXprType>
+struct nested<TensorAssignOp<LhsXprType, RhsXprType>, 1, typename eval<TensorAssignOp<LhsXprType, RhsXprType> >::type> {
+  typedef TensorAssignOp<LhsXprType, RhsXprType> type;
+};
+
+}  // end namespace internal
+
+/** The tensor assignment class.
+ * \ingroup CXX11_Tensor_Module
+ *
+ * This class represents the assignment of the values resulting from the evaluation of
+ * the rhs expression to the memory locations denoted by the lhs expression.
+ */
+template <typename LhsXprType, typename RhsXprType>
+class TensorAssignOp : public TensorBase<TensorAssignOp<LhsXprType, RhsXprType> > {
+ public:
+  typedef typename Eigen::internal::traits<TensorAssignOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename LhsXprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorAssignOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorAssignOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorAssignOp>::Index Index;
+
+  static constexpr int NumDims = Eigen::internal::traits<TensorAssignOp>::NumDimensions;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorAssignOp(LhsXprType& lhs, const RhsXprType& rhs)
+      : m_lhs_xpr(lhs), m_rhs_xpr(rhs) {}
+
+  /** \returns the nested expressions */
+  EIGEN_DEVICE_FUNC internal::remove_all_t<typename LhsXprType::Nested>& lhsExpression() const {
+    return *((internal::remove_all_t<typename LhsXprType::Nested>*)&m_lhs_xpr);
+  }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename RhsXprType::Nested>& rhsExpression() const {
+    return m_rhs_xpr;
+  }
+
+ protected:
+  internal::remove_all_t<typename LhsXprType::Nested>& m_lhs_xpr;
+  const internal::remove_all_t<typename RhsXprType::Nested>& m_rhs_xpr;
+};
+
+template <typename LeftArgType, typename RightArgType, typename Device>
+struct TensorEvaluator<const TensorAssignOp<LeftArgType, RightArgType>, Device> {
+  typedef TensorAssignOp<LeftArgType, RightArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename TensorEvaluator<RightArgType, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  static constexpr int NumDims = XprType::NumDims;
+  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
+
+  enum {
+    IsAligned =
+        int(TensorEvaluator<LeftArgType, Device>::IsAligned) & int(TensorEvaluator<RightArgType, Device>::IsAligned),
+    PacketAccess = int(TensorEvaluator<LeftArgType, Device>::PacketAccess) &
+                   int(TensorEvaluator<RightArgType, Device>::PacketAccess),
+    BlockAccess = int(TensorEvaluator<LeftArgType, Device>::BlockAccess) &
+                  int(TensorEvaluator<RightArgType, Device>::BlockAccess),
+    PreferBlockAccess = int(TensorEvaluator<LeftArgType, Device>::PreferBlockAccess) |
+                        int(TensorEvaluator<RightArgType, Device>::PreferBlockAccess),
+    RawAccess = TensorEvaluator<LeftArgType, Device>::RawAccess
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const RightArgType, Device>::TensorBlock RightTensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  TensorEvaluator(const XprType& op, const Device& device)
+      : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
+                         static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+  }
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const {
+    // The dimensions of the lhs and the rhs tensors should be equal to prevent
+    // overflows and ensure the result is fully initialized.
+    // TODO: use left impl instead if right impl dimensions are known at compile time.
+    return m_rightImpl.dimensions();
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
+    m_leftImpl.evalSubExprsIfNeeded(NULL);
+    // If the lhs provides raw access to its storage area (i.e. if m_leftImpl.data() returns a non
+    // null value), attempt to evaluate the rhs expression in place. Returns true iff in place
+    // evaluation isn't supported and the caller still needs to manually assign the values generated
+    // by the rhs to the lhs.
+    return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data());
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done](bool) {
+      m_rightImpl.evalSubExprsIfNeededAsync(m_leftImpl.data(), [done](bool need_assign) { done(need_assign); });
+    });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_leftImpl.cleanup();
+    m_rightImpl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) const {
+    m_leftImpl.coeffRef(i) = m_rightImpl.coeff(i);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) const {
+    const int LhsStoreMode = TensorEvaluator<LeftArgType, Device>::IsAligned ? Aligned : Unaligned;
+    const int RhsLoadMode = TensorEvaluator<RightArgType, Device>::IsAligned ? Aligned : Unaligned;
+    m_leftImpl.template writePacket<LhsStoreMode>(i, m_rightImpl.template packet<RhsLoadMode>(i));
+  }
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const { return m_leftImpl.coeff(index); }
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
+    return m_leftImpl.template packet<LoadMode>(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // We assume that evalPacket or evalScalar is called to perform the
+    // assignment and account for the cost of the write here, but reduce left
+    // cost by one load because we are using m_leftImpl.coeffRef.
+    TensorOpCost left = m_leftImpl.costPerCoeff(vectorized);
+    return m_rightImpl.costPerCoeff(vectorized) +
+           TensorOpCost(numext::maxi(0.0, left.bytes_loaded() - sizeof(CoeffReturnType)), left.bytes_stored(),
+                        left.compute_cycles()) +
+           TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return internal::TensorBlockResourceRequirements::merge(m_leftImpl.getResourceRequirements(),
+                                                            m_rightImpl.getResourceRequirements());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(TensorBlockDesc& desc, TensorBlockScratch& scratch) {
+    if (TensorEvaluator<LeftArgType, Device>::RawAccess && m_leftImpl.data() != NULL) {
+      // If destination has raw data access, we pass it as a potential
+      // destination for a block descriptor evaluation.
+      desc.template AddDestinationBuffer<Layout>(
+          /*dst_base=*/m_leftImpl.data() + desc.offset(),
+          /*dst_strides=*/internal::strides<Layout>(m_leftImpl.dimensions()));
+    }
+
+    RightTensorBlock block = m_rightImpl.block(desc, scratch, /*root_of_expr_ast=*/true);
+    // If block was evaluated into a destination, there is no need to do assignment.
+    if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
+      m_leftImpl.writeBlock(desc, block);
+    }
+    block.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_leftImpl.data(); }
+
+ private:
+  TensorEvaluator<LeftArgType, Device> m_leftImpl;
+  TensorEvaluator<RightArgType, Device> m_rightImpl;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
new file mode 100644
index 0000000..f6812d4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
@@ -0,0 +1,1244 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_BASE_H
+#define EIGEN_CXX11_TENSOR_TENSOR_BASE_H
+
+// clang-format off
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class TensorBase
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief The tensor base class.
+  *
+  * This class is the common parent of the Tensor and TensorMap class, thus
+  * making it possible to use either class interchangeably in expressions.
+  */
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+// FIXME: Doxygen does not like the inheritance with different template parameters
+// Since there is no doxygen documentation inside, we disable it for now
+template<typename Derived>
+class TensorBase<Derived, ReadOnlyAccessors>
+{
+  public:
+    typedef internal::traits<Derived> DerivedTraits;
+    typedef typename DerivedTraits::Scalar Scalar;
+    typedef typename DerivedTraits::Index Index;
+    typedef std::remove_const_t<Scalar> CoeffReturnType;
+    static constexpr int NumDimensions = DerivedTraits::NumDimensions;
+
+    // Generic nullary operation support.
+    template <typename CustomNullaryOp> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<CustomNullaryOp, const Derived>
+    nullaryExpr(const CustomNullaryOp& func) const {
+      return TensorCwiseNullaryOp<CustomNullaryOp, const Derived>(derived(), func);
+    }
+
+    // Coefficient-wise nullary operators
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived>
+    constant(const Scalar& value) const {
+      return nullaryExpr(internal::scalar_constant_op<Scalar>(value));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::UniformRandomGenerator<Scalar>, const Derived>
+    random() const {
+      return nullaryExpr(internal::UniformRandomGenerator<Scalar>());
+    }
+    template <typename RandomGenerator> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<RandomGenerator, const Derived>
+    random(const RandomGenerator& gen = RandomGenerator()) const {
+      return nullaryExpr(gen);
+    }
+
+    // Tensor generation
+    template <typename Generator> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorGeneratorOp<Generator, const Derived>
+    generate(const Generator& generator) const {
+      return TensorGeneratorOp<Generator, const Derived>(derived(), generator);
+    }
+
+    // Generic unary operation support.
+    template <typename CustomUnaryOp> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<CustomUnaryOp, const Derived>
+    unaryExpr(const CustomUnaryOp& func) const {
+      return TensorCwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func);
+    }
+
+    // Coefficient-wise unary operators
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived>
+    operator-() const {
+      return unaryExpr(internal::scalar_opposite_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived>
+    sqrt() const {
+      return unaryExpr(internal::scalar_sqrt_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived>
+    sign() const {
+      return unaryExpr(internal::scalar_sign_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_rsqrt_op<Scalar>, const Derived>
+    rsqrt() const {
+      return unaryExpr(internal::scalar_rsqrt_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived>
+    square() const {
+      return unaryExpr(internal::scalar_square_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived>
+    cube() const {
+      return unaryExpr(internal::scalar_cube_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived>
+    inverse() const {
+      return unaryExpr(internal::scalar_inverse_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_tanh_op<Scalar>, const Derived>
+    tanh() const {
+      return unaryExpr(internal::scalar_tanh_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, const Derived>
+    lgamma() const {
+      return unaryExpr(internal::scalar_lgamma_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived>
+    digamma() const {
+      return unaryExpr(internal::scalar_digamma_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i0_op<Scalar>, const Derived>
+    bessel_i0() const {
+      return unaryExpr(internal::scalar_bessel_i0_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i0e_op<Scalar>, const Derived>
+    bessel_i0e() const {
+      return unaryExpr(internal::scalar_bessel_i0e_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i1_op<Scalar>, const Derived>
+    bessel_i1() const {
+      return unaryExpr(internal::scalar_bessel_i1_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i1e_op<Scalar>, const Derived>
+    bessel_i1e() const {
+      return unaryExpr(internal::scalar_bessel_i1e_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_j0_op<Scalar>, const Derived>
+    bessel_j0() const {
+      return unaryExpr(internal::scalar_bessel_j0_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_y0_op<Scalar>, const Derived>
+    bessel_y0() const {
+      return unaryExpr(internal::scalar_bessel_y0_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_j1_op<Scalar>, const Derived>
+    bessel_j1() const {
+      return unaryExpr(internal::scalar_bessel_j1_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_y1_op<Scalar>, const Derived>
+    bessel_y1() const {
+      return unaryExpr(internal::scalar_bessel_y1_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k0_op<Scalar>, const Derived>
+    bessel_k0() const {
+      return unaryExpr(internal::scalar_bessel_k0_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k0e_op<Scalar>, const Derived>
+    bessel_k0e() const {
+      return unaryExpr(internal::scalar_bessel_k0e_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k1_op<Scalar>, const Derived>
+    bessel_k1() const {
+      return unaryExpr(internal::scalar_bessel_k1_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k1e_op<Scalar>, const Derived>
+    bessel_k1e() const {
+      return unaryExpr(internal::scalar_bessel_k1e_op<Scalar>());
+    }
+
+    // igamma(a = this, x = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_igamma_op<Scalar>, const Derived, const OtherDerived>
+    igamma(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_igamma_op<Scalar>());
+    }
+
+    // igamma_der_a(a = this, x = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_igamma_der_a_op<Scalar>, const Derived, const OtherDerived>
+    igamma_der_a(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_igamma_der_a_op<Scalar>());
+    }
+
+    // gamma_sample_der_alpha(alpha = this, sample = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_gamma_sample_der_alpha_op<Scalar>, const Derived, const OtherDerived>
+    gamma_sample_der_alpha(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_gamma_sample_der_alpha_op<Scalar>());
+    }
+
+    // igammac(a = this, x = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_igammac_op<Scalar>, const Derived, const OtherDerived>
+    igammac(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_igammac_op<Scalar>());
+    }
+
+    // zeta(x = this, q = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const OtherDerived>
+    zeta(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_zeta_op<Scalar>());
+    }
+
+    // polygamma(n = this, x = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_polygamma_op<Scalar>, const Derived, const OtherDerived>
+    polygamma(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_polygamma_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived>
+    erf() const {
+      return unaryExpr(internal::scalar_erf_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_erfc_op<Scalar>, const Derived>
+    erfc() const {
+      return unaryExpr(internal::scalar_erfc_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_ndtri_op<Scalar>, const Derived>
+    ndtri() const {
+      return unaryExpr(internal::scalar_ndtri_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_logistic_op<Scalar>, const Derived>
+    sigmoid() const {
+      return unaryExpr(internal::scalar_logistic_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived>
+    exp() const {
+      return unaryExpr(internal::scalar_exp_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_expm1_op<Scalar>, const Derived>
+    expm1() const {
+      return unaryExpr(internal::scalar_expm1_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived>
+    log() const {
+      return unaryExpr(internal::scalar_log_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log1p_op<Scalar>, const Derived>
+    log1p() const {
+      return unaryExpr(internal::scalar_log1p_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log2_op<Scalar>, const Derived>
+    log2() const {
+      return unaryExpr(internal::scalar_log2_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived>
+    abs() const {
+      return unaryExpr(internal::scalar_abs_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_arg_op<Scalar>, const Derived>
+    arg() const {
+      return unaryExpr(internal::scalar_arg_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_clamp_op<Scalar>, const Derived>
+    clip(Scalar min, Scalar max) const {
+      return unaryExpr(internal::scalar_clamp_op<Scalar>(min, max));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const std::conditional_t<NumTraits<CoeffReturnType>::IsComplex,
+                                                      TensorCwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Derived>,
+                                                      Derived>
+    conjugate() const {
+      return choose(Cond<NumTraits<CoeffReturnType>::IsComplex>(), unaryExpr(internal::scalar_conjugate_op<Scalar>()), derived());
+    }
+
+    template<typename ScalarExponent>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const std::enable_if_t<internal::is_arithmetic<typename NumTraits<ScalarExponent>::Real>::value,
+        TensorCwiseUnaryOp<internal::scalar_unary_pow_op<Scalar, ScalarExponent>, const Derived>>
+        pow(ScalarExponent exponent) const
+    {
+        return unaryExpr(internal::scalar_unary_pow_op<Scalar, ScalarExponent>(exponent));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_real_op<Scalar>, const Derived>
+    real() const {
+      return unaryExpr(internal::scalar_real_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_imag_op<Scalar>, const Derived>
+    imag() const {
+      return unaryExpr(internal::scalar_imag_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_sum_op<Scalar,Scalar> >, const Derived>
+    operator+ (Scalar rhs) const {
+      return unaryExpr(internal::bind2nd_op<internal::scalar_sum_op<Scalar,Scalar> >(rhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE friend
+    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_sum_op<Scalar> >, const Derived>
+    operator+ (Scalar lhs, const Derived& rhs) {
+      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_sum_op<Scalar> >(lhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_difference_op<Scalar,Scalar> >, const Derived>
+    operator- (Scalar rhs) const {
+      EIGEN_STATIC_ASSERT((NumTraits<Scalar>::IsSigned || internal::is_same<Scalar, const std::complex<float> >::value), YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return unaryExpr(internal::bind2nd_op<internal::scalar_difference_op<Scalar,Scalar> >(rhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE friend
+    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_difference_op<Scalar> >, const Derived>
+    operator- (Scalar lhs, const Derived& rhs) {
+      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_difference_op<Scalar> >(lhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_product_op<Scalar,Scalar> >, const Derived>
+    operator* (Scalar rhs) const {
+      return unaryExpr(internal::bind2nd_op<internal::scalar_product_op<Scalar,Scalar> >(rhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE friend
+    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_product_op<Scalar> >, const Derived>
+    operator* (Scalar lhs, const Derived& rhs) {
+      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_product_op<Scalar> >(lhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_quotient_op<Scalar,Scalar> >, const Derived>
+    operator/ (Scalar rhs) const {
+      return unaryExpr(internal::bind2nd_op<internal::scalar_quotient_op<Scalar,Scalar> >(rhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE friend
+    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_quotient_op<Scalar> >, const Derived>
+    operator/ (Scalar lhs, const Derived& rhs) {
+      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_quotient_op<Scalar> >(lhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_mod_op<Scalar>, const Derived>
+    operator% (Scalar rhs) const {
+      EIGEN_STATIC_ASSERT(NumTraits<Scalar>::IsInteger, YOU_MADE_A_PROGRAMMING_MISTAKE_TRY_MOD);
+      return unaryExpr(internal::scalar_mod_op<Scalar>(rhs));
+    }
+
+    template <int NanPropagation=PropagateFast>
+    EIGEN_DEVICE_FUNC
+        EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NanPropagation>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    cwiseMax(Scalar threshold) const {
+      return cwiseMax<NanPropagation>(constant(threshold));
+    }
+
+    template <int NanPropagation=PropagateFast>
+    EIGEN_DEVICE_FUNC
+        EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NanPropagation>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    cwiseMin(Scalar threshold) const {
+      return cwiseMin<NanPropagation>(constant(threshold));
+    }
+
+    template<typename NewType>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const std::conditional_t<internal::is_same<NewType, CoeffReturnType>::value,
+                                                      Derived,
+                                                      TensorConversionOp<NewType, const Derived> >
+    cast() const {
+      return choose(Cond<internal::is_same<NewType, CoeffReturnType>::value>(), derived(), TensorConversionOp<NewType, const Derived>(derived()));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_round_op<Scalar>, const Derived>
+    round() const {
+      return unaryExpr(internal::scalar_round_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_rint_op<Scalar>, const Derived>
+    rint() const {
+      return unaryExpr(internal::scalar_rint_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_ceil_op<Scalar>, const Derived>
+    ceil() const {
+      return unaryExpr(internal::scalar_ceil_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_floor_op<Scalar>, const Derived>
+    floor() const {
+      return unaryExpr(internal::scalar_floor_op<Scalar>());
+    }
+
+    // Generic binary operation support.
+    template <typename CustomBinaryOp, typename OtherDerived> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>
+    binaryExpr(const OtherDerived& other, const CustomBinaryOp& func) const {
+      return TensorCwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other, func);
+    }
+
+    // Coefficient-wise binary operators.
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const Derived, const OtherDerived>
+    operator+(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_sum_op<Scalar>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_difference_op<Scalar>, const Derived, const OtherDerived>
+    operator-(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_difference_op<Scalar>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_product_op<Scalar>, const Derived, const OtherDerived>
+    operator*(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_product_op<Scalar>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>
+    operator/(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_quotient_op<Scalar>());
+    }
+
+    template<int NaNPropagation=PropagateFast, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar, NaNPropagation>, const Derived, const OtherDerived>
+    cwiseMax(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_max_op<Scalar,Scalar, NaNPropagation>());
+    }
+
+    template<int NaNPropagation=PropagateFast, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar, NaNPropagation>, const Derived, const OtherDerived>
+    cwiseMin(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_min_op<Scalar,Scalar, NaNPropagation>());
+    }
+
+    // logical operators
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_boolean_and_op<Scalar>, const Derived, const OtherDerived>
+    operator&&(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_boolean_and_op<Scalar>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_boolean_or_op<Scalar>, const Derived, const OtherDerived>
+    operator||(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_boolean_or_op<Scalar>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_bitwise_and_op<Scalar>, const Derived, const OtherDerived>
+    operator&(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_bitwise_and_op<Scalar>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_bitwise_or_op<Scalar>, const Derived, const OtherDerived>
+    operator|(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_bitwise_or_op<Scalar>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_bitwise_xor_op<Scalar>, const Derived, const OtherDerived>
+    operator^(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_bitwise_xor_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseUnaryOp<internal::scalar_boolean_not_op<Scalar>, const Derived>
+    operator!() const {
+      return unaryExpr(internal::scalar_boolean_not_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseUnaryOp<internal::scalar_bitwise_not_op<Scalar>, const Derived>
+    operator~() const {
+      return unaryExpr(internal::scalar_bitwise_not_op<Scalar>());
+    }
+
+    // Comparisons and tests.
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const OtherDerived>
+    operator<(const TensorBase<OtherDerived, ReadOnlyAccessors>& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>());
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const OtherDerived>
+    operator<=(const TensorBase<OtherDerived, ReadOnlyAccessors>& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>());
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const OtherDerived>
+    operator>(const TensorBase<OtherDerived, ReadOnlyAccessors>& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>());
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const OtherDerived>
+    operator>=(const TensorBase<OtherDerived, ReadOnlyAccessors>& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const OtherDerived>
+    operator==(const TensorBase<OtherDerived, ReadOnlyAccessors>& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const OtherDerived>
+    operator!=(const TensorBase<OtherDerived, ReadOnlyAccessors>& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>());
+    }
+
+    // comparisons and tests for Scalars
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator<(Scalar threshold) const {
+      return operator<(constant(threshold));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator<=(Scalar threshold) const {
+      return operator<=(constant(threshold));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator>(Scalar threshold) const {
+      return operator>(constant(threshold));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator>=(Scalar threshold) const {
+      return operator>=(constant(threshold));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator==(Scalar threshold) const {
+      return operator==(constant(threshold));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator!=(Scalar threshold) const {
+      return operator!=(constant(threshold));
+    }
+
+    // Predicates.
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorConversionOp<bool, const TensorCwiseUnaryOp<internal::scalar_isnan_op<Scalar, true>, const Derived>>
+    (isnan)() const {
+      return unaryExpr(internal::scalar_isnan_op<Scalar, true>()).template cast<bool>();
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorConversionOp<bool, const TensorCwiseUnaryOp<internal::scalar_isinf_op<Scalar, true>, const Derived>>
+    (isinf)() const {
+      return unaryExpr(internal::scalar_isinf_op<Scalar, true>()).template cast<bool>();
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorConversionOp<bool, const TensorCwiseUnaryOp<internal::scalar_isfinite_op<Scalar, true>, const Derived>>
+    (isfinite)() const {
+      return unaryExpr(internal::scalar_isfinite_op<Scalar, true>()).template cast<bool>();
+    }
+
+    // Coefficient-wise ternary operators.
+    template<typename ThenDerived, typename ElseDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>
+    select(const ThenDerived& thenTensor, const ElseDerived& elseTensor) const {
+      return TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>(derived(), thenTensor.derived(), elseTensor.derived());
+    }
+
+    // Contractions.
+    typedef Eigen::IndexPair<Index> DimensionPair;
+
+    template<typename OtherDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const NoOpOutputKernel>
+    contract(const OtherDerived& other, const Dimensions& dims) const {
+      return TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const NoOpOutputKernel>(derived(), other.derived(), dims);
+    }
+
+    template<typename OtherDerived, typename Dimensions, typename OutputKernel> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const OutputKernel>
+    contract(const OtherDerived& other, const Dimensions& dims, const OutputKernel& output_kernel) const {
+      return TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const OutputKernel>(derived(), other.derived(), dims, output_kernel);
+    }
+
+    // Convolutions.
+    template<typename KernelDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>
+    convolve(const KernelDerived& kernel, const Dimensions& dims) const {
+      return TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>(derived(), kernel.derived(), dims);
+    }
+
+    // Fourier transforms
+    template <int FFTDataType, int FFTDirection, typename FFT> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorFFTOp<const FFT, const Derived, FFTDataType, FFTDirection>
+    fft(const FFT& dims) const {
+      return TensorFFTOp<const FFT, const Derived, FFTDataType, FFTDirection>(derived(), dims);
+    }
+
+    // Scan.
+    typedef TensorScanOp<internal::SumReducer<CoeffReturnType>, const Derived> TensorScanSumOp;
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorScanSumOp
+    cumsum(const Index& axis, bool exclusive = false) const {
+      return TensorScanSumOp(derived(), axis, exclusive);
+    }
+
+    typedef TensorScanOp<internal::ProdReducer<CoeffReturnType>, const Derived> TensorScanProdOp;
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorScanProdOp
+    cumprod(const Index& axis, bool exclusive = false) const {
+      return TensorScanProdOp(derived(), axis, exclusive);
+    }
+
+    template <typename Reducer>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorScanOp<Reducer, const Derived>
+    scan(const Index& axis, const Reducer& reducer, bool exclusive = false) const {
+      return TensorScanOp<Reducer, const Derived>(derived(), axis, exclusive, reducer);
+    }
+
+    // Reductions.
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::SumReducer<CoeffReturnType>, const Dims, const Derived>
+    sum(const Dims& dims) const {
+      return TensorReductionOp<internal::SumReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::SumReducer<CoeffReturnType>());
+    }
+
+    const TensorReductionOp<internal::SumReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
+    sum() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorReductionOp<internal::SumReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::SumReducer<CoeffReturnType>());
+    }
+
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const Dims, const Derived>
+    mean(const Dims& dims) const {
+      return TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MeanReducer<CoeffReturnType>());
+    }
+
+    const TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
+    mean() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MeanReducer<CoeffReturnType>());
+    }
+
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const Dims, const Derived>
+    prod(const Dims& dims) const {
+      return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::ProdReducer<CoeffReturnType>());
+    }
+
+    const TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
+    prod() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::ProdReducer<CoeffReturnType>());
+    }
+
+    template <typename Dims,int NanPropagation=PropagateFast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>
+    maximum(const Dims& dims) const {
+      return TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>(derived(), dims, internal::MaxReducer<CoeffReturnType,NanPropagation>());
+    }
+
+    template <int NanPropagation=PropagateFast>
+    const TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>
+    maximum() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MaxReducer<CoeffReturnType,NanPropagation>());
+    }
+
+    template <typename Dims,int NanPropagation=PropagateFast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>
+    minimum(const Dims& dims) const {
+      return TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>(derived(), dims, internal::MinReducer<CoeffReturnType,NanPropagation>());
+    }
+
+    template <int NanPropagation=PropagateFast>
+    const TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>
+    minimum() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MinReducer<CoeffReturnType,NanPropagation>());
+    }
+
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::AndReducer, const Dims, const std::conditional_t<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> > >
+    all(const Dims& dims) const {
+      return cast<bool>().reduce(dims, internal::AndReducer());
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::AndReducer, const DimensionList<Index, NumDimensions>, const std::conditional_t<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> > >
+    all() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return cast<bool>().reduce(in_dims, internal::AndReducer());
+    }
+
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::OrReducer, const Dims, const std::conditional_t<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> > >
+    any(const Dims& dims) const {
+      return cast<bool>().reduce(dims, internal::OrReducer());
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::OrReducer, const DimensionList<Index, NumDimensions>, const std::conditional_t<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> > >
+    any() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return cast<bool>().reduce(in_dims, internal::OrReducer());
+    }
+
+   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorPairReducerOp<
+      internal::ArgMaxPairReducer<Pair<Index, CoeffReturnType> >,
+      const array<Index, NumDimensions>, const Derived>
+    argmax() const {
+      array<Index, NumDimensions> in_dims;
+      for (Index d = 0; d < NumDimensions; ++d) in_dims[d] = d;
+      return TensorPairReducerOp<
+        internal::ArgMaxPairReducer<Pair<Index, CoeffReturnType> >,
+        const array<Index, NumDimensions>,
+        const Derived>(derived(), internal::ArgMaxPairReducer<Pair<Index, CoeffReturnType> >(), -1, in_dims);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorPairReducerOp<
+      internal::ArgMinPairReducer<Pair<Index, CoeffReturnType> >,
+      const array<Index, NumDimensions>, const Derived>
+    argmin() const {
+      array<Index, NumDimensions> in_dims;
+      for (Index d = 0; d < NumDimensions; ++d) in_dims[d] = d;
+      return TensorPairReducerOp<
+        internal::ArgMinPairReducer<Pair<Index, CoeffReturnType> >,
+        const array<Index, NumDimensions>,
+        const Derived>(derived(), internal::ArgMinPairReducer<Pair<Index, CoeffReturnType> >(), -1, in_dims);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorPairReducerOp<
+      internal::ArgMaxPairReducer<Pair<Index, CoeffReturnType> >,
+      const array<Index, 1>, const Derived>
+    argmax(const Index return_dim) const {
+      array<Index, 1> in_dims;
+      in_dims[0] = return_dim;
+      return TensorPairReducerOp<
+        internal::ArgMaxPairReducer<Pair<Index, CoeffReturnType> >,
+        const array<Index, 1>,
+        const Derived>(derived(), internal::ArgMaxPairReducer<Pair<Index, CoeffReturnType> >(), return_dim, in_dims);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorPairReducerOp<
+      internal::ArgMinPairReducer<Pair<Index, CoeffReturnType> >,
+      const array<Index, 1>, const Derived>
+    argmin(const Index return_dim) const {
+      array<Index, 1> in_dims;
+      in_dims[0] = return_dim;
+      return TensorPairReducerOp<
+        internal::ArgMinPairReducer<Pair<Index, CoeffReturnType> >,
+        const array<Index, 1>,
+        const Derived>(derived(), internal::ArgMinPairReducer<Pair<Index, CoeffReturnType> >(), return_dim, in_dims);
+    }
+
+    template <typename Reducer, typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<Reducer, const Dims, const Derived>
+    reduce(const Dims& dims, const Reducer& reducer) const {
+      return TensorReductionOp<Reducer, const Dims, const Derived>(derived(), dims, reducer);
+    }
+
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorTraceOp<const Dims, const Derived>
+    trace(const Dims& dims) const {
+      return TensorTraceOp<const Dims, const Derived>(derived(), dims);
+    }
+
+    const TensorTraceOp<const DimensionList<Index, NumDimensions>, const Derived>
+    trace() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorTraceOp<const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims);
+    }
+
+    template <typename Broadcast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorBroadcastingOp<const Broadcast, const Derived>
+    broadcast(const Broadcast& bcast) const {
+      return TensorBroadcastingOp<const Broadcast, const Derived>(derived(), bcast);
+    }
+
+    template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorConcatenationOp<Axis, const Derived, const OtherDerived>
+    concatenate(const OtherDerived& other, Axis axis) const {
+      return TensorConcatenationOp<Axis, const Derived, const OtherDerived>(derived(), other.derived(), axis);
+    }
+
+    template <typename PatchDims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorPatchOp<const PatchDims, const Derived>
+    extract_patches(const PatchDims& patch_dims) const {
+      return TensorPatchOp<const PatchDims, const Derived>(derived(), patch_dims);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
+    extract_image_patches(const Index patch_rows = 1, const Index patch_cols = 1,
+                          const Index row_stride = 1, const Index col_stride = 1,
+                          const Index in_row_stride = 1, const Index in_col_stride = 1,
+                          const PaddingType padding_type = PADDING_SAME, const Scalar padding_value = Scalar(0)) const {
+      return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride,
+                                                                 in_row_stride, in_col_stride, 1, 1, padding_type, padding_value);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
+    extract_image_patches(const Index patch_rows, const Index patch_cols,
+                          const Index row_stride, const Index col_stride,
+                          const Index in_row_stride, const Index in_col_stride,
+                          const Index row_inflate_stride, const Index col_inflate_stride,
+                          const Index padding_top, const Index padding_bottom,
+                          const Index padding_left,const Index padding_right,
+                          const Scalar padding_value) const {
+      return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride,
+                                                                 in_row_stride, in_col_stride, row_inflate_stride, col_inflate_stride,
+                                                                 padding_top, padding_bottom, padding_left, padding_right, padding_value);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>
+    extract_volume_patches(const Index patch_planes, const Index patch_rows, const Index patch_cols,
+                           const Index plane_stride = 1, const Index row_stride = 1, const Index col_stride = 1,
+                           const PaddingType padding_type = PADDING_SAME, const Scalar padding_value = Scalar(0)) const {
+      return TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>(derived(), patch_planes, patch_rows, patch_cols, plane_stride, row_stride, col_stride, 1, 1, 1, 1, 1, 1, padding_type, padding_value);
+    }
+
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>
+    extract_volume_patches(const Index patch_planes, const Index patch_rows, const Index patch_cols,
+                           const Index plane_stride, const Index row_stride, const Index col_stride,
+                           const Index plane_inflate_stride, const Index row_inflate_stride, const Index col_inflate_stride,
+                           const Index padding_top_z, const Index padding_bottom_z,
+                           const Index padding_top, const Index padding_bottom,
+                           const Index padding_left, const Index padding_right, const Scalar padding_value = Scalar(0)) const {
+      return TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>(derived(), patch_planes, patch_rows, patch_cols, plane_stride, row_stride, col_stride, 1, 1, 1, plane_inflate_stride, row_inflate_stride, col_inflate_stride, padding_top_z, padding_bottom_z, padding_top, padding_bottom, padding_left, padding_right, padding_value);
+    }
+
+    // Morphing operators.
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorLayoutSwapOp<const Derived>
+    swap_layout() const {
+      return TensorLayoutSwapOp<const Derived>(derived());
+    }
+    template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReshapingOp<const NewDimensions, const Derived>
+    reshape(const NewDimensions& newDimensions) const {
+      return TensorReshapingOp<const NewDimensions, const Derived>(derived(), newDimensions);
+    }
+    template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorSlicingOp<const StartIndices, const Sizes, const Derived>
+    slice(const StartIndices& startIndices, const Sizes& sizes) const {
+      return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes);
+    }
+    template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, const Derived>
+    stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) const {
+      return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides,
+                                const Derived>(derived(), startIndices, stopIndices, strides);
+    }
+    template <Index DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorChippingOp<DimId, const Derived>
+    chip(const Index offset) const {
+      EIGEN_STATIC_ASSERT(DimId < Derived::NumDimensions && DimId >= 0, Chip_Dim_out_of_range)
+      return TensorChippingOp<DimId, const Derived>(derived(), offset, DimId);
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorChippingOp<Dynamic, const Derived>
+    chip(const Index offset, const Index dim) const {
+      return TensorChippingOp<Dynamic, const Derived>(derived(), offset, dim);
+    }
+    template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReverseOp<const ReverseDimensions, const Derived>
+    reverse(const ReverseDimensions& rev) const {
+      return TensorReverseOp<const ReverseDimensions, const Derived>(derived(), rev);
+    }
+    template <typename Rolls> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorRollOp<const Rolls, const Derived>
+    roll(const Rolls& rolls) const {
+      return TensorRollOp<const Rolls, const Derived>(derived(), rolls);
+    }
+    template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorPaddingOp<const PaddingDimensions, const Derived>
+    pad(const PaddingDimensions& padding) const {
+      return TensorPaddingOp<const PaddingDimensions, const Derived>(derived(), padding, internal::scalar_cast_op<int, Scalar>()(0));
+    }
+    template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorPaddingOp<const PaddingDimensions, const Derived>
+    pad(const PaddingDimensions& padding, const Scalar padding_value) const {
+      return TensorPaddingOp<const PaddingDimensions, const Derived>(derived(), padding, padding_value);
+    }
+    template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorShufflingOp<const Shuffle, const Derived>
+    shuffle(const Shuffle& shfl) const {
+      return TensorShufflingOp<const Shuffle, const Derived>(derived(), shfl);
+    }
+    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorStridingOp<const Strides, const Derived>
+    stride(const Strides& strides) const {
+      return TensorStridingOp<const Strides, const Derived>(derived(), strides);
+    }
+    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorInflationOp<const Strides, const Derived>
+    inflate(const Strides& strides) const {
+      return TensorInflationOp<const Strides, const Derived>(derived(), strides);
+    }
+
+    // Returns a tensor containing index/value pairs
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorIndexPairOp<const Derived>
+    index_pairs() const {
+      return TensorIndexPairOp<const Derived>(derived());
+    }
+
+    // Support for custom unary and binary operations
+    template <typename CustomUnaryFunc>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCustomUnaryOp<const CustomUnaryFunc, const Derived> customOp(const CustomUnaryFunc& op) const {
+      return TensorCustomUnaryOp<const CustomUnaryFunc, const Derived>(derived(), op);
+    }
+    template <typename OtherDerived, typename CustomBinaryFunc>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCustomBinaryOp<const CustomBinaryFunc, const Derived, const OtherDerived> customOp(const OtherDerived& other, const CustomBinaryFunc& op) const {
+      return TensorCustomBinaryOp<const CustomBinaryFunc, const Derived, const OtherDerived>(derived(), other, op);
+    }
+
+    // Force the evaluation of the expression.
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorForcedEvalOp<const Derived> eval() const {
+      return TensorForcedEvalOp<const Derived>(derived());
+    }
+
+    // Returns a formatted tensor ready for printing to a stream
+    template<typename Format>
+    inline const TensorWithFormat<Derived,DerivedTraits::Layout,DerivedTraits::NumDimensions, Format> format(const Format& fmt) const {
+      return TensorWithFormat<Derived,DerivedTraits::Layout,DerivedTraits::NumDimensions, Format>(derived(), fmt);
+    }
+
+    #ifdef EIGEN_READONLY_TENSORBASE_PLUGIN
+    #include EIGEN_READONLY_TENSORBASE_PLUGIN
+    #endif
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  protected:
+    template <typename Scalar, int NumIndices, int Options, typename IndexType> friend class Tensor;
+    template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize;
+    // the Eigen:: prefix is required to workaround a compilation issue with nvcc 9.0
+    template <typename OtherDerived, int AccessLevel> friend class Eigen::TensorBase;
+};
+
+template<typename Derived, int AccessLevel = internal::accessors_level<Derived>::value>
+class TensorBase : public TensorBase<Derived, ReadOnlyAccessors> {
+ public:
+    typedef TensorBase<Derived, ReadOnlyAccessors> Base;
+    typedef internal::traits<Derived> DerivedTraits;
+    typedef typename DerivedTraits::Scalar Scalar;
+    typedef typename DerivedTraits::Index Index;
+    typedef Scalar CoeffReturnType;
+    static constexpr int NumDimensions = DerivedTraits::NumDimensions;
+
+    template <typename Scalar, int NumIndices, int Options, typename IndexType> friend class Tensor;
+    template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize;
+    // the Eigen:: prefix is required to workaround a compilation issue with nvcc 9.0
+    template <typename OtherDerived, int OtherAccessLevel> friend class Eigen::TensorBase;
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& setZero() {
+      return setConstant(Scalar(0));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& setConstant(const Scalar& val) {
+      return derived() = this->constant(val);
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& setRandom() {
+      return derived() = this->random();
+    }
+    template <typename RandomGenerator> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& setRandom() {
+      return derived() = this->template random<RandomGenerator>();
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& setValues(
+        const typename internal::Initializer<Derived, NumDimensions>::InitList& vals) {
+      TensorEvaluator<Derived, DefaultDevice> eval(derived(), DefaultDevice());
+      internal::initialize_tensor<Derived, NumDimensions>(eval, vals);
+      return derived();
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator+=(const OtherDerived& other) {
+      return derived() = derived() + other.derived();
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator-=(const OtherDerived& other) {
+      return derived() = derived() - other.derived();
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator*=(const OtherDerived& other) {
+      return derived() = derived() * other.derived();
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator/=(const OtherDerived& other) {
+      return derived() = derived() / other.derived();
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorLayoutSwapOp<const Derived>
+    swap_layout() const {
+      return TensorLayoutSwapOp<const Derived>(derived());
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorLayoutSwapOp<Derived>
+    swap_layout() {
+      return TensorLayoutSwapOp<Derived>(derived());
+    }
+
+    template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorConcatenationOp<const Axis, const Derived, const OtherDerived>
+    concatenate(const OtherDerived& other, const Axis& axis) const {
+      return TensorConcatenationOp<const Axis, const Derived, const OtherDerived>(derived(), other, axis);
+    }
+    template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorConcatenationOp<const Axis, Derived, OtherDerived>
+    concatenate(const OtherDerived& other, const Axis& axis) {
+      return TensorConcatenationOp<const Axis, Derived, OtherDerived>(derived(), other, axis);
+    }
+
+    template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReshapingOp<const NewDimensions, const Derived>
+    reshape(const NewDimensions& newDimensions) const {
+      return TensorReshapingOp<const NewDimensions, const Derived>(derived(), newDimensions);
+    }
+    template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorReshapingOp<const NewDimensions, Derived>
+    reshape(const NewDimensions& newDimensions) {
+      return TensorReshapingOp<const NewDimensions, Derived>(derived(), newDimensions);
+    }
+
+    template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorSlicingOp<const StartIndices, const Sizes, const Derived>
+    slice(const StartIndices& startIndices, const Sizes& sizes) const {
+      return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes);
+    }
+    template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorSlicingOp<const StartIndices, const Sizes, Derived>
+    slice(const StartIndices& startIndices, const Sizes& sizes) {
+      return TensorSlicingOp<const StartIndices, const Sizes, Derived>(derived(), startIndices, sizes);
+    }
+
+    template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, const Derived>
+    stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) const {
+      return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides,
+                                const Derived>(derived(), startIndices, stopIndices, strides);
+    }
+    template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, Derived>
+    stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) {
+      return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides,
+                                Derived>(derived(), startIndices, stopIndices, strides);
+    }
+
+    template <DenseIndex DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorChippingOp<DimId, const Derived>
+    chip(const Index offset) const {
+      EIGEN_STATIC_ASSERT(DimId < Derived::NumDimensions && DimId >= 0, Chip_Dim_out_of_range)
+      return TensorChippingOp<DimId, const Derived>(derived(), offset, DimId);
+    }
+    template <Index DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorChippingOp<DimId, Derived>
+    chip(const Index offset) {
+      EIGEN_STATIC_ASSERT(DimId < Derived::NumDimensions && DimId >= 0, Chip_Dim_out_of_range)
+      return TensorChippingOp<DimId, Derived>(derived(), offset, DimId);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorChippingOp<Dynamic, const Derived>
+    chip(const Index offset, const Index dim) const {
+      return TensorChippingOp<Dynamic, const Derived>(derived(), offset, dim);
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorChippingOp<Dynamic, Derived>
+    chip(const Index offset, const Index dim) {
+      return TensorChippingOp<Dynamic, Derived>(derived(), offset, dim);
+    }
+
+    template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReverseOp<const ReverseDimensions, const Derived>
+    reverse(const ReverseDimensions& rev) const {
+      return TensorReverseOp<const ReverseDimensions, const Derived>(derived(), rev);
+    }
+    template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorReverseOp<const ReverseDimensions, Derived>
+    reverse(const ReverseDimensions& rev) {
+      return TensorReverseOp<const ReverseDimensions, Derived>(derived(), rev);
+    }
+
+    template <typename Rolls> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorRollOp<const Rolls, const Derived>
+    roll(const Rolls& roll) const {
+      return TensorRollOp<const Rolls, const Derived>(derived(), roll);
+    }
+    template <typename Rolls> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorRollOp<const Rolls, Derived>
+    roll(const Rolls& roll) {
+      return TensorRollOp<const Rolls, Derived>(derived(), roll);
+    }
+
+    template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorShufflingOp<const Shuffle, const Derived>
+    shuffle(const Shuffle& shfl) const {
+      return TensorShufflingOp<const Shuffle, const Derived>(derived(), shfl);
+    }
+    template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorShufflingOp<const Shuffle, Derived>
+    shuffle(const Shuffle& shfl) {
+      return TensorShufflingOp<const Shuffle, Derived>(derived(), shfl);
+    }
+
+    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorStridingOp<const Strides, const Derived>
+    stride(const Strides& strides) const {
+      return TensorStridingOp<const Strides, const Derived>(derived(), strides);
+    }
+    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorStridingOp<const Strides, Derived>
+    stride(const Strides& strides) {
+      return TensorStridingOp<const Strides, Derived>(derived(), strides);
+    }
+
+    // Select the device on which to evaluate the expression.
+    template <typename DeviceType>
+    TensorDevice<Derived, DeviceType> device(const DeviceType& dev) {
+      return TensorDevice<Derived, DeviceType>(dev, derived());
+    }
+
+    // Select the async device on which to evaluate the expression.
+    template <typename DeviceType, typename DoneCallback>
+    TensorAsyncDevice<Derived, DeviceType, DoneCallback> device(const DeviceType& dev, DoneCallback done) {
+      return TensorAsyncDevice<Derived, DeviceType, DoneCallback>(dev, derived(), std::move(done));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& derived() { return *static_cast<Derived*>(this); }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+    #ifdef EIGEN_TENSORBASE_PLUGIN
+    #include EIGEN_TENSORBASE_PLUGIN
+    #endif
+
+ protected:
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TensorBase)
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(TensorBase)
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& operator=(const OtherDerived& other)
+    {
+      typedef TensorAssignOp<Derived, const OtherDerived> Assign;
+      Assign assign(derived(), other.derived());
+      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+      return derived();
+    }
+};
+#endif // EIGEN_PARSED_BY_DOXYGEN
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_BASE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
new file mode 100644
index 0000000..00bec13
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
@@ -0,0 +1,1474 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
+#define EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+// -------------------------------------------------------------------------- //
+// Forward declarations for templates defined below.
+template <typename Scalar, typename IndexType, int NumDims, int Layout>
+class TensorBlockIO;
+
+// -------------------------------------------------------------------------- //
+// Helper function to compute strides for densely stored buffer of given
+// dimensions.
+
+// TODO(ezhulenev): We compute strides many times in different evaluators, use
+// this function instead everywhere.
+template <int Layout, typename IndexType, int NumDims>
+EIGEN_ALWAYS_INLINE DSizes<IndexType, NumDims> strides(const DSizes<IndexType, NumDims>& dimensions) {
+  DSizes<IndexType, NumDims> strides;
+  if (NumDims == 0) return strides;
+
+  // TODO(ezhulenev): Use templates to unroll this loop (similar to
+  // h_array_reduce in CXX11meta.h)? Benchmark it.
+  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+    strides[0] = 1;
+    for (int i = 1; i < NumDims; ++i) {
+      strides[i] = strides[i - 1] * dimensions[i - 1];
+    }
+  } else {
+    strides[NumDims - 1] = 1;
+    for (int i = NumDims - 2; i >= 0; --i) {
+      strides[i] = strides[i + 1] * dimensions[i + 1];
+    }
+  }
+
+  return strides;
+}
+
+template <int Layout, typename IndexType, size_t NumDims>
+EIGEN_ALWAYS_INLINE DSizes<IndexType, NumDims> strides(const Eigen::array<IndexType, NumDims>& dimensions) {
+  return strides<Layout>(DSizes<IndexType, NumDims>(dimensions));
+}
+
+template <int Layout, std::ptrdiff_t... Indices>
+EIGEN_STRONG_INLINE DSizes<std::ptrdiff_t, sizeof...(Indices)> strides(const Sizes<Indices...>& sizes) {
+  return strides<Layout>(DSizes<std::ptrdiff_t, sizeof...(Indices)>(sizes));
+}
+
+// -------------------------------------------------------------------------- //
+
+// Tensor block shape type defines what are the shape preference for the blocks
+// extracted from the larger tensor.
+//
+// Example: blocks of 100 elements from the large 100x100 tensor:
+// - tensor: 100x100
+// - target_block_size: 100
+//
+// TensorBlockShapeType:
+//  - kUniformAllDims: 100 blocks of size 10x10
+//  - kSkewedInnerDims: 100 blocks of size 100x1 (or 1x100 depending on a column
+//                      or row major layout)
+enum class TensorBlockShapeType { kUniformAllDims, kSkewedInnerDims };
+
+struct TensorBlockResourceRequirements {
+  TensorBlockShapeType shape_type;  // target block shape
+  size_t size;                      // target block size
+  TensorOpCost cost_per_coeff;      // cost of computing a single block element
+
+#ifdef EIGEN_HIPCC
+  // For HIPCC, we need to explicitly declare as a "device fun", the constructor
+  // which is implicitly invoked in the "merge" / "any" routines. else HIPCC
+  // errors out complaining about the lack of a matching constructor
+  EIGEN_DEVICE_FUNC TensorBlockResourceRequirements(TensorBlockShapeType shape_type_, size_t size_, TensorOpCost cost_)
+      : shape_type(shape_type_), size(size_), cost_per_coeff(cost_) {}
+#endif
+
+  template <typename Scalar>
+  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements withShapeAndSize(TensorBlockShapeType shape_type,
+                                                                            size_t size_in_bytes, TensorOpCost cost) {
+    const size_t size = numext::maxi(size_t(1), size_in_bytes / sizeof(Scalar));
+    return {shape_type, size, cost};
+  }
+
+  template <typename Scalar>
+  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements withShapeAndSize(TensorBlockShapeType shape_type,
+                                                                            size_t size_in_bytes) {
+    // This default cost per coefficient is valid for most materialized tensor
+    // block evaluation implementations, because they typically just read
+    // coefficients from the underlying tensor storage, and write to the tensor
+    // block buffer (scratch or destination memory, reads and writes have linear
+    // access pattern). We ignore the fixed cost of block evaluation, because in
+    // practice it should negligible.
+    //
+    // Lazy block evaluation adds the cost of calling a functor for each
+    // coefficient.
+    //
+    // All non-trivial block evaluation implementations must provide their own
+    // cost approximation (e.g. shuffling inner dimension has a much higher cost
+    // because it reads memory randomly, although the total number of moved
+    // bytes is the same).
+    return withShapeAndSize<Scalar>(shape_type, size_in_bytes,
+                                    {/*bytes_loaded=*/sizeof(Scalar),
+                                     /*bytes_stored=*/sizeof(Scalar),
+                                     /*compute_cycles=*/0});
+  }
+
+  template <typename Scalar>
+  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements skewed(size_t size_in_bytes) {
+    return withShapeAndSize<Scalar>(TensorBlockShapeType::kSkewedInnerDims, size_in_bytes);
+  }
+
+  template <typename Scalar>
+  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements uniform(size_t size_in_bytes) {
+    return withShapeAndSize<Scalar>(TensorBlockShapeType::kUniformAllDims, size_in_bytes);
+  }
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE TensorBlockResourceRequirements
+  merge(const TensorBlockResourceRequirements& lhs, const TensorBlockResourceRequirements& rhs) {
+    return {merge(lhs.shape_type, rhs.shape_type),           // shape_type
+            merge(lhs.size, rhs.size),                       // size
+            merge(lhs.cost_per_coeff, rhs.cost_per_coeff)};  // cost_per_coeff
+  }
+
+  EIGEN_DEVICE_FUNC TensorBlockResourceRequirements& addCostPerCoeff(TensorOpCost cost) {
+    cost_per_coeff += cost;
+    return *this;
+  }
+
+  // This is a resource requirement that should be returned from expressions
+  // that do not have any block evaluation preference (e.g. default tensor
+  // expression with raw buffer access).
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE TensorBlockResourceRequirements any() {
+    return {TensorBlockShapeType::kUniformAllDims, 1, {0, 0, 0}};
+  }
+
+ private:
+  using Requirements = TensorBlockResourceRequirements;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE size_t merge(size_t lhs_size, size_t rhs_size) {
+    return numext::maxi(lhs_size, rhs_size);
+  }
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE TensorBlockShapeType merge(TensorBlockShapeType lhs,
+                                                                          TensorBlockShapeType rhs) {
+    return (lhs == TensorBlockShapeType::kSkewedInnerDims || rhs == TensorBlockShapeType::kSkewedInnerDims)
+               ? TensorBlockShapeType::kSkewedInnerDims
+               : TensorBlockShapeType::kUniformAllDims;
+  }
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE TensorOpCost merge(TensorOpCost lhs_cost, TensorOpCost rhs_cost) {
+    return lhs_cost + rhs_cost;
+  }
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockDescriptor specifies a block offset within a tensor and the block
+// sizes along each of the tensor dimensions.
+
+template <int NumDims, typename IndexType = Eigen::Index>
+class TensorBlockDescriptor {
+ public:
+  typedef DSizes<IndexType, NumDims> Dimensions;
+
+  // If we evaluate a Tensor assignment, and expression on the left, already has
+  // a memory buffer, then we might do performance optimization, and evaluate
+  // the root expression directly into the final output memory. Some time it's
+  // possible to reuse it for materializing subexpressions inside an expression
+  // tree, to avoid dynamic memory allocation.
+  //
+  // The pointer type of the underlying storage is erased, because passing
+  // Scalar type through all the expression evaluation layers is way too many
+  // templates. In practice destination buffer type should always match the
+  // evaluated expression scalar type.
+  class DestinationBuffer {
+   public:
+    enum DestinationBufferKind : int {
+      // The above explicit specification of "int" as the enum basetype is
+      // needed to get around a HIPCC link error ("the field type is not
+      // amp-compatible")
+      // which is issued for class members with the enum type.
+      // TODO(rocm):
+      // remove the "int" basetype once HIPCC has been fixed to not error out
+      // in the above scenario.
+
+      // Destination buffer is not defined (`m_data` == nullptr).
+      kEmpty,
+
+      // Tensor block defined by an owning tensor block descriptor can fit
+      // contiguously into the destination buffer. In this case it's safe to
+      // materialize tensor block in the destination buffer, wrap it in a
+      // TensorMap, and use to build Eigen expression on top of it.
+      kContiguous,
+
+      // Destination buffer strides do not match strides of the contiguously
+      // stored block, and it's impossible to define a TensorMap over this
+      // buffer. However if we are evaluating a root of an expression tree, we
+      // still can materialize an output into this destination, because we can
+      // guarantee that no one will ever access it through block API.
+      //
+      // In theory it is possible to build valid TensorStriding<TensorMap>
+      // expression on top of this destination buffer, however it has
+      // inefficient coeff/packet access, and defeats the purpose of fast block
+      // evaluation API.
+      kStrided
+    };
+
+    template <typename Scalar>
+    Scalar* data() const {
+      eigen_assert(m_data_type_size == sizeof(Scalar));
+      return static_cast<Scalar*>(m_data);
+    }
+
+    const Dimensions& strides() const { return m_strides; }
+    const DestinationBufferKind& kind() const { return m_kind; }
+
+   private:
+    friend class TensorBlockDescriptor<NumDims, IndexType>;
+
+    DestinationBuffer() : m_data(NULL), m_data_type_size(0), m_kind(kEmpty) {}
+
+    template <typename Scalar>
+    DestinationBuffer(Scalar* data, const Dimensions& strides, DestinationBufferKind kind)
+        : m_data(static_cast<void*>(data)), m_data_type_size(sizeof(Scalar)), m_strides(strides), m_kind(kind) {}
+
+    template <int Layout, typename Scalar>
+    static DestinationBuffer make(const TensorBlockDescriptor& desc, Scalar* data, const Dimensions& strides) {
+      return DestinationBuffer(data, strides, kind<Layout>(desc, strides));
+    }
+
+    template <int Layout>
+    static DestinationBufferKind kind(const TensorBlockDescriptor& desc, const Dimensions& strides) {
+      const Dimensions& desc_dims = desc.dimensions();
+      const Dimensions& desc_strides = internal::strides<Layout>(desc_dims);
+      for (int i = 0; i < NumDims; ++i) {
+        if (desc_dims[i] == 1) continue;
+        if (desc_strides[i] != strides[i]) return kStrided;
+      }
+      return kContiguous;
+    }
+
+    // Storage pointer is type erased, to reduce template bloat, but we still
+    // keep the size of the underlying element type for error checking.
+    void* m_data;
+    size_t m_data_type_size;
+
+    // Destination buffer dimensions always match the dimensions of a tensor
+    // block descriptor it belongs to, however strides might be different.
+    Dimensions m_strides;
+
+    DestinationBufferKind m_kind;
+  };
+
+  TensorBlockDescriptor(const IndexType offset, const Dimensions& dimensions, const DestinationBuffer& destination)
+      : m_offset(offset), m_dimensions(dimensions), m_destination(destination) {}
+
+  TensorBlockDescriptor(const IndexType offset, const Dimensions& dimensions)
+      : m_offset(offset), m_dimensions(dimensions), m_destination(DestinationBuffer()) {}
+
+  IndexType offset() const { return m_offset; }
+  const Dimensions& dimensions() const { return m_dimensions; }
+  IndexType dimension(int index) const { return m_dimensions[index]; }
+  IndexType size() const { return array_prod<IndexType>(m_dimensions); }
+
+  const DestinationBuffer& destination() const { return m_destination; }
+
+  template <int Layout, typename Scalar>
+  void AddDestinationBuffer(Scalar* dst_base, const Dimensions& dst_strides) {
+    eigen_assert(dst_base != NULL);
+    m_destination = DestinationBuffer::template make<Layout>(*this, dst_base, dst_strides);
+  }
+
+  template <int Layout, typename Scalar, typename DstStridesIndexType>
+  void AddDestinationBuffer(Scalar* dst_base, const DSizes<DstStridesIndexType, NumDims>& dst_strides) {
+    // DSizes constructor will do index type promotion if it's safe.
+    AddDestinationBuffer<Layout>(dst_base, Dimensions(dst_strides));
+  }
+
+  TensorBlockDescriptor& DropDestinationBuffer() {
+    m_destination.m_data = NULL;
+    m_destination.m_kind = DestinationBuffer::kEmpty;
+    return *this;
+  }
+
+  bool HasDestinationBuffer() const { return m_destination.kind() != DestinationBuffer::kEmpty; }
+
+  // Returns a copy of `*this` with updated offset.
+  TensorBlockDescriptor WithOffset(IndexType offset) const {
+    return TensorBlockDescriptor(offset, m_dimensions, m_destination);
+  }
+
+ private:
+  // Offset and dimensions are immutable after construction. Block descriptor
+  // can only be mutated by adding or dropping destination.
+  const IndexType m_offset;
+  const Dimensions m_dimensions;
+  DestinationBuffer m_destination;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockMapper is responsible for iterating over the blocks of a tensor.
+
+template <int NumDims, int Layout, typename IndexType = Eigen::Index>
+class TensorBlockMapper {
+  typedef TensorBlockDescriptor<NumDims, IndexType> BlockDescriptor;
+
+ public:
+  typedef DSizes<IndexType, NumDims> Dimensions;
+
+  TensorBlockMapper() = default;
+  TensorBlockMapper(const DSizes<IndexType, NumDims>& dimensions, const TensorBlockResourceRequirements& requirements)
+      : m_tensor_dimensions(dimensions), m_requirements(requirements) {
+    // Compute block dimensions and the total number of blocks.
+    InitializeBlockDimensions();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType blockCount() const { return m_total_block_count; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType blockTotalSize() const { return m_block_dimensions.TotalSize(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const DSizes<IndexType, NumDims>& blockDimensions() const {
+    return m_block_dimensions;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockDescriptor blockDescriptor(IndexType block_index) const {
+    static const bool isColMajor = Layout == static_cast<int>(ColMajor);
+
+    IndexType offset = 0;
+    DSizes<IndexType, NumDims> dimensions;
+
+    if (NumDims == 0) return BlockDescriptor(offset, dimensions);
+
+    // Iterate outer -> inner dimensions.
+    for (int i = NumDims - 1; i >= 0; --i) {
+      const int dim = isColMajor ? i : NumDims - i - 1;
+
+      const IndexType idx = block_index / m_block_strides[dim];
+      block_index -= idx * m_block_strides[dim];
+
+      const IndexType coord = idx * m_block_dimensions[dim];
+      dimensions[dim] = numext::mini(m_tensor_dimensions[dim] - coord, m_block_dimensions[dim]);
+      offset += coord * m_tensor_strides[dim];
+    }
+
+    return {offset, dimensions};
+  }
+
+ private:
+  void InitializeBlockDimensions() {
+    // Requested block shape and size.
+    const TensorBlockShapeType shape_type = m_requirements.shape_type;
+    IndexType target_block_size = numext::maxi<IndexType>(1, static_cast<IndexType>(m_requirements.size));
+
+    IndexType tensor_size = m_tensor_dimensions.TotalSize();
+
+    // Corner case: one of the dimensions is zero. Logic below is too complex
+    // to handle this case on a general basis, just use unit block size.
+    // Note: we must not yield blocks with zero dimensions (recipe for
+    // overflows/underflows, divisions by zero and NaNs later).
+    if (tensor_size == 0) {
+      for (int i = 0; i < NumDims; ++i) {
+        m_block_dimensions[i] = 1;
+      }
+      m_total_block_count = 0;
+      return;
+    }
+
+    // If tensor fits into a target block size, evaluate it as a single block.
+    if (tensor_size <= target_block_size) {
+      m_block_dimensions = m_tensor_dimensions;
+      m_total_block_count = 1;
+      // The only valid block index is `0`, and in this case we do not need
+      // to compute real strides for tensor or blocks (see blockDescriptor).
+      for (int i = 0; i < NumDims; ++i) {
+        m_tensor_strides[i] = 0;
+        m_block_strides[i] = 1;
+      }
+      return;
+    }
+
+    static const bool isColMajor = Layout == static_cast<int>(ColMajor);
+
+    // Block shape skewed towards inner dimension.
+    if (shape_type == TensorBlockShapeType::kSkewedInnerDims) {
+      IndexType coeff_to_allocate = target_block_size;
+
+      for (int i = 0; i < NumDims; ++i) {
+        const int dim = isColMajor ? i : NumDims - i - 1;
+        m_block_dimensions[dim] = numext::mini(coeff_to_allocate, m_tensor_dimensions[dim]);
+        coeff_to_allocate =
+            numext::div_ceil(coeff_to_allocate, numext::maxi(static_cast<IndexType>(1), m_block_dimensions[dim]));
+      }
+      eigen_assert(coeff_to_allocate == 1);
+
+    } else if (shape_type == TensorBlockShapeType::kUniformAllDims) {
+      // Tensor will not fit within 'target_block_size' budget: calculate tensor
+      // block dimension sizes based on "square" dimension size target.
+      const IndexType dim_size_target = convert_index<IndexType>(
+          std::pow(static_cast<float>(target_block_size), 1.0f / static_cast<float>(m_block_dimensions.rank())));
+
+      for (int i = 0; i < NumDims; ++i) {
+        // TODO(andydavis): Adjust the inner most 'block_dim_size' to make it
+        // a multiple of the packet size. Note that reducing
+        // 'block_dim_size' in this manner can increase the number of
+        // blocks, and so will amplify any per-block overhead.
+        m_block_dimensions[i] = numext::mini(dim_size_target, m_tensor_dimensions[i]);
+      }
+
+      // Add any un-allocated coefficients to inner dimension(s).
+      IndexType total_size = m_block_dimensions.TotalSize();
+      for (int i = 0; i < NumDims; ++i) {
+        const int dim = isColMajor ? i : NumDims - i - 1;
+
+        if (m_block_dimensions[dim] < m_tensor_dimensions[dim]) {
+          const IndexType total_size_other_dims = total_size / m_block_dimensions[dim];
+          const IndexType alloc_avail = numext::div_ceil<IndexType>(target_block_size, total_size_other_dims);
+          if (alloc_avail == m_block_dimensions[dim]) {
+            // Insufficient excess coefficients to allocate.
+            break;
+          }
+          m_block_dimensions[dim] = numext::mini(m_tensor_dimensions[dim], alloc_avail);
+          total_size = total_size_other_dims * m_block_dimensions[dim];
+        }
+      }
+
+    } else {
+      eigen_assert(false);  // unknown block shape
+    }
+
+    eigen_assert(m_block_dimensions.TotalSize() >=
+                 numext::mini<IndexType>(target_block_size, m_tensor_dimensions.TotalSize()));
+
+    // Calculate block counts by dimension and total block count.
+    DSizes<IndexType, NumDims> block_count;
+    for (int i = 0; i < NumDims; ++i) {
+      block_count[i] = numext::div_ceil(m_tensor_dimensions[i], m_block_dimensions[i]);
+    }
+    m_total_block_count = array_prod(block_count);
+
+    // Calculate block strides (used for enumerating blocks).
+    m_tensor_strides = strides<Layout>(m_tensor_dimensions);
+    m_block_strides = strides<Layout>(block_count);
+  }
+
+  DSizes<IndexType, NumDims> m_tensor_dimensions;
+  TensorBlockResourceRequirements m_requirements;
+
+  DSizes<IndexType, NumDims> m_block_dimensions;
+  IndexType m_total_block_count;
+
+  DSizes<IndexType, NumDims> m_tensor_strides;
+  DSizes<IndexType, NumDims> m_block_strides;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockScratchAllocator is responsible for allocating temporary buffers
+// for block evaluation (output or input block materialization). Given that
+// Eigen expression traversal order is deterministic, all temporary allocations
+// are happening in the same order, and usually have exactly the same size.
+// Scratch allocator keeps a trace of all dynamic allocations, and after the
+// first block evaluation is completed, we should be able to reuse all the
+// temporary buffers for the next block evaluation.
+
+template <typename Device>
+class TensorBlockScratchAllocator {
+ public:
+  explicit TensorBlockScratchAllocator(const Device& device) : m_device(device), m_allocation_index(0) {}
+
+  ~TensorBlockScratchAllocator() {
+    for (size_t i = 0; i < m_allocations.size(); ++i) {
+      m_device.deallocate(m_allocations[i].ptr);
+    }
+  }
+
+  void* allocate(size_t size) {
+    // TODO(ezhulenev): Remove when replaced with inlined vector.
+    if (m_allocations.capacity() == 0) m_allocations.reserve(8);
+
+    // Check if we already have an existing allocation at current index.
+    const int num_allocations = static_cast<int>(m_allocations.size());
+    const bool has_allocation = m_allocation_index < num_allocations;
+
+    // Allocation index can't be larger than the number of allocations.
+    eigen_assert(m_allocation_index <= num_allocations);
+
+    // If we have existing allocation, and its size is larger or equal to
+    // requested size, we do nothing.
+
+    // If current allocation can't fit requested size, we deallocate it, and
+    // replace with a larger allocation.
+    if (has_allocation && m_allocations[m_allocation_index].size < size) {
+      m_device.deallocate(m_allocations[m_allocation_index].ptr);
+      m_allocations[m_allocation_index].ptr = m_device.allocate(size);
+      m_allocations[m_allocation_index].size = size;
+    }
+
+    // Make a new allocation if we don't have an existing one.
+    if (!has_allocation) {
+      Allocation allocation;
+      allocation.ptr = m_device.allocate(size);
+      allocation.size = size;
+      m_allocations.push_back(allocation);
+    }
+
+    eigen_assert(m_allocations[m_allocation_index].ptr != NULL);
+    eigen_assert(m_allocations[m_allocation_index].size >= size);
+
+    return m_allocations[m_allocation_index++].ptr;
+  }
+
+  void reset() { m_allocation_index = 0; }
+
+ private:
+  struct Allocation {
+    void* ptr;
+    size_t size;
+  };
+
+  const Device& m_device;
+  int m_allocation_index;
+  // TODO(ezhulenev): This should be an inlined vector.
+  std::vector<Allocation> m_allocations;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockKind represents all possible block kinds, that can be produced by
+// TensorEvaluator::evalBlock function.
+enum TensorBlockKind {
+  // Tensor block that is a lazy expression that must be assigned to a
+  // destination using TensorBlockAssign.
+  kExpr,
+
+  // Tensor block that is a view into a memory buffer owned by an underlying
+  // Tensor expression (e.g. it can be a view into a Tensor buffer).
+  kView,
+
+  // Tensor block that was materialized in a scratch memory buffer, allocated
+  // with TensorBlockScratchAllocator. This block must be copied to a
+  // destination, similar to a block of `kExpr` type.
+  kMaterializedInScratch,
+
+  // Tensor block that was materialized directly into the final output memory
+  // buffer. For example if the left side of an assignment is a Tensor, we can
+  // directly materialize the block in the destination memory.
+  //
+  // If strides in the output buffer do not match tensor block strides, the
+  // Tensor expression will be invalid, and should not be used by
+  // TensorBlockAssign or for constructing another block expression.
+  kMaterializedInOutput
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockNotImplemented should be used to define TensorBlock typedef in
+// TensorEvaluators that do not support block evaluation.
+
+class TensorBlockNotImplemented {
+ public:
+  typedef void XprType;
+};
+
+// -------------------------------------------------------------------------- //
+// XprScalar extracts Scalar type from the Eigen expressions (if expression type
+// is not void). It's required to be able to define lazy block expression for
+// argument types, that do not support block evaluation.
+
+template <typename XprType>
+struct XprScalar {
+  typedef typename XprType::Scalar type;
+};
+template <>
+struct XprScalar<void> {
+  typedef void type;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorMaterializedBlock is a fully evaluated block of the original tensor,
+// and XprType is just a TensorMap over the data. This block type is typically
+// used to materialize blocks of tensor expressions, that can't be efficiently
+// represented as lazy Tensor expressions with fast coeff/packet operations,
+// e.g. we materialize all broadcasts into evaluated blocks.
+//
+// TensorMaterializedBlock does not own its memory buffer, it's either a memory
+// buffer that backs the original expression (e.g. block is just a view into a
+// Tensor), or a memory buffer allocated with scratch allocator, and in this
+// case the scratch allocator will deallocate it at the end of block based
+// expression execution.
+//
+// If the block was evaluated directly into the output buffer, and strides in
+// the output buffer do not match block strides, the TensorMap expression will
+// be invalid, and should never be used in block assignment or any other tensor
+// expression.
+
+template <typename Scalar, int NumDims, int Layout, typename IndexType = Eigen::Index>
+class TensorMaterializedBlock {
+ public:
+  typedef DSizes<IndexType, NumDims> Dimensions;
+  typedef TensorMap<const Tensor<Scalar, NumDims, Layout> > XprType;
+
+  TensorMaterializedBlock(TensorBlockKind kind, const Scalar* data, const Dimensions& dimensions,
+                          bool valid_expr = true)
+      : m_kind(kind), m_data(data), m_dimensions(dimensions), m_expr(m_data, m_dimensions), m_valid_expr(valid_expr) {
+    eigen_assert(m_kind == internal::TensorBlockKind::kView ||
+                 m_kind == internal::TensorBlockKind::kMaterializedInScratch ||
+                 m_kind == internal::TensorBlockKind::kMaterializedInOutput);
+  }
+
+  TensorBlockKind kind() const { return m_kind; }
+  // NOTE(ezhulenev): Returning XprType by value like in other block types
+  // causes asan failures. The theory is that XprType::Nested doesn't work
+  // properly for TensorMap.
+  const XprType& expr() const {
+    eigen_assert(m_valid_expr);
+    return m_expr;
+  }
+  const Scalar* data() const { return m_data; }
+  void cleanup() {}
+
+  typedef internal::TensorBlockDescriptor<NumDims, IndexType> TensorBlockDesc;
+
+  // TensorMaterializedBlock can be backed by different types of storage:
+  //
+  //   (1) Contiguous block of memory allocated with scratch allocator.
+  //   (2) Contiguous block of memory reused from tensor block descriptor
+  //       destination buffer.
+  //   (3) Strided block of memory reused from tensor block descriptor
+  //       destination buffer.
+  //
+  class Storage {
+   public:
+    Scalar* data() const { return m_data; }
+    const Dimensions& dimensions() const { return m_dimensions; }
+    const Dimensions& strides() const { return m_strides; }
+
+    TensorMaterializedBlock AsTensorMaterializedBlock() const {
+      return TensorMaterializedBlock(m_materialized_in_output ? internal::TensorBlockKind::kMaterializedInOutput
+                                                              : internal::TensorBlockKind::kMaterializedInScratch,
+                                     m_data, m_dimensions, !m_strided_storage);
+    }
+
+   private:
+    friend class TensorMaterializedBlock<Scalar, NumDims, Layout, IndexType>;
+
+    Storage(Scalar* data, const Dimensions& dimensions, const Dimensions& strides, bool materialized_in_output,
+            bool strided_storage)
+        : m_data(data),
+          m_dimensions(dimensions),
+          m_strides(strides),
+          m_materialized_in_output(materialized_in_output),
+          m_strided_storage(strided_storage) {}
+
+    Scalar* m_data;
+    Dimensions m_dimensions;
+    Dimensions m_strides;
+    bool m_materialized_in_output;
+    bool m_strided_storage;
+  };
+
+  // Creates a storage for materialized block either from the block descriptor
+  // destination buffer, or allocates a new buffer with scratch allocator.
+  template <typename TensorBlockScratch>
+  EIGEN_STRONG_INLINE static Storage prepareStorage(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                    bool allow_strided_storage = false) {
+    // Try to reuse destination as an output block buffer.
+    typedef typename TensorBlockDesc::DestinationBuffer DestinationBuffer;
+
+    if (desc.destination().kind() == DestinationBuffer::kContiguous) {
+      Scalar* buffer = desc.destination().template data<Scalar>();
+      desc.DropDestinationBuffer();
+      return Storage(buffer, desc.dimensions(), internal::strides<Layout>(desc.dimensions()),
+                     /*materialized_in_output=*/true,
+                     /*strided_storage=*/false);
+
+    } else if (desc.destination().kind() == DestinationBuffer::kStrided && allow_strided_storage) {
+      Scalar* buffer = desc.destination().template data<Scalar>();
+      desc.DropDestinationBuffer();
+      return Storage(buffer, desc.dimensions(), desc.destination().strides(),
+                     /*materialized_in_output=*/true, /*strided_storage=*/true);
+
+    } else {
+      void* mem = scratch.allocate(desc.size() * sizeof(Scalar));
+      return Storage(static_cast<Scalar*>(mem), desc.dimensions(), internal::strides<Layout>(desc.dimensions()),
+                     /*materialized_in_output=*/false,
+                     /*strided_storage=*/false);
+    }
+  }
+
+  // Creates a materialized block for the given descriptor from a memory buffer.
+  template <typename DataDimensions, typename TensorBlockScratch>
+  EIGEN_STRONG_INLINE static TensorMaterializedBlock materialize(const Scalar* data, const DataDimensions& data_dims,
+                                                                 TensorBlockDesc& desc, TensorBlockScratch& scratch) {
+    eigen_assert(array_size<DataDimensions>::value == desc.dimensions().size());
+
+    // If a tensor block dimensions covers a contiguous block of the underlying
+    // memory, we can skip block buffer memory allocation, and construct a block
+    // from existing `data` memory buffer.
+    //
+    // Example: (RowMajor layout)
+    //   data_dims:          [11, 12, 13, 14]
+    //   desc.dimensions():  [1,   1,  3, 14]
+    //
+    // In this case we can construct a TensorBlock starting at
+    // `data + desc.offset()`, with a `desc.dimensions()` block sizes.
+    static const bool is_col_major = Layout == ColMajor;
+
+    // Find out how many inner dimensions have a matching size.
+    int num_matching_inner_dims = 0;
+    for (int i = 0; i < NumDims; ++i) {
+      int dim = is_col_major ? i : NumDims - i - 1;
+      if (data_dims[dim] != desc.dimensions()[dim]) break;
+      ++num_matching_inner_dims;
+    }
+
+    // All the outer dimensions must be of size `1`, except a single dimension
+    // before the matching inner dimension (`3` in the example above).
+    bool can_use_direct_access = true;
+    for (int i = num_matching_inner_dims + 1; i < NumDims; ++i) {
+      int dim = is_col_major ? i : NumDims - i - 1;
+      if (desc.dimension(dim) != 1) {
+        can_use_direct_access = false;
+        break;
+      }
+    }
+
+    if (can_use_direct_access) {
+      const Scalar* block_start = data + desc.offset();
+      return TensorMaterializedBlock(internal::TensorBlockKind::kView, block_start, desc.dimensions());
+
+    } else {
+      // Reuse destination buffer or allocate new buffer with scratch allocator.
+      const Storage storage = prepareStorage(desc, scratch);
+
+      typedef internal::TensorBlockIO<Scalar, IndexType, NumDims, Layout> TensorBlockIO;
+      typedef typename TensorBlockIO::Dst TensorBlockIODst;
+      typedef typename TensorBlockIO::Src TensorBlockIOSrc;
+
+      TensorBlockIOSrc src(internal::strides<Layout>(Dimensions(data_dims)), data, desc.offset());
+      TensorBlockIODst dst(storage.dimensions(), storage.strides(), storage.data());
+
+      TensorBlockIO::Copy(dst, src);
+      return storage.AsTensorMaterializedBlock();
+    }
+  }
+
+ private:
+  TensorBlockKind m_kind;
+  const Scalar* m_data;
+  Dimensions m_dimensions;
+  XprType m_expr;
+  bool m_valid_expr;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorCwiseUnaryBlock is a lazy tensor expression block that applies UnaryOp
+// functor to the blocks produced by the underlying Tensor expression.
+
+template <typename UnaryOp, typename ArgTensorBlock>
+class TensorCwiseUnaryBlock {
+  static constexpr bool NoArgBlockAccess = internal::is_void<typename ArgTensorBlock::XprType>::value;
+
+ public:
+  typedef std::conditional_t<NoArgBlockAccess, void,
+                             TensorCwiseUnaryOp<UnaryOp, const typename ArgTensorBlock::XprType> >
+      XprType;
+
+  typedef typename XprScalar<XprType>::type Scalar;
+
+  TensorCwiseUnaryBlock(const ArgTensorBlock& arg_block, const UnaryOp& functor)
+      : m_arg_block(arg_block), m_functor(functor) {}
+
+  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
+
+  XprType expr() const { return XprType(m_arg_block.expr(), m_functor); }
+  const Scalar* data() const { return NULL; }
+  void cleanup() { m_arg_block.cleanup(); }
+
+ private:
+  ArgTensorBlock m_arg_block;
+  UnaryOp m_functor;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorCwiseBinaryBlock is a lazy tensor expression block that applies BinaryOp
+// functor to the blocks produced by the underlying Tensor expression.
+
+template <typename BinaryOp, typename LhsTensorBlock, typename RhsTensorBlock>
+class TensorCwiseBinaryBlock {
+  static constexpr bool NoArgBlockAccess = internal::is_void<typename LhsTensorBlock::XprType>::value ||
+                                           internal::is_void<typename RhsTensorBlock::XprType>::value;
+
+ public:
+  typedef std::conditional_t<
+      NoArgBlockAccess, void,
+      TensorCwiseBinaryOp<BinaryOp, const typename LhsTensorBlock::XprType, const typename RhsTensorBlock::XprType> >
+      XprType;
+
+  typedef typename XprScalar<XprType>::type Scalar;
+
+  TensorCwiseBinaryBlock(const LhsTensorBlock& left_block, const RhsTensorBlock& right_block, const BinaryOp& functor)
+      : m_left_block(left_block), m_right_block(right_block), m_functor(functor) {}
+
+  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
+
+  XprType expr() const { return XprType(m_left_block.expr(), m_right_block.expr(), m_functor); }
+
+  const Scalar* data() const { return NULL; }
+
+  void cleanup() {
+    m_left_block.cleanup();
+    m_right_block.cleanup();
+  }
+
+ private:
+  LhsTensorBlock m_left_block;
+  RhsTensorBlock m_right_block;
+  BinaryOp m_functor;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorUnaryExprBlock is a lazy tensor expression block that can construct
+// an arbitrary tensor expression from a block of the underlying type (this is a
+// generalization of the TensorCwiseUnaryBlock for arbitrary expressions).
+
+template <typename BlockFactory, typename ArgTensorBlock>
+class TensorUnaryExprBlock {
+  typedef typename ArgTensorBlock::XprType ArgXprType;
+  static constexpr bool NoArgBlockAccess = internal::is_void<ArgXprType>::value;
+
+ public:
+  typedef std::conditional_t<NoArgBlockAccess, void, typename BlockFactory::template XprType<ArgXprType>::type> XprType;
+
+  typedef typename XprScalar<XprType>::type Scalar;
+
+  TensorUnaryExprBlock(const ArgTensorBlock& arg_block, const BlockFactory& factory)
+      : m_arg_block(arg_block), m_factory(factory) {}
+
+  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
+  XprType expr() const { return m_factory.expr(m_arg_block.expr()); }
+  const Scalar* data() const { return NULL; }
+  void cleanup() { m_arg_block.cleanup(); }
+
+ private:
+  ArgTensorBlock m_arg_block;
+  BlockFactory m_factory;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorTernaryExprBlock is a lazy tensor expression block that can construct
+// an arbitrary tensor expression from three blocks of the underlying type.
+
+template <typename BlockFactory, typename Arg1TensorBlock, typename Arg2TensorBlock, typename Arg3TensorBlock>
+class TensorTernaryExprBlock {
+  typedef typename Arg1TensorBlock::XprType Arg1XprType;
+  typedef typename Arg2TensorBlock::XprType Arg2XprType;
+  typedef typename Arg3TensorBlock::XprType Arg3XprType;
+
+  static constexpr bool NoArgBlockAccess = internal::is_void<Arg1XprType>::value ||
+                                           internal::is_void<Arg2XprType>::value ||
+                                           internal::is_void<Arg3XprType>::value;
+
+ public:
+  typedef std::conditional_t<NoArgBlockAccess, void,
+                             typename BlockFactory::template XprType<Arg1XprType, Arg2XprType, Arg3XprType>::type>
+      XprType;
+
+  typedef typename XprScalar<XprType>::type Scalar;
+
+  TensorTernaryExprBlock(const Arg1TensorBlock& arg1_block, const Arg2TensorBlock& arg2_block,
+                         const Arg3TensorBlock& arg3_block, const BlockFactory& factory)
+      : m_arg1_block(arg1_block), m_arg2_block(arg2_block), m_arg3_block(arg3_block), m_factory(factory) {}
+
+  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
+  XprType expr() const { return m_factory.expr(m_arg1_block.expr(), m_arg2_block.expr(), m_arg3_block.expr()); }
+  const Scalar* data() const { return NULL; }
+  void cleanup() {
+    m_arg1_block.cleanup();
+    m_arg2_block.cleanup();
+    m_arg3_block.cleanup();
+  }
+
+ private:
+  Arg1TensorBlock m_arg1_block;
+  Arg2TensorBlock m_arg2_block;
+  Arg3TensorBlock m_arg3_block;
+  BlockFactory m_factory;
+};
+
+// -------------------------------------------------------------------------- //
+// StridedLinearBufferCopy provides a method to copy data between two linear
+// buffers with different strides, with optimized paths for scatter/gather.
+
+template <typename Scalar, typename IndexType>
+class StridedLinearBufferCopy {
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename unpacket_traits<Packet>::half HalfPacket;
+  enum {
+    Vectorizable = packet_traits<Scalar>::Vectorizable,
+    PacketSize = packet_traits<Scalar>::size,
+    HalfPacketSize = unpacket_traits<HalfPacket>::size,
+    HasHalfPacket = static_cast<int>(HalfPacketSize) < static_cast<int>(PacketSize)
+  };
+
+ public:
+  // Specifying linear copy kind statically gives ~30% speedup for small sizes.
+  enum class Kind {
+    Linear = 0,       // src_stride == 1 && dst_stride == 1
+    Scatter = 1,      // src_stride == 1 && dst_stride != 1
+    FillLinear = 2,   // src_stride == 0 && dst_stride == 1
+    FillScatter = 3,  // src_stride == 0 && dst_stride != 1
+    Gather = 4,       // dst_stride == 1
+    Random = 5        // everything else
+  };
+
+  struct Dst {
+    Dst(IndexType o, IndexType s, Scalar* d) : offset(o), stride(s), data(d) {}
+
+    IndexType offset;
+    IndexType stride;
+    Scalar* data;
+  };
+
+  struct Src {
+    Src(IndexType o, IndexType s, const Scalar* d) : offset(o), stride(s), data(d) {}
+
+    IndexType offset;
+    IndexType stride;
+    const Scalar* data;
+  };
+
+  template <typename StridedLinearBufferCopy::Kind kind>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Dst& dst, const Src& src, const size_t count) {
+    Run<kind>(count, dst.offset, dst.stride, dst.data, src.offset, src.stride, src.data);
+  }
+
+ private:
+  template <typename StridedLinearBufferCopy::Kind kind>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const IndexType count, const IndexType dst_offset,
+                                                        const IndexType dst_stride, Scalar* EIGEN_RESTRICT dst_data,
+                                                        const IndexType src_offset, const IndexType src_stride,
+                                                        const Scalar* EIGEN_RESTRICT src_data) {
+    const Scalar* src = &src_data[src_offset];
+    Scalar* dst = &dst_data[dst_offset];
+
+    if (!Vectorizable) {
+      for (Index i = 0; i < count; ++i) {
+        dst[i * dst_stride] = src[i * src_stride];
+      }
+      return;
+    }
+
+    const IndexType vectorized_size = PacketSize * (count / PacketSize);
+    IndexType i = 0;
+
+    if (kind == StridedLinearBufferCopy::Kind::Linear) {
+      // ******************************************************************** //
+      // Linear copy from `src` to `dst`.
+      const IndexType unrolled_size = (4 * PacketSize) * (count / (4 * PacketSize));
+      eigen_assert(src_stride == 1 && dst_stride == 1);
+      for (; i < unrolled_size; i += 4 * PacketSize) {
+        for (int j = 0; j < 4; ++j) {
+          Packet p = ploadu<Packet>(src + i + j * PacketSize);
+          pstoreu<Scalar, Packet>(dst + i + j * PacketSize, p);
+        }
+      }
+      for (; i < vectorized_size; i += PacketSize) {
+        Packet p = ploadu<Packet>(src + i);
+        pstoreu<Scalar, Packet>(dst + i, p);
+      }
+      if (HasHalfPacket) {
+        const IndexType vectorized_half_size = HalfPacketSize * (count / HalfPacketSize);
+        if (i < vectorized_half_size) {
+          HalfPacket p = ploadu<HalfPacket>(src + i);
+          pstoreu<Scalar, HalfPacket>(dst + i, p);
+          i += HalfPacketSize;
+        }
+      }
+      for (; i < count; ++i) {
+        dst[i] = src[i];
+      }
+      // ******************************************************************** //
+    } else if (kind == StridedLinearBufferCopy::Kind::Scatter) {
+      // Scatter from `src` to `dst`.
+      eigen_assert(src_stride == 1 && dst_stride != 1);
+      for (; i < vectorized_size; i += PacketSize) {
+        Packet p = ploadu<Packet>(src + i);
+        pscatter<Scalar, Packet>(dst + i * dst_stride, p, dst_stride);
+      }
+      if (HasHalfPacket) {
+        const IndexType vectorized_half_size = HalfPacketSize * (count / HalfPacketSize);
+        if (i < vectorized_half_size) {
+          HalfPacket p = ploadu<HalfPacket>(src + i);
+          pscatter<Scalar, HalfPacket>(dst + i * dst_stride, p, dst_stride);
+          i += HalfPacketSize;
+        }
+      }
+      for (; i < count; ++i) {
+        dst[i * dst_stride] = src[i];
+      }
+      // ******************************************************************** //
+    } else if (kind == StridedLinearBufferCopy::Kind::FillLinear) {
+      // Fill `dst` with value at `*src`.
+      eigen_assert(src_stride == 0 && dst_stride == 1);
+
+      const IndexType unrolled_size = (4 * PacketSize) * (count / (4 * PacketSize));
+      Scalar s = *src;
+      Packet p = pset1<Packet>(s);
+      for (; i < unrolled_size; i += 4 * PacketSize) {
+        for (int j = 0; j < 4; ++j) {
+          pstoreu<Scalar, Packet>(dst + i + j * PacketSize, p);
+        }
+      }
+      for (; i < vectorized_size; i += PacketSize) {
+        pstoreu<Scalar, Packet>(dst + i, p);
+      }
+      if (HasHalfPacket) {
+        const IndexType vectorized_half_size = HalfPacketSize * (count / HalfPacketSize);
+        if (i < vectorized_half_size) {
+          HalfPacket hp = pset1<HalfPacket>(s);
+          pstoreu<Scalar, HalfPacket>(dst + i, hp);
+          i += HalfPacketSize;
+        }
+      }
+      for (; i < count; ++i) {
+        dst[i] = s;
+      }
+      // ******************************************************************** //
+    } else if (kind == StridedLinearBufferCopy::Kind::FillScatter) {
+      // Scatter `*src` into `dst`.
+      eigen_assert(src_stride == 0 && dst_stride != 1);
+      Scalar s = *src;
+      Packet p = pset1<Packet>(s);
+      for (; i < vectorized_size; i += PacketSize) {
+        pscatter<Scalar, Packet>(dst + i * dst_stride, p, dst_stride);
+      }
+      if (HasHalfPacket) {
+        const IndexType vectorized_half_size = HalfPacketSize * (count / HalfPacketSize);
+        if (i < vectorized_half_size) {
+          HalfPacket hp = pset1<HalfPacket>(s);
+          pscatter<Scalar, HalfPacket>(dst + i * dst_stride, hp, dst_stride);
+          i += HalfPacketSize;
+        }
+      }
+      for (; i < count; ++i) {
+        dst[i * dst_stride] = s;
+      }
+      // ******************************************************************** //
+    } else if (kind == StridedLinearBufferCopy::Kind::Gather) {
+      // Gather from `src` into `dst`.
+      eigen_assert(dst_stride == 1);
+      for (; i < vectorized_size; i += PacketSize) {
+        Packet p = pgather<Scalar, Packet>(src + i * src_stride, src_stride);
+        pstoreu<Scalar, Packet>(dst + i, p);
+      }
+      if (HasHalfPacket) {
+        const IndexType vectorized_half_size = HalfPacketSize * (count / HalfPacketSize);
+        if (i < vectorized_half_size) {
+          HalfPacket p = pgather<Scalar, HalfPacket>(src + i * src_stride, src_stride);
+          pstoreu<Scalar, HalfPacket>(dst + i, p);
+          i += HalfPacketSize;
+        }
+      }
+      for (; i < count; ++i) {
+        dst[i] = src[i * src_stride];
+      }
+      // ******************************************************************** //
+    } else if (kind == StridedLinearBufferCopy::Kind::Random) {
+      // Random.
+      for (; i < count; ++i) {
+        dst[i * dst_stride] = src[i * src_stride];
+      }
+    } else {
+      eigen_assert(false);
+    }
+  }
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockIO copies data from `src` tensor block, to the `dst` tensor block.
+// It's possible to specify src->dst dimension mapping for the copy operation.
+// Dimensions of `dst` specify how many elements have to be copied, for the
+// `src` we need to know only stride to navigate through source memory buffer.
+
+template <typename Scalar, typename IndexType, int NumDims, int Layout>
+class TensorBlockIO {
+  static constexpr bool IsColMajor = (Layout == ColMajor);
+
+  typedef StridedLinearBufferCopy<Scalar, IndexType> LinCopy;
+
+ public:
+  typedef DSizes<IndexType, NumDims> Dimensions;
+  typedef DSizes<int, NumDims> DimensionsMap;
+
+  struct Dst {
+    Dst(const Dimensions& dst_dims, const Dimensions& dst_strides, Scalar* dst, IndexType dst_offset = 0)
+        : dims(dst_dims), strides(dst_strides), data(dst), offset(dst_offset) {}
+
+    Dimensions dims;
+    Dimensions strides;
+    Scalar* data;
+    IndexType offset;
+  };
+
+  struct Src {
+    Src(const Dimensions& src_strides, const Scalar* src, IndexType src_offset = 0)
+        : strides(src_strides), data(src), offset(src_offset) {}
+
+    Dimensions strides;
+    const Scalar* data;
+    IndexType offset;
+  };
+
+  // Copies data to `dst` from `src`, using provided dimensions mapping:
+  //
+  //   src_dimension_index = dst_to_src_dim_map[dst_dimension_index]
+  //
+  // Returns the number of copied elements.
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType Copy(const Dst& dst, const Src& src,
+                                                              const DimensionsMap& dst_to_src_dim_map) {
+    // Copy single scalar value from `src` to `dst`.
+    if (NumDims == 0) {
+      *(dst.data + dst.offset) = *(src.data + src.offset);
+      return 1;
+    }
+
+    // Both `dst` and `src` must have contiguous innermost dimension. We also
+    // accept the special case with stride '0', because it's used as a trick to
+    // implement broadcasting.
+    {
+      int inner_dim = IsColMajor ? 0 : NumDims - 1;
+      EIGEN_UNUSED_VARIABLE(inner_dim);
+      eigen_assert(dst.strides[inner_dim] == 1 || dst.strides[inner_dim] == 0);
+      eigen_assert(src.strides[inner_dim] == 1 || src.strides[inner_dim] == 0);
+    }
+
+    // Give a shorter name to `dst_to_src_dim_map`.
+    const DimensionsMap& dim_map = dst_to_src_dim_map;
+
+    // Do not squeeze reordered inner dimensions.
+    int num_squeezable_dims = NumSqueezableInnerDims(dim_map);
+
+    // NOTE: We find the innermost dimension (contiguous in memory) in the dst
+    // block, and we write data linearly into that dimension, reading it from
+    // the src. If dimensions are reordered, we might end up reading data from
+    // the src with `stride != 1`.
+    //
+    // NOTE: Random-Read/Linear-Write can be up to ~2X faster than
+    // Linear-Read/Random-Write: https://stackoverflow.com/a/54935680
+
+    // Find the innermost dimension in the dst whose size is not 1. This is the
+    // effective inner dim.
+    int num_size_one_inner_dims = 0;
+    for (int i = 0; i < num_squeezable_dims; ++i) {
+      const int dst_dim = IsColMajor ? i : NumDims - i - 1;
+      if (dst.dims[dst_dim] != 1) break;
+      num_size_one_inner_dims++;
+    }
+
+    // If all dimensions are of size 1, just copy a scalar from `src` to `dst`.
+    if (num_size_one_inner_dims == NumDims) {
+      *(dst.data + dst.offset) = *(src.data + src.offset);
+      return 1;
+    }
+
+    // Outermost dimension in the dst with `stride == 1` (contiguous in memory).
+    const int dst_stride1_dim = IsColMajor ? num_size_one_inner_dims : NumDims - num_size_one_inner_dims - 1;
+
+    // Dimension in the src that corresponds to the dst innermost dimension.
+    const int src_dim_for_dst_stride1_dim = NumDims == 0 ? 1 : dim_map[dst_stride1_dim];
+
+    // Size of the innermost dimension (length of contiguous blocks of memory).
+    IndexType dst_inner_dim_size = NumDims == 0 ? 1 : dst.dims[dst_stride1_dim];
+
+    // Squeeze multiple inner dims into one if they are contiguous in `dst` and
+    // `src` memory, so we can do less linear copy calls.
+    for (int i = num_size_one_inner_dims + 1; i < num_squeezable_dims; ++i) {
+      const int dst_dim = IsColMajor ? i : NumDims - i - 1;
+      const IndexType dst_stride = dst.strides[dst_dim];
+      const IndexType src_stride = src.strides[dim_map[dst_dim]];
+      if (dst_inner_dim_size == dst_stride && dst_stride == src_stride) {
+        dst_inner_dim_size *= dst.dims[dst_dim];
+        ++num_size_one_inner_dims;
+      } else {
+        break;
+      }
+    }
+
+    // Setup strides to read data from `src` and write to `dst`.
+    IndexType input_offset = src.offset;
+    IndexType output_offset = dst.offset;
+    IndexType input_stride = NumDims == 0 ? 1 : src.strides[src_dim_for_dst_stride1_dim];
+    IndexType output_stride = NumDims == 0 ? 1 : dst.strides[dst_stride1_dim];
+
+    const int at_least_1_dim = NumDims <= 1 ? 1 : NumDims - 1;
+    array<BlockIteratorState, at_least_1_dim> it;
+
+    // Initialize block iterator state. Squeeze away any dimension of size 1.
+    int idx = 0;  // currently initialized iterator state index
+    for (int i = num_size_one_inner_dims; i < NumDims - 1; ++i) {
+      const int dst_dim = IsColMajor ? i + 1 : NumDims - i - 2;
+      if (dst.dims[dst_dim] == 1) continue;
+
+      it[idx].size = dst.dims[dst_dim];
+      it[idx].input_stride = src.strides[dim_map[dst_dim]];
+      it[idx].output_stride = dst.strides[dst_dim];
+
+      it[idx].input_span = it[idx].input_stride * (it[idx].size - 1);
+      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
+
+      idx++;
+    }
+
+    // Iterate copying data from src to dst.
+    const IndexType block_total_size = NumDims == 0 ? 1 : dst.dims.TotalSize();
+
+#define COPY_INNER_DIM(KIND)                                                                                      \
+  IndexType num_copied = 0;                                                                                       \
+  for (num_copied = 0; num_copied < block_total_size; num_copied += dst_inner_dim_size) {                         \
+    LinCopy::template Run<KIND>(typename LinCopy::Dst(output_offset, output_stride, dst.data),                    \
+                                typename LinCopy::Src(input_offset, input_stride, src.data), dst_inner_dim_size); \
+                                                                                                                  \
+    for (int j = 0; j < idx; ++j) {                                                                               \
+      if (++it[j].count < it[j].size) {                                                                           \
+        input_offset += it[j].input_stride;                                                                       \
+        output_offset += it[j].output_stride;                                                                     \
+        break;                                                                                                    \
+      }                                                                                                           \
+      it[j].count = 0;                                                                                            \
+      input_offset -= it[j].input_span;                                                                           \
+      output_offset -= it[j].output_span;                                                                         \
+    }                                                                                                             \
+  }                                                                                                               \
+  return num_copied;
+
+    if (input_stride == 1 && output_stride == 1) {
+      COPY_INNER_DIM(LinCopy::Kind::Linear);
+    } else if (input_stride == 1 && output_stride != 1) {
+      COPY_INNER_DIM(LinCopy::Kind::Scatter);
+    } else if (input_stride == 0 && output_stride == 1) {
+      COPY_INNER_DIM(LinCopy::Kind::FillLinear);
+    } else if (input_stride == 0 && output_stride != 1) {
+      COPY_INNER_DIM(LinCopy::Kind::FillScatter);
+    } else if (output_stride == 1) {
+      COPY_INNER_DIM(LinCopy::Kind::Gather);
+    } else {
+      COPY_INNER_DIM(LinCopy::Kind::Random);
+    }
+
+#undef COPY_INNER_DIM
+  }
+
+  // Copy from `src` to `dst` with an identity src->dst dimension map. Returns
+  // the number of copied elements.
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexType Copy(const Dst& dst, const Src& src) {
+    DimensionsMap dst_to_src_map;
+    for (int i = 0; i < NumDims; ++i) dst_to_src_map[i] = i;
+    return Copy(dst, src, dst_to_src_map);
+  }
+
+ private:
+  struct BlockIteratorState {
+    BlockIteratorState() : size(0), count(0), input_stride(0), output_stride(0), input_span(0), output_span(0) {}
+
+    IndexType size;
+    IndexType count;
+    IndexType input_stride;
+    IndexType output_stride;
+    IndexType input_span;
+    IndexType output_span;
+  };
+
+  // Compute how many inner dimensions it's allowed to squeeze when doing IO
+  // between two tensor blocks. It's safe to squeeze inner dimensions, only
+  // if they are not reordered.
+  static int NumSqueezableInnerDims(const DimensionsMap& dim_map) {
+    int num_squeezable_dims = 0;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      if (dim_map[dim] != dim) break;
+      num_squeezable_dims++;
+    }
+    return num_squeezable_dims;
+  }
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockAssignment assigns a block expression of type `TensorBlockExpr` to
+// a Tensor block defined by `desc`, backed by a memory buffer at `target`.
+//
+// Currently there is no way to write from a Tensor expression to a block of
+// memory, if dimensions are reordered. If you need to do that, you should
+// materialize a Tensor block expression into a memory buffer, and then use
+// TensorBlockIO to copy data between two memory buffers with a custom
+// `target->src` dimension map (see definition above).
+//
+// Also currently the innermost dimension of `target` must have a stride '1'
+// (contiguous in memory). This restriction could be lifted with a `pscatter`,
+// but in practice it's never needed, and there is a similar TensorBlockIO
+// workaround for that.
+//
+// TODO(ezhulenev): TensorBlockAssignment is a special case of TensorBlockIO
+// where `src` is a tensor expression. Explore if it is possible to rewrite IO
+// to use expressions instead of pointers, and after that TensorBlockAssignment
+// will become an alias to IO.
+template <typename Scalar, int NumDims, typename TensorBlockExpr, typename IndexType = Eigen::Index>
+class TensorBlockAssignment {
+  // We will use coeff/packet path to evaluate block expressions.
+  typedef TensorEvaluator<const TensorBlockExpr, DefaultDevice> TensorBlockEvaluator;
+
+  typedef DSizes<IndexType, NumDims> Dimensions;
+
+  enum { Vectorizable = packet_traits<Scalar>::Vectorizable, PacketSize = packet_traits<Scalar>::size };
+
+  template <bool Vectorizable, typename Evaluator>
+  struct InnerDimAssign {
+    EIGEN_ALWAYS_INLINE static void Run(Scalar* target, IndexType count, const Evaluator& eval, IndexType eval_offset) {
+      for (IndexType i = 0; i < count; ++i) {
+        target[i] = eval.coeff(eval_offset + i);
+      }
+    }
+  };
+
+  template <typename Evaluator>
+  struct InnerDimAssign<true, Evaluator> {
+    EIGEN_ALWAYS_INLINE static void Run(Scalar* target, IndexType count, const Evaluator& eval, IndexType eval_offset) {
+      typedef typename packet_traits<Scalar>::type Packet;
+
+      const IndexType unrolled_size = (4 * PacketSize) * (count / (4 * PacketSize));
+      const IndexType vectorized_size = PacketSize * (count / PacketSize);
+      IndexType i = 0;
+
+      for (; i < unrolled_size; i += 4 * PacketSize) {
+        for (int j = 0; j < 4; ++j) {
+          const IndexType idx = eval_offset + i + j * PacketSize;
+          Packet p = eval.template packet<Unaligned>(idx);
+          pstoreu<Scalar>(target + i + j * PacketSize, p);
+        }
+      }
+
+      for (; i < vectorized_size; i += PacketSize) {
+        Packet p = eval.template packet<Unaligned>(eval_offset + i);
+        pstoreu<Scalar>(target + i, p);
+      }
+
+      for (; i < count; ++i) {
+        target[i] = eval.coeff(eval_offset + i);
+      }
+    }
+  };
+
+ public:
+  struct Target {
+    Target(const Dimensions& target_dims, const Dimensions& target_strides, Scalar* target_data,
+           IndexType target_offset = 0)
+        : dims(target_dims), strides(target_strides), data(target_data), offset(target_offset) {}
+
+    Dimensions dims;
+    Dimensions strides;
+    Scalar* data;
+    IndexType offset;
+  };
+
+  static Target target(const Dimensions& target_dims, const Dimensions& target_strides, Scalar* target_data,
+                       IndexType target_offset = 0) {
+    return Target(target_dims, target_strides, target_data, target_offset);
+  }
+
+  template <typename TargetDimsIndexType, typename TargetStridesIndexType>
+  static Target target(const DSizes<TargetDimsIndexType, NumDims>& target_dims,
+                       const DSizes<TargetStridesIndexType, NumDims>& target_strides, Scalar* target_data,
+                       IndexType target_offset = 0) {
+    // DSizes constructor will do index type promotion if it's safe.
+    return Target(Dimensions(target_dims), Dimensions(target_strides), target_data, target_offset);
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Target& target, const TensorBlockExpr& expr) {
+    // Prepare evaluator for block expression.
+    DefaultDevice default_device;
+    TensorBlockEvaluator eval(expr, default_device);
+
+    // Tensor block expression dimension should match destination dimensions.
+    eigen_assert(dimensions_match(target.dims, eval.dimensions()));
+
+    static const int Layout = TensorBlockEvaluator::Layout;
+    static const bool is_col_major = Layout == ColMajor;
+
+    // Initialize output inner dimension size based on a layout.
+    const IndexType output_size = NumDims == 0 ? 1 : target.dims.TotalSize();
+    const int inner_dim_idx = is_col_major ? 0 : NumDims - 1;
+    IndexType output_inner_dim_size = target.dims[inner_dim_idx];
+
+    // Target inner dimension stride must be '1'.
+    eigen_assert(target.strides[inner_dim_idx] == 1);
+
+    // Squeeze multiple inner dims into one if they are contiguous in `target`.
+    IndexType num_squeezed_dims = 0;
+    for (Index i = 1; i < NumDims; ++i) {
+      const Index dim = is_col_major ? i : NumDims - i - 1;
+      const IndexType target_stride = target.strides[dim];
+
+      if (output_inner_dim_size == target_stride) {
+        output_inner_dim_size *= target.dims[dim];
+        num_squeezed_dims++;
+      } else {
+        break;
+      }
+    }
+
+    // Initialize output block iterator state. Dimension in this array are
+    // always in inner_most -> outer_most order (col major layout).
+    array<BlockIteratorState, NumDims> it;
+
+    int idx = 0;  // currently initialized iterator state index
+    for (Index i = num_squeezed_dims; i < NumDims - 1; ++i) {
+      const Index dim = is_col_major ? i + 1 : NumDims - i - 2;
+
+      it[idx].count = 0;
+      it[idx].size = target.dims[dim];
+      it[idx].output_stride = target.strides[dim];
+      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
+      idx++;
+    }
+
+    // We read block expression from the beginning, and start writing data to
+    // `target` at given offset.
+    IndexType input_offset = 0;
+    IndexType output_offset = target.offset;
+
+    // Iterate copying data from `eval` to `target`.
+    for (IndexType i = 0; i < output_size; i += output_inner_dim_size) {
+      // Assign to `target` at current offset.
+      InnerDimAssign<Vectorizable && TensorBlockEvaluator::PacketAccess, TensorBlockEvaluator>::Run(
+          target.data + output_offset, output_inner_dim_size, eval, input_offset);
+
+      // Move input offset forward by the number of assigned coefficients.
+      input_offset += output_inner_dim_size;
+
+      // Update index.
+      for (int j = 0; j < idx; ++j) {
+        if (++it[j].count < it[j].size) {
+          output_offset += it[j].output_stride;
+          break;
+        }
+        it[j].count = 0;
+        output_offset -= it[j].output_span;
+      }
+    }
+  }
+
+ private:
+  struct BlockIteratorState {
+    BlockIteratorState() : count(0), size(0), output_stride(0), output_span(0) {}
+
+    IndexType count;
+    IndexType size;
+    IndexType output_stride;
+    IndexType output_span;
+  };
+};
+
+// -------------------------------------------------------------------------- //
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
new file mode 100644
index 0000000..799e513
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
@@ -0,0 +1,1001 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Broadcast, typename XprType>
+struct traits<TensorBroadcastingOp<Broadcast, XprType>> : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+  enum {
+    // Broadcast is read-only.
+    Flags = traits<XprType>::Flags & ~LvalueBit
+  };
+};
+
+template <typename Broadcast, typename XprType>
+struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense> {
+  typedef const TensorBroadcastingOp<Broadcast, XprType> EIGEN_DEVICE_REF type;
+};
+
+template <typename Broadcast, typename XprType>
+struct nested<TensorBroadcastingOp<Broadcast, XprType>, 1,
+              typename eval<TensorBroadcastingOp<Broadcast, XprType>>::type> {
+  typedef TensorBroadcastingOp<Broadcast, XprType> type;
+};
+
+template <typename Dims>
+struct is_input_scalar {
+  static const bool value = false;
+};
+template <>
+struct is_input_scalar<Sizes<>> {
+  static const bool value = true;
+};
+template <typename std::ptrdiff_t... Indices>
+struct is_input_scalar<Sizes<Indices...>> {
+  static constexpr bool value = (Sizes<Indices...>::total_size == 1);
+};
+
+}  // end namespace internal
+
+/** Tensor broadcasting class.
+ * \ingroup CXX11_Tensor_Module
+ */
+template <typename Broadcast, typename XprType>
+class TensorBroadcastingOp : public TensorBase<TensorBroadcastingOp<Broadcast, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorBroadcastingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorBroadcastingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBroadcastingOp(const XprType& expr, const Broadcast& broadcast)
+      : m_xpr(expr), m_broadcast(broadcast) {}
+
+  EIGEN_DEVICE_FUNC const Broadcast& broadcast() const { return m_broadcast; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const Broadcast m_broadcast;
+};
+
+// Eval as rvalue
+template <typename Broadcast, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device> {
+  typedef TensorBroadcastingOp<Broadcast, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+ protected:  //  all the non-static fields must have the same access control, otherwise the TensorEvaluator won't be
+             //  standard layout;
+  bool isCopy, nByOne, oneByN;
+
+ public:
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
+    PreferBlockAccess = true,
+    RawAccess = false
+  };
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  // We do block based broadcasting using a trick with 2x tensor rank and 0
+  // strides. See block method implementation for details.
+  typedef DSizes<Index, 2 * NumDims> BroadcastDimensions;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock ArgTensorBlock;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, Layout, Index> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : isCopy(false),
+        nByOne(false),
+        oneByN(false),
+        m_device(device),
+        m_broadcast(op.broadcast()),
+        m_impl(op.expression(), device) {
+    // The broadcasting op doesn't change the rank of the tensor. One can't broadcast a scalar
+    // and store the result in a scalar. Instead one should reshape the scalar into a N-D
+    // tensor with N >= 1 of 1 element first and then broadcast.
+    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    const InputDimensions& input_dims = m_impl.dimensions();
+    isCopy = true;
+    for (int i = 0; i < NumDims; ++i) {
+      eigen_assert(input_dims[i] > 0);
+      m_dimensions[i] = input_dims[i] * m_broadcast[i];
+      if (m_broadcast[i] != 1) {
+        isCopy = false;
+      }
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputStrides[0] = 1;
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1];
+        m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+      }
+    } else {
+      m_inputStrides[NumDims - 1] = 1;
+      m_outputStrides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1];
+        m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+      }
+    }
+
+    if (input_dims[0] == 1) {
+      oneByN = true;
+      for (int i = 1; i < NumDims; ++i) {
+        if (m_broadcast[i] != 1) {
+          oneByN = false;
+          break;
+        }
+      }
+    } else if (input_dims[NumDims - 1] == 1) {
+      nByOne = true;
+      for (int i = 0; i < NumDims - 1; ++i) {
+        if (m_broadcast[i] != 1) {
+          nByOne = false;
+          break;
+        }
+      }
+    }
+
+    // Handle special format like NCHW, its input shape is '[1, N..., 1]' and
+    // broadcast shape is '[N, 1..., N]'
+    if (!oneByN && !nByOne) {
+      if (input_dims[0] == 1 && input_dims[NumDims - 1] == 1 && NumDims > 2) {
+        nByOne = true;
+        oneByN = true;
+        for (int i = 1; i < NumDims - 1; ++i) {
+          if (m_broadcast[i] != 1) {
+            nByOne = false;
+            oneByN = false;
+            break;
+          }
+        }
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const {
+    if (internal::is_input_scalar<internal::remove_all_t<InputDimensions>>::value) {
+      return m_impl.coeff(0);
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      if (isCopy) {
+        return m_impl.coeff(index);
+      } else {
+        return coeffColMajor(index);
+      }
+    } else {
+      if (isCopy) {
+        return m_impl.coeff(index);
+      } else {
+        return coeffRowMajor(index);
+      }
+    }
+  }
+
+  // TODO: attempt to speed this up. The integer divisions and modulo are slow
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index indexColMajor(Index index) const {
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = NumDims - 1; i > 0; --i) {
+      const Index idx = index / m_outputStrides[i];
+      if (internal::index_statically_eq<Broadcast>(i, 1)) {
+        eigen_assert(idx < m_impl.dimensions()[i]);
+        inputIndex += idx * m_inputStrides[i];
+      } else {
+        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
+          eigen_assert(idx % m_impl.dimensions()[i] == 0);
+        } else {
+          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
+        }
+      }
+      index -= idx * m_outputStrides[i];
+    }
+    if (internal::index_statically_eq<Broadcast>(0, 1)) {
+      eigen_assert(index < m_impl.dimensions()[0]);
+      inputIndex += index;
+    } else {
+      if (internal::index_statically_eq<InputDimensions>(0, 1)) {
+        eigen_assert(index % m_impl.dimensions()[0] == 0);
+      } else {
+        inputIndex += (index % m_impl.dimensions()[0]);
+      }
+    }
+    return inputIndex;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const {
+    return m_impl.coeff(indexColMajor(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index indexRowMajor(Index index) const {
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const Index idx = index / m_outputStrides[i];
+      if (internal::index_statically_eq<Broadcast>(i, 1)) {
+        eigen_assert(idx < m_impl.dimensions()[i]);
+        inputIndex += idx * m_inputStrides[i];
+      } else {
+        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
+          eigen_assert(idx % m_impl.dimensions()[i] == 0);
+        } else {
+          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
+        }
+      }
+      index -= idx * m_outputStrides[i];
+    }
+    if (internal::index_statically_eq<Broadcast>(NumDims - 1, 1)) {
+      eigen_assert(index < m_impl.dimensions()[NumDims - 1]);
+      inputIndex += index;
+    } else {
+      if (internal::index_statically_eq<InputDimensions>(NumDims - 1, 1)) {
+        eigen_assert(index % m_impl.dimensions()[NumDims - 1] == 0);
+      } else {
+        inputIndex += (index % m_impl.dimensions()[NumDims - 1]);
+      }
+    }
+    return inputIndex;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const {
+    return m_impl.coeff(indexRowMajor(index));
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const {
+    if (internal::is_input_scalar<internal::remove_all_t<InputDimensions>>::value) {
+      return internal::pset1<PacketReturnType>(m_impl.coeff(0));
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      if (isCopy) {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+        // See PR 437: on NVIDIA P100 and K20m we observed a x3-4 speed up by enforcing
+        // unaligned loads here. The reason is unclear though.
+        return m_impl.template packet<Unaligned>(index);
+#else
+        return m_impl.template packet<LoadMode>(index);
+#endif
+      } else if (oneByN && !nByOne) {
+        return packetNByOne<LoadMode>(index);
+      } else if (!oneByN && nByOne) {
+        return packetOneByN<LoadMode>(index);
+      } else if (oneByN && nByOne) {
+        return packetOneByNByOne<LoadMode>(index);
+      } else {
+        return packetColMajor<LoadMode>(index);
+      }
+    } else {
+      if (isCopy) {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+        // See above.
+        return m_impl.template packet<Unaligned>(index);
+#else
+        return m_impl.template packet<LoadMode>(index);
+#endif
+      } else if (oneByN && !nByOne) {
+        return packetOneByN<LoadMode>(index);
+      } else if (!oneByN && nByOne) {
+        return packetNByOne<LoadMode>(index);
+      } else if (oneByN && nByOne) {
+        return packetOneByNByOne<LoadMode>(index);
+      } else {
+        return packetRowMajor<LoadMode>(index);
+      }
+    }
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByNByOne(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+    Index startDim, endDim;
+    Index inputIndex, outputOffset, batchedIndex;
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      startDim = NumDims - 1;
+      endDim = 1;
+    } else {
+      startDim = 0;
+      endDim = NumDims - 2;
+    }
+
+    batchedIndex = index % m_outputStrides[startDim];
+    inputIndex = batchedIndex / m_outputStrides[endDim];
+    outputOffset = batchedIndex % m_outputStrides[endDim];
+
+    if (outputOffset + PacketSize <= m_outputStrides[endDim]) {
+      values[0] = m_impl.coeff(inputIndex);
+      return internal::pload1<PacketReturnType>(values);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) {
+        if (outputOffset + cur < m_outputStrides[endDim]) {
+          values[i] = m_impl.coeff(inputIndex);
+        } else {
+          ++inputIndex;
+          inputIndex = (inputIndex == m_inputStrides[startDim] ? 0 : inputIndex);
+          values[i] = m_impl.coeff(inputIndex);
+          outputOffset = 0;
+          cur = 0;
+        }
+      }
+      return internal::pload<PacketReturnType>(values);
+    }
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByN(Index index) const {
+    // Consider the flattened tensor [v0, ..., vN],
+    // Concatenates m_broadcast[dim] copies,
+    //    [v0, ..., vN, v0, ..., vN, ... ]
+    // with dim == NumDims - 1 for col-major, dim == 0 for row-major.
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    // Size of flattened tensor.
+    const Index M =
+        (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_inputStrides[NumDims - 1] : m_inputStrides[0];
+    Index inputIndex = index % M;
+    if (inputIndex + PacketSize <= M) {
+      return m_impl.template packet<Unaligned>(inputIndex);
+    } else {
+      EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < PacketSize; ++i) {
+        if (inputIndex > M - 1) {
+          inputIndex = 0;
+        }
+        values[i] = m_impl.coeff(inputIndex++);
+      }
+      return internal::pload<PacketReturnType>(values);
+    }
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetNByOne(Index index) const {
+    // Consider the flattened tensor [v0, ..., vN],
+    // Interleaves m_broadcast[dim] copies,
+    //    [v0, v0, ..., v1, v1, ..., vN, vN, ... ]
+    // with dim == 0 for col-major, dim == NumDims - 1 for row-major.
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    const Index M =
+        (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_broadcast[0] : m_broadcast[NumDims - 1];
+
+    Index inputIndex = index / M;
+    Index outputOffset = index % M;
+    if (outputOffset + PacketSize <= M) {
+      return internal::pset1<PacketReturnType>(m_impl.coeff(inputIndex));
+    } else {
+      EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < PacketSize; ++i) {
+        if (outputOffset < M) {
+          values[i] = m_impl.coeff(inputIndex);
+          ++outputOffset;
+        } else {
+          values[i] = m_impl.coeff(++inputIndex);
+          outputOffset = 1;  // Next offset.
+        }
+      }
+      return internal::pload<PacketReturnType>(values);
+    }
+  }
+
+  // Ignore the LoadMode and always use unaligned loads since we can't guarantee
+  // the alignment at compile time.
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    const Index originalIndex = index;
+
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = NumDims - 1; i > 0; --i) {
+      const Index idx = index / m_outputStrides[i];
+      if (internal::index_statically_eq<Broadcast>(i, 1)) {
+        eigen_assert(idx < m_impl.dimensions()[i]);
+        inputIndex += idx * m_inputStrides[i];
+      } else {
+        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
+          eigen_assert(idx % m_impl.dimensions()[i] == 0);
+        } else {
+          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
+        }
+      }
+      index -= idx * m_outputStrides[i];
+    }
+    Index innermostLoc;
+    if (internal::index_statically_eq<Broadcast>(0, 1)) {
+      eigen_assert(index < m_impl.dimensions()[0]);
+      innermostLoc = index;
+    } else {
+      if (internal::index_statically_eq<InputDimensions>(0, 1)) {
+        eigen_assert(index % m_impl.dimensions()[0] == 0);
+        innermostLoc = 0;
+      } else {
+        innermostLoc = index % m_impl.dimensions()[0];
+      }
+    }
+    inputIndex += innermostLoc;
+
+    // TODO: This could be extended to the second dimension if we're not
+    // broadcasting alongside the first dimension, and so on.
+    if (innermostLoc + PacketSize <= m_impl.dimensions()[0]) {
+      return m_impl.template packet<Unaligned>(inputIndex);
+    } else {
+      EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+      values[0] = m_impl.coeff(inputIndex);
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < PacketSize; ++i) {
+        if (innermostLoc + i < m_impl.dimensions()[0]) {
+          values[i] = m_impl.coeff(inputIndex + i);
+        } else {
+          values[i] = coeffColMajor(originalIndex + i);
+        }
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    const Index originalIndex = index;
+
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const Index idx = index / m_outputStrides[i];
+      if (internal::index_statically_eq<Broadcast>(i, 1)) {
+        eigen_assert(idx < m_impl.dimensions()[i]);
+        inputIndex += idx * m_inputStrides[i];
+      } else {
+        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
+          eigen_assert(idx % m_impl.dimensions()[i] == 0);
+        } else {
+          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
+        }
+      }
+      index -= idx * m_outputStrides[i];
+    }
+    Index innermostLoc;
+    if (internal::index_statically_eq<Broadcast>(NumDims - 1, 1)) {
+      eigen_assert(index < m_impl.dimensions()[NumDims - 1]);
+      innermostLoc = index;
+    } else {
+      if (internal::index_statically_eq<InputDimensions>(NumDims - 1, 1)) {
+        eigen_assert(index % m_impl.dimensions()[NumDims - 1] == 0);
+        innermostLoc = 0;
+      } else {
+        innermostLoc = index % m_impl.dimensions()[NumDims - 1];
+      }
+    }
+    inputIndex += innermostLoc;
+
+    // TODO: This could be extended to the second dimension if we're not
+    // broadcasting alongside the first dimension, and so on.
+    if (innermostLoc + PacketSize <= m_impl.dimensions()[NumDims - 1]) {
+      return m_impl.template packet<Unaligned>(inputIndex);
+    } else {
+      EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+      values[0] = m_impl.coeff(inputIndex);
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < PacketSize; ++i) {
+        if (innermostLoc + i < m_impl.dimensions()[NumDims - 1]) {
+          values[i] = m_impl.coeff(inputIndex + i);
+        } else {
+          values[i] = coeffRowMajor(originalIndex + i);
+        }
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    double compute_cost = TensorOpCost::AddCost<Index>();
+    if (!isCopy && NumDims > 0) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        compute_cost += TensorOpCost::DivCost<Index>();
+        if (internal::index_statically_eq<Broadcast>(i, 1)) {
+          compute_cost += TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
+        } else {
+          if (!internal::index_statically_eq<InputDimensions>(i, 1)) {
+            compute_cost +=
+                TensorOpCost::MulCost<Index>() + TensorOpCost::ModCost<Index>() + TensorOpCost::AddCost<Index>();
+          }
+        }
+        compute_cost += TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
+      }
+    }
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    // TODO(wuke): Targeting L1 size is 30% faster than targeting L{-1} on large
+    // tensors. But this might need further tuning.
+    const size_t target_size = m_device.firstLevelCacheSize();
+    return internal::TensorBlockResourceRequirements::merge(
+        m_impl.getResourceRequirements(), internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    BlockBroadcastingParams params = blockBroadcastingParams(desc);
+
+    if (params.inner_dim_size == 0 || params.bcast_dim_size == 0) {
+      return emptyBlock();
+    }
+
+    // Prepare storage for the materialized broadcasting result.
+    const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch);
+    ScalarNoConst* materialized_output = block_storage.data();
+
+    // We potentially will need to materialize input blocks.
+    size_t materialized_input_size = 0;
+    ScalarNoConst* materialized_input = NULL;
+
+    // Initialize block broadcating iterator state for outer dimensions (outer
+    // with regard to bcast dimension). Dimension in this array are always in
+    // inner_most -> outer_most order (col major layout).
+    array<BlockBroadcastingIteratorState, NumDims> it;
+    int idx = 0;
+
+    for (int i = params.inner_dim_count + 1; i < NumDims; ++i) {
+      const Index dim = IsColMajor ? i : NumDims - 1 - i;
+      it[idx].size = params.output_dims[dim];
+      it[idx].count = 0;
+      it[idx].output_stride = m_outputStrides[dim];
+      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
+      idx++;
+    }
+
+    // Write output into the beginning of `materialized_output`.
+    Index output_offset = 0;
+
+    // We will fill output block by broadcasting along the bcast dim, and
+    // iterating over outer dimension.
+    const Index output_size = NumDims == 0 ? 1 : params.output_dims.TotalSize();
+
+    for (Index num_output_coeffs = 0; num_output_coeffs < output_size;) {
+      ScalarNoConst* bcast_output = materialized_output + num_output_coeffs;
+      Index bcast_offset = desc.offset() + output_offset;
+
+      // Broadcast along the bcast dimension.
+      num_output_coeffs += BroadcastBlockAlongBcastDim(params, bcast_offset, scratch, bcast_output, &materialized_input,
+                                                       &materialized_input_size);
+
+      // Switch to the next outer dimension.
+      for (int j = 0; j < idx; ++j) {
+        if (++it[j].count < it[j].size) {
+          output_offset += it[j].output_stride;
+          break;
+        }
+        it[j].count = 0;
+        output_offset -= it[j].output_span;
+      }
+    }
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+
+  Broadcast functor() const { return m_broadcast; }
+
+ private:
+  static constexpr bool IsColMajor = static_cast<int>(Layout) == static_cast<int>(ColMajor);
+
+  // We will build a general case block broadcasting on top of broadcasting
+  // primitive that will do broadcasting only for the inner dimension(s) along
+  // the first dimension smaller than the input size (it's called `bcast_dim`).
+  //
+  // Example:
+  //           dim:  0  1  2   (ColMajor)
+  //    input size: [9, 3, 6]
+  //    block size: [9, 2, 6]
+  //
+  // We will compute broadcasted block by iterating over the outer dimensions
+  // before `bcast_dim` (only dimension `2` in this example) and computing
+  // broadcasts along the `bcast_dim` (dimension `1` in this example).
+
+  // BlockBroadcastingParams holds precomputed parameters for broadcasting a
+  // single block along the broadcasting dimension. Sizes and strides along the
+  // `bcast_dim` might be invalid, they will be adjusted later in
+  // `BroadcastBlockAlongBcastDim`.
+  struct BlockBroadcastingParams {
+    Dimensions input_dims;      // input expression dimensions
+    Dimensions output_dims;     // output block sizes
+    Dimensions output_strides;  // output block strides
+
+    int inner_dim_count;   // count inner dimensions matching in size
+    int bcast_dim;         // broadcasting dimension index
+    Index bcast_dim_size;  // broadcasting dimension size
+    Index inner_dim_size;  // inner dimensions size
+
+    // Block sizes and strides for the input block where all dimensions before
+    // `bcast_dim` are equal to `1`.
+    Dimensions input_block_sizes;
+    Dimensions input_block_strides;
+
+    // Block sizes and strides for blocks with extra dimensions and strides `0`.
+    BroadcastDimensions bcast_block_sizes;
+    BroadcastDimensions bcast_block_strides;
+    BroadcastDimensions bcast_input_strides;
+  };
+
+  struct BlockBroadcastingIteratorState {
+    Index size;
+    Index count;
+    Index output_stride;
+    Index output_span;
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlockBroadcastingParams blockBroadcastingParams(TensorBlockDesc& desc) const {
+    BlockBroadcastingParams params;
+
+    params.input_dims = Dimensions(m_impl.dimensions());
+
+    // Output block sizes and strides.
+    params.output_dims = desc.dimensions();
+    params.output_strides = internal::strides<Layout>(params.output_dims);
+
+    // Find the broadcasting dimension (first dimension with output size smaller
+    // that the input size).
+    params.bcast_dim = 0;
+    params.bcast_dim_size = 1;
+    params.inner_dim_size = 1;
+
+    // Count the number of inner dimensions that have the same size in the block
+    // and in the broadcast expression.
+    params.inner_dim_count = 0;
+
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+
+      if (params.output_dims[dim] == m_dimensions[dim]) {
+        params.inner_dim_size *= params.output_dims[dim];
+        ++params.inner_dim_count;
+        continue;
+      }
+
+      // First non-matching dimension is the broadcasting dimension.
+      eigen_assert(params.output_dims[dim] < m_dimensions[dim]);
+      params.bcast_dim = dim;
+      params.bcast_dim_size = params.output_dims[dim];
+      break;
+    }
+
+    // Calculate the input block size for looking into the input.
+    for (int i = 0; i < params.inner_dim_count; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      params.input_block_sizes[dim] = params.input_dims[dim];
+    }
+    for (int i = params.inner_dim_count; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      params.input_block_sizes[dim] = 1;
+    }
+    params.input_block_strides = internal::strides<Layout>(params.input_block_sizes);
+
+    // Broadcast with the 0-stride trick: Create 1 extra dim for each
+    // broadcast, set the input stride to 0.
+    //
+    // When ColMajor:
+    //
+    // - bcast_block_sizes:
+    //   [d_0, b_0, d_1, b_1, ...]
+    //
+    // - bcast_block_strides:
+    //   [output_block_strides[0], output_block_strides[0] * d_0,
+    //    output_block_strides[1], output_block_strides[1] * d_1,
+    //   ...]
+    //
+    // - bcast_input_strides:
+    //   [input_block_strides[0], 0,
+    //    input_block_strides[1], 0,
+    //   ...].
+    //
+    for (int i = 0; i < params.inner_dim_count; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+
+      const int copy_dim = IsColMajor ? 2 * i : 2 * NumDims - 2 * i - 1;
+      const int broadcast_dim = IsColMajor ? copy_dim + 1 : copy_dim - 1;
+
+      params.bcast_block_sizes[copy_dim] = params.input_dims[dim];
+      params.bcast_block_sizes[broadcast_dim] = m_broadcast[dim];
+      params.bcast_block_strides[copy_dim] = params.output_strides[dim];
+      params.bcast_block_strides[broadcast_dim] = params.output_strides[dim] * params.input_dims[dim];
+      params.bcast_input_strides[copy_dim] = params.input_block_strides[dim];
+      params.bcast_input_strides[broadcast_dim] = 0;
+    }
+
+    for (int i = 2 * params.inner_dim_count; i < 2 * NumDims; ++i) {
+      const int dim = IsColMajor ? i : 2 * NumDims - i - 1;
+      params.bcast_block_sizes[dim] = 1;
+      params.bcast_block_strides[dim] = 0;
+      params.bcast_input_strides[dim] = 0;
+    }
+
+    return params;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock emptyBlock() const {
+    DSizes<Index, NumDims> dimensions;
+    for (int i = 0; i < NumDims; ++i) dimensions[i] = 0;
+    return TensorBlock(internal::TensorBlockKind::kView, NULL, dimensions);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index BroadcastBlockAlongBcastDim(
+      BlockBroadcastingParams params, Index bcast_offset, TensorBlockScratch& scratch,
+      ScalarNoConst* materialized_output, ScalarNoConst** materialized_input, size_t* materialized_input_size) const {
+    if (params.bcast_dim_size == 1) {
+      // We just need one block read using the ready-set values above.
+      return BroadcastBlock(params.input_block_sizes, params.input_block_strides, params.bcast_block_sizes,
+                            params.bcast_block_strides, params.bcast_input_strides, bcast_offset, 0, scratch,
+                            materialized_output, materialized_input, materialized_input_size);
+
+    } else if (params.input_dims[params.bcast_dim] == 1) {
+      // Broadcast bcast dimension (< NumDims) by bcast_dim_size.
+      const int broadcast_bcast_dim =
+          IsColMajor ? 2 * params.inner_dim_count + 1 : 2 * NumDims - 2 * params.inner_dim_count - 2;
+
+      params.bcast_block_sizes[broadcast_bcast_dim] = params.bcast_dim_size;
+      params.bcast_input_strides[broadcast_bcast_dim] = 0;
+      params.bcast_block_strides[broadcast_bcast_dim] = params.output_strides[params.bcast_dim];
+
+      return BroadcastBlock(params.input_block_sizes, params.input_block_strides, params.bcast_block_sizes,
+                            params.bcast_block_strides, params.bcast_input_strides, bcast_offset, 0, scratch,
+                            materialized_output, materialized_input, materialized_input_size);
+
+    } else {
+      // Keep track of the total number of the coefficients written to the
+      // output block.
+      Index num_output_coeffs = 0;
+
+      // The general case. Let's denote the output block as
+      //
+      //   x[..., a:a+bcast_dim_size, :, ..., :]
+      //
+      // where a:a+bcast_dim_size is a slice on the bcast_dim dimension
+      // (< NumDims). We need to split the a:a+bcast_dim_size into possibly 3
+      // sub-blocks:
+      //
+      // (1) a:b, where b is the smallest multiple of
+      //     input_dims[bcast_dim_start] in [a, a+bcast_dim_size].
+      //
+      // (2) b:c, where c is the largest multiple of input_dims[bcast_dim_start]
+      //     in [a, a+bcast_dim_size].
+      //
+      // (3) c:a+bcast_dim_size .
+      //
+      // Or, when b and c do not exist, we just need to process the whole block
+      // together.
+
+      // Find a.
+      const Index bcast_dim_left_index = bcast_offset / m_outputStrides[params.bcast_dim];
+
+      // Find b and c.
+      const Index input_bcast_dim_size = params.input_dims[params.bcast_dim];
+
+      // First multiple after a. This is b when <= bcast_dim_left_index +
+      // bcast_dim_size.
+      const Index first_multiple =
+          numext::div_ceil<Index>(bcast_dim_left_index, input_bcast_dim_size) * input_bcast_dim_size;
+
+      if (first_multiple <= bcast_dim_left_index + params.bcast_dim_size) {
+        // b exists, so does c. Find it.
+        const Index last_multiple =
+            (bcast_dim_left_index + params.bcast_dim_size) / input_bcast_dim_size * input_bcast_dim_size;
+        const int copy_bcast_dim =
+            IsColMajor ? 2 * params.inner_dim_count : 2 * NumDims - 2 * params.inner_dim_count - 1;
+        const int broadcast_bcast_dim =
+            IsColMajor ? 2 * params.inner_dim_count + 1 : 2 * NumDims - 2 * params.inner_dim_count - 2;
+
+        if (first_multiple > bcast_dim_left_index) {
+          const Index head_size = first_multiple - bcast_dim_left_index;
+          params.input_block_sizes[params.bcast_dim] = head_size;
+          params.bcast_block_sizes[copy_bcast_dim] = head_size;
+          params.bcast_input_strides[copy_bcast_dim] = params.input_block_strides[params.bcast_dim];
+          params.bcast_block_strides[copy_bcast_dim] = params.output_strides[params.bcast_dim];
+          params.bcast_block_sizes[broadcast_bcast_dim] = 1;
+          params.bcast_input_strides[broadcast_bcast_dim] = 0;
+          params.bcast_block_strides[broadcast_bcast_dim] =
+              params.output_strides[params.bcast_dim] * params.input_dims[params.bcast_dim];
+
+          num_output_coeffs +=
+              BroadcastBlock(params.input_block_sizes, params.input_block_strides, params.bcast_block_sizes,
+                             params.bcast_block_strides, params.bcast_input_strides, bcast_offset, 0, scratch,
+                             materialized_output, materialized_input, materialized_input_size);
+        }
+        if (first_multiple < last_multiple) {
+          params.input_block_sizes[params.bcast_dim] = input_bcast_dim_size;
+          params.bcast_block_sizes[copy_bcast_dim] = input_bcast_dim_size;
+          params.bcast_input_strides[copy_bcast_dim] = params.input_block_strides[params.bcast_dim];
+          params.bcast_block_strides[copy_bcast_dim] = params.output_strides[params.bcast_dim];
+          params.bcast_block_sizes[broadcast_bcast_dim] = (last_multiple - first_multiple) / input_bcast_dim_size;
+          params.bcast_input_strides[broadcast_bcast_dim] = 0;
+          params.bcast_block_strides[broadcast_bcast_dim] =
+              params.output_strides[params.bcast_dim] * params.input_dims[params.bcast_dim];
+          const Index offset = (first_multiple - bcast_dim_left_index) * m_outputStrides[params.bcast_dim];
+
+          num_output_coeffs +=
+              BroadcastBlock(params.input_block_sizes, params.input_block_strides, params.bcast_block_sizes,
+                             params.bcast_block_strides, params.bcast_input_strides, bcast_offset, offset, scratch,
+                             materialized_output, materialized_input, materialized_input_size);
+        }
+        if (last_multiple < bcast_dim_left_index + params.bcast_dim_size) {
+          const Index tail_size = bcast_dim_left_index + params.bcast_dim_size - last_multiple;
+          params.input_block_sizes[params.bcast_dim] = tail_size;
+          params.bcast_block_sizes[copy_bcast_dim] = tail_size;
+          params.bcast_input_strides[copy_bcast_dim] = params.input_block_strides[params.bcast_dim];
+          params.bcast_block_strides[copy_bcast_dim] = params.output_strides[params.bcast_dim];
+          params.bcast_block_sizes[broadcast_bcast_dim] = 1;
+          params.bcast_input_strides[broadcast_bcast_dim] = 0;
+          params.bcast_block_strides[broadcast_bcast_dim] =
+              params.output_strides[params.bcast_dim] * params.input_dims[params.bcast_dim];
+          const Index offset = (last_multiple - bcast_dim_left_index) * m_outputStrides[params.bcast_dim];
+
+          num_output_coeffs +=
+              BroadcastBlock(params.input_block_sizes, params.input_block_strides, params.bcast_block_sizes,
+                             params.bcast_block_strides, params.bcast_input_strides, bcast_offset, offset, scratch,
+                             materialized_output, materialized_input, materialized_input_size);
+        }
+      } else {
+        // b and c do not exist.
+        const int copy_bcast_dim =
+            IsColMajor ? 2 * params.inner_dim_count : 2 * NumDims - 2 * params.inner_dim_count - 1;
+        params.input_block_sizes[params.bcast_dim] = params.bcast_dim_size;
+        params.bcast_block_sizes[copy_bcast_dim] = params.bcast_dim_size;
+        params.bcast_input_strides[copy_bcast_dim] = params.input_block_strides[params.bcast_dim];
+        params.bcast_block_strides[copy_bcast_dim] = params.output_strides[params.bcast_dim];
+
+        num_output_coeffs +=
+            BroadcastBlock(params.input_block_sizes, params.input_block_strides, params.bcast_block_sizes,
+                           params.bcast_block_strides, params.bcast_input_strides, bcast_offset, 0, scratch,
+                           materialized_output, materialized_input, materialized_input_size);
+      }
+
+      return num_output_coeffs;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index BroadcastBlock(
+      const Dimensions& input_block_sizes, const Dimensions& input_block_strides,
+      const BroadcastDimensions& bcast_block_sizes, const BroadcastDimensions& bcast_block_strides,
+      const BroadcastDimensions& bcast_input_strides, Index bcast_offset, Index offset, TensorBlockScratch& scratch,
+      ScalarNoConst* materialized_output, ScalarNoConst** materialized_input, size_t* materialized_input_size) const {
+    // ---------------------------------------------------------------------- //
+    // Tensor block descriptor for reading block from the input.
+    const Index input_offset = bcast_offset + offset;
+    TensorBlockDesc input_desc(IsColMajor ? indexColMajor(input_offset) : indexRowMajor(input_offset),
+                               input_block_sizes);
+
+    ArgTensorBlock input_block = m_impl.block(input_desc, scratch);
+
+    // ---------------------------------------------------------------------- //
+    // Materialize input block into a temporary memory buffer only if it's not
+    // already available in the arg block.
+    const ScalarNoConst* input_buffer = NULL;
+
+    if (input_block.data() != NULL) {
+      // Input block already has raw data, there is no need to materialize it.
+      input_buffer = input_block.data();
+
+    } else {
+      // Otherwise we have to do block assignment into a temporary buffer.
+
+      // Maybe reuse previously allocated buffer, or allocate a new one with a
+      // scratch allocator.
+      const size_t input_total_size = input_block_sizes.TotalSize();
+      if (*materialized_input == NULL || *materialized_input_size < input_total_size) {
+        *materialized_input_size = input_total_size;
+        void* mem = scratch.allocate(*materialized_input_size * sizeof(Scalar));
+        *materialized_input = static_cast<ScalarNoConst*>(mem);
+      }
+
+      typedef internal::TensorBlockAssignment<ScalarNoConst, NumDims, typename ArgTensorBlock::XprType, Index>
+          TensorBlockAssignment;
+
+      TensorBlockAssignment::Run(
+          TensorBlockAssignment::target(input_block_sizes, input_block_strides, *materialized_input),
+          input_block.expr());
+
+      input_buffer = *materialized_input;
+    }
+
+    // ---------------------------------------------------------------------- //
+    // Copy data from materialized input block to the materialized output, using
+    // given broadcast strides (strides with zeroes).
+    typedef internal::TensorBlockIO<ScalarNoConst, Index, 2 * NumDims, Layout> TensorBlockIO;
+
+    typename TensorBlockIO::Src src(bcast_input_strides, input_buffer);
+    typename TensorBlockIO::Dst dst(bcast_block_sizes, bcast_block_strides, materialized_output + offset);
+
+    return TensorBlockIO::Copy(dst, src);
+  }
+
+ protected:
+  const Device EIGEN_DEVICE_REF m_device;
+  const std::remove_reference_t<Broadcast> m_broadcast;
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims> m_inputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
new file mode 100644
index 0000000..977f96f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
@@ -0,0 +1,469 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <DenseIndex DimId, typename XprType>
+struct traits<TensorChippingOp<DimId, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions - 1;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <DenseIndex DimId, typename XprType>
+struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense> {
+  typedef const TensorChippingOp<DimId, XprType> EIGEN_DEVICE_REF type;
+};
+
+template <DenseIndex DimId, typename XprType>
+struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type> {
+  typedef TensorChippingOp<DimId, XprType> type;
+};
+
+template <DenseIndex DimId>
+struct DimensionId {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DimensionId(DenseIndex dim) {
+    EIGEN_UNUSED_VARIABLE(dim);
+    eigen_assert(dim == DimId);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const { return DimId; }
+};
+template <>
+struct DimensionId<Dynamic> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DimensionId(DenseIndex dim) : actual_dim(dim) { eigen_assert(dim >= 0); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const { return actual_dim; }
+
+ private:
+  const DenseIndex actual_dim;
+};
+
+}  // end namespace internal
+
+/** A chip is a thin slice, corresponding to a column or a row in a 2-d tensor.
+ * \ingroup CXX11_Tensor_Module
+ */
+template <DenseIndex DimId, typename XprType>
+class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> > {
+ public:
+  typedef TensorBase<TensorChippingOp<DimId, XprType> > Base;
+  typedef typename Eigen::internal::traits<TensorChippingOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorChippingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorChippingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorChippingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset, const Index dim)
+      : m_xpr(expr), m_offset(offset), m_dim(dim) {
+    eigen_assert(dim < XprType::NumDimensions && dim >= 0 && "Chip_Dim_out_of_range");
+  }
+
+  EIGEN_DEVICE_FUNC const Index offset() const { return m_offset; }
+  EIGEN_DEVICE_FUNC const Index dim() const { return m_dim.actualDim(); }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorChippingOp)
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const Index m_offset;
+  const internal::DimensionId<DimId> m_dim;
+};
+
+// Eval as rvalue
+template <DenseIndex DimId, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> {
+  typedef TensorChippingOp<DimId, ArgType> XprType;
+  static constexpr int NumInputDims =
+      internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static constexpr int NumDims = NumInputDims - 1;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+
+  enum {
+    // Alignment can't be guaranteed at compile time since it depends on the
+    // slice offsets.
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
+    // Chipping of outer-most dimension is a trivial operation, because we can
+    // read and write directly from the underlying tensor using single offset.
+    IsOuterChipping = (Layout == ColMajor && DimId == NumInputDims - 1) || (Layout == RowMajor && DimId == 0),
+    // Chipping inner-most dimension.
+    IsInnerChipping = (Layout == ColMajor && DimId == 0) || (Layout == RowMajor && DimId == NumInputDims - 1),
+    // Prefer block access if the underlying expression prefers it, otherwise
+    // only if chipping is not trivial.
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess || !IsOuterChipping,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef internal::TensorBlockDescriptor<NumInputDims, Index> ArgTensorBlockDesc;
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock ArgTensorBlock;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, Layout, Index> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_dim(op.dim()), m_device(device) {
+    EIGEN_STATIC_ASSERT((NumInputDims >= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    eigen_assert(NumInputDims > m_dim.actualDim());
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    eigen_assert(op.offset() < input_dims[m_dim.actualDim()]);
+
+    int j = 0;
+    for (int i = 0; i < NumInputDims; ++i) {
+      if (i != m_dim.actualDim()) {
+        m_dimensions[j] = input_dims[i];
+        ++j;
+      }
+    }
+
+    m_stride = 1;
+    m_inputStride = 1;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < m_dim.actualDim(); ++i) {
+        m_stride *= input_dims[i];
+        m_inputStride *= input_dims[i];
+      }
+    } else {
+      for (int i = NumInputDims - 1; i > m_dim.actualDim(); --i) {
+        m_stride *= input_dims[i];
+        m_inputStride *= input_dims[i];
+      }
+    }
+    m_inputStride *= input_dims[m_dim.actualDim()];
+    m_inputOffset = m_stride * op.offset();
+
+    // Check if chipping is effectively inner or outer: products of dimensions
+    // before or after the chipped dimension is `1`.
+    Index after_chipped_dim_product = 1;
+    for (int i = static_cast<int>(m_dim.actualDim()) + 1; i < NumInputDims; ++i) {
+      after_chipped_dim_product *= input_dims[i];
+    }
+
+    Index before_chipped_dim_product = 1;
+    for (int i = 0; i < m_dim.actualDim(); ++i) {
+      before_chipped_dim_product *= input_dims[i];
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_isEffectivelyInnerChipping = before_chipped_dim_product == 1;
+      m_isEffectivelyOuterChipping = after_chipped_dim_product == 1;
+    } else {
+      m_isEffectivelyInnerChipping = after_chipped_dim_product == 1;
+      m_isEffectivelyOuterChipping = before_chipped_dim_product == 1;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType /*data*/, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_impl.coeff(srcCoeff(index));
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    if (isInnerChipping()) {
+      // m_stride is equal to 1, so let's avoid the integer division.
+      eigen_assert(m_stride == 1);
+      Index inputIndex = index * m_inputStride + m_inputOffset;
+      EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < PacketSize; ++i) {
+        values[i] = m_impl.coeff(inputIndex);
+        inputIndex += m_inputStride;
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    } else if (isOuterChipping()) {
+      // m_stride is always greater than index, so let's avoid the integer division.
+      eigen_assert(m_stride > index);
+      return m_impl.template packet<LoadMode>(index + m_inputOffset);
+    } else {
+      const Index idx = index / m_stride;
+      const Index rem = index - idx * m_stride;
+      if (rem + PacketSize <= m_stride) {
+        Index inputIndex = idx * m_inputStride + m_inputOffset + rem;
+        return m_impl.template packet<LoadMode>(inputIndex);
+      } else {
+        // Cross the stride boundary. Fallback to slow path.
+        EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+        EIGEN_UNROLL_LOOP
+        for (int i = 0; i < PacketSize; ++i) {
+          values[i] = coeff(index);
+          ++index;
+        }
+        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+        return rslt;
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    double cost = 0;
+    if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == 0) ||
+        (static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == NumInputDims - 1)) {
+      cost += TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
+    } else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumInputDims - 1) ||
+               (static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) {
+      cost += TensorOpCost::AddCost<Index>();
+    } else {
+      cost += 3 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>() + 3 * TensorOpCost::AddCost<Index>();
+    }
+
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.lastLevelCacheSize();
+    return internal::TensorBlockResourceRequirements::merge(
+        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size), m_impl.getResourceRequirements());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool root_of_expr_ast = false) const {
+    const Index chip_dim = m_dim.actualDim();
+
+    DSizes<Index, NumInputDims> input_block_dims;
+    for (int i = 0; i < NumInputDims; ++i) {
+      input_block_dims[i] = i < chip_dim ? desc.dimension(i) : i > chip_dim ? desc.dimension(i - 1) : 1;
+    }
+
+    ArgTensorBlockDesc arg_desc(srcCoeff(desc.offset()), input_block_dims);
+
+    // Try to reuse destination buffer for materializing argument block.
+    if (desc.HasDestinationBuffer()) {
+      DSizes<Index, NumInputDims> arg_destination_strides;
+      for (int i = 0; i < NumInputDims; ++i) {
+        arg_destination_strides[i] = i < chip_dim   ? desc.destination().strides()[i]
+                                     : i > chip_dim ? desc.destination().strides()[i - 1]
+                                                    : 0;  // for dimensions of size `1` stride should never be used.
+      }
+
+      arg_desc.template AddDestinationBuffer<Layout>(desc.destination().template data<ScalarNoConst>(),
+                                                     arg_destination_strides);
+    }
+
+    ArgTensorBlock arg_block = m_impl.block(arg_desc, scratch, root_of_expr_ast);
+    if (!arg_desc.HasDestinationBuffer()) desc.DropDestinationBuffer();
+
+    if (arg_block.data() != NULL) {
+      // Forward argument block buffer if possible.
+      return TensorBlock(arg_block.kind(), arg_block.data(), desc.dimensions());
+
+    } else {
+      // Assign argument block expression to a buffer.
+
+      // Prepare storage for the materialized chipping result.
+      const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch);
+
+      typedef internal::TensorBlockAssignment<ScalarNoConst, NumInputDims, typename ArgTensorBlock::XprType, Index>
+          TensorBlockAssignment;
+
+      TensorBlockAssignment::Run(
+          TensorBlockAssignment::target(arg_desc.dimensions(), internal::strides<Layout>(arg_desc.dimensions()),
+                                        block_storage.data()),
+          arg_block.expr());
+
+      return block_storage.AsTensorMaterializedBlock();
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const {
+    typename Storage::Type result = constCast(m_impl.data());
+    if (isOuterChipping() && result) {
+      return result + m_inputOffset;
+    } else {
+      return NULL;
+    }
+  }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
+    Index inputIndex;
+    if (isInnerChipping()) {
+      // m_stride is equal to 1, so let's avoid the integer division.
+      eigen_assert(m_stride == 1);
+      inputIndex = index * m_inputStride + m_inputOffset;
+    } else if (isOuterChipping()) {
+      // m_stride is always greater than index, so let's avoid the integer
+      // division.
+      eigen_assert(m_stride > index);
+      inputIndex = index + m_inputOffset;
+    } else {
+      const Index idx = index / m_stride;
+      inputIndex = idx * m_inputStride + m_inputOffset;
+      index -= idx * m_stride;
+      inputIndex += index;
+    }
+    return inputIndex;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool isInnerChipping() const {
+    return IsInnerChipping || m_isEffectivelyInnerChipping;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool isOuterChipping() const {
+    return IsOuterChipping || m_isEffectivelyOuterChipping;
+  }
+
+  Dimensions m_dimensions;
+  Index m_stride;
+  Index m_inputOffset;
+  Index m_inputStride;
+  TensorEvaluator<ArgType, Device> m_impl;
+  const internal::DimensionId<DimId> m_dim;
+  const Device EIGEN_DEVICE_REF m_device;
+
+  // If product of all dimensions after or before the chipped dimension is `1`,
+  // it is effectively the same as chipping innermost or outermost dimension.
+  bool m_isEffectivelyInnerChipping;
+  bool m_isEffectivelyOuterChipping;
+};
+
+// Eval as lvalue
+template <DenseIndex DimId, typename ArgType, typename Device>
+struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
+    : public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> {
+  typedef TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> Base;
+  typedef TensorChippingOp<DimId, ArgType> XprType;
+  static constexpr int NumInputDims =
+      internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static constexpr int NumDims = NumInputDims - 1;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
+    return this->m_impl.coeffRef(this->srcCoeff(index));
+  }
+
+  template <int StoreMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
+    if (this->isInnerChipping()) {
+      // m_stride is equal to 1, so let's avoid the integer division.
+      eigen_assert(this->m_stride == 1);
+      EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+      internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+      Index inputIndex = index * this->m_inputStride + this->m_inputOffset;
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < PacketSize; ++i) {
+        this->m_impl.coeffRef(inputIndex) = values[i];
+        inputIndex += this->m_inputStride;
+      }
+    } else if (this->isOuterChipping()) {
+      // m_stride is always greater than index, so let's avoid the integer division.
+      eigen_assert(this->m_stride > index);
+      this->m_impl.template writePacket<StoreMode>(index + this->m_inputOffset, x);
+    } else {
+      const Index idx = index / this->m_stride;
+      const Index rem = index - idx * this->m_stride;
+      if (rem + PacketSize <= this->m_stride) {
+        const Index inputIndex = idx * this->m_inputStride + this->m_inputOffset + rem;
+        this->m_impl.template writePacket<StoreMode>(inputIndex, x);
+      } else {
+        // Cross stride boundary. Fallback to slow path.
+        EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+        internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+        EIGEN_UNROLL_LOOP
+        for (int i = 0; i < PacketSize; ++i) {
+          this->coeffRef(index) = values[i];
+          ++index;
+        }
+      }
+    }
+  }
+
+  template <typename TensorBlock>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(const TensorBlockDesc& desc, const TensorBlock& block) {
+    eigen_assert(this->m_impl.data() != NULL);
+
+    const Index chip_dim = this->m_dim.actualDim();
+
+    DSizes<Index, NumInputDims> input_block_dims;
+    for (int i = 0; i < NumInputDims; ++i) {
+      input_block_dims[i] = i < chip_dim ? desc.dimension(i) : i > chip_dim ? desc.dimension(i - 1) : 1;
+    }
+
+    typedef TensorReshapingOp<const DSizes<Index, NumInputDims>, const typename TensorBlock::XprType> TensorBlockExpr;
+
+    typedef internal::TensorBlockAssignment<Scalar, NumInputDims, TensorBlockExpr, Index> TensorBlockAssign;
+
+    TensorBlockAssign::Run(
+        TensorBlockAssign::target(input_block_dims, internal::strides<Layout>(this->m_impl.dimensions()),
+                                  this->m_impl.data(), this->srcCoeff(desc.offset())),
+        block.expr().reshape(input_block_dims));
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
new file mode 100644
index 0000000..29607a7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
@@ -0,0 +1,351 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Axis, typename LhsXprType, typename RhsXprType>
+struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> > {
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef typename promote_storage_type<typename LhsXprType::Scalar, typename RhsXprType::Scalar>::ret Scalar;
+  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
+                                        typename traits<RhsXprType>::StorageKind>::ret StorageKind;
+  typedef
+      typename promote_index_type<typename traits<LhsXprType>::Index, typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef std::remove_reference_t<LhsNested> LhsNested_;
+  typedef std::remove_reference_t<RhsNested> RhsNested_;
+  static constexpr int NumDimensions = traits<LhsXprType>::NumDimensions;
+  static constexpr int Layout = traits<LhsXprType>::Layout;
+  enum { Flags = 0 };
+  typedef std::conditional_t<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
+                             typename traits<LhsXprType>::PointerType, typename traits<RhsXprType>::PointerType>
+      PointerType;
+};
+
+template <typename Axis, typename LhsXprType, typename RhsXprType>
+struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense> {
+  typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type;
+};
+
+template <typename Axis, typename LhsXprType, typename RhsXprType>
+struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1,
+              typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type> {
+  typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor concatenation class.
+ */
+template <typename Axis, typename LhsXprType, typename RhsXprType>
+class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors> {
+ public:
+  typedef TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors> Base;
+  typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar;
+  typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind;
+  typedef typename internal::traits<TensorConcatenationOp>::Index Index;
+  typedef typename internal::nested<TensorConcatenationOp>::type Nested;
+  typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
+                                                  typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis)
+      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {}
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename LhsXprType::Nested>& lhsExpression() const {
+    return m_lhs_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename RhsXprType::Nested>& rhsExpression() const {
+    return m_rhs_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC const Axis& axis() const { return m_axis; }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorConcatenationOp)
+ protected:
+  typename LhsXprType::Nested m_lhs_xpr;
+  typename RhsXprType::Nested m_rhs_xpr;
+  const Axis m_axis;
+};
+
+// Eval as rvalue
+template <typename Axis, typename LeftArgType, typename RightArgType, typename Device>
+struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> {
+  typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
+  static constexpr int RightNumDims =
+      internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess =
+        TensorEvaluator<LeftArgType, Device>::PacketAccess && TensorEvaluator<RightArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess ||
+                        TensorEvaluator<RightArgType, Device>::PreferBlockAccess,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis()) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
+                             static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) ||
+                         NumDims == 1),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT((NumDims == RightNumDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    eigen_assert(0 <= m_axis && m_axis < NumDims);
+    const Dimensions& lhs_dims = m_leftImpl.dimensions();
+    const Dimensions& rhs_dims = m_rightImpl.dimensions();
+    {
+      int i = 0;
+      for (; i < m_axis; ++i) {
+        eigen_assert(lhs_dims[i] > 0);
+        eigen_assert(lhs_dims[i] == rhs_dims[i]);
+        m_dimensions[i] = lhs_dims[i];
+      }
+      eigen_assert(lhs_dims[i] > 0);  // Now i == m_axis.
+      eigen_assert(rhs_dims[i] > 0);
+      m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
+      for (++i; i < NumDims; ++i) {
+        eigen_assert(lhs_dims[i] > 0);
+        eigen_assert(lhs_dims[i] == rhs_dims[i]);
+        m_dimensions[i] = lhs_dims[i];
+      }
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_leftStrides[0] = 1;
+      m_rightStrides[0] = 1;
+      m_outputStrides[0] = 1;
+
+      for (int j = 1; j < NumDims; ++j) {
+        m_leftStrides[j] = m_leftStrides[j - 1] * lhs_dims[j - 1];
+        m_rightStrides[j] = m_rightStrides[j - 1] * rhs_dims[j - 1];
+        m_outputStrides[j] = m_outputStrides[j - 1] * m_dimensions[j - 1];
+      }
+    } else {
+      m_leftStrides[NumDims - 1] = 1;
+      m_rightStrides[NumDims - 1] = 1;
+      m_outputStrides[NumDims - 1] = 1;
+
+      for (int j = NumDims - 2; j >= 0; --j) {
+        m_leftStrides[j] = m_leftStrides[j + 1] * lhs_dims[j + 1];
+        m_rightStrides[j] = m_rightStrides[j + 1] * rhs_dims[j + 1];
+        m_outputStrides[j] = m_outputStrides[j + 1] * m_dimensions[j + 1];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  // TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear.
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_leftImpl.evalSubExprsIfNeeded(NULL);
+    m_rightImpl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_leftImpl.cleanup();
+    m_rightImpl.cleanup();
+  }
+
+  // TODO(phli): Attempt to speed this up. The integer divisions and modulo are slow.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    // Collect dimension-wise indices (subs).
+    array<Index, NumDims> subs;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        subs[i] = index / m_outputStrides[i];
+        index -= subs[i] * m_outputStrides[i];
+      }
+      subs[0] = index;
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        subs[i] = index / m_outputStrides[i];
+        index -= subs[i] * m_outputStrides[i];
+      }
+      subs[NumDims - 1] = index;
+    }
+
+    const Dimensions& left_dims = m_leftImpl.dimensions();
+    if (subs[m_axis] < left_dims[m_axis]) {
+      Index left_index;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        left_index = subs[0];
+        EIGEN_UNROLL_LOOP
+        for (int i = 1; i < NumDims; ++i) {
+          left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
+        }
+      } else {
+        left_index = subs[NumDims - 1];
+        EIGEN_UNROLL_LOOP
+        for (int i = NumDims - 2; i >= 0; --i) {
+          left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
+        }
+      }
+      return m_leftImpl.coeff(left_index);
+    } else {
+      subs[m_axis] -= left_dims[m_axis];
+      const Dimensions& right_dims = m_rightImpl.dimensions();
+      Index right_index;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        right_index = subs[0];
+        EIGEN_UNROLL_LOOP
+        for (int i = 1; i < NumDims; ++i) {
+          right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
+        }
+      } else {
+        right_index = subs[NumDims - 1];
+        EIGEN_UNROLL_LOOP
+        for (int i = NumDims - 2; i >= 0; --i) {
+          right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
+        }
+      }
+      return m_rightImpl.coeff(right_index);
+    }
+  }
+
+  // TODO(phli): Add a real vectorization.
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    const int packetSize = PacketType<CoeffReturnType, Device>::size;
+    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < packetSize; ++i) {
+      values[i] = coeff(index + i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() +
+                                           TensorOpCost::DivCost<Index>() + TensorOpCost::ModCost<Index>());
+    const double lhs_size = m_leftImpl.dimensions().TotalSize();
+    const double rhs_size = m_rightImpl.dimensions().TotalSize();
+    return (lhs_size / (lhs_size + rhs_size)) * m_leftImpl.costPerCoeff(vectorized) +
+           (rhs_size / (lhs_size + rhs_size)) * m_rightImpl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ protected:
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims> m_leftStrides;
+  array<Index, NumDims> m_rightStrides;
+  TensorEvaluator<LeftArgType, Device> m_leftImpl;
+  TensorEvaluator<RightArgType, Device> m_rightImpl;
+  const Axis m_axis;
+};
+
+// Eval as lvalue
+template <typename Axis, typename LeftArgType, typename RightArgType, typename Device>
+struct TensorEvaluator<TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
+    : public TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> {
+  typedef TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> Base;
+  typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
+  typedef typename Base::Dimensions Dimensions;
+  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess =
+        TensorEvaluator<LeftArgType, Device>::PacketAccess && TensorEvaluator<RightArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess ||
+                        TensorEvaluator<RightArgType, Device>::PreferBlockAccess,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(XprType& op, const Device& device) : Base(op, device) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
+    // Collect dimension-wise indices (subs).
+    array<Index, Base::NumDims> subs;
+    for (int i = Base::NumDims - 1; i > 0; --i) {
+      subs[i] = index / this->m_outputStrides[i];
+      index -= subs[i] * this->m_outputStrides[i];
+    }
+    subs[0] = index;
+
+    const Dimensions& left_dims = this->m_leftImpl.dimensions();
+    if (subs[this->m_axis] < left_dims[this->m_axis]) {
+      Index left_index = subs[0];
+      for (int i = 1; i < Base::NumDims; ++i) {
+        left_index += (subs[i] % left_dims[i]) * this->m_leftStrides[i];
+      }
+      return this->m_leftImpl.coeffRef(left_index);
+    } else {
+      subs[this->m_axis] -= left_dims[this->m_axis];
+      const Dimensions& right_dims = this->m_rightImpl.dimensions();
+      Index right_index = subs[0];
+      for (int i = 1; i < Base::NumDims; ++i) {
+        right_index += (subs[i] % right_dims[i]) * this->m_rightStrides[i];
+      }
+      return this->m_rightImpl.coeffRef(right_index);
+    }
+  }
+
+  template <int StoreMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
+    const int packetSize = PacketType<CoeffReturnType, Device>::size;
+    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index + packetSize - 1 < this->dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
+    internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+    for (int i = 0; i < packetSize; ++i) {
+      coeffRef(index + i) = values[i];
+    }
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
new file mode 100644
index 0000000..c58a1c6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
@@ -0,0 +1,958 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
+struct traits<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>> {
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef typename gebp_traits<std::remove_const_t<typename LhsXprType::Scalar>,
+                               std::remove_const_t<typename RhsXprType::Scalar>>::ResScalar Scalar;
+
+  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
+                                        typename traits<RhsXprType>::StorageKind>::ret StorageKind;
+  typedef
+      typename promote_index_type<typename traits<LhsXprType>::Index, typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef std::remove_reference_t<LhsNested> LhsNested_;
+  typedef std::remove_reference_t<RhsNested> RhsNested_;
+
+  // From NumDims below.
+  static constexpr int NumDimensions =
+      traits<LhsXprType>::NumDimensions + traits<RhsXprType>::NumDimensions - 2 * array_size<Dimensions>::value;
+  static constexpr int Layout = traits<LhsXprType>::Layout;
+  typedef std::conditional_t<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
+                             typename traits<LhsXprType>::PointerType, typename traits<RhsXprType>::PointerType>
+      PointerType;
+
+  enum { Flags = 0 };
+};
+
+template <typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
+struct eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>, Eigen::Dense> {
+  typedef const TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>& type;
+};
+
+template <typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
+struct nested<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>, 1,
+              typename eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>>::type> {
+  typedef TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> type;
+};
+
+template <typename Indices_, typename LeftArgType_, typename RightArgType_, typename OutputKernelType_,
+          typename Device_>
+struct traits<
+    TensorEvaluator<const TensorContractionOp<Indices_, LeftArgType_, RightArgType_, OutputKernelType_>, Device_>> {
+  typedef Indices_ Indices;
+  typedef LeftArgType_ LeftArgType;
+  typedef RightArgType_ RightArgType;
+  typedef OutputKernelType_ OutputKernelType;
+  typedef Device_ Device;
+
+  // From NumDims below.
+  static constexpr int NumDimensions =
+      traits<LeftArgType_>::NumDimensions + traits<RightArgType_>::NumDimensions - 2 * array_size<Indices_>::value;
+};
+
+// Helper class to allocate and deallocate temporary memory for packed buffers.
+template <typename LhsScalar, typename RhsScalar>
+struct TensorContractionBlockMemAllocator {
+  typedef void* BlockMemHandle;
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC static BlockMemHandle allocate(Device& d, const Index bm, const Index bk, const Index bn,
+                                                   LhsScalar** lhs_block, RhsScalar** rhs_block) {
+    eigen_assert(lhs_block);
+    eigen_assert(rhs_block);
+    BlockSizes sz = ComputeLhsRhsBlockSizes(bm, bk, bn);
+    char* block_mem = static_cast<char*>(d.allocate(sz.lhs_size + sz.rhs_size));
+    *lhs_block = static_cast<LhsScalar*>(static_cast<void*>(block_mem));
+    *rhs_block = static_cast<RhsScalar*>(static_cast<void*>(block_mem + sz.lhs_size));
+    return block_mem;
+  }
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC static BlockMemHandle allocateSlices(Device& d, const Index bm, const Index bk, const Index bn,
+                                                         const Index num_lhs, const Index num_rhs,
+                                                         const Index num_slices, std::vector<LhsScalar*>* lhs_blocks,
+                                                         std::vector<RhsScalar*>* rhs_blocks) {
+    eigen_assert(num_slices > 0);
+    eigen_assert(num_lhs >= 0 && num_rhs >= 0);
+    eigen_assert(num_lhs == 0 || lhs_blocks);
+    eigen_assert(num_rhs == 0 || rhs_blocks);
+    BlockSizes sz = ComputeLhsRhsBlockSizes(bm, bk, bn);
+    void* block_mem = d.allocate((num_lhs * sz.lhs_size + num_rhs * sz.rhs_size) * num_slices);
+    eigen_assert(block_mem);
+    char* mem = static_cast<char*>(block_mem);
+
+    for (Index x = 0; x < num_slices; x++) {
+      if (num_lhs > 0) lhs_blocks[x].resize(num_lhs);
+      for (Index m = 0; m < num_lhs; m++) {
+        lhs_blocks[x][m] = static_cast<LhsScalar*>(static_cast<void*>(mem));
+        mem += sz.lhs_size;
+      }
+      if (num_rhs > 0) rhs_blocks[x].resize(num_rhs);
+      for (Index n = 0; n < num_rhs; n++) {
+        rhs_blocks[x][n] = static_cast<RhsScalar*>(static_cast<void*>(mem));
+        mem += sz.rhs_size;
+      }
+    }
+
+    return block_mem;
+  }
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC static void deallocate(Device& d, BlockMemHandle handle) {
+    d.deallocate(handle);
+  }
+
+ private:
+  struct BlockSizes {
+    Index lhs_size;
+    Index rhs_size;
+  };
+  EIGEN_DEVICE_FUNC static BlockSizes ComputeLhsRhsBlockSizes(const Index bm, const Index bk, const Index bn) {
+    Index align = numext::maxi(EIGEN_MAX_ALIGN_BYTES, 1);
+    BlockSizes sz;
+    sz.lhs_size = numext::div_ceil<Index>(bm * bk * sizeof(LhsScalar), align) * align;
+    sz.rhs_size = numext::div_ceil<Index>(bn * bk * sizeof(RhsScalar), align) * align;
+    return sz;
+  }
+};
+
+// WARNING: In this code we assume that Lhs and Rhs tensor expressions are in
+// ColMajor storage order. This property is guaranteed by the
+// TensorContractionOp evaluator. TensorContractionKernel specifies how we pack
+// blocks of Lhs and Rhs tensor expressions, and how we invoke matrix
+// multiplication for these blocks. Default tensor contraction uses
+// gemm_pack_rhs, gemm_pack_lhs and gebp_kernel from Eigen Core (see
+// GeneralBlockPanelKernel.h for details).
+//
+// By specializing contraction kernels we can use other low level libraries to
+// perform matrix multiplication, and still rely on Eigen contraction evaluator.
+// This also includes full support in TensorContractionThreadPool, assuming that
+// underlying gemm does not use its own threading.
+//
+// - ResScalar/LhsScalar/RhsScalar - scalar type for the result of
+//   multiplication, lhs tensor and rhs tensor respectively.
+//
+// - StorageIndex - index type for the tensor expressions. In practice almost
+//   always is Eigen::Index.
+//
+// - OutputMapper provides access to the memory of the output matrix. In
+//   practice it's always column major blas_data_mapper (it must be of ResScalar
+//   type).
+//
+// - LhsMapper/RhsMapper similarly to blas_data_mapper provide a two dimensional
+//   view into the Lhs/Rhs tensor expressions. In practice it's
+//   TensorContractionInputMapper, or some specialization of it based on the
+//   type of tensor expression (e.g. TensorImagePatchOp has optimized input
+//   mapper).
+template <typename ResScalar, typename LhsScalar, typename RhsScalar, typename StorageIndex, typename OutputMapper,
+          typename LhsMapper, typename RhsMapper>
+struct TensorContractionKernel {
+  // True if `invoke()` supports `beta` in `C <- alpha * A * B + beta * C`
+  // (otherwise beta should be always equal to 1).
+  enum { HasBeta = false };
+
+  EIGEN_DEVICE_FUNC TensorContractionKernel(StorageIndex m_, StorageIndex k_, StorageIndex n_, StorageIndex bm_,
+                                            StorageIndex bk_, StorageIndex bn_)
+      : m(m_), k(k_), n(n_), bm(bm_), bk(bk_), bn(bn_) {}
+
+  // Pack blocks of Lhs and Rhs into contiguous blocks in memory.
+  typedef LhsScalar* LhsBlock;
+  typedef RhsScalar* RhsBlock;
+
+  // Packed Lhs/Rhs block memory allocator.
+  typedef TensorContractionBlockMemAllocator<LhsScalar, RhsScalar> BlockMemAllocator;
+  typedef typename BlockMemAllocator::BlockMemHandle BlockMemHandle;
+
+  typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
+
+  typedef internal::gemm_pack_lhs<LhsScalar, StorageIndex, typename LhsMapper::SubMapper, Traits::mr,
+                                  Traits::LhsProgress, typename Traits::LhsPacket4Packing, ColMajor>
+      LhsPacker;
+
+  typedef internal::gemm_pack_rhs<RhsScalar, StorageIndex, typename RhsMapper::SubMapper, Traits::nr, ColMajor>
+      RhsPacker;
+
+  typedef internal::gebp_kernel<LhsScalar, RhsScalar, StorageIndex, OutputMapper, Traits::mr, Traits::nr,
+                                /*ConjugateLhs*/ false, /*ConjugateRhs*/ false>
+      GebpKernel;
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC BlockMemHandle allocate(Device& d, LhsBlock* lhs_block, RhsBlock* rhs_block) {
+    return BlockMemAllocator::allocate(d, bm, bk, bn, lhs_block, rhs_block);
+  }
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC BlockMemHandle allocateSlices(Device& d, const StorageIndex num_lhs, const StorageIndex num_rhs,
+                                                  const StorageIndex num_slices, std::vector<LhsBlock>* lhs_blocks,
+                                                  std::vector<RhsBlock>* rhs_blocks) {
+    return BlockMemAllocator::allocateSlices(d, bm, bk, bn, num_lhs, num_rhs, num_slices, lhs_blocks, rhs_blocks);
+  }
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC static void deallocate(Device& d, BlockMemHandle handle) {
+    BlockMemAllocator::deallocate(d, handle);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void packLhs(LhsBlock* lhsBlock, const typename LhsMapper::SubMapper& data_mapper,
+                                                   const StorageIndex depth, const StorageIndex rows) {
+    LhsPacker()(*lhsBlock, data_mapper, depth, rows, /*stride*/ 0,
+                /*offset*/ 0);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void packRhs(RhsBlock* rhsBlock, const typename RhsMapper::SubMapper& data_mapper,
+                                                   const StorageIndex depth, const StorageIndex cols) {
+    RhsPacker()(*rhsBlock, data_mapper, depth, cols);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void invoke(const OutputMapper& output_mapper, const LhsBlock& lhsBlock,
+                                                  const RhsBlock& rhsBlock, const StorageIndex rows,
+                                                  const StorageIndex depth, const StorageIndex cols,
+                                                  const ResScalar alpha, const ResScalar beta) {
+    // Default GEBP kernel does not support beta.
+    eigen_assert(beta == ResScalar(1));
+    static const int kComputeStrideFromBlockDimensions = -1;
+    GebpKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha,
+                 /*strideA*/ kComputeStrideFromBlockDimensions,
+                 /*strideB*/ kComputeStrideFromBlockDimensions,
+                 /*offsetA*/ 0, /*offsetB*/ 0);
+  }
+
+ private:
+  // These are dimensions of the original Tensors, and selected block sizes. The
+  // actual block sizes passed to all function above might be smaller because of
+  // the partial blocks at the end.
+  const StorageIndex m;
+  const StorageIndex k;
+  const StorageIndex n;
+  const StorageIndex bm;
+  const StorageIndex bk;
+  const StorageIndex bn;
+};
+
+}  // end namespace internal
+
+// Tensor contraction params that should enable to get from output matrix
+// 2-dimensional coordinates to the output tensor dimensions.
+struct TensorContractionParams {
+  // TensorContraction evaluator assumes that both tensors are in ColMajor
+  // layout, if tensors are in RowMajor evaluator swap lhs with rhs.
+  bool swapped_arguments;
+};
+
+// Output kernel allows to fuse operations into the tensor contraction.
+//
+// Examples:
+//   1. Elementwise Relu transformation following Conv2D.
+//   2. AddBias to the Conv2D output channels dimension.
+//
+// The NoOpOutputKernel implements an output kernel that does absolutely nothing.
+struct NoOpOutputKernel {
+  /**
+   * Tensor contraction evaluator calls this kernel after finishing each block
+   * of output matrix. Output blocks belong to the 2-dimensional output tensor.
+   *
+   * TensorContractionParams contains contraction dimensions information
+   * required to map output 2-d space into the expected output tensor space
+   * (potentially higher dimensional).
+   *
+   * \param[in] output_mapper Access to output tensor memory
+   * \param[in] params   Tensor contraction parameters
+   * \param[in] i        Index of a first row available through output_mapper
+   * \param[in] j        Index of a first column available through output_mapper
+   * \param[in] num_rows Number of available rows
+   * \param[in] num_cols Number of available columns
+   */
+  template <typename Index, typename Scalar>
+  EIGEN_ALWAYS_INLINE void operator()(const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,
+                                      const TensorContractionParams& params, Index i, Index j, Index num_rows,
+                                      Index num_cols) const {
+    EIGEN_UNUSED_VARIABLE(output_mapper);
+    EIGEN_UNUSED_VARIABLE(params);
+    EIGEN_UNUSED_VARIABLE(i);
+    EIGEN_UNUSED_VARIABLE(j);
+    EIGEN_UNUSED_VARIABLE(num_rows);
+    EIGEN_UNUSED_VARIABLE(num_cols);
+  }
+};
+
+/** Tensor contraction class.
+ * \ingroup CXX11_Tensor_Module
+ */
+template <typename Indices, typename LhsXprType, typename RhsXprType,
+          typename OutputKernelType = const NoOpOutputKernel>
+class TensorContractionOp
+    : public TensorBase<TensorContractionOp<Indices, LhsXprType, RhsXprType, OutputKernelType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorContractionOp>::Scalar Scalar;
+  typedef typename internal::gebp_traits<typename LhsXprType::CoeffReturnType,
+                                         typename RhsXprType::CoeffReturnType>::ResScalar CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorContractionOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorContractionOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorContractionOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionOp(const LhsXprType& lhs, const RhsXprType& rhs,
+                                                            const Indices& dims,
+                                                            const OutputKernelType& output_kernel = OutputKernelType())
+      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_indices(dims), m_output_kernel(output_kernel) {}
+
+  EIGEN_DEVICE_FUNC const Indices& indices() const { return m_indices; }
+
+  /** \returns the nested expressions */
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename LhsXprType::Nested>& lhsExpression() const {
+    return m_lhs_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename RhsXprType::Nested>& rhsExpression() const {
+    return m_rhs_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC const OutputKernelType& outputKernel() const { return m_output_kernel; }
+
+ protected:
+  typename LhsXprType::Nested m_lhs_xpr;
+  typename RhsXprType::Nested m_rhs_xpr;
+  const Indices m_indices;
+  const OutputKernelType m_output_kernel;
+};
+
+template <typename Derived>
+struct TensorContractionEvaluatorBase {
+  typedef typename internal::traits<Derived>::Indices Indices;
+  typedef typename internal::traits<Derived>::LeftArgType LeftArgType;
+  typedef typename internal::traits<Derived>::RightArgType RightArgType;
+  typedef typename internal::traits<Derived>::OutputKernelType OutputKernelType;
+  typedef typename internal::traits<Derived>::Device Device;
+
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
+  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
+  enum {
+    IsAligned = true,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = true
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  // Most of the code is assuming that both input tensors are ColMajor. If the
+  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
+  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
+  // will pretend B is LHS and A is RHS.
+  typedef std::conditional_t<static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>
+      EvalLeftArgType;
+  typedef std::conditional_t<static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>
+      EvalRightArgType;
+
+  typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluatorType;
+  typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluatorType;
+
+  static constexpr int LDims =
+      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
+  static constexpr int RDims =
+      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
+  static constexpr int ContractDims = internal::array_size<Indices>::value;
+  static constexpr int NumDims = LDims + RDims - 2 * ContractDims;
+
+  typedef array<Index, ContractDims> contract_t;
+  typedef array<Index, LDims - ContractDims> left_nocontract_t;
+  typedef array<Index, RDims - ContractDims> right_nocontract_t;
+
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  EIGEN_STRONG_INLINE TensorContractionEvaluatorBase(const XprType& op, const Device& device)
+      : m_leftImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(), op.lhsExpression(),
+                          op.rhsExpression()),
+                   device),
+        m_rightImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(), op.rhsExpression(),
+                           op.lhsExpression()),
+                    device),
+        m_device(device),
+        m_output_kernel(op.outputKernel()),
+        m_result(NULL) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
+                         static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    DSizes<Index, LDims> eval_left_dims;
+    DSizes<Index, RDims> eval_right_dims;
+    array<IndexPair<Index>, ContractDims> eval_op_indices;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      // For ColMajor, we keep using the existing dimensions
+      for (int i = 0; i < LDims; i++) {
+        eval_left_dims[i] = m_leftImpl.dimensions()[i];
+      }
+      for (int i = 0; i < RDims; i++) {
+        eval_right_dims[i] = m_rightImpl.dimensions()[i];
+      }
+      // We keep the pairs of contracting indices.
+      for (int i = 0; i < ContractDims; i++) {
+        eval_op_indices[i].first = op.indices()[i].first;
+        eval_op_indices[i].second = op.indices()[i].second;
+      }
+    } else {
+      // For RowMajor, we need to reverse the existing dimensions
+      for (int i = 0; i < LDims; i++) {
+        eval_left_dims[i] = m_leftImpl.dimensions()[LDims - i - 1];
+      }
+      for (int i = 0; i < RDims; i++) {
+        eval_right_dims[i] = m_rightImpl.dimensions()[RDims - i - 1];
+      }
+      // We need to flip all the pairs of contracting indices as well as
+      // reversing the dimensions.
+      for (int i = 0; i < ContractDims; i++) {
+        eval_op_indices[i].first = LDims - 1 - op.indices()[ContractDims - 1 - i].second;
+        eval_op_indices[i].second = RDims - 1 - op.indices()[ContractDims - 1 - i].first;
+      }
+    }
+
+    // Check for duplicate axes and make sure the first index in eval_op_indices
+    // is increasing. Using O(n^2) sorting is OK since ContractDims is small
+    for (int i = 0; i < ContractDims; i++) {
+      for (int j = i + 1; j < ContractDims; j++) {
+        eigen_assert(eval_op_indices[j].first != eval_op_indices[i].first &&
+                     eval_op_indices[j].second != eval_op_indices[i].second && "contraction axes should be unique");
+        if (eval_op_indices[j].first < eval_op_indices[i].first) {
+          numext::swap(eval_op_indices[j], eval_op_indices[i]);
+        }
+      }
+    }
+
+    array<Index, LDims> lhs_strides;
+    lhs_strides[0] = 1;
+    for (int i = 0; i < LDims - 1; ++i) {
+      lhs_strides[i + 1] = lhs_strides[i] * eval_left_dims[i];
+    }
+
+    array<Index, RDims> rhs_strides;
+    rhs_strides[0] = 1;
+    for (int i = 0; i < RDims - 1; ++i) {
+      rhs_strides[i + 1] = rhs_strides[i] * eval_right_dims[i];
+    }
+
+    if (m_i_strides.size() > 0) m_i_strides[0] = 1;
+    if (m_j_strides.size() > 0) m_j_strides[0] = 1;
+    if (m_k_strides.size() > 0) m_k_strides[0] = 1;
+
+    m_i_size = 1;
+    m_j_size = 1;
+    m_k_size = 1;
+
+    // To compute the dimension, we simply concatenate the non-contracting
+    // dimensions of the left and then the right tensor. Additionally, we also
+    // compute the strides corresponding to the left non-contracting
+    // dimensions and right non-contracting dimensions.
+    m_lhs_inner_dim_contiguous = true;
+    int dim_idx = 0;
+    Index nocontract_idx = 0;
+
+    for (int i = 0; i < LDims; i++) {
+      // find if we are contracting on index i of left tensor
+      bool contracting = false;
+      for (int j = 0; j < ContractDims; j++) {
+        if (eval_op_indices[j].first == i) {
+          contracting = true;
+          break;
+        }
+      }
+      if (!contracting) {
+        // add dimension size to output dimensions
+        m_dimensions[dim_idx] = eval_left_dims[i];
+        m_left_nocontract_strides[nocontract_idx] = lhs_strides[i];
+        if (dim_idx != i) {
+          m_lhs_inner_dim_contiguous = false;
+        }
+        if (nocontract_idx + 1 < internal::array_size<left_nocontract_t>::value) {
+          m_i_strides[nocontract_idx + 1] = m_i_strides[nocontract_idx] * eval_left_dims[i];
+        } else {
+          m_i_size = m_i_strides[nocontract_idx] * eval_left_dims[i];
+        }
+        dim_idx++;
+        nocontract_idx++;
+      }
+    }
+
+    nocontract_idx = 0;
+    for (int i = 0; i < RDims; i++) {
+      bool contracting = false;
+      // find if we are contracting on index i of right tensor
+      for (int j = 0; j < ContractDims; j++) {
+        if (eval_op_indices[j].second == i) {
+          contracting = true;
+          break;
+        }
+      }
+      if (!contracting) {
+        m_dimensions[dim_idx] = eval_right_dims[i];
+        if (nocontract_idx + 1 < internal::array_size<right_nocontract_t>::value) {
+          m_j_strides[nocontract_idx + 1] = m_j_strides[nocontract_idx] * eval_right_dims[i];
+        } else {
+          m_j_size = m_j_strides[nocontract_idx] * eval_right_dims[i];
+        }
+        m_right_nocontract_strides[nocontract_idx] = rhs_strides[i];
+        dim_idx++;
+        nocontract_idx++;
+      }
+    }
+
+    // Now compute the strides corresponding to the contracting dimensions. We
+    // assumed above that non-contracting axes are represented in the same order
+    // in the matrix as they are in the tensor. This is not the case for
+    // contracting axes. As the contracting axes must be of the same size in
+    // each tensor, we'll only look at the first tensor here.
+    m_rhs_inner_dim_contiguous = true;
+    m_rhs_inner_dim_reordered = false;
+    for (int i = 0; i < ContractDims; i++) {
+      Index left = eval_op_indices[i].first;
+      Index right = eval_op_indices[i].second;
+
+      Index size = eval_left_dims[left];
+      eigen_assert(size == eval_right_dims[right] && "Contraction axes must be same size");
+
+      if (i + 1 < static_cast<int>(internal::array_size<contract_t>::value)) {
+        m_k_strides[i + 1] = m_k_strides[i] * size;
+      } else {
+        m_k_size = m_k_strides[i] * size;
+      }
+      m_left_contracting_strides[i] = lhs_strides[left];
+      m_right_contracting_strides[i] = rhs_strides[right];
+
+      if (i > 0 && right < eval_op_indices[i - 1].second) {
+        m_rhs_inner_dim_reordered = true;
+      }
+      if (right != i) {
+        m_rhs_inner_dim_contiguous = false;
+      }
+    }
+
+    // If the layout is RowMajor, we need to reverse the m_dimensions
+    if (static_cast<int>(Layout) == static_cast<int>(RowMajor)) {
+      for (int i = 0, j = NumDims - 1; i < j; i++, j--) {
+        numext::swap(m_dimensions[i], m_dimensions[j]);
+      }
+    }
+
+    // A set of parameters that will allow output kernel to get from output
+    // tensor dimensions (i, j) into the original tensor dimensions.
+    // TODO(ezhulenev): Add parameters required to infer output tensor index for
+    // more complex contractions than 2x2 on internal dimension.
+    m_tensor_contraction_params.swapped_arguments = static_cast<int>(Layout) == RowMajor;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    m_leftImpl.evalSubExprsIfNeeded(NULL);
+    m_rightImpl.evalSubExprsIfNeeded(NULL);
+    if (data) {
+      evalTo(data);
+      return false;
+    } else {
+      m_result = static_cast<EvaluatorPointerType>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
+      evalTo(m_result);
+      return true;
+    }
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType dest, EvalSubExprsCallback done) {
+    m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done, dest](bool) {
+      m_rightImpl.evalSubExprsIfNeededAsync(nullptr, [this, done, dest](bool) {
+        if (dest) {
+          evalToAsync(dest, [done]() { done(false); });
+        } else {
+          m_result = static_cast<EvaluatorPointerType>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
+          evalToAsync(m_result, [done]() { done(true); });
+        }
+      });
+    });
+  }
+#endif  // EIGEN_USE_THREADS
+
+#ifndef TENSOR_CONTRACTION_DISPATCH
+#define TENSOR_CONTRACTION_DISPATCH(METHOD, ALIGNMENT, ARGS) \
+  if (this->m_lhs_inner_dim_contiguous) {                    \
+    if (this->m_rhs_inner_dim_contiguous) {                  \
+      if (this->m_rhs_inner_dim_reordered) {                 \
+        METHOD<true, true, true, ALIGNMENT> ARGS;            \
+      } else {                                               \
+        METHOD<true, true, false, ALIGNMENT> ARGS;           \
+      }                                                      \
+    } else {                                                 \
+      if (this->m_rhs_inner_dim_reordered) {                 \
+        METHOD<true, false, true, ALIGNMENT> ARGS;           \
+      } else {                                               \
+        METHOD<true, false, false, ALIGNMENT> ARGS;          \
+      }                                                      \
+    }                                                        \
+  } else {                                                   \
+    if (this->m_rhs_inner_dim_contiguous) {                  \
+      if (this->m_rhs_inner_dim_reordered) {                 \
+        METHOD<false, true, true, ALIGNMENT> ARGS;           \
+      } else {                                               \
+        METHOD<false, true, false, ALIGNMENT> ARGS;          \
+      }                                                      \
+    } else {                                                 \
+      if (this->m_rhs_inner_dim_reordered) {                 \
+        METHOD<false, false, true, ALIGNMENT> ARGS;          \
+      } else {                                               \
+        METHOD<false, false, false, ALIGNMENT> ARGS;         \
+      }                                                      \
+    }                                                        \
+  }
+#endif
+
+#ifndef TENSOR_CONTRACTION_ASYNC_DISPATCH
+#define TENSOR_CONTRACTION_ASYNC_DISPATCH(METHOD, DONE, ALIGNMENT, ARGS, FN) \
+  if (this->m_lhs_inner_dim_contiguous) {                                    \
+    if (this->m_rhs_inner_dim_contiguous) {                                  \
+      if (this->m_rhs_inner_dim_reordered) {                                 \
+        (new METHOD<DONE, true, true, true, ALIGNMENT> ARGS)->FN;            \
+      } else {                                                               \
+        (new METHOD<DONE, true, true, false, ALIGNMENT> ARGS)->FN;           \
+      }                                                                      \
+    } else {                                                                 \
+      if (this->m_rhs_inner_dim_reordered) {                                 \
+        (new METHOD<DONE, true, false, true, ALIGNMENT> ARGS)->FN;           \
+      } else {                                                               \
+        (new METHOD<DONE, true, false, false, ALIGNMENT> ARGS)->FN;          \
+      }                                                                      \
+    }                                                                        \
+  } else {                                                                   \
+    if (this->m_rhs_inner_dim_contiguous) {                                  \
+      if (this->m_rhs_inner_dim_reordered) {                                 \
+        (new METHOD<DONE, false, true, true, ALIGNMENT> ARGS)->FN;           \
+      } else {                                                               \
+        (new METHOD<DONE, false, true, false, ALIGNMENT> ARGS)->FN;          \
+      }                                                                      \
+    } else {                                                                 \
+      if (this->m_rhs_inner_dim_reordered) {                                 \
+        (new METHOD<DONE, false, false, true, ALIGNMENT> ARGS)->FN;          \
+      } else {                                                               \
+        (new METHOD<DONE, false, false, false, ALIGNMENT> ARGS)->FN;         \
+      }                                                                      \
+    }                                                                        \
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC void evalTo(Scalar* buffer) const {
+    static_cast<const Derived*>(this)->template evalProduct<Unaligned>(buffer);
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalToCallback>
+  void evalToAsync(Scalar* buffer, EvalToCallback done) const {
+    static_cast<const Derived*>(this)->template evalProductAsync<EvalToCallback, Unaligned>(buffer, std::move(done));
+  }
+#endif  // EIGEN_USE_THREADS
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+  void evalProductSequential(Scalar* buffer) const {
+    if (this->m_j_size == 1) {
+      this->template evalGemv<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(
+          buffer);
+    } else {
+      this->template evalGemm<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(
+          buffer);
+    }
+  }
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+#if !defined(EIGEN_HIPCC)
+  EIGEN_DEVICE_FUNC
+#endif
+      void
+      evalGemv(Scalar* buffer) const {
+    const Index rows = m_i_size;
+    const Index cols = m_k_size;
+
+    typedef std::remove_const_t<typename EvalLeftArgType::Scalar> LhsScalar;
+    typedef std::remove_const_t<typename EvalRightArgType::Scalar> RhsScalar;
+    typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
+    typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
+    const int lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size;
+    const int rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size;
+    const int lhs_alignment = LeftEvaluator::IsAligned ? Aligned : Unaligned;
+    const int rhs_alignment = RightEvaluator::IsAligned ? Aligned : Unaligned;
+    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs, LeftEvaluator, left_nocontract_t,
+                                                   contract_t, lhs_packet_size, lhs_inner_dim_contiguous, false,
+                                                   lhs_alignment>
+        LhsMapper;
+
+    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs, RightEvaluator, right_nocontract_t,
+                                                   contract_t, rhs_packet_size, rhs_inner_dim_contiguous,
+                                                   rhs_inner_dim_reordered, rhs_alignment>
+        RhsMapper;
+
+    LhsMapper lhs(m_leftImpl, m_left_nocontract_strides, m_i_strides, m_left_contracting_strides, m_k_strides);
+    RhsMapper rhs(m_rightImpl, m_right_nocontract_strides, m_j_strides, m_right_contracting_strides, m_k_strides);
+
+    const Scalar alpha(1);
+    const Index resIncr(1);
+
+    // zero out the result buffer (which must be of size at least rows * sizeof(Scalar)
+    m_device.fill(buffer, buffer + rows, Scalar(0));
+
+    internal::general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, false, RhsScalar, RhsMapper,
+                                            false>::run(rows, cols, lhs, rhs, buffer, resIncr, alpha);
+
+    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
+    m_output_kernel(OutputMapper(buffer, rows), m_tensor_contraction_params, static_cast<Index>(0),
+                    static_cast<Index>(0), rows, static_cast<Index>(1));
+  }
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+#if !defined(EIGEN_HIPCC)
+  EIGEN_DEVICE_FUNC
+#endif
+      void
+      evalGemm(Scalar* buffer) const {
+    // columns in left side, rows in right side
+    const Index k = this->m_k_size;
+    this->template evalGemmPartial<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered,
+                                   Alignment, true>(buffer, 0, k, 1);
+  }
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+  EIGEN_DEVICE_FUNC void evalGemmPartialWithoutOutputKernel(Scalar* buffer, Index k_start, Index k_end,
+                                                            int num_threads) const {
+    evalGemmPartial<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment,
+                    /*use_output_kernel*/ false>(buffer, k_start, k_end, num_threads);
+  }
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment,
+            bool use_output_kernel>
+  EIGEN_DEVICE_FUNC void evalGemmPartial(Scalar* buffer, Index k_start, Index k_end, int num_threads) const {
+    eigen_assert(k_end >= k_start && k_start >= 0 && k_end <= this->m_k_size);
+    // columns in slice on left side, rows on right side
+    const Index k_slice = k_end - k_start;
+
+    // rows in left side
+    const Index m = this->m_i_size;
+
+    // columns in right side
+    const Index n = this->m_j_size;
+
+    // define data mappers for Lhs and Rhs
+    typedef std::remove_const_t<typename EvalLeftArgType::Scalar> LhsScalar;
+    typedef std::remove_const_t<typename EvalRightArgType::Scalar> RhsScalar;
+
+    typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
+    typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
+
+    const int lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size;
+    const int rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size;
+
+    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs, LeftEvaluator, left_nocontract_t,
+                                                   contract_t, lhs_packet_size, lhs_inner_dim_contiguous, false,
+                                                   Unaligned>
+        LhsMapper;
+
+    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs, RightEvaluator, right_nocontract_t,
+                                                   contract_t, rhs_packet_size, rhs_inner_dim_contiguous,
+                                                   rhs_inner_dim_reordered, Unaligned>
+        RhsMapper;
+
+    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
+
+    typedef internal::TensorContractionKernel<Scalar, LhsScalar, RhsScalar, Index, OutputMapper, LhsMapper, RhsMapper>
+        TensorContractionKernel;
+
+    // initialize data mappers
+    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
+                  this->m_left_contracting_strides, this->m_k_strides);
+
+    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
+                  this->m_right_contracting_strides, this->m_k_strides);
+
+    OutputMapper output(buffer, m);
+
+    // Sizes of the blocks to load in cache. See the Goto paper for details.
+    internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index, internal::ShardByCol> blocking(
+        k_slice, m, n, num_threads);
+    const Index kc = blocking.kc();
+    const Index mc = numext::mini(m, blocking.mc());
+    const Index nc = numext::mini(n, blocking.nc());
+
+    typedef typename TensorContractionKernel::LhsBlock LhsBlock;
+    typedef typename TensorContractionKernel::RhsBlock RhsBlock;
+
+    LhsBlock blockA;
+    RhsBlock blockB;
+
+    TensorContractionKernel kernel(m, k_slice, n, mc, kc, nc);
+
+    typedef typename TensorContractionKernel::BlockMemHandle BlockMemHandle;
+    const BlockMemHandle packed_mem = kernel.allocate(this->m_device, &blockA, &blockB);
+
+    // If a contraction kernel does not support beta, explicitly initialize
+    // output buffer with zeroes.
+    if (!TensorContractionKernel::HasBeta) {
+      this->m_device.fill(buffer, buffer + m * n, Scalar(0));
+    }
+
+    for (Index i2 = 0; i2 < m; i2 += mc) {
+      const Index actual_mc = numext::mini(i2 + mc, m) - i2;
+      for (Index k2 = k_start; k2 < k_end; k2 += kc) {
+        // make sure we don't overshoot right edge of left matrix, then pack vertical panel
+        const Index actual_kc = numext::mini(k2 + kc, k_end) - k2;
+        kernel.packLhs(&blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+        // If kernel supports beta, there is no need to initialize output
+        // buffer with zeroes.
+        const Scalar alpha = Scalar(1);
+        const Scalar beta = (TensorContractionKernel::HasBeta && k2 == k_start) ? Scalar(0) : Scalar(1);
+
+        // series of horizontal blocks
+        for (Index j2 = 0; j2 < n; j2 += nc) {
+          // make sure we don't overshoot right edge of right matrix, then pack block
+          const Index actual_nc = numext::mini(j2 + nc, n) - j2;
+          kernel.packRhs(&blockB, rhs.getSubMapper(k2, j2), actual_kc, actual_nc);
+
+          // call gebp (matrix kernel)
+          // The parameters here are copied from Eigen's GEMM implementation
+          const OutputMapper output_mapper = output.getSubMapper(i2, j2);
+          kernel.invoke(output_mapper, blockA, blockB, actual_mc, actual_kc, actual_nc, alpha, beta);
+
+          // We are done with this [i2, j2] output block.
+          if (use_output_kernel && k2 + kc >= k_end) {
+            m_output_kernel(output_mapper, m_tensor_contraction_params, i2, j2, actual_mc, actual_nc);
+          }
+        }
+      }
+    }
+
+    kernel.deallocate(this->m_device, packed_mem);
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_leftImpl.cleanup();
+    m_rightImpl.cleanup();
+
+    if (m_result != NULL) {
+      m_device.deallocate(m_result);
+      m_result = NULL;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_result[index]; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return m_result; }
+
+ protected:
+  Dimensions m_dimensions;
+
+  contract_t m_k_strides;
+  contract_t m_left_contracting_strides;
+  contract_t m_right_contracting_strides;
+
+  bool m_lhs_inner_dim_contiguous;
+  bool m_rhs_inner_dim_contiguous;
+  bool m_rhs_inner_dim_reordered;
+
+  left_nocontract_t m_i_strides;
+  right_nocontract_t m_j_strides;
+  left_nocontract_t m_left_nocontract_strides;
+  right_nocontract_t m_right_nocontract_strides;
+
+  Index m_i_size;
+  Index m_j_size;
+  Index m_k_size;
+
+  TensorContractionParams m_tensor_contraction_params;
+
+  TensorEvaluator<EvalLeftArgType, Device> m_leftImpl;
+  TensorEvaluator<EvalRightArgType, Device> m_rightImpl;
+  const Device EIGEN_DEVICE_REF m_device;
+  OutputKernelType m_output_kernel;
+  EvaluatorPointerType m_result;
+};
+
+// evaluator for default device
+template <typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType, typename Device>
+struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device>
+    : public TensorContractionEvaluatorBase<
+          TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device>> {
+  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
+  typedef TensorContractionEvaluatorBase<Self> Base;
+
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
+  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+
+  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
+
+  // Most of the code is assuming that both input tensors are ColMajor. If the
+  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
+  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
+  // will pretend B is LHS and A is RHS.
+  typedef std::conditional_t<Layout == static_cast<int>(ColMajor), LeftArgType, RightArgType> EvalLeftArgType;
+  typedef std::conditional_t<Layout == static_cast<int>(ColMajor), RightArgType, LeftArgType> EvalRightArgType;
+
+  static constexpr int LDims =
+      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
+  static constexpr int RDims =
+      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
+  static constexpr int ContractDims = internal::array_size<Indices>::value;
+
+  typedef array<Index, ContractDims> contract_t;
+  typedef array<Index, LDims - ContractDims> left_nocontract_t;
+  typedef array<Index, RDims - ContractDims> right_nocontract_t;
+
+  static constexpr int NumDims = LDims + RDims - 2 * ContractDims;
+
+  // Could we use NumDimensions here?
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  template <int Alignment>
+  void evalProduct(Scalar* buffer) const {
+    TENSOR_CONTRACTION_DISPATCH(this->template evalProductSequential, Alignment, (buffer));
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h
new file mode 100644
index 0000000..7fbe30a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h
@@ -0,0 +1,69 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+enum { ShardByRow = 0, ShardByCol = 1 };
+
+// Default Blocking Strategy
+template <typename ResScalar, typename LhsScalar, typename RhsScalar, typename StorageIndex,
+          int ShardingType = ShardByCol>
+class TensorContractionBlocking {
+ public:
+  /*
+    adding EIGEN_DEVICE_FUNC unconditionally to 'TensorContractionBlocking' constructor in `TensorContractionBlocking.h`
+      requires adding EIGEN_DEVICE_FUNC to `computeProductBlockingSizes` in `GeneralBlockPanelKernel.h`
+      which in turn, requires adding EIGEN_DEVICE_FUNC to `evaluateProductBlockingSizesHeuristic` in
+    `GeneralBlockPanelKernel.h` which in turn, requires adding EIGEN_DEVICE_FUNC to `manage_caching_sizes` in
+    `GeneralBlockPanelKernel.h` (else HIPCC will error out)
+
+    However adding EIGEN_DEVICE_FUNC to `manage_caching_sizes` in `GeneralBlockPanelKernel.h`
+    results in NVCC erroring out with the following error
+
+    ../Eigen/src/Core/products/GeneralBlockPanelKernel.h(57): error #2901:
+       dynamic initialization is not supported for function-scope static variables within a __device__/__global__
+    function
+  */
+
+#if !defined(EIGEN_HIPCC)
+  EIGEN_DEVICE_FUNC
+#endif
+  TensorContractionBlocking(StorageIndex k, StorageIndex m, StorageIndex n, StorageIndex num_threads = 1)
+      : kc_(k), mc_(m), nc_(n) {
+    if (ShardingType == ShardByCol) {
+      computeProductBlockingSizes<LhsScalar, RhsScalar, 1>(kc_, mc_, nc_, num_threads);
+    } else {
+      computeProductBlockingSizes<LhsScalar, RhsScalar, 1>(kc_, nc_, mc_, num_threads);
+    }
+
+    const int rhs_packet_size = internal::packet_traits<RhsScalar>::size;
+    kc_ = (rhs_packet_size <= 8 || kc_ <= rhs_packet_size) ? kc_ : (kc_ / rhs_packet_size) * rhs_packet_size;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE StorageIndex kc() const { return kc_; }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE StorageIndex mc() const { return mc_; }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE StorageIndex nc() const { return nc_; }
+
+ private:
+  StorageIndex kc_;
+  StorageIndex mc_;
+  StorageIndex nc_;
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h
new file mode 100644
index 0000000..dbea8aa
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h
@@ -0,0 +1,7 @@
+
+#if defined(__clang__) || defined(__GNUC__)
+#warning \
+    "Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorContractionGpu.h file"
+#endif
+
+#include "TensorContractionGpu.h"
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h
new file mode 100644
index 0000000..62ad942
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h
@@ -0,0 +1,1387 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2015 Navdeep Jaitly <ndjaitly@google.com>
+// Copyright (C) 2014 Eric Martin <eric@ericmart.in>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_GPU_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_GPU_H
+
+#if defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Scalar, typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper,
+          bool needs_edge_check>
+__device__ EIGEN_STRONG_INLINE void EigenContractionKernelInternal(const LhsMapper lhs, const RhsMapper rhs,
+                                                                   const OutputMapper output, Scalar* lhs_shmem,
+                                                                   Scalar* rhs_shmem, const Index m_size,
+                                                                   const Index n_size, const Index k_size) {
+  const Index m_block_idx = blockIdx.x;
+  const Index n_block_idx = blockIdx.y;
+
+  const Index base_m = 64 * m_block_idx;
+  const Index base_n = 64 * n_block_idx;
+
+  // declare and initialize 64 registers for output 8x8 block
+
+  // prefetch registers
+  Scalar lhs_pf0;
+  Scalar lhs_pf1;
+  Scalar lhs_pf2;
+  Scalar lhs_pf3;
+  Scalar lhs_pf4;
+  Scalar lhs_pf5;
+  Scalar lhs_pf6;
+  Scalar lhs_pf7;
+
+  Scalar rhs_pf0;
+  Scalar rhs_pf1;
+  Scalar rhs_pf2;
+  Scalar rhs_pf3;
+  Scalar rhs_pf4;
+  Scalar rhs_pf5;
+  Scalar rhs_pf6;
+  Scalar rhs_pf7;
+
+  // shared memory is formatted
+  // (contract idx in block, nocontract idx in block, block idx)
+  // where block idx is column major. This transposition limits the number of
+  // bank conflicts when reading the LHS. The core idea is that since the contracting
+  // index is shared by both sides, then the contracting index should be in threadIdx.x.
+
+  // On the LHS, we pad each row inside of each block with an extra element. This makes
+  // each block 8 rows of 9 elements, which is 72 elements. This gives no bank conflicts
+  // on writes and very few 2-way conflicts on reads. There is an 8x8 grid of these blocks.
+
+  // On the RHS we just add 8 padding elements to the end of each block. This gives no bank
+  // conflicts on writes and also none on reads.
+
+  // storage indices
+  const Index lhs_store_idx_base = threadIdx.y * 72 + threadIdx.x * 9 + threadIdx.z;
+  const Index rhs_store_idx_base = threadIdx.y * 72 + threadIdx.z * 8 + threadIdx.x;
+
+  const Index lhs_store_idx_0 = lhs_store_idx_base + 576 * 0;
+  const Index lhs_store_idx_1 = lhs_store_idx_base + 576 * 1;
+  const Index lhs_store_idx_2 = lhs_store_idx_base + 576 * 2;
+  const Index lhs_store_idx_3 = lhs_store_idx_base + 576 * 3;
+  const Index lhs_store_idx_4 = lhs_store_idx_base + 576 * 4;
+  const Index lhs_store_idx_5 = lhs_store_idx_base + 576 * 5;
+  const Index lhs_store_idx_6 = lhs_store_idx_base + 576 * 6;
+  const Index lhs_store_idx_7 = lhs_store_idx_base + 576 * 7;
+
+  const Index rhs_store_idx_0 = rhs_store_idx_base + 576 * 0;
+  const Index rhs_store_idx_1 = rhs_store_idx_base + 576 * 1;
+  const Index rhs_store_idx_2 = rhs_store_idx_base + 576 * 2;
+  const Index rhs_store_idx_3 = rhs_store_idx_base + 576 * 3;
+  const Index rhs_store_idx_4 = rhs_store_idx_base + 576 * 4;
+  const Index rhs_store_idx_5 = rhs_store_idx_base + 576 * 5;
+  const Index rhs_store_idx_6 = rhs_store_idx_base + 576 * 6;
+  const Index rhs_store_idx_7 = rhs_store_idx_base + 576 * 7;
+
+  // in the loading code, the following variables are important:
+  // threadIdx.x: the vertical position in an 8x8 block
+  // threadIdx.y: the vertical index of the 8x8 block in the grid
+  // threadIdx.z: the horizontal position in an 8x8 block
+  // k: the horizontal index of the 8x8 block in the grid
+  //
+  // The k parameter is implicit (it was the loop counter for a loop that went
+  // from 0 to <8, but now that loop is unrolled in the below code.
+
+  const Index load_idx_vert = threadIdx.x + 8 * threadIdx.y;
+  const Index lhs_vert = base_m + load_idx_vert;
+
+#define prefetchIntoRegisters(base_k)                         \
+  {                                                           \
+    lhs_pf0 = conv(0);                                        \
+    lhs_pf1 = conv(0);                                        \
+    lhs_pf2 = conv(0);                                        \
+    lhs_pf3 = conv(0);                                        \
+    lhs_pf4 = conv(0);                                        \
+    lhs_pf5 = conv(0);                                        \
+    lhs_pf6 = conv(0);                                        \
+    lhs_pf7 = conv(0);                                        \
+                                                              \
+    rhs_pf0 = conv(0);                                        \
+    rhs_pf1 = conv(0);                                        \
+    rhs_pf2 = conv(0);                                        \
+    rhs_pf3 = conv(0);                                        \
+    rhs_pf4 = conv(0);                                        \
+    rhs_pf5 = conv(0);                                        \
+    rhs_pf6 = conv(0);                                        \
+    rhs_pf7 = conv(0);                                        \
+                                                              \
+    if (!needs_edge_check || lhs_vert < m_size) {             \
+      const Index lhs_horiz_0 = base_k + threadIdx.z + 0 * 8; \
+      const Index lhs_horiz_1 = base_k + threadIdx.z + 1 * 8; \
+      const Index lhs_horiz_2 = base_k + threadIdx.z + 2 * 8; \
+      const Index lhs_horiz_3 = base_k + threadIdx.z + 3 * 8; \
+      const Index lhs_horiz_4 = base_k + threadIdx.z + 4 * 8; \
+      const Index lhs_horiz_5 = base_k + threadIdx.z + 5 * 8; \
+      const Index lhs_horiz_6 = base_k + threadIdx.z + 6 * 8; \
+      const Index lhs_horiz_7 = base_k + threadIdx.z + 7 * 8; \
+                                                              \
+      if (!needs_edge_check || lhs_horiz_7 < k_size) {        \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                 \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                 \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                 \
+        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                 \
+        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                 \
+        lhs_pf5 = lhs(lhs_vert, lhs_horiz_5);                 \
+        lhs_pf6 = lhs(lhs_vert, lhs_horiz_6);                 \
+        lhs_pf7 = lhs(lhs_vert, lhs_horiz_7);                 \
+      } else if (lhs_horiz_6 < k_size) {                      \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                 \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                 \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                 \
+        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                 \
+        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                 \
+        lhs_pf5 = lhs(lhs_vert, lhs_horiz_5);                 \
+        lhs_pf6 = lhs(lhs_vert, lhs_horiz_6);                 \
+      } else if (lhs_horiz_5 < k_size) {                      \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                 \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                 \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                 \
+        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                 \
+        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                 \
+        lhs_pf5 = lhs(lhs_vert, lhs_horiz_5);                 \
+      } else if (lhs_horiz_4 < k_size) {                      \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                 \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                 \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                 \
+        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                 \
+        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                 \
+      } else if (lhs_horiz_3 < k_size) {                      \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                 \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                 \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                 \
+        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                 \
+      } else if (lhs_horiz_2 < k_size) {                      \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                 \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                 \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                 \
+      } else if (lhs_horiz_1 < k_size) {                      \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                 \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                 \
+      } else if (lhs_horiz_0 < k_size) {                      \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                 \
+      }                                                       \
+    }                                                         \
+                                                              \
+    const Index rhs_vert = base_k + load_idx_vert;            \
+    if (!needs_edge_check || rhs_vert < k_size) {             \
+      const Index rhs_horiz_0 = base_n + threadIdx.z + 0 * 8; \
+      const Index rhs_horiz_1 = base_n + threadIdx.z + 1 * 8; \
+      const Index rhs_horiz_2 = base_n + threadIdx.z + 2 * 8; \
+      const Index rhs_horiz_3 = base_n + threadIdx.z + 3 * 8; \
+      const Index rhs_horiz_4 = base_n + threadIdx.z + 4 * 8; \
+      const Index rhs_horiz_5 = base_n + threadIdx.z + 5 * 8; \
+      const Index rhs_horiz_6 = base_n + threadIdx.z + 6 * 8; \
+      const Index rhs_horiz_7 = base_n + threadIdx.z + 7 * 8; \
+                                                              \
+      if (rhs_horiz_7 < n_size) {                             \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                 \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                 \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                 \
+        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                 \
+        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                 \
+        rhs_pf5 = rhs(rhs_vert, rhs_horiz_5);                 \
+        rhs_pf6 = rhs(rhs_vert, rhs_horiz_6);                 \
+        rhs_pf7 = rhs(rhs_vert, rhs_horiz_7);                 \
+      } else if (rhs_horiz_6 < n_size) {                      \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                 \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                 \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                 \
+        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                 \
+        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                 \
+        rhs_pf5 = rhs(rhs_vert, rhs_horiz_5);                 \
+        rhs_pf6 = rhs(rhs_vert, rhs_horiz_6);                 \
+      } else if (rhs_horiz_5 < n_size) {                      \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                 \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                 \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                 \
+        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                 \
+        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                 \
+        rhs_pf5 = rhs(rhs_vert, rhs_horiz_5);                 \
+      } else if (rhs_horiz_4 < n_size) {                      \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                 \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                 \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                 \
+        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                 \
+        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                 \
+      } else if (rhs_horiz_3 < n_size) {                      \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                 \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                 \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                 \
+        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                 \
+      } else if (rhs_horiz_2 < n_size) {                      \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                 \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                 \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                 \
+      } else if (rhs_horiz_1 < n_size) {                      \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                 \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                 \
+      } else if (rhs_horiz_0 < n_size) {                      \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                 \
+      }                                                       \
+    }                                                         \
+  }
+
+#define writeRegToShmem()               \
+  lhs_shmem[lhs_store_idx_0] = lhs_pf0; \
+  rhs_shmem[rhs_store_idx_0] = rhs_pf0; \
+                                        \
+  lhs_shmem[lhs_store_idx_1] = lhs_pf1; \
+  rhs_shmem[rhs_store_idx_1] = rhs_pf1; \
+                                        \
+  lhs_shmem[lhs_store_idx_2] = lhs_pf2; \
+  rhs_shmem[rhs_store_idx_2] = rhs_pf2; \
+                                        \
+  lhs_shmem[lhs_store_idx_3] = lhs_pf3; \
+  rhs_shmem[rhs_store_idx_3] = rhs_pf3; \
+                                        \
+  lhs_shmem[lhs_store_idx_4] = lhs_pf4; \
+  rhs_shmem[rhs_store_idx_4] = rhs_pf4; \
+                                        \
+  lhs_shmem[lhs_store_idx_5] = lhs_pf5; \
+  rhs_shmem[rhs_store_idx_5] = rhs_pf5; \
+                                        \
+  lhs_shmem[lhs_store_idx_6] = lhs_pf6; \
+  rhs_shmem[rhs_store_idx_6] = rhs_pf6; \
+                                        \
+  lhs_shmem[lhs_store_idx_7] = lhs_pf7; \
+  rhs_shmem[rhs_store_idx_7] = rhs_pf7;
+
+  // declare and initialize result array
+#define res(i, j) _res_##i##j
+#define initResultRow(i)      \
+  Scalar res(i, 0) = conv(0); \
+  Scalar res(i, 1) = conv(0); \
+  Scalar res(i, 2) = conv(0); \
+  Scalar res(i, 3) = conv(0); \
+  Scalar res(i, 4) = conv(0); \
+  Scalar res(i, 5) = conv(0); \
+  Scalar res(i, 6) = conv(0); \
+  Scalar res(i, 7) = conv(0);
+
+  internal::scalar_cast_op<int, Scalar> conv;
+  initResultRow(0);
+  initResultRow(1);
+  initResultRow(2);
+  initResultRow(3);
+  initResultRow(4);
+  initResultRow(5);
+  initResultRow(6);
+  initResultRow(7);
+#undef initResultRow
+
+  for (Index base_k = 0; base_k < k_size; base_k += 64) {
+    // wait for previous iteration to finish with shmem. Despite common sense,
+    // the code is a bit faster with this here than at bottom of loop
+    __syncthreads();
+
+    prefetchIntoRegisters(base_k);
+    writeRegToShmem();
+
+#undef prefetchIntoRegisters
+#undef writeRegToShmem
+
+    // wait for shared mem packing to be done before starting computation
+    __syncthreads();
+
+    // compute 8x8 matrix product by outer product. This involves packing one column
+    // of LHS and one row of RHS into registers (takes 16 registers).
+
+#define lcol(i) _lcol##i
+    Scalar lcol(0);
+    Scalar lcol(1);
+    Scalar lcol(2);
+    Scalar lcol(3);
+    Scalar lcol(4);
+    Scalar lcol(5);
+    Scalar lcol(6);
+    Scalar lcol(7);
+
+#define rrow(j) _rrow##j
+    Scalar rrow(0);
+    Scalar rrow(1);
+    Scalar rrow(2);
+    Scalar rrow(3);
+    Scalar rrow(4);
+    Scalar rrow(5);
+    Scalar rrow(6);
+    Scalar rrow(7);
+
+    // Now x corresponds to k, y to m, and z to n
+    const Scalar* lhs_block = &lhs_shmem[threadIdx.x + 9 * threadIdx.y];
+    const Scalar* rhs_block = &rhs_shmem[threadIdx.x + 8 * threadIdx.z];
+
+#define lhs_element(i, j) lhs_block[72 * ((i) + 8 * (j))]
+#define rhs_element(i, j) rhs_block[72 * ((i) + 8 * (j))]
+
+#define loadData(i, j)         \
+  lcol(0) = lhs_element(0, j); \
+  rrow(0) = rhs_element(i, 0); \
+  lcol(1) = lhs_element(1, j); \
+  rrow(1) = rhs_element(i, 1); \
+  lcol(2) = lhs_element(2, j); \
+  rrow(2) = rhs_element(i, 2); \
+  lcol(3) = lhs_element(3, j); \
+  rrow(3) = rhs_element(i, 3); \
+  lcol(4) = lhs_element(4, j); \
+  rrow(4) = rhs_element(i, 4); \
+  lcol(5) = lhs_element(5, j); \
+  rrow(5) = rhs_element(i, 5); \
+  lcol(6) = lhs_element(6, j); \
+  rrow(6) = rhs_element(i, 6); \
+  lcol(7) = lhs_element(7, j); \
+  rrow(7) = rhs_element(i, 7);
+
+#define computeCol(j)             \
+  res(0, j) += lcol(0) * rrow(j); \
+  res(1, j) += lcol(1) * rrow(j); \
+  res(2, j) += lcol(2) * rrow(j); \
+  res(3, j) += lcol(3) * rrow(j); \
+  res(4, j) += lcol(4) * rrow(j); \
+  res(5, j) += lcol(5) * rrow(j); \
+  res(6, j) += lcol(6) * rrow(j); \
+  res(7, j) += lcol(7) * rrow(j);
+
+#define computePass(i) \
+  loadData(i, i);      \
+                       \
+  computeCol(0);       \
+  computeCol(1);       \
+  computeCol(2);       \
+  computeCol(3);       \
+  computeCol(4);       \
+  computeCol(5);       \
+  computeCol(6);       \
+  computeCol(7);
+
+    computePass(0);
+    computePass(1);
+    computePass(2);
+    computePass(3);
+    computePass(4);
+    computePass(5);
+    computePass(6);
+    computePass(7);
+
+#undef lcol
+#undef rrow
+#undef lhs_element
+#undef rhs_element
+#undef loadData
+#undef computeCol
+#undef computePass
+  }  // end loop over k
+
+  // we've now iterated over all of the large (ie width 64) k blocks and
+  // accumulated results in registers. At this point thread (x, y, z) contains
+  // the sum across all big k blocks of the product of little k block of index (x, y)
+  // with block of index (y, z). To compute the final output, we need to reduce
+  // the 8 threads over y by summation.
+#if defined(EIGEN_HIPCC) || (defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000)
+#define shuffleInc(i, j, mask) res(i, j) += __shfl_xor(res(i, j), mask)
+#else
+#define shuffleInc(i, j, mask) res(i, j) += __shfl_xor_sync(0xFFFFFFFF, res(i, j), mask)
+#endif
+
+#define reduceRow(i, mask) \
+  shuffleInc(i, 0, mask);  \
+  shuffleInc(i, 1, mask);  \
+  shuffleInc(i, 2, mask);  \
+  shuffleInc(i, 3, mask);  \
+  shuffleInc(i, 4, mask);  \
+  shuffleInc(i, 5, mask);  \
+  shuffleInc(i, 6, mask);  \
+  shuffleInc(i, 7, mask);
+
+#define reduceMatrix(mask) \
+  reduceRow(0, mask);      \
+  reduceRow(1, mask);      \
+  reduceRow(2, mask);      \
+  reduceRow(3, mask);      \
+  reduceRow(4, mask);      \
+  reduceRow(5, mask);      \
+  reduceRow(6, mask);      \
+  reduceRow(7, mask);
+
+  // actually perform the reduction, now each thread of index (_, y, z)
+  // contains the correct values in its registers that belong in the output
+  // block
+  reduceMatrix(1);
+  reduceMatrix(2);
+  reduceMatrix(4);
+
+#undef shuffleInc
+#undef reduceRow
+#undef reduceMatrix
+
+  // now we need to copy the 64 values into main memory. We can't split work
+  // among threads because all variables are in registers. There's 2 ways
+  // to do this:
+  // (1) have 1 thread do 64 writes from registers into global memory
+  // (2) have 1 thread do 64 writes into shared memory, and then 8 threads
+  //     each do 8 writes into global memory. We can just overwrite the shared
+  //     memory from the problem we just solved.
+  // (2) is slightly faster than (1) due to less branching and more ILP
+
+  // TODO: won't yield much gain, but could just use currently unused shared mem
+  //       and then we won't have to sync
+  // wait for shared mem to be out of use
+  __syncthreads();
+
+#define writeResultShmem(i, j) lhs_shmem[i + 8 * threadIdx.y + 64 * threadIdx.z + 512 * j] = res(i, j);
+
+#define writeRow(i)       \
+  writeResultShmem(i, 0); \
+  writeResultShmem(i, 1); \
+  writeResultShmem(i, 2); \
+  writeResultShmem(i, 3); \
+  writeResultShmem(i, 4); \
+  writeResultShmem(i, 5); \
+  writeResultShmem(i, 6); \
+  writeResultShmem(i, 7);
+
+  if (threadIdx.x == 0) {
+    writeRow(0);
+    writeRow(1);
+    writeRow(2);
+    writeRow(3);
+    writeRow(4);
+    writeRow(5);
+    writeRow(6);
+    writeRow(7);
+  }
+#undef writeResultShmem
+#undef writeRow
+
+  const int max_i_write = numext::mini((int)((m_size - base_m - threadIdx.y + 7) / 8), 8);
+  const int max_j_write = numext::mini((int)((n_size - base_n - threadIdx.z + 7) / 8), 8);
+
+  if (threadIdx.x < max_i_write) {
+    if (max_j_write == 8) {
+      // TODO: Can we trade bank conflicts for coalesced writes?
+      Scalar val0 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 0];
+      Scalar val1 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 1];
+      Scalar val2 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 2];
+      Scalar val3 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 3];
+      Scalar val4 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 4];
+      Scalar val5 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 5];
+      Scalar val6 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 6];
+      Scalar val7 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 7];
+
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 0) = val0;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 1) = val1;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 2) = val2;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 3) = val3;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 4) = val4;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 5) = val5;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 6) = val6;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 7) = val7;
+    } else {
+#pragma unroll 7
+      for (int j = 0; j < max_j_write; j++) {
+        Scalar val = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * j];
+        output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * j) = val;
+      }
+    }
+  }
+#undef res
+}
+
+template <typename Scalar, typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper>
+__global__ void
+#if defined(EIGEN_HIPCC)
+__launch_bounds__(512, 1)
+#else
+__launch_bounds__(512)
+#endif
+    EigenContractionKernel(const LhsMapper lhs, const RhsMapper rhs, const OutputMapper output, const Index m_size,
+                           const Index n_size, const Index k_size) {
+  __shared__ Scalar lhs_shmem[72 * 64];
+  __shared__ Scalar rhs_shmem[72 * 64];
+
+  const Index m_block_idx = blockIdx.x;
+  const Index n_block_idx = blockIdx.y;
+
+  const Index base_m = 64 * m_block_idx;
+  const Index base_n = 64 * n_block_idx;
+
+  if (base_m + 63 < m_size && base_n + 63 < n_size) {
+    EigenContractionKernelInternal<Scalar, Index, LhsMapper, RhsMapper, OutputMapper, false>(
+        lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size);
+  } else {
+    EigenContractionKernelInternal<Scalar, Index, LhsMapper, RhsMapper, OutputMapper, true>(
+        lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size);
+  }
+}
+
+template <typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper, bool CHECK_LHS_BOUNDARY,
+          bool CHECK_RHS_BOUNDARY>
+__device__ __forceinline__ void EigenFloatContractionKernelInternal16x16(const LhsMapper lhs, const RhsMapper rhs,
+                                                                         const OutputMapper output,
+                                                                         float2 lhs_shmem2[][16],
+                                                                         float2 rhs_shmem2[][8], const Index m_size,
+                                                                         const Index n_size, const Index k_size,
+                                                                         const Index base_m, const Index base_n) {
+  // prefetch registers
+  float4 lhs_pf0, rhs_pf0;
+
+  float4 results[4];
+  for (int i = 0; i < 4; i++) {
+    results[i].x = results[i].y = results[i].z = results[i].w = 0;
+  }
+
+#define prefetch_lhs(reg, row, col)                                 \
+  if (!CHECK_LHS_BOUNDARY) {                                        \
+    if (col < k_size) {                                             \
+      reg = lhs.template loadPacket<float4, Unaligned>(row, col);   \
+    }                                                               \
+  } else {                                                          \
+    if (col < k_size) {                                             \
+      if (row + 3 < m_size) {                                       \
+        reg = lhs.template loadPacket<float4, Unaligned>(row, col); \
+      } else if (row + 2 < m_size) {                                \
+        reg.x = lhs(row + 0, col);                                  \
+        reg.y = lhs(row + 1, col);                                  \
+        reg.z = lhs(row + 2, col);                                  \
+      } else if (row + 1 < m_size) {                                \
+        reg.x = lhs(row + 0, col);                                  \
+        reg.y = lhs(row + 1, col);                                  \
+      } else if (row < m_size) {                                    \
+        reg.x = lhs(row + 0, col);                                  \
+      }                                                             \
+    }                                                               \
+  }
+
+  Index lhs_vert = base_m + threadIdx.x * 4;
+
+  for (Index k = 0; k < k_size; k += 16) {
+    lhs_pf0 = internal::pset1<float4>(0);
+    rhs_pf0 = internal::pset1<float4>(0);
+
+    Index lhs_horiz = threadIdx.y + k;
+    prefetch_lhs(lhs_pf0, lhs_vert, lhs_horiz)
+
+        Index rhs_vert = k + (threadIdx.x % 4) * 4;
+    Index rhs_horiz0 = (threadIdx.x >> 2) + threadIdx.y * 4 + base_n;
+
+    if (!CHECK_RHS_BOUNDARY) {
+      if ((rhs_vert + 3) < k_size) {
+        // just CHECK_RHS_BOUNDARY
+        rhs_pf0 = rhs.template loadPacket<float4, Unaligned>(rhs_vert, rhs_horiz0);
+      } else if (rhs_vert + 2 < k_size) {
+        // just CHECK_RHS_BOUNDARY
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+        rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
+      } else if (rhs_vert + 1 < k_size) {
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+      } else if (rhs_vert < k_size) {
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+      }
+    } else {
+      if (rhs_horiz0 < n_size) {
+        if ((rhs_vert + 3) < k_size) {
+          rhs_pf0 = rhs.template loadPacket<float4, Unaligned>(rhs_vert, rhs_horiz0);
+        } else if ((rhs_vert + 2) < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+          rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
+        } else if ((rhs_vert + 1) < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+        } else if (rhs_vert < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        }
+      }
+    }
+    float x1, x2;
+    // TODO: The following can be a bitwise operation.
+    if ((threadIdx.x % 8) < 4) {
+      x1 = rhs_pf0.y;
+      x2 = rhs_pf0.w;
+    } else {
+      x1 = rhs_pf0.x;
+      x2 = rhs_pf0.z;
+    }
+#if defined(EIGEN_HIPCC) || (defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000)
+    x1 = __shfl_xor(x1, 4);
+    x2 = __shfl_xor(x2, 4);
+#else
+    x1 = __shfl_xor_sync(0xFFFFFFFF, x1, 4);
+    x2 = __shfl_xor_sync(0xFFFFFFFF, x2, 4);
+#endif
+    if ((threadIdx.x % 8) < 4) {
+      rhs_pf0.y = x1;
+      rhs_pf0.w = x2;
+    } else {
+      rhs_pf0.x = x1;
+      rhs_pf0.z = x2;
+    }
+
+    // We have 64 features.
+    // Row 0 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 0, 1.
+    // Row 1 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 2, 3.
+    // ...
+    // Row 31 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 62, 63
+    // Row 32 -> times (2, 6, 10, 14, 3, 7, 11, 15) for features 0, 1
+    // ...
+    rhs_shmem2[(threadIdx.x >> 3) + threadIdx.y * 2][threadIdx.x % 8] = make_float2(rhs_pf0.x, rhs_pf0.y);
+    rhs_shmem2[(threadIdx.x >> 3) + threadIdx.y * 2 + 32][threadIdx.x % 8] = make_float2(rhs_pf0.z, rhs_pf0.w);
+
+    // Row 0 (time 0) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61)
+    // Row 1 (time 1) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61)
+    // ...
+    // Row 15 (time 15) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61)
+    // Row 16 (time 0) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), ..  (62, 63)
+    // ...
+
+    lhs_shmem2[threadIdx.y][threadIdx.x] = make_float2(lhs_pf0.x, lhs_pf0.y);
+    lhs_shmem2[threadIdx.y + 16][threadIdx.x] = make_float2(lhs_pf0.z, lhs_pf0.w);
+
+#define add_vals(fl1, fl2, fr1, fr2) \
+  results[0].x += fl1.x * fr1.x;     \
+  results[0].y += fl1.y * fr1.x;     \
+  results[0].z += fl2.x * fr1.x;     \
+  results[0].w += fl2.y * fr1.x;     \
+                                     \
+  results[1].x += fl1.x * fr1.y;     \
+  results[1].y += fl1.y * fr1.y;     \
+  results[1].z += fl2.x * fr1.y;     \
+  results[1].w += fl2.y * fr1.y;     \
+                                     \
+  results[2].x += fl1.x * fr2.x;     \
+  results[2].y += fl1.y * fr2.x;     \
+  results[2].z += fl2.x * fr2.x;     \
+  results[2].w += fl2.y * fr2.x;     \
+                                     \
+  results[3].x += fl1.x * fr2.y;     \
+  results[3].y += fl1.y * fr2.y;     \
+  results[3].z += fl2.x * fr2.y;     \
+  results[3].w += fl2.y * fr2.y;
+
+    __syncthreads();
+
+// Do the multiplies.
+#pragma unroll
+    for (int koff = 0; koff < 16; koff++) {
+      // 32 x threads.
+      float2 fl1 = lhs_shmem2[koff][threadIdx.x];
+      float2 fl2 = lhs_shmem2[koff + 16][threadIdx.x];
+
+      int start_feature = threadIdx.y * 4;
+      float2 fr1 = rhs_shmem2[(start_feature >> 1) + 32 * ((koff % 4) / 2)][koff / 4 + (koff % 2) * 4];
+      float2 fr2 = rhs_shmem2[(start_feature >> 1) + 1 + 32 * ((koff % 4) / 2)][koff / 4 + (koff % 2) * 4];
+
+      add_vals(fl1, fl2, fr1, fr2)
+    }
+    __syncthreads();
+  }
+
+#undef prefetch_lhs
+#undef add_vals
+
+  Index horiz_base = threadIdx.y * 4 + base_n;
+  if (!CHECK_LHS_BOUNDARY && !CHECK_RHS_BOUNDARY) {
+    for (int i = 0; i < 4; i++) {
+      output(lhs_vert, horiz_base + i) = results[i].x;
+      output(lhs_vert + 1, horiz_base + i) = results[i].y;
+      output(lhs_vert + 2, horiz_base + i) = results[i].z;
+      output(lhs_vert + 3, horiz_base + i) = results[i].w;
+    }
+  } else if (!CHECK_RHS_BOUNDARY) {
+    // CHECK LHS
+    if (lhs_vert + 3 < m_size) {
+      for (int i = 0; i < 4; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    } else if (lhs_vert + 2 < m_size) {
+      for (int i = 0; i < 4; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+      }
+    } else if (lhs_vert + 1 < m_size) {
+      for (int i = 0; i < 4; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+      }
+    } else if (lhs_vert < m_size) {
+      for (int i = 0; i < 4; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+      }
+    }
+  } else if (!CHECK_LHS_BOUNDARY) {
+    // CHECK RHS
+    /*
+    int ncols_rem = fminf(n_size- horiz_base, 4);
+    for (int i = 0; i < ncols_rem; i++) {
+      output(lhs_vert, horiz_base + i) = results[i].x;
+      output(lhs_vert + 1, horiz_base + i) = results[i].y;
+      output(lhs_vert + 2, horiz_base + i) = results[i].z;
+      output(lhs_vert + 3, horiz_base + i) = results[i].w;
+    }*/
+    for (int i = 0; i < 4; i++) {
+      if (horiz_base + i < n_size) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    }
+  } else {
+    // CHECK both boundaries.
+    for (int i = 0; i < 4; i++) {
+      if (horiz_base + i < n_size) {
+        if (lhs_vert < m_size) output(lhs_vert, horiz_base + i) = results[i].x;
+        if (lhs_vert + 1 < m_size) output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        if (lhs_vert + 2 < m_size) output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        if (lhs_vert + 3 < m_size) output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    }
+  }
+}
+
+template <typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper, bool CHECK_LHS_BOUNDARY,
+          bool CHECK_RHS_BOUNDARY>
+__device__ __forceinline__ void EigenFloatContractionKernelInternal(const LhsMapper lhs, const RhsMapper rhs,
+                                                                    const OutputMapper output, float2 lhs_shmem2[][32],
+                                                                    float2 rhs_shmem2[][8], const Index m_size,
+                                                                    const Index n_size, const Index k_size,
+                                                                    const Index base_m, const Index base_n) {
+  // prefetch registers
+  float4 lhs_pf0, lhs_pf1, lhs_pf2, lhs_pf3;
+  float4 rhs_pf0, rhs_pf1;
+
+  float4 results[8];
+  for (int i = 0; i < 8; i++) {
+    results[i].x = results[i].y = results[i].z = results[i].w = 0;
+  }
+
+  Index lhs_vert = base_m + threadIdx.x * 4 + (threadIdx.y % 4) * 32;
+  for (Index k = 0; k < k_size; k += 32) {
+    lhs_pf0 = internal::pset1<float4>(0);
+    lhs_pf1 = internal::pset1<float4>(0);
+    lhs_pf2 = internal::pset1<float4>(0);
+    lhs_pf3 = internal::pset1<float4>(0);
+
+    rhs_pf0 = internal::pset1<float4>(0);
+    rhs_pf1 = internal::pset1<float4>(0);
+
+    if (!CHECK_LHS_BOUNDARY) {
+      if ((threadIdx.y / 4 + k + 24) < k_size) {
+        lhs_pf0 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k));
+        lhs_pf1 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 8));
+        lhs_pf2 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 16));
+        lhs_pf3 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 24));
+      } else if ((threadIdx.y / 4 + k + 16) < k_size) {
+        lhs_pf0 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k));
+        lhs_pf1 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 8));
+        lhs_pf2 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 16));
+      } else if ((threadIdx.y / 4 + k + 8) < k_size) {
+        lhs_pf0 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k));
+        lhs_pf1 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 8));
+      } else if ((threadIdx.y / 4 + k) < k_size) {
+        lhs_pf0 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k));
+      }
+    } else {
+      // just CHECK_LHS_BOUNDARY
+      if (lhs_vert + 3 < m_size) {
+        if ((threadIdx.y / 4 + k + 24) < k_size) {
+          lhs_pf0 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k));
+          lhs_pf1 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 8));
+          lhs_pf2 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 16));
+          lhs_pf3 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 24));
+        } else if ((threadIdx.y / 4 + k + 16) < k_size) {
+          lhs_pf0 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k));
+          lhs_pf1 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 8));
+          lhs_pf2 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 16));
+        } else if ((threadIdx.y / 4 + k + 8) < k_size) {
+          lhs_pf0 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k));
+          lhs_pf1 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k + 8));
+        } else if ((threadIdx.y / 4 + k) < k_size) {
+          lhs_pf0 = lhs.template loadPacket<float4, Unaligned>(lhs_vert, (threadIdx.y / 4 + k));
+        }
+      } else if (lhs_vert + 2 < m_size) {
+        if ((threadIdx.y / 4 + k + 24) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf0.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k));
+          lhs_pf0.z = lhs(lhs_vert + 2, (threadIdx.y / 4 + k));
+          lhs_pf1.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 8));
+          lhs_pf1.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 8));
+          lhs_pf1.z = lhs(lhs_vert + 2, (threadIdx.y / 4 + k + 8));
+          lhs_pf2.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 16));
+          lhs_pf2.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 16));
+          lhs_pf2.z = lhs(lhs_vert + 2, (threadIdx.y / 4 + k + 16));
+          lhs_pf3.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 24));
+          lhs_pf3.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 24));
+          lhs_pf3.z = lhs(lhs_vert + 2, (threadIdx.y / 4 + k + 24));
+        } else if ((threadIdx.y / 4 + k + 16) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf0.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k));
+          lhs_pf0.z = lhs(lhs_vert + 2, (threadIdx.y / 4 + k));
+          lhs_pf1.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 8));
+          lhs_pf1.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 8));
+          lhs_pf1.z = lhs(lhs_vert + 2, (threadIdx.y / 4 + k + 8));
+          lhs_pf2.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 16));
+          lhs_pf2.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 16));
+          lhs_pf2.z = lhs(lhs_vert + 2, (threadIdx.y / 4 + k + 16));
+        } else if ((threadIdx.y / 4 + k + 8) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf0.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k));
+          lhs_pf0.z = lhs(lhs_vert + 2, (threadIdx.y / 4 + k));
+          lhs_pf1.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 8));
+          lhs_pf1.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 8));
+          lhs_pf1.z = lhs(lhs_vert + 2, (threadIdx.y / 4 + k + 8));
+        } else if ((threadIdx.y / 4 + k) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf0.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k));
+          lhs_pf0.z = lhs(lhs_vert + 2, (threadIdx.y / 4 + k));
+        }
+      } else if (lhs_vert + 1 < m_size) {
+        if ((threadIdx.y / 4 + k + 24) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf0.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k));
+          lhs_pf1.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 8));
+          lhs_pf1.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 8));
+          lhs_pf2.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 16));
+          lhs_pf2.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 16));
+          lhs_pf3.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 24));
+          lhs_pf3.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 24));
+        } else if ((threadIdx.y / 4 + k + 16) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf0.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k));
+          lhs_pf1.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 8));
+          lhs_pf1.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 8));
+          lhs_pf2.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 16));
+          lhs_pf2.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 16));
+        } else if ((threadIdx.y / 4 + k + 8) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf0.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k));
+          lhs_pf1.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 8));
+          lhs_pf1.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k + 8));
+        } else if ((threadIdx.y / 4 + k) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf0.y = lhs(lhs_vert + 1, (threadIdx.y / 4 + k));
+        }
+      } else if (lhs_vert < m_size) {
+        if ((threadIdx.y / 4 + k + 24) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf1.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 8));
+          lhs_pf2.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 16));
+          lhs_pf3.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 24));
+        } else if ((threadIdx.y / 4 + k + 16) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf1.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 8));
+          lhs_pf2.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 16));
+        } else if ((threadIdx.y / 4 + k + 8) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+          lhs_pf1.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k + 8));
+        } else if ((threadIdx.y / 4 + k) < k_size) {
+          lhs_pf0.x = lhs(lhs_vert + 0, (threadIdx.y / 4 + k));
+        }
+      }
+    }
+    __syncthreads();
+    Index rhs_vert = k + threadIdx.x * 4;
+    Index rhs_horiz0 = threadIdx.y * 2 + base_n;
+    Index rhs_horiz1 = threadIdx.y * 2 + 1 + base_n;
+    if (!CHECK_RHS_BOUNDARY) {
+      if ((rhs_vert + 3) < k_size) {
+        // just CHECK_RHS_BOUNDARY
+        rhs_pf0 = rhs.template loadPacket<float4, Unaligned>(rhs_vert, rhs_horiz0);
+        rhs_pf1 = rhs.template loadPacket<float4, Unaligned>(rhs_vert, rhs_horiz1);
+      } else if (rhs_vert + 2 < k_size) {
+        // just CHECK_RHS_BOUNDARY
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+        rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
+        rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+        rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
+        rhs_pf1.z = rhs(rhs_vert + 2, rhs_horiz1);
+      } else if (rhs_vert + 1 < k_size) {
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+        rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+        rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
+      } else if (rhs_vert < k_size) {
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+      }
+    } else {
+      if (rhs_horiz1 < n_size) {
+        if ((rhs_vert + 3) < k_size) {
+          // just CHECK_RHS_BOUNDARY
+          rhs_pf0 = rhs.template loadPacket<float4, Unaligned>(rhs_vert, rhs_horiz0);
+          rhs_pf1 = rhs.template loadPacket<float4, Unaligned>(rhs_vert, rhs_horiz1);
+        } else if (rhs_vert + 2 < k_size) {
+          // just CHECK_RHS_BOUNDARY
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+          rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
+          rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+          rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
+          rhs_pf1.z = rhs(rhs_vert + 2, rhs_horiz1);
+        } else if (k + threadIdx.x * 4 + 1 < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+          rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+          rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
+        } else if (k + threadIdx.x * 4 < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+        }
+      } else if (rhs_horiz0 < n_size) {
+        if ((rhs_vert + 3) < k_size) {
+          // just CHECK_RHS_BOUNDARY
+          rhs_pf0 = rhs.template loadPacket<float4, Unaligned>(rhs_vert, rhs_horiz0);
+        } else if ((rhs_vert + 2) < k_size) {
+          // just CHECK_RHS_BOUNDARY
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+          rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
+        } else if ((rhs_vert + 1) < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+        } else if (rhs_vert < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        }
+      }
+    }
+    __syncthreads();
+    // Loaded. Do computation
+    // Row 0 -> times (0, 4, 8, .. 28) for features 0, 1.
+    // Row 1 -> times (0, 4, 8, .. 28) for features 2, 3.
+    // ..
+    // Row 31 -> times (0, 4, 8, .. 28) for features 62, 63
+    rhs_shmem2[threadIdx.y][threadIdx.x] = make_float2(rhs_pf0.x, rhs_pf1.x);
+    // Row 32 -> times (1, 5, 9, .. 29) for features 0, 1.
+    // Row 33 -> times (1, 5, 9, .. 29) for features 2, 3.
+    // ..
+    rhs_shmem2[threadIdx.y + 32][threadIdx.x] = make_float2(rhs_pf0.y, rhs_pf1.y);
+    // Row 64 -> times (2, 6, 10, .. 30) for features 0, 1.
+    // Row 65 -> times (2, 6, 10, .. 30) for features 2, 3.
+    rhs_shmem2[threadIdx.y + 64][threadIdx.x] = make_float2(rhs_pf0.z, rhs_pf1.z);
+    // Row 96 -> times (3, 7, 11, .. 31) for features 0, 1.
+    // Row 97 -> times (3, 7, 11, .. 31) for features 2, 3.
+    rhs_shmem2[threadIdx.y + 96][threadIdx.x] = make_float2(rhs_pf0.w, rhs_pf1.w);
+
+    // LHS.
+    // Row 0 (time 0) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61) .. (124, 125)
+    // Row 1 (time 1) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61) .. (124, 125)
+    // ...
+    // Row 8 (time 0) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), ..  (62, 63) .. (126, 127)
+    // Row 15 (time 7) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), ..  (62, 63) .. (126, 127)
+
+#define add_vals(a_feat1, a_feat2, f1, f2, f3, f4) \
+  results[0].x += a_feat1.x * f1.x;                \
+  results[1].x += a_feat1.x * f1.y;                \
+  results[2].x += a_feat1.x * f2.x;                \
+  results[3].x += a_feat1.x * f2.y;                \
+  results[4].x += a_feat1.x * f3.x;                \
+  results[5].x += a_feat1.x * f3.y;                \
+  results[6].x += a_feat1.x * f4.x;                \
+  results[7].x += a_feat1.x * f4.y;                \
+                                                   \
+  results[0].y += a_feat1.y * f1.x;                \
+  results[1].y += a_feat1.y * f1.y;                \
+  results[2].y += a_feat1.y * f2.x;                \
+  results[3].y += a_feat1.y * f2.y;                \
+  results[4].y += a_feat1.y * f3.x;                \
+  results[5].y += a_feat1.y * f3.y;                \
+  results[6].y += a_feat1.y * f4.x;                \
+  results[7].y += a_feat1.y * f4.y;                \
+                                                   \
+  results[0].z += a_feat2.x * f1.x;                \
+  results[1].z += a_feat2.x * f1.y;                \
+  results[2].z += a_feat2.x * f2.x;                \
+  results[3].z += a_feat2.x * f2.y;                \
+  results[4].z += a_feat2.x * f3.x;                \
+  results[5].z += a_feat2.x * f3.y;                \
+  results[6].z += a_feat2.x * f4.x;                \
+  results[7].z += a_feat2.x * f4.y;                \
+                                                   \
+  results[0].w += a_feat2.y * f1.x;                \
+  results[1].w += a_feat2.y * f1.y;                \
+  results[2].w += a_feat2.y * f2.x;                \
+  results[3].w += a_feat2.y * f2.y;                \
+  results[4].w += a_feat2.y * f3.x;                \
+  results[5].w += a_feat2.y * f3.y;                \
+  results[6].w += a_feat2.y * f4.x;                \
+  results[7].w += a_feat2.y * f4.y;
+
+    lhs_shmem2[threadIdx.y / 4][threadIdx.x + (threadIdx.y % 4) * 8] = make_float2(lhs_pf0.x, lhs_pf0.y);
+    lhs_shmem2[threadIdx.y / 4 + 8][threadIdx.x + (threadIdx.y % 4) * 8] = make_float2(lhs_pf1.x, lhs_pf1.y);
+    lhs_shmem2[threadIdx.y / 4 + 16][threadIdx.x + (threadIdx.y % 4) * 8] = make_float2(lhs_pf2.x, lhs_pf2.y);
+    lhs_shmem2[threadIdx.y / 4 + 24][threadIdx.x + (threadIdx.y % 4) * 8] = make_float2(lhs_pf3.x, lhs_pf3.y);
+
+    lhs_shmem2[threadIdx.y / 4 + 32][threadIdx.x + (threadIdx.y % 4) * 8] = make_float2(lhs_pf0.z, lhs_pf0.w);
+    lhs_shmem2[threadIdx.y / 4 + 40][threadIdx.x + (threadIdx.y % 4) * 8] = make_float2(lhs_pf1.z, lhs_pf1.w);
+    lhs_shmem2[threadIdx.y / 4 + 48][threadIdx.x + (threadIdx.y % 4) * 8] = make_float2(lhs_pf2.z, lhs_pf2.w);
+    lhs_shmem2[threadIdx.y / 4 + 56][threadIdx.x + (threadIdx.y % 4) * 8] = make_float2(lhs_pf3.z, lhs_pf3.w);
+
+    __syncthreads();
+
+// Do the multiplies.
+#pragma unroll
+    for (int koff = 0; koff < 32; koff++) {
+      float2 a3 = lhs_shmem2[koff][threadIdx.x + (threadIdx.y % 4) * 8];
+      float2 a4 = lhs_shmem2[koff + 32][threadIdx.x + (threadIdx.y % 4) * 8];
+
+      // first feature is at (threadIdx.y/4) * 8 last is at start + 8.
+      int start_feature = (threadIdx.y / 4) * 8;
+
+      float2 br1 = rhs_shmem2[start_feature / 2 + (koff % 4) * 32][koff / 4];
+      float2 br2 = rhs_shmem2[start_feature / 2 + 1 + (koff % 4) * 32][koff / 4];
+      float2 br3 = rhs_shmem2[start_feature / 2 + 2 + (koff % 4) * 32][koff / 4];
+      float2 br4 = rhs_shmem2[start_feature / 2 + 3 + (koff % 4) * 32][koff / 4];
+
+      add_vals(a3, a4, br1, br2, br3, br4)
+    }
+    __syncthreads();
+  }  // end loop over k
+
+#undef add_vals
+
+  __syncthreads();
+  Index horiz_base = (threadIdx.y / 4) * 8 + base_n;
+  if (!CHECK_LHS_BOUNDARY && !CHECK_RHS_BOUNDARY) {
+    for (int i = 0; i < 8; i++) {
+      output(lhs_vert, horiz_base + i) = results[i].x;
+      output(lhs_vert + 1, horiz_base + i) = results[i].y;
+      output(lhs_vert + 2, horiz_base + i) = results[i].z;
+      output(lhs_vert + 3, horiz_base + i) = results[i].w;
+    }
+  } else if (!CHECK_RHS_BOUNDARY) {
+    if (lhs_vert + 3 < m_size) {
+      for (int i = 0; i < 8; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    } else if (lhs_vert + 2 < m_size) {
+      for (int i = 0; i < 8; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+      }
+    } else if (lhs_vert + 1 < m_size) {
+      for (int i = 0; i < 8; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+      }
+    } else if (lhs_vert < m_size) {
+      for (int i = 0; i < 8; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+      }
+    }
+  } else if (!CHECK_LHS_BOUNDARY) {
+    // CHECK BOUNDARY_B
+    for (int i = 0; i < 8; i++) {
+      if (horiz_base + i < n_size) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    }
+  } else {
+    // CHECK both boundaries.
+    for (int i = 0; i < 8; i++) {
+      if (horiz_base + i < n_size) {
+        if (lhs_vert < m_size) output(lhs_vert, horiz_base + i) = results[i].x;
+        if (lhs_vert + 1 < m_size) output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        if (lhs_vert + 2 < m_size) output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        if (lhs_vert + 3 < m_size) output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    }
+  }
+}
+
+template <typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper>
+__global__ void
+#if defined(EIGEN_HIPCC)
+__launch_bounds__(256, 1)
+#else
+__launch_bounds__(256)
+#endif
+    EigenFloatContractionKernel(const LhsMapper lhs, const RhsMapper rhs, const OutputMapper output, const Index m_size,
+                                const Index n_size, const Index k_size) {
+  __shared__ float2 lhs_shmem[64 * 32];
+  __shared__ float2 rhs_shmem[128 * 8];
+
+  typedef float2 LHS_MEM[64][32];
+  typedef float2 RHS_MEM[128][8];
+
+  const Index m_block_idx = blockIdx.x;
+  const Index n_block_idx = blockIdx.y;
+
+  const Index base_m = 128 * m_block_idx;
+  const Index base_n = 64 * n_block_idx;
+
+  bool check_rhs = (base_n + 63) >= n_size;
+  bool check_lhs128 = (base_m + 127) >= m_size;
+
+  if (!check_rhs) {
+    if (!check_lhs128) {
+      // >= 128 rows left
+      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, false, false>(
+          lhs, rhs, output, *((LHS_MEM*)lhs_shmem), *((RHS_MEM*)rhs_shmem), m_size, n_size, k_size, base_m, base_n);
+    } else {
+      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, true, false>(
+          lhs, rhs, output, *((LHS_MEM*)lhs_shmem), *((RHS_MEM*)rhs_shmem), m_size, n_size, k_size, base_m, base_n);
+    }
+  } else {
+    if (!check_lhs128) {
+      // >= 128 rows left
+      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, false, true>(
+          lhs, rhs, output, *((LHS_MEM*)lhs_shmem), *((RHS_MEM*)rhs_shmem), m_size, n_size, k_size, base_m, base_n);
+    } else {
+      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, true, true>(
+          lhs, rhs, output, *((LHS_MEM*)lhs_shmem), *((RHS_MEM*)rhs_shmem), m_size, n_size, k_size, base_m, base_n);
+    }
+  }
+}
+
+template <typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper>
+__global__ void
+#if defined(EIGEN_HIPCC)
+__launch_bounds__(256, 1)
+#else
+__launch_bounds__(256)
+#endif
+    EigenFloatContractionKernel16x16(const LhsMapper lhs, const RhsMapper rhs, const OutputMapper output,
+                                     const Index m_size, const Index n_size, const Index k_size) {
+  __shared__ float2 lhs_shmem[32][16];
+  __shared__ float2 rhs_shmem[64][8];
+
+  const Index m_block_idx = blockIdx.x;
+  const Index n_block_idx = blockIdx.y;
+
+  const Index base_m = 64 * m_block_idx;
+  const Index base_n = 64 * n_block_idx;
+
+  if (base_m + 63 < m_size) {
+    if (base_n + 63 < n_size) {
+      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, false, false>(
+          lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
+    } else {
+      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, false, true>(
+          lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
+    }
+  } else {
+    if (base_n + 63 < n_size) {
+      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, true, false>(
+          lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
+    } else {
+      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, true, true>(
+          lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
+    }
+  }
+}
+
+template <typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
+struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, GpuDevice>
+    : public TensorContractionEvaluatorBase<TensorEvaluator<
+          const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, GpuDevice> > {
+  typedef GpuDevice Device;
+
+  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
+  typedef TensorContractionEvaluatorBase<Self> Base;
+
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
+  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, GpuDevice>::type PacketReturnType;
+
+  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
+
+  // Most of the code is assuming that both input tensors are ColMajor. If the
+  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
+  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
+  // will pretend B is LHS and A is RHS.
+  typedef std::conditional_t<Layout == static_cast<int>(ColMajor), LeftArgType, RightArgType> EvalLeftArgType;
+  typedef std::conditional_t<Layout == static_cast<int>(ColMajor), RightArgType, LeftArgType> EvalRightArgType;
+
+  static constexpr int LDims =
+      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
+  static constexpr int RDims =
+      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
+  static constexpr int ContractDims = internal::array_size<Indices>::value;
+
+  typedef array<Index, LDims> left_dim_mapper_t;
+  typedef array<Index, RDims> right_dim_mapper_t;
+
+  typedef array<Index, ContractDims> contract_t;
+  typedef array<Index, LDims - ContractDims> left_nocontract_t;
+  typedef array<Index, RDims - ContractDims> right_nocontract_t;
+
+  static constexpr int NumDims = LDims + RDims - 2 * ContractDims;
+
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  // typedefs needed in evalTo
+  typedef std::remove_const_t<typename EvalLeftArgType::Scalar> LhsScalar;
+  typedef std::remove_const_t<typename EvalRightArgType::Scalar> RhsScalar;
+
+  typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
+  typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
+
+  typedef typename LeftEvaluator::Dimensions LeftDimensions;
+  typedef typename RightEvaluator::Dimensions RightDimensions;
+
+  TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {
+    EIGEN_STATIC_ASSERT((internal::is_same<OutputKernelType, const NoOpOutputKernel>::value),
+                        GPU_TENSOR_CONTRACTION_DOES_NOT_SUPPORT_OUTPUT_KERNELS);
+  }
+
+  // We need to redefine this method to make nvcc happy
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
+    this->m_leftImpl.evalSubExprsIfNeeded(NULL);
+    this->m_rightImpl.evalSubExprsIfNeeded(NULL);
+    if (data) {
+      evalTo(data);
+      return false;
+    } else {
+      this->m_result = static_cast<Scalar*>(this->m_device.allocate(this->dimensions().TotalSize() * sizeof(Scalar)));
+      evalTo(this->m_result);
+      return true;
+    }
+  }
+
+  void evalTo(Scalar* buffer) const {
+    if (this->m_lhs_inner_dim_contiguous) {
+      if (this->m_rhs_inner_dim_contiguous) {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<true, true, true, Unaligned>(buffer);
+        } else {
+          evalTyped<true, true, false, Unaligned>(buffer);
+        }
+      } else {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<true, false, true, Unaligned>(buffer);
+        } else {
+          evalTyped<true, false, false, Unaligned>(buffer);
+        }
+      }
+    } else {
+      if (this->m_rhs_inner_dim_contiguous) {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<false, true, true, Unaligned>(buffer);
+        } else {
+          evalTyped<false, true, false, Unaligned>(buffer);
+        }
+      } else {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<false, false, true, Unaligned>(buffer);
+        } else {
+          evalTyped<false, false, false, Unaligned>(buffer);
+        }
+      }
+    }
+  }
+
+  template <typename LhsScalar, typename RhsScalar, typename Index, typename LhsMapper, typename RhsMapper,
+            typename OutputMapper>
+  struct LaunchKernels {
+    static void Run(const LhsMapper& lhs, const RhsMapper& rhs, const OutputMapper& output, Index m, Index n, Index k,
+                    const GpuDevice& device) {
+      const Index m_blocks = (m + 63) / 64;
+      const Index n_blocks = (n + 63) / 64;
+      const dim3 num_blocks(m_blocks, n_blocks, 1);
+      const dim3 block_size(8, 8, 8);
+      LAUNCH_GPU_KERNEL((EigenContractionKernel<Scalar, Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks,
+                        block_size, 0, device, lhs, rhs, output, m, n, k);
+    }
+  };
+
+  template <typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper>
+  struct LaunchKernels<float, float, Index, LhsMapper, RhsMapper, OutputMapper> {
+    static void Run(const LhsMapper& lhs, const RhsMapper& rhs, const OutputMapper& output, Index m, Index n, Index k,
+                    const GpuDevice& device) {
+      if (m < 768 || n < 768) {
+        const Index m_blocks = (m + 63) / 64;
+        const Index n_blocks = (n + 63) / 64;
+        const dim3 num_blocks(m_blocks, n_blocks, 1);
+        const dim3 block_size(16, 16, 1);
+        LAUNCH_GPU_KERNEL((EigenFloatContractionKernel16x16<Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks,
+                          block_size, 0, device, lhs, rhs, output, m, n, k);
+      } else {
+        const Index m_blocks = (m + 127) / 128;
+        const Index n_blocks = (n + 63) / 64;
+        const dim3 num_blocks(m_blocks, n_blocks, 1);
+        const dim3 block_size(8, 32, 1);
+        LAUNCH_GPU_KERNEL((EigenFloatContractionKernel<Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks,
+                          block_size, 0, device, lhs, rhs, output, m, n, k);
+      }
+    }
+  };
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+  void evalTyped(Scalar* buffer) const {
+    // columns in left side, rows in right side
+    const Index k = this->m_k_size;
+    EIGEN_UNUSED_VARIABLE(k)
+
+    // rows in left side
+    const Index m = this->m_i_size;
+
+    // columns in right side
+    const Index n = this->m_j_size;
+
+    // zero out the result buffer (which must be of size at least m * n * sizeof(Scalar))
+    this->m_device.fill(buffer, buffer + m * n, Scalar(0));
+
+    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs, LeftEvaluator, left_nocontract_t,
+                                                   contract_t, 4, lhs_inner_dim_contiguous, false, Unaligned>
+        LhsMapper;
+
+    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs, RightEvaluator, right_nocontract_t,
+                                                   contract_t, 4, rhs_inner_dim_contiguous, rhs_inner_dim_reordered,
+                                                   Unaligned>
+        RhsMapper;
+
+    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
+
+    // initialize data mappers
+    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
+                  this->m_left_contracting_strides, this->m_k_strides);
+
+    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
+                  this->m_right_contracting_strides, this->m_k_strides);
+
+    OutputMapper output(buffer, m);
+
+#if defined(EIGEN_USE_HIP)
+    setGpuSharedMemConfig(hipSharedMemBankSizeEightByte);
+#else
+    setGpuSharedMemConfig(cudaSharedMemBankSizeEightByte);
+#endif
+
+    LaunchKernels<LhsScalar, RhsScalar, Index, LhsMapper, RhsMapper, OutputMapper>::Run(lhs, rhs, output, m, n, k,
+                                                                                        this->m_device);
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_USE_GPU and EIGEN_GPUCC
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_GPU_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h
new file mode 100644
index 0000000..83fec99
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h
@@ -0,0 +1,537 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+enum { Rhs = 0, Lhs = 1 };
+
+/*
+ * Implementation of the Eigen blas_data_mapper class for tensors.
+ */
+/// The make pointer class is used by sycl in order to build the mapper class on the device. For other platform the
+/// default make pointer is used which is scalar * for CoeffLoader.
+template <typename Tensor, bool HasRawAccess, template <class> class MakePointer_ = MakePointer>
+struct CoeffLoader;
+
+template <typename Scalar, typename Index, int side, typename Tensor, typename nocontract_t, typename contract_t,
+          int packet_size, bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment,
+          template <class> class MakePointer_ = MakePointer>
+class BaseTensorContractionMapper;
+
+template <typename Tensor, bool HasRawAccess, template <class> class MakePointer_>
+struct CoeffLoader {
+  enum { DirectOffsets = false };
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffLoader(const Tensor& tensor) : m_tensor(tensor) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index) {
+    eigen_assert(false && "unsupported");
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const typename MakePointer_<const typename Tensor::Scalar>::Type data() const {
+    eigen_assert(false && "unsupported");
+    return NULL;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename Tensor::Scalar coeff(typename Tensor::Index index) const {
+    return m_tensor.coeff(index);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Tensor::PacketReturnType packet(typename Tensor::Index index) const {
+    return m_tensor.template packet<LoadMode>(index);
+  }
+
+ private:
+  const Tensor m_tensor;
+};
+
+template <typename Tensor, template <class> class MakePointer_>
+struct CoeffLoader<Tensor, true, MakePointer_> {
+  enum { DirectOffsets = true };
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffLoader(const Tensor& tensor) : m_data(tensor.data()) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index offset) { m_data += offset; }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const typename MakePointer_<const typename Tensor::Scalar>::Type data() const {
+    return m_data;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename Tensor::Scalar coeff(typename Tensor::Index index) const {
+    return loadConstant(m_data + index);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Tensor::PacketReturnType packet(typename Tensor::Index index) const {
+    return internal::ploadt_ro<typename Tensor::PacketReturnType, LoadMode>(m_data + index);
+  }
+
+ private:
+  typedef typename Tensor::Scalar Scalar;
+
+  typename MakePointer_<const Scalar>::Type m_data;
+};
+
+template <typename Scalar, typename Index, int side, typename Tensor, typename nocontract_t, typename contract_t,
+          int packet_size, bool inner_dim_contiguous, int Alignment, template <class> class MakePointer_ = MakePointer>
+class SimpleTensorContractionMapper {
+ public:
+  EIGEN_DEVICE_FUNC SimpleTensorContractionMapper(const Tensor& tensor, const nocontract_t& nocontract_strides,
+                                                  const nocontract_t& ij_strides, const contract_t& contract_strides,
+                                                  const contract_t& k_strides)
+      : m_tensor(tensor),
+        m_nocontract_strides(nocontract_strides),
+        m_ij_strides(ij_strides),
+        m_contract_strides(contract_strides),
+        m_k_strides(k_strides) {}
+
+  enum { DirectOffsets = CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_>::DirectOffsets };
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index offset) {
+    m_tensor.offsetBuffer(offset);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void prefetch(Index /*i*/) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(Index row) const {
+    // column major assumption
+    return operator()(row, 0);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(Index row, Index col) const {
+    return m_tensor.coeff(computeIndex(row, col));
+  }
+
+#ifdef EIGEN_MULTIDIMENSIONAL_SUBSCRIPT
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator[](Index row, Index col) const { return operator()(row, col); }
+#endif
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index computeIndex(Index row, Index col) const {
+    const bool left = (side == Lhs);
+    EIGEN_UNUSED_VARIABLE(left);  // annoying bug in g++8.1: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=85963
+    Index nocontract_val = left ? row : col;
+    Index linidx = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = static_cast<int>(array_size<nocontract_t>::value) - 1; i > 0; i--) {
+      const Index idx = nocontract_val / m_ij_strides[i];
+      linidx += idx * m_nocontract_strides[i];
+      nocontract_val -= idx * m_ij_strides[i];
+    }
+    if (array_size<typename Tensor::Dimensions>::value > array_size<contract_t>::value) {
+      if (side == Lhs && inner_dim_contiguous) {
+        eigen_assert(m_nocontract_strides[0] == 1);
+        linidx += nocontract_val;
+      } else {
+        linidx += nocontract_val * m_nocontract_strides[0];
+      }
+    }
+
+    Index contract_val = left ? col : row;
+    if (array_size<contract_t>::value > 0) {
+      EIGEN_UNROLL_LOOP
+      for (int i = static_cast<int>(array_size<contract_t>::value) - 1; i > 0; i--) {
+        const Index idx = contract_val / m_k_strides[i];
+        linidx += idx * m_contract_strides[i];
+        contract_val -= idx * m_k_strides[i];
+      }
+
+      if (side == Rhs && inner_dim_contiguous) {
+        eigen_assert(m_contract_strides[0] == 1);
+        linidx += contract_val;
+      } else {
+        linidx += contract_val * m_contract_strides[0];
+      }
+    }
+
+    return linidx;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexPair<Index> computeIndexPair(Index row, Index col,
+                                                                          const Index distance) const {
+    const bool left = (side == Lhs);
+    EIGEN_UNUSED_VARIABLE(left);  // annoying bug in g++8.1: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=85963
+    Index nocontract_val[2] = {left ? row : col, left ? row + distance : col};
+    Index linidx[2] = {0, 0};
+    if (array_size<typename Tensor::Dimensions>::value > array_size<contract_t>::value) {
+      EIGEN_UNROLL_LOOP
+      for (int i = static_cast<int>(array_size<nocontract_t>::value) - 1; i > 0; i--) {
+        const Index idx0 = nocontract_val[0] / m_ij_strides[i];
+        const Index idx1 = nocontract_val[1] / m_ij_strides[i];
+        linidx[0] += idx0 * m_nocontract_strides[i];
+        linidx[1] += idx1 * m_nocontract_strides[i];
+        nocontract_val[0] -= idx0 * m_ij_strides[i];
+        nocontract_val[1] -= idx1 * m_ij_strides[i];
+      }
+      if (side == Lhs && inner_dim_contiguous) {
+        eigen_assert(m_nocontract_strides[0] == 1);
+        linidx[0] += nocontract_val[0];
+        linidx[1] += nocontract_val[1];
+      } else {
+        linidx[0] += nocontract_val[0] * m_nocontract_strides[0];
+        linidx[1] += nocontract_val[1] * m_nocontract_strides[0];
+      }
+    }
+
+    Index contract_val[2] = {left ? col : row, left ? col : row + distance};
+    if (array_size<contract_t>::value > 0) {
+      EIGEN_UNROLL_LOOP
+      for (int i = static_cast<int>(array_size<contract_t>::value) - 1; i > 0; i--) {
+        const Index idx0 = contract_val[0] / m_k_strides[i];
+        const Index idx1 = contract_val[1] / m_k_strides[i];
+        linidx[0] += idx0 * m_contract_strides[i];
+        linidx[1] += idx1 * m_contract_strides[i];
+        contract_val[0] -= idx0 * m_k_strides[i];
+        contract_val[1] -= idx1 * m_k_strides[i];
+      }
+
+      if (side == Rhs && inner_dim_contiguous) {
+        eigen_assert(m_contract_strides[0] == 1);
+        linidx[0] += contract_val[0];
+        linidx[1] += contract_val[1];
+      } else {
+        linidx[0] += contract_val[0] * m_contract_strides[0];
+        linidx[1] += contract_val[1] * m_contract_strides[0];
+      }
+    }
+    return IndexPair<Index>(linidx[0], linidx[1]);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index firstAligned(Index size) const {
+    // Only claim alignment when we can compute the actual stride (ie when we're
+    // dealing with the lhs with inner_dim_contiguous. This is because the
+    // matrix-vector product relies on the stride when dealing with aligned inputs.
+    return (Alignment == Aligned) && (side == Lhs) && inner_dim_contiguous ? 0 : size;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index stride() const {
+    return ((side == Lhs) && inner_dim_contiguous && array_size<contract_t>::value > 0) ? m_contract_strides[0] : 1;
+  }
+
+  const CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_>& tensor() const { return m_tensor; }
+
+  const nocontract_t& nocontract_strides() const { return m_nocontract_strides; }
+  const nocontract_t& ij_strides() const { return m_ij_strides; }
+  const contract_t& contract_strides() const { return m_contract_strides; }
+  const contract_t& k_strides() const { return m_k_strides; }
+
+ protected:
+  CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_> m_tensor;
+  const nocontract_t m_nocontract_strides;
+  const nocontract_t m_ij_strides;
+  const contract_t m_contract_strides;
+  const contract_t m_k_strides;
+};
+
+template <typename Scalar, typename Index, int side, typename Tensor, typename nocontract_t, typename contract_t,
+          int packet_size, bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment,
+          template <class> class MakePointer_>
+class BaseTensorContractionMapper
+    : public SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size,
+                                           inner_dim_contiguous, Alignment, MakePointer_> {
+ public:
+  typedef SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size,
+                                        inner_dim_contiguous, Alignment, MakePointer_>
+      ParentMapper;
+
+  EIGEN_DEVICE_FUNC BaseTensorContractionMapper(const Tensor& tensor, const nocontract_t& nocontract_strides,
+                                                const nocontract_t& ij_strides, const contract_t& contract_strides,
+                                                const contract_t& k_strides)
+      : ParentMapper(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) {}
+
+  template <typename PacketT, int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      std::enable_if_t<internal::unpacket_traits<PacketT>::size == packet_size, PacketT>
+      load(Index i, Index j) const {
+    // whole method makes column major assumption
+
+    // don't need to add offsets for now (because operator handles that)
+    // current code assumes packet size must be a multiple of 2
+    EIGEN_STATIC_ASSERT(packet_size % 2 == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    if (Tensor::PacketAccess && inner_dim_contiguous && !inner_dim_reordered) {
+      const Index index = this->computeIndex(i, j);
+      eigen_assert(this->computeIndex(i + packet_size - 1, j) == index + packet_size - 1);
+      return this->m_tensor.template packet<AlignmentType>(index);
+    }
+
+    const IndexPair<Index> indexPair = this->computeIndexPair(i, j, packet_size - 1);
+    const Index first = indexPair.first;
+    const Index lastIdx = indexPair.second;
+
+    // We can always do optimized packet reads from left hand side right now, because
+    // the vertical matrix dimension on the left hand side is never contracting.
+    // On the right hand side we need to check if the contracting dimensions may have
+    // been shuffled first.
+    if (Tensor::PacketAccess && (side == Lhs || internal::array_size<contract_t>::value <= 1 || !inner_dim_reordered) &&
+        (lastIdx - first) == (packet_size - 1)) {
+      return this->m_tensor.template packet<AlignmentType>(first);
+    }
+
+    EIGEN_ALIGN_MAX Scalar data[packet_size];
+
+    data[0] = this->m_tensor.coeff(first);
+    EIGEN_UNROLL_LOOP
+    for (Index k = 1; k < packet_size - 1; k += 2) {
+      const IndexPair<Index> internal_pair = this->computeIndexPair(i + k, j, 1);
+      data[k] = this->m_tensor.coeff(internal_pair.first);
+      data[k + 1] = this->m_tensor.coeff(internal_pair.second);
+    }
+    data[packet_size - 1] = this->m_tensor.coeff(lastIdx);
+
+    return pload<PacketT>(data);
+  }
+
+  template <typename PacketT, int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      std::enable_if_t<internal::unpacket_traits<PacketT>::size != packet_size, PacketT>
+      load(Index i, Index j) const {
+    const Index requested_packet_size = internal::unpacket_traits<PacketT>::size;
+    EIGEN_ALIGN_MAX Scalar data[requested_packet_size];
+
+    const IndexPair<Index> indexPair = this->computeIndexPair(i, j, requested_packet_size - 1);
+    const Index first = indexPair.first;
+    const Index lastIdx = indexPair.second;
+
+    data[0] = this->m_tensor.coeff(first);
+    for (Index k = 1; k < requested_packet_size - 1; k += 2) {
+      const IndexPair<Index> internal_pair = this->computeIndexPair(i + k, j, 1);
+      data[k] = this->m_tensor.coeff(internal_pair.first);
+      data[k + 1] = this->m_tensor.coeff(internal_pair.second);
+    }
+    data[requested_packet_size - 1] = this->m_tensor.coeff(lastIdx);
+
+    return pload<PacketT>(data);
+  }
+
+  template <typename PacketT, int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketT loadPacket(Index i, Index j) const {
+    return this->load<PacketT, AlignmentType>(i, j);
+  }
+};
+
+template <typename Scalar, typename Index, int side, typename Tensor, typename nocontract_t, typename contract_t,
+          bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment, template <class> class MakePointer_>
+class BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous,
+                                  inner_dim_reordered, Alignment, MakePointer_>
+    : public SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1,
+                                           inner_dim_contiguous, Alignment, MakePointer_> {
+ public:
+  typedef SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous,
+                                        Alignment, MakePointer_>
+      ParentMapper;
+
+  EIGEN_DEVICE_FUNC BaseTensorContractionMapper(const Tensor& tensor, const nocontract_t& nocontract_strides,
+                                                const nocontract_t& ij_strides, const contract_t& contract_strides,
+                                                const contract_t& k_strides)
+      : ParentMapper(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) {}
+
+  template <typename PacketT, int>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketT loadPacket(Index i, Index j) const {
+    EIGEN_ALIGN_MAX Scalar data[1];
+    data[0] = this->m_tensor.coeff(this->computeIndex(i, j));
+    return pload<PacketT>(data);
+  }
+  template <typename PacketT, int>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketT load(Index i, Index j) const {
+    EIGEN_ALIGN_MAX Scalar data[1];
+    data[0] = this->m_tensor.coeff(this->computeIndex(i, j));
+    return pload<PacketT>(data);
+  }
+};
+
+template <typename Scalar, typename Index, int side, typename Tensor, typename nocontract_t, typename contract_t,
+          int packet_size, bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment,
+          template <class> class MakePointer_ = MakePointer>
+class TensorContractionSubMapper {
+ public:
+  typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size,
+                                      inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_>
+      ParentMapper;
+  typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size,
+                                     inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_>
+      Self;
+  typedef Self LinearMapper;
+  typedef Self SubMapper;
+
+  enum {
+    // We can use direct offsets iff the parent mapper supports then and we can compute the strides.
+    // TODO: we should also enable direct offsets for the Rhs case.
+    UseDirectOffsets =
+        ParentMapper::DirectOffsets && (side == Lhs) && inner_dim_contiguous && (array_size<contract_t>::value > 0)
+  };
+
+  EIGEN_DEVICE_FUNC TensorContractionSubMapper(const ParentMapper& base_mapper, Index vert_offset, Index horiz_offset)
+      : m_base_mapper(base_mapper), m_vert_offset(vert_offset), m_horiz_offset(horiz_offset) {
+    // Bake the offsets into the buffer used by the base mapper whenever possible. This avoids the need to recompute
+    // this offset every time we attempt to access a coefficient.
+    if (UseDirectOffsets) {
+      Index stride = m_base_mapper.stride();
+      m_base_mapper.offsetBuffer(vert_offset + horiz_offset * stride);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper(i, 0);
+    }
+    return m_base_mapper(i + m_vert_offset, m_horiz_offset);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i, Index j) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper(i, j);
+    }
+    return m_base_mapper(i + m_vert_offset, j + m_horiz_offset);
+  }
+
+  template <typename PacketT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT loadPacket(Index i) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper.template loadPacket<PacketT, Alignment>(i, 0);
+    }
+    return m_base_mapper.template loadPacket<PacketT, Alignment>(i + m_vert_offset, m_horiz_offset);
+  }
+
+  template <typename PacketT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT loadPacket(Index i, Index j) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper.template loadPacket<PacketT, Alignment>(i, j);
+    }
+    return m_base_mapper.template loadPacket<PacketT, Alignment>(i + m_vert_offset, j + m_horiz_offset);
+  }
+
+  template <typename PacketT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT loadPacketPartial(Index i, Index j, Index, Index = 0) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper.template loadPacket<PacketT, Alignment>(i, j);
+    }
+    return m_base_mapper.template loadPacket<PacketT, Alignment>(i + m_vert_offset, j + m_horiz_offset);
+  }
+
+  template <typename PacketT, int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT loadPacket(Index i, Index j) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper.template load<PacketT, AlignmentType>(i, j);
+    }
+    return m_base_mapper.template loadPacket<PacketT, AlignmentType>(i + m_vert_offset, j + m_horiz_offset);
+  }
+
+  template <typename PacketT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketT& p) const {
+    if (UseDirectOffsets) {
+      m_base_mapper.storePacket(i, 0, p);
+    }
+    m_base_mapper.storePacket(i + m_vert_offset, m_horiz_offset, p);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
+    if (UseDirectOffsets) {
+      return LinearMapper(m_base_mapper, i, j);
+    }
+    return LinearMapper(m_base_mapper, i + m_vert_offset, j + m_horiz_offset);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SubMapper getSubMapper(Index i, Index j) const {
+    if (UseDirectOffsets) {
+      return SubMapper(m_base_mapper, i, j);
+    }
+    return SubMapper(m_base_mapper, i + m_vert_offset, j + m_horiz_offset);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const Index stride() const { return m_base_mapper.stride(); }
+
+  template <typename PacketT, int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i) const {
+    EIGEN_STATIC_ASSERT((internal::is_same<PacketT, PacketT>::value), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    const int ActualAlignment = (AlignmentType == Aligned) && (Alignment == Aligned) ? Aligned : Unaligned;
+    if (UseDirectOffsets) {
+      return m_base_mapper.template loadPacket<PacketT, ActualAlignment>(i, 0);
+    }
+    return m_base_mapper.template loadPacket<PacketT, ActualAlignment>(i + m_vert_offset, m_horiz_offset);
+  }
+
+  template <typename PacketT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool aligned(Index) const {
+    return false;
+  }
+
+  const ParentMapper& base_mapper() const { return m_base_mapper; }
+  Index vert_offset() const { return m_vert_offset; }
+  Index horiz_offset() const { return m_horiz_offset; }
+
+ private:
+  ParentMapper m_base_mapper;
+  const Index m_vert_offset;
+  const Index m_horiz_offset;
+};
+
+template <typename Scalar_, typename Index, int side, typename Tensor, typename nocontract_t, typename contract_t,
+          int packet_size, bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment,
+          template <class> class MakePointer_ = MakePointer>
+class TensorContractionInputMapper
+    : public BaseTensorContractionMapper<Scalar_, Index, side, Tensor, nocontract_t, contract_t, packet_size,
+                                         inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> {
+ public:
+  typedef Scalar_ Scalar;
+  typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size,
+                                      inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_>
+      Base;
+  typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size,
+                                     inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_>
+      SubMapper;
+  typedef SubMapper VectorMapper;
+  typedef SubMapper LinearMapper;
+
+  EIGEN_DEVICE_FUNC TensorContractionInputMapper(const Tensor& tensor, const nocontract_t& nocontract_strides,
+                                                 const nocontract_t& ij_strides, const contract_t& contract_strides,
+                                                 const contract_t& k_strides)
+      : Base(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE SubMapper getSubMapper(Index i, Index j) const {
+    return SubMapper(*this, i, j);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
+    return LinearMapper(*this, i, j);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const {
+    return VectorMapper(*this, i, j);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_>& get_tensor() const {
+    return Base::m_tensor;
+  }
+};
+
+template <typename T>
+struct TensorContractionInputMapperTrait;
+
+template <typename Scalar_, typename Index_, int side_, typename Tensor_, typename nocontract_t_, typename contract_t_,
+          int packet_size_, bool inner_dim_contiguous_, bool inner_dim_reordered_, int Alignment_,
+          template <class> class MakePointer_>
+struct TensorContractionInputMapperTrait<
+    TensorContractionInputMapper<Scalar_, Index_, side_, Tensor_, nocontract_t_, contract_t_, packet_size_,
+                                 inner_dim_contiguous_, inner_dim_reordered_, Alignment_, MakePointer_> > {
+  typedef Tensor_ XprType;
+  static const bool inner_dim_contiguous = inner_dim_contiguous_;
+  static const bool inner_dim_reordered = inner_dim_reordered_;
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
new file mode 100644
index 0000000..fb98d5b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
@@ -0,0 +1,1653 @@
+// This file is part of Eigen, a lightweight C++ template library for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla Public License v. 2.0. If a copy of the MPL was not
+// distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * TensorContractionSycl.h
+ *
+ * \brief:
+ *  TensorContractionSycl.h, provides various tensor contraction kernel for SYCL backend
+ *
+ *****************************************************************/
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace TensorSycl {
+namespace internal {
+
+#ifndef EIGEN_SYCL_DISABLE_GEMV
+/*!
+ * \brief TVPanelSize, a template class used for setting the panel size required for launching General TensorVector
+ * contraction kernel on various hardware devices.
+ *
+ * \tparam Scalar: determines the element type of the tensor/vector
+ *
+ * \tparam StorageIndex  determines the Index type.
+ *
+ * \tparam NCWindow: determines the number of non-contracting element to be process by each work-group
+ *
+ * \tparam CFactor: determines the number of contracting element to be process by each thread
+ *
+ * \tparam NCFactor: determines the number of non-contracting element to be process by each thread
+ */
+template <typename Scalar, typename StorageIndex, StorageIndex NCWindow, StorageIndex CFactor, StorageIndex NCFactor>
+struct TVPanelSize {
+  // LocalThreadSizeC: determines total number of thread per workgroup for the contracting dimension
+  static constexpr StorageIndex LocalThreadSizeC = EIGEN_SYCL_LOCAL_THREAD_DIM0;
+  // LocalThreadSizeNC: determines total number of thread per workgroup for the non-contracting dimension
+  static constxpr StorageIndex LocalThreadSizeNC = EIGEN_SYCL_LOCAL_THREAD_DIM1;
+  // TileSizeDimNC: determines the tile size for the non-contracting dimension
+  static constexpr StorageIndex TileSizeDimNC = NCWindow / NCFactor;
+  // TileSizeDimC: determines the tile size for the contracting dimension
+  static constexpr StorageIndex TileSizeDimC = CFactor * LocalThreadSizeNC * LocalThreadSizeC;
+  // WorkLoadPerThreadNC : determines workload per thread for loading the non-contracting dimension
+  static constexpr StorageIndex WorkLoadPerThreadNC = TileSizeDimNC / LocalThreadSizeNC;
+  // WorkLoadPerThreadC: determines workload per thread for loading the non-contracting dimension
+  static constexpr StorageIndex WorkLoadPerThreadC = TileSizeDimC / LocalThreadSizeC;
+  // BC : determines if supporting bank conflict is required
+  static constexpr bool BC = false;
+};
+#endif
+
+/*!
+ * \brief TTPanelSize, a template class used for setting the panel size required for launching General Tensor Tensor
+ contraction kernel on various hardware devices.
+ *
+ * \tparam Scalar: determines the element type of the tensor
+ *
+ * \tparam StorageIndex: determines the Index type.
+ *
+ * \tparam REG_SIZE_M: determines workload per thread for loading the M dimension This can be varied based on the
+ available register on a chosen device(can be controlled by EIGEN_SYCL_REG_M macro).
+ *
+ * \tparam REG_SIZE_N: determines workload per thread for loading the N dimension This can be varied based on the
+ available register on a chosen device(can be controlled by EIGEN_SYCL_REG_N macro).
+ *
+ * \tparam TSDK: determines Tile size for dimension K. The packet size is assumed to be considered
+ */
+
+template <typename Scalar, typename StorageIndex, StorageIndex REG_SIZE_M, StorageIndex REG_SIZE_N, StorageIndex TSDK>
+struct TTPanelSize {
+  // TileSizeDimK: determines Tile size for dimension K. The packet size is assumed to be considered
+  static constexpr StorageIndex TileSizeDimK = TSDK;
+  // WorkLoadPerThreadM : determines workload per thread for loading the M dimension This can be varied based on the
+  // available register on a chosen device(can be controlled by EIGEN_SYCL_REG_M macro//
+#ifndef EIGEN_SYCL_REG_M
+  static constexpr StorageIndex WorkLoadPerThreadM = REG_SIZE_M;
+#else
+  static constexpr StorageIndex WorkLoadPerThreadM = EIGEN_SYCL_REG_M;
+#endif
+// WorkLoadPerThreadN : determines workload per thread for loading the N dimension This can be varied based on the
+// available register on a chosen device(can be controlled by EIGEN_SYCL_REG_N macro
+#ifndef EIGEN_SYCL_REG_N
+  static constexpr StorageIndex WorkLoadPerThreadN = REG_SIZE_N;
+#else
+  static constexpr StorageIndex WorkLoadPerThreadN = EIGEN_SYCL_REG_N;
+#endif
+  // LocalThreadSizeM: determines total number of thread per workgroup for the m dimension
+  static constexpr StorageIndex LocalThreadSizeM = EIGEN_SYCL_LOCAL_THREAD_DIM0;
+  // LocalThreadSizeN: determines total number of thread per workgroup for the n dimension
+  static constexpr StorageIndex LocalThreadSizeN = EIGEN_SYCL_LOCAL_THREAD_DIM1;
+  // TileSizeDimM: determines the tile size for the m dimension
+  static constexpr StorageIndex TileSizeDimM = LocalThreadSizeM * WorkLoadPerThreadM;
+  // TileSizeDimN: determines the tile size for the n dimension
+  static constexpr StorageIndex TileSizeDimN = LocalThreadSizeN * WorkLoadPerThreadN;
+  // LoadPerThreadLhs: determines workload per thread for loading Lhs Tensor. This must be divisible by packetsize
+  static constexpr StorageIndex LoadPerThreadLhs =
+      ((TileSizeDimK * WorkLoadPerThreadM * WorkLoadPerThreadN) / (TileSizeDimN));
+  // LoadPerThreadRhs: determines workload per thread for loading Rhs Tensor. This must be divisible by packetsize
+  static constexpr StorageIndex LoadPerThreadRhs =
+      ((TileSizeDimK * WorkLoadPerThreadM * WorkLoadPerThreadN) / (TileSizeDimM));
+  // BC : determines if supporting bank conflict is required
+  static constexpr bool BC = true;
+  // DoubleBuffer: determines if double buffering technique should be used (This can be disabled by
+  // EIGEN_SYCL_DISABLE_DOUBLE_BUFFER macro when the device does not have sufficient local memory)
+  static constexpr bool DoubleBuffer =
+#ifdef EIGEN_SYCL_DISABLE_DOUBLE_BUFFER
+      false;
+#else
+      true;
+#endif
+};
+
+/* !
+ * \brief contraction_type: an enum class representing the Tensor Contraction implementation algorithm. This is used to
+ * specialize the contraction algorithm based on device support for dedicated local memory.
+ */
+enum class contraction_type { local, no_local };
+/* !
+ * \brief data_source an enum class determining the location of the data in a memory hierarchy (global, local, private).
+ */
+enum class data_source { global_mem, local_mem, private_mem };
+
+/*!
+ * \brief read, a template function used for loading the data from global
+ memory. This function is used to guarantee coalesced and vectorized load whenever possible
+ *
+ * \tparam PacketLoad: determines if the each element of this tensor block should be loaded in a packet mode
+ *
+ * \param is_coalesced_layout: determines whether or not the Tensor data in a memory can be access coalesced and
+ vectorized when possible. Coalesced memory access is a key factor in Kernel performance. When a tensor is 2d and the
+ contracting dimension is 1, it is always possible to accessed tensor data coalesced and vectorized. This is the case
+ when RHS(right hand side) Tensor is transposed or when LHS(left hand side) Tensor is not transposed.
+ *
+ * \tparam PacketType:  determines the type of packet
+ *
+ * \tparam TensorMapper: determines the input tensor mapper type
+ *
+ * \tparam StorageIndex: determines the Index type
+
+ * \param tensorMapper: is the input tensor
+ *
+ * \param NCIndex: is the non-contracting dim index
+ *
+ * \param CIndex is the contracting dim index
+ *
+ * \param ld: is the leading dimension of the flattened tensor
+ */
+template <bool PacketLoad, bool is_coalesced_layout, bool, typename PacketType, typename TensorMapper,
+          typename StorageIndex>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<PacketLoad, PacketType> read(
+    const TensorMapper &tensorMapper, const StorageIndex &NCIndex, const StorageIndex &CIndex, const StorageIndex &ld) {
+  const StorageIndex row = (is_coalesced_layout) ? NCIndex : CIndex;
+  const StorageIndex col = (is_coalesced_layout) ? CIndex : NCIndex;
+  return tensorMapper.get_tensor().template packet<Unaligned>(row + (col * ld));
+}
+
+/*!
+ * \brief read, special overload of read function, when the read access is not vectorized
+ *
+ * \tparam PacketLoad: determines if the each element of this tensor block should be loaded in a packet mode
+ *
+ * \param is_coalesced_layout: determines whether or not the Tensor data in a memory can be access coalesced and
+  vectorized when possible. Coalesced memory access is a key factor in Kernel performance. When a tensor is 2d and the
+  contracting dimension is 1, it is always possible to accessed tensor data coalesced and vectorized. This is the case
+  when RHS(right hand side) Tensor is transposed or when LHS(left hand side) Tensor is not transposed.
+ *
+ * \tparam PacketType: determines the type of packet
+ *
+ * \tparam TensorMapper: determines the input tensor mapper type
+ *
+ * \tparam StorageIndex: determines the Index type
+
+ * \param tensorMapper: is the input tensor
+ *
+ * \param NCIndex: is the non-contracting dim index
+ *
+ * \param CIndex: is the contracting dim index
+ */
+template <bool PacketLoad, bool, bool IsRhs, typename PacketType, typename TensorMapper, typename StorageIndex>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<!PacketLoad, PacketType> read(
+    const TensorMapper &tensorMapper, const StorageIndex &NCIndex, const StorageIndex &CIndex, const StorageIndex &) {
+  const StorageIndex row = (IsRhs) ? CIndex : NCIndex;
+  const StorageIndex col = (IsRhs) ? NCIndex : CIndex;
+  return tensorMapper(row, col);
+}
+
+/*!
+ * \brief write, a template function used for storing the data to local memory. This function is used to guarantee
+ * coalesced and vectorized store whenever possible.
+ *
+ * \tparam StorageIndex: determines the Index type
+ *
+ * \param ld is the leading dimension of the local memory. ld is a compile time value for the local memory
+ *
+ * \tparam data_source: an enum value representing if the location of the data in a memory hierarchy.
+ *
+ * \tparam PacketType:  determines the type of packet
+ *
+ * \tparam DataScalar: determines the output data type
+ *
+ * \param packet_data: the data to be written in the local memory
+ *
+ * \param ptr: a pointer to the local memory
+ *
+ * \param CIndex is the contracting dim index
+ */
+
+template <typename StorageIndex, StorageIndex ld, data_source dt, typename PacketType, typename DataScalar>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<dt != data_source::global_mem, void> write(
+    PacketType &packet_data, DataScalar ptr) {
+  constexpr int PacketSize = Eigen::internal::unpacket_traits<PacketType>::size;
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < PacketSize; i++) {
+    *ptr = PacketWrapper<PacketType, PacketSize>::scalarize(i, packet_data);
+    ptr += ld;
+  }
+}
+
+/*!
+ * \brief Overloading the write function for storing the data to global memory, when vectorization enabled This function
+ * is used to guarantee coalesced and vectorized store whenever possible.
+ *
+ * \tparam data_source: an enum value representing if the location of the data in a memory hierarchy.
+ *
+ * \tparam PacketType:  determines the type of packet
+ *
+ * \tparam DataScalar: determines the output data type
+ *
+ * \param packet_data: the data to be written in the local memory
+ *
+ * \param ptr: a pointer to the local memory
+ */
+
+template <data_source dt, typename PacketType, typename DataScalar>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    typename std::enable_if_t<Eigen::internal::unpacket_traits<PacketType>::size != 1 && dt == data_source::global_mem,
+                              void>
+    write(PacketType &packet_data, DataScalar *ptr) {
+  ::Eigen::internal::pstoreu<DataScalar, PacketType>(ptr, packet_data);
+}
+
+/*!
+ * \brief Overloading the write function for storing the data to global memory, when vectorization is disabled.
+ *
+ * \tparam data_source: an enum value representing if the location of the data in a memory hierarchy.
+ *
+ * \tparam PacketType:  determines the type of packet
+ *
+ * \tparam DataScalar: determines the output data type
+ *
+ * \param packet_data: the data to be written in the local memory
+ *
+ * \param ptr: a pointer to the local memory
+ */
+template <data_source dt, typename PacketType, typename DataScalar>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    typename std::enable_if_t<Eigen::internal::unpacket_traits<PacketType>::size == 1 && dt == data_source::global_mem,
+                              void>
+    write(PacketType &packet_data, DataScalar *ptr) {
+  *ptr = packet_data;
+}
+
+/*!
+ * \brief check_boundary: is used to check the edge condition for non-internal blocks.
+ *
+ * \tparam is_internal: determines if the block is internal
+ */
+template <bool is_internal>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_boundary(bool) {
+  return true;
+}
+
+/*!
+ * \brief check_boundary: specialization of the check_boundary for non-internal blocks.
+ *
+ * \param cond: true when the data is in range. Otherwise false
+ */
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_boundary<false>(bool cond) {
+  return cond;
+}
+
+/*!
+ * \brief BlockProperties is a template class that provides different characteristic of a block of each Tensor processed
+ * by each workgroup.
+ *
+ * \tparam is_transposed: iff true, determines whether or not the block of the Tensor is transposed
+ *
+ * \tparam packet_load_: determines if the each element of this tensor block should be loaded in a packet mode
+ *
+ * \tparam PacketType:  determines the type of packet
+ *
+ * \tparam OutType: determines the type of each element for this block of tensor. If packet load is true, it will be
+ * packetType; Otherwise it will be scalar Type
+ *
+ * \param elements_per_access determines the size of each element based on OutType
+ *
+ * \param is_coalesced_layout  determines whether or not the Tensor data in a memory can be access coalesced and
+ * vectorized when possible. Coalesced memory access is a key factor in Kernel performance. When a tensor is 2d and the
+ * contracting dimension is 1, it is always possible to accessed tensor data coalesced and vectorized. This is the case
+ * when RHS(right hand side) Tensor is transposed or when LHS(left hand side) Tensor is not transposed.
+ *
+ * \param nc_stride determines the stride of non-contracting dimension to access the next adjustment element within the
+ * Tensor Block for each workgroup
+ *
+ * \param c_stride  determines the stride of contracting dimension to access the next adjustment element within the
+ * Tensor Block for each workgroup
+ */
+template <bool is_transposed, bool is_rhs_, bool packet_load_, typename PacketType>
+struct BlockProperties {
+  static constexpr bool packet_load = packet_load_;
+  typedef typename Eigen::internal::unpacket_traits<PacketType>::type OutScalar;
+  static constexpr bool is_rhs = is_rhs_;
+  typedef std::conditional_t<packet_load, PacketType, OutScalar> OutType;
+  static constexpr int elements_per_access = Eigen::internal::unpacket_traits<OutType>::size;
+  static constexpr bool is_coalesced_layout = !(is_transposed ^ is_rhs);
+  static constexpr int nc_stride = (is_coalesced_layout ? elements_per_access : 1);
+  static constexpr int c_stride = (is_coalesced_layout ? 1 : elements_per_access);
+};
+
+/*!
+ * \brief ThreadProperties is a template class that provides each thread's properties within a workgroup.  Please see
+ * the sycl-1.2.1 specification (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for the workgroup,
+ * work-items
+ *
+ * \tparam StorageIndex: determines the StorageIndex Type
+ *
+ * \param linearLocalThreadId: determines the linearized location of a thread within a work-group
+ *
+ * \param kGroupId: determines the logical group id in a k dimension of the flattened tensor. It will be > 1 when
+ * tall/skinny algorithm is used
+ *
+ * \param mGroupOffset: determines the logical start position of all thread within a workgroup for the m dimension of
+ * the flattened tensor.
+ *
+ * \param kGroupOffset determines the logical start position of all thread within a workgroup for the k dimension of the
+ * flattened tensor. It will be > 1 when tall/skinny algorithm is used.
+ *
+ * \param mLocalOffset: determines the logical start position of each thread within a workgroup for the m dimension of a
+ * flattened tensor. The position determines the distance of each thread within the workgroup from each other
+ * independent from their global position.
+ *
+ * \param nLocalOffset: determines the logical start position of each thread within a workgroup for the n dimension of a
+ * flattened tensor. The position determines the distance of each thread within the workgroup from each other
+ * independent from their global position.
+ *
+ * \param mGlobalOffset: determines the logical start position of each thread a thread for the m dimension on a
+ * flattened tensor
+ *
+ * \param nGlobalOffset: determines the logical start position of each thread a thread for the n dimension on a
+ * flattened tensor
+ *
+ * \param kSize : determine the number of the k elements of the flattened Tensor to be processed by each thread for the
+ * given tensor block. This is !=K dimension of Flattened Tensor when Tall/Skinny matrix is used.
+ *
+ * \param is_internal : this will determined if the thread within the work-group computes an internal block of tensor or
+ * the edge blocks. When it is internal, there is no need to check the boundaries and all the if stantement can be
+ * resolve by compiler.
+ */
+template <typename StorageIndex>
+struct ThreadProperties {
+  const StorageIndex linearLocalThreadId;
+  const StorageIndex kGroupId;
+  const StorageIndex mGroupOffset;
+  const StorageIndex nGroupOffset;
+  const StorageIndex kGroupOffset;
+  const StorageIndex mLocalOffset;
+  const StorageIndex nLocalOffset;
+  const StorageIndex mGlobalOffset;
+  const StorageIndex nGlobalOffset;
+  StorageIndex kSize;
+  const bool is_internal;
+  // this is used to adjust the last block
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ThreadProperties(
+      const StorageIndex linearLocalThreadId_, const StorageIndex kGroupId_, const StorageIndex mGroupOffset_,
+      const StorageIndex nGroupOffset_, const StorageIndex kGroupOffset_, const StorageIndex mLocalOffset_,
+      const StorageIndex nLocalOffset_, const StorageIndex mGlobalOffset_, const StorageIndex nGlobalOffset_,
+      StorageIndex kSize_, const bool is_internal_)
+      : linearLocalThreadId(linearLocalThreadId_),
+        kGroupId(kGroupId_),
+        mGroupOffset(mGroupOffset_),
+        nGroupOffset(nGroupOffset_),
+        kGroupOffset(kGroupOffset_),
+        mLocalOffset(mLocalOffset_),
+        nLocalOffset(nLocalOffset_),
+        mGlobalOffset(mGlobalOffset_),
+        nGlobalOffset(nGlobalOffset_),
+        kSize(kSize_),
+        is_internal(is_internal_) {}
+};
+
+/*!
+ * \brief TensorContractionKernel is a template class that provides Tensor -Tensor contraction operation.
+ *
+ * \tparam OutScalar: determines the output scalar type
+ *
+ * \tparam LhsScalar: determines the left-hand-side scalar type
+ *
+ * \tparam RhsScalar: determines the right-hand-side scalar type
+ *
+ * \tparam OutAccessor: determines the sycl accessor type for out put (please see the sycl-1.2.1 specification
+ (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for accessor definition)
+ *
+ * \tparam LhsMapper determines the tensor contraction mapper type for left-hand-side matrix
+ *
+ * \tparam RhsMapper determines the tensor contraction mapper type for right-hand-side matrix
+ *
+ * \tparam StorageIndex: determines the StorageIndex Type
+ *
+ * \tparam Properties: determines the Contraction Panel properties
+ *
+ * \tparam TripleDim: determines the M, K, N dimensions for the flatten tensors in order to treat them as a matrix
+ *
+ * \tparam Vectorizable: determines whether or not the vectorization is enabled for the Eigen expression.
+ *
+ * \tparam input_mapper_properties : determine if the input tensors are matrix. If they are matrix, special memory
+ access is used to guarantee that always the memory access are coalesced.
+ *
+ * \tparam IsFinal : determine if this is the final kernel. If so, the result will be written in a final output.
+ Otherwise, the result of contraction will be written iin a temporary buffer. This is the case when Tall/Skinny
+ contraction is used. So in this case, a final reduction step is required to compute final output.
+
+ * \tparam contraction_tp: it is an enum value representing whether the local memory/no local memory implementation of
+ the algorithm to be used
+ *
+ * \param scratch: local memory containing tiles of LHS and RHS tensors for each work-group
+ *
+ * \param lhs: determines the left-hand-side flattened tensor (tensor mapper)
+ *
+ * \param rhs: determines the right-hand-side flattened tensor (tensor mapper)
+ *
+ * \param out_res: determines the output tensor containing the contraction result
+ *
+ * \param groupSizeM: a logical number determining the number of work-group for m dimension
+ *
+ * \param groupSizeN: a logical number determining the number of work-group for n dimension
+ *
+ * \param numTiles: determines total number of tiles on the k dimension
+ *
+ * \param TripleDim: determines the M, K, N dimensions for the flatten tensors in order to treat them as a matrix
+ */
+template <typename OutScalar, typename LhsScalar, typename RhsScalar, typename OutAccessor, typename LhsMapper,
+          typename RhsMapper, typename StorageIndex, typename Properties, typename TripleDim, bool Vectorizable,
+          typename input_mapper_properties, bool IsFinal, contraction_type contraction_tp>
+class TensorContractionKernel {
+ public:
+  typedef typename Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketReturnType
+      PacketReturnType;
+  static constexpr int PacketSize =
+      Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketSize;
+  static constexpr bool is_lhs_transposed =
+      !::Eigen::internal::TensorContractionInputMapperTrait<LhsMapper>::inner_dim_contiguous;
+  static constexpr bool is_rhs_transposed =
+      !::Eigen::internal::TensorContractionInputMapperTrait<RhsMapper>::inner_dim_contiguous;
+
+  typedef BlockProperties<is_lhs_transposed, false, input_mapper_properties::is_lhs_matrix && Vectorizable,
+                          PacketReturnType>
+      LHSBlockProperties;
+
+  typedef BlockProperties<is_rhs_transposed, true, input_mapper_properties::is_rhs_matrix && Vectorizable,
+                          PacketReturnType>
+      RHSBlockProperties;
+
+  static constexpr StorageIndex NStride =
+      contraction_tp == contraction_type::local ? Properties::WorkLoadPerThreadN : RHSBlockProperties::nc_stride;
+
+  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
+  typedef cl::sycl::multi_ptr<OutScalar, cl::sycl::access::address_space::local_space> local_ptr;
+  typedef OutScalar * /*cl::sycl::multi_ptr<OutScalar, cl::sycl::access::address_space::private_space>*/ private_ptr;
+  typedef std::conditional_t<contraction_tp == contraction_type::local, local_ptr, private_ptr> tile_ptr;
+  static constexpr StorageIndex LSDL = contraction_tp == contraction_type::local
+                                           ? Properties::TileSizeDimM + Properties::BC
+                                           : Properties::WorkLoadPerThreadM;
+  static constexpr StorageIndex LSDR = contraction_tp == contraction_type::local
+                                           ? Properties::TileSizeDimN + Properties::BC
+                                           : Properties::WorkLoadPerThreadN;
+  static constexpr StorageIndex LocalOffset = Properties::LocalThreadSizeM * Properties::LocalThreadSizeN;
+
+  /**
+   * \brief MemHolder this is a place holder struct for creating memory hierarchy in SYCL. Inside SYCL kernel it is not
+   * allowed to have dynamic memory allocation. While the local memory is created outside of the kernel and passed to
+   * the kernel as an accessor, the private memory can only allowed to be allocated statically. Since we are abstracting
+   * the TiledMemory for both local and private memory, the MemHolder structs is used as a helper to abstract out
+   * different type of memory needed when local/no_local memory computation is called.
+   *
+   * \tparam contraction_type: it is an enum value representing whether the local memory/no local memory implementation
+   of the algorithm to be used
+   * \tparam the private memory size
+   * \param ptr the tile memory pointer type
+   */
+  template <contraction_type, StorageIndex>
+  struct MemHolder {
+    tile_ptr ptr;
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MemHolder(local_ptr block_start_ptr) : ptr(block_start_ptr) {}
+  };
+  /**
+   * \brief specialization of memHolder class when no local memory kernel is used.
+   */
+  template <StorageIndex MemSize>
+  struct MemHolder<contraction_type::no_local, MemSize> {
+    OutScalar ptr[MemSize] = {OutScalar{0}};
+  };
+  /**
+   * \brief TiledMemory: contains required memory pointer for loading  each tile of the TensorContraction panel from
+   * global memory to local/private memory when local/no_local algorithm used.
+   *
+   * \param lhs_scratch_extract : determines the LHS tile memory. It is either private or local memory based on the
+   * selected contraction_type.
+   *
+   * \param rhs_scratch_extract : determines the RHS tile memory. It is either private or local memory based on the
+   * selected contraction_type.
+   *
+   * \param lhs_extract_index: determines the position of each thread on a local memory for lhs input. When private
+   * memory is used this is set to zero as this is not applicable in case of private memory.
+   *
+   * \param rhs_extract_index: determines the position of each thread on a local memory for rhs input. When private
+   * memory is used this is set to zero as this is not applicable in case of private memory.
+   *
+   * \param lhs_scratch_compute : determines the  location to load for computation for lhs_local memory. This is the
+   * same as lhs_scratch_extract for private memory.
+   *
+   * \param rhs_scratch_compute : determines the  location to load for computation for rhs_local memory. This is the
+   * same as rhs_scratch_extract for private memory.
+   */
+  struct TiledMemory {
+    MemHolder<contraction_tp, Properties::WorkLoadPerThreadM * Properties::TileSizeDimK> lhs_scratch_extract;
+    MemHolder<contraction_tp, Properties::WorkLoadPerThreadN * Properties::TileSizeDimK> rhs_scratch_extract;
+    tile_ptr lhs_scratch_ptr_compute;
+    tile_ptr rhs_scratch_ptr_compute;
+    const std::pair<StorageIndex, StorageIndex> lhs_extract_index;
+    const std::pair<StorageIndex, StorageIndex> rhs_extract_index;
+    template <contraction_type tp = contraction_tp>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TiledMemory(const ThreadProperties<StorageIndex> &, local_ptr,
+                                                      std::enable_if_t<tp == contraction_type::no_local> * = 0)
+        : lhs_scratch_extract{},
+          rhs_scratch_extract{},
+          lhs_scratch_ptr_compute(lhs_scratch_extract.ptr),
+          rhs_scratch_ptr_compute(rhs_scratch_extract.ptr),
+          lhs_extract_index(std::pair<StorageIndex, StorageIndex>(StorageIndex{0}, StorageIndex{0})),
+          rhs_extract_index(std::pair<StorageIndex, StorageIndex>(StorageIndex{0}, StorageIndex{0})) {}
+
+    template <contraction_type tp = contraction_tp>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TiledMemory(const ThreadProperties<StorageIndex> &thread_properties,
+                                                      local_ptr block_start_ptr,
+                                                      std::enable_if_t<tp == contraction_type::local> * = 0)
+        : lhs_scratch_extract{block_start_ptr},
+          rhs_scratch_extract{lhs_scratch_extract.ptr +
+                              ((Properties::DoubleBuffer + 1) * LSDL * Properties::TileSizeDimK)},
+          lhs_scratch_ptr_compute(lhs_scratch_extract.ptr + thread_properties.mLocalOffset),
+          rhs_scratch_ptr_compute(rhs_scratch_extract.ptr + thread_properties.nLocalOffset),
+          lhs_extract_index(
+              local_id_extract<LHSBlockProperties, Properties::TileSizeDimM>(thread_properties.linearLocalThreadId)),
+          rhs_extract_index(
+              local_id_extract<RHSBlockProperties, Properties::TileSizeDimN>(thread_properties.linearLocalThreadId)) {}
+  };
+
+  Scratch scratch;
+  const LhsMapper lhs;
+  const RhsMapper rhs;
+  OutAccessor out_res;
+  const StorageIndex groupSizeM;
+  const StorageIndex groupSizeN;
+  const StorageIndex numTiles;
+  const TripleDim triple_dim;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionKernel(Scratch scratch_, const LhsMapper lhs_,
+                                                                const RhsMapper rhs_, OutAccessor out_res_,
+                                                                const StorageIndex groupSizeM_,
+                                                                const StorageIndex groupSizeN_,
+                                                                const StorageIndex numTiles_,
+                                                                const TripleDim triple_dim_)
+      : scratch(scratch_),
+        lhs(lhs_),
+        rhs(rhs_),
+        out_res(out_res_),
+        groupSizeM(groupSizeM_),
+        groupSizeN(groupSizeN_),
+        numTiles(numTiles_),
+        triple_dim(triple_dim_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionKernel(Scratch scratch_, const LhsMapper lhs_,
+                                                                const RhsMapper rhs_, OutAccessor out_res_,
+                                                                const StorageIndex groupSizeM_,
+                                                                const StorageIndex numTiles_,
+                                                                const TripleDim triple_dim_)
+      : TensorContractionKernel(scratch_, lhs_, rhs_, out_res_, groupSizeM_, 1, numTiles_, triple_dim_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) const {
+    const StorageIndex linearLocalThreadId = itemID.get_local_id(0);
+    const StorageIndex nLocalThreadId = linearLocalThreadId / Properties::LocalThreadSizeM;
+    const StorageIndex mLocalThreadId = linearLocalThreadId % Properties::LocalThreadSizeM;
+    const StorageIndex mGroupId = itemID.get_group(0) % groupSizeM;
+    const StorageIndex tmp = itemID.get_group(0) / groupSizeM;
+    const StorageIndex nGroupId = IsFinal ? tmp : tmp % groupSizeN;
+    const StorageIndex kGroupId = IsFinal ? 0 : tmp / groupSizeN;
+    const StorageIndex mGroupOffset = mGroupId * Properties::TileSizeDimM;
+    const StorageIndex nGroupOffset = nGroupId * Properties::TileSizeDimN;
+    const StorageIndex mLocalOffset = PacketSize * mLocalThreadId;
+    const StorageIndex nLocalOffset = NStride * nLocalThreadId;
+    const StorageIndex mGlobalOffset = mGroupOffset + mLocalOffset;
+    const StorageIndex nGlobalOffset = nGroupOffset + nLocalOffset;
+
+    const StorageIndex kSizePerWG = IsFinal ? triple_dim.K : numTiles * Properties::TileSizeDimK;
+    StorageIndex kGroupOffset = kGroupId * kSizePerWG;
+    const bool is_internal = triple_dim.M - mGroupOffset >= Properties::TileSizeDimM &&
+                             triple_dim.N - nGroupOffset >= Properties::TileSizeDimN &&
+                             triple_dim.K - kGroupOffset >= kSizePerWG;
+    // this is used to adjust the last block
+    StorageIndex kSize = IsFinal ? triple_dim.K : std::min(kSizePerWG, triple_dim.K - kGroupOffset);
+    // This is used to find out the lats K offset so that kGroupOffset -kSize can compute the coffset for loading to
+    // tile
+    kGroupOffset += kSize;
+
+    auto thread_properties =
+        ThreadProperties<StorageIndex>(linearLocalThreadId, kGroupId, mGroupOffset, nGroupOffset, kGroupOffset,
+                                       mLocalOffset, nLocalOffset, mGlobalOffset, nGlobalOffset, kSize, is_internal);
+
+    auto out_ptr = out_res + (IsFinal ? 0 : thread_properties.kGroupId * triple_dim.M * triple_dim.N);
+
+    (thread_properties.is_internal) ? compute_panel<true>(itemID, thread_properties, out_ptr)
+                                    : compute_panel<false>(itemID, thread_properties, out_ptr);
+  }
+  // The compute block computes the contraction operation private block for each thread and store the result in the
+  // privateRes memory of Each computation the compute block function is independent of local and no local concepts as
+  // it only compute the block on each thread's private memory space
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_block_per_tile(OutScalar *lhs_block_ptr, OutScalar *rhs_block_ptr,
+                                                                    PacketReturnType *privateRes) const {
+    StorageIndex idx = 0;
+    constexpr StorageIndex lhs_stride =
+        contraction_tp == contraction_type::local ? (PacketSize * Properties::LocalThreadSizeM) : 1;
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex wLPTN = 0; wLPTN < Properties::WorkLoadPerThreadN; wLPTN++) {
+      auto rhsPacket = PacketReturnType{*(rhs_block_ptr + wLPTN)};
+      StorageIndex lhs_index = 0;
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex wLPTM = 0; wLPTM < Properties::WorkLoadPerThreadM / PacketSize; wLPTM++) {
+        PacketReturnType lhsPack{};
+        Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, PacketSize>::set_packet(lhsPack,
+                                                                                             lhs_block_ptr + lhs_index);
+        privateRes[idx] = ::Eigen::internal::pmadd(lhsPack, rhsPacket, privateRes[idx]);
+
+        lhs_index += lhs_stride;
+        idx++;
+      }
+    }
+  }
+  // The store function write the computed contraction operation in the private memory of each thread to the global
+  // memory. The store function is independent of local and no local concepts s that it can be abstract out in the base
+  // class.
+  template <bool is_internal_block, StorageIndex PrivateNStride, typename OutPtr>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void store(OutPtr *out_ptr, PacketReturnType *privateRes,
+                                                   StorageIndex mGlobalOffset, StorageIndex nGlobalOffset) const {
+    auto chk_bound = [&](const StorageIndex &mIndex, const StorageIndex &nIndex) EIGEN_DEVICE_FUNC {
+      return (mIndex + PacketSize - 1 < triple_dim.M && nGlobalOffset + nIndex < triple_dim.N);
+    };
+    // when local memory is not used M and N are both accessed in a coalesced way. However, when local memory is
+    // available the k*N is transposed in the local to N*K therefore, each blocks operates on blockId*
+    // WorkLoadPerThreadN slice of N
+    constexpr StorageIndex GlobalNStride = contraction_tp == contraction_type::local ? 1 : Properties::LocalThreadSizeN;
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex wLPTN = 0; wLPTN < Properties::WorkLoadPerThreadN / PrivateNStride; wLPTN++) {
+      // output leading dimension
+      StorageIndex outputLD = 0;
+      // When local memory is used the PrivateNstride is always 1 because the coalesced access on N is loaded into Local
+      // memory and extracting from local to global is the same as no transposed version. However, when local memory is
+      // not used and RHS is transposed we packetize the load for RHS.
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex nId = 0; nId < PrivateNStride; nId++) {
+        StorageIndex globalRow = mGlobalOffset;
+        EIGEN_UNROLL_LOOP
+        for (StorageIndex wLPTM = 0; wLPTM < Properties::WorkLoadPerThreadM / PacketSize; wLPTM++) {
+          PacketReturnType privetOut = privateRes[wLPTM];
+          if (check_boundary<is_internal_block>(chk_bound(globalRow, nId))) {
+            // Store the final results in C. The C matrix has always M as a first StorageIndex and N as a second
+            // StorageIndex Therefore it is always coalesced layout
+            write<data_source::global_mem>(privetOut, out_ptr + outputLD + globalRow);
+          } else {
+            EIGEN_UNROLL_LOOP
+            for (StorageIndex mId = 0; mId < PacketSize; mId++) {
+              StorageIndex mOffset = globalRow + mId;
+              if (mOffset < triple_dim.M && (nGlobalOffset + nId < triple_dim.N)) {
+                out_ptr[mOffset + outputLD] =
+                    Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, PacketSize>::scalarize(mId, privetOut);
+              }
+            }
+          }
+          globalRow += (PacketSize * Properties::LocalThreadSizeM);
+        }
+        outputLD += triple_dim.M;
+        privateRes += Properties::WorkLoadPerThreadM / PacketSize;
+      }
+      out_ptr += (GlobalNStride * outputLD);
+
+      nGlobalOffset += (PrivateNStride * GlobalNStride);
+    }
+  }
+  // when no local memory is used the following extract_block will be enabled
+  template <typename InputBlockProperties, bool is_internal_block, typename Input, typename PrivateReg,
+            contraction_type contract_tp = contraction_tp>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<contract_tp == contraction_type::no_local> extract_block(
+      const Input &inpt, PrivateReg private_ptr, const std::pair<StorageIndex, StorageIndex> &,
+      const StorageIndex &ncOffset, const StorageIndex cOffset) const {
+    constexpr StorageIndex LocalThreadSizeNC =
+        InputBlockProperties::is_rhs ? Properties::LocalThreadSizeN : Properties::LocalThreadSizeM;
+    constexpr StorageIndex WorkLoadPerThreadNC =
+        InputBlockProperties::is_rhs ? Properties::WorkLoadPerThreadN : Properties::WorkLoadPerThreadM;
+    const StorageIndex &NC = InputBlockProperties::is_rhs ? triple_dim.N : triple_dim.M;
+
+    auto chk_bound = [&](const StorageIndex &CIndex, const StorageIndex &NCIndex) EIGEN_DEVICE_FUNC {
+      return ((CIndex + InputBlockProperties::c_stride - 1 < triple_dim.K) &&
+              (NCIndex + InputBlockProperties::nc_stride - 1 < NC));
+    };
+    const StorageIndex ld = InputBlockProperties::is_coalesced_layout ? NC : triple_dim.K;
+    StorageIndex cIndex = cOffset;
+
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex cId = 0; cId < Properties::TileSizeDimK / InputBlockProperties::c_stride; cId++) {
+      StorageIndex ncIndex = ncOffset;
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex ncId = 0; ncId < WorkLoadPerThreadNC / InputBlockProperties::nc_stride; ncId++) {
+        if (check_boundary<is_internal_block>(chk_bound(cIndex, ncIndex))) {
+          auto val =
+              read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
+                   InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, ncIndex, cIndex, ld);
+
+          write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : WorkLoadPerThreadNC),
+                data_source::private_mem>(val, private_ptr);
+        } else {
+          EIGEN_UNROLL_LOOP
+          for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
+            const StorageIndex ncInd = ncIndex + (InputBlockProperties::is_coalesced_layout ? i : 0);
+            const StorageIndex cInd = cIndex + (InputBlockProperties::is_coalesced_layout ? 0 : i);
+            OutScalar val =
+                (ncInd < NC && cInd < triple_dim.K)
+                    ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
+                          inpt, ncInd, cInd, ld)
+                    : OutScalar(0);
+            write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : WorkLoadPerThreadNC),
+                  data_source::private_mem>(
+                val, private_ptr + (InputBlockProperties::is_coalesced_layout ? i : 0) +
+                         ((InputBlockProperties::is_coalesced_layout ? 0 : i) * WorkLoadPerThreadNC));
+          }
+        }
+
+        // if it is lhs we have to load it packetised when the packet size is > 1, because the output is coalesced. So
+        // even if M is not accessed in a coalesced mode, we have to load packet_size number of m per thread.
+        ncIndex = (!InputBlockProperties::is_rhs && InputBlockProperties::nc_stride == 1 && PacketSize != 1)
+                      ? ncOffset + (ncId + 1) % PacketSize + ((ncId + 1) / PacketSize) * LocalThreadSizeNC
+                      : (ncIndex + InputBlockProperties::nc_stride * LocalThreadSizeNC);
+        private_ptr += InputBlockProperties::nc_stride;
+      }
+      // the previous for loop ( private_ptr += (ncId * nc_stride)) has already moved ptr with one WorkLoadPerThreadNC
+      private_ptr += (InputBlockProperties::c_stride - 1) * WorkLoadPerThreadNC;
+      cIndex += InputBlockProperties::c_stride;
+    }
+  }
+  template <typename InputBlockProperties, StorageIndex TileSizeDimNC>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::pair<StorageIndex, StorageIndex> local_id_extract(
+      const StorageIndex &linearLocalThreadId) {
+    const StorageIndex localThreadNC =
+        (InputBlockProperties::is_coalesced_layout)
+            ? linearLocalThreadId % (TileSizeDimNC / InputBlockProperties::nc_stride)
+            : linearLocalThreadId / (Properties::TileSizeDimK / InputBlockProperties::c_stride);
+    const StorageIndex localThreadC =
+        (InputBlockProperties::is_coalesced_layout)
+            ? linearLocalThreadId / (TileSizeDimNC / InputBlockProperties::nc_stride)
+            : linearLocalThreadId % (Properties::TileSizeDimK / InputBlockProperties::c_stride);
+    return std::pair<StorageIndex, StorageIndex>(localThreadNC, localThreadC);
+  }
+
+  template <bool db = Properties::DoubleBuffer, contraction_type ctp = contraction_tp>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<db && ctp == contraction_type::local> sync_mem(
+      const cl::sycl::nd_item<1> &, bool &db_offset) noexcept {
+    db_offset = !db_offset;
+  }
+
+  template <bool db = Properties::DoubleBuffer, contraction_type ctp = contraction_tp>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<!db && ctp == contraction_type::local> sync_mem(
+      const cl::sycl::nd_item<1> &itemID, bool &) noexcept {
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+  }
+
+  template <contraction_type ctp = contraction_tp>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<ctp == contraction_type::no_local> sync_mem(
+      const cl::sycl::nd_item<1> &, bool &) noexcept {
+    return;
+  }
+
+  template <bool need_sync, contraction_type ctp = contraction_tp>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<need_sync && ctp == contraction_type::no_local>
+  sync_thread(const cl::sycl::nd_item<1> &
+#ifdef EIGEN_SYCL_ARM_GPU_CACHE_OPTIMISATION
+                  itemID
+#endif
+              ) noexcept {
+#ifdef EIGEN_SYCL_ARM_GPU_CACHE_OPTIMISATION
+    itemID.barrier(cl::sycl::access::fence_spacce::local_space);
+#else
+    return;
+#endif
+  }
+  template <bool need_sync, contraction_type ctp = contraction_tp>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<need_sync && ctp == contraction_type::local>
+  sync_thread(const cl::sycl::nd_item<1> &itemID) {
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+  }
+  template <bool need_sync>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<!need_sync> sync_thread(const cl::sycl::nd_item<1> &) {
+    return;
+  }
+
+  template <bool is_internal_block>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_tile_per_panel(const cl::sycl::nd_item<1> &itemID,
+                                                                    ThreadProperties<StorageIndex> &thread_properties,
+                                                                    TiledMemory &tiled_input_block,
+                                                                    PacketReturnType *privateRes,
+                                                                    bool &db_offset) const {
+    // Tiling the Rhs block from global to local memory
+    extract_block<RHSBlockProperties, is_internal_block>(
+        rhs, tiled_input_block.rhs_scratch_extract.ptr + (db_offset * Properties::TileSizeDimK * LSDR),
+        tiled_input_block.rhs_extract_index,
+        contraction_tp == contraction_type::local ? thread_properties.nGroupOffset : thread_properties.nGlobalOffset,
+        thread_properties.kGroupOffset - thread_properties.kSize);
+
+    sync_thread<contraction_tp == contraction_type::no_local>(itemID);
+
+    // Tiling the Lhs block from global to local memory
+    extract_block<LHSBlockProperties, is_internal_block>(
+        lhs, tiled_input_block.lhs_scratch_extract.ptr + (db_offset * LSDL * Properties::TileSizeDimK),
+        tiled_input_block.lhs_extract_index,
+        contraction_tp == contraction_type::local ? thread_properties.mGroupOffset : thread_properties.mGlobalOffset,
+        thread_properties.kGroupOffset - thread_properties.kSize);
+
+    // itemID.barrier(cl::sycl::access::fence_space::local_space);
+    sync_thread<contraction_tp == contraction_type::local>(itemID);
+    // switch to compute mede
+    StorageIndex lhs_offset = (db_offset * LSDL * Properties::TileSizeDimK);
+    StorageIndex rhs_offset = (db_offset * Properties::TileSizeDimK * LSDR);
+    // Loop over the values of a single tile
+    for (StorageIndex k = 0; k < Properties::TileSizeDimK; k++) {
+      compute_block_per_tile(tiled_input_block.lhs_scratch_ptr_compute + lhs_offset,
+                             tiled_input_block.rhs_scratch_ptr_compute + rhs_offset, privateRes);
+      lhs_offset += LSDL;
+      rhs_offset += LSDR;
+    }
+    // computing the K index for the next tile
+    thread_properties.kSize -= Properties::TileSizeDimK;
+    sync_mem(itemID, db_offset);
+  }
+
+  // when local memory is available the following compute_panel will be enabled
+  template <bool is_internal_block, typename OutPtr>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_panel(const cl::sycl::nd_item<1> &itemID,
+                                                           ThreadProperties<StorageIndex> &thread_properties,
+                                                           OutPtr out_ptr) const {
+    auto tiled_input_block = TiledMemory{thread_properties, scratch.get_pointer()};
+    // Allocate register space
+    PacketReturnType privateRes[Properties::WorkLoadPerThreadM * Properties::WorkLoadPerThreadN / PacketSize] = {
+        PacketReturnType{0}};
+    bool db_offset = 0;
+
+    while (thread_properties.kSize >= Properties::TileSizeDimK) {
+      compute_tile_per_panel<is_internal_block>(itemID, thread_properties, tiled_input_block, privateRes, db_offset);
+    }
+    if (thread_properties.kSize > 0) {
+      compute_tile_per_panel<false>(itemID, thread_properties, tiled_input_block, privateRes, db_offset);
+    }
+
+    // Storing the final results in the output
+    store<is_internal_block,
+          contraction_tp == contraction_type::local ? static_cast<StorageIndex>(1) : RHSBlockProperties::nc_stride>(
+        out_ptr + thread_properties.nGlobalOffset * triple_dim.M, privateRes, thread_properties.mGlobalOffset,
+        thread_properties.nGlobalOffset);
+  }
+  // When local memory is available the following extract_block will be enabled
+  template <typename InputBlockProperties, bool is_internal_block, typename Input, typename Local,
+            contraction_type contract_tp = contraction_tp>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<contract_tp == contraction_type::local> extract_block(
+      const Input &inpt, Local local_ptr, const std::pair<StorageIndex, StorageIndex> &local_index,
+      const StorageIndex &ncOffset, const StorageIndex cOffset) const {
+    constexpr StorageIndex TileSizeDimNC =
+        InputBlockProperties::is_rhs ? Properties::TileSizeDimN : Properties::TileSizeDimM;
+    constexpr StorageIndex LoadPerThread =
+        InputBlockProperties::is_rhs ? Properties::LoadPerThreadRhs : Properties::LoadPerThreadLhs;
+    constexpr StorageIndex LSD = InputBlockProperties::is_rhs ? LSDR : LSDL;
+    static_assert(((LocalOffset % (TileSizeDimNC / InputBlockProperties::nc_stride) == 0) &&
+                   (LocalOffset % (Properties::TileSizeDimK / InputBlockProperties::c_stride) == 0)),
+                  " LocalOffset must be divisible by stride");
+    const StorageIndex &NC = InputBlockProperties::is_rhs ? triple_dim.N : triple_dim.M;
+    StorageIndex localThreadNC = local_index.first;
+    StorageIndex localThreadC = local_index.second;
+    auto chk_bound = [&](const StorageIndex &CIndex, const StorageIndex &NCIndex) EIGEN_DEVICE_FUNC {
+      return ((CIndex + InputBlockProperties::c_stride - 1 < triple_dim.K) &&
+              (NCIndex + InputBlockProperties::nc_stride - 1 < NC));
+    };
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex lPT = 0; lPT < LoadPerThread / InputBlockProperties::elements_per_access; lPT++) {
+      const StorageIndex CIndex = cOffset + (InputBlockProperties::c_stride * localThreadC);
+      const StorageIndex NCIndex = ncOffset + (InputBlockProperties::nc_stride * localThreadNC);
+      const StorageIndex ld = InputBlockProperties::is_coalesced_layout ? NC : triple_dim.K;
+      if (check_boundary<is_internal_block>(chk_bound(CIndex, NCIndex))) {
+        auto val =
+            read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
+                 InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, NCIndex, CIndex, ld);
+        write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : LSD), data_source::local_mem>(
+            val, local_ptr + (InputBlockProperties::nc_stride * localThreadNC) +
+                     (InputBlockProperties::c_stride * localThreadC * LSD));
+      } else {
+        EIGEN_UNROLL_LOOP
+        for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
+          const StorageIndex nCInd = NCIndex + (InputBlockProperties::is_coalesced_layout ? i : 0);
+          const StorageIndex cInd = CIndex + (InputBlockProperties::is_coalesced_layout ? 0 : i);
+          OutScalar val =
+              (nCInd < NC && cInd < triple_dim.K)
+                  ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
+                        inpt, nCInd, cInd, ld)
+                  : OutScalar(0);
+
+          write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : LSD), data_source::local_mem>(
+              val, local_ptr + (InputBlockProperties::nc_stride * localThreadNC) +
+                       (InputBlockProperties::is_coalesced_layout ? i : 0) +
+                       ((InputBlockProperties::c_stride * localThreadC +
+                         (InputBlockProperties::is_coalesced_layout ? 0 : i)) *
+                        LSD));
+        }
+      }
+      localThreadNC += (InputBlockProperties::is_coalesced_layout)
+                           ? LocalOffset % (TileSizeDimNC / InputBlockProperties::nc_stride)
+                           : LocalOffset / (Properties::TileSizeDimK / InputBlockProperties::c_stride);
+      localThreadC += (InputBlockProperties::is_coalesced_layout)
+                          ? LocalOffset / (TileSizeDimNC / InputBlockProperties::nc_stride)
+                          : LocalOffset % (Properties::TileSizeDimK / InputBlockProperties::c_stride);
+    }
+  }
+};
+
+#ifndef EIGEN_SYCL_DISABLE_GEMV
+
+/*!
+ * \brief GeneralVectorTensor is a template class that provides Tensor -vector contraction operation, which is a special
+ * case of Tensor Tensor contraction.
+ *
+ * \tparam OutScalar: determines the output scalar type
+ *
+ * \tparam OutAccessor: determines the sycl accessor type for out put (please see the sycl-1.2.1 specification
+ * (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for accessor definition)
+ *
+ * \tparam VectorMapper: determines the tensor contraction mapper for the vector input (can be lhs or rhs)
+ *
+ * \tparam TensorMapper: determines the tensor contraction mapper for the tensor input (can be lhs or rhs)
+ *
+ * \tparam StorageIndex: determines the StorageIndex Type
+ *
+ * \tparam Properties: determines the Contraction Panel properties
+ *
+ * \tparam KFactor: determines the number of elements in K dimension in a Tile
+ *
+ * \tparam Vectorizable: determines whether or not the vectorization is enabled for the Eigen expression.
+ *
+ * \tparam is_lhs_vec: determines whether lhs is a vector or rhs is a vector
+ *
+ * \tparam IsFinal: determine if this is the final kernel. If so, the result will be written in a final output.
+ * Otherwise, the result of contraction will be written iin a temporary buffer.
+ *
+ * \param scratch: determines the local memory containing the vector block for each work-group
+ *
+ * \param vec: determines the vector input (tensor mapper)
+ *
+ * \param mat: determines the tensor input (tensor mapper)
+ *
+ * \param out_res: determines the output vector containing the contraction result
+ *
+ * \param nonContractGroupSize: a logical number determining the number of work-group for non-contracting dimension
+ *
+ * \param nonContractDim: determines the size of non contracting dimension for the flattened tensor
+ *
+ * \param contractDim: determines the size of non contracting dimension for the flattened tensor
+ *
+ */
+template <typename OutScalar, typename OutAccessor, typename VectorMapper, typename TensorMapper, typename StorageIndex,
+          typename Properties, StorageIndex KFactor, bool Vectorizable, bool is_lhs_vec, bool IsFinal>
+struct GeneralVectorTensor {
+  typedef typename Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketReturnType
+      PacketReturnType;
+  static constexpr int PacketSize =
+      Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketSize;
+  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
+
+  static constexpr StorageIndex OutScratchOffset =
+      KFactor * Properties::LocalThreadSizeC * Properties::LocalThreadSizeNC;
+
+  // Since the access layout for a vector can always be coalesced, when LHS is a vector, we pass false and false to make
+  // sure that the !^ is true When RHS is a vector, we pass true and true to make sure that the !^ is true.
+  typedef BlockProperties<is_lhs_vec ? false : true, is_lhs_vec ? false : true, Vectorizable, PacketReturnType>
+      VecBlockProperties;
+
+  Scratch scratch;
+  const VectorMapper vec;
+  const TensorMapper mat;
+  OutAccessor out_res;
+  const StorageIndex nonContractGroupSize;
+  const StorageIndex nonContractDim;
+  const StorageIndex contractDim;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE GeneralVectorTensor(Scratch scratch_, const VectorMapper vec_,
+                                                            const TensorMapper mat_, OutAccessor out_res_,
+                                                            const StorageIndex nonContractGroupSize_,
+                                                            const StorageIndex nonContractDim_,
+                                                            const StorageIndex contractDim_)
+      : scratch(scratch_),
+        vec(vec_),
+        mat(mat_),
+        out_res(out_res_),
+        nonContractGroupSize(nonContractGroupSize_),
+        nonContractDim(nonContractDim_),
+        contractDim(contractDim_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) const {
+    auto scratch_ptr = scratch.get_pointer();
+    const StorageIndex linearLocalThreadId = itemID.get_local_id(0);
+    StorageIndex nonContractId = is_lhs_vec ? linearLocalThreadId / Properties::LocalThreadSizeC
+                                            : linearLocalThreadId % Properties::LocalThreadSizeNC;
+    StorageIndex contractId = is_lhs_vec ? linearLocalThreadId % Properties::LocalThreadSizeC
+                                         : linearLocalThreadId / Properties::LocalThreadSizeNC;
+    const StorageIndex cGroupSize = itemID.get_group_range(0) / nonContractGroupSize;
+    const StorageIndex nonContractGroupId =
+        is_lhs_vec ? itemID.get_group(0) / cGroupSize : itemID.get_group(0) % nonContractGroupSize;
+    const StorageIndex contractGroupId =
+        is_lhs_vec ? itemID.get_group(0) % cGroupSize : itemID.get_group(0) / nonContractGroupSize;
+    auto out_ptr = out_res + (IsFinal ? 0 : contractGroupId * nonContractDim);
+
+    const StorageIndex nonContractGroupOffset = nonContractGroupId * Properties::TileSizeDimNC;
+    const StorageIndex contractGroupOffset = contractGroupId * Properties::TileSizeDimC;
+    auto outScratchIndex = nonContractId + contractId * Properties::LocalThreadSizeNC;
+    const StorageIndex globalNonContractDimOffset = nonContractGroupOffset + nonContractId;
+    const StorageIndex globalContractDimOffset = contractGroupOffset + contractId;
+    auto local_output = scratch_ptr + OutScratchOffset;
+    const bool is_internal = nonContractDim - nonContractGroupOffset >= Properties::TileSizeDimNC &&
+                             contractDim - contractGroupOffset >= Properties::TileSizeDimC;
+    is_internal
+        ? compute_panel<true>(itemID, vec, mat, local_output, out_ptr,
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+                              scratch_ptr, contractGroupOffset,
+#endif
+                              nonContractGroupOffset, linearLocalThreadId, contractDim, nonContractDim, contractId,
+                              nonContractId, globalContractDimOffset, globalNonContractDimOffset, outScratchIndex)
+        : compute_panel<false>(itemID, vec, mat, local_output, out_ptr,
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+                               scratch_ptr, contractGroupOffset,
+#endif
+                               nonContractGroupOffset, linearLocalThreadId, contractDim, nonContractDim, contractId,
+                               nonContractId, globalContractDimOffset, globalNonContractDimOffset, outScratchIndex);
+  }
+  template <bool is_internal_block, typename OutPtr>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_panel(
+      const cl::sycl::nd_item<1> &itemID, const VectorMapper &vec, const TensorMapper &mat, OutScalar *local_output,
+      OutPtr out_ptr,
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+      OutScalar *scratch_ptr, const StorageIndex contractGroupOffset,
+#endif
+      const StorageIndex nonContractGroupOffset, const StorageIndex linearLocalThreadId, StorageIndex contractDim,
+      StorageIndex nonContractDim, StorageIndex contractId, StorageIndex nonContractId,
+      StorageIndex globalContractDimOffset, StorageIndex globalNonContractDimOffset, StorageIndex outScratchIndex) {
+    OutScalar outScalar[Properties::WorkLoadPerThreadNC] = {OutScalar(0)};
+    // Reading the vector
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+    const StorageIndex vectorOffset = contractGroupOffset + linearLocalThreadId;
+    extract_block<VecBlockProperties, is_internal_block, KFactor,
+                  Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC>(vec, scratch_ptr, linearLocalThreadId,
+                                                                                vectorOffset, contractDim);
+
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+    auto in_scratch_ptr = scratch_ptr + contractId;
+#endif
+
+    StorageIndex privateOffsetC = 0;
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex i = 0; i < Properties::WorkLoadPerThreadC; i++) {
+      StorageIndex privateOffsetNC = 0;
+      bool contract_conds = ((globalContractDimOffset + privateOffsetC) < contractDim);
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+      auto vecScalar = *in_scratch_ptr;
+#else
+      auto vecScalar = (check_boundary<is_internal_block>(contract_conds))
+                           ? vec(is_lhs_vec ? StorageIndex(0) : globalContractDimOffset + privateOffsetC,
+                                 is_lhs_vec ? globalContractDimOffset + privateOffsetC : StorageIndex(0))
+                           : OutScalar(0);
+#endif
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
+        auto matScalar = (check_boundary<is_internal_block>(
+                             contract_conds && ((globalNonContractDimOffset + privateOffsetNC) < nonContractDim)))
+                             ? mat(is_lhs_vec ? globalContractDimOffset + privateOffsetC
+                                              : globalNonContractDimOffset + privateOffsetNC,
+                                   is_lhs_vec ? globalNonContractDimOffset + privateOffsetNC
+                                              : globalContractDimOffset + privateOffsetC)
+                             : OutScalar(0);
+
+        outScalar[j] = ::Eigen::internal::pmadd(matScalar, vecScalar, outScalar[j]);
+        privateOffsetNC += Properties::LocalThreadSizeNC;
+      }
+      privateOffsetC += Properties::LocalThreadSizeC;
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+      in_scratch_ptr += Properties::LocalThreadSizeC;
+#endif
+    }
+
+    auto out_scratch_ptr = local_output + outScratchIndex;
+    // Each block of 16*16 element in shared memory should reduce to 16*1
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
+      *out_scratch_ptr = outScalar[j];
+
+      out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
+    }
+    if (is_lhs_vec) {
+      nonContractId = linearLocalThreadId % Properties::LocalThreadSizeNC;
+      contractId = linearLocalThreadId / Properties::LocalThreadSizeNC;
+      outScratchIndex = nonContractId + contractId * Properties::LocalThreadSizeNC;
+    }
+
+    out_scratch_ptr = local_output + outScratchIndex;
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex offset = Properties::LocalThreadSizeC >> 1; offset > 0; offset >>= 1) {
+        itemID.barrier(cl::sycl::access::fence_space::local_space);
+        if (contractId < offset) {
+          StorageIndex myNeigbourId = (Properties::LocalThreadSizeNC * offset);
+          *out_scratch_ptr += out_scratch_ptr[myNeigbourId];
+        }
+      }
+      // moving to next 16 by 16 block
+      out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
+    }
+
+    if (contractId == 0) {
+      out_scratch_ptr = local_output + nonContractId;
+      StorageIndex global_final_offset = nonContractGroupOffset + nonContractId;
+      out_ptr += global_final_offset;
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
+        if (check_boundary<is_internal_block>(global_final_offset < nonContractDim)) {
+          auto res = *out_scratch_ptr;
+
+          *out_ptr = res;
+          out_ptr += Properties::LocalThreadSizeNC;
+        }
+        // moving to next 16 by 16 block to ge the next 16 reduced elements
+        out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
+        if (!(is_internal_block)) global_final_offset += Properties::LocalThreadSizeNC;
+      }
+    }
+  }
+
+  template <typename InputBlockProperties, bool is_internal_block, int CFactor, int GroupSize, typename Input,
+            typename Local>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void extract_block(const Input &inpt, Local *local_ptr,
+                                                                  const StorageIndex &linearLocalThreadId,
+                                                                  const StorageIndex &cOffset, const StorageIndex &C) {
+    local_ptr += InputBlockProperties::c_stride * linearLocalThreadId;
+    StorageIndex cIndex = cOffset;
+    for (StorageIndex cId = 0; cId < CFactor / InputBlockProperties::c_stride; cId++) {
+      if (check_boundary<is_internal_block>(cIndex + InputBlockProperties::c_stride - 1 < C)) {
+        auto val = read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
+                        InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, StorageIndex(0),
+                                                                                              cIndex, StorageIndex(1));
+        write<StorageIndex, 1, data_source::local_mem>(val, local_ptr);
+      } else {
+        EIGEN_UNROLL_LOOP
+        for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
+          OutScalar val =
+              (cIndex + i < C)
+                  ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
+                        inpt, StorageIndex(0), cIndex + i, StorageIndex(1))
+                  : OutScalar(0);
+          write<StorageIndex, 1, data_source::local_mem>(val, local_ptr + i);
+        }
+      }
+      local_ptr += InputBlockProperties::c_stride * GroupSize;
+      cIndex += InputBlockProperties::c_stride * GroupSize;
+    }
+  }
+};
+#endif
+
+#ifndef EIGEN_SYCL_DISABLE_SCALAR
+
+/*!
+ * \brief GeneralScalarContraction is a template class that provides the scalar value of Tensor -Tensor contraction
+ * operation, when all the dimensions are contracting dimensions. This Kernel reduces two tensors to an scalar
+ *
+ * \tparam OutScalar: determines the output scalar type
+ *
+ * \tparam LhsScalar: determines the left-hand-side scalar type
+ *
+ * \tparam RhsScalar: determines the right-hand-side scalar type
+ *
+ * \tparam OutAccessor: determines the sycl accessor type for out put (please see the sycl-1.2.1 specification
+ * (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for accessor definition)
+ *
+ * \tparam LhsMapper: determines the tensor contraction mapper type for left-hand-side matrix
+ *
+ * \tparam RhsMapper: determines the tensor contraction mapper type for right-hand-side matrix
+ *
+ * \tparam StorageIndex: determines the StorageIndex Type
+ *
+ * \tparam Vectorizable: determines whether or not the vectorization is enabled for the Eigen expression.
+ *
+ * \param scratch: local memory containing tiles of LHS and RHS tensors for each work-group
+ *
+ * \param lhs: determines the left-hand-side flattened tensor (tensor mapper)
+ *
+ * \param rhs: determines the right-hand-side flattened tensor (tensor mapper)
+ *
+ * \param out_res: determines the output tensor containing the contraction result
+ *
+ * \param rng: determines the total input data size
+ */
+template <typename OutScalar, typename LhsScalar, typename RhsScalar, typename OutAccessor, typename LhsMapper,
+          typename RhsMapper, typename StorageIndex, bool Vectorizable>
+struct GeneralScalarContraction {
+  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
+  Scratch scratch;
+  const LhsMapper lhs;
+  const RhsMapper rhs;
+  OutAccessor out_res;
+  const StorageIndex rng;
+
+  EIGEN_DEVICE_FUNC GeneralScalarContraction(Scratch scratch_, const LhsMapper lhs_, const RhsMapper rhs_,
+                                             OutAccessor out_res_, const StorageIndex rng_)
+      : scratch(scratch_), lhs(lhs_), rhs(rhs_), out_res(out_res_), rng(rng_) {}
+
+  EIGEN_DEVICE_FUNC void operator()(cl::sycl::nd_item<1> itemID) const {
+    auto out_ptr = out_res;
+    OutScalar *scratch_ptr = scratch.get_pointer();
+
+    StorageIndex globalid = itemID.get_global_id(0);
+    StorageIndex localid = itemID.get_local_id(0);
+    OutScalar accumulator = OutScalar(0);
+    for (StorageIndex i = globalid; i < rng; i += itemID.get_global_range(0)) {
+      accumulator = Eigen::internal::pmadd(lhs(0, i), rhs(i, 0), accumulator);
+    }
+    auto out_scratch_ptr = scratch_ptr + localid;
+    *out_scratch_ptr = accumulator;
+    for (StorageIndex offset = itemID.get_local_range(0) >> 1; offset > 0; offset >>= 1) {
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      if (localid < offset) {
+        *out_scratch_ptr = (accumulator += out_scratch_ptr[offset]);
+      }
+    }
+    if (localid == 0) {
+      out_ptr[itemID.get_group(0)] = accumulator;
+    }
+  }
+};
+#endif
+
+}  // namespace internal
+}  // namespace TensorSycl
+
+template <typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
+struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>,
+                       Eigen::SyclDevice>
+    : public TensorContractionEvaluatorBase<TensorEvaluator<
+          const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Eigen::SyclDevice>> {
+  static_assert(std::is_same<OutputKernelType, const NoOpOutputKernel>::value,
+                "SYCL tensor contraction does not support output kernels.");
+
+  typedef Eigen::SyclDevice Device;
+
+  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
+  typedef TensorContractionEvaluatorBase<Self> Base;
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
+  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
+  typedef typename XprType::Index StorageIndex;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename Base::Storage Storage;
+  typedef typename Base::EvaluatorPointerType EvaluatorPointerType;
+  struct TripleDim {
+    const StorageIndex M;
+    const StorageIndex N;
+    const StorageIndex K;
+    TripleDim(const StorageIndex M_, const StorageIndex N_, const StorageIndex K_) : M(M_), N(N_), K(K_) {}
+  };
+  enum {
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = false,
+  };
+
+  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
+  static constexpr int LDims = Base::LDims;
+  static constexpr int RDims = Base::RDims;
+  static constexpr int ContractDims = Base::ContractDims;
+
+  typedef array<StorageIndex, LDims> left_dim_mapper_t;
+  typedef array<StorageIndex, RDims> right_dim_mapper_t;
+
+  typedef array<StorageIndex, ContractDims> contract_t;
+  typedef array<StorageIndex, LDims - ContractDims> left_nocontract_t;
+  typedef array<StorageIndex, RDims - ContractDims> right_nocontract_t;
+
+  static constexpr int NumDims = LDims + RDims - 2 * ContractDims;
+
+  typedef DSizes<StorageIndex, NumDims> Dimensions;
+
+  typedef TensorEvaluator<typename Base::EvalLeftArgType, Device> LeftEvaluator;
+  typedef TensorEvaluator<typename Base::EvalRightArgType, Device> RightEvaluator;
+  typedef std::remove_const_t<typename LeftEvaluator::CoeffReturnType> LhsScalar;
+  typedef std::remove_const_t<typename RightEvaluator::CoeffReturnType> RhsScalar;
+
+  typedef typename LeftEvaluator::Dimensions LeftDimensions;
+  typedef typename RightEvaluator::Dimensions RightDimensions;
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered>
+  struct input_mapper_propertis {
+    static constexpr bool is_lhs_matrix = (LDims == 2 && ContractDims == 1) || lhs_inner_dim_contiguous;
+    static constexpr bool is_rhs_matrix =
+        (RDims == 2 && ContractDims == 1) || (rhs_inner_dim_contiguous && !rhs_inner_dim_reordered);
+  };
+
+  TensorEvaluator(const XprType &op, const Device &device) : Base(op, device) {}
+
+  // We need to redefine this method to make nvcc happy
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(typename Base::EvaluatorPointerType data) {
+    this->m_leftImpl.evalSubExprsIfNeeded(NULL);
+    this->m_rightImpl.evalSubExprsIfNeeded(NULL);
+    if (!data) {
+      this->m_result = this->m_device.get(
+          static_cast<Scalar *>(this->m_device.allocate_temp(this->dimensions().TotalSize() * sizeof(Scalar))));
+      data = this->m_result;
+    }
+    evalToSycl(data);
+    return (this->m_result != NULL);
+  }
+  const Eigen::SyclDevice &device() const { return this->m_device; }
+  void evalToSycl(typename Base::EvaluatorPointerType buffer) const {
+    if (this->m_lhs_inner_dim_contiguous) {
+      if (this->m_rhs_inner_dim_contiguous) {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<true, true, true, Unaligned>(buffer);
+        } else {
+          evalTyped<true, true, false, Unaligned>(buffer);
+        }
+      } else {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<true, false, true, Unaligned>(buffer);
+        } else {
+          evalTyped<true, false, false, Unaligned>(buffer);
+        }
+      }
+    } else {
+      if (this->m_rhs_inner_dim_contiguous) {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<false, true, true, Unaligned>(buffer);
+        } else {
+          evalTyped<false, true, false, Unaligned>(buffer);
+        }
+      } else {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<false, false, true, Unaligned>(buffer);
+        } else {
+          evalTyped<false, false, false, Unaligned>(buffer);
+        }
+      }
+    }
+  }
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+  void evalTyped(typename Base::EvaluatorPointerType buffer) const {
+    const auto triple_dim = TripleDim{this->m_i_size, this->m_j_size, this->m_k_size};
+    typedef internal::TensorContractionInputMapper<
+        LhsScalar, StorageIndex, internal::Lhs, LeftEvaluator, left_nocontract_t, contract_t,
+        PacketType<CoeffReturnType, Device>::size, lhs_inner_dim_contiguous, false, Unaligned, MakePointer>
+        LhsMapper;
+
+    typedef internal::TensorContractionInputMapper<RhsScalar, StorageIndex, internal::Rhs, RightEvaluator,
+                                                   right_nocontract_t, contract_t,
+                                                   PacketType<CoeffReturnType, Device>::size, rhs_inner_dim_contiguous,
+                                                   rhs_inner_dim_reordered, Unaligned, MakePointer>
+        RhsMapper;
+
+    // initialize data mappers
+    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
+                  this->m_left_contracting_strides, this->m_k_strides);
+
+    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
+                  this->m_right_contracting_strides, this->m_k_strides);
+
+#ifndef EIGEN_SYCL_DISABLE_SCALAR
+    if (triple_dim.M == 1 && triple_dim.N == 1) {
+      launchSC(buffer, lhs, rhs, triple_dim.K);
+    } else
+#endif
+#ifndef EIGEN_SYCL_DISABLE_GEMV
+        if (triple_dim.M != 1 && triple_dim.N == 1) {
+      LaunchVT<false>(buffer, rhs, lhs, triple_dim.M, triple_dim.K);
+    } else if (triple_dim.M == 1 && triple_dim.N != 1) {
+      LaunchVT<true>(buffer, lhs, rhs, triple_dim.N, triple_dim.K);
+    } else  // This is equivalent of if (m!=1 && n!=1)
+#endif
+    {
+      typedef input_mapper_propertis<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered>
+          inpt_mapper_properties;
+#ifndef EIGEN_SYCL_DISABLE_SKINNY
+      bool skinny = false;
+      auto platform_name = this->device().getPlatformName();
+      // This is based on empirical calculation for AMD r9-nano and Fiji
+      if (platform_name.find("AMD") == 0) {
+        skinny = (triple_dim.M < triple_dim.K || triple_dim.N < triple_dim.K) &&
+                 ((triple_dim.M < 1024 && triple_dim.N < 1024) ||
+                  (uint64_t(triple_dim.M * triple_dim.N) < uint64_t(triple_dim.K)));
+      } else {
+        skinny = (((std::max(triple_dim.K, triple_dim.N) / std::min(triple_dim.K, triple_dim.N)) > 100) ||
+                  ((std::max(triple_dim.K, triple_dim.M) / std::min(triple_dim.K, triple_dim.M)) > 100) ||
+                  ((std::max(triple_dim.N, triple_dim.M) / std::min(triple_dim.N, triple_dim.M)) > 100));
+      }
+      if (skinny)
+        adjustTT<true, inpt_mapper_properties>(buffer, lhs, rhs, triple_dim);
+      else
+#endif  // EIGEN_SYCL_DISABLE_SKINNY
+        adjustTT<false, inpt_mapper_properties>(buffer, lhs, rhs, triple_dim);
+    }
+  }
+
+  template <bool skinny, typename input_mapper_properties, typename LhsMapper, typename RhsMapper>
+  void EIGEN_ALWAYS_INLINE adjustTT(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
+                                    const TripleDim &triple_dim) const {
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+    if (device().has_local_memory()) {
+      typedef TensorSycl::internal::TTPanelSize<CoeffReturnType, StorageIndex, 4, 4, 16> PanelParameters;
+      launchTT<TensorSycl::internal::contraction_type::local, skinny, input_mapper_properties, PanelParameters>(
+          buffer, lhs, rhs, triple_dim);
+    }
+#endif
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_OFF
+    if (!(device().has_local_memory())) {
+      typedef TensorSycl::internal::TTPanelSize<CoeffReturnType, StorageIndex, 4, 4, 4> PanelParameters;
+      launchTT<TensorSycl::internal::contraction_type::no_local, skinny, input_mapper_properties, PanelParameters>(
+          buffer, lhs, rhs, triple_dim);
+    }
+#endif
+  }
+
+  template <TensorSycl::internal::contraction_type ct, bool skinny, typename input_mapper_properties,
+            typename Properties, typename LhsMapper, typename RhsMapper>
+  void launchTT(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
+                const TripleDim &triple_dim) const {
+    const StorageIndex roundUpM = Eigen::TensorSycl::internal::roundUp(triple_dim.M, Properties::TileSizeDimM);
+    const StorageIndex roundUpN = Eigen::TensorSycl::internal::roundUp(triple_dim.N, Properties::TileSizeDimN);
+    const StorageIndex groupSizeM = roundUpM / Properties::TileSizeDimM;
+    const StorageIndex groupSizeN = roundUpN / Properties::TileSizeDimN;
+
+    const StorageIndex roundUpK = Eigen::TensorSycl::internal::roundUp(triple_dim.K, Properties::TileSizeDimK);
+    StorageIndex totalTilesK = roundUpK / Properties::TileSizeDimK;
+    StorageIndex groupSizeK =
+        skinny
+            ? std::max(std::min(totalTilesK,
+                                (StorageIndex)(device().getPowerOfTwo(device().getNumSyclMultiProcessors(), true) * 4) /
+                                    (groupSizeM * groupSizeN)),
+                       StorageIndex(1))
+            : StorageIndex(1);
+
+    const StorageIndex numTilesPerGroup = Eigen::TensorSycl::internal::roundUp(totalTilesK, groupSizeK) / groupSizeK;
+
+    const StorageIndex totalGroupSize = groupSizeM * groupSizeN * groupSizeK;
+
+    const StorageIndex localRange = Properties::LocalThreadSizeM * Properties::LocalThreadSizeN;
+    const StorageIndex globalRange = totalGroupSize * localRange;
+
+    const StorageIndex scratchSize = (ct == TensorSycl::internal::contraction_type::local)
+                                         ? ((Properties::DoubleBuffer + 1) *
+                                            (Properties::TileSizeDimM + Properties::BC) * (Properties::TileSizeDimK)) +
+                                               ((Properties::DoubleBuffer + 1) * (Properties::TileSizeDimK) *
+                                                (Properties::TileSizeDimN + Properties::BC))
+                                         : StorageIndex(1);
+
+    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
+    if (groupSizeK == 1) {
+      typedef TensorSycl::internal::TensorContractionKernel<CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType,
+                                                            LhsMapper, RhsMapper, StorageIndex, Properties, TripleDim,
+                                                            PacketAccess, input_mapper_properties, true, ct>
+          ContractKernelName;
+      device()
+          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
+              lhs, rhs, buffer, thread_range, scratchSize, groupSizeM, groupSizeN, numTilesPerGroup, triple_dim)
+          .wait();
+    } else {
+      typedef TensorSycl::internal::TensorContractionKernel<CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType,
+                                                            LhsMapper, RhsMapper, StorageIndex, Properties, TripleDim,
+                                                            PacketAccess, input_mapper_properties, false, ct>
+          ContractKernelName;
+      CoeffReturnType *temp_pointer = static_cast<CoeffReturnType *>(
+          device().allocate_temp(triple_dim.M * triple_dim.N * groupSizeK * sizeof(CoeffReturnType)));
+      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);
+
+      device()
+          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
+              lhs, rhs, tmp_global_accessor, thread_range, scratchSize, groupSizeM, groupSizeN, numTilesPerGroup,
+              triple_dim)
+          .wait();
+
+      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
+      auto op = Op();
+      typedef TensorSycl::internal::SecondStepPartialReduction<CoeffReturnType, StorageIndex, EvaluatorPointerType,
+                                                               EvaluatorPointerType, Op>
+          ReductionKernel;
+
+      device()
+          .template unary_kernel_launcher<CoeffReturnType, ReductionKernel>(
+              tmp_global_accessor, buffer,
+              cl::sycl::nd_range<1>(cl::sycl::range<1>(StorageIndex(
+                                        Eigen::TensorSycl::internal::roundUp(triple_dim.M * triple_dim.N, localRange))),
+                                    cl::sycl::range<1>(localRange)),
+              StorageIndex(1), op, StorageIndex(triple_dim.M * triple_dim.N), groupSizeK)
+          .wait();
+      device().deallocate_temp(temp_pointer);
+    }
+  }
+
+#ifndef EIGEN_SYCL_DISABLE_GEMV
+  template <bool is_lhs_vec, typename VectorMapper, typename TensorMapper, typename StorageIndex>
+  void EIGEN_ALWAYS_INLINE LaunchVT(EvaluatorPointerType buffer, const VectorMapper &vec, const TensorMapper &mat,
+                                    StorageIndex NC, StorageIndex C) const {
+    const StorageIndex nonContractDim = NC;
+    constexpr StorageIndex NCFactor = 1;
+    constexpr StorageIndex CFactor = 1;
+    constexpr StorageIndex NCWindow = 16;
+    typedef Eigen::TensorSycl::internal::TVPanelSize<CoeffReturnType, StorageIndex, NCWindow, CFactor, NCFactor>
+        Properties;
+    const StorageIndex roundUpC = Eigen::TensorSycl::internal::roundUp(C, Properties::TileSizeDimC);
+    const StorageIndex cNumGroups = roundUpC / (Properties::LocalThreadSizeC * Properties::WorkLoadPerThreadC);
+    const StorageIndex roundUpNC = Eigen::TensorSycl::internal::roundUp(nonContractDim, Properties::TileSizeDimNC);
+    const StorageIndex nCNumGroups = roundUpNC / (Properties::LocalThreadSizeNC * Properties::WorkLoadPerThreadNC);
+    const StorageIndex globalRange =
+        (roundUpNC / (Properties::WorkLoadPerThreadNC)) * (roundUpC / (Properties::WorkLoadPerThreadC));
+    const StorageIndex localRange = Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC;
+    const StorageIndex scratchSize =
+        (Properties::WorkLoadPerThreadNC + CFactor) * Properties::LocalThreadSizeC * Properties::LocalThreadSizeNC;
+    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
+    if (cNumGroups > 1) {
+      typedef Eigen::TensorSycl::internal::GeneralVectorTensor<CoeffReturnType, EvaluatorPointerType, VectorMapper,
+                                                               TensorMapper, StorageIndex, Properties, CFactor, false,
+                                                               is_lhs_vec, false>
+          ContractKernelName;
+      CoeffReturnType *temp_pointer =
+          static_cast<CoeffReturnType *>(device().allocate_temp(nonContractDim * cNumGroups * sizeof(CoeffReturnType)));
+      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);
+
+      device()
+          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
+              vec, mat, tmp_global_accessor, thread_range, scratchSize, nCNumGroups, nonContractDim, C)
+          .wait();
+
+      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
+      typedef TensorSycl::internal::SecondStepPartialReduction<CoeffReturnType, StorageIndex, EvaluatorPointerType,
+                                                               EvaluatorPointerType, Op>
+          ReductionKernel;
+
+      device()
+          .template unary_kernel_launcher<CoeffReturnType, ReductionKernel>(
+              tmp_global_accessor, buffer,
+              cl::sycl::nd_range<1>(
+                  cl::sycl::range<1>(Eigen::TensorSycl::internal::roundUp(nonContractDim, localRange)),
+                  cl::sycl::range<1>(localRange)),
+              StorageIndex(1), Op(), nonContractDim, cNumGroups)
+          .wait();
+      device().deallocate_temp(temp_pointer);
+    } else {
+      typedef Eigen::TensorSycl::internal::GeneralVectorTensor<CoeffReturnType, EvaluatorPointerType, VectorMapper,
+                                                               TensorMapper, StorageIndex, Properties, CFactor, false,
+                                                               is_lhs_vec, true>
+          ContractKernelName;
+      device()
+          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
+              vec, mat, buffer, thread_range, scratchSize, nCNumGroups, nonContractDim, C)
+          .wait();
+    }
+  }
+#endif
+
+#ifndef EIGEN_SYCL_DISABLE_SCALAR
+  template <typename LhsMapper, typename RhsMapper>
+  EIGEN_ALWAYS_INLINE void launchSC(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
+                                    StorageIndex K) const {
+    EIGEN_STATIC_ASSERT(!((EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1) &
+                          (EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 - 1)),
+                        "The Local thread size must be a power of 2 for the reduction "
+                        "operation");
+    constexpr StorageIndex local_range = EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1;
+
+    // Here we force the code not to be more than 2-step reduction: Our empirical research shows that if each thread
+    // reduces at least 512 elementss individually, we get better performance.
+    const StorageIndex num_work_group = ((K + (512 * local_range - 1)) / (512 * local_range) > 1 ? local_range : 1);
+    const StorageIndex global_range = num_work_group * local_range;
+
+    typedef Eigen::TensorSycl::internal::GeneralScalarContraction<
+        CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType, LhsMapper, RhsMapper, StorageIndex, false>
+        ContractKernelName;
+    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
+    if (num_work_group > 1) {
+      CoeffReturnType *temp_pointer =
+          static_cast<CoeffReturnType *>(device().allocate_temp(num_work_group * sizeof(CoeffReturnType)));
+      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);
+      device()
+          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(lhs, rhs, tmp_global_accessor,
+                                                                                thread_range, local_range, K)
+          .wait();
+      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
+      typedef TensorSycl::internal::SecondStepFullReducer<CoeffReturnType, Op, EvaluatorPointerType,
+                                                          EvaluatorPointerType, StorageIndex, local_range>
+          GenericRKernel;
+      device()
+          .template unary_kernel_launcher<CoeffReturnType, GenericRKernel>(
+              tmp_global_accessor, buffer,
+              cl::sycl::nd_range<1>(cl::sycl::range<1>(local_range), cl::sycl::range<1>(local_range)), local_range,
+              Op())
+          .wait();
+      device().deallocate_temp(temp_pointer);
+    } else {
+      device()
+          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(lhs, rhs, buffer, thread_range,
+                                                                                local_range, K)
+          .wait();
+    }
+  }
+#endif
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    this->m_leftImpl.cleanup();
+    this->m_rightImpl.cleanup();
+
+    if (this->m_result) {
+      this->m_device.deallocate_temp(this->m_result);
+      this->m_result = NULL;
+    }
+  }
+};
+}  // namespace Eigen
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
new file mode 100644
index 0000000..4ca98fd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
@@ -0,0 +1,1544 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H
+
+// evaluator for thread pool device
+#ifdef EIGEN_USE_THREADS
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
+struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>,
+                       ThreadPoolDevice>
+    : public TensorContractionEvaluatorBase<TensorEvaluator<
+          const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice>> {
+  typedef ThreadPoolDevice Device;
+
+  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
+  typedef TensorContractionEvaluatorBase<Self> Base;
+
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
+  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+
+  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
+
+  // Most of the code is assuming that both input tensors are ColMajor. If the
+  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
+  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
+  // will pretend B is LHS and A is RHS.
+  typedef std::conditional_t<static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>
+      EvalLeftArgType;
+  typedef std::conditional_t<static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>
+      EvalRightArgType;
+
+  static constexpr int LDims =
+      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
+  static constexpr int RDims =
+      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
+  static constexpr int ContractDims = internal::array_size<Indices>::value;
+
+  typedef array<Index, LDims> left_dim_mapper_t;
+  typedef array<Index, RDims> right_dim_mapper_t;
+
+  typedef array<Index, ContractDims> contract_t;
+  typedef array<Index, LDims - ContractDims> left_nocontract_t;
+  typedef array<Index, RDims - ContractDims> right_nocontract_t;
+
+  static constexpr int NumDims = LDims + RDims - 2 * ContractDims;
+
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  // typedefs needed in evalTo
+  typedef std::remove_const_t<typename EvalLeftArgType::Scalar> LhsScalar;
+  typedef std::remove_const_t<typename EvalRightArgType::Scalar> RhsScalar;
+  typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
+
+  typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
+  typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
+
+  TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  template <int Alignment>
+  void evalProduct(Scalar* buffer) const {
+    evalProductImpl<NoCallback, Alignment>(buffer, NoCallback());
+  }
+
+  template <typename EvalToCallback, int Alignment>
+  void evalProductAsync(Scalar* buffer, EvalToCallback done) const {
+    evalProductImpl<EvalToCallback, Alignment>(buffer, std::move(done));
+  }
+
+  template <typename DoneCallback, int Alignment>
+  void evalProductImpl(Scalar* buffer, DoneCallback done) const {
+    // This function computes a lot of heuristics in multiple steps, and it
+    // also has multiple exit points. To keep it sane, readable and all in one
+    // place, sync/async execution decision is made at runtime at the very end.
+    //
+    // (1) In sync mode we allocate Context on the stack, submit computations
+    //     to the device thread pool, and block on a barrier until it is
+    //     completed.
+    //
+    // (2) In async mode we allocate Context on the heap, and after all tasks
+    //     are finished, we call provided the done callback, and delete a
+    //     context from the heap.
+    //
+    // (*) EvalParallelContext & EvalShardedByInnerDimContext owns all the state
+    // and temporary buffers, required for executing the tensor contraction.
+    // They are responsible for cleaning it up after contraction is done.
+    static const bool IsEvalInSyncMode = std::is_same<DoneCallback, NoCallback>::value;
+
+    const Index m = this->m_i_size;
+    const Index n = this->m_j_size;
+    const Index k = this->m_k_size;
+    if (m == 0 || n == 0 || k == 0) return;
+
+    // Compute a set of algorithm parameters:
+    // - kernel block sizes (bm, bn, bk)
+    // - task grain sizes (number of kernels executed per task: gm, gn)
+    // - number of threads
+    // - sharding by row/column
+    // - parallel packing or first lhs then rhs
+    // and some derived parameters:
+    // - number of tasks (nm, nn, nk)
+    // - number of kernels (nm0, nn0)
+    // Unfortunately, all these parameters are tightly interdependent.
+    // So in some cases we first compute approximate values, then compute other
+    // values based on these approximations and then refine the approximations.
+
+    // There are lots of heuristics here. There is some reasoning behind them,
+    // but ultimately they are just tuned on contraction benchmarks for
+    // different input configurations, thread counts and instruction sets.
+    // So feel free to question any of them.
+
+    // Compute whether we want to shard by row or by column.
+    // This is a first approximation, it will be refined later. Since we don't
+    // know number of threads yet we use 2, because what's we are most
+    // interested in at this point is whether it makes sense to use
+    // parallelization at all or not.
+    bool shard_by_col = shardByCol(m, n, 2);
+
+    // First approximation of kernel blocking sizes.
+    // Again, we don't know number of threads yet, so we use 2.
+    Index bm, bn, bk;
+    if (shard_by_col) {
+      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index, internal::ShardByCol> blocking(k, m, n,
+                                                                                                              2);
+      bm = blocking.mc();
+      bn = blocking.nc();
+      bk = blocking.kc();
+    } else {
+      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index, internal::ShardByRow> blocking(k, m, n,
+                                                                                                              2);
+      bm = blocking.mc();
+      bn = blocking.nc();
+      bk = blocking.kc();
+    }
+
+    // Compute optimal number of threads.
+    // Note: we use bk instead of k here because we are interested in amount of
+    // _parallelizable_ computations, and computations are not parallelizable
+    // across k dimension.
+    const TensorOpCost cost = contractionCost(m, n, bm, bn, bk, shard_by_col, false);
+    int num_threads =
+        TensorCostModel<ThreadPoolDevice>::numThreads(static_cast<double>(n) * m, cost, this->m_device.numThreads());
+    int num_threads_by_k = numThreadsInnerDim(m, n, k);
+    if (shardByInnerDim(m, n, k, num_threads, num_threads_by_k)) {
+      // We are in the scenario where it is more effective to shard by the
+      // inner dimension.
+      if (IsEvalInSyncMode) {
+        EvalShardedByInnerDimContext<DoneCallback> ctx(this, num_threads_by_k, buffer, m, n, k, std::move(done));
+        ctx.template run<Alignment>();
+      } else {
+        auto* ctx =
+            new EvalShardedByInnerDimContext<DoneCallback>(this, num_threads_by_k, buffer, m, n, k, std::move(done));
+        ctx->template runAsync<Alignment>();
+      }
+
+      return;
+    }
+
+    // TODO(dvyukov): this is a stop-gap to prevent regressions while the cost
+    // model is not tuned. Remove this when the cost model is tuned.
+    if (n == 1) num_threads = 1;
+
+    if (num_threads == 1) {
+      TENSOR_CONTRACTION_DISPATCH(this->template evalProductSequential, Unaligned, (buffer));
+      if (!IsEvalInSyncMode) done();
+      return;
+    }
+
+    // Now that we know number of threads, recalculate sharding and blocking.
+    shard_by_col = shardByCol(m, n, num_threads);
+    if (shard_by_col) {
+      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index, internal::ShardByCol> blocking(
+          k, m, n, num_threads);
+      bm = blocking.mc();
+      bn = blocking.nc();
+      bk = blocking.kc();
+    } else {
+      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index, internal::ShardByRow> blocking(
+          k, m, n, num_threads);
+      bm = blocking.mc();
+      bn = blocking.nc();
+      bk = blocking.kc();
+    }
+
+    // Number of kernels for each dimension.
+    Index nm0 = numext::div_ceil(m, bm);
+    Index nn0 = numext::div_ceil(n, bn);
+    Index nk = numext::div_ceil(k, bk);
+
+    // Calculate task grain size (number of kernels executed per task).
+    // This task size coarsening serves two purposes:
+    // 1. It reduces per-task overheads including synchronization overheads.
+    // 2. It allows to use caches better (reuse the same packed rhs in several
+    // consecutive kernels).
+    Index gm = 1;
+    Index gn = 1;
+    // If we are sharding by column, then we prefer to reduce rows first.
+    if (shard_by_col) {
+      gm = coarsenM(m, n, bm, bn, bk, gn, num_threads, shard_by_col);
+      gn = coarsenN(m, n, bm, bn, bk, gm, num_threads, shard_by_col);
+    } else {
+      gn = coarsenN(m, n, bm, bn, bk, gm, num_threads, shard_by_col);
+      gm = coarsenM(m, n, bm, bn, bk, gn, num_threads, shard_by_col);
+    }
+    // Number of tasks in each dimension.
+    Index nm = numext::div_ceil(nm0, gm);
+    Index nn = numext::div_ceil(nn0, gn);
+
+    // If there is enough concurrency in the sharding dimension, we choose not
+    // to parallelize by the other dimension, and execute all kernels in sync
+    // mode. This reduces parallelism from the nm x nn down to nn
+    // (shard_by_col==true) or nm (shard_by_col==false).
+    const Index sharding_dim_tasks = shard_by_col ? nn : nm;
+    const int num_worker_threads = this->m_device.numThreadsInPool();
+
+    // With small number of threads we want to make sure that we do not reduce
+    // parallelism too much. With large number of threads we trade maximum
+    // parallelism for better memory locality.
+    const float oversharding_factor = num_worker_threads <= 4    ? 8.0
+                                      : num_worker_threads <= 8  ? 4.0
+                                      : num_worker_threads <= 16 ? 2.0
+                                      : num_worker_threads <= 32 ? 1.0
+                                      : num_worker_threads <= 64 ? 0.8
+                                                                 : /* num_worker_threads > 64 */ 0.6;
+
+    const bool parallelize_by_sharding_dim_only = sharding_dim_tasks >= oversharding_factor * num_worker_threads;
+
+    // Last by not least, decide whether we want to issue both lhs and rhs
+    // packing in parallel; or issue lhs packing first, and then issue rhs
+    // packing when lhs packing completes (for !shard_by_col lhs and rhs are
+    // swapped). Parallel packing allows more parallelism (for both packing and
+    // kernels), while sequential packing provides better locality (once
+    // a thread finishes rhs packing it proceed to kernels with that rhs).
+    // First, we are interested in parallel packing if there are few tasks.
+    bool parallel_pack = num_threads >= nm * nn;
+    // Also do parallel packing if all data fits into L2$.
+    if (m * bk * Index(sizeof(LhsScalar)) + n * bk * Index(sizeof(RhsScalar)) <= l2CacheSize() * num_threads)
+      parallel_pack = true;
+    // But don't do it if we will use each rhs only once. Locality seems to be
+    // more important in this case.
+    if ((shard_by_col ? nm : nn) == 1) parallel_pack = false;
+    // Also don't get in the way of parallelize_by_sharding_dim_only
+    // optimization.
+    if (parallelize_by_sharding_dim_only) parallel_pack = false;
+
+    // TODO(ezhulnev): With if constexpr we don't need SyncEvalParallelContext.
+    if (IsEvalInSyncMode) {
+#define CONTEXT_ARGS                                                                                          \
+  (this, num_threads, buffer, m, n, k, bm, bn, bk, nm, nn, nk, gm, gn, nm0, nn0, shard_by_col, parallel_pack, \
+   parallelize_by_sharding_dim_only, NoCallback())                                                            \
+      .run()
+      TENSOR_CONTRACTION_DISPATCH(SyncEvalParallelContext, Alignment, CONTEXT_ARGS);
+#undef CONTEXT_ARGS
+
+    } else {
+#define CONTEXT_ARGS                                                                                          \
+  (this, num_threads, buffer, m, n, k, bm, bn, bk, nm, nn, nk, gm, gn, nm0, nn0, shard_by_col, parallel_pack, \
+   parallelize_by_sharding_dim_only, std::move(done))
+      TENSOR_CONTRACTION_ASYNC_DISPATCH(EvalParallelContext, DoneCallback, Alignment, CONTEXT_ARGS, run());
+#undef CONTEXT_ARGS
+    }
+  }
+
+  // ------------------------------------------------------------------------ //
+
+  // Dummy struct to represent an empty DoneCallback.
+
+  struct NoCallback {
+    void operator()() { eigen_assert(false && "NoCallback should never be called"); }
+  };
+
+  // ------------------------------------------------------------------------ //
+
+  template <typename DoneCallback, typename Context>
+  class EvalParallelNotification;
+
+  // Synchronous evaluation notification that blocks caller thread in Wait().
+  template <typename Context>
+  class EvalParallelNotification<NoCallback, Context> {
+   public:
+    EvalParallelNotification(Context*, NoCallback) {}
+    void Notify() { done_.Notify(); }
+    void Wait() { done_.Wait(); }
+
+   private:
+    Eigen::Notification done_;
+  };
+
+  // Asynchronous evaluation notification that does not block in Wait().
+  template <typename DoneCallback, typename Context>
+  class EvalParallelNotification {
+   public:
+    EvalParallelNotification(Context* ctx, DoneCallback done) : ctx_(ctx), done_(std::move(done)) {}
+
+    void Notify() {
+      // Make a copy of done callback, because it will be destructed when we
+      // will delete context in the next line (EvalParallelNotification is a
+      // data member of EvalParallelContext class).
+      DoneCallback done_copy = std::move(done_);
+
+      // Delete parallel evaluation context.
+      delete ctx_;
+
+      // Now safely call the done callback.
+      done_copy();
+    }
+
+    void Wait() {}
+
+   private:
+    Context* ctx_;
+    DoneCallback done_;
+  };
+
+  // Context orchestrates sync/async parallel contraction evaluation. When it is
+  // executed in asynchronous mode, it owns all the shared state that might be
+  // accessible by block packing and kernel tasks.
+
+  template <typename DoneCallback, bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous,
+            bool rhs_inner_dim_reordered, int Alignment>
+  class EvalParallelContext {
+   public:
+    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs, LeftEvaluator, left_nocontract_t,
+                                                   contract_t, internal::packet_traits<LhsScalar>::size,
+                                                   lhs_inner_dim_contiguous, false, Unaligned>
+        LhsMapper;
+    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs, RightEvaluator, right_nocontract_t,
+                                                   contract_t, internal::packet_traits<RhsScalar>::size,
+                                                   rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Unaligned>
+        RhsMapper;
+
+    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
+
+    typedef internal::TensorContractionKernel<Scalar, LhsScalar, RhsScalar, Index, OutputMapper, LhsMapper, RhsMapper>
+        TensorContractionKernel;
+
+    typedef typename TensorContractionKernel::LhsBlock LhsBlock;
+    typedef typename TensorContractionKernel::RhsBlock RhsBlock;
+    typedef typename TensorContractionKernel::BlockMemHandle BlockMemHandle;
+
+    EvalParallelContext(const Self* self, int num_threads, Scalar* buffer, Index tm, Index tn, Index tk, Index bm,
+                        Index bn, Index bk, Index nm, Index nn, Index nk, Index gm, Index gn, Index nm0, Index nn0,
+                        bool shard_by_col, bool parallel_pack, bool parallelize_by_sharding_dim_only, DoneCallback done)
+        : created_by_thread_id_(std::this_thread::get_id()),
+          done_(this, std::move(done)),
+          device_(self->m_device),
+          lhs_(self->m_leftImpl, self->m_left_nocontract_strides, self->m_i_strides, self->m_left_contracting_strides,
+               self->m_k_strides),
+          rhs_(self->m_rightImpl, self->m_right_nocontract_strides, self->m_j_strides,
+               self->m_right_contracting_strides, self->m_k_strides),
+          buffer_(buffer),
+          output_(buffer, tm),
+          output_kernel_(self->m_output_kernel),
+          tensor_contraction_params_(self->m_tensor_contraction_params),
+          num_threads_(num_threads),
+          shard_by_col_(shard_by_col),
+          parallel_pack_(parallel_pack),
+          parallelize_by_sharding_dim_only_(parallelize_by_sharding_dim_only),
+          m_(tm),
+          n_(tn),
+          k_(tk),
+          bm_(bm),
+          bn_(bn),
+          bk_(bk),
+          nm_(nm),
+          nn_(nn),
+          nk_(nk),
+          gm_(gm),
+          gn_(gn),
+          nm0_(nm0),
+          nn0_(nn0),
+          kernel_(m_, k_, n_, bm_, bk_, bn_),
+          num_thread_local_allocations_(0),
+          // We reserve 2X more capacity for a thread local values, than the
+          // number of threads in the pool to efficiently handle task stealing
+          // by threads that are not managed by the pool.
+          thread_local_capacity(2 * (parallelize_by_sharding_dim_only_ ? device_.numThreadsInPool() : 0)),
+          // We will use only one of the Lhs/Rhs thread local storage depending
+          // on the shard_by_col value and we parallelize by sharding dim ONLY.
+          lhs_thread_local_blocks_(shard_by_col_ ? 0 : thread_local_capacity, {*this}, {*this}),
+          rhs_thread_local_blocks_(shard_by_col_ ? thread_local_capacity : 0, {*this}, {*this}) {
+      // These two options are mutually exclusive.
+      eigen_assert(!(parallel_pack && parallelize_by_sharding_dim_only));
+
+      for (Index x = 0; x < P; x++) {
+        // Normal number of notifications for k slice switch is
+        // nm_ + nn_ + nm_ * nn_. However, first P - 1 slices will receive only
+        // nm_ + nn_ notifications, because they will not receive notifications
+        // from preceding kernels.
+        state_switch_[x] =
+            x == 0 ? 1 : (parallel_pack_ ? nn_ + nm_ : (shard_by_col_ ? nn_ : nm_)) + (x == P - 1 ? nm_ * nn_ : 0);
+        state_packing_ready_[x] = parallel_pack_ ? 0 : (shard_by_col_ ? nm_ : nn_);
+        state_kernel_[x] = new std::atomic<uint8_t>*[nm_];
+        for (Index m = 0; m < nm_; m++) {
+          state_kernel_[x][m] = new std::atomic<uint8_t>[nn_];
+          // Kernels generally receive 3 notifications (previous kernel + 2
+          // packing), but the first slice won't get notifications from previous
+          // kernels.
+          for (Index n = 0; n < nn_; n++)
+            state_kernel_[x][m][n].store((x == 0 ? 0 : 1) + (parallel_pack_ ? 2 : 1), std::memory_order_relaxed);
+        }
+      }
+
+      // Allocate memory for packed rhs/lhs matrices.
+      packed_mem_ = kernel_.allocateSlices(            //
+          device_,                                     //
+          /*num_lhs=*/nm0_,                            //
+          /*num_rhs=*/nn0_,                            //
+          /*num_slices=*/std::min<Index>(nk_, P - 1),  //
+          packed_lhs_, packed_rhs_);
+
+      if (parallelize_by_sharding_dim_only_) {
+        const int num_worker_threads = device_.numThreadsInPool();
+
+        if (shard_by_col) {
+          can_use_thread_local_packed_ = new std::atomic<bool>[nn_];
+          for (int i = 0; i < nn_; ++i) can_use_thread_local_packed_[i].store(true, std::memory_order_relaxed);
+
+          Index num_blocks = num_worker_threads * gn_;
+          thread_local_pre_alocated_mem_ = kernel_.allocateSlices(  //
+              device_,                                              //
+              /*num_lhs=*/0,                                        //
+              /*num_rhs=*/num_blocks,                               //
+              /*num_slices=*/1,                                     //
+              /*lhs_blocks=*/nullptr, &rhs_thread_local_pre_allocated_);
+
+        } else {
+          can_use_thread_local_packed_ = new std::atomic<bool>[nm_];
+          for (int i = 0; i < nm_; ++i) can_use_thread_local_packed_[i].store(true, std::memory_order_relaxed);
+
+          Index num_blocks = num_worker_threads * gm_;
+          thread_local_pre_alocated_mem_ = kernel_.allocateSlices(  //
+              device_,                                              //
+              /*num_lhs=*/num_blocks,                               //
+              /*num_rhs=*/0,                                        //
+              /*num_slices=*/1, &lhs_thread_local_pre_allocated_,   //
+              /*rhs_blocks=*/nullptr);
+        }
+      }
+    }
+
+    ~EvalParallelContext() {
+      for (Index x = 0; x < P; x++) {
+        for (Index m = 0; m < nm_; m++) delete[] state_kernel_[x][m];
+        delete[] state_kernel_[x];
+      }
+      kernel_.deallocate(device_, packed_mem_);
+      if (parallelize_by_sharding_dim_only_) {
+        kernel_.deallocate(device_, thread_local_pre_alocated_mem_);
+        delete[] can_use_thread_local_packed_;
+      }
+    }
+
+    void run() {
+      // Kick off packing of the first slice.
+      signal_switch(0, 1);
+
+      // Wait for overall completion.
+      //
+      // If parallel evaluation is executed in async mode, this is a no-op, and
+      // Wait() will return immediately. In synchronous mode it will block the
+      // caller thread until it will receive notification from last task.
+      //
+      // In async mode, last task when completed will call done callback from
+      // the same thread, and will delete this context.
+      //
+      // TODO(dvyukov): This wait can lead to deadlock if contraction is
+      // evaluated in synchronous mode. If nthreads contractions are
+      // concurrently submitted from worker threads, this wait will block all
+      // worker threads and the system will deadlock.
+      done_.Wait();
+    }
+
+   private:
+    std::thread::id created_by_thread_id_;
+
+    // This notification is specialized on the type of DoneCallback and can be
+    // blocking or non-blocking.
+    EvalParallelNotification<DoneCallback, EvalParallelContext> done_;
+
+    const Device& device_;
+    LhsMapper lhs_;
+    RhsMapper rhs_;
+    Scalar* const buffer_;
+    OutputMapper output_;
+    OutputKernelType output_kernel_;
+    TensorContractionParams tensor_contraction_params_;
+    const int num_threads_;
+    const bool shard_by_col_;
+    const bool parallel_pack_;
+    const bool parallelize_by_sharding_dim_only_;
+    // Matrix sizes.
+    const Index m_;
+    const Index n_;
+    const Index k_;
+    // Block sizes.
+    const Index bm_;
+    const Index bn_;
+    const Index bk_;
+    // Number of tasks.
+    const Index nm_;
+    const Index nn_;
+    const Index nk_;
+    // Task grain sizes (number of kernels executed per task).
+    const Index gm_;
+    const Index gn_;
+    // Number of blocks (this is different from ni_/nn_ because of task size
+    // coarsening).
+    const Index nm0_;
+    const Index nn0_;
+    // Tensor contraction kernel.
+    TensorContractionKernel kernel_;
+
+    // Parallelization strategy.
+    //
+    // Blocks related to the same k block can run in parallel because they write
+    // to different output blocks. So we parallelize within k slices, this
+    // gives us parallelism level of m x n. Before we can start any kernels
+    // related to k-th slice, we need to issue m lhs packing tasks and n rhs
+    // packing tasks.
+    //
+    // However, there is a bottleneck when we are finishing kernels for k-th
+    // slice (at the very end there is only 1 runnable kernel). To mitigate this
+    // bottleneck we allow kernels from k-th and k+1-th slices to run in
+    // parallel. Note that (m, n, k) and (m, n, k+1) kernels write to the same
+    // output block, so they must not run in parallel.
+    //
+    // This gives us the following dependency graph.
+    // On each k slice we have m x n kernel tasks, m lhs packing tasks and n rhs
+    // packing tasks.
+    // Kernel (m, n, k) can start when:
+    //  - kernel (m, n, k-1) has finished
+    //  - lhs packing (m, k) has finished
+    //  - rhs packing (n, k) has finished
+    // Lhs/rhs packing can start when:
+    //  - all k-1 packing has finished (artificially imposed to limit amount of
+    //  parallel packing)
+    //
+    // On top of that we limit runnable tasks to two consecutive k slices.
+    // This is done to limit amount of memory we need for packed lhs/rhs
+    // (for each k slice we need m*bk + n*bk memory in packed_lhs_/packed_rhs_).
+    //
+    // state_switch_ tracks when we are ready to switch to the next k slice.
+    // state_kernel_[m][n] tracks when we are ready to kick off kernel (m, n).
+    // These variable are rolling over 3 consecutive k slices: first two we are
+    // actively executing + one to track completion of kernels in the second
+    // slice.
+    static constexpr Index P = 3;
+
+    // Handle to the allocated temporary storage for Lhs/Rhs blocks.
+    BlockMemHandle packed_mem_;
+    std::vector<LhsBlock> packed_lhs_[P - 1];
+    std::vector<RhsBlock> packed_rhs_[P - 1];
+
+    // If we choose to parallelize only by the sharding dimension, each thread
+    // will have its own "thread local" (not a C++ thread local storage) memory
+    // for packed_lhs or packed_rhs (shard_by_col = false of true). This memory
+    // can't be passed to a kernel that might execute on a different thread.
+    //
+    // In practice when we are ready to pack memory for the sharding dimension
+    // (rhs if shard_by_col==true) of the K-th slice, all kernels for K-1 slice
+    // already computed (99% of the time), and we can pack data into the thread
+    // local storage, and guarantee that all the kernels will be executed
+    // immediately in the same thread. This significantly increases L1 cache hit
+    // ratio and reduces pressure on the memory bus.
+    //
+    // It's still possible that kernel for the K-th slice will be ready before
+    // completion of the K-1 kernel, so we have to allocate "global" packed_lhs_
+    // and packed_rhs_ to allow kernels to be executed later on a thread
+    // different from the thread that was used for packing.
+
+    // Handle for pre-allocated thread local memory buffers.
+    BlockMemHandle thread_local_pre_alocated_mem_;
+
+    // Only one of these will be initialized depending on shard_by_col value
+    // (the size will be `num_worker_threads * num_grains_in_the_sharding_dim`).
+    std::vector<LhsBlock> lhs_thread_local_pre_allocated_;
+    std::vector<RhsBlock> rhs_thread_local_pre_allocated_;
+
+    // How many thread local blocks were already allocated.
+    std::atomic<int> num_thread_local_allocations_;
+    const int thread_local_capacity;
+
+    // We will use pre-allocated Lhs/Rhs blocks defined above, if the number of
+    // unique threads in a system is below or equal to the number of threads in
+    // a thread pool. We will fallback on dynamic memory allocation after that.
+
+    // ThreadLocalBlocks is a container for Lhs or Rhs thread local buffers. Its
+    // size is equal to the grain size in Lhs/Rhs sharding dimension.
+    template <typename BlockType>
+    class ThreadLocalBlocks {
+     public:
+      ThreadLocalBlocks() = default;
+
+      ThreadLocalBlocks(BlockType* base, size_t grain_size)
+          : is_pre_allocated_(true), thread_local_pre_allocated_base_(base), grain_size_(grain_size) {}
+
+      ThreadLocalBlocks(BlockMemHandle mem_handle, std::vector<BlockType> blocks)
+          : is_pre_allocated_(false), mem_handle_(std::move(mem_handle)), blocks_(std::move(blocks)) {}
+
+      BlockType& block(int grain_index) {
+        eigen_assert(grain_index >= 0);
+        eigen_assert(static_cast<size_t>(grain_index) < size());
+        return is_pre_allocated_ ? thread_local_pre_allocated_base_[grain_index] : blocks_[grain_index];
+      }
+
+      void Release(EvalParallelContext& ctx) const {
+        if (!is_pre_allocated_) {
+          ctx.kernel_.deallocate(ctx.device_, mem_handle_);
+        }
+      }
+
+      size_t size() const { return is_pre_allocated_ ? grain_size_ : blocks_.size(); }
+
+     private:
+      bool is_pre_allocated_;
+
+      // Reuse pre-allocated thread local buffers.
+      BlockType* thread_local_pre_allocated_base_ = nullptr;
+      size_t grain_size_ = 0;
+
+      // These will be initialized only if `is_pre_allocated == false`.
+      BlockMemHandle mem_handle_{};
+      std::vector<BlockType> blocks_;
+    };
+
+    // ThreadLocalBlocksInitialize callable does custom thread local blocks
+    // initialization, and will reuse pre-allocated buffers if possible, or will
+    // dynamically allocate new memory.
+    //
+    // Lhs/Rhs blocks might be of the same type, so we have to pass explicitly
+    // for what side do we plan to do block allocation.
+    template <typename BlockType, bool is_rhs>
+    class ThreadLocalBlocksInitialize {
+      static constexpr bool kIsLhs = !is_rhs && std::is_same<BlockType, LhsBlock>::value;
+      static const bool kIsRhs = is_rhs && std::is_same<BlockType, RhsBlock>::value;
+      static_assert(kIsLhs || kIsRhs, "Unknown block type");
+
+      using Blocks = ThreadLocalBlocks<BlockType>;
+
+     public:
+      ThreadLocalBlocksInitialize(EvalParallelContext& ctx)
+          : ctx_(ctx), num_worker_threads_(ctx_.device_.numThreadsInPool()) {}
+
+      void operator()(Blocks& blocks) {
+        const int n = ctx_.num_thread_local_allocations_.fetch_add(1, std::memory_order_relaxed);
+
+        if (n >= num_worker_threads_) {
+          ThreadLocalBlocksAllocator<is_rhs>::allocate(ctx_, blocks);
+        } else {
+          ThreadLocalBlocksAllocator<is_rhs>::reuse(ctx_, n, blocks);
+        }
+      }
+
+     private:
+      // NOTE(ezhulenev): Without 'if constexpr' we have to put calls to
+      // TensorContractionKernel::allocateSlices into template specializations.
+      // Also explicit specializations are not allowed at class scope in C++03,
+      // EvalCtx type parameter is just a workaround for that limitation.
+      template <bool pack_rhs, typename EvalCtx = EvalParallelContext>
+      struct ThreadLocalBlocksAllocator;
+
+      template <typename EvalCtx>
+      struct ThreadLocalBlocksAllocator</*pack_rhs=*/true, EvalCtx> {
+        static void allocate(EvalCtx& ctx, Blocks& blocks) {
+          std::vector<RhsBlock> rhs_blocks;
+          BlockMemHandle mem_handle = ctx.kernel_.allocateSlices(ctx.device_,
+                                                                 /*num_lhs=*/0,
+                                                                 /*num_rhs=*/ctx.gn_,
+                                                                 /*num_slices=*/1,
+                                                                 /*lhs_blocks=*/nullptr, /*rhs_blocks=*/&rhs_blocks);
+
+          blocks = ThreadLocalBlocks<RhsBlock>(std::move(mem_handle), std::move(rhs_blocks));
+        }
+
+        static void reuse(EvalCtx& ctx, int index, Blocks& blocks) {
+          RhsBlock* ptr = &ctx.rhs_thread_local_pre_allocated_[ctx.gn_ * index];
+          blocks = ThreadLocalBlocks<RhsBlock>(ptr, ctx.gn_);
+        }
+      };
+
+      template <typename EvalCtx>
+      struct ThreadLocalBlocksAllocator</*pack_rhs=*/false, EvalCtx> {
+        static void allocate(EvalCtx& ctx, Blocks& blocks) {
+          std::vector<LhsBlock> lhs_blocks;
+          BlockMemHandle mem_handle = ctx.kernel_.allocateSlices(ctx.device_,
+                                                                 /*num_lhs=*/ctx.gm_,
+                                                                 /*num_rhs=*/0,
+                                                                 /*num_slices=*/1,
+                                                                 /*lhs_blocks=*/&lhs_blocks, /*rhs_blocks=*/nullptr);
+
+          blocks = ThreadLocalBlocks<LhsBlock>(std::move(mem_handle), std::move(lhs_blocks));
+        }
+
+        static void reuse(EvalCtx& ctx, int index, Blocks& blocks) {
+          LhsBlock* ptr = &ctx.lhs_thread_local_pre_allocated_[ctx.gm_ * index];
+          blocks = ThreadLocalBlocks<LhsBlock>(ptr, ctx.gm_);
+        }
+      };
+
+      EvalParallelContext& ctx_;
+      const int num_worker_threads_;
+    };
+
+    template <typename BlockType>
+    class ThreadLocalBlocksRelease {
+     public:
+      using Blocks = ThreadLocalBlocks<BlockType>;
+      ThreadLocalBlocksRelease(EvalParallelContext& ctx) : ctx_(ctx) {}
+      void operator()(Blocks& blocks) { blocks.Release(ctx_); }
+
+     private:
+      EvalParallelContext& ctx_;
+    };
+
+    // ThreadLocalBlocks initialization callables.
+    using ThreadLocalLhsInit = ThreadLocalBlocksInitialize<LhsBlock, /*is_rhs=*/false>;
+    using ThreadLocalRhsInit = ThreadLocalBlocksInitialize<RhsBlock, /*is_rhs=*/true>;
+
+    // ThreadLocalBlocks release callables.
+    using ThreadLocalLhsRelease = ThreadLocalBlocksRelease<LhsBlock>;
+    using ThreadLocalRhsRelease = ThreadLocalBlocksRelease<RhsBlock>;
+
+    // Thread local containers for Lhs/Rhs block packs. In practice only one of
+    // them will be used, depending on the shard_by_col value.
+    Eigen::ThreadLocal<ThreadLocalBlocks<LhsBlock>, ThreadLocalLhsInit, ThreadLocalLhsRelease> lhs_thread_local_blocks_;
+    Eigen::ThreadLocal<ThreadLocalBlocks<RhsBlock>, ThreadLocalRhsInit, ThreadLocalRhsRelease> rhs_thread_local_blocks_;
+
+    // After a particular shard for Kth slice missed thread local execution
+    // opportunity (K-1 slice didn't complete kernels execution), we can no
+    // longer schedule K+1 and following slices in thread local mode, because
+    // there is no more guarantee that previous kernels were executed
+    // sequentially in the same thread (size is nn_ or nm_).
+    std::atomic<bool>* can_use_thread_local_packed_;
+
+    std::atomic<uint8_t>** state_kernel_[P];
+    // state_switch_ is frequently modified by worker threads, while other
+    // fields are read-only after constructor. Let's move it to a separate cache
+    // line to reduce cache-coherency traffic.
+    char pad_[128];
+    std::atomic<Index> state_packing_ready_[P];
+    std::atomic<Index> state_switch_[P];
+
+    LhsBlock& packed_lhs(Index m, Index k, Index m1, bool use_thread_local) {
+      if (use_thread_local) {
+        eigen_assert(!shard_by_col_);
+        ThreadLocalBlocks<LhsBlock>& blocks = lhs_thread_local_blocks_.local();
+
+        Index grain_index = m1 - m * gm_;
+        return blocks.block(
+            internal::convert_index<int>(grain_index));  // FIXME: Consider making ThreadLocalBlocks use Eigen::Index.
+      } else {
+        return packed_lhs_[k % (P - 1)][m1];
+      }
+    }
+
+    RhsBlock& packed_rhs(Index n, Index k, Index n1, bool use_thread_local) {
+      if (use_thread_local) {
+        eigen_assert(shard_by_col_);
+        ThreadLocalBlocks<RhsBlock>& blocks = rhs_thread_local_blocks_.local();
+
+        Index grain_index = n1 - n * gn_;
+        return blocks.block(
+            internal::convert_index<int>(grain_index));  // FIXME: Consider making ThreadLocalBlocks use Eigen::Index.
+      } else {
+        return packed_rhs_[k % (P - 1)][n1];
+      }
+    }
+
+    // In following two methods (pack_lhs and pack_rhs), if we know for sure
+    // that we'll be able to immediately call a kernel with packed data, and do
+    // not submit it to the thread pool, we can use thread local memory for
+    // packed data.
+    //
+    // We can only reliably check it if we are running all kernels in sync mode
+    // (parallelize only by sharding dim). If kernel for m==0 (n==0) is ready to
+    // run, it's guaranteed that all kernels with larger values of m (n) are
+    // also ready, because we execute them in the same order for all K slices.
+
+    void pack_lhs(Index m, Index k) {
+      bool use_thread_local = false;
+
+      if (parallelize_by_sharding_dim_only_ && !shard_by_col_ &&
+          can_use_thread_local_packed_[m].load(std::memory_order_relaxed)) {
+        if (state_kernel_[k % P][m][0].load(std::memory_order_relaxed) == 1) {
+          use_thread_local = true;
+        } else {
+          // If we can't guarantee that all kernels in `k` slice will be
+          // executed sequentially in current thread, it's no longer safe to use
+          // thread local memory in following slices along the k dimensions.
+          eigen_assert(k > 0);
+          can_use_thread_local_packed_[m].store(false, std::memory_order_relaxed);
+        }
+      }
+
+      const Index mend = m * gm_ + gm(m);
+      for (Index m1 = m * gm_; m1 < mend; m1++)
+        kernel_.packLhs(&packed_lhs(m, k, m1, use_thread_local), lhs_.getSubMapper(m1 * bm_, k * bk_), bk(k), bm(m1));
+
+      if (!parallel_pack_ && shard_by_col_) {
+        eigen_assert(!use_thread_local);
+        signal_packing(k);
+      } else {
+        signal_switch(k + 1);
+        for (Index n = nn_ - 1; n >= 0; n--) {
+          bool sync = parallelize_by_sharding_dim_only_ || n == 0;
+          signal_kernel(m, n, k, sync, use_thread_local);
+        }
+      }
+    }
+
+    void pack_rhs(Index n, Index k) {
+      bool use_thread_local = false;
+
+      if (parallelize_by_sharding_dim_only_ && shard_by_col_ &&
+          can_use_thread_local_packed_[n].load(std::memory_order_relaxed)) {
+        if (state_kernel_[k % P][0][n].load(std::memory_order_relaxed) == 1) {
+          use_thread_local = true;
+        } else {
+          // If we can't guarantee that all kernels in `k` slice will be
+          // executed sequentially in current thread, it's no longer safe to use
+          // thread local memory in following slices along the k dimensions.
+          eigen_assert(k > 0);
+          can_use_thread_local_packed_[n].store(false, std::memory_order_relaxed);
+        }
+      }
+
+      const Index nend = n * gn_ + gn(n);
+      for (Index n1 = n * gn_; n1 < nend; n1++) {
+        if (!TensorContractionKernel::HasBeta && k == 0) {
+          // Zero the output memory in parallel, only if contraction kernel does
+          // not support `beta`. Otherwise we will pass beta 0.0 to the first
+          // call to the `TensorContractionKernel::invoke()`.
+          //
+          // On 10000x2x10000 mm zeroing can easily take half of time. Zero (bn
+          // x m) row. Safe to do here because all kernels that will write to
+          // this memory depend on completion of this task. Note: don't call
+          // device_.fill() here. device_.fill() blocks on thread pool
+          // worker thread, which can lead to underutilization and deadlocks.
+          std::fill_n(buffer_ + n1 * bn_ * m_, bn(n1) * m_, Scalar(0));
+        }
+        kernel_.packRhs(&packed_rhs(n, k, n1, use_thread_local), rhs_.getSubMapper(k * bk_, n1 * bn_), bk(k), bn(n1));
+      }
+
+      if (parallel_pack_ || shard_by_col_) {
+        signal_switch(k + 1);
+        for (Index m = nm_ - 1; m >= 0; m--) {
+          bool sync = parallelize_by_sharding_dim_only_ || m == 0;
+          signal_kernel(m, n, k, sync, use_thread_local);
+        }
+      } else {
+        eigen_assert(!use_thread_local);
+        signal_packing(k);
+      }
+    }
+
+    void kernel(Index m, Index n, Index k, bool use_thread_local) {
+      // Note: order of iteration matters here. Iteration over m is innermost
+      // because we want to reuse the same packed rhs in consecutive tasks
+      // (rhs fits into L2$ while lhs only into L3$).
+      const Index nend = n * gn_ + gn(n);
+      const Index mend = m * gm_ + gm(m);
+
+      // NOTE: output = alpha * LHS * RHS + beta * output.
+      const Scalar alpha = Scalar(1);
+      const Scalar beta = (TensorContractionKernel::HasBeta && k == 0) ? Scalar(0) : Scalar(1);
+
+      if (shard_by_col_) {
+        for (Index n1 = n * gn_; n1 < nend; n1++) {
+          for (Index m1 = m * gm_; m1 < mend; m1++) {
+            const auto output_mapper = output_.getSubMapper(m1 * bm_, n1 * bn_);
+            kernel_.invoke(output_mapper, packed_lhs(m, k, m1, !shard_by_col_ && use_thread_local),
+                           packed_rhs(n, k, n1, shard_by_col_ && use_thread_local), bm(m1), bk(k), bn(n1), alpha, beta);
+
+            // We are done with the last task for the [m1, n1] block.
+            if (k + 1 == nk_) {
+              output_kernel_(output_mapper, tensor_contraction_params_, m1 * bm_, n1 * bn_, bm(m1), bn(n1));
+            }
+          }
+        }
+      } else {
+        for (Index m1 = m * gm_; m1 < mend; m1++)
+          for (Index n1 = n * gn_; n1 < nend; n1++) {
+            const auto output_mapper = output_.getSubMapper(m1 * bm_, n1 * bn_);
+            kernel_.invoke(output_mapper, packed_lhs(m, k, m1, !shard_by_col_ && use_thread_local),
+                           packed_rhs(n, k, n1, shard_by_col_ && use_thread_local), bm(m1), bk(k), bn(n1), alpha, beta);
+
+            // We are done with the last task for the [m1, n1] block.
+            if (k + 1 == nk_) {
+              output_kernel_(output_mapper, tensor_contraction_params_, m1 * bm_, n1 * bn_, bm(m1), bn(n1));
+            }
+          }
+      }
+      signal_kernel(m, n, k + 1, /*sync=*/false, /*use_thread_local=*/false);
+      signal_switch(k + 2);
+    }
+
+    void signal_packing(Index k) {
+      eigen_assert(!parallel_pack_);
+      Index s = state_packing_ready_[k % P].fetch_sub(1);
+      eigen_assert(s > 0);
+      if (s != 1) return;
+      state_packing_ready_[k % P] = shard_by_col_ ? nm_ : nn_;
+      enqueue_packing(k, shard_by_col_);
+    }
+
+    void signal_kernel(Index m, Index n, Index k, bool sync, bool use_thread_local) {
+      std::atomic<uint8_t>* state = &state_kernel_[k % P][m][n];
+      Index s = state->load();
+      eigen_assert(s > 0);
+      if (s != 1 && state->fetch_sub(1) != 1) {
+        eigen_assert(!use_thread_local);
+        return;
+      }
+      state->store(parallel_pack_ ? 3 : 2, std::memory_order_relaxed);
+      if (sync) {
+        kernel(m, n, k, use_thread_local);
+      } else {
+        eigen_assert(!use_thread_local);
+        device_.enqueue([this, m, n, k, use_thread_local]() { kernel(m, n, k, use_thread_local); });
+      }
+    }
+
+    void signal_switch(Index k, Index v = 1) {
+      Index s = state_switch_[k % P].fetch_sub(v);
+      eigen_assert(s >= v);
+      if (s != v) return;
+
+      // Ready to switch to the next k slice.
+      // Reset counter for the next iteration.
+      state_switch_[k % P] = (parallel_pack_ ? nm_ + nn_ : (shard_by_col_ ? nn_ : nm_)) + nm_ * nn_;
+      if (k < nk_) {
+        // Issue lhs/rhs packing. Their completion will in turn kick off
+        // kernels.
+        if (parallel_pack_) {
+          enqueue_packing(k, !shard_by_col_);
+          enqueue_packing(k, shard_by_col_);
+        } else if (shard_by_col_) {
+          enqueue_packing(k, false);
+        } else {
+          enqueue_packing(k, true);
+        }
+
+        // Termination handling.
+        // Because kernel completion signals k + 2 switch, we need to finish nk
+        // + 2 slices without issuing any tasks on nk + 1 slice. So here we
+        // pretend that all nk + 1 packing tasks just finish instantly; so that
+        // nk + 2 switch only waits for completion of nk kernels.
+      } else if (k == nk_) {
+        signal_switch(k + 1, parallel_pack_ ? nm_ + nn_ : (shard_by_col_ ? nn_ : nm_));
+      } else {
+        done_.Notify();
+      }
+    }
+
+    // Enqueue all rhs/lhs packing for k-th slice.
+    void enqueue_packing(Index k, bool rhs) { enqueue_packing_helper(0, rhs ? nn_ : nm_, k, rhs); }
+
+    void enqueue_packing_helper(Index start, Index end, Index k, bool rhs) {
+      if (end - start == 1) {
+        if (rhs)
+          pack_rhs(start, k);
+        else
+          pack_lhs(start, k);
+      } else {
+        while (end - start > 1) {
+          Index mid = (start + end) / 2;
+          device_.enqueue([this, mid, end, k, rhs]() { enqueue_packing_helper(mid, end, k, rhs); });
+          end = mid;
+        }
+
+        // Decide if we want to run first packing task (start == 0) in
+        // async mode if we parallelize only by sharding dim:
+        // (1) pack_lhs and pack_rhs call signal_switch before completing
+        //     all calls to signal_kernel, which in sync mode might lead
+        //     to the execution of the first kernel of the k+1 slice, before
+        //     completing a call to the last kernel of the k slice.
+        // (2) all pack tasks for sharded dim must be executed in a thread
+        //     pool to get pre-allocated thead local buffers.
+        bool pack_async = (start == 0) && (parallelize_by_sharding_dim_only_ && shard_by_col_ == rhs) &&
+                          (k > 0 || std::this_thread::get_id() == created_by_thread_id_);
+
+        if (pack_async) {
+          device_.enqueue([this, start, end, k, rhs]() { enqueue_packing_helper(start, end, k, rhs); });
+        } else {
+          enqueue_packing_helper(start, end, k, rhs);
+        }
+      }
+    }
+
+    // Block sizes with accounting for potentially incomplete last block.
+    Index bm(Index m) const { return m + 1 < nm0_ ? bm_ : m_ + bm_ - bm_ * nm0_; }
+    Index bn(Index n) const { return n + 1 < nn0_ ? bn_ : n_ + bn_ - bn_ * nn0_; }
+    Index bk(Index k) const { return k + 1 < nk_ ? bk_ : k_ + bk_ - bk_ * nk_; }
+    // Task grain sizes accounting for potentially incomplete last task.
+    Index gm(Index m) const { return m + 1 < nm_ ? gm_ : nm0_ + gm_ - gm_ * nm_; }
+    Index gn(Index n) const { return n + 1 < nn_ ? gn_ : nn0_ + gn_ - gn_ * nn_; }
+
+    EvalParallelContext(const EvalParallelContext&) = delete;
+    void operator=(const EvalParallelContext&) = delete;
+  };
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+  using SyncEvalParallelContext = EvalParallelContext<NoCallback, lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,
+                                                      rhs_inner_dim_reordered, Alignment>;
+
+  // ------------------------------------------------------------------------ //
+
+  // EvalShardedByInnerDimContext orchestrates sync/async contraction
+  // evaluation, when we shard by inner dimension. When it is executed in
+  // asynchronous mode, it owns all the shared state that might be accessible by
+  // block processing tasks.
+
+  template <typename DoneCallback>
+  struct EvalShardedByInnerDimContext {
+    EvalShardedByInnerDimContext(const Self* self, int num_threads, Scalar* result_buffer, Index m_size, Index n_size,
+                                 Index k_size, DoneCallback done_callback)
+        : evaluator(self),
+          m_lhs_inner_dim_contiguous(evaluator->m_lhs_inner_dim_contiguous),
+          m_rhs_inner_dim_contiguous(evaluator->m_rhs_inner_dim_contiguous),
+          m_rhs_inner_dim_reordered(evaluator->m_rhs_inner_dim_reordered),
+          result(result_buffer),
+          m(m_size),
+          n(n_size),
+          k(k_size),
+          done(std::move(done_callback)),
+          buffer_size_bytes(m * n * sizeof(Scalar)),
+          block_size(blockSize(k, num_threads)),
+          num_blocks(numext::div_ceil<Index>(k, block_size)),
+          num_pending_blocks(internal::convert_index<int>(num_blocks)),
+          l0_ranges(numext::div_ceil<Index>(num_blocks, l0_size)),
+          l0_state(l0_ranges),
+          block_buffers(num_blocks) {
+      // Keep count of pending gemm tasks for each l0 range.
+      for (int i = 0; i < l0_ranges; ++i) {
+        const Index num_pending_tasks = actualRangeSize(l0_ranges, l0_size, i);
+        l0_state.emplace_back(internal::convert_index<int>(num_pending_tasks));
+      }
+
+      // Allocate temporary buffers for each block.
+      for (Index block_idx = 0; block_idx < num_blocks; ++block_idx) {
+        Scalar* buf = block_idx == 0 ? result : static_cast<Scalar*>(evaluator->m_device.allocate(buffer_size_bytes));
+        block_buffers.emplace_back(buf);
+      }
+    }
+
+    ~EvalShardedByInnerDimContext() {
+      for (Index i = 1; i < num_blocks; ++i) {
+        evaluator->m_device.deallocate(block_buffers[i]);
+      }
+    }
+
+    template <int Alignment>
+    void run() {
+      Barrier barrier(internal::convert_index<int>(num_blocks));
+      eval<Alignment>(barrier, 0, num_blocks);
+      barrier.Wait();
+
+      // Aggregate partial sums from l0 ranges.
+      aggregateL0Blocks<Alignment>();
+
+      // Apply output kernel.
+      applyOutputKernel();
+    }
+
+    template <int Alignment>
+    void runAsync() {
+      evalAsync<Alignment>(0, num_blocks);
+    }
+
+   private:
+    // The underlying GEMM kernel assumes that k is a multiple of
+    // the packet size and subtle breakage occurs if this is violated.
+    static const Index packet_size = internal::packet_traits<RhsScalar>::size;
+
+    const Self* evaluator;  // TensorContraction evaluator
+
+    // These fields required fromTENSOR_CONTRACTION_DISPATCH macro.
+    bool m_lhs_inner_dim_contiguous;
+    bool m_rhs_inner_dim_contiguous;
+    bool m_rhs_inner_dim_reordered;
+
+    Scalar* result;
+
+    Index m;
+    Index n;
+    Index k;
+
+    DoneCallback done;
+
+    // ----------------------------------------------------------------------//
+    // Algorithm parameters.
+
+    // We will compute partial results into the buffers of this size.
+    Index buffer_size_bytes;
+
+    Index block_size;
+    Index num_blocks;
+
+    // Keep track of pending tasks when evaluate in async mode.
+    std::atomic<int> num_pending_blocks;
+
+    // We compute partial gemm results in parallel, and to get the final result
+    // we need to add them all together. For the large number of threads (>= 48)
+    // this adds a very expensive sequential step at the end.
+    //
+    // We split the [0, num_blocks) into small ranges, and when a task for the
+    // block finishes its partial gemm computation, it checks if it was the last
+    // gemm in the range, and if so, it will add all blocks of the range.
+    //
+    // After all tasks done, we need to add only these pre-aggregated blocks.
+
+    // For now we use just a single level of ranges to compute pre-aggregated
+    // partial sums, but in general we can use more layers to compute tree
+    // aggregation in parallel and reduce the size of the sequential step.
+    //
+    // TODO(ezhulenev): Add multilevel tree aggregation? Probably will make
+    // sense only if number of threads >= ~128?
+    static const Index l0_size = 4;
+    Index l0_ranges;
+
+    // Keep count of pending gemm tasks for each l0 range.
+    MaxSizeVector<std::atomic<int>> l0_state;  // [0, l0_ranges)
+
+    // Buffers allocated for each temporary block computation.
+    MaxSizeVector<Scalar*> block_buffers;  // [0, num_blocks)
+
+    template <int Alignment>
+    void processBlock(Index block_idx, Index begin, Index end) {
+      Scalar* buf = block_buffers[block_idx];
+
+      TENSOR_CONTRACTION_DISPATCH(evaluator->template evalGemmPartialWithoutOutputKernel, Alignment,
+                                  (buf, begin, end,
+                                   /*num_threads=*/internal::convert_index<int>(num_blocks)));
+
+      // Check if it was the last task in l0 range.
+      const Index l0_index = block_idx / l0_size;
+      const int v = l0_state[l0_index].fetch_sub(1);
+      eigen_assert(v >= 1);
+
+      // If we processed the last block of the range, we can aggregate all
+      // partial results into the first block of the range.
+      if (v == 1) {
+        const Index rng_size = actualRangeSize(l0_ranges, l0_size, l0_index);
+        const Index dst_block_idx = l0_index * l0_size;
+
+        if (rng_size == l0_size) {
+          addAllToBuffer<Alignment>(m * n,
+                                    /*src_buf0=*/block_buffers[dst_block_idx + 1],
+                                    /*src_buf1=*/block_buffers[dst_block_idx + 2],
+                                    /*src_buf2=*/block_buffers[dst_block_idx + 3],
+                                    /*dst_buf= */ block_buffers[dst_block_idx]);
+        } else {
+          // Aggregate blocks of potentially incomplete last range.
+          for (int i = 1; i < rng_size; ++i) {
+            addToBuffer<Alignment>(m * n,
+                                   /*src_buf=*/block_buffers[dst_block_idx + i],
+                                   /*dst_buf=*/block_buffers[dst_block_idx]);
+          }
+        }
+      }
+    }
+
+    // Aggregate partial sums from l0 ranges.
+    template <int Alignment>
+    void aggregateL0Blocks() const {
+      Index l0_index = 1;
+
+      for (; l0_index + 2 < l0_ranges; l0_index += 3) {
+        addAllToBuffer<Alignment>(m * n,
+                                  /*src_buf0=*/block_buffers[(l0_index + 0) * l0_size],
+                                  /*src_buf1=*/block_buffers[(l0_index + 1) * l0_size],
+                                  /*src_buf2=*/block_buffers[(l0_index + 2) * l0_size],
+                                  /*dst_buf= */ block_buffers[0]);
+      }
+
+      for (; l0_index < l0_ranges; ++l0_index) {
+        addToBuffer<Alignment>(m * n, block_buffers[l0_index * l0_size], block_buffers[0]);
+      }
+    }
+
+    void applyOutputKernel() const {
+      typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
+      evaluator->m_output_kernel(OutputMapper(result, m), evaluator->m_tensor_contraction_params,
+                                 static_cast<Eigen::Index>(0), static_cast<Eigen::Index>(0), m, n);
+    }
+
+    // Compute block size with accounting for potentially incomplete last block.
+    Index actualBlockSize(Index block_idx) const {
+      return block_idx + 1 < num_blocks ? block_size : k + block_size - block_size * num_blocks;
+    };
+
+    // Compute range size with accounting for potentially incomplete last range.
+    Index actualRangeSize(Index num_ranges, Index range_size, Index range_idx) const {
+      eigen_assert(range_idx < num_ranges);
+      return range_idx + 1 < num_ranges ? range_size : num_blocks + range_size - range_size * num_ranges;
+    };
+
+    template <int Alignment>
+    EIGEN_STRONG_INLINE static void addToBuffer(size_t n, const Scalar* src_buf, Scalar* tgt_buf) {
+      const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
+      size_t i = 0;
+      const size_t num_packets = n / output_packet_size;
+      for (; i < output_packet_size * num_packets; i += output_packet_size) {
+        const PacketReturnType src_val = internal::pload<PacketReturnType>(src_buf + i);
+        const PacketReturnType tgt_val = internal::ploadt<PacketReturnType, Alignment>(tgt_buf + i);
+        const PacketReturnType sum = internal::padd(src_val, tgt_val);
+        internal::pstoret<Scalar, PacketReturnType, Alignment>(tgt_buf + i, sum);
+      }
+      for (; i < n; ++i) {
+        tgt_buf[i] += src_buf[i];
+      }
+    }
+
+    template <int Alignment>
+    EIGEN_STRONG_INLINE static void addAllToBuffer(size_t n, const Scalar* src_buf0, const Scalar* src_buf1,
+                                                   const Scalar* src_buf2, Scalar* dst_buf) {
+      using ::Eigen::internal::padd;
+      using ::Eigen::internal::pload;
+      using ::Eigen::internal::ploadt;
+      using ::Eigen::internal::pstoret;
+
+      const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
+
+      size_t i = 0;
+      const size_t num_packets = n / output_packet_size;
+      for (; i < output_packet_size * num_packets; i += output_packet_size) {
+        const auto src_val0 = pload<PacketReturnType>(src_buf0 + i);
+        const auto src_val1 = pload<PacketReturnType>(src_buf1 + i);
+        const auto src_val2 = pload<PacketReturnType>(src_buf2 + i);
+
+        const auto dst_val = ploadt<PacketReturnType, Alignment>(dst_buf + i);
+        const auto sum = padd(padd(dst_val, src_val0), padd(src_val1, src_val2));
+
+        pstoret<Scalar, PacketReturnType, Alignment>(dst_buf + i, sum);
+      }
+      for (; i < n; ++i) {
+        dst_buf[i] += src_buf0[i] + src_buf1[i] + src_buf2[i];
+      }
+    }
+
+    template <int Alignment>
+    void eval(Barrier& barrier, Index start_block_idx, Index end_block_idx) {
+      while (end_block_idx - start_block_idx > 1) {
+        Index mid_block_idx = (start_block_idx + end_block_idx) / 2;
+        evaluator->m_device.enqueue([this, &barrier, mid_block_idx, end_block_idx]() {
+          eval<Alignment>(barrier, mid_block_idx, end_block_idx);
+        });
+        end_block_idx = mid_block_idx;
+      }
+
+      Index block_idx = start_block_idx;
+      Index block_start = block_idx * block_size;
+      Index block_end = block_start + actualBlockSize(block_idx);
+
+      processBlock<Alignment>(block_idx, block_start, block_end);
+      barrier.Notify();
+    }
+
+    template <int Alignment>
+    void evalAsync(Index start_block_idx, Index end_block_idx) {
+      while (end_block_idx - start_block_idx > 1) {
+        Index mid_block_idx = (start_block_idx + end_block_idx) / 2;
+        evaluator->m_device.enqueue(
+            [this, mid_block_idx, end_block_idx]() { evalAsync<Alignment>(mid_block_idx, end_block_idx); });
+        end_block_idx = mid_block_idx;
+      }
+
+      Index block_idx = start_block_idx;
+
+      Index block_start = block_idx * block_size;
+      Index block_end = block_start + actualBlockSize(block_idx);
+
+      processBlock<Alignment>(block_idx, block_start, block_end);
+
+      int v = num_pending_blocks.fetch_sub(1);
+      eigen_assert(v >= 1);
+
+      if (v == 1) {
+        // Aggregate partial sums from l0 ranges.
+        aggregateL0Blocks<Alignment>();
+
+        // Apply output kernel.
+        applyOutputKernel();
+
+        // NOTE: If we call `done` callback before deleting this (context),
+        // it might deallocate Self* pointer captured by context, and we'll
+        // fail in destructor trying to deallocate temporary buffers.
+
+        // Move done call back from context before it will be destructed.
+        DoneCallback done_copy = std::move(done);
+
+        // We are confident that we are the last one who touches context.
+        delete this;
+
+        // Now safely call the done callback.
+        done_copy();
+      }
+    }
+
+    // Cost model doesn't capture well the cost associated with constructing
+    // tensor contraction mappers and computing loop bounds in gemm_pack_lhs
+    // and gemm_pack_rhs, so we specify minimum desired block size.
+    static Index blockSize(Index k, int num_threads) {
+      const auto round_up = [=](Index index) -> Index {
+        const Index kmultiple = packet_size <= 8 ? 8 : packet_size;
+        return numext::div_ceil<Index>(index, kmultiple) * kmultiple;
+      };
+
+      const Index target_block_size = round_up(numext::div_ceil<Index>(k, num_threads));
+      const Index desired_min_block_size = 12 * packet_size;
+
+      return numext::mini<Index>(k, numext::maxi<Index>(desired_min_block_size, target_block_size));
+    }
+
+    EvalShardedByInnerDimContext(const EvalShardedByInnerDimContext&) = delete;
+    void operator=(const EvalShardedByInnerDimContext&) = delete;
+  };
+
+  // ------------------------------------------------------------------------ //
+
+  // Below are the function used by evalProductImpl heuristics, trying to select
+  // optimal parameters for parallelization algorithm.
+
+  // Decide whether we want to shard m x n contraction by columns or by rows.
+  static bool shardByCol(Index m, Index n, Index num_threads) {
+    // Note: we are comparing both n and m against Traits::nr, it is not
+    // a mistake. We are trying to figure out how both n and m will fit into
+    // the main sharding dimension.
+
+    // Sharding by column is the default
+    // ... unless there is enough data for vectorization over rows
+    if (m / num_threads >= Traits::nr &&
+        // and not enough data for vectorization over columns
+        (n / num_threads < Traits::nr ||
+         // ... or barely enough data for vectorization over columns,
+         // but it is not evenly dividable across threads
+         (n / num_threads < 4 * Traits::nr && (n % (num_threads * Traits::nr)) != 0 &&
+          // ... and it is evenly dividable across threads for rows
+          ((m % (num_threads * Traits::nr)) == 0 ||
+           // .. or it is not evenly dividable for both dimensions but
+           // there is much more data over rows so that corner effects are
+           // mitigated.
+           (m / n >= 6)))))
+      return false;
+    // Wait, or if matrices are just substantially prolonged over the other
+    // dimension.
+    if (n / num_threads < 16 * Traits::nr && m > n * 32) return false;
+    return true;
+  }
+
+  Index coarsenM(Index m, Index n, Index bm, Index bn, Index bk, Index gn, int num_threads, bool shard_by_col) const {
+    Index gm = 1;
+    Index gm1 = 1;
+    Index nm0 = numext::div_ceil(m, bm);
+    Index nm1 = nm0;
+    for (;;) {
+      // Find the next candidate for m grain size. It needs to result in
+      // different number of blocks. E.g. if we have 10 kernels, we want to try
+      // 5 and 10, but not 6, 7, 8 and 9.
+      while (gm1 <= nm0 && nm1 == numext::div_ceil(nm0, gm1)) gm1++;
+      if (gm1 > nm0) break;
+      // Check the candidate.
+      int res = checkGrain(m, n, bm, bn, bk, gm1, gn, gm, gn, num_threads, shard_by_col);
+      if (res < 0) break;
+      nm1 = numext::div_ceil(nm0, gm1);
+      if (res == 0) continue;
+      // Commit new grain size.
+      gm = gm1;
+    }
+    return gm;
+  }
+
+  Index coarsenN(Index m, Index n, Index bm, Index bn, Index bk, Index gm, int num_threads, bool shard_by_col) const {
+    Index gn = 1;
+    Index gn1 = 1;
+    Index nn0 = numext::div_ceil(n, bn);
+    Index nn1 = nn0;
+    for (;;) {
+      while (gn1 <= nn0 && nn1 == numext::div_ceil(nn0, gn1)) gn1++;
+      if (gn1 > nn0) break;
+      int res = checkGrain(m, n, bm, bn, bk, gm, gn1, gm, gn, num_threads, shard_by_col);
+      if (res < 0) break;
+      nn1 = numext::div_ceil(nn0, gn1);
+      if (res == 0) continue;
+      gn = gn1;
+    }
+    return gn;
+  }
+
+  // checkGrain checks whether grain (gm, gn) is suitable and is better than
+  // (oldgm, oldgn).
+  int checkGrain(Index m, Index n, Index bm, Index bn, Index bk, Index gm, Index gn, Index oldgm, Index oldgn,
+                 int num_threads, bool shard_by_col) const {
+    const TensorOpCost cost = contractionCost(bm * gm, bn * gn, bm, bn, bk, shard_by_col, true);
+    double taskSize = TensorCostModel<ThreadPoolDevice>::taskSize(static_cast<double>(bm) * gm * bn * gn, cost);
+    // If the task is too small, then we agree on it regardless of anything
+    // else. Otherwise synchronization overheads will dominate.
+    if (taskSize < 1) return 1;
+    // If it is too large, then we reject it and all larger tasks.
+    if (taskSize > 2) return -1;
+    // Now we are in presumably good task size range.
+    // The main deciding factor here is parallelism. Consider that we have 12
+    // kernels and 4 threads. Grains of 2, 3 and 4 all yield good task sizes.
+    // But 2/4 yield 6/3 tasks, which gives us parallelism of 0.75 (at most 3/4
+    // of cores will be busy). While grain size 3 gives us 4 tasks, which gives
+    // us parallelism of 1 (we can load all cores).
+    Index nm0 = numext::div_ceil(m, bm);
+    Index nn0 = numext::div_ceil(n, bn);
+    Index new_tasks = numext::div_ceil(nm0, gm) * numext::div_ceil(nn0, gn);
+    double new_parallelism =
+        static_cast<double>(new_tasks) / (numext::div_ceil<Index>(new_tasks, num_threads) * num_threads);
+    Index old_tasks = numext::div_ceil(nm0, oldgm) * numext::div_ceil(nn0, oldgn);
+    double old_parallelism =
+        static_cast<double>(old_tasks) / (numext::div_ceil<Index>(old_tasks, num_threads) * num_threads);
+    if (new_parallelism > old_parallelism || new_parallelism == 1) return 1;
+    return 0;
+  }
+
+  TensorOpCost contractionCost(Index m, Index n, Index bm, Index bn, Index bk, bool shard_by_col,
+                               bool prepacked) const {
+    const int packed_size = std::min<int>(PacketType<LhsScalar, Device>::size, PacketType<RhsScalar, Device>::size);
+    const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
+    const double kd = static_cast<double>(bk);
+    double compute_bandwidth = computeBandwidth(false, bm, bn, bk);
+    // Computations.
+    TensorOpCost cost = TensorOpCost(0, 0, kd * compute_bandwidth, true, packed_size);
+    // Output stores.
+    cost += TensorOpCost(0, sizeof(CoeffReturnType), 0, true, output_packet_size);
+    if (prepacked) {
+      // Packing and kernels are executed in different tasks. When we calculate
+      // task grain size we look only at kernel cost assuming that kernel
+      // is more expensive than packing.
+      return cost;
+    }
+    // Lhs/rhs loads + computations.
+    TensorOpCost lhsCost = this->m_leftImpl.costPerCoeff(true) * (kd / n);
+    TensorOpCost rhsCost = this->m_rightImpl.costPerCoeff(true) * (kd / m);
+    // Lhs packing memory cost does not contribute considerably to overall
+    // execution time because lhs is prefetched early and accessed sequentially.
+    if (shard_by_col)
+      lhsCost.dropMemoryCost();
+    else
+      rhsCost.dropMemoryCost();
+    return cost + lhsCost + rhsCost;
+  }
+
+  // Decide whether we want to shard m x k x n contraction over the inner
+  // (contraction) dimension (k).
+  static bool shardByInnerDim(Index m, Index n, Index k, int num_threads, int num_threads_by_k) {
+    std::ptrdiff_t bufsize = m * n * sizeof(Scalar);
+    bool shard_by_k = false;
+    if (n == 1 ||                                      // If mat*vec or...
+        num_threads_by_k < 2 ||                        // running single threaded or...
+        num_threads_by_k < num_threads ||              // sharding by k gives less parallelism or...
+        bufsize > l3CacheSize() / num_threads_by_k ||  // need more buffer space
+        // than L3 cache or...
+        k / num_threads_by_k < 2 * Traits::nr) {  // k per thread is tiny.
+      shard_by_k = false;
+    } else if (numext::maxi(m, n) / num_threads < Traits::nr ||  // both other dimensions are tiny or...
+                                                                 // k per thread is not small and...
+               (k / num_threads_by_k > 8 * Traits::nr &&
+                // one of the outer dimensions is tiny or sharding by k offers
+                // more parallelism.
+                (numext::mini(m, n) < 2 * Traits::nr || num_threads_by_k > num_threads))) {
+      shard_by_k = true;
+    }
+    return shard_by_k;
+  }
+
+  TensorOpCost contractionCostPerInnerDim(Index m, Index n, Index k) const {
+    // Compute cost.
+    const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
+    TensorOpCost cost(0, 0, (computeBandwidth(true, m, n, k) * m) * n, true, output_packet_size);
+    // Output stores.
+    cost += TensorOpCost(0, sizeof(CoeffReturnType), 0, true, output_packet_size);
+    TensorOpCost lhsCost = this->m_leftImpl.costPerCoeff(true) * m;
+    TensorOpCost rhsCost = this->m_rightImpl.costPerCoeff(true) * n;
+    // Since the inner gemm kernel is always sharded by column, the lhs
+    // load cost is negligible.
+    lhsCost.dropMemoryCost();
+    return cost + lhsCost + rhsCost;
+  }
+
+  int numThreadsInnerDim(Index m, Index n, Index k) const {
+    const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
+    TensorOpCost cost = contractionCostPerInnerDim(m, n, k);
+    double total_parallel_cost = TensorCostModel<ThreadPoolDevice>::totalCost(k, cost);
+    // Cost of reduction step accumulating the m*n per-thread buffers into the
+    // result.
+    double reduction_cost =
+        TensorCostModel<ThreadPoolDevice>::totalCost(m * n, TensorOpCost(2, 1, 1, true, output_packet_size));
+    int num_threads = 1;
+    double min_cost = total_parallel_cost;
+    double kPerThreadOverHead = 3000;
+    double kFixedOverHead = 100000;
+    for (int nt = 2; nt <= this->m_device.numThreads(); nt += 2) {
+      double sequential_cost = kFixedOverHead + nt * (reduction_cost + kPerThreadOverHead);
+      double parallel_cost = total_parallel_cost / nt + sequential_cost;
+      if (parallel_cost < min_cost) {
+        num_threads = nt;
+        min_cost = parallel_cost;
+      }
+    }
+    return num_threads;
+  }
+
+  double computeBandwidth(bool shard_by_col, Index bm, Index bn, Index bk) const {
+    // Peak VFMA bandwidth is 0.5. However if we have not enough data for
+    // vectorization bandwidth drops. The 4.0 and 2.0 bandwidth is determined
+    // experimentally.
+    double computeBandwidth = bk == 1                                                                          ? 4.0
+                              : (shard_by_col ? bn : bm) < Traits::nr || (shard_by_col ? bm : bn) < Traits::mr ? 2.0
+                                                                                                               : 0.5;
+#ifndef EIGEN_VECTORIZE_FMA
+    // Bandwidth of all of VFMA/MULPS/ADDPS is 0.5 on latest Intel processors.
+    // However for MULPS/ADDPS we have dependent sequence of 2 such
+    // instructions,
+    // so overall bandwidth is 1.0.
+    if (computeBandwidth == 0.5) computeBandwidth = 1.0;
+#endif
+    return computeBandwidth;
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_USE_THREADS
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
new file mode 100644
index 0000000..ccf96b7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
@@ -0,0 +1,416 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename TargetType, typename XprType>
+struct traits<TensorConversionOp<TargetType, XprType> > {
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef TargetType Scalar;
+  typedef typename traits<XprType>::StorageKind StorageKind;
+  typedef typename traits<XprType>::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = traits<XprType>::NumDimensions;
+  static constexpr int Layout = traits<XprType>::Layout;
+  enum { Flags = 0 };
+  typedef typename TypeConversion<Scalar, typename traits<XprType>::PointerType>::type PointerType;
+};
+
+template <typename TargetType, typename XprType>
+struct eval<TensorConversionOp<TargetType, XprType>, Eigen::Dense> {
+  typedef const TensorConversionOp<TargetType, XprType>& type;
+};
+
+template <typename TargetType, typename XprType>
+struct nested<TensorConversionOp<TargetType, XprType>, 1,
+              typename eval<TensorConversionOp<TargetType, XprType> >::type> {
+  typedef TensorConversionOp<TargetType, XprType> type;
+};
+
+}  // end namespace internal
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket, int SrcCoeffRatio, int TgtCoeffRatio>
+struct PacketConverter;
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
+struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 1, 1> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketConverter(const TensorEvaluator& impl) : m_impl(impl) {}
+
+  template <int LoadMode, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
+    return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<LoadMode>(index));
+  }
+
+ private:
+  const TensorEvaluator& m_impl;
+};
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
+struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 2, 1> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketConverter(const TensorEvaluator& impl) : m_impl(impl) {}
+
+  template <int LoadMode, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
+    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
+
+    SrcPacket src1 = m_impl.template packet<LoadMode>(index);
+    SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize);
+    TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2);
+    return result;
+  }
+
+ private:
+  const TensorEvaluator& m_impl;
+};
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
+struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 4, 1> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketConverter(const TensorEvaluator& impl) : m_impl(impl) {}
+
+  template <int LoadMode, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
+    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
+
+    SrcPacket src1 = m_impl.template packet<LoadMode>(index);
+    SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize);
+    SrcPacket src3 = m_impl.template packet<LoadMode>(index + 2 * SrcPacketSize);
+    SrcPacket src4 = m_impl.template packet<LoadMode>(index + 3 * SrcPacketSize);
+    TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2, src3, src4);
+    return result;
+  }
+
+ private:
+  const TensorEvaluator& m_impl;
+};
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
+struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 8, 1> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketConverter(const TensorEvaluator& impl) : m_impl(impl) {}
+
+  template <int LoadMode, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
+    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
+
+    SrcPacket src1 = m_impl.template packet<LoadMode>(index);
+    SrcPacket src2 = m_impl.template packet<LoadMode>(index + 1 * SrcPacketSize);
+    SrcPacket src3 = m_impl.template packet<LoadMode>(index + 2 * SrcPacketSize);
+    SrcPacket src4 = m_impl.template packet<LoadMode>(index + 3 * SrcPacketSize);
+    SrcPacket src5 = m_impl.template packet<LoadMode>(index + 4 * SrcPacketSize);
+    SrcPacket src6 = m_impl.template packet<LoadMode>(index + 5 * SrcPacketSize);
+    SrcPacket src7 = m_impl.template packet<LoadMode>(index + 6 * SrcPacketSize);
+    SrcPacket src8 = m_impl.template packet<LoadMode>(index + 7 * SrcPacketSize);
+    TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2, src3, src4, src5, src6, src7, src8);
+    return result;
+  }
+
+ private:
+  const TensorEvaluator& m_impl;
+};
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket, int TgtCoeffRatio>
+struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 1, TgtCoeffRatio> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketConverter(const TensorEvaluator& impl)
+      : m_impl(impl), m_maxIndex(impl.dimensions().TotalSize()) {}
+
+  template <int LoadMode, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
+    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
+    // Only call m_impl.packet() when we have direct access to the underlying data. This
+    // ensures that we don't compute the subexpression twice. We may however load some
+    // coefficients twice, but in practice this doesn't negatively impact performance.
+    if (m_impl.data() && (index + SrcPacketSize < m_maxIndex)) {
+      // Force unaligned memory loads since we can't ensure alignment anymore
+      return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<Unaligned>(index));
+    } else {
+      const int TgtPacketSize = internal::unpacket_traits<TgtPacket>::size;
+      typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
+      typedef typename internal::unpacket_traits<TgtPacket>::type TgtType;
+      internal::scalar_cast_op<SrcType, TgtType> converter;
+      EIGEN_ALIGN_MAX typename internal::unpacket_traits<TgtPacket>::type values[TgtPacketSize];
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < TgtPacketSize; ++i) {
+        values[i] = converter(m_impl.coeff(index + i));
+      }
+      TgtPacket rslt = internal::pload<TgtPacket>(values);
+      return rslt;
+    }
+  }
+
+ private:
+  const TensorEvaluator& m_impl;
+  const typename TensorEvaluator::Index m_maxIndex;
+};
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor conversion class. This class makes it possible to vectorize
+ * type casting operations when the number of scalars per packet in the source
+ * and the destination type differ
+ */
+template <typename TargetType, typename XprType>
+class TensorConversionOp : public TensorBase<TensorConversionOp<TargetType, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename internal::traits<TensorConversionOp>::Scalar Scalar;
+  typedef typename internal::traits<TensorConversionOp>::StorageKind StorageKind;
+  typedef typename internal::traits<TensorConversionOp>::Index Index;
+  typedef typename internal::nested<TensorConversionOp>::type Nested;
+  typedef Scalar CoeffReturnType;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConversionOp(const XprType& xpr) : m_xpr(xpr) {}
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+};
+
+template <bool SameType, typename Eval, typename EvalPointerType>
+struct ConversionSubExprEval {
+  static EIGEN_STRONG_INLINE bool run(Eval& impl, EvalPointerType) {
+    impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+};
+
+template <typename Eval, typename EvalPointerType>
+struct ConversionSubExprEval<true, Eval, EvalPointerType> {
+  static EIGEN_STRONG_INLINE bool run(Eval& impl, EvalPointerType data) { return impl.evalSubExprsIfNeeded(data); }
+};
+
+#ifdef EIGEN_USE_THREADS
+template <bool SameType, typename Eval, typename EvalPointerType, typename EvalSubExprsCallback>
+struct ConversionSubExprEvalAsync {
+  static EIGEN_STRONG_INLINE void run(Eval& impl, EvalPointerType, EvalSubExprsCallback done) {
+    impl.evalSubExprsIfNeededAsync(nullptr, std::move(done));
+  }
+};
+
+template <typename Eval, typename EvalPointerType, typename EvalSubExprsCallback>
+struct ConversionSubExprEvalAsync<true, Eval, EvalPointerType, EvalSubExprsCallback> {
+  static EIGEN_STRONG_INLINE void run(Eval& impl, EvalPointerType data, EvalSubExprsCallback done) {
+    impl.evalSubExprsIfNeededAsync(data, std::move(done));
+  }
+};
+#endif
+
+namespace internal {
+
+template <typename SrcType, typename TargetType, bool IsSameT>
+struct CoeffConv {
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetType run(const TensorEvaluator<ArgType, Device>& impl,
+                                                              Index index) {
+    internal::scalar_cast_op<SrcType, TargetType> converter;
+    return converter(impl.coeff(index));
+  }
+};
+
+template <typename SrcType, typename TargetType>
+struct CoeffConv<SrcType, TargetType, true> {
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetType run(const TensorEvaluator<ArgType, Device>& impl,
+                                                              Index index) {
+    return impl.coeff(index);
+  }
+};
+
+template <typename SrcPacket, typename TargetPacket, int LoadMode, bool ActuallyVectorize, bool IsSameT>
+struct PacketConv {
+  typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
+  typedef typename internal::unpacket_traits<TargetPacket>::type TargetType;
+
+  static constexpr int PacketSize = internal::unpacket_traits<TargetPacket>::size;
+
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl,
+                                                                Index index) {
+    internal::scalar_cast_op<SrcType, TargetType> converter;
+    EIGEN_ALIGN_MAX std::remove_const_t<TargetType> values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = converter(impl.coeff(index + i));
+    }
+    TargetPacket rslt = internal::pload<TargetPacket>(values);
+    return rslt;
+  }
+};
+
+template <typename SrcPacket, typename TargetPacket, int LoadMode, bool IsSameT>
+struct PacketConv<SrcPacket, TargetPacket, LoadMode, true, IsSameT> {
+  typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
+  typedef typename internal::unpacket_traits<TargetPacket>::type TargetType;
+
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl,
+                                                                Index index) {
+    const int SrcCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio;
+    const int TgtCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio;
+    PacketConverter<TensorEvaluator<ArgType, Device>, SrcPacket, TargetPacket, SrcCoeffRatio, TgtCoeffRatio> converter(
+        impl);
+    return converter.template packet<LoadMode>(index);
+  }
+};
+
+template <typename SrcPacket, typename TargetPacket, int LoadMode>
+struct PacketConv<SrcPacket, TargetPacket, LoadMode, /*ActuallyVectorize=*/false, /*IsSameT=*/true> {
+  typedef typename internal::unpacket_traits<TargetPacket>::type TargetType;
+  static constexpr int PacketSize = internal::unpacket_traits<TargetPacket>::size;
+
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl,
+                                                                Index index) {
+    EIGEN_ALIGN_MAX std::remove_const_t<TargetType> values[PacketSize];
+    for (int i = 0; i < PacketSize; ++i) values[i] = impl.coeff(index + i);
+    return internal::pload<TargetPacket>(values);
+  }
+};
+
+template <typename SrcPacket, typename TargetPacket, int LoadMode>
+struct PacketConv<SrcPacket, TargetPacket, LoadMode, /*ActuallyVectorize=*/true, /*IsSameT=*/true> {
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl,
+                                                                Index index) {
+    return impl.template packet<LoadMode>(index);
+  }
+};
+
+}  // namespace internal
+
+// Eval as rvalue
+template <typename TargetType, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorConversionOp<TargetType, ArgType>, Device> {
+  typedef TensorConversionOp<TargetType, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef TargetType Scalar;
+  typedef TargetType CoeffReturnType;
+  typedef internal::remove_all_t<typename internal::traits<ArgType>::Scalar> SrcType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename PacketType<SrcType, Device>::type PacketSourceType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  static constexpr bool IsSameType = internal::is_same<TargetType, SrcType>::value;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = false,
+    PacketAccess =
+#ifndef EIGEN_USE_SYCL
+        true,
+#else
+        TensorEvaluator<ArgType, Device>::PacketAccess &
+        internal::type_casting_traits<SrcType, TargetType>::VectorizedCast,
+#endif
+    BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    RawAccess = false
+  };
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  static constexpr int NumDims = internal::array_size<Dimensions>::value;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock ArgTensorBlock;
+
+  struct TensorConversionOpBlockFactory {
+    template <typename ArgXprType>
+    struct XprType {
+      typedef TensorConversionOp<TargetType, const ArgXprType> type;
+    };
+
+    template <typename ArgXprType>
+    typename XprType<ArgXprType>::type expr(const ArgXprType& expr) const {
+      return typename XprType<ArgXprType>::type(expr);
+    }
+  };
+
+  typedef internal::TensorUnaryExprBlock<TensorConversionOpBlockFactory, ArgTensorBlock> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_impl(op.expression(), device) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    return ConversionSubExprEval<IsSameType, TensorEvaluator<ArgType, Device>, EvaluatorPointerType>::run(m_impl, data);
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType data, EvalSubExprsCallback done) {
+    ConversionSubExprEvalAsync<IsSameType, TensorEvaluator<ArgType, Device>, EvaluatorPointerType,
+                               EvalSubExprsCallback>::run(m_impl, data, std::move(done));
+  }
+#endif
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return internal::CoeffConv<SrcType, TargetType, IsSameType>::run(m_impl, index);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    // If we are not going to do the cast, we just need to check that base
+    // TensorEvaluator has packet access. Otherwise we also need to make sure,
+    // that we have an implementation of vectorized cast.
+    const bool Vectorizable = IsSameType ? TensorEvaluator<ArgType, Device>::PacketAccess
+                                         : int(TensorEvaluator<ArgType, Device>::PacketAccess) &
+                                               int(internal::type_casting_traits<SrcType, TargetType>::VectorizedCast);
+
+    return internal::PacketConv<PacketSourceType, PacketReturnType, LoadMode, Vectorizable, IsSameType>::run(m_impl,
+                                                                                                             index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double cast_cost = TensorOpCost::CastCost<SrcType, TargetType>();
+    if (vectorized) {
+      const double SrcCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio;
+      const double TgtCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio;
+      return m_impl.costPerCoeff(vectorized) * (SrcCoeffRatio / PacketSize) +
+             TensorOpCost(0, 0, TgtCoeffRatio * (cast_cost / PacketSize));
+    } else {
+      return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, cast_cost);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return m_impl.getResourceRequirements();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    return TensorBlock(m_impl.block(desc, scratch), TensorConversionOpBlockFactory());
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  /// required by sycl in order to extract the sycl accessor
+  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+
+ protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
new file mode 100644
index 0000000..36a8d1d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
@@ -0,0 +1,1106 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Index, typename InputDims, int NumKernelDims, int Layout>
+class IndexMapper {
+ public:
+  IndexMapper(const InputDims& input_dims, const array<Index, NumKernelDims>& kernel_dims,
+              const array<Index, NumKernelDims>& indices) {
+    array<Index, NumDims> dimensions = input_dims;
+    for (int i = 0; i < NumKernelDims; ++i) {
+      const Index index = indices[i];
+      const Index input_dim = input_dims[index];
+      const Index kernel_dim = kernel_dims[i];
+      const Index result_dim = input_dim - kernel_dim + 1;
+      dimensions[index] = result_dim;
+    }
+
+    array<Index, NumDims> inputStrides;
+    array<Index, NumDims> outputStrides;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      inputStrides[0] = 1;
+      outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        inputStrides[i] = inputStrides[i - 1] * input_dims[i - 1];
+        outputStrides[i] = outputStrides[i - 1] * dimensions[i - 1];
+      }
+    } else {
+      inputStrides[NumDims - 1] = 1;
+      outputStrides[NumDims - 1] = 1;
+      for (int i = static_cast<int>(NumDims) - 2; i >= 0; --i) {
+        inputStrides[i] = inputStrides[i + 1] * input_dims[i + 1];
+        outputStrides[i] = outputStrides[i + 1] * dimensions[i + 1];
+      }
+    }
+
+    array<Index, NumDims> gpuInputDimensions;
+    array<Index, NumDims> gpuOutputDimensions;
+    array<Index, NumDims> tmp = dimensions;
+    array<Index, NumDims> ordering;
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - NumKernelDims;
+    for (int i = 0; i < NumKernelDims; ++i) {
+      const Index index = i + offset;
+      ordering[index] = indices[i];
+      tmp[indices[i]] = -1;
+      gpuInputDimensions[index] = input_dims[indices[i]];
+      gpuOutputDimensions[index] = dimensions[indices[i]];
+    }
+
+    int written = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? NumKernelDims : 0;
+    for (int i = 0; i < NumDims; ++i) {
+      if (tmp[i] >= 0) {
+        ordering[written] = i;
+        gpuInputDimensions[written] = input_dims[i];
+        gpuOutputDimensions[written] = dimensions[i];
+        ++written;
+      }
+    }
+
+    for (int i = 0; i < NumDims; ++i) {
+      m_inputStrides[i] = inputStrides[ordering[i]];
+      m_outputStrides[i] = outputStrides[ordering[i]];
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < NumDims; ++i) {
+        if (i > NumKernelDims) {
+          m_gpuInputStrides[i] = m_gpuInputStrides[i - 1] * gpuInputDimensions[i - 1];
+          m_gpuOutputStrides[i] = m_gpuOutputStrides[i - 1] * gpuOutputDimensions[i - 1];
+        } else {
+          m_gpuInputStrides[i] = 1;
+          m_gpuOutputStrides[i] = 1;
+        }
+      }
+    } else {
+      for (int i = NumDims - 1; i >= 0; --i) {
+        if (static_cast<size_t>(i + 1) < offset) {
+          m_gpuInputStrides[i] = m_gpuInputStrides[i + 1] * gpuInputDimensions[i + 1];
+          m_gpuOutputStrides[i] = m_gpuOutputStrides[i + 1] * gpuOutputDimensions[i + 1];
+        } else {
+          m_gpuInputStrides[i] = 1;
+          m_gpuOutputStrides[i] = 1;
+        }
+      }
+    }
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputPlaneToTensorInputOffset(Index p) const {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int d = NumDims - 1; d > NumKernelDims; --d) {
+        const Index idx = p / m_gpuInputStrides[d];
+        inputIndex += idx * m_inputStrides[d];
+        p -= idx * m_gpuInputStrides[d];
+      }
+      if (NumKernelDims < NumDims) {
+        inputIndex += p * m_inputStrides[NumKernelDims];
+      }
+    } else {
+      std::ptrdiff_t limit = 0;
+      if (NumKernelDims < NumDims) {
+        limit = NumDims - NumKernelDims - 1;
+      }
+      for (int d = 0; d < limit; ++d) {
+        const Index idx = p / m_gpuInputStrides[d];
+        inputIndex += idx * m_inputStrides[d];
+        p -= idx * m_gpuInputStrides[d];
+      }
+      inputIndex += p * m_inputStrides[limit];
+    }
+    return inputIndex;
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputPlaneToTensorOutputOffset(Index p) const {
+    Index outputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int d = NumDims - 1; d > NumKernelDims; --d) {
+        const Index idx = p / m_gpuOutputStrides[d];
+        outputIndex += idx * m_outputStrides[d];
+        p -= idx * m_gpuOutputStrides[d];
+      }
+      if (NumKernelDims < NumDims) {
+        outputIndex += p * m_outputStrides[NumKernelDims];
+      }
+    } else {
+      std::ptrdiff_t limit = 0;
+      if (NumKernelDims < NumDims) {
+        limit = NumDims - NumKernelDims - 1;
+      }
+      for (int d = 0; d < limit; ++d) {
+        const Index idx = p / m_gpuOutputStrides[d];
+        outputIndex += idx * m_outputStrides[d];
+        p -= idx * m_gpuOutputStrides[d];
+      }
+      outputIndex += p * m_outputStrides[limit];
+    }
+    return outputIndex;
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputKernelToTensorInputOffset(Index i) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - NumKernelDims;
+    return i * m_inputStrides[offset];
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputKernelToTensorOutputOffset(Index i) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - NumKernelDims;
+    return i * m_outputStrides[offset];
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputKernelToTensorInputOffset(Index i, Index j) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - NumKernelDims;
+    return i * m_inputStrides[offset] + j * m_inputStrides[offset + 1];
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputKernelToTensorOutputOffset(Index i, Index j) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - NumKernelDims;
+    return i * m_outputStrides[offset] + j * m_outputStrides[offset + 1];
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputKernelToTensorInputOffset(Index i, Index j, Index k) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - NumKernelDims;
+    return i * m_inputStrides[offset] + j * m_inputStrides[offset + 1] + k * m_inputStrides[offset + 2];
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputKernelToTensorOutputOffset(Index i, Index j, Index k) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - NumKernelDims;
+    return i * m_outputStrides[offset] + j * m_outputStrides[offset + 1] + k * m_outputStrides[offset + 2];
+  }
+
+ private:
+  static constexpr int NumDims = internal::array_size<InputDims>::value;
+  array<Index, NumDims> m_inputStrides;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims> m_gpuInputStrides;
+  array<Index, NumDims> m_gpuOutputStrides;
+};
+
+template <typename Dimensions, typename InputXprType, typename KernelXprType>
+struct traits<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> > {
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef typename promote_storage_type<typename InputXprType::Scalar, typename KernelXprType::Scalar>::ret Scalar;
+  typedef typename promote_storage_type<typename traits<InputXprType>::StorageKind,
+                                        typename traits<KernelXprType>::StorageKind>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<InputXprType>::Index, typename traits<KernelXprType>::Index>::type
+      Index;
+  typedef typename InputXprType::Nested LhsNested;
+  typedef typename KernelXprType::Nested RhsNested;
+  typedef std::remove_reference_t<LhsNested> LhsNested_;
+  typedef std::remove_reference_t<RhsNested> RhsNested_;
+  static constexpr int NumDimensions = traits<InputXprType>::NumDimensions;
+  static constexpr int Layout = traits<InputXprType>::Layout;
+  typedef std::conditional_t<Pointer_type_promotion<typename InputXprType::Scalar, Scalar>::val,
+                             typename traits<InputXprType>::PointerType, typename traits<KernelXprType>::PointerType>
+      PointerType;
+
+  enum { Flags = 0 };
+};
+
+template <typename Dimensions, typename InputXprType, typename KernelXprType>
+struct eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, Eigen::Dense> {
+  typedef const TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>& type;
+};
+
+template <typename Dimensions, typename InputXprType, typename KernelXprType>
+struct nested<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, 1,
+              typename eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >::type> {
+  typedef TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> type;
+};
+
+}  // end namespace internal
+
+/** Tensor convolution class.
+ * \ingroup CXX11_Tensor_Module
+ */
+template <typename Indices, typename InputXprType, typename KernelXprType>
+class TensorConvolutionOp
+    : public TensorBase<TensorConvolutionOp<Indices, InputXprType, KernelXprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorConvolutionOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename internal::promote_storage_type<typename InputXprType::CoeffReturnType,
+                                                  typename KernelXprType::CoeffReturnType>::ret CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorConvolutionOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorConvolutionOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorConvolutionOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionOp(const InputXprType& input, const KernelXprType& kernel,
+                                                            const Indices& dims)
+      : m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Indices& indices() const { return m_indices; }
+
+  /** \returns the nested expressions */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<typename InputXprType::Nested>& inputExpression()
+      const {
+    return m_input_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<typename KernelXprType::Nested>& kernelExpression()
+      const {
+    return m_kernel_xpr;
+  }
+
+ protected:
+  typename InputXprType::Nested m_input_xpr;
+  typename KernelXprType::Nested m_kernel_xpr;
+  const Indices m_indices;
+};
+
+template <typename Indices, typename InputArgType, typename KernelArgType, typename Device>
+struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, Device> {
+  typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
+
+  static constexpr int NumDims =
+      internal::array_size<typename TensorEvaluator<InputArgType, Device>::Dimensions>::value;
+  static constexpr int NumKernelDims = internal::array_size<Indices>::value;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<InputArgType, Device>::Layout;
+  enum {
+    IsAligned =
+        int(TensorEvaluator<InputArgType, Device>::IsAligned) & int(TensorEvaluator<KernelArgType, Device>::IsAligned),
+    PacketAccess = int(TensorEvaluator<InputArgType, Device>::PacketAccess) &
+                   int(TensorEvaluator<KernelArgType, Device>::PacketAccess),
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_inputImpl(op.inputExpression(), device),
+        m_kernelImpl(op.kernelExpression(), device),
+        m_kernelArg(op.kernelExpression()),
+        m_kernel(NULL),
+        m_local_kernel(false),
+        m_device(device) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Device>::Layout) ==
+                         static_cast<int>(TensorEvaluator<KernelArgType, Device>::Layout)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    const typename TensorEvaluator<InputArgType, Device>::Dimensions& input_dims = m_inputImpl.dimensions();
+    const typename TensorEvaluator<KernelArgType, Device>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputStride[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_inputStride[i] = m_inputStride[i - 1] * input_dims[i - 1];
+      }
+    } else {
+      m_inputStride[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_inputStride[i] = m_inputStride[i + 1] * input_dims[i + 1];
+      }
+    }
+
+    m_dimensions = m_inputImpl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < NumKernelDims; ++i) {
+        const Index index = op.indices()[i];
+        const Index input_dim = input_dims[index];
+        const Index kernel_dim = kernel_dims[i];
+        const Index result_dim = input_dim - kernel_dim + 1;
+        m_dimensions[index] = result_dim;
+        if (i > 0) {
+          m_kernelStride[i] = m_kernelStride[i - 1] * kernel_dims[i - 1];
+        } else {
+          m_kernelStride[0] = 1;
+        }
+        m_indexStride[i] = m_inputStride[index];
+      }
+
+      m_outputStride[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStride[i] = m_outputStride[i - 1] * m_dimensions[i - 1];
+      }
+    } else {
+      for (int i = NumKernelDims - 1; i >= 0; --i) {
+        const Index index = op.indices()[i];
+        const Index input_dim = input_dims[index];
+        const Index kernel_dim = kernel_dims[i];
+        const Index result_dim = input_dim - kernel_dim + 1;
+        m_dimensions[index] = result_dim;
+        if (i < NumKernelDims - 1) {
+          m_kernelStride[i] = m_kernelStride[i + 1] * kernel_dims[i + 1];
+        } else {
+          m_kernelStride[NumKernelDims - 1] = 1;
+        }
+        m_indexStride[i] = m_inputStride[index];
+      }
+
+      m_outputStride[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStride[i] = m_outputStride[i + 1] * m_dimensions[i + 1];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
+    m_inputImpl.evalSubExprsIfNeeded(NULL);
+    preloadKernel();
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_inputImpl.cleanup();
+    if (m_local_kernel) {
+      m_device.deallocate((void*)m_kernel);
+      m_local_kernel = false;
+    }
+    m_kernel = NULL;
+  }
+
+  void evalTo(typename XprType::Scalar* buffer) {
+    evalSubExprsIfNeeded(NULL);
+    for (int i = 0; i < dimensions().TotalSize(); ++i) {
+      buffer[i] += coeff(i);
+    }
+    cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    CoeffReturnType result = CoeffReturnType(0);
+    convolve(firstInput(index), 0, NumKernelDims - 1, result);
+    return result;
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(const Index index) const {
+    Index indices[2] = {index, index + PacketSize - 1};
+    Index startInputs[2] = {0, 0};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx0 = indices[0] / m_outputStride[i];
+        const Index idx1 = indices[1] / m_outputStride[i];
+        startInputs[0] += idx0 * m_inputStride[i];
+        startInputs[1] += idx1 * m_inputStride[i];
+        indices[0] -= idx0 * m_outputStride[i];
+        indices[1] -= idx1 * m_outputStride[i];
+      }
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx0 = indices[0] / m_outputStride[i];
+        const Index idx1 = indices[1] / m_outputStride[i];
+        startInputs[0] += idx0 * m_inputStride[i];
+        startInputs[1] += idx1 * m_inputStride[i];
+        indices[0] -= idx0 * m_outputStride[i];
+        indices[1] -= idx1 * m_outputStride[i];
+      }
+    }
+    startInputs[0] += indices[0];
+    startInputs[1] += indices[1];
+
+    if (startInputs[1] - startInputs[0] == PacketSize - 1) {
+      PacketReturnType result = internal::pset1<PacketReturnType>(0);
+      convolvePacket(startInputs[0], 0, NumKernelDims - 1, result);
+      return result;
+    } else {
+      EIGEN_ALIGN_MAX Scalar data[PacketSize];
+      data[0] = Scalar(0);
+      convolve(startInputs[0], 0, NumKernelDims - 1, data[0]);
+      for (int i = 1; i < PacketSize - 1; ++i) {
+        data[i] = Scalar(0);
+        convolve(firstInput(index + i), 0, NumKernelDims - 1, data[i]);
+      }
+      data[PacketSize - 1] = Scalar(0);
+      convolve(startInputs[1], 0, NumKernelDims - 1, data[PacketSize - 1]);
+      return internal::pload<PacketReturnType>(data);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double kernel_size = m_kernelImpl.dimensions().TotalSize();
+    // We ignore the use of fused multiply-add.
+    const double convolve_compute_cost = TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>();
+    const double firstIndex_compute_cost =
+        NumDims *
+        (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>());
+    return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +
+           kernel_size * (m_inputImpl.costPerCoeff(vectorized) + m_kernelImpl.costPerCoeff(vectorized) +
+                          TensorOpCost(0, 0, convolve_compute_cost, vectorized, PacketSize));
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ private:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
+    Index startInput = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStride[i];
+        startInput += idx * m_inputStride[i];
+        index -= idx * m_outputStride[i];
+      }
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_outputStride[i];
+        startInput += idx * m_inputStride[i];
+        index -= idx * m_outputStride[i];
+      }
+    }
+    startInput += index;
+    return startInput;
+  }
+
+  EIGEN_DEVICE_FUNC void convolve(Index firstIndex, Index firstKernel, int DimIndex, CoeffReturnType& accum) const {
+    for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) {
+      const Index input = firstIndex + j * m_indexStride[DimIndex];
+      const Index kernel = firstKernel + j * m_kernelStride[DimIndex];
+      if (DimIndex > 0) {
+        convolve(input, kernel, DimIndex - 1, accum);
+      } else {
+        accum += m_inputImpl.coeff(input) * m_kernel[kernel];
+      }
+    }
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC void convolvePacket(Index firstIndex, Index firstKernel, int DimIndex, Packet& accum) const {
+    for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) {
+      const Index input = firstIndex + j * m_indexStride[DimIndex];
+      const Index kernel = firstKernel + j * m_kernelStride[DimIndex];
+      if (DimIndex > 0) {
+        convolvePacket(input, kernel, DimIndex - 1, accum);
+      } else {
+        accum = internal::pmadd<Packet>(m_inputImpl.template packet<Unaligned>(input),
+                                        internal::pset1<Packet>(m_kernel[kernel]), accum);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void preloadKernel() {
+    // Don't make a local copy of the kernel unless we have to (i.e. it's an
+    // expression that needs to be evaluated)
+    const Scalar* in_place = m_kernelImpl.data();
+    if (in_place) {
+      m_kernel = in_place;
+      m_local_kernel = false;
+    } else {
+      size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
+      Scalar* local = (Scalar*)m_device.allocate_temp(kernel_sz);
+      typedef TensorEvalToOp<const KernelArgType> EvalTo;
+      EvalTo evalToTmp(local, m_kernelArg);
+      const bool Vectorize = internal::IsVectorizable<Device, KernelArgType>::value;
+      internal::TensorExecutor<const EvalTo, Device, Vectorize>::run(evalToTmp, m_device);
+
+      m_kernel = local;
+      m_local_kernel = true;
+    }
+  }
+
+  array<Index, NumDims> m_inputStride;
+  array<Index, NumDims> m_outputStride;
+
+  array<Index, NumKernelDims> m_indexStride;
+  array<Index, NumKernelDims> m_kernelStride;
+  TensorEvaluator<InputArgType, Device> m_inputImpl;
+  TensorEvaluator<KernelArgType, Device> m_kernelImpl;
+  Dimensions m_dimensions;
+
+  KernelArgType m_kernelArg;
+  const Scalar* m_kernel;
+  bool m_local_kernel;
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+// Use an optimized implementation of the evaluation code for GPUs whenever possible.
+#if defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
+
+template <int StaticKernelSize>
+struct GetKernelSize {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator()(const int /*kernelSize*/) const { return StaticKernelSize; }
+};
+template <>
+struct GetKernelSize<Dynamic> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator()(const int kernelSize) const { return kernelSize; }
+};
+
+template <typename InputEvaluator, typename Index, typename InputDims, int StaticKernelSize>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void EigenConvolutionKernel1D(
+    InputEvaluator eval, const internal::IndexMapper<Index, InputDims, 1, InputEvaluator::Layout> indexMapper,
+    const float* __restrict kernel, const int numPlanes, const int numX, const int maxX, const int kernelSize,
+    float* buffer) {
+#if defined(EIGEN_HIPCC)
+  HIP_DYNAMIC_SHARED(float, s)
+#else
+  extern __shared__ float s[];
+#endif
+
+  const int first_x = blockIdx.x * maxX;
+  const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
+  const int num_x_input = last_x - first_x + GetKernelSize<StaticKernelSize>()(kernelSize);
+  const int num_x_output = last_x - first_x + 1;
+
+  const int first_plane = blockIdx.y * blockDim.y;
+  const int plane_stride = blockDim.y * gridDim.y;
+
+  for (int p = first_plane + threadIdx.y; p < numPlanes; p += plane_stride) {
+    // Load inputs to shared memory
+    const int plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
+    const int plane_kernel_offset = threadIdx.y * num_x_input;
+#pragma unroll
+    for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
+      const int tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i + first_x);
+      s[i + plane_kernel_offset] = eval.coeff(tensor_index);
+    }
+
+    __syncthreads();
+
+    // Compute the convolution
+    const int plane_output_offset = indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p);
+
+#pragma unroll
+    for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
+      const int kernel_offset = plane_kernel_offset + i;
+      float result = 0.0f;
+#pragma unroll
+      for (int k = 0; k < GetKernelSize<StaticKernelSize>()(kernelSize); ++k) {
+        result += s[k + kernel_offset] * kernel[k];
+      }
+      const int tensor_index = plane_output_offset + indexMapper.mapGpuOutputKernelToTensorOutputOffset(i + first_x);
+      buffer[tensor_index] = result;
+    }
+    __syncthreads();
+  }
+};
+
+template <typename InputEvaluator, typename Index, typename InputDims, int StaticKernelSizeX, int StaticKernelSizeY>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void EigenConvolutionKernel2D(
+    InputEvaluator eval, const internal::IndexMapper<Index, InputDims, 2, InputEvaluator::Layout> indexMapper,
+    const float* __restrict kernel, const int numPlanes, const int numX, const int maxX, const int numY, const int maxY,
+    const int kernelSizeX, const int kernelSizeY, float* buffer) {
+#if defined(EIGEN_HIPCC)
+  HIP_DYNAMIC_SHARED(float, s)
+#else
+  extern __shared__ float s[];
+#endif
+
+  const int first_x = blockIdx.x * maxX;
+  const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
+  const int num_x_input = last_x - first_x + GetKernelSize<StaticKernelSizeX>()(kernelSizeX);
+  const int num_x_output = last_x - first_x + 1;
+
+  const int first_y = blockIdx.y * maxY;
+  const int last_y = (first_y + maxY < numY ? first_y + maxY : numY) - 1;
+  const int num_y_input = last_y - first_y + GetKernelSize<StaticKernelSizeY>()(kernelSizeY);
+  const int num_y_output = last_y - first_y + 1;
+
+  const int first_plane = blockIdx.z * blockDim.z;
+  const int plane_stride = blockDim.z * gridDim.z;
+
+  for (int p = first_plane + threadIdx.z; p < numPlanes; p += plane_stride) {
+    const int plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
+    const int plane_kernel_offset = threadIdx.z * num_y_input;
+
+// Load inputs to shared memory
+#pragma unroll
+    for (int j = threadIdx.y; j < num_y_input; j += blockDim.y) {
+      const int input_offset = num_x_input * (j + plane_kernel_offset);
+#pragma unroll
+      for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
+        const int tensor_index =
+            plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i + first_x, j + first_y);
+        s[i + input_offset] = eval.coeff(tensor_index);
+      }
+    }
+
+    __syncthreads();
+
+    // Convolution
+    const int plane_output_offset = indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p);
+
+#pragma unroll
+    for (int j = threadIdx.y; j < num_y_output; j += blockDim.y) {
+#pragma unroll
+      for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
+        float result = 0.0f;
+#pragma unroll
+        for (int l = 0; l < GetKernelSize<StaticKernelSizeY>()(kernelSizeY); ++l) {
+          const int kernel_offset = kernelSizeX * l;
+          const int input_offset = i + num_x_input * (j + l + plane_kernel_offset);
+#pragma unroll
+          for (int k = 0; k < GetKernelSize<StaticKernelSizeX>()(kernelSizeX); ++k) {
+            result += s[k + input_offset] * kernel[k + kernel_offset];
+          }
+        }
+        const int tensor_index =
+            plane_output_offset + indexMapper.mapGpuOutputKernelToTensorOutputOffset(i + first_x, j + first_y);
+        buffer[tensor_index] = result;
+      }
+    }
+
+    __syncthreads();
+  }
+};
+
+template <typename InputEvaluator, typename Index, typename InputDims>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void EigenConvolutionKernel3D(
+    InputEvaluator eval, const internal::IndexMapper<Index, InputDims, 3, InputEvaluator::Layout> indexMapper,
+    const float* __restrict kernel, const size_t numPlanes, const size_t numX, const size_t maxX, const size_t numY,
+    const size_t maxY, const size_t numZ, const size_t maxZ, const size_t kernelSizeX, const size_t kernelSizeY,
+    const size_t kernelSizeZ, float* buffer) {
+#if defined(EIGEN_HIPCC)
+  HIP_DYNAMIC_SHARED(float, s)
+#else
+  extern __shared__ float s[];
+#endif
+
+  // Load inputs to shared memory
+  const int first_x = blockIdx.x * maxX;
+  const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
+  const int num_x_input = last_x - first_x + kernelSizeX;
+
+  const int first_y = blockIdx.y * maxY;
+  const int last_y = (first_y + maxY < numY ? first_y + maxY : numY) - 1;
+  const int num_y_input = last_y - first_y + kernelSizeY;
+
+  const int first_z = blockIdx.z * maxZ;
+  const int last_z = (first_z + maxZ < numZ ? first_z + maxZ : numZ) - 1;
+  const int num_z_input = last_z - first_z + kernelSizeZ;
+
+  for (int p = 0; p < numPlanes; ++p) {
+    const int plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
+    const int plane_kernel_offset = 0;
+
+    for (int k = threadIdx.z; k < num_z_input; k += blockDim.z) {
+      for (int j = threadIdx.y; j < num_y_input; j += blockDim.y) {
+        for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
+          const int tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(
+                                                            i + first_x, j + first_y, k + first_z);
+          s[i + num_x_input * (j + num_y_input * (k + plane_kernel_offset))] = eval.coeff(tensor_index);
+        }
+      }
+    }
+
+    __syncthreads();
+
+    // Convolution
+    const int num_z_output = last_z - first_z + 1;
+    const int num_y_output = last_y - first_y + 1;
+    const int num_x_output = last_x - first_x + 1;
+    const int plane_output_offset = indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p);
+
+    for (int k = threadIdx.z; k < num_z_output; k += blockDim.z) {
+      for (int j = threadIdx.y; j < num_y_output; j += blockDim.y) {
+        for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
+          float result = 0.0f;
+          for (int n = 0; n < kernelSizeZ; ++n) {
+            for (int m = 0; m < kernelSizeY; ++m) {
+              for (int l = 0; l < kernelSizeX; ++l) {
+                result += s[i + l + num_x_input * (j + m + num_y_input * (k + n + plane_kernel_offset))] *
+                          kernel[l + kernelSizeX * (m + kernelSizeY * n)];
+              }
+            }
+          }
+          const int tensor_index = plane_output_offset + indexMapper.mapGpuOutputKernelToTensorOutputOffset(
+                                                             i + first_x, j + first_y, k + first_z);
+          buffer[tensor_index] = result;
+        }
+      }
+    }
+    __syncthreads();
+  }
+};
+
+template <typename Indices, typename InputArgType, typename KernelArgType>
+struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, GpuDevice> {
+  typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
+
+  static constexpr int NumDims =
+      internal::array_size<typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions>::value;
+  static constexpr int NumKernelDims = internal::array_size<Indices>::value;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions KernelDimensions;
+
+  static constexpr int Layout = TensorEvaluator<InputArgType, GpuDevice>::Layout;
+  enum {
+    IsAligned =
+        TensorEvaluator<InputArgType, GpuDevice>::IsAligned & TensorEvaluator<KernelArgType, GpuDevice>::IsAligned,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  TensorEvaluator(const XprType& op, const GpuDevice& device)
+      : m_inputImpl(op.inputExpression(), device),
+        m_kernelImpl(op.kernelExpression(), device),
+        m_kernelArg(op.kernelExpression()),
+        m_indices(op.indices()),
+        m_buf(NULL),
+        m_kernel(NULL),
+        m_local_kernel(false),
+        m_device(device) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, GpuDevice>::Layout) ==
+                         static_cast<int>(TensorEvaluator<KernelArgType, GpuDevice>::Layout)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    const typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions& input_dims = m_inputImpl.dimensions();
+    const typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
+
+    m_dimensions = m_inputImpl.dimensions();
+    for (int i = 0; i < NumKernelDims; ++i) {
+      const Index index = op.indices()[i];
+      const Index input_dim = input_dims[index];
+      const Index kernel_dim = kernel_dims[i];
+      const Index result_dim = input_dim - kernel_dim + 1;
+      m_dimensions[index] = result_dim;
+    }
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, GpuDevice>::type PacketReturnType;
+  typedef typename InputArgType::Scalar Scalar;
+  static constexpr int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
+    preloadKernel();
+    m_inputImpl.evalSubExprsIfNeeded(NULL);
+    if (data) {
+      executeEval(data);
+      return false;
+    } else {
+      m_buf = (Scalar*)m_device.allocate(dimensions().TotalSize() * sizeof(Scalar));
+      executeEval(m_buf);
+      return true;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_inputImpl.cleanup();
+    if (m_buf) {
+      m_device.deallocate(m_buf);
+      m_buf = NULL;
+    }
+    if (m_local_kernel) {
+      m_device.deallocate((void*)m_kernel);
+      m_local_kernel = false;
+    }
+    m_kernel = NULL;
+  }
+
+  EIGEN_STRONG_INLINE void preloadKernel() {
+    // Don't make a local copy of the kernel unless we have to (i.e. it's an
+    // expression that needs to be evaluated)
+    const Scalar* in_place = m_kernelImpl.data();
+    if (in_place) {
+      m_kernel = in_place;
+      m_local_kernel = false;
+    } else {
+      size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
+      Scalar* local = (Scalar*)m_device.allocate(kernel_sz);
+      typedef TensorEvalToOp<const KernelArgType> EvalTo;
+      EvalTo evalToTmp(local, m_kernelArg);
+      const bool PacketAccess = internal::IsVectorizable<GpuDevice, KernelArgType>::value;
+      internal::TensorExecutor<const EvalTo, GpuDevice, PacketAccess>::run(evalToTmp, m_device);
+
+      m_kernel = local;
+      m_local_kernel = true;
+    }
+  }
+
+  static unsigned int ceil(unsigned int num, unsigned int denom) {
+    const unsigned int rounded_toward_zero = num / denom;
+    if (num > rounded_toward_zero * denom) {
+      return rounded_toward_zero + 1;
+    }
+    return rounded_toward_zero;
+  }
+
+  void executeEval(Scalar* data) const {
+    typedef typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions InputDims;
+
+    const int maxSharedMem = m_device.sharedMemPerBlock();
+    const int maxThreadsPerBlock = m_device.maxGpuThreadsPerBlock();
+    const int maxBlocksPerProcessor = m_device.maxGpuThreadsPerMultiProcessor() / maxThreadsPerBlock;
+    const int numMultiProcessors = m_device.getNumGpuMultiProcessors();
+    const int warpSize = 32;
+
+    switch (NumKernelDims) {
+      case 1: {
+        const int kernel_size = m_kernelImpl.dimensions().TotalSize();
+
+        const int numX = dimensions()[m_indices[0]];
+        const int numP = dimensions().TotalSize() / numX;
+        int maxX;
+        dim3 block_size;
+
+        const int single_stride_dim =
+            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : m_inputImpl.dimensions().rank() - 1;
+        if (m_indices[0] == single_stride_dim) {
+          // Maximum the reuse
+          const int inner_dim = ((maxSharedMem / (sizeof(Scalar)) - kernel_size + 1 + 31) / 32) * 32;
+          maxX = numext::mini<int>(inner_dim, numX);
+          const int maxP = numext::mini<int>(maxSharedMem / ((kernel_size - 1 + maxX) * sizeof(Scalar)), numP);
+          block_size.x = numext::mini(maxThreadsPerBlock, maxX);
+          block_size.y = numext::mini<int>(maxThreadsPerBlock / block_size.x, maxP);
+        } else {
+          // Read as much as possible alongside the inner most dimension, that is the plane
+          const int inner_dim = maxSharedMem / ((warpSize + kernel_size) * sizeof(Scalar));
+          const int maxP = numext::mini<int>(inner_dim, numP);
+          maxX = numext::mini<int>(maxSharedMem / (inner_dim * sizeof(Scalar)) - kernel_size + 1, numX);
+
+          block_size.x = numext::mini(warpSize, maxX);
+          block_size.y = numext::mini<int>(maxThreadsPerBlock / block_size.x, maxP);
+        }
+
+        const int shared_mem = block_size.y * (maxX + kernel_size - 1) * sizeof(Scalar);
+        gpu_assert(shared_mem <= maxSharedMem);
+
+        const int num_x_blocks = ceil(numX, maxX);
+        const int blocksPerProcessor = numext::mini(maxBlocksPerProcessor, maxSharedMem / shared_mem);
+        const int num_y_blocks = ceil(numMultiProcessors * blocksPerProcessor, num_x_blocks);
+
+        dim3 num_blocks(num_x_blocks, numext::mini<int>(num_y_blocks, ceil(numP, block_size.y)));
+
+        const array<Index, 1> indices{m_indices[0]};
+        const array<Index, 1> kernel_dims{m_kernelImpl.dimensions()[0]};
+        internal::IndexMapper<Index, InputDims, 1, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
+        switch (kernel_size) {
+          case 4: {
+            LAUNCH_GPU_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4>),
+                              num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP,
+                              numX, maxX, 4, data);
+            break;
+          }
+          case 7: {
+            LAUNCH_GPU_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7>),
+                              num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP,
+                              numX, maxX, 7, data);
+            break;
+          }
+          default: {
+            LAUNCH_GPU_KERNEL(
+                (EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, Dynamic>),
+                num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX,
+                kernel_size, data);
+          }
+        }
+        break;
+      }
+
+      case 2: {
+        const int idxX = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 1;
+        const int idxY = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 0;
+        const int kernel_size_x = m_kernelImpl.dimensions()[idxX];
+        const int kernel_size_y = m_kernelImpl.dimensions()[idxY];
+
+        const int numX = dimensions()[m_indices[idxX]];
+        const int numY = dimensions()[m_indices[idxY]];
+        const int numP = dimensions().TotalSize() / (numX * numY);
+
+        const float scaling_factor =
+            sqrtf(static_cast<float>(maxSharedMem) / (sizeof(Scalar) * kernel_size_y * kernel_size_x));
+
+        // Snap maxX to warp size
+        int inner_dim = ((static_cast<int>(scaling_factor * kernel_size_x) - kernel_size_x + 1 + 32) / 32) * 32;
+        const int maxX = numext::mini<int>(inner_dim, numX);
+        const int maxY =
+            numext::mini<int>(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1)) - kernel_size_y + 1, numY);
+        const int maxP = numext::mini<int>(
+            maxSharedMem / ((kernel_size_x - 1 + maxX) * (kernel_size_y - 1 + maxY) * sizeof(Scalar)), numP);
+
+        dim3 block_size;
+        block_size.x = numext::mini(1024, maxX);
+        block_size.y = numext::mini<int>(1024 / block_size.x, maxY);
+        block_size.z = numext::mini<int>(1024 / (block_size.x * block_size.y), maxP);
+
+        const int shared_mem = block_size.z * (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1) * sizeof(Scalar);
+        gpu_assert(shared_mem <= maxSharedMem);
+
+        const int num_x_blocks = ceil(numX, maxX);
+        const int num_y_blocks = ceil(numY, maxY);
+        const int blocksPerProcessor = numext::mini(maxBlocksPerProcessor, maxSharedMem / shared_mem);
+        const int num_z_blocks = ceil(numMultiProcessors * blocksPerProcessor, num_x_blocks * num_y_blocks);
+
+        dim3 num_blocks(num_x_blocks, num_y_blocks, numext::mini<int>(num_z_blocks, ceil(numP, block_size.z)));
+
+        const array<Index, 2> indices{m_indices[idxX], m_indices[idxY]};
+        const array<Index, 2> kernel_dims{m_kernelImpl.dimensions()[idxX], m_kernelImpl.dimensions()[idxY]};
+        internal::IndexMapper<Index, InputDims, 2, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
+        switch (kernel_size_x) {
+          case 4: {
+            switch (kernel_size_y) {
+              case 7: {
+                LAUNCH_GPU_KERNEL(
+                    (EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4, 7>),
+                    num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX,
+                    numY, maxY, 4, 7, data);
+                break;
+              }
+              default: {
+                LAUNCH_GPU_KERNEL(
+                    (EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4, Dynamic>),
+                    num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX,
+                    numY, maxY, 4, kernel_size_y, data);
+                break;
+              }
+            }
+            break;
+          }
+          case 7: {
+            switch (kernel_size_y) {
+              case 4: {
+                LAUNCH_GPU_KERNEL(
+                    (EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7, 4>),
+                    num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX,
+                    numY, maxY, 7, 4, data);
+                break;
+              }
+              default: {
+                LAUNCH_GPU_KERNEL(
+                    (EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7, Dynamic>),
+                    num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX,
+                    numY, maxY, 7, kernel_size_y, data);
+                break;
+              }
+            }
+            break;
+          }
+          default: {
+            LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims,
+                                                        Dynamic, Dynamic>),
+                              num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP,
+                              numX, maxX, numY, maxY, kernel_size_x, kernel_size_y, data);
+            break;
+          }
+        }
+        break;
+      }
+
+      case 3: {
+        const int idxX = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 2;
+        const int idxY = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 1;
+        const int idxZ = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 2 : 0;
+
+        const int kernel_size_x = m_kernelImpl.dimensions()[idxX];
+        const int kernel_size_y = m_kernelImpl.dimensions()[idxY];
+        const int kernel_size_z = m_kernelImpl.dimensions()[idxZ];
+
+        const int numX = dimensions()[m_indices[idxX]];
+        const int numY = dimensions()[m_indices[idxY]];
+        const int numZ = dimensions()[m_indices[idxZ]];
+        const int numP = dimensions().TotalSize() / (numX * numY * numZ);
+
+        const int maxX = numext::mini<int>(
+            128, numext::mini<int>(maxSharedMem / (sizeof(Scalar) * kernel_size_y * kernel_size_z) - kernel_size_x + 1,
+                                   numX));
+        const int maxY = numext::mini<int>(
+            128, numext::mini<int>(
+                     maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1) * kernel_size_z) - kernel_size_y + 1,
+                     numY));
+        const int maxZ = numext::mini<int>(
+            128, numext::mini<int>(
+                     maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1)) -
+                         kernel_size_z + 1,
+                     numZ));
+
+        dim3 block_size;
+        block_size.x = numext::mini(32, maxX);
+        block_size.y = numext::mini(32, maxY);
+        block_size.z = numext::mini<int>(1024 / (block_size.x * block_size.y), maxZ);
+        dim3 num_blocks(ceil(numX, maxX), ceil(numY, maxY), ceil(numZ, maxZ));
+
+        const int shared_mem =
+            (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1) * (maxZ + kernel_size_z - 1) * sizeof(Scalar);
+        gpu_assert(shared_mem <= maxSharedMem);
+
+        const array<Index, 3> indices{m_indices[idxX], m_indices[idxY], m_indices[idxZ]};
+        const array<Index, 3> kernel_dims{m_kernelImpl.dimensions()[idxX], m_kernelImpl.dimensions()[idxY],
+                                          m_kernelImpl.dimensions()[idxZ]};
+        internal::IndexMapper<Index, InputDims, 3, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
+
+        LAUNCH_GPU_KERNEL((EigenConvolutionKernel3D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims>),
+                          num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX,
+                          maxX, numY, maxY, numZ, maxZ, kernel_size_x, kernel_size_y, kernel_size_z, data);
+        break;
+      }
+
+      default: {
+        EIGEN_STATIC_ASSERT((NumKernelDims >= 1 && NumKernelDims <= 3),
+                            THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    eigen_assert(m_buf);
+    eigen_assert(index < m_dimensions.TotalSize());
+    return m_buf[index];
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(const Index index) const {
+    eigen_assert(m_buf);
+    eigen_assert(index < m_dimensions.TotalSize());
+    return internal::ploadt<PacketReturnType, LoadMode>(m_buf + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // TODO(rmlarsen): For now, this is just a copy of the CPU cost
+    // model.
+    const double kernel_size = m_kernelImpl.dimensions().TotalSize();
+    // We ignore the use of fused multiply-add.
+    const double convolve_compute_cost = TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>();
+    const double firstIndex_compute_cost =
+        NumDims *
+        (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>());
+    return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +
+           kernel_size * (m_inputImpl.costPerCoeff(vectorized) + m_kernelImpl.costPerCoeff(vectorized) +
+                          TensorOpCost(0, 0, convolve_compute_cost, vectorized, PacketSize));
+  }
+
+ private:
+  TensorEvaluator<InputArgType, GpuDevice> m_inputImpl;
+  TensorEvaluator<KernelArgType, GpuDevice> m_kernelImpl;
+  KernelArgType m_kernelArg;
+  Indices m_indices;
+  Dimensions m_dimensions;
+  Scalar* m_buf;
+  const Scalar* m_kernel;
+  bool m_local_kernel;
+
+  const GpuDevice& m_device;
+};
+#endif
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
new file mode 100644
index 0000000..37cf966
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
@@ -0,0 +1,538 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum class convolution_type { CONV1D, CONV2D, CONV3D };
+template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
+          typename Kernel_accessor, typename Buffer_accessor, convolution_type Conv_Dim>
+struct EigenConvolutionKernel;
+template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
+          typename Kernel_accessor, typename Buffer_accessor>
+struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor,
+                              Buffer_accessor, convolution_type::CONV1D> {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      Local_accessor;
+  Local_accessor local_acc;
+  Evaluator device_evaluator;
+  Kernel_accessor kernel_filter;
+  Buffer_accessor buffer_acc;
+  internal::IndexMapper<Index, InputDims, 1, Evaluator::Layout> indexMapper;
+  const size_t kernelSize;
+  const cl::sycl::range<2> input_range;
+  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,
+                         Buffer_accessor buffer_acc_,
+                         internal::IndexMapper<Index, InputDims, 1, Evaluator::Layout> indexMapper_,
+                         const size_t kernelSize_, const cl::sycl::range<2> input_range_)
+      : local_acc(local_acc_),
+        device_evaluator(device_evaluator_),
+        kernel_filter(kernel_filter_),
+        buffer_acc(buffer_acc_),
+        indexMapper(indexMapper_),
+        kernelSize(kernelSize_),
+        input_range(input_range_) {}
+
+  template <typename BooleanDim2>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim2 boolean_check) const {
+    return (boolean_check[0] && boolean_check[1]);
+  }
+  void operator()(cl::sycl::nd_item<2> itemID) const {
+    auto buffer_ptr = buffer_acc;
+    auto kernel_ptr = kernel_filter;
+    // the required row to be calculated for each plane in shared memory
+    const size_t num_input = (itemID.get_local_range()[0] + kernelSize - 1);
+    const size_t plane_kernel_offset = itemID.get_local_id(1) * num_input;
+    const size_t input_offset = itemID.get_group(0) * itemID.get_local_range()[0];
+    const size_t plane_tensor_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(itemID.get_global_id(1));
+    /// fill the shared memory
+    for (size_t i = itemID.get_local_id(0); i < num_input; i += itemID.get_local_range()[0]) {
+      const size_t local_index = i + plane_kernel_offset;
+      const size_t tensor_index =
+          plane_tensor_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i + input_offset);
+
+      local_acc[local_index] =
+          (((i + input_offset) < (input_range[0] + kernelSize - 1)) && itemID.get_global_id(1) < input_range[1])
+              ? device_evaluator.coeff(tensor_index)
+              : CoeffReturnType(0);
+    }
+
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+
+    // calculate the convolution // output start x
+    const size_t first_output_start = itemID.get_group(0) * (itemID.get_local_range()[0]);
+    if (boundary_check(itemID.get_global_id() < input_range)) {
+      CoeffReturnType result = static_cast<CoeffReturnType>(0);
+      const size_t index = plane_kernel_offset + itemID.get_local_id(0);
+      for (size_t k = 0; k < kernelSize; ++k) {
+        result += (local_acc[k + index] * kernel_ptr[k]);
+      }
+      const size_t tensor_index =
+          indexMapper.mapGpuOutputPlaneToTensorOutputOffset(itemID.get_global_id(1)) +
+          indexMapper.mapGpuOutputKernelToTensorOutputOffset(itemID.get_local_id(0) + first_output_start);
+      buffer_ptr[tensor_index] = result;
+    }
+  }
+};
+
+template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
+          typename Kernel_accessor, typename Buffer_accessor>
+struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor,
+                              Buffer_accessor, convolution_type::CONV2D> {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      Local_accessor;
+  Local_accessor local_acc;
+  Evaluator device_evaluator;
+  Kernel_accessor kernel_filter;
+  Buffer_accessor buffer_acc;
+  internal::IndexMapper<Index, InputDims, 2, Evaluator::Layout> indexMapper;
+  const cl::sycl::range<2> kernel_size;
+  const cl::sycl::range<3> input_range;
+  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,
+                         Buffer_accessor buffer_acc_,
+                         internal::IndexMapper<Index, InputDims, 2, Evaluator::Layout> indexMapper_,
+                         const cl::sycl::range<2> kernel_size_, const cl::sycl::range<3> input_range_)
+      : local_acc(local_acc_),
+        device_evaluator(device_evaluator_),
+        kernel_filter(kernel_filter_),
+        buffer_acc(buffer_acc_),
+        indexMapper(indexMapper_),
+        kernel_size(kernel_size_),
+        input_range(input_range_) {}
+  template <typename BooleanDim3>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim3 boolean_check) const {
+    return (boolean_check[0] && boolean_check[1] && boolean_check[2]);
+  }
+
+  void operator()(cl::sycl::nd_item<3> itemID) const {
+    auto buffer_ptr = buffer_acc;
+    auto kernel_ptr = kernel_filter;
+    // the required row to be calculated for each plane in shared memory
+    const auto num_input = cl::sycl::range<2>{
+        (cl::sycl::range<2>(itemID.get_local_range()[0], itemID.get_local_range()[1]) + kernel_size - 1)};
+
+    const size_t plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(itemID.get_global_id(2));
+    const size_t plane_kernel_offset = itemID.get_local_id(2) * num_input[1];
+
+    const auto input_offset = cl::sycl::range<2>{itemID.get_group(0) * itemID.get_local_range()[0],
+                                                 itemID.get_group(1) * itemID.get_local_range()[1]};
+
+    // fill the local memory
+    bool in_range_dim2 = itemID.get_global_id(2) < input_range[2];
+    for (size_t j = itemID.get_local_id(1); j < num_input[1]; j += itemID.get_local_range()[1]) {
+      const size_t local_input_offset = num_input[0] * (j + plane_kernel_offset);
+      bool in_range_dim1 = ((j + input_offset[1]) < (input_range[1] + kernel_size[1] - 1));
+      for (size_t i = itemID.get_local_id(0); i < num_input[0]; i += itemID.get_local_range()[0]) {
+        const size_t local_index = i + local_input_offset;
+        const size_t tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(
+                                                             i + input_offset[0], j + input_offset[1]);
+        local_acc[local_index] =
+            (((i + input_offset[0]) < (input_range[0] + kernel_size[0] - 1)) && in_range_dim1 && in_range_dim2)
+                ? device_evaluator.coeff(tensor_index)
+                : CoeffReturnType(0);
+      }
+    }
+
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+
+    // output offset start for each thread
+    const auto output_offset = cl::sycl::range<2>{itemID.get_group(0) * itemID.get_local_range()[0],
+                                                  itemID.get_group(1) * itemID.get_local_range()[1]};
+
+    if (boundary_check(itemID.get_global_id() < input_range)) {
+      CoeffReturnType result = static_cast<CoeffReturnType>(0);
+
+      for (size_t j = 0; j < kernel_size[1]; j++) {
+        size_t kernel_offset = kernel_size[0] * j;
+        const size_t index =
+            (num_input[0] * (plane_kernel_offset + j + itemID.get_local_id(1))) + itemID.get_local_id(0);
+        for (size_t i = 0; i < kernel_size[0]; i++) {
+          result += (local_acc[i + index] * kernel_ptr[i + kernel_offset]);
+        }
+      }
+      const size_t tensor_index =
+          indexMapper.mapGpuOutputPlaneToTensorOutputOffset(itemID.get_global_id(2)) +
+          indexMapper.mapGpuOutputKernelToTensorOutputOffset(itemID.get_local_id(0) + output_offset[0],
+                                                             itemID.get_local_id(1) + output_offset[1]);
+
+      buffer_ptr[tensor_index] = result;
+    }
+  }
+};
+
+template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
+          typename Kernel_accessor, typename Buffer_accessor>
+struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor,
+                              Buffer_accessor, convolution_type::CONV3D> {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      Local_accessor;
+  Local_accessor local_acc;
+  Evaluator device_evaluator;
+  Kernel_accessor kernel_filter;
+  Buffer_accessor buffer_acc;
+  internal::IndexMapper<Index, InputDims, 3, Evaluator::Layout> indexMapper;
+  const cl::sycl::range<3> kernel_size;
+  const cl::sycl::range<3> input_range;
+  const size_t numP;
+
+  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,
+                         Buffer_accessor buffer_acc_,
+                         internal::IndexMapper<Index, InputDims, 3, Evaluator::Layout> indexMapper_,
+                         const cl::sycl::range<3> kernel_size_, const cl::sycl::range<3> input_range_,
+                         const size_t numP_)
+      : local_acc(local_acc_),
+        device_evaluator(device_evaluator_),
+        kernel_filter(kernel_filter_),
+        buffer_acc(buffer_acc_),
+        indexMapper(indexMapper_),
+        kernel_size(kernel_size_),
+        input_range(input_range_),
+        numP(numP_) {}
+  template <typename BooleanDim3>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim3 boolean_check) const {
+    return (boolean_check[0] && boolean_check[1] && boolean_check[2]);
+  }
+  void operator()(cl::sycl::nd_item<3> itemID) const {
+    auto buffer_ptr = buffer_acc;
+    auto kernel_ptr = kernel_filter;
+    const auto num_input = cl::sycl::range<3>{itemID.get_local_range() + kernel_size - 1};
+
+    const auto input_offset = cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range()};
+
+    const auto output_offset =
+        cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range() + itemID.get_local_id()};
+
+    for (size_t p = 0; p < numP; p++) {
+      /// fill the shared memory
+      const size_t plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
+      for (size_t k = itemID.get_local_id(2); k < num_input[2]; k += itemID.get_local_range()[2]) {
+        size_t local_index_dim2 = num_input[0] * num_input[1] * k;
+        bool cond_k_dim = (k + input_offset[2] < (input_range[2] + kernel_size[2] - 1));
+        for (size_t j = itemID.get_local_id(1); j < num_input[1]; j += itemID.get_local_range()[1]) {
+          bool cond_j_dim = cond_k_dim && (j + input_offset[1] < (input_range[1] + kernel_size[1] - 1));
+          size_t local_index_dim1 = (num_input[0] * j) + local_index_dim2;
+          for (size_t i = itemID.get_local_id(0); i < num_input[0]; i += itemID.get_local_range()[0]) {
+            bool conds = cond_j_dim && (i + input_offset[0] < (input_range[0] + kernel_size[0] - 1));
+            const size_t local_index = local_index_dim1 + i;
+            const size_t tensor_index =
+                plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(
+                                         i + input_offset[0], j + input_offset[1], k + input_offset[2]);
+            local_acc[local_index] = conds ? device_evaluator.coeff(tensor_index) : CoeffReturnType(0);
+          }
+        }
+      }
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+
+      // calculate the convolution
+
+      if (boundary_check(itemID.get_global_id() < input_range)) {
+        CoeffReturnType result = static_cast<CoeffReturnType>(0);
+        for (size_t k = 0; k < kernel_size[2]; k++) {
+          for (size_t j = 0; j < kernel_size[1]; j++) {
+            for (size_t i = 0; i < kernel_size[0]; i++) {
+              const size_t kernel_index = i + kernel_size[0] * (j + kernel_size[1] * k);
+              const size_t local_index =
+                  ((i + itemID.get_local_id(0)) +
+                   num_input[0] * ((j + itemID.get_local_id(1)) + num_input[1] * (k + itemID.get_local_id(2))));
+
+              result += (local_acc[local_index] * kernel_ptr[kernel_index]);
+            }
+          }
+        }
+        const size_t tensor_index =
+            indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p) +
+            indexMapper.mapGpuOutputKernelToTensorOutputOffset(output_offset[0], output_offset[1], output_offset[2]);
+        buffer_ptr[tensor_index] = result;
+      }
+
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+    }
+  }
+};
+
+template <typename Indices, typename InputArgType, typename KernelArgType>
+struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, Eigen::SyclDevice> {
+  typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
+
+  static constexpr int NumDims =
+      internal::array_size<typename TensorEvaluator<InputArgType, Eigen::SyclDevice>::Dimensions>::value;
+  static constexpr int NumKernelDims = internal::array_size<Indices>::value;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Dimensions KernelDimensions;
+  typedef const Eigen::SyclDevice Device;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Eigen::SyclDevice>::type PacketReturnType;
+  typedef typename InputArgType::Scalar Scalar;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Eigen::SyclDevice> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  typedef StorageMemory<const CoeffReturnType, Eigen::SyclDevice> KernelStorage;
+
+  static constexpr int Layout = TensorEvaluator<InputArgType, Eigen::SyclDevice>::Layout;
+  enum {
+    IsAligned = TensorEvaluator<InputArgType, Eigen::SyclDevice>::IsAligned &
+                TensorEvaluator<KernelArgType, Eigen::SyclDevice>::IsAligned,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  TensorEvaluator(const XprType &op, const Eigen::SyclDevice &device)
+      : m_inputImpl(op.inputExpression(), device),
+        m_kernelArg(op.kernelExpression()),
+        m_kernelImpl(op.kernelExpression(), device),
+        m_indices(op.indices()),
+        m_buf(NULL),
+        m_kernel(NULL),
+        m_local_kernel(false),
+        m_device(device) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Eigen::SyclDevice>::Layout) ==
+                         static_cast<int>(TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Layout)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    const typename TensorEvaluator<InputArgType, Eigen::SyclDevice>::Dimensions &input_dims = m_inputImpl.dimensions();
+    const typename TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Dimensions &kernel_dims =
+        m_kernelImpl.dimensions();
+
+    m_dimensions = m_inputImpl.dimensions();
+    for (int i = 0; i < NumKernelDims; ++i) {
+      const Index index = op.indices()[i];
+      const Index input_dim = input_dims[index];
+      const Index kernel_dim = kernel_dims[i];
+      const Index result_dim = input_dim - kernel_dim + 1;
+      m_dimensions[index] = result_dim;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC const Dimensions &dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    preloadKernel();
+    m_inputImpl.evalSubExprsIfNeeded(NULL);
+    if (data) {
+      executeEval(data);
+      return false;
+    } else {
+      m_buf = (EvaluatorPointerType)m_device.get(
+          (Scalar *)m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar)));
+      executeEval(m_buf);
+      return true;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_inputImpl.cleanup();
+    if (m_buf) {
+      m_device.deallocate_temp(m_buf);
+      m_buf = NULL;
+    }
+    if (m_local_kernel) {
+      m_device.deallocate_temp(m_kernel);
+      m_local_kernel = false;
+    }
+    m_kernel = NULL;
+  }
+  /// used by sycl in order to build the sycl buffer
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device &device() const { return m_device; }
+  /// used by sycl in order to build the sycl buffer
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return m_buf; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void preloadKernel() {
+    // Don't make a local copy of the kernel unless we have to (i.e. it's an
+    // expression that needs to be evaluated)
+    typename KernelStorage::Type in_place = m_kernelImpl.data();
+    if (in_place) {
+      m_kernel = in_place;
+      m_local_kernel = false;
+    } else {
+      ptrdiff_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
+      EvaluatorPointerType local = (EvaluatorPointerType)m_device.get((Scalar *)m_device.allocate_temp(kernel_sz));
+      typedef TensorEvalToOp<const KernelArgType> EvalTo;
+      EvalTo evalToTmp(m_device.get(local), m_kernelArg);
+      const bool PacketAccess = internal::IsVectorizable<Eigen::SyclDevice, KernelArgType>::value;
+      internal::TensorExecutor<const EvalTo, Eigen::SyclDevice, PacketAccess>::run(evalToTmp, m_device);
+      m_kernel = local;
+      m_local_kernel = true;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void executeEval(EvaluatorPointerType data) const {
+    typedef TensorEvaluator<InputArgType, Eigen::SyclDevice> InputEvaluator;
+    typedef typename InputEvaluator::Dimensions InputDims;
+    switch (NumKernelDims) {
+      case 1: {
+        const size_t numX = dimensions()[m_indices[0]];
+        const size_t numP = dimensions().TotalSize() / numX;
+        const auto input_dim = std::array<size_t, 2>{numX, numP};
+        auto global_range = cl::sycl::range<2>{1, 1};
+        auto local_range = cl::sycl::range<2>{1, 1};
+        const size_t kernel_size = m_kernelImpl.dimensions().TotalSize();
+
+        m_device.parallel_for_setup(input_dim, global_range, local_range);
+        const size_t local_memory_size = (local_range[0] + kernel_size - 1) * (local_range[1]);
+        gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock());
+        const array<Index, 1> indices{{m_indices[0]}};
+        const array<Index, 1> kernel_dims{{m_kernelImpl.dimensions()[0]}};
+        internal::IndexMapper<Index, InputDims, 1, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
+
+        typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
+                                       typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV1D>
+            ConvKernel;
+
+        m_device
+            .template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
+                m_inputImpl, m_kernel, data, cl::sycl::nd_range<2>(global_range, local_range), local_memory_size,
+                indexMapper, kernel_size, cl::sycl::range<2>(input_dim[0], input_dim[1]))
+            .wait();
+        break;
+      }
+
+      case 2: {
+        auto kernel_index = std::array<size_t, 2>{static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 1,
+                                                  static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 0};
+        auto kernel_size = cl::sycl::range<2>{(size_t)m_kernelImpl.dimensions()[kernel_index[0]],
+                                              (size_t)m_kernelImpl.dimensions()[kernel_index[1]]};
+        const size_t numX = dimensions()[m_indices[kernel_index[0]]];
+        const size_t numY = dimensions()[m_indices[kernel_index[1]]];
+        const size_t numP = dimensions().TotalSize() / (numX * numY);
+        auto input_dim = std::array<size_t, 3>{numX, numY, numP};
+
+        auto global_range = cl::sycl::range<3>{1, 1, 1};
+        auto local_range = cl::sycl::range<3>{1, 1, 1};
+
+        m_device.parallel_for_setup(input_dim, global_range, local_range);
+
+        const size_t local_memory_size =
+            (local_range[0] + kernel_size[0] - 1) * (local_range[1] + kernel_size[1] - 1) * local_range[2];
+        gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock());
+        const array<Index, 2> indices{{m_indices[kernel_index[0]], m_indices[kernel_index[1]]}};
+        const array<Index, 2> kernel_dims{
+            {m_kernelImpl.dimensions()[kernel_index[0]], m_kernelImpl.dimensions()[kernel_index[1]]}};
+        internal::IndexMapper<Index, InputDims, 2, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
+        typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
+                                       typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV2D>
+            ConvKernel;
+        m_device
+            .template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
+                m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size,
+                indexMapper, kernel_size, cl::sycl::range<3>{input_dim[0], input_dim[1], input_dim[2]})
+            .wait();
+        break;
+      }
+
+      case 3: {
+        auto kernel_index = std::array<size_t, 3>{static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 2,
+                                                  static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 1,
+                                                  static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 2 : 0};
+
+        auto kernel_size = cl::sycl::range<3>{(size_t)m_kernelImpl.dimensions()[kernel_index[0]],
+                                              (size_t)m_kernelImpl.dimensions()[kernel_index[1]],
+                                              (size_t)m_kernelImpl.dimensions()[kernel_index[2]]};
+
+        const size_t numX = dimensions()[m_indices[kernel_index[0]]];
+        const size_t numY = dimensions()[m_indices[kernel_index[1]]];
+        const size_t numZ = dimensions()[m_indices[kernel_index[2]]];
+        auto input_dim = std::array<size_t, 3>{numX, numY, numZ};
+        const size_t numP = dimensions().TotalSize() / (numX * numY * numZ);
+
+        const array<Index, 3> indices{
+            {m_indices[kernel_index[0]], m_indices[kernel_index[1]], m_indices[kernel_index[2]]}};
+        const array<Index, 3> kernel_dims{{m_kernelImpl.dimensions()[kernel_index[0]],
+                                           m_kernelImpl.dimensions()[kernel_index[1]],
+                                           m_kernelImpl.dimensions()[kernel_index[2]]}};
+
+        internal::IndexMapper<Index, InputDims, 3, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
+
+        auto global_range = cl::sycl::range<3>{1, 1, 1};
+        auto local_range = cl::sycl::range<3>{1, 1, 1};
+
+        m_device.parallel_for_setup(input_dim, global_range, local_range);
+        auto local_memory_range = (local_range + kernel_size - 1);
+        const size_t local_memory_size = local_memory_range[0] * local_memory_range[1] * local_memory_range[2];
+
+        gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock());
+        typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
+                                       typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV3D>
+            ConvKernel;
+        m_device
+            .template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
+                m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size,
+                indexMapper, kernel_size, cl::sycl::range<3>(input_dim[0], input_dim[1], input_dim[2]), numP)
+            .wait();
+        break;
+      }
+
+      default: {
+        EIGEN_STATIC_ASSERT((NumKernelDims >= 1 && NumKernelDims <= 3),
+                            THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    eigen_assert(m_buf != NULL);
+    eigen_assert(index < m_dimensions.TotalSize());
+    return m_buf[index];
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(const Index index) const {
+    eigen_assert(m_buf != NULL);
+    eigen_assert(index < m_dimensions.TotalSize());
+    return internal::ploadt<PacketReturnType, LoadMode>(m_buf + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // TODO(rmlarsen): For now, this is just a copy of the CPU cost
+    // model.
+    const double kernel_size = m_kernelImpl.dimensions().TotalSize();
+    // We ignore the use of fused multiply-add.
+    const double convolve_compute_cost = TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>();
+    const double firstIndex_compute_cost =
+        NumDims *
+        (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>());
+    return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +
+           kernel_size * (m_inputImpl.costPerCoeff(vectorized) + m_kernelImpl.costPerCoeff(vectorized) +
+                          TensorOpCost(0, 0, convolve_compute_cost, vectorized, PacketSize));
+  }
+
+ private:
+  // No assignment (copies are needed by the kernels)
+  TensorEvaluator &operator=(const TensorEvaluator &);
+  TensorEvaluator<InputArgType, Eigen::SyclDevice> m_inputImpl;
+  KernelArgType m_kernelArg;
+  TensorEvaluator<KernelArgType, Eigen::SyclDevice> m_kernelImpl;
+  Indices m_indices;
+  Dimensions m_dimensions;
+  EvaluatorPointerType m_buf;
+  typename KernelStorage::Type m_kernel;
+  bool m_local_kernel;
+  const Eigen::SyclDevice EIGEN_DEVICE_REF m_device;
+};  // namespace Eigen
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
new file mode 100644
index 0000000..d6d5860
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
@@ -0,0 +1,189 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Rasmus Munk Larsen <rmlarsen@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// Class storing the cost of evaluating a tensor expression in terms of the
+// estimated number of operand bytes loads, bytes stored, and compute cycles.
+class TensorOpCost {
+ public:
+  // TODO(rmlarsen): Fix the scalar op costs in Eigen proper. Even a simple
+  // model based on minimal reciprocal throughput numbers from Intel or
+  // Agner Fog's tables would be better than what is there now.
+  template <typename ArgType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int MulCost() {
+    return internal::functor_traits<internal::scalar_product_op<ArgType, ArgType> >::Cost;
+  }
+  template <typename ArgType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int AddCost() {
+    return internal::functor_traits<internal::scalar_sum_op<ArgType> >::Cost;
+  }
+  template <typename ArgType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int DivCost() {
+    return internal::functor_traits<internal::scalar_quotient_op<ArgType, ArgType> >::Cost;
+  }
+  template <typename ArgType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int ModCost() {
+    return internal::functor_traits<internal::scalar_mod_op<ArgType> >::Cost;
+  }
+  template <typename SrcType, typename TargetType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int CastCost() {
+    return internal::functor_traits<internal::scalar_cast_op<SrcType, TargetType> >::Cost;
+  }
+
+  EIGEN_DEVICE_FUNC TensorOpCost() : bytes_loaded_(0), bytes_stored_(0), compute_cycles_(0) {}
+  EIGEN_DEVICE_FUNC TensorOpCost(double bytes_loaded, double bytes_stored, double compute_cycles)
+      : bytes_loaded_(bytes_loaded), bytes_stored_(bytes_stored), compute_cycles_(compute_cycles) {}
+
+  EIGEN_DEVICE_FUNC TensorOpCost(double bytes_loaded, double bytes_stored, double compute_cycles, bool vectorized,
+                                 double packet_size)
+      : bytes_loaded_(bytes_loaded),
+        bytes_stored_(bytes_stored),
+        compute_cycles_(vectorized ? compute_cycles / packet_size : compute_cycles) {
+    eigen_assert(bytes_loaded >= 0 && (numext::isfinite)(bytes_loaded));
+    eigen_assert(bytes_stored >= 0 && (numext::isfinite)(bytes_stored));
+    eigen_assert(compute_cycles >= 0 && (numext::isfinite)(compute_cycles));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bytes_loaded() const { return bytes_loaded_; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bytes_stored() const { return bytes_stored_; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double compute_cycles() const { return compute_cycles_; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double total_cost(double load_cost, double store_cost,
+                                                          double compute_cost) const {
+    return load_cost * bytes_loaded_ + store_cost * bytes_stored_ + compute_cost * compute_cycles_;
+  }
+
+  // Drop memory access component. Intended for cases when memory accesses are
+  // sequential or are completely masked by computations.
+  EIGEN_DEVICE_FUNC void dropMemoryCost() {
+    bytes_loaded_ = 0;
+    bytes_stored_ = 0;
+  }
+
+  // TODO(rmlarsen): Define min in terms of total cost, not elementwise.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMin(const TensorOpCost& rhs) const {
+    double bytes_loaded = numext::mini(bytes_loaded_, rhs.bytes_loaded());
+    double bytes_stored = numext::mini(bytes_stored_, rhs.bytes_stored());
+    double compute_cycles = numext::mini(compute_cycles_, rhs.compute_cycles());
+    return TensorOpCost(bytes_loaded, bytes_stored, compute_cycles);
+  }
+
+  // TODO(rmlarsen): Define max in terms of total cost, not elementwise.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMax(const TensorOpCost& rhs) const {
+    double bytes_loaded = numext::maxi(bytes_loaded_, rhs.bytes_loaded());
+    double bytes_stored = numext::maxi(bytes_stored_, rhs.bytes_stored());
+    double compute_cycles = numext::maxi(compute_cycles_, rhs.compute_cycles());
+    return TensorOpCost(bytes_loaded, bytes_stored, compute_cycles);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost& operator+=(const TensorOpCost& rhs) {
+    bytes_loaded_ += rhs.bytes_loaded();
+    bytes_stored_ += rhs.bytes_stored();
+    compute_cycles_ += rhs.compute_cycles();
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost& operator*=(double rhs) {
+    bytes_loaded_ *= rhs;
+    bytes_stored_ *= rhs;
+    compute_cycles_ *= rhs;
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator+(TensorOpCost lhs, const TensorOpCost& rhs) {
+    lhs += rhs;
+    return lhs;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator*(TensorOpCost lhs, double rhs) {
+    lhs *= rhs;
+    return lhs;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator*(double lhs, TensorOpCost rhs) {
+    rhs *= lhs;
+    return rhs;
+  }
+
+  friend std::ostream& operator<<(std::ostream& os, const TensorOpCost& tc) {
+    return os << "[bytes_loaded = " << tc.bytes_loaded() << ", bytes_stored = " << tc.bytes_stored()
+              << ", compute_cycles = " << tc.compute_cycles() << "]";
+  }
+
+ private:
+  double bytes_loaded_;
+  double bytes_stored_;
+  double compute_cycles_;
+};
+
+// TODO(rmlarsen): Implement a policy that chooses an "optimal" number of threads
+// in [1:max_threads] instead of just switching multi-threading off for small
+// work units.
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief A cost model used to limit the number of threads used for evaluating
+ * tensor expression.
+ *
+ */
+template <typename Device>
+class TensorCostModel {
+ public:
+  // Scaling from Eigen compute cost to device cycles.
+  static const int kDeviceCyclesPerComputeCycle = 1;
+
+  // Costs in device cycles.
+  static const int kStartupCycles = 100000;
+  static const int kPerThreadCycles = 100000;
+  static const int kTaskSize = 40000;
+
+  // Returns the number of threads in [1:max_threads] to use for
+  // evaluating an expression with the given output size and cost per
+  // coefficient.
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int numThreads(double output_size, const TensorOpCost& cost_per_coeff,
+                                                              int max_threads) {
+    double cost = totalCost(output_size, cost_per_coeff);
+    double threads = (cost - kStartupCycles) / kPerThreadCycles + 0.9;
+    // Make sure we don't invoke undefined behavior when we convert to an int.
+    threads = numext::mini<double>(threads, GenericNumTraits<int>::highest());
+    return numext::mini(max_threads, numext::maxi<int>(1, static_cast<int>(threads)));
+  }
+
+  // taskSize assesses parallel task size.
+  // Value of 1.0 means ideal parallel task size. Values < 1.0 mean that task
+  // granularity needs to be increased to mitigate parallelization overheads.
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double taskSize(double output_size, const TensorOpCost& cost_per_coeff) {
+    return totalCost(output_size, cost_per_coeff) / kTaskSize;
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double totalCost(double output_size,
+                                                                const TensorOpCost& cost_per_coeff) {
+    // Cost of memory fetches from L2 cache. 64 is typical cache line size.
+    // 11 is L2 cache latency on Haswell.
+    // We don't know whether data is in L1, L2 or L3. But we are most interested
+    // in single-threaded computational time around 100us-10ms (smaller time
+    // is too small for parallelization, larger time is not interesting
+    // either because we are probably using all available threads already).
+    // And for the target time range, L2 seems to be what matters. Data set
+    // fitting into L1 is too small to take noticeable time. Data set fitting
+    // only into L3 presumably will take more than 10ms to load and process.
+    const double kLoadCycles = 1.0 / 64 * 11;
+    const double kStoreCycles = 1.0 / 64 * 11;
+    // Scaling from Eigen compute cost to device cycles.
+    return output_size * cost_per_coeff.total_cost(kLoadCycles, kStoreCycles, kDeviceCyclesPerComputeCycle);
+  }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
new file mode 100644
index 0000000..1cae60f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
@@ -0,0 +1,307 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename CustomUnaryFunc, typename XprType>
+struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType> > {
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::StorageKind StorageKind;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = traits<XprType>::NumDimensions;
+  static constexpr int Layout = traits<XprType>::Layout;
+  typedef typename traits<XprType>::PointerType PointerType;
+};
+
+template <typename CustomUnaryFunc, typename XprType>
+struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Eigen::Dense> {
+  typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType> EIGEN_DEVICE_REF type;
+};
+
+template <typename CustomUnaryFunc, typename XprType>
+struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType> > {
+  typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor custom class.
+ */
+template <typename CustomUnaryFunc, typename XprType>
+class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename internal::traits<TensorCustomUnaryOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename internal::nested<TensorCustomUnaryOp>::type Nested;
+  typedef typename internal::traits<TensorCustomUnaryOp>::StorageKind StorageKind;
+  typedef typename internal::traits<TensorCustomUnaryOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomUnaryOp(const XprType& expr, const CustomUnaryFunc& func)
+      : m_expr(expr), m_func(func) {}
+
+  EIGEN_DEVICE_FUNC const CustomUnaryFunc& func() const { return m_func; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_expr; }
+
+ protected:
+  typename XprType::Nested m_expr;
+  const CustomUnaryFunc m_func;
+};
+
+// Eval as rvalue
+template <typename CustomUnaryFunc, typename XprType, typename Device>
+struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Device> {
+  typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> ArgType;
+  typedef typename internal::traits<ArgType>::Index Index;
+  static constexpr int NumDims = internal::traits<ArgType>::NumDimensions;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef std::remove_const_t<typename ArgType::Scalar> Scalar;
+  typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<XprType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const ArgType& op, const Device& device)
+      : m_op(op), m_device(device), m_result(NULL) {
+    m_dimensions = op.func().dimensions(op.expression());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    if (data) {
+      evalTo(data);
+      return false;
+    } else {
+      m_result = static_cast<EvaluatorPointerType>(
+          m_device.get((CoeffReturnType*)m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar))));
+      evalTo(m_result);
+      return true;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    if (m_result) {
+      m_device.deallocate_temp(m_result);
+      m_result = NULL;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_result[index]; }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // TODO(rmlarsen): Extend CustomOp API to return its cost estimate.
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
+
+ protected:
+  void evalTo(EvaluatorPointerType data) {
+    TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index> > result(m_device.get(data), m_dimensions);
+    m_op.func().eval(m_op.expression(), result, m_device);
+  }
+
+  Dimensions m_dimensions;
+  const ArgType m_op;
+  const Device EIGEN_DEVICE_REF m_device;
+  EvaluatorPointerType m_result;
+};
+
+/** \class TensorCustomBinaryOp
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor custom class.
+ *
+ *
+ */
+namespace internal {
+template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> > {
+  typedef typename internal::promote_storage_type<typename LhsXprType::Scalar, typename RhsXprType::Scalar>::ret Scalar;
+  typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
+                                                  typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
+  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
+                                        typename traits<RhsXprType>::StorageKind>::ret StorageKind;
+  typedef
+      typename promote_index_type<typename traits<LhsXprType>::Index, typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef std::remove_reference_t<LhsNested> LhsNested_;
+  typedef std::remove_reference_t<RhsNested> RhsNested_;
+  static constexpr int NumDimensions = traits<LhsXprType>::NumDimensions;
+  static constexpr int Layout = traits<LhsXprType>::Layout;
+  typedef std::conditional_t<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
+                             typename traits<LhsXprType>::PointerType, typename traits<RhsXprType>::PointerType>
+      PointerType;
+};
+
+template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+struct eval<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Eigen::Dense> {
+  typedef const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>& type;
+};
+
+template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+struct nested<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> > {
+  typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> type;
+};
+
+}  // end namespace internal
+
+template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+class TensorCustomBinaryOp
+    : public TensorBase<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename internal::traits<TensorCustomBinaryOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename internal::traits<TensorCustomBinaryOp>::CoeffReturnType CoeffReturnType;
+  typedef typename internal::nested<TensorCustomBinaryOp>::type Nested;
+  typedef typename internal::traits<TensorCustomBinaryOp>::StorageKind StorageKind;
+  typedef typename internal::traits<TensorCustomBinaryOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs,
+                                                             const CustomBinaryFunc& func)
+
+      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_func(func) {}
+
+  EIGEN_DEVICE_FUNC const CustomBinaryFunc& func() const { return m_func; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename LhsXprType::Nested>& lhsExpression() const {
+    return m_lhs_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename RhsXprType::Nested>& rhsExpression() const {
+    return m_rhs_xpr;
+  }
+
+ protected:
+  typename LhsXprType::Nested m_lhs_xpr;
+  typename RhsXprType::Nested m_rhs_xpr;
+  const CustomBinaryFunc m_func;
+};
+
+// Eval as rvalue
+template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, typename Device>
+struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Device> {
+  typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> XprType;
+  typedef typename internal::traits<XprType>::Index Index;
+  static constexpr int NumDims = internal::traits<XprType>::NumDimensions;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<LhsXprType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_op(op), m_device(device), m_result(NULL) {
+    m_dimensions = op.func().dimensions(op.lhsExpression(), op.rhsExpression());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    if (data) {
+      evalTo(data);
+      return false;
+    } else {
+      m_result = static_cast<EvaluatorPointerType>(
+          m_device.get((CoeffReturnType*)m_device.allocate_temp(dimensions().TotalSize() * sizeof(CoeffReturnType))));
+      evalTo(m_result);
+      return true;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    if (m_result != NULL) {
+      m_device.deallocate_temp(m_result);
+      m_result = NULL;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_result[index]; }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // TODO(rmlarsen): Extend CustomOp API to return its cost estimate.
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
+
+ protected:
+  void evalTo(EvaluatorPointerType data) {
+    TensorMap<Tensor<CoeffReturnType, NumDims, Layout> > result(m_device.get(data), m_dimensions);
+    m_op.func().eval(m_op.lhsExpression(), m_op.rhsExpression(), result, m_device);
+  }
+
+  Dimensions m_dimensions;
+  const XprType m_op;
+  const Device EIGEN_DEVICE_REF m_device;
+  EvaluatorPointerType m_result;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h
new file mode 100644
index 0000000..1ed2089
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h
@@ -0,0 +1,138 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Pseudo expression providing an operator = that will evaluate its argument
+ * on the specified computing 'device' (GPU, thread pool, ...)
+ *
+ * Example:
+ *    C.device(EIGEN_GPU) = A + B;
+ *
+ * TODO: operator *= and /=.
+ */
+template <typename ExpressionType, typename DeviceType>
+class TensorDevice {
+ public:
+  TensorDevice(const DeviceType& device, ExpressionType& expression) : m_device(device), m_expression(expression) {}
+
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(TensorDevice)
+
+  template <typename OtherDerived>
+  EIGEN_STRONG_INLINE TensorDevice& operator=(const OtherDerived& other) {
+    typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
+    Assign assign(m_expression, other);
+    internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device);
+    return *this;
+  }
+
+  template <typename OtherDerived>
+  EIGEN_STRONG_INLINE TensorDevice& operator+=(const OtherDerived& other) {
+    typedef typename OtherDerived::Scalar Scalar;
+    typedef TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const ExpressionType, const OtherDerived> Sum;
+    Sum sum(m_expression, other);
+    typedef TensorAssignOp<ExpressionType, const Sum> Assign;
+    Assign assign(m_expression, sum);
+    internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device);
+    return *this;
+  }
+
+  template <typename OtherDerived>
+  EIGEN_STRONG_INLINE TensorDevice& operator-=(const OtherDerived& other) {
+    typedef typename OtherDerived::Scalar Scalar;
+    typedef TensorCwiseBinaryOp<internal::scalar_difference_op<Scalar>, const ExpressionType, const OtherDerived>
+        Difference;
+    Difference difference(m_expression, other);
+    typedef TensorAssignOp<ExpressionType, const Difference> Assign;
+    Assign assign(m_expression, difference);
+    internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device);
+    return *this;
+  }
+
+ protected:
+  const DeviceType& m_device;
+  ExpressionType& m_expression;
+};
+
+/** \class TensorAsyncDevice
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Pseudo expression providing an operator = that will evaluate its
+ * argument asynchronously on the specified device. Currently only
+ * ThreadPoolDevice implements proper asynchronous execution, while the default
+ * and GPU devices just run the expression synchronously and call m_done() on
+ * completion..
+ *
+ * Example:
+ *    auto done = []() { ... expression evaluation done ... };
+ *    C.device(thread_pool_device, std::move(done)) = A + B;
+ */
+
+template <typename ExpressionType, typename DeviceType, typename DoneCallback>
+class TensorAsyncDevice {
+ public:
+  TensorAsyncDevice(const DeviceType& device, ExpressionType& expression, DoneCallback done)
+      : m_device(device), m_expression(expression), m_done(std::move(done)) {}
+
+  template <typename OtherDerived>
+  EIGEN_STRONG_INLINE TensorAsyncDevice& operator=(const OtherDerived& other) {
+    typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
+    typedef internal::TensorExecutor<const Assign, DeviceType> Executor;
+
+    Assign assign(m_expression, other);
+    Executor::run(assign, m_device);
+    m_done();
+
+    return *this;
+  }
+
+ protected:
+  const DeviceType& m_device;
+  ExpressionType& m_expression;
+  DoneCallback m_done;
+};
+
+#ifdef EIGEN_USE_THREADS
+template <typename ExpressionType, typename DoneCallback>
+class TensorAsyncDevice<ExpressionType, ThreadPoolDevice, DoneCallback> {
+ public:
+  TensorAsyncDevice(const ThreadPoolDevice& device, ExpressionType& expression, DoneCallback done)
+      : m_device(device), m_expression(expression), m_done(std::move(done)) {}
+
+  template <typename OtherDerived>
+  EIGEN_STRONG_INLINE TensorAsyncDevice& operator=(const OtherDerived& other) {
+    typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
+    typedef internal::TensorAsyncExecutor<const Assign, ThreadPoolDevice, DoneCallback> Executor;
+
+    // WARNING: After assignment 'm_done' callback will be in undefined state.
+    Assign assign(m_expression, other);
+    Executor::runAsync(assign, m_device, std::move(m_done));
+
+    return *this;
+  }
+
+ protected:
+  const ThreadPoolDevice& m_device;
+  ExpressionType& m_expression;
+  DoneCallback m_done;
+};
+#endif
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
new file mode 100644
index 0000000..c2c8ed0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
@@ -0,0 +1,7 @@
+
+#if defined(__clang__) || defined(__GNUC__)
+#warning \
+    "Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorDeviceGpu.h file"
+#endif
+
+#include "TensorDeviceGpu.h"
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h
new file mode 100644
index 0000000..1f955f0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h
@@ -0,0 +1,113 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// Default device for the machine (typically a single cpu core)
+struct DefaultDevice {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
+    return internal::aligned_malloc(num_bytes);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void* buffer) const { internal::aligned_free(buffer); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate_temp(size_t num_bytes) const { return allocate(num_bytes); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate_temp(void* buffer) const { deallocate(buffer); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
+    ::memcpy(dst, src, n);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
+    memcpy(dst, src, n);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
+    memcpy(dst, src, n);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const { ::memset(buffer, c, n); }
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void fill(T* begin, T* end, const T& value) const {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+    // std::fill is not a device function, so resort to simple loop.
+    for (T* it = begin; it != end; ++it) {
+      *it = value;
+    }
+#else
+    std::fill(begin, end, value);
+#endif
+  }
+  template <typename Type>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Type get(Type data) const {
+    return data;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t numThreads() const {
+#if !defined(EIGEN_GPU_COMPILE_PHASE)
+    // Running on the host CPU
+    return 1;
+#elif defined(EIGEN_HIP_DEVICE_COMPILE)
+    // Running on a HIP device
+    return 64;
+#else
+    // Running on a CUDA device
+    return 32;
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
+#if !defined(EIGEN_GPU_COMPILE_PHASE) && !defined(SYCL_DEVICE_ONLY)
+    // Running on the host CPU
+    return l1CacheSize();
+#elif defined(EIGEN_HIP_DEVICE_COMPILE)
+    // Running on a HIP device
+    return 48 * 1024;  // FIXME: Update this number for HIP.
+#else
+    // Running on a CUDA device, return the amount of shared memory available.
+    return 48 * 1024;
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
+#if !defined(EIGEN_GPU_COMPILE_PHASE) && !defined(SYCL_DEVICE_ONLY)
+    // Running single threaded on the host CPU
+    return l3CacheSize();
+#elif defined(EIGEN_HIP_DEVICE_COMPILE)
+    // Running on a HIP device
+    return firstLevelCacheSize();  // FIXME: Update this number for HIP.
+#else
+    // Running on a CUDA device
+    return firstLevelCacheSize();
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void synchronize() const {
+    // Nothing.  Default device operations are synchronous.
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
+#if !defined(EIGEN_GPU_COMPILE_PHASE)
+    // Running single threaded on the host CPU
+    // Should return an enum that encodes the ISA supported by the CPU
+    return 1;
+#elif defined(EIGEN_HIP_DEVICE_COMPILE)
+    // Running on a HIP device
+    // return 1 as major for HIP
+    return 1;
+#else
+    // Running on a CUDA device
+    return EIGEN_CUDA_ARCH / 100;
+#endif
+  }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h
new file mode 100644
index 0000000..25656e0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h
@@ -0,0 +1,364 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_USE_GPU) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_GPU_H)
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_GPU_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#include "../../../../../Eigen/src/Core/util/GpuHipCudaDefines.inc"
+
+namespace Eigen {
+
+static const int kGpuScratchSize = 1024;
+
+// This defines an interface that GPUDevice can take to use
+// HIP / CUDA streams underneath.
+class StreamInterface {
+ public:
+  virtual ~StreamInterface() {}
+
+  virtual const gpuStream_t& stream() const = 0;
+  virtual const gpuDeviceProp_t& deviceProperties() const = 0;
+
+  // Allocate memory on the actual device where the computation will run
+  virtual void* allocate(size_t num_bytes) const = 0;
+  virtual void deallocate(void* buffer) const = 0;
+
+  // Return a scratchpad buffer of size 1k
+  virtual void* scratchpad() const = 0;
+
+  // Return a semaphore. The semaphore is initially initialized to 0, and
+  // each kernel using it is responsible for resetting to 0 upon completion
+  // to maintain the invariant that the semaphore is always equal to 0 upon
+  // each kernel start.
+  virtual unsigned int* semaphore() const = 0;
+};
+
+class GpuDeviceProperties {
+ public:
+  static const GpuDeviceProperties& instance() {
+    static const GpuDeviceProperties* kInstance = new GpuDeviceProperties();
+
+    return *kInstance;
+  }
+
+  EIGEN_STRONG_INLINE const gpuDeviceProp_t& get(int device) const { return device_properties_[device]; }
+
+ private:
+  GpuDeviceProperties() = default;
+
+  static std::vector<gpuDeviceProp_t> GetDeviceProperties() {
+    int num_devices = 0;
+    gpuError_t status = gpuGetDeviceCount(&num_devices);
+    if (status != gpuSuccess) {
+      std::cerr << "Failed to get the number of GPU devices: " << gpuGetErrorString(status) << std::endl;
+      gpu_assert(status == gpuSuccess);
+    }
+    std::vector<gpuDeviceProp_t> device_properties(num_devices);
+    for (int i = 0; i < num_devices; ++i) {
+      status = gpuGetDeviceProperties(&device_properties[i], i);
+      if (status != gpuSuccess) {
+        std::cerr << "Failed to initialize GPU device #" << i << ": " << gpuGetErrorString(status) << std::endl;
+        gpu_assert(status == gpuSuccess);
+      }
+    }
+
+    return device_properties;
+  }
+
+  std::vector<gpuDeviceProp_t> device_properties_ = GetDeviceProperties();
+};
+
+EIGEN_ALWAYS_INLINE const GpuDeviceProperties& GetGpuDeviceProperties() { return GpuDeviceProperties::instance(); }
+
+EIGEN_ALWAYS_INLINE const gpuDeviceProp_t& GetGpuDeviceProperties(int device) {
+  return GetGpuDeviceProperties().get(device);
+}
+
+static const gpuStream_t default_stream = gpuStreamDefault;
+
+class GpuStreamDevice : public StreamInterface {
+ public:
+  // Use the default stream on the current device
+  GpuStreamDevice() : stream_(&default_stream), scratch_(NULL), semaphore_(NULL) {
+    gpuError_t status = gpuGetDevice(&device_);
+    if (status != gpuSuccess) {
+      std::cerr << "Failed to get the GPU devices " << gpuGetErrorString(status) << std::endl;
+      gpu_assert(status == gpuSuccess);
+    }
+  }
+  // Use the default stream on the specified device
+  GpuStreamDevice(int device) : stream_(&default_stream), device_(device), scratch_(NULL), semaphore_(NULL) {}
+  // Use the specified stream. Note that it's the
+  // caller responsibility to ensure that the stream can run on
+  // the specified device. If no device is specified the code
+  // assumes that the stream is associated to the current gpu device.
+  GpuStreamDevice(const gpuStream_t* stream, int device = -1)
+      : stream_(stream), device_(device), scratch_(NULL), semaphore_(NULL) {
+    if (device < 0) {
+      gpuError_t status = gpuGetDevice(&device_);
+      if (status != gpuSuccess) {
+        std::cerr << "Failed to get the GPU devices " << gpuGetErrorString(status) << std::endl;
+        gpu_assert(status == gpuSuccess);
+      }
+    } else {
+      int num_devices;
+      gpuError_t err = gpuGetDeviceCount(&num_devices);
+      EIGEN_UNUSED_VARIABLE(err)
+      gpu_assert(err == gpuSuccess);
+      gpu_assert(device < num_devices);
+      device_ = device;
+    }
+  }
+
+  virtual ~GpuStreamDevice() {
+    if (scratch_) {
+      deallocate(scratch_);
+    }
+  }
+
+  const gpuStream_t& stream() const { return *stream_; }
+  const gpuDeviceProp_t& deviceProperties() const { return GetGpuDeviceProperties(device_); }
+  virtual void* allocate(size_t num_bytes) const {
+    gpuError_t err = gpuSetDevice(device_);
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+    void* result;
+    err = gpuMalloc(&result, num_bytes);
+    gpu_assert(err == gpuSuccess);
+    gpu_assert(result != NULL);
+    return result;
+  }
+  virtual void deallocate(void* buffer) const {
+    gpuError_t err = gpuSetDevice(device_);
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+    gpu_assert(buffer != NULL);
+    err = gpuFree(buffer);
+    gpu_assert(err == gpuSuccess);
+  }
+
+  virtual void* scratchpad() const {
+    if (scratch_ == NULL) {
+      scratch_ = allocate(kGpuScratchSize + sizeof(unsigned int));
+    }
+    return scratch_;
+  }
+
+  virtual unsigned int* semaphore() const {
+    if (semaphore_ == NULL) {
+      char* scratch = static_cast<char*>(scratchpad()) + kGpuScratchSize;
+      semaphore_ = reinterpret_cast<unsigned int*>(scratch);
+      gpuError_t err = gpuMemsetAsync(semaphore_, 0, sizeof(unsigned int), *stream_);
+      EIGEN_UNUSED_VARIABLE(err)
+      gpu_assert(err == gpuSuccess);
+    }
+    return semaphore_;
+  }
+
+ private:
+  const gpuStream_t* stream_;
+  int device_;
+  mutable void* scratch_;
+  mutable unsigned int* semaphore_;
+};
+
+struct GpuDevice {
+  // The StreamInterface is not owned: the caller is
+  // responsible for its initialization and eventual destruction.
+  explicit GpuDevice(const StreamInterface* stream) : stream_(stream), max_blocks_(INT_MAX) { eigen_assert(stream); }
+  explicit GpuDevice(const StreamInterface* stream, int num_blocks) : stream_(stream), max_blocks_(num_blocks) {
+    eigen_assert(stream);
+  }
+  // TODO(bsteiner): This is an internal API, we should not expose it.
+  EIGEN_STRONG_INLINE const gpuStream_t& stream() const { return stream_->stream(); }
+
+  EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const { return stream_->allocate(num_bytes); }
+
+  EIGEN_STRONG_INLINE void deallocate(void* buffer) const { stream_->deallocate(buffer); }
+
+  EIGEN_STRONG_INLINE void* allocate_temp(size_t num_bytes) const { return stream_->allocate(num_bytes); }
+
+  EIGEN_STRONG_INLINE void deallocate_temp(void* buffer) const { stream_->deallocate(buffer); }
+
+  template <typename Type>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Type get(Type data) const {
+    return data;
+  }
+
+  EIGEN_STRONG_INLINE void* scratchpad() const { return stream_->scratchpad(); }
+
+  EIGEN_STRONG_INLINE unsigned int* semaphore() const { return stream_->semaphore(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
+#ifndef EIGEN_GPU_COMPILE_PHASE
+    gpuError_t err = gpuMemcpyAsync(dst, src, n, gpuMemcpyDeviceToDevice, stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+#else
+    EIGEN_UNUSED_VARIABLE(dst);
+    EIGEN_UNUSED_VARIABLE(src);
+    EIGEN_UNUSED_VARIABLE(n);
+    eigen_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
+    gpuError_t err = gpuMemcpyAsync(dst, src, n, gpuMemcpyHostToDevice, stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+  }
+
+  EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
+    gpuError_t err = gpuMemcpyAsync(dst, src, n, gpuMemcpyDeviceToHost, stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
+#ifndef EIGEN_GPU_COMPILE_PHASE
+    gpuError_t err = gpuMemsetAsync(buffer, c, n, stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+#else
+    EIGEN_UNUSED_VARIABLE(buffer)
+    EIGEN_UNUSED_VARIABLE(c)
+    EIGEN_UNUSED_VARIABLE(n)
+    eigen_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  template <typename T>
+  EIGEN_STRONG_INLINE void fill(T* begin, T* end, const T& value) const {
+#ifndef EIGEN_GPU_COMPILE_PHASE
+    const size_t count = end - begin;
+    // Split value into bytes and run memset with stride.
+    const int value_size = sizeof(value);
+    char* buffer = (char*)begin;
+    char* value_bytes = (char*)(&value);
+    gpuError_t err;
+    EIGEN_UNUSED_VARIABLE(err)
+
+    // If all value bytes are equal, then a single memset can be much faster.
+    bool use_single_memset = true;
+    for (int i = 1; i < value_size; ++i) {
+      if (value_bytes[i] != value_bytes[0]) {
+        use_single_memset = false;
+      }
+    }
+
+    if (use_single_memset) {
+      err = gpuMemsetAsync(buffer, value_bytes[0], count * sizeof(T), stream_->stream());
+      gpu_assert(err == gpuSuccess);
+    } else {
+      for (int b = 0; b < value_size; ++b) {
+        err = gpuMemset2DAsync(buffer + b, value_size, value_bytes[b], 1, count, stream_->stream());
+        gpu_assert(err == gpuSuccess);
+      }
+    }
+#else
+    EIGEN_UNUSED_VARIABLE(begin)
+    EIGEN_UNUSED_VARIABLE(end)
+    EIGEN_UNUSED_VARIABLE(value)
+    eigen_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_STRONG_INLINE size_t numThreads() const {
+    // FIXME: Return a more accurate thread count.
+    return 32;
+  }
+
+  EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
+    // FIXME: Return a more accurate cache size.
+    return 48 * 1024;
+  }
+
+  EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
+    // We won't try to take advantage of the l2 cache for the time being, and
+    // there is no l3 cache on hip/cuda devices.
+    return firstLevelCacheSize();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void synchronize() const {
+#ifndef EIGEN_GPU_COMPILE_PHASE
+    gpuError_t err = gpuStreamSynchronize(stream_->stream());
+    if (err != gpuSuccess) {
+      std::cerr << "Error detected in GPU stream: " << gpuGetErrorString(err) << std::endl;
+      gpu_assert(err == gpuSuccess);
+    }
+#else
+    gpu_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_STRONG_INLINE int getNumGpuMultiProcessors() const { return stream_->deviceProperties().multiProcessorCount; }
+  EIGEN_STRONG_INLINE int maxGpuThreadsPerBlock() const { return stream_->deviceProperties().maxThreadsPerBlock; }
+  EIGEN_STRONG_INLINE int maxGpuThreadsPerMultiProcessor() const {
+    return stream_->deviceProperties().maxThreadsPerMultiProcessor;
+  }
+  EIGEN_STRONG_INLINE int sharedMemPerBlock() const {
+    return static_cast<int>(stream_->deviceProperties().sharedMemPerBlock);
+  }
+  EIGEN_STRONG_INLINE int majorDeviceVersion() const { return stream_->deviceProperties().major; }
+  EIGEN_STRONG_INLINE int minorDeviceVersion() const { return stream_->deviceProperties().minor; }
+
+  EIGEN_STRONG_INLINE int maxBlocks() const { return max_blocks_; }
+
+  // This function checks if the GPU runtime recorded an error for the
+  // underlying stream device.
+  inline bool ok() const {
+#ifdef EIGEN_GPUCC
+    gpuError_t error = gpuStreamQuery(stream_->stream());
+    return (error == gpuSuccess) || (error == gpuErrorNotReady);
+#else
+    return false;
+#endif
+  }
+
+ private:
+  const StreamInterface* stream_;
+  int max_blocks_;
+};
+
+#if defined(EIGEN_HIPCC)
+
+#define LAUNCH_GPU_KERNEL(kernel, gridsize, blocksize, sharedmem, device, ...)                              \
+  hipLaunchKernelGGL(kernel, dim3(gridsize), dim3(blocksize), (sharedmem), (device).stream(), __VA_ARGS__); \
+  gpu_assert(hipGetLastError() == hipSuccess);
+
+#else
+
+#define LAUNCH_GPU_KERNEL(kernel, gridsize, blocksize, sharedmem, device, ...)        \
+  (kernel)<<<(gridsize), (blocksize), (sharedmem), (device).stream()>>>(__VA_ARGS__); \
+  gpu_assert(cudaGetLastError() == cudaSuccess);
+
+#endif
+
+// FIXME: Should be device and kernel specific.
+#ifdef EIGEN_GPUCC
+static EIGEN_DEVICE_FUNC inline void setGpuSharedMemConfig(gpuSharedMemConfig config) {
+#ifndef EIGEN_GPU_COMPILE_PHASE
+  gpuError_t status = gpuDeviceSetSharedMemConfig(config);
+  EIGEN_UNUSED_VARIABLE(status)
+  gpu_assert(status == gpuSuccess);
+#else
+  EIGEN_UNUSED_VARIABLE(config)
+#endif
+}
+#endif
+
+}  // end namespace Eigen
+
+// undefine all the gpu* macros we defined at the beginning of the file
+#include "../../../../../Eigen/src/Core/util/GpuHipCudaUndefines.inc"
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_GPU_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
new file mode 100644
index 0000000..b291fed
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
@@ -0,0 +1,567 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_USE_SYCL) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H)
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H
+#include <unordered_set>
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace TensorSycl {
+namespace internal {
+
+/// Cache all the device information needed
+struct SyclDeviceInfo {
+  SyclDeviceInfo(cl::sycl::queue queue)
+      : local_mem_type(queue.get_device().template get_info<cl::sycl::info::device::local_mem_type>()),
+        max_work_item_sizes(queue.get_device().template get_info<cl::sycl::info::device::max_work_item_sizes<3>>()),
+        max_mem_alloc_size(queue.get_device().template get_info<cl::sycl::info::device::max_mem_alloc_size>()),
+        max_compute_units(queue.get_device().template get_info<cl::sycl::info::device::max_compute_units>()),
+        max_work_group_size(queue.get_device().template get_info<cl::sycl::info::device::max_work_group_size>()),
+        local_mem_size(queue.get_device().template get_info<cl::sycl::info::device::local_mem_size>()),
+        platform_name(queue.get_device().get_platform().template get_info<cl::sycl::info::platform::name>()),
+        device_name(queue.get_device().template get_info<cl::sycl::info::device::name>()),
+        device_vendor(queue.get_device().template get_info<cl::sycl::info::device::vendor>()) {}
+
+  cl::sycl::info::local_mem_type local_mem_type;
+  cl::sycl::id<3> max_work_item_sizes;
+  unsigned long max_mem_alloc_size;
+  unsigned long max_compute_units;
+  unsigned long max_work_group_size;
+  size_t local_mem_size;
+  std::string platform_name;
+  std::string device_name;
+  std::string device_vendor;
+};
+
+}  // end namespace internal
+}  // end namespace TensorSycl
+
+// All devices (even AMD CPU with intel OpenCL runtime) that support OpenCL and
+// can consume SPIR or SPIRV can use the Eigen SYCL backend and consequently
+// TensorFlow via the Eigen SYCL Backend.
+EIGEN_STRONG_INLINE auto get_sycl_supported_devices() -> decltype(cl::sycl::device::get_devices()) {
+#ifdef EIGEN_SYCL_USE_DEFAULT_SELECTOR
+  return {cl::sycl::device(cl::sycl::default_selector())};
+#else
+  std::vector<cl::sycl::device> supported_devices;
+  auto platform_list = cl::sycl::platform::get_platforms();
+  for (const auto &platform : platform_list) {
+    auto device_list = platform.get_devices();
+    auto platform_name = platform.template get_info<cl::sycl::info::platform::name>();
+    std::transform(platform_name.begin(), platform_name.end(), platform_name.begin(), ::tolower);
+    for (const auto &device : device_list) {
+      auto vendor = device.template get_info<cl::sycl::info::device::vendor>();
+      std::transform(vendor.begin(), vendor.end(), vendor.begin(), ::tolower);
+      bool unsupported_condition = (device.is_cpu() && platform_name.find("amd") != std::string::npos &&
+                                    vendor.find("apu") == std::string::npos) ||
+                                   (platform_name.find("experimental") != std::string::npos) || device.is_host();
+      if (!unsupported_condition) {
+        supported_devices.push_back(device);
+      }
+    }
+  }
+  return supported_devices;
+#endif
+}
+
+class QueueInterface {
+ public:
+  /// Creating device by using cl::sycl::selector or cl::sycl::device.
+  template <typename DeviceOrSelector>
+  explicit QueueInterface(const DeviceOrSelector &dev_or_sel, cl::sycl::async_handler handler,
+                          unsigned num_threads = std::thread::hardware_concurrency())
+      : m_queue{dev_or_sel, handler, {sycl::property::queue::in_order()}},
+        m_thread_pool(num_threads),
+        m_device_info(m_queue) {}
+
+  template <typename DeviceOrSelector>
+  explicit QueueInterface(const DeviceOrSelector &dev_or_sel,
+                          unsigned num_threads = std::thread::hardware_concurrency())
+      : QueueInterface(
+            dev_or_sel, [this](cl::sycl::exception_list l) { this->exception_caught_ = this->sycl_async_handler(l); },
+            num_threads) {}
+
+  explicit QueueInterface(const cl::sycl::queue &q, unsigned num_threads = std::thread::hardware_concurrency())
+      : m_queue(q), m_thread_pool(num_threads), m_device_info(m_queue) {}
+
+  EIGEN_STRONG_INLINE void *allocate(size_t num_bytes) const {
+#if EIGEN_MAX_ALIGN_BYTES > 0
+    return (void *)cl::sycl::aligned_alloc_device(EIGEN_MAX_ALIGN_BYTES, num_bytes, m_queue);
+#else
+    return (void *)cl::sycl::malloc_device(num_bytes, m_queue);
+#endif
+  }
+
+  EIGEN_STRONG_INLINE void *allocate_temp(size_t num_bytes) const {
+    return (void *)cl::sycl::malloc_device<uint8_t>(num_bytes, m_queue);
+  }
+
+  template <typename data_t>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE data_t *get(data_t *data) const {
+    return data;
+  }
+
+  EIGEN_STRONG_INLINE void deallocate_temp(void *p) const { deallocate(p); }
+
+  EIGEN_STRONG_INLINE void deallocate_temp(const void *p) const { deallocate_temp(const_cast<void *>(p)); }
+
+  EIGEN_STRONG_INLINE void deallocate(void *p) const { cl::sycl::free(p, m_queue); }
+
+  /// The memcpyHostToDevice is used to copy the data from host to device
+  /// The destination pointer could be deleted before the copy happened which is
+  /// why a callback function is needed. By default if none is provided, the
+  /// function is blocking.
+  EIGEN_STRONG_INLINE void memcpyHostToDevice(void *dst, const void *src, size_t n,
+                                              std::function<void()> callback) const {
+    auto e = m_queue.memcpy(dst, src, n);
+    synchronize_and_callback(e, callback);
+  }
+
+  /// The memcpyDeviceToHost is used to copy the data from device to host.
+  /// The source pointer could be deleted before the copy happened which is
+  /// why a callback function is needed. By default if none is provided, the
+  /// function is blocking.
+  EIGEN_STRONG_INLINE void memcpyDeviceToHost(void *dst, const void *src, size_t n,
+                                              std::function<void()> callback) const {
+    if (n == 0) {
+      if (callback) callback();
+      return;
+    }
+    auto e = m_queue.memcpy(dst, src, n);
+    synchronize_and_callback(e, callback);
+  }
+
+  /// The memcpy function.
+  /// No callback is required here as both arguments are on the device
+  /// and SYCL can handle the dependency.
+  EIGEN_STRONG_INLINE void memcpy(void *dst, const void *src, size_t n) const {
+    if (n == 0) {
+      return;
+    }
+    m_queue.memcpy(dst, src, n).wait();
+  }
+
+  /// the memset function.
+  /// No callback is required here as both arguments are on the device
+  /// and SYCL can handle the dependency.
+  EIGEN_STRONG_INLINE void memset(void *data, int c, size_t n) const {
+    if (n == 0) {
+      return;
+    }
+    m_queue.memset(data, c, n).wait();
+  }
+
+  template <typename T>
+  EIGEN_STRONG_INLINE void fill(T *begin, T *end, const T &value) const {
+    if (begin == end) {
+      return;
+    }
+    const size_t count = end - begin;
+    m_queue.fill(begin, value, count).wait();
+  }
+
+  template <typename OutScalar, typename sycl_kernel, typename Lhs, typename Rhs, typename OutPtr, typename Range,
+            typename Index, typename... T>
+  EIGEN_ALWAYS_INLINE cl::sycl::event binary_kernel_launcher(const Lhs &lhs, const Rhs &rhs, OutPtr outptr,
+                                                             Range thread_range, Index scratchSize, T... var) const {
+    auto kernel_functor = [=](cl::sycl::handler &cgh) {
+      typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+          LocalAccessor;
+
+      LocalAccessor scratch(cl::sycl::range<1>(scratchSize), cgh);
+      cgh.parallel_for(thread_range, sycl_kernel(scratch, lhs, rhs, outptr, var...));
+    };
+
+    return m_queue.submit(kernel_functor);
+  }
+
+  template <typename OutScalar, typename sycl_kernel, typename InPtr, typename OutPtr, typename Range, typename Index,
+            typename... T>
+  EIGEN_ALWAYS_INLINE cl::sycl::event unary_kernel_launcher(const InPtr &inptr, OutPtr &outptr, Range thread_range,
+                                                            Index scratchSize, T... var) const {
+    auto kernel_functor = [=](cl::sycl::handler &cgh) {
+      typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+          LocalAccessor;
+
+      LocalAccessor scratch(cl::sycl::range<1>(scratchSize), cgh);
+      cgh.parallel_for(thread_range, sycl_kernel(scratch, inptr, outptr, var...));
+    };
+    return m_queue.submit(kernel_functor);
+  }
+
+  template <typename OutScalar, typename sycl_kernel, typename InPtr, typename Range, typename Index, typename... T>
+  EIGEN_ALWAYS_INLINE cl::sycl::event nullary_kernel_launcher(const InPtr &inptr, Range thread_range, Index scratchSize,
+                                                              T... var) const {
+    auto kernel_functor = [=](cl::sycl::handler &cgh) {
+      typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+          LocalAccessor;
+
+      LocalAccessor scratch(cl::sycl::range<1>(scratchSize), cgh);
+      cgh.parallel_for(thread_range, sycl_kernel(scratch, inptr, var...));
+    };
+
+    return m_queue.submit(kernel_functor);
+  }
+
+  EIGEN_STRONG_INLINE void synchronize() const {
+#ifdef EIGEN_EXCEPTIONS
+    m_queue.wait_and_throw();
+#else
+    m_queue.wait();
+#endif
+  }
+
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(Index n, Index &tileSize, Index &rng, Index &GRange) const {
+    tileSize = static_cast<Index>(getNearestPowerOfTwoWorkGroupSize());
+    tileSize = std::min(static_cast<Index>(EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1),
+                        static_cast<Index>(tileSize));
+    rng = n;
+    if (rng == 0) rng = static_cast<Index>(1);
+    GRange = rng;
+    if (tileSize > GRange)
+      tileSize = GRange;
+    else if (GRange > tileSize) {
+      Index xMode = static_cast<Index>(GRange % tileSize);
+      if (xMode != 0) GRange += static_cast<Index>(tileSize - xMode);
+    }
+  }
+
+  /// This is used to prepare the number of threads and also the number of
+  /// threads per block for sycl kernels
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(const std::array<Index, 2> &input_dim, cl::sycl::range<2> &global_range,
+                                              cl::sycl::range<2> &local_range) const {
+    std::array<Index, 2> input_range = input_dim;
+    Index max_workgroup_Size = static_cast<Index>(getNearestPowerOfTwoWorkGroupSize());
+    max_workgroup_Size = std::min(static_cast<Index>(EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1),
+                                  static_cast<Index>(max_workgroup_Size));
+    Index pow_of_2 = static_cast<Index>(std::log2(max_workgroup_Size));
+    local_range[1] = static_cast<Index>(std::pow(2, static_cast<Index>(pow_of_2 / 2)));
+    input_range[1] = input_dim[1];
+    if (input_range[1] == 0) input_range[1] = static_cast<Index>(1);
+    global_range[1] = input_range[1];
+    if (local_range[1] > global_range[1])
+      local_range[1] = global_range[1];
+    else if (global_range[1] > local_range[1]) {
+      Index xMode = static_cast<Index>(global_range[1] % local_range[1]);
+      if (xMode != 0) global_range[1] += static_cast<Index>(local_range[1] - xMode);
+    }
+    local_range[0] = static_cast<Index>(max_workgroup_Size / local_range[1]);
+    input_range[0] = input_dim[0];
+    if (input_range[0] == 0) input_range[0] = static_cast<Index>(1);
+    global_range[0] = input_range[0];
+    if (local_range[0] > global_range[0])
+      local_range[0] = global_range[0];
+    else if (global_range[0] > local_range[0]) {
+      Index xMode = static_cast<Index>(global_range[0] % local_range[0]);
+      if (xMode != 0) global_range[0] += static_cast<Index>(local_range[0] - xMode);
+    }
+  }
+
+  /// This is used to prepare the number of threads and also the number of
+  /// threads per block for sycl kernels
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(const std::array<Index, 3> &input_dim, cl::sycl::range<3> &global_range,
+                                              cl::sycl::range<3> &local_range) const {
+    std::array<Index, 3> input_range = input_dim;
+    Index max_workgroup_Size = static_cast<Index>(getNearestPowerOfTwoWorkGroupSize());
+    max_workgroup_Size = std::min(static_cast<Index>(EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1),
+                                  static_cast<Index>(max_workgroup_Size));
+    Index pow_of_2 = static_cast<Index>(std::log2(max_workgroup_Size));
+    local_range[2] = static_cast<Index>(std::pow(2, static_cast<Index>(pow_of_2 / 3)));
+    input_range[2] = input_dim[2];
+    if (input_range[2] == 0) input_range[1] = static_cast<Index>(1);
+    global_range[2] = input_range[2];
+    if (local_range[2] > global_range[2])
+      local_range[2] = global_range[2];
+    else if (global_range[2] > local_range[2]) {
+      Index xMode = static_cast<Index>(global_range[2] % local_range[2]);
+      if (xMode != 0) global_range[2] += static_cast<Index>(local_range[2] - xMode);
+    }
+    pow_of_2 = static_cast<Index>(std::log2(static_cast<Index>(max_workgroup_Size / local_range[2])));
+    local_range[1] = static_cast<Index>(std::pow(2, static_cast<Index>(pow_of_2 / 2)));
+    input_range[1] = input_dim[1];
+    if (input_range[1] == 0) input_range[1] = static_cast<Index>(1);
+    global_range[1] = input_range[1];
+    if (local_range[1] > global_range[1])
+      local_range[1] = global_range[1];
+    else if (global_range[1] > local_range[1]) {
+      Index xMode = static_cast<Index>(global_range[1] % local_range[1]);
+      if (xMode != 0) global_range[1] += static_cast<Index>(local_range[1] - xMode);
+    }
+    local_range[0] = static_cast<Index>(max_workgroup_Size / (local_range[1] * local_range[2]));
+    input_range[0] = input_dim[0];
+    if (input_range[0] == 0) input_range[0] = static_cast<Index>(1);
+    global_range[0] = input_range[0];
+    if (local_range[0] > global_range[0])
+      local_range[0] = global_range[0];
+    else if (global_range[0] > local_range[0]) {
+      Index xMode = static_cast<Index>(global_range[0] % local_range[0]);
+      if (xMode != 0) global_range[0] += static_cast<Index>(local_range[0] - xMode);
+    }
+  }
+
+  EIGEN_STRONG_INLINE bool has_local_memory() const {
+#if !defined(EIGEN_SYCL_LOCAL_MEM) && defined(EIGEN_SYCL_NO_LOCAL_MEM)
+    return false;
+#elif defined(EIGEN_SYCL_LOCAL_MEM) && !defined(EIGEN_SYCL_NO_LOCAL_MEM)
+    return true;
+#else
+    return m_device_info.local_mem_type == cl::sycl::info::local_mem_type::local;
+#endif
+  }
+
+  EIGEN_STRONG_INLINE unsigned long max_buffer_size() const { return m_device_info.max_mem_alloc_size; }
+
+  EIGEN_STRONG_INLINE unsigned long getNumSyclMultiProcessors() const { return m_device_info.max_compute_units; }
+
+  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerBlock() const { return m_device_info.max_work_group_size; }
+
+  EIGEN_STRONG_INLINE cl::sycl::id<3> maxWorkItemSizes() const { return m_device_info.max_work_item_sizes; }
+
+  /// No need for sycl it should act the same as CPU version
+  EIGEN_STRONG_INLINE int majorDeviceVersion() const { return 1; }
+
+  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerMultiProcessor() const {
+    // OpenCL does not have such a concept
+    return 2;
+  }
+
+  EIGEN_STRONG_INLINE size_t sharedMemPerBlock() const { return m_device_info.local_mem_size; }
+
+  // This function returns the nearest power of 2 Work-group size which is <=
+  // maximum device workgroup size.
+  EIGEN_STRONG_INLINE size_t getNearestPowerOfTwoWorkGroupSize() const {
+    return getPowerOfTwo(m_device_info.max_work_group_size, false);
+  }
+
+  EIGEN_STRONG_INLINE std::string getPlatformName() const { return m_device_info.platform_name; }
+
+  EIGEN_STRONG_INLINE std::string getDeviceName() const { return m_device_info.device_name; }
+
+  EIGEN_STRONG_INLINE std::string getDeviceVendor() const { return m_device_info.device_vendor; }
+
+  // This function returns the nearest power of 2
+  // if roundup is true returns result>=wgsize
+  // else it return result <= wgsize
+  EIGEN_STRONG_INLINE size_t getPowerOfTwo(size_t wGSize, bool roundUp) const {
+    if (roundUp) --wGSize;
+    wGSize |= (wGSize >> 1);
+    wGSize |= (wGSize >> 2);
+    wGSize |= (wGSize >> 4);
+    wGSize |= (wGSize >> 8);
+    wGSize |= (wGSize >> 16);
+#if EIGEN_ARCH_x86_64 || EIGEN_ARCH_ARM64 || EIGEN_OS_WIN64
+    wGSize |= (wGSize >> 32);
+#endif
+    return ((!roundUp) ? (wGSize - (wGSize >> 1)) : ++wGSize);
+  }
+
+  EIGEN_STRONG_INLINE cl::sycl::queue &sycl_queue() const { return m_queue; }
+
+  // This function checks if the runtime recorded an error for the
+  // underlying stream device.
+  EIGEN_STRONG_INLINE bool ok() const {
+    if (!exception_caught_) {
+      synchronize();
+    }
+    return !exception_caught_;
+  }
+
+ protected:
+  void synchronize_and_callback(cl::sycl::event e, const std::function<void()> &callback) const {
+    if (callback) {
+      auto callback_ = [=]() {
+#ifdef EIGEN_EXCEPTIONS
+        cl::sycl::event(e).wait_and_throw();
+#else
+        cl::sycl::event(e).wait();
+#endif
+        callback();
+      };
+      m_thread_pool.Schedule(std::move(callback_));
+    } else {
+#ifdef EIGEN_EXCEPTIONS
+      m_queue.wait_and_throw();
+#else
+      m_queue.wait();
+#endif
+    }
+  }
+
+  bool sycl_async_handler(cl::sycl::exception_list exceptions) const {
+    bool exception_caught = false;
+    for (const auto &e : exceptions) {
+      if (e) {
+        exception_caught = true;
+        EIGEN_THROW_X(e);
+      }
+    }
+    return exception_caught;
+  }
+
+  /// class members:
+  bool exception_caught_ = false;
+  /// sycl queue
+  mutable cl::sycl::queue m_queue;
+  /// The thread pool is used to wait on events and call callbacks
+  /// asynchronously
+  mutable Eigen::ThreadPool m_thread_pool;
+
+  const TensorSycl::internal::SyclDeviceInfo m_device_info;
+};
+
+struct SyclDeviceBase {
+  /// QueueInterface is not owned. it is the caller's responsibility to destroy
+  /// it
+  const QueueInterface *m_queue_stream;
+  explicit SyclDeviceBase(const QueueInterface *queue_stream) : m_queue_stream(queue_stream) {}
+  EIGEN_STRONG_INLINE const QueueInterface *queue_stream() const { return m_queue_stream; }
+};
+
+// Here is a sycl device struct which accept the sycl queue interface
+// as an input
+struct SyclDevice : public SyclDeviceBase {
+  explicit SyclDevice(const QueueInterface *queue_stream) : SyclDeviceBase(queue_stream) {}
+
+  /// This is used to prepare the number of threads and also the number of
+  /// threads per block for sycl kernels
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(Index n, Index &tileSize, Index &rng, Index &GRange) const {
+    queue_stream()->parallel_for_setup(n, tileSize, rng, GRange);
+  }
+
+  /// This is used to prepare the number of threads and also the number of
+  /// threads per block for sycl kernels
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(const std::array<Index, 2> &input_dim, cl::sycl::range<2> &global_range,
+                                              cl::sycl::range<2> &local_range) const {
+    queue_stream()->parallel_for_setup(input_dim, global_range, local_range);
+  }
+
+  /// This is used to prepare the number of threads and also the number of
+  /// threads per block for sycl kernels
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(const std::array<Index, 3> &input_dim, cl::sycl::range<3> &global_range,
+                                              cl::sycl::range<3> &local_range) const {
+    queue_stream()->parallel_for_setup(input_dim, global_range, local_range);
+  }
+
+  /// allocate device memory
+  EIGEN_STRONG_INLINE void *allocate(size_t num_bytes) const { return queue_stream()->allocate(num_bytes); }
+
+  EIGEN_STRONG_INLINE void *allocate_temp(size_t num_bytes) const { return queue_stream()->allocate_temp(num_bytes); }
+
+  /// deallocate device memory
+  EIGEN_STRONG_INLINE void deallocate(void *p) const { queue_stream()->deallocate(p); }
+
+  EIGEN_STRONG_INLINE void deallocate_temp(void *buffer) const { queue_stream()->deallocate_temp(buffer); }
+
+  EIGEN_STRONG_INLINE void deallocate_temp(const void *buffer) const { queue_stream()->deallocate_temp(buffer); }
+
+  template <typename data_t>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE data_t *get(data_t *data) const {
+    return data;
+  }
+
+  // some runtime conditions that can be applied here
+  EIGEN_STRONG_INLINE bool isDeviceSuitable() const { return true; }
+
+  /// memcpyHostToDevice
+  template <typename Index>
+  EIGEN_STRONG_INLINE void memcpyHostToDevice(Index *dst, const Index *src, size_t n,
+                                              std::function<void()> callback = {}) const {
+    queue_stream()->memcpyHostToDevice(dst, src, n, callback);
+  }
+  /// memcpyDeviceToHost
+  template <typename Index>
+  EIGEN_STRONG_INLINE void memcpyDeviceToHost(void *dst, const Index *src, size_t n,
+                                              std::function<void()> callback = {}) const {
+    queue_stream()->memcpyDeviceToHost(dst, src, n, callback);
+  }
+  /// the memcpy function
+  template <typename Index>
+  EIGEN_STRONG_INLINE void memcpy(void *dst, const Index *src, size_t n) const {
+    queue_stream()->memcpy(dst, src, n);
+  }
+  /// the memset function
+  EIGEN_STRONG_INLINE void memset(void *data, int c, size_t n) const { queue_stream()->memset(data, c, n); }
+  /// the fill function
+  template <typename T>
+  EIGEN_STRONG_INLINE void fill(T *begin, T *end, const T &value) const {
+    queue_stream()->fill(begin, end, value);
+  }
+  /// returning the sycl queue
+  EIGEN_STRONG_INLINE cl::sycl::queue &sycl_queue() const { return queue_stream()->sycl_queue(); }
+
+  EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const { return 48 * 1024; }
+
+  EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
+    // We won't try to take advantage of the l2 cache for the time being, and
+    // there is no l3 cache on sycl devices.
+    return firstLevelCacheSize();
+  }
+  EIGEN_STRONG_INLINE unsigned long getNumSyclMultiProcessors() const {
+    return queue_stream()->getNumSyclMultiProcessors();
+  }
+  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerBlock() const { return queue_stream()->maxSyclThreadsPerBlock(); }
+  EIGEN_STRONG_INLINE cl::sycl::id<3> maxWorkItemSizes() const { return queue_stream()->maxWorkItemSizes(); }
+  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerMultiProcessor() const {
+    // OpenCL does not have such a concept
+    return queue_stream()->maxSyclThreadsPerMultiProcessor();
+  }
+  EIGEN_STRONG_INLINE size_t sharedMemPerBlock() const { return queue_stream()->sharedMemPerBlock(); }
+  EIGEN_STRONG_INLINE size_t getNearestPowerOfTwoWorkGroupSize() const {
+    return queue_stream()->getNearestPowerOfTwoWorkGroupSize();
+  }
+
+  EIGEN_STRONG_INLINE size_t getPowerOfTwo(size_t val, bool roundUp) const {
+    return queue_stream()->getPowerOfTwo(val, roundUp);
+  }
+  /// No need for sycl it should act the same as CPU version
+  EIGEN_STRONG_INLINE int majorDeviceVersion() const { return queue_stream()->majorDeviceVersion(); }
+
+  EIGEN_STRONG_INLINE void synchronize() const { queue_stream()->synchronize(); }
+
+  // This function checks if the runtime recorded an error for the
+  // underlying stream device.
+  EIGEN_STRONG_INLINE bool ok() const { return queue_stream()->ok(); }
+
+  EIGEN_STRONG_INLINE bool has_local_memory() const { return queue_stream()->has_local_memory(); }
+  EIGEN_STRONG_INLINE long max_buffer_size() const { return queue_stream()->max_buffer_size(); }
+  EIGEN_STRONG_INLINE std::string getPlatformName() const { return queue_stream()->getPlatformName(); }
+  EIGEN_STRONG_INLINE std::string getDeviceName() const { return queue_stream()->getDeviceName(); }
+  EIGEN_STRONG_INLINE std::string getDeviceVendor() const { return queue_stream()->getDeviceVendor(); }
+  template <typename OutScalar, typename KernelType, typename... T>
+  EIGEN_ALWAYS_INLINE cl::sycl::event binary_kernel_launcher(T... var) const {
+    return queue_stream()->template binary_kernel_launcher<OutScalar, KernelType>(var...);
+  }
+  template <typename OutScalar, typename KernelType, typename... T>
+  EIGEN_ALWAYS_INLINE cl::sycl::event unary_kernel_launcher(T... var) const {
+    return queue_stream()->template unary_kernel_launcher<OutScalar, KernelType>(var...);
+  }
+
+  template <typename OutScalar, typename KernelType, typename... T>
+  EIGEN_ALWAYS_INLINE cl::sycl::event nullary_kernel_launcher(T... var) const {
+    return queue_stream()->template nullary_kernel_launcher<OutScalar, KernelType>(var...);
+  }
+};
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
new file mode 100644
index 0000000..85c1dc8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
@@ -0,0 +1,346 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_USE_THREADS) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H)
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// An abstract interface to a device specific memory allocator.
+class Allocator {
+ public:
+  virtual ~Allocator() {}
+  virtual void* allocate(size_t num_bytes) const = 0;
+  virtual void deallocate(void* buffer) const = 0;
+};
+
+// Build a thread pool device on top the an existing pool of threads.
+struct ThreadPoolDevice {
+  // The ownership of the thread pool remains with the caller.
+  ThreadPoolDevice(ThreadPoolInterface* pool, int num_cores, Allocator* allocator = nullptr)
+      : pool_(pool), num_threads_(num_cores), allocator_(allocator) {}
+
+  EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
+    return allocator_ ? allocator_->allocate(num_bytes) : internal::aligned_malloc(num_bytes);
+  }
+
+  EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
+    if (allocator_) {
+      allocator_->deallocate(buffer);
+    } else {
+      internal::aligned_free(buffer);
+    }
+  }
+
+  EIGEN_STRONG_INLINE void* allocate_temp(size_t num_bytes) const { return allocate(num_bytes); }
+
+  EIGEN_STRONG_INLINE void deallocate_temp(void* buffer) const { deallocate(buffer); }
+
+  template <typename Type>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Type get(Type data) const {
+    return data;
+  }
+
+  EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
+#ifdef __ANDROID__
+    ::memcpy(dst, src, n);
+#else
+    // TODO(rmlarsen): Align blocks on cache lines.
+    // We have observed that going beyond 4 threads usually just wastes
+    // CPU cycles due to the threads competing for memory bandwidth, so we
+    // statically schedule at most 4 block copies here.
+    const size_t kMinBlockSize = 32768;
+    const size_t num_threads = CostModel::numThreads(n, TensorOpCost(1.0, 1.0, 0), 4);
+    if (n <= kMinBlockSize || num_threads < 2) {
+      ::memcpy(dst, src, n);
+    } else {
+      const char* src_ptr = static_cast<const char*>(src);
+      char* dst_ptr = static_cast<char*>(dst);
+      const size_t blocksize = (n + (num_threads - 1)) / num_threads;
+      Barrier barrier(static_cast<int>(num_threads - 1));
+      // Launch the last 3 blocks on worker threads.
+      for (size_t i = 1; i < num_threads; ++i) {
+        pool_->Schedule([n, i, src_ptr, dst_ptr, blocksize, &barrier] {
+          ::memcpy(dst_ptr + i * blocksize, src_ptr + i * blocksize, numext::mini(blocksize, n - (i * blocksize)));
+          barrier.Notify();
+        });
+      }
+      // Launch the first block on the main thread.
+      ::memcpy(dst_ptr, src_ptr, blocksize);
+      barrier.Wait();
+    }
+#endif
+  }
+  EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const { memcpy(dst, src, n); }
+  EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const { memcpy(dst, src, n); }
+
+  EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const { ::memset(buffer, c, n); }
+
+  template <typename T>
+  EIGEN_STRONG_INLINE void fill(T* begin, T* end, const T& value) const {
+    std::fill(begin, end, value);
+  }
+
+  EIGEN_STRONG_INLINE int numThreads() const { return num_threads_; }
+
+  // Number of threads available in the underlying thread pool. This number can
+  // be different from the value returned by numThreads().
+  EIGEN_STRONG_INLINE int numThreadsInPool() const { return pool_->NumThreads(); }
+
+  EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const { return l1CacheSize(); }
+
+  EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
+    // The l3 cache size is shared between all the cores.
+    return l3CacheSize() / num_threads_;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void synchronize() const {
+    // Nothing.  Threadpool device operations are synchronous.
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
+    // Should return an enum that encodes the ISA supported by the CPU
+    return 1;
+  }
+
+  // TODO(rmlarsen): Remove this deprecated interface when all users have been converted.
+  template <class Function, class... Args>
+  EIGEN_STRONG_INLINE void enqueueNoNotification(Function&& f, Args&&... args) const {
+    enqueue(std::forward<Function>(f), std::forward<Args>(args)...);
+  }
+
+  template <class Function, class... Args>
+  EIGEN_STRONG_INLINE void enqueue(Function&& f, Args&&... args) const {
+#if EIGEN_COMP_CXXVER >= 20
+    if constexpr (sizeof...(args) > 0) {
+      auto run_f = [f = std::forward<Function>(f), ... args = std::forward<Args>(args)]() { f(args...); };
+#else
+    if (sizeof...(args) > 0) {
+      auto run_f = [f = std::forward<Function>(f), &args...]() { f(args...); };
+#endif
+      pool_->Schedule(std::move(run_f));
+    } else {
+      pool_->Schedule(std::forward<Function>(f));
+    }
+  }
+
+  // Returns a logical thread index between 0 and pool_->NumThreads() - 1 if
+  // called from one of the threads in pool_. Returns -1 otherwise.
+  EIGEN_STRONG_INLINE int currentThreadId() const { return pool_->CurrentThreadId(); }
+
+  // WARNING: This function is synchronous and will block the calling thread.
+  //
+  // Synchronous parallelFor executes f with [0, n) arguments in parallel and
+  // waits for completion. F accepts a half-open interval [first, last). Block
+  // size is chosen based on the iteration cost and resulting parallel
+  // efficiency. If block_align is not nullptr, it is called to round up the
+  // block size.
+  void parallelFor(Index n, const TensorOpCost& cost, std::function<Index(Index)> block_align,
+                   std::function<void(Index, Index)> f) const {
+    if (EIGEN_PREDICT_FALSE(n <= 0)) {
+      return;
+      // Compute small problems directly in the caller thread.
+    } else if (n == 1 || numThreads() == 1 || CostModel::numThreads(n, cost, static_cast<int>(numThreads())) == 1) {
+      f(0, n);
+      return;
+    }
+
+    // Compute block size and total count of blocks.
+    ParallelForBlock block = CalculateParallelForBlock(n, cost, block_align);
+
+    // Recursively divide size into halves until we reach block_size.
+    // Division code rounds mid to block_size, so we are guaranteed to get
+    // block_count leaves that do actual computations.
+    Barrier barrier(static_cast<unsigned int>(block.count));
+    if (block.count <= numThreads()) {
+      // Avoid a thread hop by running the root of the tree and one block on the
+      // main thread.
+      handleRange(0, n, block.size, &barrier, pool_, f);
+    } else {
+      // Execute the root in the thread pool to avoid running work on more than
+      // numThreads() threads.
+      pool_->Schedule([this, n, &block, &barrier, &f]() { handleRange(0, n, block.size, &barrier, pool_, f); });
+    }
+
+    barrier.Wait();
+  }
+
+  // Convenience wrapper for parallelFor that does not align blocks.
+  void parallelFor(Index n, const TensorOpCost& cost, std::function<void(Index, Index)> f) const {
+    parallelFor(n, cost, nullptr, std::move(f));
+  }
+
+  // WARNING: This function is asynchronous and will not block the calling thread.
+  //
+  // Asynchronous parallelFor executes f with [0, n) arguments in parallel
+  // without waiting for completion. When the last block finished, it will call
+  // 'done' callback. F accepts a half-open interval [first, last). Block size
+  // is chosen based on the iteration cost and resulting parallel efficiency. If
+  // block_align is not nullptr, it is called to round up the block size.
+  void parallelForAsync(Index n, const TensorOpCost& cost, std::function<Index(Index)> block_align,
+                        std::function<void(Index, Index)> f, std::function<void()> done) const {
+    // Compute small problems directly in the caller thread.
+    if (n <= 1 || numThreads() == 1 || CostModel::numThreads(n, cost, static_cast<int>(numThreads())) == 1) {
+      f(0, n);
+      done();
+      return;
+    }
+
+    // Compute block size and total count of blocks.
+    ParallelForBlock block = CalculateParallelForBlock(n, cost, block_align);
+
+    ParallelForAsyncContext* const ctx = new ParallelForAsyncContext(block.count, std::move(f), std::move(done));
+
+    // Recursively divide size into halves until we reach block_size.
+    // Division code rounds mid to block_size, so we are guaranteed to get
+    // block_count leaves that do actual computations.
+    ctx->handle_range = [this, ctx, block](Index firstIdx, Index lastIdx) {
+      while (lastIdx - firstIdx > block.size) {
+        // Split into halves and schedule the second half on a different thread.
+        const Index midIdx = firstIdx + numext::div_ceil((lastIdx - firstIdx) / 2, block.size) * block.size;
+        pool_->Schedule([ctx, midIdx, lastIdx]() { ctx->handle_range(midIdx, lastIdx); });
+        lastIdx = midIdx;
+      }
+
+      // Single block or less, execute directly.
+      ctx->f(firstIdx, lastIdx);
+
+      // Delete async context if it was the last block.
+      if (ctx->count.fetch_sub(1) == 1) delete ctx;
+    };
+
+    if (block.count <= numThreads()) {
+      // Avoid a thread hop by running the root of the tree and one block on the
+      // main thread.
+      ctx->handle_range(0, n);
+    } else {
+      // Execute the root in the thread pool to avoid running work on more than
+      // numThreads() threads.
+      pool_->Schedule([ctx, n]() { ctx->handle_range(0, n); });
+    }
+  }
+
+  // Convenience wrapper for parallelForAsync that does not align blocks.
+  void parallelForAsync(Index n, const TensorOpCost& cost, std::function<void(Index, Index)> f,
+                        std::function<void()> done) const {
+    parallelForAsync(n, cost, nullptr, std::move(f), std::move(done));
+  }
+
+  // Thread pool accessor.
+  ThreadPoolInterface* getPool() const { return pool_; }
+
+  // Allocator accessor.
+  Allocator* allocator() const { return allocator_; }
+
+ private:
+  typedef TensorCostModel<ThreadPoolDevice> CostModel;
+
+  static void handleRange(Index firstIdx, Index lastIdx, Index granularity, Barrier* barrier, ThreadPoolInterface* pool,
+                          const std::function<void(Index, Index)>& f) {
+    while (lastIdx - firstIdx > granularity) {
+      // Split into halves and schedule the second half on a different thread.
+      const Index midIdx = firstIdx + numext::div_ceil((lastIdx - firstIdx) / 2, granularity) * granularity;
+      pool->Schedule([=, &f]() { handleRange(midIdx, lastIdx, granularity, barrier, pool, f); });
+      lastIdx = midIdx;
+    }
+    // Single block or less, execute directly.
+    f(firstIdx, lastIdx);
+    barrier->Notify();
+  }
+
+  // For parallelForAsync we must keep passed in closures on the heap, and
+  // delete them only after `done` callback finished.
+  struct ParallelForAsyncContext {
+    ParallelForAsyncContext(Index block_count, std::function<void(Index, Index)> block_f,
+                            std::function<void()> done_callback)
+        : count(block_count), f(std::move(block_f)), done(std::move(done_callback)) {}
+    ~ParallelForAsyncContext() { done(); }
+
+    std::atomic<Index> count;
+    std::function<void(Index, Index)> f;
+    std::function<void()> done;
+
+    std::function<void(Index, Index)> handle_range;
+  };
+
+  struct ParallelForBlock {
+    Index size;   // block size
+    Index count;  // number of blocks
+  };
+
+  // Calculates block size based on (1) the iteration cost and (2) parallel
+  // efficiency. We want blocks to be not too small to mitigate parallelization
+  // overheads; not too large to mitigate tail effect and potential load
+  // imbalance and we also want number of blocks to be evenly dividable across
+  // threads.
+  ParallelForBlock CalculateParallelForBlock(const Index n, const TensorOpCost& cost,
+                                             std::function<Index(Index)> block_align) const {
+    const double block_size_f = 1.0 / CostModel::taskSize(1, cost);
+    const Index max_oversharding_factor = 4;
+    Index block_size = numext::mini(
+        n, numext::maxi<Index>(numext::div_ceil<Index>(n, max_oversharding_factor * numThreads()), block_size_f));
+    const Index max_block_size = numext::mini(n, 2 * block_size);
+
+    if (block_align) {
+      Index new_block_size = block_align(block_size);
+      eigen_assert(new_block_size >= block_size);
+      block_size = numext::mini(n, new_block_size);
+    }
+
+    Index block_count = numext::div_ceil(n, block_size);
+
+    // Calculate parallel efficiency as fraction of total CPU time used for
+    // computations:
+    double max_efficiency =
+        static_cast<double>(block_count) / (numext::div_ceil<Index>(block_count, numThreads()) * numThreads());
+
+    // Now try to increase block size up to max_block_size as long as it
+    // doesn't decrease parallel efficiency.
+    for (Index prev_block_count = block_count; max_efficiency < 1.0 && prev_block_count > 1;) {
+      // This is the next block size that divides size into a smaller number
+      // of blocks than the current block_size.
+      Index coarser_block_size = numext::div_ceil(n, prev_block_count - 1);
+      if (block_align) {
+        Index new_block_size = block_align(coarser_block_size);
+        eigen_assert(new_block_size >= coarser_block_size);
+        coarser_block_size = numext::mini(n, new_block_size);
+      }
+      if (coarser_block_size > max_block_size) {
+        break;  // Reached max block size. Stop.
+      }
+      // Recalculate parallel efficiency.
+      const Index coarser_block_count = numext::div_ceil(n, coarser_block_size);
+      eigen_assert(coarser_block_count < prev_block_count);
+      prev_block_count = coarser_block_count;
+      const double coarser_efficiency = static_cast<double>(coarser_block_count) /
+                                        (numext::div_ceil<Index>(coarser_block_count, numThreads()) * numThreads());
+      if (coarser_efficiency + 0.01 >= max_efficiency) {
+        // Taking it.
+        block_size = coarser_block_size;
+        block_count = coarser_block_count;
+        if (max_efficiency < coarser_efficiency) {
+          max_efficiency = coarser_efficiency;
+        }
+      }
+    }
+
+    return {block_size, block_count};
+  }
+
+  ThreadPoolInterface* pool_;
+  int num_threads_;
+  Allocator* allocator_;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h
new file mode 100644
index 0000000..8f2e5af
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h
@@ -0,0 +1,117 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H
+#define EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \internal
+ *
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Special case of tensor index list used to list all the dimensions of a tensor of rank n.
+ *
+ * \sa Tensor
+ */
+template <typename Index, std::size_t Rank>
+struct DimensionList {
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const Index operator[](const Index i) const { return i; }
+};
+
+namespace internal {
+
+template <typename Index, std::size_t Rank>
+struct array_size<DimensionList<Index, Rank> > {
+  static const size_t value = Rank;
+};
+template <typename Index, std::size_t Rank>
+struct array_size<const DimensionList<Index, Rank> > {
+  static const size_t value = Rank;
+};
+
+template <DenseIndex n, typename Index, std::size_t Rank>
+const Index array_get(DimensionList<Index, Rank>&) {
+  return n;
+}
+template <DenseIndex n, typename Index, std::size_t Rank>
+const Index array_get(const DimensionList<Index, Rank>&) {
+  return n;
+}
+
+template <typename Index, std::size_t Rank>
+struct index_known_statically_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex) { return true; }
+};
+template <typename Index, std::size_t Rank>
+struct index_known_statically_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex) { return true; }
+};
+
+template <typename Index, std::size_t Rank>
+struct all_indices_known_statically_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() { return true; }
+};
+template <typename Index, std::size_t Rank>
+struct all_indices_known_statically_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() { return true; }
+};
+
+template <typename Index, std::size_t Rank>
+struct indices_statically_known_to_increase_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() { return true; }
+};
+template <typename Index, std::size_t Rank>
+struct indices_statically_known_to_increase_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() { return true; }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_eq_impl<DimensionList<Index, Rank> > {
+  static constexpr bool run(const DenseIndex i, const DenseIndex value) { return i == value; }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_eq_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { return i == value; }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_ne_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { return i != value; }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_ne_impl<const DimensionList<Index, Rank> > {
+  static constexpr bool run(const DenseIndex i, const DenseIndex value) { return i != value; }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_gt_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { return i > value; }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_gt_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { return i > value; }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_lt_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { return i < value; }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_lt_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) { return i < value; }
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
new file mode 100644
index 0000000..8aca0f3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
@@ -0,0 +1,334 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// Boilerplate code
+namespace internal {
+
+template <std::ptrdiff_t n, typename Dimension>
+struct dget {
+  static const std::ptrdiff_t value = get<n, Dimension>::value;
+};
+
+template <typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
+struct fixed_size_tensor_index_linearization_helper {
+  template <typename Dimensions>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index run(array<Index, NumIndices> const& indices,
+                                                         const Dimensions& dimensions) {
+    return array_get < RowMajor                             ? n - 1
+           : (NumIndices - n) > (indices) + dget < RowMajor ? n - 1
+                                                            : (NumIndices - n),
+           Dimensions > ::value * fixed_size_tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(
+                                      indices, dimensions);
+  }
+};
+
+template <typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
+struct fixed_size_tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor> {
+  template <typename Dimensions>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index run(array<Index, NumIndices> const&, const Dimensions&) {
+    return 0;
+  }
+};
+
+template <typename Index, std::ptrdiff_t n>
+struct fixed_size_tensor_index_extraction_helper {
+  template <typename Dimensions>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index run(const Index index, const Dimensions& dimensions) {
+    const Index mult = (index == n - 1) ? 1 : 0;
+    return array_get<n - 1>(dimensions) * mult +
+           fixed_size_tensor_index_extraction_helper<Index, n - 1>::run(index, dimensions);
+  }
+};
+
+template <typename Index>
+struct fixed_size_tensor_index_extraction_helper<Index, 0> {
+  template <typename Dimensions>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index run(const Index, const Dimensions&) {
+    return 0;
+  }
+};
+
+}  // end namespace internal
+
+/** \internal
+ *
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Fixed dimensions of a Tensor.
+ *
+ * The Sizes class encodes as part of the type the number of dimensions and the
+ * sizes corresponding to each dimension. It uses no storage space since it is
+ * entirely known at compile time.
+ *
+ * \sa Tensor
+ */
+template <typename std::ptrdiff_t... Indices>
+struct Sizes {
+  typedef internal::numeric_list<std::ptrdiff_t, Indices...> Base;
+  const Base t = Base();
+  static const std::ptrdiff_t total_size = internal::arg_prod(Indices...);
+  static const ptrdiff_t count = Base::count;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t rank() const { return Base::count; }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t TotalSize() { return internal::arg_prod(Indices...); }
+
+  EIGEN_DEVICE_FUNC Sizes() {}
+  template <typename DenseIndex>
+  explicit EIGEN_DEVICE_FUNC Sizes(const array<DenseIndex, Base::count>& /*indices*/) {
+    // TODO: Add assertion.
+  }
+  template <typename... DenseIndex>
+  EIGEN_DEVICE_FUNC Sizes(DenseIndex...) {}
+  explicit EIGEN_DEVICE_FUNC Sizes(std::initializer_list<std::ptrdiff_t> /*l*/) {
+    // TODO: Add assertion.
+  }
+
+  template <typename T>
+  Sizes& operator=(const T& /*other*/) {
+    // add assertion failure if the size of other is different
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t operator[](const std::ptrdiff_t index) const {
+    return internal::fixed_size_tensor_index_extraction_helper<std::ptrdiff_t, Base::count>::run(index, t);
+  }
+
+  template <typename DenseIndex>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptrdiff_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
+    return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, false>::run(
+        indices, t);
+  }
+  template <typename DenseIndex>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptrdiff_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
+    return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, true>::run(
+        indices, t);
+  }
+};
+
+namespace internal {
+template <typename std::ptrdiff_t... Indices>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_prod(const Sizes<Indices...>&) {
+  return Sizes<Indices...>::total_size;
+}
+}  // namespace internal
+
+// Boilerplate
+namespace internal {
+template <typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
+struct tensor_index_linearization_helper {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index run(array<Index, NumIndices> const& indices,
+                                                         array<Index, NumIndices> const& dimensions) {
+    return array_get < RowMajor ? n
+           : (NumIndices - n - 1) > (indices) + array_get < RowMajor
+               ? n
+               : (NumIndices - n - 1) >
+                     (dimensions)*tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(
+                         indices, dimensions);
+  }
+};
+
+template <typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
+struct tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index run(array<Index, NumIndices> const& indices,
+                                                         array<Index, NumIndices> const&) {
+    return array_get < RowMajor ? 0 : NumIndices - 1 > (indices);
+  }
+};
+}  // end namespace internal
+
+/** \internal
+ *
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Dynamic dimensions of a Tensor.
+ *
+ * The DSizes class is its dynamic sibling: the number of dimensions is known
+ * at compile time but the sizes are set during execution.
+ *
+ * \sa Tensor
+ */
+template <typename DenseIndex, int NumDims>
+struct DSizes : array<DenseIndex, NumDims> {
+  typedef array<DenseIndex, NumDims> Base;
+  static const int count = NumDims;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const { return NumDims; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex TotalSize() const {
+    return (NumDims == 0) ? 1 : internal::array_prod(*static_cast<const Base*>(this));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DSizes() {
+    for (int i = 0; i < NumDims; ++i) {
+      (*this)[i] = 0;
+    }
+  }
+  EIGEN_DEVICE_FUNC explicit DSizes(const array<DenseIndex, NumDims>& a) : Base(a) {}
+
+  EIGEN_DEVICE_FUNC explicit DSizes(const DenseIndex i0) {
+    eigen_assert(NumDims == 1);
+    (*this)[0] = i0;
+  }
+
+  EIGEN_DEVICE_FUNC DSizes(const DimensionList<DenseIndex, NumDims>& a) {
+    for (int i = 0; i < NumDims; ++i) {
+      (*this)[i] = a[i];
+    }
+  }
+
+  // Enable DSizes index type promotion only if we are promoting to the
+  // larger type, e.g. allow to promote dimensions of type int to long.
+  template <typename OtherIndex>
+  EIGEN_DEVICE_FUNC explicit DSizes(
+      const array<OtherIndex, NumDims>& other,
+      // Default template parameters require c++11.
+      std::enable_if_t<
+          internal::is_same<DenseIndex, typename internal::promote_index_type<DenseIndex, OtherIndex>::type>::value,
+          void*> = 0) {
+    for (int i = 0; i < NumDims; ++i) {
+      (*this)[i] = static_cast<DenseIndex>(other[i]);
+    }
+  }
+
+  template <typename FirstType, typename... OtherTypes>
+  EIGEN_DEVICE_FUNC explicit DSizes(const Eigen::IndexList<FirstType, OtherTypes...>& dimensions) {
+    for (int i = 0; i < dimensions.count; ++i) {
+      (*this)[i] = dimensions[i];
+    }
+  }
+
+  template <typename std::ptrdiff_t... Indices>
+  EIGEN_DEVICE_FUNC DSizes(const Sizes<Indices...>& a) {
+    for (int i = 0; i < NumDims; ++i) {
+      (*this)[i] = a[i];
+    }
+  }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit DSizes(DenseIndex firstDimension, DenseIndex secondDimension,
+                                                        IndexTypes... otherDimensions)
+      : Base({{firstDimension, secondDimension, otherDimensions...}}) {
+    EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 2 == NumDims, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  }
+
+  EIGEN_DEVICE_FUNC DSizes& operator=(const array<DenseIndex, NumDims>& other) {
+    *static_cast<Base*>(this) = other;
+    return *this;
+  }
+
+  // A constexpr would be so much better here
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex IndexOfColMajor(const array<DenseIndex, NumDims>& indices) const {
+    return internal::tensor_index_linearization_helper<DenseIndex, NumDims, NumDims - 1, false>::run(
+        indices, *static_cast<const Base*>(this));
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex IndexOfRowMajor(const array<DenseIndex, NumDims>& indices) const {
+    return internal::tensor_index_linearization_helper<DenseIndex, NumDims, NumDims - 1, true>::run(
+        indices, *static_cast<const Base*>(this));
+  }
+};
+
+template <typename IndexType, int NumDims>
+std::ostream& operator<<(std::ostream& os, const DSizes<IndexType, NumDims>& dims) {
+  os << "[";
+  for (int i = 0; i < NumDims; ++i) {
+    if (i > 0) os << ", ";
+    os << dims[i];
+  }
+  os << "]";
+  return os;
+}
+
+// Boilerplate
+namespace internal {
+template <typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
+struct tensor_vsize_index_linearization_helper {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index run(array<Index, NumIndices> const& indices,
+                                                         std::vector<DenseIndex> const& dimensions) {
+    return array_get < RowMajor ? n
+           : (NumIndices - n - 1) > (indices) + array_get < RowMajor
+               ? n
+               : (NumIndices - n - 1) >
+                     (dimensions)*tensor_vsize_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(
+                         indices, dimensions);
+  }
+};
+
+template <typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
+struct tensor_vsize_index_linearization_helper<Index, NumIndices, 0, RowMajor> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index run(array<Index, NumIndices> const& indices,
+                                                         std::vector<DenseIndex> const&) {
+    return array_get < RowMajor ? 0 : NumIndices - 1 > (indices);
+  }
+};
+}  // end namespace internal
+
+namespace internal {
+
+template <typename DenseIndex, int NumDims>
+struct array_size<const DSizes<DenseIndex, NumDims> > {
+  static const ptrdiff_t value = NumDims;
+};
+template <typename DenseIndex, int NumDims>
+struct array_size<DSizes<DenseIndex, NumDims> > {
+  static const ptrdiff_t value = NumDims;
+};
+template <typename std::ptrdiff_t... Indices>
+struct array_size<const Sizes<Indices...> > {
+  static const std::ptrdiff_t value = Sizes<Indices...>::count;
+};
+template <typename std::ptrdiff_t... Indices>
+struct array_size<Sizes<Indices...> > {
+  static const std::ptrdiff_t value = Sizes<Indices...>::count;
+};
+template <std::ptrdiff_t n, typename std::ptrdiff_t... Indices>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<Indices...>&) {
+  return get<n, internal::numeric_list<std::ptrdiff_t, Indices...> >::value;
+}
+template <std::ptrdiff_t n>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<>&) {
+  eigen_assert(false && "should never be called");
+  return -1;
+}
+
+template <typename Dims1, typename Dims2, ptrdiff_t n, ptrdiff_t m>
+struct sizes_match_below_dim {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Dims1&, Dims2&) { return false; }
+};
+template <typename Dims1, typename Dims2, ptrdiff_t n>
+struct sizes_match_below_dim<Dims1, Dims2, n, n> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Dims1& dims1, Dims2& dims2) {
+    return numext::equal_strict(array_get<n - 1>(dims1), array_get<n - 1>(dims2)) &&
+           sizes_match_below_dim<Dims1, Dims2, n - 1, n - 1>::run(dims1, dims2);
+  }
+};
+template <typename Dims1, typename Dims2>
+struct sizes_match_below_dim<Dims1, Dims2, 0, 0> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Dims1&, Dims2&) { return true; }
+};
+
+}  // end namespace internal
+
+template <typename Dims1, typename Dims2>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool dimensions_match(Dims1 dims1, Dims2 dims2) {
+  return internal::sizes_match_below_dim<Dims1, Dims2, internal::array_size<Dims1>::value,
+                                         internal::array_size<Dims2>::value>::run(dims1, dims2);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
new file mode 100644
index 0000000..9c83fad
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
@@ -0,0 +1,188 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
+#define EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename XprType, template <class> class MakePointer_>
+struct traits<TensorEvalToOp<XprType, MakePointer_> > {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename MakePointer_<Scalar>::Type PointerType;
+
+  enum { Flags = 0 };
+  template <class T>
+  struct MakePointer {
+    // Intermediate typedef to workaround MSVC issue.
+    typedef MakePointer_<T> MakePointerT;
+    typedef typename MakePointerT::Type Type;
+  };
+};
+
+template <typename XprType, template <class> class MakePointer_>
+struct eval<TensorEvalToOp<XprType, MakePointer_>, Eigen::Dense> {
+  typedef const TensorEvalToOp<XprType, MakePointer_>& type;
+};
+
+template <typename XprType, template <class> class MakePointer_>
+struct nested<TensorEvalToOp<XprType, MakePointer_>, 1, typename eval<TensorEvalToOp<XprType, MakePointer_> >::type> {
+  typedef TensorEvalToOp<XprType, MakePointer_> type;
+};
+
+}  // end namespace internal
+
+template <typename XprType, template <class> class MakePointer_>
+class TensorEvalToOp : public TensorBase<TensorEvalToOp<XprType, MakePointer_>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorEvalToOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
+  typedef typename MakePointer_<CoeffReturnType>::Type PointerType;
+  typedef typename Eigen::internal::nested<TensorEvalToOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorEvalToOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorEvalToOp>::Index Index;
+
+  static constexpr int NumDims = Eigen::internal::traits<TensorEvalToOp>::NumDimensions;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvalToOp(PointerType buffer, const XprType& expr)
+      : m_xpr(expr), m_buffer(buffer) {}
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_DEVICE_FUNC PointerType buffer() const { return m_buffer; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  PointerType m_buffer;
+};
+
+template <typename ArgType, typename Device, template <class> class MakePointer_>
+struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device> {
+  typedef TensorEvalToOp<ArgType, MakePointer_> XprType;
+  typedef typename ArgType::Scalar Scalar;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef typename XprType::Index Index;
+  typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  enum {
+    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = true,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = true
+  };
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  static constexpr int NumDims = internal::traits<ArgType>::NumDimensions;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock ArgTensorBlock;
+
+  typedef internal::TensorBlockAssignment<CoeffReturnType, NumDims, typename ArgTensorBlock::XprType, Index>
+      TensorBlockAssignment;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_buffer(device.get(op.buffer())), m_expression(op.expression()) {}
+
+  EIGEN_STRONG_INLINE ~TensorEvaluator() {}
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType scalar) {
+    EIGEN_UNUSED_VARIABLE(scalar);
+    eigen_assert(scalar == NULL);
+    return m_impl.evalSubExprsIfNeeded(m_buffer);
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType scalar, EvalSubExprsCallback done) {
+    EIGEN_UNUSED_VARIABLE(scalar);
+    eigen_assert(scalar == NULL);
+    m_impl.evalSubExprsIfNeededAsync(m_buffer, std::move(done));
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) const { m_buffer[i] = m_impl.coeff(i); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) const {
+    internal::pstoret<CoeffReturnType, PacketReturnType, Aligned>(
+        m_buffer + i, m_impl.template packet < TensorEvaluator<ArgType, Device>::IsAligned ? Aligned : Unaligned > (i));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return m_impl.getResourceRequirements();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(TensorBlockDesc& desc, TensorBlockScratch& scratch) {
+    // Add `m_buffer` as destination buffer to the block descriptor.
+    desc.template AddDestinationBuffer<Layout>(
+        /*dst_base=*/m_buffer + desc.offset(),
+        /*dst_strides=*/internal::strides<Layout>(m_impl.dimensions()));
+
+    ArgTensorBlock block = m_impl.block(desc, scratch, /*root_of_expr_ast=*/true);
+
+    // If block was evaluated into a destination buffer, there is no need to do
+    // an assignment.
+    if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
+      TensorBlockAssignment::Run(
+          TensorBlockAssignment::target(desc.dimensions(), internal::strides<Layout>(m_impl.dimensions()), m_buffer,
+                                        desc.offset()),
+          block.expr());
+    }
+    block.cleanup();
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_buffer[index]; }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // We assume that evalPacket or evalScalar is called to perform the
+    // assignment and account for the cost of the write here.
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_buffer; }
+  ArgType expression() const { return m_expression; }
+
+ private:
+  TensorEvaluator<ArgType, Device> m_impl;
+  EvaluatorPointerType m_buffer;
+  const ArgType m_expression;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
new file mode 100644
index 0000000..7a19d7a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
@@ -0,0 +1,859 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
+#define EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// Generic evaluator
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief The tensor evaluator class.
+ *
+ * These classes are responsible for the evaluation of the tensor expression.
+ *
+ * TODO: add support for more types of expressions, in particular expressions
+ * leading to lvalues (slicing, reshaping, etc...)
+ */
+template <typename Derived, typename Device>
+struct TensorEvaluator {
+  typedef typename Derived::Index Index;
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename Derived::Dimensions Dimensions;
+  typedef Derived XprType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename internal::traits<Derived>::template MakePointer<Scalar>::Type TensorPointerType;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  // NumDimensions is -1 for variable dim tensors
+  static constexpr int NumCoords =
+      internal::traits<Derived>::NumDimensions > 0 ? internal::traits<Derived>::NumDimensions : 0;
+  static constexpr int Layout = Derived::Layout;
+
+  enum {
+    IsAligned = Derived::IsAligned,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = internal::is_arithmetic<std::remove_const_t<Scalar>>::value,
+    PreferBlockAccess = false,
+    CoordAccess = NumCoords > 0,
+    RawAccess = true
+  };
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumCoords, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumCoords, Layout, Index> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
+      : m_data(device.get((const_cast<TensorPointerType>(m.data())))), m_dims(m.dimensions()), m_device(device) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType dest) {
+    if (!NumTraits<std::remove_const_t<Scalar>>::RequireInitialization && dest) {
+      m_device.memcpy((void*)(m_device.get(dest)), m_device.get(m_data), m_dims.TotalSize() * sizeof(Scalar));
+      return false;
+    }
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType dest, EvalSubExprsCallback done) {
+    // TODO(ezhulenev): ThreadPoolDevice memcpy is a blocking operation.
+    done(evalSubExprsIfNeeded(dest));
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    eigen_assert(m_data != NULL);
+    return m_data[index];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
+    eigen_assert(m_data != NULL);
+    return m_data[index];
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_data + index);
+  }
+
+  // Return a packet starting at `index` where `umask` specifies which elements
+  // have to be loaded. Type/size of mask depends on PacketReturnType, e.g. for
+  // Packet16f, `umask` is of type uint16_t and if a bit is 1, corresponding
+  // float element will be loaded, otherwise 0 will be loaded.
+  // Function has been templatized to enable Sfinae.
+  template <typename PacketReturnTypeT>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      std::enable_if_t<internal::unpacket_traits<PacketReturnTypeT>::masked_load_available, PacketReturnTypeT>
+      partialPacket(Index index, typename internal::unpacket_traits<PacketReturnTypeT>::mask_t umask) const {
+    return internal::ploadu<PacketReturnTypeT>(m_data + index, umask);
+  }
+
+  template <int StoreMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
+    return internal::pstoret<Scalar, PacketReturnType, StoreMode>(m_data + index, x);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
+    eigen_assert(m_data != NULL);
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      return m_data[m_dims.IndexOfColMajor(coords)];
+    } else {
+      return m_data[m_dims.IndexOfRowMajor(coords)];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(const array<DenseIndex, NumCoords>& coords) const {
+    eigen_assert(m_data != NULL);
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      return m_data[m_dims.IndexOfColMajor(coords)];
+    } else {
+      return m_data[m_dims.IndexOfRowMajor(coords)];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketType<CoeffReturnType, Device>::size);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return internal::TensorBlockResourceRequirements::any();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    eigen_assert(m_data != NULL);
+    return TensorBlock::materialize(m_data, m_dims, desc, scratch);
+  }
+
+  template <typename TensorBlock>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(const TensorBlockDesc& desc, const TensorBlock& block) {
+    eigen_assert(m_data != NULL);
+
+    typedef typename TensorBlock::XprType TensorBlockExpr;
+    typedef internal::TensorBlockAssignment<Scalar, NumCoords, TensorBlockExpr, Index> TensorBlockAssign;
+
+    TensorBlockAssign::Run(
+        TensorBlockAssign::target(desc.dimensions(), internal::strides<Layout>(m_dims), m_data, desc.offset()),
+        block.expr());
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
+
+ protected:
+  EvaluatorPointerType m_data;
+  Dimensions m_dims;
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+namespace internal {
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T loadConstant(const T* address) {
+  return *address;
+}
+// Use the texture cache on CUDA devices whenever possible
+#if defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 350
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float loadConstant(const float* address) {
+  return __ldg(address);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double loadConstant(const double* address) {
+  return __ldg(address);
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Eigen::half loadConstant(const Eigen::half* address) {
+  return Eigen::half(half_impl::raw_uint16_to_half(__ldg(&address->x)));
+}
+#endif
+
+}  // namespace internal
+
+// Default evaluator for rvalues
+template <typename Derived, typename Device>
+struct TensorEvaluator<const Derived, Device> {
+  typedef typename Derived::Index Index;
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename Derived::Dimensions Dimensions;
+  typedef const Derived XprType;
+  typedef typename internal::traits<Derived>::template MakePointer<const Scalar>::Type TensorPointerType;
+  typedef StorageMemory<const Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  // NumDimensions is -1 for variable dim tensors
+  static constexpr int NumCoords =
+      internal::traits<Derived>::NumDimensions > 0 ? internal::traits<Derived>::NumDimensions : 0;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  static constexpr int Layout = Derived::Layout;
+
+  enum {
+    IsAligned = Derived::IsAligned,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = internal::is_arithmetic<ScalarNoConst>::value,
+    PreferBlockAccess = false,
+    CoordAccess = NumCoords > 0,
+    RawAccess = true
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumCoords, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumCoords, Layout, Index> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC TensorEvaluator(const Derived& m, const Device& device)
+      : m_data(device.get(m.data())), m_dims(m.dimensions()), m_device(device) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    if (!NumTraits<std::remove_const_t<Scalar>>::RequireInitialization && data) {
+      m_device.memcpy((void*)(m_device.get(data)), m_device.get(m_data), m_dims.TotalSize() * sizeof(Scalar));
+      return false;
+    }
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType dest, EvalSubExprsCallback done) {
+    // TODO(ezhulenev): ThreadPoolDevice memcpy is a blocking operation.
+    done(evalSubExprsIfNeeded(dest));
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    eigen_assert(m_data != NULL);
+    return internal::loadConstant(m_data + index);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return internal::ploadt_ro<PacketReturnType, LoadMode>(m_data + index);
+  }
+
+  // Return a packet starting at `index` where `umask` specifies which elements
+  // have to be loaded. Type/size of mask depends on PacketReturnType, e.g. for
+  // Packet16f, `umask` is of type uint16_t and if a bit is 1, corresponding
+  // float element will be loaded, otherwise 0 will be loaded.
+  // Function has been templatized to enable Sfinae.
+  template <typename PacketReturnTypeT>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      std::enable_if_t<internal::unpacket_traits<PacketReturnTypeT>::masked_load_available, PacketReturnTypeT>
+      partialPacket(Index index, typename internal::unpacket_traits<PacketReturnTypeT>::mask_t umask) const {
+    return internal::ploadu<PacketReturnTypeT>(m_data + index, umask);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
+    eigen_assert(m_data != NULL);
+    const Index index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_dims.IndexOfColMajor(coords)
+                                                                                 : m_dims.IndexOfRowMajor(coords);
+    return internal::loadConstant(m_data + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketType<CoeffReturnType, Device>::size);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return internal::TensorBlockResourceRequirements::any();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    eigen_assert(m_data != NULL);
+    return TensorBlock::materialize(m_data, m_dims, desc, scratch);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
+
+ protected:
+  EvaluatorPointerType m_data;
+  Dimensions m_dims;
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+// -------------------- CwiseNullaryOp --------------------
+
+template <typename NullaryOp, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, ArgType>, Device> {
+  typedef TensorCwiseNullaryOp<NullaryOp, ArgType> XprType;
+
+  EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
+      : m_functor(op.functor()), m_argImpl(op.nestedExpression(), device), m_wrapper() {}
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = true,
+    PacketAccess = internal::functor_traits<NullaryOp>::PacketAccess
+#ifdef EIGEN_USE_SYCL
+                   && (PacketType<CoeffReturnType, Device>::size > 1)
+#endif
+        ,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) { return true; }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    done(true);
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {}
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const { return m_wrapper(m_functor, index); }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return m_wrapper.template packetOp<PacketReturnType, Index>(m_functor, index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketType<CoeffReturnType, Device>::size);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ private:
+  const NullaryOp m_functor;
+  TensorEvaluator<ArgType, Device> m_argImpl;
+  const internal::nullary_wrapper<CoeffReturnType, NullaryOp> m_wrapper;
+};
+
+// -------------------- CwiseUnaryOp --------------------
+
+template <typename UnaryOp, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device> {
+  typedef TensorCwiseUnaryOp<UnaryOp, ArgType> XprType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess =
+        int(TensorEvaluator<ArgType, Device>::PacketAccess) & int(internal::functor_traits<UnaryOp>::PacketAccess),
+    BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
+      : m_device(device), m_functor(op.functor()), m_argImpl(op.nestedExpression(), device) {}
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  static constexpr int NumDims = internal::array_size<Dimensions>::value;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock ArgTensorBlock;
+
+  typedef internal::TensorCwiseUnaryBlock<UnaryOp, ArgTensorBlock> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_argImpl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_argImpl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { m_argImpl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const { return m_functor(m_argImpl.coeff(index)); }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return m_functor.packetOp(m_argImpl.template packet<LoadMode>(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
+    return m_argImpl.costPerCoeff(vectorized) + TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    static const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
+    return m_argImpl.getResourceRequirements().addCostPerCoeff({0, 0, functor_cost / PacketSize});
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    return TensorBlock(m_argImpl.block(desc, scratch), m_functor);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ private:
+  const Device EIGEN_DEVICE_REF m_device;
+  const UnaryOp m_functor;
+  TensorEvaluator<ArgType, Device> m_argImpl;
+};
+
+// -------------------- CwiseBinaryOp --------------------
+
+template <typename BinaryOp, typename LeftArgType, typename RightArgType, typename Device>
+struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType>, Device> {
+  typedef TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType> XprType;
+
+  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
+  enum {
+    IsAligned =
+        int(TensorEvaluator<LeftArgType, Device>::IsAligned) & int(TensorEvaluator<RightArgType, Device>::IsAligned),
+    PacketAccess = int(TensorEvaluator<LeftArgType, Device>::PacketAccess) &
+                   int(TensorEvaluator<RightArgType, Device>::PacketAccess) &
+                   int(internal::functor_traits<BinaryOp>::PacketAccess),
+    BlockAccess = int(TensorEvaluator<LeftArgType, Device>::BlockAccess) &
+                  int(TensorEvaluator<RightArgType, Device>::BlockAccess),
+    PreferBlockAccess = int(TensorEvaluator<LeftArgType, Device>::PreferBlockAccess) |
+                        int(TensorEvaluator<RightArgType, Device>::PreferBlockAccess),
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
+      : m_device(device),
+        m_functor(op.functor()),
+        m_leftImpl(op.lhsExpression(), device),
+        m_rightImpl(op.rhsExpression(), device) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
+                             static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) ||
+                         internal::traits<XprType>::NumDimensions <= 1),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+    eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
+  }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename TensorEvaluator<LeftArgType, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const LeftArgType, Device>::TensorBlock LeftTensorBlock;
+  typedef typename TensorEvaluator<const RightArgType, Device>::TensorBlock RightTensorBlock;
+
+  typedef internal::TensorCwiseBinaryBlock<BinaryOp, LeftTensorBlock, RightTensorBlock> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const {
+    // TODO: use right impl instead if right impl dimensions are known at compile time.
+    return m_leftImpl.dimensions();
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_leftImpl.evalSubExprsIfNeeded(NULL);
+    m_rightImpl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    // TODO(ezhulenev): Evaluate two expression in parallel?
+    m_leftImpl.evalSubExprsIfNeededAsync(
+        nullptr, [this, done](bool) { m_rightImpl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); }); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_leftImpl.cleanup();
+    m_rightImpl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const {
+    return m_functor(m_leftImpl.coeff(index), m_rightImpl.coeff(index));
+  }
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return m_functor.packetOp(m_leftImpl.template packet<LoadMode>(index),
+                              m_rightImpl.template packet<LoadMode>(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
+    return m_leftImpl.costPerCoeff(vectorized) + m_rightImpl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    static const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
+    return internal::TensorBlockResourceRequirements::merge(m_leftImpl.getResourceRequirements(),
+                                                            m_rightImpl.getResourceRequirements())
+        .addCostPerCoeff({0, 0, functor_cost / PacketSize});
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    desc.DropDestinationBuffer();
+    return TensorBlock(m_leftImpl.block(desc, scratch), m_rightImpl.block(desc, scratch), m_functor);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ private:
+  const Device EIGEN_DEVICE_REF m_device;
+  const BinaryOp m_functor;
+  TensorEvaluator<LeftArgType, Device> m_leftImpl;
+  TensorEvaluator<RightArgType, Device> m_rightImpl;
+};
+
+// -------------------- CwiseTernaryOp --------------------
+
+template <typename TernaryOp, typename Arg1Type, typename Arg2Type, typename Arg3Type, typename Device>
+struct TensorEvaluator<const TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type>, Device> {
+  typedef TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type> XprType;
+
+  static constexpr int Layout = TensorEvaluator<Arg1Type, Device>::Layout;
+  enum {
+    IsAligned = TensorEvaluator<Arg1Type, Device>::IsAligned & TensorEvaluator<Arg2Type, Device>::IsAligned &
+                TensorEvaluator<Arg3Type, Device>::IsAligned,
+    PacketAccess = TensorEvaluator<Arg1Type, Device>::PacketAccess && TensorEvaluator<Arg2Type, Device>::PacketAccess &&
+                   TensorEvaluator<Arg3Type, Device>::PacketAccess && internal::functor_traits<TernaryOp>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<Arg1Type, Device>::PreferBlockAccess ||
+                        TensorEvaluator<Arg2Type, Device>::PreferBlockAccess ||
+                        TensorEvaluator<Arg3Type, Device>::PreferBlockAccess,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
+      : m_functor(op.functor()),
+        m_arg1Impl(op.arg1Expression(), device),
+        m_arg2Impl(op.arg2Expression(), device),
+        m_arg3Impl(op.arg3Expression(), device) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<Arg1Type, Device>::Layout) ==
+                             static_cast<int>(TensorEvaluator<Arg3Type, Device>::Layout) ||
+                         internal::traits<XprType>::NumDimensions <= 1),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
+                                           typename internal::traits<Arg2Type>::StorageKind>::value),
+                        STORAGE_KIND_MUST_MATCH)
+    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
+                                           typename internal::traits<Arg3Type>::StorageKind>::value),
+                        STORAGE_KIND_MUST_MATCH)
+    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index,
+                                           typename internal::traits<Arg2Type>::Index>::value),
+                        STORAGE_INDEX_MUST_MATCH)
+    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index,
+                                           typename internal::traits<Arg3Type>::Index>::value),
+                        STORAGE_INDEX_MUST_MATCH)
+
+    eigen_assert(dimensions_match(m_arg1Impl.dimensions(), m_arg2Impl.dimensions()) &&
+                 dimensions_match(m_arg1Impl.dimensions(), m_arg3Impl.dimensions()));
+  }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename TensorEvaluator<Arg1Type, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const {
+    // TODO: use arg2 or arg3 dimensions if they are known at compile time.
+    return m_arg1Impl.dimensions();
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_arg1Impl.evalSubExprsIfNeeded(NULL);
+    m_arg2Impl.evalSubExprsIfNeeded(NULL);
+    m_arg3Impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_arg1Impl.cleanup();
+    m_arg2Impl.cleanup();
+    m_arg3Impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const {
+    return m_functor(m_arg1Impl.coeff(index), m_arg2Impl.coeff(index), m_arg3Impl.coeff(index));
+  }
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return m_functor.packetOp(m_arg1Impl.template packet<LoadMode>(index), m_arg2Impl.template packet<LoadMode>(index),
+                              m_arg3Impl.template packet<LoadMode>(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double functor_cost = internal::functor_traits<TernaryOp>::Cost;
+    return m_arg1Impl.costPerCoeff(vectorized) + m_arg2Impl.costPerCoeff(vectorized) +
+           m_arg3Impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ private:
+  const TernaryOp m_functor;
+  TensorEvaluator<Arg1Type, Device> m_arg1Impl;
+  TensorEvaluator<Arg2Type, Device> m_arg2Impl;
+  TensorEvaluator<Arg3Type, Device> m_arg3Impl;
+};
+
+// -------------------- SelectOp --------------------
+
+template <typename IfArgType, typename ThenArgType, typename ElseArgType, typename Device>
+struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>, Device> {
+  typedef TensorSelectOp<IfArgType, ThenArgType, ElseArgType> XprType;
+  typedef typename XprType::Scalar Scalar;
+
+  using TernarySelectOp = internal::scalar_boolean_select_op<typename internal::traits<ThenArgType>::Scalar,
+                                                             typename internal::traits<ElseArgType>::Scalar,
+                                                             typename internal::traits<IfArgType>::Scalar>;
+  static constexpr bool TernaryPacketAccess =
+      TensorEvaluator<ThenArgType, Device>::PacketAccess && TensorEvaluator<ElseArgType, Device>::PacketAccess &&
+      TensorEvaluator<IfArgType, Device>::PacketAccess && internal::functor_traits<TernarySelectOp>::PacketAccess;
+
+  static constexpr int Layout = TensorEvaluator<IfArgType, Device>::Layout;
+  enum {
+    IsAligned = TensorEvaluator<ThenArgType, Device>::IsAligned & TensorEvaluator<ElseArgType, Device>::IsAligned,
+    PacketAccess =
+        (TensorEvaluator<ThenArgType, Device>::PacketAccess && TensorEvaluator<ElseArgType, Device>::PacketAccess) ||
+        TernaryPacketAccess,
+    BlockAccess = TensorEvaluator<IfArgType, Device>::BlockAccess &&
+                  TensorEvaluator<ThenArgType, Device>::BlockAccess &&
+                  TensorEvaluator<ElseArgType, Device>::BlockAccess,
+    PreferBlockAccess = TensorEvaluator<IfArgType, Device>::PreferBlockAccess ||
+                        TensorEvaluator<ThenArgType, Device>::PreferBlockAccess ||
+                        TensorEvaluator<ElseArgType, Device>::PreferBlockAccess,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
+      : m_condImpl(op.ifExpression(), device),
+        m_thenImpl(op.thenExpression(), device),
+        m_elseImpl(op.elseExpression(), device) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) ==
+                         static_cast<int>(TensorEvaluator<ThenArgType, Device>::Layout)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) ==
+                         static_cast<int>(TensorEvaluator<ElseArgType, Device>::Layout)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+    eigen_assert(dimensions_match(m_condImpl.dimensions(), m_thenImpl.dimensions()));
+    eigen_assert(dimensions_match(m_thenImpl.dimensions(), m_elseImpl.dimensions()));
+  }
+
+  typedef typename XprType::Index Index;
+  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename TensorEvaluator<IfArgType, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int NumDims = internal::array_size<Dimensions>::value;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const IfArgType, Device>::TensorBlock IfArgTensorBlock;
+  typedef typename TensorEvaluator<const ThenArgType, Device>::TensorBlock ThenArgTensorBlock;
+  typedef typename TensorEvaluator<const ElseArgType, Device>::TensorBlock ElseArgTensorBlock;
+
+  struct TensorSelectOpBlockFactory {
+    template <typename IfArgXprType, typename ThenArgXprType, typename ElseArgXprType>
+    struct XprType {
+      typedef TensorSelectOp<const IfArgXprType, const ThenArgXprType, const ElseArgXprType> type;
+    };
+
+    template <typename IfArgXprType, typename ThenArgXprType, typename ElseArgXprType>
+    typename XprType<IfArgXprType, ThenArgXprType, ElseArgXprType>::type expr(const IfArgXprType& if_expr,
+                                                                              const ThenArgXprType& then_expr,
+                                                                              const ElseArgXprType& else_expr) const {
+      return typename XprType<IfArgXprType, ThenArgXprType, ElseArgXprType>::type(if_expr, then_expr, else_expr);
+    }
+  };
+
+  typedef internal::TensorTernaryExprBlock<TensorSelectOpBlockFactory, IfArgTensorBlock, ThenArgTensorBlock,
+                                           ElseArgTensorBlock>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const {
+    // TODO: use then or else impl instead if they happen to be known at compile time.
+    return m_condImpl.dimensions();
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_condImpl.evalSubExprsIfNeeded(NULL);
+    m_thenImpl.evalSubExprsIfNeeded(NULL);
+    m_elseImpl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_condImpl.evalSubExprsIfNeeded(nullptr, [this, done](bool) {
+      m_thenImpl.evalSubExprsIfNeeded(
+          nullptr, [this, done](bool) { m_elseImpl.evalSubExprsIfNeeded(nullptr, [done](bool) { done(true); }); });
+    });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_condImpl.cleanup();
+    m_thenImpl.cleanup();
+    m_elseImpl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const {
+    return m_condImpl.coeff(index) ? m_thenImpl.coeff(index) : m_elseImpl.coeff(index);
+  }
+
+  template <int LoadMode, bool UseTernary = TernaryPacketAccess, std::enable_if_t<!UseTernary, bool> = true>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
+    EIGEN_ALIGN_TO_BOUNDARY(sizeof(PacketReturnType)) std::remove_const_t<Scalar> arr[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (Index i = 0; i < PacketSize; ++i) {
+      arr[i] = m_condImpl.coeff(index + i) ? Scalar(-1) : Scalar(0);
+    }
+    return TernarySelectOp().template packetOp<PacketReturnType>(m_thenImpl.template packet<LoadMode>(index),
+                                                                 m_elseImpl.template packet<LoadMode>(index),
+                                                                 internal::pload<PacketReturnType>(arr));
+  }
+
+  template <int LoadMode, bool UseTernary = TernaryPacketAccess, std::enable_if_t<UseTernary, bool> = true>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
+    return TernarySelectOp().template packetOp<PacketReturnType>(m_thenImpl.template packet<LoadMode>(index),
+                                                                 m_elseImpl.template packet<LoadMode>(index),
+                                                                 m_condImpl.template packet<LoadMode>(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return m_condImpl.costPerCoeff(vectorized) +
+           m_thenImpl.costPerCoeff(vectorized).cwiseMax(m_elseImpl.costPerCoeff(vectorized));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    auto then_req = m_thenImpl.getResourceRequirements();
+    auto else_req = m_elseImpl.getResourceRequirements();
+
+    auto merged_req = internal::TensorBlockResourceRequirements::merge(then_req, else_req);
+    merged_req.cost_per_coeff = then_req.cost_per_coeff.cwiseMax(else_req.cost_per_coeff);
+
+    return internal::TensorBlockResourceRequirements::merge(m_condImpl.getResourceRequirements(), merged_req);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    // It's unsafe to pass destination buffer to underlying expressions, because
+    // output might be aliased with one of the inputs.
+    desc.DropDestinationBuffer();
+
+    return TensorBlock(m_condImpl.block(desc, scratch), m_thenImpl.block(desc, scratch),
+                       m_elseImpl.block(desc, scratch), TensorSelectOpBlockFactory());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const {
+    m_condImpl.bind(cgh);
+    m_thenImpl.bind(cgh);
+    m_elseImpl.bind(cgh);
+  }
+#endif
+ private:
+  TensorEvaluator<IfArgType, Device> m_condImpl;
+  TensorEvaluator<ThenArgType, Device> m_thenImpl;
+  TensorEvaluator<ElseArgType, Device> m_elseImpl;
+};
+
+}  // end namespace Eigen
+
+#if defined(EIGEN_USE_SYCL) && defined(SYCL_COMPILER_IS_DPCPP)
+template <typename Derived, typename Device>
+struct cl::sycl::is_device_copyable<
+    Eigen::TensorEvaluator<Derived, Device>,
+    std::enable_if_t<!std::is_trivially_copyable<Eigen::TensorEvaluator<Derived, Device>>::value>> : std::true_type {};
+#endif
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
new file mode 100644
index 0000000..4a175fd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
@@ -0,0 +1,670 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
+#define EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**
+ * Evaluating TensorBroadcastingOp via coefficient of packet path is extremely
+ * expensive. If expression has at least one broadcast op in it, and it supports
+ * block based evaluation, we always prefer it, even for the small tensors. For
+ * all other tileable ops, block evaluation overhead for small tensors (fits
+ * into L1) is too large, and we fallback on vectorized evaluation.
+ */
+
+// TODO(ezhulenev): Add specializations for all other types of Tensor ops.
+
+template <typename Expression>
+struct ExpressionHasTensorBroadcastingOp {
+  enum { value = false };
+};
+
+template <typename LhsXprType, typename RhsXprType>
+struct ExpressionHasTensorBroadcastingOp<const TensorAssignOp<LhsXprType, RhsXprType> > {
+  enum { value = ExpressionHasTensorBroadcastingOp<RhsXprType>::value };
+};
+
+template <typename UnaryOp, typename XprType>
+struct ExpressionHasTensorBroadcastingOp<const TensorCwiseUnaryOp<UnaryOp, XprType> > {
+  enum { value = ExpressionHasTensorBroadcastingOp<XprType>::value };
+};
+
+template <typename BinaryOp, typename LhsXprType, typename RhsXprType>
+struct ExpressionHasTensorBroadcastingOp<const TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> > {
+  enum {
+    value = ExpressionHasTensorBroadcastingOp<LhsXprType>::value || ExpressionHasTensorBroadcastingOp<RhsXprType>::value
+  };
+};
+
+template <typename Broadcast, typename XprType>
+struct ExpressionHasTensorBroadcastingOp<const TensorBroadcastingOp<Broadcast, XprType> > {
+  enum { value = true };
+};
+
+// -------------------------------------------------------------------------- //
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief The tensor executor class.
+ *
+ * This class is responsible for launching the evaluation of the expression on
+ * the specified computing device.
+ *
+ * Default strategy: the expression is evaluated sequentially with a single cpu
+ * thread, without vectorization and block evaluation.
+ *
+ * @tparam Vectorizable can use packet math (SSE/AVX/etc... registers and
+ *                      instructions)
+ * @tparam Tiling       can use block based tensor evaluation
+ *                      (see TensorBlock.h)
+ */
+template <typename Expression, typename Device, bool Vectorizable, TiledEvaluation Tiling>
+class TensorExecutor {
+ public:
+  typedef typename Expression::Index StorageIndex;
+
+  // Including `unsupported/Eigen/CXX11/Tensor` in different translation units
+  // with/without `EIGEN_USE_THREADS` or `EIGEN_USE_GPU` is a potential ODR
+  // violation. If this template is instantiated with a non-default device, it
+  // means that this header file was included without defining
+  // `EIGEN_USE_THREADS`, `EIGEN_USE_GPU` or `EIGEN_USE_SYCL`.
+  static_assert(std::is_same<Device, DefaultDevice>::value,
+                "Default executor instantiated with non-default device. "
+                "You must #define EIGEN_USE_THREADS, EIGEN_USE_GPU or "
+                "EIGEN_USE_SYCL before including Eigen headers.");
+
+  static EIGEN_STRONG_INLINE void run(const Expression& expr, const Device& device = DefaultDevice()) {
+    TensorEvaluator<Expression, Device> evaluator(expr, device);
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
+    if (needs_assign) {
+      const StorageIndex size = array_prod(evaluator.dimensions());
+      for (StorageIndex i = 0; i < size; ++i) {
+        evaluator.evalScalar(i);
+      }
+    }
+    evaluator.cleanup();
+  }
+};
+
+/**
+ * Default async execution strategy is not implemented. Currently it's only
+ * available for ThreadPoolDevice (see definition below).
+ */
+template <typename Expression, typename Device, typename DoneCallback, bool Vectorizable, TiledEvaluation Tiling>
+class TensorAsyncExecutor {};
+
+/**
+ * Process all the data with a single cpu thread, using vectorized instructions.
+ */
+template <typename Expression>
+class TensorExecutor<Expression, DefaultDevice, /*Vectorizable=*/true,
+                     /*Tiling=*/TiledEvaluation::Off> {
+ public:
+  typedef typename Expression::Index StorageIndex;
+
+  static EIGEN_STRONG_INLINE void run(const Expression& expr, const DefaultDevice& device = DefaultDevice()) {
+    TensorEvaluator<Expression, DefaultDevice> evaluator(expr, device);
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
+    if (needs_assign) {
+      const StorageIndex size = array_prod(evaluator.dimensions());
+      const int PacketSize =
+          unpacket_traits<typename TensorEvaluator<Expression, DefaultDevice>::PacketReturnType>::size;
+
+      // Give compiler a strong possibility to unroll the loop. But don't insist
+      // on unrolling, because if the function is expensive compiler should not
+      // unroll the loop at the expense of inlining.
+      const StorageIndex UnrolledSize = (size / (4 * PacketSize)) * 4 * PacketSize;
+      for (StorageIndex i = 0; i < UnrolledSize; i += 4 * PacketSize) {
+        for (StorageIndex j = 0; j < 4; j++) {
+          evaluator.evalPacket(i + j * PacketSize);
+        }
+      }
+      const StorageIndex VectorizedSize = (size / PacketSize) * PacketSize;
+      for (StorageIndex i = UnrolledSize; i < VectorizedSize; i += PacketSize) {
+        evaluator.evalPacket(i);
+      }
+      for (StorageIndex i = VectorizedSize; i < size; ++i) {
+        evaluator.evalScalar(i);
+      }
+    }
+    evaluator.cleanup();
+  }
+};
+
+/**
+ * Process all the data with a single cpu thread, using blocks of data. By
+ * sizing a block to fit L1 cache we get better cache performance.
+ */
+template <typename Expression, bool Vectorizable>
+class TensorExecutor<Expression, DefaultDevice, Vectorizable,
+                     /*Tiling=*/TiledEvaluation::On> {
+ public:
+  typedef typename traits<Expression>::Scalar Scalar;
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  typedef TensorEvaluator<Expression, DefaultDevice> Evaluator;
+  typedef typename traits<Expression>::Index StorageIndex;
+
+  static constexpr int NumDims = traits<Expression>::NumDimensions;
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(const Expression& expr,
+                                                        const DefaultDevice& device = DefaultDevice()) {
+    typedef TensorBlockMapper<NumDims, Evaluator::Layout, StorageIndex> TensorBlockMapper;
+
+    typedef internal::TensorBlockDescriptor<NumDims, StorageIndex> TensorBlockDesc;
+    typedef internal::TensorBlockScratchAllocator<DefaultDevice> TensorBlockScratch;
+
+    Evaluator evaluator(expr, device);
+
+    // TODO(ezhulenev): Do not use tiling for small tensors?
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
+
+    if (needs_assign) {
+      // Query expression tree for desired block size/shape.
+      const TensorBlockResourceRequirements requirements = evaluator.getResourceRequirements();
+
+      const TensorBlockMapper block_mapper(typename TensorBlockDesc::Dimensions(evaluator.dimensions()), requirements);
+
+      // Share scratch memory allocator between all blocks.
+      TensorBlockScratch scratch(device);
+
+      const StorageIndex total_block_count = block_mapper.blockCount();
+      for (StorageIndex i = 0; i < total_block_count; ++i) {
+        TensorBlockDesc desc = block_mapper.blockDescriptor(i);
+        evaluator.evalBlock(desc, scratch);
+        scratch.reset();
+      }
+    }
+    evaluator.cleanup();
+  }
+};
+
+/**
+ * Multicore strategy: the index space is partitioned and each partition is
+ * executed on a single core.
+ *
+ * (1) TensorExecutor will submit work to the ThreadPoolDevice managed thread
+ *     pool, and will block the caller thread until all tasks are finished.
+ *
+ * (2) TensorAsyncExecutor is a non-blocking version, that will submit work to
+ *     the ThreadPoolDevice managed thread pool, and will return immediately.
+ *     It will call 'done' callback after all tasks are finished.
+ */
+#ifdef EIGEN_USE_THREADS
+
+template <typename TensorBlockMapper>
+struct TensorExecutorTilingContext {
+  TensorExecutorTilingContext() = default;
+  TensorExecutorTilingContext(const TensorBlockMapper& b_mapper, const TensorOpCost& b_cost, size_t b_aligned_size)
+      : block_mapper(b_mapper), cost(b_cost), aligned_blocksize(b_aligned_size) {}
+
+  TensorBlockMapper block_mapper;  // navigate through blocks
+  TensorOpCost cost;               // cost of computing a single block
+  size_t aligned_blocksize;        // block size after memory alignment
+};
+
+// Computes a block evaluation parameters, and allocates temporary memory buffer
+// for blocks. See TensorExecutor/TensorAsyncExecutor (Tiling=On) below.
+template <typename Evaluator, typename TensorBlockMapper, bool Vectorizable>
+TensorExecutorTilingContext<TensorBlockMapper> GetTensorExecutorTilingContext(const Evaluator& evaluator) {
+  // Query expression tree for desired block size/shape.
+  TensorBlockResourceRequirements requirements = evaluator.getResourceRequirements();
+
+  // Update target block size based on cost model.
+  double taskSize = TensorCostModel<ThreadPoolDevice>::taskSize(1, requirements.cost_per_coeff);
+  requirements.size = static_cast<size_t>(1.0 / taskSize);
+
+  TensorBlockMapper block_mapper(typename TensorBlockMapper::Dimensions(evaluator.dimensions()), requirements);
+
+  size_t block_size = block_mapper.blockTotalSize();
+  const size_t align = numext::maxi(EIGEN_MAX_ALIGN_BYTES, 1);
+  const size_t aligned_blocksize =
+      align * numext::div_ceil<size_t>(block_size * sizeof(typename Evaluator::Scalar), align);
+
+  return {block_mapper, requirements.cost_per_coeff * block_size, aligned_blocksize};
+}
+
+template <typename Evaluator, typename StorageIndex, bool Vectorizable>
+struct EvalRange {
+  static void run(Evaluator* evaluator_in, const StorageIndex firstIdx, const StorageIndex lastIdx) {
+    Evaluator evaluator = *evaluator_in;
+    eigen_assert(lastIdx >= firstIdx);
+    for (StorageIndex i = firstIdx; i < lastIdx; ++i) {
+      evaluator.evalScalar(i);
+    }
+  }
+
+  static StorageIndex alignBlockSize(StorageIndex size) { return size; }
+};
+
+template <typename Evaluator, typename StorageIndex>
+struct EvalRange<Evaluator, StorageIndex, /*Vectorizable*/ true> {
+  static constexpr int PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
+
+  static void run(Evaluator* evaluator_in, const StorageIndex firstIdx, const StorageIndex lastIdx) {
+    Evaluator evaluator = *evaluator_in;
+    eigen_assert(lastIdx >= firstIdx);
+    StorageIndex i = firstIdx;
+    if (lastIdx - firstIdx >= PacketSize) {
+      eigen_assert(firstIdx % PacketSize == 0);
+      StorageIndex last_chunk_offset = lastIdx - 4 * PacketSize;
+      // Give compiler a strong possibility to unroll the loop. But don't insist
+      // on unrolling, because if the function is expensive compiler should not
+      // unroll the loop at the expense of inlining.
+      for (; i <= last_chunk_offset; i += 4 * PacketSize) {
+        for (StorageIndex j = 0; j < 4; j++) {
+          evaluator.evalPacket(i + j * PacketSize);
+        }
+      }
+      last_chunk_offset = lastIdx - PacketSize;
+      for (; i <= last_chunk_offset; i += PacketSize) {
+        evaluator.evalPacket(i);
+      }
+    }
+    for (; i < lastIdx; ++i) {
+      evaluator.evalScalar(i);
+    }
+  }
+
+  static StorageIndex alignBlockSize(StorageIndex size) {
+    // Align block size to packet size and account for unrolling in run above.
+    if (size >= 16 * PacketSize) {
+      return (size + 4 * PacketSize - 1) & ~(4 * PacketSize - 1);
+    }
+    // Aligning to 4 * PacketSize would increase block size by more than 25%.
+    return (size + PacketSize - 1) & ~(PacketSize - 1);
+  }
+};
+
+template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
+class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, Tiling> {
+ public:
+  typedef typename Expression::Index StorageIndex;
+
+  static EIGEN_STRONG_INLINE void run(const Expression& expr, const ThreadPoolDevice& device) {
+    typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
+    typedef EvalRange<Evaluator, StorageIndex, Vectorizable> EvalRange;
+
+    Evaluator evaluator(expr, device);
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
+    if (needs_assign) {
+      const StorageIndex size = array_prod(evaluator.dimensions());
+      device.parallelFor(
+          size, evaluator.costPerCoeff(Vectorizable), EvalRange::alignBlockSize,
+          [&evaluator](StorageIndex firstIdx, StorageIndex lastIdx) { EvalRange::run(&evaluator, firstIdx, lastIdx); });
+    }
+    evaluator.cleanup();
+  }
+};
+
+template <typename Expression, bool Vectorizable>
+class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable,
+                     /*Tiling=*/TiledEvaluation::On> {
+ public:
+  typedef typename traits<Expression>::Index IndexType;
+  typedef typename traits<Expression>::Scalar Scalar;
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  static constexpr int NumDims = traits<Expression>::NumDimensions;
+
+  typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
+  typedef TensorBlockMapper<NumDims, Evaluator::Layout, IndexType> BlockMapper;
+  typedef TensorExecutorTilingContext<BlockMapper> TilingContext;
+
+  typedef internal::TensorBlockDescriptor<NumDims, IndexType> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<ThreadPoolDevice> TensorBlockScratch;
+
+  static EIGEN_STRONG_INLINE void run(const Expression& expr, const ThreadPoolDevice& device) {
+    Evaluator evaluator(expr, device);
+
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
+    if (needs_assign) {
+      const TilingContext tiling =
+          internal::GetTensorExecutorTilingContext<Evaluator, BlockMapper, Vectorizable>(evaluator);
+
+      auto eval_block = [&device, &evaluator, &tiling](IndexType firstBlockIdx, IndexType lastBlockIdx) {
+        TensorBlockScratch scratch(device);
+
+        for (IndexType block_idx = firstBlockIdx; block_idx < lastBlockIdx; ++block_idx) {
+          TensorBlockDesc desc = tiling.block_mapper.blockDescriptor(block_idx);
+          evaluator.evalBlock(desc, scratch);
+          scratch.reset();
+        }
+      };
+
+      // Evaluate small expressions directly as a single block.
+      if (tiling.block_mapper.blockCount() == 1) {
+        TensorBlockScratch scratch(device);
+        TensorBlockDesc desc(0, tiling.block_mapper.blockDimensions());
+        evaluator.evalBlock(desc, scratch);
+      } else {
+        device.parallelFor(tiling.block_mapper.blockCount(), tiling.cost, std::move(eval_block));
+      }
+    }
+    evaluator.cleanup();
+  }
+};
+
+template <typename Expression, typename DoneCallback, bool Vectorizable, TiledEvaluation Tiling>
+class TensorAsyncExecutor<Expression, ThreadPoolDevice, DoneCallback, Vectorizable, Tiling> {
+ public:
+  typedef typename Expression::Index StorageIndex;
+  typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
+
+  static EIGEN_STRONG_INLINE void runAsync(const Expression& expr, const ThreadPoolDevice& device, DoneCallback done) {
+    TensorAsyncExecutorContext* const ctx = new TensorAsyncExecutorContext(expr, device, std::move(done));
+
+    const auto on_eval_subexprs = [ctx, &device](bool need_assign) -> void {
+      if (!need_assign) {
+        delete ctx;
+        return;
+      }
+
+      typedef EvalRange<Evaluator, StorageIndex, Vectorizable> EvalRange;
+      const StorageIndex size = array_prod(ctx->evaluator.dimensions());
+      device.parallelForAsync(
+          size, ctx->evaluator.costPerCoeff(Vectorizable), EvalRange::alignBlockSize,
+          [ctx](StorageIndex firstIdx, StorageIndex lastIdx) { EvalRange::run(&ctx->evaluator, firstIdx, lastIdx); },
+          [ctx]() { delete ctx; });
+    };
+
+    ctx->evaluator.evalSubExprsIfNeededAsync(nullptr, on_eval_subexprs);
+  }
+
+ private:
+  struct TensorAsyncExecutorContext {
+    TensorAsyncExecutorContext(const Expression& expr, const ThreadPoolDevice& thread_pool, DoneCallback done)
+        : evaluator(expr, thread_pool), on_done(std::move(done)) {}
+
+    ~TensorAsyncExecutorContext() {
+      evaluator.cleanup();
+      on_done();
+    }
+
+    Evaluator evaluator;
+
+   private:
+    DoneCallback on_done;
+  };
+};
+
+template <typename Expression, typename DoneCallback, bool Vectorizable>
+class TensorAsyncExecutor<Expression, ThreadPoolDevice, DoneCallback, Vectorizable, /*Tileable*/ TiledEvaluation::On> {
+ public:
+  typedef typename traits<Expression>::Index IndexType;
+  typedef typename traits<Expression>::Scalar Scalar;
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  static constexpr int NumDims = traits<Expression>::NumDimensions;
+
+  typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
+  typedef TensorBlockMapper<NumDims, Evaluator::Layout, IndexType> BlockMapper;
+  typedef TensorExecutorTilingContext<BlockMapper> TilingContext;
+
+  typedef internal::TensorBlockDescriptor<NumDims, IndexType> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<ThreadPoolDevice> TensorBlockScratch;
+
+  static EIGEN_STRONG_INLINE void runAsync(const Expression& expr, const ThreadPoolDevice& device, DoneCallback done) {
+    TensorAsyncExecutorContext* const ctx = new TensorAsyncExecutorContext(expr, device, std::move(done));
+
+    const auto on_eval_subexprs = [ctx](bool need_assign) -> void {
+      if (!need_assign) {
+        delete ctx;
+        return;
+      }
+
+      ctx->tiling = internal::GetTensorExecutorTilingContext<Evaluator, BlockMapper, Vectorizable>(ctx->evaluator);
+
+      auto eval_block = [ctx](IndexType firstBlockIdx, IndexType lastBlockIdx) {
+        TensorBlockScratch scratch(ctx->device);
+
+        for (IndexType block_idx = firstBlockIdx; block_idx < lastBlockIdx; ++block_idx) {
+          TensorBlockDesc desc = ctx->tiling.block_mapper.blockDescriptor(block_idx);
+          ctx->evaluator.evalBlock(desc, scratch);
+          scratch.reset();
+        }
+      };
+
+      // Evaluate small expressions directly as a single block.
+      if (ctx->tiling.block_mapper.blockCount() == 1) {
+        TensorBlockScratch scratch(ctx->device);
+        TensorBlockDesc desc(0, ctx->tiling.block_mapper.blockDimensions());
+        ctx->evaluator.evalBlock(desc, scratch);
+        delete ctx;
+      } else {
+        ctx->device.parallelForAsync(ctx->tiling.block_mapper.blockCount(), ctx->tiling.cost, eval_block,
+                                     [ctx]() { delete ctx; });
+      }
+    };
+
+    ctx->evaluator.evalSubExprsIfNeededAsync(nullptr, on_eval_subexprs);
+  }
+
+ private:
+  struct TensorAsyncExecutorContext {
+    TensorAsyncExecutorContext(const Expression& expr, const ThreadPoolDevice& thread_pool, DoneCallback done)
+        : device(thread_pool), evaluator(expr, thread_pool), on_done(std::move(done)) {}
+
+    ~TensorAsyncExecutorContext() {
+      evaluator.cleanup();
+      on_done();
+    }
+
+    const ThreadPoolDevice& device;
+    Evaluator evaluator;
+    TilingContext tiling;
+
+   private:
+    DoneCallback on_done;
+  };
+};
+
+#endif  // EIGEN_USE_THREADS
+
+// GPU: the evaluation of the expression is offloaded to a GPU.
+#if defined(EIGEN_USE_GPU)
+
+template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
+class TensorExecutor<Expression, GpuDevice, Vectorizable, Tiling> {
+ public:
+  typedef typename Expression::Index StorageIndex;
+  static void run(const Expression& expr, const GpuDevice& device);
+};
+
+#if defined(EIGEN_GPUCC)
+// Returns 1 if lhs + rhs would overflow, -1 if it would underflow, otherwise 0.
+template <typename Index>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE int sum_will_overflow(Index lhs, Index rhs) {
+  const Index highest = NumTraits<Index>::highest();
+  const Index lowest = NumTraits<Index>::lowest();
+  if (lhs > 0 && rhs > 0) {
+    return lhs > highest - rhs ? 1 : 0;
+  } else if (lhs < 0 && rhs < 0) {
+    return lhs < lowest - rhs ? -1 : 0;
+  } else {
+    return 0;
+  }
+}
+
+// Returns lhs + rhs, saturating to the highest/lowest representable value on
+// overflow/underflow respectively.
+template <typename Index>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index saturate_add(Index lhs, Index rhs) {
+  const Index highest = NumTraits<Index>::highest();
+  const Index lowest = NumTraits<Index>::lowest();
+  int overflow = sum_will_overflow(lhs, rhs);
+  return overflow == 1 ? highest : overflow == -1 ? lowest : lhs + rhs;
+}
+
+// A functor that adds step_size to a given index, saturating to avoid
+// overflow/underflow. If overflow/underflow is not possible, regular addition
+// is used (for efficiency).
+template <typename Index>
+struct SafeStep {
+  // lastIdx is one past the end of the possible indexes.
+  // step_size is the value that will be added to the given index when the
+  // functor is called.
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SafeStep(Index lastIdx, Index step_size)
+      : can_overflow_(sum_will_overflow(lastIdx, step_size)), step_size_(step_size) {}
+
+  // Adds step_size to index, saturating on overflow (if overflow is possible).
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index operator()(Index index) const {
+    return can_overflow_ ? saturate_add(index, step_size_) : index + step_size_;
+  }
+
+ private:
+  const bool can_overflow_;
+  const Index step_size_;
+};
+
+template <typename Evaluator, typename StorageIndex, bool Vectorizable>
+struct EigenMetaKernelEval {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void run(Evaluator& eval, StorageIndex firstIdx, StorageIndex lastIdx,
+                                                        StorageIndex step_size) {
+    SafeStep<StorageIndex> safe_step(lastIdx, step_size);
+    for (StorageIndex i = firstIdx; i < lastIdx; i = safe_step(i)) {
+      eval.evalScalar(i);
+    }
+  }
+};
+
+template <typename Evaluator, typename StorageIndex>
+struct EigenMetaKernelEval<Evaluator, StorageIndex, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void run(Evaluator& eval, StorageIndex firstIdx, StorageIndex lastIdx,
+                                                        StorageIndex step_size) {
+    const StorageIndex PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
+    const StorageIndex vectorized_size = (lastIdx / PacketSize) * PacketSize;
+    const StorageIndex vectorized_step_size = step_size * PacketSize;
+
+    SafeStep<StorageIndex> safe_vectorized_step(vectorized_size, vectorized_step_size);
+    // Use the vector path
+    for (StorageIndex i = firstIdx * PacketSize; i < vectorized_size; i = safe_vectorized_step(i)) {
+      eval.evalPacket(i);
+    }
+    SafeStep<StorageIndex> safe_step(lastIdx, step_size);
+    for (StorageIndex i = saturate_add(vectorized_size, firstIdx); i < lastIdx; i = safe_step(i)) {
+      eval.evalScalar(i);
+    }
+  }
+};
+
+template <typename Evaluator, typename StorageIndex>
+__global__ void __launch_bounds__(1024) EigenMetaKernel(Evaluator eval, StorageIndex size) {
+  const StorageIndex first_index = blockIdx.x * blockDim.x + threadIdx.x;
+  const StorageIndex step_size = blockDim.x * gridDim.x;
+
+  const bool vectorizable = Evaluator::PacketAccess & Evaluator::IsAligned;
+  EigenMetaKernelEval<Evaluator, StorageIndex, vectorizable>::run(eval, first_index, size, step_size);
+}
+
+/*static*/
+template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
+EIGEN_STRONG_INLINE void TensorExecutor<Expression, GpuDevice, Vectorizable, Tiling>::run(const Expression& expr,
+                                                                                          const GpuDevice& device) {
+  TensorEvaluator<Expression, GpuDevice> evaluator(expr, device);
+  const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
+  if (needs_assign) {
+    const int block_size = device.maxGpuThreadsPerBlock();
+    const int max_blocks = static_cast<int>(
+        numext::mini<int64_t>(device.getNumGpuMultiProcessors() * device.maxGpuThreadsPerMultiProcessor(),
+                              NumTraits<StorageIndex>::highest()) /
+        block_size);
+    const StorageIndex size = array_prod(evaluator.dimensions());
+    // Create at least one block to ensure we don't crash with tensors of size 0.
+    const int num_blocks = numext::maxi<int>(
+        numext::mini<int>(max_blocks, static_cast<int>(numext::div_ceil<StorageIndex>(size, block_size))), 1);
+
+    LAUNCH_GPU_KERNEL((EigenMetaKernel<TensorEvaluator<Expression, GpuDevice>, StorageIndex>), num_blocks, block_size,
+                      0, device, evaluator, size);
+  }
+  evaluator.cleanup();
+}
+
+#endif  // EIGEN_GPUCC
+#endif  // EIGEN_USE_GPU
+
+// SYCL Executor policy
+#ifdef EIGEN_USE_SYCL
+
+template <typename Evaluator>
+struct ExecExprFunctorKernel {
+  typedef typename Evaluator::Index Index;
+  Evaluator evaluator;
+  const Index range;
+  template <typename Scratch>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ExecExprFunctorKernel(const Scratch, Evaluator evaluator_, const Index range_)
+      : evaluator(evaluator_), range(range_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void operator()(cl::sycl::nd_item<1> itemID) const { compute(itemID); }
+  template <bool is_vec = Evaluator::PacketAccess>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::enable_if_t<!is_vec> compute(const cl::sycl::nd_item<1>& itemID) const {
+    Index gId = static_cast<Index>(itemID.get_global_linear_id());
+    Index total_threads = itemID.get_global_range(0);
+
+    for (Index i = gId; i < range; i += total_threads) {
+      evaluator.evalScalar(i);
+    }
+  }
+  template <bool is_vec = Evaluator::PacketAccess>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::enable_if_t<is_vec> compute(const cl::sycl::nd_item<1>& itemID) const {
+    const Index vectorizedRange = (range / Evaluator::PacketSize) * Evaluator::PacketSize;
+    Index gId = static_cast<Index>(itemID.get_global_linear_id());
+    const Index step = Evaluator::PacketSize * itemID.get_global_range(0);
+    const Index start = Evaluator::PacketSize * gId;
+    for (Index i = start; i < vectorizedRange; i += step) {
+      evaluator.evalPacket(i);
+    }
+    gId += vectorizedRange;
+    for (Index i = gId; i < range; i += itemID.get_global_range(0)) {
+      evaluator.evalScalar(i);
+    }
+  }
+};
+
+template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
+class TensorExecutor<Expression, Eigen::SyclDevice, Vectorizable, Tiling> {
+ public:
+  typedef typename Expression::Index Index;
+  static EIGEN_STRONG_INLINE void run(const Expression& expr, const Eigen::SyclDevice& dev) {
+    typedef Eigen::TensorEvaluator<Expression, Eigen::SyclDevice> Evaluator;
+    Evaluator evaluator(expr, dev);
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
+    if (needs_assign) {
+      Index range, GRange, tileSize;
+      Index total_size = ::Eigen::internal::array_prod(evaluator.dimensions());
+      total_size = (total_size == 0) ? 1 : total_size;
+      const int PacketSize = Eigen::PacketType<typename Evaluator::CoeffReturnType, Eigen::SyclDevice>::size;
+      Index vectorizable_threads = static_cast<Index>(total_size / PacketSize);
+      dev.parallel_for_setup(vectorizable_threads, tileSize, range, GRange);
+      range = total_size;
+
+      dev.template nullary_kernel_launcher<typename Evaluator::CoeffReturnType, ExecExprFunctorKernel<Evaluator> >(
+             evaluator, cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), Index(1),
+             range)
+          .wait();
+    }
+    evaluator.cleanup();
+  }
+};
+
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h
new file mode 100644
index 0000000..108bc92
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h
@@ -0,0 +1,338 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_EXPR_H
+#define EIGEN_CXX11_TENSOR_TENSOR_EXPR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename NullaryOp, typename XprType>
+struct traits<TensorCwiseNullaryOp<NullaryOp, XprType> > : traits<XprType> {
+  typedef traits<XprType> XprTraits;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::Nested XprTypeNested;
+  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+  enum { Flags = 0 };
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor nullary expression.
+ *
+ * The TensorCwiseNullaryOp class applies a nullary operator to an expression.
+ * This is typically used to generate constants.
+ */
+template <typename NullaryOp, typename XprType>
+class TensorCwiseNullaryOp : public TensorBase<TensorCwiseNullaryOp<NullaryOp, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef TensorCwiseNullaryOp<NullaryOp, XprType> Nested;
+  typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseNullaryOp(const XprType& xpr, const NullaryOp& func = NullaryOp())
+      : m_xpr(xpr), m_functor(func) {}
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& nestedExpression() const { return m_xpr; }
+
+  EIGEN_DEVICE_FUNC const NullaryOp& functor() const { return m_functor; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const NullaryOp m_functor;
+};
+
+namespace internal {
+template <typename UnaryOp, typename XprType>
+struct traits<TensorCwiseUnaryOp<UnaryOp, XprType> > : traits<XprType> {
+  // TODO(phli): Add InputScalar, InputPacket.  Check references to
+  // current Scalar/Packet to see if the intent is Input or Output.
+  typedef typename result_of<UnaryOp(typename XprType::Scalar)>::type Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprType::Nested XprTypeNested;
+  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename TypeConversion<Scalar, typename XprTraits::PointerType>::type PointerType;
+};
+
+template <typename UnaryOp, typename XprType>
+struct eval<TensorCwiseUnaryOp<UnaryOp, XprType>, Eigen::Dense> {
+  typedef const TensorCwiseUnaryOp<UnaryOp, XprType>& type;
+};
+
+template <typename UnaryOp, typename XprType>
+struct nested<TensorCwiseUnaryOp<UnaryOp, XprType>, 1, typename eval<TensorCwiseUnaryOp<UnaryOp, XprType> >::type> {
+  typedef TensorCwiseUnaryOp<UnaryOp, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor unary expression.
+ *
+ * The TensorCwiseUnaryOp class represents an expression where a unary operator
+ * (e.g. cwiseSqrt) is applied to an expression.
+ */
+template <typename UnaryOp, typename XprType>
+class TensorCwiseUnaryOp : public TensorBase<TensorCwiseUnaryOp<UnaryOp, XprType>, ReadOnlyAccessors> {
+ public:
+  // TODO(phli): Add InputScalar, InputPacket.  Check references to
+  // current Scalar/Packet to see if the intent is Input or Output.
+  typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef Scalar CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorCwiseUnaryOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
+      : m_xpr(xpr), m_functor(func) {}
+
+  EIGEN_DEVICE_FUNC const UnaryOp& functor() const { return m_functor; }
+
+  /** \returns the nested expression */
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& nestedExpression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const UnaryOp m_functor;
+};
+
+namespace internal {
+template <typename BinaryOp, typename LhsXprType, typename RhsXprType>
+struct traits<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> > {
+  // Type promotion to handle the case where the types of the lhs and the rhs
+  // are different.
+  // TODO(phli): Add Lhs/RhsScalar, Lhs/RhsPacket.  Check references to
+  // current Scalar/Packet to see if the intent is Inputs or Output.
+  typedef typename result_of<BinaryOp(typename LhsXprType::Scalar, typename RhsXprType::Scalar)>::type Scalar;
+  typedef traits<LhsXprType> XprTraits;
+  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
+                                        typename traits<RhsXprType>::StorageKind>::ret StorageKind;
+  typedef
+      typename promote_index_type<typename traits<LhsXprType>::Index, typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef std::remove_reference_t<LhsNested> LhsNested_;
+  typedef std::remove_reference_t<RhsNested> RhsNested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename TypeConversion<Scalar,
+                                  std::conditional_t<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
+                                                     typename traits<LhsXprType>::PointerType,
+                                                     typename traits<RhsXprType>::PointerType> >::type PointerType;
+  enum { Flags = 0 };
+};
+
+template <typename BinaryOp, typename LhsXprType, typename RhsXprType>
+struct eval<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, Eigen::Dense> {
+  typedef const TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>& type;
+};
+
+template <typename BinaryOp, typename LhsXprType, typename RhsXprType>
+struct nested<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, 1,
+              typename eval<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> >::type> {
+  typedef TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor binary expression.
+ *
+ * The TensorCwiseBinaryOp class represents an expression where a binary
+ * operator (e.g. addition) is applied to a lhs and a rhs expression.
+ */
+template <typename BinaryOp, typename LhsXprType, typename RhsXprType>
+class TensorCwiseBinaryOp
+    : public TensorBase<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, ReadOnlyAccessors> {
+ public:
+  // TODO(phli): Add Lhs/RhsScalar, Lhs/RhsPacket.  Check references to
+  // current Scalar/Packet to see if the intent is Inputs or Output.
+  typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef Scalar CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorCwiseBinaryOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs,
+                                                            const BinaryOp& func = BinaryOp())
+      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_functor(func) {}
+
+  EIGEN_DEVICE_FUNC const BinaryOp& functor() const { return m_functor; }
+
+  /** \returns the nested expressions */
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename LhsXprType::Nested>& lhsExpression() const {
+    return m_lhs_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename RhsXprType::Nested>& rhsExpression() const {
+    return m_rhs_xpr;
+  }
+
+ protected:
+  typename LhsXprType::Nested m_lhs_xpr;
+  typename RhsXprType::Nested m_rhs_xpr;
+  const BinaryOp m_functor;
+};
+
+namespace internal {
+template <typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
+struct traits<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> > {
+  // Type promotion to handle the case where the types of the args are different.
+  typedef typename result_of<TernaryOp(typename Arg1XprType::Scalar, typename Arg2XprType::Scalar,
+                                       typename Arg3XprType::Scalar)>::type Scalar;
+  typedef traits<Arg1XprType> XprTraits;
+  typedef typename traits<Arg1XprType>::StorageKind StorageKind;
+  typedef typename traits<Arg1XprType>::Index Index;
+  typedef typename Arg1XprType::Nested Arg1Nested;
+  typedef typename Arg2XprType::Nested Arg2Nested;
+  typedef typename Arg3XprType::Nested Arg3Nested;
+  typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
+  typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
+  typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename TypeConversion<Scalar,
+                                  std::conditional_t<Pointer_type_promotion<typename Arg2XprType::Scalar, Scalar>::val,
+                                                     typename traits<Arg2XprType>::PointerType,
+                                                     typename traits<Arg3XprType>::PointerType> >::type PointerType;
+  enum { Flags = 0 };
+};
+
+template <typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
+struct eval<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, Eigen::Dense> {
+  typedef const TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>& type;
+};
+
+template <typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
+struct nested<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, 1,
+              typename eval<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> >::type> {
+  typedef TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> type;
+};
+
+}  // end namespace internal
+
+template <typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
+class TensorCwiseTernaryOp
+    : public TensorBase<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef Scalar CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorCwiseTernaryOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseTernaryOp(const Arg1XprType& arg1, const Arg2XprType& arg2,
+                                                             const Arg3XprType& arg3,
+                                                             const TernaryOp& func = TernaryOp())
+      : m_arg1_xpr(arg1), m_arg2_xpr(arg2), m_arg3_xpr(arg3), m_functor(func) {}
+
+  EIGEN_DEVICE_FUNC const TernaryOp& functor() const { return m_functor; }
+
+  /** \returns the nested expressions */
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename Arg1XprType::Nested>& arg1Expression() const {
+    return m_arg1_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename Arg2XprType::Nested>& arg2Expression() const {
+    return m_arg2_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename Arg3XprType::Nested>& arg3Expression() const {
+    return m_arg3_xpr;
+  }
+
+ protected:
+  typename Arg1XprType::Nested m_arg1_xpr;
+  typename Arg2XprType::Nested m_arg2_xpr;
+  typename Arg3XprType::Nested m_arg3_xpr;
+  const TernaryOp m_functor;
+};
+
+namespace internal {
+template <typename IfXprType, typename ThenXprType, typename ElseXprType>
+struct traits<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> > : traits<ThenXprType> {
+  typedef typename traits<ThenXprType>::Scalar Scalar;
+  typedef traits<ThenXprType> XprTraits;
+  typedef typename promote_storage_type<typename traits<ThenXprType>::StorageKind,
+                                        typename traits<ElseXprType>::StorageKind>::ret StorageKind;
+  typedef
+      typename promote_index_type<typename traits<ElseXprType>::Index, typename traits<ThenXprType>::Index>::type Index;
+  typedef typename IfXprType::Nested IfNested;
+  typedef typename ThenXprType::Nested ThenNested;
+  typedef typename ElseXprType::Nested ElseNested;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef std::conditional_t<Pointer_type_promotion<typename ThenXprType::Scalar, Scalar>::val,
+                             typename traits<ThenXprType>::PointerType, typename traits<ElseXprType>::PointerType>
+      PointerType;
+};
+
+template <typename IfXprType, typename ThenXprType, typename ElseXprType>
+struct eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, Eigen::Dense> {
+  typedef const TensorSelectOp<IfXprType, ThenXprType, ElseXprType>& type;
+};
+
+template <typename IfXprType, typename ThenXprType, typename ElseXprType>
+struct nested<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, 1,
+              typename eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >::type> {
+  typedef TensorSelectOp<IfXprType, ThenXprType, ElseXprType> type;
+};
+
+}  // end namespace internal
+
+template <typename IfXprType, typename ThenXprType, typename ElseXprType>
+class TensorSelectOp : public TensorBase<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorSelectOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename internal::promote_storage_type<typename ThenXprType::CoeffReturnType,
+                                                  typename ElseXprType::CoeffReturnType>::ret CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorSelectOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorSelectOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorSelectOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC TensorSelectOp(const IfXprType& a_condition, const ThenXprType& a_then, const ElseXprType& a_else)
+      : m_condition(a_condition), m_then(a_then), m_else(a_else) {}
+
+  EIGEN_DEVICE_FUNC const IfXprType& ifExpression() const { return m_condition; }
+
+  EIGEN_DEVICE_FUNC const ThenXprType& thenExpression() const { return m_then; }
+
+  EIGEN_DEVICE_FUNC const ElseXprType& elseExpression() const { return m_else; }
+
+ protected:
+  typename IfXprType::Nested m_condition;
+  typename ThenXprType::Nested m_then;
+  typename ElseXprType::Nested m_else;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_EXPR_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
new file mode 100644
index 0000000..141b416
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
@@ -0,0 +1,650 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Jianwei Cui <thucjw@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_FFT_H
+#define EIGEN_CXX11_TENSOR_TENSOR_FFT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <bool IsReal>
+struct MakeComplex {
+  template <typename T>
+  EIGEN_DEVICE_FUNC T operator()(const T& val) const {
+    return val;
+  }
+};
+
+template <>
+struct MakeComplex<true> {
+  template <typename T>
+  EIGEN_DEVICE_FUNC internal::make_complex_t<T> operator()(const T& val) const {
+    return internal::make_complex_t<T>(val, T(0));
+  }
+};
+
+template <int ResultType>
+struct PartOf {
+  template <typename T>
+  T operator()(const T& val) const {
+    return val;
+  }
+};
+
+template <>
+struct PartOf<RealPart> {
+  template <typename T, typename EnableIf = std::enable_if_t<NumTraits<T>::IsComplex>>
+  typename NumTraits<T>::Real operator()(const T& val) const {
+    return Eigen::numext::real(val);
+  }
+};
+
+template <>
+struct PartOf<ImagPart> {
+  template <typename T, typename EnableIf = std::enable_if_t<NumTraits<T>::IsComplex>>
+  typename NumTraits<T>::Real operator()(const T& val) const {
+    return Eigen::numext::imag(val);
+  }
+};
+
+namespace internal {
+template <typename FFT, typename XprType, int FFTResultType, int FFTDir>
+struct traits<TensorFFTOp<FFT, XprType, FFTResultType, FFTDir>> : public traits<XprType> {
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef make_complex_t<Scalar> ComplexScalar;
+  typedef typename XprTraits::Scalar InputScalar;
+  typedef std::conditional_t<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>
+      OutputScalar;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename traits<XprType>::PointerType PointerType;
+};
+
+template <typename FFT, typename XprType, int FFTResultType, int FFTDirection>
+struct eval<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>, Eigen::Dense> {
+  typedef const TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>& type;
+};
+
+template <typename FFT, typename XprType, int FFTResultType, int FFTDirection>
+struct nested<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>, 1,
+              typename eval<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>>::type> {
+  typedef TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor FFT class.
+ *
+ * TODO:
+ * Vectorize the Cooley Tukey and the Bluestein algorithm
+ * Add support for multithreaded evaluation
+ * Improve the performance on GPU
+ */
+template <typename FFT, typename XprType, int FFTResultType, int FFTDir>
+class TensorFFTOp : public TensorBase<TensorFFTOp<FFT, XprType, FFTResultType, FFTDir>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorFFTOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+  typedef std::conditional_t<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>
+      OutputScalar;
+  typedef OutputScalar CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorFFTOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorFFTOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorFFTOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFFTOp(const XprType& expr, const FFT& fft) : m_xpr(expr), m_fft(fft) {}
+
+  EIGEN_DEVICE_FUNC const FFT& fft() const { return m_fft; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const FFT m_fft;
+};
+
+// Eval as rvalue
+template <typename FFT, typename ArgType, typename Device, int FFTResultType, int FFTDir>
+struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, Device> {
+  typedef TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
+  typedef internal::traits<XprType> XprTraits;
+  typedef typename XprTraits::Scalar InputScalar;
+  typedef std::conditional_t<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>
+      OutputScalar;
+  typedef OutputScalar CoeffReturnType;
+  typedef typename PacketType<OutputScalar, Device>::type PacketReturnType;
+  static constexpr int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = true,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_fft(op.fft()), m_impl(op.expression(), device), m_data(NULL), m_device(device) {
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    for (int i = 0; i < NumDims; ++i) {
+      eigen_assert(input_dims[i] > 0);
+      m_dimensions[i] = input_dims[i];
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_strides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
+      }
+    } else {
+      m_strides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
+      }
+    }
+    m_size = m_dimensions.TotalSize();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    if (data) {
+      evalToBuf(data);
+      return false;
+    } else {
+      m_data = (EvaluatorPointerType)m_device.get(
+          (CoeffReturnType*)(m_device.allocate_temp(sizeof(CoeffReturnType) * m_size)));
+      evalToBuf(m_data);
+      return true;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    if (m_data) {
+      m_device.deallocate(m_data);
+      m_data = NULL;
+    }
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const { return m_data[index]; }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_data + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
+
+ private:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalToBuf(EvaluatorPointerType data) {
+    const bool write_to_out = internal::is_same<OutputScalar, ComplexScalar>::value;
+    ComplexScalar* buf =
+        write_to_out ? (ComplexScalar*)data : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * m_size);
+
+    for (Index i = 0; i < m_size; ++i) {
+      buf[i] = MakeComplex<internal::is_same<InputScalar, RealScalar>::value>()(m_impl.coeff(i));
+    }
+
+    for (size_t i = 0; i < m_fft.size(); ++i) {
+      Index dim = m_fft[i];
+      eigen_assert(dim >= 0 && dim < NumDims);
+      Index line_len = m_dimensions[dim];
+      eigen_assert(line_len >= 1);
+      ComplexScalar* line_buf = (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * line_len);
+      const bool is_power_of_two = isPowerOfTwo(line_len);
+      const Index good_composite = is_power_of_two ? 0 : findGoodComposite(line_len);
+      const Index log_len = is_power_of_two ? getLog2(line_len) : getLog2(good_composite);
+
+      ComplexScalar* a =
+          is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * good_composite);
+      ComplexScalar* b =
+          is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * good_composite);
+      ComplexScalar* pos_j_base_powered =
+          is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * (line_len + 1));
+      if (!is_power_of_two) {
+        // Compute twiddle factors
+        //   t_n = exp(sqrt(-1) * pi * n^2 / line_len)
+        // for n = 0, 1,..., line_len-1.
+        // For n > 2 we use the recurrence t_n = t_{n-1}^2 / t_{n-2} * t_1^2
+
+        // The recurrence is correct in exact arithmetic, but causes
+        // numerical issues for large transforms, especially in
+        // single-precision floating point. Use direct computation instead.
+        // TODO(rmlarsen): Find a way to use Eigen's vectorized sin
+        // and cosine functions here.
+        for (int j = 0; j < line_len + 1; ++j) {
+          double arg = ((EIGEN_PI * j) * j) / line_len;
+          std::complex<double> tmp(numext::cos(arg), numext::sin(arg));
+          pos_j_base_powered[j] = static_cast<ComplexScalar>(tmp);
+        }
+      }
+
+      for (Index partial_index = 0; partial_index < m_size / line_len; ++partial_index) {
+        const Index base_offset = getBaseOffsetFromIndex(partial_index, dim);
+
+        // get data into line_buf
+        const Index stride = m_strides[dim];
+        if (stride == 1) {
+          m_device.memcpy(line_buf, &buf[base_offset], line_len * sizeof(ComplexScalar));
+        } else {
+          Index offset = base_offset;
+          for (int j = 0; j < line_len; ++j, offset += stride) {
+            line_buf[j] = buf[offset];
+          }
+        }
+
+        // process the line
+        if (is_power_of_two) {
+          processDataLineCooleyTukey(line_buf, line_len, log_len);
+        } else {
+          processDataLineBluestein(line_buf, line_len, good_composite, log_len, a, b, pos_j_base_powered);
+        }
+
+        // write back
+        if (FFTDir == FFT_FORWARD && stride == 1) {
+          m_device.memcpy(&buf[base_offset], line_buf, line_len * sizeof(ComplexScalar));
+        } else {
+          Index offset = base_offset;
+          const ComplexScalar div_factor = ComplexScalar(1.0 / line_len, 0);
+          for (int j = 0; j < line_len; ++j, offset += stride) {
+            buf[offset] = (FFTDir == FFT_FORWARD) ? line_buf[j] : line_buf[j] * div_factor;
+          }
+        }
+      }
+      m_device.deallocate(line_buf);
+      if (!is_power_of_two) {
+        m_device.deallocate(a);
+        m_device.deallocate(b);
+        m_device.deallocate(pos_j_base_powered);
+      }
+    }
+
+    if (!write_to_out) {
+      for (Index i = 0; i < m_size; ++i) {
+        data[i] = PartOf<FFTResultType>()(buf[i]);
+      }
+      m_device.deallocate(buf);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static bool isPowerOfTwo(Index x) {
+    eigen_assert(x > 0);
+    return !(x & (x - 1));
+  }
+
+  // The composite number for padding, used in Bluestein's FFT algorithm
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Index findGoodComposite(Index n) {
+    Index i = 2;
+    while (i < 2 * n - 1) i *= 2;
+    return i;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Index getLog2(Index m) {
+    Index log2m = 0;
+    while (m >>= 1) log2m++;
+    return log2m;
+  }
+
+  // Call Cooley Tukey algorithm directly, data length must be power of 2
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void processDataLineCooleyTukey(ComplexScalar* line_buf, Index line_len,
+                                                                        Index log_len) {
+    eigen_assert(isPowerOfTwo(line_len));
+    scramble_FFT(line_buf, line_len);
+    compute_1D_Butterfly<FFTDir>(line_buf, line_len, log_len);
+  }
+
+  // Call Bluestein's FFT algorithm, m is a good composite number greater than (2 * n - 1), used as the padding length
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void processDataLineBluestein(ComplexScalar* line_buf, Index line_len,
+                                                                      Index good_composite, Index log_len,
+                                                                      ComplexScalar* a, ComplexScalar* b,
+                                                                      const ComplexScalar* pos_j_base_powered) {
+    Index n = line_len;
+    Index m = good_composite;
+    ComplexScalar* data = line_buf;
+
+    for (Index i = 0; i < n; ++i) {
+      if (FFTDir == FFT_FORWARD) {
+        a[i] = data[i] * numext::conj(pos_j_base_powered[i]);
+      } else {
+        a[i] = data[i] * pos_j_base_powered[i];
+      }
+    }
+    for (Index i = n; i < m; ++i) {
+      a[i] = ComplexScalar(0, 0);
+    }
+
+    for (Index i = 0; i < n; ++i) {
+      if (FFTDir == FFT_FORWARD) {
+        b[i] = pos_j_base_powered[i];
+      } else {
+        b[i] = numext::conj(pos_j_base_powered[i]);
+      }
+    }
+    for (Index i = n; i < m - n; ++i) {
+      b[i] = ComplexScalar(0, 0);
+    }
+    for (Index i = m - n; i < m; ++i) {
+      if (FFTDir == FFT_FORWARD) {
+        b[i] = pos_j_base_powered[m - i];
+      } else {
+        b[i] = numext::conj(pos_j_base_powered[m - i]);
+      }
+    }
+
+    scramble_FFT(a, m);
+    compute_1D_Butterfly<FFT_FORWARD>(a, m, log_len);
+
+    scramble_FFT(b, m);
+    compute_1D_Butterfly<FFT_FORWARD>(b, m, log_len);
+
+    for (Index i = 0; i < m; ++i) {
+      a[i] *= b[i];
+    }
+
+    scramble_FFT(a, m);
+    compute_1D_Butterfly<FFT_REVERSE>(a, m, log_len);
+
+    // Do the scaling after ifft
+    for (Index i = 0; i < m; ++i) {
+      a[i] /= m;
+    }
+
+    for (Index i = 0; i < n; ++i) {
+      if (FFTDir == FFT_FORWARD) {
+        data[i] = a[i] * numext::conj(pos_j_base_powered[i]);
+      } else {
+        data[i] = a[i] * pos_j_base_powered[i];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void scramble_FFT(ComplexScalar* data, Index n) {
+    eigen_assert(isPowerOfTwo(n));
+    Index j = 1;
+    for (Index i = 1; i < n; ++i) {
+      if (j > i) {
+        std::swap(data[j - 1], data[i - 1]);
+      }
+      Index m = n >> 1;
+      while (m >= 2 && j > m) {
+        j -= m;
+        m >>= 1;
+      }
+      j += m;
+    }
+  }
+
+  template <int Dir>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_2(ComplexScalar* data) {
+    ComplexScalar tmp = data[1];
+    data[1] = data[0] - data[1];
+    data[0] += tmp;
+  }
+
+  template <int Dir>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_4(ComplexScalar* data) {
+    ComplexScalar tmp[4];
+    tmp[0] = data[0] + data[1];
+    tmp[1] = data[0] - data[1];
+    tmp[2] = data[2] + data[3];
+    if (Dir == FFT_FORWARD) {
+      tmp[3] = ComplexScalar(0.0, -1.0) * (data[2] - data[3]);
+    } else {
+      tmp[3] = ComplexScalar(0.0, 1.0) * (data[2] - data[3]);
+    }
+    data[0] = tmp[0] + tmp[2];
+    data[1] = tmp[1] + tmp[3];
+    data[2] = tmp[0] - tmp[2];
+    data[3] = tmp[1] - tmp[3];
+  }
+
+  template <int Dir>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_8(ComplexScalar* data) {
+    ComplexScalar tmp_1[8];
+    ComplexScalar tmp_2[8];
+
+    tmp_1[0] = data[0] + data[1];
+    tmp_1[1] = data[0] - data[1];
+    tmp_1[2] = data[2] + data[3];
+    if (Dir == FFT_FORWARD) {
+      tmp_1[3] = (data[2] - data[3]) * ComplexScalar(0, -1);
+    } else {
+      tmp_1[3] = (data[2] - data[3]) * ComplexScalar(0, 1);
+    }
+    tmp_1[4] = data[4] + data[5];
+    tmp_1[5] = data[4] - data[5];
+    tmp_1[6] = data[6] + data[7];
+    if (Dir == FFT_FORWARD) {
+      tmp_1[7] = (data[6] - data[7]) * ComplexScalar(0, -1);
+    } else {
+      tmp_1[7] = (data[6] - data[7]) * ComplexScalar(0, 1);
+    }
+    tmp_2[0] = tmp_1[0] + tmp_1[2];
+    tmp_2[1] = tmp_1[1] + tmp_1[3];
+    tmp_2[2] = tmp_1[0] - tmp_1[2];
+    tmp_2[3] = tmp_1[1] - tmp_1[3];
+    tmp_2[4] = tmp_1[4] + tmp_1[6];
+// SQRT2DIV2 = sqrt(2)/2
+#define SQRT2DIV2 0.7071067811865476
+    if (Dir == FFT_FORWARD) {
+      tmp_2[5] = (tmp_1[5] + tmp_1[7]) * ComplexScalar(SQRT2DIV2, -SQRT2DIV2);
+      tmp_2[6] = (tmp_1[4] - tmp_1[6]) * ComplexScalar(0, -1);
+      tmp_2[7] = (tmp_1[5] - tmp_1[7]) * ComplexScalar(-SQRT2DIV2, -SQRT2DIV2);
+    } else {
+      tmp_2[5] = (tmp_1[5] + tmp_1[7]) * ComplexScalar(SQRT2DIV2, SQRT2DIV2);
+      tmp_2[6] = (tmp_1[4] - tmp_1[6]) * ComplexScalar(0, 1);
+      tmp_2[7] = (tmp_1[5] - tmp_1[7]) * ComplexScalar(-SQRT2DIV2, SQRT2DIV2);
+    }
+    data[0] = tmp_2[0] + tmp_2[4];
+    data[1] = tmp_2[1] + tmp_2[5];
+    data[2] = tmp_2[2] + tmp_2[6];
+    data[3] = tmp_2[3] + tmp_2[7];
+    data[4] = tmp_2[0] - tmp_2[4];
+    data[5] = tmp_2[1] - tmp_2[5];
+    data[6] = tmp_2[2] - tmp_2[6];
+    data[7] = tmp_2[3] - tmp_2[7];
+  }
+
+  template <int Dir>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_1D_merge(ComplexScalar* data, Index n, Index n_power_of_2) {
+    // Original code:
+    // RealScalar wtemp = std::sin(EIGEN_PI/n);
+    // RealScalar wpi =  -std::sin(2 * EIGEN_PI/n);
+    const RealScalar wtemp = m_sin_PI_div_n_LUT[n_power_of_2];
+    const RealScalar wpi =
+        (Dir == FFT_FORWARD) ? m_minus_sin_2_PI_div_n_LUT[n_power_of_2] : -m_minus_sin_2_PI_div_n_LUT[n_power_of_2];
+
+    const ComplexScalar wp(wtemp, wpi);
+    const ComplexScalar wp_one = wp + ComplexScalar(1, 0);
+    const ComplexScalar wp_one_2 = wp_one * wp_one;
+    const ComplexScalar wp_one_3 = wp_one_2 * wp_one;
+    const ComplexScalar wp_one_4 = wp_one_3 * wp_one;
+    const Index n2 = n / 2;
+    ComplexScalar w(1.0, 0.0);
+    for (Index i = 0; i < n2; i += 4) {
+      ComplexScalar temp0(data[i + n2] * w);
+      ComplexScalar temp1(data[i + 1 + n2] * w * wp_one);
+      ComplexScalar temp2(data[i + 2 + n2] * w * wp_one_2);
+      ComplexScalar temp3(data[i + 3 + n2] * w * wp_one_3);
+      w = w * wp_one_4;
+
+      data[i + n2] = data[i] - temp0;
+      data[i] += temp0;
+
+      data[i + 1 + n2] = data[i + 1] - temp1;
+      data[i + 1] += temp1;
+
+      data[i + 2 + n2] = data[i + 2] - temp2;
+      data[i + 2] += temp2;
+
+      data[i + 3 + n2] = data[i + 3] - temp3;
+      data[i + 3] += temp3;
+    }
+  }
+
+  template <int Dir>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_1D_Butterfly(ComplexScalar* data, Index n, Index n_power_of_2) {
+    eigen_assert(isPowerOfTwo(n));
+    if (n > 8) {
+      compute_1D_Butterfly<Dir>(data, n / 2, n_power_of_2 - 1);
+      compute_1D_Butterfly<Dir>(data + n / 2, n / 2, n_power_of_2 - 1);
+      butterfly_1D_merge<Dir>(data, n, n_power_of_2);
+    } else if (n == 8) {
+      butterfly_8<Dir>(data);
+    } else if (n == 4) {
+      butterfly_4<Dir>(data);
+    } else if (n == 2) {
+      butterfly_2<Dir>(data);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index getBaseOffsetFromIndex(Index index, Index omitted_dim) const {
+    Index result = 0;
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > omitted_dim; --i) {
+        const Index partial_m_stride = m_strides[i] / m_dimensions[omitted_dim];
+        const Index idx = index / partial_m_stride;
+        index -= idx * partial_m_stride;
+        result += idx * m_strides[i];
+      }
+      result += index;
+    } else {
+      for (Index i = 0; i < omitted_dim; ++i) {
+        const Index partial_m_stride = m_strides[i] / m_dimensions[omitted_dim];
+        const Index idx = index / partial_m_stride;
+        index -= idx * partial_m_stride;
+        result += idx * m_strides[i];
+      }
+      result += index;
+    }
+    // Value of index_coords[omitted_dim] is not determined to this step
+    return result;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index getIndexFromOffset(Index base, Index omitted_dim, Index offset) const {
+    Index result = base + offset * m_strides[omitted_dim];
+    return result;
+  }
+
+ protected:
+  Index m_size;
+  const FFT EIGEN_DEVICE_REF m_fft;
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_strides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  EvaluatorPointerType m_data;
+  const Device EIGEN_DEVICE_REF m_device;
+
+  // This will support a maximum FFT size of 2^32 for each dimension
+  // m_sin_PI_div_n_LUT[i] = (-2) * std::sin(EIGEN_PI / std::pow(2,i)) ^ 2;
+  const RealScalar m_sin_PI_div_n_LUT[32] = {RealScalar(0.0),
+                                             RealScalar(-2),
+                                             RealScalar(-0.999999999999999),
+                                             RealScalar(-0.292893218813453),
+                                             RealScalar(-0.0761204674887130),
+                                             RealScalar(-0.0192147195967696),
+                                             RealScalar(-0.00481527332780311),
+                                             RealScalar(-0.00120454379482761),
+                                             RealScalar(-3.01181303795779e-04),
+                                             RealScalar(-7.52981608554592e-05),
+                                             RealScalar(-1.88247173988574e-05),
+                                             RealScalar(-4.70619042382852e-06),
+                                             RealScalar(-1.17654829809007e-06),
+                                             RealScalar(-2.94137117780840e-07),
+                                             RealScalar(-7.35342821488550e-08),
+                                             RealScalar(-1.83835707061916e-08),
+                                             RealScalar(-4.59589268710903e-09),
+                                             RealScalar(-1.14897317243732e-09),
+                                             RealScalar(-2.87243293150586e-10),
+                                             RealScalar(-7.18108232902250e-11),
+                                             RealScalar(-1.79527058227174e-11),
+                                             RealScalar(-4.48817645568941e-12),
+                                             RealScalar(-1.12204411392298e-12),
+                                             RealScalar(-2.80511028480785e-13),
+                                             RealScalar(-7.01277571201985e-14),
+                                             RealScalar(-1.75319392800498e-14),
+                                             RealScalar(-4.38298482001247e-15),
+                                             RealScalar(-1.09574620500312e-15),
+                                             RealScalar(-2.73936551250781e-16),
+                                             RealScalar(-6.84841378126949e-17),
+                                             RealScalar(-1.71210344531737e-17),
+                                             RealScalar(-4.28025861329343e-18)};
+
+  // m_minus_sin_2_PI_div_n_LUT[i] = -std::sin(2 * EIGEN_PI / std::pow(2,i));
+  const RealScalar m_minus_sin_2_PI_div_n_LUT[32] = {RealScalar(0.0),
+                                                     RealScalar(0.0),
+                                                     RealScalar(-1.00000000000000e+00),
+                                                     RealScalar(-7.07106781186547e-01),
+                                                     RealScalar(-3.82683432365090e-01),
+                                                     RealScalar(-1.95090322016128e-01),
+                                                     RealScalar(-9.80171403295606e-02),
+                                                     RealScalar(-4.90676743274180e-02),
+                                                     RealScalar(-2.45412285229123e-02),
+                                                     RealScalar(-1.22715382857199e-02),
+                                                     RealScalar(-6.13588464915448e-03),
+                                                     RealScalar(-3.06795676296598e-03),
+                                                     RealScalar(-1.53398018628477e-03),
+                                                     RealScalar(-7.66990318742704e-04),
+                                                     RealScalar(-3.83495187571396e-04),
+                                                     RealScalar(-1.91747597310703e-04),
+                                                     RealScalar(-9.58737990959773e-05),
+                                                     RealScalar(-4.79368996030669e-05),
+                                                     RealScalar(-2.39684498084182e-05),
+                                                     RealScalar(-1.19842249050697e-05),
+                                                     RealScalar(-5.99211245264243e-06),
+                                                     RealScalar(-2.99605622633466e-06),
+                                                     RealScalar(-1.49802811316901e-06),
+                                                     RealScalar(-7.49014056584716e-07),
+                                                     RealScalar(-3.74507028292384e-07),
+                                                     RealScalar(-1.87253514146195e-07),
+                                                     RealScalar(-9.36267570730981e-08),
+                                                     RealScalar(-4.68133785365491e-08),
+                                                     RealScalar(-2.34066892682746e-08),
+                                                     RealScalar(-1.17033446341373e-08),
+                                                     RealScalar(-5.85167231706864e-09),
+                                                     RealScalar(-2.92583615853432e-09)};
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_FFT_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h
new file mode 100644
index 0000000..faa7af8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h
@@ -0,0 +1,221 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
+#define EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief The fixed sized version of the tensor class.
+ *
+ * The fixed sized equivalent of
+ * Eigen::Tensor<float, 3> t(3, 5, 7);
+ * is
+ * Eigen::TensorFixedSize<float, Sizes<3,5,7>> t;
+ */
+template <typename Scalar_, typename Dimensions_, int Options_, typename IndexType>
+class TensorFixedSize : public TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> > {
+ public:
+  typedef TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> Self;
+  typedef TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> > Base;
+  typedef typename Eigen::internal::nested<Self>::type Nested;
+  typedef typename internal::traits<Self>::StorageKind StorageKind;
+  typedef typename internal::traits<Self>::Index Index;
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef typename Base::CoeffReturnType CoeffReturnType;
+
+  static constexpr int Options = Options_;
+  static constexpr int Layout = Options_ & RowMajor ? RowMajor : ColMajor;
+
+  enum {
+    IsAligned = bool(EIGEN_MAX_ALIGN_BYTES > 0),
+    PacketAccess = (internal::packet_traits<Scalar>::size > 1),
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = true,
+    RawAccess = true
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  typedef Dimensions_ Dimensions;
+  static constexpr std::size_t NumIndices = Dimensions::count;
+
+ protected:
+  TensorStorage<Scalar, Dimensions, Options> m_storage;
+
+ public:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const { return NumIndices; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions dimensions() const { return m_storage.dimensions(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_storage.size(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() { return m_storage.data(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar* data() const { return m_storage.data(); }
+
+  // This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+  // work, because that uses base().coeffRef() - and we don't yet
+  // implement a similar class hierarchy
+  inline Self& base() { return *this; }
+  inline const Self& base() const { return *this; }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index firstIndex, IndexTypes... otherIndices) const {
+    // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return coeff(array<Index, NumIndices>{{firstIndex, otherIndices...}});
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const {
+    eigen_internal_assert(checkIndexRange(indices));
+    return m_storage.data()[linearizedIndex(indices)];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const {
+    eigen_internal_assert(index >= 0 && index < size());
+    return m_storage.data()[index];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff() const {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return m_storage.data()[0];
+  }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices) {
+    // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return coeffRef(array<Index, NumIndices>{{firstIndex, otherIndices...}});
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices) {
+    eigen_internal_assert(checkIndexRange(indices));
+    return m_storage.data()[linearizedIndex(indices)];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    eigen_internal_assert(index >= 0 && index < size());
+    return m_storage.data()[index];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef() {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return m_storage.data()[0];
+  }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) const {
+    // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return this->operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}});
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const {
+    eigen_assert(checkIndexRange(indices));
+    return coeff(indices);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const {
+    eigen_internal_assert(index >= 0 && index < size());
+    return coeff(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()() const {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return coeff();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const {
+    // The bracket operator is only for vectors, use the parenthesis operator instead.
+    EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return coeff(index);
+  }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) {
+    // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}});
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices) {
+    eigen_assert(checkIndexRange(indices));
+    return coeffRef(indices);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index index) {
+    eigen_assert(index >= 0 && index < size());
+    return coeffRef(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()() {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return coeffRef();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator[](Index index) {
+    // The bracket operator is only for vectors, use the parenthesis operator instead
+    EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return coeffRef(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFixedSize() : m_storage() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFixedSize(const Self& other) : Base(other), m_storage(other.m_storage) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFixedSize(Self&& other) : m_storage(other.m_storage) {}
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, ReadOnlyAccessors>& other) {
+    typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign;
+    Assign assign(*this, other.derived());
+    internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+  }
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, WriteAccessors>& other) {
+    typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign;
+    Assign assign(*this, other.derived());
+    internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+  }
+
+  // FIXME: check that the dimensions of other match the dimensions of *this.
+  // Unfortunately this isn't possible yet when the rhs is an expression.
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(TensorFixedSize)
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool checkIndexRange(const array<Index, NumIndices>& /*indices*/) const {
+    using internal::array_apply_and_reduce;
+    using internal::array_zip_and_reduce;
+    using internal::greater_equal_zero_op;
+    using internal::lesser_op;
+    using internal::logical_and_op;
+
+    return true;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const {
+    if (Options & RowMajor) {
+      return m_storage.dimensions().IndexOfRowMajor(indices);
+    } else {
+      return m_storage.dimensions().IndexOfColMajor(indices);
+    }
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h
new file mode 100644
index 0000000..40f5296
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h
@@ -0,0 +1,230 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#include <memory>
+
+namespace Eigen {
+
+namespace internal {
+template <typename XprType>
+struct traits<TensorForcedEvalOp<XprType>> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename traits<XprType>::StorageKind StorageKind;
+  typedef typename traits<XprType>::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+
+  enum { Flags = 0 };
+};
+
+template <typename XprType>
+struct eval<TensorForcedEvalOp<XprType>, Eigen::Dense> {
+  typedef const TensorForcedEvalOp<XprType>& type;
+};
+
+template <typename XprType>
+struct nested<TensorForcedEvalOp<XprType>, 1, typename eval<TensorForcedEvalOp<XprType>>::type> {
+  typedef TensorForcedEvalOp<XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor forced evaluation class.
+ */
+template <typename XprType>
+class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorForcedEvalOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorForcedEvalOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorForcedEvalOp(const XprType& expr) : m_xpr(expr) {}
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+};
+
+namespace internal {
+template <typename Device, typename CoeffReturnType>
+struct non_integral_type_placement_new {
+  template <typename StorageType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index numValues, StorageType m_buffer) {
+    // Initialize non-trivially constructible types.
+    if (!internal::is_arithmetic<CoeffReturnType>::value) {
+      for (Index i = 0; i < numValues; ++i) new (m_buffer + i) CoeffReturnType();
+    }
+  }
+};
+
+// SYCL does not support non-integral types
+// having new (m_buffer + i) CoeffReturnType() causes the following compiler error for SYCL Devices
+// no matching function for call to 'operator new'
+template <typename CoeffReturnType>
+struct non_integral_type_placement_new<Eigen::SyclDevice, CoeffReturnType> {
+  template <typename StorageType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index, StorageType) {}
+};
+}  // end namespace internal
+
+template <typename Device>
+class DeviceTempPointerHolder {
+ public:
+  DeviceTempPointerHolder(const Device& device, size_t size)
+      : device_(device), size_(size), ptr_(device.allocate_temp(size)) {}
+
+  ~DeviceTempPointerHolder() {
+    device_.deallocate_temp(ptr_);
+    size_ = 0;
+    ptr_ = nullptr;
+  }
+
+  void* ptr() { return ptr_; }
+
+ private:
+  Device device_;
+  size_t size_;
+  void* ptr_;
+};
+
+template <typename ArgType_, typename Device>
+struct TensorEvaluator<const TensorForcedEvalOp<ArgType_>, Device> {
+  typedef const internal::remove_all_t<ArgType_> ArgType;
+  typedef TensorForcedEvalOp<ArgType> XprType;
+  typedef typename ArgType::Scalar Scalar;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = true,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = internal::is_arithmetic<CoeffReturnType>::value,
+    PreferBlockAccess = false,
+    RawAccess = true
+  };
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  static constexpr int NumDims = internal::traits<ArgType>::NumDimensions;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims, Layout, Index> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device),
+        m_op(op.expression()),
+        m_device(device),
+        m_buffer_holder(nullptr),
+        m_buffer(nullptr) {}
+
+  ~TensorEvaluator() { cleanup(); }
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    const Index numValues = internal::array_prod(m_impl.dimensions());
+    m_buffer_holder = std::make_shared<DeviceTempPointerHolder<Device>>(m_device, numValues * sizeof(CoeffReturnType));
+    m_buffer = static_cast<EvaluatorPointerType>(m_buffer_holder->ptr());
+
+    internal::non_integral_type_placement_new<Device, CoeffReturnType>()(numValues, m_buffer);
+
+    typedef TensorEvalToOp<const std::remove_const_t<ArgType>> EvalTo;
+    EvalTo evalToTmp(m_device.get(m_buffer), m_op);
+
+    internal::TensorExecutor<const EvalTo, std::remove_const_t<Device>,
+                             /*Vectorizable=*/internal::IsVectorizable<Device, const ArgType>::value,
+                             /*Tiling=*/internal::IsTileable<Device, const ArgType>::value>::run(evalToTmp, m_device);
+
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    const Index numValues = internal::array_prod(m_impl.dimensions());
+    m_buffer_holder = std::make_shared<DeviceTempPointerHolder<Device>>(m_device, numValues * sizeof(CoeffReturnType));
+    m_buffer = static_cast<EvaluatorPointerType>(m_buffer_holder->ptr());
+
+    typedef TensorEvalToOp<const std::remove_const_t<ArgType>> EvalTo;
+    EvalTo evalToTmp(m_device.get(m_buffer), m_op);
+
+    auto on_done = std::bind([](EvalSubExprsCallback done_) { done_(true); }, std::move(done));
+    internal::TensorAsyncExecutor<
+        const EvalTo, std::remove_const_t<Device>, decltype(on_done),
+        /*Vectorizable=*/internal::IsVectorizable<Device, const ArgType>::value,
+        /*Tiling=*/internal::IsTileable<Device, const ArgType>::value>::runAsync(evalToTmp, m_device,
+                                                                                 std::move(on_done));
+  }
+#endif
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_buffer_holder = nullptr;
+    m_buffer = nullptr;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_buffer[index]; }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return internal::TensorBlockResourceRequirements::any();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    eigen_assert(m_buffer != nullptr);
+    return TensorBlock::materialize(m_buffer, m_impl.dimensions(), desc, scratch);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE EvaluatorPointerType data() const { return m_buffer; }
+
+ private:
+  TensorEvaluator<ArgType, Device> m_impl;
+  const ArgType m_op;
+  const Device EIGEN_DEVICE_REF m_device;
+  std::shared_ptr<DeviceTempPointerHolder<Device>> m_buffer_holder;
+  EvaluatorPointerType m_buffer;  // Cached copy of the value stored in m_buffer_holder.
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
new file mode 100644
index 0000000..49c20a4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
@@ -0,0 +1,215 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// MakePointer class is used as a container of the address space of the pointer
+// on the host and on the device. From the host side it generates the T* pointer
+// and when EIGEN_USE_SYCL is used it construct a buffer with a map_allocator to
+// T* m_data on the host. It is always called on the device.
+// Specialisation of MakePointer class for creating the sycl buffer with
+// map_allocator.
+template <typename T>
+struct MakePointer {
+  typedef T* Type;
+  typedef const T* ConstType;
+};
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T* constCast(const T* data) {
+  return const_cast<T*>(data);
+}
+
+// The StorageMemory class is a container of the device specific pointer
+// used for referring to a Pointer on TensorEvaluator class. While the TensorExpression
+// is a device-agnostic type and need MakePointer class for type conversion,
+// the TensorEvaluator class can be specialized for a device, hence it is possible
+// to construct different types of temporary storage memory in TensorEvaluator
+// for different devices by specializing the following StorageMemory class.
+template <typename T, typename device>
+struct StorageMemory : MakePointer<T> {};
+
+namespace internal {
+template <typename A, typename B>
+struct Pointer_type_promotion {
+  static const bool val = false;
+};
+template <typename A>
+struct Pointer_type_promotion<A, A> {
+  static const bool val = true;
+};
+template <typename A, typename B>
+struct TypeConversion {
+  typedef A* type;
+};
+}  // namespace internal
+
+template <typename PlainObjectType, int Options_ = Unaligned, template <class> class MakePointer_ = MakePointer>
+class TensorMap;
+template <typename Scalar_, int NumIndices_, int Options_ = 0, typename IndexType = DenseIndex>
+class Tensor;
+template <typename Scalar_, typename Dimensions, int Options_ = 0, typename IndexType = DenseIndex>
+class TensorFixedSize;
+template <typename PlainObjectType>
+class TensorRef;
+template <typename Derived, int AccessLevel>
+class TensorBase;
+
+template <typename NullaryOp, typename PlainObjectType>
+class TensorCwiseNullaryOp;
+template <typename UnaryOp, typename XprType>
+class TensorCwiseUnaryOp;
+template <typename BinaryOp, typename LeftXprType, typename RightXprType>
+class TensorCwiseBinaryOp;
+template <typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
+class TensorCwiseTernaryOp;
+template <typename IfXprType, typename ThenXprType, typename ElseXprType>
+class TensorSelectOp;
+template <typename Op, typename Dims, typename XprType, template <class> class MakePointer_ = MakePointer>
+class TensorReductionOp;
+template <typename XprType>
+class TensorIndexPairOp;
+template <typename ReduceOp, typename Dims, typename XprType>
+class TensorPairReducerOp;
+template <typename Axis, typename LeftXprType, typename RightXprType>
+class TensorConcatenationOp;
+template <typename Dimensions, typename LeftXprType, typename RightXprType, typename OutputKernelType>
+class TensorContractionOp;
+template <typename TargetType, typename XprType>
+class TensorConversionOp;
+template <typename Dimensions, typename InputXprType, typename KernelXprType>
+class TensorConvolutionOp;
+template <typename FFT, typename XprType, int FFTDataType, int FFTDirection>
+class TensorFFTOp;
+template <typename PatchDim, typename XprType>
+class TensorPatchOp;
+template <DenseIndex Rows, DenseIndex Cols, typename XprType>
+class TensorImagePatchOp;
+template <DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
+class TensorVolumePatchOp;
+template <typename Broadcast, typename XprType>
+class TensorBroadcastingOp;
+template <DenseIndex DimId, typename XprType>
+class TensorChippingOp;
+template <typename NewDimensions, typename XprType>
+class TensorReshapingOp;
+template <typename XprType>
+class TensorLayoutSwapOp;
+template <typename StartIndices, typename Sizes, typename XprType>
+class TensorSlicingOp;
+template <typename ReverseDimensions, typename XprType>
+class TensorReverseOp;
+template <typename Rolls, typename XprType>
+class TensorRollOp;
+template <typename PaddingDimensions, typename XprType>
+class TensorPaddingOp;
+template <typename Shuffle, typename XprType>
+class TensorShufflingOp;
+template <typename Strides, typename XprType>
+class TensorStridingOp;
+template <typename StartIndices, typename StopIndices, typename Strides, typename XprType>
+class TensorStridingSlicingOp;
+template <typename Strides, typename XprType>
+class TensorInflationOp;
+template <typename Generator, typename XprType>
+class TensorGeneratorOp;
+template <typename LeftXprType, typename RightXprType>
+class TensorAssignOp;
+template <typename Op, typename XprType>
+class TensorScanOp;
+template <typename Dims, typename XprType>
+class TensorTraceOp;
+
+template <typename CustomUnaryFunc, typename XprType>
+class TensorCustomUnaryOp;
+template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+class TensorCustomBinaryOp;
+
+template <typename XprType, template <class> class MakePointer_ = MakePointer>
+class TensorEvalToOp;
+template <typename XprType>
+class TensorForcedEvalOp;
+
+template <typename ExpressionType, typename DeviceType>
+class TensorDevice;
+template <typename ExpressionType, typename DeviceType, typename DoneCallback>
+class TensorAsyncDevice;
+template <typename Derived, typename Device>
+struct TensorEvaluator;
+
+struct NoOpOutputKernel;
+
+struct DefaultDevice;
+struct ThreadPoolDevice;
+struct GpuDevice;
+struct SyclDevice;
+
+#ifdef EIGEN_USE_SYCL
+namespace TensorSycl {
+namespace internal {
+template <typename Evaluator, typename Op>
+class GenericNondeterministicReducer;
+}
+}  // namespace TensorSycl
+#endif
+
+enum FFTResultType { RealPart = 0, ImagPart = 1, BothParts = 2 };
+
+enum FFTDirection { FFT_FORWARD = 0, FFT_REVERSE = 1 };
+
+namespace internal {
+
+template <typename Device, typename Expression>
+struct IsVectorizable {
+  static const bool value = TensorEvaluator<Expression, Device>::PacketAccess;
+};
+
+template <typename Expression>
+struct IsVectorizable<GpuDevice, Expression> {
+  static const bool value =
+      TensorEvaluator<Expression, GpuDevice>::PacketAccess && TensorEvaluator<Expression, GpuDevice>::IsAligned;
+};
+
+// Tiled evaluation strategy.
+enum TiledEvaluation {
+  Off = 0,  // tiled evaluation is not supported
+  On = 1,   // still work in progress (see TensorBlock.h)
+};
+
+template <typename Device, typename Expression>
+struct IsTileable {
+  // Check that block evaluation is supported and it's a preferred option (at
+  // least one sub-expression has much faster block evaluation, e.g.
+  // broadcasting).
+  static constexpr bool BlockAccess =
+      TensorEvaluator<Expression, Device>::BlockAccess && TensorEvaluator<Expression, Device>::PreferBlockAccess;
+
+  static const TiledEvaluation value = BlockAccess ? TiledEvaluation::On : TiledEvaluation::Off;
+};
+
+template <typename Expression, typename Device, bool Vectorizable = IsVectorizable<Device, Expression>::value,
+          TiledEvaluation Tiling = IsTileable<Device, Expression>::value>
+class TensorExecutor;
+
+template <typename Expression, typename Device, typename DoneCallback,
+          bool Vectorizable = IsVectorizable<Device, Expression>::value,
+          TiledEvaluation Tiling = IsTileable<Device, Expression>::value>
+class TensorAsyncExecutor;
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
new file mode 100644
index 0000000..7a87594
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
@@ -0,0 +1,422 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/** \internal
+ * \brief Template functor to compute the modulo between an array and a scalar.
+ */
+template <typename Scalar>
+struct scalar_mod_op {
+  EIGEN_DEVICE_FUNC scalar_mod_op(const Scalar& divisor) : m_divisor(divisor) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const { return a % m_divisor; }
+  const Scalar m_divisor;
+};
+template <typename Scalar>
+struct functor_traits<scalar_mod_op<Scalar> > {
+  enum { Cost = scalar_div_cost<Scalar, false>::value, PacketAccess = false };
+};
+
+/** \internal
+ * \brief Template functor to compute the modulo between 2 arrays.
+ */
+template <typename Scalar>
+struct scalar_mod2_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const { return a % b; }
+};
+template <typename Scalar>
+struct functor_traits<scalar_mod2_op<Scalar> > {
+  enum { Cost = scalar_div_cost<Scalar, false>::value, PacketAccess = false };
+};
+
+template <typename Scalar>
+struct scalar_fmod_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return numext::fmod(a, b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_fmod_op<Scalar> > {
+  enum {
+    Cost = 13,  // Reciprocal throughput of FPREM on Haswell.
+    PacketAccess = false
+  };
+};
+
+template <typename Reducer, typename Device>
+struct reducer_traits {
+  enum { Cost = 1, PacketAccess = false, IsStateful = false, IsExactlyAssociative = true };
+};
+
+// Standard reduction functors
+template <typename T>
+struct SumReducer {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
+    internal::scalar_sum_op<T> sum_op;
+    *accum = sum_op(*accum, t);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
+    (*accum) = padd<Packet>(*accum, p);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    internal::scalar_cast_op<int, T> conv;
+    return conv(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return pset1<Packet>(initialize());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { return accum; }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
+    return vaccum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
+    internal::scalar_sum_op<T> sum_op;
+    return sum_op(saccum, predux(vaccum));
+  }
+};
+
+template <typename T, typename Device>
+struct reducer_traits<SumReducer<T>, Device> {
+  enum {
+    Cost = NumTraits<T>::AddCost,
+    PacketAccess = PacketType<T, Device>::HasAdd,
+    IsStateful = false,
+    IsExactlyAssociative = NumTraits<T>::IsInteger
+  };
+};
+
+template <typename T>
+struct MeanReducer {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MeanReducer() : scalarCount_(0), packetCount_(0) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) {
+    internal::scalar_sum_op<T> sum_op;
+    *accum = sum_op(*accum, t);
+    scalarCount_++;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) {
+    (*accum) = padd<Packet>(*accum, p);
+    packetCount_++;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    internal::scalar_cast_op<int, T> conv;
+    return conv(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return pset1<Packet>(initialize());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
+    internal::scalar_quotient_op<T> quotient_op;
+    return quotient_op(accum, T(scalarCount_));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
+    return pdiv(vaccum, pset1<Packet>(T(packetCount_)));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
+    internal::scalar_sum_op<T> sum_op;
+    internal::scalar_quotient_op<T> quotient_op;
+    return quotient_op(sum_op(saccum, predux(vaccum)), T(scalarCount_ + packetCount_ * unpacket_traits<Packet>::size));
+  }
+
+ protected:
+  DenseIndex scalarCount_;
+  DenseIndex packetCount_;
+};
+
+template <typename T, typename Device>
+struct reducer_traits<MeanReducer<T>, Device> {
+  enum {
+    Cost = NumTraits<T>::AddCost,
+    PacketAccess = PacketType<T, Device>::HasAdd && PacketType<T, Device>::HasDiv && !NumTraits<T>::IsInteger,
+    IsStateful = true,
+    IsExactlyAssociative = NumTraits<T>::IsInteger
+  };
+};
+
+template <typename T, bool IsMax = true, bool IsInteger = true>
+struct MinMaxBottomValue {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() { return Eigen::NumTraits<T>::lowest(); }
+};
+template <typename T>
+struct MinMaxBottomValue<T, true, false> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() { return -Eigen::NumTraits<T>::infinity(); }
+};
+template <typename T>
+struct MinMaxBottomValue<T, false, true> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() { return Eigen::NumTraits<T>::highest(); }
+};
+template <typename T>
+struct MinMaxBottomValue<T, false, false> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() { return Eigen::NumTraits<T>::infinity(); }
+};
+
+template <typename T, int NaNPropagation = PropagateFast>
+struct MaxReducer {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
+    scalar_max_op<T, T, NaNPropagation> op;
+    *accum = op(t, *accum);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
+    scalar_max_op<T, T, NaNPropagation> op;
+    (*accum) = op.packetOp(*accum, p);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    return MinMaxBottomValue<T, /*IsMax=*/true, Eigen::NumTraits<T>::IsInteger>::bottom_value();
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return pset1<Packet>(initialize());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { return accum; }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
+    return vaccum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
+    scalar_max_op<T, T, NaNPropagation> op;
+    return op(saccum, op.predux(vaccum));
+  }
+};
+
+template <typename T, typename Device, int NaNPropagation>
+struct reducer_traits<MaxReducer<T, NaNPropagation>, Device> {
+  enum {
+    Cost = NumTraits<T>::AddCost,
+    PacketAccess = PacketType<T, Device>::HasMax,
+    IsStateful = false,
+    IsExactlyAssociative = (NaNPropagation != PropagateFast)
+  };
+};
+
+template <typename T, int NaNPropagation = PropagateFast>
+struct MinReducer {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
+    scalar_min_op<T, T, NaNPropagation> op;
+    *accum = op(t, *accum);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
+    scalar_min_op<T, T, NaNPropagation> op;
+    (*accum) = op.packetOp(*accum, p);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    return MinMaxBottomValue<T, /*IsMax=*/false, Eigen::NumTraits<T>::IsInteger>::bottom_value();
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return pset1<Packet>(initialize());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { return accum; }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
+    return vaccum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
+    scalar_min_op<T, T, NaNPropagation> op;
+    return op(saccum, op.predux(vaccum));
+  }
+};
+
+template <typename T, typename Device, int NaNPropagation>
+struct reducer_traits<MinReducer<T, NaNPropagation>, Device> {
+  enum {
+    Cost = NumTraits<T>::AddCost,
+    PacketAccess = PacketType<T, Device>::HasMin,
+    IsStateful = false,
+    IsExactlyAssociative = (NaNPropagation != PropagateFast)
+  };
+};
+
+template <typename T>
+struct ProdReducer {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
+    internal::scalar_product_op<T> prod_op;
+    (*accum) = prod_op(*accum, t);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
+    (*accum) = pmul<Packet>(*accum, p);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    internal::scalar_cast_op<int, T> conv;
+    return conv(1);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return pset1<Packet>(initialize());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { return accum; }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
+    return vaccum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
+    internal::scalar_product_op<T> prod_op;
+    return prod_op(saccum, predux_mul(vaccum));
+  }
+};
+
+template <typename T, typename Device>
+struct reducer_traits<ProdReducer<T>, Device> {
+  enum {
+    Cost = NumTraits<T>::MulCost,
+    PacketAccess = PacketType<T, Device>::HasMul,
+    IsStateful = false,
+    IsExactlyAssociative = true
+  };
+};
+
+struct AndReducer {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(bool t, bool* accum) const { *accum = *accum && t; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool initialize() const { return true; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool finalize(bool accum) const { return accum; }
+};
+
+template <typename Device>
+struct reducer_traits<AndReducer, Device> {
+  enum { Cost = 1, PacketAccess = false, IsStateful = false, IsExactlyAssociative = true };
+};
+
+struct OrReducer {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(bool t, bool* accum) const { *accum = *accum || t; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool initialize() const { return false; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool finalize(bool accum) const { return accum; }
+};
+
+template <typename Device>
+struct reducer_traits<OrReducer, Device> {
+  enum { Cost = 1, PacketAccess = false, IsStateful = false, IsExactlyAssociative = true };
+};
+
+// Argmin/Argmax reducers.  Returns the first occurrence if multiple locations
+// contain the same min/max value.
+template <typename T>
+struct ArgMaxPairReducer {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
+    if (t.second < accum->second) {
+      return;
+    } else if (t.second > accum->second || accum->first > t.first) {
+      *accum = t;
+    }
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    return T(0, NumTraits<typename T::second_type>::lowest());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T& accum) const { return accum; }
+};
+
+template <typename T, typename Device>
+struct reducer_traits<ArgMaxPairReducer<T>, Device> {
+  enum { Cost = NumTraits<T>::AddCost, PacketAccess = false, IsStateful = false, IsExactlyAssociative = true };
+};
+
+template <typename T>
+struct ArgMinPairReducer {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T& t, T* accum) const {
+    if (t.second > accum->second) {
+      return;
+    } else if (t.second < accum->second || accum->first > t.first) {
+      *accum = t;
+    }
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    return T(0, NumTraits<typename T::second_type>::highest());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T& accum) const { return accum; }
+};
+
+template <typename T, typename Device>
+struct reducer_traits<ArgMinPairReducer<T>, Device> {
+  enum { Cost = NumTraits<T>::AddCost, PacketAccess = false, IsStateful = false, IsExactlyAssociative = true };
+};
+
+template <typename T, typename Index, size_t NumDims>
+class GaussianGenerator {
+ public:
+  static constexpr bool PacketAccess = false;
+
+  EIGEN_DEVICE_FUNC GaussianGenerator(const array<T, NumDims>& means, const array<T, NumDims>& std_devs)
+      : m_means(means) {
+    EIGEN_UNROLL_LOOP
+    for (size_t i = 0; i < NumDims; ++i) {
+      m_two_sigmas[i] = std_devs[i] * std_devs[i] * 2;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC T operator()(const array<Index, NumDims>& coordinates) const {
+    T tmp = T(0);
+    EIGEN_UNROLL_LOOP
+    for (size_t i = 0; i < NumDims; ++i) {
+      T offset = coordinates[i] - m_means[i];
+      tmp += offset * offset / m_two_sigmas[i];
+    }
+    return numext::exp(-tmp);
+  }
+
+ private:
+  array<T, NumDims> m_means;
+  array<T, NumDims> m_two_sigmas;
+};
+
+template <typename T, typename Index, size_t NumDims>
+struct functor_traits<GaussianGenerator<T, Index, NumDims> > {
+  enum {
+    Cost = NumDims *
+               (2 * NumTraits<T>::AddCost + NumTraits<T>::MulCost + functor_traits<scalar_quotient_op<T, T> >::Cost) +
+           functor_traits<scalar_exp_op<T> >::Cost,
+    PacketAccess = GaussianGenerator<T, Index, NumDims>::PacketAccess
+  };
+};
+
+template <typename Scalar>
+struct scalar_clamp_op {
+  EIGEN_DEVICE_FUNC inline scalar_clamp_op(const Scalar& _min, const Scalar& _max) : m_min(_min), m_max(_max) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    return numext::mini(numext::maxi(x, m_min), m_max);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& x) const {
+    return internal::pmin(internal::pmax(x, pset1<Packet>(m_min)), pset1<Packet>(m_max));
+  }
+  const Scalar m_min;
+  const Scalar m_max;
+};
+template <typename Scalar>
+struct functor_traits<scalar_clamp_op<Scalar> > {
+  enum {
+    Cost = 2 * NumTraits<Scalar>::AddCost,
+    PacketAccess = (packet_traits<Scalar>::HasMin && packet_traits<Scalar>::HasMax)
+  };
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h
new file mode 100644
index 0000000..2dac337
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h
@@ -0,0 +1,269 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
+#define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Generator, typename XprType>
+struct traits<TensorGeneratorOp<Generator, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename Generator, typename XprType>
+struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense> {
+  typedef const TensorGeneratorOp<Generator, XprType>& type;
+};
+
+template <typename Generator, typename XprType>
+struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType> >::type> {
+  typedef TensorGeneratorOp<Generator, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor generator class.
+ */
+template <typename Generator, typename XprType>
+class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator)
+      : m_xpr(expr), m_generator(generator) {}
+
+  EIGEN_DEVICE_FUNC const Generator& generator() const { return m_generator; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const Generator m_generator;
+};
+
+// Eval as rvalue
+template <typename Generator, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device> {
+  typedef TensorGeneratorOp<Generator, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  static constexpr int NumDims = internal::array_size<Dimensions>::value;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = true,
+    PreferBlockAccess = true,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  typedef internal::TensorIntDivisor<Index> IndexDivisor;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims, Layout, Index> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_device(device), m_generator(op.generator()) {
+    TensorEvaluator<ArgType, Device> argImpl(op.expression(), device);
+    m_dimensions = argImpl.dimensions();
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_strides[0] = 1;
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < NumDims; ++i) {
+        m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
+        if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
+      }
+    } else {
+      m_strides[NumDims - 1] = 1;
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
+        if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) { return true; }
+  EIGEN_STRONG_INLINE void cleanup() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    array<Index, NumDims> coords;
+    extract_coordinates(index, coords);
+    return m_generator(coords);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    const int packetSize = PacketType<CoeffReturnType, Device>::size;
+    eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[packetSize];
+    for (int i = 0; i < packetSize; ++i) {
+      values[i] = coeff(index + i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.firstLevelCacheSize();
+    // TODO(ezhulenev): Generator should have a cost.
+    return internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size);
+  }
+
+  struct BlockIteratorState {
+    Index stride;
+    Index span;
+    Index size;
+    Index count;
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    static const bool is_col_major = static_cast<int>(Layout) == static_cast<int>(ColMajor);
+
+    // Compute spatial coordinates for the first block element.
+    array<Index, NumDims> coords;
+    extract_coordinates(desc.offset(), coords);
+    array<Index, NumDims> initial_coords = coords;
+
+    // Offset in the output block buffer.
+    Index offset = 0;
+
+    // Initialize output block iterator state. Dimension in this array are
+    // always in inner_most -> outer_most order (col major layout).
+    array<BlockIteratorState, NumDims> it;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = is_col_major ? i : NumDims - 1 - i;
+      it[i].size = desc.dimension(dim);
+      it[i].stride = i == 0 ? 1 : (it[i - 1].size * it[i - 1].stride);
+      it[i].span = it[i].stride * (it[i].size - 1);
+      it[i].count = 0;
+    }
+    eigen_assert(it[0].stride == 1);
+
+    // Prepare storage for the materialized generator result.
+    const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch);
+
+    CoeffReturnType* block_buffer = block_storage.data();
+
+    static const int packet_size = PacketType<CoeffReturnType, Device>::size;
+
+    static const int inner_dim = is_col_major ? 0 : NumDims - 1;
+    const Index inner_dim_size = it[0].size;
+    const Index inner_dim_vectorized = inner_dim_size - packet_size;
+
+    while (it[NumDims - 1].count < it[NumDims - 1].size) {
+      Index i = 0;
+      // Generate data for the vectorized part of the inner-most dimension.
+      for (; i <= inner_dim_vectorized; i += packet_size) {
+        for (Index j = 0; j < packet_size; ++j) {
+          array<Index, NumDims> j_coords = coords;  // Break loop dependence.
+          j_coords[inner_dim] += j;
+          *(block_buffer + offset + i + j) = m_generator(j_coords);
+        }
+        coords[inner_dim] += packet_size;
+      }
+      // Finalize non-vectorized part of the inner-most dimension.
+      for (; i < inner_dim_size; ++i) {
+        *(block_buffer + offset + i) = m_generator(coords);
+        coords[inner_dim]++;
+      }
+      coords[inner_dim] = initial_coords[inner_dim];
+
+      // For the 1d tensor we need to generate only one inner-most dimension.
+      if (NumDims == 1) break;
+
+      // Update offset.
+      for (i = 1; i < NumDims; ++i) {
+        if (++it[i].count < it[i].size) {
+          offset += it[i].stride;
+          coords[is_col_major ? i : NumDims - 1 - i]++;
+          break;
+        }
+        if (i != NumDims - 1) it[i].count = 0;
+        coords[is_col_major ? i : NumDims - 1 - i] = initial_coords[is_col_major ? i : NumDims - 1 - i];
+        offset -= it[i].span;
+      }
+    }
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const {
+    // TODO(rmlarsen): This is just a placeholder. Define interface to make
+    // generators return their cost.
+    return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>());
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void extract_coordinates(Index index, array<Index, NumDims>& coords) const {
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_fast_strides[i];
+        index -= idx * m_strides[i];
+        coords[i] = idx;
+      }
+      coords[0] = index;
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_fast_strides[i];
+        index -= idx * m_strides[i];
+        coords[i] = idx;
+      }
+      coords[NumDims - 1] = index;
+    }
+  }
+
+  const Device EIGEN_DEVICE_REF m_device;
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_strides;
+  array<IndexDivisor, NumDims> m_fast_strides;
+  Generator m_generator;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h
new file mode 100644
index 0000000..6a1240c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h
@@ -0,0 +1,36 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \cpp11 \returns an expression of the coefficient-wise betainc(\a x, \a a, \a b) to the given tensors.
+ *
+ * This function computes the regularized incomplete beta function (integral).
+ *
+ */
+template <typename ADerived, typename BDerived, typename XDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorCwiseTernaryOp<internal::scalar_betainc_op<typename XDerived::Scalar>,
+                                                                 const ADerived, const BDerived, const XDerived>
+betainc(const Eigen::TensorBase<ADerived, ReadOnlyAccessors>& a,
+        const Eigen::TensorBase<BDerived, ReadOnlyAccessors>& b,
+        const Eigen::TensorBase<XDerived, ReadOnlyAccessors>& x) {
+  return TensorCwiseTernaryOp<internal::scalar_betainc_op<typename XDerived::Scalar>, const ADerived, const BDerived,
+                              const XDerived>(a.derived(), b.derived(), x.derived(),
+                                              internal::scalar_betainc_op<typename XDerived::Scalar>());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h
new file mode 100644
index 0000000..0bdb1ab
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h
@@ -0,0 +1,413 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_IO_H
+#define EIGEN_CXX11_TENSOR_TENSOR_IO_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+struct TensorIOFormat;
+
+namespace internal {
+template <typename Tensor, std::size_t rank, typename Format, typename EnableIf = void>
+struct TensorPrinter;
+}
+
+template <typename Derived_>
+struct TensorIOFormatBase {
+  using Derived = Derived_;
+  TensorIOFormatBase(const std::vector<std::string>& separator, const std::vector<std::string>& prefix,
+                     const std::vector<std::string>& suffix, int precision = StreamPrecision, int flags = 0,
+                     const std::string& tenPrefix = "", const std::string& tenSuffix = "", const char fill = ' ')
+      : tenPrefix(tenPrefix),
+        tenSuffix(tenSuffix),
+        prefix(prefix),
+        suffix(suffix),
+        separator(separator),
+        fill(fill),
+        precision(precision),
+        flags(flags) {
+    init_spacer();
+  }
+
+  void init_spacer() {
+    if ((flags & DontAlignCols)) return;
+    spacer.resize(prefix.size());
+    spacer[0] = "";
+    int i = int(tenPrefix.length()) - 1;
+    while (i >= 0 && tenPrefix[i] != '\n') {
+      spacer[0] += ' ';
+      i--;
+    }
+
+    for (std::size_t k = 1; k < prefix.size(); k++) {
+      int j = int(prefix[k].length()) - 1;
+      while (j >= 0 && prefix[k][j] != '\n') {
+        spacer[k] += ' ';
+        j--;
+      }
+    }
+  }
+
+  std::string tenPrefix;
+  std::string tenSuffix;
+  std::vector<std::string> prefix;
+  std::vector<std::string> suffix;
+  std::vector<std::string> separator;
+  char fill;
+  int precision;
+  int flags;
+  std::vector<std::string> spacer{};
+};
+
+struct TensorIOFormatNumpy : public TensorIOFormatBase<TensorIOFormatNumpy> {
+  using Base = TensorIOFormatBase<TensorIOFormatNumpy>;
+  TensorIOFormatNumpy()
+      : Base(/*separator=*/{" ", "\n"}, /*prefix=*/{"", "["}, /*suffix=*/{"", "]"}, /*precision=*/StreamPrecision,
+             /*flags=*/0, /*tenPrefix=*/"[", /*tenSuffix=*/"]") {}
+};
+
+struct TensorIOFormatNative : public TensorIOFormatBase<TensorIOFormatNative> {
+  using Base = TensorIOFormatBase<TensorIOFormatNative>;
+  TensorIOFormatNative()
+      : Base(/*separator=*/{", ", ",\n", "\n"}, /*prefix=*/{"", "{"}, /*suffix=*/{"", "}"},
+             /*precision=*/StreamPrecision, /*flags=*/0, /*tenPrefix=*/"{", /*tenSuffix=*/"}") {}
+};
+
+struct TensorIOFormatPlain : public TensorIOFormatBase<TensorIOFormatPlain> {
+  using Base = TensorIOFormatBase<TensorIOFormatPlain>;
+  TensorIOFormatPlain()
+      : Base(/*separator=*/{" ", "\n", "\n", ""}, /*prefix=*/{""}, /*suffix=*/{""}, /*precision=*/StreamPrecision,
+             /*flags=*/0, /*tenPrefix=*/"", /*tenSuffix=*/"") {}
+};
+
+struct TensorIOFormatLegacy : public TensorIOFormatBase<TensorIOFormatLegacy> {
+  using Base = TensorIOFormatBase<TensorIOFormatLegacy>;
+  TensorIOFormatLegacy()
+      : Base(/*separator=*/{", ", "\n"}, /*prefix=*/{"", "["}, /*suffix=*/{"", "]"}, /*precision=*/StreamPrecision,
+             /*flags=*/0, /*tenPrefix=*/"", /*tenSuffix=*/"") {}
+};
+
+struct TensorIOFormat : public TensorIOFormatBase<TensorIOFormat> {
+  using Base = TensorIOFormatBase<TensorIOFormat>;
+  TensorIOFormat(const std::vector<std::string>& separator, const std::vector<std::string>& prefix,
+                 const std::vector<std::string>& suffix, int precision = StreamPrecision, int flags = 0,
+                 const std::string& tenPrefix = "", const std::string& tenSuffix = "", const char fill = ' ')
+      : Base(separator, prefix, suffix, precision, flags, tenPrefix, tenSuffix, fill) {}
+
+  static inline const TensorIOFormatNumpy Numpy() { return TensorIOFormatNumpy{}; }
+
+  static inline const TensorIOFormatPlain Plain() { return TensorIOFormatPlain{}; }
+
+  static inline const TensorIOFormatNative Native() { return TensorIOFormatNative{}; }
+
+  static inline const TensorIOFormatLegacy Legacy() { return TensorIOFormatLegacy{}; }
+};
+
+template <typename T, int Layout, int rank, typename Format>
+class TensorWithFormat;
+// specialize for Layout=ColMajor, Layout=RowMajor and rank=0.
+template <typename T, int rank, typename Format>
+class TensorWithFormat<T, RowMajor, rank, Format> {
+ public:
+  TensorWithFormat(const T& tensor, const Format& format) : t_tensor(tensor), t_format(format) {}
+
+  friend std::ostream& operator<<(std::ostream& os, const TensorWithFormat<T, RowMajor, rank, Format>& wf) {
+    // Evaluate the expression if needed
+    typedef TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice> Evaluator;
+    TensorForcedEvalOp<const T> eval = wf.t_tensor.eval();
+    Evaluator tensor(eval, DefaultDevice());
+    tensor.evalSubExprsIfNeeded(NULL);
+    internal::TensorPrinter<Evaluator, rank, Format>::run(os, tensor, wf.t_format);
+    // Cleanup.
+    tensor.cleanup();
+    return os;
+  }
+
+ protected:
+  T t_tensor;
+  Format t_format;
+};
+
+template <typename T, int rank, typename Format>
+class TensorWithFormat<T, ColMajor, rank, Format> {
+ public:
+  TensorWithFormat(const T& tensor, const Format& format) : t_tensor(tensor), t_format(format) {}
+
+  friend std::ostream& operator<<(std::ostream& os, const TensorWithFormat<T, ColMajor, rank, Format>& wf) {
+    // Switch to RowMajor storage and print afterwards
+    typedef typename T::Index IndexType;
+    std::array<IndexType, rank> shuffle;
+    std::array<IndexType, rank> id;
+    std::iota(id.begin(), id.end(), IndexType(0));
+    std::copy(id.begin(), id.end(), shuffle.rbegin());
+    auto tensor_row_major = wf.t_tensor.swap_layout().shuffle(shuffle);
+
+    // Evaluate the expression if needed
+    typedef TensorEvaluator<const TensorForcedEvalOp<const decltype(tensor_row_major)>, DefaultDevice> Evaluator;
+    TensorForcedEvalOp<const decltype(tensor_row_major)> eval = tensor_row_major.eval();
+    Evaluator tensor(eval, DefaultDevice());
+    tensor.evalSubExprsIfNeeded(NULL);
+    internal::TensorPrinter<Evaluator, rank, Format>::run(os, tensor, wf.t_format);
+    // Cleanup.
+    tensor.cleanup();
+    return os;
+  }
+
+ protected:
+  T t_tensor;
+  Format t_format;
+};
+
+template <typename T, typename Format>
+class TensorWithFormat<T, ColMajor, 0, Format> {
+ public:
+  TensorWithFormat(const T& tensor, const Format& format) : t_tensor(tensor), t_format(format) {}
+
+  friend std::ostream& operator<<(std::ostream& os, const TensorWithFormat<T, ColMajor, 0, Format>& wf) {
+    // Evaluate the expression if needed
+    typedef TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice> Evaluator;
+    TensorForcedEvalOp<const T> eval = wf.t_tensor.eval();
+    Evaluator tensor(eval, DefaultDevice());
+    tensor.evalSubExprsIfNeeded(NULL);
+    internal::TensorPrinter<Evaluator, 0, Format>::run(os, tensor, wf.t_format);
+    // Cleanup.
+    tensor.cleanup();
+    return os;
+  }
+
+ protected:
+  T t_tensor;
+  Format t_format;
+};
+
+namespace internal {
+
+// Default scalar printer.
+template <typename Scalar, typename Format, typename EnableIf = void>
+struct ScalarPrinter {
+  static void run(std::ostream& stream, const Scalar& scalar, const Format&) { stream << scalar; }
+};
+
+template <typename Scalar>
+struct ScalarPrinter<Scalar, TensorIOFormatNumpy, std::enable_if_t<NumTraits<Scalar>::IsComplex>> {
+  static void run(std::ostream& stream, const Scalar& scalar, const TensorIOFormatNumpy&) {
+    stream << numext::real(scalar) << "+" << numext::imag(scalar) << "j";
+  }
+};
+
+template <typename Scalar>
+struct ScalarPrinter<Scalar, TensorIOFormatNative, std::enable_if_t<NumTraits<Scalar>::IsComplex>> {
+  static void run(std::ostream& stream, const Scalar& scalar, const TensorIOFormatNative&) {
+    stream << "{" << numext::real(scalar) << ", " << numext::imag(scalar) << "}";
+  }
+};
+
+template <typename Tensor, std::size_t rank, typename Format, typename EnableIf>
+struct TensorPrinter {
+  using Scalar = std::remove_const_t<typename Tensor::Scalar>;
+
+  static void run(std::ostream& s, const Tensor& tensor, const Format& fmt) {
+    typedef typename Tensor::Index IndexType;
+
+    eigen_assert(Tensor::Layout == RowMajor);
+    typedef std::conditional_t<is_same<Scalar, char>::value || is_same<Scalar, unsigned char>::value ||
+                                   is_same<Scalar, numext::int8_t>::value || is_same<Scalar, numext::uint8_t>::value,
+                               int,
+                               std::conditional_t<is_same<Scalar, std::complex<char>>::value ||
+                                                      is_same<Scalar, std::complex<unsigned char>>::value ||
+                                                      is_same<Scalar, std::complex<numext::int8_t>>::value ||
+                                                      is_same<Scalar, std::complex<numext::uint8_t>>::value,
+                                                  std::complex<int>, const Scalar&>>
+        PrintType;
+
+    const IndexType total_size = array_prod(tensor.dimensions());
+
+    std::streamsize explicit_precision;
+    if (fmt.precision == StreamPrecision) {
+      explicit_precision = 0;
+    } else if (fmt.precision == FullPrecision) {
+      if (NumTraits<Scalar>::IsInteger) {
+        explicit_precision = 0;
+      } else {
+        explicit_precision = significant_decimals_impl<Scalar>::run();
+      }
+    } else {
+      explicit_precision = fmt.precision;
+    }
+
+    std::streamsize old_precision = 0;
+    if (explicit_precision) old_precision = s.precision(explicit_precision);
+
+    IndexType width = 0;
+    bool align_cols = !(fmt.flags & DontAlignCols);
+    if (align_cols) {
+      // compute the largest width
+      for (IndexType i = 0; i < total_size; i++) {
+        std::stringstream sstr;
+        sstr.copyfmt(s);
+        ScalarPrinter<Scalar, Format>::run(sstr, static_cast<PrintType>(tensor.data()[i]), fmt);
+        width = std::max<IndexType>(width, IndexType(sstr.str().length()));
+      }
+    }
+    s << fmt.tenPrefix;
+    for (IndexType i = 0; i < total_size; i++) {
+      std::array<bool, rank> is_at_end{};
+      std::array<bool, rank> is_at_begin{};
+
+      // is the ith element the end of an coeff (always true), of a row, of a matrix, ...?
+      for (std::size_t k = 0; k < rank; k++) {
+        if ((i + 1) % (std::accumulate(tensor.dimensions().rbegin(), tensor.dimensions().rbegin() + k, 1,
+                                       std::multiplies<IndexType>())) ==
+            0) {
+          is_at_end[k] = true;
+        }
+      }
+
+      // is the ith element the begin of an coeff (always true), of a row, of a matrix, ...?
+      for (std::size_t k = 0; k < rank; k++) {
+        if (i % (std::accumulate(tensor.dimensions().rbegin(), tensor.dimensions().rbegin() + k, 1,
+                                 std::multiplies<IndexType>())) ==
+            0) {
+          is_at_begin[k] = true;
+        }
+      }
+
+      // do we have a line break?
+      bool is_at_begin_after_newline = false;
+      for (std::size_t k = 0; k < rank; k++) {
+        if (is_at_begin[k]) {
+          std::size_t separator_index = (k < fmt.separator.size()) ? k : fmt.separator.size() - 1;
+          if (fmt.separator[separator_index].find('\n') != std::string::npos) {
+            is_at_begin_after_newline = true;
+          }
+        }
+      }
+
+      bool is_at_end_before_newline = false;
+      for (std::size_t k = 0; k < rank; k++) {
+        if (is_at_end[k]) {
+          std::size_t separator_index = (k < fmt.separator.size()) ? k : fmt.separator.size() - 1;
+          if (fmt.separator[separator_index].find('\n') != std::string::npos) {
+            is_at_end_before_newline = true;
+          }
+        }
+      }
+
+      std::stringstream suffix, prefix, separator;
+      for (std::size_t k = 0; k < rank; k++) {
+        std::size_t suffix_index = (k < fmt.suffix.size()) ? k : fmt.suffix.size() - 1;
+        if (is_at_end[k]) {
+          suffix << fmt.suffix[suffix_index];
+        }
+      }
+      for (std::size_t k = 0; k < rank; k++) {
+        std::size_t separator_index = (k < fmt.separator.size()) ? k : fmt.separator.size() - 1;
+        if (is_at_end[k] &&
+            (!is_at_end_before_newline || fmt.separator[separator_index].find('\n') != std::string::npos)) {
+          separator << fmt.separator[separator_index];
+        }
+      }
+      for (std::size_t k = 0; k < rank; k++) {
+        std::size_t spacer_index = (k < fmt.spacer.size()) ? k : fmt.spacer.size() - 1;
+        if (i != 0 && is_at_begin_after_newline && (!is_at_begin[k] || k == 0)) {
+          prefix << fmt.spacer[spacer_index];
+        }
+      }
+      for (int k = rank - 1; k >= 0; k--) {
+        std::size_t prefix_index = (static_cast<std::size_t>(k) < fmt.prefix.size()) ? k : fmt.prefix.size() - 1;
+        if (is_at_begin[k]) {
+          prefix << fmt.prefix[prefix_index];
+        }
+      }
+
+      s << prefix.str();
+      // So we don't mess around with formatting, output scalar to a string stream, and adjust the width/fill manually.
+      std::stringstream sstr;
+      sstr.copyfmt(s);
+      ScalarPrinter<Scalar, Format>::run(sstr, static_cast<PrintType>(tensor.data()[i]), fmt);
+      std::string scalar_str = sstr.str();
+      IndexType scalar_width = scalar_str.length();
+      if (width && scalar_width < width) {
+        std::string filler;
+        for (IndexType j = scalar_width; j < width; ++j) {
+          filler.push_back(fmt.fill);
+        }
+        s << filler;
+      }
+      s << scalar_str;
+      s << suffix.str();
+      if (i < total_size - 1) {
+        s << separator.str();
+      }
+    }
+    s << fmt.tenSuffix;
+    if (explicit_precision) s.precision(old_precision);
+  }
+};
+
+template <typename Tensor, std::size_t rank>
+struct TensorPrinter<Tensor, rank, TensorIOFormatLegacy, std::enable_if_t<rank != 0>> {
+  using Format = TensorIOFormatLegacy;
+  using Scalar = std::remove_const_t<typename Tensor::Scalar>;
+
+  static void run(std::ostream& s, const Tensor& tensor, const Format&) {
+    typedef typename Tensor::Index IndexType;
+    // backwards compatibility case: print tensor after reshaping to matrix of size dim(0) x
+    // (dim(1)*dim(2)*...*dim(rank-1)).
+    const IndexType total_size = internal::array_prod(tensor.dimensions());
+    if (total_size > 0) {
+      const IndexType first_dim = Eigen::internal::array_get<0>(tensor.dimensions());
+      Map<const Array<Scalar, Dynamic, Dynamic, Tensor::Layout>> matrix(tensor.data(), first_dim,
+                                                                        total_size / first_dim);
+      s << matrix;
+      return;
+    }
+  }
+};
+
+template <typename Tensor, typename Format>
+struct TensorPrinter<Tensor, 0, Format> {
+  static void run(std::ostream& s, const Tensor& tensor, const Format& fmt) {
+    using Scalar = std::remove_const_t<typename Tensor::Scalar>;
+
+    std::streamsize explicit_precision;
+    if (fmt.precision == StreamPrecision) {
+      explicit_precision = 0;
+    } else if (fmt.precision == FullPrecision) {
+      if (NumTraits<Scalar>::IsInteger) {
+        explicit_precision = 0;
+      } else {
+        explicit_precision = significant_decimals_impl<Scalar>::run();
+      }
+    } else {
+      explicit_precision = fmt.precision;
+    }
+
+    std::streamsize old_precision = 0;
+    if (explicit_precision) old_precision = s.precision(explicit_precision);
+    s << fmt.tenPrefix;
+    ScalarPrinter<Scalar, Format>::run(s, tensor.coeff(0), fmt);
+    s << fmt.tenSuffix;
+    if (explicit_precision) s.precision(old_precision);
+  }
+};
+
+}  // end namespace internal
+template <typename T>
+std::ostream& operator<<(std::ostream& s, const TensorBase<T, ReadOnlyAccessors>& t) {
+  s << t.format(TensorIOFormat::Plain());
+  return s;
+}
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_IO_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
new file mode 100644
index 0000000..2f94ce5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
@@ -0,0 +1,590 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
+#define EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType> {
+  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions + 1;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense> {
+  typedef const TensorImagePatchOp<Rows, Cols, XprType>& type;
+};
+
+template <DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1,
+              typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type> {
+  typedef TensorImagePatchOp<Rows, Cols, XprType> type;
+};
+
+template <typename Self, bool Vectorizable>
+struct ImagePatchCopyOp {
+  typedef typename Self::Index Index;
+  typedef typename Self::Scalar Scalar;
+  typedef typename Self::Impl Impl;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Self& self, const Index num_coeff_to_copy,
+                                                        const Index dst_index, Scalar* dst_data,
+                                                        const Index src_index) {
+    const Impl& impl = self.impl();
+    for (Index i = 0; i < num_coeff_to_copy; ++i) {
+      dst_data[dst_index + i] = impl.coeff(src_index + i);
+    }
+  }
+};
+
+template <typename Self>
+struct ImagePatchCopyOp<Self, true> {
+  typedef typename Self::Index Index;
+  typedef typename Self::Scalar Scalar;
+  typedef typename Self::Impl Impl;
+  typedef typename packet_traits<Scalar>::type Packet;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Self& self, const Index num_coeff_to_copy,
+                                                        const Index dst_index, Scalar* dst_data,
+                                                        const Index src_index) {
+    const Impl& impl = self.impl();
+    const Index packet_size = internal::unpacket_traits<Packet>::size;
+    const Index vectorized_size = (num_coeff_to_copy / packet_size) * packet_size;
+    for (Index i = 0; i < vectorized_size; i += packet_size) {
+      Packet p = impl.template packet<Unaligned>(src_index + i);
+      internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i, p);
+    }
+    for (Index i = vectorized_size; i < num_coeff_to_copy; ++i) {
+      dst_data[dst_index + i] = impl.coeff(src_index + i);
+    }
+  }
+};
+
+template <typename Self>
+struct ImagePatchPaddingOp {
+  typedef typename Self::Index Index;
+  typedef typename Self::Scalar Scalar;
+  typedef typename packet_traits<Scalar>::type Packet;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Index num_coeff_to_pad, const Scalar padding_value,
+                                                        const Index dst_index, Scalar* dst_data) {
+    const Index packet_size = internal::unpacket_traits<Packet>::size;
+    const Packet padded_packet = internal::pset1<Packet>(padding_value);
+    const Index vectorized_size = (num_coeff_to_pad / packet_size) * packet_size;
+    for (Index i = 0; i < vectorized_size; i += packet_size) {
+      internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i, padded_packet);
+    }
+    for (Index i = vectorized_size; i < num_coeff_to_pad; ++i) {
+      dst_data[dst_index + i] = padding_value;
+    }
+  }
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Patch extraction specialized for image processing.
+ * This assumes that the input has a least 3 dimensions ordered as follow:
+ *  1st dimension: channels (of size d)
+ *  2nd dimension: rows (of size r)
+ *  3rd dimension: columns (of size c)
+ *  There can be additional dimensions such as time (for video) or batch (for
+ * bulk processing after the first 3.
+ * Calling the image patch code with patch_rows and patch_cols is equivalent
+ * to calling the regular patch extraction code with parameters d, patch_rows,
+ * patch_cols, and 1 for all the additional dimensions.
+ */
+template <DenseIndex Rows, DenseIndex Cols, typename XprType>
+class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorImagePatchOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows,
+                                                           DenseIndex patch_cols, DenseIndex row_strides,
+                                                           DenseIndex col_strides, DenseIndex in_row_strides,
+                                                           DenseIndex in_col_strides, DenseIndex row_inflate_strides,
+                                                           DenseIndex col_inflate_strides, PaddingType padding_type,
+                                                           Scalar padding_value)
+      : m_xpr(expr),
+        m_patch_rows(patch_rows),
+        m_patch_cols(patch_cols),
+        m_row_strides(row_strides),
+        m_col_strides(col_strides),
+        m_in_row_strides(in_row_strides),
+        m_in_col_strides(in_col_strides),
+        m_row_inflate_strides(row_inflate_strides),
+        m_col_inflate_strides(col_inflate_strides),
+        m_padding_explicit(false),
+        m_padding_top(0),
+        m_padding_bottom(0),
+        m_padding_left(0),
+        m_padding_right(0),
+        m_padding_type(padding_type),
+        m_padding_value(padding_value) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows,
+                                                           DenseIndex patch_cols, DenseIndex row_strides,
+                                                           DenseIndex col_strides, DenseIndex in_row_strides,
+                                                           DenseIndex in_col_strides, DenseIndex row_inflate_strides,
+                                                           DenseIndex col_inflate_strides, DenseIndex padding_top,
+                                                           DenseIndex padding_bottom, DenseIndex padding_left,
+                                                           DenseIndex padding_right, Scalar padding_value)
+      : m_xpr(expr),
+        m_patch_rows(patch_rows),
+        m_patch_cols(patch_cols),
+        m_row_strides(row_strides),
+        m_col_strides(col_strides),
+        m_in_row_strides(in_row_strides),
+        m_in_col_strides(in_col_strides),
+        m_row_inflate_strides(row_inflate_strides),
+        m_col_inflate_strides(col_inflate_strides),
+        m_padding_explicit(true),
+        m_padding_top(padding_top),
+        m_padding_bottom(padding_bottom),
+        m_padding_left(padding_left),
+        m_padding_right(padding_right),
+        m_padding_type(PADDING_VALID),
+        m_padding_value(padding_value) {}
+
+  EIGEN_DEVICE_FUNC DenseIndex patch_rows() const { return m_patch_rows; }
+  EIGEN_DEVICE_FUNC DenseIndex patch_cols() const { return m_patch_cols; }
+  EIGEN_DEVICE_FUNC DenseIndex row_strides() const { return m_row_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex col_strides() const { return m_col_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex in_row_strides() const { return m_in_row_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex in_col_strides() const { return m_in_col_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
+  EIGEN_DEVICE_FUNC bool padding_explicit() const { return m_padding_explicit; }
+  EIGEN_DEVICE_FUNC DenseIndex padding_top() const { return m_padding_top; }
+  EIGEN_DEVICE_FUNC DenseIndex padding_bottom() const { return m_padding_bottom; }
+  EIGEN_DEVICE_FUNC DenseIndex padding_left() const { return m_padding_left; }
+  EIGEN_DEVICE_FUNC DenseIndex padding_right() const { return m_padding_right; }
+  EIGEN_DEVICE_FUNC PaddingType padding_type() const { return m_padding_type; }
+  EIGEN_DEVICE_FUNC Scalar padding_value() const { return m_padding_value; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const DenseIndex m_patch_rows;
+  const DenseIndex m_patch_cols;
+  const DenseIndex m_row_strides;
+  const DenseIndex m_col_strides;
+  const DenseIndex m_in_row_strides;
+  const DenseIndex m_in_col_strides;
+  const DenseIndex m_row_inflate_strides;
+  const DenseIndex m_col_inflate_strides;
+  const bool m_padding_explicit;
+  const DenseIndex m_padding_top;
+  const DenseIndex m_padding_bottom;
+  const DenseIndex m_padding_left;
+  const DenseIndex m_padding_right;
+  const PaddingType m_padding_type;
+  const Scalar m_padding_value;
+};
+
+// Eval as rvalue
+template <DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device> {
+  typedef TensorImagePatchOp<Rows, Cols, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumInputDims =
+      internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static constexpr int NumDims = NumInputDims + 1;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
+  typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device> Self;
+  typedef TensorEvaluator<ArgType, Device> Impl;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = true,
+    CoordAccess = false,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_device(device), m_impl(op.expression(), device) {
+    EIGEN_STATIC_ASSERT((NumDims >= 4), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    m_paddingValue = op.padding_value();
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+
+    // Caches a few variables.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputDepth = input_dims[0];
+      m_inputRows = input_dims[1];
+      m_inputCols = input_dims[2];
+    } else {
+      m_inputDepth = input_dims[NumInputDims - 1];
+      m_inputRows = input_dims[NumInputDims - 2];
+      m_inputCols = input_dims[NumInputDims - 3];
+    }
+
+    m_row_strides = op.row_strides();
+    m_col_strides = op.col_strides();
+
+    // Input strides and effective input/patch size
+    m_in_row_strides = op.in_row_strides();
+    m_in_col_strides = op.in_col_strides();
+    m_row_inflate_strides = op.row_inflate_strides();
+    m_col_inflate_strides = op.col_inflate_strides();
+    // The "effective" input rows and input cols are the input rows and cols
+    // after inflating them with zeros.
+    // For examples, a 2x3 matrix with row_inflate_strides and
+    // col_inflate_strides of 2 comes from:
+    //   A B C
+    //   D E F
+    //
+    // to a matrix is 3 x 5:
+    //
+    //   A . B . C
+    //   . . . . .
+    //   D . E . F
+
+    m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
+    m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
+    m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
+    m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
+
+    if (op.padding_explicit()) {
+      m_outputRows = numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) /
+                                  static_cast<float>(m_row_strides));
+      m_outputCols = numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) /
+                                  static_cast<float>(m_col_strides));
+      m_rowPaddingTop = op.padding_top();
+      m_colPaddingLeft = op.padding_left();
+    } else {
+      // Computing padding from the type
+      switch (op.padding_type()) {
+        case PADDING_VALID:
+          m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
+          m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
+          // Calculate the padding
+          m_rowPaddingTop =
+              numext::maxi<Index>(0, ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2);
+          m_colPaddingLeft =
+              numext::maxi<Index>(0, ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2);
+          break;
+        case PADDING_SAME:
+          m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
+          m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
+          // Calculate the padding
+          m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2;
+          m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2;
+          // The padding size calculation for PADDING_SAME has been updated to
+          // be consistent with how TensorFlow extracts its paddings.
+          m_rowPaddingTop = numext::maxi<Index>(0, m_rowPaddingTop);
+          m_colPaddingLeft = numext::maxi<Index>(0, m_colPaddingLeft);
+          break;
+        default:
+          eigen_assert(false && "unexpected padding");
+          m_outputCols = 0;  // Silence the uninitialized warning.
+          m_outputRows = 0;  // Silence the uninitialized warning.
+      }
+    }
+    eigen_assert(m_outputRows > 0);
+    eigen_assert(m_outputCols > 0);
+
+    // Dimensions for result of extraction.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      // ColMajor
+      // 0: depth
+      // 1: patch_rows
+      // 2: patch_cols
+      // 3: number of patches
+      // 4 and beyond: anything else (such as batch).
+      m_dimensions[0] = input_dims[0];
+      m_dimensions[1] = op.patch_rows();
+      m_dimensions[2] = op.patch_cols();
+      m_dimensions[3] = m_outputRows * m_outputCols;
+      for (int i = 4; i < NumDims; ++i) {
+        m_dimensions[i] = input_dims[i - 1];
+      }
+    } else {
+      // RowMajor
+      // NumDims-1: depth
+      // NumDims-2: patch_rows
+      // NumDims-3: patch_cols
+      // NumDims-4: number of patches
+      // NumDims-5 and beyond: anything else (such as batch).
+      m_dimensions[NumDims - 1] = input_dims[NumInputDims - 1];
+      m_dimensions[NumDims - 2] = op.patch_rows();
+      m_dimensions[NumDims - 3] = op.patch_cols();
+      m_dimensions[NumDims - 4] = m_outputRows * m_outputCols;
+      for (int i = NumDims - 5; i >= 0; --i) {
+        m_dimensions[i] = input_dims[i];
+      }
+    }
+
+    // Strides for moving the patch in various dimensions.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_colStride = m_dimensions[1];
+      m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0];
+      m_otherStride = m_patchStride * m_dimensions[3];
+    } else {
+      m_colStride = m_dimensions[NumDims - 2];
+      m_patchStride = m_colStride * m_dimensions[NumDims - 3] * m_dimensions[NumDims - 1];
+      m_otherStride = m_patchStride * m_dimensions[NumDims - 4];
+    }
+
+    // Strides for navigating through the input tensor.
+    m_rowInputStride = m_inputDepth;
+    m_colInputStride = m_inputDepth * m_inputRows;
+    m_patchInputStride = m_inputDepth * m_inputRows * m_inputCols;
+
+    // Fast representations of different variables.
+    m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
+    m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
+    m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
+    m_fastInflateRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
+    m_fastInflateColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
+    m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);
+
+    // Number of patches in the width dimension.
+    m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows);
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
+    } else {
+      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims - 1]);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    // Patch index corresponding to the passed in index.
+    const Index patchIndex = index / m_fastPatchStride;
+    // Find the offset of the element wrt the location of the first element.
+    const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;
+
+    // Other ways to index this element.
+    const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride;
+    const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
+
+    // Calculate col index in the input original tensor.
+    const Index colIndex = patch2DIndex / m_fastOutputRows;
+    const Index colOffset = patchOffset / m_fastColStride;
+    const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
+    const Index origInputCol =
+        (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInflateColStride) : 0);
+    if (inputCol < 0 || inputCol >= m_input_cols_eff ||
+        ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
+      return Scalar(m_paddingValue);
+    }
+
+    // Calculate row index in the original input tensor.
+    const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
+    const Index rowOffset = patchOffset - colOffset * m_colStride;
+    const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
+    const Index origInputRow =
+        (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInflateRowStride) : 0);
+    if (inputRow < 0 || inputRow >= m_input_rows_eff ||
+        ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
+      return Scalar(m_paddingValue);
+    }
+
+    const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+    const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
+
+    const Index inputIndex =
+        depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex * m_patchInputStride;
+    return m_impl.coeff(inputIndex);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1) {
+      return packetWithPossibleZero(index);
+    }
+
+    const Index indices[2] = {index, index + PacketSize - 1};
+    const Index patchIndex = indices[0] / m_fastPatchStride;
+    if (patchIndex != indices[1] / m_fastPatchStride) {
+      return packetWithPossibleZero(index);
+    }
+    const Index otherIndex = (NumDims == 4) ? 0 : indices[0] / m_fastOtherStride;
+    eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
+
+    // Find the offset of the element wrt the location of the first element.
+    const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth,
+                                   (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth};
+
+    const Index patch2DIndex =
+        (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
+    eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
+
+    const Index colIndex = patch2DIndex / m_fastOutputRows;
+    const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};
+
+    // Calculate col indices in the original input tensor.
+    const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] - m_colPaddingLeft,
+                                colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
+    if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
+      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
+    }
+
+    if (inputCols[0] == inputCols[1]) {
+      const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
+      const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0] * m_colStride,
+                                   patchOffsets[1] - colOffsets[1] * m_colStride};
+      eigen_assert(rowOffsets[0] <= rowOffsets[1]);
+      // Calculate row indices in the original input tensor.
+      const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] - m_rowPaddingTop,
+                                  rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
+
+      if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
+        return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
+      }
+
+      if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) {
+        // no padding
+        const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+        const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
+        const Index inputIndex =
+            depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride;
+        return m_impl.template packet<Unaligned>(inputIndex);
+      }
+    }
+
+    return packetWithPossibleZero(index);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowPaddingTop() const { return m_rowPaddingTop; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colPaddingLeft() const { return m_colPaddingLeft; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputRows() const { return m_outputRows; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputCols() const { return m_outputCols; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userRowStride() const { return m_row_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userColStride() const { return m_col_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInRowStride() const { return m_in_row_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInColStride() const { return m_in_col_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowInflateStride() const { return m_row_inflate_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colInflateStride() const { return m_col_inflate_strides; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // We conservatively estimate the cost for the code path where the computed
+    // index is inside the original image and
+    // TensorEvaluator<ArgType, Device>::CoordAccess is false.
+    const double compute_cost =
+        3 * TensorOpCost::DivCost<Index>() + 6 * TensorOpCost::MulCost<Index>() + 8 * TensorOpCost::MulCost<Index>();
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+  }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const {
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index + i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  Dimensions m_dimensions;
+
+  Index m_otherStride;
+  Index m_patchStride;
+  Index m_colStride;
+  Index m_row_strides;
+  Index m_col_strides;
+
+  Index m_in_row_strides;
+  Index m_in_col_strides;
+  Index m_row_inflate_strides;
+  Index m_col_inflate_strides;
+
+  Index m_input_rows_eff;
+  Index m_input_cols_eff;
+  Index m_patch_rows_eff;
+  Index m_patch_cols_eff;
+
+  internal::TensorIntDivisor<Index> m_fastOtherStride;
+  internal::TensorIntDivisor<Index> m_fastPatchStride;
+  internal::TensorIntDivisor<Index> m_fastColStride;
+  internal::TensorIntDivisor<Index> m_fastInflateRowStride;
+  internal::TensorIntDivisor<Index> m_fastInflateColStride;
+  internal::TensorIntDivisor<Index> m_fastInputColsEff;
+
+  Index m_rowInputStride;
+  Index m_colInputStride;
+  Index m_patchInputStride;
+
+  Index m_inputDepth;
+  Index m_inputRows;
+  Index m_inputCols;
+
+  Index m_outputRows;
+  Index m_outputCols;
+
+  Index m_rowPaddingTop;
+  Index m_colPaddingLeft;
+
+  internal::TensorIntDivisor<Index> m_fastOutputRows;
+  internal::TensorIntDivisor<Index> m_fastOutputDepth;
+
+  Scalar m_paddingValue;
+
+  const Device EIGEN_DEVICE_REF m_device;
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h
new file mode 100644
index 0000000..1071cf3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h
@@ -0,0 +1,617 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H
+#define EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <Index n>
+struct type2index {
+  static constexpr Index value = n;
+  EIGEN_DEVICE_FUNC constexpr operator Index() const { return n; }
+  EIGEN_DEVICE_FUNC void set(Index val) { eigen_assert(val == n); }
+};
+
+// This can be used with IndexPairList to get compile-time constant pairs,
+// such as IndexPairList<type2indexpair<1,2>, type2indexpair<3,4>>().
+template <Index f, Index s>
+struct type2indexpair {
+  static constexpr Index first = f;
+  static constexpr Index second = s;
+
+  constexpr EIGEN_DEVICE_FUNC operator IndexPair<Index>() const { return IndexPair<Index>(f, s); }
+
+  EIGEN_DEVICE_FUNC void set(const IndexPair<Index>& val) {
+    eigen_assert(val.first == f);
+    eigen_assert(val.second == s);
+  }
+};
+
+template <Index n>
+struct NumTraits<type2index<n>> {
+  typedef Index Real;
+  enum { IsComplex = 0, RequireInitialization = false, ReadCost = 1, AddCost = 1, MulCost = 1 };
+
+  EIGEN_DEVICE_FUNC static constexpr EIGEN_STRONG_INLINE Real epsilon() { return 0; }
+  EIGEN_DEVICE_FUNC static constexpr EIGEN_STRONG_INLINE Real dummy_precision() { return 0; }
+  EIGEN_DEVICE_FUNC static constexpr EIGEN_STRONG_INLINE Real highest() { return n; }
+  EIGEN_DEVICE_FUNC static constexpr EIGEN_STRONG_INLINE Real lowest() { return n; }
+};
+
+namespace internal {
+template <typename T>
+EIGEN_DEVICE_FUNC void update_value(T& val, Index new_val) {
+  val = internal::convert_index<T>(new_val);
+}
+template <Index n>
+EIGEN_DEVICE_FUNC void update_value(type2index<n>& val, Index new_val) {
+  val.set(new_val);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC void update_value(T& val, IndexPair<Index> new_val) {
+  val = new_val;
+}
+template <Index f, Index s>
+EIGEN_DEVICE_FUNC void update_value(type2indexpair<f, s>& val, IndexPair<Index> new_val) {
+  val.set(new_val);
+}
+
+template <typename T>
+struct is_compile_time_constant {
+  static constexpr bool value = false;
+};
+
+template <Index idx>
+struct is_compile_time_constant<type2index<idx>> {
+  static constexpr bool value = true;
+};
+template <Index idx>
+struct is_compile_time_constant<const type2index<idx>> {
+  static constexpr bool value = true;
+};
+template <Index idx>
+struct is_compile_time_constant<type2index<idx>&> {
+  static constexpr bool value = true;
+};
+template <Index idx>
+struct is_compile_time_constant<const type2index<idx>&> {
+  static constexpr bool value = true;
+};
+
+template <Index f, Index s>
+struct is_compile_time_constant<type2indexpair<f, s>> {
+  static constexpr bool value = true;
+};
+template <Index f, Index s>
+struct is_compile_time_constant<const type2indexpair<f, s>> {
+  static constexpr bool value = true;
+};
+template <Index f, Index s>
+struct is_compile_time_constant<type2indexpair<f, s>&> {
+  static constexpr bool value = true;
+};
+template <Index f, Index s>
+struct is_compile_time_constant<const type2indexpair<f, s>&> {
+  static constexpr bool value = true;
+};
+
+template <typename... T>
+struct IndexTuple;
+
+template <typename T, typename... O>
+struct IndexTuple<T, O...> {
+  EIGEN_DEVICE_FUNC constexpr IndexTuple() : head(), others() {}
+  EIGEN_DEVICE_FUNC constexpr IndexTuple(const T& v, const O... o) : head(v), others(o...) {}
+
+  static constexpr int count = 1 + sizeof...(O);
+  T head;
+  IndexTuple<O...> others;
+  typedef T Head;
+  typedef IndexTuple<O...> Other;
+};
+
+template <typename T>
+struct IndexTuple<T> {
+  EIGEN_DEVICE_FUNC constexpr IndexTuple() : head() {}
+  EIGEN_DEVICE_FUNC constexpr IndexTuple(const T& v) : head(v) {}
+
+  constexpr static int count = 1;
+  T head;
+  typedef T Head;
+};
+
+template <int N, typename... T>
+struct IndexTupleExtractor;
+
+template <int N, typename T, typename... O>
+struct IndexTupleExtractor<N, T, O...> {
+  typedef typename IndexTupleExtractor<N - 1, O...>::ValType ValType;
+
+  EIGEN_DEVICE_FUNC static constexpr ValType& get_val(IndexTuple<T, O...>& val) {
+    return IndexTupleExtractor<N - 1, O...>::get_val(val.others);
+  }
+
+  EIGEN_DEVICE_FUNC static constexpr const ValType& get_val(const IndexTuple<T, O...>& val) {
+    return IndexTupleExtractor<N - 1, O...>::get_val(val.others);
+  }
+  template <typename V>
+  EIGEN_DEVICE_FUNC static void set_val(IndexTuple<T, O...>& val, V& new_val) {
+    IndexTupleExtractor<N - 1, O...>::set_val(val.others, new_val);
+  }
+};
+
+template <typename T, typename... O>
+struct IndexTupleExtractor<0, T, O...> {
+  typedef T ValType;
+
+  EIGEN_DEVICE_FUNC static constexpr ValType& get_val(IndexTuple<T, O...>& val) { return val.head; }
+  EIGEN_DEVICE_FUNC static constexpr const ValType& get_val(const IndexTuple<T, O...>& val) { return val.head; }
+  template <typename V>
+  EIGEN_DEVICE_FUNC static void set_val(IndexTuple<T, O...>& val, V& new_val) {
+    val.head = new_val;
+  }
+};
+
+template <int N, typename T, typename... O>
+EIGEN_DEVICE_FUNC constexpr typename IndexTupleExtractor<N, T, O...>::ValType& array_get(IndexTuple<T, O...>& tuple) {
+  return IndexTupleExtractor<N, T, O...>::get_val(tuple);
+}
+template <int N, typename T, typename... O>
+EIGEN_DEVICE_FUNC constexpr const typename IndexTupleExtractor<N, T, O...>::ValType& array_get(
+    const IndexTuple<T, O...>& tuple) {
+  return IndexTupleExtractor<N, T, O...>::get_val(tuple);
+}
+template <typename T, typename... O>
+struct array_size<IndexTuple<T, O...>> {
+  static constexpr size_t value = IndexTuple<T, O...>::count;
+};
+template <typename T, typename... O>
+struct array_size<const IndexTuple<T, O...>> {
+  static constexpr size_t value = IndexTuple<T, O...>::count;
+};
+
+template <Index Idx, typename ValueT>
+struct tuple_coeff {
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr ValueT get(const Index i, const IndexTuple<T...>& t) {
+    return (i == Idx ? array_get<Idx>(t) : tuple_coeff<Idx - 1, ValueT>::get(i, t));
+  }
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static void set(const Index i, IndexTuple<T...>& t, const ValueT& value) {
+    if (i == Idx) {
+      update_value(array_get<Idx>(t), value);
+    } else {
+      tuple_coeff<Idx - 1, ValueT>::set(i, t, value);
+    }
+  }
+
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool value_known_statically(const Index i, const IndexTuple<T...>& t) {
+    return ((i == Idx) && is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value) ||
+           tuple_coeff<Idx - 1, ValueT>::value_known_statically(i, t);
+  }
+
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_known_statically(const IndexTuple<T...>& t) {
+    return is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value &&
+           tuple_coeff<Idx - 1, ValueT>::values_up_to_known_statically(t);
+  }
+
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_statically_known_to_increase(const IndexTuple<T...>& t) {
+    return is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value &&
+           is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value &&
+           array_get<Idx>(t) > array_get<Idx - 1>(t) &&
+           tuple_coeff<Idx - 1, ValueT>::values_up_to_statically_known_to_increase(t);
+  }
+};
+
+template <typename ValueT>
+struct tuple_coeff<0, ValueT> {
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr ValueT get(const Index /*i*/, const IndexTuple<T...>& t) {
+    return array_get<0>(t);
+  }
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static void set(const Index i, IndexTuple<T...>& t, const ValueT value) {
+    eigen_assert(i == 0);
+    update_value(array_get<0>(t), value);
+  }
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool value_known_statically(const Index i, const IndexTuple<T...>&) {
+    return is_compile_time_constant<typename IndexTupleExtractor<0, T...>::ValType>::value && (i == 0);
+  }
+
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_known_statically(const IndexTuple<T...>&) {
+    return is_compile_time_constant<typename IndexTupleExtractor<0, T...>::ValType>::value;
+  }
+
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_statically_known_to_increase(const IndexTuple<T...>&) {
+    return true;
+  }
+};
+}  // namespace internal
+
+/** \internal
+ *
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Set of classes used to encode a set of Tensor dimensions/indices.
+ *
+ * The indices in the list can be known at compile time or at runtime. A mix
+ * of static and dynamic indices can also be provided if needed. The tensor
+ * code will attempt to take advantage of the indices that are known at
+ * compile time to optimize the code it generates.
+ *
+ * This functionality requires a c++11 compliant compiler. If your compiler
+ * is older you need to use arrays of indices instead.
+ *
+ * Several examples are provided in the cxx11_tensor_index_list.cpp file.
+ *
+ * \sa Tensor
+ */
+
+template <typename FirstType, typename... OtherTypes>
+struct IndexList : internal::IndexTuple<FirstType, OtherTypes...> {
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr Index operator[](const Index i) const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value - 1,
+                                 Index>::get(i, *this);
+  }
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr Index get(const Index i) const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value - 1,
+                                 Index>::get(i, *this);
+  }
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void set(const Index i, const Index value) {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value - 1,
+                                 Index>::set(i, *this, value);
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr std::size_t size() const { return 1 + sizeof...(OtherTypes); };
+
+  EIGEN_DEVICE_FUNC constexpr IndexList(const internal::IndexTuple<FirstType, OtherTypes...>& other)
+      : internal::IndexTuple<FirstType, OtherTypes...>(other) {}
+  EIGEN_DEVICE_FUNC constexpr IndexList(FirstType& first, OtherTypes... other)
+      : internal::IndexTuple<FirstType, OtherTypes...>(first, other...) {}
+  EIGEN_DEVICE_FUNC constexpr IndexList() : internal::IndexTuple<FirstType, OtherTypes...>() {}
+
+  EIGEN_DEVICE_FUNC constexpr bool value_known_statically(const Index i) const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value - 1,
+                                 Index>::value_known_statically(i, *this);
+  }
+  EIGEN_DEVICE_FUNC constexpr bool all_values_known_statically() const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value - 1,
+                                 Index>::values_up_to_known_statically(*this);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr bool values_statically_known_to_increase() const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value - 1,
+                                 Index>::values_up_to_statically_known_to_increase(*this);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+std::ostream& operator<<(std::ostream& os, const IndexList<FirstType, OtherTypes...>& dims) {
+  os << "[";
+  for (size_t i = 0; i < 1 + sizeof...(OtherTypes); ++i) {
+    if (i > 0) os << ", ";
+    os << dims[i];
+  }
+  os << "]";
+  return os;
+}
+
+template <typename FirstType, typename... OtherTypes>
+constexpr IndexList<FirstType, OtherTypes...> make_index_list(FirstType val1, OtherTypes... other_vals) {
+  return IndexList<FirstType, OtherTypes...>(val1, other_vals...);
+}
+
+template <typename FirstType, typename... OtherTypes>
+struct IndexPairList : internal::IndexTuple<FirstType, OtherTypes...> {
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr IndexPair<Index> operator[](const Index i) const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value - 1,
+                                 IndexPair<Index>>::get(i, *this);
+  }
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void set(const Index i, const IndexPair<Index> value) {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value - 1,
+                                 IndexPair<Index>>::set(i, *this, value);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr IndexPairList(const internal::IndexTuple<FirstType, OtherTypes...>& other)
+      : internal::IndexTuple<FirstType, OtherTypes...>(other) {}
+  EIGEN_DEVICE_FUNC constexpr IndexPairList() : internal::IndexTuple<FirstType, OtherTypes...>() {}
+
+  EIGEN_DEVICE_FUNC constexpr bool value_known_statically(const Index i) const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value - 1,
+                                 Index>::value_known_statically(i, *this);
+  }
+};
+
+namespace internal {
+
+template <typename FirstType, typename... OtherTypes>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index array_prod(const IndexList<FirstType, OtherTypes...>& sizes) {
+  Index result = 1;
+  EIGEN_UNROLL_LOOP
+  for (size_t i = 0; i < array_size<IndexList<FirstType, OtherTypes...>>::value; ++i) {
+    result *= sizes[i];
+  }
+  return result;
+}
+
+template <typename FirstType, typename... OtherTypes>
+struct array_size<IndexList<FirstType, OtherTypes...>> {
+  static const size_t value = array_size<IndexTuple<FirstType, OtherTypes...>>::value;
+};
+template <typename FirstType, typename... OtherTypes>
+struct array_size<const IndexList<FirstType, OtherTypes...>> {
+  static const size_t value = array_size<IndexTuple<FirstType, OtherTypes...>>::value;
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct array_size<IndexPairList<FirstType, OtherTypes...>> {
+  static const size_t value = 1 + sizeof...(OtherTypes);
+};
+template <typename FirstType, typename... OtherTypes>
+struct array_size<const IndexPairList<FirstType, OtherTypes...>> {
+  static const size_t value = 1 + sizeof...(OtherTypes);
+};
+
+template <Index N, typename FirstType, typename... OtherTypes>
+EIGEN_DEVICE_FUNC constexpr Index array_get(IndexList<FirstType, OtherTypes...>& a) {
+  return IndexTupleExtractor<N, FirstType, OtherTypes...>::get_val(a);
+}
+template <Index N, typename FirstType, typename... OtherTypes>
+EIGEN_DEVICE_FUNC constexpr Index array_get(const IndexList<FirstType, OtherTypes...>& a) {
+  return IndexTupleExtractor<N, FirstType, OtherTypes...>::get_val(a);
+}
+
+template <typename T>
+struct index_known_statically_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index) { return false; }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_known_statically_impl<IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_known_statically_impl<const IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i);
+  }
+};
+
+template <typename T>
+struct all_indices_known_statically_impl {
+  static constexpr bool run() { return false; }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct all_indices_known_statically_impl<IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return IndexList<FirstType, OtherTypes...>().all_values_known_statically();
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct all_indices_known_statically_impl<const IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return IndexList<FirstType, OtherTypes...>().all_values_known_statically();
+  }
+};
+
+template <typename T>
+struct indices_statically_known_to_increase_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run() { return false; }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct indices_statically_known_to_increase_impl<IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return Eigen::IndexList<FirstType, OtherTypes...>().values_statically_known_to_increase();
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct indices_statically_known_to_increase_impl<const IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return Eigen::IndexList<FirstType, OtherTypes...>().values_statically_known_to_increase();
+  }
+};
+
+template <typename Tx>
+struct index_statically_eq_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) { return false; }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_eq_impl<IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexList<FirstType, OtherTypes...>().get(i) == value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_eq_impl<const IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexList<FirstType, OtherTypes...>().get(i) == value);
+  }
+};
+
+template <typename T>
+struct index_statically_ne_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) { return false; }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_ne_impl<IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexList<FirstType, OtherTypes...>().get(i) != value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_ne_impl<const IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexList<FirstType, OtherTypes...>().get(i) != value);
+  }
+};
+
+template <typename T>
+struct index_statically_gt_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) { return false; }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_gt_impl<IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexList<FirstType, OtherTypes...>().get(i) > value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_gt_impl<const IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexList<FirstType, OtherTypes...>().get(i) > value);
+  }
+};
+
+template <typename T>
+struct index_statically_lt_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) { return false; }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_lt_impl<IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexList<FirstType, OtherTypes...>().get(i) < value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_lt_impl<const IndexList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexList<FirstType, OtherTypes...>().get(i) < value);
+  }
+};
+
+template <typename Tx>
+struct index_pair_first_statically_eq_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) { return false; }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_pair_first_statically_eq_impl<IndexPairList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexPairList<FirstType, OtherTypes...>().operator[](i).first == value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_pair_first_statically_eq_impl<const IndexPairList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexPairList<FirstType, OtherTypes...>().operator[](i).first == value);
+  }
+};
+
+template <typename Tx>
+struct index_pair_second_statically_eq_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) { return false; }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_pair_second_statically_eq_impl<IndexPairList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexPairList<FirstType, OtherTypes...>().operator[](i).second == value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_pair_second_statically_eq_impl<const IndexPairList<FirstType, OtherTypes...>> {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &&
+           (IndexPairList<FirstType, OtherTypes...>().operator[](i).second == value);
+  }
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+namespace Eigen {
+namespace internal {
+template <typename T>
+static EIGEN_DEVICE_FUNC constexpr bool index_known_statically(Index i) {
+  return index_known_statically_impl<T>::run(i);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC constexpr bool all_indices_known_statically() {
+  return all_indices_known_statically_impl<T>::run();
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC constexpr bool indices_statically_known_to_increase() {
+  return indices_statically_known_to_increase_impl<T>::run();
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC constexpr bool index_statically_eq(Index i, Index value) {
+  return index_statically_eq_impl<T>::run(i, value);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC constexpr bool index_statically_ne(Index i, Index value) {
+  return index_statically_ne_impl<T>::run(i, value);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC constexpr bool index_statically_gt(Index i, Index value) {
+  return index_statically_gt_impl<T>::run(i, value);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC constexpr bool index_statically_lt(Index i, Index value) {
+  return index_statically_lt_impl<T>::run(i, value);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC constexpr bool index_pair_first_statically_eq(Index i, Index value) {
+  return index_pair_first_statically_eq_impl<T>::run(i, value);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC constexpr bool index_pair_second_statically_eq(Index i, Index value) {
+  return index_pair_second_statically_eq_impl<T>::run(i, value);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h
new file mode 100644
index 0000000..74a53be
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h
@@ -0,0 +1,224 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Ke Yang <yangke@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Strides, typename XprType>
+struct traits<TensorInflationOp<Strides, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename Strides, typename XprType>
+struct eval<TensorInflationOp<Strides, XprType>, Eigen::Dense> {
+  typedef const TensorInflationOp<Strides, XprType>& type;
+};
+
+template <typename Strides, typename XprType>
+struct nested<TensorInflationOp<Strides, XprType>, 1, typename eval<TensorInflationOp<Strides, XprType> >::type> {
+  typedef TensorInflationOp<Strides, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor inflation class.
+ */
+template <typename Strides, typename XprType>
+class TensorInflationOp : public TensorBase<TensorInflationOp<Strides, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorInflationOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorInflationOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorInflationOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorInflationOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorInflationOp(const XprType& expr, const Strides& strides)
+      : m_xpr(expr), m_strides(strides) {}
+
+  EIGEN_DEVICE_FUNC const Strides& strides() const { return m_strides; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const Strides m_strides;
+};
+
+// Eval as rvalue
+template <typename Strides, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device> {
+  typedef TensorInflationOp<Strides, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_strides(op.strides()) {
+    m_dimensions = m_impl.dimensions();
+    // Expand each dimension to the inflated dimension.
+    for (int i = 0; i < NumDims; ++i) {
+      m_dimensions[i] = (m_dimensions[i] - 1) * op.strides()[i] + 1;
+    }
+
+    // Remember the strides for fast division.
+    for (int i = 0; i < NumDims; ++i) {
+      m_fastStrides[i] = internal::TensorIntDivisor<Index>(m_strides[i]);
+    }
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_outputStrides[0] = 1;
+      m_inputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+        m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1];
+      }
+    } else {  // RowMajor
+      m_outputStrides[NumDims - 1] = 1;
+      m_inputStrides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+        m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  // Computes the input index given the output index. Returns true if the output
+  // index doesn't fall into a hole.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool getInputIndex(Index index, Index* inputIndex) const {
+    eigen_assert(index < dimensions().TotalSize());
+    *inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStrides[i];
+        if (idx != idx / m_fastStrides[i] * m_strides[i]) {
+          return false;
+        }
+        *inputIndex += idx / m_strides[i] * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      if (index != index / m_fastStrides[0] * m_strides[0]) {
+        return false;
+      }
+      *inputIndex += index / m_strides[0];
+      return true;
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_outputStrides[i];
+        if (idx != idx / m_fastStrides[i] * m_strides[i]) {
+          return false;
+        }
+        *inputIndex += idx / m_strides[i] * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      if (index != index / m_fastStrides[NumDims - 1] * m_strides[NumDims - 1]) {
+        return false;
+      }
+      *inputIndex += index / m_strides[NumDims - 1];
+    }
+    return true;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    Index inputIndex = 0;
+    if (getInputIndex(index, &inputIndex)) {
+      return m_impl.coeff(inputIndex);
+    } else {
+      return Scalar(0);
+    }
+  }
+
+  // TODO(yangke): optimize this function so that we can detect and produce
+  // all-zero packets
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index + i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double compute_cost = NumDims * (3 * TensorOpCost::DivCost<Index>() + 3 * TensorOpCost::MulCost<Index>() +
+                                           2 * TensorOpCost::AddCost<Index>());
+    const double input_size = m_impl.dimensions().TotalSize();
+    const double output_size = m_dimensions.TotalSize();
+    if (output_size == 0) return TensorOpCost();
+    return m_impl.costPerCoeff(vectorized) +
+           TensorOpCost(sizeof(CoeffReturnType) * input_size / output_size, 0, compute_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ protected:
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims> m_inputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  const Strides m_strides;
+  array<internal::TensorIntDivisor<Index>, NumDims> m_fastStrides;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h
new file mode 100644
index 0000000..579caac
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h
@@ -0,0 +1,78 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H
+#define EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H
+
+#include <initializer_list>
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Helper template to initialize Tensors from std::initializer_lists.
+ */
+template <typename Derived, int N>
+struct Initializer {
+  typedef std::initializer_list<typename Initializer<Derived, N - 1>::InitList> InitList;
+
+  static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
+                  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>* indices,
+                  const InitList& vals) {
+    int i = 0;
+    for (const auto& v : vals) {
+      (*indices)[traits<Derived>::NumDimensions - N] = i++;
+      Initializer<Derived, N - 1>::run(tensor, indices, v);
+    }
+  }
+};
+
+template <typename Derived>
+struct Initializer<Derived, 1> {
+  typedef std::initializer_list<typename traits<Derived>::Scalar> InitList;
+
+  static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
+                  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>* indices,
+                  const InitList& vals) {
+    int i = 0;
+    // TODO: Consider a faster approach than iterating.
+    for (const auto& v : vals) {
+      (*indices)[traits<Derived>::NumDimensions - 1] = i++;
+      tensor.coeffRef(*indices) = v;
+    }
+  }
+};
+
+template <typename Derived>
+struct Initializer<Derived, 0> {
+  typedef typename traits<Derived>::Scalar InitList;
+
+  static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
+                  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>*, const InitList& v) {
+    tensor.coeffRef(0) = v;
+  }
+};
+
+template <typename Derived, int N>
+void initialize_tensor(TensorEvaluator<Derived, DefaultDevice>& tensor,
+                       const typename Initializer<Derived, traits<Derived>::NumDimensions>::InitList& vals) {
+  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions> indices;
+  Initializer<Derived, traits<Derived>::NumDimensions>::run(tensor, &indices, vals);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h
new file mode 100644
index 0000000..ed25646
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h
@@ -0,0 +1,258 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H
+#define EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Note: result is undefined if val == 0
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::enable_if_t<sizeof(T) == 4, int> count_leading_zeros(const T val) {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+  return __clz(val);
+#elif defined(SYCL_DEVICE_ONLY)
+  return cl::sycl::clz(val);
+#elif EIGEN_COMP_MSVC
+  unsigned long index;
+  _BitScanReverse(&index, val);
+  return 31 - index;
+#else
+  EIGEN_STATIC_ASSERT(sizeof(unsigned long long) == 8, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  return __builtin_clz(static_cast<uint32_t>(val));
+#endif
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::enable_if_t<sizeof(T) == 8, int> count_leading_zeros(const T val) {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+  return __clzll(val);
+#elif defined(SYCL_DEVICE_ONLY)
+  return static_cast<int>(cl::sycl::clz(val));
+#elif EIGEN_COMP_MSVC && EIGEN_ARCH_x86_64
+  unsigned long index;
+  _BitScanReverse64(&index, val);
+  return 63 - index;
+#elif EIGEN_COMP_MSVC
+  // MSVC's _BitScanReverse64 is not available for 32bits builds.
+  unsigned int lo = (unsigned int)(val & 0xffffffff);
+  unsigned int hi = (unsigned int)((val >> 32) & 0xffffffff);
+  int n;
+  if (hi == 0)
+    n = 32 + count_leading_zeros<unsigned int>(lo);
+  else
+    n = count_leading_zeros<unsigned int>(hi);
+  return n;
+#else
+  EIGEN_STATIC_ASSERT(sizeof(unsigned long long) == 8, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  return __builtin_clzll(static_cast<uint64_t>(val));
+#endif
+}
+
+template <typename T>
+struct UnsignedTraits {
+  typedef std::conditional_t<sizeof(T) == 8, uint64_t, uint32_t> type;
+};
+
+template <typename T>
+struct DividerTraits {
+  typedef typename UnsignedTraits<T>::type type;
+  static constexpr int N = sizeof(T) * 8;
+};
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint32_t muluh(const uint32_t a, const T b) {
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+  return __umulhi(a, b);
+#elif defined(SYCL_DEVICE_ONLY)
+  return cl::sycl::mul_hi(a, static_cast<uint32_t>(b));
+#else
+  return (static_cast<uint64_t>(a) * b) >> 32;
+#endif
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint64_t muluh(const uint64_t a, const T b) {
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+  return __umul64hi(a, b);
+#elif defined(SYCL_DEVICE_ONLY)
+  return cl::sycl::mul_hi(a, static_cast<uint64_t>(b));
+#elif EIGEN_COMP_MSVC && (EIGEN_ARCH_x86_64 || EIGEN_ARCH_ARM64)
+  return __umulh(a, static_cast<uint64_t>(b));
+#elif EIGEN_HAS_BUILTIN_INT128
+  __uint128_t v = static_cast<__uint128_t>(a) * static_cast<__uint128_t>(b);
+  return static_cast<uint64_t>(v >> 64);
+#else
+  return (TensorUInt128<static_val<0>, uint64_t>(a) * TensorUInt128<static_val<0>, uint64_t>(b)).upper();
+#endif
+}
+
+template <int N, typename T>
+struct DividerHelper {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint32_t computeMultiplier(const int log_div, const T divider) {
+    EIGEN_STATIC_ASSERT(N == 32, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return static_cast<uint32_t>((static_cast<uint64_t>(1) << (N + log_div)) / divider -
+                                 (static_cast<uint64_t>(1) << N) + 1);
+  }
+};
+
+template <typename T>
+struct DividerHelper<64, T> {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint64_t computeMultiplier(const int log_div, const T divider) {
+#if EIGEN_HAS_BUILTIN_INT128 && !defined(EIGEN_GPU_COMPILE_PHASE) && !defined(SYCL_DEVICE_ONLY)
+    return static_cast<uint64_t>((static_cast<__uint128_t>(1) << (64 + log_div)) / static_cast<__uint128_t>(divider) -
+                                 (static_cast<__uint128_t>(1) << 64) + 1);
+#else
+    const uint64_t shift = 1ULL << log_div;
+    TensorUInt128<uint64_t, uint64_t> result =
+        TensorUInt128<uint64_t, static_val<0> >(shift, 0) / TensorUInt128<static_val<0>, uint64_t>(divider) -
+        TensorUInt128<static_val<1>, static_val<0> >(1, 0) + TensorUInt128<static_val<0>, static_val<1> >(1);
+    return static_cast<uint64_t>(result);
+#endif
+  }
+};
+
+/** \internal
+ *
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Fast integer division by a constant.
+ *
+ * See the paper from Granlund and Montgomery for explanation.
+ *   (at https://doi.org/10.1145/773473.178249)
+ *
+ * \sa Tensor
+ */
+template <typename T, bool div_gt_one = false>
+struct TensorIntDivisor {
+ public:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor() {
+    multiplier = 0;
+    shift1 = 0;
+    shift2 = 0;
+  }
+
+  // Must have 0 < divider < 2^31. This is relaxed to
+  // 0 < divider < 2^63 when using 64-bit indices on platforms that support
+  // the __uint128_t type.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor(const T divider) {
+    const int N = DividerTraits<T>::N;
+    eigen_assert(static_cast<typename UnsignedTraits<T>::type>(divider) < NumTraits<UnsignedType>::highest() / 2);
+    eigen_assert(divider > 0);
+
+    // fast ln2
+    const int leading_zeros = count_leading_zeros(static_cast<UnsignedType>(divider));
+    int log_div = N - leading_zeros;
+    // if divider is a power of two then log_div is 1 more than it should be.
+    if ((static_cast<typename UnsignedTraits<T>::type>(1) << (log_div - 1)) ==
+        static_cast<typename UnsignedTraits<T>::type>(divider))
+      log_div--;
+
+    multiplier = DividerHelper<N, T>::computeMultiplier(log_div, divider);
+    shift1 = log_div > 1 ? 1 : log_div;
+    shift2 = log_div > 1 ? log_div - 1 : 0;
+  }
+
+  // Must have 0 <= numerator. On platforms that don't support the __uint128_t
+  // type numerator should also be less than 2^32-1.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T divide(const T numerator) const {
+    eigen_assert(static_cast<typename UnsignedTraits<T>::type>(numerator) < NumTraits<UnsignedType>::highest() / 2);
+
+    UnsignedType t1 = muluh(multiplier, numerator);
+    UnsignedType t = (static_cast<UnsignedType>(numerator) - t1) >> shift1;
+    return (t1 + t) >> shift2;
+  }
+
+ private:
+  typedef typename DividerTraits<T>::type UnsignedType;
+  UnsignedType multiplier;
+  int32_t shift1;
+  int32_t shift2;
+};
+
+// Optimized version for signed 32 bit integers.
+// Derived from Hacker's Delight.
+// Only works for divisors strictly greater than one
+template <>
+class TensorIntDivisor<int32_t, true> {
+ public:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor() {
+    magic = 0;
+    shift = 0;
+  }
+  // Must have 2 <= divider
+  EIGEN_DEVICE_FUNC TensorIntDivisor(int32_t divider) {
+    eigen_assert(divider >= 2);
+    calcMagic(divider);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE int divide(const int32_t n) const {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+    return (__umulhi(magic, n) >> shift);
+#elif defined(SYCL_DEVICE_ONLY)
+    return (cl::sycl::mul_hi(magic, static_cast<uint32_t>(n)) >> shift);
+#else
+    uint64_t v = static_cast<uint64_t>(magic) * static_cast<uint64_t>(n);
+    return (static_cast<uint32_t>(v >> 32) >> shift);
+#endif
+  }
+
+ private:
+  // Compute the magic numbers. See Hacker's Delight section 10 for an in
+  // depth explanation.
+  EIGEN_DEVICE_FUNC void calcMagic(int32_t d) {
+    const unsigned two31 = 0x80000000;  // 2**31.
+    unsigned ad = d;
+    unsigned t = two31 + (ad >> 31);
+    unsigned anc = t - 1 - t % ad;   // Absolute value of nc.
+    int p = 31;                      // Init. p.
+    unsigned q1 = two31 / anc;       // Init. q1 = 2**p/|nc|.
+    unsigned r1 = two31 - q1 * anc;  // Init. r1 = rem(2**p, |nc|).
+    unsigned q2 = two31 / ad;        // Init. q2 = 2**p/|d|.
+    unsigned r2 = two31 - q2 * ad;   // Init. r2 = rem(2**p, |d|).
+    unsigned delta = 0;
+    do {
+      p = p + 1;
+      q1 = 2 * q1;      // Update q1 = 2**p/|nc|.
+      r1 = 2 * r1;      // Update r1 = rem(2**p, |nc|).
+      if (r1 >= anc) {  // (Must be an unsigned
+        q1 = q1 + 1;    // comparison here).
+        r1 = r1 - anc;
+      }
+      q2 = 2 * q2;     // Update q2 = 2**p/|d|.
+      r2 = 2 * r2;     // Update r2 = rem(2**p, |d|).
+      if (r2 >= ad) {  // (Must be an unsigned
+        q2 = q2 + 1;   // comparison here).
+        r2 = r2 - ad;
+      }
+      delta = ad - r2;
+    } while (q1 < delta || (q1 == delta && r1 == 0));
+
+    magic = (unsigned)(q2 + 1);
+    shift = p - 32;
+  }
+
+  uint32_t magic;
+  int32_t shift;
+};
+
+template <typename T, bool div_gt_one>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator/(const T& numerator, const TensorIntDivisor<T, div_gt_one>& divisor) {
+  return divisor.divide(numerator);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h
new file mode 100644
index 0000000..3ddcc1c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h
@@ -0,0 +1,185 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
+#define EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename XprType>
+struct traits<TensorLayoutSwapOp<XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = traits<XprType>::NumDimensions;
+  static constexpr int Layout = (traits<XprType>::Layout == ColMajor) ? RowMajor : ColMajor;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename XprType>
+struct eval<TensorLayoutSwapOp<XprType>, Eigen::Dense> {
+  typedef const TensorLayoutSwapOp<XprType>& type;
+};
+
+template <typename XprType>
+struct nested<TensorLayoutSwapOp<XprType>, 1, typename eval<TensorLayoutSwapOp<XprType> >::type> {
+  typedef TensorLayoutSwapOp<XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Swap the layout from col-major to row-major, or row-major
+ * to col-major, and invert the order of the dimensions.
+ *
+ * Beware: the dimensions are reversed by this operation. If you want to
+ * preserve the ordering of the dimensions, you need to combine this
+ * operation with a shuffle.
+ *
+ * \example:
+ * Tensor<float, 2, ColMajor> input(2, 4);
+ * Tensor<float, 2, RowMajor> output = input.swap_layout();
+ * eigen_assert(output.dimension(0) == 4);
+ * eigen_assert(output.dimension(1) == 2);
+ *
+ * array<int, 2> shuffle(1, 0);
+ * output = input.swap_layout().shuffle(shuffle);
+ * eigen_assert(output.dimension(0) == 2);
+ * eigen_assert(output.dimension(1) == 4);
+ *
+ */
+template <typename XprType>
+class TensorLayoutSwapOp : public TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors> {
+ public:
+  typedef TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors> Base;
+  typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorLayoutSwapOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorLayoutSwapOp(const XprType& expr) : m_xpr(expr) {}
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorLayoutSwapOp)
+ protected:
+  typename XprType::Nested m_xpr;
+};
+
+// Eval as rvalue
+template <typename ArgType, typename Device>
+struct TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> {
+  typedef TensorLayoutSwapOp<ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  static constexpr int Layout =
+      (TensorEvaluator<ArgType, Device>::Layout == static_cast<int>(ColMajor)) ? RowMajor : ColMajor;
+  enum {
+    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    CoordAccess = false,  // to be implemented
+    RawAccess = TensorEvaluator<ArgType, Device>::RawAccess
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_impl(op.expression(), device) {
+    for (int i = 0; i < NumDims; ++i) {
+      m_dimensions[i] = m_impl.dimensions()[NumDims - 1 - i];
+    }
+  }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) { return m_impl.evalSubExprsIfNeeded(data); }
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_impl.coeff(index); }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return m_impl.template packet<LoadMode>(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return m_impl.costPerCoeff(vectorized);
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return constCast(m_impl.data()); }
+
+  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+
+ protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+  Dimensions m_dimensions;
+};
+
+// Eval as lvalue
+template <typename ArgType, typename Device>
+struct TensorEvaluator<TensorLayoutSwapOp<ArgType>, Device>
+    : public TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> {
+  typedef TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> Base;
+  typedef TensorLayoutSwapOp<ArgType> XprType;
+
+  static constexpr int Layout =
+      (TensorEvaluator<ArgType, Device>::Layout == static_cast<int>(ColMajor)) ? RowMajor : ColMajor;
+  enum {
+    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    CoordAccess = false  // to be implemented
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
+    return this->m_impl.coeffRef(index);
+  }
+  template <int StoreMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
+    this->m_impl.template writePacket<StoreMode>(index, x);
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h
new file mode 100644
index 0000000..f8bbcfe
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h
@@ -0,0 +1,85 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_META_MACROS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_META_MACROS_H
+
+/** use this macro in sfinae selection in templated functions
+ *
+ *   template<typename T,
+ *            std::enable_if_t< isBanana<T>::value , int > = 0
+ *   >
+ *   void foo(){}
+ *
+ *   becomes =>
+ *
+ *   template<typename TopoType,
+ *           SFINAE_ENABLE_IF( isBanana<T>::value )
+ *   >
+ *   void foo(){}
+ */
+
+#define EIGEN_SFINAE_ENABLE_IF(__condition__) std::enable_if_t<(__condition__), int> = 0
+
+// Define a macro to use a reference on the host but a value on the device
+#if defined(SYCL_DEVICE_ONLY)
+#define EIGEN_DEVICE_REF
+#else
+#define EIGEN_DEVICE_REF &
+#endif
+
+// Define a macro for catching SYCL exceptions if exceptions are enabled
+#define EIGEN_SYCL_TRY_CATCH(X)                                                                                        \
+  do {                                                                                                                 \
+    EIGEN_TRY { X; }                                                                                                   \
+    EIGEN_CATCH(const cl::sycl::exception& e) {                                                                        \
+      EIGEN_THROW_X(std::runtime_error("SYCL exception at " + std::string(__FILE__) + ":" + std::to_string(__LINE__) + \
+                                       "\n" + e.what()));                                                              \
+    }                                                                                                                  \
+  } while (false)
+
+// Define a macro if local memory flags are unset or one of them is set
+// Setting both flags is the same as unsetting them
+#if (!defined(EIGEN_SYCL_LOCAL_MEM) && !defined(EIGEN_SYCL_NO_LOCAL_MEM)) || \
+    (defined(EIGEN_SYCL_LOCAL_MEM) && defined(EIGEN_SYCL_NO_LOCAL_MEM))
+#define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON 1
+#define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_OFF 1
+#elif defined(EIGEN_SYCL_LOCAL_MEM) && !defined(EIGEN_SYCL_NO_LOCAL_MEM)
+#define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON 1
+#elif !defined(EIGEN_SYCL_LOCAL_MEM) && defined(EIGEN_SYCL_NO_LOCAL_MEM)
+#define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_OFF 1
+#endif
+
+#if EIGEN_COMP_CLANG  // workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653)
+#define EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)                         \
+  using Base::operator=;                                                                \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) {      \
+    Base::operator=(other);                                                             \
+    return *this;                                                                       \
+  }                                                                                     \
+  template <typename OtherDerived>                                                      \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const OtherDerived& other) { \
+    Base::operator=(other);                                                             \
+    return *this;                                                                       \
+  }
+#else
+#define EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)
+#endif
+
+/** \internal
+ * \brief Macro to manually inherit assignment operators.
+ * This is necessary, because the implicitly defined assignment operator gets deleted when a custom operator= is
+ * defined. This also inherits template<OtherDerived> operator=(const OtherDerived&) assignments. With C++11 or later
+ * this also default-implements the copy-constructor
+ */
+#define EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(Derived) \
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)  \
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(Derived)
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
new file mode 100644
index 0000000..548edfe
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
@@ -0,0 +1,189 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_MAP_H
+#define EIGEN_CXX11_TENSOR_TENSOR_MAP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// FIXME: Use proper doxygen documentation (e.g. \tparam MakePointer_).
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief A tensor expression mapping an existing array of data.
+ *
+ */
+/// `template <class> class MakePointer_` is added to convert the host pointer to the device pointer.
+/// It is added due to the fact that for our device compiler `T*` is not allowed.
+/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer `T`.
+/// This is done through our `MakePointer_` class. By default the Type in the `MakePointer_<T>` is `T*` .
+/// Therefore, by adding the default value, we managed to convert the type and it does not break any
+/// existing code as its default value is `T*`.
+template <typename PlainObjectType, int Options_, template <class> class MakePointer_>
+class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> > {
+ public:
+  typedef TensorMap<PlainObjectType, Options_, MakePointer_> Self;
+  typedef TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> > Base;
+#ifdef EIGEN_USE_SYCL
+  typedef std::remove_reference_t<typename Eigen::internal::nested<Self>::type> Nested;
+#else
+  typedef typename Eigen::internal::nested<Self>::type Nested;
+#endif
+  typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
+  typedef typename internal::traits<PlainObjectType>::Index Index;
+  typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef typename PlainObjectType::Base::CoeffReturnType CoeffReturnType;
+
+  typedef typename MakePointer_<Scalar>::Type PointerType;
+  typedef typename MakePointer_<Scalar>::ConstType PointerConstType;
+
+  // WARN: PointerType still can be a pointer to const (const Scalar*), for
+  // example in TensorMap<Tensor<const Scalar, ...>> expression. This type of
+  // expression should be illegal, but adding this restriction is not possible
+  // in practice (see https://bitbucket.org/eigen/eigen/pull-requests/488).
+  typedef std::conditional_t<bool(internal::is_lvalue<PlainObjectType>::value),
+                             PointerType,      // use simple pointer in lvalue expressions
+                             PointerConstType  // use const pointer in rvalue expressions
+                             >
+      StoragePointerType;
+
+  // If TensorMap was constructed over rvalue expression (e.g. const Tensor),
+  // we should return a reference to const from operator() (and others), even
+  // if TensorMap itself is not const.
+  typedef std::conditional_t<bool(internal::is_lvalue<PlainObjectType>::value), Scalar&, const Scalar&> StorageRefType;
+
+  static constexpr int Options = Options_;
+
+  static constexpr Index NumIndices = PlainObjectType::NumIndices;
+  typedef typename PlainObjectType::Dimensions Dimensions;
+
+  static constexpr int Layout = PlainObjectType::Layout;
+  enum { IsAligned = ((int(Options_) & Aligned) == Aligned), CoordAccess = true, RawAccess = true };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr) : m_data(dataPtr), m_dimensions() {
+    // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT((0 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
+  }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index firstDimension,
+                                                  IndexTypes... otherDimensions)
+      : m_data(dataPtr), m_dimensions(firstDimension, otherDimensions...) {
+    // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
+    EIGEN_STATIC_ASSERT((sizeof...(otherDimensions) + 1 == NumIndices || NumIndices == Dynamic),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE)
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr,
+                                                  const array<Index, NumIndices>& dimensions)
+      : m_data(dataPtr), m_dimensions(dimensions) {}
+
+  template <typename Dimensions>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, const Dimensions& dimensions)
+      : m_data(dataPtr), m_dimensions(dimensions) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PlainObjectType& tensor)
+      : m_data(tensor.data()), m_dimensions(tensor.dimensions()) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const { return m_dimensions.rank(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_dimensions[n]; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StoragePointerType data() { return m_data; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StoragePointerType data() const { return m_data; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageRefType operator()(const array<Index, NumIndices>& indices) const {
+    if (PlainObjectType::Options & RowMajor) {
+      const Index index = m_dimensions.IndexOfRowMajor(indices);
+      return m_data[index];
+    } else {
+      const Index index = m_dimensions.IndexOfColMajor(indices);
+      return m_data[index];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageRefType operator()() const {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return m_data[0];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageRefType operator()(Index index) const {
+    eigen_internal_assert(index >= 0 && index < size());
+    return m_data[index];
+  }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageRefType operator()(Index firstIndex, Index secondIndex,
+                                                                  IndexTypes... otherIndices) const {
+    EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
+    if (PlainObjectType::Options & RowMajor) {
+      const Index index =
+          m_dimensions.IndexOfRowMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+      return m_data[index];
+    } else {
+      const Index index =
+          m_dimensions.IndexOfColMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+      return m_data[index];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageRefType operator()(const array<Index, NumIndices>& indices) {
+    if (PlainObjectType::Options & RowMajor) {
+      const Index index = m_dimensions.IndexOfRowMajor(indices);
+      return m_data[index];
+    } else {
+      const Index index = m_dimensions.IndexOfColMajor(indices);
+      return m_data[index];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageRefType operator()() {
+    EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    return m_data[0];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageRefType operator()(Index index) {
+    eigen_internal_assert(index >= 0 && index < size());
+    return m_data[index];
+  }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageRefType operator()(Index firstIndex, Index secondIndex,
+                                                                  IndexTypes... otherIndices) {
+    static_assert(sizeof...(otherIndices) + 2 == NumIndices || NumIndices == Dynamic,
+                  "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
+    eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
+    const std::size_t NumDims = sizeof...(otherIndices) + 2;
+    if (PlainObjectType::Options & RowMajor) {
+      const Index index =
+          m_dimensions.IndexOfRowMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
+      return m_data[index];
+    } else {
+      const Index index =
+          m_dimensions.IndexOfColMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
+      return m_data[index];
+    }
+  }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorMap)
+
+ private:
+  StoragePointerType m_data;
+  Dimensions m_dimensions;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_MAP_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
new file mode 100644
index 0000000..8454070
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
@@ -0,0 +1,291 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_META_H
+#define EIGEN_CXX11_TENSOR_TENSOR_META_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <bool cond>
+struct Cond {};
+
+template <typename T1, typename T2>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const T1& choose(Cond<true>, const T1& first, const T2&) {
+  return first;
+}
+
+template <typename T1, typename T2>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const T2& choose(Cond<false>, const T1&, const T2& second) {
+  return second;
+}
+
+template <size_t n>
+struct max_n_1 {
+  static const size_t size = n;
+};
+template <>
+struct max_n_1<0> {
+  static const size_t size = 1;
+};
+
+template <typename T>
+EIGEN_DEPRECATED EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE constexpr T divup(const T x, const T y) {
+  return Eigen::numext::div_ceil(x, y);
+}
+
+// Default packet types
+template <typename Scalar, typename Device>
+struct PacketType : internal::packet_traits<Scalar> {
+  typedef typename internal::packet_traits<Scalar>::type type;
+};
+
+// For CUDA packet types when using a GpuDevice
+#if defined(EIGEN_USE_GPU) && defined(EIGEN_HAS_GPU_FP16) && defined(EIGEN_GPU_COMPILE_PHASE)
+
+typedef ulonglong2 Packet4h2;
+template <>
+struct PacketType<half, GpuDevice> {
+  typedef Packet4h2 type;
+  static const int size = 8;
+  enum {
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 0,
+    HasSetLinear = 0,
+    HasBlend = 0,
+
+    HasDiv = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasExp = 1,
+    HasExpm1 = 0,
+    HasLog = 1,
+    HasLog1p = 0,
+    HasLog10 = 0,
+    HasPow = 1,
+  };
+};
+#endif
+
+#if defined(EIGEN_USE_SYCL)
+
+namespace TensorSycl {
+namespace internal {
+
+template <typename Index, Index A, Index B>
+struct PlusOp {
+  static constexpr Index Value = A + B;
+};
+
+template <typename Index, Index A, Index B>
+struct DivOp {
+  static constexpr Index Value = A / B;
+};
+
+template <typename Index, Index start, Index end, Index step, template <class Indx, Indx...> class StepOp>
+struct static_for {
+  template <typename UnaryOperator>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void loop(UnaryOperator op) {
+    op(start);
+    static_for<Index, StepOp<Index, start, step>::Value, end, step, StepOp>::loop(op);
+  }
+};
+template <typename Index, Index end, Index step, template <class Indx, Indx...> class StepOp>
+struct static_for<Index, end, end, step, StepOp> {
+  template <typename UnaryOperator>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void loop(UnaryOperator) {}
+};
+
+template <typename OutScalar, typename Device, bool Vectorizable>
+struct Vectorise {
+  static constexpr int PacketSize = 1;
+  typedef OutScalar PacketReturnType;
+};
+
+template <typename OutScalar, typename Device>
+struct Vectorise<OutScalar, Device, true> {
+  static constexpr int PacketSize = Eigen::PacketType<OutScalar, Device>::size;
+  typedef typename Eigen::PacketType<OutScalar, Device>::type PacketReturnType;
+};
+
+static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index roundUp(Index x, Index y) { return ((((x) + (y)-1) / (y)) * (y)); }
+
+}  // namespace internal
+}  // namespace TensorSycl
+
+template <>
+struct PacketType<half, SyclDevice> {
+  typedef half type;
+  static const int size = 1;
+  enum {
+    HasAdd = 0,
+    HasSub = 0,
+    HasMul = 0,
+    HasNegate = 0,
+    HasAbs = 0,
+    HasArg = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasConj = 0,
+    HasSetLinear = 0,
+    HasBlend = 0
+  };
+};
+template <typename Scalar>
+struct PacketType<Scalar, SyclDevice> : internal::default_packet_traits {
+  typedef Scalar type;
+  typedef Scalar half;
+  enum {
+    Vectorizable = 0,
+    size = 1,
+    AlignedOnScalar = 0,
+  };
+  enum {
+    HasAdd = 0,
+    HasSub = 0,
+    HasMul = 0,
+    HasNegate = 0,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasConj = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <typename Scalar>
+struct PacketType<Scalar, const SyclDevice> : PacketType<Scalar, SyclDevice> {};
+
+#ifndef EIGEN_DONT_VECTORIZE_SYCL
+#define PACKET_TYPE(CVQual, Type, val, lengths, DEV)                                 \
+  template <>                                                                        \
+  struct PacketType<CVQual Type, DEV> : internal::sycl_packet_traits<val, lengths> { \
+    typedef typename internal::packet_traits<Type>::type type;                       \
+    typedef typename internal::packet_traits<Type>::half half;                       \
+  };
+
+PACKET_TYPE(const, float, 1, 4, SyclDevice)
+PACKET_TYPE(, float, 1, 4, SyclDevice)
+PACKET_TYPE(const, float, 1, 4, const SyclDevice)
+PACKET_TYPE(, float, 1, 4, const SyclDevice)
+
+PACKET_TYPE(const, double, 0, 2, SyclDevice)
+PACKET_TYPE(, double, 0, 2, SyclDevice)
+PACKET_TYPE(const, double, 0, 2, const SyclDevice)
+PACKET_TYPE(, double, 0, 2, const SyclDevice)
+#undef PACKET_TYPE
+
+template <>
+struct PacketType<half, const SyclDevice> : PacketType<half, SyclDevice> {};
+template <>
+struct PacketType<const half, const SyclDevice> : PacketType<half, SyclDevice> {};
+#endif
+#endif
+
+// Pair mimics std::pair but works on e.g. nvcc.
+template <typename U, typename V>
+struct Pair {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
+
+  U first;
+  V second;
+
+  typedef U first_type;
+  typedef V second_type;
+
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Pair() : first(), second() {}
+
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Pair(const U& f, const V& s) : first(f), second(s) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void swap(Pair& rhs) {
+    using numext::swap;
+    swap(first, rhs.first);
+    swap(second, rhs.second);
+  }
+};
+
+template <typename U, typename V>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator==(const Pair<U, V>& x, const Pair<U, V>& y) {
+  return (x.first == y.first && x.second == y.second);
+}
+
+template <typename U, typename V>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator!=(const Pair<U, V>& x, const Pair<U, V>& y) {
+  return !(x == y);
+}
+
+// Can't use std::pairs on cuda devices
+template <typename Idx>
+struct IndexPair {
+  constexpr EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair() : first(0), second(0) {}
+  constexpr EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair(Idx f, Idx s) : first(f), second(s) {}
+
+  EIGEN_DEVICE_FUNC void set(IndexPair<Idx> val) {
+    first = val.first;
+    second = val.second;
+  }
+
+  Idx first;
+  Idx second;
+};
+
+namespace internal {
+
+template <typename IndexType, typename Index, Index First, Index... Is>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array<Index, 1 + sizeof...(Is)> customIndices2Array(
+    IndexType& idx, numeric_list<Index, First, Is...>) {
+  return {static_cast<Index>(idx[First]), static_cast<Index>(idx[Is])...};
+}
+template <typename IndexType, typename Index>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array<Index, 0> customIndices2Array(IndexType&, numeric_list<Index>) {
+  return array<Index, 0>();
+}
+
+/** Make an array (for index/dimensions) out of a custom index */
+template <typename Index, std::size_t NumIndices, typename IndexType>
+constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array<Index, NumIndices> customIndices2Array(IndexType& idx) {
+  return customIndices2Array(idx, typename gen_numeric_list<Index, NumIndices>::type{});
+}
+
+template <typename B, typename D>
+struct is_base_of {
+  typedef char (&yes)[1];
+  typedef char (&no)[2];
+
+  template <typename BB, typename DD>
+  struct Host {
+    operator BB*() const;
+    operator DD*();
+  };
+
+  template <typename T>
+  static yes check(D*, T);
+  static no check(B*, int);
+
+  static const bool value = sizeof(check(Host<B, D>(), int())) == sizeof(yes);
+};
+
+}  // namespace internal
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_META_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
new file mode 100644
index 0000000..a111eb2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
@@ -0,0 +1,980 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename NewDimensions, typename XprType>
+struct traits<TensorReshapingOp<NewDimensions, XprType>> : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = array_size<NewDimensions>::value;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename NewDimensions, typename XprType>
+struct eval<TensorReshapingOp<NewDimensions, XprType>, Eigen::Dense> {
+  typedef const TensorReshapingOp<NewDimensions, XprType> EIGEN_DEVICE_REF type;
+};
+
+template <typename NewDimensions, typename XprType>
+struct nested<TensorReshapingOp<NewDimensions, XprType>, 1,
+              typename eval<TensorReshapingOp<NewDimensions, XprType>>::type> {
+  typedef TensorReshapingOp<NewDimensions, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor reshaping class.
+ */
+template <typename NewDimensions, typename XprType>
+class TensorReshapingOp : public TensorBase<TensorReshapingOp<NewDimensions, XprType>, WriteAccessors> {
+ public:
+  typedef TensorBase<TensorReshapingOp<NewDimensions, XprType>, WriteAccessors> Base;
+  typedef typename Eigen::internal::traits<TensorReshapingOp>::Scalar Scalar;
+  typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorReshapingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorReshapingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorReshapingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReshapingOp(const XprType& expr, const NewDimensions& dims)
+      : m_xpr(expr), m_dims(dims) {}
+
+  EIGEN_DEVICE_FUNC const NewDimensions& dimensions() const { return m_dims; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorReshapingOp)
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const NewDimensions m_dims;
+};
+
+// Eval as rvalue
+template <typename NewDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> {
+  typedef TensorReshapingOp<NewDimensions, ArgType> XprType;
+  typedef NewDimensions Dimensions;
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  typedef StorageMemory<std::remove_const_t<CoeffReturnType>, Device> ConstCastStorage;
+
+  static constexpr int NumOutputDims = internal::array_size<Dimensions>::value;
+  static constexpr int NumInputDims =
+      internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+
+  enum ReshapingKind {
+    // We do not use layout information to determine reshaping kind.
+    // Depending on the layout `N` can be inner or outer dimension.
+    OneByN = 0,  // expr.reshape(1, N)
+    NByOne = 1,  // expr.reshape(N, 1)
+    Runtime = 2  // Reshape dimensions are dynamic (specified at runtime).
+  };
+
+  // clang-format off
+  static const ReshapingKind kind =
+        (NumOutputDims == 2 && internal::index_statically_eq<NewDimensions>(/*index=*/0, /*value=*/1)) ? OneByN
+      : (NumOutputDims == 2 && internal::index_statically_eq<NewDimensions>(/*index=*/1, /*value=*/1)) ? NByOne
+      : Runtime;
+  // clang-format on
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    // For trivial reshapes with raw access to underlying data we will provide
+    // zero overhead block access.
+    // TODO(ezhulenev): Consider adding block access without raw access?
+    BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess && NumInputDims > 0 && NumOutputDims > 0,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = TensorEvaluator<ArgType, Device>::RawAccess
+  };
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumOutputDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumOutputDims, Layout, Index> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_dimensions(op.dimensions()) {
+    // The total size of the reshaped tensor must be equal to the total size
+    // of the input tensor.
+    eigen_assert(internal::array_prod(m_impl.dimensions()) == internal::array_prod(op.dimensions()));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType data, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(data, std::move(done));
+  }
+#endif
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) { return m_impl.evalSubExprsIfNeeded(data); }
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_impl.coeff(index); }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return m_impl.template packet<LoadMode>(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return m_impl.costPerCoeff(vectorized);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return internal::TensorBlockResourceRequirements::any();
+  }
+
+  struct BlockIteratorState {
+    Index stride;
+    Index span;
+    Index size;
+    Index count;
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    eigen_assert(m_impl.data() != NULL);
+    eigen_assert((kind == Runtime) || (kind == OneByN && desc.dimensions()[0] == 1) ||
+                 (kind == NByOne && desc.dimensions()[1] == 1));
+
+    if (kind == OneByN || kind == NByOne) {
+      // We can guarantee at compile time that block is just a contiguous slice
+      // of the underlying expression memory buffer.
+      return TensorBlock(internal::TensorBlockKind::kView, m_impl.data() + desc.offset(), desc.dimensions());
+    } else {
+      // This will do additional runtime checks, and in the end it might be also
+      // a view, or it might be a block materialized in the temporary buffer.
+      return TensorBlock::materialize(m_impl.data(), m_dimensions, desc, scratch);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return constCast(m_impl.data()); }
+
+  EIGEN_DEVICE_FUNC const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+
+ protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+  NewDimensions m_dimensions;
+};
+
+// Eval as lvalue
+template <typename NewDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<TensorReshapingOp<NewDimensions, ArgType>, Device>
+    : public TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device>
+
+{
+  typedef TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> Base;
+  typedef TensorReshapingOp<NewDimensions, ArgType> XprType;
+  typedef NewDimensions Dimensions;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = TensorEvaluator<ArgType, Device>::RawAccess
+  };
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<TensorEvaluator::NumOutputDims, Index> TensorBlockDesc;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
+    return this->m_impl.coeffRef(index);
+  }
+
+  template <int StoreMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
+    this->m_impl.template writePacket<StoreMode>(index, x);
+  }
+
+  template <typename TensorBlock>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(const TensorBlockDesc& desc, const TensorBlock& block) {
+    eigen_assert(this->m_impl.data() != NULL);
+
+    typedef typename TensorBlock::XprType TensorBlockExpr;
+    typedef internal::TensorBlockAssignment<Scalar, TensorEvaluator::NumOutputDims, TensorBlockExpr, Index>
+        TensorBlockAssign;
+
+    TensorBlockAssign::Run(TensorBlockAssign::target(desc.dimensions(), internal::strides<Layout>(this->dimensions()),
+                                                     this->m_impl.data(), desc.offset()),
+                           block.expr());
+  }
+};
+
+/** \class TensorSlicing
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor slicing class.
+ *
+ *
+ */
+namespace internal {
+template <typename StartIndices, typename Sizes, typename XprType>
+struct traits<TensorSlicingOp<StartIndices, Sizes, XprType>> : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = array_size<StartIndices>::value;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename StartIndices, typename Sizes, typename XprType>
+struct eval<TensorSlicingOp<StartIndices, Sizes, XprType>, Eigen::Dense> {
+  typedef const TensorSlicingOp<StartIndices, Sizes, XprType> EIGEN_DEVICE_REF type;
+};
+
+template <typename StartIndices, typename Sizes, typename XprType>
+struct nested<TensorSlicingOp<StartIndices, Sizes, XprType>, 1,
+              typename eval<TensorSlicingOp<StartIndices, Sizes, XprType>>::type> {
+  typedef TensorSlicingOp<StartIndices, Sizes, XprType> type;
+};
+
+}  // end namespace internal
+
+template <typename StartIndices, typename Sizes, typename XprType>
+class TensorSlicingOp : public TensorBase<TensorSlicingOp<StartIndices, Sizes, XprType>> {
+ public:
+  typedef TensorBase<TensorSlicingOp<StartIndices, Sizes, XprType>> Base;
+  typedef typename Eigen::internal::traits<TensorSlicingOp>::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorSlicingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorSlicingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorSlicingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSlicingOp(const XprType& expr, const StartIndices& indices,
+                                                        const Sizes& sizes)
+      : m_xpr(expr), m_indices(indices), m_sizes(sizes) {}
+
+  EIGEN_DEVICE_FUNC const StartIndices& startIndices() const { return m_indices; }
+  EIGEN_DEVICE_FUNC const Sizes& sizes() const { return m_sizes; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorSlicingOp)
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const StartIndices m_indices;
+  const Sizes m_sizes;
+};
+
+namespace internal {
+
+// FIXME: Figure out the exact threshold.
+template <typename Index, typename Device, bool BlockAccess>
+struct MemcpyTriggerForSlicing {
+  EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const Device& device) : threshold_(2 * device.numThreads()) {}
+  EIGEN_DEVICE_FUNC bool operator()(Index total, Index contiguous) const {
+    const bool prefer_block_evaluation = BlockAccess && total > 32 * 1024;
+    return !prefer_block_evaluation && contiguous > threshold_;
+  }
+
+ private:
+  Index threshold_;
+};
+
+// It is very expensive to start the memcpy kernel on GPU: we therefore only
+// use it for large copies.
+#ifdef EIGEN_USE_GPU
+template <typename Index, bool BlockAccess>
+struct MemcpyTriggerForSlicing<Index, GpuDevice, BlockAccess> {
+  EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const GpuDevice&) {}
+  EIGEN_DEVICE_FUNC bool operator()(Index, Index contiguous) const { return contiguous > 4 * 1024 * 1024; }
+};
+#endif
+
+// It is very expensive to start the memcpy kernel on GPU: we therefore only
+// use it for large copies.
+#ifdef EIGEN_USE_SYCL
+template <typename Index, bool BlockAccess>
+struct MemcpyTriggerForSlicing<Index, Eigen::SyclDevice, BlockAccess> {
+  EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const SyclDevice&) {}
+  EIGEN_DEVICE_FUNC bool operator()(Index, Index contiguous) const { return contiguous > 4 * 1024 * 1024; }
+};
+#endif
+
+}  // namespace internal
+
+// Eval as rvalue
+template <typename StartIndices, typename Sizes, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> {
+  typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType;
+  static constexpr int NumDims = internal::array_size<Sizes>::value;
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef Sizes Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef StorageMemory<std::remove_const_t<CoeffReturnType>, Device> ConstCastStorage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    // Alignment can't be guaranteed at compile time since it depends on the
+    // slice offsets and sizes.
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess &&
+                  // FIXME: Temporary workaround for bug in slicing of bool tensors.
+                  !internal::is_same<std::remove_const_t<Scalar>, bool>::value,
+    PreferBlockAccess = true,
+    CoordAccess = false,
+    RawAccess = false
+  };
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  // Tensor slicing does not change the block type.
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_device(device), m_dimensions(op.sizes()), m_offsets(op.startIndices()) {
+    m_is_identity = true;
+    for (int i = 0; i < internal::array_size<Dimensions>::value; ++i) {
+      eigen_assert(m_impl.dimensions()[i] >= op.sizes()[i] + op.startIndices()[i]);
+      if (m_impl.dimensions()[i] != op.sizes()[i] || op.startIndices()[i] != 0) {
+        m_is_identity = false;
+      }
+    }
+
+    // No strides for scalars.
+    if (NumDims == 0) return;
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    const Sizes& output_dims = op.sizes();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1];
+      }
+
+      // Don't initialize m_fastOutputStrides[0] since it won't ever be accessed.
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStrides[i] = m_outputStrides[i - 1] * output_dims[i - 1];
+        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
+      }
+    } else {
+      m_inputStrides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1];
+      }
+
+      // Don't initialize m_fastOutputStrides[NumDims-1] since it won't ever be accessed.
+      m_outputStrides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStrides[i] = m_outputStrides[i + 1] * output_dims[i + 1];
+        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    if (!NumTraits<std::remove_const_t<Scalar>>::RequireInitialization && data && m_impl.data()) {
+      Index contiguous_values = 1;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        for (int i = 0; i < NumDims; ++i) {
+          contiguous_values *= dimensions()[i];
+          if (dimensions()[i] != m_impl.dimensions()[i]) {
+            break;
+          }
+        }
+      } else {
+        for (int i = NumDims - 1; i >= 0; --i) {
+          contiguous_values *= dimensions()[i];
+          if (dimensions()[i] != m_impl.dimensions()[i]) {
+            break;
+          }
+        }
+      }
+      // Use memcpy if it's going to be faster than using the regular evaluation.
+      const internal::MemcpyTriggerForSlicing<Index, Device, BlockAccess> trigger(m_device);
+      if (trigger(internal::array_prod(dimensions()), contiguous_values)) {
+        EvaluatorPointerType src = (EvaluatorPointerType)m_impl.data();
+        for (Index i = 0; i < internal::array_prod(dimensions()); i += contiguous_values) {
+          Index offset = srcCoeff(i);
+          m_device.memcpy((void*)(m_device.get(data + i)), m_device.get(src + offset),
+                          contiguous_values * sizeof(Scalar));
+        }
+        return false;
+      }
+    }
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType /*data*/, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    if (m_is_identity) {
+      return m_impl.coeff(index);
+    } else {
+      return m_impl.coeff(srcCoeff(index));
+    }
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    const int packetSize = PacketType<CoeffReturnType, Device>::size;
+    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index + packetSize - 1 < internal::array_prod(dimensions()));
+
+    if (m_is_identity) {
+      return m_impl.template packet<LoadMode>(index);
+    }
+
+    Index inputIndices[] = {0, 0};
+    Index indices[] = {index, index + packetSize - 1};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx0 = indices[0] / m_fastOutputStrides[i];
+        const Index idx1 = indices[1] / m_fastOutputStrides[i];
+        inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
+        inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
+        indices[0] -= idx0 * m_outputStrides[i];
+        indices[1] -= idx1 * m_outputStrides[i];
+      }
+      inputIndices[0] += (indices[0] + m_offsets[0]);
+      inputIndices[1] += (indices[1] + m_offsets[0]);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx0 = indices[0] / m_fastOutputStrides[i];
+        const Index idx1 = indices[1] / m_fastOutputStrides[i];
+        inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
+        inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
+        indices[0] -= idx0 * m_outputStrides[i];
+        indices[1] -= idx1 * m_outputStrides[i];
+      }
+      inputIndices[0] += (indices[0] + m_offsets[NumDims - 1]);
+      inputIndices[1] += (indices[1] + m_offsets[NumDims - 1]);
+    }
+    if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
+      PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
+      return rslt;
+    } else {
+      EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[packetSize];
+      values[0] = m_impl.coeff(inputIndices[0]);
+      values[packetSize - 1] = m_impl.coeff(inputIndices[1]);
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < packetSize - 1; ++i) {
+        values[i] = coeff(index + i);
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, m_is_identity ? 1 : NumDims);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.lastLevelCacheSize();
+    return internal::TensorBlockResourceRequirements::merge(
+        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size), m_impl.getResourceRequirements());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    TensorBlockDesc arg_desc = desc.WithOffset(srcCoeff(desc.offset()));
+    TensorBlock block = m_impl.block(arg_desc, scratch);
+    if (!arg_desc.HasDestinationBuffer()) desc.DropDestinationBuffer();
+    return block;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const {
+    typename Storage::Type result = constCast(m_impl.data());
+    if (result) {
+      Index offset = 0;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        for (int i = 0; i < NumDims; ++i) {
+          if (m_dimensions[i] != m_impl.dimensions()[i]) {
+            offset += m_offsets[i] * m_inputStrides[i];
+            for (int j = i + 1; j < NumDims; ++j) {
+              if (m_dimensions[j] > 1) {
+                return NULL;
+              }
+              offset += m_offsets[j] * m_inputStrides[j];
+            }
+            break;
+          }
+        }
+      } else {
+        for (int i = NumDims - 1; i >= 0; --i) {
+          if (m_dimensions[i] != m_impl.dimensions()[i]) {
+            offset += m_offsets[i] * m_inputStrides[i];
+            for (int j = i - 1; j >= 0; --j) {
+              if (m_dimensions[j] > 1) {
+                return NULL;
+              }
+              offset += m_offsets[j] * m_inputStrides[j];
+            }
+            break;
+          }
+        }
+      }
+      return result + offset;
+    }
+    return NULL;
+  }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += (idx + m_offsets[i]) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      inputIndex += (index + m_offsets[0]);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += (idx + m_offsets[i]) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      inputIndex += (index + m_offsets[NumDims - 1]);
+    }
+    return inputIndex;
+  }
+
+  array<Index, NumDims> m_outputStrides;
+  array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
+  array<Index, NumDims> m_inputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  const Device EIGEN_DEVICE_REF m_device;
+  Dimensions m_dimensions;
+  bool m_is_identity;
+  const StartIndices m_offsets;
+};
+
+// Eval as lvalue
+template <typename StartIndices, typename Sizes, typename ArgType, typename Device>
+struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
+    : public TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> {
+  typedef TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> Base;
+  typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType;
+  static constexpr int NumDims = internal::array_size<Sizes>::value;
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef Sizes Dimensions;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
+    PreferBlockAccess = true,
+    CoordAccess = false,
+    RawAccess = (NumDims == 1) & TensorEvaluator<ArgType, Device>::RawAccess
+  };
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
+    if (this->m_is_identity) {
+      return this->m_impl.coeffRef(index);
+    } else {
+      return this->m_impl.coeffRef(this->srcCoeff(index));
+    }
+  }
+
+  template <int StoreMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
+    if (this->m_is_identity) {
+      this->m_impl.template writePacket<StoreMode>(index, x);
+      return;
+    }
+
+    const int packetSize = PacketType<CoeffReturnType, Device>::size;
+    Index inputIndices[] = {0, 0};
+    Index indices[] = {index, index + packetSize - 1};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx0 = indices[0] / this->m_fastOutputStrides[i];
+        const Index idx1 = indices[1] / this->m_fastOutputStrides[i];
+        inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i];
+        inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i];
+        indices[0] -= idx0 * this->m_outputStrides[i];
+        indices[1] -= idx1 * this->m_outputStrides[i];
+      }
+      inputIndices[0] += (indices[0] + this->m_offsets[0]);
+      inputIndices[1] += (indices[1] + this->m_offsets[0]);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx0 = indices[0] / this->m_fastOutputStrides[i];
+        const Index idx1 = indices[1] / this->m_fastOutputStrides[i];
+        inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i];
+        inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i];
+        indices[0] -= idx0 * this->m_outputStrides[i];
+        indices[1] -= idx1 * this->m_outputStrides[i];
+      }
+      inputIndices[0] += (indices[0] + this->m_offsets[NumDims - 1]);
+      inputIndices[1] += (indices[1] + this->m_offsets[NumDims - 1]);
+    }
+    if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
+      this->m_impl.template writePacket<StoreMode>(inputIndices[0], x);
+    } else {
+      EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
+      internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+      this->m_impl.coeffRef(inputIndices[0]) = values[0];
+      this->m_impl.coeffRef(inputIndices[1]) = values[packetSize - 1];
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < packetSize - 1; ++i) {
+        this->coeffRef(index + i) = values[i];
+      }
+    }
+  }
+
+  template <typename TensorBlock>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(const TensorBlockDesc& desc, const TensorBlock& block) {
+    TensorBlockDesc arg_desc = desc.WithOffset(this->srcCoeff(desc.offset()));
+    this->m_impl.writeBlock(arg_desc, block);
+  }
+};
+
+namespace internal {
+template <typename StartIndices, typename StopIndices, typename Strides, typename XprType>
+struct traits<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>> : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = array_size<StartIndices>::value;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename StartIndices, typename StopIndices, typename Strides, typename XprType>
+struct eval<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>, Eigen::Dense> {
+  typedef const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> EIGEN_DEVICE_REF type;
+};
+
+template <typename StartIndices, typename StopIndices, typename Strides, typename XprType>
+struct nested<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>, 1,
+              typename eval<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>>::type> {
+  typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> type;
+};
+
+}  // end namespace internal
+
+template <typename StartIndices, typename StopIndices, typename Strides, typename XprType>
+class TensorStridingSlicingOp
+    : public TensorBase<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>> {
+ public:
+  typedef TensorBase<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>> Base;
+  typedef typename internal::traits<TensorStridingSlicingOp>::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename internal::nested<TensorStridingSlicingOp>::type Nested;
+  typedef typename internal::traits<TensorStridingSlicingOp>::StorageKind StorageKind;
+  typedef typename internal::traits<TensorStridingSlicingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingSlicingOp(const XprType& expr, const StartIndices& startIndices,
+                                                                const StopIndices& stopIndices, const Strides& strides)
+      : m_xpr(expr), m_startIndices(startIndices), m_stopIndices(stopIndices), m_strides(strides) {}
+
+  EIGEN_DEVICE_FUNC const StartIndices& startIndices() const { return m_startIndices; }
+  EIGEN_DEVICE_FUNC const StartIndices& stopIndices() const { return m_stopIndices; }
+  EIGEN_DEVICE_FUNC const StartIndices& strides() const { return m_strides; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorStridingSlicingOp)
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const StartIndices m_startIndices;
+  const StopIndices m_stopIndices;
+  const Strides m_strides;
+};
+
+// Eval as rvalue
+template <typename StartIndices, typename StopIndices, typename Strides, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> {
+  typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType> XprType;
+  static constexpr int NumDims = internal::array_size<Strides>::value;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  typedef Strides Dimensions;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    // Alignment can't be guaranteed at compile time since it depends on the
+    // slice offsets and sizes.
+    IsAligned = false,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_device(device), m_strides(op.strides()) {
+    // Handle degenerate intervals by gracefully clamping and allowing m_dimensions to be zero
+    DSizes<Index, NumDims> startIndicesClamped, stopIndicesClamped;
+    for (ptrdiff_t i = 0; i < internal::array_size<Dimensions>::value; ++i) {
+      eigen_assert(m_strides[i] != 0 && "0 stride is invalid");
+      if (m_strides[i] > 0) {
+        startIndicesClamped[i] = clamp(op.startIndices()[i], 0, m_impl.dimensions()[i]);
+        stopIndicesClamped[i] = clamp(op.stopIndices()[i], 0, m_impl.dimensions()[i]);
+      } else {
+        /* implies m_strides[i] < 0 by assert */
+        startIndicesClamped[i] = clamp(op.startIndices()[i], -1, m_impl.dimensions()[i] - 1);
+        stopIndicesClamped[i] = clamp(op.stopIndices()[i], -1, m_impl.dimensions()[i] - 1);
+      }
+      m_startIndices[i] = startIndicesClamped[i];
+    }
+
+    typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
+    const InputDimensions& input_dims = m_impl.dimensions();
+
+    // compute output tensor shape
+    m_is_identity = true;
+    for (int i = 0; i < NumDims; i++) {
+      Index interval = stopIndicesClamped[i] - startIndicesClamped[i];
+      if (interval == 0 || ((interval < 0) != (m_strides[i] < 0))) {
+        m_dimensions[i] = 0;
+      } else {
+        m_dimensions[i] = (interval / m_strides[i]) + (interval % m_strides[i] != 0 ? 1 : 0);
+        eigen_assert(m_dimensions[i] >= 0);
+      }
+      if (m_strides[i] != 1 || interval != m_impl.dimensions()[i]) {
+        m_is_identity = false;
+      }
+    }
+
+    Strides output_dims = m_dimensions;
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputStrides[0] = m_strides[0];
+      m_offsets[0] = startIndicesClamped[0];
+      Index previousDimProduct = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        previousDimProduct *= input_dims[i - 1];
+        m_inputStrides[i] = previousDimProduct * m_strides[i];
+        m_offsets[i] = startIndicesClamped[i] * previousDimProduct;
+      }
+
+      // Don't initialize m_fastOutputStrides[0] since it won't ever be accessed.
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStrides[i] = m_outputStrides[i - 1] * output_dims[i - 1];
+        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
+      }
+    } else {
+      m_inputStrides[NumDims - 1] = m_strides[NumDims - 1];
+      m_offsets[NumDims - 1] = startIndicesClamped[NumDims - 1];
+      Index previousDimProduct = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        previousDimProduct *= input_dims[i + 1];
+        m_inputStrides[i] = previousDimProduct * m_strides[i];
+        m_offsets[i] = startIndicesClamped[i] * previousDimProduct;
+      }
+
+      m_outputStrides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStrides[i] = m_outputStrides[i + 1] * output_dims[i + 1];
+        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    if (m_is_identity) {
+      return m_impl.coeff(index);
+    } else {
+      return m_impl.coeff(srcCoeff(index));
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, m_is_identity ? 1 : NumDims);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const { return NULL; }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i >= 0; --i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += idx * m_inputStrides[i] + m_offsets[i];
+        index -= idx * m_outputStrides[i];
+      }
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims; ++i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += idx * m_inputStrides[i] + m_offsets[i];
+        index -= idx * m_outputStrides[i];
+      }
+    }
+    return inputIndex;
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index clamp(Index value, Index min, Index max) {
+#ifndef SYCL_DEVICE_ONLY
+    return numext::maxi(min, numext::mini(max, value));
+#else
+    return cl::sycl::clamp(value, min, max);
+#endif
+  }
+
+  array<Index, NumDims> m_outputStrides;
+  array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
+  array<Index, NumDims> m_inputStrides;
+  bool m_is_identity;
+  TensorEvaluator<ArgType, Device> m_impl;
+  const Device EIGEN_DEVICE_REF m_device;
+  DSizes<Index, NumDims> m_startIndices;  // clamped startIndices
+  DSizes<Index, NumDims> m_dimensions;
+  DSizes<Index, NumDims> m_offsets;  // offset in a flattened shape
+  const Strides m_strides;
+};
+
+// Eval as lvalue
+template <typename StartIndices, typename StopIndices, typename Strides, typename ArgType, typename Device>
+struct TensorEvaluator<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device>
+    : public TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> {
+  typedef TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> Base;
+  typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType> XprType;
+  static constexpr int NumDims = internal::array_size<Strides>::value;
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    CoordAccess = TensorEvaluator<ArgType, Device>::CoordAccess,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef Strides Dimensions;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
+    if (this->m_is_identity) {
+      return this->m_impl.coeffRef(index);
+    } else {
+      return this->m_impl.coeffRef(this->srcCoeff(index));
+    }
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
new file mode 100644
index 0000000..8157596
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
@@ -0,0 +1,620 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename PaddingDimensions, typename XprType>
+struct traits<TensorPaddingOp<PaddingDimensions, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename PaddingDimensions, typename XprType>
+struct eval<TensorPaddingOp<PaddingDimensions, XprType>, Eigen::Dense> {
+  typedef const TensorPaddingOp<PaddingDimensions, XprType>& type;
+};
+
+template <typename PaddingDimensions, typename XprType>
+struct nested<TensorPaddingOp<PaddingDimensions, XprType>, 1,
+              typename eval<TensorPaddingOp<PaddingDimensions, XprType> >::type> {
+  typedef TensorPaddingOp<PaddingDimensions, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor padding class.
+ * At the moment only padding with a constant value is supported.
+ *
+ */
+template <typename PaddingDimensions, typename XprType>
+class TensorPaddingOp : public TensorBase<TensorPaddingOp<PaddingDimensions, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorPaddingOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorPaddingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorPaddingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorPaddingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPaddingOp(const XprType& expr, const PaddingDimensions& padding_dims,
+                                                        const Scalar padding_value)
+      : m_xpr(expr), m_padding_dims(padding_dims), m_padding_value(padding_value) {}
+
+  EIGEN_DEVICE_FUNC const PaddingDimensions& padding() const { return m_padding_dims; }
+  EIGEN_DEVICE_FUNC Scalar padding_value() const { return m_padding_value; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const PaddingDimensions m_padding_dims;
+  const Scalar m_padding_value;
+};
+
+// Eval as rvalue
+template <typename PaddingDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device> {
+  typedef TensorPaddingOp<PaddingDimensions, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<PaddingDimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = true,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess,
+    PreferBlockAccess = true,
+    CoordAccess = true,
+    RawAccess = false
+  };
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, Layout, Index> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_padding(op.padding()), m_paddingValue(op.padding_value()), m_device(device) {
+    // The padding op doesn't change the rank of the tensor. Directly padding a scalar would lead
+    // to a vector, which doesn't make sense. Instead one should reshape the scalar into a vector
+    // of 1 element first and then pad.
+    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    // Compute dimensions
+    m_dimensions = m_impl.dimensions();
+    for (int i = 0; i < NumDims; ++i) {
+      m_dimensions[i] += m_padding[i].first + m_padding[i].second;
+    }
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputStrides[0] = 1;
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1];
+        m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+      }
+      m_outputStrides[NumDims] = m_outputStrides[NumDims - 1] * m_dimensions[NumDims - 1];
+    } else {
+      m_inputStrides[NumDims - 1] = 1;
+      m_outputStrides[NumDims] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1];
+        m_outputStrides[i + 1] = m_outputStrides[i + 2] * m_dimensions[i + 1];
+      }
+      m_outputStrides[0] = m_outputStrides[1] * m_dimensions[0];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    eigen_assert(index < dimensions().TotalSize());
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStrides[i];
+        if (isPaddingAtIndexForDim(idx, i)) {
+          return m_paddingValue;
+        }
+        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      if (isPaddingAtIndexForDim(index, 0)) {
+        return m_paddingValue;
+      }
+      inputIndex += (index - m_padding[0].first);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_outputStrides[i + 1];
+        if (isPaddingAtIndexForDim(idx, i)) {
+          return m_paddingValue;
+        }
+        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i + 1];
+      }
+      if (isPaddingAtIndexForDim(index, NumDims - 1)) {
+        return m_paddingValue;
+      }
+      inputIndex += (index - m_padding[NumDims - 1].first);
+    }
+    return m_impl.coeff(inputIndex);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      return packetColMajor(index);
+    }
+    return packetRowMajor(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    TensorOpCost cost = m_impl.costPerCoeff(vectorized);
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims; ++i) updateCostPerDimension(cost, i, i == 0);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i >= 0; --i) updateCostPerDimension(cost, i, i == NumDims - 1);
+    }
+    return cost;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.lastLevelCacheSize();
+    return internal::TensorBlockResourceRequirements::merge(
+        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size), m_impl.getResourceRequirements());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    // If one of the dimensions is zero, return empty block view.
+    if (desc.size() == 0) {
+      return TensorBlock(internal::TensorBlockKind::kView, NULL, desc.dimensions());
+    }
+
+    static const bool IsColMajor = Layout == static_cast<int>(ColMajor);
+    const int inner_dim_idx = IsColMajor ? 0 : NumDims - 1;
+
+    Index offset = desc.offset();
+
+    // Compute offsets in the output tensor corresponding to the desc.offset().
+    DSizes<Index, NumDims> output_offsets;
+    for (int i = NumDims - 1; i > 0; --i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      const int stride_dim = IsColMajor ? dim : dim + 1;
+      output_offsets[dim] = offset / m_outputStrides[stride_dim];
+      offset -= output_offsets[dim] * m_outputStrides[stride_dim];
+    }
+    output_offsets[inner_dim_idx] = offset;
+
+    // Offsets in the input corresponding to output offsets.
+    DSizes<Index, NumDims> input_offsets = output_offsets;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      input_offsets[dim] = input_offsets[dim] - m_padding[dim].first;
+    }
+
+    // Compute offset in the input buffer (at this point it might be illegal and
+    // point outside of the input buffer, because we don't check for negative
+    // offsets, it will be autocorrected in the block iteration loop below).
+    Index input_offset = 0;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      input_offset += input_offsets[dim] * m_inputStrides[dim];
+    }
+
+    // Destination buffer and scratch buffer both indexed from 0 and have the
+    // same dimensions as the requested block (for destination buffer this
+    // property is guaranteed by `desc.destination()`).
+    Index output_offset = 0;
+    const DSizes<Index, NumDims> output_strides = internal::strides<Layout>(desc.dimensions());
+
+    // NOTE(ezhulenev): We initialize block iteration state for `NumDims - 1`
+    // dimensions, skipping innermost dimension. In theory it should be possible
+    // to squeeze matching innermost dimensions, however in practice that did
+    // not show any improvements in benchmarks. Also in practice first outer
+    // dimension usually has padding, and will prevent squeezing.
+
+    // Initialize output block iterator state. Dimension in this array are
+    // always in inner_most -> outer_most order (col major layout).
+    array<BlockIteratorState, NumDims - 1> it;
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const int dim = IsColMajor ? i + 1 : NumDims - i - 2;
+      it[i].count = 0;
+      it[i].size = desc.dimension(dim);
+
+      it[i].input_stride = m_inputStrides[dim];
+      it[i].input_span = it[i].input_stride * (it[i].size - 1);
+
+      it[i].output_stride = output_strides[dim];
+      it[i].output_span = it[i].output_stride * (it[i].size - 1);
+    }
+
+    const Index input_inner_dim_size = static_cast<Index>(m_impl.dimensions()[inner_dim_idx]);
+
+    // Total output size.
+    const Index output_size = desc.size();
+
+    // We will fill inner dimension of this size in the output. It might be
+    // larger than the inner dimension in the input, so we might have to pad
+    // before/after we copy values from the input inner dimension.
+    const Index output_inner_dim_size = desc.dimension(inner_dim_idx);
+
+    // How many values to fill with padding BEFORE reading from the input inner
+    // dimension.
+    const Index output_inner_pad_before_size =
+        input_offsets[inner_dim_idx] < 0
+            ? numext::mini(numext::abs(input_offsets[inner_dim_idx]), output_inner_dim_size)
+            : 0;
+
+    // How many values we can actually copy from the input inner dimension.
+    const Index output_inner_copy_size = numext::mini(
+        // Want to copy from input.
+        (output_inner_dim_size - output_inner_pad_before_size),
+        // Can copy from input.
+        numext::maxi(input_inner_dim_size - (input_offsets[inner_dim_idx] + output_inner_pad_before_size), Index(0)));
+
+    eigen_assert(output_inner_copy_size >= 0);
+
+    // How many values to fill with padding AFTER reading from the input inner
+    // dimension.
+    const Index output_inner_pad_after_size =
+        (output_inner_dim_size - output_inner_copy_size - output_inner_pad_before_size);
+
+    // Sanity check, sum of all sizes must be equal to the output size.
+    eigen_assert(output_inner_dim_size ==
+                 (output_inner_pad_before_size + output_inner_copy_size + output_inner_pad_after_size));
+
+    // Keep track of current coordinates and padding in the output.
+    DSizes<Index, NumDims> output_coord = output_offsets;
+    DSizes<Index, NumDims> output_padded;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
+    }
+
+    typedef internal::StridedLinearBufferCopy<ScalarNoConst, Index> LinCopy;
+
+    // Prepare storage for the materialized padding result.
+    const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch);
+
+    // TODO(ezhulenev): Squeeze multiple non-padded inner dimensions into a
+    // single logical inner dimension.
+
+    // When possible we squeeze writes for the innermost (only if non-padded)
+    // dimension with the first padded dimension. This allows to reduce the
+    // number of calls to LinCopy and better utilize vector instructions.
+    const bool squeeze_writes = NumDims > 1 &&
+                                // inner dimension is not padded
+                                (input_inner_dim_size == m_dimensions[inner_dim_idx]) &&
+                                // and equal to the block inner dimension
+                                (input_inner_dim_size == output_inner_dim_size);
+
+    const int squeeze_dim = IsColMajor ? inner_dim_idx + 1 : inner_dim_idx - 1;
+
+    // Maximum coordinate on a squeeze dimension that we can write to.
+    const Index squeeze_max_coord =
+        squeeze_writes ? numext::mini(
+                             // max non-padded element in the input
+                             static_cast<Index>(m_dimensions[squeeze_dim] - m_padding[squeeze_dim].second),
+                             // max element in the output buffer
+                             static_cast<Index>(output_offsets[squeeze_dim] + desc.dimension(squeeze_dim)))
+                       : static_cast<Index>(0);
+
+    // Iterate copying data from `m_impl.data()` to the output buffer.
+    for (Index size = 0; size < output_size;) {
+      // Detect if we are in the padded region (exclude innermost dimension).
+      bool is_padded = false;
+      for (int j = 1; j < NumDims; ++j) {
+        const int dim = IsColMajor ? j : NumDims - j - 1;
+        is_padded = output_padded[dim];
+        if (is_padded) break;
+      }
+
+      if (is_padded) {
+        // Fill single innermost dimension with padding value.
+        size += output_inner_dim_size;
+
+        LinCopy::template Run<LinCopy::Kind::FillLinear>(typename LinCopy::Dst(output_offset, 1, block_storage.data()),
+                                                         typename LinCopy::Src(0, 0, &m_paddingValue),
+                                                         output_inner_dim_size);
+
+      } else if (squeeze_writes) {
+        // Squeeze multiple reads from innermost dimensions.
+        const Index squeeze_num = squeeze_max_coord - output_coord[squeeze_dim];
+        size += output_inner_dim_size * squeeze_num;
+
+        // Copy `squeeze_num` inner dimensions from input to output.
+        LinCopy::template Run<LinCopy::Kind::Linear>(typename LinCopy::Dst(output_offset, 1, block_storage.data()),
+                                                     typename LinCopy::Src(input_offset, 1, m_impl.data()),
+                                                     output_inner_dim_size * squeeze_num);
+
+        // Update iteration state for only `squeeze_num - 1` processed inner
+        // dimensions, because we have another iteration state update at the end
+        // of the loop that will update iteration state for the last inner
+        // processed dimension.
+        it[0].count += (squeeze_num - 1);
+        input_offset += it[0].input_stride * (squeeze_num - 1);
+        output_offset += it[0].output_stride * (squeeze_num - 1);
+        output_coord[squeeze_dim] += (squeeze_num - 1);
+
+      } else {
+        // Single read from innermost dimension.
+        size += output_inner_dim_size;
+
+        {  // Fill with padding before copying from input inner dimension.
+          const Index out = output_offset;
+
+          LinCopy::template Run<LinCopy::Kind::FillLinear>(typename LinCopy::Dst(out, 1, block_storage.data()),
+                                                           typename LinCopy::Src(0, 0, &m_paddingValue),
+                                                           output_inner_pad_before_size);
+        }
+
+        {  // Copy data from input inner dimension.
+          const Index out = output_offset + output_inner_pad_before_size;
+          const Index in = input_offset + output_inner_pad_before_size;
+
+          eigen_assert(output_inner_copy_size == 0 || m_impl.data() != NULL);
+
+          LinCopy::template Run<LinCopy::Kind::Linear>(typename LinCopy::Dst(out, 1, block_storage.data()),
+                                                       typename LinCopy::Src(in, 1, m_impl.data()),
+                                                       output_inner_copy_size);
+        }
+
+        {  // Fill with padding after copying from input inner dimension.
+          const Index out = output_offset + output_inner_pad_before_size + output_inner_copy_size;
+
+          LinCopy::template Run<LinCopy::Kind::FillLinear>(typename LinCopy::Dst(out, 1, block_storage.data()),
+                                                           typename LinCopy::Src(0, 0, &m_paddingValue),
+                                                           output_inner_pad_after_size);
+        }
+      }
+
+      for (int j = 0; j < NumDims - 1; ++j) {
+        const int dim = IsColMajor ? j + 1 : NumDims - j - 2;
+
+        if (++it[j].count < it[j].size) {
+          input_offset += it[j].input_stride;
+          output_offset += it[j].output_stride;
+          output_coord[dim] += 1;
+          output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
+          break;
+        }
+        it[j].count = 0;
+        input_offset -= it[j].input_span;
+        output_offset -= it[j].output_span;
+        output_coord[dim] -= it[j].size - 1;
+        output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
+      }
+    }
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return NULL; }
+
+ private:
+  struct BlockIteratorState {
+    BlockIteratorState() : count(0), size(0), input_stride(0), input_span(0), output_stride(0), output_span(0) {}
+
+    Index count;
+    Index size;
+    Index input_stride;
+    Index input_span;
+    Index output_stride;
+    Index output_span;
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isPaddingAtIndexForDim(Index index, int dim_index) const {
+    return (!internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0) &&
+            index < m_padding[dim_index].first) ||
+           (!internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0) &&
+            index >= m_dimensions[dim_index] - m_padding[dim_index].second);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isLeftPaddingCompileTimeZero(int dim_index) const {
+    return internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isRightPaddingCompileTimeZero(int dim_index) const {
+    return internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0);
+  }
+
+  void updateCostPerDimension(TensorOpCost& cost, int i, bool first) const {
+    const double in = static_cast<double>(m_impl.dimensions()[i]);
+    const double out = in + m_padding[i].first + m_padding[i].second;
+    if (out == 0) return;
+    const double reduction = in / out;
+    cost *= reduction;
+    if (first) {
+      cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() + reduction * (1 * TensorOpCost::AddCost<Index>()));
+    } else {
+      cost += TensorOpCost(0, 0,
+                           2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() +
+                               reduction * (2 * TensorOpCost::MulCost<Index>() + 1 * TensorOpCost::DivCost<Index>()));
+    }
+  }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    const Index initialIndex = index;
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = NumDims - 1; i > 0; --i) {
+      const Index firstIdx = index;
+      const Index lastIdx = index + PacketSize - 1;
+      const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i];
+      const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i];
+      const Index lastPaddedRight = m_outputStrides[i + 1];
+
+      if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft) {
+        // all the coefficient are in the padding zone.
+        return internal::pset1<PacketReturnType>(m_paddingValue);
+      } else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
+        // all the coefficient are in the padding zone.
+        return internal::pset1<PacketReturnType>(m_paddingValue);
+      } else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) ||
+                 (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
+        // all the coefficient are between the 2 padding zones.
+        const Index idx = index / m_outputStrides[i];
+        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      } else {
+        // Every other case
+        return packetWithPossibleZero(initialIndex);
+      }
+    }
+
+    const Index lastIdx = index + PacketSize - 1;
+    const Index firstIdx = index;
+    const Index lastPaddedLeft = m_padding[0].first;
+    const Index firstPaddedRight = (m_dimensions[0] - m_padding[0].second);
+    const Index lastPaddedRight = m_outputStrides[1];
+
+    if (!isLeftPaddingCompileTimeZero(0) && lastIdx < lastPaddedLeft) {
+      // all the coefficient are in the padding zone.
+      return internal::pset1<PacketReturnType>(m_paddingValue);
+    } else if (!isRightPaddingCompileTimeZero(0) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
+      // all the coefficient are in the padding zone.
+      return internal::pset1<PacketReturnType>(m_paddingValue);
+    } else if ((isLeftPaddingCompileTimeZero(0) && isRightPaddingCompileTimeZero(0)) ||
+               (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
+      // all the coefficient are between the 2 padding zones.
+      inputIndex += (index - m_padding[0].first);
+      return m_impl.template packet<Unaligned>(inputIndex);
+    }
+    // Every other case
+    return packetWithPossibleZero(initialIndex);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    const Index initialIndex = index;
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const Index firstIdx = index;
+      const Index lastIdx = index + PacketSize - 1;
+      const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i + 1];
+      const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i + 1];
+      const Index lastPaddedRight = m_outputStrides[i];
+
+      if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft) {
+        // all the coefficient are in the padding zone.
+        return internal::pset1<PacketReturnType>(m_paddingValue);
+      } else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
+        // all the coefficient are in the padding zone.
+        return internal::pset1<PacketReturnType>(m_paddingValue);
+      } else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) ||
+                 (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
+        // all the coefficient are between the 2 padding zones.
+        const Index idx = index / m_outputStrides[i + 1];
+        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i + 1];
+      } else {
+        // Every other case
+        return packetWithPossibleZero(initialIndex);
+      }
+    }
+
+    const Index lastIdx = index + PacketSize - 1;
+    const Index firstIdx = index;
+    const Index lastPaddedLeft = m_padding[NumDims - 1].first;
+    const Index firstPaddedRight = (m_dimensions[NumDims - 1] - m_padding[NumDims - 1].second);
+    const Index lastPaddedRight = m_outputStrides[NumDims - 1];
+
+    if (!isLeftPaddingCompileTimeZero(NumDims - 1) && lastIdx < lastPaddedLeft) {
+      // all the coefficient are in the padding zone.
+      return internal::pset1<PacketReturnType>(m_paddingValue);
+    } else if (!isRightPaddingCompileTimeZero(NumDims - 1) && firstIdx >= firstPaddedRight &&
+               lastIdx < lastPaddedRight) {
+      // all the coefficient are in the padding zone.
+      return internal::pset1<PacketReturnType>(m_paddingValue);
+    } else if ((isLeftPaddingCompileTimeZero(NumDims - 1) && isRightPaddingCompileTimeZero(NumDims - 1)) ||
+               (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
+      // all the coefficient are between the 2 padding zones.
+      inputIndex += (index - m_padding[NumDims - 1].first);
+      return m_impl.template packet<Unaligned>(inputIndex);
+    }
+    // Every other case
+    return packetWithPossibleZero(initialIndex);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const {
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index + i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  Dimensions m_dimensions;
+  array<Index, NumDims + 1> m_outputStrides;
+  array<Index, NumDims> m_inputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  PaddingDimensions m_padding;
+
+  Scalar m_paddingValue;
+
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h
new file mode 100644
index 0000000..6e767a7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h
@@ -0,0 +1,258 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
+#define EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename PatchDim, typename XprType>
+struct traits<TensorPatchOp<PatchDim, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions + 1;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename PatchDim, typename XprType>
+struct eval<TensorPatchOp<PatchDim, XprType>, Eigen::Dense> {
+  typedef const TensorPatchOp<PatchDim, XprType>& type;
+};
+
+template <typename PatchDim, typename XprType>
+struct nested<TensorPatchOp<PatchDim, XprType>, 1, typename eval<TensorPatchOp<PatchDim, XprType> >::type> {
+  typedef TensorPatchOp<PatchDim, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor patch class.
+ */
+template <typename PatchDim, typename XprType>
+class TensorPatchOp : public TensorBase<TensorPatchOp<PatchDim, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorPatchOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorPatchOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorPatchOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorPatchOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPatchOp(const XprType& expr, const PatchDim& patch_dims)
+      : m_xpr(expr), m_patch_dims(patch_dims) {}
+
+  EIGEN_DEVICE_FUNC const PatchDim& patch_dims() const { return m_patch_dims; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const PatchDim m_patch_dims;
+};
+
+// Eval as rvalue
+template <typename PatchDim, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device> {
+  typedef TensorPatchOp<PatchDim, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value + 1;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    CoordAccess = false,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_impl(op.expression(), device) {
+    Index num_patches = 1;
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    const PatchDim& patch_dims = op.patch_dims();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        m_dimensions[i] = patch_dims[i];
+        num_patches *= (input_dims[i] - patch_dims[i] + 1);
+      }
+      m_dimensions[NumDims - 1] = num_patches;
+
+      m_inputStrides[0] = 1;
+      m_patchStrides[0] = 1;
+      for (int i = 1; i < NumDims - 1; ++i) {
+        m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1];
+        m_patchStrides[i] = m_patchStrides[i - 1] * (input_dims[i - 1] - patch_dims[i - 1] + 1);
+      }
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+      }
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        m_dimensions[i + 1] = patch_dims[i];
+        num_patches *= (input_dims[i] - patch_dims[i] + 1);
+      }
+      m_dimensions[0] = num_patches;
+
+      m_inputStrides[NumDims - 2] = 1;
+      m_patchStrides[NumDims - 2] = 1;
+      for (int i = NumDims - 3; i >= 0; --i) {
+        m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1];
+        m_patchStrides[i] = m_patchStrides[i + 1] * (input_dims[i + 1] - patch_dims[i + 1] + 1);
+      }
+      m_outputStrides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
+    // Find the location of the first element of the patch.
+    Index patchIndex = index / m_outputStrides[output_stride_index];
+    // Find the offset of the element wrt the location of the first element.
+    Index patchOffset = index - patchIndex * m_outputStrides[output_stride_index];
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 2; i > 0; --i) {
+        const Index patchIdx = patchIndex / m_patchStrides[i];
+        patchIndex -= patchIdx * m_patchStrides[i];
+        const Index offsetIdx = patchOffset / m_outputStrides[i];
+        patchOffset -= offsetIdx * m_outputStrides[i];
+        inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
+      }
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 2; ++i) {
+        const Index patchIdx = patchIndex / m_patchStrides[i];
+        patchIndex -= patchIdx * m_patchStrides[i];
+        const Index offsetIdx = patchOffset / m_outputStrides[i + 1];
+        patchOffset -= offsetIdx * m_outputStrides[i + 1];
+        inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
+      }
+    }
+    inputIndex += (patchIndex + patchOffset);
+    return m_impl.coeff(inputIndex);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
+    Index indices[2] = {index, index + PacketSize - 1};
+    Index patchIndices[2] = {indices[0] / m_outputStrides[output_stride_index],
+                             indices[1] / m_outputStrides[output_stride_index]};
+    Index patchOffsets[2] = {indices[0] - patchIndices[0] * m_outputStrides[output_stride_index],
+                             indices[1] - patchIndices[1] * m_outputStrides[output_stride_index]};
+
+    Index inputIndices[2] = {0, 0};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 2; i > 0; --i) {
+        const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i], patchIndices[1] / m_patchStrides[i]};
+        patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
+        patchIndices[1] -= patchIdx[1] * m_patchStrides[i];
+
+        const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i], patchOffsets[1] / m_outputStrides[i]};
+        patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i];
+        patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i];
+
+        inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
+        inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
+      }
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 2; ++i) {
+        const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i], patchIndices[1] / m_patchStrides[i]};
+        patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
+        patchIndices[1] -= patchIdx[1] * m_patchStrides[i];
+
+        const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i + 1], patchOffsets[1] / m_outputStrides[i + 1]};
+        patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i + 1];
+        patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i + 1];
+
+        inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
+        inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
+      }
+    }
+    inputIndices[0] += (patchIndices[0] + patchOffsets[0]);
+    inputIndices[1] += (patchIndices[1] + patchOffsets[1]);
+
+    if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
+      PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
+      return rslt;
+    } else {
+      EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
+      values[0] = m_impl.coeff(inputIndices[0]);
+      values[PacketSize - 1] = m_impl.coeff(inputIndices[1]);
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < PacketSize - 1; ++i) {
+        values[i] = coeff(index + i);
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double compute_cost = NumDims * (TensorOpCost::DivCost<Index>() + TensorOpCost::MulCost<Index>() +
+                                           2 * TensorOpCost::AddCost<Index>());
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ protected:
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims - 1> m_inputStrides;
+  array<Index, NumDims - 1> m_patchStrides;
+
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h
new file mode 100644
index 0000000..ffda20a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h
@@ -0,0 +1,297 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2018 Mehdi Goli <eigen@codeplay.com> Codeplay Software Ltd.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
+#define EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE uint64_t get_random_seed() {
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+  // We don't support 3d kernels since we currently only use 1 and
+  // 2d kernels.
+  gpu_assert(threadIdx.z == 0);
+  return blockIdx.x * blockDim.x + threadIdx.x + gridDim.x * blockDim.x * (blockIdx.y * blockDim.y + threadIdx.y);
+#else
+  // Rely on Eigen's random implementation.
+  return random<uint64_t>();
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE unsigned PCG_XSH_RS_generator(uint64_t* state, uint64_t stream) {
+  // TODO: Unify with the implementation in the non blocking thread pool.
+  uint64_t current = *state;
+  // Update the internal state
+  *state = current * 6364136223846793005ULL + (stream << 1 | 1);
+  // Generate the random output (using the PCG-XSH-RS scheme)
+  return static_cast<unsigned>((current ^ (current >> 22)) >> (22 + (current >> 61)));
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE uint64_t PCG_XSH_RS_state(uint64_t seed) {
+  seed = seed ? seed : get_random_seed();
+  return seed * 6364136223846793005ULL + 0xda3e39cb94b95bdbULL;
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T RandomToTypeUniform(uint64_t* state, uint64_t stream) {
+  unsigned rnd = PCG_XSH_RS_generator(state, stream);
+  return static_cast<T>(rnd);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool RandomToTypeUniform<bool>(uint64_t* state, uint64_t stream) {
+  unsigned rnd = PCG_XSH_RS_generator(state, stream);
+  return (rnd & 0x1) != 0;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half RandomToTypeUniform<Eigen::half>(uint64_t* state, uint64_t stream) {
+  // Generate 10 random bits for the mantissa, merge with exponent.
+  unsigned rnd = PCG_XSH_RS_generator(state, stream);
+  const uint16_t half_bits = static_cast<uint16_t>(rnd & 0x3ffu) | (static_cast<uint16_t>(15) << 10);
+  Eigen::half result = Eigen::numext::bit_cast<Eigen::half>(half_bits);
+  return result - Eigen::half(1.0f);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::bfloat16 RandomToTypeUniform<Eigen::bfloat16>(uint64_t* state,
+                                                                                           uint64_t stream) {
+  // Generate 7 random bits for the mantissa, merge with exponent.
+  unsigned rnd = PCG_XSH_RS_generator(state, stream);
+  const uint16_t half_bits = static_cast<uint16_t>(rnd & 0x7fu) | (static_cast<uint16_t>(127) << 7);
+  Eigen::bfloat16 result = Eigen::numext::bit_cast<Eigen::bfloat16>(half_bits);
+  return result - Eigen::bfloat16(1.0f);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float RandomToTypeUniform<float>(uint64_t* state, uint64_t stream) {
+  typedef union {
+    uint32_t raw;
+    float fp;
+  } internal;
+  internal result;
+  // Generate 23 random bits for the mantissa.
+  const unsigned rnd = PCG_XSH_RS_generator(state, stream);
+  result.raw = rnd & 0x7fffffu;
+  // Set the exponent.
+  result.raw |= (static_cast<uint32_t>(127) << 23);
+  return result.fp - 1.0f;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double RandomToTypeUniform<double>(uint64_t* state, uint64_t stream) {
+  typedef union {
+    uint64_t raw;
+    double dp;
+  } internal;
+  internal result;
+  result.raw = 0;
+  // Generate 52 random bits for the mantissa
+  // First generate the upper 20 bits
+  unsigned rnd1 = PCG_XSH_RS_generator(state, stream) & 0xfffffu;
+  // Then generate the lower 32 bits.
+  unsigned rnd2 = PCG_XSH_RS_generator(state, stream);
+  result.raw = (static_cast<uint64_t>(rnd1) << 32) | rnd2;
+  // Set the exponent
+  result.raw |= (static_cast<uint64_t>(1023) << 52);
+  // Return the final result
+  return result.dp - 1.0;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<float> RandomToTypeUniform<std::complex<float> >(uint64_t* state,
+                                                                                                    uint64_t stream) {
+  return std::complex<float>(RandomToTypeUniform<float>(state, stream), RandomToTypeUniform<float>(state, stream));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<double> RandomToTypeUniform<std::complex<double> >(uint64_t* state,
+                                                                                                      uint64_t stream) {
+  return std::complex<double>(RandomToTypeUniform<double>(state, stream), RandomToTypeUniform<double>(state, stream));
+}
+
+template <typename T>
+class UniformRandomGenerator {
+ public:
+  static constexpr bool PacketAccess = true;
+
+  // Uses the given "seed" if non-zero, otherwise uses a random seed.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UniformRandomGenerator(uint64_t seed = 0) {
+    m_state = PCG_XSH_RS_state(seed);
+#ifdef EIGEN_USE_SYCL
+    // In SYCL, the constructor runs on the CPU where thread IDs are unavailable.
+    // We initialize m_state here with just the clock seed; the per-thread
+    // component (i * 6364136223846793005ULL) is added in operator() when
+    // the thread ID becomes available, completing the PCG state setup.
+    m_exec_once = false;
+#endif
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UniformRandomGenerator(const UniformRandomGenerator& other) {
+    m_state = other.m_state;
+#ifdef EIGEN_USE_SYCL
+    m_exec_once = other.m_exec_once;
+#endif
+  }
+
+  template <typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(Index i) const {
+#ifdef EIGEN_USE_SYCL
+    if (!m_exec_once) {
+      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
+      // The (i * 6364136223846793005ULL) is the remaining part of the PCG_XSH_RS_state on the GPU side
+      m_state += (i * 6364136223846793005ULL);
+      m_exec_once = true;
+    }
+#endif
+    T result = RandomToTypeUniform<T>(&m_state, i);
+    return result;
+  }
+
+  template <typename Packet, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(Index i) const {
+    const int packetSize = internal::unpacket_traits<Packet>::size;
+    EIGEN_ALIGN_MAX T values[packetSize];
+#ifdef EIGEN_USE_SYCL
+    if (!m_exec_once) {
+      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
+      m_state += (i * 6364136223846793005ULL);
+      m_exec_once = true;
+    }
+#endif
+    EIGEN_UNROLL_LOOP
+    for (int j = 0; j < packetSize; ++j) {
+      values[j] = RandomToTypeUniform<T>(&m_state, i);
+    }
+    return internal::pload<Packet>(values);
+  }
+
+ private:
+  mutable uint64_t m_state;
+#ifdef EIGEN_USE_SYCL
+  mutable bool m_exec_once;
+#endif
+};
+
+template <typename Scalar>
+struct functor_traits<UniformRandomGenerator<Scalar> > {
+  enum {
+    // Rough estimate for floating point, multiplied by ceil(sizeof(T) / sizeof(float)).
+    Cost = 12 * NumTraits<Scalar>::AddCost * ((sizeof(Scalar) + sizeof(float) - 1) / sizeof(float)),
+    PacketAccess = UniformRandomGenerator<Scalar>::PacketAccess
+  };
+};
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T RandomToTypeNormal(uint64_t* state, uint64_t stream) {
+  // Use the ratio of uniform method to generate numbers following a normal
+  // distribution. See for example Numerical Recipes chapter 7.3.9 for the
+  // details.
+  T u, v, q;
+  do {
+    u = RandomToTypeUniform<T>(state, stream);
+    v = T(1.7156) * (RandomToTypeUniform<T>(state, stream) - T(0.5));
+    const T x = u - T(0.449871);
+    const T y = numext::abs(v) + T(0.386595);
+    q = x * x + y * (T(0.196) * y - T(0.25472) * x);
+  } while (q > T(0.27597) && (q > T(0.27846) || v * v > T(-4) * numext::log(u) * u * u));
+
+  return v / u;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<float> RandomToTypeNormal<std::complex<float> >(uint64_t* state,
+                                                                                                   uint64_t stream) {
+  return std::complex<float>(RandomToTypeNormal<float>(state, stream), RandomToTypeNormal<float>(state, stream));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<double> RandomToTypeNormal<std::complex<double> >(uint64_t* state,
+                                                                                                     uint64_t stream) {
+  return std::complex<double>(RandomToTypeNormal<double>(state, stream), RandomToTypeNormal<double>(state, stream));
+}
+
+template <typename T>
+class NormalRandomGenerator {
+ public:
+  static constexpr bool PacketAccess = true;
+
+  // Uses the given "seed" if non-zero, otherwise uses a random seed.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NormalRandomGenerator(uint64_t seed = 0) {
+    m_state = PCG_XSH_RS_state(seed);
+#ifdef EIGEN_USE_SYCL
+    // In SYCL, the constructor runs on the CPU where thread IDs are unavailable.
+    // We initialize m_state here with just the clock seed; the per-thread
+    // component (i * 6364136223846793005ULL) is added in operator() when
+    // the thread ID becomes available, completing the PCG state setup.
+    m_exec_once = false;
+#endif
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NormalRandomGenerator(const NormalRandomGenerator& other) {
+    m_state = other.m_state;
+#ifdef EIGEN_USE_SYCL
+    m_exec_once = other.m_exec_once;
+#endif
+  }
+
+  template <typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(Index i) const {
+#ifdef EIGEN_USE_SYCL
+    if (!m_exec_once) {
+      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
+      m_state += (i * 6364136223846793005ULL);
+      m_exec_once = true;
+    }
+#endif
+    T result = RandomToTypeNormal<T>(&m_state, i);
+    return result;
+  }
+
+  template <typename Packet, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(Index i) const {
+    const int packetSize = internal::unpacket_traits<Packet>::size;
+    EIGEN_ALIGN_MAX T values[packetSize];
+#ifdef EIGEN_USE_SYCL
+    if (!m_exec_once) {
+      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
+      m_state += (i * 6364136223846793005ULL);
+      m_exec_once = true;
+    }
+#endif
+    EIGEN_UNROLL_LOOP
+    for (int j = 0; j < packetSize; ++j) {
+      values[j] = RandomToTypeNormal<T>(&m_state, i);
+    }
+    return internal::pload<Packet>(values);
+  }
+
+ private:
+  mutable uint64_t m_state;
+#ifdef EIGEN_USE_SYCL
+  mutable bool m_exec_once;
+#endif
+};
+
+template <typename Scalar>
+struct functor_traits<NormalRandomGenerator<Scalar> > {
+  enum {
+    // On average, we need to generate about 3 random numbers
+    // 15 mul, 8 add, 1.5 logs
+    Cost = 3 * functor_traits<UniformRandomGenerator<Scalar> >::Cost + 15 * NumTraits<Scalar>::AddCost +
+           8 * NumTraits<Scalar>::AddCost + 3 * functor_traits<scalar_log_op<Scalar> >::Cost / 2,
+    PacketAccess = NormalRandomGenerator<Scalar>::PacketAccess
+  };
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
new file mode 100644
index 0000000..157f0cf
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
@@ -0,0 +1,1023 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2016 Mehdi Goli, Codeplay Software Ltd <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
+
+// clang is incompatible with the CUDA syntax wrt making a kernel a class friend,
+// so we'll use a macro to make clang happy.
+#ifndef KERNEL_FRIEND
+#if defined(__clang__) && (defined(__CUDA__) || defined(__HIP__))
+#define KERNEL_FRIEND friend __global__ EIGEN_HIP_LAUNCH_BOUNDS_1024
+#else
+#define KERNEL_FRIEND friend
+#endif
+#endif
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
+struct traits<TensorReductionOp<Op, Dims, XprType, MakePointer_> > : traits<XprType> {
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::Scalar Scalar;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  static constexpr int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+
+  template <class T>
+  struct MakePointer {
+    // Intermediate typedef to workaround MSVC issue.
+    typedef MakePointer_<T> MakePointerT;
+    typedef typename MakePointerT::Type Type;
+  };
+};
+
+template <typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
+struct eval<TensorReductionOp<Op, Dims, XprType, MakePointer_>, Eigen::Dense> {
+  typedef const TensorReductionOp<Op, Dims, XprType, MakePointer_>& type;
+};
+
+template <typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
+struct nested<TensorReductionOp<Op, Dims, XprType, MakePointer_>, 1,
+              typename eval<TensorReductionOp<Op, Dims, XprType, MakePointer_> >::type> {
+  typedef TensorReductionOp<Op, Dims, XprType, MakePointer_> type;
+};
+
+template <typename OutputDims>
+struct DimInitializer {
+  template <typename InputDims, typename ReducedDims>
+  EIGEN_DEVICE_FUNC static void run(const InputDims& input_dims,
+                                    const array<bool, internal::array_size<InputDims>::value>& reduced,
+                                    OutputDims* output_dims, ReducedDims* reduced_dims) {
+    const int NumInputDims = internal::array_size<InputDims>::value;
+    int outputIndex = 0;
+    int reduceIndex = 0;
+    for (int i = 0; i < NumInputDims; ++i) {
+      if (reduced[i]) {
+        (*reduced_dims)[reduceIndex] = input_dims[i];
+        ++reduceIndex;
+      } else {
+        (*output_dims)[outputIndex] = input_dims[i];
+        ++outputIndex;
+      }
+    }
+  }
+};
+
+template <>
+struct DimInitializer<Sizes<> > {
+  template <typename InputDims, typename Index, size_t Rank>
+  EIGEN_DEVICE_FUNC static void run(const InputDims& input_dims, const array<bool, Rank>&, Sizes<>*,
+                                    array<Index, Rank>* reduced_dims) {
+    const int NumInputDims = internal::array_size<InputDims>::value;
+    for (int i = 0; i < NumInputDims; ++i) {
+      (*reduced_dims)[i] = input_dims[i];
+    }
+  }
+};
+
+template <typename ReducedDims, int NumTensorDims, int Layout>
+struct are_inner_most_dims {
+  static const bool value = false;
+};
+template <typename ReducedDims, int NumTensorDims, int Layout>
+struct preserve_inner_most_dims {
+  static const bool value = false;
+};
+
+template <typename ReducedDims, int NumTensorDims>
+struct are_inner_most_dims<ReducedDims, NumTensorDims, ColMajor> {
+  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
+  static const bool tmp2 = index_statically_eq<ReducedDims>(0, 0);
+  static const bool tmp3 =
+      index_statically_eq<ReducedDims>(array_size<ReducedDims>::value - 1, array_size<ReducedDims>::value - 1);
+  static const bool value = tmp1 & tmp2 & tmp3;
+};
+template <typename ReducedDims, int NumTensorDims>
+struct are_inner_most_dims<ReducedDims, NumTensorDims, RowMajor> {
+  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
+  static const bool tmp2 = index_statically_eq<ReducedDims>(0, NumTensorDims - array_size<ReducedDims>::value);
+  static const bool tmp3 = index_statically_eq<ReducedDims>(array_size<ReducedDims>::value - 1, NumTensorDims - 1);
+  static const bool value = tmp1 & tmp2 & tmp3;
+};
+template <typename ReducedDims, int NumTensorDims>
+struct preserve_inner_most_dims<ReducedDims, NumTensorDims, ColMajor> {
+  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
+  static const bool tmp2 = index_statically_gt<ReducedDims>(0, 0);
+  static const bool value = tmp1 & tmp2;
+};
+template <typename ReducedDims, int NumTensorDims>
+struct preserve_inner_most_dims<ReducedDims, NumTensorDims, RowMajor> {
+  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
+  static const bool tmp2 = index_statically_lt<ReducedDims>(array_size<ReducedDims>::value - 1, NumTensorDims - 1);
+  static const bool value = tmp1 & tmp2;
+};
+
+template <int DimIndex, typename Self, typename Op>
+struct GenericDimReducer {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex,
+                                                           Op& reducer, typename Self::CoeffReturnType* accum) {
+    EIGEN_STATIC_ASSERT((DimIndex > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    for (int j = 0; j < self.m_reducedDims[DimIndex]; ++j) {
+      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[DimIndex];
+      GenericDimReducer<DimIndex - 1, Self, Op>::reduce(self, input, reducer, accum);
+    }
+  }
+};
+template <typename Self, typename Op>
+struct GenericDimReducer<0, Self, Op> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex,
+                                                           Op& reducer, typename Self::CoeffReturnType* accum) {
+    for (int j = 0; j < self.m_reducedDims[0]; ++j) {
+      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[0];
+      reducer.reduce(self.m_impl.coeff(input), accum);
+    }
+  }
+};
+template <typename Self, typename Op>
+struct GenericDimReducer<-1, Self, Op> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index index, Op& reducer,
+                                                           typename Self::CoeffReturnType* accum) {
+    reducer.reduce(self.m_impl.coeff(index), accum);
+  }
+};
+
+template <typename Self, typename Op,
+          bool Vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess),
+          bool UseTreeReduction = (!Self::ReducerTraits::IsStateful && !Self::ReducerTraits::IsExactlyAssociative &&
+                                   // GPU threads can quickly run out of stack space
+                                   // for moderately sized inputs.
+                                   !Self::RunningOnGPU)>
+struct InnerMostDimReducer {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(
+      const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) {
+    typename Self::CoeffReturnType accum = reducer.initialize();
+    for (typename Self::Index j = 0; j < numValuesToReduce; ++j) {
+      reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum);
+    }
+    return reducer.finalize(accum);
+  }
+};
+
+template <typename Self, typename Op>
+struct InnerMostDimReducer<Self, Op, true, false> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(
+      const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer0) {
+    using Index = typename Self::Index;
+    constexpr Index packetSize = internal::unpacket_traits<typename Self::PacketReturnType>::size;
+    Index start = 0;
+    typename Self::PacketReturnType paccum0 = reducer0.template initializePacket<typename Self::PacketReturnType>();
+    if (!Self::ReducerTraits::IsStateful && numValuesToReduce >= 4 * packetSize) {
+      const Index VectorizedSize4 = (numValuesToReduce / (4 * packetSize)) * (4 * packetSize);
+      typename Self::PacketReturnType paccum1 = reducer0.template initializePacket<typename Self::PacketReturnType>();
+      typename Self::PacketReturnType paccum2 = reducer0.template initializePacket<typename Self::PacketReturnType>();
+      typename Self::PacketReturnType paccum3 = reducer0.template initializePacket<typename Self::PacketReturnType>();
+      const Index offset0 = firstIndex;
+      const Index offset1 = firstIndex + packetSize;
+      const Index offset2 = firstIndex + 2 * packetSize;
+      const Index offset3 = firstIndex + 3 * packetSize;
+      for (Index j = 0; j < VectorizedSize4; j += 4 * packetSize) {
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(offset0 + j), &paccum0);
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(offset1 + j), &paccum1);
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(offset2 + j), &paccum2);
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(offset3 + j), &paccum3);
+      }
+      reducer0.reducePacket(paccum1, &paccum0);
+      reducer0.reducePacket(paccum2, &paccum0);
+      reducer0.reducePacket(paccum3, &paccum0);
+      start = VectorizedSize4;
+    }
+    if (start <= (numValuesToReduce - packetSize)) {
+      const Index VectorizedSize = (numValuesToReduce / packetSize) * packetSize;
+      for (Index j = start; j < VectorizedSize; j += packetSize) {
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(firstIndex + j), &paccum0);
+      }
+      start = VectorizedSize;
+    }
+    typename Self::CoeffReturnType accum = reducer0.initialize();
+    for (Index j = start; j < numValuesToReduce; ++j) {
+      reducer0.reduce(self.m_impl.coeff(firstIndex + j), &accum);
+    }
+    return reducer0.finalizeBoth(accum, paccum0);
+  }
+};
+
+#if !defined(EIGEN_HIPCC)
+
+// The following implements tree-based reduction, which improves the accuracy
+// of sum and mean reductions, since each of the n inputs only participates in
+// O(log n) additions.
+template <typename T>
+EIGEN_DEVICE_FUNC inline Index LeafSize() {
+  return 1024;
+}
+template <>
+EIGEN_DEVICE_FUNC inline Index LeafSize<half>() {
+  return 200;
+}
+template <>
+EIGEN_DEVICE_FUNC inline Index LeafSize<bfloat16>() {
+  return 128;
+}
+
+template <typename Self, typename Op>
+struct InnerMostDimReducer<Self, Op, false, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(
+      const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) {
+    const Index kLeafSize = LeafSize<typename Self::CoeffReturnType>();
+    typename Self::CoeffReturnType accum = reducer.initialize();
+    if (numValuesToReduce > kLeafSize) {
+      const typename Self::Index half = numValuesToReduce / 2;
+      // Recursively reduce the two halves.
+      reducer.reduce(reduce(self, firstIndex, half, reducer), &accum);
+      reducer.reduce(reduce(self, firstIndex + half, numValuesToReduce - half, reducer), &accum);
+      return reducer.finalize(accum);
+    } else {
+      return InnerMostDimReducer<Self, Op, false, false>::reduce(self, firstIndex, numValuesToReduce, reducer);
+    }
+  }
+};
+
+template <typename Self, typename Op>
+struct InnerMostDimReducer<Self, Op, true, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(
+      const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) {
+    const Index kLeafSize = LeafSize<typename Self::CoeffReturnType>();
+    const typename Self::Index packetSize = internal::unpacket_traits<typename Self::PacketReturnType>::size;
+    typename Self::CoeffReturnType accum = reducer.initialize();
+    if (numValuesToReduce > packetSize * kLeafSize) {
+      // Make sure the split point is aligned on a packet boundary.
+      const typename Self::Index split =
+          packetSize *
+          numext::div_ceil(firstIndex + numext::div_ceil(numValuesToReduce, typename Self::Index(2)), packetSize);
+      const typename Self::Index num_left = numext::mini(split - firstIndex, numValuesToReduce);
+      reducer.reduce(reduce(self, firstIndex, num_left, reducer), &accum);
+      if (num_left < numValuesToReduce) {
+        reducer.reduce(reduce(self, split, numValuesToReduce - num_left, reducer), &accum);
+      }
+      return reducer.finalize(accum);
+    } else {
+      return InnerMostDimReducer<Self, Op, true, false>::reduce(self, firstIndex, numValuesToReduce, reducer);
+    }
+  }
+};
+#endif
+
+template <int DimIndex, typename Self, typename Op,
+          bool vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess)>
+struct InnerMostDimPreserver {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self&, typename Self::Index, Op&,
+                                                           typename Self::PacketReturnType*) {
+    eigen_assert(false && "should never be called");
+  }
+};
+
+template <int DimIndex, typename Self, typename Op>
+struct InnerMostDimPreserver<DimIndex, Self, Op, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex,
+                                                           Op& reducer, typename Self::PacketReturnType* accum) {
+    EIGEN_STATIC_ASSERT((DimIndex > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    for (typename Self::Index j = 0; j < self.m_reducedDims[DimIndex]; ++j) {
+      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[DimIndex];
+      InnerMostDimPreserver<DimIndex - 1, Self, Op>::reduce(self, input, reducer, accum);
+    }
+  }
+};
+
+template <typename Self, typename Op>
+struct InnerMostDimPreserver<0, Self, Op, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex,
+                                                           Op& reducer0, typename Self::PacketReturnType* accum0) {
+    using Index = typename Self::Index;
+    const Index stride = self.m_reducedStrides[0];
+    const Index size = self.m_reducedDims[0];
+    if (!Self::ReducerTraits::IsStateful && size >= 16) {
+      const Index unrolled_size4 = (size / 4) * 4;
+      typename Self::PacketReturnType accum1 = reducer0.template initializePacket<typename Self::PacketReturnType>();
+      typename Self::PacketReturnType accum2 = reducer0.template initializePacket<typename Self::PacketReturnType>();
+      typename Self::PacketReturnType accum3 = reducer0.template initializePacket<typename Self::PacketReturnType>();
+      for (Index j = 0; j < unrolled_size4; j += 4) {
+        const Index input0 = firstIndex + j * stride;
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input0), accum0);
+        const Index input1 = firstIndex + (j + 1) * stride;
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input1), &accum1);
+        const Index input2 = firstIndex + (j + 2) * stride;
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input2), &accum2);
+        const Index input3 = firstIndex + (j + 3) * stride;
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input3), &accum3);
+      }
+      reducer0.reducePacket(accum1, accum0);
+      reducer0.reducePacket(accum2, accum0);
+      reducer0.reducePacket(accum3, accum0);
+      for (Index j = unrolled_size4; j < size; ++j) {
+        Index input = firstIndex + j * stride;
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input), accum0);
+      }
+    } else {
+      for (Index j = 0; j < size; ++j) {
+        Index input = firstIndex + j * stride;
+        reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input), accum0);
+      }
+    }
+  }
+};
+template <typename Self, typename Op>
+struct InnerMostDimPreserver<-1, Self, Op, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self&, typename Self::Index, Op&,
+                                                           typename Self::PacketReturnType*) {
+    eigen_assert(false && "should never be called");
+  }
+};
+
+// Default full reducer
+template <typename Self, typename Op, typename Device,
+          bool Vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess)>
+struct FullReducer {
+  static constexpr bool HasOptimizedImplementation = false;
+
+  static EIGEN_DEVICE_FUNC void run(const Self& self, Op& reducer, const Device&,
+                                    typename Self::EvaluatorPointerType output) {
+    const typename Self::Index num_coeffs = array_prod(self.m_impl.dimensions());
+    *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer);
+  }
+};
+
+#ifdef EIGEN_USE_THREADS
+// Multithreaded full reducer
+template <typename Self, typename Op, bool Vectorizable>
+struct FullReducer<Self, Op, ThreadPoolDevice, Vectorizable> {
+  static constexpr bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful;
+  static constexpr Index PacketSize = unpacket_traits<typename Self::PacketReturnType>::size;
+
+  // launch one reducer per thread and accumulate the result.
+  static void run(const Self& self, Op& reducer, const ThreadPoolDevice& device,
+                  typename Self::CoeffReturnType* output) {
+    typedef typename Self::Index Index;
+    const Index num_coeffs = array_prod(self.m_impl.dimensions());
+    if (num_coeffs == 0) {
+      *output = reducer.finalize(reducer.initialize());
+      return;
+    }
+    const TensorOpCost cost = self.m_impl.costPerCoeff(Vectorizable) +
+                              TensorOpCost(0, 0, internal::functor_traits<Op>::Cost, Vectorizable, PacketSize);
+    const Index num_threads = TensorCostModel<ThreadPoolDevice>::numThreads(num_coeffs, cost, device.numThreads());
+    if (num_threads == 1) {
+      *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer);
+      return;
+    }
+    const Index blocksize = num_coeffs / num_threads;
+    const Index numblocks = blocksize > 0 ? num_coeffs / blocksize : 0;
+    eigen_assert(num_coeffs >= numblocks * blocksize);
+
+    Barrier barrier(internal::convert_index<unsigned int>(numblocks));
+    MaxSizeVector<typename Self::CoeffReturnType> shards(numblocks, reducer.initialize());
+    for (Index i = 0; i < numblocks; ++i) {
+      auto run_shard = [i, blocksize, &self, &barrier, &shards, &reducer]() {
+        shards[i] = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, i * blocksize, blocksize, reducer);
+        barrier.Notify();
+      };
+      device.enqueue(std::move(run_shard));
+    }
+    typename Self::CoeffReturnType finalShard;
+    if (numblocks * blocksize < num_coeffs) {
+      finalShard = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, numblocks * blocksize,
+                                                                       num_coeffs - numblocks * blocksize, reducer);
+    } else {
+      finalShard = reducer.initialize();
+    }
+    barrier.Wait();
+
+    for (Index i = 0; i < numblocks; ++i) {
+      reducer.reduce(shards[i], &finalShard);
+    }
+    *output = reducer.finalize(finalShard);
+  }
+};
+
+#endif
+
+// Default inner reducer
+template <typename Self, typename Op, typename Device>
+struct InnerReducer {
+  static constexpr bool HasOptimizedImplementation = false;
+
+  EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*,
+                                    typename Self::Index, typename Self::Index) {
+    eigen_assert(false && "Not implemented");
+    return true;
+  }
+};
+
+// Default outer reducer
+template <typename Self, typename Op, typename Device>
+struct OuterReducer {
+  static constexpr bool HasOptimizedImplementation = false;
+
+  EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*,
+                                    typename Self::Index, typename Self::Index) {
+    eigen_assert(false && "Not implemented");
+    return true;
+  }
+};
+
+#ifdef EIGEN_USE_SYCL
+// Default Generic reducer
+template <typename Self, typename Op, typename Device>
+struct GenericReducer {
+  static constexpr bool HasOptimizedImplementation = false;
+
+  EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*,
+                                    typename Self::Index, typename Self::Index) {
+    eigen_assert(false && "Not implemented");
+    return true;
+  }
+};
+#endif
+
+#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
+template <int B, int N, typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernel(R, const S, I_, typename S::CoeffReturnType*,
+                                                                 unsigned int*);
+
+#if defined(EIGEN_HAS_GPU_FP16)
+template <typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitFullReduxKernelHalfFloat(
+    R, const S, I_, internal::packet_traits<half>::type*);
+template <int B, int N, typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernelHalfFloat(R, const S, I_, half*,
+                                                                          internal::packet_traits<half>::type*);
+template <int NPT, typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernelHalfFloat(R, const S, I_, I_, half*);
+
+#endif
+
+template <int NPT, typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
+
+template <int NPT, typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void OuterReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
+#endif
+
+/**
+ * For SYCL, the return type of the reduction is deduced from the initialize method of the given Op.
+ * This allows the reduction to have a different type for the accumulator than the input data type.
+ * If this is the case, the functor needs to have two reduce method: one for reducing an element of the input
+ * with the accumulator and the other for reducing two accumulators.
+ * Such a reducer can be useful for instance when the accumulator is a boolean or a bitset that checks for
+ * some properties of the input.
+ */
+template <typename Op, typename CoeffReturnType>
+struct ReductionReturnType {
+#if defined(EIGEN_USE_SYCL)
+  typedef std::remove_const_t<decltype(std::declval<Op>().initialize())> type;
+#else
+  typedef std::remove_const_t<CoeffReturnType> type;
+#endif
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor reduction class.
+ *
+ */
+template <typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
+class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType, MakePointer_>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorReductionOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorReductionOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorReductionOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorReductionOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReductionOp(const XprType& expr, const Dims& dims)
+      : m_expr(expr), m_dims(dims) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReductionOp(const XprType& expr, const Dims& dims, const Op& reducer)
+      : m_expr(expr), m_dims(dims), m_reducer(reducer) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const XprType& expression() const { return m_expr; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dims& dims() const { return m_dims; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Op& reducer() const { return m_reducer; }
+
+ protected:
+  typename XprType::Nested m_expr;
+  const Dims m_dims;
+  const Op m_reducer;
+};
+
+template <typename ArgType, typename Device>
+struct TensorReductionEvaluatorBase;
+
+// Eval as rvalue
+template <typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
+struct TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> {
+  typedef internal::reducer_traits<Op, Device> ReducerTraits;
+  typedef Dims ReducedDims;
+  typedef TensorReductionOp<Op, Dims, ArgType, MakePointer_> XprType;
+  typedef typename XprType::Index Index;
+  typedef ArgType ChildType;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
+  static constexpr int NumInputDims = internal::array_size<InputDimensions>::value;
+  static constexpr int NumReducedDims = internal::array_size<Dims>::value;
+  static constexpr int NumOutputDims = NumInputDims - NumReducedDims;
+  typedef std::conditional_t<NumOutputDims == 0, Sizes<>, DSizes<Index, NumOutputDims> > Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Self;
+  static constexpr bool InputPacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess;
+  typedef typename internal::ReductionReturnType<Op, typename XprType::CoeffReturnType>::type CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr Index PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  // Subset of strides of the input tensor for the non-reduced dimensions.
+  // Indexed by output dimensions.
+  static constexpr int NumPreservedStrides = max_n_1<NumOutputDims>::size;
+
+  // For full reductions
+#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
+  static constexpr bool RunningOnGPU = internal::is_same<Device, Eigen::GpuDevice>::value;
+  static constexpr bool RunningOnSycl = false;
+#elif defined(EIGEN_USE_SYCL)
+  static constexpr bool RunningOnSycl = internal::is_same<internal::remove_all_t<Device>, Eigen::SyclDevice>::value;
+  static constexpr bool RunningOnGPU = false;
+#else
+  static constexpr bool RunningOnGPU = false;
+  static constexpr bool RunningOnSycl = false;
+#endif
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = Self::InputPacketAccess && ReducerTraits::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = true,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  static constexpr bool ReducingInnerMostDims = internal::are_inner_most_dims<Dims, NumInputDims, Layout>::value;
+  static constexpr bool PreservingInnerMostDims = internal::preserve_inner_most_dims<Dims, NumInputDims, Layout>::value;
+  static constexpr bool RunningFullReduction = (NumOutputDims == 0);
+
+  EIGEN_STRONG_INLINE TensorReductionEvaluatorBase(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_reducer(op.reducer()), m_result(NULL), m_device(device) {
+    EIGEN_STATIC_ASSERT((NumInputDims >= NumReducedDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT((!ReducingInnerMostDims | !PreservingInnerMostDims | (NumReducedDims == NumInputDims)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    // Build the bitmap indicating if an input dimension is reduced or not.
+    for (int i = 0; i < NumInputDims; ++i) {
+      m_reduced[i] = false;
+    }
+    for (int i = 0; i < NumReducedDims; ++i) {
+      eigen_assert(op.dims()[i] >= 0);
+      eigen_assert(op.dims()[i] < NumInputDims);
+      m_reduced[op.dims()[i]] = true;
+    }
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    internal::DimInitializer<Dimensions>::run(input_dims, m_reduced, &m_dimensions, &m_reducedDims);
+
+    // Precompute output strides.
+    if (NumOutputDims > 0) {
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        m_outputStrides[0] = 1;
+        for (int i = 1; i < NumOutputDims; ++i) {
+          m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+          m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]);
+        }
+      } else {
+        m_outputStrides[static_cast<size_t>(NumOutputDims - 1)] = 1;
+        for (int i = NumOutputDims - 2; i >= 0; --i) {
+          m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+          m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]);
+        }
+      }
+    }
+
+    // Precompute input strides.
+    if (NumInputDims > 0) {
+      array<Index, NumInputDims> input_strides;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        input_strides[0] = 1;
+        for (int i = 1; i < NumInputDims; ++i) {
+          input_strides[i] = input_strides[i - 1] * input_dims[i - 1];
+        }
+      } else {
+        input_strides.back() = 1;
+        for (int i = NumInputDims - 2; i >= 0; --i) {
+          input_strides[i] = input_strides[i + 1] * input_dims[i + 1];
+        }
+      }
+
+      int outputIndex = 0;
+      int reduceIndex = 0;
+      for (int i = 0; i < NumInputDims; ++i) {
+        if (m_reduced[i]) {
+          m_reducedStrides[reduceIndex] = input_strides[i];
+          ++reduceIndex;
+        } else {
+          m_preservedStrides[outputIndex] = input_strides[i];
+          m_output_to_input_dim_map[outputIndex] = i;
+          ++outputIndex;
+        }
+      }
+    }
+
+    // Special case for full reductions
+    if (NumOutputDims == 0) {
+      m_preservedStrides[0] = internal::array_prod(input_dims);
+    }
+
+    m_numValuesToReduce = NumOutputDims == 0 ? internal::array_prod(input_dims)
+                          : (static_cast<int>(Layout) == static_cast<int>(ColMajor))
+                              ? m_preservedStrides[0]
+                              : m_preservedStrides[static_cast<size_t>(NumOutputDims - 1)];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeededCommon(EvaluatorPointerType data) {
+    // Use the FullReducer if possible.
+    if ((RunningFullReduction && RunningOnSycl) ||
+        (RunningFullReduction && internal::FullReducer<Self, Op, Device>::HasOptimizedImplementation &&
+         ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) || !RunningOnGPU))) {
+      bool need_assign = false;
+      if (!data) {
+        m_result = static_cast<EvaluatorPointerType>(
+            m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType))));
+        data = m_result;
+        need_assign = true;
+      }
+      Op reducer(m_reducer);
+      internal::FullReducer<Self, Op, Device>::run(*this, reducer, m_device, data);
+      return need_assign;
+    }
+
+    // Attempt to use an optimized reduction.
+    else if ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) || (RunningOnSycl)) {
+      bool reducing_inner_dims = true;
+      for (int i = 0; i < NumReducedDims; ++i) {
+        if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+          reducing_inner_dims &= m_reduced[i];
+        } else {
+          reducing_inner_dims &= m_reduced[NumInputDims - 1 - i];
+        }
+      }
+      if (internal::InnerReducer<Self, Op, Device>::HasOptimizedImplementation &&
+          (reducing_inner_dims || ReducingInnerMostDims)) {
+        const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
+        const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
+        if (!data) {
+          if ((num_coeffs_to_preserve < 1024 && num_values_to_reduce > num_coeffs_to_preserve &&
+               num_values_to_reduce > 128) ||
+              (RunningOnSycl)) {
+            data = static_cast<EvaluatorPointerType>(m_device.get(
+                (CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve)));
+            m_result = data;
+          } else {
+            return true;
+          }
+        }
+        Op reducer(m_reducer);
+        // For SYCL, this always returns false.
+        if (internal::InnerReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce,
+                                                          num_coeffs_to_preserve)) {
+          if (m_result) {
+            m_device.deallocate_temp(m_result);
+            m_result = NULL;
+          }
+          return true;
+        } else {
+          return (m_result != NULL);
+        }
+      }
+
+      bool preserving_inner_dims = true;
+      for (int i = 0; i < NumReducedDims; ++i) {
+        if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+          preserving_inner_dims &= m_reduced[NumInputDims - 1 - i];
+        } else {
+          preserving_inner_dims &= m_reduced[i];
+        }
+      }
+      if (internal::OuterReducer<Self, Op, Device>::HasOptimizedImplementation && preserving_inner_dims) {
+        const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
+        const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
+        if (!data) {
+          if ((num_coeffs_to_preserve < 1024 && num_values_to_reduce > num_coeffs_to_preserve &&
+               num_values_to_reduce > 32) ||
+              (RunningOnSycl)) {
+            data = static_cast<EvaluatorPointerType>(m_device.get(
+                (CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve)));
+            m_result = data;
+          } else {
+            return true;
+          }
+        }
+        Op reducer(m_reducer);
+        // For SYCL, this always returns false.
+        if (internal::OuterReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce,
+                                                          num_coeffs_to_preserve)) {
+          if (m_result) {
+            m_device.deallocate_temp(m_result);
+            m_result = NULL;
+          }
+          return true;
+        } else {
+          return (m_result != NULL);
+        }
+      }
+#if defined(EIGEN_USE_SYCL)
+      // If there is no Optimised version for SYCL, the reduction expression
+      // must break into two subexpression and use the SYCL generic Reducer on the device.
+      if (RunningOnSycl) {
+        const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
+        const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
+        if (!data) {
+          data = static_cast<EvaluatorPointerType>(
+              m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve)));
+          m_result = data;
+        }
+        Op reducer(m_reducer);
+        internal::GenericReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce,
+                                                        num_coeffs_to_preserve);
+        return (m_result != NULL);
+      }
+#endif
+    }
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType data, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(NULL, [this, data, done](bool) { done(evalSubExprsIfNeededCommon(data)); });
+  }
+#endif
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return evalSubExprsIfNeededCommon(data);
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+    if (m_result) {
+      m_device.deallocate_temp(m_result);
+      m_result = NULL;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    if ((RunningFullReduction || RunningOnGPU) && m_result) {
+      return *(m_result + index);
+    }
+    Op reducer(m_reducer);
+    if (ReducingInnerMostDims || RunningFullReduction) {
+      const Index num_values_to_reduce = (static_cast<int>(Layout) == static_cast<int>(ColMajor))
+                                             ? m_preservedStrides[0]
+                                             : m_preservedStrides[NumPreservedStrides - 1];
+      return internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstInput(index), num_values_to_reduce, reducer);
+    } else {
+      typename Self::CoeffReturnType accum = reducer.initialize();
+      internal::GenericDimReducer<NumReducedDims - 1, Self, Op>::reduce(*this, firstInput(index), reducer, &accum);
+      return reducer.finalize(accum);
+    }
+  }
+
+  // TODO(bsteiner): provide a more efficient implementation.
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    eigen_assert(index + PacketSize - 1 < Index(internal::array_prod(dimensions())));
+
+    if (RunningOnGPU && m_result) {
+      return internal::pload<PacketReturnType>(m_result + index);
+    }
+
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+    if (ReducingInnerMostDims) {
+      const Index num_values_to_reduce = (static_cast<int>(Layout) == static_cast<int>(ColMajor))
+                                             ? m_preservedStrides[0]
+                                             : m_preservedStrides[NumPreservedStrides - 1];
+      const Index firstIndex = firstInput(index);
+      for (Index i = 0; i < PacketSize; ++i) {
+        Op reducer(m_reducer);
+        values[i] = internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstIndex + i * num_values_to_reduce,
+                                                                    num_values_to_reduce, reducer);
+      }
+    } else if (PreservingInnerMostDims) {
+      const Index firstIndex = firstInput(index);
+      const int innermost_dim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : NumOutputDims - 1;
+      // TBD: extend this to the n innermost dimensions that we preserve.
+      if (((firstIndex % m_dimensions[innermost_dim]) + PacketSize - 1) < m_dimensions[innermost_dim]) {
+        Op reducer(m_reducer);
+        typename Self::PacketReturnType accum = reducer.template initializePacket<typename Self::PacketReturnType>();
+        internal::InnerMostDimPreserver<NumReducedDims - 1, Self, Op>::reduce(*this, firstIndex, reducer, &accum);
+        return reducer.finalizePacket(accum);
+      } else {
+        for (int i = 0; i < PacketSize; ++i) {
+          values[i] = coeff(index + i);
+        }
+      }
+    } else {
+      for (int i = 0; i < PacketSize; ++i) {
+        values[i] = coeff(index + i);
+      }
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  // Must be called after evalSubExprsIfNeeded().
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    if (RunningFullReduction && m_result) {
+      return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
+    } else {
+      const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
+      const double compute_cost = num_values_to_reduce * internal::functor_traits<Op>::Cost;
+      return m_impl.costPerCoeff(vectorized) * num_values_to_reduce +
+             TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
+  EIGEN_DEVICE_FUNC const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+  EIGEN_DEVICE_FUNC const Device& device() const { return m_device; }
+
+ private:
+  template <int, typename, typename>
+  friend struct internal::GenericDimReducer;
+  template <typename, typename, bool, bool>
+  friend struct internal::InnerMostDimReducer;
+  template <int, typename, typename, bool>
+  friend struct internal::InnerMostDimPreserver;
+  template <typename S, typename O, typename D, bool V>
+  friend struct internal::FullReducer;
+#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
+  template <int B, int N, typename S, typename R, typename I_>
+  KERNEL_FRIEND void internal::FullReductionKernel(R, const S, I_, typename S::CoeffReturnType*, unsigned int*);
+#if defined(EIGEN_HAS_GPU_FP16)
+  template <typename S, typename R, typename I_>
+  KERNEL_FRIEND void internal::ReductionInitFullReduxKernelHalfFloat(R, const S, I_,
+                                                                     internal::packet_traits<Eigen::half>::type*);
+  template <int B, int N, typename S, typename R, typename I_>
+  KERNEL_FRIEND void internal::FullReductionKernelHalfFloat(R, const S, I_, half*,
+                                                            internal::packet_traits<Eigen::half>::type*);
+  template <int NPT, typename S, typename R, typename I_>
+  KERNEL_FRIEND void internal::InnerReductionKernelHalfFloat(R, const S, I_, I_, half*);
+#endif
+  template <int NPT, typename S, typename R, typename I_>
+  KERNEL_FRIEND void internal::InnerReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
+
+  template <int NPT, typename S, typename R, typename I_>
+  KERNEL_FRIEND void internal::OuterReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
+#endif
+
+#if defined(EIGEN_USE_SYCL)
+  template <typename Evaluator_, typename Op__>
+  friend class TensorSycl::internal::GenericNondeterministicReducer;
+  // SYCL needs the generic reducer when the reduction is neither inner, outer, nor full.
+  template <typename, typename, typename>
+  friend struct internal::GenericReducer;
+#endif
+
+  template <typename S, typename O, typename D>
+  friend struct internal::InnerReducer;
+
+  struct BlockIteratorState {
+    Index input_dim;
+    Index output_size;
+    Index output_count;
+  };
+
+  // Returns the Index in the input tensor of the first value that needs to be
+  // used to compute the reduction at output index "index".
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
+    if (ReducingInnerMostDims) {
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        return index * m_preservedStrides[0];
+      } else {
+        return index * m_preservedStrides[NumPreservedStrides - 1];
+      }
+    }
+    // TBD: optimize the case where we preserve the innermost dimensions.
+    Index startInput = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumOutputDims - 1; i > 0; --i) {
+        // This is index_i in the output tensor.
+        const Index idx = index / m_outputStrides[i];
+        startInput += idx * m_preservedStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      if (PreservingInnerMostDims) {
+        eigen_assert(m_preservedStrides[0] == 1);
+        startInput += index;
+      } else {
+        startInput += index * m_preservedStrides[0];
+      }
+    } else {
+      for (int i = 0; i < NumOutputDims - 1; ++i) {
+        // This is index_i in the output tensor.
+        const Index idx = index / m_outputStrides[i];
+        startInput += idx * m_preservedStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      if (PreservingInnerMostDims) {
+        eigen_assert(m_preservedStrides[NumPreservedStrides - 1] == 1);
+        startInput += index;
+      } else {
+        startInput += index * m_preservedStrides[NumPreservedStrides - 1];
+      }
+    }
+    return startInput;
+  }
+
+  // Bitmap indicating if an input dimension is reduced or not.
+  array<bool, NumInputDims> m_reduced;
+  // Dimensions of the output of the operation.
+  Dimensions m_dimensions;
+  // Precomputed strides for the output tensor.
+  // Avoid zero-sized arrays, since element access fails to compile on GPU.
+  array<Index, (std::max)(NumOutputDims, 1)> m_outputStrides;
+  array<internal::TensorIntDivisor<Index>, (std::max)(NumOutputDims, 1)> m_fastOutputStrides;
+  array<Index, (std::max)(NumPreservedStrides, 1)> m_preservedStrides;
+  // Map from output to input dimension index.
+  array<Index, (std::max)(NumOutputDims, 1)> m_output_to_input_dim_map;
+  // How many values go into each reduction
+  Index m_numValuesToReduce;
+
+  // Subset of strides of the input tensor for the reduced dimensions.
+  // Indexed by reduced dimensions.
+  array<Index, NumReducedDims> m_reducedStrides;
+  // Size of the input dimensions that are reduced.
+  // Indexed by reduced dimensions.
+  array<Index, NumReducedDims> m_reducedDims;
+
+  // Evaluator for the input expression.
+  TensorEvaluator<ArgType, Device> m_impl;
+
+  // Operation to apply for computing the reduction.
+  Op m_reducer;
+
+  EvaluatorPointerType m_result;
+
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+template <typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
+struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>
+    : public TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> {
+  typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Base;
+  EIGEN_STRONG_INLINE TensorEvaluator(const typename Base::XprType& op, const Device& device) : Base(op, device) {}
+};
+
+template <typename Op, typename Dims, typename ArgType, template <class> class MakePointer_>
+struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice>
+    : public TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice> {
+  typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice>
+      Base;
+  EIGEN_STRONG_INLINE TensorEvaluator(const typename Base::XprType& op, const Eigen::SyclDevice& device)
+      : Base(op, device) {}
+  // The base coeff function uses a recursive method that is not standard layout and cannot be used in
+  // SYCL kernels, so it must be overridden.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Base::CoeffReturnType coeff(typename Base::Index index) const {
+    return *(this->data() + index);
+  }
+  // The base packet function uses a recursive method that is not standard layout and cannot be used in
+  // SYCL kernels, so it must be overridden.
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Base::PacketReturnType packet(typename Base::Index index) const {
+    return internal::pload<typename Base::PacketReturnType>(this->data() + index);
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h
new file mode 100644
index 0000000..117cbcb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h
@@ -0,0 +1,958 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_GPU_H
+#define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_GPU_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+#if defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
+// Full reducers for GPU, don't vectorize for now
+
+// Reducer function that enables multiple gpu thread to safely accumulate at the same
+// output address. It basically reads the current value of the output variable, and
+// attempts to update it with the new value. If in the meantime another gpu thread
+// updated the content of the output address it will try again.
+template <typename T, typename R>
+__device__ EIGEN_ALWAYS_INLINE void atomicReduce(T* output, T accum, R& reducer) {
+#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
+  if (sizeof(T) == 4) {
+    unsigned int oldval = *reinterpret_cast<unsigned int*>(output);
+    unsigned int newval = oldval;
+    reducer.reduce(accum, reinterpret_cast<T*>(&newval));
+    if (newval == oldval) {
+      return;
+    }
+    unsigned int readback;
+    while ((readback = atomicCAS((unsigned int*)output, oldval, newval)) != oldval) {
+      oldval = readback;
+      newval = oldval;
+      reducer.reduce(accum, reinterpret_cast<T*>(&newval));
+      if (newval == oldval) {
+        return;
+      }
+    }
+  } else if (sizeof(T) == 8) {
+    unsigned long long oldval = *reinterpret_cast<unsigned long long*>(output);
+    unsigned long long newval = oldval;
+    reducer.reduce(accum, reinterpret_cast<T*>(&newval));
+    if (newval == oldval) {
+      return;
+    }
+    unsigned long long readback;
+    while ((readback = atomicCAS(reinterpret_cast<unsigned long long*>(output), oldval, newval)) != oldval) {
+      oldval = readback;
+      newval = oldval;
+      reducer.reduce(accum, reinterpret_cast<T*>(&newval));
+      if (newval == oldval) {
+        return;
+      }
+    }
+  } else {
+    gpu_assert(0 && "Wordsize not supported");
+  }
+#else   // EIGEN_CUDA_ARCH >= 300
+  EIGEN_UNUSED_VARIABLE(output);
+  EIGEN_UNUSED_VARIABLE(accum);
+  EIGEN_UNUSED_VARIABLE(reducer);
+  gpu_assert(0 && "Shouldn't be called on unsupported device");
+#endif  // EIGEN_CUDA_ARCH >= 300
+}
+
+// We extend atomicExch to support extra data types
+template <typename Type>
+__device__ inline Type atomicExchCustom(Type* address, Type val) {
+  return atomicExch(address, val);
+}
+
+template <>
+__device__ inline double atomicExchCustom(double* address, double val) {
+  unsigned long long int* address_as_ull = reinterpret_cast<unsigned long long int*>(address);
+  return __longlong_as_double(atomicExch(address_as_ull, __double_as_longlong(val)));
+}
+
+#ifdef EIGEN_HAS_GPU_FP16
+template <typename R>
+__device__ inline void atomicReduce(half2* output, half2 accum, R& reducer) {
+  unsigned int oldval = *reinterpret_cast<unsigned int*>(output);
+  unsigned int newval = oldval;
+  reducer.reducePacket(accum, reinterpret_cast<half2*>(&newval));
+  if (newval == oldval) {
+    return;
+  }
+  unsigned int readback;
+  while ((readback = atomicCAS((unsigned int*)output, oldval, newval)) != oldval) {
+    oldval = readback;
+    newval = oldval;
+    reducer.reducePacket(accum, reinterpret_cast<half2*>(&newval));
+    if (newval == oldval) {
+      return;
+    }
+  }
+}
+#ifdef EIGEN_GPU_COMPILE_PHASE
+// reduction should be associative since reduction is not atomic in wide vector but atomic in half2 operations
+template <typename R>
+__device__ inline void atomicReduce(Packet4h2* output, Packet4h2 accum, R& reducer) {
+  half2* houtput = reinterpret_cast<half2*>(output);
+  half2* haccum = reinterpret_cast<half2*>(&accum);
+  for (int i = 0; i < 4; ++i) {
+    atomicReduce(houtput + i, *(haccum + i), reducer);
+  }
+}
+#endif  // EIGEN_GPU_COMPILE_PHASE
+#endif  // EIGEN_HAS_GPU_FP16
+
+template <>
+__device__ inline void atomicReduce(float* output, float accum, SumReducer<float>&) {
+#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
+  atomicAdd(output, accum);
+#else   // EIGEN_CUDA_ARCH >= 300
+  EIGEN_UNUSED_VARIABLE(output);
+  EIGEN_UNUSED_VARIABLE(accum);
+  gpu_assert(0 && "Shouldn't be called on unsupported device");
+#endif  // EIGEN_CUDA_ARCH >= 300
+}
+
+template <typename CoeffType, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitKernel(const CoeffType val, Index num_preserved_coeffs,
+                                                                 CoeffType* output) {
+  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+  const Index num_threads = blockDim.x * gridDim.x;
+  for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) {
+    output[i] = val;
+  }
+}
+
+template <int BlockSize, int NumPerThread, typename Self, typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernel(Reducer reducer, const Self input, Index num_coeffs,
+                                                                 typename Self::CoeffReturnType* output,
+                                                                 unsigned int* semaphore) {
+#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
+  // Initialize the output value
+  const Index first_index = blockIdx.x * BlockSize * NumPerThread + threadIdx.x;
+  if (gridDim.x == 1) {
+    if (first_index == 0) {
+      *output = reducer.initialize();
+    }
+  } else {
+    if (threadIdx.x == 0) {
+      unsigned int block = atomicCAS(semaphore, 0u, 1u);
+      if (block == 0) {
+        // We're the first block to run, initialize the output value
+        atomicExchCustom(output, reducer.initialize());
+        __threadfence();
+        atomicExch(semaphore, 2u);
+      } else {
+        // Wait for the first block to initialize the output value.
+        // Use atomicCAS here to ensure that the reads aren't cached
+        unsigned int val;
+        do {
+          val = atomicCAS(semaphore, 2u, 2u);
+        } while (val < 2u);
+      }
+    }
+  }
+
+  __syncthreads();
+
+  eigen_assert(gridDim.x == 1 || *semaphore >= 2u);
+
+  typename Self::CoeffReturnType accum = reducer.initialize();
+  Index max_iter = numext::mini<Index>(num_coeffs - first_index, NumPerThread * BlockSize);
+  for (Index i = 0; i < max_iter; i += BlockSize) {
+    const Index index = first_index + i;
+    eigen_assert(index < num_coeffs);
+    typename Self::CoeffReturnType val = input.m_impl.coeff(index);
+    reducer.reduce(val, &accum);
+  }
+
+#pragma unroll
+  for (int offset = warpSize / 2; offset > 0; offset /= 2) {
+#if defined(EIGEN_HIPCC)
+    // use std::is_floating_point to determine the type of reduced_val
+    // This is needed because when Type == double, hipcc will give a "call to __shfl_down is ambiguous" error
+    // and list the float and int versions of __shfl_down as the candidate functions.
+    if (std::is_floating_point<typename Self::CoeffReturnType>::value) {
+      reducer.reduce(__shfl_down(static_cast<float>(accum), offset, warpSize), &accum);
+    } else {
+      reducer.reduce(__shfl_down(static_cast<int>(accum), offset, warpSize), &accum);
+    }
+#elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
+    reducer.reduce(__shfl_down(accum, offset, warpSize), &accum);
+#else
+    reducer.reduce(__shfl_down_sync(0xFFFFFFFF, accum, offset, warpSize), &accum);
+#endif
+  }
+
+  if ((threadIdx.x & (warpSize - 1)) == 0) {
+    atomicReduce(output, accum, reducer);
+  }
+
+  if (gridDim.x > 1 && threadIdx.x == 0) {
+    // Let the last block reset the semaphore
+    atomicInc(semaphore, gridDim.x + 1);
+#if defined(EIGEN_HIPCC)
+    __threadfence_system();
+#endif
+  }
+#else   // EIGEN_CUDA_ARCH >= 300
+  EIGEN_UNUSED_VARIABLE(reducer);
+  EIGEN_UNUSED_VARIABLE(input);
+  EIGEN_UNUSED_VARIABLE(num_coeffs);
+  EIGEN_UNUSED_VARIABLE(output);
+  EIGEN_UNUSED_VARIABLE(semaphore);
+  gpu_assert(0 && "Shouldn't be called on unsupported device");
+#endif  // EIGEN_CUDA_ARCH >= 300
+}
+
+#ifdef EIGEN_HAS_GPU_FP16
+template <typename Self, typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitFullReduxKernelHalfFloat(Reducer reducer, const Self input,
+                                                                                   Index num_coeffs, half* scratch) {
+  eigen_assert(blockDim.x == 1);
+  eigen_assert(gridDim.x == 1);
+  typedef packet_traits<Eigen::half>::type packet_type;
+  Index packet_remainder = num_coeffs % Index(unpacket_traits<packet_type>::size);
+  if (packet_remainder != 0) {
+    half2* h2scratch = reinterpret_cast<half2*>(scratch);
+    for (Index i = num_coeffs - packet_remainder; i + 2 <= num_coeffs; i += 2) {
+      *h2scratch = __halves2half2(input.coeff(i), input.coeff(i + 1));
+      h2scratch++;
+    }
+    if ((num_coeffs & 1) != 0) {
+      half lastCoeff = input.coeff(num_coeffs - 1);
+      *h2scratch = __halves2half2(lastCoeff, reducer.initialize());
+    }
+  } else {
+    packet_type reduce = reducer.template initializePacket<packet_type>();
+    internal::pstoreu(scratch, reduce);
+  }
+}
+
+template <typename Self, typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitKernelHalfFloat(Reducer reducer, const Self /*input*/,
+                                                                          Index num_coeffs, half* output) {
+  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+  const Index num_threads = blockDim.x * gridDim.x;
+  typedef typename packet_traits<Eigen::half>::type PacketType;
+
+  const Index num_packets = num_coeffs / Index(unpacket_traits<PacketType>::size);
+  PacketType* p_output = reinterpret_cast<PacketType*>(output);
+  for (Index i = thread_id; i < num_packets; i += num_threads) {
+    p_output[i] = reducer.template initializePacket<PacketType>();
+  }
+  Index packet_remainder = num_coeffs % Index(unpacket_traits<PacketType>::size);
+  if (thread_id < packet_remainder) {
+    output[num_coeffs - packet_remainder + thread_id] = reducer.initialize();
+  }
+}
+
+template <int BlockSize, int NumPerThread, typename Self, typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernelHalfFloat(Reducer reducer, const Self input,
+                                                                          Index num_coeffs, half* output,
+                                                                          half* scratch) {
+  typedef typename packet_traits<Eigen::half>::type PacketType;
+  const int packet_width = unpacket_traits<PacketType>::size;
+  eigen_assert(NumPerThread % packet_width == 0);
+  const Index first_index = blockIdx.x * BlockSize * NumPerThread + packet_width * threadIdx.x;
+
+  // Initialize the output value if it wasn't initialized by the ReductionInitKernel
+
+  if (gridDim.x == 1) {
+    if (first_index == 0) {
+      int rem = num_coeffs % packet_width;
+      if (rem != 0) {
+        half2* p_scratch = reinterpret_cast<half2*>(scratch);
+        pstoreu(scratch, reducer.template initializePacket<PacketType>());
+        for (int i = 0; i < rem / 2; i++) {
+          *p_scratch = __halves2half2(input.coeff(num_coeffs - packet_width + 2 * i),
+                                      input.coeff(num_coeffs - packet_width + 2 * i + 1));
+          p_scratch++;
+        }
+        if ((num_coeffs & 1) != 0) {
+          half last = input.coeff(num_coeffs - 1);
+          *p_scratch = __halves2half2(last, reducer.initialize());
+        }
+      } else {
+        PacketType reduce = reducer.template initializePacket<PacketType>();
+        pstoreu(scratch, reduce);
+      }
+    }
+    __syncthreads();
+  }
+
+  PacketType accum = reducer.template initializePacket<PacketType>();
+  const Index max_iter =
+      numext::mini<Index>((num_coeffs - first_index) / packet_width, NumPerThread * BlockSize / packet_width);
+  for (Index i = 0; i < max_iter; i += BlockSize) {
+    const Index index = first_index + packet_width * i;
+    eigen_assert(index + packet_width < num_coeffs);
+    PacketType val = input.template packet<Unaligned>(index);
+    reducer.reducePacket(val, &accum);
+  }
+
+#pragma unroll
+  for (int offset = warpSize / 2; offset > 0; offset /= 2) {
+#if defined(EIGEN_HIPCC)
+    PacketType r1;
+    half2* hr = reinterpret_cast<half2*>(&r1);
+    half2* hacc = reinterpret_cast<half2*>(&accum);
+    for (int i = 0; i < packet_width / 2; i++) {
+      // FIXME : remove this workaround once we have native half/half2 support for __shfl_down
+      union {
+        int i;
+        half2 h;
+      } wka_in, wka_out;
+      wka_in.h = hacc[i];
+      wka_out.i = __shfl_down(wka_in.i, offset, warpSize);
+      hr[i] = wka_out.h;
+    }
+    reducer.reducePacket(r1, &accum);
+#elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
+    PacketType r1;
+    half2* hr = reinterpret_cast<half2*>(&r1);
+    half2* hacc = reinterpret_cast<half2*>(&accum);
+    for (int i = 0; i < packet_width / 2; i++) {
+      hr[i] = __shfl_down(hacc[i], offset, warpSize);
+    }
+    reducer.reducePacket(r1, &accum);
+#else
+    PacketType r1;
+    half2* hr = reinterpret_cast<half2*>(&r1);
+    half2* hacc = reinterpret_cast<half2*>(&accum);
+    for (int i = 0; i < packet_width / 2; i++) {
+      hr[i] = __shfl_down_sync(0xFFFFFFFF, hacc[i], (unsigned)offset, warpSize);
+    }
+    reducer.reducePacket(r1, &accum);
+
+#endif
+  }
+
+  if ((threadIdx.x & (warpSize - 1)) == 0) {
+    atomicReduce(reinterpret_cast<PacketType*>(scratch), accum, reducer);
+  }
+
+  __syncthreads();
+  half2* rv1 = reinterpret_cast<half2*>(scratch);
+  if (packet_width > 2) {
+    reducer.reducePacket(rv1[2], rv1);
+    reducer.reducePacket(rv1[3], rv1 + 1);
+    reducer.reducePacket(rv1[1], rv1);
+  }
+  if (gridDim.x == 1) {
+    if (first_index == 0) {
+      half tmp = __low2half(*rv1);
+      reducer.reduce(__high2half(*rv1), &tmp);
+      *output = tmp;
+    }
+  }
+}
+
+template <typename Op>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionCleanupKernelHalfFloat(Op reducer, half* output, half* scratch) {
+  eigen_assert(threadIdx.x == 1);
+  typedef packet_traits<Eigen::half>::type packet_type;
+  if (unpacket_traits<packet_type>::size == 1) {
+    *output = *scratch;
+  } else {
+    half2* pscratch = reinterpret_cast<half2*>(scratch);
+    half tmp = __float2half(0.f);
+    for (int i = 0; i < unpacket_traits<packet_type>::size; i += 2) {
+      reducer.reduce(__low2half(*pscratch), &tmp);
+      reducer.reduce(__high2half(*pscratch), &tmp);
+      pscratch++;
+    }
+    *output = tmp;
+  }
+}
+
+#endif  // EIGEN_HAS_GPU_FP16
+
+template <typename Self, typename Op, typename OutputType, bool PacketAccess, typename Enabled = void>
+struct FullReductionLauncher {
+  static void run(const Self&, Op&, const GpuDevice&, OutputType*, typename Self::Index) {
+    gpu_assert(false && "Should only be called on doubles, floats and half floats");
+  }
+};
+
+// Specialization for float and double
+template <typename Self, typename Op, typename OutputType, bool PacketAccess>
+struct FullReductionLauncher<
+    Self, Op, OutputType, PacketAccess,
+    std::enable_if_t<internal::is_same<float, OutputType>::value || internal::is_same<double, OutputType>::value,
+                     void>> {
+  static void run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output,
+                  typename Self::Index num_coeffs) {
+    typedef typename Self::Index Index;
+    const int block_size = 256;
+    const int num_per_thread = 128;
+    const int num_blocks = numext::div_ceil<int>(num_coeffs, block_size * num_per_thread);
+
+    unsigned int* semaphore = NULL;
+    if (num_blocks > 1) {
+      semaphore = device.semaphore();
+    }
+
+    LAUNCH_GPU_KERNEL((FullReductionKernel<block_size, num_per_thread, Self, Op, Index>), num_blocks, block_size, 0,
+                      device, reducer, self, num_coeffs, output, semaphore);
+  }
+};
+
+#ifdef EIGEN_HAS_GPU_FP16
+template <typename Self, typename Op>
+struct FullReductionLauncher<Self, Op, Eigen::half, false> {
+  static void run(const Self&, Op&, const GpuDevice&, half*, typename Self::Index) {
+    gpu_assert(false && "Should not be called since there is no packet accessor");
+  }
+};
+
+template <typename Self, typename Op>
+struct FullReductionLauncher<Self, Op, Eigen::half, true> {
+  static void run(const Self& self, Op& reducer, const GpuDevice& device, half* output,
+                  typename Self::Index num_coeffs) {
+    typedef typename Self::Index Index;
+
+    const int block_size = 256;
+    const int num_per_thread = 128;
+    const int num_blocks = numext::div_ceil<int>(num_coeffs, block_size * num_per_thread);
+    half* scratch = static_cast<half*>(device.scratchpad());
+
+    if (num_blocks > 1) {
+      // We initialize the output and the scratchpad outside the reduction kernel when we can't be sure that there
+      // won't be a race conditions between multiple thread blocks.
+      LAUNCH_GPU_KERNEL((ReductionInitFullReduxKernelHalfFloat<Self, Op, Index>), 1, 1, 0, device, reducer, self,
+                        num_coeffs, scratch);
+    }
+
+    LAUNCH_GPU_KERNEL((FullReductionKernelHalfFloat<block_size, num_per_thread, Self, Op, Index>), num_blocks,
+                      block_size, 0, device, reducer, self, num_coeffs, output, scratch);
+
+    if (num_blocks > 1) {
+      LAUNCH_GPU_KERNEL((ReductionCleanupKernelHalfFloat<Op>), 1, 1, 0, device, reducer, output, scratch);
+    }
+  }
+};
+#endif  // EIGEN_HAS_GPU_FP16
+
+template <typename Self, typename Op, bool Vectorizable>
+struct FullReducer<Self, Op, GpuDevice, Vectorizable> {
+  // Unfortunately nvidia doesn't support well exotic types such as complex,
+  // so reduce the scope of the optimized version of the code to the simple cases
+  // of doubles, floats and half floats
+#ifdef EIGEN_HAS_GPU_FP16
+  static constexpr bool HasOptimizedImplementation =
+      !Self::ReducerTraits::IsStateful && (internal::is_same<typename Self::CoeffReturnType, float>::value ||
+                                           internal::is_same<typename Self::CoeffReturnType, double>::value ||
+                                           (internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value &&
+                                            reducer_traits<Op, GpuDevice>::PacketAccess));
+#else   // EIGEN_HAS_GPU_FP16
+  static constexpr bool HasOptimizedImplementation =
+      !Self::ReducerTraits::IsStateful && (internal::is_same<typename Self::CoeffReturnType, float>::value ||
+                                           internal::is_same<typename Self::CoeffReturnType, double>::value);
+#endif  // EIGEN_HAS_GPU_FP16
+
+  template <typename OutputType>
+  static void run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output) {
+    gpu_assert(HasOptimizedImplementation && "Should only be called on doubles, floats or half floats");
+    const Index num_coeffs = array_prod(self.m_impl.dimensions());
+    // Don't crash when we're called with an input tensor of size 0.
+    if (num_coeffs == 0) {
+      return;
+    }
+
+    FullReductionLauncher<Self, Op, OutputType, reducer_traits<Op, GpuDevice>::PacketAccess>::run(self, reducer, device,
+                                                                                                  output, num_coeffs);
+  }
+};
+
+template <int NumPerThread, typename Self, typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernel(Reducer reducer, const Self input,
+                                                                  Index num_coeffs_to_reduce,
+                                                                  Index num_preserved_coeffs,
+                                                                  typename Self::CoeffReturnType* output) {
+#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
+  typedef typename Self::CoeffReturnType Type;
+  eigen_assert(blockDim.y == 1);
+  eigen_assert(blockDim.z == 1);
+  eigen_assert(gridDim.y == 1);
+  eigen_assert(gridDim.z == 1);
+
+  const int unroll_times = 16;
+  eigen_assert(NumPerThread % unroll_times == 0);
+
+  const Index input_col_blocks = numext::div_ceil<Index>(num_coeffs_to_reduce, blockDim.x * NumPerThread);
+  const Index num_input_blocks = input_col_blocks * num_preserved_coeffs;
+
+  const Index num_threads = blockDim.x * gridDim.x;
+  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+
+  // Initialize the output values if they weren't initialized by the ReductionInitKernel
+  if (gridDim.x == 1) {
+    for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) {
+      output[i] = reducer.initialize();
+    }
+    __syncthreads();
+  }
+
+  for (Index i = blockIdx.x; i < num_input_blocks; i += gridDim.x) {
+    const Index row = i / input_col_blocks;
+
+    if (row < num_preserved_coeffs) {
+      const Index col_block = i % input_col_blocks;
+      const Index col_begin = col_block * blockDim.x * NumPerThread + threadIdx.x;
+
+      Type reduced_val = reducer.initialize();
+
+      for (Index j = 0; j < NumPerThread; j += unroll_times) {
+        const Index last_col = col_begin + blockDim.x * (j + unroll_times - 1);
+        if (last_col >= num_coeffs_to_reduce) {
+          for (Index col = col_begin + blockDim.x * j; col < num_coeffs_to_reduce; col += blockDim.x) {
+            const Type val = input.m_impl.coeff(row * num_coeffs_to_reduce + col);
+            reducer.reduce(val, &reduced_val);
+          }
+          break;
+        } else {
+          // Faster version of the loop with no branches after unrolling.
+#pragma unroll
+          for (int k = 0; k < unroll_times; ++k) {
+            const Index col = col_begin + blockDim.x * (j + k);
+            reducer.reduce(input.m_impl.coeff(row * num_coeffs_to_reduce + col), &reduced_val);
+          }
+        }
+      }
+
+#pragma unroll
+      for (int offset = warpSize / 2; offset > 0; offset /= 2) {
+#if defined(EIGEN_HIPCC)
+        // use std::is_floating_point to determine the type of reduced_val
+        // This is needed because when Type == double, hipcc will give a "call to __shfl_down is ambiguous" error
+        // and list the float and int versions of __shfl_down as the candidate functions.
+        if (std::is_floating_point<Type>::value) {
+          reducer.reduce(__shfl_down(static_cast<float>(reduced_val), offset), &reduced_val);
+        } else {
+          reducer.reduce(__shfl_down(static_cast<int>(reduced_val), offset), &reduced_val);
+        }
+#elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
+        reducer.reduce(__shfl_down(reduced_val, offset), &reduced_val);
+#else
+        reducer.reduce(__shfl_down_sync(0xFFFFFFFF, reduced_val, offset), &reduced_val);
+#endif
+      }
+
+      if ((threadIdx.x & (warpSize - 1)) == 0) {
+        atomicReduce(&(output[row]), reduced_val, reducer);
+      }
+    }
+  }
+#else   // EIGEN_CUDA_ARCH >= 300
+  gpu_assert(0 && "Shouldn't be called on unsupported device");
+#endif  // EIGEN_CUDA_ARCH >= 300
+}
+
+#ifdef EIGEN_HAS_GPU_FP16
+
+template <int NumPerThread, typename Self, typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernelHalfFloat(Reducer reducer, const Self input,
+                                                                           Index num_coeffs_to_reduce,
+                                                                           Index num_preserved_coeffs, half* output) {
+  eigen_assert(blockDim.y == 1);
+  eigen_assert(blockDim.z == 1);
+  eigen_assert(gridDim.y == 1);
+  eigen_assert(gridDim.z == 1);
+
+  typedef typename packet_traits<Eigen::half>::type PacketType;
+  const int packet_width = unpacket_traits<PacketType>::size;
+  const int unroll_times = 16 / packet_width;
+  eigen_assert(NumPerThread % unroll_times == 0);
+  eigen_assert(unroll_times % 2 == 0);
+
+  const Index input_col_blocks = numext::div_ceil<Index>(num_coeffs_to_reduce, blockDim.x * NumPerThread * 2);
+  const Index num_input_blocks = numext::div_ceil<Index>(input_col_blocks * num_preserved_coeffs, 2);
+
+  const Index num_threads = blockDim.x * gridDim.x;
+  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+
+  // Initialize the output values if they weren't initialized by the ReductionInitKernel
+  if (gridDim.x == 1) {
+    Index i = packet_width * thread_id;
+    for (; i + packet_width <= num_preserved_coeffs; i += packet_width * num_threads) {
+      PacketType* poutput = reinterpret_cast<PacketType*>(output + i);
+      *poutput = reducer.template initializePacket<PacketType>();
+    }
+    if (i < num_preserved_coeffs) {
+      output[i] = reducer.initialize();
+    }
+    __syncthreads();
+  }
+
+  for (Index i = blockIdx.x; i < num_input_blocks; i += gridDim.x) {
+    const Index row = 2 * (i / input_col_blocks);  // everybody takes 2 rows
+
+    if (row + 1 < num_preserved_coeffs) {
+      const Index col_block = i % input_col_blocks;
+      const Index col_begin = packet_width * (col_block * blockDim.x * NumPerThread + threadIdx.x);
+
+      PacketType reduced_val1 = reducer.template initializePacket<PacketType>();
+      PacketType reduced_val2 = reducer.template initializePacket<PacketType>();
+
+      for (Index j = 0; j < NumPerThread; j += unroll_times) {
+        const Index last_col = col_begin + blockDim.x * (j + unroll_times - 1) * packet_width;
+        if (last_col >= num_coeffs_to_reduce) {
+          Index col = col_begin + blockDim.x * j;
+          for (; col + packet_width <= num_coeffs_to_reduce; col += blockDim.x) {
+            const PacketType val1 = input.m_impl.template packet<Unaligned>(row * num_coeffs_to_reduce + col);
+            reducer.reducePacket(val1, &reduced_val1);
+            const PacketType val2 = input.m_impl.template packet<Unaligned>((row + 1) * num_coeffs_to_reduce + col);
+            reducer.reducePacket(val2, &reduced_val2);
+          }
+          if (col < num_coeffs_to_reduce) {
+            PacketType r1 = reducer.template initializePacket<PacketType>();
+            PacketType r2 = reducer.template initializePacket<PacketType>();
+            half2* hr1 = reinterpret_cast<half2*>(&r1);
+            half2* hr2 = reinterpret_cast<half2*>(&r2);
+            while (col + 1 < num_coeffs_to_reduce) {
+              *hr1 = __halves2half2(input.m_impl.coeff(row * num_coeffs_to_reduce + col),
+                                    input.m_impl.coeff(row * num_coeffs_to_reduce + col + 1));
+              *hr2 = __halves2half2(input.m_impl.coeff((row + 1) * num_coeffs_to_reduce + col),
+                                    input.m_impl.coeff((row + 1) * num_coeffs_to_reduce + col + 1));
+              hr1++;
+              hr2++;
+              col += 2;
+            }
+            if (col < num_coeffs_to_reduce) {
+              // Peel;
+              const half last1 = input.m_impl.coeff(row * num_coeffs_to_reduce + col);
+              *hr1 = __halves2half2(last1, reducer.initialize());
+              const half last2 = input.m_impl.coeff((row + 1) * num_coeffs_to_reduce + col);
+              *hr2 = __halves2half2(last2, reducer.initialize());
+            }
+            reducer.reducePacket(r1, &reduced_val1);
+            reducer.reducePacket(r2, &reduced_val2);
+          }
+          break;
+        } else {
+          // Faster version of the loop with no branches after unrolling.
+#pragma unroll
+          for (int k = 0; k < unroll_times; ++k) {
+            const Index col = col_begin + blockDim.x * (j + k) * packet_width;
+            reducer.reducePacket(input.m_impl.template packet<Unaligned>(row * num_coeffs_to_reduce + col),
+                                 &reduced_val1);
+            reducer.reducePacket(input.m_impl.template packet<Unaligned>((row + 1) * num_coeffs_to_reduce + col),
+                                 &reduced_val2);
+          }
+        }
+      }
+
+#pragma unroll
+      for (int offset = warpSize / 2; offset > 0; offset /= 2) {
+#if defined(EIGEN_HIPCC)
+        PacketType r1;
+        PacketType r2;
+        half2* hr1 = reinterpret_cast<half2*>(&r1);
+        half2* hr2 = reinterpret_cast<half2*>(&r2);
+        half2* rv1 = reinterpret_cast<half2*>(&reduced_val1);
+        half2* rv2 = reinterpret_cast<half2*>(&reduced_val2);
+        for (int i = 0; i < packet_width / 2; i++) {
+          // FIXME : remove this workaround once we have native half/half2 support for __shfl_down
+          union {
+            int i;
+            half2 h;
+          } wka_in1, wka_out1;
+          wka_in1.h = rv1[i];
+          wka_out1.i = __shfl_down(wka_in1.i, offset, warpSize);
+          hr1[i] = wka_out1.h;
+
+          union {
+            int i;
+            half2 h;
+          } wka_in2, wka_out2;
+          wka_in2.h = rv2[i];
+          wka_out2.i = __shfl_down(wka_in2.i, offset, warpSize);
+          hr2[i] = wka_out2.h;
+        }
+        reducer.reducePacket(r1, &reduced_val1);
+        reducer.reducePacket(r2, &reduced_val2);
+#elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
+        PacketType r1;
+        PacketType r2;
+        half2* hr1 = reinterpret_cast<half2*>(&r1);
+        half2* hr2 = reinterpret_cast<half2*>(&r2);
+        half2* rv1 = reinterpret_cast<half2*>(&reduced_val1);
+        half2* rv2 = reinterpret_cast<half2*>(&reduced_val2);
+        for (int i = 0; i < packet_width / 2; i++) {
+          hr1[i] = __shfl_down(rv1[i], offset, warpSize);
+          hr2[i] = __shfl_down(rv2[i], offset, warpSize);
+        }
+        reducer.reducePacket(r1, &reduced_val1);
+        reducer.reducePacket(r2, &reduced_val2);
+#else
+        PacketType r1;
+        PacketType r2;
+        half2* hr1 = reinterpret_cast<half2*>(&r1);
+        half2* hr2 = reinterpret_cast<half2*>(&r2);
+        half2* rr1 = reinterpret_cast<half2*>(&reduced_val1);
+        half2* rr2 = reinterpret_cast<half2*>(&reduced_val2);
+        for (int j = 0; j < packet_width / 2; j++) {
+          hr1[j] = __shfl_down_sync(0xFFFFFFFF, rr1[j], (unsigned)offset, warpSize);
+          hr2[j] = __shfl_down_sync(0xFFFFFFFF, rr2[j], (unsigned)offset, warpSize);
+        }
+        reducer.reducePacket(r1, &reduced_val1);
+        reducer.reducePacket(r2, &reduced_val2);
+
+#endif
+      }
+      half2* rv1 = reinterpret_cast<half2*>(&reduced_val1);
+      half2* rv2 = reinterpret_cast<half2*>(&reduced_val2);
+      half2 val;
+      if (packet_width > 2) {
+        reducer.reducePacket(rv1[2], rv1);
+        reducer.reducePacket(rv1[3], rv1 + 1);
+        reducer.reducePacket(rv1[1], rv1);
+        reducer.reducePacket(rv2[2], rv2);
+        reducer.reducePacket(rv2[3], rv2 + 1);
+        reducer.reducePacket(rv2[1], rv2);
+      }
+      half val1 = __low2half(*rv1);
+      reducer.reduce(__high2half(*rv1), &val1);
+      half val2 = __low2half(*rv2);
+      reducer.reduce(__high2half(*rv2), &val2);
+      val = __halves2half2(val1, val2);
+      if ((threadIdx.x & (warpSize - 1)) == 0) {
+        half* loc = output + row;
+        atomicReduce(reinterpret_cast<half2*>(loc), val, reducer);
+      }
+    }
+  }
+}
+
+#endif  // EIGEN_HAS_GPU_FP16
+
+template <typename Self, typename Op, typename OutputType, bool PacketAccess, typename Enabled = void>
+struct InnerReductionLauncher {
+  static EIGEN_DEVICE_FUNC bool run(const Self&, Op&, const GpuDevice&, OutputType*, typename Self::Index,
+                                    typename Self::Index) {
+    gpu_assert(false && "Should only be called to reduce doubles, floats and half floats on a gpu device");
+    return true;
+  }
+};
+
+// Specialization for float and double
+template <typename Self, typename Op, typename OutputType, bool PacketAccess>
+struct InnerReductionLauncher<
+    Self, Op, OutputType, PacketAccess,
+    std::enable_if_t<internal::is_same<float, OutputType>::value || internal::is_same<double, OutputType>::value,
+                     void>> {
+  static bool run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output,
+                  typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
+    typedef typename Self::Index Index;
+
+    const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals;
+    const int block_size = 256;
+    const int num_per_thread = 128;
+    const int dyn_blocks = numext::div_ceil<int>(num_coeffs, block_size * num_per_thread);
+    const int max_blocks = device.getNumGpuMultiProcessors() * device.maxGpuThreadsPerMultiProcessor() / block_size;
+    const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
+
+    if (num_blocks > 1) {
+      // We initialize the outputs outside the reduction kernel when we can't be sure that there
+      // won't be a race conditions between multiple thread blocks.
+      const int dyn_blocks2 = numext::div_ceil<int>(num_preserved_vals, 1024);
+      const int max_blocks2 = device.getNumGpuMultiProcessors() * device.maxGpuThreadsPerMultiProcessor() / 1024;
+      const int num_blocks2 = numext::mini<int>(max_blocks2, dyn_blocks2);
+      LAUNCH_GPU_KERNEL((ReductionInitKernel<OutputType, Index>), num_blocks2, 1024, 0, device, reducer.initialize(),
+                        num_preserved_vals, output);
+    }
+
+    LAUNCH_GPU_KERNEL((InnerReductionKernel<num_per_thread, Self, Op, Index>), num_blocks, block_size, 0, device,
+                      reducer, self, num_coeffs_to_reduce, num_preserved_vals, output);
+
+    return false;
+  }
+};
+
+#ifdef EIGEN_HAS_GPU_FP16
+template <typename Self, typename Op>
+struct InnerReductionLauncher<Self, Op, Eigen::half, false> {
+  static bool run(const Self&, Op&, const GpuDevice&, half*, typename Self::Index, typename Self::Index) {
+    gpu_assert(false && "Should not be called since there is no packet accessor");
+    return true;
+  }
+};
+
+template <typename Self, typename Op>
+struct InnerReductionLauncher<Self, Op, Eigen::half, true> {
+  static bool run(const Self& self, Op& reducer, const GpuDevice& device, half* output,
+                  typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
+    typedef typename Self::Index Index;
+
+    if (num_preserved_vals % 2 != 0) {
+      // Not supported yet, revert to the slower code path
+      return true;
+    }
+
+    const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals;
+    const int block_size = 128;
+    const int num_per_thread = 64;
+    const int dyn_blocks = numext::div_ceil<int>(num_coeffs, block_size * num_per_thread);
+    const int max_blocks = device.getNumGpuMultiProcessors() * device.maxGpuThreadsPerMultiProcessor() / block_size;
+    const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
+
+    if (num_blocks > 1) {
+      // We initialize the outputs outside the reduction kernel when we can't be sure that there
+      // won't be a race conditions between multiple thread blocks.
+      LAUNCH_GPU_KERNEL((ReductionInitKernelHalfFloat<Self, Op, Index>), 1, 1, 0, device, reducer, self,
+                        num_preserved_vals, output);
+    }
+
+    LAUNCH_GPU_KERNEL((InnerReductionKernelHalfFloat<num_per_thread, Self, Op, Index>), num_blocks, block_size, 0,
+                      device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output);
+
+    return false;
+  }
+};
+#endif  // EIGEN_HAS_GPU_FP16
+
+template <typename Self, typename Op>
+struct InnerReducer<Self, Op, GpuDevice> {
+  // Unfortunately nvidia doesn't support well exotic types such as complex,
+  // so reduce the scope of the optimized version of the code to the simple case
+  // of floats and half floats.
+#ifdef EIGEN_HAS_GPU_FP16
+  static constexpr bool HasOptimizedImplementation =
+      !Self::ReducerTraits::IsStateful && (internal::is_same<typename Self::CoeffReturnType, float>::value ||
+                                           internal::is_same<typename Self::CoeffReturnType, double>::value ||
+                                           (internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value &&
+                                            reducer_traits<Op, GpuDevice>::PacketAccess));
+#else   // EIGEN_HAS_GPU_FP16
+  static constexpr bool HasOptimizedImplementation =
+      !Self::ReducerTraits::IsStateful && (internal::is_same<typename Self::CoeffReturnType, float>::value ||
+                                           internal::is_same<typename Self::CoeffReturnType, double>::value);
+#endif  // EIGEN_HAS_GPU_FP16
+
+  template <typename OutputType>
+  static bool run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output,
+                  typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
+    gpu_assert(HasOptimizedImplementation && "Should only be called on doubles, floats or half floats");
+    const Index num_coeffs = array_prod(self.m_impl.dimensions());
+    // Don't crash when we're called with an input tensor of size 0.
+    if (num_coeffs == 0) {
+      return true;
+    }
+    // It's faster to use the usual code.
+    if (num_coeffs_to_reduce <= 128) {
+      return true;
+    }
+
+    return InnerReductionLauncher<Self, Op, OutputType, reducer_traits<Op, GpuDevice>::PacketAccess>::run(
+        self, reducer, device, output, num_coeffs_to_reduce, num_preserved_vals);
+  }
+};
+
+template <int NumPerThread, typename Self, typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void OuterReductionKernel(Reducer reducer, const Self input,
+                                                                  Index num_coeffs_to_reduce,
+                                                                  Index num_preserved_coeffs,
+                                                                  typename Self::CoeffReturnType* output) {
+  const Index num_threads = blockDim.x * gridDim.x;
+  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+  // Initialize the output values if they weren't initialized by the ReductionInitKernel
+  if (gridDim.x == 1) {
+    for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) {
+      output[i] = reducer.initialize();
+    }
+    __syncthreads();
+  }
+
+  // Do the reduction.
+  const Index max_iter = num_preserved_coeffs * numext::div_ceil<Index>(num_coeffs_to_reduce, NumPerThread);
+  for (Index i = thread_id; i < max_iter; i += num_threads) {
+    const Index input_col = i % num_preserved_coeffs;
+    const Index input_row = (i / num_preserved_coeffs) * NumPerThread;
+    typename Self::CoeffReturnType reduced_val = reducer.initialize();
+    const Index max_row = numext::mini(input_row + NumPerThread, num_coeffs_to_reduce);
+    for (Index j = input_row; j < max_row; j++) {
+      typename Self::CoeffReturnType val = input.m_impl.coeff(j * num_preserved_coeffs + input_col);
+      reducer.reduce(val, &reduced_val);
+    }
+    atomicReduce(&(output[input_col]), reduced_val, reducer);
+  }
+}
+
+template <typename Self, typename Op>
+struct OuterReducer<Self, Op, GpuDevice> {
+  // Unfortunately nvidia doesn't support well exotic types such as complex,
+  // so reduce the scope of the optimized version of the code to the simple case
+  // of floats.
+  static constexpr bool HasOptimizedImplementation =
+      !Self::ReducerTraits::IsStateful && (internal::is_same<typename Self::CoeffReturnType, float>::value ||
+                                           internal::is_same<typename Self::CoeffReturnType, double>::value);
+  template <typename Device, typename OutputType>
+  static
+#if !defined(EIGEN_HIPCC)
+      // FIXME :  leaving this EIGEN_DEVICE_FUNC in, results in the following runtime error
+      //          (in the cxx11_tensor_reduction_gpu test)
+      //
+      // terminate called after throwing an instance of 'std::runtime_error'
+      //   what():  No device code available for function: _ZN5Eigen8internal20OuterReductionKernelIL...
+      //
+      // don't know why this happens (and why is it a runtime error instead of a compile time error)
+      //
+      // this will be fixed by HIP PR#457
+      EIGEN_DEVICE_FUNC
+#endif
+      bool
+      run(const Self&, Op&, const Device&, OutputType*, typename Self::Index, typename Self::Index) {
+    gpu_assert(false && "Should only be called to reduce doubles or floats on a gpu device");
+    return true;
+  }
+
+  static bool run(const Self& self, Op& reducer, const GpuDevice& device, float* output,
+                  typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
+    typedef typename Self::Index Index;
+
+    // It's faster to use the usual code.
+    if (num_coeffs_to_reduce <= 32) {
+      return true;
+    }
+
+    const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals;
+    const int block_size = 256;
+    const int num_per_thread = 16;
+    const int dyn_blocks = numext::div_ceil<int>(num_coeffs, block_size * num_per_thread);
+    const int max_blocks = device.getNumGpuMultiProcessors() * device.maxGpuThreadsPerMultiProcessor() / block_size;
+    const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
+
+    if (num_blocks > 1) {
+      // We initialize the outputs in the reduction kernel itself when we don't have to worry
+      // about race conditions between multiple thread blocks.
+      const int dyn_blocks2 = numext::div_ceil<int>(num_preserved_vals, 1024);
+      const int max_blocks2 = device.getNumGpuMultiProcessors() * device.maxGpuThreadsPerMultiProcessor() / 1024;
+      const int num_blocks2 = numext::mini<int>(max_blocks2, dyn_blocks2);
+      LAUNCH_GPU_KERNEL((ReductionInitKernel<float, Index>), num_blocks2, 1024, 0, device, reducer.initialize(),
+                        num_preserved_vals, output);
+    }
+
+    LAUNCH_GPU_KERNEL((OuterReductionKernel<num_per_thread, Self, Op, Index>), num_blocks, block_size, 0, device,
+                      reducer, self, num_coeffs_to_reduce, num_preserved_vals, output);
+
+    return false;
+  }
+};
+
+#endif  // defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_GPU_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
new file mode 100644
index 0000000..2b5b734
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
@@ -0,0 +1,584 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * TensorReductionSycl.h
+ *
+ * \brief:
+ *  This is the specialization of the reduction operation. Two phase reduction approach
+ * is used since the GPU does not have Global Synchronization for global memory among
+ * different work-group/thread block. To solve the problem, we need to create two kernels
+ * to reduce the data, where the first kernel reduce the data locally and each local
+ * workgroup/thread-block save the input data into global memory. In the second phase (global reduction)
+ * one work-group uses one work-group/thread-block to reduces the intermediate data into one single element.
+ * Here is an NVIDIA presentation explaining the optimized two phase reduction algorithm on GPU:
+ * https://developer.download.nvidia.com/assets/cuda/files/reduction.pdf
+ *
+ *****************************************************************/
+
+#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
+#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace TensorSycl {
+namespace internal {
+
+template <typename Op, typename CoeffReturnType, typename Index, bool Vectorizable>
+struct OpDefiner {
+  typedef typename Vectorise<CoeffReturnType, Eigen::SyclDevice, Vectorizable>::PacketReturnType PacketReturnType;
+  typedef Op type;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Op &op) { return op; }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType finalise_op(const PacketReturnType &accumulator,
+                                                                            const Index &) {
+    return accumulator;
+  }
+};
+
+template <typename CoeffReturnType, typename Index>
+struct OpDefiner<Eigen::internal::MeanReducer<CoeffReturnType>, CoeffReturnType, Index, false> {
+  typedef Eigen::internal::SumReducer<CoeffReturnType> type;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Eigen::internal::MeanReducer<CoeffReturnType> &) {
+    return type();
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType finalise_op(const CoeffReturnType &accumulator,
+                                                                           const Index &scale) {
+    ::Eigen::internal::scalar_quotient_op<CoeffReturnType> quotient_op;
+    return quotient_op(accumulator, CoeffReturnType(scale));
+  }
+};
+
+template <typename CoeffReturnType, typename Index>
+struct OpDefiner<Eigen::internal::MeanReducer<CoeffReturnType>, CoeffReturnType, Index, true> {
+  typedef typename Vectorise<CoeffReturnType, Eigen::SyclDevice, true>::PacketReturnType PacketReturnType;
+  typedef Eigen::internal::SumReducer<CoeffReturnType> type;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Eigen::internal::MeanReducer<CoeffReturnType> &) {
+    return type();
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType finalise_op(const PacketReturnType &accumulator,
+                                                                            const Index &scale) {
+    return ::Eigen::internal::pdiv(accumulator, ::Eigen::internal::pset1<PacketReturnType>(CoeffReturnType(scale)));
+  }
+};
+
+template <typename CoeffReturnType, typename OpType, typename InputAccessor, typename OutputAccessor, typename Index,
+          Index local_range>
+struct SecondStepFullReducer {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      LocalAccessor;
+  typedef OpDefiner<OpType, CoeffReturnType, Index, true> OpDef;
+  typedef typename OpDef::type Op;
+  LocalAccessor scratch;
+  InputAccessor aI;
+  OutputAccessor outAcc;
+  Op op;
+  SecondStepFullReducer(LocalAccessor scratch_, InputAccessor aI_, OutputAccessor outAcc_, OpType op_)
+      : scratch(scratch_), aI(aI_), outAcc(outAcc_), op(OpDef::get_op(op_)) {}
+
+  void operator()(cl::sycl::nd_item<1> itemID) const {
+    // Our empirical research shows that the best performance will be achieved
+    // when there is only one element per thread to reduce in the second step.
+    // in this step the second step reduction time is almost negligible.
+    // Hence, in the second step of reduction the input size is fixed to the
+    // local size, thus, there is only one element read per thread. The
+    // algorithm must be changed if the number of reduce per thread in the
+    // second step is greater than 1. Otherwise, the result will be wrong.
+    const Index localid = itemID.get_local_id(0);
+    auto aInPtr = aI + localid;
+    auto aOutPtr = outAcc;
+    CoeffReturnType *scratchptr = scratch.get_pointer();
+    CoeffReturnType accumulator = *aInPtr;
+
+    scratchptr[localid] = op.finalize(accumulator);
+    for (Index offset = itemID.get_local_range(0) / 2; offset > 0; offset /= 2) {
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      if (localid < offset) {
+        op.reduce(scratchptr[localid + offset], &accumulator);
+        scratchptr[localid] = op.finalize(accumulator);
+      }
+    }
+    if (localid == 0) *aOutPtr = op.finalize(accumulator);
+  }
+};
+
+// Full reduction first phase. In this version the vectorization is true and the reduction accept
+// any generic reducerOp  e.g( max, min, sum, mean, iamax, iamin, etc ).
+template <typename Evaluator, typename OpType, typename Evaluator::Index local_range>
+class FullReductionKernelFunctor {
+ public:
+  typedef typename Evaluator::CoeffReturnType CoeffReturnType;
+  typedef typename Evaluator::Index Index;
+  typedef OpDefiner<OpType, typename Evaluator::CoeffReturnType, Index,
+                    (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
+      OpDef;
+
+  typedef typename OpDef::type Op;
+  typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
+  typedef typename Evaluator::PacketReturnType PacketReturnType;
+  typedef std::conditional_t<(Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess), PacketReturnType,
+                             CoeffReturnType>
+      OutType;
+  typedef cl::sycl::accessor<OutType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      LocalAccessor;
+  LocalAccessor scratch;
+  Evaluator evaluator;
+  EvaluatorPointerType final_output;
+  Index rng;
+  Op op;
+
+  FullReductionKernelFunctor(LocalAccessor scratch_, Evaluator evaluator_, EvaluatorPointerType final_output_,
+                             Index rng_, OpType op_)
+      : scratch(scratch_), evaluator(evaluator_), final_output(final_output_), rng(rng_), op(OpDef::get_op(op_)) {}
+
+  void operator()(cl::sycl::nd_item<1> itemID) const { compute_reduction(itemID); }
+
+  template <bool Vect = (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<Vect> compute_reduction(
+      const cl::sycl::nd_item<1> &itemID) const {
+    auto output_ptr = final_output;
+    Index VectorizedRange = (rng / Evaluator::PacketSize) * Evaluator::PacketSize;
+    Index globalid = itemID.get_global_id(0);
+    Index localid = itemID.get_local_id(0);
+    Index step = Evaluator::PacketSize * itemID.get_global_range(0);
+    Index start = Evaluator::PacketSize * globalid;
+    // vectorizable parts
+    PacketReturnType packetAccumulator = op.template initializePacket<PacketReturnType>();
+    for (Index i = start; i < VectorizedRange; i += step) {
+      op.template reducePacket<PacketReturnType>(evaluator.impl().template packet<Unaligned>(i), &packetAccumulator);
+    }
+    globalid += VectorizedRange;
+    // non vectorizable parts
+    for (Index i = globalid; i < rng; i += itemID.get_global_range(0)) {
+      op.template reducePacket<PacketReturnType>(
+          ::Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, Evaluator::PacketSize>::convert_to_packet_type(
+              evaluator.impl().coeff(i), op.initialize()),
+          &packetAccumulator);
+    }
+    scratch[localid] = packetAccumulator =
+        OpDef::finalise_op(op.template finalizePacket<PacketReturnType>(packetAccumulator), rng);
+    // reduction parts // Local size is always power of 2
+    EIGEN_UNROLL_LOOP
+    for (Index offset = local_range / 2; offset > 0; offset /= 2) {
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      if (localid < offset) {
+        op.template reducePacket<PacketReturnType>(scratch[localid + offset], &packetAccumulator);
+        scratch[localid] = op.template finalizePacket<PacketReturnType>(packetAccumulator);
+      }
+    }
+    if (localid == 0) {
+      output_ptr[itemID.get_group(0)] =
+          op.finalizeBoth(op.initialize(), op.template finalizePacket<PacketReturnType>(packetAccumulator));
+    }
+  }
+
+  template <bool Vect = (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<!Vect> compute_reduction(
+      const cl::sycl::nd_item<1> &itemID) const {
+    auto output_ptr = final_output;
+    Index globalid = itemID.get_global_id(0);
+    Index localid = itemID.get_local_id(0);
+    // vectorizable parts
+    CoeffReturnType accumulator = op.initialize();
+    // non vectorizable parts
+    for (Index i = globalid; i < rng; i += itemID.get_global_range(0)) {
+      op.reduce(evaluator.impl().coeff(i), &accumulator);
+    }
+    scratch[localid] = accumulator = OpDef::finalise_op(op.finalize(accumulator), rng);
+
+    // reduction parts. the local size is always power of 2
+    EIGEN_UNROLL_LOOP
+    for (Index offset = local_range / 2; offset > 0; offset /= 2) {
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      if (localid < offset) {
+        op.reduce(scratch[localid + offset], &accumulator);
+        scratch[localid] = op.finalize(accumulator);
+      }
+    }
+    if (localid == 0) {
+      output_ptr[itemID.get_group(0)] = op.finalize(accumulator);
+    }
+  }
+};
+
+template <typename Evaluator, typename OpType>
+class GenericNondeterministicReducer {
+ public:
+  typedef typename Evaluator::CoeffReturnType CoeffReturnType;
+  typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
+  typedef typename Evaluator::Index Index;
+  typedef OpDefiner<OpType, CoeffReturnType, Index, false> OpDef;
+  typedef typename OpDef::type Op;
+  template <typename Scratch>
+  GenericNondeterministicReducer(Scratch, Evaluator evaluator_, EvaluatorPointerType output_accessor_, OpType functor_,
+                                 Index range_, Index num_values_to_reduce_)
+      : evaluator(evaluator_),
+        output_accessor(output_accessor_),
+        functor(OpDef::get_op(functor_)),
+        range(range_),
+        num_values_to_reduce(num_values_to_reduce_) {}
+
+  void operator()(cl::sycl::nd_item<1> itemID) const {
+    // This is to bypass the statefull condition in Eigen meanReducer
+    Op non_const_functor;
+    std::memcpy(&non_const_functor, &functor, sizeof(Op));
+    auto output_accessor_ptr = output_accessor;
+    Index globalid = static_cast<Index>(itemID.get_global_linear_id());
+    if (globalid < range) {
+      CoeffReturnType accum = functor.initialize();
+      Eigen::internal::GenericDimReducer<Evaluator::NumReducedDims - 1, Evaluator, Op>::reduce(
+          evaluator, evaluator.firstInput(globalid), non_const_functor, &accum);
+      output_accessor_ptr[globalid] = OpDef::finalise_op(functor.finalize(accum), num_values_to_reduce);
+    }
+  }
+
+ private:
+  Evaluator evaluator;
+  EvaluatorPointerType output_accessor;
+  Op functor;
+  Index range;
+  Index num_values_to_reduce;
+};
+
+enum class reduction_dim { inner_most, outer_most };
+// Default partial reduction (preserve dimensions).
+template <typename Evaluator, typename OpType, typename PannelParameters, reduction_dim rt>
+struct PartialReductionKernel {
+  typedef typename Evaluator::CoeffReturnType CoeffReturnType;
+  typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
+  typedef typename Evaluator::Index Index;
+  typedef OpDefiner<OpType, CoeffReturnType, Index, false> OpDef;
+  typedef typename OpDef::type Op;
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      ScratchAcc;
+  ScratchAcc scratch;
+  Evaluator evaluator;
+  EvaluatorPointerType output_accessor;
+  Op op;
+  const Index preserve_elements_num_groups;
+  const Index reduce_elements_num_groups;
+  const Index num_coeffs_to_preserve;
+  const Index num_coeffs_to_reduce;
+
+  PartialReductionKernel(ScratchAcc scratch_, Evaluator evaluator_, EvaluatorPointerType output_accessor_, OpType op_,
+                         const Index preserve_elements_num_groups_, const Index reduce_elements_num_groups_,
+                         const Index num_coeffs_to_preserve_, const Index num_coeffs_to_reduce_)
+      : scratch(scratch_),
+        evaluator(evaluator_),
+        output_accessor(output_accessor_),
+        op(OpDef::get_op(op_)),
+        preserve_elements_num_groups(preserve_elements_num_groups_),
+        reduce_elements_num_groups(reduce_elements_num_groups_),
+        num_coeffs_to_preserve(num_coeffs_to_preserve_),
+        num_coeffs_to_reduce(num_coeffs_to_reduce_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void element_wise_reduce(Index globalRId, Index globalPId,
+                                                                 CoeffReturnType &accumulator) const {
+    if (globalPId >= num_coeffs_to_preserve) {
+      return;
+    }
+    Index global_offset = rt == reduction_dim::outer_most ? globalPId + (globalRId * num_coeffs_to_preserve)
+                                                          : globalRId + (globalPId * num_coeffs_to_reduce);
+    Index localOffset = globalRId;
+
+    const Index per_thread_local_stride = PannelParameters::LocalThreadSizeR * reduce_elements_num_groups;
+    const Index per_thread_global_stride =
+        rt == reduction_dim::outer_most ? num_coeffs_to_preserve * per_thread_local_stride : per_thread_local_stride;
+    for (Index i = globalRId; i < num_coeffs_to_reduce; i += per_thread_local_stride) {
+      op.reduce(evaluator.impl().coeff(global_offset), &accumulator);
+      localOffset += per_thread_local_stride;
+      global_offset += per_thread_global_stride;
+    }
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) const {
+    const Index linearLocalThreadId = itemID.get_local_id(0);
+    Index pLocalThreadId = rt == reduction_dim::outer_most ? linearLocalThreadId % PannelParameters::LocalThreadSizeP
+                                                           : linearLocalThreadId / PannelParameters::LocalThreadSizeR;
+    Index rLocalThreadId = rt == reduction_dim::outer_most ? linearLocalThreadId / PannelParameters::LocalThreadSizeP
+                                                           : linearLocalThreadId % PannelParameters::LocalThreadSizeR;
+    const Index pGroupId = rt == reduction_dim::outer_most ? itemID.get_group(0) % preserve_elements_num_groups
+                                                           : itemID.get_group(0) / reduce_elements_num_groups;
+    const Index rGroupId = rt == reduction_dim::outer_most ? itemID.get_group(0) / preserve_elements_num_groups
+                                                           : itemID.get_group(0) % reduce_elements_num_groups;
+
+    Index globalPId = pGroupId * PannelParameters::LocalThreadSizeP + pLocalThreadId;
+    const Index globalRId = rGroupId * PannelParameters::LocalThreadSizeR + rLocalThreadId;
+    CoeffReturnType *scratchPtr = scratch.get_pointer();
+    auto outPtr = output_accessor + (reduce_elements_num_groups > 1 ? rGroupId * num_coeffs_to_preserve : 0);
+    CoeffReturnType accumulator = op.initialize();
+
+    element_wise_reduce(globalRId, globalPId, accumulator);
+
+    accumulator = OpDef::finalise_op(op.finalize(accumulator), num_coeffs_to_reduce);
+    scratchPtr[pLocalThreadId + rLocalThreadId * (PannelParameters::LocalThreadSizeP + PannelParameters::BC)] =
+        accumulator;
+    if (rt == reduction_dim::inner_most) {
+      pLocalThreadId = linearLocalThreadId % PannelParameters::LocalThreadSizeP;
+      rLocalThreadId = linearLocalThreadId / PannelParameters::LocalThreadSizeP;
+      globalPId = pGroupId * PannelParameters::LocalThreadSizeP + pLocalThreadId;
+    }
+
+    /* Apply the reduction operation between the current local
+     * id and the one on the other half of the vector. */
+    auto out_scratch_ptr =
+        scratchPtr + (pLocalThreadId + (rLocalThreadId * (PannelParameters::LocalThreadSizeP + PannelParameters::BC)));
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+    if (rt == reduction_dim::inner_most) {
+      accumulator = *out_scratch_ptr;
+    }
+    // The Local LocalThreadSizeR is always power of 2
+    EIGEN_UNROLL_LOOP
+    for (Index offset = PannelParameters::LocalThreadSizeR >> 1; offset > 0; offset >>= 1) {
+      if (rLocalThreadId < offset) {
+        op.reduce(out_scratch_ptr[(PannelParameters::LocalThreadSizeP + PannelParameters::BC) * offset], &accumulator);
+        // The result has already been divided for mean reducer in the
+        // previous reduction so no need to divide furthermore
+        *out_scratch_ptr = op.finalize(accumulator);
+      }
+      /* All threads collectively read from global memory into local.
+       * The barrier ensures all threads' IO is resolved before
+       * execution continues (strictly speaking, all threads within
+       * a single work-group - there is no co-ordination between
+       * work-groups, only work-items). */
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+    }
+
+    if (rLocalThreadId == 0 && (globalPId < num_coeffs_to_preserve)) {
+      outPtr[globalPId] = op.finalize(accumulator);
+    }
+  }
+};
+
+template <typename OutScalar, typename Index, typename InputAccessor, typename OutputAccessor, typename OpType>
+struct SecondStepPartialReduction {
+  typedef OpDefiner<OpType, OutScalar, Index, false> OpDef;
+  typedef typename OpDef::type Op;
+  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      ScratchAccessor;
+  InputAccessor input_accessor;
+  OutputAccessor output_accessor;
+  Op op;
+  const Index num_coeffs_to_preserve;
+  const Index num_coeffs_to_reduce;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE SecondStepPartialReduction(ScratchAccessor, InputAccessor input_accessor_,
+                                                                   OutputAccessor output_accessor_, OpType op_,
+                                                                   const Index num_coeffs_to_preserve_,
+                                                                   const Index num_coeffs_to_reduce_)
+      : input_accessor(input_accessor_),
+        output_accessor(output_accessor_),
+        op(OpDef::get_op(op_)),
+        num_coeffs_to_preserve(num_coeffs_to_preserve_),
+        num_coeffs_to_reduce(num_coeffs_to_reduce_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) const {
+    const Index globalId = itemID.get_global_id(0);
+
+    if (globalId >= num_coeffs_to_preserve) return;
+
+    auto in_ptr = input_accessor + globalId;
+
+    OutScalar accumulator = op.initialize();
+    // num_coeffs_to_reduce is not bigger that 256
+    for (Index i = 0; i < num_coeffs_to_reduce; i++) {
+      op.reduce(*in_ptr, &accumulator);
+      in_ptr += num_coeffs_to_preserve;
+    }
+    output_accessor[globalId] = op.finalize(accumulator);
+  }
+};
+
+template <typename Index, Index LTP, Index LTR, bool BC_>
+struct ReductionPannel {
+  static constexpr Index LocalThreadSizeP = LTP;
+  static constexpr Index LocalThreadSizeR = LTR;
+  static constexpr bool BC = BC_;
+};
+
+template <typename Self, typename Op, TensorSycl::internal::reduction_dim rt>
+struct PartialReducerLauncher {
+  typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
+  typedef typename Self::CoeffReturnType CoeffReturnType;
+  typedef typename Self::Storage Storage;
+  typedef typename Self::Index Index;
+  typedef ReductionPannel<typename Self::Index, EIGEN_SYCL_LOCAL_THREAD_DIM0, EIGEN_SYCL_LOCAL_THREAD_DIM1, true>
+      PannelParameters;
+
+  typedef PartialReductionKernel<Self, Op, PannelParameters, rt> SyclReducerKerneType;
+
+  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev, EvaluatorPointerType output,
+                  Index num_coeffs_to_reduce, Index num_coeffs_to_preserve) {
+    Index roundUpP = roundUp(num_coeffs_to_preserve, PannelParameters::LocalThreadSizeP);
+
+    // getPowerOfTwo makes sure local range is power of 2 and <=
+    // maxSyclThreadPerBlock this will help us to avoid extra check on the
+    // kernel
+    static_assert(!((PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR) &
+                    (PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR - 1)),
+                  "The Local thread size must be a power of 2 for the reduction "
+                  "operation");
+
+    constexpr Index localRange = PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR;
+    // In this step, we force the code not to be more than 2-step reduction:
+    // Our empirical research shows that if each thread reduces at least 64
+    // elements individually, we get better performance. However, this can change
+    // on different platforms. In this step we force the code not to be
+    // morthan step reduction: Our empirical research shows that for inner_most
+    // dim reducer, it is better to have 8 group in a reduce dimension for sizes
+    // > 1024 to achieve the best performance.
+    const Index reductionPerThread = 64;
+    Index cu = dev.getPowerOfTwo(dev.getNumSyclMultiProcessors(), true);
+    const Index pNumGroups = roundUpP / PannelParameters::LocalThreadSizeP;
+    Index rGroups = (cu + pNumGroups - 1) / pNumGroups;
+    const Index rNumGroups = num_coeffs_to_reduce > reductionPerThread * localRange ? std::min(rGroups, localRange) : 1;
+    const Index globalRange = pNumGroups * rNumGroups * localRange;
+
+    constexpr Index scratchSize =
+        PannelParameters::LocalThreadSizeR * (PannelParameters::LocalThreadSizeP + PannelParameters::BC);
+    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
+    if (rNumGroups > 1) {
+      CoeffReturnType *temp_pointer = static_cast<CoeffReturnType *>(
+          dev.allocate_temp(num_coeffs_to_preserve * rNumGroups * sizeof(CoeffReturnType)));
+      EvaluatorPointerType temp_accessor = dev.get(temp_pointer);
+      dev.template unary_kernel_launcher<CoeffReturnType, SyclReducerKerneType>(
+             self, temp_accessor, thread_range, scratchSize, reducer, pNumGroups, rNumGroups, num_coeffs_to_preserve,
+             num_coeffs_to_reduce)
+          .wait();
+      typedef SecondStepPartialReduction<CoeffReturnType, Index, EvaluatorPointerType, EvaluatorPointerType, Op>
+          SecondStepPartialReductionKernel;
+      dev.template unary_kernel_launcher<CoeffReturnType, SecondStepPartialReductionKernel>(
+             temp_accessor, output,
+             cl::sycl::nd_range<1>(cl::sycl::range<1>(pNumGroups * localRange), cl::sycl::range<1>(localRange)),
+             Index(1), reducer, num_coeffs_to_preserve, rNumGroups)
+          .wait();
+      self.device().deallocate_temp(temp_pointer);
+    } else {
+      dev.template unary_kernel_launcher<CoeffReturnType, SyclReducerKerneType>(
+             self, output, thread_range, scratchSize, reducer, pNumGroups, rNumGroups, num_coeffs_to_preserve,
+             num_coeffs_to_reduce)
+          .wait();
+    }
+    return false;
+  }
+};
+}  // namespace internal
+}  // namespace TensorSycl
+
+namespace internal {
+
+template <typename Self, typename Op, bool Vectorizable>
+struct FullReducer<Self, Op, Eigen::SyclDevice, Vectorizable> {
+  typedef typename Self::CoeffReturnType CoeffReturnType;
+  typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
+  static constexpr bool HasOptimizedImplementation = true;
+  static constexpr int PacketSize = Self::PacketAccess ? Self::PacketSize : 1;
+  static void run(const Self &self, Op &reducer, const Eigen::SyclDevice &dev, EvaluatorPointerType data) {
+    typedef std::conditional_t<Self::PacketAccess, typename Self::PacketReturnType, CoeffReturnType> OutType;
+    static_assert(!((EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1) &
+                    (EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 - 1)),
+                  "The Local thread size must be a power of 2 for the reduction "
+                  "operation");
+    constexpr Index local_range = EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1;
+
+    typename Self::Index inputSize = self.impl().dimensions().TotalSize();
+    // In this step we force the code not to be more than 2-step reduction:
+    // Our empirical research shows that if each thread reduces at least 512
+    // elements individually, we get better performance.
+    const Index reductionPerThread = 2048;
+    Index reductionGroup = dev.getPowerOfTwo(
+        (inputSize + (reductionPerThread * local_range - 1)) / (reductionPerThread * local_range), true);
+    const Index num_work_group = std::min(reductionGroup, local_range);
+    const Index global_range = num_work_group * local_range;
+
+    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
+    typedef TensorSycl::internal::FullReductionKernelFunctor<Self, Op, local_range> reduction_kernel_t;
+    if (num_work_group > 1) {
+      CoeffReturnType *temp_pointer =
+          static_cast<CoeffReturnType *>(dev.allocate_temp(num_work_group * sizeof(CoeffReturnType)));
+      typename Self::EvaluatorPointerType tmp_global_accessor = dev.get(temp_pointer);
+      dev.template unary_kernel_launcher<OutType, reduction_kernel_t>(self, tmp_global_accessor, thread_range,
+                                                                      local_range, inputSize, reducer)
+          .wait();
+      typedef TensorSycl::internal::SecondStepFullReducer<CoeffReturnType, Op, EvaluatorPointerType,
+                                                          EvaluatorPointerType, Index, local_range>
+          GenericRKernel;
+      dev.template unary_kernel_launcher<CoeffReturnType, GenericRKernel>(
+             tmp_global_accessor, data,
+             cl::sycl::nd_range<1>(cl::sycl::range<1>(num_work_group), cl::sycl::range<1>(num_work_group)),
+             num_work_group, reducer)
+          .wait();
+      dev.deallocate_temp(temp_pointer);
+    } else {
+      dev.template unary_kernel_launcher<OutType, reduction_kernel_t>(self, data, thread_range, local_range, inputSize,
+                                                                      reducer)
+          .wait();
+    }
+  }
+};
+// vectorizable inner_most most dim preserver
+// col reduction
+template <typename Self, typename Op>
+struct OuterReducer<Self, Op, Eigen::SyclDevice> {
+  static constexpr bool HasOptimizedImplementation = true;
+
+  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
+                  typename Self::EvaluatorPointerType output, typename Self::Index num_coeffs_to_reduce,
+                  typename Self::Index num_coeffs_to_preserve) {
+    return ::Eigen::TensorSycl::internal::PartialReducerLauncher<
+        Self, Op, ::Eigen::TensorSycl::internal::reduction_dim::outer_most>::run(self, reducer, dev, output,
+                                                                                 num_coeffs_to_reduce,
+                                                                                 num_coeffs_to_preserve);
+  }
+};
+// row reduction
+template <typename Self, typename Op>
+struct InnerReducer<Self, Op, Eigen::SyclDevice> {
+  static constexpr bool HasOptimizedImplementation = true;
+
+  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
+                  typename Self::EvaluatorPointerType output, typename Self::Index num_coeffs_to_reduce,
+                  typename Self::Index num_coeffs_to_preserve) {
+    return ::Eigen::TensorSycl::internal::PartialReducerLauncher<
+        Self, Op, ::Eigen::TensorSycl::internal::reduction_dim::inner_most>::run(self, reducer, dev, output,
+                                                                                 num_coeffs_to_reduce,
+                                                                                 num_coeffs_to_preserve);
+  }
+};
+
+// ArgMax uses this kernel for partial reduction.
+// TODO(@mehdi.goli): Come up with a better kernel.
+// generic partial reduction
+template <typename Self, typename Op>
+struct GenericReducer<Self, Op, Eigen::SyclDevice> {
+  static constexpr bool HasOptimizedImplementation = false;
+  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
+                  typename Self::EvaluatorPointerType output, typename Self::Index num_values_to_reduce,
+                  typename Self::Index num_coeffs_to_preserve) {
+    typename Self::Index range, GRange, tileSize;
+    dev.parallel_for_setup(num_coeffs_to_preserve, tileSize, range, GRange);
+
+    dev.template unary_kernel_launcher<typename Self::CoeffReturnType,
+                                       TensorSycl::internal::GenericNondeterministicReducer<Self, Op>>(
+           self, output, cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), Index(1),
+           reducer, range, (num_values_to_reduce != 0) ? num_values_to_reduce : static_cast<Index>(1))
+        .wait();
+    return false;
+  }
+};
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h
new file mode 100644
index 0000000..d0df443
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h
@@ -0,0 +1,390 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_REF_H
+#define EIGEN_CXX11_TENSOR_TENSOR_REF_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Dimensions, typename Scalar>
+class TensorLazyBaseEvaluator {
+ public:
+  TensorLazyBaseEvaluator() : m_refcount(0) {}
+  virtual ~TensorLazyBaseEvaluator() {}
+
+  EIGEN_DEVICE_FUNC virtual const Dimensions& dimensions() const = 0;
+  EIGEN_DEVICE_FUNC virtual const Scalar* data() const = 0;
+
+  EIGEN_DEVICE_FUNC virtual const Scalar coeff(DenseIndex index) const = 0;
+  EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex index) = 0;
+
+  void incrRefCount() { ++m_refcount; }
+  void decrRefCount() { --m_refcount; }
+  int refCount() const { return m_refcount; }
+
+ private:
+  // No copy, no assignment;
+  TensorLazyBaseEvaluator(const TensorLazyBaseEvaluator& other);
+  TensorLazyBaseEvaluator& operator=(const TensorLazyBaseEvaluator& other);
+
+  int m_refcount;
+};
+
+template <typename Dimensions, typename Expr, typename Device>
+class TensorLazyEvaluatorReadOnly
+    : public TensorLazyBaseEvaluator<Dimensions, typename TensorEvaluator<Expr, Device>::Scalar> {
+ public:
+  typedef typename TensorEvaluator<Expr, Device>::Scalar Scalar;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  typedef TensorEvaluator<Expr, Device> EvalType;
+
+  TensorLazyEvaluatorReadOnly(const Expr& expr, const Device& device) : m_impl(expr, device), m_dummy(Scalar(0)) {
+    EIGEN_STATIC_ASSERT(
+        internal::array_size<Dimensions>::value == internal::array_size<typename EvalType::Dimensions>::value,
+        "Dimension sizes must match.");
+    const auto& other_dims = m_impl.dimensions();
+    for (std::size_t i = 0; i < m_dims.size(); ++i) {
+      m_dims[i] = other_dims[i];
+    }
+    m_impl.evalSubExprsIfNeeded(NULL);
+  }
+  virtual ~TensorLazyEvaluatorReadOnly() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC virtual const Dimensions& dimensions() const { return m_dims; }
+  EIGEN_DEVICE_FUNC virtual const Scalar* data() const { return m_impl.data(); }
+
+  EIGEN_DEVICE_FUNC virtual const Scalar coeff(DenseIndex index) const { return m_impl.coeff(index); }
+  EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex /*index*/) {
+    eigen_assert(false && "can't reference the coefficient of a rvalue");
+    return m_dummy;
+  };
+
+ protected:
+  TensorEvaluator<Expr, Device> m_impl;
+  Dimensions m_dims;
+  Scalar m_dummy;
+};
+
+template <typename Dimensions, typename Expr, typename Device>
+class TensorLazyEvaluatorWritable : public TensorLazyEvaluatorReadOnly<Dimensions, Expr, Device> {
+ public:
+  typedef TensorLazyEvaluatorReadOnly<Dimensions, Expr, Device> Base;
+  typedef typename Base::Scalar Scalar;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  TensorLazyEvaluatorWritable(const Expr& expr, const Device& device) : Base(expr, device) {}
+  virtual ~TensorLazyEvaluatorWritable() {}
+
+  EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex index) { return this->m_impl.coeffRef(index); }
+};
+
+template <typename Dimensions, typename Expr, typename Device, bool IsWritable>
+class TensorLazyEvaluator : public std::conditional_t<IsWritable, TensorLazyEvaluatorWritable<Dimensions, Expr, Device>,
+                                                      TensorLazyEvaluatorReadOnly<Dimensions, const Expr, Device>> {
+ public:
+  typedef std::conditional_t<IsWritable, TensorLazyEvaluatorWritable<Dimensions, Expr, Device>,
+                             TensorLazyEvaluatorReadOnly<Dimensions, const Expr, Device>>
+      Base;
+  typedef typename Base::Scalar Scalar;
+
+  TensorLazyEvaluator(const Expr& expr, const Device& device) : Base(expr, device) {}
+  virtual ~TensorLazyEvaluator() {}
+};
+
+template <typename Derived>
+class TensorRefBase : public TensorBase<Derived> {
+ public:
+  typedef typename traits<Derived>::PlainObjectType PlainObjectType;
+  typedef typename PlainObjectType::Base Base;
+  typedef typename Eigen::internal::nested<Derived>::type Nested;
+  typedef typename traits<PlainObjectType>::StorageKind StorageKind;
+  typedef typename traits<PlainObjectType>::Index Index;
+  typedef typename traits<PlainObjectType>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef typename Base::CoeffReturnType CoeffReturnType;
+  typedef Scalar* PointerType;
+  typedef PointerType PointerArgType;
+
+  static constexpr Index NumIndices = PlainObjectType::NumIndices;
+  typedef typename PlainObjectType::Dimensions Dimensions;
+
+  static constexpr int Layout = PlainObjectType::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -----------===//
+  typedef TensorBlockNotImplemented TensorBlock;
+  //===------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorRefBase() : m_evaluator(NULL) {}
+
+  TensorRefBase(const TensorRefBase& other) : TensorBase<Derived>(other), m_evaluator(other.m_evaluator) {
+    eigen_assert(m_evaluator->refCount() > 0);
+    m_evaluator->incrRefCount();
+  }
+
+  TensorRefBase& operator=(const TensorRefBase& other) {
+    if (this != &other) {
+      unrefEvaluator();
+      m_evaluator = other.m_evaluator;
+      eigen_assert(m_evaluator->refCount() > 0);
+      m_evaluator->incrRefCount();
+    }
+    return *this;
+  }
+
+  template <typename Expression,
+            typename EnableIf = std::enable_if_t<!std::is_same<std::decay_t<Expression>, Derived>::value>>
+  EIGEN_STRONG_INLINE TensorRefBase(const Expression& expr)
+      : m_evaluator(new TensorLazyEvaluator<Dimensions, Expression, DefaultDevice,
+                                            /*IsWritable=*/!std::is_const<PlainObjectType>::value &&
+                                                bool(is_lvalue<Expression>::value)>(expr, DefaultDevice())) {
+    m_evaluator->incrRefCount();
+  }
+
+  template <typename Expression,
+            typename EnableIf = std::enable_if_t<!std::is_same<std::decay_t<Expression>, Derived>::value>>
+  EIGEN_STRONG_INLINE TensorRefBase& operator=(const Expression& expr) {
+    unrefEvaluator();
+    m_evaluator = new TensorLazyEvaluator < Dimensions, Expression, DefaultDevice,
+    /*IsWritable=*/!std::is_const<PlainObjectType>::value&& bool(is_lvalue<Expression>::value) >
+        (expr, DefaultDevice());
+    m_evaluator->incrRefCount();
+    return *this;
+  }
+
+  ~TensorRefBase() { unrefEvaluator(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const { return m_evaluator->dimensions().size(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_evaluator->dimensions()[n]; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_evaluator->dimensions(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_evaluator->dimensions().TotalSize(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar* data() const { return m_evaluator->data(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(Index index) const { return m_evaluator->coeff(index); }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(Index firstIndex, IndexTypes... otherIndices) const {
+    const std::size_t num_indices = (sizeof...(otherIndices) + 1);
+    const array<Index, num_indices> indices{{firstIndex, otherIndices...}};
+    return coeff(indices);
+  }
+
+  template <std::size_t NumIndices>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(const array<Index, NumIndices>& indices) const {
+    const Dimensions& dims = this->dimensions();
+    Index index = 0;
+    if (PlainObjectType::Options & RowMajor) {
+      index += indices[0];
+      for (size_t i = 1; i < NumIndices; ++i) {
+        index = index * dims[i] + indices[i];
+      }
+    } else {
+      index += indices[NumIndices - 1];
+      for (int i = NumIndices - 2; i >= 0; --i) {
+        index = index * dims[i] + indices[i];
+      }
+    }
+    return m_evaluator->coeff(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index index) const { return m_evaluator->coeff(index); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { return m_evaluator->coeffRef(index); }
+
+ protected:
+  TensorLazyBaseEvaluator<Dimensions, Scalar>* evaluator() { return m_evaluator; }
+
+ private:
+  EIGEN_STRONG_INLINE void unrefEvaluator() {
+    if (m_evaluator) {
+      m_evaluator->decrRefCount();
+      if (m_evaluator->refCount() == 0) {
+        delete m_evaluator;
+      }
+    }
+  }
+
+  TensorLazyBaseEvaluator<Dimensions, Scalar>* m_evaluator;
+};
+
+}  // namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief A reference to a tensor expression
+ * The expression will be evaluated lazily (as much as possible).
+ *
+ */
+template <typename PlainObjectType>
+class TensorRef : public internal::TensorRefBase<TensorRef<PlainObjectType>> {
+  typedef internal::TensorRefBase<TensorRef<PlainObjectType>> Base;
+
+ public:
+  using Scalar = typename Base::Scalar;
+  using Dimensions = typename Base::Dimensions;
+
+  EIGEN_STRONG_INLINE TensorRef() : Base() {}
+
+  EIGEN_STRONG_INLINE TensorRef(const TensorRef& other) : Base(other) {}
+
+  template <typename Expression>
+  EIGEN_STRONG_INLINE TensorRef(const Expression& expr) : Base(expr) {
+    EIGEN_STATIC_ASSERT(internal::is_lvalue<Expression>::value,
+                        "Expression must be mutable to create a mutable TensorRef<Expression>.  Did you mean "
+                        "TensorRef<const Expression>?)");
+  }
+
+  TensorRef& operator=(const TensorRef& other) { return Base::operator=(other).derived(); }
+
+  template <typename Expression>
+  EIGEN_STRONG_INLINE TensorRef& operator=(const Expression& expr) {
+    EIGEN_STATIC_ASSERT(internal::is_lvalue<Expression>::value,
+                        "Expression must be mutable to create a mutable TensorRef<Expression>.  Did you mean "
+                        "TensorRef<const Expression>?)");
+    return Base::operator=(expr).derived();
+  }
+
+  template <typename... IndexTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices) {
+    const std::size_t num_indices = (sizeof...(otherIndices) + 1);
+    const array<Index, num_indices> indices{{firstIndex, otherIndices...}};
+    return coeffRef(indices);
+  }
+
+  template <std::size_t NumIndices>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices) {
+    const Dimensions& dims = this->dimensions();
+    Index index = 0;
+    if (PlainObjectType::Options & RowMajor) {
+      index += indices[0];
+      for (size_t i = 1; i < NumIndices; ++i) {
+        index = index * dims[i] + indices[i];
+      }
+    } else {
+      index += indices[NumIndices - 1];
+      for (int i = NumIndices - 2; i >= 0; --i) {
+        index = index * dims[i] + indices[i];
+      }
+    }
+    return Base::evaluator()->coeffRef(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { return Base::evaluator()->coeffRef(index); }
+};
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief A reference to a constant tensor expression
+ * The expression will be evaluated lazily (as much as possible).
+ *
+ */
+template <typename PlainObjectType>
+class TensorRef<const PlainObjectType> : public internal::TensorRefBase<TensorRef<const PlainObjectType>> {
+  typedef internal::TensorRefBase<TensorRef<const PlainObjectType>> Base;
+
+ public:
+  EIGEN_STRONG_INLINE TensorRef() : Base() {}
+
+  EIGEN_STRONG_INLINE TensorRef(const TensorRef& other) : Base(other) {}
+
+  template <typename Expression>
+  EIGEN_STRONG_INLINE TensorRef(const Expression& expr) : Base(expr) {}
+
+  TensorRef& operator=(const TensorRef& other) { return Base::operator=(other).derived(); }
+
+  template <typename Expression>
+  EIGEN_STRONG_INLINE TensorRef& operator=(const Expression& expr) {
+    return Base::operator=(expr).derived();
+  }
+};
+
+// evaluator for rvalues
+template <typename Derived, typename Device>
+struct TensorEvaluator<const TensorRef<Derived>, Device> {
+  typedef typename Derived::Index Index;
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename Derived::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorRef<Derived>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const TensorRef<Derived>& m, const Device&) : m_ref(m) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_ref.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) { return true; }
+
+  EIGEN_STRONG_INLINE void cleanup() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_ref.coeff(index); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { return m_ref.coeffRef(index); }
+
+  EIGEN_DEVICE_FUNC const Scalar* data() const { return m_ref.data(); }
+
+ protected:
+  TensorRef<Derived> m_ref;
+};
+
+// evaluator for lvalues
+template <typename Derived, typename Device>
+struct TensorEvaluator<TensorRef<Derived>, Device> : public TensorEvaluator<const TensorRef<Derived>, Device> {
+  typedef typename Derived::Index Index;
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename Derived::Dimensions Dimensions;
+
+  typedef TensorEvaluator<const TensorRef<Derived>, Device> Base;
+
+  enum { IsAligned = false, PacketAccess = false, BlockAccess = false, PreferBlockAccess = false, RawAccess = false };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(TensorRef<Derived>& m, const Device& d) : Base(m, d) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { return this->m_ref.coeffRef(index); }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_REF_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h
new file mode 100644
index 0000000..ece85ca
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h
@@ -0,0 +1,406 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com>
+//                    Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
+#define EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename ReverseDimensions, typename XprType>
+struct traits<TensorReverseOp<ReverseDimensions, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename ReverseDimensions, typename XprType>
+struct eval<TensorReverseOp<ReverseDimensions, XprType>, Eigen::Dense> {
+  typedef const TensorReverseOp<ReverseDimensions, XprType>& type;
+};
+
+template <typename ReverseDimensions, typename XprType>
+struct nested<TensorReverseOp<ReverseDimensions, XprType>, 1,
+              typename eval<TensorReverseOp<ReverseDimensions, XprType> >::type> {
+  typedef TensorReverseOp<ReverseDimensions, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor reverse elements class.
+ *
+ */
+template <typename ReverseDimensions, typename XprType>
+class TensorReverseOp : public TensorBase<TensorReverseOp<ReverseDimensions, XprType>, WriteAccessors> {
+ public:
+  typedef TensorBase<TensorReverseOp<ReverseDimensions, XprType>, WriteAccessors> Base;
+  typedef typename Eigen::internal::traits<TensorReverseOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorReverseOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorReverseOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorReverseOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReverseOp(const XprType& expr, const ReverseDimensions& reverse_dims)
+      : m_xpr(expr), m_reverse_dims(reverse_dims) {}
+
+  EIGEN_DEVICE_FUNC const ReverseDimensions& reverse() const { return m_reverse_dims; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorReverseOp)
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const ReverseDimensions m_reverse_dims;
+};
+
+// Eval as rvalue
+template <typename ReverseDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device> {
+  typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<ReverseDimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = NumDims > 0,
+    PreferBlockAccess = true,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  typedef internal::TensorIntDivisor<Index> IndexDivisor;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock ArgTensorBlock;
+
+  typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims, Layout, Index> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_reverse(op.reverse()), m_device(device) {
+    // Reversing a scalar isn't supported yet. It would be a no-op anyway.
+    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    // Compute strides
+    m_dimensions = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_strides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
+        if (m_strides[i] > 0) m_fastStrides[i] = IndexDivisor(m_strides[i]);
+      }
+    } else {
+      m_strides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
+        if (m_strides[i] > 0) m_fastStrides[i] = IndexDivisor(m_strides[i]);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index reverseIndex(Index index) const {
+    eigen_assert(index < dimensions().TotalSize());
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        Index idx = index / m_fastStrides[i];
+        index -= idx * m_strides[i];
+        if (m_reverse[i]) {
+          idx = m_dimensions[i] - idx - 1;
+        }
+        inputIndex += idx * m_strides[i];
+      }
+      if (m_reverse[0]) {
+        inputIndex += (m_dimensions[0] - index - 1);
+      } else {
+        inputIndex += index;
+      }
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        Index idx = index / m_fastStrides[i];
+        index -= idx * m_strides[i];
+        if (m_reverse[i]) {
+          idx = m_dimensions[i] - idx - 1;
+        }
+        inputIndex += idx * m_strides[i];
+      }
+      if (m_reverse[NumDims - 1]) {
+        inputIndex += (m_dimensions[NumDims - 1] - index - 1);
+      } else {
+        inputIndex += index;
+      }
+    }
+    return inputIndex;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_impl.coeff(reverseIndex(index));
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    // TODO(ndjaitly): write a better packing routine that uses
+    // local structure.
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index + i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.lastLevelCacheSize();
+    // Block evaluation reads underlying memory in reverse order, and default
+    // cost model does not properly catch this in bytes stored/loaded.
+    return internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size).addCostPerCoeff({0, 0, 24});
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    // TODO(ezhulenev): If underlying tensor expression supports and prefers
+    // block evaluation we must use it. Currently we use coeff and packet
+    // access into the underlying tensor expression.
+    static const bool isColMajor = static_cast<int>(Layout) == static_cast<int>(ColMajor);
+
+    static const Index inner_dim_idx = isColMajor ? 0 : NumDims - 1;
+    const bool inner_dim_reversed = m_reverse[inner_dim_idx];
+
+    // Offset in the output block.
+    Index block_offset = 0;
+
+    // Offset in the input Tensor.
+    Index input_offset = reverseIndex(desc.offset());
+
+    // Initialize output block iterator state. Dimension in this array are
+    // always in inner_most -> outer_most order (col major layout).
+    array<BlockIteratorState, NumDims> it;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = isColMajor ? i : NumDims - 1 - i;
+      it[i].size = desc.dimension(dim);
+      it[i].count = 0;
+      it[i].reverse = m_reverse[dim];
+
+      it[i].block_stride = i == 0 ? 1 : (it[i - 1].size * it[i - 1].block_stride);
+      it[i].block_span = it[i].block_stride * (it[i].size - 1);
+
+      it[i].input_stride = m_strides[dim];
+      it[i].input_span = it[i].input_stride * (it[i].size - 1);
+
+      if (it[i].reverse) {
+        it[i].input_stride = -1 * it[i].input_stride;
+        it[i].input_span = -1 * it[i].input_span;
+      }
+    }
+
+    // If multiple inner dimensions have the same reverse flag, check if we can
+    // merge them into a single virtual inner dimension.
+    int effective_inner_dim = 0;
+    for (int i = 1; i < NumDims; ++i) {
+      if (it[i].reverse != it[effective_inner_dim].reverse) break;
+      if (it[i].block_stride != it[effective_inner_dim].size) break;
+      if (it[i].block_stride != numext::abs(it[i].input_stride)) break;
+
+      it[i].size = it[effective_inner_dim].size * it[i].size;
+
+      it[i].block_stride = 1;
+      it[i].input_stride = (inner_dim_reversed ? -1 : 1);
+
+      it[i].block_span = it[i].block_stride * (it[i].size - 1);
+      it[i].input_span = it[i].input_stride * (it[i].size - 1);
+
+      effective_inner_dim = i;
+    }
+
+    eigen_assert(it[effective_inner_dim].block_stride == 1);
+    eigen_assert(it[effective_inner_dim].input_stride == (inner_dim_reversed ? -1 : 1));
+
+    const Index inner_dim_size = it[effective_inner_dim].size;
+
+    // Prepare storage for the materialized reverse result.
+    const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch);
+    CoeffReturnType* block_buffer = block_storage.data();
+
+    while (it[NumDims - 1].count < it[NumDims - 1].size) {
+      // Copy inner-most dimension data from reversed location in input.
+      Index dst = block_offset;
+      Index src = input_offset;
+
+      // NOTE(ezhulenev): Adding vectorized path with internal::preverse showed
+      // worse results in benchmarks than a simple coefficient loop.
+      if (inner_dim_reversed) {
+        for (Index i = 0; i < inner_dim_size; ++i) {
+          block_buffer[dst] = m_impl.coeff(src);
+          ++dst;
+          --src;
+        }
+      } else {
+        for (Index i = 0; i < inner_dim_size; ++i) {
+          block_buffer[dst] = m_impl.coeff(src);
+          ++dst;
+          ++src;
+        }
+      }
+
+      // For the 1d tensor we need to generate only one inner-most dimension.
+      if ((NumDims - effective_inner_dim) == 1) break;
+
+      // Update offset.
+      for (Index i = effective_inner_dim + 1; i < NumDims; ++i) {
+        if (++it[i].count < it[i].size) {
+          block_offset += it[i].block_stride;
+          input_offset += it[i].input_stride;
+          break;
+        }
+        if (i != NumDims - 1) it[i].count = 0;
+        block_offset -= it[i].block_span;
+        input_offset -= it[i].input_span;
+      }
+    }
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() +
+                                     TensorOpCost::DivCost<Index>());
+    for (int i = 0; i < NumDims; ++i) {
+      if (m_reverse[i]) {
+        compute_cost += 2 * TensorOpCost::AddCost<Index>();
+      }
+    }
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost, false /* vectorized */, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
+
+ protected:
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_strides;
+  array<IndexDivisor, NumDims> m_fastStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  ReverseDimensions m_reverse;
+  const Device EIGEN_DEVICE_REF m_device;
+
+ private:
+  struct BlockIteratorState {
+    BlockIteratorState()
+        : size(0), count(0), reverse(false), block_stride(0), block_span(0), input_stride(0), input_span(0) {}
+
+    Index size;
+    Index count;
+    bool reverse;
+    Index block_stride;
+    Index block_span;
+    Index input_stride;
+    Index input_span;
+  };
+};
+
+// Eval as lvalue
+
+template <typename ReverseDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<TensorReverseOp<ReverseDimensions, ArgType>, Device>
+    : public TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device> {
+  typedef TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device> Base;
+  typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<ReverseDimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return this->m_dimensions; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) const {
+    return this->m_impl.coeffRef(this->reverseIndex(index));
+  }
+
+  template <int StoreMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    // This code is adapted from TensorMorphing.h
+    EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
+    internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      this->coeffRef(index + i) = values[i];
+    }
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorRoll.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorRoll.h
new file mode 100644
index 0000000..e4b5181
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorRoll.h
@@ -0,0 +1,361 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Tobias Wood tobias@spinicist.org.uk
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_ROLL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_ROLL_H
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename RollDimensions, typename XprType>
+struct traits<TensorRollOp<RollDimensions, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename RollDimensions, typename XprType>
+struct eval<TensorRollOp<RollDimensions, XprType>, Eigen::Dense> {
+  typedef const TensorRollOp<RollDimensions, XprType>& type;
+};
+
+template <typename RollDimensions, typename XprType>
+struct nested<TensorRollOp<RollDimensions, XprType>, 1, typename eval<TensorRollOp<RollDimensions, XprType> >::type> {
+  typedef TensorRollOp<RollDimensions, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor roll (circular shift) elements class.
+ *
+ */
+template <typename RollDimensions, typename XprType>
+class TensorRollOp : public TensorBase<TensorRollOp<RollDimensions, XprType>, WriteAccessors> {
+ public:
+  typedef TensorBase<TensorRollOp<RollDimensions, XprType>, WriteAccessors> Base;
+  typedef typename Eigen::internal::traits<TensorRollOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorRollOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorRollOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorRollOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorRollOp(const XprType& expr, const RollDimensions& roll_dims)
+      : m_xpr(expr), m_roll_dims(roll_dims) {}
+
+  EIGEN_DEVICE_FUNC const RollDimensions& roll() const { return m_roll_dims; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorRollOp)
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const RollDimensions m_roll_dims;
+};
+
+// Eval as rvalue
+template <typename RollDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorRollOp<RollDimensions, ArgType>, Device> {
+  typedef TensorRollOp<RollDimensions, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<RollDimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = NumDims > 0,
+    PreferBlockAccess = true,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  typedef internal::TensorIntDivisor<Index> IndexDivisor;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  using TensorBlockDesc = internal::TensorBlockDescriptor<NumDims, Index>;
+  using TensorBlockScratch = internal::TensorBlockScratchAllocator<Device>;
+  using ArgTensorBlock = typename TensorEvaluator<const ArgType, Device>::TensorBlock;
+  using TensorBlock = typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims, Layout, Index>;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_rolls(op.roll()), m_device(device) {
+    EIGEN_STATIC_ASSERT((NumDims > 0), Must_Have_At_Least_One_Dimension_To_Roll);
+
+    // Compute strides
+    m_dimensions = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_strides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
+        if (m_strides[i] > 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
+      }
+    } else {
+      m_strides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
+        if (m_strides[i] > 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(nullptr);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index roll(Index const i, Index const r, Index const n) const {
+    auto const tmp = (i + r) % n;
+    if (tmp < 0) {
+      return tmp + n;
+    } else {
+      return tmp;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE array<Index, NumDims> rollCoords(array<Index, NumDims> const& coords) const {
+    array<Index, NumDims> rolledCoords;
+    for (int id = 0; id < NumDims; id++) {
+      eigen_assert(coords[id] < m_dimensions[id]);
+      rolledCoords[id] = roll(coords[id], m_rolls[id], m_dimensions[id]);
+    }
+    return rolledCoords;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rollIndex(Index index) const {
+    eigen_assert(index < dimensions().TotalSize());
+    Index rolledIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        Index idx = index / m_fast_strides[i];
+        index -= idx * m_strides[i];
+        rolledIndex += roll(idx, m_rolls[i], m_dimensions[i]) * m_strides[i];
+      }
+      rolledIndex += roll(index, m_rolls[0], m_dimensions[0]);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        Index idx = index / m_fast_strides[i];
+        index -= idx * m_strides[i];
+        rolledIndex += roll(idx, m_rolls[i], m_dimensions[i]) * m_strides[i];
+      }
+      rolledIndex += roll(index, m_rolls[NumDims - 1], m_dimensions[NumDims - 1]);
+    }
+    return rolledIndex;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_impl.coeff(rollIndex(index));
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index + i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.lastLevelCacheSize();
+    return internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size).addCostPerCoeff({0, 0, 24});
+  }
+
+  struct BlockIteratorState {
+    Index stride;
+    Index span;
+    Index size;
+    Index count;
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool /*root_of_expr_ast*/ = false) const {
+    static const bool is_col_major = static_cast<int>(Layout) == static_cast<int>(ColMajor);
+
+    // Compute spatial coordinates for the first block element.
+    array<Index, NumDims> coords;
+    extract_coordinates(desc.offset(), coords);
+    array<Index, NumDims> initial_coords = coords;
+    Index offset = 0;  // Offset in the output block buffer.
+
+    // Initialize output block iterator state. Dimension in this array are
+    // always in inner_most -> outer_most order (col major layout).
+    array<BlockIteratorState, NumDims> it;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = is_col_major ? i : NumDims - 1 - i;
+      it[i].size = desc.dimension(dim);
+      it[i].stride = i == 0 ? 1 : (it[i - 1].size * it[i - 1].stride);
+      it[i].span = it[i].stride * (it[i].size - 1);
+      it[i].count = 0;
+    }
+    eigen_assert(it[0].stride == 1);
+
+    // Prepare storage for the materialized generator result.
+    const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch);
+    CoeffReturnType* block_buffer = block_storage.data();
+
+    static const int inner_dim = is_col_major ? 0 : NumDims - 1;
+    const Index inner_dim_size = it[0].size;
+
+    while (it[NumDims - 1].count < it[NumDims - 1].size) {
+      Index i = 0;
+      for (; i < inner_dim_size; ++i) {
+        auto const rolled = rollCoords(coords);
+        auto const index = is_col_major ? m_dimensions.IndexOfColMajor(rolled) : m_dimensions.IndexOfRowMajor(rolled);
+        *(block_buffer + offset + i) = m_impl.coeff(index);
+        coords[inner_dim]++;
+      }
+      coords[inner_dim] = initial_coords[inner_dim];
+
+      if (NumDims == 1) break;  // For the 1d tensor we need to generate only one inner-most dimension.
+
+      // Update offset.
+      for (i = 1; i < NumDims; ++i) {
+        if (++it[i].count < it[i].size) {
+          offset += it[i].stride;
+          coords[is_col_major ? i : NumDims - 1 - i]++;
+          break;
+        }
+        if (i != NumDims - 1) it[i].count = 0;
+        coords[is_col_major ? i : NumDims - 1 - i] = initial_coords[is_col_major ? i : NumDims - 1 - i];
+        offset -= it[i].span;
+      }
+    }
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() +
+                                     TensorOpCost::DivCost<Index>());
+    for (int i = 0; i < NumDims; ++i) {
+      compute_cost += 2 * TensorOpCost::AddCost<Index>();
+    }
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost, false /* vectorized */, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return nullptr; }
+
+ protected:
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_strides;
+  array<IndexDivisor, NumDims> m_fast_strides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  RollDimensions m_rolls;
+  const Device EIGEN_DEVICE_REF m_device;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void extract_coordinates(Index index, array<Index, NumDims>& coords) const {
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_fast_strides[i];
+        index -= idx * m_strides[i];
+        coords[i] = idx;
+      }
+      coords[0] = index;
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_fast_strides[i];
+        index -= idx * m_strides[i];
+        coords[i] = idx;
+      }
+      coords[NumDims - 1] = index;
+    }
+  }
+
+ private:
+};
+
+// Eval as lvalue
+
+template <typename RollDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<TensorRollOp<RollDimensions, ArgType>, Device>
+    : public TensorEvaluator<const TensorRollOp<RollDimensions, ArgType>, Device> {
+  typedef TensorEvaluator<const TensorRollOp<RollDimensions, ArgType>, Device> Base;
+  typedef TensorRollOp<RollDimensions, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<RollDimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,
+    RawAccess = false
+  };
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return this->m_dimensions; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) const {
+    return this->m_impl.coeffRef(this->rollIndex(index));
+  }
+
+  template <int StoreMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+    EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
+    internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      this->coeffRef(index + i) = values[i];
+    }
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_ROLL_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h
new file mode 100644
index 0000000..39d3558
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h
@@ -0,0 +1,474 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Igor Babuschkin <igor@babuschk.in>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
+#define EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Op, typename XprType>
+struct traits<TensorScanOp<Op, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename Op, typename XprType>
+struct eval<TensorScanOp<Op, XprType>, Eigen::Dense> {
+  typedef const TensorScanOp<Op, XprType>& type;
+};
+
+template <typename Op, typename XprType>
+struct nested<TensorScanOp<Op, XprType>, 1, typename eval<TensorScanOp<Op, XprType> >::type> {
+  typedef TensorScanOp<Op, XprType> type;
+};
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor scan class.
+ */
+template <typename Op, typename XprType>
+class TensorScanOp : public TensorBase<TensorScanOp<Op, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorScanOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorScanOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorScanOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorScanOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorScanOp(const XprType& expr, const Index& axis, bool exclusive = false,
+                                                     const Op& op = Op())
+      : m_expr(expr), m_axis(axis), m_accumulator(op), m_exclusive(exclusive) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index axis() const { return m_axis; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const XprType& expression() const { return m_expr; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Op accumulator() const { return m_accumulator; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool exclusive() const { return m_exclusive; }
+
+ protected:
+  typename XprType::Nested m_expr;
+  const Index m_axis;
+  const Op m_accumulator;
+  const bool m_exclusive;
+};
+
+namespace internal {
+
+template <typename Self>
+EIGEN_STRONG_INLINE void ReduceScalar(Self& self, Index offset, typename Self::CoeffReturnType* data) {
+  // Compute the scan along the axis, starting at the given offset
+  typename Self::CoeffReturnType accum = self.accumulator().initialize();
+  if (self.stride() == 1) {
+    if (self.exclusive()) {
+      for (Index curr = offset; curr < offset + self.size(); ++curr) {
+        data[curr] = self.accumulator().finalize(accum);
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+      }
+    } else {
+      for (Index curr = offset; curr < offset + self.size(); ++curr) {
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+        data[curr] = self.accumulator().finalize(accum);
+      }
+    }
+  } else {
+    if (self.exclusive()) {
+      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
+        Index curr = offset + idx3 * self.stride();
+        data[curr] = self.accumulator().finalize(accum);
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+      }
+    } else {
+      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
+        Index curr = offset + idx3 * self.stride();
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+        data[curr] = self.accumulator().finalize(accum);
+      }
+    }
+  }
+}
+
+template <typename Self>
+EIGEN_STRONG_INLINE void ReducePacket(Self& self, Index offset, typename Self::CoeffReturnType* data) {
+  using Scalar = typename Self::CoeffReturnType;
+  using Packet = typename Self::PacketReturnType;
+  // Compute the scan along the axis, starting at the calculated offset
+  Packet accum = self.accumulator().template initializePacket<Packet>();
+  if (self.stride() == 1) {
+    if (self.exclusive()) {
+      for (Index curr = offset; curr < offset + self.size(); ++curr) {
+        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
+        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
+      }
+    } else {
+      for (Index curr = offset; curr < offset + self.size(); ++curr) {
+        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
+        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
+      }
+    }
+  } else {
+    if (self.exclusive()) {
+      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
+        const Index curr = offset + idx3 * self.stride();
+        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
+        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
+      }
+    } else {
+      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
+        const Index curr = offset + idx3 * self.stride();
+        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
+        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
+      }
+    }
+  }
+}
+
+template <typename Self, bool Vectorize, bool Parallel>
+struct ReduceBlock {
+  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1, typename Self::CoeffReturnType* data) {
+    for (Index idx2 = 0; idx2 < self.stride(); idx2++) {
+      // Calculate the starting offset for the scan
+      Index offset = idx1 + idx2;
+      ReduceScalar(self, offset, data);
+    }
+  }
+};
+
+// Specialization for vectorized reduction.
+template <typename Self>
+struct ReduceBlock<Self, /*Vectorize=*/true, /*Parallel=*/false> {
+  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1, typename Self::CoeffReturnType* data) {
+    using Packet = typename Self::PacketReturnType;
+    const int PacketSize = internal::unpacket_traits<Packet>::size;
+    Index idx2 = 0;
+    for (; idx2 + PacketSize <= self.stride(); idx2 += PacketSize) {
+      // Calculate the starting offset for the packet scan
+      Index offset = idx1 + idx2;
+      ReducePacket(self, offset, data);
+    }
+    for (; idx2 < self.stride(); idx2++) {
+      // Calculate the starting offset for the scan
+      Index offset = idx1 + idx2;
+      ReduceScalar(self, offset, data);
+    }
+  }
+};
+
+// Single-threaded CPU implementation of scan
+template <typename Self, typename Reducer, typename Device,
+          bool Vectorize = (TensorEvaluator<typename Self::ChildTypeNoConst, Device>::PacketAccess &&
+                            internal::reducer_traits<Reducer, Device>::PacketAccess)>
+struct ScanLauncher {
+  void operator()(Self& self, typename Self::CoeffReturnType* data) const {
+    Index total_size = internal::array_prod(self.dimensions());
+
+    // We fix the index along the scan axis to 0 and perform a
+    // scan per remaining entry. The iteration is split into two nested
+    // loops to avoid an integer division by keeping track of each idx1 and
+    // idx2.
+    for (Index idx1 = 0; idx1 < total_size; idx1 += self.stride() * self.size()) {
+      ReduceBlock<Self, Vectorize, /*Parallel=*/false> block_reducer;
+      block_reducer(self, idx1, data);
+    }
+  }
+};
+
+#ifdef EIGEN_USE_THREADS
+
+// Adjust block_size to avoid false sharing of cachelines among
+// threads. Currently set to twice the cache line size on Intel and ARM
+// processors.
+EIGEN_STRONG_INLINE Index AdjustBlockSize(Index item_size, Index block_size) {
+  constexpr Index kBlockAlignment = 128;
+  const Index items_per_cacheline = numext::maxi<Index>(1, kBlockAlignment / item_size);
+  return items_per_cacheline * numext::div_ceil(block_size, items_per_cacheline);
+}
+
+template <typename Self>
+struct ReduceBlock<Self, /*Vectorize=*/true, /*Parallel=*/true> {
+  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1, typename Self::CoeffReturnType* data) {
+    using Scalar = typename Self::CoeffReturnType;
+    using Packet = typename Self::PacketReturnType;
+    const int PacketSize = internal::unpacket_traits<Packet>::size;
+    Index num_scalars = self.stride();
+    Index num_packets = 0;
+    if (self.stride() >= PacketSize) {
+      num_packets = self.stride() / PacketSize;
+      self.device().parallelFor(
+          num_packets,
+          TensorOpCost(PacketSize * self.size(), PacketSize * self.size(), 16 * PacketSize * self.size(), true,
+                       PacketSize),
+          // Make the shard size large enough that two neighboring threads
+          // won't write to the same cacheline of `data`.
+          [=](Index blk_size) { return AdjustBlockSize(PacketSize * sizeof(Scalar), blk_size); },
+          [&](Index first, Index last) {
+            for (Index packet = first; packet < last; ++packet) {
+              const Index idx2 = packet * PacketSize;
+              ReducePacket(self, idx1 + idx2, data);
+            }
+          });
+      num_scalars -= num_packets * PacketSize;
+    }
+    self.device().parallelFor(
+        num_scalars, TensorOpCost(self.size(), self.size(), 16 * self.size()),
+        // Make the shard size large enough that two neighboring threads
+        // won't write to the same cacheline of `data`.
+        [=](Index blk_size) { return AdjustBlockSize(sizeof(Scalar), blk_size); },
+        [&](Index first, Index last) {
+          for (Index scalar = first; scalar < last; ++scalar) {
+            const Index idx2 = num_packets * PacketSize + scalar;
+            ReduceScalar(self, idx1 + idx2, data);
+          }
+        });
+  }
+};
+
+template <typename Self>
+struct ReduceBlock<Self, /*Vectorize=*/false, /*Parallel=*/true> {
+  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1, typename Self::CoeffReturnType* data) {
+    using Scalar = typename Self::CoeffReturnType;
+    self.device().parallelFor(
+        self.stride(), TensorOpCost(self.size(), self.size(), 16 * self.size()),
+        // Make the shard size large enough that two neighboring threads
+        // won't write to the same cacheline of `data`.
+        [=](Index blk_size) { return AdjustBlockSize(sizeof(Scalar), blk_size); },
+        [&](Index first, Index last) {
+          for (Index idx2 = first; idx2 < last; ++idx2) {
+            ReduceScalar(self, idx1 + idx2, data);
+          }
+        });
+  }
+};
+
+// Specialization for multi-threaded execution.
+template <typename Self, typename Reducer, bool Vectorize>
+struct ScanLauncher<Self, Reducer, ThreadPoolDevice, Vectorize> {
+  void operator()(Self& self, typename Self::CoeffReturnType* data) {
+    using Scalar = typename Self::CoeffReturnType;
+    using Packet = typename Self::PacketReturnType;
+    const int PacketSize = internal::unpacket_traits<Packet>::size;
+    const Index total_size = internal::array_prod(self.dimensions());
+    const Index inner_block_size = self.stride() * self.size();
+    bool parallelize_by_outer_blocks = (total_size >= (self.stride() * inner_block_size));
+
+    if ((parallelize_by_outer_blocks && total_size <= 4096) ||
+        (!parallelize_by_outer_blocks && self.stride() < PacketSize)) {
+      ScanLauncher<Self, Reducer, DefaultDevice, Vectorize> launcher;
+      launcher(self, data);
+      return;
+    }
+
+    if (parallelize_by_outer_blocks) {
+      // Parallelize over outer blocks.
+      const Index num_outer_blocks = total_size / inner_block_size;
+      self.device().parallelFor(
+          num_outer_blocks,
+          TensorOpCost(inner_block_size, inner_block_size, 16 * PacketSize * inner_block_size, Vectorize, PacketSize),
+          [=](Index blk_size) { return AdjustBlockSize(inner_block_size * sizeof(Scalar), blk_size); },
+          [&](Index first, Index last) {
+            for (Index idx1 = first; idx1 < last; ++idx1) {
+              ReduceBlock<Self, Vectorize, /*Parallelize=*/false> block_reducer;
+              block_reducer(self, idx1 * inner_block_size, data);
+            }
+          });
+    } else {
+      // Parallelize over inner packets/scalars dimensions when the reduction
+      // axis is not an inner dimension.
+      ReduceBlock<Self, Vectorize, /*Parallelize=*/true> block_reducer;
+      for (Index idx1 = 0; idx1 < total_size; idx1 += self.stride() * self.size()) {
+        block_reducer(self, idx1, data);
+      }
+    }
+  }
+};
+#endif  // EIGEN_USE_THREADS
+
+#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
+
+// GPU implementation of scan
+// TODO(ibab): This placeholder implementation performs multiple scans in
+// parallel, but it would be better to use a parallel scan algorithm and
+// optimize memory access.
+template <typename Self, typename Reducer>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ScanKernel(Self self, Index total_size,
+                                                        typename Self::CoeffReturnType* data) {
+  // Compute offset as in the CPU version
+  Index val = threadIdx.x + blockIdx.x * blockDim.x;
+  Index offset = (val / self.stride()) * self.stride() * self.size() + val % self.stride();
+
+  if (offset + (self.size() - 1) * self.stride() < total_size) {
+    // Compute the scan along the axis, starting at the calculated offset
+    typename Self::CoeffReturnType accum = self.accumulator().initialize();
+    for (Index idx = 0; idx < self.size(); idx++) {
+      Index curr = offset + idx * self.stride();
+      if (self.exclusive()) {
+        data[curr] = self.accumulator().finalize(accum);
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+      } else {
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+        data[curr] = self.accumulator().finalize(accum);
+      }
+    }
+  }
+  __syncthreads();
+}
+
+template <typename Self, typename Reducer, bool Vectorize>
+struct ScanLauncher<Self, Reducer, GpuDevice, Vectorize> {
+  void operator()(const Self& self, typename Self::CoeffReturnType* data) {
+    Index total_size = internal::array_prod(self.dimensions());
+    Index num_blocks = (total_size / self.size() + 63) / 64;
+    Index block_size = 64;
+
+    LAUNCH_GPU_KERNEL((ScanKernel<Self, Reducer>), num_blocks, block_size, 0, self.device(), self, total_size, data);
+  }
+};
+#endif  // EIGEN_USE_GPU && (EIGEN_GPUCC)
+
+}  // namespace internal
+
+// Eval as rvalue
+template <typename Op, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {
+  typedef TensorScanOp<Op, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef const ArgType ChildTypeNoConst;
+  typedef const ArgType ChildType;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> Self;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    CoordAccess = false,
+    RawAccess = true
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device),
+        m_device(device),
+        m_exclusive(op.exclusive()),
+        m_accumulator(op.accumulator()),
+        m_size(m_impl.dimensions()[op.axis()]),
+        m_stride(1),
+        m_consume_dim(op.axis()),
+        m_output(NULL) {
+    // Accumulating a scalar isn't supported.
+    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    eigen_assert(op.axis() >= 0 && op.axis() < NumDims);
+
+    // Compute stride of scan axis
+    const Dimensions& dims = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < op.axis(); ++i) {
+        m_stride = m_stride * dims[i];
+      }
+    } else {
+      // dims can only be indexed through unsigned integers,
+      // so use an unsigned type to let the compiler know.
+      // This prevents spurious warnings: "'*((void*)(& evaluator)+64)[18446744073709551615]' may be used uninitialized
+      // in this function"
+      unsigned int axis = internal::convert_index<unsigned int>(op.axis());
+      for (unsigned int i = NumDims - 1; i > axis; --i) {
+        m_stride = m_stride * dims[i];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& stride() const { return m_stride; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& consume_dim() const { return m_consume_dim; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& size() const { return m_size; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Op& accumulator() const { return m_accumulator; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool exclusive() const { return m_exclusive; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& inner() const { return m_impl; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const { return m_device; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    internal::ScanLauncher<Self, Op, Device> launcher;
+    if (data) {
+      launcher(*this, data);
+      return false;
+    }
+
+    const Index total_size = internal::array_prod(dimensions());
+    m_output =
+        static_cast<EvaluatorPointerType>(m_device.get((Scalar*)m_device.allocate_temp(total_size * sizeof(Scalar))));
+    launcher(*this, m_output);
+    return true;
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_output + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return m_output; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_output[index]; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0);
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    if (m_output) {
+      m_device.deallocate_temp(m_output);
+      m_output = NULL;
+    }
+    m_impl.cleanup();
+  }
+
+ protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+  const Device EIGEN_DEVICE_REF m_device;
+  const bool m_exclusive;
+  Op m_accumulator;
+  const Index m_size;
+  Index m_stride;
+  Index m_consume_dim;
+  EvaluatorPointerType m_output;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h
new file mode 100644
index 0000000..f08b83a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h
@@ -0,0 +1,506 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * TensorScanSycl.h
+ *
+ * \brief:
+ *  Tensor Scan Sycl implement the extend  version of
+ * "Efficient parallel scan algorithms for GPUs." .for Tensor operations.
+ * The algorithm requires up to 3 stage (consequently 3 kernels) depending on
+ * the size of the tensor. In the first kernel (ScanKernelFunctor), each
+ * threads within the work-group individually reduces the allocated elements per
+ * thread in order to reduces the total number of blocks. In the next step all
+ * thread within the work-group will reduce the associated blocks into the
+ * temporary buffers. In the next kernel(ScanBlockKernelFunctor), the temporary
+ * buffer is given as an input and all the threads within a work-group scan and
+ * reduces the boundaries between the blocks (generated from the previous
+ * kernel). and write the data on the temporary buffer. If the second kernel is
+ * required, the third and final kernel (ScanAdjustmentKernelFunctor) will
+ * adjust the final result into the output buffer.
+ * The original algorithm for the parallel prefix sum can be found here:
+ *
+ * Sengupta, Shubhabrata, Mark Harris, and Michael Garland. "Efficient parallel
+ * scan algorithms for GPUs." NVIDIA, Santa Clara, CA, Tech. Rep. NVR-2008-003
+ *1, no. 1 (2008): 1-17.
+ *****************************************************************/
+
+#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_SYCL_SYCL_HPP
+#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_SYCL_SYCL_HPP
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace TensorSycl {
+namespace internal {
+
+#ifndef EIGEN_SYCL_MAX_GLOBAL_RANGE
+#define EIGEN_SYCL_MAX_GLOBAL_RANGE (EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 * 4)
+#endif
+
+template <typename index_t>
+struct ScanParameters {
+  // must be power of 2
+  static constexpr index_t ScanPerThread = 8;
+  const index_t total_size;
+  const index_t non_scan_size;
+  const index_t scan_size;
+  const index_t non_scan_stride;
+  const index_t scan_stride;
+  const index_t panel_threads;
+  const index_t group_threads;
+  const index_t block_threads;
+  const index_t elements_per_group;
+  const index_t elements_per_block;
+  const index_t loop_range;
+
+  ScanParameters(index_t total_size_, index_t non_scan_size_, index_t scan_size_, index_t non_scan_stride_,
+                 index_t scan_stride_, index_t panel_threads_, index_t group_threads_, index_t block_threads_,
+                 index_t elements_per_group_, index_t elements_per_block_, index_t loop_range_)
+      : total_size(total_size_),
+        non_scan_size(non_scan_size_),
+        scan_size(scan_size_),
+        non_scan_stride(non_scan_stride_),
+        scan_stride(scan_stride_),
+        panel_threads(panel_threads_),
+        group_threads(group_threads_),
+        block_threads(block_threads_),
+        elements_per_group(elements_per_group_),
+        elements_per_block(elements_per_block_),
+        loop_range(loop_range_) {}
+};
+
+enum class scan_step { first, second };
+template <typename Evaluator, typename CoeffReturnType, typename OutAccessor, typename Op, typename Index,
+          scan_step stp>
+struct ScanKernelFunctor {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      LocalAccessor;
+  static constexpr int PacketSize = ScanParameters<Index>::ScanPerThread / 2;
+
+  LocalAccessor scratch;
+  Evaluator dev_eval;
+  OutAccessor out_ptr;
+  OutAccessor tmp_ptr;
+  const ScanParameters<Index> scanParameters;
+  Op accumulator;
+  const bool inclusive;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScanKernelFunctor(LocalAccessor scratch_, const Evaluator dev_eval_,
+                                                          OutAccessor out_accessor_, OutAccessor temp_accessor_,
+                                                          const ScanParameters<Index> scanParameters_, Op accumulator_,
+                                                          const bool inclusive_)
+      : scratch(scratch_),
+        dev_eval(dev_eval_),
+        out_ptr(out_accessor_),
+        tmp_ptr(temp_accessor_),
+        scanParameters(scanParameters_),
+        accumulator(accumulator_),
+        inclusive(inclusive_) {}
+
+  template <scan_step sst = stp, typename Input>
+  std::enable_if_t<sst == scan_step::first, CoeffReturnType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE read(
+      const Input &inpt, Index global_id) const {
+    return inpt.coeff(global_id);
+  }
+
+  template <scan_step sst = stp, typename Input>
+  std::enable_if_t<sst != scan_step::first, CoeffReturnType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE read(
+      const Input &inpt, Index global_id) const {
+    return inpt[global_id];
+  }
+
+  template <scan_step sst = stp, typename InclusiveOp>
+  std::enable_if_t<sst == scan_step::first> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE first_step_inclusive_Operation(
+      InclusiveOp inclusive_op) const {
+    inclusive_op();
+  }
+
+  template <scan_step sst = stp, typename InclusiveOp>
+  std::enable_if_t<sst != scan_step::first> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE first_step_inclusive_Operation(
+      InclusiveOp) const {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) const {
+    for (Index loop_offset = 0; loop_offset < scanParameters.loop_range; loop_offset++) {
+      Index data_offset = (itemID.get_global_id(0) + (itemID.get_global_range(0) * loop_offset));
+      Index tmp = data_offset % scanParameters.panel_threads;
+      const Index panel_id = data_offset / scanParameters.panel_threads;
+      const Index group_id = tmp / scanParameters.group_threads;
+      tmp = tmp % scanParameters.group_threads;
+      const Index block_id = tmp / scanParameters.block_threads;
+      const Index local_id = tmp % scanParameters.block_threads;
+      // we put one element per packet in scratch_mem
+      const Index scratch_stride = scanParameters.elements_per_block / PacketSize;
+      const Index scratch_offset = (itemID.get_local_id(0) / scanParameters.block_threads) * scratch_stride;
+      CoeffReturnType private_scan[ScanParameters<Index>::ScanPerThread];
+      CoeffReturnType inclusive_scan;
+      // the actual panel size is scan_size * non_scan_size.
+      // elements_per_panel is roundup to power of 2 for binary tree
+      const Index panel_offset = panel_id * scanParameters.scan_size * scanParameters.non_scan_size;
+      const Index group_offset = group_id * scanParameters.non_scan_stride;
+      // This will be effective when the size is bigger than elements_per_block
+      const Index block_offset = block_id * scanParameters.elements_per_block * scanParameters.scan_stride;
+      const Index thread_offset = (ScanParameters<Index>::ScanPerThread * local_id * scanParameters.scan_stride);
+      const Index global_offset = panel_offset + group_offset + block_offset + thread_offset;
+      Index next_elements = 0;
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < ScanParameters<Index>::ScanPerThread; i++) {
+        Index global_id = global_offset + next_elements;
+        private_scan[i] = ((((block_id * scanParameters.elements_per_block) +
+                             (ScanParameters<Index>::ScanPerThread * local_id) + i) < scanParameters.scan_size) &&
+                           (global_id < scanParameters.total_size))
+                              ? read(dev_eval, global_id)
+                              : accumulator.initialize();
+        next_elements += scanParameters.scan_stride;
+      }
+      first_step_inclusive_Operation([&]() EIGEN_DEVICE_FUNC {
+        if (inclusive) {
+          inclusive_scan = private_scan[ScanParameters<Index>::ScanPerThread - 1];
+        }
+      });
+      // This for loop must be 2
+      EIGEN_UNROLL_LOOP
+      for (int packetIndex = 0; packetIndex < ScanParameters<Index>::ScanPerThread; packetIndex += PacketSize) {
+        Index private_offset = 1;
+        // build sum in place up the tree
+        EIGEN_UNROLL_LOOP
+        for (Index d = PacketSize >> 1; d > 0; d >>= 1) {
+          EIGEN_UNROLL_LOOP
+          for (Index l = 0; l < d; l++) {
+            Index ai = private_offset * (2 * l + 1) - 1 + packetIndex;
+            Index bi = private_offset * (2 * l + 2) - 1 + packetIndex;
+            CoeffReturnType accum = accumulator.initialize();
+            accumulator.reduce(private_scan[ai], &accum);
+            accumulator.reduce(private_scan[bi], &accum);
+            private_scan[bi] = accumulator.finalize(accum);
+          }
+          private_offset *= 2;
+        }
+        scratch[2 * local_id + (packetIndex / PacketSize) + scratch_offset] =
+            private_scan[PacketSize - 1 + packetIndex];
+        private_scan[PacketSize - 1 + packetIndex] = accumulator.initialize();
+        // traverse down tree & build scan
+        EIGEN_UNROLL_LOOP
+        for (Index d = 1; d < PacketSize; d *= 2) {
+          private_offset >>= 1;
+          EIGEN_UNROLL_LOOP
+          for (Index l = 0; l < d; l++) {
+            Index ai = private_offset * (2 * l + 1) - 1 + packetIndex;
+            Index bi = private_offset * (2 * l + 2) - 1 + packetIndex;
+            CoeffReturnType accum = accumulator.initialize();
+            accumulator.reduce(private_scan[ai], &accum);
+            accumulator.reduce(private_scan[bi], &accum);
+            private_scan[ai] = private_scan[bi];
+            private_scan[bi] = accumulator.finalize(accum);
+          }
+        }
+      }
+
+      Index offset = 1;
+      // build sum in place up the tree
+      for (Index d = scratch_stride >> 1; d > 0; d >>= 1) {
+        // Synchronise
+        itemID.barrier(cl::sycl::access::fence_space::local_space);
+        if (local_id < d) {
+          Index ai = offset * (2 * local_id + 1) - 1 + scratch_offset;
+          Index bi = offset * (2 * local_id + 2) - 1 + scratch_offset;
+          CoeffReturnType accum = accumulator.initialize();
+          accumulator.reduce(scratch[ai], &accum);
+          accumulator.reduce(scratch[bi], &accum);
+          scratch[bi] = accumulator.finalize(accum);
+        }
+        offset *= 2;
+      }
+      // Synchronise
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      // next step optimisation
+      if (local_id == 0) {
+        if (((scanParameters.elements_per_group / scanParameters.elements_per_block) > 1)) {
+          const Index temp_id = panel_id * (scanParameters.elements_per_group / scanParameters.elements_per_block) *
+                                    scanParameters.non_scan_size +
+                                group_id * (scanParameters.elements_per_group / scanParameters.elements_per_block) +
+                                block_id;
+          tmp_ptr[temp_id] = scratch[scratch_stride - 1 + scratch_offset];
+        }
+        // clear the last element
+        scratch[scratch_stride - 1 + scratch_offset] = accumulator.initialize();
+      }
+      // traverse down tree & build scan
+      for (Index d = 1; d < scratch_stride; d *= 2) {
+        offset >>= 1;
+        // Synchronise
+        itemID.barrier(cl::sycl::access::fence_space::local_space);
+        if (local_id < d) {
+          Index ai = offset * (2 * local_id + 1) - 1 + scratch_offset;
+          Index bi = offset * (2 * local_id + 2) - 1 + scratch_offset;
+          CoeffReturnType accum = accumulator.initialize();
+          accumulator.reduce(scratch[ai], &accum);
+          accumulator.reduce(scratch[bi], &accum);
+          scratch[ai] = scratch[bi];
+          scratch[bi] = accumulator.finalize(accum);
+        }
+      }
+      // Synchronise
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      // This for loop must be 2
+      EIGEN_UNROLL_LOOP
+      for (int packetIndex = 0; packetIndex < ScanParameters<Index>::ScanPerThread; packetIndex += PacketSize) {
+        EIGEN_UNROLL_LOOP
+        for (Index i = 0; i < PacketSize; i++) {
+          CoeffReturnType accum = private_scan[packetIndex + i];
+          accumulator.reduce(scratch[2 * local_id + (packetIndex / PacketSize) + scratch_offset], &accum);
+          private_scan[packetIndex + i] = accumulator.finalize(accum);
+        }
+      }
+      first_step_inclusive_Operation([&]() EIGEN_DEVICE_FUNC {
+        if (inclusive) {
+          accumulator.reduce(private_scan[ScanParameters<Index>::ScanPerThread - 1], &inclusive_scan);
+          private_scan[0] = accumulator.finalize(inclusive_scan);
+        }
+      });
+      next_elements = 0;
+      // Write the first set of private params.
+      EIGEN_UNROLL_LOOP
+      for (Index i = 0; i < ScanParameters<Index>::ScanPerThread; i++) {
+        Index global_id = global_offset + next_elements;
+        if ((((block_id * scanParameters.elements_per_block) + (ScanParameters<Index>::ScanPerThread * local_id) + i) <
+             scanParameters.scan_size) &&
+            (global_id < scanParameters.total_size)) {
+          Index private_id = (i * !inclusive) + (((i + 1) % ScanParameters<Index>::ScanPerThread) * (inclusive));
+          out_ptr[global_id] = private_scan[private_id];
+        }
+        next_elements += scanParameters.scan_stride;
+      }
+    }  // end for loop
+  }
+};
+
+template <typename CoeffReturnType, typename InAccessor, typename OutAccessor, typename Op, typename Index>
+struct ScanAdjustmentKernelFunctor {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      LocalAccessor;
+  static constexpr int PacketSize = ScanParameters<Index>::ScanPerThread / 2;
+  InAccessor in_ptr;
+  OutAccessor out_ptr;
+  const ScanParameters<Index> scanParameters;
+  Op accumulator;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScanAdjustmentKernelFunctor(LocalAccessor, InAccessor in_accessor_,
+                                                                    OutAccessor out_accessor_,
+                                                                    const ScanParameters<Index> scanParameters_,
+                                                                    Op accumulator_)
+      : in_ptr(in_accessor_), out_ptr(out_accessor_), scanParameters(scanParameters_), accumulator(accumulator_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) const {
+    for (Index loop_offset = 0; loop_offset < scanParameters.loop_range; loop_offset++) {
+      Index data_offset = (itemID.get_global_id(0) + (itemID.get_global_range(0) * loop_offset));
+      Index tmp = data_offset % scanParameters.panel_threads;
+      const Index panel_id = data_offset / scanParameters.panel_threads;
+      const Index group_id = tmp / scanParameters.group_threads;
+      tmp = tmp % scanParameters.group_threads;
+      const Index block_id = tmp / scanParameters.block_threads;
+      const Index local_id = tmp % scanParameters.block_threads;
+
+      // the actual panel size is scan_size * non_scan_size.
+      // elements_per_panel is roundup to power of 2 for binary tree
+      const Index panel_offset = panel_id * scanParameters.scan_size * scanParameters.non_scan_size;
+      const Index group_offset = group_id * scanParameters.non_scan_stride;
+      // This will be effective when the size is bigger than elements_per_block
+      const Index block_offset = block_id * scanParameters.elements_per_block * scanParameters.scan_stride;
+      const Index thread_offset = ScanParameters<Index>::ScanPerThread * local_id * scanParameters.scan_stride;
+
+      const Index global_offset = panel_offset + group_offset + block_offset + thread_offset;
+      const Index block_size = scanParameters.elements_per_group / scanParameters.elements_per_block;
+      const Index in_id = (panel_id * block_size * scanParameters.non_scan_size) + (group_id * block_size) + block_id;
+      CoeffReturnType adjust_val = in_ptr[in_id];
+
+      Index next_elements = 0;
+      EIGEN_UNROLL_LOOP
+      for (Index i = 0; i < ScanParameters<Index>::ScanPerThread; i++) {
+        Index global_id = global_offset + next_elements;
+        if ((((block_id * scanParameters.elements_per_block) + (ScanParameters<Index>::ScanPerThread * local_id) + i) <
+             scanParameters.scan_size) &&
+            (global_id < scanParameters.total_size)) {
+          CoeffReturnType accum = adjust_val;
+          accumulator.reduce(out_ptr[global_id], &accum);
+          out_ptr[global_id] = accumulator.finalize(accum);
+        }
+        next_elements += scanParameters.scan_stride;
+      }
+    }
+  }
+};
+
+template <typename Index>
+struct ScanInfo {
+  const Index &total_size;
+  const Index &scan_size;
+  const Index &panel_size;
+  const Index &non_scan_size;
+  const Index &scan_stride;
+  const Index &non_scan_stride;
+
+  Index max_elements_per_block;
+  Index block_size;
+  Index panel_threads;
+  Index group_threads;
+  Index block_threads;
+  Index elements_per_group;
+  Index elements_per_block;
+  Index loop_range;
+  Index global_range;
+  Index local_range;
+  const Eigen::SyclDevice &dev;
+  EIGEN_STRONG_INLINE ScanInfo(const Index &total_size_, const Index &scan_size_, const Index &panel_size_,
+                               const Index &non_scan_size_, const Index &scan_stride_, const Index &non_scan_stride_,
+                               const Eigen::SyclDevice &dev_)
+      : total_size(total_size_),
+        scan_size(scan_size_),
+        panel_size(panel_size_),
+        non_scan_size(non_scan_size_),
+        scan_stride(scan_stride_),
+        non_scan_stride(non_scan_stride_),
+        dev(dev_) {
+    // must be power of 2
+    local_range = std::min(Index(dev.getNearestPowerOfTwoWorkGroupSize()),
+                           Index(EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1));
+
+    max_elements_per_block = local_range * ScanParameters<Index>::ScanPerThread;
+
+    elements_per_group =
+        dev.getPowerOfTwo(Index(roundUp(Index(scan_size), ScanParameters<Index>::ScanPerThread)), true);
+    const Index elements_per_panel = elements_per_group * non_scan_size;
+    elements_per_block = std::min(Index(elements_per_group), Index(max_elements_per_block));
+    panel_threads = elements_per_panel / ScanParameters<Index>::ScanPerThread;
+    group_threads = elements_per_group / ScanParameters<Index>::ScanPerThread;
+    block_threads = elements_per_block / ScanParameters<Index>::ScanPerThread;
+    block_size = elements_per_group / elements_per_block;
+#ifdef EIGEN_SYCL_MAX_GLOBAL_RANGE
+    const Index max_threads = std::min(Index(panel_threads * panel_size), Index(EIGEN_SYCL_MAX_GLOBAL_RANGE));
+#else
+    const Index max_threads = panel_threads * panel_size;
+#endif
+    global_range = roundUp(max_threads, local_range);
+    loop_range = Index(
+        std::ceil(double(elements_per_panel * panel_size) / (global_range * ScanParameters<Index>::ScanPerThread)));
+  }
+  inline ScanParameters<Index> get_scan_parameter() {
+    return ScanParameters<Index>(total_size, non_scan_size, scan_size, non_scan_stride, scan_stride, panel_threads,
+                                 group_threads, block_threads, elements_per_group, elements_per_block, loop_range);
+  }
+  inline cl::sycl::nd_range<1> get_thread_range() {
+    return cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
+  }
+};
+
+template <typename EvaluatorPointerType, typename CoeffReturnType, typename Reducer, typename Index>
+struct SYCLAdjustBlockOffset {
+  EIGEN_STRONG_INLINE static void adjust_scan_block_offset(EvaluatorPointerType in_ptr, EvaluatorPointerType out_ptr,
+                                                           Reducer &accumulator, const Index total_size,
+                                                           const Index scan_size, const Index panel_size,
+                                                           const Index non_scan_size, const Index scan_stride,
+                                                           const Index non_scan_stride, const Eigen::SyclDevice &dev) {
+    auto scan_info =
+        ScanInfo<Index>(total_size, scan_size, panel_size, non_scan_size, scan_stride, non_scan_stride, dev);
+
+    typedef ScanAdjustmentKernelFunctor<CoeffReturnType, EvaluatorPointerType, EvaluatorPointerType, Reducer, Index>
+        AdjustFuctor;
+    dev.template unary_kernel_launcher<CoeffReturnType, AdjustFuctor>(in_ptr, out_ptr, scan_info.get_thread_range(),
+                                                                      scan_info.max_elements_per_block,
+                                                                      scan_info.get_scan_parameter(), accumulator)
+        .wait();
+  }
+};
+
+template <typename CoeffReturnType, scan_step stp>
+struct ScanLauncher_impl {
+  template <typename Input, typename EvaluatorPointerType, typename Reducer, typename Index>
+  EIGEN_STRONG_INLINE static void scan_block(Input in_ptr, EvaluatorPointerType out_ptr, Reducer &accumulator,
+                                             const Index total_size, const Index scan_size, const Index panel_size,
+                                             const Index non_scan_size, const Index scan_stride,
+                                             const Index non_scan_stride, const bool inclusive,
+                                             const Eigen::SyclDevice &dev) {
+    auto scan_info =
+        ScanInfo<Index>(total_size, scan_size, panel_size, non_scan_size, scan_stride, non_scan_stride, dev);
+    const Index temp_pointer_size = scan_info.block_size * non_scan_size * panel_size;
+    const Index scratch_size = scan_info.max_elements_per_block / (ScanParameters<Index>::ScanPerThread / 2);
+    CoeffReturnType *temp_pointer =
+        static_cast<CoeffReturnType *>(dev.allocate_temp(temp_pointer_size * sizeof(CoeffReturnType)));
+    EvaluatorPointerType tmp_global_accessor = dev.get(temp_pointer);
+
+    typedef ScanKernelFunctor<Input, CoeffReturnType, EvaluatorPointerType, Reducer, Index, stp> ScanFunctor;
+    dev.template binary_kernel_launcher<CoeffReturnType, ScanFunctor>(
+           in_ptr, out_ptr, tmp_global_accessor, scan_info.get_thread_range(), scratch_size,
+           scan_info.get_scan_parameter(), accumulator, inclusive)
+        .wait();
+
+    if (scan_info.block_size > 1) {
+      ScanLauncher_impl<CoeffReturnType, scan_step::second>::scan_block(
+          tmp_global_accessor, tmp_global_accessor, accumulator, temp_pointer_size, scan_info.block_size, panel_size,
+          non_scan_size, Index(1), scan_info.block_size, false, dev);
+
+      SYCLAdjustBlockOffset<EvaluatorPointerType, CoeffReturnType, Reducer, Index>::adjust_scan_block_offset(
+          tmp_global_accessor, out_ptr, accumulator, total_size, scan_size, panel_size, non_scan_size, scan_stride,
+          non_scan_stride, dev);
+    }
+    dev.deallocate_temp(temp_pointer);
+  }
+};
+
+}  // namespace internal
+}  // namespace TensorSycl
+namespace internal {
+template <typename Self, typename Reducer, bool vectorize>
+struct ScanLauncher<Self, Reducer, Eigen::SyclDevice, vectorize> {
+  typedef typename Self::Index Index;
+  typedef typename Self::CoeffReturnType CoeffReturnType;
+  typedef typename Self::Storage Storage;
+  typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
+  void operator()(Self &self, EvaluatorPointerType data) const {
+    const Index total_size = internal::array_prod(self.dimensions());
+    const Index scan_size = self.size();
+    const Index scan_stride = self.stride();
+    // this is the scan op (can be sum or ...)
+    auto accumulator = self.accumulator();
+    auto inclusive = !self.exclusive();
+    auto consume_dim = self.consume_dim();
+    auto dev = self.device();
+
+    auto dims = self.inner().dimensions();
+
+    Index non_scan_size = 1;
+    Index panel_size = 1;
+    if (static_cast<int>(Self::Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < consume_dim; i++) {
+        non_scan_size *= dims[i];
+      }
+      for (int i = consume_dim + 1; i < Self::NumDims; i++) {
+        panel_size *= dims[i];
+      }
+    } else {
+      for (int i = Self::NumDims - 1; i > consume_dim; i--) {
+        non_scan_size *= dims[i];
+      }
+      for (int i = consume_dim - 1; i >= 0; i--) {
+        panel_size *= dims[i];
+      }
+    }
+    const Index non_scan_stride = (scan_stride > 1) ? 1 : scan_size;
+    auto eval_impl = self.inner();
+    TensorSycl::internal::ScanLauncher_impl<CoeffReturnType, TensorSycl::internal::scan_step::first>::scan_block(
+        eval_impl, data, accumulator, total_size, scan_size, panel_size, non_scan_size, scan_stride, non_scan_stride,
+        inclusive, dev);
+  }
+};
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_SYCL_SYCL_HPP
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h
new file mode 100644
index 0000000..51f424e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h
@@ -0,0 +1,413 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Shuffle, typename XprType>
+struct traits<TensorShufflingOp<Shuffle, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename Shuffle, typename XprType>
+struct eval<TensorShufflingOp<Shuffle, XprType>, Eigen::Dense> {
+  typedef const TensorShufflingOp<Shuffle, XprType>& type;
+};
+
+template <typename Shuffle, typename XprType>
+struct nested<TensorShufflingOp<Shuffle, XprType>, 1, typename eval<TensorShufflingOp<Shuffle, XprType> >::type> {
+  typedef TensorShufflingOp<Shuffle, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor shuffling class.
+ */
+template <typename Shuffle, typename XprType>
+class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> > {
+ public:
+  typedef TensorBase<TensorShufflingOp<Shuffle, XprType> > Base;
+  typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shfl)
+      : m_xpr(expr), m_shuffle(shfl) {}
+
+  EIGEN_DEVICE_FUNC const Shuffle& shufflePermutation() const { return m_shuffle; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorShufflingOp)
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const Shuffle m_shuffle;
+};
+
+// Eval as rvalue
+template <typename Shuffle, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> {
+  typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Self;
+  typedef TensorShufflingOp<Shuffle, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess,
+    PreferBlockAccess = true,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, Layout, Index> TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_device(device), m_impl(op.expression(), device) {
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    const Shuffle& shuffle = op.shufflePermutation();
+    m_is_identity = true;
+    for (int i = 0; i < NumDims; ++i) {
+      m_shuffle[i] = static_cast<int>(shuffle[i]);
+      m_dimensions[i] = input_dims[shuffle[i]];
+      m_inverseShuffle[shuffle[i]] = i;
+      if (m_is_identity && shuffle[i] != i) {
+        m_is_identity = false;
+      }
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_unshuffledInputStrides[0] = 1;
+      m_outputStrides[0] = 1;
+
+      for (int i = 1; i < NumDims; ++i) {
+        m_unshuffledInputStrides[i] = m_unshuffledInputStrides[i - 1] * input_dims[i - 1];
+        m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+        m_fastOutputStrides[i] =
+            internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1));
+      }
+    } else {
+      m_unshuffledInputStrides[NumDims - 1] = 1;
+      m_outputStrides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_unshuffledInputStrides[i] = m_unshuffledInputStrides[i + 1] * input_dims[i + 1];
+        m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+        m_fastOutputStrides[i] =
+            internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1));
+      }
+    }
+
+    for (int i = 0; i < NumDims; ++i) {
+      m_inputStrides[i] = m_unshuffledInputStrides[shuffle[i]];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    if (m_is_identity) {
+      return m_impl.coeff(index);
+    } else {
+      return m_impl.coeff(srcCoeff(index));
+    }
+  }
+
+  template <int LoadMode, typename Self, bool ImplPacketAccess>
+  struct PacketLoader {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType Run(const Self& self, Index index) {
+      EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < PacketSize; ++i) {
+        values[i] = self.coeff(index + i);
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  };
+
+  template <int LoadMode, typename Self>
+  struct PacketLoader<LoadMode, Self, true> {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType Run(const Self& self, Index index) {
+      if (self.m_is_identity) {
+        return self.m_impl.template packet<LoadMode>(index);
+      } else {
+        EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+        EIGEN_UNROLL_LOOP
+        for (int i = 0; i < PacketSize; ++i) {
+          values[i] = self.coeff(index + i);
+        }
+        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+        return rslt;
+      }
+    }
+  };
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+    return PacketLoader<LoadMode, Self, TensorEvaluator<ArgType, Device>::PacketAccess>::Run(*this, index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    static const int inner_dim = Layout == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+
+    const size_t target_size = m_device.firstLevelCacheSize();
+    const bool inner_dim_shuffled = m_shuffle[inner_dim] != inner_dim;
+
+    // Shuffled inner dimensions leads to a random memory access, which is not
+    // captured by default cost model bytes loaded/stored. We add this cost
+    // explicitly. The number of cycles picked based on the benchmarks.
+    // TODO(ezhulenev): This number was picked based on a very questionable
+    // benchmarks, add benchmarks that are representative of real workloads.
+    using BlockRequirements = internal::TensorBlockResourceRequirements;
+    if (inner_dim_shuffled) {
+      return BlockRequirements::uniform<Scalar>(target_size).addCostPerCoeff({0, 0, NumDims * 28});
+    } else {
+      return BlockRequirements::skewed<Scalar>(target_size);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+                                                          bool root_of_expr_ast = false) const {
+    eigen_assert(m_impl.data() != NULL);
+
+    typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout> TensorBlockIO;
+    typedef typename TensorBlockIO::Dst TensorBlockIODst;
+    typedef typename TensorBlockIO::Src TensorBlockIOSrc;
+
+    const typename TensorBlock::Storage block_storage =
+        TensorBlock::prepareStorage(desc, scratch, /*allow_strided_storage=*/root_of_expr_ast);
+
+    typename TensorBlockIO::Dimensions input_strides(m_unshuffledInputStrides);
+    TensorBlockIOSrc src(input_strides, m_impl.data(), srcCoeff(desc.offset()));
+
+    TensorBlockIODst dst(block_storage.dimensions(), block_storage.strides(), block_storage.data());
+
+    typename TensorBlockIO::DimensionsMap dst_to_src_dim_map(m_shuffle);
+    TensorBlockIO::Copy(dst, src, dst_to_src_dim_map);
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double compute_cost = m_is_identity
+                                    ? TensorOpCost::AddCost<Index>()
+                                    : NumDims * (2 * TensorOpCost::AddCost<Index>() +
+                                                 2 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>());
+    return m_impl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, compute_cost, m_is_identity /* vectorized */, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index
+  GetBlockOutputIndex(Index input_index, const DSizes<Index, NumDims>& input_block_strides,
+                      const DSizes<Index, NumDims>& output_block_strides,
+                      const DSizes<internal::TensorIntDivisor<Index>, NumDims>& fast_input_block_strides) const {
+    Index output_index = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = input_index / fast_input_block_strides[i];
+        output_index += idx * output_block_strides[m_inverseShuffle[i]];
+        input_index -= idx * input_block_strides[i];
+      }
+      return output_index + input_index * output_block_strides[m_inverseShuffle[0]];
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = input_index / fast_input_block_strides[i];
+        output_index += idx * output_block_strides[m_inverseShuffle[i]];
+        input_index -= idx * input_block_strides[i];
+      }
+      return output_index + input_index * output_block_strides[m_inverseShuffle[NumDims - 1]];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += idx * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      return inputIndex + index * m_inputStrides[0];
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += idx * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      return inputIndex + index * m_inputStrides[NumDims - 1];
+    }
+  }
+
+  Dimensions m_dimensions;
+  bool m_is_identity;
+  array<int, NumDims> m_shuffle;
+  array<Index, NumDims> m_inverseShuffle;  // TODO(ezhulenev): Make it int type.
+  array<Index, NumDims> m_outputStrides;
+  array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
+  array<Index, NumDims> m_inputStrides;
+  array<Index, NumDims> m_unshuffledInputStrides;
+
+  const Device EIGEN_DEVICE_REF m_device;
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+// Eval as lvalue
+template <typename Shuffle, typename ArgType, typename Device>
+struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device>
+    : public TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> {
+  typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Base;
+
+  typedef TensorShufflingOp<Shuffle, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess,
+    PreferBlockAccess = true,
+    RawAccess = false
+  };
+
+  typedef std::remove_const_t<Scalar> ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
+    return this->m_impl.coeffRef(this->srcCoeff(index));
+  }
+
+  template <int StoreMode>
+  EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+    internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      this->coeffRef(index + i) = values[i];
+    }
+  }
+
+  template <typename TensorBlock>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(const TensorBlockDesc& desc, const TensorBlock& block) {
+    eigen_assert(this->m_impl.data() != NULL);
+
+    typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout> TensorBlockIO;
+    typedef typename TensorBlockIO::Dst TensorBlockIODst;
+    typedef typename TensorBlockIO::Src TensorBlockIOSrc;
+
+    const Scalar* block_buffer = block.data();
+
+    // TODO(ezhulenev): TensorBlockIO should be able to read from any Eigen
+    // expression with coefficient and packet access as `src`.
+    void* mem = NULL;
+    if (block_buffer == NULL) {
+      mem = this->m_device.allocate(desc.size() * sizeof(Scalar));
+      ScalarNoConst* buf = static_cast<ScalarNoConst*>(mem);
+
+      typedef internal::TensorBlockAssignment<ScalarNoConst, NumDims, typename TensorBlock::XprType, Index>
+          TensorBlockAssignment;
+
+      TensorBlockAssignment::Run(
+          TensorBlockAssignment::target(desc.dimensions(), internal::strides<Layout>(desc.dimensions()), buf),
+          block.expr());
+
+      block_buffer = buf;
+    }
+
+    // Read from block.
+    TensorBlockIOSrc src(internal::strides<Layout>(desc.dimensions()), block_buffer);
+
+    // Write to the output buffer.
+    typename TensorBlockIO::Dimensions output_strides(this->m_unshuffledInputStrides);
+    typename TensorBlockIO::Dimensions output_dimensions;
+    for (int i = 0; i < NumDims; ++i) {
+      output_dimensions[this->m_shuffle[i]] = desc.dimension(i);
+    }
+    TensorBlockIODst dst(output_dimensions, output_strides, this->m_impl.data(), this->srcCoeff(desc.offset()));
+
+    // Reorder dimensions according to the shuffle.
+    typename TensorBlockIO::DimensionsMap dst_to_src_dim_map;
+    for (int i = 0; i < NumDims; ++i) {
+      dst_to_src_dim_map[i] = static_cast<int>(this->m_inverseShuffle[i]);
+    }
+    TensorBlockIO::Copy(dst, src, dst_to_src_dim_map);
+
+    // Deallocate temporary buffer used for the block materialization.
+    if (mem != NULL) this->m_device.deallocate(mem);
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h
new file mode 100644
index 0000000..62686ce
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h
@@ -0,0 +1,143 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+// Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSORSTORAGE_H
+#define EIGEN_CXX11_TENSOR_TENSORSTORAGE_H
+
+#ifdef EIGEN_TENSOR_STORAGE_CTOR_PLUGIN
+#define EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN EIGEN_TENSOR_STORAGE_CTOR_PLUGIN;
+#else
+#define EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN
+#endif
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \internal
+ *
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Stores the data of a tensor
+ *
+ * This class stores the data of fixed-size, dynamic-size or mixed tensors
+ * in a way as compact as possible.
+ *
+ * \sa Tensor
+ */
+template <typename T, typename Dimensions, int Options>
+class TensorStorage;
+
+// Pure fixed-size storage
+template <typename T, typename FixedDimensions, int Options_>
+class TensorStorage {
+ private:
+  static constexpr std::size_t Size = FixedDimensions::total_size;
+
+  // Allocate an array of size at least one to prevent compiler warnings.
+  static constexpr std::size_t MinSize = max_n_1<Size>::size;
+  EIGEN_ALIGN_MAX T m_data[MinSize];
+
+ public:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStorage() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T* data() { return m_data; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T* data() const { return m_data; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const FixedDimensions dimensions() const { return FixedDimensions(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex size() const { return Size; }
+};
+
+// pure dynamic
+template <typename T, typename IndexType, int NumIndices_, int Options_>
+class TensorStorage<T, DSizes<IndexType, NumIndices_>, Options_> {
+ public:
+  typedef IndexType Index;
+  typedef DSizes<IndexType, NumIndices_> Dimensions;
+  typedef TensorStorage<T, DSizes<IndexType, NumIndices_>, Options_> Self;
+
+  EIGEN_DEVICE_FUNC TensorStorage() : m_data(0), m_dimensions() {
+    if (NumIndices_ == 0) {
+      m_data = internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(1);
+    }
+  }
+  EIGEN_DEVICE_FUNC TensorStorage(Index size, const array<Index, NumIndices_>& dimensions)
+      : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size)), m_dimensions(dimensions) {
+    EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN
+  }
+
+  template <typename... DenseIndex>
+  EIGEN_DEVICE_FUNC TensorStorage(DenseIndex... indices) : m_dimensions(indices...) {
+    m_data = internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(internal::array_prod(m_dimensions));
+  }
+
+  EIGEN_DEVICE_FUNC TensorStorage(const Self& other)
+      : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(
+            internal::array_prod(other.m_dimensions))),
+        m_dimensions(other.m_dimensions) {
+    internal::smart_copy(other.m_data, other.m_data + internal::array_prod(other.m_dimensions), m_data);
+  }
+  EIGEN_DEVICE_FUNC Self& operator=(const Self& other) {
+    if (this != &other) {
+      Self tmp(other);
+      this->swap(tmp);
+    }
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC TensorStorage(Self&& other) : TensorStorage() { *this = std::move(other); }
+
+  EIGEN_DEVICE_FUNC Self& operator=(Self&& other) {
+    numext::swap(m_data, other.m_data);
+    numext::swap(m_dimensions, other.m_dimensions);
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC ~TensorStorage() {
+    internal::conditional_aligned_delete_auto<T, (Options_ & DontAlign) == 0>(m_data,
+                                                                              internal::array_prod(m_dimensions));
+  }
+  EIGEN_DEVICE_FUNC void swap(Self& other) {
+    numext::swap(m_data, other.m_data);
+    numext::swap(m_dimensions, other.m_dimensions);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_DEVICE_FUNC void resize(Index size, const array<Index, NumIndices_>& nbDimensions) {
+    const Index currentSz = internal::array_prod(m_dimensions);
+    if (size != currentSz) {
+      internal::conditional_aligned_delete_auto<T, (Options_ & DontAlign) == 0>(m_data, currentSz);
+      if (size)
+        m_data = internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size);
+      else if (NumIndices_ == 0) {
+        m_data = internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(1);
+      } else
+        m_data = 0;
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+    }
+    m_dimensions = nbDimensions;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T* data() { return m_data; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T* data() const { return m_data; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
+
+ private:
+  T* m_data;
+  Dimensions m_dimensions;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSORSTORAGE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h
new file mode 100644
index 0000000..eee7c9b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h
@@ -0,0 +1,312 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Strides, typename XprType>
+struct traits<TensorStridingOp<Strides, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <typename Strides, typename XprType>
+struct eval<TensorStridingOp<Strides, XprType>, Eigen::Dense> {
+  typedef const TensorStridingOp<Strides, XprType> EIGEN_DEVICE_REF type;
+};
+
+template <typename Strides, typename XprType>
+struct nested<TensorStridingOp<Strides, XprType>, 1, typename eval<TensorStridingOp<Strides, XprType> >::type> {
+  typedef TensorStridingOp<Strides, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor striding class.
+ */
+template <typename Strides, typename XprType>
+class TensorStridingOp : public TensorBase<TensorStridingOp<Strides, XprType> > {
+ public:
+  typedef TensorBase<TensorStridingOp<Strides, XprType> > Base;
+  typedef typename Eigen::internal::traits<TensorStridingOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorStridingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorStridingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorStridingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingOp(const XprType& expr, const Strides& dims)
+      : m_xpr(expr), m_dims(dims) {}
+
+  EIGEN_DEVICE_FUNC const Strides& strides() const { return m_dims; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorStridingOp)
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const Strides m_dims;
+};
+
+// Eval as rvalue
+template <typename Strides, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device> {
+  typedef TensorStridingOp<Strides, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_impl(op.expression(), device) {
+    m_dimensions = m_impl.dimensions();
+    for (int i = 0; i < NumDims; ++i) {
+      m_dimensions[i] = Eigen::numext::ceil(static_cast<float>(m_dimensions[i]) / op.strides()[i]);
+    }
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_outputStrides[0] = 1;
+      m_inputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+        m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1];
+        m_inputStrides[i - 1] *= op.strides()[i - 1];
+      }
+      m_inputStrides[NumDims - 1] *= op.strides()[NumDims - 1];
+    } else {  // RowMajor
+      m_outputStrides[NumDims - 1] = 1;
+      m_inputStrides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+        m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1];
+        m_inputStrides[i + 1] *= op.strides()[i + 1];
+      }
+      m_inputStrides[0] *= op.strides()[0];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_impl.coeff(srcCoeff(index));
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    Index inputIndices[] = {0, 0};
+    Index indices[] = {index, index + PacketSize - 1};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx0 = indices[0] / m_outputStrides[i];
+        const Index idx1 = indices[1] / m_outputStrides[i];
+        inputIndices[0] += idx0 * m_inputStrides[i];
+        inputIndices[1] += idx1 * m_inputStrides[i];
+        indices[0] -= idx0 * m_outputStrides[i];
+        indices[1] -= idx1 * m_outputStrides[i];
+      }
+      inputIndices[0] += indices[0] * m_inputStrides[0];
+      inputIndices[1] += indices[1] * m_inputStrides[0];
+    } else {  // RowMajor
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx0 = indices[0] / m_outputStrides[i];
+        const Index idx1 = indices[1] / m_outputStrides[i];
+        inputIndices[0] += idx0 * m_inputStrides[i];
+        inputIndices[1] += idx1 * m_inputStrides[i];
+        indices[0] -= idx0 * m_outputStrides[i];
+        indices[1] -= idx1 * m_outputStrides[i];
+      }
+      inputIndices[0] += indices[0] * m_inputStrides[NumDims - 1];
+      inputIndices[1] += indices[1] * m_inputStrides[NumDims - 1];
+    }
+    if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
+      PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
+      return rslt;
+    } else {
+      EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+      values[0] = m_impl.coeff(inputIndices[0]);
+      values[PacketSize - 1] = m_impl.coeff(inputIndices[1]);
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < PacketSize - 1; ++i) {
+        values[i] = coeff(index + i);
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    double compute_cost = (NumDims - 1) * (TensorOpCost::AddCost<Index>() + TensorOpCost::MulCost<Index>() +
+                                           TensorOpCost::DivCost<Index>()) +
+                          TensorOpCost::MulCost<Index>();
+    if (vectorized) {
+      compute_cost *= 2;  // packet() computes two indices
+    }
+    const int innerDim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : (NumDims - 1);
+    return m_impl.costPerCoeff(vectorized && m_inputStrides[innerDim] == 1) +
+           // Computation is not vectorized per se, but it is done once per packet.
+           TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStrides[i];
+        inputIndex += idx * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      inputIndex += index * m_inputStrides[0];
+    } else {  // RowMajor
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_outputStrides[i];
+        inputIndex += idx * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      inputIndex += index * m_inputStrides[NumDims - 1];
+    }
+    return inputIndex;
+  }
+
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims> m_inputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+// Eval as lvalue
+template <typename Strides, typename ArgType, typename Device>
+struct TensorEvaluator<TensorStridingOp<Strides, ArgType>, Device>
+    : public TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device> {
+  typedef TensorStridingOp<Strides, ArgType> XprType;
+  typedef TensorEvaluator<const XprType, Device> Base;
+  static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    PreferBlockAccess = false,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) const {
+    return this->m_impl.coeffRef(this->srcCoeff(index));
+  }
+
+  template <int StoreMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index + PacketSize - 1 < this->dimensions().TotalSize());
+
+    Index inputIndices[] = {0, 0};
+    Index indices[] = {index, index + PacketSize - 1};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx0 = indices[0] / this->m_outputStrides[i];
+        const Index idx1 = indices[1] / this->m_outputStrides[i];
+        inputIndices[0] += idx0 * this->m_inputStrides[i];
+        inputIndices[1] += idx1 * this->m_inputStrides[i];
+        indices[0] -= idx0 * this->m_outputStrides[i];
+        indices[1] -= idx1 * this->m_outputStrides[i];
+      }
+      inputIndices[0] += indices[0] * this->m_inputStrides[0];
+      inputIndices[1] += indices[1] * this->m_inputStrides[0];
+    } else {  // RowMajor
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx0 = indices[0] / this->m_outputStrides[i];
+        const Index idx1 = indices[1] / this->m_outputStrides[i];
+        inputIndices[0] += idx0 * this->m_inputStrides[i];
+        inputIndices[1] += idx1 * this->m_inputStrides[i];
+        indices[0] -= idx0 * this->m_outputStrides[i];
+        indices[1] -= idx1 * this->m_outputStrides[i];
+      }
+      inputIndices[0] += indices[0] * this->m_inputStrides[NumDims - 1];
+      inputIndices[1] += indices[1] * this->m_inputStrides[NumDims - 1];
+    }
+    if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
+      this->m_impl.template writePacket<Unaligned>(inputIndices[0], x);
+    } else {
+      EIGEN_ALIGN_MAX Scalar values[PacketSize];
+      internal::pstore<Scalar, PacketReturnType>(values, x);
+      this->m_impl.coeffRef(inputIndices[0]) = values[0];
+      this->m_impl.coeffRef(inputIndices[1]) = values[PacketSize - 1];
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < PacketSize - 1; ++i) {
+        this->coeffRef(index + i) = values[i];
+      }
+    }
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h
new file mode 100644
index 0000000..5357a48
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h
@@ -0,0 +1,281 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gagan Goel <gagan.nith@gmail.com>
+// Copyright (C) 2017 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_TRACE_H
+#define EIGEN_CXX11_TENSOR_TENSOR_TRACE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Dims, typename XprType>
+struct traits<TensorTraceOp<Dims, XprType> > : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
+  static constexpr int Layout = XprTraits::Layout;
+  enum {
+    // Trace is read-only.
+    Flags = traits<XprType>::Flags & ~LvalueBit
+  };
+};
+
+template <typename Dims, typename XprType>
+struct eval<TensorTraceOp<Dims, XprType>, Eigen::Dense> {
+  typedef const TensorTraceOp<Dims, XprType>& type;
+};
+
+template <typename Dims, typename XprType>
+struct nested<TensorTraceOp<Dims, XprType>, 1, typename eval<TensorTraceOp<Dims, XprType> >::type> {
+  typedef TensorTraceOp<Dims, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor Trace class.
+ */
+template <typename Dims, typename XprType>
+class TensorTraceOp : public TensorBase<TensorTraceOp<Dims, XprType> > {
+ public:
+  typedef typename Eigen::internal::traits<TensorTraceOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorTraceOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorTraceOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorTraceOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorTraceOp(const XprType& expr, const Dims& dims)
+      : m_xpr(expr), m_dims(dims) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dims& dims() const { return m_dims; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<typename XprType::Nested>& expression() const {
+    return m_xpr;
+  }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const Dims m_dims;
+};
+
+// Eval as rvalue
+template <typename Dims, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorTraceOp<Dims, ArgType>, Device> {
+  typedef TensorTraceOp<Dims, ArgType> XprType;
+  static constexpr int NumInputDims =
+      internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static constexpr int NumReducedDims = internal::array_size<Dims>::value;
+  static constexpr int NumOutputDims = NumInputDims - NumReducedDims;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumOutputDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    CoordAccess = false,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_traceDim(1), m_device(device) {
+    EIGEN_STATIC_ASSERT((NumOutputDims >= 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT((NumReducedDims >= 2) || ((NumReducedDims == 0) && (NumInputDims == 0)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    for (int i = 0; i < NumInputDims; ++i) {
+      m_reduced[i] = false;
+    }
+
+    const Dims& op_dims = op.dims();
+    for (int i = 0; i < NumReducedDims; ++i) {
+      eigen_assert(op_dims[i] >= 0);
+      eigen_assert(op_dims[i] < NumInputDims);
+      m_reduced[op_dims[i]] = true;
+    }
+
+    // All the dimensions should be distinct to compute the trace
+    int num_distinct_reduce_dims = 0;
+    for (int i = 0; i < NumInputDims; ++i) {
+      if (m_reduced[i]) {
+        ++num_distinct_reduce_dims;
+      }
+    }
+
+    EIGEN_ONLY_USED_FOR_DEBUG(num_distinct_reduce_dims);
+    eigen_assert(num_distinct_reduce_dims == NumReducedDims);
+
+    // Compute the dimensions of the result.
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+
+    int output_index = 0;
+    int reduced_index = 0;
+    for (int i = 0; i < NumInputDims; ++i) {
+      if (m_reduced[i]) {
+        m_reducedDims[reduced_index] = input_dims[i];
+        if (reduced_index > 0) {
+          // All the trace dimensions must have the same size
+          eigen_assert(m_reducedDims[0] == m_reducedDims[reduced_index]);
+        }
+        ++reduced_index;
+      } else {
+        m_dimensions[output_index] = input_dims[i];
+        ++output_index;
+      }
+    }
+
+    if (NumReducedDims != 0) {
+      m_traceDim = m_reducedDims[0];
+    }
+
+    // Compute the output strides
+    if (NumOutputDims > 0) {
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        m_outputStrides[0] = 1;
+        for (int i = 1; i < NumOutputDims; ++i) {
+          m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+        }
+      } else {
+        m_outputStrides.back() = 1;
+        for (int i = NumOutputDims - 2; i >= 0; --i) {
+          m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+        }
+      }
+    }
+
+    // Compute the input strides
+    if (NumInputDims > 0) {
+      array<Index, NumInputDims> input_strides;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        input_strides[0] = 1;
+        for (int i = 1; i < NumInputDims; ++i) {
+          input_strides[i] = input_strides[i - 1] * input_dims[i - 1];
+        }
+      } else {
+        input_strides.back() = 1;
+        for (int i = NumInputDims - 2; i >= 0; --i) {
+          input_strides[i] = input_strides[i + 1] * input_dims[i + 1];
+        }
+      }
+
+      output_index = 0;
+      reduced_index = 0;
+      for (int i = 0; i < NumInputDims; ++i) {
+        if (m_reduced[i]) {
+          m_reducedStrides[reduced_index] = input_strides[i];
+          ++reduced_index;
+        } else {
+          m_preservedStrides[output_index] = input_strides[i];
+          ++output_index;
+        }
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return nullptr; }
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    // Initialize the result
+    CoeffReturnType result = internal::cast<int, CoeffReturnType>(0);
+    Index index_stride = 0;
+    for (int i = 0; i < NumReducedDims; ++i) {
+      index_stride += m_reducedStrides[i];
+    }
+
+    // If trace is requested along all dimensions, starting index would be 0
+    Index cur_index = 0;
+    if (NumOutputDims != 0) cur_index = firstInput(index);
+    for (Index i = 0; i < m_traceDim; ++i) {
+      result += m_impl.coeff(cur_index);
+      cur_index += index_stride;
+    }
+
+    return result;
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index + i);
+    }
+    PacketReturnType result = internal::ploadt<PacketReturnType, LoadMode>(values);
+    return result;
+  }
+
+ protected:
+  // Given the output index, finds the first index in the input tensor used to compute the trace
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
+    Index startInput = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumOutputDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStrides[i];
+        startInput += idx * m_preservedStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      startInput += index * m_preservedStrides[0];
+    } else {
+      for (int i = 0; i < NumOutputDims - 1; ++i) {
+        const Index idx = index / m_outputStrides[i];
+        startInput += idx * m_preservedStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      startInput += index * m_preservedStrides[NumOutputDims - 1];
+    }
+    return startInput;
+  }
+
+  Dimensions m_dimensions;
+  TensorEvaluator<ArgType, Device> m_impl;
+  // Initialize the size of the trace dimension
+  Index m_traceDim;
+  const Device EIGEN_DEVICE_REF m_device;
+  array<bool, NumInputDims> m_reduced;
+  array<Index, NumReducedDims> m_reducedDims;
+  array<Index, NumOutputDims> m_outputStrides;
+  array<Index, NumReducedDims> m_reducedStrides;
+  array<Index, NumOutputDims> m_preservedStrides;
+};
+
+}  // End namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_TRACE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h
new file mode 100644
index 0000000..5c97d52
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h
@@ -0,0 +1,232 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <typename Scalar, int Options>
+class compute_tensor_flags {
+  enum {
+    is_dynamic_size_storage = 1,
+
+    is_aligned = (((Options & DontAlign) == 0) && (
+#if EIGEN_MAX_STATIC_ALIGN_BYTES > 0
+                                                      (!is_dynamic_size_storage)
+#else
+                                                      0
+#endif
+                                                      |
+#if EIGEN_MAX_ALIGN_BYTES > 0
+                                                      is_dynamic_size_storage
+#else
+                                                      0
+#endif
+                                                      )),
+    packet_access_bit = packet_traits<Scalar>::Vectorizable && is_aligned ? PacketAccessBit : 0
+  };
+
+ public:
+  enum { ret = packet_access_bit };
+};
+
+template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
+struct traits<Tensor<Scalar_, NumIndices_, Options_, IndexType_> > {
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef IndexType_ Index;
+  static constexpr int NumDimensions = NumIndices_;
+  static constexpr int Layout = Options_ & RowMajor ? RowMajor : ColMajor;
+  enum {
+    Options = Options_,
+    Flags = compute_tensor_flags<Scalar_, Options_>::ret | (is_const<Scalar_>::value ? 0 : LvalueBit)
+  };
+  template <typename T>
+  struct MakePointer {
+    typedef T* Type;
+  };
+  typedef typename MakePointer<Scalar>::Type PointerType;
+};
+
+template <typename Scalar_, typename Dimensions, int Options_, typename IndexType_>
+struct traits<TensorFixedSize<Scalar_, Dimensions, Options_, IndexType_> > {
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef IndexType_ Index;
+  static constexpr int NumDimensions = array_size<Dimensions>::value;
+  static constexpr int Layout = Options_ & RowMajor ? RowMajor : ColMajor;
+  enum {
+    Options = Options_,
+    Flags = compute_tensor_flags<Scalar_, Options_>::ret | (is_const<Scalar_>::value ? 0 : LvalueBit)
+  };
+  template <typename T>
+  struct MakePointer {
+    typedef T* Type;
+  };
+  typedef typename MakePointer<Scalar>::Type PointerType;
+};
+
+template <typename PlainObjectType, int Options_, template <class> class MakePointer_>
+struct traits<TensorMap<PlainObjectType, Options_, MakePointer_> > : public traits<PlainObjectType> {
+  typedef traits<PlainObjectType> BaseTraits;
+  typedef typename BaseTraits::Scalar Scalar;
+  typedef typename BaseTraits::StorageKind StorageKind;
+  typedef typename BaseTraits::Index Index;
+  static constexpr int NumDimensions = BaseTraits::NumDimensions;
+  static constexpr int Layout = BaseTraits::Layout;
+  enum { Options = Options_, Flags = BaseTraits::Flags };
+  template <class T>
+  struct MakePointer {
+    // Intermediate typedef to workaround MSVC issue.
+    typedef MakePointer_<T> MakePointerT;
+    typedef typename MakePointerT::Type Type;
+  };
+  typedef typename MakePointer<Scalar>::Type PointerType;
+};
+
+template <typename PlainObjectType_>
+struct traits<TensorRef<PlainObjectType_> > : public traits<PlainObjectType_> {
+  typedef PlainObjectType_ PlainObjectType;
+  typedef traits<PlainObjectType> BaseTraits;
+  typedef typename BaseTraits::Scalar Scalar;
+  typedef typename BaseTraits::StorageKind StorageKind;
+  typedef typename BaseTraits::Index Index;
+  static constexpr int NumDimensions = BaseTraits::NumDimensions;
+  static constexpr int Layout = BaseTraits::Layout;
+  enum { Options = BaseTraits::Options, Flags = BaseTraits::Flags };
+  typedef typename BaseTraits::PointerType PointerType;
+};
+
+template <typename Scalar_, int NumIndices_, int Options, typename IndexType_>
+struct eval<Tensor<Scalar_, NumIndices_, Options, IndexType_>, Eigen::Dense> {
+  typedef const Tensor<Scalar_, NumIndices_, Options, IndexType_> EIGEN_DEVICE_REF type;
+};
+
+template <typename Scalar_, int NumIndices_, int Options, typename IndexType_>
+struct eval<const Tensor<Scalar_, NumIndices_, Options, IndexType_>, Eigen::Dense> {
+  typedef const Tensor<Scalar_, NumIndices_, Options, IndexType_> EIGEN_DEVICE_REF type;
+};
+
+template <typename Scalar_, typename Dimensions, int Options, typename IndexType_>
+struct eval<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense> {
+  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> EIGEN_DEVICE_REF type;
+};
+
+template <typename Scalar_, typename Dimensions, int Options, typename IndexType_>
+struct eval<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense> {
+  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> EIGEN_DEVICE_REF type;
+};
+
+template <typename PlainObjectType, int Options, template <class> class MakePointer>
+struct eval<TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense> {
+  typedef const TensorMap<PlainObjectType, Options, MakePointer> EIGEN_DEVICE_REF type;
+};
+
+template <typename PlainObjectType, int Options, template <class> class MakePointer>
+struct eval<const TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense> {
+  typedef const TensorMap<PlainObjectType, Options, MakePointer> EIGEN_DEVICE_REF type;
+};
+
+template <typename PlainObjectType>
+struct eval<TensorRef<PlainObjectType>, Eigen::Dense> {
+  typedef const TensorRef<PlainObjectType> EIGEN_DEVICE_REF type;
+};
+
+template <typename PlainObjectType>
+struct eval<const TensorRef<PlainObjectType>, Eigen::Dense> {
+  typedef const TensorRef<PlainObjectType> EIGEN_DEVICE_REF type;
+};
+
+// TODO: nested<> does not exist anymore in Eigen/Core, and should be removed in favor of ref_selector.
+template <typename T, int n = 1, typename PlainObject = void>
+struct nested {
+  typedef typename ref_selector<T>::type type;
+};
+
+template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
+struct nested<Tensor<Scalar_, NumIndices_, Options_, IndexType_> > {
+  typedef const Tensor<Scalar_, NumIndices_, Options_, IndexType_> EIGEN_DEVICE_REF type;
+};
+
+template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
+struct nested<const Tensor<Scalar_, NumIndices_, Options_, IndexType_> > {
+  typedef const Tensor<Scalar_, NumIndices_, Options_, IndexType_> EIGEN_DEVICE_REF type;
+};
+
+template <typename Scalar_, typename Dimensions, int Options, typename IndexType_>
+struct nested<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> > {
+  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> EIGEN_DEVICE_REF type;
+};
+
+template <typename Scalar_, typename Dimensions, int Options, typename IndexType_>
+struct nested<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> > {
+  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> EIGEN_DEVICE_REF type;
+};
+
+template <typename PlainObjectType>
+struct nested<TensorRef<PlainObjectType> > {
+  typedef const TensorRef<PlainObjectType> EIGEN_DEVICE_REF type;
+};
+
+template <typename PlainObjectType>
+struct nested<const TensorRef<PlainObjectType> > {
+  typedef const TensorRef<PlainObjectType> EIGEN_DEVICE_REF type;
+};
+
+}  // end namespace internal
+
+// Convolutional layers take in an input tensor of shape (D, R, C, B), or (D, C,
+// R, B), and convolve it with a set of filters, which can also be presented as
+// a tensor (D, K, K, M), where M is the number of filters, K is the filter
+// size, and each 3-dimensional tensor of size (D, K, K) is a filter. For
+// simplicity we assume that we always use square filters (which is usually the
+// case in images), hence the two Ks in the tensor dimension.  It also takes in
+// a few additional parameters:
+// Stride (S): The convolution stride is the offset between locations where we
+//             apply the filters.  A larger stride means that the output will be
+//             spatially smaller.
+// Padding (P): The padding we apply to the input tensor along the R and C
+//              dimensions.  This is usually used to make sure that the spatial
+//              dimensions of the output matches our intention.
+//
+// Two types of padding are often used:
+//   SAME: The pad value is computed so that the output will have size
+//         R/S and C/S.
+//   VALID: no padding is carried out.
+// When we do padding, the padded values at the padded locations are usually
+// zero.
+//
+// The output dimensions for convolution, when given all the parameters above,
+// are as follows:
+// When Padding = SAME: the output size is (B, R', C', M), where
+//   R' = ceil(float(R) / float(S))
+//   C' = ceil(float(C) / float(S))
+// where ceil is the ceiling function.  The input tensor is padded with 0 as
+// needed.  The number of padded rows and columns are computed as:
+//   Pr = ((R' - 1) * S + K - R) / 2
+//   Pc = ((C' - 1) * S + K - C) / 2
+// when the stride is 1, we have the simplified case R'=R, C'=C, Pr=Pc=(K-1)/2.
+// This is where SAME comes from - the output has the same size as the input has.
+// When Padding = VALID: the output size is computed as
+//   R' = ceil(float(R - K + 1) / float(S))
+//   C' = ceil(float(C - K + 1) / float(S))
+// and the number of padded rows and columns are computed in the same way as in
+// the SAME case.
+// When the stride is 1, we have the simplified case R'=R-K+1, C'=C-K+1, Pr=0,
+// Pc=0.
+enum PaddingType { PADDING_VALID = 1, PADDING_SAME = 2 };
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h
new file mode 100644
index 0000000..99e51c5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h
@@ -0,0 +1,229 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_UINT128_H
+#define EIGEN_CXX11_TENSOR_TENSOR_UINT128_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <uint64_t n>
+struct static_val {
+  static const uint64_t value = n;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE operator uint64_t() const { return n; }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static_val() {}
+
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static_val(const T& v) {
+    EIGEN_UNUSED_VARIABLE(v);
+    eigen_assert(v == n);
+  }
+};
+
+template <typename HIGH = uint64_t, typename LOW = uint64_t>
+struct TensorUInt128 {
+  HIGH high;
+  LOW low;
+
+  template <typename OTHER_HIGH, typename OTHER_LOW>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE TensorUInt128(const TensorUInt128<OTHER_HIGH, OTHER_LOW>& other)
+      : high(other.high), low(other.low) {
+    EIGEN_STATIC_ASSERT(sizeof(OTHER_HIGH) <= sizeof(HIGH), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT(sizeof(OTHER_LOW) <= sizeof(LOW), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  }
+
+  template <typename OTHER_HIGH, typename OTHER_LOW>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE TensorUInt128& operator=(const TensorUInt128<OTHER_HIGH, OTHER_LOW>& other) {
+    EIGEN_STATIC_ASSERT(sizeof(OTHER_HIGH) <= sizeof(HIGH), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT(sizeof(OTHER_LOW) <= sizeof(LOW), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    high = other.high;
+    low = other.low;
+    return *this;
+  }
+
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE explicit TensorUInt128(const T& x) : high(0), low(x) {
+    eigen_assert(
+        (static_cast<std::conditional_t<sizeof(T) == 8, uint64_t, uint32_t>>(x) <= NumTraits<uint64_t>::highest()));
+    eigen_assert(x >= 0);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE TensorUInt128(HIGH y, LOW x) : high(y), low(x) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE operator LOW() const { return low; }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LOW lower() const { return low; }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE HIGH upper() const { return high; }
+};
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool operator==(const TensorUInt128<HL, LL>& lhs,
+                                                      const TensorUInt128<HR, LR>& rhs) {
+  return (lhs.high == rhs.high) && (lhs.low == rhs.low);
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool operator!=(const TensorUInt128<HL, LL>& lhs,
+                                                      const TensorUInt128<HR, LR>& rhs) {
+  return (lhs.high != rhs.high) || (lhs.low != rhs.low);
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool operator>=(const TensorUInt128<HL, LL>& lhs,
+                                                      const TensorUInt128<HR, LR>& rhs) {
+  if (lhs.high != rhs.high) {
+    return lhs.high > rhs.high;
+  }
+  return lhs.low >= rhs.low;
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool operator<(const TensorUInt128<HL, LL>& lhs,
+                                                     const TensorUInt128<HR, LR>& rhs) {
+  if (lhs.high != rhs.high) {
+    return lhs.high < rhs.high;
+  }
+  return lhs.low < rhs.low;
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE TensorUInt128<uint64_t, uint64_t> operator+(const TensorUInt128<HL, LL>& lhs,
+                                                                                  const TensorUInt128<HR, LR>& rhs) {
+  TensorUInt128<uint64_t, uint64_t> result(lhs.high + rhs.high, lhs.low + rhs.low);
+  if (result.low < rhs.low) {
+    result.high += 1;
+  }
+  return result;
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE TensorUInt128<uint64_t, uint64_t> operator-(const TensorUInt128<HL, LL>& lhs,
+                                                                                  const TensorUInt128<HR, LR>& rhs) {
+  TensorUInt128<uint64_t, uint64_t> result(lhs.high - rhs.high, lhs.low - rhs.low);
+  if (result.low > lhs.low) {
+    result.high -= 1;
+  }
+  return result;
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorUInt128<uint64_t, uint64_t> operator*(
+    const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs) {
+  // Split each 128-bit integer into 4 32-bit integers, and then do the
+  // multiplications by hand as follow:
+  //   lhs      a  b  c  d
+  //   rhs      e  f  g  h
+  //           -----------
+  //           ah bh ch dh
+  //           bg cg dg
+  //           cf df
+  //           de
+  // The result is stored in 2 64bit integers, high and low.
+
+  const uint64_t LOW = 0x00000000FFFFFFFFLL;
+  const uint64_t HIGH = 0xFFFFFFFF00000000LL;
+
+  uint64_t d = lhs.low & LOW;
+  uint64_t c = (lhs.low & HIGH) >> 32LL;
+  uint64_t b = lhs.high & LOW;
+  uint64_t a = (lhs.high & HIGH) >> 32LL;
+
+  uint64_t h = rhs.low & LOW;
+  uint64_t g = (rhs.low & HIGH) >> 32LL;
+  uint64_t f = rhs.high & LOW;
+  uint64_t e = (rhs.high & HIGH) >> 32LL;
+
+  // Compute the low 32 bits of low
+  uint64_t acc = d * h;
+  uint64_t low = acc & LOW;
+  //  Compute the high 32 bits of low. Add a carry every time we wrap around
+  acc >>= 32LL;
+  uint64_t carry = 0;
+  uint64_t acc2 = acc + c * h;
+  if (acc2 < acc) {
+    carry++;
+  }
+  acc = acc2 + d * g;
+  if (acc < acc2) {
+    carry++;
+  }
+  low |= (acc << 32LL);
+
+  // Carry forward the high bits of acc to initiate the computation of the
+  // low 32 bits of high
+  acc2 = (acc >> 32LL) | (carry << 32LL);
+  carry = 0;
+
+  acc = acc2 + b * h;
+  if (acc < acc2) {
+    carry++;
+  }
+  acc2 = acc + c * g;
+  if (acc2 < acc) {
+    carry++;
+  }
+  acc = acc2 + d * f;
+  if (acc < acc2) {
+    carry++;
+  }
+  uint64_t high = acc & LOW;
+
+  // Start to compute the high 32 bits of high.
+  acc2 = (acc >> 32LL) | (carry << 32LL);
+
+  acc = acc2 + a * h;
+  acc2 = acc + b * g;
+  acc = acc2 + c * f;
+  acc2 = acc + d * e;
+  high |= (acc2 << 32LL);
+
+  return TensorUInt128<uint64_t, uint64_t>(high, low);
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorUInt128<uint64_t, uint64_t> operator/(
+    const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs) {
+  if (rhs == TensorUInt128<static_val<0>, static_val<1>>(1)) {
+    return TensorUInt128<uint64_t, uint64_t>(lhs.high, lhs.low);
+  } else if (lhs < rhs) {
+    return TensorUInt128<uint64_t, uint64_t>(0);
+  } else {
+    // calculate the biggest power of 2 times rhs that's less than or equal to lhs
+    TensorUInt128<uint64_t, uint64_t> power2(1);
+    TensorUInt128<uint64_t, uint64_t> d(rhs);
+    TensorUInt128<uint64_t, uint64_t> tmp(lhs - d);
+    while (lhs >= d) {
+      tmp = tmp - d;
+      d = d + d;
+      power2 = power2 + power2;
+    }
+
+    tmp = TensorUInt128<uint64_t, uint64_t>(lhs.high, lhs.low);
+    TensorUInt128<uint64_t, uint64_t> result(0);
+    while (power2 != TensorUInt128<static_val<0>, static_val<0>>(0)) {
+      if (tmp >= d) {
+        tmp = tmp - d;
+        result = result + power2;
+      }
+      // Shift right
+      power2 = TensorUInt128<uint64_t, uint64_t>(power2.high >> 1, (power2.low >> 1) | (power2.high << 63));
+      d = TensorUInt128<uint64_t, uint64_t>(d.high >> 1, (d.low >> 1) | (d.high << 63));
+    }
+
+    return result;
+  }
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_UINT128_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
new file mode 100644
index 0000000..61e8b04
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
@@ -0,0 +1,621 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
+#define EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct traits<TensorVolumePatchOp<Planes, Rows, Cols, XprType> > : public traits<XprType> {
+  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef std::remove_reference_t<Nested> Nested_;
+  static constexpr int NumDimensions = XprTraits::NumDimensions + 1;
+  static constexpr int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template <DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct eval<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Eigen::Dense> {
+  typedef const TensorVolumePatchOp<Planes, Rows, Cols, XprType>& type;
+};
+
+template <DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct nested<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, 1,
+              typename eval<TensorVolumePatchOp<Planes, Rows, Cols, XprType> >::type> {
+  typedef TensorVolumePatchOp<Planes, Rows, Cols, XprType> type;
+};
+
+}  // end namespace internal
+
+/**
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Patch extraction specialized for processing of volumetric data.
+ * This assumes that the input has a least 4 dimensions ordered as follows:
+ *  - channels
+ *  - planes
+ *  - rows
+ *  - columns
+ *  - (optional) additional dimensions such as time or batch size.
+ * Calling the volume patch code with patch_planes, patch_rows, and patch_cols
+ * is equivalent to calling the regular patch extraction code with parameters
+ * d, patch_planes, patch_rows, patch_cols, and 1 for all the additional
+ * dimensions.
+ */
+template <DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
+class TensorVolumePatchOp : public TensorBase<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorVolumePatchOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorVolumePatchOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorVolumePatchOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorVolumePatchOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(
+      const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols,
+      DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides, DenseIndex in_plane_strides,
+      DenseIndex in_row_strides, DenseIndex in_col_strides, DenseIndex plane_inflate_strides,
+      DenseIndex row_inflate_strides, DenseIndex col_inflate_strides, PaddingType padding_type, Scalar padding_value)
+      : m_xpr(expr),
+        m_patch_planes(patch_planes),
+        m_patch_rows(patch_rows),
+        m_patch_cols(patch_cols),
+        m_plane_strides(plane_strides),
+        m_row_strides(row_strides),
+        m_col_strides(col_strides),
+        m_in_plane_strides(in_plane_strides),
+        m_in_row_strides(in_row_strides),
+        m_in_col_strides(in_col_strides),
+        m_plane_inflate_strides(plane_inflate_strides),
+        m_row_inflate_strides(row_inflate_strides),
+        m_col_inflate_strides(col_inflate_strides),
+        m_padding_explicit(false),
+        m_padding_top_z(0),
+        m_padding_bottom_z(0),
+        m_padding_top(0),
+        m_padding_bottom(0),
+        m_padding_left(0),
+        m_padding_right(0),
+        m_padding_type(padding_type),
+        m_padding_value(padding_value) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(
+      const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols,
+      DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides, DenseIndex in_plane_strides,
+      DenseIndex in_row_strides, DenseIndex in_col_strides, DenseIndex plane_inflate_strides,
+      DenseIndex row_inflate_strides, DenseIndex col_inflate_strides, DenseIndex padding_top_z,
+      DenseIndex padding_bottom_z, DenseIndex padding_top, DenseIndex padding_bottom, DenseIndex padding_left,
+      DenseIndex padding_right, Scalar padding_value)
+      : m_xpr(expr),
+        m_patch_planes(patch_planes),
+        m_patch_rows(patch_rows),
+        m_patch_cols(patch_cols),
+        m_plane_strides(plane_strides),
+        m_row_strides(row_strides),
+        m_col_strides(col_strides),
+        m_in_plane_strides(in_plane_strides),
+        m_in_row_strides(in_row_strides),
+        m_in_col_strides(in_col_strides),
+        m_plane_inflate_strides(plane_inflate_strides),
+        m_row_inflate_strides(row_inflate_strides),
+        m_col_inflate_strides(col_inflate_strides),
+        m_padding_explicit(true),
+        m_padding_top_z(padding_top_z),
+        m_padding_bottom_z(padding_bottom_z),
+        m_padding_top(padding_top),
+        m_padding_bottom(padding_bottom),
+        m_padding_left(padding_left),
+        m_padding_right(padding_right),
+        m_padding_type(PADDING_VALID),
+        m_padding_value(padding_value) {}
+
+  EIGEN_DEVICE_FUNC DenseIndex patch_planes() const { return m_patch_planes; }
+  EIGEN_DEVICE_FUNC DenseIndex patch_rows() const { return m_patch_rows; }
+  EIGEN_DEVICE_FUNC DenseIndex patch_cols() const { return m_patch_cols; }
+  EIGEN_DEVICE_FUNC DenseIndex plane_strides() const { return m_plane_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex row_strides() const { return m_row_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex col_strides() const { return m_col_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex in_plane_strides() const { return m_in_plane_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex in_row_strides() const { return m_in_row_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex in_col_strides() const { return m_in_col_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex plane_inflate_strides() const { return m_plane_inflate_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
+  EIGEN_DEVICE_FUNC DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
+  EIGEN_DEVICE_FUNC bool padding_explicit() const { return m_padding_explicit; }
+  EIGEN_DEVICE_FUNC DenseIndex padding_top_z() const { return m_padding_top_z; }
+  EIGEN_DEVICE_FUNC DenseIndex padding_bottom_z() const { return m_padding_bottom_z; }
+  EIGEN_DEVICE_FUNC DenseIndex padding_top() const { return m_padding_top; }
+  EIGEN_DEVICE_FUNC DenseIndex padding_bottom() const { return m_padding_bottom; }
+  EIGEN_DEVICE_FUNC DenseIndex padding_left() const { return m_padding_left; }
+  EIGEN_DEVICE_FUNC DenseIndex padding_right() const { return m_padding_right; }
+  EIGEN_DEVICE_FUNC PaddingType padding_type() const { return m_padding_type; }
+  EIGEN_DEVICE_FUNC Scalar padding_value() const { return m_padding_value; }
+
+  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const DenseIndex m_patch_planes;
+  const DenseIndex m_patch_rows;
+  const DenseIndex m_patch_cols;
+  const DenseIndex m_plane_strides;
+  const DenseIndex m_row_strides;
+  const DenseIndex m_col_strides;
+  const DenseIndex m_in_plane_strides;
+  const DenseIndex m_in_row_strides;
+  const DenseIndex m_in_col_strides;
+  const DenseIndex m_plane_inflate_strides;
+  const DenseIndex m_row_inflate_strides;
+  const DenseIndex m_col_inflate_strides;
+  const bool m_padding_explicit;
+  const DenseIndex m_padding_top_z;
+  const DenseIndex m_padding_bottom_z;
+  const DenseIndex m_padding_top;
+  const DenseIndex m_padding_bottom;
+  const DenseIndex m_padding_left;
+  const DenseIndex m_padding_right;
+  const PaddingType m_padding_type;
+  const Scalar m_padding_value;
+};
+
+// Eval as rvalue
+template <DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, Device> {
+  typedef TensorVolumePatchOp<Planes, Rows, Cols, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static constexpr int NumInputDims =
+      internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static constexpr int NumDims = NumInputDims + 1;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    CoordAccess = false,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_impl(op.expression(), device) {
+    EIGEN_STATIC_ASSERT((NumDims >= 5), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    m_paddingValue = op.padding_value();
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+
+    // Cache a few variables.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputDepth = input_dims[0];
+      m_inputPlanes = input_dims[1];
+      m_inputRows = input_dims[2];
+      m_inputCols = input_dims[3];
+    } else {
+      m_inputDepth = input_dims[NumInputDims - 1];
+      m_inputPlanes = input_dims[NumInputDims - 2];
+      m_inputRows = input_dims[NumInputDims - 3];
+      m_inputCols = input_dims[NumInputDims - 4];
+    }
+
+    m_plane_strides = op.plane_strides();
+    m_row_strides = op.row_strides();
+    m_col_strides = op.col_strides();
+
+    // Input strides and effective input/patch size
+    m_in_plane_strides = op.in_plane_strides();
+    m_in_row_strides = op.in_row_strides();
+    m_in_col_strides = op.in_col_strides();
+    m_plane_inflate_strides = op.plane_inflate_strides();
+    m_row_inflate_strides = op.row_inflate_strides();
+    m_col_inflate_strides = op.col_inflate_strides();
+
+    // The "effective" spatial size after inflating data with zeros.
+    m_input_planes_eff = (m_inputPlanes - 1) * m_plane_inflate_strides + 1;
+    m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
+    m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
+    m_patch_planes_eff = op.patch_planes() + (op.patch_planes() - 1) * (m_in_plane_strides - 1);
+    m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
+    m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
+
+    if (op.padding_explicit()) {
+      m_outputPlanes =
+          numext::ceil((m_input_planes_eff + op.padding_top_z() + op.padding_bottom_z() - m_patch_planes_eff + 1.f) /
+                       static_cast<float>(m_plane_strides));
+      m_outputRows = numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) /
+                                  static_cast<float>(m_row_strides));
+      m_outputCols = numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) /
+                                  static_cast<float>(m_col_strides));
+      m_planePaddingTop = op.padding_top_z();
+      m_rowPaddingTop = op.padding_top();
+      m_colPaddingLeft = op.padding_left();
+    } else {
+      // Computing padding from the type
+      switch (op.padding_type()) {
+        case PADDING_VALID:
+          m_outputPlanes =
+              numext::ceil((m_input_planes_eff - m_patch_planes_eff + 1.f) / static_cast<float>(m_plane_strides));
+          m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
+          m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
+          m_planePaddingTop = 0;
+          m_rowPaddingTop = 0;
+          m_colPaddingLeft = 0;
+          break;
+        case PADDING_SAME: {
+          m_outputPlanes = numext::ceil(m_input_planes_eff / static_cast<float>(m_plane_strides));
+          m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
+          m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
+          const Index dz = (m_outputPlanes - 1) * m_plane_strides + m_patch_planes_eff - m_input_planes_eff;
+          const Index dy = (m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff;
+          const Index dx = (m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff;
+          m_planePaddingTop = dz / 2;
+          m_rowPaddingTop = dy / 2;
+          m_colPaddingLeft = dx / 2;
+          break;
+        }
+        default: {
+          eigen_assert(false && "unexpected padding");
+          return;
+        }
+      }
+    }
+    eigen_assert(m_outputRows > 0);
+    eigen_assert(m_outputCols > 0);
+    eigen_assert(m_outputPlanes > 0);
+
+    // Dimensions for result of extraction.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      // ColMajor
+      // 0: depth
+      // 1: patch_planes
+      // 2: patch_rows
+      // 3: patch_cols
+      // 4: number of patches
+      // 5 and beyond: anything else (such as batch).
+      m_dimensions[0] = input_dims[0];
+      m_dimensions[1] = op.patch_planes();
+      m_dimensions[2] = op.patch_rows();
+      m_dimensions[3] = op.patch_cols();
+      m_dimensions[4] = m_outputPlanes * m_outputRows * m_outputCols;
+      for (int i = 5; i < NumDims; ++i) {
+        m_dimensions[i] = input_dims[i - 1];
+      }
+    } else {
+      // RowMajor
+      // NumDims-1: depth
+      // NumDims-2: patch_planes
+      // NumDims-3: patch_rows
+      // NumDims-4: patch_cols
+      // NumDims-5: number of patches
+      // NumDims-6 and beyond: anything else (such as batch).
+      m_dimensions[NumDims - 1] = input_dims[NumInputDims - 1];
+      m_dimensions[NumDims - 2] = op.patch_planes();
+      m_dimensions[NumDims - 3] = op.patch_rows();
+      m_dimensions[NumDims - 4] = op.patch_cols();
+      m_dimensions[NumDims - 5] = m_outputPlanes * m_outputRows * m_outputCols;
+      for (int i = NumDims - 6; i >= 0; --i) {
+        m_dimensions[i] = input_dims[i];
+      }
+    }
+
+    // Strides for the output tensor.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_rowStride = m_dimensions[1];
+      m_colStride = m_dimensions[2] * m_rowStride;
+      m_patchStride = m_colStride * m_dimensions[3] * m_dimensions[0];
+      m_otherStride = m_patchStride * m_dimensions[4];
+    } else {
+      m_rowStride = m_dimensions[NumDims - 2];
+      m_colStride = m_dimensions[NumDims - 3] * m_rowStride;
+      m_patchStride = m_colStride * m_dimensions[NumDims - 4] * m_dimensions[NumDims - 1];
+      m_otherStride = m_patchStride * m_dimensions[NumDims - 5];
+    }
+
+    // Strides for navigating through the input tensor.
+    m_planeInputStride = m_inputDepth;
+    m_rowInputStride = m_inputDepth * m_inputPlanes;
+    m_colInputStride = m_inputDepth * m_inputRows * m_inputPlanes;
+    m_otherInputStride = m_inputDepth * m_inputRows * m_inputCols * m_inputPlanes;
+
+    m_outputPlanesRows = m_outputPlanes * m_outputRows;
+
+    // Fast representations of different variables.
+    m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
+
+    m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
+    m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
+    m_fastRowStride = internal::TensorIntDivisor<Index>(m_rowStride);
+    m_fastInputRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
+    m_fastInputColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
+    m_fastInputPlaneStride = internal::TensorIntDivisor<Index>(m_plane_inflate_strides);
+    m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);
+    m_fastOutputPlanes = internal::TensorIntDivisor<Index>(m_outputPlanes);
+    m_fastOutputPlanesRows = internal::TensorIntDivisor<Index>(m_outputPlanesRows);
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
+    } else {
+      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims - 1]);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType /*data*/, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    // Patch index corresponding to the passed in index.
+    const Index patchIndex = index / m_fastPatchStride;
+
+    // Spatial offset within the patch. This has to be translated into 3D
+    // coordinates within the patch.
+    const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;
+
+    // Batch, etc.
+    const Index otherIndex = (NumDims == 5) ? 0 : index / m_fastOtherStride;
+    const Index patch3DIndex = (NumDims == 5) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
+
+    // Calculate column index in the input original tensor.
+    const Index colIndex = patch3DIndex / m_fastOutputPlanesRows;
+    const Index colOffset = patchOffset / m_fastColStride;
+    const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
+    const Index origInputCol =
+        (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0);
+    if (inputCol < 0 || inputCol >= m_input_cols_eff ||
+        ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
+      return Scalar(m_paddingValue);
+    }
+
+    // Calculate row index in the original input tensor.
+    const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes;
+    const Index rowOffset = (patchOffset - colOffset * m_colStride) / m_fastRowStride;
+    const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
+    const Index origInputRow =
+        (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0);
+    if (inputRow < 0 || inputRow >= m_input_rows_eff ||
+        ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
+      return Scalar(m_paddingValue);
+    }
+
+    // Calculate plane index in the original input tensor.
+    const Index planeIndex = (patch3DIndex - m_outputPlanes * (colIndex * m_outputRows + rowIndex));
+    const Index planeOffset = patchOffset - colOffset * m_colStride - rowOffset * m_rowStride;
+    const Index inputPlane = planeIndex * m_plane_strides + planeOffset * m_in_plane_strides - m_planePaddingTop;
+    const Index origInputPlane =
+        (m_plane_inflate_strides == 1) ? inputPlane : ((inputPlane >= 0) ? (inputPlane / m_fastInputPlaneStride) : 0);
+    if (inputPlane < 0 || inputPlane >= m_input_planes_eff ||
+        ((m_plane_inflate_strides != 1) && (inputPlane != origInputPlane * m_plane_inflate_strides))) {
+      return Scalar(m_paddingValue);
+    }
+
+    const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+    const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
+
+    const Index inputIndex = depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride +
+                             origInputPlane * m_planeInputStride + otherIndex * m_otherInputStride;
+
+    return m_impl.coeff(inputIndex);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1 ||
+        m_in_plane_strides != 1 || m_plane_inflate_strides != 1) {
+      return packetWithPossibleZero(index);
+    }
+
+    const Index indices[2] = {index, index + PacketSize - 1};
+    const Index patchIndex = indices[0] / m_fastPatchStride;
+    if (patchIndex != indices[1] / m_fastPatchStride) {
+      return packetWithPossibleZero(index);
+    }
+    const Index otherIndex = (NumDims == 5) ? 0 : indices[0] / m_fastOtherStride;
+    eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
+
+    // Find the offset of the element wrt the location of the first element.
+    Index first_entry = (indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth;
+    Index second_entry = PacketSize == 1 ? first_entry : (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth;
+
+    const Index patchOffsets[2] = {first_entry, second_entry};
+
+    const Index patch3DIndex =
+        (NumDims == 5) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
+    eigen_assert(patch3DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
+
+    const Index colIndex = patch3DIndex / m_fastOutputPlanesRows;
+    const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};
+
+    // Calculate col indices in the original input tensor.
+    const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] - m_colPaddingLeft,
+                                colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
+    if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
+      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
+    }
+
+    if (inputCols[0] != inputCols[1]) {
+      return packetWithPossibleZero(index);
+    }
+
+    const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes;
+    const Index rowOffsets[2] = {(patchOffsets[0] - colOffsets[0] * m_colStride) / m_fastRowStride,
+                                 (patchOffsets[1] - colOffsets[1] * m_colStride) / m_fastRowStride};
+    eigen_assert(rowOffsets[0] <= rowOffsets[1]);
+    // Calculate row indices in the original input tensor.
+    const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] - m_rowPaddingTop,
+                                rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
+
+    if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
+      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
+    }
+
+    if (inputRows[0] != inputRows[1]) {
+      return packetWithPossibleZero(index);
+    }
+
+    const Index planeIndex = (patch3DIndex - m_outputPlanes * (colIndex * m_outputRows + rowIndex));
+    const Index planeOffsets[2] = {patchOffsets[0] - colOffsets[0] * m_colStride - rowOffsets[0] * m_rowStride,
+                                   patchOffsets[1] - colOffsets[1] * m_colStride - rowOffsets[1] * m_rowStride};
+    eigen_assert(planeOffsets[0] <= planeOffsets[1]);
+    const Index inputPlanes[2] = {planeIndex * m_plane_strides + planeOffsets[0] - m_planePaddingTop,
+                                  planeIndex * m_plane_strides + planeOffsets[1] - m_planePaddingTop};
+
+    if (inputPlanes[1] < 0 || inputPlanes[0] >= m_inputPlanes) {
+      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
+    }
+
+    if (inputPlanes[0] >= 0 && inputPlanes[1] < m_inputPlanes) {
+      // no padding
+      const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+      const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
+      const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride +
+                               m_planeInputStride * inputPlanes[0] + otherIndex * m_otherInputStride;
+      return m_impl.template packet<Unaligned>(inputIndex);
+    }
+
+    return packetWithPossibleZero(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double compute_cost =
+        10 * TensorOpCost::DivCost<Index>() + 21 * TensorOpCost::MulCost<Index>() + 8 * TensorOpCost::AddCost<Index>();
+    return TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index planePaddingTop() const { return m_planePaddingTop; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowPaddingTop() const { return m_rowPaddingTop; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colPaddingLeft() const { return m_colPaddingLeft; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputPlanes() const { return m_outputPlanes; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputRows() const { return m_outputRows; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputCols() const { return m_outputCols; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userPlaneStride() const { return m_plane_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userRowStride() const { return m_row_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userColStride() const { return m_col_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInPlaneStride() const { return m_in_plane_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInRowStride() const { return m_in_row_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInColStride() const { return m_in_col_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index planeInflateStride() const { return m_plane_inflate_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowInflateStride() const { return m_row_inflate_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colInflateStride() const { return m_col_inflate_strides; }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const {
+    EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index + i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  Dimensions m_dimensions;
+
+  // Parameters passed to the constructor.
+  Index m_plane_strides;
+  Index m_row_strides;
+  Index m_col_strides;
+
+  Index m_outputPlanes;
+  Index m_outputRows;
+  Index m_outputCols;
+
+  Index m_planePaddingTop;
+  Index m_rowPaddingTop;
+  Index m_colPaddingLeft;
+
+  Index m_in_plane_strides;
+  Index m_in_row_strides;
+  Index m_in_col_strides;
+
+  Index m_plane_inflate_strides;
+  Index m_row_inflate_strides;
+  Index m_col_inflate_strides;
+
+  // Cached input size.
+  Index m_inputDepth;
+  Index m_inputPlanes;
+  Index m_inputRows;
+  Index m_inputCols;
+
+  // Other cached variables.
+  Index m_outputPlanesRows;
+
+  // Effective input/patch post-inflation size.
+  Index m_input_planes_eff;
+  Index m_input_rows_eff;
+  Index m_input_cols_eff;
+  Index m_patch_planes_eff;
+  Index m_patch_rows_eff;
+  Index m_patch_cols_eff;
+
+  // Strides for the output tensor.
+  Index m_otherStride;
+  Index m_patchStride;
+  Index m_rowStride;
+  Index m_colStride;
+
+  // Strides for the input tensor.
+  Index m_planeInputStride;
+  Index m_rowInputStride;
+  Index m_colInputStride;
+  Index m_otherInputStride;
+
+  internal::TensorIntDivisor<Index> m_fastOtherStride;
+  internal::TensorIntDivisor<Index> m_fastPatchStride;
+  internal::TensorIntDivisor<Index> m_fastColStride;
+  internal::TensorIntDivisor<Index> m_fastRowStride;
+  internal::TensorIntDivisor<Index> m_fastInputPlaneStride;
+  internal::TensorIntDivisor<Index> m_fastInputRowStride;
+  internal::TensorIntDivisor<Index> m_fastInputColStride;
+  internal::TensorIntDivisor<Index> m_fastInputColsEff;
+  internal::TensorIntDivisor<Index> m_fastOutputPlanesRows;
+  internal::TensorIntDivisor<Index> m_fastOutputPlanes;
+  internal::TensorIntDivisor<Index> m_fastOutputDepth;
+
+  Scalar m_paddingValue;
+
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h
new file mode 100644
index 0000000..ae5c4f4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h
@@ -0,0 +1,296 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_DYNAMICSYMMETRY_H
+#define EIGEN_CXX11_TENSORSYMMETRY_DYNAMICSYMMETRY_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+class DynamicSGroup {
+ public:
+  inline explicit DynamicSGroup() : m_numIndices(1), m_elements(), m_generators(), m_globalFlags(0) {
+    m_elements.push_back(ge(Generator(0, 0, 0)));
+  }
+  inline DynamicSGroup(const DynamicSGroup& o)
+      : m_numIndices(o.m_numIndices),
+        m_elements(o.m_elements),
+        m_generators(o.m_generators),
+        m_globalFlags(o.m_globalFlags) {}
+  inline DynamicSGroup(DynamicSGroup&& o)
+      : m_numIndices(o.m_numIndices), m_elements(), m_generators(o.m_generators), m_globalFlags(o.m_globalFlags) {
+    std::swap(m_elements, o.m_elements);
+  }
+  inline DynamicSGroup& operator=(const DynamicSGroup& o) {
+    m_numIndices = o.m_numIndices;
+    m_elements = o.m_elements;
+    m_generators = o.m_generators;
+    m_globalFlags = o.m_globalFlags;
+    return *this;
+  }
+  inline DynamicSGroup& operator=(DynamicSGroup&& o) {
+    m_numIndices = o.m_numIndices;
+    std::swap(m_elements, o.m_elements);
+    m_generators = o.m_generators;
+    m_globalFlags = o.m_globalFlags;
+    return *this;
+  }
+
+  void add(int one, int two, int flags = 0);
+
+  template <typename Gen_>
+  inline void add(Gen_) {
+    add(Gen_::One, Gen_::Two, Gen_::Flags);
+  }
+  inline void addSymmetry(int one, int two) { add(one, two, 0); }
+  inline void addAntiSymmetry(int one, int two) { add(one, two, NegationFlag); }
+  inline void addHermiticity(int one, int two) { add(one, two, ConjugationFlag); }
+  inline void addAntiHermiticity(int one, int two) { add(one, two, NegationFlag | ConjugationFlag); }
+
+  template <typename Op, typename RV, typename Index, std::size_t N, typename... Args>
+  inline RV apply(const std::array<Index, N>& idx, RV initial, Args&&... args) const {
+    eigen_assert(N >= m_numIndices &&
+                 "Can only apply symmetry group to objects that have at least the required amount of indices.");
+    for (std::size_t i = 0; i < size(); i++)
+      initial = Op::run(h_permute(i, idx, typename internal::gen_numeric_list<int, N>::type()), m_elements[i].flags,
+                        initial, std::forward<Args>(args)...);
+    return initial;
+  }
+
+  template <typename Op, typename RV, typename Index, typename... Args>
+  inline RV apply(const std::vector<Index>& idx, RV initial, Args&&... args) const {
+    eigen_assert(idx.size() >= m_numIndices &&
+                 "Can only apply symmetry group to objects that have at least the required amount of indices.");
+    for (std::size_t i = 0; i < size(); i++)
+      initial = Op::run(h_permute(i, idx), m_elements[i].flags, initial, std::forward<Args>(args)...);
+    return initial;
+  }
+
+  inline int globalFlags() const { return m_globalFlags; }
+  inline std::size_t size() const { return m_elements.size(); }
+
+  template <typename Tensor_, typename... IndexTypes>
+  inline internal::tensor_symmetry_value_setter<Tensor_, DynamicSGroup> operator()(Tensor_& tensor,
+                                                                                   typename Tensor_::Index firstIndex,
+                                                                                   IndexTypes... otherIndices) const {
+    static_assert(sizeof...(otherIndices) + 1 == Tensor_::NumIndices,
+                  "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
+    return operator()(tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices>{{firstIndex, otherIndices...}});
+  }
+
+  template <typename Tensor_>
+  inline internal::tensor_symmetry_value_setter<Tensor_, DynamicSGroup> operator()(
+      Tensor_& tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices> const& indices) const {
+    return internal::tensor_symmetry_value_setter<Tensor_, DynamicSGroup>(tensor, *this, indices);
+  }
+
+ private:
+  struct GroupElement {
+    std::vector<int> representation;
+    int flags;
+    bool isId() const {
+      for (std::size_t i = 0; i < representation.size(); i++)
+        if (i != (size_t)representation[i]) return false;
+      return true;
+    }
+  };
+  struct Generator {
+    int one;
+    int two;
+    int flags;
+    constexpr Generator(int one_, int two_, int flags_) : one(one_), two(two_), flags(flags_) {}
+  };
+
+  std::size_t m_numIndices;
+  std::vector<GroupElement> m_elements;
+  std::vector<Generator> m_generators;
+  int m_globalFlags;
+
+  template <typename Index, std::size_t N, int... n>
+  inline std::array<Index, N> h_permute(std::size_t which, const std::array<Index, N>& idx,
+                                        internal::numeric_list<int, n...>) const {
+    return std::array<Index, N>{{idx[n >= m_numIndices ? n : m_elements[which].representation[n]]...}};
+  }
+
+  template <typename Index>
+  inline std::vector<Index> h_permute(std::size_t which, std::vector<Index> idx) const {
+    std::vector<Index> result;
+    result.reserve(idx.size());
+    for (auto k : m_elements[which].representation) result.push_back(idx[k]);
+    for (std::size_t i = m_numIndices; i < idx.size(); i++) result.push_back(idx[i]);
+    return result;
+  }
+
+  inline GroupElement ge(Generator const& g) const {
+    GroupElement result;
+    result.representation.reserve(m_numIndices);
+    result.flags = g.flags;
+    for (std::size_t k = 0; k < m_numIndices; k++) {
+      if (k == (std::size_t)g.one)
+        result.representation.push_back(g.two);
+      else if (k == (std::size_t)g.two)
+        result.representation.push_back(g.one);
+      else
+        result.representation.push_back(int(k));
+    }
+    return result;
+  }
+
+  GroupElement mul(GroupElement, GroupElement) const;
+  inline GroupElement mul(Generator g1, GroupElement g2) const { return mul(ge(g1), g2); }
+
+  inline GroupElement mul(GroupElement g1, Generator g2) const { return mul(g1, ge(g2)); }
+
+  inline GroupElement mul(Generator g1, Generator g2) const { return mul(ge(g1), ge(g2)); }
+
+  inline int findElement(GroupElement e) const {
+    for (auto ee : m_elements) {
+      if (ee.representation == e.representation) return ee.flags ^ e.flags;
+    }
+    return -1;
+  }
+
+  void updateGlobalFlags(int flagDiffOfSameGenerator);
+};
+
+// dynamic symmetry group that auto-adds the template parameters in the constructor
+template <typename... Gen>
+class DynamicSGroupFromTemplateArgs : public DynamicSGroup {
+ public:
+  inline DynamicSGroupFromTemplateArgs() : DynamicSGroup() { add_all(internal::type_list<Gen...>()); }
+  inline DynamicSGroupFromTemplateArgs(DynamicSGroupFromTemplateArgs const& other) : DynamicSGroup(other) {}
+  inline DynamicSGroupFromTemplateArgs(DynamicSGroupFromTemplateArgs&& other) : DynamicSGroup(other) {}
+  inline DynamicSGroupFromTemplateArgs<Gen...>& operator=(const DynamicSGroupFromTemplateArgs<Gen...>& o) {
+    DynamicSGroup::operator=(o);
+    return *this;
+  }
+  inline DynamicSGroupFromTemplateArgs<Gen...>& operator=(DynamicSGroupFromTemplateArgs<Gen...>&& o) {
+    DynamicSGroup::operator=(o);
+    return *this;
+  }
+
+ private:
+  template <typename Gen1, typename... GenNext>
+  inline void add_all(internal::type_list<Gen1, GenNext...>) {
+    add(Gen1());
+    add_all(internal::type_list<GenNext...>());
+  }
+
+  inline void add_all(internal::type_list<>) {}
+};
+
+inline DynamicSGroup::GroupElement DynamicSGroup::mul(GroupElement g1, GroupElement g2) const {
+  eigen_internal_assert(g1.representation.size() == m_numIndices);
+  eigen_internal_assert(g2.representation.size() == m_numIndices);
+
+  GroupElement result;
+  result.representation.reserve(m_numIndices);
+  for (std::size_t i = 0; i < m_numIndices; i++) {
+    int v = g2.representation[g1.representation[i]];
+    eigen_assert(v >= 0);
+    result.representation.push_back(v);
+  }
+  result.flags = g1.flags ^ g2.flags;
+  return result;
+}
+
+inline void DynamicSGroup::add(int one, int two, int flags) {
+  eigen_assert(one >= 0);
+  eigen_assert(two >= 0);
+  eigen_assert(one != two);
+
+  if ((std::size_t)one >= m_numIndices || (std::size_t)two >= m_numIndices) {
+    std::size_t newNumIndices = (one > two) ? one : two + 1;
+    for (auto& gelem : m_elements) {
+      gelem.representation.reserve(newNumIndices);
+      for (std::size_t i = m_numIndices; i < newNumIndices; i++) gelem.representation.push_back(i);
+    }
+    m_numIndices = newNumIndices;
+  }
+
+  Generator g{one, two, flags};
+  GroupElement e = ge(g);
+
+  /* special case for first generator */
+  if (m_elements.size() == 1) {
+    while (!e.isId()) {
+      m_elements.push_back(e);
+      e = mul(e, g);
+    }
+
+    if (e.flags > 0) updateGlobalFlags(e.flags);
+
+    // only add in case we didn't have identity
+    if (m_elements.size() > 1) m_generators.push_back(g);
+    return;
+  }
+
+  int p = findElement(e);
+  if (p >= 0) {
+    updateGlobalFlags(p);
+    return;
+  }
+
+  std::size_t coset_order = m_elements.size();
+  m_elements.push_back(e);
+  for (std::size_t i = 1; i < coset_order; i++) m_elements.push_back(mul(m_elements[i], e));
+  m_generators.push_back(g);
+
+  std::size_t coset_rep = coset_order;
+  do {
+    for (auto g : m_generators) {
+      e = mul(m_elements[coset_rep], g);
+      p = findElement(e);
+      if (p < 0) {
+        // element not yet in group
+        m_elements.push_back(e);
+        for (std::size_t i = 1; i < coset_order; i++) m_elements.push_back(mul(m_elements[i], e));
+      } else if (p > 0) {
+        updateGlobalFlags(p);
+      }
+    }
+    coset_rep += coset_order;
+  } while (coset_rep < m_elements.size());
+}
+
+inline void DynamicSGroup::updateGlobalFlags(int flagDiffOfSameGenerator) {
+  switch (flagDiffOfSameGenerator) {
+    case 0:
+    default:
+      // nothing happened
+      break;
+    case NegationFlag:
+      // every element is it's own negative => whole tensor is zero
+      m_globalFlags |= GlobalZeroFlag;
+      break;
+    case ConjugationFlag:
+      // every element is it's own conjugate => whole tensor is real
+      m_globalFlags |= GlobalRealFlag;
+      break;
+    case (NegationFlag | ConjugationFlag):
+      // every element is it's own negative conjugate => whole tensor is imaginary
+      m_globalFlags |= GlobalImagFlag;
+      break;
+      /* NOTE:
+       *   since GlobalZeroFlag == GlobalRealFlag | GlobalImagFlag, if one generator
+       *   causes the tensor to be real and the next one to be imaginary, this will
+       *   trivially give the correct result
+       */
+  }
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSORSYMMETRY_DYNAMICSYMMETRY_H
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/InternalHeaderCheck.h
new file mode 100644
index 0000000..b1b2e14
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/InternalHeaderCheck.h
@@ -0,0 +1,4 @@
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_MODULE_H
+#error \
+    "Please include unsupported/Eigen/CXX11/TensorSymmetry instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h
new file mode 100644
index 0000000..66a982b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h
@@ -0,0 +1,223 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_STATICSYMMETRY_H
+#define EIGEN_CXX11_TENSORSYMMETRY_STATICSYMMETRY_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename list>
+struct tensor_static_symgroup_permutate;
+
+template <int... nn>
+struct tensor_static_symgroup_permutate<numeric_list<int, nn...>> {
+  constexpr static std::size_t N = sizeof...(nn);
+
+  template <typename T>
+  constexpr static std::array<T, N> run(const std::array<T, N>& indices) {
+    return {{indices[nn]...}};
+  }
+};
+
+template <typename indices_, int flags_>
+struct tensor_static_symgroup_element {
+  typedef indices_ indices;
+  constexpr static int flags = flags_;
+};
+
+template <typename Gen, int N>
+struct tensor_static_symgroup_element_ctor {
+  typedef tensor_static_symgroup_element<typename gen_numeric_list_swapped_pair<int, N, Gen::One, Gen::Two>::type,
+                                         Gen::Flags>
+      type;
+};
+
+template <int N>
+struct tensor_static_symgroup_identity_ctor {
+  typedef tensor_static_symgroup_element<typename gen_numeric_list<int, N>::type, 0> type;
+};
+
+template <typename iib>
+struct tensor_static_symgroup_multiply_helper {
+  template <int... iia>
+  constexpr static numeric_list<int, get<iia, iib>::value...> helper(numeric_list<int, iia...>) {
+    return numeric_list<int, get<iia, iib>::value...>();
+  }
+};
+
+template <typename A, typename B>
+struct tensor_static_symgroup_multiply {
+ private:
+  typedef typename A::indices iia;
+  typedef typename B::indices iib;
+  constexpr static int ffa = A::flags;
+  constexpr static int ffb = B::flags;
+
+ public:
+  static_assert(iia::count == iib::count, "Cannot multiply symmetry elements with different number of indices.");
+
+  typedef tensor_static_symgroup_element<decltype(tensor_static_symgroup_multiply_helper<iib>::helper(iia())),
+                                         ffa ^ ffb>
+      type;
+};
+
+template <typename A, typename B>
+struct tensor_static_symgroup_equality {
+  typedef typename A::indices iia;
+  typedef typename B::indices iib;
+  constexpr static int ffa = A::flags;
+  constexpr static int ffb = B::flags;
+  static_assert(iia::count == iib::count, "Cannot compare symmetry elements with different number of indices.");
+
+  constexpr static bool value = is_same<iia, iib>::value;
+
+ private:
+  /* this should be zero if they are identical, or else the tensor
+   * will be forced to be pure real, pure imaginary or even pure zero
+   */
+  constexpr static int flags_cmp_ = ffa ^ ffb;
+
+  /* either they are not equal, then we don't care whether the flags
+   * match, or they are equal, and then we have to check
+   */
+  constexpr static bool is_zero = value && flags_cmp_ == NegationFlag;
+  constexpr static bool is_real = value && flags_cmp_ == ConjugationFlag;
+  constexpr static bool is_imag = value && flags_cmp_ == (NegationFlag | ConjugationFlag);
+
+ public:
+  constexpr static int global_flags =
+      (is_real ? GlobalRealFlag : 0) | (is_imag ? GlobalImagFlag : 0) | (is_zero ? GlobalZeroFlag : 0);
+};
+
+template <std::size_t NumIndices, typename... Gen>
+struct tensor_static_symgroup {
+  typedef StaticSGroup<Gen...> type;
+  constexpr static std::size_t size = type::static_size;
+};
+
+template <typename Index, std::size_t N, int... ii, int... jj>
+constexpr static std::array<Index, N> tensor_static_symgroup_index_permute(std::array<Index, N> idx,
+                                                                           internal::numeric_list<int, ii...>,
+                                                                           internal::numeric_list<int, jj...>) {
+  return {{idx[ii]..., idx[jj]...}};
+}
+
+template <typename Index, int... ii>
+static inline std::vector<Index> tensor_static_symgroup_index_permute(std::vector<Index> idx,
+                                                                      internal::numeric_list<int, ii...>) {
+  std::vector<Index> result{{idx[ii]...}};
+  std::size_t target_size = idx.size();
+  for (std::size_t i = result.size(); i < target_size; i++) result.push_back(idx[i]);
+  return result;
+}
+
+template <typename T>
+struct tensor_static_symgroup_do_apply;
+
+template <typename first, typename... next>
+struct tensor_static_symgroup_do_apply<internal::type_list<first, next...>> {
+  template <typename Op, typename RV, std::size_t SGNumIndices, typename Index, std::size_t NumIndices,
+            typename... Args>
+  static inline RV run(const std::array<Index, NumIndices>& idx, RV initial, Args&&... args) {
+    static_assert(NumIndices >= SGNumIndices,
+                  "Can only apply symmetry group to objects that have at least the required amount of indices.");
+    typedef typename internal::gen_numeric_list<int, NumIndices - SGNumIndices, SGNumIndices>::type remaining_indices;
+    initial = Op::run(tensor_static_symgroup_index_permute(idx, typename first::indices(), remaining_indices()),
+                      first::flags, initial, std::forward<Args>(args)...);
+    return tensor_static_symgroup_do_apply<internal::type_list<next...>>::template run<Op, RV, SGNumIndices>(
+        idx, initial, args...);
+  }
+
+  template <typename Op, typename RV, std::size_t SGNumIndices, typename Index, typename... Args>
+  static inline RV run(const std::vector<Index>& idx, RV initial, Args&&... args) {
+    eigen_assert(idx.size() >= SGNumIndices &&
+                 "Can only apply symmetry group to objects that have at least the required amount of indices.");
+    initial = Op::run(tensor_static_symgroup_index_permute(idx, typename first::indices()), first::flags, initial,
+                      std::forward<Args>(args)...);
+    return tensor_static_symgroup_do_apply<internal::type_list<next...>>::template run<Op, RV, SGNumIndices>(
+        idx, initial, args...);
+  }
+};
+
+template <EIGEN_TPL_PP_SPEC_HACK_DEF(typename, empty)>
+struct tensor_static_symgroup_do_apply<internal::type_list<EIGEN_TPL_PP_SPEC_HACK_USE(empty)>> {
+  template <typename Op, typename RV, std::size_t SGNumIndices, typename Index, std::size_t NumIndices,
+            typename... Args>
+  static inline RV run(const std::array<Index, NumIndices>&, RV initial, Args&&...) {
+    // do nothing
+    return initial;
+  }
+
+  template <typename Op, typename RV, std::size_t SGNumIndices, typename Index, typename... Args>
+  static inline RV run(const std::vector<Index>&, RV initial, Args&&...) {
+    // do nothing
+    return initial;
+  }
+};
+
+}  // end namespace internal
+
+template <typename... Gen>
+class StaticSGroup {
+  constexpr static std::size_t NumIndices = internal::tensor_symmetry_num_indices<Gen...>::value;
+  typedef internal::group_theory::enumerate_group_elements<
+      internal::tensor_static_symgroup_multiply, internal::tensor_static_symgroup_equality,
+      typename internal::tensor_static_symgroup_identity_ctor<NumIndices>::type,
+      internal::type_list<typename internal::tensor_static_symgroup_element_ctor<Gen, NumIndices>::type...>>
+      group_elements;
+  typedef typename group_elements::type ge;
+
+ public:
+  constexpr StaticSGroup() {}
+  constexpr StaticSGroup(const StaticSGroup<Gen...>&) {}
+  constexpr StaticSGroup(StaticSGroup<Gen...>&&) {}
+
+  template <typename Op, typename RV, typename Index, std::size_t N, typename... Args>
+  static inline RV apply(const std::array<Index, N>& idx, RV initial, Args&&... args) {
+    return internal::tensor_static_symgroup_do_apply<ge>::template run<Op, RV, NumIndices>(idx, initial, args...);
+  }
+
+  template <typename Op, typename RV, typename Index, typename... Args>
+  static inline RV apply(const std::vector<Index>& idx, RV initial, Args&&... args) {
+    eigen_assert(idx.size() == NumIndices);
+    return internal::tensor_static_symgroup_do_apply<ge>::template run<Op, RV, NumIndices>(idx, initial, args...);
+  }
+
+  constexpr static std::size_t static_size = ge::count;
+
+  constexpr static std::size_t size() { return ge::count; }
+  constexpr static int globalFlags() { return group_elements::global_flags; }
+
+  template <typename Tensor_, typename... IndexTypes>
+  inline internal::tensor_symmetry_value_setter<Tensor_, StaticSGroup<Gen...>> operator()(
+      Tensor_& tensor, typename Tensor_::Index firstIndex, IndexTypes... otherIndices) const {
+    static_assert(sizeof...(otherIndices) + 1 == Tensor_::NumIndices,
+                  "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
+    return operator()(tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices>{{firstIndex, otherIndices...}});
+  }
+
+  template <typename Tensor_>
+  inline internal::tensor_symmetry_value_setter<Tensor_, StaticSGroup<Gen...>> operator()(
+      Tensor_& tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices> const& indices) const {
+    return internal::tensor_symmetry_value_setter<Tensor_, StaticSGroup<Gen...>>(tensor, *this, indices);
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSORSYMMETRY_STATICSYMMETRY_H
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h
new file mode 100644
index 0000000..2c57e36
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h
@@ -0,0 +1,335 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_SYMMETRY_H
+#define EIGEN_CXX11_TENSORSYMMETRY_SYMMETRY_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum { NegationFlag = 0x01, ConjugationFlag = 0x02 };
+
+enum { GlobalRealFlag = 0x01, GlobalImagFlag = 0x02, GlobalZeroFlag = 0x03 };
+
+namespace internal {
+
+template <std::size_t NumIndices, typename... Sym>
+struct tensor_symmetry_pre_analysis;
+template <std::size_t NumIndices, typename... Sym>
+struct tensor_static_symgroup;
+template <bool instantiate, std::size_t NumIndices, typename... Sym>
+struct tensor_static_symgroup_if;
+template <typename Tensor_>
+struct tensor_symmetry_calculate_flags;
+template <typename Tensor_>
+struct tensor_symmetry_assign_value;
+template <typename... Sym>
+struct tensor_symmetry_num_indices;
+
+}  // end namespace internal
+
+template <int One_, int Two_>
+struct Symmetry {
+  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
+  constexpr static int One = One_;
+  constexpr static int Two = Two_;
+  constexpr static int Flags = 0;
+};
+
+template <int One_, int Two_>
+struct AntiSymmetry {
+  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
+  constexpr static int One = One_;
+  constexpr static int Two = Two_;
+  constexpr static int Flags = NegationFlag;
+};
+
+template <int One_, int Two_>
+struct Hermiticity {
+  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
+  constexpr static int One = One_;
+  constexpr static int Two = Two_;
+  constexpr static int Flags = ConjugationFlag;
+};
+
+template <int One_, int Two_>
+struct AntiHermiticity {
+  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
+  constexpr static int One = One_;
+  constexpr static int Two = Two_;
+  constexpr static int Flags = ConjugationFlag | NegationFlag;
+};
+
+/** \class DynamicSGroup
+ * \ingroup TensorSymmetry_Module
+ *
+ * \brief Dynamic symmetry group
+ *
+ * The %DynamicSGroup class represents a symmetry group that need not be known at
+ * compile time. It is useful if one wants to support arbitrary run-time definable
+ * symmetries for tensors, but it is also instantiated if a symmetry group is defined
+ * at compile time that would be either too large for the compiler to reasonably
+ * generate (using templates to calculate this at compile time is very inefficient)
+ * or that the compiler could generate the group but that it wouldn't make sense to
+ * unroll the loop for setting coefficients anymore.
+ */
+class DynamicSGroup;
+
+/** \internal
+ *
+ * \class DynamicSGroupFromTemplateArgs
+ * \ingroup TensorSymmetry_Module
+ *
+ * \brief Dynamic symmetry group, initialized from template arguments
+ *
+ * This class is a child class of DynamicSGroup. It uses the template arguments
+ * specified to initialize itself.
+ */
+template <typename... Gen>
+class DynamicSGroupFromTemplateArgs;
+
+/** \class StaticSGroup
+ * \ingroup TensorSymmetry_Module
+ *
+ * \brief Static symmetry group
+ *
+ * This class represents a symmetry group that is known and resolved completely
+ * at compile time. Ideally, no run-time penalty is incurred compared to the
+ * manual unrolling of the symmetry.
+ *
+ * <b><i>CAUTION:</i></b>
+ *
+ * Do not use this class directly for large symmetry groups. The compiler
+ * may run into a limit, or segfault or in the very least will take a very,
+ * very, very long time to compile the code. Use the SGroup class instead
+ * if you want a static group. That class contains logic that will
+ * automatically select the DynamicSGroup class instead if the symmetry
+ * group becomes too large. (In that case, unrolling may not even be
+ * beneficial.)
+ */
+template <typename... Gen>
+class StaticSGroup;
+
+/** \class SGroup
+ * \ingroup TensorSymmetry_Module
+ *
+ * \brief Symmetry group, initialized from template arguments
+ *
+ * This class represents a symmetry group whose generators are already
+ * known at compile time. It may or may not be resolved at compile time,
+ * depending on the estimated size of the group.
+ *
+ * \sa StaticSGroup
+ * \sa DynamicSGroup
+ */
+template <typename... Gen>
+class SGroup : public internal::tensor_symmetry_pre_analysis<internal::tensor_symmetry_num_indices<Gen...>::value,
+                                                             Gen...>::root_type {
+ public:
+  constexpr static std::size_t NumIndices = internal::tensor_symmetry_num_indices<Gen...>::value;
+  typedef typename internal::tensor_symmetry_pre_analysis<NumIndices, Gen...>::root_type Base;
+
+  // make standard constructors + assignment operators public
+  inline SGroup() : Base() {}
+  inline SGroup(const SGroup<Gen...>& other) : Base(other) {}
+  inline SGroup(SGroup<Gen...>&& other) : Base(other) {}
+  inline SGroup<Gen...>& operator=(const SGroup<Gen...>& other) {
+    Base::operator=(other);
+    return *this;
+  }
+  inline SGroup<Gen...>& operator=(SGroup<Gen...>&& other) {
+    Base::operator=(other);
+    return *this;
+  }
+
+  // all else is defined in the base class
+};
+
+namespace internal {
+
+template <typename... Sym>
+struct tensor_symmetry_num_indices {
+  constexpr static std::size_t value = 1;
+};
+
+template <int One_, int Two_, typename... Sym>
+struct tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...> {
+ private:
+  constexpr static std::size_t One = static_cast<std::size_t>(One_);
+  constexpr static std::size_t Two = static_cast<std::size_t>(Two_);
+  constexpr static std::size_t Three = tensor_symmetry_num_indices<Sym...>::value;
+
+  // don't use std::max, since it's not constexpr until C++14...
+  constexpr static std::size_t maxOneTwoPlusOne = ((One > Two) ? One : Two) + 1;
+
+ public:
+  constexpr static std::size_t value = (maxOneTwoPlusOne > Three) ? maxOneTwoPlusOne : Three;
+};
+
+template <int One_, int Two_, typename... Sym>
+struct tensor_symmetry_num_indices<AntiSymmetry<One_, Two_>, Sym...>
+    : public tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...> {};
+template <int One_, int Two_, typename... Sym>
+struct tensor_symmetry_num_indices<Hermiticity<One_, Two_>, Sym...>
+    : public tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...> {};
+template <int One_, int Two_, typename... Sym>
+struct tensor_symmetry_num_indices<AntiHermiticity<One_, Two_>, Sym...>
+    : public tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...> {};
+
+/** \internal
+ *
+ * \class tensor_symmetry_pre_analysis
+ * \ingroup TensorSymmetry_Module
+ *
+ * \brief Pre-select whether to use a static or dynamic symmetry group
+ *
+ * When a symmetry group could in principle be determined at compile time,
+ * this template implements the logic whether to actually do that or whether
+ * to rather defer that to runtime.
+ *
+ * The logic is as follows:
+ * <dl>
+ * <dt><b>No generators (trivial symmetry):</b></dt>
+ * <dd>Use a trivial static group. Ideally, this has no performance impact
+ *     compared to not using symmetry at all. In practice, this might not
+ *     be the case.</dd>
+ * <dt><b>More than 4 generators:</b></dt>
+ * <dd>Calculate the group at run time, it is likely far too large for the
+ *     compiler to be able to properly generate it in a realistic time.</dd>
+ * <dt><b>Up to and including 4 generators:</b></dt>
+ * <dd>Actually enumerate all group elements, but then check how many there
+ *     are. If there are more than 16, it is unlikely that unrolling the
+ *     loop (as is done in the static compile-time case) is sensible, so
+ *     use a dynamic group instead. If there are at most 16 elements, actually
+ *     use that static group. Note that the largest group with 4 generators
+ *     still compiles with reasonable resources.</dd>
+ * </dl>
+ *
+ * Note: Example compile time performance with g++-4.6 on an Intenl Core i5-3470
+ *       with 16 GiB RAM (all generators non-redundant and the subgroups don't
+ *       factorize):
+ *
+ *          # Generators          -O0 -ggdb               -O2
+ *          -------------------------------------------------------------------
+ *          1                 0.5 s  /   250 MiB     0.45s /   230 MiB
+ *          2                 0.5 s  /   260 MiB     0.5 s /   250 MiB
+ *          3                 0.65s  /   310 MiB     0.62s /   310 MiB
+ *          4                 2.2 s  /   860 MiB     1.7 s /   770 MiB
+ *          5               130   s  / 13000 MiB   120   s / 11000 MiB
+ *
+ * It is clear that everything is still very efficient up to 4 generators, then
+ * the memory and CPU requirements become unreasonable. Thus we only instantiate
+ * the template group theory logic if the number of generators supplied is 4 or
+ * lower, otherwise this will be forced to be done during runtime, where the
+ * algorithm is reasonably fast.
+ */
+template <std::size_t NumIndices>
+struct tensor_symmetry_pre_analysis<NumIndices> {
+  typedef StaticSGroup<> root_type;
+};
+
+template <std::size_t NumIndices, typename Gen_, typename... Gens_>
+struct tensor_symmetry_pre_analysis<NumIndices, Gen_, Gens_...> {
+  constexpr static std::size_t max_static_generators = 4;
+  constexpr static std::size_t max_static_elements = 16;
+  typedef tensor_static_symgroup_if<(sizeof...(Gens_) + 1 <= max_static_generators), NumIndices, Gen_, Gens_...> helper;
+  constexpr static std::size_t possible_size = helper::size;
+
+  typedef std::conditional_t<possible_size == 0 || possible_size >= max_static_elements,
+                             DynamicSGroupFromTemplateArgs<Gen_, Gens_...>, typename helper::type>
+      root_type;
+};
+
+template <bool instantiate, std::size_t NumIndices, typename... Gens>
+struct tensor_static_symgroup_if {
+  constexpr static std::size_t size = 0;
+  typedef void type;
+};
+
+template <std::size_t NumIndices, typename... Gens>
+struct tensor_static_symgroup_if<true, NumIndices, Gens...> : tensor_static_symgroup<NumIndices, Gens...> {};
+
+template <typename Tensor_>
+struct tensor_symmetry_assign_value {
+  typedef typename Tensor_::Index Index;
+  typedef typename Tensor_::Scalar Scalar;
+  constexpr static std::size_t NumIndices = Tensor_::NumIndices;
+
+  static inline int run(const std::array<Index, NumIndices>& transformed_indices, int transformation_flags, int dummy,
+                        Tensor_& tensor, const Scalar& value_) {
+    Scalar value(value_);
+    if (transformation_flags & ConjugationFlag) value = numext::conj(value);
+    if (transformation_flags & NegationFlag) value = -value;
+    tensor.coeffRef(transformed_indices) = value;
+    return dummy;
+  }
+};
+
+template <typename Tensor_>
+struct tensor_symmetry_calculate_flags {
+  typedef typename Tensor_::Index Index;
+  constexpr static std::size_t NumIndices = Tensor_::NumIndices;
+
+  static inline int run(const std::array<Index, NumIndices>& transformed_indices, int transform_flags,
+                        int current_flags, const std::array<Index, NumIndices>& orig_indices) {
+    if (transformed_indices == orig_indices) {
+      if (transform_flags & (ConjugationFlag | NegationFlag))
+        return current_flags | GlobalImagFlag;  // anti-hermitian diagonal
+      else if (transform_flags & ConjugationFlag)
+        return current_flags | GlobalRealFlag;  // hermitian diagonal
+      else if (transform_flags & NegationFlag)
+        return current_flags | GlobalZeroFlag;  // anti-symmetric diagonal
+    }
+    return current_flags;
+  }
+};
+
+template <typename Tensor_, typename Symmetry_, int Flags = 0>
+class tensor_symmetry_value_setter {
+ public:
+  typedef typename Tensor_::Index Index;
+  typedef typename Tensor_::Scalar Scalar;
+  constexpr static std::size_t NumIndices = Tensor_::NumIndices;
+
+  inline tensor_symmetry_value_setter(Tensor_& tensor, Symmetry_ const& symmetry,
+                                      std::array<Index, NumIndices> const& indices)
+      : m_tensor(tensor), m_symmetry(symmetry), m_indices(indices) {}
+
+  inline tensor_symmetry_value_setter<Tensor_, Symmetry_, Flags>& operator=(Scalar const& value) {
+    doAssign(value);
+    return *this;
+  }
+
+ private:
+  Tensor_& m_tensor;
+  Symmetry_ m_symmetry;
+  std::array<Index, NumIndices> m_indices;
+
+  inline void doAssign(Scalar const& value) {
+#ifdef EIGEN_TENSOR_SYMMETRY_CHECK_VALUES
+    int value_flags = m_symmetry.template apply<internal::tensor_symmetry_calculate_flags<Tensor_>, int>(
+        m_indices, m_symmetry.globalFlags(), m_indices);
+    if (value_flags & GlobalRealFlag) eigen_assert(numext::imag(value) == 0);
+    if (value_flags & GlobalImagFlag) eigen_assert(numext::real(value) == 0);
+#endif
+    m_symmetry.template apply<internal::tensor_symmetry_assign_value<Tensor_>, int>(m_indices, 0, m_tensor, value);
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSORSYMMETRY_SYMMETRY_H
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
new file mode 100644
index 0000000..aa16f3c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
@@ -0,0 +1,492 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_TEMPLATEGROUPTHEORY_H
+#define EIGEN_CXX11_TENSORSYMMETRY_TEMPLATEGROUPTHEORY_H
+
+// IWYU pragma: private
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+namespace group_theory {
+
+/** \internal
+ * \file CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
+ * This file contains C++ templates that implement group theory algorithms.
+ *
+ * The algorithms allow for a compile-time analysis of finite groups.
+ *
+ * Currently only Dimino's algorithm is implemented, which returns a list
+ * of all elements in a group given a set of (possibly redundant) generators.
+ * (One could also do that with the so-called orbital algorithm, but that
+ * is much more expensive and usually has no advantages.)
+ */
+
+/**********************************************************************
+ *                "Ok kid, here is where it gets complicated."
+ *                         - Amelia Pond in the "Doctor Who" episode
+ *                           "The Big Bang"
+ *
+ * Dimino's algorithm
+ * ==================
+ *
+ * The following is Dimino's algorithm in sequential form:
+ *
+ * Input: identity element, list of generators, equality check,
+ *        multiplication operation
+ * Output: list of group elements
+ *
+ * 1. add identity element
+ * 2. remove identities from list of generators
+ * 3. add all powers of first generator that aren't the
+ *    identity element
+ * 4. go through all remaining generators:
+ *        a. if generator is already in the list of elements
+ *                -> do nothing
+ *        b. otherwise
+ *                i.   remember current # of elements
+ *                     (i.e. the size of the current subgroup)
+ *                ii.  add all current elements (which includes
+ *                     the identity) each multiplied from right
+ *                     with the current generator to the group
+ *                iii. add all remaining cosets that are generated
+ *                     by products of the new generator with itself
+ *                     and all other generators seen so far
+ *
+ * In functional form, this is implemented as a long set of recursive
+ * templates that have a complicated relationship.
+ *
+ * The main interface for Dimino's algorithm is the template
+ * enumerate_group_elements. All lists are implemented as variadic
+ * type_list<typename...> and numeric_list<typename = int, int...>
+ * templates.
+ *
+ * 'Calling' templates is usually done via typedefs.
+ *
+ * This algorithm is an extended version of the basic version. The
+ * extension consists in the fact that each group element has a set
+ * of flags associated with it. Multiplication of two group elements
+ * with each other results in a group element whose flags are the
+ * XOR of the flags of the previous elements. Each time the algorithm
+ * notices that a group element it just calculated is already in the
+ * list of current elements, the flags of both will be compared and
+ * added to the so-called 'global flags' of the group.
+ *
+ * The rationale behind this extension is that this allows not only
+ * for the description of symmetries between tensor indices, but
+ * also allows for the description of hermiticity, antisymmetry and
+ * antihermiticity. Negation and conjugation each are specific bit
+ * in the flags value and if two different ways to reach a group
+ * element lead to two different flags, this poses a constraint on
+ * the allowed values of the resulting tensor. For example, if a
+ * group element is reach both with and without the conjugation
+ * flags, it is clear that the resulting tensor has to be real.
+ *
+ * Note that this flag mechanism is quite generic and may have other
+ * uses beyond tensor properties.
+ *
+ * IMPORTANT:
+ *     This algorithm assumes the group to be finite. If you try to
+ *     run it with a group that's infinite, the algorithm will only
+ *     terminate once you hit a compiler limit (max template depth).
+ *     Also note that trying to use this implementation to create a
+ *     very large group will probably either make you hit the same
+ *     limit, cause the compiler to segfault or at the very least
+ *     take a *really* long time (hours, days, weeks - sic!) to
+ *     compile. It is not recommended to plug in more than 4
+ *     generators, unless they are independent of each other.
+ */
+
+/** \internal
+ *
+ * \class strip_identities
+ * \ingroup CXX11_TensorSymmetry_Module
+ *
+ * \brief Cleanse a list of group elements of the identity element
+ *
+ * This template is used to make a first pass through all initial
+ * generators of Dimino's algorithm and remove the identity
+ * elements.
+ *
+ * \sa enumerate_group_elements
+ */
+template <template <typename, typename> class Equality, typename id, typename L>
+struct strip_identities;
+
+template <template <typename, typename> class Equality, typename id, typename t, typename... ts>
+struct strip_identities<Equality, id, type_list<t, ts...>> {
+  typedef std::conditional_t<
+      Equality<id, t>::value, typename strip_identities<Equality, id, type_list<ts...>>::type,
+      typename concat<type_list<t>, typename strip_identities<Equality, id, type_list<ts...>>::type>::type>
+      type;
+  constexpr static int global_flags =
+      Equality<id, t>::global_flags | strip_identities<Equality, id, type_list<ts...>>::global_flags;
+};
+
+template <template <typename, typename> class Equality, typename id EIGEN_TPL_PP_SPEC_HACK_DEFC(typename, ts)>
+struct strip_identities<Equality, id, type_list<EIGEN_TPL_PP_SPEC_HACK_USE(ts)>> {
+  typedef type_list<> type;
+  constexpr static int global_flags = 0;
+};
+
+/** \internal
+ *
+ * \class dimino_first_step_elements_helper
+ * \ingroup CXX11_TensorSymmetry_Module
+ *
+ * \brief Recursive template that adds powers of the first generator to the list of group elements
+ *
+ * This template calls itself recursively to add powers of the first
+ * generator to the list of group elements. It stops if it reaches
+ * the identity element again.
+ *
+ * \sa enumerate_group_elements, dimino_first_step_elements
+ */
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename g, typename current_element, typename elements,
+          bool dont_add_current_element  // = false
+          >
+struct dimino_first_step_elements_helper
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    :  // recursive inheritance is too difficult for Doxygen
+       public dimino_first_step_elements_helper<Multiply, Equality, id, g, typename Multiply<current_element, g>::type,
+                                                typename concat<elements, type_list<current_element>>::type,
+                                                Equality<typename Multiply<current_element, g>::type, id>::value> {
+};
+
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename g, typename current_element, typename elements>
+struct dimino_first_step_elements_helper<Multiply, Equality, id, g, current_element, elements, true>
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+{
+  typedef elements type;
+  constexpr static int global_flags = Equality<current_element, id>::global_flags;
+};
+
+/** \internal
+ *
+ * \class dimino_first_step_elements
+ * \ingroup CXX11_TensorSymmetry_Module
+ *
+ * \brief Add all powers of the first generator to the list of group elements
+ *
+ * This template takes the first non-identity generator and generates the initial
+ * list of elements which consists of all powers of that generator. For a group
+ * with just one generated, it would be enumerated after this.
+ *
+ * \sa enumerate_group_elements
+ */
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename generators>
+struct dimino_first_step_elements {
+  typedef typename get<0, generators>::type first_generator;
+  typedef typename skip<1, generators>::type next_generators;
+  typedef type_list<first_generator> generators_done;
+
+  typedef dimino_first_step_elements_helper<Multiply, Equality, id, first_generator, first_generator, type_list<id>,
+                                            false>
+      helper;
+  typedef typename helper::type type;
+  constexpr static int global_flags = helper::global_flags;
+};
+
+/** \internal
+ *
+ * \class dimino_get_coset_elements
+ * \ingroup CXX11_TensorSymmetry_Module
+ *
+ * \brief Generate all elements of a specific coset
+ *
+ * This template generates all the elements of a specific coset by
+ * multiplying all elements in the given subgroup with the new
+ * coset representative. Note that the first element of the
+ * subgroup is always the identity element, so the first element of
+ * the result of this template is going to be the coset
+ * representative itself.
+ *
+ * Note that this template accepts an additional boolean parameter
+ * that specifies whether to actually generate the coset (true) or
+ * just return an empty list (false).
+ *
+ * \sa enumerate_group_elements, dimino_add_cosets_for_rep
+ */
+template <template <typename, typename> class Multiply, typename sub_group_elements, typename new_coset_rep,
+          bool generate_coset  // = true
+          >
+struct dimino_get_coset_elements {
+  typedef typename apply_op_from_right<Multiply, new_coset_rep, sub_group_elements>::type type;
+};
+
+template <template <typename, typename> class Multiply, typename sub_group_elements, typename new_coset_rep>
+struct dimino_get_coset_elements<Multiply, sub_group_elements, new_coset_rep, false> {
+  typedef type_list<> type;
+};
+
+/** \internal
+ *
+ * \class dimino_add_cosets_for_rep
+ * \ingroup CXX11_TensorSymmetry_Module
+ *
+ * \brief Recursive template for adding coset spaces
+ *
+ * This template multiplies the coset representative with a generator
+ * from the list of previous generators. If the new element is not in
+ * the group already, it adds the corresponding coset. Finally it
+ * proceeds to call itself with the next generator from the list.
+ *
+ * \sa enumerate_group_elements, dimino_add_all_coset_spaces
+ */
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename sub_group_elements, typename elements, typename generators, typename rep_element, int sub_group_size>
+struct dimino_add_cosets_for_rep;
+
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename sub_group_elements, typename elements, typename g, typename... gs, typename rep_element,
+          int sub_group_size>
+struct dimino_add_cosets_for_rep<Multiply, Equality, id, sub_group_elements, elements, type_list<g, gs...>, rep_element,
+                                 sub_group_size> {
+  typedef typename Multiply<rep_element, g>::type new_coset_rep;
+  typedef contained_in_list_gf<Equality, new_coset_rep, elements> _cil;
+  constexpr static bool add_coset = !_cil::value;
+
+  typedef
+      typename dimino_get_coset_elements<Multiply, sub_group_elements, new_coset_rep, add_coset>::type coset_elements;
+
+  typedef dimino_add_cosets_for_rep<Multiply, Equality, id, sub_group_elements,
+                                    typename concat<elements, coset_elements>::type, type_list<gs...>, rep_element,
+                                    sub_group_size>
+      _helper;
+
+  typedef typename _helper::type type;
+  constexpr static int global_flags = _cil::global_flags | _helper::global_flags;
+
+  /* Note that we don't have to update global flags here, since
+   * we will only add these elements if they are not part of
+   * the group already. But that only happens if the coset rep
+   * is not already in the group, so the check for the coset rep
+   * will catch this.
+   */
+};
+
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename sub_group_elements, typename elements EIGEN_TPL_PP_SPEC_HACK_DEFC(typename, empty),
+          typename rep_element, int sub_group_size>
+struct dimino_add_cosets_for_rep<Multiply, Equality, id, sub_group_elements, elements,
+                                 type_list<EIGEN_TPL_PP_SPEC_HACK_USE(empty)>, rep_element, sub_group_size> {
+  typedef elements type;
+  constexpr static int global_flags = 0;
+};
+
+/** \internal
+ *
+ * \class dimino_add_all_coset_spaces
+ * \ingroup CXX11_TensorSymmetry_Module
+ *
+ * \brief Recursive template for adding all coset spaces for a new generator
+ *
+ * This template tries to go through the list of generators (with
+ * the help of the dimino_add_cosets_for_rep template) as long as
+ * it still finds elements that are not part of the group and add
+ * the corresponding cosets.
+ *
+ * \sa enumerate_group_elements, dimino_add_cosets_for_rep
+ */
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename sub_group_elements, typename elements, typename generators, int sub_group_size, int rep_pos,
+          bool stop_condition  // = false
+          >
+struct dimino_add_all_coset_spaces {
+  typedef typename get<rep_pos, elements>::type rep_element;
+  typedef dimino_add_cosets_for_rep<Multiply, Equality, id, sub_group_elements, elements, generators, rep_element,
+                                    sub_group_elements::count>
+      _ac4r;
+  typedef typename _ac4r::type new_elements;
+
+  constexpr static int new_rep_pos = rep_pos + sub_group_elements::count;
+  constexpr static bool new_stop_condition = new_rep_pos >= new_elements::count;
+
+  typedef dimino_add_all_coset_spaces<Multiply, Equality, id, sub_group_elements, new_elements, generators,
+                                      sub_group_size, new_rep_pos, new_stop_condition>
+      _helper;
+
+  typedef typename _helper::type type;
+  constexpr static int global_flags = _helper::global_flags | _ac4r::global_flags;
+};
+
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename sub_group_elements, typename elements, typename generators, int sub_group_size, int rep_pos>
+struct dimino_add_all_coset_spaces<Multiply, Equality, id, sub_group_elements, elements, generators, sub_group_size,
+                                   rep_pos, true> {
+  typedef elements type;
+  constexpr static int global_flags = 0;
+};
+
+/** \internal
+ *
+ * \class dimino_add_generator
+ * \ingroup CXX11_TensorSymmetry_Module
+ *
+ * \brief Enlarge the group by adding a new generator.
+ *
+ * It accepts a boolean parameter that determines if the generator is redundant,
+ * i.e. was already seen in the group. In that case, it reduces to a no-op.
+ *
+ * \sa enumerate_group_elements, dimino_add_all_coset_spaces
+ */
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename elements, typename generators_done, typename current_generator,
+          bool redundant  // = false
+          >
+struct dimino_add_generator {
+  /* this template is only called if the generator is not redundant
+   * => all elements of the group multiplied with the new generator
+   *    are going to be new elements of the most trivial coset space
+   */
+  typedef typename apply_op_from_right<Multiply, current_generator, elements>::type multiplied_elements;
+  typedef typename concat<elements, multiplied_elements>::type new_elements;
+
+  constexpr static int rep_pos = elements::count;
+
+  typedef dimino_add_all_coset_spaces<
+      Multiply, Equality, id,
+      elements,  // elements of previous subgroup
+      new_elements, typename concat<generators_done, type_list<current_generator>>::type,
+      elements::count,  // size of previous subgroup
+      rep_pos,
+      false  // don't stop (because rep_pos >= new_elements::count is always false at this point)
+      >
+      _helper;
+  typedef typename _helper::type type;
+  constexpr static int global_flags = _helper::global_flags;
+};
+
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename elements, typename generators_done, typename current_generator>
+struct dimino_add_generator<Multiply, Equality, id, elements, generators_done, current_generator, true> {
+  // redundant case
+  typedef elements type;
+  constexpr static int global_flags = 0;
+};
+
+/** \internal
+ *
+ * \class dimino_add_remaining_generators
+ * \ingroup CXX11_TensorSymmetry_Module
+ *
+ * \brief Recursive template that adds all remaining generators to a group
+ *
+ * Loop through the list of generators that remain and successively
+ * add them to the group.
+ *
+ * \sa enumerate_group_elements, dimino_add_generator
+ */
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename generators_done, typename remaining_generators, typename elements>
+struct dimino_add_remaining_generators {
+  typedef typename get<0, remaining_generators>::type first_generator;
+  typedef typename skip<1, remaining_generators>::type next_generators;
+
+  typedef contained_in_list_gf<Equality, first_generator, elements> _cil;
+
+  typedef dimino_add_generator<Multiply, Equality, id, elements, generators_done, first_generator, _cil::value> _helper;
+
+  typedef typename _helper::type new_elements;
+
+  typedef dimino_add_remaining_generators<Multiply, Equality, id,
+                                          typename concat<generators_done, type_list<first_generator>>::type,
+                                          next_generators, new_elements>
+      _next_iter;
+
+  typedef typename _next_iter::type type;
+  constexpr static int global_flags = _cil::global_flags | _helper::global_flags | _next_iter::global_flags;
+};
+
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename generators_done, typename elements>
+struct dimino_add_remaining_generators<Multiply, Equality, id, generators_done, type_list<>, elements> {
+  typedef elements type;
+  constexpr static int global_flags = 0;
+};
+
+/** \internal
+ *
+ * \class enumerate_group_elements_noid
+ * \ingroup CXX11_TensorSymmetry_Module
+ *
+ * \brief Helper template that implements group element enumeration
+ *
+ * This is a helper template that implements the actual enumeration
+ * of group elements. This has been split so that the list of
+ * generators can be cleansed of the identity element before
+ * performing the actual operation.
+ *
+ * \sa enumerate_group_elements
+ */
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename generators, int initial_global_flags = 0>
+struct enumerate_group_elements_noid {
+  typedef dimino_first_step_elements<Multiply, Equality, id, generators> first_step;
+  typedef typename first_step::type first_step_elements;
+
+  typedef dimino_add_remaining_generators<Multiply, Equality, id, typename first_step::generators_done,
+                                          typename first_step::next_generators,  // remaining_generators
+                                          typename first_step::type              // first_step elements
+                                          >
+      _helper;
+
+  typedef typename _helper::type type;
+  constexpr static int global_flags = initial_global_flags | first_step::global_flags | _helper::global_flags;
+};
+
+// in case when no generators are specified
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          int initial_global_flags>
+struct enumerate_group_elements_noid<Multiply, Equality, id, type_list<>, initial_global_flags> {
+  typedef type_list<id> type;
+  constexpr static int global_flags = initial_global_flags;
+};
+
+/** \internal
+ *
+ * \class enumerate_group_elements
+ * \ingroup CXX11_TensorSymmetry_Module
+ *
+ * \brief Enumerate all elements in a finite group
+ *
+ * This template enumerates all elements in a finite group. It accepts
+ * the following template parameters:
+ *
+ * \tparam Multiply      The multiplication operation that multiplies two group elements
+ *                       with each other.
+ * \tparam Equality      The equality check operation that checks if two group elements
+ *                       are equal to another.
+ * \tparam id            The identity element
+ * \tparam Generators_   A list of (possibly redundant) generators of the group
+ */
+template <template <typename, typename> class Multiply, template <typename, typename> class Equality, typename id,
+          typename Generators_>
+struct enumerate_group_elements
+    : public enumerate_group_elements_noid<Multiply, Equality, id,
+                                           typename strip_identities<Equality, id, Generators_>::type,
+                                           strip_identities<Equality, id, Generators_>::global_flags> {};
+
+}  // end namespace group_theory
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSORSYMMETRY_TEMPLATEGROUPTHEORY_H
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/util/CXX11Meta.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/util/CXX11Meta.h
new file mode 100644
index 0000000..74b47ce
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/util/CXX11Meta.h
@@ -0,0 +1,18 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11META_H
+#define EIGEN_CXX11META_H
+
+#include <vector>
+#include "../../../../../Eigen/src/Core/util/EmulateArray.h"
+
+#include "CXX11Workarounds.h"
+
+#endif  // EIGEN_CXX11META_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h
new file mode 100644
index 0000000..632f437
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h
@@ -0,0 +1,85 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11WORKAROUNDS_H
+#define EIGEN_CXX11WORKAROUNDS_H
+
+/* COMPATIBILITY CHECKS
+ * (so users of compilers that are too old get some realistic error messages)
+ */
+#if defined(__INTEL_COMPILER) && (__INTEL_COMPILER < 1310)
+#error Intel Compiler only supports required C++ features since version 13.1.
+// note that most stuff in principle works with 13.0 but when combining
+// some features, at some point 13.0 will just fail with an internal assertion
+#elif defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER) && \
+    (__GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 6))
+// G++ < 4.6 by default will continue processing the source files - even if we use #error to make
+// it error out. For this reason, we use the pragma to make sure G++ aborts at the first error
+// it sees. Unfortunately, that is still not our #error directive, but at least the output is
+// short enough the user has a chance to see that the compiler version is not sufficient for
+// the funky template mojo we use.
+#pragma GCC diagnostic error "-Wfatal-errors"
+#error GNU C++ Compiler (g++) only supports required C++ features since version 4.6.
+#endif
+
+namespace Eigen {
+
+namespace internal {
+
+/* std::get is only constexpr in C++14, not yet in C++11
+ */
+
+template <std::size_t I_, class T>
+constexpr T& array_get(std::vector<T>& a) {
+  return a[I_];
+}
+template <std::size_t I_, class T>
+constexpr T&& array_get(std::vector<T>&& a) {
+  return a[I_];
+}
+template <std::size_t I_, class T>
+constexpr T const& array_get(std::vector<T> const& a) {
+  return a[I_];
+}
+
+/* Suppose you have a template of the form
+ * template<typename T> struct X;
+ * And you want to specialize it in such a way:
+ *    template<typename S1, typename... SN> struct X<Foo<S1, SN...>> { ::: };
+ *    template<>                            struct X<Foo<>>          { ::: };
+ * This will work in Intel's compiler 13.0, but only to some extent in g++ 4.6, since
+ * g++ can only match templates called with parameter packs if the number of template
+ * arguments is not a fixed size (so inside the first specialization, referencing
+ * X<Foo<Sn...>> will fail in g++). On the other hand, g++ will accept the following:
+ *    template<typename S...> struct X<Foo<S...>> { ::: }:
+ * as an additional (!) specialization, which will then only match the empty case.
+ * But Intel's compiler 13.0 won't accept that, it will only accept the empty syntax,
+ * so we have to create a workaround for this.
+ */
+#if defined(__GNUC__) && !defined(__INTEL_COMPILER)
+#define EIGEN_TPL_PP_SPEC_HACK_DEF(mt, n) mt... n
+#define EIGEN_TPL_PP_SPEC_HACK_DEFC(mt, n) , EIGEN_TPL_PP_SPEC_HACK_DEF(mt, n)
+#define EIGEN_TPL_PP_SPEC_HACK_USE(n) n...
+#define EIGEN_TPL_PP_SPEC_HACK_USEC(n) , n...
+#else
+#define EIGEN_TPL_PP_SPEC_HACK_DEF(mt, n)
+#define EIGEN_TPL_PP_SPEC_HACK_DEFC(mt, n)
+#define EIGEN_TPL_PP_SPEC_HACK_USE(n)
+#define EIGEN_TPL_PP_SPEC_HACK_USEC(n)
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11WORKAROUNDS_H
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/EulerAngles b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/EulerAngles
new file mode 100644
index 0000000..e95dd81
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/EulerAngles
@@ -0,0 +1,44 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EULERANGLES_MODULE_H
+#define EIGEN_EULERANGLES_MODULE_H
+
+#include "../../Eigen/Core"
+#include "../../Eigen/Geometry"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+namespace Eigen {
+
+/**
+ * \defgroup EulerAngles_Module EulerAngles module
+ * \brief This module provides generic euler angles rotation.
+ *
+ * Euler angles are a way to represent 3D rotation.
+ *
+ * In order to use this module in your code, include this header:
+ * \code
+ * #include <unsupported/Eigen/EulerAngles>
+ * \endcode
+ *
+ * See \ref EulerAngles for more information.
+ *
+ */
+
+}
+
+// IWYU pragma: begin_exports
+#include "src/EulerAngles/EulerSystem.h"
+#include "src/EulerAngles/EulerAngles.h"
+// IWYU pragma: end_exports
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_EULERANGLES_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/FFT b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/FFT
new file mode 100644
index 0000000..4b7f239
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/FFT
@@ -0,0 +1,430 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Mark Borgerding mark a borgerding net
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FFT_MODULE_H
+#define EIGEN_FFT_MODULE_H
+
+#include <complex>
+#include <vector>
+#include <map>
+#include "../../Eigen/Core"
+
+/**
+ * \defgroup FFT_Module Fast Fourier Transform module
+ *
+ * \code
+ * #include <unsupported/Eigen/FFT>
+ * \endcode
+ *
+ * This module provides Fast Fourier transformation, with a configurable backend
+ * implementation.
+ *
+ * The default implementation is based on kissfft. It is a small, free, and
+ * reasonably efficient default.
+ *
+ * There are currently four implementation backend:
+ *
+ * - kissfft(https://github.com/mborgerding/kissfft) : Simple and not so fast, BSD-3-Clause.
+ *   It is a mixed-radix Fast Fourier Transform based up on the principle, "Keep It Simple, Stupid."
+ *   Notice that:kissfft fails to handle "atypically-sized" inputs(i.e., sizes with large factors),a workaround is using
+ * fftw or pocketfft.
+ * - fftw (http://www.fftw.org) : faster, GPL -- incompatible with Eigen in LGPL form, bigger code size.
+ * - MKL (https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-download.html) : fastest, free -- may be
+ * incompatible with Eigen in GPL form.
+ * - PocketFFT/DUCC (https://gitlab.mpcdf.mpg.de/mtr/pocketfft, https://gitlab.mpcdf.mpg.de/mtr/ducc) : faster than kissfft, BSD 3-clause.
+ *   It is a heavily modified implementation of FFTPack, with the following advantages:
+ *   1.strictly C++11 compliant
+ *   2.more accurate twiddle factor computation
+ *   3.very fast plan generation
+ *   4.worst case complexity for transform sizes with large prime factors is N*log(N), because Bluestein's algorithm is
+ *   According to the author, DUCC contains the "evolution" of pocketfft, though the interface is very similar.
+ * used for these cases
+ *
+ * \section FFTDesign Design
+ *
+ * The following design decisions were made concerning scaling and
+ * half-spectrum for real FFT.
+ *
+ * The intent is to facilitate generic programming and ease migrating code
+ * from  Matlab/octave.
+ * We think the default behavior of Eigen/FFT should favor correctness and
+ * generality over speed. Of course, the caller should be able to "opt-out" from this
+ * behavior and get the speed increase if they want it.
+ *
+ * 1) %Scaling:
+ * Other libraries (FFTW,IMKL,KISSFFT)  do not perform scaling, so there
+ * is a constant gain incurred after the forward&inverse transforms , so
+ * IFFT(FFT(x)) = Kx;  this is done to avoid a vector-by-value multiply.
+ * The downside is that algorithms that worked correctly in Matlab/octave
+ * don't behave the same way once implemented in C++.
+ *
+ * How Eigen/FFT differs: invertible scaling is performed so IFFT( FFT(x) ) = x.
+ *
+ * 2) Real FFT half-spectrum
+ * Other libraries use only half the frequency spectrum (plus one extra
+ * sample for the Nyquist bin) for a real FFT, the other half is the
+ * conjugate-symmetric of the first half.  This saves them a copy and some
+ * memory.  The downside is the caller needs to have special logic for the
+ * number of bins in complex vs real.
+ *
+ * How Eigen/FFT differs: The full spectrum is returned from the forward
+ * transform.  This facilitates generic template programming by obviating
+ * separate specializations for real vs complex.  On the inverse
+ * transform, only half the spectrum is actually used if the output type is real.
+ */
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+
+#ifdef EIGEN_FFTW_DEFAULT
+// FFTW: faster, GPL -- incompatible with Eigen in LGPL form, bigger code size
+#include <fftw3.h>
+#include "src/FFT/fftw_impl.h"
+namespace Eigen {
+// template <typename T> typedef struct internal::fftw_impl  default_fft_impl; this does not work
+template <typename T>
+struct default_fft_impl : public internal::fftw_impl<T> {};
+}  // namespace Eigen
+#elif defined EIGEN_MKL_DEFAULT
+// intel Math Kernel Library: fastest, free -- may be incompatible with Eigen in GPL form
+#include "src/FFT/imklfft_impl.h"
+namespace Eigen {
+template <typename T>
+struct default_fft_impl : public internal::imklfft::imklfft_impl<T> {};
+}  // namespace Eigen
+#elif defined EIGEN_POCKETFFT_DEFAULT
+// internal::pocketfft_impl: a heavily modified implementation of FFTPack, with many advantages.
+#include <pocketfft_hdronly.h>
+#include "src/FFT/pocketfft_impl.h"
+namespace Eigen {
+template <typename T>
+struct default_fft_impl : public internal::pocketfft_impl<T> {};
+}  // namespace Eigen
+#elif defined EIGEN_DUCCFFT_DEFAULT
+#include <ducc0/fft/fft.h>
+#include <ducc0/infra/string_utils.h>
+#include <ducc0/fft/fft.h>
+#include <ducc0/fft/fftnd_impl.h>
+#include "src/FFT/duccfft_impl.h"
+namespace Eigen {
+template <typename T>
+struct default_fft_impl : public internal::duccfft_impl<T> {};
+}  // namespace Eigen
+#else
+// internal::kissfft_impl:  small, free, reasonably efficient default, derived from kissfft
+#include "src/FFT/kissfft_impl.h"
+namespace Eigen {
+template <typename T>
+struct default_fft_impl : public internal::kissfft_impl<T> {};
+}  // namespace Eigen
+#endif
+
+// IWYU pragma: end_exports
+
+namespace Eigen {
+
+//
+template <typename T_SrcMat, typename T_FftIfc>
+struct fft_fwd_proxy;
+template <typename T_SrcMat, typename T_FftIfc>
+struct fft_inv_proxy;
+
+namespace internal {
+template <typename T_SrcMat, typename T_FftIfc>
+struct traits<fft_fwd_proxy<T_SrcMat, T_FftIfc> > {
+  typedef typename T_SrcMat::PlainObject ReturnType;
+};
+template <typename T_SrcMat, typename T_FftIfc>
+struct traits<fft_inv_proxy<T_SrcMat, T_FftIfc> > {
+  typedef typename T_SrcMat::PlainObject ReturnType;
+};
+}  // namespace internal
+
+template <typename T_SrcMat, typename T_FftIfc>
+struct fft_fwd_proxy : public ReturnByValue<fft_fwd_proxy<T_SrcMat, T_FftIfc> > {
+  typedef DenseIndex Index;
+
+  fft_fwd_proxy(const T_SrcMat& src, T_FftIfc& fft, Index nfft) : m_src(src), m_ifc(fft), m_nfft(nfft) {}
+
+  template <typename T_DestMat>
+  void evalTo(T_DestMat& dst) const;
+
+  Index rows() const { return m_src.rows(); }
+  Index cols() const { return m_src.cols(); }
+
+ protected:
+  const T_SrcMat& m_src;
+  T_FftIfc& m_ifc;
+  Index m_nfft;
+};
+
+template <typename T_SrcMat, typename T_FftIfc>
+struct fft_inv_proxy : public ReturnByValue<fft_inv_proxy<T_SrcMat, T_FftIfc> > {
+  typedef DenseIndex Index;
+
+  fft_inv_proxy(const T_SrcMat& src, T_FftIfc& fft, Index nfft) : m_src(src), m_ifc(fft), m_nfft(nfft) {}
+
+  template <typename T_DestMat>
+  void evalTo(T_DestMat& dst) const;
+
+  Index rows() const { return m_src.rows(); }
+  Index cols() const { return m_src.cols(); }
+
+ protected:
+  const T_SrcMat& m_src;
+  T_FftIfc& m_ifc;
+  Index m_nfft;
+};
+
+template <typename T_Scalar, typename T_Impl = default_fft_impl<T_Scalar> >
+class FFT {
+ public:
+  typedef T_Impl impl_type;
+  typedef DenseIndex Index;
+  typedef typename impl_type::Scalar Scalar;
+  typedef typename impl_type::Complex Complex;
+
+  using Flag = int;
+  static constexpr Flag Default = 0;
+  static constexpr Flag Unscaled = 1;
+  static constexpr Flag HalfSpectrum = 2;
+  static constexpr Flag Speedy = 32767;
+
+  FFT(const impl_type& impl = impl_type(), Flag flags = Default) : m_impl(impl), m_flag(flags) {
+    eigen_assert((flags == Default || flags == Unscaled || flags == HalfSpectrum || flags == Speedy) &&
+                 "invalid flags argument");
+  }
+
+  inline bool HasFlag(Flag f) const { return (m_flag & (int)f) == f; }
+
+  inline void SetFlag(Flag f) { m_flag |= (int)f; }
+
+  inline void ClearFlag(Flag f) { m_flag &= (~(int)f); }
+
+  inline void fwd(Complex* dst, const Scalar* src, Index nfft) {
+    m_impl.fwd(dst, src, static_cast<int>(nfft));
+    if (HasFlag(HalfSpectrum) == false) ReflectSpectrum(dst, nfft);
+  }
+
+  inline void fwd(Complex* dst, const Complex* src, Index nfft) { m_impl.fwd(dst, src, static_cast<int>(nfft)); }
+
+#if defined EIGEN_FFTW_DEFAULT || defined EIGEN_POCKETFFT_DEFAULT || defined EIGEN_DUCCFFT_DEFAULT || \
+    defined EIGEN_MKL_DEFAULT
+  inline void fwd2(Complex* dst, const Complex* src, int n0, int n1) { m_impl.fwd2(dst, src, n0, n1); }
+#endif
+
+  template <typename Input_>
+  inline void fwd(std::vector<Complex>& dst, const std::vector<Input_>& src) {
+    if (NumTraits<Input_>::IsComplex == 0 && HasFlag(HalfSpectrum))
+      dst.resize((src.size() >> 1) + 1);  // half the bins + Nyquist bin
+    else
+      dst.resize(src.size());
+    fwd(&dst[0], &src[0], src.size());
+  }
+
+  template <typename InputDerived, typename ComplexDerived>
+  inline void fwd(MatrixBase<ComplexDerived>& dst, const MatrixBase<InputDerived>& src, Index nfft = -1) {
+    typedef typename ComplexDerived::Scalar dst_type;
+    typedef typename InputDerived::Scalar src_type;
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(InputDerived)
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(ComplexDerived)
+    EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(ComplexDerived, InputDerived)  // size at compile-time
+    EIGEN_STATIC_ASSERT(
+        (internal::is_same<dst_type, Complex>::value),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+    EIGEN_STATIC_ASSERT(int(InputDerived::Flags) & int(ComplexDerived::Flags) & DirectAccessBit,
+                        THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES)
+
+    if (nfft < 1) nfft = src.size();
+
+    Index dst_size = nfft;
+    if (NumTraits<src_type>::IsComplex == 0 && HasFlag(HalfSpectrum)) {
+      dst_size = (nfft >> 1) + 1;
+    }
+    dst.derived().resize(dst_size);
+
+    if (src.innerStride() != 1 || src.size() < nfft) {
+      Matrix<src_type, 1, Dynamic> tmp;
+      if (src.size() < nfft) {
+        tmp.setZero(nfft);
+        tmp.block(0, 0, src.size(), 1) = src;
+      } else {
+        tmp = src;
+      }
+      if (dst.innerStride() != 1) {
+        Matrix<dst_type, 1, Dynamic> out(1, dst_size);
+        fwd(&out[0], &tmp[0], nfft);
+        dst.derived() = out;
+      } else {
+        fwd(&dst[0], &tmp[0], nfft);
+      }
+    } else {
+      if (dst.innerStride() != 1) {
+        Matrix<dst_type, 1, Dynamic> out(1, dst_size);
+        fwd(&out[0], &src[0], nfft);
+        dst.derived() = out;
+      } else {
+        fwd(&dst[0], &src[0], nfft);
+      }
+    }
+  }
+
+  template <typename InputDerived>
+  inline fft_fwd_proxy<MatrixBase<InputDerived>, FFT<T_Scalar, T_Impl> > fwd(const MatrixBase<InputDerived>& src,
+                                                                             Index nfft = -1) {
+    return fft_fwd_proxy<MatrixBase<InputDerived>, FFT<T_Scalar, T_Impl> >(src, *this, nfft);
+  }
+
+  template <typename InputDerived>
+  inline fft_inv_proxy<MatrixBase<InputDerived>, FFT<T_Scalar, T_Impl> > inv(const MatrixBase<InputDerived>& src,
+                                                                             Index nfft = -1) {
+    return fft_inv_proxy<MatrixBase<InputDerived>, FFT<T_Scalar, T_Impl> >(src, *this, nfft);
+  }
+
+  inline void inv(Complex* dst, const Complex* src, Index nfft) {
+    m_impl.inv(dst, src, static_cast<int>(nfft));
+    if (HasFlag(Unscaled) == false) scale(dst, Scalar(1. / nfft), nfft);  // scale the time series
+  }
+
+  inline void inv(Scalar* dst, const Complex* src, Index nfft) {
+    m_impl.inv(dst, src, static_cast<int>(nfft));
+    if (HasFlag(Unscaled) == false) scale(dst, Scalar(1. / nfft), nfft);  // scale the time series
+  }
+
+  template <typename OutputDerived, typename ComplexDerived>
+  inline void inv(MatrixBase<OutputDerived>& dst, const MatrixBase<ComplexDerived>& src, Index nfft = -1) {
+    typedef typename ComplexDerived::Scalar src_type;
+    typedef typename ComplexDerived::RealScalar real_type;
+    typedef typename OutputDerived::Scalar dst_type;
+    const bool realfft = (NumTraits<dst_type>::IsComplex == 0);
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(OutputDerived)
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(ComplexDerived)
+    EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(ComplexDerived, OutputDerived)  // size at compile-time
+    EIGEN_STATIC_ASSERT(
+        (internal::is_same<src_type, Complex>::value),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+    EIGEN_STATIC_ASSERT(int(OutputDerived::Flags) & int(ComplexDerived::Flags) & DirectAccessBit,
+                        THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES)
+
+    if (nfft < 1) {  // automatic FFT size determination
+      if (realfft && HasFlag(HalfSpectrum))
+        nfft = 2 * (src.size() - 1);  // assume even fft size
+      else
+        nfft = src.size();
+    }
+    dst.derived().resize(nfft);
+
+    // check for nfft that does not fit the input data size
+    Index resize_input = (realfft && HasFlag(HalfSpectrum)) ? ((nfft / 2 + 1) - src.size()) : (nfft - src.size());
+
+    if (src.innerStride() != 1 || resize_input) {
+      // if the vector is strided, then we need to copy it to a packed temporary
+      Matrix<src_type, 1, Dynamic> tmp;
+      if (resize_input) {
+        size_t ncopy = (std::min)(src.size(), src.size() + resize_input);
+        tmp.setZero(src.size() + resize_input);
+        if (realfft && HasFlag(HalfSpectrum)) {
+          // pad at the Nyquist bin
+          tmp.head(ncopy) = src.head(ncopy);
+          tmp(ncopy - 1) = real(tmp(ncopy - 1));  // enforce real-only Nyquist bin
+        } else {
+          size_t nhead, ntail;
+          nhead = 1 + ncopy / 2 - 1;  // range  [0:pi)
+          ntail = ncopy / 2 - 1;      // range (-pi:0)
+          tmp.head(nhead) = src.head(nhead);
+          tmp.tail(ntail) = src.tail(ntail);
+          if (resize_input <
+              0) {  // shrinking -- create the Nyquist bin as the average of the two bins that fold into it
+            tmp(nhead) = (src(nfft / 2) + src(src.size() - nfft / 2)) * real_type(.5);
+          } else {  // expanding -- split the old Nyquist bin into two halves
+            tmp(nhead) = src(nhead) * real_type(.5);
+            tmp(tmp.size() - nhead) = tmp(nhead);
+          }
+        }
+      } else {
+        tmp = src;
+      }
+
+      if (dst.innerStride() != 1) {
+        Matrix<dst_type, 1, Dynamic> out(1, nfft);
+        inv(&out[0], &tmp[0], nfft);
+        dst.derived() = out;
+      } else {
+        inv(&dst[0], &tmp[0], nfft);
+      }
+    } else {
+      if (dst.innerStride() != 1) {
+        Matrix<dst_type, 1, Dynamic> out(1, nfft);
+        inv(&out[0], &src[0], nfft);
+        dst.derived() = out;
+      } else {
+        inv(&dst[0], &src[0], nfft);
+      }
+    }
+  }
+
+  template <typename Output_>
+  inline void inv(std::vector<Output_>& dst, const std::vector<Complex>& src, Index nfft = -1) {
+    if (nfft < 1)
+      nfft = (NumTraits<Output_>::IsComplex == 0 && HasFlag(HalfSpectrum)) ? 2 * (src.size() - 1) : src.size();
+    dst.resize(nfft);
+    inv(&dst[0], &src[0], nfft);
+  }
+
+#if defined EIGEN_FFTW_DEFAULT || defined EIGEN_POCKETFFT_DEFAULT || defined EIGEN_DUCCFFT_DEFAULT || \
+    defined EIGEN_MKL_DEFAULT
+  inline void inv2(Complex* dst, const Complex* src, int n0, int n1) {
+    m_impl.inv2(dst, src, n0, n1);
+    if (HasFlag(Unscaled) == false) scale(dst, Scalar(1) / (n0 * n1), n0 * n1);
+  }
+#endif
+
+  inline impl_type& impl() { return m_impl; }
+
+ private:
+  template <typename T_Data>
+  inline void scale(T_Data* x, Scalar s, Index nx) {
+#if 1
+    for (int k = 0; k < nx; ++k) *x++ *= s;
+#else
+    if (((ptrdiff_t)x) & 15)
+      Matrix<T_Data, Dynamic, 1>::Map(x, nx) *= s;
+    else
+      Matrix<T_Data, Dynamic, 1>::MapAligned(x, nx) *= s;
+#endif
+  }
+
+  inline void ReflectSpectrum(Complex* freq, Index nfft) {
+    // create the implicit right-half spectrum (conjugate-mirror of the left-half)
+    Index nhbins = (nfft >> 1) + 1;
+    for (Index k = nhbins; k < nfft; ++k) freq[k] = conj(freq[nfft - k]);
+  }
+
+  impl_type m_impl;
+  int m_flag;
+};
+
+template <typename T_SrcMat, typename T_FftIfc>
+template <typename T_DestMat>
+inline void fft_fwd_proxy<T_SrcMat, T_FftIfc>::evalTo(T_DestMat& dst) const {
+  m_ifc.fwd(dst, m_src, m_nfft);
+}
+
+template <typename T_SrcMat, typename T_FftIfc>
+template <typename T_DestMat>
+inline void fft_inv_proxy<T_SrcMat, T_FftIfc>::evalTo(T_DestMat& dst) const {
+  m_ifc.inv(dst, m_src, m_nfft);
+}
+
+}  // namespace Eigen
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/IterativeSolvers b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/IterativeSolvers
new file mode 100644
index 0000000..4ae0975
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/IterativeSolvers
@@ -0,0 +1,92 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ITERATIVE_SOLVERS_MODULE_H
+#define EIGEN_ITERATIVE_SOLVERS_MODULE_H
+
+#include "../../Eigen/Sparse"
+#include "../../Eigen/Jacobi"
+#include "../../Eigen/Householder"
+
+/**
+ * \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
+ * This module aims to provide various iterative linear and non linear solver algorithms.
+ * It currently provides:
+ *  - a Householder GMRES implementation
+ *  - an IDR(s) implementation
+ *  - a BiCGSTAB(L) implementation
+ *  - a DGMRES implementation
+ *  - a MINRES implementation
+ *  - a IDRSTABL implementation
+ *
+ * Choosing the best solver for solving \c A \c x = \c b depends a lot on the preconditioner chosen as well as the
+ *properties of \c A. The following flowchart might help you.
+ * \dot width=50% 
+ * digraph g {
+ *   node [ fontname=Arial, fontsize=11];
+ *   edge [ fontname=Helvetica, fontsize=10 ];
+ *   A1[label="hermitian", shape="box"];
+ *   A2[label="positive definite", shape="box"];
+ *   CG[shape="plaintext"];
+ *   A3[label="ill conditioned", shape="box"];
+ *   A4[label="good preconditioner", shape="box"];
+ *   A5[label="flexible preconditioner", shape="box"];
+ *   A6[label="strongly indefinite", shape="box"];
+ *   A8[label="large imaginary eigenvalue", shape="box"];
+ *   A7[label="large imaginary eigenvalue",shape="box"];
+ *
+ *   SYMMLQ[shape="plaintext"];
+ *   MINRES[shape="plaintext"];
+ *   GCR[shape="plaintext"];
+ *   GMRES[shape="plaintext"];
+ *   IDRSTABL[shape="plaintext"];
+ *   IDRS[shape="plaintext"];
+ *   BICGSTABL[shape="plaintext"];
+ *   BICGSTAB[shape="plaintext"];
+ *
+ *	 A1 -> A2 [label="yes"];
+ *	 A2 -> CG [label="yes"];
+ *	 A2 -> A3 [label="no"];
+ *	 A3 -> SYMMLQ [label="yes"];
+ *	 A3 -> MINRES [label="no"];
+ *
+ *	 A1 -> A4 [label="no"];
+ *	 A4 -> A5 [label="yes"];
+ *	 A5 -> GCR [label="yes"];
+ *	 A5 -> GMRES [label="no"];
+ *
+ *	 A4 -> A6 [label="no"];
+ *	 A6 -> A8 [label="yes"];
+ *	 A6 -> A7 [label="no"];
+ *	 A7 -> BICGSTABL [label="yes"];
+ *	 A7 -> BICGSTAB [label="no"];
+ *	 A8 -> IDRSTABL [label="yes"];
+ *	 A8 -> IDRS [label="no"];
+ * }
+ * \enddot
+ * \code
+ * #include <unsupported/Eigen/IterativeSolvers>
+ * \endcode
+ */
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+#include "src/IterativeSolvers/IncompleteLU.h"
+#include "src/IterativeSolvers/GMRES.h"
+#include "src/IterativeSolvers/DGMRES.h"
+#include "src/IterativeSolvers/MINRES.h"
+#include "src/IterativeSolvers/IDRS.h"
+#include "src/IterativeSolvers/BiCGSTABL.h"
+#include "src/IterativeSolvers/IDRSTABL.h"
+// IWYU pragma: end_exports
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_ITERATIVE_SOLVERS_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/KroneckerProduct b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/KroneckerProduct
new file mode 100644
index 0000000..80432ea
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/KroneckerProduct
@@ -0,0 +1,35 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_KRONECKER_PRODUCT_MODULE_H
+#define EIGEN_KRONECKER_PRODUCT_MODULE_H
+
+#include "../../Eigen/Core"
+#include "../../Eigen/SparseCore"
+
+namespace Eigen {
+
+/**
+ * \defgroup KroneckerProduct_Module KroneckerProduct module
+ *
+ * This module contains an experimental Kronecker product implementation.
+ *
+ * \code
+ * #include <Eigen/KroneckerProduct>
+ * \endcode
+ */
+
+}  // namespace Eigen
+
+// IWYU pragma: begin_exports
+#include "src/KroneckerProduct/KroneckerTensorProduct.h"
+// IWYU pragma: end_exports
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_KRONECKER_PRODUCT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/LevenbergMarquardt b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/LevenbergMarquardt
new file mode 100644
index 0000000..72c6da2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/LevenbergMarquardt
@@ -0,0 +1,49 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LEVENBERGMARQUARDT_MODULE_H
+#define EIGEN_LEVENBERGMARQUARDT_MODULE_H
+
+// #include <vector>
+
+#include "../../Eigen/Core"
+#include "../../Eigen/Jacobi"
+#include "../../Eigen/QR"
+#include "NumericalDiff"
+
+#include "../../Eigen/SparseQR"
+
+/**
+ * \defgroup LevenbergMarquardt_Module Levenberg-Marquardt module
+ *
+ * \code
+ * #include </Eigen/LevenbergMarquardt>
+ * \endcode
+ *
+ *
+ */
+
+#include "../../Eigen/SparseCore"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+#include "src/LevenbergMarquardt/LMqrsolv.h"
+#include "src/LevenbergMarquardt/LMcovar.h"
+#include "src/LevenbergMarquardt/LMpar.h"
+#endif
+
+#include "src/LevenbergMarquardt/LevenbergMarquardt.h"
+#include "src/LevenbergMarquardt/LMonestep.h"
+// IWYU pragma: end_exports
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_LEVENBERGMARQUARDT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/MPRealSupport b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/MPRealSupport
new file mode 100644
index 0000000..322ff45
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/MPRealSupport
@@ -0,0 +1,214 @@
+// This file is part of a joint effort between Eigen, a lightweight C++ template library
+// for linear algebra, and MPFR C++, a C++ interface to MPFR library (http://www.holoborodko.com/pavel/)
+//
+// Copyright (C) 2010-2012 Pavel Holoborodko <pavel@holoborodko.com>
+// Copyright (C) 2010 Konstantin Holoborodko <konstantin@holoborodko.com>
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MPREALSUPPORT_MODULE_H
+#define EIGEN_MPREALSUPPORT_MODULE_H
+
+#include "../../Eigen/Core"
+#include <mpreal.h>
+
+namespace Eigen {
+
+/**
+  * \defgroup MPRealSupport_Module MPFRC++ Support module
+  * \code
+  * #include <Eigen/MPRealSupport>
+  * \endcode
+  *
+  * This module provides support for multi precision floating point numbers
+  * via the <a href="http://www.holoborodko.com/pavel/mpfr">MPFR C++</a>
+  * library which itself is built upon <a href="http://www.mpfr.org/">MPFR</a>/<a href="http://gmplib.org/">GMP</a>.
+  *
+  * \warning MPFR C++ is licensed under the GPL.
+  *
+  * You can find a copy of MPFR C++ that is known to be compatible in the unsupported/test/mpreal folder.
+  *
+  * Here is an example:
+  *
+\code
+#include <iostream>
+#include <Eigen/MPRealSupport>
+#include <Eigen/LU>
+using namespace mpfr;
+using namespace Eigen;
+int main()
+{
+  // set precision to 256 bits (double has only 53 bits)
+  mpreal::set_default_prec(256);
+  // Declare matrix and vector types with multi-precision scalar type
+  typedef Matrix<mpreal,Dynamic,Dynamic>  MatrixXmp;
+  typedef Matrix<mpreal,Dynamic,1>        VectorXmp;
+
+  MatrixXmp A = MatrixXmp::Random(100,100);
+  VectorXmp b = VectorXmp::Random(100);
+
+  // Solve Ax=b using LU
+  VectorXmp x = A.lu().solve(b);
+  std::cout << "relative error: " << (A*x - b).norm() / b.norm() << std::endl;
+  return 0;
+}
+\endcode
+  *
+  */
+
+template <>
+struct NumTraits<mpfr::mpreal> : GenericNumTraits<mpfr::mpreal> {
+  enum {
+    IsInteger = 0,
+    IsSigned = 1,
+    IsComplex = 0,
+    RequireInitialization = 1,
+    ReadCost = HugeCost,
+    AddCost = HugeCost,
+    MulCost = HugeCost
+  };
+
+  typedef mpfr::mpreal Real;
+  typedef mpfr::mpreal NonInteger;
+
+  static inline Real highest(long Precision = mpfr::mpreal::get_default_prec()) { return mpfr::maxval(Precision); }
+  static inline Real lowest(long Precision = mpfr::mpreal::get_default_prec()) { return -mpfr::maxval(Precision); }
+
+  // Constants
+  static inline Real Pi(long Precision = mpfr::mpreal::get_default_prec()) { return mpfr::const_pi(Precision); }
+  static inline Real Euler(long Precision = mpfr::mpreal::get_default_prec()) { return mpfr::const_euler(Precision); }
+  static inline Real Log2(long Precision = mpfr::mpreal::get_default_prec()) { return mpfr::const_log2(Precision); }
+  static inline Real Catalan(long Precision = mpfr::mpreal::get_default_prec()) {
+    return mpfr::const_catalan(Precision);
+  }
+
+  static inline Real epsilon(long Precision = mpfr::mpreal::get_default_prec()) {
+    return mpfr::machine_epsilon(Precision);
+  }
+  static inline Real epsilon(const Real& x) { return mpfr::machine_epsilon(x); }
+
+#ifdef MPREAL_HAVE_DYNAMIC_STD_NUMERIC_LIMITS
+  static inline int digits10(long Precision = mpfr::mpreal::get_default_prec()) {
+    return std::numeric_limits<Real>::digits10(Precision);
+  }
+  static inline int digits10(const Real& x) { return std::numeric_limits<Real>::digits10(x); }
+ 
+  static inline int max_digits10(long Precision = mpfr::mpreal::get_default_prec()) {
+    return std::numeric_limits<Real>::max_digits10(Precision);
+  }
+
+  static inline int digits() { return std::numeric_limits<Real>::digits(); }
+  static inline int digits(const Real& x) { return std::numeric_limits<Real>::digits(x); }
+#endif
+
+  static inline Real dummy_precision() {
+    mpfr_prec_t weak_prec = ((mpfr::mpreal::get_default_prec() - 1) * 90) / 100;
+    return mpfr::machine_epsilon(weak_prec);
+  }
+};
+
+namespace internal {
+
+template <>
+inline mpfr::mpreal random<mpfr::mpreal>() {
+  return mpfr::random();
+}
+
+template <>
+inline mpfr::mpreal random<mpfr::mpreal>(const mpfr::mpreal& a, const mpfr::mpreal& b) {
+  return a + (b - a) * random<mpfr::mpreal>();
+}
+
+inline bool isMuchSmallerThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps) {
+  return mpfr::abs(a) <= mpfr::abs(b) * eps;
+}
+
+inline bool isApprox(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps) {
+  return mpfr::isEqualFuzzy(a, b, eps);
+}
+
+inline bool isApproxOrLessThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps) {
+  return a <= b || mpfr::isEqualFuzzy(a, b, eps);
+}
+
+template <>
+inline long double cast<mpfr::mpreal, long double>(const mpfr::mpreal& x) {
+  return x.toLDouble();
+}
+
+template <>
+inline double cast<mpfr::mpreal, double>(const mpfr::mpreal& x) {
+  return x.toDouble();
+}
+
+template <>
+inline long cast<mpfr::mpreal, long>(const mpfr::mpreal& x) {
+  return x.toLong();
+}
+
+template <>
+inline int cast<mpfr::mpreal, int>(const mpfr::mpreal& x) {
+  return int(x.toLong());
+}
+
+// Specialize GEBP kernel and traits for mpreal (no need for peeling, nor complicated stuff)
+// This also permits to directly call mpfr's routines and avoid many temporaries produced by mpreal
+template <>
+class gebp_traits<mpfr::mpreal, mpfr::mpreal, false, false> {
+ public:
+  typedef mpfr::mpreal ResScalar;
+  enum {
+    Vectorizable = false,
+    LhsPacketSize = 1,
+    RhsPacketSize = 1,
+    ResPacketSize = 1,
+    NumberOfRegisters = 1,
+    nr = 1,
+    mr = 1,
+    LhsProgress = 1,
+    RhsProgress = 1
+  };
+  typedef ResScalar LhsPacket;
+  typedef ResScalar RhsPacket;
+  typedef ResScalar ResPacket;
+  typedef LhsPacket LhsPacket4Packing;
+};
+
+template <typename Index, typename DataMapper, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<mpfr::mpreal, mpfr::mpreal, Index, DataMapper, 1, 1, ConjugateLhs, ConjugateRhs> {
+  typedef mpfr::mpreal mpreal;
+
+  EIGEN_DONT_INLINE void operator()(const DataMapper& res, const mpreal* blockA, const mpreal* blockB, Index rows,
+                                    Index depth, Index cols, const mpreal& alpha, Index strideA = -1,
+                                    Index strideB = -1, Index offsetA = 0, Index offsetB = 0) {
+    if (rows == 0 || cols == 0 || depth == 0) return;
+
+    mpreal acc1(0, mpfr_get_prec(blockA[0].mpfr_srcptr())), tmp(0, mpfr_get_prec(blockA[0].mpfr_srcptr()));
+
+    if (strideA == -1) strideA = depth;
+    if (strideB == -1) strideB = depth;
+
+    for (Index i = 0; i < rows; ++i) {
+      for (Index j = 0; j < cols; ++j) {
+        const mpreal* A = blockA + i * strideA + offsetA;
+        const mpreal* B = blockB + j * strideB + offsetB;
+
+        acc1 = 0;
+        for (Index k = 0; k < depth; k++) {
+          mpfr_mul(tmp.mpfr_ptr(), A[k].mpfr_srcptr(), B[k].mpfr_srcptr(), mpreal::get_default_rnd());
+          mpfr_add(acc1.mpfr_ptr(), acc1.mpfr_ptr(), tmp.mpfr_ptr(), mpreal::get_default_rnd());
+        }
+
+        mpfr_mul(acc1.mpfr_ptr(), acc1.mpfr_srcptr(), alpha.mpfr_srcptr(), mpreal::get_default_rnd());
+        mpfr_add(res(i, j).mpfr_ptr(), res(i, j).mpfr_srcptr(), acc1.mpfr_srcptr(), mpreal::get_default_rnd());
+      }
+    }
+  }
+};
+}  // end namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_MPREALSUPPORT_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/MatrixFunctions b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/MatrixFunctions
new file mode 100644
index 0000000..845ee0b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/MatrixFunctions
@@ -0,0 +1,504 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2012 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_FUNCTIONS_MODULE_H
+#define EIGEN_MATRIX_FUNCTIONS_MODULE_H
+
+#include <cfloat>
+#include <list>
+
+#include "../../Eigen/Core"
+#include "../../Eigen/LU"
+#include "../../Eigen/Eigenvalues"
+
+/**
+ * \defgroup MatrixFunctions_Module Matrix functions module
+ * \brief This module aims to provide various methods for the computation of
+ * matrix functions.
+ *
+ * To use this module, add
+ * \code
+ * #include <unsupported/Eigen/MatrixFunctions>
+ * \endcode
+ * at the start of your source file.
+ *
+ * This module defines the following MatrixBase methods.
+ *  - \ref matrixbase_cos "MatrixBase::cos()", for computing the matrix cosine
+ *  - \ref matrixbase_cosh "MatrixBase::cosh()", for computing the matrix hyperbolic cosine
+ *  - \ref matrixbase_exp "MatrixBase::exp()", for computing the matrix exponential
+ *  - \ref matrixbase_log "MatrixBase::log()", for computing the matrix logarithm
+ *  - \ref matrixbase_pow "MatrixBase::pow()", for computing the matrix power
+ *  - \ref matrixbase_matrixfunction "MatrixBase::matrixFunction()", for computing general matrix functions
+ *  - \ref matrixbase_sin "MatrixBase::sin()", for computing the matrix sine
+ *  - \ref matrixbase_sinh "MatrixBase::sinh()", for computing the matrix hyperbolic sine
+ *  - \ref matrixbase_sqrt "MatrixBase::sqrt()", for computing the matrix square root
+ *
+ * These methods are the main entry points to this module.
+ *
+ * %Matrix functions are defined as follows.  Suppose that \f$ f \f$
+ * is an entire function (that is, a function on the complex plane
+ * that is everywhere complex differentiable).  Then its Taylor
+ * series
+ * \f[ f(0) + f'(0) x + \frac{f''(0)}{2} x^2 + \frac{f'''(0)}{3!} x^3 + \cdots \f]
+ * converges to \f$ f(x) \f$. In this case, we can define the matrix
+ * function by the same series:
+ * \f[ f(M) = f(0) + f'(0) M + \frac{f''(0)}{2} M^2 + \frac{f'''(0)}{3!} M^3 + \cdots \f]
+ *
+ */
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+#include "src/MatrixFunctions/MatrixExponential.h"
+#include "src/MatrixFunctions/MatrixFunction.h"
+#include "src/MatrixFunctions/MatrixSquareRoot.h"
+#include "src/MatrixFunctions/MatrixLogarithm.h"
+#include "src/MatrixFunctions/MatrixPower.h"
+// IWYU pragma: end_exports
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+/**
+\page matrixbaseextra_page
+\ingroup MatrixFunctions_Module
+
+\section matrixbaseextra MatrixBase methods defined in the MatrixFunctions module
+
+The remainder of the page documents the following MatrixBase methods
+which are defined in the MatrixFunctions module.
+
+
+
+\subsection matrixbase_cos MatrixBase::cos()
+
+Compute the matrix cosine.
+
+\code
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cos() const
+\endcode
+
+\param[in]  M  a square matrix.
+\returns  expression representing \f$ \cos(M) \f$.
+
+This function computes the matrix cosine. Use ArrayBase::cos() for computing the entry-wise cosine.
+
+The implementation calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cos().
+
+\sa \ref matrixbase_sin "sin()" for an example.
+
+
+
+\subsection matrixbase_cosh MatrixBase::cosh()
+
+Compute the matrix hyberbolic cosine.
+
+\code
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cosh() const
+\endcode
+
+\param[in]  M  a square matrix.
+\returns  expression representing \f$ \cosh(M) \f$
+
+This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cosh().
+
+\sa \ref matrixbase_sinh "sinh()" for an example.
+
+
+
+\subsection matrixbase_exp MatrixBase::exp()
+
+Compute the matrix exponential.
+
+\code
+const MatrixExponentialReturnValue<Derived> MatrixBase<Derived>::exp() const
+\endcode
+
+\param[in]  M  matrix whose exponential is to be computed.
+\returns    expression representing the matrix exponential of \p M.
+
+The matrix exponential of \f$ M \f$ is defined by
+\f[ \exp(M) = \sum_{k=0}^\infty \frac{M^k}{k!}. \f]
+The matrix exponential can be used to solve linear ordinary
+differential equations: the solution of \f$ y' = My \f$ with the
+initial condition \f$ y(0) = y_0 \f$ is given by
+\f$ y(t) = \exp(M) y_0 \f$.
+
+The matrix exponential is different from applying the exp function to all the entries in the matrix.
+Use ArrayBase::exp() if you want to do the latter.
+
+The cost of the computation is approximately \f$ 20 n^3 \f$ for
+matrices of size \f$ n \f$. The number 20 depends weakly on the
+norm of the matrix.
+
+The matrix exponential is computed using the scaling-and-squaring
+method combined with Pad&eacute; approximation. The matrix is first
+rescaled, then the exponential of the reduced matrix is computed
+approximant, and then the rescaling is undone by repeated
+squaring. The degree of the Pad&eacute; approximant is chosen such
+that the approximation error is less than the round-off
+error. However, errors may accumulate during the squaring phase.
+
+Details of the algorithm can be found in: Nicholas J. Higham, "The
+scaling and squaring method for the matrix exponential revisited,"
+<em>SIAM J. %Matrix Anal. Applic.</em>, <b>26</b>:1179&ndash;1193,
+2005.
+
+Example: The following program checks that
+\f[ \exp \left[ \begin{array}{ccc}
+      0 & \frac14\pi & 0 \\
+      -\frac14\pi & 0 & 0 \\
+      0 & 0 & 0
+    \end{array} \right] = \left[ \begin{array}{ccc}
+      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
+      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
+      0 & 0 & 1
+    \end{array} \right]. \f]
+This corresponds to a rotation of \f$ \frac14\pi \f$ radians around
+the z-axis.
+
+\include MatrixExponential.cpp
+Output: \verbinclude MatrixExponential.out
+
+\note \p M has to be a matrix of \c float, \c double, `long double`
+\c complex<float>, \c complex<double>, or `complex<long double>` .
+
+
+\subsection matrixbase_log MatrixBase::log()
+
+Compute the matrix logarithm.
+
+\code
+const MatrixLogarithmReturnValue<Derived> MatrixBase<Derived>::log() const
+\endcode
+
+\param[in]  M  invertible matrix whose logarithm is to be computed.
+\returns    expression representing the matrix logarithm root of \p M.
+
+The matrix logarithm of \f$ M \f$ is a matrix \f$ X \f$ such that
+\f$ \exp(X) = M \f$ where exp denotes the matrix exponential. As for
+the scalar logarithm, the equation \f$ \exp(X) = M \f$ may have
+multiple solutions; this function returns a matrix whose eigenvalues
+have imaginary part in the interval \f$ (-\pi,\pi] \f$.
+
+The matrix logarithm is different from applying the log function to all the entries in the matrix.
+Use ArrayBase::log() if you want to do the latter.
+
+In the real case, the matrix \f$ M \f$ should be invertible and
+it should have no eigenvalues which are real and negative (pairs of
+complex conjugate eigenvalues are allowed). In the complex case, it
+only needs to be invertible.
+
+This function computes the matrix logarithm using the Schur-Parlett
+algorithm as implemented by MatrixBase::matrixFunction(). The
+logarithm of an atomic block is computed by MatrixLogarithmAtomic,
+which uses direct computation for 1-by-1 and 2-by-2 blocks and an
+inverse scaling-and-squaring algorithm for bigger blocks, with the
+square roots computed by MatrixBase::sqrt().
+
+Details of the algorithm can be found in Section 11.6.2 of:
+Nicholas J. Higham,
+<em>Functions of Matrices: Theory and Computation</em>,
+SIAM 2008. ISBN 978-0-898716-46-7.
+
+Example: The following program checks that
+\f[ \log \left[ \begin{array}{ccc}
+      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
+      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
+      0 & 0 & 1
+    \end{array} \right] = \left[ \begin{array}{ccc}
+      0 & \frac14\pi & 0 \\
+      -\frac14\pi & 0 & 0 \\
+      0 & 0 & 0
+    \end{array} \right]. \f]
+This corresponds to a rotation of \f$ \frac14\pi \f$ radians around
+the z-axis. This is the inverse of the example used in the
+documentation of \ref matrixbase_exp "exp()".
+
+\include MatrixLogarithm.cpp
+Output: \verbinclude MatrixLogarithm.out
+
+\note \p M has to be a matrix of \c float, \c double, `long
+double`, \c complex<float>, \c complex<double>, or `complex<long double>`.
+
+\sa MatrixBase::exp(), MatrixBase::matrixFunction(),
+    class MatrixLogarithmAtomic, MatrixBase::sqrt().
+
+
+\subsection matrixbase_pow MatrixBase::pow()
+
+Compute the matrix raised to arbitrary real power.
+
+\code
+const MatrixPowerReturnValue<Derived> MatrixBase<Derived>::pow(RealScalar p) const
+\endcode
+
+\param[in]  M  base of the matrix power, should be a square matrix.
+\param[in]  p  exponent of the matrix power.
+
+The matrix power \f$ M^p \f$ is defined as \f$ \exp(p \log(M)) \f$,
+where exp denotes the matrix exponential, and log denotes the matrix
+logarithm. This is different from raising all the entries in the matrix
+to the p-th power. Use ArrayBase::pow() if you want to do the latter.
+
+If \p p is complex, the scalar type of \p M should be the type of \p
+p . \f$ M^p \f$ simply evaluates into \f$ \exp(p \log(M)) \f$.
+Therefore, the matrix \f$ M \f$ should meet the conditions to be an
+argument of matrix logarithm.
+
+If \p p is real, it is casted into the real scalar type of \p M. Then
+this function computes the matrix power using the Schur-Pad&eacute;
+algorithm as implemented by class MatrixPower. The exponent is split
+into integral part and fractional part, where the fractional part is
+in the interval \f$ (-1, 1) \f$. The main diagonal and the first
+super-diagonal is directly computed.
+
+If \p M is singular with a semisimple zero eigenvalue and \p p is
+positive, the Schur factor \f$ T \f$ is reordered with Givens
+rotations, i.e.
+
+\f[ T = \left[ \begin{array}{cc}
+      T_1 & T_2 \\
+      0   & 0
+    \end{array} \right] \f]
+
+where \f$ T_1 \f$ is invertible. Then \f$ T^p \f$ is given by
+
+\f[ T^p = \left[ \begin{array}{cc}
+      T_1^p & T_1^{-1} T_1^p T_2 \\
+      0     & 0
+    \end{array}. \right] \f]
+
+\warning Fractional power of a matrix with a non-semisimple zero
+eigenvalue is not well-defined. We introduce an assertion failure
+against inaccurate result, e.g. \code
+#include <unsupported/Eigen/MatrixFunctions>
+#include <iostream>
+
+int main()
+{
+  Eigen::Matrix4d A;
+  A << 0, 0, 2, 3,
+       0, 0, 4, 5,
+       0, 0, 6, 7,
+       0, 0, 8, 9;
+  std::cout << A.pow(0.37) << std::endl;
+
+  // The 1 makes eigenvalue 0 non-semisimple.
+  A.coeffRef(0, 1) = 1;
+
+  // This fails if EIGEN_NO_DEBUG is undefined.
+  std::cout << A.pow(0.37) << std::endl;
+
+  return 0;
+}
+\endcode
+
+Details of the algorithm can be found in: Nicholas J. Higham and
+Lijing Lin, "A Schur-Pad&eacute; algorithm for fractional powers of a
+matrix," <em>SIAM J. %Matrix Anal. Applic.</em>,
+<b>32(3)</b>:1056&ndash;1078, 2011.
+
+Example: The following program checks that
+\f[ \left[ \begin{array}{ccc}
+      \cos1 & -\sin1 & 0 \\
+      \sin1 & \cos1 & 0 \\
+      0 & 0 & 1
+    \end{array} \right]^{\frac14\pi} = \left[ \begin{array}{ccc}
+      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
+      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
+      0 & 0 & 1
+    \end{array} \right]. \f]
+This corresponds to \f$ \frac14\pi \f$ rotations of 1 radian around
+the z-axis.
+
+\include MatrixPower.cpp
+Output: \verbinclude MatrixPower.out
+
+MatrixBase::pow() is user-friendly. However, there are some
+circumstances under which you should use class MatrixPower directly.
+MatrixPower can save the result of Schur decomposition, so it's
+better for computing various powers for the same matrix.
+
+Example:
+\include MatrixPower_optimal.cpp
+Output: \verbinclude MatrixPower_optimal.out
+
+\note \p M has to be a matrix of \c float, \c double, `long
+double`, \c complex<float>, \c complex<double>, or
+\c complex<long double> .
+
+\sa MatrixBase::exp(), MatrixBase::log(), class MatrixPower.
+
+
+\subsection matrixbase_matrixfunction MatrixBase::matrixFunction()
+
+Compute a matrix function.
+
+\code
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::matrixFunction(typename internal::stem_function<typename
+internal::traits<Derived>::Scalar>::type f) const \endcode
+
+\param[in]  M  argument of matrix function, should be a square matrix.
+\param[in]  f  an entire function; \c f(x,n) should compute the n-th
+derivative of f at x.
+\returns  expression representing \p f applied to \p M.
+
+Suppose that \p M is a matrix whose entries have type \c Scalar.
+Then, the second argument, \p f, should be a function with prototype
+\code
+ComplexScalar f(ComplexScalar, int)
+\endcode
+where \c ComplexScalar = \c std::complex<Scalar> if \c Scalar is
+real (e.g., \c float or \c double) and \c ComplexScalar =
+\c Scalar if \c Scalar is complex. The return value of \c f(x,n)
+should be \f$ f^{(n)}(x) \f$, the n-th derivative of f at x.
+
+This routine uses the algorithm described in:
+Philip Davies and Nicholas J. Higham,
+"A Schur-Parlett algorithm for computing matrix functions",
+<em>SIAM J. %Matrix Anal. Applic.</em>, <b>25</b>:464&ndash;485, 2003.
+
+The actual work is done by the MatrixFunction class.
+
+Example: The following program checks that
+\f[ \exp \left[ \begin{array}{ccc}
+      0 & \frac14\pi & 0 \\
+      -\frac14\pi & 0 & 0 \\
+      0 & 0 & 0
+    \end{array} \right] = \left[ \begin{array}{ccc}
+      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
+      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
+      0 & 0 & 1
+    \end{array} \right]. \f]
+This corresponds to a rotation of \f$ \frac14\pi \f$ radians around
+the z-axis. This is the same example as used in the documentation
+of \ref matrixbase_exp "exp()".
+
+\include MatrixFunction.cpp
+Output: \verbinclude MatrixFunction.out
+
+Note that the function \c expfn is defined for complex numbers
+\c x, even though the matrix \c A is over the reals. Instead of
+\c expfn, we could also have used StdStemFunctions::exp:
+\code
+A.matrixFunction(StdStemFunctions<std::complex<double> >::exp, &B);
+\endcode
+
+
+
+\subsection matrixbase_sin MatrixBase::sin()
+
+Compute the matrix sine.
+
+\code
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sin() const
+\endcode
+
+\param[in]  M  a square matrix.
+\returns  expression representing \f$ \sin(M) \f$.
+
+This function computes the matrix sine. Use ArrayBase::sin() for computing the entry-wise sine.
+
+The implementation calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sin().
+
+Example: \include MatrixSine.cpp
+Output: \verbinclude MatrixSine.out
+
+
+
+\subsection matrixbase_sinh MatrixBase::sinh()
+
+Compute the matrix hyperbolic sine.
+
+\code
+MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sinh() const
+\endcode
+
+\param[in]  M  a square matrix.
+\returns  expression representing \f$ \sinh(M) \f$
+
+This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sinh().
+
+Example: \include MatrixSinh.cpp
+Output: \verbinclude MatrixSinh.out
+
+
+\subsection matrixbase_sqrt MatrixBase::sqrt()
+
+Compute the matrix square root.
+
+\code
+const MatrixSquareRootReturnValue<Derived> MatrixBase<Derived>::sqrt() const
+\endcode
+
+\param[in]  M  invertible matrix whose square root is to be computed.
+\returns    expression representing the matrix square root of \p M.
+
+The matrix square root of \f$ M \f$ is the matrix \f$ M^{1/2} \f$
+whose square is the original matrix; so if \f$ S = M^{1/2} \f$ then
+\f$ S^2 = M \f$. This is different from taking the square root of all
+the entries in the matrix; use ArrayBase::sqrt() if you want to do the
+latter.
+
+In the <b>real case</b>, the matrix \f$ M \f$ should be invertible and
+it should have no eigenvalues which are real and negative (pairs of
+complex conjugate eigenvalues are allowed). In that case, the matrix
+has a square root which is also real, and this is the square root
+computed by this function.
+
+The matrix square root is computed by first reducing the matrix to
+quasi-triangular form with the real Schur decomposition. The square
+root of the quasi-triangular matrix can then be computed directly. The
+cost is approximately \f$ 25 n^3 \f$ real flops for the real Schur
+decomposition and \f$ 3\frac13 n^3 \f$ real flops for the remainder
+(though the computation time in practice is likely more than this
+indicates).
+
+Details of the algorithm can be found in: Nicholas J. Highan,
+"Computing real square roots of a real matrix", <em>Linear Algebra
+Appl.</em>, 88/89:405&ndash;430, 1987.
+
+If the matrix is <b>positive-definite symmetric</b>, then the square
+root is also positive-definite symmetric. In this case, it is best to
+use SelfAdjointEigenSolver::operatorSqrt() to compute it.
+
+In the <b>complex case</b>, the matrix \f$ M \f$ should be invertible;
+this is a restriction of the algorithm. The square root computed by
+this algorithm is the one whose eigenvalues have an argument in the
+interval \f$ (-\frac12\pi, \frac12\pi] \f$. This is the usual branch
+cut.
+
+The computation is the same as in the real case, except that the
+complex Schur decomposition is used to reduce the matrix to a
+triangular matrix. The theoretical cost is the same. Details are in:
+&Aring;ke Bj&ouml;rck and Sven Hammarling, "A Schur method for the
+square root of a matrix", <em>Linear Algebra Appl.</em>,
+52/53:127&ndash;140, 1983.
+
+Example: The following program checks that the square root of
+\f[ \left[ \begin{array}{cc}
+              \cos(\frac13\pi) & -\sin(\frac13\pi) \\
+              \sin(\frac13\pi) & \cos(\frac13\pi)
+    \end{array} \right], \f]
+corresponding to a rotation over 60 degrees, is a rotation over 30 degrees:
+\f[ \left[ \begin{array}{cc}
+              \cos(\frac16\pi) & -\sin(\frac16\pi) \\
+              \sin(\frac16\pi) & \cos(\frac16\pi)
+    \end{array} \right]. \f]
+
+\include MatrixSquareRoot.cpp
+Output: \verbinclude MatrixSquareRoot.out
+
+\sa class RealSchur, class ComplexSchur, class MatrixSquareRoot,
+    SelfAdjointEigenSolver::operatorSqrt().
+
+*/
+
+#endif  // EIGEN_MATRIX_FUNCTIONS_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/NNLS b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/NNLS
new file mode 100644
index 0000000..fcafc08
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/NNLS
@@ -0,0 +1,388 @@
+/* Non-Negagive Least Squares Algorithm for Eigen.
+ *
+ * Copyright (C) 2021 Essex Edwards, <essex.edwards@gmail.com>
+ * Copyright (C) 2013 Hannes Matuschek, hannes.matuschek at uni-potsdam.de
+ *
+ * This Source Code Form is subject to the terms of the Mozilla
+ * Public License v. 2.0. If a copy of the MPL was not distributed
+ * with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+ */
+
+/** \defgroup nnls Non-Negative Least Squares (NNLS) Module
+ * This module provides a single class @c Eigen::NNLS implementing the NNLS algorithm.
+ * The algorithm is described in "SOLVING LEAST SQUARES PROBLEMS", by Charles L. Lawson and
+ * Richard J. Hanson, Prentice-Hall, 1974 and solves optimization problems of the form
+ *
+ * \f[ \min \left\Vert Ax-b\right\Vert_2^2\quad s.t.\, x\ge 0\,.\f]
+ *
+ * The algorithm solves the constrained least-squares problem above by iteratively improving
+ * an estimate of which constraints are active (elements of \f$x\f$ equal to zero)
+ * and which constraints are inactive (elements of \f$x\f$ greater than zero).
+ * Each iteration, an unconstrained linear least-squares problem solves for the
+ * components of \f$x\f$ in the (estimated) inactive set and the sets are updated.
+ * The unconstrained problem minimizes \f$\left\Vert A^Nx^N-b\right\Vert_2^2\f$,
+ * where \f$A^N\f$ is a matrix formed by selecting all columns of A which are
+ * in the inactive set \f$N\f$.
+ *
+ */
+
+#ifndef EIGEN_NNLS_H
+#define EIGEN_NNLS_H
+
+#include "../../Eigen/Core"
+#include "../../Eigen/QR"
+
+#include <limits>
+
+namespace Eigen {
+
+/** \ingroup nnls
+ * \class NNLS
+ * \brief Implementation of the Non-Negative Least Squares (NNLS) algorithm.
+ * \tparam MatrixType The type of the system matrix \f$A\f$.
+ *
+ * This class implements the NNLS algorithm as described in "SOLVING LEAST SQUARES PROBLEMS",
+ * Charles L. Lawson and Richard J. Hanson, Prentice-Hall, 1974. This algorithm solves a least
+ * squares problem iteratively and ensures that the solution is non-negative. I.e.
+ *
+ * \f[ \min \left\Vert Ax-b\right\Vert_2^2\quad s.t.\, x\ge 0 \f]
+ *
+ * The algorithm solves the constrained least-squares problem above by iteratively improving
+ * an estimate of which constraints are active (elements of \f$x\f$ equal to zero)
+ * and which constraints are inactive (elements of \f$x\f$ greater than zero).
+ * Each iteration, an unconstrained linear least-squares problem solves for the
+ * components of \f$x\f$ in the (estimated) inactive set and the sets are updated.
+ * The unconstrained problem minimizes \f$\left\Vert A^Nx^N-b\right\Vert_2^2\f$,
+ * where \f$A^N\f$ is a matrix formed by selecting all columns of A which are
+ * in the inactive set \f$N\f$.
+ *
+ * See <a href="https://en.wikipedia.org/wiki/Non-negative_least_squares">the
+ * wikipedia page on non-negative least squares</a> for more background information.
+ *
+ * \note Please note that it is possible to construct an NNLS problem for which the
+ *       algorithm does not converge. In practice these cases are extremely rare.
+ */
+template <class MatrixType_>
+class NNLS {
+ public:
+  typedef MatrixType_ MatrixType;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    Options = MatrixType::Options,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::Index Index;
+
+  /** Type of a row vector of the system matrix \f$A\f$. */
+  typedef Matrix<Scalar, ColsAtCompileTime, 1> SolutionVectorType;
+  /** Type of a column vector of the system matrix \f$A\f$. */
+  typedef Matrix<Scalar, RowsAtCompileTime, 1> RhsVectorType;
+  typedef Matrix<Index, ColsAtCompileTime, 1> IndicesType;
+
+  /** */
+  NNLS();
+
+  /** \brief Constructs a NNLS solver and initializes it with the given system matrix @c A.
+   * \param A Specifies the system matrix.
+   * \param max_iter Specifies the maximum number of iterations to solve the system.
+   * \param tol Specifies the precision of the optimum.
+   *        This is an absolute tolerance on the gradient of the Lagrangian, \f$A^T(Ax-b)-\lambda\f$
+   *        (with Lagrange multipliers \f$\lambda\f$).
+   */
+  NNLS(const MatrixType &A, Index max_iter = -1, Scalar tol = NumTraits<Scalar>::dummy_precision());
+
+  /** Initializes the solver with the matrix \a A for further solving NNLS problems.
+   *
+   * This function mostly initializes/computes the preconditioner. In the future
+   * we might, for instance, implement column reordering for faster matrix vector products.
+   */
+  template <typename MatrixDerived>
+  NNLS<MatrixType> &compute(const EigenBase<MatrixDerived> &A);
+
+  /** \brief Solves the NNLS problem.
+   *
+   * The dimension of @c b must be equal to the number of rows of @c A, given to the constructor.
+   *
+   * \returns The approximate solution vector \f$ x \f$. Use info() to determine if the solve was a success or not.
+   * \sa info()
+   */
+  const SolutionVectorType &solve(const RhsVectorType &b);
+
+  /** \brief Returns the solution if a problem was solved.
+   * If not, an uninitialized vector may be returned. */
+  const SolutionVectorType &x() const { return x_; }
+
+  /** \returns the tolerance threshold used by the stopping criteria.
+   * \sa setTolerance()
+   */
+  Scalar tolerance() const { return tolerance_; }
+
+  /** Sets the tolerance threshold used by the stopping criteria.
+   *
+   * This is an absolute tolerance on the gradient of the Lagrangian, \f$A^T(Ax-b)-\lambda\f$
+   * (with Lagrange multipliers \f$\lambda\f$).
+   */
+  NNLS<MatrixType> &setTolerance(const Scalar &tolerance) {
+    tolerance_ = tolerance;
+    return *this;
+  }
+
+  /** \returns the max number of iterations.
+   * It is either the value set by setMaxIterations or, by default, twice the number of columns of the matrix.
+   */
+  Index maxIterations() const { return max_iter_ < 0 ? 2 * A_.cols() : max_iter_; }
+
+  /** Sets the max number of iterations.
+   * Default is twice the number of columns of the matrix.
+   * The algorithm requires at least k iterations to produce a solution vector with k non-zero entries.
+   */
+  NNLS<MatrixType> &setMaxIterations(Index maxIters) {
+    max_iter_ = maxIters;
+    return *this;
+  }
+
+  /** \returns the number of iterations (least-squares solves) performed during the last solve */
+  Index iterations() const { return iterations_; }
+
+  /** \returns Success if the iterations converged, and an error values otherwise. */
+  ComputationInfo info() const { return info_; }
+
+ private:
+  /** \internal Adds the given index @c idx to the inactive set N and updates the QR decomposition of \f$A^N\f$. */
+  void moveToInactiveSet_(Index idx);
+
+  /** \internal Removes the given index idx from the inactive set N and updates the QR decomposition of \f$A^N\f$. */
+  void moveToActiveSet_(Index idx);
+
+  /** \internal Solves the least-squares problem \f$\left\Vert y-A^Nx\right\Vert_2^2\f$. */
+  void solveInactiveSet_(const RhsVectorType &b);
+
+ private:
+  typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime> MatrixAtAType;
+
+  /** \internal Holds the maximum number of iterations for the NNLS algorithm.
+   *  @c -1 means to use the default value. */
+  Index max_iter_;
+  /** \internal Holds the number of iterations. */
+  Index iterations_;
+  /** \internal Holds success/fail of the last solve. */
+  ComputationInfo info_;
+  /** \internal Size of the inactive set. */
+  Index numInactive_;
+  /** \internal Accuracy of the algorithm w.r.t the optimality of the solution (gradient). */
+  Scalar tolerance_;
+  /** \internal The system matrix, a copy of the one given to the constructor. */
+  MatrixType A_;
+  /** \internal Precomputed product \f$A^TA\f$. */
+  MatrixAtAType AtA_;
+  /** \internal Will hold the solution. */
+  SolutionVectorType x_;
+  /** \internal Will hold the current gradient.\f$A^Tb - A^TAx\f$ */
+  SolutionVectorType gradient_;
+  /** \internal Will hold the partial solution. */
+  SolutionVectorType y_;
+  /** \internal Precomputed product \f$A^Tb\f$. */
+  SolutionVectorType Atb_;
+  /** \internal Holds the current permutation partitioning the active and inactive sets.
+   * The first @c numInactive_ elements form the inactive set and the rest the active set. */
+  IndicesType index_sets_;
+  /** \internal QR decomposition to solve the (inactive) sub system (together with @c qrCoeffs_). */
+  MatrixType QR_;
+  /** \internal QR decomposition to solve the (inactive) sub system (together with @c QR_). */
+  SolutionVectorType qrCoeffs_;
+  /** \internal Some workspace for QR decomposition. */
+  SolutionVectorType tempSolutionVector_;
+  RhsVectorType tempRhsVector_;
+};
+
+/* ********************************************************************************************
+ * Implementation
+ * ******************************************************************************************** */
+
+template <typename MatrixType>
+NNLS<MatrixType>::NNLS()
+    : max_iter_(-1),
+      iterations_(0),
+      info_(ComputationInfo::InvalidInput),
+      numInactive_(0),
+      tolerance_(NumTraits<Scalar>::dummy_precision()) {}
+
+template <typename MatrixType>
+NNLS<MatrixType>::NNLS(const MatrixType &A, Index max_iter, Scalar tol) : max_iter_(max_iter), tolerance_(tol) {
+  compute(A);
+}
+
+template <typename MatrixType>
+template <typename MatrixDerived>
+NNLS<MatrixType> &NNLS<MatrixType>::compute(const EigenBase<MatrixDerived> &A) {
+  // Ensure Scalar type is real. The non-negativity constraint doesn't obviously extend to complex numbers.
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL);
+
+  // max_iter_: unchanged
+  iterations_ = 0;
+  info_ = ComputationInfo::Success;
+  numInactive_ = 0;
+  // tolerance: unchanged
+  A_ = A.derived();
+  AtA_.noalias() = A_.transpose() * A_;
+  x_.resize(A_.cols());
+  gradient_.resize(A_.cols());
+  y_.resize(A_.cols());
+  Atb_.resize(A_.cols());
+  index_sets_.resize(A_.cols());
+  QR_.resize(A_.rows(), A_.cols());
+  qrCoeffs_.resize(A_.cols());
+  tempSolutionVector_.resize(A_.cols());
+  tempRhsVector_.resize(A_.rows());
+
+  return *this;
+}
+
+template <typename MatrixType>
+const typename NNLS<MatrixType>::SolutionVectorType &NNLS<MatrixType>::solve(const RhsVectorType &b) {
+  // Initialize solver
+  iterations_ = 0;
+  info_ = ComputationInfo::NumericalIssue;
+  x_.setZero();
+
+  index_sets_ = IndicesType::LinSpaced(A_.cols(), 0, A_.cols() - 1);  // Identity permutation.
+  numInactive_ = 0;
+
+  // Precompute A^T*b
+  Atb_.noalias() = A_.transpose() * b;
+
+  const Index maxIterations = this->maxIterations();
+
+  // OUTER LOOP
+  while (true) {
+    // Early exit if all variables are inactive, which breaks 'maxCoeff' below.
+    if (A_.cols() == numInactive_) {
+      info_ = ComputationInfo::Success;
+      return x_;
+    }
+
+    // Find the maximum element of the gradient in the active set.
+    // If it is small or negative, then we have converged.
+    // Else, we move that variable to the inactive set.
+    gradient_.noalias() = Atb_ - AtA_ * x_;
+
+    const Index numActive = A_.cols() - numInactive_;
+    Index argmaxGradient = -1;
+    const Scalar maxGradient = gradient_(index_sets_.tail(numActive)).maxCoeff(&argmaxGradient);
+    argmaxGradient += numInactive_;  // because tail() skipped the first numInactive_ elements
+
+    if (maxGradient < tolerance_) {
+      info_ = ComputationInfo::Success;
+      return x_;
+    }
+
+    moveToInactiveSet_(argmaxGradient);
+
+    // INNER LOOP
+    while (true) {
+      // Check if max. number of iterations is reached
+      if (iterations_ >= maxIterations) {
+        info_ = ComputationInfo::NoConvergence;
+        return x_;
+      }
+
+      // Solve least-squares problem in inactive set only,
+      // this step is rather trivial as moveToInactiveSet_ & moveToActiveSet_
+      // updates the QR decomposition of inactive columns A^N.
+      // solveInactiveSet_ puts the solution in y_
+      solveInactiveSet_(b);
+      ++iterations_;  // The solve is expensive, so that is what we count as an iteration.
+
+      // Check feasibility...
+      bool feasible = true;
+      Scalar alpha = NumTraits<Scalar>::highest();
+      Index infeasibleIdx = -1;  // Which variable became infeasible first.
+      for (Index i = 0; i < numInactive_; i++) {
+        Index idx = index_sets_[i];
+        if (y_(idx) < 0) {
+          // t should always be in [0,1].
+          Scalar t = -x_(idx) / (y_(idx) - x_(idx));
+          if (alpha > t) {
+            alpha = t;
+            infeasibleIdx = i;
+            feasible = false;
+          }
+        }
+      }
+      eigen_assert(feasible || 0 <= infeasibleIdx);
+
+      // If solution is feasible, exit to outer loop
+      if (feasible) {
+        x_ = y_;
+        break;
+      }
+
+      // Infeasible solution -> interpolate to feasible one
+      for (Index i = 0; i < numInactive_; i++) {
+        Index idx = index_sets_[i];
+        x_(idx) += alpha * (y_(idx) - x_(idx));
+      }
+
+      // Remove these indices from the inactive set and update QR decomposition
+      moveToActiveSet_(infeasibleIdx);
+    }
+  }
+}
+
+template <typename MatrixType>
+void NNLS<MatrixType>::moveToInactiveSet_(Index idx) {
+  // Update permutation matrix:
+  std::swap(index_sets_(idx), index_sets_(numInactive_));
+  numInactive_++;
+
+  // Perform rank-1 update of the QR decomposition stored in QR_ & qrCoeff_
+  internal::householder_qr_inplace_update(QR_, qrCoeffs_, A_.col(index_sets_(numInactive_ - 1)), numInactive_ - 1,
+                                          tempSolutionVector_.data());
+}
+
+template <typename MatrixType>
+void NNLS<MatrixType>::moveToActiveSet_(Index idx) {
+  // swap index with last inactive one & reduce number of inactive columns
+  std::swap(index_sets_(idx), index_sets_(numInactive_ - 1));
+  numInactive_--;
+  // Update QR decomposition starting from the removed index up to the end [idx, ..., numInactive_]
+  for (Index i = idx; i < numInactive_; i++) {
+    Index col = index_sets_(i);
+    internal::householder_qr_inplace_update(QR_, qrCoeffs_, A_.col(col), i, tempSolutionVector_.data());
+  }
+}
+
+template <typename MatrixType>
+void NNLS<MatrixType>::solveInactiveSet_(const RhsVectorType &b) {
+  eigen_assert(numInactive_ > 0);
+
+  tempRhsVector_ = b;
+
+  // tmpRHS(0:numInactive_-1) := Q'*b
+  // tmpRHS(numInactive_:end) := useless stuff we would rather not compute at all.
+  tempRhsVector_.applyOnTheLeft(
+      householderSequence(QR_.leftCols(numInactive_), qrCoeffs_.head(numInactive_)).transpose());
+
+  // tempSol(0:numInactive_-1) := inv(R) * Q' * b
+  //  = the least-squares solution for the inactive variables.
+  tempSolutionVector_.head(numInactive_) =            //
+      QR_.topLeftCorner(numInactive_, numInactive_)   //
+          .template triangularView<Upper>()           //
+          .solve(tempRhsVector_.head(numInactive_));  //
+
+  // tempSol(numInactive_:end) := 0 = the value for the constrained variables.
+  tempSolutionVector_.tail(y_.size() - numInactive_).setZero();
+
+  // Back permute into original column order of A
+  y_.noalias() = index_sets_.asPermutation() * tempSolutionVector_.head(y_.size());
+}
+
+}  // namespace Eigen
+
+#endif  // EIGEN_NNLS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/NonLinearOptimization b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/NonLinearOptimization
new file mode 100644
index 0000000..486dd4a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/NonLinearOptimization
@@ -0,0 +1,141 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NONLINEAROPTIMIZATION_MODULE_H
+#define EIGEN_NONLINEAROPTIMIZATION_MODULE_H
+
+#include <vector>
+
+#include "../../Eigen/Core"
+#include "../../Eigen/Jacobi"
+#include "../../Eigen/QR"
+#include "NumericalDiff"
+
+/**
+ * \defgroup NonLinearOptimization_Module Non linear optimization module
+ *
+ * \code
+ * #include <unsupported/Eigen/NonLinearOptimization>
+ * \endcode
+ *
+ * This module provides implementation of two important algorithms in non linear
+ * optimization. In both cases, we consider a system of non linear functions. Of
+ * course, this should work, and even work very well if those functions are
+ * actually linear. But if this is so, you should probably better use other
+ * methods more fitted to this special case.
+ *
+ * One algorithm allows to find a least-squares solution of such a system
+ * (Levenberg-Marquardt algorithm) and the second one is used to find
+ * a zero for the system (Powell hybrid "dogleg" method).
+ *
+ * This code is a port of minpack (http://en.wikipedia.org/wiki/MINPACK).
+ * Minpack is a very famous, old, robust and well renowned package, written in
+ * fortran. Those implementations have been carefully tuned, tested, and used
+ * for several decades.
+ *
+ * The original fortran code was automatically translated using f2c (http://en.wikipedia.org/wiki/F2c) in C,
+ * then c++, and then cleaned by several different authors.
+ * The last one of those cleanings being our starting point :
+ * http://devernay.free.fr/hacks/cminpack.html
+ *
+ * Finally, we ported this code to Eigen, creating classes and API
+ * coherent with Eigen. When possible, we switched to Eigen
+ * implementation, such as most linear algebra (vectors, matrices, stable norms).
+ *
+ * Doing so, we were very careful to check the tests we setup at the very
+ * beginning, which ensure that the same results are found.
+ *
+ * \section Tests Tests
+ *
+ * The tests are placed in the file unsupported/test/NonLinear.cpp.
+ *
+ * There are two kinds of tests : those that come from examples bundled with cminpack.
+ * They guaranty we get the same results as the original algorithms (value for 'x',
+ * for the number of evaluations of the function, and for the number of evaluations
+ * of the Jacobian if ever).
+ *
+ * Other tests were added by myself at the very beginning of the
+ * process and check the results for Levenberg-Marquardt using the reference data
+ * on http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml. Since then i've
+ * carefully checked that the same results were obtained when modifying the
+ * code. Please note that we do not always get the exact same decimals as they do,
+ * but this is ok : they use 128bits float, and we do the tests using the C type 'double',
+ * which is 64 bits on most platforms (x86 and amd64, at least).
+ * I've performed those tests on several other implementations of Levenberg-Marquardt, and
+ * (c)minpack performs VERY well compared to those, both in accuracy and speed.
+ *
+ * The documentation for running the tests is on the wiki
+ * http://eigen.tuxfamily.org/index.php?title=Tests
+ *
+ * \section API API: overview of methods
+ *
+ * Both algorithms needs a functor computing the Jacobian. It can be computed by
+ * hand, using auto-differentiation (see \ref AutoDiff_Module), or using numerical
+ * differences (see \ref NumericalDiff_Module). For instance:
+ *\code
+ * MyFunc func;
+ * NumericalDiff<MyFunc> func_with_num_diff(func);
+ * LevenbergMarquardt<NumericalDiff<MyFunc> > lm(func_with_num_diff);
+ * \endcode
+ * For HybridNonLinearSolver, the method solveNumericalDiff() does the above wrapping for
+ * you.
+ *
+ * The methods LevenbergMarquardt.lmder1()/lmdif1()/lmstr1() and
+ * HybridNonLinearSolver.hybrj1()/hybrd1() are specific methods from the original
+ * minpack package that you probably should NOT use until you are porting a code that
+ * was previously using minpack. They just define a 'simple' API with default values
+ * for some parameters.
+ *
+ * All algorithms are provided using two APIs :
+ *     - one where the user inits the algorithm, and uses '*OneStep()' as much as he wants :
+ * this way the caller have control over the steps
+ *     - one where the user just calls a method (optimize() or solve()) which will
+ * handle the loop: init + loop until a stop condition is met. Those are provided for
+ *  convenience.
+ *
+ * As an example, the method LevenbergMarquardt::minimize() is
+ * implemented as follow:
+ * \code
+ * Status LevenbergMarquardt<FunctorType,Scalar>::minimize(FVectorType  &x, const int mode)
+ * {
+ *     Status status = minimizeInit(x, mode);
+ *     do {
+ *         status = minimizeOneStep(x, mode);
+ *     } while (status==Running);
+ *     return status;
+ * }
+ * \endcode
+ *
+ * \section examples Examples
+ *
+ * The easiest way to understand how to use this module is by looking at the many examples in the file
+ * unsupported/test/NonLinearOptimization.cpp.
+ */
+
+// IWYU pragma: begin_exports
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+#include "src/NonLinearOptimization/qrsolv.h"
+#include "src/NonLinearOptimization/r1updt.h"
+#include "src/NonLinearOptimization/r1mpyq.h"
+#include "src/NonLinearOptimization/rwupdt.h"
+#include "src/NonLinearOptimization/fdjac1.h"
+#include "src/NonLinearOptimization/lmpar.h"
+#include "src/NonLinearOptimization/dogleg.h"
+#include "src/NonLinearOptimization/covar.h"
+
+#include "src/NonLinearOptimization/chkder.h"
+
+#endif
+
+#include "src/NonLinearOptimization/HybridNonLinearSolver.h"
+#include "src/NonLinearOptimization/LevenbergMarquardt.h"
+// IWYU pragma: end_exports
+
+#endif  // EIGEN_NONLINEAROPTIMIZATION_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/NumericalDiff b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/NumericalDiff
new file mode 100644
index 0000000..ed236c8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/NumericalDiff
@@ -0,0 +1,57 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NUMERICALDIFF_MODULE_H
+#define EIGEN_NUMERICALDIFF_MODULE_H
+
+#include "../../Eigen/Core"
+
+namespace Eigen {
+
+/**
+ * \defgroup NumericalDiff_Module Numerical differentiation module
+ *
+ * \code
+ * #include <unsupported/Eigen/NumericalDiff>
+ * \endcode
+ *
+ * See http://en.wikipedia.org/wiki/Numerical_differentiation
+ *
+ * Warning : this should NOT be confused with automatic differentiation, which
+ * is a different method and has its own module in Eigen : \ref
+ * AutoDiff_Module.
+ *
+ * Currently only "Forward" and "Central" schemes are implemented. Those
+ * are basic methods, and there exist some more elaborated way of
+ * computing such approximates. They are implemented using both
+ * proprietary and free software, and usually requires linking to an
+ * external library. It is very easy for you to write a functor
+ * using such software, and the purpose is quite orthogonal to what we
+ * want to achieve with Eigen.
+ *
+ * This is why we will not provide wrappers for every great numerical
+ * differentiation software that exist, but should rather stick with those
+ * basic ones, that still are useful for testing.
+ *
+ * Also, the \ref NonLinearOptimization_Module needs this in order to
+ * provide full features compatibility with the original (c)minpack
+ * package.
+ *
+ */
+}
+
+//@{
+
+// IWYU pragma: begin_exports
+#include "src/NumericalDiff/NumericalDiff.h"
+// IWYU pragma: end_exports
+
+//@}
+
+#endif  // EIGEN_NUMERICALDIFF_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/OpenGLSupport b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/OpenGLSupport
new file mode 100644
index 0000000..f22ae56
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/OpenGLSupport
@@ -0,0 +1,350 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_OPENGL_MODULE_H
+#define EIGEN_OPENGL_MODULE_H
+
+#include "../../Eigen/Geometry"
+
+#if defined(__APPLE_CC__)
+#include <OpenGL/gl.h>
+#else
+#include <GL/gl.h>
+#endif
+
+namespace Eigen {
+
+/**
+ * \defgroup OpenGLSUpport_Module OpenGL Support module
+ *
+ * This module provides wrapper functions for a couple of OpenGL functions
+ * which simplify the way to pass Eigen's object to openGL.
+ * Here is an example:
+ *
+ * \code
+ * // You need to add path_to_eigen/unsupported to your include path.
+ * #include <Eigen/OpenGLSupport>
+ * // ...
+ * Vector3f x, y;
+ * Matrix3f rot;
+ *
+ * glVertex(y + x * rot);
+ *
+ * Quaternion q;
+ * glRotate(q);
+ *
+ * // ...
+ * \endcode
+ *
+ */
+//@{
+
+#define EIGEN_GL_FUNC_DECLARATION(FUNC)                                                                          \
+  namespace internal {                                                                                           \
+  template <typename XprType, typename Scalar = typename XprType::Scalar, int Rows = XprType::RowsAtCompileTime, \
+            int Cols = XprType::ColsAtCompileTime,                                                               \
+            bool IsGLCompatible = bool(internal::evaluator<XprType>::Flags & LinearAccessBit) &&                 \
+                                  bool(XprType::Flags & DirectAccessBit) &&                                      \
+                                  (XprType::IsVectorAtCompileTime || (XprType::Flags & RowMajorBit) == 0)>       \
+  struct EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl);                                                                 \
+                                                                                                                 \
+  template <typename XprType, typename Scalar, int Rows, int Cols>                                               \
+  struct EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl)<XprType, Scalar, Rows, Cols, false> {                            \
+    inline static void run(const XprType& p) {                                                                   \
+      EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl)<typename plain_matrix_type_column_major<XprType>::type>::run(p);    \
+    }                                                                                                            \
+  };                                                                                                             \
+  }                                                                                                              \
+                                                                                                                 \
+  template <typename Derived>                                                                                    \
+  inline void FUNC(const Eigen::DenseBase<Derived>& p) {                                                         \
+    EIGEN_CAT(EIGEN_CAT(internal::gl_, FUNC), _impl)<Derived>::run(p.derived());                                 \
+  }
+
+#define EIGEN_GL_FUNC_SPECIALIZATION_MAT(FUNC, SCALAR, ROWS, COLS, SUFFIX)           \
+  namespace internal {                                                               \
+  template <typename XprType>                                                        \
+  struct EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl)<XprType, SCALAR, ROWS, COLS, true> { \
+    inline static void run(const XprType& p) { FUNC##SUFFIX(p.data()); }             \
+  };                                                                                 \
+  }
+
+#define EIGEN_GL_FUNC_SPECIALIZATION_VEC(FUNC, SCALAR, SIZE, SUFFIX)              \
+  namespace internal {                                                            \
+  template <typename XprType>                                                     \
+  struct EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl)<XprType, SCALAR, SIZE, 1, true> { \
+    inline static void run(const XprType& p) { FUNC##SUFFIX(p.data()); }          \
+  };                                                                              \
+  template <typename XprType>                                                     \
+  struct EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl)<XprType, SCALAR, 1, SIZE, true> { \
+    inline static void run(const XprType& p) { FUNC##SUFFIX(p.data()); }          \
+  };                                                                              \
+  }
+
+EIGEN_GL_FUNC_DECLARATION(glVertex)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, int, 2, 2iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, short, 2, 2sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, float, 2, 2fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, double, 2, 2dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, int, 3, 3iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, short, 3, 3sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, float, 3, 3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, double, 3, 3dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, int, 4, 4iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, short, 4, 4sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, float, 4, 4fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex, double, 4, 4dv)
+
+EIGEN_GL_FUNC_DECLARATION(glTexCoord)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, int, 2, 2iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, short, 2, 2sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, float, 2, 2fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, double, 2, 2dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, int, 3, 3iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, short, 3, 3sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, float, 3, 3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, double, 3, 3dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, int, 4, 4iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, short, 4, 4sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, float, 4, 4fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord, double, 4, 4dv)
+
+EIGEN_GL_FUNC_DECLARATION(glColor)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, int, 2, 2iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, short, 2, 2sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, float, 2, 2fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, double, 2, 2dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, int, 3, 3iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, short, 3, 3sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, float, 3, 3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, double, 3, 3dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, int, 4, 4iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, short, 4, 4sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, float, 4, 4fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor, double, 4, 4dv)
+
+EIGEN_GL_FUNC_DECLARATION(glNormal)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal, int, 3, 3iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal, short, 3, 3sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal, float, 3, 3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal, double, 3, 3dv)
+
+inline void glScale2fv(const float* v) { glScalef(v[0], v[1], 1.f); }
+inline void glScale2dv(const double* v) { glScaled(v[0], v[1], 1.0); }
+inline void glScale3fv(const float* v) { glScalef(v[0], v[1], v[2]); }
+inline void glScale3dv(const double* v) { glScaled(v[0], v[1], v[2]); }
+
+EIGEN_GL_FUNC_DECLARATION(glScale)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale, float, 2, 2fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale, double, 2, 2dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale, float, 3, 3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale, double, 3, 3dv)
+
+template <typename Scalar>
+void glScale(const UniformScaling<Scalar>& s) {
+  glScale(Matrix<Scalar, 3, 1>::Constant(s.factor()));
+}
+
+inline void glTranslate2fv(const float* v) { glTranslatef(v[0], v[1], 0.f); }
+inline void glTranslate2dv(const double* v) { glTranslated(v[0], v[1], 0.0); }
+inline void glTranslate3fv(const float* v) { glTranslatef(v[0], v[1], v[2]); }
+inline void glTranslate3dv(const double* v) { glTranslated(v[0], v[1], v[2]); }
+
+EIGEN_GL_FUNC_DECLARATION(glTranslate)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate, float, 2, 2fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate, double, 2, 2dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate, float, 3, 3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate, double, 3, 3dv)
+
+template <typename Scalar>
+void glTranslate(const Translation<Scalar, 2>& t) {
+  glTranslate(t.vector());
+}
+template <typename Scalar>
+void glTranslate(const Translation<Scalar, 3>& t) {
+  glTranslate(t.vector());
+}
+
+EIGEN_GL_FUNC_DECLARATION(glMultMatrix)
+EIGEN_GL_FUNC_SPECIALIZATION_MAT(glMultMatrix, float, 4, 4, f)
+EIGEN_GL_FUNC_SPECIALIZATION_MAT(glMultMatrix, double, 4, 4, d)
+
+template <typename Scalar>
+void glMultMatrix(const Transform<Scalar, 3, Affine>& t) {
+  glMultMatrix(t.matrix());
+}
+template <typename Scalar>
+void glMultMatrix(const Transform<Scalar, 3, Projective>& t) {
+  glMultMatrix(t.matrix());
+}
+template <typename Scalar>
+void glMultMatrix(const Transform<Scalar, 3, AffineCompact>& t) {
+  glMultMatrix(Transform<Scalar, 3, Affine>(t).matrix());
+}
+
+EIGEN_GL_FUNC_DECLARATION(glLoadMatrix)
+EIGEN_GL_FUNC_SPECIALIZATION_MAT(glLoadMatrix, float, 4, 4, f)
+EIGEN_GL_FUNC_SPECIALIZATION_MAT(glLoadMatrix, double, 4, 4, d)
+
+template <typename Scalar>
+void glLoadMatrix(const Transform<Scalar, 3, Affine>& t) {
+  glLoadMatrix(t.matrix());
+}
+template <typename Scalar>
+void glLoadMatrix(const Transform<Scalar, 3, Projective>& t) {
+  glLoadMatrix(t.matrix());
+}
+template <typename Scalar>
+void glLoadMatrix(const Transform<Scalar, 3, AffineCompact>& t) {
+  glLoadMatrix(Transform<Scalar, 3, Affine>(t).matrix());
+}
+
+inline void glRotate(const Rotation2D<float>& rot) { glRotatef(rot.angle() * 180.f / float(EIGEN_PI), 0.f, 0.f, 1.f); }
+inline void glRotate(const Rotation2D<double>& rot) {
+  glRotated(rot.angle() * 180.0 / double(EIGEN_PI), 0.0, 0.0, 1.0);
+}
+
+template <typename Derived>
+void glRotate(const RotationBase<Derived, 3>& rot) {
+  Transform<typename Derived::Scalar, 3, Projective> tr(rot);
+  glMultMatrix(tr.matrix());
+}
+
+#define EIGEN_GL_MAKE_CONST_const const
+#define EIGEN_GL_MAKE_CONST__
+#define EIGEN_GL_EVAL(X) X
+
+#define EIGEN_GL_FUNC1_DECLARATION(FUNC, ARG1, CONST)                                                            \
+  namespace internal {                                                                                           \
+  template <typename XprType, typename Scalar = typename XprType::Scalar, int Rows = XprType::RowsAtCompileTime, \
+            int Cols = XprType::ColsAtCompileTime,                                                               \
+            bool IsGLCompatible = bool(internal::evaluator<XprType>::Flags & LinearAccessBit) &&                 \
+                                  bool(XprType::Flags & DirectAccessBit) &&                                      \
+                                  (XprType::IsVectorAtCompileTime || (XprType::Flags & RowMajorBit) == 0)>       \
+  struct EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl);                                                                 \
+                                                                                                                 \
+  template <typename XprType, typename Scalar, int Rows, int Cols>                                               \
+  struct EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl)<XprType, Scalar, Rows, Cols, false> {                            \
+    inline static void run(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) {                      \
+      EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl)<typename plain_matrix_type_column_major<XprType>::type>::run(a, p); \
+    }                                                                                                            \
+  };                                                                                                             \
+  }                                                                                                              \
+                                                                                                                 \
+  template <typename Derived>                                                                                    \
+  inline void FUNC(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) Eigen::DenseBase<Derived>& p) {            \
+    EIGEN_CAT(EIGEN_CAT(internal::gl_, FUNC), _impl)<Derived>::run(a, p.derived());                              \
+  }
+
+#define EIGEN_GL_FUNC1_SPECIALIZATION_MAT(FUNC, ARG1, CONST, SCALAR, ROWS, COLS, SUFFIX)    \
+  namespace internal {                                                                      \
+  template <typename XprType>                                                               \
+  struct EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl)<XprType, SCALAR, ROWS, COLS, true> {        \
+    inline static void run(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) { \
+      FUNC##SUFFIX(a, p.data());                                                            \
+    }                                                                                       \
+  };                                                                                        \
+  }
+
+#define EIGEN_GL_FUNC1_SPECIALIZATION_VEC(FUNC, ARG1, CONST, SCALAR, SIZE, SUFFIX)          \
+  namespace internal {                                                                      \
+  template <typename XprType>                                                               \
+  struct EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl)<XprType, SCALAR, SIZE, 1, true> {           \
+    inline static void run(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) { \
+      FUNC##SUFFIX(a, p.data());                                                            \
+    }                                                                                       \
+  };                                                                                        \
+  template <typename XprType>                                                               \
+  struct EIGEN_CAT(EIGEN_CAT(gl_, FUNC), _impl)<XprType, SCALAR, 1, SIZE, true> {           \
+    inline static void run(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) { \
+      FUNC##SUFFIX(a, p.data());                                                            \
+    }                                                                                       \
+  };                                                                                        \
+  }
+
+EIGEN_GL_FUNC1_DECLARATION(glGet, GLenum, _)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glGet, GLenum, _, float, 4, 4, Floatv)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glGet, GLenum, _, double, 4, 4, Doublev)
+
+// glUniform API
+
+#ifdef GL_VERSION_2_0
+
+inline void glUniform2fv_ei(GLint loc, const float* v) { glUniform2fv(loc, 1, v); }
+inline void glUniform2iv_ei(GLint loc, const int* v) { glUniform2iv(loc, 1, v); }
+
+inline void glUniform3fv_ei(GLint loc, const float* v) { glUniform3fv(loc, 1, v); }
+inline void glUniform3iv_ei(GLint loc, const int* v) { glUniform3iv(loc, 1, v); }
+
+inline void glUniform4fv_ei(GLint loc, const float* v) { glUniform4fv(loc, 1, v); }
+inline void glUniform4iv_ei(GLint loc, const int* v) { glUniform4iv(loc, 1, v); }
+
+inline void glUniformMatrix2fv_ei(GLint loc, const float* v) { glUniformMatrix2fv(loc, 1, false, v); }
+inline void glUniformMatrix3fv_ei(GLint loc, const float* v) { glUniformMatrix3fv(loc, 1, false, v); }
+inline void glUniformMatrix4fv_ei(GLint loc, const float* v) { glUniformMatrix4fv(loc, 1, false, v); }
+
+EIGEN_GL_FUNC1_DECLARATION(glUniform, GLint, const)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, float, 2, 2fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, int, 2, 2iv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, float, 3, 3fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, int, 3, 3iv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, float, 4, 4fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, int, 4, 4iv_ei)
+
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform, GLint, const, float, 2, 2, Matrix2fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform, GLint, const, float, 3, 3, Matrix3fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform, GLint, const, float, 4, 4, Matrix4fv_ei)
+
+#endif
+
+#ifdef GL_VERSION_2_1
+
+inline void glUniformMatrix2x3fv_ei(GLint loc, const float* v) { glUniformMatrix2x3fv(loc, 1, false, v); }
+inline void glUniformMatrix3x2fv_ei(GLint loc, const float* v) { glUniformMatrix3x2fv(loc, 1, false, v); }
+inline void glUniformMatrix2x4fv_ei(GLint loc, const float* v) { glUniformMatrix2x4fv(loc, 1, false, v); }
+inline void glUniformMatrix4x2fv_ei(GLint loc, const float* v) { glUniformMatrix4x2fv(loc, 1, false, v); }
+inline void glUniformMatrix3x4fv_ei(GLint loc, const float* v) { glUniformMatrix3x4fv(loc, 1, false, v); }
+inline void glUniformMatrix4x3fv_ei(GLint loc, const float* v) { glUniformMatrix4x3fv(loc, 1, false, v); }
+
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform, GLint, const, float, 2, 3, Matrix2x3fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform, GLint, const, float, 3, 2, Matrix3x2fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform, GLint, const, float, 2, 4, Matrix2x4fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform, GLint, const, float, 4, 2, Matrix4x2fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform, GLint, const, float, 3, 4, Matrix3x4fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform, GLint, const, float, 4, 3, Matrix4x3fv_ei)
+
+#endif
+
+#ifdef GL_VERSION_3_0
+
+inline void glUniform2uiv_ei(GLint loc, const unsigned int* v) { glUniform2uiv(loc, 1, v); }
+inline void glUniform3uiv_ei(GLint loc, const unsigned int* v) { glUniform3uiv(loc, 1, v); }
+inline void glUniform4uiv_ei(GLint loc, const unsigned int* v) { glUniform4uiv(loc, 1, v); }
+
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, unsigned int, 2, 2uiv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, unsigned int, 3, 3uiv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, unsigned int, 4, 4uiv_ei)
+
+#endif
+
+#ifdef GL_ARB_gpu_shader_fp64
+inline void glUniform2dv_ei(GLint loc, const double* v) { glUniform2dv(loc, 1, v); }
+inline void glUniform3dv_ei(GLint loc, const double* v) { glUniform3dv(loc, 1, v); }
+inline void glUniform4dv_ei(GLint loc, const double* v) { glUniform4dv(loc, 1, v); }
+
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, double, 2, 2dv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, double, 3, 3dv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform, GLint, const, double, 4, 4dv_ei)
+#endif
+
+//@}
+
+}  // namespace Eigen
+
+#endif  // EIGEN_OPENGL_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/Polynomials b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/Polynomials
new file mode 100644
index 0000000..aa9aa8e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/Polynomials
@@ -0,0 +1,140 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_POLYNOMIALS_MODULE_H
+#define EIGEN_POLYNOMIALS_MODULE_H
+
+#include "../../Eigen/Core"
+
+#include "../../Eigen/Eigenvalues"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+// Note that EIGEN_HIDE_HEAVY_CODE has to be defined per module
+#if (defined EIGEN_EXTERN_INSTANTIATIONS) && (EIGEN_EXTERN_INSTANTIATIONS >= 2)
+#ifndef EIGEN_HIDE_HEAVY_CODE
+#define EIGEN_HIDE_HEAVY_CODE
+#endif
+#elif defined EIGEN_HIDE_HEAVY_CODE
+#undef EIGEN_HIDE_HEAVY_CODE
+#endif
+
+/**
+ * \defgroup Polynomials_Module Polynomials module
+ * \brief This module provides a QR based polynomial solver.
+ *
+ * To use this module, add
+ * \code
+ * #include <unsupported/Eigen/Polynomials>
+ * \endcode
+ * at the start of your source file.
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Polynomials/PolynomialUtils.h"
+#include "src/Polynomials/Companion.h"
+#include "src/Polynomials/PolynomialSolver.h"
+// IWYU pragma: end_exports
+
+/**
+        \page polynomials Polynomials defines functions for dealing with polynomials
+        and a QR based polynomial solver.
+        \ingroup Polynomials_Module
+
+        The remainder of the page documents first the functions for evaluating, computing
+        polynomials, computing estimates about polynomials and next the QR based polynomial
+        solver.
+
+        \section polynomialUtils convenient functions to deal with polynomials
+        \subsection roots_to_monicPolynomial
+        The function
+        \code
+        void roots_to_monicPolynomial( const RootVector& rv, Polynomial& poly )
+        \endcode
+        computes the coefficients \f$ a_i \f$ of
+
+        \f$ p(x) = a_0 + a_{1}x + ... + a_{n-1}x^{n-1} + x^n \f$
+
+        where \f$ p \f$ is known through its roots i.e. \f$ p(x) = (x-r_1)(x-r_2)...(x-r_n) \f$.
+
+        \subsection poly_eval
+        The function
+        \code
+        T poly_eval( const Polynomials& poly, const T& x )
+        \endcode
+        evaluates a polynomial at a given point using stabilized H&ouml;rner method.
+
+        The following code: first computes the coefficients in the monomial basis of the monic polynomial that has the
+  provided roots; then, it evaluates the computed polynomial, using a stabilized H&ouml;rner method.
+
+        \include PolynomialUtils1.cpp
+  Output: \verbinclude PolynomialUtils1.out
+
+        \subsection Cauchy bounds
+        The function
+        \code
+        Real cauchy_max_bound( const Polynomial& poly )
+        \endcode
+        provides a maximum bound (the Cauchy one: \f$C(p)\f$) for the absolute value of a root of the given polynomial
+  i.e. \f$ \forall r_i \f$ root of \f$ p(x) = \sum_{k=0}^d a_k x^k \f$, \f$ |r_i| \le C(p) = \sum_{k=0}^{d} \left |
+  \frac{a_k}{a_d} \right | \f$ The leading coefficient \f$ p \f$: should be non zero \f$a_d \neq 0\f$.
+
+
+        The function
+        \code
+        Real cauchy_min_bound( const Polynomial& poly )
+        \endcode
+        provides a minimum bound (the Cauchy one: \f$c(p)\f$) for the absolute value of a non zero root of the given
+  polynomial i.e. \f$ \forall r_i \neq 0 \f$ root of \f$ p(x) = \sum_{k=0}^d a_k x^k \f$, \f$ |r_i| \ge c(p) = \left(
+  \sum_{k=0}^{d} \left | \frac{a_k}{a_0} \right | \right)^{-1} \f$
+
+
+
+
+        \section QR polynomial solver class
+        Computes the complex roots of a polynomial by computing the eigenvalues of the associated companion matrix with
+  the QR algorithm.
+
+        The roots of \f$ p(x) = a_0 + a_1 x + a_2 x^2 + a_{3} x^3 + x^4 \f$ are the eigenvalues of
+        \f$
+        \left [
+        \begin{array}{cccc}
+        0 & 0 &  0 & a_0 \\
+        1 & 0 &  0 & a_1 \\
+        0 & 1 &  0 & a_2 \\
+        0 & 0 &  1 & a_3
+        \end{array} \right ]
+        \f$
+
+        However, the QR algorithm is not guaranteed to converge when there are several eigenvalues with same modulus.
+
+        Therefore the current polynomial solver is guaranteed to provide a correct result only when the complex roots
+  \f$r_1,r_2,...,r_d\f$ have distinct moduli i.e.
+
+        \f$ \forall i,j \in [1;d],~ \| r_i \| \neq \| r_j \| \f$.
+
+        With 32bit (float) floating types this problem shows up frequently.
+  However, almost always, correct accuracy is reached even in these cases for 64bit
+  (double) floating types and small polynomial degree (<20).
+
+        \include PolynomialSolver1.cpp
+
+        In the above example:
+
+        -# a simple use of the polynomial solver is shown;
+        -# the accuracy problem with the QR algorithm is presented: a polynomial with almost conjugate roots is provided
+  to the solver. Those roots have almost same module therefore the QR algorithm failed to converge: the accuracy of the
+  last root is bad;
+        -# a simple way to circumvent the problem is shown: use doubles instead of floats.
+
+  Output: \verbinclude PolynomialSolver1.out
+*/
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_POLYNOMIALS_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/SparseExtra b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/SparseExtra
new file mode 100644
index 0000000..00a87c5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/SparseExtra
@@ -0,0 +1,60 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_EXTRA_MODULE_H
+#define EIGEN_SPARSE_EXTRA_MODULE_H
+
+#include "../../Eigen/Sparse"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#include <vector>
+#include <map>
+#include <unordered_map>
+#include <cstdlib>
+#include <cstring>
+#include <algorithm>
+#include <fstream>
+#include <sstream>
+
+#ifdef EIGEN_GOOGLEHASH_SUPPORT
+#include <google/dense_hash_map>
+#include <google/sparse_hash_map>
+#endif
+
+/**
+ * \defgroup SparseExtra_Module SparseExtra module
+ *
+ * This module contains some experimental features extending the sparse module:
+ * - A RandomSetter which is a wrapper object allowing to set/update a sparse matrix with random access.
+ * - A SparseInverse which calculates a sparse subset of the inverse of a sparse matrix corresponding to nonzeros of the
+ * input
+ * - MatrixMarket format(https://math.nist.gov/MatrixMarket/formats.html) readers and writers for sparse and dense
+ * matrices.
+ *
+ * \code
+ * #include <unsupported/Eigen/SparseExtra>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SparseExtra/RandomSetter.h"
+#include "src/SparseExtra/SparseInverse.h"
+
+#include "src/SparseExtra/MarketIO.h"
+
+#if !defined(_WIN32)
+#include <dirent.h>
+#include "src/SparseExtra/MatrixMarketIterator.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SPARSE_EXTRA_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/SpecialFunctions b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/SpecialFunctions
new file mode 100644
index 0000000..376b025
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/SpecialFunctions
@@ -0,0 +1,107 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_MODULE_H
+#define EIGEN_SPECIALFUNCTIONS_MODULE_H
+
+#include <math.h>
+
+#include "../../Eigen/Core"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+namespace Eigen {
+
+/**
+ * \defgroup SpecialFunctions_Module Special math functions module
+ *
+ * This module features additional coefficient-wise math functions available
+ * within the numext:: namespace for the scalar version, and as method and/or free
+ * functions of Array. Those include:
+ *
+ * - erf
+ * - erfc
+ * - lgamma
+ * - igamma
+ * - igamma_der_a
+ * - gamma_sample_der_alpha
+ * - igammac
+ * - digamma
+ * - ndtri
+ * - polygamma
+ * - zeta
+ * - betainc
+ *
+ * Bessel Functions
+ * - bessel_i0
+ * - bessel_i0e
+ * - bessel_i1
+ * - bessel_i1e
+ * - bessel_j0
+ * - bessel_j1
+ * - bessel_k0
+ * - bessel_k0e
+ * - bessel_k1
+ * - bessel_k1e
+ * - bessel_y0
+ * - bessel_y1
+ *
+ * \code
+ * #include <unsupported/Eigen/SpecialFunctions>
+ * \endcode
+ */
+//@{
+
+}  // namespace Eigen
+
+// IWYU pragma: begin_exports
+#include "src/SpecialFunctions/BesselFunctionsImpl.h"
+#include "src/SpecialFunctions/BesselFunctionsBFloat16.h"
+#include "src/SpecialFunctions/BesselFunctionsHalf.h"
+#include "src/SpecialFunctions/BesselFunctionsPacketMath.h"
+#include "src/SpecialFunctions/BesselFunctionsFunctors.h"
+#include "src/SpecialFunctions/BesselFunctionsArrayAPI.h"
+#include "src/SpecialFunctions/SpecialFunctionsImpl.h"
+#if defined(EIGEN_HIPCC)
+#include "src/SpecialFunctions/HipVectorCompatibility.h"
+#endif
+#include "src/SpecialFunctions/SpecialFunctionsBFloat16.h"
+#include "src/SpecialFunctions/SpecialFunctionsHalf.h"
+#include "src/SpecialFunctions/SpecialFunctionsPacketMath.h"
+#include "src/SpecialFunctions/SpecialFunctionsFunctors.h"
+#include "src/SpecialFunctions/SpecialFunctionsArrayAPI.h"
+
+#ifndef EIGEN_VECTORIZE_GENERIC
+// TODO(rmlarsen): Make these work with generic vectorization if possible.
+#if defined EIGEN_VECTORIZE_AVX512
+#include "src/SpecialFunctions/arch/AVX/BesselFunctions.h"
+#include "src/SpecialFunctions/arch/AVX/SpecialFunctions.h"
+#include "src/SpecialFunctions/arch/AVX512/BesselFunctions.h"
+#include "src/SpecialFunctions/arch/AVX512/SpecialFunctions.h"
+#elif defined EIGEN_VECTORIZE_AVX
+#include "src/SpecialFunctions/arch/AVX/BesselFunctions.h"
+#include "src/SpecialFunctions/arch/AVX/SpecialFunctions.h"
+#elif defined EIGEN_VECTORIZE_NEON
+#include "src/SpecialFunctions/arch/NEON/BesselFunctions.h"
+#include "src/SpecialFunctions/arch/NEON/SpecialFunctions.h"
+#endif
+
+#if defined EIGEN_VECTORIZE_GPU
+#include "src/SpecialFunctions/arch/GPU/SpecialFunctions.h"
+#endif
+#endif
+// IWYU pragma: end_exports
+
+namespace Eigen {
+//@}
+}
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SPECIALFUNCTIONS_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/Splines b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/Splines
new file mode 100644
index 0000000..632095d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/Splines
@@ -0,0 +1,36 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPLINES_MODULE_H
+#define EIGEN_SPLINES_MODULE_H
+
+namespace Eigen {
+/**
+ * \defgroup Splines_Module Spline and spline fitting module
+ *
+ * This module provides a simple multi-dimensional spline class while
+ * offering most basic functionality to fit a spline to point sets.
+ *
+ * \code
+ * #include <unsupported/Eigen/Splines>
+ * \endcode
+ */
+}
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+#include "src/Splines/SplineFwd.h"
+#include "src/Splines/Spline.h"
+#include "src/Splines/SplineFitting.h"
+// IWYU pragma: end_exports
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SPLINES_MODULE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
new file mode 100644
index 0000000..a83bdc3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
@@ -0,0 +1,76 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AUTODIFF_JACOBIAN_H
+#define EIGEN_AUTODIFF_JACOBIAN_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Functor>
+class AutoDiffJacobian : public Functor {
+ public:
+  AutoDiffJacobian() : Functor() {}
+  AutoDiffJacobian(const Functor& f) : Functor(f) {}
+
+  // forward constructors
+  template <typename... T>
+  AutoDiffJacobian(const T&... Values) : Functor(Values...) {}
+
+  typedef typename Functor::InputType InputType;
+  typedef typename Functor::ValueType ValueType;
+  typedef typename ValueType::Scalar Scalar;
+
+  enum { InputsAtCompileTime = InputType::RowsAtCompileTime, ValuesAtCompileTime = ValueType::RowsAtCompileTime };
+
+  typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
+  typedef typename JacobianType::Index Index;
+
+  typedef Matrix<Scalar, InputsAtCompileTime, 1> DerivativeType;
+  typedef AutoDiffScalar<DerivativeType> ActiveScalar;
+
+  typedef Matrix<ActiveScalar, InputsAtCompileTime, 1> ActiveInput;
+  typedef Matrix<ActiveScalar, ValuesAtCompileTime, 1> ActiveValue;
+
+  // Some compilers don't accept variadic parameters after a default parameter,
+  // i.e., we can't just write _jac=0 but we need to overload operator():
+  EIGEN_STRONG_INLINE void operator()(const InputType& x, ValueType* v) const { this->operator()(x, v, 0); }
+  template <typename... ParamsType>
+  void operator()(const InputType& x, ValueType* v, JacobianType* _jac, const ParamsType&... Params) const {
+    eigen_assert(v != 0);
+
+    if (!_jac) {
+      Functor::operator()(x, v, Params...);
+      return;
+    }
+
+    JacobianType& jac = *_jac;
+
+    ActiveInput ax = x.template cast<ActiveScalar>();
+    ActiveValue av(jac.rows());
+
+    if (InputsAtCompileTime == Dynamic)
+      for (Index j = 0; j < jac.rows(); j++) av[j].derivatives().resize(x.rows());
+
+    for (Index i = 0; i < jac.cols(); i++) ax[i].derivatives() = DerivativeType::Unit(x.rows(), i);
+
+    Functor::operator()(ax, &av, Params...);
+
+    for (Index i = 0; i < jac.rows(); i++) {
+      (*v)[i] = av[i].value();
+      jac.row(i) = av[i].derivatives();
+    }
+  }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_AUTODIFF_JACOBIAN_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
new file mode 100644
index 0000000..ea481fc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
@@ -0,0 +1,571 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AUTODIFF_SCALAR_H
+#define EIGEN_AUTODIFF_SCALAR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename DerivativeType, bool Enable>
+struct auto_diff_special_op;
+
+template <typename DerivativeType, typename OtherDerivativeType, typename EnableIf = void>
+struct maybe_coherent_pad_helper {
+  static constexpr int SizeAtCompileTime =
+      max_size_prefer_dynamic(DerivativeType::SizeAtCompileTime, OtherDerivativeType::SizeAtCompileTime);
+  using type = CoherentPadOp<DerivativeType, SizeAtCompileTime>;
+  static type pad(const DerivativeType& x, const OtherDerivativeType& y) {
+    // CoherentPadOp uses variable_if_dynamic<SizeAtCompileTime>.  In this case, `SizeAtCompileTime` might
+    // by Dynamic, so we need to take the runtime maximum of x, y.
+    return CoherentPadOp<DerivativeType, SizeAtCompileTime>(x, numext::maxi(x.size(), y.size()));
+  }
+};
+
+// Both are fixed-sized and equal, don't need to pad.
+// Both are fixed-size and this is larger than other, don't need to pad.
+template <typename DerivativeType, typename OtherDerivativeType>
+struct maybe_coherent_pad_helper<
+    DerivativeType, OtherDerivativeType,
+    std::enable_if_t<enum_ge_not_dynamic(DerivativeType::SizeAtCompileTime, OtherDerivativeType::SizeAtCompileTime)>> {
+  using type = const DerivativeType&;
+  static const DerivativeType& pad(const DerivativeType& x, const OtherDerivativeType& /*y*/) { return x; }
+};
+
+template <typename DerivativeType, typename OtherDerivativeType>
+typename maybe_coherent_pad_helper<DerivativeType, OtherDerivativeType>::type MaybeCoherentPad(
+    const DerivativeType& x, const OtherDerivativeType& y) {
+  return maybe_coherent_pad_helper<DerivativeType, OtherDerivativeType>::pad(x, y);
+}
+
+template <typename Op, typename LhsDerivativeType, typename RhsDerivativeType>
+auto MakeCoherentCwiseBinaryOp(const LhsDerivativeType& x, const RhsDerivativeType& y, Op op = Op()) {
+  const auto& lhs = MaybeCoherentPad(x, y);
+  const auto& rhs = MaybeCoherentPad(y, x);
+  return CwiseBinaryOp<Op, remove_all_t<decltype(lhs)>, remove_all_t<decltype(rhs)>>(lhs, rhs, op);
+}
+
+}  // namespace internal
+
+template <typename DerivativeType>
+class AutoDiffScalar;
+
+template <typename NewDerType>
+inline AutoDiffScalar<NewDerType> MakeAutoDiffScalar(const typename NewDerType::Scalar& value, const NewDerType& der) {
+  return AutoDiffScalar<NewDerType>(value, der);
+}
+
+/** \class AutoDiffScalar
+ * \brief A scalar type replacement with automatic differentiation capability
+ *
+ * \param DerivativeType the vector type used to store/represent the derivatives. The base scalar type
+ *                 as well as the number of derivatives to compute are determined from this type.
+ *                 Typical choices include, e.g., \c Vector4f for 4 derivatives, or \c VectorXf
+ *                 if the number of derivatives is not known at compile time, and/or, the number
+ *                 of derivatives is large.
+ *                 Note that DerivativeType can also be a reference (e.g., \c VectorXf&) to wrap a
+ *                 existing vector into an AutoDiffScalar.
+ *                 Finally, DerivativeType can also be any Eigen compatible expression.
+ *
+ * This class represents a scalar value while tracking its respective derivatives using Eigen's expression
+ * template mechanism.
+ *
+ * It supports the following list of global math function:
+ *  - std::abs, std::sqrt, std::pow, std::exp, std::log, std::sin, std::cos,
+ *  - internal::abs, internal::sqrt, numext::pow, internal::exp, internal::log, internal::sin, internal::cos,
+ *  - internal::conj, internal::real, internal::imag, numext::abs2.
+ *
+ * AutoDiffScalar can be used as the scalar type of an Eigen::Matrix object. However,
+ * in that case, the expression template mechanism only occurs at the top Matrix level,
+ * while derivatives are computed right away.
+ *
+ */
+
+template <typename DerivativeType>
+class AutoDiffScalar
+    : public internal::auto_diff_special_op<
+          DerivativeType, !internal::is_same<typename internal::traits<internal::remove_all_t<DerivativeType>>::Scalar,
+                                             typename NumTraits<typename internal::traits<
+                                                 internal::remove_all_t<DerivativeType>>::Scalar>::Real>::value> {
+ public:
+  typedef internal::auto_diff_special_op<
+      DerivativeType,
+      !internal::is_same<
+          typename internal::traits<internal::remove_all_t<DerivativeType>>::Scalar,
+          typename NumTraits<typename internal::traits<internal::remove_all_t<DerivativeType>>::Scalar>::Real>::value>
+      Base;
+  typedef internal::remove_all_t<DerivativeType> DerType;
+  typedef typename internal::traits<DerType>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real Real;
+
+  using Base::operator+;
+  using Base::operator*;
+
+  /** Default constructor without any initialization. */
+  AutoDiffScalar() {}
+
+  /** Constructs an active scalar from its \a value,
+      and initializes the \a nbDer derivatives such that it corresponds to the \a derNumber -th variable */
+  AutoDiffScalar(const Scalar& value, int nbDer, int derNumber) : m_value(value), m_derivatives(DerType::Zero(nbDer)) {
+    m_derivatives.coeffRef(derNumber) = Scalar(1);
+  }
+
+  /** Conversion from a scalar constant to an active scalar.
+   * The derivatives are set to zero. */
+  /*explicit*/ AutoDiffScalar(const Real& value) : m_value(value) {
+    if (m_derivatives.size() > 0) m_derivatives.setZero();
+  }
+
+  /** Constructs an active scalar from its \a value and derivatives \a der */
+  AutoDiffScalar(const Scalar& value, const DerType& der) : m_value(value), m_derivatives(der) {}
+
+  template <typename OtherDerType>
+  AutoDiffScalar(
+      const AutoDiffScalar<OtherDerType>& other
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+      ,
+      std::enable_if_t<
+          internal::is_same<Scalar, typename internal::traits<internal::remove_all_t<OtherDerType>>::Scalar>::value &&
+              internal::is_convertible<OtherDerType, DerType>::value,
+          void*> = 0
+#endif
+      )
+      : m_value(other.value()), m_derivatives(other.derivatives()) {
+  }
+
+  friend std::ostream& operator<<(std::ostream& s, const AutoDiffScalar& a) { return s << a.value(); }
+
+  AutoDiffScalar(const AutoDiffScalar& other) : m_value(other.value()), m_derivatives(other.derivatives()) {}
+
+  template <typename OtherDerType>
+  inline AutoDiffScalar& operator=(const AutoDiffScalar<OtherDerType>& other) {
+    m_value = other.value();
+    m_derivatives = other.derivatives();
+    return *this;
+  }
+
+  inline AutoDiffScalar& operator=(const AutoDiffScalar& other) {
+    m_value = other.value();
+    m_derivatives = other.derivatives();
+    return *this;
+  }
+
+  inline AutoDiffScalar& operator=(const Scalar& other) {
+    m_value = other;
+    if (m_derivatives.size() > 0) m_derivatives.setZero();
+    return *this;
+  }
+
+  //     inline operator const Scalar& () const { return m_value; }
+  //     inline operator Scalar& () { return m_value; }
+
+  inline const Scalar& value() const { return m_value; }
+  inline Scalar& value() { return m_value; }
+
+  inline const DerType& derivatives() const { return m_derivatives; }
+  inline DerType& derivatives() { return m_derivatives; }
+
+  inline bool operator<(const Scalar& other) const { return m_value < other; }
+  inline bool operator<=(const Scalar& other) const { return m_value <= other; }
+  inline bool operator>(const Scalar& other) const { return m_value > other; }
+  inline bool operator>=(const Scalar& other) const { return m_value >= other; }
+  inline bool operator==(const Scalar& other) const { return m_value == other; }
+  inline bool operator!=(const Scalar& other) const { return m_value != other; }
+
+  friend inline bool operator<(const Scalar& a, const AutoDiffScalar& b) { return a < b.value(); }
+  friend inline bool operator<=(const Scalar& a, const AutoDiffScalar& b) { return a <= b.value(); }
+  friend inline bool operator>(const Scalar& a, const AutoDiffScalar& b) { return a > b.value(); }
+  friend inline bool operator>=(const Scalar& a, const AutoDiffScalar& b) { return a >= b.value(); }
+  friend inline bool operator==(const Scalar& a, const AutoDiffScalar& b) { return a == b.value(); }
+  friend inline bool operator!=(const Scalar& a, const AutoDiffScalar& b) { return a != b.value(); }
+
+  template <typename OtherDerType>
+  inline bool operator<(const AutoDiffScalar<OtherDerType>& b) const {
+    return m_value < b.value();
+  }
+  template <typename OtherDerType>
+  inline bool operator<=(const AutoDiffScalar<OtherDerType>& b) const {
+    return m_value <= b.value();
+  }
+  template <typename OtherDerType>
+  inline bool operator>(const AutoDiffScalar<OtherDerType>& b) const {
+    return m_value > b.value();
+  }
+  template <typename OtherDerType>
+  inline bool operator>=(const AutoDiffScalar<OtherDerType>& b) const {
+    return m_value >= b.value();
+  }
+  template <typename OtherDerType>
+  inline bool operator==(const AutoDiffScalar<OtherDerType>& b) const {
+    return m_value == b.value();
+  }
+  template <typename OtherDerType>
+  inline bool operator!=(const AutoDiffScalar<OtherDerType>& b) const {
+    return m_value != b.value();
+  }
+
+  inline AutoDiffScalar<DerType&> operator+(const Scalar& other) const {
+    return AutoDiffScalar<DerType&>(m_value + other, m_derivatives);
+  }
+
+  friend inline AutoDiffScalar<DerType&> operator+(const Scalar& a, const AutoDiffScalar& b) {
+    return AutoDiffScalar<DerType&>(a + b.value(), b.derivatives());
+  }
+
+  inline AutoDiffScalar& operator+=(const Scalar& other) {
+    value() += other;
+    return *this;
+  }
+
+  template <typename OtherDerType>
+  inline auto operator+(const AutoDiffScalar<OtherDerType>& other) const {
+    return MakeAutoDiffScalar(
+        m_value + other.value(),
+        internal::MakeCoherentCwiseBinaryOp<internal::scalar_sum_op<Scalar>>(m_derivatives, other.derivatives()));
+  }
+
+  template <typename OtherDerType>
+  inline AutoDiffScalar& operator+=(const AutoDiffScalar<OtherDerType>& other) {
+    (*this) = (*this) + other;
+    return *this;
+  }
+
+  inline AutoDiffScalar<DerType&> operator-(const Scalar& b) const {
+    return AutoDiffScalar<DerType&>(m_value - b, m_derivatives);
+  }
+
+  friend inline AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType>> operator-(
+      const Scalar& a, const AutoDiffScalar& b) {
+    return AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType>>(a - b.value(),
+                                                                                             -b.derivatives());
+  }
+
+  inline AutoDiffScalar& operator-=(const Scalar& other) {
+    value() -= other;
+    return *this;
+  }
+
+  template <typename OtherDerType>
+  inline auto operator-(const AutoDiffScalar<OtherDerType>& other) const {
+    return MakeAutoDiffScalar(m_value - other.value(),
+                              internal::MakeCoherentCwiseBinaryOp<internal::scalar_difference_op<Scalar>>(
+                                  m_derivatives, other.derivatives()));
+  }
+
+  template <typename OtherDerType>
+  inline AutoDiffScalar& operator-=(const AutoDiffScalar<OtherDerType>& other) {
+    *this = *this - other;
+    return *this;
+  }
+
+  inline AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType>> operator-() const {
+    return AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType>>(-m_value, -m_derivatives);
+  }
+
+  inline auto operator*(const Scalar& other) const {
+    return MakeAutoDiffScalar(m_value * other, m_derivatives * other);
+  }
+
+  friend inline auto operator*(const Scalar& other, const AutoDiffScalar& a) {
+    return MakeAutoDiffScalar(a.value() * other, a.derivatives() * other);
+  }
+
+  inline auto operator/(const Scalar& other) const {
+    return MakeAutoDiffScalar(m_value / other, (m_derivatives * (Scalar(1) / other)));
+  }
+
+  friend inline auto operator/(const Scalar& other, const AutoDiffScalar& a) {
+    return MakeAutoDiffScalar(other / a.value(), a.derivatives() * (Scalar(-other) / (a.value() * a.value())));
+  }
+
+  template <typename OtherDerType>
+  inline auto operator/(const AutoDiffScalar<OtherDerType>& other) const {
+    return MakeAutoDiffScalar(m_value / other.value(),
+                              internal::MakeCoherentCwiseBinaryOp<internal::scalar_difference_op<Scalar>>(
+                                  m_derivatives * other.value(), (other.derivatives() * m_value)) *
+                                  (Scalar(1) / (other.value() * other.value())));
+  }
+
+  template <typename OtherDerType>
+  inline auto operator*(const AutoDiffScalar<OtherDerType>& other) const {
+    return MakeAutoDiffScalar(m_value * other.value(),
+                              internal::MakeCoherentCwiseBinaryOp<internal::scalar_sum_op<Scalar>>(
+                                  m_derivatives * other.value(), other.derivatives() * m_value));
+  }
+
+  inline AutoDiffScalar& operator*=(const Scalar& other) {
+    *this = *this * other;
+    return *this;
+  }
+
+  template <typename OtherDerType>
+  inline AutoDiffScalar& operator*=(const AutoDiffScalar<OtherDerType>& other) {
+    *this = *this * other;
+    return *this;
+  }
+
+  inline AutoDiffScalar& operator/=(const Scalar& other) {
+    *this = *this / other;
+    return *this;
+  }
+
+  template <typename OtherDerType>
+  inline AutoDiffScalar& operator/=(const AutoDiffScalar<OtherDerType>& other) {
+    *this = *this / other;
+    return *this;
+  }
+
+ protected:
+  Scalar m_value;
+  DerType m_derivatives;
+};
+
+namespace internal {
+
+template <typename DerivativeType>
+struct auto_diff_special_op<DerivativeType, true>
+//   : auto_diff_scalar_op<DerivativeType, typename NumTraits<Scalar>::Real,
+//                            is_same<Scalar,typename NumTraits<Scalar>::Real>::value>
+{
+  typedef remove_all_t<DerivativeType> DerType;
+  typedef typename traits<DerType>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real Real;
+
+  const AutoDiffScalar<DerivativeType>& derived() const {
+    return *static_cast<const AutoDiffScalar<DerivativeType>*>(this);
+  }
+  AutoDiffScalar<DerivativeType>& derived() { return *static_cast<AutoDiffScalar<DerivativeType>*>(this); }
+
+  inline AutoDiffScalar<DerType&> operator+(const Real& other) const {
+    return AutoDiffScalar<DerType&>(derived().value() + other, derived().derivatives());
+  }
+
+  friend inline AutoDiffScalar<DerType&> operator+(const Real& a, const AutoDiffScalar<DerivativeType>& b) {
+    return AutoDiffScalar<DerType&>(a + b.value(), b.derivatives());
+  }
+
+  inline AutoDiffScalar<DerivativeType>& operator+=(const Real& other) {
+    derived().value() += other;
+    return derived();
+  }
+
+  inline AutoDiffScalar<typename CwiseUnaryOp<bind2nd_op<scalar_product_op<Scalar, Real>>, DerType>::Type> operator*(
+      const Real& other) const {
+    return AutoDiffScalar<typename CwiseUnaryOp<bind2nd_op<scalar_product_op<Scalar, Real>>, DerType>::Type>(
+        derived().value() * other, derived().derivatives() * other);
+  }
+
+  friend inline AutoDiffScalar<typename CwiseUnaryOp<bind1st_op<scalar_product_op<Real, Scalar>>, DerType>::Type>
+  operator*(const Real& other, const AutoDiffScalar<DerivativeType>& a) {
+    return AutoDiffScalar<typename CwiseUnaryOp<bind1st_op<scalar_product_op<Real, Scalar>>, DerType>::Type>(
+        a.value() * other, a.derivatives() * other);
+  }
+
+  inline AutoDiffScalar<DerivativeType>& operator*=(const Scalar& other) {
+    *this = *this * other;
+    return derived();
+  }
+};
+
+template <typename DerivativeType>
+struct auto_diff_special_op<DerivativeType, false> {
+  void operator*() const;
+  void operator-() const;
+  void operator+() const;
+};
+
+}  // end namespace internal
+
+template <typename DerType, typename BinOp>
+struct ScalarBinaryOpTraits<AutoDiffScalar<DerType>, typename DerType::Scalar, BinOp> {
+  typedef AutoDiffScalar<DerType> ReturnType;
+};
+
+template <typename DerType, typename BinOp>
+struct ScalarBinaryOpTraits<typename DerType::Scalar, AutoDiffScalar<DerType>, BinOp> {
+  typedef AutoDiffScalar<DerType> ReturnType;
+};
+
+#define EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(FUNC, CODE)                                              \
+  template <typename DerType>                                                                        \
+  inline auto FUNC(const Eigen::AutoDiffScalar<DerType>& x) {                                        \
+    using namespace Eigen;                                                                           \
+    typedef typename Eigen::internal::traits<Eigen::internal::remove_all_t<DerType>>::Scalar Scalar; \
+    EIGEN_UNUSED_VARIABLE(sizeof(Scalar));                                                           \
+    CODE;                                                                                            \
+  }
+
+template <typename DerType>
+struct CleanedUpDerType {
+  typedef AutoDiffScalar<typename Eigen::internal::remove_all_t<DerType>::PlainObject> type;
+};
+
+template <typename DerType>
+inline const AutoDiffScalar<DerType>& conj(const AutoDiffScalar<DerType>& x) {
+  return x;
+}
+template <typename DerType>
+inline const AutoDiffScalar<DerType>& real(const AutoDiffScalar<DerType>& x) {
+  return x;
+}
+template <typename DerType>
+inline typename DerType::Scalar imag(const AutoDiffScalar<DerType>&) {
+  return 0.;
+}
+template <typename DerType, typename T>
+inline typename CleanedUpDerType<DerType>::type(min)(const AutoDiffScalar<DerType>& x, const T& y) {
+  typedef typename CleanedUpDerType<DerType>::type ADS;
+  return (x <= y ? ADS(x) : ADS(y));
+}
+template <typename DerType, typename T>
+inline typename CleanedUpDerType<DerType>::type(max)(const AutoDiffScalar<DerType>& x, const T& y) {
+  typedef typename CleanedUpDerType<DerType>::type ADS;
+  return (x >= y ? ADS(x) : ADS(y));
+}
+template <typename DerType, typename T>
+inline typename CleanedUpDerType<DerType>::type(min)(const T& x, const AutoDiffScalar<DerType>& y) {
+  typedef typename CleanedUpDerType<DerType>::type ADS;
+  return (x < y ? ADS(x) : ADS(y));
+}
+template <typename DerType, typename T>
+inline typename CleanedUpDerType<DerType>::type(max)(const T& x, const AutoDiffScalar<DerType>& y) {
+  typedef typename CleanedUpDerType<DerType>::type ADS;
+  return (x > y ? ADS(x) : ADS(y));
+}
+template <typename DerType>
+inline
+    typename CleanedUpDerType<DerType>::type(min)(const AutoDiffScalar<DerType>& x, const AutoDiffScalar<DerType>& y) {
+  return (x.value() < y.value() ? x : y);
+}
+template <typename DerType>
+inline
+    typename CleanedUpDerType<DerType>::type(max)(const AutoDiffScalar<DerType>& x, const AutoDiffScalar<DerType>& y) {
+  return (x.value() >= y.value() ? x : y);
+}
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(abs, using std::abs;
+                                    return Eigen::MakeAutoDiffScalar(abs(x.value()),
+                                                                     x.derivatives() * (x.value() < 0 ? -1 : 1));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(abs2, using numext::abs2;
+                                    return Eigen::MakeAutoDiffScalar(abs2(x.value()),
+                                                                     x.derivatives() * (Scalar(2) * x.value()));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sqrt, using std::sqrt; Scalar sqrtx = sqrt(x.value());
+                                    return Eigen::MakeAutoDiffScalar(sqrtx, x.derivatives() * (Scalar(0.5) / sqrtx));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(cos, using std::cos; using std::sin;
+                                    return Eigen::MakeAutoDiffScalar(cos(x.value()),
+                                                                     x.derivatives() * (-sin(x.value())));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sin, using std::sin; using std::cos;
+                                    return Eigen::MakeAutoDiffScalar(sin(x.value()), x.derivatives() * cos(x.value()));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(exp, using std::exp; Scalar expx = exp(x.value());
+                                    return Eigen::MakeAutoDiffScalar(expx, x.derivatives() * expx);)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(log, using std::log;
+                                    return Eigen::MakeAutoDiffScalar(log(x.value()),
+                                                                     x.derivatives() * (Scalar(1) / x.value()));)
+
+template <typename DerType>
+inline auto pow(const Eigen::AutoDiffScalar<DerType>& x,
+                const typename internal::traits<internal::remove_all_t<DerType>>::Scalar& y) {
+  using namespace Eigen;
+  using std::pow;
+  return Eigen::MakeAutoDiffScalar(pow(x.value(), y), x.derivatives() * (y * pow(x.value(), y - 1)));
+}
+
+template <typename DerTypeA, typename DerTypeB>
+inline AutoDiffScalar<Matrix<typename internal::traits<internal::remove_all_t<DerTypeA>>::Scalar, Dynamic, 1>> atan2(
+    const AutoDiffScalar<DerTypeA>& a, const AutoDiffScalar<DerTypeB>& b) {
+  using std::atan2;
+  typedef typename internal::traits<internal::remove_all_t<DerTypeA>>::Scalar Scalar;
+  typedef AutoDiffScalar<Matrix<Scalar, Dynamic, 1>> PlainADS;
+  PlainADS ret;
+  ret.value() = atan2(a.value(), b.value());
+
+  Scalar squared_hypot = a.value() * a.value() + b.value() * b.value();
+
+  // if (squared_hypot==0) the derivation is undefined and the following results in a NaN:
+  ret.derivatives() = (a.derivatives() * b.value() - a.value() * b.derivatives()) / squared_hypot;
+
+  return ret;
+}
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(tan, using std::tan; using std::cos; return Eigen::MakeAutoDiffScalar(
+                                        tan(x.value()), x.derivatives() * (Scalar(1) / numext::abs2(cos(x.value()))));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(asin, using std::sqrt; using std::asin; return Eigen::MakeAutoDiffScalar(
+                                        asin(x.value()),
+                                        x.derivatives() * (Scalar(1) / sqrt(1 - numext::abs2(x.value()))));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(acos, using std::sqrt; using std::acos; return Eigen::MakeAutoDiffScalar(
+                                        acos(x.value()),
+                                        x.derivatives() * (Scalar(-1) / sqrt(1 - numext::abs2(x.value()))));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(
+    tanh, using std::cosh; using std::tanh;
+    return Eigen::MakeAutoDiffScalar(tanh(x.value()), x.derivatives() * (Scalar(1) / numext::abs2(cosh(x.value()))));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sinh, using std::sinh; using std::cosh;
+                                    return Eigen::MakeAutoDiffScalar(sinh(x.value()),
+                                                                     x.derivatives() * cosh(x.value()));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(cosh, using std::sinh; using std::cosh;
+                                    return Eigen::MakeAutoDiffScalar(cosh(x.value()),
+                                                                     x.derivatives() * sinh(x.value()));)
+
+#undef EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY
+
+template <typename DerType>
+struct NumTraits<AutoDiffScalar<DerType>>
+    : NumTraits<typename NumTraits<typename internal::remove_all_t<DerType>::Scalar>::Real> {
+  typedef internal::remove_all_t<DerType> DerTypeCleaned;
+  typedef AutoDiffScalar<Matrix<typename NumTraits<typename DerTypeCleaned::Scalar>::Real,
+                                DerTypeCleaned::RowsAtCompileTime, DerTypeCleaned::ColsAtCompileTime, 0,
+                                DerTypeCleaned::MaxRowsAtCompileTime, DerTypeCleaned::MaxColsAtCompileTime>>
+      Real;
+  typedef AutoDiffScalar<DerType> NonInteger;
+  typedef AutoDiffScalar<DerType> Nested;
+  typedef typename NumTraits<typename DerTypeCleaned::Scalar>::Literal Literal;
+  enum { RequireInitialization = 1 };
+};
+
+namespace internal {
+template <typename DerivativeType>
+struct is_identically_zero_impl<AutoDiffScalar<DerivativeType>> {
+  static inline bool run(const AutoDiffScalar<DerivativeType>& s) {
+    const DerivativeType& derivatives = s.derivatives();
+    for (int i = 0; i < derivatives.size(); ++i) {
+      if (!numext::is_exactly_zero(derivatives[i])) {
+        return false;
+      }
+    }
+    return numext::is_exactly_zero(s.value());
+  }
+};
+}  // namespace internal
+}  // namespace Eigen
+
+namespace std {
+
+template <typename T>
+class numeric_limits<Eigen::AutoDiffScalar<T>> : public numeric_limits<typename T::Scalar> {};
+
+template <typename T>
+class numeric_limits<Eigen::AutoDiffScalar<T&>> : public numeric_limits<typename T::Scalar> {};
+
+}  // namespace std
+
+#endif  // EIGEN_AUTODIFF_SCALAR_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
new file mode 100644
index 0000000..892980e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
@@ -0,0 +1,169 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AUTODIFF_VECTOR_H
+#define EIGEN_AUTODIFF_VECTOR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/* \class AutoDiffScalar
+ * \brief A scalar type replacement with automatic differentation capability
+ *
+ * \param DerType the vector type used to store/represent the derivatives (e.g. Vector3f)
+ *
+ * This class represents a scalar value while tracking its respective derivatives.
+ *
+ * It supports the following list of global math function:
+ *  - std::abs, std::sqrt, std::pow, std::exp, std::log, std::sin, std::cos,
+ *  - internal::abs, internal::sqrt, numext::pow, internal::exp, internal::log, internal::sin, internal::cos,
+ *  - internal::conj, internal::real, internal::imag, numext::abs2.
+ *
+ * AutoDiffScalar can be used as the scalar type of an Eigen::Matrix object. However,
+ * in that case, the expression template mechanism only occurs at the top Matrix level,
+ * while derivatives are computed right away.
+ *
+ */
+template <typename ValueType, typename JacobianType>
+class AutoDiffVector {
+ public:
+  typedef typename internal::traits<ValueType>::Scalar BaseScalar;
+  typedef AutoDiffScalar<Matrix<BaseScalar, JacobianType::RowsAtCompileTime, 1> > ActiveScalar;
+  typedef ActiveScalar Scalar;
+  typedef AutoDiffScalar<typename JacobianType::ColXpr> CoeffType;
+  typedef typename JacobianType::Index Index;
+
+  inline AutoDiffVector() {}
+
+  inline AutoDiffVector(const ValueType& values) : m_values(values) { m_jacobian.setZero(); }
+
+  CoeffType operator[](Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); }
+  const CoeffType operator[](Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); }
+
+  CoeffType operator()(Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); }
+  const CoeffType operator()(Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); }
+
+  CoeffType coeffRef(Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); }
+  const CoeffType coeffRef(Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); }
+
+  Index size() const { return m_values.size(); }
+
+  // FIXME: Here we could return an expression of the sum.
+  Scalar sum() const { return Scalar(m_values.sum(), m_jacobian.rowwise().sum()); }
+
+  inline AutoDiffVector(const ValueType& values, const JacobianType& jac) : m_values(values), m_jacobian(jac) {}
+
+  template <typename OtherValueType, typename OtherJacobianType>
+  inline AutoDiffVector(const AutoDiffVector<OtherValueType, OtherJacobianType>& other)
+      : m_values(other.values()), m_jacobian(other.jacobian()) {}
+
+  inline AutoDiffVector(const AutoDiffVector& other) : m_values(other.values()), m_jacobian(other.jacobian()) {}
+
+  template <typename OtherValueType, typename OtherJacobianType>
+  inline AutoDiffVector& operator=(const AutoDiffVector<OtherValueType, OtherJacobianType>& other) {
+    m_values = other.values();
+    m_jacobian = other.jacobian();
+    return *this;
+  }
+
+  inline AutoDiffVector& operator=(const AutoDiffVector& other) {
+    m_values = other.values();
+    m_jacobian = other.jacobian();
+    return *this;
+  }
+
+  inline const ValueType& values() const { return m_values; }
+  inline ValueType& values() { return m_values; }
+
+  inline const JacobianType& jacobian() const { return m_jacobian; }
+  inline JacobianType& jacobian() { return m_jacobian; }
+
+  template <typename OtherValueType, typename OtherJacobianType>
+  inline const AutoDiffVector<
+      typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>, ValueType, OtherValueType>::Type,
+      typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>, JacobianType, OtherJacobianType>::Type>
+  operator+(const AutoDiffVector<OtherValueType, OtherJacobianType>& other) const {
+    return AutoDiffVector<
+        typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>, ValueType, OtherValueType>::Type,
+        typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>, JacobianType, OtherJacobianType>::Type>(
+        m_values + other.values(), m_jacobian + other.jacobian());
+  }
+
+  template <typename OtherValueType, typename OtherJacobianType>
+  inline AutoDiffVector& operator+=(const AutoDiffVector<OtherValueType, OtherJacobianType>& other) {
+    m_values += other.values();
+    m_jacobian += other.jacobian();
+    return *this;
+  }
+
+  template <typename OtherValueType, typename OtherJacobianType>
+  inline const AutoDiffVector<
+      typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>, ValueType, OtherValueType>::Type,
+      typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>, JacobianType, OtherJacobianType>::Type>
+  operator-(const AutoDiffVector<OtherValueType, OtherJacobianType>& other) const {
+    return AutoDiffVector<
+        typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>, ValueType, OtherValueType>::Type,
+        typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>, JacobianType, OtherJacobianType>::Type>(
+        m_values - other.values(), m_jacobian - other.jacobian());
+  }
+
+  template <typename OtherValueType, typename OtherJacobianType>
+  inline AutoDiffVector& operator-=(const AutoDiffVector<OtherValueType, OtherJacobianType>& other) {
+    m_values -= other.values();
+    m_jacobian -= other.jacobian();
+    return *this;
+  }
+
+  inline const AutoDiffVector<typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, ValueType>::Type,
+                              typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, JacobianType>::Type>
+  operator-() const {
+    return AutoDiffVector<typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, ValueType>::Type,
+                          typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, JacobianType>::Type>(
+        -m_values, -m_jacobian);
+  }
+
+  inline const AutoDiffVector<typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
+                              typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type>
+  operator*(const BaseScalar& other) const {
+    return AutoDiffVector<typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
+                          typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type>(
+        m_values * other, m_jacobian * other);
+  }
+
+  friend inline const AutoDiffVector<
+      typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
+      typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type>
+  operator*(const Scalar& other, const AutoDiffVector& v) {
+    return AutoDiffVector<typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
+                          typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type>(
+        v.values() * other, v.jacobian() * other);
+  }
+
+  inline AutoDiffVector& operator*=(const Scalar& other) {
+    m_values *= other;
+    m_jacobian *= other;
+    return *this;
+  }
+
+  template <typename OtherValueType, typename OtherJacobianType>
+  inline AutoDiffVector& operator*=(const AutoDiffVector<OtherValueType, OtherJacobianType>& other) {
+    *this = *this * other;
+    return *this;
+  }
+
+ protected:
+  ValueType m_values;
+  JacobianType m_jacobian;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_AUTODIFF_VECTOR_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/CoherentPadOp.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/CoherentPadOp.h
new file mode 100644
index 0000000..0659e9a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/CoherentPadOp.h
@@ -0,0 +1,152 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020 The Eigen Team.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COHERENT_PAD_OP_H
+#define EIGEN_COHERENT_PAD_OP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Pads a vector with zeros to a given size.
+template <typename XprType, int SizeAtCompileTime_>
+struct CoherentPadOp;
+
+template <typename XprType, int SizeAtCompileTime_>
+struct traits<CoherentPadOp<XprType, SizeAtCompileTime_>> : public traits<XprType> {
+  typedef typename internal::remove_all<XprType>::type PlainXprType;
+  typedef typename internal::ref_selector<XprType>::type XprNested;
+  typedef typename std::remove_reference_t<XprNested> XprNested_;
+  enum : int {
+    IsRowMajor = traits<PlainXprType>::Flags & RowMajorBit,
+    SizeAtCompileTime = SizeAtCompileTime_,
+    RowsAtCompileTime = IsRowMajor ? 1 : SizeAtCompileTime,
+    ColsAtCompileTime = IsRowMajor ? SizeAtCompileTime : 1,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    Flags = traits<XprType>::Flags & ~NestByRefBit,
+  };
+};
+
+// Pads a vector with zeros to a given size.
+template <typename XprType, int SizeAtCompileTime_>
+struct CoherentPadOp : public dense_xpr_base<CoherentPadOp<XprType, SizeAtCompileTime_>>::type {
+  typedef typename internal::generic_xpr_base<CoherentPadOp<XprType, SizeAtCompileTime_>>::type Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(CoherentPadOp)
+
+  using XprNested = typename traits<CoherentPadOp>::XprNested;
+  using XprNested_ = typename traits<CoherentPadOp>::XprNested_;
+  using NestedExpression = XprNested_;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoherentPadOp() = delete;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoherentPadOp(const CoherentPadOp&) = default;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoherentPadOp(CoherentPadOp&& other) = default;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoherentPadOp(const XprType& xpr, Index size) : m_xpr(xpr), m_size(size) {
+    static_assert(XprNested_::IsVectorAtCompileTime, "input type must be a vector");
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const XprNested_& nestedExpression() const { return m_xpr; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_size.value(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rows() const {
+    return traits<CoherentPadOp>::IsRowMajor ? Index(1) : size();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index cols() const {
+    return traits<CoherentPadOp>::IsRowMajor ? size() : Index(1);
+  }
+
+ private:
+  XprNested m_xpr;
+  const internal::variable_if_dynamic<Index, SizeAtCompileTime> m_size;
+};
+
+// Adapted from the Replicate evaluator.
+template <typename ArgType, int SizeAtCompileTime>
+struct unary_evaluator<CoherentPadOp<ArgType, SizeAtCompileTime>>
+    : evaluator_base<CoherentPadOp<ArgType, SizeAtCompileTime>> {
+  typedef CoherentPadOp<ArgType, SizeAtCompileTime> XprType;
+  typedef typename internal::remove_all_t<typename XprType::CoeffReturnType> CoeffReturnType;
+  typedef typename internal::nested_eval<ArgType, 1>::type ArgTypeNested;
+  typedef internal::remove_all_t<ArgTypeNested> ArgTypeNestedCleaned;
+
+  enum {
+    CoeffReadCost = evaluator<ArgTypeNestedCleaned>::CoeffReadCost,
+    LinearAccessMask = XprType::IsVectorAtCompileTime ? LinearAccessBit : 0,
+    Flags = evaluator<ArgTypeNestedCleaned>::Flags & (HereditaryBits | LinearAccessMask | RowMajorBit),
+    Alignment = evaluator<ArgTypeNestedCleaned>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit unary_evaluator(const XprType& pad)
+      : m_arg(pad.nestedExpression()), m_argImpl(m_arg), m_size(pad.nestedExpression().size()) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const {
+    EIGEN_IF_CONSTEXPR(XprType::IsRowMajor) {
+      if (col < m_size.value()) {
+        return m_argImpl.coeff(1, col);
+      }
+    }
+    else {
+      if (row < m_size.value()) {
+        return m_argImpl.coeff(row, 1);
+      }
+    }
+    return CoeffReturnType(0);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    if (index < m_size.value()) {
+      return m_argImpl.coeff(index);
+    }
+    return CoeffReturnType(0);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const {
+    // AutoDiff scalar's derivative must be a vector, which is enforced by static assert.
+    // Defer to linear access for simplicity.
+    EIGEN_IF_CONSTEXPR(XprType::IsRowMajor) { return packet(col); }
+    return packet(row);
+  }
+
+  template <int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet(Index index) const {
+    constexpr int kPacketSize = unpacket_traits<PacketType>::size;
+    if (index + kPacketSize <= m_size.value()) {
+      return m_argImpl.template packet<LoadMode, PacketType>(index);
+    } else if (index < m_size.value()) {
+      // Partial packet.
+      EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[kPacketSize];
+      const int partial = m_size.value() - index;
+      for (int i = 0; i < partial && i < kPacketSize; ++i) {
+        values[i] = m_argImpl.coeff(index + i);
+      }
+      for (int i = partial; i < kPacketSize; ++i) {
+        values[i] = CoeffReturnType(0);
+      }
+      return pload<PacketType>(values);
+    }
+    return pset1<PacketType>(CoeffReturnType(0));
+  }
+
+ protected:
+  const ArgTypeNested m_arg;
+  evaluator<ArgTypeNestedCleaned> m_argImpl;
+  const variable_if_dynamic<Index, ArgTypeNestedCleaned::SizeAtCompileTime> m_size;
+};
+
+}  // namespace internal
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CWISE_BINARY_OP_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/InternalHeaderCheck.h
new file mode 100644
index 0000000..1584337
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/AutoDiff/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_AUTODIFF_MODULE_H
+#error "Please include unsupported/Eigen/AutoDiff instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/BVH/BVAlgorithms.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/BVH/BVAlgorithms.h
new file mode 100644
index 0000000..d9e41da
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/BVH/BVAlgorithms.h
@@ -0,0 +1,291 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BVALGORITHMS_H
+#define EIGEN_BVALGORITHMS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename BVH, typename Intersector>
+bool intersect_helper(const BVH &tree, Intersector &intersector, typename BVH::Index root) {
+  typedef typename BVH::Index Index;
+  typedef typename BVH::VolumeIterator VolIter;
+  typedef typename BVH::ObjectIterator ObjIter;
+
+  VolIter vBegin = VolIter(), vEnd = VolIter();
+  ObjIter oBegin = ObjIter(), oEnd = ObjIter();
+
+  std::vector<Index> todo(1, root);
+
+  while (!todo.empty()) {
+    tree.getChildren(todo.back(), vBegin, vEnd, oBegin, oEnd);
+    todo.pop_back();
+
+    for (; vBegin != vEnd; ++vBegin)  // go through child volumes
+      if (intersector.intersectVolume(tree.getVolume(*vBegin))) todo.push_back(*vBegin);
+
+    for (; oBegin != oEnd; ++oBegin)                          // go through child objects
+      if (intersector.intersectObject(*oBegin)) return true;  // intersector said to stop query
+  }
+  return false;
+}
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+template <typename Volume1, typename Object1, typename Object2, typename Intersector>
+struct intersector_helper1 {
+  intersector_helper1(const Object2 &inStored, Intersector &in) : stored(inStored), intersector(in) {}
+  bool intersectVolume(const Volume1 &vol) { return intersector.intersectVolumeObject(vol, stored); }
+  bool intersectObject(const Object1 &obj) { return intersector.intersectObjectObject(obj, stored); }
+  Object2 stored;
+  Intersector &intersector;
+
+ private:
+  intersector_helper1 &operator=(const intersector_helper1 &);
+};
+
+template <typename Volume2, typename Object2, typename Object1, typename Intersector>
+struct intersector_helper2 {
+  intersector_helper2(const Object1 &inStored, Intersector &in) : stored(inStored), intersector(in) {}
+  bool intersectVolume(const Volume2 &vol) { return intersector.intersectObjectVolume(stored, vol); }
+  bool intersectObject(const Object2 &obj) { return intersector.intersectObjectObject(stored, obj); }
+  Object1 stored;
+  Intersector &intersector;
+
+ private:
+  intersector_helper2 &operator=(const intersector_helper2 &);
+};
+
+}  // end namespace internal
+
+/**  Given a BVH, runs the query encapsulated by \a intersector.
+  *  The Intersector type must provide the following members: \code
+     bool intersectVolume(const BVH::Volume &volume) //returns true if volume intersects the query
+     bool intersectObject(const BVH::Object &object) //returns true if the search should terminate immediately
+  \endcode
+  */
+template <typename BVH, typename Intersector>
+void BVIntersect(const BVH &tree, Intersector &intersector) {
+  internal::intersect_helper(tree, intersector, tree.getRootIndex());
+}
+
+/**  Given two BVH's, runs the query on their Cartesian product encapsulated by \a intersector.
+  *  The Intersector type must provide the following members: \code
+     bool intersectVolumeVolume(const BVH1::Volume &v1, const BVH2::Volume &v2) //returns true if product of volumes
+  intersects the query bool intersectVolumeObject(const BVH1::Volume &v1, const BVH2::Object &o2) //returns true if the
+  volume-object product intersects the query bool intersectObjectVolume(const BVH1::Object &o1, const BVH2::Volume &v2)
+  //returns true if the volume-object product intersects the query bool intersectObjectObject(const BVH1::Object &o1,
+  const BVH2::Object &o2) //returns true if the search should terminate immediately \endcode
+  */
+template <typename BVH1, typename BVH2, typename Intersector>
+void BVIntersect(const BVH1 &tree1, const BVH2 &tree2,
+                 Intersector &intersector)  // TODO: tandem descent when it makes sense
+{
+  typedef typename BVH1::Index Index1;
+  typedef typename BVH2::Index Index2;
+  typedef internal::intersector_helper1<typename BVH1::Volume, typename BVH1::Object, typename BVH2::Object,
+                                        Intersector>
+      Helper1;
+  typedef internal::intersector_helper2<typename BVH2::Volume, typename BVH2::Object, typename BVH1::Object,
+                                        Intersector>
+      Helper2;
+  typedef typename BVH1::VolumeIterator VolIter1;
+  typedef typename BVH1::ObjectIterator ObjIter1;
+  typedef typename BVH2::VolumeIterator VolIter2;
+  typedef typename BVH2::ObjectIterator ObjIter2;
+
+  VolIter1 vBegin1 = VolIter1(), vEnd1 = VolIter1();
+  ObjIter1 oBegin1 = ObjIter1(), oEnd1 = ObjIter1();
+  VolIter2 vBegin2 = VolIter2(), vEnd2 = VolIter2(), vCur2 = VolIter2();
+  ObjIter2 oBegin2 = ObjIter2(), oEnd2 = ObjIter2(), oCur2 = ObjIter2();
+
+  std::vector<std::pair<Index1, Index2> > todo(1, std::make_pair(tree1.getRootIndex(), tree2.getRootIndex()));
+
+  while (!todo.empty()) {
+    tree1.getChildren(todo.back().first, vBegin1, vEnd1, oBegin1, oEnd1);
+    tree2.getChildren(todo.back().second, vBegin2, vEnd2, oBegin2, oEnd2);
+    todo.pop_back();
+
+    for (; vBegin1 != vEnd1; ++vBegin1) {  // go through child volumes of first tree
+      const typename BVH1::Volume &vol1 = tree1.getVolume(*vBegin1);
+      for (vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) {  // go through child volumes of second tree
+        if (intersector.intersectVolumeVolume(vol1, tree2.getVolume(*vCur2)))
+          todo.push_back(std::make_pair(*vBegin1, *vCur2));
+      }
+
+      for (oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {  // go through child objects of second tree
+        Helper1 helper(*oCur2, intersector);
+        if (internal::intersect_helper(tree1, helper, *vBegin1)) return;  // intersector said to stop query
+      }
+    }
+
+    for (; oBegin1 != oEnd1; ++oBegin1) {               // go through child objects of first tree
+      for (vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) {  // go through child volumes of second tree
+        Helper2 helper(*oBegin1, intersector);
+        if (internal::intersect_helper(tree2, helper, *vCur2)) return;  // intersector said to stop query
+      }
+
+      for (oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {                    // go through child objects of second tree
+        if (intersector.intersectObjectObject(*oBegin1, *oCur2)) return;  // intersector said to stop query
+      }
+    }
+  }
+}
+
+namespace internal {
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename BVH, typename Minimizer>
+typename Minimizer::Scalar minimize_helper(const BVH &tree, Minimizer &minimizer, typename BVH::Index root,
+                                           typename Minimizer::Scalar minimum) {
+  typedef typename Minimizer::Scalar Scalar;
+  typedef typename BVH::Index Index;
+  typedef std::pair<Scalar, Index> QueueElement;  // first element is priority
+  typedef typename BVH::VolumeIterator VolIter;
+  typedef typename BVH::ObjectIterator ObjIter;
+
+  VolIter vBegin = VolIter(), vEnd = VolIter();
+  ObjIter oBegin = ObjIter(), oEnd = ObjIter();
+  std::priority_queue<QueueElement, std::vector<QueueElement>, std::greater<QueueElement> >
+      todo;  // smallest is at the top
+
+  todo.push(std::make_pair(Scalar(), root));
+
+  while (!todo.empty()) {
+    tree.getChildren(todo.top().second, vBegin, vEnd, oBegin, oEnd);
+    todo.pop();
+
+    for (; oBegin != oEnd; ++oBegin)  // go through child objects
+      minimum = (std::min)(minimum, minimizer.minimumOnObject(*oBegin));
+
+    for (; vBegin != vEnd; ++vBegin) {  // go through child volumes
+      Scalar val = minimizer.minimumOnVolume(tree.getVolume(*vBegin));
+      if (val < minimum) todo.push(std::make_pair(val, *vBegin));
+    }
+  }
+
+  return minimum;
+}
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+template <typename Volume1, typename Object1, typename Object2, typename Minimizer>
+struct minimizer_helper1 {
+  typedef typename Minimizer::Scalar Scalar;
+  minimizer_helper1(const Object2 &inStored, Minimizer &m) : stored(inStored), minimizer(m) {}
+  Scalar minimumOnVolume(const Volume1 &vol) { return minimizer.minimumOnVolumeObject(vol, stored); }
+  Scalar minimumOnObject(const Object1 &obj) { return minimizer.minimumOnObjectObject(obj, stored); }
+  Object2 stored;
+  Minimizer &minimizer;
+
+ private:
+  minimizer_helper1 &operator=(const minimizer_helper1 &);
+};
+
+template <typename Volume2, typename Object2, typename Object1, typename Minimizer>
+struct minimizer_helper2 {
+  typedef typename Minimizer::Scalar Scalar;
+  minimizer_helper2(const Object1 &inStored, Minimizer &m) : stored(inStored), minimizer(m) {}
+  Scalar minimumOnVolume(const Volume2 &vol) { return minimizer.minimumOnObjectVolume(stored, vol); }
+  Scalar minimumOnObject(const Object2 &obj) { return minimizer.minimumOnObjectObject(stored, obj); }
+  Object1 stored;
+  Minimizer &minimizer;
+
+ private:
+  minimizer_helper2 &operator=(const minimizer_helper2 &);
+};
+
+}  // end namespace internal
+
+/**  Given a BVH, runs the query encapsulated by \a minimizer.
+  *  \returns the minimum value.
+  *  The Minimizer type must provide the following members: \code
+     typedef Scalar //the numeric type of what is being minimized--not necessarily the Scalar type of the BVH (if it has
+  one) Scalar minimumOnVolume(const BVH::Volume &volume) Scalar minimumOnObject(const BVH::Object &object) \endcode
+  */
+template <typename BVH, typename Minimizer>
+typename Minimizer::Scalar BVMinimize(const BVH &tree, Minimizer &minimizer) {
+  return internal::minimize_helper(tree, minimizer, tree.getRootIndex(),
+                                   (std::numeric_limits<typename Minimizer::Scalar>::max)());
+}
+
+/**  Given two BVH's, runs the query on their cartesian product encapsulated by \a minimizer.
+  *  \returns the minimum value.
+  *  The Minimizer type must provide the following members: \code
+     typedef Scalar //the numeric type of what is being minimized--not necessarily the Scalar type of the BVH (if it has
+  one) Scalar minimumOnVolumeVolume(const BVH1::Volume &v1, const BVH2::Volume &v2) Scalar minimumOnVolumeObject(const
+  BVH1::Volume &v1, const BVH2::Object &o2) Scalar minimumOnObjectVolume(const BVH1::Object &o1, const BVH2::Volume &v2)
+     Scalar minimumOnObjectObject(const BVH1::Object &o1, const BVH2::Object &o2)
+  \endcode
+  */
+template <typename BVH1, typename BVH2, typename Minimizer>
+typename Minimizer::Scalar BVMinimize(const BVH1 &tree1, const BVH2 &tree2, Minimizer &minimizer) {
+  typedef typename Minimizer::Scalar Scalar;
+  typedef typename BVH1::Index Index1;
+  typedef typename BVH2::Index Index2;
+  typedef internal::minimizer_helper1<typename BVH1::Volume, typename BVH1::Object, typename BVH2::Object, Minimizer>
+      Helper1;
+  typedef internal::minimizer_helper2<typename BVH2::Volume, typename BVH2::Object, typename BVH1::Object, Minimizer>
+      Helper2;
+  typedef std::pair<Scalar, std::pair<Index1, Index2> > QueueElement;  // first element is priority
+  typedef typename BVH1::VolumeIterator VolIter1;
+  typedef typename BVH1::ObjectIterator ObjIter1;
+  typedef typename BVH2::VolumeIterator VolIter2;
+  typedef typename BVH2::ObjectIterator ObjIter2;
+
+  VolIter1 vBegin1 = VolIter1(), vEnd1 = VolIter1();
+  ObjIter1 oBegin1 = ObjIter1(), oEnd1 = ObjIter1();
+  VolIter2 vBegin2 = VolIter2(), vEnd2 = VolIter2(), vCur2 = VolIter2();
+  ObjIter2 oBegin2 = ObjIter2(), oEnd2 = ObjIter2(), oCur2 = ObjIter2();
+  std::priority_queue<QueueElement, std::vector<QueueElement>, std::greater<QueueElement> >
+      todo;  // smallest is at the top
+
+  Scalar minimum = (std::numeric_limits<Scalar>::max)();
+  todo.push(std::make_pair(Scalar(), std::make_pair(tree1.getRootIndex(), tree2.getRootIndex())));
+
+  while (!todo.empty()) {
+    tree1.getChildren(todo.top().second.first, vBegin1, vEnd1, oBegin1, oEnd1);
+    tree2.getChildren(todo.top().second.second, vBegin2, vEnd2, oBegin2, oEnd2);
+    todo.pop();
+
+    for (; oBegin1 != oEnd1; ++oBegin1) {               // go through child objects of first tree
+      for (oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {  // go through child objects of second tree
+        minimum = (std::min)(minimum, minimizer.minimumOnObjectObject(*oBegin1, *oCur2));
+      }
+
+      for (vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) {  // go through child volumes of second tree
+        Helper2 helper(*oBegin1, minimizer);
+        minimum = (std::min)(minimum, internal::minimize_helper(tree2, helper, *vCur2, minimum));
+      }
+    }
+
+    for (; vBegin1 != vEnd1; ++vBegin1) {  // go through child volumes of first tree
+      const typename BVH1::Volume &vol1 = tree1.getVolume(*vBegin1);
+
+      for (oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {  // go through child objects of second tree
+        Helper1 helper(*oCur2, minimizer);
+        minimum = (std::min)(minimum, internal::minimize_helper(tree1, helper, *vBegin1, minimum));
+      }
+
+      for (vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) {  // go through child volumes of second tree
+        Scalar val = minimizer.minimumOnVolumeVolume(vol1, tree2.getVolume(*vCur2));
+        if (val < minimum) todo.push(std::make_pair(val, std::make_pair(*vBegin1, *vCur2)));
+      }
+    }
+  }
+  return minimum;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BVALGORITHMS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/BVH/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/BVH/InternalHeaderCheck.h
new file mode 100644
index 0000000..7aade9b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/BVH/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_BVH_MODULE_H
+#error "Please include unsupported/Eigen/BVH instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/BVH/KdBVH.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/BVH/KdBVH.h
new file mode 100644
index 0000000..d421e6f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/BVH/KdBVH.h
@@ -0,0 +1,223 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef KDBVH_H_INCLUDED
+#define KDBVH_H_INCLUDED
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// internal pair class for the BVH--used instead of std::pair because of alignment
+template <typename Scalar, int Dim>
+struct vector_int_pair {
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar, Dim)
+  typedef Matrix<Scalar, Dim, 1> VectorType;
+
+  vector_int_pair(const VectorType &v, int i) : first(v), second(i) {}
+
+  VectorType first;
+  int second;
+};
+
+// these templates help the tree initializer get the bounding boxes either from a provided
+// iterator range or using bounding_box in a unified way
+template <typename ObjectList, typename VolumeList, typename BoxIter>
+struct get_boxes_helper {
+  void operator()(const ObjectList &objects, BoxIter boxBegin, BoxIter boxEnd, VolumeList &outBoxes) {
+    outBoxes.insert(outBoxes.end(), boxBegin, boxEnd);
+    eigen_assert(outBoxes.size() == objects.size());
+    EIGEN_ONLY_USED_FOR_DEBUG(objects);
+  }
+};
+
+template <typename ObjectList, typename VolumeList>
+struct get_boxes_helper<ObjectList, VolumeList, int> {
+  void operator()(const ObjectList &objects, int, int, VolumeList &outBoxes) {
+    outBoxes.reserve(objects.size());
+    for (int i = 0; i < (int)objects.size(); ++i) outBoxes.push_back(bounding_box(objects[i]));
+  }
+};
+
+}  // end namespace internal
+
+/** \class KdBVH
+ *  \brief A simple bounding volume hierarchy based on AlignedBox
+ *
+ *  \param Scalar_ The underlying scalar type of the bounding boxes
+ *  \param Dim_ The dimension of the space in which the hierarchy lives
+ *  \param Object_ The object type that lives in the hierarchy.  It must have value semantics.  Either
+ *                 `bounding_box(Object_)` must be defined and return an `AlignedBox<Scalar_, Dim_>` or bounding boxes
+ *                  must be provided to the tree initializer.
+ *
+ * This class provides a simple (as opposed to optimized) implementation of a bounding volume hierarchy analogous to a
+ * Kd-tree. Given a sequence of objects, it computes their bounding boxes, constructs a Kd-tree of their centers and
+ * builds a BVH with the structure of that Kd-tree.  When the elements of the tree are too expensive to be copied
+ * around, it is useful for `Object_` to be a pointer.
+ */
+template <typename Scalar_, int Dim_, typename Object_>
+class KdBVH {
+ public:
+  enum { Dim = Dim_ };
+  typedef Object_ Object;
+  typedef std::vector<Object, aligned_allocator<Object> > ObjectList;
+  typedef Scalar_ Scalar;
+  typedef AlignedBox<Scalar, Dim> Volume;
+  typedef std::vector<Volume, aligned_allocator<Volume> > VolumeList;
+  typedef int Index;
+  typedef const int *VolumeIterator;  // the iterators are just pointers into the tree's vectors
+  typedef const Object *ObjectIterator;
+
+  KdBVH() {}
+
+  /** Given an iterator range over \a Object references, constructs the BVH.  Requires that bounding_box(Object) return
+   * a Volume. */
+  template <typename Iter>
+  KdBVH(Iter begin, Iter end) {
+    init(begin, end, 0, 0);
+  }  // int is recognized by init as not being an iterator type
+
+  /** Given an iterator range over \a Object references and an iterator range over their bounding boxes, constructs the
+   * BVH */
+  template <typename OIter, typename BIter>
+  KdBVH(OIter begin, OIter end, BIter boxBegin, BIter boxEnd) {
+    init(begin, end, boxBegin, boxEnd);
+  }
+
+  /** Given an iterator range over \a Object references, constructs the BVH, overwriting whatever is in there currently.
+   * Requires that bounding_box(Object) return a Volume. */
+  template <typename Iter>
+  void init(Iter begin, Iter end) {
+    init(begin, end, 0, 0);
+  }
+
+  /** Given an iterator range over \a Object references and an iterator range over their bounding boxes,
+   * constructs the BVH, overwriting whatever is in there currently. */
+  template <typename OIter, typename BIter>
+  void init(OIter begin, OIter end, BIter boxBegin, BIter boxEnd) {
+    objects.clear();
+    boxes.clear();
+    children.clear();
+
+    objects.insert(objects.end(), begin, end);
+    int n = static_cast<int>(objects.size());
+
+    if (n < 2) return;  // if we have at most one object, we don't need any internal nodes
+
+    VolumeList objBoxes;
+    VIPairList objCenters;
+
+    // compute the bounding boxes depending on BIter type
+    internal::get_boxes_helper<ObjectList, VolumeList, BIter>()(objects, boxBegin, boxEnd, objBoxes);
+
+    objCenters.reserve(n);
+    boxes.reserve(n - 1);
+    children.reserve(2 * n - 2);
+
+    for (int i = 0; i < n; ++i) objCenters.push_back(VIPair(objBoxes[i].center(), i));
+
+    build(objCenters, 0, n, objBoxes, 0);  // the recursive part of the algorithm
+
+    ObjectList tmp(n);
+    tmp.swap(objects);
+    for (int i = 0; i < n; ++i) objects[i] = tmp[objCenters[i].second];
+  }
+
+  /** \returns the index of the root of the hierarchy */
+  inline Index getRootIndex() const { return (int)boxes.size() - 1; }
+
+  /** Given an \a index of a node, on exit, \a outVBegin and \a outVEnd range over the indices of the volume children of
+   * the node and \a outOBegin and \a outOEnd range over the object children of the node */
+  EIGEN_STRONG_INLINE void getChildren(Index index, VolumeIterator &outVBegin, VolumeIterator &outVEnd,
+                                       ObjectIterator &outOBegin, ObjectIterator &outOEnd)
+      const {  // inlining this function should open lots of optimization opportunities to the compiler
+    if (index < 0) {
+      outVBegin = outVEnd;
+      if (!objects.empty()) outOBegin = &(objects[0]);
+      outOEnd = outOBegin + objects.size();  // output all objects--necessary when the tree has only one object
+      return;
+    }
+
+    int numBoxes = static_cast<int>(boxes.size());
+
+    int idx = index * 2;
+    if (children[idx + 1] < numBoxes) {  // second index is always bigger
+      outVBegin = &(children[idx]);
+      outVEnd = outVBegin + 2;
+      outOBegin = outOEnd;
+    } else if (children[idx] >= numBoxes) {  // if both children are objects
+      outVBegin = outVEnd;
+      outOBegin = &(objects[children[idx] - numBoxes]);
+      outOEnd = outOBegin + 2;
+    } else {  // if the first child is a volume and the second is an object
+      outVBegin = &(children[idx]);
+      outVEnd = outVBegin + 1;
+      outOBegin = &(objects[children[idx + 1] - numBoxes]);
+      outOEnd = outOBegin + 1;
+    }
+  }
+
+  /** \returns the bounding box of the node at \a index */
+  inline const Volume &getVolume(Index index) const { return boxes[index]; }
+
+ private:
+  typedef internal::vector_int_pair<Scalar, Dim> VIPair;
+  typedef std::vector<VIPair, aligned_allocator<VIPair> > VIPairList;
+  typedef Matrix<Scalar, Dim, 1> VectorType;
+  struct VectorComparator  // compares vectors, or more specifically, VIPairs along a particular dimension
+  {
+    VectorComparator(int inDim) : dim(inDim) {}
+    inline bool operator()(const VIPair &v1, const VIPair &v2) const { return v1.first[dim] < v2.first[dim]; }
+    int dim;
+  };
+
+  // Build the part of the tree between objects[from] and objects[to] (not including objects[to]).
+  // This routine partitions the objCenters in [from, to) along the dimension dim, recursively constructs
+  // the two halves, and adds their parent node.  TODO: a cache-friendlier layout
+  void build(VIPairList &objCenters, int from, int to, const VolumeList &objBoxes, int dim) {
+    eigen_assert(to - from > 1);
+    if (to - from == 2) {
+      boxes.push_back(objBoxes[objCenters[from].second].merged(objBoxes[objCenters[from + 1].second]));
+      children.push_back(from + (int)objects.size() - 1);  // there are objects.size() - 1 tree nodes
+      children.push_back(from + (int)objects.size());
+    } else if (to - from == 3) {
+      int mid = from + 2;
+      std::nth_element(objCenters.begin() + from, objCenters.begin() + mid, objCenters.begin() + to,
+                       VectorComparator(dim));  // partition
+      build(objCenters, from, mid, objBoxes, (dim + 1) % Dim);
+      int idx1 = (int)boxes.size() - 1;
+      boxes.push_back(boxes[idx1].merged(objBoxes[objCenters[mid].second]));
+      children.push_back(idx1);
+      children.push_back(mid + (int)objects.size() - 1);
+    } else {
+      int mid = from + (to - from) / 2;
+      nth_element(objCenters.begin() + from, objCenters.begin() + mid, objCenters.begin() + to,
+                  VectorComparator(dim));  // partition
+      build(objCenters, from, mid, objBoxes, (dim + 1) % Dim);
+      int idx1 = (int)boxes.size() - 1;
+      build(objCenters, mid, to, objBoxes, (dim + 1) % Dim);
+      int idx2 = (int)boxes.size() - 1;
+      boxes.push_back(boxes[idx1].merged(boxes[idx2]));
+      children.push_back(idx1);
+      children.push_back(idx2);
+    }
+  }
+
+  std::vector<int> children;  // children of x are children[2x] and children[2x+1], indices bigger than boxes.size()
+                              // index into objects.
+  VolumeList boxes;
+  ObjectList objects;
+};
+
+}  // end namespace Eigen
+
+#endif  // KDBVH_H_INCLUDED
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h
new file mode 100644
index 0000000..b110aee
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h
@@ -0,0 +1,699 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 David Harmon <dharmon@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARPACKGENERALIZEDSELFADJOINTEIGENSOLVER_H
+#define EIGEN_ARPACKGENERALIZEDSELFADJOINTEIGENSOLVER_H
+
+#include "../../../../Eigen/Dense"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Scalar, typename RealScalar>
+struct arpack_wrapper;
+template <typename MatrixSolver, typename MatrixType, typename Scalar, bool BisSPD>
+struct OP;
+}  // namespace internal
+
+template <typename MatrixType, typename MatrixSolver = SimplicialLLT<MatrixType>, bool BisSPD = false>
+class ArpackGeneralizedSelfAdjointEigenSolver {
+ public:
+  /** \brief Scalar type for matrices of type \p MatrixType. */
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Index Index;
+
+  /** \brief Real scalar type for \p MatrixType.
+   *
+   * This is just \c Scalar if #Scalar is real (e.g., \c float or
+   * \c Scalar), and the type of the real part of \c Scalar if #Scalar is
+   * complex.
+   */
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  /** \brief Type for vector of eigenvalues as returned by eigenvalues().
+   *
+   * This is a column vector with entries of type #RealScalar.
+   * The length of the vector is the size of \p nbrEigenvalues.
+   */
+  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVectorType;
+
+  /** \brief Default constructor.
+   *
+   * The default constructor is for cases in which the user intends to
+   * perform decompositions via compute().
+   *
+   */
+  ArpackGeneralizedSelfAdjointEigenSolver()
+      : m_eivec(),
+        m_eivalues(),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false),
+        m_nbrConverged(0),
+        m_nbrIterations(0) {}
+
+  /** \brief Constructor; computes generalized eigenvalues of given matrix with respect to another matrix.
+   *
+   * \param[in] A Self-adjoint matrix whose eigenvalues / eigenvectors will
+   *    computed. By default, the upper triangular part is used, but can be changed
+   *    through the template parameter.
+   * \param[in] B Self-adjoint matrix for the generalized eigenvalue problem.
+   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
+   *    Must be less than the size of the input matrix, or an error is returned.
+   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
+   *    respective meanings to find the largest magnitude , smallest magnitude,
+   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
+   *    value can contain floating point value in string form, in which case the
+   *    eigenvalues closest to this value will be found.
+   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
+   *    means machine precision.
+   *
+   * This constructor calls compute(const MatrixType&, const MatrixType&, Index, string, int, RealScalar)
+   * to compute the eigenvalues of the matrix \p A with respect to \p B. The eigenvectors are computed if
+   * \p options equals #ComputeEigenvectors.
+   *
+   */
+  ArpackGeneralizedSelfAdjointEigenSolver(const MatrixType &A, const MatrixType &B, Index nbrEigenvalues,
+                                          std::string eigs_sigma = "LM", int options = ComputeEigenvectors,
+                                          RealScalar tol = 0.0)
+      : m_eivec(),
+        m_eivalues(),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false),
+        m_nbrConverged(0),
+        m_nbrIterations(0) {
+    compute(A, B, nbrEigenvalues, eigs_sigma, options, tol);
+  }
+
+  /** \brief Constructor; computes eigenvalues of given matrix.
+   *
+   * \param[in] A Self-adjoint matrix whose eigenvalues / eigenvectors will
+   *    computed. By default, the upper triangular part is used, but can be changed
+   *    through the template parameter.
+   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
+   *    Must be less than the size of the input matrix, or an error is returned.
+   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
+   *    respective meanings to find the largest magnitude , smallest magnitude,
+   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
+   *    value can contain floating point value in string form, in which case the
+   *    eigenvalues closest to this value will be found.
+   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
+   *    means machine precision.
+   *
+   * This constructor calls compute(const MatrixType&, Index, string, int, RealScalar)
+   * to compute the eigenvalues of the matrix \p A. The eigenvectors are computed if
+   * \p options equals #ComputeEigenvectors.
+   *
+   */
+
+  ArpackGeneralizedSelfAdjointEigenSolver(const MatrixType &A, Index nbrEigenvalues, std::string eigs_sigma = "LM",
+                                          int options = ComputeEigenvectors, RealScalar tol = 0.0)
+      : m_eivec(),
+        m_eivalues(),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false),
+        m_nbrConverged(0),
+        m_nbrIterations(0) {
+    compute(A, nbrEigenvalues, eigs_sigma, options, tol);
+  }
+
+  /** \brief Computes generalized eigenvalues / eigenvectors of given matrix using the external ARPACK library.
+   *
+   * \param[in]  A  Selfadjoint matrix whose eigendecomposition is to be computed.
+   * \param[in]  B  Selfadjoint matrix for generalized eigenvalues.
+   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
+   *    Must be less than the size of the input matrix, or an error is returned.
+   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
+   *    respective meanings to find the largest magnitude , smallest magnitude,
+   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
+   *    value can contain floating point value in string form, in which case the
+   *    eigenvalues closest to this value will be found.
+   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
+   *    means machine precision.
+   *
+   * \returns    Reference to \c *this
+   *
+   * This function computes the generalized eigenvalues of \p A with respect to \p B using ARPACK.  The eigenvalues()
+   * function can be used to retrieve them.  If \p options equals #ComputeEigenvectors,
+   * then the eigenvectors are also computed and can be retrieved by
+   * calling eigenvectors().
+   *
+   */
+  ArpackGeneralizedSelfAdjointEigenSolver &compute(const MatrixType &A, const MatrixType &B, Index nbrEigenvalues,
+                                                   std::string eigs_sigma = "LM", int options = ComputeEigenvectors,
+                                                   RealScalar tol = 0.0);
+
+  /** \brief Computes eigenvalues / eigenvectors of given matrix using the external ARPACK library.
+   *
+   * \param[in]  A  Selfadjoint matrix whose eigendecomposition is to be computed.
+   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
+   *    Must be less than the size of the input matrix, or an error is returned.
+   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
+   *    respective meanings to find the largest magnitude , smallest magnitude,
+   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
+   *    value can contain floating point value in string form, in which case the
+   *    eigenvalues closest to this value will be found.
+   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
+   *    means machine precision.
+   *
+   * \returns    Reference to \c *this
+   *
+   * This function computes the eigenvalues of \p A using ARPACK.  The eigenvalues()
+   * function can be used to retrieve them.  If \p options equals #ComputeEigenvectors,
+   * then the eigenvectors are also computed and can be retrieved by
+   * calling eigenvectors().
+   *
+   */
+  ArpackGeneralizedSelfAdjointEigenSolver &compute(const MatrixType &A, Index nbrEigenvalues,
+                                                   std::string eigs_sigma = "LM", int options = ComputeEigenvectors,
+                                                   RealScalar tol = 0.0);
+
+  /** \brief Returns the eigenvectors of given matrix.
+   *
+   * \returns  A const reference to the matrix whose columns are the eigenvectors.
+   *
+   * \pre The eigenvectors have been computed before.
+   *
+   * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
+   * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
+   * eigenvectors are normalized to have (Euclidean) norm equal to one. If
+   * this object was used to solve the eigenproblem for the selfadjoint
+   * matrix \f$ A \f$, then the matrix returned by this function is the
+   * matrix \f$ V \f$ in the eigendecomposition \f$ A V = D V \f$.
+   * For the generalized eigenproblem, the matrix returned is the solution \f$ A V = D B V \f$
+   *
+   * Example: \include SelfAdjointEigenSolver_eigenvectors.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_eigenvectors.out
+   *
+   * \sa eigenvalues()
+   */
+  const Matrix<Scalar, Dynamic, Dynamic> &eigenvectors() const {
+    eigen_assert(m_isInitialized && "ArpackGeneralizedSelfAdjointEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec;
+  }
+
+  /** \brief Returns the eigenvalues of given matrix.
+   *
+   * \returns A const reference to the column vector containing the eigenvalues.
+   *
+   * \pre The eigenvalues have been computed before.
+   *
+   * The eigenvalues are repeated according to their algebraic multiplicity,
+   * so there are as many eigenvalues as rows in the matrix. The eigenvalues
+   * are sorted in increasing order.
+   *
+   * Example: \include SelfAdjointEigenSolver_eigenvalues.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_eigenvalues.out
+   *
+   * \sa eigenvectors(), MatrixBase::eigenvalues()
+   */
+  const Matrix<Scalar, Dynamic, 1> &eigenvalues() const {
+    eigen_assert(m_isInitialized && "ArpackGeneralizedSelfAdjointEigenSolver is not initialized.");
+    return m_eivalues;
+  }
+
+  /** \brief Computes the positive-definite square root of the matrix.
+   *
+   * \returns the positive-definite square root of the matrix
+   *
+   * \pre The eigenvalues and eigenvectors of a positive-definite matrix
+   * have been computed before.
+   *
+   * The square root of a positive-definite matrix \f$ A \f$ is the
+   * positive-definite matrix whose square equals \f$ A \f$. This function
+   * uses the eigendecomposition \f$ A = V D V^{-1} \f$ to compute the
+   * square root as \f$ A^{1/2} = V D^{1/2} V^{-1} \f$.
+   *
+   * Example: \include SelfAdjointEigenSolver_operatorSqrt.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_operatorSqrt.out
+   *
+   * \sa operatorInverseSqrt(),
+   *     \ref MatrixFunctions_Module "MatrixFunctions Module"
+   */
+  Matrix<Scalar, Dynamic, Dynamic> operatorSqrt() const {
+    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint();
+  }
+
+  /** \brief Computes the inverse square root of the matrix.
+   *
+   * \returns the inverse positive-definite square root of the matrix
+   *
+   * \pre The eigenvalues and eigenvectors of a positive-definite matrix
+   * have been computed before.
+   *
+   * This function uses the eigendecomposition \f$ A = V D V^{-1} \f$ to
+   * compute the inverse square root as \f$ V D^{-1/2} V^{-1} \f$. This is
+   * cheaper than first computing the square root with operatorSqrt() and
+   * then its inverse with MatrixBase::inverse().
+   *
+   * Example: \include SelfAdjointEigenSolver_operatorInverseSqrt.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_operatorInverseSqrt.out
+   *
+   * \sa operatorSqrt(), MatrixBase::inverse(),
+   *     \ref MatrixFunctions_Module "MatrixFunctions Module"
+   */
+  Matrix<Scalar, Dynamic, Dynamic> operatorInverseSqrt() const {
+    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();
+  }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "ArpackGeneralizedSelfAdjointEigenSolver is not initialized.");
+    return m_info;
+  }
+
+  size_t getNbrConvergedEigenValues() const { return m_nbrConverged; }
+
+  size_t getNbrIterations() const { return m_nbrIterations; }
+
+ protected:
+  Matrix<Scalar, Dynamic, Dynamic> m_eivec;
+  Matrix<Scalar, Dynamic, 1> m_eivalues;
+  ComputationInfo m_info;
+  bool m_isInitialized;
+  bool m_eigenvectorsOk;
+
+  size_t m_nbrConverged;
+  size_t m_nbrIterations;
+};
+
+template <typename MatrixType, typename MatrixSolver, bool BisSPD>
+ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD> &
+ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>::compute(const MatrixType &A,
+                                                                                   Index nbrEigenvalues,
+                                                                                   std::string eigs_sigma, int options,
+                                                                                   RealScalar tol) {
+  MatrixType B(0, 0);
+  compute(A, B, nbrEigenvalues, eigs_sigma, options, tol);
+
+  return *this;
+}
+
+template <typename MatrixType, typename MatrixSolver, bool BisSPD>
+ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD> &
+ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>::compute(const MatrixType &A,
+                                                                                   const MatrixType &B,
+                                                                                   Index nbrEigenvalues,
+                                                                                   std::string eigs_sigma, int options,
+                                                                                   RealScalar tol) {
+  eigen_assert(A.cols() == A.rows());
+  eigen_assert(B.cols() == B.rows());
+  eigen_assert(B.rows() == 0 || A.cols() == B.rows());
+  eigen_assert((options & ~(EigVecMask | GenEigMask)) == 0 && (options & EigVecMask) != EigVecMask &&
+               "invalid option parameter");
+
+  bool isBempty = (B.rows() == 0) || (B.cols() == 0);
+
+  // For clarity, all parameters match their ARPACK name
+  //
+  // Always 0 on the first call
+  //
+  int ido = 0;
+
+  int n = (int)A.cols();
+
+  // User options: "LA", "SA", "SM", "LM", "BE"
+  //
+  char whch[3] = "LM";
+
+  // Specifies the shift if iparam[6] = { 3, 4, 5 }, not used if iparam[6] = { 1, 2 }
+  //
+  RealScalar sigma = 0.0;
+
+  if (eigs_sigma.length() >= 2 && isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1])) {
+    eigs_sigma[0] = toupper(eigs_sigma[0]);
+    eigs_sigma[1] = toupper(eigs_sigma[1]);
+
+    // In the following special case we're going to invert the problem, since solving
+    // for larger magnitude is much much faster
+    // i.e., if 'SM' is specified, we're going to really use 'LM', the default
+    //
+    if (eigs_sigma.substr(0, 2) != "SM") {
+      whch[0] = eigs_sigma[0];
+      whch[1] = eigs_sigma[1];
+    }
+  } else {
+    eigen_assert(false && "Specifying clustered eigenvalues is not yet supported!");
+
+    // If it's not scalar values, then the user may be explicitly
+    // specifying the sigma value to cluster the evs around
+    //
+    sigma = atof(eigs_sigma.c_str());
+
+    // If atof fails, it returns 0.0, which is a fine default
+    //
+  }
+
+  // "I" means normal eigenvalue problem, "G" means generalized
+  //
+  char bmat[2] = "I";
+  if (eigs_sigma.substr(0, 2) == "SM" || !(isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1])) || (!isBempty && !BisSPD))
+    bmat[0] = 'G';
+
+  // Now we determine the mode to use
+  //
+  int mode = (bmat[0] == 'G') + 1;
+  if (eigs_sigma.substr(0, 2) == "SM" || !(isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1]))) {
+    // We're going to use shift-and-invert mode, and basically find
+    // the largest eigenvalues of the inverse operator
+    //
+    mode = 3;
+  }
+
+  // The user-specified number of eigenvalues/vectors to compute
+  //
+  int nev = (int)nbrEigenvalues;
+
+  // Allocate space for ARPACK to store the residual
+  //
+  Scalar *resid = new Scalar[n];
+
+  // Number of Lanczos vectors, must satisfy nev < ncv <= n
+  // Note that this indicates that nev != n, and we cannot compute
+  // all eigenvalues of a mtrix
+  //
+  int ncv = std::min(std::max(2 * nev, 20), n);
+
+  // The working n x ncv matrix, also store the final eigenvectors (if computed)
+  //
+  Scalar *v = new Scalar[n * ncv];
+  int ldv = n;
+
+  // Working space
+  //
+  Scalar *workd = new Scalar[3 * n];
+  int lworkl = ncv * ncv + 8 * ncv;  // Must be at least this length
+  Scalar *workl = new Scalar[lworkl];
+
+  int *iparam = new int[11];
+  iparam[0] = 1;  // 1 means we let ARPACK perform the shifts, 0 means we'd have to do it
+  iparam[2] = std::max(300, (int)std::ceil(2 * n / std::max(ncv, 1)));
+  iparam[6] = mode;  // The mode, 1 is standard ev problem, 2 for generalized ev, 3 for shift-and-invert
+
+  // Used during reverse communicate to notify where arrays start
+  //
+  int *ipntr = new int[11];
+
+  // Error codes are returned in here, initial value of 0 indicates a random initial
+  // residual vector is used, any other values means resid contains the initial residual
+  // vector, possibly from a previous run
+  //
+  int info = 0;
+
+  Scalar scale = 1.0;
+
+  MatrixSolver OP;
+  if (mode == 1 || mode == 2) {
+    if (!isBempty) OP.compute(B);
+  } else if (mode == 3) {
+    if (sigma == 0.0) {
+      OP.compute(A);
+    } else {
+      // Note: We will never enter here because sigma must be 0.0
+      //
+      if (isBempty) {
+        MatrixType AminusSigmaB(A);
+        for (Index i = 0; i < A.rows(); ++i) AminusSigmaB.coeffRef(i, i) -= sigma;
+
+        OP.compute(AminusSigmaB);
+      } else {
+        MatrixType AminusSigmaB = A - sigma * B;
+        OP.compute(AminusSigmaB);
+      }
+    }
+  }
+
+  if (!(mode == 1 && isBempty) && !(mode == 2 && isBempty) && OP.info() != Success) {
+    m_info = OP.info();
+    delete[] v;
+    delete[] iparam;
+    delete[] ipntr;
+    delete[] workd;
+    delete[] workl;
+    delete[] resid;
+    m_isInitialized = false;
+    return *this;
+  }
+
+  do {
+    internal::arpack_wrapper<Scalar, RealScalar>::saupd(&ido, bmat, &n, whch, &nev, &tol, resid, &ncv, v, &ldv, iparam,
+                                                        ipntr, workd, workl, &lworkl, &info);
+
+    if (ido == -1 || ido == 1) {
+      Scalar *in = workd + ipntr[0] - 1;
+      Scalar *out = workd + ipntr[1] - 1;
+
+      if (ido == 1 && mode != 2) {
+        Scalar *out2 = workd + ipntr[2] - 1;
+        if (isBempty || mode == 1)
+          Matrix<Scalar, Dynamic, 1>::Map(out2, n) = Matrix<Scalar, Dynamic, 1>::Map(in, n);
+        else
+          Matrix<Scalar, Dynamic, 1>::Map(out2, n) = B * Matrix<Scalar, Dynamic, 1>::Map(in, n);
+
+        in = workd + ipntr[2] - 1;
+      }
+
+      if (mode == 1) {
+        if (isBempty) {
+          // OP = A
+          //
+          Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);
+        } else {
+          // OP = L^{-1}AL^{-T}
+          //
+          internal::OP<MatrixSolver, MatrixType, Scalar, BisSPD>::applyOP(OP, A, n, in, out);
+        }
+      } else if (mode == 2) {
+        if (ido == 1) Matrix<Scalar, Dynamic, 1>::Map(in, n) = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);
+
+        // OP = B^{-1} A
+        //
+        Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
+      } else if (mode == 3) {
+        // OP = (A-\sigmaB)B (\sigma could be 0, and B could be I)
+        // The B * in is already computed and stored at in if ido == 1
+        //
+        if (ido == 1 || isBempty)
+          Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
+        else
+          Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(B * Matrix<Scalar, Dynamic, 1>::Map(in, n));
+      }
+    } else if (ido == 2) {
+      Scalar *in = workd + ipntr[0] - 1;
+      Scalar *out = workd + ipntr[1] - 1;
+
+      if (isBempty || mode == 1)
+        Matrix<Scalar, Dynamic, 1>::Map(out, n) = Matrix<Scalar, Dynamic, 1>::Map(in, n);
+      else
+        Matrix<Scalar, Dynamic, 1>::Map(out, n) = B * Matrix<Scalar, Dynamic, 1>::Map(in, n);
+    }
+  } while (ido != 99);
+
+  if (info == 1)
+    m_info = NoConvergence;
+  else if (info == 3)
+    m_info = NumericalIssue;
+  else if (info < 0)
+    m_info = InvalidInput;
+  else if (info != 0)
+    eigen_assert(false && "Unknown ARPACK return value!");
+  else {
+    // Do we compute eigenvectors or not?
+    //
+    int rvec = (options & ComputeEigenvectors) == ComputeEigenvectors;
+
+    // "A" means "All", use "S" to choose specific eigenvalues (not yet supported in ARPACK))
+    //
+    char howmny[2] = "A";
+
+    // if howmny == "S", specifies the eigenvalues to compute (not implemented in ARPACK)
+    //
+    int *select = new int[ncv];
+
+    // Final eigenvalues
+    //
+    m_eivalues.resize(nev, 1);
+
+    internal::arpack_wrapper<Scalar, RealScalar>::seupd(&rvec, howmny, select, m_eivalues.data(), v, &ldv, &sigma, bmat,
+                                                        &n, whch, &nev, &tol, resid, &ncv, v, &ldv, iparam, ipntr,
+                                                        workd, workl, &lworkl, &info);
+
+    if (info == -14)
+      m_info = NoConvergence;
+    else if (info != 0)
+      m_info = InvalidInput;
+    else {
+      if (rvec) {
+        m_eivec.resize(A.rows(), nev);
+        for (int i = 0; i < nev; i++)
+          for (int j = 0; j < n; j++) m_eivec(j, i) = v[i * n + j] / scale;
+
+        if (mode == 1 && !isBempty && BisSPD)
+          internal::OP<MatrixSolver, MatrixType, Scalar, BisSPD>::project(OP, n, nev, m_eivec.data());
+
+        m_eigenvectorsOk = true;
+      }
+
+      m_nbrIterations = iparam[2];
+      m_nbrConverged = iparam[4];
+
+      m_info = Success;
+    }
+
+    delete[] select;
+  }
+
+  delete[] v;
+  delete[] iparam;
+  delete[] ipntr;
+  delete[] workd;
+  delete[] workl;
+  delete[] resid;
+
+  m_isInitialized = (m_info == Success);
+
+  return *this;
+}
+
+// Single precision
+//
+extern "C" void ssaupd_(int *ido, char *bmat, int *n, char *which, int *nev, float *tol, float *resid, int *ncv,
+                        float *v, int *ldv, int *iparam, int *ipntr, float *workd, float *workl, int *lworkl,
+                        int *info);
+
+extern "C" void sseupd_(int *rvec, char *All, int *select, float *d, float *z, int *ldz, float *sigma, char *bmat,
+                        int *n, char *which, int *nev, float *tol, float *resid, int *ncv, float *v, int *ldv,
+                        int *iparam, int *ipntr, float *workd, float *workl, int *lworkl, int *ierr);
+
+// Double precision
+//
+extern "C" void dsaupd_(int *ido, char *bmat, int *n, char *which, int *nev, double *tol, double *resid, int *ncv,
+                        double *v, int *ldv, int *iparam, int *ipntr, double *workd, double *workl, int *lworkl,
+                        int *info);
+
+extern "C" void dseupd_(int *rvec, char *All, int *select, double *d, double *z, int *ldz, double *sigma, char *bmat,
+                        int *n, char *which, int *nev, double *tol, double *resid, int *ncv, double *v, int *ldv,
+                        int *iparam, int *ipntr, double *workd, double *workl, int *lworkl, int *ierr);
+
+namespace internal {
+
+template <typename Scalar, typename RealScalar>
+struct arpack_wrapper {
+  static inline void saupd(int *ido, char *bmat, int *n, char *which, int *nev, RealScalar *tol, Scalar *resid,
+                           int *ncv, Scalar *v, int *ldv, int *iparam, int *ipntr, Scalar *workd, Scalar *workl,
+                           int *lworkl, int *info) {
+    EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
+  }
+
+  static inline void seupd(int *rvec, char *All, int *select, Scalar *d, Scalar *z, int *ldz, RealScalar *sigma,
+                           char *bmat, int *n, char *which, int *nev, RealScalar *tol, Scalar *resid, int *ncv,
+                           Scalar *v, int *ldv, int *iparam, int *ipntr, Scalar *workd, Scalar *workl, int *lworkl,
+                           int *ierr) {
+    EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
+  }
+};
+
+template <>
+struct arpack_wrapper<float, float> {
+  static inline void saupd(int *ido, char *bmat, int *n, char *which, int *nev, float *tol, float *resid, int *ncv,
+                           float *v, int *ldv, int *iparam, int *ipntr, float *workd, float *workl, int *lworkl,
+                           int *info) {
+    ssaupd_(ido, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr, workd, workl, lworkl, info);
+  }
+
+  static inline void seupd(int *rvec, char *All, int *select, float *d, float *z, int *ldz, float *sigma, char *bmat,
+                           int *n, char *which, int *nev, float *tol, float *resid, int *ncv, float *v, int *ldv,
+                           int *iparam, int *ipntr, float *workd, float *workl, int *lworkl, int *ierr) {
+    sseupd_(rvec, All, select, d, z, ldz, sigma, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr, workd,
+            workl, lworkl, ierr);
+  }
+};
+
+template <>
+struct arpack_wrapper<double, double> {
+  static inline void saupd(int *ido, char *bmat, int *n, char *which, int *nev, double *tol, double *resid, int *ncv,
+                           double *v, int *ldv, int *iparam, int *ipntr, double *workd, double *workl, int *lworkl,
+                           int *info) {
+    dsaupd_(ido, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr, workd, workl, lworkl, info);
+  }
+
+  static inline void seupd(int *rvec, char *All, int *select, double *d, double *z, int *ldz, double *sigma, char *bmat,
+                           int *n, char *which, int *nev, double *tol, double *resid, int *ncv, double *v, int *ldv,
+                           int *iparam, int *ipntr, double *workd, double *workl, int *lworkl, int *ierr) {
+    dseupd_(rvec, All, select, d, v, ldv, sigma, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr, workd,
+            workl, lworkl, ierr);
+  }
+};
+
+template <typename MatrixSolver, typename MatrixType, typename Scalar, bool BisSPD>
+struct OP {
+  static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out);
+  static inline void project(MatrixSolver &OP, int n, int k, Scalar *vecs);
+};
+
+template <typename MatrixSolver, typename MatrixType, typename Scalar>
+struct OP<MatrixSolver, MatrixType, Scalar, true> {
+  static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out) {
+    // OP = L^{-1} A L^{-T}  (B = LL^T)
+    //
+    // First solve L^T out = in
+    //
+    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixU().solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
+    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.permutationPinv() * Matrix<Scalar, Dynamic, 1>::Map(out, n);
+
+    // Then compute out = A out
+    //
+    Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(out, n);
+
+    // Then solve L out = out
+    //
+    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.permutationP() * Matrix<Scalar, Dynamic, 1>::Map(out, n);
+    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixL().solve(Matrix<Scalar, Dynamic, 1>::Map(out, n));
+  }
+
+  static inline void project(MatrixSolver &OP, int n, int k, Scalar *vecs) {
+    // Solve L^T out = in
+    //
+    Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k) =
+        OP.matrixU().solve(Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k));
+    Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k) =
+        OP.permutationPinv() * Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k);
+  }
+};
+
+template <typename MatrixSolver, typename MatrixType, typename Scalar>
+struct OP<MatrixSolver, MatrixType, Scalar, false> {
+  static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out) {
+    eigen_assert(false && "Should never be in here...");
+  }
+
+  static inline void project(MatrixSolver &OP, int n, int k, Scalar *vecs) {
+    eigen_assert(false && "Should never be in here...");
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARPACKSELFADJOINTEIGENSOLVER_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Eigenvalues/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Eigenvalues/InternalHeaderCheck.h
new file mode 100644
index 0000000..c00cb57
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Eigenvalues/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_EIGENVALUES_MODULE_H
+#error "Please include unsupported/Eigen/Eigenvalues instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/CMakeLists.txt
new file mode 100644
index 0000000..22088eb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/CMakeLists.txt
@@ -0,0 +1,6 @@
+file(GLOB Eigen_EulerAngles_SRCS "*.h")
+
+install(FILES
+  ${Eigen_EulerAngles_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/unsupported/Eigen/src/EulerAngles COMPONENT Devel
+  )
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/EulerAngles.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/EulerAngles.h
new file mode 100644
index 0000000..b0b3617
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/EulerAngles.h
@@ -0,0 +1,350 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EULERANGLESCLASS_H  // TODO: Fix previous "EIGEN_EULERANGLES_H" definition?
+#define EIGEN_EULERANGLESCLASS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+/** \class EulerAngles
+ *
+ * \ingroup EulerAngles_Module
+ *
+ * \brief Represents a rotation in a 3 dimensional space as three Euler angles.
+ *
+ * Euler rotation is a set of three rotation of three angles over three fixed axes, defined by the EulerSystem given as
+ * a template parameter.
+ *
+ * Here is how intrinsic Euler angles works:
+ *  - first, rotate the axes system over the alpha axis in angle alpha
+ *  - then, rotate the axes system over the beta axis(which was rotated in the first stage) in angle beta
+ *  - then, rotate the axes system over the gamma axis(which was rotated in the two stages above) in angle gamma
+ *
+ * \note This class support only intrinsic Euler angles for simplicity,
+ *  see EulerSystem how to easily overcome this for extrinsic systems.
+ *
+ * ### Rotation representation and conversions ###
+ *
+ * It has been proved(see Wikipedia link below) that every rotation can be represented
+ *  by Euler angles, but there is no single representation (e.g. unlike rotation matrices).
+ * Therefore, you can convert from Eigen rotation and to them
+ *  (including rotation matrices, which is not called "rotations" by Eigen design).
+ *
+ * Euler angles usually used for:
+ *  - convenient human representation of rotation, especially in interactive GUI.
+ *  - gimbal systems and robotics
+ *  - efficient encoding(i.e. 3 floats only) of rotation for network protocols.
+ *
+ * However, Euler angles are slow comparing to quaternion or matrices,
+ *  because their unnatural math definition, although it's simple for human.
+ * To overcome this, this class provide easy movement from the math friendly representation
+ *  to the human friendly representation, and vise-versa.
+ *
+ * All the user need to do is a safe simple C++ type conversion,
+ *  and this class take care for the math.
+ * Additionally, some axes related computation is done in compile time.
+ *
+ * #### Euler angles ranges in conversions ####
+ * Rotations representation as EulerAngles are not single (unlike matrices),
+ *  and even have infinite EulerAngles representations.<BR>
+ * For example, add or subtract 2*PI from either angle of EulerAngles
+ *  and you'll get the same rotation.
+ * This is the general reason for infinite representation,
+ *  but it's not the only general reason for not having a single representation.
+ *
+ * When converting rotation to EulerAngles, this class convert it to specific ranges
+ * When converting some rotation to EulerAngles, the rules for ranges are as follow:
+ * - If the rotation we converting from is an EulerAngles
+ *  (even when it represented as RotationBase explicitly), angles ranges are __undefined__.
+ * - otherwise, alpha and gamma angles will be in the range [-PI, PI].<BR>
+ *   As for Beta angle:
+ *    - If the system is Tait-Bryan, the beta angle will be in the range [-PI/2, PI/2].
+ *    - otherwise:
+ *      - If the beta axis is positive, the beta angle will be in the range [0, PI]
+ *      - If the beta axis is negative, the beta angle will be in the range [-PI, 0]
+ *
+ * \sa EulerAngles(const MatrixBase<Derived>&)
+ * \sa EulerAngles(const RotationBase<Derived, 3>&)
+ *
+ * ### Convenient user typedefs ###
+ *
+ * Convenient typedefs for EulerAngles exist for float and double scalar,
+ *  in a form of EulerAngles{A}{B}{C}{scalar},
+ *  e.g. \ref EulerAnglesXYZd, \ref EulerAnglesZYZf.
+ *
+ * Only for positive axes{+x,+y,+z} Euler systems are have convenient typedef.
+ * If you need negative axes{-x,-y,-z}, it is recommended to create you own typedef with
+ *  a word that represent what you need.
+ *
+ * ### Example ###
+ *
+ * \include EulerAngles.cpp
+ * Output: \verbinclude EulerAngles.out
+ *
+ * ### Additional reading ###
+ *
+ * If you're want to get more idea about how Euler system work in Eigen see EulerSystem.
+ *
+ * More information about Euler angles: https://en.wikipedia.org/wiki/Euler_angles
+ *
+ * \tparam Scalar_ the scalar type, i.e. the type of the angles.
+ *
+ * \tparam _System the EulerSystem to use, which represents the axes of rotation.
+ */
+template <typename Scalar_, class _System>
+class EulerAngles : public RotationBase<EulerAngles<Scalar_, _System>, 3> {
+ public:
+  typedef RotationBase<EulerAngles<Scalar_, _System>, 3> Base;
+
+  /** the scalar type of the angles */
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  /** the EulerSystem to use, which represents the axes of rotation. */
+  typedef _System System;
+
+  typedef Matrix<Scalar, 3, 3> Matrix3;      /*!< the equivalent rotation matrix type */
+  typedef Matrix<Scalar, 3, 1> Vector3;      /*!< the equivalent 3 dimension vector type */
+  typedef Quaternion<Scalar> QuaternionType; /*!< the equivalent quaternion type */
+  typedef AngleAxis<Scalar> AngleAxisType;   /*!< the equivalent angle-axis type */
+
+  /** \returns the axis vector of the first (alpha) rotation */
+  static Vector3 AlphaAxisVector() {
+    const Vector3& u = Vector3::Unit(System::AlphaAxisAbs - 1);
+    return System::IsAlphaOpposite ? -u : u;
+  }
+
+  /** \returns the axis vector of the second (beta) rotation */
+  static Vector3 BetaAxisVector() {
+    const Vector3& u = Vector3::Unit(System::BetaAxisAbs - 1);
+    return System::IsBetaOpposite ? -u : u;
+  }
+
+  /** \returns the axis vector of the third (gamma) rotation */
+  static Vector3 GammaAxisVector() {
+    const Vector3& u = Vector3::Unit(System::GammaAxisAbs - 1);
+    return System::IsGammaOpposite ? -u : u;
+  }
+
+ private:
+  Vector3 m_angles;
+
+ public:
+  /** Default constructor without initialization. */
+  EulerAngles() {}
+  /** Constructs and initialize an EulerAngles (\p alpha, \p beta, \p gamma). */
+  EulerAngles(const Scalar& alpha, const Scalar& beta, const Scalar& gamma) : m_angles(alpha, beta, gamma) {}
+
+  // TODO: Test this constructor
+  /** Constructs and initialize an EulerAngles from the array data {alpha, beta, gamma} */
+  explicit EulerAngles(const Scalar* data) : m_angles(data) {}
+
+  /** Constructs and initializes an EulerAngles from either:
+   *  - a 3x3 rotation matrix expression(i.e. pure orthogonal matrix with determinant of +1),
+   *  - a 3D vector expression representing Euler angles.
+   *
+   * \note If \p other is a 3x3 rotation matrix, the angles range rules will be as follow:<BR>
+   *  Alpha and gamma angles will be in the range [-PI, PI].<BR>
+   *  As for Beta angle:
+   *   - If the system is Tait-Bryan, the beta angle will be in the range [-PI/2, PI/2].
+   *   - otherwise:
+   *     - If the beta axis is positive, the beta angle will be in the range [0, PI]
+   *     - If the beta axis is negative, the beta angle will be in the range [-PI, 0]
+   */
+  template <typename Derived>
+  explicit EulerAngles(const MatrixBase<Derived>& other) {
+    *this = other;
+  }
+
+  /** Constructs and initialize Euler angles from a rotation \p rot.
+   *
+   * \note If \p rot is an EulerAngles (even when it represented as RotationBase explicitly),
+   *  angles ranges are __undefined__.
+   *  Otherwise, alpha and gamma angles will be in the range [-PI, PI].<BR>
+   *  As for Beta angle:
+   *   - If the system is Tait-Bryan, the beta angle will be in the range [-PI/2, PI/2].
+   *   - otherwise:
+   *     - If the beta axis is positive, the beta angle will be in the range [0, PI]
+   *     - If the beta axis is negative, the beta angle will be in the range [-PI, 0]
+   */
+  template <typename Derived>
+  EulerAngles(const RotationBase<Derived, 3>& rot) {
+    System::CalcEulerAngles(*this, rot.toRotationMatrix());
+  }
+
+  /*EulerAngles(const QuaternionType& q)
+  {
+    // TODO: Implement it in a faster way for quaternions
+    // According to http://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToEuler/
+    //  we can compute only the needed matrix cells and then convert to euler angles. (see ZYX example below)
+    // Currently we compute all matrix cells from quaternion.
+
+    // Special case only for ZYX
+    //Scalar y2 = q.y() * q.y();
+    //m_angles[0] = std::atan2(2*(q.w()*q.z() + q.x()*q.y()), (1 - 2*(y2 + q.z()*q.z())));
+    //m_angles[1] = std::asin( 2*(q.w()*q.y() - q.z()*q.x()));
+    //m_angles[2] = std::atan2(2*(q.w()*q.x() + q.y()*q.z()), (1 - 2*(q.x()*q.x() + y2)));
+  }*/
+
+  /** \returns The angle values stored in a vector (alpha, beta, gamma). */
+  const Vector3& angles() const { return m_angles; }
+  /** \returns A read-write reference to the angle values stored in a vector (alpha, beta, gamma). */
+  Vector3& angles() { return m_angles; }
+
+  /** \returns The value of the first angle. */
+  Scalar alpha() const { return m_angles[0]; }
+  /** \returns A read-write reference to the angle of the first angle. */
+  Scalar& alpha() { return m_angles[0]; }
+
+  /** \returns The value of the second angle. */
+  Scalar beta() const { return m_angles[1]; }
+  /** \returns A read-write reference to the angle of the second angle. */
+  Scalar& beta() { return m_angles[1]; }
+
+  /** \returns The value of the third angle. */
+  Scalar gamma() const { return m_angles[2]; }
+  /** \returns A read-write reference to the angle of the third angle. */
+  Scalar& gamma() { return m_angles[2]; }
+
+  /** \returns The Euler angles rotation inverse (which is as same as the negative),
+   *  (-alpha, -beta, -gamma).
+   */
+  EulerAngles inverse() const {
+    EulerAngles res;
+    res.m_angles = -m_angles;
+    return res;
+  }
+
+  /** \returns The Euler angles rotation negative (which is as same as the inverse),
+   *  (-alpha, -beta, -gamma).
+   */
+  EulerAngles operator-() const { return inverse(); }
+
+  /** Set \c *this from either:
+   *  - a 3x3 rotation matrix expression(i.e. pure orthogonal matrix with determinant of +1),
+   *  - a 3D vector expression representing Euler angles.
+   *
+   * See EulerAngles(const MatrixBase<Derived, 3>&) for more information about
+   *  angles ranges output.
+   */
+  template <class Derived>
+  EulerAngles& operator=(const MatrixBase<Derived>& other) {
+    EIGEN_STATIC_ASSERT(
+        (internal::is_same<Scalar, typename Derived::Scalar>::value),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+    internal::eulerangles_assign_impl<System, Derived>::run(*this, other.derived());
+    return *this;
+  }
+
+  // TODO: Assign and construct from another EulerAngles (with different system)
+
+  /** Set \c *this from a rotation.
+   *
+   * See EulerAngles(const RotationBase<Derived, 3>&) for more information about
+   *  angles ranges output.
+   */
+  template <typename Derived>
+  EulerAngles& operator=(const RotationBase<Derived, 3>& rot) {
+    System::CalcEulerAngles(*this, rot.toRotationMatrix());
+    return *this;
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+   * determined by \a prec.
+   *
+   * \sa MatrixBase::isApprox() */
+  bool isApprox(const EulerAngles& other, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const {
+    return angles().isApprox(other.angles(), prec);
+  }
+
+  /** \returns an equivalent 3x3 rotation matrix. */
+  Matrix3 toRotationMatrix() const {
+    // TODO: Calc it faster
+    return static_cast<QuaternionType>(*this).toRotationMatrix();
+  }
+
+  /** Convert the Euler angles to quaternion. */
+  operator QuaternionType() const {
+    return AngleAxisType(alpha(), AlphaAxisVector()) * AngleAxisType(beta(), BetaAxisVector()) *
+           AngleAxisType(gamma(), GammaAxisVector());
+  }
+
+  friend std::ostream& operator<<(std::ostream& s, const EulerAngles<Scalar, System>& eulerAngles) {
+    s << eulerAngles.angles().transpose();
+    return s;
+  }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType */
+  template <typename NewScalarType>
+  EulerAngles<NewScalarType, System> cast() const {
+    EulerAngles<NewScalarType, System> e;
+    e.angles() = angles().template cast<NewScalarType>();
+    return e;
+  }
+};
+
+#define EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(AXES, SCALAR_TYPE, SCALAR_POSTFIX) \
+  /** \ingroup EulerAngles_Module */                                         \
+  typedef EulerAngles<SCALAR_TYPE, EulerSystem##AXES> EulerAngles##AXES##SCALAR_POSTFIX;
+
+#define EIGEN_EULER_ANGLES_TYPEDEFS(SCALAR_TYPE, SCALAR_POSTFIX)      \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XYZ, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XYX, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XZY, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XZX, SCALAR_TYPE, SCALAR_POSTFIX) \
+                                                                      \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YZX, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YZY, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YXZ, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YXY, SCALAR_TYPE, SCALAR_POSTFIX) \
+                                                                      \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZXY, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZXZ, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZYX, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZYZ, SCALAR_TYPE, SCALAR_POSTFIX)
+
+EIGEN_EULER_ANGLES_TYPEDEFS(float, f)
+EIGEN_EULER_ANGLES_TYPEDEFS(double, d)
+
+// Specifically-referenced instantiations.
+/** \typedef EulerAnglesXYZd
+ * \ingroup EulerAngles_Module
+ * Euler XYZ system with type double entries.
+ */
+/** \typedef EulerAnglesZYZf
+ * \ingroup EulerAngles_Module
+ * Euler ZYZ system with type float entries.
+ */
+
+namespace internal {
+template <typename Scalar_, class _System>
+struct traits<EulerAngles<Scalar_, _System> > {
+  typedef Scalar_ Scalar;
+};
+
+// set from a rotation matrix
+template <class System, class Other>
+struct eulerangles_assign_impl<System, Other, 3, 3> {
+  typedef typename Other::Scalar Scalar;
+  static void run(EulerAngles<Scalar, System>& e, const Other& m) { System::CalcEulerAngles(e, m); }
+};
+
+// set from a vector of Euler angles
+template <class System, class Other>
+struct eulerangles_assign_impl<System, Other, 3, 1> {
+  typedef typename Other::Scalar Scalar;
+  static void run(EulerAngles<Scalar, System>& e, const Other& vec) { e.angles() = vec; }
+};
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_EULERANGLESCLASS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/EulerSystem.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/EulerSystem.h
new file mode 100644
index 0000000..c4b190b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/EulerSystem.h
@@ -0,0 +1,283 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EULERSYSTEM_H
+#define EIGEN_EULERSYSTEM_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+// Forward declarations
+template <typename Scalar_, class _System>
+class EulerAngles;
+
+namespace internal {
+// TODO: Add this trait to the Eigen internal API?
+template <int Num, bool IsPositive = (Num > 0)>
+struct Abs {
+  enum { value = Num };
+};
+
+template <int Num>
+struct Abs<Num, false> {
+  enum { value = -Num };
+};
+
+template <int Axis>
+struct IsValidAxis {
+  enum { value = Axis != 0 && Abs<Axis>::value <= 3 };
+};
+
+template <typename System, typename Other, int OtherRows = Other::RowsAtCompileTime,
+          int OtherCols = Other::ColsAtCompileTime>
+struct eulerangles_assign_impl;
+}  // namespace internal
+
+#define EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(COND, MSG) typedef char static_assertion_##MSG[(COND) ? 1 : -1]
+
+/** \brief Representation of a fixed signed rotation axis for EulerSystem.
+ *
+ * \ingroup EulerAngles_Module
+ *
+ * Values here represent:
+ *  - The axis of the rotation: X, Y or Z.
+ *  - The sign (i.e. direction of the rotation along the axis): positive(+) or negative(-)
+ *
+ * Therefore, this could express all the axes {+X,+Y,+Z,-X,-Y,-Z}
+ *
+ * For positive axis, use +EULER_{axis}, and for negative axis use -EULER_{axis}.
+ */
+enum EulerAxis {
+  EULER_X = 1, /*!< the X axis */
+  EULER_Y = 2, /*!< the Y axis */
+  EULER_Z = 3  /*!< the Z axis */
+};
+
+/** \class EulerSystem
+ *
+ * \ingroup EulerAngles_Module
+ *
+ * \brief Represents a fixed Euler rotation system.
+ *
+ * This meta-class goal is to represent the Euler system in compilation time, for EulerAngles.
+ *
+ * You can use this class to get two things:
+ *  - Build an Euler system, and then pass it as a template parameter to EulerAngles.
+ *  - Query some compile time data about an Euler system. (e.g. Whether it's Tait-Bryan)
+ *
+ * Euler rotation is a set of three rotation on fixed axes. (see \ref EulerAngles)
+ * This meta-class store constantly those signed axes. (see \ref EulerAxis)
+ *
+ * ### Types of Euler systems ###
+ *
+ * All and only valid 3 dimension Euler rotation over standard
+ *  signed axes{+X,+Y,+Z,-X,-Y,-Z} are supported:
+ *  - all axes X, Y, Z in each valid order (see below what order is valid)
+ *  - rotation over the axis is supported both over the positive and negative directions.
+ *  - both Tait-Bryan and proper/classic Euler angles (i.e. the opposite).
+ *
+ * Since EulerSystem support both positive and negative directions,
+ *  you may call this rotation distinction in other names:
+ *  - _right handed_ or _left handed_
+ *  - _counterclockwise_ or _clockwise_
+ *
+ * Notice all axed combination are valid, and would trigger a static assertion.
+ * Same unsigned axes can't be neighbors, e.g. {X,X,Y} is invalid.
+ * This yield two and only two classes:
+ *  - _Tait-Bryan_ - all unsigned axes are distinct, e.g. {X,Y,Z}
+ *  - _proper/classic Euler angles_ - The first and the third unsigned axes is equal,
+ *     and the second is different, e.g. {X,Y,X}
+ *
+ * ### Intrinsic vs extrinsic Euler systems ###
+ *
+ * Only intrinsic Euler systems are supported for simplicity.
+ *  If you want to use extrinsic Euler systems,
+ *   just use the equal intrinsic opposite order for axes and angles.
+ *  I.e axes (A,B,C) becomes (C,B,A), and angles (a,b,c) becomes (c,b,a).
+ *
+ * ### Convenient user typedefs ###
+ *
+ * Convenient typedefs for EulerSystem exist (only for positive axes Euler systems),
+ *  in a form of EulerSystem{A}{B}{C}, e.g. \ref EulerSystemXYZ.
+ *
+ * ### Additional reading ###
+ *
+ * More information about Euler angles: https://en.wikipedia.org/wiki/Euler_angles
+ *
+ * \tparam _AlphaAxis the first fixed EulerAxis
+ *
+ * \tparam _BetaAxis the second fixed EulerAxis
+ *
+ * \tparam _GammaAxis the third fixed EulerAxis
+ */
+template <int _AlphaAxis, int _BetaAxis, int _GammaAxis>
+class EulerSystem {
+ public:
+  // Defined as static constexpr rather than enum to ensure support for negative values.
+
+  /** The first rotation axis */
+  static constexpr int AlphaAxis = _AlphaAxis;
+
+  /** The second rotation axis */
+  static constexpr int BetaAxis = _BetaAxis;
+
+  /** The third rotation axis */
+  static constexpr int GammaAxis = _GammaAxis;
+
+  enum {
+    AlphaAxisAbs = internal::Abs<AlphaAxis>::value, /*!< the first rotation axis unsigned */
+    BetaAxisAbs = internal::Abs<BetaAxis>::value,   /*!< the second rotation axis unsigned */
+    GammaAxisAbs = internal::Abs<GammaAxis>::value, /*!< the third rotation axis unsigned */
+
+    IsAlphaOpposite = (AlphaAxis < 0) ? 1 : 0, /*!< whether alpha axis is negative */
+    IsBetaOpposite = (BetaAxis < 0) ? 1 : 0,   /*!< whether beta axis is negative */
+    IsGammaOpposite = (GammaAxis < 0) ? 1 : 0, /*!< whether gamma axis is negative */
+
+    // Parity is even if alpha axis X is followed by beta axis Y, or Y is followed
+    // by Z, or Z is followed by X; otherwise it is odd.
+    IsOdd = ((AlphaAxisAbs) % 3 == (BetaAxisAbs - 1) % 3) ? 0 : 1, /*!< whether the Euler system is odd */
+    IsEven = IsOdd ? 0 : 1,                                        /*!< whether the Euler system is even */
+
+    IsTaitBryan =
+        ((unsigned)AlphaAxisAbs != (unsigned)GammaAxisAbs) ? 1 : 0 /*!< whether the Euler system is Tait-Bryan */
+  };
+
+ private:
+  EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(internal::IsValidAxis<AlphaAxis>::value, ALPHA_AXIS_IS_INVALID);
+
+  EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(internal::IsValidAxis<BetaAxis>::value, BETA_AXIS_IS_INVALID);
+
+  EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(internal::IsValidAxis<GammaAxis>::value, GAMMA_AXIS_IS_INVALID);
+
+  EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT((unsigned)AlphaAxisAbs != (unsigned)BetaAxisAbs,
+                                         ALPHA_AXIS_CANT_BE_EQUAL_TO_BETA_AXIS);
+
+  EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT((unsigned)BetaAxisAbs != (unsigned)GammaAxisAbs,
+                                         BETA_AXIS_CANT_BE_EQUAL_TO_GAMMA_AXIS);
+
+  static const int
+      // I, J, K are the pivot indexes permutation for the rotation matrix, that match this Euler system.
+      // They are used in this class converters.
+      // They are always different from each other, and their possible values are: 0, 1, or 2.
+      I_ = AlphaAxisAbs - 1,
+      J_ = (AlphaAxisAbs - 1 + 1 + IsOdd) % 3, K_ = (AlphaAxisAbs - 1 + 2 - IsOdd) % 3;
+
+  // TODO: Get @mat parameter in form that avoids double evaluation.
+  template <typename Derived>
+  static void CalcEulerAngles_imp(Matrix<typename MatrixBase<Derived>::Scalar, 3, 1>& res,
+                                  const MatrixBase<Derived>& mat, internal::true_type /*isTaitBryan*/) {
+    using std::atan2;
+    using std::sqrt;
+
+    typedef typename Derived::Scalar Scalar;
+
+    const Scalar plusMinus = IsEven ? 1 : -1;
+    const Scalar minusPlus = IsOdd ? 1 : -1;
+
+    const Scalar Rsum = sqrt((mat(I_, I_) * mat(I_, I_) + mat(I_, J_) * mat(I_, J_) + mat(J_, K_) * mat(J_, K_) +
+                              mat(K_, K_) * mat(K_, K_)) /
+                             2);
+    res[1] = atan2(plusMinus * mat(I_, K_), Rsum);
+
+    // There is a singularity when cos(beta) == 0
+    if (Rsum > 4 * NumTraits<Scalar>::epsilon()) {  // cos(beta) != 0
+      res[0] = atan2(minusPlus * mat(J_, K_), mat(K_, K_));
+      res[2] = atan2(minusPlus * mat(I_, J_), mat(I_, I_));
+    } else if (plusMinus * mat(I_, K_) > 0) {               // cos(beta) == 0 and sin(beta) == 1
+      Scalar spos = mat(J_, I_) + plusMinus * mat(K_, J_);  // 2*sin(alpha + plusMinus * gamma
+      Scalar cpos = mat(J_, J_) + minusPlus * mat(K_, I_);  // 2*cos(alpha + plusMinus * gamma)
+      Scalar alphaPlusMinusGamma = atan2(spos, cpos);
+      res[0] = alphaPlusMinusGamma;
+      res[2] = 0;
+    } else {                                                              // cos(beta) == 0 and sin(beta) == -1
+      Scalar sneg = plusMinus * (mat(K_, J_) + minusPlus * mat(J_, I_));  // 2*sin(alpha + minusPlus*gamma)
+      Scalar cneg = mat(J_, J_) + plusMinus * mat(K_, I_);                // 2*cos(alpha + minusPlus*gamma)
+      Scalar alphaMinusPlusBeta = atan2(sneg, cneg);
+      res[0] = alphaMinusPlusBeta;
+      res[2] = 0;
+    }
+  }
+
+  template <typename Derived>
+  static void CalcEulerAngles_imp(Matrix<typename MatrixBase<Derived>::Scalar, 3, 1>& res,
+                                  const MatrixBase<Derived>& mat, internal::false_type /*isTaitBryan*/) {
+    using std::atan2;
+    using std::sqrt;
+
+    typedef typename Derived::Scalar Scalar;
+
+    const Scalar plusMinus = IsEven ? 1 : -1;
+    const Scalar minusPlus = IsOdd ? 1 : -1;
+
+    const Scalar Rsum = sqrt((mat(I_, J_) * mat(I_, J_) + mat(I_, K_) * mat(I_, K_) + mat(J_, I_) * mat(J_, I_) +
+                              mat(K_, I_) * mat(K_, I_)) /
+                             2);
+
+    res[1] = atan2(Rsum, mat(I_, I_));
+
+    // There is a singularity when sin(beta) == 0
+    if (Rsum > 4 * NumTraits<Scalar>::epsilon()) {  // sin(beta) != 0
+      res[0] = atan2(mat(J_, I_), minusPlus * mat(K_, I_));
+      res[2] = atan2(mat(I_, J_), plusMinus * mat(I_, K_));
+    } else if (mat(I_, I_) > 0) {                                       // sin(beta) == 0 and cos(beta) == 1
+      Scalar spos = plusMinus * mat(K_, J_) + minusPlus * mat(J_, K_);  // 2*sin(alpha + gamma)
+      Scalar cpos = mat(J_, J_) + mat(K_, K_);                          // 2*cos(alpha + gamma)
+      res[0] = atan2(spos, cpos);
+      res[2] = 0;
+    } else {                                                            // sin(beta) == 0 and cos(beta) == -1
+      Scalar sneg = plusMinus * mat(K_, J_) + plusMinus * mat(J_, K_);  // 2*sin(alpha - gamma)
+      Scalar cneg = mat(J_, J_) - mat(K_, K_);                          // 2*cos(alpha - gamma)
+      res[0] = atan2(sneg, cneg);
+      res[2] = 0;
+    }
+  }
+
+  template <typename Scalar>
+  static void CalcEulerAngles(EulerAngles<Scalar, EulerSystem>& res,
+                              const typename EulerAngles<Scalar, EulerSystem>::Matrix3& mat) {
+    CalcEulerAngles_imp(res.angles(), mat,
+                        std::conditional_t<IsTaitBryan, internal::true_type, internal::false_type>());
+
+    if (IsAlphaOpposite) res.alpha() = -res.alpha();
+
+    if (IsBetaOpposite) res.beta() = -res.beta();
+
+    if (IsGammaOpposite) res.gamma() = -res.gamma();
+  }
+
+  template <typename Scalar_, class _System>
+  friend class Eigen::EulerAngles;
+
+  template <typename System, typename Other, int OtherRows, int OtherCols>
+  friend struct internal::eulerangles_assign_impl;
+};
+
+#define EIGEN_EULER_SYSTEM_TYPEDEF(A, B, C) typedef EulerSystem<EULER_##A, EULER_##B, EULER_##C> EulerSystem##A##B##C;
+
+/** Default XYZ Euler coordinate system.
+ * \ingroup EulerAngles_Module
+ */
+EIGEN_EULER_SYSTEM_TYPEDEF(X, Y, Z)
+EIGEN_EULER_SYSTEM_TYPEDEF(X, Y, X)
+EIGEN_EULER_SYSTEM_TYPEDEF(X, Z, Y)
+EIGEN_EULER_SYSTEM_TYPEDEF(X, Z, X)
+
+EIGEN_EULER_SYSTEM_TYPEDEF(Y, Z, X)
+EIGEN_EULER_SYSTEM_TYPEDEF(Y, Z, Y)
+EIGEN_EULER_SYSTEM_TYPEDEF(Y, X, Z)
+EIGEN_EULER_SYSTEM_TYPEDEF(Y, X, Y)
+
+EIGEN_EULER_SYSTEM_TYPEDEF(Z, X, Y)
+EIGEN_EULER_SYSTEM_TYPEDEF(Z, X, Z)
+EIGEN_EULER_SYSTEM_TYPEDEF(Z, Y, X)
+EIGEN_EULER_SYSTEM_TYPEDEF(Z, Y, Z)
+}  // namespace Eigen
+
+#endif  // EIGEN_EULERSYSTEM_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/InternalHeaderCheck.h
new file mode 100644
index 0000000..0c00a30
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/EulerAngles/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_EULERANGLES_MODULE_H
+#error "Please include unsupported/Eigen/EulerAngles instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/InternalHeaderCheck.h
new file mode 100644
index 0000000..801e245
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_FFT_MODULE_H
+#error "Please include unsupported/Eigen/FFT instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/duccfft_impl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/duccfft_impl.h
new file mode 100644
index 0000000..781716d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/duccfft_impl.h
@@ -0,0 +1,71 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename _Scalar>
+struct duccfft_impl {
+  using Scalar = _Scalar;
+  using Complex = std::complex<Scalar>;
+  using shape_t = ducc0::fmav_info::shape_t;
+  using stride_t = ducc0::fmav_info::stride_t;
+
+  inline void clear() {}
+
+  inline void fwd(Complex* dst, const Scalar* src, int nfft) {
+    const shape_t axes{0};
+    ducc0::cfmav<Scalar> m_in(src, shape_t{static_cast<size_t>(nfft)});
+    ducc0::vfmav<Complex> m_out(dst, shape_t{static_cast<size_t>(nfft) / 2 + 1});
+    ducc0::r2c(m_in, m_out, axes, /*forward=*/true, /*scale=*/static_cast<Scalar>(1));
+  }
+
+  inline void fwd(Complex* dst, const Complex* src, int nfft) {
+    const shape_t axes{0};
+    ducc0::cfmav<Complex> m_in(src, shape_t{static_cast<size_t>(nfft)});
+    ducc0::vfmav<Complex> m_out(dst, shape_t{static_cast<size_t>(nfft)});
+    ducc0::c2c(m_in, m_out, axes, /*forward=*/true, /*scale=*/static_cast<Scalar>(1));
+  }
+
+  inline void inv(Scalar* dst, const Complex* src, int nfft) {
+    const shape_t axes{0};
+    ducc0::cfmav<Complex> m_in(src, shape_t{static_cast<size_t>(nfft) / 2 + 1});
+    ducc0::vfmav<Scalar> m_out(dst, shape_t{static_cast<size_t>(nfft)});
+    ducc0::c2r(m_in, m_out, axes, /*forward=*/false, /*scale=*/static_cast<Scalar>(1));
+  }
+
+  inline void inv(Complex* dst, const Complex* src, int nfft) {
+    const shape_t axes{0};
+    ducc0::cfmav<Complex> m_in(src, shape_t{static_cast<size_t>(nfft)});
+    ducc0::vfmav<Complex> m_out(dst, shape_t{static_cast<size_t>(nfft)});
+    ducc0::c2c(m_in, m_out, axes, /*forward=*/false, /*scale=*/static_cast<Scalar>(1));
+  }
+
+  inline void fwd2(Complex* dst, const Complex* src, int nfft0, int nfft1) {
+    const shape_t axes{0, 1};
+    const shape_t in_shape{static_cast<size_t>(nfft0), static_cast<size_t>(nfft1)};
+    const shape_t out_shape{static_cast<size_t>(nfft0), static_cast<size_t>(nfft1)};
+    const stride_t stride{static_cast<ptrdiff_t>(nfft1), static_cast<ptrdiff_t>(1)};
+    ducc0::cfmav<Complex> m_in(src, in_shape, stride);
+    ducc0::vfmav<Complex> m_out(dst, out_shape, stride);
+    ducc0::c2c(m_in, m_out, axes, /*forward=*/true, /*scale=*/static_cast<Scalar>(1));
+  }
+
+  inline void inv2(Complex* dst, const Complex* src, int nfft0, int nfft1) {
+    const shape_t axes{0, 1};
+    const shape_t in_shape{static_cast<size_t>(nfft0), static_cast<size_t>(nfft1)};
+    const shape_t out_shape{static_cast<size_t>(nfft0), static_cast<size_t>(nfft1)};
+    const stride_t stride{static_cast<ptrdiff_t>(nfft1), static_cast<ptrdiff_t>(1)};
+    ducc0::cfmav<Complex> m_in(src, in_shape, stride);
+    ducc0::vfmav<Complex> m_out(dst, out_shape, stride);
+    ducc0::c2c(m_in, m_out, axes, /*forward=*/false, /*scale=*/static_cast<Scalar>(1));
+  }
+};
+
+}  // namespace internal
+}  // namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/fftw_impl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/fftw_impl.h
new file mode 100644
index 0000000..588d09e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/fftw_impl.h
@@ -0,0 +1,216 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Mark Borgerding mark a borgerding net
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#include <memory>
+
+namespace Eigen {
+
+namespace internal {
+
+// FFTW uses non-const arguments,
+// so const_cast is needed for all the args it uses.
+//
+// This should be safe as long as
+// 1. we use FFTW_ESTIMATE for all our planning
+//       see the FFTW docs section 4.3.2 "Planner Flags"
+// 2. fftw_complex is compatible with std::complex
+//    This assumes std::complex<T> layout is array of size 2 with real,imag
+template <typename T>
+inline T *fftw_cast(const T *p) {
+  return const_cast<T *>(p);
+}
+
+inline fftw_complex *fftw_cast(const std::complex<double> *p) {
+  return const_cast<fftw_complex *>(reinterpret_cast<const fftw_complex *>(p));
+}
+
+inline fftwf_complex *fftw_cast(const std::complex<float> *p) {
+  return const_cast<fftwf_complex *>(reinterpret_cast<const fftwf_complex *>(p));
+}
+
+inline fftwl_complex *fftw_cast(const std::complex<long double> *p) {
+  return const_cast<fftwl_complex *>(reinterpret_cast<const fftwl_complex *>(p));
+}
+
+template <typename T>
+struct fftw_plan {};
+
+template <>
+struct fftw_plan<float> {
+  typedef float scalar_type;
+  typedef fftwf_complex complex_type;
+  std::shared_ptr<fftwf_plan_s> m_plan;
+  fftw_plan() = default;
+
+  void set_plan(fftwf_plan p) { m_plan.reset(p, fftwf_destroy_plan); }
+  inline void fwd(complex_type *dst, complex_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftwf_plan_dft_1d(nfft, src, dst, FFTW_FORWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwf_execute_dft(m_plan.get(), src, dst);
+  }
+  inline void inv(complex_type *dst, complex_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftwf_plan_dft_1d(nfft, src, dst, FFTW_BACKWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwf_execute_dft(m_plan.get(), src, dst);
+  }
+  inline void fwd(complex_type *dst, scalar_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftwf_plan_dft_r2c_1d(nfft, src, dst, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwf_execute_dft_r2c(m_plan.get(), src, dst);
+  }
+  inline void inv(scalar_type *dst, complex_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftwf_plan_dft_c2r_1d(nfft, src, dst, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwf_execute_dft_c2r(m_plan.get(), src, dst);
+  }
+
+  inline void fwd2(complex_type *dst, complex_type *src, int n0, int n1) {
+    if (m_plan == NULL)
+      set_plan(fftwf_plan_dft_2d(n0, n1, src, dst, FFTW_FORWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwf_execute_dft(m_plan.get(), src, dst);
+  }
+  inline void inv2(complex_type *dst, complex_type *src, int n0, int n1) {
+    if (m_plan == NULL)
+      set_plan(fftwf_plan_dft_2d(n0, n1, src, dst, FFTW_BACKWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwf_execute_dft(m_plan.get(), src, dst);
+  }
+};
+template <>
+struct fftw_plan<double> {
+  typedef double scalar_type;
+  typedef fftw_complex complex_type;
+  std::shared_ptr<fftw_plan_s> m_plan;
+  fftw_plan() = default;
+
+  void set_plan(::fftw_plan p) { m_plan.reset(p, fftw_destroy_plan); }
+  inline void fwd(complex_type *dst, complex_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftw_plan_dft_1d(nfft, src, dst, FFTW_FORWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftw_execute_dft(m_plan.get(), src, dst);
+  }
+  inline void inv(complex_type *dst, complex_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftw_plan_dft_1d(nfft, src, dst, FFTW_BACKWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftw_execute_dft(m_plan.get(), src, dst);
+  }
+  inline void fwd(complex_type *dst, scalar_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftw_plan_dft_r2c_1d(nfft, src, dst, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftw_execute_dft_r2c(m_plan.get(), src, dst);
+  }
+  inline void inv(scalar_type *dst, complex_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftw_plan_dft_c2r_1d(nfft, src, dst, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftw_execute_dft_c2r(m_plan.get(), src, dst);
+  }
+  inline void fwd2(complex_type *dst, complex_type *src, int n0, int n1) {
+    if (m_plan == NULL) set_plan(fftw_plan_dft_2d(n0, n1, src, dst, FFTW_FORWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftw_execute_dft(m_plan.get(), src, dst);
+  }
+  inline void inv2(complex_type *dst, complex_type *src, int n0, int n1) {
+    if (m_plan == NULL)
+      set_plan(fftw_plan_dft_2d(n0, n1, src, dst, FFTW_BACKWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftw_execute_dft(m_plan.get(), src, dst);
+  }
+};
+template <>
+struct fftw_plan<long double> {
+  typedef long double scalar_type;
+  typedef fftwl_complex complex_type;
+  std::shared_ptr<fftwl_plan_s> m_plan;
+  fftw_plan() = default;
+
+  void set_plan(fftwl_plan p) { m_plan.reset(p, fftwl_destroy_plan); }
+  inline void fwd(complex_type *dst, complex_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftwl_plan_dft_1d(nfft, src, dst, FFTW_FORWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwl_execute_dft(m_plan.get(), src, dst);
+  }
+  inline void inv(complex_type *dst, complex_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftwl_plan_dft_1d(nfft, src, dst, FFTW_BACKWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwl_execute_dft(m_plan.get(), src, dst);
+  }
+  inline void fwd(complex_type *dst, scalar_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftwl_plan_dft_r2c_1d(nfft, src, dst, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwl_execute_dft_r2c(m_plan.get(), src, dst);
+  }
+  inline void inv(scalar_type *dst, complex_type *src, int nfft) {
+    if (m_plan == NULL) set_plan(fftwl_plan_dft_c2r_1d(nfft, src, dst, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwl_execute_dft_c2r(m_plan.get(), src, dst);
+  }
+  inline void fwd2(complex_type *dst, complex_type *src, int n0, int n1) {
+    if (m_plan == NULL)
+      set_plan(fftwl_plan_dft_2d(n0, n1, src, dst, FFTW_FORWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwl_execute_dft(m_plan.get(), src, dst);
+  }
+  inline void inv2(complex_type *dst, complex_type *src, int n0, int n1) {
+    if (m_plan == NULL)
+      set_plan(fftwl_plan_dft_2d(n0, n1, src, dst, FFTW_BACKWARD, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT));
+    fftwl_execute_dft(m_plan.get(), src, dst);
+  }
+};
+
+template <typename Scalar_>
+struct fftw_impl {
+  typedef Scalar_ Scalar;
+  typedef std::complex<Scalar> Complex;
+
+  inline void clear() { m_plans.clear(); }
+
+  // complex-to-complex forward FFT
+  inline void fwd(Complex *dst, const Complex *src, int nfft) {
+    get_plan(nfft, false, dst, src).fwd(fftw_cast(dst), fftw_cast(src), nfft);
+  }
+
+  // real-to-complex forward FFT
+  inline void fwd(Complex *dst, const Scalar *src, int nfft) {
+    get_plan(nfft, false, dst, src).fwd(fftw_cast(dst), fftw_cast(src), nfft);
+  }
+
+  // 2-d complex-to-complex
+  inline void fwd2(Complex *dst, const Complex *src, int n0, int n1) {
+    get_plan(n0, n1, false, dst, src).fwd2(fftw_cast(dst), fftw_cast(src), n0, n1);
+  }
+
+  // inverse complex-to-complex
+  inline void inv(Complex *dst, const Complex *src, int nfft) {
+    get_plan(nfft, true, dst, src).inv(fftw_cast(dst), fftw_cast(src), nfft);
+  }
+
+  // half-complex to scalar
+  inline void inv(Scalar *dst, const Complex *src, int nfft) {
+    get_plan(nfft, true, dst, src).inv(fftw_cast(dst), fftw_cast(src), nfft);
+  }
+
+  // 2-d complex-to-complex
+  inline void inv2(Complex *dst, const Complex *src, int n0, int n1) {
+    get_plan(n0, n1, true, dst, src).inv2(fftw_cast(dst), fftw_cast(src), n0, n1);
+  }
+
+ protected:
+  typedef fftw_plan<Scalar> PlanData;
+
+  typedef Eigen::numext::int64_t int64_t;
+
+  typedef std::map<int64_t, PlanData> PlanMap;
+
+  PlanMap m_plans;
+
+  inline PlanData &get_plan(int nfft, bool inverse, void *dst, const void *src) {
+    bool inplace = (dst == src);
+    bool aligned = ((reinterpret_cast<size_t>(src) & 15) | (reinterpret_cast<size_t>(dst) & 15)) == 0;
+    int64_t key = ((nfft << 3) | (inverse << 2) | (inplace << 1) | aligned) << 1;
+    return m_plans[key];
+  }
+
+  inline PlanData &get_plan(int n0, int n1, bool inverse, void *dst, const void *src) {
+    bool inplace = (dst == src);
+    bool aligned = ((reinterpret_cast<size_t>(src) & 15) | (reinterpret_cast<size_t>(dst) & 15)) == 0;
+    int64_t key = (((((int64_t)n0) << 30) | (n1 << 3) | (inverse << 2) | (inplace << 1) | aligned) << 1) + 1;
+    return m_plans[key];
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/imklfft_impl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/imklfft_impl.h
new file mode 100644
index 0000000..0c5bb26
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/imklfft_impl.h
@@ -0,0 +1,248 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include <mkl_dfti.h>
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#include <complex>
+#include <memory>
+
+namespace Eigen {
+namespace internal {
+namespace imklfft {
+
+#define RUN_OR_ASSERT(EXPR, ERROR_MSG)                    \
+  {                                                       \
+    MKL_LONG status = (EXPR);                             \
+    eigen_assert(status == DFTI_NO_ERROR && (ERROR_MSG)); \
+  };
+
+inline MKL_Complex16* complex_cast(const std::complex<double>* p) {
+  return const_cast<MKL_Complex16*>(reinterpret_cast<const MKL_Complex16*>(p));
+}
+
+inline MKL_Complex8* complex_cast(const std::complex<float>* p) {
+  return const_cast<MKL_Complex8*>(reinterpret_cast<const MKL_Complex8*>(p));
+}
+
+/*
+ * Parameters:
+ * precision: enum, Precision of the transform: DFTI_SINGLE or DFTI_DOUBLE.
+ * forward_domain: enum, Forward domain of the transform: DFTI_COMPLEX or
+ * DFTI_REAL. dimension: MKL_LONG Dimension of the transform. sizes: MKL_LONG if
+ * dimension = 1.Length of the transform for a one-dimensional transform. sizes:
+ * Array of type MKL_LONG otherwise. Lengths of each dimension for a
+ * multi-dimensional transform.
+ */
+inline void configure_descriptor(std::shared_ptr<DFTI_DESCRIPTOR>& handl, enum DFTI_CONFIG_VALUE precision,
+                                 enum DFTI_CONFIG_VALUE forward_domain, MKL_LONG dimension, MKL_LONG* sizes) {
+  eigen_assert(dimension == 1 || dimension == 2 && "Transformation dimension must be less than 3.");
+
+  DFTI_DESCRIPTOR_HANDLE res = nullptr;
+  if (dimension == 1) {
+    RUN_OR_ASSERT(DftiCreateDescriptor(&res, precision, forward_domain, dimension, *sizes),
+                  "DftiCreateDescriptor failed.")
+    handl.reset(res, [](DFTI_DESCRIPTOR_HANDLE handle) { DftiFreeDescriptor(&handle); });
+    if (forward_domain == DFTI_REAL) {
+      // Set CCE storage
+      RUN_OR_ASSERT(DftiSetValue(handl.get(), DFTI_CONJUGATE_EVEN_STORAGE, DFTI_COMPLEX_COMPLEX),
+                    "DftiSetValue failed.")
+    }
+  } else {
+    RUN_OR_ASSERT(DftiCreateDescriptor(&res, precision, DFTI_COMPLEX, dimension, sizes), "DftiCreateDescriptor failed.")
+    handl.reset(res, [](DFTI_DESCRIPTOR_HANDLE handle) { DftiFreeDescriptor(&handle); });
+  }
+
+  RUN_OR_ASSERT(DftiSetValue(handl.get(), DFTI_PLACEMENT, DFTI_NOT_INPLACE), "DftiSetValue failed.")
+  RUN_OR_ASSERT(DftiCommitDescriptor(handl.get()), "DftiCommitDescriptor failed.")
+}
+
+template <typename T>
+struct plan {};
+
+template <>
+struct plan<float> {
+  typedef float scalar_type;
+  typedef MKL_Complex8 complex_type;
+
+  std::shared_ptr<DFTI_DESCRIPTOR> m_plan;
+
+  plan() = default;
+
+  enum DFTI_CONFIG_VALUE precision = DFTI_SINGLE;
+
+  inline void forward(complex_type* dst, complex_type* src, MKL_LONG nfft) {
+    if (m_plan == 0) {
+      configure_descriptor(m_plan, precision, DFTI_COMPLEX, 1, &nfft);
+    }
+    RUN_OR_ASSERT(DftiComputeForward(m_plan.get(), src, dst), "DftiComputeForward failed.")
+  }
+
+  inline void inverse(complex_type* dst, complex_type* src, MKL_LONG nfft) {
+    if (m_plan == 0) {
+      configure_descriptor(m_plan, precision, DFTI_COMPLEX, 1, &nfft);
+    }
+    RUN_OR_ASSERT(DftiComputeBackward(m_plan.get(), src, dst), "DftiComputeBackward failed.")
+  }
+
+  inline void forward(complex_type* dst, scalar_type* src, MKL_LONG nfft) {
+    if (m_plan == 0) {
+      configure_descriptor(m_plan, precision, DFTI_REAL, 1, &nfft);
+    }
+    RUN_OR_ASSERT(DftiComputeForward(m_plan.get(), src, dst), "DftiComputeForward failed.")
+  }
+
+  inline void inverse(scalar_type* dst, complex_type* src, MKL_LONG nfft) {
+    if (m_plan == 0) {
+      configure_descriptor(m_plan, precision, DFTI_REAL, 1, &nfft);
+    }
+    RUN_OR_ASSERT(DftiComputeBackward(m_plan.get(), src, dst), "DftiComputeBackward failed.")
+  }
+
+  inline void forward2(complex_type* dst, complex_type* src, int n0, int n1) {
+    if (m_plan == 0) {
+      MKL_LONG sizes[2] = {n0, n1};
+      configure_descriptor(m_plan, precision, DFTI_COMPLEX, 2, sizes);
+    }
+    RUN_OR_ASSERT(DftiComputeForward(m_plan.get(), src, dst), "DftiComputeForward failed.")
+  }
+
+  inline void inverse2(complex_type* dst, complex_type* src, int n0, int n1) {
+    if (m_plan == 0) {
+      MKL_LONG sizes[2] = {n0, n1};
+      configure_descriptor(m_plan, precision, DFTI_COMPLEX, 2, sizes);
+    }
+    RUN_OR_ASSERT(DftiComputeBackward(m_plan.get(), src, dst), "DftiComputeBackward failed.")
+  }
+};
+
+template <>
+struct plan<double> {
+  typedef double scalar_type;
+  typedef MKL_Complex16 complex_type;
+
+  std::shared_ptr<DFTI_DESCRIPTOR> m_plan;
+
+  plan() = default;
+
+  enum DFTI_CONFIG_VALUE precision = DFTI_DOUBLE;
+
+  inline void forward(complex_type* dst, complex_type* src, MKL_LONG nfft) {
+    if (m_plan == 0) {
+      configure_descriptor(m_plan, precision, DFTI_COMPLEX, 1, &nfft);
+    }
+    RUN_OR_ASSERT(DftiComputeForward(m_plan.get(), src, dst), "DftiComputeForward failed.")
+  }
+
+  inline void inverse(complex_type* dst, complex_type* src, MKL_LONG nfft) {
+    if (m_plan == 0) {
+      configure_descriptor(m_plan, precision, DFTI_COMPLEX, 1, &nfft);
+    }
+    RUN_OR_ASSERT(DftiComputeBackward(m_plan.get(), src, dst), "DftiComputeBackward failed.")
+  }
+
+  inline void forward(complex_type* dst, scalar_type* src, MKL_LONG nfft) {
+    if (m_plan == 0) {
+      configure_descriptor(m_plan, precision, DFTI_REAL, 1, &nfft);
+    }
+    RUN_OR_ASSERT(DftiComputeForward(m_plan.get(), src, dst), "DftiComputeForward failed.")
+  }
+
+  inline void inverse(scalar_type* dst, complex_type* src, MKL_LONG nfft) {
+    if (m_plan == 0) {
+      configure_descriptor(m_plan, precision, DFTI_REAL, 1, &nfft);
+    }
+    RUN_OR_ASSERT(DftiComputeBackward(m_plan.get(), src, dst), "DftiComputeBackward failed.")
+  }
+
+  inline void forward2(complex_type* dst, complex_type* src, int n0, int n1) {
+    if (m_plan == 0) {
+      MKL_LONG sizes[2] = {n0, n1};
+      configure_descriptor(m_plan, precision, DFTI_COMPLEX, 2, sizes);
+    }
+    RUN_OR_ASSERT(DftiComputeForward(m_plan.get(), src, dst), "DftiComputeForward failed.")
+  }
+
+  inline void inverse2(complex_type* dst, complex_type* src, int n0, int n1) {
+    if (m_plan == 0) {
+      MKL_LONG sizes[2] = {n0, n1};
+      configure_descriptor(m_plan, precision, DFTI_COMPLEX, 2, sizes);
+    }
+    RUN_OR_ASSERT(DftiComputeBackward(m_plan.get(), src, dst), "DftiComputeBackward failed.")
+  }
+};
+
+template <typename Scalar_>
+struct imklfft_impl {
+  typedef Scalar_ Scalar;
+  typedef std::complex<Scalar> Complex;
+
+  inline void clear() { m_plans.clear(); }
+
+  // complex-to-complex forward FFT
+  inline void fwd(Complex* dst, const Complex* src, int nfft) {
+    MKL_LONG size = nfft;
+    get_plan(nfft, dst, src).forward(complex_cast(dst), complex_cast(src), size);
+  }
+
+  // real-to-complex forward FFT
+  inline void fwd(Complex* dst, const Scalar* src, int nfft) {
+    MKL_LONG size = nfft;
+    get_plan(nfft, dst, src).forward(complex_cast(dst), const_cast<Scalar*>(src), nfft);
+  }
+
+  // 2-d complex-to-complex
+  inline void fwd2(Complex* dst, const Complex* src, int n0, int n1) {
+    get_plan(n0, n1, dst, src).forward2(complex_cast(dst), complex_cast(src), n0, n1);
+  }
+
+  // inverse complex-to-complex
+  inline void inv(Complex* dst, const Complex* src, int nfft) {
+    MKL_LONG size = nfft;
+    get_plan(nfft, dst, src).inverse(complex_cast(dst), complex_cast(src), nfft);
+  }
+
+  // half-complex to scalar
+  inline void inv(Scalar* dst, const Complex* src, int nfft) {
+    MKL_LONG size = nfft;
+    get_plan(nfft, dst, src).inverse(const_cast<Scalar*>(dst), complex_cast(src), nfft);
+  }
+
+  // 2-d complex-to-complex
+  inline void inv2(Complex* dst, const Complex* src, int n0, int n1) {
+    get_plan(n0, n1, dst, src).inverse2(complex_cast(dst), complex_cast(src), n0, n1);
+  }
+
+ private:
+  std::map<int64_t, plan<Scalar>> m_plans;
+
+  inline plan<Scalar>& get_plan(int nfft, void* dst, const void* src) {
+    int inplace = dst == src ? 1 : 0;
+    int aligned = ((reinterpret_cast<size_t>(src) & 15) | (reinterpret_cast<size_t>(dst) & 15)) == 0 ? 1 : 0;
+    int64_t key = ((nfft << 2) | (inplace << 1) | aligned) << 1;
+
+    // Create element if key does not exist.
+    return m_plans[key];
+  }
+
+  inline plan<Scalar>& get_plan(int n0, int n1, void* dst, const void* src) {
+    int inplace = (dst == src) ? 1 : 0;
+    int aligned = ((reinterpret_cast<size_t>(src) & 15) | (reinterpret_cast<size_t>(dst) & 15)) == 0 ? 1 : 0;
+    int64_t key = (((((int64_t)n0) << 31) | (n1 << 2) | (inplace << 1) | aligned) << 1) + 1;
+
+    // Create element if key does not exist.
+    return m_plans[key];
+  }
+};
+
+#undef RUN_OR_ASSERT
+
+}  // namespace imklfft
+}  // namespace internal
+}  // namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/kissfft_impl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/kissfft_impl.h
new file mode 100644
index 0000000..c7e5960
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/kissfft_impl.h
@@ -0,0 +1,416 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Mark Borgerding mark a borgerding net
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// This FFT implementation was derived from kissfft http:sourceforge.net/projects/kissfft
+// Copyright 2003-2009 Mark Borgerding
+
+template <typename Scalar_>
+struct kiss_cpx_fft {
+  typedef Scalar_ Scalar;
+  typedef std::complex<Scalar> Complex;
+  std::vector<Complex> m_twiddles;
+  std::vector<int> m_stageRadix;
+  std::vector<int> m_stageRemainder;
+  std::vector<Complex> m_scratchBuf;
+  bool m_inverse;
+
+  static const Scalar m_pi4;  // constant pi / 4
+
+  inline void make_twiddles(int nfft, bool inverse) {
+    using numext::cos;
+    using numext::sin;
+    m_inverse = inverse;
+    m_twiddles.resize(nfft);
+    Scalar phinc = m_pi4 / nfft;
+    Scalar flip = inverse ? Scalar(1) : Scalar(-1);
+    m_twiddles[0] = Complex(Scalar(1), Scalar(0));
+    if ((nfft & 1) == 0) m_twiddles[nfft / 2] = Complex(Scalar(-1), Scalar(0));
+    int i = 1;
+    for (; i * 8 < nfft; ++i) {
+      Scalar c = Scalar(cos(i * 8 * phinc));
+      Scalar s = Scalar(sin(i * 8 * phinc));
+      m_twiddles[i] = Complex(c, s * flip);
+      m_twiddles[nfft - i] = Complex(c, -s * flip);
+    }
+    for (; i * 4 < nfft; ++i) {
+      Scalar c = Scalar(cos((2 * nfft - 8 * i) * phinc));
+      Scalar s = Scalar(sin((2 * nfft - 8 * i) * phinc));
+      m_twiddles[i] = Complex(s, c * flip);
+      m_twiddles[nfft - i] = Complex(s, -c * flip);
+    }
+    for (; i * 8 < 3 * nfft; ++i) {
+      Scalar c = Scalar(cos((8 * i - 2 * nfft) * phinc));
+      Scalar s = Scalar(sin((8 * i - 2 * nfft) * phinc));
+      m_twiddles[i] = Complex(-s, c * flip);
+      m_twiddles[nfft - i] = Complex(-s, -c * flip);
+    }
+    for (; i * 2 < nfft; ++i) {
+      Scalar c = Scalar(cos((4 * nfft - 8 * i) * phinc));
+      Scalar s = Scalar(sin((4 * nfft - 8 * i) * phinc));
+      m_twiddles[i] = Complex(-c, s * flip);
+      m_twiddles[nfft - i] = Complex(-c, -s * flip);
+    }
+  }
+
+  void factorize(int nfft) {
+    // start factoring out 4's, then 2's, then 3,5,7,9,...
+    int n = nfft;
+    int p = 4;
+    do {
+      while (n % p) {
+        switch (p) {
+          case 4:
+            p = 2;
+            break;
+          case 2:
+            p = 3;
+            break;
+          default:
+            p += 2;
+            break;
+        }
+        if (p * p > n) p = n;  // impossible to have a factor > sqrt(n)
+      }
+      n /= p;
+      m_stageRadix.push_back(p);
+      m_stageRemainder.push_back(n);
+      if (p > 5) m_scratchBuf.resize(p);  // scratchbuf will be needed in bfly_generic
+    } while (n > 1);
+  }
+
+  template <typename Src_>
+  inline void work(int stage, Complex *xout, const Src_ *xin, size_t fstride, size_t in_stride) {
+    int p = m_stageRadix[stage];
+    int m = m_stageRemainder[stage];
+    Complex *Fout_beg = xout;
+    Complex *Fout_end = xout + p * m;
+
+    if (m > 1) {
+      do {
+        // recursive call:
+        // DFT of size m*p performed by doing
+        // p instances of smaller DFTs of size m,
+        // each one takes a decimated version of the input
+        work(stage + 1, xout, xin, fstride * p, in_stride);
+        xin += fstride * in_stride;
+      } while ((xout += m) != Fout_end);
+    } else {
+      do {
+        *xout = *xin;
+        xin += fstride * in_stride;
+      } while (++xout != Fout_end);
+    }
+    xout = Fout_beg;
+
+    // recombine the p smaller DFTs
+    switch (p) {
+      case 2:
+        bfly2(xout, fstride, m);
+        break;
+      case 3:
+        bfly3(xout, fstride, m);
+        break;
+      case 4:
+        bfly4(xout, fstride, m);
+        break;
+      case 5:
+        bfly5(xout, fstride, m);
+        break;
+      default:
+        bfly_generic(xout, fstride, m, p);
+        break;
+    }
+  }
+
+  inline void bfly2(Complex *Fout, const size_t fstride, int m) {
+    for (int k = 0; k < m; ++k) {
+      Complex t = Fout[m + k] * m_twiddles[k * fstride];
+      Fout[m + k] = Fout[k] - t;
+      Fout[k] += t;
+    }
+  }
+
+  inline void bfly4(Complex *Fout, const size_t fstride, const size_t m) {
+    Complex scratch[6];
+    int negative_if_inverse = m_inverse * -2 + 1;
+    for (size_t k = 0; k < m; ++k) {
+      scratch[0] = Fout[k + m] * m_twiddles[k * fstride];
+      scratch[1] = Fout[k + 2 * m] * m_twiddles[k * fstride * 2];
+      scratch[2] = Fout[k + 3 * m] * m_twiddles[k * fstride * 3];
+      scratch[5] = Fout[k] - scratch[1];
+
+      Fout[k] += scratch[1];
+      scratch[3] = scratch[0] + scratch[2];
+      scratch[4] = scratch[0] - scratch[2];
+      scratch[4] = Complex(scratch[4].imag() * negative_if_inverse, -scratch[4].real() * negative_if_inverse);
+
+      Fout[k + 2 * m] = Fout[k] - scratch[3];
+      Fout[k] += scratch[3];
+      Fout[k + m] = scratch[5] + scratch[4];
+      Fout[k + 3 * m] = scratch[5] - scratch[4];
+    }
+  }
+
+  inline void bfly3(Complex *Fout, const size_t fstride, const size_t m) {
+    size_t k = m;
+    const size_t m2 = 2 * m;
+    Complex *tw1, *tw2;
+    Complex scratch[5];
+    Complex epi3;
+    epi3 = m_twiddles[fstride * m];
+
+    tw1 = tw2 = &m_twiddles[0];
+
+    do {
+      scratch[1] = Fout[m] * *tw1;
+      scratch[2] = Fout[m2] * *tw2;
+
+      scratch[3] = scratch[1] + scratch[2];
+      scratch[0] = scratch[1] - scratch[2];
+      tw1 += fstride;
+      tw2 += fstride * 2;
+      Fout[m] = Complex(Fout->real() - Scalar(.5) * scratch[3].real(), Fout->imag() - Scalar(.5) * scratch[3].imag());
+      scratch[0] *= epi3.imag();
+      *Fout += scratch[3];
+      Fout[m2] = Complex(Fout[m].real() + scratch[0].imag(), Fout[m].imag() - scratch[0].real());
+      Fout[m] += Complex(-scratch[0].imag(), scratch[0].real());
+      ++Fout;
+    } while (--k);
+  }
+
+  inline void bfly5(Complex *Fout, const size_t fstride, const size_t m) {
+    Complex *Fout0, *Fout1, *Fout2, *Fout3, *Fout4;
+    size_t u;
+    Complex scratch[13];
+    Complex *twiddles = &m_twiddles[0];
+    Complex *tw;
+    Complex ya, yb;
+    ya = twiddles[fstride * m];
+    yb = twiddles[fstride * 2 * m];
+
+    Fout0 = Fout;
+    Fout1 = Fout0 + m;
+    Fout2 = Fout0 + 2 * m;
+    Fout3 = Fout0 + 3 * m;
+    Fout4 = Fout0 + 4 * m;
+
+    tw = twiddles;
+    for (u = 0; u < m; ++u) {
+      scratch[0] = *Fout0;
+
+      scratch[1] = *Fout1 * tw[u * fstride];
+      scratch[2] = *Fout2 * tw[2 * u * fstride];
+      scratch[3] = *Fout3 * tw[3 * u * fstride];
+      scratch[4] = *Fout4 * tw[4 * u * fstride];
+
+      scratch[7] = scratch[1] + scratch[4];
+      scratch[10] = scratch[1] - scratch[4];
+      scratch[8] = scratch[2] + scratch[3];
+      scratch[9] = scratch[2] - scratch[3];
+
+      *Fout0 += scratch[7];
+      *Fout0 += scratch[8];
+
+      scratch[5] = scratch[0] + Complex((scratch[7].real() * ya.real()) + (scratch[8].real() * yb.real()),
+                                        (scratch[7].imag() * ya.real()) + (scratch[8].imag() * yb.real()));
+
+      scratch[6] = Complex((scratch[10].imag() * ya.imag()) + (scratch[9].imag() * yb.imag()),
+                           -(scratch[10].real() * ya.imag()) - (scratch[9].real() * yb.imag()));
+
+      *Fout1 = scratch[5] - scratch[6];
+      *Fout4 = scratch[5] + scratch[6];
+
+      scratch[11] = scratch[0] + Complex((scratch[7].real() * yb.real()) + (scratch[8].real() * ya.real()),
+                                         (scratch[7].imag() * yb.real()) + (scratch[8].imag() * ya.real()));
+
+      scratch[12] = Complex(-(scratch[10].imag() * yb.imag()) + (scratch[9].imag() * ya.imag()),
+                            (scratch[10].real() * yb.imag()) - (scratch[9].real() * ya.imag()));
+
+      *Fout2 = scratch[11] + scratch[12];
+      *Fout3 = scratch[11] - scratch[12];
+
+      ++Fout0;
+      ++Fout1;
+      ++Fout2;
+      ++Fout3;
+      ++Fout4;
+    }
+  }
+
+  /* perform the butterfly for one stage of a mixed radix FFT */
+  inline void bfly_generic(Complex *Fout, const size_t fstride, int m, int p) {
+    int u, k, q1, q;
+    Complex *twiddles = &m_twiddles[0];
+    Complex t;
+    int Norig = static_cast<int>(m_twiddles.size());
+    Complex *scratchbuf = &m_scratchBuf[0];
+
+    for (u = 0; u < m; ++u) {
+      k = u;
+      for (q1 = 0; q1 < p; ++q1) {
+        scratchbuf[q1] = Fout[k];
+        k += m;
+      }
+
+      k = u;
+      for (q1 = 0; q1 < p; ++q1) {
+        int twidx = 0;
+        Fout[k] = scratchbuf[0];
+        for (q = 1; q < p; ++q) {
+          twidx += static_cast<int>(fstride) * k;
+          if (twidx >= Norig) twidx -= Norig;
+          t = scratchbuf[q] * twiddles[twidx];
+          Fout[k] += t;
+        }
+        k += m;
+      }
+    }
+  }
+};
+
+template <typename _Scalar>
+const typename kiss_cpx_fft<_Scalar>::Scalar kiss_cpx_fft<_Scalar>::m_pi4 =
+    numext::atan(kiss_cpx_fft<_Scalar>::Scalar(1));
+
+template <typename Scalar_>
+struct kissfft_impl {
+  typedef Scalar_ Scalar;
+  typedef std::complex<Scalar> Complex;
+
+  void clear() {
+    m_plans.clear();
+    m_realTwiddles.clear();
+  }
+
+  inline void fwd(Complex *dst, const Complex *src, int nfft) { get_plan(nfft, false).work(0, dst, src, 1, 1); }
+
+  inline void fwd2(Complex *dst, const Complex *src, int n0, int n1) {
+    EIGEN_UNUSED_VARIABLE(dst);
+    EIGEN_UNUSED_VARIABLE(src);
+    EIGEN_UNUSED_VARIABLE(n0);
+    EIGEN_UNUSED_VARIABLE(n1);
+  }
+
+  inline void inv2(Complex *dst, const Complex *src, int n0, int n1) {
+    EIGEN_UNUSED_VARIABLE(dst);
+    EIGEN_UNUSED_VARIABLE(src);
+    EIGEN_UNUSED_VARIABLE(n0);
+    EIGEN_UNUSED_VARIABLE(n1);
+  }
+
+  // real-to-complex forward FFT
+  // perform two FFTs of src even and src odd
+  // then twiddle to recombine them into the half-spectrum format
+  // then fill in the conjugate symmetric half
+  inline void fwd(Complex *dst, const Scalar *src, int nfft) {
+    if (nfft & 3) {
+      // use generic mode for odd
+      m_tmpBuf1.resize(nfft);
+      get_plan(nfft, false).work(0, &m_tmpBuf1[0], src, 1, 1);
+      std::copy(m_tmpBuf1.begin(), m_tmpBuf1.begin() + (nfft >> 1) + 1, dst);
+    } else {
+      int ncfft = nfft >> 1;
+      int ncfft2 = nfft >> 2;
+      Complex *rtw = real_twiddles(ncfft2);
+
+      // use optimized mode for even real
+      fwd(dst, reinterpret_cast<const Complex *>(src), ncfft);
+      Complex dc(dst[0].real() + dst[0].imag());
+      Complex nyquist(dst[0].real() - dst[0].imag());
+      int k;
+      for (k = 1; k <= ncfft2; ++k) {
+        Complex fpk = dst[k];
+        Complex fpnk = conj(dst[ncfft - k]);
+        Complex f1k = fpk + fpnk;
+        Complex f2k = fpk - fpnk;
+        Complex tw = f2k * rtw[k - 1];
+        dst[k] = (f1k + tw) * Scalar(.5);
+        dst[ncfft - k] = conj(f1k - tw) * Scalar(.5);
+      }
+      dst[0] = dc;
+      dst[ncfft] = nyquist;
+    }
+  }
+
+  // inverse complex-to-complex
+  inline void inv(Complex *dst, const Complex *src, int nfft) { get_plan(nfft, true).work(0, dst, src, 1, 1); }
+
+  // half-complex to scalar
+  inline void inv(Scalar *dst, const Complex *src, int nfft) {
+    if (nfft & 3) {
+      m_tmpBuf1.resize(nfft);
+      m_tmpBuf2.resize(nfft);
+      std::copy(src, src + (nfft >> 1) + 1, m_tmpBuf1.begin());
+      for (int k = 1; k < (nfft >> 1) + 1; ++k) m_tmpBuf1[nfft - k] = conj(m_tmpBuf1[k]);
+      inv(&m_tmpBuf2[0], &m_tmpBuf1[0], nfft);
+      for (int k = 0; k < nfft; ++k) dst[k] = m_tmpBuf2[k].real();
+    } else {
+      // optimized version for multiple of 4
+      int ncfft = nfft >> 1;
+      int ncfft2 = nfft >> 2;
+      Complex *rtw = real_twiddles(ncfft2);
+      m_tmpBuf1.resize(ncfft);
+      m_tmpBuf1[0] = Complex(src[0].real() + src[ncfft].real(), src[0].real() - src[ncfft].real());
+      for (int k = 1; k <= ncfft / 2; ++k) {
+        Complex fk = src[k];
+        Complex fnkc = conj(src[ncfft - k]);
+        Complex fek = fk + fnkc;
+        Complex tmp = fk - fnkc;
+        Complex fok = tmp * conj(rtw[k - 1]);
+        m_tmpBuf1[k] = fek + fok;
+        m_tmpBuf1[ncfft - k] = conj(fek - fok);
+      }
+      get_plan(ncfft, true).work(0, reinterpret_cast<Complex *>(dst), &m_tmpBuf1[0], 1, 1);
+    }
+  }
+
+ protected:
+  typedef kiss_cpx_fft<Scalar> PlanData;
+  typedef std::map<int, PlanData> PlanMap;
+
+  PlanMap m_plans;
+  std::map<int, std::vector<Complex> > m_realTwiddles;
+  std::vector<Complex> m_tmpBuf1;
+  std::vector<Complex> m_tmpBuf2;
+
+  inline int PlanKey(int nfft, bool isinverse) const { return (nfft << 1) | int(isinverse); }
+
+  inline PlanData &get_plan(int nfft, bool inverse) {
+    // TODO: look for PlanKey(nfft, ! inverse) and conjugate the twiddles
+    PlanData &pd = m_plans[PlanKey(nfft, inverse)];
+    if (pd.m_twiddles.size() == 0) {
+      pd.make_twiddles(nfft, inverse);
+      pd.factorize(nfft);
+    }
+    return pd;
+  }
+
+  inline Complex *real_twiddles(int ncfft2) {
+    using std::acos;
+    std::vector<Complex> &twidref = m_realTwiddles[ncfft2];  // creates new if not there
+    if ((int)twidref.size() != ncfft2) {
+      twidref.resize(ncfft2);
+      int ncfft = ncfft2 << 1;
+      Scalar pi = acos(Scalar(-1));
+      for (int k = 1; k <= ncfft2; ++k) twidref[k - 1] = exp(Complex(0, -pi * (Scalar(k) / ncfft + Scalar(.5))));
+    }
+    return &twidref[0];
+  }
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/pocketfft_impl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/pocketfft_impl.h
new file mode 100644
index 0000000..fce105b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/FFT/pocketfft_impl.h
@@ -0,0 +1,67 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename _Scalar>
+struct pocketfft_impl {
+  using Scalar = _Scalar;
+  using Complex = std::complex<Scalar>;
+  using shape_t = pocketfft::shape_t;
+  using stride_t = pocketfft::stride_t;
+
+  inline void clear() {}
+
+  inline void fwd(Complex* dst, const Scalar* src, int nfft) {
+    const shape_t shape_{static_cast<size_t>(nfft)};
+    const shape_t axes_{0};
+    const stride_t stride_in{sizeof(Scalar)};
+    const stride_t stride_out{sizeof(Complex)};
+    pocketfft::r2c(shape_, stride_in, stride_out, axes_, pocketfft::FORWARD, src, dst, static_cast<Scalar>(1));
+  }
+
+  inline void fwd(Complex* dst, const Complex* src, int nfft) {
+    const shape_t shape_{static_cast<size_t>(nfft)};
+    const shape_t axes_{0};
+    const stride_t stride_{sizeof(Complex)};
+    pocketfft::c2c(shape_, stride_, stride_, axes_, pocketfft::FORWARD, src, dst, static_cast<Scalar>(1));
+  }
+
+  inline void inv(Scalar* dst, const Complex* src, int nfft) {
+    const shape_t shape_{static_cast<size_t>(nfft)};
+    const shape_t axes_{0};
+    const stride_t stride_in{sizeof(Complex)};
+    const stride_t stride_out{sizeof(Scalar)};
+    pocketfft::c2r(shape_, stride_in, stride_out, axes_, pocketfft::BACKWARD, src, dst, static_cast<Scalar>(1));
+  }
+
+  inline void inv(Complex* dst, const Complex* src, int nfft) {
+    const shape_t shape_{static_cast<size_t>(nfft)};
+    const shape_t axes_{0};
+    const stride_t stride_{sizeof(Complex)};
+    pocketfft::c2c(shape_, stride_, stride_, axes_, pocketfft::BACKWARD, src, dst, static_cast<Scalar>(1));
+  }
+
+  inline void fwd2(Complex* dst, const Complex* src, int nfft0, int nfft1) {
+    const shape_t shape_{static_cast<size_t>(nfft0), static_cast<size_t>(nfft1)};
+    const shape_t axes_{0, 1};
+    const stride_t stride_{static_cast<ptrdiff_t>(sizeof(Complex) * nfft1), static_cast<ptrdiff_t>(sizeof(Complex))};
+    pocketfft::c2c(shape_, stride_, stride_, axes_, pocketfft::FORWARD, src, dst, static_cast<Scalar>(1));
+  }
+
+  inline void inv2(Complex* dst, const Complex* src, int nfft0, int nfft1) {
+    const shape_t shape_{static_cast<size_t>(nfft0), static_cast<size_t>(nfft1)};
+    const shape_t axes_{0, 1};
+    const stride_t stride_{static_cast<ptrdiff_t>(sizeof(Complex) * nfft1), static_cast<ptrdiff_t>(sizeof(Complex))};
+    pocketfft::c2c(shape_, stride_, stride_, axes_, pocketfft::BACKWARD, src, dst, static_cast<Scalar>(1));
+  }
+};
+
+}  // namespace internal
+}  // namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/BiCGSTABL.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/BiCGSTABL.h
new file mode 100644
index 0000000..0469a5a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/BiCGSTABL.h
@@ -0,0 +1,339 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020 Chris Schoutrop <c.e.m.schoutrop@tue.nl>
+// Copyright (C) 2020 Jens Wehner <j.wehner@esciencecenter.nl>
+// Copyright (C) 2020 Jan van Dijk <j.v.dijk@tue.nl>
+// Copyright (C) 2020 Adithya Vijaykumar
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+
+  This implementation of BiCGStab(L) is based on the papers
+      General algorithm:
+      1. G.L.G. Sleijpen, D.R. Fokkema. (1993). BiCGstab(l) for linear equations
+  involving unsymmetric matrices with complex spectrum. Electronic Transactions
+  on Numerical Analysis. Polynomial step update:
+      2. G.L.G. Sleijpen, M.B. Van Gijzen. (2010) Exploiting BiCGstab(l)
+  strategies to induce dimension reduction SIAM Journal on Scientific Computing.
+      3. Fokkema, Diederik R. Enhanced implementation of BiCGstab (l) for
+  solving linear systems of equations. Universiteit Utrecht. Mathematisch
+  Instituut, 1996
+      4. Sleijpen, G. L., & van der Vorst, H. A. (1996). Reliable updated
+  residuals in hybrid Bi-CG methods. Computing, 56(2), 141-163.
+*/
+
+#ifndef EIGEN_BICGSTABL_H
+#define EIGEN_BICGSTABL_H
+
+namespace Eigen {
+
+namespace internal {
+/**     \internal Low-level bi conjugate gradient stabilized algorithm with L
+   additional residual minimization steps \param mat The matrix A \param rhs The
+   right hand side vector b \param x On input and initial solution, on output
+   the computed solution. \param precond A preconditioner being able to
+   efficiently solve for an approximation of Ax=b (regardless of b) \param iters
+   On input the max number of iteration, on output the number of performed
+   iterations. \param tol_error On input the tolerance error, on output an
+   estimation of the relative error. \param L On input Number of additional
+   GMRES steps to take. If L is too large (~20) instabilities occur. \return
+   false in the case of numerical issue, for example a break down of BiCGSTABL.
+*/
+template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+bool bicgstabl(const MatrixType &mat, const Rhs &rhs, Dest &x, const Preconditioner &precond, Index &iters,
+               typename Dest::RealScalar &tol_error, Index L) {
+  using numext::abs;
+  using numext::sqrt;
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  const Index N = rhs.size();
+  L = L < x.rows() ? L : x.rows();
+
+  Index k = 0;
+
+  const RealScalar tol = tol_error;
+  const Index maxIters = iters;
+
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> DenseMatrixType;
+
+  DenseMatrixType rHat(N, L + 1);
+  DenseMatrixType uHat(N, L + 1);
+
+  // We start with an initial guess x_0 and let us set r_0 as (residual
+  // calculated from x_0)
+  VectorType x0 = x;
+  rHat.col(0) = rhs - mat * x0;  // r_0
+
+  x.setZero();  // This will contain the updates to the solution.
+  // rShadow is arbitrary, but must never be orthogonal to any residual.
+  VectorType rShadow = VectorType::Random(N);
+
+  VectorType x_prime = x;
+
+  // Redundant: x is already set to 0
+  // x.setZero();
+  VectorType b_prime = rHat.col(0);
+
+  // Other vectors and scalars initialization
+  Scalar rho0 = 1.0;
+  Scalar alpha = 0.0;
+  Scalar omega = 1.0;
+
+  uHat.col(0).setZero();
+
+  bool bicg_convergence = false;
+
+  const RealScalar normb = rhs.stableNorm();
+  if (internal::isApprox(normb, RealScalar(0))) {
+    x.setZero();
+    iters = 0;
+    return true;
+  }
+  RealScalar normr = rHat.col(0).stableNorm();
+  RealScalar Mx = normr;
+  RealScalar Mr = normr;
+
+  // Keep track of the solution with the lowest residual
+  RealScalar normr_min = normr;
+  VectorType x_min = x_prime + x;
+
+  // Criterion for when to apply the group-wise update, conform ref 3.
+  const RealScalar delta = 0.01;
+
+  bool compute_res = false;
+  bool update_app = false;
+
+  while (normr > tol * normb && k < maxIters) {
+    rho0 *= -omega;
+
+    for (Index j = 0; j < L; ++j) {
+      const Scalar rho1 = rShadow.dot(rHat.col(j));
+
+      if (!(numext::isfinite)(rho1) || rho0 == RealScalar(0.0)) {
+        // We cannot continue computing, return the best solution found.
+        x += x_prime;
+
+        // Check if x is better than the best stored solution thus far.
+        normr = (rhs - mat * (precond.solve(x) + x0)).stableNorm();
+
+        if (normr > normr_min || !(numext::isfinite)(normr)) {
+          // x_min is a better solution than x, return x_min
+          x = x_min;
+          normr = normr_min;
+        }
+        tol_error = normr / normb;
+        iters = k;
+        // x contains the updates to x0, add those back to obtain the solution
+        x = precond.solve(x);
+        x += x0;
+        return (normr < tol * normb);
+      }
+
+      const Scalar beta = alpha * (rho1 / rho0);
+      rho0 = rho1;
+      // Update search directions
+      uHat.leftCols(j + 1) = rHat.leftCols(j + 1) - beta * uHat.leftCols(j + 1);
+      uHat.col(j + 1) = mat * precond.solve(uHat.col(j));
+      const Scalar sigma = rShadow.dot(uHat.col(j + 1));
+      alpha = rho1 / sigma;
+      // Update residuals
+      rHat.leftCols(j + 1) -= alpha * uHat.middleCols(1, j + 1);
+      rHat.col(j + 1) = mat * precond.solve(rHat.col(j));
+      // Complete BiCG iteration by updating x
+      x += alpha * uHat.col(0);
+      normr = rHat.col(0).stableNorm();
+      // Check for early exit
+      if (normr < tol * normb) {
+        /*
+          Convergence was achieved during BiCG step.
+          Without this check BiCGStab(L) fails for trivial matrices, such as
+          when the preconditioner already is the inverse, or the input matrix is
+          identity.
+        */
+        bicg_convergence = true;
+        break;
+      } else if (normr < normr_min) {
+        // We found an x with lower residual, keep this one.
+        x_min = x + x_prime;
+        normr_min = normr;
+      }
+    }
+    if (!bicg_convergence) {
+      /*
+        The polynomial/minimize residual step.
+
+        QR Householder method for argmin is more stable than (modified)
+        Gram-Schmidt, in the sense that there is less loss of orthogonality. It
+        is more accurate than solving the normal equations, since the normal
+        equations scale with condition number squared.
+      */
+      const VectorType gamma = rHat.rightCols(L).householderQr().solve(rHat.col(0));
+      x += rHat.leftCols(L) * gamma;
+      rHat.col(0) -= rHat.rightCols(L) * gamma;
+      uHat.col(0) -= uHat.rightCols(L) * gamma;
+      normr = rHat.col(0).stableNorm();
+      omega = gamma(L - 1);
+    }
+    if (normr < normr_min) {
+      // We found an x with lower residual, keep this one.
+      x_min = x + x_prime;
+      normr_min = normr;
+    }
+
+    k++;
+
+    /*
+      Reliable update part
+
+      The recursively computed residual can deviate from the actual residual
+      after several iterations. However, computing the residual from the
+      definition costs extra MVs and should not be done at each iteration. The
+      reliable update strategy computes the true residual from the definition:
+      r=b-A*x at strategic intervals. Furthermore a "group wise update" strategy
+      is used to combine updates, which improves accuracy.
+    */
+
+    // Maximum norm of residuals since last update of x.
+    Mx = numext::maxi(Mx, normr);
+    // Maximum norm of residuals since last computation of the true residual.
+    Mr = numext::maxi(Mr, normr);
+
+    if (normr < delta * normb && normb <= Mx) {
+      update_app = true;
+    }
+
+    if (update_app || (normr < delta * Mr && normb <= Mr)) {
+      compute_res = true;
+    }
+
+    if (bicg_convergence) {
+      update_app = true;
+      compute_res = true;
+      bicg_convergence = false;
+    }
+
+    if (compute_res) {
+      // Explicitly compute residual from the definition
+
+      // This is equivalent to the shifted version of rhs - mat *
+      // (precond.solve(x)+x0)
+      rHat.col(0) = b_prime - mat * precond.solve(x);
+      normr = rHat.col(0).stableNorm();
+      Mr = normr;
+
+      if (update_app) {
+        // After the group wise update, the original problem is translated to a
+        // shifted one.
+        x_prime += x;
+        x.setZero();
+        b_prime = rHat.col(0);
+        Mx = normr;
+      }
+    }
+    if (normr < normr_min) {
+      // We found an x with lower residual, keep this one.
+      x_min = x + x_prime;
+      normr_min = normr;
+    }
+
+    compute_res = false;
+    update_app = false;
+  }
+
+  // Convert internal variable to the true solution vector x
+  x += x_prime;
+
+  normr = (rhs - mat * (precond.solve(x) + x0)).stableNorm();
+  if (normr > normr_min || !(numext::isfinite)(normr)) {
+    // x_min is a better solution than x, return x_min
+    x = x_min;
+    normr = normr_min;
+  }
+  tol_error = normr / normb;
+  iters = k;
+
+  // x contains the updates to x0, add those back to obtain the solution
+  x = precond.solve(x);
+  x += x0;
+  return true;
+}
+
+}  // namespace internal
+
+template <typename MatrixType_, typename Preconditioner_ = DiagonalPreconditioner<typename MatrixType_::Scalar>>
+class BiCGSTABL;
+
+namespace internal {
+
+template <typename MatrixType_, typename Preconditioner_>
+struct traits<Eigen::BiCGSTABL<MatrixType_, Preconditioner_>> {
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}  // namespace internal
+
+template <typename MatrixType_, typename Preconditioner_>
+class BiCGSTABL : public IterativeSolverBase<BiCGSTABL<MatrixType_, Preconditioner_>> {
+  typedef IterativeSolverBase<BiCGSTABL> Base;
+  using Base::m_error;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_iterations;
+  using Base::matrix;
+  Index m_L;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+  /** Default constructor. */
+  BiCGSTABL() : m_L(2) {}
+
+  /**
+  Initialize the solver with matrix \a A for further \c Ax=b solving.
+
+  This constructor is a shortcut for the default constructor followed
+  by a call to compute().
+
+  \warning this class stores a reference to the matrix A as well as some
+  precomputed values that depend on it. Therefore, if \a A is changed
+  this class becomes invalid. Call compute() to update it with the new
+  matrix A, or modify a copy of A.
+  */
+  template <typename MatrixDerived>
+  explicit BiCGSTABL(const EigenBase<MatrixDerived> &A) : Base(A.derived()), m_L(2) {}
+
+  /** \internal */
+  /** Loops over the number of columns of b and does the following:
+    1. sets the tolerance and maxIterations
+    2. Calls the function that has the core solver routine
+  */
+  template <typename Rhs, typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs &b, Dest &x) const {
+    m_iterations = Base::maxIterations();
+
+    m_error = Base::m_tolerance;
+
+    bool ret = internal::bicgstabl(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error, m_L);
+    m_info = (!ret) ? NumericalIssue : m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+
+  /** Sets the parameter L, indicating how many minimize residual steps are
+   * used. Default: 2 */
+  void setL(Index L) {
+    eigen_assert(L >= 1 && "L needs to be positive");
+    m_L = L;
+  }
+};
+
+}  // namespace Eigen
+
+#endif /* EIGEN_BICGSTABL_H */
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/DGMRES.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
new file mode 100644
index 0000000..74edd74
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
@@ -0,0 +1,490 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DGMRES_H
+#define EIGEN_DGMRES_H
+
+#include "../../../../Eigen/Eigenvalues"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename MatrixType_, typename Preconditioner_ = DiagonalPreconditioner<typename MatrixType_::Scalar> >
+class DGMRES;
+
+namespace internal {
+
+template <typename MatrixType_, typename Preconditioner_>
+struct traits<DGMRES<MatrixType_, Preconditioner_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+/** \brief Computes a permutation vector to have a sorted sequence
+ * \param vec The vector to reorder.
+ * \param perm gives the sorted sequence on output. Must be initialized with 0..n-1
+ * \param ncut Put  the ncut smallest elements at the end of the vector
+ * WARNING This is an expensive sort, so should be used only
+ * for small size vectors
+ * TODO: Use modified QuickSplit or std::nth_element to get the smallest values
+ */
+template <typename VectorType, typename IndexType>
+void sortWithPermutation(VectorType& vec, IndexType& perm, typename IndexType::Scalar& ncut) {
+  eigen_assert(vec.size() == perm.size());
+  bool flag;
+  for (Index k = 0; k < ncut; k++) {
+    flag = false;
+    for (Index j = 0; j < vec.size() - 1; j++) {
+      if (vec(perm(j)) < vec(perm(j + 1))) {
+        std::swap(perm(j), perm(j + 1));
+        flag = true;
+      }
+      if (!flag) break;  // The vector is in sorted order
+    }
+  }
+}
+
+}  // namespace internal
+/**
+ * \ingroup IterativeLinearSolvers_Module
+ * \brief A Restarted GMRES with deflation.
+ * This class implements a modification of the GMRES solver for
+ * sparse linear systems. The basis is built with modified
+ * Gram-Schmidt. At each restart, a few approximated eigenvectors
+ * corresponding to the smallest eigenvalues are used to build a
+ * preconditioner for the next cycle. This preconditioner
+ * for deflation can be combined with any other preconditioner,
+ * the IncompleteLUT for instance. The preconditioner is applied
+ * at right of the matrix and the combination is multiplicative.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, can be a dense or a sparse matrix.
+ * \tparam Preconditioner_ the type of the preconditioner. Default is DiagonalPreconditioner
+ * Typical usage :
+ * \code
+ * SparseMatrix<double> A;
+ * VectorXd x, b;
+ * //Fill A and b ...
+ * DGMRES<SparseMatrix<double> > solver;
+ * solver.set_restart(30); // Set restarting value
+ * solver.setEigenv(1); // Set the number of eigenvalues to deflate
+ * solver.compute(A);
+ * x = solver.solve(b);
+ * \endcode
+ *
+ * DGMRES can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+ *
+ * References :
+ * [1] D. NUENTSA WAKAM and F. PACULL, Memory Efficient Hybrid
+ *  Algebraic Solvers for Linear Systems Arising from Compressible
+ *  Flows, Computers and Fluids, In Press,
+ *  https://doi.org/10.1016/j.compfluid.2012.03.023
+ * [2] K. Burrage and J. Erhel, On the performance of various
+ * adaptive preconditioned GMRES strategies, 5(1998), 101-121.
+ * [3] J. Erhel, K. Burrage and B. Pohl, Restarted GMRES
+ *  preconditioned by deflation,J. Computational and Applied
+ *  Mathematics, 69(1996), 303-318.
+
+ *
+ */
+template <typename MatrixType_, typename Preconditioner_>
+class DGMRES : public IterativeSolverBase<DGMRES<MatrixType_, Preconditioner_> > {
+  typedef IterativeSolverBase<DGMRES> Base;
+  using Base::m_error;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_iterations;
+  using Base::m_tolerance;
+  using Base::matrix;
+
+ public:
+  using Base::_solve_impl;
+  using Base::_solve_with_guess_impl;
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+  typedef Preconditioner_ Preconditioner;
+  typedef Matrix<Scalar, Dynamic, Dynamic> DenseMatrix;
+  typedef Matrix<RealScalar, Dynamic, Dynamic> DenseRealMatrix;
+  typedef Matrix<Scalar, Dynamic, 1> DenseVector;
+  typedef Matrix<RealScalar, Dynamic, 1> DenseRealVector;
+  typedef Matrix<ComplexScalar, Dynamic, 1> ComplexVector;
+
+  /** Default constructor. */
+  DGMRES()
+      : Base(), m_restart(30), m_neig(0), m_r(0), m_maxNeig(5), m_isDeflAllocated(false), m_isDeflInitialized(false) {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+   *
+   * This constructor is a shortcut for the default constructor followed
+   * by a call to compute().
+   *
+   * \warning this class stores a reference to the matrix A as well as some
+   * precomputed values that depend on it. Therefore, if \a A is changed
+   * this class becomes invalid. Call compute() to update it with the new
+   * matrix A, or modify a copy of A.
+   */
+  template <typename MatrixDerived>
+  explicit DGMRES(const EigenBase<MatrixDerived>& A)
+      : Base(A.derived()),
+        m_restart(30),
+        m_neig(0),
+        m_r(0),
+        m_maxNeig(5),
+        m_isDeflAllocated(false),
+        m_isDeflInitialized(false) {}
+
+  ~DGMRES() {}
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const {
+    EIGEN_STATIC_ASSERT(Rhs::ColsAtCompileTime == 1 || Dest::ColsAtCompileTime == 1,
+                        YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX);
+
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+
+    dgmres(matrix(), b, x, Base::m_preconditioner);
+  }
+
+  /**
+   * Get the restart value
+   */
+  Index restart() { return m_restart; }
+
+  /**
+   * Set the restart value (default is 30)
+   */
+  void set_restart(const Index restart) { m_restart = restart; }
+
+  /**
+   * Set the number of eigenvalues to deflate at each restart
+   */
+  void setEigenv(const Index neig) {
+    m_neig = neig;
+    if (neig + 1 > m_maxNeig) m_maxNeig = neig + 1;  // To allow for complex conjugates
+  }
+
+  /**
+   * Get the size of the deflation subspace size
+   */
+  Index deflSize() { return m_r; }
+
+  /**
+   * Set the maximum size of the deflation subspace
+   */
+  void setMaxEigenv(const Index maxNeig) { m_maxNeig = maxNeig; }
+
+ protected:
+  // DGMRES algorithm
+  template <typename Rhs, typename Dest>
+  void dgmres(const MatrixType& mat, const Rhs& rhs, Dest& x, const Preconditioner& precond) const;
+  // Perform one cycle of GMRES
+  template <typename Dest>
+  Index dgmresCycle(const MatrixType& mat, const Preconditioner& precond, Dest& x, DenseVector& r0, RealScalar& beta,
+                    const RealScalar& normRhs, Index& nbIts) const;
+  // Compute data to use for deflation
+  Index dgmresComputeDeflationData(const MatrixType& mat, const Preconditioner& precond, const Index& it,
+                                   StorageIndex& neig) const;
+  // Apply deflation to a vector
+  template <typename RhsType, typename DestType>
+  Index dgmresApplyDeflation(const RhsType& In, DestType& Out) const;
+  ComplexVector schurValues(const ComplexSchur<DenseMatrix>& schurofH) const;
+  ComplexVector schurValues(const RealSchur<DenseMatrix>& schurofH) const;
+  // Init data for deflation
+  void dgmresInitDeflation(Index& rows) const;
+  mutable DenseMatrix m_V;                  // Krylov basis vectors
+  mutable DenseMatrix m_H;                  // Hessenberg matrix
+  mutable DenseMatrix m_Hes;                // Initial hessenberg matrix without Givens rotations applied
+  mutable Index m_restart;                  // Maximum size of the Krylov subspace
+  mutable DenseMatrix m_U;                  // Vectors that form the basis of the invariant subspace
+  mutable DenseMatrix m_MU;                 // matrix operator applied to m_U (for next cycles)
+  mutable DenseMatrix m_T;                  /* T=U^T*M^{-1}*A*U */
+  mutable PartialPivLU<DenseMatrix> m_luT;  // LU factorization of m_T
+  mutable StorageIndex m_neig;              // Number of eigenvalues to extract at each restart
+  mutable Index m_r;                        // Current number of deflated eigenvalues, size of m_U
+  mutable Index m_maxNeig;                  // Maximum number of eigenvalues to deflate
+  mutable RealScalar m_lambdaN;             // Modulus of the largest eigenvalue of A
+  mutable bool m_isDeflAllocated;
+  mutable bool m_isDeflInitialized;
+
+  // Adaptive strategy
+  mutable RealScalar m_smv;  // Smaller multiple of the remaining number of steps allowed
+  mutable bool m_force;      // Force the use of deflation at each restart
+};
+/**
+ * \brief Perform several cycles of restarted GMRES with modified Gram Schmidt,
+ *
+ * A right preconditioner is used combined with deflation.
+ *
+ */
+template <typename MatrixType_, typename Preconditioner_>
+template <typename Rhs, typename Dest>
+void DGMRES<MatrixType_, Preconditioner_>::dgmres(const MatrixType& mat, const Rhs& rhs, Dest& x,
+                                                  const Preconditioner& precond) const {
+  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+
+  RealScalar normRhs = rhs.norm();
+  if (normRhs <= considerAsZero) {
+    x.setZero();
+    m_error = 0;
+    return;
+  }
+
+  // Initialization
+  m_isDeflInitialized = false;
+  Index n = mat.rows();
+  DenseVector r0(n);
+  Index nbIts = 0;
+  m_H.resize(m_restart + 1, m_restart);
+  m_Hes.resize(m_restart, m_restart);
+  m_V.resize(n, m_restart + 1);
+  // Initial residual vector and initial norm
+  if (x.squaredNorm() == 0) x = precond.solve(rhs);
+  r0 = rhs - mat * x;
+  RealScalar beta = r0.norm();
+
+  m_error = beta / normRhs;
+  if (m_error < m_tolerance)
+    m_info = Success;
+  else
+    m_info = NoConvergence;
+
+  // Iterative process
+  while (nbIts < m_iterations && m_info == NoConvergence) {
+    dgmresCycle(mat, precond, x, r0, beta, normRhs, nbIts);
+
+    // Compute the new residual vector for the restart
+    if (nbIts < m_iterations && m_info == NoConvergence) {
+      r0 = rhs - mat * x;
+      beta = r0.norm();
+    }
+  }
+}
+
+/**
+ * \brief Perform one restart cycle of DGMRES
+ * \param mat The coefficient matrix
+ * \param precond The preconditioner
+ * \param x the new approximated solution
+ * \param r0 The initial residual vector
+ * \param beta The norm of the residual computed so far
+ * \param normRhs The norm of the right hand side vector
+ * \param nbIts The number of iterations
+ */
+template <typename MatrixType_, typename Preconditioner_>
+template <typename Dest>
+Index DGMRES<MatrixType_, Preconditioner_>::dgmresCycle(const MatrixType& mat, const Preconditioner& precond, Dest& x,
+                                                        DenseVector& r0, RealScalar& beta, const RealScalar& normRhs,
+                                                        Index& nbIts) const {
+  // Initialization
+  DenseVector g(m_restart + 1);  // Right hand side of the least square problem
+  g.setZero();
+  g(0) = Scalar(beta);
+  m_V.col(0) = r0 / beta;
+  m_info = NoConvergence;
+  std::vector<JacobiRotation<Scalar> > gr(m_restart);  // Givens rotations
+  Index it = 0;                                        // Number of inner iterations
+  Index n = mat.rows();
+  DenseVector tv1(n), tv2(n);  // Temporary vectors
+  while (m_info == NoConvergence && it < m_restart && nbIts < m_iterations) {
+    // Apply preconditioner(s) at right
+    if (m_isDeflInitialized) {
+      dgmresApplyDeflation(m_V.col(it), tv1);  // Deflation
+      tv2 = precond.solve(tv1);
+    } else {
+      tv2 = precond.solve(m_V.col(it));  // User's selected preconditioner
+    }
+    tv1 = mat * tv2;
+
+    // Orthogonalize it with the previous basis in the basis using modified Gram-Schmidt
+    Scalar coef;
+    for (Index i = 0; i <= it; ++i) {
+      coef = tv1.dot(m_V.col(i));
+      tv1 = tv1 - coef * m_V.col(i);
+      m_H(i, it) = coef;
+      m_Hes(i, it) = coef;
+    }
+    // Normalize the vector
+    coef = tv1.norm();
+    m_V.col(it + 1) = tv1 / coef;
+    m_H(it + 1, it) = coef;
+    //     m_Hes(it+1,it) = coef;
+
+    // FIXME: Check for happy breakdown.
+
+    // Update Hessenberg matrix with Givens rotations
+    for (Index i = 1; i <= it; ++i) {
+      m_H.col(it).applyOnTheLeft(i - 1, i, gr[i - 1].adjoint());
+    }
+    // Compute the new plane rotation
+    gr[it].makeGivens(m_H(it, it), m_H(it + 1, it));
+    // Apply the new rotation
+    m_H.col(it).applyOnTheLeft(it, it + 1, gr[it].adjoint());
+    g.applyOnTheLeft(it, it + 1, gr[it].adjoint());
+
+    beta = std::abs(g(it + 1));
+    m_error = beta / normRhs;
+    it++;
+    nbIts++;
+
+    if (m_error < m_tolerance) {
+      // The method has converged
+      m_info = Success;
+      break;
+    }
+  }
+
+  // Compute the new coefficients by solving the least square problem
+  // FIXME: Check first if the matrix is singular (zero diagonal).
+  DenseVector nrs(m_restart);
+  nrs = m_H.topLeftCorner(it, it).template triangularView<Upper>().solve(g.head(it));
+
+  // Form the new solution
+  if (m_isDeflInitialized) {
+    tv1 = m_V.leftCols(it) * nrs;
+    dgmresApplyDeflation(tv1, tv2);
+    x = x + precond.solve(tv2);
+  } else
+    x = x + precond.solve(m_V.leftCols(it) * nrs);
+
+  // Go for a new cycle and compute data for deflation
+  if (nbIts < m_iterations && m_info == NoConvergence && m_neig > 0 && (m_r + m_neig) < m_maxNeig)
+    dgmresComputeDeflationData(mat, precond, it, m_neig);
+  return 0;
+}
+
+template <typename MatrixType_, typename Preconditioner_>
+void DGMRES<MatrixType_, Preconditioner_>::dgmresInitDeflation(Index& rows) const {
+  m_U.resize(rows, m_maxNeig);
+  m_MU.resize(rows, m_maxNeig);
+  m_T.resize(m_maxNeig, m_maxNeig);
+  m_lambdaN = 0.0;
+  m_isDeflAllocated = true;
+}
+
+template <typename MatrixType_, typename Preconditioner_>
+inline typename DGMRES<MatrixType_, Preconditioner_>::ComplexVector DGMRES<MatrixType_, Preconditioner_>::schurValues(
+    const ComplexSchur<DenseMatrix>& schurofH) const {
+  return schurofH.matrixT().diagonal();
+}
+
+template <typename MatrixType_, typename Preconditioner_>
+inline typename DGMRES<MatrixType_, Preconditioner_>::ComplexVector DGMRES<MatrixType_, Preconditioner_>::schurValues(
+    const RealSchur<DenseMatrix>& schurofH) const {
+  const DenseMatrix& T = schurofH.matrixT();
+  Index it = T.rows();
+  ComplexVector eig(it);
+  Index j = 0;
+  while (j < it - 1) {
+    if (T(j + 1, j) == Scalar(0)) {
+      eig(j) = ComplexScalar(T(j, j), RealScalar(0));
+      j++;
+    } else {
+      eig(j) = ComplexScalar(T(j, j), T(j + 1, j));
+      eig(j + 1) = ComplexScalar(T(j, j + 1), T(j + 1, j + 1));
+      j++;
+    }
+  }
+  if (j < it - 1) eig(j) = ComplexScalar(T(j, j), RealScalar(0));
+  return eig;
+}
+
+template <typename MatrixType_, typename Preconditioner_>
+Index DGMRES<MatrixType_, Preconditioner_>::dgmresComputeDeflationData(const MatrixType& mat,
+                                                                       const Preconditioner& precond, const Index& it,
+                                                                       StorageIndex& neig) const {
+  // First, find the Schur form of the Hessenberg matrix H
+  std::conditional_t<NumTraits<Scalar>::IsComplex, ComplexSchur<DenseMatrix>, RealSchur<DenseMatrix> > schurofH;
+  bool computeU = true;
+  DenseMatrix matrixQ(it, it);
+  matrixQ.setIdentity();
+  schurofH.computeFromHessenberg(m_Hes.topLeftCorner(it, it), matrixQ, computeU);
+
+  ComplexVector eig(it);
+  Matrix<StorageIndex, Dynamic, 1> perm(it);
+  eig = this->schurValues(schurofH);
+
+  // Reorder the absolute values of Schur values
+  DenseRealVector modulEig(it);
+  for (Index j = 0; j < it; ++j) modulEig(j) = std::abs(eig(j));
+  perm.setLinSpaced(it, 0, internal::convert_index<StorageIndex>(it - 1));
+  internal::sortWithPermutation(modulEig, perm, neig);
+
+  if (!m_lambdaN) {
+    m_lambdaN = (std::max)(modulEig.maxCoeff(), m_lambdaN);
+  }
+  // Count the real number of extracted eigenvalues (with complex conjugates)
+  Index nbrEig = 0;
+  while (nbrEig < neig) {
+    if (eig(perm(it - nbrEig - 1)).imag() == RealScalar(0))
+      nbrEig++;
+    else
+      nbrEig += 2;
+  }
+  // Extract the  Schur vectors corresponding to the smallest Ritz values
+  DenseMatrix Sr(it, nbrEig);
+  Sr.setZero();
+  for (Index j = 0; j < nbrEig; j++) {
+    Sr.col(j) = schurofH.matrixU().col(perm(it - j - 1));
+  }
+
+  // Form the Schur vectors of the initial matrix using the Krylov basis
+  DenseMatrix X;
+  X = m_V.leftCols(it) * Sr;
+  if (m_r) {
+    // Orthogonalize X against m_U using modified Gram-Schmidt
+    for (Index j = 0; j < nbrEig; j++)
+      for (Index k = 0; k < m_r; k++) X.col(j) = X.col(j) - (m_U.col(k).dot(X.col(j))) * m_U.col(k);
+  }
+
+  // Compute m_MX = A * M^-1 * X
+  Index m = m_V.rows();
+  if (!m_isDeflAllocated) dgmresInitDeflation(m);
+  DenseMatrix MX(m, nbrEig);
+  DenseVector tv1(m);
+  for (Index j = 0; j < nbrEig; j++) {
+    tv1 = mat * X.col(j);
+    MX.col(j) = precond.solve(tv1);
+  }
+
+  // Update m_T = [U'MU U'MX; X'MU X'MX]
+  m_T.block(m_r, m_r, nbrEig, nbrEig) = X.transpose() * MX;
+  if (m_r) {
+    m_T.block(0, m_r, m_r, nbrEig) = m_U.leftCols(m_r).transpose() * MX;
+    m_T.block(m_r, 0, nbrEig, m_r) = X.transpose() * m_MU.leftCols(m_r);
+  }
+
+  // Save X into m_U and m_MX in m_MU
+  for (Index j = 0; j < nbrEig; j++) m_U.col(m_r + j) = X.col(j);
+  for (Index j = 0; j < nbrEig; j++) m_MU.col(m_r + j) = MX.col(j);
+  // Increase the size of the invariant subspace
+  m_r += nbrEig;
+
+  // Factorize m_T into m_luT
+  m_luT.compute(m_T.topLeftCorner(m_r, m_r));
+
+  // FIXME: Check if the factorization was correctly done (nonsingular matrix).
+  m_isDeflInitialized = true;
+  return 0;
+}
+template <typename MatrixType_, typename Preconditioner_>
+template <typename RhsType, typename DestType>
+Index DGMRES<MatrixType_, Preconditioner_>::dgmresApplyDeflation(const RhsType& x, DestType& y) const {
+  DenseVector x1 = m_U.leftCols(m_r).transpose() * x;
+  y = x + m_U.leftCols(m_r) * (m_lambdaN * m_luT.solve(x1) - x1);
+  return 0;
+}
+
+}  // end namespace Eigen
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/GMRES.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/GMRES.h
new file mode 100644
index 0000000..238d0ec
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/GMRES.h
@@ -0,0 +1,315 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2012, 2014 Kolja Brix <brix@igpm.rwth-aaachen.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GMRES_H
+#define EIGEN_GMRES_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**
+ * Generalized Minimal Residual Algorithm based on the
+ * Arnoldi algorithm implemented with Householder reflections.
+ *
+ * Parameters:
+ *  \param mat       matrix of linear system of equations
+ *  \param rhs       right hand side vector of linear system of equations
+ *  \param x         on input: initial guess, on output: solution
+ *  \param precond   preconditioner used
+ *  \param iters     on input: maximum number of iterations to perform
+ *                   on output: number of iterations performed
+ *  \param restart   number of iterations for a restart
+ *  \param tol_error on input: relative residual tolerance
+ *                   on output: residuum achieved
+ *
+ * \sa IterativeMethods::bicgstab()
+ *
+ *
+ * For references, please see:
+ *
+ * Saad, Y. and Schultz, M. H.
+ * GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems.
+ * SIAM J.Sci.Stat.Comp. 7, 1986, pp. 856 - 869.
+ *
+ * Saad, Y.
+ * Iterative Methods for Sparse Linear Systems.
+ * Society for Industrial and Applied Mathematics, Philadelphia, 2003.
+ *
+ * Walker, H. F.
+ * Implementations of the GMRES method.
+ * Comput.Phys.Comm. 53, 1989, pp. 311 - 320.
+ *
+ * Walker, H. F.
+ * Implementation of the GMRES Method using Householder Transformations.
+ * SIAM J.Sci.Stat.Comp. 9, 1988, pp. 152 - 163.
+ *
+ */
+template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+bool gmres(const MatrixType& mat, const Rhs& rhs, Dest& x, const Preconditioner& precond, Index& iters,
+           const Index& restart, typename Dest::RealScalar& tol_error) {
+  using std::abs;
+  using std::sqrt;
+
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> FMatrixType;
+
+  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+
+  if (rhs.norm() <= considerAsZero) {
+    x.setZero();
+    tol_error = 0;
+    return true;
+  }
+
+  RealScalar tol = tol_error;
+  const Index maxIters = iters;
+  iters = 0;
+
+  const Index m = mat.rows();
+
+  // residual and preconditioned residual
+  VectorType p0 = rhs - mat * x;
+  VectorType r0 = precond.solve(p0);
+
+  const RealScalar r0Norm = r0.norm();
+
+  // is initial guess already good enough?
+  if (r0Norm == 0) {
+    tol_error = 0;
+    return true;
+  }
+
+  // storage for Hessenberg matrix and Householder data
+  FMatrixType H = FMatrixType::Zero(m, restart + 1);
+  VectorType w = VectorType::Zero(restart + 1);
+  VectorType tau = VectorType::Zero(restart + 1);
+
+  // storage for Jacobi rotations
+  std::vector<JacobiRotation<Scalar> > G(restart);
+
+  // storage for temporaries
+  VectorType t(m), v(m), workspace(m), x_new(m);
+
+  // generate first Householder vector
+  Ref<VectorType> H0_tail = H.col(0).tail(m - 1);
+  RealScalar beta;
+  r0.makeHouseholder(H0_tail, tau.coeffRef(0), beta);
+  w(0) = Scalar(beta);
+
+  for (Index k = 1; k <= restart; ++k) {
+    ++iters;
+
+    v = VectorType::Unit(m, k - 1);
+
+    // apply Householder reflections H_{1} ... H_{k-1} to v
+    // TODO: use a HouseholderSequence
+    for (Index i = k - 1; i >= 0; --i) {
+      v.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
+    }
+
+    // apply matrix M to v:  v = mat * v;
+    t.noalias() = mat * v;
+    v = precond.solve(t);
+
+    // apply Householder reflections H_{k-1} ... H_{1} to v
+    // TODO: use a HouseholderSequence
+    for (Index i = 0; i < k; ++i) {
+      v.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
+    }
+
+    if (v.tail(m - k).norm() != 0.0) {
+      if (k <= restart) {
+        // generate new Householder vector
+        Ref<VectorType> Hk_tail = H.col(k).tail(m - k - 1);
+        v.tail(m - k).makeHouseholder(Hk_tail, tau.coeffRef(k), beta);
+
+        // apply Householder reflection H_{k} to v
+        v.tail(m - k).applyHouseholderOnTheLeft(Hk_tail, tau.coeffRef(k), workspace.data());
+      }
+    }
+
+    if (k > 1) {
+      for (Index i = 0; i < k - 1; ++i) {
+        // apply old Givens rotations to v
+        v.applyOnTheLeft(i, i + 1, G[i].adjoint());
+      }
+    }
+
+    if (k < m && v(k) != (Scalar)0) {
+      // determine next Givens rotation
+      G[k - 1].makeGivens(v(k - 1), v(k));
+
+      // apply Givens rotation to v and w
+      v.applyOnTheLeft(k - 1, k, G[k - 1].adjoint());
+      w.applyOnTheLeft(k - 1, k, G[k - 1].adjoint());
+    }
+
+    // insert coefficients into upper matrix triangle
+    H.col(k - 1).head(k) = v.head(k);
+
+    tol_error = abs(w(k)) / r0Norm;
+    bool stop = (k == m || tol_error < tol || iters == maxIters);
+
+    if (stop || k == restart) {
+      // solve upper triangular system
+      Ref<VectorType> y = w.head(k);
+      H.topLeftCorner(k, k).template triangularView<Upper>().solveInPlace(y);
+
+      // use Horner-like scheme to calculate solution vector
+      x_new.setZero();
+      for (Index i = k - 1; i >= 0; --i) {
+        x_new(i) += y(i);
+        // apply Householder reflection H_{i} to x_new
+        x_new.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
+      }
+
+      x += x_new;
+
+      if (stop) {
+        return true;
+      } else {
+        k = 0;
+
+        // reset data for restart
+        p0.noalias() = rhs - mat * x;
+        r0 = precond.solve(p0);
+
+        // clear Hessenberg matrix and Householder data
+        H.setZero();
+        w.setZero();
+        tau.setZero();
+
+        // generate first Householder vector
+        r0.makeHouseholder(H0_tail, tau.coeffRef(0), beta);
+        w(0) = Scalar(beta);
+      }
+    }
+  }
+
+  return false;
+}
+
+}  // namespace internal
+
+template <typename MatrixType_, typename Preconditioner_ = DiagonalPreconditioner<typename MatrixType_::Scalar> >
+class GMRES;
+
+namespace internal {
+
+template <typename MatrixType_, typename Preconditioner_>
+struct traits<GMRES<MatrixType_, Preconditioner_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}  // namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+ * \brief A GMRES solver for sparse square problems
+ *
+ * This class allows to solve for A.x = b sparse linear problems using a generalized minimal
+ * residual method. The vectors x and b can be either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, can be a dense or a sparse matrix.
+ * \tparam Preconditioner_ the type of the preconditioner. Default is DiagonalPreconditioner
+ *
+ * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+ * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+ * and NumTraits<Scalar>::epsilon() for the tolerance.
+ *
+ * This class can be used as the direct solver classes. Here is a typical usage example:
+ * \code
+ * int n = 10000;
+ * VectorXd x(n), b(n);
+ * SparseMatrix<double> A(n,n);
+ * // fill A and b
+ * GMRES<SparseMatrix<double> > solver(A);
+ * x = solver.solve(b);
+ * std::cout << "#iterations:     " << solver.iterations() << std::endl;
+ * std::cout << "estimated error: " << solver.error()      << std::endl;
+ * // update b, and solve again
+ * x = solver.solve(b);
+ * \endcode
+ *
+ * By default the iterations start with x=0 as an initial guess of the solution.
+ * One can control the start using the solveWithGuess() method.
+ *
+ * GMRES can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+ *
+ * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+ */
+template <typename MatrixType_, typename Preconditioner_>
+class GMRES : public IterativeSolverBase<GMRES<MatrixType_, Preconditioner_> > {
+  typedef IterativeSolverBase<GMRES> Base;
+  using Base::m_error;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_iterations;
+  using Base::matrix;
+
+ private:
+  Index m_restart;
+
+ public:
+  using Base::_solve_impl;
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+ public:
+  /** Default constructor. */
+  GMRES() : Base(), m_restart(30) {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+   *
+   * This constructor is a shortcut for the default constructor followed
+   * by a call to compute().
+   *
+   * \warning this class stores a reference to the matrix A as well as some
+   * precomputed values that depend on it. Therefore, if \a A is changed
+   * this class becomes invalid. Call compute() to update it with the new
+   * matrix A, or modify a copy of A.
+   */
+  template <typename MatrixDerived>
+  explicit GMRES(const EigenBase<MatrixDerived>& A) : Base(A.derived()), m_restart(30) {}
+
+  ~GMRES() {}
+
+  /** Get the number of iterations after that a restart is performed.
+   */
+  Index get_restart() { return m_restart; }
+
+  /** Set the number of iterations after that a restart is performed.
+   *  \param restart   number of iterations for a restarti, default is 30.
+   */
+  void set_restart(const Index restart) { m_restart = restart; }
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const {
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+    bool ret = internal::gmres(matrix(), b, x, Base::m_preconditioner, m_iterations, m_restart, m_error);
+    m_info = (!ret) ? NumericalIssue : m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+
+ protected:
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GMRES_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/IDRS.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/IDRS.h
new file mode 100644
index 0000000..09568e1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/IDRS.h
@@ -0,0 +1,395 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020 Chris Schoutrop <c.e.m.schoutrop@tue.nl>
+// Copyright (C) 2020 Jens Wehner <j.wehner@esciencecenter.nl>
+// Copyright (C) 2020 Jan van Dijk <j.v.dijk@tue.nl>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_IDRS_H
+#define EIGEN_IDRS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+/**     \internal Low-level Induced Dimension Reduction algorithm
+        \param A The matrix A
+        \param b The right hand side vector b
+        \param x On input and initial solution, on output the computed solution.
+        \param precond A preconditioner being able to efficiently solve for an
+                  approximation of Ax=b (regardless of b)
+        \param iter On input the max number of iteration, on output the number of performed iterations.
+        \param relres On input the tolerance error, on output an estimation of the relative error.
+        \param S On input Number of the dimension of the shadow space.
+                \param smoothing switches residual smoothing on.
+                \param angle small omega lead to faster convergence at the expense of numerical stability
+                \param replacement switches on a residual replacement strategy to increase accuracy of residual at the
+   expense of more Mat*vec products \return false in the case of numerical issue, for example a break down of IDRS.
+*/
+template <typename Vector, typename RealScalar>
+typename Vector::Scalar omega(const Vector& t, const Vector& s, RealScalar angle) {
+  using numext::abs;
+  typedef typename Vector::Scalar Scalar;
+  const RealScalar ns = s.stableNorm();
+  const RealScalar nt = t.stableNorm();
+  const Scalar ts = t.dot(s);
+  const RealScalar rho = abs(ts / (nt * ns));
+
+  if (rho < angle) {
+    if (ts == Scalar(0)) {
+      return Scalar(0);
+    }
+    // Original relation for om is given by
+    // om = om * angle / rho;
+    // To alleviate potential (near) division by zero this can be rewritten as
+    // om = angle * (ns / nt) * (ts / abs(ts)) = angle * (ns / nt) * sgn(ts)
+    return angle * (ns / nt) * (ts / abs(ts));
+  }
+  return ts / (nt * nt);
+}
+
+template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+bool idrs(const MatrixType& A, const Rhs& b, Dest& x, const Preconditioner& precond, Index& iter,
+          typename Dest::RealScalar& relres, Index S, bool smoothing, typename Dest::RealScalar angle,
+          bool replacement) {
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> DenseMatrixType;
+  const Index N = b.size();
+  S = S < x.rows() ? S : x.rows();
+  const RealScalar tol = relres;
+  const Index maxit = iter;
+
+  bool trueres = false;
+
+  FullPivLU<DenseMatrixType> lu_solver;
+
+  DenseMatrixType P;
+  {
+    HouseholderQR<DenseMatrixType> qr(DenseMatrixType::Random(N, S));
+    P = (qr.householderQ() * DenseMatrixType::Identity(N, S));
+  }
+
+  const RealScalar normb = b.stableNorm();
+
+  if (internal::isApprox(normb, RealScalar(0))) {
+    // Solution is the zero vector
+    x.setZero();
+    iter = 0;
+    relres = 0;
+    return true;
+  }
+  // from http://homepage.tudelft.nl/1w5b5/IDRS/manual.pdf
+  // A peak in the residual is considered dangerously high if‖ri‖/‖b‖> C(tol/epsilon).
+  // With epsilon the relative machine precision. The factor tol/epsilon corresponds
+  // to the size of a finite precision number that is so large that the absolute
+  // round-off error in this number, when propagated through the process, makes it
+  // impossible to achieve the required accuracy. The factor C accounts for the
+  // accumulation of round-off errors. This parameter has been set to 10^{-3}.
+  // mp is epsilon/C 10^3 * eps is very conservative, so normally no residual
+  // replacements will take place. It only happens if things go very wrong. Too many
+  // restarts may ruin the convergence.
+  const RealScalar mp = RealScalar(1e3) * NumTraits<Scalar>::epsilon();
+
+  // Compute initial residual
+  const RealScalar tolb = tol * normb;  // Relative tolerance
+  VectorType r = b - A * x;
+
+  VectorType x_s, r_s;
+
+  if (smoothing) {
+    x_s = x;
+    r_s = r;
+  }
+
+  RealScalar normr = r.stableNorm();
+
+  if (normr <= tolb) {
+    // Initial guess is a good enough solution
+    iter = 0;
+    relres = normr / normb;
+    return true;
+  }
+
+  DenseMatrixType G = DenseMatrixType::Zero(N, S);
+  DenseMatrixType U = DenseMatrixType::Zero(N, S);
+  DenseMatrixType M = DenseMatrixType::Identity(S, S);
+  VectorType t(N), v(N);
+  Scalar om = 1.;
+
+  // Main iteration loop, guild G-spaces:
+  iter = 0;
+
+  while (normr > tolb && iter < maxit) {
+    // New right hand size for small system:
+    VectorType f = (r.adjoint() * P).adjoint();
+
+    for (Index k = 0; k < S; ++k) {
+      // Solve small system and make v orthogonal to P:
+      // c = M(k:s,k:s)\f(k:s);
+      lu_solver.compute(M.block(k, k, S - k, S - k));
+      VectorType c = lu_solver.solve(f.segment(k, S - k));
+      // v = r - G(:,k:s)*c;
+      v = r - G.rightCols(S - k) * c;
+      // Preconditioning
+      v = precond.solve(v);
+
+      // Compute new U(:,k) and G(:,k), G(:,k) is in space G_j
+      U.col(k) = U.rightCols(S - k) * c + om * v;
+      G.col(k) = A * U.col(k);
+
+      // Bi-Orthogonalise the new basis vectors:
+      for (Index i = 0; i < k - 1; ++i) {
+        // alpha =  ( P(:,i)'*G(:,k) )/M(i,i);
+        Scalar alpha = P.col(i).dot(G.col(k)) / M(i, i);
+        G.col(k) = G.col(k) - alpha * G.col(i);
+        U.col(k) = U.col(k) - alpha * U.col(i);
+      }
+
+      // New column of M = P'*G  (first k-1 entries are zero)
+      // M(k:s,k) = (G(:,k)'*P(:,k:s))';
+      M.block(k, k, S - k, 1) = (G.col(k).adjoint() * P.rightCols(S - k)).adjoint();
+
+      if (internal::isApprox(M(k, k), Scalar(0))) {
+        return false;
+      }
+
+      // Make r orthogonal to q_i, i = 0..k-1
+      Scalar beta = f(k) / M(k, k);
+      r = r - beta * G.col(k);
+      x = x + beta * U.col(k);
+      normr = r.stableNorm();
+
+      if (replacement && normr > tolb / mp) {
+        trueres = true;
+      }
+
+      // Smoothing:
+      if (smoothing) {
+        t = r_s - r;
+        // gamma is a Scalar, but the conversion is not allowed
+        Scalar gamma = t.dot(r_s) / t.stableNorm();
+        r_s = r_s - gamma * t;
+        x_s = x_s - gamma * (x_s - x);
+        normr = r_s.stableNorm();
+      }
+
+      if (normr < tolb || iter == maxit) {
+        break;
+      }
+
+      // New f = P'*r (first k  components are zero)
+      if (k < S - 1) {
+        f.segment(k + 1, S - (k + 1)) = f.segment(k + 1, S - (k + 1)) - beta * M.block(k + 1, k, S - (k + 1), 1);
+      }
+    }  // end for
+
+    if (normr < tolb || iter == maxit) {
+      break;
+    }
+
+    // Now we have sufficient vectors in G_j to compute residual in G_j+1
+    // Note: r is already perpendicular to P so v = r
+    // Preconditioning
+    v = r;
+    v = precond.solve(v);
+
+    // Matrix-vector multiplication:
+    t = A * v;
+
+    // Computation of a new omega
+    om = internal::omega(t, r, angle);
+
+    if (om == RealScalar(0.0)) {
+      return false;
+    }
+
+    r = r - om * t;
+    x = x + om * v;
+    normr = r.stableNorm();
+
+    if (replacement && normr > tolb / mp) {
+      trueres = true;
+    }
+
+    // Residual replacement?
+    if (trueres && normr < normb) {
+      r = b - A * x;
+      trueres = false;
+    }
+
+    // Smoothing:
+    if (smoothing) {
+      t = r_s - r;
+      Scalar gamma = t.dot(r_s) / t.stableNorm();
+      r_s = r_s - gamma * t;
+      x_s = x_s - gamma * (x_s - x);
+      normr = r_s.stableNorm();
+    }
+
+    iter++;
+
+  }  // end while
+
+  if (smoothing) {
+    x = x_s;
+  }
+  relres = normr / normb;
+  return true;
+}
+
+}  // namespace internal
+
+template <typename MatrixType_, typename Preconditioner_ = DiagonalPreconditioner<typename MatrixType_::Scalar> >
+class IDRS;
+
+namespace internal {
+
+template <typename MatrixType_, typename Preconditioner_>
+struct traits<Eigen::IDRS<MatrixType_, Preconditioner_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}  // namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+ * \brief The Induced Dimension Reduction method (IDR(s)) is a short-recurrences Krylov method for sparse square
+ * problems.
+ *
+ * This class allows to solve for A.x = b sparse linear problems. The vectors x and b can be either dense or sparse.
+ * he Induced Dimension Reduction method, IDR(), is a robust and efficient short-recurrence Krylov subspace method for
+ * solving large nonsymmetric systems of linear equations.
+ *
+ * For indefinite systems IDR(S) outperforms both BiCGStab and BiCGStab(L). Additionally, IDR(S) can handle matrices
+ * with complex eigenvalues more efficiently than BiCGStab.
+ *
+ * Many problems that do not converge for BiCGSTAB converge for IDR(s) (for larger values of s). And if both methods
+ * converge the convergence for IDR(s) is typically much faster for difficult systems (for example indefinite problems).
+ *
+ * IDR(s) is a limited memory finite termination method. In exact arithmetic it converges in at most N+N/s iterations,
+ * with N the system size.  It uses a fixed number of 4+3s vector. In comparison, BiCGSTAB terminates in 2N iterations
+ * and uses 7 vectors. GMRES terminates in at most N iterations, and uses I+3 vectors, with I the number of iterations.
+ * Restarting GMRES limits the memory consumption, but destroys the finite termination property.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, can be a dense or a sparse matrix.
+ * \tparam Preconditioner_ the type of the preconditioner. Default is DiagonalPreconditioner
+ *
+ * \implsparsesolverconcept
+ *
+ * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+ * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+ * and NumTraits<Scalar>::epsilon() for the tolerance.
+ *
+ * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
+ *
+ * \b Performance: when using sparse matrices, best performance is achied for a row-major sparse matrix format.
+ * Moreover, in this case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
+ * See \ref TopicMultiThreading for details.
+ *
+ * By default the iterations start with x=0 as an initial guess of the solution.
+ * One can control the start using the solveWithGuess() method.
+ *
+ * IDR(s) can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+ *
+ * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+ */
+template <typename MatrixType_, typename Preconditioner_>
+class IDRS : public IterativeSolverBase<IDRS<MatrixType_, Preconditioner_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+ private:
+  typedef IterativeSolverBase<IDRS> Base;
+  using Base::m_error;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_iterations;
+  using Base::matrix;
+  Index m_S;
+  bool m_smoothing;
+  RealScalar m_angle;
+  bool m_residual;
+
+ public:
+  /** Default constructor. */
+  IDRS() : m_S(4), m_smoothing(false), m_angle(RealScalar(0.7)), m_residual(false) {}
+
+  /**     Initialize the solver with matrix \a A for further \c Ax=b solving.
+
+          This constructor is a shortcut for the default constructor followed
+          by a call to compute().
+
+          \warning this class stores a reference to the matrix A as well as some
+          precomputed values that depend on it. Therefore, if \a A is changed
+          this class becomes invalid. Call compute() to update it with the new
+          matrix A, or modify a copy of A.
+  */
+  template <typename MatrixDerived>
+  explicit IDRS(const EigenBase<MatrixDerived>& A)
+      : Base(A.derived()), m_S(4), m_smoothing(false), m_angle(RealScalar(0.7)), m_residual(false) {}
+
+  /** \internal */
+  /**     Loops over the number of columns of b and does the following:
+                  1. sets the tolerance and maxIterations
+                  2. Calls the function that has the core solver routine
+  */
+  template <typename Rhs, typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const {
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+
+    bool ret = internal::idrs(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error, m_S, m_smoothing, m_angle,
+                              m_residual);
+
+    m_info = (!ret) ? NumericalIssue : m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+
+  /** Sets the parameter S, indicating the dimension of the shadow space. Default is 4*/
+  void setS(Index S) {
+    if (S < 1) {
+      S = 4;
+    }
+
+    m_S = S;
+  }
+
+  /** Switches off and on smoothing.
+  Residual smoothing results in monotonically decreasing residual norms at
+  the expense of two extra vectors of storage and a few extra vector
+  operations. Although monotonic decrease of the residual norms is a
+  desirable property, the rate of convergence of the unsmoothed process and
+  the smoothed process is basically the same. Default is off */
+  void setSmoothing(bool smoothing) { m_smoothing = smoothing; }
+
+  /** The angle must be a real scalar. In IDR(s), a value for the
+  iteration parameter omega must be chosen in every s+1th step. The most
+  natural choice is to select a value to minimize the norm of the next residual.
+  This corresponds to the parameter omega = 0. In practice, this may lead to
+  values of omega that are so small that the other iteration parameters
+  cannot be computed with sufficient accuracy. In such cases it is better to
+  increase the value of omega sufficiently such that a compromise is reached
+  between accurate computations and reduction of the residual norm. The
+  parameter angle =0.7 (”maintaining the convergence strategy”)
+  results in such a compromise. */
+  void setAngle(RealScalar angle) { m_angle = angle; }
+
+  /** The parameter replace is a logical that determines whether a
+  residual replacement strategy is employed to increase the accuracy of the
+  solution. */
+  void setResidualUpdate(bool update) { m_residual = update; }
+};
+
+}  // namespace Eigen
+
+#endif /* EIGEN_IDRS_H */
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/IDRSTABL.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/IDRSTABL.h
new file mode 100644
index 0000000..a08a71d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/IDRSTABL.h
@@ -0,0 +1,476 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020 Chris Schoutrop <c.e.m.schoutrop@tue.nl>
+// Copyright (C) 2020 Mischa Senders <m.j.senders@student.tue.nl>
+// Copyright (C) 2020 Lex Kuijpers <l.kuijpers@student.tue.nl>
+// Copyright (C) 2020 Jens Wehner <j.wehner@esciencecenter.nl>
+// Copyright (C) 2020 Jan van Dijk <j.v.dijk@tue.nl>
+// Copyright (C) 2020 Adithya Vijaykumar
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+/*
+
+The IDR(S)Stab(L) method is a combination of IDR(S) and BiCGStab(L)
+
+This implementation of IDRSTABL is based on
+1. Aihara, K., Abe, K., & Ishiwata, E. (2014). A variant of IDRstab with
+reliable update strategies for solving sparse linear systems. Journal of
+Computational and Applied Mathematics, 259, 244-258.
+   doi:10.1016/j.cam.2013.08.028
+                2. Aihara, K., Abe, K., & Ishiwata, E. (2015). Preconditioned
+IDRSTABL Algorithms for Solving Nonsymmetric Linear Systems. International
+Journal of Applied Mathematics, 45(3).
+                3. Saad, Y. (2003). Iterative Methods for Sparse Linear Systems:
+Second Edition. Philadelphia, PA: SIAM.
+                4. Sonneveld, P., & Van Gijzen, M. B. (2009). IDR(s): A Family
+of Simple and Fast Algorithms for Solving Large Nonsymmetric Systems of Linear
+Equations. SIAM Journal on Scientific Computing, 31(2), 1035-1062.
+   doi:10.1137/070685804
+                5. Sonneveld, P. (2012). On the convergence behavior of IDR (s)
+and related methods. SIAM Journal on Scientific Computing, 34(5), A2576-A2598.
+
+    Right-preconditioning based on Ref. 3 is implemented here.
+*/
+
+#ifndef EIGEN_IDRSTABL_H
+#define EIGEN_IDRSTABL_H
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+bool idrstabl(const MatrixType &mat, const Rhs &rhs, Dest &x, const Preconditioner &precond, Index &iters,
+              typename Dest::RealScalar &tol_error, Index L, Index S) {
+  /*
+    Setup and type definitions.
+  */
+  using numext::abs;
+  using numext::sqrt;
+  typedef typename Dest::Scalar Scalar;
+  typedef typename Dest::RealScalar RealScalar;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> DenseMatrixType;
+
+  const Index N = x.rows();
+
+  Index k = 0;  // Iteration counter
+  const Index maxIters = iters;
+
+  const RealScalar rhs_norm = rhs.stableNorm();
+  const RealScalar tol = tol_error * rhs_norm;
+
+  if (rhs_norm == 0) {
+    /*
+      If b==0, then the exact solution is x=0.
+      rhs_norm is needed for other calculations anyways, this exit is a freebie.
+    */
+    x.setZero();
+    tol_error = 0.0;
+    return true;
+  }
+  // Construct decomposition objects beforehand.
+  FullPivLU<DenseMatrixType> lu_solver;
+
+  if (S >= N || L >= N) {
+    /*
+      The matrix is very small, or the choice of L and S is very poor
+      in that case solving directly will be best.
+    */
+    lu_solver.compute(DenseMatrixType(mat));
+    x = lu_solver.solve(rhs);
+    tol_error = (rhs - mat * x).stableNorm() / rhs_norm;
+    return true;
+  }
+
+  // Define maximum sizes to prevent any reallocation later on.
+  DenseMatrixType u(N, L + 1);
+  DenseMatrixType r(N, L + 1);
+
+  DenseMatrixType V(N * (L + 1), S);
+
+  VectorType alpha(S);
+  VectorType gamma(L);
+  VectorType update(N);
+
+  /*
+    Main IDRSTABL algorithm
+  */
+  // Set up the initial residual
+  VectorType x0 = x;
+  r.col(0) = rhs - mat * x;
+  x.setZero();  // The final solution will be x0+x
+
+  tol_error = r.col(0).stableNorm();
+
+  // FOM = Full orthogonalisation method
+  DenseMatrixType h_FOM = DenseMatrixType::Zero(S, S - 1);
+
+  // Construct an initial U matrix of size N x S
+  DenseMatrixType U(N * (L + 1), S);
+  for (Index col_index = 0; col_index < S; ++col_index) {
+    // Arnoldi-like process to generate a set of orthogonal vectors spanning
+    // {u,A*u,A*A*u,...,A^(S-1)*u}. This construction can be combined with the
+    // Full Orthogonalization Method (FOM) from Ref.3 to provide a possible
+    // early exit with no additional MV.
+    if (col_index != 0) {
+      /*
+      Modified Gram-Schmidt strategy:
+      */
+      VectorType w = mat * precond.solve(u.col(0));
+      for (Index i = 0; i < col_index; ++i) {
+        auto v = U.col(i).head(N);
+        h_FOM(i, col_index - 1) = v.dot(w);
+        w -= h_FOM(i, col_index - 1) * v;
+      }
+      u.col(0) = w;
+      h_FOM(col_index, col_index - 1) = u.col(0).stableNorm();
+
+      if (abs(h_FOM(col_index, col_index - 1)) != RealScalar(0)) {
+        /*
+        This only happens if u is NOT exactly zero. In case it is exactly zero
+        it would imply that that this u has no component in the direction of the
+        current residual.
+
+        By then setting u to zero it will not contribute any further (as it
+        should). Whereas attempting to normalize results in division by zero.
+
+        Such cases occur if:
+        1. The basis of dimension <S is sufficient to exactly solve the linear
+        system. I.e. the current residual is in span{r,Ar,...A^{m-1}r}, where
+        (m-1)<=S.
+        2. Two vectors vectors generated from r, Ar,... are (numerically)
+        parallel.
+
+        In case 1, the exact solution to the system can be obtained from the
+        "Full Orthogonalization Method" (Algorithm 6.4 in the book of Saad),
+        without any additional MV.
+
+        Contrary to what one would suspect, the comparison with ==0.0 for
+        floating-point types is intended here. Any arbitrary non-zero u is fine
+        to continue, however if u contains either NaN or Inf the algorithm will
+        break down.
+        */
+        u.col(0) /= h_FOM(col_index, col_index - 1);
+      }
+    } else {
+      u.col(0) = r.col(0);
+      u.col(0).normalize();
+    }
+
+    U.col(col_index).head(N) = u.col(0);
+  }
+
+  if (S > 1) {
+    // Check for early FOM exit.
+    Scalar beta = r.col(0).stableNorm();
+    VectorType e1 = VectorType::Zero(S - 1);
+    e1(0) = beta;
+    lu_solver.compute(h_FOM.topLeftCorner(S - 1, S - 1));
+    VectorType y = lu_solver.solve(e1);
+    VectorType x2 = x + U.topLeftCorner(N, S - 1) * y;
+
+    // Using proposition 6.7 in Saad, one MV can be saved to calculate the
+    // residual
+    RealScalar FOM_residual = (h_FOM(S - 1, S - 2) * y(S - 2) * U.col(S - 1).head(N)).stableNorm();
+
+    if (FOM_residual < tol) {
+      // Exit, the FOM algorithm was already accurate enough
+      iters = k;
+      // Convert back to the unpreconditioned solution
+      x = precond.solve(x2);
+      // x contains the updates to x0, add those back to obtain the solution
+      x += x0;
+      tol_error = FOM_residual / rhs_norm;
+      return true;
+    }
+  }
+
+  /*
+    Select an initial (N x S) matrix R0.
+    1. Generate random R0, orthonormalize the result.
+    2. This results in R0, however to save memory and compute we only need the
+    adjoint of R0. This is given by the matrix R_T.\ Additionally, the matrix
+    (mat.adjoint()*R_tilde).adjoint()=R_tilde.adjoint()*mat by the
+    anti-distributivity property of the adjoint. This results in AR_T, which is
+    constant if R_T does not have to be regenerated and can be precomputed.
+    Based on reference 4, this has zero probability in exact arithmetic.
+  */
+
+  // Original IDRSTABL and Kensuke choose S random vectors:
+  const HouseholderQR<DenseMatrixType> qr(DenseMatrixType::Random(N, S));
+  DenseMatrixType R_T = (qr.householderQ() * DenseMatrixType::Identity(N, S)).adjoint();
+  DenseMatrixType AR_T = DenseMatrixType(R_T * mat);
+
+  // Pre-allocate sigma.
+  DenseMatrixType sigma(S, S);
+
+  bool reset_while = false;  // Should the while loop be reset?
+
+  while (k < maxIters) {
+    for (Index j = 1; j <= L; ++j) {
+      /*
+        The IDR Step
+      */
+      // Construction of the sigma-matrix, and the decomposition of sigma.
+      for (Index i = 0; i < S; ++i) {
+        sigma.col(i).noalias() = AR_T * precond.solve(U.block(N * (j - 1), i, N, 1));
+      }
+
+      lu_solver.compute(sigma);
+      // Obtain the update coefficients alpha
+      if (j == 1) {
+        // alpha=inverse(sigma)*(R_T*r_0);
+        alpha.noalias() = lu_solver.solve(R_T * r.col(0));
+      } else {
+        // alpha=inverse(sigma)*(AR_T*r_{j-2})
+        alpha.noalias() = lu_solver.solve(AR_T * precond.solve(r.col(j - 2)));
+      }
+
+      // Obtain new solution and residual from this update
+      update.noalias() = U.topRows(N) * alpha;
+      r.col(0) -= mat * precond.solve(update);
+      x += update;
+
+      for (Index i = 1; i <= j - 2; ++i) {
+        // This only affects the case L>2
+        r.col(i) -= U.block(N * (i + 1), 0, N, S) * alpha;
+      }
+      if (j > 1) {
+        // r=[r;A*r_{j-2}]
+        r.col(j - 1).noalias() = mat * precond.solve(r.col(j - 2));
+      }
+      tol_error = r.col(0).stableNorm();
+
+      if (tol_error < tol) {
+        // If at this point the algorithm has converged, exit.
+        reset_while = true;
+        break;
+      }
+
+      bool break_normalization = false;
+      for (Index q = 1; q <= S; ++q) {
+        if (q == 1) {
+          // u = r;
+          u.leftCols(j + 1) = r.leftCols(j + 1);
+        } else {
+          // u=[u_1;u_2;...;u_j]
+          u.leftCols(j) = u.middleCols(1, j);
+        }
+
+        // Obtain the update coefficients beta implicitly
+        // beta=lu_sigma.solve(AR_T * u.block(N * (j - 1), 0, N, 1)
+        u.reshaped().head(u.rows() * j) -= U.topRows(N * j) * lu_solver.solve(AR_T * precond.solve(u.col(j - 1)));
+
+        // u=[u;Au_{j-1}]
+        u.col(j).noalias() = mat * precond.solve(u.col(j - 1));
+
+        // Orthonormalize u_j to the columns of V_j(:,1:q-1)
+        if (q > 1) {
+          /*
+          Modified Gram-Schmidt-like procedure to make u orthogonal to the
+          columns of V from Ref. 1.
+
+          The vector mu from Ref. 1 is obtained implicitly:
+          mu=V.block(N * j, 0, N, q - 1).adjoint() * u.block(N * j, 0, N, 1).
+          */
+          for (Index i = 0; i <= q - 2; ++i) {
+            auto v = V.col(i).segment(N * j, N);
+            Scalar h = v.squaredNorm();
+            h = v.dot(u.col(j)) / h;
+            u.reshaped().head(u.rows() * (j + 1)) -= h * V.block(0, i, N * (j + 1), 1);
+          }
+        }
+        // Normalize u and assign to a column of V
+        Scalar normalization_constant = u.col(j).stableNorm();
+        //  If u is exactly zero, this will lead to a NaN. Small, non-zero u is
+        //  fine.
+        if (normalization_constant == RealScalar(0.0)) {
+          break_normalization = true;
+          break;
+        } else {
+          u.leftCols(j + 1) /= normalization_constant;
+        }
+
+        V.block(0, q - 1, N * (j + 1), 1).noalias() = u.reshaped().head(u.rows() * (j + 1));
+      }
+
+      if (break_normalization == false) {
+        U = V;
+      }
+    }
+    if (reset_while) {
+      break;
+    }
+
+    // r=[r;mat*r_{L-1}]
+    r.col(L).noalias() = mat * precond.solve(r.col(L - 1));
+
+    /*
+            The polynomial step
+    */
+    ColPivHouseholderQR<DenseMatrixType> qr_solver(r.rightCols(L));
+    gamma.noalias() = qr_solver.solve(r.col(0));
+
+    // Update solution and residual using the "minimized residual coefficients"
+    update.noalias() = r.leftCols(L) * gamma;
+    x += update;
+    r.col(0) -= mat * precond.solve(update);
+
+    // Update iteration info
+    ++k;
+    tol_error = r.col(0).stableNorm();
+
+    if (tol_error < tol) {
+      // Slightly early exit by moving the criterion before the update of U,
+      // after the main while loop the result of that calculation would not be
+      // needed.
+      break;
+    }
+
+    /*
+    U=U0-sum(gamma_j*U_j)
+    Consider the first iteration. Then U only contains U0, so at the start of
+    the while-loop U should be U0. Therefore only the first N rows of U have to
+    be updated.
+    */
+    for (Index i = 1; i <= L; ++i) {
+      U.topRows(N) -= U.block(N * i, 0, N, S) * gamma(i - 1);
+    }
+  }
+
+  /*
+          Exit after the while loop terminated.
+  */
+  iters = k;
+  // Convert back to the unpreconditioned solution
+  x = precond.solve(x);
+  // x contains the updates to x0, add those back to obtain the solution
+  x += x0;
+  tol_error = tol_error / rhs_norm;
+  return true;
+}
+
+}  // namespace internal
+
+template <typename MatrixType_, typename Preconditioner_ = DiagonalPreconditioner<typename MatrixType_::Scalar>>
+class IDRSTABL;
+
+namespace internal {
+
+template <typename MatrixType_, typename Preconditioner_>
+struct traits<IDRSTABL<MatrixType_, Preconditioner_>> {
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}  // namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+ * \brief The IDR(s)STAB(l) is a combination of IDR(s) and BiCGSTAB(l). It is a
+ * short-recurrences Krylov method for sparse square problems. It can outperform
+ * both IDR(s) and BiCGSTAB(l). IDR(s)STAB(l) generally closely follows the
+ * optimal GMRES convergence in terms of the number of Matrix-Vector products.
+ * However, without the increasing cost per iteration of GMRES. IDR(s)STAB(l) is
+ * suitable for both indefinite systems and systems with complex eigenvalues.
+ *
+ * This class allows solving for A.x = b sparse linear problems. The vectors x
+ * and b can be either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, can be a dense or a
+ * sparse matrix. \tparam Preconditioner_ the type of the preconditioner.
+ * Default is DiagonalPreconditioner
+ *
+ * \implsparsesolverconcept
+ *
+ * The maximum number of iterations and tolerance value can be controlled via
+ * the setMaxIterations() and setTolerance() methods. The defaults are the size
+ * of the problem for the maximum number of iterations and
+ * NumTraits<Scalar>::epsilon() for the tolerance.
+ *
+ * The tolerance is the maximum relative residual error: |Ax-b|/|b| for which
+ * the linear system is considered solved.
+ *
+ * \b Performance: When using sparse matrices, best performance is achieved for
+ * a row-major sparse matrix format. Moreover, in this case multi-threading can
+ * be exploited if the user code is compiled with OpenMP enabled. See \ref
+ * TopicMultiThreading for details.
+ *
+ * By default the iterations start with x=0 as an initial guess of the solution.
+ * One can control the start using the solveWithGuess() method.
+ *
+ * IDR(s)STAB(l) can also be used in a matrix-free context, see the following
+ * \link MatrixfreeSolverExample example \endlink.
+ *
+ * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+ */
+
+template <typename MatrixType_, typename Preconditioner_>
+class IDRSTABL : public IterativeSolverBase<IDRSTABL<MatrixType_, Preconditioner_>> {
+  typedef IterativeSolverBase<IDRSTABL> Base;
+  using Base::m_error;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_iterations;
+  using Base::matrix;
+  Index m_L;
+  Index m_S;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+ public:
+  /** Default constructor. */
+  IDRSTABL() : m_L(2), m_S(4) {}
+
+  /**   Initialize the solver with matrix \a A for further \c Ax=b solving.
+
+  This constructor is a shortcut for the default constructor followed
+  by a call to compute().
+
+  \warning this class stores a reference to the matrix A as well as some
+  precomputed values that depend on it. Therefore, if \a A is changed
+  this class becomes invalid. Call compute() to update it with the new
+  matrix A, or modify a copy of A.
+          */
+  template <typename MatrixDerived>
+  explicit IDRSTABL(const EigenBase<MatrixDerived> &A) : Base(A.derived()), m_L(2), m_S(4) {}
+
+  /** \internal */
+  /**     Loops over the number of columns of b and does the following:
+                                  1. sets the tolerance and maxIterations
+                                  2. Calls the function that has the core solver
+     routine
+  */
+  template <typename Rhs, typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs &b, Dest &x) const {
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+    bool ret = internal::idrstabl(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error, m_L, m_S);
+
+    m_info = (!ret) ? NumericalIssue : m_error <= 10 * Base::m_tolerance ? Success : NoConvergence;
+  }
+
+  /** Sets the parameter L, indicating the amount of minimize residual steps are
+   * used. */
+  void setL(Index L) {
+    eigen_assert(L >= 1 && "L needs to be positive");
+    m_L = L;
+  }
+  /** Sets the parameter S, indicating the dimension of the shadow residual
+   * space.. */
+  void setS(Index S) {
+    eigen_assert(S >= 1 && "S needs to be positive");
+    m_S = S;
+  }
+};
+
+}  // namespace Eigen
+
+#endif /* EIGEN_IDRSTABL_H */
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h
new file mode 100644
index 0000000..1a2edca
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h
@@ -0,0 +1,88 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INCOMPLETE_LU_H
+#define EIGEN_INCOMPLETE_LU_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Scalar_>
+class IncompleteLU : public SparseSolverBase<IncompleteLU<Scalar_> > {
+ protected:
+  typedef SparseSolverBase<IncompleteLU<Scalar_> > Base;
+  using Base::m_isInitialized;
+
+  typedef Scalar_ Scalar;
+  typedef Matrix<Scalar, Dynamic, 1> Vector;
+  typedef typename Vector::Index Index;
+  typedef SparseMatrix<Scalar, RowMajor> FactorType;
+
+ public:
+  typedef Matrix<Scalar, Dynamic, Dynamic> MatrixType;
+
+  IncompleteLU() {}
+
+  template <typename MatrixType>
+  IncompleteLU(const MatrixType& mat) {
+    compute(mat);
+  }
+
+  Index rows() const { return m_lu.rows(); }
+  Index cols() const { return m_lu.cols(); }
+
+  template <typename MatrixType>
+  IncompleteLU& compute(const MatrixType& mat) {
+    m_lu = mat;
+    int size = mat.cols();
+    Vector diag(size);
+    for (int i = 0; i < size; ++i) {
+      typename FactorType::InnerIterator k_it(m_lu, i);
+      for (; k_it && k_it.index() < i; ++k_it) {
+        int k = k_it.index();
+        k_it.valueRef() /= diag(k);
+
+        typename FactorType::InnerIterator j_it(k_it);
+        typename FactorType::InnerIterator kj_it(m_lu, k);
+        while (kj_it && kj_it.index() <= k) ++kj_it;
+        for (++j_it; j_it;) {
+          if (kj_it.index() == j_it.index()) {
+            j_it.valueRef() -= k_it.value() * kj_it.value();
+            ++j_it;
+            ++kj_it;
+          } else if (kj_it.index() < j_it.index())
+            ++kj_it;
+          else
+            ++j_it;
+        }
+      }
+      if (k_it && k_it.index() == i)
+        diag(i) = k_it.value();
+      else
+        diag(i) = 1;
+    }
+    m_isInitialized = true;
+    return *this;
+  }
+
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const Rhs& b, Dest& x) const {
+    x = m_lu.template triangularView<UnitLower>().solve(b);
+    x = m_lu.template triangularView<Upper>().solve(x);
+  }
+
+ protected:
+  FactorType m_lu;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_INCOMPLETE_LU_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/InternalHeaderCheck.h
new file mode 100644
index 0000000..3d6ee41
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/InternalHeaderCheck.h
@@ -0,0 +1,4 @@
+#ifndef EIGEN_ITERATIVE_SOLVERS_MODULE_H
+#error \
+    "Please include unsupported/Eigen/IterativeSolvers instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/MINRES.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/MINRES.h
new file mode 100644
index 0000000..9daf568
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/MINRES.h
@@ -0,0 +1,254 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Giacomo Po <gpo@ucla.edu>
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2018 David Hyde <dabh@stanford.edu>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MINRES_H
+#define EIGEN_MINRES_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal Low-level MINRES algorithm
+ * \param mat The matrix A
+ * \param rhs The right hand side vector b
+ * \param x On input and initial solution, on output the computed solution.
+ * \param precond A right preconditioner being able to efficiently solve for an
+ *                approximation of Ax=b (regardless of b)
+ * \param iters On input the max number of iteration, on output the number of performed iterations.
+ * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+ */
+template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+EIGEN_DONT_INLINE void minres(const MatrixType& mat, const Rhs& rhs, Dest& x, const Preconditioner& precond,
+                              Index& iters, typename Dest::RealScalar& tol_error) {
+  using std::sqrt;
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+
+  // Check for zero rhs
+  const RealScalar rhsNorm2(rhs.squaredNorm());
+  if (rhsNorm2 == 0) {
+    x.setZero();
+    iters = 0;
+    tol_error = 0;
+    return;
+  }
+
+  // initialize
+  const Index maxIters(iters);                                    // initialize maxIters to iters
+  const Index N(mat.cols());                                      // the size of the matrix
+  const RealScalar threshold2(tol_error * tol_error * rhsNorm2);  // convergence threshold (compared to residualNorm2)
+
+  // Initialize preconditioned Lanczos
+  VectorType v_old(N);                // will be initialized inside loop
+  VectorType v(VectorType::Zero(N));  // initialize v
+  VectorType v_new(rhs - mat * x);    // initialize v_new
+  RealScalar residualNorm2(v_new.squaredNorm());
+  VectorType w(N);                         // will be initialized inside loop
+  VectorType w_new(precond.solve(v_new));  // initialize w_new
+                                           //            RealScalar beta; // will be initialized inside loop
+  RealScalar beta_new2(v_new.dot(w_new));
+  eigen_assert(beta_new2 >= 0.0 && "PRECONDITIONER IS NOT POSITIVE DEFINITE");
+  RealScalar beta_new(sqrt(beta_new2));
+  const RealScalar beta_one(beta_new);
+  // Initialize other variables
+  RealScalar c(1.0);  // the cosine of the Givens rotation
+  RealScalar c_old(1.0);
+  RealScalar s(0.0);                      // the sine of the Givens rotation
+  RealScalar s_old(0.0);                  // the sine of the Givens rotation
+  VectorType p_oold(N);                   // will be initialized in loop
+  VectorType p_old(VectorType::Zero(N));  // initialize p_old=0
+  VectorType p(p_old);                    // initialize p=0
+  RealScalar eta(1.0);
+
+  iters = 0;  // reset iters
+  while (iters < maxIters) {
+    // Preconditioned Lanczos
+    /* Note that there are 4 variants on the Lanczos algorithm. These are
+     * described in Paige, C. C. (1972). Computational variants of
+     * the Lanczos method for the eigenproblem. IMA Journal of Applied
+     * Mathematics, 10(3), 373-381. The current implementation corresponds
+     * to the case A(2,7) in the paper. It also corresponds to
+     * algorithm 6.14 in Y. Saad, Iterative Methods for Sparse Linear
+     * Systems, 2003 p.173. For the preconditioned version see
+     * A. Greenbaum, Iterative Methods for Solving Linear Systems, SIAM (1987).
+     */
+    const RealScalar beta(beta_new);
+    v_old = v;          // update: at first time step, this makes v_old = 0 so value of beta doesn't matter
+    v_new /= beta_new;  // overwrite v_new for next iteration
+    w_new /= beta_new;  // overwrite w_new for next iteration
+    v = v_new;          // update
+    w = w_new;          // update
+    v_new.noalias() = mat * w - beta * v_old;  // compute v_new
+    const RealScalar alpha = v_new.dot(w);
+    v_new -= alpha * v;            // overwrite v_new
+    w_new = precond.solve(v_new);  // overwrite w_new
+    beta_new2 = v_new.dot(w_new);  // compute beta_new
+    eigen_assert(beta_new2 >= 0.0 && "PRECONDITIONER IS NOT POSITIVE DEFINITE");
+    beta_new = sqrt(beta_new2);  // compute beta_new
+
+    // Givens rotation
+    const RealScalar r2 = s * alpha + c * c_old * beta;  // s, s_old, c and c_old are still from previous iteration
+    const RealScalar r3 = s_old * beta;                  // s, s_old, c and c_old are still from previous iteration
+    const RealScalar r1_hat = c * alpha - c_old * s * beta;
+    const RealScalar r1 = sqrt(std::pow(r1_hat, 2) + std::pow(beta_new, 2));
+    c_old = c;          // store for next iteration
+    s_old = s;          // store for next iteration
+    c = r1_hat / r1;    // new cosine
+    s = beta_new / r1;  // new sine
+
+    // Update solution
+    p_oold = p_old;
+    p_old = p;
+    p.noalias() = (w - r2 * p_old - r3 * p_oold) / r1;  // IS NOALIAS REQUIRED?
+    x += beta_one * c * eta * p;
+
+    /* Update the squared residual. Note that this is the estimated residual.
+    The real residual |Ax-b|^2 may be slightly larger */
+    residualNorm2 *= s * s;
+
+    if (residualNorm2 < threshold2) {
+      break;
+    }
+
+    eta = -s * eta;  // update eta
+    iters++;         // increment iteration number (for output purposes)
+  }
+
+  /* Compute error. Note that this is the estimated error. The real
+   error |Ax-b|/|b| may be slightly larger */
+  tol_error = std::sqrt(residualNorm2 / rhsNorm2);
+}
+
+}  // namespace internal
+
+template <typename MatrixType_, int UpLo_ = Lower, typename Preconditioner_ = IdentityPreconditioner>
+class MINRES;
+
+namespace internal {
+
+template <typename MatrixType_, int UpLo_, typename Preconditioner_>
+struct traits<MINRES<MatrixType_, UpLo_, Preconditioner_> > {
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}  // namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+ * \brief A minimal residual solver for sparse symmetric problems
+ *
+ * This class allows to solve for A.x = b sparse linear problems using the MINRES algorithm
+ * of Paige and Saunders (1975). The sparse matrix A must be symmetric (possibly indefinite).
+ * The vectors x and b can be either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, can be a dense or a sparse matrix.
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower,
+ *               Upper, or Lower|Upper in which the full matrix entries will be considered. Default is Lower.
+ * \tparam Preconditioner_ the type of the preconditioner. Default is DiagonalPreconditioner
+ *
+ * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+ * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+ * and NumTraits<Scalar>::epsilon() for the tolerance.
+ *
+ * This class can be used as the direct solver classes. Here is a typical usage example:
+ * \code
+ * int n = 10000;
+ * VectorXd x(n), b(n);
+ * SparseMatrix<double> A(n,n);
+ * // fill A and b
+ * MINRES<SparseMatrix<double> > mr;
+ * mr.compute(A);
+ * x = mr.solve(b);
+ * std::cout << "#iterations:     " << mr.iterations() << std::endl;
+ * std::cout << "estimated error: " << mr.error()      << std::endl;
+ * // update b, and solve again
+ * x = mr.solve(b);
+ * \endcode
+ *
+ * By default the iterations start with x=0 as an initial guess of the solution.
+ * One can control the start using the solveWithGuess() method.
+ *
+ * MINRES can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+ *
+ * \sa class ConjugateGradient, BiCGSTAB, SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+ */
+template <typename MatrixType_, int UpLo_, typename Preconditioner_>
+class MINRES : public IterativeSolverBase<MINRES<MatrixType_, UpLo_, Preconditioner_> > {
+  typedef IterativeSolverBase<MINRES> Base;
+  using Base::m_error;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_iterations;
+  using Base::matrix;
+
+ public:
+  using Base::_solve_impl;
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+  enum { UpLo = UpLo_ };
+
+ public:
+  /** Default constructor. */
+  MINRES() : Base() {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+   *
+   * This constructor is a shortcut for the default constructor followed
+   * by a call to compute().
+   *
+   * \warning this class stores a reference to the matrix A as well as some
+   * precomputed values that depend on it. Therefore, if \a A is changed
+   * this class becomes invalid. Call compute() to update it with the new
+   * matrix A, or modify a copy of A.
+   */
+  template <typename MatrixDerived>
+  explicit MINRES(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
+
+  /** Destructor. */
+  ~MINRES() {}
+
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const {
+    typedef typename Base::MatrixWrapper MatrixWrapper;
+    typedef typename Base::ActualMatrixType ActualMatrixType;
+    enum {
+      TransposeInput = (!MatrixWrapper::MatrixFree) && (UpLo == (Lower | Upper)) && (!MatrixType::IsRowMajor) &&
+                       (!NumTraits<Scalar>::IsComplex)
+    };
+    typedef std::conditional_t<TransposeInput, Transpose<const ActualMatrixType>, ActualMatrixType const&>
+        RowMajorWrapper;
+    EIGEN_STATIC_ASSERT(internal::check_implication(MatrixWrapper::MatrixFree, UpLo == (Lower | Upper)),
+                        MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY);
+    typedef std::conditional_t<UpLo == (Lower | Upper), RowMajorWrapper,
+                               typename MatrixWrapper::template ConstSelfAdjointViewReturnType<UpLo>::Type>
+        SelfAdjointWrapper;
+
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+    RowMajorWrapper row_mat(matrix());
+    internal::minres(SelfAdjointWrapper(row_mat), b, x, Base::m_preconditioner, m_iterations, m_error);
+    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+
+ protected:
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MINRES_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/Scaling.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/Scaling.h
new file mode 100644
index 0000000..045c24a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/IterativeSolvers/Scaling.h
@@ -0,0 +1,172 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire NUENTSA WAKAM <desire.nuentsa_wakam@inria.fr
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ITERSCALING_H
+#define EIGEN_ITERSCALING_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/**
+ * \ingroup IterativeLinearSolvers_Module
+ * \brief iterative scaling algorithm to equilibrate rows and column norms in matrices
+ *
+ * This class can be used as a preprocessing tool to accelerate the convergence of iterative methods
+ *
+ * This feature is  useful to limit the pivoting amount during LU/ILU factorization
+ * The  scaling strategy as presented here preserves the symmetry of the problem
+ * NOTE It is assumed that the matrix does not have empty row or column,
+ *
+ * Example with key steps
+ * \code
+ * VectorXd x(n), b(n);
+ * SparseMatrix<double> A;
+ * // fill A and b;
+ * IterScaling<SparseMatrix<double> > scal;
+ * // Compute the left and right scaling vectors. The matrix is equilibrated at output
+ * scal.computeRef(A);
+ * // Scale the right hand side
+ * b = scal.LeftScaling().cwiseProduct(b);
+ * // Now, solve the equilibrated linear system with any available solver
+ *
+ * // Scale back the computed solution
+ * x = scal.RightScaling().cwiseProduct(x);
+ * \endcode
+ *
+ * \tparam MatrixType_ the type of the matrix. It should be a real square sparsematrix
+ *
+ * References : D. Ruiz and B. Ucar, A Symmetry Preserving Algorithm for Matrix Scaling, INRIA Research report RR-7552
+ *
+ * \sa \ref IncompleteLUT
+ */
+template <typename MatrixType_>
+class IterScaling {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Index Index;
+
+ public:
+  IterScaling() { init(); }
+
+  IterScaling(const MatrixType& matrix) {
+    init();
+    compute(matrix);
+  }
+
+  ~IterScaling() {}
+
+  /**
+   * Compute the left and right diagonal matrices to scale the input matrix @p mat
+   *
+   * FIXME: This algorithm will be modified such that the diagonal elements are permuted on the diagonal.
+   *
+   * \sa LeftScaling() RightScaling()
+   */
+  void compute(const MatrixType& mat) {
+    using std::abs;
+    int m = mat.rows();
+    int n = mat.cols();
+    eigen_assert((m > 0 && m == n) && "Please give a non - empty matrix");
+    m_left.resize(m);
+    m_right.resize(n);
+    m_left.setOnes();
+    m_right.setOnes();
+    m_matrix = mat;
+    VectorXd Dr, Dc, DrRes, DcRes;  // Temporary Left and right scaling vectors
+    Dr.resize(m);
+    Dc.resize(n);
+    DrRes.resize(m);
+    DcRes.resize(n);
+    double EpsRow = 1.0, EpsCol = 1.0;
+    int its = 0;
+    do {  // Iterate until the infinite norm of each row and column is approximately 1
+      // Get the maximum value in each row and column
+      Dr.setZero();
+      Dc.setZero();
+      for (int k = 0; k < m_matrix.outerSize(); ++k) {
+        for (typename MatrixType::InnerIterator it(m_matrix, k); it; ++it) {
+          if (Dr(it.row()) < abs(it.value())) Dr(it.row()) = abs(it.value());
+
+          if (Dc(it.col()) < abs(it.value())) Dc(it.col()) = abs(it.value());
+        }
+      }
+      for (int i = 0; i < m; ++i) {
+        Dr(i) = std::sqrt(Dr(i));
+      }
+      for (int i = 0; i < n; ++i) {
+        Dc(i) = std::sqrt(Dc(i));
+      }
+      // Save the scaling factors
+      for (int i = 0; i < m; ++i) {
+        m_left(i) /= Dr(i);
+      }
+      for (int i = 0; i < n; ++i) {
+        m_right(i) /= Dc(i);
+      }
+      // Scale the rows and the columns of the matrix
+      DrRes.setZero();
+      DcRes.setZero();
+      for (int k = 0; k < m_matrix.outerSize(); ++k) {
+        for (typename MatrixType::InnerIterator it(m_matrix, k); it; ++it) {
+          it.valueRef() = it.value() / (Dr(it.row()) * Dc(it.col()));
+          // Accumulate the norms of the row and column vectors
+          if (DrRes(it.row()) < abs(it.value())) DrRes(it.row()) = abs(it.value());
+
+          if (DcRes(it.col()) < abs(it.value())) DcRes(it.col()) = abs(it.value());
+        }
+      }
+      DrRes.array() = (1 - DrRes.array()).abs();
+      EpsRow = DrRes.maxCoeff();
+      DcRes.array() = (1 - DcRes.array()).abs();
+      EpsCol = DcRes.maxCoeff();
+      its++;
+    } while ((EpsRow > m_tol || EpsCol > m_tol) && (its < m_maxits));
+    m_isInitialized = true;
+  }
+  /** Compute the left and right vectors to scale the vectors
+   * the input matrix is scaled with the computed vectors at output
+   *
+   * \sa compute()
+   */
+  void computeRef(MatrixType& mat) {
+    compute(mat);
+    mat = m_matrix;
+  }
+  /** Get the vector to scale the rows of the matrix
+   */
+  VectorXd& LeftScaling() { return m_left; }
+
+  /** Get the vector to scale the columns of the matrix
+   */
+  VectorXd& RightScaling() { return m_right; }
+
+  /** Set the tolerance for the convergence of the iterative scaling algorithm
+   */
+  void setTolerance(double tol) { m_tol = tol; }
+
+ protected:
+  void init() {
+    m_tol = 1e-10;
+    m_maxits = 5;
+    m_isInitialized = false;
+  }
+
+  MatrixType m_matrix;
+  mutable ComputationInfo m_info;
+  bool m_isInitialized;
+  VectorXd m_left;   // Left scaling vector
+  VectorXd m_right;  // Right scaling vector
+  double m_tol;
+  int m_maxits;  // Maximum number of iterations allowed
+};
+}  // namespace Eigen
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/KroneckerProduct/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/KroneckerProduct/InternalHeaderCheck.h
new file mode 100644
index 0000000..3cf30d9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/KroneckerProduct/InternalHeaderCheck.h
@@ -0,0 +1,4 @@
+#ifndef EIGEN_KRONECKER_PRODUCT_MODULE_H
+#error \
+    "Please include unsupported/Eigen/KroneckerProduct instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h
new file mode 100644
index 0000000..038bf42
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h
@@ -0,0 +1,284 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Kolja Brix <brix@igpm.rwth-aachen.de>
+// Copyright (C) 2011 Andreas Platen <andiplaten@gmx.de>
+// Copyright (C) 2012 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef KRONECKER_TENSOR_PRODUCT_H
+#define KRONECKER_TENSOR_PRODUCT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/*!
+ * \ingroup KroneckerProduct_Module
+ *
+ * \brief The base class of dense and sparse Kronecker product.
+ *
+ * \tparam Derived is the derived type.
+ */
+template <typename Derived>
+class KroneckerProductBase : public ReturnByValue<Derived> {
+ private:
+  typedef typename internal::traits<Derived> Traits;
+  typedef typename Traits::Scalar Scalar;
+
+ protected:
+  typedef typename Traits::Lhs Lhs;
+  typedef typename Traits::Rhs Rhs;
+
+ public:
+  /*! \brief Constructor. */
+  KroneckerProductBase(const Lhs& A, const Rhs& B) : m_A(A), m_B(B) {}
+
+  inline Index rows() const { return m_A.rows() * m_B.rows(); }
+  inline Index cols() const { return m_A.cols() * m_B.cols(); }
+
+  /*!
+   * This overrides ReturnByValue::coeff because this function is
+   * efficient enough.
+   */
+  Scalar coeff(Index row, Index col) const {
+    return m_A.coeff(row / m_B.rows(), col / m_B.cols()) * m_B.coeff(row % m_B.rows(), col % m_B.cols());
+  }
+
+  /*!
+   * This overrides ReturnByValue::coeff because this function is
+   * efficient enough.
+   */
+  Scalar coeff(Index i) const {
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+    return m_A.coeff(i / m_A.size()) * m_B.coeff(i % m_A.size());
+  }
+
+ protected:
+  typename Lhs::Nested m_A;
+  typename Rhs::Nested m_B;
+};
+
+/*!
+ * \ingroup KroneckerProduct_Module
+ *
+ * \brief Kronecker tensor product helper class for dense matrices
+ *
+ * This class is the return value of kroneckerProduct(MatrixBase,
+ * MatrixBase). Use the function rather than construct this class
+ * directly to avoid specifying template parameters.
+ *
+ * \tparam Lhs  Type of the left-hand side, a matrix expression.
+ * \tparam Rhs  Type of the rignt-hand side, a matrix expression.
+ */
+template <typename Lhs, typename Rhs>
+class KroneckerProduct : public KroneckerProductBase<KroneckerProduct<Lhs, Rhs> > {
+ private:
+  typedef KroneckerProductBase<KroneckerProduct> Base;
+  using Base::m_A;
+  using Base::m_B;
+
+ public:
+  /*! \brief Constructor. */
+  KroneckerProduct(const Lhs& A, const Rhs& B) : Base(A, B) {}
+
+  /*! \brief Evaluate the Kronecker tensor product. */
+  template <typename Dest>
+  void evalTo(Dest& dst) const;
+};
+
+/*!
+ * \ingroup KroneckerProduct_Module
+ *
+ * \brief Kronecker tensor product helper class for sparse matrices
+ *
+ * If at least one of the operands is a sparse matrix expression,
+ * then this class is returned and evaluates into a sparse matrix.
+ *
+ * This class is the return value of kroneckerProduct(EigenBase,
+ * EigenBase). Use the function rather than construct this class
+ * directly to avoid specifying template parameters.
+ *
+ * \tparam Lhs  Type of the left-hand side, a matrix expression.
+ * \tparam Rhs  Type of the rignt-hand side, a matrix expression.
+ */
+template <typename Lhs, typename Rhs>
+class KroneckerProductSparse : public KroneckerProductBase<KroneckerProductSparse<Lhs, Rhs> > {
+ private:
+  typedef KroneckerProductBase<KroneckerProductSparse> Base;
+  using Base::m_A;
+  using Base::m_B;
+
+ public:
+  /*! \brief Constructor. */
+  KroneckerProductSparse(const Lhs& A, const Rhs& B) : Base(A, B) {}
+
+  /*! \brief Evaluate the Kronecker tensor product. */
+  template <typename Dest>
+  void evalTo(Dest& dst) const;
+};
+
+template <typename Lhs, typename Rhs>
+template <typename Dest>
+void KroneckerProduct<Lhs, Rhs>::evalTo(Dest& dst) const {
+  const int BlockRows = Rhs::RowsAtCompileTime, BlockCols = Rhs::ColsAtCompileTime;
+  const Index Br = m_B.rows(), Bc = m_B.cols();
+  for (Index i = 0; i < m_A.rows(); ++i)
+    for (Index j = 0; j < m_A.cols(); ++j)
+      Block<Dest, BlockRows, BlockCols>(dst, i * Br, j * Bc, Br, Bc) = m_A.coeff(i, j) * m_B;
+}
+
+template <typename Lhs, typename Rhs>
+template <typename Dest>
+void KroneckerProductSparse<Lhs, Rhs>::evalTo(Dest& dst) const {
+  Index Br = m_B.rows(), Bc = m_B.cols();
+  dst.resize(this->rows(), this->cols());
+  dst.resizeNonZeros(0);
+
+  // 1 - evaluate the operands if needed:
+  typedef typename internal::nested_eval<Lhs, Dynamic>::type Lhs1;
+  typedef internal::remove_all_t<Lhs1> Lhs1Cleaned;
+  const Lhs1 lhs1(m_A);
+  typedef typename internal::nested_eval<Rhs, Dynamic>::type Rhs1;
+  typedef internal::remove_all_t<Rhs1> Rhs1Cleaned;
+  const Rhs1 rhs1(m_B);
+
+  // 2 - construct respective iterators
+  typedef Eigen::InnerIterator<Lhs1Cleaned> LhsInnerIterator;
+  typedef Eigen::InnerIterator<Rhs1Cleaned> RhsInnerIterator;
+
+  // compute number of non-zeros per innervectors of dst
+  {
+    // TODO: VectorXi is not necessarily big enough!
+    VectorXi nnzA = VectorXi::Zero(Dest::IsRowMajor ? m_A.rows() : m_A.cols());
+    for (Index kA = 0; kA < m_A.outerSize(); ++kA)
+      for (LhsInnerIterator itA(lhs1, kA); itA; ++itA) nnzA(Dest::IsRowMajor ? itA.row() : itA.col())++;
+
+    VectorXi nnzB = VectorXi::Zero(Dest::IsRowMajor ? m_B.rows() : m_B.cols());
+    for (Index kB = 0; kB < m_B.outerSize(); ++kB)
+      for (RhsInnerIterator itB(rhs1, kB); itB; ++itB) nnzB(Dest::IsRowMajor ? itB.row() : itB.col())++;
+
+    Matrix<int, Dynamic, Dynamic, ColMajor> nnzAB = nnzB * nnzA.transpose();
+    dst.reserve(VectorXi::Map(nnzAB.data(), nnzAB.size()));
+  }
+
+  for (Index kA = 0; kA < m_A.outerSize(); ++kA) {
+    for (Index kB = 0; kB < m_B.outerSize(); ++kB) {
+      for (LhsInnerIterator itA(lhs1, kA); itA; ++itA) {
+        for (RhsInnerIterator itB(rhs1, kB); itB; ++itB) {
+          Index i = itA.row() * Br + itB.row(), j = itA.col() * Bc + itB.col();
+          dst.insert(i, j) = itA.value() * itB.value();
+        }
+      }
+    }
+  }
+}
+
+namespace internal {
+
+template <typename Lhs_, typename Rhs_>
+struct traits<KroneckerProduct<Lhs_, Rhs_> > {
+  typedef remove_all_t<Lhs_> Lhs;
+  typedef remove_all_t<Rhs_> Rhs;
+  typedef typename ScalarBinaryOpTraits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
+  typedef typename promote_index_type<typename Lhs::StorageIndex, typename Rhs::StorageIndex>::type StorageIndex;
+
+  enum {
+    Rows = size_at_compile_time(traits<Lhs>::RowsAtCompileTime, traits<Rhs>::RowsAtCompileTime),
+    Cols = size_at_compile_time(traits<Lhs>::ColsAtCompileTime, traits<Rhs>::ColsAtCompileTime),
+    MaxRows = size_at_compile_time(traits<Lhs>::MaxRowsAtCompileTime, traits<Rhs>::MaxRowsAtCompileTime),
+    MaxCols = size_at_compile_time(traits<Lhs>::MaxColsAtCompileTime, traits<Rhs>::MaxColsAtCompileTime)
+  };
+
+  typedef Matrix<Scalar, Rows, Cols> ReturnType;
+};
+
+template <typename Lhs_, typename Rhs_>
+struct traits<KroneckerProductSparse<Lhs_, Rhs_> > {
+  typedef MatrixXpr XprKind;
+  typedef remove_all_t<Lhs_> Lhs;
+  typedef remove_all_t<Rhs_> Rhs;
+  typedef typename ScalarBinaryOpTraits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
+  typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind, typename traits<Rhs>::StorageKind,
+                                              scalar_product_op<typename Lhs::Scalar, typename Rhs::Scalar> >::ret
+      StorageKind;
+  typedef typename promote_index_type<typename Lhs::StorageIndex, typename Rhs::StorageIndex>::type StorageIndex;
+
+  enum {
+    LhsFlags = Lhs::Flags,
+    RhsFlags = Rhs::Flags,
+
+    RowsAtCompileTime = size_at_compile_time(traits<Lhs>::RowsAtCompileTime, traits<Rhs>::RowsAtCompileTime),
+    ColsAtCompileTime = size_at_compile_time(traits<Lhs>::ColsAtCompileTime, traits<Rhs>::ColsAtCompileTime),
+    MaxRowsAtCompileTime = size_at_compile_time(traits<Lhs>::MaxRowsAtCompileTime, traits<Rhs>::MaxRowsAtCompileTime),
+    MaxColsAtCompileTime = size_at_compile_time(traits<Lhs>::MaxColsAtCompileTime, traits<Rhs>::MaxColsAtCompileTime),
+
+    EvalToRowMajor = (int(LhsFlags) & int(RhsFlags) & RowMajorBit),
+    RemovedBits = ~(EvalToRowMajor ? 0 : RowMajorBit),
+
+    Flags = ((int(LhsFlags) | int(RhsFlags)) & HereditaryBits & RemovedBits) | EvalBeforeNestingBit,
+    CoeffReadCost = HugeCost
+  };
+
+  typedef SparseMatrix<Scalar, 0, StorageIndex> ReturnType;
+};
+
+}  // end namespace internal
+
+/*!
+ * \ingroup KroneckerProduct_Module
+ *
+ * Computes Kronecker tensor product of two dense matrices
+ *
+ * \warning If you want to replace a matrix by its Kronecker product
+ *          with some matrix, do \b NOT do this:
+ * \code
+ * A = kroneckerProduct(A,B); // bug!!! caused by aliasing effect
+ * \endcode
+ * instead, use eval() to work around this:
+ * \code
+ * A = kroneckerProduct(A,B).eval();
+ * \endcode
+ *
+ * \param a  Dense matrix a
+ * \param b  Dense matrix b
+ * \return   Kronecker tensor product of a and b
+ */
+template <typename A, typename B>
+KroneckerProduct<A, B> kroneckerProduct(const MatrixBase<A>& a, const MatrixBase<B>& b) {
+  return KroneckerProduct<A, B>(a.derived(), b.derived());
+}
+
+/*!
+ * \ingroup KroneckerProduct_Module
+ *
+ * Computes Kronecker tensor product of two matrices, at least one of
+ * which is sparse
+ *
+ * \warning If you want to replace a matrix by its Kronecker product
+ *          with some matrix, do \b NOT do this:
+ * \code
+ * A = kroneckerProduct(A,B); // bug!!! caused by aliasing effect
+ * \endcode
+ * instead, use eval() to work around this:
+ * \code
+ * A = kroneckerProduct(A,B).eval();
+ * \endcode
+ *
+ * \param a  Dense/sparse matrix a
+ * \param b  Dense/sparse matrix b
+ * \return   Kronecker tensor product of a and b, stored in a sparse
+ *           matrix
+ */
+template <typename A, typename B>
+KroneckerProductSparse<A, B> kroneckerProduct(const EigenBase<A>& a, const EigenBase<B>& b) {
+  return KroneckerProductSparse<A, B>(a.derived(), b.derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // KRONECKER_TENSOR_PRODUCT_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/CopyrightMINPACK.txt b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/CopyrightMINPACK.txt
new file mode 100644
index 0000000..ae7984d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/CopyrightMINPACK.txt
@@ -0,0 +1,52 @@
+Minpack Copyright Notice (1999) University of Chicago.  All rights reserved
+
+Redistribution and use in source and binary forms, with or
+without modification, are permitted provided that the
+following conditions are met:
+
+1. Redistributions of source code must retain the above
+copyright notice, this list of conditions and the following
+disclaimer.
+
+2. Redistributions in binary form must reproduce the above
+copyright notice, this list of conditions and the following
+disclaimer in the documentation and/or other materials
+provided with the distribution.
+
+3. The end-user documentation included with the
+redistribution, if any, must include the following
+acknowledgment:
+
+   "This product includes software developed by the
+   University of Chicago, as Operator of Argonne National
+   Laboratory.
+
+Alternately, this acknowledgment may appear in the software
+itself, if and wherever such third-party acknowledgments
+normally appear.
+
+4. WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
+WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
+UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
+THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
+OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
+OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
+OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
+USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
+THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
+DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
+UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
+BE CORRECTED.
+
+5. LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
+HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
+ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
+INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
+ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
+PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
+SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
+(INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
+EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
+POSSIBILITY OF SUCH LOSS OR DAMAGES.
+
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/InternalHeaderCheck.h
new file mode 100644
index 0000000..a9a646f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/InternalHeaderCheck.h
@@ -0,0 +1,4 @@
+#ifndef EIGEN_LEVENBERGMARQUARDT_MODULE_H
+#error \
+    "Please include unsupported/Eigen/LevenbergMarquardt instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
new file mode 100644
index 0000000..b81d5a3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
@@ -0,0 +1,80 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This code initially comes from MINPACK whose original authors are:
+// Copyright Jorge More - Argonne National Laboratory
+// Copyright Burt Garbow - Argonne National Laboratory
+// Copyright Ken Hillstrom - Argonne National Laboratory
+//
+// This Source Code Form is subject to the terms of the Minpack license
+// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
+
+#ifndef EIGEN_LMCOVAR_H
+#define EIGEN_LMCOVAR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+void covar(Matrix<Scalar, Dynamic, Dynamic>& r, const VectorXi& ipvt,
+           Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())) {
+  using std::abs;
+  /* Local variables */
+  Index i, j, k, l, ii, jj;
+  bool sing;
+  Scalar temp;
+
+  /* Function Body */
+  const Index n = r.cols();
+  const Scalar tolr = tol * abs(r(0, 0));
+  Matrix<Scalar, Dynamic, 1> wa(n);
+  eigen_assert(ipvt.size() == n);
+
+  /* form the inverse of r in the full upper triangle of r. */
+  l = -1;
+  for (k = 0; k < n; ++k)
+    if (abs(r(k, k)) > tolr) {
+      r(k, k) = 1. / r(k, k);
+      for (j = 0; j <= k - 1; ++j) {
+        temp = r(k, k) * r(j, k);
+        r(j, k) = 0.;
+        r.col(k).head(j + 1) -= r.col(j).head(j + 1) * temp;
+      }
+      l = k;
+    }
+
+  /* form the full upper triangle of the inverse of (r transpose)*r */
+  /* in the full upper triangle of r. */
+  for (k = 0; k <= l; ++k) {
+    for (j = 0; j <= k - 1; ++j) r.col(j).head(j + 1) += r.col(k).head(j + 1) * r(j, k);
+    r.col(k).head(k + 1) *= r(k, k);
+  }
+
+  /* form the full lower triangle of the covariance matrix */
+  /* in the strict lower triangle of r and in wa. */
+  for (j = 0; j < n; ++j) {
+    jj = ipvt[j];
+    sing = j > l;
+    for (i = 0; i <= j; ++i) {
+      if (sing) r(i, j) = 0.;
+      ii = ipvt[i];
+      if (ii > jj) r(ii, jj) = r(i, j);
+      if (ii < jj) r(jj, ii) = r(i, j);
+    }
+    wa[jj] = r(j, j);
+  }
+
+  /* symmetrize the covariance matrix in r. */
+  r.topLeftCorner(n, n).template triangularView<StrictlyUpper>() = r.topLeftCorner(n, n).transpose();
+  r.diagonal() = wa;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LMCOVAR_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
new file mode 100644
index 0000000..f0697ce
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
@@ -0,0 +1,187 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This code initially comes from MINPACK whose original authors are:
+// Copyright Jorge More - Argonne National Laboratory
+// Copyright Burt Garbow - Argonne National Laboratory
+// Copyright Ken Hillstrom - Argonne National Laboratory
+//
+// This Source Code Form is subject to the terms of the Minpack license
+// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
+
+#ifndef EIGEN_LMONESTEP_H
+#define EIGEN_LMONESTEP_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename FunctorType>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType>::minimizeOneStep(FVectorType &x) {
+  using std::abs;
+  using std::sqrt;
+  RealScalar temp, temp1, temp2;
+  RealScalar ratio;
+  RealScalar pnorm, xnorm, fnorm1, actred, dirder, prered;
+  eigen_assert(x.size() == n);  // check the caller is not cheating us
+
+  temp = 0.0;
+  xnorm = 0.0;
+  /* calculate the jacobian matrix. */
+  Index df_ret = m_functor.df(x, m_fjac);
+  if (df_ret < 0) return LevenbergMarquardtSpace::UserAsked;
+  if (df_ret > 0)
+    // numerical diff, we evaluated the function df_ret times
+    m_nfev += df_ret;
+  else
+    m_njev++;
+
+  /* compute the qr factorization of the jacobian. */
+  for (int j = 0; j < x.size(); ++j) m_wa2(j) = m_fjac.col(j).blueNorm();
+  QRSolver qrfac(m_fjac);
+  if (qrfac.info() != Success) {
+    m_info = NumericalIssue;
+    return LevenbergMarquardtSpace::ImproperInputParameters;
+  }
+  // Make a copy of the first factor with the associated permutation
+  m_rfactor = qrfac.matrixR();
+  m_permutation = (qrfac.colsPermutation());
+
+  /* on the first iteration and if external scaling is not used, scale according */
+  /* to the norms of the columns of the initial jacobian. */
+  if (m_iter == 1) {
+    if (!m_useExternalScaling)
+      for (Index j = 0; j < n; ++j) m_diag[j] = (m_wa2[j] == 0.) ? 1. : m_wa2[j];
+
+    /* on the first iteration, calculate the norm of the scaled x */
+    /* and initialize the step bound m_delta. */
+    xnorm = m_diag.cwiseProduct(x).stableNorm();
+    m_delta = m_factor * xnorm;
+    if (m_delta == 0.) m_delta = m_factor;
+  }
+
+  /* form (q transpose)*m_fvec and store the first n components in */
+  /* m_qtf. */
+  m_wa4 = m_fvec;
+  m_wa4 = qrfac.matrixQ().adjoint() * m_fvec;
+  m_qtf = m_wa4.head(n);
+
+  /* compute the norm of the scaled gradient. */
+  m_gnorm = 0.;
+  if (m_fnorm != 0.)
+    for (Index j = 0; j < n; ++j)
+      if (m_wa2[m_permutation.indices()[j]] != 0.)
+        m_gnorm = (std::max)(m_gnorm, abs(m_rfactor.col(j).head(j + 1).dot(m_qtf.head(j + 1) / m_fnorm) /
+                                          m_wa2[m_permutation.indices()[j]]));
+
+  /* test for convergence of the gradient norm. */
+  if (m_gnorm <= m_gtol) {
+    m_info = Success;
+    return LevenbergMarquardtSpace::CosinusTooSmall;
+  }
+
+  /* rescale if necessary. */
+  if (!m_useExternalScaling) m_diag = m_diag.cwiseMax(m_wa2);
+
+  do {
+    /* determine the levenberg-marquardt parameter. */
+    internal::lmpar2(qrfac, m_diag, m_qtf, m_delta, m_par, m_wa1);
+
+    /* store the direction p and x + p. calculate the norm of p. */
+    m_wa1 = -m_wa1;
+    m_wa2 = x + m_wa1;
+    pnorm = m_diag.cwiseProduct(m_wa1).stableNorm();
+
+    /* on the first iteration, adjust the initial step bound. */
+    if (m_iter == 1) m_delta = (std::min)(m_delta, pnorm);
+
+    /* evaluate the function at x + p and calculate its norm. */
+    if (m_functor(m_wa2, m_wa4) < 0) return LevenbergMarquardtSpace::UserAsked;
+    ++m_nfev;
+    fnorm1 = m_wa4.stableNorm();
+
+    /* compute the scaled actual reduction. */
+    actred = -1.;
+    if (Scalar(.1) * fnorm1 < m_fnorm) actred = 1. - numext::abs2(fnorm1 / m_fnorm);
+
+    /* compute the scaled predicted reduction and */
+    /* the scaled directional derivative. */
+    m_wa3 = m_rfactor.template triangularView<Upper>() * (m_permutation.inverse() * m_wa1);
+    temp1 = numext::abs2(m_wa3.stableNorm() / m_fnorm);
+    temp2 = numext::abs2(sqrt(m_par) * pnorm / m_fnorm);
+    prered = temp1 + temp2 / Scalar(.5);
+    dirder = -(temp1 + temp2);
+
+    /* compute the ratio of the actual to the predicted */
+    /* reduction. */
+    ratio = 0.;
+    if (prered != 0.) ratio = actred / prered;
+
+    /* update the step bound. */
+    if (ratio <= Scalar(.25)) {
+      if (actred >= 0.) temp = RealScalar(.5);
+      if (actred < 0.) temp = RealScalar(.5) * dirder / (dirder + RealScalar(.5) * actred);
+      if (RealScalar(.1) * fnorm1 >= m_fnorm || temp < RealScalar(.1)) temp = Scalar(.1);
+      /* Computing MIN */
+      m_delta = temp * (std::min)(m_delta, pnorm / RealScalar(.1));
+      m_par /= temp;
+    } else if (!(m_par != 0. && ratio < RealScalar(.75))) {
+      m_delta = pnorm / RealScalar(.5);
+      m_par = RealScalar(.5) * m_par;
+    }
+
+    /* test for successful iteration. */
+    if (ratio >= RealScalar(1e-4)) {
+      /* successful iteration. update x, m_fvec, and their norms. */
+      x = m_wa2;
+      m_wa2 = m_diag.cwiseProduct(x);
+      m_fvec = m_wa4;
+      xnorm = m_wa2.stableNorm();
+      m_fnorm = fnorm1;
+      ++m_iter;
+    }
+
+    /* tests for convergence. */
+    if (abs(actred) <= m_ftol && prered <= m_ftol && Scalar(.5) * ratio <= 1. && m_delta <= m_xtol * xnorm) {
+      m_info = Success;
+      return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
+    }
+    if (abs(actred) <= m_ftol && prered <= m_ftol && Scalar(.5) * ratio <= 1.) {
+      m_info = Success;
+      return LevenbergMarquardtSpace::RelativeReductionTooSmall;
+    }
+    if (m_delta <= m_xtol * xnorm) {
+      m_info = Success;
+      return LevenbergMarquardtSpace::RelativeErrorTooSmall;
+    }
+
+    /* tests for termination and stringent tolerances. */
+    if (m_nfev >= m_maxfev) {
+      m_info = NoConvergence;
+      return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
+    }
+    if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() &&
+        Scalar(.5) * ratio <= 1.) {
+      m_info = Success;
+      return LevenbergMarquardtSpace::FtolTooSmall;
+    }
+    if (m_delta <= NumTraits<Scalar>::epsilon() * xnorm) {
+      m_info = Success;
+      return LevenbergMarquardtSpace::XtolTooSmall;
+    }
+    if (m_gnorm <= NumTraits<Scalar>::epsilon()) {
+      m_info = Success;
+      return LevenbergMarquardtSpace::GtolTooSmall;
+    }
+
+  } while (ratio < Scalar(1e-4));
+
+  return LevenbergMarquardtSpace::Running;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LMONESTEP_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
new file mode 100644
index 0000000..75ca4e8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
@@ -0,0 +1,146 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This code initially comes from MINPACK whose original authors are:
+// Copyright Jorge More - Argonne National Laboratory
+// Copyright Burt Garbow - Argonne National Laboratory
+// Copyright Ken Hillstrom - Argonne National Laboratory
+//
+// This Source Code Form is subject to the terms of the Minpack license
+// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
+
+#ifndef EIGEN_LMPAR_H
+#define EIGEN_LMPAR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename QRSolver, typename VectorType>
+void lmpar2(const QRSolver &qr, const VectorType &diag, const VectorType &qtb, typename VectorType::Scalar m_delta,
+            typename VectorType::Scalar &par, VectorType &x)
+
+{
+  using std::abs;
+  using std::sqrt;
+  typedef typename QRSolver::MatrixType MatrixType;
+  typedef typename QRSolver::Scalar Scalar;
+  /* Local variables */
+  Index j;
+  Scalar fp;
+  Scalar parc, parl;
+  Index iter;
+  Scalar temp, paru;
+  Scalar gnorm;
+  Scalar dxnorm;
+
+  // Make a copy of the triangular factor.
+  // This copy is modified during call the qrsolv
+  MatrixType s;
+  s = qr.matrixR();
+
+  /* Function Body */
+  const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
+  const Index n = qr.matrixR().cols();
+  eigen_assert(n == diag.size());
+  eigen_assert(n == qtb.size());
+
+  VectorType wa1, wa2;
+
+  /* compute and store in x the gauss-newton direction. if the */
+  /* jacobian is rank-deficient, obtain a least squares solution. */
+
+  const Index rank = qr.rank();  // use a threshold
+  wa1 = qtb;
+  wa1.tail(n - rank).setZero();
+  // FIXME: There is no solve-in-place for sparse triangularView.
+  wa1.head(rank) = s.topLeftCorner(rank, rank).template triangularView<Upper>().solve(qtb.head(rank));
+
+  x = qr.colsPermutation() * wa1;
+
+  /* initialize the iteration counter. */
+  /* evaluate the function at the origin, and test */
+  /* for acceptance of the gauss-newton direction. */
+  iter = 0;
+  wa2 = diag.cwiseProduct(x);
+  dxnorm = wa2.blueNorm();
+  fp = dxnorm - m_delta;
+  if (fp <= Scalar(0.1) * m_delta) {
+    par = 0;
+    return;
+  }
+
+  /* if the jacobian is not rank deficient, the newton */
+  /* step provides a lower bound, parl, for the zero of */
+  /* the function. otherwise set this bound to zero. */
+  parl = 0.;
+  if (rank == n) {
+    wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2) / dxnorm;
+    s.topLeftCorner(n, n).transpose().template triangularView<Lower>().solveInPlace(wa1);
+    temp = wa1.blueNorm();
+    parl = fp / m_delta / temp / temp;
+  }
+
+  /* calculate an upper bound, paru, for the zero of the function. */
+  for (j = 0; j < n; ++j) wa1[j] = s.col(j).head(j + 1).dot(qtb.head(j + 1)) / diag[qr.colsPermutation().indices()(j)];
+
+  gnorm = wa1.stableNorm();
+  paru = gnorm / m_delta;
+  if (paru == 0.) paru = dwarf / (std::min)(m_delta, Scalar(0.1));
+
+  /* if the input par lies outside of the interval (parl,paru), */
+  /* set par to the closer endpoint. */
+  par = (std::max)(par, parl);
+  par = (std::min)(par, paru);
+  if (par == 0.) par = gnorm / dxnorm;
+
+  /* beginning of an iteration. */
+  while (true) {
+    ++iter;
+
+    /* evaluate the function at the current value of par. */
+    if (par == 0.) par = (std::max)(dwarf, Scalar(.001) * paru); /* Computing MAX */
+    wa1 = sqrt(par) * diag;
+
+    VectorType sdiag(n);
+    lmqrsolv(s, qr.colsPermutation(), wa1, qtb, x, sdiag);
+
+    wa2 = diag.cwiseProduct(x);
+    dxnorm = wa2.blueNorm();
+    temp = fp;
+    fp = dxnorm - m_delta;
+
+    /* if the function is small enough, accept the current value */
+    /* of par. also test for the exceptional cases where parl */
+    /* is zero or the number of iterations has reached 10. */
+    if (abs(fp) <= Scalar(0.1) * m_delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10) break;
+
+    /* compute the newton correction. */
+    wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2 / dxnorm);
+    // we could almost use this here, but the diagonal is outside qr, in sdiag[]
+    for (j = 0; j < n; ++j) {
+      wa1[j] /= sdiag[j];
+      temp = wa1[j];
+      for (Index i = j + 1; i < n; ++i) wa1[i] -= s.coeff(i, j) * temp;
+    }
+    temp = wa1.blueNorm();
+    parc = fp / m_delta / temp / temp;
+
+    /* depending on the sign of the function, update parl or paru. */
+    if (fp > 0.) parl = (std::max)(parl, par);
+    if (fp < 0.) paru = (std::min)(paru, par);
+
+    /* compute an improved estimate for par. */
+    par = (std::max)(parl, par + parc);
+  }
+  if (iter == 0) par = 0.;
+  return;
+}
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LMPAR_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h
new file mode 100644
index 0000000..49bf6e1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h
@@ -0,0 +1,178 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+//
+// This code initially comes from MINPACK whose original authors are:
+// Copyright Jorge More - Argonne National Laboratory
+// Copyright Burt Garbow - Argonne National Laboratory
+// Copyright Ken Hillstrom - Argonne National Laboratory
+//
+// This Source Code Form is subject to the terms of the Minpack license
+// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
+
+#ifndef EIGEN_LMQRSOLV_H
+#define EIGEN_LMQRSOLV_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar, int Rows, int Cols, typename PermIndex>
+void lmqrsolv(Matrix<Scalar, Rows, Cols> &s, const PermutationMatrix<Dynamic, Dynamic, PermIndex> &iPerm,
+              const Matrix<Scalar, Dynamic, 1> &diag, const Matrix<Scalar, Dynamic, 1> &qtb,
+              Matrix<Scalar, Dynamic, 1> &x, Matrix<Scalar, Dynamic, 1> &sdiag) {
+  /* Local variables */
+  Index i, j, k;
+  Scalar temp;
+  Index n = s.cols();
+  Matrix<Scalar, Dynamic, 1> wa(n);
+  JacobiRotation<Scalar> givens;
+
+  /* Function Body */
+  // the following will only change the lower triangular part of s, including
+  // the diagonal, though the diagonal is restored afterward
+
+  /*     copy r and (q transpose)*b to preserve input and initialize s. */
+  /*     in particular, save the diagonal elements of r in x. */
+  x = s.diagonal();
+  wa = qtb;
+
+  s.topLeftCorner(n, n).template triangularView<StrictlyLower>() = s.topLeftCorner(n, n).transpose();
+  /*     eliminate the diagonal matrix d using a givens rotation. */
+  for (j = 0; j < n; ++j) {
+    /*        prepare the row of d to be eliminated, locating the */
+    /*        diagonal element using p from the qr factorization. */
+    const PermIndex l = iPerm.indices()(j);
+    if (diag[l] == 0.) break;
+    sdiag.tail(n - j).setZero();
+    sdiag[j] = diag[l];
+
+    /*        the transformations to eliminate the row of d */
+    /*        modify only a single element of (q transpose)*b */
+    /*        beyond the first n, which is initially zero. */
+    Scalar qtbpj = 0.;
+    for (k = j; k < n; ++k) {
+      /*           determine a givens rotation which eliminates the */
+      /*           appropriate element in the current row of d. */
+      givens.makeGivens(-s(k, k), sdiag[k]);
+
+      /*           compute the modified diagonal element of r and */
+      /*           the modified element of ((q transpose)*b,0). */
+      s(k, k) = givens.c() * s(k, k) + givens.s() * sdiag[k];
+      temp = givens.c() * wa[k] + givens.s() * qtbpj;
+      qtbpj = -givens.s() * wa[k] + givens.c() * qtbpj;
+      wa[k] = temp;
+
+      /*           accumulate the transformation in the row of s. */
+      for (i = k + 1; i < n; ++i) {
+        temp = givens.c() * s(i, k) + givens.s() * sdiag[i];
+        sdiag[i] = -givens.s() * s(i, k) + givens.c() * sdiag[i];
+        s(i, k) = temp;
+      }
+    }
+  }
+
+  /*     solve the triangular system for z. if the system is */
+  /*     singular, then obtain a least squares solution. */
+  Index nsing;
+  for (nsing = 0; nsing < n && sdiag[nsing] != 0; nsing++) {
+  }
+
+  wa.tail(n - nsing).setZero();
+  s.topLeftCorner(nsing, nsing).transpose().template triangularView<Upper>().solveInPlace(wa.head(nsing));
+
+  // restore
+  sdiag = s.diagonal();
+  s.diagonal() = x;
+
+  /* permute the components of z back to components of x. */
+  x = iPerm * wa;
+}
+
+template <typename Scalar, int Options_, typename Index>
+void lmqrsolv(SparseMatrix<Scalar, Options_, Index> &s, const PermutationMatrix<Dynamic, Dynamic> &iPerm,
+              const Matrix<Scalar, Dynamic, 1> &diag, const Matrix<Scalar, Dynamic, 1> &qtb,
+              Matrix<Scalar, Dynamic, 1> &x, Matrix<Scalar, Dynamic, 1> &sdiag) {
+  /* Local variables */
+  typedef SparseMatrix<Scalar, RowMajor, Index> FactorType;
+  Index i, j, k, l;
+  Scalar temp;
+  Index n = s.cols();
+  Matrix<Scalar, Dynamic, 1> wa(n);
+  JacobiRotation<Scalar> givens;
+
+  /* Function Body */
+  // the following will only change the lower triangular part of s, including
+  // the diagonal, though the diagonal is restored afterward
+
+  /*     copy r and (q transpose)*b to preserve input and initialize R. */
+  wa = qtb;
+  FactorType R(s);
+  // Eliminate the diagonal matrix d using a givens rotation
+  for (j = 0; j < n; ++j) {
+    // Prepare the row of d to be eliminated, locating the
+    // diagonal element using p from the qr factorization
+    l = iPerm.indices()(j);
+    if (diag(l) == Scalar(0)) break;
+    sdiag.tail(n - j).setZero();
+    sdiag[j] = diag[l];
+    // the transformations to eliminate the row of d
+    // modify only a single element of (q transpose)*b
+    // beyond the first n, which is initially zero.
+
+    Scalar qtbpj = 0;
+    // Browse the nonzero elements of row j of the upper triangular s
+    for (k = j; k < n; ++k) {
+      typename FactorType::InnerIterator itk(R, k);
+      for (; itk; ++itk) {
+        if (itk.index() < k)
+          continue;
+        else
+          break;
+      }
+      // At this point, we have the diagonal element R(k,k)
+      //  Determine a givens rotation which eliminates
+      //  the appropriate element in the current row of d
+      givens.makeGivens(-itk.value(), sdiag(k));
+
+      // Compute the modified diagonal element of r and
+      // the modified element of ((q transpose)*b,0).
+      itk.valueRef() = givens.c() * itk.value() + givens.s() * sdiag(k);
+      temp = givens.c() * wa(k) + givens.s() * qtbpj;
+      qtbpj = -givens.s() * wa(k) + givens.c() * qtbpj;
+      wa(k) = temp;
+
+      // Accumulate the transformation in the remaining k row/column of R
+      for (++itk; itk; ++itk) {
+        i = itk.index();
+        temp = givens.c() * itk.value() + givens.s() * sdiag(i);
+        sdiag(i) = -givens.s() * itk.value() + givens.c() * sdiag(i);
+        itk.valueRef() = temp;
+      }
+    }
+  }
+
+  // Solve the triangular system for z. If the system is
+  // singular, then obtain a least squares solution
+  Index nsing;
+  for (nsing = 0; nsing < n && sdiag(nsing) != 0; nsing++) {
+  }
+
+  wa.tail(n - nsing).setZero();
+  //     x = wa;
+  wa.head(nsing) = R.topLeftCorner(nsing, nsing).template triangularView<Upper>().solve /*InPlace*/ (wa.head(nsing));
+
+  sdiag = R.diagonal();
+  // Permute the components of z back to components of x
+  x = iPerm * wa;
+}
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LMQRSOLV_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
new file mode 100644
index 0000000..c14ad63
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
@@ -0,0 +1,360 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+//
+// The algorithm of this class initially comes from MINPACK whose original authors are:
+// Copyright Jorge More - Argonne National Laboratory
+// Copyright Burt Garbow - Argonne National Laboratory
+// Copyright Ken Hillstrom - Argonne National Laboratory
+//
+// This Source Code Form is subject to the terms of the Minpack license
+// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LEVENBERGMARQUARDT_H
+#define EIGEN_LEVENBERGMARQUARDT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace LevenbergMarquardtSpace {
+enum Status {
+  NotStarted = -2,
+  Running = -1,
+  ImproperInputParameters = 0,
+  RelativeReductionTooSmall = 1,
+  RelativeErrorTooSmall = 2,
+  RelativeErrorAndReductionTooSmall = 3,
+  CosinusTooSmall = 4,
+  TooManyFunctionEvaluation = 5,
+  FtolTooSmall = 6,
+  XtolTooSmall = 7,
+  GtolTooSmall = 8,
+  UserAsked = 9
+};
+}
+
+template <typename Scalar_, int NX = Dynamic, int NY = Dynamic>
+struct DenseFunctor {
+  typedef Scalar_ Scalar;
+  enum { InputsAtCompileTime = NX, ValuesAtCompileTime = NY };
+  typedef Matrix<Scalar, InputsAtCompileTime, 1> InputType;
+  typedef Matrix<Scalar, ValuesAtCompileTime, 1> ValueType;
+  typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
+  typedef ColPivHouseholderQR<JacobianType> QRSolver;
+  const int m_inputs, m_values;
+
+  DenseFunctor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
+  DenseFunctor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
+
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values; }
+
+  // int operator()(const InputType &x, ValueType& fvec) { }
+  //  should be defined in derived classes
+
+  // int df(const InputType &x, JacobianType& fjac) { }
+  //  should be defined in derived classes
+};
+
+template <typename Scalar_, typename Index_>
+struct SparseFunctor {
+  typedef Scalar_ Scalar;
+  typedef Index_ Index;
+  typedef Matrix<Scalar, Dynamic, 1> InputType;
+  typedef Matrix<Scalar, Dynamic, 1> ValueType;
+  typedef SparseMatrix<Scalar, ColMajor, Index> JacobianType;
+  typedef SparseQR<JacobianType, COLAMDOrdering<int> > QRSolver;
+  enum { InputsAtCompileTime = Dynamic, ValuesAtCompileTime = Dynamic };
+
+  SparseFunctor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
+
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values; }
+
+  const int m_inputs, m_values;
+  // int operator()(const InputType &x, ValueType& fvec) { }
+  //  to be defined in the functor
+
+  // int df(const InputType &x, JacobianType& fjac) { }
+  //  to be defined in the functor if no automatic differentiation
+};
+namespace internal {
+template <typename QRSolver, typename VectorType>
+void lmpar2(const QRSolver &qr, const VectorType &diag, const VectorType &qtb, typename VectorType::Scalar m_delta,
+            typename VectorType::Scalar &par, VectorType &x);
+}
+/**
+ * \ingroup NonLinearOptimization_Module
+ * \brief Performs non linear optimization over a non-linear function,
+ * using a variant of the Levenberg Marquardt algorithm.
+ *
+ * Check wikipedia for more information.
+ * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
+ */
+template <typename FunctorType_>
+class LevenbergMarquardt : internal::no_assignment_operator {
+ public:
+  typedef FunctorType_ FunctorType;
+  typedef typename FunctorType::QRSolver QRSolver;
+  typedef typename FunctorType::JacobianType JacobianType;
+  typedef typename JacobianType::Scalar Scalar;
+  typedef typename JacobianType::RealScalar RealScalar;
+  typedef typename QRSolver::StorageIndex PermIndex;
+  typedef Matrix<Scalar, Dynamic, 1> FVectorType;
+  typedef PermutationMatrix<Dynamic, Dynamic, int> PermutationType;
+
+ public:
+  LevenbergMarquardt(FunctorType &functor)
+      : m_functor(functor),
+        m_nfev(0),
+        m_njev(0),
+        m_fnorm(0.0),
+        m_gnorm(0),
+        m_isInitialized(false),
+        m_info(InvalidInput) {
+    resetParameters();
+    m_useExternalScaling = false;
+  }
+
+  LevenbergMarquardtSpace::Status minimize(FVectorType &x);
+  LevenbergMarquardtSpace::Status minimizeInit(FVectorType &x);
+  LevenbergMarquardtSpace::Status minimizeOneStep(FVectorType &x);
+  LevenbergMarquardtSpace::Status lmder1(FVectorType &x, const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()));
+  static LevenbergMarquardtSpace::Status lmdif1(FunctorType &functor, FVectorType &x, Index *nfev,
+                                                const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()));
+
+  /** Sets the default parameters */
+  void resetParameters() {
+    using std::sqrt;
+
+    m_factor = 100.;
+    m_maxfev = 400;
+    m_ftol = sqrt(NumTraits<RealScalar>::epsilon());
+    m_xtol = sqrt(NumTraits<RealScalar>::epsilon());
+    m_gtol = 0.;
+    m_epsfcn = 0.;
+  }
+
+  /** Sets the tolerance for the norm of the solution vector*/
+  void setXtol(RealScalar xtol) { m_xtol = xtol; }
+
+  /** Sets the tolerance for the norm of the vector function*/
+  void setFtol(RealScalar ftol) { m_ftol = ftol; }
+
+  /** Sets the tolerance for the norm of the gradient of the error vector*/
+  void setGtol(RealScalar gtol) { m_gtol = gtol; }
+
+  /** Sets the step bound for the diagonal shift */
+  void setFactor(RealScalar factor) { m_factor = factor; }
+
+  /** Sets the error precision  */
+  void setEpsilon(RealScalar epsfcn) { m_epsfcn = epsfcn; }
+
+  /** Sets the maximum number of function evaluation */
+  void setMaxfev(Index maxfev) { m_maxfev = maxfev; }
+
+  /** Use an external Scaling. If set to true, pass a nonzero diagonal to diag() */
+  void setExternalScaling(bool value) { m_useExternalScaling = value; }
+
+  /** \returns the tolerance for the norm of the solution vector */
+  RealScalar xtol() const { return m_xtol; }
+
+  /** \returns the tolerance for the norm of the vector function */
+  RealScalar ftol() const { return m_ftol; }
+
+  /** \returns the tolerance for the norm of the gradient of the error vector */
+  RealScalar gtol() const { return m_gtol; }
+
+  /** \returns the step bound for the diagonal shift */
+  RealScalar factor() const { return m_factor; }
+
+  /** \returns the error precision */
+  RealScalar epsilon() const { return m_epsfcn; }
+
+  /** \returns the maximum number of function evaluation */
+  Index maxfev() const { return m_maxfev; }
+
+  /** \returns a reference to the diagonal of the jacobian */
+  FVectorType &diag() { return m_diag; }
+
+  /** \returns the number of iterations performed */
+  Index iterations() { return m_iter; }
+
+  /** \returns the number of functions evaluation */
+  Index nfev() { return m_nfev; }
+
+  /** \returns the number of jacobian evaluation */
+  Index njev() { return m_njev; }
+
+  /** \returns the norm of current vector function */
+  RealScalar fnorm() { return m_fnorm; }
+
+  /** \returns the norm of the gradient of the error */
+  RealScalar gnorm() { return m_gnorm; }
+
+  /** \returns the LevenbergMarquardt parameter */
+  RealScalar lm_param(void) { return m_par; }
+
+  /** \returns a reference to the  current vector function
+   */
+  FVectorType &fvec() { return m_fvec; }
+
+  /** \returns a reference to the matrix where the current Jacobian matrix is stored
+   */
+  JacobianType &jacobian() { return m_fjac; }
+
+  /** \returns a reference to the triangular matrix R from the QR of the jacobian matrix.
+   * \sa jacobian()
+   */
+  JacobianType &matrixR() { return m_rfactor; }
+
+  /** the permutation used in the QR factorization
+   */
+  PermutationType permutation() { return m_permutation; }
+
+  /**
+   * \brief Reports whether the minimization was successful
+   * \returns \c Success if the minimization was successful,
+   *         \c NumericalIssue if a numerical problem arises during the
+   *          minimization process, for example during the QR factorization
+   *         \c NoConvergence if the minimization did not converge after
+   *          the maximum number of function evaluation allowed
+   *          \c InvalidInput if the input matrix is invalid
+   */
+  ComputationInfo info() const { return m_info; }
+
+ private:
+  JacobianType m_fjac;
+  JacobianType m_rfactor;  // The triangular matrix R from the QR of the jacobian matrix m_fjac
+  FunctorType &m_functor;
+  FVectorType m_fvec, m_qtf, m_diag;
+  Index n;
+  Index m;
+  Index m_nfev;
+  Index m_njev;
+  RealScalar m_fnorm;   // Norm of the current vector function
+  RealScalar m_gnorm;   // Norm of the gradient of the error
+  RealScalar m_factor;  //
+  Index m_maxfev;       // Maximum number of function evaluation
+  RealScalar m_ftol;    // Tolerance in the norm of the vector function
+  RealScalar m_xtol;    //
+  RealScalar m_gtol;    // tolerance of the norm of the error gradient
+  RealScalar m_epsfcn;  //
+  Index m_iter;         // Number of iterations performed
+  RealScalar m_delta;
+  bool m_useExternalScaling;
+  PermutationType m_permutation;
+  FVectorType m_wa1, m_wa2, m_wa3, m_wa4;  // Temporary vectors
+  RealScalar m_par;
+  bool m_isInitialized;  // Check whether the minimization step has been called
+  ComputationInfo m_info;
+};
+
+template <typename FunctorType>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType>::minimize(FVectorType &x) {
+  LevenbergMarquardtSpace::Status status = minimizeInit(x);
+  if (status == LevenbergMarquardtSpace::ImproperInputParameters) {
+    m_isInitialized = true;
+    return status;
+  }
+  do {
+    status = minimizeOneStep(x);
+  } while (status == LevenbergMarquardtSpace::Running);
+  m_isInitialized = true;
+  return status;
+}
+
+template <typename FunctorType>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType>::minimizeInit(FVectorType &x) {
+  n = x.size();
+  m = m_functor.values();
+
+  m_wa1.resize(n);
+  m_wa2.resize(n);
+  m_wa3.resize(n);
+  m_wa4.resize(m);
+  m_fvec.resize(m);
+  // FIXME: Sparse case: allocate space for the Jacobian.
+  m_fjac.resize(m, n);
+  if (!m_useExternalScaling) m_diag.resize(n);
+  eigen_assert((!m_useExternalScaling || m_diag.size() == n) &&
+               "When m_useExternalScaling is set, the caller must provide a valid 'm_diag'");
+  m_qtf.resize(n);
+
+  /* Function Body */
+  m_nfev = 0;
+  m_njev = 0;
+
+  /*     check the input parameters for errors. */
+  if (n <= 0 || m < n || m_ftol < 0. || m_xtol < 0. || m_gtol < 0. || m_maxfev <= 0 || m_factor <= 0.) {
+    m_info = InvalidInput;
+    return LevenbergMarquardtSpace::ImproperInputParameters;
+  }
+
+  if (m_useExternalScaling)
+    for (Index j = 0; j < n; ++j)
+      if (m_diag[j] <= 0.) {
+        m_info = InvalidInput;
+        return LevenbergMarquardtSpace::ImproperInputParameters;
+      }
+
+  /*     evaluate the function at the starting point */
+  /*     and calculate its norm. */
+  m_nfev = 1;
+  if (m_functor(x, m_fvec) < 0) return LevenbergMarquardtSpace::UserAsked;
+  m_fnorm = m_fvec.stableNorm();
+
+  /*     initialize levenberg-marquardt parameter and iteration counter. */
+  m_par = 0.;
+  m_iter = 1;
+
+  return LevenbergMarquardtSpace::NotStarted;
+}
+
+template <typename FunctorType>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType>::lmder1(FVectorType &x, const Scalar tol) {
+  n = x.size();
+  m = m_functor.values();
+
+  /* check the input parameters for errors. */
+  if (n <= 0 || m < n || tol < 0.) return LevenbergMarquardtSpace::ImproperInputParameters;
+
+  resetParameters();
+  m_ftol = tol;
+  m_xtol = tol;
+  m_maxfev = 100 * (n + 1);
+
+  return minimize(x);
+}
+
+template <typename FunctorType>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType>::lmdif1(FunctorType &functor, FVectorType &x,
+                                                                        Index *nfev, const Scalar tol) {
+  Index n = x.size();
+  Index m = functor.values();
+
+  /* check the input parameters for errors. */
+  if (n <= 0 || m < n || tol < 0.) return LevenbergMarquardtSpace::ImproperInputParameters;
+
+  NumericalDiff<FunctorType> numDiff(functor);
+  // embedded LevenbergMarquardt
+  LevenbergMarquardt<NumericalDiff<FunctorType> > lm(numDiff);
+  lm.setFtol(tol);
+  lm.setXtol(tol);
+  lm.setMaxfev(200 * (n + 1));
+
+  LevenbergMarquardtSpace::Status info = LevenbergMarquardtSpace::Status(lm.minimize(x));
+  if (nfev) *nfev = lm.nfev();
+  return info;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LEVENBERGMARQUARDT_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/InternalHeaderCheck.h
new file mode 100644
index 0000000..4fc840e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/InternalHeaderCheck.h
@@ -0,0 +1,4 @@
+#ifndef EIGEN_MATRIX_FUNCTIONS_MODULE_H
+#error \
+    "Please include unsupported/Eigen/MatrixFunctions instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
new file mode 100644
index 0000000..f4e0428
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
@@ -0,0 +1,457 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009, 2010, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2011, 2013 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_EXPONENTIAL
+#define EIGEN_MATRIX_EXPONENTIAL
+
+#include "StemFunction.h"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/** \brief Scaling operator.
+ *
+ * This struct is used by CwiseUnaryOp to scale a matrix by \f$ 2^{-s} \f$.
+ */
+template <typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct MatrixExponentialScalingOp {
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  /** \brief Constructor.
+   *
+   * \param[in] squarings  The integer \f$ s \f$ in this document.
+   */
+  MatrixExponentialScalingOp(int squarings) : m_squarings(squarings) {}
+
+  /** \brief Scale a matrix coefficient.
+   *
+   * \param[in,out] x  The scalar to be scaled, becoming \f$ 2^{-s} x \f$.
+   */
+  inline const Scalar operator()(const Scalar& x) const {
+    using std::ldexp;
+    return Scalar(ldexp(Eigen::numext::real(x), -m_squarings), ldexp(Eigen::numext::imag(x), -m_squarings));
+  }
+
+ private:
+  int m_squarings;
+};
+
+template <typename Scalar>
+struct MatrixExponentialScalingOp<Scalar, /*IsComplex=*/false> {
+  /** \brief Constructor.
+   *
+   * \param[in] squarings  The integer \f$ s \f$ in this document.
+   */
+  MatrixExponentialScalingOp(int squarings) : m_squarings(squarings) {}
+
+  /** \brief Scale a matrix coefficient.
+   *
+   * \param[in,out] x  The scalar to be scaled, becoming \f$ 2^{-s} x \f$.
+   */
+  inline const Scalar operator()(const Scalar& x) const {
+    using std::ldexp;
+    return ldexp(x, -m_squarings);
+  }
+
+ private:
+  int m_squarings;
+};
+
+/** \brief Compute the (3,3)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ */
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade3(const MatA& A, MatU& U, MatV& V) {
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatA>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {120.L, 60.L, 12.L, 1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType tmp = b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  V = b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+
+/** \brief Compute the (5,5)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ */
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade5(const MatA& A, MatU& U, MatV& V) {
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {30240.L, 15120.L, 3360.L, 420.L, 30.L, 1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType A4 = A2 * A2;
+  const MatrixType tmp = b[5] * A4 + b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  V = b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+
+/** \brief Compute the (7,7)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ */
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade7(const MatA& A, MatU& U, MatV& V) {
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {17297280.L, 8648640.L, 1995840.L, 277200.L, 25200.L, 1512.L, 56.L, 1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType A4 = A2 * A2;
+  const MatrixType A6 = A4 * A2;
+  const MatrixType tmp = b[7] * A6 + b[5] * A4 + b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  V = b[6] * A6 + b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+
+/** \brief Compute the (9,9)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ */
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade9(const MatA& A, MatU& U, MatV& V) {
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {17643225600.L, 8821612800.L, 2075673600.L, 302702400.L, 30270240.L,
+                          2162160.L,     110880.L,     3960.L,       90.L,        1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType A4 = A2 * A2;
+  const MatrixType A6 = A4 * A2;
+  const MatrixType A8 = A6 * A2;
+  const MatrixType tmp =
+      b[9] * A8 + b[7] * A6 + b[5] * A4 + b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  V = b[8] * A8 + b[6] * A6 + b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+
+/** \brief Compute the (13,13)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ */
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade13(const MatA& A, MatU& U, MatV& V) {
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {64764752532480000.L,
+                          32382376266240000.L,
+                          7771770303897600.L,
+                          1187353796428800.L,
+                          129060195264000.L,
+                          10559470521600.L,
+                          670442572800.L,
+                          33522128640.L,
+                          1323241920.L,
+                          40840800.L,
+                          960960.L,
+                          16380.L,
+                          182.L,
+                          1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType A4 = A2 * A2;
+  const MatrixType A6 = A4 * A2;
+  V = b[13] * A6 + b[11] * A4 + b[9] * A2;  // used for temporary storage
+  MatrixType tmp = A6 * V;
+  tmp += b[7] * A6 + b[5] * A4 + b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  tmp = b[12] * A6 + b[10] * A4 + b[8] * A2;
+  V.noalias() = A6 * tmp;
+  V += b[6] * A6 + b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+
+/** \brief Compute the (17,17)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ *
+ *  This function activates only if your long double is double-double or quadruple.
+ */
+#if LDBL_MANT_DIG > 64
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade17(const MatA& A, MatU& U, MatV& V) {
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {830034394580628357120000.L,
+                          415017197290314178560000.L,
+                          100610229646136770560000.L,
+                          15720348382208870400000.L,
+                          1774878043152614400000.L,
+                          153822763739893248000.L,
+                          10608466464820224000.L,
+                          595373117923584000.L,
+                          27563570274240000.L,
+                          1060137318240000.L,
+                          33924394183680.L,
+                          899510451840.L,
+                          19554575040.L,
+                          341863200.L,
+                          4651200.L,
+                          46512.L,
+                          306.L,
+                          1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType A4 = A2 * A2;
+  const MatrixType A6 = A4 * A2;
+  const MatrixType A8 = A4 * A4;
+  V = b[17] * A8 + b[15] * A6 + b[13] * A4 + b[11] * A2;  // used for temporary storage
+  MatrixType tmp = A8 * V;
+  tmp += b[9] * A8 + b[7] * A6 + b[5] * A4 + b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  tmp = b[16] * A8 + b[14] * A6 + b[12] * A4 + b[10] * A2;
+  V.noalias() = tmp * A8;
+  V += b[8] * A8 + b[6] * A6 + b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+#endif
+
+template <typename MatrixType, typename RealScalar = typename NumTraits<typename traits<MatrixType>::Scalar>::Real>
+struct matrix_exp_computeUV {
+  /** \brief Compute Pad&eacute; approximant to the exponential.
+   *
+   * Computes \c U, \c V and \c squarings such that \f$ (V+U)(V-U)^{-1} \f$ is a Pad&eacute;
+   * approximant of \f$ \exp(2^{-\mbox{squarings}}M) \f$ around \f$ M = 0 \f$, where \f$ M \f$
+   * denotes the matrix \c arg. The degree of the Pad&eacute; approximant and the value of squarings
+   * are chosen such that the approximation error is no more than the round-off error.
+   */
+  static void run(const MatrixType& arg, MatrixType& U, MatrixType& V, int& squarings);
+};
+
+template <typename MatrixType>
+struct matrix_exp_computeUV<MatrixType, float> {
+  using Scalar = typename traits<MatrixType>::Scalar;
+  template <typename ArgType>
+  static void run(const ArgType& arg, MatrixType& U, MatrixType& V, int& squarings) {
+    using std::frexp;
+    using std::pow;
+    const float l1norm = arg.cwiseAbs().colwise().sum().maxCoeff();
+    squarings = 0;
+    if (l1norm < 4.258730016922831e-001f) {
+      matrix_exp_pade3(arg, U, V);
+    } else if (l1norm < 1.880152677804762e+000f) {
+      matrix_exp_pade5(arg, U, V);
+    } else {
+      const float maxnorm = 3.925724783138660f;
+      frexp(l1norm / maxnorm, &squarings);
+      if (squarings < 0) squarings = 0;
+      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<Scalar>(squarings));
+      matrix_exp_pade7(A, U, V);
+    }
+  }
+};
+
+template <typename MatrixType>
+struct matrix_exp_computeUV<MatrixType, double> {
+  using Scalar = typename traits<MatrixType>::Scalar;
+  template <typename ArgType>
+  static void run(const ArgType& arg, MatrixType& U, MatrixType& V, int& squarings) {
+    using std::frexp;
+    using std::pow;
+    const double l1norm = arg.cwiseAbs().colwise().sum().maxCoeff();
+    squarings = 0;
+    if (l1norm < 1.495585217958292e-002) {
+      matrix_exp_pade3(arg, U, V);
+    } else if (l1norm < 2.539398330063230e-001) {
+      matrix_exp_pade5(arg, U, V);
+    } else if (l1norm < 9.504178996162932e-001) {
+      matrix_exp_pade7(arg, U, V);
+    } else if (l1norm < 2.097847961257068e+000) {
+      matrix_exp_pade9(arg, U, V);
+    } else {
+      const double maxnorm = 5.371920351148152;
+      frexp(l1norm / maxnorm, &squarings);
+      if (squarings < 0) squarings = 0;
+      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<Scalar>(squarings));
+      matrix_exp_pade13(A, U, V);
+    }
+  }
+};
+
+template <typename MatrixType>
+struct matrix_exp_computeUV<MatrixType, long double> {
+  template <typename ArgType>
+  static void run(const ArgType& arg, MatrixType& U, MatrixType& V, int& squarings) {
+#if LDBL_MANT_DIG == 53  // double precision
+    matrix_exp_computeUV<MatrixType, double>::run(arg, U, V, squarings);
+
+#else
+
+    using Scalar = typename traits<MatrixType>::Scalar;
+
+    using std::frexp;
+    using std::pow;
+    const long double l1norm = arg.cwiseAbs().colwise().sum().maxCoeff();
+    squarings = 0;
+
+#if LDBL_MANT_DIG <= 64  // extended precision
+
+    if (l1norm < 4.1968497232266989671e-003L) {
+      matrix_exp_pade3(arg, U, V);
+    } else if (l1norm < 1.1848116734693823091e-001L) {
+      matrix_exp_pade5(arg, U, V);
+    } else if (l1norm < 5.5170388480686700274e-001L) {
+      matrix_exp_pade7(arg, U, V);
+    } else if (l1norm < 1.3759868875587845383e+000L) {
+      matrix_exp_pade9(arg, U, V);
+    } else {
+      const long double maxnorm = 4.0246098906697353063L;
+      frexp(l1norm / maxnorm, &squarings);
+      if (squarings < 0) squarings = 0;
+      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<Scalar>(squarings));
+      matrix_exp_pade13(A, U, V);
+    }
+
+#elif LDBL_MANT_DIG <= 106  // double-double
+
+    if (l1norm < 3.2787892205607026992947488108213e-005L) {
+      matrix_exp_pade3(arg, U, V);
+    } else if (l1norm < 6.4467025060072760084130906076332e-003L) {
+      matrix_exp_pade5(arg, U, V);
+    } else if (l1norm < 6.8988028496595374751374122881143e-002L) {
+      matrix_exp_pade7(arg, U, V);
+    } else if (l1norm < 2.7339737518502231741495857201670e-001L) {
+      matrix_exp_pade9(arg, U, V);
+    } else if (l1norm < 1.3203382096514474905666448850278e+000L) {
+      matrix_exp_pade13(arg, U, V);
+    } else {
+      const long double maxnorm = 3.2579440895405400856599663723517L;
+      frexp(l1norm / maxnorm, &squarings);
+      if (squarings < 0) squarings = 0;
+      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<Scalar>(squarings));
+      matrix_exp_pade17(A, U, V);
+    }
+
+#elif LDBL_MANT_DIG <= 113  // quadruple precision
+
+    if (l1norm < 1.639394610288918690547467954466970e-005L) {
+      matrix_exp_pade3(arg, U, V);
+    } else if (l1norm < 4.253237712165275566025884344433009e-003L) {
+      matrix_exp_pade5(arg, U, V);
+    } else if (l1norm < 5.125804063165764409885122032933142e-002L) {
+      matrix_exp_pade7(arg, U, V);
+    } else if (l1norm < 2.170000765161155195453205651889853e-001L) {
+      matrix_exp_pade9(arg, U, V);
+    } else if (l1norm < 1.125358383453143065081397882891878e+000L) {
+      matrix_exp_pade13(arg, U, V);
+    } else {
+      const long double maxnorm = 2.884233277829519311757165057717815L;
+      frexp(l1norm / maxnorm, &squarings);
+      if (squarings < 0) squarings = 0;
+      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<Scalar>(squarings));
+      matrix_exp_pade17(A, U, V);
+    }
+
+#else
+
+    // this case should be handled in compute()
+    eigen_assert(false && "Bug in MatrixExponential");
+
+#endif
+#endif  // LDBL_MANT_DIG
+  }
+};
+
+template <typename T>
+struct is_exp_known_type : false_type {};
+template <>
+struct is_exp_known_type<float> : true_type {};
+template <>
+struct is_exp_known_type<double> : true_type {};
+#if LDBL_MANT_DIG <= 113
+template <>
+struct is_exp_known_type<long double> : true_type {};
+#endif
+
+template <typename ArgType, typename ResultType>
+void matrix_exp_compute(const ArgType& arg, ResultType& result, true_type)  // natively supported scalar type
+{
+  typedef typename ArgType::PlainObject MatrixType;
+  MatrixType U, V;
+  int squarings;
+  matrix_exp_computeUV<MatrixType>::run(arg, U, V, squarings);  // Pade approximant is (U+V) / (-U+V)
+  MatrixType numer = U + V;
+  MatrixType denom = -U + V;
+  result = denom.partialPivLu().solve(numer);
+  for (int i = 0; i < squarings; i++) result *= result;  // undo scaling by repeated squaring
+}
+
+/* Computes the matrix exponential
+ *
+ * \param arg    argument of matrix exponential (should be plain object)
+ * \param result variable in which result will be stored
+ */
+template <typename ArgType, typename ResultType>
+void matrix_exp_compute(const ArgType& arg, ResultType& result, false_type)  // default
+{
+  typedef typename ArgType::PlainObject MatrixType;
+  typedef make_complex_t<typename traits<MatrixType>::Scalar> ComplexScalar;
+  result = arg.matrixFunction(internal::stem_function_exp<ComplexScalar>);
+}
+
+}  // namespace internal
+
+/** \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix exponential of some matrix (expression).
+ *
+ * \tparam Derived  Type of the argument to the matrix exponential.
+ *
+ * This class holds the argument to the matrix exponential until it is assigned or evaluated for
+ * some other reason (so the argument should not be changed in the meantime). It is the return type
+ * of MatrixBase::exp() and most of the time this is the only way it is used.
+ */
+template <typename Derived>
+struct MatrixExponentialReturnValue : public ReturnByValue<MatrixExponentialReturnValue<Derived> > {
+ public:
+  /** \brief Constructor.
+   *
+   * \param src %Matrix (expression) forming the argument of the matrix exponential.
+   */
+  MatrixExponentialReturnValue(const Derived& src) : m_src(src) {}
+
+  /** \brief Compute the matrix exponential.
+   *
+   * \param result the matrix exponential of \p src in the constructor.
+   */
+  template <typename ResultType>
+  inline void evalTo(ResultType& result) const {
+    const typename internal::nested_eval<Derived, 10>::type tmp(m_src);
+    internal::matrix_exp_compute(tmp, result, internal::is_exp_known_type<typename Derived::RealScalar>());
+  }
+
+  Index rows() const { return m_src.rows(); }
+  Index cols() const { return m_src.cols(); }
+
+ protected:
+  const typename internal::ref_selector<Derived>::type m_src;
+};
+
+namespace internal {
+template <typename Derived>
+struct traits<MatrixExponentialReturnValue<Derived> > {
+  typedef typename Derived::PlainObject ReturnType;
+};
+}  // namespace internal
+
+template <typename Derived>
+const MatrixExponentialReturnValue<Derived> MatrixBase<Derived>::exp() const {
+  eigen_assert(rows() == cols());
+  return MatrixExponentialReturnValue<Derived>(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATRIX_EXPONENTIAL
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
new file mode 100644
index 0000000..0014700
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
@@ -0,0 +1,543 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2011, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_FUNCTION_H
+#define EIGEN_MATRIX_FUNCTION_H
+
+#include "StemFunction.h"
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \brief Maximum distance allowed between eigenvalues to be considered "close". */
+static const float matrix_function_separation = 0.1f;
+
+/** \ingroup MatrixFunctions_Module
+ * \class MatrixFunctionAtomic
+ * \brief Helper class for computing matrix functions of atomic matrices.
+ *
+ * Here, an atomic matrix is a triangular matrix whose diagonal entries are close to each other.
+ */
+template <typename MatrixType>
+class MatrixFunctionAtomic {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename stem_function<Scalar>::type StemFunction;
+
+  /** \brief Constructor
+   * \param[in]  f  matrix function to compute.
+   */
+  MatrixFunctionAtomic(StemFunction f) : m_f(f) {}
+
+  /** \brief Compute matrix function of atomic matrix
+   * \param[in]  A  argument of matrix function, should be upper triangular and atomic
+   * \returns  f(A), the matrix function evaluated at the given matrix
+   */
+  MatrixType compute(const MatrixType& A);
+
+ private:
+  StemFunction* m_f;
+};
+
+template <typename MatrixType>
+typename NumTraits<typename MatrixType::Scalar>::Real matrix_function_compute_mu(const MatrixType& A) {
+  typedef typename plain_col_type<MatrixType>::type VectorType;
+  Index rows = A.rows();
+  const MatrixType N = MatrixType::Identity(rows, rows) - A;
+  VectorType e = VectorType::Ones(rows);
+  N.template triangularView<Upper>().solveInPlace(e);
+  return e.cwiseAbs().maxCoeff();
+}
+
+template <typename MatrixType>
+MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A) {
+  // TODO: Use that A is upper triangular
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  Index rows = A.rows();
+  Scalar avgEival = A.trace() / Scalar(RealScalar(rows));
+  MatrixType Ashifted = A - avgEival * MatrixType::Identity(rows, rows);
+  RealScalar mu = matrix_function_compute_mu(Ashifted);
+  MatrixType F = m_f(avgEival, 0) * MatrixType::Identity(rows, rows);
+  MatrixType P = Ashifted;
+  MatrixType Fincr;
+  for (Index s = 1; double(s) < 1.1 * double(rows) + 10.0; s++) {  // upper limit is fairly arbitrary
+    Fincr = m_f(avgEival, static_cast<int>(s)) * P;
+    F += Fincr;
+    P = Scalar(RealScalar(1) / RealScalar(s + 1)) * P * Ashifted;
+
+    // test whether Taylor series converged
+    const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff();
+    const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff();
+    if (Fincr_norm < NumTraits<Scalar>::epsilon() * F_norm) {
+      RealScalar delta = 0;
+      RealScalar rfactorial = 1;
+      for (Index r = 0; r < rows; r++) {
+        RealScalar mx = 0;
+        for (Index i = 0; i < rows; i++)
+          mx = (std::max)(mx, std::abs(m_f(Ashifted(i, i) + avgEival, static_cast<int>(s + r))));
+        if (r != 0) rfactorial *= RealScalar(r);
+        delta = (std::max)(delta, RealScalar(mx / rfactorial));
+      }
+      const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
+      if (mu * delta * P_norm < NumTraits<Scalar>::epsilon() * F_norm)  // series converged
+        break;
+    }
+  }
+  return F;
+}
+
+/** \brief Find cluster in \p clusters containing some value
+ * \param[in] key Value to find
+ * \returns Iterator to cluster containing \p key, or \c clusters.end() if no cluster in \p m_clusters
+ * contains \p key.
+ */
+template <typename Index, typename ListOfClusters>
+typename ListOfClusters::iterator matrix_function_find_cluster(Index key, ListOfClusters& clusters) {
+  typename std::list<Index>::iterator j;
+  for (typename ListOfClusters::iterator i = clusters.begin(); i != clusters.end(); ++i) {
+    j = std::find(i->begin(), i->end(), key);
+    if (j != i->end()) return i;
+  }
+  return clusters.end();
+}
+
+/** \brief Partition eigenvalues in clusters of ei'vals close to each other
+ *
+ * \param[in]  eivals    Eigenvalues
+ * \param[out] clusters  Resulting partition of eigenvalues
+ *
+ * The partition satisfies the following two properties:
+ * # Any eigenvalue in a certain cluster is at most matrix_function_separation() away from another eigenvalue
+ *   in the same cluster.
+ * # The distance between two eigenvalues in different clusters is more than matrix_function_separation().
+ * The implementation follows Algorithm 4.1 in the paper of Davies and Higham.
+ */
+template <typename EivalsType, typename Cluster>
+void matrix_function_partition_eigenvalues(const EivalsType& eivals, std::list<Cluster>& clusters) {
+  typedef typename EivalsType::RealScalar RealScalar;
+  for (Index i = 0; i < eivals.rows(); ++i) {
+    // Find cluster containing i-th ei'val, adding a new cluster if necessary
+    typename std::list<Cluster>::iterator qi = matrix_function_find_cluster(i, clusters);
+    if (qi == clusters.end()) {
+      Cluster l;
+      l.push_back(i);
+      clusters.push_back(l);
+      qi = clusters.end();
+      --qi;
+    }
+
+    // Look for other element to add to the set
+    for (Index j = i + 1; j < eivals.rows(); ++j) {
+      if (abs(eivals(j) - eivals(i)) <= RealScalar(matrix_function_separation) &&
+          std::find(qi->begin(), qi->end(), j) == qi->end()) {
+        typename std::list<Cluster>::iterator qj = matrix_function_find_cluster(j, clusters);
+        if (qj == clusters.end()) {
+          qi->push_back(j);
+        } else {
+          qi->insert(qi->end(), qj->begin(), qj->end());
+          clusters.erase(qj);
+        }
+      }
+    }
+  }
+}
+
+/** \brief Compute size of each cluster given a partitioning */
+template <typename ListOfClusters, typename Index>
+void matrix_function_compute_cluster_size(const ListOfClusters& clusters, Matrix<Index, Dynamic, 1>& clusterSize) {
+  const Index numClusters = static_cast<Index>(clusters.size());
+  clusterSize.setZero(numClusters);
+  Index clusterIndex = 0;
+  for (typename ListOfClusters::const_iterator cluster = clusters.begin(); cluster != clusters.end(); ++cluster) {
+    clusterSize[clusterIndex] = cluster->size();
+    ++clusterIndex;
+  }
+}
+
+/** \brief Compute start of each block using clusterSize */
+template <typename VectorType>
+void matrix_function_compute_block_start(const VectorType& clusterSize, VectorType& blockStart) {
+  blockStart.resize(clusterSize.rows());
+  blockStart(0) = 0;
+  for (Index i = 1; i < clusterSize.rows(); i++) {
+    blockStart(i) = blockStart(i - 1) + clusterSize(i - 1);
+  }
+}
+
+/** \brief Compute mapping of eigenvalue indices to cluster indices */
+template <typename EivalsType, typename ListOfClusters, typename VectorType>
+void matrix_function_compute_map(const EivalsType& eivals, const ListOfClusters& clusters, VectorType& eivalToCluster) {
+  eivalToCluster.resize(eivals.rows());
+  Index clusterIndex = 0;
+  for (typename ListOfClusters::const_iterator cluster = clusters.begin(); cluster != clusters.end(); ++cluster) {
+    for (Index i = 0; i < eivals.rows(); ++i) {
+      if (std::find(cluster->begin(), cluster->end(), i) != cluster->end()) {
+        eivalToCluster[i] = clusterIndex;
+      }
+    }
+    ++clusterIndex;
+  }
+}
+
+/** \brief Compute permutation which groups ei'vals in same cluster together */
+template <typename DynVectorType, typename VectorType>
+void matrix_function_compute_permutation(const DynVectorType& blockStart, const DynVectorType& eivalToCluster,
+                                         VectorType& permutation) {
+  DynVectorType indexNextEntry = blockStart;
+  permutation.resize(eivalToCluster.rows());
+  for (Index i = 0; i < eivalToCluster.rows(); i++) {
+    Index cluster = eivalToCluster[i];
+    permutation[i] = indexNextEntry[cluster];
+    ++indexNextEntry[cluster];
+  }
+}
+
+/** \brief Permute Schur decomposition in U and T according to permutation */
+template <typename VectorType, typename MatrixType>
+void matrix_function_permute_schur(VectorType& permutation, MatrixType& U, MatrixType& T) {
+  for (Index i = 0; i < permutation.rows() - 1; i++) {
+    Index j;
+    for (j = i; j < permutation.rows(); j++) {
+      if (permutation(j) == i) break;
+    }
+    eigen_assert(permutation(j) == i);
+    for (Index k = j - 1; k >= i; k--) {
+      JacobiRotation<typename MatrixType::Scalar> rotation;
+      rotation.makeGivens(T(k, k + 1), T(k + 1, k + 1) - T(k, k));
+      T.applyOnTheLeft(k, k + 1, rotation.adjoint());
+      T.applyOnTheRight(k, k + 1, rotation);
+      U.applyOnTheRight(k, k + 1, rotation);
+      std::swap(permutation.coeffRef(k), permutation.coeffRef(k + 1));
+    }
+  }
+}
+
+/** \brief Compute block diagonal part of matrix function.
+ *
+ * This routine computes the matrix function applied to the block diagonal part of \p T (which should be
+ * upper triangular), with the blocking given by \p blockStart and \p clusterSize. The matrix function of
+ * each diagonal block is computed by \p atomic. The off-diagonal parts of \p fT are set to zero.
+ */
+template <typename MatrixType, typename AtomicType, typename VectorType>
+void matrix_function_compute_block_atomic(const MatrixType& T, AtomicType& atomic, const VectorType& blockStart,
+                                          const VectorType& clusterSize, MatrixType& fT) {
+  fT.setZero(T.rows(), T.cols());
+  for (Index i = 0; i < clusterSize.rows(); ++i) {
+    fT.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i)) =
+        atomic.compute(T.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i)));
+  }
+}
+
+/** \brief Solve a triangular Sylvester equation AX + XB = C
+ *
+ * \param[in]  A  the matrix A; should be square and upper triangular
+ * \param[in]  B  the matrix B; should be square and upper triangular
+ * \param[in]  C  the matrix C; should have correct size.
+ *
+ * \returns the solution X.
+ *
+ * If A is m-by-m and B is n-by-n, then both C and X are m-by-n.  The (i,j)-th component of the Sylvester
+ * equation is
+ * \f[
+ *     \sum_{k=i}^m A_{ik} X_{kj} + \sum_{k=1}^j X_{ik} B_{kj} = C_{ij}.
+ * \f]
+ * This can be re-arranged to yield:
+ * \f[
+ *     X_{ij} = \frac{1}{A_{ii} + B_{jj}} \Bigl( C_{ij}
+ *     - \sum_{k=i+1}^m A_{ik} X_{kj} - \sum_{k=1}^{j-1} X_{ik} B_{kj} \Bigr).
+ * \f]
+ * It is assumed that A and B are such that the numerator is never zero (otherwise the Sylvester equation
+ * does not have a unique solution). In that case, these equations can be evaluated in the order
+ * \f$ i=m,\ldots,1 \f$ and \f$ j=1,\ldots,n \f$.
+ */
+template <typename MatrixType>
+MatrixType matrix_function_solve_triangular_sylvester(const MatrixType& A, const MatrixType& B, const MatrixType& C) {
+  eigen_assert(A.rows() == A.cols());
+  eigen_assert(A.isUpperTriangular());
+  eigen_assert(B.rows() == B.cols());
+  eigen_assert(B.isUpperTriangular());
+  eigen_assert(C.rows() == A.rows());
+  eigen_assert(C.cols() == B.rows());
+
+  typedef typename MatrixType::Scalar Scalar;
+
+  Index m = A.rows();
+  Index n = B.rows();
+  MatrixType X(m, n);
+
+  for (Index i = m - 1; i >= 0; --i) {
+    for (Index j = 0; j < n; ++j) {
+      // Compute AX = \sum_{k=i+1}^m A_{ik} X_{kj}
+      Scalar AX;
+      if (i == m - 1) {
+        AX = 0;
+      } else {
+        Matrix<Scalar, 1, 1> AXmatrix = A.row(i).tail(m - 1 - i) * X.col(j).tail(m - 1 - i);
+        AX = AXmatrix(0, 0);
+      }
+
+      // Compute XB = \sum_{k=1}^{j-1} X_{ik} B_{kj}
+      Scalar XB;
+      if (j == 0) {
+        XB = 0;
+      } else {
+        Matrix<Scalar, 1, 1> XBmatrix = X.row(i).head(j) * B.col(j).head(j);
+        XB = XBmatrix(0, 0);
+      }
+
+      X(i, j) = (C(i, j) - AX - XB) / (A(i, i) + B(j, j));
+    }
+  }
+  return X;
+}
+
+/** \brief Compute part of matrix function above block diagonal.
+ *
+ * This routine completes the computation of \p fT, denoting a matrix function applied to the triangular
+ * matrix \p T. It assumes that the block diagonal part of \p fT has already been computed. The part below
+ * the diagonal is zero, because \p T is upper triangular.
+ */
+template <typename MatrixType, typename VectorType>
+void matrix_function_compute_above_diagonal(const MatrixType& T, const VectorType& blockStart,
+                                            const VectorType& clusterSize, MatrixType& fT) {
+  typedef internal::traits<MatrixType> Traits;
+  typedef typename MatrixType::Scalar Scalar;
+  static const int Options = MatrixType::Options;
+  typedef Matrix<Scalar, Dynamic, Dynamic, Options, Traits::RowsAtCompileTime, Traits::ColsAtCompileTime> DynMatrixType;
+
+  for (Index k = 1; k < clusterSize.rows(); k++) {
+    for (Index i = 0; i < clusterSize.rows() - k; i++) {
+      // compute (i, i+k) block
+      DynMatrixType A = T.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i));
+      DynMatrixType B = -T.block(blockStart(i + k), blockStart(i + k), clusterSize(i + k), clusterSize(i + k));
+      DynMatrixType C = fT.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i)) *
+                        T.block(blockStart(i), blockStart(i + k), clusterSize(i), clusterSize(i + k));
+      C -= T.block(blockStart(i), blockStart(i + k), clusterSize(i), clusterSize(i + k)) *
+           fT.block(blockStart(i + k), blockStart(i + k), clusterSize(i + k), clusterSize(i + k));
+      for (Index m = i + 1; m < i + k; m++) {
+        C += fT.block(blockStart(i), blockStart(m), clusterSize(i), clusterSize(m)) *
+             T.block(blockStart(m), blockStart(i + k), clusterSize(m), clusterSize(i + k));
+        C -= T.block(blockStart(i), blockStart(m), clusterSize(i), clusterSize(m)) *
+             fT.block(blockStart(m), blockStart(i + k), clusterSize(m), clusterSize(i + k));
+      }
+      fT.block(blockStart(i), blockStart(i + k), clusterSize(i), clusterSize(i + k)) =
+          matrix_function_solve_triangular_sylvester(A, B, C);
+    }
+  }
+}
+
+/** \ingroup MatrixFunctions_Module
+ * \brief Class for computing matrix functions.
+ * \tparam  MatrixType  type of the argument of the matrix function,
+ *                      expected to be an instantiation of the Matrix class template.
+ * \tparam  AtomicType  type for computing matrix function of atomic blocks.
+ * \tparam  IsComplex   used internally to select correct specialization.
+ *
+ * This class implements the Schur-Parlett algorithm for computing matrix functions. The spectrum of the
+ * matrix is divided in clustered of eigenvalues that lies close together. This class delegates the
+ * computation of the matrix function on every block corresponding to these clusters to an object of type
+ * \p AtomicType and uses these results to compute the matrix function of the whole matrix. The class
+ * \p AtomicType should have a \p compute() member function for computing the matrix function of a block.
+ *
+ * \sa class MatrixFunctionAtomic, class MatrixLogarithmAtomic
+ */
+template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
+struct matrix_function_compute {
+  /** \brief Compute the matrix function.
+   *
+   * \param[in]  A       argument of matrix function, should be a square matrix.
+   * \param[in]  atomic  class for computing matrix function of atomic blocks.
+   * \param[out] result  the function \p f applied to \p A, as
+   * specified in the constructor.
+   *
+   * See MatrixBase::matrixFunction() for details on how this computation
+   * is implemented.
+   */
+  template <typename AtomicType, typename ResultType>
+  static void run(const MatrixType& A, AtomicType& atomic, ResultType& result);
+};
+
+/** \internal \ingroup MatrixFunctions_Module
+ * \brief Partial specialization of MatrixFunction for real matrices
+ *
+ * This converts the real matrix to a complex matrix, compute the matrix function of that matrix, and then
+ * converts the result back to a real matrix.
+ */
+template <typename MatrixType>
+struct matrix_function_compute<MatrixType, 0> {
+  template <typename MatA, typename AtomicType, typename ResultType>
+  static void run(const MatA& A, AtomicType& atomic, ResultType& result) {
+    typedef internal::traits<MatrixType> Traits;
+    typedef typename Traits::Scalar Scalar;
+    static const int Rows = Traits::RowsAtCompileTime, Cols = Traits::ColsAtCompileTime;
+    static const int MaxRows = Traits::MaxRowsAtCompileTime, MaxCols = Traits::MaxColsAtCompileTime;
+
+    typedef internal::make_complex_t<Scalar> ComplexScalar;
+    typedef Matrix<ComplexScalar, Rows, Cols, 0, MaxRows, MaxCols> ComplexMatrix;
+
+    ComplexMatrix CA = A.template cast<ComplexScalar>();
+    ComplexMatrix Cresult;
+    matrix_function_compute<ComplexMatrix>::run(CA, atomic, Cresult);
+    result = Cresult.real();
+  }
+};
+
+/** \internal \ingroup MatrixFunctions_Module
+ * \brief Partial specialization of MatrixFunction for complex matrices
+ */
+template <typename MatrixType>
+struct matrix_function_compute<MatrixType, 1> {
+  template <typename MatA, typename AtomicType, typename ResultType>
+  static void run(const MatA& A, AtomicType& atomic, ResultType& result) {
+    typedef internal::traits<MatrixType> Traits;
+
+    // compute Schur decomposition of A
+    const ComplexSchur<MatrixType> schurOfA(A);
+    eigen_assert(schurOfA.info() == Success);
+    MatrixType T = schurOfA.matrixT();
+    MatrixType U = schurOfA.matrixU();
+
+    // partition eigenvalues into clusters of ei'vals "close" to each other
+    std::list<std::list<Index> > clusters;
+    matrix_function_partition_eigenvalues(T.diagonal(), clusters);
+
+    // compute size of each cluster
+    Matrix<Index, Dynamic, 1> clusterSize;
+    matrix_function_compute_cluster_size(clusters, clusterSize);
+
+    // blockStart[i] is row index at which block corresponding to i-th cluster starts
+    Matrix<Index, Dynamic, 1> blockStart;
+    matrix_function_compute_block_start(clusterSize, blockStart);
+
+    // compute map so that eivalToCluster[i] = j means that i-th ei'val is in j-th cluster
+    Matrix<Index, Dynamic, 1> eivalToCluster;
+    matrix_function_compute_map(T.diagonal(), clusters, eivalToCluster);
+
+    // compute permutation which groups ei'vals in same cluster together
+    Matrix<Index, Traits::RowsAtCompileTime, 1> permutation;
+    matrix_function_compute_permutation(blockStart, eivalToCluster, permutation);
+
+    // permute Schur decomposition
+    matrix_function_permute_schur(permutation, U, T);
+
+    // compute result
+    MatrixType fT;  // matrix function applied to T
+    matrix_function_compute_block_atomic(T, atomic, blockStart, clusterSize, fT);
+    matrix_function_compute_above_diagonal(T, blockStart, clusterSize, fT);
+    result = U * (fT.template triangularView<Upper>() * U.adjoint());
+  }
+};
+
+}  // end of namespace internal
+
+/** \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix function of some matrix (expression).
+ *
+ * \tparam Derived  Type of the argument to the matrix function.
+ *
+ * This class holds the argument to the matrix function until it is assigned or evaluated for some other
+ * reason (so the argument should not be changed in the meantime). It is the return type of
+ * matrixBase::matrixFunction() and related functions and most of the time this is the only way it is used.
+ */
+template <typename Derived>
+class MatrixFunctionReturnValue : public ReturnByValue<MatrixFunctionReturnValue<Derived> > {
+ public:
+  typedef typename Derived::Scalar Scalar;
+  typedef typename internal::stem_function<Scalar>::type StemFunction;
+
+ protected:
+  typedef typename internal::ref_selector<Derived>::type DerivedNested;
+
+ public:
+  /** \brief Constructor.
+   *
+   * \param[in] A  %Matrix (expression) forming the argument of the matrix function.
+   * \param[in] f  Stem function for matrix function under consideration.
+   */
+  MatrixFunctionReturnValue(const Derived& A, StemFunction f) : m_A(A), m_f(f) {}
+
+  /** \brief Compute the matrix function.
+   *
+   * \param[out] result \p f applied to \p A, where \p f and \p A are as in the constructor.
+   */
+  template <typename ResultType>
+  inline void evalTo(ResultType& result) const {
+    typedef typename internal::nested_eval<Derived, 10>::type NestedEvalType;
+    typedef internal::remove_all_t<NestedEvalType> NestedEvalTypeClean;
+    typedef internal::traits<NestedEvalTypeClean> Traits;
+    typedef internal::make_complex_t<Scalar> ComplexScalar;
+    typedef Matrix<ComplexScalar, Dynamic, Dynamic, 0, Traits::RowsAtCompileTime, Traits::ColsAtCompileTime>
+        DynMatrixType;
+
+    typedef internal::MatrixFunctionAtomic<DynMatrixType> AtomicType;
+    AtomicType atomic(m_f);
+
+    internal::matrix_function_compute<typename NestedEvalTypeClean::PlainObject>::run(m_A, atomic, result);
+  }
+
+  Index rows() const { return m_A.rows(); }
+  Index cols() const { return m_A.cols(); }
+
+ private:
+  const DerivedNested m_A;
+  StemFunction* m_f;
+};
+
+namespace internal {
+template <typename Derived>
+struct traits<MatrixFunctionReturnValue<Derived> > {
+  typedef typename Derived::PlainObject ReturnType;
+};
+}  // namespace internal
+
+/********** MatrixBase methods **********/
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::matrixFunction(
+    typename internal::stem_function<typename internal::traits<Derived>::Scalar>::type f) const {
+  eigen_assert(rows() == cols());
+  return MatrixFunctionReturnValue<Derived>(derived(), f);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sin() const {
+  eigen_assert(rows() == cols());
+  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_sin<ComplexScalar>);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cos() const {
+  eigen_assert(rows() == cols());
+  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_cos<ComplexScalar>);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sinh() const {
+  eigen_assert(rows() == cols());
+  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_sinh<ComplexScalar>);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cosh() const {
+  eigen_assert(rows() == cols());
+  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_cosh<ComplexScalar>);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATRIX_FUNCTION_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
new file mode 100644
index 0000000..40e1b1d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
@@ -0,0 +1,366 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2011 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_LOGARITHM
+#define EIGEN_MATRIX_LOGARITHM
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+struct matrix_log_min_pade_degree {
+  static const int value = 3;
+};
+
+template <typename Scalar>
+struct matrix_log_max_pade_degree {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  static const int value = std::numeric_limits<RealScalar>::digits <= 24 ? 5 :  // single precision
+                               std::numeric_limits<RealScalar>::digits <= 53 ? 7
+                                                                             :  // double precision
+                               std::numeric_limits<RealScalar>::digits <= 64 ? 8
+                                                                             :  // extended precision
+                               std::numeric_limits<RealScalar>::digits <= 106 ? 10
+                                                                              :  // double-double
+                               11;                                               // quadruple precision
+};
+
+/** \brief Compute logarithm of 2x2 triangular matrix. */
+template <typename MatrixType>
+void matrix_log_compute_2x2(const MatrixType& A, MatrixType& result) {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  using std::abs;
+  using std::ceil;
+  using std::imag;
+  using std::log;
+
+  Scalar logA00 = log(A(0, 0));
+  Scalar logA11 = log(A(1, 1));
+
+  result(0, 0) = logA00;
+  result(1, 0) = Scalar(0);
+  result(1, 1) = logA11;
+
+  Scalar y = A(1, 1) - A(0, 0);
+  if (y == Scalar(0)) {
+    result(0, 1) = A(0, 1) / A(0, 0);
+  } else if ((abs(A(0, 0)) < RealScalar(0.5) * abs(A(1, 1))) || (abs(A(0, 0)) > 2 * abs(A(1, 1)))) {
+    result(0, 1) = A(0, 1) * (logA11 - logA00) / y;
+  } else {
+    // computation in previous branch is inaccurate if A(1,1) \approx A(0,0)
+    RealScalar unwindingNumber = ceil((imag(logA11 - logA00) - RealScalar(EIGEN_PI)) / RealScalar(2 * EIGEN_PI));
+    result(0, 1) = A(0, 1) * (numext::log1p(y / A(0, 0)) + Scalar(0, RealScalar(2 * EIGEN_PI) * unwindingNumber)) / y;
+  }
+}
+
+/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = float) */
+inline int matrix_log_get_pade_degree(float normTminusI) {
+  const float maxNormForPade[] = {2.5111573934555054e-1 /* degree = 3 */, 4.0535837411880493e-1, 5.3149729967117310e-1};
+  const int minPadeDegree = matrix_log_min_pade_degree<float>::value;
+  const int maxPadeDegree = matrix_log_max_pade_degree<float>::value;
+  int degree = minPadeDegree;
+  for (; degree <= maxPadeDegree; ++degree)
+    if (normTminusI <= maxNormForPade[degree - minPadeDegree]) break;
+  return degree;
+}
+
+/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = double) */
+inline int matrix_log_get_pade_degree(double normTminusI) {
+  const double maxNormForPade[] = {1.6206284795015624e-2 /* degree = 3 */, 5.3873532631381171e-2, 1.1352802267628681e-1,
+                                   1.8662860613541288e-1, 2.642960831111435e-1};
+  const int minPadeDegree = matrix_log_min_pade_degree<double>::value;
+  const int maxPadeDegree = matrix_log_max_pade_degree<double>::value;
+  int degree = minPadeDegree;
+  for (; degree <= maxPadeDegree; ++degree)
+    if (normTminusI <= maxNormForPade[degree - minPadeDegree]) break;
+  return degree;
+}
+
+/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = long double) */
+inline int matrix_log_get_pade_degree(long double normTminusI) {
+#if LDBL_MANT_DIG == 53  // double precision
+  const long double maxNormForPade[] = {1.6206284795015624e-2L /* degree = 3 */, 5.3873532631381171e-2L,
+                                        1.1352802267628681e-1L, 1.8662860613541288e-1L, 2.642960831111435e-1L};
+#elif LDBL_MANT_DIG <= 64   // extended precision
+  const long double maxNormForPade[] = {5.48256690357782863103e-3L /* degree = 3 */,
+                                        2.34559162387971167321e-2L,
+                                        5.84603923897347449857e-2L,
+                                        1.08486423756725170223e-1L,
+                                        1.68385767881294446649e-1L,
+                                        2.32777776523703892094e-1L};
+#elif LDBL_MANT_DIG <= 106  // double-double
+  const long double maxNormForPade[] = {8.58970550342939562202529664318890e-5L /* degree = 3 */,
+                                        9.34074328446359654039446552677759e-4L,
+                                        4.26117194647672175773064114582860e-3L,
+                                        1.21546224740281848743149666560464e-2L,
+                                        2.61100544998339436713088248557444e-2L,
+                                        4.66170074627052749243018566390567e-2L,
+                                        7.32585144444135027565872014932387e-2L,
+                                        1.05026503471351080481093652651105e-1L};
+#else                       // quadruple precision
+  const long double maxNormForPade[] = {4.7419931187193005048501568167858103e-5L /* degree = 3 */,
+                                        5.8853168473544560470387769480192666e-4L,
+                                        2.9216120366601315391789493628113520e-3L,
+                                        8.8415758124319434347116734705174308e-3L,
+                                        1.9850836029449446668518049562565291e-2L,
+                                        3.6688019729653446926585242192447447e-2L,
+                                        5.9290962294020186998954055264528393e-2L,
+                                        8.6998436081634343903250580992127677e-2L,
+                                        1.1880960220216759245467951592883642e-1L};
+#endif
+  const int minPadeDegree = matrix_log_min_pade_degree<long double>::value;
+  const int maxPadeDegree = matrix_log_max_pade_degree<long double>::value;
+  int degree = minPadeDegree;
+  for (; degree <= maxPadeDegree; ++degree)
+    if (normTminusI <= maxNormForPade[degree - minPadeDegree]) break;
+  return degree;
+}
+
+/* \brief Compute Pade approximation to matrix logarithm */
+template <typename MatrixType>
+void matrix_log_compute_pade(MatrixType& result, const MatrixType& T, int degree) {
+  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+  const int minPadeDegree = 3;
+  const int maxPadeDegree = 11;
+  eigen_assert(degree >= minPadeDegree && degree <= maxPadeDegree);
+  // FIXME: This creates float-conversion warnings if these are enabled.
+  // Either manually convert each value, or disable the warning locally
+  const RealScalar nodes[][maxPadeDegree] = {
+      {0.1127016653792583114820734600217600L, 0.5000000000000000000000000000000000L,  // degree 3
+       0.8872983346207416885179265399782400L},
+      {0.0694318442029737123880267555535953L, 0.3300094782075718675986671204483777L,  // degree 4
+       0.6699905217924281324013328795516223L, 0.9305681557970262876119732444464048L},
+      {0.0469100770306680036011865608503035L, 0.2307653449471584544818427896498956L,  // degree 5
+       0.5000000000000000000000000000000000L, 0.7692346550528415455181572103501044L,
+       0.9530899229693319963988134391496965L},
+      {0.0337652428984239860938492227530027L, 0.1693953067668677431693002024900473L,  // degree 6
+       0.3806904069584015456847491391596440L, 0.6193095930415984543152508608403560L,
+       0.8306046932331322568306997975099527L, 0.9662347571015760139061507772469973L},
+      {0.0254460438286207377369051579760744L, 0.1292344072003027800680676133596058L,  // degree 7
+       0.2970774243113014165466967939615193L, 0.5000000000000000000000000000000000L,
+       0.7029225756886985834533032060384807L, 0.8707655927996972199319323866403942L,
+       0.9745539561713792622630948420239256L},
+      {0.0198550717512318841582195657152635L, 0.1016667612931866302042230317620848L,  // degree 8
+       0.2372337950418355070911304754053768L, 0.4082826787521750975302619288199080L,
+       0.5917173212478249024697380711800920L, 0.7627662049581644929088695245946232L,
+       0.8983332387068133697957769682379152L, 0.9801449282487681158417804342847365L},
+      {0.0159198802461869550822118985481636L, 0.0819844463366821028502851059651326L,  // degree 9
+       0.1933142836497048013456489803292629L, 0.3378732882980955354807309926783317L,
+       0.5000000000000000000000000000000000L, 0.6621267117019044645192690073216683L,
+       0.8066857163502951986543510196707371L, 0.9180155536633178971497148940348674L,
+       0.9840801197538130449177881014518364L},
+      {0.0130467357414141399610179939577740L, 0.0674683166555077446339516557882535L,  // degree 10
+       0.1602952158504877968828363174425632L, 0.2833023029353764046003670284171079L,
+       0.4255628305091843945575869994351400L, 0.5744371694908156054424130005648600L,
+       0.7166976970646235953996329715828921L, 0.8397047841495122031171636825574368L,
+       0.9325316833444922553660483442117465L, 0.9869532642585858600389820060422260L},
+      {0.0108856709269715035980309994385713L, 0.0564687001159523504624211153480364L,  // degree 11
+       0.1349239972129753379532918739844233L, 0.2404519353965940920371371652706952L,
+       0.3652284220238275138342340072995692L, 0.5000000000000000000000000000000000L,
+       0.6347715779761724861657659927004308L, 0.7595480646034059079628628347293048L,
+       0.8650760027870246620467081260155767L, 0.9435312998840476495375788846519636L,
+       0.9891143290730284964019690005614287L}};
+
+  const RealScalar weights[][maxPadeDegree] = {
+      {0.2777777777777777777777777777777778L, 0.4444444444444444444444444444444444L,  // degree 3
+       0.2777777777777777777777777777777778L},
+      {0.1739274225687269286865319746109997L, 0.3260725774312730713134680253890003L,  // degree 4
+       0.3260725774312730713134680253890003L, 0.1739274225687269286865319746109997L},
+      {0.1184634425280945437571320203599587L, 0.2393143352496832340206457574178191L,  // degree 5
+       0.2844444444444444444444444444444444L, 0.2393143352496832340206457574178191L,
+       0.1184634425280945437571320203599587L},
+      {0.0856622461895851725201480710863665L, 0.1803807865240693037849167569188581L,  // degree 6
+       0.2339569672863455236949351719947755L, 0.2339569672863455236949351719947755L,
+       0.1803807865240693037849167569188581L, 0.0856622461895851725201480710863665L},
+      {0.0647424830844348466353057163395410L, 0.1398526957446383339507338857118898L,  // degree 7
+       0.1909150252525594724751848877444876L, 0.2089795918367346938775510204081633L,
+       0.1909150252525594724751848877444876L, 0.1398526957446383339507338857118898L,
+       0.0647424830844348466353057163395410L},
+      {0.0506142681451881295762656771549811L, 0.1111905172266872352721779972131204L,  // degree 8
+       0.1568533229389436436689811009933007L, 0.1813418916891809914825752246385978L,
+       0.1813418916891809914825752246385978L, 0.1568533229389436436689811009933007L,
+       0.1111905172266872352721779972131204L, 0.0506142681451881295762656771549811L},
+      {0.0406371941807872059859460790552618L, 0.0903240803474287020292360156214564L,  // degree 9
+       0.1303053482014677311593714347093164L, 0.1561735385200014200343152032922218L,
+       0.1651196775006298815822625346434870L, 0.1561735385200014200343152032922218L,
+       0.1303053482014677311593714347093164L, 0.0903240803474287020292360156214564L,
+       0.0406371941807872059859460790552618L},
+      {0.0333356721543440687967844049466659L, 0.0747256745752902965728881698288487L,  // degree 10
+       0.1095431812579910219977674671140816L, 0.1346333596549981775456134607847347L,
+       0.1477621123573764350869464973256692L, 0.1477621123573764350869464973256692L,
+       0.1346333596549981775456134607847347L, 0.1095431812579910219977674671140816L,
+       0.0747256745752902965728881698288487L, 0.0333356721543440687967844049466659L},
+      {0.0278342835580868332413768602212743L, 0.0627901847324523123173471496119701L,  // degree 11
+       0.0931451054638671257130488207158280L, 0.1165968822959952399592618524215876L,
+       0.1314022722551233310903444349452546L, 0.1364625433889503153572417641681711L,
+       0.1314022722551233310903444349452546L, 0.1165968822959952399592618524215876L,
+       0.0931451054638671257130488207158280L, 0.0627901847324523123173471496119701L,
+       0.0278342835580868332413768602212743L}};
+
+  MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
+  result.setZero(T.rows(), T.rows());
+  for (int k = 0; k < degree; ++k) {
+    RealScalar weight = weights[degree - minPadeDegree][k];
+    RealScalar node = nodes[degree - minPadeDegree][k];
+    result +=
+        weight *
+        (MatrixType::Identity(T.rows(), T.rows()) + node * TminusI).template triangularView<Upper>().solve(TminusI);
+  }
+}
+
+/** \brief Compute logarithm of triangular matrices with size > 2.
+ * \details This uses a inverse scale-and-square algorithm. */
+template <typename MatrixType>
+void matrix_log_compute_big(const MatrixType& A, MatrixType& result) {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  using std::pow;
+
+  int numberOfSquareRoots = 0;
+  int numberOfExtraSquareRoots = 0;
+  int degree;
+  MatrixType T = A, sqrtT;
+
+  const int maxPadeDegree = matrix_log_max_pade_degree<Scalar>::value;
+  const RealScalar maxNormForPade = RealScalar(maxPadeDegree <= 5 ? 5.3149729967117310e-1L :  // single precision
+                                                   maxPadeDegree <= 7 ? 2.6429608311114350e-1L
+                                                                      :  // double precision
+                                                   maxPadeDegree <= 8 ? 2.32777776523703892094e-1L
+                                                                      :  // extended precision
+                                                   maxPadeDegree <= 10 ? 1.05026503471351080481093652651105e-1L
+                                                                       :                       // double-double
+                                                   1.1880960220216759245467951592883642e-1L);  // quadruple precision
+
+  while (true) {
+    RealScalar normTminusI = (T - MatrixType::Identity(T.rows(), T.rows())).cwiseAbs().colwise().sum().maxCoeff();
+    if (normTminusI < maxNormForPade) {
+      degree = matrix_log_get_pade_degree(normTminusI);
+      int degree2 = matrix_log_get_pade_degree(normTminusI / RealScalar(2));
+      if ((degree - degree2 <= 1) || (numberOfExtraSquareRoots == 1)) break;
+      ++numberOfExtraSquareRoots;
+    }
+    matrix_sqrt_triangular(T, sqrtT);
+    T = sqrtT.template triangularView<Upper>();
+    ++numberOfSquareRoots;
+  }
+
+  matrix_log_compute_pade(result, T, degree);
+  result *= pow(RealScalar(2), RealScalar(numberOfSquareRoots));  // TODO: Replace by bitshift if possible.
+}
+
+/** \ingroup MatrixFunctions_Module
+ * \class MatrixLogarithmAtomic
+ * \brief Helper class for computing matrix logarithm of atomic matrices.
+ *
+ * Here, an atomic matrix is a triangular matrix whose diagonal entries are close to each other.
+ *
+ * \sa class MatrixFunctionAtomic, MatrixBase::log()
+ */
+template <typename MatrixType>
+class MatrixLogarithmAtomic {
+ public:
+  /** \brief Compute matrix logarithm of atomic matrix
+   * \param[in]  A  argument of matrix logarithm, should be upper triangular and atomic
+   * \returns  The logarithm of \p A.
+   */
+  MatrixType compute(const MatrixType& A);
+};
+
+template <typename MatrixType>
+MatrixType MatrixLogarithmAtomic<MatrixType>::compute(const MatrixType& A) {
+  using std::log;
+  MatrixType result(A.rows(), A.rows());
+  if (A.rows() == 1)
+    result(0, 0) = log(A(0, 0));
+  else if (A.rows() == 2)
+    matrix_log_compute_2x2(A, result);
+  else
+    matrix_log_compute_big(A, result);
+  return result;
+}
+
+}  // end of namespace internal
+
+/** \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix logarithm of some matrix (expression).
+ *
+ * \tparam Derived  Type of the argument to the matrix function.
+ *
+ * This class holds the argument to the matrix function until it is
+ * assigned or evaluated for some other reason (so the argument
+ * should not be changed in the meantime). It is the return type of
+ * MatrixBase::log() and most of the time this is the only way it
+ * is used.
+ */
+template <typename Derived>
+class MatrixLogarithmReturnValue : public ReturnByValue<MatrixLogarithmReturnValue<Derived> > {
+ public:
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::Index Index;
+
+ protected:
+  typedef typename internal::ref_selector<Derived>::type DerivedNested;
+
+ public:
+  /** \brief Constructor.
+   *
+   * \param[in]  A  %Matrix (expression) forming the argument of the matrix logarithm.
+   */
+  explicit MatrixLogarithmReturnValue(const Derived& A) : m_A(A) {}
+
+  /** \brief Compute the matrix logarithm.
+   *
+   * \param[out]  result  Logarithm of \c A, where \c A is as specified in the constructor.
+   */
+  template <typename ResultType>
+  inline void evalTo(ResultType& result) const {
+    typedef typename internal::nested_eval<Derived, 10>::type DerivedEvalType;
+    typedef internal::remove_all_t<DerivedEvalType> DerivedEvalTypeClean;
+    typedef internal::traits<DerivedEvalTypeClean> Traits;
+    typedef internal::make_complex_t<Scalar> ComplexScalar;
+    typedef Matrix<ComplexScalar, Dynamic, Dynamic, 0, Traits::RowsAtCompileTime, Traits::ColsAtCompileTime>
+        DynMatrixType;
+    typedef internal::MatrixLogarithmAtomic<DynMatrixType> AtomicType;
+    AtomicType atomic;
+
+    internal::matrix_function_compute<typename DerivedEvalTypeClean::PlainObject>::run(m_A, atomic, result);
+  }
+
+  Index rows() const { return m_A.rows(); }
+  Index cols() const { return m_A.cols(); }
+
+ private:
+  const DerivedNested m_A;
+};
+
+namespace internal {
+template <typename Derived>
+struct traits<MatrixLogarithmReturnValue<Derived> > {
+  typedef typename Derived::PlainObject ReturnType;
+};
+}  // namespace internal
+
+/********** MatrixBase method **********/
+
+template <typename Derived>
+const MatrixLogarithmReturnValue<Derived> MatrixBase<Derived>::log() const {
+  eigen_assert(rows() == cols());
+  return MatrixLogarithmReturnValue<Derived>(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATRIX_LOGARITHM
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
new file mode 100644
index 0000000..8e0a2f1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
@@ -0,0 +1,694 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012, 2013 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_POWER
+#define EIGEN_MATRIX_POWER
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename MatrixType>
+class MatrixPower;
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix power of some matrix.
+ *
+ * \tparam MatrixType  type of the base, a matrix.
+ *
+ * This class holds the arguments to the matrix power until it is
+ * assigned or evaluated for some other reason (so the argument
+ * should not be changed in the meantime). It is the return type of
+ * MatrixPower::operator() and related functions and most of the
+ * time this is the only way it is used.
+ */
+/* TODO: This class is only used by MatrixPower, so it should be nested
+ * into MatrixPower, like MatrixPower::ReturnValue. However, my
+ * compiler complained about unused template parameter in the
+ * following declaration in namespace internal.
+ *
+ * template<typename MatrixType>
+ * struct traits<MatrixPower<MatrixType>::ReturnValue>;
+ */
+template <typename MatrixType>
+class MatrixPowerParenthesesReturnValue : public ReturnByValue<MatrixPowerParenthesesReturnValue<MatrixType> > {
+ public:
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  /**
+   * \brief Constructor.
+   *
+   * \param[in] pow  %MatrixPower storing the base.
+   * \param[in] p    scalar, the exponent of the matrix power.
+   */
+  MatrixPowerParenthesesReturnValue(MatrixPower<MatrixType>& pow, RealScalar p) : m_pow(pow), m_p(p) {}
+
+  /**
+   * \brief Compute the matrix power.
+   *
+   * \param[out] result
+   */
+  template <typename ResultType>
+  inline void evalTo(ResultType& result) const {
+    m_pow.compute(result, m_p);
+  }
+
+  Index rows() const { return m_pow.rows(); }
+  Index cols() const { return m_pow.cols(); }
+
+ private:
+  MatrixPower<MatrixType>& m_pow;
+  const RealScalar m_p;
+};
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Class for computing matrix powers.
+ *
+ * \tparam MatrixType  type of the base, expected to be an instantiation
+ * of the Matrix class template.
+ *
+ * This class is capable of computing triangular real/complex matrices
+ * raised to a power in the interval \f$ (-1, 1) \f$.
+ *
+ * \note Currently this class is only used by MatrixPower. One may
+ * insist that this be nested into MatrixPower. This class is here to
+ * facilitate future development of triangular matrix functions.
+ */
+template <typename MatrixType>
+class MatrixPowerAtomic : internal::noncopyable {
+ private:
+  enum { RowsAtCompileTime = MatrixType::RowsAtCompileTime, MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+  typedef Block<MatrixType, Dynamic, Dynamic> ResultType;
+
+  const MatrixType& m_A;
+  RealScalar m_p;
+
+  void computePade(int degree, const MatrixType& IminusT, ResultType& res) const;
+  void compute2x2(ResultType& res, RealScalar p) const;
+  void computeBig(ResultType& res) const;
+  static int getPadeDegree(float normIminusT);
+  static int getPadeDegree(double normIminusT);
+  static int getPadeDegree(long double normIminusT);
+  static ComplexScalar computeSuperDiag(const ComplexScalar&, const ComplexScalar&, RealScalar p);
+  static RealScalar computeSuperDiag(RealScalar, RealScalar, RealScalar p);
+
+ public:
+  /**
+   * \brief Constructor.
+   *
+   * \param[in] T  the base of the matrix power.
+   * \param[in] p  the exponent of the matrix power, should be in
+   * \f$ (-1, 1) \f$.
+   *
+   * The class stores a reference to T, so it should not be changed
+   * (or destroyed) before evaluation. Only the upper triangular
+   * part of T is read.
+   */
+  MatrixPowerAtomic(const MatrixType& T, RealScalar p);
+
+  /**
+   * \brief Compute the matrix power.
+   *
+   * \param[out] res  \f$ A^p \f$ where A and p are specified in the
+   * constructor.
+   */
+  void compute(ResultType& res) const;
+};
+
+template <typename MatrixType>
+MatrixPowerAtomic<MatrixType>::MatrixPowerAtomic(const MatrixType& T, RealScalar p) : m_A(T), m_p(p) {
+  eigen_assert(T.rows() == T.cols());
+  eigen_assert(p > -1 && p < 1);
+}
+
+template <typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::compute(ResultType& res) const {
+  using std::pow;
+  switch (m_A.rows()) {
+    case 0:
+      break;
+    case 1:
+      res(0, 0) = pow(m_A(0, 0), m_p);
+      break;
+    case 2:
+      compute2x2(res, m_p);
+      break;
+    default:
+      computeBig(res);
+  }
+}
+
+template <typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::computePade(int degree, const MatrixType& IminusT, ResultType& res) const {
+  int i = 2 * degree;
+  res = (m_p - RealScalar(degree)) / RealScalar(2 * i - 2) * IminusT;
+
+  for (--i; i; --i) {
+    res = (MatrixType::Identity(IminusT.rows(), IminusT.cols()) + res)
+              .template triangularView<Upper>()
+              .solve((i == 1  ? -m_p
+                      : i & 1 ? (-m_p - RealScalar(i / 2)) / RealScalar(2 * i)
+                              : (m_p - RealScalar(i / 2)) / RealScalar(2 * i - 2)) *
+                     IminusT)
+              .eval();
+  }
+  res += MatrixType::Identity(IminusT.rows(), IminusT.cols());
+}
+
+// This function assumes that res has the correct size (see bug 614)
+template <typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::compute2x2(ResultType& res, RealScalar p) const {
+  using std::abs;
+  using std::pow;
+  res.coeffRef(0, 0) = pow(m_A.coeff(0, 0), p);
+
+  for (Index i = 1; i < m_A.cols(); ++i) {
+    res.coeffRef(i, i) = pow(m_A.coeff(i, i), p);
+    if (m_A.coeff(i - 1, i - 1) == m_A.coeff(i, i))
+      res.coeffRef(i - 1, i) = p * pow(m_A.coeff(i, i), p - 1);
+    else if (2 * abs(m_A.coeff(i - 1, i - 1)) < abs(m_A.coeff(i, i)) ||
+             2 * abs(m_A.coeff(i, i)) < abs(m_A.coeff(i - 1, i - 1)))
+      res.coeffRef(i - 1, i) =
+          (res.coeff(i, i) - res.coeff(i - 1, i - 1)) / (m_A.coeff(i, i) - m_A.coeff(i - 1, i - 1));
+    else
+      res.coeffRef(i - 1, i) = computeSuperDiag(m_A.coeff(i, i), m_A.coeff(i - 1, i - 1), p);
+    res.coeffRef(i - 1, i) *= m_A.coeff(i - 1, i);
+  }
+}
+
+template <typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::computeBig(ResultType& res) const {
+  using std::ldexp;
+  const int digits = std::numeric_limits<RealScalar>::digits;
+  const RealScalar maxNormForPade =
+      RealScalar(digits <= 24    ? 4.3386528e-1L                              // single precision
+                 : digits <= 53  ? 2.789358995219730e-1L                      // double precision
+                 : digits <= 64  ? 2.4471944416607995472e-1L                  // extended precision
+                 : digits <= 106 ? 1.1016843812851143391275867258512e-1L      // double-double
+                                 : 9.134603732914548552537150753385375e-2L);  // quadruple precision
+  MatrixType IminusT, sqrtT, T = m_A.template triangularView<Upper>();
+  RealScalar normIminusT;
+  int degree, degree2, numberOfSquareRoots = 0;
+  bool hasExtraSquareRoot = false;
+
+  for (Index i = 0; i < m_A.cols(); ++i) eigen_assert(m_A(i, i) != RealScalar(0));
+
+  while (true) {
+    IminusT = MatrixType::Identity(m_A.rows(), m_A.cols()) - T;
+    normIminusT = IminusT.cwiseAbs().colwise().sum().maxCoeff();
+    if (normIminusT < maxNormForPade) {
+      degree = getPadeDegree(normIminusT);
+      degree2 = getPadeDegree(normIminusT / 2);
+      if (degree - degree2 <= 1 || hasExtraSquareRoot) break;
+      hasExtraSquareRoot = true;
+    }
+    matrix_sqrt_triangular(T, sqrtT);
+    T = sqrtT.template triangularView<Upper>();
+    ++numberOfSquareRoots;
+  }
+  computePade(degree, IminusT, res);
+
+  for (; numberOfSquareRoots; --numberOfSquareRoots) {
+    compute2x2(res, ldexp(m_p, -numberOfSquareRoots));
+    res = res.template triangularView<Upper>() * res;
+  }
+  compute2x2(res, m_p);
+}
+
+template <typename MatrixType>
+inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(float normIminusT) {
+  const float maxNormForPade[] = {2.8064004e-1f /* degree = 3 */, 4.3386528e-1f};
+  int degree = 3;
+  for (; degree <= 4; ++degree)
+    if (normIminusT <= maxNormForPade[degree - 3]) break;
+  return degree;
+}
+
+template <typename MatrixType>
+inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(double normIminusT) {
+  const double maxNormForPade[] = {1.884160592658218e-2 /* degree = 3 */, 6.038881904059573e-2, 1.239917516308172e-1,
+                                   1.999045567181744e-1, 2.789358995219730e-1};
+  int degree = 3;
+  for (; degree <= 7; ++degree)
+    if (normIminusT <= maxNormForPade[degree - 3]) break;
+  return degree;
+}
+
+template <typename MatrixType>
+inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(long double normIminusT) {
+#if LDBL_MANT_DIG == 53
+  const int maxPadeDegree = 7;
+  const double maxNormForPade[] = {1.884160592658218e-2L /* degree = 3 */, 6.038881904059573e-2L, 1.239917516308172e-1L,
+                                   1.999045567181744e-1L, 2.789358995219730e-1L};
+#elif LDBL_MANT_DIG <= 64
+  const int maxPadeDegree = 8;
+  const long double maxNormForPade[] = {6.3854693117491799460e-3L /* degree = 3 */,
+                                        2.6394893435456973676e-2L,
+                                        6.4216043030404063729e-2L,
+                                        1.1701165502926694307e-1L,
+                                        1.7904284231268670284e-1L,
+                                        2.4471944416607995472e-1L};
+#elif LDBL_MANT_DIG <= 106
+  const int maxPadeDegree = 10;
+  const double maxNormForPade[] = {1.0007161601787493236741409687186e-4L /* degree = 3 */,
+                                   1.0007161601787493236741409687186e-3L,
+                                   4.7069769360887572939882574746264e-3L,
+                                   1.3220386624169159689406653101695e-2L,
+                                   2.8063482381631737920612944054906e-2L,
+                                   4.9625993951953473052385361085058e-2L,
+                                   7.7367040706027886224557538328171e-2L,
+                                   1.1016843812851143391275867258512e-1L};
+#else
+  const int maxPadeDegree = 10;
+  const double maxNormForPade[] = {5.524506147036624377378713555116378e-5L /* degree = 3 */,
+                                   6.640600568157479679823602193345995e-4L,
+                                   3.227716520106894279249709728084626e-3L,
+                                   9.619593944683432960546978734646284e-3L,
+                                   2.134595382433742403911124458161147e-2L,
+                                   3.908166513900489428442993794761185e-2L,
+                                   6.266780814639442865832535460550138e-2L,
+                                   9.134603732914548552537150753385375e-2L};
+#endif
+  int degree = 3;
+  for (; degree <= maxPadeDegree; ++degree)
+    if (normIminusT <= static_cast<long double>(maxNormForPade[degree - 3])) break;
+  return degree;
+}
+
+template <typename MatrixType>
+inline typename MatrixPowerAtomic<MatrixType>::ComplexScalar MatrixPowerAtomic<MatrixType>::computeSuperDiag(
+    const ComplexScalar& curr, const ComplexScalar& prev, RealScalar p) {
+  using std::ceil;
+  using std::exp;
+  using std::log;
+  using std::sinh;
+
+  ComplexScalar logCurr = log(curr);
+  ComplexScalar logPrev = log(prev);
+  RealScalar unwindingNumber =
+      ceil((numext::imag(logCurr - logPrev) - RealScalar(EIGEN_PI)) / RealScalar(2 * EIGEN_PI));
+  ComplexScalar w =
+      numext::log1p((curr - prev) / prev) / RealScalar(2) + ComplexScalar(0, RealScalar(EIGEN_PI) * unwindingNumber);
+  return RealScalar(2) * exp(RealScalar(0.5) * p * (logCurr + logPrev)) * sinh(p * w) / (curr - prev);
+}
+
+template <typename MatrixType>
+inline typename MatrixPowerAtomic<MatrixType>::RealScalar MatrixPowerAtomic<MatrixType>::computeSuperDiag(
+    RealScalar curr, RealScalar prev, RealScalar p) {
+  using std::exp;
+  using std::log;
+  using std::sinh;
+
+  RealScalar w = numext::log1p((curr - prev) / prev) / RealScalar(2);
+  return 2 * exp(p * (log(curr) + log(prev)) / 2) * sinh(p * w) / (curr - prev);
+}
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Class for computing matrix powers.
+ *
+ * \tparam MatrixType  type of the base, expected to be an instantiation
+ * of the Matrix class template.
+ *
+ * This class is capable of computing real/complex matrices raised to
+ * an arbitrary real power. Meanwhile, it saves the result of Schur
+ * decomposition if an non-integral power has even been calculated.
+ * Therefore, if you want to compute multiple (>= 2) matrix powers
+ * for the same matrix, using the class directly is more efficient than
+ * calling MatrixBase::pow().
+ *
+ * Example:
+ * \include MatrixPower_optimal.cpp
+ * Output: \verbinclude MatrixPower_optimal.out
+ */
+template <typename MatrixType>
+class MatrixPower : internal::noncopyable {
+ private:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+ public:
+  /**
+   * \brief Constructor.
+   *
+   * \param[in] A  the base of the matrix power.
+   *
+   * The class stores a reference to A, so it should not be changed
+   * (or destroyed) before evaluation.
+   */
+  explicit MatrixPower(const MatrixType& A) : m_A(A), m_conditionNumber(0), m_rank(A.cols()), m_nulls(0) {
+    eigen_assert(A.rows() == A.cols());
+  }
+
+  /**
+   * \brief Returns the matrix power.
+   *
+   * \param[in] p  exponent, a real scalar.
+   * \return The expression \f$ A^p \f$, where A is specified in the
+   * constructor.
+   */
+  const MatrixPowerParenthesesReturnValue<MatrixType> operator()(RealScalar p) {
+    return MatrixPowerParenthesesReturnValue<MatrixType>(*this, p);
+  }
+
+  /**
+   * \brief Compute the matrix power.
+   *
+   * \param[in]  p    exponent, a real scalar.
+   * \param[out] res  \f$ A^p \f$ where A is specified in the
+   * constructor.
+   */
+  template <typename ResultType>
+  void compute(ResultType& res, RealScalar p);
+
+  Index rows() const { return m_A.rows(); }
+  Index cols() const { return m_A.cols(); }
+
+ private:
+  typedef internal::make_complex_t<Scalar> ComplexScalar;
+  typedef Matrix<ComplexScalar, Dynamic, Dynamic, 0, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime>
+      ComplexMatrix;
+
+  /** \brief Reference to the base of matrix power. */
+  typename MatrixType::Nested m_A;
+
+  /** \brief Temporary storage. */
+  MatrixType m_tmp;
+
+  /** \brief Store the result of Schur decomposition. */
+  ComplexMatrix m_T, m_U;
+
+  /** \brief Store fractional power of m_T. */
+  ComplexMatrix m_fT;
+
+  /**
+   * \brief Condition number of m_A.
+   *
+   * It is initialized as 0 to avoid performing unnecessary Schur
+   * decomposition, which is the bottleneck.
+   */
+  RealScalar m_conditionNumber;
+
+  /** \brief Rank of m_A. */
+  Index m_rank;
+
+  /** \brief Rank deficiency of m_A. */
+  Index m_nulls;
+
+  /**
+   * \brief Split p into integral part and fractional part.
+   *
+   * \param[in]  p        The exponent.
+   * \param[out] p        The fractional part ranging in \f$ (-1, 1) \f$.
+   * \param[out] intpart  The integral part.
+   *
+   * Only if the fractional part is nonzero, it calls initialize().
+   */
+  void split(RealScalar& p, RealScalar& intpart);
+
+  /** \brief Perform Schur decomposition for fractional power. */
+  void initialize();
+
+  template <typename ResultType>
+  void computeIntPower(ResultType& res, RealScalar p);
+
+  template <typename ResultType>
+  void computeFracPower(ResultType& res, RealScalar p);
+
+  template <int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+  static void revertSchur(Matrix<ComplexScalar, Rows, Cols, Options, MaxRows, MaxCols>& res, const ComplexMatrix& T,
+                          const ComplexMatrix& U);
+
+  template <int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+  static void revertSchur(Matrix<RealScalar, Rows, Cols, Options, MaxRows, MaxCols>& res, const ComplexMatrix& T,
+                          const ComplexMatrix& U);
+};
+
+template <typename MatrixType>
+template <typename ResultType>
+void MatrixPower<MatrixType>::compute(ResultType& res, RealScalar p) {
+  using std::pow;
+  switch (cols()) {
+    case 0:
+      break;
+    case 1:
+      res(0, 0) = pow(m_A.coeff(0, 0), p);
+      break;
+    default:
+      RealScalar intpart;
+      split(p, intpart);
+
+      res = MatrixType::Identity(rows(), cols());
+      computeIntPower(res, intpart);
+      if (p) computeFracPower(res, p);
+  }
+}
+
+template <typename MatrixType>
+void MatrixPower<MatrixType>::split(RealScalar& p, RealScalar& intpart) {
+  using std::floor;
+  using std::pow;
+
+  intpart = floor(p);
+  p -= intpart;
+
+  // Perform Schur decomposition if it is not yet performed and the power is
+  // not an integer.
+  if (!m_conditionNumber && p) initialize();
+
+  // Choose the more stable of intpart = floor(p) and intpart = ceil(p).
+  if (p > RealScalar(0.5) && p > (1 - p) * pow(m_conditionNumber, p)) {
+    --p;
+    ++intpart;
+  }
+}
+
+template <typename MatrixType>
+void MatrixPower<MatrixType>::initialize() {
+  const ComplexSchur<MatrixType> schurOfA(m_A);
+  JacobiRotation<ComplexScalar> rot;
+  ComplexScalar eigenvalue;
+
+  m_fT.resizeLike(m_A);
+  m_T = schurOfA.matrixT();
+  m_U = schurOfA.matrixU();
+  m_conditionNumber = m_T.diagonal().array().abs().maxCoeff() / m_T.diagonal().array().abs().minCoeff();
+
+  // Move zero eigenvalues to the bottom right corner.
+  for (Index i = cols() - 1; i >= 0; --i) {
+    if (m_rank <= 2) return;
+    if (m_T.coeff(i, i) == RealScalar(0)) {
+      for (Index j = i + 1; j < m_rank; ++j) {
+        eigenvalue = m_T.coeff(j, j);
+        rot.makeGivens(m_T.coeff(j - 1, j), eigenvalue);
+        m_T.applyOnTheRight(j - 1, j, rot);
+        m_T.applyOnTheLeft(j - 1, j, rot.adjoint());
+        m_T.coeffRef(j - 1, j - 1) = eigenvalue;
+        m_T.coeffRef(j, j) = RealScalar(0);
+        m_U.applyOnTheRight(j - 1, j, rot);
+      }
+      --m_rank;
+    }
+  }
+
+  m_nulls = rows() - m_rank;
+  if (m_nulls) {
+    eigen_assert(m_T.bottomRightCorner(m_nulls, m_nulls).isZero() &&
+                 "Base of matrix power should be invertible or with a semisimple zero eigenvalue.");
+    m_fT.bottomRows(m_nulls).fill(RealScalar(0));
+  }
+}
+
+template <typename MatrixType>
+template <typename ResultType>
+void MatrixPower<MatrixType>::computeIntPower(ResultType& res, RealScalar p) {
+  using std::abs;
+  using std::fmod;
+  RealScalar pp = abs(p);
+
+  if (p < 0)
+    m_tmp = m_A.inverse();
+  else
+    m_tmp = m_A;
+
+  while (true) {
+    if (fmod(pp, 2) >= 1) res = m_tmp * res;
+    pp /= 2;
+    if (pp < 1) break;
+    m_tmp *= m_tmp;
+  }
+}
+
+template <typename MatrixType>
+template <typename ResultType>
+void MatrixPower<MatrixType>::computeFracPower(ResultType& res, RealScalar p) {
+  Block<ComplexMatrix, Dynamic, Dynamic> blockTp(m_fT, 0, 0, m_rank, m_rank);
+  eigen_assert(m_conditionNumber);
+  eigen_assert(m_rank + m_nulls == rows());
+
+  MatrixPowerAtomic<ComplexMatrix>(m_T.topLeftCorner(m_rank, m_rank), p).compute(blockTp);
+  if (m_nulls) {
+    m_fT.topRightCorner(m_rank, m_nulls) = m_T.topLeftCorner(m_rank, m_rank)
+                                               .template triangularView<Upper>()
+                                               .solve(blockTp * m_T.topRightCorner(m_rank, m_nulls));
+  }
+  revertSchur(m_tmp, m_fT, m_U);
+  res = m_tmp * res;
+}
+
+template <typename MatrixType>
+template <int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+inline void MatrixPower<MatrixType>::revertSchur(Matrix<ComplexScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
+                                                 const ComplexMatrix& T, const ComplexMatrix& U) {
+  res.noalias() = U * (T.template triangularView<Upper>() * U.adjoint());
+}
+
+template <typename MatrixType>
+template <int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+inline void MatrixPower<MatrixType>::revertSchur(Matrix<RealScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
+                                                 const ComplexMatrix& T, const ComplexMatrix& U) {
+  res.noalias() = (U * (T.template triangularView<Upper>() * U.adjoint())).real();
+}
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix power of some matrix (expression).
+ *
+ * \tparam Derived  type of the base, a matrix (expression).
+ *
+ * This class holds the arguments to the matrix power until it is
+ * assigned or evaluated for some other reason (so the argument
+ * should not be changed in the meantime). It is the return type of
+ * MatrixBase::pow() and related functions and most of the
+ * time this is the only way it is used.
+ */
+template <typename Derived>
+class MatrixPowerReturnValue : public ReturnByValue<MatrixPowerReturnValue<Derived> > {
+ public:
+  typedef typename Derived::PlainObject PlainObject;
+  typedef typename Derived::RealScalar RealScalar;
+
+  /**
+   * \brief Constructor.
+   *
+   * \param[in] A  %Matrix (expression), the base of the matrix power.
+   * \param[in] p  real scalar, the exponent of the matrix power.
+   */
+  MatrixPowerReturnValue(const Derived& A, RealScalar p) : m_A(A), m_p(p) {}
+
+  /**
+   * \brief Compute the matrix power.
+   *
+   * \param[out] result  \f$ A^p \f$ where \p A and \p p are as in the
+   * constructor.
+   */
+  template <typename ResultType>
+  inline void evalTo(ResultType& result) const {
+    MatrixPower<PlainObject>(m_A.eval()).compute(result, m_p);
+  }
+
+  Index rows() const { return m_A.rows(); }
+  Index cols() const { return m_A.cols(); }
+
+ private:
+  const Derived& m_A;
+  const RealScalar m_p;
+};
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix power of some matrix (expression).
+ *
+ * \tparam Derived  type of the base, a matrix (expression).
+ *
+ * This class holds the arguments to the matrix power until it is
+ * assigned or evaluated for some other reason (so the argument
+ * should not be changed in the meantime). It is the return type of
+ * MatrixBase::pow() and related functions and most of the
+ * time this is the only way it is used.
+ */
+template <typename Derived>
+class MatrixComplexPowerReturnValue : public ReturnByValue<MatrixComplexPowerReturnValue<Derived> > {
+ public:
+  typedef typename Derived::PlainObject PlainObject;
+  typedef internal::make_complex_t<typename Derived::Scalar> ComplexScalar;
+
+  /**
+   * \brief Constructor.
+   *
+   * \param[in] A  %Matrix (expression), the base of the matrix power.
+   * \param[in] p  complex scalar, the exponent of the matrix power.
+   */
+  MatrixComplexPowerReturnValue(const Derived& A, const ComplexScalar& p) : m_A(A), m_p(p) {}
+
+  /**
+   * \brief Compute the matrix power.
+   *
+   * Because \p p is complex, \f$ A^p \f$ is simply evaluated as \f$
+   * \exp(p \log(A)) \f$.
+   *
+   * \param[out] result  \f$ A^p \f$ where \p A and \p p are as in the
+   * constructor.
+   */
+  template <typename ResultType>
+  inline void evalTo(ResultType& result) const {
+    result = (m_p * m_A.log()).exp();
+  }
+
+  Index rows() const { return m_A.rows(); }
+  Index cols() const { return m_A.cols(); }
+
+ private:
+  const Derived& m_A;
+  const ComplexScalar m_p;
+};
+
+namespace internal {
+
+template <typename MatrixPowerType>
+struct traits<MatrixPowerParenthesesReturnValue<MatrixPowerType> > {
+  typedef typename MatrixPowerType::PlainObject ReturnType;
+};
+
+template <typename Derived>
+struct traits<MatrixPowerReturnValue<Derived> > {
+  typedef typename Derived::PlainObject ReturnType;
+};
+
+template <typename Derived>
+struct traits<MatrixComplexPowerReturnValue<Derived> > {
+  typedef typename Derived::PlainObject ReturnType;
+};
+
+}  // namespace internal
+
+template <typename Derived>
+const MatrixPowerReturnValue<Derived> MatrixBase<Derived>::pow(const RealScalar& p) const {
+  return MatrixPowerReturnValue<Derived>(derived(), p);
+}
+
+template <typename Derived>
+const MatrixComplexPowerReturnValue<Derived> MatrixBase<Derived>::pow(const internal::make_complex_t<Scalar>& p) const {
+  return MatrixComplexPowerReturnValue<Derived>(derived(), p);
+}
+
+}  // namespace Eigen
+
+#endif  // EIGEN_MATRIX_POWER
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
new file mode 100644
index 0000000..34e7531
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
@@ -0,0 +1,345 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_SQUARE_ROOT
+#define EIGEN_MATRIX_SQUARE_ROOT
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// pre:  T.block(i,i,2,2) has complex conjugate eigenvalues
+// post: sqrtT.block(i,i,2,2) is square root of T.block(i,i,2,2)
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_2x2_diagonal_block(const MatrixType& T, Index i, ResultType& sqrtT) {
+  // TODO: this 2x2 complex-conjugate eigenvalue case could reuse logic from EigenSolver if exposed.
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Matrix<Scalar, 2, 2> block = T.template block<2, 2>(i, i);
+  EigenSolver<Matrix<Scalar, 2, 2> > es(block);
+  sqrtT.template block<2, 2>(i, i) =
+      (es.eigenvectors() * es.eigenvalues().cwiseSqrt().asDiagonal() * es.eigenvectors().inverse()).real();
+}
+
+// pre:  block structure of T is such that (i,j) is a 1x1 block,
+//       all blocks of sqrtT to left of and below (i,j) are correct
+// post: sqrtT(i,j) has the correct value
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_1x1_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT) {
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Scalar tmp = (sqrtT.row(i).segment(i + 1, j - i - 1) * sqrtT.col(j).segment(i + 1, j - i - 1)).value();
+  sqrtT.coeffRef(i, j) = (T.coeff(i, j) - tmp) / (sqrtT.coeff(i, i) + sqrtT.coeff(j, j));
+}
+
+// similar to compute1x1offDiagonalBlock()
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_1x2_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT) {
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Matrix<Scalar, 1, 2> rhs = T.template block<1, 2>(i, j);
+  if (j - i > 1) rhs -= sqrtT.block(i, i + 1, 1, j - i - 1) * sqrtT.block(i + 1, j, j - i - 1, 2);
+  Matrix<Scalar, 2, 2> A = sqrtT.coeff(i, i) * Matrix<Scalar, 2, 2>::Identity();
+  A += sqrtT.template block<2, 2>(j, j).transpose();
+  sqrtT.template block<1, 2>(i, j).transpose() = A.fullPivLu().solve(rhs.transpose());
+}
+
+// similar to compute1x1offDiagonalBlock()
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_2x1_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT) {
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Matrix<Scalar, 2, 1> rhs = T.template block<2, 1>(i, j);
+  if (j - i > 2) rhs -= sqrtT.block(i, i + 2, 2, j - i - 2) * sqrtT.block(i + 2, j, j - i - 2, 1);
+  Matrix<Scalar, 2, 2> A = sqrtT.coeff(j, j) * Matrix<Scalar, 2, 2>::Identity();
+  A += sqrtT.template block<2, 2>(i, i);
+  sqrtT.template block<2, 1>(i, j) = A.fullPivLu().solve(rhs);
+}
+
+// solves the equation A X + X B = C where all matrices are 2-by-2
+template <typename MatrixType>
+void matrix_sqrt_quasi_triangular_solve_auxiliary_equation(MatrixType& X, const MatrixType& A, const MatrixType& B,
+                                                           const MatrixType& C) {
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Matrix<Scalar, 4, 4> coeffMatrix = Matrix<Scalar, 4, 4>::Zero();
+  coeffMatrix.coeffRef(0, 0) = A.coeff(0, 0) + B.coeff(0, 0);
+  coeffMatrix.coeffRef(1, 1) = A.coeff(0, 0) + B.coeff(1, 1);
+  coeffMatrix.coeffRef(2, 2) = A.coeff(1, 1) + B.coeff(0, 0);
+  coeffMatrix.coeffRef(3, 3) = A.coeff(1, 1) + B.coeff(1, 1);
+  coeffMatrix.coeffRef(0, 1) = B.coeff(1, 0);
+  coeffMatrix.coeffRef(0, 2) = A.coeff(0, 1);
+  coeffMatrix.coeffRef(1, 0) = B.coeff(0, 1);
+  coeffMatrix.coeffRef(1, 3) = A.coeff(0, 1);
+  coeffMatrix.coeffRef(2, 0) = A.coeff(1, 0);
+  coeffMatrix.coeffRef(2, 3) = B.coeff(1, 0);
+  coeffMatrix.coeffRef(3, 1) = A.coeff(1, 0);
+  coeffMatrix.coeffRef(3, 2) = B.coeff(0, 1);
+
+  Matrix<Scalar, 4, 1> rhs;
+  rhs.coeffRef(0) = C.coeff(0, 0);
+  rhs.coeffRef(1) = C.coeff(0, 1);
+  rhs.coeffRef(2) = C.coeff(1, 0);
+  rhs.coeffRef(3) = C.coeff(1, 1);
+
+  Matrix<Scalar, 4, 1> result;
+  result = coeffMatrix.fullPivLu().solve(rhs);
+
+  X.coeffRef(0, 0) = result.coeff(0);
+  X.coeffRef(0, 1) = result.coeff(1);
+  X.coeffRef(1, 0) = result.coeff(2);
+  X.coeffRef(1, 1) = result.coeff(3);
+}
+
+// similar to compute1x1offDiagonalBlock()
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_2x2_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT) {
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Matrix<Scalar, 2, 2> A = sqrtT.template block<2, 2>(i, i);
+  Matrix<Scalar, 2, 2> B = sqrtT.template block<2, 2>(j, j);
+  Matrix<Scalar, 2, 2> C = T.template block<2, 2>(i, j);
+  if (j - i > 2) C -= sqrtT.block(i, i + 2, 2, j - i - 2) * sqrtT.block(i + 2, j, j - i - 2, 2);
+  Matrix<Scalar, 2, 2> X;
+  matrix_sqrt_quasi_triangular_solve_auxiliary_equation(X, A, B, C);
+  sqrtT.template block<2, 2>(i, j) = X;
+}
+
+// pre:  T is quasi-upper-triangular and sqrtT is a zero matrix of the same size
+// post: the diagonal blocks of sqrtT are the square roots of the diagonal blocks of T
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_diagonal(const MatrixType& T, ResultType& sqrtT) {
+  using std::sqrt;
+  const Index size = T.rows();
+  for (Index i = 0; i < size; i++) {
+    if (i == size - 1 || T.coeff(i + 1, i) == 0) {
+      eigen_assert(T(i, i) >= 0);
+      sqrtT.coeffRef(i, i) = sqrt(T.coeff(i, i));
+    } else {
+      matrix_sqrt_quasi_triangular_2x2_diagonal_block(T, i, sqrtT);
+      ++i;
+    }
+  }
+}
+
+// pre:  T is quasi-upper-triangular and diagonal blocks of sqrtT are square root of diagonal blocks of T.
+// post: sqrtT is the square root of T.
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_off_diagonal(const MatrixType& T, ResultType& sqrtT) {
+  const Index size = T.rows();
+  for (Index j = 1; j < size; j++) {
+    if (T.coeff(j, j - 1) != 0)  // if T(j-1:j, j-1:j) is a 2-by-2 block
+      continue;
+    for (Index i = j - 1; i >= 0; i--) {
+      if (i > 0 && T.coeff(i, i - 1) != 0)  // if T(i-1:i, i-1:i) is a 2-by-2 block
+        continue;
+      bool iBlockIs2x2 = (i < size - 1) && (T.coeff(i + 1, i) != 0);
+      bool jBlockIs2x2 = (j < size - 1) && (T.coeff(j + 1, j) != 0);
+      if (iBlockIs2x2 && jBlockIs2x2)
+        matrix_sqrt_quasi_triangular_2x2_off_diagonal_block(T, i, j, sqrtT);
+      else if (iBlockIs2x2 && !jBlockIs2x2)
+        matrix_sqrt_quasi_triangular_2x1_off_diagonal_block(T, i, j, sqrtT);
+      else if (!iBlockIs2x2 && jBlockIs2x2)
+        matrix_sqrt_quasi_triangular_1x2_off_diagonal_block(T, i, j, sqrtT);
+      else if (!iBlockIs2x2 && !jBlockIs2x2)
+        matrix_sqrt_quasi_triangular_1x1_off_diagonal_block(T, i, j, sqrtT);
+    }
+  }
+}
+
+}  // end of namespace internal
+
+/** \ingroup MatrixFunctions_Module
+ * \brief Compute matrix square root of quasi-triangular matrix.
+ *
+ * \tparam  MatrixType  type of \p arg, the argument of matrix square root,
+ *                      expected to be an instantiation of the Matrix class template.
+ * \tparam  ResultType  type of \p result, where result is to be stored.
+ * \param[in]  arg      argument of matrix square root.
+ * \param[out] result   matrix square root of upper Hessenberg part of \p arg.
+ *
+ * This function computes the square root of the upper quasi-triangular matrix stored in the upper
+ * Hessenberg part of \p arg.  Only the upper Hessenberg part of \p result is updated, the rest is
+ * not touched.  See MatrixBase::sqrt() for details on how this computation is implemented.
+ *
+ * \sa MatrixSquareRoot, MatrixSquareRootQuasiTriangular
+ */
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular(const MatrixType& arg, ResultType& result) {
+  eigen_assert(arg.rows() == arg.cols());
+  result.resize(arg.rows(), arg.cols());
+  internal::matrix_sqrt_quasi_triangular_diagonal(arg, result);
+  internal::matrix_sqrt_quasi_triangular_off_diagonal(arg, result);
+}
+
+/** \ingroup MatrixFunctions_Module
+ * \brief Compute matrix square root of triangular matrix.
+ *
+ * \tparam  MatrixType  type of \p arg, the argument of matrix square root,
+ *                      expected to be an instantiation of the Matrix class template.
+ * \tparam  ResultType  type of \p result, where result is to be stored.
+ * \param[in]  arg      argument of matrix square root.
+ * \param[out] result   matrix square root of upper triangular part of \p arg.
+ *
+ * Only the upper triangular part (including the diagonal) of \p result is updated, the rest is not
+ * touched.  See MatrixBase::sqrt() for details on how this computation is implemented.
+ *
+ * \sa MatrixSquareRoot, MatrixSquareRootQuasiTriangular
+ */
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_triangular(const MatrixType& arg, ResultType& result) {
+  using std::sqrt;
+  typedef typename MatrixType::Scalar Scalar;
+
+  eigen_assert(arg.rows() == arg.cols());
+
+  // Compute square root of arg and store it in upper triangular part of result
+  // This uses that the square root of triangular matrices can be computed directly.
+  result.resize(arg.rows(), arg.cols());
+  for (Index i = 0; i < arg.rows(); i++) {
+    result.coeffRef(i, i) = sqrt(arg.coeff(i, i));
+  }
+  for (Index j = 1; j < arg.cols(); j++) {
+    for (Index i = j - 1; i >= 0; i--) {
+      // if i = j-1, then segment has length 0 so tmp = 0
+      Scalar tmp = (result.row(i).segment(i + 1, j - i - 1) * result.col(j).segment(i + 1, j - i - 1)).value();
+      // denominator may be zero if original matrix is singular
+      result.coeffRef(i, j) = (arg.coeff(i, j) - tmp) / (result.coeff(i, i) + result.coeff(j, j));
+    }
+  }
+}
+
+namespace internal {
+
+/** \ingroup MatrixFunctions_Module
+ * \brief Helper struct for computing matrix square roots of general matrices.
+ * \tparam  MatrixType  type of the argument of the matrix square root,
+ *                      expected to be an instantiation of the Matrix class template.
+ *
+ * \sa MatrixSquareRootTriangular, MatrixSquareRootQuasiTriangular, MatrixBase::sqrt()
+ */
+template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
+struct matrix_sqrt_compute {
+  /** \brief Compute the matrix square root
+   *
+   * \param[in]  arg     matrix whose square root is to be computed.
+   * \param[out] result  square root of \p arg.
+   *
+   * See MatrixBase::sqrt() for details on how this computation is implemented.
+   */
+  template <typename ResultType>
+  static void run(const MatrixType& arg, ResultType& result);
+};
+
+// ********** Partial specialization for real matrices **********
+
+template <typename MatrixType>
+struct matrix_sqrt_compute<MatrixType, 0> {
+  typedef typename MatrixType::PlainObject PlainType;
+  template <typename ResultType>
+  static void run(const MatrixType& arg, ResultType& result) {
+    eigen_assert(arg.rows() == arg.cols());
+
+    // Compute Schur decomposition of arg
+    const RealSchur<PlainType> schurOfA(arg);
+    const PlainType& T = schurOfA.matrixT();
+    const PlainType& U = schurOfA.matrixU();
+
+    // Compute square root of T
+    PlainType sqrtT = PlainType::Zero(arg.rows(), arg.cols());
+    matrix_sqrt_quasi_triangular(T, sqrtT);
+
+    // Compute square root of arg
+    result = U * sqrtT * U.adjoint();
+  }
+};
+
+// ********** Partial specialization for complex matrices **********
+
+template <typename MatrixType>
+struct matrix_sqrt_compute<MatrixType, 1> {
+  typedef typename MatrixType::PlainObject PlainType;
+  template <typename ResultType>
+  static void run(const MatrixType& arg, ResultType& result) {
+    eigen_assert(arg.rows() == arg.cols());
+
+    // Compute Schur decomposition of arg
+    const ComplexSchur<PlainType> schurOfA(arg);
+    const PlainType& T = schurOfA.matrixT();
+    const PlainType& U = schurOfA.matrixU();
+
+    // Compute square root of T
+    PlainType sqrtT;
+    matrix_sqrt_triangular(T, sqrtT);
+
+    // Compute square root of arg
+    result = U * (sqrtT.template triangularView<Upper>() * U.adjoint());
+  }
+};
+
+}  // end namespace internal
+
+/** \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix square root of some matrix (expression).
+ *
+ * \tparam Derived  Type of the argument to the matrix square root.
+ *
+ * This class holds the argument to the matrix square root until it
+ * is assigned or evaluated for some other reason (so the argument
+ * should not be changed in the meantime). It is the return type of
+ * MatrixBase::sqrt() and most of the time this is the only way it is
+ * used.
+ */
+template <typename Derived>
+class MatrixSquareRootReturnValue : public ReturnByValue<MatrixSquareRootReturnValue<Derived> > {
+ protected:
+  typedef typename internal::ref_selector<Derived>::type DerivedNested;
+
+ public:
+  /** \brief Constructor.
+   *
+   * \param[in]  src  %Matrix (expression) forming the argument of the
+   * matrix square root.
+   */
+  explicit MatrixSquareRootReturnValue(const Derived& src) : m_src(src) {}
+
+  /** \brief Compute the matrix square root.
+   *
+   * \param[out]  result  the matrix square root of \p src in the
+   * constructor.
+   */
+  template <typename ResultType>
+  inline void evalTo(ResultType& result) const {
+    typedef typename internal::nested_eval<Derived, 10>::type DerivedEvalType;
+    typedef internal::remove_all_t<DerivedEvalType> DerivedEvalTypeClean;
+    DerivedEvalType tmp(m_src);
+    internal::matrix_sqrt_compute<DerivedEvalTypeClean>::run(tmp, result);
+  }
+
+  Index rows() const { return m_src.rows(); }
+  Index cols() const { return m_src.cols(); }
+
+ protected:
+  const DerivedNested m_src;
+};
+
+namespace internal {
+template <typename Derived>
+struct traits<MatrixSquareRootReturnValue<Derived> > {
+  typedef typename Derived::PlainObject ReturnType;
+};
+}  // namespace internal
+
+template <typename Derived>
+const MatrixSquareRootReturnValue<Derived> MatrixBase<Derived>::sqrt() const {
+  eigen_assert(rows() == cols());
+  return MatrixSquareRootReturnValue<Derived>(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATRIX_FUNCTION
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/StemFunction.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
new file mode 100644
index 0000000..8050c37
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
@@ -0,0 +1,115 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STEM_FUNCTION
+#define EIGEN_STEM_FUNCTION
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \brief The exponential function (and its derivatives). */
+template <typename Scalar>
+Scalar stem_function_exp(Scalar x, int) {
+  using std::exp;
+  return exp(x);
+}
+
+/** \brief Cosine (and its derivatives). */
+template <typename Scalar>
+Scalar stem_function_cos(Scalar x, int n) {
+  using std::cos;
+  using std::sin;
+  Scalar res;
+
+  switch (n % 4) {
+    case 0:
+      res = std::cos(x);
+      break;
+    case 1:
+      res = -std::sin(x);
+      break;
+    case 2:
+      res = -std::cos(x);
+      break;
+    case 3:
+      res = std::sin(x);
+      break;
+  }
+  return res;
+}
+
+/** \brief Sine (and its derivatives). */
+template <typename Scalar>
+Scalar stem_function_sin(Scalar x, int n) {
+  using std::cos;
+  using std::sin;
+  Scalar res;
+
+  switch (n % 4) {
+    case 0:
+      res = std::sin(x);
+      break;
+    case 1:
+      res = std::cos(x);
+      break;
+    case 2:
+      res = -std::sin(x);
+      break;
+    case 3:
+      res = -std::cos(x);
+      break;
+  }
+  return res;
+}
+
+/** \brief Hyperbolic cosine (and its derivatives). */
+template <typename Scalar>
+Scalar stem_function_cosh(Scalar x, int n) {
+  using std::cosh;
+  using std::sinh;
+  Scalar res;
+
+  switch (n % 2) {
+    case 0:
+      res = std::cosh(x);
+      break;
+    case 1:
+      res = std::sinh(x);
+      break;
+  }
+  return res;
+}
+
+/** \brief Hyperbolic sine (and its derivatives). */
+template <typename Scalar>
+Scalar stem_function_sinh(Scalar x, int n) {
+  using std::cosh;
+  using std::sinh;
+  Scalar res;
+
+  switch (n % 2) {
+    case 0:
+      res = std::sinh(x);
+      break;
+    case 1:
+      res = std::cosh(x);
+      break;
+  }
+  return res;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_STEM_FUNCTION
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
new file mode 100644
index 0000000..079772e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -0,0 +1,545 @@
+// -*- coding: utf-8
+// vim: set fileencoding=utf-8
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HYBRIDNONLINEARSOLVER_H
+#define EIGEN_HYBRIDNONLINEARSOLVER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace HybridNonLinearSolverSpace {
+enum Status {
+  Running = -1,
+  ImproperInputParameters = 0,
+  RelativeErrorTooSmall = 1,
+  TooManyFunctionEvaluation = 2,
+  TolTooSmall = 3,
+  NotMakingProgressJacobian = 4,
+  NotMakingProgressIterations = 5,
+  UserAsked = 6
+};
+}
+
+/**
+ * \ingroup NonLinearOptimization_Module
+ * \brief Finds a zero of a system of n
+ * nonlinear functions in n variables by a modification of the Powell
+ * hybrid method ("dogleg").
+ *
+ * The user must provide a subroutine which calculates the
+ * functions. The Jacobian is either provided by the user, or approximated
+ * using a forward-difference method.
+ *
+ */
+template <typename FunctorType, typename Scalar = double>
+class HybridNonLinearSolver {
+ public:
+  typedef DenseIndex Index;
+
+  HybridNonLinearSolver(FunctorType &_functor) : functor(_functor) {
+    nfev = njev = iter = 0;
+    fnorm = 0.;
+    useExternalScaling = false;
+  }
+
+  struct Parameters {
+    Parameters()
+        : factor(Scalar(100.)),
+          maxfev(1000),
+          xtol(numext::sqrt(NumTraits<Scalar>::epsilon())),
+          nb_of_subdiagonals(-1),
+          nb_of_superdiagonals(-1),
+          epsfcn(Scalar(0.)) {}
+    Scalar factor;
+    Index maxfev;  // maximum number of function evaluation
+    Scalar xtol;
+    Index nb_of_subdiagonals;
+    Index nb_of_superdiagonals;
+    Scalar epsfcn;
+  };
+  typedef Matrix<Scalar, Dynamic, 1> FVectorType;
+  typedef Matrix<Scalar, Dynamic, Dynamic> JacobianType;
+  /* TODO: if eigen provides a triangular storage, use it here */
+  typedef Matrix<Scalar, Dynamic, Dynamic> UpperTriangularType;
+
+  HybridNonLinearSolverSpace::Status hybrj1(FVectorType &x,
+                                            const Scalar tol = numext::sqrt(NumTraits<Scalar>::epsilon()));
+
+  HybridNonLinearSolverSpace::Status solveInit(FVectorType &x);
+  HybridNonLinearSolverSpace::Status solveOneStep(FVectorType &x);
+  HybridNonLinearSolverSpace::Status solve(FVectorType &x);
+
+  HybridNonLinearSolverSpace::Status hybrd1(FVectorType &x,
+                                            const Scalar tol = numext::sqrt(NumTraits<Scalar>::epsilon()));
+
+  HybridNonLinearSolverSpace::Status solveNumericalDiffInit(FVectorType &x);
+  HybridNonLinearSolverSpace::Status solveNumericalDiffOneStep(FVectorType &x);
+  HybridNonLinearSolverSpace::Status solveNumericalDiff(FVectorType &x);
+
+  void resetParameters(void) { parameters = Parameters(); }
+  Parameters parameters;
+  FVectorType fvec, qtf, diag;
+  JacobianType fjac;
+  UpperTriangularType R;
+  Index nfev;
+  Index njev;
+  Index iter;
+  Scalar fnorm;
+  bool useExternalScaling;
+
+ private:
+  FunctorType &functor;
+  Index n;
+  Scalar sum;
+  bool sing;
+  Scalar temp;
+  Scalar delta;
+  bool jeval;
+  Index ncsuc;
+  Scalar ratio;
+  Scalar pnorm, xnorm, fnorm1;
+  Index nslow1, nslow2;
+  Index ncfail;
+  Scalar actred, prered;
+  FVectorType wa1, wa2, wa3, wa4;
+
+  HybridNonLinearSolver &operator=(const HybridNonLinearSolver &);
+};
+
+template <typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status HybridNonLinearSolver<FunctorType, Scalar>::hybrj1(FVectorType &x,
+                                                                                      const Scalar tol) {
+  n = x.size();
+
+  /* check the input parameters for errors. */
+  if (n <= 0 || tol < 0.) return HybridNonLinearSolverSpace::ImproperInputParameters;
+
+  resetParameters();
+  parameters.maxfev = 100 * (n + 1);
+  parameters.xtol = tol;
+  diag.setConstant(n, 1.);
+  useExternalScaling = true;
+  return solve(x);
+}
+
+template <typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status HybridNonLinearSolver<FunctorType, Scalar>::solveInit(FVectorType &x) {
+  n = x.size();
+
+  wa1.resize(n);
+  wa2.resize(n);
+  wa3.resize(n);
+  wa4.resize(n);
+  fvec.resize(n);
+  qtf.resize(n);
+  fjac.resize(n, n);
+  if (!useExternalScaling) diag.resize(n);
+  eigen_assert((!useExternalScaling || diag.size() == n) &&
+               "When useExternalScaling is set, the caller must provide a valid 'diag'");
+
+  /* Function Body */
+  nfev = 0;
+  njev = 0;
+
+  /*     check the input parameters for errors. */
+  if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
+    return HybridNonLinearSolverSpace::ImproperInputParameters;
+  if (useExternalScaling)
+    for (Index j = 0; j < n; ++j)
+      if (diag[j] <= 0.) return HybridNonLinearSolverSpace::ImproperInputParameters;
+
+  /*     evaluate the function at the starting point */
+  /*     and calculate its norm. */
+  nfev = 1;
+  if (functor(x, fvec) < 0) return HybridNonLinearSolverSpace::UserAsked;
+  fnorm = fvec.stableNorm();
+
+  /*     initialize iteration counter and monitors. */
+  iter = 1;
+  ncsuc = 0;
+  ncfail = 0;
+  nslow1 = 0;
+  nslow2 = 0;
+
+  return HybridNonLinearSolverSpace::Running;
+}
+
+template <typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status HybridNonLinearSolver<FunctorType, Scalar>::solveOneStep(FVectorType &x) {
+  using std::abs;
+
+  eigen_assert(x.size() == n);  // check the caller is not cheating us
+
+  Index j;
+  std::vector<JacobiRotation<Scalar> > v_givens(n), w_givens(n);
+
+  jeval = true;
+
+  /* calculate the jacobian matrix. */
+  if (functor.df(x, fjac) < 0) return HybridNonLinearSolverSpace::UserAsked;
+  ++njev;
+
+  wa2 = fjac.colwise().blueNorm();
+
+  /* on the first iteration and if external scaling is not used, scale according */
+  /* to the norms of the columns of the initial jacobian. */
+  if (iter == 1) {
+    if (!useExternalScaling)
+      for (j = 0; j < n; ++j) diag[j] = (wa2[j] == 0.) ? 1. : wa2[j];
+
+    /* on the first iteration, calculate the norm of the scaled x */
+    /* and initialize the step bound delta. */
+    xnorm = diag.cwiseProduct(x).stableNorm();
+    delta = parameters.factor * xnorm;
+    if (delta == 0.) delta = parameters.factor;
+  }
+
+  /* compute the qr factorization of the jacobian. */
+  HouseholderQR<JacobianType> qrfac(fjac);  // no pivoting:
+
+  /* copy the triangular factor of the qr factorization into r. */
+  R = qrfac.matrixQR();
+
+  /* accumulate the orthogonal factor in fjac. */
+  fjac = qrfac.householderQ();
+
+  /* form (q transpose)*fvec and store in qtf. */
+  qtf = fjac.transpose() * fvec;
+
+  /* rescale if necessary. */
+  if (!useExternalScaling) diag = diag.cwiseMax(wa2);
+
+  while (true) {
+    /* determine the direction p. */
+    internal::dogleg<Scalar>(R, diag, qtf, delta, wa1);
+
+    /* store the direction p and x + p. calculate the norm of p. */
+    wa1 = -wa1;
+    wa2 = x + wa1;
+    pnorm = diag.cwiseProduct(wa1).stableNorm();
+
+    /* on the first iteration, adjust the initial step bound. */
+    if (iter == 1) delta = (std::min)(delta, pnorm);
+
+    /* evaluate the function at x + p and calculate its norm. */
+    if (functor(wa2, wa4) < 0) return HybridNonLinearSolverSpace::UserAsked;
+    ++nfev;
+    fnorm1 = wa4.stableNorm();
+
+    /* compute the scaled actual reduction. */
+    actred = -1.;
+    if (fnorm1 < fnorm) /* Computing 2nd power */
+      actred = 1. - numext::abs2(fnorm1 / fnorm);
+
+    /* compute the scaled predicted reduction. */
+    wa3 = R.template triangularView<Upper>() * wa1 + qtf;
+    temp = wa3.stableNorm();
+    prered = 0.;
+    if (temp < fnorm) /* Computing 2nd power */
+      prered = 1. - numext::abs2(temp / fnorm);
+
+    /* compute the ratio of the actual to the predicted reduction. */
+    ratio = 0.;
+    if (prered > 0.) ratio = actred / prered;
+
+    /* update the step bound. */
+    if (ratio < Scalar(.1)) {
+      ncsuc = 0;
+      ++ncfail;
+      delta = Scalar(.5) * delta;
+    } else {
+      ncfail = 0;
+      ++ncsuc;
+      if (ratio >= Scalar(.5) || ncsuc > 1) delta = (std::max)(delta, pnorm / Scalar(.5));
+      if (abs(ratio - 1.) <= Scalar(.1)) {
+        delta = pnorm / Scalar(.5);
+      }
+    }
+
+    /* test for successful iteration. */
+    if (ratio >= Scalar(1e-4)) {
+      /* successful iteration. update x, fvec, and their norms. */
+      x = wa2;
+      wa2 = diag.cwiseProduct(x);
+      fvec = wa4;
+      xnorm = wa2.stableNorm();
+      fnorm = fnorm1;
+      ++iter;
+    }
+
+    /* determine the progress of the iteration. */
+    ++nslow1;
+    if (actred >= Scalar(.001)) nslow1 = 0;
+    if (jeval) ++nslow2;
+    if (actred >= Scalar(.1)) nslow2 = 0;
+
+    /* test for convergence. */
+    if (delta <= parameters.xtol * xnorm || fnorm == 0.) return HybridNonLinearSolverSpace::RelativeErrorTooSmall;
+
+    /* tests for termination and stringent tolerances. */
+    if (nfev >= parameters.maxfev) return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
+    if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
+      return HybridNonLinearSolverSpace::TolTooSmall;
+    if (nslow2 == 5) return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
+    if (nslow1 == 10) return HybridNonLinearSolverSpace::NotMakingProgressIterations;
+
+    /* criterion for recalculating jacobian. */
+    if (ncfail == 2) break;  // leave inner loop and go for the next outer loop iteration
+
+    /* calculate the rank one modification to the jacobian */
+    /* and update qtf if necessary. */
+    wa1 = diag.cwiseProduct(diag.cwiseProduct(wa1) / pnorm);
+    wa2 = fjac.transpose() * wa4;
+    if (ratio >= Scalar(1e-4)) qtf = wa2;
+    wa2 = (wa2 - wa3) / pnorm;
+
+    /* compute the qr factorization of the updated jacobian. */
+    internal::r1updt<Scalar>(R, wa1, v_givens, w_givens, wa2, wa3, &sing);
+    internal::r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens);
+    internal::r1mpyq<Scalar>(1, n, qtf.data(), v_givens, w_givens);
+
+    jeval = false;
+  }
+  return HybridNonLinearSolverSpace::Running;
+}
+
+template <typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status HybridNonLinearSolver<FunctorType, Scalar>::solve(FVectorType &x) {
+  HybridNonLinearSolverSpace::Status status = solveInit(x);
+  if (status == HybridNonLinearSolverSpace::ImproperInputParameters) return status;
+  while (status == HybridNonLinearSolverSpace::Running) status = solveOneStep(x);
+  return status;
+}
+
+template <typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status HybridNonLinearSolver<FunctorType, Scalar>::hybrd1(FVectorType &x,
+                                                                                      const Scalar tol) {
+  n = x.size();
+
+  /* check the input parameters for errors. */
+  if (n <= 0 || tol < 0.) return HybridNonLinearSolverSpace::ImproperInputParameters;
+
+  resetParameters();
+  parameters.maxfev = 200 * (n + 1);
+  parameters.xtol = tol;
+
+  diag.setConstant(n, 1.);
+  useExternalScaling = true;
+  return solveNumericalDiff(x);
+}
+
+template <typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status HybridNonLinearSolver<FunctorType, Scalar>::solveNumericalDiffInit(FVectorType &x) {
+  n = x.size();
+
+  if (parameters.nb_of_subdiagonals < 0) parameters.nb_of_subdiagonals = n - 1;
+  if (parameters.nb_of_superdiagonals < 0) parameters.nb_of_superdiagonals = n - 1;
+
+  wa1.resize(n);
+  wa2.resize(n);
+  wa3.resize(n);
+  wa4.resize(n);
+  qtf.resize(n);
+  fjac.resize(n, n);
+  fvec.resize(n);
+  if (!useExternalScaling) diag.resize(n);
+  eigen_assert((!useExternalScaling || diag.size() == n) &&
+               "When useExternalScaling is set, the caller must provide a valid 'diag'");
+
+  /* Function Body */
+  nfev = 0;
+  njev = 0;
+
+  /*     check the input parameters for errors. */
+  if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.nb_of_subdiagonals < 0 ||
+      parameters.nb_of_superdiagonals < 0 || parameters.factor <= 0.)
+    return HybridNonLinearSolverSpace::ImproperInputParameters;
+  if (useExternalScaling)
+    for (Index j = 0; j < n; ++j)
+      if (diag[j] <= 0.) return HybridNonLinearSolverSpace::ImproperInputParameters;
+
+  /*     evaluate the function at the starting point */
+  /*     and calculate its norm. */
+  nfev = 1;
+  if (functor(x, fvec) < 0) return HybridNonLinearSolverSpace::UserAsked;
+  fnorm = fvec.stableNorm();
+
+  /*     initialize iteration counter and monitors. */
+  iter = 1;
+  ncsuc = 0;
+  ncfail = 0;
+  nslow1 = 0;
+  nslow2 = 0;
+
+  return HybridNonLinearSolverSpace::Running;
+}
+
+template <typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status HybridNonLinearSolver<FunctorType, Scalar>::solveNumericalDiffOneStep(
+    FVectorType &x) {
+  using std::abs;
+  using std::sqrt;
+
+  eigen_assert(x.size() == n);  // check the caller is not cheating us
+
+  Index j;
+  std::vector<JacobiRotation<Scalar> > v_givens(n), w_givens(n);
+
+  jeval = true;
+  if (parameters.nb_of_subdiagonals < 0) parameters.nb_of_subdiagonals = n - 1;
+  if (parameters.nb_of_superdiagonals < 0) parameters.nb_of_superdiagonals = n - 1;
+
+  /* calculate the jacobian matrix. */
+  if (internal::fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals,
+                       parameters.epsfcn) < 0)
+    return HybridNonLinearSolverSpace::UserAsked;
+  nfev += (std::min)(parameters.nb_of_subdiagonals + parameters.nb_of_superdiagonals + 1, n);
+
+  wa2 = fjac.colwise().blueNorm();
+
+  /* on the first iteration and if external scaling is not used, scale according */
+  /* to the norms of the columns of the initial jacobian. */
+  if (iter == 1) {
+    if (!useExternalScaling)
+      for (j = 0; j < n; ++j) diag[j] = (wa2[j] == 0.) ? 1. : wa2[j];
+
+    /* on the first iteration, calculate the norm of the scaled x */
+    /* and initialize the step bound delta. */
+    xnorm = diag.cwiseProduct(x).stableNorm();
+    delta = parameters.factor * xnorm;
+    if (delta == 0.) delta = parameters.factor;
+  }
+
+  /* compute the qr factorization of the jacobian. */
+  HouseholderQR<JacobianType> qrfac(fjac);  // no pivoting:
+
+  /* copy the triangular factor of the qr factorization into r. */
+  R = qrfac.matrixQR();
+
+  /* accumulate the orthogonal factor in fjac. */
+  fjac = qrfac.householderQ();
+
+  /* form (q transpose)*fvec and store in qtf. */
+  qtf = fjac.transpose() * fvec;
+
+  /* rescale if necessary. */
+  if (!useExternalScaling) diag = diag.cwiseMax(wa2);
+
+  while (true) {
+    /* determine the direction p. */
+    internal::dogleg<Scalar>(R, diag, qtf, delta, wa1);
+
+    /* store the direction p and x + p. calculate the norm of p. */
+    wa1 = -wa1;
+    wa2 = x + wa1;
+    pnorm = diag.cwiseProduct(wa1).stableNorm();
+
+    /* on the first iteration, adjust the initial step bound. */
+    if (iter == 1) delta = (std::min)(delta, pnorm);
+
+    /* evaluate the function at x + p and calculate its norm. */
+    if (functor(wa2, wa4) < 0) return HybridNonLinearSolverSpace::UserAsked;
+    ++nfev;
+    fnorm1 = wa4.stableNorm();
+
+    /* compute the scaled actual reduction. */
+    actred = -1.;
+    if (fnorm1 < fnorm) /* Computing 2nd power */
+      actred = 1. - numext::abs2(fnorm1 / fnorm);
+
+    /* compute the scaled predicted reduction. */
+    wa3 = R.template triangularView<Upper>() * wa1 + qtf;
+    temp = wa3.stableNorm();
+    prered = 0.;
+    if (temp < fnorm) /* Computing 2nd power */
+      prered = 1. - numext::abs2(temp / fnorm);
+
+    /* compute the ratio of the actual to the predicted reduction. */
+    ratio = 0.;
+    if (prered > 0.) ratio = actred / prered;
+
+    /* update the step bound. */
+    if (ratio < Scalar(.1)) {
+      ncsuc = 0;
+      ++ncfail;
+      delta = Scalar(.5) * delta;
+    } else {
+      ncfail = 0;
+      ++ncsuc;
+      if (ratio >= Scalar(.5) || ncsuc > 1) delta = (std::max)(delta, pnorm / Scalar(.5));
+      if (abs(ratio - 1.) <= Scalar(.1)) {
+        delta = pnorm / Scalar(.5);
+      }
+    }
+
+    /* test for successful iteration. */
+    if (ratio >= Scalar(1e-4)) {
+      /* successful iteration. update x, fvec, and their norms. */
+      x = wa2;
+      wa2 = diag.cwiseProduct(x);
+      fvec = wa4;
+      xnorm = wa2.stableNorm();
+      fnorm = fnorm1;
+      ++iter;
+    }
+
+    /* determine the progress of the iteration. */
+    ++nslow1;
+    if (actred >= Scalar(.001)) nslow1 = 0;
+    if (jeval) ++nslow2;
+    if (actred >= Scalar(.1)) nslow2 = 0;
+
+    /* test for convergence. */
+    if (delta <= parameters.xtol * xnorm || fnorm == 0.) return HybridNonLinearSolverSpace::RelativeErrorTooSmall;
+
+    /* tests for termination and stringent tolerances. */
+    if (nfev >= parameters.maxfev) return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
+    if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
+      return HybridNonLinearSolverSpace::TolTooSmall;
+    if (nslow2 == 5) return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
+    if (nslow1 == 10) return HybridNonLinearSolverSpace::NotMakingProgressIterations;
+
+    /* criterion for recalculating jacobian. */
+    if (ncfail == 2) break;  // leave inner loop and go for the next outer loop iteration
+
+    /* calculate the rank one modification to the jacobian */
+    /* and update qtf if necessary. */
+    wa1 = diag.cwiseProduct(diag.cwiseProduct(wa1) / pnorm);
+    wa2 = fjac.transpose() * wa4;
+    if (ratio >= Scalar(1e-4)) qtf = wa2;
+    wa2 = (wa2 - wa3) / pnorm;
+
+    /* compute the qr factorization of the updated jacobian. */
+    internal::r1updt<Scalar>(R, wa1, v_givens, w_givens, wa2, wa3, &sing);
+    internal::r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens);
+    internal::r1mpyq<Scalar>(1, n, qtf.data(), v_givens, w_givens);
+
+    jeval = false;
+  }
+  return HybridNonLinearSolverSpace::Running;
+}
+
+template <typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status HybridNonLinearSolver<FunctorType, Scalar>::solveNumericalDiff(FVectorType &x) {
+  HybridNonLinearSolverSpace::Status status = solveNumericalDiffInit(x);
+  if (status == HybridNonLinearSolverSpace::ImproperInputParameters) return status;
+  while (status == HybridNonLinearSolverSpace::Running) status = solveNumericalDiffOneStep(x);
+  return status;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_HYBRIDNONLINEARSOLVER_H
+
+// vim: ai ts=4 sts=4 et sw=4
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/InternalHeaderCheck.h
new file mode 100644
index 0000000..d06970f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/InternalHeaderCheck.h
@@ -0,0 +1,4 @@
+#ifndef EIGEN_NONLINEAROPTIMIZATION_MODULE_H
+#error \
+    "Please include unsupported/Eigen/NonLinearOptimization instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
new file mode 100644
index 0000000..5f156ac
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -0,0 +1,587 @@
+// -*- coding: utf-8
+// vim: set fileencoding=utf-8
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LEVENBERGMARQUARDT__H
+#define EIGEN_LEVENBERGMARQUARDT__H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace LevenbergMarquardtSpace {
+enum Status {
+  NotStarted = -2,
+  Running = -1,
+  ImproperInputParameters = 0,
+  RelativeReductionTooSmall = 1,
+  RelativeErrorTooSmall = 2,
+  RelativeErrorAndReductionTooSmall = 3,
+  CosinusTooSmall = 4,
+  TooManyFunctionEvaluation = 5,
+  FtolTooSmall = 6,
+  XtolTooSmall = 7,
+  GtolTooSmall = 8,
+  UserAsked = 9
+};
+}
+
+/**
+ * \ingroup NonLinearOptimization_Module
+ * \brief Performs non linear optimization over a non-linear function,
+ * using a variant of the Levenberg Marquardt algorithm.
+ *
+ * Check wikipedia for more information.
+ * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
+ */
+template <typename FunctorType, typename Scalar = double>
+class LevenbergMarquardt {
+  static Scalar sqrt_epsilon() {
+    using std::sqrt;
+    return sqrt(NumTraits<Scalar>::epsilon());
+  }
+
+ public:
+  LevenbergMarquardt(FunctorType &_functor) : functor(_functor) {
+    nfev = njev = iter = 0;
+    fnorm = gnorm = 0.;
+    useExternalScaling = false;
+  }
+
+  typedef DenseIndex Index;
+
+  struct Parameters {
+    Parameters()
+        : factor(Scalar(100.)),
+          maxfev(400),
+          ftol(sqrt_epsilon()),
+          xtol(sqrt_epsilon()),
+          gtol(Scalar(0.)),
+          epsfcn(Scalar(0.)) {}
+    Scalar factor;
+    Index maxfev;  // maximum number of function evaluation
+    Scalar ftol;
+    Scalar xtol;
+    Scalar gtol;
+    Scalar epsfcn;
+  };
+
+  typedef Matrix<Scalar, Dynamic, 1> FVectorType;
+  typedef Matrix<Scalar, Dynamic, Dynamic> JacobianType;
+
+  LevenbergMarquardtSpace::Status lmder1(FVectorType &x, const Scalar tol = sqrt_epsilon());
+
+  LevenbergMarquardtSpace::Status minimize(FVectorType &x);
+  LevenbergMarquardtSpace::Status minimizeInit(FVectorType &x);
+  LevenbergMarquardtSpace::Status minimizeOneStep(FVectorType &x);
+
+  static LevenbergMarquardtSpace::Status lmdif1(FunctorType &functor, FVectorType &x, Index *nfev,
+                                                const Scalar tol = sqrt_epsilon());
+
+  LevenbergMarquardtSpace::Status lmstr1(FVectorType &x, const Scalar tol = sqrt_epsilon());
+
+  LevenbergMarquardtSpace::Status minimizeOptimumStorage(FVectorType &x);
+  LevenbergMarquardtSpace::Status minimizeOptimumStorageInit(FVectorType &x);
+  LevenbergMarquardtSpace::Status minimizeOptimumStorageOneStep(FVectorType &x);
+
+  void resetParameters(void) { parameters = Parameters(); }
+
+  Parameters parameters;
+  FVectorType fvec, qtf, diag;
+  JacobianType fjac;
+  PermutationMatrix<Dynamic, Dynamic> permutation;
+  Index nfev;
+  Index njev;
+  Index iter;
+  Scalar fnorm, gnorm;
+  bool useExternalScaling;
+
+  Scalar lm_param(void) { return par; }
+
+ private:
+  FunctorType &functor;
+  Index n;
+  Index m;
+  FVectorType wa1, wa2, wa3, wa4;
+
+  Scalar par, sum;
+  Scalar temp, temp1, temp2;
+  Scalar delta;
+  Scalar ratio;
+  Scalar pnorm, xnorm, fnorm1, actred, dirder, prered;
+
+  LevenbergMarquardt &operator=(const LevenbergMarquardt &);
+};
+
+template <typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType, Scalar>::lmder1(FVectorType &x, const Scalar tol) {
+  n = x.size();
+  m = functor.values();
+
+  /* check the input parameters for errors. */
+  if (n <= 0 || m < n || tol < 0.) return LevenbergMarquardtSpace::ImproperInputParameters;
+
+  resetParameters();
+  parameters.ftol = tol;
+  parameters.xtol = tol;
+  parameters.maxfev = 100 * (n + 1);
+
+  return minimize(x);
+}
+
+template <typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType, Scalar>::minimize(FVectorType &x) {
+  LevenbergMarquardtSpace::Status status = minimizeInit(x);
+  if (status == LevenbergMarquardtSpace::ImproperInputParameters) return status;
+  do {
+    status = minimizeOneStep(x);
+  } while (status == LevenbergMarquardtSpace::Running);
+  return status;
+}
+
+template <typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType, Scalar>::minimizeInit(FVectorType &x) {
+  n = x.size();
+  m = functor.values();
+
+  wa1.resize(n);
+  wa2.resize(n);
+  wa3.resize(n);
+  wa4.resize(m);
+  fvec.resize(m);
+  fjac.resize(m, n);
+  if (!useExternalScaling) diag.resize(n);
+  eigen_assert((!useExternalScaling || diag.size() == n) &&
+               "When useExternalScaling is set, the caller must provide a valid 'diag'");
+  qtf.resize(n);
+
+  /* Function Body */
+  nfev = 0;
+  njev = 0;
+
+  /*     check the input parameters for errors. */
+  if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. ||
+      parameters.maxfev <= 0 || parameters.factor <= 0.)
+    return LevenbergMarquardtSpace::ImproperInputParameters;
+
+  if (useExternalScaling)
+    for (Index j = 0; j < n; ++j)
+      if (diag[j] <= 0.) return LevenbergMarquardtSpace::ImproperInputParameters;
+
+  /*     evaluate the function at the starting point */
+  /*     and calculate its norm. */
+  nfev = 1;
+  if (functor(x, fvec) < 0) return LevenbergMarquardtSpace::UserAsked;
+  fnorm = fvec.stableNorm();
+
+  /*     initialize levenberg-marquardt parameter and iteration counter. */
+  par = 0.;
+  iter = 1;
+
+  return LevenbergMarquardtSpace::NotStarted;
+}
+
+template <typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType, Scalar>::minimizeOneStep(FVectorType &x) {
+  using std::abs;
+  using std::sqrt;
+
+  eigen_assert(x.size() == n);  // check the caller is not cheating us
+
+  /* calculate the jacobian matrix. */
+  Index df_ret = functor.df(x, fjac);
+  if (df_ret < 0) return LevenbergMarquardtSpace::UserAsked;
+  if (df_ret > 0)
+    // numerical diff, we evaluated the function df_ret times
+    nfev += df_ret;
+  else
+    njev++;
+
+  /* compute the qr factorization of the jacobian. */
+  wa2 = fjac.colwise().blueNorm();
+  ColPivHouseholderQR<JacobianType> qrfac(fjac);
+  fjac = qrfac.matrixQR();
+  permutation = qrfac.colsPermutation();
+
+  /* on the first iteration and if external scaling is not used, scale according */
+  /* to the norms of the columns of the initial jacobian. */
+  if (iter == 1) {
+    if (!useExternalScaling)
+      for (Index j = 0; j < n; ++j) diag[j] = (wa2[j] == 0.) ? 1. : wa2[j];
+
+    /* on the first iteration, calculate the norm of the scaled x */
+    /* and initialize the step bound delta. */
+    xnorm = diag.cwiseProduct(x).stableNorm();
+    delta = parameters.factor * xnorm;
+    if (delta == 0.) delta = parameters.factor;
+  }
+
+  /* form (q transpose)*fvec and store the first n components in */
+  /* qtf. */
+  wa4 = fvec;
+  wa4.applyOnTheLeft(qrfac.householderQ().adjoint());
+  qtf = wa4.head(n);
+
+  /* compute the norm of the scaled gradient. */
+  gnorm = 0.;
+  if (fnorm != 0.)
+    for (Index j = 0; j < n; ++j)
+      if (wa2[permutation.indices()[j]] != 0.)
+        gnorm = (std::max)(gnorm,
+                           abs(fjac.col(j).head(j + 1).dot(qtf.head(j + 1) / fnorm) / wa2[permutation.indices()[j]]));
+
+  /* test for convergence of the gradient norm. */
+  if (gnorm <= parameters.gtol) return LevenbergMarquardtSpace::CosinusTooSmall;
+
+  /* rescale if necessary. */
+  if (!useExternalScaling) diag = diag.cwiseMax(wa2);
+
+  do {
+    /* determine the levenberg-marquardt parameter. */
+    internal::lmpar2<Scalar>(qrfac, diag, qtf, delta, par, wa1);
+
+    /* store the direction p and x + p. calculate the norm of p. */
+    wa1 = -wa1;
+    wa2 = x + wa1;
+    pnorm = diag.cwiseProduct(wa1).stableNorm();
+
+    /* on the first iteration, adjust the initial step bound. */
+    if (iter == 1) delta = (std::min)(delta, pnorm);
+
+    /* evaluate the function at x + p and calculate its norm. */
+    if (functor(wa2, wa4) < 0) return LevenbergMarquardtSpace::UserAsked;
+    ++nfev;
+    fnorm1 = wa4.stableNorm();
+
+    /* compute the scaled actual reduction. */
+    actred = -1.;
+    if (Scalar(.1) * fnorm1 < fnorm) actred = 1. - numext::abs2(fnorm1 / fnorm);
+
+    /* compute the scaled predicted reduction and */
+    /* the scaled directional derivative. */
+    wa3.noalias() = fjac.template triangularView<Upper>() * (qrfac.colsPermutation().inverse() * wa1);
+    temp1 = numext::abs2(wa3.stableNorm() / fnorm);
+    temp2 = numext::abs2(sqrt(par) * pnorm / fnorm);
+    prered = temp1 + temp2 / Scalar(.5);
+    dirder = -(temp1 + temp2);
+
+    /* compute the ratio of the actual to the predicted */
+    /* reduction. */
+    ratio = 0.;
+    if (prered != 0.) ratio = actred / prered;
+
+    /* update the step bound. */
+    if (ratio <= Scalar(.25)) {
+      if (actred >= 0.) temp = Scalar(.5);
+      if (actred < 0.) temp = Scalar(.5) * dirder / (dirder + Scalar(.5) * actred);
+      if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1)) temp = Scalar(.1);
+      /* Computing MIN */
+      delta = temp * (std::min)(delta, pnorm / Scalar(.1));
+      par /= temp;
+    } else if (!(par != 0. && ratio < Scalar(.75))) {
+      delta = pnorm / Scalar(.5);
+      par = Scalar(.5) * par;
+    }
+
+    /* test for successful iteration. */
+    if (ratio >= Scalar(1e-4)) {
+      /* successful iteration. update x, fvec, and their norms. */
+      x = wa2;
+      wa2 = diag.cwiseProduct(x);
+      fvec = wa4;
+      xnorm = wa2.stableNorm();
+      fnorm = fnorm1;
+      ++iter;
+    }
+
+    /* tests for convergence. */
+    if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. &&
+        delta <= parameters.xtol * xnorm)
+      return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
+    if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
+      return LevenbergMarquardtSpace::RelativeReductionTooSmall;
+    if (delta <= parameters.xtol * xnorm) return LevenbergMarquardtSpace::RelativeErrorTooSmall;
+
+    /* tests for termination and stringent tolerances. */
+    if (nfev >= parameters.maxfev) return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
+    if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() &&
+        Scalar(.5) * ratio <= 1.)
+      return LevenbergMarquardtSpace::FtolTooSmall;
+    if (delta <= NumTraits<Scalar>::epsilon() * xnorm) return LevenbergMarquardtSpace::XtolTooSmall;
+    if (gnorm <= NumTraits<Scalar>::epsilon()) return LevenbergMarquardtSpace::GtolTooSmall;
+
+  } while (ratio < Scalar(1e-4));
+
+  return LevenbergMarquardtSpace::Running;
+}
+
+template <typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType, Scalar>::lmstr1(FVectorType &x, const Scalar tol) {
+  n = x.size();
+  m = functor.values();
+
+  /* check the input parameters for errors. */
+  if (n <= 0 || m < n || tol < 0.) return LevenbergMarquardtSpace::ImproperInputParameters;
+
+  resetParameters();
+  parameters.ftol = tol;
+  parameters.xtol = tol;
+  parameters.maxfev = 100 * (n + 1);
+
+  return minimizeOptimumStorage(x);
+}
+
+template <typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType, Scalar>::minimizeOptimumStorageInit(FVectorType &x) {
+  n = x.size();
+  m = functor.values();
+
+  wa1.resize(n);
+  wa2.resize(n);
+  wa3.resize(n);
+  wa4.resize(m);
+  fvec.resize(m);
+  // Only R is stored in fjac. Q is only used to compute 'qtf', which is
+  // Q.transpose()*rhs. qtf will be updated using givens rotation,
+  // instead of storing them in Q.
+  // The purpose it to only use a nxn matrix, instead of mxn here, so
+  // that we can handle cases where m>>n :
+  fjac.resize(n, n);
+  if (!useExternalScaling) diag.resize(n);
+  eigen_assert((!useExternalScaling || diag.size() == n) &&
+               "When useExternalScaling is set, the caller must provide a valid 'diag'");
+  qtf.resize(n);
+
+  /* Function Body */
+  nfev = 0;
+  njev = 0;
+
+  /*     check the input parameters for errors. */
+  if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. ||
+      parameters.maxfev <= 0 || parameters.factor <= 0.)
+    return LevenbergMarquardtSpace::ImproperInputParameters;
+
+  if (useExternalScaling)
+    for (Index j = 0; j < n; ++j)
+      if (diag[j] <= 0.) return LevenbergMarquardtSpace::ImproperInputParameters;
+
+  /*     evaluate the function at the starting point */
+  /*     and calculate its norm. */
+  nfev = 1;
+  if (functor(x, fvec) < 0) return LevenbergMarquardtSpace::UserAsked;
+  fnorm = fvec.stableNorm();
+
+  /*     initialize levenberg-marquardt parameter and iteration counter. */
+  par = 0.;
+  iter = 1;
+
+  return LevenbergMarquardtSpace::NotStarted;
+}
+
+template <typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType, Scalar>::minimizeOptimumStorageOneStep(FVectorType &x) {
+  using std::abs;
+  using std::sqrt;
+
+  eigen_assert(x.size() == n);  // check the caller is not cheating us
+
+  Index i, j;
+  bool sing;
+
+  /* compute the qr factorization of the jacobian matrix */
+  /* calculated one row at a time, while simultaneously */
+  /* forming (q transpose)*fvec and storing the first */
+  /* n components in qtf. */
+  qtf.fill(0.);
+  fjac.fill(0.);
+  Index rownb = 2;
+  for (i = 0; i < m; ++i) {
+    if (functor.df(x, wa3, rownb) < 0) return LevenbergMarquardtSpace::UserAsked;
+    internal::rwupdt<Scalar>(fjac, wa3, qtf, fvec[i]);
+    ++rownb;
+  }
+  ++njev;
+
+  /* if the jacobian is rank deficient, call qrfac to */
+  /* reorder its columns and update the components of qtf. */
+  sing = false;
+  for (j = 0; j < n; ++j) {
+    if (fjac(j, j) == 0.) sing = true;
+    wa2[j] = fjac.col(j).head(j).stableNorm();
+  }
+  permutation.setIdentity(n);
+  if (sing) {
+    wa2 = fjac.colwise().blueNorm();
+    // TODO: We have no unit test covering this code path, do not modify
+    // until it is carefully tested
+    ColPivHouseholderQR<JacobianType> qrfac(fjac);
+    fjac = qrfac.matrixQR();
+    wa1 = fjac.diagonal();
+    fjac.diagonal() = qrfac.hCoeffs();
+    permutation = qrfac.colsPermutation();
+    // TODO: Avoid this:
+    for (Index ii = 0; ii < fjac.cols(); ii++)
+      fjac.col(ii).segment(ii + 1, fjac.rows() - ii - 1) *= fjac(ii, ii);  // rescale vectors
+
+    for (j = 0; j < n; ++j) {
+      if (fjac(j, j) != 0.) {
+        sum = 0.;
+        for (i = j; i < n; ++i) sum += fjac(i, j) * qtf[i];
+        temp = -sum / fjac(j, j);
+        for (i = j; i < n; ++i) qtf[i] += fjac(i, j) * temp;
+      }
+      fjac(j, j) = wa1[j];
+    }
+  }
+
+  /* on the first iteration and if external scaling is not used, scale according */
+  /* to the norms of the columns of the initial jacobian. */
+  if (iter == 1) {
+    if (!useExternalScaling)
+      for (j = 0; j < n; ++j) diag[j] = (wa2[j] == 0.) ? 1. : wa2[j];
+
+    /* on the first iteration, calculate the norm of the scaled x */
+    /* and initialize the step bound delta. */
+    xnorm = diag.cwiseProduct(x).stableNorm();
+    delta = parameters.factor * xnorm;
+    if (delta == 0.) delta = parameters.factor;
+  }
+
+  /* compute the norm of the scaled gradient. */
+  gnorm = 0.;
+  if (fnorm != 0.)
+    for (j = 0; j < n; ++j)
+      if (wa2[permutation.indices()[j]] != 0.)
+        gnorm = (std::max)(gnorm,
+                           abs(fjac.col(j).head(j + 1).dot(qtf.head(j + 1) / fnorm) / wa2[permutation.indices()[j]]));
+
+  /* test for convergence of the gradient norm. */
+  if (gnorm <= parameters.gtol) return LevenbergMarquardtSpace::CosinusTooSmall;
+
+  /* rescale if necessary. */
+  if (!useExternalScaling) diag = diag.cwiseMax(wa2);
+
+  do {
+    /* determine the levenberg-marquardt parameter. */
+    internal::lmpar<Scalar>(fjac, permutation.indices(), diag, qtf, delta, par, wa1);
+
+    /* store the direction p and x + p. calculate the norm of p. */
+    wa1 = -wa1;
+    wa2 = x + wa1;
+    pnorm = diag.cwiseProduct(wa1).stableNorm();
+
+    /* on the first iteration, adjust the initial step bound. */
+    if (iter == 1) delta = (std::min)(delta, pnorm);
+
+    /* evaluate the function at x + p and calculate its norm. */
+    if (functor(wa2, wa4) < 0) return LevenbergMarquardtSpace::UserAsked;
+    ++nfev;
+    fnorm1 = wa4.stableNorm();
+
+    /* compute the scaled actual reduction. */
+    actred = -1.;
+    if (Scalar(.1) * fnorm1 < fnorm) actred = 1. - numext::abs2(fnorm1 / fnorm);
+
+    /* compute the scaled predicted reduction and */
+    /* the scaled directional derivative. */
+    wa3 = fjac.topLeftCorner(n, n).template triangularView<Upper>() * (permutation.inverse() * wa1);
+    temp1 = numext::abs2(wa3.stableNorm() / fnorm);
+    temp2 = numext::abs2(sqrt(par) * pnorm / fnorm);
+    prered = temp1 + temp2 / Scalar(.5);
+    dirder = -(temp1 + temp2);
+
+    /* compute the ratio of the actual to the predicted */
+    /* reduction. */
+    ratio = 0.;
+    if (prered != 0.) ratio = actred / prered;
+
+    /* update the step bound. */
+    if (ratio <= Scalar(.25)) {
+      if (actred >= 0.) temp = Scalar(.5);
+      if (actred < 0.) temp = Scalar(.5) * dirder / (dirder + Scalar(.5) * actred);
+      if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1)) temp = Scalar(.1);
+      /* Computing MIN */
+      delta = temp * (std::min)(delta, pnorm / Scalar(.1));
+      par /= temp;
+    } else if (!(par != 0. && ratio < Scalar(.75))) {
+      delta = pnorm / Scalar(.5);
+      par = Scalar(.5) * par;
+    }
+
+    /* test for successful iteration. */
+    if (ratio >= Scalar(1e-4)) {
+      /* successful iteration. update x, fvec, and their norms. */
+      x = wa2;
+      wa2 = diag.cwiseProduct(x);
+      fvec = wa4;
+      xnorm = wa2.stableNorm();
+      fnorm = fnorm1;
+      ++iter;
+    }
+
+    /* tests for convergence. */
+    if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. &&
+        delta <= parameters.xtol * xnorm)
+      return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
+    if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
+      return LevenbergMarquardtSpace::RelativeReductionTooSmall;
+    if (delta <= parameters.xtol * xnorm) return LevenbergMarquardtSpace::RelativeErrorTooSmall;
+
+    /* tests for termination and stringent tolerances. */
+    if (nfev >= parameters.maxfev) return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
+    if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() &&
+        Scalar(.5) * ratio <= 1.)
+      return LevenbergMarquardtSpace::FtolTooSmall;
+    if (delta <= NumTraits<Scalar>::epsilon() * xnorm) return LevenbergMarquardtSpace::XtolTooSmall;
+    if (gnorm <= NumTraits<Scalar>::epsilon()) return LevenbergMarquardtSpace::GtolTooSmall;
+
+  } while (ratio < Scalar(1e-4));
+
+  return LevenbergMarquardtSpace::Running;
+}
+
+template <typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType, Scalar>::minimizeOptimumStorage(FVectorType &x) {
+  LevenbergMarquardtSpace::Status status = minimizeOptimumStorageInit(x);
+  if (status == LevenbergMarquardtSpace::ImproperInputParameters) return status;
+  do {
+    status = minimizeOptimumStorageOneStep(x);
+  } while (status == LevenbergMarquardtSpace::Running);
+  return status;
+}
+
+template <typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType, Scalar>::lmdif1(FunctorType &functor, FVectorType &x,
+                                                                                Index *nfev, const Scalar tol) {
+  Index n = x.size();
+  Index m = functor.values();
+
+  /* check the input parameters for errors. */
+  if (n <= 0 || m < n || tol < 0.) return LevenbergMarquardtSpace::ImproperInputParameters;
+
+  NumericalDiff<FunctorType> numDiff(functor);
+  // embedded LevenbergMarquardt
+  LevenbergMarquardt<NumericalDiff<FunctorType>, Scalar> lm(numDiff);
+  lm.parameters.ftol = tol;
+  lm.parameters.xtol = tol;
+  lm.parameters.maxfev = 200 * (n + 1);
+
+  LevenbergMarquardtSpace::Status info = LevenbergMarquardtSpace::Status(lm.minimize(x));
+  if (nfev) *nfev = lm.nfev;
+  return info;
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LEVENBERGMARQUARDT__H
+
+// vim: ai ts=4 sts=4 et sw=4
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/chkder.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/chkder.h
new file mode 100644
index 0000000..a9311ff
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/chkder.h
@@ -0,0 +1,57 @@
+#define chkder_log10e 0.43429448190325182765
+#define chkder_factor 100.
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+void chkder(const Matrix<Scalar, Dynamic, 1> &x, const Matrix<Scalar, Dynamic, 1> &fvec,
+            const Matrix<Scalar, Dynamic, Dynamic> &fjac, Matrix<Scalar, Dynamic, 1> &xp,
+            const Matrix<Scalar, Dynamic, 1> &fvecp, int mode, Matrix<Scalar, Dynamic, 1> &err) {
+  using std::abs;
+  using std::log;
+  using std::sqrt;
+
+  typedef DenseIndex Index;
+
+  const Scalar eps = sqrt(NumTraits<Scalar>::epsilon());
+  const Scalar epsf = chkder_factor * NumTraits<Scalar>::epsilon();
+  const Scalar epslog = chkder_log10e * log(eps);
+  Scalar temp;
+
+  const Index m = fvec.size(), n = x.size();
+
+  if (mode != 2) {
+    /* mode = 1. */
+    xp.resize(n);
+    for (Index j = 0; j < n; ++j) {
+      temp = eps * abs(x[j]);
+      if (temp == 0.) temp = eps;
+      xp[j] = x[j] + temp;
+    }
+  } else {
+    /* mode = 2. */
+    err.setZero(m);
+    for (Index j = 0; j < n; ++j) {
+      temp = abs(x[j]);
+      if (temp == 0.) temp = 1.;
+      err += temp * fjac.col(j);
+    }
+    for (Index i = 0; i < m; ++i) {
+      temp = 1.;
+      if (fvec[i] != 0. && fvecp[i] != 0. && abs(fvecp[i] - fvec[i]) >= epsf * abs(fvec[i]))
+        temp = eps * abs((fvecp[i] - fvec[i]) / eps - err[i]) / (abs(fvec[i]) + abs(fvecp[i]));
+      err[i] = 1.;
+      if (temp > NumTraits<Scalar>::epsilon() && temp < eps) err[i] = (chkder_log10e * log(temp) - epslog) / epslog;
+      if (temp >= eps) err[i] = 0.;
+    }
+  }
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/covar.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/covar.h
new file mode 100644
index 0000000..311d998
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/covar.h
@@ -0,0 +1,66 @@
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+void covar(Matrix<Scalar, Dynamic, Dynamic> &r, const VectorXi &ipvt,
+           Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())) {
+  using std::abs;
+  typedef DenseIndex Index;
+
+  /* Local variables */
+  Index i, j, k, l, ii, jj;
+  bool sing;
+  Scalar temp;
+
+  /* Function Body */
+  const Index n = r.cols();
+  const Scalar tolr = tol * abs(r(0, 0));
+  Matrix<Scalar, Dynamic, 1> wa(n);
+  eigen_assert(ipvt.size() == n);
+
+  /* form the inverse of r in the full upper triangle of r. */
+  l = -1;
+  for (k = 0; k < n; ++k)
+    if (abs(r(k, k)) > tolr) {
+      r(k, k) = 1. / r(k, k);
+      for (j = 0; j <= k - 1; ++j) {
+        temp = r(k, k) * r(j, k);
+        r(j, k) = 0.;
+        r.col(k).head(j + 1) -= r.col(j).head(j + 1) * temp;
+      }
+      l = k;
+    }
+
+  /* form the full upper triangle of the inverse of (r transpose)*r */
+  /* in the full upper triangle of r. */
+  for (k = 0; k <= l; ++k) {
+    for (j = 0; j <= k - 1; ++j) r.col(j).head(j + 1) += r.col(k).head(j + 1) * r(j, k);
+    r.col(k).head(k + 1) *= r(k, k);
+  }
+
+  /* form the full lower triangle of the covariance matrix */
+  /* in the strict lower triangle of r and in wa. */
+  for (j = 0; j < n; ++j) {
+    jj = ipvt[j];
+    sing = j > l;
+    for (i = 0; i <= j; ++i) {
+      if (sing) r(i, j) = 0.;
+      ii = ipvt[i];
+      if (ii > jj) r(ii, jj) = r(i, j);
+      if (ii < jj) r(jj, ii) = r(i, j);
+    }
+    wa[jj] = r(j, j);
+  }
+
+  /* symmetrize the covariance matrix in r. */
+  r.topLeftCorner(n, n).template triangularView<StrictlyUpper>() = r.topLeftCorner(n, n).transpose();
+  r.diagonal() = wa;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/dogleg.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
new file mode 100644
index 0000000..d1abb9e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
@@ -0,0 +1,103 @@
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+void dogleg(const Matrix<Scalar, Dynamic, Dynamic> &qrfac, const Matrix<Scalar, Dynamic, 1> &diag,
+            const Matrix<Scalar, Dynamic, 1> &qtb, Scalar delta, Matrix<Scalar, Dynamic, 1> &x) {
+  using std::abs;
+  using std::sqrt;
+
+  typedef DenseIndex Index;
+
+  /* Local variables */
+  Index i, j;
+  Scalar sum, temp, alpha, bnorm;
+  Scalar gnorm, qnorm;
+  Scalar sgnorm;
+
+  /* Function Body */
+  const Scalar epsmch = NumTraits<Scalar>::epsilon();
+  const Index n = qrfac.cols();
+  eigen_assert(n == qtb.size());
+  eigen_assert(n == x.size());
+  eigen_assert(n == diag.size());
+  Matrix<Scalar, Dynamic, 1> wa1(n), wa2(n);
+
+  /* first, calculate the gauss-newton direction. */
+  for (j = n - 1; j >= 0; --j) {
+    temp = qrfac(j, j);
+    if (temp == 0.) {
+      temp = epsmch * qrfac.col(j).head(j + 1).maxCoeff();
+      if (temp == 0.) temp = epsmch;
+    }
+    if (j == n - 1)
+      x[j] = qtb[j] / temp;
+    else
+      x[j] = (qtb[j] - qrfac.row(j).tail(n - j - 1).dot(x.tail(n - j - 1))) / temp;
+  }
+
+  /* test whether the gauss-newton direction is acceptable. */
+  qnorm = diag.cwiseProduct(x).stableNorm();
+  if (qnorm <= delta) return;
+
+  // TODO : this path is not tested by Eigen unit tests
+
+  /* the gauss-newton direction is not acceptable. */
+  /* next, calculate the scaled gradient direction. */
+
+  wa1.fill(0.);
+  for (j = 0; j < n; ++j) {
+    wa1.tail(n - j) += qrfac.row(j).tail(n - j) * qtb[j];
+    wa1[j] /= diag[j];
+  }
+
+  /* calculate the norm of the scaled gradient and test for */
+  /* the special case in which the scaled gradient is zero. */
+  gnorm = wa1.stableNorm();
+  sgnorm = 0.;
+  alpha = delta / qnorm;
+  if (gnorm == 0.) goto algo_end;
+
+  /* calculate the point along the scaled gradient */
+  /* at which the quadratic is minimized. */
+  wa1.array() /= (diag * gnorm).array();
+  // TODO : once unit tests cover this part,:
+  // wa2 = qrfac.template triangularView<Upper>() * wa1;
+  for (j = 0; j < n; ++j) {
+    sum = 0.;
+    for (i = j; i < n; ++i) {
+      sum += qrfac(j, i) * wa1[i];
+    }
+    wa2[j] = sum;
+  }
+  temp = wa2.stableNorm();
+  sgnorm = gnorm / temp / temp;
+
+  /* test whether the scaled gradient direction is acceptable. */
+  alpha = 0.;
+  if (sgnorm >= delta) goto algo_end;
+
+  /* the scaled gradient direction is not acceptable. */
+  /* finally, calculate the point along the dogleg */
+  /* at which the quadratic is minimized. */
+  bnorm = qtb.stableNorm();
+  temp = bnorm / gnorm * (bnorm / qnorm) * (sgnorm / delta);
+  temp = temp - delta / qnorm * numext::abs2(sgnorm / delta) +
+         sqrt(numext::abs2(temp - delta / qnorm) +
+              (1. - numext::abs2(delta / qnorm)) * (1. - numext::abs2(sgnorm / delta)));
+  alpha = delta / qnorm * (1. - numext::abs2(sgnorm / delta)) / temp;
+algo_end:
+
+  /* form appropriate convex combination of the gauss-newton */
+  /* direction and the scaled gradient direction. */
+  temp = (1. - alpha) * (std::min)(sgnorm, delta);
+  x = temp * wa1 + alpha * x;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
new file mode 100644
index 0000000..70da3f9
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
@@ -0,0 +1,73 @@
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename FunctorType, typename Scalar>
+DenseIndex fdjac1(const FunctorType &Functor, Matrix<Scalar, Dynamic, 1> &x, Matrix<Scalar, Dynamic, 1> &fvec,
+                  Matrix<Scalar, Dynamic, Dynamic> &fjac, DenseIndex ml, DenseIndex mu, Scalar epsfcn) {
+  using std::abs;
+  using std::sqrt;
+
+  typedef DenseIndex Index;
+
+  /* Local variables */
+  Scalar h;
+  Index j, k;
+  Scalar eps, temp;
+  Index msum;
+  int iflag;
+  Index start, length;
+
+  /* Function Body */
+  const Scalar epsmch = NumTraits<Scalar>::epsilon();
+  const Index n = x.size();
+  eigen_assert(fvec.size() == n);
+  Matrix<Scalar, Dynamic, 1> wa1(n);
+  Matrix<Scalar, Dynamic, 1> wa2(n);
+
+  eps = sqrt((std::max)(epsfcn, epsmch));
+  msum = ml + mu + 1;
+  if (msum >= n) {
+    /* computation of dense approximate jacobian. */
+    for (j = 0; j < n; ++j) {
+      temp = x[j];
+      h = eps * abs(temp);
+      if (h == 0.) h = eps;
+      x[j] = temp + h;
+      iflag = Functor(x, wa1);
+      if (iflag < 0) return iflag;
+      x[j] = temp;
+      fjac.col(j) = (wa1 - fvec) / h;
+    }
+
+  } else {
+    /* computation of banded approximate jacobian. */
+    for (k = 0; k < msum; ++k) {
+      for (j = k; (msum < 0) ? (j > n) : (j < n); j += msum) {
+        wa2[j] = x[j];
+        h = eps * abs(wa2[j]);
+        if (h == 0.) h = eps;
+        x[j] = wa2[j] + h;
+      }
+      iflag = Functor(x, wa1);
+      if (iflag < 0) return iflag;
+      for (j = k; (msum < 0) ? (j > n) : (j < n); j += msum) {
+        x[j] = wa2[j];
+        h = eps * abs(wa2[j]);
+        if (h == 0.) h = eps;
+        fjac.col(j).setZero();
+        start = std::max<Index>(0, j - mu);
+        length = (std::min)(n - 1, j + ml) - start + 1;
+        fjac.col(j).segment(start, length) = (wa1.segment(start, length) - fvec.segment(start, length)) / h;
+      }
+    }
+  }
+  return 0;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
new file mode 100644
index 0000000..cbff829
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
@@ -0,0 +1,263 @@
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+void lmpar(Matrix<Scalar, Dynamic, Dynamic> &r, const VectorXi &ipvt, const Matrix<Scalar, Dynamic, 1> &diag,
+           const Matrix<Scalar, Dynamic, 1> &qtb, Scalar delta, Scalar &par, Matrix<Scalar, Dynamic, 1> &x) {
+  using std::abs;
+  using std::sqrt;
+  typedef DenseIndex Index;
+
+  /* Local variables */
+  Index i, j, l;
+  Scalar fp;
+  Scalar parc, parl;
+  Index iter;
+  Scalar temp, paru;
+  Scalar gnorm;
+  Scalar dxnorm;
+
+  /* Function Body */
+  const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
+  const Index n = r.cols();
+  eigen_assert(n == diag.size());
+  eigen_assert(n == qtb.size());
+  eigen_assert(n == x.size());
+
+  Matrix<Scalar, Dynamic, 1> wa1, wa2;
+
+  /* compute and store in x the gauss-newton direction. if the */
+  /* jacobian is rank-deficient, obtain a least squares solution. */
+  Index nsing = n - 1;
+  wa1 = qtb;
+  for (j = 0; j < n; ++j) {
+    if (r(j, j) == 0. && nsing == n - 1) nsing = j - 1;
+    if (nsing < n - 1) wa1[j] = 0.;
+  }
+  for (j = nsing; j >= 0; --j) {
+    wa1[j] /= r(j, j);
+    temp = wa1[j];
+    for (i = 0; i < j; ++i) wa1[i] -= r(i, j) * temp;
+  }
+
+  for (j = 0; j < n; ++j) x[ipvt[j]] = wa1[j];
+
+  /* initialize the iteration counter. */
+  /* evaluate the function at the origin, and test */
+  /* for acceptance of the gauss-newton direction. */
+  iter = 0;
+  wa2 = diag.cwiseProduct(x);
+  dxnorm = wa2.blueNorm();
+  fp = dxnorm - delta;
+  if (fp <= Scalar(0.1) * delta) {
+    par = 0;
+    return;
+  }
+
+  /* if the jacobian is not rank deficient, the newton */
+  /* step provides a lower bound, parl, for the zero of */
+  /* the function. otherwise set this bound to zero. */
+  parl = 0.;
+  if (nsing >= n - 1) {
+    for (j = 0; j < n; ++j) {
+      l = ipvt[j];
+      wa1[j] = diag[l] * (wa2[l] / dxnorm);
+    }
+    // Triangular solve (forward substitution):
+    for (j = 0; j < n; ++j) {
+      Scalar sum = 0.;
+      for (i = 0; i < j; ++i) sum += r(i, j) * wa1[i];
+      wa1[j] = (wa1[j] - sum) / r(j, j);
+    }
+    temp = wa1.blueNorm();
+    parl = fp / delta / temp / temp;
+  }
+
+  /* calculate an upper bound, paru, for the zero of the function. */
+  for (j = 0; j < n; ++j) wa1[j] = r.col(j).head(j + 1).dot(qtb.head(j + 1)) / diag[ipvt[j]];
+
+  gnorm = wa1.stableNorm();
+  paru = gnorm / delta;
+  if (paru == 0.) paru = dwarf / (std::min)(delta, Scalar(0.1));
+
+  /* if the input par lies outside of the interval (parl,paru), */
+  /* set par to the closer endpoint. */
+  par = (std::max)(par, parl);
+  par = (std::min)(par, paru);
+  if (par == 0.) par = gnorm / dxnorm;
+
+  /* beginning of an iteration. */
+  while (true) {
+    ++iter;
+
+    /* evaluate the function at the current value of par. */
+    if (par == 0.) par = (std::max)(dwarf, Scalar(.001) * paru); /* Computing MAX */
+    wa1 = sqrt(par) * diag;
+
+    Matrix<Scalar, Dynamic, 1> sdiag(n);
+    qrsolv<Scalar>(r, ipvt, wa1, qtb, x, sdiag);
+
+    wa2 = diag.cwiseProduct(x);
+    dxnorm = wa2.blueNorm();
+    temp = fp;
+    fp = dxnorm - delta;
+
+    /* if the function is small enough, accept the current value */
+    /* of par. also test for the exceptional cases where parl */
+    /* is zero or the number of iterations has reached 10. */
+    if (abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10) break;
+
+    /* compute the newton correction. */
+    for (j = 0; j < n; ++j) {
+      l = ipvt[j];
+      wa1[j] = diag[l] * (wa2[l] / dxnorm);
+    }
+    for (j = 0; j < n; ++j) {
+      wa1[j] /= sdiag[j];
+      temp = wa1[j];
+      for (i = j + 1; i < n; ++i) wa1[i] -= r(i, j) * temp;
+    }
+    temp = wa1.blueNorm();
+    parc = fp / delta / temp / temp;
+
+    /* depending on the sign of the function, update parl or paru. */
+    if (fp > 0.) parl = (std::max)(parl, par);
+    if (fp < 0.) paru = (std::min)(paru, par);
+
+    /* compute an improved estimate for par. */
+    /* Computing MAX */
+    par = (std::max)(parl, par + parc);
+
+    /* end of an iteration. */
+  }
+
+  /* termination. */
+  if (iter == 0) par = 0.;
+  return;
+}
+
+template <typename Scalar>
+void lmpar2(const ColPivHouseholderQR<Matrix<Scalar, Dynamic, Dynamic> > &qr, const Matrix<Scalar, Dynamic, 1> &diag,
+            const Matrix<Scalar, Dynamic, 1> &qtb, Scalar delta, Scalar &par, Matrix<Scalar, Dynamic, 1> &x)
+
+{
+  using std::abs;
+  using std::sqrt;
+  typedef DenseIndex Index;
+
+  /* Local variables */
+  Index j;
+  Scalar fp;
+  Scalar parc, parl;
+  Index iter;
+  Scalar temp, paru;
+  Scalar gnorm;
+  Scalar dxnorm;
+
+  /* Function Body */
+  const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
+  const Index n = qr.matrixQR().cols();
+  eigen_assert(n == diag.size());
+  eigen_assert(n == qtb.size());
+
+  Matrix<Scalar, Dynamic, 1> wa1, wa2;
+
+  /* compute and store in x the gauss-newton direction. if the */
+  /* jacobian is rank-deficient, obtain a least squares solution. */
+
+  const Index rank = qr.rank();  // use a threshold
+  wa1 = qtb;
+  wa1.tail(n - rank).setZero();
+  qr.matrixQR().topLeftCorner(rank, rank).template triangularView<Upper>().solveInPlace(wa1.head(rank));
+
+  x = qr.colsPermutation() * wa1;
+
+  /* initialize the iteration counter. */
+  /* evaluate the function at the origin, and test */
+  /* for acceptance of the gauss-newton direction. */
+  iter = 0;
+  wa2 = diag.cwiseProduct(x);
+  dxnorm = wa2.blueNorm();
+  fp = dxnorm - delta;
+  if (fp <= Scalar(0.1) * delta) {
+    par = 0;
+    return;
+  }
+
+  /* if the jacobian is not rank deficient, the newton */
+  /* step provides a lower bound, parl, for the zero of */
+  /* the function. otherwise set this bound to zero. */
+  parl = 0.;
+  if (rank == n) {
+    wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2) / dxnorm;
+    qr.matrixQR().topLeftCorner(n, n).transpose().template triangularView<Lower>().solveInPlace(wa1);
+    temp = wa1.blueNorm();
+    parl = fp / delta / temp / temp;
+  }
+
+  /* calculate an upper bound, paru, for the zero of the function. */
+  for (j = 0; j < n; ++j)
+    wa1[j] = qr.matrixQR().col(j).head(j + 1).dot(qtb.head(j + 1)) / diag[qr.colsPermutation().indices()(j)];
+
+  gnorm = wa1.stableNorm();
+  paru = gnorm / delta;
+  if (paru == 0.) paru = dwarf / (std::min)(delta, Scalar(0.1));
+
+  /* if the input par lies outside of the interval (parl,paru), */
+  /* set par to the closer endpoint. */
+  par = (std::max)(par, parl);
+  par = (std::min)(par, paru);
+  if (par == 0.) par = gnorm / dxnorm;
+
+  /* beginning of an iteration. */
+  Matrix<Scalar, Dynamic, Dynamic> s = qr.matrixQR();
+  while (true) {
+    ++iter;
+
+    /* evaluate the function at the current value of par. */
+    if (par == 0.) par = (std::max)(dwarf, Scalar(.001) * paru); /* Computing MAX */
+    wa1 = sqrt(par) * diag;
+
+    Matrix<Scalar, Dynamic, 1> sdiag(n);
+    qrsolv<Scalar>(s, qr.colsPermutation().indices(), wa1, qtb, x, sdiag);
+
+    wa2 = diag.cwiseProduct(x);
+    dxnorm = wa2.blueNorm();
+    temp = fp;
+    fp = dxnorm - delta;
+
+    /* if the function is small enough, accept the current value */
+    /* of par. also test for the exceptional cases where parl */
+    /* is zero or the number of iterations has reached 10. */
+    if (abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10) break;
+
+    /* compute the newton correction. */
+    wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2 / dxnorm);
+    // we could almost use this here, but the diagonal is outside qr, in sdiag[]
+    // qr.matrixQR().topLeftCorner(n, n).transpose().template triangularView<Lower>().solveInPlace(wa1);
+    for (j = 0; j < n; ++j) {
+      wa1[j] /= sdiag[j];
+      temp = wa1[j];
+      for (Index i = j + 1; i < n; ++i) wa1[i] -= s(i, j) * temp;
+    }
+    temp = wa1.blueNorm();
+    parc = fp / delta / temp / temp;
+
+    /* depending on the sign of the function, update parl or paru. */
+    if (fp > 0.) parl = (std::max)(parl, par);
+    if (fp < 0.) paru = (std::min)(paru, par);
+
+    /* compute an improved estimate for par. */
+    par = (std::max)(parl, par + parc);
+  }
+  if (iter == 0) par = 0.;
+  return;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
new file mode 100644
index 0000000..2e4d036
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
@@ -0,0 +1,89 @@
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// TODO : once qrsolv2 is removed, use ColPivHouseholderQR or PermutationMatrix instead of ipvt
+template <typename Scalar>
+void qrsolv(Matrix<Scalar, Dynamic, Dynamic> &s,
+            // TODO : use a PermutationMatrix once lmpar is no more:
+            const VectorXi &ipvt, const Matrix<Scalar, Dynamic, 1> &diag, const Matrix<Scalar, Dynamic, 1> &qtb,
+            Matrix<Scalar, Dynamic, 1> &x, Matrix<Scalar, Dynamic, 1> &sdiag)
+
+{
+  typedef DenseIndex Index;
+
+  /* Local variables */
+  Index i, j, k, l;
+  Scalar temp;
+  Index n = s.cols();
+  Matrix<Scalar, Dynamic, 1> wa(n);
+  JacobiRotation<Scalar> givens;
+
+  /* Function Body */
+  // the following will only change the lower triangular part of s, including
+  // the diagonal, though the diagonal is restored afterward
+
+  /*     copy r and (q transpose)*b to preserve input and initialize s. */
+  /*     in particular, save the diagonal elements of r in x. */
+  x = s.diagonal();
+  wa = qtb;
+
+  s.topLeftCorner(n, n).template triangularView<StrictlyLower>() = s.topLeftCorner(n, n).transpose();
+
+  /*     eliminate the diagonal matrix d using a givens rotation. */
+  for (j = 0; j < n; ++j) {
+    /*        prepare the row of d to be eliminated, locating the */
+    /*        diagonal element using p from the qr factorization. */
+    l = ipvt[j];
+    if (diag[l] == 0.) break;
+    sdiag.tail(n - j).setZero();
+    sdiag[j] = diag[l];
+
+    /*        the transformations to eliminate the row of d */
+    /*        modify only a single element of (q transpose)*b */
+    /*        beyond the first n, which is initially zero. */
+    Scalar qtbpj = 0.;
+    for (k = j; k < n; ++k) {
+      /*           determine a givens rotation which eliminates the */
+      /*           appropriate element in the current row of d. */
+      givens.makeGivens(-s(k, k), sdiag[k]);
+
+      /*           compute the modified diagonal element of r and */
+      /*           the modified element of ((q transpose)*b,0). */
+      s(k, k) = givens.c() * s(k, k) + givens.s() * sdiag[k];
+      temp = givens.c() * wa[k] + givens.s() * qtbpj;
+      qtbpj = -givens.s() * wa[k] + givens.c() * qtbpj;
+      wa[k] = temp;
+
+      /*           accumulate the transformation in the row of s. */
+      for (i = k + 1; i < n; ++i) {
+        temp = givens.c() * s(i, k) + givens.s() * sdiag[i];
+        sdiag[i] = -givens.s() * s(i, k) + givens.c() * sdiag[i];
+        s(i, k) = temp;
+      }
+    }
+  }
+
+  /*     solve the triangular system for z. if the system is */
+  /*     singular, then obtain a least squares solution. */
+  Index nsing;
+  for (nsing = 0; nsing < n && sdiag[nsing] != 0; nsing++) {
+  }
+
+  wa.tail(n - nsing).setZero();
+  s.topLeftCorner(nsing, nsing).transpose().template triangularView<Upper>().solveInPlace(wa.head(nsing));
+
+  // restore
+  sdiag = s.diagonal();
+  s.diagonal() = x;
+
+  /*     permute the components of z back to components of x. */
+  for (j = 0; j < n; ++j) x[ipvt[j]] = wa[j];
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
new file mode 100644
index 0000000..ea41315
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
@@ -0,0 +1,33 @@
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// TODO : move this to GivensQR once there's such a thing in Eigen
+
+template <typename Scalar>
+void r1mpyq(DenseIndex m, DenseIndex n, Scalar *a, const std::vector<JacobiRotation<Scalar> > &v_givens,
+            const std::vector<JacobiRotation<Scalar> > &w_givens) {
+  typedef DenseIndex Index;
+
+  /*     apply the first set of givens rotations to a. */
+  for (Index j = n - 2; j >= 0; --j)
+    for (Index i = 0; i < m; ++i) {
+      Scalar temp = v_givens[j].c() * a[i + m * j] - v_givens[j].s() * a[i + m * (n - 1)];
+      a[i + m * (n - 1)] = v_givens[j].s() * a[i + m * j] + v_givens[j].c() * a[i + m * (n - 1)];
+      a[i + m * j] = temp;
+    }
+  /*     apply the second set of givens rotations to a. */
+  for (Index j = 0; j < n - 1; ++j)
+    for (Index i = 0; i < m; ++i) {
+      Scalar temp = w_givens[j].c() * a[i + m * j] + w_givens[j].s() * a[i + m * (n - 1)];
+      a[i + m * (n - 1)] = -w_givens[j].s() * a[i + m * j] + w_givens[j].c() * a[i + m * (n - 1)];
+      a[i + m * j] = temp;
+    }
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/r1updt.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
new file mode 100644
index 0000000..201fba3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
@@ -0,0 +1,96 @@
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+void r1updt(Matrix<Scalar, Dynamic, Dynamic> &s, const Matrix<Scalar, Dynamic, 1> &u,
+            std::vector<JacobiRotation<Scalar> > &v_givens, std::vector<JacobiRotation<Scalar> > &w_givens,
+            Matrix<Scalar, Dynamic, 1> &v, Matrix<Scalar, Dynamic, 1> &w, bool *sing) {
+  typedef DenseIndex Index;
+  const JacobiRotation<Scalar> IdentityRotation = JacobiRotation<Scalar>(1, 0);
+
+  /* Local variables */
+  const Index m = s.rows();
+  const Index n = s.cols();
+  Index i, j = 1;
+  Scalar temp;
+  JacobiRotation<Scalar> givens;
+
+  // r1updt had a broader usecase, but we don't use it here. And, more
+  // importantly, we can not test it.
+  eigen_assert(m == n);
+  eigen_assert(u.size() == m);
+  eigen_assert(v.size() == n);
+  eigen_assert(w.size() == n);
+
+  /* move the nontrivial part of the last column of s into w. */
+  w[n - 1] = s(n - 1, n - 1);
+
+  /* rotate the vector v into a multiple of the n-th unit vector */
+  /* in such a way that a spike is introduced into w. */
+  for (j = n - 2; j >= 0; --j) {
+    w[j] = 0.;
+    if (v[j] != 0.) {
+      /* determine a givens rotation which eliminates the */
+      /* j-th element of v. */
+      givens.makeGivens(-v[n - 1], v[j]);
+
+      /* apply the transformation to v and store the information */
+      /* necessary to recover the givens rotation. */
+      v[n - 1] = givens.s() * v[j] + givens.c() * v[n - 1];
+      v_givens[j] = givens;
+
+      /* apply the transformation to s and extend the spike in w. */
+      for (i = j; i < m; ++i) {
+        temp = givens.c() * s(j, i) - givens.s() * w[i];
+        w[i] = givens.s() * s(j, i) + givens.c() * w[i];
+        s(j, i) = temp;
+      }
+    } else
+      v_givens[j] = IdentityRotation;
+  }
+
+  /* add the spike from the rank 1 update to w. */
+  w += v[n - 1] * u;
+
+  /* eliminate the spike. */
+  *sing = false;
+  for (j = 0; j < n - 1; ++j) {
+    if (w[j] != 0.) {
+      /* determine a givens rotation which eliminates the */
+      /* j-th element of the spike. */
+      givens.makeGivens(-s(j, j), w[j]);
+
+      /* apply the transformation to s and reduce the spike in w. */
+      for (i = j; i < m; ++i) {
+        temp = givens.c() * s(j, i) + givens.s() * w[i];
+        w[i] = -givens.s() * s(j, i) + givens.c() * w[i];
+        s(j, i) = temp;
+      }
+
+      /* store the information necessary to recover the */
+      /* givens rotation. */
+      w_givens[j] = givens;
+    } else
+      w_givens[j] = IdentityRotation;
+
+    /* test for zero diagonal elements in the output s. */
+    if (s(j, j) == 0.) {
+      *sing = true;
+    }
+  }
+  /* move w back into the last column of the output s. */
+  s(n - 1, n - 1) = w[n - 1];
+
+  if (s(j, j) == 0.) {
+    *sing = true;
+  }
+  return;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
new file mode 100644
index 0000000..8ae5848
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
@@ -0,0 +1,47 @@
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+void rwupdt(Matrix<Scalar, Dynamic, Dynamic> &r, const Matrix<Scalar, Dynamic, 1> &w, Matrix<Scalar, Dynamic, 1> &b,
+            Scalar alpha) {
+  typedef DenseIndex Index;
+
+  const Index n = r.cols();
+  eigen_assert(r.rows() >= n);
+  std::vector<JacobiRotation<Scalar> > givens(n);
+
+  /* Local variables */
+  Scalar temp, rowj;
+
+  /* Function Body */
+  for (Index j = 0; j < n; ++j) {
+    rowj = w[j];
+
+    /* apply the previous transformations to */
+    /* r(i,j), i=0,1,...,j-1, and to w(j). */
+    for (Index i = 0; i < j; ++i) {
+      temp = givens[i].c() * r(i, j) + givens[i].s() * rowj;
+      rowj = -givens[i].s() * r(i, j) + givens[i].c() * rowj;
+      r(i, j) = temp;
+    }
+
+    /* determine a givens rotation which eliminates w(j). */
+    givens[j].makeGivens(-r(j, j), rowj);
+
+    if (rowj == 0.) continue;  // givens[j] is identity
+
+    /* apply the current transformation to r(j,j), b(j), and alpha. */
+    r(j, j) = givens[j].c() * r(j, j) + givens[j].s() * rowj;
+    temp = givens[j].c() * b[j] + givens[j].s() * alpha;
+    alpha = -givens[j].s() * b[j] + givens[j].c() * alpha;
+    b[j] = temp;
+  }
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NumericalDiff/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NumericalDiff/InternalHeaderCheck.h
new file mode 100644
index 0000000..8c513d2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NumericalDiff/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_NUMERICALDIFF_MODULE_H
+#error "Please include unsupported/Eigen/NumericalDiff instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
new file mode 100644
index 0000000..0f4f929
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
@@ -0,0 +1,127 @@
+// -*- coding: utf-8
+// vim: set fileencoding=utf-8
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NUMERICAL_DIFF_H
+#define EIGEN_NUMERICAL_DIFF_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum NumericalDiffMode { Forward, Central };
+
+/**
+ * This class allows you to add a method df() to your functor, which will
+ * use numerical differentiation to compute an approximate of the
+ * derivative for the functor. Of course, if you have an analytical form
+ * for the derivative, you should rather implement df() by yourself.
+ *
+ * More information on
+ * http://en.wikipedia.org/wiki/Numerical_differentiation
+ *
+ * Currently only "Forward" and "Central" scheme are implemented.
+ */
+template <typename Functor_, NumericalDiffMode mode = Forward>
+class NumericalDiff : public Functor_ {
+ public:
+  typedef Functor_ Functor;
+  typedef typename Functor::Scalar Scalar;
+  typedef typename Functor::InputType InputType;
+  typedef typename Functor::ValueType ValueType;
+  typedef typename Functor::JacobianType JacobianType;
+
+  NumericalDiff(Scalar _epsfcn = 0.) : Functor(), epsfcn(_epsfcn) {}
+  NumericalDiff(const Functor& f, Scalar _epsfcn = 0.) : Functor(f), epsfcn(_epsfcn) {}
+
+  // forward constructors
+  template <typename T0>
+  NumericalDiff(const T0& a0) : Functor(a0), epsfcn(0) {}
+  template <typename T0, typename T1>
+  NumericalDiff(const T0& a0, const T1& a1) : Functor(a0, a1), epsfcn(0) {}
+  template <typename T0, typename T1, typename T2>
+  NumericalDiff(const T0& a0, const T1& a1, const T2& a2) : Functor(a0, a1, a2), epsfcn(0) {}
+
+  enum { InputsAtCompileTime = Functor::InputsAtCompileTime, ValuesAtCompileTime = Functor::ValuesAtCompileTime };
+
+  /**
+   * return the number of evaluation of functor
+   */
+  int df(const InputType& _x, JacobianType& jac) const {
+    using std::abs;
+    using std::sqrt;
+    /* Local variables */
+    Scalar h;
+    int nfev = 0;
+    const typename InputType::Index n = _x.size();
+    const Scalar eps = sqrt(((std::max)(epsfcn, NumTraits<Scalar>::epsilon())));
+    ValueType val1, val2;
+    InputType x = _x;
+    // TODO: We should do this only if the size is not already known.
+    val1.resize(Functor::values());
+    val2.resize(Functor::values());
+
+    // initialization
+    switch (mode) {
+      case Forward:
+        // compute f(x)
+        Functor::operator()(x, val1);
+        nfev++;
+        break;
+      case Central:
+        // do nothing
+        break;
+      default:
+        eigen_assert(false);
+    };
+
+    // Function Body
+    for (int j = 0; j < n; ++j) {
+      h = eps * abs(x[j]);
+      if (h == 0.) {
+        h = eps;
+      }
+      switch (mode) {
+        case Forward:
+          x[j] += h;
+          Functor::operator()(x, val2);
+          nfev++;
+          x[j] = _x[j];
+          jac.col(j) = (val2 - val1) / h;
+          break;
+        case Central:
+          x[j] += h;
+          Functor::operator()(x, val2);
+          nfev++;
+          x[j] -= 2 * h;
+          Functor::operator()(x, val1);
+          nfev++;
+          x[j] = _x[j];
+          jac.col(j) = (val2 - val1) / (2 * h);
+          break;
+        default:
+          eigen_assert(false);
+      };
+    }
+    return nfev;
+  }
+
+ private:
+  Scalar epsfcn;
+
+  NumericalDiff& operator=(const NumericalDiff&);
+};
+
+}  // end namespace Eigen
+
+// vim: ai ts=4 sts=4 et sw=4
+#endif  // EIGEN_NUMERICAL_DIFF_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/Companion.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/Companion.h
new file mode 100644
index 0000000..3000b4c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/Companion.h
@@ -0,0 +1,255 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPANION_H
+#define EIGEN_COMPANION_H
+
+// This file requires the user to include
+// * Eigen/Core
+// * Eigen/src/PolynomialSolver.h
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+template <int Size>
+struct decrement_if_fixed_size {
+  enum { ret = (Size == Dynamic) ? Dynamic : Size - 1 };
+};
+
+#endif
+
+template <typename Scalar_, int Deg_>
+class companion {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_, Deg_ == Dynamic ? Dynamic : Deg_)
+
+  enum { Deg = Deg_, Deg_1 = decrement_if_fixed_size<Deg>::ret };
+
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar, Deg, 1> RightColumn;
+  typedef Matrix<Scalar, Deg_1, 1> BottomLeftDiagonal;
+
+  typedef Matrix<Scalar, Deg, Deg> DenseCompanionMatrixType;
+  typedef Matrix<Scalar, Deg_, Deg_1> LeftBlock;
+  typedef Matrix<Scalar, Deg_1, Deg_1> BottomLeftBlock;
+  typedef Matrix<Scalar, 1, Deg_1> LeftBlockFirstRow;
+
+  typedef DenseIndex Index;
+
+ public:
+  EIGEN_STRONG_INLINE const Scalar_ operator()(Index row, Index col) const {
+    if (m_bl_diag.rows() > col) {
+      if (0 < row) {
+        return m_bl_diag[col];
+      } else {
+        return 0;
+      }
+    } else {
+      return m_monic[row];
+    }
+  }
+
+#ifdef EIGEN_MULTIDIMENSIONAL_SUBSCRIPT
+  EIGEN_STRONG_INLINE const Scalar_ operator[](Index row, Index col) const { return operator()(row, col); }
+#endif
+
+ public:
+  template <typename VectorType>
+  void setPolynomial(const VectorType& poly) {
+    const Index deg = poly.size() - 1;
+    m_monic = -poly.head(deg) / poly[deg];
+    m_bl_diag.setOnes(deg - 1);
+  }
+
+  template <typename VectorType>
+  companion(const VectorType& poly) {
+    setPolynomial(poly);
+  }
+
+ public:
+  DenseCompanionMatrixType denseMatrix() const {
+    const Index deg = m_monic.size();
+    const Index deg_1 = deg - 1;
+    DenseCompanionMatrixType companMat(deg, deg);
+    companMat << (LeftBlock(deg, deg_1) << LeftBlockFirstRow::Zero(1, deg_1),
+                  BottomLeftBlock::Identity(deg - 1, deg - 1) * m_bl_diag.asDiagonal())
+                     .finished(),
+        m_monic;
+    return companMat;
+  }
+
+ protected:
+  /** Helper function for the balancing algorithm.
+   * \returns true if the row and the column, having colNorm and rowNorm
+   * as norms, are balanced, false otherwise.
+   * colB and rowB are respectively the multipliers for
+   * the column and the row in order to balance them.
+   * */
+  bool balanced(RealScalar colNorm, RealScalar rowNorm, bool& isBalanced, RealScalar& colB, RealScalar& rowB);
+
+  /** Helper function for the balancing algorithm.
+   * \returns true if the row and the column, having colNorm and rowNorm
+   * as norms, are balanced, false otherwise.
+   * colB and rowB are respectively the multipliers for
+   * the column and the row in order to balance them.
+   * */
+  bool balancedR(RealScalar colNorm, RealScalar rowNorm, bool& isBalanced, RealScalar& colB, RealScalar& rowB);
+
+ public:
+  /**
+   * Balancing algorithm from B. N. PARLETT and C. REINSCH (1969)
+   * "Balancing a matrix for calculation of eigenvalues and eigenvectors"
+   * adapted to the case of companion matrices.
+   * A matrix with non zero row and non zero column is balanced
+   * for a certain norm if the i-th row and the i-th column
+   * have same norm for all i.
+   */
+  void balance();
+
+ protected:
+  RightColumn m_monic;
+  BottomLeftDiagonal m_bl_diag;
+};
+
+template <typename Scalar_, int Deg_>
+inline bool companion<Scalar_, Deg_>::balanced(RealScalar colNorm, RealScalar rowNorm, bool& isBalanced,
+                                               RealScalar& colB, RealScalar& rowB) {
+  if (RealScalar(0) == colNorm || RealScalar(0) == rowNorm || !(numext::isfinite)(colNorm) ||
+      !(numext::isfinite)(rowNorm)) {
+    return true;
+  } else {
+    // To find the balancing coefficients, if the radix is 2,
+    // one finds \f$ \sigma \f$ such that
+    //  \f$ 2^{2\sigma-1} < rowNorm / colNorm \le 2^{2\sigma+1} \f$
+    //  then the balancing coefficient for the row is \f$ 1/2^{\sigma} \f$
+    //  and the balancing coefficient for the column is \f$ 2^{\sigma} \f$
+    const RealScalar radix = RealScalar(2);
+    const RealScalar radix2 = RealScalar(4);
+
+    rowB = rowNorm / radix;
+    colB = RealScalar(1);
+    const RealScalar s = colNorm + rowNorm;
+
+    // Find sigma s.t. rowNorm / 2 <= 2^(2*sigma) * colNorm
+    RealScalar scout = colNorm;
+    while (scout < rowB) {
+      colB *= radix;
+      scout *= radix2;
+    }
+
+    // We now have an upper-bound for sigma, try to lower it.
+    // Find sigma s.t. 2^(2*sigma) * colNorm / 2 < rowNorm
+    scout = colNorm * (colB / radix) * colB;  // Avoid overflow.
+    while (scout >= rowNorm) {
+      colB /= radix;
+      scout /= radix2;
+    }
+
+    // This line is used to avoid insubstantial balancing.
+    if ((rowNorm + radix * scout) < RealScalar(0.95) * s * colB) {
+      isBalanced = false;
+      rowB = RealScalar(1) / colB;
+      return false;
+    } else {
+      return true;
+    }
+  }
+}
+
+template <typename Scalar_, int Deg_>
+inline bool companion<Scalar_, Deg_>::balancedR(RealScalar colNorm, RealScalar rowNorm, bool& isBalanced,
+                                                RealScalar& colB, RealScalar& rowB) {
+  if (RealScalar(0) == colNorm || RealScalar(0) == rowNorm) {
+    return true;
+  } else {
+    /**
+     * Set the norm of the column and the row to the geometric mean
+     * of the row and column norm
+     */
+    const RealScalar q = colNorm / rowNorm;
+    if (!isApprox(q, Scalar_(1))) {
+      rowB = sqrt(colNorm / rowNorm);
+      colB = RealScalar(1) / rowB;
+
+      isBalanced = false;
+      return false;
+    } else {
+      return true;
+    }
+  }
+}
+
+template <typename Scalar_, int Deg_>
+void companion<Scalar_, Deg_>::balance() {
+  using std::abs;
+  EIGEN_STATIC_ASSERT(Deg == Dynamic || 1 < Deg, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  const Index deg = m_monic.size();
+  const Index deg_1 = deg - 1;
+
+  bool hasConverged = false;
+  while (!hasConverged) {
+    hasConverged = true;
+    RealScalar colNorm, rowNorm;
+    RealScalar colB, rowB;
+
+    // First row, first column excluding the diagonal
+    //==============================================
+    colNorm = abs(m_bl_diag[0]);
+    rowNorm = abs(m_monic[0]);
+
+    // Compute balancing of the row and the column
+    if (!balanced(colNorm, rowNorm, hasConverged, colB, rowB)) {
+      m_bl_diag[0] *= colB;
+      m_monic[0] *= rowB;
+    }
+
+    // Middle rows and columns excluding the diagonal
+    //==============================================
+    for (Index i = 1; i < deg_1; ++i) {
+      // column norm, excluding the diagonal
+      colNorm = abs(m_bl_diag[i]);
+
+      // row norm, excluding the diagonal
+      rowNorm = abs(m_bl_diag[i - 1]) + abs(m_monic[i]);
+
+      // Compute balancing of the row and the column
+      if (!balanced(colNorm, rowNorm, hasConverged, colB, rowB)) {
+        m_bl_diag[i] *= colB;
+        m_bl_diag[i - 1] *= rowB;
+        m_monic[i] *= rowB;
+      }
+    }
+
+    // Last row, last column excluding the diagonal
+    //============================================
+    const Index ebl = m_bl_diag.size() - 1;
+    VectorBlock<RightColumn, Deg_1> headMonic(m_monic, 0, deg_1);
+    colNorm = headMonic.array().abs().sum();
+    rowNorm = abs(m_bl_diag[ebl]);
+
+    // Compute balancing of the row and the column
+    if (!balanced(colNorm, rowNorm, hasConverged, colB, rowB)) {
+      headMonic *= colB;
+      m_bl_diag[ebl] *= rowB;
+    }
+  }
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPANION_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/InternalHeaderCheck.h
new file mode 100644
index 0000000..b3aa50c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_POLYNOMIALS_MODULE_H
+#error "Please include unsupported/Eigen/Polynomials instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/PolynomialSolver.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
new file mode 100644
index 0000000..a79a49e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
@@ -0,0 +1,387 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_POLYNOMIAL_SOLVER_H
+#define EIGEN_POLYNOMIAL_SOLVER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup Polynomials_Module
+ *  \class PolynomialSolverBase.
+ *
+ * \brief Defined to be inherited by polynomial solvers: it provides
+ * convenient methods such as
+ *  - real roots,
+ *  - greatest, smallest complex roots,
+ *  - real roots with greatest, smallest absolute real value,
+ *  - greatest, smallest real roots.
+ *
+ * It stores the set of roots as a vector of complexes.
+ *
+ */
+template <typename Scalar_, int Deg_>
+class PolynomialSolverBase {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_, Deg_ == Dynamic ? Dynamic : Deg_)
+
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef internal::make_complex_t<Scalar> RootType;
+  typedef Matrix<RootType, Deg_, 1> RootsType;
+
+  typedef DenseIndex Index;
+
+ protected:
+  template <typename OtherPolynomial>
+  inline void setPolynomial(const OtherPolynomial& poly) {
+    m_roots.resize(poly.size() - 1);
+  }
+
+ public:
+  template <typename OtherPolynomial>
+  inline PolynomialSolverBase(const OtherPolynomial& poly) {
+    setPolynomial(poly());
+  }
+
+  inline PolynomialSolverBase() {}
+
+ public:
+  /** \returns the complex roots of the polynomial */
+  inline const RootsType& roots() const { return m_roots; }
+
+ public:
+  /** Clear and fills the back insertion sequence with the real roots of the polynomial
+   * i.e. the real part of the complex roots that have an imaginary part which
+   * absolute value is smaller than absImaginaryThreshold.
+   * absImaginaryThreshold takes the dummy_precision associated
+   * with the Scalar_ template parameter of the PolynomialSolver class as the default value.
+   *
+   * \param[out] bi_seq : the back insertion sequence (stl concept)
+   * \param[in]  absImaginaryThreshold : the maximum bound of the imaginary part of a complex
+   *  number that is considered as real.
+   * */
+  template <typename Stl_back_insertion_sequence>
+  inline void realRoots(Stl_back_insertion_sequence& bi_seq,
+                        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision()) const {
+    using std::abs;
+    bi_seq.clear();
+    for (Index i = 0; i < m_roots.size(); ++i) {
+      if (abs(m_roots[i].imag()) < absImaginaryThreshold) {
+        bi_seq.push_back(m_roots[i].real());
+      }
+    }
+  }
+
+ protected:
+  template <typename Predicate>
+  inline const RootType& selectComplexRoot_withRespectToNorm(Predicate& pred) const {
+    Index res = 0;
+    RealScalar norm2 = numext::abs2(m_roots[0]);
+    for (Index i = 1; i < m_roots.size(); ++i) {
+      const RealScalar currNorm2 = numext::abs2(m_roots[i]);
+      if (pred(currNorm2, norm2)) {
+        res = i;
+        norm2 = currNorm2;
+      }
+    }
+    return m_roots[res];
+  }
+
+ public:
+  /**
+   * \returns the complex root with greatest norm.
+   */
+  inline const RootType& greatestRoot() const {
+    std::greater<RealScalar> greater;
+    return selectComplexRoot_withRespectToNorm(greater);
+  }
+
+  /**
+   * \returns the complex root with smallest norm.
+   */
+  inline const RootType& smallestRoot() const {
+    std::less<RealScalar> less;
+    return selectComplexRoot_withRespectToNorm(less);
+  }
+
+ protected:
+  template <typename Predicate>
+  inline const RealScalar& selectRealRoot_withRespectToAbsRealPart(
+      Predicate& pred, bool& hasArealRoot,
+      const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision()) const {
+    using std::abs;
+    hasArealRoot = false;
+    Index res = 0;
+    RealScalar val(0);
+
+    for (Index i = 0; i < m_roots.size(); ++i) {
+      if (abs(m_roots[i].imag()) <= absImaginaryThreshold) {
+        if (!hasArealRoot) {
+          hasArealRoot = true;
+          res = i;
+          val = abs(m_roots[i].real());
+        } else {
+          const RealScalar curr = abs(m_roots[i].real());
+          if (pred(curr, val)) {
+            val = curr;
+            res = i;
+          }
+        }
+      } else if (!hasArealRoot) {
+        if (abs(m_roots[i].imag()) < abs(m_roots[res].imag())) {
+          res = i;
+        }
+      }
+    }
+    return numext::real_ref(m_roots[res]);
+  }
+
+  template <typename Predicate>
+  inline const RealScalar& selectRealRoot_withRespectToRealPart(
+      Predicate& pred, bool& hasArealRoot,
+      const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision()) const {
+    using std::abs;
+    hasArealRoot = false;
+    Index res = 0;
+    RealScalar val(0);
+
+    for (Index i = 0; i < m_roots.size(); ++i) {
+      if (abs(m_roots[i].imag()) <= absImaginaryThreshold) {
+        if (!hasArealRoot) {
+          hasArealRoot = true;
+          res = i;
+          val = m_roots[i].real();
+        } else {
+          const RealScalar curr = m_roots[i].real();
+          if (pred(curr, val)) {
+            val = curr;
+            res = i;
+          }
+        }
+      } else {
+        if (abs(m_roots[i].imag()) < abs(m_roots[res].imag())) {
+          res = i;
+        }
+      }
+    }
+    return numext::real_ref(m_roots[res]);
+  }
+
+ public:
+  /**
+   * \returns a real root with greatest absolute magnitude.
+   * A real root is defined as the real part of a complex root with absolute imaginary
+   * part smallest than absImaginaryThreshold.
+   * absImaginaryThreshold takes the dummy_precision associated
+   * with the Scalar_ template parameter of the PolynomialSolver class as the default value.
+   * If no real root is found the boolean hasArealRoot is set to false and the real part of
+   * the root with smallest absolute imaginary part is returned instead.
+   *
+   * \param[out] hasArealRoot : boolean true if a real root is found according to the
+   *  absImaginaryThreshold criterion, false otherwise.
+   * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
+   *  whether or not a root is real.
+   */
+  inline const RealScalar& absGreatestRealRoot(
+      bool& hasArealRoot, const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision()) const {
+    std::greater<RealScalar> greater;
+    return selectRealRoot_withRespectToAbsRealPart(greater, hasArealRoot, absImaginaryThreshold);
+  }
+
+  /**
+   * \returns a real root with smallest absolute magnitude.
+   * A real root is defined as the real part of a complex root with absolute imaginary
+   * part smallest than absImaginaryThreshold.
+   * absImaginaryThreshold takes the dummy_precision associated
+   * with the Scalar_ template parameter of the PolynomialSolver class as the default value.
+   * If no real root is found the boolean hasArealRoot is set to false and the real part of
+   * the root with smallest absolute imaginary part is returned instead.
+   *
+   * \param[out] hasArealRoot : boolean true if a real root is found according to the
+   *  absImaginaryThreshold criterion, false otherwise.
+   * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
+   *  whether or not a root is real.
+   */
+  inline const RealScalar& absSmallestRealRoot(
+      bool& hasArealRoot, const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision()) const {
+    std::less<RealScalar> less;
+    return selectRealRoot_withRespectToAbsRealPart(less, hasArealRoot, absImaginaryThreshold);
+  }
+
+  /**
+   * \returns the real root with greatest value.
+   * A real root is defined as the real part of a complex root with absolute imaginary
+   * part smallest than absImaginaryThreshold.
+   * absImaginaryThreshold takes the dummy_precision associated
+   * with the Scalar_ template parameter of the PolynomialSolver class as the default value.
+   * If no real root is found the boolean hasArealRoot is set to false and the real part of
+   * the root with smallest absolute imaginary part is returned instead.
+   *
+   * \param[out] hasArealRoot : boolean true if a real root is found according to the
+   *  absImaginaryThreshold criterion, false otherwise.
+   * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
+   *  whether or not a root is real.
+   */
+  inline const RealScalar& greatestRealRoot(
+      bool& hasArealRoot, const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision()) const {
+    std::greater<RealScalar> greater;
+    return selectRealRoot_withRespectToRealPart(greater, hasArealRoot, absImaginaryThreshold);
+  }
+
+  /**
+   * \returns the real root with smallest value.
+   * A real root is defined as the real part of a complex root with absolute imaginary
+   * part smallest than absImaginaryThreshold.
+   * absImaginaryThreshold takes the dummy_precision associated
+   * with the Scalar_ template parameter of the PolynomialSolver class as the default value.
+   * If no real root is found the boolean hasArealRoot is set to false and the real part of
+   * the root with smallest absolute imaginary part is returned instead.
+   *
+   * \param[out] hasArealRoot : boolean true if a real root is found according to the
+   *  absImaginaryThreshold criterion, false otherwise.
+   * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
+   *  whether or not a root is real.
+   */
+  inline const RealScalar& smallestRealRoot(
+      bool& hasArealRoot, const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision()) const {
+    std::less<RealScalar> less;
+    return selectRealRoot_withRespectToRealPart(less, hasArealRoot, absImaginaryThreshold);
+  }
+
+ protected:
+  RootsType m_roots;
+};
+
+#define EIGEN_POLYNOMIAL_SOLVER_BASE_INHERITED_TYPES(BASE) \
+  typedef typename BASE::Scalar Scalar;                    \
+  typedef typename BASE::RealScalar RealScalar;            \
+  typedef typename BASE::RootType RootType;                \
+  typedef typename BASE::RootsType RootsType;
+
+/** \ingroup Polynomials_Module
+ *
+ * \class PolynomialSolver
+ *
+ * \brief A polynomial solver
+ *
+ * Computes the complex roots of a real polynomial.
+ *
+ * \param Scalar_ the scalar type, i.e., the type of the polynomial coefficients
+ * \param Deg_ the degree of the polynomial, can be a compile time value or Dynamic.
+ *             Notice that the number of polynomial coefficients is Deg_+1.
+ *
+ * This class implements a polynomial solver and provides convenient methods such as
+ * - real roots,
+ * - greatest, smallest complex roots,
+ * - real roots with greatest, smallest absolute real value.
+ * - greatest, smallest real roots.
+ *
+ * WARNING: this polynomial solver is experimental, part of the unsupported Eigen modules.
+ *
+ *
+ * Currently a QR algorithm is used to compute the eigenvalues of the companion matrix of
+ * the polynomial to compute its roots.
+ * This supposes that the complex moduli of the roots are all distinct: e.g. there should
+ * be no multiple roots or conjugate roots for instance.
+ * With 32bit (float) floating types this problem shows up frequently.
+ * However, almost always, correct accuracy is reached even in these cases for 64bit
+ * (double) floating types and small polynomial degree (<20).
+ */
+template <typename Scalar_, int Deg_>
+class PolynomialSolver : public PolynomialSolverBase<Scalar_, Deg_> {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_, Deg_ == Dynamic ? Dynamic : Deg_)
+
+  typedef PolynomialSolverBase<Scalar_, Deg_> PS_Base;
+  EIGEN_POLYNOMIAL_SOLVER_BASE_INHERITED_TYPES(PS_Base)
+
+  typedef Matrix<Scalar, Deg_, Deg_> CompanionMatrixType;
+  typedef std::conditional_t<NumTraits<Scalar>::IsComplex, ComplexEigenSolver<CompanionMatrixType>,
+                             EigenSolver<CompanionMatrixType> >
+      EigenSolverType;
+  typedef internal::make_complex_t<Scalar_> ComplexScalar;
+
+ public:
+  /** Computes the complex roots of a new polynomial. */
+  template <typename OtherPolynomial>
+  void compute(const OtherPolynomial& poly) {
+    eigen_assert(Scalar(0) != poly[poly.size() - 1]);
+    eigen_assert(poly.size() > 1);
+    if (poly.size() > 2) {
+      internal::companion<Scalar, Deg_> companion(poly);
+      companion.balance();
+      m_eigenSolver.compute(companion.denseMatrix());
+      eigen_assert(m_eigenSolver.info() == Eigen::Success);
+      m_roots = m_eigenSolver.eigenvalues();
+      // cleanup noise in imaginary part of real roots:
+      // if the imaginary part is rather small compared to the real part
+      // and that cancelling the imaginary part yield a smaller evaluation,
+      // then it's safe to keep the real part only.
+      RealScalar coarse_prec = RealScalar(std::pow(4, poly.size() + 1)) * NumTraits<RealScalar>::epsilon();
+      for (Index i = 0; i < m_roots.size(); ++i) {
+        if (internal::isMuchSmallerThan(numext::abs(numext::imag(m_roots[i])), numext::abs(numext::real(m_roots[i])),
+                                        coarse_prec)) {
+          ComplexScalar as_real_root = ComplexScalar(numext::real(m_roots[i]));
+          if (numext::abs(poly_eval(poly, as_real_root)) <= numext::abs(poly_eval(poly, m_roots[i]))) {
+            m_roots[i] = as_real_root;
+          }
+        }
+      }
+    } else if (poly.size() == 2) {
+      m_roots.resize(1);
+      m_roots[0] = -poly[0] / poly[1];
+    }
+  }
+
+ public:
+  template <typename OtherPolynomial>
+  inline PolynomialSolver(const OtherPolynomial& poly) {
+    compute(poly);
+  }
+
+  inline PolynomialSolver() {}
+
+ protected:
+  using PS_Base::m_roots;
+  EigenSolverType m_eigenSolver;
+};
+
+template <typename Scalar_>
+class PolynomialSolver<Scalar_, 1> : public PolynomialSolverBase<Scalar_, 1> {
+ public:
+  typedef PolynomialSolverBase<Scalar_, 1> PS_Base;
+  EIGEN_POLYNOMIAL_SOLVER_BASE_INHERITED_TYPES(PS_Base)
+
+ public:
+  /** Computes the complex roots of a new polynomial. */
+  template <typename OtherPolynomial>
+  void compute(const OtherPolynomial& poly) {
+    eigen_assert(poly.size() == 2);
+    eigen_assert(Scalar(0) != poly[1]);
+    m_roots[0] = -poly[0] / poly[1];
+  }
+
+ public:
+  template <typename OtherPolynomial>
+  inline PolynomialSolver(const OtherPolynomial& poly) {
+    compute(poly);
+  }
+
+  inline PolynomialSolver() {}
+
+ protected:
+  using PS_Base::m_roots;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_POLYNOMIAL_SOLVER_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/PolynomialUtils.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
new file mode 100644
index 0000000..342ca9a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
@@ -0,0 +1,144 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_POLYNOMIAL_UTILS_H
+#define EIGEN_POLYNOMIAL_UTILS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup Polynomials_Module
+ * \returns the evaluation of the polynomial at x using Horner algorithm.
+ *
+ * \param[in] poly : the vector of coefficients of the polynomial ordered
+ *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
+ *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
+ * \param[in] x : the value to evaluate the polynomial at.
+ *
+ * \note for stability:
+ *   \f$ |x| \le 1 \f$
+ */
+template <typename Polynomials, typename T>
+inline T poly_eval_horner(const Polynomials& poly, const T& x) {
+  T val = poly[poly.size() - 1];
+  for (DenseIndex i = poly.size() - 2; i >= 0; --i) {
+    val = val * x + poly[i];
+  }
+  return val;
+}
+
+/** \ingroup Polynomials_Module
+ * \returns the evaluation of the polynomial at x using stabilized Horner algorithm.
+ *
+ * \param[in] poly : the vector of coefficients of the polynomial ordered
+ *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
+ *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
+ * \param[in] x : the value to evaluate the polynomial at.
+ */
+template <typename Polynomials, typename T>
+inline T poly_eval(const Polynomials& poly, const T& x) {
+  typedef typename NumTraits<T>::Real Real;
+
+  if (numext::abs2(x) <= Real(1)) {
+    return poly_eval_horner(poly, x);
+  } else {
+    T val = poly[0];
+    T inv_x = T(1) / x;
+    for (DenseIndex i = 1; i < poly.size(); ++i) {
+      val = val * inv_x + poly[i];
+    }
+
+    return numext::pow(x, (T)(poly.size() - 1)) * val;
+  }
+}
+
+/** \ingroup Polynomials_Module
+ * \returns a maximum bound for the absolute value of any root of the polynomial.
+ *
+ * \param[in] poly : the vector of coefficients of the polynomial ordered
+ *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
+ *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
+ *
+ *  \pre
+ *   the leading coefficient of the input polynomial poly must be non zero
+ */
+template <typename Polynomial>
+inline typename NumTraits<typename Polynomial::Scalar>::Real cauchy_max_bound(const Polynomial& poly) {
+  using std::abs;
+  typedef typename Polynomial::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real Real;
+
+  eigen_assert(Scalar(0) != poly[poly.size() - 1]);
+  const Scalar inv_leading_coeff = Scalar(1) / poly[poly.size() - 1];
+  Real cb(0);
+
+  for (DenseIndex i = 0; i < poly.size() - 1; ++i) {
+    cb += abs(poly[i] * inv_leading_coeff);
+  }
+  return cb + Real(1);
+}
+
+/** \ingroup Polynomials_Module
+ * \returns a minimum bound for the absolute value of any non zero root of the polynomial.
+ * \param[in] poly : the vector of coefficients of the polynomial ordered
+ *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
+ *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
+ */
+template <typename Polynomial>
+inline typename NumTraits<typename Polynomial::Scalar>::Real cauchy_min_bound(const Polynomial& poly) {
+  using std::abs;
+  typedef typename Polynomial::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real Real;
+
+  DenseIndex i = 0;
+  while (i < poly.size() - 1 && Scalar(0) == poly(i)) {
+    ++i;
+  }
+  if (poly.size() - 1 == i) {
+    return Real(1);
+  }
+
+  const Scalar inv_min_coeff = Scalar(1) / poly[i];
+  Real cb(1);
+  for (DenseIndex j = i + 1; j < poly.size(); ++j) {
+    cb += abs(poly[j] * inv_min_coeff);
+  }
+  return Real(1) / cb;
+}
+
+/** \ingroup Polynomials_Module
+ * Given the roots of a polynomial compute the coefficients in the
+ * monomial basis of the monic polynomial with same roots and minimal degree.
+ * If RootVector is a vector of complexes, Polynomial should also be a vector
+ * of complexes.
+ * \param[in] rv : a vector containing the roots of a polynomial.
+ * \param[out] poly : the vector of coefficients of the polynomial ordered
+ *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
+ *  e.g. \f$ 3 + x^2 \f$ is stored as a vector \f$ [ 3, 0, 1 ] \f$.
+ */
+template <typename RootVector, typename Polynomial>
+void roots_to_monicPolynomial(const RootVector& rv, Polynomial& poly) {
+  typedef typename Polynomial::Scalar Scalar;
+
+  poly.setZero(rv.size() + 1);
+  poly[0] = -rv[0];
+  poly[1] = Scalar(1);
+  for (DenseIndex i = 1; i < rv.size(); ++i) {
+    for (DenseIndex j = i + 1; j > 0; --j) {
+      poly[j] = poly[j - 1] - rv[i] * poly[j];
+    }
+    poly[0] = -rv[i] * poly[0];
+  }
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_POLYNOMIAL_UTILS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h
new file mode 100644
index 0000000..a7958af
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h
@@ -0,0 +1,931 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2013 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEBLOCKMATRIX_H
+#define EIGEN_SPARSEBLOCKMATRIX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+/** \ingroup SparseCore_Module
+ *
+ * \class BlockSparseMatrix
+ *
+ * \brief A versatile sparse matrix representation where each element is a block
+ *
+ * This class provides routines to manipulate block sparse matrices stored in a
+ * BSR-like representation. There are two main types :
+ *
+ * 1. All blocks have the same number of rows and columns, called block size
+ * in the following. In this case, if this block size is known at compile time,
+ * it can be given as a template parameter like
+ * \code
+ * BlockSparseMatrix<Scalar, 3, ColMajor> bmat(b_rows, b_cols);
+ * \endcode
+ * Here, bmat is a b_rows x b_cols block sparse matrix
+ * where each coefficient is a 3x3 dense matrix.
+ * If the block size is fixed but will be given at runtime,
+ * \code
+ * BlockSparseMatrix<Scalar, Dynamic, ColMajor> bmat(b_rows, b_cols);
+ * bmat.setBlockSize(block_size);
+ * \endcode
+ *
+ * 2. The second case is for variable-block sparse matrices.
+ * Here each block has its own dimensions. The only restriction is that all the blocks
+ * in a row (resp. a column) should have the same number of rows (resp. of columns).
+ * It is thus required in this case to describe the layout of the matrix by calling
+ * setBlockLayout(rowBlocks, colBlocks).
+ *
+ * In any of the previous case, the matrix can be filled by calling setFromTriplets().
+ * A regular sparse matrix can be converted to a block sparse matrix and vice versa.
+ * It is obviously required to describe the block layout beforehand by calling either
+ * setBlockSize() for fixed-size blocks or setBlockLayout for variable-size blocks.
+ *
+ * \tparam Scalar_ The Scalar type
+ * \tparam _BlockAtCompileTime The block layout option. It takes the following values
+ * Dynamic : block size known at runtime
+ * a numeric number : fixed-size block known at compile time
+ */
+template <typename Scalar_, int _BlockAtCompileTime = Dynamic, int Options_ = ColMajor, typename StorageIndex_ = int>
+class BlockSparseMatrix;
+
+template <typename BlockSparseMatrixT>
+class BlockSparseMatrixView;
+
+namespace internal {
+template <typename Scalar_, int _BlockAtCompileTime, int Options_, typename Index_>
+struct traits<BlockSparseMatrix<Scalar_, _BlockAtCompileTime, Options_, Index_> > {
+  typedef Scalar_ Scalar;
+  typedef Index_ Index;
+  typedef Sparse StorageKind;  // FIXME: determine where StorageKind is used.
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    BlockSize = _BlockAtCompileTime,
+    Flags = Options_ | NestByRefBit | LvalueBit,
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    SupportedAccessPatterns = InnerRandomAccessPattern
+  };
+};
+template <typename BlockSparseMatrixT>
+struct traits<BlockSparseMatrixView<BlockSparseMatrixT> > {
+  typedef Ref<
+      Matrix<typename BlockSparseMatrixT::Scalar, BlockSparseMatrixT::BlockSize, BlockSparseMatrixT::BlockSize> >
+      Scalar;
+  typedef Ref<
+      Matrix<typename BlockSparseMatrixT::RealScalar, BlockSparseMatrixT::BlockSize, BlockSparseMatrixT::BlockSize> >
+      RealScalar;
+};
+
+// Function object to sort a triplet list
+template <typename Iterator, bool IsColMajor>
+struct TripletComp {
+  typedef typename Iterator::value_type Triplet;
+  bool operator()(const Triplet& a, const Triplet& b) {
+    if (IsColMajor)
+      return ((a.col() == b.col() && a.row() < b.row()) || (a.col() < b.col()));
+    else
+      return ((a.row() == b.row() && a.col() < b.col()) || (a.row() < b.row()));
+  }
+};
+}  // end namespace internal
+
+/* Proxy to view the block sparse matrix as a regular sparse matrix */
+template <typename BlockSparseMatrixT>
+class BlockSparseMatrixView : public SparseMatrixBase<BlockSparseMatrixT> {
+ public:
+  typedef Ref<typename BlockSparseMatrixT::BlockScalar> Scalar;
+  typedef Ref<typename BlockSparseMatrixT::BlockRealScalar> RealScalar;
+  typedef typename BlockSparseMatrixT::Index Index;
+  typedef BlockSparseMatrixT Nested;
+  enum {
+    Flags = BlockSparseMatrixT::Options,
+    Options = BlockSparseMatrixT::Options,
+    RowsAtCompileTime = BlockSparseMatrixT::RowsAtCompileTime,
+    ColsAtCompileTime = BlockSparseMatrixT::ColsAtCompileTime,
+    MaxColsAtCompileTime = BlockSparseMatrixT::MaxColsAtCompileTime,
+    MaxRowsAtCompileTime = BlockSparseMatrixT::MaxRowsAtCompileTime
+  };
+
+ public:
+  BlockSparseMatrixView(const BlockSparseMatrixT& spblockmat) : m_spblockmat(spblockmat) {}
+
+  Index outerSize() const { return (Flags & RowMajorBit) == 1 ? this->rows() : this->cols(); }
+  Index cols() const { return m_spblockmat.blockCols(); }
+  Index rows() const { return m_spblockmat.blockRows(); }
+  Scalar coeff(Index row, Index col) { return m_spblockmat.coeff(row, col); }
+  Scalar coeffRef(Index row, Index col) { return m_spblockmat.coeffRef(row, col); }
+  // Wrapper to iterate over all blocks
+  class InnerIterator : public BlockSparseMatrixT::BlockInnerIterator {
+   public:
+    InnerIterator(const BlockSparseMatrixView& mat, Index outer)
+        : BlockSparseMatrixT::BlockInnerIterator(mat.m_spblockmat, outer) {}
+  };
+
+ protected:
+  const BlockSparseMatrixT& m_spblockmat;
+};
+
+// Proxy to view a regular vector as a block vector
+template <typename BlockSparseMatrixT, typename VectorType>
+class BlockVectorView {
+ public:
+  enum {
+    BlockSize = BlockSparseMatrixT::BlockSize,
+    ColsAtCompileTime = VectorType::ColsAtCompileTime,
+    RowsAtCompileTime = VectorType::RowsAtCompileTime,
+    Flags = VectorType::Flags
+  };
+  typedef Ref<const Matrix<typename BlockSparseMatrixT::Scalar, (RowsAtCompileTime == 1) ? 1 : BlockSize,
+                           (ColsAtCompileTime == 1) ? 1 : BlockSize> >
+      Scalar;
+  typedef typename BlockSparseMatrixT::Index Index;
+
+ public:
+  BlockVectorView(const BlockSparseMatrixT& spblockmat, const VectorType& vec) : m_spblockmat(spblockmat), m_vec(vec) {}
+  inline Index cols() const { return m_vec.cols(); }
+  inline Index size() const { return m_spblockmat.blockRows(); }
+  inline Scalar coeff(Index bi) const {
+    Index startRow = m_spblockmat.blockRowsIndex(bi);
+    Index rowSize = m_spblockmat.blockRowsIndex(bi + 1) - startRow;
+    return m_vec.middleRows(startRow, rowSize);
+  }
+  inline Scalar coeff(Index bi, Index j) const {
+    Index startRow = m_spblockmat.blockRowsIndex(bi);
+    Index rowSize = m_spblockmat.blockRowsIndex(bi + 1) - startRow;
+    return m_vec.block(startRow, j, rowSize, 1);
+  }
+
+ protected:
+  const BlockSparseMatrixT& m_spblockmat;
+  const VectorType& m_vec;
+};
+
+template <typename VectorType, typename Index>
+class BlockVectorReturn;
+
+// Proxy to view a regular vector as a block vector
+template <typename BlockSparseMatrixT, typename VectorType>
+class BlockVectorReturn {
+ public:
+  enum {
+    ColsAtCompileTime = VectorType::ColsAtCompileTime,
+    RowsAtCompileTime = VectorType::RowsAtCompileTime,
+    Flags = VectorType::Flags
+  };
+  typedef Ref<Matrix<typename VectorType::Scalar, RowsAtCompileTime, ColsAtCompileTime> > Scalar;
+  typedef typename BlockSparseMatrixT::Index Index;
+
+ public:
+  BlockVectorReturn(const BlockSparseMatrixT& spblockmat, VectorType& vec) : m_spblockmat(spblockmat), m_vec(vec) {}
+  inline Index size() const { return m_spblockmat.blockRows(); }
+  inline Scalar coeffRef(Index bi) {
+    Index startRow = m_spblockmat.blockRowsIndex(bi);
+    Index rowSize = m_spblockmat.blockRowsIndex(bi + 1) - startRow;
+    return m_vec.middleRows(startRow, rowSize);
+  }
+  inline Scalar coeffRef(Index bi, Index j) {
+    Index startRow = m_spblockmat.blockRowsIndex(bi);
+    Index rowSize = m_spblockmat.blockRowsIndex(bi + 1) - startRow;
+    return m_vec.block(startRow, j, rowSize, 1);
+  }
+
+ protected:
+  const BlockSparseMatrixT& m_spblockmat;
+  VectorType& m_vec;
+};
+
+// Block version of the sparse dense product
+template <typename Lhs, typename Rhs>
+class BlockSparseTimeDenseProduct;
+
+namespace internal {
+
+template <typename BlockSparseMatrixT, typename VecType>
+struct traits<BlockSparseTimeDenseProduct<BlockSparseMatrixT, VecType> > {
+  typedef Dense StorageKind;
+  typedef MatrixXpr XprKind;
+  typedef typename BlockSparseMatrixT::Scalar Scalar;
+  typedef typename BlockSparseMatrixT::Index Index;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    Flags = 0,
+    CoeffReadCost = internal::traits<BlockSparseMatrixT>::CoeffReadCost
+  };
+};
+}  // end namespace internal
+
+template <typename Lhs, typename Rhs>
+class BlockSparseTimeDenseProduct : public ProductBase<BlockSparseTimeDenseProduct<Lhs, Rhs>, Lhs, Rhs> {
+ public:
+  EIGEN_PRODUCT_PUBLIC_INTERFACE(BlockSparseTimeDenseProduct)
+
+  BlockSparseTimeDenseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs, rhs) {}
+
+  template <typename Dest>
+  void scaleAndAddTo(Dest& dest, const typename Rhs::Scalar& alpha) const {
+    BlockVectorReturn<Lhs, Dest> tmpDest(m_lhs, dest);
+    internal::sparse_time_dense_product(BlockSparseMatrixView<Lhs>(m_lhs), BlockVectorView<Lhs, Rhs>(m_lhs, m_rhs),
+                                        tmpDest, alpha);
+  }
+
+ private:
+  BlockSparseTimeDenseProduct& operator=(const BlockSparseTimeDenseProduct&);
+};
+
+template <typename Scalar_, int _BlockAtCompileTime, int Options_, typename StorageIndex_>
+class BlockSparseMatrix
+    : public SparseMatrixBase<BlockSparseMatrix<Scalar_, _BlockAtCompileTime, Options_, StorageIndex_> > {
+ public:
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef StorageIndex_ StorageIndex;
+  typedef
+      typename internal::ref_selector<BlockSparseMatrix<Scalar_, _BlockAtCompileTime, Options_, StorageIndex_> >::type
+          Nested;
+
+  enum {
+    Options = Options_,
+    Flags = Options,
+    BlockSize = _BlockAtCompileTime,
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    IsVectorAtCompileTime = 0,
+    IsColMajor = Flags & RowMajorBit ? 0 : 1
+  };
+  typedef Matrix<Scalar, _BlockAtCompileTime, _BlockAtCompileTime, IsColMajor ? ColMajor : RowMajor> BlockScalar;
+  typedef Matrix<RealScalar, _BlockAtCompileTime, _BlockAtCompileTime, IsColMajor ? ColMajor : RowMajor>
+      BlockRealScalar;
+  typedef std::conditional_t<_BlockAtCompileTime == Dynamic, Scalar, BlockScalar> BlockScalarReturnType;
+  typedef BlockSparseMatrix<Scalar, BlockSize, IsColMajor ? ColMajor : RowMajor, StorageIndex> PlainObject;
+
+ public:
+  // Default constructor
+  BlockSparseMatrix()
+      : m_innerBSize(0),
+        m_outerBSize(0),
+        m_innerOffset(0),
+        m_outerOffset(0),
+        m_nonzerosblocks(0),
+        m_values(0),
+        m_blockPtr(0),
+        m_indices(0),
+        m_outerIndex(0),
+        m_blockSize(BlockSize) {}
+
+  /**
+   * \brief Construct and resize
+   *
+   */
+  BlockSparseMatrix(Index brow, Index bcol)
+      : m_innerBSize(IsColMajor ? brow : bcol),
+        m_outerBSize(IsColMajor ? bcol : brow),
+        m_innerOffset(0),
+        m_outerOffset(0),
+        m_nonzerosblocks(0),
+        m_values(0),
+        m_blockPtr(0),
+        m_indices(0),
+        m_outerIndex(0),
+        m_blockSize(BlockSize) {}
+
+  /**
+   * \brief Copy-constructor
+   */
+  BlockSparseMatrix(const BlockSparseMatrix& other)
+      : m_innerBSize(other.m_innerBSize),
+        m_outerBSize(other.m_outerBSize),
+        m_nonzerosblocks(other.m_nonzerosblocks),
+        m_nonzeros(other.m_nonzeros),
+        m_blockPtr(0),
+        m_blockSize(other.m_blockSize) {
+    // TODO: decide whether to allow copying between variable-size and fixed-size blocks.
+    eigen_assert(m_blockSize == BlockSize && " CAN NOT COPY BETWEEN FIXED-SIZE AND VARIABLE-SIZE BLOCKS");
+
+    std::copy(other.m_innerOffset, other.m_innerOffset + m_innerBSize + 1, m_innerOffset);
+    std::copy(other.m_outerOffset, other.m_outerOffset + m_outerBSize + 1, m_outerOffset);
+    std::copy(other.m_values, other.m_values + m_nonzeros, m_values);
+
+    if (m_blockSize != Dynamic) std::copy(other.m_blockPtr, other.m_blockPtr + m_nonzerosblocks, m_blockPtr);
+
+    std::copy(other.m_indices, other.m_indices + m_nonzerosblocks, m_indices);
+    std::copy(other.m_outerIndex, other.m_outerIndex + m_outerBSize, m_outerIndex);
+  }
+
+  friend void swap(BlockSparseMatrix& first, BlockSparseMatrix& second) {
+    std::swap(first.m_innerBSize, second.m_innerBSize);
+    std::swap(first.m_outerBSize, second.m_outerBSize);
+    std::swap(first.m_innerOffset, second.m_innerOffset);
+    std::swap(first.m_outerOffset, second.m_outerOffset);
+    std::swap(first.m_nonzerosblocks, second.m_nonzerosblocks);
+    std::swap(first.m_nonzeros, second.m_nonzeros);
+    std::swap(first.m_values, second.m_values);
+    std::swap(first.m_blockPtr, second.m_blockPtr);
+    std::swap(first.m_indices, second.m_indices);
+    std::swap(first.m_outerIndex, second.m_outerIndex);
+    std::swap(first.m_BlockSize, second.m_blockSize);
+  }
+
+  BlockSparseMatrix& operator=(BlockSparseMatrix other) {
+    // Copy-and-swap paradigm ... avoid leaked data if thrown
+    swap(*this, other);
+    return *this;
+  }
+
+  // Destructor
+  ~BlockSparseMatrix() {
+    delete[] m_outerIndex;
+    delete[] m_innerOffset;
+    delete[] m_outerOffset;
+    delete[] m_indices;
+    delete[] m_blockPtr;
+    delete[] m_values;
+  }
+
+  /**
+   * \brief Constructor from a sparse matrix
+   *
+   */
+  template <typename MatrixType>
+  inline BlockSparseMatrix(const MatrixType& spmat) : m_blockSize(BlockSize) {
+    EIGEN_STATIC_ASSERT((m_blockSize != Dynamic), THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE);
+
+    *this = spmat;
+  }
+
+  /**
+   * \brief Assignment from a sparse matrix with the same storage order
+   *
+   * Convert from a sparse matrix to block sparse matrix.
+   * \warning Before calling this function, tt is necessary to call
+   * either setBlockLayout() (matrices with variable-size blocks)
+   * or setBlockSize() (for fixed-size blocks).
+   */
+  template <typename MatrixType>
+  inline BlockSparseMatrix& operator=(const MatrixType& spmat) {
+    eigen_assert((m_innerBSize != 0 && m_outerBSize != 0) &&
+                 "Trying to assign to a zero-size matrix, call resize() first");
+    eigen_assert(((MatrixType::Options & RowMajorBit) != IsColMajor) && "Wrong storage order");
+    typedef SparseMatrix<bool, MatrixType::Options, typename MatrixType::Index> MatrixPatternType;
+    MatrixPatternType blockPattern(blockRows(), blockCols());
+    m_nonzeros = 0;
+
+    // First, compute the number of nonzero blocks and their locations
+    for (StorageIndex bj = 0; bj < m_outerBSize; ++bj) {
+      // Browse each outer block and compute the structure
+      std::vector<bool> nzblocksFlag(m_innerBSize, false);  // Record the existing blocks
+      blockPattern.startVec(bj);
+      for (StorageIndex j = blockOuterIndex(bj); j < blockOuterIndex(bj + 1); ++j) {
+        typename MatrixType::InnerIterator it_spmat(spmat, j);
+        for (; it_spmat; ++it_spmat) {
+          StorageIndex bi = innerToBlock(it_spmat.index());  // Index of the current nonzero block
+          if (!nzblocksFlag[bi]) {
+            // Save the index of this nonzero block
+            nzblocksFlag[bi] = true;
+            blockPattern.insertBackByOuterInnerUnordered(bj, bi) = true;
+            // Compute the total number of nonzeros (including explicit zeros in blocks)
+            m_nonzeros += blockOuterSize(bj) * blockInnerSize(bi);
+          }
+        }
+      }  // end current outer block
+    }
+    blockPattern.finalize();
+
+    // Allocate the internal arrays
+    setBlockStructure(blockPattern);
+
+    for (StorageIndex nz = 0; nz < m_nonzeros; ++nz) m_values[nz] = Scalar(0);
+    for (StorageIndex bj = 0; bj < m_outerBSize; ++bj) {
+      // Now copy the values
+      for (StorageIndex j = blockOuterIndex(bj); j < blockOuterIndex(bj + 1); ++j) {
+        // Browse the outer block column by column (for column-major matrices)
+        typename MatrixType::InnerIterator it_spmat(spmat, j);
+        for (; it_spmat; ++it_spmat) {
+          StorageIndex idx = 0;                              // Position of this block in the column block
+          StorageIndex bi = innerToBlock(it_spmat.index());  // Index of the current nonzero block
+          // Go to the inner block where this element belongs to
+          while (bi > m_indices[m_outerIndex[bj] + idx]) ++idx;  // Not expensive for ordered blocks
+          StorageIndex idxVal;  // Get the right position in the array of values for this element
+          if (m_blockSize == Dynamic) {
+            // Offset from all blocks before ...
+            idxVal = m_blockPtr[m_outerIndex[bj] + idx];
+            // ... and offset inside the block
+            idxVal += (j - blockOuterIndex(bj)) * blockOuterSize(bj) + it_spmat.index() - m_innerOffset[bi];
+          } else {
+            // All blocks before
+            idxVal = (m_outerIndex[bj] + idx) * m_blockSize * m_blockSize;
+            // inside the block
+            idxVal += (j - blockOuterIndex(bj)) * m_blockSize + (it_spmat.index() % m_blockSize);
+          }
+          // Insert the value
+          m_values[idxVal] = it_spmat.value();
+        }  // end of this column
+      }    // end of this block
+    }      // end of this outer block
+
+    return *this;
+  }
+
+  /**
+   * \brief Set the nonzero block pattern of the matrix
+   *
+   * Given a sparse matrix describing the nonzero block pattern,
+   * this function prepares the internal pointers for values.
+   * After calling this function, any *nonzero* block (bi, bj) can be set
+   * with a simple call to coeffRef(bi,bj).
+   *
+   *
+   * \warning Before calling this function, tt is necessary to call
+   * either setBlockLayout() (matrices with variable-size blocks)
+   * or setBlockSize() (for fixed-size blocks).
+   *
+   * \param blockPattern Sparse matrix of boolean elements describing the block structure
+   *
+   * \sa setBlockLayout() \sa setBlockSize()
+   */
+  template <typename MatrixType>
+  void setBlockStructure(const MatrixType& blockPattern) {
+    resize(blockPattern.rows(), blockPattern.cols());
+    reserve(blockPattern.nonZeros());
+
+    // Browse the block pattern and set up the various pointers
+    m_outerIndex[0] = 0;
+    if (m_blockSize == Dynamic) m_blockPtr[0] = 0;
+    for (StorageIndex nz = 0; nz < m_nonzeros; ++nz) m_values[nz] = Scalar(0);
+    for (StorageIndex bj = 0; bj < m_outerBSize; ++bj) {
+      // Browse each outer block
+
+      // First, copy and save the indices of nonzero blocks
+      // FIXME : find a way to avoid this ...
+      std::vector<int> nzBlockIdx;
+      typename MatrixType::InnerIterator it(blockPattern, bj);
+      for (; it; ++it) {
+        nzBlockIdx.push_back(it.index());
+      }
+      std::sort(nzBlockIdx.begin(), nzBlockIdx.end());
+
+      // Now, fill block indices and (eventually) pointers to blocks
+      for (StorageIndex idx = 0; idx < nzBlockIdx.size(); ++idx) {
+        StorageIndex offset = m_outerIndex[bj] + idx;  // offset in m_indices
+        m_indices[offset] = nzBlockIdx[idx];
+        if (m_blockSize == Dynamic)
+          m_blockPtr[offset] = m_blockPtr[offset - 1] + blockInnerSize(nzBlockIdx[idx]) * blockOuterSize(bj);
+        // There is no blockPtr for fixed-size blocks; not needed.
+      }
+      // Save the pointer to the next outer block
+      m_outerIndex[bj + 1] = m_outerIndex[bj] + nzBlockIdx.size();
+    }
+  }
+
+  /**
+   * \brief Set the number of rows and columns blocks
+   */
+  inline void resize(Index brow, Index bcol) {
+    m_innerBSize = IsColMajor ? brow : bcol;
+    m_outerBSize = IsColMajor ? bcol : brow;
+  }
+
+  /**
+   * \brief set the block size at runtime for fixed-size block layout
+   *
+   * Call this only for fixed-size blocks
+   */
+  inline void setBlockSize(Index blockSize) { m_blockSize = blockSize; }
+
+  /**
+   * \brief Set the row and column block layouts,
+   *
+   * This function set the size of each row and column block.
+   * So this function should be used only for blocks with variable size.
+   * \param rowBlocks : Number of rows per row block
+   * \param colBlocks : Number of columns per column block
+   * \sa resize(), setBlockSize()
+   */
+  inline void setBlockLayout(const VectorXi& rowBlocks, const VectorXi& colBlocks) {
+    const VectorXi& innerBlocks = IsColMajor ? rowBlocks : colBlocks;
+    const VectorXi& outerBlocks = IsColMajor ? colBlocks : rowBlocks;
+    eigen_assert(m_innerBSize == innerBlocks.size() && "CHECK THE NUMBER OF ROW OR COLUMN BLOCKS");
+    eigen_assert(m_outerBSize == outerBlocks.size() && "CHECK THE NUMBER OF ROW OR COLUMN BLOCKS");
+    m_outerBSize = outerBlocks.size();
+    //  starting index of blocks... cumulative sums
+    m_innerOffset = new StorageIndex[m_innerBSize + 1];
+    m_outerOffset = new StorageIndex[m_outerBSize + 1];
+    m_innerOffset[0] = 0;
+    m_outerOffset[0] = 0;
+    std::partial_sum(&innerBlocks[0], &innerBlocks[m_innerBSize - 1] + 1, &m_innerOffset[1]);
+    std::partial_sum(&outerBlocks[0], &outerBlocks[m_outerBSize - 1] + 1, &m_outerOffset[1]);
+
+    // Compute the total number of nonzeros
+    m_nonzeros = 0;
+    for (StorageIndex bj = 0; bj < m_outerBSize; ++bj)
+      for (StorageIndex bi = 0; bi < m_innerBSize; ++bi) m_nonzeros += outerBlocks[bj] * innerBlocks[bi];
+  }
+
+  /**
+   * \brief Allocate the internal array of pointers to blocks and their inner indices
+   *
+   * \note For fixed-size blocks, call setBlockSize() to set the block.
+   * And For variable-size blocks, call setBlockLayout() before using this function
+   *
+   * \param nonzerosblocks Number of nonzero blocks. The total number of nonzeros is
+   * is computed in setBlockLayout() for variable-size blocks
+   * \sa setBlockSize()
+   */
+  inline void reserve(const Index nonzerosblocks) {
+    eigen_assert((m_innerBSize != 0 && m_outerBSize != 0) &&
+                 "TRYING TO RESERVE ZERO-SIZE MATRICES, CALL resize() first");
+
+    // FIXME: Should free if already allocated.
+    m_outerIndex = new StorageIndex[m_outerBSize + 1];
+
+    m_nonzerosblocks = nonzerosblocks;
+    if (m_blockSize != Dynamic) {
+      m_nonzeros = nonzerosblocks * (m_blockSize * m_blockSize);
+      m_blockPtr = 0;
+    } else {
+      // m_nonzeros  is already computed in setBlockLayout()
+      m_blockPtr = new StorageIndex[m_nonzerosblocks + 1];
+    }
+    m_indices = new StorageIndex[m_nonzerosblocks + 1];
+    m_values = new Scalar[m_nonzeros];
+  }
+
+  /**
+   * \brief Fill values in a matrix  from a triplet list.
+   *
+   * Each triplet item has a block stored in an Eigen dense matrix.
+   * The InputIterator class should provide the functions row(), col() and value()
+   *
+   * \note For fixed-size blocks, call setBlockSize() before this function.
+   *
+   * FIXME: Do not accept duplicates.
+   */
+  template <typename InputIterator>
+  void setFromTriplets(const InputIterator& begin, const InputIterator& end) {
+    eigen_assert((m_innerBSize != 0 && m_outerBSize != 0) && "ZERO BLOCKS, PLEASE CALL resize() before");
+
+    /* First, sort the triplet list
+     * FIXME: This can be unnecessarily expensive since only the inner indices have to be sorted.
+     * The best approach is like in SparseMatrix::setFromTriplets()
+     */
+    internal::TripletComp<InputIterator, IsColMajor> tripletcomp;
+    std::sort(begin, end, tripletcomp);
+
+    /* Count the number of rows and column blocks,
+     * and the number of nonzero blocks per outer dimension
+     */
+    VectorXi rowBlocks(m_innerBSize);  // Size of each block row
+    VectorXi colBlocks(m_outerBSize);  // Size of each block column
+    rowBlocks.setZero();
+    colBlocks.setZero();
+    VectorXi nzblock_outer(m_outerBSize);  // Number of nz blocks per outer vector
+    VectorXi nz_outer(m_outerBSize);       // Number of nz per outer vector...for variable-size blocks
+    nzblock_outer.setZero();
+    nz_outer.setZero();
+    for (InputIterator it(begin); it != end; ++it) {
+      eigen_assert(it->row() >= 0 && it->row() < this->blockRows() && it->col() >= 0 && it->col() < this->blockCols());
+      eigen_assert((it->value().rows() == it->value().cols() && (it->value().rows() == m_blockSize)) ||
+                   (m_blockSize == Dynamic));
+
+      if (m_blockSize == Dynamic) {
+        eigen_assert((rowBlocks[it->row()] == 0 || rowBlocks[it->row()] == it->value().rows()) &&
+                     "NON CORRESPONDING SIZES FOR ROW BLOCKS");
+        eigen_assert((colBlocks[it->col()] == 0 || colBlocks[it->col()] == it->value().cols()) &&
+                     "NON CORRESPONDING SIZES FOR COLUMN BLOCKS");
+        rowBlocks[it->row()] = it->value().rows();
+        colBlocks[it->col()] = it->value().cols();
+      }
+      nz_outer(IsColMajor ? it->col() : it->row()) += it->value().rows() * it->value().cols();
+      nzblock_outer(IsColMajor ? it->col() : it->row())++;
+    }
+    // Allocate member arrays
+    if (m_blockSize == Dynamic) setBlockLayout(rowBlocks, colBlocks);
+    StorageIndex nzblocks = nzblock_outer.sum();
+    reserve(nzblocks);
+
+    // Temporary markers
+    VectorXi block_id(m_outerBSize);  // To be used as a block marker during insertion
+
+    // Setup outer index pointers and markers
+    m_outerIndex[0] = 0;
+    if (m_blockSize == Dynamic) m_blockPtr[0] = 0;
+    for (StorageIndex bj = 0; bj < m_outerBSize; ++bj) {
+      m_outerIndex[bj + 1] = m_outerIndex[bj] + nzblock_outer(bj);
+      block_id(bj) = m_outerIndex[bj];
+      if (m_blockSize == Dynamic) {
+        m_blockPtr[m_outerIndex[bj + 1]] = m_blockPtr[m_outerIndex[bj]] + nz_outer(bj);
+      }
+    }
+
+    // Fill the matrix
+    for (InputIterator it(begin); it != end; ++it) {
+      StorageIndex outer = IsColMajor ? it->col() : it->row();
+      StorageIndex inner = IsColMajor ? it->row() : it->col();
+      m_indices[block_id(outer)] = inner;
+      StorageIndex block_size = it->value().rows() * it->value().cols();
+      StorageIndex nz_marker = blockPtr(block_id[outer]);
+      memcpy(&(m_values[nz_marker]), it->value().data(), block_size * sizeof(Scalar));
+      if (m_blockSize == Dynamic) {
+        m_blockPtr[block_id(outer) + 1] = m_blockPtr[block_id(outer)] + block_size;
+      }
+      block_id(outer)++;
+    }
+  }
+
+  /**
+   * \returns the number of rows
+   */
+  inline Index rows() const { return (IsColMajor ? innerSize() : outerSize()); }
+
+  /**
+   * \returns the number of cols
+   */
+  inline Index cols() const { return (IsColMajor ? outerSize() : innerSize()); }
+
+  inline Index innerSize() const {
+    if (m_blockSize == Dynamic)
+      return m_innerOffset[m_innerBSize];
+    else
+      return (m_innerBSize * m_blockSize);
+  }
+
+  inline Index outerSize() const {
+    if (m_blockSize == Dynamic)
+      return m_outerOffset[m_outerBSize];
+    else
+      return (m_outerBSize * m_blockSize);
+  }
+  /** \returns the number of rows grouped by blocks */
+  inline Index blockRows() const { return (IsColMajor ? m_innerBSize : m_outerBSize); }
+  /** \returns the number of columns grouped by blocks */
+  inline Index blockCols() const { return (IsColMajor ? m_outerBSize : m_innerBSize); }
+
+  inline Index outerBlocks() const { return m_outerBSize; }
+  inline Index innerBlocks() const { return m_innerBSize; }
+
+  /** \returns the block index where outer belongs to */
+  inline Index outerToBlock(Index outer) const {
+    eigen_assert(outer < outerSize() && "OUTER INDEX OUT OF BOUNDS");
+
+    if (m_blockSize != Dynamic) return (outer / m_blockSize);  // Integer division
+
+    StorageIndex b_outer = 0;
+    while (m_outerOffset[b_outer] <= outer) ++b_outer;
+    return b_outer - 1;
+  }
+  /** \returns  the block index where inner belongs to */
+  inline Index innerToBlock(Index inner) const {
+    eigen_assert(inner < innerSize() && "OUTER INDEX OUT OF BOUNDS");
+
+    if (m_blockSize != Dynamic) return (inner / m_blockSize);  // Integer division
+
+    StorageIndex b_inner = 0;
+    while (m_innerOffset[b_inner] <= inner) ++b_inner;
+    return b_inner - 1;
+  }
+
+  /**
+   *\returns a reference to the (i,j) block as an Eigen Dense Matrix
+   */
+  Ref<BlockScalar> coeffRef(Index brow, Index bcol) {
+    eigen_assert(brow < blockRows() && "BLOCK ROW INDEX OUT OF BOUNDS");
+    eigen_assert(bcol < blockCols() && "BLOCK nzblocksFlagCOLUMN OUT OF BOUNDS");
+
+    StorageIndex rsize = IsColMajor ? blockInnerSize(brow) : blockOuterSize(bcol);
+    StorageIndex csize = IsColMajor ? blockOuterSize(bcol) : blockInnerSize(brow);
+    StorageIndex inner = IsColMajor ? brow : bcol;
+    StorageIndex outer = IsColMajor ? bcol : brow;
+    StorageIndex offset = m_outerIndex[outer];
+    while (offset < m_outerIndex[outer + 1] && m_indices[offset] != inner) offset++;
+    if (m_indices[offset] == inner) {
+      return Map<BlockScalar>(&(m_values[blockPtr(offset)]), rsize, csize);
+    } else {
+      // FIXME: The block does not exist; insert it.
+      eigen_assert("DYNAMIC INSERTION IS NOT YET SUPPORTED");
+    }
+  }
+
+  /**
+   * \returns the value of the (i,j) block as an Eigen Dense Matrix
+   */
+  Map<const BlockScalar> coeff(Index brow, Index bcol) const {
+    eigen_assert(brow < blockRows() && "BLOCK ROW INDEX OUT OF BOUNDS");
+    eigen_assert(bcol < blockCols() && "BLOCK COLUMN OUT OF BOUNDS");
+
+    StorageIndex rsize = IsColMajor ? blockInnerSize(brow) : blockOuterSize(bcol);
+    StorageIndex csize = IsColMajor ? blockOuterSize(bcol) : blockInnerSize(brow);
+    StorageIndex inner = IsColMajor ? brow : bcol;
+    StorageIndex outer = IsColMajor ? bcol : brow;
+    StorageIndex offset = m_outerIndex[outer];
+    while (offset < m_outerIndex[outer + 1] && m_indices[offset] != inner) offset++;
+    if (m_indices[offset] == inner) {
+      return Map<const BlockScalar>(&(m_values[blockPtr(offset)]), rsize, csize);
+    } else
+      eigen_assert("NOT YET SUPPORTED");
+  }
+
+  // Block Matrix times vector product
+  template <typename VecType>
+  BlockSparseTimeDenseProduct<BlockSparseMatrix, VecType> operator*(const VecType& lhs) const {
+    return BlockSparseTimeDenseProduct<BlockSparseMatrix, VecType>(*this, lhs);
+  }
+
+  /** \returns the number of nonzero blocks */
+  inline Index nonZerosBlocks() const { return m_nonzerosblocks; }
+  /** \returns the total number of nonzero elements, including eventual explicit zeros in blocks */
+  inline Index nonZeros() const { return m_nonzeros; }
+
+  inline BlockScalarReturnType* valuePtr() { return static_cast<BlockScalarReturnType*>(m_values); }
+  //    inline Scalar *valuePtr(){ return m_values; }
+  inline StorageIndex* innerIndexPtr() { return m_indices; }
+  inline const StorageIndex* innerIndexPtr() const { return m_indices; }
+  inline StorageIndex* outerIndexPtr() { return m_outerIndex; }
+  inline const StorageIndex* outerIndexPtr() const { return m_outerIndex; }
+
+  /** \brief for compatibility purposes with the SparseMatrix class */
+  inline bool isCompressed() const { return true; }
+  /**
+   * \returns the starting index of the bi row block
+   */
+  inline Index blockRowsIndex(Index bi) const { return IsColMajor ? blockInnerIndex(bi) : blockOuterIndex(bi); }
+
+  /**
+   * \returns the starting index of the bj col block
+   */
+  inline Index blockColsIndex(Index bj) const { return IsColMajor ? blockOuterIndex(bj) : blockInnerIndex(bj); }
+
+  inline Index blockOuterIndex(Index bj) const {
+    return (m_blockSize == Dynamic) ? m_outerOffset[bj] : (bj * m_blockSize);
+  }
+  inline Index blockInnerIndex(Index bi) const {
+    return (m_blockSize == Dynamic) ? m_innerOffset[bi] : (bi * m_blockSize);
+  }
+
+  // Not needed.
+  inline Index blockInnerSize(Index bi) const {
+    return (m_blockSize == Dynamic) ? (m_innerOffset[bi + 1] - m_innerOffset[bi]) : m_blockSize;
+  }
+  inline Index blockOuterSize(Index bj) const {
+    return (m_blockSize == Dynamic) ? (m_outerOffset[bj + 1] - m_outerOffset[bj]) : m_blockSize;
+  }
+
+  /**
+   * \brief Browse the matrix by outer index
+   */
+  class InnerIterator;  // Browse column by column
+
+  /**
+   * \brief Browse the matrix by block outer index
+   */
+  class BlockInnerIterator;  // Browse block by block
+
+  friend std::ostream& operator<<(std::ostream& s, const BlockSparseMatrix& m) {
+    for (StorageIndex j = 0; j < m.outerBlocks(); ++j) {
+      BlockInnerIterator itb(m, j);
+      for (; itb; ++itb) {
+        s << "(" << itb.row() << ", " << itb.col() << ")\n";
+        s << itb.value() << "\n";
+      }
+    }
+    s << std::endl;
+    return s;
+  }
+
+  /**
+   * \returns the starting position of the block \p id in the array of values
+   */
+  Index blockPtr(Index id) const {
+    if (m_blockSize == Dynamic)
+      return m_blockPtr[id];
+    else
+      return id * m_blockSize * m_blockSize;
+  }
+
+ protected:
+  // To be implemented
+  // Insert a block at a particular location... need to make a room for that
+  Map<BlockScalar> insert(Index brow, Index bcol);
+
+  Index m_innerBSize;           // Number of block rows
+  Index m_outerBSize;           // Number of block columns
+  StorageIndex* m_innerOffset;  // Starting index of each inner block (size m_innerBSize+1)
+  StorageIndex* m_outerOffset;  // Starting index of each outer block (size m_outerBSize+1)
+  Index m_nonzerosblocks;       // Total nonzeros blocks (lower than  m_innerBSize x m_outerBSize)
+  Index m_nonzeros;             // Total nonzeros elements
+  Scalar* m_values;             // Values stored block column after block column (size m_nonzeros)
+  StorageIndex* m_blockPtr;     // Pointer to the beginning of each block in m_values, size m_nonzeroblocks ... null for
+                                // fixed-size blocks
+  StorageIndex* m_indices;      // Inner block indices, size m_nonzerosblocks ... OK
+  StorageIndex* m_outerIndex;   // Starting pointer of each block column in m_indices (size m_outerBSize)... OK
+  Index m_blockSize;            // Size of a block for fixed-size blocks, otherwise -1
+};
+
+template <typename Scalar_, int _BlockAtCompileTime, int Options_, typename StorageIndex_>
+class BlockSparseMatrix<Scalar_, _BlockAtCompileTime, Options_, StorageIndex_>::BlockInnerIterator {
+ public:
+  enum { Flags = Options_ };
+
+  BlockInnerIterator(const BlockSparseMatrix& mat, const Index outer)
+      : m_mat(mat), m_outer(outer), m_id(mat.m_outerIndex[outer]), m_end(mat.m_outerIndex[outer + 1]) {}
+
+  inline BlockInnerIterator& operator++() {
+    m_id++;
+    return *this;
+  }
+
+  inline const Map<const BlockScalar> value() const {
+    return Map<const BlockScalar>(&(m_mat.m_values[m_mat.blockPtr(m_id)]), rows(), cols());
+  }
+  inline Map<BlockScalar> valueRef() {
+    return Map<BlockScalar>(&(m_mat.m_values[m_mat.blockPtr(m_id)]), rows(), cols());
+  }
+  // Block inner index
+  inline Index index() const { return m_mat.m_indices[m_id]; }
+  inline Index outer() const { return m_outer; }
+  // block row index
+  inline Index row() const { return index(); }
+  // block column index
+  inline Index col() const { return outer(); }
+  // FIXME: Number of rows in the current block.
+  inline Index rows() const {
+    return (m_mat.m_blockSize == Dynamic) ? (m_mat.m_innerOffset[index() + 1] - m_mat.m_innerOffset[index()])
+                                          : m_mat.m_blockSize;
+  }
+  // Number of columns in the current block ...
+  inline Index cols() const {
+    return (m_mat.m_blockSize == Dynamic) ? (m_mat.m_outerOffset[m_outer + 1] - m_mat.m_outerOffset[m_outer])
+                                          : m_mat.m_blockSize;
+  }
+  inline operator bool() const { return (m_id < m_end); }
+
+ protected:
+  const BlockSparseMatrix<Scalar_, _BlockAtCompileTime, Options_, StorageIndex>& m_mat;
+  const Index m_outer;
+  Index m_id;
+  Index m_end;
+};
+
+template <typename Scalar_, int _BlockAtCompileTime, int Options_, typename StorageIndex_>
+class BlockSparseMatrix<Scalar_, _BlockAtCompileTime, Options_, StorageIndex_>::InnerIterator {
+ public:
+  InnerIterator(const BlockSparseMatrix& mat, Index outer)
+      : m_mat(mat),
+        m_outerB(mat.outerToBlock(outer)),
+        m_outer(outer),
+        itb(mat, mat.outerToBlock(outer)),
+        m_offset(outer - mat.blockOuterIndex(m_outerB)) {
+    if (itb) {
+      m_id = m_mat.blockInnerIndex(itb.index());
+      m_start = m_id;
+      m_end = m_mat.blockInnerIndex(itb.index() + 1);
+    }
+  }
+  inline InnerIterator& operator++() {
+    m_id++;
+    if (m_id >= m_end) {
+      ++itb;
+      if (itb) {
+        m_id = m_mat.blockInnerIndex(itb.index());
+        m_start = m_id;
+        m_end = m_mat.blockInnerIndex(itb.index() + 1);
+      }
+    }
+    return *this;
+  }
+  inline const Scalar& value() const { return itb.value().coeff(m_id - m_start, m_offset); }
+  inline Scalar& valueRef() { return itb.valueRef().coeff(m_id - m_start, m_offset); }
+  inline Index index() const { return m_id; }
+  inline Index outer() const { return m_outer; }
+  inline Index col() const { return outer(); }
+  inline Index row() const { return index(); }
+  inline operator bool() const { return itb; }
+
+ protected:
+  const BlockSparseMatrix& m_mat;
+  const Index m_outer;
+  const Index m_outerB;
+  BlockInnerIterator itb;  // Iterator through the blocks
+  const Index m_offset;    // Position of this column in the block
+  Index m_start;           // starting inner index of this block
+  Index m_id;              // current inner index in the block
+  Index m_end;             // starting inner index of the next block
+};
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSEBLOCKMATRIX_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/InternalHeaderCheck.h
new file mode 100644
index 0000000..0e55251
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPARSE_EXTRA_MODULE_H
+#error "Please include unsupported/Eigen/SparseExtra instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/MarketIO.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/MarketIO.h
new file mode 100644
index 0000000..f92622d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/MarketIO.h
@@ -0,0 +1,366 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2012 Desire NUENTSA WAKAM <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_MARKET_IO_H
+#define EIGEN_SPARSE_MARKET_IO_H
+
+#include <iostream>
+#include <vector>
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Scalar, typename StorageIndex>
+inline void GetMarketLine(const char* line, StorageIndex& i, StorageIndex& j, Scalar& value) {
+  std::stringstream sline(line);
+  sline >> i >> j >> value;
+}
+
+template <>
+inline void GetMarketLine(const char* line, int& i, int& j, float& value) {
+  std::sscanf(line, "%d %d %g", &i, &j, &value);
+}
+
+template <>
+inline void GetMarketLine(const char* line, int& i, int& j, double& value) {
+  std::sscanf(line, "%d %d %lg", &i, &j, &value);
+}
+
+template <>
+inline void GetMarketLine(const char* line, int& i, int& j, std::complex<float>& value) {
+  std::sscanf(line, "%d %d %g %g", &i, &j, &numext::real_ref(value), &numext::imag_ref(value));
+}
+
+template <>
+inline void GetMarketLine(const char* line, int& i, int& j, std::complex<double>& value) {
+  std::sscanf(line, "%d %d %lg %lg", &i, &j, &numext::real_ref(value), &numext::imag_ref(value));
+}
+
+template <typename Scalar, typename StorageIndex>
+inline void GetMarketLine(const char* line, StorageIndex& i, StorageIndex& j, std::complex<Scalar>& value) {
+  std::stringstream sline(line);
+  Scalar valR, valI;
+  sline >> i >> j >> valR >> valI;
+  value = std::complex<Scalar>(valR, valI);
+}
+
+template <typename RealScalar>
+inline void GetDenseElt(const std::string& line, RealScalar& val) {
+  std::istringstream newline(line);
+  newline >> val;
+}
+
+template <typename RealScalar>
+inline void GetDenseElt(const std::string& line, std::complex<RealScalar>& val) {
+  RealScalar valR, valI;
+  std::istringstream newline(line);
+  newline >> valR >> valI;
+  val = std::complex<RealScalar>(valR, valI);
+}
+
+template <typename Scalar>
+inline void putMarketHeader(std::string& header, int sym) {
+  header = "%%MatrixMarket matrix coordinate ";
+  if (internal::is_same<Scalar, std::complex<float> >::value ||
+      internal::is_same<Scalar, std::complex<double> >::value) {
+    header += " complex";
+    if (sym == Symmetric)
+      header += " symmetric";
+    else if (sym == SelfAdjoint)
+      header += " Hermitian";
+    else
+      header += " general";
+  } else {
+    header += " real";
+    if (sym == Symmetric)
+      header += " symmetric";
+    else
+      header += " general";
+  }
+}
+
+template <typename Scalar, typename StorageIndex>
+inline void PutMatrixElt(Scalar value, StorageIndex row, StorageIndex col, std::ofstream& out) {
+  out << row << " " << col << " " << value << "\n";
+}
+template <typename Scalar, typename StorageIndex>
+inline void PutMatrixElt(std::complex<Scalar> value, StorageIndex row, StorageIndex col, std::ofstream& out) {
+  out << row << " " << col << " " << value.real() << " " << value.imag() << "\n";
+}
+
+template <typename Scalar>
+inline void putDenseElt(Scalar value, std::ofstream& out) {
+  out << value << "\n";
+}
+template <typename Scalar>
+inline void putDenseElt(std::complex<Scalar> value, std::ofstream& out) {
+  out << value.real() << " " << value.imag() << "\n";
+}
+
+}  // end namespace internal
+
+/**
+ * \ingroup SparseExtra_Module
+ * @brief Reads the header of a matrixmarket file and determines the properties of a matrix
+ *
+ * @param filename of the file
+ * @param sym if the matrix is hermitian,symmetric or none of the latter (sym=0)
+ * @param iscomplex if the matrix has complex or real coefficients
+ * @param isdense if the matrix is dense or sparse
+ * @return true if the file was found
+ */
+inline bool getMarketHeader(const std::string& filename, int& sym, bool& iscomplex, bool& isdense) {
+  sym = 0;
+  iscomplex = false;
+  isdense = false;
+  std::ifstream in(filename.c_str(), std::ios::in);
+  if (!in) return false;
+
+  std::string line;
+  // The matrix header is always the first line in the file
+  std::getline(in, line);
+  eigen_assert(in.good());
+
+  std::stringstream fmtline(line);
+  std::string substr[5];
+  fmtline >> substr[0] >> substr[1] >> substr[2] >> substr[3] >> substr[4];
+  if (substr[2].compare("array") == 0) isdense = true;
+  if (substr[3].compare("complex") == 0) iscomplex = true;
+  if (substr[4].compare("symmetric") == 0)
+    sym = Symmetric;
+  else if (substr[4].compare("Hermitian") == 0)
+    sym = SelfAdjoint;
+
+  return true;
+}
+/**
+ * \ingroup SparseExtra_Module
+ * @brief Loads a sparse matrix from a matrixmarket format file.
+ *
+ * @tparam SparseMatrixType to read into, symmetries are not supported
+ * @param mat SparseMatrix to read into, current values are overwritten
+ * @param filename to parse matrix from
+ * @return returns true if file exists. Returns false if the parsing did not succeed.
+ */
+template <typename SparseMatrixType>
+bool loadMarket(SparseMatrixType& mat, const std::string& filename) {
+  typedef typename SparseMatrixType::Scalar Scalar;
+  typedef typename SparseMatrixType::StorageIndex StorageIndex;
+  std::ifstream input(filename.c_str(), std::ios::in);
+  if (!input) return false;
+
+  char rdbuffer[4096];
+  input.rdbuf()->pubsetbuf(rdbuffer, 4096);
+
+  const int maxBuffersize = 2048;
+  char buffer[maxBuffersize];
+
+  bool readsizes = false;
+
+  typedef Triplet<Scalar, StorageIndex> T;
+  std::vector<T> elements;
+
+  Index M(-1), N(-1), NNZ(-1);
+  Index count = 0;
+  while (input.getline(buffer, maxBuffersize)) {
+    // skip comments
+    // NOTE An appropriate test should be done on the header to get the  symmetry
+    if (buffer[0] == '%') continue;
+
+    if (!readsizes) {
+      std::stringstream line(buffer);
+      line >> M >> N >> NNZ;
+      if (M > 0 && N > 0) {
+        readsizes = true;
+        mat.resize(M, N);
+        mat.reserve(NNZ);
+        elements.reserve(NNZ);
+      }
+    } else {
+      StorageIndex i(-1), j(-1);
+      Scalar value;
+      internal::GetMarketLine(buffer, i, j, value);
+
+      i--;
+      j--;
+      if (i >= 0 && j >= 0 && i < M && j < N) {
+        ++count;
+        elements.push_back(T(i, j, value));
+      } else {
+        std::cerr << "Invalid read: " << i << "," << j << "\n";
+        return false;
+      }
+    }
+  }
+
+  mat.setFromTriplets(elements.begin(), elements.end());
+  if (count != NNZ) {
+    std::cerr << count << "!=" << NNZ << "\n";
+    return false;
+  }
+  input.close();
+  return true;
+}
+
+/**
+ * \ingroup SparseExtra_Module
+ * @brief Loads a dense Matrix or Vector from a matrixmarket file. If a statically sized matrix has to be parsed and the
+ * file contains the wrong dimensions it is undefined behaviour.
+ *
+ * @tparam DenseMatrixType to read into
+ * @param mat DenseMatrix to read into, current values are overwritten, symmetries are not supported
+ * @param filename to parse matrix from
+ * @return true if parsing was successful. Returns false if the parsing did not succeed.
+ */
+template <typename DenseType>
+bool loadMarketDense(DenseType& mat, const std::string& filename) {
+  typedef typename DenseType::Scalar Scalar;
+  std::ifstream in(filename.c_str(), std::ios::in);
+  if (!in) return false;
+
+  std::string line;
+  Index rows(0), cols(0);
+  do {  // Skip comments
+    std::getline(in, line);
+    eigen_assert(in.good());
+  } while (line[0] == '%');
+  std::istringstream newline(line);
+  newline >> rows >> cols;
+
+  bool sizes_not_positive = (rows < 1 || cols < 1);
+  bool wrong_input_rows = (DenseType::MaxRowsAtCompileTime != Dynamic && rows > DenseType::MaxRowsAtCompileTime) ||
+                          (DenseType::RowsAtCompileTime != Dynamic && rows != DenseType::RowsAtCompileTime);
+  bool wrong_input_cols = (DenseType::MaxColsAtCompileTime != Dynamic && cols > DenseType::MaxColsAtCompileTime) ||
+                          (DenseType::ColsAtCompileTime != Dynamic && cols != DenseType::ColsAtCompileTime);
+
+  if (sizes_not_positive || wrong_input_rows || wrong_input_cols) {
+    if (sizes_not_positive) {
+      std::cerr << "non-positive row or column size in file" << filename << "\n";
+    } else {
+      std::cerr << "Input matrix can not be resized to" << rows << " x " << cols << "as given in " << filename << "\n";
+    }
+    in.close();
+    return false;
+  }
+
+  mat.resize(rows, cols);
+  Index row = 0;
+  Index col = 0;
+  Index n = 0;
+  Scalar value;
+  while (std::getline(in, line) && (row < rows) && (col < cols)) {
+    internal::GetDenseElt(line, value);
+    // matrixmarket format is column major
+    mat(row, col) = value;
+    row++;
+    if (row == rows) {
+      row = 0;
+      col++;
+    }
+    n++;
+  }
+  in.close();
+  if (n != mat.size()) {
+    std::cerr << "Unable to read all elements from file " << filename << "\n";
+    return false;
+  }
+  return true;
+}
+/**
+ * \ingroup SparseExtra_Module
+ * @brief Same functionality as loadMarketDense, deprecated
+ */
+template <typename VectorType>
+bool loadMarketVector(VectorType& vec, const std::string& filename) {
+  return loadMarketDense(vec, filename);
+}
+
+/**
+ * \ingroup SparseExtra_Module
+ * @brief writes a sparse Matrix to a marketmarket format file
+ *
+ * @tparam SparseMatrixType to write to file
+ * @param mat matrix to write to file
+ * @param filename filename to write to
+ * @param sym at the moment no symmetry operations are supported
+ * @return true if writing succeeded
+ */
+template <typename SparseMatrixType>
+bool saveMarket(const SparseMatrixType& mat, const std::string& filename, int sym = 0) {
+  typedef typename SparseMatrixType::Scalar Scalar;
+  typedef typename SparseMatrixType::RealScalar RealScalar;
+  std::ofstream out(filename.c_str(), std::ios::out);
+  if (!out) return false;
+
+  out.flags(std::ios_base::scientific);
+  out.precision(std::numeric_limits<RealScalar>::digits10 + 2);
+  std::string header;
+  internal::putMarketHeader<Scalar>(header, sym);
+  out << header << std::endl;
+  out << mat.rows() << " " << mat.cols() << " " << mat.nonZeros() << "\n";
+  int count = 0;
+  EIGEN_UNUSED_VARIABLE(count);
+  for (int j = 0; j < mat.outerSize(); ++j)
+    for (typename SparseMatrixType::InnerIterator it(mat, j); it; ++it) {
+      ++count;
+      internal::PutMatrixElt(it.value(), it.row() + 1, it.col() + 1, out);
+    }
+  out.close();
+  return true;
+}
+
+/**
+ * \ingroup SparseExtra_Module
+ * @brief writes a dense Matrix or vector to a marketmarket format file
+ *
+ * @tparam DenseMatrixType to write to file
+ * @param mat matrix to write to file
+ * @param filename filename to write to
+ * @return true if writing succeeded
+ */
+
+template <typename DenseType>
+bool saveMarketDense(const DenseType& mat, const std::string& filename) {
+  typedef typename DenseType::Scalar Scalar;
+  typedef typename DenseType::RealScalar RealScalar;
+  std::ofstream out(filename.c_str(), std::ios::out);
+  if (!out) return false;
+
+  out.flags(std::ios_base::scientific);
+  out.precision(std::numeric_limits<RealScalar>::digits10 + 2);
+  if (internal::is_same<Scalar, std::complex<float> >::value || internal::is_same<Scalar, std::complex<double> >::value)
+    out << "%%MatrixMarket matrix array complex general\n";
+  else
+    out << "%%MatrixMarket matrix array real general\n";
+  out << mat.rows() << " " << mat.cols() << "\n";
+  for (Index i = 0; i < mat.cols(); i++) {
+    for (Index j = 0; j < mat.rows(); j++) {
+      internal::putDenseElt(mat(j, i), out);
+    }
+  }
+  out.close();
+  return true;
+}
+
+/**
+ * \ingroup SparseExtra_Module
+ * @brief Same functionality as saveMarketDense, deprecated
+ */
+template <typename VectorType>
+bool saveMarketVector(const VectorType& vec, const std::string& filename) {
+  return saveMarketDense(vec, filename);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPARSE_MARKET_IO_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h
new file mode 100644
index 0000000..f718d78
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h
@@ -0,0 +1,217 @@
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire NUENTSA WAKAM <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BROWSE_MATRICES_H
+#define EIGEN_BROWSE_MATRICES_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum { SPD = 0x100, NonSymmetric = 0x0 };
+
+/**
+ * @brief Iterator to browse matrices from a specified folder
+ *
+ * This is used to load all the matrices from a folder.
+ * The matrices should be in Matrix Market format
+ * It is assumed that the matrices are named as matname.mtx
+ * and matname_SPD.mtx if the matrix is Symmetric and positive definite (or Hermitian)
+ * The right hand side vectors are loaded as well, if they exist.
+ * They should be named as matname_b.mtx.
+ * Note that the right hand side for a SPD matrix is named as matname_SPD_b.mtx
+ *
+ * Sometimes a reference solution is available. In this case, it should be named as matname_x.mtx
+ *
+ * Sample code
+ * \code
+ *
+ * \endcode
+ *
+ * \tparam Scalar The scalar type
+ */
+template <typename Scalar>
+class MatrixMarketIterator {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+ public:
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef SparseMatrix<Scalar, ColMajor> MatrixType;
+
+ public:
+  MatrixMarketIterator(const std::string& folder)
+      : m_sym(0), m_isvalid(false), m_matIsLoaded(false), m_hasRhs(false), m_hasrefX(false), m_folder(folder) {
+    m_folder_id = opendir(folder.c_str());
+    if (m_folder_id) Getnextvalidmatrix();
+  }
+
+  ~MatrixMarketIterator() {
+    if (m_folder_id) closedir(m_folder_id);
+  }
+
+  inline MatrixMarketIterator& operator++() {
+    m_matIsLoaded = false;
+    m_hasrefX = false;
+    m_hasRhs = false;
+    Getnextvalidmatrix();
+    return *this;
+  }
+  inline operator bool() const { return m_isvalid; }
+
+  /** Return the sparse matrix corresponding to the current file */
+  inline MatrixType& matrix() {
+    // Read the matrix
+    if (m_matIsLoaded) return m_mat;
+
+    std::string matrix_file = m_folder + "/" + m_matname + ".mtx";
+    if (!loadMarket(m_mat, matrix_file)) {
+      std::cerr << "Warning loadMarket failed when loading \"" << matrix_file << "\"" << std::endl;
+      m_matIsLoaded = false;
+      return m_mat;
+    }
+    m_matIsLoaded = true;
+
+    if (m_sym != NonSymmetric) {
+      // Check whether we need to restore a full matrix:
+      RealScalar diag_norm = m_mat.diagonal().norm();
+      RealScalar lower_norm = m_mat.template triangularView<Lower>().norm();
+      RealScalar upper_norm = m_mat.template triangularView<Upper>().norm();
+      if (lower_norm > diag_norm && upper_norm == diag_norm) {
+        // only the lower part is stored
+        MatrixType tmp(m_mat);
+        m_mat = tmp.template selfadjointView<Lower>();
+      } else if (upper_norm > diag_norm && lower_norm == diag_norm) {
+        // only the upper part is stored
+        MatrixType tmp(m_mat);
+        m_mat = tmp.template selfadjointView<Upper>();
+      }
+    }
+    return m_mat;
+  }
+
+  /** Return the right hand side corresponding to the current matrix.
+   * If the rhs file is not provided, a random rhs is generated
+   */
+  inline VectorType& rhs() {
+    // Get the right hand side
+    if (m_hasRhs) return m_rhs;
+
+    std::string rhs_file;
+    rhs_file = m_folder + "/" + m_matname + "_b.mtx";  // The pattern is matname_b.mtx
+    m_hasRhs = Fileexists(rhs_file);
+    if (m_hasRhs) {
+      m_rhs.resize(m_mat.cols());
+      m_hasRhs = loadMarketVector(m_rhs, rhs_file);
+    }
+    if (!m_hasRhs) {
+      // Generate a random right hand side
+      if (!m_matIsLoaded) this->matrix();
+      m_refX.resize(m_mat.cols());
+      m_refX.setRandom();
+      m_rhs = m_mat * m_refX;
+      m_hasrefX = true;
+      m_hasRhs = true;
+    }
+    return m_rhs;
+  }
+
+  /** Return a reference solution
+   * If it is not provided and if the right hand side is not available
+   * then refX is randomly generated such that A*refX = b
+   * where A and b are the matrix and the rhs.
+   * Note that when a rhs is provided, refX is not available
+   */
+  inline VectorType& refX() {
+    // Check if a reference solution is provided
+    if (m_hasrefX) return m_refX;
+
+    std::string lhs_file;
+    lhs_file = m_folder + "/" + m_matname + "_x.mtx";
+    m_hasrefX = Fileexists(lhs_file);
+    if (m_hasrefX) {
+      m_refX.resize(m_mat.cols());
+      m_hasrefX = loadMarketVector(m_refX, lhs_file);
+    } else
+      m_refX.resize(0);
+    return m_refX;
+  }
+
+  inline std::string& matname() { return m_matname; }
+
+  inline int sym() { return m_sym; }
+
+  bool hasRhs() { return m_hasRhs; }
+  bool hasrefX() { return m_hasrefX; }
+  bool isFolderValid() { return bool(m_folder_id); }
+
+ protected:
+  inline bool Fileexists(std::string file) {
+    std::ifstream file_id(file.c_str());
+    if (!file_id.good()) {
+      return false;
+    } else {
+      file_id.close();
+      return true;
+    }
+  }
+
+  void Getnextvalidmatrix() {
+    m_isvalid = false;
+    // Here, we return with the next valid matrix in the folder
+    while ((m_curs_id = readdir(m_folder_id)) != NULL) {
+      m_isvalid = false;
+      std::string curfile;
+      curfile = m_folder + "/" + m_curs_id->d_name;
+      // Discard if it is a folder
+      if (m_curs_id->d_type == DT_DIR) continue;  // FIXME: This may not be available on non-BSD systems.
+
+      // Determine from the header if it is a matrix or a right hand side
+      bool isvector, iscomplex = false;
+      if (!getMarketHeader(curfile, m_sym, iscomplex, isvector)) continue;
+      if (isvector) continue;
+      if (!iscomplex) {
+        if (internal::is_same<Scalar, std::complex<float> >::value ||
+            internal::is_same<Scalar, std::complex<double> >::value)
+          continue;
+      }
+      if (iscomplex) {
+        if (internal::is_same<Scalar, float>::value || internal::is_same<Scalar, double>::value) continue;
+      }
+
+      // Get the matrix name
+      std::string filename = m_curs_id->d_name;
+      m_matname = filename.substr(0, filename.length() - 4);
+
+      // Find if the matrix is SPD
+      size_t found = m_matname.find("SPD");
+      if ((found != std::string::npos) && (m_sym != NonSymmetric)) m_sym = SPD;
+
+      m_isvalid = true;
+      break;
+    }
+  }
+  int m_sym;              // Symmetry of the matrix
+  MatrixType m_mat;       // Current matrix
+  VectorType m_rhs;       // Current vector
+  VectorType m_refX;      // The reference solution, if exists
+  std::string m_matname;  // Matrix Name
+  bool m_isvalid;
+  bool m_matIsLoaded;  // Determine if the matrix has already been loaded from the file
+  bool m_hasRhs;       // The right hand side exists
+  bool m_hasrefX;      // A reference solution is provided
+  std::string m_folder;
+  DIR* m_folder_id;
+  struct dirent* m_curs_id;
+};
+
+}  // end namespace Eigen
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/RandomSetter.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/RandomSetter.h
new file mode 100644
index 0000000..fab31f2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/RandomSetter.h
@@ -0,0 +1,308 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RANDOMSETTER_H
+#define EIGEN_RANDOMSETTER_H
+
+#if defined(EIGEN_GOOGLEHASH_SUPPORT)
+// Ensure the ::google namespace exists, required for checking existence of
+// ::google::dense_hash_map and ::google::sparse_hash_map.
+namespace google {}
+#endif
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** Represents a std::map
+ *
+ * \see RandomSetter
+ */
+template <typename Scalar>
+struct StdMapTraits {
+  typedef int KeyType;
+  typedef std::map<KeyType, Scalar> Type;
+  enum { IsSorted = 1 };
+
+  static void setInvalidKey(Type&, const KeyType&) {}
+};
+
+/** Represents a std::unordered_map
+ * \see RandomSetter
+ */
+template <typename Scalar>
+struct StdUnorderedMapTraits {
+  typedef int KeyType;
+  typedef std::unordered_map<KeyType, Scalar> Type;
+  enum { IsSorted = 0 };
+
+  static void setInvalidKey(Type&, const KeyType&) {}
+};
+
+#if defined(EIGEN_GOOGLEHASH_SUPPORT)
+
+namespace google {
+
+// Namespace work-around, since sometimes dense_hash_map and sparse_hash_map
+// are in the global namespace, and other times they are under ::google.
+using namespace ::google;
+
+template <typename KeyType, typename Scalar>
+struct DenseHashMap {
+  typedef dense_hash_map<KeyType, Scalar> type;
+};
+
+template <typename KeyType, typename Scalar>
+struct SparseHashMap {
+  typedef sparse_hash_map<KeyType, Scalar> type;
+};
+
+}  // namespace google
+
+/** Represents a google::dense_hash_map
+ *
+ * \see RandomSetter
+ */
+template <typename Scalar>
+struct GoogleDenseHashMapTraits {
+  typedef int KeyType;
+  typedef typename google::DenseHashMap<KeyType, Scalar>::type Type;
+  enum { IsSorted = 0 };
+
+  static void setInvalidKey(Type& map, const KeyType& k) { map.set_empty_key(k); }
+};
+
+/** Represents a google::sparse_hash_map
+ *
+ * \see RandomSetter
+ */
+template <typename Scalar>
+struct GoogleSparseHashMapTraits {
+  typedef int KeyType;
+  typedef typename google::SparseHashMap<KeyType, Scalar>::type Type;
+  enum { IsSorted = 0 };
+
+  static void setInvalidKey(Type&, const KeyType&) {}
+};
+#endif
+
+/** \class RandomSetter
+ * \ingroup SparseExtra_Module
+ * \brief The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access
+ *
+ * \tparam SparseMatrixType the type of the sparse matrix we are updating
+ * \tparam MapTraits a traits class representing the map implementation used for the temporary sparse storage.
+ *                  Its default value depends on the system.
+ * \tparam OuterPacketBits defines the number of rows (or columns) manage by a single map object
+ *                        as a power of two exponent.
+ *
+ * This class temporarily represents a sparse matrix object using a generic map implementation allowing for
+ * efficient random access. The conversion from the compressed representation to a hash_map object is performed
+ * in the RandomSetter constructor, while the sparse matrix is updated back at destruction time. This strategy
+ * suggest the use of nested blocks as in this example:
+ *
+ * \code
+ * SparseMatrix<double> m(rows,cols);
+ * {
+ *   RandomSetter<SparseMatrix<double> > w(m);
+ *   // don't use m but w instead with read/write random access to the coefficients:
+ *   for(;;)
+ *     w(rand(),rand()) = rand;
+ * }
+ * // when w is deleted, the data are copied back to m
+ * // and m is ready to use.
+ * \endcode
+ *
+ * Since hash_map objects are not fully sorted, representing a full matrix as a single hash_map would
+ * involve a big and costly sort to update the compressed matrix back. To overcome this issue, a RandomSetter
+ * use multiple hash_map, each representing 2^OuterPacketBits columns or rows according to the storage order.
+ * To reach optimal performance, this value should be adjusted according to the average number of nonzeros
+ * per rows/columns.
+ *
+ * The possible values for the template parameter MapTraits are:
+ *  - \b StdMapTraits: corresponds to std::map. (does not perform very well)
+ *  - \b StdUnorderedMapTraits: corresponds to std::unordered_map
+ *  - \b GoogleDenseHashMapTraits: corresponds to google::dense_hash_map (best efficiency, reasonable memory
+ * consumption)
+ *  - \b GoogleSparseHashMapTraits: corresponds to google::sparse_hash_map (best memory consumption, relatively good
+ * performance)
+ *
+ * The default map implementation depends on the availability, and the preferred order is:
+ * GoogleSparseHashMapTraits, StdUnorderedMapTraits, and finally StdMapTraits.
+ *
+ * For performance and memory consumption reasons it is highly recommended to use one of
+ * Google's hash_map implementations. To enable the support for them, you must define
+ * EIGEN_GOOGLEHASH_SUPPORT. This will include both <google/dense_hash_map> and
+ * <google/sparse_hash_map> for you.
+ *
+ * \see https://github.com/sparsehash/sparsehash
+ */
+template <typename SparseMatrixType,
+          template <typename T> class MapTraits =
+#if defined(EIGEN_GOOGLEHASH_SUPPORT)
+              GoogleDenseHashMapTraits
+#else
+              StdUnorderedMapTraits
+#endif
+          ,
+          int OuterPacketBits = 6>
+class RandomSetter {
+  typedef typename SparseMatrixType::Scalar Scalar;
+  typedef typename SparseMatrixType::StorageIndex StorageIndex;
+
+  struct ScalarWrapper {
+    ScalarWrapper() : value(0) {}
+    Scalar value;
+  };
+  typedef typename MapTraits<ScalarWrapper>::KeyType KeyType;
+  typedef typename MapTraits<ScalarWrapper>::Type HashMapType;
+  static constexpr int OuterPacketMask = (1 << OuterPacketBits) - 1;
+  enum {
+    SwapStorage = 1 - MapTraits<ScalarWrapper>::IsSorted,
+    TargetRowMajor = (SparseMatrixType::Flags & RowMajorBit) ? 1 : 0,
+    SetterRowMajor = SwapStorage ? 1 - TargetRowMajor : TargetRowMajor
+  };
+
+ public:
+  /** Constructs a random setter object from the sparse matrix \a target
+   *
+   * Note that the initial value of \a target are imported. If you want to re-set
+   * a sparse matrix from scratch, then you must set it to zero first using the
+   * setZero() function.
+   */
+  inline RandomSetter(SparseMatrixType& target) : m_target(&target) {
+    const Index outerSize = SwapStorage ? target.innerSize() : target.outerSize();
+    const Index innerSize = SwapStorage ? target.outerSize() : target.innerSize();
+    m_outerPackets = outerSize >> OuterPacketBits;
+    if (outerSize & OuterPacketMask) m_outerPackets += 1;
+    m_hashmaps = new HashMapType[m_outerPackets];
+    // compute number of bits needed to store inner indices
+    Index aux = innerSize - 1;
+    m_keyBitsOffset = 0;
+    while (aux) {
+      ++m_keyBitsOffset;
+      aux = aux >> 1;
+    }
+    KeyType ik = (1 << (OuterPacketBits + m_keyBitsOffset));
+    for (Index k = 0; k < m_outerPackets; ++k) MapTraits<ScalarWrapper>::setInvalidKey(m_hashmaps[k], ik);
+
+    // insert current coeffs
+    for (Index j = 0; j < m_target->outerSize(); ++j)
+      for (typename SparseMatrixType::InnerIterator it(*m_target, j); it; ++it)
+        (*this)(TargetRowMajor ? j : it.index(), TargetRowMajor ? it.index() : j) = it.value();
+  }
+
+  /** Destructor updating back the sparse matrix target */
+  ~RandomSetter() {
+    KeyType keyBitsMask = (1 << m_keyBitsOffset) - 1;
+    if (!SwapStorage)  // also means the map is sorted
+    {
+      m_target->setZero();
+      m_target->makeCompressed();
+      m_target->reserve(nonZeros());
+      Index prevOuter = -1;
+      for (Index k = 0; k < m_outerPackets; ++k) {
+        const Index outerOffset = (1 << OuterPacketBits) * k;
+        typename HashMapType::iterator end = m_hashmaps[k].end();
+        for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it != end; ++it) {
+          const Index outer = (it->first >> m_keyBitsOffset) + outerOffset;
+          const Index inner = it->first & keyBitsMask;
+          if (prevOuter != outer) {
+            for (Index j = prevOuter + 1; j <= outer; ++j) m_target->startVec(j);
+            prevOuter = outer;
+          }
+          m_target->insertBackByOuterInner(outer, inner) = it->second.value;
+        }
+      }
+      m_target->finalize();
+    } else {
+      VectorXi positions(m_target->outerSize());
+      positions.setZero();
+      // pass 1
+      for (Index k = 0; k < m_outerPackets; ++k) {
+        typename HashMapType::iterator end = m_hashmaps[k].end();
+        for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it != end; ++it) {
+          const Index outer = it->first & keyBitsMask;
+          ++positions[outer];
+        }
+      }
+      // prefix sum
+      StorageIndex count = 0;
+      for (Index j = 0; j < m_target->outerSize(); ++j) {
+        StorageIndex tmp = positions[j];
+        m_target->outerIndexPtr()[j] = count;
+        positions[j] = count;
+        count += tmp;
+      }
+      m_target->makeCompressed();
+      m_target->outerIndexPtr()[m_target->outerSize()] = count;
+      m_target->resizeNonZeros(count);
+      // pass 2
+      for (Index k = 0; k < m_outerPackets; ++k) {
+        const Index outerOffset = (1 << OuterPacketBits) * k;
+        typename HashMapType::iterator end = m_hashmaps[k].end();
+        for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it != end; ++it) {
+          const Index inner = (it->first >> m_keyBitsOffset) + outerOffset;
+          const Index outer = it->first & keyBitsMask;
+          // sorted insertion
+          // Note that we have to deal with at most 2^OuterPacketBits unsorted coefficients,
+          // moreover those 2^OuterPacketBits coeffs are likely to be sparse, an so only a
+          // small fraction of them have to be sorted, whence the following simple procedure:
+          Index posStart = m_target->outerIndexPtr()[outer];
+          Index i = (positions[outer]++) - 1;
+          while ((i >= posStart) && (m_target->innerIndexPtr()[i] > inner)) {
+            m_target->valuePtr()[i + 1] = m_target->valuePtr()[i];
+            m_target->innerIndexPtr()[i + 1] = m_target->innerIndexPtr()[i];
+            --i;
+          }
+          m_target->innerIndexPtr()[i + 1] = internal::convert_index<StorageIndex>(inner);
+          m_target->valuePtr()[i + 1] = it->second.value;
+        }
+      }
+    }
+    delete[] m_hashmaps;
+  }
+
+  /** \returns a reference to the coefficient at given coordinates \a row, \a col */
+  Scalar& operator()(Index row, Index col) {
+    const Index outer = SetterRowMajor ? row : col;
+    const Index inner = SetterRowMajor ? col : row;
+    const Index outerMajor = outer >> OuterPacketBits;  // index of the packet/map
+    const Index outerMinor = outer & OuterPacketMask;   // index of the inner vector in the packet
+    const KeyType key = internal::convert_index<KeyType>((outerMinor << m_keyBitsOffset) | inner);
+    return m_hashmaps[outerMajor][key].value;
+  }
+
+#ifdef EIGEN_MULTIDIMENSIONAL_SUBSCRIPT
+  /** \returns a reference to the coefficient at given coordinates \a row, \a col */
+  Scalar& operator[](Index row, Index col) { return operator()(row, col); }
+#endif
+
+  /** \returns the number of non zero coefficients
+   *
+   * \note According to the underlying map/hash_map implementation,
+   * this function might be quite expensive.
+   */
+  Index nonZeros() const {
+    Index nz = 0;
+    for (Index k = 0; k < m_outerPackets; ++k) nz += static_cast<Index>(m_hashmaps[k].size());
+    return nz;
+  }
+
+ protected:
+  HashMapType* m_hashmaps;
+  SparseMatrixType* m_target;
+  Index m_outerPackets;
+  unsigned char m_keyBitsOffset;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_RANDOMSETTER_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/SparseInverse.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/SparseInverse.h
new file mode 100644
index 0000000..142cc8f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SparseExtra/SparseInverse.h
@@ -0,0 +1,232 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022 Julian Kent <jkflying@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEINVERSE_H
+#define EIGEN_SPARSEINVERSE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#include "../../../../Eigen/Sparse"
+#include "../../../../Eigen/SparseLU"
+
+namespace Eigen {
+
+/**
+ * @brief Kahan algorithm based accumulator
+ *
+ * The Kahan sum algorithm guarantees to bound the error from floating point
+ * accumulation to a fixed value, regardless of the number of accumulations
+ * performed. Naive accumulation accumulates errors O(N), and pairwise O(logN).
+ * However pairwise also requires O(logN) memory while Kahan summation requires
+ * O(1) memory, but 4x the operations / latency.
+ *
+ * NB! Do not enable associative math optimizations, they may cause the Kahan
+ * summation to be optimized out leaving you with naive summation again.
+ *
+ */
+template <typename Scalar>
+class KahanSum {
+  // Straightforward Kahan summation for accurate accumulation of a sum of numbers
+  Scalar _sum{};
+  Scalar _correction{};
+
+ public:
+  Scalar value() { return _sum; }
+
+  void operator+=(Scalar increment) {
+    const Scalar correctedIncrement = increment + _correction;
+    const Scalar previousSum = _sum;
+    _sum += correctedIncrement;
+    _correction = correctedIncrement - (_sum - previousSum);
+  }
+};
+template <typename Scalar, Index Width = 16>
+class FABSum {
+  // https://epubs.siam.org/doi/pdf/10.1137/19M1257780
+  // Fast and Accurate Blocked Summation
+  // Uses naive summation for the fast sum, and Kahan summation for the accurate sum
+  // Theoretically SIMD sum could be changed to a tree sum which would improve accuracy
+  // over naive summation
+  KahanSum<Scalar> _totalSum;
+  Matrix<Scalar, Width, 1> _block;
+  Index _blockUsed{};
+
+ public:
+  Scalar value() { return _block.topRows(_blockUsed).sum() + _totalSum.value(); }
+
+  void operator+=(Scalar increment) {
+    _block(_blockUsed++, 0) = increment;
+    if (_blockUsed == Width) {
+      _totalSum += _block.sum();
+      _blockUsed = 0;
+    }
+  }
+};
+
+/**
+ * @brief computes an accurate dot product on two sparse vectors
+ *
+ * Uses an accurate summation algorithm for the accumulator in order to
+ * compute an accurate dot product for two sparse vectors.
+ *
+ */
+template <typename Derived, typename OtherDerived>
+typename Derived::Scalar accurateDot(const SparseMatrixBase<Derived>& A, const SparseMatrixBase<OtherDerived>& other) {
+  typedef typename Derived::Scalar Scalar;
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived, OtherDerived)
+  static_assert(internal::is_same<Scalar, typename OtherDerived::Scalar>::value, "mismatched types");
+
+  internal::evaluator<Derived> thisEval(A.derived());
+  typename Derived::ReverseInnerIterator i(thisEval, 0);
+
+  internal::evaluator<OtherDerived> otherEval(other.derived());
+  typename OtherDerived::ReverseInnerIterator j(otherEval, 0);
+
+  FABSum<Scalar> res;
+  while (i && j) {
+    if (i.index() == j.index()) {
+      res += numext::conj(i.value()) * j.value();
+      --i;
+      --j;
+    } else if (i.index() > j.index())
+      --i;
+    else
+      --j;
+  }
+  return res.value();
+}
+
+/**
+ * @brief calculate sparse subset of inverse of sparse matrix
+ *
+ * This class returns a sparse subset of the inverse of the input matrix.
+ * The nonzeros correspond to the nonzeros of the input, plus any additional
+ * elements required due to fill-in of the internal LU factorization. This is
+ * is minimized via a applying a fill-reducing permutation as part of the LU
+ * factorization.
+ *
+ * If there are specific entries of the input matrix which you need inverse
+ * values for, which are zero for the input, you need to insert entries into
+ * the input sparse matrix for them to be calculated.
+ *
+ * Due to the sensitive nature of matrix inversion, particularly on large
+ * matrices which are made possible via sparsity, high accuracy dot products
+ * based on Kahan summation are used to reduce numerical error. If you still
+ * encounter numerical errors you may with to equilibrate your matrix before
+ * calculating the inverse, as well as making sure it is actually full rank.
+ */
+template <typename Scalar>
+class SparseInverse {
+ public:
+  typedef SparseMatrix<Scalar, ColMajor> MatrixType;
+  typedef SparseMatrix<Scalar, RowMajor> RowMatrixType;
+
+  SparseInverse() {}
+
+  /**
+   * @brief This Constructor is for if you already have a factored SparseLU and would like to use it to calculate a
+   * sparse inverse.
+   *
+   * Just call this constructor with your already factored SparseLU class and you can directly call the .inverse()
+   * method to get the result.
+   */
+  SparseInverse(const SparseLU<MatrixType>& slu) { _result = computeInverse(slu); }
+
+  /**
+   * @brief Calculate the sparse inverse from a given sparse input
+   */
+  SparseInverse& compute(const SparseMatrix<Scalar>& A) {
+    SparseLU<MatrixType> slu;
+    slu.compute(A);
+    _result = computeInverse(slu);
+    return *this;
+  }
+
+  /**
+   * @brief return the already-calculated sparse inverse, or a 0x0 matrix if it could not be computed
+   */
+  const MatrixType& inverse() const { return _result; }
+
+  /**
+   * @brief Internal function to calculate the sparse inverse in a functional way
+   * @return A sparse inverse representation, or, if the decomposition didn't complete, a 0x0 matrix.
+   */
+  static MatrixType computeInverse(const SparseLU<MatrixType>& slu) {
+    if (slu.info() != Success) {
+      return MatrixType(0, 0);
+    }
+
+    // Extract from SparseLU and decompose into L, inverse D and U terms
+    Matrix<Scalar, Dynamic, 1> invD;
+    RowMatrixType Upper;
+    {
+      RowMatrixType DU = slu.matrixU().toSparse();
+      invD = DU.diagonal().cwiseInverse();
+      Upper = (invD.asDiagonal() * DU).template triangularView<StrictlyUpper>();
+    }
+    MatrixType Lower = slu.matrixL().toSparse().template triangularView<StrictlyLower>();
+
+    // Compute the inverse and reapply the permutation matrix from the LU decomposition
+    return slu.colsPermutation().transpose() * computeInverse(Upper, invD, Lower) * slu.rowsPermutation();
+  }
+
+  /**
+   * @brief Internal function to calculate the inverse from strictly upper, diagonal and strictly lower components
+   */
+  static MatrixType computeInverse(const RowMatrixType& Upper, const Matrix<Scalar, Dynamic, 1>& inverseDiagonal,
+                                   const MatrixType& Lower) {
+    // Calculate the 'minimal set', which is the nonzeros of (L+U).transpose()
+    // It could be zeroed, but we will overwrite all non-zeros anyways.
+    MatrixType colInv = Lower.transpose().template triangularView<UnitUpper>();
+    colInv += Upper.transpose();
+
+    // We also need rowmajor representation in order to do efficient row-wise dot products
+    RowMatrixType rowInv = Upper.transpose().template triangularView<UnitLower>();
+    rowInv += Lower.transpose();
+
+    // Use the Takahashi algorithm to build the supporting elements of the inverse
+    // upwards and to the left, from the bottom right element, 1 col/row at a time
+    for (Index recurseLevel = Upper.cols() - 1; recurseLevel >= 0; recurseLevel--) {
+      const auto& col = Lower.col(recurseLevel);
+      const auto& row = Upper.row(recurseLevel);
+
+      // Calculate the inverse values for the nonzeros in this column
+      typename MatrixType::ReverseInnerIterator colIter(colInv, recurseLevel);
+      for (; recurseLevel < colIter.index(); --colIter) {
+        const Scalar element = -accurateDot(col, rowInv.row(colIter.index()));
+        colIter.valueRef() = element;
+        rowInv.coeffRef(colIter.index(), recurseLevel) = element;
+      }
+
+      // Calculate the inverse values for the nonzeros in this row
+      typename RowMatrixType::ReverseInnerIterator rowIter(rowInv, recurseLevel);
+      for (; recurseLevel < rowIter.index(); --rowIter) {
+        const Scalar element = -accurateDot(row, colInv.col(rowIter.index()));
+        rowIter.valueRef() = element;
+        colInv.coeffRef(recurseLevel, rowIter.index()) = element;
+      }
+
+      // And finally the diagonal, which corresponds to both row and col iterator now
+      const Scalar diag = inverseDiagonal(recurseLevel) - accurateDot(row, colInv.col(recurseLevel));
+      rowIter.valueRef() = diag;
+      colIter.valueRef() = diag;
+    }
+
+    return colInv;
+  }
+
+ private:
+  MatrixType _result;
+};
+
+}  // namespace Eigen
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h
new file mode 100644
index 0000000..25b91b5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h
@@ -0,0 +1,276 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSELFUNCTIONS_ARRAYAPI_H
+#define EIGEN_BESSELFUNCTIONS_ARRAYAPI_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \returns an expression of the coefficient-wise i0(\a x) to the given
+ * arrays.
+ *
+ * It returns the modified Bessel function of the first kind of order zero.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of i0(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_i0()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_i0_op<typename Derived::Scalar>, const Derived>
+    bessel_i0(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_i0_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise i0e(\a x) to the given
+ * arrays.
+ *
+ * It returns the exponentially scaled modified Bessel
+ * function of the first kind of order zero.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of i0e(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_i0e()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_i0e_op<typename Derived::Scalar>, const Derived>
+    bessel_i0e(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_i0e_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise i1(\a x) to the given
+ * arrays.
+ *
+ * It returns the modified Bessel function of the first kind of order one.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of i1(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_i1()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_i1_op<typename Derived::Scalar>, const Derived>
+    bessel_i1(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_i1_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise i1e(\a x) to the given
+ * arrays.
+ *
+ * It returns the exponentially scaled modified Bessel
+ * function of the first kind of order one.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of i1e(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_i1e()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_i1e_op<typename Derived::Scalar>, const Derived>
+    bessel_i1e(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_i1e_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise k0(\a x) to the given
+ * arrays.
+ *
+ * It returns the modified Bessel function of the second kind of order zero.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of k0(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_k0()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_k0_op<typename Derived::Scalar>, const Derived>
+    bessel_k0(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_k0_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise k0e(\a x) to the given
+ * arrays.
+ *
+ * It returns the exponentially scaled modified Bessel
+ * function of the second kind of order zero.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of k0e(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_k0e()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_k0e_op<typename Derived::Scalar>, const Derived>
+    bessel_k0e(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_k0e_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise k1(\a x) to the given
+ * arrays.
+ *
+ * It returns the modified Bessel function of the second kind of order one.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of k1(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_k1()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_k1_op<typename Derived::Scalar>, const Derived>
+    bessel_k1(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_k1_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise k1e(\a x) to the given
+ * arrays.
+ *
+ * It returns the exponentially scaled modified Bessel
+ * function of the second kind of order one.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of k1e(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_k1e()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_k1e_op<typename Derived::Scalar>, const Derived>
+    bessel_k1e(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_k1e_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise j0(\a x) to the given
+ * arrays.
+ *
+ * It returns the Bessel function of the first kind of order zero.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of j0(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_j0()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_j0_op<typename Derived::Scalar>, const Derived>
+    bessel_j0(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_j0_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise y0(\a x) to the given
+ * arrays.
+ *
+ * It returns the Bessel function of the second kind of order zero.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of y0(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_y0()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_y0_op<typename Derived::Scalar>, const Derived>
+    bessel_y0(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_y0_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise j1(\a x) to the given
+ * arrays.
+ *
+ * It returns the modified Bessel function of the first kind of order one.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of j1(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_j1()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_j1_op<typename Derived::Scalar>, const Derived>
+    bessel_j1(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_j1_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+/** \returns an expression of the coefficient-wise y1(\a x) to the given
+ * arrays.
+ *
+ * It returns the Bessel function of the second kind of order one.
+ *
+ * \param x is the argument
+ *
+ * \note This function supports only float and double scalar types. To support
+ * other scalar types, the user has to provide implementations of y1(T) for
+ * any scalar type T to be supported.
+ *
+ * \sa ArrayBase::bessel_y1()
+ */
+template <typename Derived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_y1_op<typename Derived::Scalar>, const Derived>
+    bessel_y1(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<Eigen::internal::scalar_bessel_y1_op<typename Derived::Scalar>, const Derived>(
+      x.derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BESSELFUNCTIONS_ARRAYAPI_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h
new file mode 100644
index 0000000..45b70b4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h
@@ -0,0 +1,71 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSELFUNCTIONS_BFLOAT16_H
+#define EIGEN_BESSELFUNCTIONS_BFLOAT16_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace numext {
+
+#if EIGEN_HAS_C99_MATH
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i0(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_i0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i0e(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_i0e(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i1(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_i1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i1e(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_i1e(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_j0(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_j0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_j1(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_j1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_y0(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_y0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_y1(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_y1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k0(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_k0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k0e(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_k0e(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k1(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_k1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k1e(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_k1e(static_cast<float>(x)));
+}
+#endif
+
+}  // end namespace numext
+}  // end namespace Eigen
+
+#endif  // EIGEN_BESSELFUNCTIONS_BFLOAT16_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h
new file mode 100644
index 0000000..d877417
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h
@@ -0,0 +1,323 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSELFUNCTIONS_FUNCTORS_H
+#define EIGEN_BESSELFUNCTIONS_FUNCTORS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * \brief Template functor to compute the modified Bessel function of the first
+ * kind of order zero.
+ * \sa class CwiseUnaryOp, Cwise::bessel_i0()
+ */
+template <typename Scalar>
+struct scalar_bessel_i0_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_i0;
+    return bessel_i0(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_i0(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_i0_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=20 is computed.
+    // The cost is N multiplications and 2N additions. We also add
+    // the cost of an additional exp over i0e.
+    Cost = 28 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the exponentially scaled modified Bessel
+ * function of the first kind of order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_i0e()
+ */
+template <typename Scalar>
+struct scalar_bessel_i0e_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_i0e;
+    return bessel_i0e(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_i0e(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_i0e_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=20 is computed.
+    // The cost is N multiplications and 2N additions.
+    Cost = 20 * NumTraits<Scalar>::MulCost + 40 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the modified Bessel function of the first
+ * kind of order one
+ * \sa class CwiseUnaryOp, Cwise::bessel_i1()
+ */
+template <typename Scalar>
+struct scalar_bessel_i1_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_i1;
+    return bessel_i1(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_i1(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_i1_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=20 is computed.
+    // The cost is N multiplications and 2N additions. We also add
+    // the cost of an additional exp over i1e.
+    Cost = 28 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the exponentially scaled modified Bessel
+ * function of the first kind of order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_i1e()
+ */
+template <typename Scalar>
+struct scalar_bessel_i1e_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_i1e;
+    return bessel_i1e(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_i1e(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_i1e_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=20 is computed.
+    // The cost is N multiplications and 2N additions.
+    Cost = 20 * NumTraits<Scalar>::MulCost + 40 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Bessel function of the second kind of
+ * order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_j0()
+ */
+template <typename Scalar>
+struct scalar_bessel_j0_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_j0;
+    return bessel_j0(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_j0(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_j0_op<Scalar> > {
+  enum {
+    // 6 polynomial of order ~N=8 is computed.
+    // The cost is N multiplications and N additions each, along with a
+    // sine, cosine and rsqrt cost.
+    Cost = 63 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Bessel function of the second kind of
+ * order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_y0()
+ */
+template <typename Scalar>
+struct scalar_bessel_y0_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_y0;
+    return bessel_y0(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_y0(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_y0_op<Scalar> > {
+  enum {
+    // 6 polynomial of order ~N=8 is computed.
+    // The cost is N multiplications and N additions each, along with a
+    // sine, cosine, rsqrt and j0 cost.
+    Cost = 126 * NumTraits<Scalar>::MulCost + 96 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Bessel function of the first kind of
+ * order one
+ * \sa class CwiseUnaryOp, Cwise::bessel_j1()
+ */
+template <typename Scalar>
+struct scalar_bessel_j1_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_j1;
+    return bessel_j1(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_j1(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_j1_op<Scalar> > {
+  enum {
+    // 6 polynomial of order ~N=8 is computed.
+    // The cost is N multiplications and N additions each, along with a
+    // sine, cosine and rsqrt cost.
+    Cost = 63 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Bessel function of the second kind of
+ * order one
+ * \sa class CwiseUnaryOp, Cwise::bessel_j1e()
+ */
+template <typename Scalar>
+struct scalar_bessel_y1_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_y1;
+    return bessel_y1(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_y1(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_y1_op<Scalar> > {
+  enum {
+    // 6 polynomial of order ~N=8 is computed.
+    // The cost is N multiplications and N additions each, along with a
+    // sine, cosine, rsqrt and j1 cost.
+    Cost = 126 * NumTraits<Scalar>::MulCost + 96 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the modified Bessel function of the second
+ * kind of order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_k0()
+ */
+template <typename Scalar>
+struct scalar_bessel_k0_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_k0;
+    return bessel_k0(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_k0(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_k0_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=10 is computed.
+    // The cost is N multiplications and 2N additions. In addition we compute
+    // i0, a log, exp and prsqrt and sin and cos.
+    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the exponentially scaled modified Bessel
+ * function of the second kind of order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_k0e()
+ */
+template <typename Scalar>
+struct scalar_bessel_k0e_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_k0e;
+    return bessel_k0e(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_k0e(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_k0e_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=10 is computed.
+    // The cost is N multiplications and 2N additions. In addition we compute
+    // i0, a log, exp and prsqrt and sin and cos.
+    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the modified Bessel function of the
+ * second kind of order one
+ * \sa class CwiseUnaryOp, Cwise::bessel_k1()
+ */
+template <typename Scalar>
+struct scalar_bessel_k1_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_k1;
+    return bessel_k1(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_k1(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_k1_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=10 is computed.
+    // The cost is N multiplications and 2N additions. In addition we compute
+    // i1, a log, exp and prsqrt and sin and cos.
+    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the exponentially scaled modified Bessel
+ * function of the second kind of order one
+ * \sa class CwiseUnaryOp, Cwise::bessel_k1e()
+ */
+template <typename Scalar>
+struct scalar_bessel_k1e_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_k1e;
+    return bessel_k1e(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const { return internal::pbessel_k1e(x); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_k1e_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=10 is computed.
+    // The cost is N multiplications and 2N additions. In addition we compute
+    // i1, a log, exp and prsqrt and sin and cos.
+    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BESSELFUNCTIONS_FUNCTORS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h
new file mode 100644
index 0000000..ac1ea28
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h
@@ -0,0 +1,69 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSELFUNCTIONS_HALF_H
+#define EIGEN_BESSELFUNCTIONS_HALF_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace numext {
+
+#if EIGEN_HAS_C99_MATH
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i0(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_i0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i0e(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_i0e(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i1(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_i1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i1e(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_i1e(static_cast<float>(x)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_j0(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_j0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_j1(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_j1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_y0(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_y0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_y1(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_y1(static_cast<float>(x)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k0(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_k0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k0e(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_k0e(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k1(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_k1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k1e(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_k1e(static_cast<float>(x)));
+}
+#endif
+
+}  // end namespace numext
+}  // end namespace Eigen
+
+#endif  // EIGEN_BESSELFUNCTIONS_HALF_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h
new file mode 100644
index 0000000..59bdaf1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h
@@ -0,0 +1,1638 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSEL_FUNCTIONS_H
+#define EIGEN_BESSEL_FUNCTIONS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+//  Parts of this code are based on the Cephes Math Library.
+//
+//  Cephes Math Library Release 2.8:  June, 2000
+//  Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier
+//
+//  Permission has been kindly provided by the original author
+//  to incorporate the Cephes software into the Eigen codebase:
+//
+//    From: Stephen Moshier
+//    To: Eugene Brevdo
+//    Subject: Re: Permission to wrap several cephes functions in Eigen
+//
+//    Hello Eugene,
+//
+//    Thank you for writing.
+//
+//    If your licensing is similar to BSD, the formal way that has been
+//    handled is simply to add a statement to the effect that you are incorporating
+//    the Cephes software by permission of the author.
+//
+//    Good luck with your project,
+//    Steve
+
+/****************************************************************************
+ * Implementation of Bessel function, based on Cephes                       *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct bessel_i0e_retval {
+  typedef Scalar type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_i0e {
+  EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T&) { return ScalarType(0); }
+};
+
+template <typename T>
+struct generic_i0e<T, float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  i0ef.c
+     *
+     *  Modified Bessel function of order zero,
+     *  exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, i0ef();
+     *
+     * y = i0ef( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of order zero of the argument.
+     *
+     * The function is defined as i0e(x) = exp(-|x|) j0( ix ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,30        100000      3.7e-7      7.0e-8
+     * See i0f().
+     *
+     */
+
+    const float A[] = {-1.30002500998624804212E-8f, 6.04699502254191894932E-8f,  -2.67079385394061173391E-7f,
+                       1.11738753912010371815E-6f,  -4.41673835845875056359E-6f, 1.64484480707288970893E-5f,
+                       -5.75419501008210370398E-5f, 1.88502885095841655729E-4f,  -5.76375574538582365885E-4f,
+                       1.63947561694133579842E-3f,  -4.32430999505057594430E-3f, 1.05464603945949983183E-2f,
+                       -2.37374148058994688156E-2f, 4.93052842396707084878E-2f,  -9.49010970480476444210E-2f,
+                       1.71620901522208775349E-1f,  -3.04682672343198398683E-1f, 6.76795274409476084995E-1f};
+
+    const float B[] = {3.39623202570838634515E-9f, 2.26666899049817806459E-8f, 2.04891858946906374183E-7f,
+                       2.89137052083475648297E-6f, 6.88975834691682398426E-5f, 3.36911647825569408990E-3f,
+                       8.04490411014108831608E-1f};
+    T y = pabs(x);
+    T y_le_eight = internal::pchebevl<T, 18>::run(pmadd(pset1<T>(0.5f), y, pset1<T>(-2.0f)), A);
+    T y_gt_eight = pmul(internal::pchebevl<T, 7>::run(psub(pdiv(pset1<T>(32.0f), y), pset1<T>(2.0f)), B), prsqrt(y));
+    // TODO: Perhaps instead check whether all packet elements are in
+    // [-8, 8] and evaluate a branch based off of that. It's possible
+    // in practice most elements are in this region.
+    return pselect(pcmp_le(y, pset1<T>(8.0f)), y_le_eight, y_gt_eight);
+  }
+};
+
+template <typename T>
+struct generic_i0e<T, double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  i0e.c
+     *
+     *  Modified Bessel function of order zero,
+     *  exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, i0e();
+     *
+     * y = i0e( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of order zero of the argument.
+     *
+     * The function is defined as i0e(x) = exp(-|x|) j0( ix ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,30        30000       5.4e-16     1.2e-16
+     * See i0().
+     *
+     */
+
+    const double A[] = {-4.41534164647933937950E-18, 3.33079451882223809783E-17,  -2.43127984654795469359E-16,
+                        1.71539128555513303061E-15,  -1.16853328779934516808E-14, 7.67618549860493561688E-14,
+                        -4.85644678311192946090E-13, 2.95505266312963983461E-12,  -1.72682629144155570723E-11,
+                        9.67580903537323691224E-11,  -5.18979560163526290666E-10, 2.65982372468238665035E-9,
+                        -1.30002500998624804212E-8,  6.04699502254191894932E-8,   -2.67079385394061173391E-7,
+                        1.11738753912010371815E-6,   -4.41673835845875056359E-6,  1.64484480707288970893E-5,
+                        -5.75419501008210370398E-5,  1.88502885095841655729E-4,   -5.76375574538582365885E-4,
+                        1.63947561694133579842E-3,   -4.32430999505057594430E-3,  1.05464603945949983183E-2,
+                        -2.37374148058994688156E-2,  4.93052842396707084878E-2,   -9.49010970480476444210E-2,
+                        1.71620901522208775349E-1,   -3.04682672343198398683E-1,  6.76795274409476084995E-1};
+    const double B[] = {-7.23318048787475395456E-18, -4.83050448594418207126E-18, 4.46562142029675999901E-17,
+                        3.46122286769746109310E-17,  -2.82762398051658348494E-16, -3.42548561967721913462E-16,
+                        1.77256013305652638360E-15,  3.81168066935262242075E-15,  -9.55484669882830764870E-15,
+                        -4.15056934728722208663E-14, 1.54008621752140982691E-14,  3.85277838274214270114E-13,
+                        7.18012445138366623367E-13,  -1.79417853150680611778E-12, -1.32158118404477131188E-11,
+                        -3.14991652796324136454E-11, 1.18891471078464383424E-11,  4.94060238822496958910E-10,
+                        3.39623202570838634515E-9,   2.26666899049817806459E-8,   2.04891858946906374183E-7,
+                        2.89137052083475648297E-6,   6.88975834691682398426E-5,   3.36911647825569408990E-3,
+                        8.04490411014108831608E-1};
+    T y = pabs(x);
+    T y_le_eight = internal::pchebevl<T, 30>::run(pmadd(pset1<T>(0.5), y, pset1<T>(-2.0)), A);
+    T y_gt_eight = pmul(internal::pchebevl<T, 25>::run(psub(pdiv(pset1<T>(32.0), y), pset1<T>(2.0)), B), prsqrt(y));
+    // TODO: Perhaps instead check whether all packet elements are in
+    // [-8, 8] and evaluate a branch based off of that. It's possible
+    // in practice most elements are in this region.
+    return pselect(pcmp_le(y, pset1<T>(8.0)), y_le_eight, y_gt_eight);
+  }
+};
+
+template <typename T>
+struct bessel_i0e_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_i0e<T>::run(x); }
+};
+
+template <typename Scalar>
+struct bessel_i0_retval {
+  typedef Scalar type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_i0 {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    return pmul(pexp(pabs(x)), generic_i0e<T, ScalarType>::run(x));
+  }
+};
+
+template <typename T>
+struct bessel_i0_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_i0<T>::run(x); }
+};
+
+template <typename Scalar>
+struct bessel_i1e_retval {
+  typedef Scalar type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_i1e {
+  EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T&) { return ScalarType(0); }
+};
+
+template <typename T>
+struct generic_i1e<T, float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* i1ef.c
+     *
+     *  Modified Bessel function of order one,
+     *  exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, i1ef();
+     *
+     * y = i1ef( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of order one of the argument.
+     *
+     * The function is defined as i1(x) = -i exp(-|x|) j1( ix ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       1.5e-6      1.5e-7
+     * See i1().
+     *
+     */
+    const float A[] = {9.38153738649577178388E-9f,  -4.44505912879632808065E-8f, 2.00329475355213526229E-7f,
+                       -8.56872026469545474066E-7f, 3.47025130813767847674E-6f,  -1.32731636560394358279E-5f,
+                       4.78156510755005422638E-5f,  -1.61760815825896745588E-4f, 5.12285956168575772895E-4f,
+                       -1.51357245063125314899E-3f, 4.15642294431288815669E-3f,  -1.05640848946261981558E-2f,
+                       2.47264490306265168283E-2f,  -5.29459812080949914269E-2f, 1.02643658689847095384E-1f,
+                       -1.76416518357834055153E-1f, 2.52587186443633654823E-1f};
+
+    const float B[] = {-3.83538038596423702205E-9f, -2.63146884688951950684E-8f, -2.51223623787020892529E-7f,
+                       -3.88256480887769039346E-6f, -1.10588938762623716291E-4f, -9.76109749136146840777E-3f,
+                       7.78576235018280120474E-1f};
+
+    T y = pabs(x);
+    T y_le_eight = pmul(y, internal::pchebevl<T, 17>::run(pmadd(pset1<T>(0.5f), y, pset1<T>(-2.0f)), A));
+    T y_gt_eight = pmul(internal::pchebevl<T, 7>::run(psub(pdiv(pset1<T>(32.0f), y), pset1<T>(2.0f)), B), prsqrt(y));
+    // TODO: Perhaps instead check whether all packet elements are in
+    // [-8, 8] and evaluate a branch based off of that. It's possible
+    // in practice most elements are in this region.
+    y = pselect(pcmp_le(y, pset1<T>(8.0f)), y_le_eight, y_gt_eight);
+    return pselect(pcmp_lt(x, pset1<T>(0.0f)), pnegate(y), y);
+  }
+};
+
+template <typename T>
+struct generic_i1e<T, double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  i1e.c
+     *
+     *  Modified Bessel function of order one,
+     *  exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, i1e();
+     *
+     * y = i1e( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of order one of the argument.
+     *
+     * The function is defined as i1(x) = -i exp(-|x|) j1( ix ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       2.0e-15     2.0e-16
+     * See i1().
+     *
+     */
+    const double A[] = {2.77791411276104639959E-18,  -2.11142121435816608115E-17, 1.55363195773620046921E-16,
+                        -1.10559694773538630805E-15, 7.60068429473540693410E-15,  -5.04218550472791168711E-14,
+                        3.22379336594557470981E-13,  -1.98397439776494371520E-12, 1.17361862988909016308E-11,
+                        -6.66348972350202774223E-11, 3.62559028155211703701E-10,  -1.88724975172282928790E-9,
+                        9.38153738649577178388E-9,   -4.44505912879632808065E-8,  2.00329475355213526229E-7,
+                        -8.56872026469545474066E-7,  3.47025130813767847674E-6,   -1.32731636560394358279E-5,
+                        4.78156510755005422638E-5,   -1.61760815825896745588E-4,  5.12285956168575772895E-4,
+                        -1.51357245063125314899E-3,  4.15642294431288815669E-3,   -1.05640848946261981558E-2,
+                        2.47264490306265168283E-2,   -5.29459812080949914269E-2,  1.02643658689847095384E-1,
+                        -1.76416518357834055153E-1,  2.52587186443633654823E-1};
+    const double B[] = {7.51729631084210481353E-18,  4.41434832307170791151E-18,  -4.65030536848935832153E-17,
+                        -3.20952592199342395980E-17, 2.96262899764595013876E-16,  3.30820231092092828324E-16,
+                        -1.88035477551078244854E-15, -3.81440307243700780478E-15, 1.04202769841288027642E-14,
+                        4.27244001671195135429E-14,  -2.10154184277266431302E-14, -4.08355111109219731823E-13,
+                        -7.19855177624590851209E-13, 2.03562854414708950722E-12,  1.41258074366137813316E-11,
+                        3.25260358301548823856E-11,  -1.89749581235054123450E-11, -5.58974346219658380687E-10,
+                        -3.83538038596423702205E-9,  -2.63146884688951950684E-8,  -2.51223623787020892529E-7,
+                        -3.88256480887769039346E-6,  -1.10588938762623716291E-4,  -9.76109749136146840777E-3,
+                        7.78576235018280120474E-1};
+    T y = pabs(x);
+    T y_le_eight = pmul(y, internal::pchebevl<T, 29>::run(pmadd(pset1<T>(0.5), y, pset1<T>(-2.0)), A));
+    T y_gt_eight = pmul(internal::pchebevl<T, 25>::run(psub(pdiv(pset1<T>(32.0), y), pset1<T>(2.0)), B), prsqrt(y));
+    // TODO: Perhaps instead check whether all packet elements are in
+    // [-8, 8] and evaluate a branch based off of that. It's possible
+    // in practice most elements are in this region.
+    y = pselect(pcmp_le(y, pset1<T>(8.0)), y_le_eight, y_gt_eight);
+    return pselect(pcmp_lt(x, pset1<T>(0.0)), pnegate(y), y);
+  }
+};
+
+template <typename T>
+struct bessel_i1e_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_i1e<T>::run(x); }
+};
+
+template <typename T>
+struct bessel_i1_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_i1 {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    return pmul(pexp(pabs(x)), generic_i1e<T, ScalarType>::run(x));
+  }
+};
+
+template <typename T>
+struct bessel_i1_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_i1<T>::run(x); }
+};
+
+template <typename T>
+struct bessel_k0e_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_k0e {
+  EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T&) { return ScalarType(0); }
+};
+
+template <typename T>
+struct generic_k0e<T, float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  k0ef.c
+     *	Modified Bessel function, third kind, order zero,
+     *	exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, k0ef();
+     *
+     * y = k0ef( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of the third kind of order zero of the argument.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       8.1e-7      7.8e-8
+     * See k0().
+     *
+     */
+
+    const float A[] = {1.90451637722020886025E-9f, 2.53479107902614945675E-7f, 2.28621210311945178607E-5f,
+                       1.26461541144692592338E-3f, 3.59799365153615016266E-2f, 3.44289899924628486886E-1f,
+                       -5.35327393233902768720E-1f};
+
+    const float B[] = {-1.69753450938905987466E-9f, 8.57403401741422608519E-9f,  -4.66048989768794782956E-8f,
+                       2.76681363944501510342E-7f,  -1.83175552271911948767E-6f, 1.39498137188764993662E-5f,
+                       -1.28495495816278026384E-4f, 1.56988388573005337491E-3f,  -3.14481013119645005427E-2f,
+                       2.44030308206595545468E0f};
+    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = internal::pchebevl<T, 7>::run(pmadd(x, x, pset1<T>(-2.0)), A);
+    x_le_two = pmadd(generic_i0<T, float>::run(x), pnegate(plog(pmul(pset1<T>(0.5), x))), x_le_two);
+    x_le_two = pmul(pexp(x), x_le_two);
+    T x_gt_two = pmul(internal::pchebevl<T, 10>::run(psub(pdiv(pset1<T>(8.0), x), two), B), prsqrt(x));
+    return pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, pselect(pcmp_le(x, two), x_le_two, x_gt_two));
+  }
+};
+
+template <typename T>
+struct generic_k0e<T, double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  k0e.c
+     *	Modified Bessel function, third kind, order zero,
+     *	exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, k0e();
+     *
+     * y = k0e( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of the third kind of order zero of the argument.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       1.4e-15     1.4e-16
+     * See k0().
+     *
+     */
+
+    const double A[] = {1.37446543561352307156E-16, 4.25981614279661018399E-14, 1.03496952576338420167E-11,
+                        1.90451637722020886025E-9,  2.53479107902614945675E-7,  2.28621210311945178607E-5,
+                        1.26461541144692592338E-3,  3.59799365153615016266E-2,  3.44289899924628486886E-1,
+                        -5.35327393233902768720E-1};
+    const double B[] = {5.30043377268626276149E-18,  -1.64758043015242134646E-17, 5.21039150503902756861E-17,
+                        -1.67823109680541210385E-16, 5.51205597852431940784E-16,  -1.84859337734377901440E-15,
+                        6.34007647740507060557E-15,  -2.22751332699166985548E-14, 8.03289077536357521100E-14,
+                        -2.98009692317273043925E-13, 1.14034058820847496303E-12,  -4.51459788337394416547E-12,
+                        1.85594911495471785253E-11,  -7.95748924447710747776E-11, 3.57739728140030116597E-10,
+                        -1.69753450938905987466E-9,  8.57403401741422608519E-9,   -4.66048989768794782956E-8,
+                        2.76681363944501510342E-7,   -1.83175552271911948767E-6,  1.39498137188764993662E-5,
+                        -1.28495495816278026384E-4,  1.56988388573005337491E-3,   -3.14481013119645005427E-2,
+                        2.44030308206595545468E0};
+    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = internal::pchebevl<T, 10>::run(pmadd(x, x, pset1<T>(-2.0)), A);
+    x_le_two = pmadd(generic_i0<T, double>::run(x), pmul(pset1<T>(-1.0), plog(pmul(pset1<T>(0.5), x))), x_le_two);
+    x_le_two = pmul(pexp(x), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(internal::pchebevl<T, 25>::run(psub(pdiv(pset1<T>(8.0), x), two), B), prsqrt(x));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct bessel_k0e_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_k0e<T>::run(x); }
+};
+
+template <typename T>
+struct bessel_k0_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_k0 {
+  EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T&) { return ScalarType(0); }
+};
+
+template <typename T>
+struct generic_k0<T, float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  k0f.c
+     *	Modified Bessel function, third kind, order zero
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, k0f();
+     *
+     * y = k0f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns modified Bessel function of the third kind
+     * of order zero of the argument.
+     *
+     * The range is partitioned into the two intervals [0,8] and
+     * (8, infinity).  Chebyshev polynomial expansions are employed
+     * in each interval.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     * Tested at 2000 random points between 0 and 8.  Peak absolute
+     * error (relative when K0 > 1) was 1.46e-14; rms, 4.26e-15.
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       7.8e-7      8.5e-8
+     *
+     * ERROR MESSAGES:
+     *
+     *   message         condition      value returned
+     *  K0 domain          x <= 0          MAXNUM
+     *
+     */
+
+    const float A[] = {1.90451637722020886025E-9f, 2.53479107902614945675E-7f, 2.28621210311945178607E-5f,
+                       1.26461541144692592338E-3f, 3.59799365153615016266E-2f, 3.44289899924628486886E-1f,
+                       -5.35327393233902768720E-1f};
+
+    const float B[] = {-1.69753450938905987466E-9f, 8.57403401741422608519E-9f,  -4.66048989768794782956E-8f,
+                       2.76681363944501510342E-7f,  -1.83175552271911948767E-6f, 1.39498137188764993662E-5f,
+                       -1.28495495816278026384E-4f, 1.56988388573005337491E-3f,  -3.14481013119645005427E-2f,
+                       2.44030308206595545468E0f};
+    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = internal::pchebevl<T, 7>::run(pmadd(x, x, pset1<T>(-2.0)), A);
+    x_le_two = pmadd(generic_i0<T, float>::run(x), pnegate(plog(pmul(pset1<T>(0.5), x))), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two =
+        pmul(pmul(pexp(pnegate(x)), internal::pchebevl<T, 10>::run(psub(pdiv(pset1<T>(8.0), x), two), B)), prsqrt(x));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_k0<T, double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*
+     *
+     *	Modified Bessel function, third kind, order zero,
+     *	exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, k0();
+     *
+     * y = k0( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of the third kind of order zero of the argument.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       1.4e-15     1.4e-16
+     * See k0().
+     *
+     */
+    const double A[] = {1.37446543561352307156E-16, 4.25981614279661018399E-14, 1.03496952576338420167E-11,
+                        1.90451637722020886025E-9,  2.53479107902614945675E-7,  2.28621210311945178607E-5,
+                        1.26461541144692592338E-3,  3.59799365153615016266E-2,  3.44289899924628486886E-1,
+                        -5.35327393233902768720E-1};
+    const double B[] = {5.30043377268626276149E-18,  -1.64758043015242134646E-17, 5.21039150503902756861E-17,
+                        -1.67823109680541210385E-16, 5.51205597852431940784E-16,  -1.84859337734377901440E-15,
+                        6.34007647740507060557E-15,  -2.22751332699166985548E-14, 8.03289077536357521100E-14,
+                        -2.98009692317273043925E-13, 1.14034058820847496303E-12,  -4.51459788337394416547E-12,
+                        1.85594911495471785253E-11,  -7.95748924447710747776E-11, 3.57739728140030116597E-10,
+                        -1.69753450938905987466E-9,  8.57403401741422608519E-9,   -4.66048989768794782956E-8,
+                        2.76681363944501510342E-7,   -1.83175552271911948767E-6,  1.39498137188764993662E-5,
+                        -1.28495495816278026384E-4,  1.56988388573005337491E-3,   -3.14481013119645005427E-2,
+                        2.44030308206595545468E0};
+    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = internal::pchebevl<T, 10>::run(pmadd(x, x, pset1<T>(-2.0)), A);
+    x_le_two = pmadd(generic_i0<T, double>::run(x), pnegate(plog(pmul(pset1<T>(0.5), x))), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(pmul(pexp(-x), internal::pchebevl<T, 25>::run(psub(pdiv(pset1<T>(8.0), x), two), B)), prsqrt(x));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct bessel_k0_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_k0<T>::run(x); }
+};
+
+template <typename T>
+struct bessel_k1e_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_k1e {
+  EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T&) { return ScalarType(0); }
+};
+
+template <typename T>
+struct generic_k1e<T, float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* k1ef.c
+     *
+     *	Modified Bessel function, third kind, order one,
+     *	exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, k1ef();
+     *
+     * y = k1ef( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of the third kind of order one of the argument:
+     *
+     *      k1e(x) = exp(x) * k1(x).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       4.9e-7      6.7e-8
+     * See k1().
+     *
+     */
+
+    const float A[] = {-2.21338763073472585583E-8f, -2.43340614156596823496E-6f, -1.73028895751305206302E-4f,
+                       -6.97572385963986435018E-3f, -1.22611180822657148235E-1f, -3.53155960776544875667E-1f,
+                       1.52530022733894777053E0f};
+    const float B[] = {2.01504975519703286596E-9f,  -1.03457624656780970260E-8f, 5.74108412545004946722E-8f,
+                       -3.50196060308781257119E-7f, 2.40648494783721712015E-6f,  -1.93619797416608296024E-5f,
+                       1.95215518471351631108E-4f,  -2.85781685962277938680E-3f, 1.03923736576817238437E-1f,
+                       2.72062619048444266945E0f};
+    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = pdiv(internal::pchebevl<T, 7>::run(pmadd(x, x, pset1<T>(-2.0)), A), x);
+    x_le_two = pmadd(generic_i1<T, float>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
+    x_le_two = pmul(x_le_two, pexp(x));
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(internal::pchebevl<T, 10>::run(psub(pdiv(pset1<T>(8.0), x), two), B), prsqrt(x));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_k1e<T, double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  k1e.c
+     *
+     *	Modified Bessel function, third kind, order one,
+     *	exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, k1e();
+     *
+     * y = k1e( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of the third kind of order one of the argument:
+     *
+     *      k1e(x) = exp(x) * k1(x).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       7.8e-16     1.2e-16
+     * See k1().
+     *
+     */
+    const double A[] = {-7.02386347938628759343E-18, -2.42744985051936593393E-15, -6.66690169419932900609E-13,
+                        -1.41148839263352776110E-10, -2.21338763073472585583E-8,  -2.43340614156596823496E-6,
+                        -1.73028895751305206302E-4,  -6.97572385963986435018E-3,  -1.22611180822657148235E-1,
+                        -3.53155960776544875667E-1,  1.52530022733894777053E0};
+    const double B[] = {-5.75674448366501715755E-18, 1.79405087314755922667E-17,  -5.68946255844285935196E-17,
+                        1.83809354436663880070E-16,  -6.05704724837331885336E-16, 2.03870316562433424052E-15,
+                        -7.01983709041831346144E-15, 2.47715442448130437068E-14,  -8.97670518232499435011E-14,
+                        3.34841966607842919884E-13,  -1.28917396095102890680E-12, 5.13963967348173025100E-12,
+                        -2.12996783842756842877E-11, 9.21831518760500529508E-11,  -4.19035475934189648750E-10,
+                        2.01504975519703286596E-9,   -1.03457624656780970260E-8,  5.74108412545004946722E-8,
+                        -3.50196060308781257119E-7,  2.40648494783721712015E-6,   -1.93619797416608296024E-5,
+                        1.95215518471351631108E-4,   -2.85781685962277938680E-3,  1.03923736576817238437E-1,
+                        2.72062619048444266945E0};
+    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = pdiv(internal::pchebevl<T, 11>::run(pmadd(x, x, pset1<T>(-2.0)), A), x);
+    x_le_two = pmadd(generic_i1<T, double>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
+    x_le_two = pmul(x_le_two, pexp(x));
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(internal::pchebevl<T, 25>::run(psub(pdiv(pset1<T>(8.0), x), two), B), prsqrt(x));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct bessel_k1e_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_k1e<T>::run(x); }
+};
+
+template <typename T>
+struct bessel_k1_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_k1 {
+  EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T&) { return ScalarType(0); }
+};
+
+template <typename T>
+struct generic_k1<T, float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* k1f.c
+     *	Modified Bessel function, third kind, order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, k1f();
+     *
+     * y = k1f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Computes the modified Bessel function of the third kind
+     * of order one of the argument.
+     *
+     * The range is partitioned into the two intervals [0,2] and
+     * (2, infinity).  Chebyshev polynomial expansions are employed
+     * in each interval.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       4.6e-7      7.6e-8
+     *
+     * ERROR MESSAGES:
+     *
+     *   message         condition      value returned
+     * k1 domain          x <= 0          MAXNUM
+     *
+     */
+
+    const float A[] = {-2.21338763073472585583E-8f, -2.43340614156596823496E-6f, -1.73028895751305206302E-4f,
+                       -6.97572385963986435018E-3f, -1.22611180822657148235E-1f, -3.53155960776544875667E-1f,
+                       1.52530022733894777053E0f};
+    const float B[] = {2.01504975519703286596E-9f,  -1.03457624656780970260E-8f, 5.74108412545004946722E-8f,
+                       -3.50196060308781257119E-7f, 2.40648494783721712015E-6f,  -1.93619797416608296024E-5f,
+                       1.95215518471351631108E-4f,  -2.85781685962277938680E-3f, 1.03923736576817238437E-1f,
+                       2.72062619048444266945E0f};
+    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = pdiv(internal::pchebevl<T, 7>::run(pmadd(x, x, pset1<T>(-2.0)), A), x);
+    x_le_two = pmadd(generic_i1<T, float>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two =
+        pmul(pexp(pnegate(x)), pmul(internal::pchebevl<T, 10>::run(psub(pdiv(pset1<T>(8.0), x), two), B), prsqrt(x)));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_k1<T, double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  k1.c
+     *	Modified Bessel function, third kind, order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, k1f();
+     *
+     * y = k1f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Computes the modified Bessel function of the third kind
+     * of order one of the argument.
+     *
+     * The range is partitioned into the two intervals [0,2] and
+     * (2, infinity).  Chebyshev polynomial expansions are employed
+     * in each interval.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       4.6e-7      7.6e-8
+     *
+     * ERROR MESSAGES:
+     *
+     *   message         condition      value returned
+     * k1 domain          x <= 0          MAXNUM
+     *
+     */
+    const double A[] = {-7.02386347938628759343E-18, -2.42744985051936593393E-15, -6.66690169419932900609E-13,
+                        -1.41148839263352776110E-10, -2.21338763073472585583E-8,  -2.43340614156596823496E-6,
+                        -1.73028895751305206302E-4,  -6.97572385963986435018E-3,  -1.22611180822657148235E-1,
+                        -3.53155960776544875667E-1,  1.52530022733894777053E0};
+    const double B[] = {-5.75674448366501715755E-18, 1.79405087314755922667E-17,  -5.68946255844285935196E-17,
+                        1.83809354436663880070E-16,  -6.05704724837331885336E-16, 2.03870316562433424052E-15,
+                        -7.01983709041831346144E-15, 2.47715442448130437068E-14,  -8.97670518232499435011E-14,
+                        3.34841966607842919884E-13,  -1.28917396095102890680E-12, 5.13963967348173025100E-12,
+                        -2.12996783842756842877E-11, 9.21831518760500529508E-11,  -4.19035475934189648750E-10,
+                        2.01504975519703286596E-9,   -1.03457624656780970260E-8,  5.74108412545004946722E-8,
+                        -3.50196060308781257119E-7,  2.40648494783721712015E-6,   -1.93619797416608296024E-5,
+                        1.95215518471351631108E-4,   -2.85781685962277938680E-3,  1.03923736576817238437E-1,
+                        2.72062619048444266945E0};
+    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = pdiv(internal::pchebevl<T, 11>::run(pmadd(x, x, pset1<T>(-2.0)), A), x);
+    x_le_two = pmadd(generic_i1<T, double>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(pexp(-x), pmul(internal::pchebevl<T, 25>::run(psub(pdiv(pset1<T>(8.0), x), two), B), prsqrt(x)));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct bessel_k1_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_k1<T>::run(x); }
+};
+
+template <typename T>
+struct bessel_j0_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_j0 {
+  EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T&) { return ScalarType(0); }
+};
+
+template <typename T>
+struct generic_j0<T, float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* j0f.c
+     *	Bessel function of order zero
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, j0f();
+     *
+     * y = j0f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of order zero of the argument.
+     *
+     * The domain is divided into the intervals [0, 2] and
+     * (2, infinity). In the first interval the following polynomial
+     * approximation is used:
+     *
+     *
+     *        2         2         2
+     * (w - r  ) (w - r  ) (w - r  ) P(w)
+     *       1         2         3
+     *
+     *            2
+     * where w = x  and the three r's are zeros of the function.
+     *
+     * In the second interval, the modulus and phase are approximated
+     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
+     * and Phase(x) = x + 1/x R(1/x^2) - pi/4.  The function is
+     *
+     *   j0(x) = Modulus(x) cos( Phase(x) ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 2        100000      1.3e-7      3.6e-8
+     *    IEEE      2, 32       100000      1.9e-7      5.4e-8
+     *
+     */
+
+    const float JP[] = {-6.068350350393235E-008f, 6.388945720783375E-006f, -3.969646342510940E-004f,
+                        1.332913422519003E-002f, -1.729150680240724E-001f};
+    const float MO[] = {-6.838999669318810E-002f, 1.864949361379502E-001f,  -2.145007480346739E-001f,
+                        1.197549369473540E-001f,  -3.560281861530129E-003f, -4.969382655296620E-002f,
+                        -3.355424622293709E-006f, 7.978845717621440E-001f};
+    const float PH[] = {3.242077816988247E+001f,  -3.630592630518434E+001f, 1.756221482109099E+001f,
+                        -4.974978466280903E+000f, 1.001973420681837E+000f,  -1.939906941791308E-001f,
+                        6.490598792654666E-002f,  -1.249992184872738E-001f};
+    const T DR1 = pset1<T>(5.78318596294678452118f);
+    const T NEG_PIO4F = pset1<T>(-0.7853981633974483096f); /* -pi / 4 */
+    T y = pabs(x);
+    T z = pmul(y, y);
+    T y_le_two = pselect(pcmp_lt(y, pset1<T>(1.0e-3f)), pmadd(z, pset1<T>(-0.25f), pset1<T>(1.0f)),
+                         pmul(psub(z, DR1), internal::ppolevl<T, 4>::run(z, JP)));
+    T q = pdiv(pset1<T>(1.0f), y);
+    T w = prsqrt(y);
+    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO));
+    w = pmul(q, q);
+    T yn = pmadd(q, internal::ppolevl<T, 7>::run(w, PH), NEG_PIO4F);
+    T y_gt_two = pmul(p, pcos(padd(yn, y)));
+    return pselect(pcmp_le(y, pset1<T>(2.0)), y_le_two, y_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_j0<T, double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  j0.c
+     *	Bessel function of order zero
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, j0();
+     *
+     * y = j0( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of order zero of the argument.
+     *
+     * The domain is divided into the intervals [0, 5] and
+     * (5, infinity). In the first interval the following rational
+     * approximation is used:
+     *
+     *
+     *        2         2
+     * (w - r  ) (w - r  ) P (w) / Q (w)
+     *       1         2    3       8
+     *
+     *            2
+     * where w = x  and the two r's are zeros of the function.
+     *
+     * In the second interval, the Hankel asymptotic expansion
+     * is employed with two rational functions of degree 6/6
+     * and 7/7.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain     # trials      peak         rms
+     *    DEC       0, 30       10000       4.4e-17     6.3e-18
+     *    IEEE      0, 30       60000       4.2e-16     1.1e-16
+     *
+     */
+    const double PP[] = {7.96936729297347051624E-4, 8.28352392107440799803E-2, 1.23953371646414299388E0,
+                         5.44725003058768775090E0,  8.74716500199817011941E0,  5.30324038235394892183E0,
+                         9.99999999999999997821E-1};
+    const double PQ[] = {9.24408810558863637013E-4, 8.56288474354474431428E-2, 1.25352743901058953537E0,
+                         5.47097740330417105182E0,  8.76190883237069594232E0,  5.30605288235394617618E0,
+                         1.00000000000000000218E0};
+    const double QP[] = {-1.13663838898469149931E-2, -1.28252718670509318512E0, -1.95539544257735972385E1,
+                         -9.32060152123768231369E1,  -1.77681167980488050595E2, -1.47077505154951170175E2,
+                         -5.14105326766599330220E1,  -6.05014350600728481186E0};
+    const double QQ[] = {1.00000000000000000000E0, 6.43178256118178023184E1, 8.56430025976980587198E2,
+                         3.88240183605401609683E3, 7.24046774195652478189E3, 5.93072701187316984827E3,
+                         2.06209331660327847417E3, 2.42005740240291393179E2};
+    const double RP[] = {-4.79443220978201773821E9, 1.95617491946556577543E12, -2.49248344360967716204E14,
+                         9.70862251047306323952E15};
+    const double RQ[] = {1.00000000000000000000E0,  4.99563147152651017219E2,  1.73785401676374683123E5,
+                         4.84409658339962045305E7,  1.11855537045356834862E10, 2.11277520115489217587E12,
+                         3.10518229857422583814E14, 3.18121955943204943306E16, 1.71086294081043136091E18};
+    const T DR1 = pset1<T>(5.78318596294678452118E0);
+    const T DR2 = pset1<T>(3.04712623436620863991E1);
+    const T SQ2OPI = pset1<T>(7.9788456080286535587989E-1); /* sqrt(2 / pi) */
+    const T NEG_PIO4 = pset1<T>(-0.7853981633974483096);    /* pi / 4 */
+
+    T y = pabs(x);
+    T z = pmul(y, y);
+    T y_le_five = pselect(pcmp_lt(y, pset1<T>(1.0e-5)), pmadd(z, pset1<T>(-0.25), pset1<T>(1.0)),
+                          pmul(pmul(psub(z, DR1), psub(z, DR2)),
+                               pdiv(internal::ppolevl<T, 3>::run(z, RP), internal::ppolevl<T, 8>::run(z, RQ))));
+    T s = pdiv(pset1<T>(25.0), z);
+    T p = pdiv(internal::ppolevl<T, 6>::run(s, PP), internal::ppolevl<T, 6>::run(s, PQ));
+    T q = pdiv(internal::ppolevl<T, 7>::run(s, QP), internal::ppolevl<T, 7>::run(s, QQ));
+    T yn = padd(y, NEG_PIO4);
+    T w = pdiv(pset1<T>(-5.0), y);
+    p = pmadd(p, pcos(yn), pmul(w, pmul(q, psin(yn))));
+    T y_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(y)));
+    return pselect(pcmp_le(y, pset1<T>(5.0)), y_le_five, y_gt_five);
+  }
+};
+
+template <typename T>
+struct bessel_j0_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_j0<T>::run(x); }
+};
+
+template <typename T>
+struct bessel_y0_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_y0 {
+  EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T&) { return ScalarType(0); }
+};
+
+template <typename T>
+struct generic_y0<T, float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* j0f.c
+     * 	Bessel function of the second kind, order zero
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, y0f();
+     *
+     * y = y0f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of the second kind, of order
+     * zero, of the argument.
+     *
+     * The domain is divided into the intervals [0, 2] and
+     * (2, infinity). In the first interval a rational approximation
+     * R(x) is employed to compute
+     *
+     *                  2         2         2
+     * y0(x)  =  (w - r  ) (w - r  ) (w - r  ) R(x)  +  2/pi ln(x) j0(x).
+     *                 1         2         3
+     *
+     * Thus a call to j0() is required.  The three zeros are removed
+     * from R(x) to improve its numerical stability.
+     *
+     * In the second interval, the modulus and phase are approximated
+     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
+     * and Phase(x) = x + 1/x S(1/x^2) - pi/4.  Then the function is
+     *
+     *   y0(x) = Modulus(x) sin( Phase(x) ).
+     *
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *  Absolute error, when y0(x) < 1; else relative error:
+     *
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,  2       100000      2.4e-7      3.4e-8
+     *    IEEE      2, 32       100000      1.8e-7      5.3e-8
+     *
+     */
+
+    const float YP[] = {9.454583683980369E-008f, -9.413212653797057E-006f, 5.344486707214273E-004f,
+                        -1.584289289821316E-002f, 1.707584643733568E-001f};
+    const float MO[] = {-6.838999669318810E-002f, 1.864949361379502E-001f,  -2.145007480346739E-001f,
+                        1.197549369473540E-001f,  -3.560281861530129E-003f, -4.969382655296620E-002f,
+                        -3.355424622293709E-006f, 7.978845717621440E-001f};
+    const float PH[] = {3.242077816988247E+001f,  -3.630592630518434E+001f, 1.756221482109099E+001f,
+                        -4.974978466280903E+000f, 1.001973420681837E+000f,  -1.939906941791308E-001f,
+                        6.490598792654666E-002f,  -1.249992184872738E-001f};
+    const T YZ1 = pset1<T>(0.43221455686510834878f);
+    const T TWOOPI = pset1<T>(0.636619772367581343075535f); /* 2 / pi */
+    const T NEG_PIO4F = pset1<T>(-0.7853981633974483096f);  /* -pi / 4 */
+    const T NEG_MAXNUM = pset1<T>(-NumTraits<float>::infinity());
+    T z = pmul(x, x);
+    T x_le_two = pmul(TWOOPI, pmul(plog(x), generic_j0<T, float>::run(x)));
+    x_le_two = pmadd(psub(z, YZ1), internal::ppolevl<T, 4>::run(z, YP), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), NEG_MAXNUM, x_le_two);
+    T q = pdiv(pset1<T>(1.0), x);
+    T w = prsqrt(x);
+    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO));
+    T u = pmul(q, q);
+    T xn = pmadd(q, internal::ppolevl<T, 7>::run(u, PH), NEG_PIO4F);
+    T x_gt_two = pmul(p, psin(padd(xn, x)));
+    return pselect(pcmp_le(x, pset1<T>(2.0)), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_y0<T, double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  j0.c
+     *	Bessel function of the second kind, order zero
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, y0();
+     *
+     * y = y0( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of the second kind, of order
+     * zero, of the argument.
+     *
+     * The domain is divided into the intervals [0, 5] and
+     * (5, infinity). In the first interval a rational approximation
+     * R(x) is employed to compute
+     *   y0(x)  = R(x)  +   2 * log(x) * j0(x) / PI.
+     * Thus a call to j0() is required.
+     *
+     * In the second interval, the Hankel asymptotic expansion
+     * is employed with two rational functions of degree 6/6
+     * and 7/7.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *  Absolute error, when y0(x) < 1; else relative error:
+     *
+     * arithmetic   domain     # trials      peak         rms
+     *    DEC       0, 30        9400       7.0e-17     7.9e-18
+     *    IEEE      0, 30       30000       1.3e-15     1.6e-16
+     *
+     */
+    const double PP[] = {7.96936729297347051624E-4, 8.28352392107440799803E-2, 1.23953371646414299388E0,
+                         5.44725003058768775090E0,  8.74716500199817011941E0,  5.30324038235394892183E0,
+                         9.99999999999999997821E-1};
+    const double PQ[] = {9.24408810558863637013E-4, 8.56288474354474431428E-2, 1.25352743901058953537E0,
+                         5.47097740330417105182E0,  8.76190883237069594232E0,  5.30605288235394617618E0,
+                         1.00000000000000000218E0};
+    const double QP[] = {-1.13663838898469149931E-2, -1.28252718670509318512E0, -1.95539544257735972385E1,
+                         -9.32060152123768231369E1,  -1.77681167980488050595E2, -1.47077505154951170175E2,
+                         -5.14105326766599330220E1,  -6.05014350600728481186E0};
+    const double QQ[] = {1.00000000000000000000E0, 6.43178256118178023184E1, 8.56430025976980587198E2,
+                         3.88240183605401609683E3, 7.24046774195652478189E3, 5.93072701187316984827E3,
+                         2.06209331660327847417E3, 2.42005740240291393179E2};
+    const double YP[] = {1.55924367855235737965E4,   -1.46639295903971606143E7, 5.43526477051876500413E9,
+                         -9.82136065717911466409E11, 8.75906394395366999549E13, -3.46628303384729719441E15,
+                         4.42733268572569800351E16,  -1.84950800436986690637E16};
+    const double YQ[] = {1.00000000000000000000E0,  1.04128353664259848412E3,  6.26107330137134956842E5,
+                         2.68919633393814121987E8,  8.64002487103935000337E10, 2.02979612750105546709E13,
+                         3.17157752842975028269E15, 2.50596256172653059228E17};
+    const T SQ2OPI = pset1<T>(7.9788456080286535587989E-1); /* sqrt(2 / pi) */
+    const T TWOOPI = pset1<T>(0.636619772367581343075535);  /* 2 / pi */
+    const T NEG_PIO4 = pset1<T>(-0.7853981633974483096);    /* -pi / 4 */
+    const T NEG_MAXNUM = pset1<T>(-NumTraits<double>::infinity());
+
+    T z = pmul(x, x);
+    T x_le_five = pdiv(internal::ppolevl<T, 7>::run(z, YP), internal::ppolevl<T, 7>::run(z, YQ));
+    x_le_five = pmadd(pmul(TWOOPI, plog(x)), generic_j0<T, double>::run(x), x_le_five);
+    x_le_five = pselect(pcmp_le(x, pset1<T>(0.0)), NEG_MAXNUM, x_le_five);
+    T s = pdiv(pset1<T>(25.0), z);
+    T p = pdiv(internal::ppolevl<T, 6>::run(s, PP), internal::ppolevl<T, 6>::run(s, PQ));
+    T q = pdiv(internal::ppolevl<T, 7>::run(s, QP), internal::ppolevl<T, 7>::run(s, QQ));
+    T xn = padd(x, NEG_PIO4);
+    T w = pdiv(pset1<T>(5.0), x);
+    p = pmadd(p, psin(xn), pmul(w, pmul(q, pcos(xn))));
+    T x_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(x)));
+    return pselect(pcmp_le(x, pset1<T>(5.0)), x_le_five, x_gt_five);
+  }
+};
+
+template <typename T>
+struct bessel_y0_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_y0<T>::run(x); }
+};
+
+template <typename T>
+struct bessel_j1_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_j1 {
+  EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T&) { return ScalarType(0); }
+};
+
+template <typename T>
+struct generic_j1<T, float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* j1f.c
+     *	Bessel function of order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, j1f();
+     *
+     * y = j1f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of order one of the argument.
+     *
+     * The domain is divided into the intervals [0, 2] and
+     * (2, infinity). In the first interval a polynomial approximation
+     *        2
+     * (w - r  ) x P(w)
+     *       1
+     *                     2
+     * is used, where w = x  and r is the first zero of the function.
+     *
+     * In the second interval, the modulus and phase are approximated
+     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
+     * and Phase(x) = x + 1/x R(1/x^2) - 3pi/4.  The function is
+     *
+     *   j0(x) = Modulus(x) cos( Phase(x) ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain      # trials      peak       rms
+     *    IEEE      0,  2       100000       1.2e-7     2.5e-8
+     *    IEEE      2, 32       100000       2.0e-7     5.3e-8
+     *
+     *
+     */
+
+    const float JP[] = {-4.878788132172128E-009f, 6.009061827883699E-007f, -4.541343896997497E-005f,
+                        1.937383947804541E-003f, -3.405537384615824E-002f};
+    const float MO1[] = {6.913942741265801E-002f,  -2.284801500053359E-001f, 3.138238455499697E-001f,
+                         -2.102302420403875E-001f, 5.435364690523026E-003f,  1.493389585089498E-001f,
+                         4.976029650847191E-006f,  7.978845453073848E-001f};
+    const float PH1[] = {-4.497014141919556E+001f, 5.073465654089319E+001f,  -2.485774108720340E+001f,
+                         7.222973196770240E+000f,  -1.544842782180211E+000f, 3.503787691653334E-001f,
+                         -1.637986776941202E-001f, 3.749989509080821E-001f};
+    const T Z1 = pset1<T>(1.46819706421238932572E1f);
+    const T NEG_THPIO4F = pset1<T>(-2.35619449019234492885f); /* -3*pi/4 */
+
+    T y = pabs(x);
+    T z = pmul(y, y);
+    T y_le_two = pmul(psub(z, Z1), pmul(x, internal::ppolevl<T, 4>::run(z, JP)));
+    T q = pdiv(pset1<T>(1.0f), y);
+    T w = prsqrt(y);
+    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO1));
+    w = pmul(q, q);
+    T yn = pmadd(q, internal::ppolevl<T, 7>::run(w, PH1), NEG_THPIO4F);
+    T y_gt_two = pmul(p, pcos(padd(yn, y)));
+    // j1 is an odd function. This implementation differs from cephes to
+    // take this fact in to account. Cephes returns -j1(x) for y > 2 range.
+    y_gt_two = pselect(pcmp_lt(x, pset1<T>(0.0f)), pnegate(y_gt_two), y_gt_two);
+    return pselect(pcmp_le(y, pset1<T>(2.0f)), y_le_two, y_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_j1<T, double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  j1.c
+     *	Bessel function of order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, j1();
+     *
+     * y = j1( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of order one of the argument.
+     *
+     * The domain is divided into the intervals [0, 8] and
+     * (8, infinity). In the first interval a 24 term Chebyshev
+     * expansion is used. In the second, the asymptotic
+     * trigonometric representation is employed using two
+     * rational functions of degree 5/5.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain      # trials      peak         rms
+     *    DEC       0, 30       10000       4.0e-17     1.1e-17
+     *    IEEE      0, 30       30000       2.6e-16     1.1e-16
+     *
+     */
+    const double PP[] = {7.62125616208173112003E-4, 7.31397056940917570436E-2, 1.12719608129684925192E0,
+                         5.11207951146807644818E0,  8.42404590141772420927E0,  5.21451598682361504063E0,
+                         1.00000000000000000254E0};
+    const double PQ[] = {5.71323128072548699714E-4, 6.88455908754495404082E-2, 1.10514232634061696926E0,
+                         5.07386386128601488557E0,  8.39985554327604159757E0,  5.20982848682361821619E0,
+                         9.99999999999999997461E-1};
+    const double QP[] = {5.10862594750176621635E-2, 4.98213872951233449420E0, 7.58238284132545283818E1,
+                         3.66779609360150777800E2,  7.10856304998926107277E2, 5.97489612400613639965E2,
+                         2.11688757100572135698E2,  2.52070205858023719784E1};
+    const double QQ[] = {1.00000000000000000000E0, 7.42373277035675149943E1, 1.05644886038262816351E3,
+                         4.98641058337653607651E3, 9.56231892404756170795E3, 7.99704160447350683650E3,
+                         2.82619278517639096600E3, 3.36093607810698293419E2};
+    const double RP[] = {-8.99971225705559398224E8, 4.52228297998194034323E11, -7.27494245221818276015E13,
+                         3.68295732863852883286E15};
+    const double RQ[] = {1.00000000000000000000E0,  6.20836478118054335476E2,  2.56987256757748830383E5,
+                         8.35146791431949253037E7,  2.21511595479792499675E10, 4.74914122079991414898E12,
+                         7.84369607876235854894E14, 8.95222336184627338078E16, 5.32278620332680085395E18};
+    const T Z1 = pset1<T>(1.46819706421238932572E1);
+    const T Z2 = pset1<T>(4.92184563216946036703E1);
+    const T NEG_THPIO4 = pset1<T>(-2.35619449019234492885); /* -3*pi/4 */
+    const T SQ2OPI = pset1<T>(7.9788456080286535587989E-1); /* sqrt(2 / pi) */
+    T y = pabs(x);
+    T z = pmul(y, y);
+    T y_le_five = pdiv(internal::ppolevl<T, 3>::run(z, RP), internal::ppolevl<T, 8>::run(z, RQ));
+    y_le_five = pmul(pmul(pmul(y_le_five, x), psub(z, Z1)), psub(z, Z2));
+    T s = pdiv(pset1<T>(25.0), z);
+    T p = pdiv(internal::ppolevl<T, 6>::run(s, PP), internal::ppolevl<T, 6>::run(s, PQ));
+    T q = pdiv(internal::ppolevl<T, 7>::run(s, QP), internal::ppolevl<T, 7>::run(s, QQ));
+    T yn = padd(y, NEG_THPIO4);
+    T w = pdiv(pset1<T>(-5.0), y);
+    p = pmadd(p, pcos(yn), pmul(w, pmul(q, psin(yn))));
+    T y_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(y)));
+    // j1 is an odd function. This implementation differs from cephes to
+    // take this fact in to account. Cephes returns -j1(x) for y > 5 range.
+    y_gt_five = pselect(pcmp_lt(x, pset1<T>(0.0)), pnegate(y_gt_five), y_gt_five);
+    return pselect(pcmp_le(y, pset1<T>(5.0)), y_le_five, y_gt_five);
+  }
+};
+
+template <typename T>
+struct bessel_j1_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_j1<T>::run(x); }
+};
+
+template <typename T>
+struct bessel_y1_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_y1 {
+  EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T&) { return ScalarType(0); }
+};
+
+template <typename T>
+struct generic_y1<T, float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* j1f.c
+     *	Bessel function of second kind of order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, y1();
+     *
+     * y = y1( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of the second kind of order one
+     * of the argument.
+     *
+     * The domain is divided into the intervals [0, 2] and
+     * (2, infinity). In the first interval a rational approximation
+     * R(x) is employed to compute
+     *
+     *                  2
+     * y0(x)  =  (w - r  ) x R(x^2)  +  2/pi (ln(x) j1(x) - 1/x) .
+     *                 1
+     *
+     * Thus a call to j1() is required.
+     *
+     * In the second interval, the modulus and phase are approximated
+     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
+     * and Phase(x) = x + 1/x S(1/x^2) - 3pi/4.  Then the function is
+     *
+     *   y0(x) = Modulus(x) sin( Phase(x) ).
+     *
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain      # trials      peak         rms
+     *    IEEE      0,  2       100000       2.2e-7     4.6e-8
+     *    IEEE      2, 32       100000       1.9e-7     5.3e-8
+     *
+     * (error criterion relative when |y1| > 1).
+     *
+     */
+
+    const float YP[] = {8.061978323326852E-009f, -9.496460629917016E-007f, 6.719543806674249E-005f,
+                        -2.641785726447862E-003f, 4.202369946500099E-002f};
+    const float MO1[] = {6.913942741265801E-002f,  -2.284801500053359E-001f, 3.138238455499697E-001f,
+                         -2.102302420403875E-001f, 5.435364690523026E-003f,  1.493389585089498E-001f,
+                         4.976029650847191E-006f,  7.978845453073848E-001f};
+    const float PH1[] = {-4.497014141919556E+001f, 5.073465654089319E+001f,  -2.485774108720340E+001f,
+                         7.222973196770240E+000f,  -1.544842782180211E+000f, 3.503787691653334E-001f,
+                         -1.637986776941202E-001f, 3.749989509080821E-001f};
+    const T YO1 = pset1<T>(4.66539330185668857532f);
+    const T NEG_THPIO4F = pset1<T>(-2.35619449019234492885f); /* -3*pi/4 */
+    const T TWOOPI = pset1<T>(0.636619772367581343075535f);   /* 2/pi */
+    const T NEG_MAXNUM = pset1<T>(-NumTraits<float>::infinity());
+
+    T z = pmul(x, x);
+    T x_le_two = pmul(psub(z, YO1), internal::ppolevl<T, 4>::run(z, YP));
+    x_le_two = pmadd(x_le_two, x, pmul(TWOOPI, pmadd(generic_j1<T, float>::run(x), plog(x), pdiv(pset1<T>(-1.0f), x))));
+    x_le_two = pselect(pcmp_lt(x, pset1<T>(0.0f)), NEG_MAXNUM, x_le_two);
+
+    T q = pdiv(pset1<T>(1.0), x);
+    T w = prsqrt(x);
+    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO1));
+    w = pmul(q, q);
+    T xn = pmadd(q, internal::ppolevl<T, 7>::run(w, PH1), NEG_THPIO4F);
+    T x_gt_two = pmul(p, psin(padd(xn, x)));
+    return pselect(pcmp_le(x, pset1<T>(2.0)), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_y1<T, double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  j1.c
+     *	Bessel function of second kind of order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, y1();
+     *
+     * y = y1( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of the second kind of order one
+     * of the argument.
+     *
+     * The domain is divided into the intervals [0, 8] and
+     * (8, infinity). In the first interval a 25 term Chebyshev
+     * expansion is used, and a call to j1() is required.
+     * In the second, the asymptotic trigonometric representation
+     * is employed using two rational functions of degree 5/5.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain      # trials      peak         rms
+     *    DEC       0, 30       10000       8.6e-17     1.3e-17
+     *    IEEE      0, 30       30000       1.0e-15     1.3e-16
+     *
+     * (error criterion relative when |y1| > 1).
+     *
+     */
+    const double PP[] = {7.62125616208173112003E-4, 7.31397056940917570436E-2, 1.12719608129684925192E0,
+                         5.11207951146807644818E0,  8.42404590141772420927E0,  5.21451598682361504063E0,
+                         1.00000000000000000254E0};
+    const double PQ[] = {5.71323128072548699714E-4, 6.88455908754495404082E-2, 1.10514232634061696926E0,
+                         5.07386386128601488557E0,  8.39985554327604159757E0,  5.20982848682361821619E0,
+                         9.99999999999999997461E-1};
+    const double QP[] = {5.10862594750176621635E-2, 4.98213872951233449420E0, 7.58238284132545283818E1,
+                         3.66779609360150777800E2,  7.10856304998926107277E2, 5.97489612400613639965E2,
+                         2.11688757100572135698E2,  2.52070205858023719784E1};
+    const double QQ[] = {1.00000000000000000000E0, 7.42373277035675149943E1, 1.05644886038262816351E3,
+                         4.98641058337653607651E3, 9.56231892404756170795E3, 7.99704160447350683650E3,
+                         2.82619278517639096600E3, 3.36093607810698293419E2};
+    const double YP[] = {1.26320474790178026440E9,   -6.47355876379160291031E11, 1.14509511541823727583E14,
+                         -8.12770255501325109621E15, 2.02439475713594898196E17,  -7.78877196265950026825E17};
+    const double YQ[] = {1.00000000000000000000E0,  5.94301592346128195359E2,  2.35564092943068577943E5,
+                         7.34811944459721705660E7,  1.87601316108706159478E10, 3.88231277496238566008E12,
+                         6.20557727146953693363E14, 6.87141087355300489866E16, 3.97270608116560655612E18};
+    const T SQ2OPI = pset1<T>(.79788456080286535588);
+    const T NEG_THPIO4 = pset1<T>(-2.35619449019234492885); /* -3*pi/4 */
+    const T TWOOPI = pset1<T>(0.636619772367581343075535);  /* 2/pi */
+    const T NEG_MAXNUM = pset1<T>(-NumTraits<double>::infinity());
+
+    T z = pmul(x, x);
+    T x_le_five = pdiv(internal::ppolevl<T, 5>::run(z, YP), internal::ppolevl<T, 8>::run(z, YQ));
+    x_le_five =
+        pmadd(x_le_five, x, pmul(TWOOPI, pmadd(generic_j1<T, double>::run(x), plog(x), pdiv(pset1<T>(-1.0), x))));
+
+    x_le_five = pselect(pcmp_le(x, pset1<T>(0.0)), NEG_MAXNUM, x_le_five);
+    T s = pdiv(pset1<T>(25.0), z);
+    T p = pdiv(internal::ppolevl<T, 6>::run(s, PP), internal::ppolevl<T, 6>::run(s, PQ));
+    T q = pdiv(internal::ppolevl<T, 7>::run(s, QP), internal::ppolevl<T, 7>::run(s, QQ));
+    T xn = padd(x, NEG_THPIO4);
+    T w = pdiv(pset1<T>(5.0), x);
+    p = pmadd(p, psin(xn), pmul(w, pmul(q, pcos(xn))));
+    T x_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(x)));
+    return pselect(pcmp_le(x, pset1<T>(5.0)), x_le_five, x_gt_five);
+  }
+};
+
+template <typename T>
+struct bessel_y1_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T x) { return generic_y1<T>::run(x); }
+};
+
+}  // end namespace internal
+
+namespace numext {
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i0, Scalar) bessel_i0(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_i0, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i0e, Scalar) bessel_i0e(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_i0e, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i1, Scalar) bessel_i1(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_i1, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i1e, Scalar) bessel_i1e(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_i1e, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k0, Scalar) bessel_k0(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_k0, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k0e, Scalar) bessel_k0e(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_k0e, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k1, Scalar) bessel_k1(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_k1, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k1e, Scalar) bessel_k1e(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_k1e, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_j0, Scalar) bessel_j0(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_j0, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_y0, Scalar) bessel_y0(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_y0, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_j1, Scalar) bessel_j1(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_j1, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_y1, Scalar) bessel_y1(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_y1, Scalar)::run(x);
+}
+
+}  // end namespace numext
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BESSEL_FUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h
new file mode 100644
index 0000000..1c325fc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h
@@ -0,0 +1,108 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSELFUNCTIONS_PACKETMATH_H
+#define EIGEN_BESSELFUNCTIONS_PACKETMATH_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero i0(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_i0(const Packet& x) {
+  return numext::bessel_i0(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero i0e(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_i0e(const Packet& x) {
+  return numext::bessel_i0e(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one i1(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_i1(const Packet& x) {
+  return numext::bessel_i1(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one i1e(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_i1e(const Packet& x) {
+  return numext::bessel_i1e(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero j0(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_j0(const Packet& x) {
+  return numext::bessel_j0(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero j1(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_j1(const Packet& x) {
+  return numext::bessel_j1(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one y0(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_y0(const Packet& x) {
+  return numext::bessel_y0(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one y1(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_y1(const Packet& x) {
+  return numext::bessel_y1(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero k0(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_k0(const Packet& x) {
+  return numext::bessel_k0(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero k0e(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_k0e(const Packet& x) {
+  return numext::bessel_k0e(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one k1e(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_k1(const Packet& x) {
+  return numext::bessel_k1(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one k1e(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pbessel_k1e(const Packet& x) {
+  return numext::bessel_k1e(x);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BESSELFUNCTIONS_PACKETMATH_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h
new file mode 100644
index 0000000..9119335
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h
@@ -0,0 +1,71 @@
+#ifndef HIP_VECTOR_COMPATIBILITY_H
+#define HIP_VECTOR_COMPATIBILITY_H
+
+namespace hip_impl {
+template <typename, typename, unsigned int>
+struct Scalar_accessor;
+}  // end namespace hip_impl
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+#define HIP_SCALAR_ACCESSOR_BUILDER(NAME)           \
+  template <typename T, typename U, unsigned int n> \
+  struct NAME<hip_impl::Scalar_accessor<T, U, n>> : NAME<T> {};
+
+#define HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(NAME)                              \
+  template <typename T, typename U, unsigned int n>                           \
+  struct NAME##_impl<hip_impl::Scalar_accessor<T, U, n>> : NAME##_impl<T> {}; \
+  template <typename T, typename U, unsigned int n>                           \
+  struct NAME##_retval<hip_impl::Scalar_accessor<T, U, n>> : NAME##_retval<T> {};
+
+#define HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(NAME)                                \
+  template <typename T, typename U, unsigned int n, IgammaComputationMode mode> \
+  struct NAME<hip_impl::Scalar_accessor<T, U, n>, mode> : NAME<T, mode> {};
+
+#if EIGEN_HAS_C99_MATH
+HIP_SCALAR_ACCESSOR_BUILDER(betainc_helper)
+HIP_SCALAR_ACCESSOR_BUILDER(incbeta_cfe)
+
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(erf)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(erfc)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(igammac)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(lgamma)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(ndtri)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(polygamma)
+
+HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(igamma_generic_impl)
+#endif
+
+HIP_SCALAR_ACCESSOR_BUILDER(digamma_impl_maybe_poly)
+HIP_SCALAR_ACCESSOR_BUILDER(zeta_impl_series)
+
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i0)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i0e)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i1)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i1e)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_j0)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_j1)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k0)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k0e)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k1)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k1e)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_y0)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_y1)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(betainc)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(digamma)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(gamma_sample_der_alpha)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(igamma_der_a)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(igamma)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(zeta)
+
+HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(igamma_series_impl)
+HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(igammac_cf_impl)
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // HIP_VECTOR_COMPATIBILITY_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/InternalHeaderCheck.h
new file mode 100644
index 0000000..a5ef51a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/InternalHeaderCheck.h
@@ -0,0 +1,4 @@
+#ifndef EIGEN_SPECIALFUNCTIONS_MODULE_H
+#error \
+    "Please include unsupported/Eigen/SpecialFunctions instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h
new file mode 100644
index 0000000..0920d27
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h
@@ -0,0 +1,159 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_ARRAYAPI_H
+#define EIGEN_SPECIALFUNCTIONS_ARRAYAPI_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \cpp11 \returns an expression of the coefficient-wise igamma(\a a, \a x) to the given arrays.
+ *
+ * This function computes the coefficient-wise incomplete gamma function.
+ *
+ * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+ * or float/double in non c++11 mode, the user has to provide implementations of igammac(T,T) for any scalar
+ * type T to be supported.
+ *
+ * \sa Eigen::igammac(), Eigen::lgamma()
+ */
+template <typename Derived, typename ExponentDerived>
+EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_op<typename Derived::Scalar>,
+                                               const Derived, const ExponentDerived>
+igamma(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x) {
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_op<typename Derived::Scalar>, const Derived,
+                              const ExponentDerived>(a.derived(), x.derived());
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise igamma_der_a(\a a, \a x) to the given arrays.
+ *
+ * This function computes the coefficient-wise derivative of the incomplete
+ * gamma function with respect to the parameter a.
+ *
+ * \note This function supports only float and double scalar types in c++11
+ * mode. To support other scalar types,
+ * or float/double in non c++11 mode, the user has to provide implementations
+ * of igamma_der_a(T,T) for any scalar
+ * type T to be supported.
+ *
+ * \sa Eigen::igamma(), Eigen::lgamma()
+ */
+template <typename Derived, typename ExponentDerived>
+EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_der_a_op<typename Derived::Scalar>,
+                                               const Derived, const ExponentDerived>
+igamma_der_a(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x) {
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_der_a_op<typename Derived::Scalar>, const Derived,
+                              const ExponentDerived>(a.derived(), x.derived());
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise gamma_sample_der_alpha(\a alpha, \a sample) to the given
+ * arrays.
+ *
+ * This function computes the coefficient-wise derivative of the sample
+ * of a Gamma(alpha, 1) random variable with respect to the parameter alpha.
+ *
+ * \note This function supports only float and double scalar types in c++11
+ * mode. To support other scalar types,
+ * or float/double in non c++11 mode, the user has to provide implementations
+ * of gamma_sample_der_alpha(T,T) for any scalar
+ * type T to be supported.
+ *
+ * \sa Eigen::igamma(), Eigen::lgamma()
+ */
+template <typename AlphaDerived, typename SampleDerived>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseBinaryOp<Eigen::internal::scalar_gamma_sample_der_alpha_op<typename AlphaDerived::Scalar>,
+                         const AlphaDerived, const SampleDerived>
+    gamma_sample_der_alpha(const Eigen::ArrayBase<AlphaDerived>& alpha, const Eigen::ArrayBase<SampleDerived>& sample) {
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_gamma_sample_der_alpha_op<typename AlphaDerived::Scalar>,
+                              const AlphaDerived, const SampleDerived>(alpha.derived(), sample.derived());
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise igammac(\a a, \a x) to the given arrays.
+ *
+ * This function computes the coefficient-wise complementary incomplete gamma function.
+ *
+ * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+ * or float/double in non c++11 mode, the user has to provide implementations of igammac(T,T) for any scalar
+ * type T to be supported.
+ *
+ * \sa Eigen::igamma(), Eigen::lgamma()
+ */
+template <typename Derived, typename ExponentDerived>
+EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igammac_op<typename Derived::Scalar>,
+                                               const Derived, const ExponentDerived>
+igammac(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x) {
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igammac_op<typename Derived::Scalar>, const Derived,
+                              const ExponentDerived>(a.derived(), x.derived());
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise polygamma(\a n, \a x) to the given arrays.
+ *
+ * It returns the \a n -th derivative of the digamma(psi) evaluated at \c x.
+ *
+ * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+ * or float/double in non c++11 mode, the user has to provide implementations of polygamma(T,T) for any scalar
+ * type T to be supported.
+ *
+ * \sa Eigen::digamma()
+ */
+// * \warning Be careful with the order of the parameters: x.polygamma(n) is equivalent to polygamma(n,x)
+// * \sa ArrayBase::polygamma()
+template <typename DerivedN, typename DerivedX>
+EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_polygamma_op<typename DerivedX::Scalar>,
+                                               const DerivedN, const DerivedX>
+polygamma(const Eigen::ArrayBase<DerivedN>& n, const Eigen::ArrayBase<DerivedX>& x) {
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_polygamma_op<typename DerivedX::Scalar>, const DerivedN,
+                              const DerivedX>(n.derived(), x.derived());
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise betainc(\a x, \a a, \a b) to the given arrays.
+ *
+ * This function computes the regularized incomplete beta function (integral).
+ *
+ * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+ * or float/double in non c++11 mode, the user has to provide implementations of betainc(T,T,T) for any scalar
+ * type T to be supported.
+ *
+ * \sa Eigen::betainc(), Eigen::lgamma()
+ */
+template <typename ArgADerived, typename ArgBDerived, typename ArgXDerived>
+EIGEN_STRONG_INLINE const Eigen::CwiseTernaryOp<Eigen::internal::scalar_betainc_op<typename ArgXDerived::Scalar>,
+                                                const ArgADerived, const ArgBDerived, const ArgXDerived>
+betainc(const Eigen::ArrayBase<ArgADerived>& a, const Eigen::ArrayBase<ArgBDerived>& b,
+        const Eigen::ArrayBase<ArgXDerived>& x) {
+  return Eigen::CwiseTernaryOp<Eigen::internal::scalar_betainc_op<typename ArgXDerived::Scalar>, const ArgADerived,
+                               const ArgBDerived, const ArgXDerived>(a.derived(), b.derived(), x.derived());
+}
+
+/** \returns an expression of the coefficient-wise zeta(\a x, \a q) to the given arrays.
+ *
+ * It returns the Riemann zeta function of two arguments \a x and \a q:
+ *
+ * \param x is the exponent, it must be > 1
+ * \param q is the shift, it must be > 0
+ *
+ * \note This function supports only float and double scalar types. To support other scalar types, the user has
+ * to provide implementations of zeta(T,T) for any scalar type T to be supported.
+ *
+ * \sa ArrayBase::zeta()
+ */
+template <typename DerivedX, typename DerivedQ>
+EIGEN_STRONG_INLINE const
+    Eigen::CwiseBinaryOp<Eigen::internal::scalar_zeta_op<typename DerivedX::Scalar>, const DerivedX, const DerivedQ>
+    zeta(const Eigen::ArrayBase<DerivedX>& x, const Eigen::ArrayBase<DerivedQ>& q) {
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_zeta_op<typename DerivedX::Scalar>, const DerivedX,
+                              const DerivedQ>(x.derived(), q.derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPECIALFUNCTIONS_ARRAYAPI_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h
new file mode 100644
index 0000000..90babae
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h
@@ -0,0 +1,73 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_BFLOAT16_H
+#define EIGEN_SPECIALFUNCTIONS_BFLOAT16_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace numext {
+
+#if EIGEN_HAS_C99_MATH
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 lgamma(const Eigen::bfloat16& a) {
+  return Eigen::bfloat16(Eigen::numext::lgamma(static_cast<float>(a)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 digamma(const Eigen::bfloat16& a) {
+  return Eigen::bfloat16(Eigen::numext::digamma(static_cast<float>(a)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 zeta(const Eigen::bfloat16& x, const Eigen::bfloat16& q) {
+  return Eigen::bfloat16(Eigen::numext::zeta(static_cast<float>(x), static_cast<float>(q)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 polygamma(const Eigen::bfloat16& n, const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::polygamma(static_cast<float>(n), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 erf(const Eigen::bfloat16& a) {
+  return Eigen::bfloat16(Eigen::numext::erf(static_cast<float>(a)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 erfc(const Eigen::bfloat16& a) {
+  return Eigen::bfloat16(Eigen::numext::erfc(static_cast<float>(a)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 ndtri(const Eigen::bfloat16& a) {
+  return Eigen::bfloat16(Eigen::numext::ndtri(static_cast<float>(a)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 igamma(const Eigen::bfloat16& a, const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::igamma(static_cast<float>(a), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 igamma_der_a(const Eigen::bfloat16& a, const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::igamma_der_a(static_cast<float>(a), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 gamma_sample_der_alpha(const Eigen::bfloat16& alpha,
+                                                                             const Eigen::bfloat16& sample) {
+  return Eigen::bfloat16(Eigen::numext::gamma_sample_der_alpha(static_cast<float>(alpha), static_cast<float>(sample)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 igammac(const Eigen::bfloat16& a, const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::igammac(static_cast<float>(a), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 betainc(const Eigen::bfloat16& a, const Eigen::bfloat16& b,
+                                                              const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::betainc(static_cast<float>(a), static_cast<float>(b), static_cast<float>(x)));
+}
+#endif
+
+}  // end namespace numext
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPECIALFUNCTIONS_BFLOAT16_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h
new file mode 100644
index 0000000..483c9ae
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h
@@ -0,0 +1,326 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_FUNCTORS_H
+#define EIGEN_SPECIALFUNCTIONS_FUNCTORS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * \brief Template functor to compute the incomplete gamma function igamma(a, x)
+ *
+ * \sa class CwiseBinaryOp, Cwise::igamma
+ */
+template <typename Scalar>
+struct scalar_igamma_op : binary_op_base<Scalar, Scalar> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a, const Scalar& x) const {
+    using numext::igamma;
+    return igamma(a, x);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const {
+    return internal::pigamma(a, x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_igamma_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 20 * NumTraits<Scalar>::MulCost + 10 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasIGamma
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the derivative of the incomplete gamma
+ * function igamma_der_a(a, x)
+ *
+ * \sa class CwiseBinaryOp, Cwise::igamma_der_a
+ */
+template <typename Scalar>
+struct scalar_igamma_der_a_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a, const Scalar& x) const {
+    using numext::igamma_der_a;
+    return igamma_der_a(a, x);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const {
+    return internal::pigamma_der_a(a, x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_igamma_der_a_op<Scalar> > {
+  enum {
+    // 2x the cost of igamma
+    Cost = 40 * NumTraits<Scalar>::MulCost + 20 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasIGammaDerA
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the derivative of the sample
+ * of a Gamma(alpha, 1) random variable with respect to the parameter alpha
+ * gamma_sample_der_alpha(alpha, sample)
+ *
+ * \sa class CwiseBinaryOp, Cwise::gamma_sample_der_alpha
+ */
+template <typename Scalar>
+struct scalar_gamma_sample_der_alpha_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& alpha, const Scalar& sample) const {
+    using numext::gamma_sample_der_alpha;
+    return gamma_sample_der_alpha(alpha, sample);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& alpha, const Packet& sample) const {
+    return internal::pgamma_sample_der_alpha(alpha, sample);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_gamma_sample_der_alpha_op<Scalar> > {
+  enum {
+    // 2x the cost of igamma, minus the lgamma cost (the lgamma cancels out)
+    Cost = 30 * NumTraits<Scalar>::MulCost + 15 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasGammaSampleDerAlpha
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the complementary incomplete gamma function igammac(a, x)
+ *
+ * \sa class CwiseBinaryOp, Cwise::igammac
+ */
+template <typename Scalar>
+struct scalar_igammac_op : binary_op_base<Scalar, Scalar> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a, const Scalar& x) const {
+    using numext::igammac;
+    return igammac(a, x);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const {
+    return internal::pigammac(a, x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_igammac_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 20 * NumTraits<Scalar>::MulCost + 10 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasIGammac
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the incomplete beta integral betainc(a, b, x)
+ *
+ */
+template <typename Scalar>
+struct scalar_betainc_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x, const Scalar& a,
+                                                                const Scalar& b) const {
+    using numext::betainc;
+    return betainc(x, a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& x, const Packet& a, const Packet& b) const {
+    return internal::pbetainc(x, a, b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_betainc_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 400 * NumTraits<Scalar>::MulCost + 400 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBetaInc
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the natural log of the absolute
+ * value of Gamma of a scalar
+ * \sa class CwiseUnaryOp, Cwise::lgamma()
+ */
+template <typename Scalar>
+struct scalar_lgamma_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a) const {
+    using numext::lgamma;
+    return lgamma(a);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::plgamma(a); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_lgamma_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasLGamma
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute psi, the derivative of lgamma of a scalar.
+ * \sa class CwiseUnaryOp, Cwise::digamma()
+ */
+template <typename Scalar>
+struct scalar_digamma_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a) const {
+    using numext::digamma;
+    return digamma(a);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::pdigamma(a); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_digamma_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasDiGamma
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Riemann Zeta function of two arguments.
+ * \sa class CwiseUnaryOp, Cwise::zeta()
+ */
+template <typename Scalar>
+struct scalar_zeta_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x, const Scalar& q) const {
+    using numext::zeta;
+    return zeta(x, q);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x, const Packet& q) const {
+    return internal::pzeta(x, q);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_zeta_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasZeta
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the polygamma function.
+ * \sa class CwiseUnaryOp, Cwise::polygamma()
+ */
+template <typename Scalar>
+struct scalar_polygamma_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& n, const Scalar& x) const {
+    using numext::polygamma;
+    return polygamma(n, x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& n, const Packet& x) const {
+    return internal::ppolygamma(n, x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_polygamma_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasPolygamma
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the error function of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::erf()
+ */
+template <typename Scalar>
+struct scalar_erf_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a) const { return numext::erf(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return perf(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_erf_op<Scalar> > {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasErf,
+    Cost = (PacketAccess
+#ifdef EIGEN_VECTORIZE_FMA
+                // TODO(rmlarsen): Move the FMA cost model to a central location.
+                // Haswell can issue 2 add/mul/madd per cycle.
+                // 10 pmadd, 2 pmul, 1 div, 2 other
+                ? (2 * NumTraits<Scalar>::AddCost + 7 * NumTraits<Scalar>::MulCost +
+                   scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value)
+#else
+                ? (12 * NumTraits<Scalar>::AddCost + 12 * NumTraits<Scalar>::MulCost +
+                   scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value)
+#endif
+                // Assume for simplicity that this is as expensive as an exp().
+                : (functor_traits<scalar_exp_op<Scalar> >::Cost))
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Complementary Error Function
+ * of a scalar
+ * \sa class CwiseUnaryOp, Cwise::erfc()
+ */
+template <typename Scalar>
+struct scalar_erfc_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a) const {
+    using numext::erfc;
+    return erfc(a);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::perfc(a); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_erfc_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasErfc
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Inverse of the normal distribution
+ * function of a scalar
+ * \sa class CwiseUnaryOp, Cwise::ndtri()
+ */
+template <typename Scalar>
+struct scalar_ndtri_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a) const {
+    using numext::ndtri;
+    return ndtri(a);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::pndtri(a); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_ndtri_op<Scalar> > {
+  enum {
+    // On average, We are evaluating rational functions with degree N=9 in the
+    // numerator and denominator. This results in 2*N additions and 2*N
+    // multiplications.
+    Cost = 18 * NumTraits<Scalar>::MulCost + 18 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasNdtri
+  };
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPECIALFUNCTIONS_FUNCTORS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h
new file mode 100644
index 0000000..baba848
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h
@@ -0,0 +1,73 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_HALF_H
+#define EIGEN_SPECIALFUNCTIONS_HALF_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace numext {
+
+#if EIGEN_HAS_C99_MATH
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half lgamma(const Eigen::half& a) {
+  return Eigen::half(Eigen::numext::lgamma(static_cast<float>(a)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half digamma(const Eigen::half& a) {
+  return Eigen::half(Eigen::numext::digamma(static_cast<float>(a)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half zeta(const Eigen::half& x, const Eigen::half& q) {
+  return Eigen::half(Eigen::numext::zeta(static_cast<float>(x), static_cast<float>(q)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half polygamma(const Eigen::half& n, const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::polygamma(static_cast<float>(n), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half erf(const Eigen::half& a) {
+  return Eigen::half(Eigen::numext::erf(static_cast<float>(a)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half erfc(const Eigen::half& a) {
+  return Eigen::half(Eigen::numext::erfc(static_cast<float>(a)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half ndtri(const Eigen::half& a) {
+  return Eigen::half(Eigen::numext::ndtri(static_cast<float>(a)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igamma(const Eigen::half& a, const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::igamma(static_cast<float>(a), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igamma_der_a(const Eigen::half& a, const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::igamma_der_a(static_cast<float>(a), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half gamma_sample_der_alpha(const Eigen::half& alpha,
+                                                                         const Eigen::half& sample) {
+  return Eigen::half(Eigen::numext::gamma_sample_der_alpha(static_cast<float>(alpha), static_cast<float>(sample)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igammac(const Eigen::half& a, const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::igammac(static_cast<float>(a), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half betainc(const Eigen::half& a, const Eigen::half& b,
+                                                          const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::betainc(static_cast<float>(a), static_cast<float>(b), static_cast<float>(x)));
+}
+#endif
+
+}  // end namespace numext
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPECIALFUNCTIONS_HALF_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h
new file mode 100644
index 0000000..5553bbc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h
@@ -0,0 +1,2071 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIAL_FUNCTIONS_H
+#define EIGEN_SPECIAL_FUNCTIONS_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+//  Parts of this code are based on the Cephes Math Library.
+//
+//  Cephes Math Library Release 2.8:  June, 2000
+//  Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier
+//
+//  Permission has been kindly provided by the original author
+//  to incorporate the Cephes software into the Eigen codebase:
+//
+//    From: Stephen Moshier
+//    To: Eugene Brevdo
+//    Subject: Re: Permission to wrap several cephes functions in Eigen
+//
+//    Hello Eugene,
+//
+//    Thank you for writing.
+//
+//    If your licensing is similar to BSD, the formal way that has been
+//    handled is simply to add a statement to the effect that you are incorporating
+//    the Cephes software by permission of the author.
+//
+//    Good luck with your project,
+//    Steve
+
+/****************************************************************************
+ * Implementation of lgamma, requires C++11/C99                             *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct lgamma_impl {
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar) { return Scalar(0); }
+};
+
+template <typename Scalar>
+struct lgamma_retval {
+  typedef Scalar type;
+};
+
+#if EIGEN_HAS_C99_MATH
+// Since glibc 2.19
+#if defined(__GLIBC__) && ((__GLIBC__ >= 2 && __GLIBC_MINOR__ >= 19) || __GLIBC__ > 2) && \
+    (defined(_DEFAULT_SOURCE) || defined(_BSD_SOURCE) || defined(_SVID_SOURCE))
+#define EIGEN_HAS_LGAMMA_R
+#endif
+
+// Glibc versions before 2.19
+#if defined(__GLIBC__) && ((__GLIBC__ == 2 && __GLIBC_MINOR__ < 19) || __GLIBC__ < 2) && \
+    (defined(_BSD_SOURCE) || defined(_SVID_SOURCE))
+#define EIGEN_HAS_LGAMMA_R
+#endif
+
+template <>
+struct lgamma_impl<float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float run(float x) {
+#if !defined(EIGEN_GPU_COMPILE_PHASE) && defined(EIGEN_HAS_LGAMMA_R) && !defined(__APPLE__)
+    int dummy;
+    return ::lgammaf_r(x, &dummy);
+#elif defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::lgamma(x);
+#else
+    return ::lgammaf(x);
+#endif
+  }
+};
+
+template <>
+struct lgamma_impl<double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double run(double x) {
+#if !defined(EIGEN_GPU_COMPILE_PHASE) && defined(EIGEN_HAS_LGAMMA_R) && !defined(__APPLE__)
+    int dummy;
+    return ::lgamma_r(x, &dummy);
+#elif defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::lgamma(x);
+#else
+    return ::lgamma(x);
+#endif
+  }
+};
+
+#undef EIGEN_HAS_LGAMMA_R
+#endif
+
+/****************************************************************************
+ * Implementation of digamma (psi), based on Cephes                         *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct digamma_retval {
+  typedef Scalar type;
+};
+
+/*
+ *
+ * Polynomial evaluation helper for the Psi (digamma) function.
+ *
+ * digamma_impl_maybe_poly::run(s) evaluates the asymptotic Psi expansion for
+ * input Scalar s, assuming s is above 10.0.
+ *
+ * If s is above a certain threshold for the given Scalar type, zero
+ * is returned.  Otherwise the polynomial is evaluated with enough
+ * coefficients for results matching Scalar machine precision.
+ *
+ *
+ */
+template <typename Scalar>
+struct digamma_impl_maybe_poly {
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar) { return Scalar(0); }
+};
+
+template <>
+struct digamma_impl_maybe_poly<float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float run(const float s) {
+    constexpr float A[] = {-4.16666666666666666667E-3f, 3.96825396825396825397E-3f, -8.33333333333333333333E-3f,
+                           8.33333333333333333333E-2f};
+
+    float z;
+    if (s < 1.0e8f) {
+      z = 1.0f / (s * s);
+      return z * internal::ppolevl<float, 3>::run(z, A);
+    } else
+      return 0.0f;
+  }
+};
+
+template <>
+struct digamma_impl_maybe_poly<double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double run(const double s) {
+    constexpr double A[] = {8.33333333333333333333E-2,  -2.10927960927960927961E-2, 7.57575757575757575758E-3,
+                            -4.16666666666666666667E-3, 3.96825396825396825397E-3,  -8.33333333333333333333E-3,
+                            8.33333333333333333333E-2};
+
+    double z;
+    if (s < 1.0e17) {
+      z = 1.0 / (s * s);
+      return z * internal::ppolevl<double, 6>::run(z, A);
+    } else
+      return 0.0;
+  }
+};
+
+template <typename Scalar>
+struct digamma_impl {
+  EIGEN_DEVICE_FUNC static Scalar run(Scalar x) {
+    /*
+     *
+     *     Psi (digamma) function (modified for Eigen)
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, psi();
+     *
+     * y = psi( x );
+     *
+     *
+     * DESCRIPTION:
+     *
+     *              d      -
+     *   psi(x)  =  -- ln | (x)
+     *              dx
+     *
+     * is the logarithmic derivative of the gamma function.
+     * For integer x,
+     *                   n-1
+     *                    -
+     * psi(n) = -EUL  +   >  1/k.
+     *                    -
+     *                   k=1
+     *
+     * If x is negative, it is transformed to a positive argument by the
+     * reflection formula  psi(1-x) = psi(x) + pi cot(pi x).
+     * For general positive x, the argument is made greater than 10
+     * using the recurrence  psi(x+1) = psi(x) + 1/x.
+     * Then the following asymptotic expansion is applied:
+     *
+     *                           inf.   B
+     *                            -      2k
+     * psi(x) = log(x) - 1/2x -   >   -------
+     *                            -        2k
+     *                           k=1   2k x
+     *
+     * where the B2k are Bernoulli numbers.
+     *
+     * ACCURACY (float):
+     *    Relative error (except absolute when |psi| < 1):
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,30        30000       1.3e-15     1.4e-16
+     *    IEEE      -30,0       40000       1.5e-15     2.2e-16
+     *
+     * ACCURACY (double):
+     *    Absolute error,  relative when |psi| > 1 :
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      -33,0        30000      8.2e-7      1.2e-7
+     *    IEEE      0,33        100000      7.3e-7      7.7e-8
+     *
+     * ERROR MESSAGES:
+     *     message         condition      value returned
+     * psi singularity    x integer <=0      INFINITY
+     */
+
+    Scalar p, q, nz, s, w, y;
+    bool negative = false;
+
+    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
+    const Scalar m_pi = Scalar(EIGEN_PI);
+
+    const Scalar zero = Scalar(0);
+    const Scalar one = Scalar(1);
+    const Scalar half = Scalar(0.5);
+    nz = zero;
+
+    if (x <= zero) {
+      negative = true;
+      q = x;
+      p = numext::floor(q);
+      if (p == q) {
+        return nan;
+      }
+      /* Remove the zeros of tan(m_pi x)
+       * by subtracting the nearest integer from x
+       */
+      nz = q - p;
+      if (nz != half) {
+        if (nz > half) {
+          p += one;
+          nz = q - p;
+        }
+        nz = m_pi / numext::tan(m_pi * nz);
+      } else {
+        nz = zero;
+      }
+      x = one - x;
+    }
+
+    /* use the recurrence psi(x+1) = psi(x) + 1/x. */
+    s = x;
+    w = zero;
+    while (s < Scalar(10)) {
+      w += one / s;
+      s += one;
+    }
+
+    y = digamma_impl_maybe_poly<Scalar>::run(s);
+
+    y = numext::log(s) - (half / s) - y - w;
+
+    return (negative) ? y - nz : y;
+  }
+};
+
+/***************************************************************************
+ * Implementation of erfc.
+ ****************************************************************************/
+template <typename Scalar>
+struct generic_fast_erfc {
+  template <typename T>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T run(const T& x_in);
+};
+
+template <>
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_fast_erfc<float>::run(const T& x_in) {
+  constexpr float kClamp = 11.0f;
+  const T x = pmin(pmax(x_in, pset1<T>(-kClamp)), pset1<T>(kClamp));
+
+  // erfc(x) = 1 + x * S(x^2), |x| <= 1.
+  //
+  // Coefficients for S and T generated with Rminimax command:
+  // ./ratapprox --function="erfc(x)-1" --dom='[-1,1]' --type=[11,0] --num="odd"
+  //   --numF="[SG]" --denF="[SG]" --log --dispCoeff="dec"
+  constexpr float alpha[] = {5.61802298761904239654541015625e-04, -4.91381669417023658752441406250e-03,
+                             2.67075151205062866210937500000e-02, -1.12800106406211853027343750000e-01,
+                             3.76122951507568359375000000000e-01, -1.12837910652160644531250000000e+00};
+  const T x2 = pmul(x, x);
+  const T one = pset1<T>(1.0f);
+  const T erfc_small = pmadd(x, ppolevl<T, 5>::run(x2, alpha), one);
+
+  // Return early if we don't need the more expensive approximation for any
+  // entry in a.
+  const T x_abs_gt_one_mask = pcmp_lt(one, x2);
+  if (!predux_any(x_abs_gt_one_mask)) return erfc_small;
+
+  // erfc(x) = exp(-x^2) * 1/x * P(1/x^2) / Q(1/x^2), 1 < x < 9.
+  //
+  // Coefficients for P and Q generated with Rminimax command:
+  //   ./ratapprox --function="erfc(1/sqrt(x))*exp(1/x)/sqrt(x)"
+  //     --dom='[0.01,1]' --type=[3,4] --numF="[SG]" --denF="[SG]" --log
+  //     --dispCoeff="dec"
+  constexpr float gamma[] = {1.0208116471767425537109375e-01f, 4.2920666933059692382812500e-01f,
+                             3.2379078865051269531250000e-01f, 5.3971976041793823242187500e-02f};
+  constexpr float delta[] = {1.7251677811145782470703125e-02f, 3.9137163758277893066406250e-01f,
+                             1.0000000000000000000000000e+00f, 6.2173241376876831054687500e-01f,
+                             9.5662862062454223632812500e-02f};
+  const T x2_lo = twoprod_low(x, x, x2);
+  // Here we use that
+  //   exp(-x^2) = exp(-(x2+x2_lo)^2) ~= exp(-x2)*exp(-x2_lo) ~= exp(-x2)*(1-x2_lo)
+  // since x2_lo < kClamp * eps << 1 in the region we care about. This trick reduces the max error
+  // from 34 ulps to below 5 ulps.
+  const T exp2_hi = pexp(pnegate(x2));
+  const T z = pnmadd(exp2_hi, x2_lo, exp2_hi);
+  const T q2 = preciprocal(x2);
+  const T num = ppolevl<T, 3>::run(q2, gamma);
+  const T denom = pmul(x, ppolevl<T, 4>::run(q2, delta));
+  const T r = pdiv(num, denom);
+  const T maybe_two = pselect(pcmp_lt(x, pset1<T>(0.0f)), pset1<T>(2.0f), pset1<T>(0.0f));
+  const T erfc_large = pmadd(z, r, maybe_two);
+  return pselect(x_abs_gt_one_mask, erfc_large, erfc_small);
+}
+
+// Computes erf(x)/x for |x| <= 1. Used by both erf and erfc implementations.
+// Takes x2 = x^2 as input.
+//
+// PRECONDITION: x2 <= 1.
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T erf_over_x_double_small(const T& x2) {
+  // erf(x)/x =  S(x^2) / T(x^2), x^2 <= 1.
+  //
+  // Coefficients for S and T generated with Rminimax command:
+  //  ./ratapprox --function="erf(x)" --dom='[-1,1]' --type=[9,10]
+  //  --num="odd" --numF="[D]" --den="even" --denF="[D]" --log --dispCoeff="dec"
+  constexpr double alpha[] = {1.9493725660006057018823477644531294572516344487667083740234375e-04,
+                              1.8272566210022942682217328425053892715368419885635375976562500e-03,
+                              4.5303363351690106863856044583371840417385101318359375000000000e-02,
+                              1.4215015503619179981775744181504705920815467834472656250000000e-01,
+                              1.1283791670955125585606992899556644260883331298828125000000000e+00};
+  constexpr double beta[] = {2.0294484101083099089526257108317963684385176748037338256835938e-05,
+                             6.8117805899186819641732970609382391558028757572174072265625000e-04,
+                             1.0582026056098614921752165685120417037978768348693847656250000e-02,
+                             9.3252603143757495374188692949246615171432495117187500000000000e-02,
+                             4.5931062818368939559832142549566924571990966796875000000000000e-01,
+                             1.0};
+  const T num_small = ppolevl<T, 4>::run(x2, alpha);
+  const T denom_small = ppolevl<T, 5>::run(x2, beta);
+  return pdiv(num_small, denom_small);
+}
+
+// erfc(x) = exp(-x^2) * 1/x * P(1/x^2) / Q(1/x^2), 1 < x < 28.
+//
+// Coefficients for P and Q generated with Rminimax command:
+//  ./ratapprox --function="erfc(1/sqrt(x))*exp(1/x)/sqrt(x)"  --dom='[0.0013717,1]' --type=[9,9] --numF="[D]"
+//  --denF="[D]" --log --dispCoeff="dec"
+//
+// PRECONDITION: 1 < x < 28.
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T erfc_double_large(const T& x, const T& x2) {
+  constexpr double gamma[] = {1.5252844933226974316088642158462107545346952974796295166015625e-04,
+                              1.0909912393738931124520519233556115068495273590087890625000000e-02,
+                              1.0628604636755033252537572252549580298364162445068359375000000e-01,
+                              3.3492472973137982217295416376146022230386734008789062500000000e-01,
+                              4.5065776215933289750026347064704168587923049926757812500000000e-01,
+                              2.9433039130294824659017649537418037652969360351562500000000000e-01,
+                              9.8792676360600226170838311645638896152377128601074218750000000e-02,
+                              1.7095935395503719655962981960328761488199234008789062500000000e-02,
+                              1.4249109729504577659398023570247460156679153442382812500000000e-03,
+                              4.4567378313647954771875570045835956989321857690811157226562500e-05};
+  constexpr double delta[] = {2.041985103115789845773520028160419315099716186523437500000000e-03,
+                              5.316030659946043707142493417450168635696172714233398437500000e-02,
+                              3.426242193784684864077405563875799998641014099121093750000000e-01,
+                              8.565637124308049799026321124983951449394226074218750000000000e-01,
+                              1.000000000000000000000000000000000000000000000000000000000000e+00,
+                              5.968805280570776972126623149961233139038085937500000000000000e-01,
+                              1.890922854723317836356244470152887515723705291748046875000000e-01,
+                              3.152505418656005586885981983868987299501895904541015625000000e-02,
+                              2.565085751861882583380047861965067568235099315643310546875000e-03,
+                              7.899362131678837697403017248376499992446042597293853759765625e-05};
+  // Compute exp(-x^2).
+  const T x2_lo = twoprod_low(x, x, x2);
+  // Here we use that
+  //   exp(-x^2) = exp(-(x2+x2_lo)^2) ~= exp(-x2)*exp(-x2_lo) ~= exp(-x2)*(1-x2_lo)
+  // since x2_lo < kClamp *eps << 1 in the region we care about. This trick reduces the max error
+  // from 258 ulps to below 7 ulps.
+  const T exp2_hi = pexp(pnegate(x2));
+  const T z = pnmadd(exp2_hi, x2_lo, exp2_hi);
+  // Compute r = P / Q.
+  const T q2 = preciprocal(x2);
+  const T num_large = ppolevl<T, 9>::run(q2, gamma);
+  const T denom_large = pmul(x, ppolevl<T, 9>::run(q2, delta));
+  const T r = pdiv(num_large, denom_large);
+  const T maybe_two = pselect(pcmp_lt(x, pset1<T>(0.0)), pset1<T>(2.0), pset1<T>(0.0));
+  return pmadd(z, r, maybe_two);
+}
+
+template <>
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_fast_erfc<double>::run(const T& x_in) {
+  // Clamp x to [-28:28] beyond which erfc(x) is either two or zero (below the underflow threshold).
+  // This avoids having to deal with twoprod(x,x) producing NaN for sufficiently large x.
+  constexpr double kClamp = 28.0;
+  const T x = pmin(pmax(x_in, pset1<T>(-kClamp)), pset1<T>(kClamp));
+
+  // For |x| < 1, we use erfc(x) = 1 - erf(x).
+  const T x2 = pmul(x, x);
+  const T one = pset1<T>(1.0);
+  const T erfc_small = pnmadd(x, erf_over_x_double_small(x2), one);
+
+  // Return early if we don't need the more expensive approximation for any
+  // entry in a.
+  const T x_abs_gt_one_mask = pcmp_lt(one, x2);
+  if (!predux_any(x_abs_gt_one_mask)) return erfc_small;
+
+  const T erfc_large = erfc_double_large(x, x2);
+  return pselect(x_abs_gt_one_mask, erfc_large, erfc_small);
+}
+
+template <typename T>
+struct erfc_impl {
+  typedef typename unpacket_traits<T>::type Scalar;
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) { return generic_fast_erfc<Scalar>::run(x); }
+};
+
+template <typename Scalar>
+struct erfc_retval {
+  typedef Scalar type;
+};
+
+#if EIGEN_HAS_C99_MATH
+template <>
+struct erfc_impl<float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float run(const float x) {
+#if defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::erfc(x);
+#else
+    return generic_fast_erfc<float>::run(x);
+#endif
+  }
+};
+
+template <>
+struct erfc_impl<double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double run(const double x) {
+#if defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::erfc(x);
+#else
+    return generic_fast_erfc<double>::run(x);
+#endif
+  }
+};
+#endif  // EIGEN_HAS_C99_MATH
+
+/****************************************************************************
+ * Implementation of erf.
+ ****************************************************************************/
+
+template <typename Scalar>
+struct generic_fast_erf {
+  template <typename T>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T run(const T& x_in);
+};
+
+/** \internal \returns the error function of \a a (coeff-wise)
+    This uses a 11/10-degree rational interpolantand is accurate to 3 ulp for
+    normalized floats.
+
+    This implementation works on both scalars and SIMD "packets".
+*/
+template <>
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_fast_erf<float>::run(const T& x) {
+  // The monomial coefficients of the numerator polynomial (odd).
+  constexpr float alpha[] = {2.123732201653183437883853912353515625e-06f, 2.861979592125862836837768554687500000e-04f,
+                             3.658048342913389205932617187500000000e-03f, 5.243302136659622192382812500000000000e-02f,
+                             1.874160766601562500000000000000000000e-01f, 1.128379106521606445312500000000000000e+00f};
+
+  // The monomial coefficients of the denominator polynomial (even).
+  constexpr float beta[] = {3.89185734093189239501953125000e-05f, 1.14329601638019084930419921875e-03f,
+                            1.47520881146192550659179687500e-02f, 1.12945675849914550781250000000e-01f,
+                            4.99425798654556274414062500000e-01f, 1.0f};
+
+  // Since the polynomials are odd/even, we need x^2.
+  // Since erf(4) == 1 in float, we clamp x^2 to 16 to avoid
+  // computing Inf/Inf below.
+  const T x2 = pmin(pset1<T>(16.0f), pmul(x, x));
+
+  // Evaluate the numerator polynomial p.
+  T p = ppolevl<T, 5>::run(x2, alpha);
+  p = pmul(x, p);
+
+  // Evaluate the denominator polynomial p.
+  T q = ppolevl<T, 5>::run(x2, beta);
+  const T r = pdiv(p, q);
+
+  // Clamp to [-1:1].
+  return pmax(pmin(r, pset1<T>(1.0f)), pset1<T>(-1.0f));
+}
+
+template <>
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_fast_erf<double>::run(const T& x) {
+  T x2 = pmul(x, x);
+  T erf_small = pmul(x, erf_over_x_double_small(x2));
+
+  // Return early if we don't need the more expensive approximation for any
+  // entry in a.
+  const T one = pset1<T>(1.0);
+  const T x_abs_gt_one_mask = pcmp_lt(one, x2);
+  if (!predux_any(x_abs_gt_one_mask)) return erf_small;
+
+  // For |x| > 1, use erf(x) = 1 - erfc(x).
+  const T erf_large = psub(one, erfc_double_large(x, x2));
+  return pselect(x_abs_gt_one_mask, erf_large, erf_small);
+}
+
+template <typename T>
+struct erf_impl {
+  typedef typename unpacket_traits<T>::type Scalar;
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T run(const T& x) { return generic_fast_erf<Scalar>::run(x); }
+};
+
+template <typename Scalar>
+struct erf_retval {
+  typedef Scalar type;
+};
+
+#if EIGEN_HAS_C99_MATH
+template <>
+struct erf_impl<float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float run(const float x) {
+#if defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::erf(x);
+#else
+    return generic_fast_erf<float>::run(x);
+#endif
+  }
+};
+
+template <>
+struct erf_impl<double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double run(const double x) {
+#if defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::erf(x);
+#else
+    return generic_fast_erf<double>::run(x);
+#endif
+  }
+};
+#endif  // EIGEN_HAS_C99_MATH
+
+/***************************************************************************
+ * Implementation of ndtri.                                                 *
+ ****************************************************************************/
+
+/* Inverse of Normal distribution function (modified for Eigen).
+ *
+ *
+ * SYNOPSIS:
+ *
+ * double x, y, ndtri();
+ *
+ * x = ndtri( y );
+ *
+ *
+ *
+ * DESCRIPTION:
+ *
+ * Returns the argument, x, for which the area under the
+ * Gaussian probability density function (integrated from
+ * minus infinity to x) is equal to y.
+ *
+ *
+ * For small arguments 0 < y < exp(-2), the program computes
+ * z = sqrt( -2.0 * log(y) );  then the approximation is
+ * x = z - log(z)/z  - (1/z) P(1/z) / Q(1/z).
+ * There are two rational functions P/Q, one for 0 < y < exp(-32)
+ * and the other for y up to exp(-2).  For larger arguments,
+ * w = y - 0.5, and  x/sqrt(2pi) = w + w**3 R(w**2)/S(w**2)).
+ *
+ *
+ * ACCURACY:
+ *
+ *                      Relative error:
+ * arithmetic   domain        # trials      peak         rms
+ *    DEC      0.125, 1         5500       9.5e-17     2.1e-17
+ *    DEC      6e-39, 0.135     3500       5.7e-17     1.3e-17
+ *    IEEE     0.125, 1        20000       7.2e-16     1.3e-16
+ *    IEEE     3e-308, 0.135   50000       4.6e-16     9.8e-17
+ *
+ *
+ * ERROR MESSAGES:
+ *
+ *   message         condition    value returned
+ * ndtri domain       x == 0        -INF
+ * ndtri domain       x == 1         INF
+ * ndtri domain       x < 0, x > 1   NAN
+ */
+/*
+  Cephes Math Library Release 2.2: June, 1992
+  Copyright 1985, 1987, 1992 by Stephen L. Moshier
+  Direct inquiries to 30 Frost Street, Cambridge, MA 02140
+*/
+
+// TODO: Add a cheaper approximation for float.
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T flipsign(const T& should_flipsign, const T& x) {
+  typedef typename unpacket_traits<T>::type Scalar;
+  const T sign_mask = pset1<T>(Scalar(-0.0));
+  T sign_bit = pand<T>(should_flipsign, sign_mask);
+  return pxor<T>(sign_bit, x);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double flipsign<double>(const double& should_flipsign, const double& x) {
+  return should_flipsign == 0 ? x : -x;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float flipsign<float>(const float& should_flipsign, const float& x) {
+  return should_flipsign == 0 ? x : -x;
+}
+
+// We split this computation in to two so that in the scalar path
+// only one branch is evaluated (due to our template specialization of pselect
+// being an if statement.)
+
+template <typename T, typename ScalarType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_ndtri_gt_exp_neg_two(const T& b) {
+  const ScalarType p0[] = {ScalarType(-5.99633501014107895267e1), ScalarType(9.80010754185999661536e1),
+                           ScalarType(-5.66762857469070293439e1), ScalarType(1.39312609387279679503e1),
+                           ScalarType(-1.23916583867381258016e0)};
+  const ScalarType q0[] = {ScalarType(1.0),
+                           ScalarType(1.95448858338141759834e0),
+                           ScalarType(4.67627912898881538453e0),
+                           ScalarType(8.63602421390890590575e1),
+                           ScalarType(-2.25462687854119370527e2),
+                           ScalarType(2.00260212380060660359e2),
+                           ScalarType(-8.20372256168333339912e1),
+                           ScalarType(1.59056225126211695515e1),
+                           ScalarType(-1.18331621121330003142e0)};
+  const T sqrt2pi = pset1<T>(ScalarType(2.50662827463100050242e0));
+  const T half = pset1<T>(ScalarType(0.5));
+  T c, c2, ndtri_gt_exp_neg_two;
+
+  c = psub(b, half);
+  c2 = pmul(c, c);
+  ndtri_gt_exp_neg_two =
+      pmadd(c, pmul(c2, pdiv(internal::ppolevl<T, 4>::run(c2, p0), internal::ppolevl<T, 8>::run(c2, q0))), c);
+  return pmul(ndtri_gt_exp_neg_two, sqrt2pi);
+}
+
+template <typename T, typename ScalarType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_ndtri_lt_exp_neg_two(const T& b, const T& should_flipsign) {
+  /* Approximation for interval z = sqrt(-2 log a ) between 2 and 8
+   * i.e., a between exp(-2) = .135 and exp(-32) = 1.27e-14.
+   */
+  const ScalarType p1[] = {ScalarType(4.05544892305962419923e0),   ScalarType(3.15251094599893866154e1),
+                           ScalarType(5.71628192246421288162e1),   ScalarType(4.40805073893200834700e1),
+                           ScalarType(1.46849561928858024014e1),   ScalarType(2.18663306850790267539e0),
+                           ScalarType(-1.40256079171354495875e-1), ScalarType(-3.50424626827848203418e-2),
+                           ScalarType(-8.57456785154685413611e-4)};
+  const ScalarType q1[] = {ScalarType(1.0),
+                           ScalarType(1.57799883256466749731e1),
+                           ScalarType(4.53907635128879210584e1),
+                           ScalarType(4.13172038254672030440e1),
+                           ScalarType(1.50425385692907503408e1),
+                           ScalarType(2.50464946208309415979e0),
+                           ScalarType(-1.42182922854787788574e-1),
+                           ScalarType(-3.80806407691578277194e-2),
+                           ScalarType(-9.33259480895457427372e-4)};
+  /* Approximation for interval z = sqrt(-2 log a ) between 8 and 64
+   * i.e., a between exp(-32) = 1.27e-14 and exp(-2048) = 3.67e-890.
+   */
+  const ScalarType p2[] = {ScalarType(3.23774891776946035970e0),  ScalarType(6.91522889068984211695e0),
+                           ScalarType(3.93881025292474443415e0),  ScalarType(1.33303460815807542389e0),
+                           ScalarType(2.01485389549179081538e-1), ScalarType(1.23716634817820021358e-2),
+                           ScalarType(3.01581553508235416007e-4), ScalarType(2.65806974686737550832e-6),
+                           ScalarType(6.23974539184983293730e-9)};
+  const ScalarType q2[] = {ScalarType(1.0),
+                           ScalarType(6.02427039364742014255e0),
+                           ScalarType(3.67983563856160859403e0),
+                           ScalarType(1.37702099489081330271e0),
+                           ScalarType(2.16236993594496635890e-1),
+                           ScalarType(1.34204006088543189037e-2),
+                           ScalarType(3.28014464682127739104e-4),
+                           ScalarType(2.89247864745380683936e-6),
+                           ScalarType(6.79019408009981274425e-9)};
+  const T eight = pset1<T>(ScalarType(8.0));
+  const T neg_two = pset1<T>(ScalarType(-2));
+  T x, x0, x1, z;
+
+  x = psqrt(pmul(neg_two, plog(b)));
+  x0 = psub(x, pdiv(plog(x), x));
+  z = preciprocal(x);
+  x1 =
+      pmul(z, pselect(pcmp_lt(x, eight), pdiv(internal::ppolevl<T, 8>::run(z, p1), internal::ppolevl<T, 8>::run(z, q1)),
+                      pdiv(internal::ppolevl<T, 8>::run(z, p2), internal::ppolevl<T, 8>::run(z, q2))));
+  return flipsign(should_flipsign, psub(x0, x1));
+}
+
+template <typename T, typename ScalarType>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T generic_ndtri(const T& a) {
+  const T maxnum = pset1<T>(NumTraits<ScalarType>::infinity());
+  const T neg_maxnum = pset1<T>(-NumTraits<ScalarType>::infinity());
+
+  const T zero = pset1<T>(ScalarType(0));
+  const T one = pset1<T>(ScalarType(1));
+  // exp(-2)
+  const T exp_neg_two = pset1<T>(ScalarType(0.13533528323661269189));
+  T b, ndtri, should_flipsign;
+
+  should_flipsign = pcmp_le(a, psub(one, exp_neg_two));
+  b = pselect(should_flipsign, a, psub(one, a));
+
+  ndtri = pselect(pcmp_lt(exp_neg_two, b), generic_ndtri_gt_exp_neg_two<T, ScalarType>(b),
+                  generic_ndtri_lt_exp_neg_two<T, ScalarType>(b, should_flipsign));
+
+  return pselect(pcmp_eq(a, zero), neg_maxnum, pselect(pcmp_eq(one, a), maxnum, ndtri));
+}
+
+template <typename Scalar>
+struct ndtri_retval {
+  typedef Scalar type;
+};
+
+#if !EIGEN_HAS_C99_MATH
+
+template <typename Scalar>
+struct ndtri_impl {
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar) { return Scalar(0); }
+};
+
+#else
+
+template <typename Scalar>
+struct ndtri_impl {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar x) { return generic_ndtri<Scalar, Scalar>(x); }
+};
+
+#endif  // EIGEN_HAS_C99_MATH
+
+/**************************************************************************************************************
+ * Implementation of igammac (complemented incomplete gamma integral), based on Cephes but requires C++11/C99 *
+ **************************************************************************************************************/
+
+template <typename Scalar>
+struct igammac_retval {
+  typedef Scalar type;
+};
+
+// NOTE: cephes_helper is also used to implement zeta
+template <typename Scalar>
+struct cephes_helper {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar machep() {
+    eigen_assert(false && "machep not supported for this type");
+    return 0.0;
+  }
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar big() {
+    eigen_assert(false && "big not supported for this type");
+    return 0.0;
+  }
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar biginv() {
+    eigen_assert(false && "biginv not supported for this type");
+    return 0.0;
+  }
+};
+
+template <>
+struct cephes_helper<float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float machep() {
+    return NumTraits<float>::epsilon() / 2;  // 1.0 - machep == 1.0
+  }
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float big() {
+    // use epsneg (1.0 - epsneg == 1.0)
+    return 1.0f / (NumTraits<float>::epsilon() / 2);
+  }
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float biginv() {
+    // epsneg
+    return machep();
+  }
+};
+
+template <>
+struct cephes_helper<double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double machep() {
+    return NumTraits<double>::epsilon() / 2;  // 1.0 - machep == 1.0
+  }
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double big() { return 1.0 / NumTraits<double>::epsilon(); }
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double biginv() {
+    // inverse of eps
+    return NumTraits<double>::epsilon();
+  }
+};
+
+enum IgammaComputationMode { VALUE, DERIVATIVE, SAMPLE_DERIVATIVE };
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar main_igamma_term(Scalar a, Scalar x) {
+  /* Compute  x**a * exp(-x) / gamma(a)  */
+  Scalar logax = a * numext::log(x) - x - lgamma_impl<Scalar>::run(a);
+  if (logax < -numext::log(NumTraits<Scalar>::highest()) ||
+      // Assuming x and a aren't Nan.
+      (numext::isnan)(logax)) {
+    return Scalar(0);
+  }
+  return numext::exp(logax);
+}
+
+template <typename Scalar, IgammaComputationMode mode>
+EIGEN_DEVICE_FUNC int igamma_num_iterations() {
+  /* Returns the maximum number of internal iterations for igamma computation.
+   */
+  if (mode == VALUE) {
+    return 2000;
+  }
+
+  if (internal::is_same<Scalar, float>::value) {
+    return 200;
+  } else if (internal::is_same<Scalar, double>::value) {
+    return 500;
+  } else {
+    return 2000;
+  }
+}
+
+template <typename Scalar, IgammaComputationMode mode>
+struct igammac_cf_impl {
+  /* Computes igamc(a, x) or derivative (depending on the mode)
+   * using the continued fraction expansion of the complementary
+   * incomplete Gamma function.
+   *
+   * Preconditions:
+   *   a > 0
+   *   x >= 1
+   *   x >= a
+   */
+  EIGEN_DEVICE_FUNC static Scalar run(Scalar a, Scalar x) {
+    const Scalar zero = 0;
+    const Scalar one = 1;
+    const Scalar two = 2;
+    const Scalar machep = cephes_helper<Scalar>::machep();
+    const Scalar big = cephes_helper<Scalar>::big();
+    const Scalar biginv = cephes_helper<Scalar>::biginv();
+
+    if ((numext::isinf)(x)) {
+      return zero;
+    }
+
+    Scalar ax = main_igamma_term<Scalar>(a, x);
+    // This is independent of mode. If this value is zero,
+    // then the function value is zero. If the function value is zero,
+    // then we are in a neighborhood where the function value evaluates to zero,
+    // so the derivative is zero.
+    if (ax == zero) {
+      return zero;
+    }
+
+    // continued fraction
+    Scalar y = one - a;
+    Scalar z = x + y + one;
+    Scalar c = zero;
+    Scalar pkm2 = one;
+    Scalar qkm2 = x;
+    Scalar pkm1 = x + one;
+    Scalar qkm1 = z * x;
+    Scalar ans = pkm1 / qkm1;
+
+    Scalar dpkm2_da = zero;
+    Scalar dqkm2_da = zero;
+    Scalar dpkm1_da = zero;
+    Scalar dqkm1_da = -x;
+    Scalar dans_da = (dpkm1_da - ans * dqkm1_da) / qkm1;
+
+    for (int i = 0; i < igamma_num_iterations<Scalar, mode>(); i++) {
+      c += one;
+      y += one;
+      z += two;
+
+      Scalar yc = y * c;
+      Scalar pk = pkm1 * z - pkm2 * yc;
+      Scalar qk = qkm1 * z - qkm2 * yc;
+
+      Scalar dpk_da = dpkm1_da * z - pkm1 - dpkm2_da * yc + pkm2 * c;
+      Scalar dqk_da = dqkm1_da * z - qkm1 - dqkm2_da * yc + qkm2 * c;
+
+      if (qk != zero) {
+        Scalar ans_prev = ans;
+        ans = pk / qk;
+
+        Scalar dans_da_prev = dans_da;
+        dans_da = (dpk_da - ans * dqk_da) / qk;
+
+        if (mode == VALUE) {
+          if (numext::abs(ans_prev - ans) <= machep * numext::abs(ans)) {
+            break;
+          }
+        } else {
+          if (numext::abs(dans_da - dans_da_prev) <= machep) {
+            break;
+          }
+        }
+      }
+
+      pkm2 = pkm1;
+      pkm1 = pk;
+      qkm2 = qkm1;
+      qkm1 = qk;
+
+      dpkm2_da = dpkm1_da;
+      dpkm1_da = dpk_da;
+      dqkm2_da = dqkm1_da;
+      dqkm1_da = dqk_da;
+
+      if (numext::abs(pk) > big) {
+        pkm2 *= biginv;
+        pkm1 *= biginv;
+        qkm2 *= biginv;
+        qkm1 *= biginv;
+
+        dpkm2_da *= biginv;
+        dpkm1_da *= biginv;
+        dqkm2_da *= biginv;
+        dqkm1_da *= biginv;
+      }
+    }
+
+    /* Compute  x**a * exp(-x) / gamma(a)  */
+    Scalar dlogax_da = numext::log(x) - digamma_impl<Scalar>::run(a);
+    Scalar dax_da = ax * dlogax_da;
+
+    switch (mode) {
+      case VALUE:
+        return ans * ax;
+      case DERIVATIVE:
+        return ans * dax_da + dans_da * ax;
+      case SAMPLE_DERIVATIVE:
+      default:  // this is needed to suppress clang warning
+        return -(dans_da + ans * dlogax_da) * x;
+    }
+  }
+};
+
+template <typename Scalar, IgammaComputationMode mode>
+struct igamma_series_impl {
+  /* Computes igam(a, x) or its derivative (depending on the mode)
+   * using the series expansion of the incomplete Gamma function.
+   *
+   * Preconditions:
+   *   x > 0
+   *   a > 0
+   *   !(x > 1 && x > a)
+   */
+  EIGEN_DEVICE_FUNC static Scalar run(Scalar a, Scalar x) {
+    const Scalar zero = 0;
+    const Scalar one = 1;
+    const Scalar machep = cephes_helper<Scalar>::machep();
+
+    Scalar ax = main_igamma_term<Scalar>(a, x);
+
+    // This is independent of mode. If this value is zero,
+    // then the function value is zero. If the function value is zero,
+    // then we are in a neighborhood where the function value evaluates to zero,
+    // so the derivative is zero.
+    if (ax == zero) {
+      return zero;
+    }
+
+    ax /= a;
+
+    /* power series */
+    Scalar r = a;
+    Scalar c = one;
+    Scalar ans = one;
+
+    Scalar dc_da = zero;
+    Scalar dans_da = zero;
+
+    for (int i = 0; i < igamma_num_iterations<Scalar, mode>(); i++) {
+      r += one;
+      Scalar term = x / r;
+      Scalar dterm_da = -x / (r * r);
+      dc_da = term * dc_da + dterm_da * c;
+      dans_da += dc_da;
+      c *= term;
+      ans += c;
+
+      if (mode == VALUE) {
+        if (c <= machep * ans) {
+          break;
+        }
+      } else {
+        if (numext::abs(dc_da) <= machep * numext::abs(dans_da)) {
+          break;
+        }
+      }
+    }
+
+    Scalar dlogax_da = numext::log(x) - digamma_impl<Scalar>::run(a + one);
+    Scalar dax_da = ax * dlogax_da;
+
+    switch (mode) {
+      case VALUE:
+        return ans * ax;
+      case DERIVATIVE:
+        return ans * dax_da + dans_da * ax;
+      case SAMPLE_DERIVATIVE:
+      default:  // this is needed to suppress clang warning
+        return -(dans_da + ans * dlogax_da) * x / a;
+    }
+  }
+};
+
+#if !EIGEN_HAS_C99_MATH
+
+template <typename Scalar>
+struct igammac_impl {
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static Scalar run(Scalar a, Scalar x) { return Scalar(0); }
+};
+
+#else
+
+template <typename Scalar>
+struct igammac_impl {
+  EIGEN_DEVICE_FUNC static Scalar run(Scalar a, Scalar x) {
+    /*  igamc()
+     *
+     *	Incomplete gamma integral (modified for Eigen)
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double a, x, y, igamc();
+     *
+     * y = igamc( a, x );
+     *
+     * DESCRIPTION:
+     *
+     * The function is defined by
+     *
+     *
+     *  igamc(a,x)   =   1 - igam(a,x)
+     *
+     *                            inf.
+     *                              -
+     *                     1       | |  -t  a-1
+     *               =   -----     |   e   t   dt.
+     *                    -      | |
+     *                   | (a)    -
+     *                             x
+     *
+     *
+     * In this implementation both arguments must be positive.
+     * The integral is evaluated by either a power series or
+     * continued fraction expansion, depending on the relative
+     * values of a and x.
+     *
+     * ACCURACY (float):
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,30        30000       7.8e-6      5.9e-7
+     *
+     *
+     * ACCURACY (double):
+     *
+     * Tested at random a, x.
+     *                a         x                      Relative error:
+     * arithmetic   domain   domain     # trials      peak         rms
+     *    IEEE     0.5,100   0,100      200000       1.9e-14     1.7e-15
+     *    IEEE     0.01,0.5  0,100      200000       1.4e-13     1.6e-15
+     *
+     */
+    /*
+      Cephes Math Library Release 2.2: June, 1992
+      Copyright 1985, 1987, 1992 by Stephen L. Moshier
+      Direct inquiries to 30 Frost Street, Cambridge, MA 02140
+    */
+    const Scalar zero = 0;
+    const Scalar one = 1;
+    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
+
+    if ((x < zero) || (a <= zero)) {
+      // domain error
+      return nan;
+    }
+
+    if ((numext::isnan)(a) || (numext::isnan)(x)) {  // propagate nans
+      return nan;
+    }
+
+    if ((x < one) || (x < a)) {
+      return (one - igamma_series_impl<Scalar, VALUE>::run(a, x));
+    }
+
+    return igammac_cf_impl<Scalar, VALUE>::run(a, x);
+  }
+};
+
+#endif  // EIGEN_HAS_C99_MATH
+
+/************************************************************************************************
+ * Implementation of igamma (incomplete gamma integral), based on Cephes but requires C++11/C99 *
+ ************************************************************************************************/
+
+#if !EIGEN_HAS_C99_MATH
+
+template <typename Scalar, IgammaComputationMode mode>
+struct igamma_generic_impl {
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar x) { return Scalar(0); }
+};
+
+#else
+
+template <typename Scalar, IgammaComputationMode mode>
+struct igamma_generic_impl {
+  EIGEN_DEVICE_FUNC static Scalar run(Scalar a, Scalar x) {
+    /* Depending on the mode, returns
+     * - VALUE: incomplete Gamma function igamma(a, x)
+     * - DERIVATIVE: derivative of incomplete Gamma function d/da igamma(a, x)
+     * - SAMPLE_DERIVATIVE: implicit derivative of a Gamma random variable
+     * x ~ Gamma(x | a, 1), dx/da = -1 / Gamma(x | a, 1) * d igamma(a, x) / dx
+     *
+     * Derivatives are implemented by forward-mode differentiation.
+     */
+    const Scalar zero = 0;
+    const Scalar one = 1;
+    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
+
+    if (x == zero) return zero;
+
+    if ((x < zero) || (a <= zero)) {  // domain error
+      return nan;
+    }
+
+    if ((numext::isnan)(a) || (numext::isnan)(x)) {  // propagate nans
+      return nan;
+    }
+
+    if ((x > one) && (x > a)) {
+      Scalar ret = igammac_cf_impl<Scalar, mode>::run(a, x);
+      if (mode == VALUE) {
+        return one - ret;
+      } else {
+        return -ret;
+      }
+    }
+
+    return igamma_series_impl<Scalar, mode>::run(a, x);
+  }
+};
+
+#endif  // EIGEN_HAS_C99_MATH
+
+template <typename Scalar>
+struct igamma_retval {
+  typedef Scalar type;
+};
+
+template <typename Scalar>
+struct igamma_impl : igamma_generic_impl<Scalar, VALUE> {
+  /* igam()
+   * Incomplete gamma integral.
+   *
+   * The CDF of Gamma(a, 1) random variable at the point x.
+   *
+   * Accuracy estimation. For each a in [10^-2, 10^-1...10^3] we sample
+   * 50 Gamma random variables x ~ Gamma(x | a, 1), a total of 300 points.
+   * The ground truth is computed by mpmath. Mean absolute error:
+   * float: 1.26713e-05
+   * double: 2.33606e-12
+   *
+   * Cephes documentation below.
+   *
+   * SYNOPSIS:
+   *
+   * double a, x, y, igam();
+   *
+   * y = igam( a, x );
+   *
+   * DESCRIPTION:
+   *
+   * The function is defined by
+   *
+   *                           x
+   *                            -
+   *                   1       | |  -t  a-1
+   *  igam(a,x)  =   -----     |   e   t   dt.
+   *                  -      | |
+   *                 | (a)    -
+   *                           0
+   *
+   *
+   * In this implementation both arguments must be positive.
+   * The integral is evaluated by either a power series or
+   * continued fraction expansion, depending on the relative
+   * values of a and x.
+   *
+   * ACCURACY (double):
+   *
+   *                      Relative error:
+   * arithmetic   domain     # trials      peak         rms
+   *    IEEE      0,30       200000       3.6e-14     2.9e-15
+   *    IEEE      0,100      300000       9.9e-14     1.5e-14
+   *
+   *
+   * ACCURACY (float):
+   *
+   *                      Relative error:
+   * arithmetic   domain     # trials      peak         rms
+   *    IEEE      0,30        20000       7.8e-6      5.9e-7
+   *
+   */
+  /*
+    Cephes Math Library Release 2.2: June, 1992
+    Copyright 1985, 1987, 1992 by Stephen L. Moshier
+    Direct inquiries to 30 Frost Street, Cambridge, MA 02140
+  */
+
+  /* left tail of incomplete gamma function:
+   *
+   *          inf.      k
+   *   a  -x   -       x
+   *  x  e     >   ----------
+   *           -     -
+   *          k=0   | (a+k+1)
+   *
+   */
+};
+
+template <typename Scalar>
+struct igamma_der_a_retval : igamma_retval<Scalar> {};
+
+template <typename Scalar>
+struct igamma_der_a_impl : igamma_generic_impl<Scalar, DERIVATIVE> {
+  /* Derivative of the incomplete Gamma function with respect to a.
+   *
+   * Computes d/da igamma(a, x) by forward differentiation of the igamma code.
+   *
+   * Accuracy estimation. For each a in [10^-2, 10^-1...10^3] we sample
+   * 50 Gamma random variables x ~ Gamma(x | a, 1), a total of 300 points.
+   * The ground truth is computed by mpmath. Mean absolute error:
+   * float: 6.17992e-07
+   * double: 4.60453e-12
+   *
+   * Reference:
+   * R. Moore. "Algorithm AS 187: Derivatives of the incomplete gamma
+   * integral". Journal of the Royal Statistical Society. 1982
+   */
+};
+
+template <typename Scalar>
+struct gamma_sample_der_alpha_retval : igamma_retval<Scalar> {};
+
+template <typename Scalar>
+struct gamma_sample_der_alpha_impl : igamma_generic_impl<Scalar, SAMPLE_DERIVATIVE> {
+  /* Derivative of a Gamma random variable sample with respect to alpha.
+   *
+   * Consider a sample of a Gamma random variable with the concentration
+   * parameter alpha: sample ~ Gamma(alpha, 1). The reparameterization
+   * derivative that we want to compute is dsample / dalpha =
+   * d igammainv(alpha, u) / dalpha, where u = igamma(alpha, sample).
+   * However, this formula is numerically unstable and expensive, so instead
+   * we use implicit differentiation:
+   *
+   * igamma(alpha, sample) = u, where u ~ Uniform(0, 1).
+   * Apply d / dalpha to both sides:
+   * d igamma(alpha, sample) / dalpha
+   *     + d igamma(alpha, sample) / dsample * dsample/dalpha  = 0
+   * d igamma(alpha, sample) / dalpha
+   *     + Gamma(sample | alpha, 1) dsample / dalpha = 0
+   * dsample/dalpha = - (d igamma(alpha, sample) / dalpha)
+   *                   / Gamma(sample | alpha, 1)
+   *
+   * Here Gamma(sample | alpha, 1) is the PDF of the Gamma distribution
+   * (note that the derivative of the CDF w.r.t. sample is the PDF).
+   * See the reference below for more details.
+   *
+   * The derivative of igamma(alpha, sample) is computed by forward
+   * differentiation of the igamma code. Division by the Gamma PDF is performed
+   * in the same code, increasing the accuracy and speed due to cancellation
+   * of some terms.
+   *
+   * Accuracy estimation. For each alpha in [10^-2, 10^-1...10^3] we sample
+   * 50 Gamma random variables sample ~ Gamma(sample | alpha, 1), a total of 300
+   * points. The ground truth is computed by mpmath. Mean absolute error:
+   * float: 2.1686e-06
+   * double: 1.4774e-12
+   *
+   * Reference:
+   * M. Figurnov, S. Mohamed, A. Mnih "Implicit Reparameterization Gradients".
+   * 2018
+   */
+};
+
+/*****************************************************************************
+ * Implementation of Riemann zeta function of two arguments, based on Cephes *
+ *****************************************************************************/
+
+template <typename Scalar>
+struct zeta_retval {
+  typedef Scalar type;
+};
+
+template <typename Scalar>
+struct zeta_impl_series {
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Scalar) { return Scalar(0); }
+};
+
+template <>
+struct zeta_impl_series<float> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE bool run(float& a, float& b, float& s, const float x,
+                                                        const float machep) {
+    int i = 0;
+    while (i < 9) {
+      i += 1;
+      a += 1.0f;
+      b = numext::pow(a, -x);
+      s += b;
+      if (numext::abs(b / s) < machep) return true;
+    }
+
+    // Return whether we are done
+    return false;
+  }
+};
+
+template <>
+struct zeta_impl_series<double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE bool run(double& a, double& b, double& s, const double x,
+                                                        const double machep) {
+    int i = 0;
+    while ((i < 9) || (a <= 9.0)) {
+      i += 1;
+      a += 1.0;
+      b = numext::pow(a, -x);
+      s += b;
+      if (numext::abs(b / s) < machep) return true;
+    }
+
+    // Return whether we are done
+    return false;
+  }
+};
+
+template <typename Scalar>
+struct zeta_impl {
+  EIGEN_DEVICE_FUNC static Scalar run(Scalar x, Scalar q) {
+    /*							zeta.c
+     *
+     *	Riemann zeta function of two arguments
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, q, y, zeta();
+     *
+     * y = zeta( x, q );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     *
+     *
+     *                 inf.
+     *                  -        -x
+     *   zeta(x,q)  =   >   (k+q)
+     *                  -
+     *                 k=0
+     *
+     * where x > 1 and q is not a negative integer or zero.
+     * The Euler-Maclaurin summation formula is used to obtain
+     * the expansion
+     *
+     *                n
+     *                -       -x
+     * zeta(x,q)  =   >  (k+q)
+     *                -
+     *               k=1
+     *
+     *           1-x                 inf.  B   x(x+1)...(x+2j)
+     *      (n+q)           1         -     2j
+     *  +  ---------  -  -------  +   >    --------------------
+     *        x-1              x      -                   x+2j+1
+     *                   2(n+q)      j=1       (2j)! (n+q)
+     *
+     * where the B2j are Bernoulli numbers.  Note that (see zetac.c)
+     * zeta(x,1) = zetac(x) + 1.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     * Relative error for single precision:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,25        10000       6.9e-7      1.0e-7
+     *
+     * Large arguments may produce underflow in powf(), in which
+     * case the results are inaccurate.
+     *
+     * REFERENCE:
+     *
+     * Gradshteyn, I. S., and I. M. Ryzhik, Tables of Integrals,
+     * Series, and Products, p. 1073; Academic Press, 1980.
+     *
+     */
+
+    int i;
+    Scalar p, r, a, b, k, s, t, w;
+
+    const Scalar A[] = {
+        Scalar(12.0),
+        Scalar(-720.0),
+        Scalar(30240.0),
+        Scalar(-1209600.0),
+        Scalar(47900160.0),
+        Scalar(-1.8924375803183791606e9), /*1.307674368e12/691*/
+        Scalar(7.47242496e10),
+        Scalar(-2.950130727918164224e12),  /*1.067062284288e16/3617*/
+        Scalar(1.1646782814350067249e14),  /*5.109094217170944e18/43867*/
+        Scalar(-4.5979787224074726105e15), /*8.028576626982912e20/174611*/
+        Scalar(1.8152105401943546773e17),  /*1.5511210043330985984e23/854513*/
+        Scalar(-7.1661652561756670113e18)  /*1.6938241367317436694528e27/236364091*/
+    };
+
+    const Scalar maxnum = NumTraits<Scalar>::infinity();
+    const Scalar zero = Scalar(0.0), half = Scalar(0.5), one = Scalar(1.0);
+    const Scalar machep = cephes_helper<Scalar>::machep();
+    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
+
+    if (x == one) return maxnum;
+
+    if (x < one) {
+      return nan;
+    }
+
+    if (q <= zero) {
+      if (q == numext::floor(q)) {
+        if (numext::rint(Scalar(0.5) * x) == Scalar(0.5) * x) {
+          return maxnum;
+        } else {
+          return nan;
+        }
+      }
+      p = x;
+      r = numext::floor(p);
+      if (p != r) return nan;
+    }
+
+    /* Permit negative q but continue sum until n+q > +9 .
+     * This case should be handled by a reflection formula.
+     * If q<0 and x is an integer, there is a relation to
+     * the polygamma function.
+     */
+    s = numext::pow(q, -x);
+    a = q;
+    b = zero;
+    // Run the summation in a helper function that is specific to the floating precision
+    if (zeta_impl_series<Scalar>::run(a, b, s, x, machep)) {
+      return s;
+    }
+
+    // If b is zero, then the tail sum will also end up being zero.
+    // Exiting early here can prevent NaNs for some large inputs, where
+    // the tail sum computed below has term `a` which can overflow to `inf`.
+    if (numext::equal_strict(b, zero)) {
+      return s;
+    }
+
+    w = a;
+    s += b * w / (x - one);
+    s -= half * b;
+    a = one;
+    k = zero;
+
+    for (i = 0; i < 12; i++) {
+      a *= x + k;
+      b /= w;
+      t = a * b / A[i];
+      s = s + t;
+      t = numext::abs(t / s);
+      if (t < machep) {
+        break;
+      }
+      k += one;
+      a *= x + k;
+      b /= w;
+      k += one;
+    }
+    return s;
+  }
+};
+
+/****************************************************************************
+ * Implementation of polygamma function, requires C++11/C99                 *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct polygamma_retval {
+  typedef Scalar type;
+};
+
+#if !EIGEN_HAS_C99_MATH
+
+template <typename Scalar>
+struct polygamma_impl {
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(Scalar n, Scalar x) { return Scalar(0); }
+};
+
+#else
+
+template <typename Scalar>
+struct polygamma_impl {
+  EIGEN_DEVICE_FUNC static Scalar run(Scalar n, Scalar x) {
+    Scalar zero = 0.0, one = 1.0;
+    Scalar nplus = n + one;
+    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
+
+    // Check that n is a non-negative integer
+    if (numext::floor(n) != n || n < zero) {
+      return nan;
+    }
+    // Just return the digamma function for n = 0
+    else if (n == zero) {
+      return digamma_impl<Scalar>::run(x);
+    }
+    // Use the same implementation as scipy
+    else {
+      Scalar factorial = numext::exp(lgamma_impl<Scalar>::run(nplus));
+      return numext::pow(-one, nplus) * factorial * zeta_impl<Scalar>::run(nplus, x);
+    }
+  }
+};
+
+#endif  // EIGEN_HAS_C99_MATH
+
+/************************************************************************************************
+ * Implementation of betainc (incomplete beta integral), based on Cephes but requires C++11/C99 *
+ ************************************************************************************************/
+
+template <typename Scalar>
+struct betainc_retval {
+  typedef Scalar type;
+};
+
+#if !EIGEN_HAS_C99_MATH
+
+template <typename Scalar>
+struct betainc_impl {
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar b, Scalar x) { return Scalar(0); }
+};
+
+#else
+
+template <typename Scalar>
+struct betainc_impl {
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(Scalar, Scalar, Scalar) {
+    /*	betaincf.c
+     *
+     *	Incomplete beta integral
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float a, b, x, y, betaincf();
+     *
+     * y = betaincf( a, b, x );
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns incomplete beta integral of the arguments, evaluated
+     * from zero to x.  The function is defined as
+     *
+     *                  x
+     *     -            -
+     *    | (a+b)      | |  a-1     b-1
+     *  -----------    |   t   (1-t)   dt.
+     *   -     -     | |
+     *  | (a) | (b)   -
+     *                 0
+     *
+     * The domain of definition is 0 <= x <= 1.  In this
+     * implementation a and b are restricted to positive values.
+     * The integral from x to 1 may be obtained by the symmetry
+     * relation
+     *
+     *    1 - betainc( a, b, x )  =  betainc( b, a, 1-x ).
+     *
+     * The integral is evaluated by a continued fraction expansion.
+     * If a < 1, the function calls itself recursively after a
+     * transformation to increase a to a+1.
+     *
+     * ACCURACY (float):
+     *
+     * Tested at random points (a,b,x) with a and b in the indicated
+     * interval and x between 0 and 1.
+     *
+     * arithmetic   domain     # trials      peak         rms
+     * Relative error:
+     *    IEEE       0,30       10000       3.7e-5      5.1e-6
+     *    IEEE       0,100      10000       1.7e-4      2.5e-5
+     * The useful domain for relative error is limited by underflow
+     * of the single precision exponential function.
+     * Absolute error:
+     *    IEEE       0,30      100000       2.2e-5      9.6e-7
+     *    IEEE       0,100      10000       6.5e-5      3.7e-6
+     *
+     * Larger errors may occur for extreme ratios of a and b.
+     *
+     * ACCURACY (double):
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,5         10000       6.9e-15     4.5e-16
+     *    IEEE      0,85       250000       2.2e-13     1.7e-14
+     *    IEEE      0,1000      30000       5.3e-12     6.3e-13
+     *    IEEE      0,10000    250000       9.3e-11     7.1e-12
+     *    IEEE      0,100000    10000       8.7e-10     4.8e-11
+     * Outputs smaller than the IEEE gradual underflow threshold
+     * were excluded from these statistics.
+     *
+     * ERROR MESSAGES:
+     *   message         condition      value returned
+     * incbet domain      x<0, x>1          nan
+     * incbet underflow                     nan
+     */
+    return Scalar(0);
+  }
+};
+
+/* Continued fraction expansion #1 for incomplete beta integral (small_branch = True)
+ * Continued fraction expansion #2 for incomplete beta integral (small_branch = False)
+ */
+template <typename Scalar>
+struct incbeta_cfe {
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, float>::value || internal::is_same<Scalar, double>::value),
+                      THIS_TYPE_IS_NOT_SUPPORTED)
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar b, Scalar x, bool small_branch) {
+    const Scalar big = cephes_helper<Scalar>::big();
+    const Scalar machep = cephes_helper<Scalar>::machep();
+    const Scalar biginv = cephes_helper<Scalar>::biginv();
+
+    const Scalar zero = 0;
+    const Scalar one = 1;
+    const Scalar two = 2;
+
+    Scalar xk, pk, pkm1, pkm2, qk, qkm1, qkm2;
+    Scalar k1, k2, k3, k4, k5, k6, k7, k8, k26update;
+    Scalar ans;
+    int n;
+
+    const int num_iters = (internal::is_same<Scalar, float>::value) ? 100 : 300;
+    const Scalar thresh = (internal::is_same<Scalar, float>::value) ? machep : Scalar(3) * machep;
+    Scalar r = (internal::is_same<Scalar, float>::value) ? zero : one;
+
+    if (small_branch) {
+      k1 = a;
+      k2 = a + b;
+      k3 = a;
+      k4 = a + one;
+      k5 = one;
+      k6 = b - one;
+      k7 = k4;
+      k8 = a + two;
+      k26update = one;
+    } else {
+      k1 = a;
+      k2 = b - one;
+      k3 = a;
+      k4 = a + one;
+      k5 = one;
+      k6 = a + b;
+      k7 = a + one;
+      k8 = a + two;
+      k26update = -one;
+      x = x / (one - x);
+    }
+
+    pkm2 = zero;
+    qkm2 = one;
+    pkm1 = one;
+    qkm1 = one;
+    ans = one;
+    n = 0;
+
+    do {
+      xk = -(x * k1 * k2) / (k3 * k4);
+      pk = pkm1 + pkm2 * xk;
+      qk = qkm1 + qkm2 * xk;
+      pkm2 = pkm1;
+      pkm1 = pk;
+      qkm2 = qkm1;
+      qkm1 = qk;
+
+      xk = (x * k5 * k6) / (k7 * k8);
+      pk = pkm1 + pkm2 * xk;
+      qk = qkm1 + qkm2 * xk;
+      pkm2 = pkm1;
+      pkm1 = pk;
+      qkm2 = qkm1;
+      qkm1 = qk;
+
+      if (qk != zero) {
+        r = pk / qk;
+        if (numext::abs(ans - r) < numext::abs(r) * thresh) {
+          return r;
+        }
+        ans = r;
+      }
+
+      k1 += one;
+      k2 += k26update;
+      k3 += two;
+      k4 += two;
+      k5 += one;
+      k6 -= k26update;
+      k7 += two;
+      k8 += two;
+
+      if ((numext::abs(qk) + numext::abs(pk)) > big) {
+        pkm2 *= biginv;
+        pkm1 *= biginv;
+        qkm2 *= biginv;
+        qkm1 *= biginv;
+      }
+      if ((numext::abs(qk) < biginv) || (numext::abs(pk) < biginv)) {
+        pkm2 *= big;
+        pkm1 *= big;
+        qkm2 *= big;
+        qkm1 *= big;
+      }
+    } while (++n < num_iters);
+
+    return ans;
+  }
+};
+
+/* Helper functions depending on the Scalar type */
+template <typename Scalar>
+struct betainc_helper {};
+
+template <>
+struct betainc_helper<float> {
+  /* Core implementation, assumes a large (> 1.0) */
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float incbsa(float aa, float bb, float xx) {
+    float ans, a, b, t, x, onemx;
+    bool reversed_a_b = false;
+
+    onemx = 1.0f - xx;
+
+    /* see if x is greater than the mean */
+    if (xx > (aa / (aa + bb))) {
+      reversed_a_b = true;
+      a = bb;
+      b = aa;
+      t = xx;
+      x = onemx;
+    } else {
+      a = aa;
+      b = bb;
+      t = onemx;
+      x = xx;
+    }
+
+    /* Choose expansion for optimal convergence */
+    if (b > 10.0f) {
+      if (numext::abs(b * x / a) < 0.3f) {
+        t = betainc_helper<float>::incbps(a, b, x);
+        if (reversed_a_b) t = 1.0f - t;
+        return t;
+      }
+    }
+
+    ans = x * (a + b - 2.0f) / (a - 1.0f);
+    if (ans < 1.0f) {
+      ans = incbeta_cfe<float>::run(a, b, x, true /* small_branch */);
+      t = b * numext::log(t);
+    } else {
+      ans = incbeta_cfe<float>::run(a, b, x, false /* small_branch */);
+      t = (b - 1.0f) * numext::log(t);
+    }
+
+    t += a * numext::log(x) + lgamma_impl<float>::run(a + b) - lgamma_impl<float>::run(a) - lgamma_impl<float>::run(b);
+    t += numext::log(ans / a);
+    t = numext::exp(t);
+
+    if (reversed_a_b) t = 1.0f - t;
+    return t;
+  }
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float incbps(float a, float b, float x) {
+    float t, u, y, s;
+    const float machep = cephes_helper<float>::machep();
+
+    y = a * numext::log(x) + (b - 1.0f) * numext::log1p(-x) - numext::log(a);
+    y -= lgamma_impl<float>::run(a) + lgamma_impl<float>::run(b);
+    y += lgamma_impl<float>::run(a + b);
+
+    t = x / (1.0f - x);
+    s = 0.0f;
+    u = 1.0f;
+    do {
+      b -= 1.0f;
+      if (b == 0.0f) {
+        break;
+      }
+      a += 1.0f;
+      u *= t * b / a;
+      s += u;
+    } while (numext::abs(u) > machep);
+
+    return numext::exp(y) * (1.0f + s);
+  }
+};
+
+template <>
+struct betainc_impl<float> {
+  EIGEN_DEVICE_FUNC static float run(float a, float b, float x) {
+    const float nan = NumTraits<float>::quiet_NaN();
+    float ans, t;
+
+    if (a == 0.0f && b == 0.0f) return nan;
+    if (x < 0.0f || x > 1.0f) return nan;
+    if (a < 0.0f) return nan;
+    if (b < 0.0f) return nan;
+    if (a == 0.0f) return 1.0f;
+    if (b == 0.0f) return 0.0f;
+    if (x == 0.0f) return 0.0f;
+    if (x == 1.0f) return 1.0f;
+    // mtherr("betaincf", DOMAIN);
+
+    /* transformation for small aa */
+    if (a <= 1.0f) {
+      ans = betainc_helper<float>::incbsa(a + 1.0f, b, x);
+      t = a * numext::log(x) + b * numext::log1p(-x) + lgamma_impl<float>::run(a + b) -
+          lgamma_impl<float>::run(a + 1.0f) - lgamma_impl<float>::run(b);
+      return (ans + numext::exp(t));
+    } else {
+      return betainc_helper<float>::incbsa(a, b, x);
+    }
+  }
+};
+
+template <>
+struct betainc_helper<double> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE double incbps(double a, double b, double x) {
+    const double machep = cephes_helper<double>::machep();
+
+    double s, t, u, v, n, t1, z, ai;
+
+    ai = 1.0 / a;
+    u = (1.0 - b) * x;
+    v = u / (a + 1.0);
+    t1 = v;
+    t = u;
+    n = 2.0;
+    s = 0.0;
+    z = machep * ai;
+    while (numext::abs(v) > z) {
+      u = (n - b) * x / n;
+      t *= u;
+      v = t / (a + n);
+      s += v;
+      n += 1.0;
+    }
+    s += t1;
+    s += ai;
+
+    u = a * numext::log(x);
+    // TODO: gamma() is not directly implemented in Eigen.
+    /*
+    if ((a + b) < maxgam && numext::abs(u) < maxlog) {
+      t = gamma(a + b) / (gamma(a) * gamma(b));
+      s = s * t * pow(x, a);
+    }
+    */
+    t = lgamma_impl<double>::run(a + b) - lgamma_impl<double>::run(a) - lgamma_impl<double>::run(b) + u +
+        numext::log(s);
+    return s = numext::exp(t);
+  }
+};
+
+template <>
+struct betainc_impl<double> {
+  EIGEN_DEVICE_FUNC static double run(double aa, double bb, double xx) {
+    const double nan = NumTraits<double>::quiet_NaN();
+    const double machep = cephes_helper<double>::machep();
+    double a, b, t, x, xc, w, y;
+    bool reversed_a_b = false;
+
+    if (aa == 0.0 && bb == 0.0) return nan;
+    if (xx < 0.0 || xx > 1.0) return nan;
+    if (aa < 0.0) return nan;
+    if (bb < 0.0) return nan;
+    if (aa == 0.0) return 1.0;
+    if (bb == 0.0) return 0.0;
+    if (xx == 0.0) return 0.0;
+    if (xx == 1.0) return 1.0;
+    // mtherr("incbet", DOMAIN);
+
+    if ((bb * xx) <= 1.0 && xx <= 0.95) {
+      return betainc_helper<double>::incbps(aa, bb, xx);
+    }
+
+    w = 1.0 - xx;
+
+    /* Reverse a and b if x is greater than the mean. */
+    if (xx > (aa / (aa + bb))) {
+      reversed_a_b = true;
+      a = bb;
+      b = aa;
+      xc = xx;
+      x = w;
+    } else {
+      a = aa;
+      b = bb;
+      xc = w;
+      x = xx;
+    }
+
+    if (reversed_a_b && (b * x) <= 1.0 && x <= 0.95) {
+      t = betainc_helper<double>::incbps(a, b, x);
+      if (t <= machep) {
+        t = 1.0 - machep;
+      } else {
+        t = 1.0 - t;
+      }
+      return t;
+    }
+
+    /* Choose expansion for better convergence. */
+    y = x * (a + b - 2.0) - (a - 1.0);
+    if (y < 0.0) {
+      w = incbeta_cfe<double>::run(a, b, x, true /* small_branch */);
+    } else {
+      w = incbeta_cfe<double>::run(a, b, x, false /* small_branch */) / xc;
+    }
+
+    /* Multiply w by the factor
+         a      b   _             _     _
+        x  (1-x)   | (a+b) / ( a | (a) | (b) ) .   */
+
+    y = a * numext::log(x);
+    t = b * numext::log(xc);
+    // TODO: gamma is not directly implemented in Eigen.
+    /*
+    if ((a + b) < maxgam && numext::abs(y) < maxlog && numext::abs(t) < maxlog)
+    {
+      t = pow(xc, b);
+      t *= pow(x, a);
+      t /= a;
+      t *= w;
+      t *= gamma(a + b) / (gamma(a) * gamma(b));
+    } else {
+    */
+    /* Resort to logarithms.  */
+    y += t + lgamma_impl<double>::run(a + b) - lgamma_impl<double>::run(a) - lgamma_impl<double>::run(b);
+    y += numext::log(w / a);
+    t = numext::exp(y);
+
+    /* } */
+    // done:
+
+    if (reversed_a_b) {
+      if (t <= machep) {
+        t = 1.0 - machep;
+      } else {
+        t = 1.0 - t;
+      }
+    }
+    return t;
+  }
+};
+
+#endif  // EIGEN_HAS_C99_MATH
+
+}  // end namespace internal
+
+namespace numext {
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(lgamma, Scalar) lgamma(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(lgamma, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(digamma, Scalar) digamma(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(digamma, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(zeta, Scalar) zeta(const Scalar& x, const Scalar& q) {
+  return EIGEN_MATHFUNC_IMPL(zeta, Scalar)::run(x, q);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(polygamma, Scalar) polygamma(const Scalar& n, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(polygamma, Scalar)::run(n, x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(erf, Scalar) erf(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(erf, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(erfc, Scalar) erfc(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(erfc, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(ndtri, Scalar) ndtri(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(ndtri, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igamma, Scalar) igamma(const Scalar& a, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(igamma, Scalar)::run(a, x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igamma_der_a, Scalar) igamma_der_a(const Scalar& a, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(igamma_der_a, Scalar)::run(a, x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(gamma_sample_der_alpha, Scalar)
+    gamma_sample_der_alpha(const Scalar& a, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(gamma_sample_der_alpha, Scalar)::run(a, x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igammac, Scalar) igammac(const Scalar& a, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(igammac, Scalar)::run(a, x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(betainc, Scalar)
+    betainc(const Scalar& a, const Scalar& b, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(betainc, Scalar)::run(a, b, x);
+}
+
+}  // end namespace numext
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPECIAL_FUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h
new file mode 100644
index 0000000..d1470db
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h
@@ -0,0 +1,112 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_PACKETMATH_H
+#define EIGEN_SPECIALFUNCTIONS_PACKETMATH_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal \returns the ln(|gamma(\a a)|) (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plgamma(const Packet& a) {
+  using numext::lgamma;
+  return lgamma(a);
+}
+
+/** \internal \returns the derivative of lgamma, psi(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pdigamma(const Packet& a) {
+  using numext::digamma;
+  return digamma(a);
+}
+
+/** \internal \returns the zeta function of two arguments (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pzeta(const Packet& x, const Packet& q) {
+  using numext::zeta;
+  return zeta(x, q);
+}
+
+/** \internal \returns the polygamma function (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet ppolygamma(const Packet& n, const Packet& x) {
+  using numext::polygamma;
+  return polygamma(n, x);
+}
+
+/** \internal \returns the erf(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet perf(const Packet& a) {
+  using numext::erf;
+  return erf(a);
+}
+
+/** \internal \returns the erfc(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet perfc(const Packet& a) {
+  using numext::erfc;
+  return erfc(a);
+}
+
+/** \internal \returns the ndtri(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pndtri(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type ScalarType;
+  using internal::generic_ndtri;
+  return generic_ndtri<Packet, ScalarType>(a);
+}
+
+/** \internal \returns the incomplete gamma function igamma(\a a, \a x) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pigamma(const Packet& a, const Packet& x) {
+  using numext::igamma;
+  return igamma(a, x);
+}
+
+/** \internal \returns the derivative of the incomplete gamma function
+ * igamma_der_a(\a a, \a x) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pigamma_der_a(const Packet& a, const Packet& x) {
+  using numext::igamma_der_a;
+  return igamma_der_a(a, x);
+}
+
+/** \internal \returns compute the derivative of the sample
+ * of Gamma(alpha, 1) random variable with respect to the parameter a
+ * gamma_sample_der_alpha(\a alpha, \a sample) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pgamma_sample_der_alpha(const Packet& alpha, const Packet& sample) {
+  using numext::gamma_sample_der_alpha;
+  return gamma_sample_der_alpha(alpha, sample);
+}
+
+/** \internal \returns the complementary incomplete gamma function igammac(\a a, \a x) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pigammac(const Packet& a, const Packet& x) {
+  using numext::igammac;
+  return igammac(a, x);
+}
+
+/** \internal \returns the complementary incomplete gamma function betainc(\a a, \a b, \a x) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pbetainc(const Packet& a, const Packet& b, const Packet& x) {
+  using numext::betainc;
+  return betainc(a, b, x);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPECIALFUNCTIONS_PACKETMATH_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h
new file mode 100644
index 0000000..d1a39be
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h
@@ -0,0 +1,13 @@
+#ifndef EIGEN_AVX_BESSELFUNCTIONS_H
+#define EIGEN_AVX_BESSELFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+EIGEN_INSTANTIATE_BESSEL_FUNCS_F16(Packet8f, Packet8h)
+EIGEN_INSTANTIATE_BESSEL_FUNCS_BF16(Packet8f, Packet8bf)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_AVX_BESSELFUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h
new file mode 100644
index 0000000..a5a0805
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h
@@ -0,0 +1,13 @@
+#ifndef EIGEN_AVX_SPECIALFUNCTIONS_H
+#define EIGEN_AVX_SPECIALFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+EIGEN_INSTANTIATE_SPECIAL_FUNCS_F16(Packet8f, Packet8h)
+EIGEN_INSTANTIATE_SPECIAL_FUNCS_BF16(Packet8f, Packet8bf)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_AVX_SPECIALFUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h
new file mode 100644
index 0000000..63ccc2d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h
@@ -0,0 +1,13 @@
+#ifndef EIGEN_AVX512_BESSELFUNCTIONS_H
+#define EIGEN_AVX512_BESSELFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+EIGEN_INSTANTIATE_BESSEL_FUNCS_F16(Packet16f, Packet16h)
+EIGEN_INSTANTIATE_BESSEL_FUNCS_BF16(Packet16f, Packet16bf)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_AVX512_BESSELFUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h
new file mode 100644
index 0000000..d00d991
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h
@@ -0,0 +1,13 @@
+#ifndef EIGEN_AVX512_SPECIALFUNCTIONS_H
+#define EIGEN_AVX512_SPECIALFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+EIGEN_INSTANTIATE_SPECIAL_FUNCS_F16(Packet16f, Packet16h)
+EIGEN_INSTANTIATE_SPECIAL_FUNCS_BF16(Packet16f, Packet16bf)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_AVX512_SPECIALFUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h
new file mode 100644
index 0000000..8f3468b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h
@@ -0,0 +1,317 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GPU_SPECIALFUNCTIONS_H
+#define EIGEN_GPU_SPECIALFUNCTIONS_H
+
+namespace Eigen {
+
+namespace internal {
+
+// Make sure this is only available when targeting a GPU: we don't want to
+// introduce conflicts between these packet_traits definitions and the ones
+// we'll use on the host side (SSE, AVX, ...)
+#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 plgamma<float4>(const float4& a) {
+  return make_float4(lgammaf(a.x), lgammaf(a.y), lgammaf(a.z), lgammaf(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 plgamma<double2>(const double2& a) {
+  using numext::lgamma;
+  return make_double2(lgamma(a.x), lgamma(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pdigamma<float4>(const float4& a) {
+  using numext::digamma;
+  return make_float4(digamma(a.x), digamma(a.y), digamma(a.z), digamma(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pdigamma<double2>(const double2& a) {
+  using numext::digamma;
+  return make_double2(digamma(a.x), digamma(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pzeta<float4>(const float4& x, const float4& q) {
+  using numext::zeta;
+  return make_float4(zeta(x.x, q.x), zeta(x.y, q.y), zeta(x.z, q.z), zeta(x.w, q.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pzeta<double2>(const double2& x, const double2& q) {
+  using numext::zeta;
+  return make_double2(zeta(x.x, q.x), zeta(x.y, q.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ppolygamma<float4>(const float4& n, const float4& x) {
+  using numext::polygamma;
+  return make_float4(polygamma(n.x, x.x), polygamma(n.y, x.y), polygamma(n.z, x.z), polygamma(n.w, x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ppolygamma<double2>(const double2& n, const double2& x) {
+  using numext::polygamma;
+  return make_double2(polygamma(n.x, x.x), polygamma(n.y, x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 perf<float4>(const float4& a) {
+  return make_float4(erff(a.x), erff(a.y), erff(a.z), erff(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 perf<double2>(const double2& a) {
+  using numext::erf;
+  return make_double2(erf(a.x), erf(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 perfc<float4>(const float4& a) {
+  using numext::erfc;
+  return make_float4(erfc(a.x), erfc(a.y), erfc(a.z), erfc(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 perfc<double2>(const double2& a) {
+  using numext::erfc;
+  return make_double2(erfc(a.x), erfc(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pndtri<float4>(const float4& a) {
+  using numext::ndtri;
+  return make_float4(ndtri(a.x), ndtri(a.y), ndtri(a.z), ndtri(a.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pndtri<double2>(const double2& a) {
+  using numext::ndtri;
+  return make_double2(ndtri(a.x), ndtri(a.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pigamma<float4>(const float4& a, const float4& x) {
+  using numext::igamma;
+  return make_float4(igamma(a.x, x.x), igamma(a.y, x.y), igamma(a.z, x.z), igamma(a.w, x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pigamma<double2>(const double2& a, const double2& x) {
+  using numext::igamma;
+  return make_double2(igamma(a.x, x.x), igamma(a.y, x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pigamma_der_a<float4>(const float4& a, const float4& x) {
+  using numext::igamma_der_a;
+  return make_float4(igamma_der_a(a.x, x.x), igamma_der_a(a.y, x.y), igamma_der_a(a.z, x.z), igamma_der_a(a.w, x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pigamma_der_a<double2>(const double2& a, const double2& x) {
+  using numext::igamma_der_a;
+  return make_double2(igamma_der_a(a.x, x.x), igamma_der_a(a.y, x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pgamma_sample_der_alpha<float4>(const float4& alpha,
+                                                                             const float4& sample) {
+  using numext::gamma_sample_der_alpha;
+  return make_float4(gamma_sample_der_alpha(alpha.x, sample.x), gamma_sample_der_alpha(alpha.y, sample.y),
+                     gamma_sample_der_alpha(alpha.z, sample.z), gamma_sample_der_alpha(alpha.w, sample.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pgamma_sample_der_alpha<double2>(const double2& alpha,
+                                                                               const double2& sample) {
+  using numext::gamma_sample_der_alpha;
+  return make_double2(gamma_sample_der_alpha(alpha.x, sample.x), gamma_sample_der_alpha(alpha.y, sample.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pigammac<float4>(const float4& a, const float4& x) {
+  using numext::igammac;
+  return make_float4(igammac(a.x, x.x), igammac(a.y, x.y), igammac(a.z, x.z), igammac(a.w, x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pigammac<double2>(const double2& a, const double2& x) {
+  using numext::igammac;
+  return make_double2(igammac(a.x, x.x), igammac(a.y, x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbetainc<float4>(const float4& a, const float4& b, const float4& x) {
+  using numext::betainc;
+  return make_float4(betainc(a.x, b.x, x.x), betainc(a.y, b.y, x.y), betainc(a.z, b.z, x.z), betainc(a.w, b.w, x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbetainc<double2>(const double2& a, const double2& b, const double2& x) {
+  using numext::betainc;
+  return make_double2(betainc(a.x, b.x, x.x), betainc(a.y, b.y, x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i0e<float4>(const float4& x) {
+  using numext::bessel_i0e;
+  return make_float4(bessel_i0e(x.x), bessel_i0e(x.y), bessel_i0e(x.z), bessel_i0e(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_i0e<double2>(const double2& x) {
+  using numext::bessel_i0e;
+  return make_double2(bessel_i0e(x.x), bessel_i0e(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i0<float4>(const float4& x) {
+  using numext::bessel_i0;
+  return make_float4(bessel_i0(x.x), bessel_i0(x.y), bessel_i0(x.z), bessel_i0(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_i0<double2>(const double2& x) {
+  using numext::bessel_i0;
+  return make_double2(bessel_i0(x.x), bessel_i0(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i1e<float4>(const float4& x) {
+  using numext::bessel_i1e;
+  return make_float4(bessel_i1e(x.x), bessel_i1e(x.y), bessel_i1e(x.z), bessel_i1e(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_i1e<double2>(const double2& x) {
+  using numext::bessel_i1e;
+  return make_double2(bessel_i1e(x.x), bessel_i1e(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i1<float4>(const float4& x) {
+  using numext::bessel_i1;
+  return make_float4(bessel_i1(x.x), bessel_i1(x.y), bessel_i1(x.z), bessel_i1(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_i1<double2>(const double2& x) {
+  using numext::bessel_i1;
+  return make_double2(bessel_i1(x.x), bessel_i1(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k0e<float4>(const float4& x) {
+  using numext::bessel_k0e;
+  return make_float4(bessel_k0e(x.x), bessel_k0e(x.y), bessel_k0e(x.z), bessel_k0e(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_k0e<double2>(const double2& x) {
+  using numext::bessel_k0e;
+  return make_double2(bessel_k0e(x.x), bessel_k0e(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k0<float4>(const float4& x) {
+  using numext::bessel_k0;
+  return make_float4(bessel_k0(x.x), bessel_k0(x.y), bessel_k0(x.z), bessel_k0(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_k0<double2>(const double2& x) {
+  using numext::bessel_k0;
+  return make_double2(bessel_k0(x.x), bessel_k0(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k1e<float4>(const float4& x) {
+  using numext::bessel_k1e;
+  return make_float4(bessel_k1e(x.x), bessel_k1e(x.y), bessel_k1e(x.z), bessel_k1e(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_k1e<double2>(const double2& x) {
+  using numext::bessel_k1e;
+  return make_double2(bessel_k1e(x.x), bessel_k1e(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k1<float4>(const float4& x) {
+  using numext::bessel_k1;
+  return make_float4(bessel_k1(x.x), bessel_k1(x.y), bessel_k1(x.z), bessel_k1(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_k1<double2>(const double2& x) {
+  using numext::bessel_k1;
+  return make_double2(bessel_k1(x.x), bessel_k1(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_j0<float4>(const float4& x) {
+  using numext::bessel_j0;
+  return make_float4(bessel_j0(x.x), bessel_j0(x.y), bessel_j0(x.z), bessel_j0(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_j0<double2>(const double2& x) {
+  using numext::bessel_j0;
+  return make_double2(bessel_j0(x.x), bessel_j0(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_j1<float4>(const float4& x) {
+  using numext::bessel_j1;
+  return make_float4(bessel_j1(x.x), bessel_j1(x.y), bessel_j1(x.z), bessel_j1(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_j1<double2>(const double2& x) {
+  using numext::bessel_j1;
+  return make_double2(bessel_j1(x.x), bessel_j1(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_y0<float4>(const float4& x) {
+  using numext::bessel_y0;
+  return make_float4(bessel_y0(x.x), bessel_y0(x.y), bessel_y0(x.z), bessel_y0(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_y0<double2>(const double2& x) {
+  using numext::bessel_y0;
+  return make_double2(bessel_y0(x.x), bessel_y0(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_y1<float4>(const float4& x) {
+  using numext::bessel_y1;
+  return make_float4(bessel_y1(x.x), bessel_y1(x.y), bessel_y1(x.z), bessel_y1(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pbessel_y1<double2>(const double2& x) {
+  using numext::bessel_y1;
+  return make_double2(bessel_y1(x.x), bessel_y1(x.y));
+}
+
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_GPU_SPECIALFUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h
new file mode 100644
index 0000000..568abab
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h
@@ -0,0 +1,43 @@
+#ifndef EIGEN_NEON_BESSELFUNCTIONS_H
+#define EIGEN_NEON_BESSELFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+
+#define NEON_HALF_TO_FLOAT_FUNCTIONS(METHOD)                                              \
+  template <>                                                                             \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf METHOD<Packet8hf>(const Packet8hf& x) { \
+    const Packet4f lo = METHOD<Packet4f>(vcvt_f32_f16(vget_low_f16(x)));                  \
+    const Packet4f hi = METHOD<Packet4f>(vcvt_f32_f16(vget_high_f16(x)));                 \
+    return vcombine_f16(vcvt_f16_f32(lo), vcvt_f16_f32(hi));                              \
+  }                                                                                       \
+                                                                                          \
+  template <>                                                                             \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf METHOD<Packet4hf>(const Packet4hf& x) { \
+    return vcvt_f16_f32(METHOD<Packet4f>(vcvt_f32_f16(x)));                               \
+  }
+
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i0)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i0e)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i1)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i1e)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_j0)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_j1)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k0)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k0e)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k1)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k1e)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_y0)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_y1)
+
+#undef NEON_HALF_TO_FLOAT_FUNCTIONS
+#endif
+
+EIGEN_INSTANTIATE_BESSEL_FUNCS_BF16(Packet4f, Packet4bf)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_NEON_BESSELFUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h
new file mode 100644
index 0000000..c31bb66
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h
@@ -0,0 +1,33 @@
+#ifndef EIGEN_NEON_SPECIALFUNCTIONS_H
+#define EIGEN_NEON_SPECIALFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+
+#define NEON_HALF_TO_FLOAT_FUNCTIONS(METHOD)                                              \
+  template <>                                                                             \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf METHOD<Packet8hf>(const Packet8hf& x) { \
+    const Packet4f lo = METHOD<Packet4f>(vcvt_f32_f16(vget_low_f16(x)));                  \
+    const Packet4f hi = METHOD<Packet4f>(vcvt_f32_f16(vget_high_f16(x)));                 \
+    return vcombine_f16(vcvt_f16_f32(lo), vcvt_f16_f32(hi));                              \
+  }                                                                                       \
+                                                                                          \
+  template <>                                                                             \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf METHOD<Packet4hf>(const Packet4hf& x) { \
+    return vcvt_f16_f32(METHOD<Packet4f>(vcvt_f32_f16(x)));                               \
+  }
+
+NEON_HALF_TO_FLOAT_FUNCTIONS(perf)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pndtri)
+
+#undef NEON_HALF_TO_FLOAT_FUNCTIONS
+#endif
+
+EIGEN_INSTANTIATE_SPECIAL_FUNCS_BF16(Packet4f, Packet4bf)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_NEON_SPECIALFUNCTIONS_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/InternalHeaderCheck.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/InternalHeaderCheck.h
new file mode 100644
index 0000000..4a6087e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPLINES_MODULE_H
+#error "Please include unsupported/Eigen/Splines instead of including headers inside the src directory directly."
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/Spline.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/Spline.h
new file mode 100644
index 0000000..77b3522
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/Spline.h
@@ -0,0 +1,479 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPLINE_H
+#define EIGEN_SPLINE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#include "SplineFwd.h"
+
+namespace Eigen {
+/**
+ * \ingroup Splines_Module
+ * \class Spline
+ * \brief A class representing multi-dimensional spline curves.
+ *
+ * The class represents B-splines with non-uniform knot vectors. Each control
+ * point of the B-spline is associated with a basis function
+ * \f{align*}
+ *   C(u) & = \sum_{i=0}^{n}N_{i,p}(u)P_i
+ * \f}
+ *
+ * \tparam Scalar_ The underlying data type (typically float or double)
+ * \tparam Dim_ The curve dimension (e.g. 2 or 3)
+ * \tparam Degree_ Per default set to Dynamic; could be set to the actual desired
+ *                degree for optimization purposes (would result in stack allocation
+ *                of several temporary variables).
+ **/
+template <typename Scalar_, int Dim_, int Degree_>
+class Spline {
+ public:
+  typedef Scalar_ Scalar; /*!< The spline curve's scalar type. */
+  enum { Dimension = Dim_ /*!< The spline curve's dimension. */ };
+  enum { Degree = Degree_ /*!< The spline curve's degree. */ };
+
+  /** \brief The point type the spline is representing. */
+  typedef typename SplineTraits<Spline>::PointType PointType;
+
+  /** \brief The data type used to store knot vectors. */
+  typedef typename SplineTraits<Spline>::KnotVectorType KnotVectorType;
+
+  /** \brief The data type used to store parameter vectors. */
+  typedef typename SplineTraits<Spline>::ParameterVectorType ParameterVectorType;
+
+  /** \brief The data type used to store non-zero basis functions. */
+  typedef typename SplineTraits<Spline>::BasisVectorType BasisVectorType;
+
+  /** \brief The data type used to store the values of the basis function derivatives. */
+  typedef typename SplineTraits<Spline>::BasisDerivativeType BasisDerivativeType;
+
+  /** \brief The data type representing the spline's control points. */
+  typedef typename SplineTraits<Spline>::ControlPointVectorType ControlPointVectorType;
+
+  /**
+   * \brief Creates a (constant) zero spline.
+   * For Splines with dynamic degree, the resulting degree will be 0.
+   **/
+  Spline()
+      : m_knots(1, (Degree == Dynamic ? 2 : 2 * Degree + 2)),
+        m_ctrls(ControlPointVectorType::Zero(Dimension, (Degree == Dynamic ? 1 : Degree + 1))) {
+    // in theory this code can go to the initializer list but it will get pretty
+    // much unreadable ...
+    enum { MinDegree = (Degree == Dynamic ? 0 : Degree) };
+    m_knots.template segment<MinDegree + 1>(0) = Array<Scalar, 1, MinDegree + 1>::Zero();
+    m_knots.template segment<MinDegree + 1>(MinDegree + 1) = Array<Scalar, 1, MinDegree + 1>::Ones();
+  }
+
+  /**
+   * \brief Creates a spline from a knot vector and control points.
+   * \param knots The spline's knot vector.
+   * \param ctrls The spline's control point vector.
+   **/
+  template <typename OtherVectorType, typename OtherArrayType>
+  Spline(const OtherVectorType& knots, const OtherArrayType& ctrls) : m_knots(knots), m_ctrls(ctrls) {}
+
+  /**
+   * \brief Copy constructor for splines.
+   * \param spline The input spline.
+   **/
+  template <int OtherDegree>
+  Spline(const Spline<Scalar, Dimension, OtherDegree>& spline) : m_knots(spline.knots()), m_ctrls(spline.ctrls()) {}
+
+  /**
+   * \brief Returns the knots of the underlying spline.
+   **/
+  const KnotVectorType& knots() const { return m_knots; }
+
+  /**
+   * \brief Returns the ctrls of the underlying spline.
+   **/
+  const ControlPointVectorType& ctrls() const { return m_ctrls; }
+
+  /**
+   * \brief Returns the spline value at a given site \f$u\f$.
+   *
+   * The function returns
+   * \f{align*}
+   *   C(u) & = \sum_{i=0}^{n}N_{i,p}P_i
+   * \f}
+   *
+   * \param u Parameter \f$u \in [0;1]\f$ at which the spline is evaluated.
+   * \return The spline value at the given location \f$u\f$.
+   **/
+  PointType operator()(Scalar u) const;
+
+  /**
+   * \brief Evaluation of spline derivatives of up-to given order.
+   *
+   * The function returns
+   * \f{align*}
+   *   \frac{d^i}{du^i}C(u) & = \sum_{i=0}^{n} \frac{d^i}{du^i} N_{i,p}(u)P_i
+   * \f}
+   * for i ranging between 0 and order.
+   *
+   * \param u Parameter \f$u \in [0;1]\f$ at which the spline derivative is evaluated.
+   * \param order The order up to which the derivatives are computed.
+   **/
+  typename SplineTraits<Spline>::DerivativeType derivatives(Scalar u, DenseIndex order) const;
+
+  /**
+   * \copydoc Spline::derivatives
+   * Using the template version of this function is more efficieent since
+   * temporary objects are allocated on the stack whenever this is possible.
+   **/
+  template <int DerivativeOrder>
+  typename SplineTraits<Spline, DerivativeOrder>::DerivativeType derivatives(Scalar u,
+                                                                             DenseIndex order = DerivativeOrder) const;
+
+  /**
+   * \brief Computes the non-zero basis functions at the given site.
+   *
+   * Splines have local support and a point from their image is defined
+   * by exactly \f$p+1\f$ control points \f$P_i\f$ where \f$p\f$ is the
+   * spline degree.
+   *
+   * This function computes the \f$p+1\f$ non-zero basis function values
+   * for a given parameter value \f$u\f$. It returns
+   * \f{align*}{
+   *   N_{i,p}(u), \hdots, N_{i+p+1,p}(u)
+   * \f}
+   *
+   * \param u Parameter \f$u \in [0;1]\f$ at which the non-zero basis functions
+   *          are computed.
+   **/
+  typename SplineTraits<Spline>::BasisVectorType basisFunctions(Scalar u) const;
+
+  /**
+   * \brief Computes the non-zero spline basis function derivatives up to given order.
+   *
+   * The function computes
+   * \f{align*}{
+   *   \frac{d^i}{du^i} N_{i,p}(u), \hdots, \frac{d^i}{du^i} N_{i+p+1,p}(u)
+   * \f}
+   * with i ranging from 0 up to the specified order.
+   *
+   * \param u Parameter \f$u \in [0;1]\f$ at which the non-zero basis function
+   *          derivatives are computed.
+   * \param order The order up to which the basis function derivatives are computes.
+   **/
+  typename SplineTraits<Spline>::BasisDerivativeType basisFunctionDerivatives(Scalar u, DenseIndex order) const;
+
+  /**
+   * \copydoc Spline::basisFunctionDerivatives
+   * Using the template version of this function is more efficieent since
+   * temporary objects are allocated on the stack whenever this is possible.
+   **/
+  template <int DerivativeOrder>
+  typename SplineTraits<Spline, DerivativeOrder>::BasisDerivativeType basisFunctionDerivatives(
+      Scalar u, DenseIndex order = DerivativeOrder) const;
+
+  /**
+   * \brief Returns the spline degree.
+   **/
+  DenseIndex degree() const;
+
+  /**
+   * \brief Returns the span within the knot vector in which u is falling.
+   * \param u The site for which the span is determined.
+   **/
+  DenseIndex span(Scalar u) const;
+
+  /**
+   * \brief Computes the span within the provided knot vector in which u is falling.
+   **/
+  static DenseIndex Span(typename SplineTraits<Spline>::Scalar u, DenseIndex degree,
+                         const typename SplineTraits<Spline>::KnotVectorType& knots);
+
+  /**
+   * \brief Returns the spline's non-zero basis functions.
+   *
+   * The function computes and returns
+   * \f{align*}{
+   *   N_{i,p}(u), \hdots, N_{i+p+1,p}(u)
+   * \f}
+   *
+   * \param u The site at which the basis functions are computed.
+   * \param degree The degree of the underlying spline.
+   * \param knots The underlying spline's knot vector.
+   **/
+  static BasisVectorType BasisFunctions(Scalar u, DenseIndex degree, const KnotVectorType& knots);
+
+  /**
+   * \copydoc Spline::basisFunctionDerivatives
+   * \param degree The degree of the underlying spline
+   * \param knots The underlying spline's knot vector.
+   **/
+  static BasisDerivativeType BasisFunctionDerivatives(const Scalar u, const DenseIndex order, const DenseIndex degree,
+                                                      const KnotVectorType& knots);
+
+ private:
+  KnotVectorType m_knots;         /*!< Knot vector. */
+  ControlPointVectorType m_ctrls; /*!< Control points. */
+
+  template <typename DerivativeType>
+  static void BasisFunctionDerivativesImpl(const typename Spline<Scalar_, Dim_, Degree_>::Scalar u,
+                                           const DenseIndex order, const DenseIndex p,
+                                           const typename Spline<Scalar_, Dim_, Degree_>::KnotVectorType& U,
+                                           DerivativeType& N_);
+};
+
+template <typename Scalar_, int Dim_, int Degree_>
+DenseIndex Spline<Scalar_, Dim_, Degree_>::Span(
+    typename SplineTraits<Spline<Scalar_, Dim_, Degree_> >::Scalar u, DenseIndex degree,
+    const typename SplineTraits<Spline<Scalar_, Dim_, Degree_> >::KnotVectorType& knots) {
+  // Piegl & Tiller, "The NURBS Book", A2.1 (p. 68)
+  if (u <= knots(0)) return degree;
+  const Scalar* pos = std::upper_bound(knots.data() + degree - 1, knots.data() + knots.size() - degree - 1, u);
+  return static_cast<DenseIndex>(std::distance(knots.data(), pos) - 1);
+}
+
+template <typename Scalar_, int Dim_, int Degree_>
+typename Spline<Scalar_, Dim_, Degree_>::BasisVectorType Spline<Scalar_, Dim_, Degree_>::BasisFunctions(
+    typename Spline<Scalar_, Dim_, Degree_>::Scalar u, DenseIndex degree,
+    const typename Spline<Scalar_, Dim_, Degree_>::KnotVectorType& knots) {
+  const DenseIndex p = degree;
+  const DenseIndex i = Spline::Span(u, degree, knots);
+
+  const KnotVectorType& U = knots;
+
+  BasisVectorType left(p + 1);
+  left(0) = Scalar(0);
+  BasisVectorType right(p + 1);
+  right(0) = Scalar(0);
+
+  VectorBlock<BasisVectorType, Degree>(left, 1, p) =
+      u - VectorBlock<const KnotVectorType, Degree>(U, i + 1 - p, p).reverse();
+  VectorBlock<BasisVectorType, Degree>(right, 1, p) = VectorBlock<const KnotVectorType, Degree>(U, i + 1, p) - u;
+
+  BasisVectorType N(1, p + 1);
+  N(0) = Scalar(1);
+  for (DenseIndex j = 1; j <= p; ++j) {
+    Scalar saved = Scalar(0);
+    for (DenseIndex r = 0; r < j; r++) {
+      const Scalar tmp = N(r) / (right(r + 1) + left(j - r));
+      N[r] = saved + right(r + 1) * tmp;
+      saved = left(j - r) * tmp;
+    }
+    N(j) = saved;
+  }
+  return N;
+}
+
+template <typename Scalar_, int Dim_, int Degree_>
+DenseIndex Spline<Scalar_, Dim_, Degree_>::degree() const {
+  if (Degree_ == Dynamic)
+    return m_knots.size() - m_ctrls.cols() - 1;
+  else
+    return Degree_;
+}
+
+template <typename Scalar_, int Dim_, int Degree_>
+DenseIndex Spline<Scalar_, Dim_, Degree_>::span(Scalar u) const {
+  return Spline::Span(u, degree(), knots());
+}
+
+template <typename Scalar_, int Dim_, int Degree_>
+typename Spline<Scalar_, Dim_, Degree_>::PointType Spline<Scalar_, Dim_, Degree_>::operator()(Scalar u) const {
+  enum { Order = SplineTraits<Spline>::OrderAtCompileTime };
+
+  const DenseIndex span = this->span(u);
+  const DenseIndex p = degree();
+  const BasisVectorType basis_funcs = basisFunctions(u);
+
+  const Replicate<BasisVectorType, Dimension, 1> ctrl_weights(basis_funcs);
+  const Block<const ControlPointVectorType, Dimension, Order> ctrl_pts(ctrls(), 0, span - p, Dimension, p + 1);
+  return (ctrl_weights * ctrl_pts).rowwise().sum();
+}
+
+/* --------------------------------------------------------------------------------------------- */
+
+template <typename SplineType, typename DerivativeType>
+void derivativesImpl(const SplineType& spline, typename SplineType::Scalar u, DenseIndex order, DerivativeType& der) {
+  enum { Dimension = SplineTraits<SplineType>::Dimension };
+  enum { Order = SplineTraits<SplineType>::OrderAtCompileTime };
+  enum { DerivativeOrder = DerivativeType::ColsAtCompileTime };
+
+  typedef typename SplineTraits<SplineType>::ControlPointVectorType ControlPointVectorType;
+  typedef typename SplineTraits<SplineType, DerivativeOrder>::BasisDerivativeType BasisDerivativeType;
+  typedef typename BasisDerivativeType::ConstRowXpr BasisDerivativeRowXpr;
+
+  const DenseIndex p = spline.degree();
+  const DenseIndex span = spline.span(u);
+
+  const DenseIndex n = (std::min)(p, order);
+
+  der.resize(Dimension, n + 1);
+
+  // Retrieve the basis function derivatives up to the desired order...
+  const BasisDerivativeType basis_func_ders = spline.template basisFunctionDerivatives<DerivativeOrder>(u, n + 1);
+
+  // ... and perform the linear combinations of the control points.
+  for (DenseIndex der_order = 0; der_order < n + 1; ++der_order) {
+    const Replicate<BasisDerivativeRowXpr, Dimension, 1> ctrl_weights(basis_func_ders.row(der_order));
+    const Block<const ControlPointVectorType, Dimension, Order> ctrl_pts(spline.ctrls(), 0, span - p, Dimension, p + 1);
+    der.col(der_order) = (ctrl_weights * ctrl_pts).rowwise().sum();
+  }
+}
+
+template <typename Scalar_, int Dim_, int Degree_>
+typename SplineTraits<Spline<Scalar_, Dim_, Degree_> >::DerivativeType Spline<Scalar_, Dim_, Degree_>::derivatives(
+    Scalar u, DenseIndex order) const {
+  typename SplineTraits<Spline>::DerivativeType res;
+  derivativesImpl(*this, u, order, res);
+  return res;
+}
+
+template <typename Scalar_, int Dim_, int Degree_>
+template <int DerivativeOrder>
+typename SplineTraits<Spline<Scalar_, Dim_, Degree_>, DerivativeOrder>::DerivativeType
+Spline<Scalar_, Dim_, Degree_>::derivatives(Scalar u, DenseIndex order) const {
+  typename SplineTraits<Spline, DerivativeOrder>::DerivativeType res;
+  derivativesImpl(*this, u, order, res);
+  return res;
+}
+
+template <typename Scalar_, int Dim_, int Degree_>
+typename SplineTraits<Spline<Scalar_, Dim_, Degree_> >::BasisVectorType Spline<Scalar_, Dim_, Degree_>::basisFunctions(
+    Scalar u) const {
+  return Spline::BasisFunctions(u, degree(), knots());
+}
+
+/* --------------------------------------------------------------------------------------------- */
+
+template <typename Scalar_, int Dim_, int Degree_>
+template <typename DerivativeType>
+void Spline<Scalar_, Dim_, Degree_>::BasisFunctionDerivativesImpl(
+    const typename Spline<Scalar_, Dim_, Degree_>::Scalar u, const DenseIndex order, const DenseIndex p,
+    const typename Spline<Scalar_, Dim_, Degree_>::KnotVectorType& U, DerivativeType& N_) {
+  typedef Spline<Scalar_, Dim_, Degree_> SplineType;
+  enum { Order = SplineTraits<SplineType>::OrderAtCompileTime };
+
+  const DenseIndex span = SplineType::Span(u, p, U);
+
+  const DenseIndex n = (std::min)(p, order);
+
+  N_.resize(n + 1, p + 1);
+
+  BasisVectorType left = BasisVectorType::Zero(p + 1);
+  BasisVectorType right = BasisVectorType::Zero(p + 1);
+
+  Matrix<Scalar, Order, Order> ndu(p + 1, p + 1);
+
+  Scalar saved, temp;  // FIXME: These were double instead of Scalar. Was there a reason for that?
+
+  ndu(0, 0) = 1.0;
+
+  DenseIndex j;
+  for (j = 1; j <= p; ++j) {
+    left[j] = u - U[span + 1 - j];
+    right[j] = U[span + j] - u;
+    saved = 0.0;
+
+    for (DenseIndex r = 0; r < j; ++r) {
+      /* Lower triangle */
+      ndu(j, r) = right[r + 1] + left[j - r];
+      temp = ndu(r, j - 1) / ndu(j, r);
+      /* Upper triangle */
+      ndu(r, j) = static_cast<Scalar>(saved + right[r + 1] * temp);
+      saved = left[j - r] * temp;
+    }
+
+    ndu(j, j) = static_cast<Scalar>(saved);
+  }
+
+  for (j = p; j >= 0; --j) N_(0, j) = ndu(j, p);
+
+  // Compute the derivatives
+  DerivativeType a(n + 1, p + 1);
+  DenseIndex r = 0;
+  for (; r <= p; ++r) {
+    DenseIndex s1, s2;
+    s1 = 0;
+    s2 = 1;  // alternate rows in array a
+    a(0, 0) = 1.0;
+
+    // Compute the k-th derivative
+    for (DenseIndex k = 1; k <= static_cast<DenseIndex>(n); ++k) {
+      Scalar d = 0.0;
+      DenseIndex rk, pk, j1, j2;
+      rk = r - k;
+      pk = p - k;
+
+      if (r >= k) {
+        a(s2, 0) = a(s1, 0) / ndu(pk + 1, rk);
+        d = a(s2, 0) * ndu(rk, pk);
+      }
+
+      if (rk >= -1)
+        j1 = 1;
+      else
+        j1 = -rk;
+
+      if (r - 1 <= pk)
+        j2 = k - 1;
+      else
+        j2 = p - r;
+
+      for (j = j1; j <= j2; ++j) {
+        a(s2, j) = (a(s1, j) - a(s1, j - 1)) / ndu(pk + 1, rk + j);
+        d += a(s2, j) * ndu(rk + j, pk);
+      }
+
+      if (r <= pk) {
+        a(s2, k) = -a(s1, k - 1) / ndu(pk + 1, r);
+        d += a(s2, k) * ndu(r, pk);
+      }
+
+      N_(k, r) = static_cast<Scalar>(d);
+      j = s1;
+      s1 = s2;
+      s2 = j;  // Switch rows
+    }
+  }
+
+  /* Multiply through by the correct factors */
+  /* (Eq. [2.9])                             */
+  r = p;
+  for (DenseIndex k = 1; k <= static_cast<DenseIndex>(n); ++k) {
+    for (j = p; j >= 0; --j) N_(k, j) *= r;
+    r *= p - k;
+  }
+}
+
+template <typename Scalar_, int Dim_, int Degree_>
+typename SplineTraits<Spline<Scalar_, Dim_, Degree_> >::BasisDerivativeType
+Spline<Scalar_, Dim_, Degree_>::basisFunctionDerivatives(Scalar u, DenseIndex order) const {
+  typename SplineTraits<Spline<Scalar_, Dim_, Degree_> >::BasisDerivativeType der;
+  BasisFunctionDerivativesImpl(u, order, degree(), knots(), der);
+  return der;
+}
+
+template <typename Scalar_, int Dim_, int Degree_>
+template <int DerivativeOrder>
+typename SplineTraits<Spline<Scalar_, Dim_, Degree_>, DerivativeOrder>::BasisDerivativeType
+Spline<Scalar_, Dim_, Degree_>::basisFunctionDerivatives(Scalar u, DenseIndex order) const {
+  typename SplineTraits<Spline<Scalar_, Dim_, Degree_>, DerivativeOrder>::BasisDerivativeType der;
+  BasisFunctionDerivativesImpl(u, order, degree(), knots(), der);
+  return der;
+}
+
+template <typename Scalar_, int Dim_, int Degree_>
+typename SplineTraits<Spline<Scalar_, Dim_, Degree_> >::BasisDerivativeType
+Spline<Scalar_, Dim_, Degree_>::BasisFunctionDerivatives(
+    const typename Spline<Scalar_, Dim_, Degree_>::Scalar u, const DenseIndex order, const DenseIndex degree,
+    const typename Spline<Scalar_, Dim_, Degree_>::KnotVectorType& knots) {
+  typename SplineTraits<Spline>::BasisDerivativeType der;
+  BasisFunctionDerivativesImpl(u, order, degree, knots, der);
+  return der;
+}
+}  // namespace Eigen
+
+#endif  // EIGEN_SPLINE_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/SplineFitting.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/SplineFitting.h
new file mode 100644
index 0000000..f6a1111
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/SplineFitting.h
@@ -0,0 +1,393 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPLINE_FITTING_H
+#define EIGEN_SPLINE_FITTING_H
+
+#include <algorithm>
+#include <functional>
+#include <numeric>
+#include <vector>
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#include "SplineFwd.h"
+
+#include "../../../../Eigen/LU"
+#include "../../../../Eigen/QR"
+
+namespace Eigen {
+/**
+ * \brief Computes knot averages.
+ * \ingroup Splines_Module
+ *
+ * The knots are computed as
+ * \f{align*}
+ *  u_0 & = \hdots = u_p = 0 \\
+ *  u_{m-p} & = \hdots = u_{m} = 1 \\
+ *  u_{j+p} & = \frac{1}{p}\sum_{i=j}^{j+p-1}\bar{u}_i \quad\quad j=1,\hdots,n-p
+ * \f}
+ * where \f$p\f$ is the degree and \f$m+1\f$ the number knots
+ * of the desired interpolating spline.
+ *
+ * \param[in] parameters The input parameters. During interpolation one for each data point.
+ * \param[in] degree The spline degree which is used during the interpolation.
+ * \param[out] knots The output knot vector.
+ *
+ * \sa Les Piegl and Wayne Tiller, The NURBS book (2nd ed.), 1997, 9.2.1 Global Curve Interpolation to Point Data
+ **/
+template <typename KnotVectorType>
+void KnotAveraging(const KnotVectorType& parameters, DenseIndex degree, KnotVectorType& knots) {
+  knots.resize(parameters.size() + degree + 1);
+
+  for (DenseIndex j = 1; j < parameters.size() - degree; ++j) knots(j + degree) = parameters.segment(j, degree).mean();
+
+  knots.segment(0, degree + 1) = KnotVectorType::Zero(degree + 1);
+  knots.segment(knots.size() - degree - 1, degree + 1) = KnotVectorType::Ones(degree + 1);
+}
+
+/**
+ * \brief Computes knot averages when derivative constraints are present.
+ * Note that this is a technical interpretation of the referenced article
+ * since the algorithm contained therein is incorrect as written.
+ * \ingroup Splines_Module
+ *
+ * \param[in] parameters The parameters at which the interpolation B-Spline
+ *            will intersect the given interpolation points. The parameters
+ *            are assumed to be a non-decreasing sequence.
+ * \param[in] degree The degree of the interpolating B-Spline. This must be
+ *            greater than zero.
+ * \param[in] derivativeIndices The indices corresponding to parameters at
+ *            which there are derivative constraints. The indices are assumed
+ *            to be a non-decreasing sequence.
+ * \param[out] knots The calculated knot vector. These will be returned as a
+ *             non-decreasing sequence
+ *
+ * \sa Les A. Piegl, Khairan Rajab, Volha Smarodzinana. 2008.
+ * Curve interpolation with directional constraints for engineering design.
+ * Engineering with Computers
+ **/
+template <typename KnotVectorType, typename ParameterVectorType, typename IndexArray>
+void KnotAveragingWithDerivatives(const ParameterVectorType& parameters, const unsigned int degree,
+                                  const IndexArray& derivativeIndices, KnotVectorType& knots) {
+  typedef typename ParameterVectorType::Scalar Scalar;
+
+  DenseIndex numParameters = parameters.size();
+  DenseIndex numDerivatives = derivativeIndices.size();
+
+  if (numDerivatives < 1) {
+    KnotAveraging(parameters, degree, knots);
+    return;
+  }
+
+  DenseIndex startIndex;
+  DenseIndex endIndex;
+
+  DenseIndex numInternalDerivatives = numDerivatives;
+
+  if (derivativeIndices[0] == 0) {
+    startIndex = 0;
+    --numInternalDerivatives;
+  } else {
+    startIndex = 1;
+  }
+  if (derivativeIndices[numDerivatives - 1] == numParameters - 1) {
+    endIndex = numParameters - degree;
+    --numInternalDerivatives;
+  } else {
+    endIndex = numParameters - degree - 1;
+  }
+
+  // There are (endIndex - startIndex + 1) knots obtained from the averaging
+  // and 2 for the first and last parameters.
+  DenseIndex numAverageKnots = endIndex - startIndex + 3;
+  KnotVectorType averageKnots(numAverageKnots);
+  averageKnots[0] = parameters[0];
+
+  int newKnotIndex = 0;
+  for (DenseIndex i = startIndex; i <= endIndex; ++i)
+    averageKnots[++newKnotIndex] = parameters.segment(i, degree).mean();
+  averageKnots[++newKnotIndex] = parameters[numParameters - 1];
+
+  newKnotIndex = -1;
+
+  ParameterVectorType temporaryParameters(numParameters + 1);
+  KnotVectorType derivativeKnots(numInternalDerivatives);
+  for (DenseIndex i = 0; i < numAverageKnots - 1; ++i) {
+    temporaryParameters[0] = averageKnots[i];
+    ParameterVectorType parameterIndices(numParameters);
+    int temporaryParameterIndex = 1;
+    for (DenseIndex j = 0; j < numParameters; ++j) {
+      Scalar parameter = parameters[j];
+      if (parameter >= averageKnots[i] && parameter < averageKnots[i + 1]) {
+        parameterIndices[temporaryParameterIndex] = j;
+        temporaryParameters[temporaryParameterIndex++] = parameter;
+      }
+    }
+    temporaryParameters[temporaryParameterIndex] = averageKnots[i + 1];
+
+    for (int j = 0; j <= temporaryParameterIndex - 2; ++j) {
+      for (DenseIndex k = 0; k < derivativeIndices.size(); ++k) {
+        if (parameterIndices[j + 1] == derivativeIndices[k] && parameterIndices[j + 1] != 0 &&
+            parameterIndices[j + 1] != numParameters - 1) {
+          derivativeKnots[++newKnotIndex] = temporaryParameters.segment(j, 3).mean();
+          break;
+        }
+      }
+    }
+  }
+
+  KnotVectorType temporaryKnots(averageKnots.size() + derivativeKnots.size());
+
+  std::merge(averageKnots.data(), averageKnots.data() + averageKnots.size(), derivativeKnots.data(),
+             derivativeKnots.data() + derivativeKnots.size(), temporaryKnots.data());
+
+  // Number of knots (one for each point and derivative) plus spline order.
+  DenseIndex numKnots = numParameters + numDerivatives + degree + 1;
+  knots.resize(numKnots);
+
+  knots.head(degree).fill(temporaryKnots[0]);
+  knots.tail(degree).fill(temporaryKnots.template tail<1>()[0]);
+  knots.segment(degree, temporaryKnots.size()) = temporaryKnots;
+}
+
+/**
+ * \brief Computes chord length parameters which are required for spline interpolation.
+ * \ingroup Splines_Module
+ *
+ * \param[in] pts The data points to which a spline should be fit.
+ * \param[out] chord_lengths The resulting chord length vector.
+ *
+ * \sa Les Piegl and Wayne Tiller, The NURBS book (2nd ed.), 1997, 9.2.1 Global Curve Interpolation to Point Data
+ **/
+template <typename PointArrayType, typename KnotVectorType>
+void ChordLengths(const PointArrayType& pts, KnotVectorType& chord_lengths) {
+  typedef typename KnotVectorType::Scalar Scalar;
+
+  const DenseIndex n = pts.cols();
+
+  // 1. compute the column-wise norms
+  chord_lengths.resize(pts.cols());
+  chord_lengths[0] = 0;
+  chord_lengths.rightCols(n - 1) =
+      (pts.array().leftCols(n - 1) - pts.array().rightCols(n - 1)).matrix().colwise().norm();
+
+  // 2. compute the partial sums
+  std::partial_sum(chord_lengths.data(), chord_lengths.data() + n, chord_lengths.data());
+
+  // 3. normalize the data
+  chord_lengths /= chord_lengths(n - 1);
+  chord_lengths(n - 1) = Scalar(1);
+}
+
+/**
+ * \brief Spline fitting methods.
+ * \ingroup Splines_Module
+ **/
+template <typename SplineType>
+struct SplineFitting {
+  typedef typename SplineType::KnotVectorType KnotVectorType;
+  typedef typename SplineType::ParameterVectorType ParameterVectorType;
+
+  /**
+   * \brief Fits an interpolating Spline to the given data points.
+   *
+   * \param pts The points for which an interpolating spline will be computed.
+   * \param degree The degree of the interpolating spline.
+   *
+   * \returns A spline interpolating the initially provided points.
+   **/
+  template <typename PointArrayType>
+  static SplineType Interpolate(const PointArrayType& pts, DenseIndex degree);
+
+  /**
+   * \brief Fits an interpolating Spline to the given data points.
+   *
+   * \param pts The points for which an interpolating spline will be computed.
+   * \param degree The degree of the interpolating spline.
+   * \param knot_parameters The knot parameters for the interpolation.
+   *
+   * \returns A spline interpolating the initially provided points.
+   **/
+  template <typename PointArrayType>
+  static SplineType Interpolate(const PointArrayType& pts, DenseIndex degree, const KnotVectorType& knot_parameters);
+
+  /**
+   * \brief Fits an interpolating spline to the given data points and
+   * derivatives.
+   *
+   * \param points The points for which an interpolating spline will be computed.
+   * \param derivatives The desired derivatives of the interpolating spline at interpolation
+   *                    points.
+   * \param derivativeIndices An array indicating which point each derivative belongs to. This
+   *                          must be the same size as @a derivatives.
+   * \param degree The degree of the interpolating spline.
+   *
+   * \returns A spline interpolating @a points with @a derivatives at those points.
+   *
+   * \sa Les A. Piegl, Khairan Rajab, Volha Smarodzinana. 2008.
+   * Curve interpolation with directional constraints for engineering design.
+   * Engineering with Computers
+   **/
+  template <typename PointArrayType, typename IndexArray>
+  static SplineType InterpolateWithDerivatives(const PointArrayType& points, const PointArrayType& derivatives,
+                                               const IndexArray& derivativeIndices, const unsigned int degree);
+
+  /**
+   * \brief Fits an interpolating spline to the given data points and derivatives.
+   *
+   * \param points The points for which an interpolating spline will be computed.
+   * \param derivatives The desired derivatives of the interpolating spline at interpolation points.
+   * \param derivativeIndices An array indicating which point each derivative belongs to. This
+   *                          must be the same size as @a derivatives.
+   * \param degree The degree of the interpolating spline.
+   * \param parameters The parameters corresponding to the interpolation points.
+   *
+   * \returns A spline interpolating @a points with @a derivatives at those points.
+   *
+   * \sa Les A. Piegl, Khairan Rajab, Volha Smarodzinana. 2008.
+   * Curve interpolation with directional constraints for engineering design.
+   * Engineering with Computers
+   */
+  template <typename PointArrayType, typename IndexArray>
+  static SplineType InterpolateWithDerivatives(const PointArrayType& points, const PointArrayType& derivatives,
+                                               const IndexArray& derivativeIndices, const unsigned int degree,
+                                               const ParameterVectorType& parameters);
+};
+
+template <typename SplineType>
+template <typename PointArrayType>
+SplineType SplineFitting<SplineType>::Interpolate(const PointArrayType& pts, DenseIndex degree,
+                                                  const KnotVectorType& knot_parameters) {
+  typedef typename SplineType::KnotVectorType::Scalar Scalar;
+  typedef typename SplineType::ControlPointVectorType ControlPointVectorType;
+
+  typedef Matrix<Scalar, Dynamic, Dynamic> MatrixType;
+
+  KnotVectorType knots;
+  KnotAveraging(knot_parameters, degree, knots);
+
+  DenseIndex n = pts.cols();
+  MatrixType A = MatrixType::Zero(n, n);
+  for (DenseIndex i = 1; i < n - 1; ++i) {
+    const DenseIndex span = SplineType::Span(knot_parameters[i], degree, knots);
+
+    // The segment call should somehow be told the spline order at compile time.
+    A.row(i).segment(span - degree, degree + 1) = SplineType::BasisFunctions(knot_parameters[i], degree, knots);
+  }
+  A(0, 0) = 1.0;
+  A(n - 1, n - 1) = 1.0;
+
+  HouseholderQR<MatrixType> qr(A);
+
+  // Here, we are creating a temporary due to an Eigen issue.
+  ControlPointVectorType ctrls = qr.solve(MatrixType(pts.transpose())).transpose();
+
+  return SplineType(knots, ctrls);
+}
+
+template <typename SplineType>
+template <typename PointArrayType>
+SplineType SplineFitting<SplineType>::Interpolate(const PointArrayType& pts, DenseIndex degree) {
+  KnotVectorType chord_lengths;  // knot parameters
+  ChordLengths(pts, chord_lengths);
+  return Interpolate(pts, degree, chord_lengths);
+}
+
+template <typename SplineType>
+template <typename PointArrayType, typename IndexArray>
+SplineType SplineFitting<SplineType>::InterpolateWithDerivatives(const PointArrayType& points,
+                                                                 const PointArrayType& derivatives,
+                                                                 const IndexArray& derivativeIndices,
+                                                                 const unsigned int degree,
+                                                                 const ParameterVectorType& parameters) {
+  typedef typename SplineType::KnotVectorType::Scalar Scalar;
+  typedef typename SplineType::ControlPointVectorType ControlPointVectorType;
+
+  typedef Matrix<Scalar, Dynamic, Dynamic> MatrixType;
+
+  const DenseIndex n = points.cols() + derivatives.cols();
+
+  KnotVectorType knots;
+
+  KnotAveragingWithDerivatives(parameters, degree, derivativeIndices, knots);
+
+  // fill matrix
+  MatrixType A = MatrixType::Zero(n, n);
+
+  // Use these dimensions for quicker populating, then transpose for solving.
+  MatrixType b(points.rows(), n);
+
+  DenseIndex startRow;
+  DenseIndex derivativeStart;
+
+  // End derivatives.
+  if (derivativeIndices[0] == 0) {
+    A.template block<1, 2>(1, 0) << -1, 1;
+
+    Scalar y = (knots(degree + 1) - knots(0)) / degree;
+    b.col(1) = y * derivatives.col(0);
+
+    startRow = 2;
+    derivativeStart = 1;
+  } else {
+    startRow = 1;
+    derivativeStart = 0;
+  }
+  if (derivativeIndices[derivatives.cols() - 1] == points.cols() - 1) {
+    A.template block<1, 2>(n - 2, n - 2) << -1, 1;
+
+    Scalar y = (knots(knots.size() - 1) - knots(knots.size() - (degree + 2))) / degree;
+    b.col(b.cols() - 2) = y * derivatives.col(derivatives.cols() - 1);
+  }
+
+  DenseIndex row = startRow;
+  DenseIndex derivativeIndex = derivativeStart;
+  for (DenseIndex i = 1; i < parameters.size() - 1; ++i) {
+    const DenseIndex span = SplineType::Span(parameters[i], degree, knots);
+
+    if (derivativeIndex < derivativeIndices.size() && derivativeIndices[derivativeIndex] == i) {
+      A.block(row, span - degree, 2, degree + 1) =
+          SplineType::BasisFunctionDerivatives(parameters[i], 1, degree, knots);
+
+      b.col(row++) = points.col(i);
+      b.col(row++) = derivatives.col(derivativeIndex++);
+    } else {
+      A.row(row).segment(span - degree, degree + 1) = SplineType::BasisFunctions(parameters[i], degree, knots);
+      b.col(row++) = points.col(i);
+    }
+  }
+  b.col(0) = points.col(0);
+  b.col(b.cols() - 1) = points.col(points.cols() - 1);
+  A(0, 0) = 1;
+  A(n - 1, n - 1) = 1;
+
+  // Solve
+  FullPivLU<MatrixType> lu(A);
+  ControlPointVectorType controlPoints = lu.solve(MatrixType(b.transpose())).transpose();
+
+  SplineType spline(knots, controlPoints);
+
+  return spline;
+}
+
+template <typename SplineType>
+template <typename PointArrayType, typename IndexArray>
+SplineType SplineFitting<SplineType>::InterpolateWithDerivatives(const PointArrayType& points,
+                                                                 const PointArrayType& derivatives,
+                                                                 const IndexArray& derivativeIndices,
+                                                                 const unsigned int degree) {
+  ParameterVectorType parameters;
+  ChordLengths(points, parameters);
+  return InterpolateWithDerivatives(points, derivatives, derivativeIndices, degree, parameters);
+}
+}  // namespace Eigen
+
+#endif  // EIGEN_SPLINE_FITTING_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/SplineFwd.h b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/SplineFwd.h
new file mode 100644
index 0000000..ff648d4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/Eigen/src/Splines/SplineFwd.h
@@ -0,0 +1,109 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPLINES_FWD_H
+#define EIGEN_SPLINES_FWD_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+#include "../../../../Eigen/Core"
+
+namespace Eigen {
+template <typename Scalar, int Dim, int Degree = Dynamic>
+class Spline;
+
+template <typename SplineType, int DerivativeOrder = Dynamic>
+struct SplineTraits {};
+
+/**
+ * \ingroup Splines_Module
+ * \brief Compile-time attributes of the Spline class for Dynamic degree.
+ **/
+template <typename Scalar_, int Dim_, int Degree_>
+struct SplineTraits<Spline<Scalar_, Dim_, Degree_>, Dynamic> {
+  typedef Scalar_ Scalar; /*!< The spline curve's scalar type. */
+  enum { Dimension = Dim_ /*!< The spline curve's dimension. */ };
+  enum { Degree = Degree_ /*!< The spline curve's degree. */ };
+
+  enum {
+    OrderAtCompileTime = Degree_ == Dynamic ? Dynamic : Degree_ + 1 /*!< The spline curve's order at compile-time. */
+  };
+  enum {
+    NumOfDerivativesAtCompileTime = OrderAtCompileTime /*!< The number of derivatives defined for the current spline. */
+  };
+
+  enum { DerivativeMemoryLayout = Dimension == 1 ? RowMajor : ColMajor /*!< The derivative type's memory layout. */ };
+
+  /** \brief The data type used to store non-zero basis functions. */
+  typedef Array<Scalar, 1, OrderAtCompileTime> BasisVectorType;
+
+  /** \brief The data type used to store the values of the basis function derivatives. */
+  typedef Array<Scalar, Dynamic, Dynamic, RowMajor, NumOfDerivativesAtCompileTime, OrderAtCompileTime>
+      BasisDerivativeType;
+
+  /** \brief The data type used to store the spline's derivative values. */
+  typedef Array<Scalar, Dimension, Dynamic, DerivativeMemoryLayout, Dimension, NumOfDerivativesAtCompileTime>
+      DerivativeType;
+
+  /** \brief The point type the spline is representing. */
+  typedef Array<Scalar, Dimension, 1> PointType;
+
+  /** \brief The data type used to store knot vectors. */
+  typedef Array<Scalar, 1, Dynamic> KnotVectorType;
+
+  /** \brief The data type used to store parameter vectors. */
+  typedef Array<Scalar, 1, Dynamic> ParameterVectorType;
+
+  /** \brief The data type representing the spline's control points. */
+  typedef Array<Scalar, Dimension, Dynamic> ControlPointVectorType;
+};
+
+/**
+ * \ingroup Splines_Module
+ * \brief Compile-time attributes of the Spline class for fixed degree.
+ *
+ * The traits class inherits all attributes from the SplineTraits of Dynamic degree.
+ **/
+template <typename Scalar_, int Dim_, int Degree_, int _DerivativeOrder>
+struct SplineTraits<Spline<Scalar_, Dim_, Degree_>, _DerivativeOrder>
+    : public SplineTraits<Spline<Scalar_, Dim_, Degree_> > {
+  enum {
+    OrderAtCompileTime = Degree_ == Dynamic ? Dynamic : Degree_ + 1 /*!< The spline curve's order at compile-time. */
+  };
+  enum {
+    NumOfDerivativesAtCompileTime =
+        _DerivativeOrder == Dynamic
+            ? Dynamic
+            : _DerivativeOrder + 1 /*!< The number of derivatives defined for the current spline. */
+  };
+
+  enum { DerivativeMemoryLayout = Dim_ == 1 ? RowMajor : ColMajor /*!< The derivative type's memory layout. */ };
+
+  /** \brief The data type used to store the values of the basis function derivatives. */
+  typedef Array<Scalar_, Dynamic, Dynamic, RowMajor, NumOfDerivativesAtCompileTime, OrderAtCompileTime>
+      BasisDerivativeType;
+
+  /** \brief The data type used to store the spline's derivative values. */
+  typedef Array<Scalar_, Dim_, Dynamic, DerivativeMemoryLayout, Dim_, NumOfDerivativesAtCompileTime> DerivativeType;
+};
+
+/** \brief 2D float B-spline with dynamic degree. */
+typedef Spline<float, 2> Spline2f;
+
+/** \brief 3D float B-spline with dynamic degree. */
+typedef Spline<float, 3> Spline3f;
+
+/** \brief 2D double B-spline with dynamic degree. */
+typedef Spline<double, 2> Spline2d;
+
+/** \brief 3D double B-spline with dynamic degree. */
+typedef Spline<double, 3> Spline3d;
+}  // namespace Eigen
+
+#endif  // EIGEN_SPLINES_FWD_H
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/README.txt b/data_acq_and_processing/third_party/eigen/unsupported/README.txt
new file mode 100644
index 0000000..70793bf
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/README.txt
@@ -0,0 +1,50 @@
+This directory contains contributions from various users.
+They are provided "as is", without any support. Nevertheless,
+most of them are subject to be included in Eigen in the future.
+
+In order to use an unsupported module you have to do either:
+
+ - add the path_to_eigen/unsupported directory to your include path and do:
+   #include <Eigen/ModuleHeader>
+
+ - or directly do:
+   #include <unsupported/Eigen/ModuleHeader>
+
+
+If you are interested in contributing to one of them, or have other stuff
+you would like to share, feel free to contact us:
+http://eigen.tuxfamily.org/index.php?title=Main_Page#Mailing_list
+
+Any kind of contributions are much appreciated, even very preliminary ones.
+However, it:
+ - must rely on Eigen,
+ - must be highly related to math,
+ - should have some general purpose in the sense that it could
+   potentially become an official Eigen module (or be merged into another one).
+
+In doubt feel free to contact us. For instance, if your addons is very too specific
+but it shows an interesting way of using Eigen, then it could be a nice demo.
+
+
+This directory is organized as follow:
+
+unsupported/Eigen/ModuleHeader1
+unsupported/Eigen/ModuleHeader2
+unsupported/Eigen/...
+unsupported/Eigen/src/Module1/SourceFile1.h
+unsupported/Eigen/src/Module1/SourceFile2.h
+unsupported/Eigen/src/Module1/...
+unsupported/Eigen/src/Module2/SourceFile1.h
+unsupported/Eigen/src/Module2/SourceFile2.h
+unsupported/Eigen/src/Module2/...
+unsupported/Eigen/src/...
+unsupported/doc/snippets/.cpp   <- code snippets for the doc
+unsupported/doc/examples/.cpp   <- examples for the doc
+unsupported/doc/TutorialModule1.dox
+unsupported/doc/TutorialModule2.dox
+unsupported/doc/...
+unsupported/test/.cpp           <- unit test files
+
+The documentation is generated at the same time than the main Eigen documentation.
+The .html files are generated in: build_dir/doc/html/unsupported/
+
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/bench/bench_svd.cpp b/data_acq_and_processing/third_party/eigen/unsupported/bench/bench_svd.cpp
new file mode 100644
index 0000000..40a989b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/bench/bench_svd.cpp
@@ -0,0 +1,111 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
+// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
+// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
+// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/
+
+// Bench to compare the efficiency of SVD algorithms
+
+#include <iostream>
+#include <bench/BenchTimer.h>
+#include <unsupported/Eigen/SVD>
+
+using namespace Eigen;
+using namespace std;
+
+// number of computations of each algorithm before the print of the time
+#ifndef REPEAT
+#define REPEAT 10
+#endif
+
+// number of tests of the same type
+#ifndef NUMBER_SAMPLE
+#define NUMBER_SAMPLE 2
+#endif
+
+template <typename MatrixType>
+void bench_svd(const MatrixType& a = MatrixType()) {
+  MatrixType m = MatrixType::Random(a.rows(), a.cols());
+  BenchTimer timerJacobi;
+  BenchTimer timerBDC;
+  timerJacobi.reset();
+  timerBDC.reset();
+
+  cout << " Only compute Singular Values" << endl;
+  for (int k = 1; k <= NUMBER_SAMPLE; ++k) {
+    timerBDC.start();
+    for (int i = 0; i < REPEAT; ++i) {
+      BDCSVD<MatrixType> bdc_matrix(m);
+    }
+    timerBDC.stop();
+
+    timerJacobi.start();
+    for (int i = 0; i < REPEAT; ++i) {
+      JacobiSVD<MatrixType> jacobi_matrix(m);
+    }
+    timerJacobi.stop();
+
+    cout << "Sample " << k << " : " << REPEAT << " computations :  Jacobi : " << fixed << timerJacobi.value() << "s ";
+    cout << " || "
+         << " BDC : " << timerBDC.value() << "s " << endl
+         << endl;
+
+    if (timerBDC.value() >= timerJacobi.value())
+      cout << "KO : BDC is " << timerJacobi.value() / timerBDC.value() << "  times faster than Jacobi" << endl;
+    else
+      cout << "OK : BDC is " << timerJacobi.value() / timerBDC.value() << "  times faster than Jacobi" << endl;
+  }
+  cout << "       =================" << endl;
+  std::cout << std::endl;
+  timerJacobi.reset();
+  timerBDC.reset();
+  cout << " Computes rotation matrix" << endl;
+  for (int k = 1; k <= NUMBER_SAMPLE; ++k) {
+    timerBDC.start();
+    for (int i = 0; i < REPEAT; ++i) {
+      BDCSVD<MatrixType> bdc_matrix(m, ComputeFullU | ComputeFullV);
+    }
+    timerBDC.stop();
+
+    timerJacobi.start();
+    for (int i = 0; i < REPEAT; ++i) {
+      JacobiSVD<MatrixType> jacobi_matrix(m, ComputeFullU | ComputeFullV);
+    }
+    timerJacobi.stop();
+
+    cout << "Sample " << k << " : " << REPEAT << " computations :  Jacobi : " << fixed << timerJacobi.value() << "s ";
+    cout << " || "
+         << " BDC : " << timerBDC.value() << "s " << endl
+         << endl;
+
+    if (timerBDC.value() >= timerJacobi.value())
+      cout << "KO : BDC is " << timerJacobi.value() / timerBDC.value() << "  times faster than Jacobi" << endl;
+    else
+      cout << "OK : BDC is " << timerJacobi.value() / timerBDC.value() << "  times faster than Jacobi" << endl;
+  }
+  std::cout << std::endl;
+}
+
+int main(int argc, char* argv[]) {
+  std::cout << std::endl;
+
+  std::cout << "On a (Dynamic, Dynamic) (6, 6) Matrix" << std::endl;
+  bench_svd<Matrix<double, Dynamic, Dynamic> >(Matrix<double, Dynamic, Dynamic>(6, 6));
+
+  std::cout << "On a (Dynamic, Dynamic) (32, 32) Matrix" << std::endl;
+  bench_svd<Matrix<double, Dynamic, Dynamic> >(Matrix<double, Dynamic, Dynamic>(32, 32));
+
+  // std::cout<<"On a (Dynamic, Dynamic) (128, 128) Matrix" <<std::endl;
+  // bench_svd<Matrix<double,Dynamic,Dynamic> >(Matrix<double,Dynamic,Dynamic>(128, 128));
+
+  std::cout << "On a (Dynamic, Dynamic) (160, 160) Matrix" << std::endl;
+  bench_svd<Matrix<double, Dynamic, Dynamic> >(Matrix<double, Dynamic, Dynamic>(160, 160));
+
+  std::cout << "--------------------------------------------------------------------" << std::endl;
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/AutoDiff/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/AutoDiff/CMakeLists.txt
new file mode 100644
index 0000000..cb5b986
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/AutoDiff/CMakeLists.txt
@@ -0,0 +1 @@
+eigen_add_benchmark(bench_autodiff bench_autodiff.cpp)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/AutoDiff/bench_autodiff.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/AutoDiff/bench_autodiff.cpp
new file mode 100644
index 0000000..74c334f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/AutoDiff/bench_autodiff.cpp
@@ -0,0 +1,177 @@
+// Benchmarks for Eigen AutoDiff module.
+// Compares AutoDiff Jacobian computation against NumericalDiff and hand-coded Jacobians.
+
+#include <benchmark/benchmark.h>
+#include <Eigen/Core>
+#include <unsupported/Eigen/AutoDiff>
+#include <unsupported/Eigen/NumericalDiff>
+
+using namespace Eigen;
+
+// --- Small functor: Rosenbrock-like (2 inputs -> 2 outputs) ---
+struct SmallFunctor {
+  typedef Matrix<double, 2, 1> InputType;
+  typedef Matrix<double, 2, 1> ValueType;
+  typedef Matrix<double, 2, 2> JacobianType;
+
+  enum { InputsAtCompileTime = 2, ValuesAtCompileTime = 2 };
+
+  template <typename T>
+  void operator()(const Matrix<T, 2, 1>& x, Matrix<T, 2, 1>* v) const {
+    (*v)(0) = T(1) - x(0);
+    (*v)(1) = T(10) * (x(1) - x(0) * x(0));
+  }
+};
+
+// --- Medium functor: chain of operations (6 inputs -> 6 outputs) ---
+struct MediumFunctor {
+  typedef Matrix<double, 6, 1> InputType;
+  typedef Matrix<double, 6, 1> ValueType;
+  typedef Matrix<double, 6, 6> JacobianType;
+
+  enum { InputsAtCompileTime = 6, ValuesAtCompileTime = 6 };
+
+  template <typename T>
+  void operator()(const Matrix<T, 6, 1>& x, Matrix<T, 6, 1>* v) const {
+    (*v)(0) = sin(x(0)) * cos(x(1)) + x(2) * x(2);
+    (*v)(1) = exp(x(1) * T(0.1)) + x(3);
+    (*v)(2) = x(0) * x(2) - x(4) * x(5);
+    (*v)(3) = sqrt(x(3) * x(3) + T(1)) + x(0);
+    (*v)(4) = x(4) * x(4) + x(5) * x(5) + x(0) * x(1);
+    (*v)(5) = log(x(2) * x(2) + T(1)) + x(3) * x(4);
+  }
+};
+
+// --- Dynamic-size functor (N inputs -> N outputs) ---
+struct DynamicFunctor {
+  typedef Matrix<double, Dynamic, 1> InputType;
+  typedef Matrix<double, Dynamic, 1> ValueType;
+  typedef Matrix<double, Dynamic, Dynamic> JacobianType;
+
+  const int n_;
+  DynamicFunctor(int n) : n_(n) {}
+
+  enum { InputsAtCompileTime = Dynamic, ValuesAtCompileTime = Dynamic };
+
+  int inputs() const { return n_; }
+  int values() const { return n_; }
+
+  template <typename T>
+  void operator()(const Matrix<T, Dynamic, 1>& x, Matrix<T, Dynamic, 1>* v) const {
+    v->resize(n_);
+    (*v)(0) = T(1) - x(0);
+    for (int i = 1; i < n_; ++i) {
+      (*v)(i) = T(10) * (x(i) - x(i - 1) * x(i - 1));
+    }
+  }
+};
+
+// Wrapper for NumericalDiff compatibility.
+struct SmallFunctorND : SmallFunctor {
+  typedef double Scalar;
+  int inputs() const { return 2; }
+  int values() const { return 2; }
+  int operator()(const InputType& x, ValueType& v) const {
+    SmallFunctor::operator()(x, &v);
+    return 0;
+  }
+};
+
+struct MediumFunctorND : MediumFunctor {
+  typedef double Scalar;
+  int inputs() const { return 6; }
+  int values() const { return 6; }
+  int operator()(const InputType& x, ValueType& v) const {
+    MediumFunctor::operator()(x, &v);
+    return 0;
+  }
+};
+
+// --- AutoDiff Jacobian benchmarks ---
+template <typename Functor>
+static void BM_AutoDiffJacobian(benchmark::State& state, Functor func) {
+  AutoDiffJacobian<Functor> adf(func);
+  typename Functor::InputType x = Functor::InputType::Random();
+  typename Functor::ValueType v;
+  typename Functor::JacobianType jac;
+
+  for (auto _ : state) {
+    adf(x, &v, &jac);
+    benchmark::DoNotOptimize(jac.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+// --- Dynamic AutoDiff Jacobian ---
+static void BM_AutoDiffJacobian_Dynamic(benchmark::State& state) {
+  int n = state.range(0);
+  DynamicFunctor func(n);
+  AutoDiffJacobian<DynamicFunctor> adf(func);
+
+  VectorXd x = VectorXd::Random(n);
+  VectorXd v(n);
+  MatrixXd jac(n, n);
+
+  for (auto _ : state) {
+    adf(x, &v, &jac);
+    benchmark::DoNotOptimize(jac.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+// --- NumericalDiff benchmarks ---
+template <typename Functor>
+static void BM_NumericalDiffJacobian(benchmark::State& state, Functor func) {
+  NumericalDiff<Functor> ndf(func);
+  typename Functor::InputType x = Functor::InputType::Random();
+  typename Functor::JacobianType jac;
+
+  for (auto _ : state) {
+    ndf.df(x, jac);
+    benchmark::DoNotOptimize(jac.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+// --- Hand-coded Jacobian (Rosenbrock) for comparison ---
+static void BM_HandCoded_Small(benchmark::State& state) {
+  Vector2d x = Vector2d::Random();
+  Matrix2d jac;
+
+  for (auto _ : state) {
+    jac(0, 0) = -1;
+    jac(0, 1) = 0;
+    jac(1, 0) = -20 * x(0);
+    jac(1, 1) = 10;
+    benchmark::DoNotOptimize(jac.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+// --- Scalar AutoDiff evaluation (no Jacobian, just forward pass) ---
+static void BM_AutoDiffScalar_Eval(benchmark::State& state) {
+  int n = state.range(0);
+  using ADScalar = AutoDiffScalar<VectorXd>;
+  VectorXd x = VectorXd::Random(n);
+
+  for (auto _ : state) {
+    ADScalar sum(0.0, VectorXd::Zero(n));
+    for (int i = 0; i < n; ++i) {
+      ADScalar xi(x(i), n, i);
+      sum += xi * xi + sin(xi);
+    }
+    benchmark::DoNotOptimize(sum.value());
+    benchmark::DoNotOptimize(sum.derivatives().data());
+    benchmark::ClobberMemory();
+  }
+}
+
+BENCHMARK_CAPTURE(BM_AutoDiffJacobian, Small, SmallFunctor());
+BENCHMARK_CAPTURE(BM_AutoDiffJacobian, Medium, MediumFunctor());
+BENCHMARK(BM_AutoDiffJacobian_Dynamic)->Arg(2)->Arg(6)->Arg(20)->Arg(50)->Arg(100);
+
+BENCHMARK_CAPTURE(BM_NumericalDiffJacobian, Small, SmallFunctorND());
+BENCHMARK_CAPTURE(BM_NumericalDiffJacobian, Medium, MediumFunctorND());
+
+BENCHMARK(BM_HandCoded_Small);
+BENCHMARK(BM_AutoDiffScalar_Eval)->Arg(2)->Arg(6)->Arg(20)->Arg(50)->Arg(100);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/CMakeLists.txt
new file mode 100644
index 0000000..ed8c88e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/CMakeLists.txt
@@ -0,0 +1,35 @@
+cmake_minimum_required(VERSION 3.10)
+project(EigenUnsupportedBenchmarks CXX)
+
+find_package(benchmark REQUIRED)
+find_package(Threads REQUIRED)
+
+set(EIGEN_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../..")
+
+# Helper: add a Google Benchmark target (mirrors benchmarks/CMakeLists.txt).
+#   eigen_add_benchmark(name source [LIBRARIES lib1 lib2 ...] [DEFINITIONS def1 def2 ...])
+function(eigen_add_benchmark name source)
+  cmake_parse_arguments(BENCH "" "" "LIBRARIES;DEFINITIONS" ${ARGN})
+  if(NOT IS_ABSOLUTE "${source}")
+    set(source "${CMAKE_CURRENT_SOURCE_DIR}/${source}")
+  endif()
+  add_executable(${name} ${source})
+  target_include_directories(${name} PRIVATE ${EIGEN_SOURCE_DIR})
+  target_link_libraries(${name} PRIVATE benchmark::benchmark benchmark::benchmark_main
+                                        Threads::Threads)
+  if(BENCH_LIBRARIES)
+    target_link_libraries(${name} PRIVATE ${BENCH_LIBRARIES})
+  endif()
+  target_compile_options(${name} PRIVATE -O3 -DNDEBUG)
+  if(BENCH_DEFINITIONS)
+    target_compile_definitions(${name} PRIVATE ${BENCH_DEFINITIONS})
+  endif()
+endfunction()
+
+add_subdirectory(Tensor)
+add_subdirectory(MatrixFunctions)
+add_subdirectory(SpecialFunctions)
+add_subdirectory(AutoDiff)
+add_subdirectory(Splines)
+add_subdirectory(IterativeSolvers)
+add_subdirectory(KroneckerProduct)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/IterativeSolvers/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/IterativeSolvers/CMakeLists.txt
new file mode 100644
index 0000000..4ae536a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/IterativeSolvers/CMakeLists.txt
@@ -0,0 +1 @@
+eigen_add_benchmark(bench_iterative_solvers bench_iterative_solvers.cpp)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/IterativeSolvers/bench_iterative_solvers.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/IterativeSolvers/bench_iterative_solvers.cpp
new file mode 100644
index 0000000..5105d40
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/IterativeSolvers/bench_iterative_solvers.cpp
@@ -0,0 +1,209 @@
+// Benchmarks for unsupported iterative solvers: GMRES, MINRES, IDRS, IDRSTABL, BiCGSTABL, DGMRES.
+
+#include <benchmark/benchmark.h>
+#include <Eigen/Sparse>
+#include <Eigen/IterativeLinearSolvers>
+#include <unsupported/Eigen/IterativeSolvers>
+
+using namespace Eigen;
+
+typedef double Scalar;
+typedef SparseMatrix<Scalar> SpMat;
+typedef Matrix<Scalar, Dynamic, 1> Vec;
+
+// Generate a SPD sparse matrix (Laplacian-like with diagonal dominance).
+static SpMat generateSPD(int n, int bandwidth) {
+  SpMat A(n, n);
+  std::vector<Triplet<Scalar>> trips;
+  trips.reserve(n * (2 * bandwidth + 1));
+  for (int i = 0; i < n; ++i) {
+    Scalar diag = 0;
+    for (int j = std::max(0, i - bandwidth); j < std::min(n, i + bandwidth + 1); ++j) {
+      if (i != j) {
+        Scalar val = -1.0 / (1 + std::abs(i - j));
+        trips.emplace_back(i, j, val);
+        diag -= val;
+      }
+    }
+    trips.emplace_back(i, i, diag + 1.0);
+  }
+  A.setFromTriplets(trips.begin(), trips.end());
+  return A;
+}
+
+// Generate a general (non-symmetric) sparse matrix.
+static SpMat generateGeneral(int n, int bandwidth) {
+  SpMat A(n, n);
+  std::vector<Triplet<Scalar>> trips;
+  trips.reserve(n * (2 * bandwidth + 1));
+  for (int i = 0; i < n; ++i) {
+    Scalar diag = 0;
+    for (int j = std::max(0, i - bandwidth); j < std::min(n, i + bandwidth + 1); ++j) {
+      if (i != j) {
+        Scalar val = -0.5 / (1 + std::abs(i - j));
+        if (j > i) val *= 1.5;  // asymmetry
+        trips.emplace_back(i, j, val);
+        diag += std::abs(val);
+      }
+    }
+    trips.emplace_back(i, i, diag + 1.0);  // diagonal dominance
+  }
+  A.setFromTriplets(trips.begin(), trips.end());
+  return A;
+}
+
+// --- GMRES ---
+static void BM_GMRES(benchmark::State& state) {
+  int n = state.range(0);
+  int bw = state.range(1);
+  SpMat A = generateGeneral(n, bw);
+  Vec b = Vec::Random(n);
+
+  GMRES<SpMat> solver;
+  solver.setMaxIterations(1000);
+  solver.setTolerance(1e-10);
+  solver.compute(A);
+
+  for (auto _ : state) {
+    Vec x = solver.solve(b);
+    benchmark::DoNotOptimize(x.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["iterations"] = solver.iterations();
+}
+
+// --- DGMRES ---
+static void BM_DGMRES(benchmark::State& state) {
+  int n = state.range(0);
+  int bw = state.range(1);
+  SpMat A = generateGeneral(n, bw);
+  Vec b = Vec::Random(n);
+
+  DGMRES<SpMat> solver;
+  solver.setMaxIterations(1000);
+  solver.setTolerance(1e-10);
+  solver.compute(A);
+
+  for (auto _ : state) {
+    Vec x = solver.solve(b);
+    benchmark::DoNotOptimize(x.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["iterations"] = solver.iterations();
+}
+
+// --- MINRES (SPD matrices) ---
+static void BM_MINRES(benchmark::State& state) {
+  int n = state.range(0);
+  int bw = state.range(1);
+  SpMat A = generateSPD(n, bw);
+  Vec b = Vec::Random(n);
+
+  MINRES<SpMat> solver;
+  solver.setMaxIterations(1000);
+  solver.setTolerance(1e-10);
+  solver.compute(A);
+
+  for (auto _ : state) {
+    Vec x = solver.solve(b);
+    benchmark::DoNotOptimize(x.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["iterations"] = solver.iterations();
+}
+
+// --- IDRS ---
+static void BM_IDRS(benchmark::State& state) {
+  int n = state.range(0);
+  int bw = state.range(1);
+  SpMat A = generateGeneral(n, bw);
+  Vec b = Vec::Random(n);
+
+  IDRS<SpMat> solver;
+  solver.setMaxIterations(1000);
+  solver.setTolerance(1e-10);
+  solver.compute(A);
+
+  for (auto _ : state) {
+    Vec x = solver.solve(b);
+    benchmark::DoNotOptimize(x.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["iterations"] = solver.iterations();
+}
+
+// --- BiCGSTABL ---
+static void BM_BiCGSTABL(benchmark::State& state) {
+  int n = state.range(0);
+  int bw = state.range(1);
+  SpMat A = generateGeneral(n, bw);
+  Vec b = Vec::Random(n);
+
+  BiCGSTABL<SpMat> solver;
+  solver.setMaxIterations(1000);
+  solver.setTolerance(1e-10);
+  solver.compute(A);
+
+  for (auto _ : state) {
+    Vec x = solver.solve(b);
+    benchmark::DoNotOptimize(x.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["iterations"] = solver.iterations();
+}
+
+// --- Compare with CG (supported module, SPD only) ---
+static void BM_CG_Reference(benchmark::State& state) {
+  int n = state.range(0);
+  int bw = state.range(1);
+  SpMat A = generateSPD(n, bw);
+  Vec b = Vec::Random(n);
+
+  ConjugateGradient<SpMat> solver;
+  solver.setMaxIterations(1000);
+  solver.setTolerance(1e-10);
+  solver.compute(A);
+
+  for (auto _ : state) {
+    Vec x = solver.solve(b);
+    benchmark::DoNotOptimize(x.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["iterations"] = solver.iterations();
+}
+
+// --- Compare with BiCGSTAB (supported module, general) ---
+static void BM_BiCGSTAB_Reference(benchmark::State& state) {
+  int n = state.range(0);
+  int bw = state.range(1);
+  SpMat A = generateGeneral(n, bw);
+  Vec b = Vec::Random(n);
+
+  BiCGSTAB<SpMat> solver;
+  solver.setMaxIterations(1000);
+  solver.setTolerance(1e-10);
+  solver.compute(A);
+
+  for (auto _ : state) {
+    Vec x = solver.solve(b);
+    benchmark::DoNotOptimize(x.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["iterations"] = solver.iterations();
+}
+
+static void SolverSizes(::benchmark::Benchmark* b) {
+  for (int n : {1000, 10000, 100000}) {
+    for (int bw : {5, 20}) {
+      b->Args({n, bw});
+    }
+  }
+}
+
+BENCHMARK(BM_GMRES)->Apply(SolverSizes);
+BENCHMARK(BM_DGMRES)->Apply(SolverSizes);
+BENCHMARK(BM_MINRES)->Apply(SolverSizes);
+BENCHMARK(BM_IDRS)->Apply(SolverSizes);
+BENCHMARK(BM_BiCGSTABL)->Apply(SolverSizes);
+BENCHMARK(BM_CG_Reference)->Apply(SolverSizes);
+BENCHMARK(BM_BiCGSTAB_Reference)->Apply(SolverSizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/KroneckerProduct/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/KroneckerProduct/CMakeLists.txt
new file mode 100644
index 0000000..ff0d289
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/KroneckerProduct/CMakeLists.txt
@@ -0,0 +1 @@
+eigen_add_benchmark(bench_kronecker bench_kronecker.cpp)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/KroneckerProduct/bench_kronecker.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/KroneckerProduct/bench_kronecker.cpp
new file mode 100644
index 0000000..882887e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/KroneckerProduct/bench_kronecker.cpp
@@ -0,0 +1,83 @@
+// Benchmarks for Kronecker product (dense and sparse).
+
+#include <benchmark/benchmark.h>
+#include <Eigen/Core>
+#include <Eigen/Sparse>
+#include <unsupported/Eigen/KroneckerProduct>
+
+using namespace Eigen;
+
+typedef double Scalar;
+typedef Matrix<Scalar, Dynamic, Dynamic> Mat;
+typedef SparseMatrix<Scalar> SpMat;
+
+// --- Dense Kronecker product ---
+static void BM_KroneckerDense(benchmark::State& state) {
+  int na = state.range(0);
+  int nb = state.range(1);
+
+  Mat A = Mat::Random(na, na);
+  Mat B = Mat::Random(nb, nb);
+
+  for (auto _ : state) {
+    Mat C = kroneckerProduct(A, B).eval();
+    benchmark::DoNotOptimize(C.data());
+    benchmark::ClobberMemory();
+  }
+  int outSize = na * nb;
+  state.counters["output_size"] = outSize;
+}
+
+// --- Sparse Kronecker product ---
+static void BM_KroneckerSparse(benchmark::State& state) {
+  int na = state.range(0);
+  int nb = state.range(1);
+
+  // Create sparse identity-like matrices with some fill.
+  SpMat A(na, na);
+  SpMat B(nb, nb);
+
+  std::vector<Triplet<Scalar>> tripsA, tripsB;
+  for (int i = 0; i < na; ++i) {
+    tripsA.emplace_back(i, i, 2.0);
+    if (i + 1 < na) {
+      tripsA.emplace_back(i, i + 1, -1.0);
+      tripsA.emplace_back(i + 1, i, -1.0);
+    }
+  }
+  for (int i = 0; i < nb; ++i) {
+    tripsB.emplace_back(i, i, 2.0);
+    if (i + 1 < nb) {
+      tripsB.emplace_back(i, i + 1, -1.0);
+      tripsB.emplace_back(i + 1, i, -1.0);
+    }
+  }
+  A.setFromTriplets(tripsA.begin(), tripsA.end());
+  B.setFromTriplets(tripsB.begin(), tripsB.end());
+
+  for (auto _ : state) {
+    SpMat C = kroneckerProduct(A, B).eval();
+    benchmark::DoNotOptimize(C.valuePtr());
+    benchmark::ClobberMemory();
+  }
+  state.counters["output_size"] = na * nb;
+}
+
+static void KroneckerSizes(::benchmark::Benchmark* b) {
+  for (int na : {4, 8, 16}) {
+    for (int nb : {4, 8, 16}) {
+      b->Args({na, nb});
+    }
+  }
+}
+
+static void KroneckerSparseSizes(::benchmark::Benchmark* b) {
+  for (int na : {16, 32, 64, 128}) {
+    for (int nb : {16, 32, 64, 128}) {
+      b->Args({na, nb});
+    }
+  }
+}
+
+BENCHMARK(BM_KroneckerDense)->Apply(KroneckerSizes);
+BENCHMARK(BM_KroneckerSparse)->Apply(KroneckerSparseSizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/CMakeLists.txt
new file mode 100644
index 0000000..2a612ed
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/CMakeLists.txt
@@ -0,0 +1,3 @@
+eigen_add_benchmark(bench_matrix_exponential bench_matrix_exponential.cpp)
+eigen_add_benchmark(bench_matrix_logarithm bench_matrix_logarithm.cpp)
+eigen_add_benchmark(bench_matrix_power bench_matrix_power.cpp)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/bench_matrix_exponential.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/bench_matrix_exponential.cpp
new file mode 100644
index 0000000..3778b76
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/bench_matrix_exponential.cpp
@@ -0,0 +1,52 @@
+// Benchmarks for matrix exponential.
+// Critical for Sophus Lie group operations (SLAM, visual odometry).
+
+#include <benchmark/benchmark.h>
+#include <Eigen/Core>
+#include <unsupported/Eigen/MatrixFunctions>
+
+using namespace Eigen;
+
+#ifndef SCALAR
+#define SCALAR double
+#endif
+
+typedef SCALAR Scalar;
+
+static void BM_MatrixExp(benchmark::State& state) {
+  int n = state.range(0);
+  typedef Matrix<Scalar, Dynamic, Dynamic> MatrixType;
+
+  // Generate a random matrix with reasonable spectral radius.
+  MatrixType A = MatrixType::Random(n, n) / Scalar(n);
+  MatrixType result(n, n);
+
+  for (auto _ : state) {
+    result = A.exp();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+// Fixed-size specializations for Lie group sizes.
+template <int N>
+static void BM_MatrixExp_Fixed(benchmark::State& state) {
+  typedef Matrix<Scalar, N, N> MatrixType;
+
+  MatrixType A = MatrixType::Random() / Scalar(N);
+  MatrixType result;
+
+  for (auto _ : state) {
+    result = A.exp();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+// Dynamic sizes: Lie groups (2,3,4) plus larger.
+BENCHMARK(BM_MatrixExp)->Arg(2)->Arg(3)->Arg(4)->Arg(8)->Arg(16)->Arg(32)->Arg(64)->Arg(128);
+
+// Fixed-size Lie group dimensions.
+BENCHMARK(BM_MatrixExp_Fixed<2>);
+BENCHMARK(BM_MatrixExp_Fixed<3>);
+BENCHMARK(BM_MatrixExp_Fixed<4>);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/bench_matrix_logarithm.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/bench_matrix_logarithm.cpp
new file mode 100644
index 0000000..4d1c8ea
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/bench_matrix_logarithm.cpp
@@ -0,0 +1,51 @@
+// Benchmarks for matrix logarithm.
+// Inverse of matrix exponential, used for Lie group log maps.
+
+#include <benchmark/benchmark.h>
+#include <Eigen/Core>
+#include <unsupported/Eigen/MatrixFunctions>
+
+using namespace Eigen;
+
+#ifndef SCALAR
+#define SCALAR double
+#endif
+
+typedef SCALAR Scalar;
+
+static void BM_MatrixLog(benchmark::State& state) {
+  int n = state.range(0);
+  typedef Matrix<Scalar, Dynamic, Dynamic> MatrixType;
+
+  // Generate a matrix close to identity for stable log computation.
+  MatrixType A = MatrixType::Identity(n, n) + MatrixType::Random(n, n) / Scalar(n * 2);
+  // Ensure A is in the principal branch by computing exp(small matrix).
+  A = (MatrixType::Random(n, n) / Scalar(n * 4)).exp();
+  MatrixType result(n, n);
+
+  for (auto _ : state) {
+    result = A.log();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+template <int N>
+static void BM_MatrixLog_Fixed(benchmark::State& state) {
+  typedef Matrix<Scalar, N, N> MatrixType;
+
+  MatrixType A = (MatrixType::Random() / Scalar(N * 4)).exp();
+  MatrixType result;
+
+  for (auto _ : state) {
+    result = A.log();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+BENCHMARK(BM_MatrixLog)->Arg(2)->Arg(3)->Arg(4)->Arg(8)->Arg(16)->Arg(32)->Arg(64);
+
+BENCHMARK(BM_MatrixLog_Fixed<2>);
+BENCHMARK(BM_MatrixLog_Fixed<3>);
+BENCHMARK(BM_MatrixLog_Fixed<4>);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/bench_matrix_power.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/bench_matrix_power.cpp
new file mode 100644
index 0000000..a1ebea0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/MatrixFunctions/bench_matrix_power.cpp
@@ -0,0 +1,99 @@
+// Benchmarks for matrix power functions: sqrt, pow, cos, sin, cosh, sinh.
+
+#include <benchmark/benchmark.h>
+#include <Eigen/Core>
+#include <unsupported/Eigen/MatrixFunctions>
+
+using namespace Eigen;
+
+typedef double Scalar;
+typedef Matrix<Scalar, Dynamic, Dynamic> Mat;
+
+static void BM_MatrixSqrt(benchmark::State& state) {
+  int n = state.range(0);
+  // SPD matrix has well-defined sqrt.
+  Mat tmp = Mat::Random(n, n);
+  Mat A = tmp * tmp.transpose() + Mat::Identity(n, n);
+  Mat result(n, n);
+
+  for (auto _ : state) {
+    result = A.sqrt();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void BM_MatrixPow(benchmark::State& state) {
+  int n = state.range(0);
+  Mat tmp = Mat::Random(n, n);
+  Mat A = tmp * tmp.transpose() + Mat::Identity(n, n);
+  Mat result(n, n);
+  Scalar p = 2.5;
+
+  for (auto _ : state) {
+    result = A.pow(p);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void BM_MatrixCos(benchmark::State& state) {
+  int n = state.range(0);
+  Mat A = Mat::Random(n, n) / Scalar(n);
+  Mat result(n, n);
+
+  for (auto _ : state) {
+    result = A.cos();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void BM_MatrixSin(benchmark::State& state) {
+  int n = state.range(0);
+  Mat A = Mat::Random(n, n) / Scalar(n);
+  Mat result(n, n);
+
+  for (auto _ : state) {
+    result = A.sin();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void BM_MatrixCosh(benchmark::State& state) {
+  int n = state.range(0);
+  Mat A = Mat::Random(n, n) / Scalar(n);
+  Mat result(n, n);
+
+  for (auto _ : state) {
+    result = A.cosh();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void BM_MatrixSinh(benchmark::State& state) {
+  int n = state.range(0);
+  Mat A = Mat::Random(n, n) / Scalar(n);
+  Mat result(n, n);
+
+  for (auto _ : state) {
+    result = A.sinh();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void MatPowerSizes(::benchmark::Benchmark* b) {
+  for (int n : {4, 8, 16, 32, 64}) {
+    b->Arg(n);
+  }
+}
+
+BENCHMARK(BM_MatrixSqrt)->Apply(MatPowerSizes);
+BENCHMARK(BM_MatrixPow)->Apply(MatPowerSizes);
+BENCHMARK(BM_MatrixCos)->Apply(MatPowerSizes);
+BENCHMARK(BM_MatrixSin)->Apply(MatPowerSizes);
+BENCHMARK(BM_MatrixCosh)->Apply(MatPowerSizes);
+BENCHMARK(BM_MatrixSinh)->Apply(MatPowerSizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/SpecialFunctions/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/SpecialFunctions/CMakeLists.txt
new file mode 100644
index 0000000..a673413
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/SpecialFunctions/CMakeLists.txt
@@ -0,0 +1 @@
+eigen_add_benchmark(bench_special_functions bench_special_functions.cpp)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/SpecialFunctions/bench_special_functions.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/SpecialFunctions/bench_special_functions.cpp
new file mode 100644
index 0000000..dc6505d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/SpecialFunctions/bench_special_functions.cpp
@@ -0,0 +1,127 @@
+// Benchmarks for special functions beyond what bench_cwise_math.cpp covers.
+// Includes Bessel functions, two-argument functions (igamma, betainc),
+// and additional functions (lgamma, digamma, zeta, polygamma).
+
+#include <benchmark/benchmark.h>
+#include <Eigen/Core>
+#include <unsupported/Eigen/SpecialFunctions>
+
+using namespace Eigen;
+
+// Macro for unary special functions on arrays.
+#define BENCH_SPECIAL_UNARY(NAME, EXPR, LO, HI)                                                          \
+  template <typename Scalar>                                                                             \
+  static void BM_##NAME(benchmark::State& state) {                                                       \
+    const Index n = state.range(0);                                                                      \
+    using Arr = Array<Scalar, Dynamic, 1>;                                                               \
+    Arr a = (Arr::Random(n) + Scalar(1)) * Scalar((double(HI) - double(LO)) / 2.0) + Scalar(LO);         \
+    Arr b(n);                                                                                            \
+    for (auto _ : state) {                                                                               \
+      b = EXPR;                                                                                          \
+      benchmark::DoNotOptimize(b.data());                                                                \
+    }                                                                                                    \
+    state.counters["Elements/s"] = benchmark::Counter(n, benchmark::Counter::kIsIterationInvariantRate); \
+    state.SetBytesProcessed(state.iterations() * n * sizeof(Scalar) * 2);                                \
+  }
+
+// Macro for binary special functions on arrays.
+#define BENCH_SPECIAL_BINARY(NAME, EXPR, LO_A, HI_A, LO_B, HI_B)                                         \
+  template <typename Scalar>                                                                             \
+  static void BM_##NAME(benchmark::State& state) {                                                       \
+    const Index n = state.range(0);                                                                      \
+    using Arr = Array<Scalar, Dynamic, 1>;                                                               \
+    Arr a = (Arr::Random(n) + Scalar(1)) * Scalar((double(HI_A) - double(LO_A)) / 2.0) + Scalar(LO_A);   \
+    Arr b = (Arr::Random(n) + Scalar(1)) * Scalar((double(HI_B) - double(LO_B)) / 2.0) + Scalar(LO_B);   \
+    Arr c(n);                                                                                            \
+    for (auto _ : state) {                                                                               \
+      c = EXPR;                                                                                          \
+      benchmark::DoNotOptimize(c.data());                                                                \
+    }                                                                                                    \
+    state.counters["Elements/s"] = benchmark::Counter(n, benchmark::Counter::kIsIterationInvariantRate); \
+    state.SetBytesProcessed(state.iterations() * n * sizeof(Scalar) * 3);                                \
+  }
+
+// --- Unary special functions ---
+BENCH_SPECIAL_UNARY(Lgamma, Eigen::lgamma(a), 0.1, 20)
+BENCH_SPECIAL_UNARY(Digamma, Eigen::digamma(a), 0.1, 20)
+
+// --- Bessel functions (first kind) ---
+BENCH_SPECIAL_UNARY(BesselI0, Eigen::bessel_i0(a), 0, 10)
+BENCH_SPECIAL_UNARY(BesselI1, Eigen::bessel_i1(a), 0, 10)
+BENCH_SPECIAL_UNARY(BesselI0e, Eigen::bessel_i0e(a), 0, 100)
+BENCH_SPECIAL_UNARY(BesselI1e, Eigen::bessel_i1e(a), 0, 100)
+BENCH_SPECIAL_UNARY(BesselJ0, Eigen::bessel_j0(a), 0, 20)
+BENCH_SPECIAL_UNARY(BesselJ1, Eigen::bessel_j1(a), 0, 20)
+
+// --- Bessel functions (second kind) ---
+BENCH_SPECIAL_UNARY(BesselY0, Eigen::bessel_y0(a), 0.1, 20)
+BENCH_SPECIAL_UNARY(BesselY1, Eigen::bessel_y1(a), 0.1, 20)
+BENCH_SPECIAL_UNARY(BesselK0, Eigen::bessel_k0(a), 0.1, 20)
+BENCH_SPECIAL_UNARY(BesselK1, Eigen::bessel_k1(a), 0.1, 20)
+BENCH_SPECIAL_UNARY(BesselK0e, Eigen::bessel_k0e(a), 0.1, 100)
+BENCH_SPECIAL_UNARY(BesselK1e, Eigen::bessel_k1e(a), 0.1, 100)
+
+// --- Two-argument functions ---
+BENCH_SPECIAL_BINARY(Igamma, Eigen::igamma(a, b), 0.1, 10, 0.1, 10)
+BENCH_SPECIAL_BINARY(Igammac, Eigen::igammac(a, b), 0.1, 10, 0.1, 10)
+BENCH_SPECIAL_BINARY(Zeta, Eigen::zeta(a, b), 1.1, 10, 0.1, 10)
+BENCH_SPECIAL_BINARY(Polygamma, Eigen::polygamma(a, b), 1, 4, 0.1, 10)
+
+// --- Ternary: betainc ---
+template <typename Scalar>
+static void BM_Betainc(benchmark::State& state) {
+  const Index n = state.range(0);
+  using Arr = Array<Scalar, Dynamic, 1>;
+  Arr a = (Arr::Random(n) + Scalar(1)) * Scalar(2.5) + Scalar(0.5);  // [0.5, 5.5]
+  Arr b = (Arr::Random(n) + Scalar(1)) * Scalar(2.5) + Scalar(0.5);
+  Arr x = (Arr::Random(n) + Scalar(1)) * Scalar(0.5);  // [0, 1]
+  Arr result(n);
+  for (auto _ : state) {
+    result = Eigen::betainc(a, b, x);
+    benchmark::DoNotOptimize(result.data());
+  }
+  state.counters["Elements/s"] = benchmark::Counter(n, benchmark::Counter::kIsIterationInvariantRate);
+  state.SetBytesProcessed(state.iterations() * n * sizeof(Scalar) * 4);
+}
+
+static void SpecialSizes(::benchmark::Benchmark* b) {
+  for (int n : {256, 4096, 65536, 1048576}) b->Arg(n);
+}
+
+// --- Register float ---
+BENCHMARK(BM_Lgamma<float>)->Apply(SpecialSizes)->Name("Lgamma_float");
+BENCHMARK(BM_Digamma<float>)->Apply(SpecialSizes)->Name("Digamma_float");
+BENCHMARK(BM_BesselI0<float>)->Apply(SpecialSizes)->Name("BesselI0_float");
+BENCHMARK(BM_BesselI1<float>)->Apply(SpecialSizes)->Name("BesselI1_float");
+BENCHMARK(BM_BesselI0e<float>)->Apply(SpecialSizes)->Name("BesselI0e_float");
+BENCHMARK(BM_BesselI1e<float>)->Apply(SpecialSizes)->Name("BesselI1e_float");
+BENCHMARK(BM_BesselJ0<float>)->Apply(SpecialSizes)->Name("BesselJ0_float");
+BENCHMARK(BM_BesselJ1<float>)->Apply(SpecialSizes)->Name("BesselJ1_float");
+BENCHMARK(BM_BesselY0<float>)->Apply(SpecialSizes)->Name("BesselY0_float");
+BENCHMARK(BM_BesselY1<float>)->Apply(SpecialSizes)->Name("BesselY1_float");
+BENCHMARK(BM_BesselK0<float>)->Apply(SpecialSizes)->Name("BesselK0_float");
+BENCHMARK(BM_BesselK1<float>)->Apply(SpecialSizes)->Name("BesselK1_float");
+BENCHMARK(BM_BesselK0e<float>)->Apply(SpecialSizes)->Name("BesselK0e_float");
+BENCHMARK(BM_BesselK1e<float>)->Apply(SpecialSizes)->Name("BesselK1e_float");
+BENCHMARK(BM_Igamma<float>)->Apply(SpecialSizes)->Name("Igamma_float");
+BENCHMARK(BM_Igammac<float>)->Apply(SpecialSizes)->Name("Igammac_float");
+BENCHMARK(BM_Betainc<float>)->Apply(SpecialSizes)->Name("Betainc_float");
+BENCHMARK(BM_Zeta<float>)->Apply(SpecialSizes)->Name("Zeta_float");
+BENCHMARK(BM_Polygamma<float>)->Apply(SpecialSizes)->Name("Polygamma_float");
+
+// --- Register double ---
+BENCHMARK(BM_Lgamma<double>)->Apply(SpecialSizes)->Name("Lgamma_double");
+BENCHMARK(BM_Digamma<double>)->Apply(SpecialSizes)->Name("Digamma_double");
+BENCHMARK(BM_BesselI0<double>)->Apply(SpecialSizes)->Name("BesselI0_double");
+BENCHMARK(BM_BesselI1<double>)->Apply(SpecialSizes)->Name("BesselI1_double");
+BENCHMARK(BM_BesselJ0<double>)->Apply(SpecialSizes)->Name("BesselJ0_double");
+BENCHMARK(BM_BesselJ1<double>)->Apply(SpecialSizes)->Name("BesselJ1_double");
+BENCHMARK(BM_BesselY0<double>)->Apply(SpecialSizes)->Name("BesselY0_double");
+BENCHMARK(BM_BesselY1<double>)->Apply(SpecialSizes)->Name("BesselY1_double");
+BENCHMARK(BM_BesselK0<double>)->Apply(SpecialSizes)->Name("BesselK0_double");
+BENCHMARK(BM_BesselK1<double>)->Apply(SpecialSizes)->Name("BesselK1_double");
+BENCHMARK(BM_Igamma<double>)->Apply(SpecialSizes)->Name("Igamma_double");
+BENCHMARK(BM_Igammac<double>)->Apply(SpecialSizes)->Name("Igammac_double");
+BENCHMARK(BM_Betainc<double>)->Apply(SpecialSizes)->Name("Betainc_double");
+BENCHMARK(BM_Zeta<double>)->Apply(SpecialSizes)->Name("Zeta_double");
+BENCHMARK(BM_Polygamma<double>)->Apply(SpecialSizes)->Name("Polygamma_double");
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Splines/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Splines/CMakeLists.txt
new file mode 100644
index 0000000..a54acc4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Splines/CMakeLists.txt
@@ -0,0 +1 @@
+eigen_add_benchmark(bench_splines bench_splines.cpp)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Splines/bench_splines.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Splines/bench_splines.cpp
new file mode 100644
index 0000000..e422a8f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Splines/bench_splines.cpp
@@ -0,0 +1,98 @@
+// Benchmarks for Eigen Spline module.
+// Tests fitting, evaluation, and derivative computation.
+
+#include <benchmark/benchmark.h>
+#include <Eigen/Core>
+#include <unsupported/Eigen/Splines>
+
+using namespace Eigen;
+
+typedef double Scalar;
+
+// --- Spline fitting (interpolation) ---
+template <int Dim, int Degree>
+static void BM_SplineFit(benchmark::State& state) {
+  const int n = state.range(0);
+
+  typedef Spline<Scalar, Dim> SplineType;
+  typedef typename SplineType::PointType PointType;
+
+  // Generate random points.
+  Matrix<Scalar, Dim, Dynamic> pts(Dim, n);
+  pts.setRandom();
+
+  for (auto _ : state) {
+    SplineType spline = SplineFitting<SplineType>::Interpolate(pts, Degree);
+    benchmark::DoNotOptimize(spline.knots().data());
+    benchmark::ClobberMemory();
+  }
+}
+
+// --- Spline evaluation ---
+template <int Dim, int Degree>
+static void BM_SplineEval(benchmark::State& state) {
+  const int n = state.range(0);  // number of control points for fitting
+  const int neval = 1000;        // number of evaluation points
+
+  typedef Spline<Scalar, Dim> SplineType;
+
+  Matrix<Scalar, Dim, Dynamic> pts(Dim, n);
+  pts.setRandom();
+  SplineType spline = SplineFitting<SplineType>::Interpolate(pts, Degree);
+
+  // Generate evaluation parameters in [0, 1].
+  VectorXd u = VectorXd::LinSpaced(neval, 0, 1);
+
+  for (auto _ : state) {
+    for (int i = 0; i < neval; ++i) {
+      auto pt = spline(u(i));
+      benchmark::DoNotOptimize(pt.data());
+    }
+    benchmark::ClobberMemory();
+  }
+  state.counters["Evals/s"] = benchmark::Counter(neval, benchmark::Counter::kIsIterationInvariantRate);
+}
+
+// --- Spline derivative evaluation ---
+template <int Dim, int Degree>
+static void BM_SplineDerivatives(benchmark::State& state) {
+  const int n = state.range(0);
+  const int neval = 1000;
+
+  typedef Spline<Scalar, Dim> SplineType;
+
+  Matrix<Scalar, Dim, Dynamic> pts(Dim, n);
+  pts.setRandom();
+  SplineType spline = SplineFitting<SplineType>::Interpolate(pts, Degree);
+
+  VectorXd u = VectorXd::LinSpaced(neval, 0, 1);
+
+  for (auto _ : state) {
+    for (int i = 0; i < neval; ++i) {
+      auto derivs = spline.derivatives(u(i), 1);
+      benchmark::DoNotOptimize(derivs.data());
+    }
+    benchmark::ClobberMemory();
+  }
+  state.counters["Evals/s"] = benchmark::Counter(neval, benchmark::Counter::kIsIterationInvariantRate);
+}
+
+static void SplineSizes(::benchmark::Benchmark* b) {
+  for (int n : {10, 50, 200, 1000}) {
+    b->Arg(n);
+  }
+}
+
+// 2D cubic splines
+BENCHMARK(BM_SplineFit<2, 3>)->Apply(SplineSizes)->Name("SplineFit_2D_Cubic");
+BENCHMARK(BM_SplineEval<2, 3>)->Apply(SplineSizes)->Name("SplineEval_2D_Cubic");
+BENCHMARK(BM_SplineDerivatives<2, 3>)->Apply(SplineSizes)->Name("SplineDerivatives_2D_Cubic");
+
+// 3D cubic splines
+BENCHMARK(BM_SplineFit<3, 3>)->Apply(SplineSizes)->Name("SplineFit_3D_Cubic");
+BENCHMARK(BM_SplineEval<3, 3>)->Apply(SplineSizes)->Name("SplineEval_3D_Cubic");
+BENCHMARK(BM_SplineDerivatives<3, 3>)->Apply(SplineSizes)->Name("SplineDerivatives_3D_Cubic");
+
+// 2D quintic splines
+BENCHMARK(BM_SplineFit<2, 5>)->Apply(SplineSizes)->Name("SplineFit_2D_Quintic");
+BENCHMARK(BM_SplineEval<2, 5>)->Apply(SplineSizes)->Name("SplineEval_2D_Quintic");
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/CMakeLists.txt
new file mode 100644
index 0000000..4dab17c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/CMakeLists.txt
@@ -0,0 +1,8 @@
+eigen_add_benchmark(bench_contraction bench_contraction.cpp)
+eigen_add_benchmark(bench_convolution bench_convolution.cpp)
+eigen_add_benchmark(bench_reduction bench_reduction.cpp)
+eigen_add_benchmark(bench_broadcasting bench_broadcasting.cpp)
+eigen_add_benchmark(bench_shuffling bench_shuffling.cpp)
+eigen_add_benchmark(bench_tensor_fft bench_tensor_fft.cpp)
+eigen_add_benchmark(bench_morphing bench_morphing.cpp)
+eigen_add_benchmark(bench_coefficient_wise bench_coefficient_wise.cpp)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_broadcasting.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_broadcasting.cpp
new file mode 100644
index 0000000..01aed20
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_broadcasting.cpp
@@ -0,0 +1,111 @@
+// Benchmarks for Eigen Tensor broadcasting.
+// Tests broadcasting along various dimensions and ranks.
+
+#include <benchmark/benchmark.h>
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using namespace Eigen;
+
+typedef float Scalar;
+
+// --- Broadcast row vector {1,N} -> {M,N} ---
+static void BM_BroadcastRow(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> row(1, N);
+  Tensor<Scalar, 2> result(M, N);
+  row.setRandom();
+
+  Eigen::array<int, 2> bcast = {M, 1};
+
+  for (auto _ : state) {
+    result = row.broadcast(bcast);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar));
+}
+
+// --- Broadcast col vector {M,1} -> {M,N} ---
+static void BM_BroadcastCol(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> col(M, 1);
+  Tensor<Scalar, 2> result(M, N);
+  col.setRandom();
+
+  Eigen::array<int, 2> bcast = {1, N};
+
+  for (auto _ : state) {
+    result = col.broadcast(bcast);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar));
+}
+
+// --- Broadcast + element-wise add (bias addition pattern) ---
+static void BM_BroadcastAdd(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> mat(M, N);
+  Tensor<Scalar, 2> bias(1, N);
+  Tensor<Scalar, 2> result(M, N);
+  mat.setRandom();
+  bias.setRandom();
+
+  Eigen::array<int, 2> bcast = {M, 1};
+
+  for (auto _ : state) {
+    result = mat + bias.broadcast(bcast);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar) * 2);
+}
+
+// --- Rank-4 broadcast (batch x channels x 1 x 1) -> (batch x channels x H x W) ---
+static void BM_BroadcastRank4(benchmark::State& state) {
+  const int batch = state.range(0);
+  const int C = state.range(1);
+  const int H = state.range(2);
+
+  Tensor<Scalar, 4> bias(batch, C, 1, 1);
+  Tensor<Scalar, 4> result(batch, C, H, H);
+  bias.setRandom();
+
+  Eigen::array<int, 4> bcast = {1, 1, H, H};
+
+  for (auto _ : state) {
+    result = bias.broadcast(bcast);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * batch * C * H * H * sizeof(Scalar));
+}
+
+static void BroadcastSizes(::benchmark::Benchmark* b) {
+  for (int m : {64, 256, 1024}) {
+    for (int n : {64, 256, 1024}) {
+      b->Args({m, n});
+    }
+  }
+}
+
+static void Rank4Sizes(::benchmark::Benchmark* b) {
+  for (int batch : {1, 8}) {
+    for (int c : {64, 256}) {
+      for (int h : {16, 32}) {
+        b->Args({batch, c, h});
+      }
+    }
+  }
+}
+
+BENCHMARK(BM_BroadcastRow)->Apply(BroadcastSizes);
+BENCHMARK(BM_BroadcastCol)->Apply(BroadcastSizes);
+BENCHMARK(BM_BroadcastAdd)->Apply(BroadcastSizes);
+BENCHMARK(BM_BroadcastRank4)->Apply(Rank4Sizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_coefficient_wise.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_coefficient_wise.cpp
new file mode 100644
index 0000000..aed4828
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_coefficient_wise.cpp
@@ -0,0 +1,131 @@
+// Benchmarks for Eigen Tensor coefficient-wise operations.
+// Covers activation functions, normalization, and element-wise arithmetic.
+
+#include <benchmark/benchmark.h>
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using namespace Eigen;
+
+typedef float Scalar;
+
+// Macro to define a benchmark for a unary tensor operation.
+#define BENCH_TENSOR_UNARY(NAME, EXPR)                                        \
+  static void BM_##NAME(benchmark::State& state) {                            \
+    const int M = state.range(0);                                             \
+    const int N = state.range(1);                                             \
+    Tensor<Scalar, 2> a(M, N);                                                \
+    a.setRandom();                                                            \
+    Tensor<Scalar, 2> b(M, N);                                                \
+    for (auto _ : state) {                                                    \
+      b = EXPR;                                                               \
+      benchmark::DoNotOptimize(b.data());                                     \
+      benchmark::ClobberMemory();                                             \
+    }                                                                         \
+    state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar) * 2); \
+  }
+
+BENCH_TENSOR_UNARY(Exp, a.exp())
+BENCH_TENSOR_UNARY(Log, a.abs().log())
+BENCH_TENSOR_UNARY(Tanh, a.tanh())
+BENCH_TENSOR_UNARY(Sigmoid, a.sigmoid())
+BENCH_TENSOR_UNARY(ReLU, a.cwiseMax(Scalar(0)))
+BENCH_TENSOR_UNARY(Sqrt, a.abs().sqrt())
+
+// --- Element-wise binary operations ---
+static void BM_Add(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> a(M, N);
+  Tensor<Scalar, 2> b(M, N);
+  Tensor<Scalar, 2> c(M, N);
+  a.setRandom();
+  b.setRandom();
+
+  for (auto _ : state) {
+    c = a + b;
+    benchmark::DoNotOptimize(c.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar) * 3);
+}
+
+static void BM_Mul(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> a(M, N);
+  Tensor<Scalar, 2> b(M, N);
+  Tensor<Scalar, 2> c(M, N);
+  a.setRandom();
+  b.setRandom();
+
+  for (auto _ : state) {
+    c = a * b;
+    benchmark::DoNotOptimize(c.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar) * 3);
+}
+
+// --- Fused multiply-add ---
+static void BM_FMA(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> a(M, N);
+  Tensor<Scalar, 2> b(M, N);
+  Tensor<Scalar, 2> c(M, N);
+  Tensor<Scalar, 2> d(M, N);
+  a.setRandom();
+  b.setRandom();
+  c.setRandom();
+
+  for (auto _ : state) {
+    d = a * b + c;
+    benchmark::DoNotOptimize(d.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar) * 4);
+}
+
+// --- Rank-4 coefficient-wise (CNN feature maps) ---
+static void BM_ReLU_Rank4(benchmark::State& state) {
+  const int batch = state.range(0);
+  const int C = state.range(1);
+  const int H = state.range(2);
+
+  Tensor<Scalar, 4> a(batch, C, H, H);
+  Tensor<Scalar, 4> b(batch, C, H, H);
+  a.setRandom();
+
+  for (auto _ : state) {
+    b = a.cwiseMax(Scalar(0));
+    benchmark::DoNotOptimize(b.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * batch * C * H * H * sizeof(Scalar) * 2);
+}
+
+static void CwiseSizes(::benchmark::Benchmark* b) {
+  for (int size : {256, 1024}) {
+    b->Args({size, size});
+  }
+}
+
+static void Rank4Sizes(::benchmark::Benchmark* b) {
+  b->Args({32, 64, 16});
+  b->Args({8, 128, 32});
+  b->Args({1, 256, 64});
+}
+
+BENCHMARK(BM_Exp)->Apply(CwiseSizes);
+BENCHMARK(BM_Log)->Apply(CwiseSizes);
+BENCHMARK(BM_Tanh)->Apply(CwiseSizes);
+BENCHMARK(BM_Sigmoid)->Apply(CwiseSizes);
+BENCHMARK(BM_ReLU)->Apply(CwiseSizes);
+BENCHMARK(BM_Sqrt)->Apply(CwiseSizes);
+BENCHMARK(BM_Add)->Apply(CwiseSizes);
+BENCHMARK(BM_Mul)->Apply(CwiseSizes);
+BENCHMARK(BM_FMA)->Apply(CwiseSizes);
+BENCHMARK(BM_ReLU_Rank4)->Apply(Rank4Sizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_contraction.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_contraction.cpp
new file mode 100644
index 0000000..0b71ebf
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_contraction.cpp
@@ -0,0 +1,148 @@
+// Benchmarks for Eigen Tensor contraction (generalized GEMM).
+// Tests single-threaded (DefaultDevice) and multi-threaded (ThreadPoolDevice) variants.
+
+#define EIGEN_USE_THREADS
+
+#include <benchmark/benchmark.h>
+#include <unsupported/Eigen/CXX11/Tensor>
+#include <unsupported/Eigen/CXX11/ThreadPool>
+
+using namespace Eigen;
+
+#ifndef SCALAR
+#define SCALAR float
+#endif
+
+typedef SCALAR Scalar;
+
+// --- DefaultDevice contraction (rank-2, equivalent to matrix multiply) ---
+static void BM_Contraction(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+  const int K = state.range(2);
+
+  Tensor<Scalar, 2> A(M, K);
+  Tensor<Scalar, 2> B(K, N);
+  Tensor<Scalar, 2> C(M, N);
+  A.setRandom();
+  B.setRandom();
+
+  using ContractDims = Tensor<Scalar, 2>::DimensionPair;
+  Eigen::array<ContractDims, 1> contract_dims = {ContractDims(1, 0)};
+
+  for (auto _ : state) {
+    C = A.contract(B, contract_dims);
+    benchmark::DoNotOptimize(C.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["GFLOPS"] =
+      benchmark::Counter(2.0 * M * N * K, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
+}
+
+// --- ThreadPoolDevice contraction ---
+static void BM_Contraction_ThreadPool(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+  const int K = state.range(2);
+  const int threads = state.range(3);
+
+  Tensor<Scalar, 2> A(M, K);
+  Tensor<Scalar, 2> B(K, N);
+  Tensor<Scalar, 2> C(M, N);
+  A.setRandom();
+  B.setRandom();
+
+  ThreadPool tp(threads);
+  ThreadPoolDevice dev(&tp, threads);
+
+  using ContractDims = Tensor<Scalar, 2>::DimensionPair;
+  Eigen::array<ContractDims, 1> contract_dims = {ContractDims(1, 0)};
+
+  for (auto _ : state) {
+    C.device(dev) = A.contract(B, contract_dims);
+    benchmark::DoNotOptimize(C.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["GFLOPS"] =
+      benchmark::Counter(2.0 * M * N * K, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
+  state.counters["threads"] = threads;
+}
+
+// --- Rank-3 batch contraction ---
+static void BM_BatchContraction(benchmark::State& state) {
+  const int batch = state.range(0);
+  const int M = state.range(1);
+  const int N = state.range(2);
+  const int K = state.range(3);
+
+  Tensor<Scalar, 3> A(batch, M, K);
+  Tensor<Scalar, 3> B(batch, K, N);
+  Tensor<Scalar, 3> C(batch, M, N);
+  A.setRandom();
+  B.setRandom();
+
+  using ContractDims = Tensor<Scalar, 3>::DimensionPair;
+  Eigen::array<ContractDims, 1> contract_dims = {ContractDims(2, 1)};
+
+  for (auto _ : state) {
+    C = A.contract(B, contract_dims);
+    benchmark::DoNotOptimize(C.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["GFLOPS"] = benchmark::Counter(2.0 * batch * M * N * K, benchmark::Counter::kIsIterationInvariantRate,
+                                                benchmark::Counter::kIs1000);
+}
+
+// --- RowMajor contraction ---
+static void BM_Contraction_RowMajor(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+  const int K = state.range(2);
+
+  Tensor<Scalar, 2, RowMajor> A(M, K);
+  Tensor<Scalar, 2, RowMajor> B(K, N);
+  Tensor<Scalar, 2, RowMajor> C(M, N);
+  A.setRandom();
+  B.setRandom();
+
+  using ContractDims = Tensor<Scalar, 2, RowMajor>::DimensionPair;
+  Eigen::array<ContractDims, 1> contract_dims = {ContractDims(1, 0)};
+
+  for (auto _ : state) {
+    C = A.contract(B, contract_dims);
+    benchmark::DoNotOptimize(C.data());
+    benchmark::ClobberMemory();
+  }
+  state.counters["GFLOPS"] =
+      benchmark::Counter(2.0 * M * N * K, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
+}
+
+static void ContractionSizes(::benchmark::Benchmark* b) {
+  for (int size : {32, 64, 128, 256, 512, 1024}) {
+    b->Args({size, size, size});
+  }
+  // Non-square
+  b->Args({256, 256, 1024});
+  b->Args({1024, 64, 64});
+}
+
+static void ThreadPoolSizes(::benchmark::Benchmark* b) {
+  for (int size : {64, 256, 512, 1024}) {
+    for (int threads : {2, 4, 8}) {
+      b->Args({size, size, size, threads});
+    }
+  }
+}
+
+static void BatchSizes(::benchmark::Benchmark* b) {
+  for (int batch : {1, 8, 32}) {
+    for (int size : {64, 256}) {
+      b->Args({batch, size, size, size});
+    }
+  }
+}
+
+BENCHMARK(BM_Contraction)->Apply(ContractionSizes);
+BENCHMARK(BM_Contraction_RowMajor)->Apply(ContractionSizes);
+BENCHMARK(BM_Contraction_ThreadPool)->Apply(ThreadPoolSizes);
+BENCHMARK(BM_BatchContraction)->Apply(BatchSizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_convolution.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_convolution.cpp
new file mode 100644
index 0000000..46e44ee
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_convolution.cpp
@@ -0,0 +1,151 @@
+// Benchmarks for Eigen Tensor convolution (1D and 2D).
+
+#define EIGEN_USE_THREADS
+
+#include <benchmark/benchmark.h>
+#include <unsupported/Eigen/CXX11/Tensor>
+#include <unsupported/Eigen/CXX11/ThreadPool>
+
+using namespace Eigen;
+
+typedef float Scalar;
+
+// --- 1D convolution ---
+static void BM_Convolve1D(benchmark::State& state) {
+  const int input_size = state.range(0);
+  const int kernel_size = state.range(1);
+
+  Tensor<Scalar, 1> input(input_size);
+  Tensor<Scalar, 1> kernel(kernel_size);
+  input.setRandom();
+  kernel.setRandom();
+
+  Eigen::array<int, 1> dims = {0};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 1> result = input.convolve(kernel, dims);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  double flops = 2.0 * (input_size - kernel_size + 1) * kernel_size;
+  state.counters["GFLOPS"] =
+      benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
+}
+
+// --- 2D convolution ---
+static void BM_Convolve2D(benchmark::State& state) {
+  const int H = state.range(0);
+  const int W = state.range(1);
+  const int kH = state.range(2);
+  const int kW = state.range(3);
+
+  Tensor<Scalar, 2> input(H, W);
+  Tensor<Scalar, 2> kernel(kH, kW);
+  input.setRandom();
+  kernel.setRandom();
+
+  Eigen::array<int, 2> dims = {0, 1};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 2> result = input.convolve(kernel, dims);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  double flops = 2.0 * (H - kH + 1) * (W - kW + 1) * kH * kW;
+  state.counters["GFLOPS"] =
+      benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
+}
+
+// --- 2D convolution with channels (rank-3: C x H x W, convolve on H,W) ---
+static void BM_Convolve2D_Channels(benchmark::State& state) {
+  const int C = state.range(0);
+  const int H = state.range(1);
+  const int kH = state.range(2);
+
+  Tensor<Scalar, 3> input(C, H, H);
+  Tensor<Scalar, 2> kernel(kH, kH);
+  input.setRandom();
+  kernel.setRandom();
+
+  Eigen::array<int, 2> dims = {1, 2};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 3> result = input.convolve(kernel, dims);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  int outH = H - kH + 1;
+  double flops = 2.0 * C * outH * outH * kH * kH;
+  state.counters["GFLOPS"] =
+      benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
+}
+
+// --- 2D convolution with ThreadPool ---
+static void BM_Convolve2D_ThreadPool(benchmark::State& state) {
+  const int H = state.range(0);
+  const int kH = state.range(1);
+  const int threads = state.range(2);
+
+  Tensor<Scalar, 2> input(H, H);
+  Tensor<Scalar, 2> kernel(kH, kH);
+  Tensor<Scalar, 2> result(H - kH + 1, H - kH + 1);
+  input.setRandom();
+  kernel.setRandom();
+
+  ThreadPool tp(threads);
+  ThreadPoolDevice dev(&tp, threads);
+
+  Eigen::array<int, 2> dims = {0, 1};
+
+  for (auto _ : state) {
+    result.device(dev) = input.convolve(kernel, dims);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  int outH = H - kH + 1;
+  double flops = 2.0 * outH * outH * kH * kH;
+  state.counters["GFLOPS"] =
+      benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
+  state.counters["threads"] = threads;
+}
+
+static void Conv1DSizes(::benchmark::Benchmark* b) {
+  for (int input : {128, 512, 2048}) {
+    for (int kernel : {3, 5, 11}) {
+      b->Args({input, kernel});
+    }
+  }
+}
+
+static void Conv2DSizes(::benchmark::Benchmark* b) {
+  for (int hw : {32, 64, 128, 224}) {
+    for (int k : {3, 5, 7}) {
+      b->Args({hw, hw, k, k});
+    }
+  }
+}
+
+static void Conv2DChannelSizes(::benchmark::Benchmark* b) {
+  for (int c : {3, 64, 128}) {
+    for (int hw : {16, 32, 56}) {
+      for (int k : {3, 5}) {
+        b->Args({c, hw, k});
+      }
+    }
+  }
+}
+
+static void Conv2DThreadPoolSizes(::benchmark::Benchmark* b) {
+  for (int hw : {64, 128, 224}) {
+    for (int k : {3, 5}) {
+      for (int threads : {2, 4, 8}) {
+        b->Args({hw, k, threads});
+      }
+    }
+  }
+}
+
+BENCHMARK(BM_Convolve1D)->Apply(Conv1DSizes);
+BENCHMARK(BM_Convolve2D)->Apply(Conv2DSizes);
+BENCHMARK(BM_Convolve2D_Channels)->Apply(Conv2DChannelSizes);
+BENCHMARK(BM_Convolve2D_ThreadPool)->Apply(Conv2DThreadPoolSizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_morphing.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_morphing.cpp
new file mode 100644
index 0000000..8d226e7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_morphing.cpp
@@ -0,0 +1,142 @@
+// Benchmarks for Eigen Tensor morphing operations: reshape, slice, chip, pad, stride.
+
+#include <benchmark/benchmark.h>
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using namespace Eigen;
+
+typedef float Scalar;
+
+// --- Reshape (zero-cost if no evaluation needed; force eval via assignment) ---
+static void BM_Reshape(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> A(M, N);
+  A.setRandom();
+
+  Eigen::array<Index, 1> new_shape = {M * N};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 1> B = A.reshape(new_shape);
+    benchmark::DoNotOptimize(B.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar));
+}
+
+// --- Slice ---
+static void BM_Slice(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> A(M, N);
+  A.setRandom();
+
+  int sliceM = M / 2;
+  int sliceN = N / 2;
+  Eigen::array<Index, 2> offsets = {0, 0};
+  Eigen::array<Index, 2> extents = {sliceM, sliceN};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 2> B = A.slice(offsets, extents);
+    benchmark::DoNotOptimize(B.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * sliceM * sliceN * sizeof(Scalar));
+}
+
+// --- Chip (extract a sub-tensor along one dimension) ---
+static void BM_Chip(benchmark::State& state) {
+  const int D0 = state.range(0);
+  const int D1 = state.range(1);
+  const int D2 = state.range(2);
+
+  Tensor<Scalar, 3> A(D0, D1, D2);
+  A.setRandom();
+
+  for (auto _ : state) {
+    Tensor<Scalar, 2> B = A.chip(0, 0);
+    benchmark::DoNotOptimize(B.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * D1 * D2 * sizeof(Scalar));
+}
+
+// --- Pad ---
+static void BM_Pad(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+  const int padSize = state.range(2);
+
+  Tensor<Scalar, 2> A(M, N);
+  A.setRandom();
+
+  Eigen::array<std::pair<int, int>, 2> paddings;
+  paddings[0] = {padSize, padSize};
+  paddings[1] = {padSize, padSize};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 2> B = A.pad(paddings);
+    benchmark::DoNotOptimize(B.data());
+    benchmark::ClobberMemory();
+  }
+  int outM = M + 2 * padSize;
+  int outN = N + 2 * padSize;
+  state.SetBytesProcessed(state.iterations() * outM * outN * sizeof(Scalar));
+}
+
+// --- Stride ---
+static void BM_Stride(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+  const int stride = state.range(2);
+
+  Tensor<Scalar, 2> A(M, N);
+  A.setRandom();
+
+  Eigen::array<Index, 2> strides_arr = {stride, stride};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 2> B = A.stride(strides_arr);
+    benchmark::DoNotOptimize(B.data());
+    benchmark::ClobberMemory();
+  }
+  int outM = (M + stride - 1) / stride;
+  int outN = (N + stride - 1) / stride;
+  state.SetBytesProcessed(state.iterations() * outM * outN * sizeof(Scalar));
+}
+
+static void MorphSizes(::benchmark::Benchmark* b) {
+  for (int size : {256, 1024}) {
+    b->Args({size, size});
+  }
+}
+
+static void ChipSizes(::benchmark::Benchmark* b) {
+  b->Args({32, 256, 256});
+  b->Args({64, 128, 128});
+  b->Args({8, 512, 512});
+}
+
+static void PadSizes(::benchmark::Benchmark* b) {
+  for (int size : {256, 1024}) {
+    for (int pad : {1, 4, 16}) {
+      b->Args({size, size, pad});
+    }
+  }
+}
+
+static void StrideSizes(::benchmark::Benchmark* b) {
+  for (int size : {256, 1024}) {
+    for (int stride : {2, 4}) {
+      b->Args({size, size, stride});
+    }
+  }
+}
+
+BENCHMARK(BM_Reshape)->Apply(MorphSizes);
+BENCHMARK(BM_Slice)->Apply(MorphSizes);
+BENCHMARK(BM_Chip)->Apply(ChipSizes);
+BENCHMARK(BM_Pad)->Apply(PadSizes);
+BENCHMARK(BM_Stride)->Apply(StrideSizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_reduction.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_reduction.cpp
new file mode 100644
index 0000000..795c95c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_reduction.cpp
@@ -0,0 +1,158 @@
+// Benchmarks for Eigen Tensor reductions (sum, maximum, mean).
+// Tests full and partial reductions, inner vs outer dimension, DefaultDevice and ThreadPoolDevice.
+
+#define EIGEN_USE_THREADS
+
+#include <benchmark/benchmark.h>
+#include <unsupported/Eigen/CXX11/Tensor>
+#include <unsupported/Eigen/CXX11/ThreadPool>
+
+using namespace Eigen;
+
+#ifndef SCALAR
+#define SCALAR float
+#endif
+
+typedef SCALAR Scalar;
+
+// --- Full reduction (rank-2) ---
+template <typename ReduceOp>
+static void BM_FullReduction(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> A(M, N);
+  A.setRandom();
+
+  for (auto _ : state) {
+    Tensor<Scalar, 0> result = A.reduce(Eigen::array<int, 2>{0, 1}, ReduceOp());
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar));
+}
+
+// --- Partial reduction along dim 0 (inner dim, ColMajor) ---
+static void BM_ReduceInner(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> A(M, N);
+  A.setRandom();
+
+  Eigen::array<int, 1> reduce_dims = {0};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 1> result = A.sum(reduce_dims);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar));
+}
+
+// --- Partial reduction along dim 1 (outer dim, ColMajor) ---
+static void BM_ReduceOuter(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> A(M, N);
+  A.setRandom();
+
+  Eigen::array<int, 1> reduce_dims = {1};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 1> result = A.sum(reduce_dims);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar));
+}
+
+// --- Rank-4 partial reduction (batch x channels x H x W), reduce along spatial dims ---
+static void BM_ReduceSpatial(benchmark::State& state) {
+  const int batch = state.range(0);
+  const int C = state.range(1);
+  const int H = state.range(2);
+
+  Tensor<Scalar, 4> A(batch, C, H, H);
+  A.setRandom();
+
+  Eigen::array<int, 2> reduce_dims = {2, 3};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 2> result = A.sum(reduce_dims);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * batch * C * H * H * sizeof(Scalar));
+}
+
+// --- Full reduction with ThreadPoolDevice ---
+static void BM_FullReduction_ThreadPool(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+  const int threads = state.range(2);
+
+  Tensor<Scalar, 2> A(M, N);
+  Tensor<Scalar, 0> result;
+  A.setRandom();
+
+  ThreadPool tp(threads);
+  ThreadPoolDevice dev(&tp, threads);
+
+  for (auto _ : state) {
+    result.device(dev) = A.sum();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar));
+  state.counters["threads"] = threads;
+}
+
+// --- Maximum reduction (rank-2) ---
+static void BM_MaxReduction(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> A(M, N);
+  A.setRandom();
+
+  for (auto _ : state) {
+    Tensor<Scalar, 0> result = A.maximum();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar));
+}
+
+static void ReductionSizes(::benchmark::Benchmark* b) {
+  for (int size : {64, 256, 1024}) {
+    b->Args({size, size});
+  }
+}
+
+static void ThreadPoolReductionSizes(::benchmark::Benchmark* b) {
+  for (int size : {256, 1024}) {
+    for (int threads : {2, 4, 8}) {
+      b->Args({size, size, threads});
+    }
+  }
+}
+
+static void SpatialSizes(::benchmark::Benchmark* b) {
+  for (int batch : {1, 8, 32}) {
+    for (int c : {64, 128}) {
+      for (int h : {16, 32}) {
+        b->Args({batch, c, h});
+      }
+    }
+  }
+}
+
+BENCHMARK(BM_FullReduction<internal::SumReducer<Scalar>>)->Apply(ReductionSizes)->Name("SumReduction");
+BENCHMARK(BM_FullReduction<internal::MaxReducer<Scalar>>)->Apply(ReductionSizes)->Name("MaxReduction_Full");
+BENCHMARK(BM_MaxReduction)->Apply(ReductionSizes);
+BENCHMARK(BM_ReduceInner)->Apply(ReductionSizes);
+BENCHMARK(BM_ReduceOuter)->Apply(ReductionSizes);
+BENCHMARK(BM_ReduceSpatial)->Apply(SpatialSizes);
+BENCHMARK(BM_FullReduction_ThreadPool)->Apply(ThreadPoolReductionSizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_shuffling.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_shuffling.cpp
new file mode 100644
index 0000000..6296acc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_shuffling.cpp
@@ -0,0 +1,115 @@
+// Benchmarks for Eigen Tensor shuffling (transpose / permutation).
+
+#include <benchmark/benchmark.h>
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using namespace Eigen;
+
+typedef float Scalar;
+
+// --- Rank-2 transpose ---
+static void BM_Shuffle2D(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> A(M, N);
+  Tensor<Scalar, 2> B(N, M);
+  A.setRandom();
+
+  Eigen::array<int, 2> perm = {1, 0};
+
+  for (auto _ : state) {
+    B = A.shuffle(perm);
+    benchmark::DoNotOptimize(B.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar) * 2);
+}
+
+// --- Identity shuffle (no permutation, measures overhead) ---
+static void BM_ShuffleIdentity(benchmark::State& state) {
+  const int M = state.range(0);
+  const int N = state.range(1);
+
+  Tensor<Scalar, 2> A(M, N);
+  Tensor<Scalar, 2> B(M, N);
+  A.setRandom();
+
+  Eigen::array<int, 2> perm = {0, 1};
+
+  for (auto _ : state) {
+    B = A.shuffle(perm);
+    benchmark::DoNotOptimize(B.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * M * N * sizeof(Scalar) * 2);
+}
+
+// --- Rank-3 permutation ---
+static void BM_Shuffle3D(benchmark::State& state) {
+  const int D0 = state.range(0);
+  const int D1 = state.range(1);
+  const int D2 = state.range(2);
+
+  Tensor<Scalar, 3> A(D0, D1, D2);
+  A.setRandom();
+
+  // Permutation (2, 0, 1)
+  Eigen::array<int, 3> perm = {2, 0, 1};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 3> B = A.shuffle(perm);
+    benchmark::DoNotOptimize(B.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * D0 * D1 * D2 * sizeof(Scalar) * 2);
+}
+
+// --- Rank-4 permutation (NCHW -> NHWC layout conversion) ---
+static void BM_Shuffle4D_NCHW_to_NHWC(benchmark::State& state) {
+  const int N = state.range(0);
+  const int C = state.range(1);
+  const int H = state.range(2);
+
+  Tensor<Scalar, 4> A(N, C, H, H);
+  A.setRandom();
+
+  // NCHW -> NHWC: permute (0, 2, 3, 1)
+  Eigen::array<int, 4> perm = {0, 2, 3, 1};
+
+  for (auto _ : state) {
+    Tensor<Scalar, 4> B = A.shuffle(perm);
+    benchmark::DoNotOptimize(B.data());
+    benchmark::ClobberMemory();
+  }
+  state.SetBytesProcessed(state.iterations() * N * C * H * H * sizeof(Scalar) * 2);
+}
+
+static void Shuffle2DSizes(::benchmark::Benchmark* b) {
+  for (int size : {256, 1024}) {
+    b->Args({size, size});
+  }
+  b->Args({64, 4096});
+  b->Args({4096, 64});
+}
+
+static void Shuffle3DSizes(::benchmark::Benchmark* b) {
+  b->Args({64, 64, 64});
+  b->Args({128, 128, 64});
+  b->Args({32, 256, 256});
+}
+
+static void Shuffle4DSizes(::benchmark::Benchmark* b) {
+  for (int batch : {1, 8}) {
+    for (int c : {3, 64}) {
+      for (int h : {32, 64}) {
+        b->Args({batch, c, h});
+      }
+    }
+  }
+}
+
+BENCHMARK(BM_Shuffle2D)->Apply(Shuffle2DSizes);
+BENCHMARK(BM_ShuffleIdentity)->Apply(Shuffle2DSizes);
+BENCHMARK(BM_Shuffle3D)->Apply(Shuffle3DSizes);
+BENCHMARK(BM_Shuffle4D_NCHW_to_NHWC)->Apply(Shuffle4DSizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_tensor_fft.cpp b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_tensor_fft.cpp
new file mode 100644
index 0000000..26ff64b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/benchmarks/Tensor/bench_tensor_fft.cpp
@@ -0,0 +1,80 @@
+// Benchmarks for Eigen Tensor FFT.
+
+#include <benchmark/benchmark.h>
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using namespace Eigen;
+
+#ifndef SCALAR
+#define SCALAR float
+#endif
+
+typedef SCALAR Scalar;
+
+// --- 1D FFT ---
+static void BM_TensorFFT_1D(benchmark::State& state) {
+  const int N = state.range(0);
+
+  Tensor<Scalar, 1> input(N);
+  input.setRandom();
+
+  Eigen::array<int, 1> fft_dims = {0};
+
+  for (auto _ : state) {
+    Tensor<std::complex<Scalar>, 1> result = input.template fft<BothParts, FFT_FORWARD>(fft_dims);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  double mflops = 5.0 * N * std::log2(static_cast<double>(N)) / 2.0;  // real->complex
+  state.counters["MFLOPS"] =
+      benchmark::Counter(mflops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
+}
+
+// --- 2D FFT ---
+static void BM_TensorFFT_2D(benchmark::State& state) {
+  const int N = state.range(0);
+
+  Tensor<Scalar, 2> input(N, N);
+  input.setRandom();
+
+  Eigen::array<int, 2> fft_dims = {0, 1};
+
+  for (auto _ : state) {
+    Tensor<std::complex<Scalar>, 2> result = input.template fft<BothParts, FFT_FORWARD>(fft_dims);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  double total = N * N;
+  double mflops = 5.0 * total * std::log2(static_cast<double>(N));
+  state.counters["MFLOPS"] =
+      benchmark::Counter(mflops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
+}
+
+// --- 1D inverse FFT ---
+static void BM_TensorIFFT_1D(benchmark::State& state) {
+  const int N = state.range(0);
+
+  Tensor<std::complex<Scalar>, 1> input(N);
+  input.setRandom();
+
+  Eigen::array<int, 1> fft_dims = {0};
+
+  for (auto _ : state) {
+    Tensor<std::complex<Scalar>, 1> result = input.template fft<BothParts, FFT_REVERSE>(fft_dims);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+  double mflops = 5.0 * N * std::log2(static_cast<double>(N));
+  state.counters["MFLOPS"] =
+      benchmark::Counter(mflops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
+}
+
+static void FFTSizes(::benchmark::Benchmark* b) {
+  for (int n : {64, 256, 1024, 4096}) {
+    b->Arg(n);
+  }
+}
+
+BENCHMARK(BM_TensorFFT_1D)->Apply(FFTSizes);
+BENCHMARK(BM_TensorFFT_2D)->Apply(FFTSizes);
+BENCHMARK(BM_TensorIFFT_1D)->Apply(FFTSizes);
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/doc/CMakeLists.txt
new file mode 100644
index 0000000..9e9ab98
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/CMakeLists.txt
@@ -0,0 +1,4 @@
+set_directory_properties(PROPERTIES EXCLUDE_FROM_ALL TRUE)
+
+add_subdirectory(examples)
+add_subdirectory(snippets)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/Overview.dox b/data_acq_and_processing/third_party/eigen/unsupported/doc/Overview.dox
new file mode 100644
index 0000000..b3cfa96
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/Overview.dox
@@ -0,0 +1,31 @@
+/// \brief Namespace containing all symbols from the %Eigen library.
+namespace Eigen {
+
+/** \mainpage notitle
+
+This is the API documentation for %Eigen's unsupported modules.
+
+These modules are contributions from various users. They are provided "as is", without any support.
+
+Click on the \e Modules tab at the top of this page to get a list of all unsupported modules.
+
+Don't miss the <a href="../index.html">official Eigen documentation</a>.
+
+ \subpage SYCL_EIGEN "SYCL backend for Eigen"
+
+*/
+
+/*
+
+\defgroup Unsupported_modules Unsupported modules
+
+The unsupported modules are contributions from various users. They are
+provided "as is", without any support. Nevertheless, some of them are
+subject to be included in %Eigen in the future.
+
+*/
+
+/// \internal \brief Namespace containing low-level routines from the %Eigen library.
+namespace internal {}
+}
+
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/SYCL.dox b/data_acq_and_processing/third_party/eigen/unsupported/doc/SYCL.dox
new file mode 100644
index 0000000..2295adf
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/SYCL.dox
@@ -0,0 +1,9 @@
+/** \page SYCL_EIGEN Eigen SYCL Backend
+
+Useful information for Eigen SYCL Backend:
+
+- <a href="https://developer.codeplay.com/computecppce/latest/getting-started-with-eigen">Getting Started with Eigen</a> 
+
+- <a href="https://developer.codeplay.com/computecppce/latest/options-for-building-eigen-sycl">Options for Building Eigen SYCL</a>  
+
+*/
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/eigendoxy_layout.xml.in b/data_acq_and_processing/third_party/eigen/unsupported/doc/eigendoxy_layout.xml.in
new file mode 100644
index 0000000..9e3021a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/eigendoxy_layout.xml.in
@@ -0,0 +1,269 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<doxygenlayout version="1.0">
+  <!-- Generated by doxygen 1.13.0 -->
+  <!-- Navigation index tabs for HTML output -->
+  <navindex>
+    <tab type="mainpage" visible="no" title="main"/>
+    <tab type="pages" visible="yes" title="" intro=""/>
+    <tab type="topics" visible="yes" title="Chapters" intro=""/>
+    <tab type="modules" visible="yes" title="" intro="">
+      <tab type="modulelist" visible="yes" title="" intro=""/>
+      <tab type="modulemembers" visible="yes" title="" intro=""/>
+    </tab>
+    <tab type="namespaces" visible="no" title="">
+      <tab type="namespacelist" visible="yes" title="" intro=""/>
+      <tab type="namespacemembers" visible="yes" title="" intro=""/>
+    </tab>
+    <tab type="concepts" visible="yes" title="">
+    </tab>
+    <tab type="interfaces" visible="yes" title="">
+      <tab type="interfacelist" visible="yes" title="" intro=""/>
+      <tab type="interfaceindex" visible="$ALPHABETICAL_INDEX" title=""/>
+      <tab type="interfacehierarchy" visible="yes" title="" intro=""/>
+    </tab>
+    <tab type="classes" visible="yes" title="">
+      <tab type="classlist" visible="yes" title="" intro=""/>
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diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/BVH_Example.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/BVH_Example.cpp
new file mode 100644
index 0000000..8c37d71
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/BVH_Example.cpp
@@ -0,0 +1,62 @@
+#include <Eigen/StdVector>
+#include <unsupported/Eigen/BVH>
+#include <iostream>
+
+using namespace Eigen;
+typedef AlignedBox<double, 2> Box2d;
+
+namespace Eigen {
+Box2d bounding_box(const Vector2d &v) { return Box2d(v, v); }  // compute the bounding box of a single point
+}  // namespace Eigen
+
+struct PointPointMinimizer  // how to compute squared distances between points and rectangles
+{
+  PointPointMinimizer() : calls(0) {}
+  typedef double Scalar;
+
+  double minimumOnVolumeVolume(const Box2d &r1, const Box2d &r2) {
+    ++calls;
+    return r1.squaredExteriorDistance(r2);
+  }
+  double minimumOnVolumeObject(const Box2d &r, const Vector2d &v) {
+    ++calls;
+    return r.squaredExteriorDistance(v);
+  }
+  double minimumOnObjectVolume(const Vector2d &v, const Box2d &r) {
+    ++calls;
+    return r.squaredExteriorDistance(v);
+  }
+  double minimumOnObjectObject(const Vector2d &v1, const Vector2d &v2) {
+    ++calls;
+    return (v1 - v2).squaredNorm();
+  }
+
+  int calls;
+};
+
+int main() {
+  typedef std::vector<Vector2d, aligned_allocator<Vector2d> > StdVectorOfVector2d;
+  StdVectorOfVector2d redPoints, bluePoints;
+  for (int i = 0; i < 100; ++i) {  // initialize random set of red points and blue points
+    redPoints.push_back(Vector2d::Random());
+    bluePoints.push_back(Vector2d::Random());
+  }
+
+  PointPointMinimizer minimizer;
+  double minDistSq = std::numeric_limits<double>::max();
+
+  // brute force to find closest red-blue pair
+  for (int i = 0; i < (int)redPoints.size(); ++i)
+    for (int j = 0; j < (int)bluePoints.size(); ++j)
+      minDistSq = std::min(minDistSq, minimizer.minimumOnObjectObject(redPoints[i], bluePoints[j]));
+  std::cout << "Brute force distance = " << sqrt(minDistSq) << ", calls = " << minimizer.calls << std::endl;
+
+  // using BVH to find closest red-blue pair
+  minimizer.calls = 0;
+  KdBVH<double, 2, Vector2d> redTree(redPoints.begin(), redPoints.end()),
+      blueTree(bluePoints.begin(), bluePoints.end());    // construct the trees
+  minDistSq = BVMinimize(redTree, blueTree, minimizer);  // actual BVH minimization call
+  std::cout << "BVH distance         = " << sqrt(minDistSq) << ", calls = " << minimizer.calls << std::endl;
+
+  return 0;
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/CMakeLists.txt
new file mode 100644
index 0000000..74f36eb
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/CMakeLists.txt
@@ -0,0 +1,23 @@
+file(GLOB examples_SRCS "*.cpp")
+
+add_custom_target(unsupported_examples)
+
+foreach(example_src ${examples_SRCS})
+  get_filename_component(example ${example_src} NAME_WE)
+  add_executable(example_${example} ${example_src})
+  if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
+    target_link_libraries(example_${example} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
+  endif()
+  target_link_libraries(example_${example}  Eigen3::Eigen)
+  add_custom_command(
+    TARGET example_${example}
+    POST_BUILD
+    COMMAND example_${example}
+    ARGS >${CMAKE_CURRENT_BINARY_DIR}/${example}.out
+  )
+  add_dependencies(unsupported_examples example_${example})
+endforeach(example_src)
+
+if(EIGEN_TEST_SYCL)
+  add_subdirectory(SYCL)
+endif(EIGEN_TEST_SYCL)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/EulerAngles.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/EulerAngles.cpp
new file mode 100644
index 0000000..5694770
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/EulerAngles.cpp
@@ -0,0 +1,44 @@
+#include <unsupported/Eigen/EulerAngles>
+#include <iostream>
+
+using namespace Eigen;
+
+int main() {
+  // A common Euler system by many armies around the world,
+  //  where the first one is the azimuth(the angle from the north -
+  //   the same angle that is show in compass)
+  //  and the second one is elevation(the angle from the horizon)
+  //  and the third one is roll(the angle between the horizontal body
+  //   direction and the plane ground surface)
+  // Keep remembering we're using radian angles here!
+  typedef EulerSystem<-EULER_Z, EULER_Y, EULER_X> MyArmySystem;
+  typedef EulerAngles<double, MyArmySystem> MyArmyAngles;
+
+  MyArmyAngles vehicleAngles(3.14 /*PI*/ / 2, /* heading to east, notice that this angle is counter-clockwise */
+                             -0.3,            /* going down from a mountain */
+                             0.1);            /* slightly rolled to the right */
+
+  // Some Euler angles representation that our plane use.
+  EulerAnglesZYZd planeAngles(0.78474, 0.5271, -0.513794);
+
+  MyArmyAngles planeAnglesInMyArmyAngles(planeAngles);
+
+  std::cout << "vehicle angles(MyArmy):     " << vehicleAngles << std::endl;
+  std::cout << "plane angles(ZYZ):        " << planeAngles << std::endl;
+  std::cout << "plane angles(MyArmy):     " << planeAnglesInMyArmyAngles << std::endl;
+
+  // Now lets rotate the plane a little bit
+  std::cout << "==========================================================\n";
+  std::cout << "rotating plane now!\n";
+  std::cout << "==========================================================\n";
+
+  Quaterniond planeRotated = AngleAxisd(-0.342, Vector3d::UnitY()) * planeAngles;
+
+  planeAngles = planeRotated;
+  planeAnglesInMyArmyAngles = planeRotated;
+
+  std::cout << "new plane angles(ZYZ):     " << planeAngles << std::endl;
+  std::cout << "new plane angles(MyArmy): " << planeAnglesInMyArmyAngles << std::endl;
+
+  return 0;
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/FFT.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/FFT.cpp
new file mode 100644
index 0000000..bf24de5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/FFT.cpp
@@ -0,0 +1,103 @@
+//  To use the simple FFT implementation
+//  g++ -o demofft -I.. -Wall -O3 FFT.cpp
+
+//  To use the FFTW implementation
+//  g++ -o demofft -I.. -DUSE_FFTW -Wall -O3 FFT.cpp -lfftw3 -lfftw3f -lfftw3l
+
+#ifdef USE_FFTW
+#include <fftw3.h>
+#endif
+
+#include <vector>
+#include <complex>
+#include <algorithm>
+#include <iterator>
+#include <iostream>
+#include <Eigen/Core>
+#include <unsupported/Eigen/FFT>
+
+using namespace std;
+using namespace Eigen;
+
+template <typename T>
+T mag2(T a) {
+  return a * a;
+}
+template <typename T>
+T mag2(std::complex<T> a) {
+  return norm(a);
+}
+
+template <typename T>
+T mag2(const std::vector<T>& vec) {
+  T out = 0;
+  for (size_t k = 0; k < vec.size(); ++k) out += mag2(vec[k]);
+  return out;
+}
+
+template <typename T>
+T mag2(const std::vector<std::complex<T> >& vec) {
+  T out = 0;
+  for (size_t k = 0; k < vec.size(); ++k) out += mag2(vec[k]);
+  return out;
+}
+
+template <typename T>
+vector<T> operator-(const vector<T>& a, const vector<T>& b) {
+  vector<T> c(a);
+  for (size_t k = 0; k < b.size(); ++k) c[k] -= b[k];
+  return c;
+}
+
+template <typename T>
+void RandomFill(std::vector<T>& vec) {
+  for (size_t k = 0; k < vec.size(); ++k) vec[k] = T(rand()) / T(RAND_MAX) - T(.5);
+}
+
+template <typename T>
+void RandomFill(std::vector<std::complex<T> >& vec) {
+  for (size_t k = 0; k < vec.size(); ++k)
+    vec[k] = std::complex<T>(T(rand()) / T(RAND_MAX) - T(.5), T(rand()) / T(RAND_MAX) - T(.5));
+}
+
+template <typename T_time, typename T_freq>
+void fwd_inv(size_t nfft) {
+  typedef typename NumTraits<T_freq>::Real Scalar;
+  vector<T_time> timebuf(nfft);
+  RandomFill(timebuf);
+
+  vector<T_freq> freqbuf;
+  static FFT<Scalar> fft;
+  fft.fwd(freqbuf, timebuf);
+
+  vector<T_time> timebuf2;
+  fft.inv(timebuf2, freqbuf);
+
+  T_time rmse = mag2(timebuf - timebuf2) / mag2(timebuf);
+  cout << "roundtrip rmse: " << rmse << endl;
+}
+
+template <typename T_scalar>
+void two_demos(int nfft) {
+  cout << "     scalar ";
+  fwd_inv<T_scalar, std::complex<T_scalar> >(nfft);
+  cout << "    complex ";
+  fwd_inv<std::complex<T_scalar>, std::complex<T_scalar> >(nfft);
+}
+
+void demo_all_types(int nfft) {
+  cout << "nfft=" << nfft << endl;
+  cout << "   float" << endl;
+  two_demos<float>(nfft);
+  cout << "   double" << endl;
+  two_demos<double>(nfft);
+  cout << "   long double" << endl;
+  two_demos<long double>(nfft);
+}
+
+int main() {
+  demo_all_types(2 * 3 * 4 * 5 * 7);
+  demo_all_types(2 * 9 * 16 * 25);
+  demo_all_types(1024);
+  return 0;
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixExponential.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixExponential.cpp
new file mode 100644
index 0000000..eab7a5e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixExponential.cpp
@@ -0,0 +1,13 @@
+#include <unsupported/Eigen/MatrixFunctions>
+#include <iostream>
+
+using namespace Eigen;
+
+int main() {
+  const double pi = std::acos(-1.0);
+
+  MatrixXd A(3, 3);
+  A << 0, -pi / 4, 0, pi / 4, 0, 0, 0, 0, 0;
+  std::cout << "The matrix A is:\n" << A << "\n\n";
+  std::cout << "The matrix exponential of A is:\n" << A.exp() << "\n\n";
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixFunction.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixFunction.cpp
new file mode 100644
index 0000000..d72f62a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixFunction.cpp
@@ -0,0 +1,16 @@
+#include <unsupported/Eigen/MatrixFunctions>
+#include <iostream>
+
+using namespace Eigen;
+
+std::complex<double> expfn(std::complex<double> x, int) { return std::exp(x); }
+
+int main() {
+  const double pi = std::acos(-1.0);
+
+  MatrixXd A(3, 3);
+  A << 0, -pi / 4, 0, pi / 4, 0, 0, 0, 0, 0;
+
+  std::cout << "The matrix A is:\n" << A << "\n\n";
+  std::cout << "The matrix exponential of A is:\n" << A.matrixFunction(expfn) << "\n\n";
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixLogarithm.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixLogarithm.cpp
new file mode 100644
index 0000000..41b7ff6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixLogarithm.cpp
@@ -0,0 +1,12 @@
+#include <unsupported/Eigen/MatrixFunctions>
+#include <iostream>
+
+using namespace Eigen;
+
+int main() {
+  using std::sqrt;
+  MatrixXd A(3, 3);
+  A << 0.5 * sqrt(2), -0.5 * sqrt(2), 0, 0.5 * sqrt(2), 0.5 * sqrt(2), 0, 0, 0, 1;
+  std::cout << "The matrix A is:\n" << A << "\n\n";
+  std::cout << "The matrix logarithm of A is:\n" << A.log() << "\n";
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixPower.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixPower.cpp
new file mode 100644
index 0000000..35501b6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixPower.cpp
@@ -0,0 +1,16 @@
+#include <unsupported/Eigen/MatrixFunctions>
+#include <iostream>
+
+using namespace Eigen;
+
+int main() {
+  const double pi = std::acos(-1.0);
+  Matrix3d A;
+  A << cos(1), -sin(1), 0, sin(1), cos(1), 0, 0, 0, 1;
+  std::cout << "The matrix A is:\n"
+            << A
+            << "\n\n"
+               "The matrix power A^(pi/4) is:\n"
+            << A.pow(pi / 4) << std::endl;
+  return 0;
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixPower_optimal.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixPower_optimal.cpp
new file mode 100644
index 0000000..5f5f3e4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixPower_optimal.cpp
@@ -0,0 +1,25 @@
+#include <unsupported/Eigen/MatrixFunctions>
+#include <iostream>
+
+using namespace Eigen;
+
+int main() {
+  Matrix4cd A = Matrix4cd::Random();
+  MatrixPower<Matrix4cd> Apow(A);
+
+  std::cout << "The matrix A is:\n"
+            << A
+            << "\n\n"
+               "A^3.1 is:\n"
+            << Apow(3.1)
+            << "\n\n"
+               "A^3.3 is:\n"
+            << Apow(3.3)
+            << "\n\n"
+               "A^3.7 is:\n"
+            << Apow(3.7)
+            << "\n\n"
+               "A^3.9 is:\n"
+            << Apow(3.9) << std::endl;
+  return 0;
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixSine.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixSine.cpp
new file mode 100644
index 0000000..eeafa50
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixSine.cpp
@@ -0,0 +1,19 @@
+#include <unsupported/Eigen/MatrixFunctions>
+#include <iostream>
+
+using namespace Eigen;
+
+int main() {
+  MatrixXd A = MatrixXd::Random(3, 3);
+  std::cout << "A = \n" << A << "\n\n";
+
+  MatrixXd sinA = A.sin();
+  std::cout << "sin(A) = \n" << sinA << "\n\n";
+
+  MatrixXd cosA = A.cos();
+  std::cout << "cos(A) = \n" << cosA << "\n\n";
+
+  // The matrix functions satisfy sin^2(A) + cos^2(A) = I,
+  // like the scalar functions.
+  std::cout << "sin^2(A) + cos^2(A) = \n" << sinA * sinA + cosA * cosA << "\n\n";
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixSinh.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixSinh.cpp
new file mode 100644
index 0000000..1aabbb3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixSinh.cpp
@@ -0,0 +1,19 @@
+#include <unsupported/Eigen/MatrixFunctions>
+#include <iostream>
+
+using namespace Eigen;
+
+int main() {
+  MatrixXf A = MatrixXf::Random(3, 3);
+  std::cout << "A = \n" << A << "\n\n";
+
+  MatrixXf sinhA = A.sinh();
+  std::cout << "sinh(A) = \n" << sinhA << "\n\n";
+
+  MatrixXf coshA = A.cosh();
+  std::cout << "cosh(A) = \n" << coshA << "\n\n";
+
+  // The matrix functions satisfy cosh^2(A) - sinh^2(A) = I,
+  // like the scalar functions.
+  std::cout << "cosh^2(A) - sinh^2(A) = \n" << coshA * coshA - sinhA * sinhA << "\n\n";
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixSquareRoot.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixSquareRoot.cpp
new file mode 100644
index 0000000..0ac4fdc
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/MatrixSquareRoot.cpp
@@ -0,0 +1,14 @@
+#include <unsupported/Eigen/MatrixFunctions>
+#include <iostream>
+
+using namespace Eigen;
+
+int main() {
+  const double pi = std::acos(-1.0);
+
+  MatrixXd A(2, 2);
+  A << cos(pi / 3), -sin(pi / 3), sin(pi / 3), cos(pi / 3);
+  std::cout << "The matrix A is:\n" << A << "\n\n";
+  std::cout << "The matrix square root of A is:\n" << A.sqrt() << "\n\n";
+  std::cout << "The square of the last matrix is:\n" << A.sqrt() * A.sqrt() << "\n";
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/PolynomialSolver1.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/PolynomialSolver1.cpp
new file mode 100644
index 0000000..c1bf976
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/PolynomialSolver1.cpp
@@ -0,0 +1,52 @@
+#include <unsupported/Eigen/Polynomials>
+#include <vector>
+#include <iostream>
+
+using namespace Eigen;
+using namespace std;
+
+int main() {
+  typedef Matrix<double, 5, 1> Vector5d;
+
+  Vector5d roots = Vector5d::Random();
+  cout << "Roots: " << roots.transpose() << endl;
+  Eigen::Matrix<double, 6, 1> polynomial;
+  roots_to_monicPolynomial(roots, polynomial);
+
+  PolynomialSolver<double, 5> psolve(polynomial);
+  cout << "Complex roots: " << psolve.roots().transpose() << endl;
+
+  std::vector<double> realRoots;
+  psolve.realRoots(realRoots);
+  Map<Vector5d> mapRR(&realRoots[0]);
+  cout << "Real roots: " << mapRR.transpose() << endl;
+
+  cout << endl;
+  cout << "Illustration of the convergence problem with the QR algorithm: " << endl;
+  cout << "---------------------------------------------------------------" << endl;
+  Eigen::Matrix<float, 7, 1> hardCase_polynomial;
+  hardCase_polynomial << -0.957, 0.9219, 0.3516, 0.9453, -0.4023, -0.5508, -0.03125;
+  cout << "Hard case polynomial defined by floats: " << hardCase_polynomial.transpose() << endl;
+  PolynomialSolver<float, 6> psolvef(hardCase_polynomial);
+  cout << "Complex roots: " << psolvef.roots().transpose() << endl;
+  Eigen::Matrix<float, 6, 1> evals;
+  for (int i = 0; i < 6; ++i) {
+    evals[i] = std::abs(poly_eval(hardCase_polynomial, psolvef.roots()[i]));
+  }
+  cout << "Norms of the evaluations of the polynomial at the roots: " << evals.transpose() << endl << endl;
+
+  cout << "Using double's almost always solves the problem for small degrees: " << endl;
+  cout << "-------------------------------------------------------------------" << endl;
+  PolynomialSolver<double, 6> psolve6d(hardCase_polynomial.cast<double>());
+  cout << "Complex roots: " << psolve6d.roots().transpose() << endl;
+  for (int i = 0; i < 6; ++i) {
+    std::complex<float> castedRoot(psolve6d.roots()[i].real(), psolve6d.roots()[i].imag());
+    evals[i] = std::abs(poly_eval(hardCase_polynomial, castedRoot));
+  }
+  cout << "Norms of the evaluations of the polynomial at the roots: " << evals.transpose() << endl << endl;
+
+  cout.precision(10);
+  cout << "The last root in float then in double: " << psolvef.roots()[5] << "\t" << psolve6d.roots()[5] << endl;
+  std::complex<float> castedRoot(psolve6d.roots()[5].real(), psolve6d.roots()[5].imag());
+  cout << "Norm of the difference: " << std::abs(psolvef.roots()[5] - castedRoot) << endl;
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/PolynomialUtils1.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/PolynomialUtils1.cpp
new file mode 100644
index 0000000..a050b01
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/PolynomialUtils1.cpp
@@ -0,0 +1,22 @@
+#include <unsupported/Eigen/Polynomials>
+#include <iostream>
+
+using namespace Eigen;
+using namespace std;
+
+int main() {
+  Vector4d roots = Vector4d::Random();
+  cout << "Roots: " << roots.transpose() << endl;
+  Eigen::Matrix<double, 5, 1> polynomial;
+  roots_to_monicPolynomial(roots, polynomial);
+  cout << "Polynomial: ";
+  for (int i = 0; i < 4; ++i) {
+    cout << polynomial[i] << ".x^" << i << "+ ";
+  }
+  cout << polynomial[4] << ".x^4" << endl;
+  Vector4d evaluation;
+  for (int i = 0; i < 4; ++i) {
+    evaluation[i] = poly_eval(polynomial, roots[i]);
+  }
+  cout << "Evaluation of the polynomial at the roots: " << evaluation.transpose();
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/SYCL/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/SYCL/CMakeLists.txt
new file mode 100644
index 0000000..4fe94c6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/SYCL/CMakeLists.txt
@@ -0,0 +1,31 @@
+FILE(GLOB examples_SRCS "*.cpp")
+
+set(EIGEN_SYCL ON)
+list(APPEND CMAKE_EXE_LINKER_FLAGS -pthread)
+set(CMAKE_CXX_STANDARD 17)
+if(EIGEN_SYCL_ComputeCpp)
+  if(MSVC)
+    list(APPEND COMPUTECPP_USER_FLAGS -DWIN32)
+  else()
+    list(APPEND COMPUTECPP_USER_FLAGS -Wall)
+  endif()
+  # The following flags are not supported by Clang and can cause warnings
+  # if used with -Werror so they are removed here.
+  if(COMPUTECPP_USE_COMPILER_DRIVER)
+    set(CMAKE_CXX_COMPILER ${ComputeCpp_DEVICE_COMPILER_EXECUTABLE})
+    string(REPLACE "-Wlogical-op" "" CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
+    string(REPLACE "-Wno-psabi" "" CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
+  endif()
+  list(APPEND COMPUTECPP_USER_FLAGS
+      -DEIGEN_NO_ASSERTION_CHECKING=1
+      -no-serial-memop
+      -Xclang
+      -cl-mad-enable)
+endif(EIGEN_SYCL_ComputeCpp)
+
+FOREACH(example_src ${examples_SRCS})
+  GET_FILENAME_COMPONENT(example ${example_src} NAME_WE)
+  ei_add_test_internal(${example} example_${example})
+  ADD_DEPENDENCIES(unsupported_examples example_${example})
+ENDFOREACH(example_src)
+set(EIGEN_SYCL OFF)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/SYCL/CwiseMul.cpp b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/SYCL/CwiseMul.cpp
new file mode 100644
index 0000000..eac5b46
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/examples/SYCL/CwiseMul.cpp
@@ -0,0 +1,63 @@
+#include <iostream>
+#define EIGEN_USE_SYCL
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+int main() {
+  using DataType = float;
+  using IndexType = int64_t;
+  constexpr auto DataLayout = Eigen::RowMajor;
+
+  auto devices = Eigen::get_sycl_supported_devices();
+  const auto device_selector = *devices.begin();
+  Eigen::QueueInterface queueInterface(device_selector);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+
+  // create the tensors to be used in the operation
+  IndexType sizeDim1 = 3;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 3;
+  array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+
+  // initialize the tensors with the data we want manipulate to
+  Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> in2(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
+
+  // set up some random data in the tensors to be multiplied
+  in1 = in1.random();
+  in2 = in2.random();
+
+  // allocate memory for the tensors
+  DataType* gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(in1.size() * sizeof(DataType)));
+  DataType* gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(in2.size() * sizeof(DataType)));
+  DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.size() * sizeof(DataType)));
+
+  //
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
+
+  // copy the memory to the device and do the c=a*b calculation
+  sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(), (in1.size()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(), (in2.size()) * sizeof(DataType));
+  gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.size()) * sizeof(DataType));
+  sycl_device.synchronize();
+
+  // print out the results
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        std::cout << "device_out"
+                  << "(" << i << ", " << j << ", " << k << ") : " << out(i, j, k) << " vs host_out"
+                  << "(" << i << ", " << j << ", " << k << ") : " << in1(i, j, k) * in2(i, j, k) << "\n";
+      }
+    }
+  }
+  printf("c=a*b Done\n");
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/doc/snippets/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/doc/snippets/CMakeLists.txt
new file mode 100644
index 0000000..adf95a8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/doc/snippets/CMakeLists.txt
@@ -0,0 +1,26 @@
+file(GLOB snippets_SRCS "*.cpp")
+
+add_custom_target(unsupported_snippets)
+
+foreach(snippet_src ${snippets_SRCS})
+  get_filename_component(snippet ${snippet_src} NAME_WE)
+  set(compile_snippet_target compile_${snippet})
+  set(compile_snippet_src ${compile_snippet_target}.cpp)
+  file(READ ${snippet_src} snippet_source_code)
+  configure_file(${PROJECT_SOURCE_DIR}/doc/snippets/compile_snippet.cpp.in
+                 ${CMAKE_CURRENT_BINARY_DIR}/${compile_snippet_src})
+  add_executable(${compile_snippet_target}
+                 ${CMAKE_CURRENT_BINARY_DIR}/${compile_snippet_src})
+  if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
+    target_link_libraries(${compile_snippet_target} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
+  endif()
+  add_custom_command(
+    TARGET ${compile_snippet_target}
+    POST_BUILD
+    COMMAND ${compile_snippet_target}
+    ARGS >${CMAKE_CURRENT_BINARY_DIR}/${snippet}.out
+  )
+  add_dependencies(unsupported_snippets ${compile_snippet_target})
+  set_source_files_properties(${CMAKE_CURRENT_BINARY_DIR}/${compile_snippet_src}
+                              PROPERTIES OBJECT_DEPENDS ${snippet_src})
+endforeach(snippet_src)
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/BVH.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/BVH.cpp
new file mode 100644
index 0000000..54ecc5c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/BVH.cpp
@@ -0,0 +1,247 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <Eigen/StdVector>
+#include <Eigen/Geometry>
+#include <unsupported/Eigen/BVH>
+
+namespace Eigen {
+
+template <typename Scalar, int Dim>
+AlignedBox<Scalar, Dim> bounding_box(const Matrix<Scalar, Dim, 1> &v) {
+  return AlignedBox<Scalar, Dim>(v);
+}
+
+}  // namespace Eigen
+
+template <int Dim>
+struct Ball {
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(double, Dim)
+
+  typedef Matrix<double, Dim, 1> VectorType;
+
+  Ball() {}
+  Ball(const VectorType &c, double r) : center(c), radius(r) {}
+
+  VectorType center;
+  double radius;
+};
+template <int Dim>
+AlignedBox<double, Dim> bounding_box(const Ball<Dim> &b) {
+  return AlignedBox<double, Dim>(b.center.array() - b.radius, b.center.array() + b.radius);
+}
+
+inline double SQR(double x) { return x * x; }
+
+template <int Dim>
+struct BallPointStuff  // this class provides functions to be both an intersector and a minimizer, both for a ball and a
+                       // point and for two trees
+{
+  typedef double Scalar;
+  typedef Matrix<double, Dim, 1> VectorType;
+  typedef Ball<Dim> BallType;
+  typedef AlignedBox<double, Dim> BoxType;
+
+  BallPointStuff() : calls(0), count(0) {}
+  BallPointStuff(const VectorType &inP) : p(inP), calls(0), count(0) {}
+
+  bool intersectVolume(const BoxType &r) {
+    ++calls;
+    return r.contains(p);
+  }
+  bool intersectObject(const BallType &b) {
+    ++calls;
+    if ((b.center - p).squaredNorm() < SQR(b.radius)) ++count;
+    return false;  // continue
+  }
+
+  bool intersectVolumeVolume(const BoxType &r1, const BoxType &r2) {
+    ++calls;
+    return !(r1.intersection(r2)).isNull();
+  }
+  bool intersectVolumeObject(const BoxType &r, const BallType &b) {
+    ++calls;
+    return r.squaredExteriorDistance(b.center) < SQR(b.radius);
+  }
+  bool intersectObjectVolume(const BallType &b, const BoxType &r) {
+    ++calls;
+    return r.squaredExteriorDistance(b.center) < SQR(b.radius);
+  }
+  bool intersectObjectObject(const BallType &b1, const BallType &b2) {
+    ++calls;
+    if ((b1.center - b2.center).norm() < b1.radius + b2.radius) ++count;
+    return false;
+  }
+  bool intersectVolumeObject(const BoxType &r, const VectorType &v) {
+    ++calls;
+    return r.contains(v);
+  }
+  bool intersectObjectObject(const BallType &b, const VectorType &v) {
+    ++calls;
+    if ((b.center - v).squaredNorm() < SQR(b.radius)) ++count;
+    return false;
+  }
+
+  double minimumOnVolume(const BoxType &r) {
+    ++calls;
+    return r.squaredExteriorDistance(p);
+  }
+  double minimumOnObject(const BallType &b) {
+    ++calls;
+    return (std::max)(0., (b.center - p).squaredNorm() - SQR(b.radius));
+  }
+  double minimumOnVolumeVolume(const BoxType &r1, const BoxType &r2) {
+    ++calls;
+    return r1.squaredExteriorDistance(r2);
+  }
+  double minimumOnVolumeObject(const BoxType &r, const BallType &b) {
+    ++calls;
+    return SQR((std::max)(0., r.exteriorDistance(b.center) - b.radius));
+  }
+  double minimumOnObjectVolume(const BallType &b, const BoxType &r) {
+    ++calls;
+    return SQR((std::max)(0., r.exteriorDistance(b.center) - b.radius));
+  }
+  double minimumOnObjectObject(const BallType &b1, const BallType &b2) {
+    ++calls;
+    return SQR((std::max)(0., (b1.center - b2.center).norm() - b1.radius - b2.radius));
+  }
+  double minimumOnVolumeObject(const BoxType &r, const VectorType &v) {
+    ++calls;
+    return r.squaredExteriorDistance(v);
+  }
+  double minimumOnObjectObject(const BallType &b, const VectorType &v) {
+    ++calls;
+    return SQR((std::max)(0., (b.center - v).norm() - b.radius));
+  }
+
+  VectorType p;
+  int calls;
+  int count;
+};
+
+template <int Dim>
+struct TreeTest {
+  typedef Matrix<double, Dim, 1> VectorType;
+  typedef std::vector<VectorType, aligned_allocator<VectorType> > VectorTypeList;
+  typedef Ball<Dim> BallType;
+  typedef std::vector<BallType, aligned_allocator<BallType> > BallTypeList;
+  typedef AlignedBox<double, Dim> BoxType;
+
+  void testIntersect1() {
+    BallTypeList b;
+    for (int i = 0; i < 500; ++i) {
+      b.push_back(BallType(VectorType::Random(), 0.5 * internal::random(0., 1.)));
+    }
+    KdBVH<double, Dim, BallType> tree(b.begin(), b.end());
+
+    VectorType pt = VectorType::Random();
+    BallPointStuff<Dim> i1(pt), i2(pt);
+
+    for (int i = 0; i < (int)b.size(); ++i) i1.intersectObject(b[i]);
+
+    BVIntersect(tree, i2);
+
+    VERIFY(i1.count == i2.count);
+  }
+
+  void testMinimize1() {
+    BallTypeList b;
+    for (int i = 0; i < 500; ++i) {
+      b.push_back(BallType(VectorType::Random(), 0.01 * internal::random(0., 1.)));
+    }
+    KdBVH<double, Dim, BallType> tree(b.begin(), b.end());
+
+    VectorType pt = VectorType::Random();
+    BallPointStuff<Dim> i1(pt), i2(pt);
+
+    double m1 = (std::numeric_limits<double>::max)(), m2 = m1;
+
+    for (int i = 0; i < (int)b.size(); ++i) m1 = (std::min)(m1, i1.minimumOnObject(b[i]));
+
+    m2 = BVMinimize(tree, i2);
+
+    VERIFY_IS_APPROX(m1, m2);
+  }
+
+  void testIntersect2() {
+    BallTypeList b;
+    VectorTypeList v;
+
+    for (int i = 0; i < 50; ++i) {
+      b.push_back(BallType(VectorType::Random(), 0.5 * internal::random(0., 1.)));
+      for (int j = 0; j < 3; ++j) v.push_back(VectorType::Random());
+    }
+
+    KdBVH<double, Dim, BallType> tree(b.begin(), b.end());
+    KdBVH<double, Dim, VectorType> vTree(v.begin(), v.end());
+
+    BallPointStuff<Dim> i1, i2;
+
+    for (int i = 0; i < (int)b.size(); ++i)
+      for (int j = 0; j < (int)v.size(); ++j) i1.intersectObjectObject(b[i], v[j]);
+
+    BVIntersect(tree, vTree, i2);
+
+    VERIFY(i1.count == i2.count);
+  }
+
+  void testMinimize2() {
+    BallTypeList b;
+    VectorTypeList v;
+
+    for (int i = 0; i < 50; ++i) {
+      b.push_back(BallType(VectorType::Random(), 1e-7 + 1e-6 * internal::random(0., 1.)));
+      for (int j = 0; j < 3; ++j) v.push_back(VectorType::Random());
+    }
+
+    KdBVH<double, Dim, BallType> tree(b.begin(), b.end());
+    KdBVH<double, Dim, VectorType> vTree(v.begin(), v.end());
+
+    BallPointStuff<Dim> i1, i2;
+
+    double m1 = (std::numeric_limits<double>::max)(), m2 = m1;
+
+    for (int i = 0; i < (int)b.size(); ++i)
+      for (int j = 0; j < (int)v.size(); ++j) m1 = (std::min)(m1, i1.minimumOnObjectObject(b[i], v[j]));
+
+    m2 = BVMinimize(tree, vTree, i2);
+
+    VERIFY_IS_APPROX(m1, m2);
+  }
+};
+
+EIGEN_DECLARE_TEST(BVH) {
+  for (int i = 0; i < g_repeat; i++) {
+#ifdef EIGEN_TEST_PART_1
+    TreeTest<2> test2;
+    CALL_SUBTEST(test2.testIntersect1());
+    CALL_SUBTEST(test2.testMinimize1());
+    CALL_SUBTEST(test2.testIntersect2());
+    CALL_SUBTEST(test2.testMinimize2());
+#endif
+
+#ifdef EIGEN_TEST_PART_2
+    TreeTest<3> test3;
+    CALL_SUBTEST(test3.testIntersect1());
+    CALL_SUBTEST(test3.testMinimize1());
+    CALL_SUBTEST(test3.testIntersect2());
+    CALL_SUBTEST(test3.testMinimize2());
+#endif
+
+#ifdef EIGEN_TEST_PART_3
+    TreeTest<4> test4;
+    CALL_SUBTEST(test4.testIntersect1());
+    CALL_SUBTEST(test4.testMinimize1());
+    CALL_SUBTEST(test4.testIntersect2());
+    CALL_SUBTEST(test4.testMinimize2());
+#endif
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/CMakeLists.txt b/data_acq_and_processing/third_party/eigen/unsupported/test/CMakeLists.txt
new file mode 100644
index 0000000..4fb5125
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/CMakeLists.txt
@@ -0,0 +1,359 @@
+# The file split_test_helper.h was generated at first run,
+# it is now included in test/
+if(EXISTS ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h)
+  file(REMOVE ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h)
+endif()
+
+set_property(GLOBAL PROPERTY EIGEN_CURRENT_SUBPROJECT "Unsupported")
+add_custom_target(BuildUnsupported)
+
+include_directories(../../test ../../unsupported
+                    ${CMAKE_CURRENT_BINARY_DIR}/../../test)
+
+find_package (Threads)
+
+find_package(GoogleHash)
+if(GoogleHash_FOUND)
+  add_definitions("-DEIGEN_GOOGLEHASH_SUPPORT")
+  include_directories(${GOOGLEHASH_INCLUDES})
+  ei_add_property(EIGEN_TESTED_BACKENDS  "GoogleHash, ")
+else()
+  ei_add_property(EIGEN_MISSING_BACKENDS  "GoogleHash, ")
+endif()
+
+
+find_package(Adolc)
+if(Adolc_FOUND)
+  include_directories(${ADOLC_INCLUDES})
+  ei_add_property(EIGEN_TESTED_BACKENDS "Adolc, ")
+  ei_add_test(forward_adolc "" ${ADOLC_LIBRARIES})
+else()
+  ei_add_property(EIGEN_MISSING_BACKENDS "Adolc, ")
+endif()
+
+# this test seems to never have been successful on x87, so is considered to contain a FP-related bug.
+# see thread: "non-linear optimization test summary"
+ei_add_test(NonLinearOptimization)
+
+ei_add_test(NumericalDiff)
+ei_add_test(autodiff_scalar)
+ei_add_test(autodiff)
+
+ei_add_test(BVH)
+
+ei_add_test(matrix_exponential)
+ei_add_test(matrix_function)
+ei_add_test(matrix_power)
+ei_add_test(matrix_square_root)
+ei_add_test(alignedvector3)
+
+ei_add_test(FFT)
+
+ei_add_test(EulerAngles)
+
+find_package(MPREAL)
+if(MPREAL_FOUND)
+  ei_add_property(EIGEN_TESTED_BACKENDS "MPFR C++, ")
+  include_directories(${MPREAL_INCLUDES})
+  ei_add_test(mpreal_support "" "${MPREAL_LIBRARIES}" )
+else()
+  ei_add_property(EIGEN_MISSING_BACKENDS "MPFR C++, ")
+endif()
+
+ei_add_test(NNLS)
+
+ei_add_test(sparse_extra   "" "")
+
+find_package(FFTW)
+if(FFTW_FOUND)
+  ei_add_property(EIGEN_TESTED_BACKENDS "fftw, ")
+  include_directories( ${FFTW_INCLUDES} )
+  if(FFTWL_LIB)
+    ei_add_test(FFTW  "-DEIGEN_HAS_FFTWL" "${FFTW_LIBRARIES}" )
+  else()
+    ei_add_test(FFTW  "${FFTW_LIBRARIES}" )
+  endif()
+else()
+  ei_add_property(EIGEN_MISSING_BACKENDS "fftw, ")
+endif()
+
+find_path(POCKETFFT  pocketfft_hdronly.h)
+if(POCKETFFT)
+  if(EIGEN_TEST_CXX11)
+    ei_add_property(EIGEN_TESTED_BACKENDS "pocketfft, ")
+    include_directories( ${POCKETFFT} )
+    ei_add_test(pocketfft "-pthread" "${CMAKE_THREAD_LIBS_INIT}" )
+  endif()
+else()
+  ei_add_property(EIGEN_MISSING_BACKENDS "pocketfft, ")
+endif()
+
+if( NOT DUCC_ROOT AND ENV{DUCC_ROOT} )
+  set( DUCC_ROOT $ENV{DUCC_ROOT} )
+endif()
+find_path(DUCCFFT
+  NAMES "src/ducc0/fft/fft.h"
+  PATHS ${DUCC_ROOT})
+message(INFO " ${DUCC_ROOT} ${DUCCFFT}")
+if(DUCCFFT)
+  ei_add_property(EIGEN_TESTED_BACKENDS "duccfft, ")
+  include_directories( "${DUCCFFT}/src" )
+  add_library(ducc_lib "${DUCCFFT}/src/ducc0/infra/string_utils.cc" "${DUCCFFT}/src/ducc0/infra/threading.cc")
+  target_compile_definitions(ducc_lib PUBLIC "DUCC0_NO_THREADING=1")
+  ei_add_test(duccfft "-DEIGEN_DUCCFFT_DEFAULT -DDUCC0_NO_THREADING=1" "ducc_lib" )
+  set_target_properties(ducc_lib duccfft PROPERTIES CXX_STANDARD 17)
+else()
+  ei_add_property(EIGEN_MISSING_BACKENDS "duccfft, ")
+endif()
+
+
+option(EIGEN_TEST_OPENGL "Enable OpenGL support in unit tests" OFF)
+if(EIGEN_TEST_OPENGL)
+  find_package(OpenGL)
+  find_package(GLUT)
+  find_package(GLEW)
+  if(OPENGL_FOUND AND GLUT_FOUND AND GLEW_FOUND)
+    include_directories(${OPENGL_INCLUDE_DIR} ${GLUT_INCLUDE_DIR} ${GLEW_INCLUDE_DIRS})
+    ei_add_property(EIGEN_TESTED_BACKENDS "OpenGL, ")
+    set(EIGEN_GL_LIB ${GLUT_LIBRARIES} ${GLEW_LIBRARIES} ${OPENGL_LIBRARIES})
+    ei_add_test(openglsupport  "" "${EIGEN_GL_LIB}" )
+  else()
+    ei_add_property(EIGEN_MISSING_BACKENDS "OpenGL, ")
+  endif()
+else()
+    ei_add_property(EIGEN_MISSING_BACKENDS "OpenGL, ")
+endif()
+
+ei_add_test(polynomialsolver)
+ei_add_test(polynomialutils)
+ei_add_test(splines)
+ei_add_test(gmres)
+ei_add_test(dgmres)
+ei_add_test(minres)
+ei_add_test(idrs)
+ei_add_test(bicgstabl)
+ei_add_test(idrstabl)
+ei_add_test(levenberg_marquardt)
+ei_add_test(kronecker_product)
+ei_add_test(bessel_functions)
+ei_add_test(special_functions)
+ei_add_test(special_packetmath "-DEIGEN_FAST_MATH=1")
+
+if(EIGEN_TEST_SYCL)
+  set(EIGEN_SYCL ON)
+  include(SyclConfigureTesting)
+
+  ei_add_test(cxx11_tensor_sycl)
+  ei_add_test(cxx11_tensor_image_op_sycl)
+  ei_add_test(cxx11_tensor_math_sycl)
+  ei_add_test(cxx11_tensor_forced_eval_sycl)
+  ei_add_test(cxx11_tensor_broadcast_sycl)
+  ei_add_test(cxx11_tensor_device_sycl)
+  ei_add_test(cxx11_tensor_reduction_sycl)
+  ei_add_test(cxx11_tensor_morphing_sycl)
+  ei_add_test(cxx11_tensor_shuffling_sycl)
+  ei_add_test(cxx11_tensor_padding_sycl)
+  ei_add_test(cxx11_tensor_builtins_sycl)
+  ei_add_test(cxx11_tensor_contract_sycl)
+  ei_add_test(cxx11_tensor_concatenation_sycl)
+  ei_add_test(cxx11_tensor_reverse_sycl)
+  ei_add_test(cxx11_tensor_convolution_sycl)
+  ei_add_test(cxx11_tensor_striding_sycl)
+  ei_add_test(cxx11_tensor_chipping_sycl)
+  ei_add_test(cxx11_tensor_layout_swap_sycl)
+  ei_add_test(cxx11_tensor_inflation_sycl)
+  ei_add_test(cxx11_tensor_random_sycl)
+  ei_add_test(cxx11_tensor_generator_sycl)
+  ei_add_test(cxx11_tensor_patch_sycl)
+  ei_add_test(cxx11_tensor_image_patch_sycl)
+  ei_add_test(cxx11_tensor_volume_patch_sycl)
+  ei_add_test(cxx11_tensor_argmax_sycl)
+  ei_add_test(cxx11_tensor_custom_op_sycl)
+  ei_add_test(cxx11_tensor_scan_sycl)
+  ei_add_test(cxx11_tensor_of_float16_sycl)
+  set(EIGEN_SYCL OFF)
+endif()
+
+ei_add_test(cxx11_tensor_argmax)
+ei_add_test(cxx11_tensor_assign)
+ei_add_test(cxx11_tensor_block_access)
+ei_add_test(cxx11_tensor_block_eval)
+ei_add_test(cxx11_tensor_block_io)
+ei_add_test(cxx11_tensor_broadcasting)
+ei_add_test(cxx11_tensor_casts)
+ei_add_test(cxx11_tensor_chipping)
+ei_add_test(cxx11_tensor_comparisons)
+ei_add_test(cxx11_tensor_concatenation)
+ei_add_test(cxx11_tensor_const)
+ei_add_test(cxx11_tensor_contraction)
+ei_add_test(cxx11_tensor_convolution)
+ei_add_test(cxx11_tensor_custom_index)
+ei_add_test(cxx11_tensor_custom_op)
+ei_add_test(cxx11_tensor_dimension)
+ei_add_test(cxx11_tensor_empty)
+ei_add_test(cxx11_tensor_executor "-pthread" "${CMAKE_THREAD_LIBS_INIT}")
+ei_add_test(cxx11_tensor_expr)
+ei_add_test(cxx11_tensor_fft)
+ei_add_test(cxx11_tensor_fixed_size)
+ei_add_test(cxx11_tensor_forced_eval)
+ei_add_test(cxx11_tensor_generator)
+ei_add_test(cxx11_tensor_ifft)
+ei_add_test(cxx11_tensor_image_patch)
+ei_add_test(cxx11_tensor_index_list)
+ei_add_test(cxx11_tensor_inflation)
+ei_add_test(cxx11_tensor_intdiv)
+ei_add_test(cxx11_tensor_io)
+ei_add_test(cxx11_tensor_layout_swap)
+ei_add_test(cxx11_tensor_lvalue)
+ei_add_test(cxx11_tensor_map)
+ei_add_test(cxx11_tensor_math)
+ei_add_test(cxx11_tensor_mixed_indices)
+ei_add_test(cxx11_tensor_morphing)
+ei_add_test(cxx11_tensor_move)
+ei_add_test(cxx11_tensor_notification "-pthread" "${CMAKE_THREAD_LIBS_INIT}")
+ei_add_test(cxx11_tensor_of_complex)
+ei_add_test(cxx11_tensor_of_const_values)
+ei_add_test(cxx11_tensor_of_strings)
+ei_add_test(cxx11_tensor_padding)
+ei_add_test(cxx11_tensor_patch)
+ei_add_test(cxx11_tensor_random)
+ei_add_test(cxx11_tensor_reverse)
+ei_add_test(cxx11_tensor_reduction)
+ei_add_test(cxx11_tensor_ref)
+ei_add_test(cxx11_tensor_roll)
+ei_add_test(cxx11_tensor_roundings)
+ei_add_test(cxx11_tensor_scan)
+ei_add_test(cxx11_tensor_shuffling)
+ei_add_test(cxx11_tensor_simple)
+ei_add_test(cxx11_tensor_striding)
+ei_add_test(cxx11_tensor_sugar)
+ei_add_test(cxx11_tensor_thread_local "-pthread" "${CMAKE_THREAD_LIBS_INIT}")
+ei_add_test(cxx11_tensor_thread_pool "-pthread" "${CMAKE_THREAD_LIBS_INIT}")
+ei_add_test(cxx11_tensor_trace)
+ei_add_test(cxx11_tensor_volume_patch)
+#  ei_add_test(cxx11_tensor_symmetry)
+if("${CMAKE_SIZEOF_VOID_P}" EQUAL "8" AND NOT CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+  # This test requires __uint128_t which is only available on 64bit systems
+  ei_add_test(cxx11_tensor_uint128)
+endif()
+
+find_package(CUDA 9.0)
+if(CUDA_FOUND AND EIGEN_TEST_CUDA)
+  # Make sure to compile without the -pedantic, -Wundef, -Wnon-virtual-dtor
+  # and -fno-check-new flags since they trigger thousands of compilation warnings
+  # in the CUDA runtime
+  string(REPLACE "-pedantic" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
+  string(REPLACE "-Wundef" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
+  string(REPLACE "-Wnon-virtual-dtor" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
+  string(REPLACE "-fno-check-new" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
+
+  if(EIGEN_TEST_CUDA_CLANG)
+    string(APPEND CMAKE_CXX_FLAGS " --cuda-path=${CUDA_TOOLKIT_ROOT_DIR}")
+    foreach(ARCH IN LISTS EIGEN_CUDA_COMPUTE_ARCH)
+        string(APPEND CMAKE_CXX_FLAGS " --cuda-gpu-arch=sm_${ARCH}")
+    endforeach()
+    string(APPEND CMAKE_CXX_FLAGS " ${EIGEN_CUDA_CXX_FLAGS}")
+  else()
+    set(CUDA_PROPAGATE_HOST_FLAGS OFF)
+    set(NVCC_ARCH_FLAGS)
+    # Define an -arch=sm_<arch>, otherwise if GPU does not exactly match one of
+    # those in the arch list for -gencode, the kernels will fail to run with
+    #    cudaErrorNoKernelImageForDevice
+    # This can happen with newer cards (e.g. sm_75) and compiling with older
+    # versions of nvcc (e.g. 9.2) that do not support their specific arch.
+    list(LENGTH EIGEN_CUDA_COMPUTE_ARCH EIGEN_CUDA_COMPUTE_ARCH_SIZE)
+    if(EIGEN_CUDA_COMPUTE_ARCH_SIZE)
+      list(GET EIGEN_CUDA_COMPUTE_ARCH 0 EIGEN_CUDA_COMPUTE_DEFAULT)
+      set(NVCC_ARCH_FLAGS " -arch=sm_${EIGEN_CUDA_COMPUTE_DEFAULT}")
+    endif()
+    foreach(ARCH IN LISTS EIGEN_CUDA_COMPUTE_ARCH)
+      string(APPEND NVCC_ARCH_FLAGS " -gencode arch=compute_${ARCH},code=sm_${ARCH}")
+    endforeach()
+    set(CUDA_NVCC_FLAGS  "--expt-relaxed-constexpr -Xcudafe \"--display_error_number\" ${NVCC_ARCH_FLAGS} ${CUDA_NVCC_FLAGS} ${EIGEN_CUDA_CXX_FLAGS}")
+    cuda_include_directories("${CMAKE_CURRENT_BINARY_DIR}" "${CUDA_TOOLKIT_ROOT_DIR}/include")
+  endif()
+  
+  set(EIGEN_ADD_TEST_FILENAME_EXTENSION "cu")
+
+  ei_add_test(cxx11_tensor_complex_gpu)
+  ei_add_test(cxx11_tensor_complex_cwise_ops_gpu)
+  ei_add_test(cxx11_tensor_reduction_gpu)
+  ei_add_test(cxx11_tensor_argmax_gpu)
+  ei_add_test(cxx11_tensor_cast_float16_gpu)
+  ei_add_test(cxx11_tensor_scan_gpu)
+
+  set(EIGEN_CUDA_OLDEST_COMPUTE_ARCH 9999)
+  foreach(ARCH IN LISTS EIGEN_CUDA_COMPUTE_ARCH)
+    if(${ARCH} LESS ${EIGEN_CUDA_OLDEST_COMPUTE_ARCH})
+      set(EIGEN_CUDA_OLDEST_COMPUTE_ARCH ${ARCH})
+    endif()
+  endforeach()
+
+  # Contractions require arch 3.0 or higher
+  if (${EIGEN_CUDA_OLDEST_COMPUTE_ARCH} GREATER 29)
+    ei_add_test(cxx11_tensor_device)
+    ei_add_test(cxx11_tensor_gpu)
+    ei_add_test(cxx11_tensor_contract_gpu)
+    ei_add_test(cxx11_tensor_of_float16_gpu)
+  endif()
+
+  # The random number generation code requires arch 3.5 or greater.
+  if (${EIGEN_CUDA_OLDEST_COMPUTE_ARCH} GREATER 34)
+    ei_add_test(cxx11_tensor_random_gpu)
+  endif()
+
+  unset(EIGEN_ADD_TEST_FILENAME_EXTENSION)
+endif()
+
+# Add HIP specific tests
+if (EIGEN_TEST_HIP)
+
+  set(ROCM_PATH "/opt/rocm" CACHE STRING "Path to the ROCm installation.")
+
+  if (EXISTS ${ROCM_PATH}/hip)
+    set(HIP_PATH ${ROCM_PATH}/hip)
+    list(APPEND CMAKE_MODULE_PATH ${HIP_PATH}/cmake)
+  elseif (EXISTS ${ROCM_PATH}/lib/cmake/hip)
+    set(HIP_PATH ${ROCM_PATH})
+    list(APPEND CMAKE_MODULE_PATH ${HIP_PATH}/lib/cmake/hip)
+  else ()
+    message(FATAL_ERROR "EIGEN_TEST_HIP is ON, but could not find the ROCm installation under ${ROCM_PATH}")
+  endif()
+
+  find_package(HIP REQUIRED)
+  if (HIP_FOUND)
+    execute_process(COMMAND ${HIP_PATH}/bin/hipconfig --platform OUTPUT_VARIABLE HIP_PLATFORM)
+
+    if ((${HIP_PLATFORM} STREQUAL "hcc") OR (${HIP_PLATFORM} STREQUAL "amd"))
+      include_directories(${CMAKE_CURRENT_BINARY_DIR})
+      include_directories(${HIP_PATH}/include)
+
+      set(EIGEN_ADD_TEST_FILENAME_EXTENSION  "cu")
+      #
+      # complex datatype is not yet supported by HIP
+      # so leaving out those tests for now
+      #
+      # ei_add_test(cxx11_tensor_complex_gpu)
+      # ei_add_test(cxx11_tensor_complex_cwise_ops_gpu)
+      #
+      ei_add_test(cxx11_tensor_reduction_gpu)
+      ei_add_test(cxx11_tensor_argmax_gpu)
+      ei_add_test(cxx11_tensor_cast_float16_gpu)
+      ei_add_test(cxx11_tensor_scan_gpu)
+      ei_add_test(cxx11_tensor_device)
+
+      ei_add_test(cxx11_tensor_gpu)
+      ei_add_test(cxx11_tensor_contract_gpu)
+      ei_add_test(cxx11_tensor_of_float16_gpu)
+      ei_add_test(cxx11_tensor_of_bfloat16_gpu)
+      ei_add_test(cxx11_tensor_random_gpu)
+
+      unset(EIGEN_ADD_TEST_FILENAME_EXTENSION)
+
+    elseif ((${HIP_PLATFORM} STREQUAL "nvcc") OR (${HIP_PLATFORM} STREQUAL "nvidia"))
+      message(FATAL_ERROR "HIP_PLATFORM = nvcc is not supported within Eigen")
+    else ()
+      message(FATAL_ERROR "Unknown HIP_PLATFORM = ${HIP_PLATFORM}")
+    endif()
+  endif()
+endif()
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/EulerAngles.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/EulerAngles.cpp
new file mode 100644
index 0000000..a65e5f5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/EulerAngles.cpp
@@ -0,0 +1,282 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+EIGEN_DISABLE_DEPRECATED_WARNING
+
+#include <unsupported/Eigen/EulerAngles>
+
+using namespace Eigen;
+
+// Unfortunately, we need to specialize it in order to work. (We could add it in main.h test framework)
+template <typename Scalar, class System>
+bool verifyIsApprox(const Eigen::EulerAngles<Scalar, System>& a, const Eigen::EulerAngles<Scalar, System>& b) {
+  return verifyIsApprox(a.angles(), b.angles());
+}
+
+// Verify that x is in the approxed range [a, b]
+#define VERIFY_APPROXED_RANGE(a, x, b)   \
+  do {                                   \
+    VERIFY_IS_APPROX_OR_LESS_THAN(a, x); \
+    VERIFY_IS_APPROX_OR_LESS_THAN(x, b); \
+  } while (0)
+
+const char X = EULER_X;
+const char Y = EULER_Y;
+const char Z = EULER_Z;
+
+template <typename Scalar, class EulerSystem>
+void verify_euler(const EulerAngles<Scalar, EulerSystem>& e) {
+  typedef EulerAngles<Scalar, EulerSystem> EulerAnglesType;
+  typedef Matrix<Scalar, 3, 3> Matrix3;
+  typedef Matrix<Scalar, 3, 1> Vector3;
+  typedef Quaternion<Scalar> QuaternionType;
+  typedef AngleAxis<Scalar> AngleAxisType;
+
+  const Scalar ONE = Scalar(1);
+  const Scalar HALF_PI = Scalar(EIGEN_PI / 2);
+  const Scalar PI = Scalar(EIGEN_PI);
+
+  // It's very important calc the acceptable precision depending on the distance from the pole.
+  const Scalar longitudeRadius = std::abs(EulerSystem::IsTaitBryan ? std::cos(e.beta()) : std::sin(e.beta()));
+  Scalar precision = test_precision<Scalar>() / longitudeRadius;
+
+  Scalar betaRangeStart, betaRangeEnd;
+  if (EulerSystem::IsTaitBryan) {
+    betaRangeStart = -HALF_PI;
+    betaRangeEnd = HALF_PI;
+  } else {
+    if (!EulerSystem::IsBetaOpposite) {
+      betaRangeStart = 0;
+      betaRangeEnd = PI;
+    } else {
+      betaRangeStart = -PI;
+      betaRangeEnd = 0;
+    }
+  }
+
+  const Vector3 I_ = EulerAnglesType::AlphaAxisVector();
+  const Vector3 J_ = EulerAnglesType::BetaAxisVector();
+  const Vector3 K_ = EulerAnglesType::GammaAxisVector();
+
+  // Is approx checks
+  VERIFY(e.isApprox(e));
+  VERIFY_IS_APPROX(e, e);
+  VERIFY_IS_NOT_APPROX(e, EulerAnglesType(e.alpha() + ONE, e.beta() + ONE, e.gamma() + ONE));
+
+  const Matrix3 m(e);
+  VERIFY_IS_APPROX(Scalar(m.determinant()), ONE);
+
+  EulerAnglesType ebis(m);
+
+  // When no roll(acting like polar representation), we have the best precision.
+  // One of those cases is when the Euler angles are on the pole, and because it's singular case,
+  //  the computation returns no roll.
+  if (ebis.beta() == 0) precision = test_precision<Scalar>();
+
+  // Check that eabis in range
+  VERIFY_APPROXED_RANGE(-PI, ebis.alpha(), PI);
+  VERIFY_APPROXED_RANGE(betaRangeStart, ebis.beta(), betaRangeEnd);
+  VERIFY_APPROXED_RANGE(-PI, ebis.gamma(), PI);
+
+  const Matrix3 mbis(AngleAxisType(ebis.alpha(), I_) * AngleAxisType(ebis.beta(), J_) *
+                     AngleAxisType(ebis.gamma(), K_));
+  VERIFY_IS_APPROX(Scalar(mbis.determinant()), ONE);
+  VERIFY_IS_APPROX(mbis, ebis.toRotationMatrix());
+  /*std::cout << "===================\n" <<
+    "e: " << e << std::endl <<
+    "eabis: " << eabis.transpose() << std::endl <<
+    "m: " << m << std::endl <<
+    "mbis: " << mbis << std::endl <<
+    "X: " << (m * Vector3::UnitX()).transpose() << std::endl <<
+    "X: " << (mbis * Vector3::UnitX()).transpose() << std::endl;*/
+  VERIFY(m.isApprox(mbis, precision));
+
+  // Test if ea and eabis are the same
+  // Need to check both singular and non-singular cases
+  // There are two singular cases.
+  // 1. When I==K and sin(ea(1)) == 0
+  // 2. When I!=K and cos(ea(1)) == 0
+
+  // TODO: Make this test work well, and use range saturation function.
+  /*// If I==K, and ea[1]==0, then there no unique solution.
+  // The remark apply in the case where I!=K, and |ea[1]| is close to +-pi/2.
+  if( (i!=k || ea[1]!=0) && (i==k || !internal::isApprox(abs(ea[1]),Scalar(EIGEN_PI/2),test_precision<Scalar>())) )
+      VERIFY_IS_APPROX(ea, eabis);*/
+
+  // Quaternions
+  const QuaternionType q(e);
+  ebis = q;
+  const QuaternionType qbis(ebis);
+  VERIFY(internal::isApprox<Scalar>(std::abs(q.dot(qbis)), ONE, precision));
+  // VERIFY_IS_APPROX(eabis, eabis2);// Verify that the euler angles are still the same
+
+  // A suggestion for simple product test when will be supported.
+  /*EulerAnglesType e2(PI/2, PI/2, PI/2);
+  Matrix3 m2(e2);
+  VERIFY_IS_APPROX(e*e2, m*m2);*/
+}
+
+template <signed char A, signed char B, signed char C, typename Scalar>
+void verify_euler_vec(const Matrix<Scalar, 3, 1>& ea) {
+  verify_euler(EulerAngles<Scalar, EulerSystem<A, B, C> >(ea[0], ea[1], ea[2]));
+}
+
+template <signed char A, signed char B, signed char C, typename Scalar>
+void verify_euler_all_neg(const Matrix<Scalar, 3, 1>& ea) {
+  verify_euler_vec<+A, +B, +C>(ea);
+  verify_euler_vec<+A, +B, -C>(ea);
+  verify_euler_vec<+A, -B, +C>(ea);
+  verify_euler_vec<+A, -B, -C>(ea);
+
+  verify_euler_vec<-A, +B, +C>(ea);
+  verify_euler_vec<-A, +B, -C>(ea);
+  verify_euler_vec<-A, -B, +C>(ea);
+  verify_euler_vec<-A, -B, -C>(ea);
+}
+
+template <typename Scalar>
+void check_all_var(const Matrix<Scalar, 3, 1>& ea) {
+  verify_euler_all_neg<X, Y, Z>(ea);
+  verify_euler_all_neg<X, Y, X>(ea);
+  verify_euler_all_neg<X, Z, Y>(ea);
+  verify_euler_all_neg<X, Z, X>(ea);
+
+  verify_euler_all_neg<Y, Z, X>(ea);
+  verify_euler_all_neg<Y, Z, Y>(ea);
+  verify_euler_all_neg<Y, X, Z>(ea);
+  verify_euler_all_neg<Y, X, Y>(ea);
+
+  verify_euler_all_neg<Z, X, Y>(ea);
+  verify_euler_all_neg<Z, X, Z>(ea);
+  verify_euler_all_neg<Z, Y, X>(ea);
+  verify_euler_all_neg<Z, Y, Z>(ea);
+}
+
+template <typename Scalar>
+void check_singular_cases(const Scalar& singularBeta) {
+  typedef Matrix<Scalar, 3, 1> Vector3;
+  const Scalar PI = Scalar(EIGEN_PI);
+
+  for (Scalar epsilon = NumTraits<Scalar>::epsilon(); epsilon < 1; epsilon *= Scalar(1.2)) {
+    check_all_var(Vector3(PI / 4, singularBeta, PI / 3));
+    check_all_var(Vector3(PI / 4, singularBeta - epsilon, PI / 3));
+    check_all_var(Vector3(PI / 4, singularBeta - Scalar(1.5) * epsilon, PI / 3));
+    check_all_var(Vector3(PI / 4, singularBeta - 2 * epsilon, PI / 3));
+    check_all_var(Vector3(PI * Scalar(0.8), singularBeta - epsilon, Scalar(0.9) * PI));
+    check_all_var(Vector3(PI * Scalar(-0.9), singularBeta + epsilon, PI * Scalar(0.3)));
+    check_all_var(Vector3(PI * Scalar(-0.6), singularBeta + Scalar(1.5) * epsilon, PI * Scalar(0.3)));
+    check_all_var(Vector3(PI * Scalar(-0.5), singularBeta + 2 * epsilon, PI * Scalar(0.4)));
+    check_all_var(Vector3(PI * Scalar(0.9), singularBeta + epsilon, Scalar(0.8) * PI));
+  }
+
+  // This one for sanity, it had a problem with near pole cases in float scalar.
+  check_all_var(Vector3(PI * Scalar(0.8), singularBeta - Scalar(1E-6), Scalar(0.9) * PI));
+}
+
+template <typename Scalar>
+void eulerangles_manual() {
+  typedef Matrix<Scalar, 3, 1> Vector3;
+  typedef Matrix<Scalar, Dynamic, 1> VectorX;
+  const Vector3 Zero = Vector3::Zero();
+  const Scalar PI = Scalar(EIGEN_PI);
+
+  check_all_var(Zero);
+
+  // singular cases
+  check_singular_cases(PI / 2);
+  check_singular_cases(-PI / 2);
+
+  check_singular_cases(Scalar(0));
+  check_singular_cases(Scalar(-0));
+
+  check_singular_cases(PI);
+  check_singular_cases(-PI);
+
+  // non-singular cases
+  VectorX alpha = VectorX::LinSpaced(20, Scalar(-0.99) * PI, PI);
+  VectorX beta = VectorX::LinSpaced(20, Scalar(-0.49) * PI, Scalar(0.49) * PI);
+  VectorX gamma = VectorX::LinSpaced(20, Scalar(-0.99) * PI, PI);
+  for (int i = 0; i < alpha.size(); ++i) {
+    for (int j = 0; j < beta.size(); ++j) {
+      for (int k = 0; k < gamma.size(); ++k) {
+        check_all_var(Vector3(alpha(i), beta(j), gamma(k)));
+      }
+    }
+  }
+}
+
+template <typename Scalar>
+void eulerangles_rand() {
+  typedef Matrix<Scalar, 3, 3> Matrix3;
+  typedef Matrix<Scalar, 3, 1> Vector3;
+  typedef Array<Scalar, 3, 1> Array3;
+  typedef Quaternion<Scalar> Quaternionx;
+  typedef AngleAxis<Scalar> AngleAxisType;
+
+  Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI));
+  Quaternionx q1;
+  q1 = AngleAxisType(a, Vector3::Random().normalized());
+  Matrix3 m;
+  m = q1;
+
+  Vector3 ea = m.eulerAngles(0, 1, 2);
+  check_all_var(ea);
+  ea = m.eulerAngles(0, 1, 0);
+  check_all_var(ea);
+
+  // Check with purely random Quaternion:
+  q1.coeffs() = Quaternionx::Coefficients::Random().normalized();
+  m = q1;
+  ea = m.eulerAngles(0, 1, 2);
+  check_all_var(ea);
+  ea = m.eulerAngles(0, 1, 0);
+  check_all_var(ea);
+
+  // Check with random angles in range [0:pi]x[-pi:pi]x[-pi:pi].
+  ea = (Array3::Random() + Array3(1, 0, 0)) * Scalar(EIGEN_PI) * Array3(0.5, 1, 1);
+  check_all_var(ea);
+
+  ea[2] = ea[0] = internal::random<Scalar>(0, Scalar(EIGEN_PI));
+  check_all_var(ea);
+
+  ea[0] = ea[1] = internal::random<Scalar>(0, Scalar(EIGEN_PI));
+  check_all_var(ea);
+
+  ea[1] = 0;
+  check_all_var(ea);
+
+  ea.head(2).setZero();
+  check_all_var(ea);
+
+  ea.setZero();
+  check_all_var(ea);
+}
+
+EIGEN_DECLARE_TEST(EulerAngles) {
+  // Simple cast test
+  EulerAnglesXYZd onesEd(1, 1, 1);
+  EulerAnglesXYZf onesEf = onesEd.cast<float>();
+  VERIFY_IS_APPROX(onesEd, onesEf.cast<double>());
+
+  // Simple Construction from Vector3 test
+  VERIFY_IS_APPROX(onesEd, EulerAnglesXYZd(Vector3d::Ones()));
+
+  CALL_SUBTEST_1(eulerangles_manual<float>());
+  CALL_SUBTEST_2(eulerangles_manual<double>());
+
+  for (int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_3(eulerangles_rand<float>());
+    CALL_SUBTEST_4(eulerangles_rand<double>());
+  }
+
+  // TODO: Add tests for auto diff
+  // TODO: Add tests for complex numbers
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/FFT.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/FFT.cpp
new file mode 100644
index 0000000..f85461c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/FFT.cpp
@@ -0,0 +1,2 @@
+#define EIGEN_FFT_DEFAULT 1
+#include "fft_test_shared.h"
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/FFTW.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/FFTW.cpp
new file mode 100644
index 0000000..7302196
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/FFTW.cpp
@@ -0,0 +1,2 @@
+#define EIGEN_FFTW_DEFAULT 1
+#include "fft_test_shared.h"
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/NNLS.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/NNLS.cpp
new file mode 100644
index 0000000..f01b562
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/NNLS.cpp
@@ -0,0 +1,471 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) Essex Edwards <essex.edwards@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_RUNTIME_NO_MALLOC
+
+#include "main.h"
+#include <unsupported/Eigen/NNLS>
+
+/// Check that 'x' solves the NNLS optimization problem `min ||A*x-b|| s.t. 0 <= x`.
+/// The \p tolerance parameter is the absolute tolerance on the gradient, A'*(A*x-b).
+template <typename MatrixType, typename VectorB, typename VectorX, typename Scalar>
+void verify_nnls_optimality(const MatrixType &A, const VectorB &b, const VectorX &x, const Scalar tolerance) {
+  // The NNLS optimality conditions are:
+  //
+  // * 0 = A'*A*x - A'*b - lambda
+  // * 0 <= x[i] \forall i
+  // * 0 <= lambda[i] \forall i
+  // * 0 = x[i]*lambda[i] \forall i
+  //
+  // we don't know lambda, but by assuming the first optimality condition is true,
+  // we can derive it and then check the others conditions.
+  const VectorX lambda = A.transpose() * (A * x - b);
+
+  // NNLS solutions are EXACTLY not negative.
+  VERIFY_LE(0, x.minCoeff());
+
+  // Exact lambda would be non-negative, but computed lambda might leak a little
+  VERIFY_LE(-tolerance, lambda.minCoeff());
+
+  // x[i]*lambda[i] == 0 <~~> (x[i]==0) || (lambda[i] is small)
+  VERIFY(((x.array() == Scalar(0)) || (lambda.array() <= tolerance)).all());
+}
+
+template <typename MatrixType, typename VectorB, typename VectorX>
+void test_nnls_known_solution(const MatrixType &A, const VectorB &b, const VectorX &x_expected) {
+  using Scalar = typename MatrixType::Scalar;
+
+  using std::sqrt;
+  const Scalar tolerance = sqrt(Eigen::GenericNumTraits<Scalar>::epsilon());
+  Index max_iter = 5 * A.cols();  // A heuristic guess.
+  NNLS<MatrixType> nnls(A, max_iter, tolerance);
+  const VectorX x = nnls.solve(b);
+
+  VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::Success);
+  VERIFY_IS_APPROX(x, x_expected);
+  verify_nnls_optimality(A, b, x, tolerance);
+}
+
+template <typename MatrixType>
+void test_nnls_random_problem(const MatrixType &) {
+  //
+  // SETUP
+  //
+
+  Index cols = MatrixType::ColsAtCompileTime;
+  if (cols == Dynamic) cols = internal::random<Index>(1, EIGEN_TEST_MAX_SIZE);
+  Index rows = MatrixType::RowsAtCompileTime;
+  if (rows == Dynamic) rows = internal::random<Index>(cols, EIGEN_TEST_MAX_SIZE);
+  VERIFY_LE(cols, rows);  // To have a unique LS solution: cols <= rows.
+
+  // Make some sort of random test problem from a wide range of scales and condition numbers.
+  using std::pow;
+  using Scalar = typename MatrixType::Scalar;
+  const Scalar sqrtConditionNumber = pow(Scalar(10), internal::random<Scalar>(Scalar(0), Scalar(2)));
+  const Scalar scaleA = pow(Scalar(10), internal::random<Scalar>(Scalar(-3), Scalar(3)));
+  const Scalar minSingularValue = scaleA / sqrtConditionNumber;
+  const Scalar maxSingularValue = scaleA * sqrtConditionNumber;
+  MatrixType A(rows, cols);
+  generateRandomMatrixSvs(setupRangeSvs<Matrix<Scalar, Dynamic, 1>>(cols, minSingularValue, maxSingularValue), rows,
+                          cols, A);
+
+  // Make a random RHS also with a random scaling.
+  using VectorB = decltype(A.col(0).eval());
+  const Scalar scaleB = pow(Scalar(10), internal::random<Scalar>(Scalar(-3), Scalar(3)));
+  const VectorB b = scaleB * VectorB::Random(A.rows());
+
+  //
+  // ACT
+  //
+
+  using Scalar = typename MatrixType::Scalar;
+  using std::sqrt;
+  const Scalar tolerance =
+      sqrt(Eigen::GenericNumTraits<Scalar>::epsilon()) * b.cwiseAbs().maxCoeff() * A.cwiseAbs().maxCoeff();
+  Index max_iter = 5 * A.cols();  // A heuristic guess.
+  NNLS<MatrixType> nnls(A, max_iter, tolerance);
+  const typename NNLS<MatrixType>::SolutionVectorType &x = nnls.solve(b);
+
+  //
+  // VERIFY
+  //
+
+  // In fact, NNLS can fail on some problems, but they are rare in practice.
+  VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::Success);
+  verify_nnls_optimality(A, b, x, tolerance);
+}
+
+void test_nnls_handles_zero_rhs() {
+  //
+  // SETUP
+  //
+  const Index cols = internal::random<Index>(1, EIGEN_TEST_MAX_SIZE);
+  const Index rows = internal::random<Index>(cols, EIGEN_TEST_MAX_SIZE);
+  const MatrixXd A = MatrixXd::Random(rows, cols);
+  const VectorXd b = VectorXd::Zero(rows);
+
+  //
+  // ACT
+  //
+  NNLS<MatrixXd> nnls(A);
+  const VectorXd x = nnls.solve(b);
+
+  //
+  // VERIFY
+  //
+  VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::Success);
+  VERIFY_LE(nnls.iterations(), 1);  // 0 or 1 would be be fine for an edge case like this.
+  VERIFY_IS_EQUAL(x, VectorXd::Zero(cols));
+}
+
+void test_nnls_handles_Mx0_matrix() {
+  //
+  // SETUP
+  //
+  const Index rows = internal::random<Index>(1, EIGEN_TEST_MAX_SIZE);
+  const MatrixXd A(rows, 0);
+  const VectorXd b = VectorXd::Random(rows);
+
+  //
+  // ACT
+  //
+  NNLS<MatrixXd> nnls(A);
+  const VectorXd x = nnls.solve(b);
+
+  //
+  // VERIFY
+  //
+  VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::Success);
+  VERIFY_LE(nnls.iterations(), 0);
+  VERIFY_IS_EQUAL(x.size(), 0);
+}
+
+void test_nnls_handles_0x0_matrix() {
+  //
+  // SETUP
+  //
+  const MatrixXd A(0, 0);
+  const VectorXd b(0);
+
+  //
+  // ACT
+  //
+  NNLS<MatrixXd> nnls(A);
+  const VectorXd x = nnls.solve(b);
+
+  //
+  // VERIFY
+  //
+  VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::Success);
+  VERIFY_LE(nnls.iterations(), 0);
+  VERIFY_IS_EQUAL(x.size(), 0);
+}
+
+void test_nnls_handles_dependent_columns() {
+  //
+  // SETUP
+  //
+  const Index rank = internal::random<Index>(1, EIGEN_TEST_MAX_SIZE / 2);
+  const Index cols = 2 * rank;
+  const Index rows = internal::random<Index>(cols, EIGEN_TEST_MAX_SIZE);
+  const MatrixXd A = MatrixXd::Random(rows, rank) * MatrixXd::Random(rank, cols);
+  const VectorXd b = VectorXd::Random(rows);
+
+  //
+  // ACT
+  //
+  const double tolerance = 1e-8;
+  NNLS<MatrixXd> nnls(A);
+  const VectorXd &x = nnls.solve(b);
+
+  //
+  // VERIFY
+  //
+  // What should happen when the input 'A' has dependent columns?
+  // We might still succeed. Or we might not converge.
+  // Either outcome is fine. If Success is indicated,
+  // then 'x' must actually be a solution vector.
+
+  if (nnls.info() == ComputationInfo::Success) {
+    verify_nnls_optimality(A, b, x, tolerance);
+  }
+}
+
+void test_nnls_handles_wide_matrix() {
+  //
+  // SETUP
+  //
+  const Index cols = internal::random<Index>(2, EIGEN_TEST_MAX_SIZE);
+  const Index rows = internal::random<Index>(2, cols - 1);
+  const MatrixXd A = MatrixXd::Random(rows, cols);
+  const VectorXd b = VectorXd::Random(rows);
+
+  //
+  // ACT
+  //
+  const double tolerance = 1e-8;
+  NNLS<MatrixXd> nnls(A);
+  const VectorXd &x = nnls.solve(b);
+
+  //
+  // VERIFY
+  //
+  // What should happen when the input 'A' is wide?
+  // The unconstrained least-squares problem has infinitely many solutions.
+  // Subject the the non-negativity constraints,
+  // the solution might actually be unique (e.g. it is [0,0,..,0]).
+  // So, NNLS might succeed or it might fail.
+  // Either outcome is fine. If Success is indicated,
+  // then 'x' must actually be a solution vector.
+
+  if (nnls.info() == ComputationInfo::Success) {
+    verify_nnls_optimality(A, b, x, tolerance);
+  }
+}
+
+// 4x2 problem, unconstrained solution positive
+void test_nnls_known_1() {
+  Matrix<double, 4, 2> A(4, 2);
+  Matrix<double, 4, 1> b(4);
+  Matrix<double, 2, 1> x(2);
+  A << 1, 1, 2, 4, 3, 9, 4, 16;
+  b << 0.6, 2.2, 4.8, 8.4;
+  x << 0.1, 0.5;
+
+  return test_nnls_known_solution(A, b, x);
+}
+
+// 4x3 problem, unconstrained solution positive
+void test_nnls_known_2() {
+  Matrix<double, 4, 3> A(4, 3);
+  Matrix<double, 4, 1> b(4);
+  Matrix<double, 3, 1> x(3);
+
+  A << 1, 1, 1, 2, 4, 8, 3, 9, 27, 4, 16, 64;
+  b << 0.73, 3.24, 8.31, 16.72;
+  x << 0.1, 0.5, 0.13;
+
+  test_nnls_known_solution(A, b, x);
+}
+
+// Simple 4x4 problem, unconstrained solution non-negative
+void test_nnls_known_3() {
+  Matrix<double, 4, 4> A(4, 4);
+  Matrix<double, 4, 1> b(4);
+  Matrix<double, 4, 1> x(4);
+
+  A << 1, 1, 1, 1, 2, 4, 8, 16, 3, 9, 27, 81, 4, 16, 64, 256;
+  b << 0.73, 3.24, 8.31, 16.72;
+  x << 0.1, 0.5, 0.13, 0;
+
+  test_nnls_known_solution(A, b, x);
+}
+
+// Simple 4x3 problem, unconstrained solution non-negative
+void test_nnls_known_4() {
+  Matrix<double, 4, 3> A(4, 3);
+  Matrix<double, 4, 1> b(4);
+  Matrix<double, 3, 1> x(3);
+
+  A << 1, 1, 1, 2, 4, 8, 3, 9, 27, 4, 16, 64;
+  b << 0.23, 1.24, 3.81, 8.72;
+  x << 0.1, 0, 0.13;
+
+  test_nnls_known_solution(A, b, x);
+}
+
+// Simple 4x3 problem, unconstrained solution indefinite
+void test_nnls_known_5() {
+  Matrix<double, 4, 3> A(4, 3);
+  Matrix<double, 4, 1> b(4);
+  Matrix<double, 3, 1> x(3);
+
+  A << 1, 1, 1, 2, 4, 8, 3, 9, 27, 4, 16, 64;
+  b << 0.13, 0.84, 2.91, 7.12;
+  // Solution obtained by original nnls() implementation in Fortran
+  x << 0.0, 0.0, 0.1106544;
+
+  test_nnls_known_solution(A, b, x);
+}
+
+void test_nnls_small_reference_problems() {
+  test_nnls_known_1();
+  test_nnls_known_2();
+  test_nnls_known_3();
+  test_nnls_known_4();
+  test_nnls_known_5();
+}
+
+void test_nnls_with_half_precision() {
+  // The random matrix generation tools don't work with `half`,
+  // so here's a simpler setup mostly just to check that NNLS compiles & runs with custom scalar types.
+
+  using Mat = Matrix<half, 8, 2>;
+  using VecB = Matrix<half, 8, 1>;
+  using VecX = Matrix<half, 2, 1>;
+  Mat A = Mat::Random();  // full-column rank with high probability.
+  VecB b = VecB::Random();
+
+  NNLS<Mat> nnls(A, 20, half(1e-2f));
+  const VecX x = nnls.solve(b);
+
+  VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::Success);
+  verify_nnls_optimality(A, b, x, half(1e-1));
+}
+
+void test_nnls_special_case_solves_in_zero_iterations() {
+  // The particular NNLS algorithm that is implemented starts with all variables
+  // in the active set.
+  // This test builds a system where all constraints are active at the solution,
+  // so that initial guess is already correct.
+  //
+  // If the implementation changes to another algorithm that does not have this property,
+  // then this test will need to change (e.g. starting from all constraints inactive,
+  // or using ADMM, or an interior point solver).
+
+  const Index n = 10;
+  const Index m = 3 * n;
+  const VectorXd b = VectorXd::Random(m);
+  // With high probability, this is full column rank, which we need for uniqueness.
+  MatrixXd A = MatrixXd::Random(m, n);
+  // Make every column of `A` such that adding it to the active set only /increases/ the objective,
+  // this ensuring the NNLS solution is all zeros.
+  const VectorXd alignment = -(A.transpose() * b).cwiseSign();
+  A = A * alignment.asDiagonal();
+
+  NNLS<MatrixXd> nnls(A);
+  nnls.solve(b);
+
+  VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::Success);
+  VERIFY(nnls.iterations() == 0);
+}
+
+void test_nnls_special_case_solves_in_n_iterations() {
+  // The particular NNLS algorithm that is implemented starts with all variables
+  // in the active set and then adds one variable to the inactive set each iteration.
+  // This test builds a system where all variables are inactive at the solution,
+  // so it should take 'n' iterations to get there.
+  //
+  // If the implementation changes to another algorithm that does not have this property,
+  // then this test will need to change (e.g. starting from all constraints inactive,
+  // or using ADMM, or an interior point solver).
+
+  const Index n = 10;
+  const Index m = 3 * n;
+  // With high probability, this is full column rank, which we need for uniqueness.
+  const MatrixXd A = MatrixXd::Random(m, n);
+  const VectorXd x = VectorXd::Random(n).cwiseAbs().array() + 1;  // all positive.
+  const VectorXd b = A * x;
+
+  NNLS<MatrixXd> nnls(A);
+  nnls.solve(b);
+
+  VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::Success);
+  VERIFY(nnls.iterations() == n);
+}
+
+void test_nnls_returns_NoConvergence_when_maxIterations_is_too_low() {
+  // Using the special case that takes `n` iterations,
+  // from `test_nnls_special_case_solves_in_n_iterations`,
+  // we can set max iterations too low and that should cause the solve to fail.
+
+  const Index n = 10;
+  const Index m = 3 * n;
+  // With high probability, this is full column rank, which we need for uniqueness.
+  const MatrixXd A = MatrixXd::Random(m, n);
+  const VectorXd x = VectorXd::Random(n).cwiseAbs().array() + 1;  // all positive.
+  const VectorXd b = A * x;
+
+  NNLS<MatrixXd> nnls(A);
+  const Index max_iters = n - 1;
+  nnls.setMaxIterations(max_iters);
+  nnls.solve(b);
+
+  VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::NoConvergence);
+  VERIFY(nnls.iterations() == max_iters);
+}
+
+void test_nnls_default_maxIterations_is_twice_column_count() {
+  const Index cols = internal::random<Index>(1, EIGEN_TEST_MAX_SIZE);
+  const Index rows = internal::random<Index>(cols, EIGEN_TEST_MAX_SIZE);
+  const MatrixXd A = MatrixXd::Random(rows, cols);
+
+  NNLS<MatrixXd> nnls(A);
+
+  VERIFY_IS_EQUAL(nnls.maxIterations(), 2 * cols);
+}
+
+void test_nnls_does_not_allocate_during_solve() {
+  const Index cols = internal::random<Index>(1, EIGEN_TEST_MAX_SIZE);
+  const Index rows = internal::random<Index>(cols, EIGEN_TEST_MAX_SIZE);
+  const MatrixXd A = MatrixXd::Random(rows, cols);
+  const VectorXd b = VectorXd::Random(rows);
+
+  NNLS<MatrixXd> nnls(A);
+
+  internal::set_is_malloc_allowed(false);
+  nnls.solve(b);
+  internal::set_is_malloc_allowed(true);
+}
+
+void test_nnls_repeated_calls_to_compute_and_solve() {
+  const Index cols2 = internal::random<Index>(1, EIGEN_TEST_MAX_SIZE);
+  const Index rows2 = internal::random<Index>(cols2, EIGEN_TEST_MAX_SIZE);
+  const MatrixXd A2 = MatrixXd::Random(rows2, cols2);
+  const VectorXd b2 = VectorXd::Random(rows2);
+
+  NNLS<MatrixXd> nnls;
+
+  for (int i = 0; i < 4; ++i) {
+    const Index cols = internal::random<Index>(1, EIGEN_TEST_MAX_SIZE);
+    const Index rows = internal::random<Index>(cols, EIGEN_TEST_MAX_SIZE);
+    const MatrixXd A = MatrixXd::Random(rows, cols);
+
+    nnls.compute(A);
+    VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::Success);
+
+    for (int j = 0; j < 3; ++j) {
+      const VectorXd b = VectorXd::Random(rows);
+      const VectorXd x = nnls.solve(b);
+      VERIFY_IS_EQUAL(nnls.info(), ComputationInfo::Success);
+      verify_nnls_optimality(A, b, x, 1e-4);
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(NNLS) {
+  // Small matrices with known solutions:
+  CALL_SUBTEST_1(test_nnls_small_reference_problems());
+  CALL_SUBTEST_1(test_nnls_handles_Mx0_matrix());
+  CALL_SUBTEST_1(test_nnls_handles_0x0_matrix());
+
+  for (int i = 0; i < g_repeat; i++) {
+    // Essential NNLS properties, across different types.
+    CALL_SUBTEST_2(test_nnls_random_problem(MatrixXf()));
+    CALL_SUBTEST_3(test_nnls_random_problem(MatrixXd()));
+    CALL_SUBTEST_4(test_nnls_random_problem(Matrix<double, 12, 5>()));
+    CALL_SUBTEST_5(test_nnls_with_half_precision());
+
+    // Robustness tests:
+    CALL_SUBTEST_6(test_nnls_handles_zero_rhs());
+    CALL_SUBTEST_6(test_nnls_handles_dependent_columns());
+    CALL_SUBTEST_6(test_nnls_handles_wide_matrix());
+
+    // Properties specific to the implementation,
+    // not NNLS in general.
+    CALL_SUBTEST_7(test_nnls_special_case_solves_in_zero_iterations());
+    CALL_SUBTEST_7(test_nnls_special_case_solves_in_n_iterations());
+    CALL_SUBTEST_7(test_nnls_returns_NoConvergence_when_maxIterations_is_too_low());
+    CALL_SUBTEST_7(test_nnls_default_maxIterations_is_twice_column_count());
+    CALL_SUBTEST_8(test_nnls_repeated_calls_to_compute_and_solve());
+
+    // This test fails. It hits allocations in HouseholderSequence.h
+    // test_nnls_does_not_allocate_during_solve();
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/NonLinearOptimization.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/NonLinearOptimization.cpp
new file mode 100644
index 0000000..78990d7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/NonLinearOptimization.cpp
@@ -0,0 +1,1879 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+
+#include <stdio.h>
+
+#include "main.h"
+#include <unsupported/Eigen/NonLinearOptimization>
+
+// This disables some useless Warnings on MSVC.
+// It is intended to be done for this test only.
+#include <Eigen/src/Core/util/DisableStupidWarnings.h>
+
+// tolerance for checking number of iterations
+#define LM_EVAL_COUNT_TOL 2
+
+#define LM_CHECK_N_ITERS(SOLVER, NFEV, NJEV)         \
+  {                                                  \
+    VERIFY(SOLVER.nfev <= NFEV * LM_EVAL_COUNT_TOL); \
+    VERIFY(SOLVER.njev <= NJEV * LM_EVAL_COUNT_TOL); \
+  }
+
+int fcn_chkder(const VectorXd &x, VectorXd &fvec, MatrixXd &fjac, int iflag) {
+  /*      subroutine fcn for chkder example. */
+
+  int i;
+  assert(15 == fvec.size());
+  assert(3 == x.size());
+  double tmp1, tmp2, tmp3, tmp4;
+  static const double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, 3.9e-1,
+                               3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34,   2.1,    4.39};
+
+  if (iflag == 0) return 0;
+
+  if (iflag != 2)
+    for (i = 0; i < 15; i++) {
+      tmp1 = i + 1;
+      tmp2 = 16 - i - 1;
+      tmp3 = tmp1;
+      if (i >= 8) tmp3 = tmp2;
+      fvec[i] = y[i] - (x[0] + tmp1 / (x[1] * tmp2 + x[2] * tmp3));
+    }
+  else {
+    for (i = 0; i < 15; i++) {
+      tmp1 = i + 1;
+      tmp2 = 16 - i - 1;
+
+      /* error introduced into next statement for illustration. */
+      /* corrected statement should read    tmp3 = tmp1 . */
+
+      tmp3 = tmp2;
+      if (i >= 8) tmp3 = tmp2;
+      tmp4 = (x[1] * tmp2 + x[2] * tmp3);
+      tmp4 = tmp4 * tmp4;
+      fjac(i, 0) = -1.;
+      fjac(i, 1) = tmp1 * tmp2 / tmp4;
+      fjac(i, 2) = tmp1 * tmp3 / tmp4;
+    }
+  }
+  return 0;
+}
+
+void testChkder() {
+  const int m = 15, n = 3;
+  VectorXd x(n), fvec(m), xp, fvecp(m), err;
+  MatrixXd fjac(m, n);
+  VectorXi ipvt;
+
+  /*      the following values should be suitable for */
+  /*      checking the jacobian matrix. */
+  x << 9.2e-1, 1.3e-1, 5.4e-1;
+
+  internal::chkder(x, fvec, fjac, xp, fvecp, 1, err);
+  fcn_chkder(x, fvec, fjac, 1);
+  fcn_chkder(x, fvec, fjac, 2);
+  fcn_chkder(xp, fvecp, fjac, 1);
+  internal::chkder(x, fvec, fjac, xp, fvecp, 2, err);
+
+  fvecp -= fvec;
+
+  // check those
+  VectorXd fvec_ref(m), fvecp_ref(m), err_ref(m);
+  fvec_ref << -1.181606, -1.429655, -1.606344, -1.745269, -1.840654, -1.921586, -1.984141, -2.022537, -2.468977,
+      -2.827562, -3.473582, -4.437612, -6.047662, -9.267761, -18.91806;
+  fvecp_ref << -7.724666e-09, -3.432406e-09, -2.034843e-10, 2.313685e-09, 4.331078e-09, 5.984096e-09, 7.363281e-09,
+      8.53147e-09, 1.488591e-08, 2.33585e-08, 3.522012e-08, 5.301255e-08, 8.26666e-08, 1.419747e-07, 3.19899e-07;
+  err_ref << 0.1141397, 0.09943516, 0.09674474, 0.09980447, 0.1073116, 0.1220445, 0.1526814, 1, 1, 1, 1, 1, 1, 1, 1;
+
+  VERIFY_IS_APPROX(fvec, fvec_ref);
+  VERIFY_IS_APPROX(fvecp, fvecp_ref);
+  VERIFY_IS_APPROX(err, err_ref);
+}
+
+// Generic functor
+template <typename Scalar_, int NX = Dynamic, int NY = Dynamic>
+struct Functor {
+  typedef Scalar_ Scalar;
+  enum { InputsAtCompileTime = NX, ValuesAtCompileTime = NY };
+  typedef Matrix<Scalar, InputsAtCompileTime, 1> InputType;
+  typedef Matrix<Scalar, ValuesAtCompileTime, 1> ValueType;
+  typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
+
+  const int m_inputs, m_values;
+
+  Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
+  Functor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
+
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values; }
+
+  // you should define that in the subclass :
+  //  void operator() (const InputType& x, ValueType* v, JacobianType* _j=0) const;
+};
+
+struct lmder_functor : Functor<double> {
+  lmder_functor(void) : Functor<double>(3, 15) {}
+  int operator()(const VectorXd &x, VectorXd &fvec) const {
+    double tmp1, tmp2, tmp3;
+    static const double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, 3.9e-1,
+                                 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34,   2.1,    4.39};
+
+    for (int i = 0; i < values(); i++) {
+      tmp1 = i + 1;
+      tmp2 = 16 - i - 1;
+      tmp3 = (i >= 8) ? tmp2 : tmp1;
+      fvec[i] = y[i] - (x[0] + tmp1 / (x[1] * tmp2 + x[2] * tmp3));
+    }
+    return 0;
+  }
+
+  int df(const VectorXd &x, MatrixXd &fjac) const {
+    double tmp1, tmp2, tmp3, tmp4;
+    for (int i = 0; i < values(); i++) {
+      tmp1 = i + 1;
+      tmp2 = 16 - i - 1;
+      tmp3 = (i >= 8) ? tmp2 : tmp1;
+      tmp4 = (x[1] * tmp2 + x[2] * tmp3);
+      tmp4 = tmp4 * tmp4;
+      fjac(i, 0) = -1;
+      fjac(i, 1) = tmp1 * tmp2 / tmp4;
+      fjac(i, 2) = tmp1 * tmp3 / tmp4;
+    }
+    return 0;
+  }
+};
+
+void testLmder1() {
+  int n = 3, info;
+
+  VectorXd x;
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, 1.);
+
+  // do the computation
+  lmder_functor functor;
+  LevenbergMarquardt<lmder_functor> lm(functor);
+  info = lm.lmder1(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 6, 5);
+
+  // check norm
+  VERIFY_IS_APPROX(lm.fvec.blueNorm(), 0.09063596);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << 0.08241058, 1.133037, 2.343695;
+  VERIFY_IS_APPROX(x, x_ref);
+}
+
+void testLmder() {
+  const int m = 15, n = 3;
+  int info;
+  double fnorm, covfac;
+  VectorXd x;
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, 1.);
+
+  // do the computation
+  lmder_functor functor;
+  LevenbergMarquardt<lmder_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return values
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 6, 5);
+
+  // check norm
+  fnorm = lm.fvec.blueNorm();
+  VERIFY_IS_APPROX(fnorm, 0.09063596);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << 0.08241058, 1.133037, 2.343695;
+  VERIFY_IS_APPROX(x, x_ref);
+
+  // check covariance
+  covfac = fnorm * fnorm / (m - n);
+  internal::covar(lm.fjac, lm.permutation.indices());  // TODO : move this as a function of lm
+
+  MatrixXd cov_ref(n, n);
+  cov_ref << 0.0001531202, 0.002869941, -0.002656662, 0.002869941, 0.09480935, -0.09098995, -0.002656662, -0.09098995,
+      0.08778727;
+
+  //  std::cout << fjac*covfac << std::endl;
+
+  MatrixXd cov;
+  cov = covfac * lm.fjac.topLeftCorner<n, n>();
+  VERIFY_IS_APPROX(cov, cov_ref);
+  // TODO: why isn't this allowed ? :
+  // VERIFY_IS_APPROX( covfac*fjac.topLeftCorner<n,n>() , cov_ref);
+}
+
+struct hybrj_functor : Functor<double> {
+  hybrj_functor(void) : Functor<double>(9, 9) {}
+
+  int operator()(const VectorXd &x, VectorXd &fvec) {
+    double temp, temp1, temp2;
+    const VectorXd::Index n = x.size();
+    assert(fvec.size() == n);
+    for (VectorXd::Index k = 0; k < n; k++) {
+      temp = (3. - 2. * x[k]) * x[k];
+      temp1 = 0.;
+      if (k) temp1 = x[k - 1];
+      temp2 = 0.;
+      if (k != n - 1) temp2 = x[k + 1];
+      fvec[k] = temp - temp1 - 2. * temp2 + 1.;
+    }
+    return 0;
+  }
+  int df(const VectorXd &x, MatrixXd &fjac) {
+    const VectorXd::Index n = x.size();
+    assert(fjac.rows() == n);
+    assert(fjac.cols() == n);
+    for (VectorXd::Index k = 0; k < n; k++) {
+      for (VectorXd::Index j = 0; j < n; j++) fjac(k, j) = 0.;
+      fjac(k, k) = 3. - 4. * x[k];
+      if (k) fjac(k, k - 1) = -1.;
+      if (k != n - 1) fjac(k, k + 1) = -2.;
+    }
+    return 0;
+  }
+};
+
+void testHybrj1() {
+  const int n = 9;
+  int info;
+  VectorXd x(n);
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, -1.);
+
+  // do the computation
+  hybrj_functor functor;
+  HybridNonLinearSolver<hybrj_functor> solver(functor);
+  info = solver.hybrj1(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(solver, 11, 1);
+
+  // check norm
+  VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << -0.5706545, -0.6816283, -0.7017325, -0.7042129, -0.701369, -0.6918656, -0.665792, -0.5960342, -0.4164121;
+  VERIFY_IS_APPROX(x, x_ref);
+}
+
+void testHybrj() {
+  const int n = 9;
+  int info;
+  VectorXd x(n);
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, -1.);
+
+  // do the computation
+  hybrj_functor functor;
+  HybridNonLinearSolver<hybrj_functor> solver(functor);
+  solver.diag.setConstant(n, 1.);
+  solver.useExternalScaling = true;
+  info = solver.solve(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(solver, 11, 1);
+
+  // check norm
+  VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << -0.5706545, -0.6816283, -0.7017325, -0.7042129, -0.701369, -0.6918656, -0.665792, -0.5960342, -0.4164121;
+  VERIFY_IS_APPROX(x, x_ref);
+}
+
+struct hybrd_functor : Functor<double> {
+  hybrd_functor(void) : Functor<double>(9, 9) {}
+  int operator()(const VectorXd &x, VectorXd &fvec) const {
+    double temp, temp1, temp2;
+    const VectorXd::Index n = x.size();
+
+    assert(fvec.size() == n);
+    for (VectorXd::Index k = 0; k < n; k++) {
+      temp = (3. - 2. * x[k]) * x[k];
+      temp1 = 0.;
+      if (k) temp1 = x[k - 1];
+      temp2 = 0.;
+      if (k != n - 1) temp2 = x[k + 1];
+      fvec[k] = temp - temp1 - 2. * temp2 + 1.;
+    }
+    return 0;
+  }
+};
+
+void testHybrd1() {
+  int n = 9, info;
+  VectorXd x(n);
+
+  /* the following starting values provide a rough solution. */
+  x.setConstant(n, -1.);
+
+  // do the computation
+  hybrd_functor functor;
+  HybridNonLinearSolver<hybrd_functor> solver(functor);
+  info = solver.hybrd1(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  VERIFY(solver.nfev <= 20 * LM_EVAL_COUNT_TOL);
+
+  // check norm
+  VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << -0.5706545, -0.6816283, -0.7017325, -0.7042129, -0.701369, -0.6918656, -0.665792, -0.5960342, -0.4164121;
+  VERIFY_IS_APPROX(x, x_ref);
+}
+
+void testHybrd() {
+  const int n = 9;
+  int info;
+  VectorXd x;
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, -1.);
+
+  // do the computation
+  hybrd_functor functor;
+  HybridNonLinearSolver<hybrd_functor> solver(functor);
+  solver.parameters.nb_of_subdiagonals = 1;
+  solver.parameters.nb_of_superdiagonals = 1;
+  solver.diag.setConstant(n, 1.);
+  solver.useExternalScaling = true;
+  info = solver.solveNumericalDiff(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  VERIFY(solver.nfev <= 14 * LM_EVAL_COUNT_TOL);
+
+  // check norm
+  VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << -0.5706545, -0.6816283, -0.7017325, -0.7042129, -0.701369, -0.6918656, -0.665792, -0.5960342, -0.4164121;
+  VERIFY_IS_APPROX(x, x_ref);
+}
+
+struct lmstr_functor : Functor<double> {
+  lmstr_functor(void) : Functor<double>(3, 15) {}
+  int operator()(const VectorXd &x, VectorXd &fvec) {
+    /*  subroutine fcn for lmstr1 example. */
+    double tmp1, tmp2, tmp3;
+    static const double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, 3.9e-1,
+                                 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34,   2.1,    4.39};
+
+    assert(15 == fvec.size());
+    assert(3 == x.size());
+
+    for (int i = 0; i < 15; i++) {
+      tmp1 = i + 1;
+      tmp2 = 16 - i - 1;
+      tmp3 = (i >= 8) ? tmp2 : tmp1;
+      fvec[i] = y[i] - (x[0] + tmp1 / (x[1] * tmp2 + x[2] * tmp3));
+    }
+    return 0;
+  }
+  int df(const VectorXd &x, VectorXd &jac_row, VectorXd::Index rownb) {
+    assert(x.size() == 3);
+    assert(jac_row.size() == x.size());
+    double tmp1, tmp2, tmp3, tmp4;
+
+    VectorXd::Index i = rownb - 2;
+    tmp1 = i + 1;
+    tmp2 = 16 - i - 1;
+    tmp3 = (i >= 8) ? tmp2 : tmp1;
+    tmp4 = (x[1] * tmp2 + x[2] * tmp3);
+    tmp4 = tmp4 * tmp4;
+    jac_row[0] = -1;
+    jac_row[1] = tmp1 * tmp2 / tmp4;
+    jac_row[2] = tmp1 * tmp3 / tmp4;
+    return 0;
+  }
+};
+
+void testLmstr1() {
+  const int n = 3;
+  int info;
+
+  VectorXd x(n);
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, 1.);
+
+  // do the computation
+  lmstr_functor functor;
+  LevenbergMarquardt<lmstr_functor> lm(functor);
+  info = lm.lmstr1(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 6, 5);
+
+  // check norm
+  VERIFY_IS_APPROX(lm.fvec.blueNorm(), 0.09063596);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << 0.08241058, 1.133037, 2.343695;
+  VERIFY_IS_APPROX(x, x_ref);
+}
+
+void testLmstr() {
+  const int n = 3;
+  int info;
+  double fnorm;
+  VectorXd x(n);
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, 1.);
+
+  // do the computation
+  lmstr_functor functor;
+  LevenbergMarquardt<lmstr_functor> lm(functor);
+  info = lm.minimizeOptimumStorage(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return values
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 6, 5);
+
+  // check norm
+  fnorm = lm.fvec.blueNorm();
+  VERIFY_IS_APPROX(fnorm, 0.09063596);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << 0.08241058, 1.133037, 2.343695;
+  VERIFY_IS_APPROX(x, x_ref);
+}
+
+struct lmdif_functor : Functor<double> {
+  lmdif_functor(void) : Functor<double>(3, 15) {}
+  int operator()(const VectorXd &x, VectorXd &fvec) const {
+    int i;
+    double tmp1, tmp2, tmp3;
+    static const double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, 3.9e-1,
+                                 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34e0, 2.1e0,  4.39e0};
+
+    assert(x.size() == 3);
+    assert(fvec.size() == 15);
+    for (i = 0; i < 15; i++) {
+      tmp1 = i + 1;
+      tmp2 = 15 - i;
+      tmp3 = tmp1;
+
+      if (i >= 8) tmp3 = tmp2;
+      fvec[i] = y[i] - (x[0] + tmp1 / (x[1] * tmp2 + x[2] * tmp3));
+    }
+    return 0;
+  }
+};
+
+void testLmdif1() {
+  const int n = 3;
+  int info;
+
+  VectorXd x(n), fvec(15);
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, 1.);
+
+  // do the computation
+  lmdif_functor functor;
+  DenseIndex nfev = -1;  // initialize to avoid maybe-uninitialized warning
+  info = LevenbergMarquardt<lmdif_functor>::lmdif1(functor, x, &nfev);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  VERIFY(nfev <= 26 * LM_EVAL_COUNT_TOL);
+
+  // check norm
+  functor(x, fvec);
+  VERIFY_IS_APPROX(fvec.blueNorm(), 0.09063596);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << 0.0824106, 1.1330366, 2.3436947;
+  VERIFY_IS_APPROX(x, x_ref);
+}
+
+void testLmdif() {
+  const int m = 15, n = 3;
+  int info;
+  double fnorm, covfac;
+  VectorXd x(n);
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, 1.);
+
+  // do the computation
+  lmdif_functor functor;
+  NumericalDiff<lmdif_functor> numDiff(functor);
+  LevenbergMarquardt<NumericalDiff<lmdif_functor> > lm(numDiff);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return values
+  // VERIFY_IS_EQUAL(info, 1);
+  VERIFY(lm.nfev <= 26 * LM_EVAL_COUNT_TOL);
+
+  // check norm
+  fnorm = lm.fvec.blueNorm();
+  VERIFY_IS_APPROX(fnorm, 0.09063596);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << 0.08241058, 1.133037, 2.343695;
+  VERIFY_IS_APPROX(x, x_ref);
+
+  // check covariance
+  covfac = fnorm * fnorm / (m - n);
+  internal::covar(lm.fjac, lm.permutation.indices());  // TODO : move this as a function of lm
+
+  MatrixXd cov_ref(n, n);
+  cov_ref << 0.0001531202, 0.002869942, -0.002656662, 0.002869942, 0.09480937, -0.09098997, -0.002656662, -0.09098997,
+      0.08778729;
+
+  //  std::cout << fjac*covfac << std::endl;
+
+  MatrixXd cov;
+  cov = covfac * lm.fjac.topLeftCorner<n, n>();
+  VERIFY_IS_APPROX(cov, cov_ref);
+  // TODO: why isn't this allowed ? :
+  // VERIFY_IS_APPROX( covfac*fjac.topLeftCorner<n,n>() , cov_ref);
+}
+
+struct chwirut2_functor : Functor<double> {
+  chwirut2_functor(void) : Functor<double>(3, 54) {}
+  static const double m_x[54];
+  static const double m_y[54];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    int i;
+
+    assert(b.size() == 3);
+    assert(fvec.size() == 54);
+    for (i = 0; i < 54; i++) {
+      double x = m_x[i];
+      fvec[i] = exp(-b[0] * x) / (b[1] + b[2] * x) - m_y[i];
+    }
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 3);
+    assert(fjac.rows() == 54);
+    assert(fjac.cols() == 3);
+    for (int i = 0; i < 54; i++) {
+      double x = m_x[i];
+      double factor = 1. / (b[1] + b[2] * x);
+      double e = exp(-b[0] * x);
+      fjac(i, 0) = -x * e * factor;
+      fjac(i, 1) = -e * factor * factor;
+      fjac(i, 2) = -x * e * factor * factor;
+    }
+    return 0;
+  }
+};
+const double chwirut2_functor::m_x[54] = {
+    0.500E0, 1.000E0, 1.750E0, 3.750E0, 5.750E0, 0.875E0, 2.250E0, 3.250E0, 5.250E0, 0.750E0, 1.750E0,
+    2.750E0, 4.750E0, 0.625E0, 1.250E0, 2.250E0, 4.250E0, .500E0,  3.000E0, .750E0,  3.000E0, 1.500E0,
+    6.000E0, 3.000E0, 6.000E0, 1.500E0, 3.000E0, .500E0,  2.000E0, 4.000E0, .750E0,  2.000E0, 5.000E0,
+    .750E0,  2.250E0, 3.750E0, 5.750E0, 3.000E0, .750E0,  2.500E0, 4.000E0, .750E0,  2.500E0, 4.000E0,
+    .750E0,  2.500E0, 4.000E0, .500E0,  6.000E0, 3.000E0, .500E0,  2.750E0, .500E0,  1.750E0};
+const double chwirut2_functor::m_y[54] = {
+    92.9000E0, 57.1000E0, 31.0500E0, 11.5875E0, 8.0250E0,  63.6000E0, 21.4000E0, 14.2500E0, 8.4750E0,
+    63.8000E0, 26.8000E0, 16.4625E0, 7.1250E0,  67.3000E0, 41.0000E0, 21.1500E0, 8.1750E0,  81.5000E0,
+    13.1200E0, 59.9000E0, 14.6200E0, 32.9000E0, 5.4400E0,  12.5600E0, 5.4400E0,  32.0000E0, 13.9500E0,
+    75.8000E0, 20.0000E0, 10.4200E0, 59.5000E0, 21.6700E0, 8.5500E0,  62.0000E0, 20.2000E0, 7.7600E0,
+    3.7500E0,  11.8100E0, 54.7000E0, 23.7000E0, 11.5500E0, 61.3000E0, 17.7000E0, 8.7400E0,  59.2000E0,
+    16.3000E0, 8.6200E0,  81.0000E0, 4.8700E0,  14.6200E0, 81.7000E0, 17.1700E0, 81.3000E0, 28.9000E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/chwirut2.shtml
+void testNistChwirut2(void) {
+  const int n = 3;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 0.1, 0.01, 0.02;
+  // do the computation
+  chwirut2_functor functor;
+  LevenbergMarquardt<chwirut2_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 10, 8);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.6657666537E-01);
+  VERIFY_IS_APPROX(x[1], 5.1653291286E-03);
+  VERIFY_IS_APPROX(x[2], 1.2150007096E-02);
+
+  /*
+   * Second try
+   */
+  x << 0.15, 0.008, 0.010;
+  // do the computation
+  lm.resetParameters();
+  lm.parameters.ftol = 1.E6 * NumTraits<double>::epsilon();
+  lm.parameters.xtol = 1.E6 * NumTraits<double>::epsilon();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 7, 6);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.6657666537E-01);
+  VERIFY_IS_APPROX(x[1], 5.1653291286E-03);
+  VERIFY_IS_APPROX(x[2], 1.2150007096E-02);
+}
+
+struct misra1a_functor : Functor<double> {
+  misra1a_functor(void) : Functor<double>(2, 14) {}
+  static const double m_x[14];
+  static const double m_y[14];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 2);
+    assert(fvec.size() == 14);
+    for (int i = 0; i < 14; i++) {
+      fvec[i] = b[0] * (1. - exp(-b[1] * m_x[i])) - m_y[i];
+    }
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 2);
+    assert(fjac.rows() == 14);
+    assert(fjac.cols() == 2);
+    for (int i = 0; i < 14; i++) {
+      fjac(i, 0) = (1. - exp(-b[1] * m_x[i]));
+      fjac(i, 1) = (b[0] * m_x[i] * exp(-b[1] * m_x[i]));
+    }
+    return 0;
+  }
+};
+const double misra1a_functor::m_x[14] = {77.6E0,  114.9E0, 141.1E0, 190.8E0, 239.9E0, 289.0E0, 332.8E0,
+                                         378.4E0, 434.8E0, 477.3E0, 536.8E0, 593.1E0, 689.1E0, 760.0E0};
+const double misra1a_functor::m_y[14] = {10.07E0, 14.73E0, 17.94E0, 23.93E0, 29.61E0, 35.18E0, 40.02E0,
+                                         44.82E0, 50.76E0, 55.05E0, 61.01E0, 66.40E0, 75.47E0, 81.78E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/misra1a.shtml
+void testNistMisra1a(void) {
+  const int n = 2;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 500., 0.0001;
+  // do the computation
+  misra1a_functor functor;
+  LevenbergMarquardt<misra1a_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 19, 15);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
+  // check x
+  VERIFY_IS_APPROX(x[0], 2.3894212918E+02);
+  VERIFY_IS_APPROX(x[1], 5.5015643181E-04);
+
+  /*
+   * Second try
+   */
+  x << 250., 0.0005;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 5, 4);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
+  // check x
+  VERIFY_IS_APPROX(x[0], 2.3894212918E+02);
+  VERIFY_IS_APPROX(x[1], 5.5015643181E-04);
+}
+
+struct hahn1_functor : Functor<double> {
+  hahn1_functor(void) : Functor<double>(7, 236) {}
+  static const double m_x[236];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    static const double m_y[236] = {
+        .591E0,   1.547E0,  2.902E0,  2.894E0,  4.703E0,  6.307E0,  7.03E0,   7.898E0,  9.470E0,  9.484E0,  10.072E0,
+        10.163E0, 11.615E0, 12.005E0, 12.478E0, 12.982E0, 12.970E0, 13.926E0, 14.452E0, 14.404E0, 15.190E0, 15.550E0,
+        15.528E0, 15.499E0, 16.131E0, 16.438E0, 16.387E0, 16.549E0, 16.872E0, 16.830E0, 16.926E0, 16.907E0, 16.966E0,
+        17.060E0, 17.122E0, 17.311E0, 17.355E0, 17.668E0, 17.767E0, 17.803E0, 17.765E0, 17.768E0, 17.736E0, 17.858E0,
+        17.877E0, 17.912E0, 18.046E0, 18.085E0, 18.291E0, 18.357E0, 18.426E0, 18.584E0, 18.610E0, 18.870E0, 18.795E0,
+        19.111E0, .367E0,   .796E0,   0.892E0,  1.903E0,  2.150E0,  3.697E0,  5.870E0,  6.421E0,  7.422E0,  9.944E0,
+        11.023E0, 11.87E0,  12.786E0, 14.067E0, 13.974E0, 14.462E0, 14.464E0, 15.381E0, 15.483E0, 15.59E0,  16.075E0,
+        16.347E0, 16.181E0, 16.915E0, 17.003E0, 16.978E0, 17.756E0, 17.808E0, 17.868E0, 18.481E0, 18.486E0, 19.090E0,
+        16.062E0, 16.337E0, 16.345E0, 16.388E0, 17.159E0, 17.116E0, 17.164E0, 17.123E0, 17.979E0, 17.974E0, 18.007E0,
+        17.993E0, 18.523E0, 18.669E0, 18.617E0, 19.371E0, 19.330E0, 0.080E0,  0.248E0,  1.089E0,  1.418E0,  2.278E0,
+        3.624E0,  4.574E0,  5.556E0,  7.267E0,  7.695E0,  9.136E0,  9.959E0,  9.957E0,  11.600E0, 13.138E0, 13.564E0,
+        13.871E0, 13.994E0, 14.947E0, 15.473E0, 15.379E0, 15.455E0, 15.908E0, 16.114E0, 17.071E0, 17.135E0, 17.282E0,
+        17.368E0, 17.483E0, 17.764E0, 18.185E0, 18.271E0, 18.236E0, 18.237E0, 18.523E0, 18.627E0, 18.665E0, 19.086E0,
+        0.214E0,  0.943E0,  1.429E0,  2.241E0,  2.951E0,  3.782E0,  4.757E0,  5.602E0,  7.169E0,  8.920E0,  10.055E0,
+        12.035E0, 12.861E0, 13.436E0, 14.167E0, 14.755E0, 15.168E0, 15.651E0, 15.746E0, 16.216E0, 16.445E0, 16.965E0,
+        17.121E0, 17.206E0, 17.250E0, 17.339E0, 17.793E0, 18.123E0, 18.49E0,  18.566E0, 18.645E0, 18.706E0, 18.924E0,
+        19.1E0,   0.375E0,  0.471E0,  1.504E0,  2.204E0,  2.813E0,  4.765E0,  9.835E0,  10.040E0, 11.946E0, 12.596E0,
+        13.303E0, 13.922E0, 14.440E0, 14.951E0, 15.627E0, 15.639E0, 15.814E0, 16.315E0, 16.334E0, 16.430E0, 16.423E0,
+        17.024E0, 17.009E0, 17.165E0, 17.134E0, 17.349E0, 17.576E0, 17.848E0, 18.090E0, 18.276E0, 18.404E0, 18.519E0,
+        19.133E0, 19.074E0, 19.239E0, 19.280E0, 19.101E0, 19.398E0, 19.252E0, 19.89E0,  20.007E0, 19.929E0, 19.268E0,
+        19.324E0, 20.049E0, 20.107E0, 20.062E0, 20.065E0, 19.286E0, 19.972E0, 20.088E0, 20.743E0, 20.83E0,  20.935E0,
+        21.035E0, 20.93E0,  21.074E0, 21.085E0, 20.935E0};
+
+    //        int called=0; printf("call hahn1_functor with  iflag=%d, called=%d\n", iflag, called); if (iflag==1)
+    //        called++;
+
+    assert(b.size() == 7);
+    assert(fvec.size() == 236);
+    for (int i = 0; i < 236; i++) {
+      double x = m_x[i], xx = x * x, xxx = xx * x;
+      fvec[i] = (b[0] + b[1] * x + b[2] * xx + b[3] * xxx) / (1. + b[4] * x + b[5] * xx + b[6] * xxx) - m_y[i];
+    }
+    return 0;
+  }
+
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 7);
+    assert(fjac.rows() == 236);
+    assert(fjac.cols() == 7);
+    for (int i = 0; i < 236; i++) {
+      double x = m_x[i], xx = x * x, xxx = xx * x;
+      double fact = 1. / (1. + b[4] * x + b[5] * xx + b[6] * xxx);
+      fjac(i, 0) = 1. * fact;
+      fjac(i, 1) = x * fact;
+      fjac(i, 2) = xx * fact;
+      fjac(i, 3) = xxx * fact;
+      fact = -(b[0] + b[1] * x + b[2] * xx + b[3] * xxx) * fact * fact;
+      fjac(i, 4) = x * fact;
+      fjac(i, 5) = xx * fact;
+      fjac(i, 6) = xxx * fact;
+    }
+    return 0;
+  }
+};
+const double hahn1_functor::m_x[236] = {
+    24.41E0,  34.82E0,  44.09E0,  45.07E0,  54.98E0,  65.51E0,  70.53E0,  75.70E0,  89.57E0,  91.14E0,  96.40E0,
+    97.19E0,  114.26E0, 120.25E0, 127.08E0, 133.55E0, 133.61E0, 158.67E0, 172.74E0, 171.31E0, 202.14E0, 220.55E0,
+    221.05E0, 221.39E0, 250.99E0, 268.99E0, 271.80E0, 271.97E0, 321.31E0, 321.69E0, 330.14E0, 333.03E0, 333.47E0,
+    340.77E0, 345.65E0, 373.11E0, 373.79E0, 411.82E0, 419.51E0, 421.59E0, 422.02E0, 422.47E0, 422.61E0, 441.75E0,
+    447.41E0, 448.7E0,  472.89E0, 476.69E0, 522.47E0, 522.62E0, 524.43E0, 546.75E0, 549.53E0, 575.29E0, 576.00E0,
+    625.55E0, 20.15E0,  28.78E0,  29.57E0,  37.41E0,  39.12E0,  50.24E0,  61.38E0,  66.25E0,  73.42E0,  95.52E0,
+    107.32E0, 122.04E0, 134.03E0, 163.19E0, 163.48E0, 175.70E0, 179.86E0, 211.27E0, 217.78E0, 219.14E0, 262.52E0,
+    268.01E0, 268.62E0, 336.25E0, 337.23E0, 339.33E0, 427.38E0, 428.58E0, 432.68E0, 528.99E0, 531.08E0, 628.34E0,
+    253.24E0, 273.13E0, 273.66E0, 282.10E0, 346.62E0, 347.19E0, 348.78E0, 351.18E0, 450.10E0, 450.35E0, 451.92E0,
+    455.56E0, 552.22E0, 553.56E0, 555.74E0, 652.59E0, 656.20E0, 14.13E0,  20.41E0,  31.30E0,  33.84E0,  39.70E0,
+    48.83E0,  54.50E0,  60.41E0,  72.77E0,  75.25E0,  86.84E0,  94.88E0,  96.40E0,  117.37E0, 139.08E0, 147.73E0,
+    158.63E0, 161.84E0, 192.11E0, 206.76E0, 209.07E0, 213.32E0, 226.44E0, 237.12E0, 330.90E0, 358.72E0, 370.77E0,
+    372.72E0, 396.24E0, 416.59E0, 484.02E0, 495.47E0, 514.78E0, 515.65E0, 519.47E0, 544.47E0, 560.11E0, 620.77E0,
+    18.97E0,  28.93E0,  33.91E0,  40.03E0,  44.66E0,  49.87E0,  55.16E0,  60.90E0,  72.08E0,  85.15E0,  97.06E0,
+    119.63E0, 133.27E0, 143.84E0, 161.91E0, 180.67E0, 198.44E0, 226.86E0, 229.65E0, 258.27E0, 273.77E0, 339.15E0,
+    350.13E0, 362.75E0, 371.03E0, 393.32E0, 448.53E0, 473.78E0, 511.12E0, 524.70E0, 548.75E0, 551.64E0, 574.02E0,
+    623.86E0, 21.46E0,  24.33E0,  33.43E0,  39.22E0,  44.18E0,  55.02E0,  94.33E0,  96.44E0,  118.82E0, 128.48E0,
+    141.94E0, 156.92E0, 171.65E0, 190.00E0, 223.26E0, 223.88E0, 231.50E0, 265.05E0, 269.44E0, 271.78E0, 273.46E0,
+    334.61E0, 339.79E0, 349.52E0, 358.18E0, 377.98E0, 394.77E0, 429.66E0, 468.22E0, 487.27E0, 519.54E0, 523.03E0,
+    612.99E0, 638.59E0, 641.36E0, 622.05E0, 631.50E0, 663.97E0, 646.9E0,  748.29E0, 749.21E0, 750.14E0, 647.04E0,
+    646.89E0, 746.9E0,  748.43E0, 747.35E0, 749.27E0, 647.61E0, 747.78E0, 750.51E0, 851.37E0, 845.97E0, 847.54E0,
+    849.93E0, 851.61E0, 849.75E0, 850.98E0, 848.23E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/hahn1.shtml
+void testNistHahn1(void) {
+  const int n = 7;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 10., -1., .05, -.00001, -.05, .001, -.000001;
+  // do the computation
+  hahn1_functor functor;
+  LevenbergMarquardt<hahn1_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 11, 10);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.0776351733E+00);
+  VERIFY_IS_APPROX(x[1], -1.2269296921E-01);
+  VERIFY_IS_APPROX(x[2], 4.0863750610E-03);
+  VERIFY_IS_APPROX(x[3], -1.426264e-06);  // should be : -1.4262662514E-06
+  VERIFY_IS_APPROX(x[4], -5.7609940901E-03);
+  VERIFY_IS_APPROX(x[5], 2.4053735503E-04);
+  VERIFY_IS_APPROX(x[6], -1.2314450199E-07);
+
+  /*
+   * Second try
+   */
+  x << .1, -.1, .005, -.000001, -.005, .0001, -.0000001;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 11, 10);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.077640);       // should be :  1.0776351733E+00
+  VERIFY_IS_APPROX(x[1], -0.1226933);     // should be : -1.2269296921E-01
+  VERIFY_IS_APPROX(x[2], 0.004086383);    // should be : 4.0863750610E-03
+  VERIFY_IS_APPROX(x[3], -1.426277e-06);  // should be : -1.4262662514E-06
+  VERIFY_IS_APPROX(x[4], -5.7609940901E-03);
+  VERIFY_IS_APPROX(x[5], 0.00024053772);  // should be : 2.4053735503E-04
+  VERIFY_IS_APPROX(x[6], -1.231450e-07);  // should be : -1.2314450199E-07
+}
+
+struct misra1d_functor : Functor<double> {
+  misra1d_functor(void) : Functor<double>(2, 14) {}
+  static const double x[14];
+  static const double y[14];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 2);
+    assert(fvec.size() == 14);
+    for (int i = 0; i < 14; i++) {
+      fvec[i] = b[0] * b[1] * x[i] / (1. + b[1] * x[i]) - y[i];
+    }
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 2);
+    assert(fjac.rows() == 14);
+    assert(fjac.cols() == 2);
+    for (int i = 0; i < 14; i++) {
+      double den = 1. + b[1] * x[i];
+      fjac(i, 0) = b[1] * x[i] / den;
+      fjac(i, 1) = b[0] * x[i] * (den - b[1] * x[i]) / den / den;
+    }
+    return 0;
+  }
+};
+const double misra1d_functor::x[14] = {77.6E0,  114.9E0, 141.1E0, 190.8E0, 239.9E0, 289.0E0, 332.8E0,
+                                       378.4E0, 434.8E0, 477.3E0, 536.8E0, 593.1E0, 689.1E0, 760.0E0};
+const double misra1d_functor::y[14] = {10.07E0, 14.73E0, 17.94E0, 23.93E0, 29.61E0, 35.18E0, 40.02E0,
+                                       44.82E0, 50.76E0, 55.05E0, 61.01E0, 66.40E0, 75.47E0, 81.78E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/misra1d.shtml
+void testNistMisra1d(void) {
+  const int n = 2;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 500., 0.0001;
+  // do the computation
+  misra1d_functor functor;
+  LevenbergMarquardt<misra1d_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 3);
+  LM_CHECK_N_ITERS(lm, 9, 7);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
+  // check x
+  VERIFY_IS_APPROX(x[0], 4.3736970754E+02);
+  VERIFY_IS_APPROX(x[1], 3.0227324449E-04);
+
+  /*
+   * Second try
+   */
+  x << 450., 0.0003;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 4, 3);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
+  // check x
+  VERIFY_IS_APPROX(x[0], 4.3736970754E+02);
+  VERIFY_IS_APPROX(x[1], 3.0227324449E-04);
+}
+
+struct lanczos1_functor : Functor<double> {
+  lanczos1_functor(void) : Functor<double>(6, 24) {}
+  static const double x[24];
+  static const double y[24];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 6);
+    assert(fvec.size() == 24);
+    for (int i = 0; i < 24; i++)
+      fvec[i] = b[0] * exp(-b[1] * x[i]) + b[2] * exp(-b[3] * x[i]) + b[4] * exp(-b[5] * x[i]) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 6);
+    assert(fjac.rows() == 24);
+    assert(fjac.cols() == 6);
+    for (int i = 0; i < 24; i++) {
+      fjac(i, 0) = exp(-b[1] * x[i]);
+      fjac(i, 1) = -b[0] * x[i] * exp(-b[1] * x[i]);
+      fjac(i, 2) = exp(-b[3] * x[i]);
+      fjac(i, 3) = -b[2] * x[i] * exp(-b[3] * x[i]);
+      fjac(i, 4) = exp(-b[5] * x[i]);
+      fjac(i, 5) = -b[4] * x[i] * exp(-b[5] * x[i]);
+    }
+    return 0;
+  }
+};
+const double lanczos1_functor::x[24] = {0.000000000000E+00, 5.000000000000E-02, 1.000000000000E-01, 1.500000000000E-01,
+                                        2.000000000000E-01, 2.500000000000E-01, 3.000000000000E-01, 3.500000000000E-01,
+                                        4.000000000000E-01, 4.500000000000E-01, 5.000000000000E-01, 5.500000000000E-01,
+                                        6.000000000000E-01, 6.500000000000E-01, 7.000000000000E-01, 7.500000000000E-01,
+                                        8.000000000000E-01, 8.500000000000E-01, 9.000000000000E-01, 9.500000000000E-01,
+                                        1.000000000000E+00, 1.050000000000E+00, 1.100000000000E+00, 1.150000000000E+00};
+const double lanczos1_functor::y[24] = {2.513400000000E+00, 2.044333373291E+00, 1.668404436564E+00, 1.366418021208E+00,
+                                        1.123232487372E+00, 9.268897180037E-01, 7.679338563728E-01, 6.388775523106E-01,
+                                        5.337835317402E-01, 4.479363617347E-01, 3.775847884350E-01, 3.197393199326E-01,
+                                        2.720130773746E-01, 2.324965529032E-01, 1.996589546065E-01, 1.722704126914E-01,
+                                        1.493405660168E-01, 1.300700206922E-01, 1.138119324644E-01, 1.000415587559E-01,
+                                        8.833209084540E-02, 7.833544019350E-02, 6.976693743449E-02, 6.239312536719E-02};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/lanczos1.shtml
+void testNistLanczos1(void) {
+  const int n = 6;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 1.2, 0.3, 5.6, 5.5, 6.5, 7.6;
+  // do the computation
+  lanczos1_functor functor;
+  LevenbergMarquardt<lanczos1_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 2);
+  LM_CHECK_N_ITERS(lm, 79, 72);
+  // check norm^2
+  // std::cout.precision(30);
+  // std::cout << lm.fvec.squaredNorm() << "\n";
+  VERIFY(lm.fvec.squaredNorm() <= 1.44E-25);
+  // check x
+  VERIFY_IS_APPROX(x[0], 9.5100000027E-02);
+  VERIFY_IS_APPROX(x[1], 1.0000000001E+00);
+  VERIFY_IS_APPROX(x[2], 8.6070000013E-01);
+  VERIFY_IS_APPROX(x[3], 3.0000000002E+00);
+  VERIFY_IS_APPROX(x[4], 1.5575999998E+00);
+  VERIFY_IS_APPROX(x[5], 5.0000000001E+00);
+
+  /*
+   * Second try
+   */
+  x << 0.5, 0.7, 3.6, 4.2, 4., 6.3;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 2);
+  LM_CHECK_N_ITERS(lm, 9, 8);
+  // check norm^2
+  VERIFY(lm.fvec.squaredNorm() <= 1.44E-25);
+  // check x
+  VERIFY_IS_APPROX(x[0], 9.5100000027E-02);
+  VERIFY_IS_APPROX(x[1], 1.0000000001E+00);
+  VERIFY_IS_APPROX(x[2], 8.6070000013E-01);
+  VERIFY_IS_APPROX(x[3], 3.0000000002E+00);
+  VERIFY_IS_APPROX(x[4], 1.5575999998E+00);
+  VERIFY_IS_APPROX(x[5], 5.0000000001E+00);
+}
+
+struct rat42_functor : Functor<double> {
+  rat42_functor(void) : Functor<double>(3, 9) {}
+  static const double x[9];
+  static const double y[9];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 3);
+    assert(fvec.size() == 9);
+    for (int i = 0; i < 9; i++) {
+      fvec[i] = b[0] / (1. + exp(b[1] - b[2] * x[i])) - y[i];
+    }
+    return 0;
+  }
+
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 3);
+    assert(fjac.rows() == 9);
+    assert(fjac.cols() == 3);
+    for (int i = 0; i < 9; i++) {
+      double e = exp(b[1] - b[2] * x[i]);
+      fjac(i, 0) = 1. / (1. + e);
+      fjac(i, 1) = -b[0] * e / (1. + e) / (1. + e);
+      fjac(i, 2) = +b[0] * e * x[i] / (1. + e) / (1. + e);
+    }
+    return 0;
+  }
+};
+const double rat42_functor::x[9] = {9.000E0,  14.000E0, 21.000E0, 28.000E0, 42.000E0,
+                                    57.000E0, 63.000E0, 70.000E0, 79.000E0};
+const double rat42_functor::y[9] = {8.930E0,  10.800E0, 18.590E0, 22.330E0, 39.350E0,
+                                    56.110E0, 61.730E0, 64.620E0, 67.080E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/ratkowsky2.shtml
+void testNistRat42(void) {
+  const int n = 3;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 100., 1., 0.1;
+  // do the computation
+  rat42_functor functor;
+  LevenbergMarquardt<rat42_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 10, 8);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.0565229338E+00);
+  // check x
+  VERIFY_IS_APPROX(x[0], 7.2462237576E+01);
+  VERIFY_IS_APPROX(x[1], 2.6180768402E+00);
+  VERIFY_IS_APPROX(x[2], 6.7359200066E-02);
+
+  /*
+   * Second try
+   */
+  x << 75., 2.5, 0.07;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 6, 5);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.0565229338E+00);
+  // check x
+  VERIFY_IS_APPROX(x[0], 7.2462237576E+01);
+  VERIFY_IS_APPROX(x[1], 2.6180768402E+00);
+  VERIFY_IS_APPROX(x[2], 6.7359200066E-02);
+}
+
+struct MGH10_functor : Functor<double> {
+  MGH10_functor(void) : Functor<double>(3, 16) {}
+  static const double x[16];
+  static const double y[16];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 3);
+    assert(fvec.size() == 16);
+    for (int i = 0; i < 16; i++) fvec[i] = b[0] * exp(b[1] / (x[i] + b[2])) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 3);
+    assert(fjac.rows() == 16);
+    assert(fjac.cols() == 3);
+    for (int i = 0; i < 16; i++) {
+      double factor = 1. / (x[i] + b[2]);
+      double e = exp(b[1] * factor);
+      fjac(i, 0) = e;
+      fjac(i, 1) = b[0] * factor * e;
+      fjac(i, 2) = -b[1] * b[0] * factor * factor * e;
+    }
+    return 0;
+  }
+};
+const double MGH10_functor::x[16] = {5.000000E+01, 5.500000E+01, 6.000000E+01, 6.500000E+01, 7.000000E+01, 7.500000E+01,
+                                     8.000000E+01, 8.500000E+01, 9.000000E+01, 9.500000E+01, 1.000000E+02, 1.050000E+02,
+                                     1.100000E+02, 1.150000E+02, 1.200000E+02, 1.250000E+02};
+const double MGH10_functor::y[16] = {3.478000E+04, 2.861000E+04, 2.365000E+04, 1.963000E+04, 1.637000E+04, 1.372000E+04,
+                                     1.154000E+04, 9.744000E+03, 8.261000E+03, 7.030000E+03, 6.005000E+03, 5.147000E+03,
+                                     4.427000E+03, 3.820000E+03, 3.307000E+03, 2.872000E+03};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/mgh10.shtml
+void testNistMGH10(void) {
+  const int n = 3;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 2., 400000., 25000.;
+  // do the computation
+  MGH10_functor functor;
+  LevenbergMarquardt<MGH10_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 2);
+  LM_CHECK_N_ITERS(lm, 284, 249);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7945855171E+01);
+  // check x
+  VERIFY_IS_APPROX(x[0], 5.6096364710E-03);
+  VERIFY_IS_APPROX(x[1], 6.1813463463E+03);
+  VERIFY_IS_APPROX(x[2], 3.4522363462E+02);
+
+  /*
+   * Second try
+   */
+  x << 0.02, 4000., 250.;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 3);
+  LM_CHECK_N_ITERS(lm, 126, 116);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7945855171E+01);
+  // check x
+  VERIFY_IS_APPROX(x[0], 5.6096364710E-03);
+  VERIFY_IS_APPROX(x[1], 6.1813463463E+03);
+  VERIFY_IS_APPROX(x[2], 3.4522363462E+02);
+}
+
+struct BoxBOD_functor : Functor<double> {
+  BoxBOD_functor(void) : Functor<double>(2, 6) {}
+  static const double x[6];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    static const double y[6] = {109., 149., 149., 191., 213., 224.};
+    assert(b.size() == 2);
+    assert(fvec.size() == 6);
+    for (int i = 0; i < 6; i++) fvec[i] = b[0] * (1. - exp(-b[1] * x[i])) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 2);
+    assert(fjac.rows() == 6);
+    assert(fjac.cols() == 2);
+    for (int i = 0; i < 6; i++) {
+      double e = exp(-b[1] * x[i]);
+      fjac(i, 0) = 1. - e;
+      fjac(i, 1) = b[0] * x[i] * e;
+    }
+    return 0;
+  }
+};
+const double BoxBOD_functor::x[6] = {1., 2., 3., 5., 7., 10.};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/boxbod.shtml
+void testNistBoxBOD(void) {
+  const int n = 2;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 1., 1.;
+  // do the computation
+  BoxBOD_functor functor;
+  LevenbergMarquardt<BoxBOD_functor> lm(functor);
+  lm.parameters.ftol = 1.E6 * NumTraits<double>::epsilon();
+  lm.parameters.xtol = 1.E6 * NumTraits<double>::epsilon();
+  lm.parameters.factor = 10.;
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 31, 25);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 2.1380940889E+02);
+  VERIFY_IS_APPROX(x[1], 5.4723748542E-01);
+
+  /*
+   * Second try
+   */
+  x << 100., 0.75;
+  // do the computation
+  lm.resetParameters();
+  lm.parameters.ftol = NumTraits<double>::epsilon();
+  lm.parameters.xtol = NumTraits<double>::epsilon();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 20, 14);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 2.1380940889E+02);
+  VERIFY_IS_APPROX(x[1], 5.4723748542E-01);
+}
+
+struct MGH17_functor : Functor<double> {
+  MGH17_functor(void) : Functor<double>(5, 33) {}
+  static const double x[33];
+  static const double y[33];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 5);
+    assert(fvec.size() == 33);
+    for (int i = 0; i < 33; i++) fvec[i] = b[0] + b[1] * exp(-b[3] * x[i]) + b[2] * exp(-b[4] * x[i]) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 5);
+    assert(fjac.rows() == 33);
+    assert(fjac.cols() == 5);
+    for (int i = 0; i < 33; i++) {
+      fjac(i, 0) = 1.;
+      fjac(i, 1) = exp(-b[3] * x[i]);
+      fjac(i, 2) = exp(-b[4] * x[i]);
+      fjac(i, 3) = -x[i] * b[1] * exp(-b[3] * x[i]);
+      fjac(i, 4) = -x[i] * b[2] * exp(-b[4] * x[i]);
+    }
+    return 0;
+  }
+};
+const double MGH17_functor::x[33] = {0.000000E+00, 1.000000E+01, 2.000000E+01, 3.000000E+01, 4.000000E+01, 5.000000E+01,
+                                     6.000000E+01, 7.000000E+01, 8.000000E+01, 9.000000E+01, 1.000000E+02, 1.100000E+02,
+                                     1.200000E+02, 1.300000E+02, 1.400000E+02, 1.500000E+02, 1.600000E+02, 1.700000E+02,
+                                     1.800000E+02, 1.900000E+02, 2.000000E+02, 2.100000E+02, 2.200000E+02, 2.300000E+02,
+                                     2.400000E+02, 2.500000E+02, 2.600000E+02, 2.700000E+02, 2.800000E+02, 2.900000E+02,
+                                     3.000000E+02, 3.100000E+02, 3.200000E+02};
+const double MGH17_functor::y[33] = {8.440000E-01, 9.080000E-01, 9.320000E-01, 9.360000E-01, 9.250000E-01, 9.080000E-01,
+                                     8.810000E-01, 8.500000E-01, 8.180000E-01, 7.840000E-01, 7.510000E-01, 7.180000E-01,
+                                     6.850000E-01, 6.580000E-01, 6.280000E-01, 6.030000E-01, 5.800000E-01, 5.580000E-01,
+                                     5.380000E-01, 5.220000E-01, 5.060000E-01, 4.900000E-01, 4.780000E-01, 4.670000E-01,
+                                     4.570000E-01, 4.480000E-01, 4.380000E-01, 4.310000E-01, 4.240000E-01, 4.200000E-01,
+                                     4.140000E-01, 4.110000E-01, 4.060000E-01};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/mgh17.shtml
+void testNistMGH17(void) {
+  const int n = 5;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 50., 150., -100., 1., 2.;
+  // do the computation
+  MGH17_functor functor;
+  LevenbergMarquardt<MGH17_functor> lm(functor);
+  lm.parameters.ftol = NumTraits<double>::epsilon();
+  lm.parameters.xtol = NumTraits<double>::epsilon();
+  lm.parameters.maxfev = 1000;
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05);
+  // check x
+  VERIFY_IS_APPROX(x[0], 3.7541005211E-01);
+  VERIFY_IS_APPROX(x[1], 1.9358469127E+00);
+  VERIFY_IS_APPROX(x[2], -1.4646871366E+00);
+  VERIFY_IS_APPROX(x[3], 1.2867534640E-02);
+  VERIFY_IS_APPROX(x[4], 2.2122699662E-02);
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 2);
+  LM_CHECK_N_ITERS(lm, 602, 545);
+
+  /*
+   * Second try
+   */
+  x << 0.5, 1.5, -1, 0.01, 0.02;
+  // do the computation
+  lm.resetParameters();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 18, 15);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05);
+  // check x
+  VERIFY_IS_APPROX(x[0], 3.7541005211E-01);
+  VERIFY_IS_APPROX(x[1], 1.9358469127E+00);
+  VERIFY_IS_APPROX(x[2], -1.4646871366E+00);
+  VERIFY_IS_APPROX(x[3], 1.2867534640E-02);
+  VERIFY_IS_APPROX(x[4], 2.2122699662E-02);
+}
+
+struct MGH09_functor : Functor<double> {
+  MGH09_functor(void) : Functor<double>(4, 11) {}
+  static const double _x[11];
+  static const double y[11];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 4);
+    assert(fvec.size() == 11);
+    for (int i = 0; i < 11; i++) {
+      double x = _x[i], xx = x * x;
+      fvec[i] = b[0] * (xx + x * b[1]) / (xx + x * b[2] + b[3]) - y[i];
+    }
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 4);
+    assert(fjac.rows() == 11);
+    assert(fjac.cols() == 4);
+    for (int i = 0; i < 11; i++) {
+      double x = _x[i], xx = x * x;
+      double factor = 1. / (xx + x * b[2] + b[3]);
+      fjac(i, 0) = (xx + x * b[1]) * factor;
+      fjac(i, 1) = b[0] * x * factor;
+      fjac(i, 2) = -b[0] * (xx + x * b[1]) * x * factor * factor;
+      fjac(i, 3) = -b[0] * (xx + x * b[1]) * factor * factor;
+    }
+    return 0;
+  }
+};
+const double MGH09_functor::_x[11] = {4.,           2.,     1.,           5.E-1,        2.5E-01,     1.670000E-01,
+                                      1.250000E-01, 1.E-01, 8.330000E-02, 7.140000E-02, 6.250000E-02};
+const double MGH09_functor::y[11] = {1.957000E-01, 1.947000E-01, 1.735000E-01, 1.600000E-01, 8.440000E-02, 6.270000E-02,
+                                     4.560000E-02, 3.420000E-02, 3.230000E-02, 2.350000E-02, 2.460000E-02};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/mgh09.shtml
+void testNistMGH09(void) {
+  const int n = 4;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 25., 39, 41.5, 39.;
+  // do the computation
+  MGH09_functor functor;
+  LevenbergMarquardt<MGH09_functor> lm(functor);
+  lm.parameters.maxfev = 1000;
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 490, 376);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 3.0750560385E-04);
+  // check x
+  VERIFY_IS_APPROX(x[0], 0.1928077089);   // should be 1.9280693458E-01
+  VERIFY_IS_APPROX(x[1], 0.19126423573);  // should be 1.9128232873E-01
+  VERIFY_IS_APPROX(x[2], 0.12305309914);  // should be 1.2305650693E-01
+  VERIFY_IS_APPROX(x[3], 0.13605395375);  // should be 1.3606233068E-01
+
+  /*
+   * Second try
+   */
+  x << 0.25, 0.39, 0.415, 0.39;
+  // do the computation
+  lm.resetParameters();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 18, 16);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 3.0750560385E-04);
+  // check x
+  VERIFY_IS_APPROX(x[0], 0.19280781);  // should be 1.9280693458E-01
+  VERIFY_IS_APPROX(x[1], 0.19126265);  // should be 1.9128232873E-01
+  VERIFY_IS_APPROX(x[2], 0.12305280);  // should be 1.2305650693E-01
+  VERIFY_IS_APPROX(x[3], 0.13605322);  // should be 1.3606233068E-01
+}
+
+struct Bennett5_functor : Functor<double> {
+  Bennett5_functor(void) : Functor<double>(3, 154) {}
+  static const double x[154];
+  static const double y[154];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 3);
+    assert(fvec.size() == 154);
+    for (int i = 0; i < 154; i++) fvec[i] = b[0] * pow(b[1] + x[i], -1. / b[2]) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 3);
+    assert(fjac.rows() == 154);
+    assert(fjac.cols() == 3);
+    for (int i = 0; i < 154; i++) {
+      double e = pow(b[1] + x[i], -1. / b[2]);
+      fjac(i, 0) = e;
+      fjac(i, 1) = -b[0] * e / b[2] / (b[1] + x[i]);
+      fjac(i, 2) = b[0] * e * log(b[1] + x[i]) / b[2] / b[2];
+    }
+    return 0;
+  }
+};
+const double Bennett5_functor::x[154] = {
+    7.447168E0,  8.102586E0,  8.452547E0,  8.711278E0,  8.916774E0,  9.087155E0,  9.232590E0,  9.359535E0,  9.472166E0,
+    9.573384E0,  9.665293E0,  9.749461E0,  9.827092E0,  9.899128E0,  9.966321E0,  10.029280E0, 10.088510E0, 10.144430E0,
+    10.197380E0, 10.247670E0, 10.295560E0, 10.341250E0, 10.384950E0, 10.426820E0, 10.467000E0, 10.505640E0, 10.542830E0,
+    10.578690E0, 10.613310E0, 10.646780E0, 10.679150E0, 10.710520E0, 10.740920E0, 10.770440E0, 10.799100E0, 10.826970E0,
+    10.854080E0, 10.880470E0, 10.906190E0, 10.931260E0, 10.955720E0, 10.979590E0, 11.002910E0, 11.025700E0, 11.047980E0,
+    11.069770E0, 11.091100E0, 11.111980E0, 11.132440E0, 11.152480E0, 11.172130E0, 11.191410E0, 11.210310E0, 11.228870E0,
+    11.247090E0, 11.264980E0, 11.282560E0, 11.299840E0, 11.316820E0, 11.333520E0, 11.349940E0, 11.366100E0, 11.382000E0,
+    11.397660E0, 11.413070E0, 11.428240E0, 11.443200E0, 11.457930E0, 11.472440E0, 11.486750E0, 11.500860E0, 11.514770E0,
+    11.528490E0, 11.542020E0, 11.555380E0, 11.568550E0, 11.581560E0, 11.594420E0, 11.607121E0, 11.619640E0, 11.632000E0,
+    11.644210E0, 11.656280E0, 11.668200E0, 11.679980E0, 11.691620E0, 11.703130E0, 11.714510E0, 11.725760E0, 11.736880E0,
+    11.747890E0, 11.758780E0, 11.769550E0, 11.780200E0, 11.790730E0, 11.801160E0, 11.811480E0, 11.821700E0, 11.831810E0,
+    11.841820E0, 11.851730E0, 11.861550E0, 11.871270E0, 11.880890E0, 11.890420E0, 11.899870E0, 11.909220E0, 11.918490E0,
+    11.927680E0, 11.936780E0, 11.945790E0, 11.954730E0, 11.963590E0, 11.972370E0, 11.981070E0, 11.989700E0, 11.998260E0,
+    12.006740E0, 12.015150E0, 12.023490E0, 12.031760E0, 12.039970E0, 12.048100E0, 12.056170E0, 12.064180E0, 12.072120E0,
+    12.080010E0, 12.087820E0, 12.095580E0, 12.103280E0, 12.110920E0, 12.118500E0, 12.126030E0, 12.133500E0, 12.140910E0,
+    12.148270E0, 12.155570E0, 12.162830E0, 12.170030E0, 12.177170E0, 12.184270E0, 12.191320E0, 12.198320E0, 12.205270E0,
+    12.212170E0, 12.219030E0, 12.225840E0, 12.232600E0, 12.239320E0, 12.245990E0, 12.252620E0, 12.259200E0, 12.265750E0,
+    12.272240E0};
+const double Bennett5_functor::y[154] = {
+    -34.834702E0, -34.393200E0, -34.152901E0, -33.979099E0, -33.845901E0, -33.732899E0, -33.640301E0, -33.559200E0,
+    -33.486801E0, -33.423100E0, -33.365101E0, -33.313000E0, -33.260899E0, -33.217400E0, -33.176899E0, -33.139198E0,
+    -33.101601E0, -33.066799E0, -33.035000E0, -33.003101E0, -32.971298E0, -32.942299E0, -32.916302E0, -32.890202E0,
+    -32.864101E0, -32.841000E0, -32.817799E0, -32.797501E0, -32.774300E0, -32.757000E0, -32.733799E0, -32.716400E0,
+    -32.699100E0, -32.678799E0, -32.661400E0, -32.644001E0, -32.626701E0, -32.612202E0, -32.597698E0, -32.583199E0,
+    -32.568699E0, -32.554298E0, -32.539799E0, -32.525299E0, -32.510799E0, -32.499199E0, -32.487598E0, -32.473202E0,
+    -32.461601E0, -32.435501E0, -32.435501E0, -32.426800E0, -32.412300E0, -32.400799E0, -32.392101E0, -32.380501E0,
+    -32.366001E0, -32.357300E0, -32.348598E0, -32.339901E0, -32.328400E0, -32.319698E0, -32.311001E0, -32.299400E0,
+    -32.290699E0, -32.282001E0, -32.273300E0, -32.264599E0, -32.256001E0, -32.247299E0, -32.238602E0, -32.229900E0,
+    -32.224098E0, -32.215401E0, -32.203800E0, -32.198002E0, -32.189400E0, -32.183601E0, -32.174900E0, -32.169102E0,
+    -32.163300E0, -32.154598E0, -32.145901E0, -32.140099E0, -32.131401E0, -32.125599E0, -32.119801E0, -32.111198E0,
+    -32.105400E0, -32.096699E0, -32.090900E0, -32.088001E0, -32.079300E0, -32.073502E0, -32.067699E0, -32.061901E0,
+    -32.056099E0, -32.050301E0, -32.044498E0, -32.038799E0, -32.033001E0, -32.027199E0, -32.024300E0, -32.018501E0,
+    -32.012699E0, -32.004002E0, -32.001099E0, -31.995300E0, -31.989500E0, -31.983700E0, -31.977900E0, -31.972099E0,
+    -31.969299E0, -31.963501E0, -31.957701E0, -31.951900E0, -31.946100E0, -31.940300E0, -31.937401E0, -31.931601E0,
+    -31.925800E0, -31.922899E0, -31.917101E0, -31.911301E0, -31.908400E0, -31.902599E0, -31.896900E0, -31.893999E0,
+    -31.888201E0, -31.885300E0, -31.882401E0, -31.876600E0, -31.873699E0, -31.867901E0, -31.862101E0, -31.859200E0,
+    -31.856300E0, -31.850500E0, -31.844700E0, -31.841801E0, -31.838900E0, -31.833099E0, -31.830200E0, -31.827299E0,
+    -31.821600E0, -31.818701E0, -31.812901E0, -31.809999E0, -31.807100E0, -31.801300E0, -31.798401E0, -31.795500E0,
+    -31.789700E0, -31.786800E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/bennett5.shtml
+void testNistBennett5(void) {
+  const int n = 3;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << -2000., 50., 0.8;
+  // do the computation
+  Bennett5_functor functor;
+  LevenbergMarquardt<Bennett5_functor> lm(functor);
+  lm.parameters.maxfev = 1000;
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 758, 744);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.2404744073E-04);
+  // check x
+  VERIFY_IS_APPROX(x[0], -2.5235058043E+03);
+  VERIFY_IS_APPROX(x[1], 4.6736564644E+01);
+  VERIFY_IS_APPROX(x[2], 9.3218483193E-01);
+  /*
+   * Second try
+   */
+  x << -1500., 45., 0.85;
+  // do the computation
+  lm.resetParameters();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 203, 192);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.2404744073E-04);
+  // check x
+  VERIFY_IS_APPROX(x[0], -2523.3007865);  // should be -2.5235058043E+03
+  VERIFY_IS_APPROX(x[1], 46.735705771);   // should be 4.6736564644E+01);
+  VERIFY_IS_APPROX(x[2], 0.93219881891);  // should be 9.3218483193E-01);
+}
+
+struct thurber_functor : Functor<double> {
+  thurber_functor(void) : Functor<double>(7, 37) {}
+  static const double _x[37];
+  static const double _y[37];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    //        int called=0; printf("call hahn1_functor with  iflag=%d, called=%d\n", iflag, called); if (iflag==1)
+    //        called++;
+    assert(b.size() == 7);
+    assert(fvec.size() == 37);
+    for (int i = 0; i < 37; i++) {
+      double x = _x[i], xx = x * x, xxx = xx * x;
+      fvec[i] = (b[0] + b[1] * x + b[2] * xx + b[3] * xxx) / (1. + b[4] * x + b[5] * xx + b[6] * xxx) - _y[i];
+    }
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 7);
+    assert(fjac.rows() == 37);
+    assert(fjac.cols() == 7);
+    for (int i = 0; i < 37; i++) {
+      double x = _x[i], xx = x * x, xxx = xx * x;
+      double fact = 1. / (1. + b[4] * x + b[5] * xx + b[6] * xxx);
+      fjac(i, 0) = 1. * fact;
+      fjac(i, 1) = x * fact;
+      fjac(i, 2) = xx * fact;
+      fjac(i, 3) = xxx * fact;
+      fact = -(b[0] + b[1] * x + b[2] * xx + b[3] * xxx) * fact * fact;
+      fjac(i, 4) = x * fact;
+      fjac(i, 5) = xx * fact;
+      fjac(i, 6) = xxx * fact;
+    }
+    return 0;
+  }
+};
+const double thurber_functor::_x[37] = {-3.067E0, -2.981E0, -2.921E0, -2.912E0, -2.840E0, -2.797E0, -2.702E0, -2.699E0,
+                                        -2.633E0, -2.481E0, -2.363E0, -2.322E0, -1.501E0, -1.460E0, -1.274E0, -1.212E0,
+                                        -1.100E0, -1.046E0, -0.915E0, -0.714E0, -0.566E0, -0.545E0, -0.400E0, -0.309E0,
+                                        -0.109E0, -0.103E0, 0.010E0,  0.119E0,  0.377E0,  0.790E0,  0.963E0,  1.006E0,
+                                        1.115E0,  1.572E0,  1.841E0,  2.047E0,  2.200E0};
+const double thurber_functor::_y[37] = {
+    80.574E0,   84.248E0,   87.264E0,   87.195E0,   89.076E0,   89.608E0,   89.868E0,   90.101E0,
+    92.405E0,   95.854E0,   100.696E0,  101.060E0,  401.672E0,  390.724E0,  567.534E0,  635.316E0,
+    733.054E0,  759.087E0,  894.206E0,  990.785E0,  1090.109E0, 1080.914E0, 1122.643E0, 1178.351E0,
+    1260.531E0, 1273.514E0, 1288.339E0, 1327.543E0, 1353.863E0, 1414.509E0, 1425.208E0, 1421.384E0,
+    1442.962E0, 1464.350E0, 1468.705E0, 1447.894E0, 1457.628E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/thurber.shtml
+void testNistThurber(void) {
+  const int n = 7;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 1000, 1000, 400, 40, 0.7, 0.3, 0.0;
+  // do the computation
+  thurber_functor functor;
+  LevenbergMarquardt<thurber_functor> lm(functor);
+  lm.parameters.ftol = 1.E4 * NumTraits<double>::epsilon();
+  lm.parameters.xtol = 1.E4 * NumTraits<double>::epsilon();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 39, 36);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.2881396800E+03);
+  VERIFY_IS_APPROX(x[1], 1.4910792535E+03);
+  VERIFY_IS_APPROX(x[2], 5.8323836877E+02);
+  VERIFY_IS_APPROX(x[3], 7.5416644291E+01);
+  VERIFY_IS_APPROX(x[4], 9.6629502864E-01);
+  VERIFY_IS_APPROX(x[5], 3.9797285797E-01);
+  VERIFY_IS_APPROX(x[6], 4.9727297349E-02);
+
+  /*
+   * Second try
+   */
+  x << 1300, 1500, 500, 75, 1, 0.4, 0.05;
+  // do the computation
+  lm.resetParameters();
+  lm.parameters.ftol = 1.E4 * NumTraits<double>::epsilon();
+  lm.parameters.xtol = 1.E4 * NumTraits<double>::epsilon();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 29, 28);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.2881396800E+03);
+  VERIFY_IS_APPROX(x[1], 1.4910792535E+03);
+  VERIFY_IS_APPROX(x[2], 5.8323836877E+02);
+  VERIFY_IS_APPROX(x[3], 7.5416644291E+01);
+  VERIFY_IS_APPROX(x[4], 9.6629502864E-01);
+  VERIFY_IS_APPROX(x[5], 3.9797285797E-01);
+  VERIFY_IS_APPROX(x[6], 4.9727297349E-02);
+}
+
+struct rat43_functor : Functor<double> {
+  rat43_functor(void) : Functor<double>(4, 15) {}
+  static const double x[15];
+  static const double y[15];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 4);
+    assert(fvec.size() == 15);
+    for (int i = 0; i < 15; i++) fvec[i] = b[0] * pow(1. + exp(b[1] - b[2] * x[i]), -1. / b[3]) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 4);
+    assert(fjac.rows() == 15);
+    assert(fjac.cols() == 4);
+    for (int i = 0; i < 15; i++) {
+      double e = exp(b[1] - b[2] * x[i]);
+      double power = -1. / b[3];
+      fjac(i, 0) = pow(1. + e, power);
+      fjac(i, 1) = power * b[0] * e * pow(1. + e, power - 1.);
+      fjac(i, 2) = -power * b[0] * e * x[i] * pow(1. + e, power - 1.);
+      fjac(i, 3) = b[0] * power * power * log(1. + e) * pow(1. + e, power);
+    }
+    return 0;
+  }
+};
+const double rat43_functor::x[15] = {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.};
+const double rat43_functor::y[15] = {16.08,  33.83,  65.80,  97.20,  191.55, 326.20, 386.87, 520.53,
+                                     590.03, 651.92, 724.93, 699.56, 689.96, 637.56, 717.41};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/ratkowsky3.shtml
+void testNistRat43(void) {
+  const int n = 4;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 100., 10., 1., 1.;
+  // do the computation
+  rat43_functor functor;
+  LevenbergMarquardt<rat43_functor> lm(functor);
+  lm.parameters.ftol = 1.E6 * NumTraits<double>::epsilon();
+  lm.parameters.xtol = 1.E6 * NumTraits<double>::epsilon();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 27, 20);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7864049080E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 6.9964151270E+02);
+  VERIFY_IS_APPROX(x[1], 5.2771253025E+00);
+  VERIFY_IS_APPROX(x[2], 7.5962938329E-01);
+  VERIFY_IS_APPROX(x[3], 1.2792483859E+00);
+
+  /*
+   * Second try
+   */
+  x << 700., 5., 0.75, 1.3;
+  // do the computation
+  lm.resetParameters();
+  lm.parameters.ftol = 1.E5 * NumTraits<double>::epsilon();
+  lm.parameters.xtol = 1.E5 * NumTraits<double>::epsilon();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 9, 8);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7864049080E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 6.9964151270E+02);
+  VERIFY_IS_APPROX(x[1], 5.2771253025E+00);
+  VERIFY_IS_APPROX(x[2], 7.5962938329E-01);
+  VERIFY_IS_APPROX(x[3], 1.2792483859E+00);
+}
+
+struct eckerle4_functor : Functor<double> {
+  eckerle4_functor(void) : Functor<double>(3, 35) {}
+  static const double x[35];
+  static const double y[35];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 3);
+    assert(fvec.size() == 35);
+    for (int i = 0; i < 35; i++)
+      fvec[i] = b[0] / b[1] * exp(-0.5 * (x[i] - b[2]) * (x[i] - b[2]) / (b[1] * b[1])) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 3);
+    assert(fjac.rows() == 35);
+    assert(fjac.cols() == 3);
+    for (int i = 0; i < 35; i++) {
+      double b12 = b[1] * b[1];
+      double e = exp(-0.5 * (x[i] - b[2]) * (x[i] - b[2]) / b12);
+      fjac(i, 0) = e / b[1];
+      fjac(i, 1) = ((x[i] - b[2]) * (x[i] - b[2]) / b12 - 1.) * b[0] * e / b12;
+      fjac(i, 2) = (x[i] - b[2]) * e * b[0] / b[1] / b12;
+    }
+    return 0;
+  }
+};
+const double eckerle4_functor::x[35] = {400.0, 405.0, 410.0, 415.0, 420.0, 425.0, 430.0, 435.0, 436.5,
+                                        438.0, 439.5, 441.0, 442.5, 444.0, 445.5, 447.0, 448.5, 450.0,
+                                        451.5, 453.0, 454.5, 456.0, 457.5, 459.0, 460.5, 462.0, 463.5,
+                                        465.0, 470.0, 475.0, 480.0, 485.0, 490.0, 495.0, 500.0};
+const double eckerle4_functor::y[35] = {0.0001575, 0.0001699, 0.0002350, 0.0003102, 0.0004917, 0.0008710, 0.0017418,
+                                        0.0046400, 0.0065895, 0.0097302, 0.0149002, 0.0237310, 0.0401683, 0.0712559,
+                                        0.1264458, 0.2073413, 0.2902366, 0.3445623, 0.3698049, 0.3668534, 0.3106727,
+                                        0.2078154, 0.1164354, 0.0616764, 0.0337200, 0.0194023, 0.0117831, 0.0074357,
+                                        0.0022732, 0.0008800, 0.0004579, 0.0002345, 0.0001586, 0.0001143, 0.0000710};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/eckerle4.shtml
+void testNistEckerle4(void) {
+  const int n = 3;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 1., 10., 500.;
+  // do the computation
+  eckerle4_functor functor;
+  LevenbergMarquardt<eckerle4_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 18, 15);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4635887487E-03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.5543827178);
+  VERIFY_IS_APPROX(x[1], 4.0888321754);
+  VERIFY_IS_APPROX(x[2], 4.5154121844E+02);
+
+  /*
+   * Second try
+   */
+  x << 1.5, 5., 450.;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  LM_CHECK_N_ITERS(lm, 7, 6);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4635887487E-03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.5543827178);
+  VERIFY_IS_APPROX(x[1], 4.0888321754);
+  VERIFY_IS_APPROX(x[2], 4.5154121844E+02);
+}
+
+EIGEN_DECLARE_TEST(NonLinearOptimization) {
+  // Tests using the examples provided by (c)minpack
+  CALL_SUBTEST /*_1*/ (testChkder());
+  CALL_SUBTEST /*_1*/ (testLmder1());
+  CALL_SUBTEST /*_1*/ (testLmder());
+  CALL_SUBTEST /*_2*/ (testHybrj1());
+  CALL_SUBTEST /*_2*/ (testHybrj());
+  CALL_SUBTEST /*_2*/ (testHybrd1());
+  CALL_SUBTEST /*_2*/ (testHybrd());
+  CALL_SUBTEST /*_3*/ (testLmstr1());
+  CALL_SUBTEST /*_3*/ (testLmstr());
+  CALL_SUBTEST /*_3*/ (testLmdif1());
+  CALL_SUBTEST /*_3*/ (testLmdif());
+
+  // NIST tests, level of difficulty = "Lower"
+  CALL_SUBTEST /*_4*/ (testNistMisra1a());
+  CALL_SUBTEST /*_4*/ (testNistChwirut2());
+
+  // NIST tests, level of difficulty = "Average"
+  CALL_SUBTEST /*_5*/ (testNistHahn1());
+  CALL_SUBTEST /*_6*/ (testNistMisra1d());
+  CALL_SUBTEST /*_7*/ (testNistMGH17());
+  CALL_SUBTEST /*_8*/ (testNistLanczos1());
+
+  //     // NIST tests, level of difficulty = "Higher"
+  CALL_SUBTEST /*_9*/ (testNistRat42());
+  //     CALL_SUBTEST/*_10*/(testNistMGH10());
+  CALL_SUBTEST /*_11*/ (testNistBoxBOD());
+  //     CALL_SUBTEST/*_12*/(testNistMGH09());
+  CALL_SUBTEST /*_13*/ (testNistBennett5());
+  CALL_SUBTEST /*_14*/ (testNistThurber());
+  CALL_SUBTEST /*_15*/ (testNistRat43());
+  CALL_SUBTEST /*_16*/ (testNistEckerle4());
+}
+
+/*
+ * Can be useful for debugging...
+  printf("info, nfev : %d, %d\n", info, lm.nfev);
+  printf("info, nfev, njev : %d, %d, %d\n", info, solver.nfev, solver.njev);
+  printf("info, nfev : %d, %d\n", info, solver.nfev);
+  printf("x[0] : %.32g\n", x[0]);
+  printf("x[1] : %.32g\n", x[1]);
+  printf("x[2] : %.32g\n", x[2]);
+  printf("x[3] : %.32g\n", x[3]);
+  printf("fvec.blueNorm() : %.32g\n", solver.fvec.blueNorm());
+  printf("fvec.blueNorm() : %.32g\n", lm.fvec.blueNorm());
+
+  printf("info, nfev, njev : %d, %d, %d\n", info, lm.nfev, lm.njev);
+  printf("fvec.squaredNorm() : %.13g\n", lm.fvec.squaredNorm());
+  std::cout << x << std::endl;
+  std::cout.precision(9);
+  std::cout << x[0] << std::endl;
+  std::cout << x[1] << std::endl;
+  std::cout << x[2] << std::endl;
+  std::cout << x[3] << std::endl;
+*/
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/NumericalDiff.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/NumericalDiff.cpp
new file mode 100644
index 0000000..543f34f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/NumericalDiff.cpp
@@ -0,0 +1,102 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+
+#include <stdio.h>
+
+#include "main.h"
+#include <unsupported/Eigen/NumericalDiff>
+
+// Generic functor
+template <typename Scalar_, int NX = Dynamic, int NY = Dynamic>
+struct Functor {
+  typedef Scalar_ Scalar;
+  enum { InputsAtCompileTime = NX, ValuesAtCompileTime = NY };
+  typedef Matrix<Scalar, InputsAtCompileTime, 1> InputType;
+  typedef Matrix<Scalar, ValuesAtCompileTime, 1> ValueType;
+  typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
+
+  int m_inputs, m_values;
+
+  Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
+  Functor(int inputs_, int values_) : m_inputs(inputs_), m_values(values_) {}
+
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values; }
+};
+
+struct my_functor : Functor<double> {
+  my_functor(void) : Functor<double>(3, 15) {}
+  int operator()(const VectorXd &x, VectorXd &fvec) const {
+    double tmp1, tmp2, tmp3;
+    double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, 3.9e-1,
+                    3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34,   2.1,    4.39};
+
+    for (int i = 0; i < values(); i++) {
+      tmp1 = i + 1;
+      tmp2 = 16 - i - 1;
+      tmp3 = (i >= 8) ? tmp2 : tmp1;
+      fvec[i] = y[i] - (x[0] + tmp1 / (x[1] * tmp2 + x[2] * tmp3));
+    }
+    return 0;
+  }
+
+  int actual_df(const VectorXd &x, MatrixXd &fjac) const {
+    double tmp1, tmp2, tmp3, tmp4;
+    for (int i = 0; i < values(); i++) {
+      tmp1 = i + 1;
+      tmp2 = 16 - i - 1;
+      tmp3 = (i >= 8) ? tmp2 : tmp1;
+      tmp4 = (x[1] * tmp2 + x[2] * tmp3);
+      tmp4 = tmp4 * tmp4;
+      fjac(i, 0) = -1;
+      fjac(i, 1) = tmp1 * tmp2 / tmp4;
+      fjac(i, 2) = tmp1 * tmp3 / tmp4;
+    }
+    return 0;
+  }
+};
+
+void test_forward() {
+  VectorXd x(3);
+  MatrixXd jac(15, 3);
+  MatrixXd actual_jac(15, 3);
+  my_functor functor;
+
+  x << 0.082, 1.13, 2.35;
+
+  // real one
+  functor.actual_df(x, actual_jac);
+  //    std::cout << actual_jac << std::endl << std::endl;
+
+  // using NumericalDiff
+  NumericalDiff<my_functor> numDiff(functor);
+  numDiff.df(x, jac);
+  //    std::cout << jac << std::endl;
+
+  VERIFY_IS_APPROX(jac, actual_jac);
+}
+
+void test_central() {
+  VectorXd x(3);
+  MatrixXd jac(15, 3);
+  MatrixXd actual_jac(15, 3);
+  my_functor functor;
+
+  x << 0.082, 1.13, 2.35;
+
+  // real one
+  functor.actual_df(x, actual_jac);
+
+  // using NumericalDiff
+  NumericalDiff<my_functor, Central> numDiff(functor);
+  numDiff.df(x, jac);
+
+  VERIFY_IS_APPROX(jac, actual_jac);
+}
+
+EIGEN_DECLARE_TEST(NumericalDiff) {
+  CALL_SUBTEST(test_forward());
+  CALL_SUBTEST(test_central());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/alignedvector3.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/alignedvector3.cpp
new file mode 100644
index 0000000..838eaea
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/alignedvector3.cpp
@@ -0,0 +1,86 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_NO_STATIC_ASSERT
+
+#include "main.h"
+#include <unsupported/Eigen/AlignedVector3>
+
+namespace Eigen {
+
+template <typename T, typename Derived>
+T test_relative_error(const AlignedVector3<T> &a, const MatrixBase<Derived> &b) {
+  return test_relative_error(a.coeffs().template head<3>(), b);
+}
+
+}  // namespace Eigen
+
+template <typename Scalar>
+void alignedvector3() {
+  Scalar s1 = internal::random<Scalar>();
+  Scalar s2 = internal::random<Scalar>();
+  typedef Matrix<Scalar, 3, 1> RefType;
+  typedef Matrix<Scalar, 3, 3> Mat33;
+  typedef AlignedVector3<Scalar> FastType;
+  RefType r1(RefType::Random()), r2(RefType::Random()), r3(RefType::Random()), r4(RefType::Random()),
+      r5(RefType::Random());
+  FastType f1(r1), f2(r2), f3(r3), f4(r4), f5(r5);
+  Mat33 m1(Mat33::Random());
+
+  VERIFY_IS_APPROX(f1, r1);
+  VERIFY_IS_APPROX(f4, r4);
+
+  VERIFY_IS_APPROX(f4 + f1, r4 + r1);
+  VERIFY_IS_APPROX(f4 - f1, r4 - r1);
+  VERIFY_IS_APPROX(f4 + f1 - f2, r4 + r1 - r2);
+  VERIFY_IS_APPROX(f4 += f3, r4 += r3);
+  VERIFY_IS_APPROX(f4 -= f5, r4 -= r5);
+  VERIFY_IS_APPROX(f4 -= f5 + f1, r4 -= r5 + r1);
+  VERIFY_IS_APPROX(f5 + f1 - s1 * f2, r5 + r1 - s1 * r2);
+  VERIFY_IS_APPROX(f5 + f1 / s2 - s1 * f2, r5 + r1 / s2 - s1 * r2);
+
+  VERIFY_IS_APPROX(m1 * f4, m1 * r4);
+  VERIFY_IS_APPROX(f4.transpose() * m1, r4.transpose() * m1);
+
+  VERIFY_IS_APPROX(f2.dot(f3), r2.dot(r3));
+  VERIFY_IS_APPROX(f2.cross(f3), r2.cross(r3));
+  VERIFY_IS_APPROX(f2.norm(), r2.norm());
+
+  VERIFY_IS_APPROX(f2.normalized(), r2.normalized());
+
+  VERIFY_IS_APPROX((f2 + f1).normalized(), (r2 + r1).normalized());
+
+  f2.normalize();
+  r2.normalize();
+  VERIFY_IS_APPROX(f2, r2);
+
+  {
+    FastType f6 = RefType::Zero();
+    FastType f7 = FastType::Zero();
+    VERIFY_IS_APPROX(f6, f7);
+    f6 = r4 + r1;
+    VERIFY_IS_APPROX(f6, r4 + r1);
+    f6 -= Scalar(2) * r4;
+    VERIFY_IS_APPROX(f6, r1 - r4);
+  }
+
+  FastType f8, f9(0, 0, 0);
+  VERIFY_IS_APPROX(f9 - f1, -f1);
+
+  std::stringstream ss1, ss2;
+  ss1 << f1;
+  ss2 << r1;
+  VERIFY(ss1.str() == ss2.str());
+}
+
+EIGEN_DECLARE_TEST(alignedvector3) {
+  for (int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST(alignedvector3<float>());
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/autodiff.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/autodiff.cpp
new file mode 100644
index 0000000..a885a1e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/autodiff.cpp
@@ -0,0 +1,348 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <unsupported/Eigen/AutoDiff>
+
+template <typename Scalar>
+EIGEN_DONT_INLINE Scalar foo(const Scalar& x, const Scalar& y) {
+  using namespace std;
+  //   return x+std::sin(y);
+  EIGEN_ASM_COMMENT("mybegin");
+  // pow(float, int) promotes to pow(double, double)
+  return x * 2 - 1 + static_cast<Scalar>(pow(1 + x, 2)) + 2 * sqrt(y * y + 0) - 4 * sin(0 + x) + 2 * cos(y + 0) -
+         exp(Scalar(-0.5) * x * x + 0);
+  // return x+2*y*x;//x*2 -std::pow(x,2);//(2*y/x);// - y*2;
+  EIGEN_ASM_COMMENT("myend");
+}
+
+template <typename Vector>
+EIGEN_DONT_INLINE typename Vector::Scalar foo(const Vector& p) {
+  typedef typename Vector::Scalar Scalar;
+  return (p - Vector(Scalar(-1), Scalar(1.))).norm() + (p.array() * p.array()).sum() + p.dot(p);
+}
+
+template <typename Scalar_, int NX = Dynamic, int NY = Dynamic>
+struct TestFunc1 {
+  typedef Scalar_ Scalar;
+  enum { InputsAtCompileTime = NX, ValuesAtCompileTime = NY };
+  typedef Matrix<Scalar, InputsAtCompileTime, 1> InputType;
+  typedef Matrix<Scalar, ValuesAtCompileTime, 1> ValueType;
+  typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
+
+  int m_inputs, m_values;
+
+  TestFunc1() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
+  TestFunc1(int inputs_, int values_) : m_inputs(inputs_), m_values(values_) {}
+
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values; }
+
+  template <typename T>
+  void operator()(const Matrix<T, InputsAtCompileTime, 1>& x, Matrix<T, ValuesAtCompileTime, 1>* _v) const {
+    Matrix<T, ValuesAtCompileTime, 1>& v = *_v;
+
+    v[0] = 2 * x[0] * x[0] + x[0] * x[1];
+    v[1] = 3 * x[1] * x[0] + 0.5 * x[1] * x[1];
+    if (inputs() > 2) {
+      v[0] += 0.5 * x[2];
+      v[1] += x[2];
+    }
+    if (values() > 2) {
+      v[2] = 3 * x[1] * x[0] * x[0];
+    }
+    if (inputs() > 2 && values() > 2) v[2] *= x[2];
+  }
+
+  void operator()(const InputType& x, ValueType* v, JacobianType* _j) const {
+    (*this)(x, v);
+
+    if (_j) {
+      JacobianType& j = *_j;
+
+      j(0, 0) = 4 * x[0] + x[1];
+      j(1, 0) = 3 * x[1];
+
+      j(0, 1) = x[0];
+      j(1, 1) = 3 * x[0] + 2 * 0.5 * x[1];
+
+      if (inputs() > 2) {
+        j(0, 2) = 0.5;
+        j(1, 2) = 1;
+      }
+      if (values() > 2) {
+        j(2, 0) = 3 * x[1] * 2 * x[0];
+        j(2, 1) = 3 * x[0] * x[0];
+      }
+      if (inputs() > 2 && values() > 2) {
+        j(2, 0) *= x[2];
+        j(2, 1) *= x[2];
+
+        j(2, 2) = 3 * x[1] * x[0] * x[0];
+        j(2, 2) = 3 * x[1] * x[0] * x[0];
+      }
+    }
+  }
+};
+
+/* Test functor for the C++11 features. */
+template <typename Scalar>
+struct integratorFunctor {
+  typedef Matrix<Scalar, 2, 1> InputType;
+  typedef Matrix<Scalar, 2, 1> ValueType;
+
+  /*
+   * Implementation starts here.
+   */
+  integratorFunctor(const Scalar gain) : _gain(gain) {}
+  integratorFunctor(const integratorFunctor& f) : _gain(f._gain) {}
+  const Scalar _gain;
+
+  template <typename T1, typename T2>
+  void operator()(const T1& input, T2* output, const Scalar dt) const {
+    T2& o = *output;
+
+    /* Integrator to test the AD. */
+    o[0] = input[0] + input[1] * dt * _gain;
+    o[1] = input[1] * _gain;
+  }
+
+  /* Only needed for the test */
+  template <typename T1, typename T2, typename T3>
+  void operator()(const T1& input, T2* output, T3* jacobian, const Scalar dt) const {
+    T2& o = *output;
+
+    /* Integrator to test the AD. */
+    o[0] = input[0] + input[1] * dt * _gain;
+    o[1] = input[1] * _gain;
+
+    if (jacobian) {
+      T3& j = *jacobian;
+
+      j(0, 0) = 1;
+      j(0, 1) = dt * _gain;
+      j(1, 0) = 0;
+      j(1, 1) = _gain;
+    }
+  }
+};
+
+template <typename Func>
+void forward_jacobian_cpp11(const Func& f) {
+  typedef typename Func::ValueType::Scalar Scalar;
+  typedef typename Func::ValueType ValueType;
+  typedef typename Func::InputType InputType;
+  typedef typename AutoDiffJacobian<Func>::JacobianType JacobianType;
+
+  InputType x = InputType::Random(InputType::RowsAtCompileTime);
+  ValueType y, yref;
+  JacobianType j, jref;
+
+  const Scalar dt = internal::random<double>();
+
+  jref.setZero();
+  yref.setZero();
+  f(x, &yref, &jref, dt);
+
+  // std::cerr << "y, yref, jref: " << "\n";
+  // std::cerr << y.transpose() << "\n\n";
+  // std::cerr << yref << "\n\n";
+  // std::cerr << jref << "\n\n";
+
+  AutoDiffJacobian<Func> autoj(f);
+  autoj(x, &y, &j, dt);
+
+  // std::cerr << "y j (via autodiff): " << "\n";
+  // std::cerr << y.transpose() << "\n\n";
+  // std::cerr << j << "\n\n";
+
+  VERIFY_IS_APPROX(y, yref);
+  VERIFY_IS_APPROX(j, jref);
+}
+
+template <typename Func>
+void forward_jacobian(const Func& f) {
+  typename Func::InputType x = Func::InputType::Random(f.inputs());
+  typename Func::ValueType y(f.values()), yref(f.values());
+  typename Func::JacobianType j(f.values(), f.inputs()), jref(f.values(), f.inputs());
+
+  jref.setZero();
+  yref.setZero();
+  f(x, &yref, &jref);
+
+  j.setZero();
+  y.setZero();
+  AutoDiffJacobian<Func> autoj(f);
+  autoj(x, &y, &j);
+
+  VERIFY_IS_APPROX(y, yref);
+  VERIFY_IS_APPROX(j, jref);
+}
+
+// TODO also check actual derivatives!
+template <int>
+void test_autodiff_scalar() {
+  Vector2f p = Vector2f::Random();
+  typedef AutoDiffScalar<Vector2f> AD;
+  AD ax(p.x(), Vector2f::UnitX());
+  AD ay(p.y(), Vector2f::UnitY());
+  AD res = foo<AD>(ax, ay);
+  VERIFY_IS_APPROX(res.value(), foo(p.x(), p.y()));
+}
+
+// TODO also check actual derivatives!
+template <int>
+void test_autodiff_vector() {
+  Vector2f p = Vector2f::Random();
+  typedef AutoDiffScalar<Vector2f> AD;
+  typedef Matrix<AD, 2, 1> VectorAD;
+  VectorAD ap = p.cast<AD>();
+  ap.x().derivatives() = Vector2f::UnitX();
+  ap.y().derivatives() = Vector2f::UnitY();
+
+  AD res = foo<VectorAD>(ap);
+  VERIFY_IS_APPROX(res.value(), foo(p));
+}
+
+template <int>
+void test_autodiff_jacobian() {
+  CALL_SUBTEST((forward_jacobian(TestFunc1<double, 2, 2>())));
+  CALL_SUBTEST((forward_jacobian(TestFunc1<double, 2, 3>())));
+  CALL_SUBTEST((forward_jacobian(TestFunc1<double, 3, 2>())));
+  CALL_SUBTEST((forward_jacobian(TestFunc1<double, 3, 3>())));
+  CALL_SUBTEST((forward_jacobian(TestFunc1<double>(3, 3))));
+  CALL_SUBTEST((forward_jacobian_cpp11(integratorFunctor<double>(10))));
+}
+
+template <int>
+void test_autodiff_hessian() {
+  typedef AutoDiffScalar<VectorXd> AD;
+  typedef Matrix<AD, Eigen::Dynamic, 1> VectorAD;
+  typedef AutoDiffScalar<VectorAD> ADD;
+  typedef Matrix<ADD, Eigen::Dynamic, 1> VectorADD;
+  VectorADD x(2);
+  double s1 = internal::random<double>(), s2 = internal::random<double>(), s3 = internal::random<double>(),
+         s4 = internal::random<double>();
+  x(0).value() = s1;
+  x(1).value() = s2;
+
+  // set unit vectors for the derivative directions (partial derivatives of the input vector)
+  x(0).derivatives().resize(2);
+  x(0).derivatives().setZero();
+  x(0).derivatives()(0) = 1;
+  x(1).derivatives().resize(2);
+  x(1).derivatives().setZero();
+  x(1).derivatives()(1) = 1;
+
+  // repeat partial derivatives for the inner AutoDiffScalar
+  x(0).value().derivatives() = VectorXd::Unit(2, 0);
+  x(1).value().derivatives() = VectorXd::Unit(2, 1);
+
+  // set the hessian matrix to zero
+  for (int idx = 0; idx < 2; idx++) {
+    x(0).derivatives()(idx).derivatives() = VectorXd::Zero(2);
+    x(1).derivatives()(idx).derivatives() = VectorXd::Zero(2);
+  }
+
+  ADD y = sin(AD(s3) * x(0) + AD(s4) * x(1));
+
+  VERIFY_IS_APPROX(y.value().derivatives()(0), y.derivatives()(0).value());
+  VERIFY_IS_APPROX(y.value().derivatives()(1), y.derivatives()(1).value());
+  VERIFY_IS_APPROX(y.value().derivatives()(0), s3 * std::cos(s1 * s3 + s2 * s4));
+  VERIFY_IS_APPROX(y.value().derivatives()(1), s4 * std::cos(s1 * s3 + s2 * s4));
+  VERIFY_IS_APPROX(y.derivatives()(0).derivatives(), -std::sin(s1 * s3 + s2 * s4) * Vector2d(s3 * s3, s4 * s3));
+  VERIFY_IS_APPROX(y.derivatives()(1).derivatives(), -std::sin(s1 * s3 + s2 * s4) * Vector2d(s3 * s4, s4 * s4));
+
+  ADD z = x(0) * x(1);
+  VERIFY_IS_APPROX(z.derivatives()(0).derivatives(), Vector2d(0, 1));
+  VERIFY_IS_APPROX(z.derivatives()(1).derivatives(), Vector2d(1, 0));
+}
+
+double bug_1222() {
+  typedef Eigen::AutoDiffScalar<Eigen::Vector3d> AD;
+  const double _cv1_3 = 1.0;
+  const AD chi_3 = 1.0;
+  // this line did not work, because operator+ returns ADS<DerType&>, which then cannot be converted to ADS<DerType>
+  const AD denom = chi_3 + _cv1_3;
+  return denom.value();
+}
+
+double bug_1223() {
+  using std::min;
+  typedef Eigen::AutoDiffScalar<Eigen::Vector3d> AD;
+
+  const double _cv1_3 = 1.0;
+  const AD chi_3 = 1.0;
+  const AD denom = 1.0;
+
+// failed because implementation of min attempts to construct ADS<DerType&> via constructor AutoDiffScalar(const Real&
+// value) without initializing m_derivatives (which is a reference in this case)
+#define EIGEN_TEST_SPACE
+  const AD t = min EIGEN_TEST_SPACE(denom / chi_3, 1.0);
+
+  const AD t2 = min EIGEN_TEST_SPACE(denom / (chi_3 * _cv1_3), 1.0);
+
+  return t.value() + t2.value();
+}
+
+// regression test for some compilation issues with specializations of ScalarBinaryOpTraits
+void bug_1260() {
+  Matrix4d A = Matrix4d::Ones();
+  Vector4d v = Vector4d::Ones();
+  A* v;
+}
+
+// check a compilation issue with numext::max
+double bug_1261() {
+  typedef AutoDiffScalar<Matrix2d> AD;
+  typedef Matrix<AD, 2, 1> VectorAD;
+
+  VectorAD v(0., 0.);
+  const AD maxVal = v.maxCoeff();
+  const AD minVal = v.minCoeff();
+  return maxVal.value() + minVal.value();
+}
+
+double bug_1264() {
+  typedef AutoDiffScalar<Vector2d> AD;
+  const AD s = 0.;
+  const Matrix<AD, 3, 1> v1(0., 0., 0.);
+  const Matrix<AD, 3, 1> v2 = (s + 3.0) * v1;
+  return v2(0).value();
+}
+
+// check with expressions on constants
+double bug_1281() {
+  int n = 2;
+  typedef AutoDiffScalar<VectorXd> AD;
+  const AD c = 1.;
+  AD x0(2, n, 0);
+  AD y1 = (AD(c) + AD(c)) * x0;
+  y1 = x0 * (AD(c) + AD(c));
+  AD y2 = (-AD(c)) + x0;
+  y2 = x0 + (-AD(c));
+  AD y3 = (AD(c) * (-AD(c)) + AD(c)) * x0;
+  y3 = x0 * (AD(c) * (-AD(c)) + AD(c));
+  return (y1 + y2 + y3).value();
+}
+
+EIGEN_DECLARE_TEST(autodiff) {
+  for (int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1(test_autodiff_scalar<1>());
+    CALL_SUBTEST_2(test_autodiff_vector<1>());
+    CALL_SUBTEST_3(test_autodiff_jacobian<1>());
+    CALL_SUBTEST_4(test_autodiff_hessian<1>());
+  }
+
+  CALL_SUBTEST_5(bug_1222());
+  CALL_SUBTEST_5(bug_1223());
+  CALL_SUBTEST_5(bug_1260());
+  CALL_SUBTEST_5(bug_1261());
+  CALL_SUBTEST_5(bug_1281());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/autodiff_scalar.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/autodiff_scalar.cpp
new file mode 100644
index 0000000..a0bff91
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/autodiff_scalar.cpp
@@ -0,0 +1,97 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christoph Hertzberg <chtz@informatik.uni-bremen.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <unsupported/Eigen/AutoDiff>
+
+/*
+ * In this file scalar derivations are tested for correctness.
+ * TODO add more tests!
+ */
+
+template <typename Scalar>
+void check_atan2() {
+  typedef Matrix<Scalar, 1, 1> Deriv1;
+  typedef AutoDiffScalar<Deriv1> AD;
+
+  AD x(internal::random<Scalar>(-3.0, 3.0), Deriv1::UnitX());
+
+  using std::exp;
+  Scalar r = exp(internal::random<Scalar>(-10, 10));
+
+  AD s = sin(x), c = cos(x);
+  AD res = atan2(r * s, r * c);
+
+  VERIFY_IS_APPROX(res.value(), x.value());
+  VERIFY_IS_APPROX(res.derivatives(), x.derivatives());
+
+  res = atan2(r * s + 0, r * c + 0);
+  VERIFY_IS_APPROX(res.value(), x.value());
+  VERIFY_IS_APPROX(res.derivatives(), x.derivatives());
+}
+
+template <typename Scalar>
+void check_hyperbolic_functions() {
+  using std::cosh;
+  using std::sinh;
+  using std::tanh;
+  typedef Matrix<Scalar, 1, 1> Deriv1;
+  typedef AutoDiffScalar<Deriv1> AD;
+  Deriv1 p = Deriv1::Random();
+  AD val(p.x(), Deriv1::UnitX());
+
+  Scalar cosh_px = std::cosh(p.x());
+  AD res1 = tanh(val);
+  VERIFY_IS_APPROX(res1.value(), std::tanh(p.x()));
+  VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(1.0) / (cosh_px * cosh_px));
+
+  AD res2 = sinh(val);
+  VERIFY_IS_APPROX(res2.value(), std::sinh(p.x()));
+  VERIFY_IS_APPROX(res2.derivatives().x(), cosh_px);
+
+  AD res3 = cosh(val);
+  VERIFY_IS_APPROX(res3.value(), cosh_px);
+  VERIFY_IS_APPROX(res3.derivatives().x(), std::sinh(p.x()));
+
+  // Check constant values.
+  const Scalar sample_point = Scalar(1) / Scalar(3);
+  val = AD(sample_point, Deriv1::UnitX());
+  res1 = tanh(val);
+  VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(0.896629559604914));
+
+  res2 = sinh(val);
+  VERIFY_IS_APPROX(res2.derivatives().x(), Scalar(1.056071867829939));
+
+  res3 = cosh(val);
+  VERIFY_IS_APPROX(res3.derivatives().x(), Scalar(0.339540557256150));
+}
+
+template <typename Scalar>
+void check_limits_specialization() {
+  typedef Eigen::Matrix<Scalar, 1, 1> Deriv;
+  typedef Eigen::AutoDiffScalar<Deriv> AD;
+
+  typedef std::numeric_limits<AD> A;
+  typedef std::numeric_limits<Scalar> B;
+
+  // workaround "unused typedef" warning:
+  VERIFY(!bool(internal::is_same<B, A>::value));
+
+  VERIFY(bool(std::is_base_of<B, A>::value));
+}
+
+EIGEN_DECLARE_TEST(autodiff_scalar) {
+  for (int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1(check_atan2<float>());
+    CALL_SUBTEST_2(check_atan2<double>());
+    CALL_SUBTEST_3(check_hyperbolic_functions<float>());
+    CALL_SUBTEST_4(check_hyperbolic_functions<double>());
+    CALL_SUBTEST_5(check_limits_specialization<double>());
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/bessel_functions.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/bessel_functions.cpp
new file mode 100644
index 0000000..86adfc1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/bessel_functions.cpp
@@ -0,0 +1,275 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include "../Eigen/SpecialFunctions"
+
+template <typename X, typename Y>
+void verify_component_wise(const X& x, const Y& y) {
+  for (Index i = 0; i < x.size(); ++i) {
+    if ((numext::isfinite)(y(i))) {
+      VERIFY_IS_APPROX(x(i), y(i));
+    } else if ((numext::isnan)(y(i)))
+      VERIFY((numext::isnan)(x(i)));
+    else
+      VERIFY_IS_EQUAL(x(i), y(i));
+  }
+}
+
+template <typename ArrayType>
+void array_bessel_functions() {
+  // Test Bessel function i0. Reference results obtained with SciPy.
+  {
+    ArrayType x(21);
+    ArrayType expected(21);
+    ArrayType res(21);
+
+    x << -20.0, -18.0, -16.0, -14.0, -12.0, -10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0,
+        16.0, 18.0, 20.0;
+
+    expected << 4.35582826e+07, 6.21841242e+06, 8.93446228e+05, 1.29418563e+05, 1.89489253e+04, 2.81571663e+03,
+        4.27564116e+02, 6.72344070e+01, 1.13019220e+01, 2.27958530e+00, 1.00000000e+00, 2.27958530e+00, 1.13019220e+01,
+        6.72344070e+01, 4.27564116e+02, 2.81571663e+03, 1.89489253e+04, 1.29418563e+05, 8.93446228e+05, 6.21841242e+06,
+        4.35582826e+07;
+
+    CALL_SUBTEST(res = bessel_i0(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function i0e. Reference results obtained with SciPy.
+  {
+    ArrayType x(21);
+    ArrayType expected(21);
+    ArrayType res(21);
+
+    x << -20.0, -18.0, -16.0, -14.0, -12.0, -10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0,
+        16.0, 18.0, 20.0;
+
+    expected << 0.0897803118848, 0.0947062952128, 0.100544127361, 0.107615251671, 0.116426221213, 0.127833337163,
+        0.143431781857, 0.16665743264, 0.207001921224, 0.308508322554, 1.0, 0.308508322554, 0.207001921224,
+        0.16665743264, 0.143431781857, 0.127833337163, 0.116426221213, 0.107615251671, 0.100544127361, 0.0947062952128,
+        0.0897803118848;
+
+    CALL_SUBTEST(res = bessel_i0e(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function i1. Reference results obtained with SciPy.
+  {
+    ArrayType x(21);
+    ArrayType expected(21);
+    ArrayType res(21);
+
+    x << -20.0, -18.0, -16.0, -14.0, -12.0, -10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0,
+        16.0, 18.0, 20.0;
+
+    expected << -4.24549734e+07, -6.04313324e+06, -8.65059436e+05, -1.24707259e+05, -1.81413488e+04, -2.67098830e+03,
+        -3.99873137e+02, -6.13419368e+01, -9.75946515e+00, -1.59063685e+00, 0.00000000e+00, 1.59063685e+00,
+        9.75946515e+00, 6.13419368e+01, 3.99873137e+02, 2.67098830e+03, 1.81413488e+04, 1.24707259e+05, 8.65059436e+05,
+        6.04313324e+06, 4.24549734e+07;
+
+    CALL_SUBTEST(res = bessel_i1(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function i1e. Reference results obtained with SciPy.
+  {
+    ArrayType x(21);
+    ArrayType expected(21);
+    ArrayType res(21);
+
+    x << -20.0, -18.0, -16.0, -14.0, -12.0, -10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0,
+        16.0, 18.0, 20.0;
+
+    expected << -0.0875062221833, -0.092036796872, -0.0973496147565, -0.103697667463, -0.11146429929, -0.121262681384,
+        -0.134142493293, -0.152051459309, -0.178750839502, -0.215269289249, 0.0, 0.215269289249, 0.178750839502,
+        0.152051459309, 0.134142493293, 0.121262681384, 0.11146429929, 0.103697667463, 0.0973496147565, 0.092036796872,
+        0.0875062221833;
+
+    CALL_SUBTEST(res = bessel_i1e(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function j0. Reference results obtained with SciPy.
+  {
+    ArrayType x(77);
+    ArrayType expected(77);
+    ArrayType res(77);
+
+    x << -38., -37., -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23., -22., -21.,
+        -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7., -6., -5., -4., -3., -2., -1.,
+        0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.,
+        24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.;
+
+    expected << 0.11433274, 0.01086237, -0.10556738, -0.12684568, -0.03042119, 0.09727067, 0.13807901, 0.05120815,
+        -0.08636798, -0.14784876, -0.07315701, 0.07274192, 0.15599932, 0.09626678, -0.05623027, -0.16241278,
+        -0.12065148, 0.03657907, 0.16702466, 0.14662944, -0.01335581, -0.16985425, -0.17489907, -0.01422447, 0.17107348,
+        0.2069261, 0.04768931, -0.1711903, -0.24593576, -0.09033361, 0.17165081, 0.30007927, 0.15064526, -0.17759677,
+        -0.39714981, -0.26005195, 0.22389078, 0.76519769, 1., 0.76519769, 0.22389078, -0.26005195, -0.39714981,
+        -0.17759677, 0.15064526, 0.30007927, 0.17165081, -0.09033361, -0.24593576, -0.1711903, 0.04768931, 0.2069261,
+        0.17107348, -0.01422447, -0.17489907, -0.16985425, -0.01335581, 0.14662944, 0.16702466, 0.03657907, -0.12065148,
+        -0.16241278, -0.05623027, 0.09626678, 0.15599932, 0.07274192, -0.07315701, -0.14784876, -0.08636798, 0.05120815,
+        0.13807901, 0.09727067, -0.03042119, -0.12684568, -0.10556738, 0.01086237, 0.11433274;
+
+    CALL_SUBTEST(res = bessel_j0(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function j1. Reference results obtained with SciPy.
+  {
+    ArrayType x(81);
+    ArrayType expected(81);
+    ArrayType res(81);
+
+    x << -40., -39., -38., -37., -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24., -23.,
+        -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10., -9., -8., -7., -6., -5., -4., -3.,
+        -2., -1., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.,
+        22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.;
+
+    expected << -0.12603832, -0.0640561, 0.05916189, 0.13058004, 0.08232981, -0.04399094, -0.13297118, -0.10061965,
+        0.02658903, 0.13302432, 0.11875106, -0.0069342, -0.13055149, -0.13658472, -0.01504573, 0.12535025, 0.15403807,
+        0.03951932, -0.11717779, -0.17112027, -0.06683312, 0.10570143, 0.18799489, 0.09766849, -0.09039718, -0.20510404,
+        -0.13337515, 0.07031805, 0.2234471, 0.1767853, -0.04347275, -0.24531179, -0.23463635, 0.00468282, 0.27668386,
+        0.32757914, 0.06604333, -0.33905896, -0.57672481, -0.44005059, 0., 0.44005059, 0.57672481, 0.33905896,
+        -0.06604333, -0.32757914, -0.27668386, -0.00468282, 0.23463635, 0.24531179, 0.04347275, -0.1767853, -0.2234471,
+        -0.07031805, 0.13337515, 0.20510404, 0.09039718, -0.09766849, -0.18799489, -0.10570143, 0.06683312, 0.17112027,
+        0.11717779, -0.03951932, -0.15403807, -0.12535025, 0.01504573, 0.13658472, 0.13055149, 0.0069342, -0.11875106,
+        -0.13302432, -0.02658903, 0.10061965, 0.13297118, 0.04399094, -0.08232981, -0.13058004, -0.05916189, 0.0640561,
+        0.12603832;
+
+    CALL_SUBTEST(res = bessel_j1(x); verify_component_wise(res, expected););
+  }
+  // Test Bessel function k0e. Reference results obtained with SciPy.
+  {
+    ArrayType x(42);
+    ArrayType expected(42);
+    ArrayType res(42);
+
+    x << 0.25, 0.5, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.,
+        23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.;
+
+    expected << 1.97933385, 1.52410939, 1.14446308, 0.84156822, 0.6977616, 0.60929767, 0.54780756, 0.50186313,
+        0.4658451, 0.43662302, 0.41229555, 0.39163193, 0.3737955, 0.35819488, 0.34439865, 0.33208364, 0.32100235,
+        0.31096159, 0.30180802, 0.29341821, 0.28569149, 0.27854488, 0.2719092, 0.26572635, 0.25994703, 0.25452917,
+        0.2494366, 0.24463801, 0.24010616, 0.23581722, 0.23175022, 0.22788667, 0.22421014, 0.22070602, 0.21736123,
+        0.21416406, 0.21110397, 0.20817141, 0.20535778, 0.20265524, 0.20005668, 0.19755558;
+
+    CALL_SUBTEST(res = bessel_k0e(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function k0. Reference results obtained with SciPy.
+  {
+    ArrayType x(42);
+    ArrayType expected(42);
+    ArrayType res(42);
+
+    x << 0.25, 0.5, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.,
+        23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.;
+
+    expected << 1.54150675, 0.92441907, 4.21024438e-01, 1.13893873e-01, 3.47395044e-02, 1.11596761e-02, 3.69109833e-03,
+        1.24399433e-03, 4.24795742e-04, 1.46470705e-04, 5.08813130e-05, 1.77800623e-05, 6.24302055e-06, 2.20082540e-06,
+        7.78454386e-07, 2.76137082e-07, 9.81953648e-08, 3.49941166e-08, 1.24946640e-08, 4.46875334e-09, 1.60067129e-09,
+        5.74123782e-10, 2.06176797e-10, 7.41235161e-11, 2.66754511e-11, 9.60881878e-12, 3.46416156e-12, 1.24987740e-12,
+        4.51286453e-13, 1.63053459e-13, 5.89495073e-14, 2.13247750e-14, 7.71838266e-15, 2.79505752e-15, 1.01266123e-15,
+        3.67057597e-16, 1.33103515e-16, 4.82858338e-17, 1.75232770e-17, 6.36161716e-18, 2.31029936e-18, 8.39286110e-19;
+
+    CALL_SUBTEST(res = bessel_k0(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function k0e. Reference results obtained with SciPy.
+  {
+    ArrayType x(42);
+    ArrayType expected(42);
+    ArrayType res(42);
+
+    x << 0.25, 0.5, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.,
+        23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.;
+
+    expected << 1.97933385, 1.52410939, 1.14446308, 0.84156822, 0.6977616, 0.60929767, 0.54780756, 0.50186313,
+        0.4658451, 0.43662302, 0.41229555, 0.39163193, 0.3737955, 0.35819488, 0.34439865, 0.33208364, 0.32100235,
+        0.31096159, 0.30180802, 0.29341821, 0.28569149, 0.27854488, 0.2719092, 0.26572635, 0.25994703, 0.25452917,
+        0.2494366, 0.24463801, 0.24010616, 0.23581722, 0.23175022, 0.22788667, 0.22421014, 0.22070602, 0.21736123,
+        0.21416406, 0.21110397, 0.20817141, 0.20535778, 0.20265524, 0.20005668, 0.19755558;
+
+    CALL_SUBTEST(res = bessel_k0e(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function k1. Reference results obtained with SciPy.
+  {
+    ArrayType x(42);
+    ArrayType expected(42);
+    ArrayType res(42);
+
+    x << 0.25, 0.5, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.,
+        23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.;
+
+    expected << 3.74702597, 1.65644112, 6.01907230e-01, 1.39865882e-01, 4.01564311e-02, 1.24834989e-02, 4.04461345e-03,
+        1.34391972e-03, 4.54182487e-04, 1.55369212e-04, 5.36370164e-05, 1.86487735e-05, 6.52086067e-06, 2.29075746e-06,
+        8.07858841e-07, 2.85834365e-07, 1.01417294e-07, 3.60715712e-08, 1.28570417e-08, 4.59124963e-09, 1.64226697e-09,
+        5.88305797e-10, 2.11029922e-10, 7.57898116e-11, 2.72493059e-11, 9.80699893e-12, 3.53277807e-12, 1.27369078e-12,
+        4.59568940e-13, 1.65940011e-13, 5.99574032e-14, 2.16773200e-14, 7.84189960e-15, 2.83839927e-15, 1.02789171e-15,
+        3.72416929e-16, 1.34991783e-16, 4.89519373e-17, 1.77585196e-17, 6.44478588e-18, 2.33973340e-18, 8.49713195e-19;
+
+    CALL_SUBTEST(res = bessel_k1(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function k1e. Reference results obtained with SciPy.
+  {
+    ArrayType x(42);
+    ArrayType expected(42);
+    ArrayType res(42);
+
+    x << 0.25, 0.5, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.,
+        23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.;
+
+    expected << 4.81127659, 2.73100971, 1.63615349, 1.03347685, 0.80656348, 0.68157595, 0.60027386, 0.54217591,
+        0.49807158, 0.46314909, 0.43462525, 0.41076657, 0.39043094, 0.37283175, 0.35740757, 0.34374563, 0.33153489,
+        0.32053597, 0.31056123, 0.30146131, 0.29311559, 0.2854255, 0.27830958, 0.27169987, 0.26553913, 0.25977879,
+        0.25437733, 0.249299, 0.24451285, 0.23999191, 0.2357126, 0.23165413, 0.22779816, 0.22412841, 0.22063036,
+        0.21729103, 0.21409878, 0.21104314, 0.20811462, 0.20530466, 0.20260547, 0.20000997;
+
+    CALL_SUBTEST(res = bessel_k1e(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function y0. Reference results obtained with SciPy.
+  {
+    ArrayType x(42);
+    ArrayType expected(42);
+    ArrayType res(42);
+
+    x << 0.25, 0.5, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.,
+        23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.;
+
+    expected << -0.93157302, -0.44451873, 0.08825696, 0.51037567, 0.37685001, -0.01694074, -0.30851763, -0.28819468,
+        -0.02594974, 0.22352149, 0.2499367, 0.05567117, -0.16884732, -0.22523731, -0.07820786, 0.12719257, 0.2054643,
+        0.095811, -0.0926372, -0.18755216, -0.10951969, 0.0626406, 0.17020176, 0.1198876, -0.03598179, -0.15283403,
+        -0.12724943, 0.01204463, 0.13521498, 0.13183647, 0.00948116, -0.11729573, -0.13383266, -0.02874248, 0.09913483,
+        0.13340405, 0.04579799, -0.08085609, -0.13071488, -0.06066076, 0.06262353, 0.12593642;
+
+    CALL_SUBTEST(res = bessel_y0(x); verify_component_wise(res, expected););
+  }
+
+  // Test Bessel function y1. Reference results obtained with SciPy.
+  {
+    ArrayType x(42);
+    ArrayType expected(42);
+    ArrayType res(42);
+
+    x << 0.25, 0.5, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.,
+        23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.;
+
+    expected << -2.70410523, -1.47147239, -0.78121282, -0.10703243, 0.32467442, 0.39792571, 0.14786314, -0.17501034,
+        -0.30266724, -0.15806046, 0.10431458, 0.24901542, 0.16370554, -0.05709922, -0.21008141, -0.16664484, 0.02107363,
+        0.17797517, 0.16720504, 0.00815513, -0.14956011, -0.16551161, -0.03253926, 0.12340586, 0.1616692, 0.05305978,
+        -0.09882996, -0.15579655, -0.07025124, 0.07552213, 0.14803412, 0.08442557, -0.05337283, -0.13854483,
+        -0.09578012, 0.03238588, 0.12751273, 0.10445477, -0.01262946, -0.11514066, -0.11056411, -0.00579351;
+
+    CALL_SUBTEST(res = bessel_y1(x); verify_component_wise(res, expected););
+  }
+}
+
+EIGEN_DECLARE_TEST(bessel_functions) {
+  CALL_SUBTEST_1(array_bessel_functions<ArrayXf>());
+  CALL_SUBTEST_2(array_bessel_functions<ArrayXd>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/bicgstabl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/bicgstabl.cpp
new file mode 100644
index 0000000..4761e39
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/bicgstabl.cpp
@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
+// Copyright (C) 2012 Kolja Brix <brix@igpm.rwth-aaachen.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "../../test/sparse_solver.h"
+#include <Eigen/IterativeSolvers>
+
+template <typename T>
+void test_bicgstabl_T() {
+  BiCGSTABL<SparseMatrix<T>, DiagonalPreconditioner<T> > bicgstabl_colmajor_diag;
+  BiCGSTABL<SparseMatrix<T>, IncompleteLUT<T> > bicgstabl_colmajor_ilut;
+
+  // This does not change the tolerance of the test, only the tolerance of the solver.
+  bicgstabl_colmajor_diag.setTolerance(NumTraits<T>::epsilon() * 20);
+  bicgstabl_colmajor_ilut.setTolerance(NumTraits<T>::epsilon() * 20);
+
+  CALL_SUBTEST(check_sparse_square_solving(bicgstabl_colmajor_diag));
+  CALL_SUBTEST(check_sparse_square_solving(bicgstabl_colmajor_ilut));
+}
+
+EIGEN_DECLARE_TEST(bicgstabl) {
+  CALL_SUBTEST_1(test_bicgstabl_T<double>());
+  CALL_SUBTEST_2(test_bicgstabl_T<std::complex<double> >());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_argmax.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_argmax.cpp
new file mode 100644
index 0000000..b86d84c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_argmax.cpp
@@ -0,0 +1,289 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Eugene Brevdo <ebrevdo@google.com>
+//                    Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::Pair;
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_simple_index_pairs() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  tensor = (tensor + tensor.constant(0.5)).log();
+
+  Tensor<Pair<DenseIndex, float>, 4, DataLayout> index_pairs(2, 3, 5, 7);
+  index_pairs = tensor.index_pairs();
+
+  for (DenseIndex n = 0; n < 2 * 3 * 5 * 7; ++n) {
+    const Pair<DenseIndex, float>& v = index_pairs.coeff(n);
+    VERIFY_IS_EQUAL(v.first, n);
+    VERIFY_IS_EQUAL(v.second, tensor.coeff(n));
+  }
+}
+
+template <int DataLayout>
+static void test_index_pairs_dim() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  tensor = (tensor + tensor.constant(0.5)).log();
+
+  Tensor<Pair<DenseIndex, float>, 4, DataLayout> index_pairs(2, 3, 5, 7);
+
+  index_pairs = tensor.index_pairs();
+
+  for (Eigen::DenseIndex n = 0; n < tensor.size(); ++n) {
+    const Pair<DenseIndex, float>& v = index_pairs(n);  //(i, j, k, l);
+    VERIFY_IS_EQUAL(v.first, n);
+    VERIFY_IS_EQUAL(v.second, tensor(n));
+  }
+}
+
+template <int DataLayout>
+static void test_argmax_pair_reducer() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  tensor = (tensor + tensor.constant(0.5)).log();
+
+  Tensor<Pair<DenseIndex, float>, 4, DataLayout> index_pairs(2, 3, 5, 7);
+  index_pairs = tensor.index_pairs();
+
+  Tensor<Pair<DenseIndex, float>, 0, DataLayout> reduced;
+  DimensionList<DenseIndex, 4> dims;
+  reduced = index_pairs.reduce(dims, internal::ArgMaxPairReducer<Pair<DenseIndex, float> >());
+
+  Tensor<float, 0, DataLayout> maxi = tensor.maximum();
+
+  VERIFY_IS_EQUAL(maxi(), reduced(0).second);
+
+  array<DenseIndex, 3> reduce_dims;
+  for (int d = 0; d < 3; ++d) reduce_dims[d] = d;
+  Tensor<Pair<DenseIndex, float>, 1, DataLayout> reduced_by_dims(7);
+  reduced_by_dims = index_pairs.reduce(reduce_dims, internal::ArgMaxPairReducer<Pair<DenseIndex, float> >());
+
+  Tensor<float, 1, DataLayout> max_by_dims = tensor.maximum(reduce_dims);
+
+  for (int l = 0; l < 7; ++l) {
+    VERIFY_IS_EQUAL(max_by_dims(l), reduced_by_dims(l).second);
+  }
+}
+
+template <int DataLayout>
+static void test_argmin_pair_reducer() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  tensor = (tensor + tensor.constant(0.5)).log();
+
+  Tensor<Pair<DenseIndex, float>, 4, DataLayout> index_pairs(2, 3, 5, 7);
+  index_pairs = tensor.index_pairs();
+
+  Tensor<Pair<DenseIndex, float>, 0, DataLayout> reduced;
+  DimensionList<DenseIndex, 4> dims;
+  reduced = index_pairs.reduce(dims, internal::ArgMinPairReducer<Pair<DenseIndex, float> >());
+
+  Tensor<float, 0, DataLayout> mini = tensor.minimum();
+
+  VERIFY_IS_EQUAL(mini(), reduced(0).second);
+
+  array<DenseIndex, 3> reduce_dims;
+  for (int d = 0; d < 3; ++d) reduce_dims[d] = d;
+  Tensor<Pair<DenseIndex, float>, 1, DataLayout> reduced_by_dims(7);
+  reduced_by_dims = index_pairs.reduce(reduce_dims, internal::ArgMinPairReducer<Pair<DenseIndex, float> >());
+
+  Tensor<float, 1, DataLayout> min_by_dims = tensor.minimum(reduce_dims);
+
+  for (int l = 0; l < 7; ++l) {
+    VERIFY_IS_EQUAL(min_by_dims(l), reduced_by_dims(l).second);
+  }
+}
+
+template <int DataLayout>
+static void test_simple_argmax() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  tensor = (tensor + tensor.constant(0.5)).log();
+  tensor(0, 0, 0, 0) = 10.0;
+
+  Tensor<DenseIndex, 0, DataLayout> tensor_argmax;
+
+  tensor_argmax = tensor.argmax();
+
+  VERIFY_IS_EQUAL(tensor_argmax(0), 0);
+
+  tensor(1, 2, 4, 6) = 20.0;
+
+  tensor_argmax = tensor.argmax();
+
+  VERIFY_IS_EQUAL(tensor_argmax(0), 2 * 3 * 5 * 7 - 1);
+}
+
+template <int DataLayout>
+static void test_simple_argmin() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  tensor = (tensor + tensor.constant(0.5)).log();
+  tensor(0, 0, 0, 0) = -10.0;
+
+  Tensor<DenseIndex, 0, DataLayout> tensor_argmin;
+
+  tensor_argmin = tensor.argmin();
+
+  VERIFY_IS_EQUAL(tensor_argmin(0), 0);
+
+  tensor(1, 2, 4, 6) = -20.0;
+
+  tensor_argmin = tensor.argmin();
+
+  VERIFY_IS_EQUAL(tensor_argmin(0), 2 * 3 * 5 * 7 - 1);
+}
+
+template <int DataLayout>
+static void test_argmax_dim() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  std::vector<int> dims{2, 3, 5, 7};
+
+  for (int dim = 0; dim < 4; ++dim) {
+    tensor.setRandom();
+    tensor = (tensor + tensor.constant(0.5)).log();
+
+    Tensor<DenseIndex, 3, DataLayout> tensor_argmax;
+    array<DenseIndex, 4> ix;
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 3; ++j) {
+        for (int k = 0; k < 5; ++k) {
+          for (int l = 0; l < 7; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            if (ix[dim] != 0) continue;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
+            tensor(ix) = 10.0;
+          }
+        }
+      }
+    }
+
+    tensor_argmax = tensor.argmax(dim);
+
+    VERIFY_IS_EQUAL(tensor_argmax.size(), ptrdiff_t(2 * 3 * 5 * 7 / tensor.dimension(dim)));
+    for (ptrdiff_t n = 0; n < tensor_argmax.size(); ++n) {
+      // Expect max to be in the first index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_argmax.data()[n], 0);
+    }
+
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 3; ++j) {
+        for (int k = 0; k < 5; ++k) {
+          for (int l = 0; l < 7; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            if (ix[dim] != tensor.dimension(dim) - 1) continue;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
+            tensor(ix) = 20.0;
+          }
+        }
+      }
+    }
+
+    tensor_argmax = tensor.argmax(dim);
+
+    VERIFY_IS_EQUAL(tensor_argmax.size(), ptrdiff_t(2 * 3 * 5 * 7 / tensor.dimension(dim)));
+    for (ptrdiff_t n = 0; n < tensor_argmax.size(); ++n) {
+      // Expect max to be in the last index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_argmax.data()[n], tensor.dimension(dim) - 1);
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_argmin_dim() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  std::vector<int> dims{2, 3, 5, 7};
+
+  for (int dim = 0; dim < 4; ++dim) {
+    tensor.setRandom();
+    tensor = (tensor + tensor.constant(0.5)).log();
+
+    Tensor<DenseIndex, 3, DataLayout> tensor_argmin;
+    array<DenseIndex, 4> ix;
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 3; ++j) {
+        for (int k = 0; k < 5; ++k) {
+          for (int l = 0; l < 7; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            if (ix[dim] != 0) continue;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = -10.0
+            tensor(ix) = -10.0;
+          }
+        }
+      }
+    }
+
+    tensor_argmin = tensor.argmin(dim);
+
+    VERIFY_IS_EQUAL(tensor_argmin.size(), ptrdiff_t(2 * 3 * 5 * 7 / tensor.dimension(dim)));
+    for (ptrdiff_t n = 0; n < tensor_argmin.size(); ++n) {
+      // Expect min to be in the first index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_argmin.data()[n], 0);
+    }
+
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 3; ++j) {
+        for (int k = 0; k < 5; ++k) {
+          for (int l = 0; l < 7; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            if (ix[dim] != tensor.dimension(dim) - 1) continue;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = -20.0
+            tensor(ix) = -20.0;
+          }
+        }
+      }
+    }
+
+    tensor_argmin = tensor.argmin(dim);
+
+    VERIFY_IS_EQUAL(tensor_argmin.size(), ptrdiff_t(2 * 3 * 5 * 7 / tensor.dimension(dim)));
+    for (ptrdiff_t n = 0; n < tensor_argmin.size(); ++n) {
+      // Expect min to be in the last index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_argmin.data()[n], tensor.dimension(dim) - 1);
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_argmax) {
+  CALL_SUBTEST(test_simple_index_pairs<RowMajor>());
+  CALL_SUBTEST(test_simple_index_pairs<ColMajor>());
+  CALL_SUBTEST(test_index_pairs_dim<RowMajor>());
+  CALL_SUBTEST(test_index_pairs_dim<ColMajor>());
+  CALL_SUBTEST(test_argmax_pair_reducer<RowMajor>());
+  CALL_SUBTEST(test_argmax_pair_reducer<ColMajor>());
+  CALL_SUBTEST(test_argmin_pair_reducer<RowMajor>());
+  CALL_SUBTEST(test_argmin_pair_reducer<ColMajor>());
+  CALL_SUBTEST(test_simple_argmax<RowMajor>());
+  CALL_SUBTEST(test_simple_argmax<ColMajor>());
+  CALL_SUBTEST(test_simple_argmin<RowMajor>());
+  CALL_SUBTEST(test_simple_argmin<ColMajor>());
+  CALL_SUBTEST(test_argmax_dim<RowMajor>());
+  CALL_SUBTEST(test_argmax_dim<ColMajor>());
+  CALL_SUBTEST(test_argmin_dim<RowMajor>());
+  CALL_SUBTEST(test_argmin_dim<ColMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_argmax_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_argmax_gpu.cu
new file mode 100644
index 0000000..90c94c5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_argmax_gpu.cu
@@ -0,0 +1,270 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int Layout>
+void test_gpu_simple_argmax() {
+  Tensor<double, 3, Layout> in(Eigen::array<DenseIndex, 3>{72, 53, 97});
+  Tensor<DenseIndex, 0, Layout> out_max;
+  Tensor<DenseIndex, 0, Layout> out_min;
+  in.setRandom();
+  in *= in.constant(100.0);
+  in(0, 0, 0) = -1000.0;
+  in(71, 52, 96) = 1000.0;
+
+  std::size_t in_bytes = in.size() * sizeof(double);
+  std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);
+
+  double* d_in;
+  DenseIndex* d_out_max;
+  DenseIndex* d_out_min;
+  gpuMalloc((void**)(&d_in), in_bytes);
+  gpuMalloc((void**)(&d_out_max), out_bytes);
+  gpuMalloc((void**)(&d_out_min), out_bytes);
+
+  gpuMemcpy(d_in, in.data(), in_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<double, 3, Layout>, Aligned> gpu_in(d_in, Eigen::array<DenseIndex, 3>{72, 53, 97});
+  Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout>, Aligned> gpu_out_max(d_out_max);
+  Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout>, Aligned> gpu_out_min(d_out_min);
+
+  gpu_out_max.device(gpu_device) = gpu_in.argmax();
+  gpu_out_min.device(gpu_device) = gpu_in.argmin();
+
+  assert(gpuMemcpyAsync(out_max.data(), d_out_max, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) ==
+         gpuSuccess);
+  assert(gpuMemcpyAsync(out_min.data(), d_out_min, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) ==
+         gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  VERIFY_IS_EQUAL(out_max(), 72 * 53 * 97 - 1);
+  VERIFY_IS_EQUAL(out_min(), 0);
+
+  gpuFree(d_in);
+  gpuFree(d_out_max);
+  gpuFree(d_out_min);
+}
+
+template <int DataLayout>
+void test_gpu_argmax_dim() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  std::vector<int> dims;
+  dims.push_back(2);
+  dims.push_back(3);
+  dims.push_back(5);
+  dims.push_back(7);
+
+  for (int dim = 0; dim < 4; ++dim) {
+    tensor.setRandom();
+    tensor = (tensor + tensor.constant(0.5)).log();
+
+    array<DenseIndex, 3> out_shape;
+    for (int d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];
+
+    Tensor<DenseIndex, 3, DataLayout> tensor_arg(out_shape);
+
+    array<DenseIndex, 4> ix;
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 3; ++j) {
+        for (int k = 0; k < 5; ++k) {
+          for (int l = 0; l < 7; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            if (ix[dim] != 0) continue;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
+            tensor(ix) = 10.0;
+          }
+        }
+      }
+    }
+
+    std::size_t in_bytes = tensor.size() * sizeof(float);
+    std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
+
+    float* d_in;
+    DenseIndex* d_out;
+    gpuMalloc((void**)(&d_in), in_bytes);
+    gpuMalloc((void**)(&d_out), out_bytes);
+
+    gpuMemcpy(d_in, tensor.data(), in_bytes, gpuMemcpyHostToDevice);
+
+    Eigen::GpuStreamDevice stream;
+    Eigen::GpuDevice gpu_device(&stream);
+
+    Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout>, Aligned> gpu_in(d_in,
+                                                                          Eigen::array<DenseIndex, 4>{2, 3, 5, 7});
+    Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout>, Aligned> gpu_out(d_out, out_shape);
+
+    gpu_out.device(gpu_device) = gpu_in.argmax(dim);
+
+    assert(gpuMemcpyAsync(tensor_arg.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) ==
+           gpuSuccess);
+    assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+    VERIFY_IS_EQUAL(tensor_arg.size(), size_t(2 * 3 * 5 * 7 / tensor.dimension(dim)));
+
+    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+      // Expect max to be in the first index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
+    }
+
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 3; ++j) {
+        for (int k = 0; k < 5; ++k) {
+          for (int l = 0; l < 7; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            if (ix[dim] != tensor.dimension(dim) - 1) continue;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
+            tensor(ix) = 20.0;
+          }
+        }
+      }
+    }
+
+    gpuMemcpy(d_in, tensor.data(), in_bytes, gpuMemcpyHostToDevice);
+
+    gpu_out.device(gpu_device) = gpu_in.argmax(dim);
+
+    assert(gpuMemcpyAsync(tensor_arg.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) ==
+           gpuSuccess);
+    assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+      // Expect max to be in the last index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
+    }
+
+    gpuFree(d_in);
+    gpuFree(d_out);
+  }
+}
+
+template <int DataLayout>
+void test_gpu_argmin_dim() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  std::vector<int> dims;
+  dims.push_back(2);
+  dims.push_back(3);
+  dims.push_back(5);
+  dims.push_back(7);
+
+  for (int dim = 0; dim < 4; ++dim) {
+    tensor.setRandom();
+    tensor = (tensor + tensor.constant(0.5)).log();
+
+    array<DenseIndex, 3> out_shape;
+    for (int d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];
+
+    Tensor<DenseIndex, 3, DataLayout> tensor_arg(out_shape);
+
+    array<DenseIndex, 4> ix;
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 3; ++j) {
+        for (int k = 0; k < 5; ++k) {
+          for (int l = 0; l < 7; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            if (ix[dim] != 0) continue;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
+            tensor(ix) = -10.0;
+          }
+        }
+      }
+    }
+
+    std::size_t in_bytes = tensor.size() * sizeof(float);
+    std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
+
+    float* d_in;
+    DenseIndex* d_out;
+    gpuMalloc((void**)(&d_in), in_bytes);
+    gpuMalloc((void**)(&d_out), out_bytes);
+
+    gpuMemcpy(d_in, tensor.data(), in_bytes, gpuMemcpyHostToDevice);
+
+    Eigen::GpuStreamDevice stream;
+    Eigen::GpuDevice gpu_device(&stream);
+
+    Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout>, Aligned> gpu_in(d_in,
+                                                                          Eigen::array<DenseIndex, 4>{2, 3, 5, 7});
+    Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout>, Aligned> gpu_out(d_out, out_shape);
+
+    gpu_out.device(gpu_device) = gpu_in.argmin(dim);
+
+    assert(gpuMemcpyAsync(tensor_arg.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) ==
+           gpuSuccess);
+    assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+    VERIFY_IS_EQUAL(tensor_arg.size(), 2 * 3 * 5 * 7 / tensor.dimension(dim));
+
+    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+      // Expect min to be in the first index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
+    }
+
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 3; ++j) {
+        for (int k = 0; k < 5; ++k) {
+          for (int l = 0; l < 7; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            if (ix[dim] != tensor.dimension(dim) - 1) continue;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
+            tensor(ix) = -20.0;
+          }
+        }
+      }
+    }
+
+    gpuMemcpy(d_in, tensor.data(), in_bytes, gpuMemcpyHostToDevice);
+
+    gpu_out.device(gpu_device) = gpu_in.argmin(dim);
+
+    assert(gpuMemcpyAsync(tensor_arg.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) ==
+           gpuSuccess);
+    assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+      // Expect max to be in the last index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
+    }
+
+    gpuFree(d_in);
+    gpuFree(d_out);
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_argmax_gpu) {
+  CALL_SUBTEST_1(test_gpu_simple_argmax<RowMajor>());
+  CALL_SUBTEST_1(test_gpu_simple_argmax<ColMajor>());
+  CALL_SUBTEST_2(test_gpu_argmax_dim<RowMajor>());
+  CALL_SUBTEST_2(test_gpu_argmax_dim<ColMajor>());
+  CALL_SUBTEST_3(test_gpu_argmin_dim<RowMajor>());
+  CALL_SUBTEST_3(test_gpu_argmin_dim<ColMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_argmax_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_argmax_sycl.cpp
new file mode 100644
index 0000000..8f4e095
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_argmax_sycl.cpp
@@ -0,0 +1,258 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+template <typename DataType, int Layout, typename DenseIndex>
+static void test_sycl_simple_argmax(const Eigen::SyclDevice& sycl_device) {
+  Tensor<DataType, 3, Layout, DenseIndex> in(Eigen::array<DenseIndex, 3>{{2, 2, 2}});
+  Tensor<DenseIndex, 0, Layout, DenseIndex> out_max;
+  Tensor<DenseIndex, 0, Layout, DenseIndex> out_min;
+  in.setRandom();
+  in *= in.constant(static_cast<DataType>(100.0));
+  in(0, 0, 0) = static_cast<DataType>(-1000.0);
+  in(1, 1, 1) = static_cast<DataType>(1000.0);
+
+  std::size_t in_bytes = in.size() * sizeof(DataType);
+  std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);
+
+  DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
+  DenseIndex* d_out_max = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
+  DenseIndex* d_out_min = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, Layout, DenseIndex> > gpu_in(d_in,
+                                                                           Eigen::array<DenseIndex, 3>{{2, 2, 2}});
+  Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_max(d_out_max);
+  Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_min(d_out_min);
+  sycl_device.memcpyHostToDevice(d_in, in.data(), in_bytes);
+
+  gpu_out_max.device(sycl_device) = gpu_in.argmax();
+  gpu_out_min.device(sycl_device) = gpu_in.argmin();
+
+  sycl_device.memcpyDeviceToHost(out_max.data(), d_out_max, out_bytes);
+  sycl_device.memcpyDeviceToHost(out_min.data(), d_out_min, out_bytes);
+
+  VERIFY_IS_EQUAL(out_max(), 2 * 2 * 2 - 1);
+  VERIFY_IS_EQUAL(out_min(), 0);
+
+  sycl_device.deallocate(d_in);
+  sycl_device.deallocate(d_out_max);
+  sycl_device.deallocate(d_out_min);
+}
+
+template <typename DataType, int DataLayout, typename DenseIndex>
+static void test_sycl_argmax_dim(const Eigen::SyclDevice& sycl_device) {
+  DenseIndex sizeDim0 = 9;
+  DenseIndex sizeDim1 = 3;
+  DenseIndex sizeDim2 = 5;
+  DenseIndex sizeDim3 = 7;
+  Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0, sizeDim1, sizeDim2, sizeDim3);
+
+  std::vector<DenseIndex> dims;
+  dims.push_back(sizeDim0);
+  dims.push_back(sizeDim1);
+  dims.push_back(sizeDim2);
+  dims.push_back(sizeDim3);
+  for (DenseIndex dim = 0; dim < 4; ++dim) {
+    array<DenseIndex, 3> out_shape;
+    for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];
+
+    Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
+
+    array<DenseIndex, 4> ix;
+    for (DenseIndex i = 0; i < sizeDim0; ++i) {
+      for (DenseIndex j = 0; j < sizeDim1; ++j) {
+        for (DenseIndex k = 0; k < sizeDim2; ++k) {
+          for (DenseIndex l = 0; l < sizeDim3; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l)
+            // = 10.0
+            tensor(ix) = static_cast<DataType>((ix[dim] != 0) ? -1.0 : 10.0);
+          }
+        }
+      }
+    }
+
+    std::size_t in_bytes = tensor.size() * sizeof(DataType);
+    std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
+
+    DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
+    DenseIndex* d_out = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
+
+    Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(
+        d_in, Eigen::array<DenseIndex, 4>{{sizeDim0, sizeDim1, sizeDim2, sizeDim3}});
+    Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
+
+    sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
+    gpu_out.device(sycl_device) = gpu_in.argmax(dim);
+    sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
+
+    VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
+                    size_t(sizeDim0 * sizeDim1 * sizeDim2 * sizeDim3 / tensor.dimension(dim)));
+
+    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+      // Expect max to be in the first index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
+    }
+
+    sycl_device.synchronize();
+
+    for (DenseIndex i = 0; i < sizeDim0; ++i) {
+      for (DenseIndex j = 0; j < sizeDim1; ++j) {
+        for (DenseIndex k = 0; k < sizeDim2; ++k) {
+          for (DenseIndex l = 0; l < sizeDim3; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
+            tensor(ix) = static_cast<DataType>((ix[dim] != tensor.dimension(dim) - 1) ? -1.0 : 20.0);
+          }
+        }
+      }
+    }
+
+    sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
+    gpu_out.device(sycl_device) = gpu_in.argmax(dim);
+    sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
+
+    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+      // Expect max to be in the last index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
+    }
+    sycl_device.deallocate(d_in);
+    sycl_device.deallocate(d_out);
+  }
+}
+
+template <typename DataType, int DataLayout, typename DenseIndex>
+static void test_sycl_argmin_dim(const Eigen::SyclDevice& sycl_device) {
+  DenseIndex sizeDim0 = 9;
+  DenseIndex sizeDim1 = 3;
+  DenseIndex sizeDim2 = 5;
+  DenseIndex sizeDim3 = 7;
+  Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0, sizeDim1, sizeDim2, sizeDim3);
+
+  std::vector<DenseIndex> dims;
+  dims.push_back(sizeDim0);
+  dims.push_back(sizeDim1);
+  dims.push_back(sizeDim2);
+  dims.push_back(sizeDim3);
+  for (DenseIndex dim = 0; dim < 4; ++dim) {
+    array<DenseIndex, 3> out_shape;
+    for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];
+
+    Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
+
+    array<DenseIndex, 4> ix;
+    for (DenseIndex i = 0; i < sizeDim0; ++i) {
+      for (DenseIndex j = 0; j < sizeDim1; ++j) {
+        for (DenseIndex k = 0; k < sizeDim2; ++k) {
+          for (DenseIndex l = 0; l < sizeDim3; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = -10.0
+            tensor(ix) = static_cast<DataType>((ix[dim] != 0) ? 1.0 : -10.0);
+          }
+        }
+      }
+    }
+
+    std::size_t in_bytes = tensor.size() * sizeof(DataType);
+    std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
+
+    DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
+    DenseIndex* d_out = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
+
+    Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(
+        d_in, Eigen::array<DenseIndex, 4>{{sizeDim0, sizeDim1, sizeDim2, sizeDim3}});
+    Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
+
+    sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
+    gpu_out.device(sycl_device) = gpu_in.argmin(dim);
+    sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
+
+    VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
+                    size_t(sizeDim0 * sizeDim1 * sizeDim2 * sizeDim3 / tensor.dimension(dim)));
+
+    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+      // Expect max to be in the first index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
+    }
+
+    sycl_device.synchronize();
+
+    for (DenseIndex i = 0; i < sizeDim0; ++i) {
+      for (DenseIndex j = 0; j < sizeDim1; ++j) {
+        for (DenseIndex k = 0; k < sizeDim2; ++k) {
+          for (DenseIndex l = 0; l < sizeDim3; ++l) {
+            ix[0] = i;
+            ix[1] = j;
+            ix[2] = k;
+            ix[3] = l;
+            // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = -20.0
+            tensor(ix) = static_cast<DataType>((ix[dim] != tensor.dimension(dim) - 1) ? 1.0 : -20.0);
+          }
+        }
+      }
+    }
+
+    sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
+    gpu_out.device(sycl_device) = gpu_in.argmin(dim);
+    sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
+
+    for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
+      // Expect max to be in the last index of the reduced dimension
+      VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
+    }
+    sycl_device.deallocate(d_in);
+    sycl_device.deallocate(d_out);
+  }
+}
+
+template <typename DataType, typename Device_Selector>
+void sycl_argmax_test_per_device(const Device_Selector& d) {
+  QueueInterface queueInterface(d);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_sycl_simple_argmax<DataType, RowMajor, int64_t>(sycl_device);
+  test_sycl_simple_argmax<DataType, ColMajor, int64_t>(sycl_device);
+  test_sycl_argmax_dim<DataType, ColMajor, int64_t>(sycl_device);
+  test_sycl_argmax_dim<DataType, RowMajor, int64_t>(sycl_device);
+  test_sycl_argmin_dim<DataType, ColMajor, int64_t>(sycl_device);
+  test_sycl_argmin_dim<DataType, RowMajor, int64_t>(sycl_device);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_argmax_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_argmax_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_argmax_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_assign.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_assign.cpp
new file mode 100644
index 0000000..993249d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_assign.cpp
@@ -0,0 +1,359 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void test_1d() {
+  Tensor<int, 1> vec1(6);
+  Tensor<int, 1, RowMajor> vec2(6);
+  vec1(0) = 4;
+  vec2(0) = 0;
+  vec1(1) = 8;
+  vec2(1) = 1;
+  vec1(2) = 15;
+  vec2(2) = 2;
+  vec1(3) = 16;
+  vec2(3) = 3;
+  vec1(4) = 23;
+  vec2(4) = 4;
+  vec1(5) = 42;
+  vec2(5) = 5;
+
+  int col_major[6] = {0};
+  int row_major[6] = {0};
+  TensorMap<Tensor<int, 1> > vec3(col_major, 6);
+  TensorMap<Tensor<int, 1, RowMajor> > vec4(row_major, 6);
+
+  vec3 = vec1;
+  vec4 = vec2;
+
+  VERIFY_IS_EQUAL(vec3(0), 4);
+  VERIFY_IS_EQUAL(vec3(1), 8);
+  VERIFY_IS_EQUAL(vec3(2), 15);
+  VERIFY_IS_EQUAL(vec3(3), 16);
+  VERIFY_IS_EQUAL(vec3(4), 23);
+  VERIFY_IS_EQUAL(vec3(5), 42);
+
+  VERIFY_IS_EQUAL(vec4(0), 0);
+  VERIFY_IS_EQUAL(vec4(1), 1);
+  VERIFY_IS_EQUAL(vec4(2), 2);
+  VERIFY_IS_EQUAL(vec4(3), 3);
+  VERIFY_IS_EQUAL(vec4(4), 4);
+  VERIFY_IS_EQUAL(vec4(5), 5);
+
+  vec1.setZero();
+  vec2.setZero();
+  vec1 = vec3;
+  vec2 = vec4;
+
+  VERIFY_IS_EQUAL(vec1(0), 4);
+  VERIFY_IS_EQUAL(vec1(1), 8);
+  VERIFY_IS_EQUAL(vec1(2), 15);
+  VERIFY_IS_EQUAL(vec1(3), 16);
+  VERIFY_IS_EQUAL(vec1(4), 23);
+  VERIFY_IS_EQUAL(vec1(5), 42);
+
+  VERIFY_IS_EQUAL(vec2(0), 0);
+  VERIFY_IS_EQUAL(vec2(1), 1);
+  VERIFY_IS_EQUAL(vec2(2), 2);
+  VERIFY_IS_EQUAL(vec2(3), 3);
+  VERIFY_IS_EQUAL(vec2(4), 4);
+  VERIFY_IS_EQUAL(vec2(5), 5);
+}
+
+static void test_2d() {
+  Tensor<int, 2> mat1(2, 3);
+  Tensor<int, 2, RowMajor> mat2(2, 3);
+
+  mat1(0, 0) = 0;
+  mat1(0, 1) = 1;
+  mat1(0, 2) = 2;
+  mat1(1, 0) = 3;
+  mat1(1, 1) = 4;
+  mat1(1, 2) = 5;
+
+  mat2(0, 0) = 0;
+  mat2(0, 1) = 1;
+  mat2(0, 2) = 2;
+  mat2(1, 0) = 3;
+  mat2(1, 1) = 4;
+  mat2(1, 2) = 5;
+
+  int col_major[6] = {0};
+  int row_major[6] = {0};
+  TensorMap<Tensor<int, 2> > mat3(row_major, 2, 3);
+  TensorMap<Tensor<int, 2, RowMajor> > mat4(col_major, 2, 3);
+
+  mat3 = mat1;
+  mat4 = mat2;
+
+  VERIFY_IS_EQUAL(mat3(0, 0), 0);
+  VERIFY_IS_EQUAL(mat3(0, 1), 1);
+  VERIFY_IS_EQUAL(mat3(0, 2), 2);
+  VERIFY_IS_EQUAL(mat3(1, 0), 3);
+  VERIFY_IS_EQUAL(mat3(1, 1), 4);
+  VERIFY_IS_EQUAL(mat3(1, 2), 5);
+
+  VERIFY_IS_EQUAL(mat4(0, 0), 0);
+  VERIFY_IS_EQUAL(mat4(0, 1), 1);
+  VERIFY_IS_EQUAL(mat4(0, 2), 2);
+  VERIFY_IS_EQUAL(mat4(1, 0), 3);
+  VERIFY_IS_EQUAL(mat4(1, 1), 4);
+  VERIFY_IS_EQUAL(mat4(1, 2), 5);
+
+  mat1.setZero();
+  mat2.setZero();
+  mat1 = mat3;
+  mat2 = mat4;
+
+  VERIFY_IS_EQUAL(mat1(0, 0), 0);
+  VERIFY_IS_EQUAL(mat1(0, 1), 1);
+  VERIFY_IS_EQUAL(mat1(0, 2), 2);
+  VERIFY_IS_EQUAL(mat1(1, 0), 3);
+  VERIFY_IS_EQUAL(mat1(1, 1), 4);
+  VERIFY_IS_EQUAL(mat1(1, 2), 5);
+
+  VERIFY_IS_EQUAL(mat2(0, 0), 0);
+  VERIFY_IS_EQUAL(mat2(0, 1), 1);
+  VERIFY_IS_EQUAL(mat2(0, 2), 2);
+  VERIFY_IS_EQUAL(mat2(1, 0), 3);
+  VERIFY_IS_EQUAL(mat2(1, 1), 4);
+  VERIFY_IS_EQUAL(mat2(1, 2), 5);
+}
+
+static void test_3d() {
+  Tensor<int, 3> mat1(2, 3, 7);
+  Tensor<int, 3, RowMajor> mat2(2, 3, 7);
+
+  int val = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        mat1(i, j, k) = val;
+        mat2(i, j, k) = val;
+        val++;
+      }
+    }
+  }
+
+  int col_major[2 * 3 * 7] = {0};
+  int row_major[2 * 3 * 7] = {0};
+  TensorMap<Tensor<int, 3> > mat3(col_major, 2, 3, 7);
+  TensorMap<Tensor<int, 3, RowMajor> > mat4(row_major, 2, 3, 7);
+
+  mat3 = mat1;
+  mat4 = mat2;
+
+  val = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(mat3(i, j, k), val);
+        VERIFY_IS_EQUAL(mat4(i, j, k), val);
+        val++;
+      }
+    }
+  }
+
+  mat1.setZero();
+  mat2.setZero();
+  mat1 = mat3;
+  mat2 = mat4;
+
+  val = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(mat1(i, j, k), val);
+        VERIFY_IS_EQUAL(mat2(i, j, k), val);
+        val++;
+      }
+    }
+  }
+}
+
+static void test_same_type() {
+  Tensor<int, 1> orig_tensor(5);
+  Tensor<int, 1> dest_tensor(5);
+  orig_tensor.setRandom();
+  dest_tensor.setRandom();
+  int* orig_data = orig_tensor.data();
+  int* dest_data = dest_tensor.data();
+  dest_tensor = orig_tensor;
+  VERIFY_IS_EQUAL(orig_tensor.data(), orig_data);
+  VERIFY_IS_EQUAL(dest_tensor.data(), dest_data);
+  for (int i = 0; i < 5; ++i) {
+    VERIFY_IS_EQUAL(dest_tensor(i), orig_tensor(i));
+  }
+
+  TensorFixedSize<int, Sizes<5> > orig_array;
+  TensorFixedSize<int, Sizes<5> > dest_array;
+  orig_array.setRandom();
+  dest_array.setRandom();
+  orig_data = orig_array.data();
+  dest_data = dest_array.data();
+  dest_array = orig_array;
+  VERIFY_IS_EQUAL(orig_array.data(), orig_data);
+  VERIFY_IS_EQUAL(dest_array.data(), dest_data);
+  for (int i = 0; i < 5; ++i) {
+    VERIFY_IS_EQUAL(dest_array(i), orig_array(i));
+  }
+
+  int orig[5] = {1, 2, 3, 4, 5};
+  int dest[5] = {6, 7, 8, 9, 10};
+  TensorMap<Tensor<int, 1> > orig_map(orig, 5);
+  TensorMap<Tensor<int, 1> > dest_map(dest, 5);
+  orig_data = orig_map.data();
+  dest_data = dest_map.data();
+  dest_map = orig_map;
+  VERIFY_IS_EQUAL(orig_map.data(), orig_data);
+  VERIFY_IS_EQUAL(dest_map.data(), dest_data);
+  for (int i = 0; i < 5; ++i) {
+    VERIFY_IS_EQUAL(dest[i], i + 1);
+  }
+}
+
+static void test_auto_resize() {
+  Tensor<int, 1> tensor1;
+  Tensor<int, 1> tensor2(3);
+  Tensor<int, 1> tensor3(5);
+  Tensor<int, 1> tensor4(7);
+
+  Tensor<int, 1> new_tensor(5);
+  new_tensor.setRandom();
+
+  tensor1 = tensor2 = tensor3 = tensor4 = new_tensor;
+
+  VERIFY_IS_EQUAL(tensor1.dimension(0), new_tensor.dimension(0));
+  VERIFY_IS_EQUAL(tensor2.dimension(0), new_tensor.dimension(0));
+  VERIFY_IS_EQUAL(tensor3.dimension(0), new_tensor.dimension(0));
+  VERIFY_IS_EQUAL(tensor4.dimension(0), new_tensor.dimension(0));
+  for (int i = 0; i < new_tensor.dimension(0); ++i) {
+    VERIFY_IS_EQUAL(tensor1(i), new_tensor(i));
+    VERIFY_IS_EQUAL(tensor2(i), new_tensor(i));
+    VERIFY_IS_EQUAL(tensor3(i), new_tensor(i));
+    VERIFY_IS_EQUAL(tensor4(i), new_tensor(i));
+  }
+}
+
+static void test_compound_assign() {
+  Tensor<int, 1> start_tensor(10);
+  Tensor<int, 1> offset_tensor(10);
+  start_tensor.setRandom();
+  offset_tensor.setRandom();
+
+  Tensor<int, 1> tensor = start_tensor;
+  tensor += offset_tensor;
+  for (int i = 0; i < 10; ++i) {
+    VERIFY_IS_EQUAL(tensor(i), start_tensor(i) + offset_tensor(i));
+  }
+
+  tensor = start_tensor;
+  tensor -= offset_tensor;
+  for (int i = 0; i < 10; ++i) {
+    VERIFY_IS_EQUAL(tensor(i), start_tensor(i) - offset_tensor(i));
+  }
+
+  tensor = start_tensor;
+  tensor *= offset_tensor;
+  for (int i = 0; i < 10; ++i) {
+    VERIFY_IS_EQUAL(tensor(i), start_tensor(i) * offset_tensor(i));
+  }
+
+  tensor = start_tensor;
+  tensor /= offset_tensor;
+  for (int i = 0; i < 10; ++i) {
+    VERIFY_IS_EQUAL(tensor(i), start_tensor(i) / offset_tensor(i));
+  }
+}
+
+static void test_std_initializers_tensor() {
+  Tensor<int, 1> a(3);
+  a.setValues({0, 1, 2});
+  VERIFY_IS_EQUAL(a(0), 0);
+  VERIFY_IS_EQUAL(a(1), 1);
+  VERIFY_IS_EQUAL(a(2), 2);
+
+  // It fills the top-left slice.
+  a.setValues({10, 20});
+  VERIFY_IS_EQUAL(a(0), 10);
+  VERIFY_IS_EQUAL(a(1), 20);
+  VERIFY_IS_EQUAL(a(2), 2);
+
+  // Chaining.
+  Tensor<int, 1> a2(3);
+  a2 = a.setValues({100, 200, 300});
+  VERIFY_IS_EQUAL(a(0), 100);
+  VERIFY_IS_EQUAL(a(1), 200);
+  VERIFY_IS_EQUAL(a(2), 300);
+  VERIFY_IS_EQUAL(a2(0), 100);
+  VERIFY_IS_EQUAL(a2(1), 200);
+  VERIFY_IS_EQUAL(a2(2), 300);
+
+  Tensor<int, 2> b(2, 3);
+  b.setValues({{0, 1, 2}, {3, 4, 5}});
+  VERIFY_IS_EQUAL(b(0, 0), 0);
+  VERIFY_IS_EQUAL(b(0, 1), 1);
+  VERIFY_IS_EQUAL(b(0, 2), 2);
+  VERIFY_IS_EQUAL(b(1, 0), 3);
+  VERIFY_IS_EQUAL(b(1, 1), 4);
+  VERIFY_IS_EQUAL(b(1, 2), 5);
+
+  // It fills the top-left slice.
+  b.setValues({{10, 20}, {30}});
+  VERIFY_IS_EQUAL(b(0, 0), 10);
+  VERIFY_IS_EQUAL(b(0, 1), 20);
+  VERIFY_IS_EQUAL(b(0, 2), 2);
+  VERIFY_IS_EQUAL(b(1, 0), 30);
+  VERIFY_IS_EQUAL(b(1, 1), 4);
+  VERIFY_IS_EQUAL(b(1, 2), 5);
+
+  Eigen::Tensor<int, 3> c(3, 2, 4);
+  c.setValues(
+      {{{0, 1, 2, 3}, {4, 5, 6, 7}}, {{10, 11, 12, 13}, {14, 15, 16, 17}}, {{20, 21, 22, 23}, {24, 25, 26, 27}}});
+  VERIFY_IS_EQUAL(c(0, 0, 0), 0);
+  VERIFY_IS_EQUAL(c(0, 0, 1), 1);
+  VERIFY_IS_EQUAL(c(0, 0, 2), 2);
+  VERIFY_IS_EQUAL(c(0, 0, 3), 3);
+  VERIFY_IS_EQUAL(c(0, 1, 0), 4);
+  VERIFY_IS_EQUAL(c(0, 1, 1), 5);
+  VERIFY_IS_EQUAL(c(0, 1, 2), 6);
+  VERIFY_IS_EQUAL(c(0, 1, 3), 7);
+  VERIFY_IS_EQUAL(c(1, 0, 0), 10);
+  VERIFY_IS_EQUAL(c(1, 0, 1), 11);
+  VERIFY_IS_EQUAL(c(1, 0, 2), 12);
+  VERIFY_IS_EQUAL(c(1, 0, 3), 13);
+  VERIFY_IS_EQUAL(c(1, 1, 0), 14);
+  VERIFY_IS_EQUAL(c(1, 1, 1), 15);
+  VERIFY_IS_EQUAL(c(1, 1, 2), 16);
+  VERIFY_IS_EQUAL(c(1, 1, 3), 17);
+  VERIFY_IS_EQUAL(c(2, 0, 0), 20);
+  VERIFY_IS_EQUAL(c(2, 0, 1), 21);
+  VERIFY_IS_EQUAL(c(2, 0, 2), 22);
+  VERIFY_IS_EQUAL(c(2, 0, 3), 23);
+  VERIFY_IS_EQUAL(c(2, 1, 0), 24);
+  VERIFY_IS_EQUAL(c(2, 1, 1), 25);
+  VERIFY_IS_EQUAL(c(2, 1, 2), 26);
+  VERIFY_IS_EQUAL(c(2, 1, 3), 27);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_assign) {
+  CALL_SUBTEST(test_1d());
+  CALL_SUBTEST(test_2d());
+  CALL_SUBTEST(test_3d());
+  CALL_SUBTEST(test_same_type());
+  CALL_SUBTEST(test_auto_resize());
+  CALL_SUBTEST(test_compound_assign());
+  CALL_SUBTEST(test_std_initializers_tensor());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_block_access.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_block_access.cpp
new file mode 100644
index 0000000..7c5203d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_block_access.cpp
@@ -0,0 +1,523 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Andy Davis <andydavis@google.com>
+// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <algorithm>
+#include <set>
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::ColMajor;
+using Eigen::Index;
+using Eigen::RowMajor;
+using Eigen::Tensor;
+using Eigen::internal::TensorBlockShapeType;
+
+static TensorOpCost zeroCost() { return {0, 0, 0}; }
+
+template <typename T>
+static const T& choose(int layout, const T& col, const T& row) {
+  return layout == ColMajor ? col : row;
+}
+
+static TensorBlockShapeType RandomShape() {
+  return internal::random<bool>() ? TensorBlockShapeType::kUniformAllDims : TensorBlockShapeType::kSkewedInnerDims;
+}
+
+template <int NumDims>
+static size_t RandomTargetSize(const DSizes<Index, NumDims>& dims) {
+  return internal::random<size_t>(1, dims.TotalSize());
+}
+
+template <int NumDims>
+static DSizes<Index, NumDims> RandomDims() {
+  array<Index, NumDims> dims;
+  for (int i = 0; i < NumDims; ++i) {
+    dims[i] = internal::random<int>(1, 20);
+  }
+  return DSizes<Index, NumDims>(dims);
+}
+
+template <typename T>
+static T* GenerateRandomData(const Index& size) {
+  T* data = new T[size];
+  for (int i = 0; i < size; ++i) {
+    data[i] = internal::random<T>();
+  }
+  return data;
+}
+
+template <int NumDims>
+static void Debug(DSizes<Index, NumDims> dims) {
+  for (int i = 0; i < NumDims; ++i) {
+    std::cout << dims[i] << "; ";
+  }
+  std::cout << std::endl;
+}
+
+template <int Layout>
+static void test_block_mapper_sanity() {
+  typedef internal::TensorBlockMapper<2, Layout> TensorBlockMapper;
+
+  DSizes<Index, 2> tensor_dims(100, 100);
+
+  // Test uniform blocks.
+  TensorBlockMapper uniform_block_mapper(tensor_dims, {TensorBlockShapeType::kUniformAllDims, 100, zeroCost()});
+
+  VERIFY_IS_EQUAL(uniform_block_mapper.blockCount(), 100);
+  VERIFY_IS_EQUAL(uniform_block_mapper.blockTotalSize(), 100);
+
+  // 10x10 blocks
+  auto uniform_b0 = uniform_block_mapper.blockDescriptor(0);
+  VERIFY_IS_EQUAL(uniform_b0.dimensions().at(0), 10);
+  VERIFY_IS_EQUAL(uniform_b0.dimensions().at(1), 10);
+
+  // Test skewed to inner dims blocks.
+  TensorBlockMapper skewed_block_mapper(tensor_dims, {TensorBlockShapeType::kSkewedInnerDims, 100, zeroCost()});
+
+  VERIFY_IS_EQUAL(skewed_block_mapper.blockCount(), 100);
+  VERIFY_IS_EQUAL(skewed_block_mapper.blockTotalSize(), 100);
+
+  // 1x100 (100x1) rows/cols depending on a tensor layout.
+  auto skewed_b0 = skewed_block_mapper.blockDescriptor(0);
+  VERIFY_IS_EQUAL(skewed_b0.dimensions().at(0), choose(Layout, 100, 1));
+  VERIFY_IS_EQUAL(skewed_b0.dimensions().at(1), choose(Layout, 1, 100));
+}
+
+// Given a TensorBlock "visit" every element accessible though it, and a keep an
+// index in the visited set. Verify that every coeff accessed only once.
+template <int NumDims, int Layout>
+static void UpdateCoeffSet(const DSizes<Index, NumDims>& tensor_strides,
+                           const internal::TensorBlockDescriptor<NumDims>& block, Index first_coeff_index,
+                           int dim_index, std::set<Index>* visited_coeffs) {
+  const DSizes<Index, NumDims>& block_sizes = block.dimensions();
+
+  for (int i = 0; i < block_sizes[dim_index]; ++i) {
+    if (tensor_strides[dim_index] == 1) {
+      typedef std::pair<std::set<Index>::iterator, bool> ReturnType;
+      ReturnType inserted = visited_coeffs->insert(first_coeff_index + i);
+      VERIFY_IS_EQUAL(inserted.second, true);
+    } else {
+      int next_dim_index = dim_index + choose(Layout, -1, 1);
+      UpdateCoeffSet<NumDims, Layout>(tensor_strides, block, first_coeff_index, next_dim_index, visited_coeffs);
+      first_coeff_index += tensor_strides[dim_index];
+    }
+  }
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_block_mapper_maps_every_element() {
+  typedef internal::TensorBlockMapper<NumDims, Layout> TensorBlockMapper;
+
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>();
+  DSizes<Index, NumDims> strides = internal::strides<Layout>(dims);
+
+  // Keep track of elements indices available via block access.
+  std::set<Index> coeff_set;
+
+  // Try different combinations of block types and sizes.
+  TensorBlockMapper block_mapper(dims, {RandomShape(), RandomTargetSize(dims), zeroCost()});
+
+  for (int i = 0; i < block_mapper.blockCount(); ++i) {
+    auto block = block_mapper.blockDescriptor(i);
+    UpdateCoeffSet<NumDims, Layout>(strides, block, block.offset(), choose(Layout, NumDims - 1, 0), &coeff_set);
+  }
+
+  // Verify that every coefficient in the original Tensor is accessible through
+  // TensorBlock only once.
+  Index total_coeffs = dims.TotalSize();
+  VERIFY_IS_EQUAL(Index(coeff_set.size()), total_coeffs);
+  VERIFY_IS_EQUAL(*coeff_set.begin(), 0);
+  VERIFY_IS_EQUAL(*coeff_set.rbegin(), total_coeffs - 1);
+}
+
+template <int Layout, int NumDims>
+static Index GetInputIndex(Index output_index, const array<Index, NumDims>& output_to_input_dim_map,
+                           const array<Index, NumDims>& input_strides, const array<Index, NumDims>& output_strides) {
+  int input_index = 0;
+  if (Layout == ColMajor) {
+    for (int i = NumDims - 1; i > 0; --i) {
+      const Index idx = output_index / output_strides[i];
+      input_index += idx * input_strides[output_to_input_dim_map[i]];
+      output_index -= idx * output_strides[i];
+    }
+    return input_index + output_index * input_strides[output_to_input_dim_map[0]];
+  } else {
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const Index idx = output_index / output_strides[i];
+      input_index += idx * input_strides[output_to_input_dim_map[i]];
+      output_index -= idx * output_strides[i];
+    }
+    return input_index + output_index * input_strides[output_to_input_dim_map[NumDims - 1]];
+  }
+}
+
+template <int Layout, int NumDims>
+static array<Index, NumDims> ComputeStrides(const array<Index, NumDims>& sizes) {
+  array<Index, NumDims> strides;
+  if (Layout == ColMajor) {
+    strides[0] = 1;
+    for (int i = 1; i < NumDims; ++i) {
+      strides[i] = strides[i - 1] * sizes[i - 1];
+    }
+  } else {
+    strides[NumDims - 1] = 1;
+    for (int i = NumDims - 2; i >= 0; --i) {
+      strides[i] = strides[i + 1] * sizes[i + 1];
+    }
+  }
+  return strides;
+}
+
+template <typename Scalar, typename StorageIndex, int Dim>
+class EqualityChecker {
+  const Scalar* input_data;
+  const DSizes<StorageIndex, Dim>&input_dims, &input_strides, &output_dims, &output_strides;
+  void check_recursive(const Scalar* input, const Scalar* output, int depth = 0) const {
+    if (depth == Dim) {
+      VERIFY_IS_EQUAL(*input, *output);
+      return;
+    }
+
+    for (int i = 0; i < output_dims[depth]; ++i) {
+      check_recursive(input + i % input_dims[depth] * input_strides[depth], output + i * output_strides[depth],
+                      depth + 1);
+    }
+  }
+
+ public:
+  EqualityChecker(const Scalar* input_data_, const DSizes<StorageIndex, Dim>& input_dims_,
+                  const DSizes<StorageIndex, Dim>& input_strides_, const DSizes<StorageIndex, Dim>& output_dims_,
+                  const DSizes<StorageIndex, Dim>& output_strides_)
+      : input_data(input_data_),
+        input_dims(input_dims_),
+        input_strides(input_strides_),
+        output_dims(output_dims_),
+        output_strides(output_strides_) {}
+
+  void operator()(const Scalar* output_data) const { check_recursive(input_data, output_data); }
+};
+
+template <int Layout>
+static void test_uniform_block_shape() {
+  typedef internal::TensorBlockDescriptor<5> TensorBlock;
+  typedef internal::TensorBlockMapper<5, Layout> TensorBlockMapper;
+
+  {
+    // Test shape 'UniformAllDims' with uniform 'max_coeff count'.
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 5 * 5 * 5 * 5 * 5;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    for (int i = 0; i < 5; ++i) {
+      VERIFY_IS_EQUAL(5, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  }
+
+  // Test shape 'UniformAllDims' with larger 'max_coeff count' which spills
+  // partially into first inner-most dimension.
+  if (Layout == ColMajor) {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 7 * 5 * 5 * 5 * 5;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(7, block.dimensions()[0]);
+    for (int i = 1; i < 5; ++i) {
+      VERIFY_IS_EQUAL(5, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  } else {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 5 * 5 * 5 * 5 * 6;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(6, block.dimensions()[4]);
+    for (int i = 3; i >= 0; --i) {
+      VERIFY_IS_EQUAL(5, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  }
+
+  // Test shape 'UniformAllDims' with larger 'max_coeff count' which spills
+  // fully into first inner-most dimension.
+  if (Layout == ColMajor) {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 11 * 5 * 5 * 5 * 5;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(11, block.dimensions()[0]);
+    for (int i = 1; i < 5; ++i) {
+      VERIFY_IS_EQUAL(5, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  } else {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 5 * 5 * 5 * 5 * 7;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(7, block.dimensions()[4]);
+    for (int i = 3; i >= 0; --i) {
+      VERIFY_IS_EQUAL(5, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  }
+
+  // Test shape 'UniformAllDims' with larger 'max_coeff count' which spills
+  // fully into first few inner-most dimensions.
+  if (Layout == ColMajor) {
+    DSizes<Index, 5> dims(7, 5, 6, 17, 7);
+    const Index max_coeff_count = 7 * 5 * 6 * 7 * 5;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(7, block.dimensions()[0]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[1]);
+    VERIFY_IS_EQUAL(6, block.dimensions()[2]);
+    VERIFY_IS_EQUAL(7, block.dimensions()[3]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[4]);
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  } else {
+    DSizes<Index, 5> dims(7, 5, 6, 9, 7);
+    const Index max_coeff_count = 5 * 5 * 5 * 6 * 7;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(7, block.dimensions()[4]);
+    VERIFY_IS_EQUAL(6, block.dimensions()[3]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[2]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[1]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[0]);
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  }
+
+  // Test shape 'UniformAllDims' with full allocation to all dims.
+  if (Layout == ColMajor) {
+    DSizes<Index, 5> dims(7, 5, 6, 17, 7);
+    const Index max_coeff_count = 7 * 5 * 6 * 17 * 7;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(7, block.dimensions()[0]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[1]);
+    VERIFY_IS_EQUAL(6, block.dimensions()[2]);
+    VERIFY_IS_EQUAL(17, block.dimensions()[3]);
+    VERIFY_IS_EQUAL(7, block.dimensions()[4]);
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  } else {
+    DSizes<Index, 5> dims(7, 5, 6, 9, 7);
+    const Index max_coeff_count = 7 * 5 * 6 * 9 * 7;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(7, block.dimensions()[4]);
+    VERIFY_IS_EQUAL(9, block.dimensions()[3]);
+    VERIFY_IS_EQUAL(6, block.dimensions()[2]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[1]);
+    VERIFY_IS_EQUAL(7, block.dimensions()[0]);
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  }
+}
+
+template <int Layout>
+static void test_skewed_inner_dim_block_shape() {
+  typedef internal::TensorBlockDescriptor<5> TensorBlock;
+  typedef internal::TensorBlockMapper<5, Layout> TensorBlockMapper;
+
+  // Test shape 'SkewedInnerDims' with partial allocation to inner-most dim.
+  if (Layout == ColMajor) {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 10 * 1 * 1 * 1 * 1;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(10, block.dimensions()[0]);
+    for (int i = 1; i < 5; ++i) {
+      VERIFY_IS_EQUAL(1, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  } else {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 1 * 1 * 1 * 1 * 6;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(6, block.dimensions()[4]);
+    for (int i = 3; i >= 0; --i) {
+      VERIFY_IS_EQUAL(1, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  }
+
+  // Test shape 'SkewedInnerDims' with full allocation to inner-most dim.
+  if (Layout == ColMajor) {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 11 * 1 * 1 * 1 * 1;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(11, block.dimensions()[0]);
+    for (int i = 1; i < 5; ++i) {
+      VERIFY_IS_EQUAL(1, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  } else {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 1 * 1 * 1 * 1 * 7;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(7, block.dimensions()[4]);
+    for (int i = 3; i >= 0; --i) {
+      VERIFY_IS_EQUAL(1, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  }
+
+  // Test shape 'SkewedInnerDims' with full allocation to inner-most dim,
+  // and partial allocation to second inner-dim.
+  if (Layout == ColMajor) {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 11 * 3 * 1 * 1 * 1;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(11, block.dimensions()[0]);
+    VERIFY_IS_EQUAL(3, block.dimensions()[1]);
+    for (int i = 2; i < 5; ++i) {
+      VERIFY_IS_EQUAL(1, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  } else {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 1 * 1 * 1 * 15 * 7;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(7, block.dimensions()[4]);
+    VERIFY_IS_EQUAL(15, block.dimensions()[3]);
+    for (int i = 2; i >= 0; --i) {
+      VERIFY_IS_EQUAL(1, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  }
+
+  // Test shape 'SkewedInnerDims' with full allocation to inner-most dim,
+  // and partial allocation to third inner-dim.
+  if (Layout == ColMajor) {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 11 * 5 * 5 * 1 * 1;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(11, block.dimensions()[0]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[1]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[2]);
+    for (int i = 3; i < 5; ++i) {
+      VERIFY_IS_EQUAL(1, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  } else {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 1 * 1 * 5 * 17 * 7;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(7, block.dimensions()[4]);
+    VERIFY_IS_EQUAL(17, block.dimensions()[3]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[2]);
+    for (int i = 1; i >= 0; --i) {
+      VERIFY_IS_EQUAL(1, block.dimensions()[i]);
+    }
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  }
+
+  // Test shape 'SkewedInnerDims' with full allocation to all dims.
+  if (Layout == ColMajor) {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 11 * 5 * 6 * 17 * 7;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(11, block.dimensions()[0]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[1]);
+    VERIFY_IS_EQUAL(6, block.dimensions()[2]);
+    VERIFY_IS_EQUAL(17, block.dimensions()[3]);
+    VERIFY_IS_EQUAL(7, block.dimensions()[4]);
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  } else {
+    DSizes<Index, 5> dims(11, 5, 6, 17, 7);
+    const Index max_coeff_count = 11 * 5 * 6 * 17 * 7;
+    TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
+    TensorBlock block = block_mapper.blockDescriptor(0);
+    VERIFY_IS_EQUAL(7, block.dimensions()[4]);
+    VERIFY_IS_EQUAL(17, block.dimensions()[3]);
+    VERIFY_IS_EQUAL(6, block.dimensions()[2]);
+    VERIFY_IS_EQUAL(5, block.dimensions()[1]);
+    VERIFY_IS_EQUAL(11, block.dimensions()[0]);
+    VERIFY(block.dimensions().TotalSize() <= max_coeff_count);
+  }
+}
+
+template <int Layout>
+static void test_empty_dims(const internal::TensorBlockShapeType block_shape) {
+  // Test blocking of tensors with zero dimensions:
+  //  - we must not crash on asserts and divisions by zero
+  //  - we must not return block with zero dimensions
+  //    (recipe for overflows/underflows, divisions by zero and NaNs later)
+  //  - total block count must be zero
+  {
+    typedef internal::TensorBlockMapper<1, Layout> TensorBlockMapper;
+
+    DSizes<Index, 1> dims(0);
+    for (size_t max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) {
+      TensorBlockMapper block_mapper(dims, {block_shape, max_coeff_count, zeroCost()});
+      VERIFY_IS_EQUAL(block_mapper.blockCount(), 0);
+      VERIFY(block_mapper.blockTotalSize() >= 1);
+    }
+  }
+
+  {
+    typedef internal::TensorBlockMapper<2, Layout> TensorBlockMapper;
+
+    for (int dim1 = 0; dim1 < 3; ++dim1) {
+      for (int dim2 = 0; dim2 < 3; ++dim2) {
+        DSizes<Index, 2> dims(dim1, dim2);
+        for (size_t max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) {
+          TensorBlockMapper block_mapper(dims, {block_shape, max_coeff_count, zeroCost()});
+          if (dim1 * dim2 == 0) {
+            VERIFY_IS_EQUAL(block_mapper.blockCount(), 0);
+          }
+          VERIFY(block_mapper.blockTotalSize() >= 1);
+        }
+      }
+    }
+  }
+}
+
+#define TEST_LAYOUTS(NAME)        \
+  CALL_SUBTEST(NAME<ColMajor>()); \
+  CALL_SUBTEST(NAME<RowMajor>())
+
+#define TEST_LAYOUTS_AND_DIMS(TYPE, NAME)    \
+  CALL_SUBTEST((NAME<TYPE, 1, ColMajor>())); \
+  CALL_SUBTEST((NAME<TYPE, 1, RowMajor>())); \
+  CALL_SUBTEST((NAME<TYPE, 2, ColMajor>())); \
+  CALL_SUBTEST((NAME<TYPE, 2, RowMajor>())); \
+  CALL_SUBTEST((NAME<TYPE, 3, ColMajor>())); \
+  CALL_SUBTEST((NAME<TYPE, 3, RowMajor>())); \
+  CALL_SUBTEST((NAME<TYPE, 4, ColMajor>())); \
+  CALL_SUBTEST((NAME<TYPE, 4, RowMajor>())); \
+  CALL_SUBTEST((NAME<TYPE, 5, ColMajor>())); \
+  CALL_SUBTEST((NAME<TYPE, 5, RowMajor>()))
+
+#define TEST_LAYOUTS_WITH_ARG(NAME, ARG) \
+  CALL_SUBTEST(NAME<ColMajor>(ARG));     \
+  CALL_SUBTEST(NAME<RowMajor>(ARG))
+
+EIGEN_DECLARE_TEST(cxx11_tensor_block_access) {
+  TEST_LAYOUTS(test_block_mapper_sanity);
+  TEST_LAYOUTS_AND_DIMS(float, test_block_mapper_maps_every_element);
+  TEST_LAYOUTS(test_uniform_block_shape);
+  TEST_LAYOUTS(test_skewed_inner_dim_block_shape);
+  TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kUniformAllDims);
+  TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kSkewedInnerDims);
+}
+
+#undef TEST_LAYOUTS
+#undef TEST_LAYOUTS_WITH_ARG
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_block_eval.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_block_eval.cpp
new file mode 100644
index 0000000..75e2b8c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_block_eval.cpp
@@ -0,0 +1,797 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// clang-format off
+#include "main.h"
+#include <Eigen/CXX11/Tensor>
+// clang-format on
+
+using Eigen::internal::TensorBlockDescriptor;
+using Eigen::internal::TensorExecutor;
+
+// -------------------------------------------------------------------------- //
+// Utility functions to generate random tensors, blocks, and evaluate them.
+
+template <int NumDims>
+static DSizes<Index, NumDims> RandomDims(Index min, Index max) {
+  DSizes<Index, NumDims> dims;
+  for (int i = 0; i < NumDims; ++i) {
+    dims[i] = internal::random<Index>(min, max);
+  }
+  return DSizes<Index, NumDims>(dims);
+}
+
+// Block offsets and extents allows to construct a TensorSlicingOp corresponding
+// to a TensorBlockDescriptor.
+template <int NumDims>
+struct TensorBlockParams {
+  DSizes<Index, NumDims> offsets;
+  DSizes<Index, NumDims> sizes;
+  TensorBlockDescriptor<NumDims, Index> desc;
+};
+
+template <int Layout, int NumDims>
+static TensorBlockParams<NumDims> RandomBlock(DSizes<Index, NumDims> dims, Index min, Index max) {
+  // Choose random offsets and sizes along all tensor dimensions.
+  DSizes<Index, NumDims> offsets(RandomDims<NumDims>(min, max));
+  DSizes<Index, NumDims> sizes(RandomDims<NumDims>(min, max));
+
+  // Make sure that offset + size do not overflow dims.
+  for (int i = 0; i < NumDims; ++i) {
+    offsets[i] = numext::mini(dims[i] - 1, offsets[i]);
+    sizes[i] = numext::mini(sizes[i], dims[i] - offsets[i]);
+  }
+
+  Index offset = 0;
+  DSizes<Index, NumDims> strides = Eigen::internal::strides<Layout>(dims);
+  for (int i = 0; i < NumDims; ++i) {
+    offset += strides[i] * offsets[i];
+  }
+
+  return {offsets, sizes, TensorBlockDescriptor<NumDims, Index>(offset, sizes)};
+}
+
+// Generate block with block sizes skewed towards inner dimensions. This type of
+// block is required for evaluating broadcast expressions.
+template <int Layout, int NumDims>
+static TensorBlockParams<NumDims> SkewedInnerBlock(DSizes<Index, NumDims> dims) {
+  using BlockMapper = internal::TensorBlockMapper<NumDims, Layout, Index>;
+  BlockMapper block_mapper(
+      dims,
+      {internal::TensorBlockShapeType::kSkewedInnerDims, internal::random<size_t>(1, dims.TotalSize()), {0, 0, 0}});
+
+  Index total_blocks = block_mapper.blockCount();
+  Index block_index = internal::random<Index>(0, total_blocks - 1);
+  auto block = block_mapper.blockDescriptor(block_index);
+  DSizes<Index, NumDims> sizes = block.dimensions();
+
+  auto strides = internal::strides<Layout>(dims);
+  DSizes<Index, NumDims> offsets;
+
+  // Compute offsets for the first block coefficient.
+  Index index = block.offset();
+  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+    for (int i = NumDims - 1; i > 0; --i) {
+      const Index idx = index / strides[i];
+      index -= idx * strides[i];
+      offsets[i] = idx;
+    }
+    if (NumDims > 0) offsets[0] = index;
+  } else {
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const Index idx = index / strides[i];
+      index -= idx * strides[i];
+      offsets[i] = idx;
+    }
+    if (NumDims > 0) offsets[NumDims - 1] = index;
+  }
+
+  return {offsets, sizes, block};
+}
+
+template <int NumDims>
+static TensorBlockParams<NumDims> FixedSizeBlock(DSizes<Index, NumDims> dims) {
+  DSizes<Index, NumDims> offsets;
+  for (int i = 0; i < NumDims; ++i) offsets[i] = 0;
+
+  return {offsets, dims, TensorBlockDescriptor<NumDims, Index>(0, dims)};
+}
+
+inline Eigen::IndexList<Index, Eigen::type2index<1>> NByOne(Index n) {
+  Eigen::IndexList<Index, Eigen::type2index<1>> ret;
+  ret.set(0, n);
+  return ret;
+}
+inline Eigen::IndexList<Eigen::type2index<1>, Index> OneByM(Index m) {
+  Eigen::IndexList<Eigen::type2index<1>, Index> ret;
+  ret.set(1, m);
+  return ret;
+}
+
+// -------------------------------------------------------------------------- //
+// Verify that block expression evaluation produces the same result as a
+// TensorSliceOp (reading a tensor block is same to taking a tensor slice).
+
+template <typename T, int NumDims, int Layout, typename Expression, typename GenBlockParams>
+static void VerifyBlockEvaluator(Expression expr, GenBlockParams gen_block) {
+  using Device = DefaultDevice;
+  auto d = Device();
+
+  // Scratch memory allocator for block evaluation.
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+  TensorBlockScratch scratch(d);
+
+  // TensorEvaluator is needed to produce tensor blocks of the expression.
+  auto eval = TensorEvaluator<const decltype(expr), Device>(expr, d);
+  eval.evalSubExprsIfNeeded(nullptr);
+
+  // Choose a random offsets, sizes and TensorBlockDescriptor.
+  TensorBlockParams<NumDims> block_params = gen_block();
+
+  // Evaluate TensorBlock expression into a tensor.
+  Tensor<T, NumDims, Layout> block(block_params.desc.dimensions());
+
+  // Dimensions for the potential destination buffer.
+  DSizes<Index, NumDims> dst_dims;
+  if (internal::random<bool>()) {
+    dst_dims = block_params.desc.dimensions();
+  } else {
+    for (int i = 0; i < NumDims; ++i) {
+      Index extent = internal::random<Index>(0, 5);
+      dst_dims[i] = block_params.desc.dimension(i) + extent;
+    }
+  }
+
+  // Maybe use this tensor as a block desc destination.
+  Tensor<T, NumDims, Layout> dst(dst_dims);
+  dst.setZero();
+  if (internal::random<bool>()) {
+    block_params.desc.template AddDestinationBuffer<Layout>(dst.data(), internal::strides<Layout>(dst.dimensions()));
+  }
+
+  const bool root_of_expr = internal::random<bool>();
+  auto tensor_block = eval.block(block_params.desc, scratch, root_of_expr);
+
+  if (tensor_block.kind() == internal::TensorBlockKind::kMaterializedInOutput) {
+    // Copy data from destination buffer.
+    if (dimensions_match(dst.dimensions(), block.dimensions())) {
+      block = dst;
+    } else {
+      DSizes<Index, NumDims> offsets;
+      for (int i = 0; i < NumDims; ++i) offsets[i] = 0;
+      block = dst.slice(offsets, block.dimensions());
+    }
+
+  } else {
+    // Assign to block from expression.
+    auto b_expr = tensor_block.expr();
+
+    // We explicitly disable vectorization and tiling, to run a simple coefficient
+    // wise assignment loop, because it's very simple and should be correct.
+    using BlockAssign = TensorAssignOp<decltype(block), const decltype(b_expr)>;
+    using BlockExecutor = TensorExecutor<const BlockAssign, Device, false, internal::TiledEvaluation::Off>;
+    BlockExecutor::run(BlockAssign(block, b_expr), d);
+  }
+
+  // Cleanup temporary buffers owned by a tensor block.
+  tensor_block.cleanup();
+
+  // Compute a Tensor slice corresponding to a Tensor block.
+  Tensor<T, NumDims, Layout> slice(block_params.desc.dimensions());
+  auto s_expr = expr.slice(block_params.offsets, block_params.sizes);
+
+  // Explicitly use coefficient assignment to evaluate slice expression.
+  using SliceAssign = TensorAssignOp<decltype(slice), const decltype(s_expr)>;
+  using SliceExecutor = TensorExecutor<const SliceAssign, Device, false, internal::TiledEvaluation::Off>;
+  SliceExecutor::run(SliceAssign(slice, s_expr), d);
+
+  // Tensor block and tensor slice must be the same.
+  for (Index i = 0; i < block.dimensions().TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(block.coeff(i), slice.coeff(i));
+  }
+}
+
+// -------------------------------------------------------------------------- //
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_block() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  // Identity tensor expression transformation.
+  VerifyBlockEvaluator<T, NumDims, Layout>(input, [&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_unary_expr_block() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.abs(), [&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_binary_expr_block() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> lhs(dims), rhs(dims);
+  lhs.setRandom();
+  rhs.setRandom();
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(lhs * rhs, [&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_binary_with_unary_expr_block() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> lhs(dims), rhs(dims);
+  lhs.setRandom();
+  rhs.setRandom();
+
+  VerifyBlockEvaluator<T, NumDims, Layout>((lhs.abs() + rhs.abs()).sqrt(),
+                                           [&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_broadcast() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 10);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  DSizes<Index, NumDims> bcast = RandomDims<NumDims>(1, 5);
+
+  DSizes<Index, NumDims> bcasted_dims;
+  for (int i = 0; i < NumDims; ++i) bcasted_dims[i] = dims[i] * bcast[i];
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.broadcast(bcast),
+                                           [&bcasted_dims]() { return SkewedInnerBlock<Layout>(bcasted_dims); });
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.broadcast(bcast),
+                                           [&bcasted_dims]() { return RandomBlock<Layout>(bcasted_dims, 5, 10); });
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.broadcast(bcast),
+                                           [&bcasted_dims]() { return FixedSizeBlock(bcasted_dims); });
+
+  // Check that desc.destination() memory is not shared between two broadcast
+  // materializations.
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.broadcast(bcast) * input.abs().broadcast(bcast),
+                                           [&bcasted_dims]() { return SkewedInnerBlock<Layout>(bcasted_dims); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_reshape() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 10);
+
+  DSizes<Index, NumDims> shuffled = dims;
+  std::shuffle(&shuffled[0], &shuffled[NumDims - 1], std::mt19937(g_seed));
+
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.reshape(shuffled),
+                                           [&shuffled]() { return RandomBlock<Layout>(shuffled, 1, 10); });
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.reshape(shuffled),
+                                           [&shuffled]() { return SkewedInnerBlock<Layout>(shuffled); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_cast() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.template cast<int>().template cast<T>(),
+                                           [&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_select() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> lhs(dims);
+  Tensor<T, NumDims, Layout> rhs(dims);
+  Tensor<bool, NumDims, Layout> cond(dims);
+  lhs.setRandom();
+  rhs.setRandom();
+  cond.setRandom();
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(cond.select(lhs, rhs),
+                                           [&dims]() { return RandomBlock<Layout>(dims, 1, 20); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_padding() {
+  const int inner_dim = Layout == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  DSizes<Index, NumDims> pad_before = RandomDims<NumDims>(0, 4);
+  DSizes<Index, NumDims> pad_after = RandomDims<NumDims>(0, 4);
+  array<std::pair<Index, Index>, NumDims> paddings;
+  for (int i = 0; i < NumDims; ++i) {
+    paddings[i] = std::make_pair(pad_before[i], pad_after[i]);
+  }
+
+  // Test squeezing reads from inner dim.
+  if (internal::random<bool>()) {
+    pad_before[inner_dim] = 0;
+    pad_after[inner_dim] = 0;
+    paddings[inner_dim] = std::make_pair(0, 0);
+  }
+
+  DSizes<Index, NumDims> padded_dims;
+  for (int i = 0; i < NumDims; ++i) {
+    padded_dims[i] = dims[i] + pad_before[i] + pad_after[i];
+  }
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.pad(paddings),
+                                           [&padded_dims]() { return FixedSizeBlock(padded_dims); });
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.pad(paddings),
+                                           [&padded_dims]() { return RandomBlock<Layout>(padded_dims, 1, 10); });
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.pad(paddings),
+                                           [&padded_dims]() { return SkewedInnerBlock<Layout>(padded_dims); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_chipping() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  Index chip_dim = internal::random<int>(0, NumDims - 1);
+  Index chip_offset = internal::random<Index>(0, dims[chip_dim] - 2);
+
+  DSizes<Index, NumDims - 1> chipped_dims;
+  for (Index i = 0; i < chip_dim; ++i) {
+    chipped_dims[i] = dims[i];
+  }
+  for (Index i = chip_dim + 1; i < NumDims; ++i) {
+    chipped_dims[i - 1] = dims[i];
+  }
+
+  // Block buffer forwarding.
+  VerifyBlockEvaluator<T, NumDims - 1, Layout>(input.chip(chip_offset, chip_dim),
+                                               [&chipped_dims]() { return FixedSizeBlock(chipped_dims); });
+
+  VerifyBlockEvaluator<T, NumDims - 1, Layout>(input.chip(chip_offset, chip_dim),
+                                               [&chipped_dims]() { return RandomBlock<Layout>(chipped_dims, 1, 10); });
+
+  // Block expression assignment.
+  VerifyBlockEvaluator<T, NumDims - 1, Layout>(input.abs().chip(chip_offset, chip_dim),
+                                               [&chipped_dims]() { return FixedSizeBlock(chipped_dims); });
+
+  VerifyBlockEvaluator<T, NumDims - 1, Layout>(input.abs().chip(chip_offset, chip_dim),
+                                               [&chipped_dims]() { return RandomBlock<Layout>(chipped_dims, 1, 10); });
+}
+
+template <typename T, int NumDims>
+struct SimpleTensorGenerator {
+  T operator()(const array<Index, NumDims>& coords) const {
+    T result = static_cast<T>(0);
+    for (int i = 0; i < NumDims; ++i) {
+      result += static_cast<T>((i + 1) * coords[i]);
+    }
+    return result;
+  }
+};
+
+// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
+template <int NumDims>
+struct SimpleTensorGenerator<bool, NumDims> {
+  bool operator()(const array<Index, NumDims>& coords) const {
+    bool result = false;
+    for (int i = 0; i < NumDims; ++i) {
+      result ^= coords[i];
+    }
+    return result;
+  }
+};
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_generator() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  auto generator = SimpleTensorGenerator<T, NumDims>();
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.generate(generator), [&dims]() { return FixedSizeBlock(dims); });
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.generate(generator),
+                                           [&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_reverse() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  // Randomly reverse dimensions.
+  Eigen::DSizes<bool, NumDims> reverse;
+  for (int i = 0; i < NumDims; ++i) reverse[i] = internal::random<bool>();
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.reverse(reverse), [&dims]() { return FixedSizeBlock(dims); });
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.reverse(reverse),
+                                           [&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_slice() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  // Pick a random slice of an input tensor.
+  DSizes<Index, NumDims> slice_start = RandomDims<NumDims>(5, 10);
+  DSizes<Index, NumDims> slice_size = RandomDims<NumDims>(5, 10);
+
+  // Make sure that slice start + size do not overflow tensor dims.
+  for (int i = 0; i < NumDims; ++i) {
+    slice_start[i] = numext::mini(dims[i] - 1, slice_start[i]);
+    slice_size[i] = numext::mini(slice_size[i], dims[i] - slice_start[i]);
+  }
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.slice(slice_start, slice_size),
+                                           [&slice_size]() { return FixedSizeBlock(slice_size); });
+
+  VerifyBlockEvaluator<T, NumDims, Layout>(input.slice(slice_start, slice_size),
+                                           [&slice_size]() { return RandomBlock<Layout>(slice_size, 1, 10); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_eval_tensor_shuffle() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(5, 15);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  DSizes<Index, NumDims> shuffle;
+  for (int i = 0; i < NumDims; ++i) shuffle[i] = i;
+
+  do {
+    DSizes<Index, NumDims> shuffled_dims;
+    for (int i = 0; i < NumDims; ++i) shuffled_dims[i] = dims[shuffle[i]];
+
+    VerifyBlockEvaluator<T, NumDims, Layout>(input.shuffle(shuffle),
+                                             [&shuffled_dims]() { return FixedSizeBlock(shuffled_dims); });
+
+    VerifyBlockEvaluator<T, NumDims, Layout>(input.shuffle(shuffle),
+                                             [&shuffled_dims]() { return RandomBlock<Layout>(shuffled_dims, 1, 5); });
+
+    break;
+
+  } while (std::next_permutation(&shuffle[0], &shuffle[0] + NumDims));
+}
+
+template <typename T, int Layout>
+static void test_eval_tensor_reshape_with_bcast() {
+  Index dim = internal::random<Index>(1, 100);
+
+  Tensor<T, 2, Layout> lhs(1, dim);
+  Tensor<T, 2, Layout> rhs(dim, 1);
+  lhs.setRandom();
+  rhs.setRandom();
+
+  auto reshapeLhs = NByOne(dim);
+  auto reshapeRhs = OneByM(dim);
+
+  auto bcastLhs = OneByM(dim);
+  auto bcastRhs = NByOne(dim);
+
+  DSizes<Index, 2> dims(dim, dim);
+
+  VerifyBlockEvaluator<T, 2, Layout>(
+      lhs.reshape(reshapeLhs).broadcast(bcastLhs) * rhs.reshape(reshapeRhs).broadcast(bcastRhs),
+      [dims]() { return SkewedInnerBlock<Layout, 2>(dims); });
+}
+
+template <typename T, int Layout>
+static void test_eval_tensor_forced_eval() {
+  Index dim = internal::random<Index>(1, 100);
+
+  Tensor<T, 2, Layout> lhs(dim, 1);
+  Tensor<T, 2, Layout> rhs(1, dim);
+  lhs.setRandom();
+  rhs.setRandom();
+
+  auto bcastLhs = OneByM(dim);
+  auto bcastRhs = NByOne(dim);
+
+  DSizes<Index, 2> dims(dim, dim);
+
+  VerifyBlockEvaluator<T, 2, Layout>((lhs.broadcast(bcastLhs) * rhs.broadcast(bcastRhs)).eval().reshape(dims),
+                                     [dims]() { return SkewedInnerBlock<Layout, 2>(dims); });
+
+  VerifyBlockEvaluator<T, 2, Layout>((lhs.broadcast(bcastLhs) * rhs.broadcast(bcastRhs)).eval().reshape(dims),
+                                     [dims]() { return RandomBlock<Layout, 2>(dims, 1, 50); });
+}
+
+template <typename T, int Layout>
+static void test_eval_tensor_chipping_of_bcast() {
+  if (Layout != static_cast<int>(RowMajor)) return;
+
+  Index dim0 = internal::random<Index>(1, 10);
+  Index dim1 = internal::random<Index>(1, 10);
+  Index dim2 = internal::random<Index>(1, 10);
+
+  Tensor<T, 3, Layout> input(1, dim1, dim2);
+  input.setRandom();
+
+  Eigen::array<Index, 3> bcast = {{dim0, 1, 1}};
+  DSizes<Index, 2> chipped_dims(dim0, dim2);
+
+  VerifyBlockEvaluator<T, 2, Layout>(input.broadcast(bcast).chip(0, 1),
+                                     [chipped_dims]() { return FixedSizeBlock(chipped_dims); });
+
+  VerifyBlockEvaluator<T, 2, Layout>(input.broadcast(bcast).chip(0, 1),
+                                     [chipped_dims]() { return SkewedInnerBlock<Layout, 2>(chipped_dims); });
+
+  VerifyBlockEvaluator<T, 2, Layout>(input.broadcast(bcast).chip(0, 1),
+                                     [chipped_dims]() { return RandomBlock<Layout, 2>(chipped_dims, 1, 5); });
+}
+
+// -------------------------------------------------------------------------- //
+// Verify that assigning block to a Tensor expression produces the same result
+// as an assignment to TensorSliceOp (writing a block is is identical to
+// assigning one tensor to a slice of another tensor).
+
+template <typename T, int NumDims, int Layout, int NumExprDims = NumDims, typename Expression, typename GenBlockParams>
+static void VerifyBlockAssignment(Tensor<T, NumDims, Layout>& tensor, Expression expr, GenBlockParams gen_block) {
+  using Device = DefaultDevice;
+  auto d = Device();
+
+  // We use tensor evaluator as a target for block and slice assignments.
+  auto eval = TensorEvaluator<decltype(expr), Device>(expr, d);
+
+  // Generate a random block, or choose a block that fits in full expression.
+  TensorBlockParams<NumExprDims> block_params = gen_block();
+
+  // Generate random data of the selected block size.
+  Tensor<T, NumExprDims, Layout> block(block_params.desc.dimensions());
+  block.setRandom();
+
+  // ************************************************************************ //
+  // (1) Assignment from a block.
+
+  // Construct a materialize block from a random generated block tensor.
+  internal::TensorMaterializedBlock<T, NumExprDims, Layout> blk(internal::TensorBlockKind::kView, block.data(),
+                                                                block.dimensions());
+
+  // Reset all underlying tensor values to zero.
+  tensor.setZero();
+
+  // Use evaluator to write block into a tensor.
+  eval.writeBlock(block_params.desc, blk);
+
+  // Make a copy of the result after assignment.
+  Tensor<T, NumDims, Layout> block_assigned = tensor;
+
+  // ************************************************************************ //
+  // (2) Assignment to a slice
+
+  // Reset all underlying tensor values to zero.
+  tensor.setZero();
+
+  // Assign block to a slice of original expression
+  auto s_expr = expr.slice(block_params.offsets, block_params.sizes);
+
+  // Explicitly use coefficient assignment to evaluate slice expression.
+  using SliceAssign = TensorAssignOp<decltype(s_expr), const decltype(block)>;
+  using SliceExecutor = TensorExecutor<const SliceAssign, Device, false, internal::TiledEvaluation::Off>;
+  SliceExecutor::run(SliceAssign(s_expr, block), d);
+
+  // Make a copy of the result after assignment.
+  Tensor<T, NumDims, Layout> slice_assigned = tensor;
+
+  for (Index i = 0; i < tensor.dimensions().TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(block_assigned.coeff(i), slice_assigned.coeff(i));
+  }
+}
+
+// -------------------------------------------------------------------------- //
+
+template <typename T, int NumDims, int Layout>
+static void test_assign_to_tensor() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> tensor(dims);
+
+  TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims);
+
+  VerifyBlockAssignment<T, NumDims, Layout>(tensor, map, [&dims]() { return RandomBlock<Layout>(dims, 10, 20); });
+  VerifyBlockAssignment<T, NumDims, Layout>(tensor, map, [&dims]() { return FixedSizeBlock(dims); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_assign_to_tensor_reshape() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> tensor(dims);
+
+  TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims);
+
+  DSizes<Index, NumDims> shuffled = dims;
+  std::shuffle(&shuffled[0], &shuffled[NumDims - 1], std::mt19937(g_seed));
+
+  VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.reshape(shuffled),
+                                            [&shuffled]() { return RandomBlock<Layout>(shuffled, 1, 10); });
+
+  VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.reshape(shuffled),
+                                            [&shuffled]() { return SkewedInnerBlock<Layout>(shuffled); });
+
+  VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.reshape(shuffled),
+                                            [&shuffled]() { return FixedSizeBlock(shuffled); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_assign_to_tensor_chipping() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> tensor(dims);
+
+  Index chip_dim = internal::random<int>(0, NumDims - 1);
+  Index chip_offset = internal::random<Index>(0, dims[chip_dim] - 2);
+
+  DSizes<Index, NumDims - 1> chipped_dims;
+  for (Index i = 0; i < chip_dim; ++i) {
+    chipped_dims[i] = dims[i];
+  }
+  for (Index i = chip_dim + 1; i < NumDims; ++i) {
+    chipped_dims[i - 1] = dims[i];
+  }
+
+  TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims);
+
+  VerifyBlockAssignment<T, NumDims, Layout, NumDims - 1>(
+      tensor, map.chip(chip_offset, chip_dim), [&chipped_dims]() { return RandomBlock<Layout>(chipped_dims, 1, 10); });
+
+  VerifyBlockAssignment<T, NumDims, Layout, NumDims - 1>(
+      tensor, map.chip(chip_offset, chip_dim), [&chipped_dims]() { return SkewedInnerBlock<Layout>(chipped_dims); });
+
+  VerifyBlockAssignment<T, NumDims, Layout, NumDims - 1>(tensor, map.chip(chip_offset, chip_dim),
+                                                         [&chipped_dims]() { return FixedSizeBlock(chipped_dims); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_assign_to_tensor_slice() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
+  Tensor<T, NumDims, Layout> tensor(dims);
+
+  // Pick a random slice of tensor.
+  DSizes<Index, NumDims> slice_start = RandomDims<NumDims>(5, 10);
+  DSizes<Index, NumDims> slice_size = RandomDims<NumDims>(5, 10);
+
+  // Make sure that slice start + size do not overflow tensor dims.
+  for (int i = 0; i < NumDims; ++i) {
+    slice_start[i] = numext::mini(dims[i] - 1, slice_start[i]);
+    slice_size[i] = numext::mini(slice_size[i], dims[i] - slice_start[i]);
+  }
+
+  TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims);
+
+  VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.slice(slice_start, slice_size),
+                                            [&slice_size]() { return RandomBlock<Layout>(slice_size, 1, 10); });
+
+  VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.slice(slice_start, slice_size),
+                                            [&slice_size]() { return SkewedInnerBlock<Layout>(slice_size); });
+
+  VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.slice(slice_start, slice_size),
+                                            [&slice_size]() { return FixedSizeBlock(slice_size); });
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_assign_to_tensor_shuffle() {
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(5, 15);
+  Tensor<T, NumDims, Layout> tensor(dims);
+
+  DSizes<Index, NumDims> shuffle;
+  for (int i = 0; i < NumDims; ++i) shuffle[i] = i;
+
+  TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims);
+
+  do {
+    DSizes<Index, NumDims> shuffled_dims;
+    for (int i = 0; i < NumDims; ++i) shuffled_dims[i] = dims[shuffle[i]];
+
+    VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.shuffle(shuffle),
+                                              [&shuffled_dims]() { return FixedSizeBlock(shuffled_dims); });
+
+    VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.shuffle(shuffle),
+                                              [&shuffled_dims]() { return RandomBlock<Layout>(shuffled_dims, 1, 5); });
+
+  } while (std::next_permutation(&shuffle[0], &shuffle[0] + NumDims));
+}
+
+// -------------------------------------------------------------------------- //
+
+#define CALL_SUBTEST_PART(PART) CALL_SUBTEST_##PART
+
+#define CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(PART, NAME)     \
+  CALL_SUBTEST_PART(PART)((NAME<float, 1, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 2, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 3, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 4, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 5, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 1, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 2, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 4, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 4, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 5, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<int, 1, RowMajor>()));   \
+  CALL_SUBTEST_PART(PART)((NAME<int, 2, RowMajor>()));   \
+  CALL_SUBTEST_PART(PART)((NAME<int, 3, RowMajor>()));   \
+  CALL_SUBTEST_PART(PART)((NAME<int, 4, RowMajor>()));   \
+  CALL_SUBTEST_PART(PART)((NAME<int, 5, RowMajor>()));   \
+  CALL_SUBTEST_PART(PART)((NAME<int, 1, ColMajor>()));   \
+  CALL_SUBTEST_PART(PART)((NAME<int, 2, ColMajor>()));   \
+  CALL_SUBTEST_PART(PART)((NAME<int, 4, ColMajor>()));   \
+  CALL_SUBTEST_PART(PART)((NAME<int, 4, ColMajor>()));   \
+  CALL_SUBTEST_PART(PART)((NAME<int, 5, ColMajor>()));   \
+  CALL_SUBTEST_PART(PART)((NAME<bool, 1, RowMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, 2, RowMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, 3, RowMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, 4, RowMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, 5, RowMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, 1, ColMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, 2, ColMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, 4, ColMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, 4, ColMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, 5, ColMajor>()))
+
+#define CALL_SUBTESTS_DIMS_LAYOUTS(PART, NAME)           \
+  CALL_SUBTEST_PART(PART)((NAME<float, 1, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 2, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 3, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 4, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 5, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 1, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 2, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 4, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 4, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, 5, ColMajor>()))
+
+#define CALL_SUBTESTS_LAYOUTS_TYPES(PART, NAME)       \
+  CALL_SUBTEST_PART(PART)((NAME<float, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<bool, RowMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, ColMajor>()))
+
+EIGEN_DECLARE_TEST(cxx11_tensor_block_eval) {
+  // clang-format off
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(1, test_eval_tensor_block);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(1, test_eval_tensor_binary_expr_block);
+  CALL_SUBTESTS_DIMS_LAYOUTS(1, test_eval_tensor_unary_expr_block);
+  CALL_SUBTESTS_DIMS_LAYOUTS(2, test_eval_tensor_binary_with_unary_expr_block);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(2, test_eval_tensor_broadcast);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(2, test_eval_tensor_reshape);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(3, test_eval_tensor_cast);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(3, test_eval_tensor_select);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(3, test_eval_tensor_padding);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(4, test_eval_tensor_chipping);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(4, test_eval_tensor_generator);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(4, test_eval_tensor_reverse);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(5, test_eval_tensor_slice);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(5, test_eval_tensor_shuffle);
+
+  CALL_SUBTESTS_LAYOUTS_TYPES(6, test_eval_tensor_reshape_with_bcast);
+  CALL_SUBTESTS_LAYOUTS_TYPES(6, test_eval_tensor_forced_eval);
+  CALL_SUBTESTS_LAYOUTS_TYPES(6, test_eval_tensor_chipping_of_bcast);
+
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(7, test_assign_to_tensor);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(7, test_assign_to_tensor_reshape);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(7, test_assign_to_tensor_chipping);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(8, test_assign_to_tensor_slice);
+  CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(8, test_assign_to_tensor_shuffle);
+
+  // Force CMake to split this test.
+  // EIGEN_SUFFIXES;1;2;3;4;5;6;7;8
+
+  // clang-format on
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_block_io.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_block_io.cpp
new file mode 100644
index 0000000..509c10e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_block_io.cpp
@@ -0,0 +1,432 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// clang-format off
+#include "main.h"
+#include <Eigen/CXX11/Tensor>
+// clang-format on
+
+// -------------------------------------------------------------------------- //
+// A set of tests for TensorBlockIO: copying data between tensor blocks.
+
+template <int NumDims>
+static DSizes<Index, NumDims> RandomDims(Index min, Index max) {
+  DSizes<Index, NumDims> dims;
+  for (int i = 0; i < NumDims; ++i) {
+    dims[i] = internal::random<Index>(min, max);
+  }
+  return DSizes<Index, NumDims>(dims);
+}
+
+static internal::TensorBlockShapeType RandomBlockShape() {
+  return internal::random<bool>() ? internal::TensorBlockShapeType::kUniformAllDims
+                                  : internal::TensorBlockShapeType::kSkewedInnerDims;
+}
+
+template <int NumDims>
+static size_t RandomTargetBlockSize(const DSizes<Index, NumDims>& dims) {
+  return internal::random<size_t>(1, dims.TotalSize());
+}
+
+template <int Layout, int NumDims>
+static Index GetInputIndex(Index output_index, const array<Index, NumDims>& output_to_input_dim_map,
+                           const array<Index, NumDims>& input_strides, const array<Index, NumDims>& output_strides) {
+  int input_index = 0;
+  if (Layout == ColMajor) {
+    for (int i = NumDims - 1; i > 0; --i) {
+      const Index idx = output_index / output_strides[i];
+      input_index += idx * input_strides[output_to_input_dim_map[i]];
+      output_index -= idx * output_strides[i];
+    }
+    return input_index + output_index * input_strides[output_to_input_dim_map[0]];
+  } else {
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const Index idx = output_index / output_strides[i];
+      input_index += idx * input_strides[output_to_input_dim_map[i]];
+      output_index -= idx * output_strides[i];
+    }
+    return input_index + output_index * input_strides[output_to_input_dim_map[NumDims - 1]];
+  }
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_block_io_copy_data_from_source_to_target() {
+  using TensorBlockIO = internal::TensorBlockIO<T, Index, NumDims, Layout>;
+  using IODst = typename TensorBlockIO::Dst;
+  using IOSrc = typename TensorBlockIO::Src;
+
+  // Generate a random input Tensor.
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 30);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  // Write data to an output Tensor.
+  Tensor<T, NumDims, Layout> output(dims);
+
+  // Construct a tensor block mapper.
+  using TensorBlockMapper = internal::TensorBlockMapper<NumDims, Layout, Index>;
+  TensorBlockMapper block_mapper(dims, {RandomBlockShape(), RandomTargetBlockSize(dims), {0, 0, 0}});
+
+  // We will copy data from input to output through this buffer.
+  Tensor<T, NumDims, Layout> block(block_mapper.blockDimensions());
+
+  // Precompute strides for TensorBlockIO::Copy.
+  auto input_strides = internal::strides<Layout>(dims);
+  auto output_strides = internal::strides<Layout>(dims);
+
+  const T* input_data = input.data();
+  T* output_data = output.data();
+  T* block_data = block.data();
+
+  for (int i = 0; i < block_mapper.blockCount(); ++i) {
+    auto desc = block_mapper.blockDescriptor(i);
+
+    auto blk_dims = desc.dimensions();
+    auto blk_strides = internal::strides<Layout>(blk_dims);
+
+    {
+      // Read from input into a block buffer.
+      IODst dst(blk_dims, blk_strides, block_data, 0);
+      IOSrc src(input_strides, input_data, desc.offset());
+
+      TensorBlockIO::Copy(dst, src);
+    }
+
+    {
+      // Write from block buffer to output.
+      IODst dst(blk_dims, output_strides, output_data, desc.offset());
+      IOSrc src(blk_strides, block_data, 0);
+
+      TensorBlockIO::Copy(dst, src);
+    }
+  }
+
+  for (int i = 0; i < dims.TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(input_data[i], output_data[i]);
+  }
+}
+
+template <typename T, int NumDims, int Layout>
+static void test_block_io_copy_using_reordered_dimensions() {
+  // Generate a random input Tensor.
+  DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 30);
+  Tensor<T, NumDims, Layout> input(dims);
+  input.setRandom();
+
+  // Create a random dimension re-ordering/shuffle.
+  std::vector<int> shuffle;
+
+  for (int i = 0; i < NumDims; ++i) shuffle.push_back(i);
+  std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937(g_seed));
+
+  DSizes<Index, NumDims> output_tensor_dims;
+  DSizes<Index, NumDims> input_to_output_dim_map;
+  DSizes<Index, NumDims> output_to_input_dim_map;
+  for (Index i = 0; i < NumDims; ++i) {
+    output_tensor_dims[shuffle[i]] = dims[i];
+    input_to_output_dim_map[i] = shuffle[i];
+    output_to_input_dim_map[shuffle[i]] = i;
+  }
+
+  // Write data to an output Tensor.
+  Tensor<T, NumDims, Layout> output(output_tensor_dims);
+
+  // Construct a tensor block mapper.
+  // NOTE: Tensor block mapper works with shuffled dimensions.
+  using TensorBlockMapper = internal::TensorBlockMapper<NumDims, Layout, Index>;
+  TensorBlockMapper block_mapper(output_tensor_dims,
+                                 {RandomBlockShape(), RandomTargetBlockSize(output_tensor_dims), {0, 0, 0}});
+
+  // We will copy data from input to output through this buffer.
+  Tensor<T, NumDims, Layout> block(block_mapper.blockDimensions());
+
+  // Precompute strides for TensorBlockIO::Copy.
+  auto input_strides = internal::strides<Layout>(dims);
+  auto output_strides = internal::strides<Layout>(output_tensor_dims);
+
+  const T* input_data = input.data();
+  T* output_data = output.data();
+  T* block_data = block.data();
+
+  for (Index i = 0; i < block_mapper.blockCount(); ++i) {
+    auto desc = block_mapper.blockDescriptor(i);
+
+    const Index first_coeff_index =
+        GetInputIndex<Layout, NumDims>(desc.offset(), output_to_input_dim_map, input_strides, output_strides);
+
+    // NOTE: Block dimensions are in the same order as output dimensions.
+
+    using TensorBlockIO = internal::TensorBlockIO<T, Index, NumDims, Layout>;
+    using IODst = typename TensorBlockIO::Dst;
+    using IOSrc = typename TensorBlockIO::Src;
+
+    auto blk_dims = desc.dimensions();
+    auto blk_strides = internal::strides<Layout>(blk_dims);
+
+    {
+      // Read from input into a block buffer.
+      IODst dst(blk_dims, blk_strides, block_data, 0);
+      IOSrc src(input_strides, input_data, first_coeff_index);
+
+      // TODO(ezhulenev): Remove when fully switched to TensorBlock.
+      DSizes<int, NumDims> dim_map;
+      for (int j = 0; j < NumDims; ++j) dim_map[j] = static_cast<int>(output_to_input_dim_map[j]);
+      TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/dim_map);
+    }
+
+    {
+      // We need to convert block dimensions from output to input order.
+      auto dst_dims = blk_dims;
+      for (int out_dim = 0; out_dim < NumDims; ++out_dim) {
+        dst_dims[output_to_input_dim_map[out_dim]] = blk_dims[out_dim];
+      }
+
+      // Write from block buffer to output.
+      IODst dst(dst_dims, input_strides, output_data, first_coeff_index);
+      IOSrc src(blk_strides, block_data, 0);
+
+      // TODO(ezhulenev): Remove when fully switched to TensorBlock.
+      DSizes<int, NumDims> dim_map;
+      for (int j = 0; j < NumDims; ++j) dim_map[j] = static_cast<int>(input_to_output_dim_map[j]);
+      TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/dim_map);
+    }
+  }
+
+  for (Index i = 0; i < dims.TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(input_data[i], output_data[i]);
+  }
+}
+
+// This is the special case for reading data with reordering, when dimensions
+// before/after reordering are the same. Squeezing reads along inner dimensions
+// in this case is illegal, because we reorder innermost dimension.
+template <int Layout>
+static void test_block_io_copy_using_reordered_dimensions_do_not_squeeze() {
+  DSizes<Index, 3> tensor_dims(7, 9, 7);
+  DSizes<Index, 3> block_dims = tensor_dims;
+
+  DSizes<int, 3> block_to_tensor_dim;
+  block_to_tensor_dim[0] = 2;
+  block_to_tensor_dim[1] = 1;
+  block_to_tensor_dim[2] = 0;
+
+  auto tensor_strides = internal::strides<Layout>(tensor_dims);
+  auto block_strides = internal::strides<Layout>(block_dims);
+
+  Tensor<float, 3, Layout> block(block_dims);
+  Tensor<float, 3, Layout> tensor(tensor_dims);
+  tensor.setRandom();
+
+  float* tensor_data = tensor.data();
+  float* block_data = block.data();
+
+  using TensorBlockIO = internal::TensorBlockIO<float, Index, 3, Layout>;
+  using IODst = typename TensorBlockIO::Dst;
+  using IOSrc = typename TensorBlockIO::Src;
+
+  // Read from a tensor into a block.
+  IODst dst(block_dims, block_strides, block_data, 0);
+  IOSrc src(tensor_strides, tensor_data, 0);
+
+  TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/block_to_tensor_dim);
+
+  TensorMap<Tensor<float, 3, Layout> > block_tensor(block_data, block_dims);
+  TensorMap<Tensor<float, 3, Layout> > tensor_tensor(tensor_data, tensor_dims);
+
+  for (Index d0 = 0; d0 < tensor_dims[0]; ++d0) {
+    for (Index d1 = 0; d1 < tensor_dims[1]; ++d1) {
+      for (Index d2 = 0; d2 < tensor_dims[2]; ++d2) {
+        float block_value = block_tensor(d2, d1, d0);
+        float tensor_value = tensor_tensor(d0, d1, d2);
+        VERIFY_IS_EQUAL(block_value, tensor_value);
+      }
+    }
+  }
+}
+
+// This is the special case for reading data with reordering, when dimensions
+// before/after reordering are the same. Squeezing reads in this case is allowed
+// because we reorder outer dimensions.
+template <int Layout>
+static void test_block_io_copy_using_reordered_dimensions_squeeze() {
+  DSizes<Index, 4> tensor_dims(7, 5, 9, 9);
+  DSizes<Index, 4> block_dims = tensor_dims;
+
+  DSizes<int, 4> block_to_tensor_dim;
+  block_to_tensor_dim[0] = 0;
+  block_to_tensor_dim[1] = 1;
+  block_to_tensor_dim[2] = 3;
+  block_to_tensor_dim[3] = 2;
+
+  auto tensor_strides = internal::strides<Layout>(tensor_dims);
+  auto block_strides = internal::strides<Layout>(block_dims);
+
+  Tensor<float, 4, Layout> block(block_dims);
+  Tensor<float, 4, Layout> tensor(tensor_dims);
+  tensor.setRandom();
+
+  float* tensor_data = tensor.data();
+  float* block_data = block.data();
+
+  using TensorBlockIO = internal::TensorBlockIO<float, Index, 4, Layout>;
+  using IODst = typename TensorBlockIO::Dst;
+  using IOSrc = typename TensorBlockIO::Src;
+
+  // Read from a tensor into a block.
+  IODst dst(block_dims, block_strides, block_data, 0);
+  IOSrc src(tensor_strides, tensor_data, 0);
+
+  TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/block_to_tensor_dim);
+
+  TensorMap<Tensor<float, 4, Layout> > block_tensor(block_data, block_dims);
+  TensorMap<Tensor<float, 4, Layout> > tensor_tensor(tensor_data, tensor_dims);
+
+  for (Index d0 = 0; d0 < tensor_dims[0]; ++d0) {
+    for (Index d1 = 0; d1 < tensor_dims[1]; ++d1) {
+      for (Index d2 = 0; d2 < tensor_dims[2]; ++d2) {
+        for (Index d3 = 0; d3 < tensor_dims[3]; ++d3) {
+          float block_value = block_tensor(d0, d1, d3, d2);
+          float tensor_value = tensor_tensor(d0, d1, d2, d3);
+          VERIFY_IS_EQUAL(block_value, tensor_value);
+        }
+      }
+    }
+  }
+}
+
+template <int Layout>
+static void test_block_io_zero_stride() {
+  DSizes<Index, 5> rnd_dims = RandomDims<5>(1, 30);
+
+  DSizes<Index, 5> input_tensor_dims = rnd_dims;
+  input_tensor_dims[0] = 1;
+  input_tensor_dims[2] = 1;
+  input_tensor_dims[4] = 1;
+
+  Tensor<float, 5, Layout> input(input_tensor_dims);
+  input.setRandom();
+
+  DSizes<Index, 5> output_tensor_dims = rnd_dims;
+
+  auto input_tensor_strides = internal::strides<Layout>(input_tensor_dims);
+  auto output_tensor_strides = internal::strides<Layout>(output_tensor_dims);
+
+  auto input_tensor_strides_with_zeros = input_tensor_strides;
+  input_tensor_strides_with_zeros[0] = 0;
+  input_tensor_strides_with_zeros[2] = 0;
+  input_tensor_strides_with_zeros[4] = 0;
+
+  Tensor<float, 5, Layout> output(output_tensor_dims);
+  output.setRandom();
+
+  using TensorBlockIO = internal::TensorBlockIO<float, Index, 5, Layout>;
+  using IODst = typename TensorBlockIO::Dst;
+  using IOSrc = typename TensorBlockIO::Src;
+
+  // Write data from input to output with broadcasting in dims [0, 2, 4].
+  IODst dst(output_tensor_dims, output_tensor_strides, output.data(), 0);
+  IOSrc src(input_tensor_strides_with_zeros, input.data(), 0);
+  TensorBlockIO::Copy(dst, src);
+
+  for (int i = 0; i < output_tensor_dims[0]; ++i) {
+    for (int j = 0; j < output_tensor_dims[1]; ++j) {
+      for (int k = 0; k < output_tensor_dims[2]; ++k) {
+        for (int l = 0; l < output_tensor_dims[3]; ++l) {
+          for (int m = 0; m < output_tensor_dims[4]; ++m) {
+            float input_value = input(0, j, 0, l, 0);
+            float output_value = output(i, j, k, l, m);
+            VERIFY_IS_EQUAL(input_value, output_value);
+          }
+        }
+      }
+    }
+  }
+}
+
+template <int Layout>
+static void test_block_io_squeeze_ones() {
+  using TensorBlockIO = internal::TensorBlockIO<float, Index, 5, Layout>;
+  using IODst = typename TensorBlockIO::Dst;
+  using IOSrc = typename TensorBlockIO::Src;
+
+  // Total size > 1.
+  {
+    DSizes<Index, 5> block_sizes(1, 2, 1, 2, 1);
+    auto strides = internal::strides<Layout>(block_sizes);
+
+    // Create a random input tensor.
+    Tensor<float, 5> input(block_sizes);
+    input.setRandom();
+
+    Tensor<float, 5> output(block_sizes);
+
+    IODst dst(block_sizes, strides, output.data(), 0);
+    IOSrc src(strides, input.data());
+    TensorBlockIO::Copy(dst, src);
+
+    for (Index i = 0; i < block_sizes.TotalSize(); ++i) {
+      VERIFY_IS_EQUAL(output.data()[i], input.data()[i]);
+    }
+  }
+
+  // Total size == 1.
+  {
+    DSizes<Index, 5> block_sizes(1, 1, 1, 1, 1);
+    auto strides = internal::strides<Layout>(block_sizes);
+
+    // Create a random input tensor.
+    Tensor<float, 5> input(block_sizes);
+    input.setRandom();
+
+    Tensor<float, 5> output(block_sizes);
+
+    IODst dst(block_sizes, strides, output.data(), 0);
+    IOSrc src(strides, input.data());
+    TensorBlockIO::Copy(dst, src);
+
+    for (Index i = 0; i < block_sizes.TotalSize(); ++i) {
+      VERIFY_IS_EQUAL(output.data()[i], input.data()[i]);
+    }
+  }
+}
+
+#define CALL_SUBTESTS(NAME)                   \
+  CALL_SUBTEST((NAME<float, 1, RowMajor>())); \
+  CALL_SUBTEST((NAME<float, 2, RowMajor>())); \
+  CALL_SUBTEST((NAME<float, 4, RowMajor>())); \
+  CALL_SUBTEST((NAME<float, 5, RowMajor>())); \
+  CALL_SUBTEST((NAME<float, 1, ColMajor>())); \
+  CALL_SUBTEST((NAME<float, 2, ColMajor>())); \
+  CALL_SUBTEST((NAME<float, 4, ColMajor>())); \
+  CALL_SUBTEST((NAME<float, 5, ColMajor>())); \
+  CALL_SUBTEST((NAME<bool, 1, RowMajor>()));  \
+  CALL_SUBTEST((NAME<bool, 2, RowMajor>()));  \
+  CALL_SUBTEST((NAME<bool, 4, RowMajor>()));  \
+  CALL_SUBTEST((NAME<bool, 5, RowMajor>()));  \
+  CALL_SUBTEST((NAME<bool, 1, ColMajor>()));  \
+  CALL_SUBTEST((NAME<bool, 2, ColMajor>()));  \
+  CALL_SUBTEST((NAME<bool, 4, ColMajor>()));  \
+  CALL_SUBTEST((NAME<bool, 5, ColMajor>()))
+
+EIGEN_DECLARE_TEST(cxx11_tensor_block_io) {
+  // clang-format off
+  CALL_SUBTESTS(test_block_io_copy_data_from_source_to_target);
+  CALL_SUBTESTS(test_block_io_copy_using_reordered_dimensions);
+
+  CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_do_not_squeeze<RowMajor>());
+  CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_do_not_squeeze<ColMajor>());
+
+  CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_squeeze<RowMajor>());
+  CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_squeeze<ColMajor>());
+
+  CALL_SUBTEST(test_block_io_zero_stride<RowMajor>());
+  CALL_SUBTEST(test_block_io_zero_stride<ColMajor>());
+
+  CALL_SUBTEST(test_block_io_squeeze_ones<RowMajor>());
+  CALL_SUBTEST(test_block_io_squeeze_ones<ColMajor>());
+  // clang-format on
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
new file mode 100644
index 0000000..d255568
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
@@ -0,0 +1,140 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_broadcast_sycl_fixed(const Eigen::SyclDevice& sycl_device) {
+  // BROADCAST test:
+  IndexType inDim1 = 2;
+  IndexType inDim2 = 3;
+  IndexType inDim3 = 5;
+  IndexType inDim4 = 7;
+  IndexType bDim1 = 2;
+  IndexType bDim2 = 3;
+  IndexType bDim3 = 1;
+  IndexType bDim4 = 4;
+  array<IndexType, 4> in_range = {{inDim1, inDim2, inDim3, inDim4}};
+  array<IndexType, 4> broadcasts = {{bDim1, bDim2, bDim3, bDim4}};
+  array<IndexType, 4> out_range;  // = in_range * broadcasts
+  for (size_t i = 0; i < out_range.size(); ++i) out_range[i] = in_range[i] * broadcasts[i];
+
+  Tensor<DataType, 4, DataLayout, IndexType> input(in_range);
+  Tensor<DataType, 4, DataLayout, IndexType> out(out_range);
+
+  for (size_t i = 0; i < in_range.size(); ++i) VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);
+
+  for (IndexType i = 0; i < input.size(); ++i) input(i) = static_cast<DataType>(i);
+
+  DataType* gpu_in_data =
+      static_cast<DataType*>(sycl_device.allocate(input.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<TensorFixedSize<DataType, Sizes<2, 3, 5, 7>, DataLayout, IndexType>> gpu_in(gpu_in_data, in_range);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_out(gpu_out_data, out_range);
+  sycl_device.memcpyHostToDevice(gpu_in_data, input.data(), (input.dimensions().TotalSize()) * sizeof(DataType));
+  gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(DataType));
+
+  for (IndexType i = 0; i < inDim1 * bDim1; ++i) {
+    for (IndexType j = 0; j < inDim2 * bDim2; ++j) {
+      for (IndexType k = 0; k < inDim3 * bDim3; ++k) {
+        for (IndexType l = 0; l < inDim4 * bDim4; ++l) {
+          VERIFY_IS_APPROX(input(i % 2, j % 3, k % 5, l % 7), out(i, j, k, l));
+        }
+      }
+    }
+  }
+  printf("Broadcast Test with fixed size Passed\n");
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_broadcast_sycl(const Eigen::SyclDevice& sycl_device) {
+  // BROADCAST test:
+  IndexType inDim1 = 2;
+  IndexType inDim2 = 3;
+  IndexType inDim3 = 5;
+  IndexType inDim4 = 7;
+  IndexType bDim1 = 2;
+  IndexType bDim2 = 3;
+  IndexType bDim3 = 1;
+  IndexType bDim4 = 4;
+  array<IndexType, 4> in_range = {{inDim1, inDim2, inDim3, inDim4}};
+  array<IndexType, 4> broadcasts = {{bDim1, bDim2, bDim3, bDim4}};
+  array<IndexType, 4> out_range;  // = in_range * broadcasts
+  for (size_t i = 0; i < out_range.size(); ++i) out_range[i] = in_range[i] * broadcasts[i];
+
+  Tensor<DataType, 4, DataLayout, IndexType> input(in_range);
+  Tensor<DataType, 4, DataLayout, IndexType> out(out_range);
+
+  for (size_t i = 0; i < in_range.size(); ++i) VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);
+
+  for (IndexType i = 0; i < input.size(); ++i) input(i) = static_cast<DataType>(i);
+
+  DataType* gpu_in_data =
+      static_cast<DataType*>(sycl_device.allocate(input.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_in(gpu_in_data, in_range);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_out(gpu_out_data, out_range);
+  sycl_device.memcpyHostToDevice(gpu_in_data, input.data(), (input.dimensions().TotalSize()) * sizeof(DataType));
+  gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(DataType));
+
+  for (IndexType i = 0; i < inDim1 * bDim1; ++i) {
+    for (IndexType j = 0; j < inDim2 * bDim2; ++j) {
+      for (IndexType k = 0; k < inDim3 * bDim3; ++k) {
+        for (IndexType l = 0; l < inDim4 * bDim4; ++l) {
+          VERIFY_IS_APPROX(input(i % inDim1, j % inDim2, k % inDim3, l % inDim4), out(i, j, k, l));
+        }
+      }
+    }
+  }
+  printf("Broadcast Test Passed\n");
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType>
+void sycl_broadcast_test_per_device(const cl::sycl::device& d) {
+  std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
+  QueueInterface queueInterface(d);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_broadcast_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_broadcast_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_broadcast_sycl_fixed<DataType, RowMajor, int64_t>(sycl_device);
+  test_broadcast_sycl_fixed<DataType, ColMajor, int64_t>(sycl_device);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_broadcast_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_broadcast_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_broadcast_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_broadcasting.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_broadcasting.cpp
new file mode 100644
index 0000000..6f43d6a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_broadcasting.cpp
@@ -0,0 +1,312 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_simple_broadcasting() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> broadcasts;
+  broadcasts[0] = 1;
+  broadcasts[1] = 1;
+  broadcasts[2] = 1;
+  broadcasts[3] = 1;
+
+  Tensor<float, 4, DataLayout> no_broadcast;
+  no_broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(no_broadcast.dimension(0), 2);
+  VERIFY_IS_EQUAL(no_broadcast.dimension(1), 3);
+  VERIFY_IS_EQUAL(no_broadcast.dimension(2), 5);
+  VERIFY_IS_EQUAL(no_broadcast.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), no_broadcast(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  broadcasts[0] = 2;
+  broadcasts[1] = 3;
+  broadcasts[2] = 1;
+  broadcasts[3] = 4;
+  Tensor<float, 4, DataLayout> broadcast;
+  broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcast.dimension(0), 4);
+  VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
+  VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
+  VERIFY_IS_EQUAL(broadcast.dimension(3), 28);
+
+  for (int i = 0; i < 4; ++i) {
+    for (int j = 0; j < 9; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 28; ++l) {
+          VERIFY_IS_EQUAL(tensor(i % 2, j % 3, k % 5, l % 7), broadcast(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_vectorized_broadcasting() {
+  Tensor<float, 3, DataLayout> tensor(8, 3, 5);
+  tensor.setRandom();
+  array<ptrdiff_t, 3> broadcasts;
+  broadcasts[0] = 2;
+  broadcasts[1] = 3;
+  broadcasts[2] = 4;
+
+  Tensor<float, 3, DataLayout> broadcast;
+  broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcast.dimension(0), 16);
+  VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
+  VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
+
+  for (int i = 0; i < 16; ++i) {
+    for (int j = 0; j < 9; ++j) {
+      for (int k = 0; k < 20; ++k) {
+        VERIFY_IS_EQUAL(tensor(i % 8, j % 3, k % 5), broadcast(i, j, k));
+      }
+    }
+  }
+
+  tensor.resize(11, 3, 5);
+
+  tensor.setRandom();
+  broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcast.dimension(0), 22);
+  VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
+  VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
+
+  for (int i = 0; i < 22; ++i) {
+    for (int j = 0; j < 9; ++j) {
+      for (int k = 0; k < 20; ++k) {
+        VERIFY_IS_EQUAL(tensor(i % 11, j % 3, k % 5), broadcast(i, j, k));
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_static_broadcasting() {
+  Tensor<float, 3, DataLayout> tensor(8, 3, 5);
+  tensor.setRandom();
+
+  Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3>, Eigen::type2index<4>> broadcasts;
+  Tensor<float, 3, DataLayout> broadcast;
+  broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcast.dimension(0), 16);
+  VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
+  VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
+
+  for (int i = 0; i < 16; ++i) {
+    for (int j = 0; j < 9; ++j) {
+      for (int k = 0; k < 20; ++k) {
+        VERIFY_IS_EQUAL(tensor(i % 8, j % 3, k % 5), broadcast(i, j, k));
+      }
+    }
+  }
+
+  tensor.resize(11, 3, 5);
+
+  tensor.setRandom();
+  broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcast.dimension(0), 22);
+  VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
+  VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
+
+  for (int i = 0; i < 22; ++i) {
+    for (int j = 0; j < 9; ++j) {
+      for (int k = 0; k < 20; ++k) {
+        VERIFY_IS_EQUAL(tensor(i % 11, j % 3, k % 5), broadcast(i, j, k));
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_fixed_size_broadcasting() {
+  // Need to add a [] operator to the Size class for this to work
+#if 0
+  Tensor<float, 1, DataLayout> t1(10);
+  t1.setRandom();
+  TensorFixedSize<float, Sizes<1>, DataLayout> t2;
+  t2 = t2.constant(20.0f);
+
+  Tensor<float, 1, DataLayout> t3 = t1 + t2.broadcast(Eigen::array<int, 1>{{10}});
+  for (int i = 0; i < 10; ++i) {
+    VERIFY_IS_APPROX(t3(i), t1(i) + t2(0));
+  }
+
+  TensorMap<TensorFixedSize<float, Sizes<1>, DataLayout> > t4(t2.data(), {{1}});
+  Tensor<float, 1, DataLayout> t5 = t1 + t4.broadcast(Eigen::array<int, 1>{{10}});
+  for (int i = 0; i < 10; ++i) {
+    VERIFY_IS_APPROX(t5(i), t1(i) + t2(0));
+  }
+#endif
+}
+
+template <int DataLayout>
+static void test_simple_broadcasting_one_by_n() {
+  Tensor<float, 4, DataLayout> tensor(1, 13, 5, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> broadcasts;
+  broadcasts[0] = 9;
+  broadcasts[1] = 1;
+  broadcasts[2] = 1;
+  broadcasts[3] = 1;
+  Tensor<float, 4, DataLayout> broadcast;
+  broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcast.dimension(0), 9);
+  VERIFY_IS_EQUAL(broadcast.dimension(1), 13);
+  VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
+  VERIFY_IS_EQUAL(broadcast.dimension(3), 7);
+
+  for (int i = 0; i < 9; ++i) {
+    for (int j = 0; j < 13; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i % 1, j % 13, k % 5, l % 7), broadcast(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_simple_broadcasting_n_by_one() {
+  Tensor<float, 4, DataLayout> tensor(7, 3, 5, 1);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> broadcasts;
+  broadcasts[0] = 1;
+  broadcasts[1] = 1;
+  broadcasts[2] = 1;
+  broadcasts[3] = 19;
+  Tensor<float, 4, DataLayout> broadcast;
+  broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcast.dimension(0), 7);
+  VERIFY_IS_EQUAL(broadcast.dimension(1), 3);
+  VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
+  VERIFY_IS_EQUAL(broadcast.dimension(3), 19);
+
+  for (int i = 0; i < 7; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 19; ++l) {
+          VERIFY_IS_EQUAL(tensor(i % 7, j % 3, k % 5, l % 1), broadcast(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_size_one_broadcasting() {
+  Tensor<float, 1, DataLayout> tensor(1);
+  tensor.setRandom();
+  array<ptrdiff_t, 1> broadcasts = {64};
+  Tensor<float, 1, DataLayout> broadcast;
+  broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcast.dimension(0), broadcasts[0]);
+
+  for (int i = 0; i < broadcasts[0]; ++i) {
+    VERIFY_IS_EQUAL(tensor(0), broadcast(i));
+  }
+}
+
+template <int DataLayout>
+static void test_simple_broadcasting_one_by_n_by_one_1d() {
+  Tensor<float, 3, DataLayout> tensor(1, 7, 1);
+  tensor.setRandom();
+  array<ptrdiff_t, 3> broadcasts;
+  broadcasts[0] = 5;
+  broadcasts[1] = 1;
+  broadcasts[2] = 13;
+  Tensor<float, 3, DataLayout> broadcasted;
+  broadcasted = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcasted.dimension(0), 5);
+  VERIFY_IS_EQUAL(broadcasted.dimension(1), 7);
+  VERIFY_IS_EQUAL(broadcasted.dimension(2), 13);
+
+  for (int i = 0; i < 5; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      for (int k = 0; k < 13; ++k) {
+        VERIFY_IS_EQUAL(tensor(0, j % 7, 0), broadcasted(i, j, k));
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_simple_broadcasting_one_by_n_by_one_2d() {
+  Tensor<float, 4, DataLayout> tensor(1, 7, 13, 1);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> broadcasts;
+  broadcasts[0] = 5;
+  broadcasts[1] = 1;
+  broadcasts[2] = 1;
+  broadcasts[3] = 19;
+  Tensor<float, 4, DataLayout> broadcast;
+  broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcast.dimension(0), 5);
+  VERIFY_IS_EQUAL(broadcast.dimension(1), 7);
+  VERIFY_IS_EQUAL(broadcast.dimension(2), 13);
+  VERIFY_IS_EQUAL(broadcast.dimension(3), 19);
+
+  for (int i = 0; i < 5; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      for (int k = 0; k < 13; ++k) {
+        for (int l = 0; l < 19; ++l) {
+          VERIFY_IS_EQUAL(tensor(0, j % 7, k % 13, 0), broadcast(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_broadcasting) {
+  CALL_SUBTEST(test_simple_broadcasting<ColMajor>());
+  CALL_SUBTEST(test_simple_broadcasting<RowMajor>());
+  CALL_SUBTEST(test_vectorized_broadcasting<ColMajor>());
+  CALL_SUBTEST(test_vectorized_broadcasting<RowMajor>());
+  CALL_SUBTEST(test_static_broadcasting<ColMajor>());
+  CALL_SUBTEST(test_static_broadcasting<RowMajor>());
+  CALL_SUBTEST(test_fixed_size_broadcasting<ColMajor>());
+  CALL_SUBTEST(test_fixed_size_broadcasting<RowMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_one_by_n<RowMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_n_by_one<RowMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_one_by_n<ColMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_n_by_one<ColMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_1d<ColMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_2d<ColMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_1d<RowMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_2d<RowMajor>());
+  CALL_SUBTEST(test_size_one_broadcasting<ColMajor>());
+  CALL_SUBTEST(test_size_one_broadcasting<RowMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_builtins_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_builtins_sycl.cpp
new file mode 100644
index 0000000..c8e0a6d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_builtins_sycl.cpp
@@ -0,0 +1,509 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+// Functions used to compare the TensorMap implementation on the device with
+// the equivalent on the host
+namespace SYCL {
+
+template <typename T>
+T abs(T x) {
+  return cl::sycl::abs(x);
+}
+template <>
+Eigen::half abs(Eigen::half x) {
+  return Eigen::half(cl::sycl::fabs(static_cast<cl::sycl::half>(x)));
+}
+
+template <>
+float abs(float x) {
+  return cl::sycl::fabs(x);
+}
+
+template <>
+double abs(double x) {
+  return cl::sycl::fabs(x);
+}
+
+template <typename T>
+T square(T x) {
+  return x * x;
+}
+template <typename T>
+T cube(T x) {
+  return x * x * x;
+}
+template <typename T>
+T inverse(T x) {
+  return T(1) / x;
+}
+template <typename T>
+T cwiseMax(T x, T y) {
+  return cl::sycl::max(x, y);
+}
+template <>
+Eigen::half cwiseMax(Eigen::half x, Eigen::half y) {
+  return Eigen::half(cl::sycl::max(static_cast<cl::sycl::half>(x), static_cast<cl::sycl::half>(y)));
+}
+
+template <typename T>
+T cwiseMin(T x, T y) {
+  return cl::sycl::min(x, y);
+}
+template <>
+Eigen::half cwiseMin(Eigen::half x, Eigen::half y) {
+  return Eigen::half(cl::sycl::min(static_cast<cl::sycl::half>(x), static_cast<cl::sycl::half>(y)));
+}
+
+template <typename T>
+T sqrt(T x) {
+  return cl::sycl::sqrt(x);
+}
+template <>
+Eigen::half sqrt(Eigen::half x) {
+  return Eigen::half(cl::sycl::sqrt(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T rsqrt(T x) {
+  return cl::sycl::rsqrt(x);
+}
+template <>
+Eigen::half rsqrt(Eigen::half x) {
+  return Eigen::half(cl::sycl::rsqrt(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T tanh(T x) {
+  return cl::sycl::tanh(x);
+}
+template <>
+Eigen::half tanh(Eigen::half x) {
+  return Eigen::half(cl::sycl::tanh(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T exp(T x) {
+  return cl::sycl::exp(x);
+}
+template <>
+Eigen::half exp(Eigen::half x) {
+  return Eigen::half(cl::sycl::exp(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T expm1(T x) {
+  return cl::sycl::expm1(x);
+}
+template <>
+Eigen::half expm1(Eigen::half x) {
+  return Eigen::half(cl::sycl::expm1(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T log(T x) {
+  return cl::sycl::log(x);
+}
+template <>
+Eigen::half log(Eigen::half x) {
+  return Eigen::half(cl::sycl::log(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T ceil(T x) {
+  return cl::sycl::ceil(x);
+}
+template <>
+Eigen::half ceil(Eigen::half x) {
+  return Eigen::half(cl::sycl::ceil(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T floor(T x) {
+  return cl::sycl::floor(x);
+}
+template <>
+Eigen::half floor(Eigen::half x) {
+  return Eigen::half(cl::sycl::floor(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T round(T x) {
+  return cl::sycl::round(x);
+}
+template <>
+Eigen::half round(Eigen::half x) {
+  return Eigen::half(cl::sycl::round(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T log1p(T x) {
+  return cl::sycl::log1p(x);
+}
+template <>
+Eigen::half log1p(Eigen::half x) {
+  return Eigen::half(cl::sycl::log1p(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T sign(T x) {
+  return cl::sycl::sign(x);
+}
+template <>
+Eigen::half sign(Eigen::half x) {
+  return Eigen::half(cl::sycl::sign(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T isnan(T x) {
+  return cl::sycl::isnan(x);
+}
+template <>
+Eigen::half isnan(Eigen::half x) {
+  return Eigen::half(cl::sycl::isnan(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T isfinite(T x) {
+  return cl::sycl::isfinite(x);
+}
+template <>
+Eigen::half isfinite(Eigen::half x) {
+  return Eigen::half(cl::sycl::isfinite(static_cast<cl::sycl::half>(x)));
+}
+
+template <typename T>
+T isinf(T x) {
+  return cl::sycl::isinf(x);
+}
+template <>
+Eigen::half isinf(Eigen::half x) {
+  return Eigen::half(cl::sycl::isinf(static_cast<cl::sycl::half>(x)));
+}
+}  // namespace SYCL
+
+#define DECLARE_UNARY_STRUCT_NON_SYCL(FUNC)  \
+  struct op_##FUNC {                         \
+    template <typename T>                    \
+    auto operator()(const T& x) {            \
+      return SYCL::FUNC(x);                  \
+    }                                        \
+    template <typename T>                    \
+    auto operator()(const TensorMap<T>& x) { \
+      return x.FUNC();                       \
+    }                                        \
+  };
+
+DECLARE_UNARY_STRUCT_NON_SYCL(abs)
+DECLARE_UNARY_STRUCT_NON_SYCL(square)
+DECLARE_UNARY_STRUCT_NON_SYCL(cube)
+DECLARE_UNARY_STRUCT_NON_SYCL(inverse)
+
+#define DECLARE_BINARY_STRUCT_NON_SYCL(FUNC)                          \
+  struct op_##FUNC {                                                  \
+    template <typename T1, typename T2>                               \
+    auto operator()(const T1& x, const T2& y) {                       \
+      return SYCL::FUNC(x, y);                                        \
+    }                                                                 \
+    template <typename T1, typename T2>                               \
+    auto operator()(const TensorMap<T1>& x, const TensorMap<T2>& y) { \
+      return x.FUNC(y);                                               \
+    }                                                                 \
+  };
+
+DECLARE_BINARY_STRUCT_NON_SYCL(cwiseMax)
+DECLARE_BINARY_STRUCT_NON_SYCL(cwiseMin)
+
+struct EqualAssignment {
+  template <typename Lhs, typename Rhs>
+  void operator()(Lhs& lhs, const Rhs& rhs) {
+    lhs = rhs;
+  }
+};
+
+struct PlusEqualAssignment {
+  template <typename Lhs, typename Rhs>
+  void operator()(Lhs& lhs, const Rhs& rhs) {
+    lhs += rhs;
+  }
+};
+
+template <typename DataType, int DataLayout, typename Assignment, typename Operator>
+void test_unary_builtins_for_scalar(const Eigen::SyclDevice& sycl_device, const array<int64_t, 3>& tensor_range) {
+  Operator op;
+  Assignment asgn;
+  {
+    /* Assignment(out, Operator(in)) */
+    Tensor<DataType, 3, DataLayout, int64_t> in(tensor_range);
+    Tensor<DataType, 3, DataLayout, int64_t> out(tensor_range);
+    in = in.random() + DataType(0.01);
+    out = out.random() + DataType(0.01);
+    Tensor<DataType, 3, DataLayout, int64_t> reference(out);
+    DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(in.size() * sizeof(DataType)));
+    DataType* gpu_data_out = static_cast<DataType*>(sycl_device.allocate(out.size() * sizeof(DataType)));
+    TensorMap<Tensor<DataType, 3, DataLayout, int64_t>> gpu(gpu_data, tensor_range);
+    TensorMap<Tensor<DataType, 3, DataLayout, int64_t>> gpu_out(gpu_data_out, tensor_range);
+    sycl_device.memcpyHostToDevice(gpu_data, in.data(), (in.size()) * sizeof(DataType));
+    sycl_device.memcpyHostToDevice(gpu_data_out, out.data(), (out.size()) * sizeof(DataType));
+    auto device_expr = gpu_out.device(sycl_device);
+    asgn(device_expr, op(gpu));
+    sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, (out.size()) * sizeof(DataType));
+    for (int64_t i = 0; i < out.size(); ++i) {
+      DataType ver = reference(i);
+      asgn(ver, op(in(i)));
+      VERIFY_IS_APPROX(out(i), ver);
+    }
+    sycl_device.deallocate(gpu_data);
+    sycl_device.deallocate(gpu_data_out);
+  }
+  {
+    /* Assignment(out, Operator(out)) */
+    Tensor<DataType, 3, DataLayout, int64_t> out(tensor_range);
+    // Offset with 1 to avoid tiny output (< 1e-6) as they can easily fail.
+    out = out.random() + DataType(1);
+    Tensor<DataType, 3, DataLayout, int64_t> reference(out);
+    DataType* gpu_data_out = static_cast<DataType*>(sycl_device.allocate(out.size() * sizeof(DataType)));
+    TensorMap<Tensor<DataType, 3, DataLayout, int64_t>> gpu_out(gpu_data_out, tensor_range);
+    sycl_device.memcpyHostToDevice(gpu_data_out, out.data(), (out.size()) * sizeof(DataType));
+    auto device_expr = gpu_out.device(sycl_device);
+    asgn(device_expr, op(gpu_out));
+    sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, (out.size()) * sizeof(DataType));
+    for (int64_t i = 0; i < out.size(); ++i) {
+      DataType ver = reference(i);
+      asgn(ver, op(reference(i)));
+      VERIFY_IS_APPROX(out(i), ver);
+    }
+    sycl_device.deallocate(gpu_data_out);
+  }
+}
+
+#define DECLARE_UNARY_STRUCT(FUNC)                                 \
+  struct op_##FUNC {                                               \
+    template <typename T>                                          \
+    auto operator()(const T& x) -> decltype(SYCL::FUNC(x)) {       \
+      return SYCL::FUNC(x);                                        \
+    }                                                              \
+    template <typename T>                                          \
+    auto operator()(const TensorMap<T>& x) -> decltype(x.FUNC()) { \
+      return x.FUNC();                                             \
+    }                                                              \
+  };
+
+DECLARE_UNARY_STRUCT(sqrt)
+DECLARE_UNARY_STRUCT(rsqrt)
+DECLARE_UNARY_STRUCT(tanh)
+DECLARE_UNARY_STRUCT(exp)
+DECLARE_UNARY_STRUCT(expm1)
+DECLARE_UNARY_STRUCT(log)
+DECLARE_UNARY_STRUCT(ceil)
+DECLARE_UNARY_STRUCT(floor)
+DECLARE_UNARY_STRUCT(round)
+DECLARE_UNARY_STRUCT(log1p)
+DECLARE_UNARY_STRUCT(sign)
+DECLARE_UNARY_STRUCT(isnan)
+DECLARE_UNARY_STRUCT(isfinite)
+DECLARE_UNARY_STRUCT(isinf)
+
+template <typename DataType, int DataLayout, typename Assignment>
+void test_unary_builtins_for_assignement(const Eigen::SyclDevice& sycl_device, const array<int64_t, 3>& tensor_range) {
+#define RUN_UNARY_TEST(FUNC) \
+  test_unary_builtins_for_scalar<DataType, DataLayout, Assignment, op_##FUNC>(sycl_device, tensor_range)
+  RUN_UNARY_TEST(abs);
+  RUN_UNARY_TEST(sqrt);
+  RUN_UNARY_TEST(rsqrt);
+  RUN_UNARY_TEST(square);
+  RUN_UNARY_TEST(cube);
+  RUN_UNARY_TEST(inverse);
+  RUN_UNARY_TEST(tanh);
+  RUN_UNARY_TEST(exp);
+  RUN_UNARY_TEST(expm1);
+  RUN_UNARY_TEST(log);
+  RUN_UNARY_TEST(ceil);
+  RUN_UNARY_TEST(floor);
+  RUN_UNARY_TEST(round);
+  RUN_UNARY_TEST(log1p);
+  RUN_UNARY_TEST(sign);
+}
+
+template <typename DataType, int DataLayout, typename Operator>
+void test_unary_builtins_return_bool(const Eigen::SyclDevice& sycl_device, const array<int64_t, 3>& tensor_range) {
+  /* out = op(in) */
+  Operator op;
+  Tensor<DataType, 3, DataLayout, int64_t> in(tensor_range);
+  Tensor<bool, 3, DataLayout, int64_t> out(tensor_range);
+  in = in.random() + DataType(0.01);
+  DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(in.size() * sizeof(DataType)));
+  bool* gpu_data_out = static_cast<bool*>(sycl_device.allocate(out.size() * sizeof(bool)));
+  TensorMap<Tensor<DataType, 3, DataLayout, int64_t>> gpu(gpu_data, tensor_range);
+  TensorMap<Tensor<bool, 3, DataLayout, int64_t>> gpu_out(gpu_data_out, tensor_range);
+  sycl_device.memcpyHostToDevice(gpu_data, in.data(), (in.size()) * sizeof(DataType));
+  gpu_out.device(sycl_device) = op(gpu);
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, (out.size()) * sizeof(bool));
+  for (int64_t i = 0; i < out.size(); ++i) {
+    VERIFY_IS_EQUAL(out(i), op(in(i)));
+  }
+  sycl_device.deallocate(gpu_data);
+  sycl_device.deallocate(gpu_data_out);
+}
+
+template <typename DataType, int DataLayout>
+void test_unary_builtins(const Eigen::SyclDevice& sycl_device, const array<int64_t, 3>& tensor_range) {
+  test_unary_builtins_for_assignement<DataType, DataLayout, PlusEqualAssignment>(sycl_device, tensor_range);
+  test_unary_builtins_for_assignement<DataType, DataLayout, EqualAssignment>(sycl_device, tensor_range);
+  test_unary_builtins_return_bool<DataType, DataLayout, op_isnan>(sycl_device, tensor_range);
+  test_unary_builtins_return_bool<DataType, DataLayout, op_isfinite>(sycl_device, tensor_range);
+  test_unary_builtins_return_bool<DataType, DataLayout, op_isinf>(sycl_device, tensor_range);
+}
+
+template <typename DataType>
+static void test_builtin_unary_sycl(const Eigen::SyclDevice& sycl_device) {
+  int64_t sizeDim1 = 10;
+  int64_t sizeDim2 = 10;
+  int64_t sizeDim3 = 10;
+  array<int64_t, 3> tensor_range = {{sizeDim1, sizeDim2, sizeDim3}};
+
+  test_unary_builtins<DataType, RowMajor>(sycl_device, tensor_range);
+  test_unary_builtins<DataType, ColMajor>(sycl_device, tensor_range);
+}
+
+template <typename DataType, int DataLayout, typename Operator>
+void test_binary_builtins_func(const Eigen::SyclDevice& sycl_device, const array<int64_t, 3>& tensor_range) {
+  /* out = op(in_1, in_2) */
+  Operator op;
+  Tensor<DataType, 3, DataLayout, int64_t> in_1(tensor_range);
+  Tensor<DataType, 3, DataLayout, int64_t> in_2(tensor_range);
+  Tensor<DataType, 3, DataLayout, int64_t> out(tensor_range);
+  in_1 = in_1.random() + DataType(0.01);
+  in_2 = in_2.random() + DataType(0.01);
+  Tensor<DataType, 3, DataLayout, int64_t> reference(out);
+  DataType* gpu_data_1 = static_cast<DataType*>(sycl_device.allocate(in_1.size() * sizeof(DataType)));
+  DataType* gpu_data_2 = static_cast<DataType*>(sycl_device.allocate(in_2.size() * sizeof(DataType)));
+  DataType* gpu_data_out = static_cast<DataType*>(sycl_device.allocate(out.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 3, DataLayout, int64_t>> gpu_1(gpu_data_1, tensor_range);
+  TensorMap<Tensor<DataType, 3, DataLayout, int64_t>> gpu_2(gpu_data_2, tensor_range);
+  TensorMap<Tensor<DataType, 3, DataLayout, int64_t>> gpu_out(gpu_data_out, tensor_range);
+  sycl_device.memcpyHostToDevice(gpu_data_1, in_1.data(), (in_1.size()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_data_2, in_2.data(), (in_2.size()) * sizeof(DataType));
+  gpu_out.device(sycl_device) = op(gpu_1, gpu_2);
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, (out.size()) * sizeof(DataType));
+  for (int64_t i = 0; i < out.size(); ++i) {
+    VERIFY_IS_APPROX(out(i), op(in_1(i), in_2(i)));
+  }
+  sycl_device.deallocate(gpu_data_1);
+  sycl_device.deallocate(gpu_data_2);
+  sycl_device.deallocate(gpu_data_out);
+}
+
+template <typename DataType, int DataLayout, typename Operator>
+void test_binary_builtins_fixed_arg2(const Eigen::SyclDevice& sycl_device, const array<int64_t, 3>& tensor_range) {
+  /* out = op(in_1, 2) */
+  Operator op;
+  const DataType arg2(2);
+  Tensor<DataType, 3, DataLayout, int64_t> in_1(tensor_range);
+  Tensor<DataType, 3, DataLayout, int64_t> out(tensor_range);
+  in_1 = in_1.random();
+  Tensor<DataType, 3, DataLayout, int64_t> reference(out);
+  DataType* gpu_data_1 = static_cast<DataType*>(sycl_device.allocate(in_1.size() * sizeof(DataType)));
+  DataType* gpu_data_out = static_cast<DataType*>(sycl_device.allocate(out.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 3, DataLayout, int64_t>> gpu_1(gpu_data_1, tensor_range);
+  TensorMap<Tensor<DataType, 3, DataLayout, int64_t>> gpu_out(gpu_data_out, tensor_range);
+  sycl_device.memcpyHostToDevice(gpu_data_1, in_1.data(), (in_1.size()) * sizeof(DataType));
+  gpu_out.device(sycl_device) = op(gpu_1, arg2);
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, (out.size()) * sizeof(DataType));
+  for (int64_t i = 0; i < out.size(); ++i) {
+    VERIFY_IS_APPROX(out(i), op(in_1(i), arg2));
+  }
+  sycl_device.deallocate(gpu_data_1);
+  sycl_device.deallocate(gpu_data_out);
+}
+
+#define DECLARE_BINARY_STRUCT(FUNC)                                                          \
+  struct op_##FUNC {                                                                         \
+    template <typename T1, typename T2>                                                      \
+    auto operator()(const T1& x, const T2& y) -> decltype(cl::sycl::FUNC(x, y)) {            \
+      return cl::sycl::FUNC(x, y);                                                           \
+    }                                                                                        \
+    template <typename T1, typename T2>                                                      \
+    auto operator()(const TensorMap<T1>& x, const TensorMap<T2>& y) -> decltype(x.FUNC(y)) { \
+      return x.FUNC(y);                                                                      \
+    }                                                                                        \
+  };
+
+#define DECLARE_BINARY_STRUCT_OP(NAME, OPERATOR)                          \
+  struct op_##NAME {                                                      \
+    template <typename T1, typename T2>                                   \
+    auto operator()(const T1& x, const T2& y) -> decltype(x OPERATOR y) { \
+      return x OPERATOR y;                                                \
+    }                                                                     \
+  };
+
+DECLARE_BINARY_STRUCT_OP(plus, +)
+DECLARE_BINARY_STRUCT_OP(minus, -)
+DECLARE_BINARY_STRUCT_OP(times, *)
+DECLARE_BINARY_STRUCT_OP(divide, /)
+DECLARE_BINARY_STRUCT_OP(modulo, %)
+
+template <typename DataType, int DataLayout>
+void test_binary_builtins(const Eigen::SyclDevice& sycl_device, const array<int64_t, 3>& tensor_range) {
+  test_binary_builtins_func<DataType, DataLayout, op_cwiseMax>(sycl_device, tensor_range);
+  test_binary_builtins_func<DataType, DataLayout, op_cwiseMin>(sycl_device, tensor_range);
+  test_binary_builtins_func<DataType, DataLayout, op_plus>(sycl_device, tensor_range);
+  test_binary_builtins_func<DataType, DataLayout, op_minus>(sycl_device, tensor_range);
+  test_binary_builtins_func<DataType, DataLayout, op_times>(sycl_device, tensor_range);
+  test_binary_builtins_func<DataType, DataLayout, op_divide>(sycl_device, tensor_range);
+}
+
+template <typename DataType>
+static void test_floating_builtin_binary_sycl(const Eigen::SyclDevice& sycl_device) {
+  int64_t sizeDim1 = 10;
+  int64_t sizeDim2 = 10;
+  int64_t sizeDim3 = 10;
+  array<int64_t, 3> tensor_range = {{sizeDim1, sizeDim2, sizeDim3}};
+  test_binary_builtins<DataType, RowMajor>(sycl_device, tensor_range);
+  test_binary_builtins<DataType, ColMajor>(sycl_device, tensor_range);
+}
+
+template <typename DataType>
+static void test_integer_builtin_binary_sycl(const Eigen::SyclDevice& sycl_device) {
+  int64_t sizeDim1 = 10;
+  int64_t sizeDim2 = 10;
+  int64_t sizeDim3 = 10;
+  array<int64_t, 3> tensor_range = {{sizeDim1, sizeDim2, sizeDim3}};
+  test_binary_builtins_fixed_arg2<DataType, RowMajor, op_modulo>(sycl_device, tensor_range);
+  test_binary_builtins_fixed_arg2<DataType, ColMajor, op_modulo>(sycl_device, tensor_range);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_builtins_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    QueueInterface queueInterface(device);
+    Eigen::SyclDevice sycl_device(&queueInterface);
+    CALL_SUBTEST_1(test_builtin_unary_sycl<half>(sycl_device));
+    CALL_SUBTEST_2(test_floating_builtin_binary_sycl<half>(sycl_device));
+    CALL_SUBTEST_3(test_builtin_unary_sycl<float>(sycl_device));
+    CALL_SUBTEST_4(test_floating_builtin_binary_sycl<float>(sycl_device));
+    CALL_SUBTEST_5(test_integer_builtin_binary_sycl<int>(sycl_device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_cast_float16_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_cast_float16_gpu.cu
new file mode 100644
index 0000000..ce5c4c3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_cast_float16_gpu.cu
@@ -0,0 +1,73 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+void test_gpu_conversion() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  Tensor<float, 1> floats(num_elem);
+  floats.setRandom();
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::half* d_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
+  float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_half(d_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv(d_conv, num_elem);
+
+  gpu_device.memcpyHostToDevice(d_float, floats.data(), num_elem * sizeof(float));
+
+  gpu_half.device(gpu_device) = gpu_float.cast<Eigen::half>();
+  gpu_conv.device(gpu_device) = gpu_half.cast<float>();
+
+  Tensor<float, 1> initial(num_elem);
+  Tensor<float, 1> final(num_elem);
+  gpu_device.memcpyDeviceToHost(initial.data(), d_float, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(final.data(), d_conv, num_elem * sizeof(float));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_APPROX(initial(i), final(i));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_half);
+  gpu_device.deallocate(d_conv);
+}
+
+void test_fallback_conversion() {
+  int num_elem = 101;
+  Tensor<float, 1> floats(num_elem);
+  floats.setRandom();
+
+  Eigen::Tensor<Eigen::half, 1> halfs = floats.cast<Eigen::half>();
+  Eigen::Tensor<float, 1> conv = halfs.cast<float>();
+
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_APPROX(floats(i), conv(i));
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_cast_float16_gpu) {
+  CALL_SUBTEST(test_gpu_conversion());
+  CALL_SUBTEST(test_fallback_conversion());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_casts.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_casts.cpp
new file mode 100644
index 0000000..9cd377c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_casts.cpp
@@ -0,0 +1,87 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include "random_without_cast_overflow.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::Tensor;
+
+template <typename FromType, typename ToType>
+static void test_type_cast() {
+  Tensor<FromType, 2> ftensor(101, 201);
+  // Generate random values for a valid cast.
+  for (int i = 0; i < 101; ++i) {
+    for (int j = 0; j < 201; ++j) {
+      ftensor(i, j) = internal::random_without_cast_overflow<FromType, ToType>::value();
+    }
+  }
+
+  Tensor<ToType, 2> ttensor(101, 201);
+  ttensor = ftensor.template cast<ToType>();
+
+  for (int i = 0; i < 101; ++i) {
+    for (int j = 0; j < 201; ++j) {
+      const ToType ref = internal::cast<FromType, ToType>(ftensor(i, j));
+      VERIFY_IS_EQUAL(ttensor(i, j), ref);
+    }
+  }
+}
+
+template <typename Scalar, typename EnableIf = void>
+struct test_cast_runner {
+  static void run() {
+    test_type_cast<Scalar, bool>();
+    test_type_cast<Scalar, int8_t>();
+    test_type_cast<Scalar, int16_t>();
+    test_type_cast<Scalar, int32_t>();
+    test_type_cast<Scalar, int64_t>();
+    test_type_cast<Scalar, uint8_t>();
+    test_type_cast<Scalar, uint16_t>();
+    test_type_cast<Scalar, uint32_t>();
+    test_type_cast<Scalar, uint64_t>();
+    test_type_cast<Scalar, half>();
+    test_type_cast<Scalar, bfloat16>();
+    test_type_cast<Scalar, float>();
+    test_type_cast<Scalar, double>();
+    test_type_cast<Scalar, std::complex<float>>();
+    test_type_cast<Scalar, std::complex<double>>();
+  }
+};
+
+// Only certain types allow cast from std::complex<>.
+template <typename Scalar>
+struct test_cast_runner<Scalar, std::enable_if_t<NumTraits<Scalar>::IsComplex>> {
+  static void run() {
+    test_type_cast<Scalar, half>();
+    test_type_cast<Scalar, bfloat16>();
+    test_type_cast<Scalar, std::complex<float>>();
+    test_type_cast<Scalar, std::complex<double>>();
+  }
+};
+
+EIGEN_DECLARE_TEST(cxx11_tensor_casts) {
+  CALL_SUBTEST(test_cast_runner<bool>::run());
+  CALL_SUBTEST(test_cast_runner<int8_t>::run());
+  CALL_SUBTEST(test_cast_runner<int16_t>::run());
+  CALL_SUBTEST(test_cast_runner<int32_t>::run());
+  CALL_SUBTEST(test_cast_runner<int64_t>::run());
+  CALL_SUBTEST(test_cast_runner<uint8_t>::run());
+  CALL_SUBTEST(test_cast_runner<uint16_t>::run());
+  CALL_SUBTEST(test_cast_runner<uint32_t>::run());
+  CALL_SUBTEST(test_cast_runner<uint64_t>::run());
+  CALL_SUBTEST(test_cast_runner<half>::run());
+  CALL_SUBTEST(test_cast_runner<bfloat16>::run());
+  CALL_SUBTEST(test_cast_runner<float>::run());
+  CALL_SUBTEST(test_cast_runner<double>::run());
+  CALL_SUBTEST(test_cast_runner<std::complex<float>>::run());
+  CALL_SUBTEST(test_cast_runner<std::complex<double>>::run());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_chipping.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_chipping.cpp
new file mode 100644
index 0000000..dbbd89d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_chipping.cpp
@@ -0,0 +1,419 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_simple_chip() {
+  Tensor<float, 5, DataLayout> tensor(2, 3, 5, 7, 11);
+  tensor.setRandom();
+
+  Tensor<float, 4, DataLayout> chip1;
+  chip1 = tensor.template chip<0>(1);
+
+  VERIFY_IS_EQUAL(chip1.dimension(0), 3);
+  VERIFY_IS_EQUAL(chip1.dimension(1), 5);
+  VERIFY_IS_EQUAL(chip1.dimension(2), 7);
+  VERIFY_IS_EQUAL(chip1.dimension(3), 11);
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip1(i, j, k, l), tensor(1, i, j, k, l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> chip2 = tensor.template chip<1>(1);
+  VERIFY_IS_EQUAL(chip2.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip2.dimension(1), 5);
+  VERIFY_IS_EQUAL(chip2.dimension(2), 7);
+  VERIFY_IS_EQUAL(chip2.dimension(3), 11);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip2(i, j, k, l), tensor(i, 1, j, k, l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> chip3 = tensor.template chip<2>(2);
+  VERIFY_IS_EQUAL(chip3.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip3.dimension(1), 3);
+  VERIFY_IS_EQUAL(chip3.dimension(2), 7);
+  VERIFY_IS_EQUAL(chip3.dimension(3), 11);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip3(i, j, k, l), tensor(i, j, 2, k, l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> chip4(tensor.template chip<3>(5));
+  VERIFY_IS_EQUAL(chip4.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip4.dimension(1), 3);
+  VERIFY_IS_EQUAL(chip4.dimension(2), 5);
+  VERIFY_IS_EQUAL(chip4.dimension(3), 11);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip4(i, j, k, l), tensor(i, j, k, 5, l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> chip5(tensor.template chip<4>(7));
+  VERIFY_IS_EQUAL(chip5.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip5.dimension(1), 3);
+  VERIFY_IS_EQUAL(chip5.dimension(2), 5);
+  VERIFY_IS_EQUAL(chip5.dimension(3), 7);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(chip5(i, j, k, l), tensor(i, j, k, l, 7));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_dynamic_chip() {
+  Tensor<float, 5, DataLayout> tensor(2, 3, 5, 7, 11);
+  tensor.setRandom();
+
+  Tensor<float, 4, DataLayout> chip1;
+  chip1 = tensor.chip(1, 0);
+  VERIFY_IS_EQUAL(chip1.dimension(0), 3);
+  VERIFY_IS_EQUAL(chip1.dimension(1), 5);
+  VERIFY_IS_EQUAL(chip1.dimension(2), 7);
+  VERIFY_IS_EQUAL(chip1.dimension(3), 11);
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip1(i, j, k, l), tensor(1, i, j, k, l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> chip2 = tensor.chip(1, 1);
+  VERIFY_IS_EQUAL(chip2.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip2.dimension(1), 5);
+  VERIFY_IS_EQUAL(chip2.dimension(2), 7);
+  VERIFY_IS_EQUAL(chip2.dimension(3), 11);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip2(i, j, k, l), tensor(i, 1, j, k, l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> chip3 = tensor.chip(2, 2);
+  VERIFY_IS_EQUAL(chip3.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip3.dimension(1), 3);
+  VERIFY_IS_EQUAL(chip3.dimension(2), 7);
+  VERIFY_IS_EQUAL(chip3.dimension(3), 11);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip3(i, j, k, l), tensor(i, j, 2, k, l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> chip4(tensor.chip(5, 3));
+  VERIFY_IS_EQUAL(chip4.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip4.dimension(1), 3);
+  VERIFY_IS_EQUAL(chip4.dimension(2), 5);
+  VERIFY_IS_EQUAL(chip4.dimension(3), 11);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(chip4(i, j, k, l), tensor(i, j, k, 5, l));
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> chip5(tensor.chip(7, 4));
+  VERIFY_IS_EQUAL(chip5.dimension(0), 2);
+  VERIFY_IS_EQUAL(chip5.dimension(1), 3);
+  VERIFY_IS_EQUAL(chip5.dimension(2), 5);
+  VERIFY_IS_EQUAL(chip5.dimension(3), 7);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(chip5(i, j, k, l), tensor(i, j, k, l, 7));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_chip_in_expr() {
+  Tensor<float, 5, DataLayout> input1(2, 3, 5, 7, 11);
+  input1.setRandom();
+  Tensor<float, 4, DataLayout> input2(3, 5, 7, 11);
+  input2.setRandom();
+
+  Tensor<float, 4, DataLayout> result = input1.template chip<0>(0) + input2;
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          float expected = input1(0, i, j, k, l) + input2(i, j, k, l);
+          VERIFY_IS_EQUAL(result(i, j, k, l), expected);
+        }
+      }
+    }
+  }
+
+  Tensor<float, 3, DataLayout> input3(3, 7, 11);
+  input3.setRandom();
+  Tensor<float, 3, DataLayout> result2 = input1.template chip<0>(0).template chip<1>(2) + input3;
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      for (int k = 0; k < 11; ++k) {
+        float expected = input1(0, i, 2, j, k) + input3(i, j, k);
+        VERIFY_IS_EQUAL(result2(i, j, k), expected);
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_chip_as_lvalue() {
+  Tensor<float, 5, DataLayout> input1(2, 3, 5, 7, 11);
+  input1.setRandom();
+
+  Tensor<float, 4, DataLayout> input2(3, 5, 7, 11);
+  input2.setRandom();
+  Tensor<float, 5, DataLayout> tensor = input1;
+  tensor.template chip<0>(1) = input2;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (i != 1) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input2(j, k, l, m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> input3(2, 5, 7, 11);
+  input3.setRandom();
+  tensor = input1;
+  tensor.template chip<1>(1) = input3;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (j != 1) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input3(i, k, l, m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> input4(2, 3, 7, 11);
+  input4.setRandom();
+  tensor = input1;
+  tensor.template chip<2>(3) = input4;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (k != 3) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input4(i, j, l, m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> input5(2, 3, 5, 11);
+  input5.setRandom();
+  tensor = input1;
+  tensor.template chip<3>(4) = input5;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (l != 4) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input5(i, j, k, m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  Tensor<float, 4, DataLayout> input6(2, 3, 5, 7);
+  input6.setRandom();
+  tensor = input1;
+  tensor.template chip<4>(5) = input6;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (m != 5) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input6(i, j, k, l));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  Tensor<float, 5, DataLayout> input7(2, 3, 5, 7, 11);
+  input7.setRandom();
+  tensor = input1;
+  tensor.chip(0, 0) = input7.chip(0, 0);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          for (int m = 0; m < 11; ++m) {
+            if (i != 0) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input7(i, j, k, l, m));
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+static void test_chip_raw_data_col_major() {
+  Tensor<float, 5, ColMajor> tensor(2, 3, 5, 7, 11);
+  tensor.setRandom();
+
+  typedef TensorEvaluator<decltype(tensor.chip<4>(3)), DefaultDevice> Evaluator4;
+  auto chip = Evaluator4(tensor.chip<4>(3), DefaultDevice());
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          int chip_index = i + 2 * (j + 3 * (k + 5 * l));
+          VERIFY_IS_EQUAL(chip.data()[chip_index], tensor(i, j, k, l, 3));
+        }
+      }
+    }
+  }
+
+  typedef TensorEvaluator<decltype(tensor.chip<0>(0)), DefaultDevice> Evaluator0;
+  auto chip0 = Evaluator0(tensor.chip<0>(0), DefaultDevice());
+  VERIFY_IS_EQUAL(chip0.data(), static_cast<float*>(0));
+
+  typedef TensorEvaluator<decltype(tensor.chip<1>(0)), DefaultDevice> Evaluator1;
+  auto chip1 = Evaluator1(tensor.chip<1>(0), DefaultDevice());
+  VERIFY_IS_EQUAL(chip1.data(), static_cast<float*>(0));
+
+  typedef TensorEvaluator<decltype(tensor.chip<2>(0)), DefaultDevice> Evaluator2;
+  auto chip2 = Evaluator2(tensor.chip<2>(0), DefaultDevice());
+  VERIFY_IS_EQUAL(chip2.data(), static_cast<float*>(0));
+
+  typedef TensorEvaluator<decltype(tensor.chip<3>(0)), DefaultDevice> Evaluator3;
+  auto chip3 = Evaluator3(tensor.chip<3>(0), DefaultDevice());
+  VERIFY_IS_EQUAL(chip3.data(), static_cast<float*>(0));
+}
+
+static void test_chip_raw_data_row_major() {
+  Tensor<float, 5, RowMajor> tensor(11, 7, 5, 3, 2);
+  tensor.setRandom();
+
+  typedef TensorEvaluator<decltype(tensor.chip<0>(3)), DefaultDevice> Evaluator0;
+  auto chip = Evaluator0(tensor.chip<0>(3), DefaultDevice());
+  for (int i = 0; i < 7; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 2; ++l) {
+          int chip_index = l + 2 * (k + 3 * (j + 5 * i));
+          VERIFY_IS_EQUAL(chip.data()[chip_index], tensor(3, i, j, k, l));
+        }
+      }
+    }
+  }
+
+  typedef TensorEvaluator<decltype(tensor.chip<1>(0)), DefaultDevice> Evaluator1;
+  auto chip1 = Evaluator1(tensor.chip<1>(0), DefaultDevice());
+  VERIFY_IS_EQUAL(chip1.data(), static_cast<float*>(0));
+
+  typedef TensorEvaluator<decltype(tensor.chip<2>(0)), DefaultDevice> Evaluator2;
+  auto chip2 = Evaluator2(tensor.chip<2>(0), DefaultDevice());
+  VERIFY_IS_EQUAL(chip2.data(), static_cast<float*>(0));
+
+  typedef TensorEvaluator<decltype(tensor.chip<3>(0)), DefaultDevice> Evaluator3;
+  auto chip3 = Evaluator3(tensor.chip<3>(0), DefaultDevice());
+  VERIFY_IS_EQUAL(chip3.data(), static_cast<float*>(0));
+
+  typedef TensorEvaluator<decltype(tensor.chip<4>(0)), DefaultDevice> Evaluator4;
+  auto chip4 = Evaluator4(tensor.chip<4>(0), DefaultDevice());
+  VERIFY_IS_EQUAL(chip4.data(), static_cast<float*>(0));
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_chipping) {
+  CALL_SUBTEST(test_simple_chip<ColMajor>());
+  CALL_SUBTEST(test_simple_chip<RowMajor>());
+  CALL_SUBTEST(test_dynamic_chip<ColMajor>());
+  CALL_SUBTEST(test_dynamic_chip<RowMajor>());
+  CALL_SUBTEST(test_chip_in_expr<ColMajor>());
+  CALL_SUBTEST(test_chip_in_expr<RowMajor>());
+  CALL_SUBTEST(test_chip_as_lvalue<ColMajor>());
+  CALL_SUBTEST(test_chip_as_lvalue<RowMajor>());
+  CALL_SUBTEST(test_chip_raw_data_col_major());
+  CALL_SUBTEST(test_chip_raw_data_row_major());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_chipping_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_chipping_sycl.cpp
new file mode 100644
index 0000000..cab0ae8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_chipping_sycl.cpp
@@ -0,0 +1,612 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_static_chip_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  IndexType sizeDim5 = 11;
+
+  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
+  array<IndexType, 4> chip1TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
+
+  Tensor<DataType, 5, DataLayout, IndexType> tensor(tensorRange);
+  Tensor<DataType, 4, DataLayout, IndexType> chip1(chip1TensorRange);
+
+  tensor.setRandom();
+
+  const size_t tensorBuffSize = tensor.size() * sizeof(DataType);
+  const size_t chip1TensorBuffSize = chip1.size() * sizeof(DataType);
+  DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_chip1 = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));
+
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip1(gpu_data_chip1, chip1TensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
+  gpu_chip1.device(sycl_device) = gpu_tensor.template chip<0l>(1l);
+  sycl_device.memcpyDeviceToHost(chip1.data(), gpu_data_chip1, chip1TensorBuffSize);
+
+  VERIFY_IS_EQUAL(chip1.dimension(0), sizeDim2);
+  VERIFY_IS_EQUAL(chip1.dimension(1), sizeDim3);
+  VERIFY_IS_EQUAL(chip1.dimension(2), sizeDim4);
+  VERIFY_IS_EQUAL(chip1.dimension(3), sizeDim5);
+
+  for (IndexType i = 0; i < sizeDim2; ++i) {
+    for (IndexType j = 0; j < sizeDim3; ++j) {
+      for (IndexType k = 0; k < sizeDim4; ++k) {
+        for (IndexType l = 0; l < sizeDim5; ++l) {
+          VERIFY_IS_EQUAL(chip1(i, j, k, l), tensor(1l, i, j, k, l));
+        }
+      }
+    }
+  }
+
+  array<IndexType, 4> chip2TensorRange = {{sizeDim1, sizeDim3, sizeDim4, sizeDim5}};
+  Tensor<DataType, 4, DataLayout, IndexType> chip2(chip2TensorRange);
+  const size_t chip2TensorBuffSize = chip2.size() * sizeof(DataType);
+  DataType* gpu_data_chip2 = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip2(gpu_data_chip2, chip2TensorRange);
+
+  gpu_chip2.device(sycl_device) = gpu_tensor.template chip<1l>(1l);
+  sycl_device.memcpyDeviceToHost(chip2.data(), gpu_data_chip2, chip2TensorBuffSize);
+
+  VERIFY_IS_EQUAL(chip2.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(chip2.dimension(1), sizeDim3);
+  VERIFY_IS_EQUAL(chip2.dimension(2), sizeDim4);
+  VERIFY_IS_EQUAL(chip2.dimension(3), sizeDim5);
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim3; ++j) {
+      for (IndexType k = 0; k < sizeDim4; ++k) {
+        for (IndexType l = 0; l < sizeDim5; ++l) {
+          VERIFY_IS_EQUAL(chip2(i, j, k, l), tensor(i, 1l, j, k, l));
+        }
+      }
+    }
+  }
+
+  array<IndexType, 4> chip3TensorRange = {{sizeDim1, sizeDim2, sizeDim4, sizeDim5}};
+  Tensor<DataType, 4, DataLayout, IndexType> chip3(chip3TensorRange);
+  const size_t chip3TensorBuffSize = chip3.size() * sizeof(DataType);
+  DataType* gpu_data_chip3 = static_cast<DataType*>(sycl_device.allocate(chip3TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip3(gpu_data_chip3, chip3TensorRange);
+
+  gpu_chip3.device(sycl_device) = gpu_tensor.template chip<2l>(2l);
+  sycl_device.memcpyDeviceToHost(chip3.data(), gpu_data_chip3, chip3TensorBuffSize);
+
+  VERIFY_IS_EQUAL(chip3.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(chip3.dimension(1), sizeDim2);
+  VERIFY_IS_EQUAL(chip3.dimension(2), sizeDim4);
+  VERIFY_IS_EQUAL(chip3.dimension(3), sizeDim5);
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim4; ++k) {
+        for (IndexType l = 0; l < sizeDim5; ++l) {
+          VERIFY_IS_EQUAL(chip3(i, j, k, l), tensor(i, j, 2l, k, l));
+        }
+      }
+    }
+  }
+
+  array<IndexType, 4> chip4TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim5}};
+  Tensor<DataType, 4, DataLayout, IndexType> chip4(chip4TensorRange);
+  const size_t chip4TensorBuffSize = chip4.size() * sizeof(DataType);
+  DataType* gpu_data_chip4 = static_cast<DataType*>(sycl_device.allocate(chip4TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip4(gpu_data_chip4, chip4TensorRange);
+
+  gpu_chip4.device(sycl_device) = gpu_tensor.template chip<3l>(5l);
+  sycl_device.memcpyDeviceToHost(chip4.data(), gpu_data_chip4, chip4TensorBuffSize);
+
+  VERIFY_IS_EQUAL(chip4.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(chip4.dimension(1), sizeDim2);
+  VERIFY_IS_EQUAL(chip4.dimension(2), sizeDim3);
+  VERIFY_IS_EQUAL(chip4.dimension(3), sizeDim5);
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        for (IndexType l = 0; l < sizeDim5; ++l) {
+          VERIFY_IS_EQUAL(chip4(i, j, k, l), tensor(i, j, k, 5l, l));
+        }
+      }
+    }
+  }
+
+  array<IndexType, 4> chip5TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  Tensor<DataType, 4, DataLayout, IndexType> chip5(chip5TensorRange);
+  const size_t chip5TensorBuffSize = chip5.size() * sizeof(DataType);
+  DataType* gpu_data_chip5 = static_cast<DataType*>(sycl_device.allocate(chip5TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip5(gpu_data_chip5, chip5TensorRange);
+
+  gpu_chip5.device(sycl_device) = gpu_tensor.template chip<4l>(7l);
+  sycl_device.memcpyDeviceToHost(chip5.data(), gpu_data_chip5, chip5TensorBuffSize);
+
+  VERIFY_IS_EQUAL(chip5.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(chip5.dimension(1), sizeDim2);
+  VERIFY_IS_EQUAL(chip5.dimension(2), sizeDim3);
+  VERIFY_IS_EQUAL(chip5.dimension(3), sizeDim4);
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        for (IndexType l = 0; l < sizeDim4; ++l) {
+          VERIFY_IS_EQUAL(chip5(i, j, k, l), tensor(i, j, k, l, 7l));
+        }
+      }
+    }
+  }
+
+  sycl_device.deallocate(gpu_data_tensor);
+  sycl_device.deallocate(gpu_data_chip1);
+  sycl_device.deallocate(gpu_data_chip2);
+  sycl_device.deallocate(gpu_data_chip3);
+  sycl_device.deallocate(gpu_data_chip4);
+  sycl_device.deallocate(gpu_data_chip5);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_dynamic_chip_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  IndexType sizeDim5 = 11;
+
+  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
+  array<IndexType, 4> chip1TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
+
+  Tensor<DataType, 5, DataLayout, IndexType> tensor(tensorRange);
+  Tensor<DataType, 4, DataLayout, IndexType> chip1(chip1TensorRange);
+
+  tensor.setRandom();
+
+  const size_t tensorBuffSize = tensor.size() * sizeof(DataType);
+  const size_t chip1TensorBuffSize = chip1.size() * sizeof(DataType);
+  DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_chip1 = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));
+
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip1(gpu_data_chip1, chip1TensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
+  gpu_chip1.device(sycl_device) = gpu_tensor.chip(1l, 0l);
+  sycl_device.memcpyDeviceToHost(chip1.data(), gpu_data_chip1, chip1TensorBuffSize);
+
+  VERIFY_IS_EQUAL(chip1.dimension(0), sizeDim2);
+  VERIFY_IS_EQUAL(chip1.dimension(1), sizeDim3);
+  VERIFY_IS_EQUAL(chip1.dimension(2), sizeDim4);
+  VERIFY_IS_EQUAL(chip1.dimension(3), sizeDim5);
+
+  for (IndexType i = 0; i < sizeDim2; ++i) {
+    for (IndexType j = 0; j < sizeDim3; ++j) {
+      for (IndexType k = 0; k < sizeDim4; ++k) {
+        for (IndexType l = 0; l < sizeDim5; ++l) {
+          VERIFY_IS_EQUAL(chip1(i, j, k, l), tensor(1l, i, j, k, l));
+        }
+      }
+    }
+  }
+
+  array<IndexType, 4> chip2TensorRange = {{sizeDim1, sizeDim3, sizeDim4, sizeDim5}};
+  Tensor<DataType, 4, DataLayout, IndexType> chip2(chip2TensorRange);
+  const size_t chip2TensorBuffSize = chip2.size() * sizeof(DataType);
+  DataType* gpu_data_chip2 = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip2(gpu_data_chip2, chip2TensorRange);
+
+  gpu_chip2.device(sycl_device) = gpu_tensor.chip(1l, 1l);
+  sycl_device.memcpyDeviceToHost(chip2.data(), gpu_data_chip2, chip2TensorBuffSize);
+
+  VERIFY_IS_EQUAL(chip2.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(chip2.dimension(1), sizeDim3);
+  VERIFY_IS_EQUAL(chip2.dimension(2), sizeDim4);
+  VERIFY_IS_EQUAL(chip2.dimension(3), sizeDim5);
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim3; ++j) {
+      for (IndexType k = 0; k < sizeDim4; ++k) {
+        for (IndexType l = 0; l < sizeDim5; ++l) {
+          VERIFY_IS_EQUAL(chip2(i, j, k, l), tensor(i, 1l, j, k, l));
+        }
+      }
+    }
+  }
+
+  array<IndexType, 4> chip3TensorRange = {{sizeDim1, sizeDim2, sizeDim4, sizeDim5}};
+  Tensor<DataType, 4, DataLayout, IndexType> chip3(chip3TensorRange);
+  const size_t chip3TensorBuffSize = chip3.size() * sizeof(DataType);
+  DataType* gpu_data_chip3 = static_cast<DataType*>(sycl_device.allocate(chip3TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip3(gpu_data_chip3, chip3TensorRange);
+
+  gpu_chip3.device(sycl_device) = gpu_tensor.chip(2l, 2l);
+  sycl_device.memcpyDeviceToHost(chip3.data(), gpu_data_chip3, chip3TensorBuffSize);
+
+  VERIFY_IS_EQUAL(chip3.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(chip3.dimension(1), sizeDim2);
+  VERIFY_IS_EQUAL(chip3.dimension(2), sizeDim4);
+  VERIFY_IS_EQUAL(chip3.dimension(3), sizeDim5);
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim4; ++k) {
+        for (IndexType l = 0; l < sizeDim5; ++l) {
+          VERIFY_IS_EQUAL(chip3(i, j, k, l), tensor(i, j, 2l, k, l));
+        }
+      }
+    }
+  }
+
+  array<IndexType, 4> chip4TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim5}};
+  Tensor<DataType, 4, DataLayout, IndexType> chip4(chip4TensorRange);
+  const size_t chip4TensorBuffSize = chip4.size() * sizeof(DataType);
+  DataType* gpu_data_chip4 = static_cast<DataType*>(sycl_device.allocate(chip4TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip4(gpu_data_chip4, chip4TensorRange);
+
+  gpu_chip4.device(sycl_device) = gpu_tensor.chip(5l, 3l);
+  sycl_device.memcpyDeviceToHost(chip4.data(), gpu_data_chip4, chip4TensorBuffSize);
+
+  VERIFY_IS_EQUAL(chip4.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(chip4.dimension(1), sizeDim2);
+  VERIFY_IS_EQUAL(chip4.dimension(2), sizeDim3);
+  VERIFY_IS_EQUAL(chip4.dimension(3), sizeDim5);
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        for (IndexType l = 0; l < sizeDim5; ++l) {
+          VERIFY_IS_EQUAL(chip4(i, j, k, l), tensor(i, j, k, 5l, l));
+        }
+      }
+    }
+  }
+
+  array<IndexType, 4> chip5TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  Tensor<DataType, 4, DataLayout, IndexType> chip5(chip5TensorRange);
+  const size_t chip5TensorBuffSize = chip5.size() * sizeof(DataType);
+  DataType* gpu_data_chip5 = static_cast<DataType*>(sycl_device.allocate(chip5TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip5(gpu_data_chip5, chip5TensorRange);
+
+  gpu_chip5.device(sycl_device) = gpu_tensor.chip(7l, 4l);
+  sycl_device.memcpyDeviceToHost(chip5.data(), gpu_data_chip5, chip5TensorBuffSize);
+
+  VERIFY_IS_EQUAL(chip5.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(chip5.dimension(1), sizeDim2);
+  VERIFY_IS_EQUAL(chip5.dimension(2), sizeDim3);
+  VERIFY_IS_EQUAL(chip5.dimension(3), sizeDim4);
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        for (IndexType l = 0; l < sizeDim4; ++l) {
+          VERIFY_IS_EQUAL(chip5(i, j, k, l), tensor(i, j, k, l, 7l));
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data_tensor);
+  sycl_device.deallocate(gpu_data_chip1);
+  sycl_device.deallocate(gpu_data_chip2);
+  sycl_device.deallocate(gpu_data_chip3);
+  sycl_device.deallocate(gpu_data_chip4);
+  sycl_device.deallocate(gpu_data_chip5);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_chip_in_expr(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  IndexType sizeDim5 = 11;
+
+  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
+  array<IndexType, 4> chip1TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
+
+  Tensor<DataType, 5, DataLayout, IndexType> tensor(tensorRange);
+
+  Tensor<DataType, 4, DataLayout, IndexType> chip1(chip1TensorRange);
+  Tensor<DataType, 4, DataLayout, IndexType> tensor1(chip1TensorRange);
+  tensor.setRandom();
+  tensor1.setRandom();
+
+  const size_t tensorBuffSize = tensor.size() * sizeof(DataType);
+  const size_t chip1TensorBuffSize = chip1.size() * sizeof(DataType);
+  DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_chip1 = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));
+  DataType* gpu_data_tensor1 = static_cast<DataType*>(sycl_device.allocate(chip1TensorBuffSize));
+
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_chip1(gpu_data_chip1, chip1TensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_tensor1(gpu_data_tensor1, chip1TensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
+  sycl_device.memcpyHostToDevice(gpu_data_tensor1, tensor1.data(), chip1TensorBuffSize);
+  gpu_chip1.device(sycl_device) = gpu_tensor.template chip<0l>(0l) + gpu_tensor1;
+  sycl_device.memcpyDeviceToHost(chip1.data(), gpu_data_chip1, chip1TensorBuffSize);
+
+  for (int i = 0; i < sizeDim2; ++i) {
+    for (int j = 0; j < sizeDim3; ++j) {
+      for (int k = 0; k < sizeDim4; ++k) {
+        for (int l = 0; l < sizeDim5; ++l) {
+          float expected = tensor(0l, i, j, k, l) + tensor1(i, j, k, l);
+          VERIFY_IS_EQUAL(chip1(i, j, k, l), expected);
+        }
+      }
+    }
+  }
+
+  array<IndexType, 3> chip2TensorRange = {{sizeDim2, sizeDim4, sizeDim5}};
+  Tensor<DataType, 3, DataLayout, IndexType> tensor2(chip2TensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> chip2(chip2TensorRange);
+  tensor2.setRandom();
+  const size_t chip2TensorBuffSize = tensor2.size() * sizeof(DataType);
+  DataType* gpu_data_tensor2 = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
+  DataType* gpu_data_chip2 = static_cast<DataType*>(sycl_device.allocate(chip2TensorBuffSize));
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_tensor2(gpu_data_tensor2, chip2TensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_chip2(gpu_data_chip2, chip2TensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_tensor2, tensor2.data(), chip2TensorBuffSize);
+  gpu_chip2.device(sycl_device) = gpu_tensor.template chip<0l>(0l).template chip<1l>(2l) + gpu_tensor2;
+  sycl_device.memcpyDeviceToHost(chip2.data(), gpu_data_chip2, chip2TensorBuffSize);
+
+  for (int i = 0; i < sizeDim2; ++i) {
+    for (int j = 0; j < sizeDim4; ++j) {
+      for (int k = 0; k < sizeDim5; ++k) {
+        float expected = tensor(0l, i, 2l, j, k) + tensor2(i, j, k);
+        VERIFY_IS_EQUAL(chip2(i, j, k), expected);
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data_tensor);
+  sycl_device.deallocate(gpu_data_tensor1);
+  sycl_device.deallocate(gpu_data_chip1);
+  sycl_device.deallocate(gpu_data_tensor2);
+  sycl_device.deallocate(gpu_data_chip2);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_chip_as_lvalue_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  IndexType sizeDim5 = 11;
+
+  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
+  array<IndexType, 4> input2TensorRange = {{sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
+
+  Tensor<DataType, 5, DataLayout, IndexType> tensor(tensorRange);
+  Tensor<DataType, 5, DataLayout, IndexType> input1(tensorRange);
+  Tensor<DataType, 4, DataLayout, IndexType> input2(input2TensorRange);
+  input1.setRandom();
+  input2.setRandom();
+
+  const size_t tensorBuffSize = tensor.size() * sizeof(DataType);
+  const size_t input2TensorBuffSize = input2.size() * sizeof(DataType);
+  std::cout << tensorBuffSize << " , " << input2TensorBuffSize << std::endl;
+  DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_input1 = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_input2 = static_cast<DataType*>(sycl_device.allocate(input2TensorBuffSize));
+
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_input1(gpu_data_input1, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_input2(gpu_data_input2, input2TensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_input1, input1.data(), tensorBuffSize);
+  gpu_tensor.device(sycl_device) = gpu_input1;
+  sycl_device.memcpyHostToDevice(gpu_data_input2, input2.data(), input2TensorBuffSize);
+  gpu_tensor.template chip<0l>(1l).device(sycl_device) = gpu_input2;
+  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
+
+  for (int i = 0; i < sizeDim1; ++i) {
+    for (int j = 0; j < sizeDim2; ++j) {
+      for (int k = 0; k < sizeDim3; ++k) {
+        for (int l = 0; l < sizeDim4; ++l) {
+          for (int m = 0; m < sizeDim5; ++m) {
+            if (i != 1) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input2(j, k, l, m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  gpu_tensor.device(sycl_device) = gpu_input1;
+  array<IndexType, 4> input3TensorRange = {{sizeDim1, sizeDim3, sizeDim4, sizeDim5}};
+  Tensor<DataType, 4, DataLayout, IndexType> input3(input3TensorRange);
+  input3.setRandom();
+
+  const size_t input3TensorBuffSize = input3.size() * sizeof(DataType);
+  DataType* gpu_data_input3 = static_cast<DataType*>(sycl_device.allocate(input3TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_input3(gpu_data_input3, input3TensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_input3, input3.data(), input3TensorBuffSize);
+  gpu_tensor.template chip<1l>(1l).device(sycl_device) = gpu_input3;
+  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
+
+  for (int i = 0; i < sizeDim1; ++i) {
+    for (int j = 0; j < sizeDim2; ++j) {
+      for (int k = 0; k < sizeDim3; ++k) {
+        for (int l = 0; l < sizeDim4; ++l) {
+          for (int m = 0; m < sizeDim5; ++m) {
+            if (j != 1) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input3(i, k, l, m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  gpu_tensor.device(sycl_device) = gpu_input1;
+  array<IndexType, 4> input4TensorRange = {{sizeDim1, sizeDim2, sizeDim4, sizeDim5}};
+  Tensor<DataType, 4, DataLayout, IndexType> input4(input4TensorRange);
+  input4.setRandom();
+
+  const size_t input4TensorBuffSize = input4.size() * sizeof(DataType);
+  DataType* gpu_data_input4 = static_cast<DataType*>(sycl_device.allocate(input4TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_input4(gpu_data_input4, input4TensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_input4, input4.data(), input4TensorBuffSize);
+  gpu_tensor.template chip<2l>(3l).device(sycl_device) = gpu_input4;
+  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
+
+  for (int i = 0; i < sizeDim1; ++i) {
+    for (int j = 0; j < sizeDim2; ++j) {
+      for (int k = 0; k < sizeDim3; ++k) {
+        for (int l = 0; l < sizeDim4; ++l) {
+          for (int m = 0; m < sizeDim5; ++m) {
+            if (k != 3) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input4(i, j, l, m));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  gpu_tensor.device(sycl_device) = gpu_input1;
+  array<IndexType, 4> input5TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim5}};
+  Tensor<DataType, 4, DataLayout, IndexType> input5(input5TensorRange);
+  input5.setRandom();
+
+  const size_t input5TensorBuffSize = input5.size() * sizeof(DataType);
+  DataType* gpu_data_input5 = static_cast<DataType*>(sycl_device.allocate(input5TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_input5(gpu_data_input5, input5TensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_input5, input5.data(), input5TensorBuffSize);
+  gpu_tensor.template chip<3l>(4l).device(sycl_device) = gpu_input5;
+  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
+
+  for (int i = 0; i < sizeDim1; ++i) {
+    for (int j = 0; j < sizeDim2; ++j) {
+      for (int k = 0; k < sizeDim3; ++k) {
+        for (int l = 0; l < sizeDim4; ++l) {
+          for (int m = 0; m < sizeDim5; ++m) {
+            if (l != 4) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input5(i, j, k, m));
+            }
+          }
+        }
+      }
+    }
+  }
+  gpu_tensor.device(sycl_device) = gpu_input1;
+  array<IndexType, 4> input6TensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  Tensor<DataType, 4, DataLayout, IndexType> input6(input6TensorRange);
+  input6.setRandom();
+
+  const size_t input6TensorBuffSize = input6.size() * sizeof(DataType);
+  DataType* gpu_data_input6 = static_cast<DataType*>(sycl_device.allocate(input6TensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_input6(gpu_data_input6, input6TensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_input6, input6.data(), input6TensorBuffSize);
+  gpu_tensor.template chip<4l>(5l).device(sycl_device) = gpu_input6;
+  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
+
+  for (int i = 0; i < sizeDim1; ++i) {
+    for (int j = 0; j < sizeDim2; ++j) {
+      for (int k = 0; k < sizeDim3; ++k) {
+        for (int l = 0; l < sizeDim4; ++l) {
+          for (int m = 0; m < sizeDim5; ++m) {
+            if (m != 5) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input6(i, j, k, l));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  gpu_tensor.device(sycl_device) = gpu_input1;
+  Tensor<DataType, 5, DataLayout, IndexType> input7(tensorRange);
+  input7.setRandom();
+
+  DataType* gpu_data_input7 = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_input7(gpu_data_input7, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_input7, input7.data(), tensorBuffSize);
+  gpu_tensor.chip(0l, 0l).device(sycl_device) = gpu_input7.chip(0l, 0l);
+  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data_tensor, tensorBuffSize);
+
+  for (int i = 0; i < sizeDim1; ++i) {
+    for (int j = 0; j < sizeDim2; ++j) {
+      for (int k = 0; k < sizeDim3; ++k) {
+        for (int l = 0; l < sizeDim4; ++l) {
+          for (int m = 0; m < sizeDim5; ++m) {
+            if (i != 0) {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
+            } else {
+              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input7(i, j, k, l, m));
+            }
+          }
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data_tensor);
+  sycl_device.deallocate(gpu_data_input1);
+  sycl_device.deallocate(gpu_data_input2);
+  sycl_device.deallocate(gpu_data_input3);
+  sycl_device.deallocate(gpu_data_input4);
+  sycl_device.deallocate(gpu_data_input5);
+  sycl_device.deallocate(gpu_data_input6);
+  sycl_device.deallocate(gpu_data_input7);
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_chipping_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  /* test_static_chip_sycl<DataType, RowMajor, int64_t>(sycl_device);
+   test_static_chip_sycl<DataType, ColMajor, int64_t>(sycl_device);
+   test_dynamic_chip_sycl<DataType, RowMajor, int64_t>(sycl_device);
+   test_dynamic_chip_sycl<DataType, ColMajor, int64_t>(sycl_device);
+   test_chip_in_expr<DataType, RowMajor, int64_t>(sycl_device);
+   test_chip_in_expr<DataType, ColMajor, int64_t>(sycl_device);*/
+  test_chip_as_lvalue_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  // test_chip_as_lvalue_sycl<DataType, ColMajor, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_chipping_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_chipping_test_per_device<float>(device));
+    CALL_SUBTEST(sycl_chipping_test_per_device<half>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_comparisons.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_comparisons.cpp
new file mode 100644
index 0000000..17a1776
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_comparisons.cpp
@@ -0,0 +1,192 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+using Scalar = float;
+
+using TypedLTOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT, true>;
+using TypedLEOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE, true>;
+using TypedGTOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT, true>;
+using TypedGEOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE, true>;
+using TypedEQOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ, true>;
+using TypedNEOp = internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ, true>;
+
+static void test_orderings() {
+  Tensor<Scalar, 3> mat1(2, 3, 7);
+  Tensor<Scalar, 3> mat2(2, 3, 7);
+
+  mat1.setRandom();
+  mat2.setRandom();
+
+  Tensor<bool, 3> lt(2, 3, 7);
+  Tensor<bool, 3> le(2, 3, 7);
+  Tensor<bool, 3> gt(2, 3, 7);
+  Tensor<bool, 3> ge(2, 3, 7);
+
+  Tensor<Scalar, 3> typed_lt(2, 3, 7);
+  Tensor<Scalar, 3> typed_le(2, 3, 7);
+  Tensor<Scalar, 3> typed_gt(2, 3, 7);
+  Tensor<Scalar, 3> typed_ge(2, 3, 7);
+
+  lt = mat1 < mat2;
+  le = mat1 <= mat2;
+  gt = mat1 > mat2;
+  ge = mat1 >= mat2;
+
+  typed_lt = mat1.binaryExpr(mat2, TypedLTOp());
+  typed_le = mat1.binaryExpr(mat2, TypedLEOp());
+  typed_gt = mat1.binaryExpr(mat2, TypedGTOp());
+  typed_ge = mat1.binaryExpr(mat2, TypedGEOp());
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(lt(i, j, k), mat1(i, j, k) < mat2(i, j, k));
+        VERIFY_IS_EQUAL(le(i, j, k), mat1(i, j, k) <= mat2(i, j, k));
+        VERIFY_IS_EQUAL(gt(i, j, k), mat1(i, j, k) > mat2(i, j, k));
+        VERIFY_IS_EQUAL(ge(i, j, k), mat1(i, j, k) >= mat2(i, j, k));
+
+        VERIFY_IS_EQUAL(lt(i, j, k), (bool)typed_lt(i, j, k));
+        VERIFY_IS_EQUAL(le(i, j, k), (bool)typed_le(i, j, k));
+        VERIFY_IS_EQUAL(gt(i, j, k), (bool)typed_gt(i, j, k));
+        VERIFY_IS_EQUAL(ge(i, j, k), (bool)typed_ge(i, j, k));
+      }
+    }
+  }
+}
+
+static void test_equality() {
+  Tensor<Scalar, 3> mat1(2, 3, 7);
+  Tensor<Scalar, 3> mat2(2, 3, 7);
+
+  mat1.setRandom();
+  mat2.setRandom();
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        if (internal::random<bool>()) {
+          mat2(i, j, k) = mat1(i, j, k);
+        }
+      }
+    }
+  }
+
+  Tensor<bool, 3> eq(2, 3, 7);
+  Tensor<bool, 3> ne(2, 3, 7);
+
+  Tensor<Scalar, 3> typed_eq(2, 3, 7);
+  Tensor<Scalar, 3> typed_ne(2, 3, 7);
+
+  eq = (mat1 == mat2);
+  ne = (mat1 != mat2);
+
+  typed_eq = mat1.binaryExpr(mat2, TypedEQOp());
+  typed_ne = mat1.binaryExpr(mat2, TypedNEOp());
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(eq(i, j, k), mat1(i, j, k) == mat2(i, j, k));
+        VERIFY_IS_EQUAL(ne(i, j, k), mat1(i, j, k) != mat2(i, j, k));
+
+        VERIFY_IS_EQUAL(eq(i, j, k), (bool)typed_eq(i, j, k));
+        VERIFY_IS_EQUAL(ne(i, j, k), (bool)typed_ne(i, j, k));
+      }
+    }
+  }
+}
+
+static void test_isnan() {
+  Tensor<Scalar, 3> mat(2, 3, 7);
+
+  mat.setRandom();
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        if (internal::random<bool>()) {
+          mat(i, j, k) = std::numeric_limits<Scalar>::quiet_NaN();
+        }
+      }
+    }
+  }
+  Tensor<bool, 3> nan(2, 3, 7);
+  nan = (mat.isnan)();
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(nan(i, j, k), (std::isnan)(mat(i, j, k)));
+      }
+    }
+  }
+}
+
+static void test_isinf() {
+  Tensor<Scalar, 3> mat(2, 3, 7);
+
+  mat.setRandom();
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        if (internal::random<bool>()) {
+          mat(i, j, k) = std::numeric_limits<Scalar>::infinity();
+        }
+      }
+    }
+  }
+  Tensor<bool, 3> inf(2, 3, 7);
+  inf = (mat.isinf)();
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(inf(i, j, k), (std::isinf)(mat(i, j, k)));
+      }
+    }
+  }
+}
+
+static void test_isfinite() {
+  Tensor<Scalar, 3> mat(2, 3, 7);
+
+  mat.setRandom();
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        if (internal::random<bool>()) {
+          mat(i, j, k) = std::numeric_limits<Scalar>::infinity();
+        }
+        if (internal::random<bool>()) {
+          mat(i, j, k) = std::numeric_limits<Scalar>::quiet_NaN();
+        }
+      }
+    }
+  }
+  Tensor<bool, 3> inf(2, 3, 7);
+  inf = (mat.isfinite)();
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(inf(i, j, k), (std::isfinite)(mat(i, j, k)));
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_comparisons) {
+  CALL_SUBTEST(test_orderings());
+  CALL_SUBTEST(test_equality());
+  CALL_SUBTEST(test_isnan());
+  CALL_SUBTEST(test_isinf());
+  CALL_SUBTEST(test_isfinite());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_complex_cwise_ops_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_complex_cwise_ops_gpu.cu
new file mode 100644
index 0000000..6daa0a7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_complex_cwise_ops_gpu.cu
@@ -0,0 +1,90 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <typename T>
+void test_cuda_complex_cwise_ops() {
+  const int kNumItems = 2;
+  std::size_t complex_bytes = kNumItems * sizeof(std::complex<T>);
+
+  std::complex<T>* d_in1;
+  std::complex<T>* d_in2;
+  std::complex<T>* d_out;
+  cudaMalloc((void**)(&d_in1), complex_bytes);
+  cudaMalloc((void**)(&d_in2), complex_bytes);
+  cudaMalloc((void**)(&d_out), complex_bytes);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_in1(d_in1, kNumItems);
+  Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_in2(d_in2, kNumItems);
+  Eigen::TensorMap<Eigen::Tensor<std::complex<T>, 1, 0, int>, Eigen::Aligned> gpu_out(d_out, kNumItems);
+
+  const std::complex<T> a(3.14f, 2.7f);
+  const std::complex<T> b(-10.6f, 1.4f);
+
+  gpu_in1.device(gpu_device) = gpu_in1.constant(a);
+  gpu_in2.device(gpu_device) = gpu_in2.constant(b);
+
+  enum CwiseOp { Add = 0, Sub, Mul, Div, Neg, NbOps };
+
+  Tensor<std::complex<T>, 1, 0, int> actual(kNumItems);
+  for (int op = Add; op < NbOps; op++) {
+    std::complex<T> expected;
+    switch (static_cast<CwiseOp>(op)) {
+      case Add:
+        gpu_out.device(gpu_device) = gpu_in1 + gpu_in2;
+        expected = a + b;
+        break;
+      case Sub:
+        gpu_out.device(gpu_device) = gpu_in1 - gpu_in2;
+        expected = a - b;
+        break;
+      case Mul:
+        gpu_out.device(gpu_device) = gpu_in1 * gpu_in2;
+        expected = a * b;
+        break;
+      case Div:
+        gpu_out.device(gpu_device) = gpu_in1 / gpu_in2;
+        expected = a / b;
+        break;
+      case Neg:
+        gpu_out.device(gpu_device) = -gpu_in1;
+        expected = -a;
+        break;
+      case NbOps:
+        break;
+    }
+    assert(cudaMemcpyAsync(actual.data(), d_out, complex_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) ==
+           cudaSuccess);
+    assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
+
+    for (int i = 0; i < kNumItems; ++i) {
+      VERIFY_IS_APPROX(actual(i), expected);
+    }
+  }
+
+  cudaFree(d_in1);
+  cudaFree(d_in2);
+  cudaFree(d_out);
+}
+
+EIGEN_DECLARE_TEST(test_cxx11_tensor_complex_cwise_ops) {
+  CALL_SUBTEST(test_cuda_complex_cwise_ops<float>());
+  CALL_SUBTEST(test_cuda_complex_cwise_ops<double>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_complex_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_complex_gpu.cu
new file mode 100644
index 0000000..12f11e0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_complex_gpu.cu
@@ -0,0 +1,176 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+void test_cuda_nullary() {
+  Tensor<std::complex<float>, 1, 0, int> in1(2);
+  Tensor<std::complex<float>, 1, 0, int> in2(2);
+  in1.setRandom();
+  in2.setRandom();
+
+  std::size_t float_bytes = in1.size() * sizeof(float);
+  std::size_t complex_bytes = in1.size() * sizeof(std::complex<float>);
+
+  std::complex<float>* d_in1;
+  std::complex<float>* d_in2;
+  float* d_out2;
+  cudaMalloc((void**)(&d_in1), complex_bytes);
+  cudaMalloc((void**)(&d_in2), complex_bytes);
+  cudaMalloc((void**)(&d_out2), float_bytes);
+  cudaMemcpy(d_in1, in1.data(), complex_bytes, cudaMemcpyHostToDevice);
+  cudaMemcpy(d_in2, in2.data(), complex_bytes, cudaMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<std::complex<float>, 1, 0, int>, Eigen::Aligned> gpu_in1(d_in1, 2);
+  Eigen::TensorMap<Eigen::Tensor<std::complex<float>, 1, 0, int>, Eigen::Aligned> gpu_in2(d_in2, 2);
+  Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_out2(d_out2, 2);
+
+  gpu_in1.device(gpu_device) = gpu_in1.constant(std::complex<float>(3.14f, 2.7f));
+  gpu_out2.device(gpu_device) = gpu_in2.abs();
+
+  Tensor<std::complex<float>, 1, 0, int> new1(2);
+  Tensor<float, 1, 0, int> new2(2);
+
+  assert(cudaMemcpyAsync(new1.data(), d_in1, complex_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) ==
+         cudaSuccess);
+  assert(cudaMemcpyAsync(new2.data(), d_out2, float_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
+
+  assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
+
+  for (int i = 0; i < 2; ++i) {
+    VERIFY_IS_APPROX(new1(i), std::complex<float>(3.14f, 2.7f));
+    VERIFY_IS_APPROX(new2(i), std::abs(in2(i)));
+  }
+
+  cudaFree(d_in1);
+  cudaFree(d_in2);
+  cudaFree(d_out2);
+}
+
+static void test_cuda_sum_reductions() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  const int num_rows = internal::random<int>(1024, 5 * 1024);
+  const int num_cols = internal::random<int>(1024, 5 * 1024);
+
+  Tensor<std::complex<float>, 2> in(num_rows, num_cols);
+  in.setRandom();
+
+  Tensor<std::complex<float>, 0> full_redux;
+  full_redux = in.sum();
+
+  std::size_t in_bytes = in.size() * sizeof(std::complex<float>);
+  std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>);
+  std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes));
+  std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes));
+  gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
+
+  TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols);
+  TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr);
+
+  out_gpu.device(gpu_device) = in_gpu.sum();
+
+  Tensor<std::complex<float>, 0> full_redux_gpu;
+  gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
+  gpu_device.synchronize();
+
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
+
+  gpu_device.deallocate(gpu_in_ptr);
+  gpu_device.deallocate(gpu_out_ptr);
+}
+
+static void test_cuda_mean_reductions() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  const int num_rows = internal::random<int>(1024, 5 * 1024);
+  const int num_cols = internal::random<int>(1024, 5 * 1024);
+
+  Tensor<std::complex<float>, 2> in(num_rows, num_cols);
+  in.setRandom();
+
+  Tensor<std::complex<float>, 0> full_redux;
+  full_redux = in.mean();
+
+  std::size_t in_bytes = in.size() * sizeof(std::complex<float>);
+  std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>);
+  std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes));
+  std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes));
+  gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
+
+  TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols);
+  TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr);
+
+  out_gpu.device(gpu_device) = in_gpu.mean();
+
+  Tensor<std::complex<float>, 0> full_redux_gpu;
+  gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
+  gpu_device.synchronize();
+
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
+
+  gpu_device.deallocate(gpu_in_ptr);
+  gpu_device.deallocate(gpu_out_ptr);
+}
+
+static void test_cuda_product_reductions() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  const int num_rows = internal::random<int>(1024, 5 * 1024);
+  const int num_cols = internal::random<int>(1024, 5 * 1024);
+
+  Tensor<std::complex<float>, 2> in(num_rows, num_cols);
+  in.setRandom();
+
+  Tensor<std::complex<float>, 0> full_redux;
+  full_redux = in.prod();
+
+  std::size_t in_bytes = in.size() * sizeof(std::complex<float>);
+  std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>);
+  std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes));
+  std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes));
+  gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
+
+  TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols);
+  TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr);
+
+  out_gpu.device(gpu_device) = in_gpu.prod();
+
+  Tensor<std::complex<float>, 0> full_redux_gpu;
+  gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
+  gpu_device.synchronize();
+
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
+
+  gpu_device.deallocate(gpu_in_ptr);
+  gpu_device.deallocate(gpu_out_ptr);
+}
+
+EIGEN_DECLARE_TEST(test_cxx11_tensor_complex) {
+  CALL_SUBTEST(test_cuda_nullary());
+  CALL_SUBTEST(test_cuda_sum_reductions());
+  CALL_SUBTEST(test_cuda_mean_reductions());
+  CALL_SUBTEST(test_cuda_product_reductions());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_concatenation.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_concatenation.cpp
new file mode 100644
index 0000000..562ac77
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_concatenation.cpp
@@ -0,0 +1,128 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_dimension_failures() {
+  Tensor<int, 3, DataLayout> left(2, 3, 1);
+  Tensor<int, 3, DataLayout> right(3, 3, 1);
+  left.setRandom();
+  right.setRandom();
+
+  // Okay; other dimensions are equal.
+  Tensor<int, 3, DataLayout> concatenation = left.concatenate(right, 0);
+
+  // Dimension mismatches.
+  VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, 1));
+  VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, 2));
+
+  // Axis > NumDims or < 0.
+  VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, 3));
+  VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, -1));
+}
+
+template <int DataLayout>
+static void test_static_dimension_failure() {
+  Tensor<int, 2, DataLayout> left(2, 3);
+  Tensor<int, 3, DataLayout> right(2, 3, 1);
+  left.setRandom();
+  right.setRandom();
+
+#ifdef CXX11_TENSOR_CONCATENATION_STATIC_DIMENSION_FAILURE
+  // Technically compatible, but we static assert that the inputs have same
+  // NumDims.
+  Tensor<int, 3, DataLayout> concatenation = left.concatenate(right, 0);
+#endif
+
+  // This can be worked around in this case.
+  Tensor<int, 3, DataLayout> concatenation = left.reshape(Tensor<int, 3>::Dimensions(2, 3, 1)).concatenate(right, 0);
+  Tensor<int, 2, DataLayout> alternative = left.concatenate(right.reshape(Tensor<int, 2>::Dimensions(2, 3)), 0);
+}
+
+template <int DataLayout>
+static void test_simple_concatenation() {
+  Tensor<int, 3, DataLayout> left(2, 3, 1);
+  Tensor<int, 3, DataLayout> right(2, 3, 1);
+  left.setRandom();
+  right.setRandom();
+
+  Tensor<int, 3, DataLayout> concatenation = left.concatenate(right, 0);
+  VERIFY_IS_EQUAL(concatenation.dimension(0), 4);
+  VERIFY_IS_EQUAL(concatenation.dimension(1), 3);
+  VERIFY_IS_EQUAL(concatenation.dimension(2), 1);
+  for (int j = 0; j < 3; ++j) {
+    for (int i = 0; i < 2; ++i) {
+      VERIFY_IS_EQUAL(concatenation(i, j, 0), left(i, j, 0));
+    }
+    for (int i = 2; i < 4; ++i) {
+      VERIFY_IS_EQUAL(concatenation(i, j, 0), right(i - 2, j, 0));
+    }
+  }
+
+  concatenation = left.concatenate(right, 1);
+  VERIFY_IS_EQUAL(concatenation.dimension(0), 2);
+  VERIFY_IS_EQUAL(concatenation.dimension(1), 6);
+  VERIFY_IS_EQUAL(concatenation.dimension(2), 1);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(concatenation(i, j, 0), left(i, j, 0));
+    }
+    for (int j = 3; j < 6; ++j) {
+      VERIFY_IS_EQUAL(concatenation(i, j, 0), right(i, j - 3, 0));
+    }
+  }
+
+  concatenation = left.concatenate(right, 2);
+  VERIFY_IS_EQUAL(concatenation.dimension(0), 2);
+  VERIFY_IS_EQUAL(concatenation.dimension(1), 3);
+  VERIFY_IS_EQUAL(concatenation.dimension(2), 2);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(concatenation(i, j, 0), left(i, j, 0));
+      VERIFY_IS_EQUAL(concatenation(i, j, 1), right(i, j, 0));
+    }
+  }
+}
+
+// TODO(phli): Add test once we have a real vectorized implementation.
+// static void test_vectorized_concatenation() {}
+
+static void test_concatenation_as_lvalue() {
+  Tensor<int, 2> t1(2, 3);
+  Tensor<int, 2> t2(2, 3);
+  t1.setRandom();
+  t2.setRandom();
+
+  Tensor<int, 2> result(4, 3);
+  result.setRandom();
+  t1.concatenate(t2, 0) = result;
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(t1(i, j), result(i, j));
+      VERIFY_IS_EQUAL(t2(i, j), result(i + 2, j));
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_concatenation) {
+  CALL_SUBTEST(test_dimension_failures<ColMajor>());
+  CALL_SUBTEST(test_dimension_failures<RowMajor>());
+  CALL_SUBTEST(test_static_dimension_failure<ColMajor>());
+  CALL_SUBTEST(test_static_dimension_failure<RowMajor>());
+  CALL_SUBTEST(test_simple_concatenation<ColMajor>());
+  CALL_SUBTEST(test_simple_concatenation<RowMajor>());
+  // CALL_SUBTEST(test_vectorized_concatenation());
+  CALL_SUBTEST(test_concatenation_as_lvalue());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_concatenation_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_concatenation_sycl.cpp
new file mode 100644
index 0000000..fccf165
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_concatenation_sycl.cpp
@@ -0,0 +1,191 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_simple_concatenation(const Eigen::SyclDevice& sycl_device) {
+  IndexType leftDim1 = 2;
+  IndexType leftDim2 = 3;
+  IndexType leftDim3 = 1;
+  Eigen::array<IndexType, 3> leftRange = {{leftDim1, leftDim2, leftDim3}};
+  IndexType rightDim1 = 2;
+  IndexType rightDim2 = 3;
+  IndexType rightDim3 = 1;
+  Eigen::array<IndexType, 3> rightRange = {{rightDim1, rightDim2, rightDim3}};
+
+  // IndexType concatDim1 = 3;
+  //	IndexType concatDim2 = 3;
+  //	IndexType concatDim3 = 1;
+  // Eigen::array<IndexType, 3> concatRange = {{concatDim1, concatDim2, concatDim3}};
+
+  Tensor<DataType, 3, DataLayout, IndexType> left(leftRange);
+  Tensor<DataType, 3, DataLayout, IndexType> right(rightRange);
+  left.setRandom();
+  right.setRandom();
+
+  DataType* gpu_in1_data =
+      static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_in2_data =
+      static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize() * sizeof(DataType)));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, leftRange);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, rightRange);
+  sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(), (left.dimensions().TotalSize()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(), (right.dimensions().TotalSize()) * sizeof(DataType));
+  ///
+  Tensor<DataType, 3, DataLayout, IndexType> concatenation1(leftDim1 + rightDim1, leftDim2, leftDim3);
+  DataType* gpu_out_data1 =
+      static_cast<DataType*>(sycl_device.allocate(concatenation1.dimensions().TotalSize() * sizeof(DataType)));
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out1(gpu_out_data1,
+                                                                               concatenation1.dimensions());
+
+  // concatenation = left.concatenate(right, 0);
+  gpu_out1.device(sycl_device) = gpu_in1.concatenate(gpu_in2, 0);
+  sycl_device.memcpyDeviceToHost(concatenation1.data(), gpu_out_data1,
+                                 (concatenation1.dimensions().TotalSize()) * sizeof(DataType));
+
+  VERIFY_IS_EQUAL(concatenation1.dimension(0), 4);
+  VERIFY_IS_EQUAL(concatenation1.dimension(1), 3);
+  VERIFY_IS_EQUAL(concatenation1.dimension(2), 1);
+  for (IndexType j = 0; j < 3; ++j) {
+    for (IndexType i = 0; i < 2; ++i) {
+      VERIFY_IS_EQUAL(concatenation1(i, j, 0), left(i, j, 0));
+    }
+    for (IndexType i = 2; i < 4; ++i) {
+      VERIFY_IS_EQUAL(concatenation1(i, j, 0), right(i - 2, j, 0));
+    }
+  }
+
+  sycl_device.deallocate(gpu_out_data1);
+  Tensor<DataType, 3, DataLayout, IndexType> concatenation2(leftDim1, leftDim2 + rightDim2, leftDim3);
+  DataType* gpu_out_data2 =
+      static_cast<DataType*>(sycl_device.allocate(concatenation2.dimensions().TotalSize() * sizeof(DataType)));
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out2(gpu_out_data2,
+                                                                               concatenation2.dimensions());
+  gpu_out2.device(sycl_device) = gpu_in1.concatenate(gpu_in2, 1);
+  sycl_device.memcpyDeviceToHost(concatenation2.data(), gpu_out_data2,
+                                 (concatenation2.dimensions().TotalSize()) * sizeof(DataType));
+
+  // concatenation = left.concatenate(right, 1);
+  VERIFY_IS_EQUAL(concatenation2.dimension(0), 2);
+  VERIFY_IS_EQUAL(concatenation2.dimension(1), 6);
+  VERIFY_IS_EQUAL(concatenation2.dimension(2), 1);
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(concatenation2(i, j, 0), left(i, j, 0));
+    }
+    for (IndexType j = 3; j < 6; ++j) {
+      VERIFY_IS_EQUAL(concatenation2(i, j, 0), right(i, j - 3, 0));
+    }
+  }
+  sycl_device.deallocate(gpu_out_data2);
+  Tensor<DataType, 3, DataLayout, IndexType> concatenation3(leftDim1, leftDim2, leftDim3 + rightDim3);
+  DataType* gpu_out_data3 =
+      static_cast<DataType*>(sycl_device.allocate(concatenation3.dimensions().TotalSize() * sizeof(DataType)));
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out3(gpu_out_data3,
+                                                                               concatenation3.dimensions());
+  gpu_out3.device(sycl_device) = gpu_in1.concatenate(gpu_in2, 2);
+  sycl_device.memcpyDeviceToHost(concatenation3.data(), gpu_out_data3,
+                                 (concatenation3.dimensions().TotalSize()) * sizeof(DataType));
+
+  // concatenation = left.concatenate(right, 2);
+  VERIFY_IS_EQUAL(concatenation3.dimension(0), 2);
+  VERIFY_IS_EQUAL(concatenation3.dimension(1), 3);
+  VERIFY_IS_EQUAL(concatenation3.dimension(2), 2);
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(concatenation3(i, j, 0), left(i, j, 0));
+      VERIFY_IS_EQUAL(concatenation3(i, j, 1), right(i, j, 0));
+    }
+  }
+  sycl_device.deallocate(gpu_out_data3);
+  sycl_device.deallocate(gpu_in1_data);
+  sycl_device.deallocate(gpu_in2_data);
+}
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_concatenation_as_lvalue(const Eigen::SyclDevice& sycl_device) {
+  IndexType leftDim1 = 2;
+  IndexType leftDim2 = 3;
+  Eigen::array<IndexType, 2> leftRange = {{leftDim1, leftDim2}};
+
+  IndexType rightDim1 = 2;
+  IndexType rightDim2 = 3;
+  Eigen::array<IndexType, 2> rightRange = {{rightDim1, rightDim2}};
+
+  IndexType concatDim1 = 4;
+  IndexType concatDim2 = 3;
+  Eigen::array<IndexType, 2> resRange = {{concatDim1, concatDim2}};
+
+  Tensor<DataType, 2, DataLayout, IndexType> left(leftRange);
+  Tensor<DataType, 2, DataLayout, IndexType> right(rightRange);
+  Tensor<DataType, 2, DataLayout, IndexType> result(resRange);
+
+  left.setRandom();
+  right.setRandom();
+  result.setRandom();
+
+  DataType* gpu_in1_data =
+      static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_in2_data =
+      static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize() * sizeof(DataType)));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_in1(gpu_in1_data, leftRange);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_in2(gpu_in2_data, rightRange);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(gpu_out_data, resRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(), (left.dimensions().TotalSize()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(), (right.dimensions().TotalSize()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_out_data, result.data(), (result.dimensions().TotalSize()) * sizeof(DataType));
+
+  //  t1.concatenate(t2, 0) = result;
+  gpu_in1.concatenate(gpu_in2, 0).device(sycl_device) = gpu_out;
+  sycl_device.memcpyDeviceToHost(left.data(), gpu_in1_data, (left.dimensions().TotalSize()) * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(right.data(), gpu_in2_data, (right.dimensions().TotalSize()) * sizeof(DataType));
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(left(i, j), result(i, j));
+      VERIFY_IS_EQUAL(right(i, j), result(i + 2, j));
+    }
+  }
+  sycl_device.deallocate(gpu_in1_data);
+  sycl_device.deallocate(gpu_in2_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, typename Dev_selector>
+void tensorConcat_perDevice(Dev_selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_concatenation<DataType, RowMajor, int64_t>(sycl_device);
+  test_simple_concatenation<DataType, ColMajor, int64_t>(sycl_device);
+  test_concatenation_as_lvalue<DataType, ColMajor, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_concatenation_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(tensorConcat_perDevice<half>(device));
+    CALL_SUBTEST(tensorConcat_perDevice<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_const.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_const.cpp
new file mode 100644
index 0000000..78a6506
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_const.cpp
@@ -0,0 +1,56 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+using Eigen::Tensor;
+
+static void test_simple_assign() {
+  Tensor<int, 3> random(2, 3, 7);
+  random.setRandom();
+
+  TensorMap<Tensor<const int, 3> > constant(random.data(), 2, 3, 7);
+  Tensor<int, 3> result(2, 3, 7);
+  result = constant;
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL((result(i, j, k)), random(i, j, k));
+      }
+    }
+  }
+}
+
+static void test_assign_of_const_tensor() {
+  Tensor<int, 3> random(2, 3, 7);
+  random.setRandom();
+
+  TensorMap<Tensor<const int, 3> > constant1(random.data(), 2, 3, 7);
+  TensorMap<const Tensor<int, 3> > constant2(random.data(), 2, 3, 7);
+  const TensorMap<Tensor<int, 3> > constant3(random.data(), 2, 3, 7);
+
+  Tensor<int, 2> result1 = constant1.chip(0, 2);
+  Tensor<int, 2> result2 = constant2.chip(0, 2);
+  Tensor<int, 2> result3 = constant3.chip(0, 2);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL((result1(i, j)), random(i, j, 0));
+      VERIFY_IS_EQUAL((result2(i, j)), random(i, j, 0));
+      VERIFY_IS_EQUAL((result3(i, j)), random(i, j, 0));
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_const) {
+  CALL_SUBTEST(test_simple_assign());
+  CALL_SUBTEST(test_assign_of_const_tensor());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_contract_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_contract_gpu.cu
new file mode 100644
index 0000000..a41a2df
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_contract_gpu.cu
@@ -0,0 +1,210 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+typedef Tensor<float, 1>::DimensionPair DimPair;
+
+template <int DataLayout>
+void test_gpu_contraction(int m_size, int k_size, int n_size) {
+  Tensor<float, 2, DataLayout> t_left(m_size, k_size);
+  Tensor<float, 2, DataLayout> t_right(k_size, n_size);
+  Tensor<float, 2, DataLayout> t_result(m_size, n_size);
+  Tensor<float, 2, DataLayout> t_result_gpu(m_size, n_size);
+  Eigen::array<DimPair, 1> dims{DimPair(1, 0)};
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  std::size_t t_left_bytes = t_left.size() * sizeof(float);
+  std::size_t t_right_bytes = t_right.size() * sizeof(float);
+  std::size_t t_result_bytes = t_result.size() * sizeof(float);
+
+  float* d_t_left;
+  float* d_t_right;
+  float* d_t_result;
+
+  gpuMalloc((void**)(&d_t_left), t_left_bytes);
+  gpuMalloc((void**)(&d_t_right), t_right_bytes);
+  gpuMalloc((void**)(&d_t_result), t_result_bytes);
+
+  gpuMemcpy(d_t_left, t_left.data(), t_left_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_t_right, t_right.data(), t_right_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_left(d_t_left, Eigen::array<int, 2>{m_size, k_size});
+  Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_right(d_t_right, Eigen::array<int, 2>{k_size, n_size});
+  Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_result(d_t_result, Eigen::array<int, 2>{m_size, n_size});
+
+  gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims);
+  t_result = t_left.contract(t_right, dims);
+
+  gpuMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, gpuMemcpyDeviceToHost);
+  for (DenseIndex i = 0; i < t_result.size(); i++) {
+    if (fabs(t_result(i) - t_result_gpu(i)) < 1e-4f) {
+      continue;
+    }
+    if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) {
+      continue;
+    }
+    std::cout << "mismatch detected at index " << i << ": " << t_result(i) << " vs " << t_result_gpu(i) << std::endl;
+    assert(false);
+  }
+
+  gpuFree((void*)d_t_left);
+  gpuFree((void*)d_t_right);
+  gpuFree((void*)d_t_result);
+}
+
+template <int DataLayout>
+void test_scalar(int m_size, int k_size, int n_size) {
+  std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
+  // with these dimensions, the output has 300 * 140 elements, which is
+  // more than 30 * 1024, which is the number of threads in blocks on
+  // a 15 SM GK110 GPU
+  Tensor<float, 2, DataLayout> t_left(m_size, k_size);
+  Tensor<float, 2, DataLayout> t_right(k_size, n_size);
+  Tensor<float, 0, DataLayout> t_result;
+  Tensor<float, 0, DataLayout> t_result_gpu;
+  Eigen::array<DimPair, 2> dims{DimPair(0, 0), DimPair(1, 1)};
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  std::size_t t_left_bytes = t_left.size() * sizeof(float);
+  std::size_t t_right_bytes = t_right.size() * sizeof(float);
+  std::size_t t_result_bytes = sizeof(float);
+
+  float* d_t_left;
+  float* d_t_right;
+  float* d_t_result;
+
+  gpuMalloc((void**)(&d_t_left), t_left_bytes);
+  gpuMalloc((void**)(&d_t_right), t_right_bytes);
+  gpuMalloc((void**)(&d_t_result), t_result_bytes);
+
+  gpuMemcpy(d_t_left, t_left.data(), t_left_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_t_right, t_right.data(), t_right_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_left(d_t_left, m_size, k_size);
+  Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_t_right(d_t_right, k_size, n_size);
+  Eigen::TensorMap<Eigen::Tensor<float, 0, DataLayout> > gpu_t_result(d_t_result);
+
+  gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims);
+  t_result = t_left.contract(t_right, dims);
+
+  gpuMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, gpuMemcpyDeviceToHost);
+  if (fabs(t_result() - t_result_gpu()) > 1e-4f && !Eigen::internal::isApprox(t_result(), t_result_gpu(), 1e-4f)) {
+    std::cout << "mismatch detected: " << t_result() << " vs " << t_result_gpu() << std::endl;
+    assert(false);
+  }
+
+  gpuFree((void*)d_t_left);
+  gpuFree((void*)d_t_right);
+  gpuFree((void*)d_t_result);
+}
+
+template <int DataLayout>
+void test_gpu_contraction_m() {
+  for (int k = 32; k < 256; k++) {
+    test_gpu_contraction<ColMajor>(k, 128, 128);
+    test_gpu_contraction<RowMajor>(k, 128, 128);
+  }
+}
+
+template <int DataLayout>
+void test_gpu_contraction_k() {
+  for (int k = 32; k < 256; k++) {
+    test_gpu_contraction<ColMajor>(128, k, 128);
+    test_gpu_contraction<RowMajor>(128, k, 128);
+  }
+}
+
+template <int DataLayout>
+void test_gpu_contraction_n() {
+  for (int k = 32; k < 256; k++) {
+    test_gpu_contraction<ColMajor>(128, 128, k);
+    test_gpu_contraction<RowMajor>(128, 128, k);
+  }
+}
+
+template <int DataLayout>
+void test_gpu_contraction_sizes() {
+  int m_sizes[3][5] = {{31, 39, 63, 64, 65}, {127, 129, 255, 257, 511}, {512, 513, 1023, 1024, 1025}};
+
+  int n_sizes[3][5] = {{31, 39, 63, 64, 65}, {127, 129, 255, 257, 511}, {512, 513, 1023, 1024, 1025}};
+
+  int k_sizes[3][6] = {{31, 39, 63, 64, 65, 95}, {96, 127, 129, 255, 257, 511}, {512, 513, 725, 1023, 1024, 1025}};
+
+  // Some selection of specific cases.
+  //  - m changes rows each iteration
+  //  - n changes rows each 3 iterations
+  //  - k changes rows each 9 iterations
+  //  - within a row, advance once column each iteration
+  const int m_cols = 5;
+  const int n_cols = 5;
+  const int k_cols = 6;
+  int m_offset = 0;
+  int n_offset = 1;
+  int k_offset = 2;
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int l = 0; l < 3; ++l) {
+        int m = m_sizes[l][m_offset];
+        int n = n_sizes[j][n_offset];
+        int k = k_sizes[i][k_offset];
+        test_gpu_contraction<DataLayout>(m, n, k);
+        n_offset = (n_offset + 1) % n_cols;
+        k_offset = (k_offset + 1) % k_cols;
+      }
+      m_offset = (m_offset + 1) % m_cols;
+      if (j < 2) {
+        n_offset = (n_offset + n_cols - 3) % n_cols;  // Rewind 3.
+      }
+    }
+    k_offset = (k_offset + 2 * k_cols - 9) % k_cols;  // Rewind 9.
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_contract_gpu) {
+  CALL_SUBTEST_1(test_gpu_contraction<ColMajor>(128, 128, 128));
+  CALL_SUBTEST_1(test_gpu_contraction<RowMajor>(128, 128, 128));
+
+  CALL_SUBTEST_1(test_scalar<ColMajor>(128, 128, 128));
+  CALL_SUBTEST_1(test_scalar<RowMajor>(128, 128, 128));
+
+  CALL_SUBTEST_2(test_gpu_contraction_m<ColMajor>());
+  CALL_SUBTEST_3(test_gpu_contraction_m<RowMajor>());
+
+  CALL_SUBTEST_4(test_gpu_contraction_k<ColMajor>());
+  CALL_SUBTEST_5(test_gpu_contraction_k<RowMajor>());
+
+  CALL_SUBTEST_6(test_gpu_contraction_n<ColMajor>());
+  CALL_SUBTEST_7(test_gpu_contraction_n<RowMajor>());
+
+#if !defined(EIGEN_USE_HIP)
+  // disable these subtests for HIP
+  CALL_SUBTEST_8(test_gpu_contraction_sizes<ColMajor>());
+  CALL_SUBTEST_9(test_gpu_contraction_sizes<RowMajor>());
+#endif
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_contract_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_contract_sycl.cpp
new file mode 100644
index 0000000..120681b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_contract_sycl.cpp
@@ -0,0 +1,846 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include <algorithm>
+#include <chrono>
+#include <ctime>
+#include <iostream>
+
+#include "main.h"
+
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void static test_sycl_contraction(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
+  typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
+  static const DataType error_threshold = DataType(1e-4);
+  // with these dimensions, the output has 300 * 140 elements, which is
+  // more than 30 * 1024, which is the number of threads in blocks on
+  // a 15 SM GK110 GPU
+  Tensor<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size);
+  Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size);
+  Tensor<DataType, 2, DataLayout, IndexType> t_result(m_size, n_size);
+  Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(m_size, n_size);
+  Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
+  Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
+  Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
+  Eigen::array<IndexType, 2> result_dims = {{m_size, n_size}};
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
+  std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
+  std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
+
+  DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
+  DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
+  DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_result(d_t_result, result_dims);
+
+  sycl_device.memcpyHostToDevice(d_t_left, t_left.data(), t_left_bytes);
+  sycl_device.memcpyHostToDevice(d_t_right, t_right.data(), t_right_bytes);
+
+  gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
+  sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
+
+  t_result = t_left.contract(t_right, dims);
+
+  for (IndexType i = 0; i < t_result.size(); i++) {
+    if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
+      continue;
+    }
+    if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
+      continue;
+    }
+
+    std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size << ", mismatch detected at IndexType "
+              << i << ": " << t_result(i) << " vs " << t_result_gpu(i) << std::endl;
+    VERIFY_IS_APPROX(t_result_gpu(i), t_result(i));
+  }
+  sycl_device.deallocate(d_t_left);
+  sycl_device.deallocate(d_t_right);
+  sycl_device.deallocate(d_t_result);
+}
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void test_sycl_contraction_m(const Device &sycl_device) {
+  for (IndexType k = 32; k < 256; k++) {
+    test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, k, 128, 128);
+  }
+}
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void test_sycl_contraction_k(const Device &sycl_device) {
+  for (IndexType k = 32; k < 256; k++) {
+    test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, 128, k, 128);
+  }
+}
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void test_sycl_contraction_n(const Device &sycl_device) {
+  for (IndexType k = 32; k < 256; k++) {
+    test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, 128, 128, k);
+  }
+}
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void test_sycl_contraction_sizes(const Device &sycl_device) {
+  IndexType m_sizes[] = {31, 39, 63, 64, 65, 127, 129, 255, 257, 511, 512, 513, 1023, 1024, 1025};
+
+  IndexType n_sizes[] = {31, 39, 63, 64, 65, 127, 129, 255, 257, 511, 512, 513, 1023, 1024, 1025};
+
+  IndexType k_sizes[] = {31, 39, 63, 64, 65, 95, 96, 127, 129, 255, 257, 511, 512, 513, 1023, 1024, 1025};
+
+  for (IndexType i = 0; i < 15; i++) {
+    for (IndexType j = 0; j < 15; j++) {
+      for (IndexType k = 0; k < 17; k++) {
+        test_sycl_contraction<DataLayout, DataType, IndexType>(sycl_device, m_sizes[i], n_sizes[j], k_sizes[k]);
+      }
+    }
+  }
+}
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void static test_no_out_of_bounds(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
+  typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
+  static const DataType error_threshold = DataType(1e-4);
+  Tensor<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size);
+  Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size);
+  Tensor<DataType, 2, DataLayout, IndexType> t_result(m_size, n_size);
+
+  Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
+  Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
+  Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
+  Eigen::array<IndexType, 2> result_dims = {{m_size, n_size}};
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  // Allocate buffers twice as big to check for invalid read and write
+  auto padded_left_size = 2 * t_left.size();
+  auto padded_right_size = 2 * t_right.size();
+  auto padded_result_size = 2 * t_result.size();
+
+  std::size_t t_left_bytes = padded_left_size * sizeof(DataType);
+  std::size_t t_right_bytes = padded_right_size * sizeof(DataType);
+  std::size_t t_result_bytes = padded_result_size * sizeof(DataType);
+
+  DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
+  DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
+  DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
+
+  // TensorMaps are still of the same size than the Tensors
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_result(d_t_result, result_dims);
+
+  // Write nan after the actual buffer to propagate nans everywhere in case of
+  // invalid reads
+  DataType nan = std::numeric_limits<DataType>::quiet_NaN();
+  auto host_left_data = new DataType[padded_left_size];
+  std::copy_n(t_left.data(), t_left.size(), host_left_data);
+  std::fill_n(host_left_data + t_left.size(), t_left.size(), nan);
+  auto host_right_data = new DataType[padded_right_size];
+  std::copy_n(t_right.data(), t_right.size(), host_right_data);
+  std::fill_n(host_right_data + t_right.size(), t_right.size(), nan);
+  auto host_result_data = new DataType[padded_result_size];
+  std::fill_n(host_result_data, padded_result_size, nan);
+
+  sycl_device.memcpyHostToDevice(d_t_left, host_left_data, t_left_bytes);
+  sycl_device.memcpyHostToDevice(d_t_right, host_right_data, t_right_bytes);
+  sycl_device.memcpyHostToDevice(d_t_result, host_result_data, t_result_bytes);
+
+  gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
+  sycl_device.memcpyDeviceToHost(host_result_data, d_t_result, t_result_bytes);
+
+  t_result = t_left.contract(t_right, dims);
+
+  for (IndexType i = 0; i < t_result.size(); i++) {
+    if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - host_result_data[i]))) < error_threshold) {
+      continue;
+    }
+    if (Eigen::internal::isApprox(t_result(i), host_result_data[i], error_threshold)) {
+      continue;
+    }
+    if (std::isnan(host_result_data[i])) {
+      std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size
+                << ", invalid read detected at IndexType " << i << ": " << t_result(i) << " vs " << host_result_data[i]
+                << std::endl;
+    } else {
+      std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size << ", mismatch detected at IndexType "
+                << i << ": " << t_result(i) << " vs " << host_result_data[i] << std::endl;
+    }
+    VERIFY_IS_APPROX(host_result_data[i], t_result(i));
+  }
+  // Make sure that the rest of the result is still nans
+  for (IndexType i = t_result.size(); i < padded_result_size; i++) {
+    if (std::isnan(host_result_data[i])) {
+      continue;
+    }
+    std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size
+              << ", invalid write detected at IndexType " << i << ": " << host_result_data[i] << std::endl;
+    VERIFY_IS_APPROX(host_result_data[i], t_result(i));
+  }
+  sycl_device.deallocate(d_t_left);
+  sycl_device.deallocate(d_t_right);
+  sycl_device.deallocate(d_t_result);
+
+  delete[] host_left_data;
+  delete[] host_right_data;
+  delete[] host_result_data;
+}
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void test_scalar(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
+  // std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size <<
+  // ")" << std::endl;
+  // with these dimensions, the output has 300 * 140 elements, which is
+  // more than 30 * 1024, which is the number of threads in blocks on
+  // a 15 SM GK110 GPU
+  typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
+  static const DataType error_threshold = DataType(1e-4);
+  Tensor<DataType, 2, DataLayout, IndexType> t_left(m_size, k_size);
+  Tensor<DataType, 2, DataLayout, IndexType> t_right(k_size, n_size);
+  Tensor<DataType, 0, DataLayout, IndexType> t_result;
+  Tensor<DataType, 0, DataLayout, IndexType> t_result_gpu;
+  Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(1, 1)}};
+  Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
+  Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
+  t_left.setRandom();
+  t_right.setRandom();
+
+  std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
+  std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
+  std::size_t t_result_bytes = sizeof(DataType);
+
+  DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
+  DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
+  DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 0, DataLayout, IndexType>> gpu_t_result(d_t_result);
+
+  sycl_device.memcpyHostToDevice(d_t_left, t_left.data(), t_left_bytes);
+  sycl_device.memcpyHostToDevice(d_t_right, t_right.data(), t_right_bytes);
+
+  gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
+  sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
+
+  t_result = t_left.contract(t_right, dims);
+
+  if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result() - t_result_gpu()))) > error_threshold &&
+      !Eigen::internal::isApprox(t_result(), t_result_gpu(), error_threshold)) {
+    std::cout << "K: " << k_size << ", N: " << n_size << ", M: " << m_size << " : mismatch detected: " << t_result()
+              << " vs " << t_result_gpu() << std::endl;
+    VERIFY_IS_APPROX(t_result_gpu(), t_result());
+  }
+
+  sycl_device.deallocate(d_t_left);
+  sycl_device.deallocate(d_t_right);
+  sycl_device.deallocate(d_t_result);
+}
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void contraction_batch(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size,
+                       IndexType m_batch, IndexType start, IndexType limit) {
+  typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
+  static const DataType error_threshold = DataType(1e-4);
+  typedef Eigen::array<IndexType, 3> TensorDim;
+  typedef Eigen::Tensor<DataType, 3, DataLayout, IndexType> TensorType;
+  TensorDim left_dims = {{m_batch, k_size, m_size}};
+  TensorDim right_dims = {{m_batch, n_size, k_size}};
+  TensorDim res_dims = {{m_batch, m_size, n_size}};
+  Eigen::array<DimPair, 1> contract_pairs = {{DimPair(0, 1)}};
+
+  TensorType t_left(left_dims);
+  TensorType t_right(right_dims);
+  TensorType t_result_gpu(res_dims);
+  TensorType t_result(res_dims);
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
+  std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
+  std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
+
+  DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
+  DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
+  DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
+
+  Eigen::TensorMap<TensorType> gpu_t_left(d_t_left, left_dims);
+  Eigen::TensorMap<TensorType> gpu_t_right(d_t_right, right_dims);
+  Eigen::TensorMap<TensorType> gpu_t_result(d_t_result, res_dims);
+
+  sycl_device.memcpyHostToDevice(d_t_left, t_left.data(), t_left_bytes);
+  sycl_device.memcpyHostToDevice(d_t_right, t_right.data(), t_right_bytes);
+  for (int i = start; i < limit; ++i) {
+    auto x = gpu_t_left.template chip<0>(i);
+    auto y = gpu_t_right.template chip<0>(i);
+    auto z = gpu_t_result.template chip<0>(i);
+    z.device(sycl_device) = x.contract(y, contract_pairs);
+  }
+  sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
+
+  for (int i = start; i < limit; ++i) {
+    auto x = t_left.template chip<0>(i);
+    auto y = t_right.template chip<0>(i);
+    auto z = t_result.template chip<0>(i);
+    z = x.contract(y, contract_pairs);
+  }
+
+  for (IndexType i = 0; i < t_result.size(); i++) {
+    if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
+      continue;
+    }
+    if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
+      continue;
+    }
+    std::cout << "mismatch detected at IndexType " << i << ": " << t_result(i) << " vs " << t_result_gpu(i)
+              << std::endl;
+    VERIFY_IS_APPROX(t_result_gpu(i), t_result(i));
+  }
+  sycl_device.deallocate(d_t_left);
+  sycl_device.deallocate(d_t_right);
+  sycl_device.deallocate(d_t_result);
+}
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void contraction_rhs_transposed(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
+  typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
+  static const DataType error_threshold = DataType(1e-4);
+  Eigen::array<IndexType, 2> left_dims = {{m_size, k_size}};
+  Eigen::array<IndexType, 2> right_dims = {{n_size, k_size}};
+  Eigen::array<IndexType, 2> res_dims = {{m_size, n_size}};
+  Eigen::array<DimPair, 1> dims = {{DimPair(1, 1)}};
+
+  Tensor<DataType, 2, DataLayout, IndexType> t_left(left_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> t_right(right_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(res_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> t_result(res_dims);
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
+  std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
+  std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
+
+  DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
+  DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
+  DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_result(d_t_result, res_dims);
+
+  sycl_device.memcpyHostToDevice(d_t_left, t_left.data(), t_left_bytes);
+  sycl_device.memcpyHostToDevice(d_t_right, t_right.data(), t_right_bytes);
+
+  gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
+  sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
+
+  t_result = t_left.contract(t_right, dims);
+
+  for (IndexType j = 0; j < m_size; j++) {
+    for (IndexType i = 0; i < n_size; i++) {
+      if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(j, i) - t_result_gpu(j, i)))) <
+          error_threshold) {
+        continue;
+      }
+      if (Eigen::internal::isApprox(t_result(j, i), t_result_gpu(j, i), error_threshold)) {
+        continue;
+      }
+      std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size
+                << ", mismatch detected at IndexType m: " << j << " n: " << i << " CPU : " << t_result(j, i)
+                << " vs SYCL:" << t_result_gpu(j, i) << std::endl;
+      VERIFY_IS_APPROX(t_result_gpu(j, i), t_result(j, i));
+    }
+  }
+  sycl_device.deallocate(d_t_left);
+  sycl_device.deallocate(d_t_right);
+  sycl_device.deallocate(d_t_result);
+}
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void contraction_lhs_transposed(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
+  typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
+  static const DataType error_threshold = DataType(1e-4);
+  Eigen::array<IndexType, 2> left_dims = {{k_size, m_size}};
+  Eigen::array<IndexType, 2> right_dims = {{k_size, n_size}};
+  Eigen::array<IndexType, 2> res_dims = {{m_size, n_size}};
+  Eigen::array<DimPair, 1> dims = {{DimPair(0, 0)}};
+
+  Tensor<DataType, 2, DataLayout, IndexType> t_left(left_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> t_right(right_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(res_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> t_result(res_dims);
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
+  std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
+  std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
+
+  DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
+  DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
+  DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_result(d_t_result, res_dims);
+
+  sycl_device.memcpyHostToDevice(d_t_left, t_left.data(), t_left_bytes);
+  sycl_device.memcpyHostToDevice(d_t_right, t_right.data(), t_right_bytes);
+
+  gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
+  sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
+
+  t_result = t_left.contract(t_right, dims);
+
+  for (IndexType i = 0; i < t_result.size(); i++) {
+    if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
+      continue;
+    }
+    if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
+      continue;
+    }
+    std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size << ", mismatch detected at IndexType "
+              << i << ": " << t_result(i) << " vs " << t_result_gpu(i) << std::endl;
+    VERIFY_IS_APPROX(t_result_gpu(i), t_result(i));
+  }
+  sycl_device.deallocate(d_t_left);
+  sycl_device.deallocate(d_t_right);
+  sycl_device.deallocate(d_t_result);
+}
+
+template <int DataLayout, typename DataType, typename IndexType, typename Device>
+void contraction_both_transposed(const Device &sycl_device, IndexType m_size, IndexType k_size, IndexType n_size) {
+  typedef typename Tensor<DataType, 1, DataLayout, IndexType>::DimensionPair DimPair;
+  static const DataType error_threshold = DataType(1e-4);
+  Eigen::array<IndexType, 2> left_dims = {{k_size, m_size}};
+  Eigen::array<IndexType, 2> right_dims = {{n_size, k_size}};
+  Eigen::array<IndexType, 2> res_dims = {{m_size, n_size}};
+  Eigen::array<DimPair, 1> dims = {{DimPair(0, 1)}};
+
+  Tensor<DataType, 2, DataLayout, IndexType> t_left(left_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> t_right(right_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> t_result_gpu(res_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> t_result(res_dims);
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  std::size_t t_left_bytes = t_left.size() * sizeof(DataType);
+  std::size_t t_right_bytes = t_right.size() * sizeof(DataType);
+  std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
+
+  DataType *d_t_left = static_cast<DataType *>(sycl_device.allocate(t_left_bytes));
+  DataType *d_t_right = static_cast<DataType *>(sycl_device.allocate(t_right_bytes));
+  DataType *d_t_result = static_cast<DataType *>(sycl_device.allocate(t_result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_left(d_t_left, left_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_right(d_t_right, right_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_t_result(d_t_result, res_dims);
+
+  sycl_device.memcpyHostToDevice(d_t_left, t_left.data(), t_left_bytes);
+  sycl_device.memcpyHostToDevice(d_t_right, t_right.data(), t_right_bytes);
+
+  gpu_t_result.device(sycl_device) = gpu_t_left.contract(gpu_t_right, dims);
+  sycl_device.memcpyDeviceToHost(t_result_gpu.data(), d_t_result, t_result_bytes);
+
+  t_result = t_left.contract(t_right, dims);
+
+  for (IndexType i = 0; i < t_result.size(); i++) {
+    if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
+      continue;
+    }
+    if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
+      continue;
+    }
+    std::cout << "M : " << m_size << ", N : " << n_size << ", K : " << k_size << ", mismatch detected at IndexType "
+              << i << ": " << t_result(i) << " vs " << t_result_gpu(i) << std::endl;
+
+    VERIFY_IS_APPROX(t_result_gpu(i), t_result(i));
+  }
+  sycl_device.deallocate(d_t_left);
+  sycl_device.deallocate(d_t_right);
+  sycl_device.deallocate(d_t_result);
+}
+
+template <typename Dev>
+void inline tensorOutofBound(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // Test out of bound for Tensor-Tensor
+  test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 10, 1024, 1024);
+  test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 1024, 1024, 4096);
+  test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 4096, 1024, 2048);
+  test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 784, 2048, 1024);
+  test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 2048, 1024, 784);
+  test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 10, 1024, 10);
+  test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 513, 4096, 513);
+  test_no_out_of_bounds<RowMajor, DataType, IndexType>(sycl_device, 783, 1024, 783);
+  test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 784, 2048, 784);
+  test_no_out_of_bounds<ColMajor, DataType, IndexType>(sycl_device, 11, 1024, 11);
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "tensor out of bound tests finished computation at " << std::ctime(&end_time)
+            << "elapsed time: " << elapsed_seconds.count() << "s\n";
+}
+
+template <typename Dev>
+void inline tensorTensor(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // Tensor Tensor Contraction
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 128, 128, 128);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 128, 128, 128);
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "tensor tensor tests finished computation at " << std::ctime(&end_time)
+            << "elapsed time: " << elapsed_seconds.count() << "s\n";
+}
+
+template <typename Dev>
+void inline tensorTensor_m(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // Tensor Tensor Contraction
+  test_sycl_contraction_m<ColMajor, DataType, IndexType>(sycl_device);
+  test_sycl_contraction_m<RowMajor, DataType, IndexType>(sycl_device);
+
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "tensor tensor tests finished computation at " << std::ctime(&end_time)
+            << "elapsed time: " << elapsed_seconds.count() << "s\n";
+}
+
+template <typename Dev>
+void inline tensorTensor_n(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // Tensor Tensor Contraction
+  test_sycl_contraction_n<ColMajor, DataType, IndexType>(sycl_device);
+  test_sycl_contraction_n<RowMajor, DataType, IndexType>(sycl_device);
+
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "tensor tensor tests finished computation at " << std::ctime(&end_time)
+            << "elapsed time: " << elapsed_seconds.count() << "s\n";
+}
+
+template <typename Dev>
+void inline tensorTensor_k(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  test_sycl_contraction_k<ColMajor, DataType, IndexType>(sycl_device);
+  test_sycl_contraction_k<RowMajor, DataType, IndexType>(sycl_device);
+
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "tensor tensor tests finished computation at " << std::ctime(&end_time)
+            << "elapsed time: " << elapsed_seconds.count() << "s\n";
+}
+
+template <typename Dev>
+void inline tensorTensor_sizes(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // Tensor Tensor Contraction
+  test_sycl_contraction_sizes<ColMajor, DataType, IndexType>(sycl_device);
+  test_sycl_contraction_sizes<RowMajor, DataType, IndexType>(sycl_device);
+
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "tensor tensor tests finished computation at " << std::ctime(&end_time)
+            << "elapsed time: " << elapsed_seconds.count() << "s\n";
+}
+template <typename Dev>
+void inline vectorVector(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // VECTOR-VECTOR
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1025, 1, 1025);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1025, 1, 1025);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1024, 1, 1024);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1024, 1, 1024);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1023, 1, 1023);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1023, 1, 1023);
+
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "contracted tensor tests finished computation at " << std::ctime(&end_time)
+            << "elapsed time: " << elapsed_seconds.count() << "s\n";
+}
+
+template <typename Dev>
+void inline vectorTensor(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // Vector-Tensor
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 1025, 1025);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 1025, 1025);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 1024, 1024);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 1024, 1024);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 1023, 1023);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 1023, 1023);
+
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 4097, 4097);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 4097, 4097);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 4096, 4096);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 4096, 4096);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 4095, 4095);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1, 4095, 4095);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1, 802816, 32);
+
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "finished computation at " << std::ctime(&end_time) << "elapsed time: " << elapsed_seconds.count()
+            << "s\n";
+}
+
+template <typename Dev>
+void inline tensorVector(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // Matrix-Vector
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1025, 1025, 1);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1125, 1025, 1);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1224, 1024, 1);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1024, 1024, 1);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 1023, 1023, 1);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 1023, 1023, 1);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 4097, 4197, 1);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 4097, 4097, 1);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 4096, 4096, 1);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 4096, 8196, 1);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 4095, 4095, 1);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 4095, 4095, 1);
+// If the GEMV disabled it will creates one kernel to calculate the contraction.
+// Therefore the acumuation of float number will overflow the precision
+// threshold for float and cause the test to fail. While it the GMV multiple
+// kernel will be created and each one run the overflow of accumutation breaks
+// among the kernels.
+#ifndef EIGEN_SYCL_DISABLE_GEMV
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 32, 802032, 1);
+#endif
+
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "finished computation at " << std::ctime(&end_time) << "elapsed time: " << elapsed_seconds.count()
+            << "s\n";
+}
+
+template <typename Dev>
+void inline tensorScalar(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // SCALAR Contraction
+  test_scalar<ColMajor, DataType, IndexType>(sycl_device, 127, 127, 127);
+  test_scalar<RowMajor, DataType, IndexType>(sycl_device, 127, 127, 127);
+  test_scalar<ColMajor, DataType, IndexType>(sycl_device, 128, 128, 128);
+  test_scalar<RowMajor, DataType, IndexType>(sycl_device, 128, 128, 128);
+  test_scalar<ColMajor, DataType, IndexType>(sycl_device, 129, 129, 129);
+  test_scalar<RowMajor, DataType, IndexType>(sycl_device, 129, 129, 129);
+
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "finished computation at " << std::ctime(&end_time) << "elapsed time: " << elapsed_seconds.count()
+            << "s\n";
+}
+
+template <typename Dev>
+void inline skinnyTensor_row(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // Tensor Tensor Contraction
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 16, 4, 16);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 257, 131073, 257);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 256, 131072, 256);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 16, 131073, 16);
+  test_sycl_contraction<RowMajor, DataType, IndexType>(sycl_device, 17, 131072, 17);
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "finished computation at " << std::ctime(&end_time) << "elapsed time: " << elapsed_seconds.count()
+            << "s\n";
+}
+
+template <typename Dev>
+void inline skinnyTensor_col(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+  // Tensor Tensor Contraction
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 16, 4, 16);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 257, 131073, 257);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 256, 131072, 256);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 16, 131073, 16);
+  test_sycl_contraction<ColMajor, DataType, IndexType>(sycl_device, 17, 131072, 17);
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "finished computation at " << std::ctime(&end_time) << "elapsed time: " << elapsed_seconds.count()
+            << "s\n";
+}
+
+template <typename Dev>
+void inline tensor_contraction_batch_per_device(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+
+  contraction_batch<RowMajor, DataType, IndexType>(sycl_device, 64, 75, 30, 4, 0, 4);
+  contraction_batch<ColMajor, DataType, IndexType>(sycl_device, 64, 75, 30, 4, 0, 4);
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "finished computation at " << std::ctime(&end_time) << "elapsed time: " << elapsed_seconds.count()
+            << "s\n";
+}
+
+template <typename Dev>
+void inline tensor_contraction_lhs_transposed_per_device(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+
+  contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 8, 4, 8);
+  contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 32, 8, 32);
+  contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 64, 16, 64);
+  contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 784, 2048, 1024);
+  contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 1024, 10, 1024);
+  contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 4096, 1024, 1024);
+  contraction_lhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 2048, 4096, 1024);
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "finished computation at " << std::ctime(&end_time) << "elapsed time: " << elapsed_seconds.count()
+            << "s\n";
+}
+
+template <typename Dev>
+void inline tensor_contraction_rhs_transposed_per_device(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+
+  contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 16, 4, 16);
+  contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 17, 5, 17);
+  contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 32, 8, 32);
+  contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 64, 16, 64);
+  contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 10, 1024, 1024);
+  contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 1024, 1024, 4096);
+  contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 4096, 1024, 2048);
+  contraction_rhs_transposed<RowMajor, DataType, IndexType>(sycl_device, 2048, 1024, 784);
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "finished computation at " << std::ctime(&end_time) << "elapsed time: " << elapsed_seconds.count()
+            << "s\n";
+}
+
+template <typename Dev>
+void inline tensor_contraction_both_transposed_per_device(const Dev &sycl_device) {
+  typedef float DataType;
+  typedef int64_t IndexType;
+  std::chrono::time_point<std::chrono::system_clock> start, end;
+  start = std::chrono::system_clock::now();
+
+  contraction_both_transposed<RowMajor, DataType, IndexType>(sycl_device, 17, 5, 17);
+  contraction_both_transposed<RowMajor, DataType, IndexType>(sycl_device, 32, 8, 32);
+  contraction_both_transposed<RowMajor, DataType, IndexType>(sycl_device, 64, 16, 64);
+  end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+  std::time_t end_time = std::chrono::system_clock::to_time_t(end);
+  std::cout << "finished computation at " << std::ctime(&end_time) << "elapsed time: " << elapsed_seconds.count()
+            << "s\n";
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_contract_sycl) {
+  for (const auto &device : Eigen::get_sycl_supported_devices()) {
+    std::cout << "Running on " << device.template get_info<cl::sycl::info::device::name>() << std::endl;
+    QueueInterface queueInterface(device);
+    auto sycl_device = Eigen::SyclDevice(&queueInterface);
+    CALL_SUBTEST_1(tensorOutofBound(sycl_device));
+    CALL_SUBTEST_2(tensorTensor(sycl_device));
+    CALL_SUBTEST_2(tensorTensor_m(sycl_device));
+    CALL_SUBTEST_2(tensorTensor_n(sycl_device));
+    CALL_SUBTEST_2(tensorTensor_k(sycl_device));
+    CALL_SUBTEST_2(tensorTensor_sizes(sycl_device));
+    CALL_SUBTEST_3(vectorVector(sycl_device));
+    CALL_SUBTEST_4(vectorTensor(sycl_device));
+    CALL_SUBTEST_5(tensorVector(sycl_device));
+    CALL_SUBTEST_6(tensorScalar(sycl_device));
+    CALL_SUBTEST_7(skinnyTensor_row(sycl_device));
+    CALL_SUBTEST_7(skinnyTensor_col(sycl_device));
+    CALL_SUBTEST_8(tensor_contraction_batch_per_device(sycl_device));
+    CALL_SUBTEST_9(tensor_contraction_lhs_transposed_per_device(sycl_device));
+    CALL_SUBTEST_10(tensor_contraction_rhs_transposed_per_device(sycl_device));
+    CALL_SUBTEST_11(tensor_contraction_both_transposed_per_device(sycl_device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_contraction.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_contraction.cpp
new file mode 100644
index 0000000..2949c14
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_contraction.cpp
@@ -0,0 +1,567 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::DefaultDevice;
+using Eigen::Tensor;
+
+typedef Tensor<float, 1>::DimensionPair DimPair;
+
+template <int DataLayout>
+static void test_evals() {
+  Tensor<float, 2, DataLayout> mat1(2, 3);
+  Tensor<float, 2, DataLayout> mat2(2, 3);
+  Tensor<float, 2, DataLayout> mat3(3, 2);
+
+  mat1.setRandom();
+  mat2.setRandom();
+  mat3.setRandom();
+
+  Tensor<float, 2, DataLayout> mat4(3, 3);
+  mat4.setZero();
+  Eigen::array<DimPair, 1> dims3 = {{DimPair(0, 0)}};
+  typedef TensorEvaluator<decltype(mat1.contract(mat2, dims3)), DefaultDevice> Evaluator;
+  Evaluator eval(mat1.contract(mat2, dims3), DefaultDevice());
+  eval.evalTo(mat4.data());
+  EIGEN_STATIC_ASSERT(Evaluator::NumDims == 2ul, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  VERIFY_IS_EQUAL(eval.dimensions()[0], 3);
+  VERIFY_IS_EQUAL(eval.dimensions()[1], 3);
+
+  VERIFY_IS_APPROX(mat4(0, 0), mat1(0, 0) * mat2(0, 0) + mat1(1, 0) * mat2(1, 0));
+  VERIFY_IS_APPROX(mat4(0, 1), mat1(0, 0) * mat2(0, 1) + mat1(1, 0) * mat2(1, 1));
+  VERIFY_IS_APPROX(mat4(0, 2), mat1(0, 0) * mat2(0, 2) + mat1(1, 0) * mat2(1, 2));
+  VERIFY_IS_APPROX(mat4(1, 0), mat1(0, 1) * mat2(0, 0) + mat1(1, 1) * mat2(1, 0));
+  VERIFY_IS_APPROX(mat4(1, 1), mat1(0, 1) * mat2(0, 1) + mat1(1, 1) * mat2(1, 1));
+  VERIFY_IS_APPROX(mat4(1, 2), mat1(0, 1) * mat2(0, 2) + mat1(1, 1) * mat2(1, 2));
+  VERIFY_IS_APPROX(mat4(2, 0), mat1(0, 2) * mat2(0, 0) + mat1(1, 2) * mat2(1, 0));
+  VERIFY_IS_APPROX(mat4(2, 1), mat1(0, 2) * mat2(0, 1) + mat1(1, 2) * mat2(1, 1));
+  VERIFY_IS_APPROX(mat4(2, 2), mat1(0, 2) * mat2(0, 2) + mat1(1, 2) * mat2(1, 2));
+
+  Tensor<float, 2, DataLayout> mat5(2, 2);
+  mat5.setZero();
+  Eigen::array<DimPair, 1> dims4 = {{DimPair(1, 1)}};
+  typedef TensorEvaluator<decltype(mat1.contract(mat2, dims4)), DefaultDevice> Evaluator2;
+  Evaluator2 eval2(mat1.contract(mat2, dims4), DefaultDevice());
+  eval2.evalTo(mat5.data());
+  EIGEN_STATIC_ASSERT(Evaluator2::NumDims == 2ul, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  VERIFY_IS_EQUAL(eval2.dimensions()[0], 2);
+  VERIFY_IS_EQUAL(eval2.dimensions()[1], 2);
+
+  VERIFY_IS_APPROX(mat5(0, 0), mat1(0, 0) * mat2(0, 0) + mat1(0, 1) * mat2(0, 1) + mat1(0, 2) * mat2(0, 2));
+  VERIFY_IS_APPROX(mat5(0, 1), mat1(0, 0) * mat2(1, 0) + mat1(0, 1) * mat2(1, 1) + mat1(0, 2) * mat2(1, 2));
+  VERIFY_IS_APPROX(mat5(1, 0), mat1(1, 0) * mat2(0, 0) + mat1(1, 1) * mat2(0, 1) + mat1(1, 2) * mat2(0, 2));
+  VERIFY_IS_APPROX(mat5(1, 1), mat1(1, 0) * mat2(1, 0) + mat1(1, 1) * mat2(1, 1) + mat1(1, 2) * mat2(1, 2));
+
+  Tensor<float, 2, DataLayout> mat6(2, 2);
+  mat6.setZero();
+  Eigen::array<DimPair, 1> dims6 = {{DimPair(1, 0)}};
+  typedef TensorEvaluator<decltype(mat1.contract(mat3, dims6)), DefaultDevice> Evaluator3;
+  Evaluator3 eval3(mat1.contract(mat3, dims6), DefaultDevice());
+  eval3.evalTo(mat6.data());
+  EIGEN_STATIC_ASSERT(Evaluator3::NumDims == 2ul, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  VERIFY_IS_EQUAL(eval3.dimensions()[0], 2);
+  VERIFY_IS_EQUAL(eval3.dimensions()[1], 2);
+
+  VERIFY_IS_APPROX(mat6(0, 0), mat1(0, 0) * mat3(0, 0) + mat1(0, 1) * mat3(1, 0) + mat1(0, 2) * mat3(2, 0));
+  VERIFY_IS_APPROX(mat6(0, 1), mat1(0, 0) * mat3(0, 1) + mat1(0, 1) * mat3(1, 1) + mat1(0, 2) * mat3(2, 1));
+  VERIFY_IS_APPROX(mat6(1, 0), mat1(1, 0) * mat3(0, 0) + mat1(1, 1) * mat3(1, 0) + mat1(1, 2) * mat3(2, 0));
+  VERIFY_IS_APPROX(mat6(1, 1), mat1(1, 0) * mat3(0, 1) + mat1(1, 1) * mat3(1, 1) + mat1(1, 2) * mat3(2, 1));
+}
+
+template <int DataLayout>
+static void test_scalar() {
+  Tensor<float, 1, DataLayout> vec1({6});
+  Tensor<float, 1, DataLayout> vec2({6});
+
+  vec1.setRandom();
+  vec2.setRandom();
+
+  Eigen::array<DimPair, 1> dims = {{DimPair(0, 0)}};
+  Tensor<float, 0, DataLayout> scalar = vec1.contract(vec2, dims);
+
+  float expected = 0.0f;
+  for (int i = 0; i < 6; ++i) {
+    expected += vec1(i) * vec2(i);
+  }
+  VERIFY_IS_APPROX(scalar(), expected);
+}
+
+template <int DataLayout>
+static void test_multidims() {
+  Tensor<float, 3, DataLayout> mat1(2, 2, 2);
+  Tensor<float, 4, DataLayout> mat2(2, 2, 2, 2);
+
+  mat1.setRandom();
+  mat2.setRandom();
+
+  Tensor<float, 3, DataLayout> mat3(2, 2, 2);
+  mat3.setZero();
+  Eigen::array<DimPair, 2> dims = {{DimPair(1, 2), DimPair(2, 3)}};
+  typedef TensorEvaluator<decltype(mat1.contract(mat2, dims)), DefaultDevice> Evaluator;
+  Evaluator eval(mat1.contract(mat2, dims), DefaultDevice());
+  eval.evalTo(mat3.data());
+  EIGEN_STATIC_ASSERT(Evaluator::NumDims == 3ul, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  VERIFY_IS_EQUAL(eval.dimensions()[0], 2);
+  VERIFY_IS_EQUAL(eval.dimensions()[1], 2);
+  VERIFY_IS_EQUAL(eval.dimensions()[2], 2);
+
+  VERIFY_IS_APPROX(mat3(0, 0, 0), mat1(0, 0, 0) * mat2(0, 0, 0, 0) + mat1(0, 1, 0) * mat2(0, 0, 1, 0) +
+                                      mat1(0, 0, 1) * mat2(0, 0, 0, 1) + mat1(0, 1, 1) * mat2(0, 0, 1, 1));
+  VERIFY_IS_APPROX(mat3(0, 0, 1), mat1(0, 0, 0) * mat2(0, 1, 0, 0) + mat1(0, 1, 0) * mat2(0, 1, 1, 0) +
+                                      mat1(0, 0, 1) * mat2(0, 1, 0, 1) + mat1(0, 1, 1) * mat2(0, 1, 1, 1));
+  VERIFY_IS_APPROX(mat3(0, 1, 0), mat1(0, 0, 0) * mat2(1, 0, 0, 0) + mat1(0, 1, 0) * mat2(1, 0, 1, 0) +
+                                      mat1(0, 0, 1) * mat2(1, 0, 0, 1) + mat1(0, 1, 1) * mat2(1, 0, 1, 1));
+  VERIFY_IS_APPROX(mat3(0, 1, 1), mat1(0, 0, 0) * mat2(1, 1, 0, 0) + mat1(0, 1, 0) * mat2(1, 1, 1, 0) +
+                                      mat1(0, 0, 1) * mat2(1, 1, 0, 1) + mat1(0, 1, 1) * mat2(1, 1, 1, 1));
+  VERIFY_IS_APPROX(mat3(1, 0, 0), mat1(1, 0, 0) * mat2(0, 0, 0, 0) + mat1(1, 1, 0) * mat2(0, 0, 1, 0) +
+                                      mat1(1, 0, 1) * mat2(0, 0, 0, 1) + mat1(1, 1, 1) * mat2(0, 0, 1, 1));
+  VERIFY_IS_APPROX(mat3(1, 0, 1), mat1(1, 0, 0) * mat2(0, 1, 0, 0) + mat1(1, 1, 0) * mat2(0, 1, 1, 0) +
+                                      mat1(1, 0, 1) * mat2(0, 1, 0, 1) + mat1(1, 1, 1) * mat2(0, 1, 1, 1));
+  VERIFY_IS_APPROX(mat3(1, 1, 0), mat1(1, 0, 0) * mat2(1, 0, 0, 0) + mat1(1, 1, 0) * mat2(1, 0, 1, 0) +
+                                      mat1(1, 0, 1) * mat2(1, 0, 0, 1) + mat1(1, 1, 1) * mat2(1, 0, 1, 1));
+  VERIFY_IS_APPROX(mat3(1, 1, 1), mat1(1, 0, 0) * mat2(1, 1, 0, 0) + mat1(1, 1, 0) * mat2(1, 1, 1, 0) +
+                                      mat1(1, 0, 1) * mat2(1, 1, 0, 1) + mat1(1, 1, 1) * mat2(1, 1, 1, 1));
+
+  Tensor<float, 2, DataLayout> mat4(2, 2);
+  Tensor<float, 3, DataLayout> mat5(2, 2, 2);
+
+  mat4.setRandom();
+  mat5.setRandom();
+
+  Tensor<float, 1, DataLayout> mat6(2);
+  mat6.setZero();
+  Eigen::array<DimPair, 2> dims2({{DimPair(0, 1), DimPair(1, 0)}});
+  typedef TensorEvaluator<decltype(mat4.contract(mat5, dims2)), DefaultDevice> Evaluator2;
+  Evaluator2 eval2(mat4.contract(mat5, dims2), DefaultDevice());
+  eval2.evalTo(mat6.data());
+  EIGEN_STATIC_ASSERT(Evaluator2::NumDims == 1ul, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  VERIFY_IS_EQUAL(eval2.dimensions()[0], 2);
+
+  VERIFY_IS_APPROX(mat6(0), mat4(0, 0) * mat5(0, 0, 0) + mat4(1, 0) * mat5(0, 1, 0) + mat4(0, 1) * mat5(1, 0, 0) +
+                                mat4(1, 1) * mat5(1, 1, 0));
+  VERIFY_IS_APPROX(mat6(1), mat4(0, 0) * mat5(0, 0, 1) + mat4(1, 0) * mat5(0, 1, 1) + mat4(0, 1) * mat5(1, 0, 1) +
+                                mat4(1, 1) * mat5(1, 1, 1));
+}
+
+template <int DataLayout>
+static void test_holes() {
+  Tensor<float, 4, DataLayout> t1(2, 5, 7, 3);
+  Tensor<float, 5, DataLayout> t2(2, 7, 11, 13, 3);
+  t1.setRandom();
+  t2.setRandom();
+
+  Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(3, 4)}};
+  Tensor<float, 5, DataLayout> result = t1.contract(t2, dims);
+  VERIFY_IS_EQUAL(result.dimension(0), 5);
+  VERIFY_IS_EQUAL(result.dimension(1), 7);
+  VERIFY_IS_EQUAL(result.dimension(2), 7);
+  VERIFY_IS_EQUAL(result.dimension(3), 11);
+  VERIFY_IS_EQUAL(result.dimension(4), 13);
+
+  for (int i = 0; i < 5; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 5; ++l) {
+          for (int m = 0; m < 5; ++m) {
+            VERIFY_IS_APPROX(result(i, j, k, l, m),
+                             t1(0, i, j, 0) * t2(0, k, l, m, 0) + t1(1, i, j, 0) * t2(1, k, l, m, 0) +
+                                 t1(0, i, j, 1) * t2(0, k, l, m, 1) + t1(1, i, j, 1) * t2(1, k, l, m, 1) +
+                                 t1(0, i, j, 2) * t2(0, k, l, m, 2) + t1(1, i, j, 2) * t2(1, k, l, m, 2));
+          }
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_full_redux() {
+  Tensor<float, 2, DataLayout> t1(2, 2);
+  Tensor<float, 3, DataLayout> t2(2, 2, 2);
+  t1.setRandom();
+  t2.setRandom();
+
+  Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(1, 1)}};
+  Tensor<float, 1, DataLayout> result = t1.contract(t2, dims);
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_APPROX(result(0),
+                   t1(0, 0) * t2(0, 0, 0) + t1(1, 0) * t2(1, 0, 0) + t1(0, 1) * t2(0, 1, 0) + t1(1, 1) * t2(1, 1, 0));
+  VERIFY_IS_APPROX(result(1),
+                   t1(0, 0) * t2(0, 0, 1) + t1(1, 0) * t2(1, 0, 1) + t1(0, 1) * t2(0, 1, 1) + t1(1, 1) * t2(1, 1, 1));
+
+  dims[0] = DimPair(1, 0);
+  dims[1] = DimPair(2, 1);
+  result = t2.contract(t1, dims);
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_APPROX(result(0),
+                   t1(0, 0) * t2(0, 0, 0) + t1(1, 0) * t2(0, 1, 0) + t1(0, 1) * t2(0, 0, 1) + t1(1, 1) * t2(0, 1, 1));
+  VERIFY_IS_APPROX(result(1),
+                   t1(0, 0) * t2(1, 0, 0) + t1(1, 0) * t2(1, 1, 0) + t1(0, 1) * t2(1, 0, 1) + t1(1, 1) * t2(1, 1, 1));
+}
+
+template <int DataLayout>
+static void test_contraction_of_contraction() {
+  Tensor<float, 2, DataLayout> t1(2, 2);
+  Tensor<float, 2, DataLayout> t2(2, 2);
+  Tensor<float, 2, DataLayout> t3(2, 2);
+  Tensor<float, 2, DataLayout> t4(2, 2);
+  t1.setRandom();
+  t2.setRandom();
+  t3.setRandom();
+  t4.setRandom();
+
+  Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
+  auto contract1 = t1.contract(t2, dims);
+  auto diff = t3 - contract1;
+  auto contract2 = t1.contract(t4, dims);
+  Tensor<float, 2, DataLayout> result = contract2.contract(diff, dims);
+
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_EQUAL(result.dimension(1), 2);
+
+  Eigen::Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> m1(t1.data(), 2, 2), m2(t2.data(), 2, 2),
+      m3(t3.data(), 2, 2), m4(t4.data(), 2, 2);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> expected = (m1 * m4) * (m3 - m1 * m2);
+
+  VERIFY_IS_APPROX(result(0, 0), expected(0, 0));
+  VERIFY_IS_APPROX(result(0, 1), expected(0, 1));
+  VERIFY_IS_APPROX(result(1, 0), expected(1, 0));
+  VERIFY_IS_APPROX(result(1, 1), expected(1, 1));
+}
+
+template <int DataLayout>
+static void test_expr() {
+  Tensor<float, 2, DataLayout> mat1(2, 3);
+  Tensor<float, 2, DataLayout> mat2(3, 2);
+  mat1.setRandom();
+  mat2.setRandom();
+
+  Tensor<float, 2, DataLayout> mat3(2, 2);
+
+  Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
+  mat3 = mat1.contract(mat2, dims);
+
+  VERIFY_IS_APPROX(mat3(0, 0), mat1(0, 0) * mat2(0, 0) + mat1(0, 1) * mat2(1, 0) + mat1(0, 2) * mat2(2, 0));
+  VERIFY_IS_APPROX(mat3(0, 1), mat1(0, 0) * mat2(0, 1) + mat1(0, 1) * mat2(1, 1) + mat1(0, 2) * mat2(2, 1));
+  VERIFY_IS_APPROX(mat3(1, 0), mat1(1, 0) * mat2(0, 0) + mat1(1, 1) * mat2(1, 0) + mat1(1, 2) * mat2(2, 0));
+  VERIFY_IS_APPROX(mat3(1, 1), mat1(1, 0) * mat2(0, 1) + mat1(1, 1) * mat2(1, 1) + mat1(1, 2) * mat2(2, 1));
+}
+
+template <int DataLayout>
+static void test_out_of_order_contraction() {
+  Tensor<float, 3, DataLayout> mat1(2, 2, 2);
+  Tensor<float, 3, DataLayout> mat2(2, 2, 2);
+
+  mat1.setRandom();
+  mat2.setRandom();
+
+  Tensor<float, 2, DataLayout> mat3(2, 2);
+
+  Eigen::array<DimPair, 2> dims = {{DimPair(2, 0), DimPair(0, 2)}};
+  mat3 = mat1.contract(mat2, dims);
+
+  VERIFY_IS_APPROX(mat3(0, 0), mat1(0, 0, 0) * mat2(0, 0, 0) + mat1(1, 0, 0) * mat2(0, 0, 1) +
+                                   mat1(0, 0, 1) * mat2(1, 0, 0) + mat1(1, 0, 1) * mat2(1, 0, 1));
+  VERIFY_IS_APPROX(mat3(1, 0), mat1(0, 1, 0) * mat2(0, 0, 0) + mat1(1, 1, 0) * mat2(0, 0, 1) +
+                                   mat1(0, 1, 1) * mat2(1, 0, 0) + mat1(1, 1, 1) * mat2(1, 0, 1));
+  VERIFY_IS_APPROX(mat3(0, 1), mat1(0, 0, 0) * mat2(0, 1, 0) + mat1(1, 0, 0) * mat2(0, 1, 1) +
+                                   mat1(0, 0, 1) * mat2(1, 1, 0) + mat1(1, 0, 1) * mat2(1, 1, 1));
+  VERIFY_IS_APPROX(mat3(1, 1), mat1(0, 1, 0) * mat2(0, 1, 0) + mat1(1, 1, 0) * mat2(0, 1, 1) +
+                                   mat1(0, 1, 1) * mat2(1, 1, 0) + mat1(1, 1, 1) * mat2(1, 1, 1));
+
+  Eigen::array<DimPair, 2> dims2 = {{DimPair(0, 2), DimPair(2, 0)}};
+  mat3 = mat1.contract(mat2, dims2);
+
+  VERIFY_IS_APPROX(mat3(0, 0), mat1(0, 0, 0) * mat2(0, 0, 0) + mat1(1, 0, 0) * mat2(0, 0, 1) +
+                                   mat1(0, 0, 1) * mat2(1, 0, 0) + mat1(1, 0, 1) * mat2(1, 0, 1));
+  VERIFY_IS_APPROX(mat3(1, 0), mat1(0, 1, 0) * mat2(0, 0, 0) + mat1(1, 1, 0) * mat2(0, 0, 1) +
+                                   mat1(0, 1, 1) * mat2(1, 0, 0) + mat1(1, 1, 1) * mat2(1, 0, 1));
+  VERIFY_IS_APPROX(mat3(0, 1), mat1(0, 0, 0) * mat2(0, 1, 0) + mat1(1, 0, 0) * mat2(0, 1, 1) +
+                                   mat1(0, 0, 1) * mat2(1, 1, 0) + mat1(1, 0, 1) * mat2(1, 1, 1));
+  VERIFY_IS_APPROX(mat3(1, 1), mat1(0, 1, 0) * mat2(0, 1, 0) + mat1(1, 1, 0) * mat2(0, 1, 1) +
+                                   mat1(0, 1, 1) * mat2(1, 1, 0) + mat1(1, 1, 1) * mat2(1, 1, 1));
+}
+
+template <int DataLayout>
+static void test_consistency() {
+  // this does something like testing (A*B)^T = (B^T * A^T)
+
+  Tensor<float, 3, DataLayout> mat1(4, 3, 5);
+  Tensor<float, 5, DataLayout> mat2(3, 2, 1, 5, 4);
+  mat1.setRandom();
+  mat2.setRandom();
+
+  Tensor<float, 4, DataLayout> mat3(5, 2, 1, 5);
+  Tensor<float, 4, DataLayout> mat4(2, 1, 5, 5);
+
+  // contract on dimensions of size 4 and 3
+  Eigen::array<DimPair, 2> dims1 = {{DimPair(0, 4), DimPair(1, 0)}};
+  Eigen::array<DimPair, 2> dims2 = {{DimPair(4, 0), DimPair(0, 1)}};
+
+  mat3 = mat1.contract(mat2, dims1);
+  mat4 = mat2.contract(mat1, dims2);
+
+  // check that these are equal except for ordering of dimensions
+  if (DataLayout == ColMajor) {
+    for (size_t i = 0; i < 5; i++) {
+      for (size_t j = 0; j < 10; j++) {
+        VERIFY_IS_APPROX(mat3.data()[i + 5 * j], mat4.data()[j + 10 * i]);
+      }
+    }
+  } else {
+    // Row major
+    for (size_t i = 0; i < 5; i++) {
+      for (size_t j = 0; j < 10; j++) {
+        VERIFY_IS_APPROX(mat3.data()[10 * i + j], mat4.data()[i + 5 * j]);
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_large_contraction() {
+  Tensor<float, 4, DataLayout> t_left(30, 50, 8, 31);
+  Tensor<float, 5, DataLayout> t_right(8, 31, 7, 20, 10);
+  Tensor<float, 5, DataLayout> t_result(30, 50, 7, 20, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  // Add a little offset so that the results won't be close to zero.
+  t_left += t_left.constant(1.0f);
+  t_right += t_right.constant(1.0f);
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 1500, 248);
+  MapXf m_right(t_right.data(), 248, 1400);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 2> dims = {{DimPair(2, 0), DimPair(3, 1)}};
+
+  // compute results by separate methods
+  t_result = t_left.contract(t_right, dims);
+  m_result = m_left * m_right;
+
+  for (int i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY(&t_result.data()[i] != &m_result.data()[i]);
+    VERIFY_IS_APPROX(t_result.data()[i], m_result.data()[i]);
+  }
+}
+
+template <int DataLayout>
+static void test_matrix_vector() {
+  Tensor<float, 2, DataLayout> t_left(30, 50);
+  Tensor<float, 1, DataLayout> t_right(50);
+  Tensor<float, 1, DataLayout> t_result(30);
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 30, 50);
+  MapXf m_right(t_right.data(), 50, 1);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(30, 1);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 1> dims{{DimPair(1, 0)}};
+
+  // compute results by separate methods
+  t_result = t_left.contract(t_right, dims);
+  m_result = m_left * m_right;
+
+  for (int i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY(internal::isApprox(t_result(i), m_result(i, 0), 1));
+  }
+}
+
+template <int DataLayout>
+static void test_tensor_vector() {
+  Tensor<float, 3, DataLayout> t_left(7, 13, 17);
+  Tensor<float, 2, DataLayout> t_right(1, 7);
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  typedef typename Tensor<float, 1, DataLayout>::DimensionPair DimensionPair;
+  Eigen::array<DimensionPair, 1> dim_pair01{{{0, 1}}};
+  Tensor<float, 3, DataLayout> t_result = t_left.contract(t_right, dim_pair01);
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 7, 13 * 17);
+  MapXf m_right(t_right.data(), 1, 7);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result = m_left.transpose() * m_right.transpose();
+
+  for (int i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY(internal::isApprox(t_result(i), m_result(i, 0), 1));
+  }
+}
+
+template <int DataLayout>
+static void test_small_blocking_factors() {
+  Tensor<float, 4, DataLayout> t_left(30, 5, 3, 31);
+  Tensor<float, 5, DataLayout> t_right(3, 31, 7, 20, 1);
+  t_left.setRandom();
+  t_right.setRandom();
+
+  // Add a little offset so that the results won't be close to zero.
+  t_left += t_left.constant(1.0f);
+  t_right += t_right.constant(1.0f);
+
+  // Force the cache sizes, which results in smaller blocking factors.
+  Eigen::setCpuCacheSizes(896, 1920, 2944);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 2> dims = {{DimPair(2, 0), DimPair(3, 1)}};
+  Tensor<float, 5, DataLayout> t_result;
+  t_result = t_left.contract(t_right, dims);
+
+  // compute result using a simple eigen matrix product
+  Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> m_left(t_left.data(), 150, 93);
+  Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> m_right(t_right.data(), 93, 140);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result = m_left * m_right;
+
+  for (int i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY_IS_APPROX(t_result.data()[i], m_result.data()[i]);
+  }
+}
+
+template <int DataLayout>
+static void test_tensor_product() {
+  Tensor<float, 2, DataLayout> mat1(2, 3);
+  Tensor<float, 2, DataLayout> mat2(4, 1);
+  mat1.setRandom();
+  mat2.setRandom();
+
+  Eigen::array<DimPair, 0> dims;
+  Tensor<float, 4, DataLayout> result = mat1.contract(mat2, dims);
+
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_EQUAL(result.dimension(1), 3);
+  VERIFY_IS_EQUAL(result.dimension(2), 4);
+  VERIFY_IS_EQUAL(result.dimension(3), 1);
+  for (int i = 0; i < result.dimension(0); ++i) {
+    for (int j = 0; j < result.dimension(1); ++j) {
+      for (int k = 0; k < result.dimension(2); ++k) {
+        for (int l = 0; l < result.dimension(3); ++l) {
+          VERIFY_IS_APPROX(result(i, j, k, l), mat1(i, j) * mat2(k, l));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_const_inputs() {
+  Tensor<float, 2, DataLayout> in1(2, 3);
+  Tensor<float, 2, DataLayout> in2(3, 2);
+  in1.setRandom();
+  in2.setRandom();
+
+  TensorMap<Tensor<const float, 2, DataLayout>> mat1(in1.data(), 2, 3);
+  TensorMap<Tensor<const float, 2, DataLayout>> mat2(in2.data(), 3, 2);
+  Tensor<float, 2, DataLayout> mat3(2, 2);
+
+  Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
+  mat3 = mat1.contract(mat2, dims);
+
+  VERIFY_IS_APPROX(mat3(0, 0), mat1(0, 0) * mat2(0, 0) + mat1(0, 1) * mat2(1, 0) + mat1(0, 2) * mat2(2, 0));
+  VERIFY_IS_APPROX(mat3(0, 1), mat1(0, 0) * mat2(0, 1) + mat1(0, 1) * mat2(1, 1) + mat1(0, 2) * mat2(2, 1));
+  VERIFY_IS_APPROX(mat3(1, 0), mat1(1, 0) * mat2(0, 0) + mat1(1, 1) * mat2(1, 0) + mat1(1, 2) * mat2(2, 0));
+  VERIFY_IS_APPROX(mat3(1, 1), mat1(1, 0) * mat2(0, 1) + mat1(1, 1) * mat2(1, 1) + mat1(1, 2) * mat2(2, 1));
+}
+
+// Apply Sqrt to all output elements.
+struct SqrtOutputKernel {
+  template <typename Index, typename Scalar>
+  EIGEN_ALWAYS_INLINE void operator()(const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,
+                                      const TensorContractionParams&, Index, Index, Index num_rows,
+                                      Index num_cols) const {
+    for (int i = 0; i < num_rows; ++i) {
+      for (int j = 0; j < num_cols; ++j) {
+        output_mapper(i, j) = std::sqrt(output_mapper(i, j));
+      }
+    }
+  }
+};
+
+template <int DataLayout>
+static void test_large_contraction_with_output_kernel() {
+  Tensor<float, 4, DataLayout> t_left(30, 50, 8, 31);
+  Tensor<float, 5, DataLayout> t_right(8, 31, 7, 20, 10);
+  Tensor<float, 5, DataLayout> t_result(30, 50, 7, 20, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+  // Put trash in mat4 to verify contraction clears output memory.
+  t_result.setRandom();
+
+  // Add a little offset so that the results won't be close to zero.
+  t_left += t_left.constant(1.0f);
+  t_right += t_right.constant(1.0f);
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 1500, 248);
+  MapXf m_right(t_right.data(), 248, 1400);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 2> dims({{DimPair(2, 0), DimPair(3, 1)}});
+
+  // compute results by separate methods
+  t_result = t_left.contract(t_right, dims, SqrtOutputKernel());
+
+  m_result = m_left * m_right;
+
+  for (std::ptrdiff_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY(&t_result.data()[i] != &m_result.data()[i]);
+    VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i]));
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_contraction) {
+  CALL_SUBTEST_1(test_evals<ColMajor>());
+  CALL_SUBTEST_1(test_evals<RowMajor>());
+  CALL_SUBTEST_1(test_scalar<ColMajor>());
+  CALL_SUBTEST_1(test_scalar<RowMajor>());
+  CALL_SUBTEST_2(test_multidims<ColMajor>());
+  CALL_SUBTEST_2(test_multidims<RowMajor>());
+  CALL_SUBTEST_2(test_holes<ColMajor>());
+  CALL_SUBTEST_2(test_holes<RowMajor>());
+  CALL_SUBTEST_3(test_full_redux<ColMajor>());
+  CALL_SUBTEST_3(test_full_redux<RowMajor>());
+  CALL_SUBTEST_3(test_contraction_of_contraction<ColMajor>());
+  CALL_SUBTEST_3(test_contraction_of_contraction<RowMajor>());
+  CALL_SUBTEST_4(test_expr<ColMajor>());
+  CALL_SUBTEST_4(test_expr<RowMajor>());
+  CALL_SUBTEST_4(test_out_of_order_contraction<ColMajor>());
+  CALL_SUBTEST_4(test_out_of_order_contraction<RowMajor>());
+  CALL_SUBTEST_5(test_consistency<ColMajor>());
+  CALL_SUBTEST_5(test_consistency<RowMajor>());
+  CALL_SUBTEST_5(test_large_contraction<ColMajor>());
+  CALL_SUBTEST_5(test_large_contraction<RowMajor>());
+  CALL_SUBTEST_6(test_matrix_vector<ColMajor>());
+  CALL_SUBTEST_6(test_matrix_vector<RowMajor>());
+  CALL_SUBTEST_6(test_tensor_vector<ColMajor>());
+  CALL_SUBTEST_6(test_tensor_vector<RowMajor>());
+  CALL_SUBTEST_7(test_small_blocking_factors<ColMajor>());
+  CALL_SUBTEST_7(test_small_blocking_factors<RowMajor>());
+  CALL_SUBTEST_7(test_tensor_product<ColMajor>());
+  CALL_SUBTEST_7(test_tensor_product<RowMajor>());
+  CALL_SUBTEST_8(test_const_inputs<ColMajor>());
+  CALL_SUBTEST_8(test_const_inputs<RowMajor>());
+  CALL_SUBTEST_8(test_large_contraction_with_output_kernel<ColMajor>());
+  CALL_SUBTEST_8(test_large_contraction_with_output_kernel<RowMajor>());
+
+  // Force CMake to split this test.
+  // EIGEN_SUFFIXES;1;2;3;4;5;6;7;8
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_convolution.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_convolution.cpp
new file mode 100644
index 0000000..c921865
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_convolution.cpp
@@ -0,0 +1,145 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::DefaultDevice;
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_evals() {
+  Tensor<float, 2, DataLayout> input(3, 3);
+  Tensor<float, 1, DataLayout> kernel(2);
+
+  input.setRandom();
+  kernel.setRandom();
+
+  Tensor<float, 2, DataLayout> result(2, 3);
+  result.setZero();
+  Eigen::array<Tensor<float, 2>::Index, 1> dims3;
+  dims3[0] = 0;
+
+  typedef TensorEvaluator<decltype(input.convolve(kernel, dims3)), DefaultDevice> Evaluator;
+  Evaluator eval(input.convolve(kernel, dims3), DefaultDevice());
+  eval.evalTo(result.data());
+  EIGEN_STATIC_ASSERT(Evaluator::NumDims == 2ul, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  VERIFY_IS_EQUAL(eval.dimensions()[0], 2);
+  VERIFY_IS_EQUAL(eval.dimensions()[1], 3);
+
+  VERIFY_IS_APPROX(result(0, 0), input(0, 0) * kernel(0) + input(1, 0) * kernel(1));  // index 0
+  VERIFY_IS_APPROX(result(0, 1), input(0, 1) * kernel(0) + input(1, 1) * kernel(1));  // index 2
+  VERIFY_IS_APPROX(result(0, 2), input(0, 2) * kernel(0) + input(1, 2) * kernel(1));  // index 4
+  VERIFY_IS_APPROX(result(1, 0), input(1, 0) * kernel(0) + input(2, 0) * kernel(1));  // index 1
+  VERIFY_IS_APPROX(result(1, 1), input(1, 1) * kernel(0) + input(2, 1) * kernel(1));  // index 3
+  VERIFY_IS_APPROX(result(1, 2), input(1, 2) * kernel(0) + input(2, 2) * kernel(1));  // index 5
+}
+
+template <int DataLayout>
+static void test_expr() {
+  Tensor<float, 2, DataLayout> input(3, 3);
+  Tensor<float, 2, DataLayout> kernel(2, 2);
+  input.setRandom();
+  kernel.setRandom();
+
+  Tensor<float, 2, DataLayout> result(2, 2);
+  Eigen::array<ptrdiff_t, 2> dims;
+  dims[0] = 0;
+  dims[1] = 1;
+  result = input.convolve(kernel, dims);
+
+  VERIFY_IS_APPROX(result(0, 0), input(0, 0) * kernel(0, 0) + input(0, 1) * kernel(0, 1) + input(1, 0) * kernel(1, 0) +
+                                     input(1, 1) * kernel(1, 1));
+  VERIFY_IS_APPROX(result(0, 1), input(0, 1) * kernel(0, 0) + input(0, 2) * kernel(0, 1) + input(1, 1) * kernel(1, 0) +
+                                     input(1, 2) * kernel(1, 1));
+  VERIFY_IS_APPROX(result(1, 0), input(1, 0) * kernel(0, 0) + input(1, 1) * kernel(0, 1) + input(2, 0) * kernel(1, 0) +
+                                     input(2, 1) * kernel(1, 1));
+  VERIFY_IS_APPROX(result(1, 1), input(1, 1) * kernel(0, 0) + input(1, 2) * kernel(0, 1) + input(2, 1) * kernel(1, 0) +
+                                     input(2, 2) * kernel(1, 1));
+}
+
+template <int DataLayout>
+static void test_modes() {
+  Tensor<float, 1, DataLayout> input(3);
+  Tensor<float, 1, DataLayout> kernel(3);
+  input(0) = 1.0f;
+  input(1) = 2.0f;
+  input(2) = 3.0f;
+  kernel(0) = 0.5f;
+  kernel(1) = 1.0f;
+  kernel(2) = 0.0f;
+
+  Eigen::array<ptrdiff_t, 1> dims;
+  dims[0] = 0;
+  Eigen::array<std::pair<ptrdiff_t, ptrdiff_t>, 1> padding;
+
+  // Emulate VALID mode (as defined in
+  // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
+  padding[0] = std::make_pair(0, 0);
+  Tensor<float, 1, DataLayout> valid(1);
+  valid = input.pad(padding).convolve(kernel, dims);
+  VERIFY_IS_EQUAL(valid.dimension(0), 1);
+  VERIFY_IS_APPROX(valid(0), 2.5f);
+
+  // Emulate SAME mode (as defined in
+  // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
+  padding[0] = std::make_pair(1, 1);
+  Tensor<float, 1, DataLayout> same(3);
+  same = input.pad(padding).convolve(kernel, dims);
+  VERIFY_IS_EQUAL(same.dimension(0), 3);
+  VERIFY_IS_APPROX(same(0), 1.0f);
+  VERIFY_IS_APPROX(same(1), 2.5f);
+  VERIFY_IS_APPROX(same(2), 4.0f);
+
+  // Emulate FULL mode (as defined in
+  // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
+  padding[0] = std::make_pair(2, 2);
+  Tensor<float, 1, DataLayout> full(5);
+  full = input.pad(padding).convolve(kernel, dims);
+  VERIFY_IS_EQUAL(full.dimension(0), 5);
+  VERIFY_IS_APPROX(full(0), 0.0f);
+  VERIFY_IS_APPROX(full(1), 1.0f);
+  VERIFY_IS_APPROX(full(2), 2.5f);
+  VERIFY_IS_APPROX(full(3), 4.0f);
+  VERIFY_IS_APPROX(full(4), 1.5f);
+}
+
+template <int DataLayout>
+static void test_strides() {
+  Tensor<float, 1, DataLayout> input(13);
+  Tensor<float, 1, DataLayout> kernel(3);
+  input.setRandom();
+  kernel.setRandom();
+
+  Eigen::array<ptrdiff_t, 1> dims;
+  dims[0] = 0;
+  Eigen::array<ptrdiff_t, 1> stride_of_3;
+  stride_of_3[0] = 3;
+  Eigen::array<ptrdiff_t, 1> stride_of_2;
+  stride_of_2[0] = 2;
+
+  Tensor<float, 1, DataLayout> result;
+  result = input.stride(stride_of_3).convolve(kernel, dims).stride(stride_of_2);
+
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_APPROX(result(0), (input(0) * kernel(0) + input(3) * kernel(1) + input(6) * kernel(2)));
+  VERIFY_IS_APPROX(result(1), (input(6) * kernel(0) + input(9) * kernel(1) + input(12) * kernel(2)));
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_convolution) {
+  CALL_SUBTEST(test_evals<ColMajor>());
+  CALL_SUBTEST(test_evals<RowMajor>());
+  CALL_SUBTEST(test_expr<ColMajor>());
+  CALL_SUBTEST(test_expr<RowMajor>());
+  CALL_SUBTEST(test_modes<ColMajor>());
+  CALL_SUBTEST(test_modes<RowMajor>());
+  CALL_SUBTEST(test_strides<ColMajor>());
+  CALL_SUBTEST(test_strides<RowMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_convolution_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_convolution_sycl.cpp
new file mode 100644
index 0000000..350ac33
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_convolution_sycl.cpp
@@ -0,0 +1,458 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include <iostream>
+#include <chrono>
+#include <ctime>
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+#include <iomanip>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+static const float error_threshold = 1e-4f;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_larg_expr1D(const Eigen::SyclDevice& sycl_device) {
+  IndexType indim0 = 53;
+  IndexType indim1 = 55;
+  IndexType indim2 = 51;
+  IndexType outdim0 = 50;
+  IndexType outdim1 = 55;
+  IndexType outdim2 = 51;
+  Eigen::array<IndexType, 3> input_dims = {{indim0, indim1, indim2}};
+  Eigen::array<IndexType, 1> kernel_dims = {{4}};
+  Eigen::array<IndexType, 3> result_dims = {{outdim0, outdim1, outdim2}};
+
+  Tensor<DataType, 3, DataLayout, IndexType> input(input_dims);
+  Tensor<DataType, 1, DataLayout, IndexType> kernel(kernel_dims);
+  Tensor<DataType, 3, DataLayout, IndexType> result(result_dims);
+  Tensor<DataType, 3, DataLayout, IndexType> result_host(result_dims);
+
+  Eigen::array<IndexType, 1> dims3{{0}};
+
+  input.setRandom();
+  kernel.setRandom();
+  result.setZero();
+  result_host.setZero();
+
+  std::size_t input_bytes = input.size() * sizeof(DataType);
+  std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
+  std::size_t result_bytes = result.size() * sizeof(DataType);
+
+  DataType* d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
+  DataType* d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
+  DataType* d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_input(d_input, input_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_result(d_result, result_dims);
+  sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
+  sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
+
+  gpu_result.device(sycl_device) = gpu_input.convolve(gpu_kernel, dims3);
+  sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
+
+  result_host = input.convolve(kernel, dims3);
+
+  for (IndexType i = 0; i < outdim0; i++) {
+    for (IndexType j = 0; j < outdim1; j++) {
+      for (IndexType k = 0; k < outdim2; k++) {
+        if (!(Eigen::internal::isApprox(result(i, j, k), result_host(i, j, k), error_threshold))) {
+          std::cout << std::setprecision(16) << "mismatch detected at index  ( " << i << " , " << j << ", " << k
+                    << " ) "
+                    << " \t " << result(i, j, k) << " vs " << result_host(i, j, k) << std::endl;
+          assert(false);
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(d_input);
+  sycl_device.deallocate(d_kernel);
+  sycl_device.deallocate(d_result);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_larg_expr2D(const Eigen::SyclDevice& sycl_device) {
+  IndexType indim0 = 53;
+  IndexType indim1 = 55;
+  IndexType indim2 = 51;
+  IndexType outdim0 = 50;
+  IndexType outdim1 = 51;
+  IndexType outdim2 = 51;
+  Eigen::array<IndexType, 3> input_dims = {{indim0, indim1, indim2}};
+  Eigen::array<IndexType, 2> kernel_dims = {{4, 5}};
+  Eigen::array<IndexType, 3> result_dims = {{outdim0, outdim1, outdim2}};
+
+  Tensor<DataType, 3, DataLayout, IndexType> input(input_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> kernel(kernel_dims);
+  Tensor<DataType, 3, DataLayout, IndexType> result(result_dims);
+  Tensor<DataType, 3, DataLayout, IndexType> result_host(result_dims);
+
+  Eigen::array<IndexType, 2> dims3{{0, 1}};
+
+  input.setRandom();
+  kernel.setRandom();
+  result.setZero();
+  result_host.setZero();
+
+  std::size_t input_bytes = input.size() * sizeof(DataType);
+  std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
+  std::size_t result_bytes = result.size() * sizeof(DataType);
+
+  DataType* d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
+  DataType* d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
+  DataType* d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_input(d_input, input_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_result(d_result, result_dims);
+  sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
+  sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
+
+  gpu_result.device(sycl_device) = gpu_input.convolve(gpu_kernel, dims3);
+  sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
+
+  result_host = input.convolve(kernel, dims3);
+
+  for (IndexType i = 0; i < outdim0; i++) {
+    for (IndexType j = 0; j < outdim1; j++) {
+      for (IndexType k = 0; k < outdim2; k++) {
+        if (!(Eigen::internal::isApprox(result(i, j, k), result_host(i, j, k), error_threshold))) {
+          std::cout << std::setprecision(16) << "mismatch detected at index  ( " << i << " , " << j << ", " << k
+                    << " ) "
+                    << " \t " << result(i, j, k) << " vs " << result_host(i, j, k) << std::endl;
+          assert(false);
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(d_input);
+  sycl_device.deallocate(d_kernel);
+  sycl_device.deallocate(d_result);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_larg_expr3D(const Eigen::SyclDevice& sycl_device) {
+  IndexType indim0 = 53;
+  IndexType indim1 = 55;
+  IndexType indim2 = 51;
+  IndexType outdim0 = 50;
+  IndexType outdim1 = 51;
+  IndexType outdim2 = 49;
+  Eigen::array<IndexType, 3> input_dims = {{indim0, indim1, indim2}};
+  Eigen::array<IndexType, 3> kernel_dims = {{4, 5, 3}};
+  Eigen::array<IndexType, 3> result_dims = {{outdim0, outdim1, outdim2}};
+
+  Tensor<DataType, 3, DataLayout, IndexType> input(input_dims);
+  Tensor<DataType, 3, DataLayout, IndexType> kernel(kernel_dims);
+  Tensor<DataType, 3, DataLayout, IndexType> result(result_dims);
+  Tensor<DataType, 3, DataLayout, IndexType> result_host(result_dims);
+
+  Eigen::array<IndexType, 3> dims3{{0, 1, 2}};
+
+  input.setRandom();
+  kernel.setRandom();
+  result.setZero();
+  result_host.setZero();
+
+  std::size_t input_bytes = input.size() * sizeof(DataType);
+  std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
+  std::size_t result_bytes = result.size() * sizeof(DataType);
+
+  DataType* d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
+  DataType* d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
+  DataType* d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_input(d_input, input_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > gpu_result(d_result, result_dims);
+  sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
+  sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
+
+  gpu_result.device(sycl_device) = gpu_input.convolve(gpu_kernel, dims3);
+  sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
+
+  result_host = input.convolve(kernel, dims3);
+
+  for (IndexType i = 0; i < outdim0; i++) {
+    for (IndexType j = 0; j < outdim1; j++) {
+      for (IndexType k = 0; k < outdim2; k++) {
+        if (!(Eigen::internal::isApprox(result(i, j, k), result_host(i, j, k), error_threshold))) {
+          std::cout << std::setprecision(16) << "mismatch detected at index  ( " << i << " , " << j << ", " << k
+                    << " ) "
+                    << " \t " << result(i, j, k) << " vs " << result_host(i, j, k) << std::endl;
+          assert(false);
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(d_input);
+  sycl_device.deallocate(d_kernel);
+  sycl_device.deallocate(d_result);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_evals(const Eigen::SyclDevice& sycl_device) {
+  Eigen::array<IndexType, 2> input_dims = {{3, 3}};
+  Eigen::array<IndexType, 1> kernel_dims = {{2}};
+  Eigen::array<IndexType, 2> result_dims = {{2, 3}};
+
+  Tensor<DataType, 2, DataLayout, IndexType> input(input_dims);
+  Tensor<DataType, 1, DataLayout, IndexType> kernel(kernel_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> result(result_dims);
+
+  Eigen::array<IndexType, 1> dims3{{0}};
+
+  input.setRandom();
+  kernel.setRandom();
+  result.setZero();
+
+  std::size_t input_bytes = input.size() * sizeof(DataType);
+  std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
+  std::size_t result_bytes = result.size() * sizeof(DataType);
+
+  DataType* d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
+  DataType* d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
+  DataType* d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > gpu_input(d_input, input_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > gpu_result(d_result, result_dims);
+  sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
+  sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
+
+  gpu_result.device(sycl_device) = gpu_input.convolve(gpu_kernel, dims3);
+  sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
+
+  VERIFY_IS_APPROX(result(0, 0), input(0, 0) * kernel(0) + input(1, 0) * kernel(1));  // index 0
+  VERIFY_IS_APPROX(result(0, 1), input(0, 1) * kernel(0) + input(1, 1) * kernel(1));  // index 2
+  VERIFY_IS_APPROX(result(0, 2), input(0, 2) * kernel(0) + input(1, 2) * kernel(1));  // index 4
+  VERIFY_IS_APPROX(result(1, 0), input(1, 0) * kernel(0) + input(2, 0) * kernel(1));  // index 1
+  VERIFY_IS_APPROX(result(1, 1), input(1, 1) * kernel(0) + input(2, 1) * kernel(1));  // index 3
+  VERIFY_IS_APPROX(result(1, 2), input(1, 2) * kernel(0) + input(2, 2) * kernel(1));  // index 5
+
+  sycl_device.deallocate(d_input);
+  sycl_device.deallocate(d_kernel);
+  sycl_device.deallocate(d_result);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_expr(const Eigen::SyclDevice& sycl_device) {
+  Eigen::array<IndexType, 2> input_dims = {{3, 3}};
+  Eigen::array<IndexType, 2> kernel_dims = {{2, 2}};
+  Eigen::array<IndexType, 2> result_dims = {{2, 2}};
+
+  Tensor<DataType, 2, DataLayout, IndexType> input(input_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> kernel(kernel_dims);
+  Tensor<DataType, 2, DataLayout, IndexType> result(result_dims);
+
+  input.setRandom();
+  kernel.setRandom();
+  Eigen::array<IndexType, 2> dims;
+  dims[0] = 0;
+  dims[1] = 1;
+
+  std::size_t input_bytes = input.size() * sizeof(DataType);
+  std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
+  std::size_t result_bytes = result.size() * sizeof(DataType);
+
+  DataType* d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
+  DataType* d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
+  DataType* d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > gpu_input(d_input, input_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > gpu_result(d_result, result_dims);
+  sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
+  sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
+
+  gpu_result.device(sycl_device) = gpu_input.convolve(gpu_kernel, dims);
+  sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
+
+  VERIFY_IS_APPROX(result(0, 0), input(0, 0) * kernel(0, 0) + input(0, 1) * kernel(0, 1) + input(1, 0) * kernel(1, 0) +
+                                     input(1, 1) * kernel(1, 1));
+  VERIFY_IS_APPROX(result(0, 1), input(0, 1) * kernel(0, 0) + input(0, 2) * kernel(0, 1) + input(1, 1) * kernel(1, 0) +
+                                     input(1, 2) * kernel(1, 1));
+  VERIFY_IS_APPROX(result(1, 0), input(1, 0) * kernel(0, 0) + input(1, 1) * kernel(0, 1) + input(2, 0) * kernel(1, 0) +
+                                     input(2, 1) * kernel(1, 1));
+  VERIFY_IS_APPROX(result(1, 1), input(1, 1) * kernel(0, 0) + input(1, 2) * kernel(0, 1) + input(2, 1) * kernel(1, 0) +
+                                     input(2, 2) * kernel(1, 1));
+
+  sycl_device.deallocate(d_input);
+  sycl_device.deallocate(d_kernel);
+  sycl_device.deallocate(d_result);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_modes(const Eigen::SyclDevice& sycl_device) {
+  Eigen::array<IndexType, 1> input_dims = {{3}};
+  Eigen::array<IndexType, 1> kernel_dims = {{3}};
+
+  Tensor<DataType, 1, DataLayout, IndexType> input(input_dims);
+  Tensor<DataType, 1, DataLayout, IndexType> kernel(kernel_dims);
+
+  input.setRandom();
+  kernel.setRandom();
+  Eigen::array<IndexType, 1> dims;
+  dims[0] = 0;
+
+  input(0) = 1.0f;
+  input(1) = 2.0f;
+  input(2) = 3.0f;
+  kernel(0) = 0.5f;
+  kernel(1) = 1.0f;
+  kernel(2) = 0.0f;
+
+  Eigen::array<std::pair<IndexType, IndexType>, 1> padding;
+
+  // Emulate VALID mode (as defined in
+  // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
+  padding[0] = std::make_pair(0, 0);
+  Tensor<DataType, 1, DataLayout, IndexType> valid(1);
+
+  std::size_t input_bytes = input.size() * sizeof(DataType);
+  std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
+  std::size_t valid_bytes = valid.size() * sizeof(DataType);
+
+  DataType* d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
+  DataType* d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
+  DataType* d_valid = static_cast<DataType*>(sycl_device.allocate(valid_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_input(d_input, input_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_valid(d_valid, valid.dimensions());
+  sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
+  sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
+
+  gpu_valid.device(sycl_device) = gpu_input.pad(padding).convolve(gpu_kernel, dims);
+  sycl_device.memcpyDeviceToHost(valid.data(), d_valid, valid_bytes);
+
+  VERIFY_IS_EQUAL(valid.dimension(0), 1);
+  VERIFY_IS_APPROX(valid(0), 2.5f);
+
+  // Emulate SAME mode (as defined in
+  // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
+  padding[0] = std::make_pair(1, 1);
+  Tensor<DataType, 1, DataLayout, IndexType> same(3);
+  std::size_t same_bytes = same.size() * sizeof(DataType);
+  DataType* d_same = static_cast<DataType*>(sycl_device.allocate(same_bytes));
+  Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_same(d_same, same.dimensions());
+  gpu_same.device(sycl_device) = gpu_input.pad(padding).convolve(gpu_kernel, dims);
+  sycl_device.memcpyDeviceToHost(same.data(), d_same, same_bytes);
+
+  VERIFY_IS_EQUAL(same.dimension(0), 3);
+  VERIFY_IS_APPROX(same(0), 1.0f);
+  VERIFY_IS_APPROX(same(1), 2.5f);
+  VERIFY_IS_APPROX(same(2), 4.0f);
+
+  // Emulate FULL mode (as defined in
+  // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
+  padding[0] = std::make_pair(2, 2);
+
+  Tensor<DataType, 1, DataLayout, IndexType> full(5);
+  std::size_t full_bytes = full.size() * sizeof(DataType);
+  DataType* d_full = static_cast<DataType*>(sycl_device.allocate(full_bytes));
+  Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_full(d_full, full.dimensions());
+  gpu_full.device(sycl_device) = gpu_input.pad(padding).convolve(gpu_kernel, dims);
+  sycl_device.memcpyDeviceToHost(full.data(), d_full, full_bytes);
+
+  VERIFY_IS_EQUAL(full.dimension(0), 5);
+  VERIFY_IS_APPROX(full(0), 0.0f);
+  VERIFY_IS_APPROX(full(1), 1.0f);
+  VERIFY_IS_APPROX(full(2), 2.5f);
+  VERIFY_IS_APPROX(full(3), 4.0f);
+  VERIFY_IS_APPROX(full(4), 1.5f);
+
+  sycl_device.deallocate(d_input);
+  sycl_device.deallocate(d_kernel);
+  sycl_device.deallocate(d_valid);
+  sycl_device.deallocate(d_same);
+  sycl_device.deallocate(d_full);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_strides(const Eigen::SyclDevice& sycl_device) {
+  Eigen::array<IndexType, 1> input_dims = {{13}};
+  Eigen::array<IndexType, 1> kernel_dims = {{3}};
+
+  Tensor<DataType, 1, DataLayout, IndexType> input(input_dims);
+  Tensor<DataType, 1, DataLayout, IndexType> kernel(kernel_dims);
+  Tensor<DataType, 1, DataLayout, IndexType> result(2);
+
+  input.setRandom();
+  kernel.setRandom();
+  Eigen::array<IndexType, 1> dims;
+  dims[0] = 0;
+
+  Eigen::array<IndexType, 1> stride_of_3;
+  stride_of_3[0] = 3;
+  Eigen::array<IndexType, 1> stride_of_2;
+  stride_of_2[0] = 2;
+
+  std::size_t input_bytes = input.size() * sizeof(DataType);
+  std::size_t kernel_bytes = kernel.size() * sizeof(DataType);
+  std::size_t result_bytes = result.size() * sizeof(DataType);
+
+  DataType* d_input = static_cast<DataType*>(sycl_device.allocate(input_bytes));
+  DataType* d_kernel = static_cast<DataType*>(sycl_device.allocate(kernel_bytes));
+  DataType* d_result = static_cast<DataType*>(sycl_device.allocate(result_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_input(d_input, input_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_kernel(d_kernel, kernel_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 1, DataLayout, IndexType> > gpu_result(d_result, result.dimensions());
+  sycl_device.memcpyHostToDevice(d_input, input.data(), input_bytes);
+  sycl_device.memcpyHostToDevice(d_kernel, kernel.data(), kernel_bytes);
+
+  gpu_result.device(sycl_device) = gpu_input.stride(stride_of_3).convolve(gpu_kernel, dims).stride(stride_of_2);
+  sycl_device.memcpyDeviceToHost(result.data(), d_result, result_bytes);
+
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_APPROX(result(0), (input(0) * kernel(0) + input(3) * kernel(1) + input(6) * kernel(2)));
+  VERIFY_IS_APPROX(result(1), (input(6) * kernel(0) + input(9) * kernel(1) + input(12) * kernel(2)));
+}
+
+template <typename Dev_selector>
+void tensorConvolutionPerDevice(Dev_selector& s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_larg_expr1D<float, RowMajor, int64_t>(sycl_device);
+  test_larg_expr1D<float, ColMajor, int64_t>(sycl_device);
+  test_larg_expr2D<float, RowMajor, int64_t>(sycl_device);
+  test_larg_expr2D<float, ColMajor, int64_t>(sycl_device);
+  test_larg_expr3D<float, RowMajor, int64_t>(sycl_device);
+  test_larg_expr3D<float, ColMajor, int64_t>(sycl_device);
+  test_evals<float, ColMajor, int64_t>(sycl_device);
+  test_evals<float, RowMajor, int64_t>(sycl_device);
+  test_expr<float, ColMajor, int64_t>(sycl_device);
+  test_expr<float, RowMajor, int64_t>(sycl_device);
+  test_modes<float, ColMajor, int64_t>(sycl_device);
+  test_modes<float, RowMajor, int64_t>(sycl_device);
+  test_strides<float, ColMajor, int64_t>(sycl_device);
+  test_strides<float, RowMajor, int64_t>(sycl_device);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_convolution_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(tensorConvolutionPerDevice(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_custom_index.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_custom_index.cpp
new file mode 100644
index 0000000..f15da36
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_custom_index.cpp
@@ -0,0 +1,83 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <limits>
+#include <map>
+
+#include <Eigen/Dense>
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_map_as_index() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  using NormalIndex = DSizes<ptrdiff_t, 4>;
+  using CustomIndex = std::map<ptrdiff_t, ptrdiff_t>;
+  CustomIndex coeffC;
+  coeffC[0] = 1;
+  coeffC[1] = 2;
+  coeffC[2] = 4;
+  coeffC[3] = 1;
+  NormalIndex coeff(1, 2, 4, 1);
+
+  VERIFY_IS_EQUAL(tensor.coeff(coeffC), tensor.coeff(coeff));
+  VERIFY_IS_EQUAL(tensor.coeffRef(coeffC), tensor.coeffRef(coeff));
+}
+
+template <int DataLayout>
+static void test_matrix_as_index() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  using NormalIndex = DSizes<ptrdiff_t, 4>;
+  using CustomIndex = Matrix<unsigned int, 4, 1>;
+  CustomIndex coeffC(1, 2, 4, 1);
+  NormalIndex coeff(1, 2, 4, 1);
+
+  VERIFY_IS_EQUAL(tensor.coeff(coeffC), tensor.coeff(coeff));
+  VERIFY_IS_EQUAL(tensor.coeffRef(coeffC), tensor.coeffRef(coeff));
+}
+
+template <int DataLayout>
+static void test_varlist_as_index() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  DSizes<ptrdiff_t, 4> coeff(1, 2, 4, 1);
+
+  VERIFY_IS_EQUAL(tensor.coeff({1, 2, 4, 1}), tensor.coeff(coeff));
+  VERIFY_IS_EQUAL(tensor.coeffRef({1, 2, 4, 1}), tensor.coeffRef(coeff));
+}
+
+template <int DataLayout>
+static void test_sizes_as_index() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  DSizes<ptrdiff_t, 4> coeff(1, 2, 4, 1);
+  Sizes<1, 2, 4, 1> coeffC;
+
+  VERIFY_IS_EQUAL(tensor.coeff(coeffC), tensor.coeff(coeff));
+  VERIFY_IS_EQUAL(tensor.coeffRef(coeffC), tensor.coeffRef(coeff));
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_custom_index) {
+  test_map_as_index<ColMajor>();
+  test_map_as_index<RowMajor>();
+  test_matrix_as_index<ColMajor>();
+  test_matrix_as_index<RowMajor>();
+  test_varlist_as_index<ColMajor>();
+  test_varlist_as_index<RowMajor>();
+  test_sizes_as_index<ColMajor>();
+  test_sizes_as_index<RowMajor>();
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_custom_op.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_custom_op.cpp
new file mode 100644
index 0000000..493da48
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_custom_op.cpp
@@ -0,0 +1,102 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+struct InsertZeros {
+  DSizes<DenseIndex, 2> dimensions(const Tensor<float, 2>& input) const {
+    DSizes<DenseIndex, 2> result;
+    result[0] = input.dimension(0) * 2;
+    result[1] = input.dimension(1) * 2;
+    return result;
+  }
+
+  template <typename Output, typename Device>
+  void eval(const Tensor<float, 2>& input, Output& output, const Device& device) const {
+    array<DenseIndex, 2> strides;
+    strides[0] = 2;
+    strides[1] = 2;
+    output.stride(strides).device(device) = input;
+
+    Eigen::DSizes<DenseIndex, 2> offsets(1, 1);
+    Eigen::DSizes<DenseIndex, 2> extents(output.dimension(0) - 1, output.dimension(1) - 1);
+    output.slice(offsets, extents).stride(strides).device(device) = input.constant(0.0f);
+  }
+};
+
+static void test_custom_unary_op() {
+  Tensor<float, 2> tensor(3, 5);
+  tensor.setRandom();
+
+  Tensor<float, 2> result = tensor.customOp(InsertZeros());
+  VERIFY_IS_EQUAL(result.dimension(0), 6);
+  VERIFY_IS_EQUAL(result.dimension(1), 10);
+
+  for (int i = 0; i < 6; i += 2) {
+    for (int j = 0; j < 10; j += 2) {
+      VERIFY_IS_EQUAL(result(i, j), tensor(i / 2, j / 2));
+    }
+  }
+  for (int i = 1; i < 6; i += 2) {
+    for (int j = 1; j < 10; j += 2) {
+      VERIFY_IS_EQUAL(result(i, j), 0);
+    }
+  }
+}
+
+struct BatchMatMul {
+  DSizes<DenseIndex, 3> dimensions(const Tensor<float, 3>& input1, const Tensor<float, 3>& input2) const {
+    DSizes<DenseIndex, 3> result;
+    result[0] = input1.dimension(0);
+    result[1] = input2.dimension(1);
+    result[2] = input2.dimension(2);
+    return result;
+  }
+
+  template <typename Output, typename Device>
+  void eval(const Tensor<float, 3>& input1, const Tensor<float, 3>& input2, Output& output,
+            const Device& device) const {
+    typedef Tensor<float, 3>::DimensionPair DimPair;
+    array<DimPair, 1> dims;
+    dims[0] = DimPair(1, 0);
+    for (int i = 0; i < output.dimension(2); ++i) {
+      output.template chip<2>(i).device(device) = input1.chip<2>(i).contract(input2.chip<2>(i), dims);
+    }
+  }
+};
+
+static void test_custom_binary_op() {
+  Tensor<float, 3> tensor1(2, 3, 5);
+  tensor1.setRandom();
+  Tensor<float, 3> tensor2(3, 7, 5);
+  tensor2.setRandom();
+
+  Tensor<float, 3> result = tensor1.customOp(tensor2, BatchMatMul());
+  for (int i = 0; i < 5; ++i) {
+    typedef Tensor<float, 3>::DimensionPair DimPair;
+    array<DimPair, 1> dims;
+    dims[0] = DimPair(1, 0);
+    Tensor<float, 2> reference = tensor1.chip<2>(i).contract(tensor2.chip<2>(i), dims);
+    TensorRef<Tensor<float, 2> > val = result.chip<2>(i);
+    for (int j = 0; j < 2; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(val(j, k), reference(j, k));
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_custom_op) {
+  CALL_SUBTEST(test_custom_unary_op());
+  CALL_SUBTEST(test_custom_binary_op());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_custom_op_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_custom_op_sycl.cpp
new file mode 100644
index 0000000..eb9ef6e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_custom_op_sycl.cpp
@@ -0,0 +1,170 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+template <typename TensorType>
+struct InsertZeros {
+  DSizes<DenseIndex, 2> dimensions(const TensorType& input) const {
+    DSizes<DenseIndex, 2> result;
+    result[0] = input.dimension(0) * 2;
+    result[1] = input.dimension(1) * 2;
+    return result;
+  }
+
+  template <typename Output, typename Device>
+  void eval(const TensorType& input, Output& output, const Device& device) const {
+    array<DenseIndex, 2> strides;
+    strides[0] = 2;
+    strides[1] = 2;
+    output.stride(strides).device(device) = input;
+
+    Eigen::DSizes<DenseIndex, 2> offsets(1, 1);
+    Eigen::DSizes<DenseIndex, 2> extents(output.dimension(0) - 1, output.dimension(1) - 1);
+    output.slice(offsets, extents).stride(strides).device(device) = input.constant(0.0f);
+  }
+};
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_custom_unary_op_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 3;
+  IndexType sizeDim2 = 5;
+  Eigen::array<IndexType, 2> tensorRange = {{sizeDim1, sizeDim2}};
+  Eigen::array<IndexType, 2> tensorResultRange = {{6, 10}};
+
+  Eigen::Tensor<DataType, 2, DataLayout, IndexType> in1(tensorRange);
+  Eigen::Tensor<DataType, 2, DataLayout, IndexType> out(tensorResultRange);
+
+  DataType* gpu_in1_data =
+      static_cast<DataType*>(sycl_device.allocate(in1.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(DataType)));
+
+  typedef Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > TensorType;
+  TensorType gpu_in1(gpu_in1_data, tensorRange);
+  TensorType gpu_out(gpu_out_data, tensorResultRange);
+
+  in1.setRandom();
+  sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(), (in1.dimensions().TotalSize()) * sizeof(DataType));
+  gpu_out.device(sycl_device) = gpu_in1.customOp(InsertZeros<TensorType>());
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(DataType));
+
+  VERIFY_IS_EQUAL(out.dimension(0), 6);
+  VERIFY_IS_EQUAL(out.dimension(1), 10);
+
+  for (int i = 0; i < 6; i += 2) {
+    for (int j = 0; j < 10; j += 2) {
+      VERIFY_IS_EQUAL(out(i, j), in1(i / 2, j / 2));
+    }
+  }
+  for (int i = 1; i < 6; i += 2) {
+    for (int j = 1; j < 10; j += 2) {
+      VERIFY_IS_EQUAL(out(i, j), 0);
+    }
+  }
+  sycl_device.deallocate(gpu_in1_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename TensorType>
+struct BatchMatMul {
+  DSizes<DenseIndex, 3> dimensions(const TensorType& input1, const TensorType& input2) const {
+    DSizes<DenseIndex, 3> result;
+    result[0] = input1.dimension(0);
+    result[1] = input2.dimension(1);
+    result[2] = input2.dimension(2);
+    return result;
+  }
+
+  template <typename Output, typename Device>
+  void eval(const TensorType& input1, const TensorType& input2, Output& output, const Device& device) const {
+    typedef typename TensorType::DimensionPair DimPair;
+    array<DimPair, 1> dims;
+    dims[0] = DimPair(1, 0);
+    for (int64_t i = 0; i < output.dimension(2); ++i) {
+      output.template chip<2>(i).device(device) = input1.template chip<2>(i).contract(input2.template chip<2>(i), dims);
+    }
+  }
+};
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_custom_binary_op_sycl(const Eigen::SyclDevice& sycl_device) {
+  Eigen::array<IndexType, 3> tensorRange1 = {{2, 3, 5}};
+  Eigen::array<IndexType, 3> tensorRange2 = {{3, 7, 5}};
+  Eigen::array<IndexType, 3> tensorResultRange = {{2, 7, 5}};
+
+  Eigen::Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange1);
+  Eigen::Tensor<DataType, 3, DataLayout, IndexType> in2(tensorRange2);
+  Eigen::Tensor<DataType, 3, DataLayout, IndexType> out(tensorResultRange);
+
+  DataType* gpu_in1_data =
+      static_cast<DataType*>(sycl_device.allocate(in1.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_in2_data =
+      static_cast<DataType*>(sycl_device.allocate(in2.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(DataType)));
+
+  typedef Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType> > TensorType;
+  TensorType gpu_in1(gpu_in1_data, tensorRange1);
+  TensorType gpu_in2(gpu_in2_data, tensorRange2);
+  TensorType gpu_out(gpu_out_data, tensorResultRange);
+
+  in1.setRandom();
+  in2.setRandom();
+
+  sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(), (in1.dimensions().TotalSize()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(), (in2.dimensions().TotalSize()) * sizeof(DataType));
+
+  gpu_out.device(sycl_device) = gpu_in1.customOp(gpu_in2, BatchMatMul<TensorType>());
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(DataType));
+
+  for (IndexType i = 0; i < 5; ++i) {
+    typedef typename Eigen::Tensor<DataType, 3, DataLayout, IndexType>::DimensionPair DimPair;
+    array<DimPair, 1> dims;
+    dims[0] = DimPair(1, 0);
+    Eigen::Tensor<DataType, 2, DataLayout, IndexType> reference =
+        in1.template chip<2>(i).contract(in2.template chip<2>(i), dims);
+    TensorRef<Eigen::Tensor<DataType, 2, DataLayout, IndexType> > val = out.template chip<2>(i);
+    for (IndexType j = 0; j < 2; ++j) {
+      for (IndexType k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(val(j, k), reference(j, k));
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_in1_data);
+  sycl_device.deallocate(gpu_in2_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, typename Dev_selector>
+void custom_op_perDevice(Dev_selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_custom_unary_op_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_custom_unary_op_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_custom_binary_op_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_custom_binary_op_sycl<DataType, RowMajor, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_custom_op_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(custom_op_perDevice<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_device.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_device.cu
new file mode 100644
index 0000000..b4ba23e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_device.cu
@@ -0,0 +1,436 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include "OffByOneScalar.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+// Context for evaluation on cpu
+struct CPUContext {
+  CPUContext(const Eigen::Tensor<float, 3>& in1, Eigen::Tensor<float, 3>& in2, Eigen::Tensor<float, 3>& out)
+      : in1_(in1), in2_(in2), out_(out), kernel_1d_(2), kernel_2d_(2, 2), kernel_3d_(2, 2, 2) {
+    kernel_1d_(0) = 3.14f;
+    kernel_1d_(1) = 2.7f;
+
+    kernel_2d_(0, 0) = 3.14f;
+    kernel_2d_(1, 0) = 2.7f;
+    kernel_2d_(0, 1) = 0.2f;
+    kernel_2d_(1, 1) = 7.0f;
+
+    kernel_3d_(0, 0, 0) = 3.14f;
+    kernel_3d_(0, 1, 0) = 2.7f;
+    kernel_3d_(0, 0, 1) = 0.2f;
+    kernel_3d_(0, 1, 1) = 7.0f;
+    kernel_3d_(1, 0, 0) = -1.0f;
+    kernel_3d_(1, 1, 0) = -0.3f;
+    kernel_3d_(1, 0, 1) = -0.7f;
+    kernel_3d_(1, 1, 1) = -0.5f;
+  }
+
+  const Eigen::DefaultDevice& device() const { return cpu_device_; }
+
+  const Eigen::Tensor<float, 3>& in1() const { return in1_; }
+  const Eigen::Tensor<float, 3>& in2() const { return in2_; }
+  Eigen::Tensor<float, 3>& out() { return out_; }
+  const Eigen::Tensor<float, 1>& kernel1d() const { return kernel_1d_; }
+  const Eigen::Tensor<float, 2>& kernel2d() const { return kernel_2d_; }
+  const Eigen::Tensor<float, 3>& kernel3d() const { return kernel_3d_; }
+
+ private:
+  const Eigen::Tensor<float, 3>& in1_;
+  const Eigen::Tensor<float, 3>& in2_;
+  Eigen::Tensor<float, 3>& out_;
+
+  Eigen::Tensor<float, 1> kernel_1d_;
+  Eigen::Tensor<float, 2> kernel_2d_;
+  Eigen::Tensor<float, 3> kernel_3d_;
+
+  Eigen::DefaultDevice cpu_device_;
+};
+
+// Context for evaluation on GPU
+struct GPUContext {
+  GPUContext(const Eigen::TensorMap<Eigen::Tensor<float, 3>>& in1, Eigen::TensorMap<Eigen::Tensor<float, 3>>& in2,
+             Eigen::TensorMap<Eigen::Tensor<float, 3>>& out)
+      : in1_(in1), in2_(in2), out_(out), gpu_device_(&stream_) {
+    assert(gpuMalloc((void**)(&kernel_1d_), 2 * sizeof(float)) == gpuSuccess);
+    float kernel_1d_val[] = {3.14f, 2.7f};
+    assert(gpuMemcpy(kernel_1d_, kernel_1d_val, 2 * sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
+
+    assert(gpuMalloc((void**)(&kernel_2d_), 4 * sizeof(float)) == gpuSuccess);
+    float kernel_2d_val[] = {3.14f, 2.7f, 0.2f, 7.0f};
+    assert(gpuMemcpy(kernel_2d_, kernel_2d_val, 4 * sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
+
+    assert(gpuMalloc((void**)(&kernel_3d_), 8 * sizeof(float)) == gpuSuccess);
+    float kernel_3d_val[] = {3.14f, -1.0f, 2.7f, -0.3f, 0.2f, -0.7f, 7.0f, -0.5f};
+    assert(gpuMemcpy(kernel_3d_, kernel_3d_val, 8 * sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
+  }
+  ~GPUContext() {
+    assert(gpuFree(kernel_1d_) == gpuSuccess);
+    assert(gpuFree(kernel_2d_) == gpuSuccess);
+    assert(gpuFree(kernel_3d_) == gpuSuccess);
+  }
+
+  const Eigen::GpuDevice& device() const { return gpu_device_; }
+
+  const Eigen::TensorMap<Eigen::Tensor<float, 3>>& in1() const { return in1_; }
+  const Eigen::TensorMap<Eigen::Tensor<float, 3>>& in2() const { return in2_; }
+  Eigen::TensorMap<Eigen::Tensor<float, 3>>& out() { return out_; }
+  Eigen::TensorMap<Eigen::Tensor<float, 1>> kernel1d() const {
+    return Eigen::TensorMap<Eigen::Tensor<float, 1>>(kernel_1d_, 2);
+  }
+  Eigen::TensorMap<Eigen::Tensor<float, 2>> kernel2d() const {
+    return Eigen::TensorMap<Eigen::Tensor<float, 2>>(kernel_2d_, 2, 2);
+  }
+  Eigen::TensorMap<Eigen::Tensor<float, 3>> kernel3d() const {
+    return Eigen::TensorMap<Eigen::Tensor<float, 3>>(kernel_3d_, 2, 2, 2);
+  }
+
+ private:
+  const Eigen::TensorMap<Eigen::Tensor<float, 3>>& in1_;
+  const Eigen::TensorMap<Eigen::Tensor<float, 3>>& in2_;
+  Eigen::TensorMap<Eigen::Tensor<float, 3>>& out_;
+
+  float* kernel_1d_;
+  float* kernel_2d_;
+  float* kernel_3d_;
+
+  Eigen::GpuStreamDevice stream_;
+  Eigen::GpuDevice gpu_device_;
+};
+
+// The actual expression to evaluate
+template <typename Context>
+void test_contextual_eval(Context* context) {
+  context->out().device(context->device()) = context->in1() + context->in2() * 3.14f + context->in1().constant(2.718f);
+}
+
+template <typename Context>
+void test_forced_contextual_eval(Context* context) {
+  context->out().device(context->device()) =
+      (context->in1() + context->in2()).eval() * 3.14f + context->in1().constant(2.718f);
+}
+
+template <typename Context>
+void test_compound_assignment(Context* context) {
+  context->out().device(context->device()) = context->in1().constant(2.718f);
+  context->out().device(context->device()) += context->in1() + context->in2() * 3.14f;
+}
+
+template <typename Context>
+void test_contraction(Context* context) {
+  Eigen::array<std::pair<int, int>, 2> dims;
+  dims[0] = std::make_pair(1, 1);
+  dims[1] = std::make_pair(2, 2);
+
+  Eigen::array<int, 2> shape{40, 50 * 70};
+
+  Eigen::DSizes<int, 2> indices(0, 0);
+  Eigen::DSizes<int, 2> sizes(40, 40);
+
+  context->out().reshape(shape).slice(indices, sizes).device(context->device()) =
+      context->in1().contract(context->in2(), dims);
+}
+
+template <typename Context>
+void test_1d_convolution(Context* context) {
+  Eigen::DSizes<int, 3> indices(0, 0, 0);
+  Eigen::DSizes<int, 3> sizes(40, 49, 70);
+
+  Eigen::array<int, 1> dims{1};
+  context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel1d(), dims);
+}
+
+template <typename Context>
+void test_2d_convolution(Context* context) {
+  Eigen::DSizes<int, 3> indices(0, 0, 0);
+  Eigen::DSizes<int, 3> sizes(40, 49, 69);
+
+  Eigen::array<int, 2> dims{1, 2};
+  context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel2d(), dims);
+}
+
+template <typename Context>
+void test_3d_convolution(Context* context) {
+  Eigen::DSizes<int, 3> indices(0, 0, 0);
+  Eigen::DSizes<int, 3> sizes(39, 49, 69);
+
+  Eigen::array<int, 3> dims{0, 1, 2};
+  context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel3d(), dims);
+}
+
+// Helper method to synchronize device.
+template <typename Device>
+void synchronize(Device& device) { /*nothing*/
+}
+template <>
+void synchronize(Eigen::GpuDevice& device) {
+  device.synchronize();
+}
+
+template <typename DataType, typename TensorDevice>
+void test_device_memory(const TensorDevice& device) {
+  int count = 100;
+  Eigen::array<int, 1> tensorRange{count};
+  Eigen::Tensor<DataType, 1> host(tensorRange);
+  Eigen::Tensor<DataType, 1> expected(tensorRange);
+  DataType* device_data = static_cast<DataType*>(device.allocate(count * sizeof(DataType)));
+
+  // memset
+  const char byte_value = static_cast<char>(0xAB);
+  device.memset(device_data, byte_value, count * sizeof(DataType));
+  device.memcpyDeviceToHost(host.data(), device_data, count * sizeof(DataType));
+  synchronize(device);
+  memset(expected.data(), byte_value, count * sizeof(DataType));
+  for (size_t i = 0; i < count; i++) {
+    VERIFY_IS_EQUAL(host(i), expected(i));
+  }
+
+  // fill
+  DataType fill_value = DataType(7);
+  std::fill_n(expected.data(), count, fill_value);
+  device.fill(device_data, device_data + count, fill_value);
+  device.memcpyDeviceToHost(host.data(), device_data, count * sizeof(DataType));
+  synchronize(device);
+  for (int i = 0; i < count; i++) {
+    VERIFY_IS_EQUAL(host(i), expected(i));
+  }
+
+  device.deallocate(device_data);
+}
+
+void test_cpu() {
+  Eigen::Tensor<float, 3> in1(40, 50, 70);
+  Eigen::Tensor<float, 3> in2(40, 50, 70);
+  Eigen::Tensor<float, 3> out(40, 50, 70);
+
+  in1 = in1.random() + in1.constant(10.0f);
+  in2 = in2.random() + in2.constant(10.0f);
+
+  CPUContext context(in1, in2, out);
+  test_contextual_eval(&context);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 50; ++j) {
+      for (int k = 0; k < 70; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f + 2.718f);
+      }
+    }
+  }
+
+  test_forced_contextual_eval(&context);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 50; ++j) {
+      for (int k = 0; k < 70; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) + in2(i, j, k)) * 3.14f + 2.718f);
+      }
+    }
+  }
+
+  test_compound_assignment(&context);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 50; ++j) {
+      for (int k = 0; k < 70; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f + 2.718f);
+      }
+    }
+  }
+
+  test_contraction(&context);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 40; ++j) {
+      const float result = out(i, j, 0);
+      float expected = 0;
+      for (int k = 0; k < 50; ++k) {
+        for (int l = 0; l < 70; ++l) {
+          expected += in1(i, k, l) * in2(j, k, l);
+        }
+      }
+      VERIFY_IS_APPROX(expected, result);
+    }
+  }
+
+  test_1d_convolution(&context);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 49; ++j) {
+      for (int k = 0; k < 70; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f));
+      }
+    }
+  }
+
+  test_2d_convolution(&context);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 49; ++j) {
+      for (int k = 0; k < 69; ++k) {
+        const float result = out(i, j, k);
+        const float expected =
+            (in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f) + (in1(i, j, k + 1) * 0.2f + in1(i, j + 1, k + 1) * 7.0f);
+        if (fabs(expected) < 1e-4f && fabs(result) < 1e-4f) {
+          continue;
+        }
+        VERIFY_IS_APPROX(expected, result);
+      }
+    }
+  }
+
+  test_3d_convolution(&context);
+  for (int i = 0; i < 39; ++i) {
+    for (int j = 0; j < 49; ++j) {
+      for (int k = 0; k < 69; ++k) {
+        const float result = out(i, j, k);
+        const float expected =
+            (in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f + in1(i, j, k + 1) * 0.2f + in1(i, j + 1, k + 1) * 7.0f) +
+            (in1(i + 1, j, k) * -1.0f + in1(i + 1, j + 1, k) * -0.3f + in1(i + 1, j, k + 1) * -0.7f +
+             in1(i + 1, j + 1, k + 1) * -0.5f);
+        if (fabs(expected) < 1e-4f && fabs(result) < 1e-4f) {
+          continue;
+        }
+        VERIFY_IS_APPROX(expected, result);
+      }
+    }
+  }
+
+  test_device_memory<float>(context.device());
+  test_device_memory<OffByOneScalar<int>>(context.device());
+}
+
+void test_gpu() {
+  Eigen::Tensor<float, 3> in1(40, 50, 70);
+  Eigen::Tensor<float, 3> in2(40, 50, 70);
+  Eigen::Tensor<float, 3> out(40, 50, 70);
+  in1 = in1.random() + in1.constant(10.0f);
+  in2 = in2.random() + in2.constant(10.0f);
+
+  std::size_t in1_bytes = in1.size() * sizeof(float);
+  std::size_t in2_bytes = in2.size() * sizeof(float);
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_in1;
+  float* d_in2;
+  float* d_out;
+  gpuMalloc((void**)(&d_in1), in1_bytes);
+  gpuMalloc((void**)(&d_in2), in2_bytes);
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_in2, in2.data(), in2_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(d_in1, 40, 50, 70);
+  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(d_in2, 40, 50, 70);
+  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(d_out, 40, 50, 70);
+
+  GPUContext context(gpu_in1, gpu_in2, gpu_out);
+  test_contextual_eval(&context);
+  assert(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 50; ++j) {
+      for (int k = 0; k < 70; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f + 2.718f);
+      }
+    }
+  }
+
+  test_forced_contextual_eval(&context);
+  assert(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 50; ++j) {
+      for (int k = 0; k < 70; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) + in2(i, j, k)) * 3.14f + 2.718f);
+      }
+    }
+  }
+
+  test_compound_assignment(&context);
+  assert(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 50; ++j) {
+      for (int k = 0; k < 70; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f + 2.718f);
+      }
+    }
+  }
+
+  test_contraction(&context);
+  assert(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 40; ++j) {
+      const float result = out(i, j, 0);
+      float expected = 0;
+      for (int k = 0; k < 50; ++k) {
+        for (int l = 0; l < 70; ++l) {
+          expected += in1(i, k, l) * in2(j, k, l);
+        }
+      }
+      VERIFY_IS_APPROX(expected, result);
+    }
+  }
+
+  test_1d_convolution(&context);
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, context.device().stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(context.device().stream()) == gpuSuccess);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 49; ++j) {
+      for (int k = 0; k < 70; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f));
+      }
+    }
+  }
+
+  test_2d_convolution(&context);
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, context.device().stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(context.device().stream()) == gpuSuccess);
+  for (int i = 0; i < 40; ++i) {
+    for (int j = 0; j < 49; ++j) {
+      for (int k = 0; k < 69; ++k) {
+        const float result = out(i, j, k);
+        const float expected =
+            (in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f + in1(i, j, k + 1) * 0.2f + in1(i, j + 1, k + 1) * 7.0f);
+        VERIFY_IS_APPROX(expected, result);
+      }
+    }
+  }
+
+#if !defined(EIGEN_USE_HIP)
+  // disable this test on the HIP platform
+  // 3D tensor convolutions seem to hang on the HIP platform
+
+  test_3d_convolution(&context);
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, context.device().stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(context.device().stream()) == gpuSuccess);
+  for (int i = 0; i < 39; ++i) {
+    for (int j = 0; j < 49; ++j) {
+      for (int k = 0; k < 69; ++k) {
+        const float result = out(i, j, k);
+        const float expected = (in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f + in1(i, j, k + 1) * 0.2f +
+                                in1(i, j + 1, k + 1) * 7.0f + in1(i + 1, j, k) * -1.0f + in1(i + 1, j + 1, k) * -0.3f +
+                                in1(i + 1, j, k + 1) * -0.7f + in1(i + 1, j + 1, k + 1) * -0.5f);
+        VERIFY_IS_APPROX(expected, result);
+      }
+    }
+  }
+
+#endif
+
+  test_device_memory<float>(context.device());
+  test_device_memory<OffByOneScalar<int>>(context.device());
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_device) {
+  CALL_SUBTEST_1(test_cpu());
+  CALL_SUBTEST_2(test_gpu());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_device_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_device_sycl.cpp
new file mode 100644
index 0000000..d190815
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_device_sycl.cpp
@@ -0,0 +1,90 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include "OffByOneScalar.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+#include <stdint.h>
+#include <iostream>
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_device_memory(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 100;
+  array<IndexType, 1> tensorRange = {{sizeDim1}};
+  Tensor<DataType, 1, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 1, DataLayout, IndexType> in1(tensorRange);
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.size() * sizeof(DataType)));
+
+  // memset
+  memset(in1.data(), 1, in1.size() * sizeof(DataType));
+  sycl_device.memset(gpu_in_data, 1, in.size() * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(in.data(), gpu_in_data, in.size() * sizeof(DataType));
+  for (IndexType i = 0; i < in.size(); i++) {
+    VERIFY_IS_EQUAL(in(i), in1(i));
+  }
+
+  // fill
+  DataType value = DataType(7);
+  std::fill_n(in1.data(), in1.size(), value);
+  sycl_device.fill(gpu_in_data, gpu_in_data + in.size(), value);
+  sycl_device.memcpyDeviceToHost(in.data(), gpu_in_data, in.size() * sizeof(DataType));
+  for (IndexType i = 0; i < in.size(); i++) {
+    VERIFY_IS_EQUAL(in(i), in1(i));
+  }
+
+  sycl_device.deallocate(gpu_in_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_device_exceptions(const Eigen::SyclDevice& sycl_device) {
+  VERIFY(sycl_device.ok());
+  IndexType sizeDim1 = 100;
+  array<IndexType, 1> tensorDims = {{sizeDim1}};
+  DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(sizeDim1 * sizeof(DataType)));
+  sycl_device.memset(gpu_data, 1, sizeDim1 * sizeof(DataType));
+
+  TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> in(gpu_data, tensorDims);
+  TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> out(gpu_data, tensorDims);
+  out.device(sycl_device) = in / in.constant(0);
+
+  sycl_device.synchronize();
+  VERIFY(!sycl_device.ok());
+  sycl_device.deallocate(gpu_data);
+}
+
+template <typename DataType>
+void sycl_device_test_per_device(const cl::sycl::device& d) {
+  std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
+  QueueInterface queueInterface(d);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_device_memory<DataType, RowMajor, int64_t>(sycl_device);
+  test_device_memory<DataType, ColMajor, int64_t>(sycl_device);
+  /// this test throw an exception. enable it if you want to see the exception
+  // test_device_exceptions<DataType, RowMajor>(sycl_device);
+  /// this test throw an exception. enable it if you want to see the exception
+  // test_device_exceptions<DataType, ColMajor>(sycl_device);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_device_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_device_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_device_test_per_device<float>(device));
+    CALL_SUBTEST(sycl_device_test_per_device<OffByOneScalar<int>>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_dimension.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_dimension.cpp
new file mode 100644
index 0000000..3423a66
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_dimension.cpp
@@ -0,0 +1,82 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+static void test_dynamic_size() {
+  Eigen::DSizes<int, 3> dimensions(2, 3, 7);
+
+  VERIFY_IS_EQUAL((int)Eigen::internal::array_get<0>(dimensions), 2);
+  VERIFY_IS_EQUAL((int)Eigen::internal::array_get<1>(dimensions), 3);
+  VERIFY_IS_EQUAL((int)Eigen::internal::array_get<2>(dimensions), 7);
+  VERIFY_IS_EQUAL((int)dimensions.TotalSize(), 2 * 3 * 7);
+  VERIFY_IS_EQUAL((int)dimensions[0], 2);
+  VERIFY_IS_EQUAL((int)dimensions[1], 3);
+  VERIFY_IS_EQUAL((int)dimensions[2], 7);
+}
+
+static void test_fixed_size() {
+  Eigen::Sizes<2, 3, 7> dimensions;
+
+  VERIFY_IS_EQUAL((int)Eigen::internal::array_get<0>(dimensions), 2);
+  VERIFY_IS_EQUAL((int)Eigen::internal::array_get<1>(dimensions), 3);
+  VERIFY_IS_EQUAL((int)Eigen::internal::array_get<2>(dimensions), 7);
+  VERIFY_IS_EQUAL((int)dimensions.TotalSize(), 2 * 3 * 7);
+}
+
+static void test_match() {
+  Eigen::DSizes<unsigned int, 3> dyn((unsigned int)2, (unsigned int)3, (unsigned int)7);
+  Eigen::Sizes<2, 3, 7> stat;
+  VERIFY_IS_EQUAL(Eigen::dimensions_match(dyn, stat), true);
+
+  Eigen::DSizes<int, 3> dyn1(2, 3, 7);
+  Eigen::DSizes<int, 2> dyn2(2, 3);
+  VERIFY_IS_EQUAL(Eigen::dimensions_match(dyn1, dyn2), false);
+}
+
+static void test_rank_zero() {
+  Eigen::Sizes<> scalar;
+  VERIFY_IS_EQUAL((int)scalar.TotalSize(), 1);
+  VERIFY_IS_EQUAL((int)scalar.rank(), 0);
+  VERIFY_IS_EQUAL((int)internal::array_prod(scalar), 1);
+
+  Eigen::DSizes<ptrdiff_t, 0> dscalar;
+  VERIFY_IS_EQUAL((int)dscalar.TotalSize(), 1);
+  VERIFY_IS_EQUAL((int)dscalar.rank(), 0);
+}
+
+static void test_index_type_promotion() {
+  Eigen::DSizes<int, 3> src0(1, 2, 3);
+  Eigen::array<int, 3> src1;
+  src1[0] = 4;
+  src1[1] = 5;
+  src1[2] = 6;
+
+  Eigen::DSizes<long, 3> dst0(src0);
+  Eigen::DSizes<long, 3> dst1(src1);
+
+  VERIFY_IS_EQUAL(dst0[0], 1L);
+  VERIFY_IS_EQUAL(dst0[1], 2L);
+  VERIFY_IS_EQUAL(dst0[2], 3L);
+  VERIFY_IS_EQUAL(dst1[0], 4L);
+  VERIFY_IS_EQUAL(dst1[1], 5L);
+  VERIFY_IS_EQUAL(dst1[2], 6L);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_dimension) {
+  CALL_SUBTEST(test_dynamic_size());
+  CALL_SUBTEST(test_fixed_size());
+  CALL_SUBTEST(test_match());
+  CALL_SUBTEST(test_rank_zero());
+  CALL_SUBTEST(test_index_type_promotion());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_empty.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_empty.cpp
new file mode 100644
index 0000000..c90cf36
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_empty.cpp
@@ -0,0 +1,35 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+static void test_empty_tensor() {
+  Tensor<float, 2> source;
+  Tensor<float, 2> tgt1 = source;
+  Tensor<float, 2> tgt2(source);
+  Tensor<float, 2> tgt3;
+  tgt3 = tgt1;
+  tgt3 = tgt2;
+}
+
+static void test_empty_fixed_size_tensor() {
+  TensorFixedSize<float, Sizes<0> > source;
+  TensorFixedSize<float, Sizes<0> > tgt1 = source;
+  TensorFixedSize<float, Sizes<0> > tgt2(source);
+  TensorFixedSize<float, Sizes<0> > tgt3;
+  tgt3 = tgt1;
+  tgt3 = tgt2;
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_empty) {
+  CALL_SUBTEST(test_empty_tensor());
+  CALL_SUBTEST(test_empty_fixed_size_tensor());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_executor.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_executor.cpp
new file mode 100644
index 0000000..228fa9e
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+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_executor.cpp
@@ -0,0 +1,704 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_USE_THREADS
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::ColMajor;
+using Eigen::RowMajor;
+using Eigen::Tensor;
+using Eigen::internal::TiledEvaluation;
+
+// A set of tests to verify that different TensorExecutor strategies yields the
+// same results for all the ops, supporting tiled evaluation.
+
+// Default assignment that does no use block evaluation or vectorization.
+// We assume that default coefficient evaluation is well tested and correct.
+template <typename Dst, typename Expr>
+void DefaultAssign(Dst& dst, Expr expr) {
+  using Assign = Eigen::TensorAssignOp<Dst, const Expr>;
+  using Executor = Eigen::internal::TensorExecutor<const Assign, DefaultDevice,
+                                                   /*Vectorizable=*/false,
+                                                   /*Tiling=*/TiledEvaluation::Off>;
+
+  Executor::run(Assign(dst, expr), DefaultDevice());
+}
+
+// Assignment with specified device and tiling strategy.
+template <bool Vectorizable, TiledEvaluation Tiling, typename Device, typename Dst, typename Expr>
+void DeviceAssign(Device& d, Dst& dst, Expr expr) {
+  using Assign = Eigen::TensorAssignOp<Dst, const Expr>;
+  using Executor = Eigen::internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+  Executor::run(Assign(dst, expr), d);
+}
+
+template <int NumDims>
+static array<Index, NumDims> RandomDims(int min_dim = 1, int max_dim = 20) {
+  array<Index, NumDims> dims;
+  for (int i = 0; i < NumDims; ++i) {
+    dims[i] = internal::random<int>(min_dim, max_dim);
+  }
+  return dims;
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_unary_expr(Device d) {
+  static constexpr int Options = 0 | Layout;
+
+  // Pick a large enough tensor size to bypass small tensor block evaluation
+  // optimization.
+  auto dims = RandomDims<NumDims>(50 / NumDims, 100 / NumDims);
+
+  Tensor<T, NumDims, Options, Index> src(dims);
+  Tensor<T, NumDims, Options, Index> dst(dims);
+
+  src.setRandom();
+  const auto expr = src.square();
+
+  using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+  using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+  Executor::run(Assign(dst, expr), d);
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    T square = src.coeff(i) * src.coeff(i);
+    VERIFY_IS_EQUAL(square, dst.coeff(i));
+  }
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_binary_expr(Device d) {
+  static constexpr int Options = 0 | Layout;
+
+  // Pick a large enough tensor size to bypass small tensor block evaluation
+  // optimization.
+  auto dims = RandomDims<NumDims>(50 / NumDims, 100 / NumDims);
+
+  Tensor<T, NumDims, Options, Index> lhs(dims);
+  Tensor<T, NumDims, Options, Index> rhs(dims);
+  Tensor<T, NumDims, Options, Index> dst(dims);
+
+  lhs.setRandom();
+  rhs.setRandom();
+
+  const auto expr = lhs + rhs;
+
+  using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+  using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+  Executor::run(Assign(dst, expr), d);
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    T sum = lhs.coeff(i) + rhs.coeff(i);
+    VERIFY_IS_EQUAL(sum, dst.coeff(i));
+  }
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_broadcasting(Device d) {
+  static constexpr int Options = 0 | Layout;
+
+  auto dims = RandomDims<NumDims>(1, 10);
+  Tensor<T, NumDims, Options, Index> src(dims);
+  src.setRandom();
+
+  const auto broadcasts = RandomDims<NumDims>(1, 7);
+  const auto expr = src.broadcast(broadcasts);
+
+  // We assume that broadcasting on a default device is tested and correct, so
+  // we can rely on it to verify correctness of tensor executor and tiling.
+  Tensor<T, NumDims, Options, Index> golden;
+  golden = expr;
+
+  // Now do the broadcasting using configured tensor executor.
+  Tensor<T, NumDims, Options, Index> dst(golden.dimensions());
+
+  using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+  using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+  Executor::run(Assign(dst, expr), d);
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+  }
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+struct test_execute_chipping_rvalue_runner {
+  template <int ChipDim>
+  static std::enable_if_t<0 <= ChipDim, void> run_dim(Device& d, const array<Index, NumDims>& dims,
+                                                      const Tensor<T, NumDims, Layout, Index>& src) {
+    const auto offset = internal::random<Index>(0, dims[(ChipDim)] - 1);
+    const auto expr = src.template chip<ChipDim>(offset);
+
+    Tensor<T, NumDims - 1, Layout, Index> golden;
+    golden = expr;
+
+    Tensor<T, NumDims - 1, Layout, Index> dst(golden.dimensions());
+
+    using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+    using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+    Executor::run(Assign(dst, expr), d);
+
+    for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+      VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+    }
+
+    // Recursively reduce chip dimension.
+    run_dim<ChipDim - 1>(d, dims, src);
+  }
+
+  template <int ChipDim>
+      static std::enable_if_t <
+      ChipDim<0, void> run_dim(Device&, const array<Index, NumDims>&, const Tensor<T, NumDims, Layout, Index>&) {}
+
+  static void run(Device d) {
+    auto dims = RandomDims<NumDims>(1, 10);
+    Tensor<T, NumDims, Layout, Index> src(dims);
+    src.setRandom();
+    run_dim<NumDims - 1>(d, dims, src);
+  }
+};
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_chipping_rvalue(Device d) {
+  test_execute_chipping_rvalue_runner<T, NumDims, Device, Vectorizable, Tiling, Layout>::run(d);
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+struct test_execute_chipping_lvalue_runner {
+  template <int ChipDim>
+  static std::enable_if_t<0 <= ChipDim> run_dim(Device& d, const array<Index, NumDims>& dims) {
+    /* Generate random data that we'll assign to the chipped tensor dim. */
+    array<Index, NumDims - 1> src_dims;
+    for (int i = 0; i < NumDims - 1; ++i) {
+      int dim = i < (ChipDim) ? i : i + 1;
+      src_dims[i] = dims[dim];
+    }
+
+    Tensor<T, NumDims - 1, Layout, Index> src(src_dims);
+    src.setRandom();
+
+    const auto offset = internal::random<Index>(0, dims[(ChipDim)] - 1);
+
+    Tensor<T, NumDims, Layout, Index> random(dims);
+    random.setZero();
+
+    Tensor<T, NumDims, Layout, Index> golden(dims);
+    golden = random;
+    golden.template chip<(ChipDim)>(offset) = src;
+
+    Tensor<T, NumDims, Layout, Index> dst(dims);
+    dst = random;
+    auto expr = dst.template chip<(ChipDim)>(offset);
+
+    using Assign = TensorAssignOp<decltype(expr), const decltype(src)>;
+    using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+    Executor::run(Assign(expr, src), d);
+
+    for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+      VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+    }
+
+    run_dim<ChipDim - 1>(d, dims);
+  }
+
+  template <int ChipDim>
+      static std::enable_if_t < ChipDim<0, void> run_dim(Device&, const array<Index, NumDims>&) {}
+
+  static void run(Device d) {
+    auto dims = RandomDims<NumDims>(1, 10);
+    run_dim<NumDims - 1>(d, dims);
+  }
+};
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_chipping_lvalue(Device d) {
+  test_execute_chipping_lvalue_runner<T, NumDims, Device, Vectorizable, Tiling, Layout>::run(d);
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_shuffle_rvalue(Device d) {
+  static constexpr int Options = 0 | Layout;
+
+  auto dims = RandomDims<NumDims>(1, 10);
+  Tensor<T, NumDims, Options, Index> src(dims);
+  src.setRandom();
+
+  DSizes<Index, NumDims> shuffle;
+  for (int i = 0; i < NumDims; ++i) shuffle[i] = i;
+
+  // Test all possible shuffle permutations.
+  do {
+    DSizes<Index, NumDims> shuffled_dims;
+    for (int i = 0; i < NumDims; ++i) {
+      shuffled_dims[i] = dims[shuffle[i]];
+    }
+
+    const auto expr = src.shuffle(shuffle);
+
+    // We assume that shuffling on a default device is tested and correct, so
+    // we can rely on it to verify correctness of tensor executor and tiling.
+    Tensor<T, NumDims, Options, Index> golden(shuffled_dims);
+    DefaultAssign(golden, expr);
+
+    // Now do the shuffling using configured tensor executor.
+    Tensor<T, NumDims, Options, Index> dst(shuffled_dims);
+    DeviceAssign<Vectorizable, Tiling>(d, dst, expr);
+
+    for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+      VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+    }
+
+  } while (std::next_permutation(&shuffle[0], &shuffle[0] + NumDims));
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_shuffle_lvalue(Device d) {
+  static constexpr int Options = 0 | Layout;
+
+  auto dims = RandomDims<NumDims>(5, 10);
+  Tensor<T, NumDims, Options, Index> src(dims);
+  src.setRandom();
+
+  DSizes<Index, NumDims> shuffle;
+  for (int i = 0; i < NumDims; ++i) shuffle[i] = i;
+
+  // Test all possible shuffle permutations.
+  do {
+    DSizes<Index, NumDims> shuffled_dims;
+    for (int i = 0; i < NumDims; ++i) shuffled_dims[shuffle[i]] = dims[i];
+
+    // We assume that shuffling on a default device is tested and correct, so
+    // we can rely on it to verify correctness of tensor executor and tiling.
+    Tensor<T, NumDims, Options, Index> golden(shuffled_dims);
+    auto golden_shuffle = golden.shuffle(shuffle);
+    DefaultAssign(golden_shuffle, src);
+
+    // Now do the shuffling using configured tensor executor.
+    Tensor<T, NumDims, Options, Index> dst(shuffled_dims);
+    auto dst_shuffle = dst.shuffle(shuffle);
+    DeviceAssign<Vectorizable, Tiling>(d, dst_shuffle, src);
+
+    for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+      VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+    }
+
+  } while (std::next_permutation(&shuffle[0], &shuffle[0] + NumDims));
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_reshape(Device d) {
+  static_assert(NumDims >= 2, "NumDims must be greater or equal than 2");
+
+  static constexpr int ReshapedDims = NumDims - 1;
+  static constexpr int Options = 0 | Layout;
+
+  auto dims = RandomDims<NumDims>(5, 10);
+  Tensor<T, NumDims, Options, Index> src(dims);
+  src.setRandom();
+
+  // Multiple 0th dimension and then shuffle.
+  std::vector<Index> shuffle;
+  for (int i = 0; i < ReshapedDims; ++i) shuffle.push_back(i);
+  std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937());
+
+  DSizes<Index, ReshapedDims> reshaped_dims;
+  reshaped_dims[shuffle[0]] = dims[0] * dims[1];
+  for (int i = 1; i < ReshapedDims; ++i) reshaped_dims[shuffle[i]] = dims[i + 1];
+
+  Tensor<T, ReshapedDims, Options, Index> golden = src.reshape(reshaped_dims);
+
+  // Now reshape using configured tensor executor.
+  Tensor<T, ReshapedDims, Options, Index> dst(golden.dimensions());
+
+  auto expr = src.reshape(reshaped_dims);
+
+  using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+  using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+  Executor::run(Assign(dst, expr), d);
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+  }
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_slice_rvalue(Device d) {
+  static_assert(NumDims >= 2, "NumDims must be greater or equal than 2");
+  static constexpr int Options = 0 | Layout;
+
+  auto dims = RandomDims<NumDims>(5, 10);
+  Tensor<T, NumDims, Options, Index> src(dims);
+  src.setRandom();
+
+  // Pick a random slice of src tensor.
+  auto slice_start = DSizes<Index, NumDims>(RandomDims<NumDims>());
+  auto slice_size = DSizes<Index, NumDims>(RandomDims<NumDims>());
+
+  // Make sure that slice start + size do not overflow tensor dims.
+  for (int i = 0; i < NumDims; ++i) {
+    slice_start[i] = numext::mini(dims[i] - 1, slice_start[i]);
+    slice_size[i] = numext::mini(slice_size[i], dims[i] - slice_start[i]);
+  }
+
+  Tensor<T, NumDims, Options, Index> golden = src.slice(slice_start, slice_size);
+
+  // Now reshape using configured tensor executor.
+  Tensor<T, NumDims, Options, Index> dst(golden.dimensions());
+
+  auto expr = src.slice(slice_start, slice_size);
+
+  using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+  using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+  Executor::run(Assign(dst, expr), d);
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+  }
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_slice_lvalue(Device d) {
+  static_assert(NumDims >= 2, "NumDims must be greater or equal than 2");
+  static constexpr int Options = 0 | Layout;
+
+  auto dims = RandomDims<NumDims>(5, 10);
+  Tensor<T, NumDims, Options, Index> src(dims);
+  src.setRandom();
+
+  // Pick a random slice of src tensor.
+  auto slice_start = DSizes<Index, NumDims>(RandomDims<NumDims>(1, 10));
+  auto slice_size = DSizes<Index, NumDims>(RandomDims<NumDims>(1, 10));
+
+  // Make sure that slice start + size do not overflow tensor dims.
+  for (int i = 0; i < NumDims; ++i) {
+    slice_start[i] = numext::mini(dims[i] - 1, slice_start[i]);
+    slice_size[i] = numext::mini(slice_size[i], dims[i] - slice_start[i]);
+  }
+
+  Tensor<T, NumDims, Options, Index> slice(slice_size);
+  slice.setRandom();
+
+  // Assign a slice using default executor.
+  Tensor<T, NumDims, Options, Index> golden = src;
+  golden.slice(slice_start, slice_size) = slice;
+
+  // And using configured execution strategy.
+  Tensor<T, NumDims, Options, Index> dst = src;
+  auto expr = dst.slice(slice_start, slice_size);
+
+  using Assign = TensorAssignOp<decltype(expr), const decltype(slice)>;
+  using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+  Executor::run(Assign(expr, slice), d);
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+  }
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_broadcasting_of_forced_eval(Device d) {
+  static constexpr int Options = 0 | Layout;
+
+  auto dims = RandomDims<NumDims>(1, 10);
+  Tensor<T, NumDims, Options, Index> src(dims);
+  src.setRandom();
+
+  const auto broadcasts = RandomDims<NumDims>(1, 7);
+  const auto expr = src.square().eval().broadcast(broadcasts);
+
+  // We assume that broadcasting on a default device is tested and correct, so
+  // we can rely on it to verify correctness of tensor executor and tiling.
+  Tensor<T, NumDims, Options, Index> golden;
+  golden = expr;
+
+  // Now do the broadcasting using configured tensor executor.
+  Tensor<T, NumDims, Options, Index> dst(golden.dimensions());
+
+  using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+  using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+  Executor::run(Assign(dst, expr), d);
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+  }
+}
+
+template <typename T, int NumDims>
+struct DummyGenerator {
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T operator()(const array<Index, NumDims>& dims) const {
+    T result = static_cast<T>(0);
+    for (int i = 0; i < NumDims; ++i) {
+      result += static_cast<T>((i + 1) * dims[i]);
+    }
+    return result;
+  }
+};
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_generator_op(Device d) {
+  static constexpr int Options = 0 | Layout;
+
+  auto dims = RandomDims<NumDims>(20, 30);
+  Tensor<T, NumDims, Options, Index> src(dims);
+  src.setRandom();
+
+  const auto expr = src.generate(DummyGenerator<T, NumDims>());
+
+  // We assume that generator on a default device is tested and correct, so
+  // we can rely on it to verify correctness of tensor executor and tiling.
+  Tensor<T, NumDims, Options, Index> golden;
+  golden = expr;
+
+  // Now do the broadcasting using configured tensor executor.
+  Tensor<T, NumDims, Options, Index> dst(golden.dimensions());
+
+  using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+  using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+  Executor::run(Assign(dst, expr), d);
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+  }
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_execute_reverse_rvalue(Device d) {
+  static constexpr int Options = 0 | Layout;
+
+  auto dims = RandomDims<NumDims>(1, numext::pow(1000000.0, 1.0 / NumDims));
+  Tensor<T, NumDims, Options, Index> src(dims);
+  src.setRandom();
+
+  // Reverse half of the dimensions.
+  Eigen::array<bool, NumDims> reverse;
+  for (int i = 0; i < NumDims; ++i) reverse[i] = internal::random<bool>();
+
+  const auto expr = src.reverse(reverse);
+
+  // We assume that reversing on a default device is tested and correct, so
+  // we can rely on it to verify correctness of tensor executor and tiling.
+  Tensor<T, NumDims, Options, Index> golden;
+  golden = expr;
+
+  // Now do the reversing using configured tensor executor.
+  Tensor<T, NumDims, Options, Index> dst(golden.dimensions());
+
+  using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+  using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
+
+  Executor::run(Assign(dst, expr), d);
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
+  }
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_async_execute_unary_expr(Device d) {
+  static constexpr int Options = 0 | Layout;
+
+  // Pick a large enough tensor size to bypass small tensor block evaluation
+  // optimization.
+  auto dims = RandomDims<NumDims>(50 / NumDims, 100 / NumDims);
+
+  Tensor<T, NumDims, Options, Index> src(dims);
+  Tensor<T, NumDims, Options, Index> dst(dims);
+
+  src.setRandom();
+  const auto expr = src.square();
+
+  Eigen::Barrier done(1);
+  auto on_done = [&done]() { done.Notify(); };
+
+  using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+  using DoneCallback = decltype(on_done);
+  using Executor = internal::TensorAsyncExecutor<const Assign, Device, DoneCallback, Vectorizable, Tiling>;
+
+  Executor::runAsync(Assign(dst, expr), d, on_done);
+  done.Wait();
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    T square = src.coeff(i) * src.coeff(i);
+    VERIFY_IS_EQUAL(square, dst.coeff(i));
+  }
+}
+
+template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
+void test_async_execute_binary_expr(Device d) {
+  static constexpr int Options = 0 | Layout;
+
+  // Pick a large enough tensor size to bypass small tensor block evaluation
+  // optimization.
+  auto dims = RandomDims<NumDims>(50 / NumDims, 100 / NumDims);
+
+  Tensor<T, NumDims, Options, Index> lhs(dims);
+  Tensor<T, NumDims, Options, Index> rhs(dims);
+  Tensor<T, NumDims, Options, Index> dst(dims);
+
+  lhs.setRandom();
+  rhs.setRandom();
+
+  const auto expr = lhs + rhs;
+
+  Eigen::Barrier done(1);
+  auto on_done = [&done]() { done.Notify(); };
+
+  using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
+  using DoneCallback = decltype(on_done);
+  using Executor = internal::TensorAsyncExecutor<const Assign, Device, DoneCallback, Vectorizable, Tiling>;
+
+  Executor::runAsync(Assign(dst, expr), d, on_done);
+  done.Wait();
+
+  for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
+    T sum = lhs.coeff(i) + rhs.coeff(i);
+    VERIFY_IS_EQUAL(sum, dst.coeff(i));
+  }
+}
+
+#ifndef EIGEN_DONT_VECTORIZE
+#define EIGEN_DONT_VECTORIZE 0
+#endif
+#define VECTORIZABLE(T, VAL) !EIGEN_DONT_VECTORIZE&& Eigen::internal::packet_traits<T>::Vectorizable&& VAL
+
+#define CALL_SUBTEST_PART(PART) CALL_SUBTEST_##PART
+
+#define CALL_SUBTEST_COMBINATIONS(PART, NAME, T, NUM_DIMS)                                                            \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, false, TiledEvaluation::Off, ColMajor>(default_device))); \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, false, TiledEvaluation::On, ColMajor>(default_device)));  \
+  CALL_SUBTEST_PART(PART)                                                                                             \
+  ((NAME<T, NUM_DIMS, DefaultDevice, VECTORIZABLE(T, true), TiledEvaluation::Off, ColMajor>(default_device)));        \
+  CALL_SUBTEST_PART(PART)                                                                                             \
+  ((NAME<T, NUM_DIMS, DefaultDevice, VECTORIZABLE(T, true), TiledEvaluation::On, ColMajor>(default_device)));         \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, false, TiledEvaluation::Off, RowMajor>(default_device))); \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, false, TiledEvaluation::On, RowMajor>(default_device)));  \
+  CALL_SUBTEST_PART(PART)                                                                                             \
+  ((NAME<T, NUM_DIMS, DefaultDevice, VECTORIZABLE(T, true), TiledEvaluation::Off, RowMajor>(default_device)));        \
+  CALL_SUBTEST_PART(PART)                                                                                             \
+  ((NAME<T, NUM_DIMS, DefaultDevice, VECTORIZABLE(T, true), TiledEvaluation::On, RowMajor>(default_device)));         \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::Off, ColMajor>(tp_device)));   \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::On, ColMajor>(tp_device)));    \
+  CALL_SUBTEST_PART(PART)                                                                                             \
+  ((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(T, true), TiledEvaluation::Off, ColMajor>(tp_device)));          \
+  CALL_SUBTEST_PART(PART)                                                                                             \
+  ((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(T, true), TiledEvaluation::On, ColMajor>(tp_device)));           \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::Off, RowMajor>(tp_device)));   \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::On, RowMajor>(tp_device)));    \
+  CALL_SUBTEST_PART(PART)                                                                                             \
+  ((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(T, true), TiledEvaluation::Off, RowMajor>(tp_device)));          \
+  CALL_SUBTEST_PART(PART)                                                                                             \
+  ((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(T, true), TiledEvaluation::On, RowMajor>(tp_device)))
+
+// NOTE: Currently only ThreadPoolDevice supports async expression evaluation.
+#define CALL_ASYNC_SUBTEST_COMBINATIONS(PART, NAME, T, NUM_DIMS)                                                    \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::Off, ColMajor>(tp_device))); \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::On, ColMajor>(tp_device)));  \
+  CALL_SUBTEST_PART(PART)                                                                                           \
+  ((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(T, true), TiledEvaluation::Off, ColMajor>(tp_device)));        \
+  CALL_SUBTEST_PART(PART)                                                                                           \
+  ((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(T, true), TiledEvaluation::On, ColMajor>(tp_device)));         \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::Off, RowMajor>(tp_device))); \
+  CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::On, RowMajor>(tp_device)));  \
+  CALL_SUBTEST_PART(PART)                                                                                           \
+  ((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(T, true), TiledEvaluation::Off, RowMajor>(tp_device)));        \
+  CALL_SUBTEST_PART(PART)                                                                                           \
+  ((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(T, true), TiledEvaluation::On, RowMajor>(tp_device)))
+
+EIGEN_DECLARE_TEST(cxx11_tensor_executor) {
+  Eigen::DefaultDevice default_device;
+  // Default device is unused in ASYNC tests.
+  EIGEN_UNUSED_VARIABLE(default_device);
+
+  const auto num_threads = internal::random<int>(20, 24);
+  Eigen::ThreadPool tp(num_threads);
+  Eigen::ThreadPoolDevice tp_device(&tp, num_threads);
+
+  CALL_SUBTEST_COMBINATIONS(1, test_execute_unary_expr, float, 3);
+  CALL_SUBTEST_COMBINATIONS(1, test_execute_unary_expr, float, 4);
+  CALL_SUBTEST_COMBINATIONS(1, test_execute_unary_expr, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(2, test_execute_binary_expr, float, 3);
+  CALL_SUBTEST_COMBINATIONS(2, test_execute_binary_expr, float, 4);
+  CALL_SUBTEST_COMBINATIONS(2, test_execute_binary_expr, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(3, test_execute_broadcasting, float, 3);
+  CALL_SUBTEST_COMBINATIONS(3, test_execute_broadcasting, float, 4);
+  CALL_SUBTEST_COMBINATIONS(3, test_execute_broadcasting, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(4, test_execute_chipping_rvalue, float, 3);
+  CALL_SUBTEST_COMBINATIONS(4, test_execute_chipping_rvalue, float, 4);
+  CALL_SUBTEST_COMBINATIONS(4, test_execute_chipping_rvalue, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(5, test_execute_chipping_lvalue, float, 3);
+  CALL_SUBTEST_COMBINATIONS(5, test_execute_chipping_lvalue, float, 4);
+  CALL_SUBTEST_COMBINATIONS(5, test_execute_chipping_lvalue, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(6, test_execute_shuffle_rvalue, float, 3);
+  CALL_SUBTEST_COMBINATIONS(6, test_execute_shuffle_rvalue, float, 4);
+  CALL_SUBTEST_COMBINATIONS(6, test_execute_shuffle_rvalue, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(7, test_execute_shuffle_lvalue, float, 3);
+  CALL_SUBTEST_COMBINATIONS(7, test_execute_shuffle_lvalue, float, 4);
+  CALL_SUBTEST_COMBINATIONS(7, test_execute_shuffle_lvalue, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(9, test_execute_reshape, float, 2);
+  CALL_SUBTEST_COMBINATIONS(9, test_execute_reshape, float, 3);
+  CALL_SUBTEST_COMBINATIONS(9, test_execute_reshape, float, 4);
+  CALL_SUBTEST_COMBINATIONS(9, test_execute_reshape, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(10, test_execute_slice_rvalue, float, 2);
+  CALL_SUBTEST_COMBINATIONS(10, test_execute_slice_rvalue, float, 3);
+  CALL_SUBTEST_COMBINATIONS(10, test_execute_slice_rvalue, float, 4);
+  CALL_SUBTEST_COMBINATIONS(10, test_execute_slice_rvalue, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(11, test_execute_slice_lvalue, float, 2);
+  CALL_SUBTEST_COMBINATIONS(11, test_execute_slice_lvalue, float, 3);
+  CALL_SUBTEST_COMBINATIONS(11, test_execute_slice_lvalue, float, 4);
+  CALL_SUBTEST_COMBINATIONS(11, test_execute_slice_lvalue, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 2);
+  CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 3);
+  CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 4);
+  CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(13, test_execute_generator_op, float, 2);
+  CALL_SUBTEST_COMBINATIONS(13, test_execute_generator_op, float, 3);
+  CALL_SUBTEST_COMBINATIONS(13, test_execute_generator_op, float, 4);
+  CALL_SUBTEST_COMBINATIONS(13, test_execute_generator_op, float, 5);
+
+  CALL_SUBTEST_COMBINATIONS(14, test_execute_reverse_rvalue, float, 1);
+  CALL_SUBTEST_COMBINATIONS(14, test_execute_reverse_rvalue, float, 2);
+  CALL_SUBTEST_COMBINATIONS(14, test_execute_reverse_rvalue, float, 3);
+  CALL_SUBTEST_COMBINATIONS(14, test_execute_reverse_rvalue, float, 4);
+  CALL_SUBTEST_COMBINATIONS(14, test_execute_reverse_rvalue, float, 5);
+
+  CALL_ASYNC_SUBTEST_COMBINATIONS(15, test_async_execute_unary_expr, float, 3);
+  CALL_ASYNC_SUBTEST_COMBINATIONS(15, test_async_execute_unary_expr, float, 4);
+  CALL_ASYNC_SUBTEST_COMBINATIONS(15, test_async_execute_unary_expr, float, 5);
+
+  CALL_ASYNC_SUBTEST_COMBINATIONS(16, test_async_execute_binary_expr, float, 3);
+  CALL_ASYNC_SUBTEST_COMBINATIONS(16, test_async_execute_binary_expr, float, 4);
+  CALL_ASYNC_SUBTEST_COMBINATIONS(16, test_async_execute_binary_expr, float, 5);
+
+  // Force CMake to split this test.
+  // EIGEN_SUFFIXES;1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_expr.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_expr.cpp
new file mode 100644
index 0000000..cf0d8ee
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_expr.cpp
@@ -0,0 +1,476 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include <numeric>
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void test_1d() {
+  Tensor<float, 1> vec1(6);
+  Tensor<float, 1, RowMajor> vec2(6);
+
+  vec1(0) = 4.0;
+  vec2(0) = 0.0;
+  vec1(1) = 8.0;
+  vec2(1) = 1.0;
+  vec1(2) = 15.0;
+  vec2(2) = 2.0;
+  vec1(3) = 16.0;
+  vec2(3) = 3.0;
+  vec1(4) = 23.0;
+  vec2(4) = 4.0;
+  vec1(5) = 42.0;
+  vec2(5) = 5.0;
+
+  float data3[6];
+  TensorMap<Tensor<float, 1>> vec3(data3, 6);
+  vec3 = vec1.sqrt();
+  float data4[6];
+  TensorMap<Tensor<float, 1, RowMajor>> vec4(data4, 6);
+  vec4 = vec2.square();
+  float data5[6];
+  TensorMap<Tensor<float, 1, RowMajor>> vec5(data5, 6);
+  vec5 = vec2.cube();
+
+  VERIFY_IS_APPROX(vec3(0), sqrtf(4.0));
+  VERIFY_IS_APPROX(vec3(1), sqrtf(8.0));
+  VERIFY_IS_APPROX(vec3(2), sqrtf(15.0));
+  VERIFY_IS_APPROX(vec3(3), sqrtf(16.0));
+  VERIFY_IS_APPROX(vec3(4), sqrtf(23.0));
+  VERIFY_IS_APPROX(vec3(5), sqrtf(42.0));
+
+  VERIFY_IS_APPROX(vec4(0), 0.0f);
+  VERIFY_IS_APPROX(vec4(1), 1.0f);
+  VERIFY_IS_APPROX(vec4(2), 2.0f * 2.0f);
+  VERIFY_IS_APPROX(vec4(3), 3.0f * 3.0f);
+  VERIFY_IS_APPROX(vec4(4), 4.0f * 4.0f);
+  VERIFY_IS_APPROX(vec4(5), 5.0f * 5.0f);
+
+  VERIFY_IS_APPROX(vec5(0), 0.0f);
+  VERIFY_IS_APPROX(vec5(1), 1.0f);
+  VERIFY_IS_APPROX(vec5(2), 2.0f * 2.0f * 2.0f);
+  VERIFY_IS_APPROX(vec5(3), 3.0f * 3.0f * 3.0f);
+  VERIFY_IS_APPROX(vec5(4), 4.0f * 4.0f * 4.0f);
+  VERIFY_IS_APPROX(vec5(5), 5.0f * 5.0f * 5.0f);
+
+  vec3 = vec1 + vec2;
+  VERIFY_IS_APPROX(vec3(0), 4.0f + 0.0f);
+  VERIFY_IS_APPROX(vec3(1), 8.0f + 1.0f);
+  VERIFY_IS_APPROX(vec3(2), 15.0f + 2.0f);
+  VERIFY_IS_APPROX(vec3(3), 16.0f + 3.0f);
+  VERIFY_IS_APPROX(vec3(4), 23.0f + 4.0f);
+  VERIFY_IS_APPROX(vec3(5), 42.0f + 5.0f);
+}
+
+static void test_2d() {
+  float data1[6];
+  TensorMap<Tensor<float, 2>> mat1(data1, 2, 3);
+  float data2[6];
+  TensorMap<Tensor<float, 2, RowMajor>> mat2(data2, 2, 3);
+
+  mat1(0, 0) = 0.0;
+  mat1(0, 1) = 1.0;
+  mat1(0, 2) = 2.0;
+  mat1(1, 0) = 3.0;
+  mat1(1, 1) = 4.0;
+  mat1(1, 2) = 5.0;
+
+  mat2(0, 0) = -0.0;
+  mat2(0, 1) = -1.0;
+  mat2(0, 2) = -2.0;
+  mat2(1, 0) = -3.0;
+  mat2(1, 1) = -4.0;
+  mat2(1, 2) = -5.0;
+
+  Tensor<float, 2> mat3(2, 3);
+  Tensor<float, 2, RowMajor> mat4(2, 3);
+  mat3 = mat1.abs();
+  mat4 = mat2.abs();
+
+  VERIFY_IS_APPROX(mat3(0, 0), 0.0f);
+  VERIFY_IS_APPROX(mat3(0, 1), 1.0f);
+  VERIFY_IS_APPROX(mat3(0, 2), 2.0f);
+  VERIFY_IS_APPROX(mat3(1, 0), 3.0f);
+  VERIFY_IS_APPROX(mat3(1, 1), 4.0f);
+  VERIFY_IS_APPROX(mat3(1, 2), 5.0f);
+
+  VERIFY_IS_APPROX(mat4(0, 0), 0.0f);
+  VERIFY_IS_APPROX(mat4(0, 1), 1.0f);
+  VERIFY_IS_APPROX(mat4(0, 2), 2.0f);
+  VERIFY_IS_APPROX(mat4(1, 0), 3.0f);
+  VERIFY_IS_APPROX(mat4(1, 1), 4.0f);
+  VERIFY_IS_APPROX(mat4(1, 2), 5.0f);
+}
+
+static void test_3d() {
+  Tensor<float, 3> mat1(2, 3, 7);
+  Tensor<float, 3, RowMajor> mat2(2, 3, 7);
+
+  float val = 1.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        mat1(i, j, k) = val;
+        mat2(i, j, k) = val;
+        val += 1.0f;
+      }
+    }
+  }
+
+  Tensor<float, 3> mat3(2, 3, 7);
+  mat3 = mat1 + mat1;
+  Tensor<float, 3, RowMajor> mat4(2, 3, 7);
+  mat4 = mat2 * 3.14f;
+  Tensor<float, 3> mat5(2, 3, 7);
+  mat5 = (mat1 + mat1.constant(1)).inverse().log();
+  Tensor<float, 3, RowMajor> mat6(2, 3, 7);
+  mat6 = mat2.pow(0.5f) * 3.14f;
+  Tensor<float, 3> mat7(2, 3, 7);
+  mat7 = mat1.cwiseMax(mat5 * 2.0f).exp();
+  Tensor<float, 3, RowMajor> mat8(2, 3, 7);
+  mat8 = (-mat2).exp() * 3.14f;
+  Tensor<float, 3, RowMajor> mat9(2, 3, 7);
+  mat9 = mat2 + 3.14f;
+  Tensor<float, 3, RowMajor> mat10(2, 3, 7);
+  mat10 = mat2 - 3.14f;
+  Tensor<float, 3, RowMajor> mat11(2, 3, 7);
+  mat11 = mat2 / 3.14f;
+
+  val = 1.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(mat3(i, j, k), val + val);
+        VERIFY_IS_APPROX(mat4(i, j, k), val * 3.14f);
+        VERIFY_IS_APPROX(mat5(i, j, k), logf(1.0f / (val + 1)));
+        VERIFY_IS_APPROX(mat6(i, j, k), sqrtf(val) * 3.14f);
+        VERIFY_IS_APPROX(mat7(i, j, k), expf((std::max)(val, mat5(i, j, k) * 2.0f)));
+        VERIFY_IS_APPROX(mat8(i, j, k), expf(-val) * 3.14f);
+        VERIFY_IS_APPROX(mat9(i, j, k), val + 3.14f);
+        VERIFY_IS_APPROX(mat10(i, j, k), val - 3.14f);
+        VERIFY_IS_APPROX(mat11(i, j, k), val / 3.14f);
+        val += 1.0f;
+      }
+    }
+  }
+}
+
+static void test_constants() {
+  Tensor<float, 3> mat1(2, 3, 7);
+  Tensor<float, 3> mat2(2, 3, 7);
+  Tensor<float, 3> mat3(2, 3, 7);
+
+  float val = 1.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        mat1(i, j, k) = val;
+        val += 1.0f;
+      }
+    }
+  }
+  mat2 = mat1.constant(3.14f);
+  mat3 = mat1.cwiseMax(7.3f).exp();
+
+  val = 1.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(mat2(i, j, k), 3.14f);
+        VERIFY_IS_APPROX(mat3(i, j, k), expf((std::max)(val, 7.3f)));
+        val += 1.0f;
+      }
+    }
+  }
+}
+
+static void test_boolean() {
+  const int kSize = 31;
+  Tensor<int, 1> vec(kSize);
+  std::iota(vec.data(), vec.data() + kSize, 0);
+
+  // Test ||.
+  Tensor<bool, 1> bool1 = (vec < vec.constant(1) || vec > vec.constant(4)).cast<bool>();
+  for (int i = 0; i < kSize; ++i) {
+    bool expected = i < 1 || i > 4;
+    VERIFY_IS_EQUAL(bool1[i], expected);
+  }
+
+  // Test &&, including cast of operand vec.
+  Tensor<bool, 1> bool2 = vec.cast<bool>() && (vec < vec.constant(4)).cast<bool>();
+  for (int i = 0; i < kSize; ++i) {
+    bool expected = bool(i) && i < 4;
+    VERIFY_IS_EQUAL(bool2[i], expected);
+  }
+
+  // Compilation tests:
+  // Test Tensor<bool> against results of cast or comparison; verifies that
+  // CoeffReturnType is set to match Op return type of bool for Unary and Binary
+  // Ops.
+  Tensor<bool, 1> bool3 = vec.cast<bool>() && bool2;
+  bool3 = (vec < vec.constant(4)).cast<bool>() && bool2;
+}
+
+static void test_functors() {
+  Tensor<float, 3> mat1(2, 3, 7);
+  Tensor<float, 3> mat2(2, 3, 7);
+  Tensor<float, 3> mat3(2, 3, 7);
+
+  float val = 1.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        mat1(i, j, k) = val;
+        val += 1.0f;
+      }
+    }
+  }
+  mat2 = mat1.inverse().unaryExpr(&asinf);
+  mat3 = mat1.unaryExpr(&tanhf);
+
+  val = 1.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(mat2(i, j, k), asinf(1.0f / mat1(i, j, k)));
+        VERIFY_IS_APPROX(mat3(i, j, k), tanhf(mat1(i, j, k)));
+        val += 1.0f;
+      }
+    }
+  }
+}
+
+static void test_type_casting() {
+  Tensor<bool, 3> mat1(2, 3, 7);
+  Tensor<float, 3> mat2(2, 3, 7);
+  Tensor<double, 3> mat3(2, 3, 7);
+  mat1.setRandom();
+  mat2.setRandom();
+
+  mat3 = mat1.cast<double>();
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(mat3(i, j, k), mat1(i, j, k) ? 1.0 : 0.0);
+      }
+    }
+  }
+
+  mat3 = mat2.cast<double>();
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(mat3(i, j, k), static_cast<double>(mat2(i, j, k)));
+      }
+    }
+  }
+}
+
+static void test_select() {
+  using TypedGTOp = internal::scalar_cmp_op<float, float, internal::cmp_GT, true>;
+
+  Tensor<float, 3> selector(2, 3, 7);
+  Tensor<float, 3> mat1(2, 3, 7);
+  Tensor<float, 3> mat2(2, 3, 7);
+  Tensor<float, 3> result(2, 3, 7);
+
+  selector.setRandom();
+  mat1.setRandom();
+  mat2.setRandom();
+
+  // test select with a boolean condition
+  result = (selector > selector.constant(0.5f)).select(mat1, mat2);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(result(i, j, k), (selector(i, j, k) > 0.5f) ? mat1(i, j, k) : mat2(i, j, k));
+      }
+    }
+  }
+
+  // test select with a typed condition
+  result = selector.binaryExpr(selector.constant(0.5f), TypedGTOp()).select(mat1, mat2);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(result(i, j, k), (selector(i, j, k) > 0.5f) ? mat1(i, j, k) : mat2(i, j, k));
+      }
+    }
+  }
+}
+
+// Nan propagation does currently not work like one would expect from std::max/std::min,
+// so we disable it for now
+#if !EIGEN_ARCH_ARM_OR_ARM64
+template <typename Scalar>
+void test_minmax_nan_propagation_templ() {
+  for (int size = 1; size < 17; ++size) {
+    const Scalar kNaN = std::numeric_limits<Scalar>::quiet_NaN();
+    const Scalar kInf = std::numeric_limits<Scalar>::infinity();
+    const Scalar kZero(0);
+    Tensor<Scalar, 1> vec_full_nan(size);
+    Tensor<Scalar, 1> vec_one_nan(size);
+    Tensor<Scalar, 1> vec_zero(size);
+    vec_full_nan.setConstant(kNaN);
+    vec_zero.setZero();
+    vec_one_nan.setZero();
+    vec_one_nan(size / 2) = kNaN;
+
+    auto verify_all_nan = [&](const Tensor<Scalar, 1>& v) {
+      for (int i = 0; i < size; ++i) {
+        VERIFY((numext::isnan)(v(i)));
+      }
+    };
+
+    auto verify_all_zero = [&](const Tensor<Scalar, 1>& v) {
+      for (int i = 0; i < size; ++i) {
+        VERIFY_IS_EQUAL(v(i), Scalar(0));
+      }
+    };
+
+    // Test NaN propagating max.
+    // max(nan, nan) = nan
+    // max(nan, 0) = nan
+    // max(0, nan) = nan
+    // max(0, 0) = 0
+    verify_all_nan(vec_full_nan.template cwiseMax<PropagateNaN>(kNaN));
+    verify_all_nan(vec_full_nan.template cwiseMax<PropagateNaN>(vec_full_nan));
+    verify_all_nan(vec_full_nan.template cwiseMax<PropagateNaN>(kZero));
+    verify_all_nan(vec_full_nan.template cwiseMax<PropagateNaN>(vec_zero));
+    verify_all_nan(vec_zero.template cwiseMax<PropagateNaN>(kNaN));
+    verify_all_nan(vec_zero.template cwiseMax<PropagateNaN>(vec_full_nan));
+    verify_all_zero(vec_zero.template cwiseMax<PropagateNaN>(kZero));
+    verify_all_zero(vec_zero.template cwiseMax<PropagateNaN>(vec_zero));
+
+    // Test number propagating max.
+    // max(nan, nan) = nan
+    // max(nan, 0) = 0
+    // max(0, nan) = 0
+    // max(0, 0) = 0
+    verify_all_nan(vec_full_nan.template cwiseMax<PropagateNumbers>(kNaN));
+    verify_all_nan(vec_full_nan.template cwiseMax<PropagateNumbers>(vec_full_nan));
+    verify_all_zero(vec_full_nan.template cwiseMax<PropagateNumbers>(kZero));
+    verify_all_zero(vec_full_nan.template cwiseMax<PropagateNumbers>(vec_zero));
+    verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(kNaN));
+    verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(vec_full_nan));
+    verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(kZero));
+    verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(vec_zero));
+
+    // Test NaN propagating min.
+    // min(nan, nan) = nan
+    // min(nan, 0) = nan
+    // min(0, nan) = nan
+    // min(0, 0) = 0
+    verify_all_nan(vec_full_nan.template cwiseMin<PropagateNaN>(kNaN));
+    verify_all_nan(vec_full_nan.template cwiseMin<PropagateNaN>(vec_full_nan));
+    verify_all_nan(vec_full_nan.template cwiseMin<PropagateNaN>(kZero));
+    verify_all_nan(vec_full_nan.template cwiseMin<PropagateNaN>(vec_zero));
+    verify_all_nan(vec_zero.template cwiseMin<PropagateNaN>(kNaN));
+    verify_all_nan(vec_zero.template cwiseMin<PropagateNaN>(vec_full_nan));
+    verify_all_zero(vec_zero.template cwiseMin<PropagateNaN>(kZero));
+    verify_all_zero(vec_zero.template cwiseMin<PropagateNaN>(vec_zero));
+
+    // Test number propagating min.
+    // min(nan, nan) = nan
+    // min(nan, 0) = 0
+    // min(0, nan) = 0
+    // min(0, 0) = 0
+    verify_all_nan(vec_full_nan.template cwiseMin<PropagateNumbers>(kNaN));
+    verify_all_nan(vec_full_nan.template cwiseMin<PropagateNumbers>(vec_full_nan));
+    verify_all_zero(vec_full_nan.template cwiseMin<PropagateNumbers>(kZero));
+    verify_all_zero(vec_full_nan.template cwiseMin<PropagateNumbers>(vec_zero));
+    verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(kNaN));
+    verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(vec_full_nan));
+    verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(kZero));
+    verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(vec_zero));
+
+    // Test min and max reduction
+    Tensor<Scalar, 0> val;
+    val = vec_zero.minimum();
+    VERIFY_IS_EQUAL(val(), kZero);
+    val = vec_zero.template minimum<PropagateNaN>();
+    VERIFY_IS_EQUAL(val(), kZero);
+    val = vec_zero.template minimum<PropagateNumbers>();
+    VERIFY_IS_EQUAL(val(), kZero);
+    val = vec_zero.maximum();
+    VERIFY_IS_EQUAL(val(), kZero);
+    val = vec_zero.template maximum<PropagateNaN>();
+    VERIFY_IS_EQUAL(val(), kZero);
+    val = vec_zero.template maximum<PropagateNumbers>();
+    VERIFY_IS_EQUAL(val(), kZero);
+
+    // Test NaN propagation for tensor of all NaNs.
+    val = vec_full_nan.template minimum<PropagateNaN>();
+    VERIFY((numext::isnan)(val()));
+    val = vec_full_nan.template minimum<PropagateNumbers>();
+    VERIFY_IS_EQUAL(val(), kInf);
+    val = vec_full_nan.template maximum<PropagateNaN>();
+    VERIFY((numext::isnan)(val()));
+    val = vec_full_nan.template maximum<PropagateNumbers>();
+    VERIFY_IS_EQUAL(val(), -kInf);
+
+    // Test NaN propagation for tensor with a single NaN.
+    val = vec_one_nan.template minimum<PropagateNaN>();
+    VERIFY((numext::isnan)(val()));
+    val = vec_one_nan.template minimum<PropagateNumbers>();
+    VERIFY_IS_EQUAL(val(), (size == 1 ? kInf : kZero));
+    val = vec_one_nan.template maximum<PropagateNaN>();
+    VERIFY((numext::isnan)(val()));
+    val = vec_one_nan.template maximum<PropagateNumbers>();
+    VERIFY_IS_EQUAL(val(), (size == 1 ? -kInf : kZero));
+  }
+}
+#else
+template <typename Scalar>
+void test_minmax_nan_propagation_templ() {}
+#endif
+
+static void test_clip() {
+  Tensor<float, 1> vec(6);
+  vec(0) = 4.0;
+  vec(1) = 8.0;
+  vec(2) = 15.0;
+  vec(3) = 16.0;
+  vec(4) = 23.0;
+  vec(5) = 42.0;
+
+  float kMin = 20;
+  float kMax = 30;
+
+  Tensor<float, 1> vec_clipped(6);
+  vec_clipped = vec.clip(kMin, kMax);
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_EQUAL(vec_clipped(i), numext::mini(numext::maxi(vec(i), kMin), kMax));
+  }
+}
+
+static void test_minmax_nan_propagation() {
+  test_minmax_nan_propagation_templ<float>();
+  test_minmax_nan_propagation_templ<double>();
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_expr) {
+  CALL_SUBTEST(test_1d());
+  CALL_SUBTEST(test_2d());
+  CALL_SUBTEST(test_3d());
+  CALL_SUBTEST(test_constants());
+  CALL_SUBTEST(test_boolean());
+  CALL_SUBTEST(test_functors());
+  CALL_SUBTEST(test_type_casting());
+  CALL_SUBTEST(test_select());
+  CALL_SUBTEST(test_clip());
+  CALL_SUBTEST(test_minmax_nan_propagation());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_fft.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_fft.cpp
new file mode 100644
index 0000000..70bba2e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_fft.cpp
@@ -0,0 +1,296 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Jianwei Cui <thucjw@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_fft_2D_golden() {
+  Tensor<float, 2, DataLayout> input(2, 3);
+  input(0, 0) = 1;
+  input(0, 1) = 2;
+  input(0, 2) = 3;
+  input(1, 0) = 4;
+  input(1, 1) = 5;
+  input(1, 2) = 6;
+
+  array<ptrdiff_t, 2> fft;
+  fft[0] = 0;
+  fft[1] = 1;
+
+  Tensor<std::complex<float>, 2, DataLayout> output = input.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft);
+
+  std::complex<float> output_golden[6];  // in ColMajor order
+  output_golden[0] = std::complex<float>(21, 0);
+  output_golden[1] = std::complex<float>(-9, 0);
+  output_golden[2] = std::complex<float>(-3, 1.73205);
+  output_golden[3] = std::complex<float>(0, 0);
+  output_golden[4] = std::complex<float>(-3, -1.73205);
+  output_golden[5] = std::complex<float>(0, 0);
+
+  std::complex<float> c_offset = std::complex<float>(1.0, 1.0);
+
+  if (DataLayout == ColMajor) {
+    VERIFY_IS_APPROX(output(0) + c_offset, output_golden[0] + c_offset);
+    VERIFY_IS_APPROX(output(1) + c_offset, output_golden[1] + c_offset);
+    VERIFY_IS_APPROX(output(2) + c_offset, output_golden[2] + c_offset);
+    VERIFY_IS_APPROX(output(3) + c_offset, output_golden[3] + c_offset);
+    VERIFY_IS_APPROX(output(4) + c_offset, output_golden[4] + c_offset);
+    VERIFY_IS_APPROX(output(5) + c_offset, output_golden[5] + c_offset);
+  } else {
+    VERIFY_IS_APPROX(output(0) + c_offset, output_golden[0] + c_offset);
+    VERIFY_IS_APPROX(output(1) + c_offset, output_golden[2] + c_offset);
+    VERIFY_IS_APPROX(output(2) + c_offset, output_golden[4] + c_offset);
+    VERIFY_IS_APPROX(output(3) + c_offset, output_golden[1] + c_offset);
+    VERIFY_IS_APPROX(output(4) + c_offset, output_golden[3] + c_offset);
+    VERIFY_IS_APPROX(output(5) + c_offset, output_golden[5] + c_offset);
+  }
+}
+
+static void test_fft_complex_input_golden() {
+  Tensor<std::complex<float>, 1, ColMajor> input(5);
+  input(0) = std::complex<float>(1, 1);
+  input(1) = std::complex<float>(2, 2);
+  input(2) = std::complex<float>(3, 3);
+  input(3) = std::complex<float>(4, 4);
+  input(4) = std::complex<float>(5, 5);
+
+  array<ptrdiff_t, 1> fft;
+  fft[0] = 0;
+
+  Tensor<std::complex<float>, 1, ColMajor> forward_output_both_parts = input.fft<BothParts, FFT_FORWARD>(fft);
+  Tensor<std::complex<float>, 1, ColMajor> reverse_output_both_parts = input.fft<BothParts, FFT_REVERSE>(fft);
+
+  Tensor<float, 1, ColMajor> forward_output_real_part = input.fft<RealPart, FFT_FORWARD>(fft);
+  Tensor<float, 1, ColMajor> reverse_output_real_part = input.fft<RealPart, FFT_REVERSE>(fft);
+
+  Tensor<float, 1, ColMajor> forward_output_imag_part = input.fft<ImagPart, FFT_FORWARD>(fft);
+  Tensor<float, 1, ColMajor> reverse_output_imag_part = input.fft<ImagPart, FFT_REVERSE>(fft);
+
+  VERIFY_IS_EQUAL(forward_output_both_parts.dimension(0), input.dimension(0));
+  VERIFY_IS_EQUAL(reverse_output_both_parts.dimension(0), input.dimension(0));
+
+  VERIFY_IS_EQUAL(forward_output_real_part.dimension(0), input.dimension(0));
+  VERIFY_IS_EQUAL(reverse_output_real_part.dimension(0), input.dimension(0));
+
+  VERIFY_IS_EQUAL(forward_output_imag_part.dimension(0), input.dimension(0));
+  VERIFY_IS_EQUAL(reverse_output_imag_part.dimension(0), input.dimension(0));
+
+  std::complex<float> forward_golden_result[5];
+  std::complex<float> reverse_golden_result[5];
+
+  forward_golden_result[0] = std::complex<float>(15.000000000000000, +15.000000000000000);
+  forward_golden_result[1] = std::complex<float>(-5.940954801177935, +0.940954801177934);
+  forward_golden_result[2] = std::complex<float>(-3.312299240582266, -1.687700759417735);
+  forward_golden_result[3] = std::complex<float>(-1.687700759417735, -3.312299240582266);
+  forward_golden_result[4] = std::complex<float>(0.940954801177934, -5.940954801177935);
+
+  reverse_golden_result[0] = std::complex<float>(3.000000000000000, +3.000000000000000);
+  reverse_golden_result[1] = std::complex<float>(0.188190960235587, -1.188190960235587);
+  reverse_golden_result[2] = std::complex<float>(-0.337540151883547, -0.662459848116453);
+  reverse_golden_result[3] = std::complex<float>(-0.662459848116453, -0.337540151883547);
+  reverse_golden_result[4] = std::complex<float>(-1.188190960235587, +0.188190960235587);
+
+  for (int i = 0; i < 5; ++i) {
+    VERIFY_IS_APPROX(forward_output_both_parts(i), forward_golden_result[i]);
+    VERIFY_IS_APPROX(forward_output_real_part(i), forward_golden_result[i].real());
+    VERIFY_IS_APPROX(forward_output_imag_part(i), forward_golden_result[i].imag());
+  }
+
+  for (int i = 0; i < 5; ++i) {
+    VERIFY_IS_APPROX(reverse_output_both_parts(i), reverse_golden_result[i]);
+    VERIFY_IS_APPROX(reverse_output_real_part(i), reverse_golden_result[i].real());
+    VERIFY_IS_APPROX(reverse_output_imag_part(i), reverse_golden_result[i].imag());
+  }
+}
+
+static void test_fft_real_input_golden() {
+  Tensor<float, 1, ColMajor> input(5);
+  input(0) = 1.0;
+  input(1) = 2.0;
+  input(2) = 3.0;
+  input(3) = 4.0;
+  input(4) = 5.0;
+
+  array<ptrdiff_t, 1> fft;
+  fft[0] = 0;
+
+  Tensor<std::complex<float>, 1, ColMajor> forward_output_both_parts = input.fft<BothParts, FFT_FORWARD>(fft);
+  Tensor<std::complex<float>, 1, ColMajor> reverse_output_both_parts = input.fft<BothParts, FFT_REVERSE>(fft);
+
+  Tensor<float, 1, ColMajor> forward_output_real_part = input.fft<RealPart, FFT_FORWARD>(fft);
+  Tensor<float, 1, ColMajor> reverse_output_real_part = input.fft<RealPart, FFT_REVERSE>(fft);
+
+  Tensor<float, 1, ColMajor> forward_output_imag_part = input.fft<ImagPart, FFT_FORWARD>(fft);
+  Tensor<float, 1, ColMajor> reverse_output_imag_part = input.fft<ImagPart, FFT_REVERSE>(fft);
+
+  VERIFY_IS_EQUAL(forward_output_both_parts.dimension(0), input.dimension(0));
+  VERIFY_IS_EQUAL(reverse_output_both_parts.dimension(0), input.dimension(0));
+
+  VERIFY_IS_EQUAL(forward_output_real_part.dimension(0), input.dimension(0));
+  VERIFY_IS_EQUAL(reverse_output_real_part.dimension(0), input.dimension(0));
+
+  VERIFY_IS_EQUAL(forward_output_imag_part.dimension(0), input.dimension(0));
+  VERIFY_IS_EQUAL(reverse_output_imag_part.dimension(0), input.dimension(0));
+
+  std::complex<float> forward_golden_result[5];
+  std::complex<float> reverse_golden_result[5];
+
+  forward_golden_result[0] = std::complex<float>(15, 0);
+  forward_golden_result[1] = std::complex<float>(-2.5, +3.44095480117793);
+  forward_golden_result[2] = std::complex<float>(-2.5, +0.81229924058227);
+  forward_golden_result[3] = std::complex<float>(-2.5, -0.81229924058227);
+  forward_golden_result[4] = std::complex<float>(-2.5, -3.44095480117793);
+
+  reverse_golden_result[0] = std::complex<float>(3.0, 0);
+  reverse_golden_result[1] = std::complex<float>(-0.5, -0.688190960235587);
+  reverse_golden_result[2] = std::complex<float>(-0.5, -0.162459848116453);
+  reverse_golden_result[3] = std::complex<float>(-0.5, +0.162459848116453);
+  reverse_golden_result[4] = std::complex<float>(-0.5, +0.688190960235587);
+
+  std::complex<float> c_offset(1.0, 1.0);
+  float r_offset = 1.0;
+
+  for (int i = 0; i < 5; ++i) {
+    VERIFY_IS_APPROX(forward_output_both_parts(i) + c_offset, forward_golden_result[i] + c_offset);
+    VERIFY_IS_APPROX(forward_output_real_part(i) + r_offset, forward_golden_result[i].real() + r_offset);
+    VERIFY_IS_APPROX(forward_output_imag_part(i) + r_offset, forward_golden_result[i].imag() + r_offset);
+  }
+
+  for (int i = 0; i < 5; ++i) {
+    VERIFY_IS_APPROX(reverse_output_both_parts(i) + c_offset, reverse_golden_result[i] + c_offset);
+    VERIFY_IS_APPROX(reverse_output_real_part(i) + r_offset, reverse_golden_result[i].real() + r_offset);
+    VERIFY_IS_APPROX(reverse_output_imag_part(i) + r_offset, reverse_golden_result[i].imag() + r_offset);
+  }
+}
+
+template <int DataLayout, typename RealScalar, bool isComplexInput, int FFTResultType, int FFTDirection, int TensorRank>
+static void test_fft_real_input_energy() {
+  Eigen::DSizes<ptrdiff_t, TensorRank> dimensions;
+  ptrdiff_t total_size = 1;
+  for (int i = 0; i < TensorRank; ++i) {
+    dimensions[i] = rand() % 20 + 1;
+    total_size *= dimensions[i];
+  }
+  const DSizes<ptrdiff_t, TensorRank> arr = dimensions;
+
+  typedef std::conditional_t<isComplexInput == true, std::complex<RealScalar>, RealScalar> InputScalar;
+
+  Tensor<InputScalar, TensorRank, DataLayout> input;
+  input.resize(arr);
+  input.setRandom();
+
+  array<ptrdiff_t, TensorRank> fft;
+  for (int i = 0; i < TensorRank; ++i) {
+    fft[i] = i;
+  }
+
+  typedef std::conditional_t<FFTResultType == Eigen::BothParts, std::complex<RealScalar>, RealScalar> OutputScalar;
+  Tensor<OutputScalar, TensorRank, DataLayout> output;
+  output = input.template fft<FFTResultType, FFTDirection>(fft);
+
+  for (int i = 0; i < TensorRank; ++i) {
+    VERIFY_IS_EQUAL(output.dimension(i), input.dimension(i));
+  }
+
+  RealScalar energy_original = 0.0;
+  RealScalar energy_after_fft = 0.0;
+
+  for (int i = 0; i < total_size; ++i) {
+    energy_original += numext::abs2(input(i));
+  }
+
+  for (int i = 0; i < total_size; ++i) {
+    energy_after_fft += numext::abs2(output(i));
+  }
+
+  if (FFTDirection == FFT_FORWARD) {
+    VERIFY_IS_APPROX(energy_original, energy_after_fft / total_size);
+  } else {
+    VERIFY_IS_APPROX(energy_original, energy_after_fft * total_size);
+  }
+}
+
+template <typename RealScalar>
+static void test_fft_non_power_of_2_round_trip(int exponent) {
+  int n = (1 << exponent) + 1;
+
+  Eigen::DSizes<ptrdiff_t, 1> dimensions;
+  dimensions[0] = n;
+  const DSizes<ptrdiff_t, 1> arr = dimensions;
+  Tensor<RealScalar, 1, ColMajor, ptrdiff_t> input;
+
+  input.resize(arr);
+  input.setRandom();
+
+  array<int, 1> fft;
+  fft[0] = 0;
+
+  Tensor<std::complex<RealScalar>, 1, ColMajor> forward = input.template fft<BothParts, FFT_FORWARD>(fft);
+
+  Tensor<RealScalar, 1, ColMajor, ptrdiff_t> output = forward.template fft<RealPart, FFT_REVERSE>(fft);
+
+  for (int i = 0; i < n; ++i) {
+    RealScalar tol = test_precision<RealScalar>() * (std::abs(input[i]) + std::abs(output[i]) + 1);
+    VERIFY_IS_APPROX_OR_LESS_THAN(std::abs(input[i] - output[i]), tol);
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_fft) {
+  test_fft_complex_input_golden();
+  test_fft_real_input_golden();
+
+  test_fft_2D_golden<ColMajor>();
+  test_fft_2D_golden<RowMajor>();
+
+  test_fft_real_input_energy<ColMajor, float, true, Eigen::BothParts, FFT_FORWARD, 1>();
+  test_fft_real_input_energy<ColMajor, double, true, Eigen::BothParts, FFT_FORWARD, 1>();
+  test_fft_real_input_energy<ColMajor, float, false, Eigen::BothParts, FFT_FORWARD, 1>();
+  test_fft_real_input_energy<ColMajor, double, false, Eigen::BothParts, FFT_FORWARD, 1>();
+
+  test_fft_real_input_energy<ColMajor, float, true, Eigen::BothParts, FFT_FORWARD, 2>();
+  test_fft_real_input_energy<ColMajor, double, true, Eigen::BothParts, FFT_FORWARD, 2>();
+  test_fft_real_input_energy<ColMajor, float, false, Eigen::BothParts, FFT_FORWARD, 2>();
+  test_fft_real_input_energy<ColMajor, double, false, Eigen::BothParts, FFT_FORWARD, 2>();
+
+  test_fft_real_input_energy<ColMajor, float, true, Eigen::BothParts, FFT_FORWARD, 3>();
+  test_fft_real_input_energy<ColMajor, double, true, Eigen::BothParts, FFT_FORWARD, 3>();
+  test_fft_real_input_energy<ColMajor, float, false, Eigen::BothParts, FFT_FORWARD, 3>();
+  test_fft_real_input_energy<ColMajor, double, false, Eigen::BothParts, FFT_FORWARD, 3>();
+
+  test_fft_real_input_energy<ColMajor, float, true, Eigen::BothParts, FFT_FORWARD, 4>();
+  test_fft_real_input_energy<ColMajor, double, true, Eigen::BothParts, FFT_FORWARD, 4>();
+  test_fft_real_input_energy<ColMajor, float, false, Eigen::BothParts, FFT_FORWARD, 4>();
+  test_fft_real_input_energy<ColMajor, double, false, Eigen::BothParts, FFT_FORWARD, 4>();
+
+  test_fft_real_input_energy<RowMajor, float, true, Eigen::BothParts, FFT_FORWARD, 1>();
+  test_fft_real_input_energy<RowMajor, double, true, Eigen::BothParts, FFT_FORWARD, 1>();
+  test_fft_real_input_energy<RowMajor, float, false, Eigen::BothParts, FFT_FORWARD, 1>();
+  test_fft_real_input_energy<RowMajor, double, false, Eigen::BothParts, FFT_FORWARD, 1>();
+
+  test_fft_real_input_energy<RowMajor, float, true, Eigen::BothParts, FFT_FORWARD, 2>();
+  test_fft_real_input_energy<RowMajor, double, true, Eigen::BothParts, FFT_FORWARD, 2>();
+  test_fft_real_input_energy<RowMajor, float, false, Eigen::BothParts, FFT_FORWARD, 2>();
+  test_fft_real_input_energy<RowMajor, double, false, Eigen::BothParts, FFT_FORWARD, 2>();
+
+  test_fft_real_input_energy<RowMajor, float, true, Eigen::BothParts, FFT_FORWARD, 3>();
+  test_fft_real_input_energy<RowMajor, double, true, Eigen::BothParts, FFT_FORWARD, 3>();
+  test_fft_real_input_energy<RowMajor, float, false, Eigen::BothParts, FFT_FORWARD, 3>();
+  test_fft_real_input_energy<RowMajor, double, false, Eigen::BothParts, FFT_FORWARD, 3>();
+
+  test_fft_real_input_energy<RowMajor, float, true, Eigen::BothParts, FFT_FORWARD, 4>();
+  test_fft_real_input_energy<RowMajor, double, true, Eigen::BothParts, FFT_FORWARD, 4>();
+  test_fft_real_input_energy<RowMajor, float, false, Eigen::BothParts, FFT_FORWARD, 4>();
+  test_fft_real_input_energy<RowMajor, double, false, Eigen::BothParts, FFT_FORWARD, 4>();
+
+  test_fft_non_power_of_2_round_trip<float>(7);
+  test_fft_non_power_of_2_round_trip<double>(7);
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_fixed_size.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_fixed_size.cpp
new file mode 100644
index 0000000..7cfc18d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_fixed_size.cpp
@@ -0,0 +1,263 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void test_0d() {
+  TensorFixedSize<float, Sizes<> > scalar1;
+  TensorFixedSize<float, Sizes<>, RowMajor> scalar2;
+  VERIFY_IS_EQUAL(scalar1.rank(), 0);
+  VERIFY_IS_EQUAL(scalar1.size(), 1);
+  VERIFY_IS_EQUAL(internal::array_prod(scalar1.dimensions()), 1);
+
+  scalar1() = 7.0;
+  scalar2() = 13.0;
+
+  // Test against shallow copy.
+  TensorFixedSize<float, Sizes<> > copy = scalar1;
+  VERIFY_IS_NOT_EQUAL(scalar1.data(), copy.data());
+  VERIFY_IS_APPROX(scalar1(), copy());
+  copy = scalar1;
+  VERIFY_IS_NOT_EQUAL(scalar1.data(), copy.data());
+  VERIFY_IS_APPROX(scalar1(), copy());
+
+  TensorFixedSize<float, Sizes<> > scalar3 = scalar1.sqrt();
+  TensorFixedSize<float, Sizes<>, RowMajor> scalar4 = scalar2.sqrt();
+  VERIFY_IS_EQUAL(scalar3.rank(), 0);
+  VERIFY_IS_APPROX(scalar3(), sqrtf(7.0));
+  VERIFY_IS_APPROX(scalar4(), sqrtf(13.0));
+
+  scalar3 = scalar1 + scalar2;
+  VERIFY_IS_APPROX(scalar3(), 7.0f + 13.0f);
+}
+
+static void test_1d() {
+  TensorFixedSize<float, Sizes<6> > vec1;
+  TensorFixedSize<float, Sizes<6>, RowMajor> vec2;
+
+  VERIFY_IS_EQUAL((vec1.size()), 6);
+  //  VERIFY_IS_EQUAL((vec1.dimensions()[0]), 6);
+  //  VERIFY_IS_EQUAL((vec1.dimension(0)), 6);
+
+  vec1(0) = 4.0;
+  vec2(0) = 0.0;
+  vec1(1) = 8.0;
+  vec2(1) = 1.0;
+  vec1(2) = 15.0;
+  vec2(2) = 2.0;
+  vec1(3) = 16.0;
+  vec2(3) = 3.0;
+  vec1(4) = 23.0;
+  vec2(4) = 4.0;
+  vec1(5) = 42.0;
+  vec2(5) = 5.0;
+
+  // Test against shallow copy.
+  TensorFixedSize<float, Sizes<6> > copy = vec1;
+  VERIFY_IS_NOT_EQUAL(vec1.data(), copy.data());
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_APPROX(vec1(i), copy(i));
+  }
+  copy = vec1;
+  VERIFY_IS_NOT_EQUAL(vec1.data(), copy.data());
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_APPROX(vec1(i), copy(i));
+  }
+
+  TensorFixedSize<float, Sizes<6> > vec3 = vec1.sqrt();
+  TensorFixedSize<float, Sizes<6>, RowMajor> vec4 = vec2.sqrt();
+
+  VERIFY_IS_EQUAL((vec3.size()), 6);
+  VERIFY_IS_EQUAL(vec3.rank(), 1);
+  //  VERIFY_IS_EQUAL((vec3.dimensions()[0]), 6);
+  //  VERIFY_IS_EQUAL((vec3.dimension(0)), 6);
+
+  VERIFY_IS_APPROX(vec3(0), sqrtf(4.0));
+  VERIFY_IS_APPROX(vec3(1), sqrtf(8.0));
+  VERIFY_IS_APPROX(vec3(2), sqrtf(15.0));
+  VERIFY_IS_APPROX(vec3(3), sqrtf(16.0));
+  VERIFY_IS_APPROX(vec3(4), sqrtf(23.0));
+  VERIFY_IS_APPROX(vec3(5), sqrtf(42.0));
+
+  VERIFY_IS_APPROX(vec4(0), sqrtf(0.0));
+  VERIFY_IS_APPROX(vec4(1), sqrtf(1.0));
+  VERIFY_IS_APPROX(vec4(2), sqrtf(2.0));
+  VERIFY_IS_APPROX(vec4(3), sqrtf(3.0));
+  VERIFY_IS_APPROX(vec4(4), sqrtf(4.0));
+  VERIFY_IS_APPROX(vec4(5), sqrtf(5.0));
+
+  vec3 = vec1 + vec2;
+  VERIFY_IS_APPROX(vec3(0), 4.0f + 0.0f);
+  VERIFY_IS_APPROX(vec3(1), 8.0f + 1.0f);
+  VERIFY_IS_APPROX(vec3(2), 15.0f + 2.0f);
+  VERIFY_IS_APPROX(vec3(3), 16.0f + 3.0f);
+  VERIFY_IS_APPROX(vec3(4), 23.0f + 4.0f);
+  VERIFY_IS_APPROX(vec3(5), 42.0f + 5.0f);
+}
+
+static void test_tensor_map() {
+  TensorFixedSize<float, Sizes<6> > vec1;
+  TensorFixedSize<float, Sizes<6>, RowMajor> vec2;
+
+  vec1(0) = 4.0;
+  vec2(0) = 0.0;
+  vec1(1) = 8.0;
+  vec2(1) = 1.0;
+  vec1(2) = 15.0;
+  vec2(2) = 2.0;
+  vec1(3) = 16.0;
+  vec2(3) = 3.0;
+  vec1(4) = 23.0;
+  vec2(4) = 4.0;
+  vec1(5) = 42.0;
+  vec2(5) = 5.0;
+
+  float data3[6];
+  TensorMap<TensorFixedSize<float, Sizes<6> > > vec3(data3, 6);
+  vec3 = vec1.sqrt() + vec2;
+
+  VERIFY_IS_APPROX(vec3(0), sqrtf(4.0));
+  VERIFY_IS_APPROX(vec3(1), sqrtf(8.0) + 1.0f);
+  VERIFY_IS_APPROX(vec3(2), sqrtf(15.0) + 2.0f);
+  VERIFY_IS_APPROX(vec3(3), sqrtf(16.0) + 3.0f);
+  VERIFY_IS_APPROX(vec3(4), sqrtf(23.0) + 4.0f);
+  VERIFY_IS_APPROX(vec3(5), sqrtf(42.0) + 5.0f);
+}
+
+static void test_2d() {
+  float data1[6];
+  TensorMap<TensorFixedSize<float, Sizes<2, 3> > > mat1(data1, 2, 3);
+  float data2[6];
+  TensorMap<TensorFixedSize<float, Sizes<2, 3>, RowMajor> > mat2(data2, 2, 3);
+
+  VERIFY_IS_EQUAL((mat1.size()), 2 * 3);
+  VERIFY_IS_EQUAL(mat1.rank(), 2);
+  //  VERIFY_IS_EQUAL((mat1.dimension(0)), 2);
+  //  VERIFY_IS_EQUAL((mat1.dimension(1)), 3);
+
+  mat1(0, 0) = 0.0;
+  mat1(0, 1) = 1.0;
+  mat1(0, 2) = 2.0;
+  mat1(1, 0) = 3.0;
+  mat1(1, 1) = 4.0;
+  mat1(1, 2) = 5.0;
+
+  mat2(0, 0) = -0.0;
+  mat2(0, 1) = -1.0;
+  mat2(0, 2) = -2.0;
+  mat2(1, 0) = -3.0;
+  mat2(1, 1) = -4.0;
+  mat2(1, 2) = -5.0;
+
+  TensorFixedSize<float, Sizes<2, 3> > mat3;
+  TensorFixedSize<float, Sizes<2, 3>, RowMajor> mat4;
+  mat3 = mat1.abs();
+  mat4 = mat2.abs();
+
+  VERIFY_IS_EQUAL((mat3.size()), 2 * 3);
+  //  VERIFY_IS_EQUAL((mat3.dimension(0)), 2);
+  //  VERIFY_IS_EQUAL((mat3.dimension(1)), 3);
+
+  VERIFY_IS_APPROX(mat3(0, 0), 0.0f);
+  VERIFY_IS_APPROX(mat3(0, 1), 1.0f);
+  VERIFY_IS_APPROX(mat3(0, 2), 2.0f);
+  VERIFY_IS_APPROX(mat3(1, 0), 3.0f);
+  VERIFY_IS_APPROX(mat3(1, 1), 4.0f);
+  VERIFY_IS_APPROX(mat3(1, 2), 5.0f);
+
+  VERIFY_IS_APPROX(mat4(0, 0), 0.0f);
+  VERIFY_IS_APPROX(mat4(0, 1), 1.0f);
+  VERIFY_IS_APPROX(mat4(0, 2), 2.0f);
+  VERIFY_IS_APPROX(mat4(1, 0), 3.0f);
+  VERIFY_IS_APPROX(mat4(1, 1), 4.0f);
+  VERIFY_IS_APPROX(mat4(1, 2), 5.0f);
+}
+
+static void test_3d() {
+  TensorFixedSize<float, Sizes<2, 3, 7> > mat1;
+  TensorFixedSize<float, Sizes<2, 3, 7>, RowMajor> mat2;
+
+  VERIFY_IS_EQUAL((mat1.size()), 2 * 3 * 7);
+  VERIFY_IS_EQUAL(mat1.rank(), 3);
+  //  VERIFY_IS_EQUAL((mat1.dimension(0)), 2);
+  //  VERIFY_IS_EQUAL((mat1.dimension(1)), 3);
+  //  VERIFY_IS_EQUAL((mat1.dimension(2)), 7);
+
+  float val = 0.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        mat1(i, j, k) = val;
+        mat2(i, j, k) = val;
+        val += 1.0f;
+      }
+    }
+  }
+
+  TensorFixedSize<float, Sizes<2, 3, 7> > mat3;
+  mat3 = mat1.sqrt();
+  TensorFixedSize<float, Sizes<2, 3, 7>, RowMajor> mat4;
+  mat4 = mat2.sqrt();
+
+  VERIFY_IS_EQUAL((mat3.size()), 2 * 3 * 7);
+  //  VERIFY_IS_EQUAL((mat3.dimension(0)), 2);
+  //  VERIFY_IS_EQUAL((mat3.dimension(1)), 3);
+  //  VERIFY_IS_EQUAL((mat3.dimension(2)), 7);
+
+  val = 0.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(mat3(i, j, k), sqrtf(val));
+        VERIFY_IS_APPROX(mat4(i, j, k), sqrtf(val));
+        val += 1.0f;
+      }
+    }
+  }
+}
+
+static void test_array() {
+  TensorFixedSize<float, Sizes<2, 3, 7> > mat1;
+  float val = 0.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        mat1(i, j, k) = val;
+        val += 1.0f;
+      }
+    }
+  }
+
+  TensorFixedSize<float, Sizes<2, 3, 7> > mat3;
+  mat3 = mat1.pow(3.5f);
+
+  val = 0.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(mat3(i, j, k), powf(val, 3.5f));
+        val += 1.0f;
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_fixed_size) {
+  CALL_SUBTEST(test_0d());
+  CALL_SUBTEST(test_1d());
+  CALL_SUBTEST(test_tensor_map());
+  CALL_SUBTEST(test_2d());
+  CALL_SUBTEST(test_3d());
+  CALL_SUBTEST(test_array());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_forced_eval.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_forced_eval.cpp
new file mode 100644
index 0000000..4eb7d91
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_forced_eval.cpp
@@ -0,0 +1,75 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/Core>
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::MatrixXf;
+using Eigen::Tensor;
+
+static void test_simple() {
+  MatrixXf m1(3, 3);
+  MatrixXf m2(3, 3);
+  m1.setRandom();
+  m2.setRandom();
+
+  TensorMap<Tensor<float, 2> > mat1(m1.data(), 3, 3);
+  TensorMap<Tensor<float, 2> > mat2(m2.data(), 3, 3);
+
+  Tensor<float, 2> mat3(3, 3);
+  mat3 = mat1;
+
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+  Eigen::array<DimPair, 1> dims;
+  dims[0] = DimPair(1, 0);
+
+  mat3 = mat3.contract(mat2, dims).eval();
+
+  VERIFY_IS_APPROX(mat3(0, 0), (m1 * m2).eval()(0, 0));
+  VERIFY_IS_APPROX(mat3(0, 1), (m1 * m2).eval()(0, 1));
+  VERIFY_IS_APPROX(mat3(0, 2), (m1 * m2).eval()(0, 2));
+  VERIFY_IS_APPROX(mat3(1, 0), (m1 * m2).eval()(1, 0));
+  VERIFY_IS_APPROX(mat3(1, 1), (m1 * m2).eval()(1, 1));
+  VERIFY_IS_APPROX(mat3(1, 2), (m1 * m2).eval()(1, 2));
+  VERIFY_IS_APPROX(mat3(2, 0), (m1 * m2).eval()(2, 0));
+  VERIFY_IS_APPROX(mat3(2, 1), (m1 * m2).eval()(2, 1));
+  VERIFY_IS_APPROX(mat3(2, 2), (m1 * m2).eval()(2, 2));
+}
+
+static void test_const() {
+  MatrixXf input(3, 3);
+  input.setRandom();
+  MatrixXf output = input;
+  output.rowwise() -= input.colwise().maxCoeff();
+
+  Eigen::array<int, 1> depth_dim;
+  depth_dim[0] = 0;
+  Tensor<float, 2>::Dimensions dims2d;
+  dims2d[0] = 1;
+  dims2d[1] = 3;
+  Eigen::array<int, 2> bcast;
+  bcast[0] = 3;
+  bcast[1] = 1;
+  const TensorMap<const Tensor<float, 2> > input_tensor(input.data(), 3, 3);
+  Tensor<float, 2> output_tensor =
+      (input_tensor - input_tensor.maximum(depth_dim).eval().reshape(dims2d).broadcast(bcast));
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_APPROX(output(i, j), output_tensor(i, j));
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_forced_eval) {
+  CALL_SUBTEST(test_simple());
+  CALL_SUBTEST(test_const());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
new file mode 100644
index 0000000..19a3c79
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
@@ -0,0 +1,78 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+template <typename DataType, int DataLayout, typename IndexType>
+void test_forced_eval_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 100;
+  IndexType sizeDim2 = 20;
+  IndexType sizeDim3 = 20;
+  Eigen::array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  Eigen::Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
+  Eigen::Tensor<DataType, 3, DataLayout, IndexType> in2(tensorRange);
+  Eigen::Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
+
+  DataType* gpu_in1_data =
+      static_cast<DataType*>(sycl_device.allocate(in1.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_in2_data =
+      static_cast<DataType*>(sycl_device.allocate(in2.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(DataType)));
+
+  in1 = in1.random() + in1.constant(static_cast<DataType>(10.0f));
+  in2 = in2.random() + in2.constant(static_cast<DataType>(10.0f));
+
+  // creating TensorMap from tensor
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
+  sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(), (in1.dimensions().TotalSize()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(), (in2.dimensions().TotalSize()) * sizeof(DataType));
+  /// c=(a+b)*b
+  gpu_out.device(sycl_device) = (gpu_in1 + gpu_in2).eval() * gpu_in2;
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(DataType));
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) + in2(i, j, k)) * in2(i, j, k));
+      }
+    }
+  }
+  printf("(a+b)*b Test Passed\n");
+  sycl_device.deallocate(gpu_in1_data);
+  sycl_device.deallocate(gpu_in2_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, typename Dev_selector>
+void tensorForced_evalperDevice(Dev_selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_forced_eval_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_forced_eval_sycl<DataType, ColMajor, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_forced_eval_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(tensorForced_evalperDevice<float>(device));
+    CALL_SUBTEST(tensorForced_evalperDevice<half>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_generator.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_generator.cpp
new file mode 100644
index 0000000..e4aec1b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_generator.cpp
@@ -0,0 +1,82 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+struct Generator1D {
+  Generator1D() {}
+
+  float operator()(const array<Eigen::DenseIndex, 1>& coordinates) const { return coordinates[0]; }
+};
+
+template <int DataLayout>
+static void test_1D() {
+  Tensor<float, 1> vec(6);
+  Tensor<float, 1> result = vec.generate(Generator1D());
+
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_EQUAL(result(i), i);
+  }
+}
+
+struct Generator2D {
+  Generator2D() {}
+
+  float operator()(const array<Eigen::DenseIndex, 2>& coordinates) const {
+    return 3 * coordinates[0] + 11 * coordinates[1];
+  }
+};
+
+template <int DataLayout>
+static void test_2D() {
+  Tensor<float, 2> matrix(512, 512);
+  Tensor<float, 2> result = matrix.generate(Generator2D());
+
+  for (int i = 0; i < 512; ++i) {
+    for (int j = 0; j < 512; ++j) {
+      VERIFY_IS_EQUAL(result(i, j), 3 * i + 11 * j);
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_gaussian() {
+  int rows = 32;
+  int cols = 48;
+  array<float, 2> means;
+  means[0] = rows / 2.0f;
+  means[1] = cols / 2.0f;
+  array<float, 2> std_devs;
+  std_devs[0] = 3.14f;
+  std_devs[1] = 2.7f;
+  internal::GaussianGenerator<float, Eigen::DenseIndex, 2> gaussian_gen(means, std_devs);
+
+  Tensor<float, 2> matrix(rows, cols);
+  Tensor<float, 2> result = matrix.generate(gaussian_gen);
+
+  for (int i = 0; i < rows; ++i) {
+    for (int j = 0; j < cols; ++j) {
+      float g_rows = powf(rows / 2.0f - i, 2) / (3.14f * 3.14f) * 0.5f;
+      float g_cols = powf(cols / 2.0f - j, 2) / (2.7f * 2.7f) * 0.5f;
+      float gaussian = expf(-g_rows - g_cols);
+      VERIFY_IS_EQUAL(result(i, j), gaussian);
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_generator) {
+  CALL_SUBTEST(test_1D<ColMajor>());
+  CALL_SUBTEST(test_1D<RowMajor>());
+  CALL_SUBTEST(test_2D<ColMajor>());
+  CALL_SUBTEST(test_2D<RowMajor>());
+  CALL_SUBTEST(test_gaussian<ColMajor>());
+  CALL_SUBTEST(test_gaussian<RowMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_generator_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_generator_sycl.cpp
new file mode 100644
index 0000000..335bf25
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_generator_sycl.cpp
@@ -0,0 +1,140 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+static const float error_threshold = 1e-8f;
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+struct Generator1D {
+  Generator1D() {}
+
+  float operator()(const array<Eigen::DenseIndex, 1>& coordinates) const { return coordinates[0]; }
+};
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_1D_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 6;
+  array<IndexType, 1> tensorRange = {{sizeDim1}};
+  Tensor<DataType, 1, DataLayout, IndexType> vec(tensorRange);
+  Tensor<DataType, 1, DataLayout, IndexType> result(tensorRange);
+
+  const size_t tensorBuffSize = vec.size() * sizeof(DataType);
+  DataType* gpu_data_vec = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+
+  TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> gpu_vec(gpu_data_vec, tensorRange);
+  TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> gpu_result(gpu_data_result, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_vec, vec.data(), tensorBuffSize);
+  gpu_result.device(sycl_device) = gpu_vec.generate(Generator1D());
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
+
+  for (IndexType i = 0; i < 6; ++i) {
+    VERIFY_IS_EQUAL(result(i), i);
+  }
+}
+
+struct Generator2D {
+  Generator2D() {}
+
+  float operator()(const array<Eigen::DenseIndex, 2>& coordinates) const {
+    return 3 * coordinates[0] + 11 * coordinates[1];
+  }
+};
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_2D_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 5;
+  IndexType sizeDim2 = 7;
+  array<IndexType, 2> tensorRange = {{sizeDim1, sizeDim2}};
+  Tensor<DataType, 2, DataLayout, IndexType> matrix(tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> result(tensorRange);
+
+  const size_t tensorBuffSize = matrix.size() * sizeof(DataType);
+  DataType* gpu_data_matrix = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_matrix(gpu_data_matrix, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_result(gpu_data_result, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_matrix, matrix.data(), tensorBuffSize);
+  gpu_result.device(sycl_device) = gpu_matrix.generate(Generator2D());
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
+
+  for (IndexType i = 0; i < 5; ++i) {
+    for (IndexType j = 0; j < 5; ++j) {
+      VERIFY_IS_EQUAL(result(i, j), 3 * i + 11 * j);
+    }
+  }
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_gaussian_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType rows = 32;
+  IndexType cols = 48;
+  array<DataType, 2> means;
+  means[0] = rows / 2.0f;
+  means[1] = cols / 2.0f;
+  array<DataType, 2> std_devs;
+  std_devs[0] = 3.14f;
+  std_devs[1] = 2.7f;
+  internal::GaussianGenerator<DataType, Eigen::DenseIndex, 2> gaussian_gen(means, std_devs);
+
+  array<IndexType, 2> tensorRange = {{rows, cols}};
+  Tensor<DataType, 2, DataLayout, IndexType> matrix(tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> result(tensorRange);
+
+  const size_t tensorBuffSize = matrix.size() * sizeof(DataType);
+  DataType* gpu_data_matrix = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_matrix(gpu_data_matrix, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_result(gpu_data_result, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_matrix, matrix.data(), tensorBuffSize);
+  gpu_result.device(sycl_device) = gpu_matrix.generate(gaussian_gen);
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize);
+
+  for (IndexType i = 0; i < rows; ++i) {
+    for (IndexType j = 0; j < cols; ++j) {
+      DataType g_rows = powf(rows / 2.0f - i, 2) / (3.14f * 3.14f) * 0.5f;
+      DataType g_cols = powf(cols / 2.0f - j, 2) / (2.7f * 2.7f) * 0.5f;
+      DataType gaussian = expf(-g_rows - g_cols);
+      Eigen::internal::isApprox(result(i, j), gaussian, error_threshold);
+    }
+  }
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_generator_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_1D_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_1D_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_2D_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_2D_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_gaussian_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_gaussian_sycl<DataType, ColMajor, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_generator_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_generator_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_gpu.cu
new file mode 100644
index 0000000..c4d0be1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_gpu.cu
@@ -0,0 +1,1561 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+void test_gpu_nullary() {
+  Tensor<float, 1, 0, int> in1(2);
+  Tensor<float, 1, 0, int> in2(2);
+  in1.setRandom();
+  in2.setRandom();
+
+  std::size_t tensor_bytes = in1.size() * sizeof(float);
+
+  float* d_in1;
+  float* d_in2;
+  gpuMalloc((void**)(&d_in1), tensor_bytes);
+  gpuMalloc((void**)(&d_in2), tensor_bytes);
+  gpuMemcpy(d_in1, in1.data(), tensor_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_in2, in2.data(), tensor_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_in1(d_in1, 2);
+  Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_in2(d_in2, 2);
+
+  gpu_in1.device(gpu_device) = gpu_in1.constant(3.14f);
+  gpu_in2.device(gpu_device) = gpu_in2.random();
+
+  Tensor<float, 1, 0, int> new1(2);
+  Tensor<float, 1, 0, int> new2(2);
+
+  assert(gpuMemcpyAsync(new1.data(), d_in1, tensor_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuMemcpyAsync(new2.data(), d_in2, tensor_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 2; ++i) {
+    VERIFY_IS_APPROX(new1(i), 3.14f);
+    VERIFY_IS_NOT_EQUAL(new2(i), in2(i));
+  }
+
+  gpuFree(d_in1);
+  gpuFree(d_in2);
+}
+
+// Tests that there are no indexing overflows when computing tensors with the
+// max representable size.
+template <typename IndexType, IndexType N = (std::numeric_limits<IndexType>::max)()>
+void test_gpu_nullary_max_size() {
+  typedef int8_t DataType;
+  typedef Tensor<DataType, 1, 0, IndexType> TensorType;
+  typedef Eigen::array<IndexType, 1> ArrayType;
+
+  const IndexType n = N;
+  TensorType in1((ArrayType(n)));
+  in1.setZero();
+
+  std::size_t in1_bytes = in1.size() * sizeof(DataType);
+
+  DataType* d_in1;
+  gpuMalloc((void**)(&d_in1), in1_bytes);
+
+  gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<TensorType> gpu_in1(d_in1, ArrayType(n));
+
+  gpu_in1.device(gpu_device) = gpu_in1.constant(123);
+
+  TensorType new1((ArrayType(n)));
+
+  assert(gpuMemcpyAsync(new1.data(), d_in1, in1_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (IndexType i = 0; i < n; ++i) {
+    VERIFY_IS_EQUAL(new1(ArrayType(i)), 123);
+  }
+
+  gpuFree(d_in1);
+}
+
+void test_gpu_elementwise_small() {
+  Tensor<float, 1> in1(Eigen::array<Eigen::DenseIndex, 1>{2});
+  Tensor<float, 1> in2(Eigen::array<Eigen::DenseIndex, 1>{2});
+  Tensor<float, 1> out(Eigen::array<Eigen::DenseIndex, 1>{2});
+  in1.setRandom();
+  in2.setRandom();
+
+  std::size_t in1_bytes = in1.size() * sizeof(float);
+  std::size_t in2_bytes = in2.size() * sizeof(float);
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_in1;
+  float* d_in2;
+  float* d_out;
+  gpuMalloc((void**)(&d_in1), in1_bytes);
+  gpuMalloc((void**)(&d_in2), in2_bytes);
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_in2, in2.data(), in2_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in1(d_in1, Eigen::array<Eigen::DenseIndex, 1>{2});
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in2(d_in2, Eigen::array<Eigen::DenseIndex, 1>{2});
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_out(d_out, Eigen::array<Eigen::DenseIndex, 1>{2});
+
+  gpu_out.device(gpu_device) = gpu_in1 + gpu_in2;
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 2; ++i) {
+    VERIFY_IS_APPROX(out(Eigen::array<Eigen::DenseIndex, 1>{i}),
+                     in1(Eigen::array<Eigen::DenseIndex, 1>{i}) + in2(Eigen::array<Eigen::DenseIndex, 1>{i}));
+  }
+
+  gpuFree(d_in1);
+  gpuFree(d_in2);
+  gpuFree(d_out);
+}
+
+void test_gpu_elementwise() {
+  Tensor<float, 3> in1(Eigen::array<Eigen::DenseIndex, 3>{72, 53, 97});
+  Tensor<float, 3> in2(Eigen::array<Eigen::DenseIndex, 3>{72, 53, 97});
+  Tensor<float, 3> in3(Eigen::array<Eigen::DenseIndex, 3>{72, 53, 97});
+  Tensor<float, 3> out(Eigen::array<Eigen::DenseIndex, 3>{72, 53, 97});
+  in1.setRandom();
+  in2.setRandom();
+  in3.setRandom();
+
+  std::size_t in1_bytes = in1.size() * sizeof(float);
+  std::size_t in2_bytes = in2.size() * sizeof(float);
+  std::size_t in3_bytes = in3.size() * sizeof(float);
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_in1;
+  float* d_in2;
+  float* d_in3;
+  float* d_out;
+  gpuMalloc((void**)(&d_in1), in1_bytes);
+  gpuMalloc((void**)(&d_in2), in2_bytes);
+  gpuMalloc((void**)(&d_in3), in3_bytes);
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_in2, in2.data(), in2_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_in3, in3.data(), in3_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in1(d_in1, Eigen::array<Eigen::DenseIndex, 3>{72, 53, 97});
+  Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, Eigen::array<Eigen::DenseIndex, 3>{72, 53, 97});
+  Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in3(d_in3, Eigen::array<Eigen::DenseIndex, 3>{72, 53, 97});
+  Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, Eigen::array<Eigen::DenseIndex, 3>{72, 53, 97});
+
+  gpu_out.device(gpu_device) = gpu_in1 + gpu_in2 * gpu_in3;
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 72; ++i) {
+    for (int j = 0; j < 53; ++j) {
+      for (int k = 0; k < 97; ++k) {
+        VERIFY_IS_APPROX(
+            out(Eigen::array<Eigen::DenseIndex, 3>{i, j, k}),
+            in1(Eigen::array<Eigen::DenseIndex, 3>{i, j, k}) +
+                in2(Eigen::array<Eigen::DenseIndex, 3>{i, j, k}) * in3(Eigen::array<Eigen::DenseIndex, 3>{i, j, k}));
+      }
+    }
+  }
+
+  gpuFree(d_in1);
+  gpuFree(d_in2);
+  gpuFree(d_in3);
+  gpuFree(d_out);
+}
+
+void test_gpu_props() {
+  Tensor<float, 1> in1(200);
+  Tensor<bool, 1> out(200);
+  in1.setRandom();
+
+  std::size_t in1_bytes = in1.size() * sizeof(float);
+  std::size_t out_bytes = out.size() * sizeof(bool);
+
+  float* d_in1;
+  bool* d_out;
+  gpuMalloc((void**)(&d_in1), in1_bytes);
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_in1(d_in1, 200);
+  Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_out(d_out, 200);
+
+  gpu_out.device(gpu_device) = (gpu_in1.isnan)();
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 200; ++i) {
+    VERIFY_IS_EQUAL(out(i), (std::isnan)(in1(i)));
+  }
+
+  gpuFree(d_in1);
+  gpuFree(d_out);
+}
+
+void test_gpu_reduction() {
+  Tensor<float, 4> in1(72, 53, 97, 113);
+  Tensor<float, 2> out(72, 97);
+  in1.setRandom();
+
+  std::size_t in1_bytes = in1.size() * sizeof(float);
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_in1;
+  float* d_out;
+  gpuMalloc((void**)(&d_in1), in1_bytes);
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 4> > gpu_in1(d_in1, 72, 53, 97, 113);
+  Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72, 97);
+
+  array<Eigen::DenseIndex, 2> reduction_axis;
+  reduction_axis[0] = 1;
+  reduction_axis[1] = 3;
+
+  gpu_out.device(gpu_device) = gpu_in1.maximum(reduction_axis);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 72; ++i) {
+    for (int j = 0; j < 97; ++j) {
+      float expected = 0;
+      for (int k = 0; k < 53; ++k) {
+        for (int l = 0; l < 113; ++l) {
+          expected = std::max<float>(expected, in1(i, k, j, l));
+        }
+      }
+      VERIFY_IS_APPROX(out(i, j), expected);
+    }
+  }
+
+  gpuFree(d_in1);
+  gpuFree(d_out);
+}
+
+template <int DataLayout>
+void test_gpu_contraction() {
+  // with these dimensions, the output has 300 * 140 elements, which is
+  // more than 30 * 1024, which is the number of threads in blocks on
+  // a 15 SM GK110 GPU
+  Tensor<float, 4, DataLayout> t_left(6, 50, 3, 31);
+  Tensor<float, 5, DataLayout> t_right(Eigen::array<Eigen::DenseIndex, 5>{3, 31, 7, 20, 1});
+  Tensor<float, 5, DataLayout> t_result(Eigen::array<Eigen::DenseIndex, 5>{6, 50, 7, 20, 1});
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  std::size_t t_left_bytes = t_left.size() * sizeof(float);
+  std::size_t t_right_bytes = t_right.size() * sizeof(float);
+  std::size_t t_result_bytes = t_result.size() * sizeof(float);
+
+  float* d_t_left;
+  float* d_t_right;
+  float* d_t_result;
+
+  gpuMalloc((void**)(&d_t_left), t_left_bytes);
+  gpuMalloc((void**)(&d_t_right), t_right_bytes);
+  gpuMalloc((void**)(&d_t_result), t_result_bytes);
+
+  gpuMemcpy(d_t_left, t_left.data(), t_left_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_t_right, t_right.data(), t_right_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_t_left(d_t_left, 6, 50, 3, 31);
+  Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_t_right(d_t_right, 3, 31, 7, 20, 1);
+  Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_t_result(d_t_result, 6, 50, 7, 20, 1);
+
+  typedef Eigen::Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> > MapXf;
+  MapXf m_left(t_left.data(), 300, 93);
+  MapXf m_right(t_right.data(), 93, 140);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(300, 140);
+
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+  Eigen::array<DimPair, 2> dims;
+  dims[0] = DimPair(2, 0);
+  dims[1] = DimPair(3, 1);
+
+  m_result = m_left * m_right;
+  gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims);
+
+  gpuMemcpy(t_result.data(), d_t_result, t_result_bytes, gpuMemcpyDeviceToHost);
+
+  for (DenseIndex i = 0; i < t_result.size(); i++) {
+    if (fabs(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) {
+      std::cout << "mismatch detected at index " << i << ": " << t_result.data()[i] << " vs " << m_result.data()[i]
+                << std::endl;
+      assert(false);
+    }
+  }
+
+  gpuFree(d_t_left);
+  gpuFree(d_t_right);
+  gpuFree(d_t_result);
+}
+
+template <int DataLayout>
+void test_gpu_convolution_1d() {
+  Tensor<float, 4, DataLayout> input(74, 37, 11, 137);
+  Tensor<float, 1, DataLayout> kernel(4);
+  Tensor<float, 4, DataLayout> out(74, 34, 11, 137);
+  input = input.constant(10.0f) + input.random();
+  kernel = kernel.constant(7.0f) + kernel.random();
+
+  std::size_t input_bytes = input.size() * sizeof(float);
+  std::size_t kernel_bytes = kernel.size() * sizeof(float);
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_input;
+  float* d_kernel;
+  float* d_out;
+  gpuMalloc((void**)(&d_input), input_bytes);
+  gpuMalloc((void**)(&d_kernel), kernel_bytes);
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  gpuMemcpy(d_input, input.data(), input_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_kernel, kernel.data(), kernel_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_input(d_input, 74, 37, 11, 137);
+  Eigen::TensorMap<Eigen::Tensor<float, 1, DataLayout> > gpu_kernel(d_kernel, 4);
+  Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_out(d_out, 74, 34, 11, 137);
+
+  Eigen::array<Eigen::DenseIndex, 1> dims{1};
+  gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 74; ++i) {
+    for (int j = 0; j < 34; ++j) {
+      for (int k = 0; k < 11; ++k) {
+        for (int l = 0; l < 137; ++l) {
+          const float result = out(i, j, k, l);
+          const float expected = input(i, j + 0, k, l) * kernel(0) + input(i, j + 1, k, l) * kernel(1) +
+                                 input(i, j + 2, k, l) * kernel(2) + input(i, j + 3, k, l) * kernel(3);
+          VERIFY_IS_APPROX(result, expected);
+        }
+      }
+    }
+  }
+
+  gpuFree(d_input);
+  gpuFree(d_kernel);
+  gpuFree(d_out);
+}
+
+void test_gpu_convolution_inner_dim_col_major_1d() {
+  Tensor<float, 4, ColMajor> input(74, 9, 11, 7);
+  Tensor<float, 1, ColMajor> kernel(4);
+  Tensor<float, 4, ColMajor> out(71, 9, 11, 7);
+  input = input.constant(10.0f) + input.random();
+  kernel = kernel.constant(7.0f) + kernel.random();
+
+  std::size_t input_bytes = input.size() * sizeof(float);
+  std::size_t kernel_bytes = kernel.size() * sizeof(float);
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_input;
+  float* d_kernel;
+  float* d_out;
+  gpuMalloc((void**)(&d_input), input_bytes);
+  gpuMalloc((void**)(&d_kernel), kernel_bytes);
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  gpuMemcpy(d_input, input.data(), input_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_kernel, kernel.data(), kernel_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 4, ColMajor> > gpu_input(d_input, 74, 9, 11, 7);
+  Eigen::TensorMap<Eigen::Tensor<float, 1, ColMajor> > gpu_kernel(d_kernel, 4);
+  Eigen::TensorMap<Eigen::Tensor<float, 4, ColMajor> > gpu_out(d_out, 71, 9, 11, 7);
+
+  Eigen::array<Eigen::DenseIndex, 1> dims{0};
+  gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 71; ++i) {
+    for (int j = 0; j < 9; ++j) {
+      for (int k = 0; k < 11; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          const float result = out(i, j, k, l);
+          const float expected = input(i + 0, j, k, l) * kernel(0) + input(i + 1, j, k, l) * kernel(1) +
+                                 input(i + 2, j, k, l) * kernel(2) + input(i + 3, j, k, l) * kernel(3);
+          VERIFY_IS_APPROX(result, expected);
+        }
+      }
+    }
+  }
+
+  gpuFree(d_input);
+  gpuFree(d_kernel);
+  gpuFree(d_out);
+}
+
+void test_gpu_convolution_inner_dim_row_major_1d() {
+  Tensor<float, 4, RowMajor> input(7, 9, 11, 74);
+  Tensor<float, 1, RowMajor> kernel(4);
+  Tensor<float, 4, RowMajor> out(7, 9, 11, 71);
+  input = input.constant(10.0f) + input.random();
+  kernel = kernel.constant(7.0f) + kernel.random();
+
+  std::size_t input_bytes = input.size() * sizeof(float);
+  std::size_t kernel_bytes = kernel.size() * sizeof(float);
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_input;
+  float* d_kernel;
+  float* d_out;
+  gpuMalloc((void**)(&d_input), input_bytes);
+  gpuMalloc((void**)(&d_kernel), kernel_bytes);
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  gpuMemcpy(d_input, input.data(), input_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_kernel, kernel.data(), kernel_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 4, RowMajor> > gpu_input(d_input, 7, 9, 11, 74);
+  Eigen::TensorMap<Eigen::Tensor<float, 1, RowMajor> > gpu_kernel(d_kernel, 4);
+  Eigen::TensorMap<Eigen::Tensor<float, 4, RowMajor> > gpu_out(d_out, 7, 9, 11, 71);
+
+  Eigen::array<Eigen::DenseIndex, 1> dims{3};
+  gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 7; ++i) {
+    for (int j = 0; j < 9; ++j) {
+      for (int k = 0; k < 11; ++k) {
+        for (int l = 0; l < 71; ++l) {
+          const float result = out(i, j, k, l);
+          const float expected = input(i, j, k, l + 0) * kernel(0) + input(i, j, k, l + 1) * kernel(1) +
+                                 input(i, j, k, l + 2) * kernel(2) + input(i, j, k, l + 3) * kernel(3);
+          VERIFY_IS_APPROX(result, expected);
+        }
+      }
+    }
+  }
+
+  gpuFree(d_input);
+  gpuFree(d_kernel);
+  gpuFree(d_out);
+}
+
+template <int DataLayout>
+void test_gpu_convolution_2d() {
+  Tensor<float, 4, DataLayout> input(74, 37, 11, 137);
+  Tensor<float, 2, DataLayout> kernel(3, 4);
+  Tensor<float, 4, DataLayout> out(74, 35, 8, 137);
+  input = input.constant(10.0f) + input.random();
+  kernel = kernel.constant(7.0f) + kernel.random();
+
+  std::size_t input_bytes = input.size() * sizeof(float);
+  std::size_t kernel_bytes = kernel.size() * sizeof(float);
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_input;
+  float* d_kernel;
+  float* d_out;
+  gpuMalloc((void**)(&d_input), input_bytes);
+  gpuMalloc((void**)(&d_kernel), kernel_bytes);
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  gpuMemcpy(d_input, input.data(), input_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_kernel, kernel.data(), kernel_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_input(d_input, 74, 37, 11, 137);
+  Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> > gpu_kernel(d_kernel, 3, 4);
+  Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout> > gpu_out(d_out, 74, 35, 8, 137);
+
+  Eigen::array<Eigen::DenseIndex, 2> dims{1, 2};
+  gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 74; ++i) {
+    for (int j = 0; j < 35; ++j) {
+      for (int k = 0; k < 8; ++k) {
+        for (int l = 0; l < 137; ++l) {
+          const float result = out(i, j, k, l);
+          const float expected = input(i, j + 0, k + 0, l) * kernel(0, 0) + input(i, j + 1, k + 0, l) * kernel(1, 0) +
+                                 input(i, j + 2, k + 0, l) * kernel(2, 0) + input(i, j + 0, k + 1, l) * kernel(0, 1) +
+                                 input(i, j + 1, k + 1, l) * kernel(1, 1) + input(i, j + 2, k + 1, l) * kernel(2, 1) +
+                                 input(i, j + 0, k + 2, l) * kernel(0, 2) + input(i, j + 1, k + 2, l) * kernel(1, 2) +
+                                 input(i, j + 2, k + 2, l) * kernel(2, 2) + input(i, j + 0, k + 3, l) * kernel(0, 3) +
+                                 input(i, j + 1, k + 3, l) * kernel(1, 3) + input(i, j + 2, k + 3, l) * kernel(2, 3);
+          VERIFY_IS_APPROX(result, expected);
+        }
+      }
+    }
+  }
+
+  gpuFree(d_input);
+  gpuFree(d_kernel);
+  gpuFree(d_out);
+}
+
+template <int DataLayout>
+void test_gpu_convolution_3d() {
+  Tensor<float, 5, DataLayout> input(Eigen::array<Eigen::DenseIndex, 5>{74, 37, 11, 137, 17});
+  Tensor<float, 3, DataLayout> kernel(3, 4, 2);
+  Tensor<float, 5, DataLayout> out(Eigen::array<Eigen::DenseIndex, 5>{74, 35, 8, 136, 17});
+  input = input.constant(10.0f) + input.random();
+  kernel = kernel.constant(7.0f) + kernel.random();
+
+  std::size_t input_bytes = input.size() * sizeof(float);
+  std::size_t kernel_bytes = kernel.size() * sizeof(float);
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_input;
+  float* d_kernel;
+  float* d_out;
+  gpuMalloc((void**)(&d_input), input_bytes);
+  gpuMalloc((void**)(&d_kernel), kernel_bytes);
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  gpuMemcpy(d_input, input.data(), input_bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_kernel, kernel.data(), kernel_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_input(d_input, 74, 37, 11, 137, 17);
+  Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> > gpu_kernel(d_kernel, 3, 4, 2);
+  Eigen::TensorMap<Eigen::Tensor<float, 5, DataLayout> > gpu_out(d_out, 74, 35, 8, 136, 17);
+
+  Eigen::array<Eigen::DenseIndex, 3> dims{1, 2, 3};
+  gpu_out.device(gpu_device) = gpu_input.convolve(gpu_kernel, dims);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 74; ++i) {
+    for (int j = 0; j < 35; ++j) {
+      for (int k = 0; k < 8; ++k) {
+        for (int l = 0; l < 136; ++l) {
+          for (int m = 0; m < 17; ++m) {
+            const float result = out(i, j, k, l, m);
+            const float expected = input(i, j + 0, k + 0, l + 0, m) * kernel(0, 0, 0) +
+                                   input(i, j + 1, k + 0, l + 0, m) * kernel(1, 0, 0) +
+                                   input(i, j + 2, k + 0, l + 0, m) * kernel(2, 0, 0) +
+                                   input(i, j + 0, k + 1, l + 0, m) * kernel(0, 1, 0) +
+                                   input(i, j + 1, k + 1, l + 0, m) * kernel(1, 1, 0) +
+                                   input(i, j + 2, k + 1, l + 0, m) * kernel(2, 1, 0) +
+                                   input(i, j + 0, k + 2, l + 0, m) * kernel(0, 2, 0) +
+                                   input(i, j + 1, k + 2, l + 0, m) * kernel(1, 2, 0) +
+                                   input(i, j + 2, k + 2, l + 0, m) * kernel(2, 2, 0) +
+                                   input(i, j + 0, k + 3, l + 0, m) * kernel(0, 3, 0) +
+                                   input(i, j + 1, k + 3, l + 0, m) * kernel(1, 3, 0) +
+                                   input(i, j + 2, k + 3, l + 0, m) * kernel(2, 3, 0) +
+                                   input(i, j + 0, k + 0, l + 1, m) * kernel(0, 0, 1) +
+                                   input(i, j + 1, k + 0, l + 1, m) * kernel(1, 0, 1) +
+                                   input(i, j + 2, k + 0, l + 1, m) * kernel(2, 0, 1) +
+                                   input(i, j + 0, k + 1, l + 1, m) * kernel(0, 1, 1) +
+                                   input(i, j + 1, k + 1, l + 1, m) * kernel(1, 1, 1) +
+                                   input(i, j + 2, k + 1, l + 1, m) * kernel(2, 1, 1) +
+                                   input(i, j + 0, k + 2, l + 1, m) * kernel(0, 2, 1) +
+                                   input(i, j + 1, k + 2, l + 1, m) * kernel(1, 2, 1) +
+                                   input(i, j + 2, k + 2, l + 1, m) * kernel(2, 2, 1) +
+                                   input(i, j + 0, k + 3, l + 1, m) * kernel(0, 3, 1) +
+                                   input(i, j + 1, k + 3, l + 1, m) * kernel(1, 3, 1) +
+                                   input(i, j + 2, k + 3, l + 1, m) * kernel(2, 3, 1);
+            VERIFY_IS_APPROX(result, expected);
+          }
+        }
+      }
+    }
+  }
+
+  gpuFree(d_input);
+  gpuFree(d_kernel);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_lgamma(const Scalar stddev) {
+  Tensor<Scalar, 2> in(72, 97);
+  in.setRandom();
+  in *= in.constant(stddev);
+  Tensor<Scalar, 2> out(72, 97);
+  out.setZero();
+
+  std::size_t bytes = in.size() * sizeof(Scalar);
+
+  Scalar* d_in;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_in), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_in, in.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_in(d_in, 72, 97);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 72, 97);
+
+  gpu_out.device(gpu_device) = gpu_in.lgamma();
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 72; ++i) {
+    for (int j = 0; j < 97; ++j) {
+      VERIFY_IS_APPROX(out(i, j), (std::lgamma)(in(i, j)));
+    }
+  }
+
+  gpuFree(d_in);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_digamma() {
+  Tensor<Scalar, 1> in(7);
+  Tensor<Scalar, 1> out(7);
+  Tensor<Scalar, 1> expected_out(7);
+  out.setZero();
+
+  in(0) = Scalar(1);
+  in(1) = Scalar(1.5);
+  in(2) = Scalar(4);
+  in(3) = Scalar(-10.5);
+  in(4) = Scalar(10000.5);
+  in(5) = Scalar(0);
+  in(6) = Scalar(-1);
+
+  expected_out(0) = Scalar(-0.5772156649015329);
+  expected_out(1) = Scalar(0.03648997397857645);
+  expected_out(2) = Scalar(1.2561176684318);
+  expected_out(3) = Scalar(2.398239129535781);
+  expected_out(4) = Scalar(9.210340372392849);
+  expected_out(5) = std::numeric_limits<Scalar>::quiet_NaN();
+  expected_out(6) = std::numeric_limits<Scalar>::quiet_NaN();
+
+  std::size_t bytes = in.size() * sizeof(Scalar);
+
+  Scalar* d_in;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_in), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_in, in.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in(d_in, 7);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 7);
+
+  gpu_out.device(gpu_device) = gpu_in.digamma();
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 7; ++i) {
+    VERIFY_IS_CWISE_APPROX(out(i), expected_out(i));
+  }
+
+  gpuFree(d_in);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_zeta() {
+  Tensor<Scalar, 1> in_x(6);
+  Tensor<Scalar, 1> in_q(6);
+  Tensor<Scalar, 1> out(6);
+  Tensor<Scalar, 1> expected_out(6);
+  out.setZero();
+
+  in_x(0) = Scalar(1);
+  in_x(1) = Scalar(1.5);
+  in_x(2) = Scalar(4);
+  in_x(3) = Scalar(-10.5);
+  in_x(4) = Scalar(10000.5);
+  in_x(5) = Scalar(3);
+
+  in_q(0) = Scalar(1.2345);
+  in_q(1) = Scalar(2);
+  in_q(2) = Scalar(1.5);
+  in_q(3) = Scalar(3);
+  in_q(4) = Scalar(1.0001);
+  in_q(5) = Scalar(-2.5);
+
+  expected_out(0) = std::numeric_limits<Scalar>::infinity();
+  expected_out(1) = Scalar(1.61237534869);
+  expected_out(2) = Scalar(0.234848505667);
+  expected_out(3) = std::numeric_limits<Scalar>::quiet_NaN();
+  expected_out(4) = Scalar(0.367879440865);
+  expected_out(5) = Scalar(0.054102025820864097);
+
+  std::size_t bytes = in_x.size() * sizeof(Scalar);
+
+  Scalar* d_in_x;
+  Scalar* d_in_q;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_in_x), bytes);
+  gpuMalloc((void**)(&d_in_q), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_in_x, in_x.data(), bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_in_q, in_q.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 6);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_q(d_in_q, 6);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 6);
+
+  gpu_out.device(gpu_device) = gpu_in_x.zeta(gpu_in_q);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_CWISE_APPROX(out(i), expected_out(i));
+  }
+
+  gpuFree(d_in_x);
+  gpuFree(d_in_q);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_polygamma() {
+  Tensor<Scalar, 1> in_x(7);
+  Tensor<Scalar, 1> in_n(7);
+  Tensor<Scalar, 1> out(7);
+  Tensor<Scalar, 1> expected_out(7);
+  out.setZero();
+
+  in_n(0) = Scalar(1);
+  in_n(1) = Scalar(1);
+  in_n(2) = Scalar(1);
+  in_n(3) = Scalar(17);
+  in_n(4) = Scalar(31);
+  in_n(5) = Scalar(28);
+  in_n(6) = Scalar(8);
+
+  in_x(0) = Scalar(2);
+  in_x(1) = Scalar(3);
+  in_x(2) = Scalar(25.5);
+  in_x(3) = Scalar(4.7);
+  in_x(4) = Scalar(11.8);
+  in_x(5) = Scalar(17.7);
+  in_x(6) = Scalar(30.2);
+
+  expected_out(0) = Scalar(0.644934066848);
+  expected_out(1) = Scalar(0.394934066848);
+  expected_out(2) = Scalar(0.0399946696496);
+  expected_out(3) = Scalar(293.334565435);
+  expected_out(4) = Scalar(0.445487887616);
+  expected_out(5) = Scalar(-2.47810300902e-07);
+  expected_out(6) = Scalar(-8.29668781082e-09);
+
+  std::size_t bytes = in_x.size() * sizeof(Scalar);
+
+  Scalar* d_in_x;
+  Scalar* d_in_n;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_in_x), bytes);
+  gpuMalloc((void**)(&d_in_n), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_in_x, in_x.data(), bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_in_n, in_n.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 7);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_n(d_in_n, 7);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 7);
+
+  gpu_out.device(gpu_device) = gpu_in_n.polygamma(gpu_in_x);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 7; ++i) {
+    VERIFY_IS_APPROX(out(i), expected_out(i));
+  }
+
+  gpuFree(d_in_x);
+  gpuFree(d_in_n);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_igamma() {
+  Tensor<Scalar, 2> a(6, 6);
+  Tensor<Scalar, 2> x(6, 6);
+  Tensor<Scalar, 2> out(6, 6);
+  out.setZero();
+
+  Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)};
+  Scalar x_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)};
+
+  for (int i = 0; i < 6; ++i) {
+    for (int j = 0; j < 6; ++j) {
+      a(i, j) = a_s[i];
+      x(i, j) = x_s[j];
+    }
+  }
+
+  Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
+  Scalar igamma_s[][6] = {
+      {0.0, nan, nan, nan, nan, nan},
+      {0.0, 0.6321205588285578, 0.7768698398515702, 0.9816843611112658, 9.999500016666262e-05, 1.0},
+      {0.0, 0.4275932955291202, 0.608374823728911, 0.9539882943107686, 7.522076445089201e-07, 1.0},
+      {0.0, 0.01898815687615381, 0.06564245437845008, 0.5665298796332909, 4.166333347221828e-18, 1.0},
+      {0.0, 0.9999780593618628, 0.9999899967080838, 0.9999996219837988, 0.9991370418689945, 1.0},
+      {0.0, 0.0, 0.0, 0.0, 0.0, 0.5042041932513908}};
+
+  std::size_t bytes = a.size() * sizeof(Scalar);
+
+  Scalar* d_a;
+  Scalar* d_x;
+  Scalar* d_out;
+  assert(gpuMalloc((void**)(&d_a), bytes) == gpuSuccess);
+  assert(gpuMalloc((void**)(&d_x), bytes) == gpuSuccess);
+  assert(gpuMalloc((void**)(&d_out), bytes) == gpuSuccess);
+
+  gpuMemcpy(d_a, a.data(), bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_x, x.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_a(d_a, 6, 6);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_x(d_x, 6, 6);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 6, 6);
+
+  gpu_out.device(gpu_device) = gpu_a.igamma(gpu_x);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 6; ++i) {
+    for (int j = 0; j < 6; ++j) {
+      if ((std::isnan)(igamma_s[i][j])) {
+        VERIFY((std::isnan)(out(i, j)));
+      } else {
+        VERIFY_IS_APPROX(out(i, j), igamma_s[i][j]);
+      }
+    }
+  }
+
+  gpuFree(d_a);
+  gpuFree(d_x);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_igammac() {
+  Tensor<Scalar, 2> a(6, 6);
+  Tensor<Scalar, 2> x(6, 6);
+  Tensor<Scalar, 2> out(6, 6);
+  out.setZero();
+
+  Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)};
+  Scalar x_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)};
+
+  for (int i = 0; i < 6; ++i) {
+    for (int j = 0; j < 6; ++j) {
+      a(i, j) = a_s[i];
+      x(i, j) = x_s[j];
+    }
+  }
+
+  Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
+  Scalar igammac_s[][6] = {
+      {nan, nan, nan, nan, nan, nan},
+      {1.0, 0.36787944117144233, 0.22313016014842982, 0.018315638888734182, 0.9999000049998333, 0.0},
+      {1.0, 0.5724067044708798, 0.3916251762710878, 0.04601170568923136, 0.9999992477923555, 0.0},
+      {1.0, 0.9810118431238462, 0.9343575456215499, 0.4334701203667089, 1.0, 0.0},
+      {1.0, 2.1940638138146658e-05, 1.0003291916285e-05, 3.7801620118431334e-07, 0.0008629581310054535, 0.0},
+      {1.0, 1.0, 1.0, 1.0, 1.0, 0.49579580674813944}};
+
+  std::size_t bytes = a.size() * sizeof(Scalar);
+
+  Scalar* d_a;
+  Scalar* d_x;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_a), bytes);
+  gpuMalloc((void**)(&d_x), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_a, a.data(), bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_x, x.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_a(d_a, 6, 6);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_x(d_x, 6, 6);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 6, 6);
+
+  gpu_out.device(gpu_device) = gpu_a.igammac(gpu_x);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 6; ++i) {
+    for (int j = 0; j < 6; ++j) {
+      if ((std::isnan)(igammac_s[i][j])) {
+        VERIFY((std::isnan)(out(i, j)));
+      } else {
+        VERIFY_IS_APPROX(out(i, j), igammac_s[i][j]);
+      }
+    }
+  }
+
+  gpuFree(d_a);
+  gpuFree(d_x);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_erf(const Scalar stddev) {
+  Tensor<Scalar, 2> in(72, 97);
+  in.setRandom();
+  in *= in.constant(stddev);
+  Tensor<Scalar, 2> out(72, 97);
+  out.setZero();
+
+  std::size_t bytes = in.size() * sizeof(Scalar);
+
+  Scalar* d_in;
+  Scalar* d_out;
+  assert(gpuMalloc((void**)(&d_in), bytes) == gpuSuccess);
+  assert(gpuMalloc((void**)(&d_out), bytes) == gpuSuccess);
+
+  gpuMemcpy(d_in, in.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_in(d_in, 72, 97);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 72, 97);
+
+  gpu_out.device(gpu_device) = gpu_in.erf();
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 72; ++i) {
+    for (int j = 0; j < 97; ++j) {
+      VERIFY_IS_APPROX(out(i, j), (std::erf)(in(i, j)));
+    }
+  }
+
+  gpuFree(d_in);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_erfc(const Scalar stddev) {
+  Tensor<Scalar, 2> in(72, 97);
+  in.setRandom();
+  in *= in.constant(stddev);
+  Tensor<Scalar, 2> out(72, 97);
+  out.setZero();
+
+  std::size_t bytes = in.size() * sizeof(Scalar);
+
+  Scalar* d_in;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_in), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_in, in.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_in(d_in, 72, 97);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 2> > gpu_out(d_out, 72, 97);
+
+  gpu_out.device(gpu_device) = gpu_in.erfc();
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 72; ++i) {
+    for (int j = 0; j < 97; ++j) {
+      VERIFY_IS_APPROX(out(i, j), (std::erfc)(in(i, j)));
+    }
+  }
+
+  gpuFree(d_in);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_ndtri() {
+  Tensor<Scalar, 1> in_x(9);
+  Tensor<Scalar, 1> out(9);
+  Tensor<Scalar, 1> expected_out(9);
+  out.setZero();
+
+  in_x(0) = Scalar(1);
+  in_x(1) = Scalar(0.);
+  in_x(2) = Scalar(0.5);
+  in_x(3) = Scalar(0.2);
+  in_x(4) = Scalar(0.8);
+  in_x(5) = Scalar(0.9);
+  in_x(6) = Scalar(0.1);
+  in_x(7) = Scalar(0.99);
+  in_x(8) = Scalar(0.01);
+
+  expected_out(0) = std::numeric_limits<Scalar>::infinity();
+  expected_out(1) = -std::numeric_limits<Scalar>::infinity();
+  expected_out(2) = Scalar(0.0);
+  expected_out(3) = Scalar(-0.8416212335729142);
+  expected_out(4) = Scalar(0.8416212335729142);
+  expected_out(5) = Scalar(1.2815515655446004);
+  expected_out(6) = Scalar(-1.2815515655446004);
+  expected_out(7) = Scalar(2.3263478740408408);
+  expected_out(8) = Scalar(-2.3263478740408408);
+
+  std::size_t bytes = in_x.size() * sizeof(Scalar);
+
+  Scalar* d_in_x;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_in_x), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_in_x, in_x.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 6);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 6);
+
+  gpu_out.device(gpu_device) = gpu_in_x.ndtri();
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_CWISE_APPROX(out(i), expected_out(i));
+  }
+
+  gpuFree(d_in_x);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_betainc() {
+  Tensor<Scalar, 1> in_x(125);
+  Tensor<Scalar, 1> in_a(125);
+  Tensor<Scalar, 1> in_b(125);
+  Tensor<Scalar, 1> out(125);
+  Tensor<Scalar, 1> expected_out(125);
+  out.setZero();
+
+  Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
+
+  Array<Scalar, 1, Dynamic> x(125);
+  Array<Scalar, 1, Dynamic> a(125);
+  Array<Scalar, 1, Dynamic> b(125);
+  Array<Scalar, 1, Dynamic> v(125);
+
+  a << 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+      0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+      0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+      0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+      0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+      0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+      0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999,
+      0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379,
+      31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+      31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+      31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+      31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+      999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999,
+      999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999,
+      999.999;
+
+  b << 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+      0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+      31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0,
+      0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999,
+      0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+      31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379,
+      0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999,
+      0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999,
+      999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379,
+      0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999,
+      31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999,
+      999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+      0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379,
+      31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999;
+
+  x << -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1,
+      0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5,
+      0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1,
+      -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2,
+      0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8,
+      1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1;
+
+  v << nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
+      nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.47972119876364683, 0.5, 0.5202788012363533, nan, nan,
+      0.9518683957740043, 0.9789663010413743, 0.9931729188073435, nan, nan, 0.999995949033062, 0.9999999999993698,
+      0.9999999999999999, nan, nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan,
+      nan, nan, 0.006827081192655869, 0.0210336989586256, 0.04813160422599567, nan, nan, 0.20014344256217678,
+      0.5000000000000001, 0.7998565574378232, nan, nan, 0.9991401428435834, 0.999999999698403, 0.9999999999999999, nan,
+      nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan, nan, nan,
+      1.0646600232370887e-25, 6.301722877826246e-13, 4.050966937974938e-06, nan, nan, 7.864342668429763e-23,
+      3.015969667594166e-10, 0.0008598571564165444, nan, nan, 6.031987710123844e-08, 0.5000000000000007,
+      0.9999999396801229, nan, nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan,
+      nan, nan, 0.0, 7.029920380986636e-306, 2.2450728208591345e-101, nan, nan, 0.0, 9.275871147869727e-302,
+      1.2232913026152827e-97, nan, nan, 0.0, 3.0891393081932924e-252, 2.9303043666183996e-60, nan, nan,
+      2.248913486879199e-196, 0.5000000000004947, 0.9999999999999999, nan;
+
+  for (int i = 0; i < 125; ++i) {
+    in_x(i) = x(i);
+    in_a(i) = a(i);
+    in_b(i) = b(i);
+    expected_out(i) = v(i);
+  }
+
+  std::size_t bytes = in_x.size() * sizeof(Scalar);
+
+  Scalar* d_in_x;
+  Scalar* d_in_a;
+  Scalar* d_in_b;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_in_x), bytes);
+  gpuMalloc((void**)(&d_in_a), bytes);
+  gpuMalloc((void**)(&d_in_b), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_in_x, in_x.data(), bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_in_a, in_a.data(), bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_in_b, in_b.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 125);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_a(d_in_a, 125);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_b(d_in_b, 125);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 125);
+
+  gpu_out.device(gpu_device) = betainc(gpu_in_a, gpu_in_b, gpu_in_x);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 125; ++i) {
+    VERIFY_IS_CWISE_APPROX(out(i), expected_out(i));
+  }
+
+  gpuFree(d_in_x);
+  gpuFree(d_in_a);
+  gpuFree(d_in_b);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_i0e() {
+  Tensor<Scalar, 1> in_x(21);
+  Tensor<Scalar, 1> out(21);
+  Tensor<Scalar, 1> expected_out(21);
+  out.setZero();
+
+  Array<Scalar, 1, Dynamic> in_x_array(21);
+  Array<Scalar, 1, Dynamic> expected_out_array(21);
+
+  in_x_array << -20.0, -18.0, -16.0, -14.0, -12.0, -10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0,
+      14.0, 16.0, 18.0, 20.0;
+
+  expected_out_array << 0.0897803118848, 0.0947062952128, 0.100544127361, 0.107615251671, 0.116426221213,
+      0.127833337163, 0.143431781857, 0.16665743264, 0.207001921224, 0.308508322554, 1.0, 0.308508322554,
+      0.207001921224, 0.16665743264, 0.143431781857, 0.127833337163, 0.116426221213, 0.107615251671, 0.100544127361,
+      0.0947062952128, 0.0897803118848;
+
+  for (int i = 0; i < 21; ++i) {
+    in_x(i) = in_x_array(i);
+    expected_out(i) = expected_out_array(i);
+  }
+
+  std::size_t bytes = in_x.size() * sizeof(Scalar);
+
+  Scalar* d_in;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_in), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_in, in_x.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in(d_in, 21);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 21);
+
+  gpu_out.device(gpu_device) = gpu_in.bessel_i0e();
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 21; ++i) {
+    VERIFY_IS_APPROX(out(i), expected_out(i));
+  }
+
+  gpuFree(d_in);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_i1e() {
+  Tensor<Scalar, 1> in_x(21);
+  Tensor<Scalar, 1> out(21);
+  Tensor<Scalar, 1> expected_out(21);
+  out.setZero();
+
+  Array<Scalar, 1, Dynamic> in_x_array(21);
+  Array<Scalar, 1, Dynamic> expected_out_array(21);
+
+  in_x_array << -20.0, -18.0, -16.0, -14.0, -12.0, -10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0,
+      14.0, 16.0, 18.0, 20.0;
+
+  expected_out_array << -0.0875062221833, -0.092036796872, -0.0973496147565, -0.103697667463, -0.11146429929,
+      -0.121262681384, -0.134142493293, -0.152051459309, -0.178750839502, -0.215269289249, 0.0, 0.215269289249,
+      0.178750839502, 0.152051459309, 0.134142493293, 0.121262681384, 0.11146429929, 0.103697667463, 0.0973496147565,
+      0.092036796872, 0.0875062221833;
+
+  for (int i = 0; i < 21; ++i) {
+    in_x(i) = in_x_array(i);
+    expected_out(i) = expected_out_array(i);
+  }
+
+  std::size_t bytes = in_x.size() * sizeof(Scalar);
+
+  Scalar* d_in;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_in), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_in, in_x.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in(d_in, 21);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 21);
+
+  gpu_out.device(gpu_device) = gpu_in.bessel_i1e();
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 21; ++i) {
+    VERIFY_IS_APPROX(out(i), expected_out(i));
+  }
+
+  gpuFree(d_in);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_igamma_der_a() {
+  Tensor<Scalar, 1> in_x(30);
+  Tensor<Scalar, 1> in_a(30);
+  Tensor<Scalar, 1> out(30);
+  Tensor<Scalar, 1> expected_out(30);
+  out.setZero();
+
+  Array<Scalar, 1, Dynamic> in_a_array(30);
+  Array<Scalar, 1, Dynamic> in_x_array(30);
+  Array<Scalar, 1, Dynamic> expected_out_array(30);
+
+  // See special_functions.cpp for the Python code that generates the test data.
+
+  in_a_array << 0.01, 0.01, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0,
+      10.0, 100.0, 100.0, 100.0, 100.0, 100.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0;
+
+  in_x_array << 1.25668890405e-26, 1.17549435082e-38, 1.20938905072e-05, 1.17549435082e-38, 1.17549435082e-38,
+      5.66572070696e-16, 0.0132865061065, 0.0200034203853, 6.29263709118e-17, 1.37160367764e-06, 0.333412038288,
+      1.18135687766, 0.580629033777, 0.170631439426, 0.786686768458, 7.63873279537, 13.1944344379, 11.896042354,
+      10.5830172417, 10.5020942233, 92.8918587747, 95.003720371, 86.3715926467, 96.0330217672, 82.6389930677,
+      968.702906754, 969.463546828, 1001.79726022, 955.047416547, 1044.27458568;
+
+  expected_out_array << -32.7256441441, -36.4394150514, -9.66467612263, -36.4394150514, -36.4394150514, -1.0891900302,
+      -2.66351229645, -2.48666868596, -0.929700494428, -3.56327722764, -0.455320135314, -0.391437214323,
+      -0.491352055991, -0.350454834292, -0.471773162921, -0.104084440522, -0.0723646747909, -0.0992828975532,
+      -0.121638215446, -0.122619605294, -0.0317670267286, -0.0359974812869, -0.0154359225363, -0.0375775365921,
+      -0.00794899153653, -0.00777303219211, -0.00796085782042, -0.0125850719397, -0.00455500206958, -0.00476436993148;
+
+  for (int i = 0; i < 30; ++i) {
+    in_x(i) = in_x_array(i);
+    in_a(i) = in_a_array(i);
+    expected_out(i) = expected_out_array(i);
+  }
+
+  std::size_t bytes = in_x.size() * sizeof(Scalar);
+
+  Scalar* d_a;
+  Scalar* d_x;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_a), bytes);
+  gpuMalloc((void**)(&d_x), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_a, in_a.data(), bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_x, in_x.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_a(d_a, 30);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_x(d_x, 30);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 30);
+
+  gpu_out.device(gpu_device) = gpu_a.igamma_der_a(gpu_x);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 30; ++i) {
+    VERIFY_IS_APPROX(out(i), expected_out(i));
+  }
+
+  gpuFree(d_a);
+  gpuFree(d_x);
+  gpuFree(d_out);
+}
+
+template <typename Scalar>
+void test_gpu_gamma_sample_der_alpha() {
+  Tensor<Scalar, 1> in_alpha(30);
+  Tensor<Scalar, 1> in_sample(30);
+  Tensor<Scalar, 1> out(30);
+  Tensor<Scalar, 1> expected_out(30);
+  out.setZero();
+
+  Array<Scalar, 1, Dynamic> in_alpha_array(30);
+  Array<Scalar, 1, Dynamic> in_sample_array(30);
+  Array<Scalar, 1, Dynamic> expected_out_array(30);
+
+  // See special_functions.cpp for the Python code that generates the test data.
+
+  in_alpha_array << 0.01, 0.01, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0,
+      10.0, 10.0, 100.0, 100.0, 100.0, 100.0, 100.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0;
+
+  in_sample_array << 1.25668890405e-26, 1.17549435082e-38, 1.20938905072e-05, 1.17549435082e-38, 1.17549435082e-38,
+      5.66572070696e-16, 0.0132865061065, 0.0200034203853, 6.29263709118e-17, 1.37160367764e-06, 0.333412038288,
+      1.18135687766, 0.580629033777, 0.170631439426, 0.786686768458, 7.63873279537, 13.1944344379, 11.896042354,
+      10.5830172417, 10.5020942233, 92.8918587747, 95.003720371, 86.3715926467, 96.0330217672, 82.6389930677,
+      968.702906754, 969.463546828, 1001.79726022, 955.047416547, 1044.27458568;
+
+  expected_out_array << 7.42424742367e-23, 1.02004297287e-34, 0.0130155240738, 1.02004297287e-34, 1.02004297287e-34,
+      1.96505168277e-13, 0.525575786243, 0.713903991771, 2.32077561808e-14, 0.000179348049886, 0.635500453302,
+      1.27561284917, 0.878125852156, 0.41565819538, 1.03606488534, 0.885964824887, 1.16424049334, 1.10764479598,
+      1.04590810812, 1.04193666963, 0.965193152414, 0.976217589464, 0.93008035061, 0.98153216096, 0.909196397698,
+      0.98434963993, 0.984738050206, 1.00106492525, 0.97734200649, 1.02198794179;
+
+  for (int i = 0; i < 30; ++i) {
+    in_alpha(i) = in_alpha_array(i);
+    in_sample(i) = in_sample_array(i);
+    expected_out(i) = expected_out_array(i);
+  }
+
+  std::size_t bytes = in_alpha.size() * sizeof(Scalar);
+
+  Scalar* d_alpha;
+  Scalar* d_sample;
+  Scalar* d_out;
+  gpuMalloc((void**)(&d_alpha), bytes);
+  gpuMalloc((void**)(&d_sample), bytes);
+  gpuMalloc((void**)(&d_out), bytes);
+
+  gpuMemcpy(d_alpha, in_alpha.data(), bytes, gpuMemcpyHostToDevice);
+  gpuMemcpy(d_sample, in_sample.data(), bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_alpha(d_alpha, 30);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_sample(d_sample, 30);
+  Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 30);
+
+  gpu_out.device(gpu_device) = gpu_alpha.gamma_sample_der_alpha(gpu_sample);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  for (int i = 0; i < 30; ++i) {
+    VERIFY_IS_APPROX(out(i), expected_out(i));
+  }
+
+  gpuFree(d_alpha);
+  gpuFree(d_sample);
+  gpuFree(d_out);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_gpu) {
+  CALL_SUBTEST_1(test_gpu_nullary());
+  CALL_SUBTEST_1(test_gpu_nullary_max_size<int16_t>());
+  CALL_SUBTEST_1(test_gpu_nullary_max_size<int32_t>());
+  CALL_SUBTEST_1((test_gpu_nullary_max_size<int64_t, (std::numeric_limits<int32_t>::max)() + 100ll>()));
+  CALL_SUBTEST_1(test_gpu_elementwise_small());
+  CALL_SUBTEST_1(test_gpu_elementwise());
+  CALL_SUBTEST_1(test_gpu_props());
+  CALL_SUBTEST_2(test_gpu_reduction());
+  CALL_SUBTEST_3(test_gpu_contraction<ColMajor>());
+  CALL_SUBTEST_3(test_gpu_contraction<RowMajor>());
+  CALL_SUBTEST_4(test_gpu_convolution_1d<ColMajor>());
+  CALL_SUBTEST_4(test_gpu_convolution_1d<RowMajor>());
+  CALL_SUBTEST_4(test_gpu_convolution_inner_dim_col_major_1d());
+  CALL_SUBTEST_4(test_gpu_convolution_inner_dim_row_major_1d());
+  CALL_SUBTEST_5(test_gpu_convolution_2d<ColMajor>());
+  CALL_SUBTEST_5(test_gpu_convolution_2d<RowMajor>());
+#if !defined(EIGEN_USE_HIP)
+  // disable these tests on HIP for now.
+  // they hang..need to investigate and fix
+  CALL_SUBTEST_6(test_gpu_convolution_3d<ColMajor>());
+  CALL_SUBTEST_7(test_gpu_convolution_3d<RowMajor>());
+#endif
+
+  // std::erf, std::erfc, and so on where only added in c++11. We use them
+  // as a golden reference to validate the results produced by Eigen. Therefore
+  // we can only run these tests if we use a c++11 compiler.
+  CALL_SUBTEST_8(test_gpu_lgamma<float>(1.0f));
+  CALL_SUBTEST_8(test_gpu_lgamma<float>(100.0f));
+  CALL_SUBTEST_8(test_gpu_lgamma<float>(0.01f));
+  CALL_SUBTEST_8(test_gpu_lgamma<float>(0.001f));
+
+  CALL_SUBTEST_8(test_gpu_lgamma<double>(1.0));
+  CALL_SUBTEST_8(test_gpu_lgamma<double>(100.0));
+  CALL_SUBTEST_8(test_gpu_lgamma<double>(0.01));
+  CALL_SUBTEST_8(test_gpu_lgamma<double>(0.001));
+
+  CALL_SUBTEST_8(test_gpu_erf<float>(1.0f));
+  CALL_SUBTEST_8(test_gpu_erf<float>(100.0f));
+  CALL_SUBTEST_8(test_gpu_erf<float>(0.01f));
+  CALL_SUBTEST_8(test_gpu_erf<float>(0.001f));
+
+  CALL_SUBTEST_8(test_gpu_erfc<float>(1.0f));
+  // CALL_SUBTEST_8(test_gpu_erfc<float>(100.0f));
+  CALL_SUBTEST_8(test_gpu_erfc<float>(5.0f));  // GPU erfc lacks precision for large inputs
+  CALL_SUBTEST_8(test_gpu_erfc<float>(0.01f));
+  CALL_SUBTEST_8(test_gpu_erfc<float>(0.001f));
+
+  CALL_SUBTEST_8(test_gpu_erf<double>(1.0));
+  CALL_SUBTEST_8(test_gpu_erf<double>(100.0));
+  CALL_SUBTEST_8(test_gpu_erf<double>(0.01));
+  CALL_SUBTEST_8(test_gpu_erf<double>(0.001));
+
+  CALL_SUBTEST_8(test_gpu_erfc<double>(1.0));
+  // CALL_SUBTEST_8(test_gpu_erfc<double>(100.0));
+  CALL_SUBTEST_8(test_gpu_erfc<double>(5.0));  // GPU erfc lacks precision for large inputs
+  CALL_SUBTEST_8(test_gpu_erfc<double>(0.01));
+  CALL_SUBTEST_8(test_gpu_erfc<double>(0.001));
+
+#if !defined(EIGEN_USE_HIP)
+  // disable these tests on HIP for now.
+
+  CALL_SUBTEST_9(test_gpu_ndtri<float>());
+  CALL_SUBTEST_9(test_gpu_ndtri<double>());
+
+  CALL_SUBTEST_9(test_gpu_digamma<float>());
+  CALL_SUBTEST_9(test_gpu_digamma<double>());
+
+  CALL_SUBTEST_9(test_gpu_polygamma<float>());
+  CALL_SUBTEST_9(test_gpu_polygamma<double>());
+
+  CALL_SUBTEST_9(test_gpu_zeta<float>());
+  CALL_SUBTEST_9(test_gpu_zeta<double>());
+#endif
+
+  CALL_SUBTEST_9(test_gpu_igamma<float>());
+  CALL_SUBTEST_9(test_gpu_igammac<float>());
+
+  CALL_SUBTEST_9(test_gpu_igamma<double>());
+  CALL_SUBTEST_9(test_gpu_igammac<double>());
+
+#if !defined(EIGEN_USE_HIP)
+  // disable these tests on HIP for now.
+  CALL_SUBTEST_9(test_gpu_betainc<float>());
+  CALL_SUBTEST_9(test_gpu_betainc<double>());
+
+  CALL_SUBTEST_9(test_gpu_i0e<float>());
+  CALL_SUBTEST_9(test_gpu_i0e<double>());
+
+  CALL_SUBTEST_9(test_gpu_i1e<float>());
+  CALL_SUBTEST_9(test_gpu_i1e<double>());
+
+  CALL_SUBTEST_9(test_gpu_i1e<float>());
+  CALL_SUBTEST_9(test_gpu_i1e<double>());
+
+  CALL_SUBTEST_9(test_gpu_igamma_der_a<float>());
+  CALL_SUBTEST_9(test_gpu_igamma_der_a<double>());
+
+  CALL_SUBTEST_9(test_gpu_gamma_sample_der_alpha<float>());
+  CALL_SUBTEST_9(test_gpu_gamma_sample_der_alpha<double>());
+#endif
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_ifft.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_ifft.cpp
new file mode 100644
index 0000000..3433aea
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_ifft.cpp
@@ -0,0 +1,157 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Jianwei Cui <thucjw@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <complex>
+#include <cmath>
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_1D_fft_ifft_invariant(int sequence_length) {
+  Tensor<double, 1, DataLayout> tensor(sequence_length);
+  tensor.setRandom();
+
+  array<int, 1> fft;
+  fft[0] = 0;
+
+  Tensor<std::complex<double>, 1, DataLayout> tensor_after_fft;
+  Tensor<std::complex<double>, 1, DataLayout> tensor_after_fft_ifft;
+
+  tensor_after_fft = tensor.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft);
+  tensor_after_fft_ifft = tensor_after_fft.template fft<Eigen::BothParts, Eigen::FFT_REVERSE>(fft);
+
+  VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), sequence_length);
+  VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), sequence_length);
+
+  for (int i = 0; i < sequence_length; ++i) {
+    VERIFY_IS_APPROX(static_cast<float>(tensor(i)), static_cast<float>(std::real(tensor_after_fft_ifft(i))));
+  }
+}
+
+template <int DataLayout>
+static void test_2D_fft_ifft_invariant(int dim0, int dim1) {
+  Tensor<double, 2, DataLayout> tensor(dim0, dim1);
+  tensor.setRandom();
+
+  array<int, 2> fft;
+  fft[0] = 0;
+  fft[1] = 1;
+
+  Tensor<std::complex<double>, 2, DataLayout> tensor_after_fft;
+  Tensor<std::complex<double>, 2, DataLayout> tensor_after_fft_ifft;
+
+  tensor_after_fft = tensor.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft);
+  tensor_after_fft_ifft = tensor_after_fft.template fft<Eigen::BothParts, Eigen::FFT_REVERSE>(fft);
+
+  VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), dim0);
+  VERIFY_IS_EQUAL(tensor_after_fft.dimension(1), dim1);
+  VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), dim0);
+  VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(1), dim1);
+
+  for (int i = 0; i < dim0; ++i) {
+    for (int j = 0; j < dim1; ++j) {
+      // std::cout << "[" << i << "][" << j << "]" <<  "  Original data: " << tensor(i,j) << " Transformed data:" <<
+      // tensor_after_fft_ifft(i,j) << std::endl;
+      VERIFY_IS_APPROX(static_cast<float>(tensor(i, j)), static_cast<float>(std::real(tensor_after_fft_ifft(i, j))));
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_3D_fft_ifft_invariant(int dim0, int dim1, int dim2) {
+  Tensor<double, 3, DataLayout> tensor(dim0, dim1, dim2);
+  tensor.setRandom();
+
+  array<int, 3> fft;
+  fft[0] = 0;
+  fft[1] = 1;
+  fft[2] = 2;
+
+  Tensor<std::complex<double>, 3, DataLayout> tensor_after_fft;
+  Tensor<std::complex<double>, 3, DataLayout> tensor_after_fft_ifft;
+
+  tensor_after_fft = tensor.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft);
+  tensor_after_fft_ifft = tensor_after_fft.template fft<Eigen::BothParts, Eigen::FFT_REVERSE>(fft);
+
+  VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), dim0);
+  VERIFY_IS_EQUAL(tensor_after_fft.dimension(1), dim1);
+  VERIFY_IS_EQUAL(tensor_after_fft.dimension(2), dim2);
+  VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), dim0);
+  VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(1), dim1);
+  VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(2), dim2);
+
+  for (int i = 0; i < dim0; ++i) {
+    for (int j = 0; j < dim1; ++j) {
+      for (int k = 0; k < dim2; ++k) {
+        VERIFY_IS_APPROX(static_cast<float>(tensor(i, j, k)),
+                         static_cast<float>(std::real(tensor_after_fft_ifft(i, j, k))));
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_sub_fft_ifft_invariant(int dim0, int dim1, int dim2, int dim3) {
+  Tensor<double, 4, DataLayout> tensor(dim0, dim1, dim2, dim3);
+  tensor.setRandom();
+
+  array<int, 2> fft;
+  fft[0] = 2;
+  fft[1] = 0;
+
+  Tensor<std::complex<double>, 4, DataLayout> tensor_after_fft;
+  Tensor<double, 4, DataLayout> tensor_after_fft_ifft;
+
+  tensor_after_fft = tensor.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft);
+  tensor_after_fft_ifft = tensor_after_fft.template fft<Eigen::RealPart, Eigen::FFT_REVERSE>(fft);
+
+  VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), dim0);
+  VERIFY_IS_EQUAL(tensor_after_fft.dimension(1), dim1);
+  VERIFY_IS_EQUAL(tensor_after_fft.dimension(2), dim2);
+  VERIFY_IS_EQUAL(tensor_after_fft.dimension(3), dim3);
+  VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), dim0);
+  VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(1), dim1);
+  VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(2), dim2);
+  VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(3), dim3);
+
+  for (int i = 0; i < dim0; ++i) {
+    for (int j = 0; j < dim1; ++j) {
+      for (int k = 0; k < dim2; ++k) {
+        for (int l = 0; l < dim3; ++l) {
+          VERIFY_IS_APPROX(static_cast<float>(tensor(i, j, k, l)),
+                           static_cast<float>(tensor_after_fft_ifft(i, j, k, l)));
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_ifft) {
+  CALL_SUBTEST(test_1D_fft_ifft_invariant<ColMajor>(4));
+  CALL_SUBTEST(test_1D_fft_ifft_invariant<ColMajor>(16));
+  CALL_SUBTEST(test_1D_fft_ifft_invariant<ColMajor>(32));
+  CALL_SUBTEST(test_1D_fft_ifft_invariant<ColMajor>(1024 * 1024));
+
+  CALL_SUBTEST(test_2D_fft_ifft_invariant<ColMajor>(4, 4));
+  CALL_SUBTEST(test_2D_fft_ifft_invariant<ColMajor>(8, 16));
+  CALL_SUBTEST(test_2D_fft_ifft_invariant<ColMajor>(16, 32));
+  CALL_SUBTEST(test_2D_fft_ifft_invariant<ColMajor>(1024, 1024));
+
+  CALL_SUBTEST(test_3D_fft_ifft_invariant<ColMajor>(4, 4, 4));
+  CALL_SUBTEST(test_3D_fft_ifft_invariant<ColMajor>(8, 16, 32));
+  CALL_SUBTEST(test_3D_fft_ifft_invariant<ColMajor>(16, 4, 8));
+  CALL_SUBTEST(test_3D_fft_ifft_invariant<ColMajor>(256, 256, 256));
+
+  CALL_SUBTEST(test_sub_fft_ifft_invariant<ColMajor>(4, 4, 4, 4));
+  CALL_SUBTEST(test_sub_fft_ifft_invariant<ColMajor>(8, 16, 32, 64));
+  CALL_SUBTEST(test_sub_fft_ifft_invariant<ColMajor>(16, 4, 8, 12));
+  CALL_SUBTEST(test_sub_fft_ifft_invariant<ColMajor>(64, 64, 64, 64));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_image_op_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_image_op_sycl.cpp
new file mode 100644
index 0000000..2aedaf6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_image_op_sycl.cpp
@@ -0,0 +1,100 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_image_op_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 245;
+  IndexType sizeDim2 = 343;
+  IndexType sizeDim3 = 577;
+
+  array<IndexType, 3> input_range = {{sizeDim1, sizeDim2, sizeDim3}};
+  array<IndexType, 3> slice_range = {{sizeDim1 - 1, sizeDim2, sizeDim3}};
+
+  Tensor<DataType, 3, DataLayout, IndexType> tensor1(input_range);
+  Tensor<DataType, 3, DataLayout, IndexType> tensor2(input_range);
+  Tensor<DataType, 3, DataLayout, IndexType> tensor3(slice_range);
+  Tensor<DataType, 3, DataLayout, IndexType> tensor3_cpu(slice_range);
+
+  typedef Eigen::DSizes<IndexType, 3> Index3;
+  Index3 strides1(1L, 1L, 1L);
+  Index3 indicesStart1(1L, 0L, 0L);
+  Index3 indicesStop1(sizeDim1, sizeDim2, sizeDim3);
+
+  Index3 strides2(1L, 1L, 1L);
+  Index3 indicesStart2(0L, 0L, 0L);
+  Index3 indicesStop2(sizeDim1 - 1, sizeDim2, sizeDim3);
+  Eigen::DSizes<IndexType, 3> sizes(sizeDim1 - 1, sizeDim2, sizeDim3);
+
+  tensor1.setRandom();
+  tensor2.setRandom();
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size() * sizeof(DataType)));
+  DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(tensor3.size() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, input_range);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, input_range);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu3(gpu_data3, slice_range);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(), (tensor1.size()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_data2, tensor2.data(), (tensor2.size()) * sizeof(DataType));
+  gpu3.device(sycl_device) = gpu1.slice(indicesStart1, sizes) - gpu2.slice(indicesStart2, sizes);
+  sycl_device.memcpyDeviceToHost(tensor3.data(), gpu_data3, (tensor3.size()) * sizeof(DataType));
+
+  tensor3_cpu = tensor1.stridedSlice(indicesStart1, indicesStop1, strides1) -
+                tensor2.stridedSlice(indicesStart2, indicesStop2, strides2);
+
+  for (IndexType i = 0; i < slice_range[0]; ++i) {
+    for (IndexType j = 0; j < slice_range[1]; ++j) {
+      for (IndexType k = 0; k < slice_range[2]; ++k) {
+        VERIFY_IS_EQUAL(tensor3_cpu(i, j, k), tensor3(i, j, k));
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+  sycl_device.deallocate(gpu_data3);
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_computing_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_image_op_sycl<DataType, RowMajor, int64_t>(sycl_device);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_image_op_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_computing_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
+#ifdef EIGEN_SYCL_DOUBLE_SUPPORT
+    CALL_SUBTEST(sycl_computing_test_per_device<double>(device));
+#endif
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_image_patch.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_image_patch.cpp
new file mode 100644
index 0000000..55d3c6e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_image_patch.cpp
@@ -0,0 +1,811 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+void test_simple_patch() {
+  Tensor<float, 4> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout();
+  VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3));
+  VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0));
+
+  // Single pixel patch: ColMajor
+  Tensor<float, 5> single_pixel_patch;
+  single_pixel_patch = tensor.extract_image_patches(1, 1);
+  VERIFY_IS_EQUAL(single_pixel_patch.dimension(0), 2);
+  VERIFY_IS_EQUAL(single_pixel_patch.dimension(1), 1);
+  VERIFY_IS_EQUAL(single_pixel_patch.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_pixel_patch.dimension(3), 3 * 5);
+  VERIFY_IS_EQUAL(single_pixel_patch.dimension(4), 7);
+
+  // Single pixel patch: RowMajor
+  Tensor<float, 5, RowMajor> single_pixel_patch_row_major;
+  single_pixel_patch_row_major = tensor_row_major.extract_image_patches(1, 1);
+  VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(0), 7);
+  VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(1), 3 * 5);
+  VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(3), 1);
+  VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(4), 2);
+
+  for (int i = 0; i < tensor.size(); ++i) {
+    // ColMajor
+    if (tensor.data()[i] != single_pixel_patch.data()[i]) {
+      std::cout << "Mismatch detected at index " << i << " : " << tensor.data()[i] << " vs "
+                << single_pixel_patch.data()[i] << std::endl;
+    }
+    VERIFY_IS_EQUAL(single_pixel_patch.data()[i], tensor.data()[i]);
+    // RowMajor
+    if (tensor_row_major.data()[i] != single_pixel_patch_row_major.data()[i]) {
+      std::cout << "Mismatch detected at index " << i << " : " << tensor.data()[i] << " vs "
+                << single_pixel_patch_row_major.data()[i] << std::endl;
+    }
+    VERIFY_IS_EQUAL(single_pixel_patch_row_major.data()[i], tensor_row_major.data()[i]);
+    VERIFY_IS_EQUAL(tensor.data()[i], tensor_row_major.data()[i]);
+    VERIFY_IS_EQUAL(single_pixel_patch.data()[i], single_pixel_patch_row_major.data()[i]);
+  }
+
+  // Entire image patch: ColMajor
+  Tensor<float, 5> entire_image_patch;
+  entire_image_patch = tensor.extract_image_patches(3, 5);
+  VERIFY_IS_EQUAL(entire_image_patch.dimension(0), 2);
+  VERIFY_IS_EQUAL(entire_image_patch.dimension(1), 3);
+  VERIFY_IS_EQUAL(entire_image_patch.dimension(2), 5);
+  VERIFY_IS_EQUAL(entire_image_patch.dimension(3), 3 * 5);
+  VERIFY_IS_EQUAL(entire_image_patch.dimension(4), 7);
+
+  // Entire image patch: RowMajor
+  Tensor<float, 5, RowMajor> entire_image_patch_row_major;
+  entire_image_patch_row_major = tensor_row_major.extract_image_patches(3, 5);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(0), 7);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(1), 3 * 5);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(2), 5);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(3), 3);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(4), 2);
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      int patchId = i + 3 * j;
+      for (int r = 0; r < 3; ++r) {
+        for (int c = 0; c < 5; ++c) {
+          for (int d = 0; d < 2; ++d) {
+            for (int b = 0; b < 7; ++b) {
+              float expected = 0.0f;
+              float expected_row_major = 0.0f;
+              if (r - 1 + i >= 0 && c - 2 + j >= 0 && r - 1 + i < 3 && c - 2 + j < 5) {
+                expected = tensor(d, r - 1 + i, c - 2 + j, b);
+                expected_row_major = tensor_row_major(b, c - 2 + j, r - 1 + i, d);
+              }
+              // ColMajor
+              if (entire_image_patch(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(entire_image_patch(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (entire_image_patch_row_major(b, patchId, c, r, d) != expected_row_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(entire_image_patch_row_major(b, patchId, c, r, d), expected_row_major);
+              // Check that ColMajor and RowMajor agree.
+              VERIFY_IS_EQUAL(expected, expected_row_major);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  // 2D patch: ColMajor
+  Tensor<float, 5> twod_patch;
+  twod_patch = tensor.extract_image_patches(2, 2);
+  VERIFY_IS_EQUAL(twod_patch.dimension(0), 2);
+  VERIFY_IS_EQUAL(twod_patch.dimension(1), 2);
+  VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
+  VERIFY_IS_EQUAL(twod_patch.dimension(3), 3 * 5);
+  VERIFY_IS_EQUAL(twod_patch.dimension(4), 7);
+
+  // 2D patch: RowMajor
+  Tensor<float, 5, RowMajor> twod_patch_row_major;
+  twod_patch_row_major = tensor_row_major.extract_image_patches(2, 2);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(0), 7);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(1), 3 * 5);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(2), 2);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(3), 2);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(4), 2);
+
+  // Based on the calculation described in TensorTraits.h, padding happens to be 0.
+  int row_padding = 0;
+  int col_padding = 0;
+  int stride = 1;
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      int patchId = i + 3 * j;
+      for (int r = 0; r < 2; ++r) {
+        for (int c = 0; c < 2; ++c) {
+          for (int d = 0; d < 2; ++d) {
+            for (int b = 0; b < 7; ++b) {
+              float expected = 0.0f;
+              float expected_row_major = 0.0f;
+              int row_offset = r * stride + i - row_padding;
+              int col_offset = c * stride + j - col_padding;
+              // ColMajor
+              if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor.dimension(1) &&
+                  col_offset < tensor.dimension(2)) {
+                expected = tensor(d, row_offset, col_offset, b);
+              }
+              if (twod_patch(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(twod_patch(d, r, c, patchId, b), expected);
+
+              // RowMajor
+              if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_row_major.dimension(2) &&
+                  col_offset < tensor_row_major.dimension(1)) {
+                expected_row_major = tensor_row_major(b, col_offset, row_offset, d);
+              }
+              if (twod_patch_row_major(b, patchId, c, r, d) != expected_row_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(twod_patch_row_major(b, patchId, c, r, d), expected_row_major);
+              // Check that ColMajor and RowMajor agree.
+              VERIFY_IS_EQUAL(expected, expected_row_major);
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+// Verifies VALID padding (no padding) with incrementing values.
+void test_patch_padding_valid() {
+  int input_depth = 3;
+  int input_rows = 3;
+  int input_cols = 3;
+  int input_batches = 1;
+  int ksize = 2;   // Corresponds to the Rows and Cols for tensor.extract_image_patches<>.
+  int stride = 2;  // Only same stride is supported.
+  Tensor<float, 4> tensor(input_depth, input_rows, input_cols, input_batches);
+  // Initializes tensor with incrementing numbers.
+  for (int i = 0; i < tensor.size(); ++i) {
+    tensor.data()[i] = i + 1;
+  }
+  // ColMajor
+  Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
+
+  VERIFY_IS_EQUAL(result.dimension(0), input_depth);    // depth
+  VERIFY_IS_EQUAL(result.dimension(1), ksize);          // kernel rows
+  VERIFY_IS_EQUAL(result.dimension(2), ksize);          // kernel cols
+  VERIFY_IS_EQUAL(result.dimension(3), 1);              // number of patches
+  VERIFY_IS_EQUAL(result.dimension(4), input_batches);  // number of batches
+
+  // RowMajor
+  Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout();
+  VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3));
+  VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0));
+
+  Tensor<float, 5, RowMajor> result_row_major =
+      tensor_row_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
+  VERIFY_IS_EQUAL(result.dimension(0), result_row_major.dimension(4));
+  VERIFY_IS_EQUAL(result.dimension(1), result_row_major.dimension(3));
+  VERIFY_IS_EQUAL(result.dimension(2), result_row_major.dimension(2));
+  VERIFY_IS_EQUAL(result.dimension(3), result_row_major.dimension(1));
+  VERIFY_IS_EQUAL(result.dimension(4), result_row_major.dimension(0));
+
+  // No padding is carried out.
+  int row_padding = 0;
+  int col_padding = 0;
+
+  for (int i = 0; (i + stride + ksize - 1) < input_rows; i += stride) {    // input rows
+    for (int j = 0; (j + stride + ksize - 1) < input_cols; j += stride) {  // input cols
+      int patchId = i + input_rows * j;
+      for (int r = 0; r < ksize; ++r) {                // patch rows
+        for (int c = 0; c < ksize; ++c) {              // patch cols
+          for (int d = 0; d < input_depth; ++d) {      // depth
+            for (int b = 0; b < input_batches; ++b) {  // batch
+              float expected = 0.0f;
+              float expected_row_major = 0.0f;
+              int row_offset = r + i - row_padding;
+              int col_offset = c + j - col_padding;
+              if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) {
+                expected = tensor(d, row_offset, col_offset, b);
+                expected_row_major = tensor_row_major(b, col_offset, row_offset, d);
+              }
+              // ColMajor
+              if (result(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (result_row_major(b, patchId, c, r, d) != expected_row_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major);
+              // Check that ColMajor and RowMajor agree.
+              VERIFY_IS_EQUAL(expected, expected_row_major);
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+// Verifies VALID padding (no padding) with the same value.
+void test_patch_padding_valid_same_value() {
+  int input_depth = 1;
+  int input_rows = 5;
+  int input_cols = 5;
+  int input_batches = 2;
+  int ksize = 3;   // Corresponds to the Rows and Cols for tensor.extract_image_patches<>.
+  int stride = 2;  // Only same stride is supported.
+  // ColMajor
+  Tensor<float, 4> tensor(input_depth, input_rows, input_cols, input_batches);
+  tensor = tensor.constant(11.0f);
+  Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
+
+  VERIFY_IS_EQUAL(result.dimension(0), input_depth);    // depth
+  VERIFY_IS_EQUAL(result.dimension(1), ksize);          // kernel rows
+  VERIFY_IS_EQUAL(result.dimension(2), ksize);          // kernel cols
+  VERIFY_IS_EQUAL(result.dimension(3), 4);              // number of patches
+  VERIFY_IS_EQUAL(result.dimension(4), input_batches);  // number of batches
+
+  // RowMajor
+  Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout();
+  VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3));
+  VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0));
+
+  Tensor<float, 5, RowMajor> result_row_major =
+      tensor_row_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
+  VERIFY_IS_EQUAL(result.dimension(0), result_row_major.dimension(4));
+  VERIFY_IS_EQUAL(result.dimension(1), result_row_major.dimension(3));
+  VERIFY_IS_EQUAL(result.dimension(2), result_row_major.dimension(2));
+  VERIFY_IS_EQUAL(result.dimension(3), result_row_major.dimension(1));
+  VERIFY_IS_EQUAL(result.dimension(4), result_row_major.dimension(0));
+
+  // No padding is carried out.
+  int row_padding = 0;
+  int col_padding = 0;
+
+  for (int i = 0; (i + stride + ksize - 1) <= input_rows; i += stride) {    // input rows
+    for (int j = 0; (j + stride + ksize - 1) <= input_cols; j += stride) {  // input cols
+      int patchId = i + input_rows * j;
+      for (int r = 0; r < ksize; ++r) {                // patch rows
+        for (int c = 0; c < ksize; ++c) {              // patch cols
+          for (int d = 0; d < input_depth; ++d) {      // depth
+            for (int b = 0; b < input_batches; ++b) {  // batch
+              float expected = 0.0f;
+              float expected_row_major = 0.0f;
+              int row_offset = r + i - row_padding;
+              int col_offset = c + j - col_padding;
+              if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) {
+                expected = tensor(d, row_offset, col_offset, b);
+                expected_row_major = tensor_row_major(b, col_offset, row_offset, d);
+              }
+              // ColMajor
+              if (result(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (result_row_major(b, patchId, c, r, d) != expected_row_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major);
+              // Check that ColMajor and RowMajor agree.
+              VERIFY_IS_EQUAL(expected, expected_row_major);
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+// Verifies SAME padding.
+void test_patch_padding_same() {
+  int input_depth = 3;
+  int input_rows = 4;
+  int input_cols = 2;
+  int input_batches = 1;
+  int ksize = 2;   // Corresponds to the Rows and Cols for tensor.extract_image_patches<>.
+  int stride = 2;  // Only same stride is supported.
+  // ColMajor
+  Tensor<float, 4> tensor(input_depth, input_rows, input_cols, input_batches);
+  // Initializes tensor with incrementing numbers.
+  for (int i = 0; i < tensor.size(); ++i) {
+    tensor.data()[i] = i + 1;
+  }
+  Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, stride, stride, PADDING_SAME);
+
+  VERIFY_IS_EQUAL(result.dimension(0), input_depth);    // depth
+  VERIFY_IS_EQUAL(result.dimension(1), ksize);          // kernel rows
+  VERIFY_IS_EQUAL(result.dimension(2), ksize);          // kernel cols
+  VERIFY_IS_EQUAL(result.dimension(3), 2);              // number of patches
+  VERIFY_IS_EQUAL(result.dimension(4), input_batches);  // number of batches
+
+  // RowMajor
+  Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout();
+  VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3));
+  VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0));
+
+  Tensor<float, 5, RowMajor> result_row_major =
+      tensor_row_major.extract_image_patches(ksize, ksize, stride, stride, PADDING_SAME);
+  VERIFY_IS_EQUAL(result.dimension(0), result_row_major.dimension(4));
+  VERIFY_IS_EQUAL(result.dimension(1), result_row_major.dimension(3));
+  VERIFY_IS_EQUAL(result.dimension(2), result_row_major.dimension(2));
+  VERIFY_IS_EQUAL(result.dimension(3), result_row_major.dimension(1));
+  VERIFY_IS_EQUAL(result.dimension(4), result_row_major.dimension(0));
+
+  // Based on the calculation described in TensorTraits.h, padding happens to be
+  // 0.
+  int row_padding = 0;
+  int col_padding = 0;
+
+  for (int i = 0; (i + stride + ksize - 1) <= input_rows; i += stride) {    // input rows
+    for (int j = 0; (j + stride + ksize - 1) <= input_cols; j += stride) {  // input cols
+      int patchId = i + input_rows * j;
+      for (int r = 0; r < ksize; ++r) {                // patch rows
+        for (int c = 0; c < ksize; ++c) {              // patch cols
+          for (int d = 0; d < input_depth; ++d) {      // depth
+            for (int b = 0; b < input_batches; ++b) {  // batch
+              float expected = 0.0f;
+              float expected_row_major = 0.0f;
+              int row_offset = r * stride + i - row_padding;
+              int col_offset = c * stride + j - col_padding;
+              if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) {
+                expected = tensor(d, row_offset, col_offset, b);
+                expected_row_major = tensor_row_major(b, col_offset, row_offset, d);
+              }
+              // ColMajor
+              if (result(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (result_row_major(b, patchId, c, r, d) != expected_row_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major);
+              // Check that ColMajor and RowMajor agree.
+              VERIFY_IS_EQUAL(expected, expected_row_major);
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+// Verifies that SAME padding, when computed as negative values, will be clipped
+// to zero.
+void test_patch_padding_same_negative_padding_clip_to_zero() {
+  int input_depth = 1;
+  int input_rows = 15;
+  int input_cols = 1;
+  int input_batches = 1;
+  int ksize = 1;  // Corresponds to the Rows and Cols for
+                  // tensor.extract_image_patches<>.
+  int row_stride = 5;
+  int col_stride = 1;
+  // ColMajor
+  Tensor<float, 4> tensor(input_depth, input_rows, input_cols, input_batches);
+  // Initializes tensor with incrementing numbers.
+  for (int i = 0; i < tensor.size(); ++i) {
+    tensor.data()[i] = i + 1;
+  }
+  Tensor<float, 5> result = tensor.extract_image_patches(ksize, ksize, row_stride, col_stride, 1, 1, PADDING_SAME);
+  // row padding will be computed as -2 originally and then be clipped to 0.
+  VERIFY_IS_EQUAL(result.coeff(0), 1.0f);
+  VERIFY_IS_EQUAL(result.coeff(1), 6.0f);
+  VERIFY_IS_EQUAL(result.coeff(2), 11.0f);
+
+  VERIFY_IS_EQUAL(result.dimension(0), input_depth);    // depth
+  VERIFY_IS_EQUAL(result.dimension(1), ksize);          // kernel rows
+  VERIFY_IS_EQUAL(result.dimension(2), ksize);          // kernel cols
+  VERIFY_IS_EQUAL(result.dimension(3), 3);              // number of patches
+  VERIFY_IS_EQUAL(result.dimension(4), input_batches);  // number of batches
+
+  // RowMajor
+  Tensor<float, 4, RowMajor> tensor_row_major = tensor.swap_layout();
+  VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3));
+  VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0));
+
+  Tensor<float, 5, RowMajor> result_row_major =
+      tensor_row_major.extract_image_patches(ksize, ksize, row_stride, col_stride, 1, 1, PADDING_SAME);
+  VERIFY_IS_EQUAL(result_row_major.coeff(0), 1.0f);
+  VERIFY_IS_EQUAL(result_row_major.coeff(1), 6.0f);
+  VERIFY_IS_EQUAL(result_row_major.coeff(2), 11.0f);
+
+  VERIFY_IS_EQUAL(result.dimension(0), result_row_major.dimension(4));
+  VERIFY_IS_EQUAL(result.dimension(1), result_row_major.dimension(3));
+  VERIFY_IS_EQUAL(result.dimension(2), result_row_major.dimension(2));
+  VERIFY_IS_EQUAL(result.dimension(3), result_row_major.dimension(1));
+  VERIFY_IS_EQUAL(result.dimension(4), result_row_major.dimension(0));
+}
+
+void test_patch_no_extra_dim() {
+  Tensor<float, 3> tensor(2, 3, 5);
+  tensor.setRandom();
+  Tensor<float, 3, RowMajor> tensor_row_major = tensor.swap_layout();
+  VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(0));
+
+  // Single pixel patch: ColMajor
+  Tensor<float, 4> single_pixel_patch;
+  single_pixel_patch = tensor.extract_image_patches(1, 1);
+  VERIFY_IS_EQUAL(single_pixel_patch.dimension(0), 2);
+  VERIFY_IS_EQUAL(single_pixel_patch.dimension(1), 1);
+  VERIFY_IS_EQUAL(single_pixel_patch.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_pixel_patch.dimension(3), 3 * 5);
+
+  // Single pixel patch: RowMajor
+  Tensor<float, 4, RowMajor> single_pixel_patch_row_major;
+  single_pixel_patch_row_major = tensor_row_major.extract_image_patches(1, 1);
+  VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(0), 3 * 5);
+  VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(1), 1);
+  VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(3), 2);
+
+  for (int i = 0; i < tensor.size(); ++i) {
+    // ColMajor
+    if (tensor.data()[i] != single_pixel_patch.data()[i]) {
+      std::cout << "Mismatch detected at index " << i << " : " << tensor.data()[i] << " vs "
+                << single_pixel_patch.data()[i] << std::endl;
+    }
+    VERIFY_IS_EQUAL(single_pixel_patch.data()[i], tensor.data()[i]);
+    // RowMajor
+    if (tensor_row_major.data()[i] != single_pixel_patch_row_major.data()[i]) {
+      std::cout << "Mismatch detected at index " << i << " : " << tensor.data()[i] << " vs "
+                << single_pixel_patch_row_major.data()[i] << std::endl;
+    }
+    VERIFY_IS_EQUAL(single_pixel_patch_row_major.data()[i], tensor_row_major.data()[i]);
+    VERIFY_IS_EQUAL(tensor.data()[i], tensor_row_major.data()[i]);
+    VERIFY_IS_EQUAL(single_pixel_patch.data()[i], single_pixel_patch_row_major.data()[i]);
+  }
+
+  // Entire image patch: ColMajor
+  Tensor<float, 4> entire_image_patch;
+  entire_image_patch = tensor.extract_image_patches(3, 5);
+  VERIFY_IS_EQUAL(entire_image_patch.dimension(0), 2);
+  VERIFY_IS_EQUAL(entire_image_patch.dimension(1), 3);
+  VERIFY_IS_EQUAL(entire_image_patch.dimension(2), 5);
+  VERIFY_IS_EQUAL(entire_image_patch.dimension(3), 3 * 5);
+
+  // Entire image patch: RowMajor
+  Tensor<float, 4, RowMajor> entire_image_patch_row_major;
+  entire_image_patch_row_major = tensor_row_major.extract_image_patches(3, 5);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(0), 3 * 5);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(1), 5);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(2), 3);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(3), 2);
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      int patchId = i + 3 * j;
+      for (int r = 0; r < 3; ++r) {
+        for (int c = 0; c < 5; ++c) {
+          for (int d = 0; d < 2; ++d) {
+            float expected = 0.0f;
+            float expected_row_major = 0.0f;
+            if (r - 1 + i >= 0 && c - 2 + j >= 0 && r - 1 + i < 3 && c - 2 + j < 5) {
+              expected = tensor(d, r - 1 + i, c - 2 + j);
+              expected_row_major = tensor_row_major(c - 2 + j, r - 1 + i, d);
+            }
+            // ColMajor
+            if (entire_image_patch(d, r, c, patchId) != expected) {
+              std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d
+                        << std::endl;
+            }
+            VERIFY_IS_EQUAL(entire_image_patch(d, r, c, patchId), expected);
+            // RowMajor
+            if (entire_image_patch_row_major(patchId, c, r, d) != expected_row_major) {
+              std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d
+                        << std::endl;
+            }
+            VERIFY_IS_EQUAL(entire_image_patch_row_major(patchId, c, r, d), expected_row_major);
+            // Check that ColMajor and RowMajor agree.
+            VERIFY_IS_EQUAL(expected, expected_row_major);
+          }
+        }
+      }
+    }
+  }
+
+  // 2D patch: ColMajor
+  Tensor<float, 4> twod_patch;
+  twod_patch = tensor.extract_image_patches(2, 2);
+  VERIFY_IS_EQUAL(twod_patch.dimension(0), 2);
+  VERIFY_IS_EQUAL(twod_patch.dimension(1), 2);
+  VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
+  VERIFY_IS_EQUAL(twod_patch.dimension(3), 3 * 5);
+
+  // 2D patch: RowMajor
+  Tensor<float, 4, RowMajor> twod_patch_row_major;
+  twod_patch_row_major = tensor_row_major.extract_image_patches(2, 2);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(0), 3 * 5);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(1), 2);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(2), 2);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(3), 2);
+
+  // Based on the calculation described in TensorTraits.h, padding happens to be 0.
+  int row_padding = 0;
+  int col_padding = 0;
+  int stride = 1;
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      int patchId = i + 3 * j;
+      for (int r = 0; r < 2; ++r) {
+        for (int c = 0; c < 2; ++c) {
+          for (int d = 0; d < 2; ++d) {
+            float expected = 0.0f;
+            float expected_row_major = 0.0f;
+            int row_offset = r * stride + i - row_padding;
+            int col_offset = c * stride + j - col_padding;
+            // ColMajor
+            if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor.dimension(1) &&
+                col_offset < tensor.dimension(2)) {
+              expected = tensor(d, row_offset, col_offset);
+            }
+            if (twod_patch(d, r, c, patchId) != expected) {
+              std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d
+                        << std::endl;
+            }
+            VERIFY_IS_EQUAL(twod_patch(d, r, c, patchId), expected);
+            // RowMajor
+            if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_row_major.dimension(1) &&
+                col_offset < tensor_row_major.dimension(0)) {
+              expected_row_major = tensor_row_major(col_offset, row_offset, d);
+            }
+            if (twod_patch_row_major(patchId, c, r, d) != expected_row_major) {
+              std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d
+                        << std::endl;
+            }
+            VERIFY_IS_EQUAL(twod_patch_row_major(patchId, c, r, d), expected_row_major);
+            // Check that ColMajor and RowMajor agree.
+            VERIFY_IS_EQUAL(expected, expected_row_major);
+          }
+        }
+      }
+    }
+  }
+}
+
+void test_imagenet_patches() {
+  // Test the code on typical configurations used by the 'imagenet' benchmarks at
+  // https://github.com/soumith/convnet-benchmarks
+  // ColMajor
+  Tensor<float, 4> l_in(3, 128, 128, 16);
+  l_in.setRandom();
+  Tensor<float, 5> l_out = l_in.extract_image_patches(11, 11);
+  VERIFY_IS_EQUAL(l_out.dimension(0), 3);
+  VERIFY_IS_EQUAL(l_out.dimension(1), 11);
+  VERIFY_IS_EQUAL(l_out.dimension(2), 11);
+  VERIFY_IS_EQUAL(l_out.dimension(3), 128 * 128);
+  VERIFY_IS_EQUAL(l_out.dimension(4), 16);
+
+  // RowMajor
+  Tensor<float, 5, RowMajor> l_out_row_major = l_in.swap_layout().extract_image_patches(11, 11);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 16);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 128 * 128);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 11);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 11);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 3);
+
+  for (int b = 0; b < 16; ++b) {
+    for (int i = 0; i < 128; ++i) {
+      for (int j = 0; j < 128; ++j) {
+        int patchId = i + 128 * j;
+        for (int c = 0; c < 11; ++c) {
+          for (int r = 0; r < 11; ++r) {
+            for (int d = 0; d < 3; ++d) {
+              float expected = 0.0f;
+              if (r - 5 + i >= 0 && c - 5 + j >= 0 && r - 5 + i < 128 && c - 5 + j < 128) {
+                expected = l_in(d, r - 5 + i, c - 5 + j, b);
+              }
+              // ColMajor
+              if (l_out(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (l_out_row_major(b, patchId, c, r, d) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  // ColMajor
+  l_in.resize(16, 64, 64, 32);
+  l_in.setRandom();
+  l_out = l_in.extract_image_patches(9, 9);
+  VERIFY_IS_EQUAL(l_out.dimension(0), 16);
+  VERIFY_IS_EQUAL(l_out.dimension(1), 9);
+  VERIFY_IS_EQUAL(l_out.dimension(2), 9);
+  VERIFY_IS_EQUAL(l_out.dimension(3), 64 * 64);
+  VERIFY_IS_EQUAL(l_out.dimension(4), 32);
+
+  // RowMajor
+  l_out_row_major = l_in.swap_layout().extract_image_patches(9, 9);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 64 * 64);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 9);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 9);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 16);
+
+  for (int b = 0; b < 32; ++b) {
+    for (int i = 0; i < 64; ++i) {
+      for (int j = 0; j < 64; ++j) {
+        int patchId = i + 64 * j;
+        for (int c = 0; c < 9; ++c) {
+          for (int r = 0; r < 9; ++r) {
+            for (int d = 0; d < 16; ++d) {
+              float expected = 0.0f;
+              if (r - 4 + i >= 0 && c - 4 + j >= 0 && r - 4 + i < 64 && c - 4 + j < 64) {
+                expected = l_in(d, r - 4 + i, c - 4 + j, b);
+              }
+              // ColMajor
+              if (l_out(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (l_out_row_major(b, patchId, c, r, d) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  // ColMajor
+  l_in.resize(32, 16, 16, 32);
+  l_in.setRandom();
+  l_out = l_in.extract_image_patches(7, 7);
+  VERIFY_IS_EQUAL(l_out.dimension(0), 32);
+  VERIFY_IS_EQUAL(l_out.dimension(1), 7);
+  VERIFY_IS_EQUAL(l_out.dimension(2), 7);
+  VERIFY_IS_EQUAL(l_out.dimension(3), 16 * 16);
+  VERIFY_IS_EQUAL(l_out.dimension(4), 32);
+
+  // RowMajor
+  l_out_row_major = l_in.swap_layout().extract_image_patches(7, 7);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 16 * 16);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 7);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 7);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 32);
+
+  for (int b = 0; b < 32; ++b) {
+    for (int i = 0; i < 16; ++i) {
+      for (int j = 0; j < 16; ++j) {
+        int patchId = i + 16 * j;
+        for (int c = 0; c < 7; ++c) {
+          for (int r = 0; r < 7; ++r) {
+            for (int d = 0; d < 32; ++d) {
+              float expected = 0.0f;
+              if (r - 3 + i >= 0 && c - 3 + j >= 0 && r - 3 + i < 16 && c - 3 + j < 16) {
+                expected = l_in(d, r - 3 + i, c - 3 + j, b);
+              }
+              // ColMajor
+              if (l_out(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (l_out_row_major(b, patchId, c, r, d) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  // ColMajor
+  l_in.resize(64, 13, 13, 32);
+  l_in.setRandom();
+  l_out = l_in.extract_image_patches(3, 3);
+  VERIFY_IS_EQUAL(l_out.dimension(0), 64);
+  VERIFY_IS_EQUAL(l_out.dimension(1), 3);
+  VERIFY_IS_EQUAL(l_out.dimension(2), 3);
+  VERIFY_IS_EQUAL(l_out.dimension(3), 13 * 13);
+  VERIFY_IS_EQUAL(l_out.dimension(4), 32);
+
+  // RowMajor
+  l_out_row_major = l_in.swap_layout().extract_image_patches(3, 3);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 13 * 13);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 3);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 3);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 64);
+
+  for (int b = 0; b < 32; ++b) {
+    for (int i = 0; i < 13; ++i) {
+      for (int j = 0; j < 13; ++j) {
+        int patchId = i + 13 * j;
+        for (int c = 0; c < 3; ++c) {
+          for (int r = 0; r < 3; ++r) {
+            for (int d = 0; d < 64; ++d) {
+              float expected = 0.0f;
+              if (r - 1 + i >= 0 && c - 1 + j >= 0 && r - 1 + i < 13 && c - 1 + j < 13) {
+                expected = l_in(d, r - 1 + i, c - 1 + j, b);
+              }
+              // ColMajor
+              if (l_out(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (l_out_row_major(b, patchId, c, r, d) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected);
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_image_patch) {
+  CALL_SUBTEST_1(test_simple_patch());
+  CALL_SUBTEST_2(test_patch_no_extra_dim());
+  CALL_SUBTEST_3(test_patch_padding_valid());
+  CALL_SUBTEST_4(test_patch_padding_valid_same_value());
+  CALL_SUBTEST_5(test_patch_padding_same());
+  CALL_SUBTEST_6(test_imagenet_patches());
+  CALL_SUBTEST_7(test_patch_padding_same_negative_padding_clip_to_zero());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_image_patch_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_image_patch_sycl.cpp
new file mode 100644
index 0000000..9cbf26c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_image_patch_sycl.cpp
@@ -0,0 +1,1155 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+static const int DataLayout = ColMajor;
+
+template <typename DataType, typename IndexType>
+static void test_simple_image_patch_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  array<IndexType, 4> tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  array<IndexType, 4> tensorRowMajorRange = {{sizeDim4, sizeDim3, sizeDim2, sizeDim1}};
+  Tensor<DataType, 4, DataLayout, IndexType> tensor_col_major(tensorColMajorRange);
+  Tensor<DataType, 4, RowMajor, IndexType> tensor_row_major(tensorRowMajorRange);
+  tensor_col_major.setRandom();
+
+  DataType* gpu_data_col_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size() * sizeof(DataType)));
+  DataType* gpu_data_row_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
+  TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),
+                                 (tensor_col_major.size()) * sizeof(DataType));
+  gpu_row_major.device(sycl_device) = gpu_col_major.swap_layout();
+  sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major,
+                                 (tensor_col_major.size()) * sizeof(DataType));
+
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(0), tensor_row_major.dimension(3));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(1), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(2), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(3), tensor_row_major.dimension(0));
+
+  // Single pixel patch: ColMajor
+  array<IndexType, 5> patchColMajorTensorRange = {{sizeDim1, 1, 1, sizeDim2 * sizeDim3, sizeDim4}};
+  Tensor<DataType, 5, DataLayout, IndexType> single_patch_col_major(patchColMajorTensorRange);
+  size_t patchTensorBuffSize = single_patch_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_single_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_single_patch_col_major(gpu_data_single_patch_col_major,
+                                                                                   patchColMajorTensorRange);
+  gpu_single_patch_col_major.device(sycl_device) = gpu_col_major.extract_image_patches(1, 1);
+  sycl_device.memcpyDeviceToHost(single_patch_col_major.data(), gpu_data_single_patch_col_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(single_patch_col_major.dimension(0), 2);
+  VERIFY_IS_EQUAL(single_patch_col_major.dimension(1), 1);
+  VERIFY_IS_EQUAL(single_patch_col_major.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_patch_col_major.dimension(3), 3 * 5);
+  VERIFY_IS_EQUAL(single_patch_col_major.dimension(4), 7);
+
+  // Single pixel patch: RowMajor
+  array<IndexType, 5> patchRowMajorTensorRange = {{sizeDim4, sizeDim2 * sizeDim3, 1, 1, sizeDim1}};
+  Tensor<DataType, 5, RowMajor, IndexType> single_patch_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = single_patch_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_single_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_single_patch_row_major(gpu_data_single_patch_row_major,
+                                                                                 patchRowMajorTensorRange);
+  gpu_single_patch_row_major.device(sycl_device) = gpu_row_major.extract_image_patches(1, 1);
+  sycl_device.memcpyDeviceToHost(single_patch_row_major.data(), gpu_data_single_patch_row_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(single_patch_row_major.dimension(0), 7);
+  VERIFY_IS_EQUAL(single_patch_row_major.dimension(1), 3 * 5);
+  VERIFY_IS_EQUAL(single_patch_row_major.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_patch_row_major.dimension(3), 1);
+  VERIFY_IS_EQUAL(single_patch_row_major.dimension(4), 2);
+
+  for (IndexType i = 0; i < tensor_col_major.size(); ++i) {
+    // ColMajor
+    if (tensor_col_major.data()[i] != single_patch_col_major.data()[i]) {
+      std::cout << "Mismatch detected at index colmajor " << i << " : " << tensor_col_major.data()[i] << " vs "
+                << single_patch_col_major.data()[i] << std::endl;
+    }
+    VERIFY_IS_EQUAL(single_patch_col_major.data()[i], tensor_col_major.data()[i]);
+    // RowMajor
+    if (tensor_row_major.data()[i] != single_patch_row_major.data()[i]) {
+      std::cout << "Mismatch detected at index row major" << i << " : " << tensor_row_major.data()[i] << " vs "
+                << single_patch_row_major.data()[i] << std::endl;
+    }
+    VERIFY_IS_EQUAL(single_patch_row_major.data()[i], tensor_row_major.data()[i]);
+    VERIFY_IS_EQUAL(tensor_col_major.data()[i], tensor_row_major.data()[i]);
+    VERIFY_IS_EQUAL(single_patch_col_major.data()[i], single_patch_row_major.data()[i]);
+  }
+
+  // Entire image patch: ColMajor
+  patchColMajorTensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim2 * sizeDim3, sizeDim4}};
+  Tensor<DataType, 5, DataLayout, IndexType> entire_image_patch_col_major(patchColMajorTensorRange);
+  patchTensorBuffSize = entire_image_patch_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_entire_image_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_entire_image_patch_col_major(
+      gpu_data_entire_image_patch_col_major, patchColMajorTensorRange);
+  gpu_entire_image_patch_col_major.device(sycl_device) = gpu_col_major.extract_image_patches(3, 5);
+  sycl_device.memcpyDeviceToHost(entire_image_patch_col_major.data(), gpu_data_entire_image_patch_col_major,
+                                 patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(0), 2);
+  VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(1), 3);
+  VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(2), 5);
+  VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(3), 3 * 5);
+  VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(4), 7);
+
+  // Entire image patch: RowMajor
+  patchRowMajorTensorRange = {{sizeDim4, sizeDim2 * sizeDim3, sizeDim3, sizeDim2, sizeDim1}};
+  Tensor<DataType, 5, RowMajor, IndexType> entire_image_patch_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = entire_image_patch_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_entire_image_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_entire_image_patch_row_major(
+      gpu_data_entire_image_patch_row_major, patchRowMajorTensorRange);
+  gpu_entire_image_patch_row_major.device(sycl_device) = gpu_row_major.extract_image_patches(3, 5);
+  sycl_device.memcpyDeviceToHost(entire_image_patch_row_major.data(), gpu_data_entire_image_patch_row_major,
+                                 patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(0), 7);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(1), 3 * 5);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(2), 5);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(3), 3);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(4), 2);
+
+  for (IndexType i = 0; i < 3; ++i) {
+    for (IndexType j = 0; j < 5; ++j) {
+      IndexType patchId = i + 3 * j;
+      for (IndexType r = 0; r < 3; ++r) {
+        for (IndexType c = 0; c < 5; ++c) {
+          for (IndexType d = 0; d < 2; ++d) {
+            for (IndexType b = 0; b < 7; ++b) {
+              DataType expected_col_major = 0.0f;
+              DataType expected_row_major = 0.0f;
+              if (r - 1 + i >= 0 && c - 2 + j >= 0 && r - 1 + i < 3 && c - 2 + j < 5) {
+                expected_col_major = tensor_col_major(d, r - 1 + i, c - 2 + j, b);
+                expected_row_major = tensor_row_major(b, c - 2 + j, r - 1 + i, d);
+              }
+              // ColMajor
+              if (entire_image_patch_col_major(d, r, c, patchId, b) != expected_col_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(entire_image_patch_col_major(d, r, c, patchId, b), expected_col_major);
+              // RowMajor
+              if (entire_image_patch_row_major(b, patchId, c, r, d) != expected_row_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(entire_image_patch_row_major(b, patchId, c, r, d), expected_row_major);
+              // Check that ColMajor and RowMajor agree.
+              VERIFY_IS_EQUAL(expected_col_major, expected_row_major);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  // 2D patch: ColMajor
+  patchColMajorTensorRange = {{sizeDim1, 2, 2, sizeDim2 * sizeDim3, sizeDim4}};
+  Tensor<DataType, 5, DataLayout, IndexType> twod_patch_col_major(patchColMajorTensorRange);
+  patchTensorBuffSize = twod_patch_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_twod_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_twod_patch_col_major(gpu_data_twod_patch_col_major,
+                                                                                 patchColMajorTensorRange);
+  gpu_twod_patch_col_major.device(sycl_device) = gpu_col_major.extract_image_patches(2, 2);
+  sycl_device.memcpyDeviceToHost(twod_patch_col_major.data(), gpu_data_twod_patch_col_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(twod_patch_col_major.dimension(0), 2);
+  VERIFY_IS_EQUAL(twod_patch_col_major.dimension(1), 2);
+  VERIFY_IS_EQUAL(twod_patch_col_major.dimension(2), 2);
+  VERIFY_IS_EQUAL(twod_patch_col_major.dimension(3), 3 * 5);
+  VERIFY_IS_EQUAL(twod_patch_col_major.dimension(4), 7);
+
+  // 2D patch: RowMajor
+  patchRowMajorTensorRange = {{sizeDim4, sizeDim2 * sizeDim3, 2, 2, sizeDim1}};
+  Tensor<DataType, 5, RowMajor, IndexType> twod_patch_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = twod_patch_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_twod_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_twod_patch_row_major(gpu_data_twod_patch_row_major,
+                                                                               patchRowMajorTensorRange);
+  gpu_twod_patch_row_major.device(sycl_device) = gpu_row_major.extract_image_patches(2, 2);
+  sycl_device.memcpyDeviceToHost(twod_patch_row_major.data(), gpu_data_twod_patch_row_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(0), 7);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(1), 3 * 5);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(2), 2);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(3), 2);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(4), 2);
+
+  // Based on the calculation described in TensorTraits.h, padding happens to be 0.
+  IndexType row_padding = 0;
+  IndexType col_padding = 0;
+  IndexType stride = 1;
+
+  for (IndexType i = 0; i < 3; ++i) {
+    for (IndexType j = 0; j < 5; ++j) {
+      IndexType patchId = i + 3 * j;
+      for (IndexType r = 0; r < 2; ++r) {
+        for (IndexType c = 0; c < 2; ++c) {
+          for (IndexType d = 0; d < 2; ++d) {
+            for (IndexType b = 0; b < 7; ++b) {
+              DataType expected_col_major = 0.0f;
+              DataType expected_row_major = 0.0f;
+              IndexType row_offset = r * stride + i - row_padding;
+              IndexType col_offset = c * stride + j - col_padding;
+              // ColMajor
+              if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_col_major.dimension(1) &&
+                  col_offset < tensor_col_major.dimension(2)) {
+                expected_col_major = tensor_col_major(d, row_offset, col_offset, b);
+              }
+              if (twod_patch_col_major(d, r, c, patchId, b) != expected_col_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(twod_patch_col_major(d, r, c, patchId, b), expected_col_major);
+
+              // RowMajor
+              if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_row_major.dimension(2) &&
+                  col_offset < tensor_row_major.dimension(1)) {
+                expected_row_major = tensor_row_major(b, col_offset, row_offset, d);
+              }
+              if (twod_patch_row_major(b, patchId, c, r, d) != expected_row_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(twod_patch_row_major(b, patchId, c, r, d), expected_row_major);
+              // Check that ColMajor and RowMajor agree.
+              VERIFY_IS_EQUAL(expected_col_major, expected_row_major);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  sycl_device.deallocate(gpu_data_col_major);
+  sycl_device.deallocate(gpu_data_row_major);
+  sycl_device.deallocate(gpu_data_single_patch_col_major);
+  sycl_device.deallocate(gpu_data_single_patch_row_major);
+  sycl_device.deallocate(gpu_data_entire_image_patch_col_major);
+  sycl_device.deallocate(gpu_data_entire_image_patch_row_major);
+  sycl_device.deallocate(gpu_data_twod_patch_col_major);
+  sycl_device.deallocate(gpu_data_twod_patch_row_major);
+}
+
+// Verifies VALID padding (no padding) with incrementing values.
+template <typename DataType, typename IndexType>
+static void test_patch_padding_valid_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType input_depth = 3;
+  IndexType input_rows = 3;
+  IndexType input_cols = 3;
+  IndexType input_batches = 1;
+  IndexType ksize = 2;   // Corresponds to the Rows and Cols for tensor.extract_image_patches<>.
+  IndexType stride = 2;  // Only same stride is supported.
+
+  array<IndexType, 4> tensorColMajorRange = {{input_depth, input_rows, input_cols, input_batches}};
+  array<IndexType, 4> tensorRowMajorRange = {{input_batches, input_cols, input_rows, input_depth}};
+  Tensor<DataType, 4, DataLayout, IndexType> tensor_col_major(tensorColMajorRange);
+  Tensor<DataType, 4, RowMajor, IndexType> tensor_row_major(tensorRowMajorRange);
+
+  DataType* gpu_data_col_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size() * sizeof(DataType)));
+  DataType* gpu_data_row_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
+  TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),
+                                 (tensor_col_major.size()) * sizeof(DataType));
+  gpu_row_major.device(sycl_device) = gpu_col_major.swap_layout();
+  sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major,
+                                 (tensor_col_major.size()) * sizeof(DataType));
+
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(0), tensor_row_major.dimension(3));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(1), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(2), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(3), tensor_row_major.dimension(0));
+
+  // Initializes tensor with incrementing numbers.
+  for (IndexType i = 0; i < tensor_col_major.size(); ++i) {
+    tensor_col_major.data()[i] = i + 1;
+  }
+  // ColMajor
+  array<IndexType, 5> patchColMajorTensorRange = {{input_depth, ksize, ksize, 1, input_batches}};
+  Tensor<DataType, 5, DataLayout, IndexType> result_col_major(patchColMajorTensorRange);
+  size_t patchTensorBuffSize = result_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_result_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_result_col_major(gpu_data_result_col_major,
+                                                                             patchColMajorTensorRange);
+  gpu_result_col_major.device(sycl_device) =
+      gpu_col_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
+  sycl_device.memcpyDeviceToHost(result_col_major.data(), gpu_data_result_col_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(result_col_major.dimension(0), input_depth);    // depth
+  VERIFY_IS_EQUAL(result_col_major.dimension(1), ksize);          // kernel rows
+  VERIFY_IS_EQUAL(result_col_major.dimension(2), ksize);          // kernel cols
+  VERIFY_IS_EQUAL(result_col_major.dimension(3), 1);              // number of patches
+  VERIFY_IS_EQUAL(result_col_major.dimension(4), input_batches);  // number of batches
+
+  // RowMajor
+  array<IndexType, 5> patchRowMajorTensorRange = {{input_batches, 1, ksize, ksize, input_depth}};
+  Tensor<DataType, 5, RowMajor, IndexType> result_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = result_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_result_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_result_row_major(gpu_data_result_row_major,
+                                                                           patchRowMajorTensorRange);
+  gpu_result_row_major.device(sycl_device) =
+      gpu_row_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
+  sycl_device.memcpyDeviceToHost(result_row_major.data(), gpu_data_result_row_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(result_col_major.dimension(0), result_row_major.dimension(4));
+  VERIFY_IS_EQUAL(result_col_major.dimension(1), result_row_major.dimension(3));
+  VERIFY_IS_EQUAL(result_col_major.dimension(2), result_row_major.dimension(2));
+  VERIFY_IS_EQUAL(result_col_major.dimension(3), result_row_major.dimension(1));
+  VERIFY_IS_EQUAL(result_col_major.dimension(4), result_row_major.dimension(0));
+
+  // No padding is carried out.
+  IndexType row_padding = 0;
+  IndexType col_padding = 0;
+
+  for (IndexType i = 0; (i + stride + ksize - 1) < input_rows; i += stride) {    // input rows
+    for (IndexType j = 0; (j + stride + ksize - 1) < input_cols; j += stride) {  // input cols
+      IndexType patchId = i + input_rows * j;
+      for (IndexType r = 0; r < ksize; ++r) {                // patch rows
+        for (IndexType c = 0; c < ksize; ++c) {              // patch cols
+          for (IndexType d = 0; d < input_depth; ++d) {      // depth
+            for (IndexType b = 0; b < input_batches; ++b) {  // batch
+              DataType expected_col_major = 0.0f;
+              DataType expected_row_major = 0.0f;
+              IndexType row_offset = r + i - row_padding;
+              IndexType col_offset = c + j - col_padding;
+              if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) {
+                expected_col_major = tensor_col_major(d, row_offset, col_offset, b);
+                expected_row_major = tensor_row_major(b, col_offset, row_offset, d);
+              }
+              // ColMajor
+              if (result_col_major(d, r, c, patchId, b) != expected_col_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result_col_major(d, r, c, patchId, b), expected_col_major);
+              // RowMajor
+              if (result_row_major(b, patchId, c, r, d) != expected_row_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major);
+              // Check that ColMajor and RowMajor agree.
+              VERIFY_IS_EQUAL(expected_col_major, expected_row_major);
+            }
+          }
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data_col_major);
+  sycl_device.deallocate(gpu_data_row_major);
+  sycl_device.deallocate(gpu_data_result_col_major);
+  sycl_device.deallocate(gpu_data_result_row_major);
+}
+
+// Verifies VALID padding (no padding) with the same value.
+template <typename DataType, typename IndexType>
+static void test_patch_padding_valid_same_value_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType input_depth = 1;
+  IndexType input_rows = 5;
+  IndexType input_cols = 5;
+  IndexType input_batches = 2;
+  IndexType ksize = 3;   // Corresponds to the Rows and Cols for tensor.extract_image_patches<>.
+  IndexType stride = 2;  // Only same stride is supported.
+  // ColMajor
+
+  array<IndexType, 4> tensorColMajorRange = {{input_depth, input_rows, input_cols, input_batches}};
+  array<IndexType, 4> tensorRowMajorRange = {{input_batches, input_cols, input_rows, input_depth}};
+  Tensor<DataType, 4, DataLayout, IndexType> tensor_col_major(tensorColMajorRange);
+  Tensor<DataType, 4, RowMajor, IndexType> tensor_row_major(tensorRowMajorRange);
+
+  DataType* gpu_data_col_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size() * sizeof(DataType)));
+  DataType* gpu_data_row_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
+  TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
+  gpu_col_major.device(sycl_device) = gpu_col_major.constant(11.0f);
+  gpu_row_major.device(sycl_device) = gpu_col_major.swap_layout();
+  sycl_device.memcpyDeviceToHost(tensor_col_major.data(), gpu_data_col_major,
+                                 (tensor_col_major.size()) * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major,
+                                 (tensor_row_major.size()) * sizeof(DataType));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(0), tensor_row_major.dimension(3));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(1), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(2), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(3), tensor_row_major.dimension(0));
+
+  array<IndexType, 5> patchColMajorTensorRange = {{input_depth, ksize, ksize, 4, input_batches}};
+  Tensor<DataType, 5, DataLayout, IndexType> result_col_major(patchColMajorTensorRange);
+  size_t patchTensorBuffSize = result_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_result_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_result_col_major(gpu_data_result_col_major,
+                                                                             patchColMajorTensorRange);
+  gpu_result_col_major.device(sycl_device) =
+      gpu_col_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
+  sycl_device.memcpyDeviceToHost(result_col_major.data(), gpu_data_result_col_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(result_col_major.dimension(0), input_depth);    // depth
+  VERIFY_IS_EQUAL(result_col_major.dimension(1), ksize);          // kernel rows
+  VERIFY_IS_EQUAL(result_col_major.dimension(2), ksize);          // kernel cols
+  VERIFY_IS_EQUAL(result_col_major.dimension(3), 4);              // number of patches
+  VERIFY_IS_EQUAL(result_col_major.dimension(4), input_batches);  // number of batches
+
+  // RowMajor
+  array<IndexType, 5> patchRowMajorTensorRange = {{input_batches, 4, ksize, ksize, input_depth}};
+  Tensor<DataType, 5, RowMajor, IndexType> result_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = result_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_result_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_result_row_major(gpu_data_result_row_major,
+                                                                           patchRowMajorTensorRange);
+  gpu_result_row_major.device(sycl_device) =
+      gpu_row_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID);
+  sycl_device.memcpyDeviceToHost(result_row_major.data(), gpu_data_result_row_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(result_col_major.dimension(0), result_row_major.dimension(4));
+  VERIFY_IS_EQUAL(result_col_major.dimension(1), result_row_major.dimension(3));
+  VERIFY_IS_EQUAL(result_col_major.dimension(2), result_row_major.dimension(2));
+  VERIFY_IS_EQUAL(result_col_major.dimension(3), result_row_major.dimension(1));
+  VERIFY_IS_EQUAL(result_col_major.dimension(4), result_row_major.dimension(0));
+
+  // No padding is carried out.
+  IndexType row_padding = 0;
+  IndexType col_padding = 0;
+
+  for (IndexType i = 0; (i + stride + ksize - 1) <= input_rows; i += stride) {    // input rows
+    for (IndexType j = 0; (j + stride + ksize - 1) <= input_cols; j += stride) {  // input cols
+      IndexType patchId = i + input_rows * j;
+      for (IndexType r = 0; r < ksize; ++r) {                // patch rows
+        for (IndexType c = 0; c < ksize; ++c) {              // patch cols
+          for (IndexType d = 0; d < input_depth; ++d) {      // depth
+            for (IndexType b = 0; b < input_batches; ++b) {  // batch
+              DataType expected_col_major = 0.0f;
+              DataType expected_row_major = 0.0f;
+              IndexType row_offset = r + i - row_padding;
+              IndexType col_offset = c + j - col_padding;
+              if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) {
+                expected_col_major = tensor_col_major(d, row_offset, col_offset, b);
+                expected_row_major = tensor_row_major(b, col_offset, row_offset, d);
+              }
+              // ColMajor
+              if (result_col_major(d, r, c, patchId, b) != expected_col_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result_col_major(d, r, c, patchId, b), expected_col_major);
+              // RowMajor
+              if (result_row_major(b, patchId, c, r, d) != expected_row_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major);
+              // Check that ColMajor and RowMajor agree.
+              VERIFY_IS_EQUAL(expected_col_major, expected_row_major);
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+// Verifies SAME padding.
+template <typename DataType, typename IndexType>
+static void test_patch_padding_same_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType input_depth = 3;
+  IndexType input_rows = 4;
+  IndexType input_cols = 2;
+  IndexType input_batches = 1;
+  IndexType ksize = 2;   // Corresponds to the Rows and Cols for tensor.extract_image_patches<>.
+  IndexType stride = 2;  // Only same stride is supported.
+
+  // ColMajor
+  array<IndexType, 4> tensorColMajorRange = {{input_depth, input_rows, input_cols, input_batches}};
+  array<IndexType, 4> tensorRowMajorRange = {{input_batches, input_cols, input_rows, input_depth}};
+  Tensor<DataType, 4, DataLayout, IndexType> tensor_col_major(tensorColMajorRange);
+  Tensor<DataType, 4, RowMajor, IndexType> tensor_row_major(tensorRowMajorRange);
+
+  DataType* gpu_data_col_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size() * sizeof(DataType)));
+  DataType* gpu_data_row_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
+  TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),
+                                 (tensor_col_major.size()) * sizeof(DataType));
+  gpu_row_major.device(sycl_device) = gpu_col_major.swap_layout();
+  sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major,
+                                 (tensor_col_major.size()) * sizeof(DataType));
+
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(0), tensor_row_major.dimension(3));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(1), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(2), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(3), tensor_row_major.dimension(0));
+
+  // Initializes tensor with incrementing numbers.
+  for (IndexType i = 0; i < tensor_col_major.size(); ++i) {
+    tensor_col_major.data()[i] = i + 1;
+  }
+
+  array<IndexType, 5> patchColMajorTensorRange = {{input_depth, ksize, ksize, 2, input_batches}};
+  Tensor<DataType, 5, DataLayout, IndexType> result_col_major(patchColMajorTensorRange);
+  size_t patchTensorBuffSize = result_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_result_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_result_col_major(gpu_data_result_col_major,
+                                                                             patchColMajorTensorRange);
+  gpu_result_col_major.device(sycl_device) =
+      gpu_col_major.extract_image_patches(ksize, ksize, stride, stride, PADDING_SAME);
+  sycl_device.memcpyDeviceToHost(result_col_major.data(), gpu_data_result_col_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(result_col_major.dimension(0), input_depth);    // depth
+  VERIFY_IS_EQUAL(result_col_major.dimension(1), ksize);          // kernel rows
+  VERIFY_IS_EQUAL(result_col_major.dimension(2), ksize);          // kernel cols
+  VERIFY_IS_EQUAL(result_col_major.dimension(3), 2);              // number of patches
+  VERIFY_IS_EQUAL(result_col_major.dimension(4), input_batches);  // number of batches
+
+  // RowMajor
+
+  array<IndexType, 5> patchRowMajorTensorRange = {{input_batches, 2, ksize, ksize, input_depth}};
+  Tensor<DataType, 5, RowMajor, IndexType> result_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = result_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_result_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_result_row_major(gpu_data_result_row_major,
+                                                                           patchRowMajorTensorRange);
+  gpu_result_row_major.device(sycl_device) =
+      gpu_row_major.extract_image_patches(ksize, ksize, stride, stride, PADDING_SAME);
+  sycl_device.memcpyDeviceToHost(result_row_major.data(), gpu_data_result_row_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(result_col_major.dimension(0), result_row_major.dimension(4));
+  VERIFY_IS_EQUAL(result_col_major.dimension(1), result_row_major.dimension(3));
+  VERIFY_IS_EQUAL(result_col_major.dimension(2), result_row_major.dimension(2));
+  VERIFY_IS_EQUAL(result_col_major.dimension(3), result_row_major.dimension(1));
+  VERIFY_IS_EQUAL(result_col_major.dimension(4), result_row_major.dimension(0));
+
+  // Based on the calculation described in TensorTraits.h, padding happens to be 0.
+  IndexType row_padding = 0;
+  IndexType col_padding = 0;
+
+  for (IndexType i = 0; (i + stride + ksize - 1) <= input_rows; i += stride) {    // input rows
+    for (IndexType j = 0; (j + stride + ksize - 1) <= input_cols; j += stride) {  // input cols
+      IndexType patchId = i + input_rows * j;
+      for (IndexType r = 0; r < ksize; ++r) {                // patch rows
+        for (IndexType c = 0; c < ksize; ++c) {              // patch cols
+          for (IndexType d = 0; d < input_depth; ++d) {      // depth
+            for (IndexType b = 0; b < input_batches; ++b) {  // batch
+              DataType expected_col_major = 0.0f;
+              DataType expected_row_major = 0.0f;
+              IndexType row_offset = r * stride + i - row_padding;
+              IndexType col_offset = c * stride + j - col_padding;
+              if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) {
+                expected_col_major = tensor_col_major(d, row_offset, col_offset, b);
+                expected_row_major = tensor_row_major(b, col_offset, row_offset, d);
+              }
+              // ColMajor
+              if (result_col_major(d, r, c, patchId, b) != expected_col_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result_col_major(d, r, c, patchId, b), expected_col_major);
+              // RowMajor
+              if (result_row_major(b, patchId, c, r, d) != expected_row_major) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major);
+              // Check that ColMajor and RowMajor agree.
+              VERIFY_IS_EQUAL(expected_col_major, expected_row_major);
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+template <typename DataType, typename IndexType>
+static void test_patch_no_extra_dim_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+
+  // ColMajor
+  array<IndexType, 3> tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  array<IndexType, 3> tensorRowMajorRange = {{sizeDim3, sizeDim2, sizeDim1}};
+  Tensor<DataType, 3, DataLayout, IndexType> tensor_col_major(tensorColMajorRange);
+  tensor_col_major.setRandom();
+  Tensor<DataType, 3, RowMajor, IndexType> tensor_row_major(tensorRowMajorRange);
+
+  DataType* gpu_data_col_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size() * sizeof(DataType)));
+  DataType* gpu_data_row_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 3, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
+  TensorMap<Tensor<DataType, 3, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),
+                                 (tensor_col_major.size()) * sizeof(DataType));
+  gpu_row_major.device(sycl_device) = gpu_col_major.swap_layout();
+  sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major,
+                                 (tensor_row_major.size()) * sizeof(DataType));
+
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(0), tensor_row_major.dimension(2));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(1), tensor_row_major.dimension(1));
+  VERIFY_IS_EQUAL(tensor_col_major.dimension(2), tensor_row_major.dimension(0));
+
+  // Single pixel patch: ColMajor
+  array<IndexType, 4> patchColMajorTensorRange = {{sizeDim1, 1, 1, sizeDim2 * sizeDim3}};
+  Tensor<DataType, 4, DataLayout, IndexType> single_patch_col_major(patchColMajorTensorRange);
+  size_t patchTensorBuffSize = single_patch_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_single_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_single_patch_col_major(gpu_data_single_patch_col_major,
+                                                                                   patchColMajorTensorRange);
+  gpu_single_patch_col_major.device(sycl_device) = gpu_col_major.extract_image_patches(1, 1);
+  sycl_device.memcpyDeviceToHost(single_patch_col_major.data(), gpu_data_single_patch_col_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(single_patch_col_major.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(single_patch_col_major.dimension(1), 1);
+  VERIFY_IS_EQUAL(single_patch_col_major.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_patch_col_major.dimension(3), sizeDim2 * sizeDim3);
+
+  // Single pixel patch: RowMajor
+  array<IndexType, 4> patchRowMajorTensorRange = {{sizeDim2 * sizeDim3, 1, 1, sizeDim1}};
+  Tensor<DataType, 4, RowMajor, IndexType> single_patch_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = single_patch_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_single_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_single_patch_row_major(gpu_data_single_patch_row_major,
+                                                                                 patchRowMajorTensorRange);
+  gpu_single_patch_row_major.device(sycl_device) = gpu_row_major.extract_image_patches(1, 1);
+  sycl_device.memcpyDeviceToHost(single_patch_row_major.data(), gpu_data_single_patch_row_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(single_patch_row_major.dimension(0), sizeDim2 * sizeDim3);
+  VERIFY_IS_EQUAL(single_patch_row_major.dimension(1), 1);
+  VERIFY_IS_EQUAL(single_patch_row_major.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_patch_row_major.dimension(3), sizeDim1);
+
+  for (IndexType i = 0; i < tensor_col_major.size(); ++i) {
+    // ColMajor
+    if (tensor_col_major.data()[i] != single_patch_col_major.data()[i]) {
+      std::cout << "Mismatch detected at index " << i << " : " << tensor_col_major.data()[i] << " vs "
+                << single_patch_col_major.data()[i] << std::endl;
+    }
+    VERIFY_IS_EQUAL(single_patch_col_major.data()[i], tensor_col_major.data()[i]);
+    // RowMajor
+    if (tensor_row_major.data()[i] != single_patch_row_major.data()[i]) {
+      std::cout << "Mismatch detected at index " << i << " : " << tensor_col_major.data()[i] << " vs "
+                << single_patch_row_major.data()[i] << std::endl;
+    }
+    VERIFY_IS_EQUAL(single_patch_row_major.data()[i], tensor_row_major.data()[i]);
+    VERIFY_IS_EQUAL(tensor_col_major.data()[i], tensor_row_major.data()[i]);
+    VERIFY_IS_EQUAL(single_patch_col_major.data()[i], single_patch_row_major.data()[i]);
+  }
+
+  // Entire image patch: ColMajor
+  patchColMajorTensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim2 * sizeDim3}};
+  Tensor<DataType, 4, DataLayout, IndexType> entire_image_patch_col_major(patchColMajorTensorRange);
+  patchTensorBuffSize = entire_image_patch_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_entire_image_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_entire_image_patch_col_major(
+      gpu_data_entire_image_patch_col_major, patchColMajorTensorRange);
+  gpu_entire_image_patch_col_major.device(sycl_device) = gpu_col_major.extract_image_patches(3, 5);
+  sycl_device.memcpyDeviceToHost(entire_image_patch_col_major.data(), gpu_data_entire_image_patch_col_major,
+                                 patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(0), 2);
+  VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(1), 3);
+  VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(2), 5);
+  VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(3), 3 * 5);
+
+  // Entire image patch: RowMajor
+  patchRowMajorTensorRange = {{sizeDim2 * sizeDim3, sizeDim3, sizeDim2, sizeDim1}};
+  Tensor<DataType, 4, RowMajor, IndexType> entire_image_patch_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = entire_image_patch_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_entire_image_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_entire_image_patch_row_major(
+      gpu_data_entire_image_patch_row_major, patchRowMajorTensorRange);
+  gpu_entire_image_patch_row_major.device(sycl_device) = gpu_row_major.extract_image_patches(3, 5);
+  sycl_device.memcpyDeviceToHost(entire_image_patch_row_major.data(), gpu_data_entire_image_patch_row_major,
+                                 patchTensorBuffSize);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(0), 3 * 5);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(1), 5);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(2), 3);
+  VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(3), 2);
+
+  for (IndexType i = 0; i < 3; ++i) {
+    for (IndexType j = 0; j < 5; ++j) {
+      IndexType patchId = i + 3 * j;
+      for (IndexType r = 0; r < 3; ++r) {
+        for (IndexType c = 0; c < 5; ++c) {
+          for (IndexType d = 0; d < 2; ++d) {
+            DataType expected_col_major = 0.0f;
+            DataType expected_row_major = 0.0f;
+            if (r - 1 + i >= 0 && c - 2 + j >= 0 && r - 1 + i < 3 && c - 2 + j < 5) {
+              expected_col_major = tensor_col_major(d, r - 1 + i, c - 2 + j);
+              expected_row_major = tensor_row_major(c - 2 + j, r - 1 + i, d);
+            }
+            // ColMajor
+            if (entire_image_patch_col_major(d, r, c, patchId) != expected_col_major) {
+              std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d
+                        << std::endl;
+            }
+            VERIFY_IS_EQUAL(entire_image_patch_col_major(d, r, c, patchId), expected_col_major);
+            // RowMajor
+            if (entire_image_patch_row_major(patchId, c, r, d) != expected_row_major) {
+              std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d
+                        << std::endl;
+            }
+            VERIFY_IS_EQUAL(entire_image_patch_row_major(patchId, c, r, d), expected_row_major);
+            // Check that ColMajor and RowMajor agree.
+            VERIFY_IS_EQUAL(expected_col_major, expected_row_major);
+          }
+        }
+      }
+    }
+  }
+
+  // 2D patch: ColMajor
+  patchColMajorTensorRange = {{sizeDim1, 2, 2, sizeDim2 * sizeDim3}};
+  Tensor<DataType, 4, DataLayout, IndexType> twod_patch_col_major(patchColMajorTensorRange);
+  patchTensorBuffSize = twod_patch_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_twod_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_twod_patch_col_major(gpu_data_twod_patch_col_major,
+                                                                                 patchColMajorTensorRange);
+  gpu_twod_patch_col_major.device(sycl_device) = gpu_col_major.extract_image_patches(2, 2);
+  sycl_device.memcpyDeviceToHost(twod_patch_col_major.data(), gpu_data_twod_patch_col_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(twod_patch_col_major.dimension(0), 2);
+  VERIFY_IS_EQUAL(twod_patch_col_major.dimension(1), 2);
+  VERIFY_IS_EQUAL(twod_patch_col_major.dimension(2), 2);
+  VERIFY_IS_EQUAL(twod_patch_col_major.dimension(3), 3 * 5);
+
+  // 2D patch: RowMajor
+  patchRowMajorTensorRange = {{sizeDim2 * sizeDim3, 2, 2, sizeDim1}};
+  Tensor<DataType, 4, RowMajor, IndexType> twod_patch_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = twod_patch_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_twod_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_twod_patch_row_major(gpu_data_twod_patch_row_major,
+                                                                               patchRowMajorTensorRange);
+  gpu_twod_patch_row_major.device(sycl_device) = gpu_row_major.extract_image_patches(2, 2);
+  sycl_device.memcpyDeviceToHost(twod_patch_row_major.data(), gpu_data_twod_patch_row_major, patchTensorBuffSize);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(0), 3 * 5);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(1), 2);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(2), 2);
+  VERIFY_IS_EQUAL(twod_patch_row_major.dimension(3), 2);
+
+  // Based on the calculation described in TensorTraits.h, padding happens to be 0.
+  IndexType row_padding = 0;
+  IndexType col_padding = 0;
+  IndexType stride = 1;
+
+  for (IndexType i = 0; i < 3; ++i) {
+    for (IndexType j = 0; j < 5; ++j) {
+      IndexType patchId = i + 3 * j;
+      for (IndexType r = 0; r < 2; ++r) {
+        for (IndexType c = 0; c < 2; ++c) {
+          for (IndexType d = 0; d < 2; ++d) {
+            DataType expected_col_major = 0.0f;
+            DataType expected_row_major = 0.0f;
+            IndexType row_offset = r * stride + i - row_padding;
+            IndexType col_offset = c * stride + j - col_padding;
+            // ColMajor
+            if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_col_major.dimension(1) &&
+                col_offset < tensor_col_major.dimension(2)) {
+              expected_col_major = tensor_col_major(d, row_offset, col_offset);
+            }
+            if (twod_patch_col_major(d, r, c, patchId) != expected_col_major) {
+              std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d
+                        << std::endl;
+            }
+            VERIFY_IS_EQUAL(twod_patch_col_major(d, r, c, patchId), expected_col_major);
+            // RowMajor
+            if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_row_major.dimension(1) &&
+                col_offset < tensor_row_major.dimension(0)) {
+              expected_row_major = tensor_row_major(col_offset, row_offset, d);
+            }
+            if (twod_patch_row_major(patchId, c, r, d) != expected_row_major) {
+              std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d
+                        << std::endl;
+            }
+            VERIFY_IS_EQUAL(twod_patch_row_major(patchId, c, r, d), expected_row_major);
+            // Check that ColMajor and RowMajor agree.
+            VERIFY_IS_EQUAL(expected_col_major, expected_row_major);
+          }
+        }
+      }
+    }
+  }
+
+  sycl_device.deallocate(gpu_data_col_major);
+  sycl_device.deallocate(gpu_data_row_major);
+  sycl_device.deallocate(gpu_data_single_patch_col_major);
+  sycl_device.deallocate(gpu_data_single_patch_row_major);
+  sycl_device.deallocate(gpu_data_entire_image_patch_col_major);
+  sycl_device.deallocate(gpu_data_entire_image_patch_row_major);
+  sycl_device.deallocate(gpu_data_twod_patch_col_major);
+  sycl_device.deallocate(gpu_data_twod_patch_row_major);
+}
+
+template <typename DataType, typename IndexType>
+static void test_imagenet_patches_sycl(const Eigen::SyclDevice& sycl_device) {
+  // Test the code on typical configurations used by the 'imagenet' benchmarks at
+  // https://github.com/soumith/convnet-benchmarks
+  // ColMajor
+  IndexType sizeDim1 = 3;
+  IndexType sizeDim2 = 128;
+  IndexType sizeDim3 = 128;
+  IndexType sizeDim4 = 16;
+  array<IndexType, 4> tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  Tensor<DataType, 4, DataLayout, IndexType> l_in_col_major(tensorColMajorRange);
+  l_in_col_major.setRandom();
+
+  DataType* gpu_data_l_in_col_major =
+      static_cast<DataType*>(sycl_device.allocate(l_in_col_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_l_in_col_major(gpu_data_l_in_col_major, tensorColMajorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_l_in_col_major, l_in_col_major.data(),
+                                 (l_in_col_major.size()) * sizeof(DataType));
+
+  array<IndexType, 5> patchTensorRange = {{sizeDim1, 11, 11, sizeDim2 * sizeDim3, sizeDim4}};
+  Tensor<DataType, 5, DataLayout, IndexType> l_out_col_major(patchTensorRange);
+  size_t patchTensorBuffSize = l_out_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_l_out_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_l_out_col_major(gpu_data_l_out_col_major, patchTensorRange);
+  gpu_l_out_col_major.device(sycl_device) = gpu_l_in_col_major.extract_image_patches(11, 11);
+  sycl_device.memcpyDeviceToHost(l_out_col_major.data(), gpu_data_l_out_col_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(1), 11);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(2), 11);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(3), sizeDim2 * sizeDim3);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(4), sizeDim4);
+
+  // RowMajor
+  patchTensorRange = {{sizeDim4, sizeDim2 * sizeDim3, 11, 11, sizeDim1}};
+  Tensor<DataType, 5, RowMajor, IndexType> l_out_row_major(patchTensorRange);
+  patchTensorBuffSize = l_out_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_l_out_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_l_out_row_major(gpu_data_l_out_row_major, patchTensorRange);
+  gpu_l_out_row_major.device(sycl_device) = gpu_l_in_col_major.swap_layout().extract_image_patches(11, 11);
+  sycl_device.memcpyDeviceToHost(l_out_row_major.data(), gpu_data_l_out_row_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(0), sizeDim4);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(1), sizeDim2 * sizeDim3);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 11);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 11);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(4), sizeDim1);
+
+  for (IndexType b = 0; b < 16; ++b) {
+    for (IndexType i = 0; i < 128; ++i) {
+      for (IndexType j = 0; j < 128; ++j) {
+        IndexType patchId = i + 128 * j;
+        for (IndexType c = 0; c < 11; ++c) {
+          for (IndexType r = 0; r < 11; ++r) {
+            for (IndexType d = 0; d < 3; ++d) {
+              DataType expected = 0.0f;
+              if (r - 5 + i >= 0 && c - 5 + j >= 0 && r - 5 + i < 128 && c - 5 + j < 128) {
+                expected = l_in_col_major(d, r - 5 + i, c - 5 + j, b);
+              }
+              // ColMajor
+              if (l_out_col_major(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_col_major(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (l_out_row_major(b, patchId, c, r, d) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  // ColMajor
+  sycl_device.deallocate(gpu_data_l_in_col_major);
+  sycl_device.deallocate(gpu_data_l_out_col_major);
+  sizeDim1 = 16;
+  sizeDim2 = 64;
+  sizeDim3 = 64;
+  sizeDim4 = 32;
+  tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  l_in_col_major.resize(tensorColMajorRange);
+  l_in_col_major.setRandom();
+  gpu_data_l_in_col_major = static_cast<DataType*>(sycl_device.allocate(l_in_col_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_l_in_col_major_resize1(gpu_data_l_in_col_major,
+                                                                                 tensorColMajorRange);
+
+  patchTensorRange = {{sizeDim1, 9, 9, sizeDim2 * sizeDim3, sizeDim4}};
+  l_out_col_major.resize(patchTensorRange);
+  patchTensorBuffSize = l_out_col_major.size() * sizeof(DataType);
+  gpu_data_l_out_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_l_out_col_major_resize1(gpu_data_l_out_col_major,
+                                                                                    patchTensorRange);
+  sycl_device.memcpyHostToDevice(gpu_data_l_in_col_major, l_in_col_major.data(),
+                                 (l_in_col_major.size()) * sizeof(DataType));
+  gpu_l_out_col_major_resize1.device(sycl_device) = gpu_l_in_col_major_resize1.extract_image_patches(9, 9);
+  sycl_device.memcpyDeviceToHost(l_out_col_major.data(), gpu_data_l_out_col_major, patchTensorBuffSize);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(0), 16);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(1), 9);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(2), 9);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(3), 64 * 64);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(4), 32);
+
+  // RowMajor
+  sycl_device.deallocate(gpu_data_l_out_row_major);
+  patchTensorRange = {{sizeDim4, sizeDim2 * sizeDim3, 9, 9, sizeDim1}};
+  l_out_row_major.resize(patchTensorRange);
+  patchTensorBuffSize = l_out_row_major.size() * sizeof(DataType);
+  gpu_data_l_out_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_l_out_row_major_resize1(gpu_data_l_out_row_major,
+                                                                                  patchTensorRange);
+  gpu_l_out_row_major_resize1.device(sycl_device) =
+      gpu_l_in_col_major_resize1.swap_layout().extract_image_patches(9, 9);
+  sycl_device.memcpyDeviceToHost(l_out_row_major.data(), gpu_data_l_out_row_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 64 * 64);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 9);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 9);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 16);
+
+  for (IndexType b = 0; b < 32; ++b) {
+    for (IndexType i = 0; i < 64; ++i) {
+      for (IndexType j = 0; j < 64; ++j) {
+        IndexType patchId = i + 64 * j;
+        for (IndexType c = 0; c < 9; ++c) {
+          for (IndexType r = 0; r < 9; ++r) {
+            for (IndexType d = 0; d < 16; ++d) {
+              DataType expected = 0.0f;
+              if (r - 4 + i >= 0 && c - 4 + j >= 0 && r - 4 + i < 64 && c - 4 + j < 64) {
+                expected = l_in_col_major(d, r - 4 + i, c - 4 + j, b);
+              }
+              // ColMajor
+              if (l_out_col_major(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_col_major(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (l_out_row_major(b, patchId, c, r, d) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  // ColMajor
+
+  sycl_device.deallocate(gpu_data_l_in_col_major);
+  sycl_device.deallocate(gpu_data_l_out_col_major);
+  sizeDim1 = 32;
+  sizeDim2 = 16;
+  sizeDim3 = 16;
+  sizeDim4 = 32;
+  tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  l_in_col_major.resize(tensorColMajorRange);
+  l_in_col_major.setRandom();
+  gpu_data_l_in_col_major = static_cast<DataType*>(sycl_device.allocate(l_in_col_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_l_in_col_major_resize2(gpu_data_l_in_col_major,
+                                                                                 tensorColMajorRange);
+
+  patchTensorRange = {{sizeDim1, 7, 7, sizeDim2 * sizeDim3, sizeDim4}};
+  l_out_col_major.resize(patchTensorRange);
+  patchTensorBuffSize = l_out_col_major.size() * sizeof(DataType);
+  gpu_data_l_out_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_l_out_col_major_resize2(gpu_data_l_out_col_major,
+                                                                                    patchTensorRange);
+  sycl_device.memcpyHostToDevice(gpu_data_l_in_col_major, l_in_col_major.data(),
+                                 (l_in_col_major.size()) * sizeof(DataType));
+  gpu_l_out_col_major_resize2.device(sycl_device) = gpu_l_in_col_major_resize2.extract_image_patches(7, 7);
+  sycl_device.memcpyDeviceToHost(l_out_col_major.data(), gpu_data_l_out_col_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(0), 32);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(1), 7);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(2), 7);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(3), 16 * 16);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(4), 32);
+
+  // RowMajor
+  sycl_device.deallocate(gpu_data_l_out_row_major);
+  patchTensorRange = {{sizeDim4, sizeDim2 * sizeDim3, 7, 7, sizeDim1}};
+  l_out_row_major.resize(patchTensorRange);
+  patchTensorBuffSize = l_out_row_major.size() * sizeof(DataType);
+  gpu_data_l_out_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_l_out_row_major_resize2(gpu_data_l_out_row_major,
+                                                                                  patchTensorRange);
+  gpu_l_out_row_major_resize2.device(sycl_device) =
+      gpu_l_in_col_major_resize2.swap_layout().extract_image_patches(7, 7);
+  sycl_device.memcpyDeviceToHost(l_out_row_major.data(), gpu_data_l_out_row_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 16 * 16);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 7);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 7);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 32);
+
+  for (IndexType b = 0; b < 32; ++b) {
+    for (IndexType i = 0; i < 16; ++i) {
+      for (IndexType j = 0; j < 16; ++j) {
+        IndexType patchId = i + 16 * j;
+        for (IndexType c = 0; c < 7; ++c) {
+          for (IndexType r = 0; r < 7; ++r) {
+            for (IndexType d = 0; d < 32; ++d) {
+              DataType expected = 0.0f;
+              if (r - 3 + i >= 0 && c - 3 + j >= 0 && r - 3 + i < 16 && c - 3 + j < 16) {
+                expected = l_in_col_major(d, r - 3 + i, c - 3 + j, b);
+              }
+              // ColMajor
+              if (l_out_col_major(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_col_major(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (l_out_row_major(b, patchId, c, r, d) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  // ColMajor
+  sycl_device.deallocate(gpu_data_l_in_col_major);
+  sycl_device.deallocate(gpu_data_l_out_col_major);
+  sizeDim1 = 64;
+  sizeDim2 = 13;
+  sizeDim3 = 13;
+  sizeDim4 = 32;
+  tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  l_in_col_major.resize(tensorColMajorRange);
+  l_in_col_major.setRandom();
+  gpu_data_l_in_col_major = static_cast<DataType*>(sycl_device.allocate(l_in_col_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_l_in_col_major_resize3(gpu_data_l_in_col_major,
+                                                                                 tensorColMajorRange);
+
+  patchTensorRange = {{sizeDim1, 3, 3, sizeDim2 * sizeDim3, sizeDim4}};
+  l_out_col_major.resize(patchTensorRange);
+  patchTensorBuffSize = l_out_col_major.size() * sizeof(DataType);
+  gpu_data_l_out_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_l_out_col_major_resize3(gpu_data_l_out_col_major,
+                                                                                    patchTensorRange);
+  sycl_device.memcpyHostToDevice(gpu_data_l_in_col_major, l_in_col_major.data(),
+                                 (l_in_col_major.size()) * sizeof(DataType));
+  gpu_l_out_col_major_resize3.device(sycl_device) = gpu_l_in_col_major_resize3.extract_image_patches(3, 3);
+  sycl_device.memcpyDeviceToHost(l_out_col_major.data(), gpu_data_l_out_col_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(0), 64);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(1), 3);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(2), 3);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(3), 13 * 13);
+  VERIFY_IS_EQUAL(l_out_col_major.dimension(4), 32);
+
+  // RowMajor
+  sycl_device.deallocate(gpu_data_l_out_row_major);
+  patchTensorRange = {{sizeDim4, sizeDim2 * sizeDim3, 3, 3, sizeDim1}};
+  l_out_row_major.resize(patchTensorRange);
+  patchTensorBuffSize = l_out_row_major.size() * sizeof(DataType);
+  gpu_data_l_out_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_l_out_row_major_resize3(gpu_data_l_out_row_major,
+                                                                                  patchTensorRange);
+  gpu_l_out_row_major_resize3.device(sycl_device) =
+      gpu_l_in_col_major_resize3.swap_layout().extract_image_patches(3, 3);
+  sycl_device.memcpyDeviceToHost(l_out_row_major.data(), gpu_data_l_out_row_major, patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 13 * 13);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 3);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 3);
+  VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 64);
+
+  for (IndexType b = 0; b < 32; ++b) {
+    for (IndexType i = 0; i < 13; ++i) {
+      for (IndexType j = 0; j < 13; ++j) {
+        IndexType patchId = i + 13 * j;
+        for (IndexType c = 0; c < 3; ++c) {
+          for (IndexType r = 0; r < 3; ++r) {
+            for (IndexType d = 0; d < 64; ++d) {
+              DataType expected = 0.0f;
+              if (r - 1 + i >= 0 && c - 1 + j >= 0 && r - 1 + i < 13 && c - 1 + j < 13) {
+                expected = l_in_col_major(d, r - 1 + i, c - 1 + j, b);
+              }
+              // ColMajor
+              if (l_out_col_major(d, r, c, patchId, b) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_col_major(d, r, c, patchId, b), expected);
+              // RowMajor
+              if (l_out_row_major(b, patchId, c, r, d) != expected) {
+                std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c
+                          << " d=" << d << " b=" << b << std::endl;
+              }
+              VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected);
+            }
+          }
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data_l_in_col_major);
+  sycl_device.deallocate(gpu_data_l_out_col_major);
+  sycl_device.deallocate(gpu_data_l_out_row_major);
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_tensor_image_patch_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_image_patch_sycl<DataType, int64_t>(sycl_device);
+  test_patch_padding_valid_sycl<DataType, int64_t>(sycl_device);
+  test_patch_padding_valid_same_value_sycl<DataType, int64_t>(sycl_device);
+  test_patch_padding_same_sycl<DataType, int64_t>(sycl_device);
+  test_patch_no_extra_dim_sycl<DataType, int64_t>(sycl_device);
+  test_imagenet_patches_sycl<DataType, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_image_patch_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_tensor_image_patch_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_index_list.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_index_list.cpp
new file mode 100644
index 0000000..9e0b755
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_index_list.cpp
@@ -0,0 +1,424 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+static void test_static_index_list() {
+  Tensor<float, 4> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  constexpr auto reduction_axis = make_index_list(0, 1, 2);
+  VERIFY_IS_EQUAL(internal::array_get<0>(reduction_axis), 0);
+  VERIFY_IS_EQUAL(internal::array_get<1>(reduction_axis), 1);
+  VERIFY_IS_EQUAL(internal::array_get<2>(reduction_axis), 2);
+  VERIFY_IS_EQUAL(static_cast<Index>(reduction_axis[0]), 0);
+  VERIFY_IS_EQUAL(static_cast<Index>(reduction_axis[1]), 1);
+  VERIFY_IS_EQUAL(static_cast<Index>(reduction_axis[2]), 2);
+
+  VERIFY_IS_EQUAL(reduction_axis.size(), std::size_t(3));
+
+  EIGEN_STATIC_ASSERT((internal::array_get<0>(reduction_axis) == 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::array_get<1>(reduction_axis) == 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::array_get<2>(reduction_axis) == 2), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  Tensor<float, 1> result = tensor.sum(reduction_axis);
+  for (int i = 0; i < result.size(); ++i) {
+    float expected = 0.0f;
+    for (int j = 0; j < 2; ++j) {
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 5; ++l) {
+          expected += tensor(j, k, l, i);
+        }
+      }
+    }
+    VERIFY_IS_APPROX(result(i), expected);
+  }
+}
+
+static void test_type2index_list() {
+  Tensor<float, 5> tensor(2, 3, 5, 7, 11);
+  tensor.setRandom();
+  tensor += tensor.constant(10.0f);
+
+  typedef Eigen::IndexList<Eigen::type2index<0>> Dims0;
+  typedef Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1>> Dims1;
+  typedef Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1>, Eigen::type2index<2>> Dims2;
+  typedef Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1>, Eigen::type2index<2>, Eigen::type2index<3>>
+      Dims3;
+  typedef Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1>, Eigen::type2index<2>, Eigen::type2index<3>,
+                           Eigen::type2index<4>>
+      Dims4;
+
+#if 0
+  EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<Dims0>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<Dims1>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<Dims2>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<Dims3>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<Dims4>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+#endif
+
+  EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims0, 1, ColMajor>::value == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims1, 2, ColMajor>::value == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims2, 3, ColMajor>::value == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims3, 4, ColMajor>::value == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims4, 5, ColMajor>::value == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims0, 1, RowMajor>::value == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims1, 2, RowMajor>::value == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims2, 3, RowMajor>::value == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims3, 4, RowMajor>::value == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::are_inner_most_dims<Dims4, 5, RowMajor>::value == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  const Dims0 reduction_axis0;
+  Tensor<float, 4> result0 = tensor.sum(reduction_axis0);
+  for (int m = 0; m < 11; ++m) {
+    for (int l = 0; l < 7; ++l) {
+      for (int k = 0; k < 5; ++k) {
+        for (int j = 0; j < 3; ++j) {
+          float expected = 0.0f;
+          for (int i = 0; i < 2; ++i) {
+            expected += tensor(i, j, k, l, m);
+          }
+          VERIFY_IS_APPROX(result0(j, k, l, m), expected);
+        }
+      }
+    }
+  }
+
+  const Dims1 reduction_axis1;
+  Tensor<float, 3> result1 = tensor.sum(reduction_axis1);
+  for (int m = 0; m < 11; ++m) {
+    for (int l = 0; l < 7; ++l) {
+      for (int k = 0; k < 5; ++k) {
+        float expected = 0.0f;
+        for (int j = 0; j < 3; ++j) {
+          for (int i = 0; i < 2; ++i) {
+            expected += tensor(i, j, k, l, m);
+          }
+        }
+        VERIFY_IS_APPROX(result1(k, l, m), expected);
+      }
+    }
+  }
+
+  const Dims2 reduction_axis2;
+  Tensor<float, 2> result2 = tensor.sum(reduction_axis2);
+  for (int m = 0; m < 11; ++m) {
+    for (int l = 0; l < 7; ++l) {
+      float expected = 0.0f;
+      for (int k = 0; k < 5; ++k) {
+        for (int j = 0; j < 3; ++j) {
+          for (int i = 0; i < 2; ++i) {
+            expected += tensor(i, j, k, l, m);
+          }
+        }
+      }
+      VERIFY_IS_APPROX(result2(l, m), expected);
+    }
+  }
+
+  const Dims3 reduction_axis3;
+  Tensor<float, 1> result3 = tensor.sum(reduction_axis3);
+  for (int m = 0; m < 11; ++m) {
+    float expected = 0.0f;
+    for (int l = 0; l < 7; ++l) {
+      for (int k = 0; k < 5; ++k) {
+        for (int j = 0; j < 3; ++j) {
+          for (int i = 0; i < 2; ++i) {
+            expected += tensor(i, j, k, l, m);
+          }
+        }
+      }
+    }
+    VERIFY_IS_APPROX(result3(m), expected);
+  }
+
+  const Dims4 reduction_axis4;
+  Tensor<float, 0> result4 = tensor.sum(reduction_axis4);
+  float expected = 0.0f;
+  for (int m = 0; m < 11; ++m) {
+    for (int l = 0; l < 7; ++l) {
+      for (int k = 0; k < 5; ++k) {
+        for (int j = 0; j < 3; ++j) {
+          for (int i = 0; i < 2; ++i) {
+            expected += tensor(i, j, k, l, m);
+          }
+        }
+      }
+    }
+  }
+  VERIFY_IS_APPROX(result4(), expected);
+}
+
+static void test_type2indexpair_list() {
+  Tensor<float, 5> tensor(2, 3, 5, 7, 11);
+  tensor.setRandom();
+  tensor += tensor.constant(10.0f);
+
+  typedef Eigen::IndexPairList<Eigen::type2indexpair<0, 10>> Dims0;
+  typedef Eigen::IndexPairList<Eigen::type2indexpair<0, 10>, Eigen::type2indexpair<1, 11>, Eigen::type2indexpair<2, 12>>
+      Dims2_a;
+  typedef Eigen::IndexPairList<Eigen::type2indexpair<0, 10>, Eigen::IndexPair<Index>, Eigen::type2indexpair<2, 12>>
+      Dims2_b;
+  typedef Eigen::IndexPairList<Eigen::IndexPair<Index>, Eigen::type2indexpair<1, 11>, Eigen::IndexPair<Index>> Dims2_c;
+
+  Dims2_a d2_a;
+
+  Dims2_b d2_b;
+  d2_b.set(1, Eigen::IndexPair<Index>(1, 11));
+
+  Dims2_c d2_c;
+  d2_c.set(0, Eigen::IndexPair<Index>(Eigen::IndexPair<Index>(0, 10)));
+  d2_c.set(1, Eigen::IndexPair<Index>(1, 11));  // setting type2indexpair to correct value.
+  d2_c.set(2, Eigen::IndexPair<Index>(2, 12));
+
+  VERIFY_IS_EQUAL(d2_a[0].first, 0);
+  VERIFY_IS_EQUAL(d2_a[0].second, 10);
+  VERIFY_IS_EQUAL(d2_a[1].first, 1);
+  VERIFY_IS_EQUAL(d2_a[1].second, 11);
+  VERIFY_IS_EQUAL(d2_a[2].first, 2);
+  VERIFY_IS_EQUAL(d2_a[2].second, 12);
+
+  VERIFY_IS_EQUAL(d2_b[0].first, 0);
+  VERIFY_IS_EQUAL(d2_b[0].second, 10);
+  VERIFY_IS_EQUAL(d2_b[1].first, 1);
+  VERIFY_IS_EQUAL(d2_b[1].second, 11);
+  VERIFY_IS_EQUAL(d2_b[2].first, 2);
+  VERIFY_IS_EQUAL(d2_b[2].second, 12);
+
+  VERIFY_IS_EQUAL(d2_c[0].first, 0);
+  VERIFY_IS_EQUAL(d2_c[0].second, 10);
+  VERIFY_IS_EQUAL(d2_c[1].first, 1);
+  VERIFY_IS_EQUAL(d2_c[1].second, 11);
+  VERIFY_IS_EQUAL(d2_c[2].first, 2);
+  VERIFY_IS_EQUAL(d2_c[2].second, 12);
+
+  EIGEN_STATIC_ASSERT((d2_a.value_known_statically(0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((d2_a.value_known_statically(1) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((d2_a.value_known_statically(2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((d2_b.value_known_statically(0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((d2_b.value_known_statically(1) == false), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((d2_b.value_known_statically(2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((d2_c.value_known_statically(0) == false), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((d2_c.value_known_statically(1) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((d2_c.value_known_statically(2) == false), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims0>(0, 0) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims0>(0, 1) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(0, 0) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(0, 1) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(1, 1) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(1, 2) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(2, 2) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_a>(2, 3) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(0, 0) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(0, 1) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(1, 1) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(1, 2) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(2, 2) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_b>(2, 3) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(0, 0) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(0, 1) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(1, 1) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(1, 2) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(2, 2) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_first_statically_eq<Dims2_c>(2, 3) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims0>(0, 10) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims0>(0, 11) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(0, 10) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(0, 11) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(1, 11) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(1, 12) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(2, 12) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_a>(2, 13) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(0, 10) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(0, 11) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(1, 11) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(1, 12) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(2, 12) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_b>(2, 13) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(0, 10) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(0, 11) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(1, 11) == true),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(1, 12) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(2, 12) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((Eigen::internal::index_pair_second_statically_eq<Dims2_c>(2, 13) == false),
+                      YOU_MADE_A_PROGRAMMING_MISTAKE);
+}
+
+static void test_dynamic_index_list() {
+  Tensor<float, 4> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  int dim1 = 2;
+  int dim2 = 1;
+  int dim3 = 0;
+
+  auto reduction_axis = make_index_list(dim1, dim2, dim3);
+
+  VERIFY_IS_EQUAL(internal::array_get<0>(reduction_axis), 2);
+  VERIFY_IS_EQUAL(internal::array_get<1>(reduction_axis), 1);
+  VERIFY_IS_EQUAL(internal::array_get<2>(reduction_axis), 0);
+  VERIFY_IS_EQUAL(static_cast<Index>(reduction_axis[0]), 2);
+  VERIFY_IS_EQUAL(static_cast<Index>(reduction_axis[1]), 1);
+  VERIFY_IS_EQUAL(static_cast<Index>(reduction_axis[2]), 0);
+
+  Tensor<float, 1> result = tensor.sum(reduction_axis);
+  for (int i = 0; i < result.size(); ++i) {
+    float expected = 0.0f;
+    for (int j = 0; j < 2; ++j) {
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 5; ++l) {
+          expected += tensor(j, k, l, i);
+        }
+      }
+    }
+    VERIFY_IS_APPROX(result(i), expected);
+  }
+}
+
+static void test_mixed_index_list() {
+  Tensor<float, 4> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  int dim2 = 1;
+  int dim4 = 3;
+
+  auto reduction_axis = make_index_list(0, dim2, 2, dim4);
+
+  VERIFY_IS_EQUAL(internal::array_get<0>(reduction_axis), 0);
+  VERIFY_IS_EQUAL(internal::array_get<1>(reduction_axis), 1);
+  VERIFY_IS_EQUAL(internal::array_get<2>(reduction_axis), 2);
+  VERIFY_IS_EQUAL(internal::array_get<3>(reduction_axis), 3);
+  VERIFY_IS_EQUAL(static_cast<Index>(reduction_axis[0]), 0);
+  VERIFY_IS_EQUAL(static_cast<Index>(reduction_axis[1]), 1);
+  VERIFY_IS_EQUAL(static_cast<Index>(reduction_axis[2]), 2);
+  VERIFY_IS_EQUAL(static_cast<Index>(reduction_axis[3]), 3);
+
+  typedef IndexList<type2index<0>, int, type2index<2>, int> ReductionIndices;
+  ReductionIndices reduction_indices;
+  reduction_indices.set(1, 1);
+  reduction_indices.set(3, 3);
+  EIGEN_STATIC_ASSERT((internal::array_get<0>(reduction_indices) == 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::array_get<2>(reduction_indices) == 2), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::index_known_statically<ReductionIndices>(0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::index_known_statically<ReductionIndices>(2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionIndices>(0, 0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionIndices>(2, 2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+#if 0
+  EIGEN_STATIC_ASSERT((internal::all_indices_known_statically<ReductionIndices>() == false), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<ReductionIndices>() == false), YOU_MADE_A_PROGRAMMING_MISTAKE);
+#endif
+
+  typedef IndexList<type2index<0>, type2index<1>, type2index<2>, type2index<3>> ReductionList;
+  ReductionList reduction_list;
+  EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionList>(0, 0) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionList>(1, 1) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionList>(2, 2) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::index_statically_eq<ReductionList>(3, 3) == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+#if 0
+  EIGEN_STATIC_ASSERT((internal::all_indices_known_statically<ReductionList>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::indices_statically_known_to_increase<ReductionList>() == true), YOU_MADE_A_PROGRAMMING_MISTAKE);
+#endif
+
+  Tensor<float, 0> result1 = tensor.sum(reduction_axis);
+  Tensor<float, 0> result2 = tensor.sum(reduction_indices);
+  Tensor<float, 0> result3 = tensor.sum(reduction_list);
+
+  float expected = 0.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          expected += tensor(i, j, k, l);
+        }
+      }
+    }
+  }
+  VERIFY_IS_APPROX(result1(), expected);
+  VERIFY_IS_APPROX(result2(), expected);
+  VERIFY_IS_APPROX(result3(), expected);
+}
+
+static void test_dim_check() {
+  Eigen::IndexList<Eigen::type2index<1>, int> dim1;
+  dim1.set(1, 2);
+  Eigen::IndexList<Eigen::type2index<1>, int> dim2;
+  dim2.set(1, 2);
+  VERIFY(dimensions_match(dim1, dim2));
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_index_list) {
+  CALL_SUBTEST(test_static_index_list());
+  CALL_SUBTEST(test_type2index_list());
+  CALL_SUBTEST(test_type2indexpair_list());
+  CALL_SUBTEST(test_dynamic_index_list());
+  CALL_SUBTEST(test_mixed_index_list());
+  CALL_SUBTEST(test_dim_check());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_inflation.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_inflation.cpp
new file mode 100644
index 0000000..32662ab
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_inflation.cpp
@@ -0,0 +1,75 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Ke Yang <yangke@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_simple_inflation() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> strides;
+
+  strides[0] = 1;
+  strides[1] = 1;
+  strides[2] = 1;
+  strides[3] = 1;
+
+  Tensor<float, 4, DataLayout> no_stride;
+  no_stride = tensor.inflate(strides);
+
+  VERIFY_IS_EQUAL(no_stride.dimension(0), 2);
+  VERIFY_IS_EQUAL(no_stride.dimension(1), 3);
+  VERIFY_IS_EQUAL(no_stride.dimension(2), 5);
+  VERIFY_IS_EQUAL(no_stride.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), no_stride(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  strides[0] = 2;
+  strides[1] = 4;
+  strides[2] = 2;
+  strides[3] = 3;
+  Tensor<float, 4, DataLayout> inflated;
+  inflated = tensor.inflate(strides);
+
+  VERIFY_IS_EQUAL(inflated.dimension(0), 3);
+  VERIFY_IS_EQUAL(inflated.dimension(1), 9);
+  VERIFY_IS_EQUAL(inflated.dimension(2), 9);
+  VERIFY_IS_EQUAL(inflated.dimension(3), 19);
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 9; ++j) {
+      for (int k = 0; k < 9; ++k) {
+        for (int l = 0; l < 19; ++l) {
+          if (i % 2 == 0 && j % 4 == 0 && k % 2 == 0 && l % 3 == 0) {
+            VERIFY_IS_EQUAL(inflated(i, j, k, l), tensor(i / 2, j / 4, k / 2, l / 3));
+          } else {
+            VERIFY_IS_EQUAL(0, inflated(i, j, k, l));
+          }
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_inflation) {
+  CALL_SUBTEST(test_simple_inflation<ColMajor>());
+  CALL_SUBTEST(test_simple_inflation<RowMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_inflation_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_inflation_sycl.cpp
new file mode 100644
index 0000000..2475a35
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_inflation_sycl.cpp
@@ -0,0 +1,130 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+// Inflation Definition for each dimension the inflated val would be
+//((dim-1)*strid[dim] +1)
+
+// for 1 dimension vector of size 3 with value (4,4,4) with the inflated stride value of 3 would be changed to
+// tensor of size (2*3) +1 = 7 with the value of
+// (4, 0, 0, 4, 0, 0, 4).
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_simple_inflation_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
+  Tensor<DataType, 4, DataLayout, IndexType> no_stride(tensorRange);
+  tensor.setRandom();
+
+  array<IndexType, 4> strides;
+  strides[0] = 1;
+  strides[1] = 1;
+  strides[2] = 1;
+  strides[3] = 1;
+
+  const size_t tensorBuffSize = tensor.size() * sizeof(DataType);
+  DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_no_stride = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_no_stride(gpu_data_no_stride, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
+  gpu_no_stride.device(sycl_device) = gpu_tensor.inflate(strides);
+  sycl_device.memcpyDeviceToHost(no_stride.data(), gpu_data_no_stride, tensorBuffSize);
+
+  VERIFY_IS_EQUAL(no_stride.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(no_stride.dimension(1), sizeDim2);
+  VERIFY_IS_EQUAL(no_stride.dimension(2), sizeDim3);
+  VERIFY_IS_EQUAL(no_stride.dimension(3), sizeDim4);
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 5; ++k) {
+        for (IndexType l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), no_stride(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  strides[0] = 2;
+  strides[1] = 4;
+  strides[2] = 2;
+  strides[3] = 3;
+
+  IndexType inflatedSizeDim1 = 3;
+  IndexType inflatedSizeDim2 = 9;
+  IndexType inflatedSizeDim3 = 9;
+  IndexType inflatedSizeDim4 = 19;
+  array<IndexType, 4> inflatedTensorRange = {{inflatedSizeDim1, inflatedSizeDim2, inflatedSizeDim3, inflatedSizeDim4}};
+
+  Tensor<DataType, 4, DataLayout, IndexType> inflated(inflatedTensorRange);
+
+  const size_t inflatedTensorBuffSize = inflated.size() * sizeof(DataType);
+  DataType* gpu_data_inflated = static_cast<DataType*>(sycl_device.allocate(inflatedTensorBuffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_inflated(gpu_data_inflated, inflatedTensorRange);
+  gpu_inflated.device(sycl_device) = gpu_tensor.inflate(strides);
+  sycl_device.memcpyDeviceToHost(inflated.data(), gpu_data_inflated, inflatedTensorBuffSize);
+
+  VERIFY_IS_EQUAL(inflated.dimension(0), inflatedSizeDim1);
+  VERIFY_IS_EQUAL(inflated.dimension(1), inflatedSizeDim2);
+  VERIFY_IS_EQUAL(inflated.dimension(2), inflatedSizeDim3);
+  VERIFY_IS_EQUAL(inflated.dimension(3), inflatedSizeDim4);
+
+  for (IndexType i = 0; i < inflatedSizeDim1; ++i) {
+    for (IndexType j = 0; j < inflatedSizeDim2; ++j) {
+      for (IndexType k = 0; k < inflatedSizeDim3; ++k) {
+        for (IndexType l = 0; l < inflatedSizeDim4; ++l) {
+          if (i % strides[0] == 0 && j % strides[1] == 0 && k % strides[2] == 0 && l % strides[3] == 0) {
+            VERIFY_IS_EQUAL(inflated(i, j, k, l),
+                            tensor(i / strides[0], j / strides[1], k / strides[2], l / strides[3]));
+          } else {
+            VERIFY_IS_EQUAL(0, inflated(i, j, k, l));
+          }
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data_tensor);
+  sycl_device.deallocate(gpu_data_no_stride);
+  sycl_device.deallocate(gpu_data_inflated);
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_inflation_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_inflation_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_simple_inflation_sycl<DataType, ColMajor, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_inflation_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_inflation_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_inflation_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_intdiv.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_intdiv.cpp
new file mode 100644
index 0000000..433ca6e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_intdiv.cpp
@@ -0,0 +1,135 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+void test_signed_32bit() {
+  // Divide by one
+  const Eigen::internal::TensorIntDivisor<int32_t, false> div_by_one(1);
+
+  for (int32_t j = 0; j < 25000; ++j) {
+    const int32_t fast_div = j / div_by_one;
+    const int32_t slow_div = j / 1;
+    VERIFY_IS_EQUAL(fast_div, slow_div);
+  }
+
+  // Standard divide by 2 or more
+  for (int32_t i = 2; i < 25000; ++i) {
+    const Eigen::internal::TensorIntDivisor<int32_t, false> div(i);
+
+    for (int32_t j = 0; j < 25000; ++j) {
+      const int32_t fast_div = j / div;
+      const int32_t slow_div = j / i;
+      VERIFY_IS_EQUAL(fast_div, slow_div);
+    }
+  }
+
+  // Optimized divide by 2 or more
+  for (int32_t i = 2; i < 25000; ++i) {
+    const Eigen::internal::TensorIntDivisor<int32_t, true> div(i);
+
+    for (int32_t j = 0; j < 25000; ++j) {
+      const int32_t fast_div = j / div;
+      const int32_t slow_div = j / i;
+      VERIFY_IS_EQUAL(fast_div, slow_div);
+    }
+  }
+}
+
+void test_unsigned_32bit() {
+  for (uint32_t i = 1; i < 25000; ++i) {
+    const Eigen::internal::TensorIntDivisor<uint32_t> div(i);
+
+    for (uint32_t j = 0; j < 25000; ++j) {
+      const uint32_t fast_div = j / div;
+      const uint32_t slow_div = j / i;
+      VERIFY_IS_EQUAL(fast_div, slow_div);
+    }
+  }
+}
+
+void test_signed_64bit() {
+  for (int64_t i = 1; i < 25000; ++i) {
+    const Eigen::internal::TensorIntDivisor<int64_t> div(i);
+
+    for (int64_t j = 0; j < 25000; ++j) {
+      const int64_t fast_div = j / div;
+      const int64_t slow_div = j / i;
+      VERIFY_IS_EQUAL(fast_div, slow_div);
+    }
+  }
+}
+
+void test_unsigned_64bit() {
+  for (uint64_t i = 1; i < 25000; ++i) {
+    const Eigen::internal::TensorIntDivisor<uint64_t> div(i);
+
+    for (uint64_t j = 0; j < 25000; ++j) {
+      const uint64_t fast_div = j / div;
+      const uint64_t slow_div = j / i;
+      VERIFY_IS_EQUAL(fast_div, slow_div);
+    }
+  }
+}
+
+void test_powers_32bit() {
+  for (int expon = 1; expon < 31; expon++) {
+    int32_t div = (1 << expon);
+    for (int num_expon = 0; num_expon < 32; num_expon++) {
+      int32_t start_num = (1 << num_expon) - 100;
+      int32_t end_num = (1 << num_expon) + 100;
+      if (start_num < 0) start_num = 0;
+      for (int32_t num = start_num; num < end_num; num++) {
+        Eigen::internal::TensorIntDivisor<int32_t> divider = Eigen::internal::TensorIntDivisor<int32_t>(div);
+        int32_t result = num / div;
+        int32_t result_op = divider.divide(num);
+        VERIFY_IS_EQUAL(result_op, result);
+      }
+    }
+  }
+}
+
+void test_powers_64bit() {
+  for (int expon = 0; expon < 63; expon++) {
+    int64_t div = (1ull << expon);
+    for (int num_expon = 0; num_expon < 63; num_expon++) {
+      int64_t start_num = (1ull << num_expon) - 10;
+      int64_t end_num = (1ull << num_expon) + 10;
+      if (start_num < 0) start_num = 0;
+      for (int64_t num = start_num; num < end_num; num++) {
+        Eigen::internal::TensorIntDivisor<int64_t> divider(div);
+        int64_t result = num / div;
+        int64_t result_op = divider.divide(num);
+        VERIFY_IS_EQUAL(result_op, result);
+      }
+    }
+  }
+}
+
+void test_specific() {
+  // A particular combination that was previously failing
+  int64_t div = 209715200;
+  int64_t num = 3238002688ll;
+  Eigen::internal::TensorIntDivisor<int64_t> divider(div);
+  int64_t result = num / div;
+  int64_t result_op = divider.divide(num);
+  VERIFY_IS_EQUAL(result, result_op);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_intdiv) {
+  CALL_SUBTEST_1(test_signed_32bit());
+  CALL_SUBTEST_2(test_unsigned_32bit());
+  CALL_SUBTEST_3(test_signed_64bit());
+  CALL_SUBTEST_4(test_unsigned_64bit());
+  CALL_SUBTEST_5(test_powers_32bit());
+  CALL_SUBTEST_6(test_powers_64bit());
+  CALL_SUBTEST_7(test_specific());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_io.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_io.cpp
new file mode 100644
index 0000000..27b3230
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_io.cpp
@@ -0,0 +1,152 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#include "main.h"
+
+#include <sstream>
+#include <Eigen/CXX11/Tensor>
+
+template <typename Scalar, int rank, int Layout>
+struct test_tensor_ostream_impl {};
+
+template <typename Scalar, int Layout>
+struct test_tensor_ostream_impl<Scalar, 0, Layout> {
+  static void run() {
+    Eigen::Tensor<Scalar, 0> t;
+    t.setValues(1);
+    std::ostringstream os;
+    os << t.format(Eigen::TensorIOFormat::Plain());
+    VERIFY(os.str() == "1");
+  }
+};
+
+template <typename Scalar, int Layout>
+struct test_tensor_ostream_impl<Scalar, 1, Layout> {
+  static void run() {
+    Eigen::Tensor<Scalar, 1> t = {3};
+    t.setValues({1, 2, 3});
+    std::ostringstream os;
+    os << t.format(Eigen::TensorIOFormat::Plain());
+    VERIFY(os.str() == "1 2 3");
+  }
+};
+
+template <typename Scalar, int Layout>
+struct test_tensor_ostream_impl<Scalar, 2, Layout> {
+  static void run() {
+    Eigen::Tensor<Scalar, 2> t = {3, 2};
+    t.setValues({{1, 2}, {3, 4}, {5, 6}});
+    std::ostringstream os;
+    os << t.format(Eigen::TensorIOFormat::Plain());
+    VERIFY(os.str() == "1 2\n3 4\n5 6");
+  }
+};
+
+template <typename Scalar, int Layout>
+struct test_tensor_ostream_impl<Scalar, 3, Layout> {
+  static void run() {
+    Eigen::Tensor<Scalar, 3> t = {4, 3, 2};
+    t.setValues({{{1, 2}, {3, 4}, {5, 6}},
+                 {{7, 8}, {9, 10}, {11, 12}},
+                 {{13, 14}, {15, 16}, {17, 18}},
+                 {{19, 20}, {21, 22}, {23, 24}}});
+    std::ostringstream os;
+    os << t.format(Eigen::TensorIOFormat::Plain());
+    VERIFY(os.str() == " 1  2\n 3  4\n 5  6\n\n 7  8\n 9 10\n11 12\n\n13 14\n15 16\n17 18\n\n19 20\n21 22\n23 24");
+  }
+};
+
+template <int Layout>
+struct test_tensor_ostream_impl<bool, 2, Layout> {
+  static void run() {
+    Eigen::Tensor<bool, 2> t = {3, 2};
+    t.setValues({{false, true}, {true, false}, {false, false}});
+    std::ostringstream os;
+    os << t.format(Eigen::TensorIOFormat::Plain());
+    VERIFY(os.str() == "0 1\n1 0\n0 0");
+  }
+};
+
+template <typename Scalar, int Layout>
+struct test_tensor_ostream_impl<std::complex<Scalar>, 2, Layout> {
+  static void run() {
+    Eigen::Tensor<std::complex<Scalar>, 2> t = {3, 2};
+    t.setValues({{std::complex<Scalar>(1, 2), std::complex<Scalar>(12, 3)},
+                 {std::complex<Scalar>(-4, 2), std::complex<Scalar>(0, 5)},
+                 {std::complex<Scalar>(-1, 4), std::complex<Scalar>(5, 27)}});
+    std::ostringstream os;
+    os << t.format(Eigen::TensorIOFormat::Plain());
+    VERIFY(os.str() == " (1,2) (12,3)\n(-4,2)  (0,5)\n(-1,4) (5,27)");
+
+    os.str("");
+    os.clear();
+    os << t.format(Eigen::TensorIOFormat::Numpy());
+    VERIFY(os.str() == "[[ 1+2j 12+3j]\n [-4+2j  0+5j]\n [-1+4j 5+27j]]");
+
+    os.str("");
+    os.clear();
+    os << t.format(Eigen::TensorIOFormat::Native());
+    VERIFY(os.str() == "{{ {1, 2}, {12, 3}},\n {{-4, 2},  {0, 5}},\n {{-1, 4}, {5, 27}}}");
+  }
+};
+
+template <typename Scalar, int rank, int Layout>
+void test_tensor_ostream() {
+  test_tensor_ostream_impl<Scalar, rank, Layout>::run();
+}
+
+void test_const_tensor_ostream() {
+  Eigen::Tensor<float, 0> t;
+  t.setValues(1);
+  const Eigen::TensorMap<Eigen::Tensor<const float, 0, Eigen::RowMajor>, Eigen::Unaligned> t_const(
+      t.data(), Eigen::DSizes<Eigen::DenseIndex, 0>{});
+  std::ostringstream os;
+  os << t_const.format(Eigen::TensorIOFormat::Plain());
+  VERIFY(os.str() == "1");
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_io) {
+  CALL_SUBTEST((test_tensor_ostream<float, 0, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<float, 1, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<float, 2, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<float, 3, Eigen::ColMajor>()));
+
+  CALL_SUBTEST((test_tensor_ostream<double, 0, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<double, 1, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<double, 2, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<double, 3, Eigen::ColMajor>()));
+
+  CALL_SUBTEST((test_tensor_ostream<int, 0, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<int, 1, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<int, 2, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<int, 3, Eigen::ColMajor>()));
+
+  CALL_SUBTEST((test_tensor_ostream<float, 0, Eigen::RowMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<float, 1, Eigen::RowMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<float, 2, Eigen::RowMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<float, 3, Eigen::RowMajor>()));
+
+  CALL_SUBTEST((test_tensor_ostream<double, 0, Eigen::RowMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<double, 1, Eigen::RowMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<double, 2, Eigen::RowMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<double, 3, Eigen::RowMajor>()));
+
+  CALL_SUBTEST((test_tensor_ostream<int, 0, Eigen::RowMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<int, 1, Eigen::RowMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<int, 2, Eigen::RowMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<int, 3, Eigen::RowMajor>()));
+
+  CALL_SUBTEST((test_tensor_ostream<bool, 2, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<bool, 2, Eigen::RowMajor>()));
+
+  CALL_SUBTEST((test_tensor_ostream<std::complex<double>, 2, Eigen::ColMajor>()));
+  CALL_SUBTEST((test_tensor_ostream<std::complex<float>, 2, Eigen::ColMajor>()));
+
+  // Test printing TensorMap with const elements.
+  CALL_SUBTEST((test_const_tensor_ostream()));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_layout_swap.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_layout_swap.cpp
new file mode 100644
index 0000000..50fb3ae
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_layout_swap.cpp
@@ -0,0 +1,56 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+static void test_simple_swap() {
+  Tensor<float, 3, ColMajor> tensor(2, 3, 7);
+  tensor.setRandom();
+
+  Tensor<float, 3, RowMajor> tensor2 = tensor.swap_layout();
+  VERIFY_IS_EQUAL(tensor.dimension(0), tensor2.dimension(2));
+  VERIFY_IS_EQUAL(tensor.dimension(1), tensor2.dimension(1));
+  VERIFY_IS_EQUAL(tensor.dimension(2), tensor2.dimension(0));
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(tensor(i, j, k), tensor2(k, j, i));
+      }
+    }
+  }
+}
+
+static void test_swap_as_lvalue() {
+  Tensor<float, 3, ColMajor> tensor(2, 3, 7);
+  tensor.setRandom();
+
+  Tensor<float, 3, RowMajor> tensor2(7, 3, 2);
+  tensor2.swap_layout() = tensor;
+  VERIFY_IS_EQUAL(tensor.dimension(0), tensor2.dimension(2));
+  VERIFY_IS_EQUAL(tensor.dimension(1), tensor2.dimension(1));
+  VERIFY_IS_EQUAL(tensor.dimension(2), tensor2.dimension(0));
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(tensor(i, j, k), tensor2(k, j, i));
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_layout_swap) {
+  CALL_SUBTEST(test_simple_swap());
+  CALL_SUBTEST(test_swap_as_lvalue());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_layout_swap_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_layout_swap_sycl.cpp
new file mode 100644
index 0000000..066221e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_layout_swap_sycl.cpp
@@ -0,0 +1,120 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <typename DataType, typename IndexType>
+static void test_simple_swap_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 7;
+  array<IndexType, 3> tensorColRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  array<IndexType, 3> tensorRowRange = {{sizeDim3, sizeDim2, sizeDim1}};
+
+  Tensor<DataType, 3, ColMajor, IndexType> tensor1(tensorColRange);
+  Tensor<DataType, 3, RowMajor, IndexType> tensor2(tensorRowRange);
+  tensor1.setRandom();
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 3, ColMajor, IndexType>> gpu1(gpu_data1, tensorColRange);
+  TensorMap<Tensor<DataType, 3, RowMajor, IndexType>> gpu2(gpu_data2, tensorRowRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(), (tensor1.size()) * sizeof(DataType));
+  gpu2.device(sycl_device) = gpu1.swap_layout();
+  sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2, (tensor2.size()) * sizeof(DataType));
+
+  //  Tensor<float, 3, ColMajor> tensor(2,3,7);
+  // tensor.setRandom();
+
+  //  Tensor<float, 3, RowMajor> tensor2 = tensor.swap_layout();
+  VERIFY_IS_EQUAL(tensor1.dimension(0), tensor2.dimension(2));
+  VERIFY_IS_EQUAL(tensor1.dimension(1), tensor2.dimension(1));
+  VERIFY_IS_EQUAL(tensor1.dimension(2), tensor2.dimension(0));
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(tensor1(i, j, k), tensor2(k, j, i));
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+}
+
+template <typename DataType, typename IndexType>
+static void test_swap_as_lvalue_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 7;
+  array<IndexType, 3> tensorColRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  array<IndexType, 3> tensorRowRange = {{sizeDim3, sizeDim2, sizeDim1}};
+
+  Tensor<DataType, 3, ColMajor, IndexType> tensor1(tensorColRange);
+  Tensor<DataType, 3, RowMajor, IndexType> tensor2(tensorRowRange);
+  tensor1.setRandom();
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 3, ColMajor, IndexType>> gpu1(gpu_data1, tensorColRange);
+  TensorMap<Tensor<DataType, 3, RowMajor, IndexType>> gpu2(gpu_data2, tensorRowRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(), (tensor1.size()) * sizeof(DataType));
+  gpu2.swap_layout().device(sycl_device) = gpu1;
+  sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2, (tensor2.size()) * sizeof(DataType));
+
+  //  Tensor<float, 3, ColMajor> tensor(2,3,7);
+  //  tensor.setRandom();
+
+  // Tensor<float, 3, RowMajor> tensor2(7,3,2);
+  //  tensor2.swap_layout() = tensor;
+  VERIFY_IS_EQUAL(tensor1.dimension(0), tensor2.dimension(2));
+  VERIFY_IS_EQUAL(tensor1.dimension(1), tensor2.dimension(1));
+  VERIFY_IS_EQUAL(tensor1.dimension(2), tensor2.dimension(0));
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(tensor1(i, j, k), tensor2(k, j, i));
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_tensor_layout_swap_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_swap_sycl<DataType, int64_t>(sycl_device);
+  test_swap_as_lvalue_sycl<DataType, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_layout_swap_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_tensor_layout_swap_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_tensor_layout_swap_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_lvalue.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_lvalue.cpp
new file mode 100644
index 0000000..d70c7e0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_lvalue.cpp
@@ -0,0 +1,36 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void test_compound_assignment() {
+  Tensor<float, 3> mat1(2, 3, 7);
+  Tensor<float, 3> mat2(2, 3, 7);
+  Tensor<float, 3> mat3(2, 3, 7);
+
+  mat1.setRandom();
+  mat2.setRandom();
+  mat3 = mat1;
+  mat3 += mat2;
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(mat3(i, j, k), mat1(i, j, k) + mat2(i, j, k));
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_lvalue) { CALL_SUBTEST(test_compound_assignment()); }
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_map.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_map.cpp
new file mode 100644
index 0000000..25176f4
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_map.cpp
@@ -0,0 +1,322 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void test_0d() {
+  Tensor<int, 0> scalar1;
+  Tensor<int, 0, RowMajor> scalar2;
+
+  TensorMap<const Tensor<int, 0> > scalar3(scalar1.data());
+  TensorMap<const Tensor<int, 0, RowMajor> > scalar4(scalar2.data());
+
+  scalar1() = 7;
+  scalar2() = 13;
+
+  VERIFY_IS_EQUAL(scalar1.rank(), 0);
+  VERIFY_IS_EQUAL(scalar1.size(), 1);
+
+  VERIFY_IS_EQUAL(scalar3(), 7);
+  VERIFY_IS_EQUAL(scalar4(), 13);
+}
+
+static void test_1d() {
+  Tensor<int, 1> vec1(6);
+  Tensor<int, 1, RowMajor> vec2(6);
+
+  TensorMap<const Tensor<int, 1> > vec3(vec1.data(), 6);
+  TensorMap<const Tensor<int, 1, RowMajor> > vec4(vec2.data(), 6);
+
+  vec1(0) = 4;
+  vec2(0) = 0;
+  vec1(1) = 8;
+  vec2(1) = 1;
+  vec1(2) = 15;
+  vec2(2) = 2;
+  vec1(3) = 16;
+  vec2(3) = 3;
+  vec1(4) = 23;
+  vec2(4) = 4;
+  vec1(5) = 42;
+  vec2(5) = 5;
+
+  VERIFY_IS_EQUAL(vec1.rank(), 1);
+  VERIFY_IS_EQUAL(vec1.size(), 6);
+  VERIFY_IS_EQUAL(vec1.dimension(0), 6);
+
+  VERIFY_IS_EQUAL(vec3(0), 4);
+  VERIFY_IS_EQUAL(vec3(1), 8);
+  VERIFY_IS_EQUAL(vec3(2), 15);
+  VERIFY_IS_EQUAL(vec3(3), 16);
+  VERIFY_IS_EQUAL(vec3(4), 23);
+  VERIFY_IS_EQUAL(vec3(5), 42);
+
+  VERIFY_IS_EQUAL(vec4(0), 0);
+  VERIFY_IS_EQUAL(vec4(1), 1);
+  VERIFY_IS_EQUAL(vec4(2), 2);
+  VERIFY_IS_EQUAL(vec4(3), 3);
+  VERIFY_IS_EQUAL(vec4(4), 4);
+  VERIFY_IS_EQUAL(vec4(5), 5);
+}
+
+static void test_2d() {
+  Tensor<int, 2> mat1(2, 3);
+  Tensor<int, 2, RowMajor> mat2(2, 3);
+
+  mat1(0, 0) = 0;
+  mat1(0, 1) = 1;
+  mat1(0, 2) = 2;
+  mat1(1, 0) = 3;
+  mat1(1, 1) = 4;
+  mat1(1, 2) = 5;
+
+  mat2(0, 0) = 0;
+  mat2(0, 1) = 1;
+  mat2(0, 2) = 2;
+  mat2(1, 0) = 3;
+  mat2(1, 1) = 4;
+  mat2(1, 2) = 5;
+
+  TensorMap<const Tensor<int, 2> > mat3(mat1.data(), 2, 3);
+  TensorMap<const Tensor<int, 2, RowMajor> > mat4(mat2.data(), 2, 3);
+
+  VERIFY_IS_EQUAL(mat3.rank(), 2);
+  VERIFY_IS_EQUAL(mat3.size(), 6);
+  VERIFY_IS_EQUAL(mat3.dimension(0), 2);
+  VERIFY_IS_EQUAL(mat3.dimension(1), 3);
+
+  VERIFY_IS_EQUAL(mat4.rank(), 2);
+  VERIFY_IS_EQUAL(mat4.size(), 6);
+  VERIFY_IS_EQUAL(mat4.dimension(0), 2);
+  VERIFY_IS_EQUAL(mat4.dimension(1), 3);
+
+  VERIFY_IS_EQUAL(mat3(0, 0), 0);
+  VERIFY_IS_EQUAL(mat3(0, 1), 1);
+  VERIFY_IS_EQUAL(mat3(0, 2), 2);
+  VERIFY_IS_EQUAL(mat3(1, 0), 3);
+  VERIFY_IS_EQUAL(mat3(1, 1), 4);
+  VERIFY_IS_EQUAL(mat3(1, 2), 5);
+
+  VERIFY_IS_EQUAL(mat4(0, 0), 0);
+  VERIFY_IS_EQUAL(mat4(0, 1), 1);
+  VERIFY_IS_EQUAL(mat4(0, 2), 2);
+  VERIFY_IS_EQUAL(mat4(1, 0), 3);
+  VERIFY_IS_EQUAL(mat4(1, 1), 4);
+  VERIFY_IS_EQUAL(mat4(1, 2), 5);
+}
+
+static void test_3d() {
+  Tensor<int, 3> mat1(2, 3, 7);
+  Tensor<int, 3, RowMajor> mat2(2, 3, 7);
+
+  int val = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        mat1(i, j, k) = val;
+        mat2(i, j, k) = val;
+        val++;
+      }
+    }
+  }
+
+  TensorMap<const Tensor<int, 3> > mat3(mat1.data(), 2, 3, 7);
+  TensorMap<const Tensor<int, 3, RowMajor> > mat4(mat2.data(), 2, 3, 7);
+
+  VERIFY_IS_EQUAL(mat3.rank(), 3);
+  VERIFY_IS_EQUAL(mat3.size(), 2 * 3 * 7);
+  VERIFY_IS_EQUAL(mat3.dimension(0), 2);
+  VERIFY_IS_EQUAL(mat3.dimension(1), 3);
+  VERIFY_IS_EQUAL(mat3.dimension(2), 7);
+
+  VERIFY_IS_EQUAL(mat4.rank(), 3);
+  VERIFY_IS_EQUAL(mat4.size(), 2 * 3 * 7);
+  VERIFY_IS_EQUAL(mat4.dimension(0), 2);
+  VERIFY_IS_EQUAL(mat4.dimension(1), 3);
+  VERIFY_IS_EQUAL(mat4.dimension(2), 7);
+
+  val = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(mat3(i, j, k), val);
+        VERIFY_IS_EQUAL(mat4(i, j, k), val);
+        val++;
+      }
+    }
+  }
+}
+
+static void test_from_tensor() {
+  Tensor<int, 3> mat1(2, 3, 7);
+  Tensor<int, 3, RowMajor> mat2(2, 3, 7);
+
+  int val = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        mat1(i, j, k) = val;
+        mat2(i, j, k) = val;
+        val++;
+      }
+    }
+  }
+
+  TensorMap<Tensor<int, 3> > mat3(mat1);
+  TensorMap<Tensor<int, 3, RowMajor> > mat4(mat2);
+
+  VERIFY_IS_EQUAL(mat3.rank(), 3);
+  VERIFY_IS_EQUAL(mat3.size(), 2 * 3 * 7);
+  VERIFY_IS_EQUAL(mat3.dimension(0), 2);
+  VERIFY_IS_EQUAL(mat3.dimension(1), 3);
+  VERIFY_IS_EQUAL(mat3.dimension(2), 7);
+
+  VERIFY_IS_EQUAL(mat4.rank(), 3);
+  VERIFY_IS_EQUAL(mat4.size(), 2 * 3 * 7);
+  VERIFY_IS_EQUAL(mat4.dimension(0), 2);
+  VERIFY_IS_EQUAL(mat4.dimension(1), 3);
+  VERIFY_IS_EQUAL(mat4.dimension(2), 7);
+
+  val = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(mat3(i, j, k), val);
+        VERIFY_IS_EQUAL(mat4(i, j, k), val);
+        val++;
+      }
+    }
+  }
+
+  TensorFixedSize<int, Sizes<2, 3, 7> > mat5;
+
+  val = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        array<ptrdiff_t, 3> coords;
+        coords[0] = i;
+        coords[1] = j;
+        coords[2] = k;
+        mat5(coords) = val;
+        val++;
+      }
+    }
+  }
+
+  TensorMap<TensorFixedSize<int, Sizes<2, 3, 7> > > mat6(mat5);
+
+  VERIFY_IS_EQUAL(mat6.rank(), 3);
+  VERIFY_IS_EQUAL(mat6.size(), 2 * 3 * 7);
+  VERIFY_IS_EQUAL(mat6.dimension(0), 2);
+  VERIFY_IS_EQUAL(mat6.dimension(1), 3);
+  VERIFY_IS_EQUAL(mat6.dimension(2), 7);
+
+  val = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(mat6(i, j, k), val);
+        val++;
+      }
+    }
+  }
+}
+
+static int f(const TensorMap<Tensor<int, 3> >& tensor) {
+  //  Size<0> empty;
+  EIGEN_STATIC_ASSERT((internal::array_size<Sizes<> >::value == 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT((internal::array_size<DSizes<int, 0> >::value == 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  Tensor<int, 0> result = tensor.sum();
+  return result();
+}
+
+static void test_casting() {
+  Tensor<int, 3> tensor(2, 3, 7);
+
+  int val = 0;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        tensor(i, j, k) = val;
+        val++;
+      }
+    }
+  }
+
+  TensorMap<Tensor<int, 3> > map(tensor);
+  int sum1 = f(map);
+  int sum2 = f(tensor);
+
+  VERIFY_IS_EQUAL(sum1, sum2);
+  VERIFY_IS_EQUAL(sum1, 861);
+}
+
+template <typename T>
+static const T& add_const(T& value) {
+  return value;
+}
+
+static void test_0d_const_tensor() {
+  Tensor<int, 0> scalar1;
+  Tensor<int, 0, RowMajor> scalar2;
+
+  TensorMap<const Tensor<int, 0> > scalar3(add_const(scalar1).data());
+  TensorMap<const Tensor<int, 0, RowMajor> > scalar4(add_const(scalar2).data());
+
+  scalar1() = 7;
+  scalar2() = 13;
+
+  VERIFY_IS_EQUAL(scalar1.rank(), 0);
+  VERIFY_IS_EQUAL(scalar1.size(), 1);
+
+  VERIFY_IS_EQUAL(scalar3(), 7);
+  VERIFY_IS_EQUAL(scalar4(), 13);
+}
+
+static void test_0d_const_tensor_map() {
+  Tensor<int, 0> scalar1;
+  Tensor<int, 0, RowMajor> scalar2;
+
+  const TensorMap<Tensor<int, 0> > scalar3(scalar1.data());
+  const TensorMap<Tensor<int, 0, RowMajor> > scalar4(scalar2.data());
+
+  // Although TensorMap is constant, we still can write to the underlying
+  // storage, because we map over non-constant Tensor.
+  scalar3() = 7;
+  scalar4() = 13;
+
+  VERIFY_IS_EQUAL(scalar1(), 7);
+  VERIFY_IS_EQUAL(scalar2(), 13);
+
+  // Pointer to the underlying storage is also non-const.
+  scalar3.data()[0] = 8;
+  scalar4.data()[0] = 14;
+
+  VERIFY_IS_EQUAL(scalar1(), 8);
+  VERIFY_IS_EQUAL(scalar2(), 14);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_map) {
+  CALL_SUBTEST(test_0d());
+  CALL_SUBTEST(test_1d());
+  CALL_SUBTEST(test_2d());
+  CALL_SUBTEST(test_3d());
+
+  CALL_SUBTEST(test_from_tensor());
+  CALL_SUBTEST(test_casting());
+
+  CALL_SUBTEST(test_0d_const_tensor());
+  CALL_SUBTEST(test_0d_const_tensor_map());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_math.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_math.cpp
new file mode 100644
index 0000000..8184b88
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_math.cpp
@@ -0,0 +1,42 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void test_tanh() {
+  Tensor<float, 1> vec1(6);
+  vec1.setRandom();
+
+  Tensor<float, 1> vec2 = vec1.tanh();
+
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_APPROX(vec2(i), tanhf(vec1(i)));
+  }
+}
+
+static void test_sigmoid() {
+  Tensor<float, 1> vec1(6);
+  vec1.setRandom();
+
+  Tensor<float, 1> vec2 = vec1.sigmoid();
+
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_APPROX(vec2(i), 1.0f / (1.0f + std::exp(-vec1(i))));
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_math) {
+  CALL_SUBTEST(test_tanh());
+  CALL_SUBTEST(test_sigmoid());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_math_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_math_sycl.cpp
new file mode 100644
index 0000000..7152a58
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_math_sycl.cpp
@@ -0,0 +1,102 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_tanh_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 4;
+  IndexType sizeDim2 = 4;
+  IndexType sizeDim3 = 1;
+  array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out_cpu(tensorRange);
+
+  in = in.random();
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(in.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(out.size() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, in.data(), (in.size()) * sizeof(DataType));
+  gpu2.device(sycl_device) = gpu1.tanh();
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data2, (out.size()) * sizeof(DataType));
+
+  out_cpu = in.tanh();
+
+  for (int i = 0; i < in.size(); ++i) {
+    VERIFY_IS_APPROX(out(i), out_cpu(i));
+  }
+}
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_sigmoid_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 4;
+  IndexType sizeDim2 = 4;
+  IndexType sizeDim3 = 1;
+  array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out_cpu(tensorRange);
+
+  in = in.random();
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(in.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(out.size() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, in.data(), (in.size()) * sizeof(DataType));
+  gpu2.device(sycl_device) = gpu1.sigmoid();
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data2, (out.size()) * sizeof(DataType));
+
+  out_cpu = in.sigmoid();
+
+  for (int i = 0; i < in.size(); ++i) {
+    VERIFY_IS_APPROX(out(i), out_cpu(i));
+  }
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_computing_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_tanh_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_tanh_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_sigmoid_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_sigmoid_sycl<DataType, ColMajor, int64_t>(sycl_device);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_math_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_computing_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_mixed_indices.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_mixed_indices.cpp
new file mode 100644
index 0000000..be6c486
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_mixed_indices.cpp
@@ -0,0 +1,53 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+static void test_simple() {
+  Tensor<float, 1, ColMajor> vec1(6);
+  Tensor<float, 1, ColMajor, int> vec2(6);
+
+  vec1(0) = 4.0;
+  vec2(0) = 0.0;
+  vec1(1) = 8.0;
+  vec2(1) = 1.0;
+  vec1(2) = 15.0;
+  vec2(2) = 2.0;
+  vec1(3) = 16.0;
+  vec2(3) = 3.0;
+  vec1(4) = 23.0;
+  vec2(4) = 4.0;
+  vec1(5) = 42.0;
+  vec2(5) = 5.0;
+
+  float data3[6];
+  TensorMap<Tensor<float, 1, ColMajor>> vec3(data3, 6);
+  vec3 = vec1.sqrt();
+  float data4[6];
+  TensorMap<Tensor<float, 1, ColMajor, int>> vec4(data4, 6);
+  vec4 = vec2.square();
+
+  VERIFY_IS_APPROX(vec3(0), sqrtf(4.0));
+  VERIFY_IS_APPROX(vec3(1), sqrtf(8.0));
+  VERIFY_IS_APPROX(vec3(2), sqrtf(15.0));
+  VERIFY_IS_APPROX(vec3(3), sqrtf(16.0));
+  VERIFY_IS_APPROX(vec3(4), sqrtf(23.0));
+  VERIFY_IS_APPROX(vec3(5), sqrtf(42.0));
+
+  VERIFY_IS_APPROX(vec4(0), 0.0f);
+  VERIFY_IS_APPROX(vec4(1), 1.0f);
+  VERIFY_IS_APPROX(vec4(2), 2.0f * 2.0f);
+  VERIFY_IS_APPROX(vec4(3), 3.0f * 3.0f);
+  VERIFY_IS_APPROX(vec4(4), 4.0f * 4.0f);
+  VERIFY_IS_APPROX(vec4(5), 5.0f * 5.0f);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_mixed_indices) { CALL_SUBTEST(test_simple()); }
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_morphing.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_morphing.cpp
new file mode 100644
index 0000000..55d4291
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_morphing.cpp
@@ -0,0 +1,551 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <typename>
+static void test_simple_reshape() {
+  Tensor<float, 5> tensor1(2, 3, 1, 7, 1);
+  tensor1.setRandom();
+
+  Tensor<float, 3> tensor2(2, 3, 7);
+  Tensor<float, 2> tensor3(6, 7);
+  Tensor<float, 2> tensor4(2, 21);
+
+  Tensor<float, 3>::Dimensions dim1(2, 3, 7);
+  tensor2 = tensor1.reshape(dim1);
+  Tensor<float, 2>::Dimensions dim2(6, 7);
+  tensor3 = tensor1.reshape(dim2);
+  Tensor<float, 2>::Dimensions dim3(2, 21);
+  tensor4 = tensor1.reshape(dim1).reshape(dim3);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor2(i, j, k));
+        VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor3(i + 2 * j, k));
+        VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor4(i, j + 3 * k));
+      }
+    }
+  }
+}
+
+template <typename>
+static void test_static_reshape() {
+  using Eigen::type2index;
+
+  Tensor<float, 5> tensor(2, 3, 1, 7, 1);
+  tensor.setRandom();
+
+  // New dimensions: [2, 3, 7]
+  Eigen::IndexList<type2index<2>, type2index<3>, type2index<7>> dim;
+  Tensor<float, 3> reshaped = tensor.reshape(static_cast<Eigen::DSizes<ptrdiff_t, 3>>(dim));
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(tensor(i, j, 0, k, 0), reshaped(i, j, k));
+      }
+    }
+  }
+}
+
+template <typename>
+static void test_reshape_in_expr() {
+  MatrixXf m1(2, 3 * 5 * 7 * 11);
+  MatrixXf m2(3 * 5 * 7 * 11, 13);
+  m1.setRandom();
+  m2.setRandom();
+  MatrixXf m3 = m1 * m2;
+
+  TensorMap<Tensor<float, 5>> tensor1(m1.data(), 2, 3, 5, 7, 11);
+  TensorMap<Tensor<float, 5>> tensor2(m2.data(), 3, 5, 7, 11, 13);
+  Tensor<float, 2>::Dimensions newDims1(2, 3 * 5 * 7 * 11);
+  Tensor<float, 2>::Dimensions newDims2(3 * 5 * 7 * 11, 13);
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+  array<DimPair, 1> contract_along{{DimPair(1, 0)}};
+  Tensor<float, 2> tensor3(2, 13);
+  tensor3 = tensor1.reshape(newDims1).contract(tensor2.reshape(newDims2), contract_along);
+
+  Map<MatrixXf> res(tensor3.data(), 2, 13);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 13; ++j) {
+      VERIFY_IS_APPROX(res(i, j), m3(i, j));
+    }
+  }
+}
+
+template <typename>
+static void test_reshape_as_lvalue() {
+  Tensor<float, 3> tensor(2, 3, 7);
+  tensor.setRandom();
+
+  Tensor<float, 2> tensor2d(6, 7);
+  Tensor<float, 3>::Dimensions dim(2, 3, 7);
+  tensor2d.reshape(dim) = tensor;
+
+  float scratch[2 * 3 * 1 * 7 * 1];
+  TensorMap<Tensor<float, 5>> tensor5d(scratch, 2, 3, 1, 7, 1);
+  tensor5d.reshape(dim).device(Eigen::DefaultDevice()) = tensor;
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(tensor2d(i + 2 * j, k), tensor(i, j, k));
+        VERIFY_IS_EQUAL(tensor5d(i, j, 0, k, 0), tensor(i, j, k));
+      }
+    }
+  }
+}
+
+template <typename T, int DataLayout>
+static void test_simple_slice() {
+  Tensor<T, 5, DataLayout> tensor(2, 3, 5, 7, 11);
+  tensor.setRandom();
+
+  Tensor<T, 5, DataLayout> slice1(1, 1, 1, 1, 1);
+  Eigen::DSizes<ptrdiff_t, 5> indices(1, 2, 3, 4, 5);
+  Eigen::DSizes<ptrdiff_t, 5> sizes(1, 1, 1, 1, 1);
+  slice1 = tensor.slice(indices, sizes);
+  VERIFY_IS_EQUAL(slice1(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));
+
+  Tensor<T, 5, DataLayout> slice2(1, 1, 2, 2, 3);
+  Eigen::DSizes<ptrdiff_t, 5> indices2(1, 1, 3, 4, 5);
+  Eigen::DSizes<ptrdiff_t, 5> sizes2(1, 1, 2, 2, 3);
+  slice2 = tensor.slice(indices2, sizes2);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 2; ++j) {
+      for (int k = 0; k < 3; ++k) {
+        VERIFY_IS_EQUAL(slice2(0, 0, i, j, k), tensor(1, 1, 3 + i, 4 + j, 5 + k));
+      }
+    }
+  }
+}
+
+template <typename T>
+static void test_const_slice() {
+  const T b[1] = {42};
+  TensorMap<Tensor<const T, 1>> m(b, 1);
+  DSizes<DenseIndex, 1> offsets;
+  offsets[0] = 0;
+  TensorRef<const Tensor<const T, 1>> slice_ref(m.slice(offsets, m.dimensions()));
+  VERIFY_IS_EQUAL(slice_ref(0), 42);
+}
+
+template <typename T, int DataLayout>
+static void test_slice_in_expr() {
+  typedef Matrix<T, Dynamic, Dynamic, DataLayout> Mtx;
+  Mtx m1(7, 7);
+  Mtx m2(3, 3);
+  m1.setRandom();
+  m2.setRandom();
+
+  Mtx m3 = m1.block(1, 2, 3, 3) * m2.block(0, 2, 3, 1);
+
+  TensorMap<Tensor<T, 2, DataLayout>> tensor1(m1.data(), 7, 7);
+  TensorMap<Tensor<T, 2, DataLayout>> tensor2(m2.data(), 3, 3);
+  Tensor<T, 2, DataLayout> tensor3(3, 1);
+  typedef typename Tensor<T, 1>::DimensionPair DimPair;
+  array<DimPair, 1> contract_along{{DimPair(1, 0)}};
+
+  Eigen::DSizes<ptrdiff_t, 2> indices1(1, 2);
+  Eigen::DSizes<ptrdiff_t, 2> sizes1(3, 3);
+  Eigen::DSizes<ptrdiff_t, 2> indices2(0, 2);
+  Eigen::DSizes<ptrdiff_t, 2> sizes2(3, 1);
+  tensor3 = tensor1.slice(indices1, sizes1).contract(tensor2.slice(indices2, sizes2), contract_along);
+
+  Map<Mtx> res(tensor3.data(), 3, 1);
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 1; ++j) {
+      VERIFY_IS_APPROX(res(i, j), m3(i, j));
+    }
+  }
+
+  // Take an arbitrary slice of an arbitrarily sized tensor.
+  TensorMap<Tensor<const T, 2, DataLayout>> tensor4(m1.data(), 7, 7);
+  Tensor<T, 1, DataLayout> tensor6 =
+      tensor4.reshape(DSizes<ptrdiff_t, 1>(7 * 7)).exp().slice(DSizes<ptrdiff_t, 1>(0), DSizes<ptrdiff_t, 1>(35));
+  for (int i = 0; i < 35; ++i) {
+    VERIFY_IS_APPROX(tensor6(i), expf(tensor4.data()[i]));
+  }
+}
+
+template <typename T, int DataLayout>
+static void test_slice_as_lvalue() {
+  Tensor<T, 3, DataLayout> tensor1(2, 2, 7);
+  tensor1.setRandom();
+  Tensor<T, 3, DataLayout> tensor2(2, 2, 7);
+  tensor2.setRandom();
+  Tensor<T, 3, DataLayout> tensor3(4, 3, 5);
+  tensor3.setRandom();
+  Tensor<T, 3, DataLayout> tensor4(4, 3, 2);
+  tensor4.setRandom();
+  Tensor<T, 3, DataLayout> tensor5(10, 13, 12);
+  tensor5.setRandom();
+
+  Tensor<T, 3, DataLayout> result(4, 5, 7);
+  Eigen::DSizes<ptrdiff_t, 3> sizes12(2, 2, 7);
+  Eigen::DSizes<ptrdiff_t, 3> first_slice(0, 0, 0);
+  result.slice(first_slice, sizes12) = tensor1;
+  Eigen::DSizes<ptrdiff_t, 3> second_slice(2, 0, 0);
+  result.slice(second_slice, sizes12).device(Eigen::DefaultDevice()) = tensor2;
+
+  Eigen::DSizes<ptrdiff_t, 3> sizes3(4, 3, 5);
+  Eigen::DSizes<ptrdiff_t, 3> third_slice(0, 2, 0);
+  result.slice(third_slice, sizes3) = tensor3;
+
+  Eigen::DSizes<ptrdiff_t, 3> sizes4(4, 3, 2);
+  Eigen::DSizes<ptrdiff_t, 3> fourth_slice(0, 2, 5);
+  result.slice(fourth_slice, sizes4) = tensor4;
+
+  for (int j = 0; j < 2; ++j) {
+    for (int k = 0; k < 7; ++k) {
+      for (int i = 0; i < 2; ++i) {
+        VERIFY_IS_EQUAL(result(i, j, k), tensor1(i, j, k));
+        VERIFY_IS_EQUAL(result(i + 2, j, k), tensor2(i, j, k));
+      }
+    }
+  }
+  for (int i = 0; i < 4; ++i) {
+    for (int j = 2; j < 5; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        VERIFY_IS_EQUAL(result(i, j, k), tensor3(i, j - 2, k));
+      }
+      for (int k = 5; k < 7; ++k) {
+        VERIFY_IS_EQUAL(result(i, j, k), tensor4(i, j - 2, k - 5));
+      }
+    }
+  }
+
+  Eigen::DSizes<ptrdiff_t, 3> sizes5(4, 5, 7);
+  Eigen::DSizes<ptrdiff_t, 3> fifth_slice(0, 0, 0);
+  result.slice(fifth_slice, sizes5) = tensor5.slice(fifth_slice, sizes5);
+  for (int i = 0; i < 4; ++i) {
+    for (int j = 2; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(result(i, j, k), tensor5(i, j, k));
+      }
+    }
+  }
+}
+
+template <typename T, int DataLayout>
+static void test_slice_raw_data() {
+  Tensor<T, 4, DataLayout> tensor(3, 5, 7, 11);
+  tensor.setRandom();
+
+  Eigen::DSizes<ptrdiff_t, 4> offsets(1, 2, 3, 4);
+  Eigen::DSizes<ptrdiff_t, 4> extents(1, 1, 1, 1);
+  typedef TensorEvaluator<decltype(tensor.slice(offsets, extents)), DefaultDevice> SliceEvaluator;
+  auto slice1 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
+  VERIFY_IS_EQUAL(slice1.dimensions().TotalSize(), 1);
+  VERIFY_IS_EQUAL(slice1.data()[0], tensor(1, 2, 3, 4));
+
+  if (DataLayout == ColMajor) {
+    extents = Eigen::DSizes<ptrdiff_t, 4>(2, 1, 1, 1);
+    auto slice2 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
+    VERIFY_IS_EQUAL(slice2.dimensions().TotalSize(), 2);
+    VERIFY_IS_EQUAL(slice2.data()[0], tensor(1, 2, 3, 4));
+    VERIFY_IS_EQUAL(slice2.data()[1], tensor(2, 2, 3, 4));
+  } else {
+    extents = Eigen::DSizes<ptrdiff_t, 4>(1, 1, 1, 2);
+    auto slice2 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
+    VERIFY_IS_EQUAL(slice2.dimensions().TotalSize(), 2);
+    VERIFY_IS_EQUAL(slice2.data()[0], tensor(1, 2, 3, 4));
+    VERIFY_IS_EQUAL(slice2.data()[1], tensor(1, 2, 3, 5));
+  }
+
+  extents = Eigen::DSizes<ptrdiff_t, 4>(1, 2, 1, 1);
+  auto slice3 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
+  VERIFY_IS_EQUAL(slice3.dimensions().TotalSize(), 2);
+  VERIFY_IS_EQUAL(slice3.data(), static_cast<T*>(0));
+
+  if (DataLayout == ColMajor) {
+    offsets = Eigen::DSizes<ptrdiff_t, 4>(0, 2, 3, 4);
+    extents = Eigen::DSizes<ptrdiff_t, 4>(3, 2, 1, 1);
+    auto slice4 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
+    VERIFY_IS_EQUAL(slice4.dimensions().TotalSize(), 6);
+    for (int i = 0; i < 3; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        VERIFY_IS_EQUAL(slice4.data()[i + 3 * j], tensor(i, 2 + j, 3, 4));
+      }
+    }
+  } else {
+    offsets = Eigen::DSizes<ptrdiff_t, 4>(1, 2, 3, 0);
+    extents = Eigen::DSizes<ptrdiff_t, 4>(1, 1, 2, 11);
+    auto slice4 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
+    VERIFY_IS_EQUAL(slice4.dimensions().TotalSize(), 22);
+    for (int l = 0; l < 11; ++l) {
+      for (int k = 0; k < 2; ++k) {
+        VERIFY_IS_EQUAL(slice4.data()[l + 11 * k], tensor(1, 2, 3 + k, l));
+      }
+    }
+  }
+
+  if (DataLayout == ColMajor) {
+    offsets = Eigen::DSizes<ptrdiff_t, 4>(0, 0, 0, 4);
+    extents = Eigen::DSizes<ptrdiff_t, 4>(3, 5, 7, 2);
+    auto slice5 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
+    VERIFY_IS_EQUAL(slice5.dimensions().TotalSize(), 210);
+    for (int i = 0; i < 3; ++i) {
+      for (int j = 0; j < 5; ++j) {
+        for (int k = 0; k < 7; ++k) {
+          for (int l = 0; l < 2; ++l) {
+            int slice_index = i + 3 * (j + 5 * (k + 7 * l));
+            VERIFY_IS_EQUAL(slice5.data()[slice_index], tensor(i, j, k, l + 4));
+          }
+        }
+      }
+    }
+  } else {
+    offsets = Eigen::DSizes<ptrdiff_t, 4>(1, 0, 0, 0);
+    extents = Eigen::DSizes<ptrdiff_t, 4>(2, 5, 7, 11);
+    auto slice5 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
+    VERIFY_IS_EQUAL(slice5.dimensions().TotalSize(), 770);
+    for (int l = 0; l < 11; ++l) {
+      for (int k = 0; k < 7; ++k) {
+        for (int j = 0; j < 5; ++j) {
+          for (int i = 0; i < 2; ++i) {
+            int slice_index = l + 11 * (k + 7 * (j + 5 * i));
+            VERIFY_IS_EQUAL(slice5.data()[slice_index], tensor(i + 1, j, k, l));
+          }
+        }
+      }
+    }
+  }
+
+  offsets = Eigen::DSizes<ptrdiff_t, 4>(0, 0, 0, 0);
+  extents = Eigen::DSizes<ptrdiff_t, 4>(3, 5, 7, 11);
+  auto slice6 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
+  VERIFY_IS_EQUAL(slice6.dimensions().TotalSize(), 3 * 5 * 7 * 11);
+  VERIFY_IS_EQUAL(slice6.data(), tensor.data());
+}
+
+template <typename T, int DataLayout>
+static void test_strided_slice() {
+  typedef Tensor<T, 5, DataLayout> Tensor5f;
+  typedef Eigen::DSizes<Eigen::DenseIndex, 5> Index5;
+  typedef Tensor<T, 2, DataLayout> Tensor2f;
+  typedef Eigen::DSizes<Eigen::DenseIndex, 2> Index2;
+  Tensor<T, 5, DataLayout> tensor(2, 3, 5, 7, 11);
+  Tensor<T, 2, DataLayout> tensor2(7, 11);
+  tensor.setRandom();
+  tensor2.setRandom();
+
+  if (true) {
+    Tensor2f slice(2, 3);
+    Index2 strides(-2, -1);
+    Index2 indicesStart(5, 7);
+    Index2 indicesStop(0, 4);
+    slice = tensor2.stridedSlice(indicesStart, indicesStop, strides);
+    for (int j = 0; j < 2; ++j) {
+      for (int k = 0; k < 3; ++k) {
+        VERIFY_IS_EQUAL(slice(j, k), tensor2(5 - 2 * j, 7 - k));
+      }
+    }
+  }
+
+  if (true) {
+    Tensor2f slice(0, 1);
+    Index2 strides(1, 1);
+    Index2 indicesStart(5, 4);
+    Index2 indicesStop(5, 5);
+    slice = tensor2.stridedSlice(indicesStart, indicesStop, strides);
+  }
+
+  if (true) {  // test clamped degenerate interavls
+    Tensor2f slice(7, 11);
+    Index2 strides(1, -1);
+    Index2 indicesStart(-3, 20);  // should become 0,10
+    Index2 indicesStop(20, -11);  // should become 11, -1
+    slice = tensor2.stridedSlice(indicesStart, indicesStop, strides);
+    for (int j = 0; j < 7; ++j) {
+      for (int k = 0; k < 11; ++k) {
+        VERIFY_IS_EQUAL(slice(j, k), tensor2(j, 10 - k));
+      }
+    }
+  }
+
+  if (true) {
+    Tensor5f slice1(1, 1, 1, 1, 1);
+    Eigen::DSizes<Eigen::DenseIndex, 5> indicesStart(1, 2, 3, 4, 5);
+    Eigen::DSizes<Eigen::DenseIndex, 5> indicesStop(2, 3, 4, 5, 6);
+    Eigen::DSizes<Eigen::DenseIndex, 5> strides(1, 1, 1, 1, 1);
+    slice1 = tensor.stridedSlice(indicesStart, indicesStop, strides);
+    VERIFY_IS_EQUAL(slice1(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));
+  }
+
+  if (true) {
+    Tensor5f slice(1, 1, 2, 2, 3);
+    Index5 start(1, 1, 3, 4, 5);
+    Index5 stop(2, 2, 5, 6, 8);
+    Index5 strides(1, 1, 1, 1, 1);
+    slice = tensor.stridedSlice(start, stop, strides);
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        for (int k = 0; k < 3; ++k) {
+          VERIFY_IS_EQUAL(slice(0, 0, i, j, k), tensor(1, 1, 3 + i, 4 + j, 5 + k));
+        }
+      }
+    }
+  }
+
+  if (true) {
+    Tensor5f slice(1, 1, 2, 2, 3);
+    Index5 strides3(1, 1, -2, 1, -1);
+    Index5 indices3Start(1, 1, 4, 4, 7);
+    Index5 indices3Stop(2, 2, 0, 6, 4);
+    slice = tensor.stridedSlice(indices3Start, indices3Stop, strides3);
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        for (int k = 0; k < 3; ++k) {
+          VERIFY_IS_EQUAL(slice(0, 0, i, j, k), tensor(1, 1, 4 - 2 * i, 4 + j, 7 - k));
+        }
+      }
+    }
+  }
+
+  if (false) {  // tests degenerate interval
+    Tensor5f slice(1, 1, 2, 2, 3);
+    Index5 strides3(1, 1, 2, 1, 1);
+    Index5 indices3Start(1, 1, 4, 4, 7);
+    Index5 indices3Stop(2, 2, 0, 6, 4);
+    slice = tensor.stridedSlice(indices3Start, indices3Stop, strides3);
+  }
+}
+
+template <typename T, int DataLayout>
+static void test_strided_slice_write() {
+  typedef Tensor<T, 2, DataLayout> Tensor2f;
+  typedef Eigen::DSizes<Eigen::DenseIndex, 2> Index2;
+
+  Tensor<T, 2, DataLayout> tensor(7, 11), tensor2(7, 11);
+  tensor.setRandom();
+  tensor2 = tensor;
+  Tensor2f slice(2, 3);
+
+  slice.setRandom();
+
+  Index2 strides(1, 1);
+  Index2 indicesStart(3, 4);
+  Index2 indicesStop(5, 7);
+  Index2 lengths(2, 3);
+
+  tensor.slice(indicesStart, lengths) = slice;
+  tensor2.stridedSlice(indicesStart, indicesStop, strides) = slice;
+
+  for (int i = 0; i < 7; i++)
+    for (int j = 0; j < 11; j++) {
+      VERIFY_IS_EQUAL(tensor(i, j), tensor2(i, j));
+    }
+}
+
+template <typename T, int DataLayout>
+static void test_composition() {
+  Eigen::Tensor<T, 2, DataLayout> matrix(7, 11);
+  matrix.setRandom();
+
+  const DSizes<ptrdiff_t, 3> newDims(1, 1, 11);
+  Eigen::Tensor<T, 3, DataLayout> tensor =
+      matrix.slice(DSizes<ptrdiff_t, 2>(2, 0), DSizes<ptrdiff_t, 2>(1, 11)).reshape(newDims);
+
+  VERIFY_IS_EQUAL(tensor.dimensions().TotalSize(), 11);
+  VERIFY_IS_EQUAL(tensor.dimension(0), 1);
+  VERIFY_IS_EQUAL(tensor.dimension(1), 1);
+  VERIFY_IS_EQUAL(tensor.dimension(2), 11);
+  for (int i = 0; i < 11; ++i) {
+    VERIFY_IS_EQUAL(tensor(0, 0, i), matrix(2, i));
+  }
+}
+
+template <typename T, int DataLayout>
+static void test_empty_slice() {
+  Tensor<T, 3, DataLayout> tensor(2, 3, 5);
+  tensor.setRandom();
+  Tensor<T, 3, DataLayout> copy = tensor;
+
+  // empty size in first dimension
+  Eigen::DSizes<ptrdiff_t, 3> indices1(1, 2, 3);
+  Eigen::DSizes<ptrdiff_t, 3> sizes1(0, 1, 2);
+  Tensor<T, 3, DataLayout> slice1(0, 1, 2);
+  slice1.setRandom();
+  tensor.slice(indices1, sizes1) = slice1;
+
+  // empty size in second dimension
+  Eigen::DSizes<ptrdiff_t, 3> indices2(1, 2, 3);
+  Eigen::DSizes<ptrdiff_t, 3> sizes2(1, 0, 2);
+  Tensor<T, 3, DataLayout> slice2(1, 0, 2);
+  slice2.setRandom();
+  tensor.slice(indices2, sizes2) = slice2;
+
+  // empty size in third dimension
+  Eigen::DSizes<ptrdiff_t, 3> indices3(1, 2, 3);
+  Eigen::DSizes<ptrdiff_t, 3> sizes3(1, 1, 0);
+  Tensor<T, 3, DataLayout> slice3(1, 1, 0);
+  slice3.setRandom();
+  tensor.slice(indices3, sizes3) = slice3;
+
+  // empty size in first and second dimension
+  Eigen::DSizes<ptrdiff_t, 3> indices4(1, 2, 3);
+  Eigen::DSizes<ptrdiff_t, 3> sizes4(0, 0, 2);
+  Tensor<T, 3, DataLayout> slice4(0, 0, 2);
+  slice4.setRandom();
+  tensor.slice(indices4, sizes4) = slice4;
+
+  // empty size in second and third dimension
+  Eigen::DSizes<ptrdiff_t, 3> indices5(1, 2, 3);
+  Eigen::DSizes<ptrdiff_t, 3> sizes5(1, 0, 0);
+  Tensor<T, 3, DataLayout> slice5(1, 0, 0);
+  slice5.setRandom();
+  tensor.slice(indices5, sizes5) = slice5;
+
+  // empty size in all dimensions
+  Eigen::DSizes<ptrdiff_t, 3> indices6(1, 2, 3);
+  Eigen::DSizes<ptrdiff_t, 3> sizes6(0, 0, 0);
+  Tensor<T, 3, DataLayout> slice6(0, 0, 0);
+  slice6.setRandom();
+  tensor.slice(indices6, sizes6) = slice6;
+
+  // none of these operations should change the tensor's components
+  // because all of the rvalue slices have at least one zero dimension
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        VERIFY_IS_EQUAL(tensor(i, j, k), copy(i, j, k));
+      }
+    }
+  }
+}
+
+#define CALL_SUBTEST_PART(PART) CALL_SUBTEST_##PART
+
+#define CALL_SUBTESTS_TYPES_LAYOUTS(PART, NAME)       \
+  CALL_SUBTEST_PART(PART)((NAME<float, ColMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<float, RowMajor>())); \
+  CALL_SUBTEST_PART(PART)((NAME<bool, ColMajor>()));  \
+  CALL_SUBTEST_PART(PART)((NAME<bool, RowMajor>()))
+
+EIGEN_DECLARE_TEST(cxx11_tensor_morphing) {
+  CALL_SUBTEST_1(test_simple_reshape<void>());
+  CALL_SUBTEST_1(test_static_reshape<void>());
+  CALL_SUBTEST_1(test_reshape_as_lvalue<void>());
+  CALL_SUBTEST_1(test_reshape_in_expr<void>());
+  CALL_SUBTEST_1(test_const_slice<float>());
+
+  CALL_SUBTESTS_TYPES_LAYOUTS(2, test_simple_slice);
+  CALL_SUBTESTS_TYPES_LAYOUTS(3, test_slice_as_lvalue);
+  CALL_SUBTESTS_TYPES_LAYOUTS(4, test_slice_raw_data);
+  CALL_SUBTESTS_TYPES_LAYOUTS(5, test_strided_slice_write);
+  CALL_SUBTESTS_TYPES_LAYOUTS(6, test_strided_slice);
+  CALL_SUBTESTS_TYPES_LAYOUTS(7, test_composition);
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_morphing_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_morphing_sycl.cpp
new file mode 100644
index 0000000..7242b8e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_morphing_sycl.cpp
@@ -0,0 +1,371 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_simple_reshape(const Eigen::SyclDevice& sycl_device) {
+  typename Tensor<DataType, 5, DataLayout, IndexType>::Dimensions dim1(2, 3, 1, 7, 1);
+  typename Tensor<DataType, 3, DataLayout, IndexType>::Dimensions dim2(2, 3, 7);
+  typename Tensor<DataType, 2, DataLayout, IndexType>::Dimensions dim3(6, 7);
+  typename Tensor<DataType, 2, DataLayout, IndexType>::Dimensions dim4(2, 21);
+
+  Tensor<DataType, 5, DataLayout, IndexType> tensor1(dim1);
+  Tensor<DataType, 3, DataLayout, IndexType> tensor2(dim2);
+  Tensor<DataType, 2, DataLayout, IndexType> tensor3(dim3);
+  Tensor<DataType, 2, DataLayout, IndexType> tensor4(dim4);
+
+  tensor1.setRandom();
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size() * sizeof(DataType)));
+  DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(tensor3.size() * sizeof(DataType)));
+  DataType* gpu_data4 = static_cast<DataType*>(sycl_device.allocate(tensor4.size() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu1(gpu_data1, dim1);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, dim2);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu3(gpu_data3, dim3);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu4(gpu_data4, dim4);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(), (tensor1.size()) * sizeof(DataType));
+
+  gpu2.device(sycl_device) = gpu1.reshape(dim2);
+  sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2, (tensor1.size()) * sizeof(DataType));
+
+  gpu3.device(sycl_device) = gpu1.reshape(dim3);
+  sycl_device.memcpyDeviceToHost(tensor3.data(), gpu_data3, (tensor3.size()) * sizeof(DataType));
+
+  gpu4.device(sycl_device) = gpu1.reshape(dim2).reshape(dim4);
+  sycl_device.memcpyDeviceToHost(tensor4.data(), gpu_data4, (tensor4.size()) * sizeof(DataType));
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor2(i, j, k));  /// ColMajor
+        if (static_cast<int>(DataLayout) == static_cast<int>(ColMajor)) {
+          VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor3(i + 2 * j, k));  /// ColMajor
+          VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor4(i, j + 3 * k));  /// ColMajor
+        } else {
+          // VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor2(i,j,k));      /// RowMajor
+          VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor4(i, j * 7 + k));  /// RowMajor
+          VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor3(i * 3 + j, k));  /// RowMajor
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+  sycl_device.deallocate(gpu_data3);
+  sycl_device.deallocate(gpu_data4);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_reshape_as_lvalue(const Eigen::SyclDevice& sycl_device) {
+  typename Tensor<DataType, 3, DataLayout, IndexType>::Dimensions dim1(2, 3, 7);
+  typename Tensor<DataType, 2, DataLayout, IndexType>::Dimensions dim2(6, 7);
+  typename Tensor<DataType, 5, DataLayout, IndexType>::Dimensions dim3(2, 3, 1, 7, 1);
+  Tensor<DataType, 3, DataLayout, IndexType> tensor(dim1);
+  Tensor<DataType, 2, DataLayout, IndexType> tensor2d(dim2);
+  Tensor<DataType, 5, DataLayout, IndexType> tensor5d(dim3);
+
+  tensor.setRandom();
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2d.size() * sizeof(DataType)));
+  DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(tensor5d.size() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, dim1);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu2(gpu_data2, dim2);
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu3(gpu_data3, dim3);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), (tensor.size()) * sizeof(DataType));
+
+  gpu2.reshape(dim1).device(sycl_device) = gpu1;
+  sycl_device.memcpyDeviceToHost(tensor2d.data(), gpu_data2, (tensor2d.size()) * sizeof(DataType));
+
+  gpu3.reshape(dim1).device(sycl_device) = gpu1;
+  sycl_device.memcpyDeviceToHost(tensor5d.data(), gpu_data3, (tensor5d.size()) * sizeof(DataType));
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(tensor5d(i, j, 0, k, 0), tensor(i, j, k));
+        if (static_cast<int>(DataLayout) == static_cast<int>(ColMajor)) {
+          VERIFY_IS_EQUAL(tensor2d(i + 2 * j, k), tensor(i, j, k));  /// ColMajor
+        } else {
+          VERIFY_IS_EQUAL(tensor2d(i * 3 + j, k), tensor(i, j, k));  /// RowMajor
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+  sycl_device.deallocate(gpu_data3);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_simple_slice(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  IndexType sizeDim5 = 11;
+  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
+  Tensor<DataType, 5, DataLayout, IndexType> tensor(tensorRange);
+  tensor.setRandom();
+  array<IndexType, 5> slice1_range = {{1, 1, 1, 1, 1}};
+  Tensor<DataType, 5, DataLayout, IndexType> slice1(slice1_range);
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(slice1.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu2(gpu_data2, slice1_range);
+  Eigen::DSizes<IndexType, 5> indices(1, 2, 3, 4, 5);
+  Eigen::DSizes<IndexType, 5> sizes(1, 1, 1, 1, 1);
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), (tensor.size()) * sizeof(DataType));
+  gpu2.device(sycl_device) = gpu1.slice(indices, sizes);
+  sycl_device.memcpyDeviceToHost(slice1.data(), gpu_data2, (slice1.size()) * sizeof(DataType));
+  VERIFY_IS_EQUAL(slice1(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));
+
+  array<IndexType, 5> slice2_range = {{1, 1, 2, 2, 3}};
+  Tensor<DataType, 5, DataLayout, IndexType> slice2(slice2_range);
+  DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(slice2.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu3(gpu_data3, slice2_range);
+  Eigen::DSizes<IndexType, 5> indices2(1, 1, 3, 4, 5);
+  Eigen::DSizes<IndexType, 5> sizes2(1, 1, 2, 2, 3);
+  gpu3.device(sycl_device) = gpu1.slice(indices2, sizes2);
+  sycl_device.memcpyDeviceToHost(slice2.data(), gpu_data3, (slice2.size()) * sizeof(DataType));
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 2; ++j) {
+      for (IndexType k = 0; k < 3; ++k) {
+        VERIFY_IS_EQUAL(slice2(0, 0, i, j, k), tensor(1, 1, 3 + i, 4 + j, 5 + k));
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+  sycl_device.deallocate(gpu_data3);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_strided_slice_as_rhs_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  IndexType sizeDim5 = 11;
+  typedef Eigen::DSizes<IndexType, 5> Index5;
+  Index5 strides(1L, 1L, 1L, 1L, 1L);
+  Index5 indicesStart(1L, 2L, 3L, 4L, 5L);
+  Index5 indicesStop(2L, 3L, 4L, 5L, 6L);
+  Index5 lengths(1L, 1L, 1L, 1L, 1L);
+
+  array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
+  Tensor<DataType, 5, DataLayout, IndexType> tensor(tensorRange);
+  tensor.setRandom();
+
+  array<IndexType, 5> slice1_range = {{1, 1, 1, 1, 1}};
+  Tensor<DataType, 5, DataLayout, IndexType> slice1(slice1_range);
+  Tensor<DataType, 5, DataLayout, IndexType> slice_stride1(slice1_range);
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(slice1.size() * sizeof(DataType)));
+  DataType* gpu_data_stride2 = static_cast<DataType*>(sycl_device.allocate(slice_stride1.size() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu2(gpu_data2, slice1_range);
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_stride2(gpu_data_stride2, slice1_range);
+
+  Eigen::DSizes<IndexType, 5> indices(1, 2, 3, 4, 5);
+  Eigen::DSizes<IndexType, 5> sizes(1, 1, 1, 1, 1);
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), (tensor.size()) * sizeof(DataType));
+  gpu2.device(sycl_device) = gpu1.slice(indices, sizes);
+  sycl_device.memcpyDeviceToHost(slice1.data(), gpu_data2, (slice1.size()) * sizeof(DataType));
+
+  gpu_stride2.device(sycl_device) = gpu1.stridedSlice(indicesStart, indicesStop, strides);
+  sycl_device.memcpyDeviceToHost(slice_stride1.data(), gpu_data_stride2, (slice_stride1.size()) * sizeof(DataType));
+
+  VERIFY_IS_EQUAL(slice1(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));
+  VERIFY_IS_EQUAL(slice_stride1(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));
+
+  array<IndexType, 5> slice2_range = {{1, 1, 2, 2, 3}};
+  Tensor<DataType, 5, DataLayout, IndexType> slice2(slice2_range);
+  Tensor<DataType, 5, DataLayout, IndexType> strideSlice2(slice2_range);
+
+  DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(slice2.size() * sizeof(DataType)));
+  DataType* gpu_data_stride3 = static_cast<DataType*>(sycl_device.allocate(strideSlice2.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu3(gpu_data3, slice2_range);
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_stride3(gpu_data_stride3, slice2_range);
+  Eigen::DSizes<IndexType, 5> indices2(1, 1, 3, 4, 5);
+  Eigen::DSizes<IndexType, 5> sizes2(1, 1, 2, 2, 3);
+  Index5 strides2(1L, 1L, 1L, 1L, 1L);
+  Index5 indicesStart2(1L, 1L, 3L, 4L, 5L);
+  Index5 indicesStop2(2L, 2L, 5L, 6L, 8L);
+
+  gpu3.device(sycl_device) = gpu1.slice(indices2, sizes2);
+  sycl_device.memcpyDeviceToHost(slice2.data(), gpu_data3, (slice2.size()) * sizeof(DataType));
+
+  gpu_stride3.device(sycl_device) = gpu1.stridedSlice(indicesStart2, indicesStop2, strides2);
+  sycl_device.memcpyDeviceToHost(strideSlice2.data(), gpu_data_stride3, (strideSlice2.size()) * sizeof(DataType));
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 2; ++j) {
+      for (IndexType k = 0; k < 3; ++k) {
+        VERIFY_IS_EQUAL(slice2(0, 0, i, j, k), tensor(1, 1, 3 + i, 4 + j, 5 + k));
+        VERIFY_IS_EQUAL(strideSlice2(0, 0, i, j, k), tensor(1, 1, 3 + i, 4 + j, 5 + k));
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+  sycl_device.deallocate(gpu_data3);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_strided_slice_write_sycl(const Eigen::SyclDevice& sycl_device) {
+  typedef Tensor<DataType, 2, DataLayout, IndexType> Tensor2f;
+  typedef Eigen::DSizes<IndexType, 2> Index2;
+  IndexType sizeDim1 = 7L;
+  IndexType sizeDim2 = 11L;
+  array<IndexType, 2> tensorRange = {{sizeDim1, sizeDim2}};
+  Tensor<DataType, 2, DataLayout, IndexType> tensor(tensorRange), tensor2(tensorRange);
+  IndexType sliceDim1 = 2;
+  IndexType sliceDim2 = 3;
+  array<IndexType, 2> sliceRange = {{sliceDim1, sliceDim2}};
+  Tensor2f slice(sliceRange);
+  Index2 strides(1L, 1L);
+  Index2 indicesStart(3L, 4L);
+  Index2 indicesStop(5L, 7L);
+  Index2 lengths(2L, 3L);
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size() * sizeof(DataType)));
+  DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(slice.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu3(gpu_data3, sliceRange);
+
+  tensor.setRandom();
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), (tensor.size()) * sizeof(DataType));
+  gpu2.device(sycl_device) = gpu1;
+
+  slice.setRandom();
+  sycl_device.memcpyHostToDevice(gpu_data3, slice.data(), (slice.size()) * sizeof(DataType));
+
+  gpu1.slice(indicesStart, lengths).device(sycl_device) = gpu3;
+  gpu2.stridedSlice(indicesStart, indicesStop, strides).device(sycl_device) = gpu3;
+  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data1, (tensor.size()) * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2, (tensor2.size()) * sizeof(DataType));
+
+  for (IndexType i = 0; i < sizeDim1; i++)
+    for (IndexType j = 0; j < sizeDim2; j++) {
+      VERIFY_IS_EQUAL(tensor(i, j), tensor2(i, j));
+    }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+  sycl_device.deallocate(gpu_data3);
+}
+
+template <typename OutIndex, typename DSizes>
+Eigen::array<OutIndex, DSizes::count> To32BitDims(const DSizes& in) {
+  Eigen::array<OutIndex, DSizes::count> out;
+  for (int i = 0; i < DSizes::count; ++i) {
+    out[i] = in[i];
+  }
+  return out;
+}
+
+template <class DataType, int DataLayout, typename IndexType, typename ConvertedIndexType>
+int run_eigen(const SyclDevice& sycl_device) {
+  using TensorI64 = Tensor<DataType, 5, DataLayout, IndexType>;
+  using TensorI32 = Tensor<DataType, 5, DataLayout, ConvertedIndexType>;
+  using TensorMI64 = TensorMap<TensorI64>;
+  using TensorMI32 = TensorMap<TensorI32>;
+  Eigen::array<IndexType, 5> tensor_range{{4, 1, 1, 1, 6}};
+  Eigen::array<IndexType, 5> slice_range{{4, 1, 1, 1, 3}};
+
+  TensorI64 out_tensor_gpu(tensor_range);
+  TensorI64 out_tensor_cpu(tensor_range);
+  out_tensor_cpu.setRandom();
+
+  TensorI64 sub_tensor(slice_range);
+  sub_tensor.setRandom();
+
+  DataType* out_gpu_data = static_cast<DataType*>(sycl_device.allocate(out_tensor_cpu.size() * sizeof(DataType)));
+  DataType* sub_gpu_data = static_cast<DataType*>(sycl_device.allocate(sub_tensor.size() * sizeof(DataType)));
+  TensorMI64 out_gpu(out_gpu_data, tensor_range);
+  TensorMI64 sub_gpu(sub_gpu_data, slice_range);
+
+  sycl_device.memcpyHostToDevice(out_gpu_data, out_tensor_cpu.data(), out_tensor_cpu.size() * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(sub_gpu_data, sub_tensor.data(), sub_tensor.size() * sizeof(DataType));
+
+  Eigen::array<ConvertedIndexType, 5> slice_offset_32{{0, 0, 0, 0, 3}};
+  Eigen::array<ConvertedIndexType, 5> slice_range_32{{4, 1, 1, 1, 3}};
+  TensorMI32 out_cpu_32(out_tensor_cpu.data(), To32BitDims<ConvertedIndexType>(out_tensor_cpu.dimensions()));
+  TensorMI32 sub_cpu_32(sub_tensor.data(), To32BitDims<ConvertedIndexType>(sub_tensor.dimensions()));
+  TensorMI32 out_gpu_32(out_gpu.data(), To32BitDims<ConvertedIndexType>(out_gpu.dimensions()));
+  TensorMI32 sub_gpu_32(sub_gpu.data(), To32BitDims<ConvertedIndexType>(sub_gpu.dimensions()));
+
+  out_gpu_32.slice(slice_offset_32, slice_range_32).device(sycl_device) = sub_gpu_32;
+
+  out_cpu_32.slice(slice_offset_32, slice_range_32) = sub_cpu_32;
+
+  sycl_device.memcpyDeviceToHost(out_tensor_gpu.data(), out_gpu_data, out_tensor_cpu.size() * sizeof(DataType));
+  int has_err = 0;
+  for (IndexType i = 0; i < out_tensor_cpu.size(); ++i) {
+    auto exp = out_tensor_cpu(i);
+    auto val = out_tensor_gpu(i);
+    if (val != exp) {
+      std::cout << "#" << i << " got " << val << " but expected " << exp << std::endl;
+      has_err = 1;
+    }
+  }
+  sycl_device.deallocate(out_gpu_data);
+  sycl_device.deallocate(sub_gpu_data);
+  return has_err;
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_morphing_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_slice<DataType, RowMajor, int64_t>(sycl_device);
+  test_simple_slice<DataType, ColMajor, int64_t>(sycl_device);
+  test_simple_reshape<DataType, RowMajor, int64_t>(sycl_device);
+  test_simple_reshape<DataType, ColMajor, int64_t>(sycl_device);
+  test_reshape_as_lvalue<DataType, RowMajor, int64_t>(sycl_device);
+  test_reshape_as_lvalue<DataType, ColMajor, int64_t>(sycl_device);
+  test_strided_slice_write_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_strided_slice_write_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_strided_slice_as_rhs_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_strided_slice_as_rhs_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  run_eigen<float, RowMajor, long, int>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_morphing_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_morphing_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_morphing_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_move.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_move.cpp
new file mode 100644
index 0000000..a21ebb1
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_move.cpp
@@ -0,0 +1,68 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Viktor Csomor <viktor.csomor@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+#include <utility>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void calc_indices(int i, int& x, int& y, int& z) {
+  x = i / 4;
+  y = (i % 4) / 2;
+  z = i % 2;
+}
+
+static void test_move() {
+  int x;
+  int y;
+  int z;
+
+  Tensor<int, 3> tensor1(2, 2, 2);
+  Tensor<int, 3, RowMajor> tensor2(2, 2, 2);
+
+  for (int i = 0; i < 8; i++) {
+    calc_indices(i, x, y, z);
+    tensor1(x, y, z) = i;
+    tensor2(x, y, z) = 2 * i;
+  }
+
+  // Invokes the move constructor.
+  Tensor<int, 3> moved_tensor1 = std::move(tensor1);
+  Tensor<int, 3, RowMajor> moved_tensor2 = std::move(tensor2);
+
+  VERIFY_IS_EQUAL(tensor1.size(), 0);
+  VERIFY_IS_EQUAL(tensor2.size(), 0);
+
+  for (int i = 0; i < 8; i++) {
+    calc_indices(i, x, y, z);
+    VERIFY_IS_EQUAL(moved_tensor1(x, y, z), i);
+    VERIFY_IS_EQUAL(moved_tensor2(x, y, z), 2 * i);
+  }
+
+  Tensor<int, 3> moved_tensor3(2, 2, 2);
+  Tensor<int, 3, RowMajor> moved_tensor4(2, 2, 2);
+
+  moved_tensor3.setZero();
+  moved_tensor4.setZero();
+
+  // Invokes the move assignment operator.
+  moved_tensor3 = std::move(moved_tensor1);
+  moved_tensor4 = std::move(moved_tensor2);
+
+  for (int i = 0; i < 8; i++) {
+    calc_indices(i, x, y, z);
+    VERIFY_IS_EQUAL(moved_tensor3(x, y, z), i);
+    VERIFY_IS_EQUAL(moved_tensor4(x, y, z), 2 * i);
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_move) { CALL_SUBTEST(test_move()); }
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_notification.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_notification.cpp
new file mode 100644
index 0000000..2173866
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_notification.cpp
@@ -0,0 +1,67 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Vijay Vasudevan <vrv@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_USE_THREADS
+
+#include <atomic>
+
+#include <stdlib.h>
+#include "main.h"
+#include <Eigen/CXX11/Tensor>
+
+static void test_notification_single() {
+  ThreadPool thread_pool(1);
+
+  std::atomic<int> counter(0);
+  Eigen::Notification n;
+  auto func = [&n, &counter]() {
+    n.Wait();
+    ++counter;
+  };
+  thread_pool.Schedule(func);
+  std::this_thread::sleep_for(std::chrono::milliseconds(1000));
+
+  // The thread should be waiting for the notification.
+  VERIFY_IS_EQUAL(counter, 0);
+
+  // Unblock the thread
+  n.Notify();
+
+  std::this_thread::sleep_for(std::chrono::milliseconds(1000));
+
+  // Verify the counter has been incremented
+  VERIFY_IS_EQUAL(counter, 1);
+}
+
+// Like test_notification_single() but enqueues multiple threads to
+// validate that all threads get notified by Notify().
+static void test_notification_multiple() {
+  ThreadPool thread_pool(1);
+
+  std::atomic<int> counter(0);
+  Eigen::Notification n;
+  auto func = [&n, &counter]() {
+    n.Wait();
+    ++counter;
+  };
+  thread_pool.Schedule(func);
+  thread_pool.Schedule(func);
+  thread_pool.Schedule(func);
+  thread_pool.Schedule(func);
+  std::this_thread::sleep_for(std::chrono::milliseconds(1000));
+  VERIFY_IS_EQUAL(counter, 0);
+  n.Notify();
+  std::this_thread::sleep_for(std::chrono::milliseconds(1000));
+  VERIFY_IS_EQUAL(counter, 4);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_notification) {
+  CALL_SUBTEST(test_notification_single());
+  CALL_SUBTEST(test_notification_multiple());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_bfloat16_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_bfloat16_gpu.cu
new file mode 100644
index 0000000..722c92f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_bfloat16_gpu.cu
@@ -0,0 +1,461 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2021 Rohit Santhanam <rohit.santhanam@amd.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <typename>
+void test_gpu_numext() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  bool* d_res_bfloat16 = (bool*)gpu_device.allocate(num_elem * sizeof(bool));
+  bool* d_res_float = (bool*)gpu_device.allocate(num_elem * sizeof(bool));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_bfloat16(d_res_bfloat16, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
+  gpu_res_float.device(gpu_device) = gpu_float.unaryExpr(Eigen::internal::scalar_isnan_op<float>());
+  // Test bfloat16 specific isnan op.
+  gpu_res_bfloat16.device(gpu_device) =
+      gpu_float.cast<Eigen::bfloat16>().unaryExpr(Eigen::internal::scalar_isnan_op<Eigen::bfloat16>());
+
+  Tensor<bool, 1> bfloat16_prec(num_elem);
+  Tensor<bool, 1> full_prec(num_elem);
+  gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, num_elem * sizeof(bool));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(bool));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_EQUAL(full_prec(i), bfloat16_prec(i));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_res_bfloat16);
+  gpu_device.deallocate(d_res_float);
+}
+
+#ifdef EIGEN_HAS_GPU_BF16
+
+template <typename>
+void test_gpu_conversion() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::bfloat16* d_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16));
+  float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_bfloat16(d_bfloat16, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv(d_conv, num_elem);
+
+  gpu_float.device(gpu_device) = gpu_float.random();
+  gpu_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>();
+  gpu_conv.device(gpu_device) = gpu_bfloat16.cast<float>();
+
+  Tensor<float, 1> initial(num_elem);
+  Tensor<float, 1> final(num_elem);
+  gpu_device.memcpyDeviceToHost(initial.data(), d_float, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(final.data(), d_conv, num_elem * sizeof(float));
+
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_APPROX(static_cast<Eigen::bfloat16>(initial(i)), static_cast<Eigen::bfloat16>(final(i)));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_bfloat16);
+  gpu_device.deallocate(d_conv);
+}
+
+template <typename>
+void test_gpu_unary() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_bfloat16 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_bfloat16(d_res_bfloat16, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
+  gpu_float.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().cast<float>();
+  gpu_res_float.device(gpu_device) = gpu_float.abs();
+  gpu_res_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().abs().cast<float>();
+
+  Tensor<float, 1> bfloat16_prec(num_elem);
+  Tensor<float, 1> full_prec(num_elem);
+  gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_APPROX(full_prec(i), bfloat16_prec(i));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_res_bfloat16);
+  gpu_device.deallocate(d_res_float);
+}
+
+template <typename>
+void test_gpu_elementwise() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_bfloat16 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_bfloat16(d_res_bfloat16, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  gpu_float1.device(gpu_device) = gpu_float1.random();
+  gpu_float1.device(gpu_device) = gpu_float1.cast<Eigen::bfloat16>().cast<float>();
+  gpu_float2.device(gpu_device) = gpu_float2.random();
+  gpu_float2.device(gpu_device) = gpu_float2.cast<Eigen::bfloat16>().cast<float>();
+  gpu_res_float.device(gpu_device) = (gpu_float1 + gpu_float2) * gpu_float1;
+  gpu_res_bfloat16.device(gpu_device) =
+      ((gpu_float1.cast<Eigen::bfloat16>() + gpu_float2.cast<Eigen::bfloat16>()) * gpu_float1.cast<Eigen::bfloat16>())
+          .cast<float>();
+
+  Tensor<float, 1> bfloat16_prec(num_elem);
+  Tensor<float, 1> full_prec(num_elem);
+  gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_APPROX(static_cast<Eigen::bfloat16>(full_prec(i)), static_cast<Eigen::bfloat16>(bfloat16_prec(i)));
+  }
+
+  gpu_device.deallocate(d_float1);
+  gpu_device.deallocate(d_float2);
+  gpu_device.deallocate(d_res_bfloat16);
+  gpu_device.deallocate(d_res_float);
+}
+
+template <typename>
+void test_gpu_trancendental() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_float3 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::bfloat16* d_res1_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16));
+  Eigen::bfloat16* d_res1_float = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16));
+  Eigen::bfloat16* d_res2_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16));
+  Eigen::bfloat16* d_res2_float = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16));
+  Eigen::bfloat16* d_res3_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16));
+  Eigen::bfloat16* d_res3_float = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float3(d_float3, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res1_bfloat16(d_res1_bfloat16, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res2_bfloat16(d_res2_bfloat16, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res3_bfloat16(d_res3_bfloat16, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res4_bfloat16(d_res3_bfloat16, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res4_float(d_res3_float, num_elem);
+
+  gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
+  gpu_float1.device(gpu_device) = gpu_float1.cast<Eigen::bfloat16>().cast<float>();
+  gpu_float2.device(gpu_device) = gpu_float2.random() + gpu_float1.constant(0.5f);
+  gpu_float2.device(gpu_device) = gpu_float2.cast<Eigen::bfloat16>().cast<float>();
+  gpu_float3.device(gpu_device) = gpu_float3.random();
+  gpu_float3.device(gpu_device) = gpu_float3.cast<Eigen::bfloat16>().cast<float>();
+  gpu_res1_float.device(gpu_device) = gpu_float1.exp().cast<Eigen::bfloat16>();
+  gpu_res2_float.device(gpu_device) = gpu_float2.log().cast<Eigen::bfloat16>();
+  gpu_res3_float.device(gpu_device) = gpu_float3.log1p().cast<Eigen::bfloat16>();
+  gpu_res4_float.device(gpu_device) = gpu_float3.expm1().cast<Eigen::bfloat16>();
+
+  gpu_res1_bfloat16.device(gpu_device) = gpu_float1.cast<Eigen::bfloat16>();
+  gpu_res1_bfloat16.device(gpu_device) = gpu_res1_bfloat16.exp();
+
+  gpu_res2_bfloat16.device(gpu_device) = gpu_float2.cast<Eigen::bfloat16>();
+  gpu_res2_bfloat16.device(gpu_device) = gpu_res2_bfloat16.log();
+
+  gpu_res3_bfloat16.device(gpu_device) = gpu_float3.cast<Eigen::bfloat16>();
+  gpu_res3_bfloat16.device(gpu_device) = gpu_res3_bfloat16.log1p();
+
+  gpu_res3_bfloat16.device(gpu_device) = gpu_float3.cast<Eigen::bfloat16>();
+  gpu_res3_bfloat16.device(gpu_device) = gpu_res3_bfloat16.expm1();
+
+  Tensor<float, 1> input1(num_elem);
+  Tensor<Eigen::bfloat16, 1> bfloat16_prec1(num_elem);
+  Tensor<Eigen::bfloat16, 1> full_prec1(num_elem);
+  Tensor<float, 1> input2(num_elem);
+  Tensor<Eigen::bfloat16, 1> bfloat16_prec2(num_elem);
+  Tensor<Eigen::bfloat16, 1> full_prec2(num_elem);
+  Tensor<float, 1> input3(num_elem);
+  Tensor<Eigen::bfloat16, 1> bfloat16_prec3(num_elem);
+  Tensor<Eigen::bfloat16, 1> full_prec3(num_elem);
+  gpu_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(input2.data(), d_float2, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(input3.data(), d_float3, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(bfloat16_prec1.data(), d_res1_bfloat16, num_elem * sizeof(Eigen::bfloat16));
+  gpu_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem * sizeof(Eigen::bfloat16));
+  gpu_device.memcpyDeviceToHost(bfloat16_prec2.data(), d_res2_bfloat16, num_elem * sizeof(Eigen::bfloat16));
+  gpu_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem * sizeof(Eigen::bfloat16));
+  gpu_device.memcpyDeviceToHost(bfloat16_prec3.data(), d_res3_bfloat16, num_elem * sizeof(Eigen::bfloat16));
+  gpu_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem * sizeof(Eigen::bfloat16));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_APPROX(full_prec1(i), bfloat16_prec1(i));
+  }
+  for (int i = 0; i < num_elem; ++i) {
+    if (std::abs(input2(i) - 1.f) < 0.05f)  // log lacks accuracy nearby 1
+      VERIFY_IS_APPROX(full_prec2(i) + Eigen::bfloat16(0.1f), bfloat16_prec2(i) + Eigen::bfloat16(0.1f));
+    else
+      VERIFY_IS_APPROX(full_prec2(i), bfloat16_prec2(i));
+  }
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_APPROX(full_prec3(i), bfloat16_prec3(i));
+  }
+  gpu_device.deallocate(d_float1);
+  gpu_device.deallocate(d_float2);
+  gpu_device.deallocate(d_float3);
+  gpu_device.deallocate(d_res1_bfloat16);
+  gpu_device.deallocate(d_res1_float);
+  gpu_device.deallocate(d_res2_bfloat16);
+  gpu_device.deallocate(d_res2_float);
+  gpu_device.deallocate(d_res3_float);
+  gpu_device.deallocate(d_res3_bfloat16);
+}
+
+template <typename>
+void test_gpu_contractions() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int rows = 23;
+  int cols = 23;
+  int num_elem = rows * cols;
+
+  float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::bfloat16* d_res_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16));
+  Eigen::bfloat16* d_res_float = (Eigen::bfloat16*)gpu_device.allocate(num_elem * sizeof(Eigen::bfloat16));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1(d_float1, rows, cols);
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2(d_float2, rows, cols);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 2>, Eigen::Aligned> gpu_res_bfloat16(d_res_bfloat16, rows, cols);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 2>, Eigen::Aligned> gpu_res_float(d_res_float, rows, cols);
+
+  gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
+  gpu_float2.device(gpu_device) = gpu_float2.random() - gpu_float2.constant(0.5f);
+
+  typedef Tensor<float, 2>::DimensionPair DimPair;
+  Eigen::array<DimPair, 1> dims(DimPair(1, 0));
+  gpu_res_float.device(gpu_device) = gpu_float1.contract(gpu_float2, dims).cast<Eigen::bfloat16>();
+  gpu_res_bfloat16.device(gpu_device) =
+      gpu_float1.cast<Eigen::bfloat16>().contract(gpu_float2.cast<Eigen::bfloat16>(), dims);
+
+  Tensor<Eigen::bfloat16, 2> bfloat16_prec(rows, cols);
+  Tensor<Eigen::bfloat16, 2> full_prec(rows, cols);
+  gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, num_elem * sizeof(Eigen::bfloat16));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(Eigen::bfloat16));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < rows; ++i) {
+    for (int j = 0; j < cols; ++j) {
+      if (numext::abs(full_prec(i, j) - bfloat16_prec(i, j)) > Eigen::bfloat16(1e-2f)) {
+        VERIFY_IS_APPROX(full_prec(i, j), bfloat16_prec(i, j));
+      }
+    }
+  }
+
+  gpu_device.deallocate(d_float1);
+  gpu_device.deallocate(d_float2);
+  gpu_device.deallocate(d_res_bfloat16);
+  gpu_device.deallocate(d_res_float);
+}
+
+template <typename>
+void test_gpu_reductions(int size1, int size2, int redux) {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = size1 * size2;
+  int result_size = (redux == 1 ? size1 : size2);
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::bfloat16* d_res_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(result_size * sizeof(Eigen::bfloat16));
+  Eigen::bfloat16* d_res_float = (Eigen::bfloat16*)gpu_device.allocate(result_size * sizeof(Eigen::bfloat16));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float(d_float, size1, size2);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res_bfloat16(d_res_bfloat16, result_size);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 1>, Eigen::Aligned> gpu_res_float(d_res_float, result_size);
+
+  gpu_float.device(gpu_device) = gpu_float.random() * 2.0f;
+
+  Eigen::array<int, 1> redux_dim = {redux};
+  gpu_res_float.device(gpu_device) = gpu_float.sum(redux_dim).cast<Eigen::bfloat16>();
+  gpu_res_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().sum(redux_dim);
+
+  Tensor<Eigen::bfloat16, 1> bfloat16_prec(result_size);
+  Tensor<Eigen::bfloat16, 1> full_prec(result_size);
+  gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, result_size * sizeof(Eigen::bfloat16));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, result_size * sizeof(Eigen::bfloat16));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < result_size; ++i) {
+    VERIFY_IS_APPROX(full_prec(i), bfloat16_prec(i));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_res_bfloat16);
+  gpu_device.deallocate(d_res_float);
+}
+
+template <typename>
+void test_gpu_reductions() {
+  test_gpu_reductions<void>(13, 13, 0);
+  test_gpu_reductions<void>(13, 13, 1);
+
+  test_gpu_reductions<void>(35, 36, 0);
+  test_gpu_reductions<void>(35, 36, 1);
+
+  test_gpu_reductions<void>(36, 35, 0);
+  test_gpu_reductions<void>(36, 35, 1);
+}
+
+template <typename>
+void test_gpu_full_reductions() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int size = 13;
+  int num_elem = size * size;
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::bfloat16* d_res_bfloat16 = (Eigen::bfloat16*)gpu_device.allocate(1 * sizeof(Eigen::bfloat16));
+  Eigen::bfloat16* d_res_float = (Eigen::bfloat16*)gpu_device.allocate(1 * sizeof(Eigen::bfloat16));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float(d_float, size, size);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 0>, Eigen::Aligned> gpu_res_bfloat16(d_res_bfloat16);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::bfloat16, 0>, Eigen::Aligned> gpu_res_float(d_res_float);
+
+  gpu_float.device(gpu_device) = gpu_float.random();
+
+  gpu_res_float.device(gpu_device) = gpu_float.sum().cast<Eigen::bfloat16>();
+  gpu_res_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().sum();
+
+  Tensor<Eigen::bfloat16, 0> bfloat16_prec;
+  Tensor<Eigen::bfloat16, 0> full_prec;
+  gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, sizeof(Eigen::bfloat16));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::bfloat16));
+  gpu_device.synchronize();
+
+  VERIFY_IS_APPROX(full_prec(), bfloat16_prec());
+
+  gpu_res_float.device(gpu_device) = gpu_float.maximum().cast<Eigen::bfloat16>();
+  gpu_res_bfloat16.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().maximum();
+  gpu_device.memcpyDeviceToHost(bfloat16_prec.data(), d_res_bfloat16, sizeof(Eigen::bfloat16));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::bfloat16));
+  gpu_device.synchronize();
+
+  VERIFY_IS_APPROX(full_prec(), bfloat16_prec());
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_res_bfloat16);
+  gpu_device.deallocate(d_res_float);
+}
+
+template <typename>
+void test_gpu_forced_evals() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_bfloat16_1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_bfloat16_2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_bfloat16_1(d_res_bfloat16_1, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Unaligned> gpu_res_bfloat16_2(d_res_bfloat16_2, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  Eigen::array<int, 1> no_bcast;
+  no_bcast[0] = 1;
+
+  gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
+  gpu_float.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().cast<float>();
+  gpu_res_float.device(gpu_device) = gpu_float.abs();
+  gpu_res_bfloat16_1.device(gpu_device) = gpu_float.cast<Eigen::bfloat16>().abs().eval().cast<float>();
+  gpu_res_bfloat16_2.device(gpu_device) =
+      gpu_float.cast<Eigen::bfloat16>().abs().broadcast(no_bcast).eval().cast<float>();
+
+  Tensor<float, 1> bfloat16_prec1(num_elem);
+  Tensor<float, 1> bfloat16_prec2(num_elem);
+  Tensor<float, 1> full_prec(num_elem);
+  gpu_device.memcpyDeviceToHost(bfloat16_prec1.data(), d_res_bfloat16_1, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(bfloat16_prec2.data(), d_res_bfloat16_2, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_APPROX(full_prec(i), bfloat16_prec1(i));
+    VERIFY_IS_APPROX(full_prec(i), bfloat16_prec2(i));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_res_bfloat16_1);
+  gpu_device.deallocate(d_res_bfloat16_2);
+  gpu_device.deallocate(d_res_float);
+}
+
+#endif
+
+EIGEN_DECLARE_TEST(cxx11_tensor_of_bfloat16_gpu) {
+  CALL_SUBTEST_1(test_gpu_numext<void>());
+
+// The reduction unit tests have been excluded until a working
+// implementation to expand the accumulator data type to float32
+// is available.
+// TODO: add reduction unit tests
+#ifdef EIGEN_HAS_GPU_BF16
+  CALL_SUBTEST_2(test_gpu_conversion<void>());
+  CALL_SUBTEST_3(test_gpu_unary<void>());
+  CALL_SUBTEST_4(test_gpu_elementwise<void>());
+  CALL_SUBTEST_5(test_gpu_trancendental<void>());
+  CALL_SUBTEST_6(test_gpu_contractions<void>());
+  CALL_SUBTEST_7(test_gpu_reductions<void>());
+  CALL_SUBTEST_8(test_gpu_full_reductions<void>());
+  CALL_SUBTEST_9(test_gpu_forced_evals<void>());
+#else
+  std::cout << "bfloat16 floats are not supported by this version of gpu: skipping the test" << std::endl;
+#endif
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_complex.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_complex.cpp
new file mode 100644
index 0000000..d34edbd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_complex.cpp
@@ -0,0 +1,108 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+static void test_additions() {
+  Tensor<std::complex<float>, 1> data1(3);
+  Tensor<std::complex<float>, 1> data2(3);
+  for (int i = 0; i < 3; ++i) {
+    data1(i) = std::complex<float>(i, -i);
+    data2(i) = std::complex<float>(i, 7 * i);
+  }
+
+  Tensor<std::complex<float>, 1> sum = data1 + data2;
+  for (int i = 0; i < 3; ++i) {
+    VERIFY_IS_EQUAL(sum(i), std::complex<float>(2 * i, 6 * i));
+  }
+}
+
+static void test_abs() {
+  Tensor<std::complex<float>, 1> data1(3);
+  Tensor<std::complex<double>, 1> data2(3);
+  data1.setRandom();
+  data2.setRandom();
+
+  Tensor<float, 1> abs1 = data1.abs();
+  Tensor<double, 1> abs2 = data2.abs();
+  for (int i = 0; i < 3; ++i) {
+    VERIFY_IS_APPROX(abs1(i), std::abs(data1(i)));
+    VERIFY_IS_APPROX(abs2(i), std::abs(data2(i)));
+  }
+}
+
+static void test_arg() {
+  Tensor<std::complex<float>, 1> data1(3);
+  Tensor<std::complex<double>, 1> data2(3);
+  data1.setRandom();
+  data2.setRandom();
+
+  Tensor<float, 1> arg1 = data1.arg();
+  Tensor<double, 1> arg2 = data2.arg();
+  for (int i = 0; i < 3; ++i) {
+    VERIFY_IS_APPROX(arg1(i), std::arg(data1(i)));
+    VERIFY_IS_APPROX(arg2(i), std::arg(data2(i)));
+  }
+}
+
+static void test_conjugate() {
+  Tensor<std::complex<float>, 1> data1(3);
+  Tensor<std::complex<double>, 1> data2(3);
+  Tensor<int, 1> data3(3);
+  data1.setRandom();
+  data2.setRandom();
+  data3.setRandom();
+
+  Tensor<std::complex<float>, 1> conj1 = data1.conjugate();
+  Tensor<std::complex<double>, 1> conj2 = data2.conjugate();
+  Tensor<int, 1> conj3 = data3.conjugate();
+  for (int i = 0; i < 3; ++i) {
+    VERIFY_IS_APPROX(conj1(i), std::conj(data1(i)));
+    VERIFY_IS_APPROX(conj2(i), std::conj(data2(i)));
+    VERIFY_IS_APPROX(conj3(i), data3(i));
+  }
+}
+
+static void test_contractions() {
+  Tensor<std::complex<float>, 4> t_left(30, 50, 8, 31);
+  Tensor<std::complex<float>, 5> t_right(8, 31, 7, 20, 10);
+  Tensor<std::complex<float>, 5> t_result(30, 50, 7, 20, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  typedef Map<Matrix<std::complex<float>, Dynamic, Dynamic>> MapXcf;
+  MapXcf m_left(t_left.data(), 1500, 248);
+  MapXcf m_right(t_right.data(), 248, 1400);
+  Matrix<std::complex<float>, Dynamic, Dynamic> m_result(1500, 1400);
+
+  // This contraction should be equivalent to a regular matrix multiplication
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+  Eigen::array<DimPair, 2> dims;
+  dims[0] = DimPair(2, 0);
+  dims[1] = DimPair(3, 1);
+  t_result = t_left.contract(t_right, dims);
+  m_result = m_left * m_right;
+  for (int i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY_IS_APPROX(t_result.data()[i], m_result.data()[i]);
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_of_complex) {
+  CALL_SUBTEST(test_additions());
+  CALL_SUBTEST(test_abs());
+  CALL_SUBTEST(test_arg());
+  CALL_SUBTEST(test_conjugate());
+  CALL_SUBTEST(test_contractions());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_const_values.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_const_values.cpp
new file mode 100644
index 0000000..6c76cf3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_const_values.cpp
@@ -0,0 +1,98 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void test_assign() {
+  float data1[6];
+  TensorMap<Tensor<const float, 2>> mat1(data1, 2, 3);
+  float data2[6];
+  const TensorMap<Tensor<float, 2>> mat2(data2, 2, 3);
+
+  for (int i = 0; i < 6; ++i) {
+    data1[i] = i;
+    data2[i] = -i;
+  }
+
+  Tensor<float, 2> rslt1;
+  rslt1 = mat1;
+  Tensor<float, 2> rslt2;
+  rslt2 = mat2;
+
+  Tensor<float, 2> rslt3 = mat1;
+  Tensor<float, 2> rslt4 = mat2;
+
+  Tensor<float, 2> rslt5(mat1);
+  Tensor<float, 2> rslt6(mat2);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_APPROX(rslt1(i, j), static_cast<float>(i + 2 * j));
+      VERIFY_IS_APPROX(rslt2(i, j), static_cast<float>(-i - 2 * j));
+      VERIFY_IS_APPROX(rslt3(i, j), static_cast<float>(i + 2 * j));
+      VERIFY_IS_APPROX(rslt4(i, j), static_cast<float>(-i - 2 * j));
+      VERIFY_IS_APPROX(rslt5(i, j), static_cast<float>(i + 2 * j));
+      VERIFY_IS_APPROX(rslt6(i, j), static_cast<float>(-i - 2 * j));
+    }
+  }
+}
+
+static void test_plus() {
+  float data1[6];
+  TensorMap<Tensor<const float, 2>> mat1(data1, 2, 3);
+  float data2[6];
+  TensorMap<Tensor<float, 2>> mat2(data2, 2, 3);
+
+  for (int i = 0; i < 6; ++i) {
+    data1[i] = i;
+    data2[i] = -i;
+  }
+
+  Tensor<float, 2> sum1;
+  sum1 = mat1 + mat2;
+  Tensor<float, 2> sum2;
+  sum2 = mat2 + mat1;
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_APPROX(sum1(i, j), 0.0f);
+      VERIFY_IS_APPROX(sum2(i, j), 0.0f);
+    }
+  }
+}
+
+static void test_plus_equal() {
+  float data1[6];
+  TensorMap<Tensor<const float, 2>> mat1(data1, 2, 3);
+  float data2[6];
+  TensorMap<Tensor<float, 2>> mat2(data2, 2, 3);
+
+  for (int i = 0; i < 6; ++i) {
+    data1[i] = i;
+    data2[i] = -i;
+  }
+  mat2 += mat1;
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_APPROX(mat2(i, j), 0.0f);
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_of_const_values) {
+  CALL_SUBTEST(test_assign());
+  CALL_SUBTEST(test_plus());
+  CALL_SUBTEST(test_plus_equal());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_float16_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_float16_gpu.cu
new file mode 100644
index 0000000..88482de
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_float16_gpu.cu
@@ -0,0 +1,462 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <typename>
+void test_gpu_numext() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  bool* d_res_half = (bool*)gpu_device.allocate(num_elem * sizeof(bool));
+  bool* d_res_float = (bool*)gpu_device.allocate(num_elem * sizeof(bool));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
+  gpu_res_float.device(gpu_device) = gpu_float.unaryExpr(Eigen::internal::scalar_isnan_op<float>());
+  gpu_res_half.device(gpu_device) =
+      gpu_float.cast<Eigen::half>().unaryExpr(Eigen::internal::scalar_isnan_op<Eigen::half>());
+
+  Tensor<bool, 1> half_prec(num_elem);
+  Tensor<bool, 1> full_prec(num_elem);
+  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(bool));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(bool));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking numext " << i << std::endl;
+    VERIFY_IS_EQUAL(full_prec(i), half_prec(i));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_res_half);
+  gpu_device.deallocate(d_res_float);
+}
+
+#ifdef EIGEN_HAS_GPU_FP16
+
+template <typename>
+void test_gpu_conversion() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::half* d_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
+  float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_half(d_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv(d_conv, num_elem);
+
+  gpu_float.device(gpu_device) = gpu_float.random();
+  gpu_half.device(gpu_device) = gpu_float.cast<Eigen::half>();
+  gpu_conv.device(gpu_device) = gpu_half.cast<float>();
+
+  Tensor<float, 1> initial(num_elem);
+  Tensor<float, 1> final(num_elem);
+  gpu_device.memcpyDeviceToHost(initial.data(), d_float, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(final.data(), d_conv, num_elem * sizeof(float));
+
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_APPROX(initial(i), final(i));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_half);
+  gpu_device.deallocate(d_conv);
+}
+
+template <typename>
+void test_gpu_unary() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
+  gpu_res_float.device(gpu_device) = gpu_float.abs();
+  gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().cast<float>();
+
+  Tensor<float, 1> half_prec(num_elem);
+  Tensor<float, 1> full_prec(num_elem);
+  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking unary " << i << std::endl;
+    VERIFY_IS_APPROX(full_prec(i), half_prec(i));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_res_half);
+  gpu_device.deallocate(d_res_float);
+}
+
+template <typename>
+void test_gpu_elementwise() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  gpu_float1.device(gpu_device) = gpu_float1.random();
+  gpu_float2.device(gpu_device) = gpu_float2.random();
+  gpu_res_float.device(gpu_device) = (gpu_float1 + gpu_float2) * gpu_float1;
+  gpu_res_half.device(gpu_device) =
+      ((gpu_float1.cast<Eigen::half>() + gpu_float2.cast<Eigen::half>()) * gpu_float1.cast<Eigen::half>())
+          .cast<float>();
+
+  Tensor<float, 1> half_prec(num_elem);
+  Tensor<float, 1> full_prec(num_elem);
+  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking elemwise " << i << ": full prec = " << full_prec(i) << " vs half prec = " << half_prec(i)
+              << std::endl;
+    VERIFY_IS_APPROX(static_cast<Eigen::half>(full_prec(i)), static_cast<Eigen::half>(half_prec(i)));
+  }
+
+  gpu_device.deallocate(d_float1);
+  gpu_device.deallocate(d_float2);
+  gpu_device.deallocate(d_res_half);
+  gpu_device.deallocate(d_res_float);
+}
+
+template <typename>
+void test_gpu_trancendental() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_float3 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::half* d_res1_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res1_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res2_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res2_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res3_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res3_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float3(d_float3, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_half(d_res1_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_half(d_res2_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_half(d_res3_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_half(d_res3_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_float(d_res3_float, num_elem);
+
+  gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
+  gpu_float2.device(gpu_device) = gpu_float2.random() + gpu_float1.constant(0.5f);
+  gpu_float3.device(gpu_device) = gpu_float3.random();
+  gpu_res1_float.device(gpu_device) = gpu_float1.exp().cast<Eigen::half>();
+  gpu_res2_float.device(gpu_device) = gpu_float2.log().cast<Eigen::half>();
+  gpu_res3_float.device(gpu_device) = gpu_float3.log1p().cast<Eigen::half>();
+  gpu_res4_float.device(gpu_device) = gpu_float3.expm1().cast<Eigen::half>();
+
+  gpu_res1_half.device(gpu_device) = gpu_float1.cast<Eigen::half>();
+  gpu_res1_half.device(gpu_device) = gpu_res1_half.exp();
+
+  gpu_res2_half.device(gpu_device) = gpu_float2.cast<Eigen::half>();
+  gpu_res2_half.device(gpu_device) = gpu_res2_half.log();
+
+  gpu_res3_half.device(gpu_device) = gpu_float3.cast<Eigen::half>();
+  gpu_res3_half.device(gpu_device) = gpu_res3_half.log1p();
+
+  gpu_res3_half.device(gpu_device) = gpu_float3.cast<Eigen::half>();
+  gpu_res3_half.device(gpu_device) = gpu_res3_half.expm1();
+
+  Tensor<float, 1> input1(num_elem);
+  Tensor<Eigen::half, 1> half_prec1(num_elem);
+  Tensor<Eigen::half, 1> full_prec1(num_elem);
+  Tensor<float, 1> input2(num_elem);
+  Tensor<Eigen::half, 1> half_prec2(num_elem);
+  Tensor<Eigen::half, 1> full_prec2(num_elem);
+  Tensor<float, 1> input3(num_elem);
+  Tensor<Eigen::half, 1> half_prec3(num_elem);
+  Tensor<Eigen::half, 1> full_prec3(num_elem);
+  gpu_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(input2.data(), d_float2, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(input3.data(), d_float3, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem * sizeof(Eigen::half));
+  gpu_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem * sizeof(Eigen::half));
+  gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res2_half, num_elem * sizeof(Eigen::half));
+  gpu_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem * sizeof(Eigen::half));
+  gpu_device.memcpyDeviceToHost(half_prec3.data(), d_res3_half, num_elem * sizeof(Eigen::half));
+  gpu_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem * sizeof(Eigen::half));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking elemwise exp " << i << " input = " << input1(i) << " full = " << full_prec1(i)
+              << " half = " << half_prec1(i) << std::endl;
+    VERIFY_IS_APPROX(full_prec1(i), half_prec1(i));
+  }
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking elemwise log " << i << " input = " << input2(i) << " full = " << full_prec2(i)
+              << " half = " << half_prec2(i) << std::endl;
+    if (std::abs(input2(i) - 1.f) < 0.05f)  // log lacks accuracy nearby 1
+      VERIFY_IS_APPROX(full_prec2(i) + Eigen::half(0.1f), half_prec2(i) + Eigen::half(0.1f));
+    else
+      VERIFY_IS_APPROX(full_prec2(i), half_prec2(i));
+  }
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking elemwise plog1 " << i << " input = " << input3(i) << " full = " << full_prec3(i)
+              << " half = " << half_prec3(i) << std::endl;
+    VERIFY_IS_APPROX(full_prec3(i), half_prec3(i));
+  }
+  gpu_device.deallocate(d_float1);
+  gpu_device.deallocate(d_float2);
+  gpu_device.deallocate(d_float3);
+  gpu_device.deallocate(d_res1_half);
+  gpu_device.deallocate(d_res1_float);
+  gpu_device.deallocate(d_res2_half);
+  gpu_device.deallocate(d_res2_float);
+  gpu_device.deallocate(d_res3_float);
+  gpu_device.deallocate(d_res3_half);
+}
+
+template <typename>
+void test_gpu_contractions() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int rows = 23;
+  int cols = 23;
+  int num_elem = rows * cols;
+
+  float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1(d_float1, rows, cols);
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2(d_float2, rows, cols);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_half(d_res_half, rows, cols);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_float(d_res_float, rows, cols);
+
+  gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
+  gpu_float2.device(gpu_device) = gpu_float2.random() - gpu_float2.constant(0.5f);
+
+  typedef Tensor<float, 2>::DimensionPair DimPair;
+  Eigen::array<DimPair, 1> dims{DimPair(1, 0)};
+  gpu_res_float.device(gpu_device) = gpu_float1.contract(gpu_float2, dims).cast<Eigen::half>();
+  gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().contract(gpu_float2.cast<Eigen::half>(), dims);
+
+  Tensor<Eigen::half, 2> half_prec(rows, cols);
+  Tensor<Eigen::half, 2> full_prec(rows, cols);
+  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(Eigen::half));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(Eigen::half));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < rows; ++i) {
+    for (int j = 0; j < cols; ++j) {
+      std::cout << "Checking contract " << i << " " << j << full_prec(i, j) << " " << half_prec(i, j) << std::endl;
+      if (numext::abs(full_prec(i, j) - half_prec(i, j)) > Eigen::half(1e-2f)) {
+        VERIFY_IS_APPROX(full_prec(i, j), half_prec(i, j));
+      }
+    }
+  }
+
+  gpu_device.deallocate(d_float1);
+  gpu_device.deallocate(d_float2);
+  gpu_device.deallocate(d_res_half);
+  gpu_device.deallocate(d_res_float);
+}
+
+template <typename>
+void test_gpu_reductions(int size1, int size2, int redux) {
+  std::cout << "Reducing " << size1 << " by " << size2 << " tensor along dim " << redux << std::endl;
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = size1 * size2;
+  int result_size = (redux == 1 ? size1 : size2);
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half));
+  Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float(d_float, size1, size2);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_half(d_res_half, result_size);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_float(d_res_float, result_size);
+
+  gpu_float.device(gpu_device) = gpu_float.random() * 2.0f;
+
+  Eigen::array<int, 1> redux_dim = {redux};
+  gpu_res_float.device(gpu_device) = gpu_float.sum(redux_dim).cast<Eigen::half>();
+  gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().sum(redux_dim);
+
+  Tensor<Eigen::half, 1> half_prec(result_size);
+  Tensor<Eigen::half, 1> full_prec(result_size);
+  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, result_size * sizeof(Eigen::half));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, result_size * sizeof(Eigen::half));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < result_size; ++i) {
+    std::cout << "EXPECTED " << full_prec(i) << " GOT " << half_prec(i) << std::endl;
+    VERIFY_IS_APPROX(full_prec(i), half_prec(i));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_res_half);
+  gpu_device.deallocate(d_res_float);
+}
+
+template <typename>
+void test_gpu_reductions() {
+  test_gpu_reductions<void>(13, 13, 0);
+  test_gpu_reductions<void>(13, 13, 1);
+
+  test_gpu_reductions<void>(35, 36, 0);
+  test_gpu_reductions<void>(35, 36, 1);
+
+  test_gpu_reductions<void>(36, 35, 0);
+  test_gpu_reductions<void>(36, 35, 1);
+}
+
+template <typename>
+void test_gpu_full_reductions() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int size = 13;
+  int num_elem = size * size;
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half));
+  Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float(d_float, size, size);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_half(d_res_half);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_float(d_res_float);
+
+  gpu_float.device(gpu_device) = gpu_float.random();
+
+  gpu_res_float.device(gpu_device) = gpu_float.sum().cast<Eigen::half>();
+  gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().sum();
+
+  Tensor<Eigen::half, 0> half_prec;
+  Tensor<Eigen::half, 0> full_prec;
+  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half));
+  gpu_device.synchronize();
+
+  VERIFY_IS_APPROX(full_prec(), half_prec());
+
+  gpu_res_float.device(gpu_device) = gpu_float.maximum().cast<Eigen::half>();
+  gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().maximum();
+  gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half));
+  gpu_device.synchronize();
+
+  VERIFY_IS_APPROX(full_prec(), half_prec());
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_res_half);
+  gpu_device.deallocate(d_res_float);
+}
+
+template <typename>
+void test_gpu_forced_evals() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+  int num_elem = 101;
+
+  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_half1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_half2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+  float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half1(d_res_half1, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Unaligned> gpu_res_half2(d_res_half2, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  Eigen::array<int, 1> no_bcast;
+  no_bcast[0] = 1;
+
+  gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
+  gpu_res_float.device(gpu_device) = gpu_float.abs();
+  gpu_res_half1.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().eval().cast<float>();
+  gpu_res_half2.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().broadcast(no_bcast).eval().cast<float>();
+
+  Tensor<float, 1> half_prec1(num_elem);
+  Tensor<float, 1> half_prec2(num_elem);
+  Tensor<float, 1> full_prec(num_elem);
+  gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res_half1, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res_half2, num_elem * sizeof(float));
+  gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
+  gpu_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking forced eval " << i << full_prec(i) << " vs " << half_prec1(i) << " vs " << half_prec2(i)
+              << std::endl;
+    VERIFY_IS_APPROX(full_prec(i), half_prec1(i));
+    VERIFY_IS_APPROX(full_prec(i), half_prec2(i));
+  }
+
+  gpu_device.deallocate(d_float);
+  gpu_device.deallocate(d_res_half1);
+  gpu_device.deallocate(d_res_half2);
+  gpu_device.deallocate(d_res_float);
+}
+#endif
+
+EIGEN_DECLARE_TEST(cxx11_tensor_of_float16_gpu) {
+  CALL_SUBTEST_1(test_gpu_numext<void>());
+
+#ifdef EIGEN_HAS_GPU_FP16
+  CALL_SUBTEST_1(test_gpu_conversion<void>());
+  CALL_SUBTEST_1(test_gpu_unary<void>());
+  CALL_SUBTEST_1(test_gpu_elementwise<void>());
+  CALL_SUBTEST_1(test_gpu_trancendental<void>());
+  CALL_SUBTEST_2(test_gpu_contractions<void>());
+  CALL_SUBTEST_3(test_gpu_reductions<void>());
+  CALL_SUBTEST_4(test_gpu_full_reductions<void>());
+  CALL_SUBTEST_5(test_gpu_forced_evals<void>());
+#else
+  std::cout << "Half floats are not supported by this version of gpu: skipping the test" << std::endl;
+#endif
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_float16_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_float16_sycl.cpp
new file mode 100644
index 0000000..7de39e7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_float16_sycl.cpp
@@ -0,0 +1,392 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2023
+// Alejandro Acosta  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_SYCL
+#define EIGEN_SYCL_HALF_SUPPORT
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+
+void test_gpu_numext(const Eigen::SyclDevice& sycl_device) {
+  int num_elem = 101;
+
+  float* d_float = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float)));
+  bool* d_res_half = static_cast<bool*>(sycl_device.allocate(num_elem * sizeof(bool)));
+  bool* d_res_float = static_cast<bool*>(sycl_device.allocate(num_elem * sizeof(bool)));
+
+  Eigen::TensorMap<Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Tensor<bool, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem);
+  Eigen::TensorMap<Tensor<bool, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  gpu_float.device(sycl_device) = gpu_float.random() - gpu_float.constant(0.5f);
+  gpu_res_float.device(sycl_device) = gpu_float.unaryExpr(Eigen::internal::scalar_isnan_op<float>());
+  gpu_res_half.device(sycl_device) =
+      gpu_float.cast<Eigen::half>().unaryExpr(Eigen::internal::scalar_isnan_op<Eigen::half>());
+
+  Tensor<bool, 1> half_prec(num_elem);
+  Tensor<bool, 1> full_prec(num_elem);
+
+  sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(bool));
+  sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(bool));
+
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking numext " << i << std::endl;
+    VERIFY_IS_EQUAL(full_prec(i), half_prec(i));
+  }
+}
+
+void test_gpu_conversion(const Eigen::SyclDevice& sycl_device) {
+  int num_elem = 101;
+
+  float* d_float = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float)));
+  Eigen::half* d_half = static_cast<Eigen::half*>(sycl_device.allocate(num_elem * sizeof(Eigen::half)));
+  float* d_conv = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float)));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_half(d_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv(d_conv, num_elem);
+
+  gpu_float.device(sycl_device) = gpu_float.random();
+  gpu_half.device(sycl_device) = gpu_float.cast<Eigen::half>();
+  gpu_conv.device(sycl_device) = gpu_half.cast<float>();
+
+  Tensor<float, 1> initial(num_elem);
+  Tensor<float, 1> final(num_elem);
+  sycl_device.memcpyDeviceToHost(initial.data(), d_float, num_elem * sizeof(float));
+  sycl_device.memcpyDeviceToHost(final.data(), d_conv, num_elem * sizeof(float));
+
+  for (int i = 0; i < num_elem; ++i) {
+    VERIFY_IS_APPROX(initial(i), final(i));
+  }
+}
+
+void test_gpu_unary(const Eigen::SyclDevice& sycl_device) {
+  int num_elem = 101;
+
+  float* d_float = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  float* d_res_half = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  float* d_res_float = (float*)sycl_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  gpu_float.device(sycl_device) = gpu_float.random() - gpu_float.constant(0.5f);
+  gpu_res_float.device(sycl_device) = gpu_float.abs();
+  gpu_res_half.device(sycl_device) = gpu_float.cast<Eigen::half>().abs().cast<float>();
+
+  Tensor<float, 1> half_prec(num_elem);
+  Tensor<float, 1> full_prec(num_elem);
+  sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(float));
+  sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
+  sycl_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking unary " << i << std::endl;
+    VERIFY_IS_APPROX(full_prec(i), half_prec(i));
+  }
+}
+
+void test_gpu_elementwise(const Eigen::SyclDevice& sycl_device) {
+  int num_elem = 101;
+
+  float* d_float1 = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float)));
+  float* d_float2 = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float)));
+  float* d_res_half = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float)));
+  float* d_res_float = static_cast<float*>(sycl_device.allocate(num_elem * sizeof(float)));
+
+  Eigen::TensorMap<Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem);
+  Eigen::TensorMap<Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem);
+  Eigen::TensorMap<Tensor<float, 1>, Eigen::Aligned> gpu_res_half(d_res_half, num_elem);
+  Eigen::TensorMap<Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  gpu_float1.device(sycl_device) = gpu_float1.random();
+  gpu_float2.device(sycl_device) = gpu_float2.random();
+  gpu_res_float.device(sycl_device) = (gpu_float1 + gpu_float2) * gpu_float1;
+  gpu_res_half.device(sycl_device) =
+      ((gpu_float1.cast<Eigen::half>() + gpu_float2.cast<Eigen::half>()) * gpu_float1.cast<Eigen::half>())
+          .cast<float>();
+
+  Tensor<float, 1> half_prec(num_elem);
+  Tensor<float, 1> full_prec(num_elem);
+
+  sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(float));
+  sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
+
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking elemwise " << i << ": full prec = " << full_prec(i) << " vs half prec = " << half_prec(i)
+              << std::endl;
+    VERIFY_IS_APPROX(static_cast<Eigen::half>(full_prec(i)), static_cast<Eigen::half>(half_prec(i)));
+  }
+}
+
+void test_gpu_trancendental(const Eigen::SyclDevice& sycl_device) {
+  int num_elem = 101;
+
+  float* d_float1 = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  float* d_float2 = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  float* d_float3 = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  Eigen::half* d_res1_half = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res1_float = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res2_half = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res2_float = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res3_half = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res3_float = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float3(d_float3, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_half(d_res1_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_half(d_res2_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_half(d_res3_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_half(d_res3_half, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_float(d_res3_float, num_elem);
+
+  gpu_float1.device(sycl_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
+  gpu_float2.device(sycl_device) = gpu_float2.random() + gpu_float1.constant(0.5f);
+  gpu_float3.device(sycl_device) = gpu_float3.random();
+  gpu_res1_float.device(sycl_device) = gpu_float1.exp().cast<Eigen::half>();
+  gpu_res2_float.device(sycl_device) = gpu_float2.log().cast<Eigen::half>();
+  gpu_res3_float.device(sycl_device) = gpu_float3.log1p().cast<Eigen::half>();
+  gpu_res4_float.device(sycl_device) = gpu_float3.expm1().cast<Eigen::half>();
+
+  gpu_res1_half.device(sycl_device) = gpu_float1.cast<Eigen::half>();
+  gpu_res1_half.device(sycl_device) = gpu_res1_half.exp();
+
+  gpu_res2_half.device(sycl_device) = gpu_float2.cast<Eigen::half>();
+  gpu_res2_half.device(sycl_device) = gpu_res2_half.log();
+
+  gpu_res3_half.device(sycl_device) = gpu_float3.cast<Eigen::half>();
+  gpu_res3_half.device(sycl_device) = gpu_res3_half.log1p();
+
+  gpu_res3_half.device(sycl_device) = gpu_float3.cast<Eigen::half>();
+  gpu_res3_half.device(sycl_device) = gpu_res3_half.expm1();
+
+  Tensor<float, 1> input1(num_elem);
+  Tensor<Eigen::half, 1> half_prec1(num_elem);
+  Tensor<Eigen::half, 1> full_prec1(num_elem);
+  Tensor<float, 1> input2(num_elem);
+  Tensor<Eigen::half, 1> half_prec2(num_elem);
+  Tensor<Eigen::half, 1> full_prec2(num_elem);
+  Tensor<float, 1> input3(num_elem);
+  Tensor<Eigen::half, 1> half_prec3(num_elem);
+  Tensor<Eigen::half, 1> full_prec3(num_elem);
+  sycl_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem * sizeof(float));
+  sycl_device.memcpyDeviceToHost(input2.data(), d_float2, num_elem * sizeof(float));
+  sycl_device.memcpyDeviceToHost(input3.data(), d_float3, num_elem * sizeof(float));
+  sycl_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem * sizeof(Eigen::half));
+  sycl_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem * sizeof(Eigen::half));
+  sycl_device.memcpyDeviceToHost(half_prec2.data(), d_res2_half, num_elem * sizeof(Eigen::half));
+  sycl_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem * sizeof(Eigen::half));
+  sycl_device.memcpyDeviceToHost(half_prec3.data(), d_res3_half, num_elem * sizeof(Eigen::half));
+  sycl_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem * sizeof(Eigen::half));
+  sycl_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking elemwise exp " << i << " input = " << input1(i) << " full = " << full_prec1(i)
+              << " half = " << half_prec1(i) << std::endl;
+    VERIFY_IS_APPROX(full_prec1(i), half_prec1(i));
+  }
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking elemwise log " << i << " input = " << input2(i) << " full = " << full_prec2(i)
+              << " half = " << half_prec2(i) << std::endl;
+    if (std::abs(input2(i) - 1.f) < 0.05f)  // log lacks accuracy nearby 1
+      VERIFY_IS_APPROX(full_prec2(i) + Eigen::half(0.1f), half_prec2(i) + Eigen::half(0.1f));
+    else
+      VERIFY_IS_APPROX(full_prec2(i), half_prec2(i));
+  }
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking elemwise plog1 " << i << " input = " << input3(i) << " full = " << full_prec3(i)
+              << " half = " << half_prec3(i) << std::endl;
+    VERIFY_IS_APPROX(full_prec3(i), half_prec3(i));
+  }
+}
+
+void test_gpu_contractions(const Eigen::SyclDevice& sycl_device) {
+  int rows = 23;
+  int cols = 23;
+  int num_elem = rows * cols;
+
+  float* d_float1 = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  float* d_float2 = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  Eigen::half* d_res_half = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half));
+  Eigen::half* d_res_float = (Eigen::half*)sycl_device.allocate(num_elem * sizeof(Eigen::half));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1(d_float1, rows, cols);
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2(d_float2, rows, cols);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_half(d_res_half, rows, cols);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_float(d_res_float, rows, cols);
+
+  gpu_float1.device(sycl_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
+  gpu_float2.device(sycl_device) = gpu_float2.random() - gpu_float2.constant(0.5f);
+
+  typedef typename Tensor<float, 2>::DimensionPair DimPair;
+  Eigen::array<DimPair, 1> dims;
+  gpu_res_float.device(sycl_device) = gpu_float1.contract(gpu_float2, dims).cast<Eigen::half>();
+  gpu_res_half.device(sycl_device) = gpu_float1.cast<Eigen::half>().contract(gpu_float2.cast<Eigen::half>(), dims);
+
+  Tensor<Eigen::half, 2> half_prec(rows, cols);
+  Tensor<Eigen::half, 2> full_prec(rows, cols);
+  sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem * sizeof(Eigen::half));
+  sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(Eigen::half));
+  sycl_device.synchronize();
+
+  for (int i = 0; i < rows; ++i) {
+    for (int j = 0; j < cols; ++j) {
+      std::cout << "Checking contract " << i << " " << j << full_prec(i, j) << " " << half_prec(i, j) << std::endl;
+      if (numext::abs(full_prec(i, j) - half_prec(i, j)) > Eigen::half(1e-2f)) {
+        VERIFY_IS_APPROX(full_prec(i, j), half_prec(i, j));
+      }
+    }
+  }
+}
+
+void test_gpu_reductions(const Eigen::SyclDevice& sycl_device, int size1, int size2, int redux) {
+  std::cout << "Reducing " << size1 << " by " << size2 << " tensor along dim " << redux << std::endl;
+
+  int num_elem = size1 * size2;
+  int result_size = (redux == 1 ? size1 : size2);
+
+  float* d_float = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  Eigen::half* d_res_half = (Eigen::half*)sycl_device.allocate(result_size * sizeof(Eigen::half));
+  Eigen::half* d_res_float = (Eigen::half*)sycl_device.allocate(result_size * sizeof(Eigen::half));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float(d_float, size1, size2);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_half(d_res_half, result_size);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_float(d_res_float, result_size);
+
+  gpu_float.device(sycl_device) = gpu_float.random() * 2.0f;
+
+  Eigen::array<int, 1> redux_dim = {redux};
+  gpu_res_float.device(sycl_device) = gpu_float.sum(redux_dim).cast<Eigen::half>();
+  gpu_res_half.device(sycl_device) = gpu_float.cast<Eigen::half>().sum(redux_dim);
+
+  Tensor<Eigen::half, 1> half_prec(result_size);
+  Tensor<Eigen::half, 1> full_prec(result_size);
+  sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, result_size * sizeof(Eigen::half));
+  sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, result_size * sizeof(Eigen::half));
+  sycl_device.synchronize();
+
+  for (int i = 0; i < result_size; ++i) {
+    std::cout << "EXPECTED " << full_prec(i) << " GOT " << half_prec(i) << std::endl;
+    VERIFY_IS_APPROX(full_prec(i), half_prec(i));
+  }
+}
+
+void test_gpu_reductions(const Eigen::SyclDevice& sycl_device) {
+  test_gpu_reductions(sycl_device, 13, 13, 0);
+  test_gpu_reductions(sycl_device, 13, 13, 1);
+
+  test_gpu_reductions(sycl_device, 35, 36, 0);
+  test_gpu_reductions(sycl_device, 35, 36, 1);
+
+  test_gpu_reductions(sycl_device, 36, 35, 0);
+  test_gpu_reductions(sycl_device, 36, 35, 1);
+}
+
+void test_gpu_full_reductions(const Eigen::SyclDevice& sycl_device) {
+  int size = 13;
+  int num_elem = size * size;
+
+  float* d_float = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  Eigen::half* d_res_half = (Eigen::half*)sycl_device.allocate(1 * sizeof(Eigen::half));
+  Eigen::half* d_res_float = (Eigen::half*)sycl_device.allocate(1 * sizeof(Eigen::half));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float(d_float, size, size);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_half(d_res_half);
+  Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_float(d_res_float);
+
+  gpu_float.device(sycl_device) = gpu_float.random();
+
+  gpu_res_float.device(sycl_device) = gpu_float.sum().cast<Eigen::half>();
+  gpu_res_half.device(sycl_device) = gpu_float.cast<Eigen::half>().sum();
+
+  Tensor<Eigen::half, 0> half_prec;
+  Tensor<Eigen::half, 0> full_prec;
+  sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half));
+  sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half));
+  sycl_device.synchronize();
+
+  VERIFY_IS_APPROX(full_prec(), half_prec());
+
+  gpu_res_float.device(sycl_device) = gpu_float.maximum().cast<Eigen::half>();
+  gpu_res_half.device(sycl_device) = gpu_float.cast<Eigen::half>().maximum();
+  sycl_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half));
+  sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half));
+  sycl_device.synchronize();
+
+  VERIFY_IS_APPROX(full_prec(), half_prec());
+}
+
+void test_gpu_forced_evals(const Eigen::SyclDevice& sycl_device) {
+  int num_elem = 101;
+
+  float* d_float = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  float* d_res_half1 = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  float* d_res_half2 = (float*)sycl_device.allocate(num_elem * sizeof(float));
+  float* d_res_float = (float*)sycl_device.allocate(num_elem * sizeof(float));
+
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(d_float, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half1(d_res_half1, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Unaligned> gpu_res_half2(d_res_half2, num_elem);
+  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(d_res_float, num_elem);
+
+  Eigen::array<int, 1> no_bcast;
+  no_bcast[0] = 1;
+
+  gpu_float.device(sycl_device) = gpu_float.random() - gpu_float.constant(0.5f);
+  gpu_res_float.device(sycl_device) = gpu_float.abs();
+  gpu_res_half1.device(sycl_device) = gpu_float.cast<Eigen::half>().abs().eval().cast<float>();
+  gpu_res_half2.device(sycl_device) = gpu_float.cast<Eigen::half>().abs().broadcast(no_bcast).eval().cast<float>();
+
+  Tensor<float, 1> half_prec1(num_elem);
+  Tensor<float, 1> half_prec2(num_elem);
+  Tensor<float, 1> full_prec(num_elem);
+  sycl_device.memcpyDeviceToHost(half_prec1.data(), d_res_half1, num_elem * sizeof(float));
+  sycl_device.memcpyDeviceToHost(half_prec2.data(), d_res_half2, num_elem * sizeof(float));
+  sycl_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem * sizeof(float));
+  sycl_device.synchronize();
+
+  for (int i = 0; i < num_elem; ++i) {
+    std::cout << "Checking forced eval " << i << full_prec(i) << " vs " << half_prec1(i) << " vs " << half_prec2(i)
+              << std::endl;
+    VERIFY_IS_APPROX(full_prec(i), half_prec1(i));
+    VERIFY_IS_APPROX(full_prec(i), half_prec2(i));
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_of_float16_sycl) {
+  for (const auto& s : Eigen::get_sycl_supported_devices()) {
+    QueueInterface queueInterface(s);
+    auto sycl_device = Eigen::SyclDevice(&queueInterface);
+
+    CALL_SUBTEST_1(test_gpu_numext(sycl_device));
+    CALL_SUBTEST_1(test_gpu_conversion(sycl_device));
+    CALL_SUBTEST_1(test_gpu_unary(sycl_device));
+    CALL_SUBTEST_1(test_gpu_elementwise(sycl_device));
+    CALL_SUBTEST_1(test_gpu_trancendental(sycl_device));
+    CALL_SUBTEST_2(test_gpu_contractions(sycl_device));
+    CALL_SUBTEST_3(test_gpu_reductions(sycl_device));
+    CALL_SUBTEST_4(test_gpu_full_reductions(sycl_device));
+    CALL_SUBTEST_5(test_gpu_forced_evals(sycl_device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_strings.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_strings.cpp
new file mode 100644
index 0000000..f7db25c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_of_strings.cpp
@@ -0,0 +1,141 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+static void test_assign() {
+  std::string data1[6];
+  TensorMap<Tensor<std::string, 2>> mat1(data1, 2, 3);
+  std::string data2[6];
+  const TensorMap<Tensor<const std::string, 2>> mat2(data2, 2, 3);
+
+  for (int i = 0; i < 6; ++i) {
+    std::ostringstream s1;
+    s1 << "abc" << i * 3;
+    data1[i] = s1.str();
+    std::ostringstream s2;
+    s2 << "def" << i * 5;
+    data2[i] = s2.str();
+  }
+
+  Tensor<std::string, 2> rslt1;
+  rslt1 = mat1;
+  Tensor<std::string, 2> rslt2;
+  rslt2 = mat2;
+
+  Tensor<std::string, 2> rslt3 = mat1;
+  Tensor<std::string, 2> rslt4 = mat2;
+
+  Tensor<std::string, 2> rslt5(mat1);
+  Tensor<std::string, 2> rslt6(mat2);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(rslt1(i, j), data1[i + 2 * j]);
+      VERIFY_IS_EQUAL(rslt2(i, j), data2[i + 2 * j]);
+      VERIFY_IS_EQUAL(rslt3(i, j), data1[i + 2 * j]);
+      VERIFY_IS_EQUAL(rslt4(i, j), data2[i + 2 * j]);
+      VERIFY_IS_EQUAL(rslt5(i, j), data1[i + 2 * j]);
+      VERIFY_IS_EQUAL(rslt6(i, j), data2[i + 2 * j]);
+    }
+  }
+}
+
+static void test_concat() {
+  Tensor<std::string, 2> t1(2, 3);
+  Tensor<std::string, 2> t2(2, 3);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      std::ostringstream s1;
+      s1 << "abc" << i + j * 2;
+      t1(i, j) = s1.str();
+      std::ostringstream s2;
+      s2 << "def" << i * 5 + j * 32;
+      t2(i, j) = s2.str();
+    }
+  }
+
+  Tensor<std::string, 2> result = t1.concatenate(t2, 1);
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_EQUAL(result.dimension(1), 6);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(result(i, j), t1(i, j));
+      VERIFY_IS_EQUAL(result(i, j + 3), t2(i, j));
+    }
+  }
+}
+
+static void test_slices() {
+  Tensor<std::string, 2> data(2, 6);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      std::ostringstream s1;
+      s1 << "abc" << i + j * 2;
+      data(i, j) = s1.str();
+    }
+  }
+
+  const Eigen::DSizes<ptrdiff_t, 2> half_size(2, 3);
+  const Eigen::DSizes<ptrdiff_t, 2> first_half(0, 0);
+  const Eigen::DSizes<ptrdiff_t, 2> second_half(0, 3);
+
+  Tensor<std::string, 2> t1 = data.slice(first_half, half_size);
+  Tensor<std::string, 2> t2 = data.slice(second_half, half_size);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(data(i, j), t1(i, j));
+      VERIFY_IS_EQUAL(data(i, j + 3), t2(i, j));
+    }
+  }
+}
+
+static void test_additions() {
+  Tensor<std::string, 1> data1(3);
+  Tensor<std::string, 1> data2(3);
+  for (int i = 0; i < 3; ++i) {
+    data1(i) = "abc";
+    std::ostringstream s1;
+    s1 << i;
+    data2(i) = s1.str();
+  }
+
+  Tensor<std::string, 1> sum = data1 + data2;
+  for (int i = 0; i < 3; ++i) {
+    std::ostringstream concat;
+    concat << "abc" << i;
+    std::string expected = concat.str();
+    VERIFY_IS_EQUAL(sum(i), expected);
+  }
+}
+
+static void test_initialization() {
+  Tensor<std::string, 2> a(2, 3);
+  a.setConstant(std::string("foo"));
+  for (int i = 0; i < 2 * 3; ++i) {
+    VERIFY_IS_EQUAL(a(i), std::string("foo"));
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_of_strings) {
+  // Beware: none of this is likely to ever work on a GPU.
+  CALL_SUBTEST(test_assign());
+  CALL_SUBTEST(test_concat());
+  CALL_SUBTEST(test_slices());
+  CALL_SUBTEST(test_additions());
+  CALL_SUBTEST(test_initialization());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_padding.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_padding.cpp
new file mode 100644
index 0000000..6c5ddf0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_padding.cpp
@@ -0,0 +1,90 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_simple_padding() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  array<std::pair<ptrdiff_t, ptrdiff_t>, 4> paddings;
+  paddings[0] = std::make_pair(0, 0);
+  paddings[1] = std::make_pair(2, 1);
+  paddings[2] = std::make_pair(3, 4);
+  paddings[3] = std::make_pair(0, 0);
+
+  Tensor<float, 4, DataLayout> padded;
+  padded = tensor.pad(paddings);
+
+  VERIFY_IS_EQUAL(padded.dimension(0), 2 + 0);
+  VERIFY_IS_EQUAL(padded.dimension(1), 3 + 3);
+  VERIFY_IS_EQUAL(padded.dimension(2), 5 + 7);
+  VERIFY_IS_EQUAL(padded.dimension(3), 7 + 0);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 6; ++j) {
+      for (int k = 0; k < 12; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          if (j >= 2 && j < 5 && k >= 3 && k < 8) {
+            VERIFY_IS_EQUAL(padded(i, j, k, l), tensor(i, j - 2, k - 3, l));
+          } else {
+            VERIFY_IS_EQUAL(padded(i, j, k, l), 0.0f);
+          }
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_padded_expr() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  array<std::pair<ptrdiff_t, ptrdiff_t>, 4> paddings;
+  paddings[0] = std::make_pair(0, 0);
+  paddings[1] = std::make_pair(2, 1);
+  paddings[2] = std::make_pair(3, 4);
+  paddings[3] = std::make_pair(0, 0);
+
+  Eigen::DSizes<ptrdiff_t, 2> reshape_dims;
+  reshape_dims[0] = 12;
+  reshape_dims[1] = 84;
+
+  Tensor<float, 2, DataLayout> result;
+  result = tensor.pad(paddings).reshape(reshape_dims);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 6; ++j) {
+      for (int k = 0; k < 12; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          const float result_value =
+              DataLayout == ColMajor ? result(i + 2 * j, k + 12 * l) : result(j + 6 * i, l + 7 * k);
+          if (j >= 2 && j < 5 && k >= 3 && k < 8) {
+            VERIFY_IS_EQUAL(result_value, tensor(i, j - 2, k - 3, l));
+          } else {
+            VERIFY_IS_EQUAL(result_value, 0.0f);
+          }
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_padding) {
+  CALL_SUBTEST(test_simple_padding<ColMajor>());
+  CALL_SUBTEST(test_simple_padding<RowMajor>());
+  CALL_SUBTEST(test_padded_expr<ColMajor>());
+  CALL_SUBTEST(test_padded_expr<RowMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_padding_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_padding_sycl.cpp
new file mode 100644
index 0000000..ae4d98a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_padding_sycl.cpp
@@ -0,0 +1,148 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_simple_padding(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+
+  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
+  tensor.setRandom();
+
+  array<std::pair<IndexType, IndexType>, 4> paddings;
+  paddings[0] = std::make_pair(0, 0);
+  paddings[1] = std::make_pair(2, 1);
+  paddings[2] = std::make_pair(3, 4);
+  paddings[3] = std::make_pair(0, 0);
+
+  IndexType padedSizeDim1 = 2;
+  IndexType padedSizeDim2 = 6;
+  IndexType padedSizeDim3 = 12;
+  IndexType padedSizeDim4 = 7;
+  array<IndexType, 4> padedtensorRange = {{padedSizeDim1, padedSizeDim2, padedSizeDim3, padedSizeDim4}};
+
+  Tensor<DataType, 4, DataLayout, IndexType> padded(padedtensorRange);
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(padded.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu2(gpu_data2, padedtensorRange);
+
+  VERIFY_IS_EQUAL(padded.dimension(0), 2 + 0);
+  VERIFY_IS_EQUAL(padded.dimension(1), 3 + 3);
+  VERIFY_IS_EQUAL(padded.dimension(2), 5 + 7);
+  VERIFY_IS_EQUAL(padded.dimension(3), 7 + 0);
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), (tensor.size()) * sizeof(DataType));
+  gpu2.device(sycl_device) = gpu1.pad(paddings);
+  sycl_device.memcpyDeviceToHost(padded.data(), gpu_data2, (padded.size()) * sizeof(DataType));
+  for (IndexType i = 0; i < padedSizeDim1; ++i) {
+    for (IndexType j = 0; j < padedSizeDim2; ++j) {
+      for (IndexType k = 0; k < padedSizeDim3; ++k) {
+        for (IndexType l = 0; l < padedSizeDim4; ++l) {
+          if (j >= 2 && j < 5 && k >= 3 && k < 8) {
+            VERIFY_IS_EQUAL(padded(i, j, k, l), tensor(i, j - 2, k - 3, l));
+          } else {
+            VERIFY_IS_EQUAL(padded(i, j, k, l), 0.0f);
+          }
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_padded_expr(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+
+  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
+  tensor.setRandom();
+
+  array<std::pair<IndexType, IndexType>, 4> paddings;
+  paddings[0] = std::make_pair(0, 0);
+  paddings[1] = std::make_pair(2, 1);
+  paddings[2] = std::make_pair(3, 4);
+  paddings[3] = std::make_pair(0, 0);
+
+  Eigen::DSizes<IndexType, 2> reshape_dims;
+  reshape_dims[0] = 12;
+  reshape_dims[1] = 84;
+
+  Tensor<DataType, 2, DataLayout, IndexType> result(reshape_dims);
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(result.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu2(gpu_data2, reshape_dims);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), (tensor.size()) * sizeof(DataType));
+  gpu2.device(sycl_device) = gpu1.pad(paddings).reshape(reshape_dims);
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_data2, (result.size()) * sizeof(DataType));
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 6; ++j) {
+      for (IndexType k = 0; k < 12; ++k) {
+        for (IndexType l = 0; l < 7; ++l) {
+          const float result_value =
+              DataLayout == ColMajor ? result(i + 2 * j, k + 12 * l) : result(j + 6 * i, l + 7 * k);
+          if (j >= 2 && j < 5 && k >= 3 && k < 8) {
+            VERIFY_IS_EQUAL(result_value, tensor(i, j - 2, k - 3, l));
+          } else {
+            VERIFY_IS_EQUAL(result_value, 0.0f);
+          }
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_padding_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_padding<DataType, RowMajor, int64_t>(sycl_device);
+  test_simple_padding<DataType, ColMajor, int64_t>(sycl_device);
+  test_padded_expr<DataType, RowMajor, int64_t>(sycl_device);
+  test_padded_expr<DataType, ColMajor, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_padding_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_padding_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_padding_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_patch.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_patch.cpp
new file mode 100644
index 0000000..59b03e7
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_patch.cpp
@@ -0,0 +1,170 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_simple_patch() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> patch_dims;
+
+  patch_dims[0] = 1;
+  patch_dims[1] = 1;
+  patch_dims[2] = 1;
+  patch_dims[3] = 1;
+
+  Tensor<float, 5, DataLayout> no_patch;
+  no_patch = tensor.extract_patches(patch_dims);
+
+  if (DataLayout == ColMajor) {
+    VERIFY_IS_EQUAL(no_patch.dimension(0), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(1), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(2), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(3), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(4), tensor.size());
+  } else {
+    VERIFY_IS_EQUAL(no_patch.dimension(0), tensor.size());
+    VERIFY_IS_EQUAL(no_patch.dimension(1), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(2), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(3), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(4), 1);
+  }
+
+  for (int i = 0; i < tensor.size(); ++i) {
+    VERIFY_IS_EQUAL(tensor.data()[i], no_patch.data()[i]);
+  }
+
+  patch_dims[0] = 2;
+  patch_dims[1] = 3;
+  patch_dims[2] = 5;
+  patch_dims[3] = 7;
+  Tensor<float, 5, DataLayout> single_patch;
+  single_patch = tensor.extract_patches(patch_dims);
+
+  if (DataLayout == ColMajor) {
+    VERIFY_IS_EQUAL(single_patch.dimension(0), 2);
+    VERIFY_IS_EQUAL(single_patch.dimension(1), 3);
+    VERIFY_IS_EQUAL(single_patch.dimension(2), 5);
+    VERIFY_IS_EQUAL(single_patch.dimension(3), 7);
+    VERIFY_IS_EQUAL(single_patch.dimension(4), 1);
+  } else {
+    VERIFY_IS_EQUAL(single_patch.dimension(0), 1);
+    VERIFY_IS_EQUAL(single_patch.dimension(1), 2);
+    VERIFY_IS_EQUAL(single_patch.dimension(2), 3);
+    VERIFY_IS_EQUAL(single_patch.dimension(3), 5);
+    VERIFY_IS_EQUAL(single_patch.dimension(4), 7);
+  }
+
+  for (int i = 0; i < tensor.size(); ++i) {
+    VERIFY_IS_EQUAL(tensor.data()[i], single_patch.data()[i]);
+  }
+
+  patch_dims[0] = 1;
+  patch_dims[1] = 2;
+  patch_dims[2] = 2;
+  patch_dims[3] = 1;
+  Tensor<float, 5, DataLayout> twod_patch;
+  twod_patch = tensor.extract_patches(patch_dims);
+
+  if (DataLayout == ColMajor) {
+    VERIFY_IS_EQUAL(twod_patch.dimension(0), 1);
+    VERIFY_IS_EQUAL(twod_patch.dimension(1), 2);
+    VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
+    VERIFY_IS_EQUAL(twod_patch.dimension(3), 1);
+    VERIFY_IS_EQUAL(twod_patch.dimension(4), 2 * 2 * 4 * 7);
+  } else {
+    VERIFY_IS_EQUAL(twod_patch.dimension(0), 2 * 2 * 4 * 7);
+    VERIFY_IS_EQUAL(twod_patch.dimension(1), 1);
+    VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
+    VERIFY_IS_EQUAL(twod_patch.dimension(3), 2);
+    VERIFY_IS_EQUAL(twod_patch.dimension(4), 1);
+  }
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 2; ++j) {
+      for (int k = 0; k < 4; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          int patch_loc;
+          if (DataLayout == ColMajor) {
+            patch_loc = i + 2 * (j + 2 * (k + 4 * l));
+          } else {
+            patch_loc = l + 7 * (k + 4 * (j + 2 * i));
+          }
+          for (int x = 0; x < 2; ++x) {
+            for (int y = 0; y < 2; ++y) {
+              if (DataLayout == ColMajor) {
+                VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l), twod_patch(0, x, y, 0, patch_loc));
+              } else {
+                VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l), twod_patch(patch_loc, 0, x, y, 0));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  patch_dims[0] = 1;
+  patch_dims[1] = 2;
+  patch_dims[2] = 3;
+  patch_dims[3] = 5;
+  Tensor<float, 5, DataLayout> threed_patch;
+  threed_patch = tensor.extract_patches(patch_dims);
+
+  if (DataLayout == ColMajor) {
+    VERIFY_IS_EQUAL(threed_patch.dimension(0), 1);
+    VERIFY_IS_EQUAL(threed_patch.dimension(1), 2);
+    VERIFY_IS_EQUAL(threed_patch.dimension(2), 3);
+    VERIFY_IS_EQUAL(threed_patch.dimension(3), 5);
+    VERIFY_IS_EQUAL(threed_patch.dimension(4), 2 * 2 * 3 * 3);
+  } else {
+    VERIFY_IS_EQUAL(threed_patch.dimension(0), 2 * 2 * 3 * 3);
+    VERIFY_IS_EQUAL(threed_patch.dimension(1), 1);
+    VERIFY_IS_EQUAL(threed_patch.dimension(2), 2);
+    VERIFY_IS_EQUAL(threed_patch.dimension(3), 3);
+    VERIFY_IS_EQUAL(threed_patch.dimension(4), 5);
+  }
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 2; ++j) {
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 3; ++l) {
+          int patch_loc;
+          if (DataLayout == ColMajor) {
+            patch_loc = i + 2 * (j + 2 * (k + 3 * l));
+          } else {
+            patch_loc = l + 3 * (k + 3 * (j + 2 * i));
+          }
+          for (int x = 0; x < 2; ++x) {
+            for (int y = 0; y < 3; ++y) {
+              for (int z = 0; z < 5; ++z) {
+                if (DataLayout == ColMajor) {
+                  VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l + z), threed_patch(0, x, y, z, patch_loc));
+                } else {
+                  VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l + z), threed_patch(patch_loc, 0, x, y, z));
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_patch) {
+  CALL_SUBTEST(test_simple_patch<ColMajor>());
+  CALL_SUBTEST(test_simple_patch<RowMajor>());
+  //   CALL_SUBTEST(test_expr_shuffling());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_patch_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_patch_sycl.cpp
new file mode 100644
index 0000000..b1be9ff
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_patch_sycl.cpp
@@ -0,0 +1,249 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_simple_patch_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  array<IndexType, 5> patchTensorRange;
+  if (DataLayout == ColMajor) {
+    patchTensorRange = {{1, 1, 1, 1, sizeDim1 * sizeDim2 * sizeDim3 * sizeDim4}};
+  } else {
+    patchTensorRange = {{sizeDim1 * sizeDim2 * sizeDim3 * sizeDim4, 1, 1, 1, 1}};
+  }
+
+  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
+  Tensor<DataType, 5, DataLayout, IndexType> no_patch(patchTensorRange);
+
+  tensor.setRandom();
+
+  array<ptrdiff_t, 4> patch_dims;
+  patch_dims[0] = 1;
+  patch_dims[1] = 1;
+  patch_dims[2] = 1;
+  patch_dims[3] = 1;
+
+  const size_t tensorBuffSize = tensor.size() * sizeof(DataType);
+  size_t patchTensorBuffSize = no_patch.size() * sizeof(DataType);
+  DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+  DataType* gpu_data_no_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_no_patch(gpu_data_no_patch, patchTensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
+  gpu_no_patch.device(sycl_device) = gpu_tensor.extract_patches(patch_dims);
+  sycl_device.memcpyDeviceToHost(no_patch.data(), gpu_data_no_patch, patchTensorBuffSize);
+
+  if (DataLayout == ColMajor) {
+    VERIFY_IS_EQUAL(no_patch.dimension(0), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(1), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(2), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(3), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(4), tensor.size());
+  } else {
+    VERIFY_IS_EQUAL(no_patch.dimension(0), tensor.size());
+    VERIFY_IS_EQUAL(no_patch.dimension(1), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(2), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(3), 1);
+    VERIFY_IS_EQUAL(no_patch.dimension(4), 1);
+  }
+
+  for (int i = 0; i < tensor.size(); ++i) {
+    VERIFY_IS_EQUAL(tensor.data()[i], no_patch.data()[i]);
+  }
+
+  patch_dims[0] = 2;
+  patch_dims[1] = 3;
+  patch_dims[2] = 5;
+  patch_dims[3] = 7;
+
+  if (DataLayout == ColMajor) {
+    patchTensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, 1}};
+  } else {
+    patchTensorRange = {{1, sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  }
+  Tensor<DataType, 5, DataLayout, IndexType> single_patch(patchTensorRange);
+  patchTensorBuffSize = single_patch.size() * sizeof(DataType);
+  DataType* gpu_data_single_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_single_patch(gpu_data_single_patch, patchTensorRange);
+
+  gpu_single_patch.device(sycl_device) = gpu_tensor.extract_patches(patch_dims);
+  sycl_device.memcpyDeviceToHost(single_patch.data(), gpu_data_single_patch, patchTensorBuffSize);
+
+  if (DataLayout == ColMajor) {
+    VERIFY_IS_EQUAL(single_patch.dimension(0), 2);
+    VERIFY_IS_EQUAL(single_patch.dimension(1), 3);
+    VERIFY_IS_EQUAL(single_patch.dimension(2), 5);
+    VERIFY_IS_EQUAL(single_patch.dimension(3), 7);
+    VERIFY_IS_EQUAL(single_patch.dimension(4), 1);
+  } else {
+    VERIFY_IS_EQUAL(single_patch.dimension(0), 1);
+    VERIFY_IS_EQUAL(single_patch.dimension(1), 2);
+    VERIFY_IS_EQUAL(single_patch.dimension(2), 3);
+    VERIFY_IS_EQUAL(single_patch.dimension(3), 5);
+    VERIFY_IS_EQUAL(single_patch.dimension(4), 7);
+  }
+
+  for (int i = 0; i < tensor.size(); ++i) {
+    VERIFY_IS_EQUAL(tensor.data()[i], single_patch.data()[i]);
+  }
+  patch_dims[0] = 1;
+  patch_dims[1] = 2;
+  patch_dims[2] = 2;
+  patch_dims[3] = 1;
+
+  if (DataLayout == ColMajor) {
+    patchTensorRange = {{1, 2, 2, 1, 2 * 2 * 4 * 7}};
+  } else {
+    patchTensorRange = {{2 * 2 * 4 * 7, 1, 2, 2, 1}};
+  }
+  Tensor<DataType, 5, DataLayout, IndexType> twod_patch(patchTensorRange);
+  patchTensorBuffSize = twod_patch.size() * sizeof(DataType);
+  DataType* gpu_data_twod_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_twod_patch(gpu_data_twod_patch, patchTensorRange);
+
+  gpu_twod_patch.device(sycl_device) = gpu_tensor.extract_patches(patch_dims);
+  sycl_device.memcpyDeviceToHost(twod_patch.data(), gpu_data_twod_patch, patchTensorBuffSize);
+
+  if (DataLayout == ColMajor) {
+    VERIFY_IS_EQUAL(twod_patch.dimension(0), 1);
+    VERIFY_IS_EQUAL(twod_patch.dimension(1), 2);
+    VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
+    VERIFY_IS_EQUAL(twod_patch.dimension(3), 1);
+    VERIFY_IS_EQUAL(twod_patch.dimension(4), 2 * 2 * 4 * 7);
+  } else {
+    VERIFY_IS_EQUAL(twod_patch.dimension(0), 2 * 2 * 4 * 7);
+    VERIFY_IS_EQUAL(twod_patch.dimension(1), 1);
+    VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
+    VERIFY_IS_EQUAL(twod_patch.dimension(3), 2);
+    VERIFY_IS_EQUAL(twod_patch.dimension(4), 1);
+  }
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 2; ++j) {
+      for (int k = 0; k < 4; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          int patch_loc;
+          if (DataLayout == ColMajor) {
+            patch_loc = i + 2 * (j + 2 * (k + 4 * l));
+          } else {
+            patch_loc = l + 7 * (k + 4 * (j + 2 * i));
+          }
+          for (int x = 0; x < 2; ++x) {
+            for (int y = 0; y < 2; ++y) {
+              if (DataLayout == ColMajor) {
+                VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l), twod_patch(0, x, y, 0, patch_loc));
+              } else {
+                VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l), twod_patch(patch_loc, 0, x, y, 0));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  patch_dims[0] = 1;
+  patch_dims[1] = 2;
+  patch_dims[2] = 3;
+  patch_dims[3] = 5;
+
+  if (DataLayout == ColMajor) {
+    patchTensorRange = {{1, 2, 3, 5, 2 * 2 * 3 * 3}};
+  } else {
+    patchTensorRange = {{2 * 2 * 3 * 3, 1, 2, 3, 5}};
+  }
+  Tensor<DataType, 5, DataLayout, IndexType> threed_patch(patchTensorRange);
+  patchTensorBuffSize = threed_patch.size() * sizeof(DataType);
+  DataType* gpu_data_threed_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_threed_patch(gpu_data_threed_patch, patchTensorRange);
+
+  gpu_threed_patch.device(sycl_device) = gpu_tensor.extract_patches(patch_dims);
+  sycl_device.memcpyDeviceToHost(threed_patch.data(), gpu_data_threed_patch, patchTensorBuffSize);
+
+  if (DataLayout == ColMajor) {
+    VERIFY_IS_EQUAL(threed_patch.dimension(0), 1);
+    VERIFY_IS_EQUAL(threed_patch.dimension(1), 2);
+    VERIFY_IS_EQUAL(threed_patch.dimension(2), 3);
+    VERIFY_IS_EQUAL(threed_patch.dimension(3), 5);
+    VERIFY_IS_EQUAL(threed_patch.dimension(4), 2 * 2 * 3 * 3);
+  } else {
+    VERIFY_IS_EQUAL(threed_patch.dimension(0), 2 * 2 * 3 * 3);
+    VERIFY_IS_EQUAL(threed_patch.dimension(1), 1);
+    VERIFY_IS_EQUAL(threed_patch.dimension(2), 2);
+    VERIFY_IS_EQUAL(threed_patch.dimension(3), 3);
+    VERIFY_IS_EQUAL(threed_patch.dimension(4), 5);
+  }
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 2; ++j) {
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 3; ++l) {
+          int patch_loc;
+          if (DataLayout == ColMajor) {
+            patch_loc = i + 2 * (j + 2 * (k + 3 * l));
+          } else {
+            patch_loc = l + 3 * (k + 3 * (j + 2 * i));
+          }
+          for (int x = 0; x < 2; ++x) {
+            for (int y = 0; y < 3; ++y) {
+              for (int z = 0; z < 5; ++z) {
+                if (DataLayout == ColMajor) {
+                  VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l + z), threed_patch(0, x, y, z, patch_loc));
+                } else {
+                  VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l + z), threed_patch(patch_loc, 0, x, y, z));
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data_tensor);
+  sycl_device.deallocate(gpu_data_no_patch);
+  sycl_device.deallocate(gpu_data_single_patch);
+  sycl_device.deallocate(gpu_data_twod_patch);
+  sycl_device.deallocate(gpu_data_threed_patch);
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_tensor_patch_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_patch_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_simple_patch_sycl<DataType, ColMajor, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_patch_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_tensor_patch_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_tensor_patch_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_random.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_random.cpp
new file mode 100644
index 0000000..585a3f8
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_random.cpp
@@ -0,0 +1,80 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+template <typename Scalar>
+static void test_default() {
+  Tensor<Scalar, 1> vec(6);
+  vec.setRandom();
+
+  // Fixme: we should check that the generated numbers follow a uniform
+  // distribution instead.
+  for (int i = 1; i < 6; ++i) {
+    VERIFY_IS_NOT_EQUAL(vec(i), vec(i - 1));
+  }
+}
+
+template <typename Scalar>
+static void test_normal() {
+  Tensor<Scalar, 1> vec(6);
+  vec.template setRandom<Eigen::internal::NormalRandomGenerator<Scalar>>();
+
+  // Fixme: we should check that the generated numbers follow a gaussian
+  // distribution instead.
+  for (int i = 1; i < 6; ++i) {
+    VERIFY_IS_NOT_EQUAL(vec(i), vec(i - 1));
+  }
+}
+
+struct MyGenerator {
+  MyGenerator() {}
+  MyGenerator(const MyGenerator&) {}
+
+  // Return a random value to be used.  "element_location" is the
+  // location of the entry to set in the tensor, it can typically
+  // be ignored.
+  int operator()(Eigen::DenseIndex element_location, Eigen::DenseIndex /*unused*/ = 0) const {
+    return static_cast<int>(3 * element_location);
+  }
+
+  // Same as above but generates several numbers at a time.
+  internal::packet_traits<int>::type packetOp(Eigen::DenseIndex packet_location,
+                                              Eigen::DenseIndex /*unused*/ = 0) const {
+    const int packetSize = internal::packet_traits<int>::size;
+    EIGEN_ALIGN_MAX int values[packetSize];
+    for (int i = 0; i < packetSize; ++i) {
+      values[i] = static_cast<int>(3 * (packet_location + i));
+    }
+    return internal::pload<typename internal::packet_traits<int>::type>(values);
+  }
+};
+
+static void test_custom() {
+  Tensor<int, 1> vec(6);
+  vec.setRandom<MyGenerator>();
+
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_EQUAL(vec(i), 3 * i);
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_random) {
+  CALL_SUBTEST((test_default<float>()));
+  CALL_SUBTEST((test_normal<float>()));
+  CALL_SUBTEST((test_default<double>()));
+  CALL_SUBTEST((test_normal<double>()));
+  CALL_SUBTEST((test_default<Eigen::half>()));
+  CALL_SUBTEST((test_normal<Eigen::half>()));
+  CALL_SUBTEST((test_default<Eigen::bfloat16>()));
+  CALL_SUBTEST((test_normal<Eigen::bfloat16>()));
+  CALL_SUBTEST(test_custom());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_random_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_random_gpu.cu
new file mode 100644
index 0000000..6edea1c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_random_gpu.cu
@@ -0,0 +1,78 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <Eigen/CXX11/Tensor>
+
+void test_gpu_random_uniform() {
+  Tensor<float, 2> out(72, 97);
+  out.setZero();
+
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_out;
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72, 97);
+
+  gpu_out.device(gpu_device) = gpu_out.random();
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+
+  // For now we just check this code doesn't crash.
+  // TODO: come up with a valid test of randomness
+}
+
+void test_gpu_random_normal() {
+  Tensor<float, 2> out(72, 97);
+  out.setZero();
+
+  std::size_t out_bytes = out.size() * sizeof(float);
+
+  float* d_out;
+  gpuMalloc((void**)(&d_out), out_bytes);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72, 97);
+
+  Eigen::internal::NormalRandomGenerator<float> gen(true);
+  gpu_out.device(gpu_device) = gpu_out.random(gen);
+
+  assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
+  assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
+}
+
+static void test_complex() {
+  Tensor<std::complex<float>, 1> vec(6);
+  vec.setRandom();
+
+  // Fixme: we should check that the generated numbers follow a uniform
+  // distribution instead.
+  for (int i = 1; i < 6; ++i) {
+    VERIFY_IS_NOT_EQUAL(vec(i), vec(i - 1));
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_random_gpu) {
+  CALL_SUBTEST(test_gpu_random_uniform());
+  CALL_SUBTEST(test_gpu_random_normal());
+  CALL_SUBTEST(test_complex());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_random_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_random_sycl.cpp
new file mode 100644
index 0000000..0d29a5d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_random_sycl.cpp
@@ -0,0 +1,84 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_sycl_random_uniform(const Eigen::SyclDevice& sycl_device) {
+  Tensor<DataType, 2, DataLayout, IndexType> out(72, 97);
+  out.setZero();
+
+  std::size_t out_bytes = out.size() * sizeof(DataType);
+
+  IndexType sizeDim0 = 72;
+  IndexType sizeDim1 = 97;
+
+  array<IndexType, 2> tensorRange = {{sizeDim0, sizeDim1}};
+
+  DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange);
+
+  gpu_out.device(sycl_device) = gpu_out.random();
+  sycl_device.memcpyDeviceToHost(out.data(), d_out, out_bytes);
+
+  // For now we just check the code doesn't crash.
+  // TODO: come up with a valid test of randomness
+  sycl_device.deallocate(d_out);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_sycl_random_normal(const Eigen::SyclDevice& sycl_device) {
+  Tensor<DataType, 2, DataLayout, IndexType> out(72, 97);
+  out.setZero();
+  std::size_t out_bytes = out.size() * sizeof(DataType);
+
+  IndexType sizeDim0 = 72;
+  IndexType sizeDim1 = 97;
+
+  array<IndexType, 2> tensorRange = {{sizeDim0, sizeDim1}};
+
+  DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange);
+  Eigen::internal::NormalRandomGenerator<DataType> gen(true);
+  gpu_out.device(sycl_device) = gpu_out.random(gen);
+  sycl_device.memcpyDeviceToHost(out.data(), d_out, out_bytes);
+
+  // For now we just check the code doesn't crash.
+  // TODO: come up with a valid test of randomness
+  sycl_device.deallocate(d_out);
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_random_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_sycl_random_uniform<DataType, RowMajor, int64_t>(sycl_device);
+  test_sycl_random_uniform<DataType, ColMajor, int64_t>(sycl_device);
+  test_sycl_random_normal<DataType, RowMajor, int64_t>(sycl_device);
+  test_sycl_random_normal<DataType, ColMajor, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_random_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_random_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_random_test_per_device<float>(device));
+#ifdef EIGEN_SYCL_DOUBLE_SUPPORT
+    CALL_SUBTEST(sycl_random_test_per_device<double>(device));
+#endif
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reduction.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reduction.cpp
new file mode 100644
index 0000000..0fdebe2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reduction.cpp
@@ -0,0 +1,520 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <limits>
+#include <numeric>
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_trivial_reductions() {
+  {
+    Tensor<float, 0, DataLayout> tensor;
+    tensor.setRandom();
+    array<ptrdiff_t, 0> reduction_axis;
+
+    Tensor<float, 0, DataLayout> result = tensor.sum(reduction_axis);
+    VERIFY_IS_EQUAL(result(), tensor());
+  }
+
+  {
+    Tensor<float, 1, DataLayout> tensor(7);
+    tensor.setRandom();
+    array<ptrdiff_t, 0> reduction_axis;
+
+    Tensor<float, 1, DataLayout> result = tensor.sum(reduction_axis);
+    VERIFY_IS_EQUAL(result.dimension(0), 7);
+    for (int i = 0; i < 7; ++i) {
+      VERIFY_IS_EQUAL(result(i), tensor(i));
+    }
+  }
+
+  {
+    Tensor<float, 2, DataLayout> tensor(2, 3);
+    tensor.setRandom();
+    array<ptrdiff_t, 0> reduction_axis;
+
+    Tensor<float, 2, DataLayout> result = tensor.sum(reduction_axis);
+    VERIFY_IS_EQUAL(result.dimension(0), 2);
+    VERIFY_IS_EQUAL(result.dimension(1), 3);
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 3; ++j) {
+        VERIFY_IS_EQUAL(result(i, j), tensor(i, j));
+      }
+    }
+  }
+}
+
+template <typename Scalar, int DataLayout>
+static void test_simple_reductions() {
+  Tensor<Scalar, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  // Add a little offset so that the product reductions won't be close to zero.
+  tensor += tensor.constant(Scalar(0.5f));
+  array<ptrdiff_t, 2> reduction_axis2;
+  reduction_axis2[0] = 1;
+  reduction_axis2[1] = 3;
+
+  Tensor<Scalar, 2, DataLayout> result = tensor.sum(reduction_axis2);
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_EQUAL(result.dimension(1), 5);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      Scalar sum = Scalar(0.0f);
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          sum += tensor(i, k, j, l);
+        }
+      }
+      VERIFY_IS_APPROX(result(i, j), sum);
+    }
+  }
+
+  {
+    Tensor<Scalar, 0, DataLayout> sum1 = tensor.sum();
+    VERIFY_IS_EQUAL(sum1.rank(), 0);
+
+    array<ptrdiff_t, 4> reduction_axis4;
+    reduction_axis4[0] = 0;
+    reduction_axis4[1] = 1;
+    reduction_axis4[2] = 2;
+    reduction_axis4[3] = 3;
+    Tensor<Scalar, 0, DataLayout> sum2 = tensor.sum(reduction_axis4);
+    VERIFY_IS_EQUAL(sum2.rank(), 0);
+
+    VERIFY_IS_APPROX(sum1(), sum2());
+  }
+
+  reduction_axis2[0] = 0;
+  reduction_axis2[1] = 2;
+  result = tensor.prod(reduction_axis2);
+  VERIFY_IS_EQUAL(result.dimension(0), 3);
+  VERIFY_IS_EQUAL(result.dimension(1), 7);
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      Scalar prod = Scalar(1.0f);
+      for (int k = 0; k < 2; ++k) {
+        for (int l = 0; l < 5; ++l) {
+          prod *= tensor(k, i, l, j);
+        }
+      }
+      VERIFY_IS_APPROX(result(i, j), prod);
+    }
+  }
+
+  {
+    Tensor<Scalar, 0, DataLayout> prod1 = tensor.prod();
+    VERIFY_IS_EQUAL(prod1.rank(), 0);
+
+    array<ptrdiff_t, 4> reduction_axis4;
+    reduction_axis4[0] = 0;
+    reduction_axis4[1] = 1;
+    reduction_axis4[2] = 2;
+    reduction_axis4[3] = 3;
+    Tensor<Scalar, 0, DataLayout> prod2 = tensor.prod(reduction_axis4);
+    VERIFY_IS_EQUAL(prod2.rank(), 0);
+
+    VERIFY_IS_APPROX(prod1(), prod2());
+  }
+
+  reduction_axis2[0] = 0;
+  reduction_axis2[1] = 2;
+  result = tensor.maximum(reduction_axis2);
+  VERIFY_IS_EQUAL(result.dimension(0), 3);
+  VERIFY_IS_EQUAL(result.dimension(1), 7);
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      Scalar max_val = std::numeric_limits<Scalar>::lowest();
+      for (int k = 0; k < 2; ++k) {
+        for (int l = 0; l < 5; ++l) {
+          max_val = (std::max)(max_val, tensor(k, i, l, j));
+        }
+      }
+      VERIFY_IS_APPROX(result(i, j), max_val);
+    }
+  }
+
+  {
+    Tensor<Scalar, 0, DataLayout> max1 = tensor.maximum();
+    VERIFY_IS_EQUAL(max1.rank(), 0);
+
+    array<ptrdiff_t, 4> reduction_axis4;
+    reduction_axis4[0] = 0;
+    reduction_axis4[1] = 1;
+    reduction_axis4[2] = 2;
+    reduction_axis4[3] = 3;
+    Tensor<Scalar, 0, DataLayout> max2 = tensor.maximum(reduction_axis4);
+    VERIFY_IS_EQUAL(max2.rank(), 0);
+
+    VERIFY_IS_APPROX(max1(), max2());
+  }
+
+  reduction_axis2[0] = 0;
+  reduction_axis2[1] = 1;
+  result = tensor.minimum(reduction_axis2);
+  VERIFY_IS_EQUAL(result.dimension(0), 5);
+  VERIFY_IS_EQUAL(result.dimension(1), 7);
+  for (int i = 0; i < 5; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      Scalar min_val = (std::numeric_limits<Scalar>::max)();
+      for (int k = 0; k < 2; ++k) {
+        for (int l = 0; l < 3; ++l) {
+          min_val = (std::min)(min_val, tensor(k, l, i, j));
+        }
+      }
+      VERIFY_IS_APPROX(result(i, j), min_val);
+    }
+  }
+
+  {
+    Tensor<Scalar, 0, DataLayout> min1 = tensor.minimum();
+    VERIFY_IS_EQUAL(min1.rank(), 0);
+
+    array<ptrdiff_t, 4> reduction_axis4;
+    reduction_axis4[0] = 0;
+    reduction_axis4[1] = 1;
+    reduction_axis4[2] = 2;
+    reduction_axis4[3] = 3;
+    Tensor<Scalar, 0, DataLayout> min2 = tensor.minimum(reduction_axis4);
+    VERIFY_IS_EQUAL(min2.rank(), 0);
+
+    VERIFY_IS_APPROX(min1(), min2());
+  }
+
+  reduction_axis2[0] = 0;
+  reduction_axis2[1] = 1;
+  result = tensor.mean(reduction_axis2);
+  VERIFY_IS_EQUAL(result.dimension(0), 5);
+  VERIFY_IS_EQUAL(result.dimension(1), 7);
+  for (int i = 0; i < 5; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      Scalar sum = Scalar(0.0f);
+      int count = 0;
+      for (int k = 0; k < 2; ++k) {
+        for (int l = 0; l < 3; ++l) {
+          sum += tensor(k, l, i, j);
+          ++count;
+        }
+      }
+      VERIFY_IS_APPROX(result(i, j), sum / Scalar(count));
+    }
+  }
+
+  {
+    Tensor<Scalar, 0, DataLayout> mean1 = tensor.mean();
+    VERIFY_IS_EQUAL(mean1.rank(), 0);
+
+    array<ptrdiff_t, 4> reduction_axis4;
+    reduction_axis4[0] = 0;
+    reduction_axis4[1] = 1;
+    reduction_axis4[2] = 2;
+    reduction_axis4[3] = 3;
+    Tensor<Scalar, 0, DataLayout> mean2 = tensor.mean(reduction_axis4);
+    VERIFY_IS_EQUAL(mean2.rank(), 0);
+
+    VERIFY_IS_APPROX(mean1(), mean2());
+  }
+
+  {
+    Tensor<int, 1> ints(10);
+    std::iota(ints.data(), ints.data() + ints.dimension(0), 0);
+
+    TensorFixedSize<bool, Sizes<>> all_;
+    all_ = ints.all();
+    VERIFY(!all_());
+    all_ = (ints >= ints.constant(0)).all();
+    VERIFY(all_());
+
+    TensorFixedSize<bool, Sizes<>> any;
+    any = (ints > ints.constant(10)).any();
+    VERIFY(!any());
+    any = (ints < ints.constant(1)).any();
+    VERIFY(any());
+  }
+}
+
+template <int DataLayout>
+static void test_reductions_in_expr() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 2> reduction_axis2;
+  reduction_axis2[0] = 1;
+  reduction_axis2[1] = 3;
+
+  Tensor<float, 2, DataLayout> result(2, 5);
+  result = result.constant(1.0f) - tensor.sum(reduction_axis2);
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_EQUAL(result.dimension(1), 5);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      float sum = 0.0f;
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          sum += tensor(i, k, j, l);
+        }
+      }
+      VERIFY_IS_APPROX(result(i, j), 1.0f - sum);
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_full_reductions() {
+  Tensor<float, 2, DataLayout> tensor(2, 3);
+  tensor.setRandom();
+  array<ptrdiff_t, 2> reduction_axis;
+  reduction_axis[0] = 0;
+  reduction_axis[1] = 1;
+
+  Tensor<float, 0, DataLayout> result = tensor.sum(reduction_axis);
+  VERIFY_IS_EQUAL(result.rank(), 0);
+
+  float sum = 0.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      sum += tensor(i, j);
+    }
+  }
+  VERIFY_IS_APPROX(result(0), sum);
+
+  result = tensor.square().sum(reduction_axis).sqrt();
+  VERIFY_IS_EQUAL(result.rank(), 0);
+
+  sum = 0.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      sum += tensor(i, j) * tensor(i, j);
+    }
+  }
+  VERIFY_IS_APPROX(result(), sqrtf(sum));
+}
+
+struct UserReducer {
+  static const bool PacketAccess = false;
+  UserReducer(float offset) : offset_(offset) {}
+  void reduce(const float val, float* accum) { *accum += val * val; }
+  float initialize() const { return 0; }
+  float finalize(const float accum) const { return 1.0f / (accum + offset_); }
+
+ private:
+  const float offset_;
+};
+
+template <int DataLayout>
+static void test_user_defined_reductions() {
+  Tensor<float, 2, DataLayout> tensor(5, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 1> reduction_axis;
+  reduction_axis[0] = 1;
+
+  UserReducer reducer(10.0f);
+  Tensor<float, 1, DataLayout> result = tensor.reduce(reduction_axis, reducer);
+  VERIFY_IS_EQUAL(result.dimension(0), 5);
+  for (int i = 0; i < 5; ++i) {
+    float expected = 10.0f;
+    for (int j = 0; j < 7; ++j) {
+      expected += tensor(i, j) * tensor(i, j);
+    }
+    expected = 1.0f / expected;
+    VERIFY_IS_APPROX(result(i), expected);
+  }
+}
+
+template <int DataLayout>
+static void test_tensor_maps() {
+  int inputs[2 * 3 * 5 * 7];
+  TensorMap<Tensor<int, 4, DataLayout>> tensor_map(inputs, 2, 3, 5, 7);
+  TensorMap<Tensor<const int, 4, DataLayout>> tensor_map_const(inputs, 2, 3, 5, 7);
+  const TensorMap<Tensor<const int, 4, DataLayout>> tensor_map_const_const(inputs, 2, 3, 5, 7);
+
+  tensor_map.setRandom();
+  array<ptrdiff_t, 2> reduction_axis;
+  reduction_axis[0] = 1;
+  reduction_axis[1] = 3;
+
+  Tensor<int, 2, DataLayout> result = tensor_map.sum(reduction_axis);
+  Tensor<int, 2, DataLayout> result2 = tensor_map_const.sum(reduction_axis);
+  Tensor<int, 2, DataLayout> result3 = tensor_map_const_const.sum(reduction_axis);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      int sum = 0;
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          sum += tensor_map(i, k, j, l);
+        }
+      }
+      VERIFY_IS_EQUAL(result(i, j), sum);
+      VERIFY_IS_EQUAL(result2(i, j), sum);
+      VERIFY_IS_EQUAL(result3(i, j), sum);
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_static_dims() {
+  Tensor<float, 4, DataLayout> in(72, 53, 97, 113);
+  Tensor<float, 2, DataLayout> out(72, 97);
+  in.setRandom();
+
+  Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<3>> reduction_axis;
+
+  out = in.maximum(reduction_axis);
+
+  for (int i = 0; i < 72; ++i) {
+    for (int j = 0; j < 97; ++j) {
+      float expected = -1e10f;
+      for (int k = 0; k < 53; ++k) {
+        for (int l = 0; l < 113; ++l) {
+          expected = (std::max)(expected, in(i, k, j, l));
+        }
+      }
+      VERIFY_IS_EQUAL(out(i, j), expected);
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_innermost_last_dims() {
+  Tensor<float, 4, DataLayout> in(72, 53, 97, 113);
+  Tensor<float, 2, DataLayout> out(97, 113);
+  in.setRandom();
+
+  // Reduce on the innermost dimensions.
+  // This triggers the use of packets for ColMajor.
+  Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1>> reduction_axis;
+
+  out = in.maximum(reduction_axis);
+
+  for (int i = 0; i < 97; ++i) {
+    for (int j = 0; j < 113; ++j) {
+      float expected = -1e10f;
+      for (int k = 0; k < 53; ++k) {
+        for (int l = 0; l < 72; ++l) {
+          expected = (std::max)(expected, in(l, k, i, j));
+        }
+      }
+      VERIFY_IS_EQUAL(out(i, j), expected);
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_innermost_first_dims() {
+  Tensor<float, 4, DataLayout> in(72, 53, 97, 113);
+  Tensor<float, 2, DataLayout> out(72, 53);
+  in.setRandom();
+
+  // Reduce on the innermost dimensions.
+  // This triggers the use of packets for RowMajor.
+  Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3>> reduction_axis;
+
+  out = in.maximum(reduction_axis);
+
+  for (int i = 0; i < 72; ++i) {
+    for (int j = 0; j < 53; ++j) {
+      float expected = -1e10f;
+      for (int k = 0; k < 97; ++k) {
+        for (int l = 0; l < 113; ++l) {
+          expected = (std::max)(expected, in(i, j, k, l));
+        }
+      }
+      VERIFY_IS_EQUAL(out(i, j), expected);
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_reduce_middle_dims() {
+  Tensor<float, 4, DataLayout> in(72, 53, 97, 113);
+  Tensor<float, 2, DataLayout> out(72, 53);
+  in.setRandom();
+
+  // Reduce on the innermost dimensions.
+  // This triggers the use of packets for RowMajor.
+  Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<2>> reduction_axis;
+
+  out = in.maximum(reduction_axis);
+
+  for (int i = 0; i < 72; ++i) {
+    for (int j = 0; j < 113; ++j) {
+      float expected = -1e10f;
+      for (int k = 0; k < 53; ++k) {
+        for (int l = 0; l < 97; ++l) {
+          expected = (std::max)(expected, in(i, k, l, j));
+        }
+      }
+      VERIFY_IS_EQUAL(out(i, j), expected);
+    }
+  }
+}
+
+template <typename ScalarType, int num_elements, int max_mean>
+void test_sum_accuracy() {
+  Tensor<double, 1> double_tensor(num_elements);
+  Tensor<ScalarType, 1> tensor(num_elements);
+  for (double prescribed_mean = 0; prescribed_mean <= max_mean;
+       prescribed_mean = numext::maxi(1.0, prescribed_mean * 3.99)) {
+    // FIXME: NormalRandomGenerator doesn't work in bfloat and half.
+    double_tensor.setRandom<Eigen::internal::NormalRandomGenerator<double>>();
+    double_tensor += double_tensor.constant(prescribed_mean);
+    tensor = double_tensor.cast<ScalarType>();
+
+    Tensor<ScalarType, 0> sum;
+    sum = tensor.sum();
+
+    // Compute the reference value in double precision.
+    double expected_sum = 0.0;
+    double abs_sum = 0.0;
+    for (int i = 0; i < num_elements; ++i) {
+      expected_sum += static_cast<double>(tensor(i));
+      abs_sum += static_cast<double>(numext::abs(tensor(i)));
+    }
+    // Test against probabilistic forward error bound. In reality, the error is much smaller
+    // when we use tree summation.
+    double err = Eigen::numext::abs(static_cast<double>(sum()) - expected_sum);
+    double tol = numext::sqrt(static_cast<double>(num_elements)) *
+                 static_cast<double>(NumTraits<ScalarType>::epsilon()) * abs_sum;
+    VERIFY_LE(err, tol);
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_reduction) {
+  CALL_SUBTEST(test_trivial_reductions<ColMajor>());
+  CALL_SUBTEST(test_trivial_reductions<RowMajor>());
+  CALL_SUBTEST((test_simple_reductions<float, ColMajor>()));
+  CALL_SUBTEST((test_simple_reductions<float, RowMajor>()));
+  CALL_SUBTEST((test_simple_reductions<Eigen::half, ColMajor>()));
+  CALL_SUBTEST((test_simple_reductions<Eigen::bfloat16, ColMajor>()));
+  CALL_SUBTEST(test_reductions_in_expr<ColMajor>());
+  CALL_SUBTEST(test_reductions_in_expr<RowMajor>());
+  CALL_SUBTEST(test_full_reductions<ColMajor>());
+  CALL_SUBTEST(test_full_reductions<RowMajor>());
+  CALL_SUBTEST(test_user_defined_reductions<ColMajor>());
+  CALL_SUBTEST(test_user_defined_reductions<RowMajor>());
+  CALL_SUBTEST(test_tensor_maps<ColMajor>());
+  CALL_SUBTEST(test_tensor_maps<RowMajor>());
+  CALL_SUBTEST(test_static_dims<ColMajor>());
+  CALL_SUBTEST(test_static_dims<RowMajor>());
+  CALL_SUBTEST(test_innermost_last_dims<ColMajor>());
+  CALL_SUBTEST(test_innermost_last_dims<RowMajor>());
+  CALL_SUBTEST(test_innermost_first_dims<ColMajor>());
+  CALL_SUBTEST(test_innermost_first_dims<RowMajor>());
+  CALL_SUBTEST(test_reduce_middle_dims<ColMajor>());
+  CALL_SUBTEST(test_reduce_middle_dims<RowMajor>());
+  CALL_SUBTEST((test_sum_accuracy<float, 10 * 1024 * 1024, 8 * 1024>()));
+  CALL_SUBTEST((test_sum_accuracy<Eigen::bfloat16, 10 * 1024 * 1024, 8 * 1024>()));
+  // The range of half is limited to 65519 when using round-to-even,
+  // so we are severely limited in the size and mean of the tensors
+  // we can reduce without overflow.
+  CALL_SUBTEST((test_sum_accuracy<Eigen::half, 4 * 1024, 16>()));
+  CALL_SUBTEST((test_sum_accuracy<Eigen::half, 10 * 1024 * 1024, 0>()));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reduction_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reduction_gpu.cu
new file mode 100644
index 0000000..24cc5be
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reduction_gpu.cu
@@ -0,0 +1,151 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+template <typename Type, int DataLayout>
+static void test_full_reductions() {
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  const int num_rows = internal::random<int>(1024, 5 * 1024);
+  const int num_cols = internal::random<int>(1024, 5 * 1024);
+
+  Tensor<Type, 2, DataLayout> in(num_rows, num_cols);
+  in.setRandom();
+
+  Tensor<Type, 0, DataLayout> full_redux;
+  full_redux = in.sum();
+
+  std::size_t in_bytes = in.size() * sizeof(Type);
+  std::size_t out_bytes = full_redux.size() * sizeof(Type);
+  Type* gpu_in_ptr = static_cast<Type*>(gpu_device.allocate(in_bytes));
+  Type* gpu_out_ptr = static_cast<Type*>(gpu_device.allocate(out_bytes));
+  gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
+
+  TensorMap<Tensor<Type, 2, DataLayout> > in_gpu(gpu_in_ptr, num_rows, num_cols);
+  TensorMap<Tensor<Type, 0, DataLayout> > out_gpu(gpu_out_ptr);
+
+  out_gpu.device(gpu_device) = in_gpu.sum();
+
+  Tensor<Type, 0, DataLayout> full_redux_gpu;
+  gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
+  gpu_device.synchronize();
+
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
+
+  gpu_device.deallocate(gpu_in_ptr);
+  gpu_device.deallocate(gpu_out_ptr);
+}
+
+template <typename Type, int DataLayout>
+static void test_first_dim_reductions() {
+  int dim_x = 33;
+  int dim_y = 1;
+  int dim_z = 128;
+
+  Tensor<Type, 3, DataLayout> in(dim_x, dim_y, dim_z);
+  in.setRandom();
+
+  Eigen::array<int, 1> red_axis;
+  red_axis[0] = 0;
+  Tensor<Type, 2, DataLayout> redux = in.sum(red_axis);
+
+  // Create device
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice dev(&stream);
+
+  // Create data(T)
+  Type* in_data = (Type*)dev.allocate(dim_x * dim_y * dim_z * sizeof(Type));
+  Type* out_data = (Type*)dev.allocate(dim_z * dim_y * sizeof(Type));
+  Eigen::TensorMap<Eigen::Tensor<Type, 3, DataLayout> > gpu_in(in_data, dim_x, dim_y, dim_z);
+  Eigen::TensorMap<Eigen::Tensor<Type, 2, DataLayout> > gpu_out(out_data, dim_y, dim_z);
+
+  // Perform operation
+  dev.memcpyHostToDevice(in_data, in.data(), in.size() * sizeof(Type));
+  gpu_out.device(dev) = gpu_in.sum(red_axis);
+  gpu_out.device(dev) += gpu_in.sum(red_axis);
+  Tensor<Type, 2, DataLayout> redux_gpu(dim_y, dim_z);
+  dev.memcpyDeviceToHost(redux_gpu.data(), out_data, gpu_out.size() * sizeof(Type));
+  dev.synchronize();
+
+  // Check that the CPU and GPU reductions return the same result.
+  for (int i = 0; i < gpu_out.size(); ++i) {
+    VERIFY_IS_APPROX(2 * redux(i), redux_gpu(i));
+  }
+
+  dev.deallocate(in_data);
+  dev.deallocate(out_data);
+}
+
+template <typename Type, int DataLayout>
+static void test_last_dim_reductions() {
+  int dim_x = 128;
+  int dim_y = 1;
+  int dim_z = 33;
+
+  Tensor<Type, 3, DataLayout> in(dim_x, dim_y, dim_z);
+  in.setRandom();
+
+  Eigen::array<int, 1> red_axis;
+  red_axis[0] = 2;
+  Tensor<Type, 2, DataLayout> redux = in.sum(red_axis);
+
+  // Create device
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice dev(&stream);
+
+  // Create data
+  Type* in_data = (Type*)dev.allocate(dim_x * dim_y * dim_z * sizeof(Type));
+  Type* out_data = (Type*)dev.allocate(dim_x * dim_y * sizeof(Type));
+  Eigen::TensorMap<Eigen::Tensor<Type, 3, DataLayout> > gpu_in(in_data, dim_x, dim_y, dim_z);
+  Eigen::TensorMap<Eigen::Tensor<Type, 2, DataLayout> > gpu_out(out_data, dim_x, dim_y);
+
+  // Perform operation
+  dev.memcpyHostToDevice(in_data, in.data(), in.size() * sizeof(Type));
+  gpu_out.device(dev) = gpu_in.sum(red_axis);
+  gpu_out.device(dev) += gpu_in.sum(red_axis);
+  Tensor<Type, 2, DataLayout> redux_gpu(dim_x, dim_y);
+  dev.memcpyDeviceToHost(redux_gpu.data(), out_data, gpu_out.size() * sizeof(Type));
+  dev.synchronize();
+
+  // Check that the CPU and GPU reductions return the same result.
+  for (int i = 0; i < gpu_out.size(); ++i) {
+    VERIFY_IS_APPROX(2 * redux(i), redux_gpu(i));
+  }
+
+  dev.deallocate(in_data);
+  dev.deallocate(out_data);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_reduction_gpu) {
+  CALL_SUBTEST_1((test_full_reductions<float, ColMajor>()));
+  CALL_SUBTEST_1((test_full_reductions<double, ColMajor>()));
+  CALL_SUBTEST_2((test_full_reductions<float, RowMajor>()));
+  CALL_SUBTEST_2((test_full_reductions<double, RowMajor>()));
+
+  CALL_SUBTEST_3((test_first_dim_reductions<float, ColMajor>()));
+  CALL_SUBTEST_3((test_first_dim_reductions<double, ColMajor>()));
+  CALL_SUBTEST_4((test_first_dim_reductions<float, RowMajor>()));
+  // Outer reductions of doubles aren't supported just yet.
+  //  CALL_SUBTEST_4((test_first_dim_reductions<double, RowMajor>()))
+
+  CALL_SUBTEST_5((test_last_dim_reductions<float, ColMajor>()));
+  // Outer reductions of doubles aren't supported just yet.
+  //  CALL_SUBTEST_5((test_last_dim_reductions<double, ColMajor>()));
+  CALL_SUBTEST_6((test_last_dim_reductions<float, RowMajor>()));
+  CALL_SUBTEST_6((test_last_dim_reductions<double, RowMajor>()));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reduction_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reduction_sycl.cpp
new file mode 100644
index 0000000..9276953
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reduction_sycl.cpp
@@ -0,0 +1,857 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+
+#include <unsupported/Eigen/CXX11/Tensor>
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_full_reductions_sum_sycl(const Eigen::SyclDevice& sycl_device) {
+  const IndexType num_rows = 753;
+  const IndexType num_cols = 537;
+  array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
+
+  array<IndexType, 2> outRange = {{1, 1}};
+
+  Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> full_redux(outRange);
+  Tensor<DataType, 2, DataLayout, IndexType> full_redux_gpu(outRange);
+
+  in.setRandom();
+  auto dim = DSizes<IndexType, 2>(1, 1);
+  full_redux = in.sum().reshape(dim);
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      (DataType*)sycl_device.allocate(sizeof(DataType) * (full_redux_gpu.dimensions().TotalSize()));
+
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> out_gpu(gpu_out_data, outRange);
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.sum().reshape(dim);
+  sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data,
+                                 (full_redux_gpu.dimensions().TotalSize()) * sizeof(DataType));
+  // Check that the CPU and GPU reductions return the same result.
+  std::cout << "SYCL FULL :" << full_redux_gpu(0, 0) << ", CPU FULL: " << full_redux(0, 0) << "\n";
+  VERIFY_IS_APPROX(full_redux_gpu(0, 0), full_redux(0, 0));
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_full_reductions_sum_with_offset_sycl(const Eigen::SyclDevice& sycl_device) {
+  using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
+  using scalar_tensor = Tensor<DataType, 0, DataLayout, IndexType>;
+  const IndexType num_rows = 64;
+  const IndexType num_cols = 64;
+  array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
+  const IndexType n_elems = internal::array_prod(tensor_range);
+
+  data_tensor in(tensor_range);
+  scalar_tensor full_redux;
+  scalar_tensor full_redux_gpu;
+
+  in.setRandom();
+  array<IndexType, 2> tensor_offset_range(tensor_range);
+  tensor_offset_range[0] -= 1;
+
+  const IndexType offset = 64;
+  TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
+  full_redux = in_offset.sum();
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
+  DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(sizeof(DataType)));
+
+  TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
+  TensorMap<scalar_tensor> out_gpu(gpu_out_data);
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), n_elems * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.sum();
+  sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
+
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_full_reductions_max_sycl(const Eigen::SyclDevice& sycl_device) {
+  const IndexType num_rows = 4096;
+  const IndexType num_cols = 4096;
+  array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
+
+  Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 0, DataLayout, IndexType> full_redux;
+  Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
+
+  in.setRandom();
+
+  full_redux = in.maximum();
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data = (DataType*)sycl_device.allocate(sizeof(DataType));
+
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 0, DataLayout, IndexType>> out_gpu(gpu_out_data);
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.maximum();
+  sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
+  VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_full_reductions_max_with_offset_sycl(const Eigen::SyclDevice& sycl_device) {
+  using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
+  using scalar_tensor = Tensor<DataType, 0, DataLayout, IndexType>;
+  const IndexType num_rows = 64;
+  const IndexType num_cols = 64;
+  array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
+  const IndexType n_elems = internal::array_prod(tensor_range);
+
+  data_tensor in(tensor_range);
+  scalar_tensor full_redux;
+  scalar_tensor full_redux_gpu;
+
+  in.setRandom();
+  array<IndexType, 2> tensor_offset_range(tensor_range);
+  tensor_offset_range[0] -= 1;
+  // Set the initial value to be the max.
+  // As we don't include this in the reduction the result should not be 2.
+  in(0) = static_cast<DataType>(2);
+
+  const IndexType offset = 64;
+  TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
+  full_redux = in_offset.maximum();
+  VERIFY_IS_NOT_EQUAL(full_redux(), in(0));
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
+  DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(sizeof(DataType)));
+
+  TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
+  TensorMap<scalar_tensor> out_gpu(gpu_out_data);
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), n_elems * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.maximum();
+  sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
+
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_full_reductions_mean_sycl(const Eigen::SyclDevice& sycl_device) {
+  const IndexType num_rows = 4096;
+  const IndexType num_cols = 4096;
+  array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
+  array<IndexType, 1> argRange = {{num_cols}};
+  Eigen::array<IndexType, 1> red_axis;
+  red_axis[0] = 0;
+  //  red_axis[1]=1;
+  Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> in_arg1(tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> in_arg2(tensorRange);
+  Tensor<bool, 1, DataLayout, IndexType> out_arg_cpu(argRange);
+  Tensor<bool, 1, DataLayout, IndexType> out_arg_gpu(argRange);
+  Tensor<bool, 1, DataLayout, IndexType> out_arg_gpu_helper(argRange);
+  Tensor<DataType, 0, DataLayout, IndexType> full_redux;
+  Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
+
+  in.setRandom();
+  in_arg1.setRandom();
+  in_arg2.setRandom();
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_in_arg1_data =
+      static_cast<DataType*>(sycl_device.allocate(in_arg1.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_in_arg2_data =
+      static_cast<DataType*>(sycl_device.allocate(in_arg2.dimensions().TotalSize() * sizeof(DataType)));
+  bool* gpu_out_arg__gpu_helper_data =
+      static_cast<bool*>(sycl_device.allocate(out_arg_gpu.dimensions().TotalSize() * sizeof(DataType)));
+  bool* gpu_out_arg_data =
+      static_cast<bool*>(sycl_device.allocate(out_arg_gpu.dimensions().TotalSize() * sizeof(DataType)));
+
+  DataType* gpu_out_data = (DataType*)sycl_device.allocate(sizeof(DataType));
+
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_Arg1_gpu(gpu_in_arg1_data, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_Arg2_gpu(gpu_in_arg2_data, tensorRange);
+  TensorMap<Tensor<bool, 1, DataLayout, IndexType>> out_Argout_gpu(gpu_out_arg_data, argRange);
+  TensorMap<Tensor<bool, 1, DataLayout, IndexType>> out_Argout_gpu_helper(gpu_out_arg__gpu_helper_data, argRange);
+  TensorMap<Tensor<DataType, 0, DataLayout, IndexType>> out_gpu(gpu_out_data);
+
+  // CPU VERSION
+  out_arg_cpu =
+      (in_arg1.argmax(1) == in_arg2.argmax(1)).select(out_arg_cpu.constant(true), out_arg_cpu.constant(false));
+  full_redux = (out_arg_cpu.template cast<float>()).reduce(red_axis, Eigen::internal::MeanReducer<DataType>());
+
+  // GPU VERSION
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_in_arg1_data, in_arg1.data(),
+                                 (in_arg1.dimensions().TotalSize()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_in_arg2_data, in_arg2.data(),
+                                 (in_arg2.dimensions().TotalSize()) * sizeof(DataType));
+  out_Argout_gpu_helper.device(sycl_device) = (in_Arg1_gpu.argmax(1) == in_Arg2_gpu.argmax(1));
+  out_Argout_gpu.device(sycl_device) =
+      (out_Argout_gpu_helper).select(out_Argout_gpu.constant(true), out_Argout_gpu.constant(false));
+  out_gpu.device(sycl_device) =
+      (out_Argout_gpu.template cast<float>()).reduce(red_axis, Eigen::internal::MeanReducer<DataType>());
+  sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
+  // Check that the CPU and GPU reductions return the same result.
+  std::cout << "SYCL : " << full_redux_gpu() << " , CPU : " << full_redux() << '\n';
+  VERIFY_IS_EQUAL(full_redux_gpu(), full_redux());
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_in_arg1_data);
+  sycl_device.deallocate(gpu_in_arg2_data);
+  sycl_device.deallocate(gpu_out_arg__gpu_helper_data);
+  sycl_device.deallocate(gpu_out_arg_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_full_reductions_mean_with_offset_sycl(const Eigen::SyclDevice& sycl_device) {
+  using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
+  using scalar_tensor = Tensor<DataType, 0, DataLayout, IndexType>;
+  const IndexType num_rows = 64;
+  const IndexType num_cols = 64;
+  array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
+  const IndexType n_elems = internal::array_prod(tensor_range);
+
+  data_tensor in(tensor_range);
+  scalar_tensor full_redux;
+  scalar_tensor full_redux_gpu;
+
+  in.setRandom();
+  array<IndexType, 2> tensor_offset_range(tensor_range);
+  tensor_offset_range[0] -= 1;
+
+  const IndexType offset = 64;
+  TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
+  full_redux = in_offset.mean();
+  VERIFY_IS_NOT_EQUAL(full_redux(), in(0));
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
+  DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(sizeof(DataType)));
+
+  TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
+  TensorMap<scalar_tensor> out_gpu(gpu_out_data);
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), n_elems * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.mean();
+  sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
+
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_full_reductions_mean_with_odd_offset_sycl(const Eigen::SyclDevice& sycl_device) {
+  // This is a particular case which illustrates a possible problem when the
+  // number of local threads in a workgroup is even, but is not a power of two.
+  using data_tensor = Tensor<DataType, 1, DataLayout, IndexType>;
+  using scalar_tensor = Tensor<DataType, 0, DataLayout, IndexType>;
+  // 2177 = (17 * 128) + 1 gives rise to 18 local threads.
+  // 8708 = 4 * 2177 = 4 * (17 * 128) + 4 uses 18 vectorised local threads.
+  const IndexType n_elems = 8707;
+  array<IndexType, 1> tensor_range = {{n_elems}};
+
+  data_tensor in(tensor_range);
+  DataType full_redux;
+  DataType full_redux_gpu;
+  TensorMap<scalar_tensor> red_cpu(&full_redux);
+  TensorMap<scalar_tensor> red_gpu(&full_redux_gpu);
+
+  const DataType const_val = static_cast<DataType>(0.6391);
+  in = in.constant(const_val);
+
+  Eigen::IndexList<Eigen::type2index<0>> red_axis;
+  red_cpu = in.reduce(red_axis, Eigen::internal::MeanReducer<DataType>());
+  VERIFY_IS_APPROX(const_val, red_cpu());
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
+  DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(sizeof(DataType)));
+
+  TensorMap<data_tensor> in_gpu(gpu_in_data, tensor_range);
+  TensorMap<scalar_tensor> out_gpu(gpu_out_data);
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), n_elems * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.reduce(red_axis, Eigen::internal::MeanReducer<DataType>());
+  sycl_device.memcpyDeviceToHost(red_gpu.data(), gpu_out_data, sizeof(DataType));
+
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux_gpu, full_redux);
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_full_reductions_min_sycl(const Eigen::SyclDevice& sycl_device) {
+  const IndexType num_rows = 876;
+  const IndexType num_cols = 953;
+  array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
+
+  Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 0, DataLayout, IndexType> full_redux;
+  Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
+
+  in.setRandom();
+
+  full_redux = in.minimum();
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data = (DataType*)sycl_device.allocate(sizeof(DataType));
+
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 0, DataLayout, IndexType>> out_gpu(gpu_out_data);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.minimum();
+  sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_full_reductions_min_with_offset_sycl(const Eigen::SyclDevice& sycl_device) {
+  using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
+  using scalar_tensor = Tensor<DataType, 0, DataLayout, IndexType>;
+  const IndexType num_rows = 64;
+  const IndexType num_cols = 64;
+  array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
+  const IndexType n_elems = internal::array_prod(tensor_range);
+
+  data_tensor in(tensor_range);
+  scalar_tensor full_redux;
+  scalar_tensor full_redux_gpu;
+
+  in.setRandom();
+  array<IndexType, 2> tensor_offset_range(tensor_range);
+  tensor_offset_range[0] -= 1;
+  // Set the initial value to be the min.
+  // As we don't include this in the reduction the result should not be -2.
+  in(0) = static_cast<DataType>(-2);
+
+  const IndexType offset = 64;
+  TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
+  full_redux = in_offset.minimum();
+  VERIFY_IS_NOT_EQUAL(full_redux(), in(0));
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
+  DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(sizeof(DataType)));
+
+  TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
+  TensorMap<scalar_tensor> out_gpu(gpu_out_data);
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), n_elems * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.minimum();
+  sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
+
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_first_dim_reductions_max_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType dim_x = 145;
+  IndexType dim_y = 1;
+  IndexType dim_z = 67;
+
+  array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
+  Eigen::array<IndexType, 1> red_axis;
+  red_axis[0] = 0;
+  array<IndexType, 2> reduced_tensorRange = {{dim_y, dim_z}};
+
+  Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
+
+  in.setRandom();
+
+  redux = in.maximum(red_axis);
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> out_gpu(gpu_out_data, reduced_tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.maximum(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize() * sizeof(DataType));
+
+  // Check that the CPU and GPU reductions return the same result.
+  for (IndexType j = 0; j < reduced_tensorRange[0]; j++)
+    for (IndexType k = 0; k < reduced_tensorRange[1]; k++) VERIFY_IS_APPROX(redux_gpu(j, k), redux(j, k));
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_first_dim_reductions_max_with_offset_sycl(const Eigen::SyclDevice& sycl_device) {
+  using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
+  using reduced_tensor = Tensor<DataType, 1, DataLayout, IndexType>;
+
+  const IndexType num_rows = 64;
+  const IndexType num_cols = 64;
+  array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
+  array<IndexType, 1> reduced_range = {{num_cols}};
+  const IndexType n_elems = internal::array_prod(tensor_range);
+  const IndexType n_reduced = num_cols;
+
+  data_tensor in(tensor_range);
+  reduced_tensor redux;
+  reduced_tensor redux_gpu(reduced_range);
+
+  in.setRandom();
+  array<IndexType, 2> tensor_offset_range(tensor_range);
+  tensor_offset_range[0] -= 1;
+  // Set maximum value outside of the considered range.
+  for (IndexType i = 0; i < n_reduced; i++) {
+    in(i) = static_cast<DataType>(2);
+  }
+
+  Eigen::array<IndexType, 1> red_axis;
+  red_axis[0] = 0;
+
+  const IndexType offset = 64;
+  TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
+  redux = in_offset.maximum(red_axis);
+  for (IndexType i = 0; i < n_reduced; i++) {
+    VERIFY_IS_NOT_EQUAL(redux(i), in(i));
+  }
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
+  DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(n_reduced * sizeof(DataType)));
+
+  TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
+  TensorMap<reduced_tensor> out_gpu(gpu_out_data, reduced_range);
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), n_elems * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.maximum(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, n_reduced * sizeof(DataType));
+
+  // Check that the CPU and GPU reductions return the same result.
+  for (IndexType i = 0; i < n_reduced; i++) {
+    VERIFY_IS_APPROX(redux_gpu(i), redux(i));
+  }
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_last_dim_reductions_max_with_offset_sycl(const Eigen::SyclDevice& sycl_device) {
+  using data_tensor = Tensor<DataType, 2, DataLayout, IndexType>;
+  using reduced_tensor = Tensor<DataType, 1, DataLayout, IndexType>;
+
+  const IndexType num_rows = 64;
+  const IndexType num_cols = 64;
+  array<IndexType, 2> tensor_range = {{num_rows, num_cols}};
+  array<IndexType, 1> full_reduced_range = {{num_rows}};
+  array<IndexType, 1> reduced_range = {{num_rows - 1}};
+  const IndexType n_elems = internal::array_prod(tensor_range);
+  const IndexType n_reduced = reduced_range[0];
+
+  data_tensor in(tensor_range);
+  reduced_tensor redux(full_reduced_range);
+  reduced_tensor redux_gpu(reduced_range);
+
+  in.setRandom();
+  redux.setZero();
+  array<IndexType, 2> tensor_offset_range(tensor_range);
+  tensor_offset_range[0] -= 1;
+  // Set maximum value outside of the considered range.
+  for (IndexType i = 0; i < n_reduced; i++) {
+    in(i) = static_cast<DataType>(2);
+  }
+
+  Eigen::array<IndexType, 1> red_axis;
+  red_axis[0] = 1;
+
+  const IndexType offset = 64;
+  // Introduce an offset in both the input and the output.
+  TensorMap<data_tensor> in_offset(in.data() + offset, tensor_offset_range);
+  TensorMap<reduced_tensor> red_offset(redux.data() + 1, reduced_range);
+  red_offset = in_offset.maximum(red_axis);
+
+  // Check that the first value hasn't been changed and that the reduced values
+  // are not equal to the previously set maximum in the input outside the range.
+  VERIFY_IS_EQUAL(redux(0), static_cast<DataType>(0));
+  for (IndexType i = 0; i < n_reduced; i++) {
+    VERIFY_IS_NOT_EQUAL(red_offset(i), in(i));
+  }
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(n_elems * sizeof(DataType)));
+  DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate((n_reduced + 1) * sizeof(DataType)));
+
+  TensorMap<data_tensor> in_gpu(gpu_in_data + offset, tensor_offset_range);
+  TensorMap<reduced_tensor> out_gpu(gpu_out_data + 1, reduced_range);
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), n_elems * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.maximum(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu.data(), out_gpu.data(), n_reduced * sizeof(DataType));
+
+  // Check that the CPU and GPU reductions return the same result.
+  for (IndexType i = 0; i < n_reduced; i++) {
+    VERIFY_IS_APPROX(redux_gpu(i), red_offset(i));
+  }
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_first_dim_reductions_sum_sycl(const Eigen::SyclDevice& sycl_device, IndexType dim_x, IndexType dim_y) {
+  array<IndexType, 2> tensorRange = {{dim_x, dim_y}};
+  Eigen::array<IndexType, 1> red_axis;
+  red_axis[0] = 0;
+  array<IndexType, 1> reduced_tensorRange = {{dim_y}};
+
+  Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 1, DataLayout, IndexType> redux(reduced_tensorRange);
+  Tensor<DataType, 1, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
+
+  in.setRandom();
+  redux = in.sum(red_axis);
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> out_gpu(gpu_out_data, reduced_tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.sum(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize() * sizeof(DataType));
+
+  // Check that the CPU and GPU reductions return the same result.
+  for (IndexType i = 0; i < redux.size(); i++) {
+    VERIFY_IS_APPROX(redux_gpu.data()[i], redux.data()[i]);
+  }
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_first_dim_reductions_mean_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType dim_x = 145;
+  IndexType dim_y = 1;
+  IndexType dim_z = 67;
+
+  array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
+  Eigen::array<IndexType, 1> red_axis;
+  red_axis[0] = 0;
+  array<IndexType, 2> reduced_tensorRange = {{dim_y, dim_z}};
+
+  Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
+
+  in.setRandom();
+
+  redux = in.mean(red_axis);
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> out_gpu(gpu_out_data, reduced_tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.mean(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize() * sizeof(DataType));
+
+  // Check that the CPU and GPU reductions return the same result.
+  for (IndexType j = 0; j < reduced_tensorRange[0]; j++)
+    for (IndexType k = 0; k < reduced_tensorRange[1]; k++) VERIFY_IS_APPROX(redux_gpu(j, k), redux(j, k));
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_last_dim_reductions_mean_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType dim_x = 64;
+  IndexType dim_y = 1;
+  IndexType dim_z = 32;
+
+  array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
+  Eigen::array<IndexType, 1> red_axis;
+  red_axis[0] = 2;
+  array<IndexType, 2> reduced_tensorRange = {{dim_x, dim_y}};
+
+  Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
+
+  in.setRandom();
+
+  redux = in.mean(red_axis);
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> out_gpu(gpu_out_data, reduced_tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.mean(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize() * sizeof(DataType));
+  // Check that the CPU and GPU reductions return the same result.
+  for (IndexType j = 0; j < reduced_tensorRange[0]; j++)
+    for (IndexType k = 0; k < reduced_tensorRange[1]; k++) VERIFY_IS_APPROX(redux_gpu(j, k), redux(j, k));
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_last_dim_reductions_sum_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType dim_x = 64;
+  IndexType dim_y = 1;
+  IndexType dim_z = 32;
+
+  array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
+  Eigen::array<IndexType, 1> red_axis;
+  red_axis[0] = 2;
+  array<IndexType, 2> reduced_tensorRange = {{dim_x, dim_y}};
+
+  Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
+  Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
+
+  in.setRandom();
+
+  redux = in.sum(red_axis);
+
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> out_gpu(gpu_out_data, reduced_tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), (in.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.sum(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize() * sizeof(DataType));
+  // Check that the CPU and GPU reductions return the same result.
+  for (IndexType j = 0; j < reduced_tensorRange[0]; j++)
+    for (IndexType k = 0; k < reduced_tensorRange[1]; k++) VERIFY_IS_APPROX(redux_gpu(j, k), redux(j, k));
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_last_reductions_sum_sycl(const Eigen::SyclDevice& sycl_device) {
+  auto tensorRange = Sizes<64, 32>(64, 32);
+  // auto red_axis =  Sizes<0,1>(0,1);
+  Eigen::IndexList<Eigen::type2index<1>> red_axis;
+  auto reduced_tensorRange = Sizes<64>(64);
+  TensorFixedSize<DataType, Sizes<64, 32>, DataLayout> in_fix;
+  TensorFixedSize<DataType, Sizes<64>, DataLayout> redux_fix;
+  TensorFixedSize<DataType, Sizes<64>, DataLayout> redux_gpu_fix;
+
+  in_fix.setRandom();
+
+  redux_fix = in_fix.sum(red_axis);
+
+  DataType* gpu_in_data =
+      static_cast<DataType*>(sycl_device.allocate(in_fix.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(redux_gpu_fix.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<TensorFixedSize<DataType, Sizes<64, 32>, DataLayout>> in_gpu_fix(gpu_in_data, tensorRange);
+  TensorMap<TensorFixedSize<DataType, Sizes<64>, DataLayout>> out_gpu_fix(gpu_out_data, reduced_tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in_fix.data(), (in_fix.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu_fix.device(sycl_device) = in_gpu_fix.sum(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu_fix.data(), gpu_out_data,
+                                 redux_gpu_fix.dimensions().TotalSize() * sizeof(DataType));
+  // Check that the CPU and GPU reductions return the same result.
+  for (IndexType j = 0; j < reduced_tensorRange[0]; j++) {
+    VERIFY_IS_APPROX(redux_gpu_fix(j), redux_fix(j));
+  }
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_last_reductions_mean_sycl(const Eigen::SyclDevice& sycl_device) {
+  auto tensorRange = Sizes<64, 32>(64, 32);
+  Eigen::IndexList<Eigen::type2index<1>> red_axis;
+  auto reduced_tensorRange = Sizes<64>(64);
+  TensorFixedSize<DataType, Sizes<64, 32>, DataLayout> in_fix;
+  TensorFixedSize<DataType, Sizes<64>, DataLayout> redux_fix;
+  TensorFixedSize<DataType, Sizes<64>, DataLayout> redux_gpu_fix;
+
+  in_fix.setRandom();
+  redux_fix = in_fix.mean(red_axis);
+
+  DataType* gpu_in_data =
+      static_cast<DataType*>(sycl_device.allocate(in_fix.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(redux_gpu_fix.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<TensorFixedSize<DataType, Sizes<64, 32>, DataLayout>> in_gpu_fix(gpu_in_data, tensorRange);
+  TensorMap<TensorFixedSize<DataType, Sizes<64>, DataLayout>> out_gpu_fix(gpu_out_data, reduced_tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in_fix.data(), (in_fix.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu_fix.device(sycl_device) = in_gpu_fix.mean(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu_fix.data(), gpu_out_data,
+                                 redux_gpu_fix.dimensions().TotalSize() * sizeof(DataType));
+  sycl_device.synchronize();
+  // Check that the CPU and GPU reductions return the same result.
+  for (IndexType j = 0; j < reduced_tensorRange[0]; j++) {
+    VERIFY_IS_APPROX(redux_gpu_fix(j), redux_fix(j));
+  }
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+// SYCL supports a generic case of reduction where the accumulator is a
+// different type than the input data This is an example on how to get if a
+// Tensor contains nan and/or inf in one reduction
+template <typename InT, typename OutT>
+struct CustomReducer {
+  static const bool PacketAccess = false;
+  static const bool IsStateful = false;
+
+  static constexpr OutT InfBit = 1;
+  static constexpr OutT NanBit = 2;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const InT x, OutT* accum) const {
+    if (Eigen::numext::isinf(x))
+      *accum |= InfBit;
+    else if (Eigen::numext::isnan(x))
+      *accum |= NanBit;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const OutT x, OutT* accum) const { *accum |= x; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE OutT initialize() const { return OutT(0); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE OutT finalize(const OutT accum) const { return accum; }
+};
+
+template <typename DataType, typename AccumType, int DataLayout, typename IndexType>
+static void test_full_reductions_custom_sycl(const Eigen::SyclDevice& sycl_device) {
+  constexpr IndexType InSize = 64;
+  auto tensorRange = Sizes<InSize>(InSize);
+  Eigen::IndexList<Eigen::type2index<0>> dims;
+  auto reduced_tensorRange = Sizes<>();
+  TensorFixedSize<DataType, Sizes<InSize>, DataLayout> in_fix;
+  TensorFixedSize<AccumType, Sizes<>, DataLayout> redux_gpu_fix;
+
+  CustomReducer<DataType, AccumType> reducer;
+
+  in_fix.setRandom();
+
+  size_t in_size_bytes = in_fix.dimensions().TotalSize() * sizeof(DataType);
+  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in_size_bytes));
+  AccumType* gpu_out_data = static_cast<AccumType*>(sycl_device.allocate(sizeof(AccumType)));
+
+  TensorMap<TensorFixedSize<DataType, Sizes<InSize>, DataLayout>> in_gpu_fix(gpu_in_data, tensorRange);
+  TensorMap<TensorFixedSize<AccumType, Sizes<>, DataLayout>> out_gpu_fix(gpu_out_data, reduced_tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in_fix.data(), in_size_bytes);
+  out_gpu_fix.device(sycl_device) = in_gpu_fix.reduce(dims, reducer);
+  sycl_device.memcpyDeviceToHost(redux_gpu_fix.data(), gpu_out_data, sizeof(AccumType));
+  VERIFY_IS_EQUAL(redux_gpu_fix(0), AccumType(0));
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, typename Dev>
+void sycl_reduction_test_full_per_device(const Dev& sycl_device) {
+  test_full_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_full_reductions_sum_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_full_reductions_min_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_full_reductions_min_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_full_reductions_max_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_full_reductions_max_sycl<DataType, RowMajor, int64_t>(sycl_device);
+
+  test_full_reductions_mean_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_full_reductions_mean_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_full_reductions_custom_sycl<DataType, int, RowMajor, int64_t>(sycl_device);
+  test_full_reductions_custom_sycl<DataType, int, ColMajor, int64_t>(sycl_device);
+  sycl_device.synchronize();
+}
+
+template <typename DataType, typename Dev>
+void sycl_reduction_full_offset_per_device(const Dev& sycl_device) {
+  test_full_reductions_sum_with_offset_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_full_reductions_sum_with_offset_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_full_reductions_min_with_offset_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_full_reductions_min_with_offset_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_full_reductions_max_with_offset_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_full_reductions_max_with_offset_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_full_reductions_mean_with_offset_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_full_reductions_mean_with_offset_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_full_reductions_mean_with_odd_offset_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  sycl_device.synchronize();
+}
+
+template <typename DataType, typename Dev>
+void sycl_reduction_test_first_dim_per_device(const Dev& sycl_device) {
+  test_first_dim_reductions_sum_sycl<DataType, ColMajor, int64_t>(sycl_device, 4197, 4097);
+  test_first_dim_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device, 4197, 4097);
+  test_first_dim_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device, 129, 8);
+  test_first_dim_reductions_max_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_first_dim_reductions_max_with_offset_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  sycl_device.synchronize();
+}
+
+template <typename DataType, typename Dev>
+void sycl_reduction_test_last_dim_per_device(const Dev& sycl_device) {
+  test_last_dim_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_last_dim_reductions_max_with_offset_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_last_reductions_sum_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_last_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_last_reductions_mean_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_last_reductions_mean_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  sycl_device.synchronize();
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_reduction_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    std::cout << "Running on " << device.template get_info<cl::sycl::info::device::name>() << std::endl;
+    QueueInterface queueInterface(device);
+    auto sycl_device = Eigen::SyclDevice(&queueInterface);
+    CALL_SUBTEST_1(sycl_reduction_test_full_per_device<float>(sycl_device));
+    CALL_SUBTEST_2(sycl_reduction_full_offset_per_device<float>(sycl_device));
+    CALL_SUBTEST_3(sycl_reduction_test_first_dim_per_device<float>(sycl_device));
+    CALL_SUBTEST_4(sycl_reduction_test_last_dim_per_device<float>(sycl_device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_ref.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_ref.cpp
new file mode 100644
index 0000000..8d6821a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_ref.cpp
@@ -0,0 +1,243 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void test_simple_lvalue_ref() {
+  Tensor<int, 1> input(6);
+  input.setRandom();
+
+  TensorRef<Tensor<int, 1>> ref3(input);
+  TensorRef<Tensor<int, 1>> ref4 = input;
+
+  VERIFY_IS_EQUAL(ref3.data(), input.data());
+  VERIFY_IS_EQUAL(ref4.data(), input.data());
+
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_EQUAL(ref3(i), input(i));
+    VERIFY_IS_EQUAL(ref4(i), input(i));
+  }
+
+  for (int i = 0; i < 6; ++i) {
+    ref3.coeffRef(i) = i;
+  }
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_EQUAL(input(i), i);
+  }
+  for (int i = 0; i < 6; ++i) {
+    ref4.coeffRef(i) = -i * 2;
+  }
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_EQUAL(input(i), -i * 2);
+  }
+}
+
+static void test_simple_rvalue_ref() {
+  Tensor<int, 1> input1(6);
+  input1.setRandom();
+  Tensor<int, 1> input2(6);
+  input2.setRandom();
+
+  TensorRef<const Tensor<int, 1>> ref3(input1 + input2);
+  TensorRef<const Tensor<int, 1>> ref4 = input1 + input2;
+
+  VERIFY_IS_NOT_EQUAL(ref3.data(), input1.data());
+  VERIFY_IS_NOT_EQUAL(ref4.data(), input1.data());
+  VERIFY_IS_NOT_EQUAL(ref3.data(), input2.data());
+  VERIFY_IS_NOT_EQUAL(ref4.data(), input2.data());
+
+  for (int i = 0; i < 6; ++i) {
+    VERIFY_IS_EQUAL(ref3(i), input1(i) + input2(i));
+    VERIFY_IS_EQUAL(ref4(i), input1(i) + input2(i));
+  }
+}
+
+static void test_multiple_dims() {
+  Tensor<float, 3> input(3, 5, 7);
+  input.setRandom();
+
+  TensorRef<Tensor<float, 3>> ref(input);
+  VERIFY_IS_EQUAL(ref.data(), input.data());
+  VERIFY_IS_EQUAL(ref.dimension(0), 3);
+  VERIFY_IS_EQUAL(ref.dimension(1), 5);
+  VERIFY_IS_EQUAL(ref.dimension(2), 7);
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(ref(i, j, k), input(i, j, k));
+      }
+    }
+  }
+}
+
+static void test_slice() {
+  Tensor<float, 5> tensor(2, 3, 5, 7, 11);
+  tensor.setRandom();
+
+  Eigen::DSizes<ptrdiff_t, 5> indices(1, 2, 3, 4, 5);
+  Eigen::DSizes<ptrdiff_t, 5> sizes(1, 1, 1, 1, 1);
+  TensorRef<Tensor<float, 5>> slice = tensor.slice(indices, sizes);
+  VERIFY_IS_EQUAL(slice(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));
+
+  Eigen::DSizes<ptrdiff_t, 5> indices2(1, 1, 3, 4, 5);
+  Eigen::DSizes<ptrdiff_t, 5> sizes2(1, 1, 2, 2, 3);
+  slice = tensor.slice(indices2, sizes2);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 2; ++j) {
+      for (int k = 0; k < 3; ++k) {
+        VERIFY_IS_EQUAL(slice(0, 0, i, j, k), tensor(1, 1, 3 + i, 4 + j, 5 + k));
+      }
+    }
+  }
+
+  Eigen::DSizes<ptrdiff_t, 5> indices3(0, 0, 0, 0, 0);
+  Eigen::DSizes<ptrdiff_t, 5> sizes3(2, 3, 1, 1, 1);
+  slice = tensor.slice(indices3, sizes3);
+  VERIFY_IS_EQUAL(slice.data(), tensor.data());
+}
+
+static void test_ref_of_trace() {
+  Tensor<int, 2> input(6, 6);
+  input.setRandom();
+  int trace = 0;
+  for (int i = 0; i < 6; ++i) {
+    trace += input(i, i);
+  }
+  TensorRef<const Tensor<int, 0>> ref(input.trace());
+  VERIFY_IS_EQUAL(ref.coeff(0), trace);
+}
+
+static void test_ref_of_ref() {
+  Tensor<float, 3> input(3, 5, 7);
+  input.setRandom();
+
+  TensorRef<Tensor<float, 3>> ref(input);
+  TensorRef<Tensor<float, 3>> ref_of_ref(ref);
+  TensorRef<Tensor<float, 3>> ref_of_ref2;
+  ref_of_ref2 = ref;
+
+  VERIFY_IS_EQUAL(ref_of_ref.data(), input.data());
+  VERIFY_IS_EQUAL(ref_of_ref.dimension(0), 3);
+  VERIFY_IS_EQUAL(ref_of_ref.dimension(1), 5);
+  VERIFY_IS_EQUAL(ref_of_ref.dimension(2), 7);
+
+  VERIFY_IS_EQUAL(ref_of_ref2.data(), input.data());
+  VERIFY_IS_EQUAL(ref_of_ref2.dimension(0), 3);
+  VERIFY_IS_EQUAL(ref_of_ref2.dimension(1), 5);
+  VERIFY_IS_EQUAL(ref_of_ref2.dimension(2), 7);
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(ref_of_ref(i, j, k), input(i, j, k));
+        VERIFY_IS_EQUAL(ref_of_ref2(i, j, k), input(i, j, k));
+      }
+    }
+  }
+}
+
+static void test_ref_in_expr() {
+  Tensor<float, 3> input(3, 5, 7);
+  input.setRandom();
+  TensorRef<Tensor<float, 3>> input_ref(input);
+
+  Tensor<float, 3> result(3, 5, 7);
+  result.setRandom();
+  TensorRef<const Tensor<float, 3>> result_ref(result);
+
+  Tensor<float, 3> bias(3, 5, 7);
+  bias.setRandom();
+
+  result_ref = input_ref + bias;
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(result_ref(i, j, k), input(i, j, k) + bias(i, j, k));
+        VERIFY_IS_NOT_EQUAL(result(i, j, k), input(i, j, k) + bias(i, j, k));
+      }
+    }
+  }
+
+  result = result_ref;
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_EQUAL(result(i, j, k), input(i, j, k) + bias(i, j, k));
+      }
+    }
+  }
+}
+
+static void test_coeff_ref() {
+  Tensor<float, 5> tensor(2, 3, 5, 7, 11);
+  tensor.setRandom();
+  Tensor<float, 5> original = tensor;
+
+  TensorRef<Tensor<float, 4>> slice = tensor.chip(7, 4);
+  slice.coeffRef(0, 0, 0, 0) = 1.0f;
+  slice.coeffRef(1, 0, 0, 0) += 2.0f;
+
+  VERIFY_IS_EQUAL(tensor(0, 0, 0, 0, 7), 1.0f);
+  VERIFY_IS_EQUAL(tensor(1, 0, 0, 0, 7), original(1, 0, 0, 0, 7) + 2.0f);
+}
+
+static void test_nested_ops_with_ref() {
+  Tensor<float, 4> t(2, 3, 5, 7);
+  t.setRandom();
+  TensorMap<Tensor<const float, 4>> m(t.data(), 2, 3, 5, 7);
+  array<std::pair<ptrdiff_t, ptrdiff_t>, 4> paddings;
+  paddings[0] = std::make_pair(0, 0);
+  paddings[1] = std::make_pair(2, 1);
+  paddings[2] = std::make_pair(3, 4);
+  paddings[3] = std::make_pair(0, 0);
+  DSizes<Eigen::DenseIndex, 4> shuffle_dims(0, 1, 2, 3);
+  TensorRef<const Tensor<const float, 4>> ref(m.pad(paddings));
+  array<std::pair<ptrdiff_t, ptrdiff_t>, 4> trivial;
+  trivial[0] = std::make_pair(0, 0);
+  trivial[1] = std::make_pair(0, 0);
+  trivial[2] = std::make_pair(0, 0);
+  trivial[3] = std::make_pair(0, 0);
+  Tensor<float, 4> padded = ref.shuffle(shuffle_dims).pad(trivial);
+  VERIFY_IS_EQUAL(padded.dimension(0), 2 + 0);
+  VERIFY_IS_EQUAL(padded.dimension(1), 3 + 3);
+  VERIFY_IS_EQUAL(padded.dimension(2), 5 + 7);
+  VERIFY_IS_EQUAL(padded.dimension(3), 7 + 0);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 6; ++j) {
+      for (int k = 0; k < 12; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          if (j >= 2 && j < 5 && k >= 3 && k < 8) {
+            VERIFY_IS_EQUAL(padded(i, j, k, l), t(i, j - 2, k - 3, l));
+          } else {
+            VERIFY_IS_EQUAL(padded(i, j, k, l), 0.0f);
+          }
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_ref) {
+  CALL_SUBTEST(test_simple_lvalue_ref());
+  CALL_SUBTEST(test_simple_rvalue_ref());
+  CALL_SUBTEST(test_multiple_dims());
+  CALL_SUBTEST(test_slice());
+  CALL_SUBTEST(test_ref_of_trace());
+  CALL_SUBTEST(test_ref_of_ref());
+  CALL_SUBTEST(test_ref_in_expr());
+  CALL_SUBTEST(test_coeff_ref());
+  CALL_SUBTEST(test_nested_ops_with_ref());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reverse.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reverse.cpp
new file mode 100644
index 0000000..150c71b
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reverse.cpp
@@ -0,0 +1,179 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com and
+//                    Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_simple_reverse() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  array<bool, 4> dim_rev;
+  dim_rev[0] = false;
+  dim_rev[1] = true;
+  dim_rev[2] = true;
+  dim_rev[3] = false;
+
+  Tensor<float, 4, DataLayout> reversed_tensor;
+  reversed_tensor = tensor.reverse(dim_rev);
+
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2);
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3);
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5);
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), reversed_tensor(i, 2 - j, 4 - k, l));
+        }
+      }
+    }
+  }
+
+  dim_rev[0] = true;
+  dim_rev[1] = false;
+  dim_rev[2] = false;
+  dim_rev[3] = false;
+
+  reversed_tensor = tensor.reverse(dim_rev);
+
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2);
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3);
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5);
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), reversed_tensor(1 - i, j, k, l));
+        }
+      }
+    }
+  }
+
+  dim_rev[0] = true;
+  dim_rev[1] = false;
+  dim_rev[2] = false;
+  dim_rev[3] = true;
+
+  reversed_tensor = tensor.reverse(dim_rev);
+
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2);
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3);
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5);
+  VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), reversed_tensor(1 - i, j, k, 6 - l));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_expr_reverse(bool LValue) {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  array<bool, 4> dim_rev;
+  dim_rev[0] = false;
+  dim_rev[1] = true;
+  dim_rev[2] = false;
+  dim_rev[3] = true;
+
+  Tensor<float, 4, DataLayout> expected(2, 3, 5, 7);
+  if (LValue) {
+    expected.reverse(dim_rev) = tensor;
+  } else {
+    expected = tensor.reverse(dim_rev);
+  }
+
+  Tensor<float, 4, DataLayout> result(2, 3, 5, 7);
+
+  array<ptrdiff_t, 4> src_slice_dim;
+  src_slice_dim[0] = 2;
+  src_slice_dim[1] = 3;
+  src_slice_dim[2] = 1;
+  src_slice_dim[3] = 7;
+  array<ptrdiff_t, 4> src_slice_start;
+  src_slice_start[0] = 0;
+  src_slice_start[1] = 0;
+  src_slice_start[2] = 0;
+  src_slice_start[3] = 0;
+  array<ptrdiff_t, 4> dst_slice_dim = src_slice_dim;
+  array<ptrdiff_t, 4> dst_slice_start = src_slice_start;
+
+  for (int i = 0; i < 5; ++i) {
+    if (LValue) {
+      result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev) = tensor.slice(src_slice_start, src_slice_dim);
+    } else {
+      result.slice(dst_slice_start, dst_slice_dim) = tensor.slice(src_slice_start, src_slice_dim).reverse(dim_rev);
+    }
+    src_slice_start[2] += 1;
+    dst_slice_start[2] += 1;
+  }
+
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_EQUAL(result.dimension(1), 3);
+  VERIFY_IS_EQUAL(result.dimension(2), 5);
+  VERIFY_IS_EQUAL(result.dimension(3), 7);
+
+  for (int i = 0; i < expected.dimension(0); ++i) {
+    for (int j = 0; j < expected.dimension(1); ++j) {
+      for (int k = 0; k < expected.dimension(2); ++k) {
+        for (int l = 0; l < expected.dimension(3); ++l) {
+          VERIFY_IS_EQUAL(result(i, j, k, l), expected(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  dst_slice_start[2] = 0;
+  result.setRandom();
+  for (int i = 0; i < 5; ++i) {
+    if (LValue) {
+      result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev) = tensor.slice(dst_slice_start, dst_slice_dim);
+    } else {
+      result.slice(dst_slice_start, dst_slice_dim) = tensor.reverse(dim_rev).slice(dst_slice_start, dst_slice_dim);
+    }
+    dst_slice_start[2] += 1;
+  }
+
+  for (int i = 0; i < expected.dimension(0); ++i) {
+    for (int j = 0; j < expected.dimension(1); ++j) {
+      for (int k = 0; k < expected.dimension(2); ++k) {
+        for (int l = 0; l < expected.dimension(3); ++l) {
+          VERIFY_IS_EQUAL(result(i, j, k, l), expected(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_reverse) {
+  CALL_SUBTEST(test_simple_reverse<ColMajor>());
+  CALL_SUBTEST(test_simple_reverse<RowMajor>());
+  CALL_SUBTEST(test_expr_reverse<ColMajor>(true));
+  CALL_SUBTEST(test_expr_reverse<RowMajor>(true));
+  CALL_SUBTEST(test_expr_reverse<ColMajor>(false));
+  CALL_SUBTEST(test_expr_reverse<RowMajor>(false));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reverse_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reverse_sycl.cpp
new file mode 100644
index 0000000..c9c6172
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_reverse_sycl.cpp
@@ -0,0 +1,232 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_simple_reverse(const Eigen::SyclDevice& sycl_device) {
+  IndexType dim1 = 2;
+  IndexType dim2 = 3;
+  IndexType dim3 = 5;
+  IndexType dim4 = 7;
+
+  array<IndexType, 4> tensorRange = {{dim1, dim2, dim3, dim4}};
+  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
+  Tensor<DataType, 4, DataLayout, IndexType> reversed_tensor(tensorRange);
+  tensor.setRandom();
+
+  array<bool, 4> dim_rev;
+  dim_rev[0] = false;
+  dim_rev[1] = true;
+  dim_rev[2] = true;
+  dim_rev[3] = false;
+
+  DataType* gpu_in_data =
+      static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data =
+      static_cast<DataType*>(sycl_device.allocate(reversed_tensor.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> > out_gpu(gpu_out_data, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(), (tensor.dimensions().TotalSize()) * sizeof(DataType));
+  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
+  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data,
+                                 reversed_tensor.dimensions().TotalSize() * sizeof(DataType));
+  // Check that the CPU and GPU reductions return the same result.
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 5; ++k) {
+        for (IndexType l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), reversed_tensor(i, 2 - j, 4 - k, l));
+        }
+      }
+    }
+  }
+  dim_rev[0] = true;
+  dim_rev[1] = false;
+  dim_rev[2] = false;
+  dim_rev[3] = false;
+
+  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
+  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data,
+                                 reversed_tensor.dimensions().TotalSize() * sizeof(DataType));
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 5; ++k) {
+        for (IndexType l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), reversed_tensor(1 - i, j, k, l));
+        }
+      }
+    }
+  }
+
+  dim_rev[0] = true;
+  dim_rev[1] = false;
+  dim_rev[2] = false;
+  dim_rev[3] = true;
+  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
+  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data,
+                                 reversed_tensor.dimensions().TotalSize() * sizeof(DataType));
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 5; ++k) {
+        for (IndexType l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), reversed_tensor(1 - i, j, k, 6 - l));
+        }
+      }
+    }
+  }
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_expr_reverse(const Eigen::SyclDevice& sycl_device, bool LValue) {
+  IndexType dim1 = 2;
+  IndexType dim2 = 3;
+  IndexType dim3 = 5;
+  IndexType dim4 = 7;
+
+  array<IndexType, 4> tensorRange = {{dim1, dim2, dim3, dim4}};
+  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
+  Tensor<DataType, 4, DataLayout, IndexType> expected(tensorRange);
+  Tensor<DataType, 4, DataLayout, IndexType> result(tensorRange);
+  tensor.setRandom();
+
+  array<bool, 4> dim_rev;
+  dim_rev[0] = false;
+  dim_rev[1] = true;
+  dim_rev[2] = false;
+  dim_rev[3] = true;
+
+  DataType* gpu_in_data =
+      static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data_expected =
+      static_cast<DataType*>(sycl_device.allocate(expected.dimensions().TotalSize() * sizeof(DataType)));
+  DataType* gpu_out_data_result =
+      static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> > out_gpu_expected(gpu_out_data_expected, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> > out_gpu_result(gpu_out_data_result, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(), (tensor.dimensions().TotalSize()) * sizeof(DataType));
+
+  if (LValue) {
+    out_gpu_expected.reverse(dim_rev).device(sycl_device) = in_gpu;
+  } else {
+    out_gpu_expected.device(sycl_device) = in_gpu.reverse(dim_rev);
+  }
+  sycl_device.memcpyDeviceToHost(expected.data(), gpu_out_data_expected,
+                                 expected.dimensions().TotalSize() * sizeof(DataType));
+
+  array<IndexType, 4> src_slice_dim;
+  src_slice_dim[0] = 2;
+  src_slice_dim[1] = 3;
+  src_slice_dim[2] = 1;
+  src_slice_dim[3] = 7;
+  array<IndexType, 4> src_slice_start;
+  src_slice_start[0] = 0;
+  src_slice_start[1] = 0;
+  src_slice_start[2] = 0;
+  src_slice_start[3] = 0;
+  array<IndexType, 4> dst_slice_dim = src_slice_dim;
+  array<IndexType, 4> dst_slice_start = src_slice_start;
+
+  for (IndexType i = 0; i < 5; ++i) {
+    if (LValue) {
+      out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
+          in_gpu.slice(src_slice_start, src_slice_dim);
+    } else {
+      out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
+          in_gpu.slice(src_slice_start, src_slice_dim).reverse(dim_rev);
+    }
+    src_slice_start[2] += 1;
+    dst_slice_start[2] += 1;
+  }
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_out_data_result,
+                                 result.dimensions().TotalSize() * sizeof(DataType));
+
+  for (IndexType i = 0; i < expected.dimension(0); ++i) {
+    for (IndexType j = 0; j < expected.dimension(1); ++j) {
+      for (IndexType k = 0; k < expected.dimension(2); ++k) {
+        for (IndexType l = 0; l < expected.dimension(3); ++l) {
+          VERIFY_IS_EQUAL(result(i, j, k, l), expected(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  dst_slice_start[2] = 0;
+  result.setRandom();
+  sycl_device.memcpyHostToDevice(gpu_out_data_result, result.data(),
+                                 (result.dimensions().TotalSize()) * sizeof(DataType));
+  for (IndexType i = 0; i < 5; ++i) {
+    if (LValue) {
+      out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
+          in_gpu.slice(dst_slice_start, dst_slice_dim);
+    } else {
+      out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
+          in_gpu.reverse(dim_rev).slice(dst_slice_start, dst_slice_dim);
+    }
+    dst_slice_start[2] += 1;
+  }
+  sycl_device.memcpyDeviceToHost(result.data(), gpu_out_data_result,
+                                 result.dimensions().TotalSize() * sizeof(DataType));
+
+  for (IndexType i = 0; i < expected.dimension(0); ++i) {
+    for (IndexType j = 0; j < expected.dimension(1); ++j) {
+      for (IndexType k = 0; k < expected.dimension(2); ++k) {
+        for (IndexType l = 0; l < expected.dimension(3); ++l) {
+          VERIFY_IS_EQUAL(result(i, j, k, l), expected(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+template <typename DataType>
+void sycl_reverse_test_per_device(const cl::sycl::device& d) {
+  QueueInterface queueInterface(d);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_reverse<DataType, RowMajor, int64_t>(sycl_device);
+  test_simple_reverse<DataType, ColMajor, int64_t>(sycl_device);
+  test_expr_reverse<DataType, RowMajor, int64_t>(sycl_device, false);
+  test_expr_reverse<DataType, ColMajor, int64_t>(sycl_device, false);
+  test_expr_reverse<DataType, RowMajor, int64_t>(sycl_device, true);
+  test_expr_reverse<DataType, ColMajor, int64_t>(sycl_device, true);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_reverse_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    std::cout << "Running on " << device.get_info<cl::sycl::info::device::name>() << std::endl;
+    CALL_SUBTEST_1(sycl_reverse_test_per_device<short>(device));
+    CALL_SUBTEST_2(sycl_reverse_test_per_device<int>(device));
+    CALL_SUBTEST_3(sycl_reverse_test_per_device<unsigned int>(device));
+#ifdef EIGEN_SYCL_DOUBLE_SUPPORT
+    CALL_SUBTEST_4(sycl_reverse_test_per_device<double>(device));
+#endif
+    CALL_SUBTEST_5(sycl_reverse_test_per_device<half>(device));
+    CALL_SUBTEST_6(sycl_reverse_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_roll.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_roll.cpp
new file mode 100644
index 0000000..59f5efe
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_roll.cpp
@@ -0,0 +1,156 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2024 Tobias Wood tobias@spinicist.org.uk
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_simple_roll() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  array<Index, 4> dim_roll;
+  dim_roll[0] = 0;
+  dim_roll[1] = 1;
+  dim_roll[2] = 4;
+  dim_roll[3] = 8;
+
+  Tensor<float, 4, DataLayout> rolled_tensor;
+  rolled_tensor = tensor.roll(dim_roll);
+
+  VERIFY_IS_EQUAL(rolled_tensor.dimension(0), 2);
+  VERIFY_IS_EQUAL(rolled_tensor.dimension(1), 3);
+  VERIFY_IS_EQUAL(rolled_tensor.dimension(2), 5);
+  VERIFY_IS_EQUAL(rolled_tensor.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, (j + 1) % 3, (k + 4) % 5, (l + 8) % 7), rolled_tensor(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  dim_roll[0] = -3;
+  dim_roll[1] = -2;
+  dim_roll[2] = -1;
+  dim_roll[3] = 0;
+
+  rolled_tensor = tensor.roll(dim_roll);
+
+  VERIFY_IS_EQUAL(rolled_tensor.dimension(0), 2);
+  VERIFY_IS_EQUAL(rolled_tensor.dimension(1), 3);
+  VERIFY_IS_EQUAL(rolled_tensor.dimension(2), 5);
+  VERIFY_IS_EQUAL(rolled_tensor.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor((i + 1) % 2, (j + 1) % 3, (k + 4) % 5, l), rolled_tensor(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_expr_roll(bool LValue) {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  array<bool, 4> dim_roll;
+  dim_roll[0] = 2;
+  dim_roll[1] = 1;
+  dim_roll[2] = 0;
+  dim_roll[3] = 3;
+
+  Tensor<float, 4, DataLayout> expected(tensor.dimensions());
+  if (LValue) {
+    expected.roll(dim_roll) = tensor;
+  } else {
+    expected = tensor.roll(dim_roll);
+  }
+
+  Tensor<float, 4, DataLayout> result(tensor.dimensions());
+
+  array<ptrdiff_t, 4> src_slice_dim;
+  src_slice_dim[0] = tensor.dimension(0);
+  src_slice_dim[1] = tensor.dimension(1);
+  src_slice_dim[2] = 1;
+  src_slice_dim[3] = tensor.dimension(3);
+  array<ptrdiff_t, 4> src_slice_start;
+  src_slice_start[0] = 0;
+  src_slice_start[1] = 0;
+  src_slice_start[2] = 0;
+  src_slice_start[3] = 0;
+  array<ptrdiff_t, 4> dst_slice_dim = src_slice_dim;
+  array<ptrdiff_t, 4> dst_slice_start = src_slice_start;
+
+  for (int i = 0; i < tensor.dimension(2); ++i) {
+    if (LValue) {
+      result.slice(dst_slice_start, dst_slice_dim).roll(dim_roll) = tensor.slice(src_slice_start, src_slice_dim);
+    } else {
+      result.slice(dst_slice_start, dst_slice_dim) = tensor.slice(src_slice_start, src_slice_dim).roll(dim_roll);
+    }
+    src_slice_start[2] += 1;
+    dst_slice_start[2] += 1;
+  }
+
+  VERIFY_IS_EQUAL(result.dimension(0), tensor.dimension(0));
+  VERIFY_IS_EQUAL(result.dimension(1), tensor.dimension(1));
+  VERIFY_IS_EQUAL(result.dimension(2), tensor.dimension(2));
+  VERIFY_IS_EQUAL(result.dimension(3), tensor.dimension(3));
+
+  for (int i = 0; i < expected.dimension(0); ++i) {
+    for (int j = 0; j < expected.dimension(1); ++j) {
+      for (int k = 0; k < expected.dimension(2); ++k) {
+        for (int l = 0; l < expected.dimension(3); ++l) {
+          VERIFY_IS_EQUAL(result(i, j, k, l), expected(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  dst_slice_start[2] = 0;
+  result.setRandom();
+  for (int i = 0; i < tensor.dimension(2); ++i) {
+    if (LValue) {
+      result.slice(dst_slice_start, dst_slice_dim).roll(dim_roll) = tensor.slice(dst_slice_start, dst_slice_dim);
+    } else {
+      result.slice(dst_slice_start, dst_slice_dim) = tensor.roll(dim_roll).slice(dst_slice_start, dst_slice_dim);
+    }
+    dst_slice_start[2] += 1;
+  }
+
+  for (int i = 0; i < expected.dimension(0); ++i) {
+    for (int j = 0; j < expected.dimension(1); ++j) {
+      for (int k = 0; k < expected.dimension(2); ++k) {
+        for (int l = 0; l < expected.dimension(3); ++l) {
+          VERIFY_IS_EQUAL(result(i, j, k, l), expected(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_roll) {
+  CALL_SUBTEST(test_simple_roll<ColMajor>());
+  CALL_SUBTEST(test_simple_roll<RowMajor>());
+  CALL_SUBTEST(test_expr_roll<ColMajor>(true));
+  CALL_SUBTEST(test_expr_roll<RowMajor>(true));
+  CALL_SUBTEST(test_expr_roll<ColMajor>(false));
+  CALL_SUBTEST(test_expr_roll<RowMajor>(false));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_roundings.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_roundings.cpp
new file mode 100644
index 0000000..2c1181a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_roundings.cpp
@@ -0,0 +1,57 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+static void test_float_rounding() {
+  Tensor<float, 2> ftensor(20, 30);
+  ftensor = ftensor.random() * 100.f;
+
+  Tensor<float, 2> result = ftensor.round();
+
+  for (int i = 0; i < 20; ++i) {
+    for (int j = 0; j < 30; ++j) {
+      VERIFY_IS_EQUAL(result(i, j), numext::round(ftensor(i, j)));
+    }
+  }
+}
+
+static void test_float_flooring() {
+  Tensor<float, 2> ftensor(20, 30);
+  ftensor = ftensor.random() * 100.f;
+
+  Tensor<float, 2> result = ftensor.floor();
+
+  for (int i = 0; i < 20; ++i) {
+    for (int j = 0; j < 30; ++j) {
+      VERIFY_IS_EQUAL(result(i, j), numext::floor(ftensor(i, j)));
+    }
+  }
+}
+
+static void test_float_ceiling() {
+  Tensor<float, 2> ftensor(20, 30);
+  ftensor = ftensor.random() * 100.f;
+
+  Tensor<float, 2> result = ftensor.ceil();
+
+  for (int i = 0; i < 20; ++i) {
+    for (int j = 0; j < 30; ++j) {
+      VERIFY_IS_EQUAL(result(i, j), numext::ceil(ftensor(i, j)));
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_roundings) {
+  CALL_SUBTEST(test_float_rounding());
+  CALL_SUBTEST(test_float_ceiling());
+  CALL_SUBTEST(test_float_flooring());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_scan.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_scan.cpp
new file mode 100644
index 0000000..50ec63d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_scan.cpp
@@ -0,0 +1,108 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Igor Babuschkin <igor@babuschk.in>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <limits>
+#include <numeric>
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout, typename Type = float, bool Exclusive = false>
+static void test_1d_scan() {
+  int size = 50;
+  Tensor<Type, 1, DataLayout> tensor(size);
+  tensor.setRandom();
+  Tensor<Type, 1, DataLayout> result = tensor.cumsum(0, Exclusive);
+
+  VERIFY_IS_EQUAL(tensor.dimension(0), result.dimension(0));
+
+  float accum = 0;
+  for (int i = 0; i < size; i++) {
+    if (Exclusive) {
+      VERIFY_IS_EQUAL(result(i), accum);
+      accum += tensor(i);
+    } else {
+      accum += tensor(i);
+      VERIFY_IS_EQUAL(result(i), accum);
+    }
+  }
+
+  accum = 1;
+  result = tensor.cumprod(0, Exclusive);
+  for (int i = 0; i < size; i++) {
+    if (Exclusive) {
+      VERIFY_IS_EQUAL(result(i), accum);
+      accum *= tensor(i);
+    } else {
+      accum *= tensor(i);
+      VERIFY_IS_EQUAL(result(i), accum);
+    }
+  }
+}
+
+template <int DataLayout, typename Type = float>
+static void test_4d_scan() {
+  int size = 5;
+  Tensor<Type, 4, DataLayout> tensor(size, size, size, size);
+  tensor.setRandom();
+
+  Tensor<Type, 4, DataLayout> result(size, size, size, size);
+
+  result = tensor.cumsum(0);
+  float accum = 0;
+  for (int i = 0; i < size; i++) {
+    accum += tensor(i, 1, 2, 3);
+    VERIFY_IS_EQUAL(result(i, 1, 2, 3), accum);
+  }
+  result = tensor.cumsum(1);
+  accum = 0;
+  for (int i = 0; i < size; i++) {
+    accum += tensor(1, i, 2, 3);
+    VERIFY_IS_EQUAL(result(1, i, 2, 3), accum);
+  }
+  result = tensor.cumsum(2);
+  accum = 0;
+  for (int i = 0; i < size; i++) {
+    accum += tensor(1, 2, i, 3);
+    VERIFY_IS_EQUAL(result(1, 2, i, 3), accum);
+  }
+  result = tensor.cumsum(3);
+  accum = 0;
+  for (int i = 0; i < size; i++) {
+    accum += tensor(1, 2, 3, i);
+    VERIFY_IS_EQUAL(result(1, 2, 3, i), accum);
+  }
+}
+
+template <int DataLayout>
+static void test_tensor_maps() {
+  int inputs[20];
+  TensorMap<Tensor<int, 1, DataLayout> > tensor_map(inputs, 20);
+  tensor_map.setRandom();
+
+  Tensor<int, 1, DataLayout> result = tensor_map.cumsum(0);
+
+  int accum = 0;
+  for (int i = 0; i < 20; ++i) {
+    accum += tensor_map(i);
+    VERIFY_IS_EQUAL(result(i), accum);
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_scan) {
+  CALL_SUBTEST((test_1d_scan<ColMajor, float, true>()));
+  CALL_SUBTEST((test_1d_scan<ColMajor, float, false>()));
+  CALL_SUBTEST((test_1d_scan<RowMajor, float, true>()));
+  CALL_SUBTEST((test_1d_scan<RowMajor, float, false>()));
+  CALL_SUBTEST(test_4d_scan<ColMajor>());
+  CALL_SUBTEST(test_4d_scan<RowMajor>());
+  CALL_SUBTEST(test_tensor_maps<ColMajor>());
+  CALL_SUBTEST(test_tensor_maps<RowMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_scan_gpu.cu b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_scan_gpu.cu
new file mode 100644
index 0000000..b94ab31
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_scan_gpu.cu
@@ -0,0 +1,72 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_GPU
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+typedef Tensor<float, 1>::DimensionPair DimPair;
+
+template <int DataLayout>
+void test_gpu_cumsum(int m_size, int k_size, int n_size) {
+  std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
+  Tensor<float, 3, DataLayout> t_input(m_size, k_size, n_size);
+  Tensor<float, 3, DataLayout> t_result(m_size, k_size, n_size);
+  Tensor<float, 3, DataLayout> t_result_gpu(m_size, k_size, n_size);
+
+  t_input.setRandom();
+
+  std::size_t t_input_bytes = t_input.size() * sizeof(float);
+  std::size_t t_result_bytes = t_result.size() * sizeof(float);
+
+  float* d_t_input;
+  float* d_t_result;
+
+  gpuMalloc((void**)(&d_t_input), t_input_bytes);
+  gpuMalloc((void**)(&d_t_result), t_result_bytes);
+
+  gpuMemcpy(d_t_input, t_input.data(), t_input_bytes, gpuMemcpyHostToDevice);
+
+  Eigen::GpuStreamDevice stream;
+  Eigen::GpuDevice gpu_device(&stream);
+
+  Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> > gpu_t_input(d_t_input,
+                                                                     Eigen::array<int, 3>{m_size, k_size, n_size});
+  Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> > gpu_t_result(d_t_result,
+                                                                      Eigen::array<int, 3>{m_size, k_size, n_size});
+
+  gpu_t_result.device(gpu_device) = gpu_t_input.cumsum(1);
+  t_result = t_input.cumsum(1);
+
+  gpuMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, gpuMemcpyDeviceToHost);
+  for (DenseIndex i = 0; i < t_result.size(); i++) {
+    if (fabs(t_result(i) - t_result_gpu(i)) < 1e-4f) {
+      continue;
+    }
+    if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) {
+      continue;
+    }
+    std::cout << "mismatch detected at index " << i << ": " << t_result(i) << " vs " << t_result_gpu(i) << std::endl;
+    assert(false);
+  }
+
+  gpuFree((void*)d_t_input);
+  gpuFree((void*)d_t_result);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_scan_gpu) {
+  CALL_SUBTEST_1(test_gpu_cumsum<ColMajor>(128, 128, 128));
+  CALL_SUBTEST_2(test_gpu_cumsum<RowMajor>(128, 128, 128));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_scan_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_scan_sycl.cpp
new file mode 100644
index 0000000..13f7c9e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_scan_sycl.cpp
@@ -0,0 +1,112 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+typedef Tensor<float, 1>::DimensionPair DimPair;
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_sycl_cumsum(const Eigen::SyclDevice& sycl_device, IndexType m_size, IndexType k_size, IndexType n_size,
+                      int consume_dim, bool exclusive) {
+  static const DataType error_threshold = 1e-4f;
+  std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << " consume_dim : " << consume_dim << ")"
+            << std::endl;
+  Tensor<DataType, 3, DataLayout, IndexType> t_input(m_size, k_size, n_size);
+  Tensor<DataType, 3, DataLayout, IndexType> t_result(m_size, k_size, n_size);
+  Tensor<DataType, 3, DataLayout, IndexType> t_result_gpu(m_size, k_size, n_size);
+
+  t_input.setRandom();
+  std::size_t t_input_bytes = t_input.size() * sizeof(DataType);
+  std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
+
+  DataType* gpu_data_in = static_cast<DataType*>(sycl_device.allocate(t_input_bytes));
+  DataType* gpu_data_out = static_cast<DataType*>(sycl_device.allocate(t_result_bytes));
+
+  array<IndexType, 3> tensorRange = {{m_size, k_size, n_size}};
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_t_input(gpu_data_in, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_t_result(gpu_data_out, tensorRange);
+  sycl_device.memcpyHostToDevice(gpu_data_in, t_input.data(), t_input_bytes);
+  sycl_device.memcpyHostToDevice(gpu_data_out, t_input.data(), t_input_bytes);
+
+  gpu_t_result.device(sycl_device) = gpu_t_input.cumsum(consume_dim, exclusive);
+
+  t_result = t_input.cumsum(consume_dim, exclusive);
+
+  sycl_device.memcpyDeviceToHost(t_result_gpu.data(), gpu_data_out, t_result_bytes);
+  sycl_device.synchronize();
+
+  for (IndexType i = 0; i < t_result.size(); i++) {
+    if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
+      continue;
+    }
+    if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
+      continue;
+    }
+    std::cout << "mismatch detected at index " << i << " CPU : " << t_result(i) << " vs SYCL : " << t_result_gpu(i)
+              << std::endl;
+    assert(false);
+  }
+  sycl_device.deallocate(gpu_data_in);
+  sycl_device.deallocate(gpu_data_out);
+}
+
+template <typename DataType, typename Dev>
+void sycl_scan_test_exclusive_dim0_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, true);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, true);
+}
+template <typename DataType, typename Dev>
+void sycl_scan_test_exclusive_dim1_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, true);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, true);
+}
+template <typename DataType, typename Dev>
+void sycl_scan_test_exclusive_dim2_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, true);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, true);
+}
+template <typename DataType, typename Dev>
+void sycl_scan_test_inclusive_dim0_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, false);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, false);
+}
+template <typename DataType, typename Dev>
+void sycl_scan_test_inclusive_dim1_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, false);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, false);
+}
+template <typename DataType, typename Dev>
+void sycl_scan_test_inclusive_dim2_per_device(const Dev& sycl_device) {
+  test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, false);
+  test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, false);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_scan_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    std::cout << "Running on " << device.template get_info<cl::sycl::info::device::name>() << std::endl;
+    QueueInterface queueInterface(device);
+    auto sycl_device = Eigen::SyclDevice(&queueInterface);
+    CALL_SUBTEST_1(sycl_scan_test_exclusive_dim0_per_device<float>(sycl_device));
+    CALL_SUBTEST_2(sycl_scan_test_exclusive_dim1_per_device<float>(sycl_device));
+    CALL_SUBTEST_3(sycl_scan_test_exclusive_dim2_per_device<float>(sycl_device));
+    CALL_SUBTEST_4(sycl_scan_test_inclusive_dim0_per_device<float>(sycl_device));
+    CALL_SUBTEST_5(sycl_scan_test_inclusive_dim1_per_device<float>(sycl_device));
+    CALL_SUBTEST_6(sycl_scan_test_inclusive_dim2_per_device<float>(sycl_device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_shuffling.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_shuffling.cpp
new file mode 100644
index 0000000..4e6fb73
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_shuffling.cpp
@@ -0,0 +1,270 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_simple_shuffling() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> shuffles;
+  shuffles[0] = 0;
+  shuffles[1] = 1;
+  shuffles[2] = 2;
+  shuffles[3] = 3;
+
+  Tensor<float, 4, DataLayout> no_shuffle;
+  no_shuffle = tensor.shuffle(shuffles);
+
+  VERIFY_IS_EQUAL(no_shuffle.dimension(0), 2);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(1), 3);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(2), 5);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), no_shuffle(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  shuffles[0] = 2;
+  shuffles[1] = 3;
+  shuffles[2] = 1;
+  shuffles[3] = 0;
+  Tensor<float, 4, DataLayout> shuffle;
+  shuffle = tensor.shuffle(shuffles);
+
+  VERIFY_IS_EQUAL(shuffle.dimension(0), 5);
+  VERIFY_IS_EQUAL(shuffle.dimension(1), 7);
+  VERIFY_IS_EQUAL(shuffle.dimension(2), 3);
+  VERIFY_IS_EQUAL(shuffle.dimension(3), 2);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, l, j, i));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_expr_shuffling() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  array<ptrdiff_t, 4> shuffles;
+  shuffles[0] = 2;
+  shuffles[1] = 3;
+  shuffles[2] = 1;
+  shuffles[3] = 0;
+  Tensor<float, 4, DataLayout> expected;
+  expected = tensor.shuffle(shuffles);
+
+  Tensor<float, 4, DataLayout> result(5, 7, 3, 2);
+
+  array<ptrdiff_t, 4> src_slice_dim{{2, 3, 1, 7}};
+  array<ptrdiff_t, 4> src_slice_start{{0, 0, 0, 0}};
+  array<ptrdiff_t, 4> dst_slice_dim{{1, 7, 3, 2}};
+  array<ptrdiff_t, 4> dst_slice_start{{0, 0, 0, 0}};
+
+  for (int i = 0; i < 5; ++i) {
+    result.slice(dst_slice_start, dst_slice_dim) = tensor.slice(src_slice_start, src_slice_dim).shuffle(shuffles);
+    src_slice_start[2] += 1;
+    dst_slice_start[0] += 1;
+  }
+
+  VERIFY_IS_EQUAL(result.dimension(0), 5);
+  VERIFY_IS_EQUAL(result.dimension(1), 7);
+  VERIFY_IS_EQUAL(result.dimension(2), 3);
+  VERIFY_IS_EQUAL(result.dimension(3), 2);
+
+  for (int i = 0; i < expected.dimension(0); ++i) {
+    for (int j = 0; j < expected.dimension(1); ++j) {
+      for (int k = 0; k < expected.dimension(2); ++k) {
+        for (int l = 0; l < expected.dimension(3); ++l) {
+          VERIFY_IS_EQUAL(result(i, j, k, l), expected(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  dst_slice_start[0] = 0;
+  result.setRandom();
+  for (int i = 0; i < 5; ++i) {
+    result.slice(dst_slice_start, dst_slice_dim) = tensor.shuffle(shuffles).slice(dst_slice_start, dst_slice_dim);
+    dst_slice_start[0] += 1;
+  }
+
+  for (int i = 0; i < expected.dimension(0); ++i) {
+    for (int j = 0; j < expected.dimension(1); ++j) {
+      for (int k = 0; k < expected.dimension(2); ++k) {
+        for (int l = 0; l < expected.dimension(3); ++l) {
+          VERIFY_IS_EQUAL(result(i, j, k, l), expected(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_shuffling_as_value() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> shuffles;
+  shuffles[2] = 0;
+  shuffles[3] = 1;
+  shuffles[1] = 2;
+  shuffles[0] = 3;
+  Tensor<float, 4, DataLayout> shuffle(5, 7, 3, 2);
+  shuffle.shuffle(shuffles) = tensor;
+
+  VERIFY_IS_EQUAL(shuffle.dimension(0), 5);
+  VERIFY_IS_EQUAL(shuffle.dimension(1), 7);
+  VERIFY_IS_EQUAL(shuffle.dimension(2), 3);
+  VERIFY_IS_EQUAL(shuffle.dimension(3), 2);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, l, j, i));
+        }
+      }
+    }
+  }
+
+  array<ptrdiff_t, 4> no_shuffle;
+  no_shuffle[0] = 0;
+  no_shuffle[1] = 1;
+  no_shuffle[2] = 2;
+  no_shuffle[3] = 3;
+  Tensor<float, 4, DataLayout> shuffle2(5, 7, 3, 2);
+  shuffle2.shuffle(shuffles) = tensor.shuffle(no_shuffle);
+  for (int i = 0; i < 5; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 2; ++l) {
+          VERIFY_IS_EQUAL(shuffle2(i, j, k, l), shuffle(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_shuffle_unshuffle() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+
+  // Choose a random permutation.
+  array<ptrdiff_t, 4> shuffles;
+  for (int i = 0; i < 4; ++i) {
+    shuffles[i] = i;
+  }
+  array<ptrdiff_t, 4> shuffles_inverse;
+  for (int i = 0; i < 4; ++i) {
+    const ptrdiff_t index = internal::random<ptrdiff_t>(i, 3);
+    shuffles_inverse[shuffles[index]] = i;
+    std::swap(shuffles[i], shuffles[index]);
+  }
+
+  Tensor<float, 4, DataLayout> shuffle;
+  shuffle = tensor.shuffle(shuffles).shuffle(shuffles_inverse);
+
+  VERIFY_IS_EQUAL(shuffle.dimension(0), 2);
+  VERIFY_IS_EQUAL(shuffle.dimension(1), 3);
+  VERIFY_IS_EQUAL(shuffle.dimension(2), 5);
+  VERIFY_IS_EQUAL(shuffle.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_empty_shuffling() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 0, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> shuffles;
+  shuffles[0] = 0;
+  shuffles[1] = 1;
+  shuffles[2] = 2;
+  shuffles[3] = 3;
+
+  Tensor<float, 4, DataLayout> no_shuffle;
+  no_shuffle = tensor.shuffle(shuffles);
+
+  VERIFY_IS_EQUAL(no_shuffle.dimension(0), 2);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(1), 3);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(2), 0);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 0; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), no_shuffle(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  shuffles[0] = 2;
+  shuffles[1] = 3;
+  shuffles[2] = 1;
+  shuffles[3] = 0;
+  Tensor<float, 4, DataLayout> shuffle;
+  shuffle = tensor.shuffle(shuffles);
+
+  VERIFY_IS_EQUAL(shuffle.dimension(0), 0);
+  VERIFY_IS_EQUAL(shuffle.dimension(1), 7);
+  VERIFY_IS_EQUAL(shuffle.dimension(2), 3);
+  VERIFY_IS_EQUAL(shuffle.dimension(3), 2);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 0; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, l, j, i));
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_shuffling) {
+  CALL_SUBTEST(test_simple_shuffling<ColMajor>());
+  CALL_SUBTEST(test_simple_shuffling<RowMajor>());
+  CALL_SUBTEST(test_expr_shuffling<ColMajor>());
+  CALL_SUBTEST(test_expr_shuffling<RowMajor>());
+  CALL_SUBTEST(test_shuffling_as_value<ColMajor>());
+  CALL_SUBTEST(test_shuffling_as_value<RowMajor>());
+  CALL_SUBTEST(test_shuffle_unshuffle<ColMajor>());
+  CALL_SUBTEST(test_shuffle_unshuffle<RowMajor>());
+  CALL_SUBTEST(test_empty_shuffling<ColMajor>());
+  CALL_SUBTEST(test_empty_shuffling<RowMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_shuffling_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_shuffling_sycl.cpp
new file mode 100644
index 0000000..0f88f52
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_shuffling_sycl.cpp
@@ -0,0 +1,114 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
+  Tensor<DataType, 4, DataLayout, IndexType> no_shuffle(tensorRange);
+  tensor.setRandom();
+
+  const size_t buffSize = tensor.size() * sizeof(DataType);
+  array<IndexType, 4> shuffles;
+  shuffles[0] = 0;
+  shuffles[1] = 1;
+  shuffles[2] = 2;
+  shuffles[3] = 3;
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(buffSize));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(buffSize));
+
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), buffSize);
+
+  gpu2.device(sycl_device) = gpu1.shuffle(shuffles);
+  sycl_device.memcpyDeviceToHost(no_shuffle.data(), gpu_data2, buffSize);
+  sycl_device.synchronize();
+
+  VERIFY_IS_EQUAL(no_shuffle.dimension(0), sizeDim1);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(1), sizeDim2);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(2), sizeDim3);
+  VERIFY_IS_EQUAL(no_shuffle.dimension(3), sizeDim4);
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        for (IndexType l = 0; l < sizeDim4; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), no_shuffle(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  shuffles[0] = 2;
+  shuffles[1] = 3;
+  shuffles[2] = 1;
+  shuffles[3] = 0;
+  array<IndexType, 4> tensorrangeShuffle = {{sizeDim3, sizeDim4, sizeDim2, sizeDim1}};
+  Tensor<DataType, 4, DataLayout, IndexType> shuffle(tensorrangeShuffle);
+  DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(buffSize));
+  TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu3(gpu_data3, tensorrangeShuffle);
+
+  gpu3.device(sycl_device) = gpu1.shuffle(shuffles);
+  sycl_device.memcpyDeviceToHost(shuffle.data(), gpu_data3, buffSize);
+  sycl_device.synchronize();
+
+  VERIFY_IS_EQUAL(shuffle.dimension(0), sizeDim3);
+  VERIFY_IS_EQUAL(shuffle.dimension(1), sizeDim4);
+  VERIFY_IS_EQUAL(shuffle.dimension(2), sizeDim2);
+  VERIFY_IS_EQUAL(shuffle.dimension(3), sizeDim1);
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        for (IndexType l = 0; l < sizeDim4; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, l, j, i));
+        }
+      }
+    }
+  }
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_shuffling_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_shuffling_sycl<DataType, RowMajor, int64_t>(sycl_device);
+  test_simple_shuffling_sycl<DataType, ColMajor, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_shuffling_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_shuffling_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_shuffling_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_simple.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_simple.cpp
new file mode 100644
index 0000000..1686af3
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_simple.cpp
@@ -0,0 +1,332 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void test_0d() {
+  Tensor<int, 0> scalar1;
+  Tensor<int, 0, RowMajor> scalar2;
+  Tensor<int, 0> scalar3;
+  Tensor<int, 0, RowMajor> scalar4;
+
+  scalar3.resize();
+  scalar4.resize();
+
+  scalar1() = 7;
+  scalar2() = 13;
+  scalar3.setValues(17);
+  scalar4.setZero();
+
+  VERIFY_IS_EQUAL(scalar1.rank(), 0);
+  VERIFY_IS_EQUAL(scalar1.size(), 1);
+
+  VERIFY_IS_EQUAL(scalar1(), 7);
+  VERIFY_IS_EQUAL(scalar2(), 13);
+  VERIFY_IS_EQUAL(scalar3(), 17);
+  VERIFY_IS_EQUAL(scalar4(), 0);
+
+  Tensor<int, 0> scalar5(scalar1);
+
+  VERIFY_IS_EQUAL(scalar5(), 7);
+  VERIFY_IS_EQUAL(scalar5.data()[0], 7);
+}
+
+static void test_1d() {
+  Tensor<int, 1> vec1(6);
+  Tensor<int, 1, RowMajor> vec2(6);
+  Tensor<int, 1> vec3;
+  Tensor<int, 1, RowMajor> vec4;
+
+  vec3.resize(6);
+  vec4.resize(6);
+
+  vec1(0) = 4;
+  vec2(0) = 0;
+  vec3(0) = 5;
+  vec1(1) = 8;
+  vec2(1) = 1;
+  vec3(1) = 4;
+  vec1(2) = 15;
+  vec2(2) = 2;
+  vec3(2) = 3;
+  vec1(3) = 16;
+  vec2(3) = 3;
+  vec3(3) = 2;
+  vec1(4) = 23;
+  vec2(4) = 4;
+  vec3(4) = 1;
+  vec1(5) = 42;
+  vec2(5) = 5;
+  vec3(5) = 0;
+  vec4.setZero();
+
+  VERIFY_IS_EQUAL((vec1.rank()), 1);
+  VERIFY_IS_EQUAL((vec1.size()), 6);
+  VERIFY_IS_EQUAL((vec1.dimensions()[0]), 6);
+
+  VERIFY_IS_EQUAL((vec1[0]), 4);
+  VERIFY_IS_EQUAL((vec1[1]), 8);
+  VERIFY_IS_EQUAL((vec1[2]), 15);
+  VERIFY_IS_EQUAL((vec1[3]), 16);
+  VERIFY_IS_EQUAL((vec1[4]), 23);
+  VERIFY_IS_EQUAL((vec1[5]), 42);
+
+  VERIFY_IS_EQUAL((vec2[0]), 0);
+  VERIFY_IS_EQUAL((vec2[1]), 1);
+  VERIFY_IS_EQUAL((vec2[2]), 2);
+  VERIFY_IS_EQUAL((vec2[3]), 3);
+  VERIFY_IS_EQUAL((vec2[4]), 4);
+  VERIFY_IS_EQUAL((vec2[5]), 5);
+
+  VERIFY_IS_EQUAL((vec3[0]), 5);
+  VERIFY_IS_EQUAL((vec3[1]), 4);
+  VERIFY_IS_EQUAL((vec3[2]), 3);
+  VERIFY_IS_EQUAL((vec3[3]), 2);
+  VERIFY_IS_EQUAL((vec3[4]), 1);
+  VERIFY_IS_EQUAL((vec3[5]), 0);
+
+  VERIFY_IS_EQUAL((vec4[0]), 0);
+  VERIFY_IS_EQUAL((vec4[1]), 0);
+  VERIFY_IS_EQUAL((vec4[2]), 0);
+  VERIFY_IS_EQUAL((vec4[3]), 0);
+  VERIFY_IS_EQUAL((vec4[4]), 0);
+  VERIFY_IS_EQUAL((vec4[5]), 0);
+
+  Tensor<int, 1> vec5(vec1);
+
+  VERIFY_IS_EQUAL((vec5(0)), 4);
+  VERIFY_IS_EQUAL((vec5(1)), 8);
+  VERIFY_IS_EQUAL((vec5(2)), 15);
+  VERIFY_IS_EQUAL((vec5(3)), 16);
+  VERIFY_IS_EQUAL((vec5(4)), 23);
+  VERIFY_IS_EQUAL((vec5(5)), 42);
+
+  VERIFY_IS_EQUAL((vec5.data()[0]), 4);
+  VERIFY_IS_EQUAL((vec5.data()[1]), 8);
+  VERIFY_IS_EQUAL((vec5.data()[2]), 15);
+  VERIFY_IS_EQUAL((vec5.data()[3]), 16);
+  VERIFY_IS_EQUAL((vec5.data()[4]), 23);
+  VERIFY_IS_EQUAL((vec5.data()[5]), 42);
+}
+
+static void test_2d() {
+  Tensor<int, 2> mat1(2, 3);
+  Tensor<int, 2, RowMajor> mat2(2, 3);
+
+  mat1(0, 0) = 0;
+  mat1(0, 1) = 1;
+  mat1(0, 2) = 2;
+  mat1(1, 0) = 3;
+  mat1(1, 1) = 4;
+  mat1(1, 2) = 5;
+
+  mat2(0, 0) = 0;
+  mat2(0, 1) = 1;
+  mat2(0, 2) = 2;
+  mat2(1, 0) = 3;
+  mat2(1, 1) = 4;
+  mat2(1, 2) = 5;
+
+  VERIFY_IS_EQUAL((mat1.rank()), 2);
+  VERIFY_IS_EQUAL((mat1.size()), 6);
+  VERIFY_IS_EQUAL((mat1.dimensions()[0]), 2);
+  VERIFY_IS_EQUAL((mat1.dimensions()[1]), 3);
+
+  VERIFY_IS_EQUAL((mat2.rank()), 2);
+  VERIFY_IS_EQUAL((mat2.size()), 6);
+  VERIFY_IS_EQUAL((mat2.dimensions()[0]), 2);
+  VERIFY_IS_EQUAL((mat2.dimensions()[1]), 3);
+
+  VERIFY_IS_EQUAL((mat1.data()[0]), 0);
+  VERIFY_IS_EQUAL((mat1.data()[1]), 3);
+  VERIFY_IS_EQUAL((mat1.data()[2]), 1);
+  VERIFY_IS_EQUAL((mat1.data()[3]), 4);
+  VERIFY_IS_EQUAL((mat1.data()[4]), 2);
+  VERIFY_IS_EQUAL((mat1.data()[5]), 5);
+
+  VERIFY_IS_EQUAL((mat2.data()[0]), 0);
+  VERIFY_IS_EQUAL((mat2.data()[1]), 1);
+  VERIFY_IS_EQUAL((mat2.data()[2]), 2);
+  VERIFY_IS_EQUAL((mat2.data()[3]), 3);
+  VERIFY_IS_EQUAL((mat2.data()[4]), 4);
+  VERIFY_IS_EQUAL((mat2.data()[5]), 5);
+}
+
+static void test_3d() {
+  Tensor<int, 3> epsilon(3, 3, 3);
+  epsilon.setZero();
+  epsilon(0, 1, 2) = epsilon(2, 0, 1) = epsilon(1, 2, 0) = 1;
+  epsilon(2, 1, 0) = epsilon(0, 2, 1) = epsilon(1, 0, 2) = -1;
+
+  VERIFY_IS_EQUAL((epsilon.size()), 27);
+  VERIFY_IS_EQUAL((epsilon.dimensions()[0]), 3);
+  VERIFY_IS_EQUAL((epsilon.dimensions()[1]), 3);
+  VERIFY_IS_EQUAL((epsilon.dimensions()[2]), 3);
+
+  VERIFY_IS_EQUAL((epsilon(0, 0, 0)), 0);
+  VERIFY_IS_EQUAL((epsilon(0, 0, 1)), 0);
+  VERIFY_IS_EQUAL((epsilon(0, 0, 2)), 0);
+  VERIFY_IS_EQUAL((epsilon(0, 1, 0)), 0);
+  VERIFY_IS_EQUAL((epsilon(0, 1, 1)), 0);
+  VERIFY_IS_EQUAL((epsilon(0, 2, 0)), 0);
+  VERIFY_IS_EQUAL((epsilon(0, 2, 2)), 0);
+  VERIFY_IS_EQUAL((epsilon(1, 0, 0)), 0);
+  VERIFY_IS_EQUAL((epsilon(1, 0, 1)), 0);
+  VERIFY_IS_EQUAL((epsilon(1, 1, 0)), 0);
+  VERIFY_IS_EQUAL((epsilon(1, 1, 1)), 0);
+  VERIFY_IS_EQUAL((epsilon(1, 1, 2)), 0);
+  VERIFY_IS_EQUAL((epsilon(1, 2, 1)), 0);
+  VERIFY_IS_EQUAL((epsilon(1, 2, 2)), 0);
+  VERIFY_IS_EQUAL((epsilon(2, 0, 0)), 0);
+  VERIFY_IS_EQUAL((epsilon(2, 0, 2)), 0);
+  VERIFY_IS_EQUAL((epsilon(2, 1, 1)), 0);
+  VERIFY_IS_EQUAL((epsilon(2, 1, 2)), 0);
+  VERIFY_IS_EQUAL((epsilon(2, 2, 0)), 0);
+  VERIFY_IS_EQUAL((epsilon(2, 2, 1)), 0);
+  VERIFY_IS_EQUAL((epsilon(2, 2, 2)), 0);
+
+  VERIFY_IS_EQUAL((epsilon(0, 1, 2)), 1);
+  VERIFY_IS_EQUAL((epsilon(2, 0, 1)), 1);
+  VERIFY_IS_EQUAL((epsilon(1, 2, 0)), 1);
+  VERIFY_IS_EQUAL((epsilon(2, 1, 0)), -1);
+  VERIFY_IS_EQUAL((epsilon(0, 2, 1)), -1);
+  VERIFY_IS_EQUAL((epsilon(1, 0, 2)), -1);
+
+  array<Eigen::DenseIndex, 3> dims;
+  dims[0] = 2;
+  dims[1] = 3;
+  dims[2] = 4;
+  Tensor<int, 3> t1(dims);
+  Tensor<int, 3, RowMajor> t2(dims);
+
+  VERIFY_IS_EQUAL((t1.size()), 24);
+  VERIFY_IS_EQUAL((t1.dimensions()[0]), 2);
+  VERIFY_IS_EQUAL((t1.dimensions()[1]), 3);
+  VERIFY_IS_EQUAL((t1.dimensions()[2]), 4);
+
+  VERIFY_IS_EQUAL((t2.size()), 24);
+  VERIFY_IS_EQUAL((t2.dimensions()[0]), 2);
+  VERIFY_IS_EQUAL((t2.dimensions()[1]), 3);
+  VERIFY_IS_EQUAL((t2.dimensions()[2]), 4);
+
+  for (int i = 0; i < 2; i++) {
+    for (int j = 0; j < 3; j++) {
+      for (int k = 0; k < 4; k++) {
+        t1(i, j, k) = 100 * i + 10 * j + k;
+        t2(i, j, k) = 100 * i + 10 * j + k;
+      }
+    }
+  }
+
+  VERIFY_IS_EQUAL((t1.data()[0]), 0);
+  VERIFY_IS_EQUAL((t1.data()[1]), 100);
+  VERIFY_IS_EQUAL((t1.data()[2]), 10);
+  VERIFY_IS_EQUAL((t1.data()[3]), 110);
+  VERIFY_IS_EQUAL((t1.data()[4]), 20);
+  VERIFY_IS_EQUAL((t1.data()[5]), 120);
+  VERIFY_IS_EQUAL((t1.data()[6]), 1);
+  VERIFY_IS_EQUAL((t1.data()[7]), 101);
+  VERIFY_IS_EQUAL((t1.data()[8]), 11);
+  VERIFY_IS_EQUAL((t1.data()[9]), 111);
+  VERIFY_IS_EQUAL((t1.data()[10]), 21);
+  VERIFY_IS_EQUAL((t1.data()[11]), 121);
+  VERIFY_IS_EQUAL((t1.data()[12]), 2);
+  VERIFY_IS_EQUAL((t1.data()[13]), 102);
+  VERIFY_IS_EQUAL((t1.data()[14]), 12);
+  VERIFY_IS_EQUAL((t1.data()[15]), 112);
+  VERIFY_IS_EQUAL((t1.data()[16]), 22);
+  VERIFY_IS_EQUAL((t1.data()[17]), 122);
+  VERIFY_IS_EQUAL((t1.data()[18]), 3);
+  VERIFY_IS_EQUAL((t1.data()[19]), 103);
+  VERIFY_IS_EQUAL((t1.data()[20]), 13);
+  VERIFY_IS_EQUAL((t1.data()[21]), 113);
+  VERIFY_IS_EQUAL((t1.data()[22]), 23);
+  VERIFY_IS_EQUAL((t1.data()[23]), 123);
+
+  VERIFY_IS_EQUAL((t2.data()[0]), 0);
+  VERIFY_IS_EQUAL((t2.data()[1]), 1);
+  VERIFY_IS_EQUAL((t2.data()[2]), 2);
+  VERIFY_IS_EQUAL((t2.data()[3]), 3);
+  VERIFY_IS_EQUAL((t2.data()[4]), 10);
+  VERIFY_IS_EQUAL((t2.data()[5]), 11);
+  VERIFY_IS_EQUAL((t2.data()[6]), 12);
+  VERIFY_IS_EQUAL((t2.data()[7]), 13);
+  VERIFY_IS_EQUAL((t2.data()[8]), 20);
+  VERIFY_IS_EQUAL((t2.data()[9]), 21);
+  VERIFY_IS_EQUAL((t2.data()[10]), 22);
+  VERIFY_IS_EQUAL((t2.data()[11]), 23);
+  VERIFY_IS_EQUAL((t2.data()[12]), 100);
+  VERIFY_IS_EQUAL((t2.data()[13]), 101);
+  VERIFY_IS_EQUAL((t2.data()[14]), 102);
+  VERIFY_IS_EQUAL((t2.data()[15]), 103);
+  VERIFY_IS_EQUAL((t2.data()[16]), 110);
+  VERIFY_IS_EQUAL((t2.data()[17]), 111);
+  VERIFY_IS_EQUAL((t2.data()[18]), 112);
+  VERIFY_IS_EQUAL((t2.data()[19]), 113);
+  VERIFY_IS_EQUAL((t2.data()[20]), 120);
+  VERIFY_IS_EQUAL((t2.data()[21]), 121);
+  VERIFY_IS_EQUAL((t2.data()[22]), 122);
+  VERIFY_IS_EQUAL((t2.data()[23]), 123);
+}
+
+static void test_simple_assign() {
+  Tensor<int, 3> epsilon(3, 3, 3);
+  epsilon.setZero();
+  epsilon(0, 1, 2) = epsilon(2, 0, 1) = epsilon(1, 2, 0) = 1;
+  epsilon(2, 1, 0) = epsilon(0, 2, 1) = epsilon(1, 0, 2) = -1;
+
+  Tensor<int, 3> e2(3, 3, 3);
+  e2.setZero();
+  VERIFY_IS_EQUAL((e2(1, 2, 0)), 0);
+
+  e2 = epsilon;
+  VERIFY_IS_EQUAL((e2(1, 2, 0)), 1);
+  VERIFY_IS_EQUAL((e2(0, 1, 2)), 1);
+  VERIFY_IS_EQUAL((e2(2, 0, 1)), 1);
+  VERIFY_IS_EQUAL((e2(2, 1, 0)), -1);
+  VERIFY_IS_EQUAL((e2(0, 2, 1)), -1);
+  VERIFY_IS_EQUAL((e2(1, 0, 2)), -1);
+}
+
+static void test_resize() {
+  Tensor<int, 3> epsilon;
+  epsilon.resize(2, 3, 7);
+  VERIFY_IS_EQUAL(epsilon.dimension(0), 2);
+  VERIFY_IS_EQUAL(epsilon.dimension(1), 3);
+  VERIFY_IS_EQUAL(epsilon.dimension(2), 7);
+  VERIFY_IS_EQUAL(epsilon.size(), 2 * 3 * 7);
+
+  const int* old_data = epsilon.data();
+  epsilon.resize(3, 2, 7);
+  VERIFY_IS_EQUAL(epsilon.dimension(0), 3);
+  VERIFY_IS_EQUAL(epsilon.dimension(1), 2);
+  VERIFY_IS_EQUAL(epsilon.dimension(2), 7);
+  VERIFY_IS_EQUAL(epsilon.size(), 2 * 3 * 7);
+  VERIFY_IS_EQUAL(epsilon.data(), old_data);
+
+  epsilon.resize(3, 5, 7);
+  VERIFY_IS_EQUAL(epsilon.dimension(0), 3);
+  VERIFY_IS_EQUAL(epsilon.dimension(1), 5);
+  VERIFY_IS_EQUAL(epsilon.dimension(2), 7);
+  VERIFY_IS_EQUAL(epsilon.size(), 3 * 5 * 7);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_simple) {
+  CALL_SUBTEST(test_0d());
+  CALL_SUBTEST(test_1d());
+  CALL_SUBTEST(test_2d());
+  CALL_SUBTEST(test_3d());
+  CALL_SUBTEST(test_simple_assign());
+  CALL_SUBTEST(test_resize());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_striding.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_striding.cpp
new file mode 100644
index 0000000..4f3cd0d
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_striding.cpp
@@ -0,0 +1,114 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_simple_striding() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> strides;
+  strides[0] = 1;
+  strides[1] = 1;
+  strides[2] = 1;
+  strides[3] = 1;
+
+  Tensor<float, 4, DataLayout> no_stride;
+  no_stride = tensor.stride(strides);
+
+  VERIFY_IS_EQUAL(no_stride.dimension(0), 2);
+  VERIFY_IS_EQUAL(no_stride.dimension(1), 3);
+  VERIFY_IS_EQUAL(no_stride.dimension(2), 5);
+  VERIFY_IS_EQUAL(no_stride.dimension(3), 7);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), no_stride(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  strides[0] = 2;
+  strides[1] = 4;
+  strides[2] = 2;
+  strides[3] = 3;
+  Tensor<float, 4, DataLayout> stride;
+  stride = tensor.stride(strides);
+
+  VERIFY_IS_EQUAL(stride.dimension(0), 1);
+  VERIFY_IS_EQUAL(stride.dimension(1), 1);
+  VERIFY_IS_EQUAL(stride.dimension(2), 3);
+  VERIFY_IS_EQUAL(stride.dimension(3), 3);
+
+  for (int i = 0; i < 1; ++i) {
+    for (int j = 0; j < 1; ++j) {
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 3; ++l) {
+          VERIFY_IS_EQUAL(tensor(2 * i, 4 * j, 2 * k, 3 * l), stride(i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_striding_as_lvalue() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> strides;
+  strides[0] = 2;
+  strides[1] = 4;
+  strides[2] = 2;
+  strides[3] = 3;
+
+  Tensor<float, 4, DataLayout> result(3, 12, 10, 21);
+  result.stride(strides) = tensor;
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), result(2 * i, 4 * j, 2 * k, 3 * l));
+        }
+      }
+    }
+  }
+
+  array<ptrdiff_t, 4> no_strides;
+  no_strides[0] = 1;
+  no_strides[1] = 1;
+  no_strides[2] = 1;
+  no_strides[3] = 1;
+  Tensor<float, 4, DataLayout> result2(3, 12, 10, 21);
+  result2.stride(strides) = tensor.stride(no_strides);
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), result2(2 * i, 4 * j, 2 * k, 3 * l));
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_striding) {
+  CALL_SUBTEST(test_simple_striding<ColMajor>());
+  CALL_SUBTEST(test_simple_striding<RowMajor>());
+  CALL_SUBTEST(test_striding_as_lvalue<ColMajor>());
+  CALL_SUBTEST(test_striding_as_lvalue<RowMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_striding_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_striding_sycl.cpp
new file mode 100644
index 0000000..e488c0e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_striding_sycl.cpp
@@ -0,0 +1,193 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include <iostream>
+#include <chrono>
+#include <ctime>
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_simple_striding(const Eigen::SyclDevice& sycl_device) {
+  Eigen::array<IndexType, 4> tensor_dims = {{2, 3, 5, 7}};
+  Eigen::array<IndexType, 4> stride_dims = {{1, 1, 3, 3}};
+
+  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensor_dims);
+  Tensor<DataType, 4, DataLayout, IndexType> no_stride(tensor_dims);
+  Tensor<DataType, 4, DataLayout, IndexType> stride(stride_dims);
+
+  std::size_t tensor_bytes = tensor.size() * sizeof(DataType);
+  std::size_t no_stride_bytes = no_stride.size() * sizeof(DataType);
+  std::size_t stride_bytes = stride.size() * sizeof(DataType);
+  DataType* d_tensor = static_cast<DataType*>(sycl_device.allocate(tensor_bytes));
+  DataType* d_no_stride = static_cast<DataType*>(sycl_device.allocate(no_stride_bytes));
+  DataType* d_stride = static_cast<DataType*>(sycl_device.allocate(stride_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_tensor(d_tensor, tensor_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_no_stride(d_no_stride, tensor_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_stride(d_stride, stride_dims);
+
+  tensor.setRandom();
+  array<IndexType, 4> strides;
+  strides[0] = 1;
+  strides[1] = 1;
+  strides[2] = 1;
+  strides[3] = 1;
+  sycl_device.memcpyHostToDevice(d_tensor, tensor.data(), tensor_bytes);
+  gpu_no_stride.device(sycl_device) = gpu_tensor.stride(strides);
+  sycl_device.memcpyDeviceToHost(no_stride.data(), d_no_stride, no_stride_bytes);
+
+  // no_stride = tensor.stride(strides);
+
+  VERIFY_IS_EQUAL(no_stride.dimension(0), 2);
+  VERIFY_IS_EQUAL(no_stride.dimension(1), 3);
+  VERIFY_IS_EQUAL(no_stride.dimension(2), 5);
+  VERIFY_IS_EQUAL(no_stride.dimension(3), 7);
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 5; ++k) {
+        for (IndexType l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), no_stride(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  strides[0] = 2;
+  strides[1] = 4;
+  strides[2] = 2;
+  strides[3] = 3;
+  // Tensor<float, 4, DataLayout> stride;
+  //   stride = tensor.stride(strides);
+
+  gpu_stride.device(sycl_device) = gpu_tensor.stride(strides);
+  sycl_device.memcpyDeviceToHost(stride.data(), d_stride, stride_bytes);
+
+  VERIFY_IS_EQUAL(stride.dimension(0), 1);
+  VERIFY_IS_EQUAL(stride.dimension(1), 1);
+  VERIFY_IS_EQUAL(stride.dimension(2), 3);
+  VERIFY_IS_EQUAL(stride.dimension(3), 3);
+
+  for (IndexType i = 0; i < 1; ++i) {
+    for (IndexType j = 0; j < 1; ++j) {
+      for (IndexType k = 0; k < 3; ++k) {
+        for (IndexType l = 0; l < 3; ++l) {
+          VERIFY_IS_EQUAL(tensor(2 * i, 4 * j, 2 * k, 3 * l), stride(i, j, k, l));
+        }
+      }
+    }
+  }
+
+  sycl_device.deallocate(d_tensor);
+  sycl_device.deallocate(d_no_stride);
+  sycl_device.deallocate(d_stride);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_striding_as_lvalue(const Eigen::SyclDevice& sycl_device) {
+  Eigen::array<IndexType, 4> tensor_dims = {{2, 3, 5, 7}};
+  Eigen::array<IndexType, 4> stride_dims = {{3, 12, 10, 21}};
+
+  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensor_dims);
+  Tensor<DataType, 4, DataLayout, IndexType> no_stride(stride_dims);
+  Tensor<DataType, 4, DataLayout, IndexType> stride(stride_dims);
+
+  std::size_t tensor_bytes = tensor.size() * sizeof(DataType);
+  std::size_t no_stride_bytes = no_stride.size() * sizeof(DataType);
+  std::size_t stride_bytes = stride.size() * sizeof(DataType);
+
+  DataType* d_tensor = static_cast<DataType*>(sycl_device.allocate(tensor_bytes));
+  DataType* d_no_stride = static_cast<DataType*>(sycl_device.allocate(no_stride_bytes));
+  DataType* d_stride = static_cast<DataType*>(sycl_device.allocate(stride_bytes));
+
+  Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_tensor(d_tensor, tensor_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_no_stride(d_no_stride, stride_dims);
+  Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_stride(d_stride, stride_dims);
+
+  // Tensor<float, 4, DataLayout> tensor(2,3,5,7);
+  tensor.setRandom();
+  array<IndexType, 4> strides;
+  strides[0] = 2;
+  strides[1] = 4;
+  strides[2] = 2;
+  strides[3] = 3;
+
+  //  Tensor<float, 4, DataLayout> result(3, 12, 10, 21);
+  //  result.stride(strides) = tensor;
+  sycl_device.memcpyHostToDevice(d_tensor, tensor.data(), tensor_bytes);
+  gpu_stride.stride(strides).device(sycl_device) = gpu_tensor;
+  sycl_device.memcpyDeviceToHost(stride.data(), d_stride, stride_bytes);
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 5; ++k) {
+        for (IndexType l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), stride(2 * i, 4 * j, 2 * k, 3 * l));
+        }
+      }
+    }
+  }
+
+  array<IndexType, 4> no_strides;
+  no_strides[0] = 1;
+  no_strides[1] = 1;
+  no_strides[2] = 1;
+  no_strides[3] = 1;
+  //  Tensor<float, 4, DataLayout> result2(3, 12, 10, 21);
+  //  result2.stride(strides) = tensor.stride(no_strides);
+
+  gpu_no_stride.stride(strides).device(sycl_device) = gpu_tensor.stride(no_strides);
+  sycl_device.memcpyDeviceToHost(no_stride.data(), d_no_stride, no_stride_bytes);
+
+  for (IndexType i = 0; i < 2; ++i) {
+    for (IndexType j = 0; j < 3; ++j) {
+      for (IndexType k = 0; k < 5; ++k) {
+        for (IndexType l = 0; l < 7; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), no_stride(2 * i, 4 * j, 2 * k, 3 * l));
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(d_tensor);
+  sycl_device.deallocate(d_no_stride);
+  sycl_device.deallocate(d_stride);
+}
+
+template <typename Dev_selector>
+void tensorStridingPerDevice(Dev_selector& s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_simple_striding<float, ColMajor, int64_t>(sycl_device);
+  test_simple_striding<float, RowMajor, int64_t>(sycl_device);
+  test_striding_as_lvalue<float, ColMajor, int64_t>(sycl_device);
+  test_striding_as_lvalue<float, RowMajor, int64_t>(sycl_device);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_striding_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(tensorStridingPerDevice(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_sugar.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_sugar.cpp
new file mode 100644
index 0000000..9c4d878
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_sugar.cpp
@@ -0,0 +1,78 @@
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::RowMajor;
+using Eigen::Tensor;
+
+static void test_comparison_sugar() {
+  // we already trust comparisons between tensors, we're simply checking that
+  // the sugared versions are doing the same thing
+  Tensor<int, 3> t(6, 7, 5);
+
+  t.setRandom();
+  // make sure we have at least one value == 0
+  t(0, 0, 0) = 0;
+
+  Tensor<bool, 0> b;
+
+#define TEST_TENSOR_EQUAL(e1, e2) \
+  b = ((e1) == (e2)).all();       \
+  VERIFY(b())
+
+#define TEST_OP(op) TEST_TENSOR_EQUAL(t op 0, t op t.constant(0))
+
+  TEST_OP(==);
+  TEST_OP(!=);
+  TEST_OP(<=);
+  TEST_OP(>=);
+  TEST_OP(<);
+  TEST_OP(>);
+#undef TEST_OP
+#undef TEST_TENSOR_EQUAL
+}
+
+static void test_scalar_sugar_add_mul() {
+  Tensor<float, 3> A(6, 7, 5);
+  Tensor<float, 3> B(6, 7, 5);
+  A.setRandom();
+  B.setRandom();
+
+  const float alpha = 0.43f;
+  const float beta = 0.21f;
+  const float gamma = 0.14f;
+
+  Tensor<float, 3> R = A.constant(gamma) + A * A.constant(alpha) + B * B.constant(beta);
+  Tensor<float, 3> S = A * alpha + B * beta + gamma;
+  Tensor<float, 3> T = gamma + alpha * A + beta * B;
+
+  for (int i = 0; i < 6 * 7 * 5; ++i) {
+    VERIFY_IS_APPROX(R(i), S(i));
+    VERIFY_IS_APPROX(R(i), T(i));
+  }
+}
+
+static void test_scalar_sugar_sub_div() {
+  Tensor<float, 3> A(6, 7, 5);
+  Tensor<float, 3> B(6, 7, 5);
+  A.setRandom();
+  B.setRandom();
+
+  const float alpha = 0.43f;
+  const float beta = 0.21f;
+  const float gamma = 0.14f;
+  const float delta = 0.32f;
+
+  Tensor<float, 3> R = A.constant(gamma) - A / A.constant(alpha) - B.constant(beta) / B - A.constant(delta);
+  Tensor<float, 3> S = gamma - A / alpha - beta / B - delta;
+
+  for (int i = 0; i < 6 * 7 * 5; ++i) {
+    VERIFY_IS_APPROX(R(i), S(i));
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_sugar) {
+  CALL_SUBTEST(test_comparison_sugar());
+  CALL_SUBTEST(test_scalar_sugar_add_mul());
+  CALL_SUBTEST(test_scalar_sugar_sub_div());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_sycl.cpp
new file mode 100644
index 0000000..842a510
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_sycl.cpp
@@ -0,0 +1,348 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_sycl_mem_transfers(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 5;
+  IndexType sizeDim2 = 5;
+  IndexType sizeDim3 = 1;
+  array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out1(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out2(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out3(tensorRange);
+
+  in1 = in1.random();
+
+  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(in1.size() * sizeof(DataType)));
+  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(out1.size() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data1, in1.data(), (in1.size()) * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_data2, in1.data(), (in1.size()) * sizeof(DataType));
+  gpu1.device(sycl_device) = gpu1 * 3.14f;
+  gpu2.device(sycl_device) = gpu2 * 2.7f;
+  sycl_device.memcpyDeviceToHost(out1.data(), gpu_data1, (out1.size()) * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(out2.data(), gpu_data1, (out2.size()) * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(out3.data(), gpu_data2, (out3.size()) * sizeof(DataType));
+  sycl_device.synchronize();
+
+  for (IndexType i = 0; i < in1.size(); ++i) {
+    //  std::cout << "SYCL DATA : " << out1(i) << "  vs  CPU DATA : " << in1(i) * 3.14f << "\n";
+    VERIFY_IS_APPROX(out1(i), in1(i) * 3.14f);
+    VERIFY_IS_APPROX(out2(i), in1(i) * 3.14f);
+    VERIFY_IS_APPROX(out3(i), in1(i) * 2.7f);
+  }
+
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_sycl_mem_sync(const Eigen::SyclDevice& sycl_device) {
+  IndexType size = 20;
+  array<IndexType, 1> tensorRange = {{size}};
+  Tensor<DataType, 1, DataLayout, IndexType> in1(tensorRange);
+  Tensor<DataType, 1, DataLayout, IndexType> in2(tensorRange);
+  Tensor<DataType, 1, DataLayout, IndexType> out(tensorRange);
+
+  in1 = in1.random();
+  in2 = in1;
+
+  DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(in1.size() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> gpu1(gpu_data, tensorRange);
+  sycl_device.memcpyHostToDevice(gpu_data, in1.data(), (in1.size()) * sizeof(DataType));
+  sycl_device.synchronize();
+  in1.setZero();
+
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data, out.size() * sizeof(DataType));
+  sycl_device.synchronize();
+
+  for (IndexType i = 0; i < in1.size(); ++i) {
+    VERIFY_IS_APPROX(out(i), in2(i));
+  }
+
+  sycl_device.deallocate(gpu_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_sycl_mem_sync_offsets(const Eigen::SyclDevice& sycl_device) {
+  using tensor_type = Tensor<DataType, 1, DataLayout, IndexType>;
+  IndexType full_size = 32;
+  IndexType half_size = full_size / 2;
+  array<IndexType, 1> tensorRange = {{full_size}};
+  tensor_type in1(tensorRange);
+  tensor_type out(tensorRange);
+
+  DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(full_size * sizeof(DataType)));
+  TensorMap<tensor_type> gpu1(gpu_data, tensorRange);
+
+  in1 = in1.random();
+  // Copy all data to device, then permute on copy back to host
+  sycl_device.memcpyHostToDevice(gpu_data, in1.data(), full_size * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data + half_size, half_size * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(out.data() + half_size, gpu_data, half_size * sizeof(DataType));
+
+  for (IndexType i = 0; i < half_size; ++i) {
+    VERIFY_IS_APPROX(out(i), in1(i + half_size));
+    VERIFY_IS_APPROX(out(i + half_size), in1(i));
+  }
+
+  in1 = in1.random();
+  out.setZero();
+  // Permute copies to device, then copy all back to host
+  sycl_device.memcpyHostToDevice(gpu_data + half_size, in1.data(), half_size * sizeof(DataType));
+  sycl_device.memcpyHostToDevice(gpu_data, in1.data() + half_size, half_size * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data, full_size * sizeof(DataType));
+
+  for (IndexType i = 0; i < half_size; ++i) {
+    VERIFY_IS_APPROX(out(i), in1(i + half_size));
+    VERIFY_IS_APPROX(out(i + half_size), in1(i));
+  }
+
+  in1 = in1.random();
+  out.setZero();
+  DataType* gpu_data_out = static_cast<DataType*>(sycl_device.allocate(full_size * sizeof(DataType)));
+  TensorMap<tensor_type> gpu2(gpu_data_out, tensorRange);
+  // Copy all to device, permute copies on device, then copy all back to host
+  sycl_device.memcpyHostToDevice(gpu_data, in1.data(), full_size * sizeof(DataType));
+  sycl_device.memcpy(gpu_data_out + half_size, gpu_data, half_size * sizeof(DataType));
+  sycl_device.memcpy(gpu_data_out, gpu_data + half_size, half_size * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, full_size * sizeof(DataType));
+
+  for (IndexType i = 0; i < half_size; ++i) {
+    VERIFY_IS_APPROX(out(i), in1(i + half_size));
+    VERIFY_IS_APPROX(out(i + half_size), in1(i));
+  }
+
+  sycl_device.deallocate(gpu_data_out);
+  sycl_device.deallocate(gpu_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_sycl_memset_offsets(const Eigen::SyclDevice& sycl_device) {
+  using tensor_type = Tensor<DataType, 1, DataLayout, IndexType>;
+  IndexType full_size = 32;
+  IndexType half_size = full_size / 2;
+  array<IndexType, 1> tensorRange = {{full_size}};
+  tensor_type cpu_out(tensorRange);
+  tensor_type out(tensorRange);
+
+  cpu_out.setZero();
+
+  std::memset(cpu_out.data(), 0, half_size * sizeof(DataType));
+  std::memset(cpu_out.data() + half_size, 1, half_size * sizeof(DataType));
+
+  DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(full_size * sizeof(DataType)));
+  TensorMap<tensor_type> gpu1(gpu_data, tensorRange);
+
+  sycl_device.memset(gpu_data, 0, half_size * sizeof(DataType));
+  sycl_device.memset(gpu_data + half_size, 1, half_size * sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_data, full_size * sizeof(DataType));
+
+  for (IndexType i = 0; i < full_size; ++i) {
+    VERIFY_IS_APPROX(out(i), cpu_out(i));
+  }
+
+  sycl_device.deallocate(gpu_data);
+}
+
+template <typename DataType, int DataLayout, typename IndexType>
+void test_sycl_computations(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim1 = 100;
+  IndexType sizeDim2 = 10;
+  IndexType sizeDim3 = 20;
+  array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
+  Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> in2(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> in3(tensorRange);
+  Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
+
+  in2 = in2.random();
+  in3 = in3.random();
+
+  DataType* gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(in1.size() * sizeof(DataType)));
+  DataType* gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(in2.size() * sizeof(DataType)));
+  DataType* gpu_in3_data = static_cast<DataType*>(sycl_device.allocate(in3.size() * sizeof(DataType)));
+  DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.size() * sizeof(DataType)));
+
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in3(gpu_in3_data, tensorRange);
+  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
+
+  /// a=1.2f
+  gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
+  sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data, (in1.size()) * sizeof(DataType));
+  sycl_device.synchronize();
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        VERIFY_IS_APPROX(in1(i, j, k), 1.2f);
+      }
+    }
+  }
+  printf("a=1.2f Test passed\n");
+
+  /// a=b*1.2f
+  gpu_out.device(sycl_device) = gpu_in1 * 1.2f;
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.size()) * sizeof(DataType));
+  sycl_device.synchronize();
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) * 1.2f);
+      }
+    }
+  }
+  printf("a=b*1.2f Test Passed\n");
+
+  /// c=a*b
+  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(), (in2.size()) * sizeof(DataType));
+  gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.size()) * sizeof(DataType));
+  sycl_device.synchronize();
+
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) * in2(i, j, k));
+      }
+    }
+  }
+  printf("c=a*b Test Passed\n");
+
+  /// c=a+b
+  gpu_out.device(sycl_device) = gpu_in1 + gpu_in2;
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.size()) * sizeof(DataType));
+  sycl_device.synchronize();
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k));
+      }
+    }
+  }
+  printf("c=a+b Test Passed\n");
+
+  /// c=a*a
+  gpu_out.device(sycl_device) = gpu_in1 * gpu_in1;
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.size()) * sizeof(DataType));
+  sycl_device.synchronize();
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) * in1(i, j, k));
+      }
+    }
+  }
+  printf("c= a*a Test Passed\n");
+
+  // a*3.14f + b*2.7f
+  gpu_out.device(sycl_device) = gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f);
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.size()) * sizeof(DataType));
+  sycl_device.synchronize();
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) * 3.14f + in2(i, j, k) * 2.7f);
+      }
+    }
+  }
+  printf("a*3.14f + b*2.7f Test Passed\n");
+
+  /// d= (a>0.5? b:c)
+  sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(), (in3.size()) * sizeof(DataType));
+  gpu_out.device(sycl_device) = (gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.size()) * sizeof(DataType));
+  sycl_device.synchronize();
+  for (IndexType i = 0; i < sizeDim1; ++i) {
+    for (IndexType j = 0; j < sizeDim2; ++j) {
+      for (IndexType k = 0; k < sizeDim3; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) > 0.5f) ? in2(i, j, k) : in3(i, j, k));
+      }
+    }
+  }
+  printf("d= (a>0.5? b:c) Test Passed\n");
+  sycl_device.deallocate(gpu_in1_data);
+  sycl_device.deallocate(gpu_in2_data);
+  sycl_device.deallocate(gpu_in3_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+template <typename Scalar1, typename Scalar2, int DataLayout, typename IndexType>
+static void test_sycl_cast(const Eigen::SyclDevice& sycl_device) {
+  IndexType size = 20;
+  array<IndexType, 1> tensorRange = {{size}};
+  Tensor<Scalar1, 1, DataLayout, IndexType> in(tensorRange);
+  Tensor<Scalar2, 1, DataLayout, IndexType> out(tensorRange);
+  Tensor<Scalar2, 1, DataLayout, IndexType> out_host(tensorRange);
+
+  in = in.random();
+
+  Scalar1* gpu_in_data = static_cast<Scalar1*>(sycl_device.allocate(in.size() * sizeof(Scalar1)));
+  Scalar2* gpu_out_data = static_cast<Scalar2*>(sycl_device.allocate(out.size() * sizeof(Scalar2)));
+
+  TensorMap<Tensor<Scalar1, 1, DataLayout, IndexType>> gpu_in(gpu_in_data, tensorRange);
+  TensorMap<Tensor<Scalar2, 1, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(), (in.size()) * sizeof(Scalar1));
+  gpu_out.device(sycl_device) = gpu_in.template cast<Scalar2>();
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, out.size() * sizeof(Scalar2));
+  out_host = in.template cast<Scalar2>();
+  for (IndexType i = 0; i < size; i++) {
+    VERIFY_IS_APPROX(out(i), out_host(i));
+  }
+  printf("cast Test Passed\n");
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+template <typename DataType, typename dev_Selector>
+void sycl_computing_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  test_sycl_mem_transfers<DataType, RowMajor, int64_t>(sycl_device);
+  test_sycl_computations<DataType, RowMajor, int64_t>(sycl_device);
+  test_sycl_mem_sync<DataType, RowMajor, int64_t>(sycl_device);
+  test_sycl_mem_sync_offsets<DataType, RowMajor, int64_t>(sycl_device);
+  test_sycl_memset_offsets<DataType, RowMajor, int64_t>(sycl_device);
+  test_sycl_mem_transfers<DataType, ColMajor, int64_t>(sycl_device);
+  test_sycl_computations<DataType, ColMajor, int64_t>(sycl_device);
+  test_sycl_mem_sync<DataType, ColMajor, int64_t>(sycl_device);
+  test_sycl_cast<DataType, int, RowMajor, int64_t>(sycl_device);
+  test_sycl_cast<DataType, int, ColMajor, int64_t>(sycl_device);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_symmetry.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_symmetry.cpp
new file mode 100644
index 0000000..dce3030
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_symmetry.cpp
@@ -0,0 +1,799 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+#include <Eigen/CXX11/TensorSymmetry>
+
+#include <map>
+#include <set>
+
+using Eigen::AntiHermiticity;
+using Eigen::AntiSymmetry;
+using Eigen::DynamicSGroup;
+using Eigen::Hermiticity;
+using Eigen::SGroup;
+using Eigen::StaticSGroup;
+using Eigen::Symmetry;
+using Eigen::Tensor;
+
+using Eigen::ConjugationFlag;
+using Eigen::GlobalImagFlag;
+using Eigen::GlobalRealFlag;
+using Eigen::GlobalZeroFlag;
+using Eigen::NegationFlag;
+
+// helper function to determine if the compiler instantiated a static
+// or dynamic symmetry group
+template <typename... Sym>
+bool isDynGroup(StaticSGroup<Sym...> const& dummy) {
+  (void)dummy;
+  return false;
+}
+
+bool isDynGroup(DynamicSGroup const& dummy) {
+  (void)dummy;
+  return true;
+}
+
+// helper class for checking that the symmetry groups are correct
+struct checkIdx {
+  template <typename ArrType>
+  static inline int doCheck_(ArrType e, int flags, int dummy, std::set<uint64_t>& found,
+                             std::map<uint64_t, int> const& expected) {
+    // use decimal representation of value
+    uint64_t value = e[0];
+    for (std::size_t i = 1; i < e.size(); i++) value = value * 10 + e[i];
+
+    // we want to make sure that we find each element
+    auto it = expected.find(value);
+    VERIFY((it != expected.end()));
+    VERIFY_IS_EQUAL(it->second, flags);
+
+    // we want to make sure we only have each element once;
+    // set::insert returns true for the second part of the pair
+    // if the element was really inserted and not already there
+    auto p = found.insert(value);
+    VERIFY((p.second));
+
+    return dummy;
+  }
+
+  static inline int run(std::vector<int> e, int flags, int dummy, std::set<uint64_t>& found,
+                        std::map<uint64_t, int> const& expected) {
+    return doCheck_(e, flags, dummy, found, expected);
+  }
+
+  template <std::size_t N>
+  static inline int run(std::array<int, N> e, int flags, int dummy, std::set<uint64_t>& found,
+                        std::map<uint64_t, int> const& expected) {
+    return doCheck_(e, flags, dummy, found, expected);
+  }
+};
+
+static void test_symgroups_static() {
+  std::array<int, 7> identity{{0, 1, 2, 3, 4, 5, 6}};
+
+  // Simple static symmetry group
+  StaticSGroup<AntiSymmetry<0, 1>, Hermiticity<0, 2>> group;
+
+  std::set<uint64_t> found;
+  std::map<uint64_t, int> expected;
+  expected[123456] = 0;
+  expected[1023456] = NegationFlag;
+  expected[2103456] = ConjugationFlag;
+  expected[1203456] = ConjugationFlag | NegationFlag;
+  expected[2013456] = ConjugationFlag | NegationFlag;
+  expected[213456] = ConjugationFlag;
+
+  VERIFY_IS_EQUAL(group.size(), 6u);
+  VERIFY_IS_EQUAL(group.globalFlags(), GlobalImagFlag);
+  group.apply<checkIdx, int>(identity, 0, found, expected);
+  VERIFY_IS_EQUAL(found.size(), 6u);
+}
+
+static void test_symgroups_dynamic() {
+  std::vector<int> identity;
+  for (int i = 0; i <= 6; i++) identity.push_back(i);
+
+  // Simple dynamic symmetry group
+  DynamicSGroup group;
+  group.add(0, 1, NegationFlag);
+  group.add(0, 2, ConjugationFlag);
+
+  VERIFY_IS_EQUAL(group.size(), 6u);
+  VERIFY_IS_EQUAL(group.globalFlags(), GlobalImagFlag);
+
+  std::set<uint64_t> found;
+  std::map<uint64_t, int> expected;
+  expected[123456] = 0;
+  expected[1023456] = NegationFlag;
+  expected[2103456] = ConjugationFlag;
+  expected[1203456] = ConjugationFlag | NegationFlag;
+  expected[2013456] = ConjugationFlag | NegationFlag;
+  expected[213456] = ConjugationFlag;
+
+  VERIFY_IS_EQUAL(group.size(), 6u);
+  VERIFY_IS_EQUAL(group.globalFlags(), GlobalImagFlag);
+  group.apply<checkIdx, int>(identity, 0, found, expected);
+  VERIFY_IS_EQUAL(found.size(), 6u);
+}
+
+static void test_symgroups_selection() {
+  std::array<int, 7> identity7{{0, 1, 2, 3, 4, 5, 6}};
+  std::array<int, 10> identity10{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}};
+
+  {
+    // Do the same test as in test_symgroups_static but
+    // require selection via SGroup
+    SGroup<AntiSymmetry<0, 1>, Hermiticity<0, 2>> group;
+
+    std::set<uint64_t> found;
+    std::map<uint64_t, int> expected;
+    expected[123456] = 0;
+    expected[1023456] = NegationFlag;
+    expected[2103456] = ConjugationFlag;
+    expected[1203456] = ConjugationFlag | NegationFlag;
+    expected[2013456] = ConjugationFlag | NegationFlag;
+    expected[213456] = ConjugationFlag;
+
+    VERIFY(!isDynGroup(group));
+    VERIFY_IS_EQUAL(group.size(), 6u);
+    VERIFY_IS_EQUAL(group.globalFlags(), GlobalImagFlag);
+    group.apply<checkIdx, int>(identity7, 0, found, expected);
+    VERIFY_IS_EQUAL(found.size(), 6u);
+  }
+
+  {
+    // simple factorizing group: 5 generators, 2^5 = 32 elements
+    // selection should make this dynamic, although static group
+    // can still be reasonably generated
+    SGroup<Symmetry<0, 1>, Symmetry<2, 3>, Symmetry<4, 5>, Symmetry<6, 7>, Symmetry<8, 9>> group;
+
+    std::set<uint64_t> found;
+    std::map<uint64_t, int> expected;
+    expected[123456789] = 0;
+    expected[123456798] = 0;
+    expected[123457689] = 0;
+    expected[123457698] = 0;
+    expected[123546789] = 0;
+    expected[123546798] = 0;
+    expected[123547689] = 0;
+    expected[123547698] = 0;
+    expected[132456789] = 0;
+    expected[132456798] = 0;
+    expected[132457689] = 0;
+    expected[132457698] = 0;
+    expected[132546789] = 0;
+    expected[132546798] = 0;
+    expected[132547689] = 0;
+    expected[132547698] = 0;
+    expected[1023456789] = 0;
+    expected[1023456798] = 0;
+    expected[1023457689] = 0;
+    expected[1023457698] = 0;
+    expected[1023546789] = 0;
+    expected[1023546798] = 0;
+    expected[1023547689] = 0;
+    expected[1023547698] = 0;
+    expected[1032456789] = 0;
+    expected[1032456798] = 0;
+    expected[1032457689] = 0;
+    expected[1032457698] = 0;
+    expected[1032546789] = 0;
+    expected[1032546798] = 0;
+    expected[1032547689] = 0;
+    expected[1032547698] = 0;
+
+    VERIFY(isDynGroup(group));
+    VERIFY_IS_EQUAL(group.size(), 32u);
+    VERIFY_IS_EQUAL(group.globalFlags(), 0);
+    group.apply<checkIdx, int>(identity10, 0, found, expected);
+    VERIFY_IS_EQUAL(found.size(), 32u);
+
+    // no verify that we could also generate a static group
+    // with these generators
+    found.clear();
+    StaticSGroup<Symmetry<0, 1>, Symmetry<2, 3>, Symmetry<4, 5>, Symmetry<6, 7>, Symmetry<8, 9>> group_static;
+    VERIFY_IS_EQUAL(group_static.size(), 32u);
+    VERIFY_IS_EQUAL(group_static.globalFlags(), 0);
+    group_static.apply<checkIdx, int>(identity10, 0, found, expected);
+    VERIFY_IS_EQUAL(found.size(), 32u);
+  }
+
+  {
+    // try to create a HUGE group
+    SGroup<Symmetry<0, 1>, Symmetry<1, 2>, Symmetry<2, 3>, Symmetry<3, 4>, Symmetry<4, 5>, Symmetry<5, 6>> group;
+
+    std::set<uint64_t> found;
+    uint64_t pre_expected[5040] = {
+        123456,  1023456, 213456,  2013456, 1203456, 2103456, 132456,  1032456, 312456,  3012456, 1302456, 3102456,
+        231456,  2031456, 321456,  3021456, 2301456, 3201456, 1230456, 2130456, 1320456, 3120456, 2310456, 3210456,
+        124356,  1024356, 214356,  2014356, 1204356, 2104356, 142356,  1042356, 412356,  4012356, 1402356, 4102356,
+        241356,  2041356, 421356,  4021356, 2401356, 4201356, 1240356, 2140356, 1420356, 4120356, 2410356, 4210356,
+        134256,  1034256, 314256,  3014256, 1304256, 3104256, 143256,  1043256, 413256,  4013256, 1403256, 4103256,
+        341256,  3041256, 431256,  4031256, 3401256, 4301256, 1340256, 3140256, 1430256, 4130256, 3410256, 4310256,
+        234156,  2034156, 324156,  3024156, 2304156, 3204156, 243156,  2043156, 423156,  4023156, 2403156, 4203156,
+        342156,  3042156, 432156,  4032156, 3402156, 4302156, 2340156, 3240156, 2430156, 4230156, 3420156, 4320156,
+        1234056, 2134056, 1324056, 3124056, 2314056, 3214056, 1243056, 2143056, 1423056, 4123056, 2413056, 4213056,
+        1342056, 3142056, 1432056, 4132056, 3412056, 4312056, 2341056, 3241056, 2431056, 4231056, 3421056, 4321056,
+        123546,  1023546, 213546,  2013546, 1203546, 2103546, 132546,  1032546, 312546,  3012546, 1302546, 3102546,
+        231546,  2031546, 321546,  3021546, 2301546, 3201546, 1230546, 2130546, 1320546, 3120546, 2310546, 3210546,
+        125346,  1025346, 215346,  2015346, 1205346, 2105346, 152346,  1052346, 512346,  5012346, 1502346, 5102346,
+        251346,  2051346, 521346,  5021346, 2501346, 5201346, 1250346, 2150346, 1520346, 5120346, 2510346, 5210346,
+        135246,  1035246, 315246,  3015246, 1305246, 3105246, 153246,  1053246, 513246,  5013246, 1503246, 5103246,
+        351246,  3051246, 531246,  5031246, 3501246, 5301246, 1350246, 3150246, 1530246, 5130246, 3510246, 5310246,
+        235146,  2035146, 325146,  3025146, 2305146, 3205146, 253146,  2053146, 523146,  5023146, 2503146, 5203146,
+        352146,  3052146, 532146,  5032146, 3502146, 5302146, 2350146, 3250146, 2530146, 5230146, 3520146, 5320146,
+        1235046, 2135046, 1325046, 3125046, 2315046, 3215046, 1253046, 2153046, 1523046, 5123046, 2513046, 5213046,
+        1352046, 3152046, 1532046, 5132046, 3512046, 5312046, 2351046, 3251046, 2531046, 5231046, 3521046, 5321046,
+        124536,  1024536, 214536,  2014536, 1204536, 2104536, 142536,  1042536, 412536,  4012536, 1402536, 4102536,
+        241536,  2041536, 421536,  4021536, 2401536, 4201536, 1240536, 2140536, 1420536, 4120536, 2410536, 4210536,
+        125436,  1025436, 215436,  2015436, 1205436, 2105436, 152436,  1052436, 512436,  5012436, 1502436, 5102436,
+        251436,  2051436, 521436,  5021436, 2501436, 5201436, 1250436, 2150436, 1520436, 5120436, 2510436, 5210436,
+        145236,  1045236, 415236,  4015236, 1405236, 4105236, 154236,  1054236, 514236,  5014236, 1504236, 5104236,
+        451236,  4051236, 541236,  5041236, 4501236, 5401236, 1450236, 4150236, 1540236, 5140236, 4510236, 5410236,
+        245136,  2045136, 425136,  4025136, 2405136, 4205136, 254136,  2054136, 524136,  5024136, 2504136, 5204136,
+        452136,  4052136, 542136,  5042136, 4502136, 5402136, 2450136, 4250136, 2540136, 5240136, 4520136, 5420136,
+        1245036, 2145036, 1425036, 4125036, 2415036, 4215036, 1254036, 2154036, 1524036, 5124036, 2514036, 5214036,
+        1452036, 4152036, 1542036, 5142036, 4512036, 5412036, 2451036, 4251036, 2541036, 5241036, 4521036, 5421036,
+        134526,  1034526, 314526,  3014526, 1304526, 3104526, 143526,  1043526, 413526,  4013526, 1403526, 4103526,
+        341526,  3041526, 431526,  4031526, 3401526, 4301526, 1340526, 3140526, 1430526, 4130526, 3410526, 4310526,
+        135426,  1035426, 315426,  3015426, 1305426, 3105426, 153426,  1053426, 513426,  5013426, 1503426, 5103426,
+        351426,  3051426, 531426,  5031426, 3501426, 5301426, 1350426, 3150426, 1530426, 5130426, 3510426, 5310426,
+        145326,  1045326, 415326,  4015326, 1405326, 4105326, 154326,  1054326, 514326,  5014326, 1504326, 5104326,
+        451326,  4051326, 541326,  5041326, 4501326, 5401326, 1450326, 4150326, 1540326, 5140326, 4510326, 5410326,
+        345126,  3045126, 435126,  4035126, 3405126, 4305126, 354126,  3054126, 534126,  5034126, 3504126, 5304126,
+        453126,  4053126, 543126,  5043126, 4503126, 5403126, 3450126, 4350126, 3540126, 5340126, 4530126, 5430126,
+        1345026, 3145026, 1435026, 4135026, 3415026, 4315026, 1354026, 3154026, 1534026, 5134026, 3514026, 5314026,
+        1453026, 4153026, 1543026, 5143026, 4513026, 5413026, 3451026, 4351026, 3541026, 5341026, 4531026, 5431026,
+        234516,  2034516, 324516,  3024516, 2304516, 3204516, 243516,  2043516, 423516,  4023516, 2403516, 4203516,
+        342516,  3042516, 432516,  4032516, 3402516, 4302516, 2340516, 3240516, 2430516, 4230516, 3420516, 4320516,
+        235416,  2035416, 325416,  3025416, 2305416, 3205416, 253416,  2053416, 523416,  5023416, 2503416, 5203416,
+        352416,  3052416, 532416,  5032416, 3502416, 5302416, 2350416, 3250416, 2530416, 5230416, 3520416, 5320416,
+        245316,  2045316, 425316,  4025316, 2405316, 4205316, 254316,  2054316, 524316,  5024316, 2504316, 5204316,
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+        456321,  4056321, 546321,  5046321, 4506321, 5406321, 465321,  4065321, 645321,  6045321, 4605321, 6405321,
+        564321,  5064321, 654321,  6054321, 5604321, 6504321, 4560321, 5460321, 4650321, 6450321, 5640321, 6540321,
+        3456021, 4356021, 3546021, 5346021, 4536021, 5436021, 3465021, 4365021, 3645021, 6345021, 4635021, 6435021,
+        3564021, 5364021, 3654021, 6354021, 5634021, 6534021, 4563021, 5463021, 4653021, 6453021, 5643021, 6543021,
+        2345601, 3245601, 2435601, 4235601, 3425601, 4325601, 2354601, 3254601, 2534601, 5234601, 3524601, 5324601,
+        2453601, 4253601, 2543601, 5243601, 4523601, 5423601, 3452601, 4352601, 3542601, 5342601, 4532601, 5432601,
+        2346501, 3246501, 2436501, 4236501, 3426501, 4326501, 2364501, 3264501, 2634501, 6234501, 3624501, 6324501,
+        2463501, 4263501, 2643501, 6243501, 4623501, 6423501, 3462501, 4362501, 3642501, 6342501, 4632501, 6432501,
+        2356401, 3256401, 2536401, 5236401, 3526401, 5326401, 2365401, 3265401, 2635401, 6235401, 3625401, 6325401,
+        2563401, 5263401, 2653401, 6253401, 5623401, 6523401, 3562401, 5362401, 3652401, 6352401, 5632401, 6532401,
+        2456301, 4256301, 2546301, 5246301, 4526301, 5426301, 2465301, 4265301, 2645301, 6245301, 4625301, 6425301,
+        2564301, 5264301, 2654301, 6254301, 5624301, 6524301, 4562301, 5462301, 4652301, 6452301, 5642301, 6542301,
+        3456201, 4356201, 3546201, 5346201, 4536201, 5436201, 3465201, 4365201, 3645201, 6345201, 4635201, 6435201,
+        3564201, 5364201, 3654201, 6354201, 5634201, 6534201, 4563201, 5463201, 4653201, 6453201, 5643201, 6543201,
+        1234560, 2134560, 1324560, 3124560, 2314560, 3214560, 1243560, 2143560, 1423560, 4123560, 2413560, 4213560,
+        1342560, 3142560, 1432560, 4132560, 3412560, 4312560, 2341560, 3241560, 2431560, 4231560, 3421560, 4321560,
+        1235460, 2135460, 1325460, 3125460, 2315460, 3215460, 1253460, 2153460, 1523460, 5123460, 2513460, 5213460,
+        1352460, 3152460, 1532460, 5132460, 3512460, 5312460, 2351460, 3251460, 2531460, 5231460, 3521460, 5321460,
+        1245360, 2145360, 1425360, 4125360, 2415360, 4215360, 1254360, 2154360, 1524360, 5124360, 2514360, 5214360,
+        1452360, 4152360, 1542360, 5142360, 4512360, 5412360, 2451360, 4251360, 2541360, 5241360, 4521360, 5421360,
+        1345260, 3145260, 1435260, 4135260, 3415260, 4315260, 1354260, 3154260, 1534260, 5134260, 3514260, 5314260,
+        1453260, 4153260, 1543260, 5143260, 4513260, 5413260, 3451260, 4351260, 3541260, 5341260, 4531260, 5431260,
+        2345160, 3245160, 2435160, 4235160, 3425160, 4325160, 2354160, 3254160, 2534160, 5234160, 3524160, 5324160,
+        2453160, 4253160, 2543160, 5243160, 4523160, 5423160, 3452160, 4352160, 3542160, 5342160, 4532160, 5432160,
+        1234650, 2134650, 1324650, 3124650, 2314650, 3214650, 1243650, 2143650, 1423650, 4123650, 2413650, 4213650,
+        1342650, 3142650, 1432650, 4132650, 3412650, 4312650, 2341650, 3241650, 2431650, 4231650, 3421650, 4321650,
+        1236450, 2136450, 1326450, 3126450, 2316450, 3216450, 1263450, 2163450, 1623450, 6123450, 2613450, 6213450,
+        1362450, 3162450, 1632450, 6132450, 3612450, 6312450, 2361450, 3261450, 2631450, 6231450, 3621450, 6321450,
+        1246350, 2146350, 1426350, 4126350, 2416350, 4216350, 1264350, 2164350, 1624350, 6124350, 2614350, 6214350,
+        1462350, 4162350, 1642350, 6142350, 4612350, 6412350, 2461350, 4261350, 2641350, 6241350, 4621350, 6421350,
+        1346250, 3146250, 1436250, 4136250, 3416250, 4316250, 1364250, 3164250, 1634250, 6134250, 3614250, 6314250,
+        1463250, 4163250, 1643250, 6143250, 4613250, 6413250, 3461250, 4361250, 3641250, 6341250, 4631250, 6431250,
+        2346150, 3246150, 2436150, 4236150, 3426150, 4326150, 2364150, 3264150, 2634150, 6234150, 3624150, 6324150,
+        2463150, 4263150, 2643150, 6243150, 4623150, 6423150, 3462150, 4362150, 3642150, 6342150, 4632150, 6432150,
+        1235640, 2135640, 1325640, 3125640, 2315640, 3215640, 1253640, 2153640, 1523640, 5123640, 2513640, 5213640,
+        1352640, 3152640, 1532640, 5132640, 3512640, 5312640, 2351640, 3251640, 2531640, 5231640, 3521640, 5321640,
+        1236540, 2136540, 1326540, 3126540, 2316540, 3216540, 1263540, 2163540, 1623540, 6123540, 2613540, 6213540,
+        1362540, 3162540, 1632540, 6132540, 3612540, 6312540, 2361540, 3261540, 2631540, 6231540, 3621540, 6321540,
+        1256340, 2156340, 1526340, 5126340, 2516340, 5216340, 1265340, 2165340, 1625340, 6125340, 2615340, 6215340,
+        1562340, 5162340, 1652340, 6152340, 5612340, 6512340, 2561340, 5261340, 2651340, 6251340, 5621340, 6521340,
+        1356240, 3156240, 1536240, 5136240, 3516240, 5316240, 1365240, 3165240, 1635240, 6135240, 3615240, 6315240,
+        1563240, 5163240, 1653240, 6153240, 5613240, 6513240, 3561240, 5361240, 3651240, 6351240, 5631240, 6531240,
+        2356140, 3256140, 2536140, 5236140, 3526140, 5326140, 2365140, 3265140, 2635140, 6235140, 3625140, 6325140,
+        2563140, 5263140, 2653140, 6253140, 5623140, 6523140, 3562140, 5362140, 3652140, 6352140, 5632140, 6532140,
+        1245630, 2145630, 1425630, 4125630, 2415630, 4215630, 1254630, 2154630, 1524630, 5124630, 2514630, 5214630,
+        1452630, 4152630, 1542630, 5142630, 4512630, 5412630, 2451630, 4251630, 2541630, 5241630, 4521630, 5421630,
+        1246530, 2146530, 1426530, 4126530, 2416530, 4216530, 1264530, 2164530, 1624530, 6124530, 2614530, 6214530,
+        1462530, 4162530, 1642530, 6142530, 4612530, 6412530, 2461530, 4261530, 2641530, 6241530, 4621530, 6421530,
+        1256430, 2156430, 1526430, 5126430, 2516430, 5216430, 1265430, 2165430, 1625430, 6125430, 2615430, 6215430,
+        1562430, 5162430, 1652430, 6152430, 5612430, 6512430, 2561430, 5261430, 2651430, 6251430, 5621430, 6521430,
+        1456230, 4156230, 1546230, 5146230, 4516230, 5416230, 1465230, 4165230, 1645230, 6145230, 4615230, 6415230,
+        1564230, 5164230, 1654230, 6154230, 5614230, 6514230, 4561230, 5461230, 4651230, 6451230, 5641230, 6541230,
+        2456130, 4256130, 2546130, 5246130, 4526130, 5426130, 2465130, 4265130, 2645130, 6245130, 4625130, 6425130,
+        2564130, 5264130, 2654130, 6254130, 5624130, 6524130, 4562130, 5462130, 4652130, 6452130, 5642130, 6542130,
+        1345620, 3145620, 1435620, 4135620, 3415620, 4315620, 1354620, 3154620, 1534620, 5134620, 3514620, 5314620,
+        1453620, 4153620, 1543620, 5143620, 4513620, 5413620, 3451620, 4351620, 3541620, 5341620, 4531620, 5431620,
+        1346520, 3146520, 1436520, 4136520, 3416520, 4316520, 1364520, 3164520, 1634520, 6134520, 3614520, 6314520,
+        1463520, 4163520, 1643520, 6143520, 4613520, 6413520, 3461520, 4361520, 3641520, 6341520, 4631520, 6431520,
+        1356420, 3156420, 1536420, 5136420, 3516420, 5316420, 1365420, 3165420, 1635420, 6135420, 3615420, 6315420,
+        1563420, 5163420, 1653420, 6153420, 5613420, 6513420, 3561420, 5361420, 3651420, 6351420, 5631420, 6531420,
+        1456320, 4156320, 1546320, 5146320, 4516320, 5416320, 1465320, 4165320, 1645320, 6145320, 4615320, 6415320,
+        1564320, 5164320, 1654320, 6154320, 5614320, 6514320, 4561320, 5461320, 4651320, 6451320, 5641320, 6541320,
+        3456120, 4356120, 3546120, 5346120, 4536120, 5436120, 3465120, 4365120, 3645120, 6345120, 4635120, 6435120,
+        3564120, 5364120, 3654120, 6354120, 5634120, 6534120, 4563120, 5463120, 4653120, 6453120, 5643120, 6543120,
+        2345610, 3245610, 2435610, 4235610, 3425610, 4325610, 2354610, 3254610, 2534610, 5234610, 3524610, 5324610,
+        2453610, 4253610, 2543610, 5243610, 4523610, 5423610, 3452610, 4352610, 3542610, 5342610, 4532610, 5432610,
+        2346510, 3246510, 2436510, 4236510, 3426510, 4326510, 2364510, 3264510, 2634510, 6234510, 3624510, 6324510,
+        2463510, 4263510, 2643510, 6243510, 4623510, 6423510, 3462510, 4362510, 3642510, 6342510, 4632510, 6432510,
+        2356410, 3256410, 2536410, 5236410, 3526410, 5326410, 2365410, 3265410, 2635410, 6235410, 3625410, 6325410,
+        2563410, 5263410, 2653410, 6253410, 5623410, 6523410, 3562410, 5362410, 3652410, 6352410, 5632410, 6532410,
+        2456310, 4256310, 2546310, 5246310, 4526310, 5426310, 2465310, 4265310, 2645310, 6245310, 4625310, 6425310,
+        2564310, 5264310, 2654310, 6254310, 5624310, 6524310, 4562310, 5462310, 4652310, 6452310, 5642310, 6542310,
+        3456210, 4356210, 3546210, 5346210, 4536210, 5436210, 3465210, 4365210, 3645210, 6345210, 4635210, 6435210,
+        3564210, 5364210, 3654210, 6354210, 5634210, 6534210, 4563210, 5463210, 4653210, 6453210, 5643210, 6543210};
+    std::map<uint64_t, int> expected;
+    for (std::size_t i = 0; i < 5040; i++) expected[pre_expected[i]] = 0;  // flags are 0, everything is symmetric here
+
+    VERIFY(isDynGroup(group));
+    VERIFY_IS_EQUAL(group.size(), 5040u);
+    VERIFY_IS_EQUAL(group.globalFlags(), 0);
+    group.apply<checkIdx, int>(identity7, 0, found, expected);
+    VERIFY_IS_EQUAL(found.size(), 5040u);
+  }
+}
+
+static void test_tensor_epsilon() {
+  SGroup<AntiSymmetry<0, 1>, AntiSymmetry<1, 2>> sym;
+  Tensor<int, 3> epsilon(3, 3, 3);
+
+  epsilon.setZero();
+  sym(epsilon, 0, 1, 2) = 1;
+
+  for (int i = 0; i < 3; i++) {
+    for (int j = 0; j < 3; j++) {
+      for (int k = 0; k < 3; k++) {
+        VERIFY_IS_EQUAL((epsilon(i, j, k)), (-(j - i) * (k - j) * (i - k) / 2));
+      }
+    }
+  }
+}
+
+static void test_tensor_sym() {
+  SGroup<Symmetry<0, 1>, Symmetry<2, 3>> sym;
+  Tensor<int, 4> t(10, 10, 10, 10);
+
+  t.setZero();
+
+  for (int l = 0; l < 10; l++) {
+    for (int k = l; k < 10; k++) {
+      for (int j = 0; j < 10; j++) {
+        for (int i = j; i < 10; i++) {
+          sym(t, i, j, k, l) = (i + j) * (k + l);
+        }
+      }
+    }
+  }
+
+  for (int l = 0; l < 10; l++) {
+    for (int k = 0; k < 10; k++) {
+      for (int j = 0; j < 10; j++) {
+        for (int i = 0; i < 10; i++) {
+          VERIFY_IS_EQUAL((t(i, j, k, l)), ((i + j) * (k + l)));
+        }
+      }
+    }
+  }
+}
+
+static void test_tensor_asym() {
+  SGroup<AntiSymmetry<0, 1>, AntiSymmetry<2, 3>> sym;
+  Tensor<int, 4> t(10, 10, 10, 10);
+
+  t.setZero();
+
+  for (int l = 0; l < 10; l++) {
+    for (int k = l + 1; k < 10; k++) {
+      for (int j = 0; j < 10; j++) {
+        for (int i = j + 1; i < 10; i++) {
+          sym(t, i, j, k, l) = ((i * j) + (k * l));
+        }
+      }
+    }
+  }
+
+  for (int l = 0; l < 10; l++) {
+    for (int k = 0; k < 10; k++) {
+      for (int j = 0; j < 10; j++) {
+        for (int i = 0; i < 10; i++) {
+          if (i < j && k < l)
+            VERIFY_IS_EQUAL((t(i, j, k, l)), (((i * j) + (k * l))));
+          else if (i > j && k > l)
+            VERIFY_IS_EQUAL((t(i, j, k, l)), (((i * j) + (k * l))));
+          else if (i < j && k > l)
+            VERIFY_IS_EQUAL((t(i, j, k, l)), (-((i * j) + (k * l))));
+          else if (i > j && k < l)
+            VERIFY_IS_EQUAL((t(i, j, k, l)), (-((i * j) + (k * l))));
+          else
+            VERIFY_IS_EQUAL((t(i, j, k, l)), 0);
+        }
+      }
+    }
+  }
+}
+
+static void test_tensor_dynsym() {
+  DynamicSGroup sym;
+  sym.addSymmetry(0, 1);
+  sym.addSymmetry(2, 3);
+  Tensor<int, 4> t(10, 10, 10, 10);
+
+  t.setZero();
+
+  for (int l = 0; l < 10; l++) {
+    for (int k = l; k < 10; k++) {
+      for (int j = 0; j < 10; j++) {
+        for (int i = j; i < 10; i++) {
+          sym(t, i, j, k, l) = (i + j) * (k + l);
+        }
+      }
+    }
+  }
+
+  for (int l = 0; l < 10; l++) {
+    for (int k = 0; k < 10; k++) {
+      for (int j = 0; j < 10; j++) {
+        for (int i = 0; i < 10; i++) {
+          VERIFY_IS_EQUAL((t(i, j, k, l)), ((i + j) * (k + l)));
+        }
+      }
+    }
+  }
+}
+
+static void test_tensor_randacc() {
+  SGroup<Symmetry<0, 1>, Symmetry<2, 3>> sym;
+  Tensor<int, 4> t(10, 10, 10, 10);
+
+  t.setZero();
+
+  // set elements 1 million times, that way we access the
+  // entire matrix
+  for (int n = 0; n < 1000000; n++) {
+    int i = rand() % 10;
+    int j = rand() % 10;
+    int k = rand() % 10;
+    int l = rand() % 10;
+    // only access those indices in a given order
+    if (i < j) std::swap(i, j);
+    if (k < l) std::swap(k, l);
+    sym(t, i, j, k, l) = (i + j) * (k + l);
+  }
+
+  for (int l = 0; l < 10; l++) {
+    for (int k = 0; k < 10; k++) {
+      for (int j = 0; j < 10; j++) {
+        for (int i = 0; i < 10; i++) {
+          VERIFY_IS_EQUAL((t(i, j, k, l)), ((i + j) * (k + l)));
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_symmetry) {
+  CALL_SUBTEST(test_symgroups_static());
+  CALL_SUBTEST(test_symgroups_dynamic());
+  CALL_SUBTEST(test_symgroups_selection());
+  CALL_SUBTEST(test_tensor_epsilon());
+  CALL_SUBTEST(test_tensor_sym());
+  CALL_SUBTEST(test_tensor_asym());
+  CALL_SUBTEST(test_tensor_dynsym());
+  CALL_SUBTEST(test_tensor_randacc());
+}
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_thread_local.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_thread_local.cpp
new file mode 100644
index 0000000..32ba2cd
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_thread_local.cpp
@@ -0,0 +1,144 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_USE_THREADS
+
+#include <iostream>
+#include <unordered_set>
+
+#include "main.h"
+#include <Eigen/ThreadPool>
+
+struct Counter {
+  Counter() = default;
+
+  void inc() {
+    // Check that mutation happens only in a thread that created this counter.
+    VERIFY_IS_EQUAL(std::this_thread::get_id(), created_by);
+    counter_value++;
+  }
+  int value() { return counter_value; }
+
+  std::thread::id created_by;
+  int counter_value = 0;
+};
+
+struct InitCounter {
+  void operator()(Counter& counter) { counter.created_by = std::this_thread::get_id(); }
+};
+
+void test_simple_thread_local() {
+  int num_threads = internal::random<int>(4, 32);
+  Eigen::ThreadPool thread_pool(num_threads);
+  Eigen::ThreadLocal<Counter, InitCounter> counter(num_threads, InitCounter());
+
+  int num_tasks = 3 * num_threads;
+  Eigen::Barrier barrier(num_tasks);
+
+  for (int i = 0; i < num_tasks; ++i) {
+    thread_pool.Schedule([&counter, &barrier]() {
+      Counter& local = counter.local();
+      local.inc();
+
+      std::this_thread::sleep_for(std::chrono::milliseconds(100));
+      barrier.Notify();
+    });
+  }
+
+  barrier.Wait();
+
+  counter.ForEach([](std::thread::id, Counter& cnt) { VERIFY_IS_EQUAL(cnt.value(), 3); });
+}
+
+void test_zero_sized_thread_local() {
+  Eigen::ThreadLocal<Counter, InitCounter> counter(0, InitCounter());
+
+  Counter& local = counter.local();
+  local.inc();
+
+  int total = 0;
+  counter.ForEach([&total](std::thread::id, Counter& cnt) {
+    total += cnt.value();
+    VERIFY_IS_EQUAL(cnt.value(), 1);
+  });
+
+  VERIFY_IS_EQUAL(total, 1);
+}
+
+// All thread local values fits into the lock-free storage.
+void test_large_number_of_tasks_no_spill() {
+  int num_threads = internal::random<int>(4, 32);
+  Eigen::ThreadPool thread_pool(num_threads);
+  Eigen::ThreadLocal<Counter, InitCounter> counter(num_threads, InitCounter());
+
+  int num_tasks = 10000;
+  Eigen::Barrier barrier(num_tasks);
+
+  for (int i = 0; i < num_tasks; ++i) {
+    thread_pool.Schedule([&counter, &barrier]() {
+      Counter& local = counter.local();
+      local.inc();
+      barrier.Notify();
+    });
+  }
+
+  barrier.Wait();
+
+  int total = 0;
+  std::unordered_set<std::thread::id> unique_threads;
+
+  counter.ForEach([&](std::thread::id id, Counter& cnt) {
+    total += cnt.value();
+    unique_threads.insert(id);
+  });
+
+  VERIFY_IS_EQUAL(total, num_tasks);
+  // Not all threads in a pool might be woken up to execute submitted tasks.
+  // Also thread_pool.Schedule() might use current thread if queue is full.
+  VERIFY_IS_EQUAL(unique_threads.size() <= (static_cast<size_t>(num_threads + 1)), true);
+}
+
+// Lock free thread local storage is too small to fit all the unique threads,
+// and it spills to a map guarded by a mutex.
+void test_large_number_of_tasks_with_spill() {
+  int num_threads = internal::random<int>(4, 32);
+  Eigen::ThreadPool thread_pool(num_threads);
+  Eigen::ThreadLocal<Counter, InitCounter> counter(1, InitCounter());
+
+  int num_tasks = 10000;
+  Eigen::Barrier barrier(num_tasks);
+
+  for (int i = 0; i < num_tasks; ++i) {
+    thread_pool.Schedule([&counter, &barrier]() {
+      Counter& local = counter.local();
+      local.inc();
+      barrier.Notify();
+    });
+  }
+
+  barrier.Wait();
+
+  int total = 0;
+  std::unordered_set<std::thread::id> unique_threads;
+
+  counter.ForEach([&](std::thread::id id, Counter& cnt) {
+    total += cnt.value();
+    unique_threads.insert(id);
+  });
+
+  VERIFY_IS_EQUAL(total, num_tasks);
+  // Not all threads in a pool might be woken up to execute submitted tasks.
+  // Also thread_pool.Schedule() might use current thread if queue is full.
+  VERIFY_IS_EQUAL(unique_threads.size() <= (static_cast<size_t>(num_threads + 1)), true);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_thread_local) {
+  CALL_SUBTEST(test_simple_thread_local());
+  CALL_SUBTEST(test_zero_sized_thread_local());
+  CALL_SUBTEST(test_large_number_of_tasks_no_spill());
+  CALL_SUBTEST(test_large_number_of_tasks_with_spill());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_thread_pool.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_thread_pool.cpp
new file mode 100644
index 0000000..a566d7e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_thread_pool.cpp
@@ -0,0 +1,776 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_USE_THREADS
+
+#include "main.h"
+#include <iostream>
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+class TestAllocator : public Allocator {
+ public:
+  ~TestAllocator() EIGEN_OVERRIDE {}
+  EIGEN_DEVICE_FUNC void* allocate(size_t num_bytes) const EIGEN_OVERRIDE {
+    const_cast<TestAllocator*>(this)->alloc_count_++;
+    return internal::aligned_malloc(num_bytes);
+  }
+  EIGEN_DEVICE_FUNC void deallocate(void* buffer) const EIGEN_OVERRIDE {
+    const_cast<TestAllocator*>(this)->dealloc_count_++;
+    internal::aligned_free(buffer);
+  }
+
+  int alloc_count() const { return alloc_count_; }
+  int dealloc_count() const { return dealloc_count_; }
+
+ private:
+  int alloc_count_ = 0;
+  int dealloc_count_ = 0;
+};
+
+void test_multithread_elementwise() {
+  Tensor<float, 3> in1(200, 30, 70);
+  Tensor<float, 3> in2(200, 30, 70);
+  Tensor<double, 3> out(200, 30, 70);
+
+  in1.setRandom();
+  in2.setRandom();
+
+  Eigen::ThreadPool tp(internal::random<int>(3, 11));
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
+  out.device(thread_pool_device) = (in1 + in2 * 3.14f).cast<double>();
+
+  for (int i = 0; i < 200; ++i) {
+    for (int j = 0; j < 30; ++j) {
+      for (int k = 0; k < 70; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), static_cast<double>(in1(i, j, k) + in2(i, j, k) * 3.14f));
+      }
+    }
+  }
+}
+
+void test_async_multithread_elementwise() {
+  Tensor<float, 3> in1(200, 30, 70);
+  Tensor<float, 3> in2(200, 30, 70);
+  Tensor<double, 3> out(200, 30, 70);
+
+  in1.setRandom();
+  in2.setRandom();
+
+  Eigen::ThreadPool tp(internal::random<int>(3, 11));
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
+
+  Eigen::Barrier b(1);
+  out.device(thread_pool_device, [&b]() { b.Notify(); }) = (in1 + in2 * 3.14f).cast<double>();
+  b.Wait();
+
+  for (int i = 0; i < 200; ++i) {
+    for (int j = 0; j < 30; ++j) {
+      for (int k = 0; k < 70; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), static_cast<double>(in1(i, j, k) + in2(i, j, k) * 3.14f));
+      }
+    }
+  }
+}
+
+void test_multithread_chip() {
+  Tensor<float, 5> in(2, 3, 5, 7, 11);
+  Tensor<float, 4> out(3, 5, 7, 11);
+
+  in.setRandom();
+
+  Eigen::ThreadPool tp(internal::random<int>(3, 11));
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
+
+  out.device(thread_pool_device) = in.chip(1, 0);
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(out(i, j, k, l), in(1, i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+void test_async_multithread_chip() {
+  Tensor<float, 5> in(2, 3, 5, 7, 11);
+  Tensor<float, 4> out(3, 5, 7, 11);
+
+  in.setRandom();
+
+  Eigen::ThreadPool tp(internal::random<int>(3, 11));
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
+
+  Eigen::Barrier b(1);
+  out.device(thread_pool_device, [&b]() { b.Notify(); }) = in.chip(1, 0);
+  b.Wait();
+
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(out(i, j, k, l), in(1, i, j, k, l));
+        }
+      }
+    }
+  }
+}
+
+void test_multithread_volume_patch() {
+  Tensor<float, 5> in(4, 2, 3, 5, 7);
+  Tensor<float, 6> out(4, 1, 1, 1, 2 * 3 * 5, 7);
+
+  in.setRandom();
+
+  Eigen::ThreadPool tp(internal::random<int>(3, 11));
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
+
+  out.device(thread_pool_device) = in.extract_volume_patches(1, 1, 1);
+
+  for (int i = 0; i < in.size(); ++i) {
+    VERIFY_IS_EQUAL(in.data()[i], out.data()[i]);
+  }
+}
+
+void test_async_multithread_volume_patch() {
+  Tensor<float, 5> in(4, 2, 3, 5, 7);
+  Tensor<float, 6> out(4, 1, 1, 1, 2 * 3 * 5, 7);
+
+  in.setRandom();
+
+  Eigen::ThreadPool tp(internal::random<int>(3, 11));
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
+
+  Eigen::Barrier b(1);
+  out.device(thread_pool_device, [&b]() { b.Notify(); }) = in.extract_volume_patches(1, 1, 1);
+  b.Wait();
+
+  for (int i = 0; i < in.size(); ++i) {
+    VERIFY_IS_EQUAL(in.data()[i], out.data()[i]);
+  }
+}
+
+void test_multithread_compound_assignment() {
+  Tensor<float, 3> in1(2, 3, 7);
+  Tensor<float, 3> in2(2, 3, 7);
+  Tensor<float, 3> out(2, 3, 7);
+
+  in1.setRandom();
+  in2.setRandom();
+
+  Eigen::ThreadPool tp(internal::random<int>(3, 11));
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
+  out.device(thread_pool_device) = in1;
+  out.device(thread_pool_device) += in2 * 3.14f;
+
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f);
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+void test_multithread_contraction() {
+  Tensor<float, 4, DataLayout> t_left(30, 50, 37, 31);
+  Tensor<float, 5, DataLayout> t_right(37, 31, 70, 2, 10);
+  Tensor<float, 5, DataLayout> t_result(30, 50, 70, 2, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+
+  // this contraction should be equivalent to a single matrix multiplication
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+  Eigen::array<DimPair, 2> dims({{DimPair(2, 0), DimPair(3, 1)}});
+
+  typedef Map<Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 1500, 1147);
+  MapXf m_right(t_right.data(), 1147, 1400);
+  Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400);
+
+  Eigen::ThreadPool tp(4);
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, 4);
+
+  // compute results by separate methods
+  t_result.device(thread_pool_device) = t_left.contract(t_right, dims);
+  m_result = m_left * m_right;
+
+  for (ptrdiff_t i = 0; i < t_result.size(); i++) {
+    VERIFY(&t_result.data()[i] != &m_result.data()[i]);
+    if (fabsf(t_result(i) - m_result(i)) < 1e-4f) {
+      continue;
+    }
+    if (Eigen::internal::isApprox(t_result(i), m_result(i), 1e-4f)) {
+      continue;
+    }
+    std::cout << "mismatch detected at index " << i << ": " << t_result(i) << " vs " << m_result(i) << std::endl;
+    assert(false);
+  }
+}
+
+template <int DataLayout>
+void test_contraction_corner_cases() {
+  Tensor<float, 2, DataLayout> t_left(32, 500);
+  Tensor<float, 2, DataLayout> t_right(32, 28 * 28);
+  Tensor<float, 2, DataLayout> t_result(500, 28 * 28);
+
+  t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f;
+  t_right = (t_right.constant(-0.6f) + t_right.random()) * 2.0f;
+  t_result = t_result.constant(NAN);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+  Eigen::array<DimPair, 1> dims{{DimPair(0, 0)}};
+
+  typedef Map<Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 32, 500);
+  MapXf m_right(t_right.data(), 32, 28 * 28);
+  Matrix<float, Dynamic, Dynamic, DataLayout> m_result(500, 28 * 28);
+
+  Eigen::ThreadPool tp(12);
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, 12);
+
+  // compute results by separate methods
+  t_result.device(thread_pool_device) = t_left.contract(t_right, dims);
+  m_result = m_left.transpose() * m_right;
+
+  for (ptrdiff_t i = 0; i < t_result.size(); i++) {
+    assert(!(numext::isnan)(t_result.data()[i]));
+    if (fabsf(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) {
+      std::cout << "mismatch detected at index " << i << " : " << t_result.data()[i] << " vs " << m_result.data()[i]
+                << std::endl;
+      assert(false);
+    }
+  }
+
+  t_left.resize(32, 1);
+  t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f;
+  t_result.resize(1, 28 * 28);
+  t_result = t_result.constant(NAN);
+  t_result.device(thread_pool_device) = t_left.contract(t_right, dims);
+  new (&m_left) MapXf(t_left.data(), 32, 1);
+  m_result = m_left.transpose() * m_right;
+  for (ptrdiff_t i = 0; i < t_result.size(); i++) {
+    assert(!(numext::isnan)(t_result.data()[i]));
+    if (fabsf(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) {
+      std::cout << "mismatch detected: " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl;
+      assert(false);
+    }
+  }
+
+  t_left.resize(32, 500);
+  t_right.resize(32, 4);
+  t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f;
+  t_right = (t_right.constant(-0.6f) + t_right.random()) * 2.0f;
+  t_result.resize(500, 4);
+  t_result = t_result.constant(NAN);
+  t_result.device(thread_pool_device) = t_left.contract(t_right, dims);
+  new (&m_left) MapXf(t_left.data(), 32, 500);
+  new (&m_right) MapXf(t_right.data(), 32, 4);
+  m_result = m_left.transpose() * m_right;
+  for (ptrdiff_t i = 0; i < t_result.size(); i++) {
+    assert(!(numext::isnan)(t_result.data()[i]));
+    if (fabsf(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) {
+      std::cout << "mismatch detected: " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl;
+      assert(false);
+    }
+  }
+
+  t_left.resize(32, 1);
+  t_right.resize(32, 4);
+  t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f;
+  t_right = (t_right.constant(-0.6f) + t_right.random()) * 2.0f;
+  t_result.resize(1, 4);
+  t_result = t_result.constant(NAN);
+  t_result.device(thread_pool_device) = t_left.contract(t_right, dims);
+  new (&m_left) MapXf(t_left.data(), 32, 1);
+  new (&m_right) MapXf(t_right.data(), 32, 4);
+  m_result = m_left.transpose() * m_right;
+  for (ptrdiff_t i = 0; i < t_result.size(); i++) {
+    assert(!(numext::isnan)(t_result.data()[i]));
+    if (fabsf(t_result.data()[i] - m_result.data()[i]) >= 1e-4f) {
+      std::cout << "mismatch detected: " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl;
+      assert(false);
+    }
+  }
+}
+
+template <int DataLayout>
+void test_multithread_contraction_agrees_with_singlethread() {
+  int contract_size = internal::random<int>(1, 5000);
+
+  Tensor<float, 3, DataLayout> left(internal::random<int>(1, 80), contract_size, internal::random<int>(1, 100));
+
+  Tensor<float, 4, DataLayout> right(internal::random<int>(1, 25), internal::random<int>(1, 37), contract_size,
+                                     internal::random<int>(1, 51));
+
+  left.setRandom();
+  right.setRandom();
+
+  // add constants to shift values away from 0 for more precision
+  left += left.constant(1.5f);
+  right += right.constant(1.5f);
+
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+  Eigen::array<DimPair, 1> dims({{DimPair(1, 2)}});
+
+  Eigen::ThreadPool tp(internal::random<int>(2, 11));
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(2, 11));
+
+  Tensor<float, 5, DataLayout> st_result;
+  st_result = left.contract(right, dims);
+
+  Tensor<float, 5, DataLayout> tp_result(st_result.dimensions());
+  tp_result.device(thread_pool_device) = left.contract(right, dims);
+
+  VERIFY(dimensions_match(st_result.dimensions(), tp_result.dimensions()));
+  for (ptrdiff_t i = 0; i < st_result.size(); i++) {
+    // if both of the values are very small, then do nothing (because the test will fail
+    // due to numerical precision issues when values are small)
+    if (numext::abs(st_result.data()[i] - tp_result.data()[i]) >= 1e-4f) {
+      VERIFY_IS_APPROX(st_result.data()[i], tp_result.data()[i]);
+    }
+  }
+}
+
+// Apply Sqrt to all output elements.
+struct SqrtOutputKernel {
+  template <typename Index, typename Scalar>
+  EIGEN_ALWAYS_INLINE void operator()(const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,
+                                      const TensorContractionParams&, Index, Index, Index num_rows,
+                                      Index num_cols) const {
+    for (int i = 0; i < num_rows; ++i) {
+      for (int j = 0; j < num_cols; ++j) {
+        output_mapper(i, j) = std::sqrt(output_mapper(i, j));
+      }
+    }
+  }
+};
+
+template <int DataLayout>
+static void test_multithread_contraction_with_output_kernel() {
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+
+  const int num_threads = internal::random<int>(2, 11);
+  ThreadPool threads(num_threads);
+  Eigen::ThreadPoolDevice device(&threads, num_threads);
+
+  Tensor<float, 4, DataLayout> t_left(30, 50, 8, 31);
+  Tensor<float, 5, DataLayout> t_right(8, 31, 7, 20, 10);
+  Tensor<float, 5, DataLayout> t_result(30, 50, 7, 20, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+  // Put trash in mat4 to verify contraction clears output memory.
+  t_result.setRandom();
+
+  // Add a little offset so that the results won't be close to zero.
+  t_left += t_left.constant(1.0f);
+  t_right += t_right.constant(1.0f);
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 1500, 248);
+  MapXf m_right(t_right.data(), 248, 1400);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 2> dims({{DimPair(2, 0), DimPair(3, 1)}});
+
+  // compute results by separate methods
+  t_result.device(device) = t_left.contract(t_right, dims, SqrtOutputKernel());
+
+  m_result = m_left * m_right;
+
+  for (Index i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY(&t_result.data()[i] != &m_result.data()[i]);
+    VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i]));
+  }
+}
+
+template <int DataLayout>
+void test_async_multithread_contraction_agrees_with_singlethread() {
+  int contract_size = internal::random<int>(100, 500);
+
+  Tensor<float, 3, DataLayout> left(internal::random<int>(10, 40), contract_size, internal::random<int>(10, 40));
+
+  Tensor<float, 4, DataLayout> right(internal::random<int>(1, 20), internal::random<int>(1, 20), contract_size,
+                                     internal::random<int>(1, 20));
+
+  left.setRandom();
+  right.setRandom();
+
+  // add constants to shift values away from 0 for more precision
+  left += left.constant(1.5f);
+  right += right.constant(1.5f);
+
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+  Eigen::array<DimPair, 1> dims({{DimPair(1, 2)}});
+
+  Eigen::ThreadPool tp(internal::random<int>(2, 11));
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(8, 32));
+
+  Tensor<float, 5, DataLayout> st_result;
+  st_result = left.contract(right, dims);
+
+  Tensor<float, 5, DataLayout> tp_result(st_result.dimensions());
+
+  Eigen::Barrier barrier(1);
+  tp_result.device(thread_pool_device, [&barrier]() { barrier.Notify(); }) = left.contract(right, dims);
+  barrier.Wait();
+
+  VERIFY(dimensions_match(st_result.dimensions(), tp_result.dimensions()));
+  for (ptrdiff_t i = 0; i < st_result.size(); i++) {
+    // if both of the values are very small, then do nothing (because the test
+    // will fail due to numerical precision issues when values are small)
+    if (numext::abs(st_result.data()[i] - tp_result.data()[i]) >= 1e-4f) {
+      VERIFY_IS_APPROX(st_result.data()[i], tp_result.data()[i]);
+    }
+  }
+}
+
+// We are triggering 'evalShardedByInnerDim' optimization.
+template <int DataLayout>
+static void test_sharded_by_inner_dim_contraction() {
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+
+  const int num_threads = internal::random<int>(4, 16);
+  ThreadPool threads(num_threads);
+  Eigen::ThreadPoolDevice device(&threads, num_threads);
+
+  Tensor<float, 2, DataLayout> t_left(2, 10000);
+  Tensor<float, 2, DataLayout> t_right(10000, 10);
+  Tensor<float, 2, DataLayout> t_result(2, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+  // Put trash in t_result to verify contraction clears output memory.
+  t_result.setRandom();
+
+  // Add a little offset so that the results won't be close to zero.
+  t_left += t_left.constant(1.0f);
+  t_right += t_right.constant(1.0f);
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 2, 10000);
+  MapXf m_right(t_right.data(), 10000, 10);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(2, 10);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 1> dims({{DimPair(1, 0)}});
+
+  // compute results by separate methods
+  t_result.device(device) = t_left.contract(t_right, dims);
+  m_result = m_left * m_right;
+
+  for (Index i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY_IS_APPROX(t_result.data()[i], m_result.data()[i]);
+  }
+}
+
+// We are triggering 'evalShardedByInnerDim' optimization with output kernel.
+template <int DataLayout>
+static void test_sharded_by_inner_dim_contraction_with_output_kernel() {
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+
+  const int num_threads = internal::random<int>(4, 16);
+  ThreadPool threads(num_threads);
+  Eigen::ThreadPoolDevice device(&threads, num_threads);
+
+  Tensor<float, 2, DataLayout> t_left(2, 10000);
+  Tensor<float, 2, DataLayout> t_right(10000, 10);
+  Tensor<float, 2, DataLayout> t_result(2, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+  // Put trash in t_result to verify contraction clears output memory.
+  t_result.setRandom();
+
+  // Add a little offset so that the results won't be close to zero.
+  t_left += t_left.constant(1.0f);
+  t_right += t_right.constant(1.0f);
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 2, 10000);
+  MapXf m_right(t_right.data(), 10000, 10);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(2, 10);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 1> dims({{DimPair(1, 0)}});
+
+  // compute results by separate methods
+  t_result.device(device) = t_left.contract(t_right, dims, SqrtOutputKernel());
+  m_result = m_left * m_right;
+
+  for (Index i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i]));
+  }
+}
+
+// We are triggering 'evalShardedByInnerDim' optimization.
+template <int DataLayout>
+static void test_async_sharded_by_inner_dim_contraction() {
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+
+  const int num_threads = internal::random<int>(4, 16);
+  ThreadPool threads(num_threads);
+  Eigen::ThreadPoolDevice device(&threads, num_threads);
+
+  Tensor<float, 2, DataLayout> t_left(2, 10000);
+  Tensor<float, 2, DataLayout> t_right(10000, 10);
+  Tensor<float, 2, DataLayout> t_result(2, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+  // Put trash in t_result to verify contraction clears output memory.
+  t_result.setRandom();
+
+  // Add a little offset so that the results won't be close to zero.
+  t_left += t_left.constant(1.0f);
+  t_right += t_right.constant(1.0f);
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 2, 10000);
+  MapXf m_right(t_right.data(), 10000, 10);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(2, 10);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 1> dims({{DimPair(1, 0)}});
+
+  // compute results by separate methods
+  Eigen::Barrier barrier(1);
+  t_result.device(device, [&barrier]() { barrier.Notify(); }) = t_left.contract(t_right, dims);
+  barrier.Wait();
+
+  m_result = m_left * m_right;
+
+  for (Index i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY_IS_APPROX(t_result.data()[i], m_result.data()[i]);
+  }
+}
+
+// We are triggering 'evalShardedByInnerDim' optimization with output kernel.
+template <int DataLayout>
+static void test_async_sharded_by_inner_dim_contraction_with_output_kernel() {
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+
+  const int num_threads = internal::random<int>(4, 16);
+  ThreadPool threads(num_threads);
+  Eigen::ThreadPoolDevice device(&threads, num_threads);
+
+  Tensor<float, 2, DataLayout> t_left(2, 10000);
+  Tensor<float, 2, DataLayout> t_right(10000, 10);
+  Tensor<float, 2, DataLayout> t_result(2, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+  // Put trash in t_result to verify contraction clears output memory.
+  t_result.setRandom();
+
+  // Add a little offset so that the results won't be close to zero.
+  t_left += t_left.constant(1.0f);
+  t_right += t_right.constant(1.0f);
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 2, 10000);
+  MapXf m_right(t_right.data(), 10000, 10);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(2, 10);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 1> dims({{DimPair(1, 0)}});
+
+  // compute results by separate methods
+  Eigen::Barrier barrier(1);
+  t_result.device(device, [&barrier]() { barrier.Notify(); }) = t_left.contract(t_right, dims, SqrtOutputKernel());
+  barrier.Wait();
+  m_result = m_left * m_right;
+
+  for (Index i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i]));
+  }
+}
+
+template <int DataLayout>
+void test_full_contraction() {
+  int contract_size1 = internal::random<int>(1, 500);
+  int contract_size2 = internal::random<int>(1, 500);
+
+  Tensor<float, 2, DataLayout> left(contract_size1, contract_size2);
+  Tensor<float, 2, DataLayout> right(contract_size1, contract_size2);
+  left.setRandom();
+  right.setRandom();
+
+  // add constants to shift values away from 0 for more precision
+  left += left.constant(1.5f);
+  right += right.constant(1.5f);
+
+  typedef Tensor<float, 2>::DimensionPair DimPair;
+  Eigen::array<DimPair, 2> dims({{DimPair(0, 0), DimPair(1, 1)}});
+
+  Eigen::ThreadPool tp(internal::random<int>(2, 11));
+  Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(2, 11));
+
+  Tensor<float, 0, DataLayout> st_result;
+  st_result = left.contract(right, dims);
+
+  Tensor<float, 0, DataLayout> tp_result;
+  tp_result.device(thread_pool_device) = left.contract(right, dims);
+
+  VERIFY(dimensions_match(st_result.dimensions(), tp_result.dimensions()));
+  // if both of the values are very small, then do nothing (because the test will fail
+  // due to numerical precision issues when values are small)
+  if (numext::abs(st_result() - tp_result()) >= 1e-4f) {
+    VERIFY_IS_APPROX(st_result(), tp_result());
+  }
+}
+
+template <int DataLayout>
+void test_multithreaded_reductions() {
+  const int num_threads = internal::random<int>(3, 11);
+  ThreadPool thread_pool(num_threads);
+  Eigen::ThreadPoolDevice thread_pool_device(&thread_pool, num_threads);
+
+  const int num_rows = internal::random<int>(13, 732);
+  const int num_cols = internal::random<int>(13, 732);
+  Tensor<float, 2, DataLayout> t1(num_rows, num_cols);
+  t1.setRandom();
+
+  Tensor<float, 0, DataLayout> full_redux;
+  full_redux = t1.sum();
+
+  Tensor<float, 0, DataLayout> full_redux_tp;
+  full_redux_tp.device(thread_pool_device) = t1.sum();
+
+  // Check that the single threaded and the multi threaded reductions return
+  // the same result.
+  VERIFY_IS_APPROX(full_redux(), full_redux_tp());
+}
+
+void test_memcpy() {
+  for (int i = 0; i < 5; ++i) {
+    const int num_threads = internal::random<int>(3, 11);
+    Eigen::ThreadPool tp(num_threads);
+    Eigen::ThreadPoolDevice thread_pool_device(&tp, num_threads);
+
+    const int size = internal::random<int>(13, 7632);
+    Tensor<float, 1> t1(size);
+    t1.setRandom();
+    std::vector<float> result(size);
+    thread_pool_device.memcpy(&result[0], t1.data(), size * sizeof(float));
+    for (int j = 0; j < size; j++) {
+      VERIFY_IS_EQUAL(t1(j), result[j]);
+    }
+  }
+}
+
+void test_multithread_random() {
+  Eigen::ThreadPool tp(2);
+  Eigen::ThreadPoolDevice device(&tp, 2);
+  Tensor<float, 1> t(1 << 20);
+  t.device(device) = t.random<Eigen::internal::NormalRandomGenerator<float>>();
+}
+
+template <int DataLayout>
+void test_multithread_shuffle(Allocator* allocator) {
+  Tensor<float, 4, DataLayout> tensor(17, 5, 7, 11);
+  tensor.setRandom();
+
+  const int num_threads = internal::random<int>(2, 11);
+  ThreadPool threads(num_threads);
+  Eigen::ThreadPoolDevice device(&threads, num_threads, allocator);
+
+  Tensor<float, 4, DataLayout> shuffle(7, 5, 11, 17);
+  array<ptrdiff_t, 4> shuffles = {{2, 1, 3, 0}};
+  shuffle.device(device) = tensor.shuffle(shuffles);
+
+  for (int i = 0; i < 17; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      for (int k = 0; k < 7; ++k) {
+        for (int l = 0; l < 11; ++l) {
+          VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, j, l, i));
+        }
+      }
+    }
+  }
+}
+
+void test_threadpool_allocate(TestAllocator* allocator) {
+  const int num_threads = internal::random<int>(2, 11);
+  const int num_allocs = internal::random<int>(2, 11);
+  ThreadPool threads(num_threads);
+  Eigen::ThreadPoolDevice device(&threads, num_threads, allocator);
+
+  for (int a = 0; a < num_allocs; ++a) {
+    void* ptr = device.allocate(512);
+    device.deallocate(ptr);
+  }
+  VERIFY(allocator != NULL);
+  VERIFY_IS_EQUAL(allocator->alloc_count(), num_allocs);
+  VERIFY_IS_EQUAL(allocator->dealloc_count(), num_allocs);
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_thread_pool) {
+  CALL_SUBTEST_1(test_multithread_elementwise());
+  CALL_SUBTEST_1(test_async_multithread_elementwise());
+  CALL_SUBTEST_1(test_multithread_compound_assignment());
+
+  CALL_SUBTEST_2(test_multithread_contraction<ColMajor>());
+  CALL_SUBTEST_2(test_multithread_contraction<RowMajor>());
+
+  CALL_SUBTEST_3(test_multithread_chip());
+  CALL_SUBTEST_3(test_async_multithread_chip());
+
+  CALL_SUBTEST_4(test_multithread_volume_patch());
+  CALL_SUBTEST_4(test_async_multithread_volume_patch());
+
+  CALL_SUBTEST_5(test_multithread_contraction_agrees_with_singlethread<ColMajor>());
+  CALL_SUBTEST_5(test_multithread_contraction_agrees_with_singlethread<RowMajor>());
+  CALL_SUBTEST_5(test_multithread_contraction_with_output_kernel<ColMajor>());
+  CALL_SUBTEST_5(test_multithread_contraction_with_output_kernel<RowMajor>());
+
+  CALL_SUBTEST_6(test_async_multithread_contraction_agrees_with_singlethread<ColMajor>());
+  CALL_SUBTEST_6(test_async_multithread_contraction_agrees_with_singlethread<RowMajor>());
+
+  // Test EvalShardedByInnerDimContext parallelization strategy.
+  CALL_SUBTEST_7(test_sharded_by_inner_dim_contraction<ColMajor>());
+  CALL_SUBTEST_7(test_sharded_by_inner_dim_contraction<RowMajor>());
+  CALL_SUBTEST_7(test_sharded_by_inner_dim_contraction_with_output_kernel<ColMajor>());
+  CALL_SUBTEST_7(test_sharded_by_inner_dim_contraction_with_output_kernel<RowMajor>());
+
+  CALL_SUBTEST_8(test_async_sharded_by_inner_dim_contraction<ColMajor>());
+  CALL_SUBTEST_8(test_async_sharded_by_inner_dim_contraction<RowMajor>());
+  CALL_SUBTEST_8(test_async_sharded_by_inner_dim_contraction_with_output_kernel<ColMajor>());
+  CALL_SUBTEST_8(test_async_sharded_by_inner_dim_contraction_with_output_kernel<RowMajor>());
+
+  // Exercise various cases that have been problematic in the past.
+  CALL_SUBTEST_9(test_contraction_corner_cases<ColMajor>());
+  CALL_SUBTEST_9(test_contraction_corner_cases<RowMajor>());
+
+  CALL_SUBTEST_10(test_full_contraction<ColMajor>());
+  CALL_SUBTEST_10(test_full_contraction<RowMajor>());
+
+  CALL_SUBTEST_11(test_multithreaded_reductions<ColMajor>());
+  CALL_SUBTEST_11(test_multithreaded_reductions<RowMajor>());
+
+  CALL_SUBTEST_12(test_memcpy());
+  CALL_SUBTEST_12(test_multithread_random());
+
+  TestAllocator test_allocator;
+  CALL_SUBTEST_13(test_multithread_shuffle<ColMajor>(NULL));
+  CALL_SUBTEST_13(test_multithread_shuffle<RowMajor>(&test_allocator));
+  CALL_SUBTEST_13(test_threadpool_allocate(&test_allocator));
+
+  // Force CMake to split this test.
+  // EIGEN_SUFFIXES;1;2;3;4;5;6;7;8;9;10;11;12;13
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_trace.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_trace.cpp
new file mode 100644
index 0000000..5b95839
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_trace.cpp
@@ -0,0 +1,168 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gagan Goel <gagan.nith@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::Tensor;
+
+template <int DataLayout>
+static void test_0D_trace() {
+  Tensor<float, 0, DataLayout> tensor;
+  tensor.setRandom();
+  array<ptrdiff_t, 0> dims;
+  Tensor<float, 0, DataLayout> result = tensor.trace(dims);
+  VERIFY_IS_EQUAL(result(), tensor());
+}
+
+template <int DataLayout>
+static void test_all_dimensions_trace() {
+  Tensor<float, 3, DataLayout> tensor1(5, 5, 5);
+  tensor1.setRandom();
+  Tensor<float, 0, DataLayout> result1 = tensor1.trace();
+  VERIFY_IS_EQUAL(result1.rank(), 0);
+  float sum = 0.0f;
+  for (int i = 0; i < 5; ++i) {
+    sum += tensor1(i, i, i);
+  }
+  VERIFY_IS_EQUAL(result1(), sum);
+
+  Tensor<float, 5, DataLayout> tensor2(7, 7, 7, 7, 7);
+  tensor2.setRandom();
+  array<ptrdiff_t, 5> dims = {{2, 1, 0, 3, 4}};
+  Tensor<float, 0, DataLayout> result2 = tensor2.trace(dims);
+  VERIFY_IS_EQUAL(result2.rank(), 0);
+  sum = 0.0f;
+  for (int i = 0; i < 7; ++i) {
+    sum += tensor2(i, i, i, i, i);
+  }
+  VERIFY_IS_EQUAL(result2(), sum);
+}
+
+template <int DataLayout>
+static void test_simple_trace() {
+  Tensor<float, 3, DataLayout> tensor1(3, 5, 3);
+  tensor1.setRandom();
+  array<ptrdiff_t, 2> dims1 = {{0, 2}};
+  Tensor<float, 1, DataLayout> result1 = tensor1.trace(dims1);
+  VERIFY_IS_EQUAL(result1.rank(), 1);
+  VERIFY_IS_EQUAL(result1.dimension(0), 5);
+  float sum = 0.0f;
+  for (int i = 0; i < 5; ++i) {
+    sum = 0.0f;
+    for (int j = 0; j < 3; ++j) {
+      sum += tensor1(j, i, j);
+    }
+    VERIFY_IS_EQUAL(result1(i), sum);
+  }
+
+  Tensor<float, 4, DataLayout> tensor2(5, 5, 7, 7);
+  tensor2.setRandom();
+  array<ptrdiff_t, 2> dims2 = {{2, 3}};
+  Tensor<float, 2, DataLayout> result2 = tensor2.trace(dims2);
+  VERIFY_IS_EQUAL(result2.rank(), 2);
+  VERIFY_IS_EQUAL(result2.dimension(0), 5);
+  VERIFY_IS_EQUAL(result2.dimension(1), 5);
+  for (int i = 0; i < 5; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      sum = 0.0f;
+      for (int k = 0; k < 7; ++k) {
+        sum += tensor2(i, j, k, k);
+      }
+      VERIFY_IS_EQUAL(result2(i, j), sum);
+    }
+  }
+
+  array<ptrdiff_t, 2> dims3 = {{1, 0}};
+  Tensor<float, 2, DataLayout> result3 = tensor2.trace(dims3);
+  VERIFY_IS_EQUAL(result3.rank(), 2);
+  VERIFY_IS_EQUAL(result3.dimension(0), 7);
+  VERIFY_IS_EQUAL(result3.dimension(1), 7);
+  for (int i = 0; i < 7; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      sum = 0.0f;
+      for (int k = 0; k < 5; ++k) {
+        sum += tensor2(k, k, i, j);
+      }
+      VERIFY_IS_EQUAL(result3(i, j), sum);
+    }
+  }
+
+  Tensor<float, 5, DataLayout> tensor3(3, 7, 3, 7, 3);
+  tensor3.setRandom();
+  array<ptrdiff_t, 3> dims4 = {{0, 2, 4}};
+  Tensor<float, 2, DataLayout> result4 = tensor3.trace(dims4);
+  VERIFY_IS_EQUAL(result4.rank(), 2);
+  VERIFY_IS_EQUAL(result4.dimension(0), 7);
+  VERIFY_IS_EQUAL(result4.dimension(1), 7);
+  for (int i = 0; i < 7; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      sum = 0.0f;
+      for (int k = 0; k < 3; ++k) {
+        sum += tensor3(k, i, k, j, k);
+      }
+      VERIFY_IS_EQUAL(result4(i, j), sum);
+    }
+  }
+
+  Tensor<float, 5, DataLayout> tensor4(3, 7, 4, 7, 5);
+  tensor4.setRandom();
+  array<ptrdiff_t, 2> dims5 = {{1, 3}};
+  Tensor<float, 3, DataLayout> result5 = tensor4.trace(dims5);
+  VERIFY_IS_EQUAL(result5.rank(), 3);
+  VERIFY_IS_EQUAL(result5.dimension(0), 3);
+  VERIFY_IS_EQUAL(result5.dimension(1), 4);
+  VERIFY_IS_EQUAL(result5.dimension(2), 5);
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 4; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        sum = 0.0f;
+        for (int l = 0; l < 7; ++l) {
+          sum += tensor4(i, l, j, l, k);
+        }
+        VERIFY_IS_EQUAL(result5(i, j, k), sum);
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_trace_in_expr() {
+  Tensor<float, 4, DataLayout> tensor(2, 3, 5, 3);
+  tensor.setRandom();
+  array<ptrdiff_t, 2> dims = {{1, 3}};
+  Tensor<float, 2, DataLayout> result(2, 5);
+  result = result.constant(1.0f) - tensor.trace(dims);
+  VERIFY_IS_EQUAL(result.rank(), 2);
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_EQUAL(result.dimension(1), 5);
+  float sum = 0.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      sum = 0.0f;
+      for (int k = 0; k < 3; ++k) {
+        sum += tensor(i, k, j, k);
+      }
+      VERIFY_IS_EQUAL(result(i, j), 1.0f - sum);
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_trace) {
+  CALL_SUBTEST(test_0D_trace<ColMajor>());
+  CALL_SUBTEST(test_0D_trace<RowMajor>());
+  CALL_SUBTEST(test_all_dimensions_trace<ColMajor>());
+  CALL_SUBTEST(test_all_dimensions_trace<RowMajor>());
+  CALL_SUBTEST(test_simple_trace<ColMajor>());
+  CALL_SUBTEST(test_simple_trace<RowMajor>());
+  CALL_SUBTEST(test_trace_in_expr<ColMajor>());
+  CALL_SUBTEST(test_trace_in_expr<RowMajor>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_uint128.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_uint128.cpp
new file mode 100644
index 0000000..4470f25
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_uint128.cpp
@@ -0,0 +1,157 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+#if EIGEN_COMP_MSVC || !defined(__SIZEOF_INT128__)
+#define EIGEN_NO_INT128
+#else
+typedef __uint128_t uint128_t;
+#endif
+
+// Only run the test on compilers that support 128bit integers natively
+#ifndef EIGEN_NO_INT128
+
+using Eigen::internal::static_val;
+using Eigen::internal::TensorUInt128;
+
+void VERIFY_EQUAL(TensorUInt128<uint64_t, uint64_t> actual, uint128_t expected) {
+  bool matchl = actual.lower() == static_cast<uint64_t>(expected);
+  bool matchh = actual.upper() == static_cast<uint64_t>(expected >> 64);
+  if (!matchl || !matchh) {
+    const char* testname = g_test_stack.back().c_str();
+    std::cerr << "Test " << testname << " failed in " << __FILE__ << " (" << __LINE__ << ")" << std::endl;
+    abort();
+  }
+}
+
+void test_add() {
+  uint64_t incr = internal::random<uint64_t>(1, 9999999999);
+  for (uint64_t i1 = 0; i1 < 100; ++i1) {
+    for (uint64_t i2 = 1; i2 < 100 * incr; i2 += incr) {
+      TensorUInt128<uint64_t, uint64_t> i(i1, i2);
+      uint128_t a = (static_cast<uint128_t>(i1) << 64) + static_cast<uint128_t>(i2);
+      for (uint64_t j1 = 0; j1 < 100; ++j1) {
+        for (uint64_t j2 = 1; j2 < 100 * incr; j2 += incr) {
+          TensorUInt128<uint64_t, uint64_t> j(j1, j2);
+          uint128_t b = (static_cast<uint128_t>(j1) << 64) + static_cast<uint128_t>(j2);
+          TensorUInt128<uint64_t, uint64_t> actual = i + j;
+          uint128_t expected = a + b;
+          VERIFY_EQUAL(actual, expected);
+        }
+      }
+    }
+  }
+}
+
+void test_sub() {
+  uint64_t incr = internal::random<uint64_t>(1, 9999999999);
+  for (uint64_t i1 = 0; i1 < 100; ++i1) {
+    for (uint64_t i2 = 1; i2 < 100 * incr; i2 += incr) {
+      TensorUInt128<uint64_t, uint64_t> i(i1, i2);
+      uint128_t a = (static_cast<uint128_t>(i1) << 64) + static_cast<uint128_t>(i2);
+      for (uint64_t j1 = 0; j1 < 100; ++j1) {
+        for (uint64_t j2 = 1; j2 < 100 * incr; j2 += incr) {
+          TensorUInt128<uint64_t, uint64_t> j(j1, j2);
+          uint128_t b = (static_cast<uint128_t>(j1) << 64) + static_cast<uint128_t>(j2);
+          TensorUInt128<uint64_t, uint64_t> actual = i - j;
+          uint128_t expected = a - b;
+          VERIFY_EQUAL(actual, expected);
+        }
+      }
+    }
+  }
+}
+
+void test_mul() {
+  uint64_t incr = internal::random<uint64_t>(1, 9999999999);
+  for (uint64_t i1 = 0; i1 < 100; ++i1) {
+    for (uint64_t i2 = 1; i2 < 100 * incr; i2 += incr) {
+      TensorUInt128<uint64_t, uint64_t> i(i1, i2);
+      uint128_t a = (static_cast<uint128_t>(i1) << 64) + static_cast<uint128_t>(i2);
+      for (uint64_t j1 = 0; j1 < 100; ++j1) {
+        for (uint64_t j2 = 1; j2 < 100 * incr; j2 += incr) {
+          TensorUInt128<uint64_t, uint64_t> j(j1, j2);
+          uint128_t b = (static_cast<uint128_t>(j1) << 64) + static_cast<uint128_t>(j2);
+          TensorUInt128<uint64_t, uint64_t> actual = i * j;
+          uint128_t expected = a * b;
+          VERIFY_EQUAL(actual, expected);
+        }
+      }
+    }
+  }
+}
+
+void test_div() {
+  uint64_t incr = internal::random<uint64_t>(1, 9999999999);
+  for (uint64_t i1 = 0; i1 < 100; ++i1) {
+    for (uint64_t i2 = 1; i2 < 100 * incr; i2 += incr) {
+      TensorUInt128<uint64_t, uint64_t> i(i1, i2);
+      uint128_t a = (static_cast<uint128_t>(i1) << 64) + static_cast<uint128_t>(i2);
+      for (uint64_t j1 = 0; j1 < 100; ++j1) {
+        for (uint64_t j2 = 1; j2 < 100 * incr; j2 += incr) {
+          TensorUInt128<uint64_t, uint64_t> j(j1, j2);
+          uint128_t b = (static_cast<uint128_t>(j1) << 64) + static_cast<uint128_t>(j2);
+          TensorUInt128<uint64_t, uint64_t> actual = i / j;
+          uint128_t expected = a / b;
+          VERIFY_EQUAL(actual, expected);
+        }
+      }
+    }
+  }
+}
+
+void test_misc1() {
+  uint64_t incr = internal::random<uint64_t>(1, 9999999999);
+  for (uint64_t i2 = 1; i2 < 100 * incr; i2 += incr) {
+    TensorUInt128<static_val<0>, uint64_t> i(0, i2);
+    uint128_t a = static_cast<uint128_t>(i2);
+    for (uint64_t j2 = 1; j2 < 100 * incr; j2 += incr) {
+      TensorUInt128<static_val<0>, uint64_t> j(0, j2);
+      uint128_t b = static_cast<uint128_t>(j2);
+      uint64_t actual = (i * j).upper();
+      uint64_t expected = (a * b) >> 64;
+      VERIFY_IS_EQUAL(actual, expected);
+    }
+  }
+}
+
+void test_misc2() {
+  int64_t incr = internal::random<int64_t>(1, 100);
+  for (int64_t log_div = 0; log_div < 63; ++log_div) {
+    for (int64_t divider = 1; divider <= 1000000 * incr; divider += incr) {
+      uint64_t expected = (static_cast<uint128_t>(1) << (64 + log_div)) / static_cast<uint128_t>(divider) -
+                          (static_cast<uint128_t>(1) << 64) + 1;
+      uint64_t shift = 1ULL << log_div;
+
+      TensorUInt128<uint64_t, uint64_t> result =
+          (TensorUInt128<uint64_t, static_val<0> >(shift, 0) / TensorUInt128<static_val<0>, uint64_t>(divider) -
+           TensorUInt128<static_val<1>, static_val<0> >(1, 0) + TensorUInt128<static_val<0>, static_val<1> >(1));
+      uint64_t actual = static_cast<uint64_t>(result);
+      VERIFY_IS_EQUAL(actual, expected);
+    }
+  }
+}
+#endif
+
+EIGEN_DECLARE_TEST(cxx11_tensor_uint128) {
+#ifdef EIGEN_NO_INT128
+  // Skip the test on compilers that don't support 128bit integers natively
+  return;
+#else
+  CALL_SUBTEST_1(test_add());
+  CALL_SUBTEST_2(test_sub());
+  CALL_SUBTEST_3(test_mul());
+  CALL_SUBTEST_4(test_div());
+  CALL_SUBTEST_5(test_misc1());
+  CALL_SUBTEST_6(test_misc2());
+#endif
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_volume_patch.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_volume_patch.cpp
new file mode 100644
index 0000000..9a04861
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_volume_patch.cpp
@@ -0,0 +1,107 @@
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+static void test_single_voxel_patch() {
+  Tensor<float, 5> tensor(4, 2, 3, 5, 7);
+  tensor.setRandom();
+  Tensor<float, 5, RowMajor> tensor_row_major = tensor.swap_layout();
+
+  Tensor<float, 6> single_voxel_patch;
+  single_voxel_patch = tensor.extract_volume_patches(1, 1, 1);
+  VERIFY_IS_EQUAL(single_voxel_patch.dimension(0), 4);
+  VERIFY_IS_EQUAL(single_voxel_patch.dimension(1), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch.dimension(3), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch.dimension(4), 2 * 3 * 5);
+  VERIFY_IS_EQUAL(single_voxel_patch.dimension(5), 7);
+
+  Tensor<float, 6, RowMajor> single_voxel_patch_row_major;
+  single_voxel_patch_row_major = tensor_row_major.extract_volume_patches(1, 1, 1);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(0), 7);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(1), 2 * 3 * 5);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(3), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(4), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(5), 4);
+
+  for (int i = 0; i < tensor.size(); ++i) {
+    VERIFY_IS_EQUAL(tensor.data()[i], single_voxel_patch.data()[i]);
+    VERIFY_IS_EQUAL(tensor_row_major.data()[i], single_voxel_patch_row_major.data()[i]);
+    VERIFY_IS_EQUAL(tensor.data()[i], tensor_row_major.data()[i]);
+  }
+}
+
+static void test_entire_volume_patch() {
+  const int depth = 4;
+  const int patch_z = 2;
+  const int patch_y = 3;
+  const int patch_x = 5;
+  const int batch = 7;
+
+  Tensor<float, 5> tensor(depth, patch_z, patch_y, patch_x, batch);
+  tensor.setRandom();
+  Tensor<float, 5, RowMajor> tensor_row_major = tensor.swap_layout();
+
+  Tensor<float, 6> entire_volume_patch;
+  entire_volume_patch = tensor.extract_volume_patches(patch_z, patch_y, patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch.dimension(0), depth);
+  VERIFY_IS_EQUAL(entire_volume_patch.dimension(1), patch_z);
+  VERIFY_IS_EQUAL(entire_volume_patch.dimension(2), patch_y);
+  VERIFY_IS_EQUAL(entire_volume_patch.dimension(3), patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch.dimension(4), patch_z * patch_y * patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch.dimension(5), batch);
+
+  Tensor<float, 6, RowMajor> entire_volume_patch_row_major;
+  entire_volume_patch_row_major = tensor_row_major.extract_volume_patches(patch_z, patch_y, patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(0), batch);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(1), patch_z * patch_y * patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(2), patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(3), patch_y);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(4), patch_z);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(5), depth);
+
+  const int dz = patch_z - 1;
+  const int dy = patch_y - 1;
+  const int dx = patch_x - 1;
+
+  const int forward_pad_z = dz / 2;
+  const int forward_pad_y = dy / 2;
+  const int forward_pad_x = dx / 2;
+
+  for (int pz = 0; pz < patch_z; pz++) {
+    for (int py = 0; py < patch_y; py++) {
+      for (int px = 0; px < patch_x; px++) {
+        const int patchId = pz + patch_z * (py + px * patch_y);
+        for (int z = 0; z < patch_z; z++) {
+          for (int y = 0; y < patch_y; y++) {
+            for (int x = 0; x < patch_x; x++) {
+              for (int b = 0; b < batch; b++) {
+                for (int d = 0; d < depth; d++) {
+                  float expected = 0.0f;
+                  float expected_row_major = 0.0f;
+                  const int eff_z = z - forward_pad_z + pz;
+                  const int eff_y = y - forward_pad_y + py;
+                  const int eff_x = x - forward_pad_x + px;
+                  if (eff_z >= 0 && eff_y >= 0 && eff_x >= 0 && eff_z < patch_z && eff_y < patch_y && eff_x < patch_x) {
+                    expected = tensor(d, eff_z, eff_y, eff_x, b);
+                    expected_row_major = tensor_row_major(b, eff_x, eff_y, eff_z, d);
+                  }
+                  VERIFY_IS_EQUAL(entire_volume_patch(d, z, y, x, patchId, b), expected);
+                  VERIFY_IS_EQUAL(entire_volume_patch_row_major(b, patchId, x, y, z, d), expected_row_major);
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_volume_patch) {
+  CALL_SUBTEST(test_single_voxel_patch());
+  CALL_SUBTEST(test_entire_volume_patch());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_volume_patch_sycl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_volume_patch_sycl.cpp
new file mode 100644
index 0000000..6a4248e
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/cxx11_tensor_volume_patch_sycl.cpp
@@ -0,0 +1,229 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+static const int DataLayout = ColMajor;
+
+template <typename DataType, typename IndexType>
+static void test_single_voxel_patch_sycl(const Eigen::SyclDevice& sycl_device) {
+  IndexType sizeDim0 = 4;
+  IndexType sizeDim1 = 2;
+  IndexType sizeDim2 = 3;
+  IndexType sizeDim3 = 5;
+  IndexType sizeDim4 = 7;
+  array<IndexType, 5> tensorColMajorRange = {{sizeDim0, sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
+  array<IndexType, 5> tensorRowMajorRange = {{sizeDim4, sizeDim3, sizeDim2, sizeDim1, sizeDim0}};
+  Tensor<DataType, 5, DataLayout, IndexType> tensor_col_major(tensorColMajorRange);
+  Tensor<DataType, 5, RowMajor, IndexType> tensor_row_major(tensorRowMajorRange);
+  tensor_col_major.setRandom();
+
+  DataType* gpu_data_col_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size() * sizeof(DataType)));
+  DataType* gpu_data_row_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 5, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),
+                                 (tensor_col_major.size()) * sizeof(DataType));
+  gpu_row_major.device(sycl_device) = gpu_col_major.swap_layout();
+
+  // single volume patch: ColMajor
+  array<IndexType, 6> patchColMajorTensorRange = {{sizeDim0, 1, 1, 1, sizeDim1 * sizeDim2 * sizeDim3, sizeDim4}};
+  Tensor<DataType, 6, DataLayout, IndexType> single_voxel_patch_col_major(patchColMajorTensorRange);
+  size_t patchTensorBuffSize = single_voxel_patch_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_single_voxel_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 6, DataLayout, IndexType>> gpu_single_voxel_patch_col_major(
+      gpu_data_single_voxel_patch_col_major, patchColMajorTensorRange);
+  gpu_single_voxel_patch_col_major.device(sycl_device) = gpu_col_major.extract_volume_patches(1, 1, 1);
+  sycl_device.memcpyDeviceToHost(single_voxel_patch_col_major.data(), gpu_data_single_voxel_patch_col_major,
+                                 patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(0), 4);
+  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(1), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(3), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(4), 2 * 3 * 5);
+  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(5), 7);
+
+  array<IndexType, 6> patchRowMajorTensorRange = {{sizeDim4, sizeDim1 * sizeDim2 * sizeDim3, 1, 1, 1, sizeDim0}};
+  Tensor<DataType, 6, RowMajor, IndexType> single_voxel_patch_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = single_voxel_patch_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_single_voxel_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 6, RowMajor, IndexType>> gpu_single_voxel_patch_row_major(
+      gpu_data_single_voxel_patch_row_major, patchRowMajorTensorRange);
+  gpu_single_voxel_patch_row_major.device(sycl_device) = gpu_row_major.extract_volume_patches(1, 1, 1);
+  sycl_device.memcpyDeviceToHost(single_voxel_patch_row_major.data(), gpu_data_single_voxel_patch_row_major,
+                                 patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(0), 7);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(1), 2 * 3 * 5);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(2), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(3), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(4), 1);
+  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(5), 4);
+
+  sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major,
+                                 (tensor_col_major.size()) * sizeof(DataType));
+  for (IndexType i = 0; i < tensor_col_major.size(); ++i) {
+    VERIFY_IS_EQUAL(tensor_col_major.data()[i], single_voxel_patch_col_major.data()[i]);
+    VERIFY_IS_EQUAL(tensor_row_major.data()[i], single_voxel_patch_row_major.data()[i]);
+    VERIFY_IS_EQUAL(tensor_col_major.data()[i], tensor_row_major.data()[i]);
+  }
+
+  sycl_device.deallocate(gpu_data_col_major);
+  sycl_device.deallocate(gpu_data_row_major);
+  sycl_device.deallocate(gpu_data_single_voxel_patch_col_major);
+  sycl_device.deallocate(gpu_data_single_voxel_patch_row_major);
+}
+
+template <typename DataType, typename IndexType>
+static void test_entire_volume_patch_sycl(const Eigen::SyclDevice& sycl_device) {
+  const int depth = 4;
+  const int patch_z = 2;
+  const int patch_y = 3;
+  const int patch_x = 5;
+  const int batch = 7;
+
+  array<IndexType, 5> tensorColMajorRange = {{depth, patch_z, patch_y, patch_x, batch}};
+  array<IndexType, 5> tensorRowMajorRange = {{batch, patch_x, patch_y, patch_z, depth}};
+  Tensor<DataType, 5, DataLayout, IndexType> tensor_col_major(tensorColMajorRange);
+  Tensor<DataType, 5, RowMajor, IndexType> tensor_row_major(tensorRowMajorRange);
+  tensor_col_major.setRandom();
+
+  DataType* gpu_data_col_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size() * sizeof(DataType)));
+  DataType* gpu_data_row_major =
+      static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size() * sizeof(DataType)));
+  TensorMap<Tensor<DataType, 5, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
+  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),
+                                 (tensor_col_major.size()) * sizeof(DataType));
+  gpu_row_major.device(sycl_device) = gpu_col_major.swap_layout();
+  sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major,
+                                 (tensor_col_major.size()) * sizeof(DataType));
+
+  // single volume patch: ColMajor
+  array<IndexType, 6> patchColMajorTensorRange = {
+      {depth, patch_z, patch_y, patch_x, patch_z * patch_y * patch_x, batch}};
+  Tensor<DataType, 6, DataLayout, IndexType> entire_volume_patch_col_major(patchColMajorTensorRange);
+  size_t patchTensorBuffSize = entire_volume_patch_col_major.size() * sizeof(DataType);
+  DataType* gpu_data_entire_volume_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 6, DataLayout, IndexType>> gpu_entire_volume_patch_col_major(
+      gpu_data_entire_volume_patch_col_major, patchColMajorTensorRange);
+  gpu_entire_volume_patch_col_major.device(sycl_device) =
+      gpu_col_major.extract_volume_patches(patch_z, patch_y, patch_x);
+  sycl_device.memcpyDeviceToHost(entire_volume_patch_col_major.data(), gpu_data_entire_volume_patch_col_major,
+                                 patchTensorBuffSize);
+
+  //  Tensor<float, 5> tensor(depth, patch_z, patch_y, patch_x, batch);
+  //  tensor.setRandom();
+  //  Tensor<float, 5, RowMajor> tensor_row_major = tensor.swap_layout();
+
+  // Tensor<float, 6> entire_volume_patch;
+  // entire_volume_patch = tensor.extract_volume_patches(patch_z, patch_y, patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(0), depth);
+  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(1), patch_z);
+  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(2), patch_y);
+  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(3), patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(4), patch_z * patch_y * patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(5), batch);
+
+  //  Tensor<float, 6, RowMajor> entire_volume_patch_row_major;
+  // entire_volume_patch_row_major = tensor_row_major.extract_volume_patches(patch_z, patch_y, patch_x);
+
+  array<IndexType, 6> patchRowMajorTensorRange = {
+      {batch, patch_z * patch_y * patch_x, patch_x, patch_y, patch_z, depth}};
+  Tensor<DataType, 6, RowMajor, IndexType> entire_volume_patch_row_major(patchRowMajorTensorRange);
+  patchTensorBuffSize = entire_volume_patch_row_major.size() * sizeof(DataType);
+  DataType* gpu_data_entire_volume_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+  TensorMap<Tensor<DataType, 6, RowMajor, IndexType>> gpu_entire_volume_patch_row_major(
+      gpu_data_entire_volume_patch_row_major, patchRowMajorTensorRange);
+  gpu_entire_volume_patch_row_major.device(sycl_device) =
+      gpu_row_major.extract_volume_patches(patch_z, patch_y, patch_x);
+  sycl_device.memcpyDeviceToHost(entire_volume_patch_row_major.data(), gpu_data_entire_volume_patch_row_major,
+                                 patchTensorBuffSize);
+
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(0), batch);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(1), patch_z * patch_y * patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(2), patch_x);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(3), patch_y);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(4), patch_z);
+  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(5), depth);
+
+  const int dz = patch_z - 1;
+  const int dy = patch_y - 1;
+  const int dx = patch_x - 1;
+
+  const int forward_pad_z = dz / 2;
+  const int forward_pad_y = dy / 2;
+  const int forward_pad_x = dx / 2;
+
+  for (int pz = 0; pz < patch_z; pz++) {
+    for (int py = 0; py < patch_y; py++) {
+      for (int px = 0; px < patch_x; px++) {
+        const int patchId = pz + patch_z * (py + px * patch_y);
+        for (int z = 0; z < patch_z; z++) {
+          for (int y = 0; y < patch_y; y++) {
+            for (int x = 0; x < patch_x; x++) {
+              for (int b = 0; b < batch; b++) {
+                for (int d = 0; d < depth; d++) {
+                  float expected = 0.0f;
+                  float expected_row_major = 0.0f;
+                  const int eff_z = z - forward_pad_z + pz;
+                  const int eff_y = y - forward_pad_y + py;
+                  const int eff_x = x - forward_pad_x + px;
+                  if (eff_z >= 0 && eff_y >= 0 && eff_x >= 0 && eff_z < patch_z && eff_y < patch_y && eff_x < patch_x) {
+                    expected = tensor_col_major(d, eff_z, eff_y, eff_x, b);
+                    expected_row_major = tensor_row_major(b, eff_x, eff_y, eff_z, d);
+                  }
+                  VERIFY_IS_EQUAL(entire_volume_patch_col_major(d, z, y, x, patchId, b), expected);
+                  VERIFY_IS_EQUAL(entire_volume_patch_row_major(b, patchId, x, y, z, d), expected_row_major);
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  sycl_device.deallocate(gpu_data_col_major);
+  sycl_device.deallocate(gpu_data_row_major);
+  sycl_device.deallocate(gpu_data_entire_volume_patch_col_major);
+  sycl_device.deallocate(gpu_data_entire_volume_patch_row_major);
+}
+
+template <typename DataType, typename dev_Selector>
+void sycl_tensor_volume_patch_test_per_device(dev_Selector s) {
+  QueueInterface queueInterface(s);
+  auto sycl_device = Eigen::SyclDevice(&queueInterface);
+  std::cout << "Running on " << s.template get_info<cl::sycl::info::device::name>() << std::endl;
+  test_single_voxel_patch_sycl<DataType, int64_t>(sycl_device);
+  test_entire_volume_patch_sycl<DataType, int64_t>(sycl_device);
+}
+EIGEN_DECLARE_TEST(cxx11_tensor_volume_patch_sycl) {
+  for (const auto& device : Eigen::get_sycl_supported_devices()) {
+    CALL_SUBTEST(sycl_tensor_volume_patch_test_per_device<half>(device));
+    CALL_SUBTEST(sycl_tensor_volume_patch_test_per_device<float>(device));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/dgmres.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/dgmres.cpp
new file mode 100644
index 0000000..940800c
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/dgmres.cpp
@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
+// Copyright (C) 2012 desire Nuentsa <desire.nuentsa_wakam@inria.fr
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "../../test/sparse_solver.h"
+#include <unsupported/Eigen/IterativeSolvers>
+
+template <typename T>
+void test_dgmres_T() {
+  DGMRES<SparseMatrix<T>, DiagonalPreconditioner<T> > dgmres_colmajor_diag;
+  DGMRES<SparseMatrix<T>, IdentityPreconditioner> dgmres_colmajor_I;
+  DGMRES<SparseMatrix<T>, IncompleteLUT<T> > dgmres_colmajor_ilut;
+  // GMRES<SparseMatrix<T>, SSORPreconditioner<T> >     dgmres_colmajor_ssor;
+
+  CALL_SUBTEST(check_sparse_square_solving(dgmres_colmajor_diag));
+  //   CALL_SUBTEST( check_sparse_square_solving(dgmres_colmajor_I)     );
+  CALL_SUBTEST(check_sparse_square_solving(dgmres_colmajor_ilut));
+  // CALL_SUBTEST( check_sparse_square_solving(dgmres_colmajor_ssor)     );
+}
+
+EIGEN_DECLARE_TEST(dgmres) {
+  CALL_SUBTEST_1(test_dgmres_T<double>());
+  CALL_SUBTEST_2(test_dgmres_T<std::complex<double> >());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/duccfft.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/duccfft.cpp
new file mode 100644
index 0000000..4247663
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/duccfft.cpp
@@ -0,0 +1,4 @@
+#define EIGEN_DUCCFFT_DEFAULT 1
+#include <ducc0/fft/fft.h>         // Needs to be included before main.h
+#include <ducc0/fft/fftnd_impl.h>  // Same requirement
+#include "fft_test_shared.h"
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/fft_test_shared.h b/data_acq_and_processing/third_party/eigen/unsupported/test/fft_test_shared.h
new file mode 100644
index 0000000..d7380be
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/fft_test_shared.h
@@ -0,0 +1,311 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Mark Borgerding mark a borgerding net
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <unsupported/Eigen/FFT>
+
+template <typename T>
+inline std::complex<T> RandomCpx() {
+  return std::complex<T>((T)(rand() / (T)RAND_MAX - .5), (T)(rand() / (T)RAND_MAX - .5));
+}
+
+using namespace std;
+using namespace Eigen;
+
+template <typename T>
+inline complex<long double> promote(complex<T> x) {
+  return complex<long double>((long double)x.real(), (long double)x.imag());
+}
+
+inline complex<long double> promote(float x) { return complex<long double>((long double)x); }
+inline complex<long double> promote(double x) { return complex<long double>((long double)x); }
+inline complex<long double> promote(long double x) { return complex<long double>((long double)x); }
+
+template <typename VT1, typename VT2>
+long double fft_rmse(const VT1& fftbuf, const VT2& timebuf) {
+  long double totalpower = 0;
+  long double difpower = 0;
+  long double pi = acos((long double)-1);
+  for (size_t k0 = 0; k0 < (size_t)fftbuf.size(); ++k0) {
+    complex<long double> acc = 0;
+    long double phinc = (long double)(-2.) * k0 * pi / timebuf.size();
+    for (size_t k1 = 0; k1 < (size_t)timebuf.size(); ++k1) {
+      acc += promote(timebuf[k1]) * exp(complex<long double>(0, k1 * phinc));
+    }
+    totalpower += numext::abs2(acc);
+    complex<long double> x = promote(fftbuf[k0]);
+    complex<long double> dif = acc - x;
+    difpower += numext::abs2(dif);
+    // cerr << k0 << "\t" << acc << "\t" <<  x << "\t" << sqrt(numext::abs2(dif)) << endl;
+  }
+  // cerr << "rmse:" << sqrt(difpower/totalpower) << endl;
+  return sqrt(difpower / totalpower);
+}
+
+template <typename VT1, typename VT2>
+long double dif_rmse(const VT1 buf1, const VT2 buf2) {
+  long double totalpower = 0;
+  long double difpower = 0;
+  size_t n = (min)(buf1.size(), buf2.size());
+  for (size_t k = 0; k < n; ++k) {
+    totalpower += (long double)((numext::abs2(buf1[k]) + numext::abs2(buf2[k])) / 2);
+    difpower += (long double)(numext::abs2(buf1[k] - buf2[k]));
+  }
+  return sqrt(difpower / totalpower);
+}
+
+enum { StdVectorContainer, EigenVectorContainer };
+
+template <int Container, typename Scalar>
+struct VectorType;
+
+template <typename Scalar>
+struct VectorType<StdVectorContainer, Scalar> {
+  typedef vector<Scalar> type;
+};
+
+template <typename Scalar>
+struct VectorType<EigenVectorContainer, Scalar> {
+  typedef Matrix<Scalar, Dynamic, 1> type;
+};
+
+template <int Container, typename T>
+void test_scalar_generic(int nfft) {
+  typedef typename FFT<T>::Complex Complex;
+  typedef typename FFT<T>::Scalar Scalar;
+  typedef typename VectorType<Container, Scalar>::type ScalarVector;
+  typedef typename VectorType<Container, Complex>::type ComplexVector;
+
+  FFT<T> fft;
+  ScalarVector tbuf(nfft);
+  ComplexVector freqBuf;
+  for (int k = 0; k < nfft; ++k) tbuf[k] = (T)(rand() / (double)RAND_MAX - .5);
+
+  // make sure it DOESN'T give the right full spectrum answer
+  // if we've asked for half-spectrum
+  fft.SetFlag(fft.HalfSpectrum);
+  fft.fwd(freqBuf, tbuf);
+  VERIFY((size_t)freqBuf.size() == (size_t)((nfft >> 1) + 1));
+  VERIFY(T(fft_rmse(freqBuf, tbuf)) < test_precision<T>());  // gross check
+
+  fft.ClearFlag(fft.HalfSpectrum);
+  fft.fwd(freqBuf, tbuf);
+  VERIFY((size_t)freqBuf.size() == (size_t)nfft);
+  VERIFY(T(fft_rmse(freqBuf, tbuf)) < test_precision<T>());  // gross check
+
+  if (nfft & 1) return;  // odd FFTs get the wrong size inverse FFT
+
+  ScalarVector tbuf2;
+  fft.inv(tbuf2, freqBuf);
+  VERIFY(T(dif_rmse(tbuf, tbuf2)) < test_precision<T>());  // gross check
+
+  // verify that the Unscaled flag takes effect
+  ScalarVector tbuf3;
+  fft.SetFlag(fft.Unscaled);
+
+  fft.inv(tbuf3, freqBuf);
+
+  for (int k = 0; k < nfft; ++k) tbuf3[k] *= T(1. / nfft);
+
+  // for (size_t i=0;i<(size_t) tbuf.size();++i)
+  //     cout << "freqBuf=" << freqBuf[i] << " in2=" << tbuf3[i] << " -  in=" << tbuf[i] << " => " << (tbuf3[i] -
+  //     tbuf[i] ) <<  endl;
+
+  VERIFY(T(dif_rmse(tbuf, tbuf3)) < test_precision<T>());  // gross check
+
+  // verify that ClearFlag works
+  fft.ClearFlag(fft.Unscaled);
+  fft.inv(tbuf2, freqBuf);
+  VERIFY(T(dif_rmse(tbuf, tbuf2)) < test_precision<T>());  // gross check
+}
+
+template <typename T>
+void test_scalar(int nfft) {
+  test_scalar_generic<StdVectorContainer, T>(nfft);
+  // test_scalar_generic<EigenVectorContainer,T>(nfft);
+}
+
+template <int Container, typename T>
+void test_complex_generic(int nfft) {
+  typedef typename FFT<T>::Complex Complex;
+  typedef typename VectorType<Container, Complex>::type ComplexVector;
+
+  FFT<T> fft;
+
+  ComplexVector inbuf(nfft);
+  ComplexVector outbuf;
+  ComplexVector buf3;
+  for (int k = 0; k < nfft; ++k)
+    inbuf[k] = Complex((T)(rand() / (double)RAND_MAX - .5), (T)(rand() / (double)RAND_MAX - .5));
+  fft.fwd(outbuf, inbuf);
+
+  VERIFY(T(fft_rmse(outbuf, inbuf)) < test_precision<T>());  // gross check
+  fft.inv(buf3, outbuf);
+
+  VERIFY(T(dif_rmse(inbuf, buf3)) < test_precision<T>());  // gross check
+
+  // verify that the Unscaled flag takes effect
+  ComplexVector buf4;
+  fft.SetFlag(fft.Unscaled);
+  fft.inv(buf4, outbuf);
+  for (int k = 0; k < nfft; ++k) buf4[k] *= T(1. / nfft);
+  VERIFY(T(dif_rmse(inbuf, buf4)) < test_precision<T>());  // gross check
+
+  // verify that ClearFlag works
+  fft.ClearFlag(fft.Unscaled);
+  fft.inv(buf3, outbuf);
+  VERIFY(T(dif_rmse(inbuf, buf3)) < test_precision<T>());  // gross check
+}
+
+template <typename T>
+void test_complex_strided(int nfft) {
+  typedef typename FFT<T>::Complex Complex;
+  typedef typename Eigen::Vector<Complex, Dynamic> ComplexVector;
+  constexpr int kInputStride = 3;
+  constexpr int kOutputStride = 7;
+  constexpr int kInvOutputStride = 13;
+
+  FFT<T> fft;
+
+  ComplexVector inbuf(nfft * kInputStride);
+  inbuf.setRandom();
+  ComplexVector outbuf(nfft * kOutputStride);
+  outbuf.setRandom();
+  ComplexVector invoutbuf(nfft * kInvOutputStride);
+  invoutbuf.setRandom();
+
+  using StridedComplexVector = Map<ComplexVector, /*MapOptions=*/0, InnerStride<Dynamic>>;
+  StridedComplexVector input(inbuf.data(), nfft, InnerStride<Dynamic>(kInputStride));
+  StridedComplexVector output(outbuf.data(), nfft, InnerStride<Dynamic>(kOutputStride));
+  StridedComplexVector inv_output(invoutbuf.data(), nfft, InnerStride<Dynamic>(kInvOutputStride));
+
+  for (int k = 0; k < nfft; ++k)
+    input[k] = Complex((T)(rand() / (double)RAND_MAX - .5), (T)(rand() / (double)RAND_MAX - .5));
+  fft.fwd(output, input);
+
+  VERIFY(T(fft_rmse(output, input)) < test_precision<T>());  // gross check
+  fft.inv(inv_output, output);
+  VERIFY(T(dif_rmse(inv_output, input)) < test_precision<T>());  // gross check
+}
+
+template <typename T>
+void test_complex(int nfft) {
+  test_complex_generic<StdVectorContainer, T>(nfft);
+  test_complex_generic<EigenVectorContainer, T>(nfft);
+  test_complex_strided<T>(nfft);
+}
+
+template <typename T, int nrows, int ncols>
+void test_complex2d() {
+  typedef typename Eigen::FFT<T>::Complex Complex;
+  FFT<T> fft;
+  Eigen::Matrix<Complex, nrows, ncols> src, src2, dst, dst2;
+
+  src = Eigen::Matrix<Complex, nrows, ncols>::Random();
+  // src =  Eigen::Matrix<Complex,nrows,ncols>::Identity();
+
+  for (int k = 0; k < ncols; k++) {
+    Eigen::Matrix<Complex, nrows, 1> tmpOut;
+    fft.fwd(tmpOut, src.col(k));
+    dst2.col(k) = tmpOut;
+  }
+
+  for (int k = 0; k < nrows; k++) {
+    Eigen::Matrix<Complex, 1, ncols> tmpOut;
+    fft.fwd(tmpOut, dst2.row(k));
+    dst2.row(k) = tmpOut;
+  }
+
+  fft.fwd2(dst.data(), src.data(), ncols, nrows);
+  fft.inv2(src2.data(), dst.data(), ncols, nrows);
+  VERIFY((src - src2).norm() < test_precision<T>());
+  VERIFY((dst - dst2).norm() < test_precision<T>());
+}
+
+inline void test_return_by_value(int len) {
+  VectorXf in;
+  VectorXf in1;
+  in.setRandom(len);
+  VectorXcf out1, out2;
+  FFT<float> fft;
+
+  fft.SetFlag(fft.HalfSpectrum);
+
+  fft.fwd(out1, in);
+  out2 = fft.fwd(in);
+  VERIFY((out1 - out2).norm() < test_precision<float>());
+  in1 = fft.inv(out1);
+  VERIFY((in1 - in).norm() < test_precision<float>());
+}
+
+EIGEN_DECLARE_TEST(FFTW) {
+  CALL_SUBTEST(test_return_by_value(32));
+  CALL_SUBTEST(test_complex<float>(32));
+  CALL_SUBTEST(test_complex<double>(32));
+  CALL_SUBTEST(test_complex<float>(256));
+  CALL_SUBTEST(test_complex<double>(256));
+  CALL_SUBTEST(test_complex<float>(3 * 8));
+  CALL_SUBTEST(test_complex<double>(3 * 8));
+  CALL_SUBTEST(test_complex<float>(5 * 32));
+  CALL_SUBTEST(test_complex<double>(5 * 32));
+  CALL_SUBTEST(test_complex<float>(2 * 3 * 4));
+  CALL_SUBTEST(test_complex<double>(2 * 3 * 4));
+  CALL_SUBTEST(test_complex<float>(2 * 3 * 4 * 5));
+  CALL_SUBTEST(test_complex<double>(2 * 3 * 4 * 5));
+  CALL_SUBTEST(test_complex<float>(2 * 3 * 4 * 5 * 7));
+  CALL_SUBTEST(test_complex<double>(2 * 3 * 4 * 5 * 7));
+
+  CALL_SUBTEST(test_scalar<float>(32));
+  CALL_SUBTEST(test_scalar<double>(32));
+  CALL_SUBTEST(test_scalar<float>(45));
+  CALL_SUBTEST(test_scalar<double>(45));
+  CALL_SUBTEST(test_scalar<float>(50));
+  CALL_SUBTEST(test_scalar<double>(50));
+  CALL_SUBTEST(test_scalar<float>(256));
+  CALL_SUBTEST(test_scalar<double>(256));
+  CALL_SUBTEST(test_scalar<float>(2 * 3 * 4 * 5 * 7));
+  CALL_SUBTEST(test_scalar<double>(2 * 3 * 4 * 5 * 7));
+
+#if defined EIGEN_HAS_FFTWL || defined EIGEN_POCKETFFT_DEFAULT || defined EIGEN_DUCCFFT_DEFAULT
+  CALL_SUBTEST(test_complex<long double>(32));
+  CALL_SUBTEST(test_complex<long double>(256));
+  CALL_SUBTEST(test_complex<long double>(3 * 8));
+  CALL_SUBTEST(test_complex<long double>(5 * 32));
+  CALL_SUBTEST(test_complex<long double>(2 * 3 * 4));
+  CALL_SUBTEST(test_complex<long double>(2 * 3 * 4 * 5));
+  CALL_SUBTEST(test_complex<long double>(2 * 3 * 4 * 5 * 7));
+
+  CALL_SUBTEST(test_scalar<long double>(32));
+  CALL_SUBTEST(test_scalar<long double>(45));
+  CALL_SUBTEST(test_scalar<long double>(50));
+  CALL_SUBTEST(test_scalar<long double>(256));
+  CALL_SUBTEST(test_scalar<long double>(2 * 3 * 4 * 5 * 7));
+
+  CALL_SUBTEST((test_complex2d<long double, 2 * 3 * 4, 2 * 3 * 4>()));
+  CALL_SUBTEST((test_complex2d<long double, 3 * 4 * 5, 3 * 4 * 5>()));
+  CALL_SUBTEST((test_complex2d<long double, 24, 60>()));
+  CALL_SUBTEST((test_complex2d<long double, 60, 24>()));
+// fail to build since Eigen limit the stack allocation size,too big here.
+// CALL_SUBTEST( ( test_complex2d<long double, 256, 256> () ) );
+#endif
+#if defined EIGEN_FFTW_DEFAULT || defined EIGEN_POCKETFFT_DEFAULT || defined EIGEN_DUCCFFT_DEFAULT || \
+    defined EIGEN_MKL_DEFAULT
+  CALL_SUBTEST((test_complex2d<float, 24, 24>()));
+  CALL_SUBTEST((test_complex2d<float, 60, 60>()));
+  CALL_SUBTEST((test_complex2d<float, 24, 60>()));
+  CALL_SUBTEST((test_complex2d<float, 60, 24>()));
+#endif
+#if defined EIGEN_FFTW_DEFAULT || defined EIGEN_POCKETFFT_DEFAULT || defined EIGEN_DUCCFFT_DEFAULT || \
+    defined EIGEN_MKL_DEFAULT
+  CALL_SUBTEST((test_complex2d<double, 24, 24>()));
+  CALL_SUBTEST((test_complex2d<double, 60, 60>()));
+  CALL_SUBTEST((test_complex2d<double, 24, 60>()));
+  CALL_SUBTEST((test_complex2d<double, 60, 24>()));
+#endif
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/forward_adolc.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/forward_adolc.cpp
new file mode 100644
index 0000000..5d129a0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/forward_adolc.cpp
@@ -0,0 +1,127 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <Eigen/Dense>
+
+#define NUMBER_DIRECTIONS 16
+#include <unsupported/Eigen/AdolcForward>
+
+template <typename Vector>
+EIGEN_DONT_INLINE typename Vector::Scalar foo(const Vector& p) {
+  typedef typename Vector::Scalar Scalar;
+  return (p - Vector(Scalar(-1), Scalar(1.))).norm() + (p.array().sqrt().abs() * p.array().sin()).sum() + p.dot(p);
+}
+
+template <typename Scalar_, int NX = Dynamic, int NY = Dynamic>
+struct TestFunc1 {
+  typedef Scalar_ Scalar;
+  enum { InputsAtCompileTime = NX, ValuesAtCompileTime = NY };
+  typedef Matrix<Scalar, InputsAtCompileTime, 1> InputType;
+  typedef Matrix<Scalar, ValuesAtCompileTime, 1> ValueType;
+  typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
+
+  int m_inputs, m_values;
+
+  TestFunc1() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
+  TestFunc1(int inputs_, int values_) : m_inputs(inputs_), m_values(values_) {}
+
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values; }
+
+  template <typename T>
+  void operator()(const Matrix<T, InputsAtCompileTime, 1>& x, Matrix<T, ValuesAtCompileTime, 1>* _v) const {
+    Matrix<T, ValuesAtCompileTime, 1>& v = *_v;
+
+    v[0] = 2 * x[0] * x[0] + x[0] * x[1];
+    v[1] = 3 * x[1] * x[0] + 0.5 * x[1] * x[1];
+    if (inputs() > 2) {
+      v[0] += 0.5 * x[2];
+      v[1] += x[2];
+    }
+    if (values() > 2) {
+      v[2] = 3 * x[1] * x[0] * x[0];
+    }
+    if (inputs() > 2 && values() > 2) v[2] *= x[2];
+  }
+
+  void operator()(const InputType& x, ValueType* v, JacobianType* _j) const {
+    (*this)(x, v);
+
+    if (_j) {
+      JacobianType& j = *_j;
+
+      j(0, 0) = 4 * x[0] + x[1];
+      j(1, 0) = 3 * x[1];
+
+      j(0, 1) = x[0];
+      j(1, 1) = 3 * x[0] + 2 * 0.5 * x[1];
+
+      if (inputs() > 2) {
+        j(0, 2) = 0.5;
+        j(1, 2) = 1;
+      }
+      if (values() > 2) {
+        j(2, 0) = 3 * x[1] * 2 * x[0];
+        j(2, 1) = 3 * x[0] * x[0];
+      }
+      if (inputs() > 2 && values() > 2) {
+        j(2, 0) *= x[2];
+        j(2, 1) *= x[2];
+
+        j(2, 2) = 3 * x[1] * x[0] * x[0];
+        j(2, 2) = 3 * x[1] * x[0] * x[0];
+      }
+    }
+  }
+};
+
+template <typename Func>
+void adolc_forward_jacobian(const Func& f) {
+  typename Func::InputType x = Func::InputType::Random(f.inputs());
+  typename Func::ValueType y(f.values()), yref(f.values());
+  typename Func::JacobianType j(f.values(), f.inputs()), jref(f.values(), f.inputs());
+
+  jref.setZero();
+  yref.setZero();
+  f(x, &yref, &jref);
+  //     std::cerr << y.transpose() << "\n\n";;
+  //     std::cerr << j << "\n\n";;
+
+  j.setZero();
+  y.setZero();
+  AdolcForwardJacobian<Func> autoj(f);
+  autoj(x, &y, &j);
+  //     std::cerr << y.transpose() << "\n\n";;
+  //     std::cerr << j << "\n\n";;
+
+  VERIFY_IS_APPROX(y, yref);
+  VERIFY_IS_APPROX(j, jref);
+}
+
+EIGEN_DECLARE_TEST(forward_adolc) {
+  adtl::setNumDir(NUMBER_DIRECTIONS);
+
+  for (int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST((adolc_forward_jacobian(TestFunc1<double, 2, 2>())));
+    CALL_SUBTEST((adolc_forward_jacobian(TestFunc1<double, 2, 3>())));
+    CALL_SUBTEST((adolc_forward_jacobian(TestFunc1<double, 3, 2>())));
+    CALL_SUBTEST((adolc_forward_jacobian(TestFunc1<double, 3, 3>())));
+    CALL_SUBTEST((adolc_forward_jacobian(TestFunc1<double>(3, 3))));
+  }
+
+  {
+    // simple instantiation tests
+    Matrix<adtl::adouble, 2, 1> x;
+    foo(x);
+    Matrix<adtl::adouble, Dynamic, Dynamic> A(4, 4);
+    ;
+    A.selfadjointView<Lower>().eigenvalues();
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/gmres.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/gmres.cpp
new file mode 100644
index 0000000..225bb96
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/gmres.cpp
@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
+// Copyright (C) 2012 Kolja Brix <brix@igpm.rwth-aaachen.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "../../test/sparse_solver.h"
+#include <Eigen/IterativeSolvers>
+
+template <typename T>
+void test_gmres_T() {
+  GMRES<SparseMatrix<T>, DiagonalPreconditioner<T> > gmres_colmajor_diag;
+  GMRES<SparseMatrix<T>, IdentityPreconditioner> gmres_colmajor_I;
+  GMRES<SparseMatrix<T>, IncompleteLUT<T> > gmres_colmajor_ilut;
+  // GMRES<SparseMatrix<T>, SSORPreconditioner<T> >     gmres_colmajor_ssor;
+
+  CALL_SUBTEST(check_sparse_square_solving(gmres_colmajor_diag));
+  //   CALL_SUBTEST( check_sparse_square_solving(gmres_colmajor_I)     );
+  CALL_SUBTEST(check_sparse_square_solving(gmres_colmajor_ilut));
+  // CALL_SUBTEST( check_sparse_square_solving(gmres_colmajor_ssor)     );
+}
+
+EIGEN_DECLARE_TEST(gmres) {
+  CALL_SUBTEST_1(test_gmres_T<double>());
+  CALL_SUBTEST_2(test_gmres_T<std::complex<double> >());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/idrs.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/idrs.cpp
new file mode 100644
index 0000000..9a2e4a5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/idrs.cpp
@@ -0,0 +1,26 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
+// Copyright (C) 2012 Kolja Brix <brix@igpm.rwth-aaachen.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "../../test/sparse_solver.h"
+#include <Eigen/IterativeSolvers>
+
+template <typename T>
+void test_idrs_T() {
+  IDRS<SparseMatrix<T>, DiagonalPreconditioner<T> > idrs_colmajor_diag;
+  IDRS<SparseMatrix<T>, IncompleteLUT<T> > idrs_colmajor_ilut;
+
+  CALL_SUBTEST(check_sparse_square_solving(idrs_colmajor_diag));
+  CALL_SUBTEST(check_sparse_square_solving(idrs_colmajor_ilut));
+}
+
+EIGEN_DECLARE_TEST(idrs) {
+  CALL_SUBTEST_1(test_idrs_T<double>());
+  CALL_SUBTEST_2(test_idrs_T<std::complex<double> >());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/idrstabl.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/idrstabl.cpp
new file mode 100644
index 0000000..7e40dd6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/idrstabl.cpp
@@ -0,0 +1,28 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "../../test/sparse_solver.h"
+#include <unsupported/Eigen/IterativeSolvers>
+
+template <typename T>
+void test_idrstabl_T() {
+  IDRSTABL<SparseMatrix<T>, DiagonalPreconditioner<T> > idrstabl_colmajor_diag;
+  IDRSTABL<SparseMatrix<T>, IncompleteLUT<T> > idrstabl_colmajor_ilut;
+
+  idrstabl_colmajor_diag.setTolerance(NumTraits<T>::epsilon() * 4);
+  idrstabl_colmajor_ilut.setTolerance(NumTraits<T>::epsilon() * 4);
+
+  CALL_SUBTEST(check_sparse_square_solving(idrstabl_colmajor_diag));
+  CALL_SUBTEST(check_sparse_square_solving(idrstabl_colmajor_ilut));
+}
+
+EIGEN_DECLARE_TEST(idrstabl) {
+  CALL_SUBTEST_1((test_idrstabl_T<double>()));
+  CALL_SUBTEST_2((test_idrstabl_T<std::complex<double> >()));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/kronecker_product.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/kronecker_product.cpp
new file mode 100644
index 0000000..d7396e0
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/kronecker_product.cpp
@@ -0,0 +1,242 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Kolja Brix <brix@igpm.rwth-aachen.de>
+// Copyright (C) 2011 Andreas Platen <andiplaten@gmx.de>
+// Copyright (C) 2012 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifdef EIGEN_TEST_PART_1
+
+#include "sparse.h"
+#include <Eigen/SparseExtra>
+#include <Eigen/KroneckerProduct>
+
+template <typename MatrixType>
+void check_dimension(const MatrixType& ab, const int rows, const int cols) {
+  VERIFY_IS_EQUAL(ab.rows(), rows);
+  VERIFY_IS_EQUAL(ab.cols(), cols);
+}
+
+template <typename MatrixType>
+void check_kronecker_product(const MatrixType& ab) {
+  VERIFY_IS_EQUAL(ab.rows(), 6);
+  VERIFY_IS_EQUAL(ab.cols(), 6);
+  VERIFY_IS_EQUAL(ab.size(), 36);
+  VERIFY_IS_APPROX(ab.coeff(0, 0), -0.4017367630386106);
+  VERIFY_IS_APPROX(ab.coeff(0, 1), 0.1056863433932735);
+  VERIFY_IS_APPROX(ab.coeff(0, 2), -0.7255206194554212);
+  VERIFY_IS_APPROX(ab.coeff(0, 3), 0.1908653336744706);
+  VERIFY_IS_APPROX(ab.coeff(0, 4), 0.350864567234111);
+  VERIFY_IS_APPROX(ab.coeff(0, 5), -0.0923032108308013);
+  VERIFY_IS_APPROX(ab.coeff(1, 0), 0.415417514804677);
+  VERIFY_IS_APPROX(ab.coeff(1, 1), -0.2369227701722048);
+  VERIFY_IS_APPROX(ab.coeff(1, 2), 0.7502275131458511);
+  VERIFY_IS_APPROX(ab.coeff(1, 3), -0.4278731019742696);
+  VERIFY_IS_APPROX(ab.coeff(1, 4), -0.3628129162264507);
+  VERIFY_IS_APPROX(ab.coeff(1, 5), 0.2069210808481275);
+  VERIFY_IS_APPROX(ab.coeff(2, 0), 0.05465890160863986);
+  VERIFY_IS_APPROX(ab.coeff(2, 1), -0.2634092511419858);
+  VERIFY_IS_APPROX(ab.coeff(2, 2), 0.09871180285793758);
+  VERIFY_IS_APPROX(ab.coeff(2, 3), -0.4757066334017702);
+  VERIFY_IS_APPROX(ab.coeff(2, 4), -0.04773740823058334);
+  VERIFY_IS_APPROX(ab.coeff(2, 5), 0.2300535609645254);
+  VERIFY_IS_APPROX(ab.coeff(3, 0), -0.8172945853260133);
+  VERIFY_IS_APPROX(ab.coeff(3, 1), 0.2150086428359221);
+  VERIFY_IS_APPROX(ab.coeff(3, 2), 0.5825113847292743);
+  VERIFY_IS_APPROX(ab.coeff(3, 3), -0.1532433770097174);
+  VERIFY_IS_APPROX(ab.coeff(3, 4), -0.329383387282399);
+  VERIFY_IS_APPROX(ab.coeff(3, 5), 0.08665207912033064);
+  VERIFY_IS_APPROX(ab.coeff(4, 0), 0.8451267514863225);
+  VERIFY_IS_APPROX(ab.coeff(4, 1), -0.481996458918977);
+  VERIFY_IS_APPROX(ab.coeff(4, 2), -0.6023482390791535);
+  VERIFY_IS_APPROX(ab.coeff(4, 3), 0.3435339347164565);
+  VERIFY_IS_APPROX(ab.coeff(4, 4), 0.3406002157428891);
+  VERIFY_IS_APPROX(ab.coeff(4, 5), -0.1942526344200915);
+  VERIFY_IS_APPROX(ab.coeff(5, 0), 0.1111982482925399);
+  VERIFY_IS_APPROX(ab.coeff(5, 1), -0.5358806424754169);
+  VERIFY_IS_APPROX(ab.coeff(5, 2), -0.07925446559335647);
+  VERIFY_IS_APPROX(ab.coeff(5, 3), 0.3819388757769038);
+  VERIFY_IS_APPROX(ab.coeff(5, 4), 0.04481475387219876);
+  VERIFY_IS_APPROX(ab.coeff(5, 5), -0.2159688616158057);
+}
+
+template <typename MatrixType>
+void check_sparse_kronecker_product(const MatrixType& ab) {
+  VERIFY_IS_EQUAL(ab.rows(), 12);
+  VERIFY_IS_EQUAL(ab.cols(), 10);
+  VERIFY_IS_EQUAL(ab.nonZeros(), 3 * 2);
+  VERIFY_IS_APPROX(ab.coeff(3, 0), -0.04);
+  VERIFY_IS_APPROX(ab.coeff(5, 1), 0.05);
+  VERIFY_IS_APPROX(ab.coeff(0, 6), -0.08);
+  VERIFY_IS_APPROX(ab.coeff(2, 7), 0.10);
+  VERIFY_IS_APPROX(ab.coeff(6, 8), 0.12);
+  VERIFY_IS_APPROX(ab.coeff(8, 9), -0.15);
+}
+
+EIGEN_DECLARE_TEST(kronecker_product) {
+  // DM = dense matrix; SM = sparse matrix
+
+  Matrix<double, 2, 3> DM_a;
+  SparseMatrix<double> SM_a(2, 3);
+  SM_a.insert(0, 0) = DM_a.coeffRef(0, 0) = -0.4461540300782201;
+  SM_a.insert(0, 1) = DM_a.coeffRef(0, 1) = -0.8057364375283049;
+  SM_a.insert(0, 2) = DM_a.coeffRef(0, 2) = 0.3896572459516341;
+  SM_a.insert(1, 0) = DM_a.coeffRef(1, 0) = -0.9076572187376921;
+  SM_a.insert(1, 1) = DM_a.coeffRef(1, 1) = 0.6469156566545853;
+  SM_a.insert(1, 2) = DM_a.coeffRef(1, 2) = -0.3658010398782789;
+
+  MatrixXd DM_b(3, 2);
+  SparseMatrix<double> SM_b(3, 2);
+  SM_b.insert(0, 0) = DM_b.coeffRef(0, 0) = 0.9004440976767099;
+  SM_b.insert(0, 1) = DM_b.coeffRef(0, 1) = -0.2368830858139832;
+  SM_b.insert(1, 0) = DM_b.coeffRef(1, 0) = -0.9311078389941825;
+  SM_b.insert(1, 1) = DM_b.coeffRef(1, 1) = 0.5310335762980047;
+  SM_b.insert(2, 0) = DM_b.coeffRef(2, 0) = -0.1225112806872035;
+  SM_b.insert(2, 1) = DM_b.coeffRef(2, 1) = 0.5903998022741264;
+
+  SparseMatrix<double, RowMajor> SM_row_a(SM_a), SM_row_b(SM_b);
+
+  // test DM_fixedSize = kroneckerProduct(DM_block,DM)
+  Matrix<double, 6, 6> DM_fix_ab = kroneckerProduct(DM_a.topLeftCorner<2, 3>(), DM_b);
+
+  CALL_SUBTEST(check_kronecker_product(DM_fix_ab));
+  CALL_SUBTEST(check_kronecker_product(kroneckerProduct(DM_a.topLeftCorner<2, 3>(), DM_b)));
+
+  for (int i = 0; i < DM_fix_ab.rows(); ++i)
+    for (int j = 0; j < DM_fix_ab.cols(); ++j)
+      VERIFY_IS_APPROX(kroneckerProduct(DM_a, DM_b).coeff(i, j), DM_fix_ab(i, j));
+
+  // test DM_block = kroneckerProduct(DM,DM)
+  MatrixXd DM_block_ab(10, 15);
+  DM_block_ab.block<6, 6>(2, 5) = kroneckerProduct(DM_a, DM_b);
+  CALL_SUBTEST(check_kronecker_product(DM_block_ab.block<6, 6>(2, 5)));
+
+  // test DM = kroneckerProduct(DM,DM)
+  MatrixXd DM_ab = kroneckerProduct(DM_a, DM_b);
+  CALL_SUBTEST(check_kronecker_product(DM_ab));
+  CALL_SUBTEST(check_kronecker_product(kroneckerProduct(DM_a, DM_b)));
+
+  // test SM = kroneckerProduct(SM,DM)
+  SparseMatrix<double> SM_ab = kroneckerProduct(SM_a, DM_b);
+  CALL_SUBTEST(check_kronecker_product(SM_ab));
+  SparseMatrix<double, RowMajor> SM_ab2 = kroneckerProduct(SM_a, DM_b);
+  CALL_SUBTEST(check_kronecker_product(SM_ab2));
+  CALL_SUBTEST(check_kronecker_product(kroneckerProduct(SM_a, DM_b)));
+
+  // test SM = kroneckerProduct(DM,SM)
+  SM_ab.setZero();
+  SM_ab.insert(0, 0) = 37.0;
+  SM_ab = kroneckerProduct(DM_a, SM_b);
+  CALL_SUBTEST(check_kronecker_product(SM_ab));
+  SM_ab2.setZero();
+  SM_ab2.insert(0, 0) = 37.0;
+  SM_ab2 = kroneckerProduct(DM_a, SM_b);
+  CALL_SUBTEST(check_kronecker_product(SM_ab2));
+  CALL_SUBTEST(check_kronecker_product(kroneckerProduct(DM_a, SM_b)));
+
+  // test SM = kroneckerProduct(SM,SM)
+  SM_ab.resize(2, 33);
+  SM_ab.insert(0, 0) = 37.0;
+  SM_ab = kroneckerProduct(SM_a, SM_b);
+  CALL_SUBTEST(check_kronecker_product(SM_ab));
+  SM_ab2.resize(5, 11);
+  SM_ab2.insert(0, 0) = 37.0;
+  SM_ab2 = kroneckerProduct(SM_a, SM_b);
+  CALL_SUBTEST(check_kronecker_product(SM_ab2));
+  CALL_SUBTEST(check_kronecker_product(kroneckerProduct(SM_a, SM_b)));
+
+  // test SM = kroneckerProduct(SM,SM) with sparse pattern
+  SM_a.resize(4, 5);
+  SM_b.resize(3, 2);
+  SM_a.resizeNonZeros(0);
+  SM_b.resizeNonZeros(0);
+  SM_a.insert(1, 0) = -0.1;
+  SM_a.insert(0, 3) = -0.2;
+  SM_a.insert(2, 4) = 0.3;
+  SM_a.finalize();
+
+  SM_b.insert(0, 0) = 0.4;
+  SM_b.insert(2, 1) = -0.5;
+  SM_b.finalize();
+  SM_ab.resize(1, 1);
+  SM_ab.insert(0, 0) = 37.0;
+  SM_ab = kroneckerProduct(SM_a, SM_b);
+  CALL_SUBTEST(check_sparse_kronecker_product(SM_ab));
+
+  // test dimension of result of DM = kroneckerProduct(DM,DM)
+  MatrixXd DM_a2 = Eigen::MatrixXd::Random(2, 1);
+  MatrixXd DM_b2 = Eigen::MatrixXd::Random(5, 4);
+  MatrixXd DM_ab2 = kroneckerProduct(DM_a2, DM_b2);
+  CALL_SUBTEST(check_dimension(DM_ab2, 2 * 5, 1 * 4));
+  DM_a2 = Eigen::MatrixXd::Random(10, 9);
+  DM_b2 = Eigen::MatrixXd::Random(4, 8);
+  DM_ab2 = kroneckerProduct(DM_a2, DM_b2);
+  CALL_SUBTEST(check_dimension(DM_ab2, 10 * 4, 9 * 8));
+
+  for (int i = 0; i < g_repeat; i++) {
+    double density = Eigen::internal::random<double>(0.01, 0.5);
+    int ra = Eigen::internal::random<int>(1, 50);
+    int ca = Eigen::internal::random<int>(1, 50);
+    int rb = Eigen::internal::random<int>(1, 50);
+    int cb = Eigen::internal::random<int>(1, 50);
+    SparseMatrix<float, ColMajor> sA(ra, ca), sB(rb, cb), sC;
+    SparseMatrix<float, RowMajor> sC2;
+    MatrixXf dA(ra, ca), dB(rb, cb), dC;
+    initSparse(density, dA, sA);
+    initSparse(density, dB, sB);
+
+    sC = kroneckerProduct(sA, sB);
+    dC = kroneckerProduct(dA, dB);
+    VERIFY_IS_APPROX(MatrixXf(sC), dC);
+
+    sC = kroneckerProduct(sA.transpose(), sB);
+    dC = kroneckerProduct(dA.transpose(), dB);
+    VERIFY_IS_APPROX(MatrixXf(sC), dC);
+
+    sC = kroneckerProduct(sA.transpose(), sB.transpose());
+    dC = kroneckerProduct(dA.transpose(), dB.transpose());
+    VERIFY_IS_APPROX(MatrixXf(sC), dC);
+
+    sC = kroneckerProduct(sA, sB.transpose());
+    dC = kroneckerProduct(dA, dB.transpose());
+    VERIFY_IS_APPROX(MatrixXf(sC), dC);
+
+    sC2 = kroneckerProduct(sA, sB);
+    dC = kroneckerProduct(dA, dB);
+    VERIFY_IS_APPROX(MatrixXf(sC2), dC);
+
+    sC2 = kroneckerProduct(dA, sB);
+    dC = kroneckerProduct(dA, dB);
+    VERIFY_IS_APPROX(MatrixXf(sC2), dC);
+
+    sC2 = kroneckerProduct(sA, dB);
+    dC = kroneckerProduct(dA, dB);
+    VERIFY_IS_APPROX(MatrixXf(sC2), dC);
+
+    sC2 = kroneckerProduct(2 * sA, sB);
+    dC = kroneckerProduct(2 * dA, dB);
+    VERIFY_IS_APPROX(MatrixXf(sC2), dC);
+  }
+}
+
+#endif
+
+#ifdef EIGEN_TEST_PART_2
+
+// simply check that for a dense kronecker product, sparse module is not needed
+#include "main.h"
+#include <Eigen/KroneckerProduct>
+
+EIGEN_DECLARE_TEST(kronecker_product) {
+  MatrixXd a(2, 2), b(3, 3), c;
+  a.setRandom();
+  b.setRandom();
+  c = kroneckerProduct(a, b);
+  VERIFY_IS_APPROX(c.block(3, 3, 3, 3), a(1, 1) * b);
+}
+
+#endif
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/levenberg_marquardt.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/levenberg_marquardt.cpp
new file mode 100644
index 0000000..45edcc2
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/levenberg_marquardt.cpp
@@ -0,0 +1,1555 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+// Copyright (C) 2012 desire Nuentsa <desire.nuentsa_wakam@inria.fr
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// FIXME: These tests all check for hard-coded values. Ideally, parameters and start estimates should be randomized.
+
+#include <stdio.h>
+
+#include "main.h"
+#include <unsupported/Eigen/LevenbergMarquardt>
+
+// This disables some useless Warnings on MSVC.
+// It is intended to be done for this test only.
+#include <Eigen/src/Core/util/DisableStupidWarnings.h>
+
+using std::sqrt;
+
+// tolerance for checking number of iterations
+#define LM_EVAL_COUNT_TOL 2
+
+struct lmder_functor : DenseFunctor<double> {
+  lmder_functor(void) : DenseFunctor<double>(3, 15) {}
+  int operator()(const VectorXd &x, VectorXd &fvec) const {
+    double tmp1, tmp2, tmp3;
+    static const double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, 3.9e-1,
+                                 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34,   2.1,    4.39};
+
+    for (int i = 0; i < values(); i++) {
+      tmp1 = i + 1;
+      tmp2 = 16 - i - 1;
+      tmp3 = (i >= 8) ? tmp2 : tmp1;
+      fvec[i] = y[i] - (x[0] + tmp1 / (x[1] * tmp2 + x[2] * tmp3));
+    }
+    return 0;
+  }
+
+  int df(const VectorXd &x, MatrixXd &fjac) const {
+    double tmp1, tmp2, tmp3, tmp4;
+    for (int i = 0; i < values(); i++) {
+      tmp1 = i + 1;
+      tmp2 = 16 - i - 1;
+      tmp3 = (i >= 8) ? tmp2 : tmp1;
+      tmp4 = (x[1] * tmp2 + x[2] * tmp3);
+      tmp4 = tmp4 * tmp4;
+      fjac(i, 0) = -1;
+      fjac(i, 1) = tmp1 * tmp2 / tmp4;
+      fjac(i, 2) = tmp1 * tmp3 / tmp4;
+    }
+    return 0;
+  }
+};
+
+void testLmder1() {
+  int n = 3, info;
+
+  VectorXd x;
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, 1.);
+
+  // do the computation
+  lmder_functor functor;
+  LevenbergMarquardt<lmder_functor> lm(functor);
+  info = lm.lmder1(x);
+  EIGEN_UNUSED_VARIABLE(info)
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 6);
+  // VERIFY_IS_EQUAL(lm.njev(), 5);
+
+  // check norm
+  VERIFY_IS_APPROX(lm.fvec().blueNorm(), 0.09063596);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << 0.08241058, 1.133037, 2.343695;
+  VERIFY_IS_APPROX(x, x_ref);
+}
+
+void testLmder() {
+  const int m = 15, n = 3;
+  int info;
+  double fnorm, covfac;
+  VectorXd x;
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, 1.);
+
+  // do the computation
+  lmder_functor functor;
+  LevenbergMarquardt<lmder_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return values
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 6);
+  // VERIFY_IS_EQUAL(lm.njev(), 5);
+
+  // check norm
+  fnorm = lm.fvec().blueNorm();
+  VERIFY_IS_APPROX(fnorm, 0.09063596);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << 0.08241058, 1.133037, 2.343695;
+  VERIFY_IS_APPROX(x, x_ref);
+
+  // check covariance
+  covfac = fnorm * fnorm / (m - n);
+  internal::covar(lm.matrixR(), lm.permutation().indices());  // TODO : move this as a function of lm
+
+  MatrixXd cov_ref(n, n);
+  cov_ref << 0.0001531202, 0.002869941, -0.002656662, 0.002869941, 0.09480935, -0.09098995, -0.002656662, -0.09098995,
+      0.08778727;
+
+  //  std::cout << fjac*covfac << std::endl;
+
+  MatrixXd cov;
+  cov = covfac * lm.matrixR().topLeftCorner<n, n>();
+  VERIFY_IS_APPROX(cov, cov_ref);
+  // TODO: why isn't this allowed ? :
+  // VERIFY_IS_APPROX( covfac*fjac.topLeftCorner<n,n>() , cov_ref);
+}
+
+struct lmdif_functor : DenseFunctor<double> {
+  lmdif_functor(void) : DenseFunctor<double>(3, 15) {}
+  int operator()(const VectorXd &x, VectorXd &fvec) const {
+    int i;
+    double tmp1, tmp2, tmp3;
+    static const double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, 3.9e-1,
+                                 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34e0, 2.1e0,  4.39e0};
+
+    assert(x.size() == 3);
+    assert(fvec.size() == 15);
+    for (i = 0; i < 15; i++) {
+      tmp1 = i + 1;
+      tmp2 = 15 - i;
+      tmp3 = tmp1;
+
+      if (i >= 8) tmp3 = tmp2;
+      fvec[i] = y[i] - (x[0] + tmp1 / (x[1] * tmp2 + x[2] * tmp3));
+    }
+    return 0;
+  }
+};
+
+void testLmdif1() {
+  const int n = 3;
+  int info;
+
+  VectorXd x(n), fvec(15);
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, 1.);
+
+  // do the computation
+  lmdif_functor functor;
+  DenseIndex nfev;
+  info = LevenbergMarquardt<lmdif_functor>::lmdif1(functor, x, &nfev);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  //   VERIFY_IS_EQUAL(nfev, 26);
+
+  // check norm
+  functor(x, fvec);
+  VERIFY_IS_APPROX(fvec.blueNorm(), 0.09063596);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << 0.0824106, 1.1330366, 2.3436947;
+  VERIFY_IS_APPROX(x, x_ref);
+}
+
+void testLmdif() {
+  const int m = 15, n = 3;
+  int info;
+  double fnorm, covfac;
+  VectorXd x(n);
+
+  /* the following starting values provide a rough fit. */
+  x.setConstant(n, 1.);
+
+  // do the computation
+  lmdif_functor functor;
+  NumericalDiff<lmdif_functor> numDiff(functor);
+  LevenbergMarquardt<NumericalDiff<lmdif_functor> > lm(numDiff);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return values
+  // VERIFY_IS_EQUAL(info, 1);
+  //   VERIFY_IS_EQUAL(lm.nfev(), 26);
+
+  // check norm
+  fnorm = lm.fvec().blueNorm();
+  VERIFY_IS_APPROX(fnorm, 0.09063596);
+
+  // check x
+  VectorXd x_ref(n);
+  x_ref << 0.08241058, 1.133037, 2.343695;
+  VERIFY_IS_APPROX(x, x_ref);
+
+  // check covariance
+  covfac = fnorm * fnorm / (m - n);
+  internal::covar(lm.matrixR(), lm.permutation().indices());  // TODO : move this as a function of lm
+
+  MatrixXd cov_ref(n, n);
+  cov_ref << 0.0001531202, 0.002869942, -0.002656662, 0.002869942, 0.09480937, -0.09098997, -0.002656662, -0.09098997,
+      0.08778729;
+
+  //  std::cout << fjac*covfac << std::endl;
+
+  MatrixXd cov;
+  cov = covfac * lm.matrixR().topLeftCorner<n, n>();
+  VERIFY_IS_APPROX(cov, cov_ref);
+  // TODO: why isn't this allowed ? :
+  // VERIFY_IS_APPROX( covfac*fjac.topLeftCorner<n,n>() , cov_ref);
+}
+
+struct chwirut2_functor : DenseFunctor<double> {
+  chwirut2_functor(void) : DenseFunctor<double>(3, 54) {}
+  static const double m_x[54];
+  static const double m_y[54];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    int i;
+
+    assert(b.size() == 3);
+    assert(fvec.size() == 54);
+    for (i = 0; i < 54; i++) {
+      double x = m_x[i];
+      fvec[i] = exp(-b[0] * x) / (b[1] + b[2] * x) - m_y[i];
+    }
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 3);
+    assert(fjac.rows() == 54);
+    assert(fjac.cols() == 3);
+    for (int i = 0; i < 54; i++) {
+      double x = m_x[i];
+      double factor = 1. / (b[1] + b[2] * x);
+      double e = exp(-b[0] * x);
+      fjac(i, 0) = -x * e * factor;
+      fjac(i, 1) = -e * factor * factor;
+      fjac(i, 2) = -x * e * factor * factor;
+    }
+    return 0;
+  }
+};
+const double chwirut2_functor::m_x[54] = {
+    0.500E0, 1.000E0, 1.750E0, 3.750E0, 5.750E0, 0.875E0, 2.250E0, 3.250E0, 5.250E0, 0.750E0, 1.750E0,
+    2.750E0, 4.750E0, 0.625E0, 1.250E0, 2.250E0, 4.250E0, .500E0,  3.000E0, .750E0,  3.000E0, 1.500E0,
+    6.000E0, 3.000E0, 6.000E0, 1.500E0, 3.000E0, .500E0,  2.000E0, 4.000E0, .750E0,  2.000E0, 5.000E0,
+    .750E0,  2.250E0, 3.750E0, 5.750E0, 3.000E0, .750E0,  2.500E0, 4.000E0, .750E0,  2.500E0, 4.000E0,
+    .750E0,  2.500E0, 4.000E0, .500E0,  6.000E0, 3.000E0, .500E0,  2.750E0, .500E0,  1.750E0};
+const double chwirut2_functor::m_y[54] = {
+    92.9000E0, 57.1000E0, 31.0500E0, 11.5875E0, 8.0250E0,  63.6000E0, 21.4000E0, 14.2500E0, 8.4750E0,
+    63.8000E0, 26.8000E0, 16.4625E0, 7.1250E0,  67.3000E0, 41.0000E0, 21.1500E0, 8.1750E0,  81.5000E0,
+    13.1200E0, 59.9000E0, 14.6200E0, 32.9000E0, 5.4400E0,  12.5600E0, 5.4400E0,  32.0000E0, 13.9500E0,
+    75.8000E0, 20.0000E0, 10.4200E0, 59.5000E0, 21.6700E0, 8.5500E0,  62.0000E0, 20.2000E0, 7.7600E0,
+    3.7500E0,  11.8100E0, 54.7000E0, 23.7000E0, 11.5500E0, 61.3000E0, 17.7000E0, 8.7400E0,  59.2000E0,
+    16.3000E0, 8.6200E0,  81.0000E0, 4.8700E0,  14.6200E0, 81.7000E0, 17.1700E0, 81.3000E0, 28.9000E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/chwirut2.shtml
+void testNistChwirut2(void) {
+  const int n = 3;
+  LevenbergMarquardtSpace::Status info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 0.1, 0.01, 0.02;
+  // do the computation
+  chwirut2_functor functor;
+  LevenbergMarquardt<chwirut2_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  //   VERIFY_IS_EQUAL(lm.nfev(), 10);
+  // VERIFY_IS_EQUAL(lm.njev(), 8);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.1304802941E+02);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.6657666537E-01);
+  VERIFY_IS_APPROX(x[1], 5.1653291286E-03);
+  VERIFY_IS_APPROX(x[2], 1.2150007096E-02);
+
+  /*
+   * Second try
+   */
+  x << 0.15, 0.008, 0.010;
+  // do the computation
+  lm.resetParameters();
+  lm.setFtol(1.E6 * NumTraits<double>::epsilon());
+  lm.setXtol(1.E6 * NumTraits<double>::epsilon());
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  //   VERIFY_IS_EQUAL(lm.nfev(), 7);
+  // VERIFY_IS_EQUAL(lm.njev(), 6);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.1304802941E+02);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.6657666537E-01);
+  VERIFY_IS_APPROX(x[1], 5.1653291286E-03);
+  VERIFY_IS_APPROX(x[2], 1.2150007096E-02);
+}
+
+struct misra1a_functor : DenseFunctor<double> {
+  misra1a_functor(void) : DenseFunctor<double>(2, 14) {}
+  static const double m_x[14];
+  static const double m_y[14];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 2);
+    assert(fvec.size() == 14);
+    for (int i = 0; i < 14; i++) {
+      fvec[i] = b[0] * (1. - exp(-b[1] * m_x[i])) - m_y[i];
+    }
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 2);
+    assert(fjac.rows() == 14);
+    assert(fjac.cols() == 2);
+    for (int i = 0; i < 14; i++) {
+      fjac(i, 0) = (1. - exp(-b[1] * m_x[i]));
+      fjac(i, 1) = (b[0] * m_x[i] * exp(-b[1] * m_x[i]));
+    }
+    return 0;
+  }
+};
+const double misra1a_functor::m_x[14] = {77.6E0,  114.9E0, 141.1E0, 190.8E0, 239.9E0, 289.0E0, 332.8E0,
+                                         378.4E0, 434.8E0, 477.3E0, 536.8E0, 593.1E0, 689.1E0, 760.0E0};
+const double misra1a_functor::m_y[14] = {10.07E0, 14.73E0, 17.94E0, 23.93E0, 29.61E0, 35.18E0, 40.02E0,
+                                         44.82E0, 50.76E0, 55.05E0, 61.01E0, 66.40E0, 75.47E0, 81.78E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/misra1a.shtml
+void testNistMisra1a(void) {
+  const int n = 2;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 500., 0.0001;
+  // do the computation
+  misra1a_functor functor;
+  LevenbergMarquardt<misra1a_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 19);
+  // VERIFY_IS_EQUAL(lm.njev(), 15);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.2455138894E-01);
+  // check x
+  VERIFY_IS_APPROX(x[0], 2.3894212918E+02);
+  VERIFY_IS_APPROX(x[1], 5.5015643181E-04);
+
+  /*
+   * Second try
+   */
+  x << 250., 0.0005;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 5);
+  // VERIFY_IS_EQUAL(lm.njev(), 4);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.2455138894E-01);
+  // check x
+  VERIFY_IS_APPROX(x[0], 2.3894212918E+02);
+  VERIFY_IS_APPROX(x[1], 5.5015643181E-04);
+}
+
+struct hahn1_functor : DenseFunctor<double> {
+  hahn1_functor(void) : DenseFunctor<double>(7, 236) {}
+  static const double m_x[236];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    static const double m_y[236] = {
+        .591E0,   1.547E0,  2.902E0,  2.894E0,  4.703E0,  6.307E0,  7.03E0,   7.898E0,  9.470E0,  9.484E0,  10.072E0,
+        10.163E0, 11.615E0, 12.005E0, 12.478E0, 12.982E0, 12.970E0, 13.926E0, 14.452E0, 14.404E0, 15.190E0, 15.550E0,
+        15.528E0, 15.499E0, 16.131E0, 16.438E0, 16.387E0, 16.549E0, 16.872E0, 16.830E0, 16.926E0, 16.907E0, 16.966E0,
+        17.060E0, 17.122E0, 17.311E0, 17.355E0, 17.668E0, 17.767E0, 17.803E0, 17.765E0, 17.768E0, 17.736E0, 17.858E0,
+        17.877E0, 17.912E0, 18.046E0, 18.085E0, 18.291E0, 18.357E0, 18.426E0, 18.584E0, 18.610E0, 18.870E0, 18.795E0,
+        19.111E0, .367E0,   .796E0,   0.892E0,  1.903E0,  2.150E0,  3.697E0,  5.870E0,  6.421E0,  7.422E0,  9.944E0,
+        11.023E0, 11.87E0,  12.786E0, 14.067E0, 13.974E0, 14.462E0, 14.464E0, 15.381E0, 15.483E0, 15.59E0,  16.075E0,
+        16.347E0, 16.181E0, 16.915E0, 17.003E0, 16.978E0, 17.756E0, 17.808E0, 17.868E0, 18.481E0, 18.486E0, 19.090E0,
+        16.062E0, 16.337E0, 16.345E0, 16.388E0, 17.159E0, 17.116E0, 17.164E0, 17.123E0, 17.979E0, 17.974E0, 18.007E0,
+        17.993E0, 18.523E0, 18.669E0, 18.617E0, 19.371E0, 19.330E0, 0.080E0,  0.248E0,  1.089E0,  1.418E0,  2.278E0,
+        3.624E0,  4.574E0,  5.556E0,  7.267E0,  7.695E0,  9.136E0,  9.959E0,  9.957E0,  11.600E0, 13.138E0, 13.564E0,
+        13.871E0, 13.994E0, 14.947E0, 15.473E0, 15.379E0, 15.455E0, 15.908E0, 16.114E0, 17.071E0, 17.135E0, 17.282E0,
+        17.368E0, 17.483E0, 17.764E0, 18.185E0, 18.271E0, 18.236E0, 18.237E0, 18.523E0, 18.627E0, 18.665E0, 19.086E0,
+        0.214E0,  0.943E0,  1.429E0,  2.241E0,  2.951E0,  3.782E0,  4.757E0,  5.602E0,  7.169E0,  8.920E0,  10.055E0,
+        12.035E0, 12.861E0, 13.436E0, 14.167E0, 14.755E0, 15.168E0, 15.651E0, 15.746E0, 16.216E0, 16.445E0, 16.965E0,
+        17.121E0, 17.206E0, 17.250E0, 17.339E0, 17.793E0, 18.123E0, 18.49E0,  18.566E0, 18.645E0, 18.706E0, 18.924E0,
+        19.1E0,   0.375E0,  0.471E0,  1.504E0,  2.204E0,  2.813E0,  4.765E0,  9.835E0,  10.040E0, 11.946E0, 12.596E0,
+        13.303E0, 13.922E0, 14.440E0, 14.951E0, 15.627E0, 15.639E0, 15.814E0, 16.315E0, 16.334E0, 16.430E0, 16.423E0,
+        17.024E0, 17.009E0, 17.165E0, 17.134E0, 17.349E0, 17.576E0, 17.848E0, 18.090E0, 18.276E0, 18.404E0, 18.519E0,
+        19.133E0, 19.074E0, 19.239E0, 19.280E0, 19.101E0, 19.398E0, 19.252E0, 19.89E0,  20.007E0, 19.929E0, 19.268E0,
+        19.324E0, 20.049E0, 20.107E0, 20.062E0, 20.065E0, 19.286E0, 19.972E0, 20.088E0, 20.743E0, 20.83E0,  20.935E0,
+        21.035E0, 20.93E0,  21.074E0, 21.085E0, 20.935E0};
+
+    //        int called=0; printf("call hahn1_functor with  iflag=%d, called=%d\n", iflag, called); if (iflag==1)
+    //        called++;
+
+    assert(b.size() == 7);
+    assert(fvec.size() == 236);
+    for (int i = 0; i < 236; i++) {
+      double x = m_x[i], xx = x * x, xxx = xx * x;
+      fvec[i] = (b[0] + b[1] * x + b[2] * xx + b[3] * xxx) / (1. + b[4] * x + b[5] * xx + b[6] * xxx) - m_y[i];
+    }
+    return 0;
+  }
+
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 7);
+    assert(fjac.rows() == 236);
+    assert(fjac.cols() == 7);
+    for (int i = 0; i < 236; i++) {
+      double x = m_x[i], xx = x * x, xxx = xx * x;
+      double fact = 1. / (1. + b[4] * x + b[5] * xx + b[6] * xxx);
+      fjac(i, 0) = 1. * fact;
+      fjac(i, 1) = x * fact;
+      fjac(i, 2) = xx * fact;
+      fjac(i, 3) = xxx * fact;
+      fact = -(b[0] + b[1] * x + b[2] * xx + b[3] * xxx) * fact * fact;
+      fjac(i, 4) = x * fact;
+      fjac(i, 5) = xx * fact;
+      fjac(i, 6) = xxx * fact;
+    }
+    return 0;
+  }
+};
+const double hahn1_functor::m_x[236] = {
+    24.41E0,  34.82E0,  44.09E0,  45.07E0,  54.98E0,  65.51E0,  70.53E0,  75.70E0,  89.57E0,  91.14E0,  96.40E0,
+    97.19E0,  114.26E0, 120.25E0, 127.08E0, 133.55E0, 133.61E0, 158.67E0, 172.74E0, 171.31E0, 202.14E0, 220.55E0,
+    221.05E0, 221.39E0, 250.99E0, 268.99E0, 271.80E0, 271.97E0, 321.31E0, 321.69E0, 330.14E0, 333.03E0, 333.47E0,
+    340.77E0, 345.65E0, 373.11E0, 373.79E0, 411.82E0, 419.51E0, 421.59E0, 422.02E0, 422.47E0, 422.61E0, 441.75E0,
+    447.41E0, 448.7E0,  472.89E0, 476.69E0, 522.47E0, 522.62E0, 524.43E0, 546.75E0, 549.53E0, 575.29E0, 576.00E0,
+    625.55E0, 20.15E0,  28.78E0,  29.57E0,  37.41E0,  39.12E0,  50.24E0,  61.38E0,  66.25E0,  73.42E0,  95.52E0,
+    107.32E0, 122.04E0, 134.03E0, 163.19E0, 163.48E0, 175.70E0, 179.86E0, 211.27E0, 217.78E0, 219.14E0, 262.52E0,
+    268.01E0, 268.62E0, 336.25E0, 337.23E0, 339.33E0, 427.38E0, 428.58E0, 432.68E0, 528.99E0, 531.08E0, 628.34E0,
+    253.24E0, 273.13E0, 273.66E0, 282.10E0, 346.62E0, 347.19E0, 348.78E0, 351.18E0, 450.10E0, 450.35E0, 451.92E0,
+    455.56E0, 552.22E0, 553.56E0, 555.74E0, 652.59E0, 656.20E0, 14.13E0,  20.41E0,  31.30E0,  33.84E0,  39.70E0,
+    48.83E0,  54.50E0,  60.41E0,  72.77E0,  75.25E0,  86.84E0,  94.88E0,  96.40E0,  117.37E0, 139.08E0, 147.73E0,
+    158.63E0, 161.84E0, 192.11E0, 206.76E0, 209.07E0, 213.32E0, 226.44E0, 237.12E0, 330.90E0, 358.72E0, 370.77E0,
+    372.72E0, 396.24E0, 416.59E0, 484.02E0, 495.47E0, 514.78E0, 515.65E0, 519.47E0, 544.47E0, 560.11E0, 620.77E0,
+    18.97E0,  28.93E0,  33.91E0,  40.03E0,  44.66E0,  49.87E0,  55.16E0,  60.90E0,  72.08E0,  85.15E0,  97.06E0,
+    119.63E0, 133.27E0, 143.84E0, 161.91E0, 180.67E0, 198.44E0, 226.86E0, 229.65E0, 258.27E0, 273.77E0, 339.15E0,
+    350.13E0, 362.75E0, 371.03E0, 393.32E0, 448.53E0, 473.78E0, 511.12E0, 524.70E0, 548.75E0, 551.64E0, 574.02E0,
+    623.86E0, 21.46E0,  24.33E0,  33.43E0,  39.22E0,  44.18E0,  55.02E0,  94.33E0,  96.44E0,  118.82E0, 128.48E0,
+    141.94E0, 156.92E0, 171.65E0, 190.00E0, 223.26E0, 223.88E0, 231.50E0, 265.05E0, 269.44E0, 271.78E0, 273.46E0,
+    334.61E0, 339.79E0, 349.52E0, 358.18E0, 377.98E0, 394.77E0, 429.66E0, 468.22E0, 487.27E0, 519.54E0, 523.03E0,
+    612.99E0, 638.59E0, 641.36E0, 622.05E0, 631.50E0, 663.97E0, 646.9E0,  748.29E0, 749.21E0, 750.14E0, 647.04E0,
+    646.89E0, 746.9E0,  748.43E0, 747.35E0, 749.27E0, 647.61E0, 747.78E0, 750.51E0, 851.37E0, 845.97E0, 847.54E0,
+    849.93E0, 851.61E0, 849.75E0, 850.98E0, 848.23E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/hahn1.shtml
+void testNistHahn1(void) {
+  const int n = 7;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 10., -1., .05, -.00001, -.05, .001, -.000001;
+  // do the computation
+  hahn1_functor functor;
+  LevenbergMarquardt<hahn1_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 11);
+  // VERIFY_IS_EQUAL(lm.njev(), 10);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.5324382854E+00);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.0776351733E+00);
+  VERIFY_IS_APPROX(x[1], -1.2269296921E-01);
+  VERIFY_IS_APPROX(x[2], 4.0863750610E-03);
+  VERIFY_IS_APPROX(x[3], -1.426264e-06);  // should be : -1.4262662514E-06
+  VERIFY_IS_APPROX(x[4], -5.7609940901E-03);
+  VERIFY_IS_APPROX(x[5], 2.4053735503E-04);
+  VERIFY_IS_APPROX(x[6], -1.2314450199E-07);
+
+  /*
+   * Second try
+   */
+  x << .1, -.1, .005, -.000001, -.005, .0001, -.0000001;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  //   VERIFY_IS_EQUAL(lm.nfev(), 11);
+  // VERIFY_IS_EQUAL(lm.njev(), 10);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.5324382854E+00);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.077640);       // should be :  1.0776351733E+00
+  VERIFY_IS_APPROX(x[1], -0.1226933);     // should be : -1.2269296921E-01
+  VERIFY_IS_APPROX(x[2], 0.004086383);    // should be : 4.0863750610E-03
+  VERIFY_IS_APPROX(x[3], -1.426277e-06);  // should be : -1.4262662514E-06
+  VERIFY_IS_APPROX(x[4], -5.7609940901E-03);
+  VERIFY_IS_APPROX(x[5], 0.00024053772);  // should be : 2.4053735503E-04
+  VERIFY_IS_APPROX(x[6], -1.231450e-07);  // should be : -1.2314450199E-07
+}
+
+struct misra1d_functor : DenseFunctor<double> {
+  misra1d_functor(void) : DenseFunctor<double>(2, 14) {}
+  static const double x[14];
+  static const double y[14];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 2);
+    assert(fvec.size() == 14);
+    for (int i = 0; i < 14; i++) {
+      fvec[i] = b[0] * b[1] * x[i] / (1. + b[1] * x[i]) - y[i];
+    }
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 2);
+    assert(fjac.rows() == 14);
+    assert(fjac.cols() == 2);
+    for (int i = 0; i < 14; i++) {
+      double den = 1. + b[1] * x[i];
+      fjac(i, 0) = b[1] * x[i] / den;
+      fjac(i, 1) = b[0] * x[i] * (den - b[1] * x[i]) / den / den;
+    }
+    return 0;
+  }
+};
+const double misra1d_functor::x[14] = {77.6E0,  114.9E0, 141.1E0, 190.8E0, 239.9E0, 289.0E0, 332.8E0,
+                                       378.4E0, 434.8E0, 477.3E0, 536.8E0, 593.1E0, 689.1E0, 760.0E0};
+const double misra1d_functor::y[14] = {10.07E0, 14.73E0, 17.94E0, 23.93E0, 29.61E0, 35.18E0, 40.02E0,
+                                       44.82E0, 50.76E0, 55.05E0, 61.01E0, 66.40E0, 75.47E0, 81.78E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/misra1d.shtml
+void testNistMisra1d(void) {
+  const int n = 2;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 500., 0.0001;
+  // do the computation
+  misra1d_functor functor;
+  LevenbergMarquardt<misra1d_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 9);
+  // VERIFY_IS_EQUAL(lm.njev(), 7);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6419295283E-02);
+  // check x
+  VERIFY_IS_APPROX(x[0], 4.3736970754E+02);
+  VERIFY_IS_APPROX(x[1], 3.0227324449E-04);
+
+  /*
+   * Second try
+   */
+  x << 450., 0.0003;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 4);
+  // VERIFY_IS_EQUAL(lm.njev(), 3);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6419295283E-02);
+  // check x
+  VERIFY_IS_APPROX(x[0], 4.3736970754E+02);
+  VERIFY_IS_APPROX(x[1], 3.0227324449E-04);
+}
+
+struct lanczos1_functor : DenseFunctor<double> {
+  lanczos1_functor(void) : DenseFunctor<double>(6, 24) {}
+  static const double x[24];
+  static const double y[24];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 6);
+    assert(fvec.size() == 24);
+    for (int i = 0; i < 24; i++)
+      fvec[i] = b[0] * exp(-b[1] * x[i]) + b[2] * exp(-b[3] * x[i]) + b[4] * exp(-b[5] * x[i]) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 6);
+    assert(fjac.rows() == 24);
+    assert(fjac.cols() == 6);
+    for (int i = 0; i < 24; i++) {
+      fjac(i, 0) = exp(-b[1] * x[i]);
+      fjac(i, 1) = -b[0] * x[i] * exp(-b[1] * x[i]);
+      fjac(i, 2) = exp(-b[3] * x[i]);
+      fjac(i, 3) = -b[2] * x[i] * exp(-b[3] * x[i]);
+      fjac(i, 4) = exp(-b[5] * x[i]);
+      fjac(i, 5) = -b[4] * x[i] * exp(-b[5] * x[i]);
+    }
+    return 0;
+  }
+};
+const double lanczos1_functor::x[24] = {0.000000000000E+00, 5.000000000000E-02, 1.000000000000E-01, 1.500000000000E-01,
+                                        2.000000000000E-01, 2.500000000000E-01, 3.000000000000E-01, 3.500000000000E-01,
+                                        4.000000000000E-01, 4.500000000000E-01, 5.000000000000E-01, 5.500000000000E-01,
+                                        6.000000000000E-01, 6.500000000000E-01, 7.000000000000E-01, 7.500000000000E-01,
+                                        8.000000000000E-01, 8.500000000000E-01, 9.000000000000E-01, 9.500000000000E-01,
+                                        1.000000000000E+00, 1.050000000000E+00, 1.100000000000E+00, 1.150000000000E+00};
+const double lanczos1_functor::y[24] = {2.513400000000E+00, 2.044333373291E+00, 1.668404436564E+00, 1.366418021208E+00,
+                                        1.123232487372E+00, 9.268897180037E-01, 7.679338563728E-01, 6.388775523106E-01,
+                                        5.337835317402E-01, 4.479363617347E-01, 3.775847884350E-01, 3.197393199326E-01,
+                                        2.720130773746E-01, 2.324965529032E-01, 1.996589546065E-01, 1.722704126914E-01,
+                                        1.493405660168E-01, 1.300700206922E-01, 1.138119324644E-01, 1.000415587559E-01,
+                                        8.833209084540E-02, 7.833544019350E-02, 6.976693743449E-02, 6.239312536719E-02};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/lanczos1.shtml
+void testNistLanczos1(void) {
+  const int n = 6;
+  LevenbergMarquardtSpace::Status info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 1.2, 0.3, 5.6, 5.5, 6.5, 7.6;
+  // do the computation
+  lanczos1_functor functor;
+  LevenbergMarquardt<lanczos1_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeErrorTooSmall);
+  // VERIFY_IS_EQUAL(lm.nfev(), 79);
+  // VERIFY_IS_EQUAL(lm.njev(), 72);
+  // check norm^2
+  VERIFY(lm.fvec().squaredNorm() <= 1.44E-25);
+  // check x
+  VERIFY_IS_APPROX(x[0], 9.5100000027E-02);
+  VERIFY_IS_APPROX(x[1], 1.0000000001E+00);
+  VERIFY_IS_APPROX(x[2], 8.6070000013E-01);
+  VERIFY_IS_APPROX(x[3], 3.0000000002E+00);
+  VERIFY_IS_APPROX(x[4], 1.5575999998E+00);
+  VERIFY_IS_APPROX(x[5], 5.0000000001E+00);
+
+  /*
+   * Second try
+   */
+  x << 0.5, 0.7, 3.6, 4.2, 4., 6.3;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeErrorTooSmall);
+  // VERIFY_IS_EQUAL(lm.nfev(), 9);
+  // VERIFY_IS_EQUAL(lm.njev(), 8);
+  // check norm^2
+  VERIFY(lm.fvec().squaredNorm() <= 1.44E-25);
+  // check x
+  VERIFY_IS_APPROX(x[0], 9.5100000027E-02);
+  VERIFY_IS_APPROX(x[1], 1.0000000001E+00);
+  VERIFY_IS_APPROX(x[2], 8.6070000013E-01);
+  VERIFY_IS_APPROX(x[3], 3.0000000002E+00);
+  VERIFY_IS_APPROX(x[4], 1.5575999998E+00);
+  VERIFY_IS_APPROX(x[5], 5.0000000001E+00);
+}
+
+struct rat42_functor : DenseFunctor<double> {
+  rat42_functor(void) : DenseFunctor<double>(3, 9) {}
+  static const double x[9];
+  static const double y[9];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 3);
+    assert(fvec.size() == 9);
+    for (int i = 0; i < 9; i++) {
+      fvec[i] = b[0] / (1. + exp(b[1] - b[2] * x[i])) - y[i];
+    }
+    return 0;
+  }
+
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 3);
+    assert(fjac.rows() == 9);
+    assert(fjac.cols() == 3);
+    for (int i = 0; i < 9; i++) {
+      double e = exp(b[1] - b[2] * x[i]);
+      fjac(i, 0) = 1. / (1. + e);
+      fjac(i, 1) = -b[0] * e / (1. + e) / (1. + e);
+      fjac(i, 2) = +b[0] * e * x[i] / (1. + e) / (1. + e);
+    }
+    return 0;
+  }
+};
+const double rat42_functor::x[9] = {9.000E0,  14.000E0, 21.000E0, 28.000E0, 42.000E0,
+                                    57.000E0, 63.000E0, 70.000E0, 79.000E0};
+const double rat42_functor::y[9] = {8.930E0,  10.800E0, 18.590E0, 22.330E0, 39.350E0,
+                                    56.110E0, 61.730E0, 64.620E0, 67.080E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/ratkowsky2.shtml
+void testNistRat42(void) {
+  const int n = 3;
+  LevenbergMarquardtSpace::Status info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 100., 1., 0.1;
+  // do the computation
+  rat42_functor functor;
+  LevenbergMarquardt<rat42_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeReductionTooSmall);
+  // VERIFY_IS_EQUAL(lm.nfev(), 10);
+  // VERIFY_IS_EQUAL(lm.njev(), 8);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.0565229338E+00);
+  // check x
+  VERIFY_IS_APPROX(x[0], 7.2462237576E+01);
+  VERIFY_IS_APPROX(x[1], 2.6180768402E+00);
+  VERIFY_IS_APPROX(x[2], 6.7359200066E-02);
+
+  /*
+   * Second try
+   */
+  x << 75., 2.5, 0.07;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeReductionTooSmall);
+  // VERIFY_IS_EQUAL(lm.nfev(), 6);
+  // VERIFY_IS_EQUAL(lm.njev(), 5);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.0565229338E+00);
+  // check x
+  VERIFY_IS_APPROX(x[0], 7.2462237576E+01);
+  VERIFY_IS_APPROX(x[1], 2.6180768402E+00);
+  VERIFY_IS_APPROX(x[2], 6.7359200066E-02);
+}
+
+struct MGH10_functor : DenseFunctor<double> {
+  MGH10_functor(void) : DenseFunctor<double>(3, 16) {}
+  static const double x[16];
+  static const double y[16];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 3);
+    assert(fvec.size() == 16);
+    for (int i = 0; i < 16; i++) fvec[i] = b[0] * exp(b[1] / (x[i] + b[2])) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 3);
+    assert(fjac.rows() == 16);
+    assert(fjac.cols() == 3);
+    for (int i = 0; i < 16; i++) {
+      double factor = 1. / (x[i] + b[2]);
+      double e = exp(b[1] * factor);
+      fjac(i, 0) = e;
+      fjac(i, 1) = b[0] * factor * e;
+      fjac(i, 2) = -b[1] * b[0] * factor * factor * e;
+    }
+    return 0;
+  }
+};
+const double MGH10_functor::x[16] = {5.000000E+01, 5.500000E+01, 6.000000E+01, 6.500000E+01, 7.000000E+01, 7.500000E+01,
+                                     8.000000E+01, 8.500000E+01, 9.000000E+01, 9.500000E+01, 1.000000E+02, 1.050000E+02,
+                                     1.100000E+02, 1.150000E+02, 1.200000E+02, 1.250000E+02};
+const double MGH10_functor::y[16] = {3.478000E+04, 2.861000E+04, 2.365000E+04, 1.963000E+04, 1.637000E+04, 1.372000E+04,
+                                     1.154000E+04, 9.744000E+03, 8.261000E+03, 7.030000E+03, 6.005000E+03, 5.147000E+03,
+                                     4.427000E+03, 3.820000E+03, 3.307000E+03, 2.872000E+03};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/mgh10.shtml
+void testNistMGH10(void) {
+  const int n = 3;
+  LevenbergMarquardtSpace::Status info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 2., 400000., 25000.;
+  // do the computation
+  MGH10_functor functor;
+  LevenbergMarquardt<MGH10_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+  // ++g_test_level;
+  // VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeReductionTooSmall);
+  // --g_test_level;
+  // was: VERIFY_IS_EQUAL(info, 1);
+
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.7945855171E+01);
+  // check x
+  VERIFY_IS_APPROX(x[0], 5.6096364710E-03);
+  VERIFY_IS_APPROX(x[1], 6.1813463463E+03);
+  VERIFY_IS_APPROX(x[2], 3.4522363462E+02);
+
+  // check return value
+
+  // ++g_test_level;
+  // VERIFY_IS_EQUAL(lm.nfev(), 284 );
+  // VERIFY_IS_EQUAL(lm.njev(), 249 );
+  // --g_test_level;
+  VERIFY(lm.nfev() < 284 * LM_EVAL_COUNT_TOL);
+  VERIFY(lm.njev() < 249 * LM_EVAL_COUNT_TOL);
+
+  /*
+   * Second try
+   */
+  x << 0.02, 4000., 250.;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+  // ++g_test_level;
+  // VERIFY_IS_EQUAL(info, LevenbergMarquardtSpace::RelativeReductionTooSmall);
+  // // was: VERIFY_IS_EQUAL(info, 1);
+  // --g_test_level;
+
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.7945855171E+01);
+  // check x
+  VERIFY_IS_APPROX(x[0], 5.6096364710E-03);
+  VERIFY_IS_APPROX(x[1], 6.1813463463E+03);
+  VERIFY_IS_APPROX(x[2], 3.4522363462E+02);
+
+  // check return value
+  // ++g_test_level;
+  // VERIFY_IS_EQUAL(lm.nfev(), 126);
+  // VERIFY_IS_EQUAL(lm.njev(), 116);
+  // --g_test_level;
+  VERIFY(lm.nfev() < 126 * LM_EVAL_COUNT_TOL);
+  VERIFY(lm.njev() < 116 * LM_EVAL_COUNT_TOL);
+}
+
+struct BoxBOD_functor : DenseFunctor<double> {
+  BoxBOD_functor(void) : DenseFunctor<double>(2, 6) {}
+  static const double x[6];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    static const double y[6] = {109., 149., 149., 191., 213., 224.};
+    assert(b.size() == 2);
+    assert(fvec.size() == 6);
+    for (int i = 0; i < 6; i++) fvec[i] = b[0] * (1. - exp(-b[1] * x[i])) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 2);
+    assert(fjac.rows() == 6);
+    assert(fjac.cols() == 2);
+    for (int i = 0; i < 6; i++) {
+      double e = exp(-b[1] * x[i]);
+      fjac(i, 0) = 1. - e;
+      fjac(i, 1) = b[0] * x[i] * e;
+    }
+    return 0;
+  }
+};
+const double BoxBOD_functor::x[6] = {1., 2., 3., 5., 7., 10.};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/boxbod.shtml
+void testNistBoxBOD(void) {
+  const int n = 2;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 1., 1.;
+  // do the computation
+  BoxBOD_functor functor;
+  LevenbergMarquardt<BoxBOD_functor> lm(functor);
+  lm.setFtol(1.E6 * NumTraits<double>::epsilon());
+  lm.setXtol(1.E6 * NumTraits<double>::epsilon());
+  lm.setFactor(10);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.1680088766E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 2.1380940889E+02);
+  VERIFY_IS_APPROX(x[1], 5.4723748542E-01);
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY(lm.nfev() < 31); // 31
+  // VERIFY(lm.njev() < 25); // 25
+
+  /*
+   * Second try
+   */
+  x << 100., 0.75;
+  // do the computation
+  lm.resetParameters();
+  lm.setFtol(NumTraits<double>::epsilon());
+  lm.setXtol(NumTraits<double>::epsilon());
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // ++g_test_level;
+  // VERIFY_IS_EQUAL(lm.nfev(), 16 );
+  // VERIFY_IS_EQUAL(lm.njev(), 15 );
+  // --g_test_level;
+  VERIFY(lm.nfev() < 16 * LM_EVAL_COUNT_TOL);
+  VERIFY(lm.njev() < 15 * LM_EVAL_COUNT_TOL);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.1680088766E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 2.1380940889E+02);
+  VERIFY_IS_APPROX(x[1], 5.4723748542E-01);
+}
+
+struct MGH17_functor : DenseFunctor<double> {
+  MGH17_functor(void) : DenseFunctor<double>(5, 33) {}
+  static const double x[33];
+  static const double y[33];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 5);
+    assert(fvec.size() == 33);
+    for (int i = 0; i < 33; i++) fvec[i] = b[0] + b[1] * exp(-b[3] * x[i]) + b[2] * exp(-b[4] * x[i]) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 5);
+    assert(fjac.rows() == 33);
+    assert(fjac.cols() == 5);
+    for (int i = 0; i < 33; i++) {
+      fjac(i, 0) = 1.;
+      fjac(i, 1) = exp(-b[3] * x[i]);
+      fjac(i, 2) = exp(-b[4] * x[i]);
+      fjac(i, 3) = -x[i] * b[1] * exp(-b[3] * x[i]);
+      fjac(i, 4) = -x[i] * b[2] * exp(-b[4] * x[i]);
+    }
+    return 0;
+  }
+};
+const double MGH17_functor::x[33] = {0.000000E+00, 1.000000E+01, 2.000000E+01, 3.000000E+01, 4.000000E+01, 5.000000E+01,
+                                     6.000000E+01, 7.000000E+01, 8.000000E+01, 9.000000E+01, 1.000000E+02, 1.100000E+02,
+                                     1.200000E+02, 1.300000E+02, 1.400000E+02, 1.500000E+02, 1.600000E+02, 1.700000E+02,
+                                     1.800000E+02, 1.900000E+02, 2.000000E+02, 2.100000E+02, 2.200000E+02, 2.300000E+02,
+                                     2.400000E+02, 2.500000E+02, 2.600000E+02, 2.700000E+02, 2.800000E+02, 2.900000E+02,
+                                     3.000000E+02, 3.100000E+02, 3.200000E+02};
+const double MGH17_functor::y[33] = {8.440000E-01, 9.080000E-01, 9.320000E-01, 9.360000E-01, 9.250000E-01, 9.080000E-01,
+                                     8.810000E-01, 8.500000E-01, 8.180000E-01, 7.840000E-01, 7.510000E-01, 7.180000E-01,
+                                     6.850000E-01, 6.580000E-01, 6.280000E-01, 6.030000E-01, 5.800000E-01, 5.580000E-01,
+                                     5.380000E-01, 5.220000E-01, 5.060000E-01, 4.900000E-01, 4.780000E-01, 4.670000E-01,
+                                     4.570000E-01, 4.480000E-01, 4.380000E-01, 4.310000E-01, 4.240000E-01, 4.200000E-01,
+                                     4.140000E-01, 4.110000E-01, 4.060000E-01};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/mgh17.shtml
+void testNistMGH17(void) {
+  const int n = 5;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 50., 150., -100., 1., 2.;
+  // do the computation
+  MGH17_functor functor;
+  LevenbergMarquardt<MGH17_functor> lm(functor);
+  lm.setFtol(NumTraits<double>::epsilon());
+  lm.setXtol(NumTraits<double>::epsilon());
+  lm.setMaxfev(1000);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.4648946975E-05);
+  // check x
+  VERIFY_IS_APPROX(x[0], 3.7541005211E-01);
+  VERIFY_IS_APPROX(x[1], 1.9358469127E+00);
+  VERIFY_IS_APPROX(x[2], -1.4646871366E+00);
+  VERIFY_IS_APPROX(x[3], 1.2867534640E-02);
+  VERIFY_IS_APPROX(x[4], 2.2122699662E-02);
+
+  // check return value
+  //   VERIFY_IS_EQUAL(info, 2);  //FIXME Use (lm.info() == Success)
+  // VERIFY(lm.nfev() < 700 ); // 602
+  // VERIFY(lm.njev() < 600 ); // 545
+
+  /*
+   * Second try
+   */
+  x << 0.5, 1.5, -1, 0.01, 0.02;
+  // do the computation
+  lm.resetParameters();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 18);
+  // VERIFY_IS_EQUAL(lm.njev(), 15);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.4648946975E-05);
+  // check x
+  VERIFY_IS_APPROX(x[0], 3.7541005211E-01);
+  VERIFY_IS_APPROX(x[1], 1.9358469127E+00);
+  VERIFY_IS_APPROX(x[2], -1.4646871366E+00);
+  VERIFY_IS_APPROX(x[3], 1.2867534640E-02);
+  VERIFY_IS_APPROX(x[4], 2.2122699662E-02);
+}
+
+struct MGH09_functor : DenseFunctor<double> {
+  MGH09_functor(void) : DenseFunctor<double>(4, 11) {}
+  static const double _x[11];
+  static const double y[11];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 4);
+    assert(fvec.size() == 11);
+    for (int i = 0; i < 11; i++) {
+      double x = _x[i], xx = x * x;
+      fvec[i] = b[0] * (xx + x * b[1]) / (xx + x * b[2] + b[3]) - y[i];
+    }
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 4);
+    assert(fjac.rows() == 11);
+    assert(fjac.cols() == 4);
+    for (int i = 0; i < 11; i++) {
+      double x = _x[i], xx = x * x;
+      double factor = 1. / (xx + x * b[2] + b[3]);
+      fjac(i, 0) = (xx + x * b[1]) * factor;
+      fjac(i, 1) = b[0] * x * factor;
+      fjac(i, 2) = -b[0] * (xx + x * b[1]) * x * factor * factor;
+      fjac(i, 3) = -b[0] * (xx + x * b[1]) * factor * factor;
+    }
+    return 0;
+  }
+};
+const double MGH09_functor::_x[11] = {4.,           2.,     1.,           5.E-1,        2.5E-01,     1.670000E-01,
+                                      1.250000E-01, 1.E-01, 8.330000E-02, 7.140000E-02, 6.250000E-02};
+const double MGH09_functor::y[11] = {1.957000E-01, 1.947000E-01, 1.735000E-01, 1.600000E-01, 8.440000E-02, 6.270000E-02,
+                                     4.560000E-02, 3.420000E-02, 3.230000E-02, 2.350000E-02, 2.460000E-02};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/mgh09.shtml
+void testNistMGH09(void) {
+  const int n = 4;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 25., 39, 41.5, 39.;
+  // do the computation
+  MGH09_functor functor;
+  LevenbergMarquardt<MGH09_functor> lm(functor);
+  lm.setMaxfev(1000);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 3.0750560385E-04);
+  // check x
+  VERIFY_IS_APPROX(x[0], 0.1928077089);   // should be 1.9280693458E-01
+  VERIFY_IS_APPROX(x[1], 0.19126423573);  // should be 1.9128232873E-01
+  VERIFY_IS_APPROX(x[2], 0.12305309914);  // should be 1.2305650693E-01
+  VERIFY_IS_APPROX(x[3], 0.13605395375);  // should be 1.3606233068E-01
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY(lm.nfev() < 510 ); // 490
+  // VERIFY(lm.njev() < 400 ); // 376
+
+  /*
+   * Second try
+   */
+  x << 0.25, 0.39, 0.415, 0.39;
+  // do the computation
+  lm.resetParameters();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 18);
+  // VERIFY_IS_EQUAL(lm.njev(), 16);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 3.0750560385E-04);
+  // check x
+  VERIFY_IS_APPROX(x[0], 0.19280781);  // should be 1.9280693458E-01
+  VERIFY_IS_APPROX(x[1], 0.19126265);  // should be 1.9128232873E-01
+  VERIFY_IS_APPROX(x[2], 0.12305280);  // should be 1.2305650693E-01
+  VERIFY_IS_APPROX(x[3], 0.13605322);  // should be 1.3606233068E-01
+}
+
+struct Bennett5_functor : DenseFunctor<double> {
+  Bennett5_functor(void) : DenseFunctor<double>(3, 154) {}
+  static const double x[154];
+  static const double y[154];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 3);
+    assert(fvec.size() == 154);
+    for (int i = 0; i < 154; i++) fvec[i] = b[0] * pow(b[1] + x[i], -1. / b[2]) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 3);
+    assert(fjac.rows() == 154);
+    assert(fjac.cols() == 3);
+    for (int i = 0; i < 154; i++) {
+      double e = pow(b[1] + x[i], -1. / b[2]);
+      fjac(i, 0) = e;
+      fjac(i, 1) = -b[0] * e / b[2] / (b[1] + x[i]);
+      fjac(i, 2) = b[0] * e * log(b[1] + x[i]) / b[2] / b[2];
+    }
+    return 0;
+  }
+};
+const double Bennett5_functor::x[154] = {
+    7.447168E0,  8.102586E0,  8.452547E0,  8.711278E0,  8.916774E0,  9.087155E0,  9.232590E0,  9.359535E0,  9.472166E0,
+    9.573384E0,  9.665293E0,  9.749461E0,  9.827092E0,  9.899128E0,  9.966321E0,  10.029280E0, 10.088510E0, 10.144430E0,
+    10.197380E0, 10.247670E0, 10.295560E0, 10.341250E0, 10.384950E0, 10.426820E0, 10.467000E0, 10.505640E0, 10.542830E0,
+    10.578690E0, 10.613310E0, 10.646780E0, 10.679150E0, 10.710520E0, 10.740920E0, 10.770440E0, 10.799100E0, 10.826970E0,
+    10.854080E0, 10.880470E0, 10.906190E0, 10.931260E0, 10.955720E0, 10.979590E0, 11.002910E0, 11.025700E0, 11.047980E0,
+    11.069770E0, 11.091100E0, 11.111980E0, 11.132440E0, 11.152480E0, 11.172130E0, 11.191410E0, 11.210310E0, 11.228870E0,
+    11.247090E0, 11.264980E0, 11.282560E0, 11.299840E0, 11.316820E0, 11.333520E0, 11.349940E0, 11.366100E0, 11.382000E0,
+    11.397660E0, 11.413070E0, 11.428240E0, 11.443200E0, 11.457930E0, 11.472440E0, 11.486750E0, 11.500860E0, 11.514770E0,
+    11.528490E0, 11.542020E0, 11.555380E0, 11.568550E0, 11.581560E0, 11.594420E0, 11.607121E0, 11.619640E0, 11.632000E0,
+    11.644210E0, 11.656280E0, 11.668200E0, 11.679980E0, 11.691620E0, 11.703130E0, 11.714510E0, 11.725760E0, 11.736880E0,
+    11.747890E0, 11.758780E0, 11.769550E0, 11.780200E0, 11.790730E0, 11.801160E0, 11.811480E0, 11.821700E0, 11.831810E0,
+    11.841820E0, 11.851730E0, 11.861550E0, 11.871270E0, 11.880890E0, 11.890420E0, 11.899870E0, 11.909220E0, 11.918490E0,
+    11.927680E0, 11.936780E0, 11.945790E0, 11.954730E0, 11.963590E0, 11.972370E0, 11.981070E0, 11.989700E0, 11.998260E0,
+    12.006740E0, 12.015150E0, 12.023490E0, 12.031760E0, 12.039970E0, 12.048100E0, 12.056170E0, 12.064180E0, 12.072120E0,
+    12.080010E0, 12.087820E0, 12.095580E0, 12.103280E0, 12.110920E0, 12.118500E0, 12.126030E0, 12.133500E0, 12.140910E0,
+    12.148270E0, 12.155570E0, 12.162830E0, 12.170030E0, 12.177170E0, 12.184270E0, 12.191320E0, 12.198320E0, 12.205270E0,
+    12.212170E0, 12.219030E0, 12.225840E0, 12.232600E0, 12.239320E0, 12.245990E0, 12.252620E0, 12.259200E0, 12.265750E0,
+    12.272240E0};
+const double Bennett5_functor::y[154] = {
+    -34.834702E0, -34.393200E0, -34.152901E0, -33.979099E0, -33.845901E0, -33.732899E0, -33.640301E0, -33.559200E0,
+    -33.486801E0, -33.423100E0, -33.365101E0, -33.313000E0, -33.260899E0, -33.217400E0, -33.176899E0, -33.139198E0,
+    -33.101601E0, -33.066799E0, -33.035000E0, -33.003101E0, -32.971298E0, -32.942299E0, -32.916302E0, -32.890202E0,
+    -32.864101E0, -32.841000E0, -32.817799E0, -32.797501E0, -32.774300E0, -32.757000E0, -32.733799E0, -32.716400E0,
+    -32.699100E0, -32.678799E0, -32.661400E0, -32.644001E0, -32.626701E0, -32.612202E0, -32.597698E0, -32.583199E0,
+    -32.568699E0, -32.554298E0, -32.539799E0, -32.525299E0, -32.510799E0, -32.499199E0, -32.487598E0, -32.473202E0,
+    -32.461601E0, -32.435501E0, -32.435501E0, -32.426800E0, -32.412300E0, -32.400799E0, -32.392101E0, -32.380501E0,
+    -32.366001E0, -32.357300E0, -32.348598E0, -32.339901E0, -32.328400E0, -32.319698E0, -32.311001E0, -32.299400E0,
+    -32.290699E0, -32.282001E0, -32.273300E0, -32.264599E0, -32.256001E0, -32.247299E0, -32.238602E0, -32.229900E0,
+    -32.224098E0, -32.215401E0, -32.203800E0, -32.198002E0, -32.189400E0, -32.183601E0, -32.174900E0, -32.169102E0,
+    -32.163300E0, -32.154598E0, -32.145901E0, -32.140099E0, -32.131401E0, -32.125599E0, -32.119801E0, -32.111198E0,
+    -32.105400E0, -32.096699E0, -32.090900E0, -32.088001E0, -32.079300E0, -32.073502E0, -32.067699E0, -32.061901E0,
+    -32.056099E0, -32.050301E0, -32.044498E0, -32.038799E0, -32.033001E0, -32.027199E0, -32.024300E0, -32.018501E0,
+    -32.012699E0, -32.004002E0, -32.001099E0, -31.995300E0, -31.989500E0, -31.983700E0, -31.977900E0, -31.972099E0,
+    -31.969299E0, -31.963501E0, -31.957701E0, -31.951900E0, -31.946100E0, -31.940300E0, -31.937401E0, -31.931601E0,
+    -31.925800E0, -31.922899E0, -31.917101E0, -31.911301E0, -31.908400E0, -31.902599E0, -31.896900E0, -31.893999E0,
+    -31.888201E0, -31.885300E0, -31.882401E0, -31.876600E0, -31.873699E0, -31.867901E0, -31.862101E0, -31.859200E0,
+    -31.856300E0, -31.850500E0, -31.844700E0, -31.841801E0, -31.838900E0, -31.833099E0, -31.830200E0, -31.827299E0,
+    -31.821600E0, -31.818701E0, -31.812901E0, -31.809999E0, -31.807100E0, -31.801300E0, -31.798401E0, -31.795500E0,
+    -31.789700E0, -31.786800E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/bennett5.shtml
+void testNistBennett5(void) {
+  const int n = 3;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << -2000., 50., 0.8;
+  // do the computation
+  Bennett5_functor functor;
+  LevenbergMarquardt<Bennett5_functor> lm(functor);
+  lm.setMaxfev(1000);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 758);
+  // VERIFY_IS_EQUAL(lm.njev(), 744);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.2404744073E-04);
+  // check x
+  VERIFY_IS_APPROX(x[0], -2.5235058043E+03);
+  VERIFY_IS_APPROX(x[1], 4.6736564644E+01);
+  VERIFY_IS_APPROX(x[2], 9.3218483193E-01);
+  /*
+   * Second try
+   */
+  x << -1500., 45., 0.85;
+  // do the computation
+  lm.resetParameters();
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 203);
+  // VERIFY_IS_EQUAL(lm.njev(), 192);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.2404744073E-04);
+  // check x
+  VERIFY_IS_APPROX(x[0], -2523.3007865);  // should be -2.5235058043E+03
+  VERIFY_IS_APPROX(x[1], 46.735705771);   // should be 4.6736564644E+01);
+  VERIFY_IS_APPROX(x[2], 0.93219881891);  // should be 9.3218483193E-01);
+}
+
+struct thurber_functor : DenseFunctor<double> {
+  thurber_functor(void) : DenseFunctor<double>(7, 37) {}
+  static const double _x[37];
+  static const double _y[37];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    //        int called=0; printf("call hahn1_functor with  iflag=%d, called=%d\n", iflag, called); if (iflag==1)
+    //        called++;
+    assert(b.size() == 7);
+    assert(fvec.size() == 37);
+    for (int i = 0; i < 37; i++) {
+      double x = _x[i], xx = x * x, xxx = xx * x;
+      fvec[i] = (b[0] + b[1] * x + b[2] * xx + b[3] * xxx) / (1. + b[4] * x + b[5] * xx + b[6] * xxx) - _y[i];
+    }
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 7);
+    assert(fjac.rows() == 37);
+    assert(fjac.cols() == 7);
+    for (int i = 0; i < 37; i++) {
+      double x = _x[i], xx = x * x, xxx = xx * x;
+      double fact = 1. / (1. + b[4] * x + b[5] * xx + b[6] * xxx);
+      fjac(i, 0) = 1. * fact;
+      fjac(i, 1) = x * fact;
+      fjac(i, 2) = xx * fact;
+      fjac(i, 3) = xxx * fact;
+      fact = -(b[0] + b[1] * x + b[2] * xx + b[3] * xxx) * fact * fact;
+      fjac(i, 4) = x * fact;
+      fjac(i, 5) = xx * fact;
+      fjac(i, 6) = xxx * fact;
+    }
+    return 0;
+  }
+};
+const double thurber_functor::_x[37] = {-3.067E0, -2.981E0, -2.921E0, -2.912E0, -2.840E0, -2.797E0, -2.702E0, -2.699E0,
+                                        -2.633E0, -2.481E0, -2.363E0, -2.322E0, -1.501E0, -1.460E0, -1.274E0, -1.212E0,
+                                        -1.100E0, -1.046E0, -0.915E0, -0.714E0, -0.566E0, -0.545E0, -0.400E0, -0.309E0,
+                                        -0.109E0, -0.103E0, 0.010E0,  0.119E0,  0.377E0,  0.790E0,  0.963E0,  1.006E0,
+                                        1.115E0,  1.572E0,  1.841E0,  2.047E0,  2.200E0};
+const double thurber_functor::_y[37] = {
+    80.574E0,   84.248E0,   87.264E0,   87.195E0,   89.076E0,   89.608E0,   89.868E0,   90.101E0,
+    92.405E0,   95.854E0,   100.696E0,  101.060E0,  401.672E0,  390.724E0,  567.534E0,  635.316E0,
+    733.054E0,  759.087E0,  894.206E0,  990.785E0,  1090.109E0, 1080.914E0, 1122.643E0, 1178.351E0,
+    1260.531E0, 1273.514E0, 1288.339E0, 1327.543E0, 1353.863E0, 1414.509E0, 1425.208E0, 1421.384E0,
+    1442.962E0, 1464.350E0, 1468.705E0, 1447.894E0, 1457.628E0};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/thurber.shtml
+void testNistThurber(void) {
+  const int n = 7;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 1000, 1000, 400, 40, 0.7, 0.3, 0.0;
+  // do the computation
+  thurber_functor functor;
+  LevenbergMarquardt<thurber_functor> lm(functor);
+  lm.setFtol(1.E4 * NumTraits<double>::epsilon());
+  lm.setXtol(1.E4 * NumTraits<double>::epsilon());
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 39);
+  // VERIFY_IS_EQUAL(lm.njev(), 36);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6427082397E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.2881396800E+03);
+  VERIFY_IS_APPROX(x[1], 1.4910792535E+03);
+  VERIFY_IS_APPROX(x[2], 5.8323836877E+02);
+  VERIFY_IS_APPROX(x[3], 7.5416644291E+01);
+  VERIFY_IS_APPROX(x[4], 9.6629502864E-01);
+  VERIFY_IS_APPROX(x[5], 3.9797285797E-01);
+  VERIFY_IS_APPROX(x[6], 4.9727297349E-02);
+
+  /*
+   * Second try
+   */
+  x << 1300, 1500, 500, 75, 1, 0.4, 0.05;
+  // do the computation
+  lm.resetParameters();
+  lm.setFtol(1.E4 * NumTraits<double>::epsilon());
+  lm.setXtol(1.E4 * NumTraits<double>::epsilon());
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 29);
+  // VERIFY_IS_EQUAL(lm.njev(), 28);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.6427082397E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.2881396800E+03);
+  VERIFY_IS_APPROX(x[1], 1.4910792535E+03);
+  VERIFY_IS_APPROX(x[2], 5.8323836877E+02);
+  VERIFY_IS_APPROX(x[3], 7.5416644291E+01);
+  VERIFY_IS_APPROX(x[4], 9.6629502864E-01);
+  VERIFY_IS_APPROX(x[5], 3.9797285797E-01);
+  VERIFY_IS_APPROX(x[6], 4.9727297349E-02);
+}
+
+struct rat43_functor : DenseFunctor<double> {
+  rat43_functor(void) : DenseFunctor<double>(4, 15) {}
+  static const double x[15];
+  static const double y[15];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 4);
+    assert(fvec.size() == 15);
+    for (int i = 0; i < 15; i++) fvec[i] = b[0] * pow(1. + exp(b[1] - b[2] * x[i]), -1. / b[3]) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 4);
+    assert(fjac.rows() == 15);
+    assert(fjac.cols() == 4);
+    for (int i = 0; i < 15; i++) {
+      double e = exp(b[1] - b[2] * x[i]);
+      double power = -1. / b[3];
+      fjac(i, 0) = pow(1. + e, power);
+      fjac(i, 1) = power * b[0] * e * pow(1. + e, power - 1.);
+      fjac(i, 2) = -power * b[0] * e * x[i] * pow(1. + e, power - 1.);
+      fjac(i, 3) = b[0] * power * power * log(1. + e) * pow(1. + e, power);
+    }
+    return 0;
+  }
+};
+const double rat43_functor::x[15] = {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.};
+const double rat43_functor::y[15] = {16.08,  33.83,  65.80,  97.20,  191.55, 326.20, 386.87, 520.53,
+                                     590.03, 651.92, 724.93, 699.56, 689.96, 637.56, 717.41};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/ratkowsky3.shtml
+void testNistRat43(void) {
+  const int n = 4;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 100., 10., 1., 1.;
+  // do the computation
+  rat43_functor functor;
+  LevenbergMarquardt<rat43_functor> lm(functor);
+  lm.setFtol(1.E6 * NumTraits<double>::epsilon());
+  lm.setXtol(1.E6 * NumTraits<double>::epsilon());
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 27);
+  // VERIFY_IS_EQUAL(lm.njev(), 20);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.7864049080E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 6.9964151270E+02);
+  VERIFY_IS_APPROX(x[1], 5.2771253025E+00);
+  VERIFY_IS_APPROX(x[2], 7.5962938329E-01);
+  VERIFY_IS_APPROX(x[3], 1.2792483859E+00);
+
+  /*
+   * Second try
+   */
+  x << 700., 5., 0.75, 1.3;
+  // do the computation
+  lm.resetParameters();
+  lm.setFtol(1.E5 * NumTraits<double>::epsilon());
+  lm.setXtol(1.E5 * NumTraits<double>::epsilon());
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 9);
+  // VERIFY_IS_EQUAL(lm.njev(), 8);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.7864049080E+03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 6.9964151270E+02);
+  VERIFY_IS_APPROX(x[1], 5.2771253025E+00);
+  VERIFY_IS_APPROX(x[2], 7.5962938329E-01);
+  VERIFY_IS_APPROX(x[3], 1.2792483859E+00);
+}
+
+struct eckerle4_functor : DenseFunctor<double> {
+  eckerle4_functor(void) : DenseFunctor<double>(3, 35) {}
+  static const double x[35];
+  static const double y[35];
+  int operator()(const VectorXd &b, VectorXd &fvec) {
+    assert(b.size() == 3);
+    assert(fvec.size() == 35);
+    for (int i = 0; i < 35; i++)
+      fvec[i] = b[0] / b[1] * exp(-0.5 * (x[i] - b[2]) * (x[i] - b[2]) / (b[1] * b[1])) - y[i];
+    return 0;
+  }
+  int df(const VectorXd &b, MatrixXd &fjac) {
+    assert(b.size() == 3);
+    assert(fjac.rows() == 35);
+    assert(fjac.cols() == 3);
+    for (int i = 0; i < 35; i++) {
+      double b12 = b[1] * b[1];
+      double e = exp(-0.5 * (x[i] - b[2]) * (x[i] - b[2]) / b12);
+      fjac(i, 0) = e / b[1];
+      fjac(i, 1) = ((x[i] - b[2]) * (x[i] - b[2]) / b12 - 1.) * b[0] * e / b12;
+      fjac(i, 2) = (x[i] - b[2]) * e * b[0] / b[1] / b12;
+    }
+    return 0;
+  }
+};
+const double eckerle4_functor::x[35] = {400.0, 405.0, 410.0, 415.0, 420.0, 425.0, 430.0, 435.0, 436.5,
+                                        438.0, 439.5, 441.0, 442.5, 444.0, 445.5, 447.0, 448.5, 450.0,
+                                        451.5, 453.0, 454.5, 456.0, 457.5, 459.0, 460.5, 462.0, 463.5,
+                                        465.0, 470.0, 475.0, 480.0, 485.0, 490.0, 495.0, 500.0};
+const double eckerle4_functor::y[35] = {0.0001575, 0.0001699, 0.0002350, 0.0003102, 0.0004917, 0.0008710, 0.0017418,
+                                        0.0046400, 0.0065895, 0.0097302, 0.0149002, 0.0237310, 0.0401683, 0.0712559,
+                                        0.1264458, 0.2073413, 0.2902366, 0.3445623, 0.3698049, 0.3668534, 0.3106727,
+                                        0.2078154, 0.1164354, 0.0616764, 0.0337200, 0.0194023, 0.0117831, 0.0074357,
+                                        0.0022732, 0.0008800, 0.0004579, 0.0002345, 0.0001586, 0.0001143, 0.0000710};
+
+// http://www.itl.nist.gov/div898/strd/nls/data/eckerle4.shtml
+void testNistEckerle4(void) {
+  const int n = 3;
+  int info;
+
+  VectorXd x(n);
+
+  /*
+   * First try
+   */
+  x << 1., 10., 500.;
+  // do the computation
+  eckerle4_functor functor;
+  LevenbergMarquardt<eckerle4_functor> lm(functor);
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 18);
+  // VERIFY_IS_EQUAL(lm.njev(), 15);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.4635887487E-03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.5543827178);
+  VERIFY_IS_APPROX(x[1], 4.0888321754);
+  VERIFY_IS_APPROX(x[2], 4.5154121844E+02);
+
+  /*
+   * Second try
+   */
+  x << 1.5, 5., 450.;
+  // do the computation
+  info = lm.minimize(x);
+  EIGEN_UNUSED_VARIABLE(info)
+
+  // check return value
+  // VERIFY_IS_EQUAL(info, 1);
+  // VERIFY_IS_EQUAL(lm.nfev(), 7);
+  // VERIFY_IS_EQUAL(lm.njev(), 6);
+  // check norm^2
+  VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.4635887487E-03);
+  // check x
+  VERIFY_IS_APPROX(x[0], 1.5543827178);
+  VERIFY_IS_APPROX(x[1], 4.0888321754);
+  VERIFY_IS_APPROX(x[2], 4.5154121844E+02);
+}
+
+EIGEN_DECLARE_TEST(levenberg_marquardt) {
+  // Tests using the examples provided by (c)minpack
+  CALL_SUBTEST(testLmder1());
+  CALL_SUBTEST(testLmder());
+  CALL_SUBTEST(testLmdif1());
+  //     CALL_SUBTEST(testLmstr1());
+  //     CALL_SUBTEST(testLmstr());
+  CALL_SUBTEST(testLmdif());
+
+  // NIST tests, level of difficulty = "Lower"
+  CALL_SUBTEST(testNistMisra1a());
+  CALL_SUBTEST(testNistChwirut2());
+
+  // NIST tests, level of difficulty = "Average"
+  CALL_SUBTEST(testNistHahn1());
+  CALL_SUBTEST(testNistMisra1d());
+  CALL_SUBTEST(testNistMGH17());
+  CALL_SUBTEST(testNistLanczos1());
+
+  //     // NIST tests, level of difficulty = "Higher"
+  CALL_SUBTEST(testNistRat42());
+  CALL_SUBTEST(testNistMGH10());
+  CALL_SUBTEST(testNistBoxBOD());
+  //     CALL_SUBTEST(testNistMGH09());
+  CALL_SUBTEST(testNistBennett5());
+  CALL_SUBTEST(testNistThurber());
+  CALL_SUBTEST(testNistRat43());
+  CALL_SUBTEST(testNistEckerle4());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_exponential.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_exponential.cpp
new file mode 100644
index 0000000..0a52169
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_exponential.cpp
@@ -0,0 +1,128 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "matrix_functions.h"
+
+double binom(int n, int k) {
+  double res = 1;
+  for (int i = 0; i < k; i++) res = res * (n - k + i + 1) / (i + 1);
+  return res;
+}
+
+template <typename T>
+T expfn(T x, int) {
+  return std::exp(x);
+}
+
+template <typename T>
+void test2dRotation(double tol) {
+  Matrix<T, 2, 2> A, B, C;
+  T angle;
+
+  A << 0, 1, -1, 0;
+  for (int i = 0; i <= 20; i++) {
+    angle = static_cast<T>(pow(10, i / 5. - 2));
+    B << std::cos(angle), std::sin(angle), -std::sin(angle), std::cos(angle);
+
+    C = (angle * A).matrixFunction(expfn);
+    std::cout << "test2dRotation: i = " << i << "   error funm = " << relerr(C, B);
+    VERIFY(C.isApprox(B, static_cast<T>(tol)));
+
+    C = (angle * A).exp();
+    std::cout << "   error expm = " << relerr(C, B) << "\n";
+    VERIFY(C.isApprox(B, static_cast<T>(tol)));
+  }
+}
+
+template <typename T>
+void test2dHyperbolicRotation(double tol) {
+  Matrix<std::complex<T>, 2, 2> A, B, C;
+  std::complex<T> imagUnit(0, 1);
+  T angle, ch, sh;
+
+  for (int i = 0; i <= 20; i++) {
+    angle = static_cast<T>((i - 10) / 2.0);
+    ch = std::cosh(angle);
+    sh = std::sinh(angle);
+    A << 0, angle * imagUnit, -angle * imagUnit, 0;
+    B << ch, sh * imagUnit, -sh * imagUnit, ch;
+
+    C = A.matrixFunction(expfn);
+    std::cout << "test2dHyperbolicRotation: i = " << i << "   error funm = " << relerr(C, B);
+    VERIFY(C.isApprox(B, static_cast<T>(tol)));
+
+    C = A.exp();
+    std::cout << "   error expm = " << relerr(C, B) << "\n";
+    VERIFY(C.isApprox(B, static_cast<T>(tol)));
+  }
+}
+
+template <typename T>
+void testPascal(double tol) {
+  for (int size = 1; size < 20; size++) {
+    Matrix<T, Dynamic, Dynamic> A(size, size), B(size, size), C(size, size);
+    A.setZero();
+    for (int i = 0; i < size - 1; i++) A(i + 1, i) = static_cast<T>(i + 1);
+    B.setZero();
+    for (int i = 0; i < size; i++)
+      for (int j = 0; j <= i; j++) B(i, j) = static_cast<T>(binom(i, j));
+
+    C = A.matrixFunction(expfn);
+    std::cout << "testPascal: size = " << size << "   error funm = " << relerr(C, B);
+    VERIFY(C.isApprox(B, static_cast<T>(tol)));
+
+    C = A.exp();
+    std::cout << "   error expm = " << relerr(C, B) << "\n";
+    VERIFY(C.isApprox(B, static_cast<T>(tol)));
+  }
+}
+
+template <typename MatrixType>
+void randomTest(const MatrixType& m, double tol) {
+  /* this test covers the following files:
+     Inverse.h
+  */
+  typename MatrixType::Index rows = m.rows();
+  typename MatrixType::Index cols = m.cols();
+  MatrixType m1(rows, cols), m2(rows, cols), identity = MatrixType::Identity(rows, cols);
+
+  typedef typename NumTraits<typename internal::traits<MatrixType>::Scalar>::Real RealScalar;
+
+  for (int i = 0; i < g_repeat; i++) {
+    m1 = MatrixType::Random(rows, cols);
+
+    m2 = m1.matrixFunction(expfn) * (-m1).matrixFunction(expfn);
+    std::cout << "randomTest: error funm = " << relerr(identity, m2);
+    VERIFY(identity.isApprox(m2, static_cast<RealScalar>(tol)));
+
+    m2 = m1.exp() * (-m1).exp();
+    std::cout << "   error expm = " << relerr(identity, m2) << "\n";
+    VERIFY(identity.isApprox(m2, static_cast<RealScalar>(tol)));
+  }
+}
+
+EIGEN_DECLARE_TEST(matrix_exponential) {
+  CALL_SUBTEST_2(test2dRotation<double>(1e-13));
+  CALL_SUBTEST_1(test2dRotation<float>(2e-5));  // was 1e-5, relaxed for clang 2.8 / linux / x86-64
+  CALL_SUBTEST_8(test2dRotation<long double>(1e-13));
+  CALL_SUBTEST_2(test2dHyperbolicRotation<double>(1e-14));
+  CALL_SUBTEST_1(test2dHyperbolicRotation<float>(1e-5));
+  CALL_SUBTEST_8(test2dHyperbolicRotation<long double>(1e-14));
+  CALL_SUBTEST_6(testPascal<float>(1e-6));
+  CALL_SUBTEST_5(testPascal<double>(1e-15));
+  CALL_SUBTEST_2(randomTest(Matrix2d(), 1e-13));
+  CALL_SUBTEST_7(randomTest(Matrix<double, 3, 3, RowMajor>(), 1e-13));
+  CALL_SUBTEST_3(randomTest(Matrix4cd(), 1e-13));
+  CALL_SUBTEST_4(randomTest(MatrixXd(8, 8), 1e-13));
+  CALL_SUBTEST_1(randomTest(Matrix2f(), 1e-4));
+  CALL_SUBTEST_5(randomTest(Matrix3cf(), 1e-4));
+  CALL_SUBTEST_1(randomTest(Matrix4f(), 1e-4));
+  CALL_SUBTEST_6(randomTest(MatrixXf(8, 8), 1e-4));
+  CALL_SUBTEST_9(randomTest(Matrix<long double, Dynamic, Dynamic>(7, 7), 1e-13));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_function.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_function.cpp
new file mode 100644
index 0000000..6588527
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_function.cpp
@@ -0,0 +1,210 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <unsupported/Eigen/MatrixFunctions>
+
+// Variant of VERIFY_IS_APPROX which uses absolute error instead of
+// relative error.
+#define VERIFY_IS_APPROX_ABS(a, b) VERIFY(test_isApprox_abs(a, b))
+
+template <typename Type1, typename Type2>
+inline bool test_isApprox_abs(const Type1& a, const Type2& b) {
+  return ((a - b).array().abs() < test_precision<typename Type1::RealScalar>()).all();
+}
+
+// Returns a matrix with eigenvalues clustered around 0, 1 and 2.
+template <typename MatrixType>
+MatrixType randomMatrixWithRealEivals(const Index size) {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  MatrixType diag = MatrixType::Zero(size, size);
+  for (Index i = 0; i < size; ++i) {
+    diag(i, i) =
+        Scalar(RealScalar(internal::random<int>(0, 2))) + internal::random<Scalar>() * Scalar(RealScalar(0.01));
+  }
+  MatrixType A = MatrixType::Random(size, size);
+  HouseholderQR<MatrixType> QRofA(A);
+  return QRofA.householderQ().inverse() * diag * QRofA.householderQ();
+}
+
+template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
+struct randomMatrixWithImagEivals {
+  // Returns a matrix with eigenvalues clustered around 0 and +/- i.
+  static MatrixType run(const Index size);
+};
+
+// Partial specialization for real matrices
+template <typename MatrixType>
+struct randomMatrixWithImagEivals<MatrixType, 0> {
+  static MatrixType run(const Index size) {
+    typedef typename MatrixType::Scalar Scalar;
+    MatrixType diag = MatrixType::Zero(size, size);
+    Index i = 0;
+    while (i < size) {
+      Index randomInt = internal::random<Index>(-1, 1);
+      if (randomInt == 0 || i == size - 1) {
+        diag(i, i) = internal::random<Scalar>() * Scalar(0.01);
+        ++i;
+      } else {
+        Scalar alpha = Scalar(randomInt) + internal::random<Scalar>() * Scalar(0.01);
+        diag(i, i + 1) = alpha;
+        diag(i + 1, i) = -alpha;
+        i += 2;
+      }
+    }
+    MatrixType A = MatrixType::Random(size, size);
+    HouseholderQR<MatrixType> QRofA(A);
+    return QRofA.householderQ().inverse() * diag * QRofA.householderQ();
+  }
+};
+
+// Partial specialization for complex matrices
+template <typename MatrixType>
+struct randomMatrixWithImagEivals<MatrixType, 1> {
+  static MatrixType run(const Index size) {
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    const Scalar imagUnit(0, 1);
+    MatrixType diag = MatrixType::Zero(size, size);
+    for (Index i = 0; i < size; ++i) {
+      diag(i, i) = Scalar(RealScalar(internal::random<Index>(-1, 1))) * imagUnit +
+                   internal::random<Scalar>() * Scalar(RealScalar(0.01));
+    }
+    MatrixType A = MatrixType::Random(size, size);
+    HouseholderQR<MatrixType> QRofA(A);
+    return QRofA.householderQ().inverse() * diag * QRofA.householderQ();
+  }
+};
+
+template <typename MatrixType>
+void testMatrixExponential(const MatrixType& A) {
+  typedef typename internal::traits<MatrixType>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef std::complex<RealScalar> ComplexScalar;
+
+  VERIFY_IS_APPROX(A.exp(), A.matrixFunction(internal::stem_function_exp<ComplexScalar>));
+}
+
+template <typename MatrixType>
+void testMatrixLogarithm(const MatrixType& A) {
+  typedef typename internal::traits<MatrixType>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  MatrixType scaledA;
+  RealScalar maxImagPartOfSpectrum = A.eigenvalues().imag().cwiseAbs().maxCoeff();
+  if (maxImagPartOfSpectrum >= RealScalar(0.9L * EIGEN_PI))
+    scaledA = A * RealScalar(0.9L * EIGEN_PI) / maxImagPartOfSpectrum;
+  else
+    scaledA = A;
+
+  // identity X.exp().log() = X only holds if Im(lambda) < pi for all eigenvalues of X
+  MatrixType expA = scaledA.exp();
+  MatrixType logExpA = expA.log();
+  VERIFY_IS_APPROX(logExpA, scaledA);
+}
+
+template <typename MatrixType>
+void testHyperbolicFunctions(const MatrixType& A) {
+  // Need to use absolute error because of possible cancellation when
+  // adding/subtracting expA and expmA.
+  VERIFY_IS_APPROX_ABS(A.sinh(), (A.exp() - (-A).exp()) / 2);
+  VERIFY_IS_APPROX_ABS(A.cosh(), (A.exp() + (-A).exp()) / 2);
+}
+
+template <typename MatrixType>
+void testGonioFunctions(const MatrixType& A) {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef std::complex<RealScalar> ComplexScalar;
+  typedef Matrix<ComplexScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime, MatrixType::Options>
+      ComplexMatrix;
+
+  ComplexScalar imagUnit(0, 1);
+  ComplexScalar two(2, 0);
+
+  ComplexMatrix Ac = A.template cast<ComplexScalar>();
+
+  ComplexMatrix exp_iA = (imagUnit * Ac).exp();
+  ComplexMatrix exp_miA = (-imagUnit * Ac).exp();
+
+  ComplexMatrix sinAc = A.sin().template cast<ComplexScalar>();
+  VERIFY_IS_APPROX_ABS(sinAc, (exp_iA - exp_miA) / (two * imagUnit));
+
+  ComplexMatrix cosAc = A.cos().template cast<ComplexScalar>();
+  VERIFY_IS_APPROX_ABS(cosAc, (exp_iA + exp_miA) / 2);
+}
+
+template <typename MatrixType>
+void testMatrix(const MatrixType& A) {
+  testMatrixExponential(A);
+  testMatrixLogarithm(A);
+  testHyperbolicFunctions(A);
+  testGonioFunctions(A);
+}
+
+template <typename MatrixType>
+void testMatrixType(const MatrixType& m) {
+  // Matrices with clustered eigenvalue lead to different code paths
+  // in MatrixFunction.h and are thus useful for testing.
+
+  const Index size = m.rows();
+  for (int i = 0; i < g_repeat; i++) {
+    testMatrix(MatrixType::Random(size, size).eval());
+    testMatrix(randomMatrixWithRealEivals<MatrixType>(size));
+    testMatrix(randomMatrixWithImagEivals<MatrixType>::run(size));
+  }
+}
+
+template <typename MatrixType>
+void testMapRef(const MatrixType& A) {
+  // Test if passing Ref and Map objects is possible
+  // (Regression test for Bug #1796)
+  Index size = A.rows();
+  MatrixType X;
+  X.setRandom(size, size);
+  MatrixType Y(size, size);
+  Ref<MatrixType> R(Y);
+  Ref<const MatrixType> Rc(X);
+  Map<MatrixType> M(Y.data(), size, size);
+  Map<const MatrixType> Mc(X.data(), size, size);
+
+  X = X * X;  // make sure sqrt is possible
+  Y = X.sqrt();
+  R = Rc.sqrt();
+  M = Mc.sqrt();
+  Y = X.exp();
+  R = Rc.exp();
+  M = Mc.exp();
+  X = Y;  // make sure log is possible
+  Y = X.log();
+  R = Rc.log();
+  M = Mc.log();
+
+  Y = X.cos() + Rc.cos() + Mc.cos();
+  Y = X.sin() + Rc.sin() + Mc.sin();
+
+  Y = X.cosh() + Rc.cosh() + Mc.cosh();
+  Y = X.sinh() + Rc.sinh() + Mc.sinh();
+}
+
+EIGEN_DECLARE_TEST(matrix_function) {
+  CALL_SUBTEST_1(testMatrixType(Matrix<float, 1, 1>()));
+  CALL_SUBTEST_2(testMatrixType(Matrix3cf()));
+  CALL_SUBTEST_3(testMatrixType(MatrixXf(8, 8)));
+  CALL_SUBTEST_4(testMatrixType(Matrix2d()));
+  CALL_SUBTEST_5(testMatrixType(Matrix<double, 5, 5, RowMajor>()));
+  CALL_SUBTEST_6(testMatrixType(Matrix4cd()));
+  CALL_SUBTEST_7(testMatrixType(MatrixXd(13, 13)));
+
+  CALL_SUBTEST_1(testMapRef(Matrix<float, 1, 1>()));
+  CALL_SUBTEST_2(testMapRef(Matrix3cf()));
+  CALL_SUBTEST_3(testMapRef(MatrixXf(8, 8)));
+  CALL_SUBTEST_7(testMapRef(MatrixXd(13, 13)));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_functions.h b/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_functions.h
new file mode 100644
index 0000000..cd5b908
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_functions.h
@@ -0,0 +1,56 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <unsupported/Eigen/MatrixFunctions>
+
+// For complex matrices, any matrix is fine.
+template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
+struct processTriangularMatrix {
+  static void run(MatrixType&, MatrixType&, const MatrixType&) {}
+};
+
+// For real matrices, make sure none of the eigenvalues are negative.
+template <typename MatrixType>
+struct processTriangularMatrix<MatrixType, 0> {
+  static void run(MatrixType& m, MatrixType& T, const MatrixType& U) {
+    const Index size = m.cols();
+
+    for (Index i = 0; i < size; ++i) {
+      if (i == size - 1 || T.coeff(i + 1, i) == 0)
+        T.coeffRef(i, i) = std::abs(T.coeff(i, i));
+      else
+        ++i;
+    }
+    m = U * T * U.transpose();
+  }
+};
+
+template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
+struct generateTestMatrix;
+
+template <typename MatrixType>
+struct generateTestMatrix<MatrixType, 0> {
+  static void run(MatrixType& result, typename MatrixType::Index size) {
+    result = MatrixType::Random(size, size);
+    RealSchur<MatrixType> schur(result);
+    MatrixType T = schur.matrixT();
+    processTriangularMatrix<MatrixType>::run(result, T, schur.matrixU());
+  }
+};
+
+template <typename MatrixType>
+struct generateTestMatrix<MatrixType, 1> {
+  static void run(MatrixType& result, typename MatrixType::Index size) { result = MatrixType::Random(size, size); }
+};
+
+template <typename Derived, typename OtherDerived>
+typename Derived::RealScalar relerr(const MatrixBase<Derived>& A, const MatrixBase<OtherDerived>& B) {
+  return std::sqrt((A - B).cwiseAbs2().sum() / (std::min)(A.cwiseAbs2().sum(), B.cwiseAbs2().sum()));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_power.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_power.cpp
new file mode 100644
index 0000000..e17d35a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_power.cpp
@@ -0,0 +1,197 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012, 2013 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "matrix_functions.h"
+
+template <typename T>
+void test2dRotation(const T& tol) {
+  Matrix<T, 2, 2> A, B, C;
+  T angle, c, s;
+
+  A << 0, 1, -1, 0;
+  MatrixPower<Matrix<T, 2, 2> > Apow(A);
+
+  for (int i = 0; i <= 20; ++i) {
+    angle = std::pow(T(10), T(i - 10) / T(5.));
+    c = std::cos(angle);
+    s = std::sin(angle);
+    B << c, s, -s, c;
+
+    C = Apow(std::ldexp(angle, 1) / T(EIGEN_PI));
+    std::cout << "test2dRotation: i = " << i << "   error powerm = " << relerr(C, B) << '\n';
+    VERIFY(C.isApprox(B, tol));
+  }
+}
+
+template <typename T>
+void test2dHyperbolicRotation(const T& tol) {
+  Matrix<std::complex<T>, 2, 2> A, B, C;
+  T angle, ch = std::cosh((T)1);
+  std::complex<T> ish(0, std::sinh((T)1));
+
+  A << ch, ish, -ish, ch;
+  MatrixPower<Matrix<std::complex<T>, 2, 2> > Apow(A);
+
+  for (int i = 0; i <= 20; ++i) {
+    angle = std::ldexp(static_cast<T>(i - 10), -1);
+    ch = std::cosh(angle);
+    ish = std::complex<T>(0, std::sinh(angle));
+    B << ch, ish, -ish, ch;
+
+    C = Apow(angle);
+    std::cout << "test2dHyperbolicRotation: i = " << i << "   error powerm = " << relerr(C, B) << '\n';
+    VERIFY(C.isApprox(B, tol));
+  }
+}
+
+template <typename T>
+void test3dRotation(const T& tol) {
+  Matrix<T, 3, 1> v;
+  T angle;
+
+  for (int i = 0; i <= 20; ++i) {
+    v = Matrix<T, 3, 1>::Random();
+    v.normalize();
+    angle = std::pow(T(10), T(i - 10) / T(5.));
+    VERIFY(AngleAxis<T>(angle, v).matrix().isApprox(AngleAxis<T>(1, v).matrix().pow(angle), tol));
+  }
+}
+
+template <typename MatrixType>
+void testGeneral(const MatrixType& m, const typename MatrixType::RealScalar& tol) {
+  typedef typename MatrixType::RealScalar RealScalar;
+  MatrixType m1, m2, m3, m4, m5;
+  RealScalar x, y;
+
+  for (int i = 0; i < g_repeat; ++i) {
+    generateTestMatrix<MatrixType>::run(m1, m.rows());
+    MatrixPower<MatrixType> mpow(m1);
+
+    x = internal::random<RealScalar>();
+    y = internal::random<RealScalar>();
+    m2 = mpow(x);
+    m3 = mpow(y);
+
+    m4 = mpow(x + y);
+    m5.noalias() = m2 * m3;
+    VERIFY(m4.isApprox(m5, tol));
+
+    m4 = mpow(x * y);
+    m5 = m2.pow(y);
+    VERIFY(m4.isApprox(m5, tol));
+
+    m4 = (std::abs(x) * m1).pow(y);
+    m5 = std::pow(std::abs(x), y) * m3;
+    VERIFY(m4.isApprox(m5, tol));
+  }
+}
+
+template <typename MatrixType>
+void testSingular(const MatrixType& m_const, const typename MatrixType::RealScalar& tol) {
+  // we need to pass by reference in order to prevent errors with
+  // MSVC for aligned data types ...
+  MatrixType& m = const_cast<MatrixType&>(m_const);
+
+  const int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex;
+  typedef std::conditional_t<IsComplex, TriangularView<MatrixType, Upper>, const MatrixType&> TriangularType;
+  std::conditional_t<IsComplex, ComplexSchur<MatrixType>, RealSchur<MatrixType> > schur;
+  MatrixType T;
+
+  for (int i = 0; i < g_repeat; ++i) {
+    m.setRandom();
+    m.col(0).fill(0);
+
+    schur.compute(m);
+    T = schur.matrixT();
+    const MatrixType& U = schur.matrixU();
+    processTriangularMatrix<MatrixType>::run(m, T, U);
+    MatrixPower<MatrixType> mpow(m);
+
+    T = T.sqrt();
+    VERIFY(mpow(0.5L).isApprox(U * (TriangularType(T) * U.adjoint()), tol));
+
+    T = T.sqrt();
+    VERIFY(mpow(0.25L).isApprox(U * (TriangularType(T) * U.adjoint()), tol));
+
+    T = T.sqrt();
+    VERIFY(mpow(0.125L).isApprox(U * (TriangularType(T) * U.adjoint()), tol));
+  }
+}
+
+template <typename MatrixType>
+void testLogThenExp(const MatrixType& m_const, const typename MatrixType::RealScalar& tol) {
+  // we need to pass by reference in order to prevent errors with
+  // MSVC for aligned data types ...
+  MatrixType& m = const_cast<MatrixType&>(m_const);
+
+  typedef typename MatrixType::Scalar Scalar;
+  Scalar x;
+
+  for (int i = 0; i < g_repeat; ++i) {
+    generateTestMatrix<MatrixType>::run(m, m.rows());
+    x = internal::random<Scalar>();
+    VERIFY(m.pow(x).isApprox((x * m.log()).exp(), tol));
+  }
+}
+
+typedef Matrix<double, 3, 3, RowMajor> Matrix3dRowMajor;
+typedef Matrix<long double, 3, 3> Matrix3e;
+typedef Matrix<long double, Dynamic, Dynamic> MatrixXe;
+
+EIGEN_DECLARE_TEST(matrix_power) {
+  CALL_SUBTEST_2(test2dRotation<double>(1e-13));
+  CALL_SUBTEST_1(test2dRotation<float>(2e-5f));  // was 1e-5, relaxed for clang 2.8 / linux / x86-64
+  CALL_SUBTEST_9(test2dRotation<long double>(1e-13L));
+  CALL_SUBTEST_2(test2dHyperbolicRotation<double>(1e-14));
+  CALL_SUBTEST_1(test2dHyperbolicRotation<float>(1e-5f));
+  CALL_SUBTEST_9(test2dHyperbolicRotation<long double>(1e-14L));
+
+  CALL_SUBTEST_10(test3dRotation<double>(1e-13));
+  CALL_SUBTEST_11(test3dRotation<float>(1e-5f));
+  CALL_SUBTEST_12(test3dRotation<long double>(1e-13L));
+
+  CALL_SUBTEST_2(testGeneral(Matrix2d(), 1e-13));
+  CALL_SUBTEST_7(testGeneral(Matrix3dRowMajor(), 1e-13));
+  CALL_SUBTEST_3(testGeneral(Matrix4cd(), 1e-13));
+  CALL_SUBTEST_4(testGeneral(MatrixXd(8, 8), 2e-12));
+  CALL_SUBTEST_1(testGeneral(Matrix2f(), 1e-4f));
+  CALL_SUBTEST_5(testGeneral(Matrix3cf(), 1e-4f));
+  CALL_SUBTEST_8(testGeneral(Matrix4f(), 1e-4f));
+  CALL_SUBTEST_6(testGeneral(MatrixXf(2, 2), 1e-3f));  // see bug 614
+  CALL_SUBTEST_9(testGeneral(MatrixXe(7, 7), 1e-12L));
+  CALL_SUBTEST_10(testGeneral(Matrix3d(), 1e-13));
+  CALL_SUBTEST_11(testGeneral(Matrix3f(), 1e-4f));
+  CALL_SUBTEST_12(testGeneral(Matrix3e(), 1e-13L));
+
+  CALL_SUBTEST_2(testSingular(Matrix2d(), 1e-13));
+  CALL_SUBTEST_7(testSingular(Matrix3dRowMajor(), 1e-13));
+  CALL_SUBTEST_3(testSingular(Matrix4cd(), 1e-13));
+  CALL_SUBTEST_4(testSingular(MatrixXd(8, 8), 2e-12));
+  CALL_SUBTEST_1(testSingular(Matrix2f(), 1e-4f));
+  CALL_SUBTEST_5(testSingular(Matrix3cf(), 1e-4f));
+  CALL_SUBTEST_8(testSingular(Matrix4f(), 1e-4f));
+  CALL_SUBTEST_6(testSingular(MatrixXf(2, 2), 1e-3f));
+  CALL_SUBTEST_9(testSingular(MatrixXe(7, 7), 1e-12L));
+  CALL_SUBTEST_10(testSingular(Matrix3d(), 1e-13));
+  CALL_SUBTEST_11(testSingular(Matrix3f(), 1e-4f));
+  CALL_SUBTEST_12(testSingular(Matrix3e(), 1e-13L));
+
+  CALL_SUBTEST_2(testLogThenExp(Matrix2d(), 1e-13));
+  CALL_SUBTEST_7(testLogThenExp(Matrix3dRowMajor(), 1e-13));
+  CALL_SUBTEST_3(testLogThenExp(Matrix4cd(), 1e-13));
+  CALL_SUBTEST_4(testLogThenExp(MatrixXd(8, 8), 2e-12));
+  CALL_SUBTEST_1(testLogThenExp(Matrix2f(), 1e-4f));
+  CALL_SUBTEST_5(testLogThenExp(Matrix3cf(), 1e-4f));
+  CALL_SUBTEST_8(testLogThenExp(Matrix4f(), 1e-4f));
+  CALL_SUBTEST_6(testLogThenExp(MatrixXf(2, 2), 1e-3f));
+  CALL_SUBTEST_9(testLogThenExp(MatrixXe(7, 7), 1e-12L));
+  CALL_SUBTEST_10(testLogThenExp(Matrix3d(), 1e-13));
+  CALL_SUBTEST_11(testLogThenExp(Matrix3f(), 1e-4f));
+  CALL_SUBTEST_12(testLogThenExp(Matrix3e(), 1e-13L));
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_square_root.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_square_root.cpp
new file mode 100644
index 0000000..c7f8fbe
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/matrix_square_root.cpp
@@ -0,0 +1,29 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "matrix_functions.h"
+
+template <typename MatrixType>
+void testMatrixSqrt(const MatrixType& m) {
+  MatrixType A;
+  generateTestMatrix<MatrixType>::run(A, m.rows());
+  MatrixType sqrtA = A.sqrt();
+  VERIFY_IS_APPROX(sqrtA * sqrtA, A);
+}
+
+EIGEN_DECLARE_TEST(matrix_square_root) {
+  for (int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1(testMatrixSqrt(Matrix3cf()));
+    CALL_SUBTEST_2(testMatrixSqrt(MatrixXcd(12, 12)));
+    CALL_SUBTEST_3(testMatrixSqrt(Matrix4f()));
+    CALL_SUBTEST_4(testMatrixSqrt(Matrix<double, Dynamic, Dynamic, RowMajor>(9, 9)));
+    CALL_SUBTEST_5(testMatrixSqrt(Matrix<float, 1, 1>()));
+    CALL_SUBTEST_5(testMatrixSqrt(Matrix<std::complex<float>, 1, 1>()));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/minres.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/minres.cpp
new file mode 100644
index 0000000..dfd5b9a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/minres.cpp
@@ -0,0 +1,41 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Giacomo Po <gpo@ucla.edu>
+// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#include <cmath>
+
+#include "../../test/sparse_solver.h"
+#include <Eigen/IterativeSolvers>
+
+template <typename T>
+void test_minres_T() {
+  // Identity preconditioner
+  MINRES<SparseMatrix<T>, Lower, IdentityPreconditioner> minres_colmajor_lower_I;
+  MINRES<SparseMatrix<T>, Upper, IdentityPreconditioner> minres_colmajor_upper_I;
+
+  // Diagonal preconditioner
+  MINRES<SparseMatrix<T>, Lower, DiagonalPreconditioner<T> > minres_colmajor_lower_diag;
+  MINRES<SparseMatrix<T>, Upper, DiagonalPreconditioner<T> > minres_colmajor_upper_diag;
+  MINRES<SparseMatrix<T>, Lower | Upper, DiagonalPreconditioner<T> > minres_colmajor_uplo_diag;
+
+  // call tests for SPD matrix
+  CALL_SUBTEST(check_sparse_spd_solving(minres_colmajor_lower_I));
+  CALL_SUBTEST(check_sparse_spd_solving(minres_colmajor_upper_I));
+
+  CALL_SUBTEST(check_sparse_spd_solving(minres_colmajor_lower_diag));
+  CALL_SUBTEST(check_sparse_spd_solving(minres_colmajor_upper_diag));
+  CALL_SUBTEST(check_sparse_spd_solving(minres_colmajor_uplo_diag));
+
+  // TO DO: symmetric semi-definite matrix
+  // TO DO: symmetric indefinite matrix
+}
+
+EIGEN_DECLARE_TEST(minres) {
+  CALL_SUBTEST_1(test_minres_T<double>());
+  //  CALL_SUBTEST_2(test_minres_T<std::complex<double> >());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/mklfft.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/mklfft.cpp
new file mode 100644
index 0000000..631dd20
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/mklfft.cpp
@@ -0,0 +1,2 @@
+#define EIGEN_MKL_DEFAULT 1
+#include "fft_test_shared.h"
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/mpreal_support.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/mpreal_support.cpp
new file mode 100644
index 0000000..1598f7f
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/mpreal_support.cpp
@@ -0,0 +1,69 @@
+#include <mpreal.h>  // Must be included before main.h.
+#include "main.h"
+#include <Eigen/MPRealSupport>
+#include <Eigen/LU>
+#include <Eigen/Eigenvalues>
+#include <sstream>
+
+using namespace mpfr;
+using namespace Eigen;
+
+EIGEN_DECLARE_TEST(mpreal_support) {
+  // set precision to 256 bits (double has only 53 bits)
+  mpreal::set_default_prec(256);
+  typedef Matrix<mpreal, Eigen::Dynamic, Eigen::Dynamic> MatrixXmp;
+  typedef Matrix<std::complex<mpreal>, Eigen::Dynamic, Eigen::Dynamic> MatrixXcmp;
+
+  std::cerr << "epsilon =         " << NumTraits<mpreal>::epsilon() << "\n";
+  std::cerr << "dummy_precision = " << NumTraits<mpreal>::dummy_precision() << "\n";
+  std::cerr << "highest =         " << NumTraits<mpreal>::highest() << "\n";
+  std::cerr << "lowest =          " << NumTraits<mpreal>::lowest() << "\n";
+  std::cerr << "digits10 =        " << NumTraits<mpreal>::digits10() << "\n";
+  std::cerr << "max_digits10 =    " << NumTraits<mpreal>::max_digits10() << "\n";
+
+  for (int i = 0; i < g_repeat; i++) {
+    int s = Eigen::internal::random<int>(1, 100);
+    MatrixXmp A = MatrixXmp::Random(s, s);
+    MatrixXmp B = MatrixXmp::Random(s, s);
+    MatrixXmp S = A.adjoint() * A;
+    MatrixXmp X;
+    MatrixXcmp Ac = MatrixXcmp::Random(s, s);
+    MatrixXcmp Bc = MatrixXcmp::Random(s, s);
+    MatrixXcmp Sc = Ac.adjoint() * Ac;
+    MatrixXcmp Xc;
+
+    // Basic stuffs
+    VERIFY_IS_APPROX(A.real(), A);
+    VERIFY(Eigen::internal::isApprox(A.array().abs2().sum(), A.squaredNorm()));
+    VERIFY_IS_APPROX(A.array().exp(), exp(A.array()));
+    VERIFY_IS_APPROX(A.array().abs2().sqrt(), A.array().abs());
+    VERIFY_IS_APPROX(A.array().sin(), sin(A.array()));
+    VERIFY_IS_APPROX(A.array().cos(), cos(A.array()));
+
+    // Cholesky
+    X = S.selfadjointView<Lower>().llt().solve(B);
+    VERIFY_IS_APPROX((S.selfadjointView<Lower>() * X).eval(), B);
+
+    Xc = Sc.selfadjointView<Lower>().llt().solve(Bc);
+    VERIFY_IS_APPROX((Sc.selfadjointView<Lower>() * Xc).eval(), Bc);
+
+    // partial LU
+    X = A.lu().solve(B);
+    VERIFY_IS_APPROX((A * X).eval(), B);
+
+    // symmetric eigenvalues
+    SelfAdjointEigenSolver<MatrixXmp> eig(S);
+    VERIFY_IS_EQUAL(eig.info(), Success);
+    VERIFY(
+        (S.selfadjointView<Lower>() * eig.eigenvectors())
+            .isApprox(eig.eigenvectors() * eig.eigenvalues().asDiagonal(), NumTraits<mpreal>::dummy_precision() * 1e3));
+  }
+
+  {
+    MatrixXmp A(8, 3);
+    A.setRandom();
+    // test output (interesting things happen in this code)
+    std::stringstream stream;
+    stream << A;
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/openglsupport.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/openglsupport.cpp
new file mode 100644
index 0000000..c70552a
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/openglsupport.cpp
@@ -0,0 +1,594 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include <main.h>
+#include <iostream>
+#include <string>
+
+#if defined(__APPLE_CC__)
+// Prevent deprecation warnings caused by GLEW on MacOS.
+#define GL_SILENCE_DEPRECATION 1
+#endif
+#include <GL/glew.h>
+#include <Eigen/OpenGLSupport>
+#if defined(__APPLE_CC__)
+#include <GLUT/glut.h>
+#else
+#include <GL/freeglut.h>
+#endif
+
+using namespace Eigen;
+
+#define VERIFY_MATRIX(CODE, REF)                 \
+  {                                              \
+    glMatrixMode(GL_MODELVIEW);                  \
+    glLoadIdentity();                            \
+    CODE;                                        \
+    Matrix<float, 4, 4, ColMajor> m;             \
+    m.setZero();                                 \
+    glGet(GL_MODELVIEW_MATRIX, m);               \
+    if (!(REF).cast<float>().isApprox(m)) {      \
+      std::cerr << "Expected:\n"                 \
+                << ((REF).cast<float>()) << "\n" \
+                << "got\n"                       \
+                << m << "\n\n";                  \
+    }                                            \
+    VERIFY_IS_APPROX((REF).cast<float>(), m);    \
+  }
+
+#define VERIFY_UNIFORM(SUFFIX, NAME, TYPE)                     \
+  {                                                            \
+    TYPE value;                                                \
+    value.setRandom();                                         \
+    TYPE data;                                                 \
+    int loc = glGetUniformLocation(prg_id, #NAME);             \
+    VERIFY((loc != -1) && "uniform not found");                \
+    glUniform(loc, value);                                     \
+    EIGEN_CAT(glGetUniform, SUFFIX)(prg_id, loc, data.data()); \
+    if (!value.isApprox(data)) {                               \
+      std::cerr << "Expected:\n"                               \
+                << value << "\n"                               \
+                << "got\n"                                     \
+                << data << "\n\n";                             \
+    }                                                          \
+    VERIFY_IS_APPROX(value, data);                             \
+  }
+
+#define VERIFY_UNIFORMi(NAME, TYPE)                                        \
+  {                                                                        \
+    TYPE value = TYPE::Random().eval().cast<float>().cast<TYPE::Scalar>(); \
+    TYPE data;                                                             \
+    int loc = glGetUniformLocation(prg_id, #NAME);                         \
+    VERIFY((loc != -1) && "uniform not found");                            \
+    glUniform(loc, value);                                                 \
+    glGetUniformiv(prg_id, loc, (GLint*)data.data());                      \
+    if (!value.isApprox(data)) {                                           \
+      std::cerr << "Expected:\n"                                           \
+                << value << "\n"                                           \
+                << "got\n"                                                 \
+                << data << "\n\n";                                         \
+    }                                                                      \
+    VERIFY_IS_APPROX(value, data);                                         \
+  }
+
+void printProgramInfoLog(GLuint objectID) {
+  int infologLength, charsWritten;
+  GLchar* infoLog;
+  glGetProgramiv(objectID, GL_INFO_LOG_LENGTH, &infologLength);
+  if (infologLength > 0) {
+    infoLog = new GLchar[infologLength];
+    glGetProgramInfoLog(objectID, infologLength, &charsWritten, infoLog);
+    if (charsWritten > 0) std::cerr << "Program info : \n" << infoLog << std::endl;
+    delete[] infoLog;
+  }
+}
+
+void printShaderInfoLog(GLuint objectID) {
+  int infologLength, charsWritten;
+  GLchar* infoLog;
+  glGetShaderiv(objectID, GL_INFO_LOG_LENGTH, &infologLength);
+  if (infologLength > 0) {
+    infoLog = new GLchar[infologLength];
+    glGetShaderInfoLog(objectID, infologLength, &charsWritten, infoLog);
+    if (charsWritten > 0) std::cerr << "Shader info : \n" << infoLog << std::endl;
+    delete[] infoLog;
+  }
+}
+
+GLint createProgram(const char* vtx, const char* frg, bool print_errors = true) {
+  GLint prg_id = glCreateProgram();
+  GLint vtx_id = glCreateShader(GL_VERTEX_SHADER);
+  GLint frg_id = glCreateShader(GL_FRAGMENT_SHADER);
+  GLint ok;
+
+  glShaderSource(vtx_id, 1, &vtx, 0);
+  glCompileShader(vtx_id);
+  glGetShaderiv(vtx_id, GL_COMPILE_STATUS, &ok);
+  if (!ok) {
+    if (print_errors) {
+      std::cerr << "vtx compilation failed\n";
+      std::cerr << "Source:\n" << vtx << "\n";
+      printShaderInfoLog(vtx_id);
+    }
+    glDeleteShader(vtx_id);
+    return GL_ZERO;
+  }
+
+  glShaderSource(frg_id, 1, &frg, 0);
+  glCompileShader(frg_id);
+  glGetShaderiv(frg_id, GL_COMPILE_STATUS, &ok);
+  if (!ok) {
+    if (print_errors) {
+      std::cerr << "frg compilation failed.\n";
+      std::cerr << "Source:\n" << frg << "\n";
+      printShaderInfoLog(frg_id);
+    }
+    glDeleteShader(vtx_id);
+    glDeleteShader(frg_id);
+    return GL_ZERO;
+  }
+
+  glAttachShader(prg_id, vtx_id);
+  glAttachShader(prg_id, frg_id);
+  glLinkProgram(prg_id);
+
+  // Delete shaders once linked.
+  glDeleteShader(vtx_id);
+  glDeleteShader(frg_id);
+  glGetProgramiv(prg_id, GL_LINK_STATUS, &ok);
+  if (!ok) {
+    if (print_errors) {
+      std::cerr << "linking failed.\n";
+      printProgramInfoLog(prg_id);
+    }
+    glDeleteProgram(prg_id);
+    return GL_ZERO;
+  }
+
+  glUseProgram(prg_id);
+  return prg_id;
+}
+
+GLint createProgram(const std::string& vtx, const std::string& frg, bool print_errors = true) {
+  return createProgram(vtx.c_str(), frg.c_str(), print_errors);
+}
+
+std::string getGlslVersionString(int gl_major_version, int gl_minor_version) {
+  switch (gl_major_version) {
+    case 2:
+      switch (gl_minor_version) {
+        case 0:
+          return "#version 110";
+        case 1:
+          return "#version 120";
+      }
+      break;
+    case 3:
+      switch (gl_minor_version) {
+        case 0:
+          return "#version 130";
+        case 1:
+          return "#version 140";
+        case 2:
+          return "#version 150";
+        case 3:
+          return "#version 330";
+      }
+      break;
+    case 4:
+      switch (gl_minor_version) {
+        case 0:
+          return "#version 400";
+        case 1:
+          return "#version 410";
+        case 2:
+          return "#version 420";
+        case 3:
+          return "#version 430";
+        case 4:
+          return "#version 440";
+        case 5:
+          return "#version 450";
+        case 6:
+          return "#version 460";
+      }
+      break;
+  }
+  return "";
+}
+
+void find_and_replace(std::string& str, const std::string& find, const std::string& replace) {
+  size_t loc = 0;
+  size_t flen = find.length();
+  size_t rlen = replace.length();
+  while ((loc = str.find(find, loc)) != std::string::npos) {
+    str.replace(loc, flen, replace);
+    loc += rlen;
+  }
+}
+
+// Finds and replaces a set of substrings in a string.
+std::string format(const std::string& str, const std::vector<std::string>& find,
+                   const std::vector<std::string>& replace) {
+  std::string out = str;
+  for (std::size_t i = 0; i < find.size(); ++i) {
+    find_and_replace(out, find[i], replace[i]);
+  }
+  return out;
+}
+
+// GLUT display function that runs test.  Must be run within the display loop
+// in order to properly destroy resources.
+void openglsupport_test_loop() {
+  // Get context info.
+  const GLubyte* gl_version_string = glGetString(GL_VERSION);
+  std::cerr << "GL version: " << gl_version_string << std::endl;
+  std::cerr << "GLSL version: " << glGetString(GL_SHADING_LANGUAGE_VERSION) << std::endl;
+  // Parse version from string since GL_MAJOR_VERSION is only supported in GL 3.0+.
+  // Version string guaranteed to be <major>.<minor><vendor extension>.
+  GLint gl_major_version = gl_version_string[0] - '0';
+  GLint gl_minor_version = gl_version_string[2] - '0';
+  bool legacy_gl = gl_major_version < 3 || (gl_major_version == 3 && gl_minor_version < 2);
+
+  // Fixed-function pipeline removed in OpenGL 3.2.
+  if (legacy_gl) {
+    // Draw a basic triangle.
+    Vector3f v3f;
+    Matrix3f rot;
+    glBegin(GL_POINTS);
+    {
+      glVertex(v3f);
+      glVertex(2 * v3f + v3f);
+      glVertex(rot * v3f);
+    }
+    glEnd();
+
+    // 4x4 matrices
+    Matrix4f mf44;
+    mf44.setRandom();
+    VERIFY_MATRIX(glLoadMatrix(mf44), mf44);
+    VERIFY_MATRIX(glMultMatrix(mf44), mf44);
+    Matrix4d md44;
+    md44.setRandom();
+    VERIFY_MATRIX(glLoadMatrix(md44), md44);
+    VERIFY_MATRIX(glMultMatrix(md44), md44);
+
+    // Quaternion
+    Quaterniond qd(AngleAxisd(internal::random<double>(), Vector3d::Random()));
+    VERIFY_MATRIX(glRotate(qd), Projective3d(qd).matrix());
+
+    Quaternionf qf(AngleAxisf(internal::random<double>(), Vector3f::Random()));
+    VERIFY_MATRIX(glRotate(qf), Projective3f(qf).matrix());
+
+    // 3D Transform
+    Transform<float, 3, AffineCompact> acf3;
+    acf3.matrix().setRandom();
+    VERIFY_MATRIX(glLoadMatrix(acf3), Projective3f(acf3).matrix());
+    VERIFY_MATRIX(glMultMatrix(acf3), Projective3f(acf3).matrix());
+
+    Transform<float, 3, Affine> af3(acf3);
+    VERIFY_MATRIX(glLoadMatrix(af3), Projective3f(af3).matrix());
+    VERIFY_MATRIX(glMultMatrix(af3), Projective3f(af3).matrix());
+
+    Transform<float, 3, Projective> pf3;
+    pf3.matrix().setRandom();
+    VERIFY_MATRIX(glLoadMatrix(pf3), Projective3f(pf3).matrix());
+    VERIFY_MATRIX(glMultMatrix(pf3), Projective3f(pf3).matrix());
+
+    Transform<double, 3, AffineCompact> acd3;
+    acd3.matrix().setRandom();
+    VERIFY_MATRIX(glLoadMatrix(acd3), Projective3d(acd3).matrix());
+    VERIFY_MATRIX(glMultMatrix(acd3), Projective3d(acd3).matrix());
+
+    Transform<double, 3, Affine> ad3(acd3);
+    VERIFY_MATRIX(glLoadMatrix(ad3), Projective3d(ad3).matrix());
+    VERIFY_MATRIX(glMultMatrix(ad3), Projective3d(ad3).matrix());
+
+    Transform<double, 3, Projective> pd3;
+    pd3.matrix().setRandom();
+    VERIFY_MATRIX(glLoadMatrix(pd3), Projective3d(pd3).matrix());
+    VERIFY_MATRIX(glMultMatrix(pd3), Projective3d(pd3).matrix());
+
+    // translations (2D and 3D)
+    {
+      Vector2f vf2;
+      vf2.setRandom();
+      Vector3f vf23;
+      vf23 << vf2, 0;
+      VERIFY_MATRIX(glTranslate(vf2), Projective3f(Translation3f(vf23)).matrix());
+      Vector2d vd2;
+      vd2.setRandom();
+      Vector3d vd23;
+      vd23 << vd2, 0;
+      VERIFY_MATRIX(glTranslate(vd2), Projective3d(Translation3d(vd23)).matrix());
+
+      Vector3f vf3;
+      vf3.setRandom();
+      VERIFY_MATRIX(glTranslate(vf3), Projective3f(Translation3f(vf3)).matrix());
+      Vector3d vd3;
+      vd3.setRandom();
+      VERIFY_MATRIX(glTranslate(vd3), Projective3d(Translation3d(vd3)).matrix());
+
+      Translation<float, 3> tf3;
+      tf3.vector().setRandom();
+      VERIFY_MATRIX(glTranslate(tf3), Projective3f(tf3).matrix());
+
+      Translation<double, 3> td3;
+      td3.vector().setRandom();
+      VERIFY_MATRIX(glTranslate(td3), Projective3d(td3).matrix());
+    }
+
+    // scaling (2D and 3D)
+    {
+      Vector2f vf2;
+      vf2.setRandom();
+      Vector3f vf23;
+      vf23 << vf2, 1;
+      VERIFY_MATRIX(glScale(vf2), Projective3f(Scaling(vf23)).matrix());
+      Vector2d vd2;
+      vd2.setRandom();
+      Vector3d vd23;
+      vd23 << vd2, 1;
+      VERIFY_MATRIX(glScale(vd2), Projective3d(Scaling(vd23)).matrix());
+
+      Vector3f vf3;
+      vf3.setRandom();
+      VERIFY_MATRIX(glScale(vf3), Projective3f(Scaling(vf3)).matrix());
+      Vector3d vd3;
+      vd3.setRandom();
+      VERIFY_MATRIX(glScale(vd3), Projective3d(Scaling(vd3)).matrix());
+
+      UniformScaling<float> usf(internal::random<float>());
+      VERIFY_MATRIX(glScale(usf), Projective3f(usf).matrix());
+
+      UniformScaling<double> usd(internal::random<double>());
+      VERIFY_MATRIX(glScale(usd), Projective3d(usd).matrix());
+    }
+  } else {
+    std::cerr << "Warning: fixed-function pipeline was not tested.\n";
+  }
+
+  // Dynamic shader substitution variables.
+  // Modern shaders require a version string, and newer runtimes fail to
+  // compile old GLSL versions. Thus, we dynamically set the GLSL version
+  // string based on runtime. Also, pre OpenGL 3.0, the output gl_FragColor was
+  // built-in. This was deprecated in OpenGL 3.0, requiring us to explicitly
+  // define the output variable.
+  std::vector<std::string> glsl_vars;
+  glsl_vars.push_back("${GLSL_VERSION}");
+  glsl_vars.push_back("${FRAG_OUTPUT_DECLARATION}");
+  glsl_vars.push_back("${FRAG_OUTPUT_VARIABLE}");
+
+  std::vector<std::string> glsl_vals;
+  glsl_vals.push_back(getGlslVersionString(gl_major_version, gl_minor_version));
+  if (gl_major_version >= 3) {
+    glsl_vals.push_back("out vec4 fragColor;");
+    glsl_vals.push_back("fragColor");
+  } else {
+    glsl_vals.push_back("");
+    glsl_vals.push_back("gl_FragColor");
+  }
+
+  // uniform
+  {
+    // vertex shader.
+    std::string vtx = format(
+        "${GLSL_VERSION}\n"
+        "void main(void) {\n"
+        "  gl_Position = vec4(0,0,0,1);\n"
+        "}\n",
+        glsl_vars, glsl_vals);
+
+#ifdef GL_VERSION_2_0
+    if (GLEW_VERSION_2_0 && GL_VERSION_2_0) {
+      std::string frg = format(
+          "${GLSL_VERSION}\n"
+          "uniform vec2 v2f;\n"
+          "uniform vec3 v3f;\n"
+          "uniform vec4 v4f;\n"
+          "uniform ivec2 v2i;\n"
+          "uniform ivec3 v3i;\n"
+          "uniform ivec4 v4i;\n"
+          "uniform mat2 m2f;\n"
+          "uniform mat3 m3f;\n"
+          "uniform mat4 m4f;\n"
+          "${FRAG_OUTPUT_DECLARATION}\n"
+          "void main(void) { \n"
+          "  ${FRAG_OUTPUT_VARIABLE} = "
+          "vec4(v2f[0]+v3f[0]+v4f[0])+vec4(v2i[0]+v3i[0]+v4i[0])+vec4(m2f[0][0]+m3f[0][0]+m4f[0][0]);\n"
+          "}\n",
+          glsl_vars, glsl_vals);
+
+      GLint prg_id = createProgram(vtx, frg);
+      VERIFY(prg_id > 0 && "Failed to create program.");
+      VERIFY_UNIFORM(fv, v2f, Vector2f);
+      VERIFY_UNIFORM(fv, v3f, Vector3f);
+      VERIFY_UNIFORM(fv, v4f, Vector4f);
+      VERIFY_UNIFORMi(v2i, Vector2i);
+      VERIFY_UNIFORMi(v3i, Vector3i);
+      VERIFY_UNIFORMi(v4i, Vector4i);
+      VERIFY_UNIFORM(fv, m2f, Matrix2f);
+      VERIFY_UNIFORM(fv, m3f, Matrix3f);
+      VERIFY_UNIFORM(fv, m4f, Matrix4f);
+      glDeleteProgram(prg_id);
+    } else
+#endif
+      std::cerr << "Warning: opengl 2.0 was not tested.\n";
+
+#ifdef GL_VERSION_2_1
+    if (GLEW_VERSION_2_1 && GL_VERSION_2_1 &&
+        (gl_major_version > 2 || (gl_major_version == 2 && gl_minor_version >= 1))) {
+      std::string frg = format(
+          "${GLSL_VERSION}\n"
+          "uniform mat2x3 m23f;\n"
+          "uniform mat3x2 m32f;\n"
+          "uniform mat2x4 m24f;\n"
+          "uniform mat4x2 m42f;\n"
+          "uniform mat3x4 m34f;\n"
+          "uniform mat4x3 m43f;\n"
+          "${FRAG_OUTPUT_DECLARATION}\n"
+          "void main(void) {\n"
+          "  ${FRAG_OUTPUT_VARIABLE} = vec4(m23f[0][0]+m32f[0][0]+m24f[0][0]+m42f[0][0]+m34f[0][0]+m43f[0][0]);\n"
+          "}\n",
+          glsl_vars, glsl_vals);
+
+      GLint prg_id = createProgram(vtx, frg);
+      VERIFY(prg_id > 0 && "Failed to create program.");
+      typedef Matrix<float, 2, 3> Matrix23f;
+      typedef Matrix<float, 3, 2> Matrix32f;
+      typedef Matrix<float, 2, 4> Matrix24f;
+      typedef Matrix<float, 4, 2> Matrix42f;
+      typedef Matrix<float, 3, 4> Matrix34f;
+      typedef Matrix<float, 4, 3> Matrix43f;
+
+      VERIFY_UNIFORM(fv, m23f, Matrix23f);
+      VERIFY_UNIFORM(fv, m32f, Matrix32f);
+      VERIFY_UNIFORM(fv, m24f, Matrix24f);
+      VERIFY_UNIFORM(fv, m42f, Matrix42f);
+      VERIFY_UNIFORM(fv, m34f, Matrix34f);
+      VERIFY_UNIFORM(fv, m43f, Matrix43f);
+      glDeleteProgram(prg_id);
+    } else
+#endif
+      std::cerr << "Warning: opengl 2.1 was not tested.\n";
+
+#ifdef GL_VERSION_3_0
+    if (GLEW_VERSION_3_0 && GL_VERSION_3_0 && gl_major_version >= 3) {
+      std::string frg = format(
+          "${GLSL_VERSION}\n"
+          "uniform uvec2 v2ui;\n"
+          "uniform uvec3 v3ui;\n"
+          "uniform uvec4 v4ui;\n"
+          "${FRAG_OUTPUT_DECLARATION}\n"
+          "void main(void) {\n"
+          "  ${FRAG_OUTPUT_VARIABLE} = vec4(v2ui[0]+v3ui[0]+v4ui[0]);\n"
+          "}\n",
+          glsl_vars, glsl_vals);
+
+      GLint prg_id = createProgram(vtx, frg);
+      VERIFY(prg_id > 0 && "Failed to create program.");
+      typedef Matrix<unsigned int, 2, 1> Vector2ui;
+      typedef Matrix<unsigned int, 3, 1> Vector3ui;
+      typedef Matrix<unsigned int, 4, 1> Vector4ui;
+
+      VERIFY_UNIFORMi(v2ui, Vector2ui);
+      VERIFY_UNIFORMi(v3ui, Vector3ui);
+      VERIFY_UNIFORMi(v4ui, Vector4ui);
+      glDeleteProgram(prg_id);
+    } else
+#endif
+      std::cerr << "Warning: opengl 3.0 was not tested.\n";
+
+    // dvecn supported if >= 4.1 or ARB_vertex_attrib_64bit
+    bool has_fp64_native = (gl_major_version == 4 && gl_minor_version >= 1);
+    bool has_fp64_extension = false;
+#ifdef GLEW_ARB_gpu_shader_fp64
+    if (GLEW_ARB_gpu_shader_fp64) {
+      // Check that extension can actually be compiled.
+      if (has_fp64_extension) {
+        std::string frg = format(
+            "${GLSL_VERSION}\n"
+            "#extension GL_ARB_gpu_shader_fp64 : enable\n"
+            "uniform dvec2 dv2;\n"
+            "${FRAG_OUTPUT_DECLARATION}\n"
+            "void main(void) {\n"
+            "  ${FRAG_OUTPUT_VARIABLE} = vec4(dv2.x, dv2.y, dv2.x, dv2.y);\n"
+            "}\n",
+            glsl_vars, glsl_vals);
+        GLint prg_id = createProgram(vtx, frg, /*print_errors=*/false);
+        if (prg_id) {
+          has_fp64_extension = true;
+          glDeleteProgram(prg_id);
+        }
+      }
+    }
+#endif
+
+    if (has_fp64_native || has_fp64_extension) {
+      std::vector<std::string> glsl_vars_with_extension = glsl_vars;
+      glsl_vars_with_extension.push_back("${GLSL_EXTENSIONS}");
+      std::vector<std::string> glsl_vals_with_extension = glsl_vals;
+      if (has_fp64_extension) {
+        glsl_vals_with_extension.push_back("#extension GL_ARB_gpu_shader_fp64 : enable");
+      } else {
+        glsl_vals_with_extension.push_back("");
+      }
+
+      std::string frg = format(
+          "${GLSL_VERSION}\n"
+          "${GLSL_EXTENSIONS}\n"
+          "uniform dvec2 v2d;\n"
+          "uniform dvec3 v3d;\n"
+          "uniform dvec4 v4d;\n"
+          "${FRAG_OUTPUT_DECLARATION}\n"
+          "void main(void) {\n"
+          "  ${FRAG_OUTPUT_VARIABLE} = vec4(v2d[0]+v3d[0]+v4d[0]);\n"
+          "}\n",
+          glsl_vars_with_extension, glsl_vals_with_extension);
+
+      GLint prg_id = createProgram(vtx, frg);
+      VERIFY(prg_id > 0 && "Failed to create program.");
+      VERIFY_UNIFORM(dv, v2d, Vector2d);
+      VERIFY_UNIFORM(dv, v3d, Vector3d);
+      VERIFY_UNIFORM(dv, v4d, Vector4d);
+      glDeleteProgram(prg_id);
+    } else
+      std::cerr << "Warning: dvec (fp64) was not tested.\n";
+  }
+
+  // Exit loop - Leaving main loop is supported by freeglut, otherwise we
+  // are forced to exit.
+#ifdef FREEGLUT
+  glutLeaveMainLoop();
+  // Trigger another display loop iteration. Otherwise, it just hangs.
+  glutPostRedisplay();
+#else
+  exit(0);
+#endif
+}
+
+EIGEN_DECLARE_TEST(openglsupport) {
+  int argc = 0;
+  glutInit(&argc, 0);
+
+  GLint glut_display_mode = GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH;
+
+#ifndef EIGEN_LEGACY_OPENGL
+  // Initialize 3.2+ OpenGL context.
+#if defined(__APPLE_CC__)
+  glut_display_mode |= GLUT_3_2_CORE_PROFILE;
+#elif defined(FREEGLUT)
+  glutInitContextVersion(3, 2);
+  glutInitContextFlags(GLUT_FORWARD_COMPATIBLE);
+  glutInitContextProfile(GLUT_CORE_PROFILE);
+#endif
+#endif
+
+  glutInitDisplayMode(glut_display_mode);
+  glutInitWindowPosition(0, 0);
+  glutInitWindowSize(10, 10);
+
+  int window = glutCreateWindow("Eigen");
+  if (window <= 0) {
+    std::cerr << "Error: Unable to create GLUT Window.\n";
+    exit(1);
+  }
+
+  glewExperimental = GL_TRUE;
+  if (glewInit() != GLEW_OK) {
+    std::cerr << "Warning: Failed to initialize GLEW.\n";
+    exit(1);
+  }
+
+  // Run test in display, otherwise GLUT fails to clean up and leads to memory
+  // access errors on exit.
+  glutDisplayFunc(openglsupport_test_loop);
+  glutMainLoop();
+  glutDestroyWindow(window);
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/pocketfft.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/pocketfft.cpp
new file mode 100644
index 0000000..5e8a0b6
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/pocketfft.cpp
@@ -0,0 +1,2 @@
+#define EIGEN_POCKETFFT_DEFAULT 1
+#include "fft_test_shared.h"
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/polynomialsolver.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/polynomialsolver.cpp
new file mode 100644
index 0000000..fc66c42
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/polynomialsolver.cpp
@@ -0,0 +1,206 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <unsupported/Eigen/Polynomials>
+#include <iostream>
+#include <algorithm>
+
+using namespace std;
+
+namespace Eigen {
+namespace internal {
+template <int Size>
+struct increment_if_fixed_size {
+  enum { ret = (Size == Dynamic) ? Dynamic : Size + 1 };
+};
+}  // namespace internal
+}  // namespace Eigen
+
+template <typename PolynomialType>
+PolynomialType polyder(const PolynomialType& p) {
+  typedef typename PolynomialType::Scalar Scalar;
+  PolynomialType res(p.size());
+  for (Index i = 1; i < p.size(); ++i) res[i - 1] = p[i] * Scalar(i);
+  res[p.size() - 1] = 0.;
+  return res;
+}
+
+template <int Deg, typename POLYNOMIAL, typename SOLVER>
+bool aux_evalSolver(const POLYNOMIAL& pols, SOLVER& psolve) {
+  typedef typename POLYNOMIAL::Scalar Scalar;
+  typedef typename POLYNOMIAL::RealScalar RealScalar;
+
+  typedef typename SOLVER::RootsType RootsType;
+  typedef Matrix<RealScalar, Deg, 1> EvalRootsType;
+
+  const Index deg = pols.size() - 1;
+
+  // Test template constructor from coefficient vector
+  SOLVER solve_constr(pols);
+
+  psolve.compute(pols);
+  const RootsType& roots(psolve.roots());
+  EvalRootsType evr(deg);
+  POLYNOMIAL pols_der = polyder(pols);
+  EvalRootsType der(deg);
+  for (int i = 0; i < roots.size(); ++i) {
+    evr[i] = std::abs(poly_eval(pols, roots[i]));
+    der[i] = numext::maxi(RealScalar(1.), std::abs(poly_eval(pols_der, roots[i])));
+  }
+
+  // we need to divide by the magnitude of the derivative because
+  // with a high derivative is very small error in the value of the root
+  // yiels a very large error in the polynomial evaluation.
+  bool evalToZero = (evr.cwiseQuotient(der)).isZero(test_precision<Scalar>());
+  if (!evalToZero) {
+    cerr << "WRONG root: " << endl;
+    cerr << "Polynomial: " << pols.transpose() << endl;
+    cerr << "Roots found: " << roots.transpose() << endl;
+    cerr << "Abs value of the polynomial at the roots: " << evr.transpose() << endl;
+    cerr << endl;
+  }
+
+  std::vector<RealScalar> rootModuli(roots.size());
+  Map<EvalRootsType> aux(&rootModuli[0], roots.size());
+  aux = roots.array().abs();
+  std::sort(rootModuli.begin(), rootModuli.end());
+  bool distinctModuli = true;
+  for (size_t i = 1; i < rootModuli.size() && distinctModuli; ++i) {
+    if (internal::isApprox(rootModuli[i], rootModuli[i - 1])) {
+      distinctModuli = false;
+    }
+  }
+  VERIFY(evalToZero || !distinctModuli);
+
+  return distinctModuli;
+}
+
+template <int Deg, typename POLYNOMIAL>
+void evalSolver(const POLYNOMIAL& pols) {
+  typedef typename POLYNOMIAL::Scalar Scalar;
+
+  typedef PolynomialSolver<Scalar, Deg> PolynomialSolverType;
+
+  PolynomialSolverType psolve;
+  aux_evalSolver<Deg, POLYNOMIAL, PolynomialSolverType>(pols, psolve);
+}
+
+template <int Deg, typename POLYNOMIAL, typename ROOTS, typename REAL_ROOTS>
+void evalSolverSugarFunction(const POLYNOMIAL& pols, const ROOTS& roots, const REAL_ROOTS& real_roots) {
+  using std::sqrt;
+  typedef typename POLYNOMIAL::Scalar Scalar;
+  typedef typename POLYNOMIAL::RealScalar RealScalar;
+
+  typedef PolynomialSolver<Scalar, Deg> PolynomialSolverType;
+
+  PolynomialSolverType psolve;
+  if (aux_evalSolver<Deg, POLYNOMIAL, PolynomialSolverType>(pols, psolve)) {
+    // It is supposed that
+    //  1) the roots found are correct
+    //  2) the roots have distinct moduli
+
+    // Test realRoots
+    std::vector<RealScalar> calc_realRoots;
+    psolve.realRoots(calc_realRoots, test_precision<RealScalar>());
+    VERIFY_IS_EQUAL(calc_realRoots.size(), (size_t)real_roots.size());
+
+    const RealScalar psPrec = sqrt(test_precision<RealScalar>());
+
+    for (size_t i = 0; i < calc_realRoots.size(); ++i) {
+      bool found = false;
+      for (size_t j = 0; j < calc_realRoots.size() && !found; ++j) {
+        if (internal::isApprox(calc_realRoots[i], real_roots[j], psPrec)) {
+          found = true;
+        }
+      }
+      VERIFY(found);
+    }
+
+    // Test greatestRoot
+    VERIFY(internal::isApprox(roots.array().abs().maxCoeff(), abs(psolve.greatestRoot()), psPrec));
+
+    // Test smallestRoot
+    VERIFY(internal::isApprox(roots.array().abs().minCoeff(), abs(psolve.smallestRoot()), psPrec));
+
+    bool hasRealRoot;
+    // Test absGreatestRealRoot
+    RealScalar r = psolve.absGreatestRealRoot(hasRealRoot, test_precision<RealScalar>());
+    VERIFY(hasRealRoot == (real_roots.size() > 0));
+    if (hasRealRoot) {
+      VERIFY(internal::isApprox(real_roots.array().abs().maxCoeff(), abs(r), psPrec));
+    }
+
+    // Test absSmallestRealRoot
+    r = psolve.absSmallestRealRoot(hasRealRoot, test_precision<RealScalar>());
+    VERIFY(hasRealRoot == (real_roots.size() > 0));
+    if (hasRealRoot) {
+      VERIFY(internal::isApprox(real_roots.array().abs().minCoeff(), abs(r), psPrec));
+    }
+
+    // Test greatestRealRoot
+    r = psolve.greatestRealRoot(hasRealRoot, test_precision<RealScalar>());
+    VERIFY(hasRealRoot == (real_roots.size() > 0));
+    if (hasRealRoot) {
+      VERIFY(internal::isApprox(real_roots.array().maxCoeff(), r, psPrec));
+    }
+
+    // Test smallestRealRoot
+    r = psolve.smallestRealRoot(hasRealRoot, test_precision<RealScalar>());
+    VERIFY(hasRealRoot == (real_roots.size() > 0));
+    if (hasRealRoot) {
+      VERIFY(internal::isApprox(real_roots.array().minCoeff(), r, psPrec));
+    }
+  }
+}
+
+template <typename Scalar_, int Deg_>
+void polynomialsolver(int deg) {
+  typedef typename NumTraits<Scalar_>::Real RealScalar;
+  typedef internal::increment_if_fixed_size<Deg_> Dim;
+  typedef Matrix<Scalar_, Dim::ret, 1> PolynomialType;
+  typedef Matrix<Scalar_, Deg_, 1> EvalRootsType;
+  typedef Matrix<RealScalar, Deg_, 1> RealRootsType;
+
+  cout << "Standard cases" << endl;
+  PolynomialType pols = PolynomialType::Random(deg + 1);
+  evalSolver<Deg_, PolynomialType>(pols);
+
+  cout << "Hard cases" << endl;
+  Scalar_ multipleRoot = internal::random<Scalar_>();
+  EvalRootsType allRoots = EvalRootsType::Constant(deg, multipleRoot);
+  roots_to_monicPolynomial(allRoots, pols);
+  evalSolver<Deg_, PolynomialType>(pols);
+
+  cout << "Test sugar" << endl;
+  RealRootsType realRoots = RealRootsType::Random(deg);
+  // sort by ascending absolute value to mitigate precision lost during polynomial expansion
+  std::sort(realRoots.begin(), realRoots.end(),
+            [](RealScalar a, RealScalar b) { return numext::abs(a) < numext::abs(b); });
+  roots_to_monicPolynomial(realRoots, pols);
+  evalSolverSugarFunction<Deg_>(pols, realRoots.template cast<std::complex<RealScalar> >().eval(), realRoots);
+}
+
+EIGEN_DECLARE_TEST(polynomialsolver) {
+  for (int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1((polynomialsolver<float, 1>(1)));
+    CALL_SUBTEST_2((polynomialsolver<double, 2>(2)));
+    CALL_SUBTEST_3((polynomialsolver<double, 3>(3)));
+    CALL_SUBTEST_4((polynomialsolver<float, 4>(4)));
+    CALL_SUBTEST_5((polynomialsolver<double, 5>(5)));
+    CALL_SUBTEST_6((polynomialsolver<float, 6>(6)));
+    CALL_SUBTEST_7((polynomialsolver<float, 7>(7)));
+    CALL_SUBTEST_8((polynomialsolver<double, 8>(8)));
+
+    CALL_SUBTEST_9((polynomialsolver<float, Dynamic>(internal::random<int>(9, 13))));
+    CALL_SUBTEST_10((polynomialsolver<double, Dynamic>(internal::random<int>(9, 13))));
+    CALL_SUBTEST_11((polynomialsolver<float, Dynamic>(1)));
+    CALL_SUBTEST_12((polynomialsolver<std::complex<double>, Dynamic>(internal::random<int>(2, 13))));
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/polynomialutils.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/polynomialutils.cpp
new file mode 100644
index 0000000..f57c874
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/polynomialutils.cpp
@@ -0,0 +1,102 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <unsupported/Eigen/Polynomials>
+#include <iostream>
+
+using namespace std;
+
+namespace Eigen {
+namespace internal {
+template <int Size>
+struct increment_if_fixed_size {
+  enum { ret = (Size == Dynamic) ? Dynamic : Size + 1 };
+};
+}  // namespace internal
+}  // namespace Eigen
+
+template <typename Scalar_, int Deg_>
+void realRoots_to_monicPolynomial_test(int deg) {
+  typedef internal::increment_if_fixed_size<Deg_> Dim;
+  typedef Matrix<Scalar_, Dim::ret, 1> PolynomialType;
+  typedef Matrix<Scalar_, Deg_, 1> EvalRootsType;
+
+  PolynomialType pols(deg + 1);
+  EvalRootsType roots = EvalRootsType::Random(deg);
+  roots_to_monicPolynomial(roots, pols);
+
+  EvalRootsType evr(deg);
+  for (int i = 0; i < roots.size(); ++i) {
+    evr[i] = std::abs(poly_eval(pols, roots[i]));
+  }
+
+  bool evalToZero = evr.isZero(test_precision<Scalar_>());
+  if (!evalToZero) {
+    cerr << evr.transpose() << endl;
+  }
+  VERIFY(evalToZero);
+}
+
+template <typename Scalar_>
+void realRoots_to_monicPolynomial_scalar() {
+  CALL_SUBTEST_2((realRoots_to_monicPolynomial_test<Scalar_, 2>(2)));
+  CALL_SUBTEST_3((realRoots_to_monicPolynomial_test<Scalar_, 3>(3)));
+  CALL_SUBTEST_4((realRoots_to_monicPolynomial_test<Scalar_, 4>(4)));
+  CALL_SUBTEST_5((realRoots_to_monicPolynomial_test<Scalar_, 5>(5)));
+  CALL_SUBTEST_6((realRoots_to_monicPolynomial_test<Scalar_, 6>(6)));
+  CALL_SUBTEST_7((realRoots_to_monicPolynomial_test<Scalar_, 7>(7)));
+  CALL_SUBTEST_8((realRoots_to_monicPolynomial_test<Scalar_, 17>(17)));
+
+  CALL_SUBTEST_9((realRoots_to_monicPolynomial_test<Scalar_, Dynamic>(internal::random<int>(18, 26))));
+}
+
+template <typename Scalar_, int Deg_>
+void CauchyBounds(int deg) {
+  typedef internal::increment_if_fixed_size<Deg_> Dim;
+  typedef Matrix<Scalar_, Dim::ret, 1> PolynomialType;
+  typedef Matrix<Scalar_, Deg_, 1> EvalRootsType;
+
+  PolynomialType pols(deg + 1);
+  EvalRootsType roots = EvalRootsType::Random(deg);
+  roots_to_monicPolynomial(roots, pols);
+  Scalar_ M = cauchy_max_bound(pols);
+  Scalar_ m = cauchy_min_bound(pols);
+  Scalar_ Max = roots.array().abs().maxCoeff();
+  Scalar_ min = roots.array().abs().minCoeff();
+  bool eval = (M >= Max) && (m <= min);
+  if (!eval) {
+    cerr << "Roots: " << roots << endl;
+    cerr << "Bounds: (" << m << ", " << M << ")" << endl;
+    cerr << "Min,Max: (" << min << ", " << Max << ")" << endl;
+  }
+  VERIFY(eval);
+}
+
+template <typename Scalar_>
+void CauchyBounds_scalar() {
+  CALL_SUBTEST_2((CauchyBounds<Scalar_, 2>(2)));
+  CALL_SUBTEST_3((CauchyBounds<Scalar_, 3>(3)));
+  CALL_SUBTEST_4((CauchyBounds<Scalar_, 4>(4)));
+  CALL_SUBTEST_5((CauchyBounds<Scalar_, 5>(5)));
+  CALL_SUBTEST_6((CauchyBounds<Scalar_, 6>(6)));
+  CALL_SUBTEST_7((CauchyBounds<Scalar_, 7>(7)));
+  CALL_SUBTEST_8((CauchyBounds<Scalar_, 17>(17)));
+
+  CALL_SUBTEST_9((CauchyBounds<Scalar_, Dynamic>(internal::random<int>(18, 26))));
+}
+
+EIGEN_DECLARE_TEST(polynomialutils) {
+  for (int i = 0; i < g_repeat; i++) {
+    realRoots_to_monicPolynomial_scalar<double>();
+    realRoots_to_monicPolynomial_scalar<float>();
+    CauchyBounds_scalar<double>();
+    CauchyBounds_scalar<float>();
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/sparse_extra.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/sparse_extra.cpp
new file mode 100644
index 0000000..4bf5452
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/sparse_extra.cpp
@@ -0,0 +1,254 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include <cstdlib>
+#include <string>
+
+#include "sparse.h"
+
+#ifdef min
+#undef min
+#endif
+
+#ifdef max
+#undef max
+#endif
+
+#include <Eigen/SparseExtra>
+
+// Read from an environment variable TEST_TMPDIR, if available,
+// and append the provided filename. Defaults to local directory.
+std::string GetTestTempFilename(const char* filename) {
+  const char* test_tmpdir = std::getenv("TEST_TMPDIR");
+  if (test_tmpdir == nullptr) {
+    return std::string(filename);
+  }
+  return std::string(test_tmpdir) + std::string("/") + std::string(filename);
+}
+
+template <typename SetterType, typename DenseType, typename Scalar, int Options>
+bool test_random_setter(SparseMatrix<Scalar, Options>& sm, const DenseType& ref,
+                        const std::vector<Vector2i>& nonzeroCoords) {
+  {
+    sm.setZero();
+    SetterType w(sm);
+    std::vector<Vector2i> remaining = nonzeroCoords;
+    while (!remaining.empty()) {
+      int i = internal::random<int>(0, static_cast<int>(remaining.size()) - 1);
+      w(remaining[i].x(), remaining[i].y()) = ref.coeff(remaining[i].x(), remaining[i].y());
+      remaining[i] = remaining.back();
+      remaining.pop_back();
+    }
+  }
+  return sm.isApprox(ref);
+}
+
+template <typename SparseMatrixType>
+void sparse_extra(const SparseMatrixType& ref) {
+  const Index rows = ref.rows();
+  const Index cols = ref.cols();
+  typedef typename SparseMatrixType::Scalar Scalar;
+  enum { Flags = SparseMatrixType::Flags };
+
+  double density = (std::max)(8. / (rows * cols), 0.01);
+  typedef Matrix<Scalar, Dynamic, Dynamic> DenseMatrix;
+  typedef Matrix<Scalar, Dynamic, 1> DenseVector;
+  Scalar eps = 1e-6;
+
+  SparseMatrixType m(rows, cols);
+  DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
+  DenseVector vec1 = DenseVector::Random(rows);
+
+  std::vector<Vector2i> zeroCoords;
+  std::vector<Vector2i> nonzeroCoords;
+  initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
+
+  if (zeroCoords.size() == 0 || nonzeroCoords.size() == 0) return;
+
+  // test coeff and coeffRef
+  for (int i = 0; i < (int)zeroCoords.size(); ++i) {
+    VERIFY_IS_MUCH_SMALLER_THAN(m.coeff(zeroCoords[i].x(), zeroCoords[i].y()), eps);
+    if (internal::is_same<SparseMatrixType, SparseMatrix<Scalar, Flags> >::value)
+      VERIFY_RAISES_ASSERT(m.coeffRef(zeroCoords[0].x(), zeroCoords[0].y()) = 5);
+  }
+  VERIFY_IS_APPROX(m, refMat);
+
+  m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+  refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+
+  VERIFY_IS_APPROX(m, refMat);
+
+  // random setter
+  //   {
+  //     m.setZero();
+  //     VERIFY_IS_NOT_APPROX(m, refMat);
+  //     SparseSetter<SparseMatrixType, RandomAccessPattern> w(m);
+  //     std::vector<Vector2i> remaining = nonzeroCoords;
+  //     while(!remaining.empty())
+  //     {
+  //       int i = internal::random<int>(0,remaining.size()-1);
+  //       w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
+  //       remaining[i] = remaining.back();
+  //       remaining.pop_back();
+  //     }
+  //   }
+  //   VERIFY_IS_APPROX(m, refMat);
+
+  VERIFY((test_random_setter<RandomSetter<SparseMatrixType, StdMapTraits> >(m, refMat, nonzeroCoords)));
+  VERIFY((test_random_setter<RandomSetter<SparseMatrixType, StdUnorderedMapTraits> >(m, refMat, nonzeroCoords)));
+#ifdef EIGEN_GOOGLEHASH_SUPPORT
+  VERIFY((test_random_setter<RandomSetter<SparseMatrixType, GoogleDenseHashMapTraits> >(m, refMat, nonzeroCoords)));
+  VERIFY((test_random_setter<RandomSetter<SparseMatrixType, GoogleSparseHashMapTraits> >(m, refMat, nonzeroCoords)));
+#endif
+
+  // test RandomSetter
+  /*{
+    SparseMatrixType m1(rows,cols), m2(rows,cols);
+    DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
+    initSparse<Scalar>(density, refM1, m1);
+    {
+      Eigen::RandomSetter<SparseMatrixType > setter(m2);
+      for (int j=0; j<m1.outerSize(); ++j)
+        for (typename SparseMatrixType::InnerIterator i(m1,j); i; ++i)
+          setter(i.index(), j) = i.value();
+    }
+    VERIFY_IS_APPROX(m1, m2);
+  }*/
+}
+
+template <typename SparseMatrixType>
+void check_marketio() {
+  typedef Matrix<typename SparseMatrixType::Scalar, Dynamic, Dynamic> DenseMatrix;
+  Index rows = internal::random<Index>(1, 100);
+  Index cols = internal::random<Index>(1, 100);
+  SparseMatrixType m1, m2;
+  m1 = DenseMatrix::Random(rows, cols).sparseView();
+  std::string filename = GetTestTempFilename("sparse_extra.mtx");
+  saveMarket(m1, filename);
+  loadMarket(m2, filename);
+  VERIFY_IS_EQUAL(DenseMatrix(m1), DenseMatrix(m2));
+}
+
+template <typename VectorType>
+void check_marketio_vector() {
+  Index size = internal::random<Index>(1, 100);
+  VectorType v1, v2;
+  v1 = VectorType::Random(size);
+  std::string filename = GetTestTempFilename("vector_extra.mtx");
+  saveMarketVector(v1, filename);
+  loadMarketVector(v2, filename);
+  VERIFY_IS_EQUAL(v1, v2);
+}
+
+template <typename DenseMatrixType>
+void check_marketio_dense() {
+  Index rows = DenseMatrixType::MaxRowsAtCompileTime;
+  if (DenseMatrixType::MaxRowsAtCompileTime == Dynamic) {
+    rows = internal::random<Index>(1, 100);
+  } else if (DenseMatrixType::RowsAtCompileTime == Dynamic) {
+    rows = internal::random<Index>(1, DenseMatrixType::MaxRowsAtCompileTime);
+  }
+
+  Index cols = DenseMatrixType::MaxColsAtCompileTime;
+  if (DenseMatrixType::MaxColsAtCompileTime == Dynamic) {
+    cols = internal::random<Index>(1, 100);
+  } else if (DenseMatrixType::ColsAtCompileTime == Dynamic) {
+    cols = internal::random<Index>(1, DenseMatrixType::MaxColsAtCompileTime);
+  }
+
+  DenseMatrixType m1, m2;
+  m1 = DenseMatrixType::Random(rows, cols);
+  std::string filename = GetTestTempFilename("dense_extra.mtx");
+  saveMarketDense(m1, filename);
+  loadMarketDense(m2, filename);
+  VERIFY_IS_EQUAL(m1, m2);
+}
+
+template <typename Scalar>
+void check_sparse_inverse() {
+  typedef SparseMatrix<Scalar> MatrixType;
+
+  Matrix<Scalar, -1, -1> A;
+  A.resize(1000, 1000);
+  A.fill(0);
+  A.setIdentity();
+  A.col(0).array() += 1;
+  A.row(0).array() += 2;
+  A.col(2).array() += 3;
+  A.row(7).array() += 3;
+  A.col(9).array() += 3;
+  A.block(3, 4, 4, 2).array() += 9;
+  A.middleRows(10, 50).array() += 3;
+  A.middleCols(50, 50).array() += 40;
+  A.block(500, 300, 40, 20).array() += 10;
+  A.transposeInPlace();
+
+  Eigen::SparseLU<MatrixType> slu;
+  slu.compute(A.sparseView());
+  Matrix<Scalar, -1, -1> Id(A.rows(), A.cols());
+  Id.setIdentity();
+  Matrix<Scalar, -1, -1> inv = slu.solve(Id);
+
+  const MatrixType sparseInv = Eigen::SparseInverse<Scalar>().compute(A.sparseView()).inverse();
+
+  Scalar sumdiff = 0;  // Check the diff only of the non-zero elements
+  for (Eigen::Index j = 0; j < A.cols(); j++) {
+    for (typename MatrixType::InnerIterator iter(sparseInv, j); iter; ++iter) {
+      const Scalar diff = std::abs(inv(iter.row(), iter.col()) - iter.value());
+      VERIFY_IS_APPROX_OR_LESS_THAN(diff, 1e-11);
+
+      if (iter.value() != 0) {
+        sumdiff += diff;
+      }
+    }
+  }
+
+  VERIFY_IS_APPROX_OR_LESS_THAN(sumdiff, 1e-10);
+}
+
+EIGEN_DECLARE_TEST(sparse_extra) {
+  for (int i = 0; i < g_repeat; i++) {
+    int s = Eigen::internal::random<int>(1, 50);
+    CALL_SUBTEST_1(sparse_extra(SparseMatrix<double>(8, 8)));
+    CALL_SUBTEST_2(sparse_extra(SparseMatrix<std::complex<double> >(s, s)));
+    CALL_SUBTEST_1(sparse_extra(SparseMatrix<double>(s, s)));
+
+    CALL_SUBTEST_3((check_marketio<SparseMatrix<float, ColMajor, int> >()));
+    CALL_SUBTEST_3((check_marketio<SparseMatrix<double, ColMajor, int> >()));
+    CALL_SUBTEST_3((check_marketio<SparseMatrix<std::complex<float>, ColMajor, int> >()));
+    CALL_SUBTEST_3((check_marketio<SparseMatrix<std::complex<double>, ColMajor, int> >()));
+    CALL_SUBTEST_3((check_marketio<SparseMatrix<float, ColMajor, long int> >()));
+    CALL_SUBTEST_3((check_marketio<SparseMatrix<double, ColMajor, long int> >()));
+    CALL_SUBTEST_3((check_marketio<SparseMatrix<std::complex<float>, ColMajor, long int> >()));
+    CALL_SUBTEST_3((check_marketio<SparseMatrix<std::complex<double>, ColMajor, long int> >()));
+
+    CALL_SUBTEST_4((check_marketio_dense<Matrix<float, Dynamic, Dynamic> >()));
+    CALL_SUBTEST_4((check_marketio_dense<Matrix<float, Dynamic, Dynamic, RowMajor> >()));
+    CALL_SUBTEST_4((check_marketio_dense<Matrix<double, Dynamic, Dynamic> >()));
+    CALL_SUBTEST_4((check_marketio_dense<Matrix<std::complex<float>, Dynamic, Dynamic> >()));
+    CALL_SUBTEST_4((check_marketio_dense<Matrix<std::complex<double>, Dynamic, Dynamic> >()));
+    CALL_SUBTEST_4((check_marketio_dense<Matrix<float, Dynamic, 3> >()));
+    CALL_SUBTEST_4((check_marketio_dense<Matrix<double, 3, Dynamic> >()));
+    CALL_SUBTEST_4((check_marketio_dense<Matrix<double, 3, 4> >()));
+    CALL_SUBTEST_4((check_marketio_dense<Matrix<double, Dynamic, Dynamic, ColMajor, 5, 5> >()));
+
+    CALL_SUBTEST_5((check_marketio_vector<Matrix<float, 1, Dynamic> >()));
+    CALL_SUBTEST_5((check_marketio_vector<Matrix<double, 1, Dynamic> >()));
+    CALL_SUBTEST_5((check_marketio_vector<Matrix<std::complex<float>, 1, Dynamic> >()));
+    CALL_SUBTEST_5((check_marketio_vector<Matrix<std::complex<double>, 1, Dynamic> >()));
+    CALL_SUBTEST_5((check_marketio_vector<Matrix<float, Dynamic, 1> >()));
+    CALL_SUBTEST_5((check_marketio_vector<Matrix<double, Dynamic, 1> >()));
+    CALL_SUBTEST_5((check_marketio_vector<Matrix<std::complex<float>, Dynamic, 1> >()));
+    CALL_SUBTEST_5((check_marketio_vector<Matrix<std::complex<double>, Dynamic, 1> >()));
+
+    CALL_SUBTEST_6((check_sparse_inverse<double>()));
+
+    TEST_SET_BUT_UNUSED_VARIABLE(s);
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/special_functions.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/special_functions.cpp
new file mode 100644
index 0000000..0abad05
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/special_functions.cpp
@@ -0,0 +1,425 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include <limits.h>
+#include "main.h"
+#include "../Eigen/SpecialFunctions"
+
+// Workaround to allow "implicit" conversions from double to Scalar via comma-initialization.
+template <typename Derived>
+Eigen::CommaInitializer<Derived> operator<<(Eigen::DenseBase<Derived>& dense, double v) {
+  return (dense << static_cast<typename Derived::Scalar>(v));
+}
+
+template <typename XprType>
+Eigen::CommaInitializer<XprType>& operator,(Eigen::CommaInitializer<XprType>& ci, double v) {
+  return (ci, static_cast<typename XprType::Scalar>(v));
+}
+
+template <typename X, typename Y>
+void verify_component_wise(const X& x, const Y& y) {
+  for (Index i = 0; i < x.size(); ++i) {
+    if ((numext::isfinite)(y(i)))
+      VERIFY_IS_APPROX(x(i), y(i));
+    else if ((numext::isnan)(y(i)))
+      VERIFY((numext::isnan)(x(i)));
+    else
+      VERIFY_IS_EQUAL(x(i), y(i));
+  }
+}
+
+template <typename ArrayType>
+void array_special_functions() {
+  using std::abs;
+  using std::sqrt;
+  typedef typename ArrayType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  Scalar plusinf = std::numeric_limits<Scalar>::infinity();
+  Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
+
+  Index rows = internal::random<Index>(1, 30);
+  Index cols = 1;
+
+  // API
+  {
+    ArrayType m1 = ArrayType::Random(rows, cols);
+#if EIGEN_HAS_C99_MATH
+    VERIFY_IS_APPROX(m1.lgamma(), lgamma(m1));
+    VERIFY_IS_APPROX(m1.digamma(), digamma(m1));
+    VERIFY_IS_APPROX(m1.erf(), erf(m1));
+    VERIFY_IS_APPROX(m1.erfc(), erfc(m1));
+#endif  // EIGEN_HAS_C99_MATH
+  }
+
+#if EIGEN_HAS_C99_MATH
+  // check special functions (comparing against numpy implementation)
+  if (!NumTraits<Scalar>::IsComplex) {
+    {
+      ArrayType m1 = ArrayType::Random(rows, cols);
+      ArrayType m2 = ArrayType::Random(rows, cols);
+
+      // Test various properties of igamma & igammac.  These are normalized
+      // gamma integrals where
+      //   igammac(a, x) = Gamma(a, x) / Gamma(a)
+      //   igamma(a, x) = gamma(a, x) / Gamma(a)
+      // where Gamma and gamma are considered the standard unnormalized
+      // upper and lower incomplete gamma functions, respectively.
+      ArrayType a = m1.abs() + Scalar(2);
+      ArrayType x = m2.abs() + Scalar(2);
+      ArrayType zero = ArrayType::Zero(rows, cols);
+      ArrayType one = ArrayType::Constant(rows, cols, Scalar(1.0));
+      ArrayType a_m1 = a - one;
+      ArrayType Gamma_a_x = Eigen::igammac(a, x) * a.lgamma().exp();
+      ArrayType Gamma_a_m1_x = Eigen::igammac(a_m1, x) * a_m1.lgamma().exp();
+      ArrayType gamma_a_x = Eigen::igamma(a, x) * a.lgamma().exp();
+      ArrayType gamma_a_m1_x = Eigen::igamma(a_m1, x) * a_m1.lgamma().exp();
+
+      // Gamma(a, 0) == Gamma(a)
+      VERIFY_IS_APPROX(Eigen::igammac(a, zero), one);
+
+      // Gamma(a, x) + gamma(a, x) == Gamma(a)
+      VERIFY_IS_APPROX(Gamma_a_x + gamma_a_x, a.lgamma().exp());
+
+      // Gamma(a, x) == (a - 1) * Gamma(a-1, x) + x^(a-1) * exp(-x)
+      VERIFY_IS_APPROX(Gamma_a_x, (a - Scalar(1)) * Gamma_a_m1_x + x.pow(a - Scalar(1)) * (-x).exp());
+
+      // gamma(a, x) == (a - 1) * gamma(a-1, x) - x^(a-1) * exp(-x)
+      VERIFY_IS_APPROX(gamma_a_x, (a - Scalar(1)) * gamma_a_m1_x - x.pow(a - Scalar(1)) * (-x).exp());
+    }
+    {
+      // Verify for large a and x that values are between 0 and 1.
+      ArrayType m1 = ArrayType::Random(rows, cols);
+      ArrayType m2 = ArrayType::Random(rows, cols);
+      int max_exponent = std::numeric_limits<Scalar>::max_exponent10;
+      ArrayType a = m1.abs() * Scalar(pow(10., max_exponent - 1));
+      ArrayType x = m2.abs() * Scalar(pow(10., max_exponent - 1));
+      for (int i = 0; i < a.size(); ++i) {
+        Scalar igam = numext::igamma(a(i), x(i));
+        VERIFY(0 <= igam);
+        VERIFY(igam <= 1);
+      }
+    }
+
+    {
+      // Check exact values of igamma and igammac against a third party calculation.
+      Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)};
+      Scalar x_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)};
+
+      // location i*6+j corresponds to a_s[i], x_s[j].
+      Scalar igamma_s[][6] = {
+          {Scalar(0.0), nan, nan, nan, nan, nan},
+          {Scalar(0.0), Scalar(0.6321205588285578), Scalar(0.7768698398515702), Scalar(0.9816843611112658),
+           Scalar(9.999500016666262e-05), Scalar(1.0)},
+          {Scalar(0.0), Scalar(0.4275932955291202), Scalar(0.608374823728911), Scalar(0.9539882943107686),
+           Scalar(7.522076445089201e-07), Scalar(1.0)},
+          {Scalar(0.0), Scalar(0.01898815687615381), Scalar(0.06564245437845008), Scalar(0.5665298796332909),
+           Scalar(4.166333347221828e-18), Scalar(1.0)},
+          {Scalar(0.0), Scalar(0.9999780593618628), Scalar(0.9999899967080838), Scalar(0.9999996219837988),
+           Scalar(0.9991370418689945), Scalar(1.0)},
+          {Scalar(0.0), Scalar(0.0), Scalar(0.0), Scalar(0.0), Scalar(0.0), Scalar(0.5042041932513908)}};
+      Scalar igammac_s[][6] = {
+          {nan, nan, nan, nan, nan, nan},
+          {Scalar(1.0), Scalar(0.36787944117144233), Scalar(0.22313016014842982), Scalar(0.018315638888734182),
+           Scalar(0.9999000049998333), Scalar(0.0)},
+          {Scalar(1.0), Scalar(0.5724067044708798), Scalar(0.3916251762710878), Scalar(0.04601170568923136),
+           Scalar(0.9999992477923555), Scalar(0.0)},
+          {Scalar(1.0), Scalar(0.9810118431238462), Scalar(0.9343575456215499), Scalar(0.4334701203667089), Scalar(1.0),
+           Scalar(0.0)},
+          {Scalar(1.0), Scalar(2.1940638138146658e-05), Scalar(1.0003291916285e-05), Scalar(3.7801620118431334e-07),
+           Scalar(0.0008629581310054535), Scalar(0.0)},
+          {Scalar(1.0), Scalar(1.0), Scalar(1.0), Scalar(1.0), Scalar(1.0), Scalar(0.49579580674813944)}};
+
+      for (int i = 0; i < 6; ++i) {
+        for (int j = 0; j < 6; ++j) {
+          if ((std::isnan)(igamma_s[i][j])) {
+            VERIFY((std::isnan)(numext::igamma(a_s[i], x_s[j])));
+          } else {
+            VERIFY_IS_APPROX(numext::igamma(a_s[i], x_s[j]), igamma_s[i][j]);
+          }
+
+          if ((std::isnan)(igammac_s[i][j])) {
+            VERIFY((std::isnan)(numext::igammac(a_s[i], x_s[j])));
+          } else {
+            VERIFY_IS_APPROX(numext::igammac(a_s[i], x_s[j]), igammac_s[i][j]);
+          }
+        }
+      }
+    }
+  }
+#endif  // EIGEN_HAS_C99_MATH
+
+  // Check the ndtri function against scipy.special.ndtri
+  {
+    ArrayType x(11), res(11), ref(11);
+    x << 0.5, 0.2, 0.8, 0.9, 0.1, 0.99, 0.01, 0, 1, -0.01, 1.01;
+    ref << 0., -0.8416212335729142, 0.8416212335729142, 1.2815515655446004, -1.2815515655446004, 2.3263478740408408,
+        -2.3263478740408408, -plusinf, plusinf, nan, nan;
+    CALL_SUBTEST(verify_component_wise(ref, ref););
+    CALL_SUBTEST(res = x.ndtri(); verify_component_wise(res, ref););
+    CALL_SUBTEST(res = ndtri(x); verify_component_wise(res, ref););
+
+    // ndtri(normal_cdf(x)) ~= x
+    CALL_SUBTEST(ArrayType m1 = ArrayType::Random(32); using std::sqrt;
+
+                 ArrayType cdf_val = (m1 / Scalar(sqrt(2.))).erf(); cdf_val = (cdf_val + Scalar(1)) / Scalar(2);
+                 verify_component_wise(cdf_val.ndtri(), m1););
+  }
+
+  // Check the zeta function against scipy.special.zeta
+  {
+    ArrayType x(11), q(11), res(11), ref(11);
+    x << 1.5, 4, 10.5, 10000.5, 3, 1, 0.9, 2, 3, 4, 2000;
+    q << 2, 1.5, 3, 1.0001, -2.5, 1.2345, 1.2345, -1, -2, -3, 2000;
+    ref << 1.61237534869, 0.234848505667, 1.03086757337e-5, 0.367879440865, 0.054102025820864097, plusinf, nan, plusinf,
+        nan, plusinf, 0;
+    CALL_SUBTEST(verify_component_wise(ref, ref););
+    CALL_SUBTEST(res = x.zeta(q); verify_component_wise(res, ref););
+    CALL_SUBTEST(res = zeta(x, q); verify_component_wise(res, ref););
+  }
+
+  // digamma
+  {
+    ArrayType x(9), res(9), ref(9);
+    x << 1, 1.5, 4, -10.5, 10000.5, 0, -1, -2, -3;
+    ref << -0.5772156649015329, 0.03648997397857645, 1.2561176684318, 2.398239129535781, 9.210340372392849, nan, nan,
+        nan, nan;
+    CALL_SUBTEST(verify_component_wise(ref, ref););
+
+    CALL_SUBTEST(res = x.digamma(); verify_component_wise(res, ref););
+    CALL_SUBTEST(res = digamma(x); verify_component_wise(res, ref););
+  }
+
+#if EIGEN_HAS_C99_MATH
+  {
+    ArrayType n(16), x(16), res(16), ref(16);
+    n << 1, 1, 1, 1.5, 17, 31, 28, 8, 42, 147, 170, -1, 0, 1, 2, 3;
+    x << 2, 3, 25.5, 1.5, 4.7, 11.8, 17.7, 30.2, 15.8, 54.1, 64, -1, -2, -3, -4, -5;
+    ref << 0.644934066848, 0.394934066848, 0.0399946696496, nan, 293.334565435, 0.445487887616, -2.47810300902e-07,
+        -8.29668781082e-09, -0.434562276666, 0.567742190178, -0.0108615497927, nan, nan, plusinf, nan, plusinf;
+    CALL_SUBTEST(verify_component_wise(ref, ref););
+
+    if (sizeof(RealScalar) >= 8) {  // double
+      // Reason for commented line: http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1232
+      //       CALL_SUBTEST( res = x.polygamma(n); verify_component_wise(res, ref); );
+      CALL_SUBTEST(res = polygamma(n, x); verify_component_wise(res, ref););
+    } else {
+      //       CALL_SUBTEST( res = x.polygamma(n); verify_component_wise(res.head(8), ref.head(8)); );
+      CALL_SUBTEST(res = polygamma(n, x); verify_component_wise(res.head(8), ref.head(8)););
+    }
+  }
+#endif
+
+#if EIGEN_HAS_C99_MATH
+  {
+    // Inputs and ground truth generated with scipy via:
+    //   a = np.logspace(-3, 3, 5) - 1e-3
+    //   b = np.logspace(-3, 3, 5) - 1e-3
+    //   x = np.linspace(-0.1, 1.1, 5)
+    //   (full_a, full_b, full_x) = np.vectorize(lambda a, b, x: (a, b, x))(*np.ix_(a, b, x))
+    //   full_a = full_a.flatten().tolist()
+    //   full_b = full_b.flatten().tolist()
+    //   full_x = full_x.flatten().tolist()
+    //   v = scipy.special.betainc(full_a, full_b, full_x).flatten().tolist()
+    // Note in Eigen, we call betainc with arguments in the order (a, b, x);
+    ArrayType a(125);
+    ArrayType b(125);
+    ArrayType x(125);
+    ArrayType v(125);
+    ArrayType res(125);
+
+    a << 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+        0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+        0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+        0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+        0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+        0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+        0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999,
+        0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379,
+        31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+        31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+        31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+        31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+        31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, 999.999,
+        999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999,
+        999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999;
+
+    b << 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
+        0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+        31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0,
+        0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999,
+        0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379,
+        31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379,
+        0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999,
+        0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999,
+        999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379,
+        0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999,
+        31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999,
+        999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379,
+        0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999,
+        31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999,
+        999.999, 999.999, 999.999;
+
+    x << -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1,
+        0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2,
+        0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5,
+        0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8,
+        1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1,
+        -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1;
+
+    v << nan, nan, nan, nan, nan, nan, 1.0, 1.0, 1.0, nan, nan, 1.0, 1.0, 1.0, nan, nan, 1.0, 1.0, 1.0, nan, nan, 1.0,
+        1.0, 1.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.47972119876364683, 0.4999999999999997, 0.5202788012363533, nan,
+        nan, 0.9518683957740043, 0.9789663010413745, 0.993172918807344, nan, nan, 0.999995949033062, 0.99999999999937,
+        1.0, nan, nan, 1.0, 1.0, 1.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0068270811926558735, 0.0210336989586256,
+        0.048131604225995696, nan, nan, 0.20014344256217687, 0.5000000000000002, 0.7998565574378237, nan, nan,
+        0.9991401428435834, 0.9999999996984031, 1.0, nan, nan, 1.0, 1.0, 1.0, nan, nan, 0.0, 0.0, 0.0, nan, nan,
+        1.0646600232370862e-25, 6.30172287782625e-13, 4.050966937974916e-06, nan, nan, 7.864342668429712e-23,
+        3.015969667594142e-10, 0.0008598571564165379, nan, nan, 6.031987710123845e-08, 0.5000000000000001,
+        0.9999999396801229, nan, nan, 1.0, 1.0, 1.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 7.029920380993552e-306,
+        2.245072820861537e-101, nan, nan, 0.0, 9.275871147875753e-302, 1.223291302616039e-97, nan, nan, 0.0,
+        3.089139308197642e-252, 2.9303043666230076e-60, nan, nan, 2.248913486881819e-196, 0.5000000000000036, 1.0, nan;
+
+    CALL_SUBTEST(res = betainc(a, b, x); verify_component_wise(res, v););
+  }
+
+  // Test various properties of betainc
+  {
+    ArrayType m1 = ArrayType::Random(32);
+    ArrayType m2 = ArrayType::Random(32);
+    ArrayType m3 = ArrayType::Random(32);
+    ArrayType one = ArrayType::Constant(32, Scalar(1.0));
+    const Scalar eps = std::numeric_limits<Scalar>::epsilon();
+    ArrayType a = (m1 * Scalar(4)).exp();
+    ArrayType b = (m2 * Scalar(4)).exp();
+    ArrayType x = m3.abs();
+
+    // betainc(a, 1, x) == x**a
+    CALL_SUBTEST(ArrayType test = betainc(a, one, x); ArrayType expected = x.pow(a);
+                 verify_component_wise(test, expected););
+
+    // betainc(1, b, x) == 1 - (1 - x)**b
+    CALL_SUBTEST(ArrayType test = betainc(one, b, x); ArrayType expected = one - (one - x).pow(b);
+                 verify_component_wise(test, expected););
+
+    // betainc(a, b, x) == 1 - betainc(b, a, 1-x)
+    CALL_SUBTEST(ArrayType test = betainc(a, b, x) + betainc(b, a, one - x); ArrayType expected = one;
+                 verify_component_wise(test, expected););
+
+    // betainc(a+1, b, x) = betainc(a, b, x) - x**a * (1 - x)**b / (a * beta(a, b))
+    CALL_SUBTEST(
+        ArrayType num = x.pow(a) * (one - x).pow(b);
+        ArrayType denom = a * (a.lgamma() + b.lgamma() - (a + b).lgamma()).exp();
+        // Add eps to rhs and lhs so that component-wise test doesn't result in
+        // nans when both outputs are zeros.
+        ArrayType expected = betainc(a, b, x) - num / denom + eps;
+        ArrayType test = betainc(a + one, b, x) + eps; if (sizeof(Scalar) >= 8) {  // double
+          verify_component_wise(test, expected);
+        } else {
+          // Reason for limited test: http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1232
+          verify_component_wise(test.head(8), expected.head(8));
+        });
+
+    // betainc(a, b+1, x) = betainc(a, b, x) + x**a * (1 - x)**b / (b * beta(a, b))
+    CALL_SUBTEST(
+        // Add eps to rhs and lhs so that component-wise test doesn't result in
+        // nans when both outputs are zeros.
+        ArrayType num = x.pow(a) * (one - x).pow(b);
+        ArrayType denom = b * (a.lgamma() + b.lgamma() - (a + b).lgamma()).exp();
+        ArrayType expected = betainc(a, b, x) + num / denom + eps; ArrayType test = betainc(a, b + one, x) + eps;
+        verify_component_wise(test, expected););
+  }
+#endif  // EIGEN_HAS_C99_MATH
+
+  /* Code to generate the data for the following two test cases.
+  N = 5
+  np.random.seed(3)
+
+  a = np.logspace(-2, 3, 6)
+  a = np.ravel(np.tile(np.reshape(a, [-1, 1]), [1, N]))
+  x = np.random.gamma(a, 1.0)
+  x = np.maximum(x, np.finfo(np.float32).tiny)
+
+  def igamma(a, x):
+    return mpmath.gammainc(a, 0, x, regularized=True)
+
+  def igamma_der_a(a, x):
+    res = mpmath.diff(lambda a_prime: igamma(a_prime, x), a)
+    return np.float64(res)
+
+  def gamma_sample_der_alpha(a, x):
+    igamma_x = igamma(a, x)
+    def igammainv_of_igamma(a_prime):
+      return mpmath.findroot(lambda x_prime: igamma(a_prime, x_prime) -
+          igamma_x, x, solver='newton')
+    return np.float64(mpmath.diff(igammainv_of_igamma, a))
+
+  v_igamma_der_a = np.vectorize(igamma_der_a)(a, x)
+  v_gamma_sample_der_alpha = np.vectorize(gamma_sample_der_alpha)(a, x)
+*/
+
+#if EIGEN_HAS_C99_MATH
+  // Test igamma_der_a
+  {
+    ArrayType a(30);
+    ArrayType x(30);
+    ArrayType res(30);
+    ArrayType v(30);
+
+    a << 0.01, 0.01, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0, 10.0,
+        100.0, 100.0, 100.0, 100.0, 100.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0;
+
+    x << 1.25668890405e-26, 1.17549435082e-38, 1.20938905072e-05, 1.17549435082e-38, 1.17549435082e-38,
+        5.66572070696e-16, 0.0132865061065, 0.0200034203853, 6.29263709118e-17, 1.37160367764e-06, 0.333412038288,
+        1.18135687766, 0.580629033777, 0.170631439426, 0.786686768458, 7.63873279537, 13.1944344379, 11.896042354,
+        10.5830172417, 10.5020942233, 92.8918587747, 95.003720371, 86.3715926467, 96.0330217672, 82.6389930677,
+        968.702906754, 969.463546828, 1001.79726022, 955.047416547, 1044.27458568;
+
+    v << -32.7256441441, -36.4394150514, -9.66467612263, -36.4394150514, -36.4394150514, -1.0891900302, -2.66351229645,
+        -2.48666868596, -0.929700494428, -3.56327722764, -0.455320135314, -0.391437214323, -0.491352055991,
+        -0.350454834292, -0.471773162921, -0.104084440522, -0.0723646747909, -0.0992828975532, -0.121638215446,
+        -0.122619605294, -0.0317670267286, -0.0359974812869, -0.0154359225363, -0.0375775365921, -0.00794899153653,
+        -0.00777303219211, -0.00796085782042, -0.0125850719397, -0.00455500206958, -0.00476436993148;
+
+    CALL_SUBTEST(res = igamma_der_a(a, x); verify_component_wise(res, v););
+  }
+
+  // Test gamma_sample_der_alpha
+  {
+    ArrayType alpha(30);
+    ArrayType sample(30);
+    ArrayType res(30);
+    ArrayType v(30);
+
+    alpha << 0.01, 0.01, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0, 10.0,
+        10.0, 100.0, 100.0, 100.0, 100.0, 100.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0;
+
+    sample << 1.25668890405e-26, 1.17549435082e-38, 1.20938905072e-05, 1.17549435082e-38, 1.17549435082e-38,
+        5.66572070696e-16, 0.0132865061065, 0.0200034203853, 6.29263709118e-17, 1.37160367764e-06, 0.333412038288,
+        1.18135687766, 0.580629033777, 0.170631439426, 0.786686768458, 7.63873279537, 13.1944344379, 11.896042354,
+        10.5830172417, 10.5020942233, 92.8918587747, 95.003720371, 86.3715926467, 96.0330217672, 82.6389930677,
+        968.702906754, 969.463546828, 1001.79726022, 955.047416547, 1044.27458568;
+
+    v << 7.42424742367e-23, 1.02004297287e-34, 0.0130155240738, 1.02004297287e-34, 1.02004297287e-34, 1.96505168277e-13,
+        0.525575786243, 0.713903991771, 2.32077561808e-14, 0.000179348049886, 0.635500453302, 1.27561284917,
+        0.878125852156, 0.41565819538, 1.03606488534, 0.885964824887, 1.16424049334, 1.10764479598, 1.04590810812,
+        1.04193666963, 0.965193152414, 0.976217589464, 0.93008035061, 0.98153216096, 0.909196397698, 0.98434963993,
+        0.984738050206, 1.00106492525, 0.97734200649, 1.02198794179;
+
+    CALL_SUBTEST(res = gamma_sample_der_alpha(alpha, sample); verify_component_wise(res, v););
+  }
+#endif  // EIGEN_HAS_C99_MATH
+}
+
+EIGEN_DECLARE_TEST(special_functions) {
+  CALL_SUBTEST_1(array_special_functions<ArrayXf>());
+  CALL_SUBTEST_2(array_special_functions<ArrayXd>());
+  // TODO(cantonios): half/bfloat16 don't have enough precision to reproduce results above.
+  // CALL_SUBTEST_3(array_special_functions<ArrayX<Eigen::half>>());
+  // CALL_SUBTEST_4(array_special_functions<ArrayX<Eigen::bfloat16>>());
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/special_packetmath.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/special_packetmath.cpp
new file mode 100644
index 0000000..57f00d5
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/special_packetmath.cpp
@@ -0,0 +1,160 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include <limits>
+#include "packetmath_test_shared.h"
+#include "../Eigen/SpecialFunctions"
+
+#if EIGEN_ARCH_ARM
+// Note: 32-bit arm always flushes subnormals to zero.
+#define MAYBE_FLUSH(op)                                                      \
+  [](Scalar x) {                                                             \
+    Scalar y = static_cast<Scalar>(op(x));                                   \
+    if (Eigen::numext::abs(y) < (std::numeric_limits<decltype(y)>::min)()) { \
+      y = y * decltype(y)(0); /* Preserve sign. */                           \
+    }                                                                        \
+    return y;                                                                \
+  }
+#else
+#define MAYBE_FLUSH(op) op
+#endif
+
+template <typename Scalar, typename Packet>
+void packetmath_real() {
+  using std::abs;
+  typedef internal::packet_traits<Scalar> PacketTraits;
+  const int PacketSize = internal::unpacket_traits<Packet>::size;
+
+  const int size = PacketSize * 4;
+  EIGEN_ALIGN_MAX Scalar data1[PacketSize * 4] = {};
+  EIGEN_ALIGN_MAX Scalar data2[PacketSize * 4] = {};
+  EIGEN_ALIGN_MAX Scalar ref[PacketSize * 4] = {};
+
+#if EIGEN_HAS_C99_MATH
+  {
+    data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
+    test::packet_helper<internal::packet_traits<Scalar>::HasLGamma, Packet> h;
+    h.store(data2, internal::plgamma(h.load(data1)));
+    VERIFY((numext::isnan)(data2[0]));
+  }
+  if (internal::packet_traits<Scalar>::HasErf) {
+    data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
+    test::packet_helper<internal::packet_traits<Scalar>::HasErf, Packet> h;
+    h.store(data2, internal::perf(h.load(data1)));
+    VERIFY((numext::isnan)(data2[0]));
+  }
+  {
+    data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
+    test::packet_helper<internal::packet_traits<Scalar>::HasErfc, Packet> h;
+    h.store(data2, internal::perfc(h.load(data1)));
+    VERIFY((numext::isnan)(data2[0]));
+  }
+  {
+    for (int i = 0; i < size; ++i) {
+      data1[i] = internal::random<Scalar>(Scalar(0), Scalar(1));
+    }
+    CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasNdtri, numext::ndtri, internal::pndtri);
+  }
+#endif  // EIGEN_HAS_C99_MATH
+
+  // For bessel_i*e and bessel_j*, the valid range is negative reals.
+  {
+    const int max_exponent = numext::mini(std::numeric_limits<Scalar>::max_exponent10 - 1, 6);
+    for (int i = 0; i < size; ++i) {
+      data1[i] = internal::random<Scalar>(Scalar(-1), Scalar(1)) *
+                 Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-max_exponent), Scalar(max_exponent))));
+      data2[i] = internal::random<Scalar>(Scalar(-1), Scalar(1)) *
+                 Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-max_exponent), Scalar(max_exponent))));
+    }
+
+    CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_i0e, internal::pbessel_i0e);
+    CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_i1e, internal::pbessel_i1e);
+    CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_j0, internal::pbessel_j0);
+    CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_j1, internal::pbessel_j1);
+  }
+
+  // Use a smaller data range for the bessel_i* as these can become very large.
+  // Following #1693, we also restrict this range further to avoid inf's due to
+  // differences in pexp and exp.
+  for (int i = 0; i < size; ++i) {
+    data1[i] = internal::random<Scalar>(Scalar(0.01), Scalar(1)) *
+               Scalar(std::pow(Scalar(9), internal::random<Scalar>(Scalar(-1), Scalar(2))));
+    data2[i] = internal::random<Scalar>(Scalar(0.01), Scalar(1)) *
+               Scalar(std::pow(Scalar(9), internal::random<Scalar>(Scalar(-1), Scalar(2))));
+  }
+  CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_i0, internal::pbessel_i0);
+  CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_i1, internal::pbessel_i1);
+
+  // y_i, and k_i are valid for x > 0.
+  {
+    const int max_exponent = numext::mini(std::numeric_limits<Scalar>::max_exponent10 - 1, 5);
+    for (int i = 0; i < size; ++i) {
+      data1[i] = internal::random<Scalar>(Scalar(0.01), Scalar(1)) *
+                 Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-2), Scalar(max_exponent))));
+      data2[i] = internal::random<Scalar>(Scalar(0.01), Scalar(1)) *
+                 Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-2), Scalar(max_exponent))));
+    }
+  }
+
+  // TODO(srvasude): Re-enable this test once properly investigated why the
+  // scalar and vector paths differ.
+  // CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_y0, internal::pbessel_y0);
+  CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_y1, internal::pbessel_y1);
+  CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_k0e, internal::pbessel_k0e);
+  CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_k1e, internal::pbessel_k1e);
+
+  // Following #1693, we restrict the range for exp to avoid zeroing out too
+  // fast.
+  for (int i = 0; i < size; ++i) {
+    data1[i] = internal::random<Scalar>(Scalar(0.01), Scalar(1)) *
+               Scalar(std::pow(Scalar(9), internal::random<Scalar>(Scalar(-1), Scalar(2))));
+    data2[i] = internal::random<Scalar>(Scalar(0.01), Scalar(1)) *
+               Scalar(std::pow(Scalar(9), internal::random<Scalar>(Scalar(-1), Scalar(2))));
+  }
+  CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_k0, internal::pbessel_k0);
+  CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_k1, internal::pbessel_k1);
+
+  for (int i = 0; i < size; ++i) {
+    data1[i] = internal::random<Scalar>(Scalar(0.01), Scalar(1)) *
+               Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-1), Scalar(2))));
+    data2[i] = internal::random<Scalar>(Scalar(0.01), Scalar(1)) *
+               Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-1), Scalar(2))));
+  }
+
+#if EIGEN_HAS_C99_MATH
+  CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasLGamma, std::lgamma, internal::plgamma);
+  CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasErf, std::erf, internal::perf);
+  // FIXME(rmlarsen): This test occasionally fails due to difference in tiny subnormal results
+  // near the underflow boundary. I am not sure which version is correct.
+  CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasErfc, MAYBE_FLUSH(std::erfc), internal::perfc);
+#endif
+}
+
+namespace Eigen {
+namespace test {
+
+template <typename Scalar, typename PacketType, bool IsComplex, bool IsInteger>
+struct runall {
+  static void run() { packetmath_real<Scalar, PacketType>(); }
+};
+
+}  // namespace test
+}  // namespace Eigen
+
+EIGEN_DECLARE_TEST(special_packetmath) {
+  g_first_pass = true;
+  for (int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1(test::runner<float>::run());
+    CALL_SUBTEST_2(test::runner<double>::run());
+    CALL_SUBTEST_3(test::runner<Eigen::half>::run());
+    CALL_SUBTEST_4(test::runner<Eigen::bfloat16>::run());
+    g_first_pass = false;
+  }
+}
diff --git a/data_acq_and_processing/third_party/eigen/unsupported/test/splines.cpp b/data_acq_and_processing/third_party/eigen/unsupported/test/splines.cpp
new file mode 100644
index 0000000..8083718
--- /dev/null
+++ b/data_acq_and_processing/third_party/eigen/unsupported/test/splines.cpp
@@ -0,0 +1,205 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010-2011 Hauke Heibel <heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <unsupported/Eigen/Splines>
+
+namespace Eigen {
+
+// lets do some explicit instantiations and thus
+// force the compilation of all spline functions...
+template class Spline<double, 2, Dynamic>;
+template class Spline<double, 3, Dynamic>;
+
+template class Spline<double, 2, 2>;
+template class Spline<double, 2, 3>;
+template class Spline<double, 2, 4>;
+template class Spline<double, 2, 5>;
+
+template class Spline<float, 2, Dynamic>;
+template class Spline<float, 3, Dynamic>;
+
+template class Spline<float, 3, 2>;
+template class Spline<float, 3, 3>;
+template class Spline<float, 3, 4>;
+template class Spline<float, 3, 5>;
+
+}  // namespace Eigen
+
+Spline<double, 2, Dynamic> closed_spline2d() {
+  RowVectorXd knots(12);
+  knots << 0, 0, 0, 0, 0.867193179093898, 1.660330955342408, 2.605084834823134, 3.484154586374428, 4.252699478956276,
+      4.252699478956276, 4.252699478956276, 4.252699478956276;
+
+  MatrixXd ctrls(8, 2);
+  ctrls << -0.370967741935484, 0.236842105263158, -0.231401860693277, 0.442245185027632, 0.344361228532831,
+      0.773369994120753, 0.828990216203802, 0.106550882647595, 0.407270163678382, -1.043452922172848,
+      -0.488467813584053, -0.390098582530090, -0.494657189446427, 0.054804824897884, -0.370967741935484,
+      0.236842105263158;
+  ctrls.transposeInPlace();
+
+  return Spline<double, 2, Dynamic>(knots, ctrls);
+}
+
+/* create a reference spline */
+Spline<double, 3, Dynamic> spline3d() {
+  RowVectorXd knots(11);
+  knots << 0, 0, 0, 0.118997681558377, 0.162611735194631, 0.498364051982143, 0.655098003973841, 0.679702676853675,
+      1.000000000000000, 1.000000000000000, 1.000000000000000;
+
+  MatrixXd ctrls(8, 3);
+  ctrls << 0.959743958516081, 0.340385726666133, 0.585267750979777, 0.223811939491137, 0.751267059305653,
+      0.255095115459269, 0.505957051665142, 0.699076722656686, 0.890903252535799, 0.959291425205444, 0.547215529963803,
+      0.138624442828679, 0.149294005559057, 0.257508254123736, 0.840717255983663, 0.254282178971531, 0.814284826068816,
+      0.243524968724989, 0.929263623187228, 0.349983765984809, 0.196595250431208, 0.251083857976031, 0.616044676146639,
+      0.473288848902729;
+  ctrls.transposeInPlace();
+
+  return Spline<double, 3, Dynamic>(knots, ctrls);
+}
+
+/* compares evaluations against known results */
+void eval_spline3d() {
+  Spline3d spline = spline3d();
+
+  RowVectorXd u(10);
+  u << 0.351659507062997, 0.830828627896291, 0.585264091152724, 0.549723608291140, 0.917193663829810, 0.285839018820374,
+      0.757200229110721, 0.753729094278495, 0.380445846975357, 0.567821640725221;
+
+  MatrixXd pts(10, 3);
+  pts << 0.707620811535916, 0.510258911240815, 0.417485437023409, 0.603422256426978, 0.529498282727551,
+      0.270351549348981, 0.228364197569334, 0.423745615677815, 0.637687289287490, 0.275556796335168, 0.350856706427970,
+      0.684295784598905, 0.514519311047655, 0.525077224890754, 0.351628308305896, 0.724152914315666, 0.574461155457304,
+      0.469860285484058, 0.529365063753288, 0.613328702656816, 0.237837040141739, 0.522469395136878, 0.619099658652895,
+      0.237139665242069, 0.677357023849552, 0.480655768435853, 0.422227610314397, 0.247046593173758, 0.380604672404750,
+      0.670065791405019;
+  pts.transposeInPlace();
+
+  for (int i = 0; i < u.size(); ++i) {
+    Vector3d pt = spline(u(i));
+    VERIFY((pt - pts.col(i)).norm() < 1e-14);
+  }
+}
+
+/* compares evaluations on corner cases */
+void eval_spline3d_onbrks() {
+  Spline3d spline = spline3d();
+
+  RowVectorXd u = spline.knots();
+
+  MatrixXd pts(11, 3);
+  pts << 0.959743958516081, 0.340385726666133, 0.585267750979777, 0.959743958516081, 0.340385726666133,
+      0.585267750979777, 0.959743958516081, 0.340385726666133, 0.585267750979777, 0.430282980289940, 0.713074680056118,
+      0.720373307943349, 0.558074875553060, 0.681617921034459, 0.804417124839942, 0.407076008291750, 0.349707710518163,
+      0.617275937419545, 0.240037008286602, 0.738739390398014, 0.324554153129411, 0.302434111480572, 0.781162443963899,
+      0.240177089094644, 0.251083857976031, 0.616044676146639, 0.473288848902729, 0.251083857976031, 0.616044676146639,
+      0.473288848902729, 0.251083857976031, 0.616044676146639, 0.473288848902729;
+  pts.transposeInPlace();
+
+  for (int i = 0; i < u.size(); ++i) {
+    Vector3d pt = spline(u(i));
+    VERIFY((pt - pts.col(i)).norm() < 1e-14);
+  }
+}
+
+void eval_closed_spline2d() {
+  Spline2d spline = closed_spline2d();
+
+  RowVectorXd u(12);
+  u << 0, 0.332457030395796, 0.356467130532952, 0.453562180176215, 0.648017921874804, 0.973770235555003,
+      1.882577647219307, 2.289408593930498, 3.511951429883045, 3.884149321369450, 4.236261590369414, 4.252699478956276;
+
+  MatrixXd pts(12, 2);
+  pts << -0.370967741935484, 0.236842105263158, -0.152576775123250, 0.448975001279334, -0.133417538277668,
+      0.461615613865667, -0.053199060826740, 0.507630360006299, 0.114249591147281, 0.570414135097409, 0.377810316891987,
+      0.560497102875315, 0.665052120135908, -0.157557441109611, 0.516006487053228, -0.559763292174825,
+      -0.379486035348887, -0.331959640488223, -0.462034726249078, -0.039105670080824, -0.378730600917982,
+      0.225127015099919, -0.370967741935484, 0.236842105263158;
+  pts.transposeInPlace();
+
+  for (int i = 0; i < u.size(); ++i) {
+    Vector2d pt = spline(u(i));
+    VERIFY((pt - pts.col(i)).norm() < 1e-14);
+  }
+}
+
+void check_global_interpolation2d() {
+  typedef Spline2d::PointType PointType;
+  typedef Spline2d::KnotVectorType KnotVectorType;
+  typedef Spline2d::ControlPointVectorType ControlPointVectorType;
+
+  ControlPointVectorType points = ControlPointVectorType::Random(2, 100);
+
+  KnotVectorType chord_lengths;  // knot parameters
+  Eigen::ChordLengths(points, chord_lengths);
+
+  // interpolation without knot parameters
+  {
+    const Spline2d spline = SplineFitting<Spline2d>::Interpolate(points, 3);
+
+    for (Eigen::DenseIndex i = 0; i < points.cols(); ++i) {
+      PointType pt = spline(chord_lengths(i));
+      PointType ref = points.col(i);
+      VERIFY((pt - ref).matrix().norm() < 1e-14);
+    }
+  }
+
+  // interpolation with given knot parameters
+  {
+    const Spline2d spline = SplineFitting<Spline2d>::Interpolate(points, 3, chord_lengths);
+
+    for (Eigen::DenseIndex i = 0; i < points.cols(); ++i) {
+      PointType pt = spline(chord_lengths(i));
+      PointType ref = points.col(i);
+      VERIFY((pt - ref).matrix().norm() < 1e-14);
+    }
+  }
+}
+
+void check_global_interpolation_with_derivatives2d() {
+  typedef Spline2d::PointType PointType;
+  typedef Spline2d::KnotVectorType KnotVectorType;
+
+  const Eigen::DenseIndex numPoints = 100;
+  const unsigned int dimension = 2;
+  const unsigned int degree = 3;
+
+  ArrayXXd points = ArrayXXd::Random(dimension, numPoints);
+
+  KnotVectorType knots;
+  Eigen::ChordLengths(points, knots);
+
+  ArrayXXd derivatives = ArrayXXd::Random(dimension, numPoints);
+  VectorXd derivativeIndices(numPoints);
+
+  for (Eigen::DenseIndex i = 0; i < numPoints; ++i) derivativeIndices(i) = static_cast<double>(i);
+
+  const Spline2d spline =
+      SplineFitting<Spline2d>::InterpolateWithDerivatives(points, derivatives, derivativeIndices, degree);
+
+  for (Eigen::DenseIndex i = 0; i < points.cols(); ++i) {
+    PointType point = spline(knots(i));
+    PointType referencePoint = points.col(i);
+    VERIFY_IS_APPROX(point, referencePoint);
+    PointType derivative = spline.derivatives(knots(i), 1).col(1);
+    PointType referenceDerivative = derivatives.col(i);
+    VERIFY_IS_APPROX(derivative, referenceDerivative);
+  }
+}
+
+EIGEN_DECLARE_TEST(splines) {
+  for (int i = 0; i < g_repeat; ++i) {
+    CALL_SUBTEST(eval_spline3d());
+    CALL_SUBTEST(eval_spline3d_onbrks());
+    CALL_SUBTEST(eval_closed_spline2d());
+    CALL_SUBTEST(check_global_interpolation2d());
+    CALL_SUBTEST(check_global_interpolation_with_derivatives2d());
+  }
+}
diff --git a/data_acq_and_processing/third_party/nlohmann/json.hpp b/data_acq_and_processing/third_party/nlohmann/json.hpp
new file mode 100644
index 0000000..8b72ea6
--- /dev/null
+++ b/data_acq_and_processing/third_party/nlohmann/json.hpp
@@ -0,0 +1,24765 @@
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+/****************************************************************************\
+ * Note on documentation: The source files contain links to the online      *
+ * documentation of the public API at https://json.nlohmann.me. This URL    *
+ * contains the most recent documentation and should also be applicable to  *
+ * previous versions; documentation for deprecated functions is not         *
+ * removed, but marked deprecated. See "Generate documentation" section in  *
+ * file docs/README.md.                                                     *
+\****************************************************************************/
+
+#ifndef INCLUDE_NLOHMANN_JSON_HPP_
+#define INCLUDE_NLOHMANN_JSON_HPP_
+
+#include <algorithm> // all_of, find, for_each
+#include <cstddef> // nullptr_t, ptrdiff_t, size_t
+#include <functional> // hash, less
+#include <initializer_list> // initializer_list
+#ifndef JSON_NO_IO
+    #include <iosfwd> // istream, ostream
+#endif  // JSON_NO_IO
+#include <iterator> // random_access_iterator_tag
+#include <memory> // unique_ptr
+#include <string> // string, stoi, to_string
+#include <utility> // declval, forward, move, pair, swap
+#include <vector> // vector
+
+// #include <nlohmann/adl_serializer.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <utility>
+
+// #include <nlohmann/detail/abi_macros.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+// This file contains all macro definitions affecting or depending on the ABI
+
+#ifndef JSON_SKIP_LIBRARY_VERSION_CHECK
+    #if defined(NLOHMANN_JSON_VERSION_MAJOR) && defined(NLOHMANN_JSON_VERSION_MINOR) && defined(NLOHMANN_JSON_VERSION_PATCH)
+        #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 3
+            #warning "Already included a different version of the library!"
+        #endif
+    #endif
+#endif
+
+#define NLOHMANN_JSON_VERSION_MAJOR 3   // NOLINT(modernize-macro-to-enum)
+#define NLOHMANN_JSON_VERSION_MINOR 11  // NOLINT(modernize-macro-to-enum)
+#define NLOHMANN_JSON_VERSION_PATCH 3   // NOLINT(modernize-macro-to-enum)
+
+#ifndef JSON_DIAGNOSTICS
+    #define JSON_DIAGNOSTICS 0
+#endif
+
+#ifndef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
+    #define JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON 0
+#endif
+
+#if JSON_DIAGNOSTICS
+    #define NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS _diag
+#else
+    #define NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS
+#endif
+
+#if JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
+    #define NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON _ldvcmp
+#else
+    #define NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON
+#endif
+
+#ifndef NLOHMANN_JSON_NAMESPACE_NO_VERSION
+    #define NLOHMANN_JSON_NAMESPACE_NO_VERSION 0
+#endif
+
+// Construct the namespace ABI tags component
+#define NLOHMANN_JSON_ABI_TAGS_CONCAT_EX(a, b) json_abi ## a ## b
+#define NLOHMANN_JSON_ABI_TAGS_CONCAT(a, b) \
+    NLOHMANN_JSON_ABI_TAGS_CONCAT_EX(a, b)
+
+#define NLOHMANN_JSON_ABI_TAGS                                       \
+    NLOHMANN_JSON_ABI_TAGS_CONCAT(                                   \
+            NLOHMANN_JSON_ABI_TAG_DIAGNOSTICS,                       \
+            NLOHMANN_JSON_ABI_TAG_LEGACY_DISCARDED_VALUE_COMPARISON)
+
+// Construct the namespace version component
+#define NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT_EX(major, minor, patch) \
+    _v ## major ## _ ## minor ## _ ## patch
+#define NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT(major, minor, patch) \
+    NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT_EX(major, minor, patch)
+
+#if NLOHMANN_JSON_NAMESPACE_NO_VERSION
+#define NLOHMANN_JSON_NAMESPACE_VERSION
+#else
+#define NLOHMANN_JSON_NAMESPACE_VERSION                                 \
+    NLOHMANN_JSON_NAMESPACE_VERSION_CONCAT(NLOHMANN_JSON_VERSION_MAJOR, \
+                                           NLOHMANN_JSON_VERSION_MINOR, \
+                                           NLOHMANN_JSON_VERSION_PATCH)
+#endif
+
+// Combine namespace components
+#define NLOHMANN_JSON_NAMESPACE_CONCAT_EX(a, b) a ## b
+#define NLOHMANN_JSON_NAMESPACE_CONCAT(a, b) \
+    NLOHMANN_JSON_NAMESPACE_CONCAT_EX(a, b)
+
+#ifndef NLOHMANN_JSON_NAMESPACE
+#define NLOHMANN_JSON_NAMESPACE               \
+    nlohmann::NLOHMANN_JSON_NAMESPACE_CONCAT( \
+            NLOHMANN_JSON_ABI_TAGS,           \
+            NLOHMANN_JSON_NAMESPACE_VERSION)
+#endif
+
+#ifndef NLOHMANN_JSON_NAMESPACE_BEGIN
+#define NLOHMANN_JSON_NAMESPACE_BEGIN                \
+    namespace nlohmann                               \
+    {                                                \
+    inline namespace NLOHMANN_JSON_NAMESPACE_CONCAT( \
+                NLOHMANN_JSON_ABI_TAGS,              \
+                NLOHMANN_JSON_NAMESPACE_VERSION)     \
+    {
+#endif
+
+#ifndef NLOHMANN_JSON_NAMESPACE_END
+#define NLOHMANN_JSON_NAMESPACE_END                                     \
+    }  /* namespace (inline namespace) NOLINT(readability/namespace) */ \
+    }  // namespace nlohmann
+#endif
+
+// #include <nlohmann/detail/conversions/from_json.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <algorithm> // transform
+#include <array> // array
+#include <forward_list> // forward_list
+#include <iterator> // inserter, front_inserter, end
+#include <map> // map
+#include <string> // string
+#include <tuple> // tuple, make_tuple
+#include <type_traits> // is_arithmetic, is_same, is_enum, underlying_type, is_convertible
+#include <unordered_map> // unordered_map
+#include <utility> // pair, declval
+#include <valarray> // valarray
+
+// #include <nlohmann/detail/exceptions.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cstddef> // nullptr_t
+#include <exception> // exception
+#if JSON_DIAGNOSTICS
+    #include <numeric> // accumulate
+#endif
+#include <stdexcept> // runtime_error
+#include <string> // to_string
+#include <vector> // vector
+
+// #include <nlohmann/detail/value_t.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <array> // array
+#include <cstddef> // size_t
+#include <cstdint> // uint8_t
+#include <string> // string
+
+// #include <nlohmann/detail/macro_scope.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <utility> // declval, pair
+// #include <nlohmann/detail/meta/detected.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <type_traits>
+
+// #include <nlohmann/detail/meta/void_t.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+template<typename ...Ts> struct make_void
+{
+    using type = void;
+};
+template<typename ...Ts> using void_t = typename make_void<Ts...>::type;
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+// https://en.cppreference.com/w/cpp/experimental/is_detected
+struct nonesuch
+{
+    nonesuch() = delete;
+    ~nonesuch() = delete;
+    nonesuch(nonesuch const&) = delete;
+    nonesuch(nonesuch const&&) = delete;
+    void operator=(nonesuch const&) = delete;
+    void operator=(nonesuch&&) = delete;
+};
+
+template<class Default,
+         class AlwaysVoid,
+         template<class...> class Op,
+         class... Args>
+struct detector
+{
+    using value_t = std::false_type;
+    using type = Default;
+};
+
+template<class Default, template<class...> class Op, class... Args>
+struct detector<Default, void_t<Op<Args...>>, Op, Args...>
+{
+    using value_t = std::true_type;
+    using type = Op<Args...>;
+};
+
+template<template<class...> class Op, class... Args>
+using is_detected = typename detector<nonesuch, void, Op, Args...>::value_t;
+
+template<template<class...> class Op, class... Args>
+struct is_detected_lazy : is_detected<Op, Args...> { };
+
+template<template<class...> class Op, class... Args>
+using detected_t = typename detector<nonesuch, void, Op, Args...>::type;
+
+template<class Default, template<class...> class Op, class... Args>
+using detected_or = detector<Default, void, Op, Args...>;
+
+template<class Default, template<class...> class Op, class... Args>
+using detected_or_t = typename detected_or<Default, Op, Args...>::type;
+
+template<class Expected, template<class...> class Op, class... Args>
+using is_detected_exact = std::is_same<Expected, detected_t<Op, Args...>>;
+
+template<class To, template<class...> class Op, class... Args>
+using is_detected_convertible =
+    std::is_convertible<detected_t<Op, Args...>, To>;
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/thirdparty/hedley/hedley.hpp>
+
+
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2016-2021 Evan Nemerson <evan@nemerson.com>
+// SPDX-License-Identifier: MIT
+
+/* Hedley - https://nemequ.github.io/hedley
+ * Created by Evan Nemerson <evan@nemerson.com>
+ */
+
+#if !defined(JSON_HEDLEY_VERSION) || (JSON_HEDLEY_VERSION < 15)
+#if defined(JSON_HEDLEY_VERSION)
+    #undef JSON_HEDLEY_VERSION
+#endif
+#define JSON_HEDLEY_VERSION 15
+
+#if defined(JSON_HEDLEY_STRINGIFY_EX)
+    #undef JSON_HEDLEY_STRINGIFY_EX
+#endif
+#define JSON_HEDLEY_STRINGIFY_EX(x) #x
+
+#if defined(JSON_HEDLEY_STRINGIFY)
+    #undef JSON_HEDLEY_STRINGIFY
+#endif
+#define JSON_HEDLEY_STRINGIFY(x) JSON_HEDLEY_STRINGIFY_EX(x)
+
+#if defined(JSON_HEDLEY_CONCAT_EX)
+    #undef JSON_HEDLEY_CONCAT_EX
+#endif
+#define JSON_HEDLEY_CONCAT_EX(a,b) a##b
+
+#if defined(JSON_HEDLEY_CONCAT)
+    #undef JSON_HEDLEY_CONCAT
+#endif
+#define JSON_HEDLEY_CONCAT(a,b) JSON_HEDLEY_CONCAT_EX(a,b)
+
+#if defined(JSON_HEDLEY_CONCAT3_EX)
+    #undef JSON_HEDLEY_CONCAT3_EX
+#endif
+#define JSON_HEDLEY_CONCAT3_EX(a,b,c) a##b##c
+
+#if defined(JSON_HEDLEY_CONCAT3)
+    #undef JSON_HEDLEY_CONCAT3
+#endif
+#define JSON_HEDLEY_CONCAT3(a,b,c) JSON_HEDLEY_CONCAT3_EX(a,b,c)
+
+#if defined(JSON_HEDLEY_VERSION_ENCODE)
+    #undef JSON_HEDLEY_VERSION_ENCODE
+#endif
+#define JSON_HEDLEY_VERSION_ENCODE(major,minor,revision) (((major) * 1000000) + ((minor) * 1000) + (revision))
+
+#if defined(JSON_HEDLEY_VERSION_DECODE_MAJOR)
+    #undef JSON_HEDLEY_VERSION_DECODE_MAJOR
+#endif
+#define JSON_HEDLEY_VERSION_DECODE_MAJOR(version) ((version) / 1000000)
+
+#if defined(JSON_HEDLEY_VERSION_DECODE_MINOR)
+    #undef JSON_HEDLEY_VERSION_DECODE_MINOR
+#endif
+#define JSON_HEDLEY_VERSION_DECODE_MINOR(version) (((version) % 1000000) / 1000)
+
+#if defined(JSON_HEDLEY_VERSION_DECODE_REVISION)
+    #undef JSON_HEDLEY_VERSION_DECODE_REVISION
+#endif
+#define JSON_HEDLEY_VERSION_DECODE_REVISION(version) ((version) % 1000)
+
+#if defined(JSON_HEDLEY_GNUC_VERSION)
+    #undef JSON_HEDLEY_GNUC_VERSION
+#endif
+#if defined(__GNUC__) && defined(__GNUC_PATCHLEVEL__)
+    #define JSON_HEDLEY_GNUC_VERSION JSON_HEDLEY_VERSION_ENCODE(__GNUC__, __GNUC_MINOR__, __GNUC_PATCHLEVEL__)
+#elif defined(__GNUC__)
+    #define JSON_HEDLEY_GNUC_VERSION JSON_HEDLEY_VERSION_ENCODE(__GNUC__, __GNUC_MINOR__, 0)
+#endif
+
+#if defined(JSON_HEDLEY_GNUC_VERSION_CHECK)
+    #undef JSON_HEDLEY_GNUC_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_GNUC_VERSION)
+    #define JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_GNUC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_MSVC_VERSION)
+    #undef JSON_HEDLEY_MSVC_VERSION
+#endif
+#if defined(_MSC_FULL_VER) && (_MSC_FULL_VER >= 140000000) && !defined(__ICL)
+    #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_FULL_VER / 10000000, (_MSC_FULL_VER % 10000000) / 100000, (_MSC_FULL_VER % 100000) / 100)
+#elif defined(_MSC_FULL_VER) && !defined(__ICL)
+    #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_FULL_VER / 1000000, (_MSC_FULL_VER % 1000000) / 10000, (_MSC_FULL_VER % 10000) / 10)
+#elif defined(_MSC_VER) && !defined(__ICL)
+    #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_VER / 100, _MSC_VER % 100, 0)
+#endif
+
+#if defined(JSON_HEDLEY_MSVC_VERSION_CHECK)
+    #undef JSON_HEDLEY_MSVC_VERSION_CHECK
+#endif
+#if !defined(JSON_HEDLEY_MSVC_VERSION)
+    #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (0)
+#elif defined(_MSC_VER) && (_MSC_VER >= 1400)
+    #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_FULL_VER >= ((major * 10000000) + (minor * 100000) + (patch)))
+#elif defined(_MSC_VER) && (_MSC_VER >= 1200)
+    #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_FULL_VER >= ((major * 1000000) + (minor * 10000) + (patch)))
+#else
+    #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_VER >= ((major * 100) + (minor)))
+#endif
+
+#if defined(JSON_HEDLEY_INTEL_VERSION)
+    #undef JSON_HEDLEY_INTEL_VERSION
+#endif
+#if defined(__INTEL_COMPILER) && defined(__INTEL_COMPILER_UPDATE) && !defined(__ICL)
+    #define JSON_HEDLEY_INTEL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER / 100, __INTEL_COMPILER % 100, __INTEL_COMPILER_UPDATE)
+#elif defined(__INTEL_COMPILER) && !defined(__ICL)
+    #define JSON_HEDLEY_INTEL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER / 100, __INTEL_COMPILER % 100, 0)
+#endif
+
+#if defined(JSON_HEDLEY_INTEL_VERSION_CHECK)
+    #undef JSON_HEDLEY_INTEL_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_INTEL_VERSION)
+    #define JSON_HEDLEY_INTEL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_INTEL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_INTEL_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_INTEL_CL_VERSION)
+    #undef JSON_HEDLEY_INTEL_CL_VERSION
+#endif
+#if defined(__INTEL_COMPILER) && defined(__INTEL_COMPILER_UPDATE) && defined(__ICL)
+    #define JSON_HEDLEY_INTEL_CL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER, __INTEL_COMPILER_UPDATE, 0)
+#endif
+
+#if defined(JSON_HEDLEY_INTEL_CL_VERSION_CHECK)
+    #undef JSON_HEDLEY_INTEL_CL_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_INTEL_CL_VERSION)
+    #define JSON_HEDLEY_INTEL_CL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_INTEL_CL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_INTEL_CL_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_PGI_VERSION)
+    #undef JSON_HEDLEY_PGI_VERSION
+#endif
+#if defined(__PGI) && defined(__PGIC__) && defined(__PGIC_MINOR__) && defined(__PGIC_PATCHLEVEL__)
+    #define JSON_HEDLEY_PGI_VERSION JSON_HEDLEY_VERSION_ENCODE(__PGIC__, __PGIC_MINOR__, __PGIC_PATCHLEVEL__)
+#endif
+
+#if defined(JSON_HEDLEY_PGI_VERSION_CHECK)
+    #undef JSON_HEDLEY_PGI_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_PGI_VERSION)
+    #define JSON_HEDLEY_PGI_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_PGI_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_PGI_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_SUNPRO_VERSION)
+    #undef JSON_HEDLEY_SUNPRO_VERSION
+#endif
+#if defined(__SUNPRO_C) && (__SUNPRO_C > 0x1000)
+    #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((((__SUNPRO_C >> 16) & 0xf) * 10) + ((__SUNPRO_C >> 12) & 0xf), (((__SUNPRO_C >> 8) & 0xf) * 10) + ((__SUNPRO_C >> 4) & 0xf), (__SUNPRO_C & 0xf) * 10)
+#elif defined(__SUNPRO_C)
+    #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((__SUNPRO_C >> 8) & 0xf, (__SUNPRO_C >> 4) & 0xf, (__SUNPRO_C) & 0xf)
+#elif defined(__SUNPRO_CC) && (__SUNPRO_CC > 0x1000)
+    #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((((__SUNPRO_CC >> 16) & 0xf) * 10) + ((__SUNPRO_CC >> 12) & 0xf), (((__SUNPRO_CC >> 8) & 0xf) * 10) + ((__SUNPRO_CC >> 4) & 0xf), (__SUNPRO_CC & 0xf) * 10)
+#elif defined(__SUNPRO_CC)
+    #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((__SUNPRO_CC >> 8) & 0xf, (__SUNPRO_CC >> 4) & 0xf, (__SUNPRO_CC) & 0xf)
+#endif
+
+#if defined(JSON_HEDLEY_SUNPRO_VERSION_CHECK)
+    #undef JSON_HEDLEY_SUNPRO_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_SUNPRO_VERSION)
+    #define JSON_HEDLEY_SUNPRO_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_SUNPRO_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_SUNPRO_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION)
+    #undef JSON_HEDLEY_EMSCRIPTEN_VERSION
+#endif
+#if defined(__EMSCRIPTEN__)
+    #define JSON_HEDLEY_EMSCRIPTEN_VERSION JSON_HEDLEY_VERSION_ENCODE(__EMSCRIPTEN_major__, __EMSCRIPTEN_minor__, __EMSCRIPTEN_tiny__)
+#endif
+
+#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK)
+    #undef JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION)
+    #define JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_EMSCRIPTEN_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_ARM_VERSION)
+    #undef JSON_HEDLEY_ARM_VERSION
+#endif
+#if defined(__CC_ARM) && defined(__ARMCOMPILER_VERSION)
+    #define JSON_HEDLEY_ARM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ARMCOMPILER_VERSION / 1000000, (__ARMCOMPILER_VERSION % 1000000) / 10000, (__ARMCOMPILER_VERSION % 10000) / 100)
+#elif defined(__CC_ARM) && defined(__ARMCC_VERSION)
+    #define JSON_HEDLEY_ARM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ARMCC_VERSION / 1000000, (__ARMCC_VERSION % 1000000) / 10000, (__ARMCC_VERSION % 10000) / 100)
+#endif
+
+#if defined(JSON_HEDLEY_ARM_VERSION_CHECK)
+    #undef JSON_HEDLEY_ARM_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_ARM_VERSION)
+    #define JSON_HEDLEY_ARM_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_ARM_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_ARM_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_IBM_VERSION)
+    #undef JSON_HEDLEY_IBM_VERSION
+#endif
+#if defined(__ibmxl__)
+    #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ibmxl_version__, __ibmxl_release__, __ibmxl_modification__)
+#elif defined(__xlC__) && defined(__xlC_ver__)
+    #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__xlC__ >> 8, __xlC__ & 0xff, (__xlC_ver__ >> 8) & 0xff)
+#elif defined(__xlC__)
+    #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__xlC__ >> 8, __xlC__ & 0xff, 0)
+#endif
+
+#if defined(JSON_HEDLEY_IBM_VERSION_CHECK)
+    #undef JSON_HEDLEY_IBM_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_IBM_VERSION)
+    #define JSON_HEDLEY_IBM_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_IBM_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_IBM_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_TI_VERSION)
+    #undef JSON_HEDLEY_TI_VERSION
+#endif
+#if \
+    defined(__TI_COMPILER_VERSION__) && \
+    ( \
+      defined(__TMS470__) || defined(__TI_ARM__) || \
+      defined(__MSP430__) || \
+      defined(__TMS320C2000__) \
+    )
+#if (__TI_COMPILER_VERSION__ >= 16000000)
+    #define JSON_HEDLEY_TI_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
+#endif
+#endif
+
+#if defined(JSON_HEDLEY_TI_VERSION_CHECK)
+    #undef JSON_HEDLEY_TI_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_TI_VERSION)
+    #define JSON_HEDLEY_TI_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_TI_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_TI_CL2000_VERSION)
+    #undef JSON_HEDLEY_TI_CL2000_VERSION
+#endif
+#if defined(__TI_COMPILER_VERSION__) && defined(__TMS320C2000__)
+    #define JSON_HEDLEY_TI_CL2000_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
+#endif
+
+#if defined(JSON_HEDLEY_TI_CL2000_VERSION_CHECK)
+    #undef JSON_HEDLEY_TI_CL2000_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_TI_CL2000_VERSION)
+    #define JSON_HEDLEY_TI_CL2000_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL2000_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_TI_CL2000_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_TI_CL430_VERSION)
+    #undef JSON_HEDLEY_TI_CL430_VERSION
+#endif
+#if defined(__TI_COMPILER_VERSION__) && defined(__MSP430__)
+    #define JSON_HEDLEY_TI_CL430_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
+#endif
+
+#if defined(JSON_HEDLEY_TI_CL430_VERSION_CHECK)
+    #undef JSON_HEDLEY_TI_CL430_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_TI_CL430_VERSION)
+    #define JSON_HEDLEY_TI_CL430_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL430_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_TI_CL430_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_TI_ARMCL_VERSION)
+    #undef JSON_HEDLEY_TI_ARMCL_VERSION
+#endif
+#if defined(__TI_COMPILER_VERSION__) && (defined(__TMS470__) || defined(__TI_ARM__))
+    #define JSON_HEDLEY_TI_ARMCL_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
+#endif
+
+#if defined(JSON_HEDLEY_TI_ARMCL_VERSION_CHECK)
+    #undef JSON_HEDLEY_TI_ARMCL_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_TI_ARMCL_VERSION)
+    #define JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_ARMCL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_TI_CL6X_VERSION)
+    #undef JSON_HEDLEY_TI_CL6X_VERSION
+#endif
+#if defined(__TI_COMPILER_VERSION__) && defined(__TMS320C6X__)
+    #define JSON_HEDLEY_TI_CL6X_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
+#endif
+
+#if defined(JSON_HEDLEY_TI_CL6X_VERSION_CHECK)
+    #undef JSON_HEDLEY_TI_CL6X_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_TI_CL6X_VERSION)
+    #define JSON_HEDLEY_TI_CL6X_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL6X_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_TI_CL6X_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_TI_CL7X_VERSION)
+    #undef JSON_HEDLEY_TI_CL7X_VERSION
+#endif
+#if defined(__TI_COMPILER_VERSION__) && defined(__C7000__)
+    #define JSON_HEDLEY_TI_CL7X_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
+#endif
+
+#if defined(JSON_HEDLEY_TI_CL7X_VERSION_CHECK)
+    #undef JSON_HEDLEY_TI_CL7X_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_TI_CL7X_VERSION)
+    #define JSON_HEDLEY_TI_CL7X_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL7X_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_TI_CL7X_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_TI_CLPRU_VERSION)
+    #undef JSON_HEDLEY_TI_CLPRU_VERSION
+#endif
+#if defined(__TI_COMPILER_VERSION__) && defined(__PRU__)
+    #define JSON_HEDLEY_TI_CLPRU_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000))
+#endif
+
+#if defined(JSON_HEDLEY_TI_CLPRU_VERSION_CHECK)
+    #undef JSON_HEDLEY_TI_CLPRU_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_TI_CLPRU_VERSION)
+    #define JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CLPRU_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_CRAY_VERSION)
+    #undef JSON_HEDLEY_CRAY_VERSION
+#endif
+#if defined(_CRAYC)
+    #if defined(_RELEASE_PATCHLEVEL)
+        #define JSON_HEDLEY_CRAY_VERSION JSON_HEDLEY_VERSION_ENCODE(_RELEASE_MAJOR, _RELEASE_MINOR, _RELEASE_PATCHLEVEL)
+    #else
+        #define JSON_HEDLEY_CRAY_VERSION JSON_HEDLEY_VERSION_ENCODE(_RELEASE_MAJOR, _RELEASE_MINOR, 0)
+    #endif
+#endif
+
+#if defined(JSON_HEDLEY_CRAY_VERSION_CHECK)
+    #undef JSON_HEDLEY_CRAY_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_CRAY_VERSION)
+    #define JSON_HEDLEY_CRAY_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_CRAY_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_CRAY_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_IAR_VERSION)
+    #undef JSON_HEDLEY_IAR_VERSION
+#endif
+#if defined(__IAR_SYSTEMS_ICC__)
+    #if __VER__ > 1000
+        #define JSON_HEDLEY_IAR_VERSION JSON_HEDLEY_VERSION_ENCODE((__VER__ / 1000000), ((__VER__ / 1000) % 1000), (__VER__ % 1000))
+    #else
+        #define JSON_HEDLEY_IAR_VERSION JSON_HEDLEY_VERSION_ENCODE(__VER__ / 100, __VER__ % 100, 0)
+    #endif
+#endif
+
+#if defined(JSON_HEDLEY_IAR_VERSION_CHECK)
+    #undef JSON_HEDLEY_IAR_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_IAR_VERSION)
+    #define JSON_HEDLEY_IAR_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_IAR_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_IAR_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_TINYC_VERSION)
+    #undef JSON_HEDLEY_TINYC_VERSION
+#endif
+#if defined(__TINYC__)
+    #define JSON_HEDLEY_TINYC_VERSION JSON_HEDLEY_VERSION_ENCODE(__TINYC__ / 1000, (__TINYC__ / 100) % 10, __TINYC__ % 100)
+#endif
+
+#if defined(JSON_HEDLEY_TINYC_VERSION_CHECK)
+    #undef JSON_HEDLEY_TINYC_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_TINYC_VERSION)
+    #define JSON_HEDLEY_TINYC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TINYC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_TINYC_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_DMC_VERSION)
+    #undef JSON_HEDLEY_DMC_VERSION
+#endif
+#if defined(__DMC__)
+    #define JSON_HEDLEY_DMC_VERSION JSON_HEDLEY_VERSION_ENCODE(__DMC__ >> 8, (__DMC__ >> 4) & 0xf, __DMC__ & 0xf)
+#endif
+
+#if defined(JSON_HEDLEY_DMC_VERSION_CHECK)
+    #undef JSON_HEDLEY_DMC_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_DMC_VERSION)
+    #define JSON_HEDLEY_DMC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_DMC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_DMC_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_COMPCERT_VERSION)
+    #undef JSON_HEDLEY_COMPCERT_VERSION
+#endif
+#if defined(__COMPCERT_VERSION__)
+    #define JSON_HEDLEY_COMPCERT_VERSION JSON_HEDLEY_VERSION_ENCODE(__COMPCERT_VERSION__ / 10000, (__COMPCERT_VERSION__ / 100) % 100, __COMPCERT_VERSION__ % 100)
+#endif
+
+#if defined(JSON_HEDLEY_COMPCERT_VERSION_CHECK)
+    #undef JSON_HEDLEY_COMPCERT_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_COMPCERT_VERSION)
+    #define JSON_HEDLEY_COMPCERT_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_COMPCERT_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_COMPCERT_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_PELLES_VERSION)
+    #undef JSON_HEDLEY_PELLES_VERSION
+#endif
+#if defined(__POCC__)
+    #define JSON_HEDLEY_PELLES_VERSION JSON_HEDLEY_VERSION_ENCODE(__POCC__ / 100, __POCC__ % 100, 0)
+#endif
+
+#if defined(JSON_HEDLEY_PELLES_VERSION_CHECK)
+    #undef JSON_HEDLEY_PELLES_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_PELLES_VERSION)
+    #define JSON_HEDLEY_PELLES_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_PELLES_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_PELLES_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_MCST_LCC_VERSION)
+    #undef JSON_HEDLEY_MCST_LCC_VERSION
+#endif
+#if defined(__LCC__) && defined(__LCC_MINOR__)
+    #define JSON_HEDLEY_MCST_LCC_VERSION JSON_HEDLEY_VERSION_ENCODE(__LCC__ / 100, __LCC__ % 100, __LCC_MINOR__)
+#endif
+
+#if defined(JSON_HEDLEY_MCST_LCC_VERSION_CHECK)
+    #undef JSON_HEDLEY_MCST_LCC_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_MCST_LCC_VERSION)
+    #define JSON_HEDLEY_MCST_LCC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_MCST_LCC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_MCST_LCC_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_GCC_VERSION)
+    #undef JSON_HEDLEY_GCC_VERSION
+#endif
+#if \
+    defined(JSON_HEDLEY_GNUC_VERSION) && \
+    !defined(__clang__) && \
+    !defined(JSON_HEDLEY_INTEL_VERSION) && \
+    !defined(JSON_HEDLEY_PGI_VERSION) && \
+    !defined(JSON_HEDLEY_ARM_VERSION) && \
+    !defined(JSON_HEDLEY_CRAY_VERSION) && \
+    !defined(JSON_HEDLEY_TI_VERSION) && \
+    !defined(JSON_HEDLEY_TI_ARMCL_VERSION) && \
+    !defined(JSON_HEDLEY_TI_CL430_VERSION) && \
+    !defined(JSON_HEDLEY_TI_CL2000_VERSION) && \
+    !defined(JSON_HEDLEY_TI_CL6X_VERSION) && \
+    !defined(JSON_HEDLEY_TI_CL7X_VERSION) && \
+    !defined(JSON_HEDLEY_TI_CLPRU_VERSION) && \
+    !defined(__COMPCERT__) && \
+    !defined(JSON_HEDLEY_MCST_LCC_VERSION)
+    #define JSON_HEDLEY_GCC_VERSION JSON_HEDLEY_GNUC_VERSION
+#endif
+
+#if defined(JSON_HEDLEY_GCC_VERSION_CHECK)
+    #undef JSON_HEDLEY_GCC_VERSION_CHECK
+#endif
+#if defined(JSON_HEDLEY_GCC_VERSION)
+    #define JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_GCC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch))
+#else
+    #define JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) (0)
+#endif
+
+#if defined(JSON_HEDLEY_HAS_ATTRIBUTE)
+    #undef JSON_HEDLEY_HAS_ATTRIBUTE
+#endif
+#if \
+  defined(__has_attribute) && \
+  ( \
+    (!defined(JSON_HEDLEY_IAR_VERSION) || JSON_HEDLEY_IAR_VERSION_CHECK(8,5,9)) \
+  )
+#  define JSON_HEDLEY_HAS_ATTRIBUTE(attribute) __has_attribute(attribute)
+#else
+#  define JSON_HEDLEY_HAS_ATTRIBUTE(attribute) (0)
+#endif
+
+#if defined(JSON_HEDLEY_GNUC_HAS_ATTRIBUTE)
+    #undef JSON_HEDLEY_GNUC_HAS_ATTRIBUTE
+#endif
+#if defined(__has_attribute)
+    #define JSON_HEDLEY_GNUC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_HAS_ATTRIBUTE(attribute)
+#else
+    #define JSON_HEDLEY_GNUC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_GCC_HAS_ATTRIBUTE)
+    #undef JSON_HEDLEY_GCC_HAS_ATTRIBUTE
+#endif
+#if defined(__has_attribute)
+    #define JSON_HEDLEY_GCC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_HAS_ATTRIBUTE(attribute)
+#else
+    #define JSON_HEDLEY_GCC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_HAS_CPP_ATTRIBUTE)
+    #undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE
+#endif
+#if \
+    defined(__has_cpp_attribute) && \
+    defined(__cplusplus) && \
+    (!defined(JSON_HEDLEY_SUNPRO_VERSION) || JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0))
+    #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute) __has_cpp_attribute(attribute)
+#else
+    #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute) (0)
+#endif
+
+#if defined(JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS)
+    #undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS
+#endif
+#if !defined(__cplusplus) || !defined(__has_cpp_attribute)
+    #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) (0)
+#elif \
+    !defined(JSON_HEDLEY_PGI_VERSION) && \
+    !defined(JSON_HEDLEY_IAR_VERSION) && \
+    (!defined(JSON_HEDLEY_SUNPRO_VERSION) || JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0)) && \
+    (!defined(JSON_HEDLEY_MSVC_VERSION) || JSON_HEDLEY_MSVC_VERSION_CHECK(19,20,0))
+    #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) JSON_HEDLEY_HAS_CPP_ATTRIBUTE(ns::attribute)
+#else
+    #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) (0)
+#endif
+
+#if defined(JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE)
+    #undef JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE
+#endif
+#if defined(__has_cpp_attribute) && defined(__cplusplus)
+    #define JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) __has_cpp_attribute(attribute)
+#else
+    #define JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE)
+    #undef JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE
+#endif
+#if defined(__has_cpp_attribute) && defined(__cplusplus)
+    #define JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) __has_cpp_attribute(attribute)
+#else
+    #define JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_HAS_BUILTIN)
+    #undef JSON_HEDLEY_HAS_BUILTIN
+#endif
+#if defined(__has_builtin)
+    #define JSON_HEDLEY_HAS_BUILTIN(builtin) __has_builtin(builtin)
+#else
+    #define JSON_HEDLEY_HAS_BUILTIN(builtin) (0)
+#endif
+
+#if defined(JSON_HEDLEY_GNUC_HAS_BUILTIN)
+    #undef JSON_HEDLEY_GNUC_HAS_BUILTIN
+#endif
+#if defined(__has_builtin)
+    #define JSON_HEDLEY_GNUC_HAS_BUILTIN(builtin,major,minor,patch) __has_builtin(builtin)
+#else
+    #define JSON_HEDLEY_GNUC_HAS_BUILTIN(builtin,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_GCC_HAS_BUILTIN)
+    #undef JSON_HEDLEY_GCC_HAS_BUILTIN
+#endif
+#if defined(__has_builtin)
+    #define JSON_HEDLEY_GCC_HAS_BUILTIN(builtin,major,minor,patch) __has_builtin(builtin)
+#else
+    #define JSON_HEDLEY_GCC_HAS_BUILTIN(builtin,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_HAS_FEATURE)
+    #undef JSON_HEDLEY_HAS_FEATURE
+#endif
+#if defined(__has_feature)
+    #define JSON_HEDLEY_HAS_FEATURE(feature) __has_feature(feature)
+#else
+    #define JSON_HEDLEY_HAS_FEATURE(feature) (0)
+#endif
+
+#if defined(JSON_HEDLEY_GNUC_HAS_FEATURE)
+    #undef JSON_HEDLEY_GNUC_HAS_FEATURE
+#endif
+#if defined(__has_feature)
+    #define JSON_HEDLEY_GNUC_HAS_FEATURE(feature,major,minor,patch) __has_feature(feature)
+#else
+    #define JSON_HEDLEY_GNUC_HAS_FEATURE(feature,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_GCC_HAS_FEATURE)
+    #undef JSON_HEDLEY_GCC_HAS_FEATURE
+#endif
+#if defined(__has_feature)
+    #define JSON_HEDLEY_GCC_HAS_FEATURE(feature,major,minor,patch) __has_feature(feature)
+#else
+    #define JSON_HEDLEY_GCC_HAS_FEATURE(feature,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_HAS_EXTENSION)
+    #undef JSON_HEDLEY_HAS_EXTENSION
+#endif
+#if defined(__has_extension)
+    #define JSON_HEDLEY_HAS_EXTENSION(extension) __has_extension(extension)
+#else
+    #define JSON_HEDLEY_HAS_EXTENSION(extension) (0)
+#endif
+
+#if defined(JSON_HEDLEY_GNUC_HAS_EXTENSION)
+    #undef JSON_HEDLEY_GNUC_HAS_EXTENSION
+#endif
+#if defined(__has_extension)
+    #define JSON_HEDLEY_GNUC_HAS_EXTENSION(extension,major,minor,patch) __has_extension(extension)
+#else
+    #define JSON_HEDLEY_GNUC_HAS_EXTENSION(extension,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_GCC_HAS_EXTENSION)
+    #undef JSON_HEDLEY_GCC_HAS_EXTENSION
+#endif
+#if defined(__has_extension)
+    #define JSON_HEDLEY_GCC_HAS_EXTENSION(extension,major,minor,patch) __has_extension(extension)
+#else
+    #define JSON_HEDLEY_GCC_HAS_EXTENSION(extension,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE)
+    #undef JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE
+#endif
+#if defined(__has_declspec_attribute)
+    #define JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute) __has_declspec_attribute(attribute)
+#else
+    #define JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute) (0)
+#endif
+
+#if defined(JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE)
+    #undef JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE
+#endif
+#if defined(__has_declspec_attribute)
+    #define JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) __has_declspec_attribute(attribute)
+#else
+    #define JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE)
+    #undef JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE
+#endif
+#if defined(__has_declspec_attribute)
+    #define JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) __has_declspec_attribute(attribute)
+#else
+    #define JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_HAS_WARNING)
+    #undef JSON_HEDLEY_HAS_WARNING
+#endif
+#if defined(__has_warning)
+    #define JSON_HEDLEY_HAS_WARNING(warning) __has_warning(warning)
+#else
+    #define JSON_HEDLEY_HAS_WARNING(warning) (0)
+#endif
+
+#if defined(JSON_HEDLEY_GNUC_HAS_WARNING)
+    #undef JSON_HEDLEY_GNUC_HAS_WARNING
+#endif
+#if defined(__has_warning)
+    #define JSON_HEDLEY_GNUC_HAS_WARNING(warning,major,minor,patch) __has_warning(warning)
+#else
+    #define JSON_HEDLEY_GNUC_HAS_WARNING(warning,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_GCC_HAS_WARNING)
+    #undef JSON_HEDLEY_GCC_HAS_WARNING
+#endif
+#if defined(__has_warning)
+    #define JSON_HEDLEY_GCC_HAS_WARNING(warning,major,minor,patch) __has_warning(warning)
+#else
+    #define JSON_HEDLEY_GCC_HAS_WARNING(warning,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if \
+    (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) || \
+    defined(__clang__) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) || \
+    JSON_HEDLEY_PGI_VERSION_CHECK(18,4,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,7,0) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(2,0,1) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,1,0) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,0,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+    JSON_HEDLEY_CRAY_VERSION_CHECK(5,0,0) || \
+    JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,17) || \
+    JSON_HEDLEY_SUNPRO_VERSION_CHECK(8,0,0) || \
+    (JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) && defined(__C99_PRAGMA_OPERATOR))
+    #define JSON_HEDLEY_PRAGMA(value) _Pragma(#value)
+#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0)
+    #define JSON_HEDLEY_PRAGMA(value) __pragma(value)
+#else
+    #define JSON_HEDLEY_PRAGMA(value)
+#endif
+
+#if defined(JSON_HEDLEY_DIAGNOSTIC_PUSH)
+    #undef JSON_HEDLEY_DIAGNOSTIC_PUSH
+#endif
+#if defined(JSON_HEDLEY_DIAGNOSTIC_POP)
+    #undef JSON_HEDLEY_DIAGNOSTIC_POP
+#endif
+#if defined(__clang__)
+    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("clang diagnostic push")
+    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("clang diagnostic pop")
+#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("warning(push)")
+    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("warning(pop)")
+#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("GCC diagnostic push")
+    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("GCC diagnostic pop")
+#elif \
+    JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_PUSH __pragma(warning(push))
+    #define JSON_HEDLEY_DIAGNOSTIC_POP __pragma(warning(pop))
+#elif JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("push")
+    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("pop")
+#elif \
+    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,4,0) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,1,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("diag_push")
+    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("diag_pop")
+#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,90,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("warning(push)")
+    #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("warning(pop)")
+#else
+    #define JSON_HEDLEY_DIAGNOSTIC_PUSH
+    #define JSON_HEDLEY_DIAGNOSTIC_POP
+#endif
+
+/* JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_ is for
+   HEDLEY INTERNAL USE ONLY.  API subject to change without notice. */
+#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_)
+    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_
+#endif
+#if defined(__cplusplus)
+#  if JSON_HEDLEY_HAS_WARNING("-Wc++98-compat")
+#    if JSON_HEDLEY_HAS_WARNING("-Wc++17-extensions")
+#      if JSON_HEDLEY_HAS_WARNING("-Wc++1z-extensions")
+#        define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \
+    JSON_HEDLEY_DIAGNOSTIC_PUSH \
+    _Pragma("clang diagnostic ignored \"-Wc++98-compat\"") \
+    _Pragma("clang diagnostic ignored \"-Wc++17-extensions\"") \
+    _Pragma("clang diagnostic ignored \"-Wc++1z-extensions\"") \
+    xpr \
+    JSON_HEDLEY_DIAGNOSTIC_POP
+#      else
+#        define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \
+    JSON_HEDLEY_DIAGNOSTIC_PUSH \
+    _Pragma("clang diagnostic ignored \"-Wc++98-compat\"") \
+    _Pragma("clang diagnostic ignored \"-Wc++17-extensions\"") \
+    xpr \
+    JSON_HEDLEY_DIAGNOSTIC_POP
+#      endif
+#    else
+#      define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \
+    JSON_HEDLEY_DIAGNOSTIC_PUSH \
+    _Pragma("clang diagnostic ignored \"-Wc++98-compat\"") \
+    xpr \
+    JSON_HEDLEY_DIAGNOSTIC_POP
+#    endif
+#  endif
+#endif
+#if !defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(x) x
+#endif
+
+#if defined(JSON_HEDLEY_CONST_CAST)
+    #undef JSON_HEDLEY_CONST_CAST
+#endif
+#if defined(__cplusplus)
+#  define JSON_HEDLEY_CONST_CAST(T, expr) (const_cast<T>(expr))
+#elif \
+  JSON_HEDLEY_HAS_WARNING("-Wcast-qual") || \
+  JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0) || \
+  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
+#  define JSON_HEDLEY_CONST_CAST(T, expr) (__extension__ ({ \
+        JSON_HEDLEY_DIAGNOSTIC_PUSH \
+        JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL \
+        ((T) (expr)); \
+        JSON_HEDLEY_DIAGNOSTIC_POP \
+    }))
+#else
+#  define JSON_HEDLEY_CONST_CAST(T, expr) ((T) (expr))
+#endif
+
+#if defined(JSON_HEDLEY_REINTERPRET_CAST)
+    #undef JSON_HEDLEY_REINTERPRET_CAST
+#endif
+#if defined(__cplusplus)
+    #define JSON_HEDLEY_REINTERPRET_CAST(T, expr) (reinterpret_cast<T>(expr))
+#else
+    #define JSON_HEDLEY_REINTERPRET_CAST(T, expr) ((T) (expr))
+#endif
+
+#if defined(JSON_HEDLEY_STATIC_CAST)
+    #undef JSON_HEDLEY_STATIC_CAST
+#endif
+#if defined(__cplusplus)
+    #define JSON_HEDLEY_STATIC_CAST(T, expr) (static_cast<T>(expr))
+#else
+    #define JSON_HEDLEY_STATIC_CAST(T, expr) ((T) (expr))
+#endif
+
+#if defined(JSON_HEDLEY_CPP_CAST)
+    #undef JSON_HEDLEY_CPP_CAST
+#endif
+#if defined(__cplusplus)
+#  if JSON_HEDLEY_HAS_WARNING("-Wold-style-cast")
+#    define JSON_HEDLEY_CPP_CAST(T, expr) \
+    JSON_HEDLEY_DIAGNOSTIC_PUSH \
+    _Pragma("clang diagnostic ignored \"-Wold-style-cast\"") \
+    ((T) (expr)) \
+    JSON_HEDLEY_DIAGNOSTIC_POP
+#  elif JSON_HEDLEY_IAR_VERSION_CHECK(8,3,0)
+#    define JSON_HEDLEY_CPP_CAST(T, expr) \
+    JSON_HEDLEY_DIAGNOSTIC_PUSH \
+    _Pragma("diag_suppress=Pe137") \
+    JSON_HEDLEY_DIAGNOSTIC_POP
+#  else
+#    define JSON_HEDLEY_CPP_CAST(T, expr) ((T) (expr))
+#  endif
+#else
+#  define JSON_HEDLEY_CPP_CAST(T, expr) (expr)
+#endif
+
+#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED)
+    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED
+#endif
+#if JSON_HEDLEY_HAS_WARNING("-Wdeprecated-declarations")
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("clang diagnostic ignored \"-Wdeprecated-declarations\"")
+#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("warning(disable:1478 1786)")
+#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED __pragma(warning(disable:1478 1786))
+#elif JSON_HEDLEY_PGI_VERSION_CHECK(20,7,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1215,1216,1444,1445")
+#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1215,1444")
+#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("GCC diagnostic ignored \"-Wdeprecated-declarations\"")
+#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED __pragma(warning(disable:4996))
+#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1215,1444")
+#elif \
+    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1291,1718")
+#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) && !defined(__cplusplus)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("error_messages(off,E_DEPRECATED_ATT,E_DEPRECATED_ATT_MESS)")
+#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) && defined(__cplusplus)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("error_messages(off,symdeprecated,symdeprecated2)")
+#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress=Pe1444,Pe1215")
+#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,90,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("warn(disable:2241)")
+#else
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED
+#endif
+
+#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS)
+    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS
+#endif
+#if JSON_HEDLEY_HAS_WARNING("-Wunknown-pragmas")
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("clang diagnostic ignored \"-Wunknown-pragmas\"")
+#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("warning(disable:161)")
+#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS __pragma(warning(disable:161))
+#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 1675")
+#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("GCC diagnostic ignored \"-Wunknown-pragmas\"")
+#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS __pragma(warning(disable:4068))
+#elif \
+    JSON_HEDLEY_TI_VERSION_CHECK(16,9,0) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,3,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 163")
+#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 163")
+#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress=Pe161")
+#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 161")
+#else
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS
+#endif
+
+#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES)
+    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES
+#endif
+#if JSON_HEDLEY_HAS_WARNING("-Wunknown-attributes")
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("clang diagnostic ignored \"-Wunknown-attributes\"")
+#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("GCC diagnostic ignored \"-Wdeprecated-declarations\"")
+#elif JSON_HEDLEY_INTEL_VERSION_CHECK(17,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("warning(disable:1292)")
+#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES __pragma(warning(disable:1292))
+#elif JSON_HEDLEY_MSVC_VERSION_CHECK(19,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES __pragma(warning(disable:5030))
+#elif JSON_HEDLEY_PGI_VERSION_CHECK(20,7,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1097,1098")
+#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1097")
+#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,14,0) && defined(__cplusplus)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("error_messages(off,attrskipunsup)")
+#elif \
+    JSON_HEDLEY_TI_VERSION_CHECK(18,1,0) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,3,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1173")
+#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress=Pe1097")
+#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1097")
+#else
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES
+#endif
+
+#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL)
+    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL
+#endif
+#if JSON_HEDLEY_HAS_WARNING("-Wcast-qual")
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma("clang diagnostic ignored \"-Wcast-qual\"")
+#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma("warning(disable:2203 2331)")
+#elif JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma("GCC diagnostic ignored \"-Wcast-qual\"")
+#else
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL
+#endif
+
+#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION)
+    #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION
+#endif
+#if JSON_HEDLEY_HAS_WARNING("-Wunused-function")
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma("clang diagnostic ignored \"-Wunused-function\"")
+#elif JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma("GCC diagnostic ignored \"-Wunused-function\"")
+#elif JSON_HEDLEY_MSVC_VERSION_CHECK(1,0,0)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION __pragma(warning(disable:4505))
+#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma("diag_suppress 3142")
+#else
+    #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION
+#endif
+
+#if defined(JSON_HEDLEY_DEPRECATED)
+    #undef JSON_HEDLEY_DEPRECATED
+#endif
+#if defined(JSON_HEDLEY_DEPRECATED_FOR)
+    #undef JSON_HEDLEY_DEPRECATED_FOR
+#endif
+#if \
+    JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_DEPRECATED(since) __declspec(deprecated("Since " # since))
+    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __declspec(deprecated("Since " #since "; use " #replacement))
+#elif \
+    (JSON_HEDLEY_HAS_EXTENSION(attribute_deprecated_with_message) && !defined(JSON_HEDLEY_IAR_VERSION)) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(4,5,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0) || \
+    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) || \
+    JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \
+    JSON_HEDLEY_TI_VERSION_CHECK(18,1,0) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(18,1,0) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,3,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,3,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_DEPRECATED(since) __attribute__((__deprecated__("Since " #since)))
+    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __attribute__((__deprecated__("Since " #since "; use " #replacement)))
+#elif defined(__cplusplus) && (__cplusplus >= 201402L)
+    #define JSON_HEDLEY_DEPRECATED(since) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[deprecated("Since " #since)]])
+    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[deprecated("Since " #since "; use " #replacement)]])
+#elif \
+    JSON_HEDLEY_HAS_ATTRIBUTE(deprecated) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \
+    JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)
+    #define JSON_HEDLEY_DEPRECATED(since) __attribute__((__deprecated__))
+    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __attribute__((__deprecated__))
+#elif \
+    JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \
+    JSON_HEDLEY_PELLES_VERSION_CHECK(6,50,0) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_DEPRECATED(since) __declspec(deprecated)
+    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __declspec(deprecated)
+#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
+    #define JSON_HEDLEY_DEPRECATED(since) _Pragma("deprecated")
+    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) _Pragma("deprecated")
+#else
+    #define JSON_HEDLEY_DEPRECATED(since)
+    #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement)
+#endif
+
+#if defined(JSON_HEDLEY_UNAVAILABLE)
+    #undef JSON_HEDLEY_UNAVAILABLE
+#endif
+#if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(warning) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_UNAVAILABLE(available_since) __attribute__((__warning__("Not available until " #available_since)))
+#else
+    #define JSON_HEDLEY_UNAVAILABLE(available_since)
+#endif
+
+#if defined(JSON_HEDLEY_WARN_UNUSED_RESULT)
+    #undef JSON_HEDLEY_WARN_UNUSED_RESULT
+#endif
+#if defined(JSON_HEDLEY_WARN_UNUSED_RESULT_MSG)
+    #undef JSON_HEDLEY_WARN_UNUSED_RESULT_MSG
+#endif
+#if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(warn_unused_result) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+    (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0) && defined(__cplusplus)) || \
+    JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_WARN_UNUSED_RESULT __attribute__((__warn_unused_result__))
+    #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) __attribute__((__warn_unused_result__))
+#elif (JSON_HEDLEY_HAS_CPP_ATTRIBUTE(nodiscard) >= 201907L)
+    #define JSON_HEDLEY_WARN_UNUSED_RESULT JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]])
+    #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard(msg)]])
+#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE(nodiscard)
+    #define JSON_HEDLEY_WARN_UNUSED_RESULT JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]])
+    #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]])
+#elif defined(_Check_return_) /* SAL */
+    #define JSON_HEDLEY_WARN_UNUSED_RESULT _Check_return_
+    #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) _Check_return_
+#else
+    #define JSON_HEDLEY_WARN_UNUSED_RESULT
+    #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg)
+#endif
+
+#if defined(JSON_HEDLEY_SENTINEL)
+    #undef JSON_HEDLEY_SENTINEL
+#endif
+#if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(sentinel) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(5,4,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_SENTINEL(position) __attribute__((__sentinel__(position)))
+#else
+    #define JSON_HEDLEY_SENTINEL(position)
+#endif
+
+#if defined(JSON_HEDLEY_NO_RETURN)
+    #undef JSON_HEDLEY_NO_RETURN
+#endif
+#if JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
+    #define JSON_HEDLEY_NO_RETURN __noreturn
+#elif \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_NO_RETURN __attribute__((__noreturn__))
+#elif defined(__STDC_VERSION__) && __STDC_VERSION__ >= 201112L
+    #define JSON_HEDLEY_NO_RETURN _Noreturn
+#elif defined(__cplusplus) && (__cplusplus >= 201103L)
+    #define JSON_HEDLEY_NO_RETURN JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[noreturn]])
+#elif \
+    JSON_HEDLEY_HAS_ATTRIBUTE(noreturn) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,2,0) || \
+    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+    JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
+    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+    JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)
+    #define JSON_HEDLEY_NO_RETURN __attribute__((__noreturn__))
+#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)
+    #define JSON_HEDLEY_NO_RETURN _Pragma("does_not_return")
+#elif \
+    JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_NO_RETURN __declspec(noreturn)
+#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,0,0) && defined(__cplusplus)
+    #define JSON_HEDLEY_NO_RETURN _Pragma("FUNC_NEVER_RETURNS;")
+#elif JSON_HEDLEY_COMPCERT_VERSION_CHECK(3,2,0)
+    #define JSON_HEDLEY_NO_RETURN __attribute((noreturn))
+#elif JSON_HEDLEY_PELLES_VERSION_CHECK(9,0,0)
+    #define JSON_HEDLEY_NO_RETURN __declspec(noreturn)
+#else
+    #define JSON_HEDLEY_NO_RETURN
+#endif
+
+#if defined(JSON_HEDLEY_NO_ESCAPE)
+    #undef JSON_HEDLEY_NO_ESCAPE
+#endif
+#if JSON_HEDLEY_HAS_ATTRIBUTE(noescape)
+    #define JSON_HEDLEY_NO_ESCAPE __attribute__((__noescape__))
+#else
+    #define JSON_HEDLEY_NO_ESCAPE
+#endif
+
+#if defined(JSON_HEDLEY_UNREACHABLE)
+    #undef JSON_HEDLEY_UNREACHABLE
+#endif
+#if defined(JSON_HEDLEY_UNREACHABLE_RETURN)
+    #undef JSON_HEDLEY_UNREACHABLE_RETURN
+#endif
+#if defined(JSON_HEDLEY_ASSUME)
+    #undef JSON_HEDLEY_ASSUME
+#endif
+#if \
+    JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_ASSUME(expr) __assume(expr)
+#elif JSON_HEDLEY_HAS_BUILTIN(__builtin_assume)
+    #define JSON_HEDLEY_ASSUME(expr) __builtin_assume(expr)
+#elif \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0)
+    #if defined(__cplusplus)
+        #define JSON_HEDLEY_ASSUME(expr) std::_nassert(expr)
+    #else
+        #define JSON_HEDLEY_ASSUME(expr) _nassert(expr)
+    #endif
+#endif
+#if \
+    (JSON_HEDLEY_HAS_BUILTIN(__builtin_unreachable) && (!defined(JSON_HEDLEY_ARM_VERSION))) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(4,5,0) || \
+    JSON_HEDLEY_PGI_VERSION_CHECK(18,10,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_IBM_VERSION_CHECK(13,1,5) || \
+    JSON_HEDLEY_CRAY_VERSION_CHECK(10,0,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_UNREACHABLE() __builtin_unreachable()
+#elif defined(JSON_HEDLEY_ASSUME)
+    #define JSON_HEDLEY_UNREACHABLE() JSON_HEDLEY_ASSUME(0)
+#endif
+#if !defined(JSON_HEDLEY_ASSUME)
+    #if defined(JSON_HEDLEY_UNREACHABLE)
+        #define JSON_HEDLEY_ASSUME(expr) JSON_HEDLEY_STATIC_CAST(void, ((expr) ? 1 : (JSON_HEDLEY_UNREACHABLE(), 1)))
+    #else
+        #define JSON_HEDLEY_ASSUME(expr) JSON_HEDLEY_STATIC_CAST(void, expr)
+    #endif
+#endif
+#if defined(JSON_HEDLEY_UNREACHABLE)
+    #if  \
+        JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \
+        JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0)
+        #define JSON_HEDLEY_UNREACHABLE_RETURN(value) return (JSON_HEDLEY_STATIC_CAST(void, JSON_HEDLEY_ASSUME(0)), (value))
+    #else
+        #define JSON_HEDLEY_UNREACHABLE_RETURN(value) JSON_HEDLEY_UNREACHABLE()
+    #endif
+#else
+    #define JSON_HEDLEY_UNREACHABLE_RETURN(value) return (value)
+#endif
+#if !defined(JSON_HEDLEY_UNREACHABLE)
+    #define JSON_HEDLEY_UNREACHABLE() JSON_HEDLEY_ASSUME(0)
+#endif
+
+JSON_HEDLEY_DIAGNOSTIC_PUSH
+#if JSON_HEDLEY_HAS_WARNING("-Wpedantic")
+    #pragma clang diagnostic ignored "-Wpedantic"
+#endif
+#if JSON_HEDLEY_HAS_WARNING("-Wc++98-compat-pedantic") && defined(__cplusplus)
+    #pragma clang diagnostic ignored "-Wc++98-compat-pedantic"
+#endif
+#if JSON_HEDLEY_GCC_HAS_WARNING("-Wvariadic-macros",4,0,0)
+    #if defined(__clang__)
+        #pragma clang diagnostic ignored "-Wvariadic-macros"
+    #elif defined(JSON_HEDLEY_GCC_VERSION)
+        #pragma GCC diagnostic ignored "-Wvariadic-macros"
+    #endif
+#endif
+#if defined(JSON_HEDLEY_NON_NULL)
+    #undef JSON_HEDLEY_NON_NULL
+#endif
+#if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(nonnull) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0)
+    #define JSON_HEDLEY_NON_NULL(...) __attribute__((__nonnull__(__VA_ARGS__)))
+#else
+    #define JSON_HEDLEY_NON_NULL(...)
+#endif
+JSON_HEDLEY_DIAGNOSTIC_POP
+
+#if defined(JSON_HEDLEY_PRINTF_FORMAT)
+    #undef JSON_HEDLEY_PRINTF_FORMAT
+#endif
+#if defined(__MINGW32__) && JSON_HEDLEY_GCC_HAS_ATTRIBUTE(format,4,4,0) && !defined(__USE_MINGW_ANSI_STDIO)
+    #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(ms_printf, string_idx, first_to_check)))
+#elif defined(__MINGW32__) && JSON_HEDLEY_GCC_HAS_ATTRIBUTE(format,4,4,0) && defined(__USE_MINGW_ANSI_STDIO)
+    #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(gnu_printf, string_idx, first_to_check)))
+#elif \
+    JSON_HEDLEY_HAS_ATTRIBUTE(format) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0) || \
+    JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
+    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(__printf__, string_idx, first_to_check)))
+#elif JSON_HEDLEY_PELLES_VERSION_CHECK(6,0,0)
+    #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __declspec(vaformat(printf,string_idx,first_to_check))
+#else
+    #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check)
+#endif
+
+#if defined(JSON_HEDLEY_CONSTEXPR)
+    #undef JSON_HEDLEY_CONSTEXPR
+#endif
+#if defined(__cplusplus)
+    #if __cplusplus >= 201103L
+        #define JSON_HEDLEY_CONSTEXPR JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(constexpr)
+    #endif
+#endif
+#if !defined(JSON_HEDLEY_CONSTEXPR)
+    #define JSON_HEDLEY_CONSTEXPR
+#endif
+
+#if defined(JSON_HEDLEY_PREDICT)
+    #undef JSON_HEDLEY_PREDICT
+#endif
+#if defined(JSON_HEDLEY_LIKELY)
+    #undef JSON_HEDLEY_LIKELY
+#endif
+#if defined(JSON_HEDLEY_UNLIKELY)
+    #undef JSON_HEDLEY_UNLIKELY
+#endif
+#if defined(JSON_HEDLEY_UNPREDICTABLE)
+    #undef JSON_HEDLEY_UNPREDICTABLE
+#endif
+#if JSON_HEDLEY_HAS_BUILTIN(__builtin_unpredictable)
+    #define JSON_HEDLEY_UNPREDICTABLE(expr) __builtin_unpredictable((expr))
+#endif
+#if \
+  (JSON_HEDLEY_HAS_BUILTIN(__builtin_expect_with_probability) && !defined(JSON_HEDLEY_PGI_VERSION)) || \
+  JSON_HEDLEY_GCC_VERSION_CHECK(9,0,0) || \
+  JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+#  define JSON_HEDLEY_PREDICT(expr, value, probability) __builtin_expect_with_probability(  (expr), (value), (probability))
+#  define JSON_HEDLEY_PREDICT_TRUE(expr, probability)   __builtin_expect_with_probability(!!(expr),    1   , (probability))
+#  define JSON_HEDLEY_PREDICT_FALSE(expr, probability)  __builtin_expect_with_probability(!!(expr),    0   , (probability))
+#  define JSON_HEDLEY_LIKELY(expr)                      __builtin_expect                 (!!(expr),    1                  )
+#  define JSON_HEDLEY_UNLIKELY(expr)                    __builtin_expect                 (!!(expr),    0                  )
+#elif \
+  (JSON_HEDLEY_HAS_BUILTIN(__builtin_expect) && !defined(JSON_HEDLEY_INTEL_CL_VERSION)) || \
+  JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0) || \
+  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+  (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0) && defined(__cplusplus)) || \
+  JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+  JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
+  JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+  JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,7,0) || \
+  JSON_HEDLEY_TI_CL430_VERSION_CHECK(3,1,0) || \
+  JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,1,0) || \
+  JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \
+  JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+  JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+  JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,27) || \
+  JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \
+  JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+#  define JSON_HEDLEY_PREDICT(expr, expected, probability) \
+    (((probability) >= 0.9) ? __builtin_expect((expr), (expected)) : (JSON_HEDLEY_STATIC_CAST(void, expected), (expr)))
+#  define JSON_HEDLEY_PREDICT_TRUE(expr, probability) \
+    (__extension__ ({ \
+        double hedley_probability_ = (probability); \
+        ((hedley_probability_ >= 0.9) ? __builtin_expect(!!(expr), 1) : ((hedley_probability_ <= 0.1) ? __builtin_expect(!!(expr), 0) : !!(expr))); \
+    }))
+#  define JSON_HEDLEY_PREDICT_FALSE(expr, probability) \
+    (__extension__ ({ \
+        double hedley_probability_ = (probability); \
+        ((hedley_probability_ >= 0.9) ? __builtin_expect(!!(expr), 0) : ((hedley_probability_ <= 0.1) ? __builtin_expect(!!(expr), 1) : !!(expr))); \
+    }))
+#  define JSON_HEDLEY_LIKELY(expr)   __builtin_expect(!!(expr), 1)
+#  define JSON_HEDLEY_UNLIKELY(expr) __builtin_expect(!!(expr), 0)
+#else
+#  define JSON_HEDLEY_PREDICT(expr, expected, probability) (JSON_HEDLEY_STATIC_CAST(void, expected), (expr))
+#  define JSON_HEDLEY_PREDICT_TRUE(expr, probability) (!!(expr))
+#  define JSON_HEDLEY_PREDICT_FALSE(expr, probability) (!!(expr))
+#  define JSON_HEDLEY_LIKELY(expr) (!!(expr))
+#  define JSON_HEDLEY_UNLIKELY(expr) (!!(expr))
+#endif
+#if !defined(JSON_HEDLEY_UNPREDICTABLE)
+    #define JSON_HEDLEY_UNPREDICTABLE(expr) JSON_HEDLEY_PREDICT(expr, 1, 0.5)
+#endif
+
+#if defined(JSON_HEDLEY_MALLOC)
+    #undef JSON_HEDLEY_MALLOC
+#endif
+#if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(malloc) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+    JSON_HEDLEY_IBM_VERSION_CHECK(12,1,0) || \
+    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_MALLOC __attribute__((__malloc__))
+#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)
+    #define JSON_HEDLEY_MALLOC _Pragma("returns_new_memory")
+#elif \
+    JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_MALLOC __declspec(restrict)
+#else
+    #define JSON_HEDLEY_MALLOC
+#endif
+
+#if defined(JSON_HEDLEY_PURE)
+    #undef JSON_HEDLEY_PURE
+#endif
+#if \
+  JSON_HEDLEY_HAS_ATTRIBUTE(pure) || \
+  JSON_HEDLEY_GCC_VERSION_CHECK(2,96,0) || \
+  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+  JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
+  JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+  JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
+  JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+  (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+  JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+  (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+  JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+  (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+  JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+  (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+  JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
+  JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+  JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+  JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \
+  JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+#  define JSON_HEDLEY_PURE __attribute__((__pure__))
+#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)
+#  define JSON_HEDLEY_PURE _Pragma("does_not_write_global_data")
+#elif defined(__cplusplus) && \
+    ( \
+      JSON_HEDLEY_TI_CL430_VERSION_CHECK(2,0,1) || \
+      JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0) || \
+      JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) \
+    )
+#  define JSON_HEDLEY_PURE _Pragma("FUNC_IS_PURE;")
+#else
+#  define JSON_HEDLEY_PURE
+#endif
+
+#if defined(JSON_HEDLEY_CONST)
+    #undef JSON_HEDLEY_CONST
+#endif
+#if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(const) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(2,5,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+    JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
+    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+    JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_CONST __attribute__((__const__))
+#elif \
+    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0)
+    #define JSON_HEDLEY_CONST _Pragma("no_side_effect")
+#else
+    #define JSON_HEDLEY_CONST JSON_HEDLEY_PURE
+#endif
+
+#if defined(JSON_HEDLEY_RESTRICT)
+    #undef JSON_HEDLEY_RESTRICT
+#endif
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) && !defined(__cplusplus)
+    #define JSON_HEDLEY_RESTRICT restrict
+#elif \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \
+    JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+    JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
+    JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,4) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,1,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,14,0) && defined(__cplusplus)) || \
+    JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) || \
+    defined(__clang__) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_RESTRICT __restrict
+#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,3,0) && !defined(__cplusplus)
+    #define JSON_HEDLEY_RESTRICT _Restrict
+#else
+    #define JSON_HEDLEY_RESTRICT
+#endif
+
+#if defined(JSON_HEDLEY_INLINE)
+    #undef JSON_HEDLEY_INLINE
+#endif
+#if \
+    (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) || \
+    (defined(__cplusplus) && (__cplusplus >= 199711L))
+    #define JSON_HEDLEY_INLINE inline
+#elif \
+    defined(JSON_HEDLEY_GCC_VERSION) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(6,2,0)
+    #define JSON_HEDLEY_INLINE __inline__
+#elif \
+    JSON_HEDLEY_MSVC_VERSION_CHECK(12,0,0) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,1,0) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(3,1,0) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_INLINE __inline
+#else
+    #define JSON_HEDLEY_INLINE
+#endif
+
+#if defined(JSON_HEDLEY_ALWAYS_INLINE)
+    #undef JSON_HEDLEY_ALWAYS_INLINE
+#endif
+#if \
+  JSON_HEDLEY_HAS_ATTRIBUTE(always_inline) || \
+  JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \
+  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+  JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
+  JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+  JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
+  JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+  (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+  JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+  (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+  JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+  (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+  JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+  (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+  JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
+  JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+  JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+  JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \
+  JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)
+#  define JSON_HEDLEY_ALWAYS_INLINE __attribute__((__always_inline__)) JSON_HEDLEY_INLINE
+#elif \
+  JSON_HEDLEY_MSVC_VERSION_CHECK(12,0,0) || \
+  JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+#  define JSON_HEDLEY_ALWAYS_INLINE __forceinline
+#elif defined(__cplusplus) && \
+    ( \
+      JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+      JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+      JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+      JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \
+      JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+      JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) \
+    )
+#  define JSON_HEDLEY_ALWAYS_INLINE _Pragma("FUNC_ALWAYS_INLINE;")
+#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
+#  define JSON_HEDLEY_ALWAYS_INLINE _Pragma("inline=forced")
+#else
+#  define JSON_HEDLEY_ALWAYS_INLINE JSON_HEDLEY_INLINE
+#endif
+
+#if defined(JSON_HEDLEY_NEVER_INLINE)
+    #undef JSON_HEDLEY_NEVER_INLINE
+#endif
+#if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(noinline) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+    JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \
+    JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \
+    (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \
+    (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \
+    (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \
+    (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \
+    JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \
+    JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \
+    JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0)
+    #define JSON_HEDLEY_NEVER_INLINE __attribute__((__noinline__))
+#elif \
+    JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_NEVER_INLINE __declspec(noinline)
+#elif JSON_HEDLEY_PGI_VERSION_CHECK(10,2,0)
+    #define JSON_HEDLEY_NEVER_INLINE _Pragma("noinline")
+#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,0,0) && defined(__cplusplus)
+    #define JSON_HEDLEY_NEVER_INLINE _Pragma("FUNC_CANNOT_INLINE;")
+#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
+    #define JSON_HEDLEY_NEVER_INLINE _Pragma("inline=never")
+#elif JSON_HEDLEY_COMPCERT_VERSION_CHECK(3,2,0)
+    #define JSON_HEDLEY_NEVER_INLINE __attribute((noinline))
+#elif JSON_HEDLEY_PELLES_VERSION_CHECK(9,0,0)
+    #define JSON_HEDLEY_NEVER_INLINE __declspec(noinline)
+#else
+    #define JSON_HEDLEY_NEVER_INLINE
+#endif
+
+#if defined(JSON_HEDLEY_PRIVATE)
+    #undef JSON_HEDLEY_PRIVATE
+#endif
+#if defined(JSON_HEDLEY_PUBLIC)
+    #undef JSON_HEDLEY_PUBLIC
+#endif
+#if defined(JSON_HEDLEY_IMPORT)
+    #undef JSON_HEDLEY_IMPORT
+#endif
+#if defined(_WIN32) || defined(__CYGWIN__)
+#  define JSON_HEDLEY_PRIVATE
+#  define JSON_HEDLEY_PUBLIC   __declspec(dllexport)
+#  define JSON_HEDLEY_IMPORT   __declspec(dllimport)
+#else
+#  if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(visibility) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \
+    JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+    JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \
+    ( \
+      defined(__TI_EABI__) && \
+      ( \
+        (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \
+        JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) \
+      ) \
+    ) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+#    define JSON_HEDLEY_PRIVATE __attribute__((__visibility__("hidden")))
+#    define JSON_HEDLEY_PUBLIC  __attribute__((__visibility__("default")))
+#  else
+#    define JSON_HEDLEY_PRIVATE
+#    define JSON_HEDLEY_PUBLIC
+#  endif
+#  define JSON_HEDLEY_IMPORT    extern
+#endif
+
+#if defined(JSON_HEDLEY_NO_THROW)
+    #undef JSON_HEDLEY_NO_THROW
+#endif
+#if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(nothrow) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_NO_THROW __attribute__((__nothrow__))
+#elif \
+    JSON_HEDLEY_MSVC_VERSION_CHECK(13,1,0) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0)
+    #define JSON_HEDLEY_NO_THROW __declspec(nothrow)
+#else
+    #define JSON_HEDLEY_NO_THROW
+#endif
+
+#if defined(JSON_HEDLEY_FALL_THROUGH)
+    #undef JSON_HEDLEY_FALL_THROUGH
+#endif
+#if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(fallthrough) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(7,0,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_FALL_THROUGH __attribute__((__fallthrough__))
+#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(clang,fallthrough)
+    #define JSON_HEDLEY_FALL_THROUGH JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[clang::fallthrough]])
+#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE(fallthrough)
+    #define JSON_HEDLEY_FALL_THROUGH JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[fallthrough]])
+#elif defined(__fallthrough) /* SAL */
+    #define JSON_HEDLEY_FALL_THROUGH __fallthrough
+#else
+    #define JSON_HEDLEY_FALL_THROUGH
+#endif
+
+#if defined(JSON_HEDLEY_RETURNS_NON_NULL)
+    #undef JSON_HEDLEY_RETURNS_NON_NULL
+#endif
+#if \
+    JSON_HEDLEY_HAS_ATTRIBUTE(returns_nonnull) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_RETURNS_NON_NULL __attribute__((__returns_nonnull__))
+#elif defined(_Ret_notnull_) /* SAL */
+    #define JSON_HEDLEY_RETURNS_NON_NULL _Ret_notnull_
+#else
+    #define JSON_HEDLEY_RETURNS_NON_NULL
+#endif
+
+#if defined(JSON_HEDLEY_ARRAY_PARAM)
+    #undef JSON_HEDLEY_ARRAY_PARAM
+#endif
+#if \
+    defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) && \
+    !defined(__STDC_NO_VLA__) && \
+    !defined(__cplusplus) && \
+    !defined(JSON_HEDLEY_PGI_VERSION) && \
+    !defined(JSON_HEDLEY_TINYC_VERSION)
+    #define JSON_HEDLEY_ARRAY_PARAM(name) (name)
+#else
+    #define JSON_HEDLEY_ARRAY_PARAM(name)
+#endif
+
+#if defined(JSON_HEDLEY_IS_CONSTANT)
+    #undef JSON_HEDLEY_IS_CONSTANT
+#endif
+#if defined(JSON_HEDLEY_REQUIRE_CONSTEXPR)
+    #undef JSON_HEDLEY_REQUIRE_CONSTEXPR
+#endif
+/* JSON_HEDLEY_IS_CONSTEXPR_ is for
+   HEDLEY INTERNAL USE ONLY.  API subject to change without notice. */
+#if defined(JSON_HEDLEY_IS_CONSTEXPR_)
+    #undef JSON_HEDLEY_IS_CONSTEXPR_
+#endif
+#if \
+    JSON_HEDLEY_HAS_BUILTIN(__builtin_constant_p) || \
+    JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \
+    JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+    JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,19) || \
+    JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \
+    JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \
+    JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \
+    (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0) && !defined(__cplusplus)) || \
+    JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \
+    JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10)
+    #define JSON_HEDLEY_IS_CONSTANT(expr) __builtin_constant_p(expr)
+#endif
+#if !defined(__cplusplus)
+#  if \
+       JSON_HEDLEY_HAS_BUILTIN(__builtin_types_compatible_p) || \
+       JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \
+       JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+       JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \
+       JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \
+       JSON_HEDLEY_ARM_VERSION_CHECK(5,4,0) || \
+       JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,24)
+#if defined(__INTPTR_TYPE__)
+    #define JSON_HEDLEY_IS_CONSTEXPR_(expr) __builtin_types_compatible_p(__typeof__((1 ? (void*) ((__INTPTR_TYPE__) ((expr) * 0)) : (int*) 0)), int*)
+#else
+    #include <stdint.h>
+    #define JSON_HEDLEY_IS_CONSTEXPR_(expr) __builtin_types_compatible_p(__typeof__((1 ? (void*) ((intptr_t) ((expr) * 0)) : (int*) 0)), int*)
+#endif
+#  elif \
+       ( \
+          defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) && \
+          !defined(JSON_HEDLEY_SUNPRO_VERSION) && \
+          !defined(JSON_HEDLEY_PGI_VERSION) && \
+          !defined(JSON_HEDLEY_IAR_VERSION)) || \
+       (JSON_HEDLEY_HAS_EXTENSION(c_generic_selections) && !defined(JSON_HEDLEY_IAR_VERSION)) || \
+       JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) || \
+       JSON_HEDLEY_INTEL_VERSION_CHECK(17,0,0) || \
+       JSON_HEDLEY_IBM_VERSION_CHECK(12,1,0) || \
+       JSON_HEDLEY_ARM_VERSION_CHECK(5,3,0)
+#if defined(__INTPTR_TYPE__)
+    #define JSON_HEDLEY_IS_CONSTEXPR_(expr) _Generic((1 ? (void*) ((__INTPTR_TYPE__) ((expr) * 0)) : (int*) 0), int*: 1, void*: 0)
+#else
+    #include <stdint.h>
+    #define JSON_HEDLEY_IS_CONSTEXPR_(expr) _Generic((1 ? (void*) ((intptr_t) * 0) : (int*) 0), int*: 1, void*: 0)
+#endif
+#  elif \
+       defined(JSON_HEDLEY_GCC_VERSION) || \
+       defined(JSON_HEDLEY_INTEL_VERSION) || \
+       defined(JSON_HEDLEY_TINYC_VERSION) || \
+       defined(JSON_HEDLEY_TI_ARMCL_VERSION) || \
+       JSON_HEDLEY_TI_CL430_VERSION_CHECK(18,12,0) || \
+       defined(JSON_HEDLEY_TI_CL2000_VERSION) || \
+       defined(JSON_HEDLEY_TI_CL6X_VERSION) || \
+       defined(JSON_HEDLEY_TI_CL7X_VERSION) || \
+       defined(JSON_HEDLEY_TI_CLPRU_VERSION) || \
+       defined(__clang__)
+#    define JSON_HEDLEY_IS_CONSTEXPR_(expr) ( \
+        sizeof(void) != \
+        sizeof(*( \
+                  1 ? \
+                  ((void*) ((expr) * 0L) ) : \
+((struct { char v[sizeof(void) * 2]; } *) 1) \
+                ) \
+              ) \
+                                            )
+#  endif
+#endif
+#if defined(JSON_HEDLEY_IS_CONSTEXPR_)
+    #if !defined(JSON_HEDLEY_IS_CONSTANT)
+        #define JSON_HEDLEY_IS_CONSTANT(expr) JSON_HEDLEY_IS_CONSTEXPR_(expr)
+    #endif
+    #define JSON_HEDLEY_REQUIRE_CONSTEXPR(expr) (JSON_HEDLEY_IS_CONSTEXPR_(expr) ? (expr) : (-1))
+#else
+    #if !defined(JSON_HEDLEY_IS_CONSTANT)
+        #define JSON_HEDLEY_IS_CONSTANT(expr) (0)
+    #endif
+    #define JSON_HEDLEY_REQUIRE_CONSTEXPR(expr) (expr)
+#endif
+
+#if defined(JSON_HEDLEY_BEGIN_C_DECLS)
+    #undef JSON_HEDLEY_BEGIN_C_DECLS
+#endif
+#if defined(JSON_HEDLEY_END_C_DECLS)
+    #undef JSON_HEDLEY_END_C_DECLS
+#endif
+#if defined(JSON_HEDLEY_C_DECL)
+    #undef JSON_HEDLEY_C_DECL
+#endif
+#if defined(__cplusplus)
+    #define JSON_HEDLEY_BEGIN_C_DECLS extern "C" {
+    #define JSON_HEDLEY_END_C_DECLS }
+    #define JSON_HEDLEY_C_DECL extern "C"
+#else
+    #define JSON_HEDLEY_BEGIN_C_DECLS
+    #define JSON_HEDLEY_END_C_DECLS
+    #define JSON_HEDLEY_C_DECL
+#endif
+
+#if defined(JSON_HEDLEY_STATIC_ASSERT)
+    #undef JSON_HEDLEY_STATIC_ASSERT
+#endif
+#if \
+  !defined(__cplusplus) && ( \
+      (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L)) || \
+      (JSON_HEDLEY_HAS_FEATURE(c_static_assert) && !defined(JSON_HEDLEY_INTEL_CL_VERSION)) || \
+      JSON_HEDLEY_GCC_VERSION_CHECK(6,0,0) || \
+      JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \
+      defined(_Static_assert) \
+    )
+#  define JSON_HEDLEY_STATIC_ASSERT(expr, message) _Static_assert(expr, message)
+#elif \
+  (defined(__cplusplus) && (__cplusplus >= 201103L)) || \
+  JSON_HEDLEY_MSVC_VERSION_CHECK(16,0,0) || \
+  JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+#  define JSON_HEDLEY_STATIC_ASSERT(expr, message) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(static_assert(expr, message))
+#else
+#  define JSON_HEDLEY_STATIC_ASSERT(expr, message)
+#endif
+
+#if defined(JSON_HEDLEY_NULL)
+    #undef JSON_HEDLEY_NULL
+#endif
+#if defined(__cplusplus)
+    #if __cplusplus >= 201103L
+        #define JSON_HEDLEY_NULL JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(nullptr)
+    #elif defined(NULL)
+        #define JSON_HEDLEY_NULL NULL
+    #else
+        #define JSON_HEDLEY_NULL JSON_HEDLEY_STATIC_CAST(void*, 0)
+    #endif
+#elif defined(NULL)
+    #define JSON_HEDLEY_NULL NULL
+#else
+    #define JSON_HEDLEY_NULL ((void*) 0)
+#endif
+
+#if defined(JSON_HEDLEY_MESSAGE)
+    #undef JSON_HEDLEY_MESSAGE
+#endif
+#if JSON_HEDLEY_HAS_WARNING("-Wunknown-pragmas")
+#  define JSON_HEDLEY_MESSAGE(msg) \
+    JSON_HEDLEY_DIAGNOSTIC_PUSH \
+    JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS \
+    JSON_HEDLEY_PRAGMA(message msg) \
+    JSON_HEDLEY_DIAGNOSTIC_POP
+#elif \
+  JSON_HEDLEY_GCC_VERSION_CHECK(4,4,0) || \
+  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
+#  define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message msg)
+#elif JSON_HEDLEY_CRAY_VERSION_CHECK(5,0,0)
+#  define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(_CRI message msg)
+#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0)
+#  define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message(msg))
+#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,0,0)
+#  define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message(msg))
+#else
+#  define JSON_HEDLEY_MESSAGE(msg)
+#endif
+
+#if defined(JSON_HEDLEY_WARNING)
+    #undef JSON_HEDLEY_WARNING
+#endif
+#if JSON_HEDLEY_HAS_WARNING("-Wunknown-pragmas")
+#  define JSON_HEDLEY_WARNING(msg) \
+    JSON_HEDLEY_DIAGNOSTIC_PUSH \
+    JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS \
+    JSON_HEDLEY_PRAGMA(clang warning msg) \
+    JSON_HEDLEY_DIAGNOSTIC_POP
+#elif \
+  JSON_HEDLEY_GCC_VERSION_CHECK(4,8,0) || \
+  JSON_HEDLEY_PGI_VERSION_CHECK(18,4,0) || \
+  JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0)
+#  define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_PRAGMA(GCC warning msg)
+#elif \
+  JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) || \
+  JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+#  define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_PRAGMA(message(msg))
+#else
+#  define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_MESSAGE(msg)
+#endif
+
+#if defined(JSON_HEDLEY_REQUIRE)
+    #undef JSON_HEDLEY_REQUIRE
+#endif
+#if defined(JSON_HEDLEY_REQUIRE_MSG)
+    #undef JSON_HEDLEY_REQUIRE_MSG
+#endif
+#if JSON_HEDLEY_HAS_ATTRIBUTE(diagnose_if)
+#  if JSON_HEDLEY_HAS_WARNING("-Wgcc-compat")
+#    define JSON_HEDLEY_REQUIRE(expr) \
+    JSON_HEDLEY_DIAGNOSTIC_PUSH \
+    _Pragma("clang diagnostic ignored \"-Wgcc-compat\"") \
+    __attribute__((diagnose_if(!(expr), #expr, "error"))) \
+    JSON_HEDLEY_DIAGNOSTIC_POP
+#    define JSON_HEDLEY_REQUIRE_MSG(expr,msg) \
+    JSON_HEDLEY_DIAGNOSTIC_PUSH \
+    _Pragma("clang diagnostic ignored \"-Wgcc-compat\"") \
+    __attribute__((diagnose_if(!(expr), msg, "error"))) \
+    JSON_HEDLEY_DIAGNOSTIC_POP
+#  else
+#    define JSON_HEDLEY_REQUIRE(expr) __attribute__((diagnose_if(!(expr), #expr, "error")))
+#    define JSON_HEDLEY_REQUIRE_MSG(expr,msg) __attribute__((diagnose_if(!(expr), msg, "error")))
+#  endif
+#else
+#  define JSON_HEDLEY_REQUIRE(expr)
+#  define JSON_HEDLEY_REQUIRE_MSG(expr,msg)
+#endif
+
+#if defined(JSON_HEDLEY_FLAGS)
+    #undef JSON_HEDLEY_FLAGS
+#endif
+#if JSON_HEDLEY_HAS_ATTRIBUTE(flag_enum) && (!defined(__cplusplus) || JSON_HEDLEY_HAS_WARNING("-Wbitfield-enum-conversion"))
+    #define JSON_HEDLEY_FLAGS __attribute__((__flag_enum__))
+#else
+    #define JSON_HEDLEY_FLAGS
+#endif
+
+#if defined(JSON_HEDLEY_FLAGS_CAST)
+    #undef JSON_HEDLEY_FLAGS_CAST
+#endif
+#if JSON_HEDLEY_INTEL_VERSION_CHECK(19,0,0)
+#  define JSON_HEDLEY_FLAGS_CAST(T, expr) (__extension__ ({ \
+        JSON_HEDLEY_DIAGNOSTIC_PUSH \
+        _Pragma("warning(disable:188)") \
+        ((T) (expr)); \
+        JSON_HEDLEY_DIAGNOSTIC_POP \
+    }))
+#else
+#  define JSON_HEDLEY_FLAGS_CAST(T, expr) JSON_HEDLEY_STATIC_CAST(T, expr)
+#endif
+
+#if defined(JSON_HEDLEY_EMPTY_BASES)
+    #undef JSON_HEDLEY_EMPTY_BASES
+#endif
+#if \
+    (JSON_HEDLEY_MSVC_VERSION_CHECK(19,0,23918) && !JSON_HEDLEY_MSVC_VERSION_CHECK(20,0,0)) || \
+    JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0)
+    #define JSON_HEDLEY_EMPTY_BASES __declspec(empty_bases)
+#else
+    #define JSON_HEDLEY_EMPTY_BASES
+#endif
+
+/* Remaining macros are deprecated. */
+
+#if defined(JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK)
+    #undef JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK
+#endif
+#if defined(__clang__)
+    #define JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK(major,minor,patch) (0)
+#else
+    #define JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK(major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch)
+#endif
+
+#if defined(JSON_HEDLEY_CLANG_HAS_ATTRIBUTE)
+    #undef JSON_HEDLEY_CLANG_HAS_ATTRIBUTE
+#endif
+#define JSON_HEDLEY_CLANG_HAS_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_ATTRIBUTE(attribute)
+
+#if defined(JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE)
+    #undef JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE
+#endif
+#define JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute)
+
+#if defined(JSON_HEDLEY_CLANG_HAS_BUILTIN)
+    #undef JSON_HEDLEY_CLANG_HAS_BUILTIN
+#endif
+#define JSON_HEDLEY_CLANG_HAS_BUILTIN(builtin) JSON_HEDLEY_HAS_BUILTIN(builtin)
+
+#if defined(JSON_HEDLEY_CLANG_HAS_FEATURE)
+    #undef JSON_HEDLEY_CLANG_HAS_FEATURE
+#endif
+#define JSON_HEDLEY_CLANG_HAS_FEATURE(feature) JSON_HEDLEY_HAS_FEATURE(feature)
+
+#if defined(JSON_HEDLEY_CLANG_HAS_EXTENSION)
+    #undef JSON_HEDLEY_CLANG_HAS_EXTENSION
+#endif
+#define JSON_HEDLEY_CLANG_HAS_EXTENSION(extension) JSON_HEDLEY_HAS_EXTENSION(extension)
+
+#if defined(JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE)
+    #undef JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE
+#endif
+#define JSON_HEDLEY_CLANG_HAS_DECLSPEC_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute)
+
+#if defined(JSON_HEDLEY_CLANG_HAS_WARNING)
+    #undef JSON_HEDLEY_CLANG_HAS_WARNING
+#endif
+#define JSON_HEDLEY_CLANG_HAS_WARNING(warning) JSON_HEDLEY_HAS_WARNING(warning)
+
+#endif /* !defined(JSON_HEDLEY_VERSION) || (JSON_HEDLEY_VERSION < X) */
+
+
+// This file contains all internal macro definitions (except those affecting ABI)
+// You MUST include macro_unscope.hpp at the end of json.hpp to undef all of them
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+
+// exclude unsupported compilers
+#if !defined(JSON_SKIP_UNSUPPORTED_COMPILER_CHECK)
+    #if defined(__clang__)
+        #if (__clang_major__ * 10000 + __clang_minor__ * 100 + __clang_patchlevel__) < 30400
+            #error "unsupported Clang version - see https://github.com/nlohmann/json#supported-compilers"
+        #endif
+    #elif defined(__GNUC__) && !(defined(__ICC) || defined(__INTEL_COMPILER))
+        #if (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) < 40800
+            #error "unsupported GCC version - see https://github.com/nlohmann/json#supported-compilers"
+        #endif
+    #endif
+#endif
+
+// C++ language standard detection
+// if the user manually specified the used c++ version this is skipped
+#if !defined(JSON_HAS_CPP_20) && !defined(JSON_HAS_CPP_17) && !defined(JSON_HAS_CPP_14) && !defined(JSON_HAS_CPP_11)
+    #if (defined(__cplusplus) && __cplusplus >= 202002L) || (defined(_MSVC_LANG) && _MSVC_LANG >= 202002L)
+        #define JSON_HAS_CPP_20
+        #define JSON_HAS_CPP_17
+        #define JSON_HAS_CPP_14
+    #elif (defined(__cplusplus) && __cplusplus >= 201703L) || (defined(_HAS_CXX17) && _HAS_CXX17 == 1) // fix for issue #464
+        #define JSON_HAS_CPP_17
+        #define JSON_HAS_CPP_14
+    #elif (defined(__cplusplus) && __cplusplus >= 201402L) || (defined(_HAS_CXX14) && _HAS_CXX14 == 1)
+        #define JSON_HAS_CPP_14
+    #endif
+    // the cpp 11 flag is always specified because it is the minimal required version
+    #define JSON_HAS_CPP_11
+#endif
+
+#ifdef __has_include
+    #if __has_include(<version>)
+        #include <version>
+    #endif
+#endif
+
+#if !defined(JSON_HAS_FILESYSTEM) && !defined(JSON_HAS_EXPERIMENTAL_FILESYSTEM)
+    #ifdef JSON_HAS_CPP_17
+        #if defined(__cpp_lib_filesystem)
+            #define JSON_HAS_FILESYSTEM 1
+        #elif defined(__cpp_lib_experimental_filesystem)
+            #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1
+        #elif !defined(__has_include)
+            #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1
+        #elif __has_include(<filesystem>)
+            #define JSON_HAS_FILESYSTEM 1
+        #elif __has_include(<experimental/filesystem>)
+            #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 1
+        #endif
+
+        // std::filesystem does not work on MinGW GCC 8: https://sourceforge.net/p/mingw-w64/bugs/737/
+        #if defined(__MINGW32__) && defined(__GNUC__) && __GNUC__ == 8
+            #undef JSON_HAS_FILESYSTEM
+            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
+        #endif
+
+        // no filesystem support before GCC 8: https://en.cppreference.com/w/cpp/compiler_support
+        #if defined(__GNUC__) && !defined(__clang__) && __GNUC__ < 8
+            #undef JSON_HAS_FILESYSTEM
+            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
+        #endif
+
+        // no filesystem support before Clang 7: https://en.cppreference.com/w/cpp/compiler_support
+        #if defined(__clang_major__) && __clang_major__ < 7
+            #undef JSON_HAS_FILESYSTEM
+            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
+        #endif
+
+        // no filesystem support before MSVC 19.14: https://en.cppreference.com/w/cpp/compiler_support
+        #if defined(_MSC_VER) && _MSC_VER < 1914
+            #undef JSON_HAS_FILESYSTEM
+            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
+        #endif
+
+        // no filesystem support before iOS 13
+        #if defined(__IPHONE_OS_VERSION_MIN_REQUIRED) && __IPHONE_OS_VERSION_MIN_REQUIRED < 130000
+            #undef JSON_HAS_FILESYSTEM
+            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
+        #endif
+
+        // no filesystem support before macOS Catalina
+        #if defined(__MAC_OS_X_VERSION_MIN_REQUIRED) && __MAC_OS_X_VERSION_MIN_REQUIRED < 101500
+            #undef JSON_HAS_FILESYSTEM
+            #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
+        #endif
+    #endif
+#endif
+
+#ifndef JSON_HAS_EXPERIMENTAL_FILESYSTEM
+    #define JSON_HAS_EXPERIMENTAL_FILESYSTEM 0
+#endif
+
+#ifndef JSON_HAS_FILESYSTEM
+    #define JSON_HAS_FILESYSTEM 0
+#endif
+
+#ifndef JSON_HAS_THREE_WAY_COMPARISON
+    #if defined(__cpp_impl_three_way_comparison) && __cpp_impl_three_way_comparison >= 201907L \
+        && defined(__cpp_lib_three_way_comparison) && __cpp_lib_three_way_comparison >= 201907L
+        #define JSON_HAS_THREE_WAY_COMPARISON 1
+    #else
+        #define JSON_HAS_THREE_WAY_COMPARISON 0
+    #endif
+#endif
+
+#ifndef JSON_HAS_RANGES
+    // ranges header shipping in GCC 11.1.0 (released 2021-04-27) has syntax error
+    #if defined(__GLIBCXX__) && __GLIBCXX__ == 20210427
+        #define JSON_HAS_RANGES 0
+    #elif defined(__cpp_lib_ranges)
+        #define JSON_HAS_RANGES 1
+    #else
+        #define JSON_HAS_RANGES 0
+    #endif
+#endif
+
+#ifndef JSON_HAS_STATIC_RTTI
+    #if !defined(_HAS_STATIC_RTTI) || _HAS_STATIC_RTTI != 0
+        #define JSON_HAS_STATIC_RTTI 1
+    #else
+        #define JSON_HAS_STATIC_RTTI 0
+    #endif
+#endif
+
+#ifdef JSON_HAS_CPP_17
+    #define JSON_INLINE_VARIABLE inline
+#else
+    #define JSON_INLINE_VARIABLE
+#endif
+
+#if JSON_HEDLEY_HAS_ATTRIBUTE(no_unique_address)
+    #define JSON_NO_UNIQUE_ADDRESS [[no_unique_address]]
+#else
+    #define JSON_NO_UNIQUE_ADDRESS
+#endif
+
+// disable documentation warnings on clang
+#if defined(__clang__)
+    #pragma clang diagnostic push
+    #pragma clang diagnostic ignored "-Wdocumentation"
+    #pragma clang diagnostic ignored "-Wdocumentation-unknown-command"
+#endif
+
+// allow disabling exceptions
+#if (defined(__cpp_exceptions) || defined(__EXCEPTIONS) || defined(_CPPUNWIND)) && !defined(JSON_NOEXCEPTION)
+    #define JSON_THROW(exception) throw exception
+    #define JSON_TRY try
+    #define JSON_CATCH(exception) catch(exception)
+    #define JSON_INTERNAL_CATCH(exception) catch(exception)
+#else
+    #include <cstdlib>
+    #define JSON_THROW(exception) std::abort()
+    #define JSON_TRY if(true)
+    #define JSON_CATCH(exception) if(false)
+    #define JSON_INTERNAL_CATCH(exception) if(false)
+#endif
+
+// override exception macros
+#if defined(JSON_THROW_USER)
+    #undef JSON_THROW
+    #define JSON_THROW JSON_THROW_USER
+#endif
+#if defined(JSON_TRY_USER)
+    #undef JSON_TRY
+    #define JSON_TRY JSON_TRY_USER
+#endif
+#if defined(JSON_CATCH_USER)
+    #undef JSON_CATCH
+    #define JSON_CATCH JSON_CATCH_USER
+    #undef JSON_INTERNAL_CATCH
+    #define JSON_INTERNAL_CATCH JSON_CATCH_USER
+#endif
+#if defined(JSON_INTERNAL_CATCH_USER)
+    #undef JSON_INTERNAL_CATCH
+    #define JSON_INTERNAL_CATCH JSON_INTERNAL_CATCH_USER
+#endif
+
+// allow overriding assert
+#if !defined(JSON_ASSERT)
+    #include <cassert> // assert
+    #define JSON_ASSERT(x) assert(x)
+#endif
+
+// allow to access some private functions (needed by the test suite)
+#if defined(JSON_TESTS_PRIVATE)
+    #define JSON_PRIVATE_UNLESS_TESTED public
+#else
+    #define JSON_PRIVATE_UNLESS_TESTED private
+#endif
+
+/*!
+@brief macro to briefly define a mapping between an enum and JSON
+@def NLOHMANN_JSON_SERIALIZE_ENUM
+@since version 3.4.0
+*/
+#define NLOHMANN_JSON_SERIALIZE_ENUM(ENUM_TYPE, ...)                                            \
+    template<typename BasicJsonType>                                                            \
+    inline void to_json(BasicJsonType& j, const ENUM_TYPE& e)                                   \
+    {                                                                                           \
+        static_assert(std::is_enum<ENUM_TYPE>::value, #ENUM_TYPE " must be an enum!");          \
+        static const std::pair<ENUM_TYPE, BasicJsonType> m[] = __VA_ARGS__;                     \
+        auto it = std::find_if(std::begin(m), std::end(m),                                      \
+                               [e](const std::pair<ENUM_TYPE, BasicJsonType>& ej_pair) -> bool  \
+        {                                                                                       \
+            return ej_pair.first == e;                                                          \
+        });                                                                                     \
+        j = ((it != std::end(m)) ? it : std::begin(m))->second;                                 \
+    }                                                                                           \
+    template<typename BasicJsonType>                                                            \
+    inline void from_json(const BasicJsonType& j, ENUM_TYPE& e)                                 \
+    {                                                                                           \
+        static_assert(std::is_enum<ENUM_TYPE>::value, #ENUM_TYPE " must be an enum!");          \
+        static const std::pair<ENUM_TYPE, BasicJsonType> m[] = __VA_ARGS__;                     \
+        auto it = std::find_if(std::begin(m), std::end(m),                                      \
+                               [&j](const std::pair<ENUM_TYPE, BasicJsonType>& ej_pair) -> bool \
+        {                                                                                       \
+            return ej_pair.second == j;                                                         \
+        });                                                                                     \
+        e = ((it != std::end(m)) ? it : std::begin(m))->first;                                  \
+    }
+
+// Ugly macros to avoid uglier copy-paste when specializing basic_json. They
+// may be removed in the future once the class is split.
+
+#define NLOHMANN_BASIC_JSON_TPL_DECLARATION                                \
+    template<template<typename, typename, typename...> class ObjectType,   \
+             template<typename, typename...> class ArrayType,              \
+             class StringType, class BooleanType, class NumberIntegerType, \
+             class NumberUnsignedType, class NumberFloatType,              \
+             template<typename> class AllocatorType,                       \
+             template<typename, typename = void> class JSONSerializer,     \
+             class BinaryType,                                             \
+             class CustomBaseClass>
+
+#define NLOHMANN_BASIC_JSON_TPL                                            \
+    basic_json<ObjectType, ArrayType, StringType, BooleanType,             \
+    NumberIntegerType, NumberUnsignedType, NumberFloatType,                \
+    AllocatorType, JSONSerializer, BinaryType, CustomBaseClass>
+
+// Macros to simplify conversion from/to types
+
+#define NLOHMANN_JSON_EXPAND( x ) x
+#define NLOHMANN_JSON_GET_MACRO(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58, _59, _60, _61, _62, _63, _64, NAME,...) NAME
+#define NLOHMANN_JSON_PASTE(...) NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_GET_MACRO(__VA_ARGS__, \
+        NLOHMANN_JSON_PASTE64, \
+        NLOHMANN_JSON_PASTE63, \
+        NLOHMANN_JSON_PASTE62, \
+        NLOHMANN_JSON_PASTE61, \
+        NLOHMANN_JSON_PASTE60, \
+        NLOHMANN_JSON_PASTE59, \
+        NLOHMANN_JSON_PASTE58, \
+        NLOHMANN_JSON_PASTE57, \
+        NLOHMANN_JSON_PASTE56, \
+        NLOHMANN_JSON_PASTE55, \
+        NLOHMANN_JSON_PASTE54, \
+        NLOHMANN_JSON_PASTE53, \
+        NLOHMANN_JSON_PASTE52, \
+        NLOHMANN_JSON_PASTE51, \
+        NLOHMANN_JSON_PASTE50, \
+        NLOHMANN_JSON_PASTE49, \
+        NLOHMANN_JSON_PASTE48, \
+        NLOHMANN_JSON_PASTE47, \
+        NLOHMANN_JSON_PASTE46, \
+        NLOHMANN_JSON_PASTE45, \
+        NLOHMANN_JSON_PASTE44, \
+        NLOHMANN_JSON_PASTE43, \
+        NLOHMANN_JSON_PASTE42, \
+        NLOHMANN_JSON_PASTE41, \
+        NLOHMANN_JSON_PASTE40, \
+        NLOHMANN_JSON_PASTE39, \
+        NLOHMANN_JSON_PASTE38, \
+        NLOHMANN_JSON_PASTE37, \
+        NLOHMANN_JSON_PASTE36, \
+        NLOHMANN_JSON_PASTE35, \
+        NLOHMANN_JSON_PASTE34, \
+        NLOHMANN_JSON_PASTE33, \
+        NLOHMANN_JSON_PASTE32, \
+        NLOHMANN_JSON_PASTE31, \
+        NLOHMANN_JSON_PASTE30, \
+        NLOHMANN_JSON_PASTE29, \
+        NLOHMANN_JSON_PASTE28, \
+        NLOHMANN_JSON_PASTE27, \
+        NLOHMANN_JSON_PASTE26, \
+        NLOHMANN_JSON_PASTE25, \
+        NLOHMANN_JSON_PASTE24, \
+        NLOHMANN_JSON_PASTE23, \
+        NLOHMANN_JSON_PASTE22, \
+        NLOHMANN_JSON_PASTE21, \
+        NLOHMANN_JSON_PASTE20, \
+        NLOHMANN_JSON_PASTE19, \
+        NLOHMANN_JSON_PASTE18, \
+        NLOHMANN_JSON_PASTE17, \
+        NLOHMANN_JSON_PASTE16, \
+        NLOHMANN_JSON_PASTE15, \
+        NLOHMANN_JSON_PASTE14, \
+        NLOHMANN_JSON_PASTE13, \
+        NLOHMANN_JSON_PASTE12, \
+        NLOHMANN_JSON_PASTE11, \
+        NLOHMANN_JSON_PASTE10, \
+        NLOHMANN_JSON_PASTE9, \
+        NLOHMANN_JSON_PASTE8, \
+        NLOHMANN_JSON_PASTE7, \
+        NLOHMANN_JSON_PASTE6, \
+        NLOHMANN_JSON_PASTE5, \
+        NLOHMANN_JSON_PASTE4, \
+        NLOHMANN_JSON_PASTE3, \
+        NLOHMANN_JSON_PASTE2, \
+        NLOHMANN_JSON_PASTE1)(__VA_ARGS__))
+#define NLOHMANN_JSON_PASTE2(func, v1) func(v1)
+#define NLOHMANN_JSON_PASTE3(func, v1, v2) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE2(func, v2)
+#define NLOHMANN_JSON_PASTE4(func, v1, v2, v3) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE3(func, v2, v3)
+#define NLOHMANN_JSON_PASTE5(func, v1, v2, v3, v4) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE4(func, v2, v3, v4)
+#define NLOHMANN_JSON_PASTE6(func, v1, v2, v3, v4, v5) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE5(func, v2, v3, v4, v5)
+#define NLOHMANN_JSON_PASTE7(func, v1, v2, v3, v4, v5, v6) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE6(func, v2, v3, v4, v5, v6)
+#define NLOHMANN_JSON_PASTE8(func, v1, v2, v3, v4, v5, v6, v7) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE7(func, v2, v3, v4, v5, v6, v7)
+#define NLOHMANN_JSON_PASTE9(func, v1, v2, v3, v4, v5, v6, v7, v8) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE8(func, v2, v3, v4, v5, v6, v7, v8)
+#define NLOHMANN_JSON_PASTE10(func, v1, v2, v3, v4, v5, v6, v7, v8, v9) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE9(func, v2, v3, v4, v5, v6, v7, v8, v9)
+#define NLOHMANN_JSON_PASTE11(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE10(func, v2, v3, v4, v5, v6, v7, v8, v9, v10)
+#define NLOHMANN_JSON_PASTE12(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE11(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11)
+#define NLOHMANN_JSON_PASTE13(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE12(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12)
+#define NLOHMANN_JSON_PASTE14(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE13(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13)
+#define NLOHMANN_JSON_PASTE15(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE14(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14)
+#define NLOHMANN_JSON_PASTE16(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE15(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)
+#define NLOHMANN_JSON_PASTE17(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE16(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16)
+#define NLOHMANN_JSON_PASTE18(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE17(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17)
+#define NLOHMANN_JSON_PASTE19(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE18(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18)
+#define NLOHMANN_JSON_PASTE20(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE19(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19)
+#define NLOHMANN_JSON_PASTE21(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE20(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20)
+#define NLOHMANN_JSON_PASTE22(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE21(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21)
+#define NLOHMANN_JSON_PASTE23(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE22(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22)
+#define NLOHMANN_JSON_PASTE24(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE23(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23)
+#define NLOHMANN_JSON_PASTE25(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE24(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24)
+#define NLOHMANN_JSON_PASTE26(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE25(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25)
+#define NLOHMANN_JSON_PASTE27(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE26(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26)
+#define NLOHMANN_JSON_PASTE28(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE27(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27)
+#define NLOHMANN_JSON_PASTE29(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE28(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28)
+#define NLOHMANN_JSON_PASTE30(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE29(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29)
+#define NLOHMANN_JSON_PASTE31(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE30(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30)
+#define NLOHMANN_JSON_PASTE32(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE31(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31)
+#define NLOHMANN_JSON_PASTE33(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE32(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32)
+#define NLOHMANN_JSON_PASTE34(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE33(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33)
+#define NLOHMANN_JSON_PASTE35(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE34(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34)
+#define NLOHMANN_JSON_PASTE36(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE35(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35)
+#define NLOHMANN_JSON_PASTE37(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE36(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36)
+#define NLOHMANN_JSON_PASTE38(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE37(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37)
+#define NLOHMANN_JSON_PASTE39(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE38(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38)
+#define NLOHMANN_JSON_PASTE40(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE39(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39)
+#define NLOHMANN_JSON_PASTE41(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE40(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40)
+#define NLOHMANN_JSON_PASTE42(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE41(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41)
+#define NLOHMANN_JSON_PASTE43(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE42(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42)
+#define NLOHMANN_JSON_PASTE44(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE43(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43)
+#define NLOHMANN_JSON_PASTE45(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE44(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44)
+#define NLOHMANN_JSON_PASTE46(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE45(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45)
+#define NLOHMANN_JSON_PASTE47(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE46(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46)
+#define NLOHMANN_JSON_PASTE48(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE47(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47)
+#define NLOHMANN_JSON_PASTE49(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE48(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48)
+#define NLOHMANN_JSON_PASTE50(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE49(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49)
+#define NLOHMANN_JSON_PASTE51(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE50(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50)
+#define NLOHMANN_JSON_PASTE52(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE51(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51)
+#define NLOHMANN_JSON_PASTE53(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE52(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52)
+#define NLOHMANN_JSON_PASTE54(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE53(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53)
+#define NLOHMANN_JSON_PASTE55(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE54(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54)
+#define NLOHMANN_JSON_PASTE56(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE55(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55)
+#define NLOHMANN_JSON_PASTE57(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE56(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56)
+#define NLOHMANN_JSON_PASTE58(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE57(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57)
+#define NLOHMANN_JSON_PASTE59(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE58(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58)
+#define NLOHMANN_JSON_PASTE60(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE59(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59)
+#define NLOHMANN_JSON_PASTE61(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE60(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60)
+#define NLOHMANN_JSON_PASTE62(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE61(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61)
+#define NLOHMANN_JSON_PASTE63(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE62(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62)
+#define NLOHMANN_JSON_PASTE64(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62, v63) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE63(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62, v63)
+
+#define NLOHMANN_JSON_TO(v1) nlohmann_json_j[#v1] = nlohmann_json_t.v1;
+#define NLOHMANN_JSON_FROM(v1) nlohmann_json_j.at(#v1).get_to(nlohmann_json_t.v1);
+#define NLOHMANN_JSON_FROM_WITH_DEFAULT(v1) nlohmann_json_t.v1 = nlohmann_json_j.value(#v1, nlohmann_json_default_obj.v1);
+
+/*!
+@brief macro
+@def NLOHMANN_DEFINE_TYPE_INTRUSIVE
+@since version 3.9.0
+*/
+#define NLOHMANN_DEFINE_TYPE_INTRUSIVE(Type, ...)  \
+    friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
+    friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) }
+
+#define NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(Type, ...)  \
+    friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
+    friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
+
+#define NLOHMANN_DEFINE_TYPE_INTRUSIVE_ONLY_SERIALIZE(Type, ...)  \
+    friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) }
+
+/*!
+@brief macro
+@def NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE
+@since version 3.9.0
+*/
+#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Type, ...)  \
+    inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
+    inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) }
+
+#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_ONLY_SERIALIZE(Type, ...)  \
+    inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) }
+
+#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_WITH_DEFAULT(Type, ...)  \
+    inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
+    inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
+
+// inspired from https://stackoverflow.com/a/26745591
+// allows to call any std function as if (e.g. with begin):
+// using std::begin; begin(x);
+//
+// it allows using the detected idiom to retrieve the return type
+// of such an expression
+#define NLOHMANN_CAN_CALL_STD_FUNC_IMPL(std_name)                                 \
+    namespace detail {                                                            \
+    using std::std_name;                                                          \
+    \
+    template<typename... T>                                                       \
+    using result_of_##std_name = decltype(std_name(std::declval<T>()...));        \
+    }                                                                             \
+    \
+    namespace detail2 {                                                           \
+    struct std_name##_tag                                                         \
+    {                                                                             \
+    };                                                                            \
+    \
+    template<typename... T>                                                       \
+    std_name##_tag std_name(T&&...);                                              \
+    \
+    template<typename... T>                                                       \
+    using result_of_##std_name = decltype(std_name(std::declval<T>()...));        \
+    \
+    template<typename... T>                                                       \
+    struct would_call_std_##std_name                                              \
+    {                                                                             \
+        static constexpr auto const value = ::nlohmann::detail::                  \
+                                            is_detected_exact<std_name##_tag, result_of_##std_name, T...>::value; \
+    };                                                                            \
+    } /* namespace detail2 */ \
+    \
+    template<typename... T>                                                       \
+    struct would_call_std_##std_name : detail2::would_call_std_##std_name<T...>   \
+    {                                                                             \
+    }
+
+#ifndef JSON_USE_IMPLICIT_CONVERSIONS
+    #define JSON_USE_IMPLICIT_CONVERSIONS 1
+#endif
+
+#if JSON_USE_IMPLICIT_CONVERSIONS
+    #define JSON_EXPLICIT
+#else
+    #define JSON_EXPLICIT explicit
+#endif
+
+#ifndef JSON_DISABLE_ENUM_SERIALIZATION
+    #define JSON_DISABLE_ENUM_SERIALIZATION 0
+#endif
+
+#ifndef JSON_USE_GLOBAL_UDLS
+    #define JSON_USE_GLOBAL_UDLS 1
+#endif
+
+#if JSON_HAS_THREE_WAY_COMPARISON
+    #include <compare> // partial_ordering
+#endif
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+///////////////////////////
+// JSON type enumeration //
+///////////////////////////
+
+/*!
+@brief the JSON type enumeration
+
+This enumeration collects the different JSON types. It is internally used to
+distinguish the stored values, and the functions @ref basic_json::is_null(),
+@ref basic_json::is_object(), @ref basic_json::is_array(),
+@ref basic_json::is_string(), @ref basic_json::is_boolean(),
+@ref basic_json::is_number() (with @ref basic_json::is_number_integer(),
+@ref basic_json::is_number_unsigned(), and @ref basic_json::is_number_float()),
+@ref basic_json::is_discarded(), @ref basic_json::is_primitive(), and
+@ref basic_json::is_structured() rely on it.
+
+@note There are three enumeration entries (number_integer, number_unsigned, and
+number_float), because the library distinguishes these three types for numbers:
+@ref basic_json::number_unsigned_t is used for unsigned integers,
+@ref basic_json::number_integer_t is used for signed integers, and
+@ref basic_json::number_float_t is used for floating-point numbers or to
+approximate integers which do not fit in the limits of their respective type.
+
+@sa see @ref basic_json::basic_json(const value_t value_type) -- create a JSON
+value with the default value for a given type
+
+@since version 1.0.0
+*/
+enum class value_t : std::uint8_t
+{
+    null,             ///< null value
+    object,           ///< object (unordered set of name/value pairs)
+    array,            ///< array (ordered collection of values)
+    string,           ///< string value
+    boolean,          ///< boolean value
+    number_integer,   ///< number value (signed integer)
+    number_unsigned,  ///< number value (unsigned integer)
+    number_float,     ///< number value (floating-point)
+    binary,           ///< binary array (ordered collection of bytes)
+    discarded         ///< discarded by the parser callback function
+};
+
+/*!
+@brief comparison operator for JSON types
+
+Returns an ordering that is similar to Python:
+- order: null < boolean < number < object < array < string < binary
+- furthermore, each type is not smaller than itself
+- discarded values are not comparable
+- binary is represented as a b"" string in python and directly comparable to a
+  string; however, making a binary array directly comparable with a string would
+  be surprising behavior in a JSON file.
+
+@since version 1.0.0
+*/
+#if JSON_HAS_THREE_WAY_COMPARISON
+    inline std::partial_ordering operator<=>(const value_t lhs, const value_t rhs) noexcept // *NOPAD*
+#else
+    inline bool operator<(const value_t lhs, const value_t rhs) noexcept
+#endif
+{
+    static constexpr std::array<std::uint8_t, 9> order = {{
+            0 /* null */, 3 /* object */, 4 /* array */, 5 /* string */,
+            1 /* boolean */, 2 /* integer */, 2 /* unsigned */, 2 /* float */,
+            6 /* binary */
+        }
+    };
+
+    const auto l_index = static_cast<std::size_t>(lhs);
+    const auto r_index = static_cast<std::size_t>(rhs);
+#if JSON_HAS_THREE_WAY_COMPARISON
+    if (l_index < order.size() && r_index < order.size())
+    {
+        return order[l_index] <=> order[r_index]; // *NOPAD*
+    }
+    return std::partial_ordering::unordered;
+#else
+    return l_index < order.size() && r_index < order.size() && order[l_index] < order[r_index];
+#endif
+}
+
+// GCC selects the built-in operator< over an operator rewritten from
+// a user-defined spaceship operator
+// Clang, MSVC, and ICC select the rewritten candidate
+// (see GCC bug https://gcc.gnu.org/bugzilla/show_bug.cgi?id=105200)
+#if JSON_HAS_THREE_WAY_COMPARISON && defined(__GNUC__)
+inline bool operator<(const value_t lhs, const value_t rhs) noexcept
+{
+    return std::is_lt(lhs <=> rhs); // *NOPAD*
+}
+#endif
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/string_escape.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/*!
+@brief replace all occurrences of a substring by another string
+
+@param[in,out] s  the string to manipulate; changed so that all
+               occurrences of @a f are replaced with @a t
+@param[in]     f  the substring to replace with @a t
+@param[in]     t  the string to replace @a f
+
+@pre The search string @a f must not be empty. **This precondition is
+enforced with an assertion.**
+
+@since version 2.0.0
+*/
+template<typename StringType>
+inline void replace_substring(StringType& s, const StringType& f,
+                              const StringType& t)
+{
+    JSON_ASSERT(!f.empty());
+    for (auto pos = s.find(f);                // find first occurrence of f
+            pos != StringType::npos;          // make sure f was found
+            s.replace(pos, f.size(), t),      // replace with t, and
+            pos = s.find(f, pos + t.size()))  // find next occurrence of f
+    {}
+}
+
+/*!
+ * @brief string escaping as described in RFC 6901 (Sect. 4)
+ * @param[in] s string to escape
+ * @return    escaped string
+ *
+ * Note the order of escaping "~" to "~0" and "/" to "~1" is important.
+ */
+template<typename StringType>
+inline StringType escape(StringType s)
+{
+    replace_substring(s, StringType{"~"}, StringType{"~0"});
+    replace_substring(s, StringType{"/"}, StringType{"~1"});
+    return s;
+}
+
+/*!
+ * @brief string unescaping as described in RFC 6901 (Sect. 4)
+ * @param[in] s string to unescape
+ * @return    unescaped string
+ *
+ * Note the order of escaping "~1" to "/" and "~0" to "~" is important.
+ */
+template<typename StringType>
+static void unescape(StringType& s)
+{
+    replace_substring(s, StringType{"~1"}, StringType{"/"});
+    replace_substring(s, StringType{"~0"}, StringType{"~"});
+}
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/input/position_t.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cstddef> // size_t
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/// struct to capture the start position of the current token
+struct position_t
+{
+    /// the total number of characters read
+    std::size_t chars_read_total = 0;
+    /// the number of characters read in the current line
+    std::size_t chars_read_current_line = 0;
+    /// the number of lines read
+    std::size_t lines_read = 0;
+
+    /// conversion to size_t to preserve SAX interface
+    constexpr operator size_t() const
+    {
+        return chars_read_total;
+    }
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/cpp_future.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2018 The Abseil Authors
+// SPDX-License-Identifier: MIT
+
+
+
+#include <array> // array
+#include <cstddef> // size_t
+#include <type_traits> // conditional, enable_if, false_type, integral_constant, is_constructible, is_integral, is_same, remove_cv, remove_reference, true_type
+#include <utility> // index_sequence, make_index_sequence, index_sequence_for
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+template<typename T>
+using uncvref_t = typename std::remove_cv<typename std::remove_reference<T>::type>::type;
+
+#ifdef JSON_HAS_CPP_14
+
+// the following utilities are natively available in C++14
+using std::enable_if_t;
+using std::index_sequence;
+using std::make_index_sequence;
+using std::index_sequence_for;
+
+#else
+
+// alias templates to reduce boilerplate
+template<bool B, typename T = void>
+using enable_if_t = typename std::enable_if<B, T>::type;
+
+// The following code is taken from https://github.com/abseil/abseil-cpp/blob/10cb35e459f5ecca5b2ff107635da0bfa41011b4/absl/utility/utility.h
+// which is part of Google Abseil (https://github.com/abseil/abseil-cpp), licensed under the Apache License 2.0.
+
+//// START OF CODE FROM GOOGLE ABSEIL
+
+// integer_sequence
+//
+// Class template representing a compile-time integer sequence. An instantiation
+// of `integer_sequence<T, Ints...>` has a sequence of integers encoded in its
+// type through its template arguments (which is a common need when
+// working with C++11 variadic templates). `absl::integer_sequence` is designed
+// to be a drop-in replacement for C++14's `std::integer_sequence`.
+//
+// Example:
+//
+//   template< class T, T... Ints >
+//   void user_function(integer_sequence<T, Ints...>);
+//
+//   int main()
+//   {
+//     // user_function's `T` will be deduced to `int` and `Ints...`
+//     // will be deduced to `0, 1, 2, 3, 4`.
+//     user_function(make_integer_sequence<int, 5>());
+//   }
+template <typename T, T... Ints>
+struct integer_sequence
+{
+    using value_type = T;
+    static constexpr std::size_t size() noexcept
+    {
+        return sizeof...(Ints);
+    }
+};
+
+// index_sequence
+//
+// A helper template for an `integer_sequence` of `size_t`,
+// `absl::index_sequence` is designed to be a drop-in replacement for C++14's
+// `std::index_sequence`.
+template <size_t... Ints>
+using index_sequence = integer_sequence<size_t, Ints...>;
+
+namespace utility_internal
+{
+
+template <typename Seq, size_t SeqSize, size_t Rem>
+struct Extend;
+
+// Note that SeqSize == sizeof...(Ints). It's passed explicitly for efficiency.
+template <typename T, T... Ints, size_t SeqSize>
+struct Extend<integer_sequence<T, Ints...>, SeqSize, 0>
+{
+    using type = integer_sequence < T, Ints..., (Ints + SeqSize)... >;
+};
+
+template <typename T, T... Ints, size_t SeqSize>
+struct Extend<integer_sequence<T, Ints...>, SeqSize, 1>
+{
+    using type = integer_sequence < T, Ints..., (Ints + SeqSize)..., 2 * SeqSize >;
+};
+
+// Recursion helper for 'make_integer_sequence<T, N>'.
+// 'Gen<T, N>::type' is an alias for 'integer_sequence<T, 0, 1, ... N-1>'.
+template <typename T, size_t N>
+struct Gen
+{
+    using type =
+        typename Extend < typename Gen < T, N / 2 >::type, N / 2, N % 2 >::type;
+};
+
+template <typename T>
+struct Gen<T, 0>
+{
+    using type = integer_sequence<T>;
+};
+
+}  // namespace utility_internal
+
+// Compile-time sequences of integers
+
+// make_integer_sequence
+//
+// This template alias is equivalent to
+// `integer_sequence<int, 0, 1, ..., N-1>`, and is designed to be a drop-in
+// replacement for C++14's `std::make_integer_sequence`.
+template <typename T, T N>
+using make_integer_sequence = typename utility_internal::Gen<T, N>::type;
+
+// make_index_sequence
+//
+// This template alias is equivalent to `index_sequence<0, 1, ..., N-1>`,
+// and is designed to be a drop-in replacement for C++14's
+// `std::make_index_sequence`.
+template <size_t N>
+using make_index_sequence = make_integer_sequence<size_t, N>;
+
+// index_sequence_for
+//
+// Converts a typename pack into an index sequence of the same length, and
+// is designed to be a drop-in replacement for C++14's
+// `std::index_sequence_for()`
+template <typename... Ts>
+using index_sequence_for = make_index_sequence<sizeof...(Ts)>;
+
+//// END OF CODE FROM GOOGLE ABSEIL
+
+#endif
+
+// dispatch utility (taken from ranges-v3)
+template<unsigned N> struct priority_tag : priority_tag < N - 1 > {};
+template<> struct priority_tag<0> {};
+
+// taken from ranges-v3
+template<typename T>
+struct static_const
+{
+    static JSON_INLINE_VARIABLE constexpr T value{};
+};
+
+#ifndef JSON_HAS_CPP_17
+    template<typename T>
+    constexpr T static_const<T>::value;
+#endif
+
+template<typename T, typename... Args>
+inline constexpr std::array<T, sizeof...(Args)> make_array(Args&& ... args)
+{
+    return std::array<T, sizeof...(Args)> {{static_cast<T>(std::forward<Args>(args))...}};
+}
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <limits> // numeric_limits
+#include <type_traits> // false_type, is_constructible, is_integral, is_same, true_type
+#include <utility> // declval
+#include <tuple> // tuple
+#include <string> // char_traits
+
+// #include <nlohmann/detail/iterators/iterator_traits.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <iterator> // random_access_iterator_tag
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+// #include <nlohmann/detail/meta/void_t.hpp>
+
+// #include <nlohmann/detail/meta/cpp_future.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+template<typename It, typename = void>
+struct iterator_types {};
+
+template<typename It>
+struct iterator_types <
+    It,
+    void_t<typename It::difference_type, typename It::value_type, typename It::pointer,
+    typename It::reference, typename It::iterator_category >>
+{
+    using difference_type = typename It::difference_type;
+    using value_type = typename It::value_type;
+    using pointer = typename It::pointer;
+    using reference = typename It::reference;
+    using iterator_category = typename It::iterator_category;
+};
+
+// This is required as some compilers implement std::iterator_traits in a way that
+// doesn't work with SFINAE. See https://github.com/nlohmann/json/issues/1341.
+template<typename T, typename = void>
+struct iterator_traits
+{
+};
+
+template<typename T>
+struct iterator_traits < T, enable_if_t < !std::is_pointer<T>::value >>
+            : iterator_types<T>
+{
+};
+
+template<typename T>
+struct iterator_traits<T*, enable_if_t<std::is_object<T>::value>>
+{
+    using iterator_category = std::random_access_iterator_tag;
+    using value_type = T;
+    using difference_type = ptrdiff_t;
+    using pointer = T*;
+    using reference = T&;
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/call_std/begin.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+
+NLOHMANN_CAN_CALL_STD_FUNC_IMPL(begin);
+
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/meta/call_std/end.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+
+NLOHMANN_CAN_CALL_STD_FUNC_IMPL(end);
+
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/meta/cpp_future.hpp>
+
+// #include <nlohmann/detail/meta/detected.hpp>
+
+// #include <nlohmann/json_fwd.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+#ifndef INCLUDE_NLOHMANN_JSON_FWD_HPP_
+    #define INCLUDE_NLOHMANN_JSON_FWD_HPP_
+
+    #include <cstdint> // int64_t, uint64_t
+    #include <map> // map
+    #include <memory> // allocator
+    #include <string> // string
+    #include <vector> // vector
+
+    // #include <nlohmann/detail/abi_macros.hpp>
+
+
+    /*!
+    @brief namespace for Niels Lohmann
+    @see https://github.com/nlohmann
+    @since version 1.0.0
+    */
+    NLOHMANN_JSON_NAMESPACE_BEGIN
+
+    /*!
+    @brief default JSONSerializer template argument
+
+    This serializer ignores the template arguments and uses ADL
+    ([argument-dependent lookup](https://en.cppreference.com/w/cpp/language/adl))
+    for serialization.
+    */
+    template<typename T = void, typename SFINAE = void>
+    struct adl_serializer;
+
+    /// a class to store JSON values
+    /// @sa https://json.nlohmann.me/api/basic_json/
+    template<template<typename U, typename V, typename... Args> class ObjectType =
+    std::map,
+    template<typename U, typename... Args> class ArrayType = std::vector,
+    class StringType = std::string, class BooleanType = bool,
+    class NumberIntegerType = std::int64_t,
+    class NumberUnsignedType = std::uint64_t,
+    class NumberFloatType = double,
+    template<typename U> class AllocatorType = std::allocator,
+    template<typename T, typename SFINAE = void> class JSONSerializer =
+    adl_serializer,
+    class BinaryType = std::vector<std::uint8_t>, // cppcheck-suppress syntaxError
+    class CustomBaseClass = void>
+    class basic_json;
+
+    /// @brief JSON Pointer defines a string syntax for identifying a specific value within a JSON document
+    /// @sa https://json.nlohmann.me/api/json_pointer/
+    template<typename RefStringType>
+    class json_pointer;
+
+    /*!
+    @brief default specialization
+    @sa https://json.nlohmann.me/api/json/
+    */
+    using json = basic_json<>;
+
+    /// @brief a minimal map-like container that preserves insertion order
+    /// @sa https://json.nlohmann.me/api/ordered_map/
+    template<class Key, class T, class IgnoredLess, class Allocator>
+    struct ordered_map;
+
+    /// @brief specialization that maintains the insertion order of object keys
+    /// @sa https://json.nlohmann.me/api/ordered_json/
+    using ordered_json = basic_json<nlohmann::ordered_map>;
+
+    NLOHMANN_JSON_NAMESPACE_END
+
+#endif  // INCLUDE_NLOHMANN_JSON_FWD_HPP_
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+/*!
+@brief detail namespace with internal helper functions
+
+This namespace collects functions that should not be exposed,
+implementations of some @ref basic_json methods, and meta-programming helpers.
+
+@since version 2.1.0
+*/
+namespace detail
+{
+
+/////////////
+// helpers //
+/////////////
+
+// Note to maintainers:
+//
+// Every trait in this file expects a non CV-qualified type.
+// The only exceptions are in the 'aliases for detected' section
+// (i.e. those of the form: decltype(T::member_function(std::declval<T>())))
+//
+// In this case, T has to be properly CV-qualified to constraint the function arguments
+// (e.g. to_json(BasicJsonType&, const T&))
+
+template<typename> struct is_basic_json : std::false_type {};
+
+NLOHMANN_BASIC_JSON_TPL_DECLARATION
+struct is_basic_json<NLOHMANN_BASIC_JSON_TPL> : std::true_type {};
+
+// used by exceptions create() member functions
+// true_type for pointer to possibly cv-qualified basic_json or std::nullptr_t
+// false_type otherwise
+template<typename BasicJsonContext>
+struct is_basic_json_context :
+    std::integral_constant < bool,
+    is_basic_json<typename std::remove_cv<typename std::remove_pointer<BasicJsonContext>::type>::type>::value
+    || std::is_same<BasicJsonContext, std::nullptr_t>::value >
+{};
+
+//////////////////////
+// json_ref helpers //
+//////////////////////
+
+template<typename>
+class json_ref;
+
+template<typename>
+struct is_json_ref : std::false_type {};
+
+template<typename T>
+struct is_json_ref<json_ref<T>> : std::true_type {};
+
+//////////////////////////
+// aliases for detected //
+//////////////////////////
+
+template<typename T>
+using mapped_type_t = typename T::mapped_type;
+
+template<typename T>
+using key_type_t = typename T::key_type;
+
+template<typename T>
+using value_type_t = typename T::value_type;
+
+template<typename T>
+using difference_type_t = typename T::difference_type;
+
+template<typename T>
+using pointer_t = typename T::pointer;
+
+template<typename T>
+using reference_t = typename T::reference;
+
+template<typename T>
+using iterator_category_t = typename T::iterator_category;
+
+template<typename T, typename... Args>
+using to_json_function = decltype(T::to_json(std::declval<Args>()...));
+
+template<typename T, typename... Args>
+using from_json_function = decltype(T::from_json(std::declval<Args>()...));
+
+template<typename T, typename U>
+using get_template_function = decltype(std::declval<T>().template get<U>());
+
+// trait checking if JSONSerializer<T>::from_json(json const&, udt&) exists
+template<typename BasicJsonType, typename T, typename = void>
+struct has_from_json : std::false_type {};
+
+// trait checking if j.get<T> is valid
+// use this trait instead of std::is_constructible or std::is_convertible,
+// both rely on, or make use of implicit conversions, and thus fail when T
+// has several constructors/operator= (see https://github.com/nlohmann/json/issues/958)
+template <typename BasicJsonType, typename T>
+struct is_getable
+{
+    static constexpr bool value = is_detected<get_template_function, const BasicJsonType&, T>::value;
+};
+
+template<typename BasicJsonType, typename T>
+struct has_from_json < BasicJsonType, T, enable_if_t < !is_basic_json<T>::value >>
+{
+    using serializer = typename BasicJsonType::template json_serializer<T, void>;
+
+    static constexpr bool value =
+        is_detected_exact<void, from_json_function, serializer,
+        const BasicJsonType&, T&>::value;
+};
+
+// This trait checks if JSONSerializer<T>::from_json(json const&) exists
+// this overload is used for non-default-constructible user-defined-types
+template<typename BasicJsonType, typename T, typename = void>
+struct has_non_default_from_json : std::false_type {};
+
+template<typename BasicJsonType, typename T>
+struct has_non_default_from_json < BasicJsonType, T, enable_if_t < !is_basic_json<T>::value >>
+{
+    using serializer = typename BasicJsonType::template json_serializer<T, void>;
+
+    static constexpr bool value =
+        is_detected_exact<T, from_json_function, serializer,
+        const BasicJsonType&>::value;
+};
+
+// This trait checks if BasicJsonType::json_serializer<T>::to_json exists
+// Do not evaluate the trait when T is a basic_json type, to avoid template instantiation infinite recursion.
+template<typename BasicJsonType, typename T, typename = void>
+struct has_to_json : std::false_type {};
+
+template<typename BasicJsonType, typename T>
+struct has_to_json < BasicJsonType, T, enable_if_t < !is_basic_json<T>::value >>
+{
+    using serializer = typename BasicJsonType::template json_serializer<T, void>;
+
+    static constexpr bool value =
+        is_detected_exact<void, to_json_function, serializer, BasicJsonType&,
+        T>::value;
+};
+
+template<typename T>
+using detect_key_compare = typename T::key_compare;
+
+template<typename T>
+struct has_key_compare : std::integral_constant<bool, is_detected<detect_key_compare, T>::value> {};
+
+// obtains the actual object key comparator
+template<typename BasicJsonType>
+struct actual_object_comparator
+{
+    using object_t = typename BasicJsonType::object_t;
+    using object_comparator_t = typename BasicJsonType::default_object_comparator_t;
+    using type = typename std::conditional < has_key_compare<object_t>::value,
+          typename object_t::key_compare, object_comparator_t>::type;
+};
+
+template<typename BasicJsonType>
+using actual_object_comparator_t = typename actual_object_comparator<BasicJsonType>::type;
+
+/////////////////
+// char_traits //
+/////////////////
+
+// Primary template of char_traits calls std char_traits
+template<typename T>
+struct char_traits : std::char_traits<T>
+{};
+
+// Explicitly define char traits for unsigned char since it is not standard
+template<>
+struct char_traits<unsigned char> : std::char_traits<char>
+{
+    using char_type = unsigned char;
+    using int_type = uint64_t;
+
+    // Redefine to_int_type function
+    static int_type to_int_type(char_type c) noexcept
+    {
+        return static_cast<int_type>(c);
+    }
+
+    static char_type to_char_type(int_type i) noexcept
+    {
+        return static_cast<char_type>(i);
+    }
+
+    static constexpr int_type eof() noexcept
+    {
+        return static_cast<int_type>(EOF);
+    }
+};
+
+// Explicitly define char traits for signed char since it is not standard
+template<>
+struct char_traits<signed char> : std::char_traits<char>
+{
+    using char_type = signed char;
+    using int_type = uint64_t;
+
+    // Redefine to_int_type function
+    static int_type to_int_type(char_type c) noexcept
+    {
+        return static_cast<int_type>(c);
+    }
+
+    static char_type to_char_type(int_type i) noexcept
+    {
+        return static_cast<char_type>(i);
+    }
+
+    static constexpr int_type eof() noexcept
+    {
+        return static_cast<int_type>(EOF);
+    }
+};
+
+///////////////////
+// is_ functions //
+///////////////////
+
+// https://en.cppreference.com/w/cpp/types/conjunction
+template<class...> struct conjunction : std::true_type { };
+template<class B> struct conjunction<B> : B { };
+template<class B, class... Bn>
+struct conjunction<B, Bn...>
+: std::conditional<static_cast<bool>(B::value), conjunction<Bn...>, B>::type {};
+
+// https://en.cppreference.com/w/cpp/types/negation
+template<class B> struct negation : std::integral_constant < bool, !B::value > { };
+
+// Reimplementation of is_constructible and is_default_constructible, due to them being broken for
+// std::pair and std::tuple until LWG 2367 fix (see https://cplusplus.github.io/LWG/lwg-defects.html#2367).
+// This causes compile errors in e.g. clang 3.5 or gcc 4.9.
+template <typename T>
+struct is_default_constructible : std::is_default_constructible<T> {};
+
+template <typename T1, typename T2>
+struct is_default_constructible<std::pair<T1, T2>>
+            : conjunction<is_default_constructible<T1>, is_default_constructible<T2>> {};
+
+template <typename T1, typename T2>
+struct is_default_constructible<const std::pair<T1, T2>>
+            : conjunction<is_default_constructible<T1>, is_default_constructible<T2>> {};
+
+template <typename... Ts>
+struct is_default_constructible<std::tuple<Ts...>>
+            : conjunction<is_default_constructible<Ts>...> {};
+
+template <typename... Ts>
+struct is_default_constructible<const std::tuple<Ts...>>
+            : conjunction<is_default_constructible<Ts>...> {};
+
+template <typename T, typename... Args>
+struct is_constructible : std::is_constructible<T, Args...> {};
+
+template <typename T1, typename T2>
+struct is_constructible<std::pair<T1, T2>> : is_default_constructible<std::pair<T1, T2>> {};
+
+template <typename T1, typename T2>
+struct is_constructible<const std::pair<T1, T2>> : is_default_constructible<const std::pair<T1, T2>> {};
+
+template <typename... Ts>
+struct is_constructible<std::tuple<Ts...>> : is_default_constructible<std::tuple<Ts...>> {};
+
+template <typename... Ts>
+struct is_constructible<const std::tuple<Ts...>> : is_default_constructible<const std::tuple<Ts...>> {};
+
+template<typename T, typename = void>
+struct is_iterator_traits : std::false_type {};
+
+template<typename T>
+struct is_iterator_traits<iterator_traits<T>>
+{
+  private:
+    using traits = iterator_traits<T>;
+
+  public:
+    static constexpr auto value =
+        is_detected<value_type_t, traits>::value &&
+        is_detected<difference_type_t, traits>::value &&
+        is_detected<pointer_t, traits>::value &&
+        is_detected<iterator_category_t, traits>::value &&
+        is_detected<reference_t, traits>::value;
+};
+
+template<typename T>
+struct is_range
+{
+  private:
+    using t_ref = typename std::add_lvalue_reference<T>::type;
+
+    using iterator = detected_t<result_of_begin, t_ref>;
+    using sentinel = detected_t<result_of_end, t_ref>;
+
+    // to be 100% correct, it should use https://en.cppreference.com/w/cpp/iterator/input_or_output_iterator
+    // and https://en.cppreference.com/w/cpp/iterator/sentinel_for
+    // but reimplementing these would be too much work, as a lot of other concepts are used underneath
+    static constexpr auto is_iterator_begin =
+        is_iterator_traits<iterator_traits<iterator>>::value;
+
+  public:
+    static constexpr bool value = !std::is_same<iterator, nonesuch>::value && !std::is_same<sentinel, nonesuch>::value && is_iterator_begin;
+};
+
+template<typename R>
+using iterator_t = enable_if_t<is_range<R>::value, result_of_begin<decltype(std::declval<R&>())>>;
+
+template<typename T>
+using range_value_t = value_type_t<iterator_traits<iterator_t<T>>>;
+
+// The following implementation of is_complete_type is taken from
+// https://blogs.msdn.microsoft.com/vcblog/2015/12/02/partial-support-for-expression-sfinae-in-vs-2015-update-1/
+// and is written by Xiang Fan who agreed to using it in this library.
+
+template<typename T, typename = void>
+struct is_complete_type : std::false_type {};
+
+template<typename T>
+struct is_complete_type<T, decltype(void(sizeof(T)))> : std::true_type {};
+
+template<typename BasicJsonType, typename CompatibleObjectType,
+         typename = void>
+struct is_compatible_object_type_impl : std::false_type {};
+
+template<typename BasicJsonType, typename CompatibleObjectType>
+struct is_compatible_object_type_impl <
+    BasicJsonType, CompatibleObjectType,
+    enable_if_t < is_detected<mapped_type_t, CompatibleObjectType>::value&&
+    is_detected<key_type_t, CompatibleObjectType>::value >>
+{
+    using object_t = typename BasicJsonType::object_t;
+
+    // macOS's is_constructible does not play well with nonesuch...
+    static constexpr bool value =
+        is_constructible<typename object_t::key_type,
+        typename CompatibleObjectType::key_type>::value &&
+        is_constructible<typename object_t::mapped_type,
+        typename CompatibleObjectType::mapped_type>::value;
+};
+
+template<typename BasicJsonType, typename CompatibleObjectType>
+struct is_compatible_object_type
+    : is_compatible_object_type_impl<BasicJsonType, CompatibleObjectType> {};
+
+template<typename BasicJsonType, typename ConstructibleObjectType,
+         typename = void>
+struct is_constructible_object_type_impl : std::false_type {};
+
+template<typename BasicJsonType, typename ConstructibleObjectType>
+struct is_constructible_object_type_impl <
+    BasicJsonType, ConstructibleObjectType,
+    enable_if_t < is_detected<mapped_type_t, ConstructibleObjectType>::value&&
+    is_detected<key_type_t, ConstructibleObjectType>::value >>
+{
+    using object_t = typename BasicJsonType::object_t;
+
+    static constexpr bool value =
+        (is_default_constructible<ConstructibleObjectType>::value &&
+         (std::is_move_assignable<ConstructibleObjectType>::value ||
+          std::is_copy_assignable<ConstructibleObjectType>::value) &&
+         (is_constructible<typename ConstructibleObjectType::key_type,
+          typename object_t::key_type>::value &&
+          std::is_same <
+          typename object_t::mapped_type,
+          typename ConstructibleObjectType::mapped_type >::value)) ||
+        (has_from_json<BasicJsonType,
+         typename ConstructibleObjectType::mapped_type>::value ||
+         has_non_default_from_json <
+         BasicJsonType,
+         typename ConstructibleObjectType::mapped_type >::value);
+};
+
+template<typename BasicJsonType, typename ConstructibleObjectType>
+struct is_constructible_object_type
+    : is_constructible_object_type_impl<BasicJsonType,
+      ConstructibleObjectType> {};
+
+template<typename BasicJsonType, typename CompatibleStringType>
+struct is_compatible_string_type
+{
+    static constexpr auto value =
+        is_constructible<typename BasicJsonType::string_t, CompatibleStringType>::value;
+};
+
+template<typename BasicJsonType, typename ConstructibleStringType>
+struct is_constructible_string_type
+{
+    // launder type through decltype() to fix compilation failure on ICPC
+#ifdef __INTEL_COMPILER
+    using laundered_type = decltype(std::declval<ConstructibleStringType>());
+#else
+    using laundered_type = ConstructibleStringType;
+#endif
+
+    static constexpr auto value =
+        conjunction <
+        is_constructible<laundered_type, typename BasicJsonType::string_t>,
+        is_detected_exact<typename BasicJsonType::string_t::value_type,
+        value_type_t, laundered_type >>::value;
+};
+
+template<typename BasicJsonType, typename CompatibleArrayType, typename = void>
+struct is_compatible_array_type_impl : std::false_type {};
+
+template<typename BasicJsonType, typename CompatibleArrayType>
+struct is_compatible_array_type_impl <
+    BasicJsonType, CompatibleArrayType,
+    enable_if_t <
+    is_detected<iterator_t, CompatibleArrayType>::value&&
+    is_iterator_traits<iterator_traits<detected_t<iterator_t, CompatibleArrayType>>>::value&&
+// special case for types like std::filesystem::path whose iterator's value_type are themselves
+// c.f. https://github.com/nlohmann/json/pull/3073
+    !std::is_same<CompatibleArrayType, detected_t<range_value_t, CompatibleArrayType>>::value >>
+{
+    static constexpr bool value =
+        is_constructible<BasicJsonType,
+        range_value_t<CompatibleArrayType>>::value;
+};
+
+template<typename BasicJsonType, typename CompatibleArrayType>
+struct is_compatible_array_type
+    : is_compatible_array_type_impl<BasicJsonType, CompatibleArrayType> {};
+
+template<typename BasicJsonType, typename ConstructibleArrayType, typename = void>
+struct is_constructible_array_type_impl : std::false_type {};
+
+template<typename BasicJsonType, typename ConstructibleArrayType>
+struct is_constructible_array_type_impl <
+    BasicJsonType, ConstructibleArrayType,
+    enable_if_t<std::is_same<ConstructibleArrayType,
+    typename BasicJsonType::value_type>::value >>
+            : std::true_type {};
+
+template<typename BasicJsonType, typename ConstructibleArrayType>
+struct is_constructible_array_type_impl <
+    BasicJsonType, ConstructibleArrayType,
+    enable_if_t < !std::is_same<ConstructibleArrayType,
+    typename BasicJsonType::value_type>::value&&
+    !is_compatible_string_type<BasicJsonType, ConstructibleArrayType>::value&&
+    is_default_constructible<ConstructibleArrayType>::value&&
+(std::is_move_assignable<ConstructibleArrayType>::value ||
+ std::is_copy_assignable<ConstructibleArrayType>::value)&&
+is_detected<iterator_t, ConstructibleArrayType>::value&&
+is_iterator_traits<iterator_traits<detected_t<iterator_t, ConstructibleArrayType>>>::value&&
+is_detected<range_value_t, ConstructibleArrayType>::value&&
+// special case for types like std::filesystem::path whose iterator's value_type are themselves
+// c.f. https://github.com/nlohmann/json/pull/3073
+!std::is_same<ConstructibleArrayType, detected_t<range_value_t, ConstructibleArrayType>>::value&&
+        is_complete_type <
+        detected_t<range_value_t, ConstructibleArrayType >>::value >>
+{
+    using value_type = range_value_t<ConstructibleArrayType>;
+
+    static constexpr bool value =
+        std::is_same<value_type,
+        typename BasicJsonType::array_t::value_type>::value ||
+        has_from_json<BasicJsonType,
+        value_type>::value ||
+        has_non_default_from_json <
+        BasicJsonType,
+        value_type >::value;
+};
+
+template<typename BasicJsonType, typename ConstructibleArrayType>
+struct is_constructible_array_type
+    : is_constructible_array_type_impl<BasicJsonType, ConstructibleArrayType> {};
+
+template<typename RealIntegerType, typename CompatibleNumberIntegerType,
+         typename = void>
+struct is_compatible_integer_type_impl : std::false_type {};
+
+template<typename RealIntegerType, typename CompatibleNumberIntegerType>
+struct is_compatible_integer_type_impl <
+    RealIntegerType, CompatibleNumberIntegerType,
+    enable_if_t < std::is_integral<RealIntegerType>::value&&
+    std::is_integral<CompatibleNumberIntegerType>::value&&
+    !std::is_same<bool, CompatibleNumberIntegerType>::value >>
+{
+    // is there an assert somewhere on overflows?
+    using RealLimits = std::numeric_limits<RealIntegerType>;
+    using CompatibleLimits = std::numeric_limits<CompatibleNumberIntegerType>;
+
+    static constexpr auto value =
+        is_constructible<RealIntegerType,
+        CompatibleNumberIntegerType>::value &&
+        CompatibleLimits::is_integer &&
+        RealLimits::is_signed == CompatibleLimits::is_signed;
+};
+
+template<typename RealIntegerType, typename CompatibleNumberIntegerType>
+struct is_compatible_integer_type
+    : is_compatible_integer_type_impl<RealIntegerType,
+      CompatibleNumberIntegerType> {};
+
+template<typename BasicJsonType, typename CompatibleType, typename = void>
+struct is_compatible_type_impl: std::false_type {};
+
+template<typename BasicJsonType, typename CompatibleType>
+struct is_compatible_type_impl <
+    BasicJsonType, CompatibleType,
+    enable_if_t<is_complete_type<CompatibleType>::value >>
+{
+    static constexpr bool value =
+        has_to_json<BasicJsonType, CompatibleType>::value;
+};
+
+template<typename BasicJsonType, typename CompatibleType>
+struct is_compatible_type
+    : is_compatible_type_impl<BasicJsonType, CompatibleType> {};
+
+template<typename T1, typename T2>
+struct is_constructible_tuple : std::false_type {};
+
+template<typename T1, typename... Args>
+struct is_constructible_tuple<T1, std::tuple<Args...>> : conjunction<is_constructible<T1, Args>...> {};
+
+template<typename BasicJsonType, typename T>
+struct is_json_iterator_of : std::false_type {};
+
+template<typename BasicJsonType>
+struct is_json_iterator_of<BasicJsonType, typename BasicJsonType::iterator> : std::true_type {};
+
+template<typename BasicJsonType>
+struct is_json_iterator_of<BasicJsonType, typename BasicJsonType::const_iterator> : std::true_type
+{};
+
+// checks if a given type T is a template specialization of Primary
+template<template <typename...> class Primary, typename T>
+struct is_specialization_of : std::false_type {};
+
+template<template <typename...> class Primary, typename... Args>
+struct is_specialization_of<Primary, Primary<Args...>> : std::true_type {};
+
+template<typename T>
+using is_json_pointer = is_specialization_of<::nlohmann::json_pointer, uncvref_t<T>>;
+
+// checks if A and B are comparable using Compare functor
+template<typename Compare, typename A, typename B, typename = void>
+struct is_comparable : std::false_type {};
+
+template<typename Compare, typename A, typename B>
+struct is_comparable<Compare, A, B, void_t<
+decltype(std::declval<Compare>()(std::declval<A>(), std::declval<B>())),
+decltype(std::declval<Compare>()(std::declval<B>(), std::declval<A>()))
+>> : std::true_type {};
+
+template<typename T>
+using detect_is_transparent = typename T::is_transparent;
+
+// type trait to check if KeyType can be used as object key (without a BasicJsonType)
+// see is_usable_as_basic_json_key_type below
+template<typename Comparator, typename ObjectKeyType, typename KeyTypeCVRef, bool RequireTransparentComparator = true,
+         bool ExcludeObjectKeyType = RequireTransparentComparator, typename KeyType = uncvref_t<KeyTypeCVRef>>
+using is_usable_as_key_type = typename std::conditional <
+                              is_comparable<Comparator, ObjectKeyType, KeyTypeCVRef>::value
+                              && !(ExcludeObjectKeyType && std::is_same<KeyType,
+                                   ObjectKeyType>::value)
+                              && (!RequireTransparentComparator
+                                  || is_detected <detect_is_transparent, Comparator>::value)
+                              && !is_json_pointer<KeyType>::value,
+                              std::true_type,
+                              std::false_type >::type;
+
+// type trait to check if KeyType can be used as object key
+// true if:
+//   - KeyType is comparable with BasicJsonType::object_t::key_type
+//   - if ExcludeObjectKeyType is true, KeyType is not BasicJsonType::object_t::key_type
+//   - the comparator is transparent or RequireTransparentComparator is false
+//   - KeyType is not a JSON iterator or json_pointer
+template<typename BasicJsonType, typename KeyTypeCVRef, bool RequireTransparentComparator = true,
+         bool ExcludeObjectKeyType = RequireTransparentComparator, typename KeyType = uncvref_t<KeyTypeCVRef>>
+using is_usable_as_basic_json_key_type = typename std::conditional <
+        is_usable_as_key_type<typename BasicJsonType::object_comparator_t,
+        typename BasicJsonType::object_t::key_type, KeyTypeCVRef,
+        RequireTransparentComparator, ExcludeObjectKeyType>::value
+        && !is_json_iterator_of<BasicJsonType, KeyType>::value,
+        std::true_type,
+        std::false_type >::type;
+
+template<typename ObjectType, typename KeyType>
+using detect_erase_with_key_type = decltype(std::declval<ObjectType&>().erase(std::declval<KeyType>()));
+
+// type trait to check if object_t has an erase() member functions accepting KeyType
+template<typename BasicJsonType, typename KeyType>
+using has_erase_with_key_type = typename std::conditional <
+                                is_detected <
+                                detect_erase_with_key_type,
+                                typename BasicJsonType::object_t, KeyType >::value,
+                                std::true_type,
+                                std::false_type >::type;
+
+// a naive helper to check if a type is an ordered_map (exploits the fact that
+// ordered_map inherits capacity() from std::vector)
+template <typename T>
+struct is_ordered_map
+{
+    using one = char;
+
+    struct two
+    {
+        char x[2]; // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
+    };
+
+    template <typename C> static one test( decltype(&C::capacity) ) ;
+    template <typename C> static two test(...);
+
+    enum { value = sizeof(test<T>(nullptr)) == sizeof(char) }; // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
+};
+
+// to avoid useless casts (see https://github.com/nlohmann/json/issues/2893#issuecomment-889152324)
+template < typename T, typename U, enable_if_t < !std::is_same<T, U>::value, int > = 0 >
+T conditional_static_cast(U value)
+{
+    return static_cast<T>(value);
+}
+
+template<typename T, typename U, enable_if_t<std::is_same<T, U>::value, int> = 0>
+T conditional_static_cast(U value)
+{
+    return value;
+}
+
+template<typename... Types>
+using all_integral = conjunction<std::is_integral<Types>...>;
+
+template<typename... Types>
+using all_signed = conjunction<std::is_signed<Types>...>;
+
+template<typename... Types>
+using all_unsigned = conjunction<std::is_unsigned<Types>...>;
+
+// there's a disjunction trait in another PR; replace when merged
+template<typename... Types>
+using same_sign = std::integral_constant < bool,
+      all_signed<Types...>::value || all_unsigned<Types...>::value >;
+
+template<typename OfType, typename T>
+using never_out_of_range = std::integral_constant < bool,
+      (std::is_signed<OfType>::value && (sizeof(T) < sizeof(OfType)))
+      || (same_sign<OfType, T>::value && sizeof(OfType) == sizeof(T)) >;
+
+template<typename OfType, typename T,
+         bool OfTypeSigned = std::is_signed<OfType>::value,
+         bool TSigned = std::is_signed<T>::value>
+struct value_in_range_of_impl2;
+
+template<typename OfType, typename T>
+struct value_in_range_of_impl2<OfType, T, false, false>
+{
+    static constexpr bool test(T val)
+    {
+        using CommonType = typename std::common_type<OfType, T>::type;
+        return static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
+    }
+};
+
+template<typename OfType, typename T>
+struct value_in_range_of_impl2<OfType, T, true, false>
+{
+    static constexpr bool test(T val)
+    {
+        using CommonType = typename std::common_type<OfType, T>::type;
+        return static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
+    }
+};
+
+template<typename OfType, typename T>
+struct value_in_range_of_impl2<OfType, T, false, true>
+{
+    static constexpr bool test(T val)
+    {
+        using CommonType = typename std::common_type<OfType, T>::type;
+        return val >= 0 && static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
+    }
+};
+
+template<typename OfType, typename T>
+struct value_in_range_of_impl2<OfType, T, true, true>
+{
+    static constexpr bool test(T val)
+    {
+        using CommonType = typename std::common_type<OfType, T>::type;
+        return static_cast<CommonType>(val) >= static_cast<CommonType>((std::numeric_limits<OfType>::min)())
+               && static_cast<CommonType>(val) <= static_cast<CommonType>((std::numeric_limits<OfType>::max)());
+    }
+};
+
+template<typename OfType, typename T,
+         bool NeverOutOfRange = never_out_of_range<OfType, T>::value,
+         typename = detail::enable_if_t<all_integral<OfType, T>::value>>
+struct value_in_range_of_impl1;
+
+template<typename OfType, typename T>
+struct value_in_range_of_impl1<OfType, T, false>
+{
+    static constexpr bool test(T val)
+    {
+        return value_in_range_of_impl2<OfType, T>::test(val);
+    }
+};
+
+template<typename OfType, typename T>
+struct value_in_range_of_impl1<OfType, T, true>
+{
+    static constexpr bool test(T /*val*/)
+    {
+        return true;
+    }
+};
+
+template<typename OfType, typename T>
+inline constexpr bool value_in_range_of(T val)
+{
+    return value_in_range_of_impl1<OfType, T>::test(val);
+}
+
+template<bool Value>
+using bool_constant = std::integral_constant<bool, Value>;
+
+///////////////////////////////////////////////////////////////////////////////
+// is_c_string
+///////////////////////////////////////////////////////////////////////////////
+
+namespace impl
+{
+
+template<typename T>
+inline constexpr bool is_c_string()
+{
+    using TUnExt = typename std::remove_extent<T>::type;
+    using TUnCVExt = typename std::remove_cv<TUnExt>::type;
+    using TUnPtr = typename std::remove_pointer<T>::type;
+    using TUnCVPtr = typename std::remove_cv<TUnPtr>::type;
+    return
+        (std::is_array<T>::value && std::is_same<TUnCVExt, char>::value)
+        || (std::is_pointer<T>::value && std::is_same<TUnCVPtr, char>::value);
+}
+
+}  // namespace impl
+
+// checks whether T is a [cv] char */[cv] char[] C string
+template<typename T>
+struct is_c_string : bool_constant<impl::is_c_string<T>()> {};
+
+template<typename T>
+using is_c_string_uncvref = is_c_string<uncvref_t<T>>;
+
+///////////////////////////////////////////////////////////////////////////////
+// is_transparent
+///////////////////////////////////////////////////////////////////////////////
+
+namespace impl
+{
+
+template<typename T>
+inline constexpr bool is_transparent()
+{
+    return is_detected<detect_is_transparent, T>::value;
+}
+
+}  // namespace impl
+
+// checks whether T has a member named is_transparent
+template<typename T>
+struct is_transparent : bool_constant<impl::is_transparent<T>()> {};
+
+///////////////////////////////////////////////////////////////////////////////
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/string_concat.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cstring> // strlen
+#include <string> // string
+#include <utility> // forward
+
+// #include <nlohmann/detail/meta/cpp_future.hpp>
+
+// #include <nlohmann/detail/meta/detected.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+inline std::size_t concat_length()
+{
+    return 0;
+}
+
+template<typename... Args>
+inline std::size_t concat_length(const char* cstr, const Args& ... rest);
+
+template<typename StringType, typename... Args>
+inline std::size_t concat_length(const StringType& str, const Args& ... rest);
+
+template<typename... Args>
+inline std::size_t concat_length(const char /*c*/, const Args& ... rest)
+{
+    return 1 + concat_length(rest...);
+}
+
+template<typename... Args>
+inline std::size_t concat_length(const char* cstr, const Args& ... rest)
+{
+    // cppcheck-suppress ignoredReturnValue
+    return ::strlen(cstr) + concat_length(rest...);
+}
+
+template<typename StringType, typename... Args>
+inline std::size_t concat_length(const StringType& str, const Args& ... rest)
+{
+    return str.size() + concat_length(rest...);
+}
+
+template<typename OutStringType>
+inline void concat_into(OutStringType& /*out*/)
+{}
+
+template<typename StringType, typename Arg>
+using string_can_append = decltype(std::declval<StringType&>().append(std::declval < Arg && > ()));
+
+template<typename StringType, typename Arg>
+using detect_string_can_append = is_detected<string_can_append, StringType, Arg>;
+
+template<typename StringType, typename Arg>
+using string_can_append_op = decltype(std::declval<StringType&>() += std::declval < Arg && > ());
+
+template<typename StringType, typename Arg>
+using detect_string_can_append_op = is_detected<string_can_append_op, StringType, Arg>;
+
+template<typename StringType, typename Arg>
+using string_can_append_iter = decltype(std::declval<StringType&>().append(std::declval<const Arg&>().begin(), std::declval<const Arg&>().end()));
+
+template<typename StringType, typename Arg>
+using detect_string_can_append_iter = is_detected<string_can_append_iter, StringType, Arg>;
+
+template<typename StringType, typename Arg>
+using string_can_append_data = decltype(std::declval<StringType&>().append(std::declval<const Arg&>().data(), std::declval<const Arg&>().size()));
+
+template<typename StringType, typename Arg>
+using detect_string_can_append_data = is_detected<string_can_append_data, StringType, Arg>;
+
+template < typename OutStringType, typename Arg, typename... Args,
+           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
+                         && detect_string_can_append_op<OutStringType, Arg>::value, int > = 0 >
+inline void concat_into(OutStringType& out, Arg && arg, Args && ... rest);
+
+template < typename OutStringType, typename Arg, typename... Args,
+           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
+                         && !detect_string_can_append_op<OutStringType, Arg>::value
+                         && detect_string_can_append_iter<OutStringType, Arg>::value, int > = 0 >
+inline void concat_into(OutStringType& out, const Arg& arg, Args && ... rest);
+
+template < typename OutStringType, typename Arg, typename... Args,
+           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
+                         && !detect_string_can_append_op<OutStringType, Arg>::value
+                         && !detect_string_can_append_iter<OutStringType, Arg>::value
+                         && detect_string_can_append_data<OutStringType, Arg>::value, int > = 0 >
+inline void concat_into(OutStringType& out, const Arg& arg, Args && ... rest);
+
+template<typename OutStringType, typename Arg, typename... Args,
+         enable_if_t<detect_string_can_append<OutStringType, Arg>::value, int> = 0>
+inline void concat_into(OutStringType& out, Arg && arg, Args && ... rest)
+{
+    out.append(std::forward<Arg>(arg));
+    concat_into(out, std::forward<Args>(rest)...);
+}
+
+template < typename OutStringType, typename Arg, typename... Args,
+           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
+                         && detect_string_can_append_op<OutStringType, Arg>::value, int > >
+inline void concat_into(OutStringType& out, Arg&& arg, Args&& ... rest)
+{
+    out += std::forward<Arg>(arg);
+    concat_into(out, std::forward<Args>(rest)...);
+}
+
+template < typename OutStringType, typename Arg, typename... Args,
+           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
+                         && !detect_string_can_append_op<OutStringType, Arg>::value
+                         && detect_string_can_append_iter<OutStringType, Arg>::value, int > >
+inline void concat_into(OutStringType& out, const Arg& arg, Args&& ... rest)
+{
+    out.append(arg.begin(), arg.end());
+    concat_into(out, std::forward<Args>(rest)...);
+}
+
+template < typename OutStringType, typename Arg, typename... Args,
+           enable_if_t < !detect_string_can_append<OutStringType, Arg>::value
+                         && !detect_string_can_append_op<OutStringType, Arg>::value
+                         && !detect_string_can_append_iter<OutStringType, Arg>::value
+                         && detect_string_can_append_data<OutStringType, Arg>::value, int > >
+inline void concat_into(OutStringType& out, const Arg& arg, Args&& ... rest)
+{
+    out.append(arg.data(), arg.size());
+    concat_into(out, std::forward<Args>(rest)...);
+}
+
+template<typename OutStringType = std::string, typename... Args>
+inline OutStringType concat(Args && ... args)
+{
+    OutStringType str;
+    str.reserve(concat_length(args...));
+    concat_into(str, std::forward<Args>(args)...);
+    return str;
+}
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+////////////////
+// exceptions //
+////////////////
+
+/// @brief general exception of the @ref basic_json class
+/// @sa https://json.nlohmann.me/api/basic_json/exception/
+class exception : public std::exception
+{
+  public:
+    /// returns the explanatory string
+    const char* what() const noexcept override
+    {
+        return m.what();
+    }
+
+    /// the id of the exception
+    const int id; // NOLINT(cppcoreguidelines-non-private-member-variables-in-classes)
+
+  protected:
+    JSON_HEDLEY_NON_NULL(3)
+    exception(int id_, const char* what_arg) : id(id_), m(what_arg) {} // NOLINT(bugprone-throw-keyword-missing)
+
+    static std::string name(const std::string& ename, int id_)
+    {
+        return concat("[json.exception.", ename, '.', std::to_string(id_), "] ");
+    }
+
+    static std::string diagnostics(std::nullptr_t /*leaf_element*/)
+    {
+        return "";
+    }
+
+    template<typename BasicJsonType>
+    static std::string diagnostics(const BasicJsonType* leaf_element)
+    {
+#if JSON_DIAGNOSTICS
+        std::vector<std::string> tokens;
+        for (const auto* current = leaf_element; current != nullptr && current->m_parent != nullptr; current = current->m_parent)
+        {
+            switch (current->m_parent->type())
+            {
+                case value_t::array:
+                {
+                    for (std::size_t i = 0; i < current->m_parent->m_data.m_value.array->size(); ++i)
+                    {
+                        if (&current->m_parent->m_data.m_value.array->operator[](i) == current)
+                        {
+                            tokens.emplace_back(std::to_string(i));
+                            break;
+                        }
+                    }
+                    break;
+                }
+
+                case value_t::object:
+                {
+                    for (const auto& element : *current->m_parent->m_data.m_value.object)
+                    {
+                        if (&element.second == current)
+                        {
+                            tokens.emplace_back(element.first.c_str());
+                            break;
+                        }
+                    }
+                    break;
+                }
+
+                case value_t::null: // LCOV_EXCL_LINE
+                case value_t::string: // LCOV_EXCL_LINE
+                case value_t::boolean: // LCOV_EXCL_LINE
+                case value_t::number_integer: // LCOV_EXCL_LINE
+                case value_t::number_unsigned: // LCOV_EXCL_LINE
+                case value_t::number_float: // LCOV_EXCL_LINE
+                case value_t::binary: // LCOV_EXCL_LINE
+                case value_t::discarded: // LCOV_EXCL_LINE
+                default:   // LCOV_EXCL_LINE
+                    break; // LCOV_EXCL_LINE
+            }
+        }
+
+        if (tokens.empty())
+        {
+            return "";
+        }
+
+        auto str = std::accumulate(tokens.rbegin(), tokens.rend(), std::string{},
+                                   [](const std::string & a, const std::string & b)
+        {
+            return concat(a, '/', detail::escape(b));
+        });
+        return concat('(', str, ") ");
+#else
+        static_cast<void>(leaf_element);
+        return "";
+#endif
+    }
+
+  private:
+    /// an exception object as storage for error messages
+    std::runtime_error m;
+};
+
+/// @brief exception indicating a parse error
+/// @sa https://json.nlohmann.me/api/basic_json/parse_error/
+class parse_error : public exception
+{
+  public:
+    /*!
+    @brief create a parse error exception
+    @param[in] id_       the id of the exception
+    @param[in] pos       the position where the error occurred (or with
+                         chars_read_total=0 if the position cannot be
+                         determined)
+    @param[in] what_arg  the explanatory string
+    @return parse_error object
+    */
+    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
+    static parse_error create(int id_, const position_t& pos, const std::string& what_arg, BasicJsonContext context)
+    {
+        const std::string w = concat(exception::name("parse_error", id_), "parse error",
+                                     position_string(pos), ": ", exception::diagnostics(context), what_arg);
+        return {id_, pos.chars_read_total, w.c_str()};
+    }
+
+    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
+    static parse_error create(int id_, std::size_t byte_, const std::string& what_arg, BasicJsonContext context)
+    {
+        const std::string w = concat(exception::name("parse_error", id_), "parse error",
+                                     (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""),
+                                     ": ", exception::diagnostics(context), what_arg);
+        return {id_, byte_, w.c_str()};
+    }
+
+    /*!
+    @brief byte index of the parse error
+
+    The byte index of the last read character in the input file.
+
+    @note For an input with n bytes, 1 is the index of the first character and
+          n+1 is the index of the terminating null byte or the end of file.
+          This also holds true when reading a byte vector (CBOR or MessagePack).
+    */
+    const std::size_t byte;
+
+  private:
+    parse_error(int id_, std::size_t byte_, const char* what_arg)
+        : exception(id_, what_arg), byte(byte_) {}
+
+    static std::string position_string(const position_t& pos)
+    {
+        return concat(" at line ", std::to_string(pos.lines_read + 1),
+                      ", column ", std::to_string(pos.chars_read_current_line));
+    }
+};
+
+/// @brief exception indicating errors with iterators
+/// @sa https://json.nlohmann.me/api/basic_json/invalid_iterator/
+class invalid_iterator : public exception
+{
+  public:
+    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
+    static invalid_iterator create(int id_, const std::string& what_arg, BasicJsonContext context)
+    {
+        const std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg);
+        return {id_, w.c_str()};
+    }
+
+  private:
+    JSON_HEDLEY_NON_NULL(3)
+    invalid_iterator(int id_, const char* what_arg)
+        : exception(id_, what_arg) {}
+};
+
+/// @brief exception indicating executing a member function with a wrong type
+/// @sa https://json.nlohmann.me/api/basic_json/type_error/
+class type_error : public exception
+{
+  public:
+    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
+    static type_error create(int id_, const std::string& what_arg, BasicJsonContext context)
+    {
+        const std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg);
+        return {id_, w.c_str()};
+    }
+
+  private:
+    JSON_HEDLEY_NON_NULL(3)
+    type_error(int id_, const char* what_arg) : exception(id_, what_arg) {}
+};
+
+/// @brief exception indicating access out of the defined range
+/// @sa https://json.nlohmann.me/api/basic_json/out_of_range/
+class out_of_range : public exception
+{
+  public:
+    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
+    static out_of_range create(int id_, const std::string& what_arg, BasicJsonContext context)
+    {
+        const std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg);
+        return {id_, w.c_str()};
+    }
+
+  private:
+    JSON_HEDLEY_NON_NULL(3)
+    out_of_range(int id_, const char* what_arg) : exception(id_, what_arg) {}
+};
+
+/// @brief exception indicating other library errors
+/// @sa https://json.nlohmann.me/api/basic_json/other_error/
+class other_error : public exception
+{
+  public:
+    template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
+    static other_error create(int id_, const std::string& what_arg, BasicJsonContext context)
+    {
+        const std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg);
+        return {id_, w.c_str()};
+    }
+
+  private:
+    JSON_HEDLEY_NON_NULL(3)
+    other_error(int id_, const char* what_arg) : exception(id_, what_arg) {}
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/cpp_future.hpp>
+
+// #include <nlohmann/detail/meta/identity_tag.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+// dispatching helper struct
+template <class T> struct identity_tag {};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/meta/std_fs.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+
+#if JSON_HAS_EXPERIMENTAL_FILESYSTEM
+#include <experimental/filesystem>
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+namespace std_fs = std::experimental::filesystem;
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+#elif JSON_HAS_FILESYSTEM
+#include <filesystem>
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+namespace std_fs = std::filesystem;
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+#endif
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+// #include <nlohmann/detail/string_concat.hpp>
+
+// #include <nlohmann/detail/value_t.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+template<typename BasicJsonType>
+inline void from_json(const BasicJsonType& j, typename std::nullptr_t& n)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_null()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be null, but is ", j.type_name()), &j));
+    }
+    n = nullptr;
+}
+
+// overloads for basic_json template parameters
+template < typename BasicJsonType, typename ArithmeticType,
+           enable_if_t < std::is_arithmetic<ArithmeticType>::value&&
+                         !std::is_same<ArithmeticType, typename BasicJsonType::boolean_t>::value,
+                         int > = 0 >
+void get_arithmetic_value(const BasicJsonType& j, ArithmeticType& val)
+{
+    switch (static_cast<value_t>(j))
+    {
+        case value_t::number_unsigned:
+        {
+            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_unsigned_t*>());
+            break;
+        }
+        case value_t::number_integer:
+        {
+            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_integer_t*>());
+            break;
+        }
+        case value_t::number_float:
+        {
+            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_float_t*>());
+            break;
+        }
+
+        case value_t::null:
+        case value_t::object:
+        case value_t::array:
+        case value_t::string:
+        case value_t::boolean:
+        case value_t::binary:
+        case value_t::discarded:
+        default:
+            JSON_THROW(type_error::create(302, concat("type must be number, but is ", j.type_name()), &j));
+    }
+}
+
+template<typename BasicJsonType>
+inline void from_json(const BasicJsonType& j, typename BasicJsonType::boolean_t& b)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_boolean()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be boolean, but is ", j.type_name()), &j));
+    }
+    b = *j.template get_ptr<const typename BasicJsonType::boolean_t*>();
+}
+
+template<typename BasicJsonType>
+inline void from_json(const BasicJsonType& j, typename BasicJsonType::string_t& s)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_string()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be string, but is ", j.type_name()), &j));
+    }
+    s = *j.template get_ptr<const typename BasicJsonType::string_t*>();
+}
+
+template <
+    typename BasicJsonType, typename StringType,
+    enable_if_t <
+        std::is_assignable<StringType&, const typename BasicJsonType::string_t>::value
+        && is_detected_exact<typename BasicJsonType::string_t::value_type, value_type_t, StringType>::value
+        && !std::is_same<typename BasicJsonType::string_t, StringType>::value
+        && !is_json_ref<StringType>::value, int > = 0 >
+inline void from_json(const BasicJsonType& j, StringType& s)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_string()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be string, but is ", j.type_name()), &j));
+    }
+
+    s = *j.template get_ptr<const typename BasicJsonType::string_t*>();
+}
+
+template<typename BasicJsonType>
+inline void from_json(const BasicJsonType& j, typename BasicJsonType::number_float_t& val)
+{
+    get_arithmetic_value(j, val);
+}
+
+template<typename BasicJsonType>
+inline void from_json(const BasicJsonType& j, typename BasicJsonType::number_unsigned_t& val)
+{
+    get_arithmetic_value(j, val);
+}
+
+template<typename BasicJsonType>
+inline void from_json(const BasicJsonType& j, typename BasicJsonType::number_integer_t& val)
+{
+    get_arithmetic_value(j, val);
+}
+
+#if !JSON_DISABLE_ENUM_SERIALIZATION
+template<typename BasicJsonType, typename EnumType,
+         enable_if_t<std::is_enum<EnumType>::value, int> = 0>
+inline void from_json(const BasicJsonType& j, EnumType& e)
+{
+    typename std::underlying_type<EnumType>::type val;
+    get_arithmetic_value(j, val);
+    e = static_cast<EnumType>(val);
+}
+#endif  // JSON_DISABLE_ENUM_SERIALIZATION
+
+// forward_list doesn't have an insert method
+template<typename BasicJsonType, typename T, typename Allocator,
+         enable_if_t<is_getable<BasicJsonType, T>::value, int> = 0>
+inline void from_json(const BasicJsonType& j, std::forward_list<T, Allocator>& l)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
+    }
+    l.clear();
+    std::transform(j.rbegin(), j.rend(),
+                   std::front_inserter(l), [](const BasicJsonType & i)
+    {
+        return i.template get<T>();
+    });
+}
+
+// valarray doesn't have an insert method
+template<typename BasicJsonType, typename T,
+         enable_if_t<is_getable<BasicJsonType, T>::value, int> = 0>
+inline void from_json(const BasicJsonType& j, std::valarray<T>& l)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
+    }
+    l.resize(j.size());
+    std::transform(j.begin(), j.end(), std::begin(l),
+                   [](const BasicJsonType & elem)
+    {
+        return elem.template get<T>();
+    });
+}
+
+template<typename BasicJsonType, typename T, std::size_t N>
+auto from_json(const BasicJsonType& j, T (&arr)[N])  // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
+-> decltype(j.template get<T>(), void())
+{
+    for (std::size_t i = 0; i < N; ++i)
+    {
+        arr[i] = j.at(i).template get<T>();
+    }
+}
+
+template<typename BasicJsonType>
+inline void from_json_array_impl(const BasicJsonType& j, typename BasicJsonType::array_t& arr, priority_tag<3> /*unused*/)
+{
+    arr = *j.template get_ptr<const typename BasicJsonType::array_t*>();
+}
+
+template<typename BasicJsonType, typename T, std::size_t N>
+auto from_json_array_impl(const BasicJsonType& j, std::array<T, N>& arr,
+                          priority_tag<2> /*unused*/)
+-> decltype(j.template get<T>(), void())
+{
+    for (std::size_t i = 0; i < N; ++i)
+    {
+        arr[i] = j.at(i).template get<T>();
+    }
+}
+
+template<typename BasicJsonType, typename ConstructibleArrayType,
+         enable_if_t<
+             std::is_assignable<ConstructibleArrayType&, ConstructibleArrayType>::value,
+             int> = 0>
+auto from_json_array_impl(const BasicJsonType& j, ConstructibleArrayType& arr, priority_tag<1> /*unused*/)
+-> decltype(
+    arr.reserve(std::declval<typename ConstructibleArrayType::size_type>()),
+    j.template get<typename ConstructibleArrayType::value_type>(),
+    void())
+{
+    using std::end;
+
+    ConstructibleArrayType ret;
+    ret.reserve(j.size());
+    std::transform(j.begin(), j.end(),
+                   std::inserter(ret, end(ret)), [](const BasicJsonType & i)
+    {
+        // get<BasicJsonType>() returns *this, this won't call a from_json
+        // method when value_type is BasicJsonType
+        return i.template get<typename ConstructibleArrayType::value_type>();
+    });
+    arr = std::move(ret);
+}
+
+template<typename BasicJsonType, typename ConstructibleArrayType,
+         enable_if_t<
+             std::is_assignable<ConstructibleArrayType&, ConstructibleArrayType>::value,
+             int> = 0>
+inline void from_json_array_impl(const BasicJsonType& j, ConstructibleArrayType& arr,
+                                 priority_tag<0> /*unused*/)
+{
+    using std::end;
+
+    ConstructibleArrayType ret;
+    std::transform(
+        j.begin(), j.end(), std::inserter(ret, end(ret)),
+        [](const BasicJsonType & i)
+    {
+        // get<BasicJsonType>() returns *this, this won't call a from_json
+        // method when value_type is BasicJsonType
+        return i.template get<typename ConstructibleArrayType::value_type>();
+    });
+    arr = std::move(ret);
+}
+
+template < typename BasicJsonType, typename ConstructibleArrayType,
+           enable_if_t <
+               is_constructible_array_type<BasicJsonType, ConstructibleArrayType>::value&&
+               !is_constructible_object_type<BasicJsonType, ConstructibleArrayType>::value&&
+               !is_constructible_string_type<BasicJsonType, ConstructibleArrayType>::value&&
+               !std::is_same<ConstructibleArrayType, typename BasicJsonType::binary_t>::value&&
+               !is_basic_json<ConstructibleArrayType>::value,
+               int > = 0 >
+auto from_json(const BasicJsonType& j, ConstructibleArrayType& arr)
+-> decltype(from_json_array_impl(j, arr, priority_tag<3> {}),
+j.template get<typename ConstructibleArrayType::value_type>(),
+void())
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
+    }
+
+    from_json_array_impl(j, arr, priority_tag<3> {});
+}
+
+template < typename BasicJsonType, typename T, std::size_t... Idx >
+std::array<T, sizeof...(Idx)> from_json_inplace_array_impl(BasicJsonType&& j,
+        identity_tag<std::array<T, sizeof...(Idx)>> /*unused*/, index_sequence<Idx...> /*unused*/)
+{
+    return { { std::forward<BasicJsonType>(j).at(Idx).template get<T>()... } };
+}
+
+template < typename BasicJsonType, typename T, std::size_t N >
+auto from_json(BasicJsonType&& j, identity_tag<std::array<T, N>> tag)
+-> decltype(from_json_inplace_array_impl(std::forward<BasicJsonType>(j), tag, make_index_sequence<N> {}))
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
+    }
+
+    return from_json_inplace_array_impl(std::forward<BasicJsonType>(j), tag, make_index_sequence<N> {});
+}
+
+template<typename BasicJsonType>
+inline void from_json(const BasicJsonType& j, typename BasicJsonType::binary_t& bin)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_binary()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be binary, but is ", j.type_name()), &j));
+    }
+
+    bin = *j.template get_ptr<const typename BasicJsonType::binary_t*>();
+}
+
+template<typename BasicJsonType, typename ConstructibleObjectType,
+         enable_if_t<is_constructible_object_type<BasicJsonType, ConstructibleObjectType>::value, int> = 0>
+inline void from_json(const BasicJsonType& j, ConstructibleObjectType& obj)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_object()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be object, but is ", j.type_name()), &j));
+    }
+
+    ConstructibleObjectType ret;
+    const auto* inner_object = j.template get_ptr<const typename BasicJsonType::object_t*>();
+    using value_type = typename ConstructibleObjectType::value_type;
+    std::transform(
+        inner_object->begin(), inner_object->end(),
+        std::inserter(ret, ret.begin()),
+        [](typename BasicJsonType::object_t::value_type const & p)
+    {
+        return value_type(p.first, p.second.template get<typename ConstructibleObjectType::mapped_type>());
+    });
+    obj = std::move(ret);
+}
+
+// overload for arithmetic types, not chosen for basic_json template arguments
+// (BooleanType, etc..); note: Is it really necessary to provide explicit
+// overloads for boolean_t etc. in case of a custom BooleanType which is not
+// an arithmetic type?
+template < typename BasicJsonType, typename ArithmeticType,
+           enable_if_t <
+               std::is_arithmetic<ArithmeticType>::value&&
+               !std::is_same<ArithmeticType, typename BasicJsonType::number_unsigned_t>::value&&
+               !std::is_same<ArithmeticType, typename BasicJsonType::number_integer_t>::value&&
+               !std::is_same<ArithmeticType, typename BasicJsonType::number_float_t>::value&&
+               !std::is_same<ArithmeticType, typename BasicJsonType::boolean_t>::value,
+               int > = 0 >
+inline void from_json(const BasicJsonType& j, ArithmeticType& val)
+{
+    switch (static_cast<value_t>(j))
+    {
+        case value_t::number_unsigned:
+        {
+            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_unsigned_t*>());
+            break;
+        }
+        case value_t::number_integer:
+        {
+            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_integer_t*>());
+            break;
+        }
+        case value_t::number_float:
+        {
+            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::number_float_t*>());
+            break;
+        }
+        case value_t::boolean:
+        {
+            val = static_cast<ArithmeticType>(*j.template get_ptr<const typename BasicJsonType::boolean_t*>());
+            break;
+        }
+
+        case value_t::null:
+        case value_t::object:
+        case value_t::array:
+        case value_t::string:
+        case value_t::binary:
+        case value_t::discarded:
+        default:
+            JSON_THROW(type_error::create(302, concat("type must be number, but is ", j.type_name()), &j));
+    }
+}
+
+template<typename BasicJsonType, typename... Args, std::size_t... Idx>
+std::tuple<Args...> from_json_tuple_impl_base(BasicJsonType&& j, index_sequence<Idx...> /*unused*/)
+{
+    return std::make_tuple(std::forward<BasicJsonType>(j).at(Idx).template get<Args>()...);
+}
+
+template < typename BasicJsonType, class A1, class A2 >
+std::pair<A1, A2> from_json_tuple_impl(BasicJsonType&& j, identity_tag<std::pair<A1, A2>> /*unused*/, priority_tag<0> /*unused*/)
+{
+    return {std::forward<BasicJsonType>(j).at(0).template get<A1>(),
+            std::forward<BasicJsonType>(j).at(1).template get<A2>()};
+}
+
+template<typename BasicJsonType, typename A1, typename A2>
+inline void from_json_tuple_impl(BasicJsonType&& j, std::pair<A1, A2>& p, priority_tag<1> /*unused*/)
+{
+    p = from_json_tuple_impl(std::forward<BasicJsonType>(j), identity_tag<std::pair<A1, A2>> {}, priority_tag<0> {});
+}
+
+template<typename BasicJsonType, typename... Args>
+std::tuple<Args...> from_json_tuple_impl(BasicJsonType&& j, identity_tag<std::tuple<Args...>> /*unused*/, priority_tag<2> /*unused*/)
+{
+    return from_json_tuple_impl_base<BasicJsonType, Args...>(std::forward<BasicJsonType>(j), index_sequence_for<Args...> {});
+}
+
+template<typename BasicJsonType, typename... Args>
+inline void from_json_tuple_impl(BasicJsonType&& j, std::tuple<Args...>& t, priority_tag<3> /*unused*/)
+{
+    t = from_json_tuple_impl_base<BasicJsonType, Args...>(std::forward<BasicJsonType>(j), index_sequence_for<Args...> {});
+}
+
+template<typename BasicJsonType, typename TupleRelated>
+auto from_json(BasicJsonType&& j, TupleRelated&& t)
+-> decltype(from_json_tuple_impl(std::forward<BasicJsonType>(j), std::forward<TupleRelated>(t), priority_tag<3> {}))
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
+    }
+
+    return from_json_tuple_impl(std::forward<BasicJsonType>(j), std::forward<TupleRelated>(t), priority_tag<3> {});
+}
+
+template < typename BasicJsonType, typename Key, typename Value, typename Compare, typename Allocator,
+           typename = enable_if_t < !std::is_constructible <
+                                        typename BasicJsonType::string_t, Key >::value >>
+inline void from_json(const BasicJsonType& j, std::map<Key, Value, Compare, Allocator>& m)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
+    }
+    m.clear();
+    for (const auto& p : j)
+    {
+        if (JSON_HEDLEY_UNLIKELY(!p.is_array()))
+        {
+            JSON_THROW(type_error::create(302, concat("type must be array, but is ", p.type_name()), &j));
+        }
+        m.emplace(p.at(0).template get<Key>(), p.at(1).template get<Value>());
+    }
+}
+
+template < typename BasicJsonType, typename Key, typename Value, typename Hash, typename KeyEqual, typename Allocator,
+           typename = enable_if_t < !std::is_constructible <
+                                        typename BasicJsonType::string_t, Key >::value >>
+inline void from_json(const BasicJsonType& j, std::unordered_map<Key, Value, Hash, KeyEqual, Allocator>& m)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_array()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be array, but is ", j.type_name()), &j));
+    }
+    m.clear();
+    for (const auto& p : j)
+    {
+        if (JSON_HEDLEY_UNLIKELY(!p.is_array()))
+        {
+            JSON_THROW(type_error::create(302, concat("type must be array, but is ", p.type_name()), &j));
+        }
+        m.emplace(p.at(0).template get<Key>(), p.at(1).template get<Value>());
+    }
+}
+
+#if JSON_HAS_FILESYSTEM || JSON_HAS_EXPERIMENTAL_FILESYSTEM
+template<typename BasicJsonType>
+inline void from_json(const BasicJsonType& j, std_fs::path& p)
+{
+    if (JSON_HEDLEY_UNLIKELY(!j.is_string()))
+    {
+        JSON_THROW(type_error::create(302, concat("type must be string, but is ", j.type_name()), &j));
+    }
+    p = *j.template get_ptr<const typename BasicJsonType::string_t*>();
+}
+#endif
+
+struct from_json_fn
+{
+    template<typename BasicJsonType, typename T>
+    auto operator()(const BasicJsonType& j, T&& val) const
+    noexcept(noexcept(from_json(j, std::forward<T>(val))))
+    -> decltype(from_json(j, std::forward<T>(val)))
+    {
+        return from_json(j, std::forward<T>(val));
+    }
+};
+
+}  // namespace detail
+
+#ifndef JSON_HAS_CPP_17
+/// namespace to hold default `from_json` function
+/// to see why this is required:
+/// http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2015/n4381.html
+namespace // NOLINT(cert-dcl59-cpp,fuchsia-header-anon-namespaces,google-build-namespaces)
+{
+#endif
+JSON_INLINE_VARIABLE constexpr const auto& from_json = // NOLINT(misc-definitions-in-headers)
+    detail::static_const<detail::from_json_fn>::value;
+#ifndef JSON_HAS_CPP_17
+}  // namespace
+#endif
+
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/conversions/to_json.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <algorithm> // copy
+#include <iterator> // begin, end
+#include <string> // string
+#include <tuple> // tuple, get
+#include <type_traits> // is_same, is_constructible, is_floating_point, is_enum, underlying_type
+#include <utility> // move, forward, declval, pair
+#include <valarray> // valarray
+#include <vector> // vector
+
+// #include <nlohmann/detail/iterators/iteration_proxy.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cstddef> // size_t
+#include <iterator> // input_iterator_tag
+#include <string> // string, to_string
+#include <tuple> // tuple_size, get, tuple_element
+#include <utility> // move
+
+#if JSON_HAS_RANGES
+    #include <ranges> // enable_borrowed_range
+#endif
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+// #include <nlohmann/detail/value_t.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+template<typename string_type>
+void int_to_string( string_type& target, std::size_t value )
+{
+    // For ADL
+    using std::to_string;
+    target = to_string(value);
+}
+template<typename IteratorType> class iteration_proxy_value
+{
+  public:
+    using difference_type = std::ptrdiff_t;
+    using value_type = iteration_proxy_value;
+    using pointer = value_type *;
+    using reference = value_type &;
+    using iterator_category = std::input_iterator_tag;
+    using string_type = typename std::remove_cv< typename std::remove_reference<decltype( std::declval<IteratorType>().key() ) >::type >::type;
+
+  private:
+    /// the iterator
+    IteratorType anchor{};
+    /// an index for arrays (used to create key names)
+    std::size_t array_index = 0;
+    /// last stringified array index
+    mutable std::size_t array_index_last = 0;
+    /// a string representation of the array index
+    mutable string_type array_index_str = "0";
+    /// an empty string (to return a reference for primitive values)
+    string_type empty_str{};
+
+  public:
+    explicit iteration_proxy_value() = default;
+    explicit iteration_proxy_value(IteratorType it, std::size_t array_index_ = 0)
+    noexcept(std::is_nothrow_move_constructible<IteratorType>::value
+             && std::is_nothrow_default_constructible<string_type>::value)
+        : anchor(std::move(it))
+        , array_index(array_index_)
+    {}
+
+    iteration_proxy_value(iteration_proxy_value const&) = default;
+    iteration_proxy_value& operator=(iteration_proxy_value const&) = default;
+    // older GCCs are a bit fussy and require explicit noexcept specifiers on defaulted functions
+    iteration_proxy_value(iteration_proxy_value&&)
+    noexcept(std::is_nothrow_move_constructible<IteratorType>::value
+             && std::is_nothrow_move_constructible<string_type>::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations)
+    iteration_proxy_value& operator=(iteration_proxy_value&&)
+    noexcept(std::is_nothrow_move_assignable<IteratorType>::value
+             && std::is_nothrow_move_assignable<string_type>::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations)
+    ~iteration_proxy_value() = default;
+
+    /// dereference operator (needed for range-based for)
+    const iteration_proxy_value& operator*() const
+    {
+        return *this;
+    }
+
+    /// increment operator (needed for range-based for)
+    iteration_proxy_value& operator++()
+    {
+        ++anchor;
+        ++array_index;
+
+        return *this;
+    }
+
+    iteration_proxy_value operator++(int)& // NOLINT(cert-dcl21-cpp)
+    {
+        auto tmp = iteration_proxy_value(anchor, array_index);
+        ++anchor;
+        ++array_index;
+        return tmp;
+    }
+
+    /// equality operator (needed for InputIterator)
+    bool operator==(const iteration_proxy_value& o) const
+    {
+        return anchor == o.anchor;
+    }
+
+    /// inequality operator (needed for range-based for)
+    bool operator!=(const iteration_proxy_value& o) const
+    {
+        return anchor != o.anchor;
+    }
+
+    /// return key of the iterator
+    const string_type& key() const
+    {
+        JSON_ASSERT(anchor.m_object != nullptr);
+
+        switch (anchor.m_object->type())
+        {
+            // use integer array index as key
+            case value_t::array:
+            {
+                if (array_index != array_index_last)
+                {
+                    int_to_string( array_index_str, array_index );
+                    array_index_last = array_index;
+                }
+                return array_index_str;
+            }
+
+            // use key from the object
+            case value_t::object:
+                return anchor.key();
+
+            // use an empty key for all primitive types
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+                return empty_str;
+        }
+    }
+
+    /// return value of the iterator
+    typename IteratorType::reference value() const
+    {
+        return anchor.value();
+    }
+};
+
+/// proxy class for the items() function
+template<typename IteratorType> class iteration_proxy
+{
+  private:
+    /// the container to iterate
+    typename IteratorType::pointer container = nullptr;
+
+  public:
+    explicit iteration_proxy() = default;
+
+    /// construct iteration proxy from a container
+    explicit iteration_proxy(typename IteratorType::reference cont) noexcept
+        : container(&cont) {}
+
+    iteration_proxy(iteration_proxy const&) = default;
+    iteration_proxy& operator=(iteration_proxy const&) = default;
+    iteration_proxy(iteration_proxy&&) noexcept = default;
+    iteration_proxy& operator=(iteration_proxy&&) noexcept = default;
+    ~iteration_proxy() = default;
+
+    /// return iterator begin (needed for range-based for)
+    iteration_proxy_value<IteratorType> begin() const noexcept
+    {
+        return iteration_proxy_value<IteratorType>(container->begin());
+    }
+
+    /// return iterator end (needed for range-based for)
+    iteration_proxy_value<IteratorType> end() const noexcept
+    {
+        return iteration_proxy_value<IteratorType>(container->end());
+    }
+};
+
+// Structured Bindings Support
+// For further reference see https://blog.tartanllama.xyz/structured-bindings/
+// And see https://github.com/nlohmann/json/pull/1391
+template<std::size_t N, typename IteratorType, enable_if_t<N == 0, int> = 0>
+auto get(const nlohmann::detail::iteration_proxy_value<IteratorType>& i) -> decltype(i.key())
+{
+    return i.key();
+}
+// Structured Bindings Support
+// For further reference see https://blog.tartanllama.xyz/structured-bindings/
+// And see https://github.com/nlohmann/json/pull/1391
+template<std::size_t N, typename IteratorType, enable_if_t<N == 1, int> = 0>
+auto get(const nlohmann::detail::iteration_proxy_value<IteratorType>& i) -> decltype(i.value())
+{
+    return i.value();
+}
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// The Addition to the STD Namespace is required to add
+// Structured Bindings Support to the iteration_proxy_value class
+// For further reference see https://blog.tartanllama.xyz/structured-bindings/
+// And see https://github.com/nlohmann/json/pull/1391
+namespace std
+{
+
+#if defined(__clang__)
+    // Fix: https://github.com/nlohmann/json/issues/1401
+    #pragma clang diagnostic push
+    #pragma clang diagnostic ignored "-Wmismatched-tags"
+#endif
+template<typename IteratorType>
+class tuple_size<::nlohmann::detail::iteration_proxy_value<IteratorType>> // NOLINT(cert-dcl58-cpp)
+            : public std::integral_constant<std::size_t, 2> {};
+
+template<std::size_t N, typename IteratorType>
+class tuple_element<N, ::nlohmann::detail::iteration_proxy_value<IteratorType >> // NOLINT(cert-dcl58-cpp)
+{
+  public:
+    using type = decltype(
+                     get<N>(std::declval <
+                            ::nlohmann::detail::iteration_proxy_value<IteratorType >> ()));
+};
+#if defined(__clang__)
+    #pragma clang diagnostic pop
+#endif
+
+}  // namespace std
+
+#if JSON_HAS_RANGES
+    template <typename IteratorType>
+    inline constexpr bool ::std::ranges::enable_borrowed_range<::nlohmann::detail::iteration_proxy<IteratorType>> = true;
+#endif
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/cpp_future.hpp>
+
+// #include <nlohmann/detail/meta/std_fs.hpp>
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+// #include <nlohmann/detail/value_t.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+//////////////////
+// constructors //
+//////////////////
+
+/*
+ * Note all external_constructor<>::construct functions need to call
+ * j.m_data.m_value.destroy(j.m_data.m_type) to avoid a memory leak in case j contains an
+ * allocated value (e.g., a string). See bug issue
+ * https://github.com/nlohmann/json/issues/2865 for more information.
+ */
+
+template<value_t> struct external_constructor;
+
+template<>
+struct external_constructor<value_t::boolean>
+{
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, typename BasicJsonType::boolean_t b) noexcept
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::boolean;
+        j.m_data.m_value = b;
+        j.assert_invariant();
+    }
+};
+
+template<>
+struct external_constructor<value_t::string>
+{
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, const typename BasicJsonType::string_t& s)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::string;
+        j.m_data.m_value = s;
+        j.assert_invariant();
+    }
+
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, typename BasicJsonType::string_t&& s)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::string;
+        j.m_data.m_value = std::move(s);
+        j.assert_invariant();
+    }
+
+    template < typename BasicJsonType, typename CompatibleStringType,
+               enable_if_t < !std::is_same<CompatibleStringType, typename BasicJsonType::string_t>::value,
+                             int > = 0 >
+    static void construct(BasicJsonType& j, const CompatibleStringType& str)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::string;
+        j.m_data.m_value.string = j.template create<typename BasicJsonType::string_t>(str);
+        j.assert_invariant();
+    }
+};
+
+template<>
+struct external_constructor<value_t::binary>
+{
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, const typename BasicJsonType::binary_t& b)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::binary;
+        j.m_data.m_value = typename BasicJsonType::binary_t(b);
+        j.assert_invariant();
+    }
+
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, typename BasicJsonType::binary_t&& b)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::binary;
+        j.m_data.m_value = typename BasicJsonType::binary_t(std::move(b));
+        j.assert_invariant();
+    }
+};
+
+template<>
+struct external_constructor<value_t::number_float>
+{
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, typename BasicJsonType::number_float_t val) noexcept
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::number_float;
+        j.m_data.m_value = val;
+        j.assert_invariant();
+    }
+};
+
+template<>
+struct external_constructor<value_t::number_unsigned>
+{
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, typename BasicJsonType::number_unsigned_t val) noexcept
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::number_unsigned;
+        j.m_data.m_value = val;
+        j.assert_invariant();
+    }
+};
+
+template<>
+struct external_constructor<value_t::number_integer>
+{
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, typename BasicJsonType::number_integer_t val) noexcept
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::number_integer;
+        j.m_data.m_value = val;
+        j.assert_invariant();
+    }
+};
+
+template<>
+struct external_constructor<value_t::array>
+{
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, const typename BasicJsonType::array_t& arr)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = arr;
+        j.set_parents();
+        j.assert_invariant();
+    }
+
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, typename BasicJsonType::array_t&& arr)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = std::move(arr);
+        j.set_parents();
+        j.assert_invariant();
+    }
+
+    template < typename BasicJsonType, typename CompatibleArrayType,
+               enable_if_t < !std::is_same<CompatibleArrayType, typename BasicJsonType::array_t>::value,
+                             int > = 0 >
+    static void construct(BasicJsonType& j, const CompatibleArrayType& arr)
+    {
+        using std::begin;
+        using std::end;
+
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value.array = j.template create<typename BasicJsonType::array_t>(begin(arr), end(arr));
+        j.set_parents();
+        j.assert_invariant();
+    }
+
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, const std::vector<bool>& arr)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = value_t::array;
+        j.m_data.m_value.array->reserve(arr.size());
+        for (const bool x : arr)
+        {
+            j.m_data.m_value.array->push_back(x);
+            j.set_parent(j.m_data.m_value.array->back());
+        }
+        j.assert_invariant();
+    }
+
+    template<typename BasicJsonType, typename T,
+             enable_if_t<std::is_convertible<T, BasicJsonType>::value, int> = 0>
+    static void construct(BasicJsonType& j, const std::valarray<T>& arr)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = value_t::array;
+        j.m_data.m_value.array->resize(arr.size());
+        if (arr.size() > 0)
+        {
+            std::copy(std::begin(arr), std::end(arr), j.m_data.m_value.array->begin());
+        }
+        j.set_parents();
+        j.assert_invariant();
+    }
+};
+
+template<>
+struct external_constructor<value_t::object>
+{
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, const typename BasicJsonType::object_t& obj)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::object;
+        j.m_data.m_value = obj;
+        j.set_parents();
+        j.assert_invariant();
+    }
+
+    template<typename BasicJsonType>
+    static void construct(BasicJsonType& j, typename BasicJsonType::object_t&& obj)
+    {
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::object;
+        j.m_data.m_value = std::move(obj);
+        j.set_parents();
+        j.assert_invariant();
+    }
+
+    template < typename BasicJsonType, typename CompatibleObjectType,
+               enable_if_t < !std::is_same<CompatibleObjectType, typename BasicJsonType::object_t>::value, int > = 0 >
+    static void construct(BasicJsonType& j, const CompatibleObjectType& obj)
+    {
+        using std::begin;
+        using std::end;
+
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::object;
+        j.m_data.m_value.object = j.template create<typename BasicJsonType::object_t>(begin(obj), end(obj));
+        j.set_parents();
+        j.assert_invariant();
+    }
+};
+
+/////////////
+// to_json //
+/////////////
+
+template<typename BasicJsonType, typename T,
+         enable_if_t<std::is_same<T, typename BasicJsonType::boolean_t>::value, int> = 0>
+inline void to_json(BasicJsonType& j, T b) noexcept
+{
+    external_constructor<value_t::boolean>::construct(j, b);
+}
+
+template < typename BasicJsonType, typename BoolRef,
+           enable_if_t <
+               ((std::is_same<std::vector<bool>::reference, BoolRef>::value
+                 && !std::is_same <std::vector<bool>::reference, typename BasicJsonType::boolean_t&>::value)
+                || (std::is_same<std::vector<bool>::const_reference, BoolRef>::value
+                    && !std::is_same <detail::uncvref_t<std::vector<bool>::const_reference>,
+                                      typename BasicJsonType::boolean_t >::value))
+               && std::is_convertible<const BoolRef&, typename BasicJsonType::boolean_t>::value, int > = 0 >
+inline void to_json(BasicJsonType& j, const BoolRef& b) noexcept
+{
+    external_constructor<value_t::boolean>::construct(j, static_cast<typename BasicJsonType::boolean_t>(b));
+}
+
+template<typename BasicJsonType, typename CompatibleString,
+         enable_if_t<std::is_constructible<typename BasicJsonType::string_t, CompatibleString>::value, int> = 0>
+inline void to_json(BasicJsonType& j, const CompatibleString& s)
+{
+    external_constructor<value_t::string>::construct(j, s);
+}
+
+template<typename BasicJsonType>
+inline void to_json(BasicJsonType& j, typename BasicJsonType::string_t&& s)
+{
+    external_constructor<value_t::string>::construct(j, std::move(s));
+}
+
+template<typename BasicJsonType, typename FloatType,
+         enable_if_t<std::is_floating_point<FloatType>::value, int> = 0>
+inline void to_json(BasicJsonType& j, FloatType val) noexcept
+{
+    external_constructor<value_t::number_float>::construct(j, static_cast<typename BasicJsonType::number_float_t>(val));
+}
+
+template<typename BasicJsonType, typename CompatibleNumberUnsignedType,
+         enable_if_t<is_compatible_integer_type<typename BasicJsonType::number_unsigned_t, CompatibleNumberUnsignedType>::value, int> = 0>
+inline void to_json(BasicJsonType& j, CompatibleNumberUnsignedType val) noexcept
+{
+    external_constructor<value_t::number_unsigned>::construct(j, static_cast<typename BasicJsonType::number_unsigned_t>(val));
+}
+
+template<typename BasicJsonType, typename CompatibleNumberIntegerType,
+         enable_if_t<is_compatible_integer_type<typename BasicJsonType::number_integer_t, CompatibleNumberIntegerType>::value, int> = 0>
+inline void to_json(BasicJsonType& j, CompatibleNumberIntegerType val) noexcept
+{
+    external_constructor<value_t::number_integer>::construct(j, static_cast<typename BasicJsonType::number_integer_t>(val));
+}
+
+#if !JSON_DISABLE_ENUM_SERIALIZATION
+template<typename BasicJsonType, typename EnumType,
+         enable_if_t<std::is_enum<EnumType>::value, int> = 0>
+inline void to_json(BasicJsonType& j, EnumType e) noexcept
+{
+    using underlying_type = typename std::underlying_type<EnumType>::type;
+    external_constructor<value_t::number_integer>::construct(j, static_cast<underlying_type>(e));
+}
+#endif  // JSON_DISABLE_ENUM_SERIALIZATION
+
+template<typename BasicJsonType>
+inline void to_json(BasicJsonType& j, const std::vector<bool>& e)
+{
+    external_constructor<value_t::array>::construct(j, e);
+}
+
+template < typename BasicJsonType, typename CompatibleArrayType,
+           enable_if_t < is_compatible_array_type<BasicJsonType,
+                         CompatibleArrayType>::value&&
+                         !is_compatible_object_type<BasicJsonType, CompatibleArrayType>::value&&
+                         !is_compatible_string_type<BasicJsonType, CompatibleArrayType>::value&&
+                         !std::is_same<typename BasicJsonType::binary_t, CompatibleArrayType>::value&&
+                         !is_basic_json<CompatibleArrayType>::value,
+                         int > = 0 >
+inline void to_json(BasicJsonType& j, const CompatibleArrayType& arr)
+{
+    external_constructor<value_t::array>::construct(j, arr);
+}
+
+template<typename BasicJsonType>
+inline void to_json(BasicJsonType& j, const typename BasicJsonType::binary_t& bin)
+{
+    external_constructor<value_t::binary>::construct(j, bin);
+}
+
+template<typename BasicJsonType, typename T,
+         enable_if_t<std::is_convertible<T, BasicJsonType>::value, int> = 0>
+inline void to_json(BasicJsonType& j, const std::valarray<T>& arr)
+{
+    external_constructor<value_t::array>::construct(j, std::move(arr));
+}
+
+template<typename BasicJsonType>
+inline void to_json(BasicJsonType& j, typename BasicJsonType::array_t&& arr)
+{
+    external_constructor<value_t::array>::construct(j, std::move(arr));
+}
+
+template < typename BasicJsonType, typename CompatibleObjectType,
+           enable_if_t < is_compatible_object_type<BasicJsonType, CompatibleObjectType>::value&& !is_basic_json<CompatibleObjectType>::value, int > = 0 >
+inline void to_json(BasicJsonType& j, const CompatibleObjectType& obj)
+{
+    external_constructor<value_t::object>::construct(j, obj);
+}
+
+template<typename BasicJsonType>
+inline void to_json(BasicJsonType& j, typename BasicJsonType::object_t&& obj)
+{
+    external_constructor<value_t::object>::construct(j, std::move(obj));
+}
+
+template <
+    typename BasicJsonType, typename T, std::size_t N,
+    enable_if_t < !std::is_constructible<typename BasicJsonType::string_t,
+                  const T(&)[N]>::value, // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
+                  int > = 0 >
+inline void to_json(BasicJsonType& j, const T(&arr)[N]) // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
+{
+    external_constructor<value_t::array>::construct(j, arr);
+}
+
+template < typename BasicJsonType, typename T1, typename T2, enable_if_t < std::is_constructible<BasicJsonType, T1>::value&& std::is_constructible<BasicJsonType, T2>::value, int > = 0 >
+inline void to_json(BasicJsonType& j, const std::pair<T1, T2>& p)
+{
+    j = { p.first, p.second };
+}
+
+// for https://github.com/nlohmann/json/pull/1134
+template<typename BasicJsonType, typename T,
+         enable_if_t<std::is_same<T, iteration_proxy_value<typename BasicJsonType::iterator>>::value, int> = 0>
+inline void to_json(BasicJsonType& j, const T& b)
+{
+    j = { {b.key(), b.value()} };
+}
+
+template<typename BasicJsonType, typename Tuple, std::size_t... Idx>
+inline void to_json_tuple_impl(BasicJsonType& j, const Tuple& t, index_sequence<Idx...> /*unused*/)
+{
+    j = { std::get<Idx>(t)... };
+}
+
+template<typename BasicJsonType, typename T, enable_if_t<is_constructible_tuple<BasicJsonType, T>::value, int > = 0>
+inline void to_json(BasicJsonType& j, const T& t)
+{
+    to_json_tuple_impl(j, t, make_index_sequence<std::tuple_size<T>::value> {});
+}
+
+#if JSON_HAS_FILESYSTEM || JSON_HAS_EXPERIMENTAL_FILESYSTEM
+template<typename BasicJsonType>
+inline void to_json(BasicJsonType& j, const std_fs::path& p)
+{
+    j = p.string();
+}
+#endif
+
+struct to_json_fn
+{
+    template<typename BasicJsonType, typename T>
+    auto operator()(BasicJsonType& j, T&& val) const noexcept(noexcept(to_json(j, std::forward<T>(val))))
+    -> decltype(to_json(j, std::forward<T>(val)), void())
+    {
+        return to_json(j, std::forward<T>(val));
+    }
+};
+}  // namespace detail
+
+#ifndef JSON_HAS_CPP_17
+/// namespace to hold default `to_json` function
+/// to see why this is required:
+/// http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2015/n4381.html
+namespace // NOLINT(cert-dcl59-cpp,fuchsia-header-anon-namespaces,google-build-namespaces)
+{
+#endif
+JSON_INLINE_VARIABLE constexpr const auto& to_json = // NOLINT(misc-definitions-in-headers)
+    detail::static_const<detail::to_json_fn>::value;
+#ifndef JSON_HAS_CPP_17
+}  // namespace
+#endif
+
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/meta/identity_tag.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+
+/// @sa https://json.nlohmann.me/api/adl_serializer/
+template<typename ValueType, typename>
+struct adl_serializer
+{
+    /// @brief convert a JSON value to any value type
+    /// @sa https://json.nlohmann.me/api/adl_serializer/from_json/
+    template<typename BasicJsonType, typename TargetType = ValueType>
+    static auto from_json(BasicJsonType && j, TargetType& val) noexcept(
+        noexcept(::nlohmann::from_json(std::forward<BasicJsonType>(j), val)))
+    -> decltype(::nlohmann::from_json(std::forward<BasicJsonType>(j), val), void())
+    {
+        ::nlohmann::from_json(std::forward<BasicJsonType>(j), val);
+    }
+
+    /// @brief convert a JSON value to any value type
+    /// @sa https://json.nlohmann.me/api/adl_serializer/from_json/
+    template<typename BasicJsonType, typename TargetType = ValueType>
+    static auto from_json(BasicJsonType && j) noexcept(
+    noexcept(::nlohmann::from_json(std::forward<BasicJsonType>(j), detail::identity_tag<TargetType> {})))
+    -> decltype(::nlohmann::from_json(std::forward<BasicJsonType>(j), detail::identity_tag<TargetType> {}))
+    {
+        return ::nlohmann::from_json(std::forward<BasicJsonType>(j), detail::identity_tag<TargetType> {});
+    }
+
+    /// @brief convert any value type to a JSON value
+    /// @sa https://json.nlohmann.me/api/adl_serializer/to_json/
+    template<typename BasicJsonType, typename TargetType = ValueType>
+    static auto to_json(BasicJsonType& j, TargetType && val) noexcept(
+        noexcept(::nlohmann::to_json(j, std::forward<TargetType>(val))))
+    -> decltype(::nlohmann::to_json(j, std::forward<TargetType>(val)), void())
+    {
+        ::nlohmann::to_json(j, std::forward<TargetType>(val));
+    }
+};
+
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/byte_container_with_subtype.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cstdint> // uint8_t, uint64_t
+#include <tuple> // tie
+#include <utility> // move
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+
+/// @brief an internal type for a backed binary type
+/// @sa https://json.nlohmann.me/api/byte_container_with_subtype/
+template<typename BinaryType>
+class byte_container_with_subtype : public BinaryType
+{
+  public:
+    using container_type = BinaryType;
+    using subtype_type = std::uint64_t;
+
+    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
+    byte_container_with_subtype() noexcept(noexcept(container_type()))
+        : container_type()
+    {}
+
+    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
+    byte_container_with_subtype(const container_type& b) noexcept(noexcept(container_type(b)))
+        : container_type(b)
+    {}
+
+    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
+    byte_container_with_subtype(container_type&& b) noexcept(noexcept(container_type(std::move(b))))
+        : container_type(std::move(b))
+    {}
+
+    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
+    byte_container_with_subtype(const container_type& b, subtype_type subtype_) noexcept(noexcept(container_type(b)))
+        : container_type(b)
+        , m_subtype(subtype_)
+        , m_has_subtype(true)
+    {}
+
+    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/byte_container_with_subtype/
+    byte_container_with_subtype(container_type&& b, subtype_type subtype_) noexcept(noexcept(container_type(std::move(b))))
+        : container_type(std::move(b))
+        , m_subtype(subtype_)
+        , m_has_subtype(true)
+    {}
+
+    bool operator==(const byte_container_with_subtype& rhs) const
+    {
+        return std::tie(static_cast<const BinaryType&>(*this), m_subtype, m_has_subtype) ==
+               std::tie(static_cast<const BinaryType&>(rhs), rhs.m_subtype, rhs.m_has_subtype);
+    }
+
+    bool operator!=(const byte_container_with_subtype& rhs) const
+    {
+        return !(rhs == *this);
+    }
+
+    /// @brief sets the binary subtype
+    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/set_subtype/
+    void set_subtype(subtype_type subtype_) noexcept
+    {
+        m_subtype = subtype_;
+        m_has_subtype = true;
+    }
+
+    /// @brief return the binary subtype
+    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/subtype/
+    constexpr subtype_type subtype() const noexcept
+    {
+        return m_has_subtype ? m_subtype : static_cast<subtype_type>(-1);
+    }
+
+    /// @brief return whether the value has a subtype
+    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/has_subtype/
+    constexpr bool has_subtype() const noexcept
+    {
+        return m_has_subtype;
+    }
+
+    /// @brief clears the binary subtype
+    /// @sa https://json.nlohmann.me/api/byte_container_with_subtype/clear_subtype/
+    void clear_subtype() noexcept
+    {
+        m_subtype = 0;
+        m_has_subtype = false;
+    }
+
+  private:
+    subtype_type m_subtype = 0;
+    bool m_has_subtype = false;
+};
+
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/conversions/from_json.hpp>
+
+// #include <nlohmann/detail/conversions/to_json.hpp>
+
+// #include <nlohmann/detail/exceptions.hpp>
+
+// #include <nlohmann/detail/hash.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cstdint> // uint8_t
+#include <cstddef> // size_t
+#include <functional> // hash
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+// #include <nlohmann/detail/value_t.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+// boost::hash_combine
+inline std::size_t combine(std::size_t seed, std::size_t h) noexcept
+{
+    seed ^= h + 0x9e3779b9 + (seed << 6U) + (seed >> 2U);
+    return seed;
+}
+
+/*!
+@brief hash a JSON value
+
+The hash function tries to rely on std::hash where possible. Furthermore, the
+type of the JSON value is taken into account to have different hash values for
+null, 0, 0U, and false, etc.
+
+@tparam BasicJsonType basic_json specialization
+@param j JSON value to hash
+@return hash value of j
+*/
+template<typename BasicJsonType>
+std::size_t hash(const BasicJsonType& j)
+{
+    using string_t = typename BasicJsonType::string_t;
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+
+    const auto type = static_cast<std::size_t>(j.type());
+    switch (j.type())
+    {
+        case BasicJsonType::value_t::null:
+        case BasicJsonType::value_t::discarded:
+        {
+            return combine(type, 0);
+        }
+
+        case BasicJsonType::value_t::object:
+        {
+            auto seed = combine(type, j.size());
+            for (const auto& element : j.items())
+            {
+                const auto h = std::hash<string_t> {}(element.key());
+                seed = combine(seed, h);
+                seed = combine(seed, hash(element.value()));
+            }
+            return seed;
+        }
+
+        case BasicJsonType::value_t::array:
+        {
+            auto seed = combine(type, j.size());
+            for (const auto& element : j)
+            {
+                seed = combine(seed, hash(element));
+            }
+            return seed;
+        }
+
+        case BasicJsonType::value_t::string:
+        {
+            const auto h = std::hash<string_t> {}(j.template get_ref<const string_t&>());
+            return combine(type, h);
+        }
+
+        case BasicJsonType::value_t::boolean:
+        {
+            const auto h = std::hash<bool> {}(j.template get<bool>());
+            return combine(type, h);
+        }
+
+        case BasicJsonType::value_t::number_integer:
+        {
+            const auto h = std::hash<number_integer_t> {}(j.template get<number_integer_t>());
+            return combine(type, h);
+        }
+
+        case BasicJsonType::value_t::number_unsigned:
+        {
+            const auto h = std::hash<number_unsigned_t> {}(j.template get<number_unsigned_t>());
+            return combine(type, h);
+        }
+
+        case BasicJsonType::value_t::number_float:
+        {
+            const auto h = std::hash<number_float_t> {}(j.template get<number_float_t>());
+            return combine(type, h);
+        }
+
+        case BasicJsonType::value_t::binary:
+        {
+            auto seed = combine(type, j.get_binary().size());
+            const auto h = std::hash<bool> {}(j.get_binary().has_subtype());
+            seed = combine(seed, h);
+            seed = combine(seed, static_cast<std::size_t>(j.get_binary().subtype()));
+            for (const auto byte : j.get_binary())
+            {
+                seed = combine(seed, std::hash<std::uint8_t> {}(byte));
+            }
+            return seed;
+        }
+
+        default:                   // LCOV_EXCL_LINE
+            JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+            return 0;              // LCOV_EXCL_LINE
+    }
+}
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/input/binary_reader.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <algorithm> // generate_n
+#include <array> // array
+#include <cmath> // ldexp
+#include <cstddef> // size_t
+#include <cstdint> // uint8_t, uint16_t, uint32_t, uint64_t
+#include <cstdio> // snprintf
+#include <cstring> // memcpy
+#include <iterator> // back_inserter
+#include <limits> // numeric_limits
+#include <string> // char_traits, string
+#include <utility> // make_pair, move
+#include <vector> // vector
+
+// #include <nlohmann/detail/exceptions.hpp>
+
+// #include <nlohmann/detail/input/input_adapters.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <array> // array
+#include <cstddef> // size_t
+#include <cstring> // strlen
+#include <iterator> // begin, end, iterator_traits, random_access_iterator_tag, distance, next
+#include <memory> // shared_ptr, make_shared, addressof
+#include <numeric> // accumulate
+#include <string> // string, char_traits
+#include <type_traits> // enable_if, is_base_of, is_pointer, is_integral, remove_pointer
+#include <utility> // pair, declval
+
+#ifndef JSON_NO_IO
+    #include <cstdio>   // FILE *
+    #include <istream>  // istream
+#endif                  // JSON_NO_IO
+
+// #include <nlohmann/detail/iterators/iterator_traits.hpp>
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/// the supported input formats
+enum class input_format_t { json, cbor, msgpack, ubjson, bson, bjdata };
+
+////////////////////
+// input adapters //
+////////////////////
+
+#ifndef JSON_NO_IO
+/*!
+Input adapter for stdio file access. This adapter read only 1 byte and do not use any
+ buffer. This adapter is a very low level adapter.
+*/
+class file_input_adapter
+{
+  public:
+    using char_type = char;
+
+    JSON_HEDLEY_NON_NULL(2)
+    explicit file_input_adapter(std::FILE* f) noexcept
+        : m_file(f)
+    {
+        JSON_ASSERT(m_file != nullptr);
+    }
+
+    // make class move-only
+    file_input_adapter(const file_input_adapter&) = delete;
+    file_input_adapter(file_input_adapter&&) noexcept = default;
+    file_input_adapter& operator=(const file_input_adapter&) = delete;
+    file_input_adapter& operator=(file_input_adapter&&) = delete;
+    ~file_input_adapter() = default;
+
+    std::char_traits<char>::int_type get_character() noexcept
+    {
+        return std::fgetc(m_file);
+    }
+
+  private:
+    /// the file pointer to read from
+    std::FILE* m_file;
+};
+
+/*!
+Input adapter for a (caching) istream. Ignores a UFT Byte Order Mark at
+beginning of input. Does not support changing the underlying std::streambuf
+in mid-input. Maintains underlying std::istream and std::streambuf to support
+subsequent use of standard std::istream operations to process any input
+characters following those used in parsing the JSON input.  Clears the
+std::istream flags; any input errors (e.g., EOF) will be detected by the first
+subsequent call for input from the std::istream.
+*/
+class input_stream_adapter
+{
+  public:
+    using char_type = char;
+
+    ~input_stream_adapter()
+    {
+        // clear stream flags; we use underlying streambuf I/O, do not
+        // maintain ifstream flags, except eof
+        if (is != nullptr)
+        {
+            is->clear(is->rdstate() & std::ios::eofbit);
+        }
+    }
+
+    explicit input_stream_adapter(std::istream& i)
+        : is(&i), sb(i.rdbuf())
+    {}
+
+    // delete because of pointer members
+    input_stream_adapter(const input_stream_adapter&) = delete;
+    input_stream_adapter& operator=(input_stream_adapter&) = delete;
+    input_stream_adapter& operator=(input_stream_adapter&&) = delete;
+
+    input_stream_adapter(input_stream_adapter&& rhs) noexcept
+        : is(rhs.is), sb(rhs.sb)
+    {
+        rhs.is = nullptr;
+        rhs.sb = nullptr;
+    }
+
+    // std::istream/std::streambuf use std::char_traits<char>::to_int_type, to
+    // ensure that std::char_traits<char>::eof() and the character 0xFF do not
+    // end up as the same value, e.g. 0xFFFFFFFF.
+    std::char_traits<char>::int_type get_character()
+    {
+        auto res = sb->sbumpc();
+        // set eof manually, as we don't use the istream interface.
+        if (JSON_HEDLEY_UNLIKELY(res == std::char_traits<char>::eof()))
+        {
+            is->clear(is->rdstate() | std::ios::eofbit);
+        }
+        return res;
+    }
+
+  private:
+    /// the associated input stream
+    std::istream* is = nullptr;
+    std::streambuf* sb = nullptr;
+};
+#endif  // JSON_NO_IO
+
+// General-purpose iterator-based adapter. It might not be as fast as
+// theoretically possible for some containers, but it is extremely versatile.
+template<typename IteratorType>
+class iterator_input_adapter
+{
+  public:
+    using char_type = typename std::iterator_traits<IteratorType>::value_type;
+
+    iterator_input_adapter(IteratorType first, IteratorType last)
+        : current(std::move(first)), end(std::move(last))
+    {}
+
+    typename char_traits<char_type>::int_type get_character()
+    {
+        if (JSON_HEDLEY_LIKELY(current != end))
+        {
+            auto result = char_traits<char_type>::to_int_type(*current);
+            std::advance(current, 1);
+            return result;
+        }
+
+        return char_traits<char_type>::eof();
+    }
+
+  private:
+    IteratorType current;
+    IteratorType end;
+
+    template<typename BaseInputAdapter, size_t T>
+    friend struct wide_string_input_helper;
+
+    bool empty() const
+    {
+        return current == end;
+    }
+};
+
+template<typename BaseInputAdapter, size_t T>
+struct wide_string_input_helper;
+
+template<typename BaseInputAdapter>
+struct wide_string_input_helper<BaseInputAdapter, 4>
+{
+    // UTF-32
+    static void fill_buffer(BaseInputAdapter& input,
+                            std::array<std::char_traits<char>::int_type, 4>& utf8_bytes,
+                            size_t& utf8_bytes_index,
+                            size_t& utf8_bytes_filled)
+    {
+        utf8_bytes_index = 0;
+
+        if (JSON_HEDLEY_UNLIKELY(input.empty()))
+        {
+            utf8_bytes[0] = std::char_traits<char>::eof();
+            utf8_bytes_filled = 1;
+        }
+        else
+        {
+            // get the current character
+            const auto wc = input.get_character();
+
+            // UTF-32 to UTF-8 encoding
+            if (wc < 0x80)
+            {
+                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
+                utf8_bytes_filled = 1;
+            }
+            else if (wc <= 0x7FF)
+            {
+                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xC0u | ((static_cast<unsigned int>(wc) >> 6u) & 0x1Fu));
+                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
+                utf8_bytes_filled = 2;
+            }
+            else if (wc <= 0xFFFF)
+            {
+                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xE0u | ((static_cast<unsigned int>(wc) >> 12u) & 0x0Fu));
+                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 6u) & 0x3Fu));
+                utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
+                utf8_bytes_filled = 3;
+            }
+            else if (wc <= 0x10FFFF)
+            {
+                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xF0u | ((static_cast<unsigned int>(wc) >> 18u) & 0x07u));
+                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 12u) & 0x3Fu));
+                utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 6u) & 0x3Fu));
+                utf8_bytes[3] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
+                utf8_bytes_filled = 4;
+            }
+            else
+            {
+                // unknown character
+                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
+                utf8_bytes_filled = 1;
+            }
+        }
+    }
+};
+
+template<typename BaseInputAdapter>
+struct wide_string_input_helper<BaseInputAdapter, 2>
+{
+    // UTF-16
+    static void fill_buffer(BaseInputAdapter& input,
+                            std::array<std::char_traits<char>::int_type, 4>& utf8_bytes,
+                            size_t& utf8_bytes_index,
+                            size_t& utf8_bytes_filled)
+    {
+        utf8_bytes_index = 0;
+
+        if (JSON_HEDLEY_UNLIKELY(input.empty()))
+        {
+            utf8_bytes[0] = std::char_traits<char>::eof();
+            utf8_bytes_filled = 1;
+        }
+        else
+        {
+            // get the current character
+            const auto wc = input.get_character();
+
+            // UTF-16 to UTF-8 encoding
+            if (wc < 0x80)
+            {
+                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
+                utf8_bytes_filled = 1;
+            }
+            else if (wc <= 0x7FF)
+            {
+                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xC0u | ((static_cast<unsigned int>(wc) >> 6u)));
+                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
+                utf8_bytes_filled = 2;
+            }
+            else if (0xD800 > wc || wc >= 0xE000)
+            {
+                utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xE0u | ((static_cast<unsigned int>(wc) >> 12u)));
+                utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((static_cast<unsigned int>(wc) >> 6u) & 0x3Fu));
+                utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | (static_cast<unsigned int>(wc) & 0x3Fu));
+                utf8_bytes_filled = 3;
+            }
+            else
+            {
+                if (JSON_HEDLEY_UNLIKELY(!input.empty()))
+                {
+                    const auto wc2 = static_cast<unsigned int>(input.get_character());
+                    const auto charcode = 0x10000u + (((static_cast<unsigned int>(wc) & 0x3FFu) << 10u) | (wc2 & 0x3FFu));
+                    utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(0xF0u | (charcode >> 18u));
+                    utf8_bytes[1] = static_cast<std::char_traits<char>::int_type>(0x80u | ((charcode >> 12u) & 0x3Fu));
+                    utf8_bytes[2] = static_cast<std::char_traits<char>::int_type>(0x80u | ((charcode >> 6u) & 0x3Fu));
+                    utf8_bytes[3] = static_cast<std::char_traits<char>::int_type>(0x80u | (charcode & 0x3Fu));
+                    utf8_bytes_filled = 4;
+                }
+                else
+                {
+                    utf8_bytes[0] = static_cast<std::char_traits<char>::int_type>(wc);
+                    utf8_bytes_filled = 1;
+                }
+            }
+        }
+    }
+};
+
+// Wraps another input adapter to convert wide character types into individual bytes.
+template<typename BaseInputAdapter, typename WideCharType>
+class wide_string_input_adapter
+{
+  public:
+    using char_type = char;
+
+    wide_string_input_adapter(BaseInputAdapter base)
+        : base_adapter(base) {}
+
+    typename std::char_traits<char>::int_type get_character() noexcept
+    {
+        // check if buffer needs to be filled
+        if (utf8_bytes_index == utf8_bytes_filled)
+        {
+            fill_buffer<sizeof(WideCharType)>();
+
+            JSON_ASSERT(utf8_bytes_filled > 0);
+            JSON_ASSERT(utf8_bytes_index == 0);
+        }
+
+        // use buffer
+        JSON_ASSERT(utf8_bytes_filled > 0);
+        JSON_ASSERT(utf8_bytes_index < utf8_bytes_filled);
+        return utf8_bytes[utf8_bytes_index++];
+    }
+
+  private:
+    BaseInputAdapter base_adapter;
+
+    template<size_t T>
+    void fill_buffer()
+    {
+        wide_string_input_helper<BaseInputAdapter, T>::fill_buffer(base_adapter, utf8_bytes, utf8_bytes_index, utf8_bytes_filled);
+    }
+
+    /// a buffer for UTF-8 bytes
+    std::array<std::char_traits<char>::int_type, 4> utf8_bytes = {{0, 0, 0, 0}};
+
+    /// index to the utf8_codes array for the next valid byte
+    std::size_t utf8_bytes_index = 0;
+    /// number of valid bytes in the utf8_codes array
+    std::size_t utf8_bytes_filled = 0;
+};
+
+template<typename IteratorType, typename Enable = void>
+struct iterator_input_adapter_factory
+{
+    using iterator_type = IteratorType;
+    using char_type = typename std::iterator_traits<iterator_type>::value_type;
+    using adapter_type = iterator_input_adapter<iterator_type>;
+
+    static adapter_type create(IteratorType first, IteratorType last)
+    {
+        return adapter_type(std::move(first), std::move(last));
+    }
+};
+
+template<typename T>
+struct is_iterator_of_multibyte
+{
+    using value_type = typename std::iterator_traits<T>::value_type;
+    enum
+    {
+        value = sizeof(value_type) > 1
+    };
+};
+
+template<typename IteratorType>
+struct iterator_input_adapter_factory<IteratorType, enable_if_t<is_iterator_of_multibyte<IteratorType>::value>>
+{
+    using iterator_type = IteratorType;
+    using char_type = typename std::iterator_traits<iterator_type>::value_type;
+    using base_adapter_type = iterator_input_adapter<iterator_type>;
+    using adapter_type = wide_string_input_adapter<base_adapter_type, char_type>;
+
+    static adapter_type create(IteratorType first, IteratorType last)
+    {
+        return adapter_type(base_adapter_type(std::move(first), std::move(last)));
+    }
+};
+
+// General purpose iterator-based input
+template<typename IteratorType>
+typename iterator_input_adapter_factory<IteratorType>::adapter_type input_adapter(IteratorType first, IteratorType last)
+{
+    using factory_type = iterator_input_adapter_factory<IteratorType>;
+    return factory_type::create(first, last);
+}
+
+// Convenience shorthand from container to iterator
+// Enables ADL on begin(container) and end(container)
+// Encloses the using declarations in namespace for not to leak them to outside scope
+
+namespace container_input_adapter_factory_impl
+{
+
+using std::begin;
+using std::end;
+
+template<typename ContainerType, typename Enable = void>
+struct container_input_adapter_factory {};
+
+template<typename ContainerType>
+struct container_input_adapter_factory< ContainerType,
+       void_t<decltype(begin(std::declval<ContainerType>()), end(std::declval<ContainerType>()))>>
+       {
+           using adapter_type = decltype(input_adapter(begin(std::declval<ContainerType>()), end(std::declval<ContainerType>())));
+
+           static adapter_type create(const ContainerType& container)
+{
+    return input_adapter(begin(container), end(container));
+}
+       };
+
+}  // namespace container_input_adapter_factory_impl
+
+template<typename ContainerType>
+typename container_input_adapter_factory_impl::container_input_adapter_factory<ContainerType>::adapter_type input_adapter(const ContainerType& container)
+{
+    return container_input_adapter_factory_impl::container_input_adapter_factory<ContainerType>::create(container);
+}
+
+#ifndef JSON_NO_IO
+// Special cases with fast paths
+inline file_input_adapter input_adapter(std::FILE* file)
+{
+    return file_input_adapter(file);
+}
+
+inline input_stream_adapter input_adapter(std::istream& stream)
+{
+    return input_stream_adapter(stream);
+}
+
+inline input_stream_adapter input_adapter(std::istream&& stream)
+{
+    return input_stream_adapter(stream);
+}
+#endif  // JSON_NO_IO
+
+using contiguous_bytes_input_adapter = decltype(input_adapter(std::declval<const char*>(), std::declval<const char*>()));
+
+// Null-delimited strings, and the like.
+template < typename CharT,
+           typename std::enable_if <
+               std::is_pointer<CharT>::value&&
+               !std::is_array<CharT>::value&&
+               std::is_integral<typename std::remove_pointer<CharT>::type>::value&&
+               sizeof(typename std::remove_pointer<CharT>::type) == 1,
+               int >::type = 0 >
+contiguous_bytes_input_adapter input_adapter(CharT b)
+{
+    auto length = std::strlen(reinterpret_cast<const char*>(b));
+    const auto* ptr = reinterpret_cast<const char*>(b);
+    return input_adapter(ptr, ptr + length);
+}
+
+template<typename T, std::size_t N>
+auto input_adapter(T (&array)[N]) -> decltype(input_adapter(array, array + N)) // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
+{
+    return input_adapter(array, array + N);
+}
+
+// This class only handles inputs of input_buffer_adapter type.
+// It's required so that expressions like {ptr, len} can be implicitly cast
+// to the correct adapter.
+class span_input_adapter
+{
+  public:
+    template < typename CharT,
+               typename std::enable_if <
+                   std::is_pointer<CharT>::value&&
+                   std::is_integral<typename std::remove_pointer<CharT>::type>::value&&
+                   sizeof(typename std::remove_pointer<CharT>::type) == 1,
+                   int >::type = 0 >
+    span_input_adapter(CharT b, std::size_t l)
+        : ia(reinterpret_cast<const char*>(b), reinterpret_cast<const char*>(b) + l) {}
+
+    template<class IteratorType,
+             typename std::enable_if<
+                 std::is_same<typename iterator_traits<IteratorType>::iterator_category, std::random_access_iterator_tag>::value,
+                 int>::type = 0>
+    span_input_adapter(IteratorType first, IteratorType last)
+        : ia(input_adapter(first, last)) {}
+
+    contiguous_bytes_input_adapter&& get()
+    {
+        return std::move(ia); // NOLINT(hicpp-move-const-arg,performance-move-const-arg)
+    }
+
+  private:
+    contiguous_bytes_input_adapter ia;
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/input/json_sax.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cstddef>
+#include <string> // string
+#include <utility> // move
+#include <vector> // vector
+
+// #include <nlohmann/detail/exceptions.hpp>
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/string_concat.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+
+/*!
+@brief SAX interface
+
+This class describes the SAX interface used by @ref nlohmann::json::sax_parse.
+Each function is called in different situations while the input is parsed. The
+boolean return value informs the parser whether to continue processing the
+input.
+*/
+template<typename BasicJsonType>
+struct json_sax
+{
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+    using string_t = typename BasicJsonType::string_t;
+    using binary_t = typename BasicJsonType::binary_t;
+
+    /*!
+    @brief a null value was read
+    @return whether parsing should proceed
+    */
+    virtual bool null() = 0;
+
+    /*!
+    @brief a boolean value was read
+    @param[in] val  boolean value
+    @return whether parsing should proceed
+    */
+    virtual bool boolean(bool val) = 0;
+
+    /*!
+    @brief an integer number was read
+    @param[in] val  integer value
+    @return whether parsing should proceed
+    */
+    virtual bool number_integer(number_integer_t val) = 0;
+
+    /*!
+    @brief an unsigned integer number was read
+    @param[in] val  unsigned integer value
+    @return whether parsing should proceed
+    */
+    virtual bool number_unsigned(number_unsigned_t val) = 0;
+
+    /*!
+    @brief a floating-point number was read
+    @param[in] val  floating-point value
+    @param[in] s    raw token value
+    @return whether parsing should proceed
+    */
+    virtual bool number_float(number_float_t val, const string_t& s) = 0;
+
+    /*!
+    @brief a string value was read
+    @param[in] val  string value
+    @return whether parsing should proceed
+    @note It is safe to move the passed string value.
+    */
+    virtual bool string(string_t& val) = 0;
+
+    /*!
+    @brief a binary value was read
+    @param[in] val  binary value
+    @return whether parsing should proceed
+    @note It is safe to move the passed binary value.
+    */
+    virtual bool binary(binary_t& val) = 0;
+
+    /*!
+    @brief the beginning of an object was read
+    @param[in] elements  number of object elements or -1 if unknown
+    @return whether parsing should proceed
+    @note binary formats may report the number of elements
+    */
+    virtual bool start_object(std::size_t elements) = 0;
+
+    /*!
+    @brief an object key was read
+    @param[in] val  object key
+    @return whether parsing should proceed
+    @note It is safe to move the passed string.
+    */
+    virtual bool key(string_t& val) = 0;
+
+    /*!
+    @brief the end of an object was read
+    @return whether parsing should proceed
+    */
+    virtual bool end_object() = 0;
+
+    /*!
+    @brief the beginning of an array was read
+    @param[in] elements  number of array elements or -1 if unknown
+    @return whether parsing should proceed
+    @note binary formats may report the number of elements
+    */
+    virtual bool start_array(std::size_t elements) = 0;
+
+    /*!
+    @brief the end of an array was read
+    @return whether parsing should proceed
+    */
+    virtual bool end_array() = 0;
+
+    /*!
+    @brief a parse error occurred
+    @param[in] position    the position in the input where the error occurs
+    @param[in] last_token  the last read token
+    @param[in] ex          an exception object describing the error
+    @return whether parsing should proceed (must return false)
+    */
+    virtual bool parse_error(std::size_t position,
+                             const std::string& last_token,
+                             const detail::exception& ex) = 0;
+
+    json_sax() = default;
+    json_sax(const json_sax&) = default;
+    json_sax(json_sax&&) noexcept = default;
+    json_sax& operator=(const json_sax&) = default;
+    json_sax& operator=(json_sax&&) noexcept = default;
+    virtual ~json_sax() = default;
+};
+
+namespace detail
+{
+/*!
+@brief SAX implementation to create a JSON value from SAX events
+
+This class implements the @ref json_sax interface and processes the SAX events
+to create a JSON value which makes it basically a DOM parser. The structure or
+hierarchy of the JSON value is managed by the stack `ref_stack` which contains
+a pointer to the respective array or object for each recursion depth.
+
+After successful parsing, the value that is passed by reference to the
+constructor contains the parsed value.
+
+@tparam BasicJsonType  the JSON type
+*/
+template<typename BasicJsonType>
+class json_sax_dom_parser
+{
+  public:
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+    using string_t = typename BasicJsonType::string_t;
+    using binary_t = typename BasicJsonType::binary_t;
+
+    /*!
+    @param[in,out] r  reference to a JSON value that is manipulated while
+                       parsing
+    @param[in] allow_exceptions_  whether parse errors yield exceptions
+    */
+    explicit json_sax_dom_parser(BasicJsonType& r, const bool allow_exceptions_ = true)
+        : root(r), allow_exceptions(allow_exceptions_)
+    {}
+
+    // make class move-only
+    json_sax_dom_parser(const json_sax_dom_parser&) = delete;
+    json_sax_dom_parser(json_sax_dom_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
+    json_sax_dom_parser& operator=(const json_sax_dom_parser&) = delete;
+    json_sax_dom_parser& operator=(json_sax_dom_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
+    ~json_sax_dom_parser() = default;
+
+    bool null()
+    {
+        handle_value(nullptr);
+        return true;
+    }
+
+    bool boolean(bool val)
+    {
+        handle_value(val);
+        return true;
+    }
+
+    bool number_integer(number_integer_t val)
+    {
+        handle_value(val);
+        return true;
+    }
+
+    bool number_unsigned(number_unsigned_t val)
+    {
+        handle_value(val);
+        return true;
+    }
+
+    bool number_float(number_float_t val, const string_t& /*unused*/)
+    {
+        handle_value(val);
+        return true;
+    }
+
+    bool string(string_t& val)
+    {
+        handle_value(val);
+        return true;
+    }
+
+    bool binary(binary_t& val)
+    {
+        handle_value(std::move(val));
+        return true;
+    }
+
+    bool start_object(std::size_t len)
+    {
+        ref_stack.push_back(handle_value(BasicJsonType::value_t::object));
+
+        if (JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
+        {
+            JSON_THROW(out_of_range::create(408, concat("excessive object size: ", std::to_string(len)), ref_stack.back()));
+        }
+
+        return true;
+    }
+
+    bool key(string_t& val)
+    {
+        JSON_ASSERT(!ref_stack.empty());
+        JSON_ASSERT(ref_stack.back()->is_object());
+
+        // add null at given key and store the reference for later
+        object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val));
+        return true;
+    }
+
+    bool end_object()
+    {
+        JSON_ASSERT(!ref_stack.empty());
+        JSON_ASSERT(ref_stack.back()->is_object());
+
+        ref_stack.back()->set_parents();
+        ref_stack.pop_back();
+        return true;
+    }
+
+    bool start_array(std::size_t len)
+    {
+        ref_stack.push_back(handle_value(BasicJsonType::value_t::array));
+
+        if (JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
+        {
+            JSON_THROW(out_of_range::create(408, concat("excessive array size: ", std::to_string(len)), ref_stack.back()));
+        }
+
+        return true;
+    }
+
+    bool end_array()
+    {
+        JSON_ASSERT(!ref_stack.empty());
+        JSON_ASSERT(ref_stack.back()->is_array());
+
+        ref_stack.back()->set_parents();
+        ref_stack.pop_back();
+        return true;
+    }
+
+    template<class Exception>
+    bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/,
+                     const Exception& ex)
+    {
+        errored = true;
+        static_cast<void>(ex);
+        if (allow_exceptions)
+        {
+            JSON_THROW(ex);
+        }
+        return false;
+    }
+
+    constexpr bool is_errored() const
+    {
+        return errored;
+    }
+
+  private:
+    /*!
+    @invariant If the ref stack is empty, then the passed value will be the new
+               root.
+    @invariant If the ref stack contains a value, then it is an array or an
+               object to which we can add elements
+    */
+    template<typename Value>
+    JSON_HEDLEY_RETURNS_NON_NULL
+    BasicJsonType* handle_value(Value&& v)
+    {
+        if (ref_stack.empty())
+        {
+            root = BasicJsonType(std::forward<Value>(v));
+            return &root;
+        }
+
+        JSON_ASSERT(ref_stack.back()->is_array() || ref_stack.back()->is_object());
+
+        if (ref_stack.back()->is_array())
+        {
+            ref_stack.back()->m_data.m_value.array->emplace_back(std::forward<Value>(v));
+            return &(ref_stack.back()->m_data.m_value.array->back());
+        }
+
+        JSON_ASSERT(ref_stack.back()->is_object());
+        JSON_ASSERT(object_element);
+        *object_element = BasicJsonType(std::forward<Value>(v));
+        return object_element;
+    }
+
+    /// the parsed JSON value
+    BasicJsonType& root;
+    /// stack to model hierarchy of values
+    std::vector<BasicJsonType*> ref_stack {};
+    /// helper to hold the reference for the next object element
+    BasicJsonType* object_element = nullptr;
+    /// whether a syntax error occurred
+    bool errored = false;
+    /// whether to throw exceptions in case of errors
+    const bool allow_exceptions = true;
+};
+
+template<typename BasicJsonType>
+class json_sax_dom_callback_parser
+{
+  public:
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+    using string_t = typename BasicJsonType::string_t;
+    using binary_t = typename BasicJsonType::binary_t;
+    using parser_callback_t = typename BasicJsonType::parser_callback_t;
+    using parse_event_t = typename BasicJsonType::parse_event_t;
+
+    json_sax_dom_callback_parser(BasicJsonType& r,
+                                 const parser_callback_t cb,
+                                 const bool allow_exceptions_ = true)
+        : root(r), callback(cb), allow_exceptions(allow_exceptions_)
+    {
+        keep_stack.push_back(true);
+    }
+
+    // make class move-only
+    json_sax_dom_callback_parser(const json_sax_dom_callback_parser&) = delete;
+    json_sax_dom_callback_parser(json_sax_dom_callback_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
+    json_sax_dom_callback_parser& operator=(const json_sax_dom_callback_parser&) = delete;
+    json_sax_dom_callback_parser& operator=(json_sax_dom_callback_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
+    ~json_sax_dom_callback_parser() = default;
+
+    bool null()
+    {
+        handle_value(nullptr);
+        return true;
+    }
+
+    bool boolean(bool val)
+    {
+        handle_value(val);
+        return true;
+    }
+
+    bool number_integer(number_integer_t val)
+    {
+        handle_value(val);
+        return true;
+    }
+
+    bool number_unsigned(number_unsigned_t val)
+    {
+        handle_value(val);
+        return true;
+    }
+
+    bool number_float(number_float_t val, const string_t& /*unused*/)
+    {
+        handle_value(val);
+        return true;
+    }
+
+    bool string(string_t& val)
+    {
+        handle_value(val);
+        return true;
+    }
+
+    bool binary(binary_t& val)
+    {
+        handle_value(std::move(val));
+        return true;
+    }
+
+    bool start_object(std::size_t len)
+    {
+        // check callback for object start
+        const bool keep = callback(static_cast<int>(ref_stack.size()), parse_event_t::object_start, discarded);
+        keep_stack.push_back(keep);
+
+        auto val = handle_value(BasicJsonType::value_t::object, true);
+        ref_stack.push_back(val.second);
+
+        // check object limit
+        if (ref_stack.back() && JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
+        {
+            JSON_THROW(out_of_range::create(408, concat("excessive object size: ", std::to_string(len)), ref_stack.back()));
+        }
+
+        return true;
+    }
+
+    bool key(string_t& val)
+    {
+        BasicJsonType k = BasicJsonType(val);
+
+        // check callback for key
+        const bool keep = callback(static_cast<int>(ref_stack.size()), parse_event_t::key, k);
+        key_keep_stack.push_back(keep);
+
+        // add discarded value at given key and store the reference for later
+        if (keep && ref_stack.back())
+        {
+            object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val) = discarded);
+        }
+
+        return true;
+    }
+
+    bool end_object()
+    {
+        if (ref_stack.back())
+        {
+            if (!callback(static_cast<int>(ref_stack.size()) - 1, parse_event_t::object_end, *ref_stack.back()))
+            {
+                // discard object
+                *ref_stack.back() = discarded;
+            }
+            else
+            {
+                ref_stack.back()->set_parents();
+            }
+        }
+
+        JSON_ASSERT(!ref_stack.empty());
+        JSON_ASSERT(!keep_stack.empty());
+        ref_stack.pop_back();
+        keep_stack.pop_back();
+
+        if (!ref_stack.empty() && ref_stack.back() && ref_stack.back()->is_structured())
+        {
+            // remove discarded value
+            for (auto it = ref_stack.back()->begin(); it != ref_stack.back()->end(); ++it)
+            {
+                if (it->is_discarded())
+                {
+                    ref_stack.back()->erase(it);
+                    break;
+                }
+            }
+        }
+
+        return true;
+    }
+
+    bool start_array(std::size_t len)
+    {
+        const bool keep = callback(static_cast<int>(ref_stack.size()), parse_event_t::array_start, discarded);
+        keep_stack.push_back(keep);
+
+        auto val = handle_value(BasicJsonType::value_t::array, true);
+        ref_stack.push_back(val.second);
+
+        // check array limit
+        if (ref_stack.back() && JSON_HEDLEY_UNLIKELY(len != static_cast<std::size_t>(-1) && len > ref_stack.back()->max_size()))
+        {
+            JSON_THROW(out_of_range::create(408, concat("excessive array size: ", std::to_string(len)), ref_stack.back()));
+        }
+
+        return true;
+    }
+
+    bool end_array()
+    {
+        bool keep = true;
+
+        if (ref_stack.back())
+        {
+            keep = callback(static_cast<int>(ref_stack.size()) - 1, parse_event_t::array_end, *ref_stack.back());
+            if (keep)
+            {
+                ref_stack.back()->set_parents();
+            }
+            else
+            {
+                // discard array
+                *ref_stack.back() = discarded;
+            }
+        }
+
+        JSON_ASSERT(!ref_stack.empty());
+        JSON_ASSERT(!keep_stack.empty());
+        ref_stack.pop_back();
+        keep_stack.pop_back();
+
+        // remove discarded value
+        if (!keep && !ref_stack.empty() && ref_stack.back()->is_array())
+        {
+            ref_stack.back()->m_data.m_value.array->pop_back();
+        }
+
+        return true;
+    }
+
+    template<class Exception>
+    bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/,
+                     const Exception& ex)
+    {
+        errored = true;
+        static_cast<void>(ex);
+        if (allow_exceptions)
+        {
+            JSON_THROW(ex);
+        }
+        return false;
+    }
+
+    constexpr bool is_errored() const
+    {
+        return errored;
+    }
+
+  private:
+    /*!
+    @param[in] v  value to add to the JSON value we build during parsing
+    @param[in] skip_callback  whether we should skip calling the callback
+               function; this is required after start_array() and
+               start_object() SAX events, because otherwise we would call the
+               callback function with an empty array or object, respectively.
+
+    @invariant If the ref stack is empty, then the passed value will be the new
+               root.
+    @invariant If the ref stack contains a value, then it is an array or an
+               object to which we can add elements
+
+    @return pair of boolean (whether value should be kept) and pointer (to the
+            passed value in the ref_stack hierarchy; nullptr if not kept)
+    */
+    template<typename Value>
+    std::pair<bool, BasicJsonType*> handle_value(Value&& v, const bool skip_callback = false)
+    {
+        JSON_ASSERT(!keep_stack.empty());
+
+        // do not handle this value if we know it would be added to a discarded
+        // container
+        if (!keep_stack.back())
+        {
+            return {false, nullptr};
+        }
+
+        // create value
+        auto value = BasicJsonType(std::forward<Value>(v));
+
+        // check callback
+        const bool keep = skip_callback || callback(static_cast<int>(ref_stack.size()), parse_event_t::value, value);
+
+        // do not handle this value if we just learnt it shall be discarded
+        if (!keep)
+        {
+            return {false, nullptr};
+        }
+
+        if (ref_stack.empty())
+        {
+            root = std::move(value);
+            return {true, & root};
+        }
+
+        // skip this value if we already decided to skip the parent
+        // (https://github.com/nlohmann/json/issues/971#issuecomment-413678360)
+        if (!ref_stack.back())
+        {
+            return {false, nullptr};
+        }
+
+        // we now only expect arrays and objects
+        JSON_ASSERT(ref_stack.back()->is_array() || ref_stack.back()->is_object());
+
+        // array
+        if (ref_stack.back()->is_array())
+        {
+            ref_stack.back()->m_data.m_value.array->emplace_back(std::move(value));
+            return {true, & (ref_stack.back()->m_data.m_value.array->back())};
+        }
+
+        // object
+        JSON_ASSERT(ref_stack.back()->is_object());
+        // check if we should store an element for the current key
+        JSON_ASSERT(!key_keep_stack.empty());
+        const bool store_element = key_keep_stack.back();
+        key_keep_stack.pop_back();
+
+        if (!store_element)
+        {
+            return {false, nullptr};
+        }
+
+        JSON_ASSERT(object_element);
+        *object_element = std::move(value);
+        return {true, object_element};
+    }
+
+    /// the parsed JSON value
+    BasicJsonType& root;
+    /// stack to model hierarchy of values
+    std::vector<BasicJsonType*> ref_stack {};
+    /// stack to manage which values to keep
+    std::vector<bool> keep_stack {};
+    /// stack to manage which object keys to keep
+    std::vector<bool> key_keep_stack {};
+    /// helper to hold the reference for the next object element
+    BasicJsonType* object_element = nullptr;
+    /// whether a syntax error occurred
+    bool errored = false;
+    /// callback function
+    const parser_callback_t callback = nullptr;
+    /// whether to throw exceptions in case of errors
+    const bool allow_exceptions = true;
+    /// a discarded value for the callback
+    BasicJsonType discarded = BasicJsonType::value_t::discarded;
+};
+
+template<typename BasicJsonType>
+class json_sax_acceptor
+{
+  public:
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+    using string_t = typename BasicJsonType::string_t;
+    using binary_t = typename BasicJsonType::binary_t;
+
+    bool null()
+    {
+        return true;
+    }
+
+    bool boolean(bool /*unused*/)
+    {
+        return true;
+    }
+
+    bool number_integer(number_integer_t /*unused*/)
+    {
+        return true;
+    }
+
+    bool number_unsigned(number_unsigned_t /*unused*/)
+    {
+        return true;
+    }
+
+    bool number_float(number_float_t /*unused*/, const string_t& /*unused*/)
+    {
+        return true;
+    }
+
+    bool string(string_t& /*unused*/)
+    {
+        return true;
+    }
+
+    bool binary(binary_t& /*unused*/)
+    {
+        return true;
+    }
+
+    bool start_object(std::size_t /*unused*/ = static_cast<std::size_t>(-1))
+    {
+        return true;
+    }
+
+    bool key(string_t& /*unused*/)
+    {
+        return true;
+    }
+
+    bool end_object()
+    {
+        return true;
+    }
+
+    bool start_array(std::size_t /*unused*/ = static_cast<std::size_t>(-1))
+    {
+        return true;
+    }
+
+    bool end_array()
+    {
+        return true;
+    }
+
+    bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/, const detail::exception& /*unused*/)
+    {
+        return false;
+    }
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/input/lexer.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <array> // array
+#include <clocale> // localeconv
+#include <cstddef> // size_t
+#include <cstdio> // snprintf
+#include <cstdlib> // strtof, strtod, strtold, strtoll, strtoull
+#include <initializer_list> // initializer_list
+#include <string> // char_traits, string
+#include <utility> // move
+#include <vector> // vector
+
+// #include <nlohmann/detail/input/input_adapters.hpp>
+
+// #include <nlohmann/detail/input/position_t.hpp>
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+///////////
+// lexer //
+///////////
+
+template<typename BasicJsonType>
+class lexer_base
+{
+  public:
+    /// token types for the parser
+    enum class token_type
+    {
+        uninitialized,    ///< indicating the scanner is uninitialized
+        literal_true,     ///< the `true` literal
+        literal_false,    ///< the `false` literal
+        literal_null,     ///< the `null` literal
+        value_string,     ///< a string -- use get_string() for actual value
+        value_unsigned,   ///< an unsigned integer -- use get_number_unsigned() for actual value
+        value_integer,    ///< a signed integer -- use get_number_integer() for actual value
+        value_float,      ///< an floating point number -- use get_number_float() for actual value
+        begin_array,      ///< the character for array begin `[`
+        begin_object,     ///< the character for object begin `{`
+        end_array,        ///< the character for array end `]`
+        end_object,       ///< the character for object end `}`
+        name_separator,   ///< the name separator `:`
+        value_separator,  ///< the value separator `,`
+        parse_error,      ///< indicating a parse error
+        end_of_input,     ///< indicating the end of the input buffer
+        literal_or_value  ///< a literal or the begin of a value (only for diagnostics)
+    };
+
+    /// return name of values of type token_type (only used for errors)
+    JSON_HEDLEY_RETURNS_NON_NULL
+    JSON_HEDLEY_CONST
+    static const char* token_type_name(const token_type t) noexcept
+    {
+        switch (t)
+        {
+            case token_type::uninitialized:
+                return "<uninitialized>";
+            case token_type::literal_true:
+                return "true literal";
+            case token_type::literal_false:
+                return "false literal";
+            case token_type::literal_null:
+                return "null literal";
+            case token_type::value_string:
+                return "string literal";
+            case token_type::value_unsigned:
+            case token_type::value_integer:
+            case token_type::value_float:
+                return "number literal";
+            case token_type::begin_array:
+                return "'['";
+            case token_type::begin_object:
+                return "'{'";
+            case token_type::end_array:
+                return "']'";
+            case token_type::end_object:
+                return "'}'";
+            case token_type::name_separator:
+                return "':'";
+            case token_type::value_separator:
+                return "','";
+            case token_type::parse_error:
+                return "<parse error>";
+            case token_type::end_of_input:
+                return "end of input";
+            case token_type::literal_or_value:
+                return "'[', '{', or a literal";
+            // LCOV_EXCL_START
+            default: // catch non-enum values
+                return "unknown token";
+                // LCOV_EXCL_STOP
+        }
+    }
+};
+/*!
+@brief lexical analysis
+
+This class organizes the lexical analysis during JSON deserialization.
+*/
+template<typename BasicJsonType, typename InputAdapterType>
+class lexer : public lexer_base<BasicJsonType>
+{
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+    using string_t = typename BasicJsonType::string_t;
+    using char_type = typename InputAdapterType::char_type;
+    using char_int_type = typename char_traits<char_type>::int_type;
+
+  public:
+    using token_type = typename lexer_base<BasicJsonType>::token_type;
+
+    explicit lexer(InputAdapterType&& adapter, bool ignore_comments_ = false) noexcept
+        : ia(std::move(adapter))
+        , ignore_comments(ignore_comments_)
+        , decimal_point_char(static_cast<char_int_type>(get_decimal_point()))
+    {}
+
+    // delete because of pointer members
+    lexer(const lexer&) = delete;
+    lexer(lexer&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
+    lexer& operator=(lexer&) = delete;
+    lexer& operator=(lexer&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
+    ~lexer() = default;
+
+  private:
+    /////////////////////
+    // locales
+    /////////////////////
+
+    /// return the locale-dependent decimal point
+    JSON_HEDLEY_PURE
+    static char get_decimal_point() noexcept
+    {
+        const auto* loc = localeconv();
+        JSON_ASSERT(loc != nullptr);
+        return (loc->decimal_point == nullptr) ? '.' : *(loc->decimal_point);
+    }
+
+    /////////////////////
+    // scan functions
+    /////////////////////
+
+    /*!
+    @brief get codepoint from 4 hex characters following `\u`
+
+    For input "\u c1 c2 c3 c4" the codepoint is:
+      (c1 * 0x1000) + (c2 * 0x0100) + (c3 * 0x0010) + c4
+    = (c1 << 12) + (c2 << 8) + (c3 << 4) + (c4 << 0)
+
+    Furthermore, the possible characters '0'..'9', 'A'..'F', and 'a'..'f'
+    must be converted to the integers 0x0..0x9, 0xA..0xF, 0xA..0xF, resp. The
+    conversion is done by subtracting the offset (0x30, 0x37, and 0x57)
+    between the ASCII value of the character and the desired integer value.
+
+    @return codepoint (0x0000..0xFFFF) or -1 in case of an error (e.g. EOF or
+            non-hex character)
+    */
+    int get_codepoint()
+    {
+        // this function only makes sense after reading `\u`
+        JSON_ASSERT(current == 'u');
+        int codepoint = 0;
+
+        const auto factors = { 12u, 8u, 4u, 0u };
+        for (const auto factor : factors)
+        {
+            get();
+
+            if (current >= '0' && current <= '9')
+            {
+                codepoint += static_cast<int>((static_cast<unsigned int>(current) - 0x30u) << factor);
+            }
+            else if (current >= 'A' && current <= 'F')
+            {
+                codepoint += static_cast<int>((static_cast<unsigned int>(current) - 0x37u) << factor);
+            }
+            else if (current >= 'a' && current <= 'f')
+            {
+                codepoint += static_cast<int>((static_cast<unsigned int>(current) - 0x57u) << factor);
+            }
+            else
+            {
+                return -1;
+            }
+        }
+
+        JSON_ASSERT(0x0000 <= codepoint && codepoint <= 0xFFFF);
+        return codepoint;
+    }
+
+    /*!
+    @brief check if the next byte(s) are inside a given range
+
+    Adds the current byte and, for each passed range, reads a new byte and
+    checks if it is inside the range. If a violation was detected, set up an
+    error message and return false. Otherwise, return true.
+
+    @param[in] ranges  list of integers; interpreted as list of pairs of
+                       inclusive lower and upper bound, respectively
+
+    @pre The passed list @a ranges must have 2, 4, or 6 elements; that is,
+         1, 2, or 3 pairs. This precondition is enforced by an assertion.
+
+    @return true if and only if no range violation was detected
+    */
+    bool next_byte_in_range(std::initializer_list<char_int_type> ranges)
+    {
+        JSON_ASSERT(ranges.size() == 2 || ranges.size() == 4 || ranges.size() == 6);
+        add(current);
+
+        for (auto range = ranges.begin(); range != ranges.end(); ++range)
+        {
+            get();
+            if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range))) // NOLINT(bugprone-inc-dec-in-conditions)
+            {
+                add(current);
+            }
+            else
+            {
+                error_message = "invalid string: ill-formed UTF-8 byte";
+                return false;
+            }
+        }
+
+        return true;
+    }
+
+    /*!
+    @brief scan a string literal
+
+    This function scans a string according to Sect. 7 of RFC 8259. While
+    scanning, bytes are escaped and copied into buffer token_buffer. Then the
+    function returns successfully, token_buffer is *not* null-terminated (as it
+    may contain \0 bytes), and token_buffer.size() is the number of bytes in the
+    string.
+
+    @return token_type::value_string if string could be successfully scanned,
+            token_type::parse_error otherwise
+
+    @note In case of errors, variable error_message contains a textual
+          description.
+    */
+    token_type scan_string()
+    {
+        // reset token_buffer (ignore opening quote)
+        reset();
+
+        // we entered the function by reading an open quote
+        JSON_ASSERT(current == '\"');
+
+        while (true)
+        {
+            // get next character
+            switch (get())
+            {
+                // end of file while parsing string
+                case char_traits<char_type>::eof():
+                {
+                    error_message = "invalid string: missing closing quote";
+                    return token_type::parse_error;
+                }
+
+                // closing quote
+                case '\"':
+                {
+                    return token_type::value_string;
+                }
+
+                // escapes
+                case '\\':
+                {
+                    switch (get())
+                    {
+                        // quotation mark
+                        case '\"':
+                            add('\"');
+                            break;
+                        // reverse solidus
+                        case '\\':
+                            add('\\');
+                            break;
+                        // solidus
+                        case '/':
+                            add('/');
+                            break;
+                        // backspace
+                        case 'b':
+                            add('\b');
+                            break;
+                        // form feed
+                        case 'f':
+                            add('\f');
+                            break;
+                        // line feed
+                        case 'n':
+                            add('\n');
+                            break;
+                        // carriage return
+                        case 'r':
+                            add('\r');
+                            break;
+                        // tab
+                        case 't':
+                            add('\t');
+                            break;
+
+                        // unicode escapes
+                        case 'u':
+                        {
+                            const int codepoint1 = get_codepoint();
+                            int codepoint = codepoint1; // start with codepoint1
+
+                            if (JSON_HEDLEY_UNLIKELY(codepoint1 == -1))
+                            {
+                                error_message = "invalid string: '\\u' must be followed by 4 hex digits";
+                                return token_type::parse_error;
+                            }
+
+                            // check if code point is a high surrogate
+                            if (0xD800 <= codepoint1 && codepoint1 <= 0xDBFF)
+                            {
+                                // expect next \uxxxx entry
+                                if (JSON_HEDLEY_LIKELY(get() == '\\' && get() == 'u'))
+                                {
+                                    const int codepoint2 = get_codepoint();
+
+                                    if (JSON_HEDLEY_UNLIKELY(codepoint2 == -1))
+                                    {
+                                        error_message = "invalid string: '\\u' must be followed by 4 hex digits";
+                                        return token_type::parse_error;
+                                    }
+
+                                    // check if codepoint2 is a low surrogate
+                                    if (JSON_HEDLEY_LIKELY(0xDC00 <= codepoint2 && codepoint2 <= 0xDFFF))
+                                    {
+                                        // overwrite codepoint
+                                        codepoint = static_cast<int>(
+                                                        // high surrogate occupies the most significant 22 bits
+                                                        (static_cast<unsigned int>(codepoint1) << 10u)
+                                                        // low surrogate occupies the least significant 15 bits
+                                                        + static_cast<unsigned int>(codepoint2)
+                                                        // there is still the 0xD800, 0xDC00 and 0x10000 noise
+                                                        // in the result, so we have to subtract with:
+                                                        // (0xD800 << 10) + DC00 - 0x10000 = 0x35FDC00
+                                                        - 0x35FDC00u);
+                                    }
+                                    else
+                                    {
+                                        error_message = "invalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFF";
+                                        return token_type::parse_error;
+                                    }
+                                }
+                                else
+                                {
+                                    error_message = "invalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFF";
+                                    return token_type::parse_error;
+                                }
+                            }
+                            else
+                            {
+                                if (JSON_HEDLEY_UNLIKELY(0xDC00 <= codepoint1 && codepoint1 <= 0xDFFF))
+                                {
+                                    error_message = "invalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFF";
+                                    return token_type::parse_error;
+                                }
+                            }
+
+                            // result of the above calculation yields a proper codepoint
+                            JSON_ASSERT(0x00 <= codepoint && codepoint <= 0x10FFFF);
+
+                            // translate codepoint into bytes
+                            if (codepoint < 0x80)
+                            {
+                                // 1-byte characters: 0xxxxxxx (ASCII)
+                                add(static_cast<char_int_type>(codepoint));
+                            }
+                            else if (codepoint <= 0x7FF)
+                            {
+                                // 2-byte characters: 110xxxxx 10xxxxxx
+                                add(static_cast<char_int_type>(0xC0u | (static_cast<unsigned int>(codepoint) >> 6u)));
+                                add(static_cast<char_int_type>(0x80u | (static_cast<unsigned int>(codepoint) & 0x3Fu)));
+                            }
+                            else if (codepoint <= 0xFFFF)
+                            {
+                                // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
+                                add(static_cast<char_int_type>(0xE0u | (static_cast<unsigned int>(codepoint) >> 12u)));
+                                add(static_cast<char_int_type>(0x80u | ((static_cast<unsigned int>(codepoint) >> 6u) & 0x3Fu)));
+                                add(static_cast<char_int_type>(0x80u | (static_cast<unsigned int>(codepoint) & 0x3Fu)));
+                            }
+                            else
+                            {
+                                // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
+                                add(static_cast<char_int_type>(0xF0u | (static_cast<unsigned int>(codepoint) >> 18u)));
+                                add(static_cast<char_int_type>(0x80u | ((static_cast<unsigned int>(codepoint) >> 12u) & 0x3Fu)));
+                                add(static_cast<char_int_type>(0x80u | ((static_cast<unsigned int>(codepoint) >> 6u) & 0x3Fu)));
+                                add(static_cast<char_int_type>(0x80u | (static_cast<unsigned int>(codepoint) & 0x3Fu)));
+                            }
+
+                            break;
+                        }
+
+                        // other characters after escape
+                        default:
+                            error_message = "invalid string: forbidden character after backslash";
+                            return token_type::parse_error;
+                    }
+
+                    break;
+                }
+
+                // invalid control characters
+                case 0x00:
+                {
+                    error_message = "invalid string: control character U+0000 (NUL) must be escaped to \\u0000";
+                    return token_type::parse_error;
+                }
+
+                case 0x01:
+                {
+                    error_message = "invalid string: control character U+0001 (SOH) must be escaped to \\u0001";
+                    return token_type::parse_error;
+                }
+
+                case 0x02:
+                {
+                    error_message = "invalid string: control character U+0002 (STX) must be escaped to \\u0002";
+                    return token_type::parse_error;
+                }
+
+                case 0x03:
+                {
+                    error_message = "invalid string: control character U+0003 (ETX) must be escaped to \\u0003";
+                    return token_type::parse_error;
+                }
+
+                case 0x04:
+                {
+                    error_message = "invalid string: control character U+0004 (EOT) must be escaped to \\u0004";
+                    return token_type::parse_error;
+                }
+
+                case 0x05:
+                {
+                    error_message = "invalid string: control character U+0005 (ENQ) must be escaped to \\u0005";
+                    return token_type::parse_error;
+                }
+
+                case 0x06:
+                {
+                    error_message = "invalid string: control character U+0006 (ACK) must be escaped to \\u0006";
+                    return token_type::parse_error;
+                }
+
+                case 0x07:
+                {
+                    error_message = "invalid string: control character U+0007 (BEL) must be escaped to \\u0007";
+                    return token_type::parse_error;
+                }
+
+                case 0x08:
+                {
+                    error_message = "invalid string: control character U+0008 (BS) must be escaped to \\u0008 or \\b";
+                    return token_type::parse_error;
+                }
+
+                case 0x09:
+                {
+                    error_message = "invalid string: control character U+0009 (HT) must be escaped to \\u0009 or \\t";
+                    return token_type::parse_error;
+                }
+
+                case 0x0A:
+                {
+                    error_message = "invalid string: control character U+000A (LF) must be escaped to \\u000A or \\n";
+                    return token_type::parse_error;
+                }
+
+                case 0x0B:
+                {
+                    error_message = "invalid string: control character U+000B (VT) must be escaped to \\u000B";
+                    return token_type::parse_error;
+                }
+
+                case 0x0C:
+                {
+                    error_message = "invalid string: control character U+000C (FF) must be escaped to \\u000C or \\f";
+                    return token_type::parse_error;
+                }
+
+                case 0x0D:
+                {
+                    error_message = "invalid string: control character U+000D (CR) must be escaped to \\u000D or \\r";
+                    return token_type::parse_error;
+                }
+
+                case 0x0E:
+                {
+                    error_message = "invalid string: control character U+000E (SO) must be escaped to \\u000E";
+                    return token_type::parse_error;
+                }
+
+                case 0x0F:
+                {
+                    error_message = "invalid string: control character U+000F (SI) must be escaped to \\u000F";
+                    return token_type::parse_error;
+                }
+
+                case 0x10:
+                {
+                    error_message = "invalid string: control character U+0010 (DLE) must be escaped to \\u0010";
+                    return token_type::parse_error;
+                }
+
+                case 0x11:
+                {
+                    error_message = "invalid string: control character U+0011 (DC1) must be escaped to \\u0011";
+                    return token_type::parse_error;
+                }
+
+                case 0x12:
+                {
+                    error_message = "invalid string: control character U+0012 (DC2) must be escaped to \\u0012";
+                    return token_type::parse_error;
+                }
+
+                case 0x13:
+                {
+                    error_message = "invalid string: control character U+0013 (DC3) must be escaped to \\u0013";
+                    return token_type::parse_error;
+                }
+
+                case 0x14:
+                {
+                    error_message = "invalid string: control character U+0014 (DC4) must be escaped to \\u0014";
+                    return token_type::parse_error;
+                }
+
+                case 0x15:
+                {
+                    error_message = "invalid string: control character U+0015 (NAK) must be escaped to \\u0015";
+                    return token_type::parse_error;
+                }
+
+                case 0x16:
+                {
+                    error_message = "invalid string: control character U+0016 (SYN) must be escaped to \\u0016";
+                    return token_type::parse_error;
+                }
+
+                case 0x17:
+                {
+                    error_message = "invalid string: control character U+0017 (ETB) must be escaped to \\u0017";
+                    return token_type::parse_error;
+                }
+
+                case 0x18:
+                {
+                    error_message = "invalid string: control character U+0018 (CAN) must be escaped to \\u0018";
+                    return token_type::parse_error;
+                }
+
+                case 0x19:
+                {
+                    error_message = "invalid string: control character U+0019 (EM) must be escaped to \\u0019";
+                    return token_type::parse_error;
+                }
+
+                case 0x1A:
+                {
+                    error_message = "invalid string: control character U+001A (SUB) must be escaped to \\u001A";
+                    return token_type::parse_error;
+                }
+
+                case 0x1B:
+                {
+                    error_message = "invalid string: control character U+001B (ESC) must be escaped to \\u001B";
+                    return token_type::parse_error;
+                }
+
+                case 0x1C:
+                {
+                    error_message = "invalid string: control character U+001C (FS) must be escaped to \\u001C";
+                    return token_type::parse_error;
+                }
+
+                case 0x1D:
+                {
+                    error_message = "invalid string: control character U+001D (GS) must be escaped to \\u001D";
+                    return token_type::parse_error;
+                }
+
+                case 0x1E:
+                {
+                    error_message = "invalid string: control character U+001E (RS) must be escaped to \\u001E";
+                    return token_type::parse_error;
+                }
+
+                case 0x1F:
+                {
+                    error_message = "invalid string: control character U+001F (US) must be escaped to \\u001F";
+                    return token_type::parse_error;
+                }
+
+                // U+0020..U+007F (except U+0022 (quote) and U+005C (backspace))
+                case 0x20:
+                case 0x21:
+                case 0x23:
+                case 0x24:
+                case 0x25:
+                case 0x26:
+                case 0x27:
+                case 0x28:
+                case 0x29:
+                case 0x2A:
+                case 0x2B:
+                case 0x2C:
+                case 0x2D:
+                case 0x2E:
+                case 0x2F:
+                case 0x30:
+                case 0x31:
+                case 0x32:
+                case 0x33:
+                case 0x34:
+                case 0x35:
+                case 0x36:
+                case 0x37:
+                case 0x38:
+                case 0x39:
+                case 0x3A:
+                case 0x3B:
+                case 0x3C:
+                case 0x3D:
+                case 0x3E:
+                case 0x3F:
+                case 0x40:
+                case 0x41:
+                case 0x42:
+                case 0x43:
+                case 0x44:
+                case 0x45:
+                case 0x46:
+                case 0x47:
+                case 0x48:
+                case 0x49:
+                case 0x4A:
+                case 0x4B:
+                case 0x4C:
+                case 0x4D:
+                case 0x4E:
+                case 0x4F:
+                case 0x50:
+                case 0x51:
+                case 0x52:
+                case 0x53:
+                case 0x54:
+                case 0x55:
+                case 0x56:
+                case 0x57:
+                case 0x58:
+                case 0x59:
+                case 0x5A:
+                case 0x5B:
+                case 0x5D:
+                case 0x5E:
+                case 0x5F:
+                case 0x60:
+                case 0x61:
+                case 0x62:
+                case 0x63:
+                case 0x64:
+                case 0x65:
+                case 0x66:
+                case 0x67:
+                case 0x68:
+                case 0x69:
+                case 0x6A:
+                case 0x6B:
+                case 0x6C:
+                case 0x6D:
+                case 0x6E:
+                case 0x6F:
+                case 0x70:
+                case 0x71:
+                case 0x72:
+                case 0x73:
+                case 0x74:
+                case 0x75:
+                case 0x76:
+                case 0x77:
+                case 0x78:
+                case 0x79:
+                case 0x7A:
+                case 0x7B:
+                case 0x7C:
+                case 0x7D:
+                case 0x7E:
+                case 0x7F:
+                {
+                    add(current);
+                    break;
+                }
+
+                // U+0080..U+07FF: bytes C2..DF 80..BF
+                case 0xC2:
+                case 0xC3:
+                case 0xC4:
+                case 0xC5:
+                case 0xC6:
+                case 0xC7:
+                case 0xC8:
+                case 0xC9:
+                case 0xCA:
+                case 0xCB:
+                case 0xCC:
+                case 0xCD:
+                case 0xCE:
+                case 0xCF:
+                case 0xD0:
+                case 0xD1:
+                case 0xD2:
+                case 0xD3:
+                case 0xD4:
+                case 0xD5:
+                case 0xD6:
+                case 0xD7:
+                case 0xD8:
+                case 0xD9:
+                case 0xDA:
+                case 0xDB:
+                case 0xDC:
+                case 0xDD:
+                case 0xDE:
+                case 0xDF:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!next_byte_in_range({0x80, 0xBF})))
+                    {
+                        return token_type::parse_error;
+                    }
+                    break;
+                }
+
+                // U+0800..U+0FFF: bytes E0 A0..BF 80..BF
+                case 0xE0:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0xA0, 0xBF, 0x80, 0xBF}))))
+                    {
+                        return token_type::parse_error;
+                    }
+                    break;
+                }
+
+                // U+1000..U+CFFF: bytes E1..EC 80..BF 80..BF
+                // U+E000..U+FFFF: bytes EE..EF 80..BF 80..BF
+                case 0xE1:
+                case 0xE2:
+                case 0xE3:
+                case 0xE4:
+                case 0xE5:
+                case 0xE6:
+                case 0xE7:
+                case 0xE8:
+                case 0xE9:
+                case 0xEA:
+                case 0xEB:
+                case 0xEC:
+                case 0xEE:
+                case 0xEF:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0xBF, 0x80, 0xBF}))))
+                    {
+                        return token_type::parse_error;
+                    }
+                    break;
+                }
+
+                // U+D000..U+D7FF: bytes ED 80..9F 80..BF
+                case 0xED:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0x9F, 0x80, 0xBF}))))
+                    {
+                        return token_type::parse_error;
+                    }
+                    break;
+                }
+
+                // U+10000..U+3FFFF F0 90..BF 80..BF 80..BF
+                case 0xF0:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x90, 0xBF, 0x80, 0xBF, 0x80, 0xBF}))))
+                    {
+                        return token_type::parse_error;
+                    }
+                    break;
+                }
+
+                // U+40000..U+FFFFF F1..F3 80..BF 80..BF 80..BF
+                case 0xF1:
+                case 0xF2:
+                case 0xF3:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0xBF, 0x80, 0xBF, 0x80, 0xBF}))))
+                    {
+                        return token_type::parse_error;
+                    }
+                    break;
+                }
+
+                // U+100000..U+10FFFF F4 80..8F 80..BF 80..BF
+                case 0xF4:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0x8F, 0x80, 0xBF, 0x80, 0xBF}))))
+                    {
+                        return token_type::parse_error;
+                    }
+                    break;
+                }
+
+                // remaining bytes (80..C1 and F5..FF) are ill-formed
+                default:
+                {
+                    error_message = "invalid string: ill-formed UTF-8 byte";
+                    return token_type::parse_error;
+                }
+            }
+        }
+    }
+
+    /*!
+     * @brief scan a comment
+     * @return whether comment could be scanned successfully
+     */
+    bool scan_comment()
+    {
+        switch (get())
+        {
+            // single-line comments skip input until a newline or EOF is read
+            case '/':
+            {
+                while (true)
+                {
+                    switch (get())
+                    {
+                        case '\n':
+                        case '\r':
+                        case char_traits<char_type>::eof():
+                        case '\0':
+                            return true;
+
+                        default:
+                            break;
+                    }
+                }
+            }
+
+            // multi-line comments skip input until */ is read
+            case '*':
+            {
+                while (true)
+                {
+                    switch (get())
+                    {
+                        case char_traits<char_type>::eof():
+                        case '\0':
+                        {
+                            error_message = "invalid comment; missing closing '*/'";
+                            return false;
+                        }
+
+                        case '*':
+                        {
+                            switch (get())
+                            {
+                                case '/':
+                                    return true;
+
+                                default:
+                                {
+                                    unget();
+                                    continue;
+                                }
+                            }
+                        }
+
+                        default:
+                            continue;
+                    }
+                }
+            }
+
+            // unexpected character after reading '/'
+            default:
+            {
+                error_message = "invalid comment; expecting '/' or '*' after '/'";
+                return false;
+            }
+        }
+    }
+
+    JSON_HEDLEY_NON_NULL(2)
+    static void strtof(float& f, const char* str, char** endptr) noexcept
+    {
+        f = std::strtof(str, endptr);
+    }
+
+    JSON_HEDLEY_NON_NULL(2)
+    static void strtof(double& f, const char* str, char** endptr) noexcept
+    {
+        f = std::strtod(str, endptr);
+    }
+
+    JSON_HEDLEY_NON_NULL(2)
+    static void strtof(long double& f, const char* str, char** endptr) noexcept
+    {
+        f = std::strtold(str, endptr);
+    }
+
+    /*!
+    @brief scan a number literal
+
+    This function scans a string according to Sect. 6 of RFC 8259.
+
+    The function is realized with a deterministic finite state machine derived
+    from the grammar described in RFC 8259. Starting in state "init", the
+    input is read and used to determined the next state. Only state "done"
+    accepts the number. State "error" is a trap state to model errors. In the
+    table below, "anything" means any character but the ones listed before.
+
+    state    | 0        | 1-9      | e E      | +       | -       | .        | anything
+    ---------|----------|----------|----------|---------|---------|----------|-----------
+    init     | zero     | any1     | [error]  | [error] | minus   | [error]  | [error]
+    minus    | zero     | any1     | [error]  | [error] | [error] | [error]  | [error]
+    zero     | done     | done     | exponent | done    | done    | decimal1 | done
+    any1     | any1     | any1     | exponent | done    | done    | decimal1 | done
+    decimal1 | decimal2 | decimal2 | [error]  | [error] | [error] | [error]  | [error]
+    decimal2 | decimal2 | decimal2 | exponent | done    | done    | done     | done
+    exponent | any2     | any2     | [error]  | sign    | sign    | [error]  | [error]
+    sign     | any2     | any2     | [error]  | [error] | [error] | [error]  | [error]
+    any2     | any2     | any2     | done     | done    | done    | done     | done
+
+    The state machine is realized with one label per state (prefixed with
+    "scan_number_") and `goto` statements between them. The state machine
+    contains cycles, but any cycle can be left when EOF is read. Therefore,
+    the function is guaranteed to terminate.
+
+    During scanning, the read bytes are stored in token_buffer. This string is
+    then converted to a signed integer, an unsigned integer, or a
+    floating-point number.
+
+    @return token_type::value_unsigned, token_type::value_integer, or
+            token_type::value_float if number could be successfully scanned,
+            token_type::parse_error otherwise
+
+    @note The scanner is independent of the current locale. Internally, the
+          locale's decimal point is used instead of `.` to work with the
+          locale-dependent converters.
+    */
+    token_type scan_number()  // lgtm [cpp/use-of-goto]
+    {
+        // reset token_buffer to store the number's bytes
+        reset();
+
+        // the type of the parsed number; initially set to unsigned; will be
+        // changed if minus sign, decimal point or exponent is read
+        token_type number_type = token_type::value_unsigned;
+
+        // state (init): we just found out we need to scan a number
+        switch (current)
+        {
+            case '-':
+            {
+                add(current);
+                goto scan_number_minus;
+            }
+
+            case '0':
+            {
+                add(current);
+                goto scan_number_zero;
+            }
+
+            case '1':
+            case '2':
+            case '3':
+            case '4':
+            case '5':
+            case '6':
+            case '7':
+            case '8':
+            case '9':
+            {
+                add(current);
+                goto scan_number_any1;
+            }
+
+            // all other characters are rejected outside scan_number()
+            default:            // LCOV_EXCL_LINE
+                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+        }
+
+scan_number_minus:
+        // state: we just parsed a leading minus sign
+        number_type = token_type::value_integer;
+        switch (get())
+        {
+            case '0':
+            {
+                add(current);
+                goto scan_number_zero;
+            }
+
+            case '1':
+            case '2':
+            case '3':
+            case '4':
+            case '5':
+            case '6':
+            case '7':
+            case '8':
+            case '9':
+            {
+                add(current);
+                goto scan_number_any1;
+            }
+
+            default:
+            {
+                error_message = "invalid number; expected digit after '-'";
+                return token_type::parse_error;
+            }
+        }
+
+scan_number_zero:
+        // state: we just parse a zero (maybe with a leading minus sign)
+        switch (get())
+        {
+            case '.':
+            {
+                add(decimal_point_char);
+                goto scan_number_decimal1;
+            }
+
+            case 'e':
+            case 'E':
+            {
+                add(current);
+                goto scan_number_exponent;
+            }
+
+            default:
+                goto scan_number_done;
+        }
+
+scan_number_any1:
+        // state: we just parsed a number 0-9 (maybe with a leading minus sign)
+        switch (get())
+        {
+            case '0':
+            case '1':
+            case '2':
+            case '3':
+            case '4':
+            case '5':
+            case '6':
+            case '7':
+            case '8':
+            case '9':
+            {
+                add(current);
+                goto scan_number_any1;
+            }
+
+            case '.':
+            {
+                add(decimal_point_char);
+                goto scan_number_decimal1;
+            }
+
+            case 'e':
+            case 'E':
+            {
+                add(current);
+                goto scan_number_exponent;
+            }
+
+            default:
+                goto scan_number_done;
+        }
+
+scan_number_decimal1:
+        // state: we just parsed a decimal point
+        number_type = token_type::value_float;
+        switch (get())
+        {
+            case '0':
+            case '1':
+            case '2':
+            case '3':
+            case '4':
+            case '5':
+            case '6':
+            case '7':
+            case '8':
+            case '9':
+            {
+                add(current);
+                goto scan_number_decimal2;
+            }
+
+            default:
+            {
+                error_message = "invalid number; expected digit after '.'";
+                return token_type::parse_error;
+            }
+        }
+
+scan_number_decimal2:
+        // we just parsed at least one number after a decimal point
+        switch (get())
+        {
+            case '0':
+            case '1':
+            case '2':
+            case '3':
+            case '4':
+            case '5':
+            case '6':
+            case '7':
+            case '8':
+            case '9':
+            {
+                add(current);
+                goto scan_number_decimal2;
+            }
+
+            case 'e':
+            case 'E':
+            {
+                add(current);
+                goto scan_number_exponent;
+            }
+
+            default:
+                goto scan_number_done;
+        }
+
+scan_number_exponent:
+        // we just parsed an exponent
+        number_type = token_type::value_float;
+        switch (get())
+        {
+            case '+':
+            case '-':
+            {
+                add(current);
+                goto scan_number_sign;
+            }
+
+            case '0':
+            case '1':
+            case '2':
+            case '3':
+            case '4':
+            case '5':
+            case '6':
+            case '7':
+            case '8':
+            case '9':
+            {
+                add(current);
+                goto scan_number_any2;
+            }
+
+            default:
+            {
+                error_message =
+                    "invalid number; expected '+', '-', or digit after exponent";
+                return token_type::parse_error;
+            }
+        }
+
+scan_number_sign:
+        // we just parsed an exponent sign
+        switch (get())
+        {
+            case '0':
+            case '1':
+            case '2':
+            case '3':
+            case '4':
+            case '5':
+            case '6':
+            case '7':
+            case '8':
+            case '9':
+            {
+                add(current);
+                goto scan_number_any2;
+            }
+
+            default:
+            {
+                error_message = "invalid number; expected digit after exponent sign";
+                return token_type::parse_error;
+            }
+        }
+
+scan_number_any2:
+        // we just parsed a number after the exponent or exponent sign
+        switch (get())
+        {
+            case '0':
+            case '1':
+            case '2':
+            case '3':
+            case '4':
+            case '5':
+            case '6':
+            case '7':
+            case '8':
+            case '9':
+            {
+                add(current);
+                goto scan_number_any2;
+            }
+
+            default:
+                goto scan_number_done;
+        }
+
+scan_number_done:
+        // unget the character after the number (we only read it to know that
+        // we are done scanning a number)
+        unget();
+
+        char* endptr = nullptr; // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
+        errno = 0;
+
+        // try to parse integers first and fall back to floats
+        if (number_type == token_type::value_unsigned)
+        {
+            const auto x = std::strtoull(token_buffer.data(), &endptr, 10);
+
+            // we checked the number format before
+            JSON_ASSERT(endptr == token_buffer.data() + token_buffer.size());
+
+            if (errno == 0)
+            {
+                value_unsigned = static_cast<number_unsigned_t>(x);
+                if (value_unsigned == x)
+                {
+                    return token_type::value_unsigned;
+                }
+            }
+        }
+        else if (number_type == token_type::value_integer)
+        {
+            const auto x = std::strtoll(token_buffer.data(), &endptr, 10);
+
+            // we checked the number format before
+            JSON_ASSERT(endptr == token_buffer.data() + token_buffer.size());
+
+            if (errno == 0)
+            {
+                value_integer = static_cast<number_integer_t>(x);
+                if (value_integer == x)
+                {
+                    return token_type::value_integer;
+                }
+            }
+        }
+
+        // this code is reached if we parse a floating-point number or if an
+        // integer conversion above failed
+        strtof(value_float, token_buffer.data(), &endptr);
+
+        // we checked the number format before
+        JSON_ASSERT(endptr == token_buffer.data() + token_buffer.size());
+
+        return token_type::value_float;
+    }
+
+    /*!
+    @param[in] literal_text  the literal text to expect
+    @param[in] length        the length of the passed literal text
+    @param[in] return_type   the token type to return on success
+    */
+    JSON_HEDLEY_NON_NULL(2)
+    token_type scan_literal(const char_type* literal_text, const std::size_t length,
+                            token_type return_type)
+    {
+        JSON_ASSERT(char_traits<char_type>::to_char_type(current) == literal_text[0]);
+        for (std::size_t i = 1; i < length; ++i)
+        {
+            if (JSON_HEDLEY_UNLIKELY(char_traits<char_type>::to_char_type(get()) != literal_text[i]))
+            {
+                error_message = "invalid literal";
+                return token_type::parse_error;
+            }
+        }
+        return return_type;
+    }
+
+    /////////////////////
+    // input management
+    /////////////////////
+
+    /// reset token_buffer; current character is beginning of token
+    void reset() noexcept
+    {
+        token_buffer.clear();
+        token_string.clear();
+        token_string.push_back(char_traits<char_type>::to_char_type(current));
+    }
+
+    /*
+    @brief get next character from the input
+
+    This function provides the interface to the used input adapter. It does
+    not throw in case the input reached EOF, but returns a
+    `char_traits<char>::eof()` in that case.  Stores the scanned characters
+    for use in error messages.
+
+    @return character read from the input
+    */
+    char_int_type get()
+    {
+        ++position.chars_read_total;
+        ++position.chars_read_current_line;
+
+        if (next_unget)
+        {
+            // just reset the next_unget variable and work with current
+            next_unget = false;
+        }
+        else
+        {
+            current = ia.get_character();
+        }
+
+        if (JSON_HEDLEY_LIKELY(current != char_traits<char_type>::eof()))
+        {
+            token_string.push_back(char_traits<char_type>::to_char_type(current));
+        }
+
+        if (current == '\n')
+        {
+            ++position.lines_read;
+            position.chars_read_current_line = 0;
+        }
+
+        return current;
+    }
+
+    /*!
+    @brief unget current character (read it again on next get)
+
+    We implement unget by setting variable next_unget to true. The input is not
+    changed - we just simulate ungetting by modifying chars_read_total,
+    chars_read_current_line, and token_string. The next call to get() will
+    behave as if the unget character is read again.
+    */
+    void unget()
+    {
+        next_unget = true;
+
+        --position.chars_read_total;
+
+        // in case we "unget" a newline, we have to also decrement the lines_read
+        if (position.chars_read_current_line == 0)
+        {
+            if (position.lines_read > 0)
+            {
+                --position.lines_read;
+            }
+        }
+        else
+        {
+            --position.chars_read_current_line;
+        }
+
+        if (JSON_HEDLEY_LIKELY(current != char_traits<char_type>::eof()))
+        {
+            JSON_ASSERT(!token_string.empty());
+            token_string.pop_back();
+        }
+    }
+
+    /// add a character to token_buffer
+    void add(char_int_type c)
+    {
+        token_buffer.push_back(static_cast<typename string_t::value_type>(c));
+    }
+
+  public:
+    /////////////////////
+    // value getters
+    /////////////////////
+
+    /// return integer value
+    constexpr number_integer_t get_number_integer() const noexcept
+    {
+        return value_integer;
+    }
+
+    /// return unsigned integer value
+    constexpr number_unsigned_t get_number_unsigned() const noexcept
+    {
+        return value_unsigned;
+    }
+
+    /// return floating-point value
+    constexpr number_float_t get_number_float() const noexcept
+    {
+        return value_float;
+    }
+
+    /// return current string value (implicitly resets the token; useful only once)
+    string_t& get_string()
+    {
+        return token_buffer;
+    }
+
+    /////////////////////
+    // diagnostics
+    /////////////////////
+
+    /// return position of last read token
+    constexpr position_t get_position() const noexcept
+    {
+        return position;
+    }
+
+    /// return the last read token (for errors only).  Will never contain EOF
+    /// (an arbitrary value that is not a valid char value, often -1), because
+    /// 255 may legitimately occur.  May contain NUL, which should be escaped.
+    std::string get_token_string() const
+    {
+        // escape control characters
+        std::string result;
+        for (const auto c : token_string)
+        {
+            if (static_cast<unsigned char>(c) <= '\x1F')
+            {
+                // escape control characters
+                std::array<char, 9> cs{{}};
+                static_cast<void>((std::snprintf)(cs.data(), cs.size(), "<U+%.4X>", static_cast<unsigned char>(c))); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
+                result += cs.data();
+            }
+            else
+            {
+                // add character as is
+                result.push_back(static_cast<std::string::value_type>(c));
+            }
+        }
+
+        return result;
+    }
+
+    /// return syntax error message
+    JSON_HEDLEY_RETURNS_NON_NULL
+    constexpr const char* get_error_message() const noexcept
+    {
+        return error_message;
+    }
+
+    /////////////////////
+    // actual scanner
+    /////////////////////
+
+    /*!
+    @brief skip the UTF-8 byte order mark
+    @return true iff there is no BOM or the correct BOM has been skipped
+    */
+    bool skip_bom()
+    {
+        if (get() == 0xEF)
+        {
+            // check if we completely parse the BOM
+            return get() == 0xBB && get() == 0xBF;
+        }
+
+        // the first character is not the beginning of the BOM; unget it to
+        // process is later
+        unget();
+        return true;
+    }
+
+    void skip_whitespace()
+    {
+        do
+        {
+            get();
+        }
+        while (current == ' ' || current == '\t' || current == '\n' || current == '\r');
+    }
+
+    token_type scan()
+    {
+        // initially, skip the BOM
+        if (position.chars_read_total == 0 && !skip_bom())
+        {
+            error_message = "invalid BOM; must be 0xEF 0xBB 0xBF if given";
+            return token_type::parse_error;
+        }
+
+        // read next character and ignore whitespace
+        skip_whitespace();
+
+        // ignore comments
+        while (ignore_comments && current == '/')
+        {
+            if (!scan_comment())
+            {
+                return token_type::parse_error;
+            }
+
+            // skip following whitespace
+            skip_whitespace();
+        }
+
+        switch (current)
+        {
+            // structural characters
+            case '[':
+                return token_type::begin_array;
+            case ']':
+                return token_type::end_array;
+            case '{':
+                return token_type::begin_object;
+            case '}':
+                return token_type::end_object;
+            case ':':
+                return token_type::name_separator;
+            case ',':
+                return token_type::value_separator;
+
+            // literals
+            case 't':
+            {
+                std::array<char_type, 4> true_literal = {{static_cast<char_type>('t'), static_cast<char_type>('r'), static_cast<char_type>('u'), static_cast<char_type>('e')}};
+                return scan_literal(true_literal.data(), true_literal.size(), token_type::literal_true);
+            }
+            case 'f':
+            {
+                std::array<char_type, 5> false_literal = {{static_cast<char_type>('f'), static_cast<char_type>('a'), static_cast<char_type>('l'), static_cast<char_type>('s'), static_cast<char_type>('e')}};
+                return scan_literal(false_literal.data(), false_literal.size(), token_type::literal_false);
+            }
+            case 'n':
+            {
+                std::array<char_type, 4> null_literal = {{static_cast<char_type>('n'), static_cast<char_type>('u'), static_cast<char_type>('l'), static_cast<char_type>('l')}};
+                return scan_literal(null_literal.data(), null_literal.size(), token_type::literal_null);
+            }
+
+            // string
+            case '\"':
+                return scan_string();
+
+            // number
+            case '-':
+            case '0':
+            case '1':
+            case '2':
+            case '3':
+            case '4':
+            case '5':
+            case '6':
+            case '7':
+            case '8':
+            case '9':
+                return scan_number();
+
+            // end of input (the null byte is needed when parsing from
+            // string literals)
+            case '\0':
+            case char_traits<char_type>::eof():
+                return token_type::end_of_input;
+
+            // error
+            default:
+                error_message = "invalid literal";
+                return token_type::parse_error;
+        }
+    }
+
+  private:
+    /// input adapter
+    InputAdapterType ia;
+
+    /// whether comments should be ignored (true) or signaled as errors (false)
+    const bool ignore_comments = false;
+
+    /// the current character
+    char_int_type current = char_traits<char_type>::eof();
+
+    /// whether the next get() call should just return current
+    bool next_unget = false;
+
+    /// the start position of the current token
+    position_t position {};
+
+    /// raw input token string (for error messages)
+    std::vector<char_type> token_string {};
+
+    /// buffer for variable-length tokens (numbers, strings)
+    string_t token_buffer {};
+
+    /// a description of occurred lexer errors
+    const char* error_message = "";
+
+    // number values
+    number_integer_t value_integer = 0;
+    number_unsigned_t value_unsigned = 0;
+    number_float_t value_float = 0;
+
+    /// the decimal point
+    const char_int_type decimal_point_char = '.';
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/is_sax.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cstdint> // size_t
+#include <utility> // declval
+#include <string> // string
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+// #include <nlohmann/detail/meta/detected.hpp>
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+template<typename T>
+using null_function_t = decltype(std::declval<T&>().null());
+
+template<typename T>
+using boolean_function_t =
+    decltype(std::declval<T&>().boolean(std::declval<bool>()));
+
+template<typename T, typename Integer>
+using number_integer_function_t =
+    decltype(std::declval<T&>().number_integer(std::declval<Integer>()));
+
+template<typename T, typename Unsigned>
+using number_unsigned_function_t =
+    decltype(std::declval<T&>().number_unsigned(std::declval<Unsigned>()));
+
+template<typename T, typename Float, typename String>
+using number_float_function_t = decltype(std::declval<T&>().number_float(
+                                    std::declval<Float>(), std::declval<const String&>()));
+
+template<typename T, typename String>
+using string_function_t =
+    decltype(std::declval<T&>().string(std::declval<String&>()));
+
+template<typename T, typename Binary>
+using binary_function_t =
+    decltype(std::declval<T&>().binary(std::declval<Binary&>()));
+
+template<typename T>
+using start_object_function_t =
+    decltype(std::declval<T&>().start_object(std::declval<std::size_t>()));
+
+template<typename T, typename String>
+using key_function_t =
+    decltype(std::declval<T&>().key(std::declval<String&>()));
+
+template<typename T>
+using end_object_function_t = decltype(std::declval<T&>().end_object());
+
+template<typename T>
+using start_array_function_t =
+    decltype(std::declval<T&>().start_array(std::declval<std::size_t>()));
+
+template<typename T>
+using end_array_function_t = decltype(std::declval<T&>().end_array());
+
+template<typename T, typename Exception>
+using parse_error_function_t = decltype(std::declval<T&>().parse_error(
+        std::declval<std::size_t>(), std::declval<const std::string&>(),
+        std::declval<const Exception&>()));
+
+template<typename SAX, typename BasicJsonType>
+struct is_sax
+{
+  private:
+    static_assert(is_basic_json<BasicJsonType>::value,
+                  "BasicJsonType must be of type basic_json<...>");
+
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+    using string_t = typename BasicJsonType::string_t;
+    using binary_t = typename BasicJsonType::binary_t;
+    using exception_t = typename BasicJsonType::exception;
+
+  public:
+    static constexpr bool value =
+        is_detected_exact<bool, null_function_t, SAX>::value &&
+        is_detected_exact<bool, boolean_function_t, SAX>::value &&
+        is_detected_exact<bool, number_integer_function_t, SAX, number_integer_t>::value &&
+        is_detected_exact<bool, number_unsigned_function_t, SAX, number_unsigned_t>::value &&
+        is_detected_exact<bool, number_float_function_t, SAX, number_float_t, string_t>::value &&
+        is_detected_exact<bool, string_function_t, SAX, string_t>::value &&
+        is_detected_exact<bool, binary_function_t, SAX, binary_t>::value &&
+        is_detected_exact<bool, start_object_function_t, SAX>::value &&
+        is_detected_exact<bool, key_function_t, SAX, string_t>::value &&
+        is_detected_exact<bool, end_object_function_t, SAX>::value &&
+        is_detected_exact<bool, start_array_function_t, SAX>::value &&
+        is_detected_exact<bool, end_array_function_t, SAX>::value &&
+        is_detected_exact<bool, parse_error_function_t, SAX, exception_t>::value;
+};
+
+template<typename SAX, typename BasicJsonType>
+struct is_sax_static_asserts
+{
+  private:
+    static_assert(is_basic_json<BasicJsonType>::value,
+                  "BasicJsonType must be of type basic_json<...>");
+
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+    using string_t = typename BasicJsonType::string_t;
+    using binary_t = typename BasicJsonType::binary_t;
+    using exception_t = typename BasicJsonType::exception;
+
+  public:
+    static_assert(is_detected_exact<bool, null_function_t, SAX>::value,
+                  "Missing/invalid function: bool null()");
+    static_assert(is_detected_exact<bool, boolean_function_t, SAX>::value,
+                  "Missing/invalid function: bool boolean(bool)");
+    static_assert(is_detected_exact<bool, boolean_function_t, SAX>::value,
+                  "Missing/invalid function: bool boolean(bool)");
+    static_assert(
+        is_detected_exact<bool, number_integer_function_t, SAX,
+        number_integer_t>::value,
+        "Missing/invalid function: bool number_integer(number_integer_t)");
+    static_assert(
+        is_detected_exact<bool, number_unsigned_function_t, SAX,
+        number_unsigned_t>::value,
+        "Missing/invalid function: bool number_unsigned(number_unsigned_t)");
+    static_assert(is_detected_exact<bool, number_float_function_t, SAX,
+                  number_float_t, string_t>::value,
+                  "Missing/invalid function: bool number_float(number_float_t, const string_t&)");
+    static_assert(
+        is_detected_exact<bool, string_function_t, SAX, string_t>::value,
+        "Missing/invalid function: bool string(string_t&)");
+    static_assert(
+        is_detected_exact<bool, binary_function_t, SAX, binary_t>::value,
+        "Missing/invalid function: bool binary(binary_t&)");
+    static_assert(is_detected_exact<bool, start_object_function_t, SAX>::value,
+                  "Missing/invalid function: bool start_object(std::size_t)");
+    static_assert(is_detected_exact<bool, key_function_t, SAX, string_t>::value,
+                  "Missing/invalid function: bool key(string_t&)");
+    static_assert(is_detected_exact<bool, end_object_function_t, SAX>::value,
+                  "Missing/invalid function: bool end_object()");
+    static_assert(is_detected_exact<bool, start_array_function_t, SAX>::value,
+                  "Missing/invalid function: bool start_array(std::size_t)");
+    static_assert(is_detected_exact<bool, end_array_function_t, SAX>::value,
+                  "Missing/invalid function: bool end_array()");
+    static_assert(
+        is_detected_exact<bool, parse_error_function_t, SAX, exception_t>::value,
+        "Missing/invalid function: bool parse_error(std::size_t, const "
+        "std::string&, const exception&)");
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+// #include <nlohmann/detail/string_concat.hpp>
+
+// #include <nlohmann/detail/value_t.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/// how to treat CBOR tags
+enum class cbor_tag_handler_t
+{
+    error,   ///< throw a parse_error exception in case of a tag
+    ignore,  ///< ignore tags
+    store    ///< store tags as binary type
+};
+
+/*!
+@brief determine system byte order
+
+@return true if and only if system's byte order is little endian
+
+@note from https://stackoverflow.com/a/1001328/266378
+*/
+static inline bool little_endianness(int num = 1) noexcept
+{
+    return *reinterpret_cast<char*>(&num) == 1;
+}
+
+///////////////////
+// binary reader //
+///////////////////
+
+/*!
+@brief deserialization of CBOR, MessagePack, and UBJSON values
+*/
+template<typename BasicJsonType, typename InputAdapterType, typename SAX = json_sax_dom_parser<BasicJsonType>>
+class binary_reader
+{
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+    using string_t = typename BasicJsonType::string_t;
+    using binary_t = typename BasicJsonType::binary_t;
+    using json_sax_t = SAX;
+    using char_type = typename InputAdapterType::char_type;
+    using char_int_type = typename char_traits<char_type>::int_type;
+
+  public:
+    /*!
+    @brief create a binary reader
+
+    @param[in] adapter  input adapter to read from
+    */
+    explicit binary_reader(InputAdapterType&& adapter, const input_format_t format = input_format_t::json) noexcept : ia(std::move(adapter)), input_format(format)
+    {
+        (void)detail::is_sax_static_asserts<SAX, BasicJsonType> {};
+    }
+
+    // make class move-only
+    binary_reader(const binary_reader&) = delete;
+    binary_reader(binary_reader&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
+    binary_reader& operator=(const binary_reader&) = delete;
+    binary_reader& operator=(binary_reader&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor)
+    ~binary_reader() = default;
+
+    /*!
+    @param[in] format  the binary format to parse
+    @param[in] sax_    a SAX event processor
+    @param[in] strict  whether to expect the input to be consumed completed
+    @param[in] tag_handler  how to treat CBOR tags
+
+    @return whether parsing was successful
+    */
+    JSON_HEDLEY_NON_NULL(3)
+    bool sax_parse(const input_format_t format,
+                   json_sax_t* sax_,
+                   const bool strict = true,
+                   const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error)
+    {
+        sax = sax_;
+        bool result = false;
+
+        switch (format)
+        {
+            case input_format_t::bson:
+                result = parse_bson_internal();
+                break;
+
+            case input_format_t::cbor:
+                result = parse_cbor_internal(true, tag_handler);
+                break;
+
+            case input_format_t::msgpack:
+                result = parse_msgpack_internal();
+                break;
+
+            case input_format_t::ubjson:
+            case input_format_t::bjdata:
+                result = parse_ubjson_internal();
+                break;
+
+            case input_format_t::json: // LCOV_EXCL_LINE
+            default:            // LCOV_EXCL_LINE
+                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+        }
+
+        // strict mode: next byte must be EOF
+        if (result && strict)
+        {
+            if (input_format == input_format_t::ubjson || input_format == input_format_t::bjdata)
+            {
+                get_ignore_noop();
+            }
+            else
+            {
+                get();
+            }
+
+            if (JSON_HEDLEY_UNLIKELY(current != char_traits<char_type>::eof()))
+            {
+                return sax->parse_error(chars_read, get_token_string(), parse_error::create(110, chars_read,
+                                        exception_message(input_format, concat("expected end of input; last byte: 0x", get_token_string()), "value"), nullptr));
+            }
+        }
+
+        return result;
+    }
+
+  private:
+    //////////
+    // BSON //
+    //////////
+
+    /*!
+    @brief Reads in a BSON-object and passes it to the SAX-parser.
+    @return whether a valid BSON-value was passed to the SAX parser
+    */
+    bool parse_bson_internal()
+    {
+        std::int32_t document_size{};
+        get_number<std::int32_t, true>(input_format_t::bson, document_size);
+
+        if (JSON_HEDLEY_UNLIKELY(!sax->start_object(static_cast<std::size_t>(-1))))
+        {
+            return false;
+        }
+
+        if (JSON_HEDLEY_UNLIKELY(!parse_bson_element_list(/*is_array*/false)))
+        {
+            return false;
+        }
+
+        return sax->end_object();
+    }
+
+    /*!
+    @brief Parses a C-style string from the BSON input.
+    @param[in,out] result  A reference to the string variable where the read
+                            string is to be stored.
+    @return `true` if the \x00-byte indicating the end of the string was
+             encountered before the EOF; false` indicates an unexpected EOF.
+    */
+    bool get_bson_cstr(string_t& result)
+    {
+        auto out = std::back_inserter(result);
+        while (true)
+        {
+            get();
+            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::bson, "cstring")))
+            {
+                return false;
+            }
+            if (current == 0x00)
+            {
+                return true;
+            }
+            *out++ = static_cast<typename string_t::value_type>(current);
+        }
+    }
+
+    /*!
+    @brief Parses a zero-terminated string of length @a len from the BSON
+           input.
+    @param[in] len  The length (including the zero-byte at the end) of the
+                    string to be read.
+    @param[in,out] result  A reference to the string variable where the read
+                            string is to be stored.
+    @tparam NumberType The type of the length @a len
+    @pre len >= 1
+    @return `true` if the string was successfully parsed
+    */
+    template<typename NumberType>
+    bool get_bson_string(const NumberType len, string_t& result)
+    {
+        if (JSON_HEDLEY_UNLIKELY(len < 1))
+        {
+            auto last_token = get_token_string();
+            return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
+                                    exception_message(input_format_t::bson, concat("string length must be at least 1, is ", std::to_string(len)), "string"), nullptr));
+        }
+
+        return get_string(input_format_t::bson, len - static_cast<NumberType>(1), result) && get() != char_traits<char_type>::eof();
+    }
+
+    /*!
+    @brief Parses a byte array input of length @a len from the BSON input.
+    @param[in] len  The length of the byte array to be read.
+    @param[in,out] result  A reference to the binary variable where the read
+                            array is to be stored.
+    @tparam NumberType The type of the length @a len
+    @pre len >= 0
+    @return `true` if the byte array was successfully parsed
+    */
+    template<typename NumberType>
+    bool get_bson_binary(const NumberType len, binary_t& result)
+    {
+        if (JSON_HEDLEY_UNLIKELY(len < 0))
+        {
+            auto last_token = get_token_string();
+            return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
+                                    exception_message(input_format_t::bson, concat("byte array length cannot be negative, is ", std::to_string(len)), "binary"), nullptr));
+        }
+
+        // All BSON binary values have a subtype
+        std::uint8_t subtype{};
+        get_number<std::uint8_t>(input_format_t::bson, subtype);
+        result.set_subtype(subtype);
+
+        return get_binary(input_format_t::bson, len, result);
+    }
+
+    /*!
+    @brief Read a BSON document element of the given @a element_type.
+    @param[in] element_type The BSON element type, c.f. http://bsonspec.org/spec.html
+    @param[in] element_type_parse_position The position in the input stream,
+               where the `element_type` was read.
+    @warning Not all BSON element types are supported yet. An unsupported
+             @a element_type will give rise to a parse_error.114:
+             Unsupported BSON record type 0x...
+    @return whether a valid BSON-object/array was passed to the SAX parser
+    */
+    bool parse_bson_element_internal(const char_int_type element_type,
+                                     const std::size_t element_type_parse_position)
+    {
+        switch (element_type)
+        {
+            case 0x01: // double
+            {
+                double number{};
+                return get_number<double, true>(input_format_t::bson, number) && sax->number_float(static_cast<number_float_t>(number), "");
+            }
+
+            case 0x02: // string
+            {
+                std::int32_t len{};
+                string_t value;
+                return get_number<std::int32_t, true>(input_format_t::bson, len) && get_bson_string(len, value) && sax->string(value);
+            }
+
+            case 0x03: // object
+            {
+                return parse_bson_internal();
+            }
+
+            case 0x04: // array
+            {
+                return parse_bson_array();
+            }
+
+            case 0x05: // binary
+            {
+                std::int32_t len{};
+                binary_t value;
+                return get_number<std::int32_t, true>(input_format_t::bson, len) && get_bson_binary(len, value) && sax->binary(value);
+            }
+
+            case 0x08: // boolean
+            {
+                return sax->boolean(get() != 0);
+            }
+
+            case 0x0A: // null
+            {
+                return sax->null();
+            }
+
+            case 0x10: // int32
+            {
+                std::int32_t value{};
+                return get_number<std::int32_t, true>(input_format_t::bson, value) && sax->number_integer(value);
+            }
+
+            case 0x12: // int64
+            {
+                std::int64_t value{};
+                return get_number<std::int64_t, true>(input_format_t::bson, value) && sax->number_integer(value);
+            }
+
+            default: // anything else not supported (yet)
+            {
+                std::array<char, 3> cr{{}};
+                static_cast<void>((std::snprintf)(cr.data(), cr.size(), "%.2hhX", static_cast<unsigned char>(element_type))); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
+                const std::string cr_str{cr.data()};
+                return sax->parse_error(element_type_parse_position, cr_str,
+                                        parse_error::create(114, element_type_parse_position, concat("Unsupported BSON record type 0x", cr_str), nullptr));
+            }
+        }
+    }
+
+    /*!
+    @brief Read a BSON element list (as specified in the BSON-spec)
+
+    The same binary layout is used for objects and arrays, hence it must be
+    indicated with the argument @a is_array which one is expected
+    (true --> array, false --> object).
+
+    @param[in] is_array Determines if the element list being read is to be
+                        treated as an object (@a is_array == false), or as an
+                        array (@a is_array == true).
+    @return whether a valid BSON-object/array was passed to the SAX parser
+    */
+    bool parse_bson_element_list(const bool is_array)
+    {
+        string_t key;
+
+        while (auto element_type = get())
+        {
+            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::bson, "element list")))
+            {
+                return false;
+            }
+
+            const std::size_t element_type_parse_position = chars_read;
+            if (JSON_HEDLEY_UNLIKELY(!get_bson_cstr(key)))
+            {
+                return false;
+            }
+
+            if (!is_array && !sax->key(key))
+            {
+                return false;
+            }
+
+            if (JSON_HEDLEY_UNLIKELY(!parse_bson_element_internal(element_type, element_type_parse_position)))
+            {
+                return false;
+            }
+
+            // get_bson_cstr only appends
+            key.clear();
+        }
+
+        return true;
+    }
+
+    /*!
+    @brief Reads an array from the BSON input and passes it to the SAX-parser.
+    @return whether a valid BSON-array was passed to the SAX parser
+    */
+    bool parse_bson_array()
+    {
+        std::int32_t document_size{};
+        get_number<std::int32_t, true>(input_format_t::bson, document_size);
+
+        if (JSON_HEDLEY_UNLIKELY(!sax->start_array(static_cast<std::size_t>(-1))))
+        {
+            return false;
+        }
+
+        if (JSON_HEDLEY_UNLIKELY(!parse_bson_element_list(/*is_array*/true)))
+        {
+            return false;
+        }
+
+        return sax->end_array();
+    }
+
+    //////////
+    // CBOR //
+    //////////
+
+    /*!
+    @param[in] get_char  whether a new character should be retrieved from the
+                         input (true) or whether the last read character should
+                         be considered instead (false)
+    @param[in] tag_handler how CBOR tags should be treated
+
+    @return whether a valid CBOR value was passed to the SAX parser
+    */
+    bool parse_cbor_internal(const bool get_char,
+                             const cbor_tag_handler_t tag_handler)
+    {
+        switch (get_char ? get() : current)
+        {
+            // EOF
+            case char_traits<char_type>::eof():
+                return unexpect_eof(input_format_t::cbor, "value");
+
+            // Integer 0x00..0x17 (0..23)
+            case 0x00:
+            case 0x01:
+            case 0x02:
+            case 0x03:
+            case 0x04:
+            case 0x05:
+            case 0x06:
+            case 0x07:
+            case 0x08:
+            case 0x09:
+            case 0x0A:
+            case 0x0B:
+            case 0x0C:
+            case 0x0D:
+            case 0x0E:
+            case 0x0F:
+            case 0x10:
+            case 0x11:
+            case 0x12:
+            case 0x13:
+            case 0x14:
+            case 0x15:
+            case 0x16:
+            case 0x17:
+                return sax->number_unsigned(static_cast<number_unsigned_t>(current));
+
+            case 0x18: // Unsigned integer (one-byte uint8_t follows)
+            {
+                std::uint8_t number{};
+                return get_number(input_format_t::cbor, number) && sax->number_unsigned(number);
+            }
+
+            case 0x19: // Unsigned integer (two-byte uint16_t follows)
+            {
+                std::uint16_t number{};
+                return get_number(input_format_t::cbor, number) && sax->number_unsigned(number);
+            }
+
+            case 0x1A: // Unsigned integer (four-byte uint32_t follows)
+            {
+                std::uint32_t number{};
+                return get_number(input_format_t::cbor, number) && sax->number_unsigned(number);
+            }
+
+            case 0x1B: // Unsigned integer (eight-byte uint64_t follows)
+            {
+                std::uint64_t number{};
+                return get_number(input_format_t::cbor, number) && sax->number_unsigned(number);
+            }
+
+            // Negative integer -1-0x00..-1-0x17 (-1..-24)
+            case 0x20:
+            case 0x21:
+            case 0x22:
+            case 0x23:
+            case 0x24:
+            case 0x25:
+            case 0x26:
+            case 0x27:
+            case 0x28:
+            case 0x29:
+            case 0x2A:
+            case 0x2B:
+            case 0x2C:
+            case 0x2D:
+            case 0x2E:
+            case 0x2F:
+            case 0x30:
+            case 0x31:
+            case 0x32:
+            case 0x33:
+            case 0x34:
+            case 0x35:
+            case 0x36:
+            case 0x37:
+                return sax->number_integer(static_cast<std::int8_t>(0x20 - 1 - current));
+
+            case 0x38: // Negative integer (one-byte uint8_t follows)
+            {
+                std::uint8_t number{};
+                return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast<number_integer_t>(-1) - number);
+            }
+
+            case 0x39: // Negative integer -1-n (two-byte uint16_t follows)
+            {
+                std::uint16_t number{};
+                return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast<number_integer_t>(-1) - number);
+            }
+
+            case 0x3A: // Negative integer -1-n (four-byte uint32_t follows)
+            {
+                std::uint32_t number{};
+                return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast<number_integer_t>(-1) - number);
+            }
+
+            case 0x3B: // Negative integer -1-n (eight-byte uint64_t follows)
+            {
+                std::uint64_t number{};
+                return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast<number_integer_t>(-1)
+                        - static_cast<number_integer_t>(number));
+            }
+
+            // Binary data (0x00..0x17 bytes follow)
+            case 0x40:
+            case 0x41:
+            case 0x42:
+            case 0x43:
+            case 0x44:
+            case 0x45:
+            case 0x46:
+            case 0x47:
+            case 0x48:
+            case 0x49:
+            case 0x4A:
+            case 0x4B:
+            case 0x4C:
+            case 0x4D:
+            case 0x4E:
+            case 0x4F:
+            case 0x50:
+            case 0x51:
+            case 0x52:
+            case 0x53:
+            case 0x54:
+            case 0x55:
+            case 0x56:
+            case 0x57:
+            case 0x58: // Binary data (one-byte uint8_t for n follows)
+            case 0x59: // Binary data (two-byte uint16_t for n follow)
+            case 0x5A: // Binary data (four-byte uint32_t for n follow)
+            case 0x5B: // Binary data (eight-byte uint64_t for n follow)
+            case 0x5F: // Binary data (indefinite length)
+            {
+                binary_t b;
+                return get_cbor_binary(b) && sax->binary(b);
+            }
+
+            // UTF-8 string (0x00..0x17 bytes follow)
+            case 0x60:
+            case 0x61:
+            case 0x62:
+            case 0x63:
+            case 0x64:
+            case 0x65:
+            case 0x66:
+            case 0x67:
+            case 0x68:
+            case 0x69:
+            case 0x6A:
+            case 0x6B:
+            case 0x6C:
+            case 0x6D:
+            case 0x6E:
+            case 0x6F:
+            case 0x70:
+            case 0x71:
+            case 0x72:
+            case 0x73:
+            case 0x74:
+            case 0x75:
+            case 0x76:
+            case 0x77:
+            case 0x78: // UTF-8 string (one-byte uint8_t for n follows)
+            case 0x79: // UTF-8 string (two-byte uint16_t for n follow)
+            case 0x7A: // UTF-8 string (four-byte uint32_t for n follow)
+            case 0x7B: // UTF-8 string (eight-byte uint64_t for n follow)
+            case 0x7F: // UTF-8 string (indefinite length)
+            {
+                string_t s;
+                return get_cbor_string(s) && sax->string(s);
+            }
+
+            // array (0x00..0x17 data items follow)
+            case 0x80:
+            case 0x81:
+            case 0x82:
+            case 0x83:
+            case 0x84:
+            case 0x85:
+            case 0x86:
+            case 0x87:
+            case 0x88:
+            case 0x89:
+            case 0x8A:
+            case 0x8B:
+            case 0x8C:
+            case 0x8D:
+            case 0x8E:
+            case 0x8F:
+            case 0x90:
+            case 0x91:
+            case 0x92:
+            case 0x93:
+            case 0x94:
+            case 0x95:
+            case 0x96:
+            case 0x97:
+                return get_cbor_array(
+                           conditional_static_cast<std::size_t>(static_cast<unsigned int>(current) & 0x1Fu), tag_handler);
+
+            case 0x98: // array (one-byte uint8_t for n follows)
+            {
+                std::uint8_t len{};
+                return get_number(input_format_t::cbor, len) && get_cbor_array(static_cast<std::size_t>(len), tag_handler);
+            }
+
+            case 0x99: // array (two-byte uint16_t for n follow)
+            {
+                std::uint16_t len{};
+                return get_number(input_format_t::cbor, len) && get_cbor_array(static_cast<std::size_t>(len), tag_handler);
+            }
+
+            case 0x9A: // array (four-byte uint32_t for n follow)
+            {
+                std::uint32_t len{};
+                return get_number(input_format_t::cbor, len) && get_cbor_array(conditional_static_cast<std::size_t>(len), tag_handler);
+            }
+
+            case 0x9B: // array (eight-byte uint64_t for n follow)
+            {
+                std::uint64_t len{};
+                return get_number(input_format_t::cbor, len) && get_cbor_array(conditional_static_cast<std::size_t>(len), tag_handler);
+            }
+
+            case 0x9F: // array (indefinite length)
+                return get_cbor_array(static_cast<std::size_t>(-1), tag_handler);
+
+            // map (0x00..0x17 pairs of data items follow)
+            case 0xA0:
+            case 0xA1:
+            case 0xA2:
+            case 0xA3:
+            case 0xA4:
+            case 0xA5:
+            case 0xA6:
+            case 0xA7:
+            case 0xA8:
+            case 0xA9:
+            case 0xAA:
+            case 0xAB:
+            case 0xAC:
+            case 0xAD:
+            case 0xAE:
+            case 0xAF:
+            case 0xB0:
+            case 0xB1:
+            case 0xB2:
+            case 0xB3:
+            case 0xB4:
+            case 0xB5:
+            case 0xB6:
+            case 0xB7:
+                return get_cbor_object(conditional_static_cast<std::size_t>(static_cast<unsigned int>(current) & 0x1Fu), tag_handler);
+
+            case 0xB8: // map (one-byte uint8_t for n follows)
+            {
+                std::uint8_t len{};
+                return get_number(input_format_t::cbor, len) && get_cbor_object(static_cast<std::size_t>(len), tag_handler);
+            }
+
+            case 0xB9: // map (two-byte uint16_t for n follow)
+            {
+                std::uint16_t len{};
+                return get_number(input_format_t::cbor, len) && get_cbor_object(static_cast<std::size_t>(len), tag_handler);
+            }
+
+            case 0xBA: // map (four-byte uint32_t for n follow)
+            {
+                std::uint32_t len{};
+                return get_number(input_format_t::cbor, len) && get_cbor_object(conditional_static_cast<std::size_t>(len), tag_handler);
+            }
+
+            case 0xBB: // map (eight-byte uint64_t for n follow)
+            {
+                std::uint64_t len{};
+                return get_number(input_format_t::cbor, len) && get_cbor_object(conditional_static_cast<std::size_t>(len), tag_handler);
+            }
+
+            case 0xBF: // map (indefinite length)
+                return get_cbor_object(static_cast<std::size_t>(-1), tag_handler);
+
+            case 0xC6: // tagged item
+            case 0xC7:
+            case 0xC8:
+            case 0xC9:
+            case 0xCA:
+            case 0xCB:
+            case 0xCC:
+            case 0xCD:
+            case 0xCE:
+            case 0xCF:
+            case 0xD0:
+            case 0xD1:
+            case 0xD2:
+            case 0xD3:
+            case 0xD4:
+            case 0xD8: // tagged item (1 bytes follow)
+            case 0xD9: // tagged item (2 bytes follow)
+            case 0xDA: // tagged item (4 bytes follow)
+            case 0xDB: // tagged item (8 bytes follow)
+            {
+                switch (tag_handler)
+                {
+                    case cbor_tag_handler_t::error:
+                    {
+                        auto last_token = get_token_string();
+                        return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
+                                                exception_message(input_format_t::cbor, concat("invalid byte: 0x", last_token), "value"), nullptr));
+                    }
+
+                    case cbor_tag_handler_t::ignore:
+                    {
+                        // ignore binary subtype
+                        switch (current)
+                        {
+                            case 0xD8:
+                            {
+                                std::uint8_t subtype_to_ignore{};
+                                get_number(input_format_t::cbor, subtype_to_ignore);
+                                break;
+                            }
+                            case 0xD9:
+                            {
+                                std::uint16_t subtype_to_ignore{};
+                                get_number(input_format_t::cbor, subtype_to_ignore);
+                                break;
+                            }
+                            case 0xDA:
+                            {
+                                std::uint32_t subtype_to_ignore{};
+                                get_number(input_format_t::cbor, subtype_to_ignore);
+                                break;
+                            }
+                            case 0xDB:
+                            {
+                                std::uint64_t subtype_to_ignore{};
+                                get_number(input_format_t::cbor, subtype_to_ignore);
+                                break;
+                            }
+                            default:
+                                break;
+                        }
+                        return parse_cbor_internal(true, tag_handler);
+                    }
+
+                    case cbor_tag_handler_t::store:
+                    {
+                        binary_t b;
+                        // use binary subtype and store in binary container
+                        switch (current)
+                        {
+                            case 0xD8:
+                            {
+                                std::uint8_t subtype{};
+                                get_number(input_format_t::cbor, subtype);
+                                b.set_subtype(detail::conditional_static_cast<typename binary_t::subtype_type>(subtype));
+                                break;
+                            }
+                            case 0xD9:
+                            {
+                                std::uint16_t subtype{};
+                                get_number(input_format_t::cbor, subtype);
+                                b.set_subtype(detail::conditional_static_cast<typename binary_t::subtype_type>(subtype));
+                                break;
+                            }
+                            case 0xDA:
+                            {
+                                std::uint32_t subtype{};
+                                get_number(input_format_t::cbor, subtype);
+                                b.set_subtype(detail::conditional_static_cast<typename binary_t::subtype_type>(subtype));
+                                break;
+                            }
+                            case 0xDB:
+                            {
+                                std::uint64_t subtype{};
+                                get_number(input_format_t::cbor, subtype);
+                                b.set_subtype(detail::conditional_static_cast<typename binary_t::subtype_type>(subtype));
+                                break;
+                            }
+                            default:
+                                return parse_cbor_internal(true, tag_handler);
+                        }
+                        get();
+                        return get_cbor_binary(b) && sax->binary(b);
+                    }
+
+                    default:                 // LCOV_EXCL_LINE
+                        JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+                        return false;        // LCOV_EXCL_LINE
+                }
+            }
+
+            case 0xF4: // false
+                return sax->boolean(false);
+
+            case 0xF5: // true
+                return sax->boolean(true);
+
+            case 0xF6: // null
+                return sax->null();
+
+            case 0xF9: // Half-Precision Float (two-byte IEEE 754)
+            {
+                const auto byte1_raw = get();
+                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "number")))
+                {
+                    return false;
+                }
+                const auto byte2_raw = get();
+                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "number")))
+                {
+                    return false;
+                }
+
+                const auto byte1 = static_cast<unsigned char>(byte1_raw);
+                const auto byte2 = static_cast<unsigned char>(byte2_raw);
+
+                // code from RFC 7049, Appendix D, Figure 3:
+                // As half-precision floating-point numbers were only added
+                // to IEEE 754 in 2008, today's programming platforms often
+                // still only have limited support for them. It is very
+                // easy to include at least decoding support for them even
+                // without such support. An example of a small decoder for
+                // half-precision floating-point numbers in the C language
+                // is shown in Fig. 3.
+                const auto half = static_cast<unsigned int>((byte1 << 8u) + byte2);
+                const double val = [&half]
+                {
+                    const int exp = (half >> 10u) & 0x1Fu;
+                    const unsigned int mant = half & 0x3FFu;
+                    JSON_ASSERT(0 <= exp&& exp <= 32);
+                    JSON_ASSERT(mant <= 1024);
+                    switch (exp)
+                    {
+                        case 0:
+                            return std::ldexp(mant, -24);
+                        case 31:
+                            return (mant == 0)
+                            ? std::numeric_limits<double>::infinity()
+                            : std::numeric_limits<double>::quiet_NaN();
+                        default:
+                            return std::ldexp(mant + 1024, exp - 25);
+                    }
+                }();
+                return sax->number_float((half & 0x8000u) != 0
+                                         ? static_cast<number_float_t>(-val)
+                                         : static_cast<number_float_t>(val), "");
+            }
+
+            case 0xFA: // Single-Precision Float (four-byte IEEE 754)
+            {
+                float number{};
+                return get_number(input_format_t::cbor, number) && sax->number_float(static_cast<number_float_t>(number), "");
+            }
+
+            case 0xFB: // Double-Precision Float (eight-byte IEEE 754)
+            {
+                double number{};
+                return get_number(input_format_t::cbor, number) && sax->number_float(static_cast<number_float_t>(number), "");
+            }
+
+            default: // anything else (0xFF is handled inside the other types)
+            {
+                auto last_token = get_token_string();
+                return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
+                                        exception_message(input_format_t::cbor, concat("invalid byte: 0x", last_token), "value"), nullptr));
+            }
+        }
+    }
+
+    /*!
+    @brief reads a CBOR string
+
+    This function first reads starting bytes to determine the expected
+    string length and then copies this number of bytes into a string.
+    Additionally, CBOR's strings with indefinite lengths are supported.
+
+    @param[out] result  created string
+
+    @return whether string creation completed
+    */
+    bool get_cbor_string(string_t& result)
+    {
+        if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "string")))
+        {
+            return false;
+        }
+
+        switch (current)
+        {
+            // UTF-8 string (0x00..0x17 bytes follow)
+            case 0x60:
+            case 0x61:
+            case 0x62:
+            case 0x63:
+            case 0x64:
+            case 0x65:
+            case 0x66:
+            case 0x67:
+            case 0x68:
+            case 0x69:
+            case 0x6A:
+            case 0x6B:
+            case 0x6C:
+            case 0x6D:
+            case 0x6E:
+            case 0x6F:
+            case 0x70:
+            case 0x71:
+            case 0x72:
+            case 0x73:
+            case 0x74:
+            case 0x75:
+            case 0x76:
+            case 0x77:
+            {
+                return get_string(input_format_t::cbor, static_cast<unsigned int>(current) & 0x1Fu, result);
+            }
+
+            case 0x78: // UTF-8 string (one-byte uint8_t for n follows)
+            {
+                std::uint8_t len{};
+                return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result);
+            }
+
+            case 0x79: // UTF-8 string (two-byte uint16_t for n follow)
+            {
+                std::uint16_t len{};
+                return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result);
+            }
+
+            case 0x7A: // UTF-8 string (four-byte uint32_t for n follow)
+            {
+                std::uint32_t len{};
+                return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result);
+            }
+
+            case 0x7B: // UTF-8 string (eight-byte uint64_t for n follow)
+            {
+                std::uint64_t len{};
+                return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result);
+            }
+
+            case 0x7F: // UTF-8 string (indefinite length)
+            {
+                while (get() != 0xFF)
+                {
+                    string_t chunk;
+                    if (!get_cbor_string(chunk))
+                    {
+                        return false;
+                    }
+                    result.append(chunk);
+                }
+                return true;
+            }
+
+            default:
+            {
+                auto last_token = get_token_string();
+                return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read,
+                                        exception_message(input_format_t::cbor, concat("expected length specification (0x60-0x7B) or indefinite string type (0x7F); last byte: 0x", last_token), "string"), nullptr));
+            }
+        }
+    }
+
+    /*!
+    @brief reads a CBOR byte array
+
+    This function first reads starting bytes to determine the expected
+    byte array length and then copies this number of bytes into the byte array.
+    Additionally, CBOR's byte arrays with indefinite lengths are supported.
+
+    @param[out] result  created byte array
+
+    @return whether byte array creation completed
+    */
+    bool get_cbor_binary(binary_t& result)
+    {
+        if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "binary")))
+        {
+            return false;
+        }
+
+        switch (current)
+        {
+            // Binary data (0x00..0x17 bytes follow)
+            case 0x40:
+            case 0x41:
+            case 0x42:
+            case 0x43:
+            case 0x44:
+            case 0x45:
+            case 0x46:
+            case 0x47:
+            case 0x48:
+            case 0x49:
+            case 0x4A:
+            case 0x4B:
+            case 0x4C:
+            case 0x4D:
+            case 0x4E:
+            case 0x4F:
+            case 0x50:
+            case 0x51:
+            case 0x52:
+            case 0x53:
+            case 0x54:
+            case 0x55:
+            case 0x56:
+            case 0x57:
+            {
+                return get_binary(input_format_t::cbor, static_cast<unsigned int>(current) & 0x1Fu, result);
+            }
+
+            case 0x58: // Binary data (one-byte uint8_t for n follows)
+            {
+                std::uint8_t len{};
+                return get_number(input_format_t::cbor, len) &&
+                       get_binary(input_format_t::cbor, len, result);
+            }
+
+            case 0x59: // Binary data (two-byte uint16_t for n follow)
+            {
+                std::uint16_t len{};
+                return get_number(input_format_t::cbor, len) &&
+                       get_binary(input_format_t::cbor, len, result);
+            }
+
+            case 0x5A: // Binary data (four-byte uint32_t for n follow)
+            {
+                std::uint32_t len{};
+                return get_number(input_format_t::cbor, len) &&
+                       get_binary(input_format_t::cbor, len, result);
+            }
+
+            case 0x5B: // Binary data (eight-byte uint64_t for n follow)
+            {
+                std::uint64_t len{};
+                return get_number(input_format_t::cbor, len) &&
+                       get_binary(input_format_t::cbor, len, result);
+            }
+
+            case 0x5F: // Binary data (indefinite length)
+            {
+                while (get() != 0xFF)
+                {
+                    binary_t chunk;
+                    if (!get_cbor_binary(chunk))
+                    {
+                        return false;
+                    }
+                    result.insert(result.end(), chunk.begin(), chunk.end());
+                }
+                return true;
+            }
+
+            default:
+            {
+                auto last_token = get_token_string();
+                return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read,
+                                        exception_message(input_format_t::cbor, concat("expected length specification (0x40-0x5B) or indefinite binary array type (0x5F); last byte: 0x", last_token), "binary"), nullptr));
+            }
+        }
+    }
+
+    /*!
+    @param[in] len  the length of the array or static_cast<std::size_t>(-1) for an
+                    array of indefinite size
+    @param[in] tag_handler how CBOR tags should be treated
+    @return whether array creation completed
+    */
+    bool get_cbor_array(const std::size_t len,
+                        const cbor_tag_handler_t tag_handler)
+    {
+        if (JSON_HEDLEY_UNLIKELY(!sax->start_array(len)))
+        {
+            return false;
+        }
+
+        if (len != static_cast<std::size_t>(-1))
+        {
+            for (std::size_t i = 0; i < len; ++i)
+            {
+                if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(true, tag_handler)))
+                {
+                    return false;
+                }
+            }
+        }
+        else
+        {
+            while (get() != 0xFF)
+            {
+                if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(false, tag_handler)))
+                {
+                    return false;
+                }
+            }
+        }
+
+        return sax->end_array();
+    }
+
+    /*!
+    @param[in] len  the length of the object or static_cast<std::size_t>(-1) for an
+                    object of indefinite size
+    @param[in] tag_handler how CBOR tags should be treated
+    @return whether object creation completed
+    */
+    bool get_cbor_object(const std::size_t len,
+                         const cbor_tag_handler_t tag_handler)
+    {
+        if (JSON_HEDLEY_UNLIKELY(!sax->start_object(len)))
+        {
+            return false;
+        }
+
+        if (len != 0)
+        {
+            string_t key;
+            if (len != static_cast<std::size_t>(-1))
+            {
+                for (std::size_t i = 0; i < len; ++i)
+                {
+                    get();
+                    if (JSON_HEDLEY_UNLIKELY(!get_cbor_string(key) || !sax->key(key)))
+                    {
+                        return false;
+                    }
+
+                    if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(true, tag_handler)))
+                    {
+                        return false;
+                    }
+                    key.clear();
+                }
+            }
+            else
+            {
+                while (get() != 0xFF)
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!get_cbor_string(key) || !sax->key(key)))
+                    {
+                        return false;
+                    }
+
+                    if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(true, tag_handler)))
+                    {
+                        return false;
+                    }
+                    key.clear();
+                }
+            }
+        }
+
+        return sax->end_object();
+    }
+
+    /////////////
+    // MsgPack //
+    /////////////
+
+    /*!
+    @return whether a valid MessagePack value was passed to the SAX parser
+    */
+    bool parse_msgpack_internal()
+    {
+        switch (get())
+        {
+            // EOF
+            case char_traits<char_type>::eof():
+                return unexpect_eof(input_format_t::msgpack, "value");
+
+            // positive fixint
+            case 0x00:
+            case 0x01:
+            case 0x02:
+            case 0x03:
+            case 0x04:
+            case 0x05:
+            case 0x06:
+            case 0x07:
+            case 0x08:
+            case 0x09:
+            case 0x0A:
+            case 0x0B:
+            case 0x0C:
+            case 0x0D:
+            case 0x0E:
+            case 0x0F:
+            case 0x10:
+            case 0x11:
+            case 0x12:
+            case 0x13:
+            case 0x14:
+            case 0x15:
+            case 0x16:
+            case 0x17:
+            case 0x18:
+            case 0x19:
+            case 0x1A:
+            case 0x1B:
+            case 0x1C:
+            case 0x1D:
+            case 0x1E:
+            case 0x1F:
+            case 0x20:
+            case 0x21:
+            case 0x22:
+            case 0x23:
+            case 0x24:
+            case 0x25:
+            case 0x26:
+            case 0x27:
+            case 0x28:
+            case 0x29:
+            case 0x2A:
+            case 0x2B:
+            case 0x2C:
+            case 0x2D:
+            case 0x2E:
+            case 0x2F:
+            case 0x30:
+            case 0x31:
+            case 0x32:
+            case 0x33:
+            case 0x34:
+            case 0x35:
+            case 0x36:
+            case 0x37:
+            case 0x38:
+            case 0x39:
+            case 0x3A:
+            case 0x3B:
+            case 0x3C:
+            case 0x3D:
+            case 0x3E:
+            case 0x3F:
+            case 0x40:
+            case 0x41:
+            case 0x42:
+            case 0x43:
+            case 0x44:
+            case 0x45:
+            case 0x46:
+            case 0x47:
+            case 0x48:
+            case 0x49:
+            case 0x4A:
+            case 0x4B:
+            case 0x4C:
+            case 0x4D:
+            case 0x4E:
+            case 0x4F:
+            case 0x50:
+            case 0x51:
+            case 0x52:
+            case 0x53:
+            case 0x54:
+            case 0x55:
+            case 0x56:
+            case 0x57:
+            case 0x58:
+            case 0x59:
+            case 0x5A:
+            case 0x5B:
+            case 0x5C:
+            case 0x5D:
+            case 0x5E:
+            case 0x5F:
+            case 0x60:
+            case 0x61:
+            case 0x62:
+            case 0x63:
+            case 0x64:
+            case 0x65:
+            case 0x66:
+            case 0x67:
+            case 0x68:
+            case 0x69:
+            case 0x6A:
+            case 0x6B:
+            case 0x6C:
+            case 0x6D:
+            case 0x6E:
+            case 0x6F:
+            case 0x70:
+            case 0x71:
+            case 0x72:
+            case 0x73:
+            case 0x74:
+            case 0x75:
+            case 0x76:
+            case 0x77:
+            case 0x78:
+            case 0x79:
+            case 0x7A:
+            case 0x7B:
+            case 0x7C:
+            case 0x7D:
+            case 0x7E:
+            case 0x7F:
+                return sax->number_unsigned(static_cast<number_unsigned_t>(current));
+
+            // fixmap
+            case 0x80:
+            case 0x81:
+            case 0x82:
+            case 0x83:
+            case 0x84:
+            case 0x85:
+            case 0x86:
+            case 0x87:
+            case 0x88:
+            case 0x89:
+            case 0x8A:
+            case 0x8B:
+            case 0x8C:
+            case 0x8D:
+            case 0x8E:
+            case 0x8F:
+                return get_msgpack_object(conditional_static_cast<std::size_t>(static_cast<unsigned int>(current) & 0x0Fu));
+
+            // fixarray
+            case 0x90:
+            case 0x91:
+            case 0x92:
+            case 0x93:
+            case 0x94:
+            case 0x95:
+            case 0x96:
+            case 0x97:
+            case 0x98:
+            case 0x99:
+            case 0x9A:
+            case 0x9B:
+            case 0x9C:
+            case 0x9D:
+            case 0x9E:
+            case 0x9F:
+                return get_msgpack_array(conditional_static_cast<std::size_t>(static_cast<unsigned int>(current) & 0x0Fu));
+
+            // fixstr
+            case 0xA0:
+            case 0xA1:
+            case 0xA2:
+            case 0xA3:
+            case 0xA4:
+            case 0xA5:
+            case 0xA6:
+            case 0xA7:
+            case 0xA8:
+            case 0xA9:
+            case 0xAA:
+            case 0xAB:
+            case 0xAC:
+            case 0xAD:
+            case 0xAE:
+            case 0xAF:
+            case 0xB0:
+            case 0xB1:
+            case 0xB2:
+            case 0xB3:
+            case 0xB4:
+            case 0xB5:
+            case 0xB6:
+            case 0xB7:
+            case 0xB8:
+            case 0xB9:
+            case 0xBA:
+            case 0xBB:
+            case 0xBC:
+            case 0xBD:
+            case 0xBE:
+            case 0xBF:
+            case 0xD9: // str 8
+            case 0xDA: // str 16
+            case 0xDB: // str 32
+            {
+                string_t s;
+                return get_msgpack_string(s) && sax->string(s);
+            }
+
+            case 0xC0: // nil
+                return sax->null();
+
+            case 0xC2: // false
+                return sax->boolean(false);
+
+            case 0xC3: // true
+                return sax->boolean(true);
+
+            case 0xC4: // bin 8
+            case 0xC5: // bin 16
+            case 0xC6: // bin 32
+            case 0xC7: // ext 8
+            case 0xC8: // ext 16
+            case 0xC9: // ext 32
+            case 0xD4: // fixext 1
+            case 0xD5: // fixext 2
+            case 0xD6: // fixext 4
+            case 0xD7: // fixext 8
+            case 0xD8: // fixext 16
+            {
+                binary_t b;
+                return get_msgpack_binary(b) && sax->binary(b);
+            }
+
+            case 0xCA: // float 32
+            {
+                float number{};
+                return get_number(input_format_t::msgpack, number) && sax->number_float(static_cast<number_float_t>(number), "");
+            }
+
+            case 0xCB: // float 64
+            {
+                double number{};
+                return get_number(input_format_t::msgpack, number) && sax->number_float(static_cast<number_float_t>(number), "");
+            }
+
+            case 0xCC: // uint 8
+            {
+                std::uint8_t number{};
+                return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number);
+            }
+
+            case 0xCD: // uint 16
+            {
+                std::uint16_t number{};
+                return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number);
+            }
+
+            case 0xCE: // uint 32
+            {
+                std::uint32_t number{};
+                return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number);
+            }
+
+            case 0xCF: // uint 64
+            {
+                std::uint64_t number{};
+                return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number);
+            }
+
+            case 0xD0: // int 8
+            {
+                std::int8_t number{};
+                return get_number(input_format_t::msgpack, number) && sax->number_integer(number);
+            }
+
+            case 0xD1: // int 16
+            {
+                std::int16_t number{};
+                return get_number(input_format_t::msgpack, number) && sax->number_integer(number);
+            }
+
+            case 0xD2: // int 32
+            {
+                std::int32_t number{};
+                return get_number(input_format_t::msgpack, number) && sax->number_integer(number);
+            }
+
+            case 0xD3: // int 64
+            {
+                std::int64_t number{};
+                return get_number(input_format_t::msgpack, number) && sax->number_integer(number);
+            }
+
+            case 0xDC: // array 16
+            {
+                std::uint16_t len{};
+                return get_number(input_format_t::msgpack, len) && get_msgpack_array(static_cast<std::size_t>(len));
+            }
+
+            case 0xDD: // array 32
+            {
+                std::uint32_t len{};
+                return get_number(input_format_t::msgpack, len) && get_msgpack_array(conditional_static_cast<std::size_t>(len));
+            }
+
+            case 0xDE: // map 16
+            {
+                std::uint16_t len{};
+                return get_number(input_format_t::msgpack, len) && get_msgpack_object(static_cast<std::size_t>(len));
+            }
+
+            case 0xDF: // map 32
+            {
+                std::uint32_t len{};
+                return get_number(input_format_t::msgpack, len) && get_msgpack_object(conditional_static_cast<std::size_t>(len));
+            }
+
+            // negative fixint
+            case 0xE0:
+            case 0xE1:
+            case 0xE2:
+            case 0xE3:
+            case 0xE4:
+            case 0xE5:
+            case 0xE6:
+            case 0xE7:
+            case 0xE8:
+            case 0xE9:
+            case 0xEA:
+            case 0xEB:
+            case 0xEC:
+            case 0xED:
+            case 0xEE:
+            case 0xEF:
+            case 0xF0:
+            case 0xF1:
+            case 0xF2:
+            case 0xF3:
+            case 0xF4:
+            case 0xF5:
+            case 0xF6:
+            case 0xF7:
+            case 0xF8:
+            case 0xF9:
+            case 0xFA:
+            case 0xFB:
+            case 0xFC:
+            case 0xFD:
+            case 0xFE:
+            case 0xFF:
+                return sax->number_integer(static_cast<std::int8_t>(current));
+
+            default: // anything else
+            {
+                auto last_token = get_token_string();
+                return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
+                                        exception_message(input_format_t::msgpack, concat("invalid byte: 0x", last_token), "value"), nullptr));
+            }
+        }
+    }
+
+    /*!
+    @brief reads a MessagePack string
+
+    This function first reads starting bytes to determine the expected
+    string length and then copies this number of bytes into a string.
+
+    @param[out] result  created string
+
+    @return whether string creation completed
+    */
+    bool get_msgpack_string(string_t& result)
+    {
+        if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::msgpack, "string")))
+        {
+            return false;
+        }
+
+        switch (current)
+        {
+            // fixstr
+            case 0xA0:
+            case 0xA1:
+            case 0xA2:
+            case 0xA3:
+            case 0xA4:
+            case 0xA5:
+            case 0xA6:
+            case 0xA7:
+            case 0xA8:
+            case 0xA9:
+            case 0xAA:
+            case 0xAB:
+            case 0xAC:
+            case 0xAD:
+            case 0xAE:
+            case 0xAF:
+            case 0xB0:
+            case 0xB1:
+            case 0xB2:
+            case 0xB3:
+            case 0xB4:
+            case 0xB5:
+            case 0xB6:
+            case 0xB7:
+            case 0xB8:
+            case 0xB9:
+            case 0xBA:
+            case 0xBB:
+            case 0xBC:
+            case 0xBD:
+            case 0xBE:
+            case 0xBF:
+            {
+                return get_string(input_format_t::msgpack, static_cast<unsigned int>(current) & 0x1Fu, result);
+            }
+
+            case 0xD9: // str 8
+            {
+                std::uint8_t len{};
+                return get_number(input_format_t::msgpack, len) && get_string(input_format_t::msgpack, len, result);
+            }
+
+            case 0xDA: // str 16
+            {
+                std::uint16_t len{};
+                return get_number(input_format_t::msgpack, len) && get_string(input_format_t::msgpack, len, result);
+            }
+
+            case 0xDB: // str 32
+            {
+                std::uint32_t len{};
+                return get_number(input_format_t::msgpack, len) && get_string(input_format_t::msgpack, len, result);
+            }
+
+            default:
+            {
+                auto last_token = get_token_string();
+                return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read,
+                                        exception_message(input_format_t::msgpack, concat("expected length specification (0xA0-0xBF, 0xD9-0xDB); last byte: 0x", last_token), "string"), nullptr));
+            }
+        }
+    }
+
+    /*!
+    @brief reads a MessagePack byte array
+
+    This function first reads starting bytes to determine the expected
+    byte array length and then copies this number of bytes into a byte array.
+
+    @param[out] result  created byte array
+
+    @return whether byte array creation completed
+    */
+    bool get_msgpack_binary(binary_t& result)
+    {
+        // helper function to set the subtype
+        auto assign_and_return_true = [&result](std::int8_t subtype)
+        {
+            result.set_subtype(static_cast<std::uint8_t>(subtype));
+            return true;
+        };
+
+        switch (current)
+        {
+            case 0xC4: // bin 8
+            {
+                std::uint8_t len{};
+                return get_number(input_format_t::msgpack, len) &&
+                       get_binary(input_format_t::msgpack, len, result);
+            }
+
+            case 0xC5: // bin 16
+            {
+                std::uint16_t len{};
+                return get_number(input_format_t::msgpack, len) &&
+                       get_binary(input_format_t::msgpack, len, result);
+            }
+
+            case 0xC6: // bin 32
+            {
+                std::uint32_t len{};
+                return get_number(input_format_t::msgpack, len) &&
+                       get_binary(input_format_t::msgpack, len, result);
+            }
+
+            case 0xC7: // ext 8
+            {
+                std::uint8_t len{};
+                std::int8_t subtype{};
+                return get_number(input_format_t::msgpack, len) &&
+                       get_number(input_format_t::msgpack, subtype) &&
+                       get_binary(input_format_t::msgpack, len, result) &&
+                       assign_and_return_true(subtype);
+            }
+
+            case 0xC8: // ext 16
+            {
+                std::uint16_t len{};
+                std::int8_t subtype{};
+                return get_number(input_format_t::msgpack, len) &&
+                       get_number(input_format_t::msgpack, subtype) &&
+                       get_binary(input_format_t::msgpack, len, result) &&
+                       assign_and_return_true(subtype);
+            }
+
+            case 0xC9: // ext 32
+            {
+                std::uint32_t len{};
+                std::int8_t subtype{};
+                return get_number(input_format_t::msgpack, len) &&
+                       get_number(input_format_t::msgpack, subtype) &&
+                       get_binary(input_format_t::msgpack, len, result) &&
+                       assign_and_return_true(subtype);
+            }
+
+            case 0xD4: // fixext 1
+            {
+                std::int8_t subtype{};
+                return get_number(input_format_t::msgpack, subtype) &&
+                       get_binary(input_format_t::msgpack, 1, result) &&
+                       assign_and_return_true(subtype);
+            }
+
+            case 0xD5: // fixext 2
+            {
+                std::int8_t subtype{};
+                return get_number(input_format_t::msgpack, subtype) &&
+                       get_binary(input_format_t::msgpack, 2, result) &&
+                       assign_and_return_true(subtype);
+            }
+
+            case 0xD6: // fixext 4
+            {
+                std::int8_t subtype{};
+                return get_number(input_format_t::msgpack, subtype) &&
+                       get_binary(input_format_t::msgpack, 4, result) &&
+                       assign_and_return_true(subtype);
+            }
+
+            case 0xD7: // fixext 8
+            {
+                std::int8_t subtype{};
+                return get_number(input_format_t::msgpack, subtype) &&
+                       get_binary(input_format_t::msgpack, 8, result) &&
+                       assign_and_return_true(subtype);
+            }
+
+            case 0xD8: // fixext 16
+            {
+                std::int8_t subtype{};
+                return get_number(input_format_t::msgpack, subtype) &&
+                       get_binary(input_format_t::msgpack, 16, result) &&
+                       assign_and_return_true(subtype);
+            }
+
+            default:           // LCOV_EXCL_LINE
+                return false;  // LCOV_EXCL_LINE
+        }
+    }
+
+    /*!
+    @param[in] len  the length of the array
+    @return whether array creation completed
+    */
+    bool get_msgpack_array(const std::size_t len)
+    {
+        if (JSON_HEDLEY_UNLIKELY(!sax->start_array(len)))
+        {
+            return false;
+        }
+
+        for (std::size_t i = 0; i < len; ++i)
+        {
+            if (JSON_HEDLEY_UNLIKELY(!parse_msgpack_internal()))
+            {
+                return false;
+            }
+        }
+
+        return sax->end_array();
+    }
+
+    /*!
+    @param[in] len  the length of the object
+    @return whether object creation completed
+    */
+    bool get_msgpack_object(const std::size_t len)
+    {
+        if (JSON_HEDLEY_UNLIKELY(!sax->start_object(len)))
+        {
+            return false;
+        }
+
+        string_t key;
+        for (std::size_t i = 0; i < len; ++i)
+        {
+            get();
+            if (JSON_HEDLEY_UNLIKELY(!get_msgpack_string(key) || !sax->key(key)))
+            {
+                return false;
+            }
+
+            if (JSON_HEDLEY_UNLIKELY(!parse_msgpack_internal()))
+            {
+                return false;
+            }
+            key.clear();
+        }
+
+        return sax->end_object();
+    }
+
+    ////////////
+    // UBJSON //
+    ////////////
+
+    /*!
+    @param[in] get_char  whether a new character should be retrieved from the
+                         input (true, default) or whether the last read
+                         character should be considered instead
+
+    @return whether a valid UBJSON value was passed to the SAX parser
+    */
+    bool parse_ubjson_internal(const bool get_char = true)
+    {
+        return get_ubjson_value(get_char ? get_ignore_noop() : current);
+    }
+
+    /*!
+    @brief reads a UBJSON string
+
+    This function is either called after reading the 'S' byte explicitly
+    indicating a string, or in case of an object key where the 'S' byte can be
+    left out.
+
+    @param[out] result   created string
+    @param[in] get_char  whether a new character should be retrieved from the
+                         input (true, default) or whether the last read
+                         character should be considered instead
+
+    @return whether string creation completed
+    */
+    bool get_ubjson_string(string_t& result, const bool get_char = true)
+    {
+        if (get_char)
+        {
+            get();  // TODO(niels): may we ignore N here?
+        }
+
+        if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "value")))
+        {
+            return false;
+        }
+
+        switch (current)
+        {
+            case 'U':
+            {
+                std::uint8_t len{};
+                return get_number(input_format, len) && get_string(input_format, len, result);
+            }
+
+            case 'i':
+            {
+                std::int8_t len{};
+                return get_number(input_format, len) && get_string(input_format, len, result);
+            }
+
+            case 'I':
+            {
+                std::int16_t len{};
+                return get_number(input_format, len) && get_string(input_format, len, result);
+            }
+
+            case 'l':
+            {
+                std::int32_t len{};
+                return get_number(input_format, len) && get_string(input_format, len, result);
+            }
+
+            case 'L':
+            {
+                std::int64_t len{};
+                return get_number(input_format, len) && get_string(input_format, len, result);
+            }
+
+            case 'u':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                std::uint16_t len{};
+                return get_number(input_format, len) && get_string(input_format, len, result);
+            }
+
+            case 'm':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                std::uint32_t len{};
+                return get_number(input_format, len) && get_string(input_format, len, result);
+            }
+
+            case 'M':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                std::uint64_t len{};
+                return get_number(input_format, len) && get_string(input_format, len, result);
+            }
+
+            default:
+                break;
+        }
+        auto last_token = get_token_string();
+        std::string message;
+
+        if (input_format != input_format_t::bjdata)
+        {
+            message = "expected length type specification (U, i, I, l, L); last byte: 0x" + last_token;
+        }
+        else
+        {
+            message = "expected length type specification (U, i, u, I, m, l, M, L); last byte: 0x" + last_token;
+        }
+        return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read, exception_message(input_format, message, "string"), nullptr));
+    }
+
+    /*!
+    @param[out] dim  an integer vector storing the ND array dimensions
+    @return whether reading ND array size vector is successful
+    */
+    bool get_ubjson_ndarray_size(std::vector<size_t>& dim)
+    {
+        std::pair<std::size_t, char_int_type> size_and_type;
+        size_t dimlen = 0;
+        bool no_ndarray = true;
+
+        if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_type(size_and_type, no_ndarray)))
+        {
+            return false;
+        }
+
+        if (size_and_type.first != npos)
+        {
+            if (size_and_type.second != 0)
+            {
+                if (size_and_type.second != 'N')
+                {
+                    for (std::size_t i = 0; i < size_and_type.first; ++i)
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_value(dimlen, no_ndarray, size_and_type.second)))
+                        {
+                            return false;
+                        }
+                        dim.push_back(dimlen);
+                    }
+                }
+            }
+            else
+            {
+                for (std::size_t i = 0; i < size_and_type.first; ++i)
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_value(dimlen, no_ndarray)))
+                    {
+                        return false;
+                    }
+                    dim.push_back(dimlen);
+                }
+            }
+        }
+        else
+        {
+            while (current != ']')
+            {
+                if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_value(dimlen, no_ndarray, current)))
+                {
+                    return false;
+                }
+                dim.push_back(dimlen);
+                get_ignore_noop();
+            }
+        }
+        return true;
+    }
+
+    /*!
+    @param[out] result  determined size
+    @param[in,out] is_ndarray  for input, `true` means already inside an ndarray vector
+                               or ndarray dimension is not allowed; `false` means ndarray
+                               is allowed; for output, `true` means an ndarray is found;
+                               is_ndarray can only return `true` when its initial value
+                               is `false`
+    @param[in] prefix  type marker if already read, otherwise set to 0
+
+    @return whether size determination completed
+    */
+    bool get_ubjson_size_value(std::size_t& result, bool& is_ndarray, char_int_type prefix = 0)
+    {
+        if (prefix == 0)
+        {
+            prefix = get_ignore_noop();
+        }
+
+        switch (prefix)
+        {
+            case 'U':
+            {
+                std::uint8_t number{};
+                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
+                {
+                    return false;
+                }
+                result = static_cast<std::size_t>(number);
+                return true;
+            }
+
+            case 'i':
+            {
+                std::int8_t number{};
+                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
+                {
+                    return false;
+                }
+                if (number < 0)
+                {
+                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read,
+                                            exception_message(input_format, "count in an optimized container must be positive", "size"), nullptr));
+                }
+                result = static_cast<std::size_t>(number); // NOLINT(bugprone-signed-char-misuse,cert-str34-c): number is not a char
+                return true;
+            }
+
+            case 'I':
+            {
+                std::int16_t number{};
+                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
+                {
+                    return false;
+                }
+                if (number < 0)
+                {
+                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read,
+                                            exception_message(input_format, "count in an optimized container must be positive", "size"), nullptr));
+                }
+                result = static_cast<std::size_t>(number);
+                return true;
+            }
+
+            case 'l':
+            {
+                std::int32_t number{};
+                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
+                {
+                    return false;
+                }
+                if (number < 0)
+                {
+                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read,
+                                            exception_message(input_format, "count in an optimized container must be positive", "size"), nullptr));
+                }
+                result = static_cast<std::size_t>(number);
+                return true;
+            }
+
+            case 'L':
+            {
+                std::int64_t number{};
+                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
+                {
+                    return false;
+                }
+                if (number < 0)
+                {
+                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read,
+                                            exception_message(input_format, "count in an optimized container must be positive", "size"), nullptr));
+                }
+                if (!value_in_range_of<std::size_t>(number))
+                {
+                    return sax->parse_error(chars_read, get_token_string(), out_of_range::create(408,
+                                            exception_message(input_format, "integer value overflow", "size"), nullptr));
+                }
+                result = static_cast<std::size_t>(number);
+                return true;
+            }
+
+            case 'u':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                std::uint16_t number{};
+                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
+                {
+                    return false;
+                }
+                result = static_cast<std::size_t>(number);
+                return true;
+            }
+
+            case 'm':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                std::uint32_t number{};
+                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
+                {
+                    return false;
+                }
+                result = conditional_static_cast<std::size_t>(number);
+                return true;
+            }
+
+            case 'M':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                std::uint64_t number{};
+                if (JSON_HEDLEY_UNLIKELY(!get_number(input_format, number)))
+                {
+                    return false;
+                }
+                if (!value_in_range_of<std::size_t>(number))
+                {
+                    return sax->parse_error(chars_read, get_token_string(), out_of_range::create(408,
+                                            exception_message(input_format, "integer value overflow", "size"), nullptr));
+                }
+                result = detail::conditional_static_cast<std::size_t>(number);
+                return true;
+            }
+
+            case '[':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                if (is_ndarray) // ndarray dimensional vector can only contain integers, and can not embed another array
+                {
+                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read, exception_message(input_format, "ndarray dimensional vector is not allowed", "size"), nullptr));
+                }
+                std::vector<size_t> dim;
+                if (JSON_HEDLEY_UNLIKELY(!get_ubjson_ndarray_size(dim)))
+                {
+                    return false;
+                }
+                if (dim.size() == 1 || (dim.size() == 2 && dim.at(0) == 1)) // return normal array size if 1D row vector
+                {
+                    result = dim.at(dim.size() - 1);
+                    return true;
+                }
+                if (!dim.empty())  // if ndarray, convert to an object in JData annotated array format
+                {
+                    for (auto i : dim) // test if any dimension in an ndarray is 0, if so, return a 1D empty container
+                    {
+                        if ( i == 0 )
+                        {
+                            result = 0;
+                            return true;
+                        }
+                    }
+
+                    string_t key = "_ArraySize_";
+                    if (JSON_HEDLEY_UNLIKELY(!sax->start_object(3) || !sax->key(key) || !sax->start_array(dim.size())))
+                    {
+                        return false;
+                    }
+                    result = 1;
+                    for (auto i : dim)
+                    {
+                        result *= i;
+                        if (result == 0 || result == npos) // because dim elements shall not have zeros, result = 0 means overflow happened; it also can't be npos as it is used to initialize size in get_ubjson_size_type()
+                        {
+                            return sax->parse_error(chars_read, get_token_string(), out_of_range::create(408, exception_message(input_format, "excessive ndarray size caused overflow", "size"), nullptr));
+                        }
+                        if (JSON_HEDLEY_UNLIKELY(!sax->number_unsigned(static_cast<number_unsigned_t>(i))))
+                        {
+                            return false;
+                        }
+                    }
+                    is_ndarray = true;
+                    return sax->end_array();
+                }
+                result = 0;
+                return true;
+            }
+
+            default:
+                break;
+        }
+        auto last_token = get_token_string();
+        std::string message;
+
+        if (input_format != input_format_t::bjdata)
+        {
+            message = "expected length type specification (U, i, I, l, L) after '#'; last byte: 0x" + last_token;
+        }
+        else
+        {
+            message = "expected length type specification (U, i, u, I, m, l, M, L) after '#'; last byte: 0x" + last_token;
+        }
+        return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read, exception_message(input_format, message, "size"), nullptr));
+    }
+
+    /*!
+    @brief determine the type and size for a container
+
+    In the optimized UBJSON format, a type and a size can be provided to allow
+    for a more compact representation.
+
+    @param[out] result  pair of the size and the type
+    @param[in] inside_ndarray  whether the parser is parsing an ND array dimensional vector
+
+    @return whether pair creation completed
+    */
+    bool get_ubjson_size_type(std::pair<std::size_t, char_int_type>& result, bool inside_ndarray = false)
+    {
+        result.first = npos; // size
+        result.second = 0; // type
+        bool is_ndarray = false;
+
+        get_ignore_noop();
+
+        if (current == '$')
+        {
+            result.second = get();  // must not ignore 'N', because 'N' maybe the type
+            if (input_format == input_format_t::bjdata
+                    && JSON_HEDLEY_UNLIKELY(std::binary_search(bjd_optimized_type_markers.begin(), bjd_optimized_type_markers.end(), result.second)))
+            {
+                auto last_token = get_token_string();
+                return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
+                                        exception_message(input_format, concat("marker 0x", last_token, " is not a permitted optimized array type"), "type"), nullptr));
+            }
+
+            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "type")))
+            {
+                return false;
+            }
+
+            get_ignore_noop();
+            if (JSON_HEDLEY_UNLIKELY(current != '#'))
+            {
+                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "value")))
+                {
+                    return false;
+                }
+                auto last_token = get_token_string();
+                return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
+                                        exception_message(input_format, concat("expected '#' after type information; last byte: 0x", last_token), "size"), nullptr));
+            }
+
+            const bool is_error = get_ubjson_size_value(result.first, is_ndarray);
+            if (input_format == input_format_t::bjdata && is_ndarray)
+            {
+                if (inside_ndarray)
+                {
+                    return sax->parse_error(chars_read, get_token_string(), parse_error::create(112, chars_read,
+                                            exception_message(input_format, "ndarray can not be recursive", "size"), nullptr));
+                }
+                result.second |= (1 << 8); // use bit 8 to indicate ndarray, all UBJSON and BJData markers should be ASCII letters
+            }
+            return is_error;
+        }
+
+        if (current == '#')
+        {
+            const bool is_error = get_ubjson_size_value(result.first, is_ndarray);
+            if (input_format == input_format_t::bjdata && is_ndarray)
+            {
+                return sax->parse_error(chars_read, get_token_string(), parse_error::create(112, chars_read,
+                                        exception_message(input_format, "ndarray requires both type and size", "size"), nullptr));
+            }
+            return is_error;
+        }
+
+        return true;
+    }
+
+    /*!
+    @param prefix  the previously read or set type prefix
+    @return whether value creation completed
+    */
+    bool get_ubjson_value(const char_int_type prefix)
+    {
+        switch (prefix)
+        {
+            case char_traits<char_type>::eof():  // EOF
+                return unexpect_eof(input_format, "value");
+
+            case 'T':  // true
+                return sax->boolean(true);
+            case 'F':  // false
+                return sax->boolean(false);
+
+            case 'Z':  // null
+                return sax->null();
+
+            case 'U':
+            {
+                std::uint8_t number{};
+                return get_number(input_format, number) && sax->number_unsigned(number);
+            }
+
+            case 'i':
+            {
+                std::int8_t number{};
+                return get_number(input_format, number) && sax->number_integer(number);
+            }
+
+            case 'I':
+            {
+                std::int16_t number{};
+                return get_number(input_format, number) && sax->number_integer(number);
+            }
+
+            case 'l':
+            {
+                std::int32_t number{};
+                return get_number(input_format, number) && sax->number_integer(number);
+            }
+
+            case 'L':
+            {
+                std::int64_t number{};
+                return get_number(input_format, number) && sax->number_integer(number);
+            }
+
+            case 'u':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                std::uint16_t number{};
+                return get_number(input_format, number) && sax->number_unsigned(number);
+            }
+
+            case 'm':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                std::uint32_t number{};
+                return get_number(input_format, number) && sax->number_unsigned(number);
+            }
+
+            case 'M':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                std::uint64_t number{};
+                return get_number(input_format, number) && sax->number_unsigned(number);
+            }
+
+            case 'h':
+            {
+                if (input_format != input_format_t::bjdata)
+                {
+                    break;
+                }
+                const auto byte1_raw = get();
+                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "number")))
+                {
+                    return false;
+                }
+                const auto byte2_raw = get();
+                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "number")))
+                {
+                    return false;
+                }
+
+                const auto byte1 = static_cast<unsigned char>(byte1_raw);
+                const auto byte2 = static_cast<unsigned char>(byte2_raw);
+
+                // code from RFC 7049, Appendix D, Figure 3:
+                // As half-precision floating-point numbers were only added
+                // to IEEE 754 in 2008, today's programming platforms often
+                // still only have limited support for them. It is very
+                // easy to include at least decoding support for them even
+                // without such support. An example of a small decoder for
+                // half-precision floating-point numbers in the C language
+                // is shown in Fig. 3.
+                const auto half = static_cast<unsigned int>((byte2 << 8u) + byte1);
+                const double val = [&half]
+                {
+                    const int exp = (half >> 10u) & 0x1Fu;
+                    const unsigned int mant = half & 0x3FFu;
+                    JSON_ASSERT(0 <= exp&& exp <= 32);
+                    JSON_ASSERT(mant <= 1024);
+                    switch (exp)
+                    {
+                        case 0:
+                            return std::ldexp(mant, -24);
+                        case 31:
+                            return (mant == 0)
+                            ? std::numeric_limits<double>::infinity()
+                            : std::numeric_limits<double>::quiet_NaN();
+                        default:
+                            return std::ldexp(mant + 1024, exp - 25);
+                    }
+                }();
+                return sax->number_float((half & 0x8000u) != 0
+                                         ? static_cast<number_float_t>(-val)
+                                         : static_cast<number_float_t>(val), "");
+            }
+
+            case 'd':
+            {
+                float number{};
+                return get_number(input_format, number) && sax->number_float(static_cast<number_float_t>(number), "");
+            }
+
+            case 'D':
+            {
+                double number{};
+                return get_number(input_format, number) && sax->number_float(static_cast<number_float_t>(number), "");
+            }
+
+            case 'H':
+            {
+                return get_ubjson_high_precision_number();
+            }
+
+            case 'C':  // char
+            {
+                get();
+                if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "char")))
+                {
+                    return false;
+                }
+                if (JSON_HEDLEY_UNLIKELY(current > 127))
+                {
+                    auto last_token = get_token_string();
+                    return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read,
+                                            exception_message(input_format, concat("byte after 'C' must be in range 0x00..0x7F; last byte: 0x", last_token), "char"), nullptr));
+                }
+                string_t s(1, static_cast<typename string_t::value_type>(current));
+                return sax->string(s);
+            }
+
+            case 'S':  // string
+            {
+                string_t s;
+                return get_ubjson_string(s) && sax->string(s);
+            }
+
+            case '[':  // array
+                return get_ubjson_array();
+
+            case '{':  // object
+                return get_ubjson_object();
+
+            default: // anything else
+                break;
+        }
+        auto last_token = get_token_string();
+        return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read, exception_message(input_format, "invalid byte: 0x" + last_token, "value"), nullptr));
+    }
+
+    /*!
+    @return whether array creation completed
+    */
+    bool get_ubjson_array()
+    {
+        std::pair<std::size_t, char_int_type> size_and_type;
+        if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_type(size_and_type)))
+        {
+            return false;
+        }
+
+        // if bit-8 of size_and_type.second is set to 1, encode bjdata ndarray as an object in JData annotated array format (https://github.com/NeuroJSON/jdata):
+        // {"_ArrayType_" : "typeid", "_ArraySize_" : [n1, n2, ...], "_ArrayData_" : [v1, v2, ...]}
+
+        if (input_format == input_format_t::bjdata && size_and_type.first != npos && (size_and_type.second & (1 << 8)) != 0)
+        {
+            size_and_type.second &= ~(static_cast<char_int_type>(1) << 8);  // use bit 8 to indicate ndarray, here we remove the bit to restore the type marker
+            auto it = std::lower_bound(bjd_types_map.begin(), bjd_types_map.end(), size_and_type.second, [](const bjd_type & p, char_int_type t)
+            {
+                return p.first < t;
+            });
+            string_t key = "_ArrayType_";
+            if (JSON_HEDLEY_UNLIKELY(it == bjd_types_map.end() || it->first != size_and_type.second))
+            {
+                auto last_token = get_token_string();
+                return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
+                                        exception_message(input_format, "invalid byte: 0x" + last_token, "type"), nullptr));
+            }
+
+            string_t type = it->second; // sax->string() takes a reference
+            if (JSON_HEDLEY_UNLIKELY(!sax->key(key) || !sax->string(type)))
+            {
+                return false;
+            }
+
+            if (size_and_type.second == 'C')
+            {
+                size_and_type.second = 'U';
+            }
+
+            key = "_ArrayData_";
+            if (JSON_HEDLEY_UNLIKELY(!sax->key(key) || !sax->start_array(size_and_type.first) ))
+            {
+                return false;
+            }
+
+            for (std::size_t i = 0; i < size_and_type.first; ++i)
+            {
+                if (JSON_HEDLEY_UNLIKELY(!get_ubjson_value(size_and_type.second)))
+                {
+                    return false;
+                }
+            }
+
+            return (sax->end_array() && sax->end_object());
+        }
+
+        if (size_and_type.first != npos)
+        {
+            if (JSON_HEDLEY_UNLIKELY(!sax->start_array(size_and_type.first)))
+            {
+                return false;
+            }
+
+            if (size_and_type.second != 0)
+            {
+                if (size_and_type.second != 'N')
+                {
+                    for (std::size_t i = 0; i < size_and_type.first; ++i)
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!get_ubjson_value(size_and_type.second)))
+                        {
+                            return false;
+                        }
+                    }
+                }
+            }
+            else
+            {
+                for (std::size_t i = 0; i < size_and_type.first; ++i)
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal()))
+                    {
+                        return false;
+                    }
+                }
+            }
+        }
+        else
+        {
+            if (JSON_HEDLEY_UNLIKELY(!sax->start_array(static_cast<std::size_t>(-1))))
+            {
+                return false;
+            }
+
+            while (current != ']')
+            {
+                if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal(false)))
+                {
+                    return false;
+                }
+                get_ignore_noop();
+            }
+        }
+
+        return sax->end_array();
+    }
+
+    /*!
+    @return whether object creation completed
+    */
+    bool get_ubjson_object()
+    {
+        std::pair<std::size_t, char_int_type> size_and_type;
+        if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_type(size_and_type)))
+        {
+            return false;
+        }
+
+        // do not accept ND-array size in objects in BJData
+        if (input_format == input_format_t::bjdata && size_and_type.first != npos && (size_and_type.second & (1 << 8)) != 0)
+        {
+            auto last_token = get_token_string();
+            return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read,
+                                    exception_message(input_format, "BJData object does not support ND-array size in optimized format", "object"), nullptr));
+        }
+
+        string_t key;
+        if (size_and_type.first != npos)
+        {
+            if (JSON_HEDLEY_UNLIKELY(!sax->start_object(size_and_type.first)))
+            {
+                return false;
+            }
+
+            if (size_and_type.second != 0)
+            {
+                for (std::size_t i = 0; i < size_and_type.first; ++i)
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!get_ubjson_string(key) || !sax->key(key)))
+                    {
+                        return false;
+                    }
+                    if (JSON_HEDLEY_UNLIKELY(!get_ubjson_value(size_and_type.second)))
+                    {
+                        return false;
+                    }
+                    key.clear();
+                }
+            }
+            else
+            {
+                for (std::size_t i = 0; i < size_and_type.first; ++i)
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!get_ubjson_string(key) || !sax->key(key)))
+                    {
+                        return false;
+                    }
+                    if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal()))
+                    {
+                        return false;
+                    }
+                    key.clear();
+                }
+            }
+        }
+        else
+        {
+            if (JSON_HEDLEY_UNLIKELY(!sax->start_object(static_cast<std::size_t>(-1))))
+            {
+                return false;
+            }
+
+            while (current != '}')
+            {
+                if (JSON_HEDLEY_UNLIKELY(!get_ubjson_string(key, false) || !sax->key(key)))
+                {
+                    return false;
+                }
+                if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal()))
+                {
+                    return false;
+                }
+                get_ignore_noop();
+                key.clear();
+            }
+        }
+
+        return sax->end_object();
+    }
+
+    // Note, no reader for UBJSON binary types is implemented because they do
+    // not exist
+
+    bool get_ubjson_high_precision_number()
+    {
+        // get size of following number string
+        std::size_t size{};
+        bool no_ndarray = true;
+        auto res = get_ubjson_size_value(size, no_ndarray);
+        if (JSON_HEDLEY_UNLIKELY(!res))
+        {
+            return res;
+        }
+
+        // get number string
+        std::vector<char> number_vector;
+        for (std::size_t i = 0; i < size; ++i)
+        {
+            get();
+            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format, "number")))
+            {
+                return false;
+            }
+            number_vector.push_back(static_cast<char>(current));
+        }
+
+        // parse number string
+        using ia_type = decltype(detail::input_adapter(number_vector));
+        auto number_lexer = detail::lexer<BasicJsonType, ia_type>(detail::input_adapter(number_vector), false);
+        const auto result_number = number_lexer.scan();
+        const auto number_string = number_lexer.get_token_string();
+        const auto result_remainder = number_lexer.scan();
+
+        using token_type = typename detail::lexer_base<BasicJsonType>::token_type;
+
+        if (JSON_HEDLEY_UNLIKELY(result_remainder != token_type::end_of_input))
+        {
+            return sax->parse_error(chars_read, number_string, parse_error::create(115, chars_read,
+                                    exception_message(input_format, concat("invalid number text: ", number_lexer.get_token_string()), "high-precision number"), nullptr));
+        }
+
+        switch (result_number)
+        {
+            case token_type::value_integer:
+                return sax->number_integer(number_lexer.get_number_integer());
+            case token_type::value_unsigned:
+                return sax->number_unsigned(number_lexer.get_number_unsigned());
+            case token_type::value_float:
+                return sax->number_float(number_lexer.get_number_float(), std::move(number_string));
+            case token_type::uninitialized:
+            case token_type::literal_true:
+            case token_type::literal_false:
+            case token_type::literal_null:
+            case token_type::value_string:
+            case token_type::begin_array:
+            case token_type::begin_object:
+            case token_type::end_array:
+            case token_type::end_object:
+            case token_type::name_separator:
+            case token_type::value_separator:
+            case token_type::parse_error:
+            case token_type::end_of_input:
+            case token_type::literal_or_value:
+            default:
+                return sax->parse_error(chars_read, number_string, parse_error::create(115, chars_read,
+                                        exception_message(input_format, concat("invalid number text: ", number_lexer.get_token_string()), "high-precision number"), nullptr));
+        }
+    }
+
+    ///////////////////////
+    // Utility functions //
+    ///////////////////////
+
+    /*!
+    @brief get next character from the input
+
+    This function provides the interface to the used input adapter. It does
+    not throw in case the input reached EOF, but returns a -'ve valued
+    `char_traits<char_type>::eof()` in that case.
+
+    @return character read from the input
+    */
+    char_int_type get()
+    {
+        ++chars_read;
+        return current = ia.get_character();
+    }
+
+    /*!
+    @return character read from the input after ignoring all 'N' entries
+    */
+    char_int_type get_ignore_noop()
+    {
+        do
+        {
+            get();
+        }
+        while (current == 'N');
+
+        return current;
+    }
+
+    /*
+    @brief read a number from the input
+
+    @tparam NumberType the type of the number
+    @param[in] format   the current format (for diagnostics)
+    @param[out] result  number of type @a NumberType
+
+    @return whether conversion completed
+
+    @note This function needs to respect the system's endianness, because
+          bytes in CBOR, MessagePack, and UBJSON are stored in network order
+          (big endian) and therefore need reordering on little endian systems.
+          On the other hand, BSON and BJData use little endian and should reorder
+          on big endian systems.
+    */
+    template<typename NumberType, bool InputIsLittleEndian = false>
+    bool get_number(const input_format_t format, NumberType& result)
+    {
+        // step 1: read input into array with system's byte order
+        std::array<std::uint8_t, sizeof(NumberType)> vec{};
+        for (std::size_t i = 0; i < sizeof(NumberType); ++i)
+        {
+            get();
+            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(format, "number")))
+            {
+                return false;
+            }
+
+            // reverse byte order prior to conversion if necessary
+            if (is_little_endian != (InputIsLittleEndian || format == input_format_t::bjdata))
+            {
+                vec[sizeof(NumberType) - i - 1] = static_cast<std::uint8_t>(current);
+            }
+            else
+            {
+                vec[i] = static_cast<std::uint8_t>(current); // LCOV_EXCL_LINE
+            }
+        }
+
+        // step 2: convert array into number of type T and return
+        std::memcpy(&result, vec.data(), sizeof(NumberType));
+        return true;
+    }
+
+    /*!
+    @brief create a string by reading characters from the input
+
+    @tparam NumberType the type of the number
+    @param[in] format the current format (for diagnostics)
+    @param[in] len number of characters to read
+    @param[out] result string created by reading @a len bytes
+
+    @return whether string creation completed
+
+    @note We can not reserve @a len bytes for the result, because @a len
+          may be too large. Usually, @ref unexpect_eof() detects the end of
+          the input before we run out of string memory.
+    */
+    template<typename NumberType>
+    bool get_string(const input_format_t format,
+                    const NumberType len,
+                    string_t& result)
+    {
+        bool success = true;
+        for (NumberType i = 0; i < len; i++)
+        {
+            get();
+            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(format, "string")))
+            {
+                success = false;
+                break;
+            }
+            result.push_back(static_cast<typename string_t::value_type>(current));
+        }
+        return success;
+    }
+
+    /*!
+    @brief create a byte array by reading bytes from the input
+
+    @tparam NumberType the type of the number
+    @param[in] format the current format (for diagnostics)
+    @param[in] len number of bytes to read
+    @param[out] result byte array created by reading @a len bytes
+
+    @return whether byte array creation completed
+
+    @note We can not reserve @a len bytes for the result, because @a len
+          may be too large. Usually, @ref unexpect_eof() detects the end of
+          the input before we run out of memory.
+    */
+    template<typename NumberType>
+    bool get_binary(const input_format_t format,
+                    const NumberType len,
+                    binary_t& result)
+    {
+        bool success = true;
+        for (NumberType i = 0; i < len; i++)
+        {
+            get();
+            if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(format, "binary")))
+            {
+                success = false;
+                break;
+            }
+            result.push_back(static_cast<std::uint8_t>(current));
+        }
+        return success;
+    }
+
+    /*!
+    @param[in] format   the current format (for diagnostics)
+    @param[in] context  further context information (for diagnostics)
+    @return whether the last read character is not EOF
+    */
+    JSON_HEDLEY_NON_NULL(3)
+    bool unexpect_eof(const input_format_t format, const char* context) const
+    {
+        if (JSON_HEDLEY_UNLIKELY(current == char_traits<char_type>::eof()))
+        {
+            return sax->parse_error(chars_read, "<end of file>",
+                                    parse_error::create(110, chars_read, exception_message(format, "unexpected end of input", context), nullptr));
+        }
+        return true;
+    }
+
+    /*!
+    @return a string representation of the last read byte
+    */
+    std::string get_token_string() const
+    {
+        std::array<char, 3> cr{{}};
+        static_cast<void>((std::snprintf)(cr.data(), cr.size(), "%.2hhX", static_cast<unsigned char>(current))); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
+        return std::string{cr.data()};
+    }
+
+    /*!
+    @param[in] format   the current format
+    @param[in] detail   a detailed error message
+    @param[in] context  further context information
+    @return a message string to use in the parse_error exceptions
+    */
+    std::string exception_message(const input_format_t format,
+                                  const std::string& detail,
+                                  const std::string& context) const
+    {
+        std::string error_msg = "syntax error while parsing ";
+
+        switch (format)
+        {
+            case input_format_t::cbor:
+                error_msg += "CBOR";
+                break;
+
+            case input_format_t::msgpack:
+                error_msg += "MessagePack";
+                break;
+
+            case input_format_t::ubjson:
+                error_msg += "UBJSON";
+                break;
+
+            case input_format_t::bson:
+                error_msg += "BSON";
+                break;
+
+            case input_format_t::bjdata:
+                error_msg += "BJData";
+                break;
+
+            case input_format_t::json: // LCOV_EXCL_LINE
+            default:            // LCOV_EXCL_LINE
+                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+        }
+
+        return concat(error_msg, ' ', context, ": ", detail);
+    }
+
+  private:
+    static JSON_INLINE_VARIABLE constexpr std::size_t npos = static_cast<std::size_t>(-1);
+
+    /// input adapter
+    InputAdapterType ia;
+
+    /// the current character
+    char_int_type current = char_traits<char_type>::eof();
+
+    /// the number of characters read
+    std::size_t chars_read = 0;
+
+    /// whether we can assume little endianness
+    const bool is_little_endian = little_endianness();
+
+    /// input format
+    const input_format_t input_format = input_format_t::json;
+
+    /// the SAX parser
+    json_sax_t* sax = nullptr;
+
+    // excluded markers in bjdata optimized type
+#define JSON_BINARY_READER_MAKE_BJD_OPTIMIZED_TYPE_MARKERS_ \
+    make_array<char_int_type>('F', 'H', 'N', 'S', 'T', 'Z', '[', '{')
+
+#define JSON_BINARY_READER_MAKE_BJD_TYPES_MAP_ \
+    make_array<bjd_type>(                      \
+    bjd_type{'C', "char"},                     \
+    bjd_type{'D', "double"},                   \
+    bjd_type{'I', "int16"},                    \
+    bjd_type{'L', "int64"},                    \
+    bjd_type{'M', "uint64"},                   \
+    bjd_type{'U', "uint8"},                    \
+    bjd_type{'d', "single"},                   \
+    bjd_type{'i', "int8"},                     \
+    bjd_type{'l', "int32"},                    \
+    bjd_type{'m', "uint32"},                   \
+    bjd_type{'u', "uint16"})
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    // lookup tables
+    // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+    const decltype(JSON_BINARY_READER_MAKE_BJD_OPTIMIZED_TYPE_MARKERS_) bjd_optimized_type_markers =
+        JSON_BINARY_READER_MAKE_BJD_OPTIMIZED_TYPE_MARKERS_;
+
+    using bjd_type = std::pair<char_int_type, string_t>;
+    // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+    const decltype(JSON_BINARY_READER_MAKE_BJD_TYPES_MAP_) bjd_types_map =
+        JSON_BINARY_READER_MAKE_BJD_TYPES_MAP_;
+
+#undef JSON_BINARY_READER_MAKE_BJD_OPTIMIZED_TYPE_MARKERS_
+#undef JSON_BINARY_READER_MAKE_BJD_TYPES_MAP_
+};
+
+#ifndef JSON_HAS_CPP_17
+    template<typename BasicJsonType, typename InputAdapterType, typename SAX>
+    constexpr std::size_t binary_reader<BasicJsonType, InputAdapterType, SAX>::npos;
+#endif
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/input/input_adapters.hpp>
+
+// #include <nlohmann/detail/input/lexer.hpp>
+
+// #include <nlohmann/detail/input/parser.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cmath> // isfinite
+#include <cstdint> // uint8_t
+#include <functional> // function
+#include <string> // string
+#include <utility> // move
+#include <vector> // vector
+
+// #include <nlohmann/detail/exceptions.hpp>
+
+// #include <nlohmann/detail/input/input_adapters.hpp>
+
+// #include <nlohmann/detail/input/json_sax.hpp>
+
+// #include <nlohmann/detail/input/lexer.hpp>
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/is_sax.hpp>
+
+// #include <nlohmann/detail/string_concat.hpp>
+
+// #include <nlohmann/detail/value_t.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+////////////
+// parser //
+////////////
+
+enum class parse_event_t : std::uint8_t
+{
+    /// the parser read `{` and started to process a JSON object
+    object_start,
+    /// the parser read `}` and finished processing a JSON object
+    object_end,
+    /// the parser read `[` and started to process a JSON array
+    array_start,
+    /// the parser read `]` and finished processing a JSON array
+    array_end,
+    /// the parser read a key of a value in an object
+    key,
+    /// the parser finished reading a JSON value
+    value
+};
+
+template<typename BasicJsonType>
+using parser_callback_t =
+    std::function<bool(int /*depth*/, parse_event_t /*event*/, BasicJsonType& /*parsed*/)>;
+
+/*!
+@brief syntax analysis
+
+This class implements a recursive descent parser.
+*/
+template<typename BasicJsonType, typename InputAdapterType>
+class parser
+{
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+    using string_t = typename BasicJsonType::string_t;
+    using lexer_t = lexer<BasicJsonType, InputAdapterType>;
+    using token_type = typename lexer_t::token_type;
+
+  public:
+    /// a parser reading from an input adapter
+    explicit parser(InputAdapterType&& adapter,
+                    const parser_callback_t<BasicJsonType> cb = nullptr,
+                    const bool allow_exceptions_ = true,
+                    const bool skip_comments = false)
+        : callback(cb)
+        , m_lexer(std::move(adapter), skip_comments)
+        , allow_exceptions(allow_exceptions_)
+    {
+        // read first token
+        get_token();
+    }
+
+    /*!
+    @brief public parser interface
+
+    @param[in] strict      whether to expect the last token to be EOF
+    @param[in,out] result  parsed JSON value
+
+    @throw parse_error.101 in case of an unexpected token
+    @throw parse_error.102 if to_unicode fails or surrogate error
+    @throw parse_error.103 if to_unicode fails
+    */
+    void parse(const bool strict, BasicJsonType& result)
+    {
+        if (callback)
+        {
+            json_sax_dom_callback_parser<BasicJsonType> sdp(result, callback, allow_exceptions);
+            sax_parse_internal(&sdp);
+
+            // in strict mode, input must be completely read
+            if (strict && (get_token() != token_type::end_of_input))
+            {
+                sdp.parse_error(m_lexer.get_position(),
+                                m_lexer.get_token_string(),
+                                parse_error::create(101, m_lexer.get_position(),
+                                                    exception_message(token_type::end_of_input, "value"), nullptr));
+            }
+
+            // in case of an error, return discarded value
+            if (sdp.is_errored())
+            {
+                result = value_t::discarded;
+                return;
+            }
+
+            // set top-level value to null if it was discarded by the callback
+            // function
+            if (result.is_discarded())
+            {
+                result = nullptr;
+            }
+        }
+        else
+        {
+            json_sax_dom_parser<BasicJsonType> sdp(result, allow_exceptions);
+            sax_parse_internal(&sdp);
+
+            // in strict mode, input must be completely read
+            if (strict && (get_token() != token_type::end_of_input))
+            {
+                sdp.parse_error(m_lexer.get_position(),
+                                m_lexer.get_token_string(),
+                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_of_input, "value"), nullptr));
+            }
+
+            // in case of an error, return discarded value
+            if (sdp.is_errored())
+            {
+                result = value_t::discarded;
+                return;
+            }
+        }
+
+        result.assert_invariant();
+    }
+
+    /*!
+    @brief public accept interface
+
+    @param[in] strict  whether to expect the last token to be EOF
+    @return whether the input is a proper JSON text
+    */
+    bool accept(const bool strict = true)
+    {
+        json_sax_acceptor<BasicJsonType> sax_acceptor;
+        return sax_parse(&sax_acceptor, strict);
+    }
+
+    template<typename SAX>
+    JSON_HEDLEY_NON_NULL(2)
+    bool sax_parse(SAX* sax, const bool strict = true)
+    {
+        (void)detail::is_sax_static_asserts<SAX, BasicJsonType> {};
+        const bool result = sax_parse_internal(sax);
+
+        // strict mode: next byte must be EOF
+        if (result && strict && (get_token() != token_type::end_of_input))
+        {
+            return sax->parse_error(m_lexer.get_position(),
+                                    m_lexer.get_token_string(),
+                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_of_input, "value"), nullptr));
+        }
+
+        return result;
+    }
+
+  private:
+    template<typename SAX>
+    JSON_HEDLEY_NON_NULL(2)
+    bool sax_parse_internal(SAX* sax)
+    {
+        // stack to remember the hierarchy of structured values we are parsing
+        // true = array; false = object
+        std::vector<bool> states;
+        // value to avoid a goto (see comment where set to true)
+        bool skip_to_state_evaluation = false;
+
+        while (true)
+        {
+            if (!skip_to_state_evaluation)
+            {
+                // invariant: get_token() was called before each iteration
+                switch (last_token)
+                {
+                    case token_type::begin_object:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!sax->start_object(static_cast<std::size_t>(-1))))
+                        {
+                            return false;
+                        }
+
+                        // closing } -> we are done
+                        if (get_token() == token_type::end_object)
+                        {
+                            if (JSON_HEDLEY_UNLIKELY(!sax->end_object()))
+                            {
+                                return false;
+                            }
+                            break;
+                        }
+
+                        // parse key
+                        if (JSON_HEDLEY_UNLIKELY(last_token != token_type::value_string))
+                        {
+                            return sax->parse_error(m_lexer.get_position(),
+                                                    m_lexer.get_token_string(),
+                                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::value_string, "object key"), nullptr));
+                        }
+                        if (JSON_HEDLEY_UNLIKELY(!sax->key(m_lexer.get_string())))
+                        {
+                            return false;
+                        }
+
+                        // parse separator (:)
+                        if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::name_separator))
+                        {
+                            return sax->parse_error(m_lexer.get_position(),
+                                                    m_lexer.get_token_string(),
+                                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::name_separator, "object separator"), nullptr));
+                        }
+
+                        // remember we are now inside an object
+                        states.push_back(false);
+
+                        // parse values
+                        get_token();
+                        continue;
+                    }
+
+                    case token_type::begin_array:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!sax->start_array(static_cast<std::size_t>(-1))))
+                        {
+                            return false;
+                        }
+
+                        // closing ] -> we are done
+                        if (get_token() == token_type::end_array)
+                        {
+                            if (JSON_HEDLEY_UNLIKELY(!sax->end_array()))
+                            {
+                                return false;
+                            }
+                            break;
+                        }
+
+                        // remember we are now inside an array
+                        states.push_back(true);
+
+                        // parse values (no need to call get_token)
+                        continue;
+                    }
+
+                    case token_type::value_float:
+                    {
+                        const auto res = m_lexer.get_number_float();
+
+                        if (JSON_HEDLEY_UNLIKELY(!std::isfinite(res)))
+                        {
+                            return sax->parse_error(m_lexer.get_position(),
+                                                    m_lexer.get_token_string(),
+                                                    out_of_range::create(406, concat("number overflow parsing '", m_lexer.get_token_string(), '\''), nullptr));
+                        }
+
+                        if (JSON_HEDLEY_UNLIKELY(!sax->number_float(res, m_lexer.get_string())))
+                        {
+                            return false;
+                        }
+
+                        break;
+                    }
+
+                    case token_type::literal_false:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!sax->boolean(false)))
+                        {
+                            return false;
+                        }
+                        break;
+                    }
+
+                    case token_type::literal_null:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!sax->null()))
+                        {
+                            return false;
+                        }
+                        break;
+                    }
+
+                    case token_type::literal_true:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!sax->boolean(true)))
+                        {
+                            return false;
+                        }
+                        break;
+                    }
+
+                    case token_type::value_integer:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!sax->number_integer(m_lexer.get_number_integer())))
+                        {
+                            return false;
+                        }
+                        break;
+                    }
+
+                    case token_type::value_string:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!sax->string(m_lexer.get_string())))
+                        {
+                            return false;
+                        }
+                        break;
+                    }
+
+                    case token_type::value_unsigned:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!sax->number_unsigned(m_lexer.get_number_unsigned())))
+                        {
+                            return false;
+                        }
+                        break;
+                    }
+
+                    case token_type::parse_error:
+                    {
+                        // using "uninitialized" to avoid "expected" message
+                        return sax->parse_error(m_lexer.get_position(),
+                                                m_lexer.get_token_string(),
+                                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::uninitialized, "value"), nullptr));
+                    }
+                    case token_type::end_of_input:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(m_lexer.get_position().chars_read_total == 1))
+                        {
+                            return sax->parse_error(m_lexer.get_position(),
+                                                    m_lexer.get_token_string(),
+                                                    parse_error::create(101, m_lexer.get_position(),
+                                                            "attempting to parse an empty input; check that your input string or stream contains the expected JSON", nullptr));
+                        }
+
+                        return sax->parse_error(m_lexer.get_position(),
+                                                m_lexer.get_token_string(),
+                                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::literal_or_value, "value"), nullptr));
+                    }
+                    case token_type::uninitialized:
+                    case token_type::end_array:
+                    case token_type::end_object:
+                    case token_type::name_separator:
+                    case token_type::value_separator:
+                    case token_type::literal_or_value:
+                    default: // the last token was unexpected
+                    {
+                        return sax->parse_error(m_lexer.get_position(),
+                                                m_lexer.get_token_string(),
+                                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::literal_or_value, "value"), nullptr));
+                    }
+                }
+            }
+            else
+            {
+                skip_to_state_evaluation = false;
+            }
+
+            // we reached this line after we successfully parsed a value
+            if (states.empty())
+            {
+                // empty stack: we reached the end of the hierarchy: done
+                return true;
+            }
+
+            if (states.back())  // array
+            {
+                // comma -> next value
+                if (get_token() == token_type::value_separator)
+                {
+                    // parse a new value
+                    get_token();
+                    continue;
+                }
+
+                // closing ]
+                if (JSON_HEDLEY_LIKELY(last_token == token_type::end_array))
+                {
+                    if (JSON_HEDLEY_UNLIKELY(!sax->end_array()))
+                    {
+                        return false;
+                    }
+
+                    // We are done with this array. Before we can parse a
+                    // new value, we need to evaluate the new state first.
+                    // By setting skip_to_state_evaluation to false, we
+                    // are effectively jumping to the beginning of this if.
+                    JSON_ASSERT(!states.empty());
+                    states.pop_back();
+                    skip_to_state_evaluation = true;
+                    continue;
+                }
+
+                return sax->parse_error(m_lexer.get_position(),
+                                        m_lexer.get_token_string(),
+                                        parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_array, "array"), nullptr));
+            }
+
+            // states.back() is false -> object
+
+            // comma -> next value
+            if (get_token() == token_type::value_separator)
+            {
+                // parse key
+                if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::value_string))
+                {
+                    return sax->parse_error(m_lexer.get_position(),
+                                            m_lexer.get_token_string(),
+                                            parse_error::create(101, m_lexer.get_position(), exception_message(token_type::value_string, "object key"), nullptr));
+                }
+
+                if (JSON_HEDLEY_UNLIKELY(!sax->key(m_lexer.get_string())))
+                {
+                    return false;
+                }
+
+                // parse separator (:)
+                if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::name_separator))
+                {
+                    return sax->parse_error(m_lexer.get_position(),
+                                            m_lexer.get_token_string(),
+                                            parse_error::create(101, m_lexer.get_position(), exception_message(token_type::name_separator, "object separator"), nullptr));
+                }
+
+                // parse values
+                get_token();
+                continue;
+            }
+
+            // closing }
+            if (JSON_HEDLEY_LIKELY(last_token == token_type::end_object))
+            {
+                if (JSON_HEDLEY_UNLIKELY(!sax->end_object()))
+                {
+                    return false;
+                }
+
+                // We are done with this object. Before we can parse a
+                // new value, we need to evaluate the new state first.
+                // By setting skip_to_state_evaluation to false, we
+                // are effectively jumping to the beginning of this if.
+                JSON_ASSERT(!states.empty());
+                states.pop_back();
+                skip_to_state_evaluation = true;
+                continue;
+            }
+
+            return sax->parse_error(m_lexer.get_position(),
+                                    m_lexer.get_token_string(),
+                                    parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_object, "object"), nullptr));
+        }
+    }
+
+    /// get next token from lexer
+    token_type get_token()
+    {
+        return last_token = m_lexer.scan();
+    }
+
+    std::string exception_message(const token_type expected, const std::string& context)
+    {
+        std::string error_msg = "syntax error ";
+
+        if (!context.empty())
+        {
+            error_msg += concat("while parsing ", context, ' ');
+        }
+
+        error_msg += "- ";
+
+        if (last_token == token_type::parse_error)
+        {
+            error_msg += concat(m_lexer.get_error_message(), "; last read: '",
+                                m_lexer.get_token_string(), '\'');
+        }
+        else
+        {
+            error_msg += concat("unexpected ", lexer_t::token_type_name(last_token));
+        }
+
+        if (expected != token_type::uninitialized)
+        {
+            error_msg += concat("; expected ", lexer_t::token_type_name(expected));
+        }
+
+        return error_msg;
+    }
+
+  private:
+    /// callback function
+    const parser_callback_t<BasicJsonType> callback = nullptr;
+    /// the type of the last read token
+    token_type last_token = token_type::uninitialized;
+    /// the lexer
+    lexer_t m_lexer;
+    /// whether to throw exceptions in case of errors
+    const bool allow_exceptions = true;
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/iterators/internal_iterator.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+// #include <nlohmann/detail/iterators/primitive_iterator.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cstddef> // ptrdiff_t
+#include <limits>  // numeric_limits
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/*
+@brief an iterator for primitive JSON types
+
+This class models an iterator for primitive JSON types (boolean, number,
+string). It's only purpose is to allow the iterator/const_iterator classes
+to "iterate" over primitive values. Internally, the iterator is modeled by
+a `difference_type` variable. Value begin_value (`0`) models the begin,
+end_value (`1`) models past the end.
+*/
+class primitive_iterator_t
+{
+  private:
+    using difference_type = std::ptrdiff_t;
+    static constexpr difference_type begin_value = 0;
+    static constexpr difference_type end_value = begin_value + 1;
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    /// iterator as signed integer type
+    difference_type m_it = (std::numeric_limits<std::ptrdiff_t>::min)();
+
+  public:
+    constexpr difference_type get_value() const noexcept
+    {
+        return m_it;
+    }
+
+    /// set iterator to a defined beginning
+    void set_begin() noexcept
+    {
+        m_it = begin_value;
+    }
+
+    /// set iterator to a defined past the end
+    void set_end() noexcept
+    {
+        m_it = end_value;
+    }
+
+    /// return whether the iterator can be dereferenced
+    constexpr bool is_begin() const noexcept
+    {
+        return m_it == begin_value;
+    }
+
+    /// return whether the iterator is at end
+    constexpr bool is_end() const noexcept
+    {
+        return m_it == end_value;
+    }
+
+    friend constexpr bool operator==(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept
+    {
+        return lhs.m_it == rhs.m_it;
+    }
+
+    friend constexpr bool operator<(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept
+    {
+        return lhs.m_it < rhs.m_it;
+    }
+
+    primitive_iterator_t operator+(difference_type n) noexcept
+    {
+        auto result = *this;
+        result += n;
+        return result;
+    }
+
+    friend constexpr difference_type operator-(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept
+    {
+        return lhs.m_it - rhs.m_it;
+    }
+
+    primitive_iterator_t& operator++() noexcept
+    {
+        ++m_it;
+        return *this;
+    }
+
+    primitive_iterator_t operator++(int)& noexcept // NOLINT(cert-dcl21-cpp)
+    {
+        auto result = *this;
+        ++m_it;
+        return result;
+    }
+
+    primitive_iterator_t& operator--() noexcept
+    {
+        --m_it;
+        return *this;
+    }
+
+    primitive_iterator_t operator--(int)& noexcept // NOLINT(cert-dcl21-cpp)
+    {
+        auto result = *this;
+        --m_it;
+        return result;
+    }
+
+    primitive_iterator_t& operator+=(difference_type n) noexcept
+    {
+        m_it += n;
+        return *this;
+    }
+
+    primitive_iterator_t& operator-=(difference_type n) noexcept
+    {
+        m_it -= n;
+        return *this;
+    }
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/*!
+@brief an iterator value
+
+@note This structure could easily be a union, but MSVC currently does not allow
+unions members with complex constructors, see https://github.com/nlohmann/json/pull/105.
+*/
+template<typename BasicJsonType> struct internal_iterator
+{
+    /// iterator for JSON objects
+    typename BasicJsonType::object_t::iterator object_iterator {};
+    /// iterator for JSON arrays
+    typename BasicJsonType::array_t::iterator array_iterator {};
+    /// generic iterator for all other types
+    primitive_iterator_t primitive_iterator {};
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/iterators/iter_impl.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <iterator> // iterator, random_access_iterator_tag, bidirectional_iterator_tag, advance, next
+#include <type_traits> // conditional, is_const, remove_const
+
+// #include <nlohmann/detail/exceptions.hpp>
+
+// #include <nlohmann/detail/iterators/internal_iterator.hpp>
+
+// #include <nlohmann/detail/iterators/primitive_iterator.hpp>
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/cpp_future.hpp>
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+// #include <nlohmann/detail/value_t.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+// forward declare, to be able to friend it later on
+template<typename IteratorType> class iteration_proxy;
+template<typename IteratorType> class iteration_proxy_value;
+
+/*!
+@brief a template for a bidirectional iterator for the @ref basic_json class
+This class implements a both iterators (iterator and const_iterator) for the
+@ref basic_json class.
+@note An iterator is called *initialized* when a pointer to a JSON value has
+      been set (e.g., by a constructor or a copy assignment). If the iterator is
+      default-constructed, it is *uninitialized* and most methods are undefined.
+      **The library uses assertions to detect calls on uninitialized iterators.**
+@requirement The class satisfies the following concept requirements:
+-
+[BidirectionalIterator](https://en.cppreference.com/w/cpp/named_req/BidirectionalIterator):
+  The iterator that can be moved can be moved in both directions (i.e.
+  incremented and decremented).
+@since version 1.0.0, simplified in version 2.0.9, change to bidirectional
+       iterators in version 3.0.0 (see https://github.com/nlohmann/json/issues/593)
+*/
+template<typename BasicJsonType>
+class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-special-member-functions)
+{
+    /// the iterator with BasicJsonType of different const-ness
+    using other_iter_impl = iter_impl<typename std::conditional<std::is_const<BasicJsonType>::value, typename std::remove_const<BasicJsonType>::type, const BasicJsonType>::type>;
+    /// allow basic_json to access private members
+    friend other_iter_impl;
+    friend BasicJsonType;
+    friend iteration_proxy<iter_impl>;
+    friend iteration_proxy_value<iter_impl>;
+
+    using object_t = typename BasicJsonType::object_t;
+    using array_t = typename BasicJsonType::array_t;
+    // make sure BasicJsonType is basic_json or const basic_json
+    static_assert(is_basic_json<typename std::remove_const<BasicJsonType>::type>::value,
+                  "iter_impl only accepts (const) basic_json");
+    // superficial check for the LegacyBidirectionalIterator named requirement
+    static_assert(std::is_base_of<std::bidirectional_iterator_tag, std::bidirectional_iterator_tag>::value
+                  &&  std::is_base_of<std::bidirectional_iterator_tag, typename std::iterator_traits<typename array_t::iterator>::iterator_category>::value,
+                  "basic_json iterator assumes array and object type iterators satisfy the LegacyBidirectionalIterator named requirement.");
+
+  public:
+    /// The std::iterator class template (used as a base class to provide typedefs) is deprecated in C++17.
+    /// The C++ Standard has never required user-defined iterators to derive from std::iterator.
+    /// A user-defined iterator should provide publicly accessible typedefs named
+    /// iterator_category, value_type, difference_type, pointer, and reference.
+    /// Note that value_type is required to be non-const, even for constant iterators.
+    using iterator_category = std::bidirectional_iterator_tag;
+
+    /// the type of the values when the iterator is dereferenced
+    using value_type = typename BasicJsonType::value_type;
+    /// a type to represent differences between iterators
+    using difference_type = typename BasicJsonType::difference_type;
+    /// defines a pointer to the type iterated over (value_type)
+    using pointer = typename std::conditional<std::is_const<BasicJsonType>::value,
+          typename BasicJsonType::const_pointer,
+          typename BasicJsonType::pointer>::type;
+    /// defines a reference to the type iterated over (value_type)
+    using reference =
+        typename std::conditional<std::is_const<BasicJsonType>::value,
+        typename BasicJsonType::const_reference,
+        typename BasicJsonType::reference>::type;
+
+    iter_impl() = default;
+    ~iter_impl() = default;
+    iter_impl(iter_impl&&) noexcept = default;
+    iter_impl& operator=(iter_impl&&) noexcept = default;
+
+    /*!
+    @brief constructor for a given JSON instance
+    @param[in] object  pointer to a JSON object for this iterator
+    @pre object != nullptr
+    @post The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    explicit iter_impl(pointer object) noexcept : m_object(object)
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+            {
+                m_it.object_iterator = typename object_t::iterator();
+                break;
+            }
+
+            case value_t::array:
+            {
+                m_it.array_iterator = typename array_t::iterator();
+                break;
+            }
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                m_it.primitive_iterator = primitive_iterator_t();
+                break;
+            }
+        }
+    }
+
+    /*!
+    @note The conventional copy constructor and copy assignment are implicitly
+          defined. Combined with the following converting constructor and
+          assignment, they support: (1) copy from iterator to iterator, (2)
+          copy from const iterator to const iterator, and (3) conversion from
+          iterator to const iterator. However conversion from const iterator
+          to iterator is not defined.
+    */
+
+    /*!
+    @brief const copy constructor
+    @param[in] other const iterator to copy from
+    @note This copy constructor had to be defined explicitly to circumvent a bug
+          occurring on msvc v19.0 compiler (VS 2015) debug build. For more
+          information refer to: https://github.com/nlohmann/json/issues/1608
+    */
+    iter_impl(const iter_impl<const BasicJsonType>& other) noexcept
+        : m_object(other.m_object), m_it(other.m_it)
+    {}
+
+    /*!
+    @brief converting assignment
+    @param[in] other const iterator to copy from
+    @return const/non-const iterator
+    @note It is not checked whether @a other is initialized.
+    */
+    iter_impl& operator=(const iter_impl<const BasicJsonType>& other) noexcept
+    {
+        if (&other != this)
+        {
+            m_object = other.m_object;
+            m_it = other.m_it;
+        }
+        return *this;
+    }
+
+    /*!
+    @brief converting constructor
+    @param[in] other  non-const iterator to copy from
+    @note It is not checked whether @a other is initialized.
+    */
+    iter_impl(const iter_impl<typename std::remove_const<BasicJsonType>::type>& other) noexcept
+        : m_object(other.m_object), m_it(other.m_it)
+    {}
+
+    /*!
+    @brief converting assignment
+    @param[in] other  non-const iterator to copy from
+    @return const/non-const iterator
+    @note It is not checked whether @a other is initialized.
+    */
+    iter_impl& operator=(const iter_impl<typename std::remove_const<BasicJsonType>::type>& other) noexcept // NOLINT(cert-oop54-cpp)
+    {
+        m_object = other.m_object;
+        m_it = other.m_it;
+        return *this;
+    }
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    /*!
+    @brief set the iterator to the first value
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    void set_begin() noexcept
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+            {
+                m_it.object_iterator = m_object->m_data.m_value.object->begin();
+                break;
+            }
+
+            case value_t::array:
+            {
+                m_it.array_iterator = m_object->m_data.m_value.array->begin();
+                break;
+            }
+
+            case value_t::null:
+            {
+                // set to end so begin()==end() is true: null is empty
+                m_it.primitive_iterator.set_end();
+                break;
+            }
+
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                m_it.primitive_iterator.set_begin();
+                break;
+            }
+        }
+    }
+
+    /*!
+    @brief set the iterator past the last value
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    void set_end() noexcept
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+            {
+                m_it.object_iterator = m_object->m_data.m_value.object->end();
+                break;
+            }
+
+            case value_t::array:
+            {
+                m_it.array_iterator = m_object->m_data.m_value.array->end();
+                break;
+            }
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                m_it.primitive_iterator.set_end();
+                break;
+            }
+        }
+    }
+
+  public:
+    /*!
+    @brief return a reference to the value pointed to by the iterator
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    reference operator*() const
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+            {
+                JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end());
+                return m_it.object_iterator->second;
+            }
+
+            case value_t::array:
+            {
+                JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end());
+                return *m_it.array_iterator;
+            }
+
+            case value_t::null:
+                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
+
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.is_begin()))
+                {
+                    return *m_object;
+                }
+
+                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
+            }
+        }
+    }
+
+    /*!
+    @brief dereference the iterator
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    pointer operator->() const
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+            {
+                JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end());
+                return &(m_it.object_iterator->second);
+            }
+
+            case value_t::array:
+            {
+                JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end());
+                return &*m_it.array_iterator;
+            }
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.is_begin()))
+                {
+                    return m_object;
+                }
+
+                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
+            }
+        }
+    }
+
+    /*!
+    @brief post-increment (it++)
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    iter_impl operator++(int)& // NOLINT(cert-dcl21-cpp)
+    {
+        auto result = *this;
+        ++(*this);
+        return result;
+    }
+
+    /*!
+    @brief pre-increment (++it)
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    iter_impl& operator++()
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+            {
+                std::advance(m_it.object_iterator, 1);
+                break;
+            }
+
+            case value_t::array:
+            {
+                std::advance(m_it.array_iterator, 1);
+                break;
+            }
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                ++m_it.primitive_iterator;
+                break;
+            }
+        }
+
+        return *this;
+    }
+
+    /*!
+    @brief post-decrement (it--)
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    iter_impl operator--(int)& // NOLINT(cert-dcl21-cpp)
+    {
+        auto result = *this;
+        --(*this);
+        return result;
+    }
+
+    /*!
+    @brief pre-decrement (--it)
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    iter_impl& operator--()
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+            {
+                std::advance(m_it.object_iterator, -1);
+                break;
+            }
+
+            case value_t::array:
+            {
+                std::advance(m_it.array_iterator, -1);
+                break;
+            }
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                --m_it.primitive_iterator;
+                break;
+            }
+        }
+
+        return *this;
+    }
+
+    /*!
+    @brief comparison: equal
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    template < typename IterImpl, detail::enable_if_t < (std::is_same<IterImpl, iter_impl>::value || std::is_same<IterImpl, other_iter_impl>::value), std::nullptr_t > = nullptr >
+    bool operator==(const IterImpl& other) const
+    {
+        // if objects are not the same, the comparison is undefined
+        if (JSON_HEDLEY_UNLIKELY(m_object != other.m_object))
+        {
+            JSON_THROW(invalid_iterator::create(212, "cannot compare iterators of different containers", m_object));
+        }
+
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+                return (m_it.object_iterator == other.m_it.object_iterator);
+
+            case value_t::array:
+                return (m_it.array_iterator == other.m_it.array_iterator);
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+                return (m_it.primitive_iterator == other.m_it.primitive_iterator);
+        }
+    }
+
+    /*!
+    @brief comparison: not equal
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    template < typename IterImpl, detail::enable_if_t < (std::is_same<IterImpl, iter_impl>::value || std::is_same<IterImpl, other_iter_impl>::value), std::nullptr_t > = nullptr >
+    bool operator!=(const IterImpl& other) const
+    {
+        return !operator==(other);
+    }
+
+    /*!
+    @brief comparison: smaller
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    bool operator<(const iter_impl& other) const
+    {
+        // if objects are not the same, the comparison is undefined
+        if (JSON_HEDLEY_UNLIKELY(m_object != other.m_object))
+        {
+            JSON_THROW(invalid_iterator::create(212, "cannot compare iterators of different containers", m_object));
+        }
+
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+                JSON_THROW(invalid_iterator::create(213, "cannot compare order of object iterators", m_object));
+
+            case value_t::array:
+                return (m_it.array_iterator < other.m_it.array_iterator);
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+                return (m_it.primitive_iterator < other.m_it.primitive_iterator);
+        }
+    }
+
+    /*!
+    @brief comparison: less than or equal
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    bool operator<=(const iter_impl& other) const
+    {
+        return !other.operator < (*this);
+    }
+
+    /*!
+    @brief comparison: greater than
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    bool operator>(const iter_impl& other) const
+    {
+        return !operator<=(other);
+    }
+
+    /*!
+    @brief comparison: greater than or equal
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    bool operator>=(const iter_impl& other) const
+    {
+        return !operator<(other);
+    }
+
+    /*!
+    @brief add to iterator
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    iter_impl& operator+=(difference_type i)
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+                JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object));
+
+            case value_t::array:
+            {
+                std::advance(m_it.array_iterator, i);
+                break;
+            }
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                m_it.primitive_iterator += i;
+                break;
+            }
+        }
+
+        return *this;
+    }
+
+    /*!
+    @brief subtract from iterator
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    iter_impl& operator-=(difference_type i)
+    {
+        return operator+=(-i);
+    }
+
+    /*!
+    @brief add to iterator
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    iter_impl operator+(difference_type i) const
+    {
+        auto result = *this;
+        result += i;
+        return result;
+    }
+
+    /*!
+    @brief addition of distance and iterator
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    friend iter_impl operator+(difference_type i, const iter_impl& it)
+    {
+        auto result = it;
+        result += i;
+        return result;
+    }
+
+    /*!
+    @brief subtract from iterator
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    iter_impl operator-(difference_type i) const
+    {
+        auto result = *this;
+        result -= i;
+        return result;
+    }
+
+    /*!
+    @brief return difference
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    difference_type operator-(const iter_impl& other) const
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+                JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object));
+
+            case value_t::array:
+                return m_it.array_iterator - other.m_it.array_iterator;
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+                return m_it.primitive_iterator - other.m_it.primitive_iterator;
+        }
+    }
+
+    /*!
+    @brief access to successor
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    reference operator[](difference_type n) const
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        switch (m_object->m_data.m_type)
+        {
+            case value_t::object:
+                JSON_THROW(invalid_iterator::create(208, "cannot use operator[] for object iterators", m_object));
+
+            case value_t::array:
+                return *std::next(m_it.array_iterator, n);
+
+            case value_t::null:
+                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
+
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.get_value() == -n))
+                {
+                    return *m_object;
+                }
+
+                JSON_THROW(invalid_iterator::create(214, "cannot get value", m_object));
+            }
+        }
+    }
+
+    /*!
+    @brief return the key of an object iterator
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    const typename object_t::key_type& key() const
+    {
+        JSON_ASSERT(m_object != nullptr);
+
+        if (JSON_HEDLEY_LIKELY(m_object->is_object()))
+        {
+            return m_it.object_iterator->first;
+        }
+
+        JSON_THROW(invalid_iterator::create(207, "cannot use key() for non-object iterators", m_object));
+    }
+
+    /*!
+    @brief return the value of an iterator
+    @pre The iterator is initialized; i.e. `m_object != nullptr`.
+    */
+    reference value() const
+    {
+        return operator*();
+    }
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    /// associated JSON instance
+    pointer m_object = nullptr;
+    /// the actual iterator of the associated instance
+    internal_iterator<typename std::remove_const<BasicJsonType>::type> m_it {};
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/iterators/iteration_proxy.hpp>
+
+// #include <nlohmann/detail/iterators/json_reverse_iterator.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <cstddef> // ptrdiff_t
+#include <iterator> // reverse_iterator
+#include <utility> // declval
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+//////////////////////
+// reverse_iterator //
+//////////////////////
+
+/*!
+@brief a template for a reverse iterator class
+
+@tparam Base the base iterator type to reverse. Valid types are @ref
+iterator (to create @ref reverse_iterator) and @ref const_iterator (to
+create @ref const_reverse_iterator).
+
+@requirement The class satisfies the following concept requirements:
+-
+[BidirectionalIterator](https://en.cppreference.com/w/cpp/named_req/BidirectionalIterator):
+  The iterator that can be moved can be moved in both directions (i.e.
+  incremented and decremented).
+- [OutputIterator](https://en.cppreference.com/w/cpp/named_req/OutputIterator):
+  It is possible to write to the pointed-to element (only if @a Base is
+  @ref iterator).
+
+@since version 1.0.0
+*/
+template<typename Base>
+class json_reverse_iterator : public std::reverse_iterator<Base>
+{
+  public:
+    using difference_type = std::ptrdiff_t;
+    /// shortcut to the reverse iterator adapter
+    using base_iterator = std::reverse_iterator<Base>;
+    /// the reference type for the pointed-to element
+    using reference = typename Base::reference;
+
+    /// create reverse iterator from iterator
+    explicit json_reverse_iterator(const typename base_iterator::iterator_type& it) noexcept
+        : base_iterator(it) {}
+
+    /// create reverse iterator from base class
+    explicit json_reverse_iterator(const base_iterator& it) noexcept : base_iterator(it) {}
+
+    /// post-increment (it++)
+    json_reverse_iterator operator++(int)& // NOLINT(cert-dcl21-cpp)
+    {
+        return static_cast<json_reverse_iterator>(base_iterator::operator++(1));
+    }
+
+    /// pre-increment (++it)
+    json_reverse_iterator& operator++()
+    {
+        return static_cast<json_reverse_iterator&>(base_iterator::operator++());
+    }
+
+    /// post-decrement (it--)
+    json_reverse_iterator operator--(int)& // NOLINT(cert-dcl21-cpp)
+    {
+        return static_cast<json_reverse_iterator>(base_iterator::operator--(1));
+    }
+
+    /// pre-decrement (--it)
+    json_reverse_iterator& operator--()
+    {
+        return static_cast<json_reverse_iterator&>(base_iterator::operator--());
+    }
+
+    /// add to iterator
+    json_reverse_iterator& operator+=(difference_type i)
+    {
+        return static_cast<json_reverse_iterator&>(base_iterator::operator+=(i));
+    }
+
+    /// add to iterator
+    json_reverse_iterator operator+(difference_type i) const
+    {
+        return static_cast<json_reverse_iterator>(base_iterator::operator+(i));
+    }
+
+    /// subtract from iterator
+    json_reverse_iterator operator-(difference_type i) const
+    {
+        return static_cast<json_reverse_iterator>(base_iterator::operator-(i));
+    }
+
+    /// return difference
+    difference_type operator-(const json_reverse_iterator& other) const
+    {
+        return base_iterator(*this) - base_iterator(other);
+    }
+
+    /// access to successor
+    reference operator[](difference_type n) const
+    {
+        return *(this->operator+(n));
+    }
+
+    /// return the key of an object iterator
+    auto key() const -> decltype(std::declval<Base>().key())
+    {
+        auto it = --this->base();
+        return it.key();
+    }
+
+    /// return the value of an iterator
+    reference value() const
+    {
+        auto it = --this->base();
+        return it.operator * ();
+    }
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/iterators/primitive_iterator.hpp>
+
+// #include <nlohmann/detail/json_custom_base_class.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <type_traits> // conditional, is_same
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/*!
+@brief Default base class of the @ref basic_json class.
+
+So that the correct implementations of the copy / move ctors / assign operators
+of @ref basic_json do not require complex case distinctions
+(no base class / custom base class used as customization point),
+@ref basic_json always has a base class.
+By default, this class is used because it is empty and thus has no effect
+on the behavior of @ref basic_json.
+*/
+struct json_default_base {};
+
+template<class T>
+using json_base_class = typename std::conditional <
+                        std::is_same<T, void>::value,
+                        json_default_base,
+                        T
+                        >::type;
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/json_pointer.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <algorithm> // all_of
+#include <cctype> // isdigit
+#include <cerrno> // errno, ERANGE
+#include <cstdlib> // strtoull
+#ifndef JSON_NO_IO
+    #include <iosfwd> // ostream
+#endif  // JSON_NO_IO
+#include <limits> // max
+#include <numeric> // accumulate
+#include <string> // string
+#include <utility> // move
+#include <vector> // vector
+
+// #include <nlohmann/detail/exceptions.hpp>
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/string_concat.hpp>
+
+// #include <nlohmann/detail/string_escape.hpp>
+
+// #include <nlohmann/detail/value_t.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+
+/// @brief JSON Pointer defines a string syntax for identifying a specific value within a JSON document
+/// @sa https://json.nlohmann.me/api/json_pointer/
+template<typename RefStringType>
+class json_pointer
+{
+    // allow basic_json to access private members
+    NLOHMANN_BASIC_JSON_TPL_DECLARATION
+    friend class basic_json;
+
+    template<typename>
+    friend class json_pointer;
+
+    template<typename T>
+    struct string_t_helper
+    {
+        using type = T;
+    };
+
+    NLOHMANN_BASIC_JSON_TPL_DECLARATION
+    struct string_t_helper<NLOHMANN_BASIC_JSON_TPL>
+    {
+        using type = StringType;
+    };
+
+  public:
+    // for backwards compatibility accept BasicJsonType
+    using string_t = typename string_t_helper<RefStringType>::type;
+
+    /// @brief create JSON pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/json_pointer/
+    explicit json_pointer(const string_t& s = "")
+        : reference_tokens(split(s))
+    {}
+
+    /// @brief return a string representation of the JSON pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/to_string/
+    string_t to_string() const
+    {
+        return std::accumulate(reference_tokens.begin(), reference_tokens.end(),
+                               string_t{},
+                               [](const string_t& a, const string_t& b)
+        {
+            return detail::concat(a, '/', detail::escape(b));
+        });
+    }
+
+    /// @brief return a string representation of the JSON pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_string/
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, to_string())
+    operator string_t() const
+    {
+        return to_string();
+    }
+
+#ifndef JSON_NO_IO
+    /// @brief write string representation of the JSON pointer to stream
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ltlt/
+    friend std::ostream& operator<<(std::ostream& o, const json_pointer& ptr)
+    {
+        o << ptr.to_string();
+        return o;
+    }
+#endif
+
+    /// @brief append another JSON pointer at the end of this JSON pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slasheq/
+    json_pointer& operator/=(const json_pointer& ptr)
+    {
+        reference_tokens.insert(reference_tokens.end(),
+                                ptr.reference_tokens.begin(),
+                                ptr.reference_tokens.end());
+        return *this;
+    }
+
+    /// @brief append an unescaped reference token at the end of this JSON pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slasheq/
+    json_pointer& operator/=(string_t token)
+    {
+        push_back(std::move(token));
+        return *this;
+    }
+
+    /// @brief append an array index at the end of this JSON pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slasheq/
+    json_pointer& operator/=(std::size_t array_idx)
+    {
+        return *this /= std::to_string(array_idx);
+    }
+
+    /// @brief create a new JSON pointer by appending the right JSON pointer at the end of the left JSON pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slash/
+    friend json_pointer operator/(const json_pointer& lhs,
+                                  const json_pointer& rhs)
+    {
+        return json_pointer(lhs) /= rhs;
+    }
+
+    /// @brief create a new JSON pointer by appending the unescaped token at the end of the JSON pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slash/
+    friend json_pointer operator/(const json_pointer& lhs, string_t token) // NOLINT(performance-unnecessary-value-param)
+    {
+        return json_pointer(lhs) /= std::move(token);
+    }
+
+    /// @brief create a new JSON pointer by appending the array-index-token at the end of the JSON pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_slash/
+    friend json_pointer operator/(const json_pointer& lhs, std::size_t array_idx)
+    {
+        return json_pointer(lhs) /= array_idx;
+    }
+
+    /// @brief returns the parent of this JSON pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/parent_pointer/
+    json_pointer parent_pointer() const
+    {
+        if (empty())
+        {
+            return *this;
+        }
+
+        json_pointer res = *this;
+        res.pop_back();
+        return res;
+    }
+
+    /// @brief remove last reference token
+    /// @sa https://json.nlohmann.me/api/json_pointer/pop_back/
+    void pop_back()
+    {
+        if (JSON_HEDLEY_UNLIKELY(empty()))
+        {
+            JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", nullptr));
+        }
+
+        reference_tokens.pop_back();
+    }
+
+    /// @brief return last reference token
+    /// @sa https://json.nlohmann.me/api/json_pointer/back/
+    const string_t& back() const
+    {
+        if (JSON_HEDLEY_UNLIKELY(empty()))
+        {
+            JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", nullptr));
+        }
+
+        return reference_tokens.back();
+    }
+
+    /// @brief append an unescaped token at the end of the reference pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/push_back/
+    void push_back(const string_t& token)
+    {
+        reference_tokens.push_back(token);
+    }
+
+    /// @brief append an unescaped token at the end of the reference pointer
+    /// @sa https://json.nlohmann.me/api/json_pointer/push_back/
+    void push_back(string_t&& token)
+    {
+        reference_tokens.push_back(std::move(token));
+    }
+
+    /// @brief return whether pointer points to the root document
+    /// @sa https://json.nlohmann.me/api/json_pointer/empty/
+    bool empty() const noexcept
+    {
+        return reference_tokens.empty();
+    }
+
+  private:
+    /*!
+    @param[in] s  reference token to be converted into an array index
+
+    @return integer representation of @a s
+
+    @throw parse_error.106  if an array index begins with '0'
+    @throw parse_error.109  if an array index begins not with a digit
+    @throw out_of_range.404 if string @a s could not be converted to an integer
+    @throw out_of_range.410 if an array index exceeds size_type
+    */
+    template<typename BasicJsonType>
+    static typename BasicJsonType::size_type array_index(const string_t& s)
+    {
+        using size_type = typename BasicJsonType::size_type;
+
+        // error condition (cf. RFC 6901, Sect. 4)
+        if (JSON_HEDLEY_UNLIKELY(s.size() > 1 && s[0] == '0'))
+        {
+            JSON_THROW(detail::parse_error::create(106, 0, detail::concat("array index '", s, "' must not begin with '0'"), nullptr));
+        }
+
+        // error condition (cf. RFC 6901, Sect. 4)
+        if (JSON_HEDLEY_UNLIKELY(s.size() > 1 && !(s[0] >= '1' && s[0] <= '9')))
+        {
+            JSON_THROW(detail::parse_error::create(109, 0, detail::concat("array index '", s, "' is not a number"), nullptr));
+        }
+
+        const char* p = s.c_str();
+        char* p_end = nullptr;
+        errno = 0; // strtoull doesn't reset errno
+        const unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int)
+        if (p == p_end // invalid input or empty string
+                || errno == ERANGE // out of range
+                || JSON_HEDLEY_UNLIKELY(static_cast<std::size_t>(p_end - p) != s.size())) // incomplete read
+        {
+            JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", s, "'"), nullptr));
+        }
+
+        // only triggered on special platforms (like 32bit), see also
+        // https://github.com/nlohmann/json/pull/2203
+        if (res >= static_cast<unsigned long long>((std::numeric_limits<size_type>::max)()))  // NOLINT(runtime/int)
+        {
+            JSON_THROW(detail::out_of_range::create(410, detail::concat("array index ", s, " exceeds size_type"), nullptr));   // LCOV_EXCL_LINE
+        }
+
+        return static_cast<size_type>(res);
+    }
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    json_pointer top() const
+    {
+        if (JSON_HEDLEY_UNLIKELY(empty()))
+        {
+            JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", nullptr));
+        }
+
+        json_pointer result = *this;
+        result.reference_tokens = {reference_tokens[0]};
+        return result;
+    }
+
+  private:
+    /*!
+    @brief create and return a reference to the pointed to value
+
+    @complexity Linear in the number of reference tokens.
+
+    @throw parse_error.109 if array index is not a number
+    @throw type_error.313 if value cannot be unflattened
+    */
+    template<typename BasicJsonType>
+    BasicJsonType& get_and_create(BasicJsonType& j) const
+    {
+        auto* result = &j;
+
+        // in case no reference tokens exist, return a reference to the JSON value
+        // j which will be overwritten by a primitive value
+        for (const auto& reference_token : reference_tokens)
+        {
+            switch (result->type())
+            {
+                case detail::value_t::null:
+                {
+                    if (reference_token == "0")
+                    {
+                        // start a new array if reference token is 0
+                        result = &result->operator[](0);
+                    }
+                    else
+                    {
+                        // start a new object otherwise
+                        result = &result->operator[](reference_token);
+                    }
+                    break;
+                }
+
+                case detail::value_t::object:
+                {
+                    // create an entry in the object
+                    result = &result->operator[](reference_token);
+                    break;
+                }
+
+                case detail::value_t::array:
+                {
+                    // create an entry in the array
+                    result = &result->operator[](array_index<BasicJsonType>(reference_token));
+                    break;
+                }
+
+                /*
+                The following code is only reached if there exists a reference
+                token _and_ the current value is primitive. In this case, we have
+                an error situation, because primitive values may only occur as
+                single value; that is, with an empty list of reference tokens.
+                */
+                case detail::value_t::string:
+                case detail::value_t::boolean:
+                case detail::value_t::number_integer:
+                case detail::value_t::number_unsigned:
+                case detail::value_t::number_float:
+                case detail::value_t::binary:
+                case detail::value_t::discarded:
+                default:
+                    JSON_THROW(detail::type_error::create(313, "invalid value to unflatten", &j));
+            }
+        }
+
+        return *result;
+    }
+
+    /*!
+    @brief return a reference to the pointed to value
+
+    @note This version does not throw if a value is not present, but tries to
+          create nested values instead. For instance, calling this function
+          with pointer `"/this/that"` on a null value is equivalent to calling
+          `operator[]("this").operator[]("that")` on that value, effectively
+          changing the null value to an object.
+
+    @param[in] ptr  a JSON value
+
+    @return reference to the JSON value pointed to by the JSON pointer
+
+    @complexity Linear in the length of the JSON pointer.
+
+    @throw parse_error.106   if an array index begins with '0'
+    @throw parse_error.109   if an array index was not a number
+    @throw out_of_range.404  if the JSON pointer can not be resolved
+    */
+    template<typename BasicJsonType>
+    BasicJsonType& get_unchecked(BasicJsonType* ptr) const
+    {
+        for (const auto& reference_token : reference_tokens)
+        {
+            // convert null values to arrays or objects before continuing
+            if (ptr->is_null())
+            {
+                // check if reference token is a number
+                const bool nums =
+                    std::all_of(reference_token.begin(), reference_token.end(),
+                                [](const unsigned char x)
+                {
+                    return std::isdigit(x);
+                });
+
+                // change value to array for numbers or "-" or to object otherwise
+                *ptr = (nums || reference_token == "-")
+                       ? detail::value_t::array
+                       : detail::value_t::object;
+            }
+
+            switch (ptr->type())
+            {
+                case detail::value_t::object:
+                {
+                    // use unchecked object access
+                    ptr = &ptr->operator[](reference_token);
+                    break;
+                }
+
+                case detail::value_t::array:
+                {
+                    if (reference_token == "-")
+                    {
+                        // explicitly treat "-" as index beyond the end
+                        ptr = &ptr->operator[](ptr->m_data.m_value.array->size());
+                    }
+                    else
+                    {
+                        // convert array index to number; unchecked access
+                        ptr = &ptr->operator[](array_index<BasicJsonType>(reference_token));
+                    }
+                    break;
+                }
+
+                case detail::value_t::null:
+                case detail::value_t::string:
+                case detail::value_t::boolean:
+                case detail::value_t::number_integer:
+                case detail::value_t::number_unsigned:
+                case detail::value_t::number_float:
+                case detail::value_t::binary:
+                case detail::value_t::discarded:
+                default:
+                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
+            }
+        }
+
+        return *ptr;
+    }
+
+    /*!
+    @throw parse_error.106   if an array index begins with '0'
+    @throw parse_error.109   if an array index was not a number
+    @throw out_of_range.402  if the array index '-' is used
+    @throw out_of_range.404  if the JSON pointer can not be resolved
+    */
+    template<typename BasicJsonType>
+    BasicJsonType& get_checked(BasicJsonType* ptr) const
+    {
+        for (const auto& reference_token : reference_tokens)
+        {
+            switch (ptr->type())
+            {
+                case detail::value_t::object:
+                {
+                    // note: at performs range check
+                    ptr = &ptr->at(reference_token);
+                    break;
+                }
+
+                case detail::value_t::array:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
+                    {
+                        // "-" always fails the range check
+                        JSON_THROW(detail::out_of_range::create(402, detail::concat(
+                                "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()),
+                                ") is out of range"), ptr));
+                    }
+
+                    // note: at performs range check
+                    ptr = &ptr->at(array_index<BasicJsonType>(reference_token));
+                    break;
+                }
+
+                case detail::value_t::null:
+                case detail::value_t::string:
+                case detail::value_t::boolean:
+                case detail::value_t::number_integer:
+                case detail::value_t::number_unsigned:
+                case detail::value_t::number_float:
+                case detail::value_t::binary:
+                case detail::value_t::discarded:
+                default:
+                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
+            }
+        }
+
+        return *ptr;
+    }
+
+    /*!
+    @brief return a const reference to the pointed to value
+
+    @param[in] ptr  a JSON value
+
+    @return const reference to the JSON value pointed to by the JSON
+    pointer
+
+    @throw parse_error.106   if an array index begins with '0'
+    @throw parse_error.109   if an array index was not a number
+    @throw out_of_range.402  if the array index '-' is used
+    @throw out_of_range.404  if the JSON pointer can not be resolved
+    */
+    template<typename BasicJsonType>
+    const BasicJsonType& get_unchecked(const BasicJsonType* ptr) const
+    {
+        for (const auto& reference_token : reference_tokens)
+        {
+            switch (ptr->type())
+            {
+                case detail::value_t::object:
+                {
+                    // use unchecked object access
+                    ptr = &ptr->operator[](reference_token);
+                    break;
+                }
+
+                case detail::value_t::array:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
+                    {
+                        // "-" cannot be used for const access
+                        JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_data.m_value.array->size()), ") is out of range"), ptr));
+                    }
+
+                    // use unchecked array access
+                    ptr = &ptr->operator[](array_index<BasicJsonType>(reference_token));
+                    break;
+                }
+
+                case detail::value_t::null:
+                case detail::value_t::string:
+                case detail::value_t::boolean:
+                case detail::value_t::number_integer:
+                case detail::value_t::number_unsigned:
+                case detail::value_t::number_float:
+                case detail::value_t::binary:
+                case detail::value_t::discarded:
+                default:
+                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
+            }
+        }
+
+        return *ptr;
+    }
+
+    /*!
+    @throw parse_error.106   if an array index begins with '0'
+    @throw parse_error.109   if an array index was not a number
+    @throw out_of_range.402  if the array index '-' is used
+    @throw out_of_range.404  if the JSON pointer can not be resolved
+    */
+    template<typename BasicJsonType>
+    const BasicJsonType& get_checked(const BasicJsonType* ptr) const
+    {
+        for (const auto& reference_token : reference_tokens)
+        {
+            switch (ptr->type())
+            {
+                case detail::value_t::object:
+                {
+                    // note: at performs range check
+                    ptr = &ptr->at(reference_token);
+                    break;
+                }
+
+                case detail::value_t::array:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
+                    {
+                        // "-" always fails the range check
+                        JSON_THROW(detail::out_of_range::create(402, detail::concat(
+                                "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()),
+                                ") is out of range"), ptr));
+                    }
+
+                    // note: at performs range check
+                    ptr = &ptr->at(array_index<BasicJsonType>(reference_token));
+                    break;
+                }
+
+                case detail::value_t::null:
+                case detail::value_t::string:
+                case detail::value_t::boolean:
+                case detail::value_t::number_integer:
+                case detail::value_t::number_unsigned:
+                case detail::value_t::number_float:
+                case detail::value_t::binary:
+                case detail::value_t::discarded:
+                default:
+                    JSON_THROW(detail::out_of_range::create(404, detail::concat("unresolved reference token '", reference_token, "'"), ptr));
+            }
+        }
+
+        return *ptr;
+    }
+
+    /*!
+    @throw parse_error.106   if an array index begins with '0'
+    @throw parse_error.109   if an array index was not a number
+    */
+    template<typename BasicJsonType>
+    bool contains(const BasicJsonType* ptr) const
+    {
+        for (const auto& reference_token : reference_tokens)
+        {
+            switch (ptr->type())
+            {
+                case detail::value_t::object:
+                {
+                    if (!ptr->contains(reference_token))
+                    {
+                        // we did not find the key in the object
+                        return false;
+                    }
+
+                    ptr = &ptr->operator[](reference_token);
+                    break;
+                }
+
+                case detail::value_t::array:
+                {
+                    if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
+                    {
+                        // "-" always fails the range check
+                        return false;
+                    }
+                    if (JSON_HEDLEY_UNLIKELY(reference_token.size() == 1 && !("0" <= reference_token && reference_token <= "9")))
+                    {
+                        // invalid char
+                        return false;
+                    }
+                    if (JSON_HEDLEY_UNLIKELY(reference_token.size() > 1))
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(!('1' <= reference_token[0] && reference_token[0] <= '9')))
+                        {
+                            // first char should be between '1' and '9'
+                            return false;
+                        }
+                        for (std::size_t i = 1; i < reference_token.size(); i++)
+                        {
+                            if (JSON_HEDLEY_UNLIKELY(!('0' <= reference_token[i] && reference_token[i] <= '9')))
+                            {
+                                // other char should be between '0' and '9'
+                                return false;
+                            }
+                        }
+                    }
+
+                    const auto idx = array_index<BasicJsonType>(reference_token);
+                    if (idx >= ptr->size())
+                    {
+                        // index out of range
+                        return false;
+                    }
+
+                    ptr = &ptr->operator[](idx);
+                    break;
+                }
+
+                case detail::value_t::null:
+                case detail::value_t::string:
+                case detail::value_t::boolean:
+                case detail::value_t::number_integer:
+                case detail::value_t::number_unsigned:
+                case detail::value_t::number_float:
+                case detail::value_t::binary:
+                case detail::value_t::discarded:
+                default:
+                {
+                    // we do not expect primitive values if there is still a
+                    // reference token to process
+                    return false;
+                }
+            }
+        }
+
+        // no reference token left means we found a primitive value
+        return true;
+    }
+
+    /*!
+    @brief split the string input to reference tokens
+
+    @note This function is only called by the json_pointer constructor.
+          All exceptions below are documented there.
+
+    @throw parse_error.107  if the pointer is not empty or begins with '/'
+    @throw parse_error.108  if character '~' is not followed by '0' or '1'
+    */
+    static std::vector<string_t> split(const string_t& reference_string)
+    {
+        std::vector<string_t> result;
+
+        // special case: empty reference string -> no reference tokens
+        if (reference_string.empty())
+        {
+            return result;
+        }
+
+        // check if nonempty reference string begins with slash
+        if (JSON_HEDLEY_UNLIKELY(reference_string[0] != '/'))
+        {
+            JSON_THROW(detail::parse_error::create(107, 1, detail::concat("JSON pointer must be empty or begin with '/' - was: '", reference_string, "'"), nullptr));
+        }
+
+        // extract the reference tokens:
+        // - slash: position of the last read slash (or end of string)
+        // - start: position after the previous slash
+        for (
+            // search for the first slash after the first character
+            std::size_t slash = reference_string.find_first_of('/', 1),
+            // set the beginning of the first reference token
+            start = 1;
+            // we can stop if start == 0 (if slash == string_t::npos)
+            start != 0;
+            // set the beginning of the next reference token
+            // (will eventually be 0 if slash == string_t::npos)
+            start = (slash == string_t::npos) ? 0 : slash + 1,
+            // find next slash
+            slash = reference_string.find_first_of('/', start))
+        {
+            // use the text between the beginning of the reference token
+            // (start) and the last slash (slash).
+            auto reference_token = reference_string.substr(start, slash - start);
+
+            // check reference tokens are properly escaped
+            for (std::size_t pos = reference_token.find_first_of('~');
+                    pos != string_t::npos;
+                    pos = reference_token.find_first_of('~', pos + 1))
+            {
+                JSON_ASSERT(reference_token[pos] == '~');
+
+                // ~ must be followed by 0 or 1
+                if (JSON_HEDLEY_UNLIKELY(pos == reference_token.size() - 1 ||
+                                         (reference_token[pos + 1] != '0' &&
+                                          reference_token[pos + 1] != '1')))
+                {
+                    JSON_THROW(detail::parse_error::create(108, 0, "escape character '~' must be followed with '0' or '1'", nullptr));
+                }
+            }
+
+            // finally, store the reference token
+            detail::unescape(reference_token);
+            result.push_back(reference_token);
+        }
+
+        return result;
+    }
+
+  private:
+    /*!
+    @param[in] reference_string  the reference string to the current value
+    @param[in] value             the value to consider
+    @param[in,out] result        the result object to insert values to
+
+    @note Empty objects or arrays are flattened to `null`.
+    */
+    template<typename BasicJsonType>
+    static void flatten(const string_t& reference_string,
+                        const BasicJsonType& value,
+                        BasicJsonType& result)
+    {
+        switch (value.type())
+        {
+            case detail::value_t::array:
+            {
+                if (value.m_data.m_value.array->empty())
+                {
+                    // flatten empty array as null
+                    result[reference_string] = nullptr;
+                }
+                else
+                {
+                    // iterate array and use index as reference string
+                    for (std::size_t i = 0; i < value.m_data.m_value.array->size(); ++i)
+                    {
+                        flatten(detail::concat(reference_string, '/', std::to_string(i)),
+                                value.m_data.m_value.array->operator[](i), result);
+                    }
+                }
+                break;
+            }
+
+            case detail::value_t::object:
+            {
+                if (value.m_data.m_value.object->empty())
+                {
+                    // flatten empty object as null
+                    result[reference_string] = nullptr;
+                }
+                else
+                {
+                    // iterate object and use keys as reference string
+                    for (const auto& element : *value.m_data.m_value.object)
+                    {
+                        flatten(detail::concat(reference_string, '/', detail::escape(element.first)), element.second, result);
+                    }
+                }
+                break;
+            }
+
+            case detail::value_t::null:
+            case detail::value_t::string:
+            case detail::value_t::boolean:
+            case detail::value_t::number_integer:
+            case detail::value_t::number_unsigned:
+            case detail::value_t::number_float:
+            case detail::value_t::binary:
+            case detail::value_t::discarded:
+            default:
+            {
+                // add primitive value with its reference string
+                result[reference_string] = value;
+                break;
+            }
+        }
+    }
+
+    /*!
+    @param[in] value  flattened JSON
+
+    @return unflattened JSON
+
+    @throw parse_error.109 if array index is not a number
+    @throw type_error.314  if value is not an object
+    @throw type_error.315  if object values are not primitive
+    @throw type_error.313  if value cannot be unflattened
+    */
+    template<typename BasicJsonType>
+    static BasicJsonType
+    unflatten(const BasicJsonType& value)
+    {
+        if (JSON_HEDLEY_UNLIKELY(!value.is_object()))
+        {
+            JSON_THROW(detail::type_error::create(314, "only objects can be unflattened", &value));
+        }
+
+        BasicJsonType result;
+
+        // iterate the JSON object values
+        for (const auto& element : *value.m_data.m_value.object)
+        {
+            if (JSON_HEDLEY_UNLIKELY(!element.second.is_primitive()))
+            {
+                JSON_THROW(detail::type_error::create(315, "values in object must be primitive", &element.second));
+            }
+
+            // assign value to reference pointed to by JSON pointer; Note that if
+            // the JSON pointer is "" (i.e., points to the whole value), function
+            // get_and_create returns a reference to result itself. An assignment
+            // will then create a primitive value.
+            json_pointer(element.first).get_and_create(result) = element.second;
+        }
+
+        return result;
+    }
+
+    // can't use conversion operator because of ambiguity
+    json_pointer<string_t> convert() const&
+    {
+        json_pointer<string_t> result;
+        result.reference_tokens = reference_tokens;
+        return result;
+    }
+
+    json_pointer<string_t> convert()&&
+    {
+        json_pointer<string_t> result;
+        result.reference_tokens = std::move(reference_tokens);
+        return result;
+    }
+
+  public:
+#if JSON_HAS_THREE_WAY_COMPARISON
+    /// @brief compares two JSON pointers for equality
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
+    template<typename RefStringTypeRhs>
+    bool operator==(const json_pointer<RefStringTypeRhs>& rhs) const noexcept
+    {
+        return reference_tokens == rhs.reference_tokens;
+    }
+
+    /// @brief compares JSON pointer and string for equality
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator==(json_pointer))
+    bool operator==(const string_t& rhs) const
+    {
+        return *this == json_pointer(rhs);
+    }
+
+    /// @brief 3-way compares two JSON pointers
+    template<typename RefStringTypeRhs>
+    std::strong_ordering operator<=>(const json_pointer<RefStringTypeRhs>& rhs) const noexcept // *NOPAD*
+    {
+        return  reference_tokens <=> rhs.reference_tokens; // *NOPAD*
+    }
+#else
+    /// @brief compares two JSON pointers for equality
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
+    template<typename RefStringTypeLhs, typename RefStringTypeRhs>
+    // NOLINTNEXTLINE(readability-redundant-declaration)
+    friend bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
+                           const json_pointer<RefStringTypeRhs>& rhs) noexcept;
+
+    /// @brief compares JSON pointer and string for equality
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
+    template<typename RefStringTypeLhs, typename StringType>
+    // NOLINTNEXTLINE(readability-redundant-declaration)
+    friend bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
+                           const StringType& rhs);
+
+    /// @brief compares string and JSON pointer for equality
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_eq/
+    template<typename RefStringTypeRhs, typename StringType>
+    // NOLINTNEXTLINE(readability-redundant-declaration)
+    friend bool operator==(const StringType& lhs,
+                           const json_pointer<RefStringTypeRhs>& rhs);
+
+    /// @brief compares two JSON pointers for inequality
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_ne/
+    template<typename RefStringTypeLhs, typename RefStringTypeRhs>
+    // NOLINTNEXTLINE(readability-redundant-declaration)
+    friend bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
+                           const json_pointer<RefStringTypeRhs>& rhs) noexcept;
+
+    /// @brief compares JSON pointer and string for inequality
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_ne/
+    template<typename RefStringTypeLhs, typename StringType>
+    // NOLINTNEXTLINE(readability-redundant-declaration)
+    friend bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
+                           const StringType& rhs);
+
+    /// @brief compares string and JSON pointer for inequality
+    /// @sa https://json.nlohmann.me/api/json_pointer/operator_ne/
+    template<typename RefStringTypeRhs, typename StringType>
+    // NOLINTNEXTLINE(readability-redundant-declaration)
+    friend bool operator!=(const StringType& lhs,
+                           const json_pointer<RefStringTypeRhs>& rhs);
+
+    /// @brief compares two JSON pointer for less-than
+    template<typename RefStringTypeLhs, typename RefStringTypeRhs>
+    // NOLINTNEXTLINE(readability-redundant-declaration)
+    friend bool operator<(const json_pointer<RefStringTypeLhs>& lhs,
+                          const json_pointer<RefStringTypeRhs>& rhs) noexcept;
+#endif
+
+  private:
+    /// the reference tokens
+    std::vector<string_t> reference_tokens;
+};
+
+#if !JSON_HAS_THREE_WAY_COMPARISON
+// functions cannot be defined inside class due to ODR violations
+template<typename RefStringTypeLhs, typename RefStringTypeRhs>
+inline bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
+                       const json_pointer<RefStringTypeRhs>& rhs) noexcept
+{
+    return lhs.reference_tokens == rhs.reference_tokens;
+}
+
+template<typename RefStringTypeLhs,
+         typename StringType = typename json_pointer<RefStringTypeLhs>::string_t>
+JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator==(json_pointer, json_pointer))
+inline bool operator==(const json_pointer<RefStringTypeLhs>& lhs,
+                       const StringType& rhs)
+{
+    return lhs == json_pointer<RefStringTypeLhs>(rhs);
+}
+
+template<typename RefStringTypeRhs,
+         typename StringType = typename json_pointer<RefStringTypeRhs>::string_t>
+JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator==(json_pointer, json_pointer))
+inline bool operator==(const StringType& lhs,
+                       const json_pointer<RefStringTypeRhs>& rhs)
+{
+    return json_pointer<RefStringTypeRhs>(lhs) == rhs;
+}
+
+template<typename RefStringTypeLhs, typename RefStringTypeRhs>
+inline bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
+                       const json_pointer<RefStringTypeRhs>& rhs) noexcept
+{
+    return !(lhs == rhs);
+}
+
+template<typename RefStringTypeLhs,
+         typename StringType = typename json_pointer<RefStringTypeLhs>::string_t>
+JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator!=(json_pointer, json_pointer))
+inline bool operator!=(const json_pointer<RefStringTypeLhs>& lhs,
+                       const StringType& rhs)
+{
+    return !(lhs == rhs);
+}
+
+template<typename RefStringTypeRhs,
+         typename StringType = typename json_pointer<RefStringTypeRhs>::string_t>
+JSON_HEDLEY_DEPRECATED_FOR(3.11.2, operator!=(json_pointer, json_pointer))
+inline bool operator!=(const StringType& lhs,
+                       const json_pointer<RefStringTypeRhs>& rhs)
+{
+    return !(lhs == rhs);
+}
+
+template<typename RefStringTypeLhs, typename RefStringTypeRhs>
+inline bool operator<(const json_pointer<RefStringTypeLhs>& lhs,
+                      const json_pointer<RefStringTypeRhs>& rhs) noexcept
+{
+    return lhs.reference_tokens < rhs.reference_tokens;
+}
+#endif
+
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/json_ref.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <initializer_list>
+#include <utility>
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+template<typename BasicJsonType>
+class json_ref
+{
+  public:
+    using value_type = BasicJsonType;
+
+    json_ref(value_type&& value)
+        : owned_value(std::move(value))
+    {}
+
+    json_ref(const value_type& value)
+        : value_ref(&value)
+    {}
+
+    json_ref(std::initializer_list<json_ref> init)
+        : owned_value(init)
+    {}
+
+    template <
+        class... Args,
+        enable_if_t<std::is_constructible<value_type, Args...>::value, int> = 0 >
+    json_ref(Args && ... args)
+        : owned_value(std::forward<Args>(args)...)
+    {}
+
+    // class should be movable only
+    json_ref(json_ref&&) noexcept = default;
+    json_ref(const json_ref&) = delete;
+    json_ref& operator=(const json_ref&) = delete;
+    json_ref& operator=(json_ref&&) = delete;
+    ~json_ref() = default;
+
+    value_type moved_or_copied() const
+    {
+        if (value_ref == nullptr)
+        {
+            return std::move(owned_value);
+        }
+        return *value_ref;
+    }
+
+    value_type const& operator*() const
+    {
+        return value_ref ? *value_ref : owned_value;
+    }
+
+    value_type const* operator->() const
+    {
+        return &** this;
+    }
+
+  private:
+    mutable value_type owned_value = nullptr;
+    value_type const* value_ref = nullptr;
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/string_concat.hpp>
+
+// #include <nlohmann/detail/string_escape.hpp>
+
+// #include <nlohmann/detail/meta/cpp_future.hpp>
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+// #include <nlohmann/detail/output/binary_writer.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <algorithm> // reverse
+#include <array> // array
+#include <map> // map
+#include <cmath> // isnan, isinf
+#include <cstdint> // uint8_t, uint16_t, uint32_t, uint64_t
+#include <cstring> // memcpy
+#include <limits> // numeric_limits
+#include <string> // string
+#include <utility> // move
+#include <vector> // vector
+
+// #include <nlohmann/detail/input/binary_reader.hpp>
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/output/output_adapters.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <algorithm> // copy
+#include <cstddef> // size_t
+#include <iterator> // back_inserter
+#include <memory> // shared_ptr, make_shared
+#include <string> // basic_string
+#include <vector> // vector
+
+#ifndef JSON_NO_IO
+    #include <ios>      // streamsize
+    #include <ostream>  // basic_ostream
+#endif  // JSON_NO_IO
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/// abstract output adapter interface
+template<typename CharType> struct output_adapter_protocol
+{
+    virtual void write_character(CharType c) = 0;
+    virtual void write_characters(const CharType* s, std::size_t length) = 0;
+    virtual ~output_adapter_protocol() = default;
+
+    output_adapter_protocol() = default;
+    output_adapter_protocol(const output_adapter_protocol&) = default;
+    output_adapter_protocol(output_adapter_protocol&&) noexcept = default;
+    output_adapter_protocol& operator=(const output_adapter_protocol&) = default;
+    output_adapter_protocol& operator=(output_adapter_protocol&&) noexcept = default;
+};
+
+/// a type to simplify interfaces
+template<typename CharType>
+using output_adapter_t = std::shared_ptr<output_adapter_protocol<CharType>>;
+
+/// output adapter for byte vectors
+template<typename CharType, typename AllocatorType = std::allocator<CharType>>
+class output_vector_adapter : public output_adapter_protocol<CharType>
+{
+  public:
+    explicit output_vector_adapter(std::vector<CharType, AllocatorType>& vec) noexcept
+        : v(vec)
+    {}
+
+    void write_character(CharType c) override
+    {
+        v.push_back(c);
+    }
+
+    JSON_HEDLEY_NON_NULL(2)
+    void write_characters(const CharType* s, std::size_t length) override
+    {
+        v.insert(v.end(), s, s + length);
+    }
+
+  private:
+    std::vector<CharType, AllocatorType>& v;
+};
+
+#ifndef JSON_NO_IO
+/// output adapter for output streams
+template<typename CharType>
+class output_stream_adapter : public output_adapter_protocol<CharType>
+{
+  public:
+    explicit output_stream_adapter(std::basic_ostream<CharType>& s) noexcept
+        : stream(s)
+    {}
+
+    void write_character(CharType c) override
+    {
+        stream.put(c);
+    }
+
+    JSON_HEDLEY_NON_NULL(2)
+    void write_characters(const CharType* s, std::size_t length) override
+    {
+        stream.write(s, static_cast<std::streamsize>(length));
+    }
+
+  private:
+    std::basic_ostream<CharType>& stream;
+};
+#endif  // JSON_NO_IO
+
+/// output adapter for basic_string
+template<typename CharType, typename StringType = std::basic_string<CharType>>
+class output_string_adapter : public output_adapter_protocol<CharType>
+{
+  public:
+    explicit output_string_adapter(StringType& s) noexcept
+        : str(s)
+    {}
+
+    void write_character(CharType c) override
+    {
+        str.push_back(c);
+    }
+
+    JSON_HEDLEY_NON_NULL(2)
+    void write_characters(const CharType* s, std::size_t length) override
+    {
+        str.append(s, length);
+    }
+
+  private:
+    StringType& str;
+};
+
+template<typename CharType, typename StringType = std::basic_string<CharType>>
+class output_adapter
+{
+  public:
+    template<typename AllocatorType = std::allocator<CharType>>
+    output_adapter(std::vector<CharType, AllocatorType>& vec)
+        : oa(std::make_shared<output_vector_adapter<CharType, AllocatorType>>(vec)) {}
+
+#ifndef JSON_NO_IO
+    output_adapter(std::basic_ostream<CharType>& s)
+        : oa(std::make_shared<output_stream_adapter<CharType>>(s)) {}
+#endif  // JSON_NO_IO
+
+    output_adapter(StringType& s)
+        : oa(std::make_shared<output_string_adapter<CharType, StringType>>(s)) {}
+
+    operator output_adapter_t<CharType>()
+    {
+        return oa;
+    }
+
+  private:
+    output_adapter_t<CharType> oa = nullptr;
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/string_concat.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+///////////////////
+// binary writer //
+///////////////////
+
+/*!
+@brief serialization to CBOR and MessagePack values
+*/
+template<typename BasicJsonType, typename CharType>
+class binary_writer
+{
+    using string_t = typename BasicJsonType::string_t;
+    using binary_t = typename BasicJsonType::binary_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+
+  public:
+    /*!
+    @brief create a binary writer
+
+    @param[in] adapter  output adapter to write to
+    */
+    explicit binary_writer(output_adapter_t<CharType> adapter) : oa(std::move(adapter))
+    {
+        JSON_ASSERT(oa);
+    }
+
+    /*!
+    @param[in] j  JSON value to serialize
+    @pre       j.type() == value_t::object
+    */
+    void write_bson(const BasicJsonType& j)
+    {
+        switch (j.type())
+        {
+            case value_t::object:
+            {
+                write_bson_object(*j.m_data.m_value.object);
+                break;
+            }
+
+            case value_t::null:
+            case value_t::array:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                JSON_THROW(type_error::create(317, concat("to serialize to BSON, top-level type must be object, but is ", j.type_name()), &j));
+            }
+        }
+    }
+
+    /*!
+    @param[in] j  JSON value to serialize
+    */
+    void write_cbor(const BasicJsonType& j)
+    {
+        switch (j.type())
+        {
+            case value_t::null:
+            {
+                oa->write_character(to_char_type(0xF6));
+                break;
+            }
+
+            case value_t::boolean:
+            {
+                oa->write_character(j.m_data.m_value.boolean
+                                    ? to_char_type(0xF5)
+                                    : to_char_type(0xF4));
+                break;
+            }
+
+            case value_t::number_integer:
+            {
+                if (j.m_data.m_value.number_integer >= 0)
+                {
+                    // CBOR does not differentiate between positive signed
+                    // integers and unsigned integers. Therefore, we used the
+                    // code from the value_t::number_unsigned case here.
+                    if (j.m_data.m_value.number_integer <= 0x17)
+                    {
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
+                    {
+                        oa->write_character(to_char_type(0x18));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)())
+                    {
+                        oa->write_character(to_char_type(0x19));
+                        write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)())
+                    {
+                        oa->write_character(to_char_type(0x1A));
+                        write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_integer));
+                    }
+                    else
+                    {
+                        oa->write_character(to_char_type(0x1B));
+                        write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_integer));
+                    }
+                }
+                else
+                {
+                    // The conversions below encode the sign in the first
+                    // byte, and the value is converted to a positive number.
+                    const auto positive_number = -1 - j.m_data.m_value.number_integer;
+                    if (j.m_data.m_value.number_integer >= -24)
+                    {
+                        write_number(static_cast<std::uint8_t>(0x20 + positive_number));
+                    }
+                    else if (positive_number <= (std::numeric_limits<std::uint8_t>::max)())
+                    {
+                        oa->write_character(to_char_type(0x38));
+                        write_number(static_cast<std::uint8_t>(positive_number));
+                    }
+                    else if (positive_number <= (std::numeric_limits<std::uint16_t>::max)())
+                    {
+                        oa->write_character(to_char_type(0x39));
+                        write_number(static_cast<std::uint16_t>(positive_number));
+                    }
+                    else if (positive_number <= (std::numeric_limits<std::uint32_t>::max)())
+                    {
+                        oa->write_character(to_char_type(0x3A));
+                        write_number(static_cast<std::uint32_t>(positive_number));
+                    }
+                    else
+                    {
+                        oa->write_character(to_char_type(0x3B));
+                        write_number(static_cast<std::uint64_t>(positive_number));
+                    }
+                }
+                break;
+            }
+
+            case value_t::number_unsigned:
+            {
+                if (j.m_data.m_value.number_unsigned <= 0x17)
+                {
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_unsigned));
+                }
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x18));
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_unsigned));
+                }
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x19));
+                    write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_unsigned));
+                }
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x1A));
+                    write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_unsigned));
+                }
+                else
+                {
+                    oa->write_character(to_char_type(0x1B));
+                    write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_unsigned));
+                }
+                break;
+            }
+
+            case value_t::number_float:
+            {
+                if (std::isnan(j.m_data.m_value.number_float))
+                {
+                    // NaN is 0xf97e00 in CBOR
+                    oa->write_character(to_char_type(0xF9));
+                    oa->write_character(to_char_type(0x7E));
+                    oa->write_character(to_char_type(0x00));
+                }
+                else if (std::isinf(j.m_data.m_value.number_float))
+                {
+                    // Infinity is 0xf97c00, -Infinity is 0xf9fc00
+                    oa->write_character(to_char_type(0xf9));
+                    oa->write_character(j.m_data.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC));
+                    oa->write_character(to_char_type(0x00));
+                }
+                else
+                {
+                    write_compact_float(j.m_data.m_value.number_float, detail::input_format_t::cbor);
+                }
+                break;
+            }
+
+            case value_t::string:
+            {
+                // step 1: write control byte and the string length
+                const auto N = j.m_data.m_value.string->size();
+                if (N <= 0x17)
+                {
+                    write_number(static_cast<std::uint8_t>(0x60 + N));
+                }
+                else if (N <= (std::numeric_limits<std::uint8_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x78));
+                    write_number(static_cast<std::uint8_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x79));
+                    write_number(static_cast<std::uint16_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x7A));
+                    write_number(static_cast<std::uint32_t>(N));
+                }
+                // LCOV_EXCL_START
+                else if (N <= (std::numeric_limits<std::uint64_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x7B));
+                    write_number(static_cast<std::uint64_t>(N));
+                }
+                // LCOV_EXCL_STOP
+
+                // step 2: write the string
+                oa->write_characters(
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.string->c_str()),
+                    j.m_data.m_value.string->size());
+                break;
+            }
+
+            case value_t::array:
+            {
+                // step 1: write control byte and the array size
+                const auto N = j.m_data.m_value.array->size();
+                if (N <= 0x17)
+                {
+                    write_number(static_cast<std::uint8_t>(0x80 + N));
+                }
+                else if (N <= (std::numeric_limits<std::uint8_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x98));
+                    write_number(static_cast<std::uint8_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x99));
+                    write_number(static_cast<std::uint16_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x9A));
+                    write_number(static_cast<std::uint32_t>(N));
+                }
+                // LCOV_EXCL_START
+                else if (N <= (std::numeric_limits<std::uint64_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x9B));
+                    write_number(static_cast<std::uint64_t>(N));
+                }
+                // LCOV_EXCL_STOP
+
+                // step 2: write each element
+                for (const auto& el : *j.m_data.m_value.array)
+                {
+                    write_cbor(el);
+                }
+                break;
+            }
+
+            case value_t::binary:
+            {
+                if (j.m_data.m_value.binary->has_subtype())
+                {
+                    if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint8_t>::max)())
+                    {
+                        write_number(static_cast<std::uint8_t>(0xd8));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.binary->subtype()));
+                    }
+                    else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint16_t>::max)())
+                    {
+                        write_number(static_cast<std::uint8_t>(0xd9));
+                        write_number(static_cast<std::uint16_t>(j.m_data.m_value.binary->subtype()));
+                    }
+                    else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint32_t>::max)())
+                    {
+                        write_number(static_cast<std::uint8_t>(0xda));
+                        write_number(static_cast<std::uint32_t>(j.m_data.m_value.binary->subtype()));
+                    }
+                    else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint64_t>::max)())
+                    {
+                        write_number(static_cast<std::uint8_t>(0xdb));
+                        write_number(static_cast<std::uint64_t>(j.m_data.m_value.binary->subtype()));
+                    }
+                }
+
+                // step 1: write control byte and the binary array size
+                const auto N = j.m_data.m_value.binary->size();
+                if (N <= 0x17)
+                {
+                    write_number(static_cast<std::uint8_t>(0x40 + N));
+                }
+                else if (N <= (std::numeric_limits<std::uint8_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x58));
+                    write_number(static_cast<std::uint8_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x59));
+                    write_number(static_cast<std::uint16_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x5A));
+                    write_number(static_cast<std::uint32_t>(N));
+                }
+                // LCOV_EXCL_START
+                else if (N <= (std::numeric_limits<std::uint64_t>::max)())
+                {
+                    oa->write_character(to_char_type(0x5B));
+                    write_number(static_cast<std::uint64_t>(N));
+                }
+                // LCOV_EXCL_STOP
+
+                // step 2: write each element
+                oa->write_characters(
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.binary->data()),
+                    N);
+
+                break;
+            }
+
+            case value_t::object:
+            {
+                // step 1: write control byte and the object size
+                const auto N = j.m_data.m_value.object->size();
+                if (N <= 0x17)
+                {
+                    write_number(static_cast<std::uint8_t>(0xA0 + N));
+                }
+                else if (N <= (std::numeric_limits<std::uint8_t>::max)())
+                {
+                    oa->write_character(to_char_type(0xB8));
+                    write_number(static_cast<std::uint8_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
+                {
+                    oa->write_character(to_char_type(0xB9));
+                    write_number(static_cast<std::uint16_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
+                {
+                    oa->write_character(to_char_type(0xBA));
+                    write_number(static_cast<std::uint32_t>(N));
+                }
+                // LCOV_EXCL_START
+                else if (N <= (std::numeric_limits<std::uint64_t>::max)())
+                {
+                    oa->write_character(to_char_type(0xBB));
+                    write_number(static_cast<std::uint64_t>(N));
+                }
+                // LCOV_EXCL_STOP
+
+                // step 2: write each element
+                for (const auto& el : *j.m_data.m_value.object)
+                {
+                    write_cbor(el.first);
+                    write_cbor(el.second);
+                }
+                break;
+            }
+
+            case value_t::discarded:
+            default:
+                break;
+        }
+    }
+
+    /*!
+    @param[in] j  JSON value to serialize
+    */
+    void write_msgpack(const BasicJsonType& j)
+    {
+        switch (j.type())
+        {
+            case value_t::null: // nil
+            {
+                oa->write_character(to_char_type(0xC0));
+                break;
+            }
+
+            case value_t::boolean: // true and false
+            {
+                oa->write_character(j.m_data.m_value.boolean
+                                    ? to_char_type(0xC3)
+                                    : to_char_type(0xC2));
+                break;
+            }
+
+            case value_t::number_integer:
+            {
+                if (j.m_data.m_value.number_integer >= 0)
+                {
+                    // MessagePack does not differentiate between positive
+                    // signed integers and unsigned integers. Therefore, we used
+                    // the code from the value_t::number_unsigned case here.
+                    if (j.m_data.m_value.number_unsigned < 128)
+                    {
+                        // positive fixnum
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
+                    {
+                        // uint 8
+                        oa->write_character(to_char_type(0xCC));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
+                    {
+                        // uint 16
+                        oa->write_character(to_char_type(0xCD));
+                        write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
+                    {
+                        // uint 32
+                        oa->write_character(to_char_type(0xCE));
+                        write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
+                    {
+                        // uint 64
+                        oa->write_character(to_char_type(0xCF));
+                        write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_integer));
+                    }
+                }
+                else
+                {
+                    if (j.m_data.m_value.number_integer >= -32)
+                    {
+                        // negative fixnum
+                        write_number(static_cast<std::int8_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int8_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
+                    {
+                        // int 8
+                        oa->write_character(to_char_type(0xD0));
+                        write_number(static_cast<std::int8_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int16_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
+                    {
+                        // int 16
+                        oa->write_character(to_char_type(0xD1));
+                        write_number(static_cast<std::int16_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int32_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
+                    {
+                        // int 32
+                        oa->write_character(to_char_type(0xD2));
+                        write_number(static_cast<std::int32_t>(j.m_data.m_value.number_integer));
+                    }
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int64_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
+                    {
+                        // int 64
+                        oa->write_character(to_char_type(0xD3));
+                        write_number(static_cast<std::int64_t>(j.m_data.m_value.number_integer));
+                    }
+                }
+                break;
+            }
+
+            case value_t::number_unsigned:
+            {
+                if (j.m_data.m_value.number_unsigned < 128)
+                {
+                    // positive fixnum
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
+                }
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
+                {
+                    // uint 8
+                    oa->write_character(to_char_type(0xCC));
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
+                }
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
+                {
+                    // uint 16
+                    oa->write_character(to_char_type(0xCD));
+                    write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_integer));
+                }
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
+                {
+                    // uint 32
+                    oa->write_character(to_char_type(0xCE));
+                    write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_integer));
+                }
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
+                {
+                    // uint 64
+                    oa->write_character(to_char_type(0xCF));
+                    write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_integer));
+                }
+                break;
+            }
+
+            case value_t::number_float:
+            {
+                write_compact_float(j.m_data.m_value.number_float, detail::input_format_t::msgpack);
+                break;
+            }
+
+            case value_t::string:
+            {
+                // step 1: write control byte and the string length
+                const auto N = j.m_data.m_value.string->size();
+                if (N <= 31)
+                {
+                    // fixstr
+                    write_number(static_cast<std::uint8_t>(0xA0 | N));
+                }
+                else if (N <= (std::numeric_limits<std::uint8_t>::max)())
+                {
+                    // str 8
+                    oa->write_character(to_char_type(0xD9));
+                    write_number(static_cast<std::uint8_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
+                {
+                    // str 16
+                    oa->write_character(to_char_type(0xDA));
+                    write_number(static_cast<std::uint16_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
+                {
+                    // str 32
+                    oa->write_character(to_char_type(0xDB));
+                    write_number(static_cast<std::uint32_t>(N));
+                }
+
+                // step 2: write the string
+                oa->write_characters(
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.string->c_str()),
+                    j.m_data.m_value.string->size());
+                break;
+            }
+
+            case value_t::array:
+            {
+                // step 1: write control byte and the array size
+                const auto N = j.m_data.m_value.array->size();
+                if (N <= 15)
+                {
+                    // fixarray
+                    write_number(static_cast<std::uint8_t>(0x90 | N));
+                }
+                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
+                {
+                    // array 16
+                    oa->write_character(to_char_type(0xDC));
+                    write_number(static_cast<std::uint16_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
+                {
+                    // array 32
+                    oa->write_character(to_char_type(0xDD));
+                    write_number(static_cast<std::uint32_t>(N));
+                }
+
+                // step 2: write each element
+                for (const auto& el : *j.m_data.m_value.array)
+                {
+                    write_msgpack(el);
+                }
+                break;
+            }
+
+            case value_t::binary:
+            {
+                // step 0: determine if the binary type has a set subtype to
+                // determine whether or not to use the ext or fixext types
+                const bool use_ext = j.m_data.m_value.binary->has_subtype();
+
+                // step 1: write control byte and the byte string length
+                const auto N = j.m_data.m_value.binary->size();
+                if (N <= (std::numeric_limits<std::uint8_t>::max)())
+                {
+                    std::uint8_t output_type{};
+                    bool fixed = true;
+                    if (use_ext)
+                    {
+                        switch (N)
+                        {
+                            case 1:
+                                output_type = 0xD4; // fixext 1
+                                break;
+                            case 2:
+                                output_type = 0xD5; // fixext 2
+                                break;
+                            case 4:
+                                output_type = 0xD6; // fixext 4
+                                break;
+                            case 8:
+                                output_type = 0xD7; // fixext 8
+                                break;
+                            case 16:
+                                output_type = 0xD8; // fixext 16
+                                break;
+                            default:
+                                output_type = 0xC7; // ext 8
+                                fixed = false;
+                                break;
+                        }
+
+                    }
+                    else
+                    {
+                        output_type = 0xC4; // bin 8
+                        fixed = false;
+                    }
+
+                    oa->write_character(to_char_type(output_type));
+                    if (!fixed)
+                    {
+                        write_number(static_cast<std::uint8_t>(N));
+                    }
+                }
+                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
+                {
+                    const std::uint8_t output_type = use_ext
+                                                     ? 0xC8 // ext 16
+                                                     : 0xC5; // bin 16
+
+                    oa->write_character(to_char_type(output_type));
+                    write_number(static_cast<std::uint16_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
+                {
+                    const std::uint8_t output_type = use_ext
+                                                     ? 0xC9 // ext 32
+                                                     : 0xC6; // bin 32
+
+                    oa->write_character(to_char_type(output_type));
+                    write_number(static_cast<std::uint32_t>(N));
+                }
+
+                // step 1.5: if this is an ext type, write the subtype
+                if (use_ext)
+                {
+                    write_number(static_cast<std::int8_t>(j.m_data.m_value.binary->subtype()));
+                }
+
+                // step 2: write the byte string
+                oa->write_characters(
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.binary->data()),
+                    N);
+
+                break;
+            }
+
+            case value_t::object:
+            {
+                // step 1: write control byte and the object size
+                const auto N = j.m_data.m_value.object->size();
+                if (N <= 15)
+                {
+                    // fixmap
+                    write_number(static_cast<std::uint8_t>(0x80 | (N & 0xF)));
+                }
+                else if (N <= (std::numeric_limits<std::uint16_t>::max)())
+                {
+                    // map 16
+                    oa->write_character(to_char_type(0xDE));
+                    write_number(static_cast<std::uint16_t>(N));
+                }
+                else if (N <= (std::numeric_limits<std::uint32_t>::max)())
+                {
+                    // map 32
+                    oa->write_character(to_char_type(0xDF));
+                    write_number(static_cast<std::uint32_t>(N));
+                }
+
+                // step 2: write each element
+                for (const auto& el : *j.m_data.m_value.object)
+                {
+                    write_msgpack(el.first);
+                    write_msgpack(el.second);
+                }
+                break;
+            }
+
+            case value_t::discarded:
+            default:
+                break;
+        }
+    }
+
+    /*!
+    @param[in] j  JSON value to serialize
+    @param[in] use_count   whether to use '#' prefixes (optimized format)
+    @param[in] use_type    whether to use '$' prefixes (optimized format)
+    @param[in] add_prefix  whether prefixes need to be used for this value
+    @param[in] use_bjdata  whether write in BJData format, default is false
+    */
+    void write_ubjson(const BasicJsonType& j, const bool use_count,
+                      const bool use_type, const bool add_prefix = true,
+                      const bool use_bjdata = false)
+    {
+        switch (j.type())
+        {
+            case value_t::null:
+            {
+                if (add_prefix)
+                {
+                    oa->write_character(to_char_type('Z'));
+                }
+                break;
+            }
+
+            case value_t::boolean:
+            {
+                if (add_prefix)
+                {
+                    oa->write_character(j.m_data.m_value.boolean
+                                        ? to_char_type('T')
+                                        : to_char_type('F'));
+                }
+                break;
+            }
+
+            case value_t::number_integer:
+            {
+                write_number_with_ubjson_prefix(j.m_data.m_value.number_integer, add_prefix, use_bjdata);
+                break;
+            }
+
+            case value_t::number_unsigned:
+            {
+                write_number_with_ubjson_prefix(j.m_data.m_value.number_unsigned, add_prefix, use_bjdata);
+                break;
+            }
+
+            case value_t::number_float:
+            {
+                write_number_with_ubjson_prefix(j.m_data.m_value.number_float, add_prefix, use_bjdata);
+                break;
+            }
+
+            case value_t::string:
+            {
+                if (add_prefix)
+                {
+                    oa->write_character(to_char_type('S'));
+                }
+                write_number_with_ubjson_prefix(j.m_data.m_value.string->size(), true, use_bjdata);
+                oa->write_characters(
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.string->c_str()),
+                    j.m_data.m_value.string->size());
+                break;
+            }
+
+            case value_t::array:
+            {
+                if (add_prefix)
+                {
+                    oa->write_character(to_char_type('['));
+                }
+
+                bool prefix_required = true;
+                if (use_type && !j.m_data.m_value.array->empty())
+                {
+                    JSON_ASSERT(use_count);
+                    const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata);
+                    const bool same_prefix = std::all_of(j.begin() + 1, j.end(),
+                                                         [this, first_prefix, use_bjdata](const BasicJsonType & v)
+                    {
+                        return ubjson_prefix(v, use_bjdata) == first_prefix;
+                    });
+
+                    std::vector<CharType> bjdx = {'[', '{', 'S', 'H', 'T', 'F', 'N', 'Z'}; // excluded markers in bjdata optimized type
+
+                    if (same_prefix && !(use_bjdata && std::find(bjdx.begin(), bjdx.end(), first_prefix) != bjdx.end()))
+                    {
+                        prefix_required = false;
+                        oa->write_character(to_char_type('$'));
+                        oa->write_character(first_prefix);
+                    }
+                }
+
+                if (use_count)
+                {
+                    oa->write_character(to_char_type('#'));
+                    write_number_with_ubjson_prefix(j.m_data.m_value.array->size(), true, use_bjdata);
+                }
+
+                for (const auto& el : *j.m_data.m_value.array)
+                {
+                    write_ubjson(el, use_count, use_type, prefix_required, use_bjdata);
+                }
+
+                if (!use_count)
+                {
+                    oa->write_character(to_char_type(']'));
+                }
+
+                break;
+            }
+
+            case value_t::binary:
+            {
+                if (add_prefix)
+                {
+                    oa->write_character(to_char_type('['));
+                }
+
+                if (use_type && !j.m_data.m_value.binary->empty())
+                {
+                    JSON_ASSERT(use_count);
+                    oa->write_character(to_char_type('$'));
+                    oa->write_character('U');
+                }
+
+                if (use_count)
+                {
+                    oa->write_character(to_char_type('#'));
+                    write_number_with_ubjson_prefix(j.m_data.m_value.binary->size(), true, use_bjdata);
+                }
+
+                if (use_type)
+                {
+                    oa->write_characters(
+                        reinterpret_cast<const CharType*>(j.m_data.m_value.binary->data()),
+                        j.m_data.m_value.binary->size());
+                }
+                else
+                {
+                    for (size_t i = 0; i < j.m_data.m_value.binary->size(); ++i)
+                    {
+                        oa->write_character(to_char_type('U'));
+                        oa->write_character(j.m_data.m_value.binary->data()[i]);
+                    }
+                }
+
+                if (!use_count)
+                {
+                    oa->write_character(to_char_type(']'));
+                }
+
+                break;
+            }
+
+            case value_t::object:
+            {
+                if (use_bjdata && j.m_data.m_value.object->size() == 3 && j.m_data.m_value.object->find("_ArrayType_") != j.m_data.m_value.object->end() && j.m_data.m_value.object->find("_ArraySize_") != j.m_data.m_value.object->end() && j.m_data.m_value.object->find("_ArrayData_") != j.m_data.m_value.object->end())
+                {
+                    if (!write_bjdata_ndarray(*j.m_data.m_value.object, use_count, use_type))  // decode bjdata ndarray in the JData format (https://github.com/NeuroJSON/jdata)
+                    {
+                        break;
+                    }
+                }
+
+                if (add_prefix)
+                {
+                    oa->write_character(to_char_type('{'));
+                }
+
+                bool prefix_required = true;
+                if (use_type && !j.m_data.m_value.object->empty())
+                {
+                    JSON_ASSERT(use_count);
+                    const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata);
+                    const bool same_prefix = std::all_of(j.begin(), j.end(),
+                                                         [this, first_prefix, use_bjdata](const BasicJsonType & v)
+                    {
+                        return ubjson_prefix(v, use_bjdata) == first_prefix;
+                    });
+
+                    std::vector<CharType> bjdx = {'[', '{', 'S', 'H', 'T', 'F', 'N', 'Z'}; // excluded markers in bjdata optimized type
+
+                    if (same_prefix && !(use_bjdata && std::find(bjdx.begin(), bjdx.end(), first_prefix) != bjdx.end()))
+                    {
+                        prefix_required = false;
+                        oa->write_character(to_char_type('$'));
+                        oa->write_character(first_prefix);
+                    }
+                }
+
+                if (use_count)
+                {
+                    oa->write_character(to_char_type('#'));
+                    write_number_with_ubjson_prefix(j.m_data.m_value.object->size(), true, use_bjdata);
+                }
+
+                for (const auto& el : *j.m_data.m_value.object)
+                {
+                    write_number_with_ubjson_prefix(el.first.size(), true, use_bjdata);
+                    oa->write_characters(
+                        reinterpret_cast<const CharType*>(el.first.c_str()),
+                        el.first.size());
+                    write_ubjson(el.second, use_count, use_type, prefix_required, use_bjdata);
+                }
+
+                if (!use_count)
+                {
+                    oa->write_character(to_char_type('}'));
+                }
+
+                break;
+            }
+
+            case value_t::discarded:
+            default:
+                break;
+        }
+    }
+
+  private:
+    //////////
+    // BSON //
+    //////////
+
+    /*!
+    @return The size of a BSON document entry header, including the id marker
+            and the entry name size (and its null-terminator).
+    */
+    static std::size_t calc_bson_entry_header_size(const string_t& name, const BasicJsonType& j)
+    {
+        const auto it = name.find(static_cast<typename string_t::value_type>(0));
+        if (JSON_HEDLEY_UNLIKELY(it != BasicJsonType::string_t::npos))
+        {
+            JSON_THROW(out_of_range::create(409, concat("BSON key cannot contain code point U+0000 (at byte ", std::to_string(it), ")"), &j));
+            static_cast<void>(j);
+        }
+
+        return /*id*/ 1ul + name.size() + /*zero-terminator*/1u;
+    }
+
+    /*!
+    @brief Writes the given @a element_type and @a name to the output adapter
+    */
+    void write_bson_entry_header(const string_t& name,
+                                 const std::uint8_t element_type)
+    {
+        oa->write_character(to_char_type(element_type)); // boolean
+        oa->write_characters(
+            reinterpret_cast<const CharType*>(name.c_str()),
+            name.size() + 1u);
+    }
+
+    /*!
+    @brief Writes a BSON element with key @a name and boolean value @a value
+    */
+    void write_bson_boolean(const string_t& name,
+                            const bool value)
+    {
+        write_bson_entry_header(name, 0x08);
+        oa->write_character(value ? to_char_type(0x01) : to_char_type(0x00));
+    }
+
+    /*!
+    @brief Writes a BSON element with key @a name and double value @a value
+    */
+    void write_bson_double(const string_t& name,
+                           const double value)
+    {
+        write_bson_entry_header(name, 0x01);
+        write_number<double>(value, true);
+    }
+
+    /*!
+    @return The size of the BSON-encoded string in @a value
+    */
+    static std::size_t calc_bson_string_size(const string_t& value)
+    {
+        return sizeof(std::int32_t) + value.size() + 1ul;
+    }
+
+    /*!
+    @brief Writes a BSON element with key @a name and string value @a value
+    */
+    void write_bson_string(const string_t& name,
+                           const string_t& value)
+    {
+        write_bson_entry_header(name, 0x02);
+
+        write_number<std::int32_t>(static_cast<std::int32_t>(value.size() + 1ul), true);
+        oa->write_characters(
+            reinterpret_cast<const CharType*>(value.c_str()),
+            value.size() + 1);
+    }
+
+    /*!
+    @brief Writes a BSON element with key @a name and null value
+    */
+    void write_bson_null(const string_t& name)
+    {
+        write_bson_entry_header(name, 0x0A);
+    }
+
+    /*!
+    @return The size of the BSON-encoded integer @a value
+    */
+    static std::size_t calc_bson_integer_size(const std::int64_t value)
+    {
+        return (std::numeric_limits<std::int32_t>::min)() <= value && value <= (std::numeric_limits<std::int32_t>::max)()
+               ? sizeof(std::int32_t)
+               : sizeof(std::int64_t);
+    }
+
+    /*!
+    @brief Writes a BSON element with key @a name and integer @a value
+    */
+    void write_bson_integer(const string_t& name,
+                            const std::int64_t value)
+    {
+        if ((std::numeric_limits<std::int32_t>::min)() <= value && value <= (std::numeric_limits<std::int32_t>::max)())
+        {
+            write_bson_entry_header(name, 0x10); // int32
+            write_number<std::int32_t>(static_cast<std::int32_t>(value), true);
+        }
+        else
+        {
+            write_bson_entry_header(name, 0x12); // int64
+            write_number<std::int64_t>(static_cast<std::int64_t>(value), true);
+        }
+    }
+
+    /*!
+    @return The size of the BSON-encoded unsigned integer in @a j
+    */
+    static constexpr std::size_t calc_bson_unsigned_size(const std::uint64_t value) noexcept
+    {
+        return (value <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
+               ? sizeof(std::int32_t)
+               : sizeof(std::int64_t);
+    }
+
+    /*!
+    @brief Writes a BSON element with key @a name and unsigned @a value
+    */
+    void write_bson_unsigned(const string_t& name,
+                             const BasicJsonType& j)
+    {
+        if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
+        {
+            write_bson_entry_header(name, 0x10 /* int32 */);
+            write_number<std::int32_t>(static_cast<std::int32_t>(j.m_data.m_value.number_unsigned), true);
+        }
+        else if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
+        {
+            write_bson_entry_header(name, 0x12 /* int64 */);
+            write_number<std::int64_t>(static_cast<std::int64_t>(j.m_data.m_value.number_unsigned), true);
+        }
+        else
+        {
+            JSON_THROW(out_of_range::create(407, concat("integer number ", std::to_string(j.m_data.m_value.number_unsigned), " cannot be represented by BSON as it does not fit int64"), &j));
+        }
+    }
+
+    /*!
+    @brief Writes a BSON element with key @a name and object @a value
+    */
+    void write_bson_object_entry(const string_t& name,
+                                 const typename BasicJsonType::object_t& value)
+    {
+        write_bson_entry_header(name, 0x03); // object
+        write_bson_object(value);
+    }
+
+    /*!
+    @return The size of the BSON-encoded array @a value
+    */
+    static std::size_t calc_bson_array_size(const typename BasicJsonType::array_t& value)
+    {
+        std::size_t array_index = 0ul;
+
+        const std::size_t embedded_document_size = std::accumulate(std::begin(value), std::end(value), static_cast<std::size_t>(0), [&array_index](std::size_t result, const typename BasicJsonType::array_t::value_type & el)
+        {
+            return result + calc_bson_element_size(std::to_string(array_index++), el);
+        });
+
+        return sizeof(std::int32_t) + embedded_document_size + 1ul;
+    }
+
+    /*!
+    @return The size of the BSON-encoded binary array @a value
+    */
+    static std::size_t calc_bson_binary_size(const typename BasicJsonType::binary_t& value)
+    {
+        return sizeof(std::int32_t) + value.size() + 1ul;
+    }
+
+    /*!
+    @brief Writes a BSON element with key @a name and array @a value
+    */
+    void write_bson_array(const string_t& name,
+                          const typename BasicJsonType::array_t& value)
+    {
+        write_bson_entry_header(name, 0x04); // array
+        write_number<std::int32_t>(static_cast<std::int32_t>(calc_bson_array_size(value)), true);
+
+        std::size_t array_index = 0ul;
+
+        for (const auto& el : value)
+        {
+            write_bson_element(std::to_string(array_index++), el);
+        }
+
+        oa->write_character(to_char_type(0x00));
+    }
+
+    /*!
+    @brief Writes a BSON element with key @a name and binary value @a value
+    */
+    void write_bson_binary(const string_t& name,
+                           const binary_t& value)
+    {
+        write_bson_entry_header(name, 0x05);
+
+        write_number<std::int32_t>(static_cast<std::int32_t>(value.size()), true);
+        write_number(value.has_subtype() ? static_cast<std::uint8_t>(value.subtype()) : static_cast<std::uint8_t>(0x00));
+
+        oa->write_characters(reinterpret_cast<const CharType*>(value.data()), value.size());
+    }
+
+    /*!
+    @brief Calculates the size necessary to serialize the JSON value @a j with its @a name
+    @return The calculated size for the BSON document entry for @a j with the given @a name.
+    */
+    static std::size_t calc_bson_element_size(const string_t& name,
+            const BasicJsonType& j)
+    {
+        const auto header_size = calc_bson_entry_header_size(name, j);
+        switch (j.type())
+        {
+            case value_t::object:
+                return header_size + calc_bson_object_size(*j.m_data.m_value.object);
+
+            case value_t::array:
+                return header_size + calc_bson_array_size(*j.m_data.m_value.array);
+
+            case value_t::binary:
+                return header_size + calc_bson_binary_size(*j.m_data.m_value.binary);
+
+            case value_t::boolean:
+                return header_size + 1ul;
+
+            case value_t::number_float:
+                return header_size + 8ul;
+
+            case value_t::number_integer:
+                return header_size + calc_bson_integer_size(j.m_data.m_value.number_integer);
+
+            case value_t::number_unsigned:
+                return header_size + calc_bson_unsigned_size(j.m_data.m_value.number_unsigned);
+
+            case value_t::string:
+                return header_size + calc_bson_string_size(*j.m_data.m_value.string);
+
+            case value_t::null:
+                return header_size + 0ul;
+
+            // LCOV_EXCL_START
+            case value_t::discarded:
+            default:
+                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert)
+                return 0ul;
+                // LCOV_EXCL_STOP
+        }
+    }
+
+    /*!
+    @brief Serializes the JSON value @a j to BSON and associates it with the
+           key @a name.
+    @param name The name to associate with the JSON entity @a j within the
+                current BSON document
+    */
+    void write_bson_element(const string_t& name,
+                            const BasicJsonType& j)
+    {
+        switch (j.type())
+        {
+            case value_t::object:
+                return write_bson_object_entry(name, *j.m_data.m_value.object);
+
+            case value_t::array:
+                return write_bson_array(name, *j.m_data.m_value.array);
+
+            case value_t::binary:
+                return write_bson_binary(name, *j.m_data.m_value.binary);
+
+            case value_t::boolean:
+                return write_bson_boolean(name, j.m_data.m_value.boolean);
+
+            case value_t::number_float:
+                return write_bson_double(name, j.m_data.m_value.number_float);
+
+            case value_t::number_integer:
+                return write_bson_integer(name, j.m_data.m_value.number_integer);
+
+            case value_t::number_unsigned:
+                return write_bson_unsigned(name, j);
+
+            case value_t::string:
+                return write_bson_string(name, *j.m_data.m_value.string);
+
+            case value_t::null:
+                return write_bson_null(name);
+
+            // LCOV_EXCL_START
+            case value_t::discarded:
+            default:
+                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert)
+                return;
+                // LCOV_EXCL_STOP
+        }
+    }
+
+    /*!
+    @brief Calculates the size of the BSON serialization of the given
+           JSON-object @a j.
+    @param[in] value  JSON value to serialize
+    @pre       value.type() == value_t::object
+    */
+    static std::size_t calc_bson_object_size(const typename BasicJsonType::object_t& value)
+    {
+        const std::size_t document_size = std::accumulate(value.begin(), value.end(), static_cast<std::size_t>(0),
+                                          [](size_t result, const typename BasicJsonType::object_t::value_type & el)
+        {
+            return result += calc_bson_element_size(el.first, el.second);
+        });
+
+        return sizeof(std::int32_t) + document_size + 1ul;
+    }
+
+    /*!
+    @param[in] value  JSON value to serialize
+    @pre       value.type() == value_t::object
+    */
+    void write_bson_object(const typename BasicJsonType::object_t& value)
+    {
+        write_number<std::int32_t>(static_cast<std::int32_t>(calc_bson_object_size(value)), true);
+
+        for (const auto& el : value)
+        {
+            write_bson_element(el.first, el.second);
+        }
+
+        oa->write_character(to_char_type(0x00));
+    }
+
+    //////////
+    // CBOR //
+    //////////
+
+    static constexpr CharType get_cbor_float_prefix(float /*unused*/)
+    {
+        return to_char_type(0xFA);  // Single-Precision Float
+    }
+
+    static constexpr CharType get_cbor_float_prefix(double /*unused*/)
+    {
+        return to_char_type(0xFB);  // Double-Precision Float
+    }
+
+    /////////////
+    // MsgPack //
+    /////////////
+
+    static constexpr CharType get_msgpack_float_prefix(float /*unused*/)
+    {
+        return to_char_type(0xCA);  // float 32
+    }
+
+    static constexpr CharType get_msgpack_float_prefix(double /*unused*/)
+    {
+        return to_char_type(0xCB);  // float 64
+    }
+
+    ////////////
+    // UBJSON //
+    ////////////
+
+    // UBJSON: write number (floating point)
+    template<typename NumberType, typename std::enable_if<
+                 std::is_floating_point<NumberType>::value, int>::type = 0>
+    void write_number_with_ubjson_prefix(const NumberType n,
+                                         const bool add_prefix,
+                                         const bool use_bjdata)
+    {
+        if (add_prefix)
+        {
+            oa->write_character(get_ubjson_float_prefix(n));
+        }
+        write_number(n, use_bjdata);
+    }
+
+    // UBJSON: write number (unsigned integer)
+    template<typename NumberType, typename std::enable_if<
+                 std::is_unsigned<NumberType>::value, int>::type = 0>
+    void write_number_with_ubjson_prefix(const NumberType n,
+                                         const bool add_prefix,
+                                         const bool use_bjdata)
+    {
+        if (n <= static_cast<std::uint64_t>((std::numeric_limits<std::int8_t>::max)()))
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('i'));  // int8
+            }
+            write_number(static_cast<std::uint8_t>(n), use_bjdata);
+        }
+        else if (n <= (std::numeric_limits<std::uint8_t>::max)())
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('U'));  // uint8
+            }
+            write_number(static_cast<std::uint8_t>(n), use_bjdata);
+        }
+        else if (n <= static_cast<std::uint64_t>((std::numeric_limits<std::int16_t>::max)()))
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('I'));  // int16
+            }
+            write_number(static_cast<std::int16_t>(n), use_bjdata);
+        }
+        else if (use_bjdata && n <= static_cast<uint64_t>((std::numeric_limits<uint16_t>::max)()))
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('u'));  // uint16 - bjdata only
+            }
+            write_number(static_cast<std::uint16_t>(n), use_bjdata);
+        }
+        else if (n <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('l'));  // int32
+            }
+            write_number(static_cast<std::int32_t>(n), use_bjdata);
+        }
+        else if (use_bjdata && n <= static_cast<uint64_t>((std::numeric_limits<uint32_t>::max)()))
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('m'));  // uint32 - bjdata only
+            }
+            write_number(static_cast<std::uint32_t>(n), use_bjdata);
+        }
+        else if (n <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('L'));  // int64
+            }
+            write_number(static_cast<std::int64_t>(n), use_bjdata);
+        }
+        else if (use_bjdata && n <= (std::numeric_limits<uint64_t>::max)())
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('M'));  // uint64 - bjdata only
+            }
+            write_number(static_cast<std::uint64_t>(n), use_bjdata);
+        }
+        else
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('H'));  // high-precision number
+            }
+
+            const auto number = BasicJsonType(n).dump();
+            write_number_with_ubjson_prefix(number.size(), true, use_bjdata);
+            for (std::size_t i = 0; i < number.size(); ++i)
+            {
+                oa->write_character(to_char_type(static_cast<std::uint8_t>(number[i])));
+            }
+        }
+    }
+
+    // UBJSON: write number (signed integer)
+    template < typename NumberType, typename std::enable_if <
+                   std::is_signed<NumberType>::value&&
+                   !std::is_floating_point<NumberType>::value, int >::type = 0 >
+    void write_number_with_ubjson_prefix(const NumberType n,
+                                         const bool add_prefix,
+                                         const bool use_bjdata)
+    {
+        if ((std::numeric_limits<std::int8_t>::min)() <= n && n <= (std::numeric_limits<std::int8_t>::max)())
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('i'));  // int8
+            }
+            write_number(static_cast<std::int8_t>(n), use_bjdata);
+        }
+        else if (static_cast<std::int64_t>((std::numeric_limits<std::uint8_t>::min)()) <= n && n <= static_cast<std::int64_t>((std::numeric_limits<std::uint8_t>::max)()))
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('U'));  // uint8
+            }
+            write_number(static_cast<std::uint8_t>(n), use_bjdata);
+        }
+        else if ((std::numeric_limits<std::int16_t>::min)() <= n && n <= (std::numeric_limits<std::int16_t>::max)())
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('I'));  // int16
+            }
+            write_number(static_cast<std::int16_t>(n), use_bjdata);
+        }
+        else if (use_bjdata && (static_cast<std::int64_t>((std::numeric_limits<std::uint16_t>::min)()) <= n && n <= static_cast<std::int64_t>((std::numeric_limits<std::uint16_t>::max)())))
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('u'));  // uint16 - bjdata only
+            }
+            write_number(static_cast<uint16_t>(n), use_bjdata);
+        }
+        else if ((std::numeric_limits<std::int32_t>::min)() <= n && n <= (std::numeric_limits<std::int32_t>::max)())
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('l'));  // int32
+            }
+            write_number(static_cast<std::int32_t>(n), use_bjdata);
+        }
+        else if (use_bjdata && (static_cast<std::int64_t>((std::numeric_limits<std::uint32_t>::min)()) <= n && n <= static_cast<std::int64_t>((std::numeric_limits<std::uint32_t>::max)())))
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('m'));  // uint32 - bjdata only
+            }
+            write_number(static_cast<uint32_t>(n), use_bjdata);
+        }
+        else if ((std::numeric_limits<std::int64_t>::min)() <= n && n <= (std::numeric_limits<std::int64_t>::max)())
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('L'));  // int64
+            }
+            write_number(static_cast<std::int64_t>(n), use_bjdata);
+        }
+        // LCOV_EXCL_START
+        else
+        {
+            if (add_prefix)
+            {
+                oa->write_character(to_char_type('H'));  // high-precision number
+            }
+
+            const auto number = BasicJsonType(n).dump();
+            write_number_with_ubjson_prefix(number.size(), true, use_bjdata);
+            for (std::size_t i = 0; i < number.size(); ++i)
+            {
+                oa->write_character(to_char_type(static_cast<std::uint8_t>(number[i])));
+            }
+        }
+        // LCOV_EXCL_STOP
+    }
+
+    /*!
+    @brief determine the type prefix of container values
+    */
+    CharType ubjson_prefix(const BasicJsonType& j, const bool use_bjdata) const noexcept
+    {
+        switch (j.type())
+        {
+            case value_t::null:
+                return 'Z';
+
+            case value_t::boolean:
+                return j.m_data.m_value.boolean ? 'T' : 'F';
+
+            case value_t::number_integer:
+            {
+                if ((std::numeric_limits<std::int8_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
+                {
+                    return 'i';
+                }
+                if ((std::numeric_limits<std::uint8_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
+                {
+                    return 'U';
+                }
+                if ((std::numeric_limits<std::int16_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
+                {
+                    return 'I';
+                }
+                if (use_bjdata && ((std::numeric_limits<std::uint16_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)()))
+                {
+                    return 'u';
+                }
+                if ((std::numeric_limits<std::int32_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
+                {
+                    return 'l';
+                }
+                if (use_bjdata && ((std::numeric_limits<std::uint32_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)()))
+                {
+                    return 'm';
+                }
+                if ((std::numeric_limits<std::int64_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
+                {
+                    return 'L';
+                }
+                // anything else is treated as high-precision number
+                return 'H'; // LCOV_EXCL_LINE
+            }
+
+            case value_t::number_unsigned:
+            {
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int8_t>::max)()))
+                {
+                    return 'i';
+                }
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint8_t>::max)()))
+                {
+                    return 'U';
+                }
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int16_t>::max)()))
+                {
+                    return 'I';
+                }
+                if (use_bjdata && j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint16_t>::max)()))
+                {
+                    return 'u';
+                }
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
+                {
+                    return 'l';
+                }
+                if (use_bjdata && j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint32_t>::max)()))
+                {
+                    return 'm';
+                }
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
+                {
+                    return 'L';
+                }
+                if (use_bjdata && j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
+                {
+                    return 'M';
+                }
+                // anything else is treated as high-precision number
+                return 'H'; // LCOV_EXCL_LINE
+            }
+
+            case value_t::number_float:
+                return get_ubjson_float_prefix(j.m_data.m_value.number_float);
+
+            case value_t::string:
+                return 'S';
+
+            case value_t::array: // fallthrough
+            case value_t::binary:
+                return '[';
+
+            case value_t::object:
+                return '{';
+
+            case value_t::discarded:
+            default:  // discarded values
+                return 'N';
+        }
+    }
+
+    static constexpr CharType get_ubjson_float_prefix(float /*unused*/)
+    {
+        return 'd';  // float 32
+    }
+
+    static constexpr CharType get_ubjson_float_prefix(double /*unused*/)
+    {
+        return 'D';  // float 64
+    }
+
+    /*!
+    @return false if the object is successfully converted to a bjdata ndarray, true if the type or size is invalid
+    */
+    bool write_bjdata_ndarray(const typename BasicJsonType::object_t& value, const bool use_count, const bool use_type)
+    {
+        std::map<string_t, CharType> bjdtype = {{"uint8", 'U'},  {"int8", 'i'},  {"uint16", 'u'}, {"int16", 'I'},
+            {"uint32", 'm'}, {"int32", 'l'}, {"uint64", 'M'}, {"int64", 'L'}, {"single", 'd'}, {"double", 'D'}, {"char", 'C'}
+        };
+
+        string_t key = "_ArrayType_";
+        auto it = bjdtype.find(static_cast<string_t>(value.at(key)));
+        if (it == bjdtype.end())
+        {
+            return true;
+        }
+        CharType dtype = it->second;
+
+        key = "_ArraySize_";
+        std::size_t len = (value.at(key).empty() ? 0 : 1);
+        for (const auto& el : value.at(key))
+        {
+            len *= static_cast<std::size_t>(el.m_data.m_value.number_unsigned);
+        }
+
+        key = "_ArrayData_";
+        if (value.at(key).size() != len)
+        {
+            return true;
+        }
+
+        oa->write_character('[');
+        oa->write_character('$');
+        oa->write_character(dtype);
+        oa->write_character('#');
+
+        key = "_ArraySize_";
+        write_ubjson(value.at(key), use_count, use_type, true,  true);
+
+        key = "_ArrayData_";
+        if (dtype == 'U' || dtype == 'C')
+        {
+            for (const auto& el : value.at(key))
+            {
+                write_number(static_cast<std::uint8_t>(el.m_data.m_value.number_unsigned), true);
+            }
+        }
+        else if (dtype == 'i')
+        {
+            for (const auto& el : value.at(key))
+            {
+                write_number(static_cast<std::int8_t>(el.m_data.m_value.number_integer), true);
+            }
+        }
+        else if (dtype == 'u')
+        {
+            for (const auto& el : value.at(key))
+            {
+                write_number(static_cast<std::uint16_t>(el.m_data.m_value.number_unsigned), true);
+            }
+        }
+        else if (dtype == 'I')
+        {
+            for (const auto& el : value.at(key))
+            {
+                write_number(static_cast<std::int16_t>(el.m_data.m_value.number_integer), true);
+            }
+        }
+        else if (dtype == 'm')
+        {
+            for (const auto& el : value.at(key))
+            {
+                write_number(static_cast<std::uint32_t>(el.m_data.m_value.number_unsigned), true);
+            }
+        }
+        else if (dtype == 'l')
+        {
+            for (const auto& el : value.at(key))
+            {
+                write_number(static_cast<std::int32_t>(el.m_data.m_value.number_integer), true);
+            }
+        }
+        else if (dtype == 'M')
+        {
+            for (const auto& el : value.at(key))
+            {
+                write_number(static_cast<std::uint64_t>(el.m_data.m_value.number_unsigned), true);
+            }
+        }
+        else if (dtype == 'L')
+        {
+            for (const auto& el : value.at(key))
+            {
+                write_number(static_cast<std::int64_t>(el.m_data.m_value.number_integer), true);
+            }
+        }
+        else if (dtype == 'd')
+        {
+            for (const auto& el : value.at(key))
+            {
+                write_number(static_cast<float>(el.m_data.m_value.number_float), true);
+            }
+        }
+        else if (dtype == 'D')
+        {
+            for (const auto& el : value.at(key))
+            {
+                write_number(static_cast<double>(el.m_data.m_value.number_float), true);
+            }
+        }
+        return false;
+    }
+
+    ///////////////////////
+    // Utility functions //
+    ///////////////////////
+
+    /*
+    @brief write a number to output input
+    @param[in] n number of type @a NumberType
+    @param[in] OutputIsLittleEndian Set to true if output data is
+                                 required to be little endian
+    @tparam NumberType the type of the number
+
+    @note This function needs to respect the system's endianness, because bytes
+          in CBOR, MessagePack, and UBJSON are stored in network order (big
+          endian) and therefore need reordering on little endian systems.
+          On the other hand, BSON and BJData use little endian and should reorder
+          on big endian systems.
+    */
+    template<typename NumberType>
+    void write_number(const NumberType n, const bool OutputIsLittleEndian = false)
+    {
+        // step 1: write number to array of length NumberType
+        std::array<CharType, sizeof(NumberType)> vec{};
+        std::memcpy(vec.data(), &n, sizeof(NumberType));
+
+        // step 2: write array to output (with possible reordering)
+        if (is_little_endian != OutputIsLittleEndian)
+        {
+            // reverse byte order prior to conversion if necessary
+            std::reverse(vec.begin(), vec.end());
+        }
+
+        oa->write_characters(vec.data(), sizeof(NumberType));
+    }
+
+    void write_compact_float(const number_float_t n, detail::input_format_t format)
+    {
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wfloat-equal"
+#endif
+        if (static_cast<double>(n) >= static_cast<double>(std::numeric_limits<float>::lowest()) &&
+                static_cast<double>(n) <= static_cast<double>((std::numeric_limits<float>::max)()) &&
+                static_cast<double>(static_cast<float>(n)) == static_cast<double>(n))
+        {
+            oa->write_character(format == detail::input_format_t::cbor
+                                ? get_cbor_float_prefix(static_cast<float>(n))
+                                : get_msgpack_float_prefix(static_cast<float>(n)));
+            write_number(static_cast<float>(n));
+        }
+        else
+        {
+            oa->write_character(format == detail::input_format_t::cbor
+                                ? get_cbor_float_prefix(n)
+                                : get_msgpack_float_prefix(n));
+            write_number(n);
+        }
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+    }
+
+  public:
+    // The following to_char_type functions are implement the conversion
+    // between uint8_t and CharType. In case CharType is not unsigned,
+    // such a conversion is required to allow values greater than 128.
+    // See <https://github.com/nlohmann/json/issues/1286> for a discussion.
+    template < typename C = CharType,
+               enable_if_t < std::is_signed<C>::value && std::is_signed<char>::value > * = nullptr >
+    static constexpr CharType to_char_type(std::uint8_t x) noexcept
+    {
+        return *reinterpret_cast<char*>(&x);
+    }
+
+    template < typename C = CharType,
+               enable_if_t < std::is_signed<C>::value && std::is_unsigned<char>::value > * = nullptr >
+    static CharType to_char_type(std::uint8_t x) noexcept
+    {
+        static_assert(sizeof(std::uint8_t) == sizeof(CharType), "size of CharType must be equal to std::uint8_t");
+        static_assert(std::is_trivial<CharType>::value, "CharType must be trivial");
+        CharType result;
+        std::memcpy(&result, &x, sizeof(x));
+        return result;
+    }
+
+    template<typename C = CharType,
+             enable_if_t<std::is_unsigned<C>::value>* = nullptr>
+    static constexpr CharType to_char_type(std::uint8_t x) noexcept
+    {
+        return x;
+    }
+
+    template < typename InputCharType, typename C = CharType,
+               enable_if_t <
+                   std::is_signed<C>::value &&
+                   std::is_signed<char>::value &&
+                   std::is_same<char, typename std::remove_cv<InputCharType>::type>::value
+                   > * = nullptr >
+    static constexpr CharType to_char_type(InputCharType x) noexcept
+    {
+        return x;
+    }
+
+  private:
+    /// whether we can assume little endianness
+    const bool is_little_endian = little_endianness();
+
+    /// the output
+    output_adapter_t<CharType> oa = nullptr;
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/output/output_adapters.hpp>
+
+// #include <nlohmann/detail/output/serializer.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2008-2009 Björn Hoehrmann <bjoern@hoehrmann.de>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <algorithm> // reverse, remove, fill, find, none_of
+#include <array> // array
+#include <clocale> // localeconv, lconv
+#include <cmath> // labs, isfinite, isnan, signbit
+#include <cstddef> // size_t, ptrdiff_t
+#include <cstdint> // uint8_t
+#include <cstdio> // snprintf
+#include <limits> // numeric_limits
+#include <string> // string, char_traits
+#include <iomanip> // setfill, setw
+#include <type_traits> // is_same
+#include <utility> // move
+
+// #include <nlohmann/detail/conversions/to_chars.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2009 Florian Loitsch <https://florian.loitsch.com/>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <array> // array
+#include <cmath>   // signbit, isfinite
+#include <cstdint> // intN_t, uintN_t
+#include <cstring> // memcpy, memmove
+#include <limits> // numeric_limits
+#include <type_traits> // conditional
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/*!
+@brief implements the Grisu2 algorithm for binary to decimal floating-point
+conversion.
+
+This implementation is a slightly modified version of the reference
+implementation which may be obtained from
+http://florian.loitsch.com/publications (bench.tar.gz).
+
+The code is distributed under the MIT license, Copyright (c) 2009 Florian Loitsch.
+
+For a detailed description of the algorithm see:
+
+[1] Loitsch, "Printing Floating-Point Numbers Quickly and Accurately with
+    Integers", Proceedings of the ACM SIGPLAN 2010 Conference on Programming
+    Language Design and Implementation, PLDI 2010
+[2] Burger, Dybvig, "Printing Floating-Point Numbers Quickly and Accurately",
+    Proceedings of the ACM SIGPLAN 1996 Conference on Programming Language
+    Design and Implementation, PLDI 1996
+*/
+namespace dtoa_impl
+{
+
+template<typename Target, typename Source>
+Target reinterpret_bits(const Source source)
+{
+    static_assert(sizeof(Target) == sizeof(Source), "size mismatch");
+
+    Target target;
+    std::memcpy(&target, &source, sizeof(Source));
+    return target;
+}
+
+struct diyfp // f * 2^e
+{
+    static constexpr int kPrecision = 64; // = q
+
+    std::uint64_t f = 0;
+    int e = 0;
+
+    constexpr diyfp(std::uint64_t f_, int e_) noexcept : f(f_), e(e_) {}
+
+    /*!
+    @brief returns x - y
+    @pre x.e == y.e and x.f >= y.f
+    */
+    static diyfp sub(const diyfp& x, const diyfp& y) noexcept
+    {
+        JSON_ASSERT(x.e == y.e);
+        JSON_ASSERT(x.f >= y.f);
+
+        return {x.f - y.f, x.e};
+    }
+
+    /*!
+    @brief returns x * y
+    @note The result is rounded. (Only the upper q bits are returned.)
+    */
+    static diyfp mul(const diyfp& x, const diyfp& y) noexcept
+    {
+        static_assert(kPrecision == 64, "internal error");
+
+        // Computes:
+        //  f = round((x.f * y.f) / 2^q)
+        //  e = x.e + y.e + q
+
+        // Emulate the 64-bit * 64-bit multiplication:
+        //
+        // p = u * v
+        //   = (u_lo + 2^32 u_hi) (v_lo + 2^32 v_hi)
+        //   = (u_lo v_lo         ) + 2^32 ((u_lo v_hi         ) + (u_hi v_lo         )) + 2^64 (u_hi v_hi         )
+        //   = (p0                ) + 2^32 ((p1                ) + (p2                )) + 2^64 (p3                )
+        //   = (p0_lo + 2^32 p0_hi) + 2^32 ((p1_lo + 2^32 p1_hi) + (p2_lo + 2^32 p2_hi)) + 2^64 (p3                )
+        //   = (p0_lo             ) + 2^32 (p0_hi + p1_lo + p2_lo                      ) + 2^64 (p1_hi + p2_hi + p3)
+        //   = (p0_lo             ) + 2^32 (Q                                          ) + 2^64 (H                 )
+        //   = (p0_lo             ) + 2^32 (Q_lo + 2^32 Q_hi                           ) + 2^64 (H                 )
+        //
+        // (Since Q might be larger than 2^32 - 1)
+        //
+        //   = (p0_lo + 2^32 Q_lo) + 2^64 (Q_hi + H)
+        //
+        // (Q_hi + H does not overflow a 64-bit int)
+        //
+        //   = p_lo + 2^64 p_hi
+
+        const std::uint64_t u_lo = x.f & 0xFFFFFFFFu;
+        const std::uint64_t u_hi = x.f >> 32u;
+        const std::uint64_t v_lo = y.f & 0xFFFFFFFFu;
+        const std::uint64_t v_hi = y.f >> 32u;
+
+        const std::uint64_t p0 = u_lo * v_lo;
+        const std::uint64_t p1 = u_lo * v_hi;
+        const std::uint64_t p2 = u_hi * v_lo;
+        const std::uint64_t p3 = u_hi * v_hi;
+
+        const std::uint64_t p0_hi = p0 >> 32u;
+        const std::uint64_t p1_lo = p1 & 0xFFFFFFFFu;
+        const std::uint64_t p1_hi = p1 >> 32u;
+        const std::uint64_t p2_lo = p2 & 0xFFFFFFFFu;
+        const std::uint64_t p2_hi = p2 >> 32u;
+
+        std::uint64_t Q = p0_hi + p1_lo + p2_lo;
+
+        // The full product might now be computed as
+        //
+        // p_hi = p3 + p2_hi + p1_hi + (Q >> 32)
+        // p_lo = p0_lo + (Q << 32)
+        //
+        // But in this particular case here, the full p_lo is not required.
+        // Effectively we only need to add the highest bit in p_lo to p_hi (and
+        // Q_hi + 1 does not overflow).
+
+        Q += std::uint64_t{1} << (64u - 32u - 1u); // round, ties up
+
+        const std::uint64_t h = p3 + p2_hi + p1_hi + (Q >> 32u);
+
+        return {h, x.e + y.e + 64};
+    }
+
+    /*!
+    @brief normalize x such that the significand is >= 2^(q-1)
+    @pre x.f != 0
+    */
+    static diyfp normalize(diyfp x) noexcept
+    {
+        JSON_ASSERT(x.f != 0);
+
+        while ((x.f >> 63u) == 0)
+        {
+            x.f <<= 1u;
+            x.e--;
+        }
+
+        return x;
+    }
+
+    /*!
+    @brief normalize x such that the result has the exponent E
+    @pre e >= x.e and the upper e - x.e bits of x.f must be zero.
+    */
+    static diyfp normalize_to(const diyfp& x, const int target_exponent) noexcept
+    {
+        const int delta = x.e - target_exponent;
+
+        JSON_ASSERT(delta >= 0);
+        JSON_ASSERT(((x.f << delta) >> delta) == x.f);
+
+        return {x.f << delta, target_exponent};
+    }
+};
+
+struct boundaries
+{
+    diyfp w;
+    diyfp minus;
+    diyfp plus;
+};
+
+/*!
+Compute the (normalized) diyfp representing the input number 'value' and its
+boundaries.
+
+@pre value must be finite and positive
+*/
+template<typename FloatType>
+boundaries compute_boundaries(FloatType value)
+{
+    JSON_ASSERT(std::isfinite(value));
+    JSON_ASSERT(value > 0);
+
+    // Convert the IEEE representation into a diyfp.
+    //
+    // If v is denormal:
+    //      value = 0.F * 2^(1 - bias) = (          F) * 2^(1 - bias - (p-1))
+    // If v is normalized:
+    //      value = 1.F * 2^(E - bias) = (2^(p-1) + F) * 2^(E - bias - (p-1))
+
+    static_assert(std::numeric_limits<FloatType>::is_iec559,
+                  "internal error: dtoa_short requires an IEEE-754 floating-point implementation");
+
+    constexpr int      kPrecision = std::numeric_limits<FloatType>::digits; // = p (includes the hidden bit)
+    constexpr int      kBias      = std::numeric_limits<FloatType>::max_exponent - 1 + (kPrecision - 1);
+    constexpr int      kMinExp    = 1 - kBias;
+    constexpr std::uint64_t kHiddenBit = std::uint64_t{1} << (kPrecision - 1); // = 2^(p-1)
+
+    using bits_type = typename std::conditional<kPrecision == 24, std::uint32_t, std::uint64_t >::type;
+
+    const auto bits = static_cast<std::uint64_t>(reinterpret_bits<bits_type>(value));
+    const std::uint64_t E = bits >> (kPrecision - 1);
+    const std::uint64_t F = bits & (kHiddenBit - 1);
+
+    const bool is_denormal = E == 0;
+    const diyfp v = is_denormal
+                    ? diyfp(F, kMinExp)
+                    : diyfp(F + kHiddenBit, static_cast<int>(E) - kBias);
+
+    // Compute the boundaries m- and m+ of the floating-point value
+    // v = f * 2^e.
+    //
+    // Determine v- and v+, the floating-point predecessor and successor if v,
+    // respectively.
+    //
+    //      v- = v - 2^e        if f != 2^(p-1) or e == e_min                (A)
+    //         = v - 2^(e-1)    if f == 2^(p-1) and e > e_min                (B)
+    //
+    //      v+ = v + 2^e
+    //
+    // Let m- = (v- + v) / 2 and m+ = (v + v+) / 2. All real numbers _strictly_
+    // between m- and m+ round to v, regardless of how the input rounding
+    // algorithm breaks ties.
+    //
+    //      ---+-------------+-------------+-------------+-------------+---  (A)
+    //         v-            m-            v             m+            v+
+    //
+    //      -----------------+------+------+-------------+-------------+---  (B)
+    //                       v-     m-     v             m+            v+
+
+    const bool lower_boundary_is_closer = F == 0 && E > 1;
+    const diyfp m_plus = diyfp(2 * v.f + 1, v.e - 1);
+    const diyfp m_minus = lower_boundary_is_closer
+                          ? diyfp(4 * v.f - 1, v.e - 2)  // (B)
+                          : diyfp(2 * v.f - 1, v.e - 1); // (A)
+
+    // Determine the normalized w+ = m+.
+    const diyfp w_plus = diyfp::normalize(m_plus);
+
+    // Determine w- = m- such that e_(w-) = e_(w+).
+    const diyfp w_minus = diyfp::normalize_to(m_minus, w_plus.e);
+
+    return {diyfp::normalize(v), w_minus, w_plus};
+}
+
+// Given normalized diyfp w, Grisu needs to find a (normalized) cached
+// power-of-ten c, such that the exponent of the product c * w = f * 2^e lies
+// within a certain range [alpha, gamma] (Definition 3.2 from [1])
+//
+//      alpha <= e = e_c + e_w + q <= gamma
+//
+// or
+//
+//      f_c * f_w * 2^alpha <= f_c 2^(e_c) * f_w 2^(e_w) * 2^q
+//                          <= f_c * f_w * 2^gamma
+//
+// Since c and w are normalized, i.e. 2^(q-1) <= f < 2^q, this implies
+//
+//      2^(q-1) * 2^(q-1) * 2^alpha <= c * w * 2^q < 2^q * 2^q * 2^gamma
+//
+// or
+//
+//      2^(q - 2 + alpha) <= c * w < 2^(q + gamma)
+//
+// The choice of (alpha,gamma) determines the size of the table and the form of
+// the digit generation procedure. Using (alpha,gamma)=(-60,-32) works out well
+// in practice:
+//
+// The idea is to cut the number c * w = f * 2^e into two parts, which can be
+// processed independently: An integral part p1, and a fractional part p2:
+//
+//      f * 2^e = ( (f div 2^-e) * 2^-e + (f mod 2^-e) ) * 2^e
+//              = (f div 2^-e) + (f mod 2^-e) * 2^e
+//              = p1 + p2 * 2^e
+//
+// The conversion of p1 into decimal form requires a series of divisions and
+// modulos by (a power of) 10. These operations are faster for 32-bit than for
+// 64-bit integers, so p1 should ideally fit into a 32-bit integer. This can be
+// achieved by choosing
+//
+//      -e >= 32   or   e <= -32 := gamma
+//
+// In order to convert the fractional part
+//
+//      p2 * 2^e = p2 / 2^-e = d[-1] / 10^1 + d[-2] / 10^2 + ...
+//
+// into decimal form, the fraction is repeatedly multiplied by 10 and the digits
+// d[-i] are extracted in order:
+//
+//      (10 * p2) div 2^-e = d[-1]
+//      (10 * p2) mod 2^-e = d[-2] / 10^1 + ...
+//
+// The multiplication by 10 must not overflow. It is sufficient to choose
+//
+//      10 * p2 < 16 * p2 = 2^4 * p2 <= 2^64.
+//
+// Since p2 = f mod 2^-e < 2^-e,
+//
+//      -e <= 60   or   e >= -60 := alpha
+
+constexpr int kAlpha = -60;
+constexpr int kGamma = -32;
+
+struct cached_power // c = f * 2^e ~= 10^k
+{
+    std::uint64_t f;
+    int e;
+    int k;
+};
+
+/*!
+For a normalized diyfp w = f * 2^e, this function returns a (normalized) cached
+power-of-ten c = f_c * 2^e_c, such that the exponent of the product w * c
+satisfies (Definition 3.2 from [1])
+
+     alpha <= e_c + e + q <= gamma.
+*/
+inline cached_power get_cached_power_for_binary_exponent(int e)
+{
+    // Now
+    //
+    //      alpha <= e_c + e + q <= gamma                                    (1)
+    //      ==> f_c * 2^alpha <= c * 2^e * 2^q
+    //
+    // and since the c's are normalized, 2^(q-1) <= f_c,
+    //
+    //      ==> 2^(q - 1 + alpha) <= c * 2^(e + q)
+    //      ==> 2^(alpha - e - 1) <= c
+    //
+    // If c were an exact power of ten, i.e. c = 10^k, one may determine k as
+    //
+    //      k = ceil( log_10( 2^(alpha - e - 1) ) )
+    //        = ceil( (alpha - e - 1) * log_10(2) )
+    //
+    // From the paper:
+    // "In theory the result of the procedure could be wrong since c is rounded,
+    //  and the computation itself is approximated [...]. In practice, however,
+    //  this simple function is sufficient."
+    //
+    // For IEEE double precision floating-point numbers converted into
+    // normalized diyfp's w = f * 2^e, with q = 64,
+    //
+    //      e >= -1022      (min IEEE exponent)
+    //           -52        (p - 1)
+    //           -52        (p - 1, possibly normalize denormal IEEE numbers)
+    //           -11        (normalize the diyfp)
+    //         = -1137
+    //
+    // and
+    //
+    //      e <= +1023      (max IEEE exponent)
+    //           -52        (p - 1)
+    //           -11        (normalize the diyfp)
+    //         = 960
+    //
+    // This binary exponent range [-1137,960] results in a decimal exponent
+    // range [-307,324]. One does not need to store a cached power for each
+    // k in this range. For each such k it suffices to find a cached power
+    // such that the exponent of the product lies in [alpha,gamma].
+    // This implies that the difference of the decimal exponents of adjacent
+    // table entries must be less than or equal to
+    //
+    //      floor( (gamma - alpha) * log_10(2) ) = 8.
+    //
+    // (A smaller distance gamma-alpha would require a larger table.)
+
+    // NB:
+    // Actually this function returns c, such that -60 <= e_c + e + 64 <= -34.
+
+    constexpr int kCachedPowersMinDecExp = -300;
+    constexpr int kCachedPowersDecStep = 8;
+
+    static constexpr std::array<cached_power, 79> kCachedPowers =
+    {
+        {
+            { 0xAB70FE17C79AC6CA, -1060, -300 },
+            { 0xFF77B1FCBEBCDC4F, -1034, -292 },
+            { 0xBE5691EF416BD60C, -1007, -284 },
+            { 0x8DD01FAD907FFC3C,  -980, -276 },
+            { 0xD3515C2831559A83,  -954, -268 },
+            { 0x9D71AC8FADA6C9B5,  -927, -260 },
+            { 0xEA9C227723EE8BCB,  -901, -252 },
+            { 0xAECC49914078536D,  -874, -244 },
+            { 0x823C12795DB6CE57,  -847, -236 },
+            { 0xC21094364DFB5637,  -821, -228 },
+            { 0x9096EA6F3848984F,  -794, -220 },
+            { 0xD77485CB25823AC7,  -768, -212 },
+            { 0xA086CFCD97BF97F4,  -741, -204 },
+            { 0xEF340A98172AACE5,  -715, -196 },
+            { 0xB23867FB2A35B28E,  -688, -188 },
+            { 0x84C8D4DFD2C63F3B,  -661, -180 },
+            { 0xC5DD44271AD3CDBA,  -635, -172 },
+            { 0x936B9FCEBB25C996,  -608, -164 },
+            { 0xDBAC6C247D62A584,  -582, -156 },
+            { 0xA3AB66580D5FDAF6,  -555, -148 },
+            { 0xF3E2F893DEC3F126,  -529, -140 },
+            { 0xB5B5ADA8AAFF80B8,  -502, -132 },
+            { 0x87625F056C7C4A8B,  -475, -124 },
+            { 0xC9BCFF6034C13053,  -449, -116 },
+            { 0x964E858C91BA2655,  -422, -108 },
+            { 0xDFF9772470297EBD,  -396, -100 },
+            { 0xA6DFBD9FB8E5B88F,  -369,  -92 },
+            { 0xF8A95FCF88747D94,  -343,  -84 },
+            { 0xB94470938FA89BCF,  -316,  -76 },
+            { 0x8A08F0F8BF0F156B,  -289,  -68 },
+            { 0xCDB02555653131B6,  -263,  -60 },
+            { 0x993FE2C6D07B7FAC,  -236,  -52 },
+            { 0xE45C10C42A2B3B06,  -210,  -44 },
+            { 0xAA242499697392D3,  -183,  -36 },
+            { 0xFD87B5F28300CA0E,  -157,  -28 },
+            { 0xBCE5086492111AEB,  -130,  -20 },
+            { 0x8CBCCC096F5088CC,  -103,  -12 },
+            { 0xD1B71758E219652C,   -77,   -4 },
+            { 0x9C40000000000000,   -50,    4 },
+            { 0xE8D4A51000000000,   -24,   12 },
+            { 0xAD78EBC5AC620000,     3,   20 },
+            { 0x813F3978F8940984,    30,   28 },
+            { 0xC097CE7BC90715B3,    56,   36 },
+            { 0x8F7E32CE7BEA5C70,    83,   44 },
+            { 0xD5D238A4ABE98068,   109,   52 },
+            { 0x9F4F2726179A2245,   136,   60 },
+            { 0xED63A231D4C4FB27,   162,   68 },
+            { 0xB0DE65388CC8ADA8,   189,   76 },
+            { 0x83C7088E1AAB65DB,   216,   84 },
+            { 0xC45D1DF942711D9A,   242,   92 },
+            { 0x924D692CA61BE758,   269,  100 },
+            { 0xDA01EE641A708DEA,   295,  108 },
+            { 0xA26DA3999AEF774A,   322,  116 },
+            { 0xF209787BB47D6B85,   348,  124 },
+            { 0xB454E4A179DD1877,   375,  132 },
+            { 0x865B86925B9BC5C2,   402,  140 },
+            { 0xC83553C5C8965D3D,   428,  148 },
+            { 0x952AB45CFA97A0B3,   455,  156 },
+            { 0xDE469FBD99A05FE3,   481,  164 },
+            { 0xA59BC234DB398C25,   508,  172 },
+            { 0xF6C69A72A3989F5C,   534,  180 },
+            { 0xB7DCBF5354E9BECE,   561,  188 },
+            { 0x88FCF317F22241E2,   588,  196 },
+            { 0xCC20CE9BD35C78A5,   614,  204 },
+            { 0x98165AF37B2153DF,   641,  212 },
+            { 0xE2A0B5DC971F303A,   667,  220 },
+            { 0xA8D9D1535CE3B396,   694,  228 },
+            { 0xFB9B7CD9A4A7443C,   720,  236 },
+            { 0xBB764C4CA7A44410,   747,  244 },
+            { 0x8BAB8EEFB6409C1A,   774,  252 },
+            { 0xD01FEF10A657842C,   800,  260 },
+            { 0x9B10A4E5E9913129,   827,  268 },
+            { 0xE7109BFBA19C0C9D,   853,  276 },
+            { 0xAC2820D9623BF429,   880,  284 },
+            { 0x80444B5E7AA7CF85,   907,  292 },
+            { 0xBF21E44003ACDD2D,   933,  300 },
+            { 0x8E679C2F5E44FF8F,   960,  308 },
+            { 0xD433179D9C8CB841,   986,  316 },
+            { 0x9E19DB92B4E31BA9,  1013,  324 },
+        }
+    };
+
+    // This computation gives exactly the same results for k as
+    //      k = ceil((kAlpha - e - 1) * 0.30102999566398114)
+    // for |e| <= 1500, but doesn't require floating-point operations.
+    // NB: log_10(2) ~= 78913 / 2^18
+    JSON_ASSERT(e >= -1500);
+    JSON_ASSERT(e <=  1500);
+    const int f = kAlpha - e - 1;
+    const int k = (f * 78913) / (1 << 18) + static_cast<int>(f > 0);
+
+    const int index = (-kCachedPowersMinDecExp + k + (kCachedPowersDecStep - 1)) / kCachedPowersDecStep;
+    JSON_ASSERT(index >= 0);
+    JSON_ASSERT(static_cast<std::size_t>(index) < kCachedPowers.size());
+
+    const cached_power cached = kCachedPowers[static_cast<std::size_t>(index)];
+    JSON_ASSERT(kAlpha <= cached.e + e + 64);
+    JSON_ASSERT(kGamma >= cached.e + e + 64);
+
+    return cached;
+}
+
+/*!
+For n != 0, returns k, such that pow10 := 10^(k-1) <= n < 10^k.
+For n == 0, returns 1 and sets pow10 := 1.
+*/
+inline int find_largest_pow10(const std::uint32_t n, std::uint32_t& pow10)
+{
+    // LCOV_EXCL_START
+    if (n >= 1000000000)
+    {
+        pow10 = 1000000000;
+        return 10;
+    }
+    // LCOV_EXCL_STOP
+    if (n >= 100000000)
+    {
+        pow10 = 100000000;
+        return  9;
+    }
+    if (n >= 10000000)
+    {
+        pow10 = 10000000;
+        return  8;
+    }
+    if (n >= 1000000)
+    {
+        pow10 = 1000000;
+        return  7;
+    }
+    if (n >= 100000)
+    {
+        pow10 = 100000;
+        return  6;
+    }
+    if (n >= 10000)
+    {
+        pow10 = 10000;
+        return  5;
+    }
+    if (n >= 1000)
+    {
+        pow10 = 1000;
+        return  4;
+    }
+    if (n >= 100)
+    {
+        pow10 = 100;
+        return  3;
+    }
+    if (n >= 10)
+    {
+        pow10 = 10;
+        return  2;
+    }
+
+    pow10 = 1;
+    return 1;
+}
+
+inline void grisu2_round(char* buf, int len, std::uint64_t dist, std::uint64_t delta,
+                         std::uint64_t rest, std::uint64_t ten_k)
+{
+    JSON_ASSERT(len >= 1);
+    JSON_ASSERT(dist <= delta);
+    JSON_ASSERT(rest <= delta);
+    JSON_ASSERT(ten_k > 0);
+
+    //               <--------------------------- delta ---->
+    //                                  <---- dist --------->
+    // --------------[------------------+-------------------]--------------
+    //               M-                 w                   M+
+    //
+    //                                  ten_k
+    //                                <------>
+    //                                       <---- rest ---->
+    // --------------[------------------+----+--------------]--------------
+    //                                  w    V
+    //                                       = buf * 10^k
+    //
+    // ten_k represents a unit-in-the-last-place in the decimal representation
+    // stored in buf.
+    // Decrement buf by ten_k while this takes buf closer to w.
+
+    // The tests are written in this order to avoid overflow in unsigned
+    // integer arithmetic.
+
+    while (rest < dist
+            && delta - rest >= ten_k
+            && (rest + ten_k < dist || dist - rest > rest + ten_k - dist))
+    {
+        JSON_ASSERT(buf[len - 1] != '0');
+        buf[len - 1]--;
+        rest += ten_k;
+    }
+}
+
+/*!
+Generates V = buffer * 10^decimal_exponent, such that M- <= V <= M+.
+M- and M+ must be normalized and share the same exponent -60 <= e <= -32.
+*/
+inline void grisu2_digit_gen(char* buffer, int& length, int& decimal_exponent,
+                             diyfp M_minus, diyfp w, diyfp M_plus)
+{
+    static_assert(kAlpha >= -60, "internal error");
+    static_assert(kGamma <= -32, "internal error");
+
+    // Generates the digits (and the exponent) of a decimal floating-point
+    // number V = buffer * 10^decimal_exponent in the range [M-, M+]. The diyfp's
+    // w, M- and M+ share the same exponent e, which satisfies alpha <= e <= gamma.
+    //
+    //               <--------------------------- delta ---->
+    //                                  <---- dist --------->
+    // --------------[------------------+-------------------]--------------
+    //               M-                 w                   M+
+    //
+    // Grisu2 generates the digits of M+ from left to right and stops as soon as
+    // V is in [M-,M+].
+
+    JSON_ASSERT(M_plus.e >= kAlpha);
+    JSON_ASSERT(M_plus.e <= kGamma);
+
+    std::uint64_t delta = diyfp::sub(M_plus, M_minus).f; // (significand of (M+ - M-), implicit exponent is e)
+    std::uint64_t dist  = diyfp::sub(M_plus, w      ).f; // (significand of (M+ - w ), implicit exponent is e)
+
+    // Split M+ = f * 2^e into two parts p1 and p2 (note: e < 0):
+    //
+    //      M+ = f * 2^e
+    //         = ((f div 2^-e) * 2^-e + (f mod 2^-e)) * 2^e
+    //         = ((p1        ) * 2^-e + (p2        )) * 2^e
+    //         = p1 + p2 * 2^e
+
+    const diyfp one(std::uint64_t{1} << -M_plus.e, M_plus.e);
+
+    auto p1 = static_cast<std::uint32_t>(M_plus.f >> -one.e); // p1 = f div 2^-e (Since -e >= 32, p1 fits into a 32-bit int.)
+    std::uint64_t p2 = M_plus.f & (one.f - 1);                    // p2 = f mod 2^-e
+
+    // 1)
+    //
+    // Generate the digits of the integral part p1 = d[n-1]...d[1]d[0]
+
+    JSON_ASSERT(p1 > 0);
+
+    std::uint32_t pow10{};
+    const int k = find_largest_pow10(p1, pow10);
+
+    //      10^(k-1) <= p1 < 10^k, pow10 = 10^(k-1)
+    //
+    //      p1 = (p1 div 10^(k-1)) * 10^(k-1) + (p1 mod 10^(k-1))
+    //         = (d[k-1]         ) * 10^(k-1) + (p1 mod 10^(k-1))
+    //
+    //      M+ = p1                                             + p2 * 2^e
+    //         = d[k-1] * 10^(k-1) + (p1 mod 10^(k-1))          + p2 * 2^e
+    //         = d[k-1] * 10^(k-1) + ((p1 mod 10^(k-1)) * 2^-e + p2) * 2^e
+    //         = d[k-1] * 10^(k-1) + (                         rest) * 2^e
+    //
+    // Now generate the digits d[n] of p1 from left to right (n = k-1,...,0)
+    //
+    //      p1 = d[k-1]...d[n] * 10^n + d[n-1]...d[0]
+    //
+    // but stop as soon as
+    //
+    //      rest * 2^e = (d[n-1]...d[0] * 2^-e + p2) * 2^e <= delta * 2^e
+
+    int n = k;
+    while (n > 0)
+    {
+        // Invariants:
+        //      M+ = buffer * 10^n + (p1 + p2 * 2^e)    (buffer = 0 for n = k)
+        //      pow10 = 10^(n-1) <= p1 < 10^n
+        //
+        const std::uint32_t d = p1 / pow10;  // d = p1 div 10^(n-1)
+        const std::uint32_t r = p1 % pow10;  // r = p1 mod 10^(n-1)
+        //
+        //      M+ = buffer * 10^n + (d * 10^(n-1) + r) + p2 * 2^e
+        //         = (buffer * 10 + d) * 10^(n-1) + (r + p2 * 2^e)
+        //
+        JSON_ASSERT(d <= 9);
+        buffer[length++] = static_cast<char>('0' + d); // buffer := buffer * 10 + d
+        //
+        //      M+ = buffer * 10^(n-1) + (r + p2 * 2^e)
+        //
+        p1 = r;
+        n--;
+        //
+        //      M+ = buffer * 10^n + (p1 + p2 * 2^e)
+        //      pow10 = 10^n
+        //
+
+        // Now check if enough digits have been generated.
+        // Compute
+        //
+        //      p1 + p2 * 2^e = (p1 * 2^-e + p2) * 2^e = rest * 2^e
+        //
+        // Note:
+        // Since rest and delta share the same exponent e, it suffices to
+        // compare the significands.
+        const std::uint64_t rest = (std::uint64_t{p1} << -one.e) + p2;
+        if (rest <= delta)
+        {
+            // V = buffer * 10^n, with M- <= V <= M+.
+
+            decimal_exponent += n;
+
+            // We may now just stop. But instead look if the buffer could be
+            // decremented to bring V closer to w.
+            //
+            // pow10 = 10^n is now 1 ulp in the decimal representation V.
+            // The rounding procedure works with diyfp's with an implicit
+            // exponent of e.
+            //
+            //      10^n = (10^n * 2^-e) * 2^e = ulp * 2^e
+            //
+            const std::uint64_t ten_n = std::uint64_t{pow10} << -one.e;
+            grisu2_round(buffer, length, dist, delta, rest, ten_n);
+
+            return;
+        }
+
+        pow10 /= 10;
+        //
+        //      pow10 = 10^(n-1) <= p1 < 10^n
+        // Invariants restored.
+    }
+
+    // 2)
+    //
+    // The digits of the integral part have been generated:
+    //
+    //      M+ = d[k-1]...d[1]d[0] + p2 * 2^e
+    //         = buffer            + p2 * 2^e
+    //
+    // Now generate the digits of the fractional part p2 * 2^e.
+    //
+    // Note:
+    // No decimal point is generated: the exponent is adjusted instead.
+    //
+    // p2 actually represents the fraction
+    //
+    //      p2 * 2^e
+    //          = p2 / 2^-e
+    //          = d[-1] / 10^1 + d[-2] / 10^2 + ...
+    //
+    // Now generate the digits d[-m] of p1 from left to right (m = 1,2,...)
+    //
+    //      p2 * 2^e = d[-1]d[-2]...d[-m] * 10^-m
+    //                      + 10^-m * (d[-m-1] / 10^1 + d[-m-2] / 10^2 + ...)
+    //
+    // using
+    //
+    //      10^m * p2 = ((10^m * p2) div 2^-e) * 2^-e + ((10^m * p2) mod 2^-e)
+    //                = (                   d) * 2^-e + (                   r)
+    //
+    // or
+    //      10^m * p2 * 2^e = d + r * 2^e
+    //
+    // i.e.
+    //
+    //      M+ = buffer + p2 * 2^e
+    //         = buffer + 10^-m * (d + r * 2^e)
+    //         = (buffer * 10^m + d) * 10^-m + 10^-m * r * 2^e
+    //
+    // and stop as soon as 10^-m * r * 2^e <= delta * 2^e
+
+    JSON_ASSERT(p2 > delta);
+
+    int m = 0;
+    for (;;)
+    {
+        // Invariant:
+        //      M+ = buffer * 10^-m + 10^-m * (d[-m-1] / 10 + d[-m-2] / 10^2 + ...) * 2^e
+        //         = buffer * 10^-m + 10^-m * (p2                                 ) * 2^e
+        //         = buffer * 10^-m + 10^-m * (1/10 * (10 * p2)                   ) * 2^e
+        //         = buffer * 10^-m + 10^-m * (1/10 * ((10*p2 div 2^-e) * 2^-e + (10*p2 mod 2^-e)) * 2^e
+        //
+        JSON_ASSERT(p2 <= (std::numeric_limits<std::uint64_t>::max)() / 10);
+        p2 *= 10;
+        const std::uint64_t d = p2 >> -one.e;     // d = (10 * p2) div 2^-e
+        const std::uint64_t r = p2 & (one.f - 1); // r = (10 * p2) mod 2^-e
+        //
+        //      M+ = buffer * 10^-m + 10^-m * (1/10 * (d * 2^-e + r) * 2^e
+        //         = buffer * 10^-m + 10^-m * (1/10 * (d + r * 2^e))
+        //         = (buffer * 10 + d) * 10^(-m-1) + 10^(-m-1) * r * 2^e
+        //
+        JSON_ASSERT(d <= 9);
+        buffer[length++] = static_cast<char>('0' + d); // buffer := buffer * 10 + d
+        //
+        //      M+ = buffer * 10^(-m-1) + 10^(-m-1) * r * 2^e
+        //
+        p2 = r;
+        m++;
+        //
+        //      M+ = buffer * 10^-m + 10^-m * p2 * 2^e
+        // Invariant restored.
+
+        // Check if enough digits have been generated.
+        //
+        //      10^-m * p2 * 2^e <= delta * 2^e
+        //              p2 * 2^e <= 10^m * delta * 2^e
+        //                    p2 <= 10^m * delta
+        delta *= 10;
+        dist  *= 10;
+        if (p2 <= delta)
+        {
+            break;
+        }
+    }
+
+    // V = buffer * 10^-m, with M- <= V <= M+.
+
+    decimal_exponent -= m;
+
+    // 1 ulp in the decimal representation is now 10^-m.
+    // Since delta and dist are now scaled by 10^m, we need to do the
+    // same with ulp in order to keep the units in sync.
+    //
+    //      10^m * 10^-m = 1 = 2^-e * 2^e = ten_m * 2^e
+    //
+    const std::uint64_t ten_m = one.f;
+    grisu2_round(buffer, length, dist, delta, p2, ten_m);
+
+    // By construction this algorithm generates the shortest possible decimal
+    // number (Loitsch, Theorem 6.2) which rounds back to w.
+    // For an input number of precision p, at least
+    //
+    //      N = 1 + ceil(p * log_10(2))
+    //
+    // decimal digits are sufficient to identify all binary floating-point
+    // numbers (Matula, "In-and-Out conversions").
+    // This implies that the algorithm does not produce more than N decimal
+    // digits.
+    //
+    //      N = 17 for p = 53 (IEEE double precision)
+    //      N = 9  for p = 24 (IEEE single precision)
+}
+
+/*!
+v = buf * 10^decimal_exponent
+len is the length of the buffer (number of decimal digits)
+The buffer must be large enough, i.e. >= max_digits10.
+*/
+JSON_HEDLEY_NON_NULL(1)
+inline void grisu2(char* buf, int& len, int& decimal_exponent,
+                   diyfp m_minus, diyfp v, diyfp m_plus)
+{
+    JSON_ASSERT(m_plus.e == m_minus.e);
+    JSON_ASSERT(m_plus.e == v.e);
+
+    //  --------(-----------------------+-----------------------)--------    (A)
+    //          m-                      v                       m+
+    //
+    //  --------------------(-----------+-----------------------)--------    (B)
+    //                      m-          v                       m+
+    //
+    // First scale v (and m- and m+) such that the exponent is in the range
+    // [alpha, gamma].
+
+    const cached_power cached = get_cached_power_for_binary_exponent(m_plus.e);
+
+    const diyfp c_minus_k(cached.f, cached.e); // = c ~= 10^-k
+
+    // The exponent of the products is = v.e + c_minus_k.e + q and is in the range [alpha,gamma]
+    const diyfp w       = diyfp::mul(v,       c_minus_k);
+    const diyfp w_minus = diyfp::mul(m_minus, c_minus_k);
+    const diyfp w_plus  = diyfp::mul(m_plus,  c_minus_k);
+
+    //  ----(---+---)---------------(---+---)---------------(---+---)----
+    //          w-                      w                       w+
+    //          = c*m-                  = c*v                   = c*m+
+    //
+    // diyfp::mul rounds its result and c_minus_k is approximated too. w, w- and
+    // w+ are now off by a small amount.
+    // In fact:
+    //
+    //      w - v * 10^k < 1 ulp
+    //
+    // To account for this inaccuracy, add resp. subtract 1 ulp.
+    //
+    //  --------+---[---------------(---+---)---------------]---+--------
+    //          w-  M-                  w                   M+  w+
+    //
+    // Now any number in [M-, M+] (bounds included) will round to w when input,
+    // regardless of how the input rounding algorithm breaks ties.
+    //
+    // And digit_gen generates the shortest possible such number in [M-, M+].
+    // Note that this does not mean that Grisu2 always generates the shortest
+    // possible number in the interval (m-, m+).
+    const diyfp M_minus(w_minus.f + 1, w_minus.e);
+    const diyfp M_plus (w_plus.f  - 1, w_plus.e );
+
+    decimal_exponent = -cached.k; // = -(-k) = k
+
+    grisu2_digit_gen(buf, len, decimal_exponent, M_minus, w, M_plus);
+}
+
+/*!
+v = buf * 10^decimal_exponent
+len is the length of the buffer (number of decimal digits)
+The buffer must be large enough, i.e. >= max_digits10.
+*/
+template<typename FloatType>
+JSON_HEDLEY_NON_NULL(1)
+void grisu2(char* buf, int& len, int& decimal_exponent, FloatType value)
+{
+    static_assert(diyfp::kPrecision >= std::numeric_limits<FloatType>::digits + 3,
+                  "internal error: not enough precision");
+
+    JSON_ASSERT(std::isfinite(value));
+    JSON_ASSERT(value > 0);
+
+    // If the neighbors (and boundaries) of 'value' are always computed for double-precision
+    // numbers, all float's can be recovered using strtod (and strtof). However, the resulting
+    // decimal representations are not exactly "short".
+    //
+    // The documentation for 'std::to_chars' (https://en.cppreference.com/w/cpp/utility/to_chars)
+    // says "value is converted to a string as if by std::sprintf in the default ("C") locale"
+    // and since sprintf promotes floats to doubles, I think this is exactly what 'std::to_chars'
+    // does.
+    // On the other hand, the documentation for 'std::to_chars' requires that "parsing the
+    // representation using the corresponding std::from_chars function recovers value exactly". That
+    // indicates that single precision floating-point numbers should be recovered using
+    // 'std::strtof'.
+    //
+    // NB: If the neighbors are computed for single-precision numbers, there is a single float
+    //     (7.0385307e-26f) which can't be recovered using strtod. The resulting double precision
+    //     value is off by 1 ulp.
+#if 0 // NOLINT(readability-avoid-unconditional-preprocessor-if)
+    const boundaries w = compute_boundaries(static_cast<double>(value));
+#else
+    const boundaries w = compute_boundaries(value);
+#endif
+
+    grisu2(buf, len, decimal_exponent, w.minus, w.w, w.plus);
+}
+
+/*!
+@brief appends a decimal representation of e to buf
+@return a pointer to the element following the exponent.
+@pre -1000 < e < 1000
+*/
+JSON_HEDLEY_NON_NULL(1)
+JSON_HEDLEY_RETURNS_NON_NULL
+inline char* append_exponent(char* buf, int e)
+{
+    JSON_ASSERT(e > -1000);
+    JSON_ASSERT(e <  1000);
+
+    if (e < 0)
+    {
+        e = -e;
+        *buf++ = '-';
+    }
+    else
+    {
+        *buf++ = '+';
+    }
+
+    auto k = static_cast<std::uint32_t>(e);
+    if (k < 10)
+    {
+        // Always print at least two digits in the exponent.
+        // This is for compatibility with printf("%g").
+        *buf++ = '0';
+        *buf++ = static_cast<char>('0' + k);
+    }
+    else if (k < 100)
+    {
+        *buf++ = static_cast<char>('0' + k / 10);
+        k %= 10;
+        *buf++ = static_cast<char>('0' + k);
+    }
+    else
+    {
+        *buf++ = static_cast<char>('0' + k / 100);
+        k %= 100;
+        *buf++ = static_cast<char>('0' + k / 10);
+        k %= 10;
+        *buf++ = static_cast<char>('0' + k);
+    }
+
+    return buf;
+}
+
+/*!
+@brief prettify v = buf * 10^decimal_exponent
+
+If v is in the range [10^min_exp, 10^max_exp) it will be printed in fixed-point
+notation. Otherwise it will be printed in exponential notation.
+
+@pre min_exp < 0
+@pre max_exp > 0
+*/
+JSON_HEDLEY_NON_NULL(1)
+JSON_HEDLEY_RETURNS_NON_NULL
+inline char* format_buffer(char* buf, int len, int decimal_exponent,
+                           int min_exp, int max_exp)
+{
+    JSON_ASSERT(min_exp < 0);
+    JSON_ASSERT(max_exp > 0);
+
+    const int k = len;
+    const int n = len + decimal_exponent;
+
+    // v = buf * 10^(n-k)
+    // k is the length of the buffer (number of decimal digits)
+    // n is the position of the decimal point relative to the start of the buffer.
+
+    if (k <= n && n <= max_exp)
+    {
+        // digits[000]
+        // len <= max_exp + 2
+
+        std::memset(buf + k, '0', static_cast<size_t>(n) - static_cast<size_t>(k));
+        // Make it look like a floating-point number (#362, #378)
+        buf[n + 0] = '.';
+        buf[n + 1] = '0';
+        return buf + (static_cast<size_t>(n) + 2);
+    }
+
+    if (0 < n && n <= max_exp)
+    {
+        // dig.its
+        // len <= max_digits10 + 1
+
+        JSON_ASSERT(k > n);
+
+        std::memmove(buf + (static_cast<size_t>(n) + 1), buf + n, static_cast<size_t>(k) - static_cast<size_t>(n));
+        buf[n] = '.';
+        return buf + (static_cast<size_t>(k) + 1U);
+    }
+
+    if (min_exp < n && n <= 0)
+    {
+        // 0.[000]digits
+        // len <= 2 + (-min_exp - 1) + max_digits10
+
+        std::memmove(buf + (2 + static_cast<size_t>(-n)), buf, static_cast<size_t>(k));
+        buf[0] = '0';
+        buf[1] = '.';
+        std::memset(buf + 2, '0', static_cast<size_t>(-n));
+        return buf + (2U + static_cast<size_t>(-n) + static_cast<size_t>(k));
+    }
+
+    if (k == 1)
+    {
+        // dE+123
+        // len <= 1 + 5
+
+        buf += 1;
+    }
+    else
+    {
+        // d.igitsE+123
+        // len <= max_digits10 + 1 + 5
+
+        std::memmove(buf + 2, buf + 1, static_cast<size_t>(k) - 1);
+        buf[1] = '.';
+        buf += 1 + static_cast<size_t>(k);
+    }
+
+    *buf++ = 'e';
+    return append_exponent(buf, n - 1);
+}
+
+}  // namespace dtoa_impl
+
+/*!
+@brief generates a decimal representation of the floating-point number value in [first, last).
+
+The format of the resulting decimal representation is similar to printf's %g
+format. Returns an iterator pointing past-the-end of the decimal representation.
+
+@note The input number must be finite, i.e. NaN's and Inf's are not supported.
+@note The buffer must be large enough.
+@note The result is NOT null-terminated.
+*/
+template<typename FloatType>
+JSON_HEDLEY_NON_NULL(1, 2)
+JSON_HEDLEY_RETURNS_NON_NULL
+char* to_chars(char* first, const char* last, FloatType value)
+{
+    static_cast<void>(last); // maybe unused - fix warning
+    JSON_ASSERT(std::isfinite(value));
+
+    // Use signbit(value) instead of (value < 0) since signbit works for -0.
+    if (std::signbit(value))
+    {
+        value = -value;
+        *first++ = '-';
+    }
+
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wfloat-equal"
+#endif
+    if (value == 0) // +-0
+    {
+        *first++ = '0';
+        // Make it look like a floating-point number (#362, #378)
+        *first++ = '.';
+        *first++ = '0';
+        return first;
+    }
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+
+    JSON_ASSERT(last - first >= std::numeric_limits<FloatType>::max_digits10);
+
+    // Compute v = buffer * 10^decimal_exponent.
+    // The decimal digits are stored in the buffer, which needs to be interpreted
+    // as an unsigned decimal integer.
+    // len is the length of the buffer, i.e. the number of decimal digits.
+    int len = 0;
+    int decimal_exponent = 0;
+    dtoa_impl::grisu2(first, len, decimal_exponent, value);
+
+    JSON_ASSERT(len <= std::numeric_limits<FloatType>::max_digits10);
+
+    // Format the buffer like printf("%.*g", prec, value)
+    constexpr int kMinExp = -4;
+    // Use digits10 here to increase compatibility with version 2.
+    constexpr int kMaxExp = std::numeric_limits<FloatType>::digits10;
+
+    JSON_ASSERT(last - first >= kMaxExp + 2);
+    JSON_ASSERT(last - first >= 2 + (-kMinExp - 1) + std::numeric_limits<FloatType>::max_digits10);
+    JSON_ASSERT(last - first >= std::numeric_limits<FloatType>::max_digits10 + 6);
+
+    return dtoa_impl::format_buffer(first, len, decimal_exponent, kMinExp, kMaxExp);
+}
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/exceptions.hpp>
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/cpp_future.hpp>
+
+// #include <nlohmann/detail/output/binary_writer.hpp>
+
+// #include <nlohmann/detail/output/output_adapters.hpp>
+
+// #include <nlohmann/detail/string_concat.hpp>
+
+// #include <nlohmann/detail/value_t.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+///////////////////
+// serialization //
+///////////////////
+
+/// how to treat decoding errors
+enum class error_handler_t
+{
+    strict,  ///< throw a type_error exception in case of invalid UTF-8
+    replace, ///< replace invalid UTF-8 sequences with U+FFFD
+    ignore   ///< ignore invalid UTF-8 sequences
+};
+
+template<typename BasicJsonType>
+class serializer
+{
+    using string_t = typename BasicJsonType::string_t;
+    using number_float_t = typename BasicJsonType::number_float_t;
+    using number_integer_t = typename BasicJsonType::number_integer_t;
+    using number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+    using binary_char_t = typename BasicJsonType::binary_t::value_type;
+    static constexpr std::uint8_t UTF8_ACCEPT = 0;
+    static constexpr std::uint8_t UTF8_REJECT = 1;
+
+  public:
+    /*!
+    @param[in] s  output stream to serialize to
+    @param[in] ichar  indentation character to use
+    @param[in] error_handler_  how to react on decoding errors
+    */
+    serializer(output_adapter_t<char> s, const char ichar,
+               error_handler_t error_handler_ = error_handler_t::strict)
+        : o(std::move(s))
+        , loc(std::localeconv())
+        , thousands_sep(loc->thousands_sep == nullptr ? '\0' : std::char_traits<char>::to_char_type(* (loc->thousands_sep)))
+        , decimal_point(loc->decimal_point == nullptr ? '\0' : std::char_traits<char>::to_char_type(* (loc->decimal_point)))
+        , indent_char(ichar)
+        , indent_string(512, indent_char)
+        , error_handler(error_handler_)
+    {}
+
+    // delete because of pointer members
+    serializer(const serializer&) = delete;
+    serializer& operator=(const serializer&) = delete;
+    serializer(serializer&&) = delete;
+    serializer& operator=(serializer&&) = delete;
+    ~serializer() = default;
+
+    /*!
+    @brief internal implementation of the serialization function
+
+    This function is called by the public member function dump and organizes
+    the serialization internally. The indentation level is propagated as
+    additional parameter. In case of arrays and objects, the function is
+    called recursively.
+
+    - strings and object keys are escaped using `escape_string()`
+    - integer numbers are converted implicitly via `operator<<`
+    - floating-point numbers are converted to a string using `"%g"` format
+    - binary values are serialized as objects containing the subtype and the
+      byte array
+
+    @param[in] val               value to serialize
+    @param[in] pretty_print      whether the output shall be pretty-printed
+    @param[in] ensure_ascii If @a ensure_ascii is true, all non-ASCII characters
+    in the output are escaped with `\uXXXX` sequences, and the result consists
+    of ASCII characters only.
+    @param[in] indent_step       the indent level
+    @param[in] current_indent    the current indent level (only used internally)
+    */
+    void dump(const BasicJsonType& val,
+              const bool pretty_print,
+              const bool ensure_ascii,
+              const unsigned int indent_step,
+              const unsigned int current_indent = 0)
+    {
+        switch (val.m_data.m_type)
+        {
+            case value_t::object:
+            {
+                if (val.m_data.m_value.object->empty())
+                {
+                    o->write_characters("{}", 2);
+                    return;
+                }
+
+                if (pretty_print)
+                {
+                    o->write_characters("{\n", 2);
+
+                    // variable to hold indentation for recursive calls
+                    const auto new_indent = current_indent + indent_step;
+                    if (JSON_HEDLEY_UNLIKELY(indent_string.size() < new_indent))
+                    {
+                        indent_string.resize(indent_string.size() * 2, ' ');
+                    }
+
+                    // first n-1 elements
+                    auto i = val.m_data.m_value.object->cbegin();
+                    for (std::size_t cnt = 0; cnt < val.m_data.m_value.object->size() - 1; ++cnt, ++i)
+                    {
+                        o->write_characters(indent_string.c_str(), new_indent);
+                        o->write_character('\"');
+                        dump_escaped(i->first, ensure_ascii);
+                        o->write_characters("\": ", 3);
+                        dump(i->second, true, ensure_ascii, indent_step, new_indent);
+                        o->write_characters(",\n", 2);
+                    }
+
+                    // last element
+                    JSON_ASSERT(i != val.m_data.m_value.object->cend());
+                    JSON_ASSERT(std::next(i) == val.m_data.m_value.object->cend());
+                    o->write_characters(indent_string.c_str(), new_indent);
+                    o->write_character('\"');
+                    dump_escaped(i->first, ensure_ascii);
+                    o->write_characters("\": ", 3);
+                    dump(i->second, true, ensure_ascii, indent_step, new_indent);
+
+                    o->write_character('\n');
+                    o->write_characters(indent_string.c_str(), current_indent);
+                    o->write_character('}');
+                }
+                else
+                {
+                    o->write_character('{');
+
+                    // first n-1 elements
+                    auto i = val.m_data.m_value.object->cbegin();
+                    for (std::size_t cnt = 0; cnt < val.m_data.m_value.object->size() - 1; ++cnt, ++i)
+                    {
+                        o->write_character('\"');
+                        dump_escaped(i->first, ensure_ascii);
+                        o->write_characters("\":", 2);
+                        dump(i->second, false, ensure_ascii, indent_step, current_indent);
+                        o->write_character(',');
+                    }
+
+                    // last element
+                    JSON_ASSERT(i != val.m_data.m_value.object->cend());
+                    JSON_ASSERT(std::next(i) == val.m_data.m_value.object->cend());
+                    o->write_character('\"');
+                    dump_escaped(i->first, ensure_ascii);
+                    o->write_characters("\":", 2);
+                    dump(i->second, false, ensure_ascii, indent_step, current_indent);
+
+                    o->write_character('}');
+                }
+
+                return;
+            }
+
+            case value_t::array:
+            {
+                if (val.m_data.m_value.array->empty())
+                {
+                    o->write_characters("[]", 2);
+                    return;
+                }
+
+                if (pretty_print)
+                {
+                    o->write_characters("[\n", 2);
+
+                    // variable to hold indentation for recursive calls
+                    const auto new_indent = current_indent + indent_step;
+                    if (JSON_HEDLEY_UNLIKELY(indent_string.size() < new_indent))
+                    {
+                        indent_string.resize(indent_string.size() * 2, ' ');
+                    }
+
+                    // first n-1 elements
+                    for (auto i = val.m_data.m_value.array->cbegin();
+                            i != val.m_data.m_value.array->cend() - 1; ++i)
+                    {
+                        o->write_characters(indent_string.c_str(), new_indent);
+                        dump(*i, true, ensure_ascii, indent_step, new_indent);
+                        o->write_characters(",\n", 2);
+                    }
+
+                    // last element
+                    JSON_ASSERT(!val.m_data.m_value.array->empty());
+                    o->write_characters(indent_string.c_str(), new_indent);
+                    dump(val.m_data.m_value.array->back(), true, ensure_ascii, indent_step, new_indent);
+
+                    o->write_character('\n');
+                    o->write_characters(indent_string.c_str(), current_indent);
+                    o->write_character(']');
+                }
+                else
+                {
+                    o->write_character('[');
+
+                    // first n-1 elements
+                    for (auto i = val.m_data.m_value.array->cbegin();
+                            i != val.m_data.m_value.array->cend() - 1; ++i)
+                    {
+                        dump(*i, false, ensure_ascii, indent_step, current_indent);
+                        o->write_character(',');
+                    }
+
+                    // last element
+                    JSON_ASSERT(!val.m_data.m_value.array->empty());
+                    dump(val.m_data.m_value.array->back(), false, ensure_ascii, indent_step, current_indent);
+
+                    o->write_character(']');
+                }
+
+                return;
+            }
+
+            case value_t::string:
+            {
+                o->write_character('\"');
+                dump_escaped(*val.m_data.m_value.string, ensure_ascii);
+                o->write_character('\"');
+                return;
+            }
+
+            case value_t::binary:
+            {
+                if (pretty_print)
+                {
+                    o->write_characters("{\n", 2);
+
+                    // variable to hold indentation for recursive calls
+                    const auto new_indent = current_indent + indent_step;
+                    if (JSON_HEDLEY_UNLIKELY(indent_string.size() < new_indent))
+                    {
+                        indent_string.resize(indent_string.size() * 2, ' ');
+                    }
+
+                    o->write_characters(indent_string.c_str(), new_indent);
+
+                    o->write_characters("\"bytes\": [", 10);
+
+                    if (!val.m_data.m_value.binary->empty())
+                    {
+                        for (auto i = val.m_data.m_value.binary->cbegin();
+                                i != val.m_data.m_value.binary->cend() - 1; ++i)
+                        {
+                            dump_integer(*i);
+                            o->write_characters(", ", 2);
+                        }
+                        dump_integer(val.m_data.m_value.binary->back());
+                    }
+
+                    o->write_characters("],\n", 3);
+                    o->write_characters(indent_string.c_str(), new_indent);
+
+                    o->write_characters("\"subtype\": ", 11);
+                    if (val.m_data.m_value.binary->has_subtype())
+                    {
+                        dump_integer(val.m_data.m_value.binary->subtype());
+                    }
+                    else
+                    {
+                        o->write_characters("null", 4);
+                    }
+                    o->write_character('\n');
+                    o->write_characters(indent_string.c_str(), current_indent);
+                    o->write_character('}');
+                }
+                else
+                {
+                    o->write_characters("{\"bytes\":[", 10);
+
+                    if (!val.m_data.m_value.binary->empty())
+                    {
+                        for (auto i = val.m_data.m_value.binary->cbegin();
+                                i != val.m_data.m_value.binary->cend() - 1; ++i)
+                        {
+                            dump_integer(*i);
+                            o->write_character(',');
+                        }
+                        dump_integer(val.m_data.m_value.binary->back());
+                    }
+
+                    o->write_characters("],\"subtype\":", 12);
+                    if (val.m_data.m_value.binary->has_subtype())
+                    {
+                        dump_integer(val.m_data.m_value.binary->subtype());
+                        o->write_character('}');
+                    }
+                    else
+                    {
+                        o->write_characters("null}", 5);
+                    }
+                }
+                return;
+            }
+
+            case value_t::boolean:
+            {
+                if (val.m_data.m_value.boolean)
+                {
+                    o->write_characters("true", 4);
+                }
+                else
+                {
+                    o->write_characters("false", 5);
+                }
+                return;
+            }
+
+            case value_t::number_integer:
+            {
+                dump_integer(val.m_data.m_value.number_integer);
+                return;
+            }
+
+            case value_t::number_unsigned:
+            {
+                dump_integer(val.m_data.m_value.number_unsigned);
+                return;
+            }
+
+            case value_t::number_float:
+            {
+                dump_float(val.m_data.m_value.number_float);
+                return;
+            }
+
+            case value_t::discarded:
+            {
+                o->write_characters("<discarded>", 11);
+                return;
+            }
+
+            case value_t::null:
+            {
+                o->write_characters("null", 4);
+                return;
+            }
+
+            default:            // LCOV_EXCL_LINE
+                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+        }
+    }
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    /*!
+    @brief dump escaped string
+
+    Escape a string by replacing certain special characters by a sequence of an
+    escape character (backslash) and another character and other control
+    characters by a sequence of "\u" followed by a four-digit hex
+    representation. The escaped string is written to output stream @a o.
+
+    @param[in] s  the string to escape
+    @param[in] ensure_ascii  whether to escape non-ASCII characters with
+                             \uXXXX sequences
+
+    @complexity Linear in the length of string @a s.
+    */
+    void dump_escaped(const string_t& s, const bool ensure_ascii)
+    {
+        std::uint32_t codepoint{};
+        std::uint8_t state = UTF8_ACCEPT;
+        std::size_t bytes = 0;  // number of bytes written to string_buffer
+
+        // number of bytes written at the point of the last valid byte
+        std::size_t bytes_after_last_accept = 0;
+        std::size_t undumped_chars = 0;
+
+        for (std::size_t i = 0; i < s.size(); ++i)
+        {
+            const auto byte = static_cast<std::uint8_t>(s[i]);
+
+            switch (decode(state, codepoint, byte))
+            {
+                case UTF8_ACCEPT:  // decode found a new code point
+                {
+                    switch (codepoint)
+                    {
+                        case 0x08: // backspace
+                        {
+                            string_buffer[bytes++] = '\\';
+                            string_buffer[bytes++] = 'b';
+                            break;
+                        }
+
+                        case 0x09: // horizontal tab
+                        {
+                            string_buffer[bytes++] = '\\';
+                            string_buffer[bytes++] = 't';
+                            break;
+                        }
+
+                        case 0x0A: // newline
+                        {
+                            string_buffer[bytes++] = '\\';
+                            string_buffer[bytes++] = 'n';
+                            break;
+                        }
+
+                        case 0x0C: // formfeed
+                        {
+                            string_buffer[bytes++] = '\\';
+                            string_buffer[bytes++] = 'f';
+                            break;
+                        }
+
+                        case 0x0D: // carriage return
+                        {
+                            string_buffer[bytes++] = '\\';
+                            string_buffer[bytes++] = 'r';
+                            break;
+                        }
+
+                        case 0x22: // quotation mark
+                        {
+                            string_buffer[bytes++] = '\\';
+                            string_buffer[bytes++] = '\"';
+                            break;
+                        }
+
+                        case 0x5C: // reverse solidus
+                        {
+                            string_buffer[bytes++] = '\\';
+                            string_buffer[bytes++] = '\\';
+                            break;
+                        }
+
+                        default:
+                        {
+                            // escape control characters (0x00..0x1F) or, if
+                            // ensure_ascii parameter is used, non-ASCII characters
+                            if ((codepoint <= 0x1F) || (ensure_ascii && (codepoint >= 0x7F)))
+                            {
+                                if (codepoint <= 0xFFFF)
+                                {
+                                    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
+                                    static_cast<void>((std::snprintf)(string_buffer.data() + bytes, 7, "\\u%04x",
+                                                                      static_cast<std::uint16_t>(codepoint)));
+                                    bytes += 6;
+                                }
+                                else
+                                {
+                                    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
+                                    static_cast<void>((std::snprintf)(string_buffer.data() + bytes, 13, "\\u%04x\\u%04x",
+                                                                      static_cast<std::uint16_t>(0xD7C0u + (codepoint >> 10u)),
+                                                                      static_cast<std::uint16_t>(0xDC00u + (codepoint & 0x3FFu))));
+                                    bytes += 12;
+                                }
+                            }
+                            else
+                            {
+                                // copy byte to buffer (all previous bytes
+                                // been copied have in default case above)
+                                string_buffer[bytes++] = s[i];
+                            }
+                            break;
+                        }
+                    }
+
+                    // write buffer and reset index; there must be 13 bytes
+                    // left, as this is the maximal number of bytes to be
+                    // written ("\uxxxx\uxxxx\0") for one code point
+                    if (string_buffer.size() - bytes < 13)
+                    {
+                        o->write_characters(string_buffer.data(), bytes);
+                        bytes = 0;
+                    }
+
+                    // remember the byte position of this accept
+                    bytes_after_last_accept = bytes;
+                    undumped_chars = 0;
+                    break;
+                }
+
+                case UTF8_REJECT:  // decode found invalid UTF-8 byte
+                {
+                    switch (error_handler)
+                    {
+                        case error_handler_t::strict:
+                        {
+                            JSON_THROW(type_error::create(316, concat("invalid UTF-8 byte at index ", std::to_string(i), ": 0x", hex_bytes(byte | 0)), nullptr));
+                        }
+
+                        case error_handler_t::ignore:
+                        case error_handler_t::replace:
+                        {
+                            // in case we saw this character the first time, we
+                            // would like to read it again, because the byte
+                            // may be OK for itself, but just not OK for the
+                            // previous sequence
+                            if (undumped_chars > 0)
+                            {
+                                --i;
+                            }
+
+                            // reset length buffer to the last accepted index;
+                            // thus removing/ignoring the invalid characters
+                            bytes = bytes_after_last_accept;
+
+                            if (error_handler == error_handler_t::replace)
+                            {
+                                // add a replacement character
+                                if (ensure_ascii)
+                                {
+                                    string_buffer[bytes++] = '\\';
+                                    string_buffer[bytes++] = 'u';
+                                    string_buffer[bytes++] = 'f';
+                                    string_buffer[bytes++] = 'f';
+                                    string_buffer[bytes++] = 'f';
+                                    string_buffer[bytes++] = 'd';
+                                }
+                                else
+                                {
+                                    string_buffer[bytes++] = detail::binary_writer<BasicJsonType, char>::to_char_type('\xEF');
+                                    string_buffer[bytes++] = detail::binary_writer<BasicJsonType, char>::to_char_type('\xBF');
+                                    string_buffer[bytes++] = detail::binary_writer<BasicJsonType, char>::to_char_type('\xBD');
+                                }
+
+                                // write buffer and reset index; there must be 13 bytes
+                                // left, as this is the maximal number of bytes to be
+                                // written ("\uxxxx\uxxxx\0") for one code point
+                                if (string_buffer.size() - bytes < 13)
+                                {
+                                    o->write_characters(string_buffer.data(), bytes);
+                                    bytes = 0;
+                                }
+
+                                bytes_after_last_accept = bytes;
+                            }
+
+                            undumped_chars = 0;
+
+                            // continue processing the string
+                            state = UTF8_ACCEPT;
+                            break;
+                        }
+
+                        default:            // LCOV_EXCL_LINE
+                            JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+                    }
+                    break;
+                }
+
+                default:  // decode found yet incomplete multi-byte code point
+                {
+                    if (!ensure_ascii)
+                    {
+                        // code point will not be escaped - copy byte to buffer
+                        string_buffer[bytes++] = s[i];
+                    }
+                    ++undumped_chars;
+                    break;
+                }
+            }
+        }
+
+        // we finished processing the string
+        if (JSON_HEDLEY_LIKELY(state == UTF8_ACCEPT))
+        {
+            // write buffer
+            if (bytes > 0)
+            {
+                o->write_characters(string_buffer.data(), bytes);
+            }
+        }
+        else
+        {
+            // we finish reading, but do not accept: string was incomplete
+            switch (error_handler)
+            {
+                case error_handler_t::strict:
+                {
+                    JSON_THROW(type_error::create(316, concat("incomplete UTF-8 string; last byte: 0x", hex_bytes(static_cast<std::uint8_t>(s.back() | 0))), nullptr));
+                }
+
+                case error_handler_t::ignore:
+                {
+                    // write all accepted bytes
+                    o->write_characters(string_buffer.data(), bytes_after_last_accept);
+                    break;
+                }
+
+                case error_handler_t::replace:
+                {
+                    // write all accepted bytes
+                    o->write_characters(string_buffer.data(), bytes_after_last_accept);
+                    // add a replacement character
+                    if (ensure_ascii)
+                    {
+                        o->write_characters("\\ufffd", 6);
+                    }
+                    else
+                    {
+                        o->write_characters("\xEF\xBF\xBD", 3);
+                    }
+                    break;
+                }
+
+                default:            // LCOV_EXCL_LINE
+                    JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+            }
+        }
+    }
+
+  private:
+    /*!
+    @brief count digits
+
+    Count the number of decimal (base 10) digits for an input unsigned integer.
+
+    @param[in] x  unsigned integer number to count its digits
+    @return    number of decimal digits
+    */
+    inline unsigned int count_digits(number_unsigned_t x) noexcept
+    {
+        unsigned int n_digits = 1;
+        for (;;)
+        {
+            if (x < 10)
+            {
+                return n_digits;
+            }
+            if (x < 100)
+            {
+                return n_digits + 1;
+            }
+            if (x < 1000)
+            {
+                return n_digits + 2;
+            }
+            if (x < 10000)
+            {
+                return n_digits + 3;
+            }
+            x = x / 10000u;
+            n_digits += 4;
+        }
+    }
+
+    /*!
+     * @brief convert a byte to a uppercase hex representation
+     * @param[in] byte byte to represent
+     * @return representation ("00".."FF")
+     */
+    static std::string hex_bytes(std::uint8_t byte)
+    {
+        std::string result = "FF";
+        constexpr const char* nibble_to_hex = "0123456789ABCDEF";
+        result[0] = nibble_to_hex[byte / 16];
+        result[1] = nibble_to_hex[byte % 16];
+        return result;
+    }
+
+    // templates to avoid warnings about useless casts
+    template <typename NumberType, enable_if_t<std::is_signed<NumberType>::value, int> = 0>
+    bool is_negative_number(NumberType x)
+    {
+        return x < 0;
+    }
+
+    template < typename NumberType, enable_if_t <std::is_unsigned<NumberType>::value, int > = 0 >
+    bool is_negative_number(NumberType /*unused*/)
+    {
+        return false;
+    }
+
+    /*!
+    @brief dump an integer
+
+    Dump a given integer to output stream @a o. Works internally with
+    @a number_buffer.
+
+    @param[in] x  integer number (signed or unsigned) to dump
+    @tparam NumberType either @a number_integer_t or @a number_unsigned_t
+    */
+    template < typename NumberType, detail::enable_if_t <
+                   std::is_integral<NumberType>::value ||
+                   std::is_same<NumberType, number_unsigned_t>::value ||
+                   std::is_same<NumberType, number_integer_t>::value ||
+                   std::is_same<NumberType, binary_char_t>::value,
+                   int > = 0 >
+    void dump_integer(NumberType x)
+    {
+        static constexpr std::array<std::array<char, 2>, 100> digits_to_99
+        {
+            {
+                {{'0', '0'}}, {{'0', '1'}}, {{'0', '2'}}, {{'0', '3'}}, {{'0', '4'}}, {{'0', '5'}}, {{'0', '6'}}, {{'0', '7'}}, {{'0', '8'}}, {{'0', '9'}},
+                {{'1', '0'}}, {{'1', '1'}}, {{'1', '2'}}, {{'1', '3'}}, {{'1', '4'}}, {{'1', '5'}}, {{'1', '6'}}, {{'1', '7'}}, {{'1', '8'}}, {{'1', '9'}},
+                {{'2', '0'}}, {{'2', '1'}}, {{'2', '2'}}, {{'2', '3'}}, {{'2', '4'}}, {{'2', '5'}}, {{'2', '6'}}, {{'2', '7'}}, {{'2', '8'}}, {{'2', '9'}},
+                {{'3', '0'}}, {{'3', '1'}}, {{'3', '2'}}, {{'3', '3'}}, {{'3', '4'}}, {{'3', '5'}}, {{'3', '6'}}, {{'3', '7'}}, {{'3', '8'}}, {{'3', '9'}},
+                {{'4', '0'}}, {{'4', '1'}}, {{'4', '2'}}, {{'4', '3'}}, {{'4', '4'}}, {{'4', '5'}}, {{'4', '6'}}, {{'4', '7'}}, {{'4', '8'}}, {{'4', '9'}},
+                {{'5', '0'}}, {{'5', '1'}}, {{'5', '2'}}, {{'5', '3'}}, {{'5', '4'}}, {{'5', '5'}}, {{'5', '6'}}, {{'5', '7'}}, {{'5', '8'}}, {{'5', '9'}},
+                {{'6', '0'}}, {{'6', '1'}}, {{'6', '2'}}, {{'6', '3'}}, {{'6', '4'}}, {{'6', '5'}}, {{'6', '6'}}, {{'6', '7'}}, {{'6', '8'}}, {{'6', '9'}},
+                {{'7', '0'}}, {{'7', '1'}}, {{'7', '2'}}, {{'7', '3'}}, {{'7', '4'}}, {{'7', '5'}}, {{'7', '6'}}, {{'7', '7'}}, {{'7', '8'}}, {{'7', '9'}},
+                {{'8', '0'}}, {{'8', '1'}}, {{'8', '2'}}, {{'8', '3'}}, {{'8', '4'}}, {{'8', '5'}}, {{'8', '6'}}, {{'8', '7'}}, {{'8', '8'}}, {{'8', '9'}},
+                {{'9', '0'}}, {{'9', '1'}}, {{'9', '2'}}, {{'9', '3'}}, {{'9', '4'}}, {{'9', '5'}}, {{'9', '6'}}, {{'9', '7'}}, {{'9', '8'}}, {{'9', '9'}},
+            }
+        };
+
+        // special case for "0"
+        if (x == 0)
+        {
+            o->write_character('0');
+            return;
+        }
+
+        // use a pointer to fill the buffer
+        auto buffer_ptr = number_buffer.begin(); // NOLINT(llvm-qualified-auto,readability-qualified-auto,cppcoreguidelines-pro-type-vararg,hicpp-vararg)
+
+        number_unsigned_t abs_value;
+
+        unsigned int n_chars{};
+
+        if (is_negative_number(x))
+        {
+            *buffer_ptr = '-';
+            abs_value = remove_sign(static_cast<number_integer_t>(x));
+
+            // account one more byte for the minus sign
+            n_chars = 1 + count_digits(abs_value);
+        }
+        else
+        {
+            abs_value = static_cast<number_unsigned_t>(x);
+            n_chars = count_digits(abs_value);
+        }
+
+        // spare 1 byte for '\0'
+        JSON_ASSERT(n_chars < number_buffer.size() - 1);
+
+        // jump to the end to generate the string from backward,
+        // so we later avoid reversing the result
+        buffer_ptr += n_chars;
+
+        // Fast int2ascii implementation inspired by "Fastware" talk by Andrei Alexandrescu
+        // See: https://www.youtube.com/watch?v=o4-CwDo2zpg
+        while (abs_value >= 100)
+        {
+            const auto digits_index = static_cast<unsigned>((abs_value % 100));
+            abs_value /= 100;
+            *(--buffer_ptr) = digits_to_99[digits_index][1];
+            *(--buffer_ptr) = digits_to_99[digits_index][0];
+        }
+
+        if (abs_value >= 10)
+        {
+            const auto digits_index = static_cast<unsigned>(abs_value);
+            *(--buffer_ptr) = digits_to_99[digits_index][1];
+            *(--buffer_ptr) = digits_to_99[digits_index][0];
+        }
+        else
+        {
+            *(--buffer_ptr) = static_cast<char>('0' + abs_value);
+        }
+
+        o->write_characters(number_buffer.data(), n_chars);
+    }
+
+    /*!
+    @brief dump a floating-point number
+
+    Dump a given floating-point number to output stream @a o. Works internally
+    with @a number_buffer.
+
+    @param[in] x  floating-point number to dump
+    */
+    void dump_float(number_float_t x)
+    {
+        // NaN / inf
+        if (!std::isfinite(x))
+        {
+            o->write_characters("null", 4);
+            return;
+        }
+
+        // If number_float_t is an IEEE-754 single or double precision number,
+        // use the Grisu2 algorithm to produce short numbers which are
+        // guaranteed to round-trip, using strtof and strtod, resp.
+        //
+        // NB: The test below works if <long double> == <double>.
+        static constexpr bool is_ieee_single_or_double
+            = (std::numeric_limits<number_float_t>::is_iec559 && std::numeric_limits<number_float_t>::digits == 24 && std::numeric_limits<number_float_t>::max_exponent == 128) ||
+              (std::numeric_limits<number_float_t>::is_iec559 && std::numeric_limits<number_float_t>::digits == 53 && std::numeric_limits<number_float_t>::max_exponent == 1024);
+
+        dump_float(x, std::integral_constant<bool, is_ieee_single_or_double>());
+    }
+
+    void dump_float(number_float_t x, std::true_type /*is_ieee_single_or_double*/)
+    {
+        auto* begin = number_buffer.data();
+        auto* end = ::nlohmann::detail::to_chars(begin, begin + number_buffer.size(), x);
+
+        o->write_characters(begin, static_cast<size_t>(end - begin));
+    }
+
+    void dump_float(number_float_t x, std::false_type /*is_ieee_single_or_double*/)
+    {
+        // get number of digits for a float -> text -> float round-trip
+        static constexpr auto d = std::numeric_limits<number_float_t>::max_digits10;
+
+        // the actual conversion
+        // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
+        std::ptrdiff_t len = (std::snprintf)(number_buffer.data(), number_buffer.size(), "%.*g", d, x);
+
+        // negative value indicates an error
+        JSON_ASSERT(len > 0);
+        // check if buffer was large enough
+        JSON_ASSERT(static_cast<std::size_t>(len) < number_buffer.size());
+
+        // erase thousands separator
+        if (thousands_sep != '\0')
+        {
+            // NOLINTNEXTLINE(readability-qualified-auto,llvm-qualified-auto): std::remove returns an iterator, see https://github.com/nlohmann/json/issues/3081
+            const auto end = std::remove(number_buffer.begin(), number_buffer.begin() + len, thousands_sep);
+            std::fill(end, number_buffer.end(), '\0');
+            JSON_ASSERT((end - number_buffer.begin()) <= len);
+            len = (end - number_buffer.begin());
+        }
+
+        // convert decimal point to '.'
+        if (decimal_point != '\0' && decimal_point != '.')
+        {
+            // NOLINTNEXTLINE(readability-qualified-auto,llvm-qualified-auto): std::find returns an iterator, see https://github.com/nlohmann/json/issues/3081
+            const auto dec_pos = std::find(number_buffer.begin(), number_buffer.end(), decimal_point);
+            if (dec_pos != number_buffer.end())
+            {
+                *dec_pos = '.';
+            }
+        }
+
+        o->write_characters(number_buffer.data(), static_cast<std::size_t>(len));
+
+        // determine if we need to append ".0"
+        const bool value_is_int_like =
+            std::none_of(number_buffer.begin(), number_buffer.begin() + len + 1,
+                         [](char c)
+        {
+            return c == '.' || c == 'e';
+        });
+
+        if (value_is_int_like)
+        {
+            o->write_characters(".0", 2);
+        }
+    }
+
+    /*!
+    @brief check whether a string is UTF-8 encoded
+
+    The function checks each byte of a string whether it is UTF-8 encoded. The
+    result of the check is stored in the @a state parameter. The function must
+    be called initially with state 0 (accept). State 1 means the string must
+    be rejected, because the current byte is not allowed. If the string is
+    completely processed, but the state is non-zero, the string ended
+    prematurely; that is, the last byte indicated more bytes should have
+    followed.
+
+    @param[in,out] state  the state of the decoding
+    @param[in,out] codep  codepoint (valid only if resulting state is UTF8_ACCEPT)
+    @param[in] byte       next byte to decode
+    @return               new state
+
+    @note The function has been edited: a std::array is used.
+
+    @copyright Copyright (c) 2008-2009 Bjoern Hoehrmann <bjoern@hoehrmann.de>
+    @sa http://bjoern.hoehrmann.de/utf-8/decoder/dfa/
+    */
+    static std::uint8_t decode(std::uint8_t& state, std::uint32_t& codep, const std::uint8_t byte) noexcept
+    {
+        static const std::array<std::uint8_t, 400> utf8d =
+        {
+            {
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 00..1F
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 20..3F
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 40..5F
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 60..7F
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, // 80..9F
+                7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, // A0..BF
+                8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, // C0..DF
+                0xA, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x4, 0x3, 0x3, // E0..EF
+                0xB, 0x6, 0x6, 0x6, 0x5, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, // F0..FF
+                0x0, 0x1, 0x2, 0x3, 0x5, 0x8, 0x7, 0x1, 0x1, 0x1, 0x4, 0x6, 0x1, 0x1, 0x1, 0x1, // s0..s0
+                1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, // s1..s2
+                1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, // s3..s4
+                1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, // s5..s6
+                1, 3, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 // s7..s8
+            }
+        };
+
+        JSON_ASSERT(byte < utf8d.size());
+        const std::uint8_t type = utf8d[byte];
+
+        codep = (state != UTF8_ACCEPT)
+                ? (byte & 0x3fu) | (codep << 6u)
+                : (0xFFu >> type) & (byte);
+
+        const std::size_t index = 256u + static_cast<size_t>(state) * 16u + static_cast<size_t>(type);
+        JSON_ASSERT(index < utf8d.size());
+        state = utf8d[index];
+        return state;
+    }
+
+    /*
+     * Overload to make the compiler happy while it is instantiating
+     * dump_integer for number_unsigned_t.
+     * Must never be called.
+     */
+    number_unsigned_t remove_sign(number_unsigned_t x)
+    {
+        JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+        return x; // LCOV_EXCL_LINE
+    }
+
+    /*
+     * Helper function for dump_integer
+     *
+     * This function takes a negative signed integer and returns its absolute
+     * value as unsigned integer. The plus/minus shuffling is necessary as we can
+     * not directly remove the sign of an arbitrary signed integer as the
+     * absolute values of INT_MIN and INT_MAX are usually not the same. See
+     * #1708 for details.
+     */
+    inline number_unsigned_t remove_sign(number_integer_t x) noexcept
+    {
+        JSON_ASSERT(x < 0 && x < (std::numeric_limits<number_integer_t>::max)()); // NOLINT(misc-redundant-expression)
+        return static_cast<number_unsigned_t>(-(x + 1)) + 1;
+    }
+
+  private:
+    /// the output of the serializer
+    output_adapter_t<char> o = nullptr;
+
+    /// a (hopefully) large enough character buffer
+    std::array<char, 64> number_buffer{{}};
+
+    /// the locale
+    const std::lconv* loc = nullptr;
+    /// the locale's thousand separator character
+    const char thousands_sep = '\0';
+    /// the locale's decimal point character
+    const char decimal_point = '\0';
+
+    /// string buffer
+    std::array<char, 512> string_buffer{{}};
+
+    /// the indentation character
+    const char indent_char;
+    /// the indentation string
+    string_t indent_string;
+
+    /// error_handler how to react on decoding errors
+    const error_handler_t error_handler;
+};
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
+// #include <nlohmann/detail/value_t.hpp>
+
+// #include <nlohmann/json_fwd.hpp>
+
+// #include <nlohmann/ordered_map.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <functional> // equal_to, less
+#include <initializer_list> // initializer_list
+#include <iterator> // input_iterator_tag, iterator_traits
+#include <memory> // allocator
+#include <stdexcept> // for out_of_range
+#include <type_traits> // enable_if, is_convertible
+#include <utility> // pair
+#include <vector> // vector
+
+// #include <nlohmann/detail/macro_scope.hpp>
+
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+
+/// ordered_map: a minimal map-like container that preserves insertion order
+/// for use within nlohmann::basic_json<ordered_map>
+template <class Key, class T, class IgnoredLess = std::less<Key>,
+          class Allocator = std::allocator<std::pair<const Key, T>>>
+                  struct ordered_map : std::vector<std::pair<const Key, T>, Allocator>
+{
+    using key_type = Key;
+    using mapped_type = T;
+    using Container = std::vector<std::pair<const Key, T>, Allocator>;
+    using iterator = typename Container::iterator;
+    using const_iterator = typename Container::const_iterator;
+    using size_type = typename Container::size_type;
+    using value_type = typename Container::value_type;
+#ifdef JSON_HAS_CPP_14
+    using key_compare = std::equal_to<>;
+#else
+    using key_compare = std::equal_to<Key>;
+#endif
+
+    // Explicit constructors instead of `using Container::Container`
+    // otherwise older compilers choke on it (GCC <= 5.5, xcode <= 9.4)
+    ordered_map() noexcept(noexcept(Container())) : Container{} {}
+    explicit ordered_map(const Allocator& alloc) noexcept(noexcept(Container(alloc))) : Container{alloc} {}
+    template <class It>
+    ordered_map(It first, It last, const Allocator& alloc = Allocator())
+        : Container{first, last, alloc} {}
+    ordered_map(std::initializer_list<value_type> init, const Allocator& alloc = Allocator() )
+        : Container{init, alloc} {}
+
+    std::pair<iterator, bool> emplace(const key_type& key, T&& t)
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return {it, false};
+            }
+        }
+        Container::emplace_back(key, std::forward<T>(t));
+        return {std::prev(this->end()), true};
+    }
+
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
+    std::pair<iterator, bool> emplace(KeyType && key, T && t)
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return {it, false};
+            }
+        }
+        Container::emplace_back(std::forward<KeyType>(key), std::forward<T>(t));
+        return {std::prev(this->end()), true};
+    }
+
+    T& operator[](const key_type& key)
+    {
+        return emplace(key, T{}).first->second;
+    }
+
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
+    T & operator[](KeyType && key)
+    {
+        return emplace(std::forward<KeyType>(key), T{}).first->second;
+    }
+
+    const T& operator[](const key_type& key) const
+    {
+        return at(key);
+    }
+
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
+    const T & operator[](KeyType && key) const
+    {
+        return at(std::forward<KeyType>(key));
+    }
+
+    T& at(const key_type& key)
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return it->second;
+            }
+        }
+
+        JSON_THROW(std::out_of_range("key not found"));
+    }
+
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
+    T & at(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward)
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return it->second;
+            }
+        }
+
+        JSON_THROW(std::out_of_range("key not found"));
+    }
+
+    const T& at(const key_type& key) const
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return it->second;
+            }
+        }
+
+        JSON_THROW(std::out_of_range("key not found"));
+    }
+
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
+    const T & at(KeyType && key) const // NOLINT(cppcoreguidelines-missing-std-forward)
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return it->second;
+            }
+        }
+
+        JSON_THROW(std::out_of_range("key not found"));
+    }
+
+    size_type erase(const key_type& key)
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                // Since we cannot move const Keys, re-construct them in place
+                for (auto next = it; ++next != this->end(); ++it)
+                {
+                    it->~value_type(); // Destroy but keep allocation
+                    new (&*it) value_type{std::move(*next)};
+                }
+                Container::pop_back();
+                return 1;
+            }
+        }
+        return 0;
+    }
+
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
+    size_type erase(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward)
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                // Since we cannot move const Keys, re-construct them in place
+                for (auto next = it; ++next != this->end(); ++it)
+                {
+                    it->~value_type(); // Destroy but keep allocation
+                    new (&*it) value_type{std::move(*next)};
+                }
+                Container::pop_back();
+                return 1;
+            }
+        }
+        return 0;
+    }
+
+    iterator erase(iterator pos)
+    {
+        return erase(pos, std::next(pos));
+    }
+
+    iterator erase(iterator first, iterator last)
+    {
+        if (first == last)
+        {
+            return first;
+        }
+
+        const auto elements_affected = std::distance(first, last);
+        const auto offset = std::distance(Container::begin(), first);
+
+        // This is the start situation. We need to delete elements_affected
+        // elements (3 in this example: e, f, g), and need to return an
+        // iterator past the last deleted element (h in this example).
+        // Note that offset is the distance from the start of the vector
+        // to first. We will need this later.
+
+        // [ a, b, c, d, e, f, g, h, i, j ]
+        //               ^        ^
+        //             first    last
+
+        // Since we cannot move const Keys, we re-construct them in place.
+        // We start at first and re-construct (viz. copy) the elements from
+        // the back of the vector. Example for first iteration:
+
+        //               ,--------.
+        //               v        |   destroy e and re-construct with h
+        // [ a, b, c, d, e, f, g, h, i, j ]
+        //               ^        ^
+        //               it       it + elements_affected
+
+        for (auto it = first; std::next(it, elements_affected) != Container::end(); ++it)
+        {
+            it->~value_type(); // destroy but keep allocation
+            new (&*it) value_type{std::move(*std::next(it, elements_affected))}; // "move" next element to it
+        }
+
+        // [ a, b, c, d, h, i, j, h, i, j ]
+        //               ^        ^
+        //             first    last
+
+        // remove the unneeded elements at the end of the vector
+        Container::resize(this->size() - static_cast<size_type>(elements_affected));
+
+        // [ a, b, c, d, h, i, j ]
+        //               ^        ^
+        //             first    last
+
+        // first is now pointing past the last deleted element, but we cannot
+        // use this iterator, because it may have been invalidated by the
+        // resize call. Instead, we can return begin() + offset.
+        return Container::begin() + offset;
+    }
+
+    size_type count(const key_type& key) const
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return 1;
+            }
+        }
+        return 0;
+    }
+
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
+    size_type count(KeyType && key) const // NOLINT(cppcoreguidelines-missing-std-forward)
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return 1;
+            }
+        }
+        return 0;
+    }
+
+    iterator find(const key_type& key)
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return it;
+            }
+        }
+        return Container::end();
+    }
+
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
+    iterator find(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward)
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return it;
+            }
+        }
+        return Container::end();
+    }
+
+    const_iterator find(const key_type& key) const
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, key))
+            {
+                return it;
+            }
+        }
+        return Container::end();
+    }
+
+    std::pair<iterator, bool> insert( value_type&& value )
+    {
+        return emplace(value.first, std::move(value.second));
+    }
+
+    std::pair<iterator, bool> insert( const value_type& value )
+    {
+        for (auto it = this->begin(); it != this->end(); ++it)
+        {
+            if (m_compare(it->first, value.first))
+            {
+                return {it, false};
+            }
+        }
+        Container::push_back(value);
+        return {--this->end(), true};
+    }
+
+    template<typename InputIt>
+    using require_input_iter = typename std::enable_if<std::is_convertible<typename std::iterator_traits<InputIt>::iterator_category,
+            std::input_iterator_tag>::value>::type;
+
+    template<typename InputIt, typename = require_input_iter<InputIt>>
+    void insert(InputIt first, InputIt last)
+    {
+        for (auto it = first; it != last; ++it)
+        {
+            insert(*it);
+        }
+    }
+
+private:
+    JSON_NO_UNIQUE_ADDRESS key_compare m_compare = key_compare();
+};
+
+NLOHMANN_JSON_NAMESPACE_END
+
+
+#if defined(JSON_HAS_CPP_17)
+    #if JSON_HAS_STATIC_RTTI
+        #include <any>
+    #endif
+    #include <string_view>
+#endif
+
+/*!
+@brief namespace for Niels Lohmann
+@see https://github.com/nlohmann
+@since version 1.0.0
+*/
+NLOHMANN_JSON_NAMESPACE_BEGIN
+
+/*!
+@brief a class to store JSON values
+
+@internal
+@invariant The member variables @a m_value and @a m_type have the following
+relationship:
+- If `m_type == value_t::object`, then `m_value.object != nullptr`.
+- If `m_type == value_t::array`, then `m_value.array != nullptr`.
+- If `m_type == value_t::string`, then `m_value.string != nullptr`.
+The invariants are checked by member function assert_invariant().
+
+@note ObjectType trick from https://stackoverflow.com/a/9860911
+@endinternal
+
+@since version 1.0.0
+
+@nosubgrouping
+*/
+NLOHMANN_BASIC_JSON_TPL_DECLARATION
+class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-special-member-functions)
+    : public ::nlohmann::detail::json_base_class<CustomBaseClass>
+{
+  private:
+    template<detail::value_t> friend struct detail::external_constructor;
+
+    template<typename>
+    friend class ::nlohmann::json_pointer;
+    // can be restored when json_pointer backwards compatibility is removed
+    // friend ::nlohmann::json_pointer<StringType>;
+
+    template<typename BasicJsonType, typename InputType>
+    friend class ::nlohmann::detail::parser;
+    friend ::nlohmann::detail::serializer<basic_json>;
+    template<typename BasicJsonType>
+    friend class ::nlohmann::detail::iter_impl;
+    template<typename BasicJsonType, typename CharType>
+    friend class ::nlohmann::detail::binary_writer;
+    template<typename BasicJsonType, typename InputType, typename SAX>
+    friend class ::nlohmann::detail::binary_reader;
+    template<typename BasicJsonType>
+    friend class ::nlohmann::detail::json_sax_dom_parser;
+    template<typename BasicJsonType>
+    friend class ::nlohmann::detail::json_sax_dom_callback_parser;
+    friend class ::nlohmann::detail::exception;
+
+    /// workaround type for MSVC
+    using basic_json_t = NLOHMANN_BASIC_JSON_TPL;
+    using json_base_class_t = ::nlohmann::detail::json_base_class<CustomBaseClass>;
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    // convenience aliases for types residing in namespace detail;
+    using lexer = ::nlohmann::detail::lexer_base<basic_json>;
+
+    template<typename InputAdapterType>
+    static ::nlohmann::detail::parser<basic_json, InputAdapterType> parser(
+        InputAdapterType adapter,
+        detail::parser_callback_t<basic_json>cb = nullptr,
+        const bool allow_exceptions = true,
+        const bool ignore_comments = false
+                                 )
+    {
+        return ::nlohmann::detail::parser<basic_json, InputAdapterType>(std::move(adapter),
+                std::move(cb), allow_exceptions, ignore_comments);
+    }
+
+  private:
+    using primitive_iterator_t = ::nlohmann::detail::primitive_iterator_t;
+    template<typename BasicJsonType>
+    using internal_iterator = ::nlohmann::detail::internal_iterator<BasicJsonType>;
+    template<typename BasicJsonType>
+    using iter_impl = ::nlohmann::detail::iter_impl<BasicJsonType>;
+    template<typename Iterator>
+    using iteration_proxy = ::nlohmann::detail::iteration_proxy<Iterator>;
+    template<typename Base> using json_reverse_iterator = ::nlohmann::detail::json_reverse_iterator<Base>;
+
+    template<typename CharType>
+    using output_adapter_t = ::nlohmann::detail::output_adapter_t<CharType>;
+
+    template<typename InputType>
+    using binary_reader = ::nlohmann::detail::binary_reader<basic_json, InputType>;
+    template<typename CharType> using binary_writer = ::nlohmann::detail::binary_writer<basic_json, CharType>;
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    using serializer = ::nlohmann::detail::serializer<basic_json>;
+
+  public:
+    using value_t = detail::value_t;
+    /// JSON Pointer, see @ref nlohmann::json_pointer
+    using json_pointer = ::nlohmann::json_pointer<StringType>;
+    template<typename T, typename SFINAE>
+    using json_serializer = JSONSerializer<T, SFINAE>;
+    /// how to treat decoding errors
+    using error_handler_t = detail::error_handler_t;
+    /// how to treat CBOR tags
+    using cbor_tag_handler_t = detail::cbor_tag_handler_t;
+    /// helper type for initializer lists of basic_json values
+    using initializer_list_t = std::initializer_list<detail::json_ref<basic_json>>;
+
+    using input_format_t = detail::input_format_t;
+    /// SAX interface type, see @ref nlohmann::json_sax
+    using json_sax_t = json_sax<basic_json>;
+
+    ////////////////
+    // exceptions //
+    ////////////////
+
+    /// @name exceptions
+    /// Classes to implement user-defined exceptions.
+    /// @{
+
+    using exception = detail::exception;
+    using parse_error = detail::parse_error;
+    using invalid_iterator = detail::invalid_iterator;
+    using type_error = detail::type_error;
+    using out_of_range = detail::out_of_range;
+    using other_error = detail::other_error;
+
+    /// @}
+
+    /////////////////////
+    // container types //
+    /////////////////////
+
+    /// @name container types
+    /// The canonic container types to use @ref basic_json like any other STL
+    /// container.
+    /// @{
+
+    /// the type of elements in a basic_json container
+    using value_type = basic_json;
+
+    /// the type of an element reference
+    using reference = value_type&;
+    /// the type of an element const reference
+    using const_reference = const value_type&;
+
+    /// a type to represent differences between iterators
+    using difference_type = std::ptrdiff_t;
+    /// a type to represent container sizes
+    using size_type = std::size_t;
+
+    /// the allocator type
+    using allocator_type = AllocatorType<basic_json>;
+
+    /// the type of an element pointer
+    using pointer = typename std::allocator_traits<allocator_type>::pointer;
+    /// the type of an element const pointer
+    using const_pointer = typename std::allocator_traits<allocator_type>::const_pointer;
+
+    /// an iterator for a basic_json container
+    using iterator = iter_impl<basic_json>;
+    /// a const iterator for a basic_json container
+    using const_iterator = iter_impl<const basic_json>;
+    /// a reverse iterator for a basic_json container
+    using reverse_iterator = json_reverse_iterator<typename basic_json::iterator>;
+    /// a const reverse iterator for a basic_json container
+    using const_reverse_iterator = json_reverse_iterator<typename basic_json::const_iterator>;
+
+    /// @}
+
+    /// @brief returns the allocator associated with the container
+    /// @sa https://json.nlohmann.me/api/basic_json/get_allocator/
+    static allocator_type get_allocator()
+    {
+        return allocator_type();
+    }
+
+    /// @brief returns version information on the library
+    /// @sa https://json.nlohmann.me/api/basic_json/meta/
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json meta()
+    {
+        basic_json result;
+
+        result["copyright"] = "(C) 2013-2023 Niels Lohmann";
+        result["name"] = "JSON for Modern C++";
+        result["url"] = "https://github.com/nlohmann/json";
+        result["version"]["string"] =
+            detail::concat(std::to_string(NLOHMANN_JSON_VERSION_MAJOR), '.',
+                           std::to_string(NLOHMANN_JSON_VERSION_MINOR), '.',
+                           std::to_string(NLOHMANN_JSON_VERSION_PATCH));
+        result["version"]["major"] = NLOHMANN_JSON_VERSION_MAJOR;
+        result["version"]["minor"] = NLOHMANN_JSON_VERSION_MINOR;
+        result["version"]["patch"] = NLOHMANN_JSON_VERSION_PATCH;
+
+#ifdef _WIN32
+        result["platform"] = "win32";
+#elif defined __linux__
+        result["platform"] = "linux";
+#elif defined __APPLE__
+        result["platform"] = "apple";
+#elif defined __unix__
+        result["platform"] = "unix";
+#else
+        result["platform"] = "unknown";
+#endif
+
+#if defined(__ICC) || defined(__INTEL_COMPILER)
+        result["compiler"] = {{"family", "icc"}, {"version", __INTEL_COMPILER}};
+#elif defined(__clang__)
+        result["compiler"] = {{"family", "clang"}, {"version", __clang_version__}};
+#elif defined(__GNUC__) || defined(__GNUG__)
+        result["compiler"] = {{"family", "gcc"}, {"version", detail::concat(
+                    std::to_string(__GNUC__), '.',
+                    std::to_string(__GNUC_MINOR__), '.',
+                    std::to_string(__GNUC_PATCHLEVEL__))
+            }
+        };
+#elif defined(__HP_cc) || defined(__HP_aCC)
+        result["compiler"] = "hp"
+#elif defined(__IBMCPP__)
+        result["compiler"] = {{"family", "ilecpp"}, {"version", __IBMCPP__}};
+#elif defined(_MSC_VER)
+        result["compiler"] = {{"family", "msvc"}, {"version", _MSC_VER}};
+#elif defined(__PGI)
+        result["compiler"] = {{"family", "pgcpp"}, {"version", __PGI}};
+#elif defined(__SUNPRO_CC)
+        result["compiler"] = {{"family", "sunpro"}, {"version", __SUNPRO_CC}};
+#else
+        result["compiler"] = {{"family", "unknown"}, {"version", "unknown"}};
+#endif
+
+#if defined(_MSVC_LANG)
+        result["compiler"]["c++"] = std::to_string(_MSVC_LANG);
+#elif defined(__cplusplus)
+        result["compiler"]["c++"] = std::to_string(__cplusplus);
+#else
+        result["compiler"]["c++"] = "unknown";
+#endif
+        return result;
+    }
+
+    ///////////////////////////
+    // JSON value data types //
+    ///////////////////////////
+
+    /// @name JSON value data types
+    /// The data types to store a JSON value. These types are derived from
+    /// the template arguments passed to class @ref basic_json.
+    /// @{
+
+    /// @brief default object key comparator type
+    /// The actual object key comparator type (@ref object_comparator_t) may be
+    /// different.
+    /// @sa https://json.nlohmann.me/api/basic_json/default_object_comparator_t/
+#if defined(JSON_HAS_CPP_14)
+    // use of transparent comparator avoids unnecessary repeated construction of temporaries
+    // in functions involving lookup by key with types other than object_t::key_type (aka. StringType)
+    using default_object_comparator_t = std::less<>;
+#else
+    using default_object_comparator_t = std::less<StringType>;
+#endif
+
+    /// @brief a type for an object
+    /// @sa https://json.nlohmann.me/api/basic_json/object_t/
+    using object_t = ObjectType<StringType,
+          basic_json,
+          default_object_comparator_t,
+          AllocatorType<std::pair<const StringType,
+          basic_json>>>;
+
+    /// @brief a type for an array
+    /// @sa https://json.nlohmann.me/api/basic_json/array_t/
+    using array_t = ArrayType<basic_json, AllocatorType<basic_json>>;
+
+    /// @brief a type for a string
+    /// @sa https://json.nlohmann.me/api/basic_json/string_t/
+    using string_t = StringType;
+
+    /// @brief a type for a boolean
+    /// @sa https://json.nlohmann.me/api/basic_json/boolean_t/
+    using boolean_t = BooleanType;
+
+    /// @brief a type for a number (integer)
+    /// @sa https://json.nlohmann.me/api/basic_json/number_integer_t/
+    using number_integer_t = NumberIntegerType;
+
+    /// @brief a type for a number (unsigned)
+    /// @sa https://json.nlohmann.me/api/basic_json/number_unsigned_t/
+    using number_unsigned_t = NumberUnsignedType;
+
+    /// @brief a type for a number (floating-point)
+    /// @sa https://json.nlohmann.me/api/basic_json/number_float_t/
+    using number_float_t = NumberFloatType;
+
+    /// @brief a type for a packed binary type
+    /// @sa https://json.nlohmann.me/api/basic_json/binary_t/
+    using binary_t = nlohmann::byte_container_with_subtype<BinaryType>;
+
+    /// @brief object key comparator type
+    /// @sa https://json.nlohmann.me/api/basic_json/object_comparator_t/
+    using object_comparator_t = detail::actual_object_comparator_t<basic_json>;
+
+    /// @}
+
+  private:
+
+    /// helper for exception-safe object creation
+    template<typename T, typename... Args>
+    JSON_HEDLEY_RETURNS_NON_NULL
+    static T* create(Args&& ... args)
+    {
+        AllocatorType<T> alloc;
+        using AllocatorTraits = std::allocator_traits<AllocatorType<T>>;
+
+        auto deleter = [&](T * obj)
+        {
+            AllocatorTraits::deallocate(alloc, obj, 1);
+        };
+        std::unique_ptr<T, decltype(deleter)> obj(AllocatorTraits::allocate(alloc, 1), deleter);
+        AllocatorTraits::construct(alloc, obj.get(), std::forward<Args>(args)...);
+        JSON_ASSERT(obj != nullptr);
+        return obj.release();
+    }
+
+    ////////////////////////
+    // JSON value storage //
+    ////////////////////////
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    /*!
+    @brief a JSON value
+
+    The actual storage for a JSON value of the @ref basic_json class. This
+    union combines the different storage types for the JSON value types
+    defined in @ref value_t.
+
+    JSON type | value_t type    | used type
+    --------- | --------------- | ------------------------
+    object    | object          | pointer to @ref object_t
+    array     | array           | pointer to @ref array_t
+    string    | string          | pointer to @ref string_t
+    boolean   | boolean         | @ref boolean_t
+    number    | number_integer  | @ref number_integer_t
+    number    | number_unsigned | @ref number_unsigned_t
+    number    | number_float    | @ref number_float_t
+    binary    | binary          | pointer to @ref binary_t
+    null      | null            | *no value is stored*
+
+    @note Variable-length types (objects, arrays, and strings) are stored as
+    pointers. The size of the union should not exceed 64 bits if the default
+    value types are used.
+
+    @since version 1.0.0
+    */
+    union json_value
+    {
+        /// object (stored with pointer to save storage)
+        object_t* object;
+        /// array (stored with pointer to save storage)
+        array_t* array;
+        /// string (stored with pointer to save storage)
+        string_t* string;
+        /// binary (stored with pointer to save storage)
+        binary_t* binary;
+        /// boolean
+        boolean_t boolean;
+        /// number (integer)
+        number_integer_t number_integer;
+        /// number (unsigned integer)
+        number_unsigned_t number_unsigned;
+        /// number (floating-point)
+        number_float_t number_float;
+
+        /// default constructor (for null values)
+        json_value() = default;
+        /// constructor for booleans
+        json_value(boolean_t v) noexcept : boolean(v) {}
+        /// constructor for numbers (integer)
+        json_value(number_integer_t v) noexcept : number_integer(v) {}
+        /// constructor for numbers (unsigned)
+        json_value(number_unsigned_t v) noexcept : number_unsigned(v) {}
+        /// constructor for numbers (floating-point)
+        json_value(number_float_t v) noexcept : number_float(v) {}
+        /// constructor for empty values of a given type
+        json_value(value_t t)
+        {
+            switch (t)
+            {
+                case value_t::object:
+                {
+                    object = create<object_t>();
+                    break;
+                }
+
+                case value_t::array:
+                {
+                    array = create<array_t>();
+                    break;
+                }
+
+                case value_t::string:
+                {
+                    string = create<string_t>("");
+                    break;
+                }
+
+                case value_t::binary:
+                {
+                    binary = create<binary_t>();
+                    break;
+                }
+
+                case value_t::boolean:
+                {
+                    boolean = static_cast<boolean_t>(false);
+                    break;
+                }
+
+                case value_t::number_integer:
+                {
+                    number_integer = static_cast<number_integer_t>(0);
+                    break;
+                }
+
+                case value_t::number_unsigned:
+                {
+                    number_unsigned = static_cast<number_unsigned_t>(0);
+                    break;
+                }
+
+                case value_t::number_float:
+                {
+                    number_float = static_cast<number_float_t>(0.0);
+                    break;
+                }
+
+                case value_t::null:
+                {
+                    object = nullptr;  // silence warning, see #821
+                    break;
+                }
+
+                case value_t::discarded:
+                default:
+                {
+                    object = nullptr;  // silence warning, see #821
+                    if (JSON_HEDLEY_UNLIKELY(t == value_t::null))
+                    {
+                        JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.11.3", nullptr)); // LCOV_EXCL_LINE
+                    }
+                    break;
+                }
+            }
+        }
+
+        /// constructor for strings
+        json_value(const string_t& value) : string(create<string_t>(value)) {}
+
+        /// constructor for rvalue strings
+        json_value(string_t&& value) : string(create<string_t>(std::move(value))) {}
+
+        /// constructor for objects
+        json_value(const object_t& value) : object(create<object_t>(value)) {}
+
+        /// constructor for rvalue objects
+        json_value(object_t&& value) : object(create<object_t>(std::move(value))) {}
+
+        /// constructor for arrays
+        json_value(const array_t& value) : array(create<array_t>(value)) {}
+
+        /// constructor for rvalue arrays
+        json_value(array_t&& value) : array(create<array_t>(std::move(value))) {}
+
+        /// constructor for binary arrays
+        json_value(const typename binary_t::container_type& value) : binary(create<binary_t>(value)) {}
+
+        /// constructor for rvalue binary arrays
+        json_value(typename binary_t::container_type&& value) : binary(create<binary_t>(std::move(value))) {}
+
+        /// constructor for binary arrays (internal type)
+        json_value(const binary_t& value) : binary(create<binary_t>(value)) {}
+
+        /// constructor for rvalue binary arrays (internal type)
+        json_value(binary_t&& value) : binary(create<binary_t>(std::move(value))) {}
+
+        void destroy(value_t t)
+        {
+            if (
+                (t == value_t::object && object == nullptr) ||
+                (t == value_t::array && array == nullptr) ||
+                (t == value_t::string && string == nullptr) ||
+                (t == value_t::binary && binary == nullptr)
+            )
+            {
+                //not initialized (e.g. due to exception in the ctor)
+                return;
+            }
+            if (t == value_t::array || t == value_t::object)
+            {
+                // flatten the current json_value to a heap-allocated stack
+                std::vector<basic_json> stack;
+
+                // move the top-level items to stack
+                if (t == value_t::array)
+                {
+                    stack.reserve(array->size());
+                    std::move(array->begin(), array->end(), std::back_inserter(stack));
+                }
+                else
+                {
+                    stack.reserve(object->size());
+                    for (auto&& it : *object)
+                    {
+                        stack.push_back(std::move(it.second));
+                    }
+                }
+
+                while (!stack.empty())
+                {
+                    // move the last item to local variable to be processed
+                    basic_json current_item(std::move(stack.back()));
+                    stack.pop_back();
+
+                    // if current_item is array/object, move
+                    // its children to the stack to be processed later
+                    if (current_item.is_array())
+                    {
+                        std::move(current_item.m_data.m_value.array->begin(), current_item.m_data.m_value.array->end(), std::back_inserter(stack));
+
+                        current_item.m_data.m_value.array->clear();
+                    }
+                    else if (current_item.is_object())
+                    {
+                        for (auto&& it : *current_item.m_data.m_value.object)
+                        {
+                            stack.push_back(std::move(it.second));
+                        }
+
+                        current_item.m_data.m_value.object->clear();
+                    }
+
+                    // it's now safe that current_item get destructed
+                    // since it doesn't have any children
+                }
+            }
+
+            switch (t)
+            {
+                case value_t::object:
+                {
+                    AllocatorType<object_t> alloc;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, object);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, object, 1);
+                    break;
+                }
+
+                case value_t::array:
+                {
+                    AllocatorType<array_t> alloc;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, array);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, array, 1);
+                    break;
+                }
+
+                case value_t::string:
+                {
+                    AllocatorType<string_t> alloc;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, string);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, string, 1);
+                    break;
+                }
+
+                case value_t::binary:
+                {
+                    AllocatorType<binary_t> alloc;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, binary);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, binary, 1);
+                    break;
+                }
+
+                case value_t::null:
+                case value_t::boolean:
+                case value_t::number_integer:
+                case value_t::number_unsigned:
+                case value_t::number_float:
+                case value_t::discarded:
+                default:
+                {
+                    break;
+                }
+            }
+        }
+    };
+
+  private:
+    /*!
+    @brief checks the class invariants
+
+    This function asserts the class invariants. It needs to be called at the
+    end of every constructor to make sure that created objects respect the
+    invariant. Furthermore, it has to be called each time the type of a JSON
+    value is changed, because the invariant expresses a relationship between
+    @a m_type and @a m_value.
+
+    Furthermore, the parent relation is checked for arrays and objects: If
+    @a check_parents true and the value is an array or object, then the
+    container's elements must have the current value as parent.
+
+    @param[in] check_parents  whether the parent relation should be checked.
+               The value is true by default and should only be set to false
+               during destruction of objects when the invariant does not
+               need to hold.
+    */
+    void assert_invariant(bool check_parents = true) const noexcept
+    {
+        JSON_ASSERT(m_data.m_type != value_t::object || m_data.m_value.object != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::array || m_data.m_value.array != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::string || m_data.m_value.string != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::binary || m_data.m_value.binary != nullptr);
+
+#if JSON_DIAGNOSTICS
+        JSON_TRY
+        {
+            // cppcheck-suppress assertWithSideEffect
+            JSON_ASSERT(!check_parents || !is_structured() || std::all_of(begin(), end(), [this](const basic_json & j)
+            {
+                return j.m_parent == this;
+            }));
+        }
+        JSON_CATCH(...) {} // LCOV_EXCL_LINE
+#endif
+        static_cast<void>(check_parents);
+    }
+
+    void set_parents()
+    {
+#if JSON_DIAGNOSTICS
+        switch (m_data.m_type)
+        {
+            case value_t::array:
+            {
+                for (auto& element : *m_data.m_value.array)
+                {
+                    element.m_parent = this;
+                }
+                break;
+            }
+
+            case value_t::object:
+            {
+                for (auto& element : *m_data.m_value.object)
+                {
+                    element.second.m_parent = this;
+                }
+                break;
+            }
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+                break;
+        }
+#endif
+    }
+
+    iterator set_parents(iterator it, typename iterator::difference_type count_set_parents)
+    {
+#if JSON_DIAGNOSTICS
+        for (typename iterator::difference_type i = 0; i < count_set_parents; ++i)
+        {
+            (it + i)->m_parent = this;
+        }
+#else
+        static_cast<void>(count_set_parents);
+#endif
+        return it;
+    }
+
+    reference set_parent(reference j, std::size_t old_capacity = static_cast<std::size_t>(-1))
+    {
+#if JSON_DIAGNOSTICS
+        if (old_capacity != static_cast<std::size_t>(-1))
+        {
+            // see https://github.com/nlohmann/json/issues/2838
+            JSON_ASSERT(type() == value_t::array);
+            if (JSON_HEDLEY_UNLIKELY(m_data.m_value.array->capacity() != old_capacity))
+            {
+                // capacity has changed: update all parents
+                set_parents();
+                return j;
+            }
+        }
+
+        // ordered_json uses a vector internally, so pointers could have
+        // been invalidated; see https://github.com/nlohmann/json/issues/2962
+#ifdef JSON_HEDLEY_MSVC_VERSION
+#pragma warning(push )
+#pragma warning(disable : 4127) // ignore warning to replace if with if constexpr
+#endif
+        if (detail::is_ordered_map<object_t>::value)
+        {
+            set_parents();
+            return j;
+        }
+#ifdef JSON_HEDLEY_MSVC_VERSION
+#pragma warning( pop )
+#endif
+
+        j.m_parent = this;
+#else
+        static_cast<void>(j);
+        static_cast<void>(old_capacity);
+#endif
+        return j;
+    }
+
+  public:
+    //////////////////////////
+    // JSON parser callback //
+    //////////////////////////
+
+    /// @brief parser event types
+    /// @sa https://json.nlohmann.me/api/basic_json/parse_event_t/
+    using parse_event_t = detail::parse_event_t;
+
+    /// @brief per-element parser callback type
+    /// @sa https://json.nlohmann.me/api/basic_json/parser_callback_t/
+    using parser_callback_t = detail::parser_callback_t<basic_json>;
+
+    //////////////////
+    // constructors //
+    //////////////////
+
+    /// @name constructors and destructors
+    /// Constructors of class @ref basic_json, copy/move constructor, copy
+    /// assignment, static functions creating objects, and the destructor.
+    /// @{
+
+    /// @brief create an empty value with a given type
+    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
+    basic_json(const value_t v)
+        : m_data(v)
+    {
+        assert_invariant();
+    }
+
+    /// @brief create a null object
+    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
+    basic_json(std::nullptr_t = nullptr) noexcept // NOLINT(bugprone-exception-escape)
+        : basic_json(value_t::null)
+    {
+        assert_invariant();
+    }
+
+    /// @brief create a JSON value from compatible types
+    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
+    template < typename CompatibleType,
+               typename U = detail::uncvref_t<CompatibleType>,
+               detail::enable_if_t <
+                   !detail::is_basic_json<U>::value && detail::is_compatible_type<basic_json_t, U>::value, int > = 0 >
+    basic_json(CompatibleType && val) noexcept(noexcept( // NOLINT(bugprone-forwarding-reference-overload,bugprone-exception-escape)
+                JSONSerializer<U>::to_json(std::declval<basic_json_t&>(),
+                                           std::forward<CompatibleType>(val))))
+    {
+        JSONSerializer<U>::to_json(*this, std::forward<CompatibleType>(val));
+        set_parents();
+        assert_invariant();
+    }
+
+    /// @brief create a JSON value from an existing one
+    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
+    template < typename BasicJsonType,
+               detail::enable_if_t <
+                   detail::is_basic_json<BasicJsonType>::value&& !std::is_same<basic_json, BasicJsonType>::value, int > = 0 >
+    basic_json(const BasicJsonType& val)
+    {
+        using other_boolean_t = typename BasicJsonType::boolean_t;
+        using other_number_float_t = typename BasicJsonType::number_float_t;
+        using other_number_integer_t = typename BasicJsonType::number_integer_t;
+        using other_number_unsigned_t = typename BasicJsonType::number_unsigned_t;
+        using other_string_t = typename BasicJsonType::string_t;
+        using other_object_t = typename BasicJsonType::object_t;
+        using other_array_t = typename BasicJsonType::array_t;
+        using other_binary_t = typename BasicJsonType::binary_t;
+
+        switch (val.type())
+        {
+            case value_t::boolean:
+                JSONSerializer<other_boolean_t>::to_json(*this, val.template get<other_boolean_t>());
+                break;
+            case value_t::number_float:
+                JSONSerializer<other_number_float_t>::to_json(*this, val.template get<other_number_float_t>());
+                break;
+            case value_t::number_integer:
+                JSONSerializer<other_number_integer_t>::to_json(*this, val.template get<other_number_integer_t>());
+                break;
+            case value_t::number_unsigned:
+                JSONSerializer<other_number_unsigned_t>::to_json(*this, val.template get<other_number_unsigned_t>());
+                break;
+            case value_t::string:
+                JSONSerializer<other_string_t>::to_json(*this, val.template get_ref<const other_string_t&>());
+                break;
+            case value_t::object:
+                JSONSerializer<other_object_t>::to_json(*this, val.template get_ref<const other_object_t&>());
+                break;
+            case value_t::array:
+                JSONSerializer<other_array_t>::to_json(*this, val.template get_ref<const other_array_t&>());
+                break;
+            case value_t::binary:
+                JSONSerializer<other_binary_t>::to_json(*this, val.template get_ref<const other_binary_t&>());
+                break;
+            case value_t::null:
+                *this = nullptr;
+                break;
+            case value_t::discarded:
+                m_data.m_type = value_t::discarded;
+                break;
+            default:            // LCOV_EXCL_LINE
+                JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+        }
+        JSON_ASSERT(m_data.m_type == val.type());
+        set_parents();
+        assert_invariant();
+    }
+
+    /// @brief create a container (array or object) from an initializer list
+    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
+    basic_json(initializer_list_t init,
+               bool type_deduction = true,
+               value_t manual_type = value_t::array)
+    {
+        // check if each element is an array with two elements whose first
+        // element is a string
+        bool is_an_object = std::all_of(init.begin(), init.end(),
+                                        [](const detail::json_ref<basic_json>& element_ref)
+        {
+            // The cast is to ensure op[size_type] is called, bearing in mind size_type may not be int;
+            // (many string types can be constructed from 0 via its null-pointer guise, so we get a
+            // broken call to op[key_type], the wrong semantics and a 4804 warning on Windows)
+            return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[static_cast<size_type>(0)].is_string();
+        });
+
+        // adjust type if type deduction is not wanted
+        if (!type_deduction)
+        {
+            // if array is wanted, do not create an object though possible
+            if (manual_type == value_t::array)
+            {
+                is_an_object = false;
+            }
+
+            // if object is wanted but impossible, throw an exception
+            if (JSON_HEDLEY_UNLIKELY(manual_type == value_t::object && !is_an_object))
+            {
+                JSON_THROW(type_error::create(301, "cannot create object from initializer list", nullptr));
+            }
+        }
+
+        if (is_an_object)
+        {
+            // the initializer list is a list of pairs -> create object
+            m_data.m_type = value_t::object;
+            m_data.m_value = value_t::object;
+
+            for (auto& element_ref : init)
+            {
+                auto element = element_ref.moved_or_copied();
+                m_data.m_value.object->emplace(
+                    std::move(*((*element.m_data.m_value.array)[0].m_data.m_value.string)),
+                    std::move((*element.m_data.m_value.array)[1]));
+            }
+        }
+        else
+        {
+            // the initializer list describes an array -> create array
+            m_data.m_type = value_t::array;
+            m_data.m_value.array = create<array_t>(init.begin(), init.end());
+        }
+
+        set_parents();
+        assert_invariant();
+    }
+
+    /// @brief explicitly create a binary array (without subtype)
+    /// @sa https://json.nlohmann.me/api/basic_json/binary/
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json binary(const typename binary_t::container_type& init)
+    {
+        auto res = basic_json();
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = init;
+        return res;
+    }
+
+    /// @brief explicitly create a binary array (with subtype)
+    /// @sa https://json.nlohmann.me/api/basic_json/binary/
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json binary(const typename binary_t::container_type& init, typename binary_t::subtype_type subtype)
+    {
+        auto res = basic_json();
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = binary_t(init, subtype);
+        return res;
+    }
+
+    /// @brief explicitly create a binary array
+    /// @sa https://json.nlohmann.me/api/basic_json/binary/
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json binary(typename binary_t::container_type&& init)
+    {
+        auto res = basic_json();
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = std::move(init);
+        return res;
+    }
+
+    /// @brief explicitly create a binary array (with subtype)
+    /// @sa https://json.nlohmann.me/api/basic_json/binary/
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json binary(typename binary_t::container_type&& init, typename binary_t::subtype_type subtype)
+    {
+        auto res = basic_json();
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = binary_t(std::move(init), subtype);
+        return res;
+    }
+
+    /// @brief explicitly create an array from an initializer list
+    /// @sa https://json.nlohmann.me/api/basic_json/array/
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json array(initializer_list_t init = {})
+    {
+        return basic_json(init, false, value_t::array);
+    }
+
+    /// @brief explicitly create an object from an initializer list
+    /// @sa https://json.nlohmann.me/api/basic_json/object/
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json object(initializer_list_t init = {})
+    {
+        return basic_json(init, false, value_t::object);
+    }
+
+    /// @brief construct an array with count copies of given value
+    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
+    basic_json(size_type cnt, const basic_json& val):
+        m_data{cnt, val}
+    {
+        set_parents();
+        assert_invariant();
+    }
+
+    /// @brief construct a JSON container given an iterator range
+    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
+    template < class InputIT, typename std::enable_if <
+                   std::is_same<InputIT, typename basic_json_t::iterator>::value ||
+                   std::is_same<InputIT, typename basic_json_t::const_iterator>::value, int >::type = 0 >
+    basic_json(InputIT first, InputIT last)
+    {
+        JSON_ASSERT(first.m_object != nullptr);
+        JSON_ASSERT(last.m_object != nullptr);
+
+        // make sure iterator fits the current value
+        if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object))
+        {
+            JSON_THROW(invalid_iterator::create(201, "iterators are not compatible", nullptr));
+        }
+
+        // copy type from first iterator
+        m_data.m_type = first.m_object->m_data.m_type;
+
+        // check if iterator range is complete for primitive values
+        switch (m_data.m_type)
+        {
+            case value_t::boolean:
+            case value_t::number_float:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::string:
+            {
+                if (JSON_HEDLEY_UNLIKELY(!first.m_it.primitive_iterator.is_begin()
+                                         || !last.m_it.primitive_iterator.is_end()))
+                {
+                    JSON_THROW(invalid_iterator::create(204, "iterators out of range", first.m_object));
+                }
+                break;
+            }
+
+            case value_t::null:
+            case value_t::object:
+            case value_t::array:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+                break;
+        }
+
+        switch (m_data.m_type)
+        {
+            case value_t::number_integer:
+            {
+                m_data.m_value.number_integer = first.m_object->m_data.m_value.number_integer;
+                break;
+            }
+
+            case value_t::number_unsigned:
+            {
+                m_data.m_value.number_unsigned = first.m_object->m_data.m_value.number_unsigned;
+                break;
+            }
+
+            case value_t::number_float:
+            {
+                m_data.m_value.number_float = first.m_object->m_data.m_value.number_float;
+                break;
+            }
+
+            case value_t::boolean:
+            {
+                m_data.m_value.boolean = first.m_object->m_data.m_value.boolean;
+                break;
+            }
+
+            case value_t::string:
+            {
+                m_data.m_value = *first.m_object->m_data.m_value.string;
+                break;
+            }
+
+            case value_t::object:
+            {
+                m_data.m_value.object = create<object_t>(first.m_it.object_iterator,
+                                        last.m_it.object_iterator);
+                break;
+            }
+
+            case value_t::array:
+            {
+                m_data.m_value.array = create<array_t>(first.m_it.array_iterator,
+                                                       last.m_it.array_iterator);
+                break;
+            }
+
+            case value_t::binary:
+            {
+                m_data.m_value = *first.m_object->m_data.m_value.binary;
+                break;
+            }
+
+            case value_t::null:
+            case value_t::discarded:
+            default:
+                JSON_THROW(invalid_iterator::create(206, detail::concat("cannot construct with iterators from ", first.m_object->type_name()), first.m_object));
+        }
+
+        set_parents();
+        assert_invariant();
+    }
+
+    ///////////////////////////////////////
+    // other constructors and destructor //
+    ///////////////////////////////////////
+
+    template<typename JsonRef,
+             detail::enable_if_t<detail::conjunction<detail::is_json_ref<JsonRef>,
+                                 std::is_same<typename JsonRef::value_type, basic_json>>::value, int> = 0 >
+    basic_json(const JsonRef& ref) : basic_json(ref.moved_or_copied()) {}
+
+    /// @brief copy constructor
+    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
+    basic_json(const basic_json& other)
+        : json_base_class_t(other)
+    {
+        m_data.m_type = other.m_data.m_type;
+        // check of passed value is valid
+        other.assert_invariant();
+
+        switch (m_data.m_type)
+        {
+            case value_t::object:
+            {
+                m_data.m_value = *other.m_data.m_value.object;
+                break;
+            }
+
+            case value_t::array:
+            {
+                m_data.m_value = *other.m_data.m_value.array;
+                break;
+            }
+
+            case value_t::string:
+            {
+                m_data.m_value = *other.m_data.m_value.string;
+                break;
+            }
+
+            case value_t::boolean:
+            {
+                m_data.m_value = other.m_data.m_value.boolean;
+                break;
+            }
+
+            case value_t::number_integer:
+            {
+                m_data.m_value = other.m_data.m_value.number_integer;
+                break;
+            }
+
+            case value_t::number_unsigned:
+            {
+                m_data.m_value = other.m_data.m_value.number_unsigned;
+                break;
+            }
+
+            case value_t::number_float:
+            {
+                m_data.m_value = other.m_data.m_value.number_float;
+                break;
+            }
+
+            case value_t::binary:
+            {
+                m_data.m_value = *other.m_data.m_value.binary;
+                break;
+            }
+
+            case value_t::null:
+            case value_t::discarded:
+            default:
+                break;
+        }
+
+        set_parents();
+        assert_invariant();
+    }
+
+    /// @brief move constructor
+    /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
+    basic_json(basic_json&& other) noexcept
+        : json_base_class_t(std::forward<json_base_class_t>(other)),
+          m_data(std::move(other.m_data))
+    {
+        // check that passed value is valid
+        other.assert_invariant(false);
+
+        // invalidate payload
+        other.m_data.m_type = value_t::null;
+        other.m_data.m_value = {};
+
+        set_parents();
+        assert_invariant();
+    }
+
+    /// @brief copy assignment
+    /// @sa https://json.nlohmann.me/api/basic_json/operator=/
+    basic_json& operator=(basic_json other) noexcept (
+        std::is_nothrow_move_constructible<value_t>::value&&
+        std::is_nothrow_move_assignable<value_t>::value&&
+        std::is_nothrow_move_constructible<json_value>::value&&
+        std::is_nothrow_move_assignable<json_value>::value&&
+        std::is_nothrow_move_assignable<json_base_class_t>::value
+    )
+    {
+        // check that passed value is valid
+        other.assert_invariant();
+
+        using std::swap;
+        swap(m_data.m_type, other.m_data.m_type);
+        swap(m_data.m_value, other.m_data.m_value);
+        json_base_class_t::operator=(std::move(other));
+
+        set_parents();
+        assert_invariant();
+        return *this;
+    }
+
+    /// @brief destructor
+    /// @sa https://json.nlohmann.me/api/basic_json/~basic_json/
+    ~basic_json() noexcept
+    {
+        assert_invariant(false);
+    }
+
+    /// @}
+
+  public:
+    ///////////////////////
+    // object inspection //
+    ///////////////////////
+
+    /// @name object inspection
+    /// Functions to inspect the type of a JSON value.
+    /// @{
+
+    /// @brief serialization
+    /// @sa https://json.nlohmann.me/api/basic_json/dump/
+    string_t dump(const int indent = -1,
+                  const char indent_char = ' ',
+                  const bool ensure_ascii = false,
+                  const error_handler_t error_handler = error_handler_t::strict) const
+    {
+        string_t result;
+        serializer s(detail::output_adapter<char, string_t>(result), indent_char, error_handler);
+
+        if (indent >= 0)
+        {
+            s.dump(*this, true, ensure_ascii, static_cast<unsigned int>(indent));
+        }
+        else
+        {
+            s.dump(*this, false, ensure_ascii, 0);
+        }
+
+        return result;
+    }
+
+    /// @brief return the type of the JSON value (explicit)
+    /// @sa https://json.nlohmann.me/api/basic_json/type/
+    constexpr value_t type() const noexcept
+    {
+        return m_data.m_type;
+    }
+
+    /// @brief return whether type is primitive
+    /// @sa https://json.nlohmann.me/api/basic_json/is_primitive/
+    constexpr bool is_primitive() const noexcept
+    {
+        return is_null() || is_string() || is_boolean() || is_number() || is_binary();
+    }
+
+    /// @brief return whether type is structured
+    /// @sa https://json.nlohmann.me/api/basic_json/is_structured/
+    constexpr bool is_structured() const noexcept
+    {
+        return is_array() || is_object();
+    }
+
+    /// @brief return whether value is null
+    /// @sa https://json.nlohmann.me/api/basic_json/is_null/
+    constexpr bool is_null() const noexcept
+    {
+        return m_data.m_type == value_t::null;
+    }
+
+    /// @brief return whether value is a boolean
+    /// @sa https://json.nlohmann.me/api/basic_json/is_boolean/
+    constexpr bool is_boolean() const noexcept
+    {
+        return m_data.m_type == value_t::boolean;
+    }
+
+    /// @brief return whether value is a number
+    /// @sa https://json.nlohmann.me/api/basic_json/is_number/
+    constexpr bool is_number() const noexcept
+    {
+        return is_number_integer() || is_number_float();
+    }
+
+    /// @brief return whether value is an integer number
+    /// @sa https://json.nlohmann.me/api/basic_json/is_number_integer/
+    constexpr bool is_number_integer() const noexcept
+    {
+        return m_data.m_type == value_t::number_integer || m_data.m_type == value_t::number_unsigned;
+    }
+
+    /// @brief return whether value is an unsigned integer number
+    /// @sa https://json.nlohmann.me/api/basic_json/is_number_unsigned/
+    constexpr bool is_number_unsigned() const noexcept
+    {
+        return m_data.m_type == value_t::number_unsigned;
+    }
+
+    /// @brief return whether value is a floating-point number
+    /// @sa https://json.nlohmann.me/api/basic_json/is_number_float/
+    constexpr bool is_number_float() const noexcept
+    {
+        return m_data.m_type == value_t::number_float;
+    }
+
+    /// @brief return whether value is an object
+    /// @sa https://json.nlohmann.me/api/basic_json/is_object/
+    constexpr bool is_object() const noexcept
+    {
+        return m_data.m_type == value_t::object;
+    }
+
+    /// @brief return whether value is an array
+    /// @sa https://json.nlohmann.me/api/basic_json/is_array/
+    constexpr bool is_array() const noexcept
+    {
+        return m_data.m_type == value_t::array;
+    }
+
+    /// @brief return whether value is a string
+    /// @sa https://json.nlohmann.me/api/basic_json/is_string/
+    constexpr bool is_string() const noexcept
+    {
+        return m_data.m_type == value_t::string;
+    }
+
+    /// @brief return whether value is a binary array
+    /// @sa https://json.nlohmann.me/api/basic_json/is_binary/
+    constexpr bool is_binary() const noexcept
+    {
+        return m_data.m_type == value_t::binary;
+    }
+
+    /// @brief return whether value is discarded
+    /// @sa https://json.nlohmann.me/api/basic_json/is_discarded/
+    constexpr bool is_discarded() const noexcept
+    {
+        return m_data.m_type == value_t::discarded;
+    }
+
+    /// @brief return the type of the JSON value (implicit)
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_value_t/
+    constexpr operator value_t() const noexcept
+    {
+        return m_data.m_type;
+    }
+
+    /// @}
+
+  private:
+    //////////////////
+    // value access //
+    //////////////////
+
+    /// get a boolean (explicit)
+    boolean_t get_impl(boolean_t* /*unused*/) const
+    {
+        if (JSON_HEDLEY_LIKELY(is_boolean()))
+        {
+            return m_data.m_value.boolean;
+        }
+
+        JSON_THROW(type_error::create(302, detail::concat("type must be boolean, but is ", type_name()), this));
+    }
+
+    /// get a pointer to the value (object)
+    object_t* get_impl_ptr(object_t* /*unused*/) noexcept
+    {
+        return is_object() ? m_data.m_value.object : nullptr;
+    }
+
+    /// get a pointer to the value (object)
+    constexpr const object_t* get_impl_ptr(const object_t* /*unused*/) const noexcept
+    {
+        return is_object() ? m_data.m_value.object : nullptr;
+    }
+
+    /// get a pointer to the value (array)
+    array_t* get_impl_ptr(array_t* /*unused*/) noexcept
+    {
+        return is_array() ? m_data.m_value.array : nullptr;
+    }
+
+    /// get a pointer to the value (array)
+    constexpr const array_t* get_impl_ptr(const array_t* /*unused*/) const noexcept
+    {
+        return is_array() ? m_data.m_value.array : nullptr;
+    }
+
+    /// get a pointer to the value (string)
+    string_t* get_impl_ptr(string_t* /*unused*/) noexcept
+    {
+        return is_string() ? m_data.m_value.string : nullptr;
+    }
+
+    /// get a pointer to the value (string)
+    constexpr const string_t* get_impl_ptr(const string_t* /*unused*/) const noexcept
+    {
+        return is_string() ? m_data.m_value.string : nullptr;
+    }
+
+    /// get a pointer to the value (boolean)
+    boolean_t* get_impl_ptr(boolean_t* /*unused*/) noexcept
+    {
+        return is_boolean() ? &m_data.m_value.boolean : nullptr;
+    }
+
+    /// get a pointer to the value (boolean)
+    constexpr const boolean_t* get_impl_ptr(const boolean_t* /*unused*/) const noexcept
+    {
+        return is_boolean() ? &m_data.m_value.boolean : nullptr;
+    }
+
+    /// get a pointer to the value (integer number)
+    number_integer_t* get_impl_ptr(number_integer_t* /*unused*/) noexcept
+    {
+        return is_number_integer() ? &m_data.m_value.number_integer : nullptr;
+    }
+
+    /// get a pointer to the value (integer number)
+    constexpr const number_integer_t* get_impl_ptr(const number_integer_t* /*unused*/) const noexcept
+    {
+        return is_number_integer() ? &m_data.m_value.number_integer : nullptr;
+    }
+
+    /// get a pointer to the value (unsigned number)
+    number_unsigned_t* get_impl_ptr(number_unsigned_t* /*unused*/) noexcept
+    {
+        return is_number_unsigned() ? &m_data.m_value.number_unsigned : nullptr;
+    }
+
+    /// get a pointer to the value (unsigned number)
+    constexpr const number_unsigned_t* get_impl_ptr(const number_unsigned_t* /*unused*/) const noexcept
+    {
+        return is_number_unsigned() ? &m_data.m_value.number_unsigned : nullptr;
+    }
+
+    /// get a pointer to the value (floating-point number)
+    number_float_t* get_impl_ptr(number_float_t* /*unused*/) noexcept
+    {
+        return is_number_float() ? &m_data.m_value.number_float : nullptr;
+    }
+
+    /// get a pointer to the value (floating-point number)
+    constexpr const number_float_t* get_impl_ptr(const number_float_t* /*unused*/) const noexcept
+    {
+        return is_number_float() ? &m_data.m_value.number_float : nullptr;
+    }
+
+    /// get a pointer to the value (binary)
+    binary_t* get_impl_ptr(binary_t* /*unused*/) noexcept
+    {
+        return is_binary() ? m_data.m_value.binary : nullptr;
+    }
+
+    /// get a pointer to the value (binary)
+    constexpr const binary_t* get_impl_ptr(const binary_t* /*unused*/) const noexcept
+    {
+        return is_binary() ? m_data.m_value.binary : nullptr;
+    }
+
+    /*!
+    @brief helper function to implement get_ref()
+
+    This function helps to implement get_ref() without code duplication for
+    const and non-const overloads
+
+    @tparam ThisType will be deduced as `basic_json` or `const basic_json`
+
+    @throw type_error.303 if ReferenceType does not match underlying value
+    type of the current JSON
+    */
+    template<typename ReferenceType, typename ThisType>
+    static ReferenceType get_ref_impl(ThisType& obj)
+    {
+        // delegate the call to get_ptr<>()
+        auto* ptr = obj.template get_ptr<typename std::add_pointer<ReferenceType>::type>();
+
+        if (JSON_HEDLEY_LIKELY(ptr != nullptr))
+        {
+            return *ptr;
+        }
+
+        JSON_THROW(type_error::create(303, detail::concat("incompatible ReferenceType for get_ref, actual type is ", obj.type_name()), &obj));
+    }
+
+  public:
+    /// @name value access
+    /// Direct access to the stored value of a JSON value.
+    /// @{
+
+    /// @brief get a pointer value (implicit)
+    /// @sa https://json.nlohmann.me/api/basic_json/get_ptr/
+    template<typename PointerType, typename std::enable_if<
+                 std::is_pointer<PointerType>::value, int>::type = 0>
+    auto get_ptr() noexcept -> decltype(std::declval<basic_json_t&>().get_impl_ptr(std::declval<PointerType>()))
+    {
+        // delegate the call to get_impl_ptr<>()
+        return get_impl_ptr(static_cast<PointerType>(nullptr));
+    }
+
+    /// @brief get a pointer value (implicit)
+    /// @sa https://json.nlohmann.me/api/basic_json/get_ptr/
+    template < typename PointerType, typename std::enable_if <
+                   std::is_pointer<PointerType>::value&&
+                   std::is_const<typename std::remove_pointer<PointerType>::type>::value, int >::type = 0 >
+    constexpr auto get_ptr() const noexcept -> decltype(std::declval<const basic_json_t&>().get_impl_ptr(std::declval<PointerType>()))
+    {
+        // delegate the call to get_impl_ptr<>() const
+        return get_impl_ptr(static_cast<PointerType>(nullptr));
+    }
+
+  private:
+    /*!
+    @brief get a value (explicit)
+
+    Explicit type conversion between the JSON value and a compatible value
+    which is [CopyConstructible](https://en.cppreference.com/w/cpp/named_req/CopyConstructible)
+    and [DefaultConstructible](https://en.cppreference.com/w/cpp/named_req/DefaultConstructible).
+    The value is converted by calling the @ref json_serializer<ValueType>
+    `from_json()` method.
+
+    The function is equivalent to executing
+    @code {.cpp}
+    ValueType ret;
+    JSONSerializer<ValueType>::from_json(*this, ret);
+    return ret;
+    @endcode
+
+    This overloads is chosen if:
+    - @a ValueType is not @ref basic_json,
+    - @ref json_serializer<ValueType> has a `from_json()` method of the form
+      `void from_json(const basic_json&, ValueType&)`, and
+    - @ref json_serializer<ValueType> does not have a `from_json()` method of
+      the form `ValueType from_json(const basic_json&)`
+
+    @tparam ValueType the returned value type
+
+    @return copy of the JSON value, converted to @a ValueType
+
+    @throw what @ref json_serializer<ValueType> `from_json()` method throws
+
+    @liveexample{The example below shows several conversions from JSON values
+    to other types. There a few things to note: (1) Floating-point numbers can
+    be converted to integers\, (2) A JSON array can be converted to a standard
+    `std::vector<short>`\, (3) A JSON object can be converted to C++
+    associative containers such as `std::unordered_map<std::string\,
+    json>`.,get__ValueType_const}
+
+    @since version 2.1.0
+    */
+    template < typename ValueType,
+               detail::enable_if_t <
+                   detail::is_default_constructible<ValueType>::value&&
+                   detail::has_from_json<basic_json_t, ValueType>::value,
+                   int > = 0 >
+    ValueType get_impl(detail::priority_tag<0> /*unused*/) const noexcept(noexcept(
+                JSONSerializer<ValueType>::from_json(std::declval<const basic_json_t&>(), std::declval<ValueType&>())))
+    {
+        auto ret = ValueType();
+        JSONSerializer<ValueType>::from_json(*this, ret);
+        return ret;
+    }
+
+    /*!
+    @brief get a value (explicit); special case
+
+    Explicit type conversion between the JSON value and a compatible value
+    which is **not** [CopyConstructible](https://en.cppreference.com/w/cpp/named_req/CopyConstructible)
+    and **not** [DefaultConstructible](https://en.cppreference.com/w/cpp/named_req/DefaultConstructible).
+    The value is converted by calling the @ref json_serializer<ValueType>
+    `from_json()` method.
+
+    The function is equivalent to executing
+    @code {.cpp}
+    return JSONSerializer<ValueType>::from_json(*this);
+    @endcode
+
+    This overloads is chosen if:
+    - @a ValueType is not @ref basic_json and
+    - @ref json_serializer<ValueType> has a `from_json()` method of the form
+      `ValueType from_json(const basic_json&)`
+
+    @note If @ref json_serializer<ValueType> has both overloads of
+    `from_json()`, this one is chosen.
+
+    @tparam ValueType the returned value type
+
+    @return copy of the JSON value, converted to @a ValueType
+
+    @throw what @ref json_serializer<ValueType> `from_json()` method throws
+
+    @since version 2.1.0
+    */
+    template < typename ValueType,
+               detail::enable_if_t <
+                   detail::has_non_default_from_json<basic_json_t, ValueType>::value,
+                   int > = 0 >
+    ValueType get_impl(detail::priority_tag<1> /*unused*/) const noexcept(noexcept(
+                JSONSerializer<ValueType>::from_json(std::declval<const basic_json_t&>())))
+    {
+        return JSONSerializer<ValueType>::from_json(*this);
+    }
+
+    /*!
+    @brief get special-case overload
+
+    This overloads converts the current @ref basic_json in a different
+    @ref basic_json type
+
+    @tparam BasicJsonType == @ref basic_json
+
+    @return a copy of *this, converted into @a BasicJsonType
+
+    @complexity Depending on the implementation of the called `from_json()`
+                method.
+
+    @since version 3.2.0
+    */
+    template < typename BasicJsonType,
+               detail::enable_if_t <
+                   detail::is_basic_json<BasicJsonType>::value,
+                   int > = 0 >
+    BasicJsonType get_impl(detail::priority_tag<2> /*unused*/) const
+    {
+        return *this;
+    }
+
+    /*!
+    @brief get special-case overload
+
+    This overloads avoids a lot of template boilerplate, it can be seen as the
+    identity method
+
+    @tparam BasicJsonType == @ref basic_json
+
+    @return a copy of *this
+
+    @complexity Constant.
+
+    @since version 2.1.0
+    */
+    template<typename BasicJsonType,
+             detail::enable_if_t<
+                 std::is_same<BasicJsonType, basic_json_t>::value,
+                 int> = 0>
+    basic_json get_impl(detail::priority_tag<3> /*unused*/) const
+    {
+        return *this;
+    }
+
+    /*!
+    @brief get a pointer value (explicit)
+    @copydoc get()
+    */
+    template<typename PointerType,
+             detail::enable_if_t<
+                 std::is_pointer<PointerType>::value,
+                 int> = 0>
+    constexpr auto get_impl(detail::priority_tag<4> /*unused*/) const noexcept
+    -> decltype(std::declval<const basic_json_t&>().template get_ptr<PointerType>())
+    {
+        // delegate the call to get_ptr
+        return get_ptr<PointerType>();
+    }
+
+  public:
+    /*!
+    @brief get a (pointer) value (explicit)
+
+    Performs explicit type conversion between the JSON value and a compatible value if required.
+
+    - If the requested type is a pointer to the internally stored JSON value that pointer is returned.
+    No copies are made.
+
+    - If the requested type is the current @ref basic_json, or a different @ref basic_json convertible
+    from the current @ref basic_json.
+
+    - Otherwise the value is converted by calling the @ref json_serializer<ValueType> `from_json()`
+    method.
+
+    @tparam ValueTypeCV the provided value type
+    @tparam ValueType the returned value type
+
+    @return copy of the JSON value, converted to @tparam ValueType if necessary
+
+    @throw what @ref json_serializer<ValueType> `from_json()` method throws if conversion is required
+
+    @since version 2.1.0
+    */
+    template < typename ValueTypeCV, typename ValueType = detail::uncvref_t<ValueTypeCV>>
+#if defined(JSON_HAS_CPP_14)
+    constexpr
+#endif
+    auto get() const noexcept(
+    noexcept(std::declval<const basic_json_t&>().template get_impl<ValueType>(detail::priority_tag<4> {})))
+    -> decltype(std::declval<const basic_json_t&>().template get_impl<ValueType>(detail::priority_tag<4> {}))
+    {
+        // we cannot static_assert on ValueTypeCV being non-const, because
+        // there is support for get<const basic_json_t>(), which is why we
+        // still need the uncvref
+        static_assert(!std::is_reference<ValueTypeCV>::value,
+                      "get() cannot be used with reference types, you might want to use get_ref()");
+        return get_impl<ValueType>(detail::priority_tag<4> {});
+    }
+
+    /*!
+    @brief get a pointer value (explicit)
+
+    Explicit pointer access to the internally stored JSON value. No copies are
+    made.
+
+    @warning The pointer becomes invalid if the underlying JSON object
+    changes.
+
+    @tparam PointerType pointer type; must be a pointer to @ref array_t, @ref
+    object_t, @ref string_t, @ref boolean_t, @ref number_integer_t,
+    @ref number_unsigned_t, or @ref number_float_t.
+
+    @return pointer to the internally stored JSON value if the requested
+    pointer type @a PointerType fits to the JSON value; `nullptr` otherwise
+
+    @complexity Constant.
+
+    @liveexample{The example below shows how pointers to internal values of a
+    JSON value can be requested. Note that no type conversions are made and a
+    `nullptr` is returned if the value and the requested pointer type does not
+    match.,get__PointerType}
+
+    @sa see @ref get_ptr() for explicit pointer-member access
+
+    @since version 1.0.0
+    */
+    template<typename PointerType, typename std::enable_if<
+                 std::is_pointer<PointerType>::value, int>::type = 0>
+    auto get() noexcept -> decltype(std::declval<basic_json_t&>().template get_ptr<PointerType>())
+    {
+        // delegate the call to get_ptr
+        return get_ptr<PointerType>();
+    }
+
+    /// @brief get a value (explicit)
+    /// @sa https://json.nlohmann.me/api/basic_json/get_to/
+    template < typename ValueType,
+               detail::enable_if_t <
+                   !detail::is_basic_json<ValueType>::value&&
+                   detail::has_from_json<basic_json_t, ValueType>::value,
+                   int > = 0 >
+    ValueType & get_to(ValueType& v) const noexcept(noexcept(
+                JSONSerializer<ValueType>::from_json(std::declval<const basic_json_t&>(), v)))
+    {
+        JSONSerializer<ValueType>::from_json(*this, v);
+        return v;
+    }
+
+    // specialization to allow calling get_to with a basic_json value
+    // see https://github.com/nlohmann/json/issues/2175
+    template<typename ValueType,
+             detail::enable_if_t <
+                 detail::is_basic_json<ValueType>::value,
+                 int> = 0>
+    ValueType & get_to(ValueType& v) const
+    {
+        v = *this;
+        return v;
+    }
+
+    template <
+        typename T, std::size_t N,
+        typename Array = T (&)[N], // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
+        detail::enable_if_t <
+            detail::has_from_json<basic_json_t, Array>::value, int > = 0 >
+    Array get_to(T (&v)[N]) const // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays)
+    noexcept(noexcept(JSONSerializer<Array>::from_json(
+                          std::declval<const basic_json_t&>(), v)))
+    {
+        JSONSerializer<Array>::from_json(*this, v);
+        return v;
+    }
+
+    /// @brief get a reference value (implicit)
+    /// @sa https://json.nlohmann.me/api/basic_json/get_ref/
+    template<typename ReferenceType, typename std::enable_if<
+                 std::is_reference<ReferenceType>::value, int>::type = 0>
+    ReferenceType get_ref()
+    {
+        // delegate call to get_ref_impl
+        return get_ref_impl<ReferenceType>(*this);
+    }
+
+    /// @brief get a reference value (implicit)
+    /// @sa https://json.nlohmann.me/api/basic_json/get_ref/
+    template < typename ReferenceType, typename std::enable_if <
+                   std::is_reference<ReferenceType>::value&&
+                   std::is_const<typename std::remove_reference<ReferenceType>::type>::value, int >::type = 0 >
+    ReferenceType get_ref() const
+    {
+        // delegate call to get_ref_impl
+        return get_ref_impl<ReferenceType>(*this);
+    }
+
+    /*!
+    @brief get a value (implicit)
+
+    Implicit type conversion between the JSON value and a compatible value.
+    The call is realized by calling @ref get() const.
+
+    @tparam ValueType non-pointer type compatible to the JSON value, for
+    instance `int` for JSON integer numbers, `bool` for JSON booleans, or
+    `std::vector` types for JSON arrays. The character type of @ref string_t
+    as well as an initializer list of this type is excluded to avoid
+    ambiguities as these types implicitly convert to `std::string`.
+
+    @return copy of the JSON value, converted to type @a ValueType
+
+    @throw type_error.302 in case passed type @a ValueType is incompatible
+    to the JSON value type (e.g., the JSON value is of type boolean, but a
+    string is requested); see example below
+
+    @complexity Linear in the size of the JSON value.
+
+    @liveexample{The example below shows several conversions from JSON values
+    to other types. There a few things to note: (1) Floating-point numbers can
+    be converted to integers\, (2) A JSON array can be converted to a standard
+    `std::vector<short>`\, (3) A JSON object can be converted to C++
+    associative containers such as `std::unordered_map<std::string\,
+    json>`.,operator__ValueType}
+
+    @since version 1.0.0
+    */
+    template < typename ValueType, typename std::enable_if <
+                   detail::conjunction <
+                       detail::negation<std::is_pointer<ValueType>>,
+                       detail::negation<std::is_same<ValueType, std::nullptr_t>>,
+                       detail::negation<std::is_same<ValueType, detail::json_ref<basic_json>>>,
+                                        detail::negation<std::is_same<ValueType, typename string_t::value_type>>,
+                                        detail::negation<detail::is_basic_json<ValueType>>,
+                                        detail::negation<std::is_same<ValueType, std::initializer_list<typename string_t::value_type>>>,
+#if defined(JSON_HAS_CPP_17) && (defined(__GNUC__) || (defined(_MSC_VER) && _MSC_VER >= 1910 && _MSC_VER <= 1914))
+                                                detail::negation<std::is_same<ValueType, std::string_view>>,
+#endif
+#if defined(JSON_HAS_CPP_17) && JSON_HAS_STATIC_RTTI
+                                                detail::negation<std::is_same<ValueType, std::any>>,
+#endif
+                                                detail::is_detected_lazy<detail::get_template_function, const basic_json_t&, ValueType>
+                                                >::value, int >::type = 0 >
+                                        JSON_EXPLICIT operator ValueType() const
+    {
+        // delegate the call to get<>() const
+        return get<ValueType>();
+    }
+
+    /// @brief get a binary value
+    /// @sa https://json.nlohmann.me/api/basic_json/get_binary/
+    binary_t& get_binary()
+    {
+        if (!is_binary())
+        {
+            JSON_THROW(type_error::create(302, detail::concat("type must be binary, but is ", type_name()), this));
+        }
+
+        return *get_ptr<binary_t*>();
+    }
+
+    /// @brief get a binary value
+    /// @sa https://json.nlohmann.me/api/basic_json/get_binary/
+    const binary_t& get_binary() const
+    {
+        if (!is_binary())
+        {
+            JSON_THROW(type_error::create(302, detail::concat("type must be binary, but is ", type_name()), this));
+        }
+
+        return *get_ptr<const binary_t*>();
+    }
+
+    /// @}
+
+    ////////////////////
+    // element access //
+    ////////////////////
+
+    /// @name element access
+    /// Access to the JSON value.
+    /// @{
+
+    /// @brief access specified array element with bounds checking
+    /// @sa https://json.nlohmann.me/api/basic_json/at/
+    reference at(size_type idx)
+    {
+        // at only works for arrays
+        if (JSON_HEDLEY_LIKELY(is_array()))
+        {
+            JSON_TRY
+            {
+                return set_parent(m_data.m_value.array->at(idx));
+            }
+            JSON_CATCH (std::out_of_range&)
+            {
+                // create better exception explanation
+                JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), this));
+            }
+        }
+        else
+        {
+            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
+        }
+    }
+
+    /// @brief access specified array element with bounds checking
+    /// @sa https://json.nlohmann.me/api/basic_json/at/
+    const_reference at(size_type idx) const
+    {
+        // at only works for arrays
+        if (JSON_HEDLEY_LIKELY(is_array()))
+        {
+            JSON_TRY
+            {
+                return m_data.m_value.array->at(idx);
+            }
+            JSON_CATCH (std::out_of_range&)
+            {
+                // create better exception explanation
+                JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), this));
+            }
+        }
+        else
+        {
+            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
+        }
+    }
+
+    /// @brief access specified object element with bounds checking
+    /// @sa https://json.nlohmann.me/api/basic_json/at/
+    reference at(const typename object_t::key_type& key)
+    {
+        // at only works for objects
+        if (JSON_HEDLEY_UNLIKELY(!is_object()))
+        {
+            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
+        }
+
+        auto it = m_data.m_value.object->find(key);
+        if (it == m_data.m_value.object->end())
+        {
+            JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this));
+        }
+        return set_parent(it->second);
+    }
+
+    /// @brief access specified object element with bounds checking
+    /// @sa https://json.nlohmann.me/api/basic_json/at/
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
+    reference at(KeyType && key)
+    {
+        // at only works for objects
+        if (JSON_HEDLEY_UNLIKELY(!is_object()))
+        {
+            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
+        }
+
+        auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+        if (it == m_data.m_value.object->end())
+        {
+            JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward<KeyType>(key)), "' not found"), this));
+        }
+        return set_parent(it->second);
+    }
+
+    /// @brief access specified object element with bounds checking
+    /// @sa https://json.nlohmann.me/api/basic_json/at/
+    const_reference at(const typename object_t::key_type& key) const
+    {
+        // at only works for objects
+        if (JSON_HEDLEY_UNLIKELY(!is_object()))
+        {
+            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
+        }
+
+        auto it = m_data.m_value.object->find(key);
+        if (it == m_data.m_value.object->end())
+        {
+            JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this));
+        }
+        return it->second;
+    }
+
+    /// @brief access specified object element with bounds checking
+    /// @sa https://json.nlohmann.me/api/basic_json/at/
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
+    const_reference at(KeyType && key) const
+    {
+        // at only works for objects
+        if (JSON_HEDLEY_UNLIKELY(!is_object()))
+        {
+            JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
+        }
+
+        auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+        if (it == m_data.m_value.object->end())
+        {
+            JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward<KeyType>(key)), "' not found"), this));
+        }
+        return it->second;
+    }
+
+    /// @brief access specified array element
+    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
+    reference operator[](size_type idx)
+    {
+        // implicitly convert null value to an empty array
+        if (is_null())
+        {
+            m_data.m_type = value_t::array;
+            m_data.m_value.array = create<array_t>();
+            assert_invariant();
+        }
+
+        // operator[] only works for arrays
+        if (JSON_HEDLEY_LIKELY(is_array()))
+        {
+            // fill up array with null values if given idx is outside range
+            if (idx >= m_data.m_value.array->size())
+            {
+#if JSON_DIAGNOSTICS
+                // remember array size & capacity before resizing
+                const auto old_size = m_data.m_value.array->size();
+                const auto old_capacity = m_data.m_value.array->capacity();
+#endif
+                m_data.m_value.array->resize(idx + 1);
+
+#if JSON_DIAGNOSTICS
+                if (JSON_HEDLEY_UNLIKELY(m_data.m_value.array->capacity() != old_capacity))
+                {
+                    // capacity has changed: update all parents
+                    set_parents();
+                }
+                else
+                {
+                    // set parent for values added above
+                    set_parents(begin() + static_cast<typename iterator::difference_type>(old_size), static_cast<typename iterator::difference_type>(idx + 1 - old_size));
+                }
+#endif
+                assert_invariant();
+            }
+
+            return m_data.m_value.array->operator[](idx);
+        }
+
+        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this));
+    }
+
+    /// @brief access specified array element
+    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
+    const_reference operator[](size_type idx) const
+    {
+        // const operator[] only works for arrays
+        if (JSON_HEDLEY_LIKELY(is_array()))
+        {
+            return m_data.m_value.array->operator[](idx);
+        }
+
+        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this));
+    }
+
+    /// @brief access specified object element
+    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
+    reference operator[](typename object_t::key_type key)
+    {
+        // implicitly convert null value to an empty object
+        if (is_null())
+        {
+            m_data.m_type = value_t::object;
+            m_data.m_value.object = create<object_t>();
+            assert_invariant();
+        }
+
+        // operator[] only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            auto result = m_data.m_value.object->emplace(std::move(key), nullptr);
+            return set_parent(result.first->second);
+        }
+
+        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a string argument with ", type_name()), this));
+    }
+
+    /// @brief access specified object element
+    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
+    const_reference operator[](const typename object_t::key_type& key) const
+    {
+        // const operator[] only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            auto it = m_data.m_value.object->find(key);
+            JSON_ASSERT(it != m_data.m_value.object->end());
+            return it->second;
+        }
+
+        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a string argument with ", type_name()), this));
+    }
+
+    // these two functions resolve a (const) char * ambiguity affecting Clang and MSVC
+    // (they seemingly cannot be constrained to resolve the ambiguity)
+    template<typename T>
+    reference operator[](T* key)
+    {
+        return operator[](typename object_t::key_type(key));
+    }
+
+    template<typename T>
+    const_reference operator[](T* key) const
+    {
+        return operator[](typename object_t::key_type(key));
+    }
+
+    /// @brief access specified object element
+    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int > = 0 >
+    reference operator[](KeyType && key)
+    {
+        // implicitly convert null value to an empty object
+        if (is_null())
+        {
+            m_data.m_type = value_t::object;
+            m_data.m_value.object = create<object_t>();
+            assert_invariant();
+        }
+
+        // operator[] only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            auto result = m_data.m_value.object->emplace(std::forward<KeyType>(key), nullptr);
+            return set_parent(result.first->second);
+        }
+
+        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a string argument with ", type_name()), this));
+    }
+
+    /// @brief access specified object element
+    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int > = 0 >
+    const_reference operator[](KeyType && key) const
+    {
+        // const operator[] only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+            JSON_ASSERT(it != m_data.m_value.object->end());
+            return it->second;
+        }
+
+        JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a string argument with ", type_name()), this));
+    }
+
+  private:
+    template<typename KeyType>
+    using is_comparable_with_object_key = detail::is_comparable <
+        object_comparator_t, const typename object_t::key_type&, KeyType >;
+
+    template<typename ValueType>
+    using value_return_type = std::conditional <
+        detail::is_c_string_uncvref<ValueType>::value,
+        string_t, typename std::decay<ValueType>::type >;
+
+  public:
+    /// @brief access specified object element with default value
+    /// @sa https://json.nlohmann.me/api/basic_json/value/
+    template < class ValueType, detail::enable_if_t <
+                   !detail::is_transparent<object_comparator_t>::value
+                   && detail::is_getable<basic_json_t, ValueType>::value
+                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
+    ValueType value(const typename object_t::key_type& key, const ValueType& default_value) const
+    {
+        // value only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            // if key is found, return value and given default value otherwise
+            const auto it = find(key);
+            if (it != end())
+            {
+                return it->template get<ValueType>();
+            }
+
+            return default_value;
+        }
+
+        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
+    }
+
+    /// @brief access specified object element with default value
+    /// @sa https://json.nlohmann.me/api/basic_json/value/
+    template < class ValueType, class ReturnType = typename value_return_type<ValueType>::type,
+               detail::enable_if_t <
+                   !detail::is_transparent<object_comparator_t>::value
+                   && detail::is_getable<basic_json_t, ReturnType>::value
+                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
+    ReturnType value(const typename object_t::key_type& key, ValueType && default_value) const
+    {
+        // value only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            // if key is found, return value and given default value otherwise
+            const auto it = find(key);
+            if (it != end())
+            {
+                return it->template get<ReturnType>();
+            }
+
+            return std::forward<ValueType>(default_value);
+        }
+
+        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
+    }
+
+    /// @brief access specified object element with default value
+    /// @sa https://json.nlohmann.me/api/basic_json/value/
+    template < class ValueType, class KeyType, detail::enable_if_t <
+                   detail::is_transparent<object_comparator_t>::value
+                   && !detail::is_json_pointer<KeyType>::value
+                   && is_comparable_with_object_key<KeyType>::value
+                   && detail::is_getable<basic_json_t, ValueType>::value
+                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
+    ValueType value(KeyType && key, const ValueType& default_value) const
+    {
+        // value only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            // if key is found, return value and given default value otherwise
+            const auto it = find(std::forward<KeyType>(key));
+            if (it != end())
+            {
+                return it->template get<ValueType>();
+            }
+
+            return default_value;
+        }
+
+        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
+    }
+
+    /// @brief access specified object element via JSON Pointer with default value
+    /// @sa https://json.nlohmann.me/api/basic_json/value/
+    template < class ValueType, class KeyType, class ReturnType = typename value_return_type<ValueType>::type,
+               detail::enable_if_t <
+                   detail::is_transparent<object_comparator_t>::value
+                   && !detail::is_json_pointer<KeyType>::value
+                   && is_comparable_with_object_key<KeyType>::value
+                   && detail::is_getable<basic_json_t, ReturnType>::value
+                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
+    ReturnType value(KeyType && key, ValueType && default_value) const
+    {
+        // value only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            // if key is found, return value and given default value otherwise
+            const auto it = find(std::forward<KeyType>(key));
+            if (it != end())
+            {
+                return it->template get<ReturnType>();
+            }
+
+            return std::forward<ValueType>(default_value);
+        }
+
+        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
+    }
+
+    /// @brief access specified object element via JSON Pointer with default value
+    /// @sa https://json.nlohmann.me/api/basic_json/value/
+    template < class ValueType, detail::enable_if_t <
+                   detail::is_getable<basic_json_t, ValueType>::value
+                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
+    ValueType value(const json_pointer& ptr, const ValueType& default_value) const
+    {
+        // value only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            // if pointer resolves a value, return it or use default value
+            JSON_TRY
+            {
+                return ptr.get_checked(this).template get<ValueType>();
+            }
+            JSON_INTERNAL_CATCH (out_of_range&)
+            {
+                return default_value;
+            }
+        }
+
+        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
+    }
+
+    /// @brief access specified object element via JSON Pointer with default value
+    /// @sa https://json.nlohmann.me/api/basic_json/value/
+    template < class ValueType, class ReturnType = typename value_return_type<ValueType>::type,
+               detail::enable_if_t <
+                   detail::is_getable<basic_json_t, ReturnType>::value
+                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
+    ReturnType value(const json_pointer& ptr, ValueType && default_value) const
+    {
+        // value only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            // if pointer resolves a value, return it or use default value
+            JSON_TRY
+            {
+                return ptr.get_checked(this).template get<ReturnType>();
+            }
+            JSON_INTERNAL_CATCH (out_of_range&)
+            {
+                return std::forward<ValueType>(default_value);
+            }
+        }
+
+        JSON_THROW(type_error::create(306, detail::concat("cannot use value() with ", type_name()), this));
+    }
+
+    template < class ValueType, class BasicJsonType, detail::enable_if_t <
+                   detail::is_basic_json<BasicJsonType>::value
+                   && detail::is_getable<basic_json_t, ValueType>::value
+                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
+    ValueType value(const ::nlohmann::json_pointer<BasicJsonType>& ptr, const ValueType& default_value) const
+    {
+        return value(ptr.convert(), default_value);
+    }
+
+    template < class ValueType, class BasicJsonType, class ReturnType = typename value_return_type<ValueType>::type,
+               detail::enable_if_t <
+                   detail::is_basic_json<BasicJsonType>::value
+                   && detail::is_getable<basic_json_t, ReturnType>::value
+                   && !std::is_same<value_t, detail::uncvref_t<ValueType>>::value, int > = 0 >
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
+    ReturnType value(const ::nlohmann::json_pointer<BasicJsonType>& ptr, ValueType && default_value) const
+    {
+        return value(ptr.convert(), std::forward<ValueType>(default_value));
+    }
+
+    /// @brief access the first element
+    /// @sa https://json.nlohmann.me/api/basic_json/front/
+    reference front()
+    {
+        return *begin();
+    }
+
+    /// @brief access the first element
+    /// @sa https://json.nlohmann.me/api/basic_json/front/
+    const_reference front() const
+    {
+        return *cbegin();
+    }
+
+    /// @brief access the last element
+    /// @sa https://json.nlohmann.me/api/basic_json/back/
+    reference back()
+    {
+        auto tmp = end();
+        --tmp;
+        return *tmp;
+    }
+
+    /// @brief access the last element
+    /// @sa https://json.nlohmann.me/api/basic_json/back/
+    const_reference back() const
+    {
+        auto tmp = cend();
+        --tmp;
+        return *tmp;
+    }
+
+    /// @brief remove element given an iterator
+    /// @sa https://json.nlohmann.me/api/basic_json/erase/
+    template < class IteratorType, detail::enable_if_t <
+                   std::is_same<IteratorType, typename basic_json_t::iterator>::value ||
+                   std::is_same<IteratorType, typename basic_json_t::const_iterator>::value, int > = 0 >
+    IteratorType erase(IteratorType pos)
+    {
+        // make sure iterator fits the current value
+        if (JSON_HEDLEY_UNLIKELY(this != pos.m_object))
+        {
+            JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", this));
+        }
+
+        IteratorType result = end();
+
+        switch (m_data.m_type)
+        {
+            case value_t::boolean:
+            case value_t::number_float:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::string:
+            case value_t::binary:
+            {
+                if (JSON_HEDLEY_UNLIKELY(!pos.m_it.primitive_iterator.is_begin()))
+                {
+                    JSON_THROW(invalid_iterator::create(205, "iterator out of range", this));
+                }
+
+                if (is_string())
+                {
+                    AllocatorType<string_t> alloc;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.string);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.string, 1);
+                    m_data.m_value.string = nullptr;
+                }
+                else if (is_binary())
+                {
+                    AllocatorType<binary_t> alloc;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.binary);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.binary, 1);
+                    m_data.m_value.binary = nullptr;
+                }
+
+                m_data.m_type = value_t::null;
+                assert_invariant();
+                break;
+            }
+
+            case value_t::object:
+            {
+                result.m_it.object_iterator = m_data.m_value.object->erase(pos.m_it.object_iterator);
+                break;
+            }
+
+            case value_t::array:
+            {
+                result.m_it.array_iterator = m_data.m_value.array->erase(pos.m_it.array_iterator);
+                break;
+            }
+
+            case value_t::null:
+            case value_t::discarded:
+            default:
+                JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
+        }
+
+        return result;
+    }
+
+    /// @brief remove elements given an iterator range
+    /// @sa https://json.nlohmann.me/api/basic_json/erase/
+    template < class IteratorType, detail::enable_if_t <
+                   std::is_same<IteratorType, typename basic_json_t::iterator>::value ||
+                   std::is_same<IteratorType, typename basic_json_t::const_iterator>::value, int > = 0 >
+    IteratorType erase(IteratorType first, IteratorType last)
+    {
+        // make sure iterator fits the current value
+        if (JSON_HEDLEY_UNLIKELY(this != first.m_object || this != last.m_object))
+        {
+            JSON_THROW(invalid_iterator::create(203, "iterators do not fit current value", this));
+        }
+
+        IteratorType result = end();
+
+        switch (m_data.m_type)
+        {
+            case value_t::boolean:
+            case value_t::number_float:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::string:
+            case value_t::binary:
+            {
+                if (JSON_HEDLEY_LIKELY(!first.m_it.primitive_iterator.is_begin()
+                                       || !last.m_it.primitive_iterator.is_end()))
+                {
+                    JSON_THROW(invalid_iterator::create(204, "iterators out of range", this));
+                }
+
+                if (is_string())
+                {
+                    AllocatorType<string_t> alloc;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.string);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.string, 1);
+                    m_data.m_value.string = nullptr;
+                }
+                else if (is_binary())
+                {
+                    AllocatorType<binary_t> alloc;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.binary);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.binary, 1);
+                    m_data.m_value.binary = nullptr;
+                }
+
+                m_data.m_type = value_t::null;
+                assert_invariant();
+                break;
+            }
+
+            case value_t::object:
+            {
+                result.m_it.object_iterator = m_data.m_value.object->erase(first.m_it.object_iterator,
+                                              last.m_it.object_iterator);
+                break;
+            }
+
+            case value_t::array:
+            {
+                result.m_it.array_iterator = m_data.m_value.array->erase(first.m_it.array_iterator,
+                                             last.m_it.array_iterator);
+                break;
+            }
+
+            case value_t::null:
+            case value_t::discarded:
+            default:
+                JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
+        }
+
+        return result;
+    }
+
+  private:
+    template < typename KeyType, detail::enable_if_t <
+                   detail::has_erase_with_key_type<basic_json_t, KeyType>::value, int > = 0 >
+    size_type erase_internal(KeyType && key)
+    {
+        // this erase only works for objects
+        if (JSON_HEDLEY_UNLIKELY(!is_object()))
+        {
+            JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
+        }
+
+        return m_data.m_value.object->erase(std::forward<KeyType>(key));
+    }
+
+    template < typename KeyType, detail::enable_if_t <
+                   !detail::has_erase_with_key_type<basic_json_t, KeyType>::value, int > = 0 >
+    size_type erase_internal(KeyType && key)
+    {
+        // this erase only works for objects
+        if (JSON_HEDLEY_UNLIKELY(!is_object()))
+        {
+            JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
+        }
+
+        const auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+        if (it != m_data.m_value.object->end())
+        {
+            m_data.m_value.object->erase(it);
+            return 1;
+        }
+        return 0;
+    }
+
+  public:
+
+    /// @brief remove element from a JSON object given a key
+    /// @sa https://json.nlohmann.me/api/basic_json/erase/
+    size_type erase(const typename object_t::key_type& key)
+    {
+        // the indirection via erase_internal() is added to avoid making this
+        // function a template and thus de-rank it during overload resolution
+        return erase_internal(key);
+    }
+
+    /// @brief remove element from a JSON object given a key
+    /// @sa https://json.nlohmann.me/api/basic_json/erase/
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
+    size_type erase(KeyType && key)
+    {
+        return erase_internal(std::forward<KeyType>(key));
+    }
+
+    /// @brief remove element from a JSON array given an index
+    /// @sa https://json.nlohmann.me/api/basic_json/erase/
+    void erase(const size_type idx)
+    {
+        // this erase only works for arrays
+        if (JSON_HEDLEY_LIKELY(is_array()))
+        {
+            if (JSON_HEDLEY_UNLIKELY(idx >= size()))
+            {
+                JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), this));
+            }
+
+            m_data.m_value.array->erase(m_data.m_value.array->begin() + static_cast<difference_type>(idx));
+        }
+        else
+        {
+            JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
+        }
+    }
+
+    /// @}
+
+    ////////////
+    // lookup //
+    ////////////
+
+    /// @name lookup
+    /// @{
+
+    /// @brief find an element in a JSON object
+    /// @sa https://json.nlohmann.me/api/basic_json/find/
+    iterator find(const typename object_t::key_type& key)
+    {
+        auto result = end();
+
+        if (is_object())
+        {
+            result.m_it.object_iterator = m_data.m_value.object->find(key);
+        }
+
+        return result;
+    }
+
+    /// @brief find an element in a JSON object
+    /// @sa https://json.nlohmann.me/api/basic_json/find/
+    const_iterator find(const typename object_t::key_type& key) const
+    {
+        auto result = cend();
+
+        if (is_object())
+        {
+            result.m_it.object_iterator = m_data.m_value.object->find(key);
+        }
+
+        return result;
+    }
+
+    /// @brief find an element in a JSON object
+    /// @sa https://json.nlohmann.me/api/basic_json/find/
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
+    iterator find(KeyType && key)
+    {
+        auto result = end();
+
+        if (is_object())
+        {
+            result.m_it.object_iterator = m_data.m_value.object->find(std::forward<KeyType>(key));
+        }
+
+        return result;
+    }
+
+    /// @brief find an element in a JSON object
+    /// @sa https://json.nlohmann.me/api/basic_json/find/
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
+    const_iterator find(KeyType && key) const
+    {
+        auto result = cend();
+
+        if (is_object())
+        {
+            result.m_it.object_iterator = m_data.m_value.object->find(std::forward<KeyType>(key));
+        }
+
+        return result;
+    }
+
+    /// @brief returns the number of occurrences of a key in a JSON object
+    /// @sa https://json.nlohmann.me/api/basic_json/count/
+    size_type count(const typename object_t::key_type& key) const
+    {
+        // return 0 for all nonobject types
+        return is_object() ? m_data.m_value.object->count(key) : 0;
+    }
+
+    /// @brief returns the number of occurrences of a key in a JSON object
+    /// @sa https://json.nlohmann.me/api/basic_json/count/
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
+    size_type count(KeyType && key) const
+    {
+        // return 0 for all nonobject types
+        return is_object() ? m_data.m_value.object->count(std::forward<KeyType>(key)) : 0;
+    }
+
+    /// @brief check the existence of an element in a JSON object
+    /// @sa https://json.nlohmann.me/api/basic_json/contains/
+    bool contains(const typename object_t::key_type& key) const
+    {
+        return is_object() && m_data.m_value.object->find(key) != m_data.m_value.object->end();
+    }
+
+    /// @brief check the existence of an element in a JSON object
+    /// @sa https://json.nlohmann.me/api/basic_json/contains/
+    template<class KeyType, detail::enable_if_t<
+                 detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
+    bool contains(KeyType && key) const
+    {
+        return is_object() && m_data.m_value.object->find(std::forward<KeyType>(key)) != m_data.m_value.object->end();
+    }
+
+    /// @brief check the existence of an element in a JSON object given a JSON pointer
+    /// @sa https://json.nlohmann.me/api/basic_json/contains/
+    bool contains(const json_pointer& ptr) const
+    {
+        return ptr.contains(this);
+    }
+
+    template<typename BasicJsonType, detail::enable_if_t<detail::is_basic_json<BasicJsonType>::value, int> = 0>
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
+    bool contains(const typename ::nlohmann::json_pointer<BasicJsonType>& ptr) const
+    {
+        return ptr.contains(this);
+    }
+
+    /// @}
+
+    ///////////////
+    // iterators //
+    ///////////////
+
+    /// @name iterators
+    /// @{
+
+    /// @brief returns an iterator to the first element
+    /// @sa https://json.nlohmann.me/api/basic_json/begin/
+    iterator begin() noexcept
+    {
+        iterator result(this);
+        result.set_begin();
+        return result;
+    }
+
+    /// @brief returns an iterator to the first element
+    /// @sa https://json.nlohmann.me/api/basic_json/begin/
+    const_iterator begin() const noexcept
+    {
+        return cbegin();
+    }
+
+    /// @brief returns a const iterator to the first element
+    /// @sa https://json.nlohmann.me/api/basic_json/cbegin/
+    const_iterator cbegin() const noexcept
+    {
+        const_iterator result(this);
+        result.set_begin();
+        return result;
+    }
+
+    /// @brief returns an iterator to one past the last element
+    /// @sa https://json.nlohmann.me/api/basic_json/end/
+    iterator end() noexcept
+    {
+        iterator result(this);
+        result.set_end();
+        return result;
+    }
+
+    /// @brief returns an iterator to one past the last element
+    /// @sa https://json.nlohmann.me/api/basic_json/end/
+    const_iterator end() const noexcept
+    {
+        return cend();
+    }
+
+    /// @brief returns an iterator to one past the last element
+    /// @sa https://json.nlohmann.me/api/basic_json/cend/
+    const_iterator cend() const noexcept
+    {
+        const_iterator result(this);
+        result.set_end();
+        return result;
+    }
+
+    /// @brief returns an iterator to the reverse-beginning
+    /// @sa https://json.nlohmann.me/api/basic_json/rbegin/
+    reverse_iterator rbegin() noexcept
+    {
+        return reverse_iterator(end());
+    }
+
+    /// @brief returns an iterator to the reverse-beginning
+    /// @sa https://json.nlohmann.me/api/basic_json/rbegin/
+    const_reverse_iterator rbegin() const noexcept
+    {
+        return crbegin();
+    }
+
+    /// @brief returns an iterator to the reverse-end
+    /// @sa https://json.nlohmann.me/api/basic_json/rend/
+    reverse_iterator rend() noexcept
+    {
+        return reverse_iterator(begin());
+    }
+
+    /// @brief returns an iterator to the reverse-end
+    /// @sa https://json.nlohmann.me/api/basic_json/rend/
+    const_reverse_iterator rend() const noexcept
+    {
+        return crend();
+    }
+
+    /// @brief returns a const reverse iterator to the last element
+    /// @sa https://json.nlohmann.me/api/basic_json/crbegin/
+    const_reverse_iterator crbegin() const noexcept
+    {
+        return const_reverse_iterator(cend());
+    }
+
+    /// @brief returns a const reverse iterator to one before the first
+    /// @sa https://json.nlohmann.me/api/basic_json/crend/
+    const_reverse_iterator crend() const noexcept
+    {
+        return const_reverse_iterator(cbegin());
+    }
+
+  public:
+    /// @brief wrapper to access iterator member functions in range-based for
+    /// @sa https://json.nlohmann.me/api/basic_json/items/
+    /// @deprecated This function is deprecated since 3.1.0 and will be removed in
+    ///             version 4.0.0 of the library. Please use @ref items() instead;
+    ///             that is, replace `json::iterator_wrapper(j)` with `j.items()`.
+    JSON_HEDLEY_DEPRECATED_FOR(3.1.0, items())
+    static iteration_proxy<iterator> iterator_wrapper(reference ref) noexcept
+    {
+        return ref.items();
+    }
+
+    /// @brief wrapper to access iterator member functions in range-based for
+    /// @sa https://json.nlohmann.me/api/basic_json/items/
+    /// @deprecated This function is deprecated since 3.1.0 and will be removed in
+    ///         version 4.0.0 of the library. Please use @ref items() instead;
+    ///         that is, replace `json::iterator_wrapper(j)` with `j.items()`.
+    JSON_HEDLEY_DEPRECATED_FOR(3.1.0, items())
+    static iteration_proxy<const_iterator> iterator_wrapper(const_reference ref) noexcept
+    {
+        return ref.items();
+    }
+
+    /// @brief helper to access iterator member functions in range-based for
+    /// @sa https://json.nlohmann.me/api/basic_json/items/
+    iteration_proxy<iterator> items() noexcept
+    {
+        return iteration_proxy<iterator>(*this);
+    }
+
+    /// @brief helper to access iterator member functions in range-based for
+    /// @sa https://json.nlohmann.me/api/basic_json/items/
+    iteration_proxy<const_iterator> items() const noexcept
+    {
+        return iteration_proxy<const_iterator>(*this);
+    }
+
+    /// @}
+
+    //////////////
+    // capacity //
+    //////////////
+
+    /// @name capacity
+    /// @{
+
+    /// @brief checks whether the container is empty.
+    /// @sa https://json.nlohmann.me/api/basic_json/empty/
+    bool empty() const noexcept
+    {
+        switch (m_data.m_type)
+        {
+            case value_t::null:
+            {
+                // null values are empty
+                return true;
+            }
+
+            case value_t::array:
+            {
+                // delegate call to array_t::empty()
+                return m_data.m_value.array->empty();
+            }
+
+            case value_t::object:
+            {
+                // delegate call to object_t::empty()
+                return m_data.m_value.object->empty();
+            }
+
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                // all other types are nonempty
+                return false;
+            }
+        }
+    }
+
+    /// @brief returns the number of elements
+    /// @sa https://json.nlohmann.me/api/basic_json/size/
+    size_type size() const noexcept
+    {
+        switch (m_data.m_type)
+        {
+            case value_t::null:
+            {
+                // null values are empty
+                return 0;
+            }
+
+            case value_t::array:
+            {
+                // delegate call to array_t::size()
+                return m_data.m_value.array->size();
+            }
+
+            case value_t::object:
+            {
+                // delegate call to object_t::size()
+                return m_data.m_value.object->size();
+            }
+
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                // all other types have size 1
+                return 1;
+            }
+        }
+    }
+
+    /// @brief returns the maximum possible number of elements
+    /// @sa https://json.nlohmann.me/api/basic_json/max_size/
+    size_type max_size() const noexcept
+    {
+        switch (m_data.m_type)
+        {
+            case value_t::array:
+            {
+                // delegate call to array_t::max_size()
+                return m_data.m_value.array->max_size();
+            }
+
+            case value_t::object:
+            {
+                // delegate call to object_t::max_size()
+                return m_data.m_value.object->max_size();
+            }
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                // all other types have max_size() == size()
+                return size();
+            }
+        }
+    }
+
+    /// @}
+
+    ///////////////
+    // modifiers //
+    ///////////////
+
+    /// @name modifiers
+    /// @{
+
+    /// @brief clears the contents
+    /// @sa https://json.nlohmann.me/api/basic_json/clear/
+    void clear() noexcept
+    {
+        switch (m_data.m_type)
+        {
+            case value_t::number_integer:
+            {
+                m_data.m_value.number_integer = 0;
+                break;
+            }
+
+            case value_t::number_unsigned:
+            {
+                m_data.m_value.number_unsigned = 0;
+                break;
+            }
+
+            case value_t::number_float:
+            {
+                m_data.m_value.number_float = 0.0;
+                break;
+            }
+
+            case value_t::boolean:
+            {
+                m_data.m_value.boolean = false;
+                break;
+            }
+
+            case value_t::string:
+            {
+                m_data.m_value.string->clear();
+                break;
+            }
+
+            case value_t::binary:
+            {
+                m_data.m_value.binary->clear();
+                break;
+            }
+
+            case value_t::array:
+            {
+                m_data.m_value.array->clear();
+                break;
+            }
+
+            case value_t::object:
+            {
+                m_data.m_value.object->clear();
+                break;
+            }
+
+            case value_t::null:
+            case value_t::discarded:
+            default:
+                break;
+        }
+    }
+
+    /// @brief add an object to an array
+    /// @sa https://json.nlohmann.me/api/basic_json/push_back/
+    void push_back(basic_json&& val)
+    {
+        // push_back only works for null objects or arrays
+        if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_array())))
+        {
+            JSON_THROW(type_error::create(308, detail::concat("cannot use push_back() with ", type_name()), this));
+        }
+
+        // transform null object into an array
+        if (is_null())
+        {
+            m_data.m_type = value_t::array;
+            m_data.m_value = value_t::array;
+            assert_invariant();
+        }
+
+        // add element to array (move semantics)
+        const auto old_capacity = m_data.m_value.array->capacity();
+        m_data.m_value.array->push_back(std::move(val));
+        set_parent(m_data.m_value.array->back(), old_capacity);
+        // if val is moved from, basic_json move constructor marks it null, so we do not call the destructor
+    }
+
+    /// @brief add an object to an array
+    /// @sa https://json.nlohmann.me/api/basic_json/operator+=/
+    reference operator+=(basic_json&& val)
+    {
+        push_back(std::move(val));
+        return *this;
+    }
+
+    /// @brief add an object to an array
+    /// @sa https://json.nlohmann.me/api/basic_json/push_back/
+    void push_back(const basic_json& val)
+    {
+        // push_back only works for null objects or arrays
+        if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_array())))
+        {
+            JSON_THROW(type_error::create(308, detail::concat("cannot use push_back() with ", type_name()), this));
+        }
+
+        // transform null object into an array
+        if (is_null())
+        {
+            m_data.m_type = value_t::array;
+            m_data.m_value = value_t::array;
+            assert_invariant();
+        }
+
+        // add element to array
+        const auto old_capacity = m_data.m_value.array->capacity();
+        m_data.m_value.array->push_back(val);
+        set_parent(m_data.m_value.array->back(), old_capacity);
+    }
+
+    /// @brief add an object to an array
+    /// @sa https://json.nlohmann.me/api/basic_json/operator+=/
+    reference operator+=(const basic_json& val)
+    {
+        push_back(val);
+        return *this;
+    }
+
+    /// @brief add an object to an object
+    /// @sa https://json.nlohmann.me/api/basic_json/push_back/
+    void push_back(const typename object_t::value_type& val)
+    {
+        // push_back only works for null objects or objects
+        if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_object())))
+        {
+            JSON_THROW(type_error::create(308, detail::concat("cannot use push_back() with ", type_name()), this));
+        }
+
+        // transform null object into an object
+        if (is_null())
+        {
+            m_data.m_type = value_t::object;
+            m_data.m_value = value_t::object;
+            assert_invariant();
+        }
+
+        // add element to object
+        auto res = m_data.m_value.object->insert(val);
+        set_parent(res.first->second);
+    }
+
+    /// @brief add an object to an object
+    /// @sa https://json.nlohmann.me/api/basic_json/operator+=/
+    reference operator+=(const typename object_t::value_type& val)
+    {
+        push_back(val);
+        return *this;
+    }
+
+    /// @brief add an object to an object
+    /// @sa https://json.nlohmann.me/api/basic_json/push_back/
+    void push_back(initializer_list_t init)
+    {
+        if (is_object() && init.size() == 2 && (*init.begin())->is_string())
+        {
+            basic_json&& key = init.begin()->moved_or_copied();
+            push_back(typename object_t::value_type(
+                          std::move(key.get_ref<string_t&>()), (init.begin() + 1)->moved_or_copied()));
+        }
+        else
+        {
+            push_back(basic_json(init));
+        }
+    }
+
+    /// @brief add an object to an object
+    /// @sa https://json.nlohmann.me/api/basic_json/operator+=/
+    reference operator+=(initializer_list_t init)
+    {
+        push_back(init);
+        return *this;
+    }
+
+    /// @brief add an object to an array
+    /// @sa https://json.nlohmann.me/api/basic_json/emplace_back/
+    template<class... Args>
+    reference emplace_back(Args&& ... args)
+    {
+        // emplace_back only works for null objects or arrays
+        if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_array())))
+        {
+            JSON_THROW(type_error::create(311, detail::concat("cannot use emplace_back() with ", type_name()), this));
+        }
+
+        // transform null object into an array
+        if (is_null())
+        {
+            m_data.m_type = value_t::array;
+            m_data.m_value = value_t::array;
+            assert_invariant();
+        }
+
+        // add element to array (perfect forwarding)
+        const auto old_capacity = m_data.m_value.array->capacity();
+        m_data.m_value.array->emplace_back(std::forward<Args>(args)...);
+        return set_parent(m_data.m_value.array->back(), old_capacity);
+    }
+
+    /// @brief add an object to an object if key does not exist
+    /// @sa https://json.nlohmann.me/api/basic_json/emplace/
+    template<class... Args>
+    std::pair<iterator, bool> emplace(Args&& ... args)
+    {
+        // emplace only works for null objects or arrays
+        if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_object())))
+        {
+            JSON_THROW(type_error::create(311, detail::concat("cannot use emplace() with ", type_name()), this));
+        }
+
+        // transform null object into an object
+        if (is_null())
+        {
+            m_data.m_type = value_t::object;
+            m_data.m_value = value_t::object;
+            assert_invariant();
+        }
+
+        // add element to array (perfect forwarding)
+        auto res = m_data.m_value.object->emplace(std::forward<Args>(args)...);
+        set_parent(res.first->second);
+
+        // create result iterator and set iterator to the result of emplace
+        auto it = begin();
+        it.m_it.object_iterator = res.first;
+
+        // return pair of iterator and boolean
+        return {it, res.second};
+    }
+
+    /// Helper for insertion of an iterator
+    /// @note: This uses std::distance to support GCC 4.8,
+    ///        see https://github.com/nlohmann/json/pull/1257
+    template<typename... Args>
+    iterator insert_iterator(const_iterator pos, Args&& ... args)
+    {
+        iterator result(this);
+        JSON_ASSERT(m_data.m_value.array != nullptr);
+
+        auto insert_pos = std::distance(m_data.m_value.array->begin(), pos.m_it.array_iterator);
+        m_data.m_value.array->insert(pos.m_it.array_iterator, std::forward<Args>(args)...);
+        result.m_it.array_iterator = m_data.m_value.array->begin() + insert_pos;
+
+        // This could have been written as:
+        // result.m_it.array_iterator = m_data.m_value.array->insert(pos.m_it.array_iterator, cnt, val);
+        // but the return value of insert is missing in GCC 4.8, so it is written this way instead.
+
+        set_parents();
+        return result;
+    }
+
+    /// @brief inserts element into array
+    /// @sa https://json.nlohmann.me/api/basic_json/insert/
+    iterator insert(const_iterator pos, const basic_json& val)
+    {
+        // insert only works for arrays
+        if (JSON_HEDLEY_LIKELY(is_array()))
+        {
+            // check if iterator pos fits to this JSON value
+            if (JSON_HEDLEY_UNLIKELY(pos.m_object != this))
+            {
+                JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", this));
+            }
+
+            // insert to array and return iterator
+            return insert_iterator(pos, val);
+        }
+
+        JSON_THROW(type_error::create(309, detail::concat("cannot use insert() with ", type_name()), this));
+    }
+
+    /// @brief inserts element into array
+    /// @sa https://json.nlohmann.me/api/basic_json/insert/
+    iterator insert(const_iterator pos, basic_json&& val)
+    {
+        return insert(pos, val);
+    }
+
+    /// @brief inserts copies of element into array
+    /// @sa https://json.nlohmann.me/api/basic_json/insert/
+    iterator insert(const_iterator pos, size_type cnt, const basic_json& val)
+    {
+        // insert only works for arrays
+        if (JSON_HEDLEY_LIKELY(is_array()))
+        {
+            // check if iterator pos fits to this JSON value
+            if (JSON_HEDLEY_UNLIKELY(pos.m_object != this))
+            {
+                JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", this));
+            }
+
+            // insert to array and return iterator
+            return insert_iterator(pos, cnt, val);
+        }
+
+        JSON_THROW(type_error::create(309, detail::concat("cannot use insert() with ", type_name()), this));
+    }
+
+    /// @brief inserts range of elements into array
+    /// @sa https://json.nlohmann.me/api/basic_json/insert/
+    iterator insert(const_iterator pos, const_iterator first, const_iterator last)
+    {
+        // insert only works for arrays
+        if (JSON_HEDLEY_UNLIKELY(!is_array()))
+        {
+            JSON_THROW(type_error::create(309, detail::concat("cannot use insert() with ", type_name()), this));
+        }
+
+        // check if iterator pos fits to this JSON value
+        if (JSON_HEDLEY_UNLIKELY(pos.m_object != this))
+        {
+            JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", this));
+        }
+
+        // check if range iterators belong to the same JSON object
+        if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object))
+        {
+            JSON_THROW(invalid_iterator::create(210, "iterators do not fit", this));
+        }
+
+        if (JSON_HEDLEY_UNLIKELY(first.m_object == this))
+        {
+            JSON_THROW(invalid_iterator::create(211, "passed iterators may not belong to container", this));
+        }
+
+        // insert to array and return iterator
+        return insert_iterator(pos, first.m_it.array_iterator, last.m_it.array_iterator);
+    }
+
+    /// @brief inserts elements from initializer list into array
+    /// @sa https://json.nlohmann.me/api/basic_json/insert/
+    iterator insert(const_iterator pos, initializer_list_t ilist)
+    {
+        // insert only works for arrays
+        if (JSON_HEDLEY_UNLIKELY(!is_array()))
+        {
+            JSON_THROW(type_error::create(309, detail::concat("cannot use insert() with ", type_name()), this));
+        }
+
+        // check if iterator pos fits to this JSON value
+        if (JSON_HEDLEY_UNLIKELY(pos.m_object != this))
+        {
+            JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", this));
+        }
+
+        // insert to array and return iterator
+        return insert_iterator(pos, ilist.begin(), ilist.end());
+    }
+
+    /// @brief inserts range of elements into object
+    /// @sa https://json.nlohmann.me/api/basic_json/insert/
+    void insert(const_iterator first, const_iterator last)
+    {
+        // insert only works for objects
+        if (JSON_HEDLEY_UNLIKELY(!is_object()))
+        {
+            JSON_THROW(type_error::create(309, detail::concat("cannot use insert() with ", type_name()), this));
+        }
+
+        // check if range iterators belong to the same JSON object
+        if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object))
+        {
+            JSON_THROW(invalid_iterator::create(210, "iterators do not fit", this));
+        }
+
+        // passed iterators must belong to objects
+        if (JSON_HEDLEY_UNLIKELY(!first.m_object->is_object()))
+        {
+            JSON_THROW(invalid_iterator::create(202, "iterators first and last must point to objects", this));
+        }
+
+        m_data.m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator);
+    }
+
+    /// @brief updates a JSON object from another object, overwriting existing keys
+    /// @sa https://json.nlohmann.me/api/basic_json/update/
+    void update(const_reference j, bool merge_objects = false)
+    {
+        update(j.begin(), j.end(), merge_objects);
+    }
+
+    /// @brief updates a JSON object from another object, overwriting existing keys
+    /// @sa https://json.nlohmann.me/api/basic_json/update/
+    void update(const_iterator first, const_iterator last, bool merge_objects = false)
+    {
+        // implicitly convert null value to an empty object
+        if (is_null())
+        {
+            m_data.m_type = value_t::object;
+            m_data.m_value.object = create<object_t>();
+            assert_invariant();
+        }
+
+        if (JSON_HEDLEY_UNLIKELY(!is_object()))
+        {
+            JSON_THROW(type_error::create(312, detail::concat("cannot use update() with ", type_name()), this));
+        }
+
+        // check if range iterators belong to the same JSON object
+        if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object))
+        {
+            JSON_THROW(invalid_iterator::create(210, "iterators do not fit", this));
+        }
+
+        // passed iterators must belong to objects
+        if (JSON_HEDLEY_UNLIKELY(!first.m_object->is_object()))
+        {
+            JSON_THROW(type_error::create(312, detail::concat("cannot use update() with ", first.m_object->type_name()), first.m_object));
+        }
+
+        for (auto it = first; it != last; ++it)
+        {
+            if (merge_objects && it.value().is_object())
+            {
+                auto it2 = m_data.m_value.object->find(it.key());
+                if (it2 != m_data.m_value.object->end())
+                {
+                    it2->second.update(it.value(), true);
+                    continue;
+                }
+            }
+            m_data.m_value.object->operator[](it.key()) = it.value();
+#if JSON_DIAGNOSTICS
+            m_data.m_value.object->operator[](it.key()).m_parent = this;
+#endif
+        }
+    }
+
+    /// @brief exchanges the values
+    /// @sa https://json.nlohmann.me/api/basic_json/swap/
+    void swap(reference other) noexcept (
+        std::is_nothrow_move_constructible<value_t>::value&&
+        std::is_nothrow_move_assignable<value_t>::value&&
+        std::is_nothrow_move_constructible<json_value>::value&& // NOLINT(cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
+        std::is_nothrow_move_assignable<json_value>::value
+    )
+    {
+        std::swap(m_data.m_type, other.m_data.m_type);
+        std::swap(m_data.m_value, other.m_data.m_value);
+
+        set_parents();
+        other.set_parents();
+        assert_invariant();
+    }
+
+    /// @brief exchanges the values
+    /// @sa https://json.nlohmann.me/api/basic_json/swap/
+    friend void swap(reference left, reference right) noexcept (
+        std::is_nothrow_move_constructible<value_t>::value&&
+        std::is_nothrow_move_assignable<value_t>::value&&
+        std::is_nothrow_move_constructible<json_value>::value&& // NOLINT(cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
+        std::is_nothrow_move_assignable<json_value>::value
+    )
+    {
+        left.swap(right);
+    }
+
+    /// @brief exchanges the values
+    /// @sa https://json.nlohmann.me/api/basic_json/swap/
+    void swap(array_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
+    {
+        // swap only works for arrays
+        if (JSON_HEDLEY_LIKELY(is_array()))
+        {
+            using std::swap;
+            swap(*(m_data.m_value.array), other);
+        }
+        else
+        {
+            JSON_THROW(type_error::create(310, detail::concat("cannot use swap(array_t&) with ", type_name()), this));
+        }
+    }
+
+    /// @brief exchanges the values
+    /// @sa https://json.nlohmann.me/api/basic_json/swap/
+    void swap(object_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
+    {
+        // swap only works for objects
+        if (JSON_HEDLEY_LIKELY(is_object()))
+        {
+            using std::swap;
+            swap(*(m_data.m_value.object), other);
+        }
+        else
+        {
+            JSON_THROW(type_error::create(310, detail::concat("cannot use swap(object_t&) with ", type_name()), this));
+        }
+    }
+
+    /// @brief exchanges the values
+    /// @sa https://json.nlohmann.me/api/basic_json/swap/
+    void swap(string_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
+    {
+        // swap only works for strings
+        if (JSON_HEDLEY_LIKELY(is_string()))
+        {
+            using std::swap;
+            swap(*(m_data.m_value.string), other);
+        }
+        else
+        {
+            JSON_THROW(type_error::create(310, detail::concat("cannot use swap(string_t&) with ", type_name()), this));
+        }
+    }
+
+    /// @brief exchanges the values
+    /// @sa https://json.nlohmann.me/api/basic_json/swap/
+    void swap(binary_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
+    {
+        // swap only works for strings
+        if (JSON_HEDLEY_LIKELY(is_binary()))
+        {
+            using std::swap;
+            swap(*(m_data.m_value.binary), other);
+        }
+        else
+        {
+            JSON_THROW(type_error::create(310, detail::concat("cannot use swap(binary_t&) with ", type_name()), this));
+        }
+    }
+
+    /// @brief exchanges the values
+    /// @sa https://json.nlohmann.me/api/basic_json/swap/
+    void swap(typename binary_t::container_type& other) // NOLINT(bugprone-exception-escape)
+    {
+        // swap only works for strings
+        if (JSON_HEDLEY_LIKELY(is_binary()))
+        {
+            using std::swap;
+            swap(*(m_data.m_value.binary), other);
+        }
+        else
+        {
+            JSON_THROW(type_error::create(310, detail::concat("cannot use swap(binary_t::container_type&) with ", type_name()), this));
+        }
+    }
+
+    /// @}
+
+    //////////////////////////////////////////
+    // lexicographical comparison operators //
+    //////////////////////////////////////////
+
+    /// @name lexicographical comparison operators
+    /// @{
+
+    // note parentheses around operands are necessary; see
+    // https://github.com/nlohmann/json/issues/1530
+#define JSON_IMPLEMENT_OPERATOR(op, null_result, unordered_result, default_result)                       \
+    const auto lhs_type = lhs.type();                                                                    \
+    const auto rhs_type = rhs.type();                                                                    \
+    \
+    if (lhs_type == rhs_type) /* NOLINT(readability/braces) */                                           \
+    {                                                                                                    \
+        switch (lhs_type)                                                                                \
+        {                                                                                                \
+            case value_t::array:                                                                         \
+                return (*lhs.m_data.m_value.array) op (*rhs.m_data.m_value.array);                                     \
+                \
+            case value_t::object:                                                                        \
+                return (*lhs.m_data.m_value.object) op (*rhs.m_data.m_value.object);                                   \
+                \
+            case value_t::null:                                                                          \
+                return (null_result);                                                                    \
+                \
+            case value_t::string:                                                                        \
+                return (*lhs.m_data.m_value.string) op (*rhs.m_data.m_value.string);                                   \
+                \
+            case value_t::boolean:                                                                       \
+                return (lhs.m_data.m_value.boolean) op (rhs.m_data.m_value.boolean);                                   \
+                \
+            case value_t::number_integer:                                                                \
+                return (lhs.m_data.m_value.number_integer) op (rhs.m_data.m_value.number_integer);                     \
+                \
+            case value_t::number_unsigned:                                                               \
+                return (lhs.m_data.m_value.number_unsigned) op (rhs.m_data.m_value.number_unsigned);                   \
+                \
+            case value_t::number_float:                                                                  \
+                return (lhs.m_data.m_value.number_float) op (rhs.m_data.m_value.number_float);                         \
+                \
+            case value_t::binary:                                                                        \
+                return (*lhs.m_data.m_value.binary) op (*rhs.m_data.m_value.binary);                                   \
+                \
+            case value_t::discarded:                                                                     \
+            default:                                                                                     \
+                return (unordered_result);                                                               \
+        }                                                                                                \
+    }                                                                                                    \
+    else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_float)                   \
+    {                                                                                                    \
+        return static_cast<number_float_t>(lhs.m_data.m_value.number_integer) op rhs.m_data.m_value.number_float;      \
+    }                                                                                                    \
+    else if (lhs_type == value_t::number_float && rhs_type == value_t::number_integer)                   \
+    {                                                                                                    \
+        return lhs.m_data.m_value.number_float op static_cast<number_float_t>(rhs.m_data.m_value.number_integer);      \
+    }                                                                                                    \
+    else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_float)                  \
+    {                                                                                                    \
+        return static_cast<number_float_t>(lhs.m_data.m_value.number_unsigned) op rhs.m_data.m_value.number_float;     \
+    }                                                                                                    \
+    else if (lhs_type == value_t::number_float && rhs_type == value_t::number_unsigned)                  \
+    {                                                                                                    \
+        return lhs.m_data.m_value.number_float op static_cast<number_float_t>(rhs.m_data.m_value.number_unsigned);     \
+    }                                                                                                    \
+    else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_integer)                \
+    {                                                                                                    \
+        return static_cast<number_integer_t>(lhs.m_data.m_value.number_unsigned) op rhs.m_data.m_value.number_integer; \
+    }                                                                                                    \
+    else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_unsigned)                \
+    {                                                                                                    \
+        return lhs.m_data.m_value.number_integer op static_cast<number_integer_t>(rhs.m_data.m_value.number_unsigned); \
+    }                                                                                                    \
+    else if(compares_unordered(lhs, rhs))\
+    {\
+        return (unordered_result);\
+    }\
+    \
+    return (default_result);
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    // returns true if:
+    // - any operand is NaN and the other operand is of number type
+    // - any operand is discarded
+    // in legacy mode, discarded values are considered ordered if
+    // an operation is computed as an odd number of inverses of others
+    static bool compares_unordered(const_reference lhs, const_reference rhs, bool inverse = false) noexcept
+    {
+        if ((lhs.is_number_float() && std::isnan(lhs.m_data.m_value.number_float) && rhs.is_number())
+                || (rhs.is_number_float() && std::isnan(rhs.m_data.m_value.number_float) && lhs.is_number()))
+        {
+            return true;
+        }
+#if JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
+        return (lhs.is_discarded() || rhs.is_discarded()) && !inverse;
+#else
+        static_cast<void>(inverse);
+        return lhs.is_discarded() || rhs.is_discarded();
+#endif
+    }
+
+  private:
+    bool compares_unordered(const_reference rhs, bool inverse = false) const noexcept
+    {
+        return compares_unordered(*this, rhs, inverse);
+    }
+
+  public:
+#if JSON_HAS_THREE_WAY_COMPARISON
+    /// @brief comparison: equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_eq/
+    bool operator==(const_reference rhs) const noexcept
+    {
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wfloat-equal"
+#endif
+        const_reference lhs = *this;
+        JSON_IMPLEMENT_OPERATOR( ==, true, false, false)
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+    }
+
+    /// @brief comparison: equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_eq/
+    template<typename ScalarType>
+    requires std::is_scalar_v<ScalarType>
+    bool operator==(ScalarType rhs) const noexcept
+    {
+        return *this == basic_json(rhs);
+    }
+
+    /// @brief comparison: not equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ne/
+    bool operator!=(const_reference rhs) const noexcept
+    {
+        if (compares_unordered(rhs, true))
+        {
+            return false;
+        }
+        return !operator==(rhs);
+    }
+
+    /// @brief comparison: 3-way
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_spaceship/
+    std::partial_ordering operator<=>(const_reference rhs) const noexcept // *NOPAD*
+    {
+        const_reference lhs = *this;
+        // default_result is used if we cannot compare values. In that case,
+        // we compare types.
+        JSON_IMPLEMENT_OPERATOR(<=>, // *NOPAD*
+                                std::partial_ordering::equivalent,
+                                std::partial_ordering::unordered,
+                                lhs_type <=> rhs_type) // *NOPAD*
+    }
+
+    /// @brief comparison: 3-way
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_spaceship/
+    template<typename ScalarType>
+    requires std::is_scalar_v<ScalarType>
+    std::partial_ordering operator<=>(ScalarType rhs) const noexcept // *NOPAD*
+    {
+        return *this <=> basic_json(rhs); // *NOPAD*
+    }
+
+#if JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
+    // all operators that are computed as an odd number of inverses of others
+    // need to be overloaded to emulate the legacy comparison behavior
+
+    /// @brief comparison: less than or equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_le/
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON)
+    bool operator<=(const_reference rhs) const noexcept
+    {
+        if (compares_unordered(rhs, true))
+        {
+            return false;
+        }
+        return !(rhs < *this);
+    }
+
+    /// @brief comparison: less than or equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_le/
+    template<typename ScalarType>
+    requires std::is_scalar_v<ScalarType>
+    bool operator<=(ScalarType rhs) const noexcept
+    {
+        return *this <= basic_json(rhs);
+    }
+
+    /// @brief comparison: greater than or equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ge/
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON)
+    bool operator>=(const_reference rhs) const noexcept
+    {
+        if (compares_unordered(rhs, true))
+        {
+            return false;
+        }
+        return !(*this < rhs);
+    }
+
+    /// @brief comparison: greater than or equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ge/
+    template<typename ScalarType>
+    requires std::is_scalar_v<ScalarType>
+    bool operator>=(ScalarType rhs) const noexcept
+    {
+        return *this >= basic_json(rhs);
+    }
+#endif
+#else
+    /// @brief comparison: equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_eq/
+    friend bool operator==(const_reference lhs, const_reference rhs) noexcept
+    {
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wfloat-equal"
+#endif
+        JSON_IMPLEMENT_OPERATOR( ==, true, false, false)
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+    }
+
+    /// @brief comparison: equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_eq/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator==(const_reference lhs, ScalarType rhs) noexcept
+    {
+        return lhs == basic_json(rhs);
+    }
+
+    /// @brief comparison: equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_eq/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator==(ScalarType lhs, const_reference rhs) noexcept
+    {
+        return basic_json(lhs) == rhs;
+    }
+
+    /// @brief comparison: not equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ne/
+    friend bool operator!=(const_reference lhs, const_reference rhs) noexcept
+    {
+        if (compares_unordered(lhs, rhs, true))
+        {
+            return false;
+        }
+        return !(lhs == rhs);
+    }
+
+    /// @brief comparison: not equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ne/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator!=(const_reference lhs, ScalarType rhs) noexcept
+    {
+        return lhs != basic_json(rhs);
+    }
+
+    /// @brief comparison: not equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ne/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator!=(ScalarType lhs, const_reference rhs) noexcept
+    {
+        return basic_json(lhs) != rhs;
+    }
+
+    /// @brief comparison: less than
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_lt/
+    friend bool operator<(const_reference lhs, const_reference rhs) noexcept
+    {
+        // default_result is used if we cannot compare values. In that case,
+        // we compare types. Note we have to call the operator explicitly,
+        // because MSVC has problems otherwise.
+        JSON_IMPLEMENT_OPERATOR( <, false, false, operator<(lhs_type, rhs_type))
+    }
+
+    /// @brief comparison: less than
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_lt/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator<(const_reference lhs, ScalarType rhs) noexcept
+    {
+        return lhs < basic_json(rhs);
+    }
+
+    /// @brief comparison: less than
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_lt/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator<(ScalarType lhs, const_reference rhs) noexcept
+    {
+        return basic_json(lhs) < rhs;
+    }
+
+    /// @brief comparison: less than or equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_le/
+    friend bool operator<=(const_reference lhs, const_reference rhs) noexcept
+    {
+        if (compares_unordered(lhs, rhs, true))
+        {
+            return false;
+        }
+        return !(rhs < lhs);
+    }
+
+    /// @brief comparison: less than or equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_le/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator<=(const_reference lhs, ScalarType rhs) noexcept
+    {
+        return lhs <= basic_json(rhs);
+    }
+
+    /// @brief comparison: less than or equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_le/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator<=(ScalarType lhs, const_reference rhs) noexcept
+    {
+        return basic_json(lhs) <= rhs;
+    }
+
+    /// @brief comparison: greater than
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_gt/
+    friend bool operator>(const_reference lhs, const_reference rhs) noexcept
+    {
+        // double inverse
+        if (compares_unordered(lhs, rhs))
+        {
+            return false;
+        }
+        return !(lhs <= rhs);
+    }
+
+    /// @brief comparison: greater than
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_gt/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator>(const_reference lhs, ScalarType rhs) noexcept
+    {
+        return lhs > basic_json(rhs);
+    }
+
+    /// @brief comparison: greater than
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_gt/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator>(ScalarType lhs, const_reference rhs) noexcept
+    {
+        return basic_json(lhs) > rhs;
+    }
+
+    /// @brief comparison: greater than or equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ge/
+    friend bool operator>=(const_reference lhs, const_reference rhs) noexcept
+    {
+        if (compares_unordered(lhs, rhs, true))
+        {
+            return false;
+        }
+        return !(lhs < rhs);
+    }
+
+    /// @brief comparison: greater than or equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ge/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator>=(const_reference lhs, ScalarType rhs) noexcept
+    {
+        return lhs >= basic_json(rhs);
+    }
+
+    /// @brief comparison: greater than or equal
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ge/
+    template<typename ScalarType, typename std::enable_if<
+                 std::is_scalar<ScalarType>::value, int>::type = 0>
+    friend bool operator>=(ScalarType lhs, const_reference rhs) noexcept
+    {
+        return basic_json(lhs) >= rhs;
+    }
+#endif
+
+#undef JSON_IMPLEMENT_OPERATOR
+
+    /// @}
+
+    ///////////////////
+    // serialization //
+    ///////////////////
+
+    /// @name serialization
+    /// @{
+#ifndef JSON_NO_IO
+    /// @brief serialize to stream
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ltlt/
+    friend std::ostream& operator<<(std::ostream& o, const basic_json& j)
+    {
+        // read width member and use it as indentation parameter if nonzero
+        const bool pretty_print = o.width() > 0;
+        const auto indentation = pretty_print ? o.width() : 0;
+
+        // reset width to 0 for subsequent calls to this stream
+        o.width(0);
+
+        // do the actual serialization
+        serializer s(detail::output_adapter<char>(o), o.fill());
+        s.dump(j, pretty_print, false, static_cast<unsigned int>(indentation));
+        return o;
+    }
+
+    /// @brief serialize to stream
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_ltlt/
+    /// @deprecated This function is deprecated since 3.0.0 and will be removed in
+    ///             version 4.0.0 of the library. Please use
+    ///             operator<<(std::ostream&, const basic_json&) instead; that is,
+    ///             replace calls like `j >> o;` with `o << j;`.
+    JSON_HEDLEY_DEPRECATED_FOR(3.0.0, operator<<(std::ostream&, const basic_json&))
+    friend std::ostream& operator>>(const basic_json& j, std::ostream& o)
+    {
+        return o << j;
+    }
+#endif  // JSON_NO_IO
+    /// @}
+
+    /////////////////////
+    // deserialization //
+    /////////////////////
+
+    /// @name deserialization
+    /// @{
+
+    /// @brief deserialize from a compatible input
+    /// @sa https://json.nlohmann.me/api/basic_json/parse/
+    template<typename InputType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json parse(InputType&& i,
+                            const parser_callback_t cb = nullptr,
+                            const bool allow_exceptions = true,
+                            const bool ignore_comments = false)
+    {
+        basic_json result;
+        parser(detail::input_adapter(std::forward<InputType>(i)), cb, allow_exceptions, ignore_comments).parse(true, result);
+        return result;
+    }
+
+    /// @brief deserialize from a pair of character iterators
+    /// @sa https://json.nlohmann.me/api/basic_json/parse/
+    template<typename IteratorType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json parse(IteratorType first,
+                            IteratorType last,
+                            const parser_callback_t cb = nullptr,
+                            const bool allow_exceptions = true,
+                            const bool ignore_comments = false)
+    {
+        basic_json result;
+        parser(detail::input_adapter(std::move(first), std::move(last)), cb, allow_exceptions, ignore_comments).parse(true, result);
+        return result;
+    }
+
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, parse(ptr, ptr + len))
+    static basic_json parse(detail::span_input_adapter&& i,
+                            const parser_callback_t cb = nullptr,
+                            const bool allow_exceptions = true,
+                            const bool ignore_comments = false)
+    {
+        basic_json result;
+        parser(i.get(), cb, allow_exceptions, ignore_comments).parse(true, result);
+        return result;
+    }
+
+    /// @brief check if the input is valid JSON
+    /// @sa https://json.nlohmann.me/api/basic_json/accept/
+    template<typename InputType>
+    static bool accept(InputType&& i,
+                       const bool ignore_comments = false)
+    {
+        return parser(detail::input_adapter(std::forward<InputType>(i)), nullptr, false, ignore_comments).accept(true);
+    }
+
+    /// @brief check if the input is valid JSON
+    /// @sa https://json.nlohmann.me/api/basic_json/accept/
+    template<typename IteratorType>
+    static bool accept(IteratorType first, IteratorType last,
+                       const bool ignore_comments = false)
+    {
+        return parser(detail::input_adapter(std::move(first), std::move(last)), nullptr, false, ignore_comments).accept(true);
+    }
+
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, accept(ptr, ptr + len))
+    static bool accept(detail::span_input_adapter&& i,
+                       const bool ignore_comments = false)
+    {
+        return parser(i.get(), nullptr, false, ignore_comments).accept(true);
+    }
+
+    /// @brief generate SAX events
+    /// @sa https://json.nlohmann.me/api/basic_json/sax_parse/
+    template <typename InputType, typename SAX>
+    JSON_HEDLEY_NON_NULL(2)
+    static bool sax_parse(InputType&& i, SAX* sax,
+                          input_format_t format = input_format_t::json,
+                          const bool strict = true,
+                          const bool ignore_comments = false)
+    {
+        auto ia = detail::input_adapter(std::forward<InputType>(i));
+        return format == input_format_t::json
+               ? parser(std::move(ia), nullptr, true, ignore_comments).sax_parse(sax, strict)
+               : detail::binary_reader<basic_json, decltype(ia), SAX>(std::move(ia), format).sax_parse(format, sax, strict);
+    }
+
+    /// @brief generate SAX events
+    /// @sa https://json.nlohmann.me/api/basic_json/sax_parse/
+    template<class IteratorType, class SAX>
+    JSON_HEDLEY_NON_NULL(3)
+    static bool sax_parse(IteratorType first, IteratorType last, SAX* sax,
+                          input_format_t format = input_format_t::json,
+                          const bool strict = true,
+                          const bool ignore_comments = false)
+    {
+        auto ia = detail::input_adapter(std::move(first), std::move(last));
+        return format == input_format_t::json
+               ? parser(std::move(ia), nullptr, true, ignore_comments).sax_parse(sax, strict)
+               : detail::binary_reader<basic_json, decltype(ia), SAX>(std::move(ia), format).sax_parse(format, sax, strict);
+    }
+
+    /// @brief generate SAX events
+    /// @sa https://json.nlohmann.me/api/basic_json/sax_parse/
+    /// @deprecated This function is deprecated since 3.8.0 and will be removed in
+    ///             version 4.0.0 of the library. Please use
+    ///             sax_parse(ptr, ptr + len) instead.
+    template <typename SAX>
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, sax_parse(ptr, ptr + len, ...))
+    JSON_HEDLEY_NON_NULL(2)
+    static bool sax_parse(detail::span_input_adapter&& i, SAX* sax,
+                          input_format_t format = input_format_t::json,
+                          const bool strict = true,
+                          const bool ignore_comments = false)
+    {
+        auto ia = i.get();
+        return format == input_format_t::json
+               // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
+               ? parser(std::move(ia), nullptr, true, ignore_comments).sax_parse(sax, strict)
+               // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
+               : detail::binary_reader<basic_json, decltype(ia), SAX>(std::move(ia), format).sax_parse(format, sax, strict);
+    }
+#ifndef JSON_NO_IO
+    /// @brief deserialize from stream
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_gtgt/
+    /// @deprecated This stream operator is deprecated since 3.0.0 and will be removed in
+    ///             version 4.0.0 of the library. Please use
+    ///             operator>>(std::istream&, basic_json&) instead; that is,
+    ///             replace calls like `j << i;` with `i >> j;`.
+    JSON_HEDLEY_DEPRECATED_FOR(3.0.0, operator>>(std::istream&, basic_json&))
+    friend std::istream& operator<<(basic_json& j, std::istream& i)
+    {
+        return operator>>(i, j);
+    }
+
+    /// @brief deserialize from stream
+    /// @sa https://json.nlohmann.me/api/basic_json/operator_gtgt/
+    friend std::istream& operator>>(std::istream& i, basic_json& j)
+    {
+        parser(detail::input_adapter(i)).parse(false, j);
+        return i;
+    }
+#endif  // JSON_NO_IO
+    /// @}
+
+    ///////////////////////////
+    // convenience functions //
+    ///////////////////////////
+
+    /// @brief return the type as string
+    /// @sa https://json.nlohmann.me/api/basic_json/type_name/
+    JSON_HEDLEY_RETURNS_NON_NULL
+    const char* type_name() const noexcept
+    {
+        switch (m_data.m_type)
+        {
+            case value_t::null:
+                return "null";
+            case value_t::object:
+                return "object";
+            case value_t::array:
+                return "array";
+            case value_t::string:
+                return "string";
+            case value_t::boolean:
+                return "boolean";
+            case value_t::binary:
+                return "binary";
+            case value_t::discarded:
+                return "discarded";
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            default:
+                return "number";
+        }
+    }
+
+  JSON_PRIVATE_UNLESS_TESTED:
+    //////////////////////
+    // member variables //
+    //////////////////////
+
+    struct data
+    {
+        /// the type of the current element
+        value_t m_type = value_t::null;
+
+        /// the value of the current element
+        json_value m_value = {};
+
+        data(const value_t v)
+            : m_type(v), m_value(v)
+        {
+        }
+
+        data(size_type cnt, const basic_json& val)
+            : m_type(value_t::array)
+        {
+            m_value.array = create<array_t>(cnt, val);
+        }
+
+        data() noexcept = default;
+        data(data&&) noexcept = default;
+        data(const data&) noexcept = delete;
+        data& operator=(data&&) noexcept = delete;
+        data& operator=(const data&) noexcept = delete;
+
+        ~data() noexcept
+        {
+            m_value.destroy(m_type);
+        }
+    };
+
+    data m_data = {};
+
+#if JSON_DIAGNOSTICS
+    /// a pointer to a parent value (for debugging purposes)
+    basic_json* m_parent = nullptr;
+#endif
+
+    //////////////////////////////////////////
+    // binary serialization/deserialization //
+    //////////////////////////////////////////
+
+    /// @name binary serialization/deserialization support
+    /// @{
+
+  public:
+    /// @brief create a CBOR serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_cbor/
+    static std::vector<std::uint8_t> to_cbor(const basic_json& j)
+    {
+        std::vector<std::uint8_t> result;
+        to_cbor(j, result);
+        return result;
+    }
+
+    /// @brief create a CBOR serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_cbor/
+    static void to_cbor(const basic_json& j, detail::output_adapter<std::uint8_t> o)
+    {
+        binary_writer<std::uint8_t>(o).write_cbor(j);
+    }
+
+    /// @brief create a CBOR serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_cbor/
+    static void to_cbor(const basic_json& j, detail::output_adapter<char> o)
+    {
+        binary_writer<char>(o).write_cbor(j);
+    }
+
+    /// @brief create a MessagePack serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_msgpack/
+    static std::vector<std::uint8_t> to_msgpack(const basic_json& j)
+    {
+        std::vector<std::uint8_t> result;
+        to_msgpack(j, result);
+        return result;
+    }
+
+    /// @brief create a MessagePack serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_msgpack/
+    static void to_msgpack(const basic_json& j, detail::output_adapter<std::uint8_t> o)
+    {
+        binary_writer<std::uint8_t>(o).write_msgpack(j);
+    }
+
+    /// @brief create a MessagePack serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_msgpack/
+    static void to_msgpack(const basic_json& j, detail::output_adapter<char> o)
+    {
+        binary_writer<char>(o).write_msgpack(j);
+    }
+
+    /// @brief create a UBJSON serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_ubjson/
+    static std::vector<std::uint8_t> to_ubjson(const basic_json& j,
+            const bool use_size = false,
+            const bool use_type = false)
+    {
+        std::vector<std::uint8_t> result;
+        to_ubjson(j, result, use_size, use_type);
+        return result;
+    }
+
+    /// @brief create a UBJSON serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_ubjson/
+    static void to_ubjson(const basic_json& j, detail::output_adapter<std::uint8_t> o,
+                          const bool use_size = false, const bool use_type = false)
+    {
+        binary_writer<std::uint8_t>(o).write_ubjson(j, use_size, use_type);
+    }
+
+    /// @brief create a UBJSON serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_ubjson/
+    static void to_ubjson(const basic_json& j, detail::output_adapter<char> o,
+                          const bool use_size = false, const bool use_type = false)
+    {
+        binary_writer<char>(o).write_ubjson(j, use_size, use_type);
+    }
+
+    /// @brief create a BJData serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_bjdata/
+    static std::vector<std::uint8_t> to_bjdata(const basic_json& j,
+            const bool use_size = false,
+            const bool use_type = false)
+    {
+        std::vector<std::uint8_t> result;
+        to_bjdata(j, result, use_size, use_type);
+        return result;
+    }
+
+    /// @brief create a BJData serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_bjdata/
+    static void to_bjdata(const basic_json& j, detail::output_adapter<std::uint8_t> o,
+                          const bool use_size = false, const bool use_type = false)
+    {
+        binary_writer<std::uint8_t>(o).write_ubjson(j, use_size, use_type, true, true);
+    }
+
+    /// @brief create a BJData serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_bjdata/
+    static void to_bjdata(const basic_json& j, detail::output_adapter<char> o,
+                          const bool use_size = false, const bool use_type = false)
+    {
+        binary_writer<char>(o).write_ubjson(j, use_size, use_type, true, true);
+    }
+
+    /// @brief create a BSON serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_bson/
+    static std::vector<std::uint8_t> to_bson(const basic_json& j)
+    {
+        std::vector<std::uint8_t> result;
+        to_bson(j, result);
+        return result;
+    }
+
+    /// @brief create a BSON serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_bson/
+    static void to_bson(const basic_json& j, detail::output_adapter<std::uint8_t> o)
+    {
+        binary_writer<std::uint8_t>(o).write_bson(j);
+    }
+
+    /// @brief create a BSON serialization of a given JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/to_bson/
+    static void to_bson(const basic_json& j, detail::output_adapter<char> o)
+    {
+        binary_writer<char>(o).write_bson(j);
+    }
+
+    /// @brief create a JSON value from an input in CBOR format
+    /// @sa https://json.nlohmann.me/api/basic_json/from_cbor/
+    template<typename InputType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json from_cbor(InputType&& i,
+                                const bool strict = true,
+                                const bool allow_exceptions = true,
+                                const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = detail::input_adapter(std::forward<InputType>(i));
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::cbor).sax_parse(input_format_t::cbor, &sdp, strict, tag_handler);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    /// @brief create a JSON value from an input in CBOR format
+    /// @sa https://json.nlohmann.me/api/basic_json/from_cbor/
+    template<typename IteratorType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json from_cbor(IteratorType first, IteratorType last,
+                                const bool strict = true,
+                                const bool allow_exceptions = true,
+                                const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = detail::input_adapter(std::move(first), std::move(last));
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::cbor).sax_parse(input_format_t::cbor, &sdp, strict, tag_handler);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    template<typename T>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_cbor(ptr, ptr + len))
+    static basic_json from_cbor(const T* ptr, std::size_t len,
+                                const bool strict = true,
+                                const bool allow_exceptions = true,
+                                const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error)
+    {
+        return from_cbor(ptr, ptr + len, strict, allow_exceptions, tag_handler);
+    }
+
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_cbor(ptr, ptr + len))
+    static basic_json from_cbor(detail::span_input_adapter&& i,
+                                const bool strict = true,
+                                const bool allow_exceptions = true,
+                                const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = i.get();
+        // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::cbor).sax_parse(input_format_t::cbor, &sdp, strict, tag_handler);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    /// @brief create a JSON value from an input in MessagePack format
+    /// @sa https://json.nlohmann.me/api/basic_json/from_msgpack/
+    template<typename InputType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json from_msgpack(InputType&& i,
+                                   const bool strict = true,
+                                   const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = detail::input_adapter(std::forward<InputType>(i));
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::msgpack).sax_parse(input_format_t::msgpack, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    /// @brief create a JSON value from an input in MessagePack format
+    /// @sa https://json.nlohmann.me/api/basic_json/from_msgpack/
+    template<typename IteratorType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json from_msgpack(IteratorType first, IteratorType last,
+                                   const bool strict = true,
+                                   const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = detail::input_adapter(std::move(first), std::move(last));
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::msgpack).sax_parse(input_format_t::msgpack, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    template<typename T>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_msgpack(ptr, ptr + len))
+    static basic_json from_msgpack(const T* ptr, std::size_t len,
+                                   const bool strict = true,
+                                   const bool allow_exceptions = true)
+    {
+        return from_msgpack(ptr, ptr + len, strict, allow_exceptions);
+    }
+
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_msgpack(ptr, ptr + len))
+    static basic_json from_msgpack(detail::span_input_adapter&& i,
+                                   const bool strict = true,
+                                   const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = i.get();
+        // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::msgpack).sax_parse(input_format_t::msgpack, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    /// @brief create a JSON value from an input in UBJSON format
+    /// @sa https://json.nlohmann.me/api/basic_json/from_ubjson/
+    template<typename InputType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json from_ubjson(InputType&& i,
+                                  const bool strict = true,
+                                  const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = detail::input_adapter(std::forward<InputType>(i));
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::ubjson).sax_parse(input_format_t::ubjson, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    /// @brief create a JSON value from an input in UBJSON format
+    /// @sa https://json.nlohmann.me/api/basic_json/from_ubjson/
+    template<typename IteratorType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json from_ubjson(IteratorType first, IteratorType last,
+                                  const bool strict = true,
+                                  const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = detail::input_adapter(std::move(first), std::move(last));
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::ubjson).sax_parse(input_format_t::ubjson, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    template<typename T>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_ubjson(ptr, ptr + len))
+    static basic_json from_ubjson(const T* ptr, std::size_t len,
+                                  const bool strict = true,
+                                  const bool allow_exceptions = true)
+    {
+        return from_ubjson(ptr, ptr + len, strict, allow_exceptions);
+    }
+
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_ubjson(ptr, ptr + len))
+    static basic_json from_ubjson(detail::span_input_adapter&& i,
+                                  const bool strict = true,
+                                  const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = i.get();
+        // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::ubjson).sax_parse(input_format_t::ubjson, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    /// @brief create a JSON value from an input in BJData format
+    /// @sa https://json.nlohmann.me/api/basic_json/from_bjdata/
+    template<typename InputType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json from_bjdata(InputType&& i,
+                                  const bool strict = true,
+                                  const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = detail::input_adapter(std::forward<InputType>(i));
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::bjdata).sax_parse(input_format_t::bjdata, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    /// @brief create a JSON value from an input in BJData format
+    /// @sa https://json.nlohmann.me/api/basic_json/from_bjdata/
+    template<typename IteratorType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json from_bjdata(IteratorType first, IteratorType last,
+                                  const bool strict = true,
+                                  const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = detail::input_adapter(std::move(first), std::move(last));
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::bjdata).sax_parse(input_format_t::bjdata, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    /// @brief create a JSON value from an input in BSON format
+    /// @sa https://json.nlohmann.me/api/basic_json/from_bson/
+    template<typename InputType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json from_bson(InputType&& i,
+                                const bool strict = true,
+                                const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = detail::input_adapter(std::forward<InputType>(i));
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::bson).sax_parse(input_format_t::bson, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    /// @brief create a JSON value from an input in BSON format
+    /// @sa https://json.nlohmann.me/api/basic_json/from_bson/
+    template<typename IteratorType>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json from_bson(IteratorType first, IteratorType last,
+                                const bool strict = true,
+                                const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = detail::input_adapter(std::move(first), std::move(last));
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::bson).sax_parse(input_format_t::bson, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+
+    template<typename T>
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_bson(ptr, ptr + len))
+    static basic_json from_bson(const T* ptr, std::size_t len,
+                                const bool strict = true,
+                                const bool allow_exceptions = true)
+    {
+        return from_bson(ptr, ptr + len, strict, allow_exceptions);
+    }
+
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_bson(ptr, ptr + len))
+    static basic_json from_bson(detail::span_input_adapter&& i,
+                                const bool strict = true,
+                                const bool allow_exceptions = true)
+    {
+        basic_json result;
+        detail::json_sax_dom_parser<basic_json> sdp(result, allow_exceptions);
+        auto ia = i.get();
+        // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg)
+        const bool res = binary_reader<decltype(ia)>(std::move(ia), input_format_t::bson).sax_parse(input_format_t::bson, &sdp, strict);
+        return res ? result : basic_json(value_t::discarded);
+    }
+    /// @}
+
+    //////////////////////////
+    // JSON Pointer support //
+    //////////////////////////
+
+    /// @name JSON Pointer functions
+    /// @{
+
+    /// @brief access specified element via JSON Pointer
+    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
+    reference operator[](const json_pointer& ptr)
+    {
+        return ptr.get_unchecked(this);
+    }
+
+    template<typename BasicJsonType, detail::enable_if_t<detail::is_basic_json<BasicJsonType>::value, int> = 0>
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
+    reference operator[](const ::nlohmann::json_pointer<BasicJsonType>& ptr)
+    {
+        return ptr.get_unchecked(this);
+    }
+
+    /// @brief access specified element via JSON Pointer
+    /// @sa https://json.nlohmann.me/api/basic_json/operator%5B%5D/
+    const_reference operator[](const json_pointer& ptr) const
+    {
+        return ptr.get_unchecked(this);
+    }
+
+    template<typename BasicJsonType, detail::enable_if_t<detail::is_basic_json<BasicJsonType>::value, int> = 0>
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
+    const_reference operator[](const ::nlohmann::json_pointer<BasicJsonType>& ptr) const
+    {
+        return ptr.get_unchecked(this);
+    }
+
+    /// @brief access specified element via JSON Pointer
+    /// @sa https://json.nlohmann.me/api/basic_json/at/
+    reference at(const json_pointer& ptr)
+    {
+        return ptr.get_checked(this);
+    }
+
+    template<typename BasicJsonType, detail::enable_if_t<detail::is_basic_json<BasicJsonType>::value, int> = 0>
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
+    reference at(const ::nlohmann::json_pointer<BasicJsonType>& ptr)
+    {
+        return ptr.get_checked(this);
+    }
+
+    /// @brief access specified element via JSON Pointer
+    /// @sa https://json.nlohmann.me/api/basic_json/at/
+    const_reference at(const json_pointer& ptr) const
+    {
+        return ptr.get_checked(this);
+    }
+
+    template<typename BasicJsonType, detail::enable_if_t<detail::is_basic_json<BasicJsonType>::value, int> = 0>
+    JSON_HEDLEY_DEPRECATED_FOR(3.11.0, basic_json::json_pointer or nlohmann::json_pointer<basic_json::string_t>) // NOLINT(readability/alt_tokens)
+    const_reference at(const ::nlohmann::json_pointer<BasicJsonType>& ptr) const
+    {
+        return ptr.get_checked(this);
+    }
+
+    /// @brief return flattened JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/flatten/
+    basic_json flatten() const
+    {
+        basic_json result(value_t::object);
+        json_pointer::flatten("", *this, result);
+        return result;
+    }
+
+    /// @brief unflatten a previously flattened JSON value
+    /// @sa https://json.nlohmann.me/api/basic_json/unflatten/
+    basic_json unflatten() const
+    {
+        return json_pointer::unflatten(*this);
+    }
+
+    /// @}
+
+    //////////////////////////
+    // JSON Patch functions //
+    //////////////////////////
+
+    /// @name JSON Patch functions
+    /// @{
+
+    /// @brief applies a JSON patch in-place without copying the object
+    /// @sa https://json.nlohmann.me/api/basic_json/patch/
+    void patch_inplace(const basic_json& json_patch)
+    {
+        basic_json& result = *this;
+        // the valid JSON Patch operations
+        enum class patch_operations {add, remove, replace, move, copy, test, invalid};
+
+        const auto get_op = [](const std::string & op)
+        {
+            if (op == "add")
+            {
+                return patch_operations::add;
+            }
+            if (op == "remove")
+            {
+                return patch_operations::remove;
+            }
+            if (op == "replace")
+            {
+                return patch_operations::replace;
+            }
+            if (op == "move")
+            {
+                return patch_operations::move;
+            }
+            if (op == "copy")
+            {
+                return patch_operations::copy;
+            }
+            if (op == "test")
+            {
+                return patch_operations::test;
+            }
+
+            return patch_operations::invalid;
+        };
+
+        // wrapper for "add" operation; add value at ptr
+        const auto operation_add = [&result](json_pointer & ptr, basic_json val)
+        {
+            // adding to the root of the target document means replacing it
+            if (ptr.empty())
+            {
+                result = val;
+                return;
+            }
+
+            // make sure the top element of the pointer exists
+            json_pointer const top_pointer = ptr.top();
+            if (top_pointer != ptr)
+            {
+                result.at(top_pointer);
+            }
+
+            // get reference to parent of JSON pointer ptr
+            const auto last_path = ptr.back();
+            ptr.pop_back();
+            // parent must exist when performing patch add per RFC6902 specs
+            basic_json& parent = result.at(ptr);
+
+            switch (parent.m_data.m_type)
+            {
+                case value_t::null:
+                case value_t::object:
+                {
+                    // use operator[] to add value
+                    parent[last_path] = val;
+                    break;
+                }
+
+                case value_t::array:
+                {
+                    if (last_path == "-")
+                    {
+                        // special case: append to back
+                        parent.push_back(val);
+                    }
+                    else
+                    {
+                        const auto idx = json_pointer::template array_index<basic_json_t>(last_path);
+                        if (JSON_HEDLEY_UNLIKELY(idx > parent.size()))
+                        {
+                            // avoid undefined behavior
+                            JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), &parent));
+                        }
+
+                        // default case: insert add offset
+                        parent.insert(parent.begin() + static_cast<difference_type>(idx), val);
+                    }
+                    break;
+                }
+
+                // if there exists a parent it cannot be primitive
+                case value_t::string: // LCOV_EXCL_LINE
+                case value_t::boolean: // LCOV_EXCL_LINE
+                case value_t::number_integer: // LCOV_EXCL_LINE
+                case value_t::number_unsigned: // LCOV_EXCL_LINE
+                case value_t::number_float: // LCOV_EXCL_LINE
+                case value_t::binary: // LCOV_EXCL_LINE
+                case value_t::discarded: // LCOV_EXCL_LINE
+                default:            // LCOV_EXCL_LINE
+                    JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
+            }
+        };
+
+        // wrapper for "remove" operation; remove value at ptr
+        const auto operation_remove = [this, & result](json_pointer & ptr)
+        {
+            // get reference to parent of JSON pointer ptr
+            const auto last_path = ptr.back();
+            ptr.pop_back();
+            basic_json& parent = result.at(ptr);
+
+            // remove child
+            if (parent.is_object())
+            {
+                // perform range check
+                auto it = parent.find(last_path);
+                if (JSON_HEDLEY_LIKELY(it != parent.end()))
+                {
+                    parent.erase(it);
+                }
+                else
+                {
+                    JSON_THROW(out_of_range::create(403, detail::concat("key '", last_path, "' not found"), this));
+                }
+            }
+            else if (parent.is_array())
+            {
+                // note erase performs range check
+                parent.erase(json_pointer::template array_index<basic_json_t>(last_path));
+            }
+        };
+
+        // type check: top level value must be an array
+        if (JSON_HEDLEY_UNLIKELY(!json_patch.is_array()))
+        {
+            JSON_THROW(parse_error::create(104, 0, "JSON patch must be an array of objects", &json_patch));
+        }
+
+        // iterate and apply the operations
+        for (const auto& val : json_patch)
+        {
+            // wrapper to get a value for an operation
+            const auto get_value = [&val](const std::string & op,
+                                          const std::string & member,
+                                          bool string_type) -> basic_json &
+            {
+                // find value
+                auto it = val.m_data.m_value.object->find(member);
+
+                // context-sensitive error message
+                const auto error_msg = (op == "op") ? "operation" : detail::concat("operation '", op, '\'');
+
+                // check if desired value is present
+                if (JSON_HEDLEY_UNLIKELY(it == val.m_data.m_value.object->end()))
+                {
+                    // NOLINTNEXTLINE(performance-inefficient-string-concatenation)
+                    JSON_THROW(parse_error::create(105, 0, detail::concat(error_msg, " must have member '", member, "'"), &val));
+                }
+
+                // check if result is of type string
+                if (JSON_HEDLEY_UNLIKELY(string_type && !it->second.is_string()))
+                {
+                    // NOLINTNEXTLINE(performance-inefficient-string-concatenation)
+                    JSON_THROW(parse_error::create(105, 0, detail::concat(error_msg, " must have string member '", member, "'"), &val));
+                }
+
+                // no error: return value
+                return it->second;
+            };
+
+            // type check: every element of the array must be an object
+            if (JSON_HEDLEY_UNLIKELY(!val.is_object()))
+            {
+                JSON_THROW(parse_error::create(104, 0, "JSON patch must be an array of objects", &val));
+            }
+
+            // collect mandatory members
+            const auto op = get_value("op", "op", true).template get<std::string>();
+            const auto path = get_value(op, "path", true).template get<std::string>();
+            json_pointer ptr(path);
+
+            switch (get_op(op))
+            {
+                case patch_operations::add:
+                {
+                    operation_add(ptr, get_value("add", "value", false));
+                    break;
+                }
+
+                case patch_operations::remove:
+                {
+                    operation_remove(ptr);
+                    break;
+                }
+
+                case patch_operations::replace:
+                {
+                    // the "path" location must exist - use at()
+                    result.at(ptr) = get_value("replace", "value", false);
+                    break;
+                }
+
+                case patch_operations::move:
+                {
+                    const auto from_path = get_value("move", "from", true).template get<std::string>();
+                    json_pointer from_ptr(from_path);
+
+                    // the "from" location must exist - use at()
+                    basic_json const v = result.at(from_ptr);
+
+                    // The move operation is functionally identical to a
+                    // "remove" operation on the "from" location, followed
+                    // immediately by an "add" operation at the target
+                    // location with the value that was just removed.
+                    operation_remove(from_ptr);
+                    operation_add(ptr, v);
+                    break;
+                }
+
+                case patch_operations::copy:
+                {
+                    const auto from_path = get_value("copy", "from", true).template get<std::string>();
+                    const json_pointer from_ptr(from_path);
+
+                    // the "from" location must exist - use at()
+                    basic_json const v = result.at(from_ptr);
+
+                    // The copy is functionally identical to an "add"
+                    // operation at the target location using the value
+                    // specified in the "from" member.
+                    operation_add(ptr, v);
+                    break;
+                }
+
+                case patch_operations::test:
+                {
+                    bool success = false;
+                    JSON_TRY
+                    {
+                        // check if "value" matches the one at "path"
+                        // the "path" location must exist - use at()
+                        success = (result.at(ptr) == get_value("test", "value", false));
+                    }
+                    JSON_INTERNAL_CATCH (out_of_range&)
+                    {
+                        // ignore out of range errors: success remains false
+                    }
+
+                    // throw an exception if test fails
+                    if (JSON_HEDLEY_UNLIKELY(!success))
+                    {
+                        JSON_THROW(other_error::create(501, detail::concat("unsuccessful: ", val.dump()), &val));
+                    }
+
+                    break;
+                }
+
+                case patch_operations::invalid:
+                default:
+                {
+                    // op must be "add", "remove", "replace", "move", "copy", or
+                    // "test"
+                    JSON_THROW(parse_error::create(105, 0, detail::concat("operation value '", op, "' is invalid"), &val));
+                }
+            }
+        }
+    }
+
+    /// @brief applies a JSON patch to a copy of the current object
+    /// @sa https://json.nlohmann.me/api/basic_json/patch/
+    basic_json patch(const basic_json& json_patch) const
+    {
+        basic_json result = *this;
+        result.patch_inplace(json_patch);
+        return result;
+    }
+
+    /// @brief creates a diff as a JSON patch
+    /// @sa https://json.nlohmann.me/api/basic_json/diff/
+    JSON_HEDLEY_WARN_UNUSED_RESULT
+    static basic_json diff(const basic_json& source, const basic_json& target,
+                           const std::string& path = "")
+    {
+        // the patch
+        basic_json result(value_t::array);
+
+        // if the values are the same, return empty patch
+        if (source == target)
+        {
+            return result;
+        }
+
+        if (source.type() != target.type())
+        {
+            // different types: replace value
+            result.push_back(
+            {
+                {"op", "replace"}, {"path", path}, {"value", target}
+            });
+            return result;
+        }
+
+        switch (source.type())
+        {
+            case value_t::array:
+            {
+                // first pass: traverse common elements
+                std::size_t i = 0;
+                while (i < source.size() && i < target.size())
+                {
+                    // recursive call to compare array values at index i
+                    auto temp_diff = diff(source[i], target[i], detail::concat(path, '/', std::to_string(i)));
+                    result.insert(result.end(), temp_diff.begin(), temp_diff.end());
+                    ++i;
+                }
+
+                // We now reached the end of at least one array
+                // in a second pass, traverse the remaining elements
+
+                // remove my remaining elements
+                const auto end_index = static_cast<difference_type>(result.size());
+                while (i < source.size())
+                {
+                    // add operations in reverse order to avoid invalid
+                    // indices
+                    result.insert(result.begin() + end_index, object(
+                    {
+                        {"op", "remove"},
+                        {"path", detail::concat(path, '/', std::to_string(i))}
+                    }));
+                    ++i;
+                }
+
+                // add other remaining elements
+                while (i < target.size())
+                {
+                    result.push_back(
+                    {
+                        {"op", "add"},
+                        {"path", detail::concat(path, "/-")},
+                        {"value", target[i]}
+                    });
+                    ++i;
+                }
+
+                break;
+            }
+
+            case value_t::object:
+            {
+                // first pass: traverse this object's elements
+                for (auto it = source.cbegin(); it != source.cend(); ++it)
+                {
+                    // escape the key name to be used in a JSON patch
+                    const auto path_key = detail::concat(path, '/', detail::escape(it.key()));
+
+                    if (target.find(it.key()) != target.end())
+                    {
+                        // recursive call to compare object values at key it
+                        auto temp_diff = diff(it.value(), target[it.key()], path_key);
+                        result.insert(result.end(), temp_diff.begin(), temp_diff.end());
+                    }
+                    else
+                    {
+                        // found a key that is not in o -> remove it
+                        result.push_back(object(
+                        {
+                            {"op", "remove"}, {"path", path_key}
+                        }));
+                    }
+                }
+
+                // second pass: traverse other object's elements
+                for (auto it = target.cbegin(); it != target.cend(); ++it)
+                {
+                    if (source.find(it.key()) == source.end())
+                    {
+                        // found a key that is not in this -> add it
+                        const auto path_key = detail::concat(path, '/', detail::escape(it.key()));
+                        result.push_back(
+                        {
+                            {"op", "add"}, {"path", path_key},
+                            {"value", it.value()}
+                        });
+                    }
+                }
+
+                break;
+            }
+
+            case value_t::null:
+            case value_t::string:
+            case value_t::boolean:
+            case value_t::number_integer:
+            case value_t::number_unsigned:
+            case value_t::number_float:
+            case value_t::binary:
+            case value_t::discarded:
+            default:
+            {
+                // both primitive type: replace value
+                result.push_back(
+                {
+                    {"op", "replace"}, {"path", path}, {"value", target}
+                });
+                break;
+            }
+        }
+
+        return result;
+    }
+    /// @}
+
+    ////////////////////////////////
+    // JSON Merge Patch functions //
+    ////////////////////////////////
+
+    /// @name JSON Merge Patch functions
+    /// @{
+
+    /// @brief applies a JSON Merge Patch
+    /// @sa https://json.nlohmann.me/api/basic_json/merge_patch/
+    void merge_patch(const basic_json& apply_patch)
+    {
+        if (apply_patch.is_object())
+        {
+            if (!is_object())
+            {
+                *this = object();
+            }
+            for (auto it = apply_patch.begin(); it != apply_patch.end(); ++it)
+            {
+                if (it.value().is_null())
+                {
+                    erase(it.key());
+                }
+                else
+                {
+                    operator[](it.key()).merge_patch(it.value());
+                }
+            }
+        }
+        else
+        {
+            *this = apply_patch;
+        }
+    }
+
+    /// @}
+};
+
+/// @brief user-defined to_string function for JSON values
+/// @sa https://json.nlohmann.me/api/basic_json/to_string/
+NLOHMANN_BASIC_JSON_TPL_DECLARATION
+std::string to_string(const NLOHMANN_BASIC_JSON_TPL& j)
+{
+    return j.dump();
+}
+
+inline namespace literals
+{
+inline namespace json_literals
+{
+
+/// @brief user-defined string literal for JSON values
+/// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json/
+JSON_HEDLEY_NON_NULL(1)
+#if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0)
+    inline nlohmann::json operator ""_json(const char* s, std::size_t n)
+#else
+    inline nlohmann::json operator "" _json(const char* s, std::size_t n)
+#endif
+{
+    return nlohmann::json::parse(s, s + n);
+}
+
+/// @brief user-defined string literal for JSON pointer
+/// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json_pointer/
+JSON_HEDLEY_NON_NULL(1)
+#if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0)
+    inline nlohmann::json::json_pointer operator ""_json_pointer(const char* s, std::size_t n)
+#else
+    inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n)
+#endif
+{
+    return nlohmann::json::json_pointer(std::string(s, n));
+}
+
+}  // namespace json_literals
+}  // namespace literals
+NLOHMANN_JSON_NAMESPACE_END
+
+///////////////////////
+// nonmember support //
+///////////////////////
+
+namespace std // NOLINT(cert-dcl58-cpp)
+{
+
+/// @brief hash value for JSON objects
+/// @sa https://json.nlohmann.me/api/basic_json/std_hash/
+NLOHMANN_BASIC_JSON_TPL_DECLARATION
+struct hash<nlohmann::NLOHMANN_BASIC_JSON_TPL> // NOLINT(cert-dcl58-cpp)
+{
+    std::size_t operator()(const nlohmann::NLOHMANN_BASIC_JSON_TPL& j) const
+    {
+        return nlohmann::detail::hash(j);
+    }
+};
+
+// specialization for std::less<value_t>
+template<>
+struct less< ::nlohmann::detail::value_t> // do not remove the space after '<', see https://github.com/nlohmann/json/pull/679
+{
+    /*!
+    @brief compare two value_t enum values
+    @since version 3.0.0
+    */
+    bool operator()(::nlohmann::detail::value_t lhs,
+                    ::nlohmann::detail::value_t rhs) const noexcept
+    {
+#if JSON_HAS_THREE_WAY_COMPARISON
+        return std::is_lt(lhs <=> rhs); // *NOPAD*
+#else
+        return ::nlohmann::detail::operator<(lhs, rhs);
+#endif
+    }
+};
+
+// C++20 prohibit function specialization in the std namespace.
+#ifndef JSON_HAS_CPP_20
+
+/// @brief exchanges the values of two JSON objects
+/// @sa https://json.nlohmann.me/api/basic_json/std_swap/
+NLOHMANN_BASIC_JSON_TPL_DECLARATION
+inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC_JSON_TPL& j2) noexcept(  // NOLINT(readability-inconsistent-declaration-parameter-name, cert-dcl58-cpp)
+    is_nothrow_move_constructible<nlohmann::NLOHMANN_BASIC_JSON_TPL>::value&&                          // NOLINT(misc-redundant-expression,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
+    is_nothrow_move_assignable<nlohmann::NLOHMANN_BASIC_JSON_TPL>::value)
+{
+    j1.swap(j2);
+}
+
+#endif
+
+}  // namespace std
+
+#if JSON_USE_GLOBAL_UDLS
+    #if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0)
+        using nlohmann::literals::json_literals::operator ""_json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+        using nlohmann::literals::json_literals::operator ""_json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+    #else
+        using nlohmann::literals::json_literals::operator "" _json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+        using nlohmann::literals::json_literals::operator "" _json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+    #endif
+#endif
+
+// #include <nlohmann/detail/macro_unscope.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+// restore clang diagnostic settings
+#if defined(__clang__)
+    #pragma clang diagnostic pop
+#endif
+
+// clean up
+#undef JSON_ASSERT
+#undef JSON_INTERNAL_CATCH
+#undef JSON_THROW
+#undef JSON_PRIVATE_UNLESS_TESTED
+#undef NLOHMANN_BASIC_JSON_TPL_DECLARATION
+#undef NLOHMANN_BASIC_JSON_TPL
+#undef JSON_EXPLICIT
+#undef NLOHMANN_CAN_CALL_STD_FUNC_IMPL
+#undef JSON_INLINE_VARIABLE
+#undef JSON_NO_UNIQUE_ADDRESS
+#undef JSON_DISABLE_ENUM_SERIALIZATION
+#undef JSON_USE_GLOBAL_UDLS
+
+#ifndef JSON_TEST_KEEP_MACROS
+    #undef JSON_CATCH
+    #undef JSON_TRY
+    #undef JSON_HAS_CPP_11
+    #undef JSON_HAS_CPP_14
+    #undef JSON_HAS_CPP_17
+    #undef JSON_HAS_CPP_20
+    #undef JSON_HAS_FILESYSTEM
+    #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
+    #undef JSON_HAS_THREE_WAY_COMPARISON
+    #undef JSON_HAS_RANGES
+    #undef JSON_HAS_STATIC_RTTI
+    #undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
+#endif
+
+// #include <nlohmann/thirdparty/hedley/hedley_undef.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#undef JSON_HEDLEY_ALWAYS_INLINE
+#undef JSON_HEDLEY_ARM_VERSION
+#undef JSON_HEDLEY_ARM_VERSION_CHECK
+#undef JSON_HEDLEY_ARRAY_PARAM
+#undef JSON_HEDLEY_ASSUME
+#undef JSON_HEDLEY_BEGIN_C_DECLS
+#undef JSON_HEDLEY_CLANG_HAS_ATTRIBUTE
+#undef JSON_HEDLEY_CLANG_HAS_BUILTIN
+#undef JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE
+#undef JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE
+#undef JSON_HEDLEY_CLANG_HAS_EXTENSION
+#undef JSON_HEDLEY_CLANG_HAS_FEATURE
+#undef JSON_HEDLEY_CLANG_HAS_WARNING
+#undef JSON_HEDLEY_COMPCERT_VERSION
+#undef JSON_HEDLEY_COMPCERT_VERSION_CHECK
+#undef JSON_HEDLEY_CONCAT
+#undef JSON_HEDLEY_CONCAT3
+#undef JSON_HEDLEY_CONCAT3_EX
+#undef JSON_HEDLEY_CONCAT_EX
+#undef JSON_HEDLEY_CONST
+#undef JSON_HEDLEY_CONSTEXPR
+#undef JSON_HEDLEY_CONST_CAST
+#undef JSON_HEDLEY_CPP_CAST
+#undef JSON_HEDLEY_CRAY_VERSION
+#undef JSON_HEDLEY_CRAY_VERSION_CHECK
+#undef JSON_HEDLEY_C_DECL
+#undef JSON_HEDLEY_DEPRECATED
+#undef JSON_HEDLEY_DEPRECATED_FOR
+#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL
+#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_
+#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED
+#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES
+#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS
+#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION
+#undef JSON_HEDLEY_DIAGNOSTIC_POP
+#undef JSON_HEDLEY_DIAGNOSTIC_PUSH
+#undef JSON_HEDLEY_DMC_VERSION
+#undef JSON_HEDLEY_DMC_VERSION_CHECK
+#undef JSON_HEDLEY_EMPTY_BASES
+#undef JSON_HEDLEY_EMSCRIPTEN_VERSION
+#undef JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK
+#undef JSON_HEDLEY_END_C_DECLS
+#undef JSON_HEDLEY_FLAGS
+#undef JSON_HEDLEY_FLAGS_CAST
+#undef JSON_HEDLEY_GCC_HAS_ATTRIBUTE
+#undef JSON_HEDLEY_GCC_HAS_BUILTIN
+#undef JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE
+#undef JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE
+#undef JSON_HEDLEY_GCC_HAS_EXTENSION
+#undef JSON_HEDLEY_GCC_HAS_FEATURE
+#undef JSON_HEDLEY_GCC_HAS_WARNING
+#undef JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK
+#undef JSON_HEDLEY_GCC_VERSION
+#undef JSON_HEDLEY_GCC_VERSION_CHECK
+#undef JSON_HEDLEY_GNUC_HAS_ATTRIBUTE
+#undef JSON_HEDLEY_GNUC_HAS_BUILTIN
+#undef JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE
+#undef JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE
+#undef JSON_HEDLEY_GNUC_HAS_EXTENSION
+#undef JSON_HEDLEY_GNUC_HAS_FEATURE
+#undef JSON_HEDLEY_GNUC_HAS_WARNING
+#undef JSON_HEDLEY_GNUC_VERSION
+#undef JSON_HEDLEY_GNUC_VERSION_CHECK
+#undef JSON_HEDLEY_HAS_ATTRIBUTE
+#undef JSON_HEDLEY_HAS_BUILTIN
+#undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE
+#undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS
+#undef JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE
+#undef JSON_HEDLEY_HAS_EXTENSION
+#undef JSON_HEDLEY_HAS_FEATURE
+#undef JSON_HEDLEY_HAS_WARNING
+#undef JSON_HEDLEY_IAR_VERSION
+#undef JSON_HEDLEY_IAR_VERSION_CHECK
+#undef JSON_HEDLEY_IBM_VERSION
+#undef JSON_HEDLEY_IBM_VERSION_CHECK
+#undef JSON_HEDLEY_IMPORT
+#undef JSON_HEDLEY_INLINE
+#undef JSON_HEDLEY_INTEL_CL_VERSION
+#undef JSON_HEDLEY_INTEL_CL_VERSION_CHECK
+#undef JSON_HEDLEY_INTEL_VERSION
+#undef JSON_HEDLEY_INTEL_VERSION_CHECK
+#undef JSON_HEDLEY_IS_CONSTANT
+#undef JSON_HEDLEY_IS_CONSTEXPR_
+#undef JSON_HEDLEY_LIKELY
+#undef JSON_HEDLEY_MALLOC
+#undef JSON_HEDLEY_MCST_LCC_VERSION
+#undef JSON_HEDLEY_MCST_LCC_VERSION_CHECK
+#undef JSON_HEDLEY_MESSAGE
+#undef JSON_HEDLEY_MSVC_VERSION
+#undef JSON_HEDLEY_MSVC_VERSION_CHECK
+#undef JSON_HEDLEY_NEVER_INLINE
+#undef JSON_HEDLEY_NON_NULL
+#undef JSON_HEDLEY_NO_ESCAPE
+#undef JSON_HEDLEY_NO_RETURN
+#undef JSON_HEDLEY_NO_THROW
+#undef JSON_HEDLEY_NULL
+#undef JSON_HEDLEY_PELLES_VERSION
+#undef JSON_HEDLEY_PELLES_VERSION_CHECK
+#undef JSON_HEDLEY_PGI_VERSION
+#undef JSON_HEDLEY_PGI_VERSION_CHECK
+#undef JSON_HEDLEY_PREDICT
+#undef JSON_HEDLEY_PRINTF_FORMAT
+#undef JSON_HEDLEY_PRIVATE
+#undef JSON_HEDLEY_PUBLIC
+#undef JSON_HEDLEY_PURE
+#undef JSON_HEDLEY_REINTERPRET_CAST
+#undef JSON_HEDLEY_REQUIRE
+#undef JSON_HEDLEY_REQUIRE_CONSTEXPR
+#undef JSON_HEDLEY_REQUIRE_MSG
+#undef JSON_HEDLEY_RESTRICT
+#undef JSON_HEDLEY_RETURNS_NON_NULL
+#undef JSON_HEDLEY_SENTINEL
+#undef JSON_HEDLEY_STATIC_ASSERT
+#undef JSON_HEDLEY_STATIC_CAST
+#undef JSON_HEDLEY_STRINGIFY
+#undef JSON_HEDLEY_STRINGIFY_EX
+#undef JSON_HEDLEY_SUNPRO_VERSION
+#undef JSON_HEDLEY_SUNPRO_VERSION_CHECK
+#undef JSON_HEDLEY_TINYC_VERSION
+#undef JSON_HEDLEY_TINYC_VERSION_CHECK
+#undef JSON_HEDLEY_TI_ARMCL_VERSION
+#undef JSON_HEDLEY_TI_ARMCL_VERSION_CHECK
+#undef JSON_HEDLEY_TI_CL2000_VERSION
+#undef JSON_HEDLEY_TI_CL2000_VERSION_CHECK
+#undef JSON_HEDLEY_TI_CL430_VERSION
+#undef JSON_HEDLEY_TI_CL430_VERSION_CHECK
+#undef JSON_HEDLEY_TI_CL6X_VERSION
+#undef JSON_HEDLEY_TI_CL6X_VERSION_CHECK
+#undef JSON_HEDLEY_TI_CL7X_VERSION
+#undef JSON_HEDLEY_TI_CL7X_VERSION_CHECK
+#undef JSON_HEDLEY_TI_CLPRU_VERSION
+#undef JSON_HEDLEY_TI_CLPRU_VERSION_CHECK
+#undef JSON_HEDLEY_TI_VERSION
+#undef JSON_HEDLEY_TI_VERSION_CHECK
+#undef JSON_HEDLEY_UNAVAILABLE
+#undef JSON_HEDLEY_UNLIKELY
+#undef JSON_HEDLEY_UNPREDICTABLE
+#undef JSON_HEDLEY_UNREACHABLE
+#undef JSON_HEDLEY_UNREACHABLE_RETURN
+#undef JSON_HEDLEY_VERSION
+#undef JSON_HEDLEY_VERSION_DECODE_MAJOR
+#undef JSON_HEDLEY_VERSION_DECODE_MINOR
+#undef JSON_HEDLEY_VERSION_DECODE_REVISION
+#undef JSON_HEDLEY_VERSION_ENCODE
+#undef JSON_HEDLEY_WARNING
+#undef JSON_HEDLEY_WARN_UNUSED_RESULT
+#undef JSON_HEDLEY_WARN_UNUSED_RESULT_MSG
+#undef JSON_HEDLEY_FALL_THROUGH
+
+
+
+#endif  // INCLUDE_NLOHMANN_JSON_HPP_
diff --git a/python_app/__init__.py b/python_app/__init__.py
new file mode 100644
index 0000000..0266a75
--- /dev/null
+++ b/python_app/__init__.py
@@ -0,0 +1 @@
+"""Python application package for radar GUI, storage, orchestration, and hardware adapters."""
diff --git a/python_app/docs/README.md b/python_app/docs/README.md
new file mode 100644
index 0000000..190a398
--- /dev/null
+++ b/python_app/docs/README.md
@@ -0,0 +1,22 @@
+# Python App Documentation
+
+## Назначение
+Каталог `python_app/docs` содержит техническую документацию по Python-части радарной системы: GUI, orchestration, storage, hardware adapters и эксплуатационные сценарии.
+
+## Карта документов
+- `architecture_overview.md`: общая модульная архитектура и границы ответственности.
+- `runtime_data_flow.md`: потоки данных от acquisition до отображения и snapshot.
+- `gui_architecture.md`: структура GUI, mixin-контроллеры, plotting/runtime helpers.
+- `hardware_layer.md`: слой работы с LibreVNA и переключателями.
+- `storage_formats.md`: форматы NPZ/binary/numpy snapshot и naming.
+- `shm_protocol_and_readers.md`: shared-memory ring контракт и декодирование payload.
+- `preprocessing_and_capture_workflow.md`: калибровка/референс и последовательный захват.
+- `operations_runbook.md`: запуск, диагностика, типовые инциденты.
+- `module_reference.md`: справочник ключевых Python-модулей.
+
+## Границы
+- Документация описывает Python-часть проекта в `python_app`.
+- C++ конвейер (`data_acq_and_processing`) описывается только в части интеграционных точек с Python.
+
+## Версия документации
+Актуально для состояния репозитория после рефакторинга: разделения `models`, `storage/npz`, `orchestration/shm`, `gui/plotting`, `gui/runtime`, `gui/controllers/sections`.
diff --git a/python_app/docs/architecture_overview.md b/python_app/docs/architecture_overview.md
new file mode 100644
index 0000000..abc8afc
--- /dev/null
+++ b/python_app/docs/architecture_overview.md
@@ -0,0 +1,46 @@
+# Архитектурный обзор Python-части
+
+## 1. Верхнеуровневая схема
+Python-часть разделена на следующие пакеты:
+- `gui`: desktop UI на PyQt6 + pyqtgraph.
+- `models`: dataclass-модели runtime-конфига и датасетов.
+- `orchestration`: управление runtime-конфигом, процессами и SHM-ридерами.
+- `storage`: persistent storage для calibration/reference и runtime snapshots.
+- `hardware_full`: Python-обертки над LibreVNA и switch drivers.
+- `workflows`: сценарии калибровки/референса/последовательного захвата.
+- `scripts`: эксплуатационные и отладочные скрипты.
+
+## 2. Ключевые архитектурные решения
+- GUI оставлен единой точкой входа (`AppWindow`), но тяжелая логика вынесена в `sections`, `plotting` и `runtime` helpers.
+- Конфиг разделен на schema/codec/validation:
+  - `run_config_schema.py`
+  - `run_config_codec.py`
+  - `run_config_validation.py`
+  - `run_config_model.py` как фасадный модуль.
+- Storage разделен на подпакет `storage/npz/*`:
+  - `paths.py`
+  - `serialize.py`
+  - `snapshot_numpy.py`
+  - `store.py`
+- SHM-декодирование разделено на `orchestration/shm/*`:
+  - `binary_cursor.py`
+  - `decoder.py`
+  - `ring_reader.py`
+  - `shm_reader.py` как фасад.
+- LibreVNA service переведен на backend-подход (`librevna_backends.py`) с orchestration-оберткой `librevna_service.py`.
+
+## 3. Инварианты
+- Формат `run_config.json` сохраняется совместимым с C++ pipeline.
+- Форматы snapshot (`binary`, `numpy-directory-v1`) сохраняются.
+- Сигнатуры ключевых пользовательских входов (`gui/main.py`, scripts) сохраняются.
+- Данные в `python_app/data` и runtime-файлы в `python_app/runtime` не реорганизуются автоматически.
+
+## 4. Основные зависимости
+- GUI: `PyQt6`, `pyqtgraph`, `numpy`.
+- Hardware: `libusb1` через драйвер в `hardware_full/librevna_driver`.
+- Storage/processing tools: `numpy`, stdlib JSON/Path/mmap/subprocess.
+
+## 5. Точки расширения
+- Новые processing modes: через live-config + GUI section + C++ processor.
+- Новые storage backends: реализовать `StoreApi`.
+- Новые hardware adapters: добавить backend/driver и подключить в service/factory.
diff --git a/python_app/docs/gui_architecture.md b/python_app/docs/gui_architecture.md
new file mode 100644
index 0000000..b158c76
--- /dev/null
+++ b/python_app/docs/gui_architecture.md
@@ -0,0 +1,43 @@
+# Архитектура GUI
+
+## 1. Состав
+- `gui/main.py`: entrypoint приложения.
+- `gui/app_window.py`: компоновка миксинов и базовой инфраструктуры.
+- `gui/controllers/*`: orchestration-логика.
+- `gui/controllers/sections/*`: построение UI-групп (Pipeline, Processing, Radar, Switches и т.д.).
+- `gui/plotting/*`: чистая логика B-scan расчетов и cache helpers.
+- `gui/runtime/*`: runtime history/constraint helpers.
+- `gui/preprocess_dialog.py`: окно последовательного захвата calibration/reference.
+
+## 2. Mixin роли
+- `AppWindowUiMixin`: сборка виджетов и layout.
+- `AppWindowConfigMixin`: сборка `RunConfigModel`, live settings и лимиты устройства.
+- `AppWindowPipelineMixin`: старт/стоп процессов, polling ring readers, history lifecycle.
+- `AppWindowPlotMixin`: выбор режима отрисовки и рендер.
+- `AppWindowPreprocessMixin`: workflow для calibration/reference capture.
+- `AppWindowSnapshotMixin`: сохранение runtime snapshot.
+
+## 3. Состояния AppWindow
+Ключевые поля состояния:
+- readers: `_raw_reader`, `_pre_reader`, `_result_reader`
+- history: `_raw_history`, `_pre_history`, `_result_history`
+- bscan cache: `_bscan_history_by_combo`, `_bscan_depth_axis_by_combo`, `_bscan_render_signature`
+- single capture flags: `_single_capture_*`
+- radar limits: `_radar_limits`
+
+## 4. UI composition
+Секции строятся через builders в `gui/controllers/sections`:
+- `build_pipeline_group`
+- `build_hardware_actions_group`
+- `build_data_actions_group`
+- `build_preprocess_summary_group`
+- `build_processing_group`
+- `build_radar_group`
+- `build_switch_group`
+
+## 5. Рекомендации по расширению GUI
+- Не добавлять тяжелую математику в mixin-файлы.
+- Для новых параметров processing mode:
+  - добавить поле в live config,
+  - добавить controls в `processing_section`,
+  - добавить обработку в plotting/runtime helpers.
diff --git a/python_app/docs/hardware_layer.md b/python_app/docs/hardware_layer.md
new file mode 100644
index 0000000..21b0244
--- /dev/null
+++ b/python_app/docs/hardware_layer.md
@@ -0,0 +1,35 @@
+# Hardware layer
+
+## 1. LibreVNA
+- `hardware_full/librevna_service.py`: orchestration facade.
+- `hardware_full/librevna_backends.py`:
+  - `NativeLibreVnaBackend`
+  - `MockLibreVnaBackend`
+- `hardware_full/librevna_driver/*`: низкоуровневый USB/protocol stack.
+
+### Native flow
+1. `LibreVnaService.open()`
+2. `configure(RadarSweepModel)`
+3. `acquire_s21()`
+4. `close()`
+
+### Device limits
+- `read_device_limits()` возвращает min/max frequency, IFBW, power и max points.
+- GUI применяет лимиты для clamp/labels и live B-scan frequency bounds.
+
+## 2. Switches
+- `switch_service.py`: выбор mock/native драйвера по конфигурации.
+- `switch_drivers/*`:
+  - `MockSwitchDriver`
+  - `H7992Driver`
+  - `HMC349ADriver`
+  - `gpio_uapi.py` (Linux GPIO v2 wrapper)
+
+### Инварианты
+- Позиции валидируются на уровне драйвера.
+- Драйверы требуют `open()` перед `switch_to()`.
+- Для native GPIO ошибки ОС конвертируются в Python `RuntimeError` с контекстом.
+
+## 3. Расширение hardware слоя
+- Новый радар: добавить backend с тем же контрактом, подключить в service.
+- Новый switch: реализовать `SwitchDriverProtocol` и добавить ветку в `SwitchService._build_driver()`.
diff --git a/python_app/docs/module_reference.md b/python_app/docs/module_reference.md
new file mode 100644
index 0000000..dd54483
--- /dev/null
+++ b/python_app/docs/module_reference.md
@@ -0,0 +1,50 @@
+# Module reference
+
+## `python_app.gui`
+- `main.py`: GUI entrypoint.
+- `app_window.py`: composition root.
+- `controllers/app_window_*_mixin.py`: функциональные части окна.
+- `controllers/sections/*`: построители UI-секций.
+- `plotting/bscan_math.py`: математика B-scan.
+- `plotting/bscan_history.py`: cache/signature для B-scan.
+- `runtime/history.py`: сигнатуры run/history merge.
+- `runtime/constraints.py`: режимные ограничения.
+
+## `python_app.models`
+- `dataset_model.py`: dataclass-модели sweep/result данных.
+- `run_config_schema.py`: dataclass-schema run config.
+- `run_config_codec.py`: encode/decode/load.
+- `run_config_validation.py`: payload checks/parsing helpers.
+- `run_config_model.py`: фасадный экспорт.
+
+## `python_app.orchestration`
+- `config_writer.py`: runtime config + bundle files.
+- `live_processing_config.py`: live processing JSON writer.
+- `process_supervisor.py`: управление процессами C++ pipeline.
+- `shm/*`: shared-memory чтение и декодирование.
+- `shm_reader.py`: фасадный экспорт.
+
+## `python_app.storage`
+- `store_api.py`: абстракция хранилища.
+- `npz/store.py`: `NpzStore`.
+- `npz/serialize.py`: binary serializers.
+- `npz/snapshot_numpy.py`: snapshot selection/writers.
+- `npz/paths.py`: naming/path helpers.
+- `npz_store.py`: фасадный экспорт.
+
+## `python_app.hardware_full`
+- `librevna_service.py`: high-level service.
+- `librevna_backends.py`: native/mock adapters.
+- `librevna_driver/*`: USB/protocol/session/controller stack.
+- `switch_service.py`: switch facade.
+- `switch_drivers/*`: native/mock switch implementations.
+
+## `python_app.workflows`
+- `sequential_capture_workflow.py`: последовательный capture session.
+- `calibration_workflow.py`, `reference_workflow.py`: helper workflows.
+
+## `python_app.scripts`
+- `check_snapshot_numpy.py`: snapshot validator + plots.
+- `manual_smoke_run.py`: локальный smoke сценарий.
+- `hardware_raw_orchestrator_test.py`: raw ring visualization utility.
+- `vna_only_raw_test.py`: direct VNA check utility.
diff --git a/python_app/docs/operations_runbook.md b/python_app/docs/operations_runbook.md
new file mode 100644
index 0000000..8d1e21a
--- /dev/null
+++ b/python_app/docs/operations_runbook.md
@@ -0,0 +1,49 @@
+# Operations runbook
+
+## 1. Подготовка
+- Собрать C++ binaries (`make`).
+- Проверить `run_config.json` и доступность hardware.
+- Для native режима убедиться в правах на USB/GPIO.
+
+## 2. Запуск GUI
+```bash
+python3 -m python_app.gui.main
+```
+
+## 3. Базовый сценарий live run
+1. Проверить Radar/Switches config.
+2. Выбрать calibration/reference sets.
+3. Нажать `Start`.
+4. Проверить обновление history и графика.
+5. Нажать `Stop`.
+
+## 4. Сохранение snapshot
+1. Указать `Path`, `Name`, `Last N`.
+2. Нажать `Save Numpy Snapshot`.
+3. Проверить `manifest.json` и каталоги `raw/preprocessed/results`.
+
+## 5. Проверка snapshot
+```bash
+python3 python_app/scripts/check_snapshot_numpy.py <snapshot_dir>
+```
+
+## 6. Конвертация snapshot для vna_system
+```bash
+python3 python_app/scripts/convert_snapshot_to_vna_history.py \
+  <snapshot_dir> \
+  -o <output_json> \
+  --input 0 \
+  --output-index 0
+```
+
+Полученный JSON содержит `sweep_history` и загружается в `vna_system` через кнопку загрузки истории у B-scan графика.
+
+## 7. Типовые проблемы
+- Пустой график: проверить processing mode и наличие trace payloads.
+- Нет запуска в B-scan: проверить ограничение combo для native switches.
+- Ошибки лимитов радара: проверить соединение с устройством и serial.
+- Процессы не стартуют: смотреть crash reports в GUI log.
+
+## 8. Безопасная остановка
+- Использовать `Stop` в UI.
+- При закрытии окна вызывается стоп всех процессов и закрытие dialogs/readers.
diff --git a/python_app/docs/preprocessing_and_capture_workflow.md b/python_app/docs/preprocessing_and_capture_workflow.md
new file mode 100644
index 0000000..dbf1805
--- /dev/null
+++ b/python_app/docs/preprocessing_and_capture_workflow.md
@@ -0,0 +1,32 @@
+# Preprocessing and capture workflow
+
+## 1. Назначение
+До запуска live-pipeline необходимо выбрать calibration/reference set, покрывающие целевые run combos.
+
+## 2. Процесс в GUI
+1. Открыть `Preprocessing Panel`.
+2. Выбрать или создать имя набора (`set_name`).
+3. Запустить `Start Calibration Sequence` или `Start Reference Sequence`.
+4. Для каждого combo нажимать `Capture Current Combo`.
+5. По завершении данные сохраняются в `NpzStore`.
+
+## 3. Internal flow
+- `AppWindowPreprocessMixin` создает `SequentialCaptureSession`.
+- Session управляет:
+  - `LibreVnaService`
+  - input/output `SwitchService`
+  - последовательным обходом full combo matrix.
+- После полной матрицы вызывается `finalize(store)`.
+
+## 4. Связь с live run
+`_start_run()` проверяет:
+- выбраны ли calibration/reference sets,
+- покрывают ли выбранные sets все run combos (`has_combo_coverage`),
+- доступны ли bundle-файлы для preprocessor.
+
+## 5. Диагностика
+Типовые ошибки:
+- set already exists,
+- incomplete capture sequence,
+- hardware not available,
+- mismatch requested combos vs stored combos.
diff --git a/python_app/docs/runtime_data_flow.md b/python_app/docs/runtime_data_flow.md
new file mode 100644
index 0000000..2a0328b
--- /dev/null
+++ b/python_app/docs/runtime_data_flow.md
@@ -0,0 +1,39 @@
+# Потоки данных runtime
+
+## 1. Непрерывный acquisition pipeline
+1. `AppWindowPipelineMixin._start_run()` строит `RunConfigModel` из UI.
+2. `ConfigWriter` пишет runtime config и bundles calibration/reference.
+3. `ProcessSupervisor` запускает:
+   - `data_processor`
+   - `data_preprocessor`
+   - `sweep_orchestrator`
+4. GUI открывает `ShmRingReader` для:
+   - raw tap
+   - preprocessed tap
+   - results
+5. Таймер GUI (`QTimer`) вызывает `_poll_rings()`:
+   - чтение raw/preprocessed/results
+   - запись в history deques
+   - рендер либо trace-lines, либо B-scan heatmap.
+
+## 2. B-scan path
+- Источник: `preprocessed` history ring.
+- Кэш-сигнатура: `build_bscan_signature()` учитывает live-параметры и tail history.
+- Ребилд: `rebuild_bscan_history_from_preprocessed()` + `compute_bscan_profile()`.
+- Рендер: `ImageItem` с LUT и уровнями (`bscan_lookup_table`, `bscan_levels`).
+
+## 3. Single capture path
+- `_start_single_capture()` запускает pipeline в non-continuous режиме.
+- GUI ждет collection после стартового timestamp и валидного trace payload.
+- По завершении выполняется `_stop_run()`.
+
+## 4. Snapshot path
+- `AppWindowSnapshotMixin._save_snapshot()` предварительно дренирует ring buffers.
+- `NpzStore.save_runtime_snapshot_numpy()`:
+  - выбирает aligned histories (`select_aligned_histories`),
+  - пишет `raw/preprocessed/results` + `manifest.json`.
+
+## 5. Failure handling
+- Process crashes читаются через `ProcessSupervisor.collect_crash_reports()`.
+- Reader/parsing errors логируются в GUI runtime log.
+- Hardware/validation errors показываются через `_show_error()` и лог.
diff --git a/python_app/docs/shm_protocol_and_readers.md b/python_app/docs/shm_protocol_and_readers.md
new file mode 100644
index 0000000..9d8566b
--- /dev/null
+++ b/python_app/docs/shm_protocol_and_readers.md
@@ -0,0 +1,28 @@
+# SHM protocol and readers
+
+## 1. Ring format
+Python reader ожидает ring header:
+- magic: `RDRRING2`
+- version: `1`
+- fields capacity/slot_size/write_seq/read_seq.
+
+Ring files находятся в `/dev/shm/<ring_name_without_slash>`.
+
+## 2. Reader components
+- `orchestration/shm/binary_cursor.py`: primitive cursor API.
+- `orchestration/shm/decoder.py`: decode trace/result payloads.
+- `orchestration/shm/ring_reader.py`: mmap-based ring consumption.
+- `orchestration/shm_reader.py`: фасад для импортов.
+
+## 3. Consumption semantics
+- `pop_payload()` возвращает `None`, если новых payload нет.
+- При sequence mismatch reader выполняет fast-forward read pointer.
+- `drop_all()` принудительно дропает unread payloads.
+
+## 4. Error cases
+- missing ring file -> `FileNotFoundError`
+- magic/version mismatch -> `RuntimeError`
+- bad payload magic/kind -> `ValueError`
+
+## 5. Integration
+`AppWindowPipelineMixin` использует по одному reader на raw/preprocessed/results ring.
diff --git a/python_app/docs/storage_formats.md b/python_app/docs/storage_formats.md
new file mode 100644
index 0000000..d73c6f7
--- /dev/null
+++ b/python_app/docs/storage_formats.md
@@ -0,0 +1,41 @@
+# Storage formats
+
+## 1. Calibration/reference sets
+Хранятся в `python_app/data/{calibration|reference}/{radar_key}`:
+- `<set_name>.npz`
+- `<set_name>.json`
+
+`radar_key` формируется из sweep и power параметров (`radar_key_from_config`).
+
+## 2. Binary collection serialization
+`storage/npz/serialize.py`:
+- `RAW_MAGIC = 0x31574152`
+- `PREPROC_MAGIC = 0x31525050`
+- `RESULT_MAGIC = 0x314C5352`
+
+Поддерживается сериализация:
+- trace collections (`serialize_trace_collection`)
+- result collections (`serialize_result_collection`)
+
+## 3. Runtime numpy snapshot (`numpy-directory-v1`)
+Структура:
+- `manifest.json`
+- `raw/<collection_dir>/...`
+- `preprocessed/<collection_dir>/...`
+- `results/<collection_dir>/block_*/...`
+
+Коллекция: `0003_id22_ns11999302655222`.
+
+## 4. History selection
+Перед записью snapshot вызывается `select_aligned_histories()`:
+- primary mode: `aligned_by_collection_and_occurrence`
+- fallback: `independent_tail`
+
+Это предотвращает рассинхронизацию raw/preprocessed/results при сохранении.
+
+## 5. Обратное чтение
+Для валидации snapshot используется `scripts/check_snapshot_numpy.py`:
+- проверка shape/finite,
+- сравнение коллекций,
+- построение графиков сравнения,
+- plot всех switch states для одной коллекции.
diff --git a/python_app/gui/__init__.py b/python_app/gui/__init__.py
new file mode 100644
index 0000000..7edeb15
--- /dev/null
+++ b/python_app/gui/__init__.py
@@ -0,0 +1 @@
+"""GUI package for radar system runtime control and visualization."""
diff --git a/python_app/gui/app_window.py b/python_app/gui/app_window.py
new file mode 100644
index 0000000..0022c3e
--- /dev/null
+++ b/python_app/gui/app_window.py
@@ -0,0 +1,137 @@
+"""Main GUI window composed from focused mixins."""
+
+from __future__ import annotations
+
+from collections import deque
+import json
+from pathlib import Path
+
+from PyQt6.QtCore import QTimer
+from PyQt6.QtWidgets import QMainWindow, QMessageBox
+
+from python_app.gui.controllers.app_window_config_mixin import AppWindowConfigMixin
+from python_app.gui.controllers.app_window_pipeline_mixin import AppWindowPipelineMixin
+from python_app.gui.controllers.app_window_plot_mixin import AppWindowPlotMixin
+from python_app.gui.controllers.app_window_preprocess_mixin import AppWindowPreprocessMixin
+from python_app.gui.controllers.app_window_snapshot_mixin import AppWindowSnapshotMixin
+from python_app.gui.controllers.app_window_ui_mixin import AppWindowUiMixin
+from python_app.gui.preprocess_dialog import PreprocessDialog
+from python_app.models.dataset_model import ResultCollection, SweepCollection
+from python_app.models.run_config_model import RunConfigModel
+from python_app.orchestration.config_writer import ConfigWriter
+from python_app.orchestration.live_processing_config import ProcessingLiveConfigWriter
+from python_app.orchestration.process_supervisor import ProcessSupervisor
+from python_app.orchestration.shm_reader import ShmRingReader
+from python_app.storage.npz_store import NpzStore
+from python_app.workflows.sequential_capture_workflow import SequentialCaptureSession
+
+
+class AppWindow(
+    AppWindowUiMixin,
+    AppWindowConfigMixin,
+    AppWindowPreprocessMixin,
+    AppWindowPlotMixin,
+    AppWindowPipelineMixin,
+    AppWindowSnapshotMixin,
+    QMainWindow,
+):
+    """Top-level application window coordinating UI and acquisition runtime."""
+
+    def __init__(self, project_root: Path) -> None:
+        """Initialize application state, services, UI, and polling timer."""
+        super().__init__()
+        self._project_root = project_root
+        self._defaults_config_path = project_root / "run_config.json"
+        self._defaults_config = RunConfigModel.load_from_path(self._defaults_config_path)
+
+        self._store = NpzStore(project_root / "python_app/data")
+        self._config_writer = ConfigWriter(project_root / "python_app/runtime")
+        self._supervisor = ProcessSupervisor(project_root)
+        self._live_config_writer = ProcessingLiveConfigWriter(project_root / "python_app/runtime/processing_live.json")
+
+        self._raw_reader: ShmRingReader | None = None
+        self._pre_reader: ShmRingReader | None = None
+        self._result_reader: ShmRingReader | None = None
+
+        self._preprocess_dialog: PreprocessDialog | None = None
+        self._selected_calibration_set = str(self._defaults_config.preprocess.calibration_set)
+        self._selected_reference_set = str(self._defaults_config.preprocess.reference_set)
+
+        self._capture_session: SequentialCaptureSession | None = None
+        self._resume_pipeline_after_capture = False
+        self._single_capture_active = False
+        self._single_capture_start_ns: int | None = None
+        self._single_capture_seen_raw = False
+        self._single_capture_target_collection_id: int | None = None
+
+        self._raw_history: deque[SweepCollection] = deque(maxlen=512)
+        self._pre_history: deque[SweepCollection] = deque(maxlen=512)
+        result_history_limit = max(
+            1,
+            min(
+                int(self._defaults_config.rings.preprocessed.capacity),
+                int(self._defaults_config.rings.results.capacity),
+                50,
+            ),
+        )
+        self._result_history: deque[ResultCollection] = deque(maxlen=result_history_limit)
+        self._history_command_seq = self._load_history_command_seq(self._live_config_writer.path)
+        self._bscan_history_limit = result_history_limit
+        self._bscan_history_by_combo = {}
+        self._bscan_depth_axis_by_combo = {}
+        self._bscan_history_floor_collection_id = 0
+        self._bscan_render_signature = None
+        self._phase_viewbox = None
+        self._history_run_signature = None
+        self._radar_limits: dict[str, float | int] | None = None
+
+        self._max_pop_per_poll = 256
+        self._max_pop_per_snapshot_drain = 4096
+
+        self._timer = QTimer(self)
+        self._timer.setInterval(50)
+        self._timer.timeout.connect(self._poll_rings)
+
+        self._build_ui()
+        self._refresh_preprocess_summary_labels()
+        if self._radar_mode.currentText() == "native":
+            self._refresh_radar_limits_from_device()
+        else:
+            self._apply_radar_limits_to_ui(None)
+        self._write_live_processing_config()
+        self._timer.start()
+
+    def _log(self, text: str) -> None:
+        """Append a line to the runtime log panel."""
+        self._log_box.appendPlainText(text)
+
+    @staticmethod
+    def _load_history_command_seq(config_path: Path) -> int:
+        """Load previously used live-command sequence from runtime config file."""
+        try:
+            payload = json.loads(config_path.read_text(encoding="utf-8"))
+        except Exception:  # noqa: BLE001
+            return 0
+
+        raw_value = payload.get("history_command_seq", 0)
+        if isinstance(raw_value, bool):
+            return 0
+        if isinstance(raw_value, (int, float)):
+            return max(0, int(raw_value))
+        return 0
+
+    def _show_error(self, message: str) -> None:
+        """Log and present an error in a modal dialog."""
+        self._log(f"ERROR: {message}")
+        QMessageBox.critical(self, "Error", message)
+
+    def closeEvent(self, event) -> None:  # noqa: N802
+        """Ensure workers and dialogs are closed before window destruction."""
+        try:
+            self._resume_pipeline_after_capture = False
+            self._abort_capture_sequence(resume_pipeline=False)
+            self._stop_all_processes()
+            if self._preprocess_dialog is not None:
+                self._preprocess_dialog.close()
+        finally:
+            super().closeEvent(event)
diff --git a/python_app/gui/controllers/__init__.py b/python_app/gui/controllers/__init__.py
new file mode 100644
index 0000000..77adf32
--- /dev/null
+++ b/python_app/gui/controllers/__init__.py
@@ -0,0 +1 @@
+"""Controller mixins and orchestration helpers for GUI windows."""
diff --git a/python_app/gui/controllers/app_window_config_mixin.py b/python_app/gui/controllers/app_window_config_mixin.py
new file mode 100644
index 0000000..4fac590
--- /dev/null
+++ b/python_app/gui/controllers/app_window_config_mixin.py
@@ -0,0 +1,356 @@
+"""Configuration and live-processing binding mixin for the main window."""
+
+from __future__ import annotations
+
+from python_app.hardware_full.librevna_service import LibreVnaService
+from python_app.models.run_config_model import ComboModel, RunConfigModel
+from python_app.orchestration.config_writer import parse_combos_from_text
+from python_app.orchestration.live_processing_config import ProcessingLiveConfig
+from python_app.storage.npz_store import radar_key_from_config
+
+
+class AppWindowConfigMixin:
+    """Builds runtime config models from current UI state."""
+
+    def _save_current_config(self) -> None:
+        """Persist currently selected GUI settings into root run_config.json."""
+        try:
+            config = self._build_config()
+            self._config_writer.write(config, self._defaults_config_path)
+            self._defaults_config = config.clone()
+            self._log(f"Current config saved: {self._defaults_config_path}")
+        except Exception as exc:  # noqa: BLE001
+            self._show_error(f"Failed to save current config: {exc}")
+
+    def _build_config(self) -> RunConfigModel:
+        """Build `RunConfigModel` from current GUI widget values."""
+        config = self._defaults_config.clone()
+
+        config.radar.serial = self._serial_input.text().strip()
+        config.radar.driver_mode = self._radar_mode.currentText()
+
+        config.radar.sweep.start_hz = float(self._start_hz_input.text().strip())
+        config.radar.sweep.stop_hz = float(self._stop_hz_input.text().strip())
+        config.radar.sweep.points = int(self._points_input.text().strip())
+        config.radar.sweep.if_bandwidth_hz = float(self._ifbw_input.text().strip())
+        config.radar.sweep.power_dbm = float(self._power_input.text().strip())
+
+        config.input_switch.driver_mode = self._input_mode.currentText()
+        config.output_switch.driver_mode = self._output_mode.currentText()
+
+        config.input_switch.driver = self._input_driver.currentText()
+        config.output_switch.driver = self._output_driver.currentText()
+        config.input_switch.radar_port = 2
+        config.output_switch.radar_port = 1
+
+        config.input_switch.positions = int(self._input_positions.text().strip())
+        config.output_switch.positions = int(self._output_positions.text().strip())
+
+        config.input_switch.gpio_chip = self._input_gpio_chip.text().strip()
+        config.input_switch.pin_a = int(self._input_pin_a.text().strip())
+        config.input_switch.pin_b = int(self._input_pin_b.text().strip())
+        config.input_switch.invert_logic = self._input_invert_logic.currentText() == "true"
+
+        config.output_switch.gpio_chip = self._output_gpio_chip.text().strip()
+        config.output_switch.pin_a = int(self._output_pin_a.text().strip())
+        config.output_switch.pin_b = int(self._output_pin_b.text().strip())
+        config.output_switch.invert_logic = self._output_invert_logic.currentText() == "true"
+
+        config.runtime.settling_ms = int(self._settling_ms.text().strip())
+        config.runtime.processing_live_config_path = str(self._live_config_writer.path)
+
+        combo_text = self._combos_text.text()
+        config.combos = parse_combos_from_text(combo_text)
+        config.ensure_combos()
+        if self._switches_are_effectively_static(config):
+            config.combos = [ComboModel(input=0, output=0)]
+
+        config.preprocess.calibration_set = self._selected_calibration_set
+        config.preprocess.reference_set = self._selected_reference_set
+        return config
+
+    def _radar_key(self, config: RunConfigModel) -> str:
+        """Build radar key used by calibration/reference storage lookup."""
+        return radar_key_from_config(
+            model_name=config.radar.model,
+            serial=config.radar.serial,
+            sweep_start_hz=config.radar.sweep.start_hz,
+            sweep_stop_hz=config.radar.sweep.stop_hz,
+            sweep_points=config.radar.sweep.points,
+            ifbw_hz=config.radar.sweep.if_bandwidth_hz,
+            power_dbm=config.radar.sweep.power_dbm,
+        )
+
+    def _live_processing_config(self, *, history_command: str = "none") -> ProcessingLiveConfig:
+        """Build live processing config from current processing widgets."""
+        self._sync_bscan_frequency_limits_with_radar()
+        return ProcessingLiveConfig(
+            processor_mode=self._processing_mode.currentText(),
+            gain_db=float(self._processing_gain_db.value()),
+            phase_deg=float(self._processing_phase_deg.value()),
+            bscan_axis=self._bscan_axis.currentText(),
+            bscan_cut_m=float(self._bscan_cut_m.value()),
+            bscan_max_depth_m=float(self._bscan_max_depth_m.value()),
+            bscan_gain=float(self._bscan_gain.value()),
+            bscan_start_freq_mhz=float(self._bscan_start_freq_mhz.value()),
+            bscan_stop_freq_mhz=float(self._bscan_stop_freq_mhz.value()),
+            history_command_seq=int(self._history_command_seq),
+            history_command=str(history_command),
+        )
+
+    def _write_live_processing_config(self, *, history_command: str = "none", bump_history_seq: bool = False) -> None:
+        """Persist current live processing config to runtime JSON file."""
+        if bump_history_seq:
+            self._history_command_seq += 1
+        self._live_config_writer.write(self._live_processing_config(history_command=history_command))
+
+    def _on_processing_live_settings_changed(self, *_args) -> None:
+        """Handle live-processing setting changes and trigger redraw when needed."""
+        try:
+            self._write_live_processing_config()
+            if self._processing_mode.currentText() == "bscan":
+                self._drain_results_until_quiet(timeout_s=0.25, poll_s=0.01)
+                self._sync_bscan_history_from_results()
+                self._draw_bscan_heatmap_from_history()
+            elif self._result_history:
+                self._draw_results(self._result_history[-1])
+        except Exception as exc:  # noqa: BLE001
+            self._show_error(f"Failed to update live processing settings: {exc}")
+
+    def _on_processing_mode_changed(self, mode: str) -> None:
+        """Switch processing parameter page and refresh corresponding visualization."""
+        mode_to_page = {
+            "pass_through": 0,
+            "bscan": 1,
+        }
+        self._set_plot_mode(mode)
+        self._processing_mode_pages.setCurrentIndex(mode_to_page.get(mode, 0))
+        current_page = self._processing_mode_pages.currentWidget()
+        if current_page is not None:
+            self._processing_mode_pages.setFixedHeight(current_page.sizeHint().height())
+        self._processing_mode_pages.updateGeometry()
+        self._on_processing_live_settings_changed()
+
+    def _on_bscan_clear_history_clicked(self) -> None:
+        """Permanently clear all runtime histories, ring backlogs, and B-scan cache."""
+        self._apply_bscan_history_deletion(remove_last_only=False)
+
+    def _on_bscan_remove_last_sweep_clicked(self) -> None:
+        """Permanently delete the latest sweep from runtime histories and rings."""
+        self._apply_bscan_history_deletion(remove_last_only=True)
+
+    def _apply_bscan_history_deletion(self, *, remove_last_only: bool) -> None:
+        """Apply destructive B-scan history deletion via C++ processor history commands."""
+        resume_acquisition = self._supervisor.is_running()
+        history_command = "remove_last" if remove_last_only else "clear_all"
+        action = "last sweep removed" if remove_last_only else "history fully cleared"
+        dropped_results = 0
+
+        try:
+            if resume_acquisition:
+                self._stop_run()
+
+            if self._result_reader is not None:
+                dropped_results = self._result_reader.drop_all()
+
+            if remove_last_only:
+                if self._result_history:
+                    self._result_history.pop()
+            else:
+                self._result_history.clear()
+            self._clear_bscan_plot_history()
+
+            self._write_live_processing_config(history_command=history_command, bump_history_seq=True)
+
+            if self._supervisor.is_processor_running():
+                self._drain_results_until_quiet(timeout_s=0.35, poll_s=0.01)
+
+            self._update_history_indicator()
+            self._redraw_after_history_deletion()
+            if resume_acquisition:
+                self._start_run()
+            self._log(f"B-scan {action}; dropped pending results={dropped_results}")
+        except Exception as exc:  # noqa: BLE001
+            self._show_error(f"Failed to delete B-scan history: {exc}")
+
+    def _redraw_after_history_deletion(self) -> None:
+        """Refresh plot immediately after destructive history deletion."""
+        if self._processing_mode.currentText() == "bscan":
+            if self._result_history:
+                self._sync_bscan_history_from_results()
+            if not self._draw_bscan_heatmap_from_history():
+                self._plot.clear()
+            return
+        if self._result_history:
+            self._draw_results(self._result_history[-1])
+            return
+        self._plot.clear()
+
+    def _on_radar_identity_changed(self, *_args) -> None:
+        """Refresh device limits when radar identity/mode changes."""
+        if self._radar_mode.currentText() != "native":
+            self._apply_radar_limits_to_ui(None)
+            return
+        changed = self._refresh_radar_limits_from_device()
+        if changed:
+            self._on_processing_live_settings_changed()
+
+    def _on_radar_sweep_limits_changed(self) -> None:
+        """Clamp B-scan frequency bounds after sweep start/stop edits."""
+        if self._sync_bscan_frequency_limits_with_radar():
+            self._on_processing_live_settings_changed()
+
+    def _refresh_radar_limits_from_device(self) -> bool:
+        """Query native LibreVNA limits and apply them to GUI fields."""
+        serial = self._serial_input.text().strip()
+        radar_service = LibreVnaService(serial=serial or None)
+        if not radar_service.driver_available:
+            self._fallback_to_mock_mode("LibreVNA Python driver is not available for device limits query")
+            return False
+
+        try:
+            limits = radar_service.read_device_limits()
+        except Exception as exc:  # noqa: BLE001
+            self._fallback_to_mock_mode(f"Failed to query LibreVNA limits: {exc}")
+            return False
+
+        return self._apply_radar_limits_to_ui(limits)
+
+    def _fallback_to_mock_mode(self, reason: str) -> None:
+        """Fallback to mock mode when native limits cannot be queried."""
+        self._log(f"{reason}; switched radar mode to mock")
+        if self._radar_mode.currentText() != "mock":
+            was_blocked = self._radar_mode.blockSignals(True)
+            self._radar_mode.setCurrentText("mock")
+            self._radar_mode.blockSignals(was_blocked)
+        self._apply_radar_limits_to_ui(None)
+
+    def _apply_radar_limits_to_ui(self, limits: dict[str, float | int] | None) -> bool:
+        """Apply optional radar limits and clamp dependent GUI fields."""
+        if limits is None:
+            self._radar_limits = None
+            self._radar_start_label.setText("Start Hz")
+            self._radar_stop_label.setText("Stop Hz")
+            self._radar_points_label.setText("Points")
+            self._radar_ifbw_label.setText("IF BW Hz")
+            self._radar_power_label.setText("Stimulus Power dBm")
+            if self._radar_mode.currentText() == "native":
+                self._radar_limits_hint.setText("Device limits unavailable in native mode (device not connected).")
+            else:
+                self._radar_limits_hint.setText("Mock mode: device limits are not applied.")
+            self._power_input.setToolTip("Device power limits are available only in native mode.")
+            return False
+
+        min_freq_hz = float(limits["min_frequency_hz"])
+        max_freq_hz = float(limits["max_frequency_hz"])
+        min_ifbw_hz = float(limits["min_ifbw_hz"])
+        max_ifbw_hz = float(limits["max_ifbw_hz"])
+        max_points = int(limits["max_points"])
+        min_power_dbm = float(limits["min_power_dbm"])
+        max_power_dbm = float(limits["max_power_dbm"])
+
+        self._radar_limits = limits
+
+        self._radar_start_label.setText(f"Start Hz ({min_freq_hz:g}..{max_freq_hz:g})")
+        self._radar_stop_label.setText(f"Stop Hz ({min_freq_hz:g}..{max_freq_hz:g})")
+        self._radar_points_label.setText(f"Points (1..{max_points:d})")
+        self._radar_ifbw_label.setText(f"IF BW Hz ({min_ifbw_hz:g}..{max_ifbw_hz:g})")
+        self._radar_power_label.setText(f"Stimulus Power dBm ({min_power_dbm:g}..{max_power_dbm:g})")
+        self._radar_limits_hint.setText(
+            f"Limits: Freq {min_freq_hz:g}..{max_freq_hz:g} Hz, Points 1..{max_points:d}, "
+            f"IF BW {min_ifbw_hz:g}..{max_ifbw_hz:g} Hz, Power {min_power_dbm:g}..{max_power_dbm:g} dBm."
+        )
+
+        changed = False
+        prev_start = self._start_hz_input.text().strip()
+        prev_stop = self._stop_hz_input.text().strip()
+        prev_points = self._points_input.text().strip()
+        prev_ifbw = self._ifbw_input.text().strip()
+        prev_power = self._power_input.text().strip()
+
+        start_hz = self._clamp_line_edit_float(self._start_hz_input, min_freq_hz, max_freq_hz)
+        stop_hz = self._clamp_line_edit_float(self._stop_hz_input, min_freq_hz, max_freq_hz)
+        if start_hz > stop_hz:
+            stop_hz = start_hz
+            self._stop_hz_input.setText(f"{stop_hz:g}")
+            changed = True
+
+        points = self._clamp_line_edit_int(self._points_input, 1, max_points)
+        ifbw = self._clamp_line_edit_float(self._ifbw_input, min_ifbw_hz, max_ifbw_hz)
+        power = self._clamp_line_edit_float(self._power_input, min_power_dbm, max_power_dbm)
+        self._power_input.setToolTip(f"Device range: {min_power_dbm:g}..{max_power_dbm:g} dBm")
+
+        changed = (
+            changed
+            or prev_start != self._start_hz_input.text().strip()
+            or prev_stop != self._stop_hz_input.text().strip()
+            or prev_points != self._points_input.text().strip()
+            or prev_ifbw != self._ifbw_input.text().strip()
+            or prev_power != self._power_input.text().strip()
+        )
+
+        self._sync_bscan_frequency_limits_with_radar()
+        return changed
+
+    @staticmethod
+    def _clamp_line_edit_float(widget, min_value: float, max_value: float) -> float:
+        """Clamp float line-edit value to inclusive range and rewrite widget text."""
+        try:
+            value = float(widget.text().strip())
+        except ValueError:
+            value = min_value
+        value = min(max(value, min_value), max_value)
+        widget.setText(f"{value:g}")
+        return value
+
+    @staticmethod
+    def _clamp_line_edit_int(widget, min_value: int, max_value: int) -> int:
+        """Clamp integer line-edit value to inclusive range and rewrite widget text."""
+        try:
+            value = int(float(widget.text().strip()))
+        except ValueError:
+            value = min_value
+        value = min(max(value, min_value), max_value)
+        widget.setText(str(value))
+        return value
+
+    def _sync_bscan_frequency_limits_with_radar(self) -> bool:
+        """Synchronize B-scan start/stop MHz widget ranges with radar sweep bounds."""
+        try:
+            radar_start_hz = float(self._start_hz_input.text().strip())
+            radar_stop_hz = float(self._stop_hz_input.text().strip())
+        except ValueError:
+            return False
+
+        radar_min_mhz = min(radar_start_hz, radar_stop_hz) / 1_000_000.0
+        radar_max_mhz = max(radar_start_hz, radar_stop_hz) / 1_000_000.0
+
+        changed = False
+        for widget in (self._bscan_start_freq_mhz, self._bscan_stop_freq_mhz):
+            if widget.minimum() != radar_min_mhz or widget.maximum() != radar_max_mhz:
+                changed = True
+            widget.blockSignals(True)
+            widget.setRange(radar_min_mhz, radar_max_mhz)
+            widget.blockSignals(False)
+
+        clamped_start_mhz = min(max(self._bscan_start_freq_mhz.value(), radar_min_mhz), radar_max_mhz)
+        clamped_stop_mhz = min(max(self._bscan_stop_freq_mhz.value(), radar_min_mhz), radar_max_mhz)
+        if clamped_start_mhz != self._bscan_start_freq_mhz.value():
+            changed = True
+            self._bscan_start_freq_mhz.blockSignals(True)
+            self._bscan_start_freq_mhz.setValue(clamped_start_mhz)
+            self._bscan_start_freq_mhz.blockSignals(False)
+        if clamped_stop_mhz != self._bscan_stop_freq_mhz.value():
+            changed = True
+            self._bscan_stop_freq_mhz.blockSignals(True)
+            self._bscan_stop_freq_mhz.setValue(clamped_stop_mhz)
+            self._bscan_stop_freq_mhz.blockSignals(False)
+
+        return changed
+
+    @staticmethod
+    def _switches_are_effectively_static(config: RunConfigModel) -> bool:
+        """Return `True` when switch setup effectively yields one fixed combo."""
+        has_single_position = config.input_switch.positions <= 1 and config.output_switch.positions <= 1
+        both_mock = config.input_switch.driver_mode == "mock" and config.output_switch.driver_mode == "mock"
+        return has_single_position or both_mock
diff --git a/python_app/gui/controllers/app_window_pipeline_mixin.py b/python_app/gui/controllers/app_window_pipeline_mixin.py
new file mode 100644
index 0000000..8050468
--- /dev/null
+++ b/python_app/gui/controllers/app_window_pipeline_mixin.py
@@ -0,0 +1,380 @@
+"""Pipeline runtime lifecycle mixin for the main GUI window."""
+
+from __future__ import annotations
+
+import time
+
+from python_app.gui.runtime.constraints import validate_processing_mode_constraints
+from python_app.gui.runtime.history import build_run_history_signature, record_result_history
+from python_app.hardware_full.librevna_service import LibreVnaService
+from python_app.models.dataset_model import ComboKey, ResultCollection, SweepCollection
+from python_app.models.run_config_model import RunConfigModel
+from python_app.orchestration.shm_reader import ShmRingReader
+
+
+class AppWindowPipelineMixin:
+    """Controls start/stop, readers, and periodic polling of pipeline rings."""
+
+    def _start_single_capture(self) -> None:
+        """Start acquisition in single-capture mode."""
+        self._start_run(single_capture=True)
+
+    def _start_run(self, *, single_capture: bool = False) -> None:
+        """Start pipeline processes and ring readers."""
+        if self._capture_session is not None:
+            self._show_error("Cannot start pipeline during active capture sequence")
+            return
+        if self._supervisor.is_running():
+            self._show_error("Pipeline is already running")
+            return
+
+        try:
+            processor_was_running = self._supervisor.is_processor_running()
+            if not processor_was_running:
+                self._reset_runtime_history()
+
+            config = self._build_config()
+            self._validate_processing_mode_constraints(config)
+            run_signature = self._build_run_history_signature(config)
+            radar_key = self._radar_key(config)
+
+            if not config.preprocess.calibration_set or not config.preprocess.reference_set:
+                raise RuntimeError("Select calibration and reference sets in Preprocessing Panel before Start")
+
+            combo_keys = [ComboKey(input_pos=combo.input, output_pos=combo.output) for combo in config.combos]
+
+            if not self._store.has_combo_coverage(
+                "calibration", radar_key, config.preprocess.calibration_set, combo_keys
+            ):
+                raise RuntimeError("Selected calibration set does not cover requested run combos")
+            if not self._store.has_combo_coverage(
+                "reference", radar_key, config.preprocess.reference_set, combo_keys
+            ):
+                raise RuntimeError("Selected reference set does not cover requested run combos")
+
+            calibration_bundle, reference_bundle = self._config_writer.prepare_bundles(
+                self._store,
+                radar_key,
+                config.preprocess.calibration_set,
+                config.preprocess.reference_set,
+            )
+            config.preprocess.calibration_bundle_path = str(calibration_bundle)
+            config.preprocess.reference_bundle_path = str(reference_bundle)
+            config.runtime.continuous = not single_capture
+
+            if not single_capture:
+                self._prepare_radar_for_native_acquisition(config)
+
+            config_path = self._config_writer.write(config, self._project_root / "python_app/runtime/run_config.json")
+
+            if not single_capture:
+                should_reset_history = (
+                    self._history_run_signature is not None and self._history_run_signature != run_signature
+                )
+                if should_reset_history:
+                    self._reset_runtime_history()
+                    self._log("History reset because run settings changed")
+                self._history_run_signature = run_signature
+
+            self._supervisor.start(config_path)
+            self._close_readers()
+            self._raw_reader = ShmRingReader(config.rings.raw_tap.name)
+            self._pre_reader = ShmRingReader(config.rings.preprocessed_tap.name)
+            self._result_reader = ShmRingReader(config.rings.results.name)
+            self._single_capture_active = single_capture
+            self._single_capture_start_ns = None
+            self._single_capture_seen_raw = False
+            self._single_capture_target_collection_id = None
+
+            self._drop_pending_ring_payloads(include_results=not single_capture)
+            if single_capture:
+                self._single_capture_start_ns = time.monotonic_ns()
+
+            if single_capture:
+                self._status_label.setText("Status: single capture running")
+                self._log("Single capture started")
+            else:
+                self._status_label.setText("Status: running")
+                self._log("Pipeline started")
+        except Exception as exc:  # noqa: BLE001
+            self._single_capture_active = False
+            self._single_capture_start_ns = None
+            self._stop_all_processes()
+            self._show_error(f"Failed to start pipeline: {exc}")
+
+    def _apply_radar_settings(self) -> None:
+        """Apply current radar settings by preconfiguring native device."""
+        if self._capture_session is not None:
+            self._show_error("Finish or abort capture sequence before applying radar settings")
+            return
+
+        was_running = self._supervisor.is_running()
+        if was_running:
+            self._stop_run()
+
+        try:
+            if self._radar_mode.currentText() == "native":
+                self._refresh_radar_limits_from_device()
+            config = self._build_config()
+            self._prepare_radar_for_native_acquisition(config)
+            self._log("Radar settings applied")
+        except Exception as exc:  # noqa: BLE001
+            self._show_error(f"Failed to apply radar settings: {exc}")
+        finally:
+            if was_running:
+                self._start_run()
+
+    def _prepare_radar_for_native_acquisition(self, config: RunConfigModel) -> None:
+        """Preconfigure native LibreVNA using current sweep settings."""
+        if config.radar.driver_mode != "native":
+            self._log("Radar pre-configuration skipped (mock mode)")
+            return
+
+        radar_service = LibreVnaService(serial=config.radar.serial or None)
+        if not radar_service.driver_available:
+            raise RuntimeError("LibreVNA Python driver is not available for native pre-configuration")
+
+        try:
+            radar_service.open()
+            radar_service.configure(config.radar.sweep)
+        finally:
+            radar_service.close()
+
+        self._log("Radar pre-configured via Python driver")
+
+    def _stop_run(self) -> None:
+        """Stop acquisition-side processes and close readers as needed."""
+        was_running = self._supervisor.is_running()
+        if was_running:
+            self._supervisor.stop_orchestrator()
+            self._drain_rings_until_quiet(timeout_s=0.35, poll_s=0.02)
+            self._supervisor.stop_preprocessor()
+            self._drain_rings_until_quiet(timeout_s=0.25, poll_s=0.02)
+        else:
+            self._supervisor.stop()
+            self._drain_rings_once_for_history()
+        keep_results_reader = self._supervisor.is_processor_running()
+        self._close_readers(keep_results=keep_results_reader)
+        self._single_capture_active = False
+        self._single_capture_start_ns = None
+        self._single_capture_seen_raw = False
+        self._single_capture_target_collection_id = None
+        self._update_history_indicator()
+        self._status_label.setText("Status: idle")
+        if was_running:
+            if keep_results_reader:
+                self._log("Acquisition stopped (data_processor kept running)")
+            else:
+                self._log("Pipeline stopped")
+
+    def _stop_all_processes(self) -> None:
+        """Stop all managed pipeline processes and close all readers."""
+        was_running = self._supervisor.is_running() or self._supervisor.is_processor_running()
+        self._supervisor.stop_all()
+        self._drain_rings_until_quiet(timeout_s=0.25, poll_s=0.02)
+        self._close_readers(keep_results=False)
+        self._single_capture_active = False
+        self._single_capture_start_ns = None
+        self._single_capture_seen_raw = False
+        self._single_capture_target_collection_id = None
+        self._update_history_indicator()
+        self._status_label.setText("Status: idle")
+        if was_running:
+            self._log("All pipeline processes stopped")
+
+    def _close_readers(self, *, keep_results: bool = False) -> None:
+        """Close active ring readers."""
+        if self._raw_reader is not None:
+            self._raw_reader.close()
+            self._raw_reader = None
+        if self._pre_reader is not None:
+            self._pre_reader.close()
+            self._pre_reader = None
+        if not keep_results and self._result_reader is not None:
+            self._result_reader.close()
+            self._result_reader = None
+
+    def _poll_rings(self) -> None:
+        """Poll readers, ingest history, and trigger rendering."""
+        for report in self._supervisor.collect_crash_reports():
+            self._status_label.setText("Status: error")
+            self._log(report)
+
+        try:
+            if self._raw_reader is not None:
+                self._read_all_raw()
+            self._read_all_preprocessed()
+            result_latest = self._read_all_results() if self._result_reader is not None else None
+            self._update_history_indicator()
+
+            if self._single_capture_active:
+                if self._finish_single_capture_if_ready(result_latest):
+                    return
+                return
+
+            self._draw_preferred_collection(result_latest=result_latest)
+        except Exception as exc:  # noqa: BLE001
+            self._log(f"Reader error: {exc}")
+
+    def _finish_single_capture_if_ready(self, result_latest: ResultCollection | None) -> bool:
+        """Finalize single capture when new result matching start criteria is available."""
+        if not self._single_capture_active:
+            return False
+        if result_latest is None:
+            return False
+        if self._single_capture_start_ns is None:
+            return False
+        if not self._single_capture_seen_raw:
+            return False
+        if (
+            self._single_capture_target_collection_id is not None
+            and result_latest.collection_id < self._single_capture_target_collection_id
+        ):
+            return False
+        if result_latest.monotonic_ns < self._single_capture_start_ns:
+            return False
+        if not self._result_collection_has_trace(result_latest):
+            return False
+
+        self._draw_results(result_latest)
+        self._log("Single capture completed")
+        self._stop_run()
+        return True
+
+    def _read_all_raw(self) -> SweepCollection | None:
+        """Read available raw collections from raw ring."""
+        assert self._raw_reader is not None
+        latest: SweepCollection | None = None
+        for _ in range(self._max_pop_per_poll):
+            collection = self._raw_reader.pop_raw_collection()
+            if collection is None:
+                break
+            self._raw_history.append(collection)
+            latest = collection
+            if self._single_capture_active and self._single_capture_start_ns is not None:
+                if collection.monotonic_ns >= self._single_capture_start_ns:
+                    self._single_capture_seen_raw = True
+                    if self._single_capture_target_collection_id is None:
+                        self._single_capture_target_collection_id = collection.collection_id
+        return latest
+
+    def _read_all_preprocessed(self) -> None:
+        """Read available preprocessed collections from preprocessed ring."""
+        if self._pre_reader is None:
+            return
+
+        for _ in range(self._max_pop_per_poll):
+            collection = self._pre_reader.pop_preprocessed_collection()
+            if collection is None:
+                break
+            self._pre_history.append(collection)
+
+    def _read_all_results(self) -> ResultCollection | None:
+        """Read available result collections from results ring."""
+        assert self._result_reader is not None
+        latest: ResultCollection | None = None
+        for _ in range(self._max_pop_per_poll):
+            collection = self._result_reader.pop_result_collection()
+            if collection is None:
+                break
+            if self._record_result_history(collection):
+                latest = collection
+        return latest
+
+    def _record_result_history(self, collection: ResultCollection) -> bool:
+        """Merge collection into result history preserving de-dup semantics."""
+        return record_result_history(self._result_history, collection)
+
+    def _drain_rings_once_for_history(self) -> None:
+        """Perform one non-blocking read pass to extend histories."""
+        if self._raw_reader is not None:
+            self._read_all_raw()
+        self._read_all_preprocessed()
+        if self._result_reader is not None:
+            self._read_all_results()
+
+    def _drain_rings_until_quiet(self, *, timeout_s: float, poll_s: float) -> None:
+        """Drain rings until history sizes stabilize or timeout expires."""
+        deadline = time.monotonic() + timeout_s
+        stable_rounds = 0
+        previous = (
+            len(self._raw_history),
+            len(self._pre_history),
+            len(self._result_history),
+        )
+
+        while time.monotonic() < deadline and stable_rounds < 2:
+            self._drain_rings_once_for_history()
+            current = (
+                len(self._raw_history),
+                len(self._pre_history),
+                len(self._result_history),
+            )
+            if current == previous:
+                stable_rounds += 1
+            else:
+                stable_rounds = 0
+                previous = current
+            time.sleep(poll_s)
+
+    def _drain_results_until_quiet(self, *, timeout_s: float, poll_s: float) -> None:
+        """Drain only results ring until size stabilizes or timeout expires."""
+        if self._result_reader is None:
+            return
+
+        deadline = time.monotonic() + timeout_s
+        stable_rounds = 0
+
+        while time.monotonic() < deadline and stable_rounds < 2:
+            latest = self._read_all_results()
+            if latest is None:
+                stable_rounds += 1
+            else:
+                stable_rounds = 0
+            time.sleep(poll_s)
+
+    def _update_history_indicator(self) -> None:
+        """Update UI label with current history buffer sizes."""
+        self._history_label.setText(
+            f"History: raw={len(self._raw_history)}, "
+            f"preprocessed={len(self._pre_history)}, "
+            f"results={len(self._result_history)}"
+        )
+
+    def _reset_runtime_history(self) -> None:
+        """Reset runtime history and B-scan caches."""
+        self._replace_runtime_history(retained_raw=[], retained_pre=[], retained_result=[])
+        self._bscan_history_floor_collection_id = 0
+        self._clear_bscan_plot_history()
+        self._update_history_indicator()
+
+    def _replace_runtime_history(
+        self,
+        *,
+        retained_raw: list[SweepCollection],
+        retained_pre: list[SweepCollection],
+        retained_result: list[ResultCollection],
+    ) -> None:
+        """Replace history deques with provided retained tails."""
+        raw_tail = retained_raw[-self._raw_history.maxlen :] if self._raw_history.maxlen is not None else retained_raw
+        pre_tail = retained_pre[-self._pre_history.maxlen :] if self._pre_history.maxlen is not None else retained_pre
+        result_tail = (
+            retained_result[-self._result_history.maxlen :]
+            if self._result_history.maxlen is not None
+            else retained_result
+        )
+
+        self._raw_history.clear()
+        self._pre_history.clear()
+        self._result_history.clear()
+        self._raw_history.extend(raw_tail)
+        self._pre_history.extend(pre_tail)
+        self._result_history.extend(result_tail)
+
+    def _build_run_history_signature(self, config: RunConfigModel) -> tuple[object, ...]:
+        """Build signature used to decide when history should be reset."""
+        return build_run_history_signature(config)
+
+    def _validate_processing_mode_constraints(self, config: RunConfigModel) -> None:
+        """Validate processing-mode constraints for run start."""
+        validate_processing_mode_constraints(self._processing_mode.currentText(), config)
diff --git a/python_app/gui/controllers/app_window_plot_mixin.py b/python_app/gui/controllers/app_window_plot_mixin.py
new file mode 100644
index 0000000..51cda45
--- /dev/null
+++ b/python_app/gui/controllers/app_window_plot_mixin.py
@@ -0,0 +1,378 @@
+"""Plot rendering mixin for processed radar result collections."""
+
+from __future__ import annotations
+
+from PyQt6.QtCore import QRectF, Qt
+import numpy as np
+import pyqtgraph as pg
+
+from python_app.gui.plotting.bscan_history import (
+    build_bscan_signature,
+    pick_bscan_display_key,
+    rebuild_bscan_history_from_results,
+)
+from python_app.gui.plotting.bscan_math import (
+    bscan_levels,
+    bscan_lookup_table,
+    build_lut,
+)
+from python_app.models.dataset_model import ResultCollection, TraceData
+
+
+class AppWindowPlotMixin:
+    """Renders result collections on the main pyqtgraph plot."""
+
+    def _draw_preferred_collection(
+        self,
+        *,
+        result_latest: ResultCollection | None,
+    ) -> None:
+        """Draw latest available result collection if present."""
+        if result_latest is None:
+            return
+        self._draw_results(result_latest)
+
+    def _draw_results(self, collection: ResultCollection) -> bool:
+        """Draw collection based on currently selected processing mode."""
+        if self._processing_mode.currentText() == "bscan":
+            return self._draw_bscan_heatmap(collection)
+        return self._draw_trace_lines(collection)
+
+    def _show_magnitude_curves(self) -> bool:
+        """Return whether magnitude curves should be rendered."""
+        return self._show_magnitude_checkbox.isChecked()
+
+    def _show_phase_curves(self) -> bool:
+        """Return whether phase curves should be rendered."""
+        return self._show_phase_checkbox.isChecked()
+
+    def _on_trace_visibility_changed(self, *_args) -> None:
+        """Redraw pass-through traces when magnitude/phase toggles changed."""
+        if self._processing_mode.currentText() == "bscan":
+            return
+        if self._result_history:
+            self._draw_results(self._result_history[-1])
+            return
+        self._clear_trace_plots()
+
+    def _clear_trace_plots(self) -> None:
+        """Clear pass-through magnitude and phase plots."""
+        self._trace_magnitude_plot.clear()
+        self._trace_phase_plot.clear()
+
+    def _draw_trace_lines(self, collection: ResultCollection) -> bool:
+        """Draw result payload traces as stacked magnitude/phase plots."""
+        show_magnitude = self._show_magnitude_curves()
+        show_phase = self._show_phase_curves()
+        magnitude_plot = self._trace_magnitude_plot
+        phase_plot = self._trace_phase_plot
+
+        magnitude_plot.setVisible(show_magnitude)
+        phase_plot.setVisible(show_phase)
+        self._clear_trace_plots()
+        if not show_magnitude and not show_phase:
+            return False
+
+        if show_magnitude:
+            mag_item = magnitude_plot.getPlotItem()
+            magnitude_plot.getViewBox().invertY(False)
+            magnitude_plot.getViewBox().enableAutoRange(x=True, y=True)
+            mag_item.showAxis("left", show=True)
+            mag_item.showAxis("bottom", show=not show_phase)
+            magnitude_plot.setLabel("left", "Magnitude", units="dB")
+            if not show_phase:
+                magnitude_plot.setLabel("bottom", "Frequency", units="Hz")
+
+        if show_phase:
+            phase_item = phase_plot.getPlotItem()
+            phase_plot.getViewBox().invertY(False)
+            phase_plot.getViewBox().enableAutoRange(x=True, y=False)
+            phase_item.showAxis("left", show=True)
+            phase_item.showAxis("bottom", show=True)
+            phase_plot.setLabel("left", "Phase", units="deg")
+            phase_plot.setLabel("bottom", "Frequency", units="Hz")
+
+        palette = [
+            "#4cc9f0",
+            "#f72585",
+            "#b8f2e6",
+            "#ffd166",
+            "#90be6d",
+            "#ff595e",
+            "#6a4c93",
+            "#1982c4",
+        ]
+
+        color_index = 0
+        has_data = False
+        x_min = np.inf
+        x_max = -np.inf
+        for block in collection.blocks:
+            for payload in block.payloads:
+                if payload.kind != 1 or payload.trace.size == 0:
+                    continue
+                if payload.frequency_hz.size == 0 or payload.frequency_hz.size != payload.trace.size:
+                    continue
+
+                local_x_min = float(np.min(payload.frequency_hz))
+                local_x_max = float(np.max(payload.frequency_hz))
+                x_min = min(x_min, local_x_min)
+                x_max = max(x_max, local_x_max)
+                color = palette[color_index % len(palette)]
+
+                if show_magnitude:
+                    magnitude_values = 20.0 * np.log10(np.maximum(np.abs(payload.trace), 1e-12))
+                    magnitude_curve = pg.PlotCurveItem(
+                        payload.frequency_hz,
+                        magnitude_values,
+                        pen=pg.mkPen(color, width=1.4),
+                    )
+                    magnitude_plot.addItem(magnitude_curve)
+                    has_data = True
+
+                if show_phase:
+                    phase_values = np.degrees(np.angle(payload.trace))
+                    phase_curve = pg.PlotCurveItem(
+                        payload.frequency_hz,
+                        phase_values,
+                        pen=pg.mkPen(color, width=1.2, style=Qt.PenStyle.DashLine),
+                    )
+                    phase_plot.addItem(phase_curve)
+                    has_data = True
+
+                color_index += 1
+
+        if has_data:
+            if np.isfinite(x_min) and np.isfinite(x_max):
+                if show_magnitude:
+                    magnitude_plot.setXRange(x_min, x_max, padding=0.02)
+                if show_phase:
+                    phase_plot.setXRange(x_min, x_max, padding=0.02)
+            if show_phase:
+                phase_plot.setYRange(-180.0, 180.0, padding=0.02)
+        return has_data
+
+    def _draw_bscan_heatmap(self, _collection: ResultCollection) -> bool:
+        """Draw B-scan image rebuilt from processed result history."""
+        self._disable_phase_axis()
+        self._sync_bscan_history_from_results()
+        return self._draw_bscan_heatmap_from_history()
+
+    def _draw_bscan_heatmap_from_history(self) -> bool:
+        """Render B-scan heatmap from currently cached history arrays."""
+        display_key = self._pick_bscan_display_key()
+        if display_key is None:
+            return False
+
+        history = self._bscan_history_by_combo.get(display_key)
+        depth_axis = self._bscan_depth_axis_by_combo.get(display_key)
+        if not history or depth_axis is None:
+            return False
+
+        sweeps = np.vstack(history).astype(np.float32, copy=False)
+        if sweeps.size == 0:
+            return False
+
+        depth_min = float(np.min(depth_axis))
+        depth_max = float(np.max(depth_axis))
+        depth_span = max(depth_max - depth_min, 1e-6)
+        sweep_count = sweeps.shape[0]
+        sweep_width = float(max(sweep_count, 1))
+        x_min = 0.5
+        x_max = x_min + sweep_width
+
+        image_item = pg.ImageItem(axisOrder="row-major")
+        image_item.setImage(sweeps.T, autoLevels=False)
+        image_item.setRect(QRectF(x_min, depth_min, sweep_width, depth_span))
+
+        axis_mode = self._bscan_axis.currentText()
+        image_item.setLookupTable(self._bscan_lookup_table(axis_mode))
+        image_item.setLevels(self._bscan_levels(sweeps, axis_mode))
+
+        self._plot.clear()
+        view_box = self._plot.getViewBox()
+        view_box.invertY(True)
+        view_box.enableAutoRange(x=False, y=False)
+        self._plot.getPlotItem().showAxis("left", show=True)
+        self._plot.getPlotItem().showAxis("bottom", show=True)
+        self._plot.setLabel("bottom", "Sweep #")
+        self._plot.setLabel("left", "Depth", units="m")
+        self._plot.addItem(image_item)
+        self._plot.setXRange(x_min, x_max, padding=0.02)
+        self._plot.setYRange(depth_min, depth_max, padding=0.02)
+        self._plot.setTitle(f"B-scan in{display_key[0]}/out{display_key[1]} | sweeps={sweep_count}")
+        return True
+
+    def _sync_bscan_history_from_results(self) -> None:
+        """Rebuild B-scan history cache when live params or inputs changed."""
+        self._advance_bscan_floor_to_cpp_window()
+        signature = self._bscan_signature()
+        if signature == self._bscan_render_signature:
+            return
+        self._rebuild_bscan_history_from_results()
+        self._bscan_render_signature = signature
+
+    def _bscan_signature(self) -> tuple[object, ...]:
+        """Build state signature for B-scan history cache invalidation."""
+        live_config = self._live_processing_config()
+        result_history = list(self._result_history)
+        return build_bscan_signature(
+            live_config=live_config,
+            result_history=result_history,
+            history_limit=self._bscan_history_limit,
+            floor_collection_id=self._bscan_history_floor_collection_id,
+        )
+
+    def _rebuild_bscan_history_from_results(self) -> None:
+        """Recompute B-scan history cache from results history buffer."""
+        result_history = list(self._result_history)
+        history_by_combo, depth_axis_by_combo = rebuild_bscan_history_from_results(
+            result_history=result_history,
+            history_limit=self._bscan_history_limit,
+            floor_collection_id=self._bscan_history_floor_collection_id,
+        )
+        self._bscan_history_by_combo = history_by_combo
+        self._bscan_depth_axis_by_combo = depth_axis_by_combo
+
+    def _pick_bscan_display_key(self) -> tuple[int, int] | None:
+        """Choose combo history key to render."""
+        return pick_bscan_display_key(self._bscan_history_by_combo)
+
+    def _bscan_lookup_table(self, axis_mode: str) -> np.ndarray:
+        """Return lookup table for current B-scan axis mode."""
+        return bscan_lookup_table(axis_mode)
+
+    @staticmethod
+    def _build_lut(stops: list[str], *, size: int = 256) -> np.ndarray:
+        """Backward-compatible wrapper around LUT builder."""
+        return build_lut(stops, size=size)
+
+    @staticmethod
+    def _bscan_levels(sweeps: np.ndarray, axis_mode: str) -> tuple[float, float]:
+        """Return display levels for B-scan image."""
+        return bscan_levels(sweeps, axis_mode)
+
+    def _clear_bscan_plot_history(self) -> None:
+        """Drop cached B-scan history and invalidate cache signature."""
+        self._bscan_history_by_combo.clear()
+        self._bscan_depth_axis_by_combo.clear()
+        self._bscan_render_signature = None
+
+    def _advance_bscan_floor_to_cpp_window(self) -> None:
+        """Clamp B-scan source history to C++ available replay window."""
+        if not self._result_history:
+            return
+
+        cpp_window_limit = min(
+            int(self._defaults_config.rings.preprocessed.capacity),
+            int(self._defaults_config.rings.results.capacity),
+        )
+        cpp_window_limit = max(1, cpp_window_limit)
+        latest_collection_id = int(self._result_history[-1].collection_id)
+        current_floor = int(self._bscan_history_floor_collection_id)
+
+        # Collection ids restart from 1 on new C++ run; release floor only while
+        # acquisition is running, so manual "remove last" behavior in stopped mode
+        # remains deterministic.
+        if latest_collection_id < current_floor and self._supervisor.is_running():
+            self._bscan_history_floor_collection_id = 0
+            current_floor = 0
+
+        floor_candidate = max(0, latest_collection_id - cpp_window_limit)
+        if floor_candidate > current_floor:
+            self._bscan_history_floor_collection_id = floor_candidate
+
+    def _ensure_phase_view_box(self) -> pg.ViewBox:
+        """Create or return secondary right-axis ViewBox for phase curves."""
+        plot_item = self._plot.getPlotItem()
+        phase_view_box = self._phase_viewbox
+        if phase_view_box is None:
+            phase_view_box = pg.ViewBox()
+            self._phase_viewbox = phase_view_box
+            plot_item.scene().addItem(phase_view_box)
+            plot_item.getAxis("right").linkToView(phase_view_box)
+            phase_view_box.setXLink(plot_item.vb)
+            plot_item.vb.sigResized.connect(self._update_phase_view_box_geometry)
+        self._update_phase_view_box_geometry()
+        return phase_view_box
+
+    def _update_phase_view_box_geometry(self) -> None:
+        """Keep right-axis ViewBox geometry in sync with main plot ViewBox."""
+        phase_view_box = self._phase_viewbox
+        if phase_view_box is None:
+            return
+        plot_item = self._plot.getPlotItem()
+        phase_view_box.setGeometry(plot_item.vb.sceneBoundingRect())
+        phase_view_box.linkedViewChanged(plot_item.vb, phase_view_box.XAxis)
+
+    def _clear_phase_overlay(self) -> None:
+        """Remove all phase curves from secondary ViewBox."""
+        self._trace_phase_plot.clear()
+
+    def _disable_phase_axis(self) -> None:
+        """Hide right axis and clear phase overlay when phase is not rendered."""
+        self._clear_phase_overlay()
+
+    def _result_collection_has_trace(self, collection: ResultCollection) -> bool:
+        """Return `True` when collection contains at least one trace payload."""
+        for block in collection.blocks:
+            for payload in block.payloads:
+                if payload.kind == 1 and payload.trace.size > 0:
+                    return True
+        return False
+
+    def _draw_single_trace(self, trace: TraceData, title: str) -> None:
+        """Draw one trace on stacked magnitude/phase plots."""
+        show_magnitude = self._show_magnitude_curves()
+        show_phase = self._show_phase_curves()
+        magnitude_plot = self._trace_magnitude_plot
+        phase_plot = self._trace_phase_plot
+
+        magnitude_plot.setVisible(show_magnitude)
+        phase_plot.setVisible(show_phase)
+        self._clear_trace_plots()
+        if not show_magnitude and not show_phase:
+            return
+
+        if show_magnitude:
+            magnitude_plot.getViewBox().invertY(False)
+            magnitude_plot.getViewBox().enableAutoRange(x=True, y=True)
+            magnitude_plot.getPlotItem().showAxis("bottom", show=not show_phase)
+            magnitude_plot.setLabel("left", "Magnitude", units="dB")
+            magnitude_plot.setTitle(title)
+            if not show_phase:
+                magnitude_plot.setLabel("bottom", "Frequency", units="Hz")
+        if show_phase:
+            phase_plot.getViewBox().invertY(False)
+            phase_plot.getViewBox().enableAutoRange(x=True, y=False)
+            phase_plot.getPlotItem().showAxis("bottom", show=True)
+            phase_plot.setLabel("left", "Phase", units="deg")
+            phase_plot.setLabel("bottom", "Frequency", units="Hz")
+            phase_plot.setTitle(title)
+
+        if show_magnitude:
+            magnitude_db = 20.0 * np.log10(np.maximum(np.abs(trace.s21), 1e-12))
+            magnitude_curve = pg.PlotCurveItem(
+                trace.frequency_hz,
+                magnitude_db,
+                pen=pg.mkPen("#ffd166", width=1.8),
+            )
+            magnitude_plot.addItem(magnitude_curve)
+
+        if show_phase:
+            phase_deg = np.degrees(np.angle(trace.s21))
+            phase_curve = pg.PlotCurveItem(
+                trace.frequency_hz,
+                phase_deg,
+                pen=pg.mkPen("#80ed99", width=1.4, style=Qt.PenStyle.DashLine),
+            )
+            phase_plot.addItem(phase_curve)
+            phase_plot.setYRange(-180.0, 180.0, padding=0.02)
+
+        if np.size(trace.frequency_hz) > 1:
+            x_min = float(np.min(trace.frequency_hz))
+            x_max = float(np.max(trace.frequency_hz))
+            if show_magnitude:
+                magnitude_plot.setXRange(x_min, x_max, padding=0.02)
+            if show_phase:
+                phase_plot.setXRange(x_min, x_max, padding=0.02)
diff --git a/python_app/gui/controllers/app_window_preprocess_mixin.py b/python_app/gui/controllers/app_window_preprocess_mixin.py
new file mode 100644
index 0000000..6fa05d1
--- /dev/null
+++ b/python_app/gui/controllers/app_window_preprocess_mixin.py
@@ -0,0 +1,224 @@
+"""Preprocessing-set selection and sequential capture workflow mixin."""
+
+from __future__ import annotations
+
+from python_app.gui.preprocess_dialog import PreprocessDialog
+from python_app.workflows.sequential_capture_workflow import SequentialCaptureSession
+
+
+class AppWindowPreprocessMixin:
+    """Handles calibration/reference set management and capture workflow."""
+
+    def _open_preprocess_panel(self) -> None:
+        """Open preprocessing dialog and refresh available sets."""
+        dialog = self._ensure_preprocess_dialog()
+        try:
+            self._refresh_sets()
+            self._update_capture_dialog_state()
+        except Exception as exc:  # noqa: BLE001
+            self._show_error(f"Failed to open preprocessing panel: {exc}")
+            return
+
+        dialog.show()
+        dialog.raise_()
+        dialog.activateWindow()
+
+    def _ensure_preprocess_dialog(self) -> PreprocessDialog:
+        """Create preprocessing dialog lazily and wire its signals once."""
+        if self._preprocess_dialog is not None:
+            return self._preprocess_dialog
+
+        dialog = PreprocessDialog(self)
+        dialog.refresh_requested.connect(self._refresh_sets)
+        dialog.selection_changed.connect(self._on_preprocess_selection_changed)
+        dialog.start_sequence_requested.connect(self._start_capture_sequence)
+        dialog.capture_next_requested.connect(self._capture_next_combo)
+        dialog.abort_sequence_requested.connect(self._abort_capture_sequence)
+        self._preprocess_dialog = dialog
+        self._update_capture_dialog_state()
+        return dialog
+
+    def _on_preprocess_selection_changed(self, calibration_set: str, reference_set: str) -> None:
+        """Persist selected preprocessing set names from dialog."""
+        self._selected_calibration_set = calibration_set.strip()
+        self._selected_reference_set = reference_set.strip()
+        self._refresh_preprocess_summary_labels()
+
+    def _refresh_preprocess_summary_labels(self) -> None:
+        """Update compact summary labels in the main window."""
+        self._selected_calibration_label.setText(self._selected_calibration_set or "<not selected>")
+        self._selected_reference_label.setText(self._selected_reference_set or "<not selected>")
+
+    def _refresh_sets(self) -> None:
+        """Refresh calibration/reference set lists for current radar key."""
+        config = self._build_config()
+        radar_key = self._radar_key(config)
+        calibration_sets = self._store.list_sets("calibration", radar_key)
+        reference_sets = self._store.list_sets("reference", radar_key)
+
+        dialog = self._ensure_preprocess_dialog()
+        dialog.set_calibration_sets(calibration_sets)
+        dialog.set_reference_sets(reference_sets)
+
+        if self._selected_calibration_set not in calibration_sets:
+            self._selected_calibration_set = calibration_sets[0] if calibration_sets else ""
+        if self._selected_reference_set not in reference_sets:
+            self._selected_reference_set = reference_sets[0] if reference_sets else ""
+
+        dialog.set_selected_sets(self._selected_calibration_set, self._selected_reference_set)
+        self._refresh_preprocess_summary_labels()
+        self._log(f"Set lists refreshed for key={radar_key}")
+
+    def _start_capture_sequence(self, kind: str) -> None:
+        """Start sequential capture session for requested preprocessing kind."""
+        if self._capture_session is not None:
+            self._show_error("Another capture sequence is already active")
+            return
+
+        dialog = self._ensure_preprocess_dialog()
+        set_name = dialog.set_name()
+        if not set_name:
+            self._show_error("Set name is required")
+            return
+
+        was_running = self._supervisor.is_running()
+        if was_running:
+            self._log("Pipeline paused for exclusive hardware capture")
+            self._stop_run()
+
+        self._resume_pipeline_after_capture = was_running
+
+        try:
+            config = self._build_config()
+            radar_key = self._radar_key(config)
+            existing_sets = self._store.list_sets(kind, radar_key)
+            if set_name in existing_sets:
+                raise RuntimeError(f"Set '{set_name}' already exists for {kind} and cannot be overwritten")
+
+            session = SequentialCaptureSession(config=config, kind=kind, set_name=set_name)
+            session.open()
+            self._capture_session = session
+
+            dialog.clear_capture_log()
+            dialog.set_status(f"{kind.title()} sequence started")
+            self._update_capture_dialog_state()
+            self._log(f"{kind.title()} sequence started for set={set_name}; fill all N*M combos")
+        except Exception as exc:  # noqa: BLE001
+            self._cleanup_capture_session()
+            self._show_error(f"Failed to start {kind} sequence: {exc}")
+            self._resume_pipeline_if_needed()
+
+    def _capture_next_combo(self) -> None:
+        """Capture next combo in active sequential capture session."""
+        session = self._capture_session
+        if session is None:
+            self._show_error("No active capture sequence")
+            return
+
+        dialog = self._ensure_preprocess_dialog()
+
+        try:
+            trace = session.capture_current_combo()
+            state = session.state()
+            tx_label, rx_label = dialog.antenna_labels()
+
+            dialog.append_capture_log_entry(
+                kind=session.kind,
+                captured_count=state.captured_count,
+                total_count=state.total_count,
+                input_pos=trace.combo.input_pos,
+                output_pos=trace.combo.output_pos,
+                tx_label=tx_label,
+                rx_label=rx_label,
+            )
+
+            dialog.draw_last_trace(trace, title=f"{session.kind.title()} captured")
+            self._draw_single_trace(trace, title=f"{session.kind.title()} last trace")
+
+            self._log(
+                f"{session.kind.title()} capture: {state.captured_count}/{state.total_count} | "
+                f"input={trace.combo.input_pos} output={trace.combo.output_pos}"
+            )
+
+            if session.is_complete():
+                radar_key, collection = session.finalize(self._store)
+                set_name = session.set_name
+                kind = session.kind
+                self._cleanup_capture_session()
+
+                if kind == "calibration":
+                    self._selected_calibration_set = set_name
+                else:
+                    self._selected_reference_set = set_name
+
+                self._refresh_sets()
+                dialog.set_status(f"{kind.title()} set saved: {set_name} ({len(collection.traces)} traces)")
+                self._log(f"{kind.title()} sequence completed and saved: set={set_name}, key={radar_key}")
+                self._resume_pipeline_if_needed()
+            else:
+                self._update_capture_dialog_state()
+        except Exception as exc:  # noqa: BLE001
+            self._show_error(f"Failed to capture combo: {exc}")
+            self._abort_capture_sequence()
+
+    def _abort_capture_sequence(self, *, resume_pipeline: bool = True) -> None:
+        """Abort active capture session and optionally resume pipeline."""
+        if self._capture_session is None:
+            return
+
+        kind = self._capture_session.kind
+        self._cleanup_capture_session()
+        dialog = self._ensure_preprocess_dialog()
+        dialog.set_status(f"{kind.title()} sequence aborted")
+        self._log(f"{kind.title()} sequence aborted")
+        if resume_pipeline:
+            self._resume_pipeline_if_needed()
+
+    def _update_capture_dialog_state(self) -> None:
+        """Sync dialog state widgets with active capture session."""
+        if self._preprocess_dialog is None:
+            return
+
+        if self._capture_session is None:
+            self._preprocess_dialog.set_capture_state(
+                kind=None,
+                captured_count=0,
+                total_count=0,
+                next_input=None,
+                next_output=None,
+            )
+            return
+
+        state = self._capture_session.state()
+        next_input = None
+        next_output = None
+        if state.current_combo is not None:
+            next_input = state.current_combo.input
+            next_output = state.current_combo.output
+
+        self._preprocess_dialog.set_capture_state(
+            kind=state.kind,
+            captured_count=state.captured_count,
+            total_count=state.total_count,
+            next_input=next_input,
+            next_output=next_output,
+        )
+
+    def _cleanup_capture_session(self) -> None:
+        """Close and clear current capture session object."""
+        if self._capture_session is not None:
+            self._capture_session.close()
+            self._capture_session = None
+        self._update_capture_dialog_state()
+
+    def _resume_pipeline_if_needed(self) -> None:
+        """Resume acquisition pipeline if it was paused for capture session."""
+        should_resume = self._resume_pipeline_after_capture
+        self._resume_pipeline_after_capture = False
+        if not should_resume:
+            return
+
+        try:
+            self._start_run()
+        except Exception as exc:  # noqa: BLE001
+            self._show_error(f"Failed to resume pipeline after capture: {exc}")
diff --git a/python_app/gui/controllers/app_window_snapshot_mixin.py b/python_app/gui/controllers/app_window_snapshot_mixin.py
new file mode 100644
index 0000000..07bbea3
--- /dev/null
+++ b/python_app/gui/controllers/app_window_snapshot_mixin.py
@@ -0,0 +1,119 @@
+"""Snapshot and ring-drain helper mixin for the main GUI window."""
+
+from __future__ import annotations
+
+from pathlib import Path
+import time
+
+from PyQt6.QtWidgets import QFileDialog
+
+
+class AppWindowSnapshotMixin:
+    """Saves runtime data snapshots and maintains ring-reader freshness."""
+
+    def _save_snapshot(self) -> None:
+        """Save runtime snapshot in numpy-directory format."""
+        self._drain_runtime_rings_for_snapshot()
+
+        if not self._raw_history and not self._pre_history and not self._result_history:
+            self._show_error("No runtime data is available for save")
+            return
+
+        try:
+            last_n = int(self._save_count.value())
+            output_root = Path(self._save_path_input.text().strip()).expanduser()
+            snapshot_name = self._save_name_input.text().strip()
+            snapshot_dir, summary = self._store.save_runtime_snapshot_numpy(
+                output_root,
+                snapshot_name,
+                list(self._raw_history),
+                list(self._pre_history),
+                list(self._result_history),
+                last_n,
+            )
+            self._log(
+                f"Saved numpy snapshot: {snapshot_dir} "
+                f"(raw={summary.get('raw_count', 0)}, "
+                f"preprocessed={summary.get('preprocessed_count', 0)}, "
+                f"results={summary.get('result_count', 0)}, "
+                f"mode={summary.get('selection_mode', 'unknown')})"
+            )
+        except Exception as exc:  # noqa: BLE001
+            self._show_error(f"Failed to save snapshot: {exc}")
+
+    def _browse_save_path(self) -> None:
+        """Open directory picker for snapshot output path."""
+        selected = QFileDialog.getExistingDirectory(
+            self,
+            "Select Snapshot Directory",
+            self._save_path_input.text().strip() or str(self._project_root),
+        )
+        if selected:
+            self._save_path_input.setText(selected)
+
+    def _drain_runtime_rings_for_snapshot(self) -> None:
+        """Drain readers before snapshot to reduce partial-history races."""
+        if self._raw_reader is None and self._pre_reader is None and self._result_reader is None:
+            return
+        try:
+            start_raw_count = len(self._raw_history)
+            start_pre_count = len(self._pre_history)
+            start_result_count = len(self._result_history)
+            deadline = time.monotonic() + 0.8
+
+            while True:
+                progress = False
+
+                if self._raw_reader is not None:
+                    for _ in range(self._max_pop_per_snapshot_drain):
+                        collection = self._raw_reader.pop_raw_collection()
+                        if collection is None:
+                            break
+                        self._raw_history.append(collection)
+                        progress = True
+
+                if self._pre_reader is not None:
+                    for _ in range(self._max_pop_per_snapshot_drain):
+                        collection = self._pre_reader.pop_preprocessed_collection()
+                        if collection is None:
+                            break
+                        self._pre_history.append(collection)
+                        progress = True
+
+                if self._result_reader is not None:
+                    for _ in range(self._max_pop_per_snapshot_drain):
+                        collection = self._result_reader.pop_result_collection()
+                        if collection is None:
+                            break
+                        self._record_result_history(collection)
+                        progress = True
+
+                if progress:
+                    continue
+
+                missing_raw = self._raw_reader is not None and len(self._raw_history) == start_raw_count
+                missing_pre = self._pre_reader is not None and len(self._pre_history) == start_pre_count
+                got_results = len(self._result_history) > start_result_count
+
+                if got_results and (missing_raw or missing_pre) and time.monotonic() < deadline:
+                    time.sleep(0.01)
+                    continue
+                break
+        except Exception as exc:  # noqa: BLE001
+            self._log(f"Snapshot drain warning: {exc}")
+
+    def _drop_pending_ring_payloads(self, *, include_results: bool = True) -> None:
+        """Drop unread payloads from active readers."""
+        dropped_raw = self._raw_reader.drop_all() if self._raw_reader is not None else 0
+        dropped_pre = self._pre_reader.drop_all() if self._pre_reader is not None else 0
+        dropped_results = 0
+        if include_results and self._result_reader is not None:
+            dropped_results = self._result_reader.drop_all()
+
+        if dropped_raw or dropped_pre or dropped_results:
+            self._log(
+                "Single capture ring reset: "
+                f"dropped raw={dropped_raw}, "
+                f"preprocessed={dropped_pre}, "
+                f"results={dropped_results}"
+            )
diff --git a/python_app/gui/controllers/app_window_ui_mixin.py b/python_app/gui/controllers/app_window_ui_mixin.py
new file mode 100644
index 0000000..e008a25
--- /dev/null
+++ b/python_app/gui/controllers/app_window_ui_mixin.py
@@ -0,0 +1,192 @@
+"""UI construction mixin for the main radar control window."""
+
+from __future__ import annotations
+
+from PyQt6.QtWidgets import (
+    QComboBox,
+    QFrame,
+    QGroupBox,
+    QHBoxLayout,
+    QLabel,
+    QPlainTextEdit,
+    QPushButton,
+    QScrollArea,
+    QStackedWidget,
+    QVBoxLayout,
+    QWidget,
+)
+import pyqtgraph as pg
+
+from python_app.gui.controllers.sections import (
+    build_data_actions_group,
+    build_hardware_actions_group,
+    build_pipeline_group,
+    build_preprocess_summary_group,
+    build_processing_group,
+    build_radar_group,
+    build_switch_group,
+)
+
+
+class AppWindowUiMixin:
+    """Builds and wires all static UI widgets."""
+
+    def _build_ui(self) -> None:
+        """Build main window widgets, plot area, and settings panel."""
+        self.setWindowTitle("Radar System Control")
+        root = QWidget(self)
+        self.setCentralWidget(root)
+
+        layout = QHBoxLayout(root)
+        layout.setContentsMargins(12, 12, 12, 12)
+        layout.setSpacing(14)
+
+        self._plot_stack = QStackedWidget(root)
+
+        self._plot = pg.PlotWidget(background="#0f141c")
+        self._plot.showGrid(x=True, y=True, alpha=0.2)
+        self._plot.setLabel("bottom", "Frequency", units="Hz")
+        self._plot.setLabel("left", "Magnitude", units="dB")
+        self._plot_stack.addWidget(self._plot)
+
+        self._trace_plots_container = QWidget(root)
+        trace_layout = QVBoxLayout(self._trace_plots_container)
+        trace_layout.setContentsMargins(0, 0, 0, 0)
+        trace_layout.setSpacing(6)
+
+        self._trace_magnitude_plot = pg.PlotWidget(background="#0f141c")
+        self._trace_magnitude_plot.showGrid(x=True, y=True, alpha=0.2)
+        self._trace_magnitude_plot.setLabel("left", "Magnitude", units="dB")
+        self._trace_magnitude_plot.getPlotItem().showAxis("bottom", show=False)
+        self._trace_magnitude_plot.getPlotItem().setDownsampling(mode="peak")
+        self._trace_magnitude_plot.getPlotItem().setClipToView(True)
+        trace_layout.addWidget(self._trace_magnitude_plot, stretch=1)
+
+        self._trace_phase_plot = pg.PlotWidget(background="#0f141c")
+        self._trace_phase_plot.showGrid(x=True, y=True, alpha=0.2)
+        self._trace_phase_plot.setLabel("left", "Phase", units="deg")
+        self._trace_phase_plot.setLabel("bottom", "Frequency", units="Hz")
+        self._trace_phase_plot.getPlotItem().setDownsampling(mode="peak")
+        self._trace_phase_plot.getPlotItem().setClipToView(True)
+        trace_layout.addWidget(self._trace_phase_plot, stretch=1)
+
+        self._plot_stack.addWidget(self._trace_plots_container)
+        self._plot_stack.setCurrentWidget(self._trace_plots_container)
+        layout.addWidget(self._plot_stack, stretch=11)
+
+        self._settings_toggle_button = QPushButton("<")
+        self._settings_toggle_button.setObjectName("settingsToggleButton")
+        self._settings_toggle_button.setFixedWidth(26)
+        self._settings_toggle_button.clicked.connect(self._toggle_settings_panel)
+        layout.addWidget(self._settings_toggle_button, stretch=0)
+
+        self._settings_panel = QWidget(root)
+        self._settings_panel.setMinimumWidth(659)
+        right_layout = QVBoxLayout(self._settings_panel)
+        right_layout.setContentsMargins(0, 0, 0, 0)
+        right_layout.setSpacing(10)
+
+        # Build log widget early so error handlers can safely write during UI construction.
+        self._log_box = QPlainTextEdit(self._settings_panel)
+        self._log_box.setReadOnly(True)
+        self._log_box.setMinimumHeight(170)
+
+        pipeline_group = self._build_pipeline_group()
+        hardware_actions_group = self._build_hardware_actions_group()
+        data_actions_group = self._build_data_actions_group()
+        preprocess_summary_group = self._build_preprocess_summary_group()
+        radar_group = self._build_radar_group()
+        processing_group = self._build_processing_group()
+        switch_group = self._build_switch_group()
+
+        controls = QWidget(self._settings_panel)
+        controls_layout = QVBoxLayout(controls)
+        controls_layout.setContentsMargins(0, 0, 0, 0)
+        controls_layout.setSpacing(10)
+        controls_layout.addWidget(pipeline_group)
+        controls_layout.addWidget(hardware_actions_group)
+        controls_layout.addWidget(data_actions_group)
+        controls_layout.addWidget(preprocess_summary_group)
+        controls_layout.addWidget(processing_group)
+        controls_layout.addWidget(radar_group)
+        controls_layout.addWidget(switch_group)
+        controls_layout.addStretch(1)
+
+        scroll = QScrollArea(self._settings_panel)
+        scroll.setWidgetResizable(True)
+        scroll.setFrameShape(QFrame.Shape.NoFrame)
+        scroll.setWidget(controls)
+        right_layout.addWidget(scroll, stretch=1)
+
+        self._status_label = QLabel("Status: idle", self._settings_panel)
+        self._status_label.setObjectName("statusLabel")
+        right_layout.addWidget(self._status_label)
+
+        self._history_label = QLabel("History: raw=0, preprocessed=0, results=0", self._settings_panel)
+        self._history_label.setObjectName("hintLabel")
+        right_layout.addWidget(self._history_label)
+
+        right_layout.addWidget(self._log_box, stretch=0)
+
+        layout.addWidget(self._settings_panel, stretch=8)
+        self._set_settings_panel_visible(True)
+        self.resize(1650, 940)
+
+    def _toggle_settings_panel(self) -> None:
+        """Toggle settings panel visibility."""
+        self._set_settings_panel_visible(not self._settings_panel.isVisible())
+
+    def _set_settings_panel_visible(self, visible: bool) -> None:
+        """Set settings panel visibility and update toggle button glyph."""
+        self._settings_panel.setVisible(visible)
+        if visible:
+            self._settings_toggle_button.setText(">")
+            self._settings_toggle_button.setToolTip("Hide settings panel")
+        else:
+            self._settings_toggle_button.setText("<")
+            self._settings_toggle_button.setToolTip("Show settings panel")
+
+    def _set_plot_mode(self, mode: str) -> None:
+        """Switch visible plot surface based on processing mode."""
+        if mode == "bscan":
+            self._plot_stack.setCurrentWidget(self._plot)
+            return
+        self._plot_stack.setCurrentWidget(self._trace_plots_container)
+
+    def _build_pipeline_group(self) -> QGroupBox:
+        """Build pipeline controls section."""
+        return build_pipeline_group(self)
+
+    def _build_hardware_actions_group(self) -> QGroupBox:
+        """Build hardware actions section."""
+        return build_hardware_actions_group(self)
+
+    def _build_data_actions_group(self) -> QGroupBox:
+        """Build data actions section."""
+        return build_data_actions_group(self)
+
+    def _build_preprocess_summary_group(self) -> QGroupBox:
+        """Build selected preprocess sets summary section."""
+        return build_preprocess_summary_group(self)
+
+    def _build_processing_group(self) -> QGroupBox:
+        """Build processing mode section."""
+        return build_processing_group(self)
+
+    def _build_radar_group(self) -> QGroupBox:
+        """Build radar settings section."""
+        return build_radar_group(self)
+
+    def _build_switch_group(self) -> QGroupBox:
+        """Build switch settings section."""
+        return build_switch_group(self)
+
+    @staticmethod
+    def _set_combo_current_text(combo: QComboBox, value: str) -> None:
+        """Select combo item by text, appending it when missing."""
+        index = combo.findText(value)
+        if index >= 0:
+            combo.setCurrentIndex(index)
+            return
+        combo.addItem(value)
+        combo.setCurrentIndex(combo.count() - 1)
diff --git a/python_app/gui/controllers/sections/__init__.py b/python_app/gui/controllers/sections/__init__.py
new file mode 100644
index 0000000..62d0256
--- /dev/null
+++ b/python_app/gui/controllers/sections/__init__.py
@@ -0,0 +1,19 @@
+"""Composable UI section builders used by AppWindow UI mixin."""
+
+from python_app.gui.controllers.sections.data_actions_section import build_data_actions_group
+from python_app.gui.controllers.sections.hardware_actions_section import build_hardware_actions_group
+from python_app.gui.controllers.sections.pipeline_section import build_pipeline_group
+from python_app.gui.controllers.sections.preprocess_summary_section import build_preprocess_summary_group
+from python_app.gui.controllers.sections.processing_section import build_processing_group
+from python_app.gui.controllers.sections.radar_section import build_radar_group
+from python_app.gui.controllers.sections.switch_section import build_switch_group
+
+__all__ = [
+    "build_data_actions_group",
+    "build_hardware_actions_group",
+    "build_pipeline_group",
+    "build_preprocess_summary_group",
+    "build_processing_group",
+    "build_radar_group",
+    "build_switch_group",
+]
diff --git a/python_app/gui/controllers/sections/data_actions_section.py b/python_app/gui/controllers/sections/data_actions_section.py
new file mode 100644
index 0000000..7844de7
--- /dev/null
+++ b/python_app/gui/controllers/sections/data_actions_section.py
@@ -0,0 +1,46 @@
+"""Builder for snapshot and storage actions section."""
+
+from __future__ import annotations
+
+from PyQt6.QtWidgets import QGroupBox, QHBoxLayout, QLabel, QLineEdit, QPushButton, QSpinBox, QVBoxLayout
+
+
+def build_data_actions_group(owner) -> QGroupBox:
+    """Create snapshot save controls section."""
+    group = QGroupBox("Data Actions")
+    layout = QVBoxLayout(group)
+    layout.setSpacing(8)
+
+    save_row = QHBoxLayout()
+    save_row.setSpacing(8)
+
+    save_button = QPushButton("Save Numpy Snapshot")
+    save_button.clicked.connect(owner._save_snapshot)
+    owner._save_count = QSpinBox()
+    owner._save_count.setMinimum(1)
+    owner._save_count.setMaximum(10_000)
+    owner._save_count.setValue(10)
+
+    save_row.addWidget(save_button)
+    save_row.addWidget(QLabel("Last N"))
+    save_row.addWidget(owner._save_count)
+    save_row.addStretch(1)
+    layout.addLayout(save_row)
+
+    path_row = QHBoxLayout()
+    path_row.setSpacing(8)
+    owner._save_path_input = QLineEdit(str(owner._project_root / "python_app/data/snapshots"))
+    browse_button = QPushButton("Browse")
+    browse_button.clicked.connect(owner._browse_save_path)
+    path_row.addWidget(QLabel("Path"))
+    path_row.addWidget(owner._save_path_input, stretch=1)
+    path_row.addWidget(browse_button)
+    layout.addLayout(path_row)
+
+    name_row = QHBoxLayout()
+    name_row.setSpacing(8)
+    owner._save_name_input = QLineEdit("snapshot_manual")
+    name_row.addWidget(QLabel("Name"))
+    name_row.addWidget(owner._save_name_input, stretch=1)
+    layout.addLayout(name_row)
+    return group
diff --git a/python_app/gui/controllers/sections/hardware_actions_section.py b/python_app/gui/controllers/sections/hardware_actions_section.py
new file mode 100644
index 0000000..70d5c87
--- /dev/null
+++ b/python_app/gui/controllers/sections/hardware_actions_section.py
@@ -0,0 +1,25 @@
+"""Builder for hardware actions section."""
+
+from __future__ import annotations
+
+from PyQt6.QtWidgets import QGroupBox, QHBoxLayout, QPushButton
+
+
+def build_hardware_actions_group(owner) -> QGroupBox:
+    """Create hardware action buttons section."""
+    group = QGroupBox("Hardware Actions")
+    layout = QHBoxLayout(group)
+    layout.setSpacing(8)
+
+    apply_radar_button = QPushButton("Apply Radar Settings")
+    apply_radar_button.clicked.connect(owner._apply_radar_settings)
+    layout.addWidget(apply_radar_button)
+
+    save_config_button = QPushButton("Save Current Config")
+    save_config_button.clicked.connect(owner._save_current_config)
+    layout.addWidget(save_config_button)
+
+    preprocess_button = QPushButton("Preprocessing Panel")
+    preprocess_button.clicked.connect(owner._open_preprocess_panel)
+    layout.addWidget(preprocess_button)
+    return group
diff --git a/python_app/gui/controllers/sections/pipeline_section.py b/python_app/gui/controllers/sections/pipeline_section.py
new file mode 100644
index 0000000..65ee644
--- /dev/null
+++ b/python_app/gui/controllers/sections/pipeline_section.py
@@ -0,0 +1,34 @@
+"""Builder for pipeline control section."""
+
+from __future__ import annotations
+
+from PyQt6.QtWidgets import QGroupBox, QHBoxLayout, QLabel, QPushButton, QVBoxLayout
+
+
+def build_pipeline_group(owner) -> QGroupBox:
+    """Create Start/Single/Stop controls section."""
+    group = QGroupBox("Pipeline")
+    layout = QVBoxLayout(group)
+    layout.setSpacing(8)
+
+    action_row = QHBoxLayout()
+    action_row.setSpacing(8)
+
+    start_button = QPushButton("Start")
+    start_button.clicked.connect(owner._start_run)
+    action_row.addWidget(start_button)
+
+    single_button = QPushButton("Single Capture")
+    single_button.clicked.connect(owner._start_single_capture)
+    action_row.addWidget(single_button)
+
+    stop_button = QPushButton("Stop")
+    stop_button.clicked.connect(owner._stop_run)
+    action_row.addWidget(stop_button)
+
+    layout.addLayout(action_row)
+
+    hint = QLabel("Start continuous run or single processed collection capture.")
+    hint.setObjectName("hintLabel")
+    layout.addWidget(hint)
+    return group
diff --git a/python_app/gui/controllers/sections/preprocess_summary_section.py b/python_app/gui/controllers/sections/preprocess_summary_section.py
new file mode 100644
index 0000000..7f49521
--- /dev/null
+++ b/python_app/gui/controllers/sections/preprocess_summary_section.py
@@ -0,0 +1,19 @@
+"""Builder for preprocess set summary section."""
+
+from __future__ import annotations
+
+from PyQt6.QtWidgets import QFormLayout, QGroupBox, QLabel
+
+
+def build_preprocess_summary_group(owner) -> QGroupBox:
+    """Create selected calibration/reference summary section."""
+    group = QGroupBox("Selected Preprocess Sets")
+    form = QFormLayout(group)
+    form.setFieldGrowthPolicy(QFormLayout.FieldGrowthPolicy.AllNonFixedFieldsGrow)
+
+    owner._selected_calibration_label = QLabel("<not selected>")
+    owner._selected_reference_label = QLabel("<not selected>")
+
+    form.addRow("Calibration", owner._selected_calibration_label)
+    form.addRow("Reference", owner._selected_reference_label)
+    return group
diff --git a/python_app/gui/controllers/sections/processing_section.py b/python_app/gui/controllers/sections/processing_section.py
new file mode 100644
index 0000000..d57c08e
--- /dev/null
+++ b/python_app/gui/controllers/sections/processing_section.py
@@ -0,0 +1,134 @@
+"""Builder for processing mode and live-parameter section."""
+
+from __future__ import annotations
+
+from PyQt6.QtWidgets import (
+    QCheckBox,
+    QComboBox,
+    QDoubleSpinBox,
+    QFormLayout,
+    QGroupBox,
+    QHBoxLayout,
+    QPushButton,
+    QSizePolicy,
+    QStackedWidget,
+    QWidget,
+)
+
+
+def build_processing_group(owner) -> QGroupBox:
+    """Create processing mode section with pass-through and B-scan pages."""
+    group = QGroupBox("Processing")
+    form = QFormLayout(group)
+    form.setFieldGrowthPolicy(QFormLayout.FieldGrowthPolicy.AllNonFixedFieldsGrow)
+
+    owner._processing_mode = QComboBox()
+    owner._processing_mode.addItems(["pass_through", "bscan"])
+
+    owner._processing_mode_pages = QStackedWidget(group)
+    owner._processing_mode_pages.setSizePolicy(QSizePolicy.Policy.Preferred, QSizePolicy.Policy.Fixed)
+
+    pass_through_page = QWidget(owner._processing_mode_pages)
+    pass_through_page.setSizePolicy(QSizePolicy.Policy.Preferred, QSizePolicy.Policy.Fixed)
+    pass_through_form = QFormLayout(pass_through_page)
+    pass_through_form.setFieldGrowthPolicy(QFormLayout.FieldGrowthPolicy.AllNonFixedFieldsGrow)
+
+    owner._processing_gain_db = QDoubleSpinBox()
+    owner._processing_gain_db.setDecimals(2)
+    owner._processing_gain_db.setRange(-40.0, 40.0)
+    owner._processing_gain_db.setSingleStep(0.25)
+    owner._processing_gain_db.setValue(0.0)
+
+    owner._processing_phase_deg = QDoubleSpinBox()
+    owner._processing_phase_deg.setDecimals(1)
+    owner._processing_phase_deg.setRange(-180.0, 180.0)
+    owner._processing_phase_deg.setSingleStep(1.0)
+    owner._processing_phase_deg.setValue(0.0)
+
+    owner._show_magnitude_checkbox = QCheckBox("Show magnitude")
+    owner._show_magnitude_checkbox.setChecked(True)
+
+    owner._show_phase_checkbox = QCheckBox("Show phase")
+    owner._show_phase_checkbox.setChecked(True)
+
+    pass_through_form.addRow("Gain dB (live)", owner._processing_gain_db)
+    pass_through_form.addRow("Phase deg (live)", owner._processing_phase_deg)
+    pass_through_form.addRow(owner._show_magnitude_checkbox)
+    pass_through_form.addRow(owner._show_phase_checkbox)
+    owner._processing_mode_pages.addWidget(pass_through_page)
+
+    bscan_page = QWidget(owner._processing_mode_pages)
+    bscan_page.setSizePolicy(QSizePolicy.Policy.Preferred, QSizePolicy.Policy.Fixed)
+    bscan_form = QFormLayout(bscan_page)
+    bscan_form.setFieldGrowthPolicy(QFormLayout.FieldGrowthPolicy.AllNonFixedFieldsGrow)
+
+    owner._bscan_axis = QComboBox()
+    owner._bscan_axis.addItems(["abs", "real", "phase"])
+
+    owner._bscan_cut_m = QDoubleSpinBox()
+    owner._bscan_cut_m.setDecimals(3)
+    owner._bscan_cut_m.setRange(0.0, 2.0)
+    owner._bscan_cut_m.setSingleStep(0.001)
+    owner._bscan_cut_m.setValue(0.824)
+
+    owner._bscan_max_depth_m = QDoubleSpinBox()
+    owner._bscan_max_depth_m.setDecimals(1)
+    owner._bscan_max_depth_m.setRange(0.1, 5.0)
+    owner._bscan_max_depth_m.setSingleStep(0.1)
+    owner._bscan_max_depth_m.setValue(1.0)
+
+    owner._bscan_gain = QDoubleSpinBox()
+    owner._bscan_gain.setDecimals(1)
+    owner._bscan_gain.setRange(0.0, 3.0)
+    owner._bscan_gain.setSingleStep(0.1)
+    owner._bscan_gain.setValue(1.0)
+
+    owner._bscan_start_freq_mhz = QDoubleSpinBox()
+    owner._bscan_start_freq_mhz.setDecimals(1)
+    owner._bscan_start_freq_mhz.setRange(100.0, 8800.0)
+    owner._bscan_start_freq_mhz.setSingleStep(10.0)
+    owner._bscan_start_freq_mhz.setValue(100.0)
+
+    owner._bscan_stop_freq_mhz = QDoubleSpinBox()
+    owner._bscan_stop_freq_mhz.setDecimals(1)
+    owner._bscan_stop_freq_mhz.setRange(100.0, 8800.0)
+    owner._bscan_stop_freq_mhz.setSingleStep(10.0)
+    owner._bscan_stop_freq_mhz.setValue(8800.0)
+
+    owner._bscan_clear_history_button = QPushButton("Clear B-scan History")
+    owner._bscan_clear_history_button.clicked.connect(owner._on_bscan_clear_history_clicked)
+    owner._bscan_remove_last_button = QPushButton("Remove Last Sweep")
+    owner._bscan_remove_last_button.clicked.connect(owner._on_bscan_remove_last_sweep_clicked)
+    bscan_actions = QWidget(owner._processing_mode_pages)
+    bscan_actions_layout = QHBoxLayout(bscan_actions)
+    bscan_actions_layout.setContentsMargins(0, 0, 0, 0)
+    bscan_actions_layout.setSpacing(8)
+    bscan_actions_layout.addWidget(owner._bscan_remove_last_button)
+    bscan_actions_layout.addWidget(owner._bscan_clear_history_button)
+
+    bscan_form.addRow("Axis", owner._bscan_axis)
+    bscan_form.addRow("Cut m", owner._bscan_cut_m)
+    bscan_form.addRow("Max depth m", owner._bscan_max_depth_m)
+    bscan_form.addRow("Gain", owner._bscan_gain)
+    bscan_form.addRow("Start MHz", owner._bscan_start_freq_mhz)
+    bscan_form.addRow("Stop MHz", owner._bscan_stop_freq_mhz)
+    bscan_form.addRow(bscan_actions)
+    owner._processing_mode_pages.addWidget(bscan_page)
+
+    owner._processing_mode.currentTextChanged.connect(owner._on_processing_mode_changed)
+    owner._processing_gain_db.valueChanged.connect(owner._on_processing_live_settings_changed)
+    owner._processing_phase_deg.valueChanged.connect(owner._on_processing_live_settings_changed)
+    owner._show_magnitude_checkbox.toggled.connect(owner._on_trace_visibility_changed)
+    owner._show_phase_checkbox.toggled.connect(owner._on_trace_visibility_changed)
+    form.addRow("Mode", owner._processing_mode)
+    form.addRow(owner._processing_mode_pages)
+
+    owner._bscan_axis.currentTextChanged.connect(owner._on_processing_live_settings_changed)
+    owner._bscan_cut_m.valueChanged.connect(owner._on_processing_live_settings_changed)
+    owner._bscan_max_depth_m.valueChanged.connect(owner._on_processing_live_settings_changed)
+    owner._bscan_gain.valueChanged.connect(owner._on_processing_live_settings_changed)
+    owner._bscan_start_freq_mhz.valueChanged.connect(owner._on_processing_live_settings_changed)
+    owner._bscan_stop_freq_mhz.valueChanged.connect(owner._on_processing_live_settings_changed)
+
+    owner._on_processing_mode_changed(owner._processing_mode.currentText())
+    return group
diff --git a/python_app/gui/controllers/sections/radar_section.py b/python_app/gui/controllers/sections/radar_section.py
new file mode 100644
index 0000000..c651fca
--- /dev/null
+++ b/python_app/gui/controllers/sections/radar_section.py
@@ -0,0 +1,51 @@
+"""Builder for radar settings section."""
+
+from __future__ import annotations
+
+from PyQt6.QtWidgets import QComboBox, QFormLayout, QGroupBox, QLabel, QLineEdit
+
+
+def build_radar_group(owner) -> QGroupBox:
+    """Create radar settings controls and labels."""
+    group = QGroupBox("Radar")
+    form = QFormLayout(group)
+    form.setFieldGrowthPolicy(QFormLayout.FieldGrowthPolicy.AllNonFixedFieldsGrow)
+    defaults = owner._defaults_config.radar
+
+    owner._serial_input = QLineEdit(defaults.serial)
+    owner._serial_input.setPlaceholderText("Optional: empty = auto-detect first LibreVNA")
+
+    owner._radar_mode = QComboBox()
+    owner._radar_mode.addItems(["mock", "native"])
+    owner._radar_mode.setToolTip("mock: synthetic signal, native: real LibreVNA hardware")
+    owner._set_combo_current_text(owner._radar_mode, defaults.driver_mode)
+
+    owner._start_hz_input = QLineEdit(f"{defaults.sweep.start_hz:g}")
+    owner._stop_hz_input = QLineEdit(f"{defaults.sweep.stop_hz:g}")
+    owner._points_input = QLineEdit(str(defaults.sweep.points))
+    owner._ifbw_input = QLineEdit(f"{defaults.sweep.if_bandwidth_hz:g}")
+    owner._power_input = QLineEdit(f"{defaults.sweep.power_dbm:g}")
+    owner._power_input.setToolTip("Device power limits are available only in native mode.")
+    owner._serial_input.editingFinished.connect(owner._on_radar_identity_changed)
+    owner._radar_mode.currentTextChanged.connect(owner._on_radar_identity_changed)
+    owner._start_hz_input.editingFinished.connect(owner._on_radar_sweep_limits_changed)
+    owner._stop_hz_input.editingFinished.connect(owner._on_radar_sweep_limits_changed)
+
+    owner._radar_start_label = QLabel("Start Hz")
+    owner._radar_stop_label = QLabel("Stop Hz")
+    owner._radar_points_label = QLabel("Points")
+    owner._radar_ifbw_label = QLabel("IF BW Hz")
+    owner._radar_power_label = QLabel("Stimulus Power dBm")
+
+    owner._radar_limits_hint = QLabel("Mock mode: device limits are not applied.")
+    owner._radar_limits_hint.setObjectName("hintLabel")
+
+    form.addRow("Serial", owner._serial_input)
+    form.addRow("Mode", owner._radar_mode)
+    form.addRow(owner._radar_start_label, owner._start_hz_input)
+    form.addRow(owner._radar_stop_label, owner._stop_hz_input)
+    form.addRow(owner._radar_points_label, owner._points_input)
+    form.addRow(owner._radar_ifbw_label, owner._ifbw_input)
+    form.addRow(owner._radar_power_label, owner._power_input)
+    form.addRow(owner._radar_limits_hint)
+    return group
diff --git a/python_app/gui/controllers/sections/switch_section.py b/python_app/gui/controllers/sections/switch_section.py
new file mode 100644
index 0000000..fcc99f5
--- /dev/null
+++ b/python_app/gui/controllers/sections/switch_section.py
@@ -0,0 +1,82 @@
+"""Builder for switch and combo settings section."""
+
+from __future__ import annotations
+
+from PyQt6.QtWidgets import QComboBox, QFormLayout, QGroupBox, QHBoxLayout, QLineEdit, QVBoxLayout
+
+
+def build_switch_group(owner) -> QGroupBox:
+    """Create input/output switch controls and run combos settings."""
+    group = QGroupBox("Switches")
+    layout = QVBoxLayout(group)
+    input_defaults = owner._defaults_config.input_switch
+    output_defaults = owner._defaults_config.output_switch
+
+    global_form = QFormLayout()
+    global_form.setFieldGrowthPolicy(QFormLayout.FieldGrowthPolicy.AllNonFixedFieldsGrow)
+    owner._settling_ms = QLineEdit(str(owner._defaults_config.runtime.settling_ms))
+    owner._combos_text = QLineEdit("")
+    owner._combos_text.setPlaceholderText("input:output,input:output or empty for full")
+    global_form.addRow("Settling ms", owner._settling_ms)
+    global_form.addRow("Run combos", owner._combos_text)
+    layout.addLayout(global_form)
+
+    switch_columns = QHBoxLayout()
+
+    input_group = QGroupBox("Input Switch (Radar Port 2)")
+    input_form = QFormLayout(input_group)
+    input_form.setFieldGrowthPolicy(QFormLayout.FieldGrowthPolicy.AllNonFixedFieldsGrow)
+
+    owner._input_mode = QComboBox()
+    owner._input_mode.addItems(["mock", "native"])
+    owner._set_combo_current_text(owner._input_mode, input_defaults.driver_mode)
+    owner._input_driver = QComboBox()
+    owner._input_driver.addItems(["hmc349a", "h7992"])
+    owner._set_combo_current_text(owner._input_driver, input_defaults.driver)
+    owner._input_positions = QLineEdit(str(input_defaults.positions))
+    owner._input_gpio_chip = QLineEdit(input_defaults.gpio_chip)
+    owner._input_pin_a = QLineEdit(str(input_defaults.pin_a))
+    owner._input_pin_b = QLineEdit(str(input_defaults.pin_b))
+    owner._input_invert_logic = QComboBox()
+    owner._input_invert_logic.addItems(["false", "true"])
+    owner._set_combo_current_text(owner._input_invert_logic, "true" if input_defaults.invert_logic else "false")
+
+    input_form.addRow("Mode", owner._input_mode)
+    input_form.addRow("Driver", owner._input_driver)
+    input_form.addRow("Positions", owner._input_positions)
+    input_form.addRow("GPIO chip", owner._input_gpio_chip)
+    input_form.addRow("Pin A", owner._input_pin_a)
+    input_form.addRow("Pin B", owner._input_pin_b)
+    input_form.addRow("Invert logic", owner._input_invert_logic)
+
+    output_group = QGroupBox("Output Switch (Radar Port 1)")
+    output_form = QFormLayout(output_group)
+    output_form.setFieldGrowthPolicy(QFormLayout.FieldGrowthPolicy.AllNonFixedFieldsGrow)
+
+    owner._output_mode = QComboBox()
+    owner._output_mode.addItems(["mock", "native"])
+    owner._set_combo_current_text(owner._output_mode, output_defaults.driver_mode)
+    owner._output_driver = QComboBox()
+    owner._output_driver.addItems(["h7992", "hmc349a"])
+    owner._set_combo_current_text(owner._output_driver, output_defaults.driver)
+    owner._output_positions = QLineEdit(str(output_defaults.positions))
+    owner._output_gpio_chip = QLineEdit(output_defaults.gpio_chip)
+    owner._output_pin_a = QLineEdit(str(output_defaults.pin_a))
+    owner._output_pin_b = QLineEdit(str(output_defaults.pin_b))
+    owner._output_invert_logic = QComboBox()
+    owner._output_invert_logic.addItems(["false", "true"])
+    owner._set_combo_current_text(owner._output_invert_logic, "true" if output_defaults.invert_logic else "false")
+
+    output_form.addRow("Mode", owner._output_mode)
+    output_form.addRow("Driver", owner._output_driver)
+    output_form.addRow("Positions", owner._output_positions)
+    output_form.addRow("GPIO chip", owner._output_gpio_chip)
+    output_form.addRow("Pin A", owner._output_pin_a)
+    output_form.addRow("Pin B", owner._output_pin_b)
+    output_form.addRow("Invert logic", owner._output_invert_logic)
+
+    switch_columns.addWidget(input_group)
+    switch_columns.addWidget(output_group)
+    layout.addLayout(switch_columns)
+
+    return group
diff --git a/python_app/gui/main.py b/python_app/gui/main.py
new file mode 100644
index 0000000..5bc6bc9
--- /dev/null
+++ b/python_app/gui/main.py
@@ -0,0 +1,31 @@
+"""Application entry point for the PyQt GUI."""
+
+from __future__ import annotations
+
+from pathlib import Path
+import sys
+
+import pyqtgraph as pg
+from PyQt6.QtWidgets import QApplication
+
+# Ensure imports are resolved when started as a script.
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from python_app.gui.app_window import AppWindow
+from python_app.gui.theme import apply_dark_theme
+
+
+def main() -> int:
+    """Run Qt event loop and show main radar control window."""
+    app = QApplication(sys.argv)
+    apply_dark_theme(app)
+    pg.setConfigOptions(antialias=True, foreground="#dbe4f1")
+    window = AppWindow(PROJECT_ROOT)
+    window.show()
+    return app.exec()
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/python_app/gui/plotting/__init__.py b/python_app/gui/plotting/__init__.py
new file mode 100644
index 0000000..c066fbf
--- /dev/null
+++ b/python_app/gui/plotting/__init__.py
@@ -0,0 +1,19 @@
+"""Plotting helpers for trace and B-scan visualization."""
+
+from python_app.gui.plotting.bscan_history import (
+    build_bscan_signature,
+    pick_bscan_display_key,
+    rebuild_bscan_history_from_results,
+)
+from python_app.gui.plotting.bscan_math import (
+    bscan_levels,
+    bscan_lookup_table,
+)
+
+__all__ = [
+    "bscan_levels",
+    "bscan_lookup_table",
+    "build_bscan_signature",
+    "pick_bscan_display_key",
+    "rebuild_bscan_history_from_results",
+]
diff --git a/python_app/gui/plotting/bscan_history.py b/python_app/gui/plotting/bscan_history.py
new file mode 100644
index 0000000..2b277f3
--- /dev/null
+++ b/python_app/gui/plotting/bscan_history.py
@@ -0,0 +1,114 @@
+"""Helpers for B-scan history signatures and cache rebuilding."""
+
+from __future__ import annotations
+
+from collections import deque
+
+import numpy as np
+
+from python_app.models.dataset_model import ResultCollection
+from python_app.orchestration.live_processing_config import ProcessingLiveConfig
+
+
+def _result_tail(
+    *,
+    result_history: list[ResultCollection],
+    history_limit: int,
+    floor_collection_id: int,
+) -> list[ResultCollection]:
+    """Return filtered and de-duplicated result-history tail for B-scan usage."""
+    filtered = [
+        collection
+        for collection in result_history[-history_limit:]
+        if int(collection.collection_id) > int(floor_collection_id)
+    ]
+    unique_tail: list[ResultCollection] = []
+    seen_keys: set[tuple[int, int]] = set()
+    for collection in filtered:
+        key = (int(collection.collection_id), int(collection.monotonic_ns))
+        if key in seen_keys:
+            continue
+        seen_keys.add(key)
+        unique_tail.append(collection)
+    return unique_tail
+
+
+def build_bscan_signature(
+    live_config: ProcessingLiveConfig,
+    result_history: list[ResultCollection],
+    history_limit: int,
+    floor_collection_id: int,
+) -> tuple[object, ...]:
+    """Build deterministic signature used to detect B-scan cache invalidation."""
+    result_tail = _result_tail(
+        result_history=result_history,
+        history_limit=history_limit,
+        floor_collection_id=floor_collection_id,
+    )
+    return (
+        str(live_config.bscan_axis),
+        float(live_config.bscan_cut_m),
+        float(live_config.bscan_max_depth_m),
+        float(live_config.bscan_gain),
+        float(live_config.bscan_start_freq_mhz),
+        float(live_config.bscan_stop_freq_mhz),
+        int(floor_collection_id),
+        tuple((int(collection.collection_id), int(collection.monotonic_ns), len(collection.blocks)) for collection in result_tail),
+    )
+
+
+def rebuild_bscan_history_from_results(
+    result_history: list[ResultCollection],
+    history_limit: int,
+    floor_collection_id: int,
+) -> tuple[dict[tuple[int, int], deque[np.ndarray]], dict[tuple[int, int], np.ndarray]]:
+    """Rebuild B-scan history and depth axes from processed result payloads."""
+    history_by_combo: dict[tuple[int, int], deque[np.ndarray]] = {}
+    depth_axis_by_combo: dict[tuple[int, int], np.ndarray] = {}
+
+    result_tail = _result_tail(
+        result_history=result_history,
+        history_limit=history_limit,
+        floor_collection_id=floor_collection_id,
+    )
+
+    for collection in result_tail:
+        for block in collection.blocks:
+            key = (block.combo.input_pos, block.combo.output_pos)
+            for payload in block.payloads:
+                if payload.kind != 1 or payload.processing_name != "bscan":
+                    continue
+                if payload.frequency_hz.size == 0 or payload.trace.size == 0:
+                    continue
+                if payload.frequency_hz.size != payload.trace.size:
+                    continue
+
+                depth_axis = np.asarray(payload.frequency_hz, dtype=np.float32)
+                amplitudes = np.asarray(np.real(payload.trace), dtype=np.float32)
+                if depth_axis.size == 0 or amplitudes.size == 0:
+                    continue
+
+                history = history_by_combo.get(key)
+                stored_axis = depth_axis_by_combo.get(key)
+                if (
+                    history is None
+                    or stored_axis is None
+                    or stored_axis.shape != depth_axis.shape
+                    or not np.allclose(stored_axis, depth_axis, rtol=1e-4, atol=1e-6)
+                ):
+                    history = deque(maxlen=history_limit)
+                    history_by_combo[key] = history
+                    depth_axis_by_combo[key] = depth_axis.copy()
+
+                history.append(amplitudes.copy())
+
+    return history_by_combo, depth_axis_by_combo
+
+
+def pick_bscan_display_key(
+    history_by_combo: dict[tuple[int, int], deque[np.ndarray]],
+) -> tuple[int, int] | None:
+    """Choose combo key to display when multiple histories are present."""
+    if not history_by_combo:
+        return None
+    return next(iter(history_by_combo.keys()))
diff --git a/python_app/gui/plotting/bscan_math.py b/python_app/gui/plotting/bscan_math.py
new file mode 100644
index 0000000..476f128
--- /dev/null
+++ b/python_app/gui/plotting/bscan_math.py
@@ -0,0 +1,37 @@
+"""Color-scaling helpers for B-scan visualization."""
+
+from __future__ import annotations
+
+import numpy as np
+import pyqtgraph as pg
+
+def bscan_lookup_table(axis_mode: str) -> np.ndarray:
+    """Build B-scan colormap table for selected axis mode."""
+    if axis_mode == "abs":
+        return build_lut(["#440154", "#31688e", "#35b779", "#fde725"])
+    return build_lut(["#2166ac", "#67a9cf", "#f7f7f7", "#ef8a62", "#b2182b"])
+
+
+def build_lut(stops: list[str], *, size: int = 256) -> np.ndarray:
+    """Interpolate hex color stops into 8-bit RGB LUT array."""
+    stop_positions = np.linspace(0.0, 1.0, num=len(stops), dtype=np.float32)
+    sample_positions = np.linspace(0.0, 1.0, num=size, dtype=np.float32)
+    stop_colors = np.asarray([pg.mkColor(value).getRgb()[:3] for value in stops], dtype=np.float32)
+
+    lut = np.empty((size, 3), dtype=np.uint8)
+    for channel in range(3):
+        lut[:, channel] = np.interp(sample_positions, stop_positions, stop_colors[:, channel]).astype(np.uint8)
+    return lut
+
+
+def bscan_levels(sweeps: np.ndarray, axis_mode: str) -> tuple[float, float]:
+    """Compute image levels for B-scan data based on axis mode."""
+    min_value = float(np.min(sweeps))
+    max_value = float(np.max(sweeps))
+    if axis_mode == "abs":
+        if max_value <= min_value:
+            return min_value, min_value + 1e-6
+        return min_value, max_value
+
+    max_abs = max(abs(min_value), abs(max_value), 1e-6)
+    return -max_abs, max_abs
diff --git a/python_app/gui/preprocess_dialog.py b/python_app/gui/preprocess_dialog.py
new file mode 100644
index 0000000..77d026b
--- /dev/null
+++ b/python_app/gui/preprocess_dialog.py
@@ -0,0 +1,252 @@
+"""Dialog for calibration/reference set selection and sequential capture."""
+
+from __future__ import annotations
+
+from PyQt6.QtCore import pyqtSignal
+from PyQt6.QtWidgets import (
+    QComboBox,
+    QDialog,
+    QFormLayout,
+    QGridLayout,
+    QGroupBox,
+    QHBoxLayout,
+    QLabel,
+    QLineEdit,
+    QPlainTextEdit,
+    QPushButton,
+    QVBoxLayout,
+)
+import pyqtgraph as pg
+import numpy as np
+
+from python_app.models.dataset_model import TraceData
+
+
+class PreprocessDialog(QDialog):
+    """Standalone dialog for preprocessing capture workflows."""
+
+    refresh_requested = pyqtSignal()
+    selection_changed = pyqtSignal(str, str)
+    start_sequence_requested = pyqtSignal(str)
+    capture_next_requested = pyqtSignal()
+    abort_sequence_requested = pyqtSignal()
+
+    def __init__(self, parent=None) -> None:
+        """Create dialog and build all widgets."""
+        super().__init__(parent)
+        self.setWindowTitle("Preprocessing Setup")
+        self.resize(1040, 760)
+        self._build_ui()
+
+    def _build_ui(self) -> None:
+        """Build dialog layout, controls, and preview plot."""
+        layout = QVBoxLayout(self)
+
+        sets_group = QGroupBox("Calibration / Reference Sets", self)
+        sets_layout = QGridLayout(sets_group)
+
+        self._set_name_input = QLineEdit("set_001", sets_group)
+        self._calibration_combo = QComboBox(sets_group)
+        self._reference_combo = QComboBox(sets_group)
+
+        refresh_button = QPushButton("Refresh Sets", sets_group)
+        refresh_button.clicked.connect(self.refresh_requested.emit)
+
+        self._calibration_combo.currentTextChanged.connect(self._emit_selection_changed)
+        self._reference_combo.currentTextChanged.connect(self._emit_selection_changed)
+
+        sets_layout.addWidget(QLabel("Set name"), 0, 0)
+        sets_layout.addWidget(self._set_name_input, 0, 1)
+        sets_layout.addWidget(refresh_button, 0, 2)
+
+        sets_layout.addWidget(QLabel("Calibration set"), 1, 0)
+        sets_layout.addWidget(self._calibration_combo, 1, 1, 1, 2)
+
+        sets_layout.addWidget(QLabel("Reference set"), 2, 0)
+        sets_layout.addWidget(self._reference_combo, 2, 1, 1, 2)
+
+        layout.addWidget(sets_group)
+
+        sequence_group = QGroupBox("Sequential Capture (Fill Full N*M)", self)
+        sequence_layout = QGridLayout(sequence_group)
+
+        self._active_kind_label = QLabel("<none>", sequence_group)
+        self._progress_label = QLabel("0 / 0", sequence_group)
+        self._combo_label = QLabel("<none>", sequence_group)
+
+        self._tx_antenna_label_input = QLineEdit(sequence_group)
+        self._rx_antenna_label_input = QLineEdit(sequence_group)
+        self._tx_antenna_label_input.setPlaceholderText("e.g. TX_A")
+        self._rx_antenna_label_input.setPlaceholderText("e.g. RX_B")
+
+        start_calibration_button = QPushButton("Start Calibration Sequence", sequence_group)
+        start_calibration_button.clicked.connect(lambda: self.start_sequence_requested.emit("calibration"))
+
+        start_reference_button = QPushButton("Start Reference Sequence", sequence_group)
+        start_reference_button.clicked.connect(lambda: self.start_sequence_requested.emit("reference"))
+
+        self._capture_next_button = QPushButton("Capture Current Combo", sequence_group)
+        self._capture_next_button.clicked.connect(self.capture_next_requested.emit)
+        self._capture_next_button.setEnabled(False)
+
+        self._abort_button = QPushButton("Abort Sequence", sequence_group)
+        self._abort_button.clicked.connect(self.abort_sequence_requested.emit)
+        self._abort_button.setEnabled(False)
+
+        button_row = QHBoxLayout()
+        button_row.addWidget(start_calibration_button)
+        button_row.addWidget(start_reference_button)
+        button_row.addWidget(self._capture_next_button)
+        button_row.addWidget(self._abort_button)
+
+        sequence_layout.addWidget(QLabel("Active type"), 0, 0)
+        sequence_layout.addWidget(self._active_kind_label, 0, 1)
+        sequence_layout.addWidget(QLabel("Progress"), 1, 0)
+        sequence_layout.addWidget(self._progress_label, 1, 1)
+        sequence_layout.addWidget(QLabel("Current combo"), 2, 0)
+        sequence_layout.addWidget(self._combo_label, 2, 1)
+        sequence_layout.addWidget(QLabel("TX antenna label"), 3, 0)
+        sequence_layout.addWidget(self._tx_antenna_label_input, 3, 1)
+        sequence_layout.addWidget(QLabel("RX antenna label"), 4, 0)
+        sequence_layout.addWidget(self._rx_antenna_label_input, 4, 1)
+        sequence_layout.addLayout(button_row, 5, 0, 1, 2)
+
+        self._capture_log = QPlainTextEdit(sequence_group)
+        self._capture_log.setReadOnly(True)
+        self._capture_log.setPlaceholderText("Capture history per combo")
+        sequence_layout.addWidget(self._capture_log, 6, 0, 1, 2)
+
+        layout.addWidget(sequence_group)
+
+        self._status = QLabel("Ready", self)
+        layout.addWidget(self._status)
+
+        self._plot = pg.PlotWidget(background="#101418")
+        self._plot.showGrid(x=True, y=True, alpha=0.2)
+        self._plot.setLabel("bottom", "Frequency", units="Hz")
+        self._plot.setLabel("left", "Magnitude", units="dB")
+        layout.addWidget(self._plot, stretch=1)
+
+    def set_name(self) -> str:
+        """Return requested target set name."""
+        return self._set_name_input.text().strip()
+
+    def calibration_set(self) -> str:
+        """Return currently selected calibration set."""
+        return self._calibration_combo.currentText().strip()
+
+    def reference_set(self) -> str:
+        """Return currently selected reference set."""
+        return self._reference_combo.currentText().strip()
+
+    def antenna_labels(self) -> tuple[str, str]:
+        """Return optional TX/RX user labels used in capture logs."""
+        return self._tx_antenna_label_input.text().strip(), self._rx_antenna_label_input.text().strip()
+
+    def clear_capture_log(self) -> None:
+        """Clear capture history text box."""
+        self._capture_log.clear()
+
+    def append_capture_log_entry(
+        self,
+        *,
+        kind: str,
+        captured_count: int,
+        total_count: int,
+        input_pos: int,
+        output_pos: int,
+        tx_label: str,
+        rx_label: str,
+    ) -> None:
+        """Append one capture progress row to dialog log."""
+        tx_info = tx_label or "-"
+        rx_info = rx_label or "-"
+        self._capture_log.appendPlainText(
+            f"{kind}: {captured_count}/{total_count} | "
+            f"input={input_pos} output={output_pos} | "
+            f"TX={tx_info} RX={rx_info}"
+        )
+
+    def set_capture_state(
+        self,
+        *,
+        kind: str | None,
+        captured_count: int,
+        total_count: int,
+        next_input: int | None,
+        next_output: int | None,
+    ) -> None:
+        """Update sequence progress/status widgets."""
+        if kind is None:
+            self._active_kind_label.setText("<none>")
+            self._progress_label.setText("0 / 0")
+            self._combo_label.setText("<none>")
+            self._capture_next_button.setEnabled(False)
+            self._abort_button.setEnabled(False)
+            return
+
+        self._active_kind_label.setText(kind)
+        self._progress_label.setText(f"{captured_count} / {total_count}")
+        if next_input is None or next_output is None:
+            self._combo_label.setText("<complete>")
+            self._capture_next_button.setEnabled(False)
+            self._abort_button.setEnabled(True)
+        else:
+            self._combo_label.setText(f"input={next_input}, output={next_output}")
+            self._capture_next_button.setEnabled(True)
+            self._abort_button.setEnabled(True)
+
+    def set_calibration_sets(self, names: list[str]) -> None:
+        """Replace calibration set choices while preserving current selection when possible."""
+        self._set_combo_items(self._calibration_combo, names, self.calibration_set())
+
+    def set_reference_sets(self, names: list[str]) -> None:
+        """Replace reference set choices while preserving current selection when possible."""
+        self._set_combo_items(self._reference_combo, names, self.reference_set())
+
+    def set_selected_sets(self, calibration_set: str, reference_set: str) -> None:
+        """Apply selected set names to both comboboxes and emit selection update."""
+        if calibration_set:
+            index = self._calibration_combo.findText(calibration_set)
+            if index >= 0:
+                self._calibration_combo.setCurrentIndex(index)
+
+        if reference_set:
+            index = self._reference_combo.findText(reference_set)
+            if index >= 0:
+                self._reference_combo.setCurrentIndex(index)
+
+        self._emit_selection_changed()
+
+    def set_status(self, message: str) -> None:
+        """Set short human-readable status line."""
+        self._status.setText(message)
+
+    def draw_last_trace(self, trace: TraceData, title: str) -> None:
+        """Draw the latest captured sweep trace in dB scale."""
+        magnitude_db = 20.0 * np.log10(np.maximum(np.abs(trace.s21), 1e-12))
+        self._plot.clear()
+        self._plot.plot(
+            trace.frequency_hz,
+            magnitude_db,
+            pen=pg.mkPen("#4cc9f0", width=1.8),
+        )
+        combo = trace.combo
+        self._status.setText(
+            f"{title}: input={combo.input_pos}, output={combo.output_pos}, points={trace.frequency_hz.size}"
+        )
+
+    def _emit_selection_changed(self) -> None:
+        """Emit current calibration/reference selection."""
+        self.selection_changed.emit(self.calibration_set(), self.reference_set())
+
+    @staticmethod
+    def _set_combo_items(combo: QComboBox, names: list[str], current_text: str) -> None:
+        """Replace combo contents and keep previous value when still available."""
+        combo.clear()
+        combo.addItems(names)
+        if not current_text:
+            return
+        index = combo.findText(current_text)
+        if index >= 0:
+            combo.setCurrentIndex(index)
diff --git a/python_app/gui/runtime/__init__.py b/python_app/gui/runtime/__init__.py
new file mode 100644
index 0000000..ef2a80a
--- /dev/null
+++ b/python_app/gui/runtime/__init__.py
@@ -0,0 +1,13 @@
+"""Runtime helpers for GUI polling, history management, and constraints."""
+
+from python_app.gui.runtime.constraints import validate_processing_mode_constraints
+from python_app.gui.runtime.history import (
+    build_run_history_signature,
+    record_result_history,
+)
+
+__all__ = [
+    "build_run_history_signature",
+    "record_result_history",
+    "validate_processing_mode_constraints",
+]
diff --git a/python_app/gui/runtime/constraints.py b/python_app/gui/runtime/constraints.py
new file mode 100644
index 0000000..9ef7117
--- /dev/null
+++ b/python_app/gui/runtime/constraints.py
@@ -0,0 +1,21 @@
+"""Validation helpers for GUI processing mode constraints."""
+
+from __future__ import annotations
+
+from python_app.models.run_config_model import RunConfigModel
+
+
+def validate_processing_mode_constraints(processing_mode: str, config: RunConfigModel) -> None:
+    """Validate mode-specific constraints for current run configuration."""
+    if processing_mode != "bscan":
+        return
+
+    any_native_switch = config.input_switch.driver_mode == "native" or config.output_switch.driver_mode == "native"
+    if not any_native_switch:
+        return
+
+    combo_count = len({(int(combo.input), int(combo.output)) for combo in config.combos})
+    if combo_count != 1:
+        raise RuntimeError(
+            f"B-scan with native switches requires exactly one run combo (now {combo_count})"
+        )
diff --git a/python_app/gui/runtime/history.py b/python_app/gui/runtime/history.py
new file mode 100644
index 0000000..799af97
--- /dev/null
+++ b/python_app/gui/runtime/history.py
@@ -0,0 +1,53 @@
+"""Helpers for GUI-side runtime history management."""
+
+from __future__ import annotations
+
+from collections import deque
+
+from python_app.models.dataset_model import ResultCollection
+from python_app.models.run_config_model import RunConfigModel
+
+
+def record_result_history(
+    result_history: deque[ResultCollection],
+    collection: ResultCollection,
+) -> bool:
+    """Append new result or replace existing entry by stable collection key."""
+    for index in range(len(result_history) - 1, -1, -1):
+        existing = result_history[index]
+        if (
+            existing.collection_id == collection.collection_id
+            and existing.monotonic_ns == collection.monotonic_ns
+        ):
+            result_history[index] = collection
+            return True
+
+    result_history.append(collection)
+    return True
+
+
+def build_run_history_signature(
+    config: RunConfigModel,
+) -> tuple[object, ...]:
+    """Build deterministic signature to detect run-settings changes (excluding live processing params)."""
+    combos_signature = tuple((int(combo.input), int(combo.output)) for combo in config.combos)
+    return (
+        str(config.radar.driver_mode),
+        str(config.radar.serial),
+        float(config.radar.sweep.start_hz),
+        float(config.radar.sweep.stop_hz),
+        int(config.radar.sweep.points),
+        float(config.radar.sweep.if_bandwidth_hz),
+        float(config.radar.sweep.power_dbm),
+        str(config.input_switch.driver_mode),
+        str(config.input_switch.driver),
+        int(config.input_switch.positions),
+        bool(config.input_switch.invert_logic),
+        str(config.output_switch.driver_mode),
+        str(config.output_switch.driver),
+        int(config.output_switch.positions),
+        bool(config.output_switch.invert_logic),
+        str(config.preprocess.calibration_set),
+        str(config.preprocess.reference_set),
+        combos_signature,
+    )
diff --git a/python_app/gui/theme.py b/python_app/gui/theme.py
new file mode 100644
index 0000000..e27c5ff
--- /dev/null
+++ b/python_app/gui/theme.py
@@ -0,0 +1,125 @@
+"""Application-wide Qt palette and stylesheet configuration."""
+
+from __future__ import annotations
+
+from PyQt6.QtGui import QColor, QPalette
+from PyQt6.QtWidgets import QApplication, QStyleFactory
+
+
+_DARK_STYLESHEET = """
+QMainWindow, QDialog {
+    background-color: #0f131a;
+}
+
+QWidget {
+    color: #e7edf7;
+    font-size: 13px;
+}
+
+QGroupBox {
+    background-color: #151b24;
+    border: 1px solid #263142;
+    border-radius: 10px;
+    margin-top: 14px;
+    padding: 10px;
+}
+
+QGroupBox::title {
+    subcontrol-origin: margin;
+    left: 10px;
+    padding: 0 6px;
+    color: #9fb4ce;
+    font-weight: 600;
+}
+
+QPushButton {
+    background-color: #1d2735;
+    border: 1px solid #314055;
+    border-radius: 8px;
+    padding: 7px 12px;
+}
+
+QPushButton:hover {
+    background-color: #243246;
+}
+
+QPushButton:pressed {
+    background-color: #1a2432;
+}
+
+QPushButton:disabled {
+    color: #6b7d95;
+    background-color: #151d27;
+    border-color: #232e3d;
+}
+
+QPushButton#settingsToggleButton {
+    min-width: 26px;
+    max-width: 26px;
+    padding: 6px 0px;
+    border-radius: 7px;
+    font-weight: 700;
+}
+
+QLineEdit,
+QPlainTextEdit,
+QComboBox,
+QSpinBox,
+QDoubleSpinBox {
+    background-color: #101721;
+    border: 1px solid #2e3b4e;
+    border-radius: 7px;
+    padding: 5px 8px;
+    selection-background-color: #2f7ee6;
+}
+
+QLineEdit:focus,
+QPlainTextEdit:focus,
+QComboBox:focus,
+QSpinBox:focus,
+QDoubleSpinBox:focus {
+    border: 1px solid #5d88bd;
+}
+
+QComboBox::drop-down {
+    border: none;
+    width: 18px;
+}
+
+QScrollArea {
+    border: none;
+    background: transparent;
+}
+
+QLabel#statusLabel {
+    color: #94b4d9;
+    font-weight: 600;
+    padding: 2px 1px;
+}
+
+QLabel#hintLabel {
+    color: #7f94af;
+}
+"""
+
+
+def apply_dark_theme(app: QApplication) -> None:
+    """Apply a minimal modern dark theme shared by all windows."""
+    app.setStyle(QStyleFactory.create("Fusion"))
+
+    palette = QPalette()
+    palette.setColor(QPalette.ColorRole.Window, QColor("#0f131a"))
+    palette.setColor(QPalette.ColorRole.WindowText, QColor("#e7edf7"))
+    palette.setColor(QPalette.ColorRole.Base, QColor("#101721"))
+    palette.setColor(QPalette.ColorRole.AlternateBase, QColor("#151b24"))
+    palette.setColor(QPalette.ColorRole.ToolTipBase, QColor("#151b24"))
+    palette.setColor(QPalette.ColorRole.ToolTipText, QColor("#e7edf7"))
+    palette.setColor(QPalette.ColorRole.Text, QColor("#e7edf7"))
+    palette.setColor(QPalette.ColorRole.Button, QColor("#1d2735"))
+    palette.setColor(QPalette.ColorRole.ButtonText, QColor("#e7edf7"))
+    palette.setColor(QPalette.ColorRole.BrightText, QColor("#ffffff"))
+    palette.setColor(QPalette.ColorRole.Link, QColor("#5d88bd"))
+    palette.setColor(QPalette.ColorRole.Highlight, QColor("#2f7ee6"))
+    palette.setColor(QPalette.ColorRole.HighlightedText, QColor("#ffffff"))
+    app.setPalette(palette)
+    app.setStyleSheet(_DARK_STYLESHEET)
diff --git a/python_app/hardware_full/__init__.py b/python_app/hardware_full/__init__.py
new file mode 100644
index 0000000..12b049e
--- /dev/null
+++ b/python_app/hardware_full/__init__.py
@@ -0,0 +1 @@
+"""Hardware integration package for radar and switch devices."""
diff --git a/python_app/hardware_full/librevna_backends.py b/python_app/hardware_full/librevna_backends.py
new file mode 100644
index 0000000..3bfd97c
--- /dev/null
+++ b/python_app/hardware_full/librevna_backends.py
@@ -0,0 +1,163 @@
+"""Backend adapters used by :mod:`python_app.hardware_full.librevna_service`."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+import math
+from typing import Any, Protocol
+
+import numpy as np
+
+from python_app.models.run_config_model import RadarSweepModel
+
+
+class LibreVnaBackend(Protocol):
+    """Minimal backend contract required by `LibreVnaService`."""
+
+    def open(self) -> None:
+        """Open backend resources."""
+
+    def close(self) -> None:
+        """Close backend resources."""
+
+    @property
+    def is_open(self) -> bool:
+        """Return whether backend currently holds open runtime resources."""
+
+    def configure(self, sweep: RadarSweepModel) -> None:
+        """Apply sweep settings."""
+
+    def read_device_limits(self) -> dict[str, float | int]:
+        """Query runtime device limits."""
+
+    def acquire_s21(self) -> tuple[np.ndarray, np.ndarray]:
+        """Acquire one S21 trace."""
+
+
+@dataclass(slots=True)
+class NativeLibreVnaBackend:
+    """Native backend using direct USB LibreVNA Python driver."""
+
+    serial: str | None
+    strict_protocol_version: int
+    _device: Any | None = field(init=False, default=None, repr=False)
+    _settings: RadarSweepModel | None = field(init=False, default=None, repr=False)
+    _libre_vna_device: type[Any] | None = field(init=False, default=None, repr=False)
+    _vna_sweep_settings: type[Any] | None = field(init=False, default=None, repr=False)
+
+    def __post_init__(self) -> None:
+        """Import native driver classes lazily and initialize backend state."""
+        from python_app.hardware_full.librevna_driver import LibreVNADevice, VNASweepSettings
+
+        self._device: LibreVNADevice | None = None
+        self._settings: RadarSweepModel | None = None
+        self._libre_vna_device = LibreVNADevice
+        self._vna_sweep_settings = VNASweepSettings
+
+    def open(self) -> None:
+        """Open device connection if it is not already open."""
+        if self._device is not None:
+            return
+
+        self._device = self._libre_vna_device()
+        self._device.connect(serial=self.serial, strict_protocol_version=self.strict_protocol_version, timeout_s=2.0)
+
+    @property
+    def is_open(self) -> bool:
+        """Return `True` when native device connection is open."""
+        return self._device is not None
+
+    def close(self) -> None:
+        """Close device connection when open."""
+        if self._device is None:
+            return
+        self._device.disconnect()
+        self._device = None
+
+    def configure(self, sweep: RadarSweepModel) -> None:
+        """Store and apply sweep settings to connected hardware."""
+        self._settings = sweep
+        if self._device is None:
+            return
+
+        settings = self._vna_sweep_settings(
+            f_start_hz=sweep.start_hz,
+            f_stop_hz=sweep.stop_hz,
+            points=sweep.points,
+            if_bandwidth_hz=sweep.if_bandwidth_hz,
+            power_start_dbm=sweep.power_dbm,
+            power_stop_dbm=sweep.power_dbm,
+            excited_ports=(1, 2),
+        )
+        self._device.vna.configure(settings)
+
+    def read_device_limits(self) -> dict[str, float | int]:
+        """Read frequency/IFBW/power/points limits from connected device."""
+        if self._device is None:
+            raise RuntimeError("Failed to connect to LibreVNA device")
+
+        device_info = self._device.get_device_info()
+        limits = device_info.limits
+        return {
+            "min_frequency_hz": float(limits.min_frequency_hz),
+            "max_frequency_hz": float(limits.max_frequency_hz),
+            "min_ifbw_hz": float(limits.min_ifbw_hz),
+            "max_ifbw_hz": float(limits.max_ifbw_hz),
+            "max_points": int(limits.max_points),
+            "min_power_dbm": float(limits.min_power_dbm),
+            "max_power_dbm": float(limits.max_power_dbm),
+        }
+
+    def acquire_s21(self) -> tuple[np.ndarray, np.ndarray]:
+        """Acquire one S21 sweep from hardware."""
+        if self._settings is None:
+            raise RuntimeError("Radar service is not configured")
+        if self._device is None:
+            raise RuntimeError("Device not found")
+
+        result = self._device.vna.acquire(expected_points=self._settings.points, timeout_s=20.0)
+        return np.asarray(result.x, dtype=np.float32), np.asarray(result.trace("s21"), dtype=np.complex64)
+
+
+@dataclass(slots=True)
+class MockLibreVnaBackend:
+    """Synthetic backend used for local development and tests."""
+    _settings: RadarSweepModel | None = field(init=False, default=None, repr=False)
+    _mock_phase: float = field(init=False, default=0.0, repr=False)
+
+    def __post_init__(self) -> None:
+        """Initialize mock backend state."""
+        self._settings = None
+        self._mock_phase = 0.0
+
+    def open(self) -> None:
+        """No-op for mock backend."""
+
+    def close(self) -> None:
+        """No-op for mock backend."""
+
+    @property
+    def is_open(self) -> bool:
+        """Mock backend does not hold external resources."""
+        return False
+
+    def configure(self, sweep: RadarSweepModel) -> None:
+        """Store sweep settings used by synthetic acquisition."""
+        self._settings = sweep
+
+    def read_device_limits(self) -> dict[str, float | int]:
+        """Mock backend does not support native device limits queries."""
+        raise RuntimeError("LibreVNA Python driver is not available")
+
+    def acquire_s21(self) -> tuple[np.ndarray, np.ndarray]:
+        """Generate synthetic S21 values using deterministic phase envelope."""
+        if self._settings is None:
+            raise RuntimeError("Radar service is not configured")
+
+        points = self._settings.points
+        freq = np.linspace(self._settings.start_hz, self._settings.stop_hz, points, dtype=np.float32)
+        phase = (2.0 * math.pi * np.linspace(0.0, 1.0, points, dtype=np.float32)) + self._mock_phase
+        envelope = 0.6 + 0.4 * np.sin(phase * 0.5)
+        s21 = (envelope * np.cos(phase) + 1j * envelope * np.sin(phase)).astype(np.complex64)
+        self._mock_phase += 0.05
+        return freq, s21
diff --git a/python_app/hardware_full/librevna_driver/__init__.py b/python_app/hardware_full/librevna_driver/__init__.py
new file mode 100644
index 0000000..118490e
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/__init__.py
@@ -0,0 +1,72 @@
+"""Python driver for direct USB control of LibreVNA (protocol v14)."""
+
+import logging
+
+from .device import LibreVNADevice
+from .enums import (
+    HardwareFamily,
+    PacketType,
+    SParameter,
+    SweepKind,
+    SweepScale,
+    SyncMode,
+)
+from .logging_utils import DEFAULT_LOG_LEVEL, configure_logging
+from .exceptions import (
+    CRCError,
+    DeviceDisconnectedError,
+    IncompleteSweepError,
+    LibreVNAError,
+    NackError,
+    ParseError,
+    ProtocolVersionMismatch,
+    TimeoutError,
+    UnsupportedHardwareError,
+)
+from .models import (
+    DeviceConfigVariant,
+    DeviceInfo,
+    DeviceLimits,
+    DeviceStatus,
+    Packet,
+    StreamHandle,
+    SweepResult,
+    USBDeviceDescriptor,
+    VNADatapointPacket,
+    VNARawPoint,
+    VNASweepSettings,
+)
+
+__all__ = [
+    "CRCError",
+    "DEFAULT_LOG_LEVEL",
+    "DeviceConfigVariant",
+    "DeviceDisconnectedError",
+    "DeviceInfo",
+    "DeviceLimits",
+    "DeviceStatus",
+    "HardwareFamily",
+    "IncompleteSweepError",
+    "LibreVNADevice",
+    "LibreVNAError",
+    "NackError",
+    "Packet",
+    "PacketType",
+    "ParseError",
+    "ProtocolVersionMismatch",
+    "configure_logging",
+    "SParameter",
+    "StreamHandle",
+    "SweepKind",
+    "SweepResult",
+    "SweepScale",
+    "SyncMode",
+    "TimeoutError",
+    "USBDeviceDescriptor",
+    "UnsupportedHardwareError",
+    "VNADatapointPacket",
+    "VNARawPoint",
+    "VNASweepSettings",
+]
+
+logging.getLogger(__name__).addHandler(logging.NullHandler())
diff --git a/python_app/hardware_full/librevna_driver/api/__init__.py b/python_app/hardware_full/librevna_driver/api/__init__.py
new file mode 100644
index 0000000..56ea479
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/api/__init__.py
@@ -0,0 +1,9 @@
+"""Public controller classes."""
+
+from .config import ConfigController
+from .vna import VNAController
+
+__all__ = [
+    "ConfigController",
+    "VNAController",
+]
diff --git a/python_app/hardware_full/librevna_driver/api/config.py b/python_app/hardware_full/librevna_driver/api/config.py
new file mode 100644
index 0000000..c52cc4a
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/api/config.py
@@ -0,0 +1,54 @@
+"""Device configuration controller."""
+
+from __future__ import annotations
+
+import logging
+
+from ..enums import PacketType
+from ..exceptions import ParseError
+from ..models import DeviceConfigVariant, Packet
+from ..protocol import parse_device_config
+from ..session import LibreVNASession
+
+logger = logging.getLogger(__name__)
+
+
+class ConfigController:
+    """Device configuration read/write/reset operations."""
+
+    def __init__(self, session: LibreVNASession) -> None:
+        """Bind controller to an active session."""
+        self._session = session
+
+    def get(self, *, timeout_s: float = 1.0) -> DeviceConfigVariant:
+        """Read configuration block for active hardware family."""
+        packet = self._session.request(
+            PacketType.REQUEST_DEVICE_CONFIGURATION,
+            PacketType.DEVICE_CONFIGURATION,
+            timeout_s=timeout_s,
+        )
+        if not isinstance(packet.payload, (bytes, bytearray, memoryview)):
+            raise ParseError("DeviceConfiguration payload has unexpected type")
+        cfg = parse_device_config(bytes(packet.payload), self._session.hardware_family)
+        logger.info(
+            "Loaded device configuration for family=%s fields=%d",
+            cfg.family.name,
+            len(cfg.values),
+        )
+        return cfg
+
+    def set(self, cfg: DeviceConfigVariant, *, timeout_s: float = 1.0) -> None:
+        """Write configuration block for active hardware family."""
+        if cfg.family != self._session.hardware_family:
+            raise ParseError("DeviceConfigVariant family does not match connected hardware family")
+        logger.info(
+            "Writing device configuration for family=%s fields=%d",
+            cfg.family.name,
+            len(cfg.values),
+        )
+        self._session.send(Packet(PacketType.DEVICE_CONFIGURATION, cfg), require_ack=True, timeout_s=timeout_s)
+
+    def reset(self, *, timeout_s: float = 1.0) -> None:
+        """Reset device configuration to firmware defaults."""
+        logger.warning("Resetting device configuration to firmware defaults")
+        self._session.send(Packet(PacketType.RESET_DEVICE_CONFIGURATION), require_ack=True, timeout_s=timeout_s)
diff --git a/python_app/hardware_full/librevna_driver/api/vna.py b/python_app/hardware_full/librevna_driver/api/vna.py
new file mode 100644
index 0000000..5cff8b8
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/api/vna.py
@@ -0,0 +1,99 @@
+"""VNA high-level controller for direct protocol packets."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+import logging
+from typing import Callable
+
+from ..enums import PacketType
+from ..exceptions import ParseError
+from ..models import Packet, StreamHandle, SweepResult, VNADatapointPacket, VNARawPoint, VNASweepSettings
+from ..session import LibreVNASession
+from ..sweep.assembler import assemble_vna_sweep, datapoint_to_raw_point
+
+logger = logging.getLogger(__name__)
+
+
+class VNAController:
+    """VNA operations built on direct packet protocol."""
+
+    def __init__(self, session: LibreVNASession) -> None:
+        """Bind VNA controller to active session."""
+        self._session = session
+        self._settings: VNASweepSettings | None = None
+
+    def configure(self, settings: VNASweepSettings) -> None:
+        """Send `SweepSettings` packet to configure VNA operation."""
+        effective = settings
+        if effective.sync_mode is None:
+            effective = replace(effective, sync_mode=self._session.default_sync_mode)
+        self._settings = effective
+
+        self._session.clear_queue(PacketType.VNA_DATAPOINT)
+        self._session.send(Packet(PacketType.SWEEP_SETTINGS, effective), require_ack=True)
+        logger.info(
+            "VNA configured: kind=%s start=%.3fHz stop=%.3fHz points=%d ifbw=%.3fHz ports=%s",
+            effective.kind.value,
+            effective.f_start_hz,
+            effective.f_stop_hz,
+            effective.points,
+            effective.if_bandwidth_hz,
+            effective.excited_ports,
+        )
+
+    def acquire(self, *, expected_points: int | None = None, timeout_s: float = 10.0) -> SweepResult:
+        """Acquire one complete sweep and return assembled complex traces."""
+        if self._settings is None:
+            raise ParseError("VNA is not configured. Call vna.configure() before acquire().")
+
+        settings = self._settings
+        points_target = expected_points if expected_points is not None else settings.points
+        if points_target <= 0:
+            raise ValueError("expected_points must be > 0")
+
+        logger.info(
+            "VNA acquire start: expected_points=%d timeout=%.2fs standby=%s",
+            points_target,
+            timeout_s,
+            settings.standby,
+        )
+        self._session.clear_queue(PacketType.VNA_DATAPOINT)
+        if settings.standby:
+            self._session.send(Packet(PacketType.INITIATE_SWEEP), require_ack=True)
+
+        ordered = self._session.collect_indexed_payloads(
+            packet_type=PacketType.VNA_DATAPOINT,
+            expected_points=points_target,
+            timeout_s=timeout_s,
+            payload_type=VNADatapointPacket,
+            payload_error="Expected decoded VNADatapointPacket payload",
+        )
+
+        device_info = self._session.get_device_info()
+        result = assemble_vna_sweep(
+            ordered,
+            settings,
+            num_ports=device_info.num_ports,
+            expected_points=points_target,
+        )
+        logger.info("VNA acquire complete: received_points=%d", len(result.x))
+        return result
+
+    def stream(self, callback: Callable[[VNARawPoint], None]) -> StreamHandle:
+        """Subscribe callback for every incoming VNA datapoint packet."""
+        if self._settings is None:
+            raise ParseError("VNA is not configured. Call vna.configure() before stream().")
+
+        settings = self._settings
+        num_ports = self._session.get_device_info().num_ports
+
+        def _on_packet(packet: Packet) -> None:
+            """Decode datapoint packet and forward mapped raw point to callback."""
+            payload = packet.payload
+            if not isinstance(payload, VNADatapointPacket):
+                raise ParseError("Expected decoded VNADatapointPacket payload")
+            callback(datapoint_to_raw_point(payload, settings, num_ports=num_ports))
+
+        logger.info("VNA stream subscription started")
+        return self._session.subscribe(PacketType.VNA_DATAPOINT, _on_packet)
diff --git a/python_app/hardware_full/librevna_driver/device.py b/python_app/hardware_full/librevna_driver/device.py
new file mode 100644
index 0000000..bc8426c
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/device.py
@@ -0,0 +1,115 @@
+"""Main user-facing LibreVNA direct-USB device class."""
+
+from __future__ import annotations
+
+import logging
+from types import TracebackType
+
+from .api.config import ConfigController
+from .api.vna import VNAController
+from .enums import PacketType
+from .models import DeviceInfo, DeviceStatus, Packet, USBDeviceDescriptor
+from .session import LibreVNASession
+
+logger = logging.getLogger(__name__)
+
+
+class LibreVNADevice:
+    """Main entry point for direct USB control of LibreVNA."""
+
+    def __init__(self) -> None:
+        """Create device facade with session-backed VNA/config controllers."""
+        self._session = LibreVNASession()
+
+        self.vna = VNAController(self._session)
+        self.config = ConfigController(self._session)
+
+    def __enter__(self) -> LibreVNADevice:
+        """Return self to support context-managed lifetime in host applications."""
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        tb: TracebackType | None,
+    ) -> None:
+        """Always close USB session when leaving context manager block."""
+        self.disconnect()
+
+    @property
+    def is_connected(self) -> bool:
+        """Return connection state of underlying USB session."""
+        return self._session.is_connected
+
+    @property
+    def connected_serial(self) -> str | None:
+        """Return currently connected serial number when available."""
+        return self._session.connected_serial
+
+    @staticmethod
+    def list_devices() -> list[USBDeviceDescriptor]:
+        """Return all currently discoverable LibreVNA USB devices."""
+        devices = LibreVNASession.list_devices()
+        logger.debug("Discovered %d LibreVNA USB device(s)", len(devices))
+        return devices
+
+    def connect(
+        self,
+        serial: str | None = None,
+        strict_protocol_version: int = 14,
+        timeout_s: float = 1.0,
+    ) -> None:
+        """Connect to a LibreVNA device by optional serial number."""
+        logger.info(
+            "Connecting to LibreVNA (serial=%s, strict_protocol=%d, timeout=%.2fs)",
+            serial,
+            strict_protocol_version,
+            timeout_s,
+        )
+        self._session.connect(
+            serial=serial,
+            strict_protocol_version=strict_protocol_version,
+            timeout_s=timeout_s,
+        )
+        logger.info("Connected to LibreVNA (serial=%s)", self.connected_serial)
+
+    def disconnect(self) -> None:
+        """Disconnect USB transport and clear runtime state."""
+        logger.info("Disconnecting LibreVNA session")
+        self._session.disconnect()
+        logger.info("LibreVNA session disconnected")
+
+    def send(self, packet: Packet, *, require_ack: bool = True, timeout_s: float = 0.5) -> None:
+        """Send low-level protocol packet."""
+        logger.debug(
+            "Sending packet %s (require_ack=%s, timeout=%.2fs)",
+            packet.type.name,
+            require_ack,
+            timeout_s,
+        )
+        self._session.send(packet, require_ack=require_ack, timeout_s=timeout_s)
+
+    def request(
+        self,
+        packet_type: PacketType,
+        response_type: PacketType,
+        *,
+        timeout_s: float = 1.0,
+    ) -> Packet:
+        """Send no-payload request packet and wait for a response packet."""
+        logger.debug(
+            "Request packet=%s response=%s timeout=%.2fs",
+            packet_type.name,
+            response_type.name,
+            timeout_s,
+        )
+        return self._session.request(packet_type, response_type, timeout_s=timeout_s)
+
+    def get_device_info(self) -> DeviceInfo:
+        """Return cached device info from handshake."""
+        return self._session.get_device_info()
+
+    def get_device_status(self, *, timeout_s: float = 1.0) -> DeviceStatus:
+        """Query and return current device status."""
+        return self._session.get_device_status(timeout_s=timeout_s)
diff --git a/python_app/hardware_full/librevna_driver/enums.py b/python_app/hardware_full/librevna_driver/enums.py
new file mode 100644
index 0000000..e336428
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/enums.py
@@ -0,0 +1,87 @@
+"""Enumerations used by the LibreVNA direct-USB driver."""
+
+from __future__ import annotations
+
+from enum import Enum, IntEnum
+
+
+class PacketType(IntEnum):
+    """Packet identifiers defined by LibreVNA protocol v14."""
+
+    NONE = 0
+    SWEEP_SETTINGS = 2
+    MANUAL_STATUS = 3
+    MANUAL_CONTROL = 4
+    DEVICE_INFO = 5
+    FIRMWARE_PACKET = 6
+    ACK = 7
+    CLEAR_FLASH = 8
+    PERFORM_FIRMWARE_UPDATE = 9
+    NACK = 10
+    REFERENCE = 11
+    GENERATOR = 12
+    SPECTRUM_ANALYZER_SETTINGS = 13
+    SPECTRUM_ANALYZER_RESULT = 14
+    REQUEST_DEVICE_INFO = 15
+    REQUEST_SOURCE_CAL = 16
+    REQUEST_RECEIVER_CAL = 17
+    SOURCE_CAL_POINT = 18
+    RECEIVER_CAL_POINT = 19
+    SET_IDLE = 20
+    REQUEST_FREQUENCY_CORRECTION = 21
+    FREQUENCY_CORRECTION = 22
+    REQUEST_DEVICE_CONFIGURATION = 23
+    DEVICE_CONFIGURATION = 24
+    DEVICE_STATUS = 25
+    REQUEST_DEVICE_STATUS = 26
+    VNA_DATAPOINT = 27
+    SET_TRIGGER = 28
+    CLEAR_TRIGGER = 29
+    STOP_STATUS_UPDATES = 30
+    START_STATUS_UPDATES = 31
+    INITIATE_SWEEP = 32
+    PERFORM_ACTION = 33
+    RESET_DEVICE_CONFIGURATION = 34
+
+
+class SyncMode(IntEnum):
+    """Synchronization mode encoded in sweep settings."""
+
+    DISABLED = 0
+    USB = 1
+    EXTERNAL_REFERENCE = 2
+    EXTERNAL_TRIGGER = 3
+
+
+class HardwareFamily(IntEnum):
+    """Known LibreVNA hardware families."""
+
+    V1 = 0x01
+    VD0 = 0xD0
+    VE0 = 0xE0
+    VFE = 0xFE
+    VFF = 0xFF
+    UNKNOWN = 0x00
+
+
+class SweepKind(str, Enum):
+    """Logical VNA sweep mode."""
+
+    FREQUENCY = "frequency"
+    POWER = "power"
+
+
+class SweepScale(str, Enum):
+    """Frequency axis spacing."""
+
+    LIN = "lin"
+    LOG = "log"
+
+
+class SParameter(str, Enum):
+    """Canonical 2-port S-parameters."""
+
+    S11 = "S11"
+    S12 = "S12"
+    S21 = "S21"
+    S22 = "S22"
diff --git a/python_app/hardware_full/librevna_driver/exceptions.py b/python_app/hardware_full/librevna_driver/exceptions.py
new file mode 100644
index 0000000..38e220d
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/exceptions.py
@@ -0,0 +1,39 @@
+"""Project exceptions for direct USB and protocol operations."""
+
+from __future__ import annotations
+
+
+class LibreVNAError(Exception):
+    """Base error for all library exceptions."""
+
+
+class ProtocolVersionMismatch(LibreVNAError):
+    """Raised when device protocol version differs from required version."""
+
+
+class CRCError(LibreVNAError):
+    """Raised when packet CRC validation fails."""
+
+
+class NackError(LibreVNAError):
+    """Raised when the device explicitly responds with NACK."""
+
+
+class TimeoutError(LibreVNAError):
+    """Raised when waiting for packet/ack times out."""
+
+
+class DeviceDisconnectedError(LibreVNAError):
+    """Raised when USB transport is disconnected or unavailable."""
+
+
+class IncompleteSweepError(LibreVNAError):
+    """Raised when a sweep does not contain the required points."""
+
+
+class ParseError(LibreVNAError):
+    """Raised when packet decoding or payload parsing fails."""
+
+
+class UnsupportedHardwareError(LibreVNAError):
+    """Raised when a requested operation is unsupported for hardware family."""
diff --git a/python_app/hardware_full/librevna_driver/logging_utils.py b/python_app/hardware_full/librevna_driver/logging_utils.py
new file mode 100644
index 0000000..9e3e639
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/logging_utils.py
@@ -0,0 +1,52 @@
+"""Logging helpers for applications embedding ``librevna_driver``.
+
+The library uses standard ``logging`` module loggers under the
+``librevna_driver`` namespace and never configures global logging implicitly.
+Use :func:`configure_logging` in scripts/services when you want a convenient
+default console setup.
+"""
+
+from __future__ import annotations
+
+import logging
+
+_LOGGER_NAMESPACE = "librevna_driver"
+_DEFAULT_FORMAT = (
+    "%(asctime)s | %(levelname)-8s | %(name)s | %(message)s"
+)
+DEFAULT_LOG_LEVEL = "INFO"
+
+
+def configure_logging(
+    level: int | str | None = None,
+    *,
+    fmt: str = _DEFAULT_FORMAT,
+    datefmt: str | None = "%Y-%m-%d %H:%M:%S",
+) -> None:
+    """Configure package logger with one stream handler.
+
+    This helper affects only the ``librevna_driver`` logger tree and is safe to
+    call repeatedly (previous handlers attached by this function are replaced).
+    When ``level`` is ``None``, :data:`DEFAULT_LOG_LEVEL` is used.
+    """
+
+    effective_level = level if level is not None else DEFAULT_LOG_LEVEL
+
+    logger = logging.getLogger(_LOGGER_NAMESPACE)
+    logger.handlers.clear()
+    handler = logging.StreamHandler()
+    handler.setFormatter(logging.Formatter(fmt=fmt, datefmt=datefmt))
+    logger.addHandler(handler)
+    logger.setLevel(_parse_level(effective_level))
+    logger.propagate = False
+
+
+def _parse_level(level: int | str) -> int:
+    """Parse numeric or textual log level into logging constant."""
+    if isinstance(level, int):
+        return level
+
+    normalized = level.strip().upper()
+    if normalized in logging.getLevelNamesMapping():
+        return logging.getLevelNamesMapping()[normalized]
+    raise ValueError(f"Unknown logging level: {level!r}")
diff --git a/python_app/hardware_full/librevna_driver/models.py b/python_app/hardware_full/librevna_driver/models.py
new file mode 100644
index 0000000..d06cd69
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/models.py
@@ -0,0 +1,255 @@
+"""Public datamodels and low-level packet payload representations."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+import logging
+from threading import Event, Thread
+from typing import Any, Callable
+
+import numpy as np
+
+from .enums import HardwareFamily, PacketType, SweepKind, SweepScale, SyncMode
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(slots=True)
+class Packet:
+    """Generic protocol packet container."""
+
+    type: PacketType
+    payload: Any = b""
+
+
+@dataclass(slots=True)
+class USBDeviceDescriptor:
+    """USB device descriptor exposed by transport discovery."""
+
+    serial: str
+    vendor_id: int
+    product_id: int
+
+
+@dataclass(slots=True)
+class DeviceLimits:
+    """Hardware capability limits reported by the instrument."""
+
+    min_frequency_hz: float
+    max_frequency_hz: float
+    max_frequency_harmonic_hz: float
+    min_ifbw_hz: float
+    max_ifbw_hz: float
+    max_points: int
+    min_power_dbm: float
+    max_power_dbm: float
+    min_rbw_hz: float
+    max_rbw_hz: float
+    max_amplitude_points: int
+    max_dwell_time_s: float
+
+
+@dataclass(slots=True)
+class DeviceInfo:
+    """Device identity and capabilities."""
+
+    protocol_version: int
+    firmware_major: int
+    firmware_minor: int
+    firmware_patch: int
+    firmware_version: str
+    hardware_version: int
+    hardware_revision: str
+    hardware_family: HardwareFamily
+    limits: DeviceLimits
+    num_ports: int
+
+
+@dataclass(slots=True)
+class DeviceStatus:
+    """Current runtime status and telemetry."""
+
+    family: HardwareFamily
+    source_locked: bool | None
+    lo_locked: bool | None
+    adc_overload: bool | None
+    unlevel: bool | None
+    temperatures_c: list[float] = field(default_factory=list)
+    raw: dict[str, int | float | bool] = field(default_factory=dict)
+
+
+@dataclass(slots=True)
+class VNASweepSettings:
+    """Configuration for one VNA sweep setup packet."""
+
+    kind: SweepKind = SweepKind.FREQUENCY
+    f_start_hz: float = 1_000_000.0
+    f_stop_hz: float = 6_000_000_000.0
+    points: int = 501
+    if_bandwidth_hz: float = 1_000.0
+    power_start_dbm: float = -10.0
+    power_stop_dbm: float = -10.0
+    excited_ports: tuple[int, ...] = (1, 2)
+    sweep_scale: SweepScale = SweepScale.LIN
+    dwell_s: float = 0.0
+    suppress_invalid_peaks: bool = True
+    fixed_power_setting: bool = False
+    standby: bool = True
+    sync_mode: SyncMode | None = None
+    sync_master: bool = False
+
+    def __post_init__(self) -> None:
+        """Validate configuration before transmitting to the device."""
+        if self.points <= 0:
+            raise ValueError("points must be > 0")
+        if self.if_bandwidth_hz <= 0:
+            raise ValueError("if_bandwidth_hz must be > 0")
+        if self.dwell_s < 0:
+            raise ValueError("dwell_s must be >= 0")
+        if not self.excited_ports:
+            raise ValueError("excited_ports must not be empty")
+        if len(set(self.excited_ports)) != len(self.excited_ports):
+            raise ValueError("excited_ports must not contain duplicates")
+        if any(port <= 0 for port in self.excited_ports):
+            raise ValueError("excited_ports must use 1-based positive port numbers")
+        if self.f_stop_hz < self.f_start_hz:
+            raise ValueError("f_stop_hz must be >= f_start_hz")
+        if self.kind == SweepKind.POWER and self.power_stop_dbm < self.power_start_dbm:
+            raise ValueError("power_stop_dbm must be >= power_start_dbm for power sweep")
+        if self.kind == SweepKind.POWER and self.f_start_hz != self.f_stop_hz:
+            raise ValueError("power sweep requires f_start_hz == f_stop_hz")
+
+
+@dataclass(slots=True)
+class DeviceConfigVariant:
+    """Family-specific device configuration fields."""
+
+    family: HardwareFamily
+    values: dict[str, int | float | bool] = field(default_factory=dict)
+
+
+@dataclass(slots=True)
+class VNADatapointPacket:
+    """Decoded low-level VNADatapoint payload."""
+
+    frequency_or_time: int
+    cdbm: int
+    point_number: int
+    real: np.ndarray
+    imag: np.ndarray
+    flags: np.ndarray
+
+
+@dataclass(slots=True)
+class VNARawPoint:
+    """Normalized VNA datapoint used by streaming callback API."""
+
+    point_number: int
+    frequency_hz: float | None
+    time_s: float | None
+    power_dbm: float | None
+    measurements: dict[str, complex] = field(default_factory=dict)
+    z0: float = 50.0
+
+
+class StreamHandle:
+    """Handle returned from streaming subscriptions."""
+
+    def __init__(
+        self,
+        stop_event: Event,
+        close_callback: Callable[[], None],
+        reader_thread: Thread | None = None,
+    ) -> None:
+        """Create stream handle with stop event and unsubscribe callback."""
+        self._stop_event = stop_event
+        self._close_callback = close_callback
+        self._reader_thread = reader_thread
+
+    def close(self, *, join_timeout_s: float | None = 1.0) -> None:
+        """Stop streaming and release internal resources."""
+        logger.debug("Closing stream handle (join_timeout_s=%s)", join_timeout_s)
+        if not self._stop_event.is_set():
+            self._stop_event.set()
+            self._close_callback()
+        if self._reader_thread is not None and self._reader_thread.is_alive():
+            self._reader_thread.join(timeout=join_timeout_s)
+        logger.debug("Stream handle closed")
+
+
+@dataclass(slots=True)
+class SweepResult:
+    """Container for complex VNA traces sharing one X-axis."""
+
+    x: np.ndarray
+    traces: dict[str, np.ndarray] = field(default_factory=dict)
+    x_label: str = "frequency_hz"
+
+    def __post_init__(self) -> None:
+        """Normalize dtypes and validate shape compatibility."""
+        self.x = np.asarray(self.x, dtype=np.float64)
+        normalized: dict[str, np.ndarray] = {}
+        for key, values in self.traces.items():
+            arr = np.asarray(values, dtype=np.complex128)
+            if arr.shape != self.x.shape:
+                raise ValueError(
+                    f"Trace '{key}' shape {arr.shape} does not match axis shape {self.x.shape}"
+                )
+            normalized[key.strip().lower()] = arr
+        self.traces = normalized
+
+    @property
+    def s11(self) -> np.ndarray | None:
+        """Return `S11` trace when available."""
+        return self.traces.get("s11")
+
+    @property
+    def s21(self) -> np.ndarray | None:
+        """Return `S21` trace when available."""
+        return self.traces.get("s21")
+
+    @property
+    def s12(self) -> np.ndarray | None:
+        """Return `S12` trace when available."""
+        return self.traces.get("s12")
+
+    @property
+    def s22(self) -> np.ndarray | None:
+        """Return `S22` trace when available."""
+        return self.traces.get("s22")
+
+    def trace(self, parameter: str) -> np.ndarray:
+        """Return complex trace by parameter name."""
+        key = parameter.strip().lower()
+        if key not in self.traces:
+            raise KeyError(f"Trace '{parameter}' is not available")
+        return self.traces[key]
+
+    def real(self, parameter: str) -> np.ndarray:
+        """Return real part of selected trace."""
+        return self.trace(parameter).real
+
+    def imag(self, parameter: str) -> np.ndarray:
+        """Return imaginary part of selected trace."""
+        return self.trace(parameter).imag
+
+    def to_npz(self, path: str) -> None:
+        """Save result as NumPy `.npz` archive."""
+        data: dict[str, np.ndarray] = {self.x_label: self.x}
+        data.update(self.traces)
+        np.savez(path, **data)
+
+    def to_csv(self, path: str) -> None:
+        """Save result as CSV with `<trace>_real`/`<trace>_imag` columns."""
+        columns: list[np.ndarray] = [self.x]
+        headers: list[str] = [self.x_label]
+        for name, values in sorted(self.traces.items()):
+            columns.append(values.real)
+            columns.append(values.imag)
+            headers.append(f"{name}_real")
+            headers.append(f"{name}_imag")
+        matrix = np.column_stack(columns)
+        np.savetxt(path, matrix, delimiter=",", header=",".join(headers), comments="")
+
+
+PacketPayload = Any
diff --git a/python_app/hardware_full/librevna_driver/protocol/__init__.py b/python_app/hardware_full/librevna_driver/protocol/__init__.py
new file mode 100644
index 0000000..f295c84
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/protocol/__init__.py
@@ -0,0 +1,31 @@
+"""Protocol framing and payload codecs for LibreVNA packet protocol v14."""
+
+from .codec import (
+    decode_packet_payload,
+    decode_vna_datapoint_payload,
+    encode_device_config_payload,
+    encode_packet_payload,
+    encode_sweep_settings_payload,
+    ensure_no_payload_types,
+    NO_PAYLOAD_PACKET_TYPES,
+    parse_device_config,
+    parse_device_info,
+    parse_device_status,
+)
+from .frame import FrameScanner, decode_frame, encode_frame
+
+__all__ = [
+    "FrameScanner",
+    "NO_PAYLOAD_PACKET_TYPES",
+    "decode_frame",
+    "decode_packet_payload",
+    "decode_vna_datapoint_payload",
+    "encode_device_config_payload",
+    "encode_frame",
+    "encode_packet_payload",
+    "encode_sweep_settings_payload",
+    "ensure_no_payload_types",
+    "parse_device_config",
+    "parse_device_info",
+    "parse_device_status",
+]
diff --git a/python_app/hardware_full/librevna_driver/protocol/codec.py b/python_app/hardware_full/librevna_driver/protocol/codec.py
new file mode 100644
index 0000000..5a31c8f
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/protocol/codec.py
@@ -0,0 +1,407 @@
+"""Payload encoders/decoders for LibreVNA protocol packets."""
+
+from __future__ import annotations
+
+import ctypes
+import logging
+import struct
+
+import numpy as np
+
+from ..enums import HardwareFamily, PacketType
+from ..exceptions import ParseError, UnsupportedHardwareError
+from ..models import (
+    DeviceConfigVariant,
+    DeviceInfo,
+    DeviceLimits,
+    DeviceStatus,
+    Packet,
+    VNADatapointPacket,
+    VNASweepSettings,
+)
+from .structs import DeviceStatusUnion
+
+logger = logging.getLogger(__name__)
+
+
+_DEVICE_INFO_STRUCT = struct.Struct("<HBBBBcQQIIHhhIIBQBH")
+_SWEEP_SETTINGS_STRUCT = struct.Struct("<QQHIhBHhH")
+_DEVICE_CONFIG_V1_STRUCT = struct.Struct("<IBHB")
+_DEVICE_CONFIG_VFF_STRUCT = struct.Struct("<IIIBH")
+_DEVICE_CONFIG_VFE_STRUCT = struct.Struct("<H")
+_DEVICE_CONFIG_VD0_STRUCT = struct.Struct("<HIB")
+
+_DEVICE_STATUS_VARIANT_BY_FAMILY: dict[HardwareFamily, str] = {
+    HardwareFamily.V1: "V1",
+    HardwareFamily.VFF: "VFF",
+    HardwareFamily.VFE: "VFE",
+    HardwareFamily.VD0: "VD0",
+    HardwareFamily.VE0: "VD0",
+}
+
+
+def _family_from_hardware_version(hardware_version: int) -> HardwareFamily:
+    """Map hardware version byte to typed hardware family enum."""
+    try:
+        return HardwareFamily(hardware_version)
+    except ValueError as exc:
+        raise UnsupportedHardwareError(
+            f"Unsupported hardware_version in DeviceInfo: 0x{hardware_version:02X}"
+        ) from exc
+
+
+def _ensure_payload_length(payload: bytes, expected: int, *, packet_name: str) -> None:
+    """Validate exact payload length for fixed-size packet structures."""
+    if len(payload) != expected:
+        raise ParseError(
+            f"{packet_name} payload length mismatch: expected {expected}, got {len(payload)}"
+        )
+
+
+def parse_device_info(payload: bytes) -> DeviceInfo:
+    """Parse `DeviceInfo` packet payload into typed model."""
+    _ensure_payload_length(payload, _DEVICE_INFO_STRUCT.size, packet_name="DeviceInfo")
+
+    (
+        protocol_version,
+        fw_major,
+        fw_minor,
+        fw_patch,
+        hardware_version,
+        hw_revision_raw,
+        min_freq,
+        max_freq,
+        min_ifbw,
+        max_ifbw,
+        max_points,
+        min_cdbm,
+        max_cdbm,
+        min_rbw,
+        max_rbw,
+        max_amplitude_points,
+        max_harmonic,
+        num_ports,
+        max_dwell_time_us,
+    ) = _DEVICE_INFO_STRUCT.unpack(payload)
+
+    family = _family_from_hardware_version(hardware_version)
+    hw_revision = hw_revision_raw.decode("ascii", errors="replace")
+
+    limits = DeviceLimits(
+        min_frequency_hz=float(min_freq),
+        max_frequency_hz=float(max_freq),
+        max_frequency_harmonic_hz=float(max_harmonic),
+        min_ifbw_hz=float(min_ifbw),
+        max_ifbw_hz=float(max_ifbw),
+        max_points=int(max_points),
+        min_power_dbm=float(min_cdbm) / 100.0,
+        max_power_dbm=float(max_cdbm) / 100.0,
+        min_rbw_hz=float(min_rbw),
+        max_rbw_hz=float(max_rbw),
+        max_amplitude_points=int(max_amplitude_points),
+        max_dwell_time_s=float(max_dwell_time_us) * 1e-6,
+    )
+
+    device_info = DeviceInfo(
+        protocol_version=int(protocol_version),
+        firmware_major=int(fw_major),
+        firmware_minor=int(fw_minor),
+        firmware_patch=int(fw_patch),
+        firmware_version=f"{fw_major}.{fw_minor}.{fw_patch}",
+        hardware_version=int(hardware_version),
+        hardware_revision=hw_revision,
+        hardware_family=family,
+        limits=limits,
+        num_ports=int(num_ports),
+    )
+    logger.debug(
+        "Decoded DeviceInfo: protocol=%d fw=%s family=%s ports=%d",
+        device_info.protocol_version,
+        device_info.firmware_version,
+        device_info.hardware_family.name,
+        device_info.num_ports,
+    )
+    return device_info
+
+
+def _device_status_variant_name_for_family(family: HardwareFamily) -> str:
+    """Resolve `DeviceStatusUnion` variant name for specific hardware family."""
+    try:
+        return _DEVICE_STATUS_VARIANT_BY_FAMILY[family]
+    except KeyError as exc:
+        raise UnsupportedHardwareError(
+            f"Unsupported hardware family for DeviceStatus: {family!r}"
+        ) from exc
+
+
+def _structure_fields_dict(struct_obj: ctypes.Structure) -> dict[str, int | float | bool]:
+    """Convert ctypes structure fields into plain Python dictionary."""
+    values: dict[str, int | float | bool] = {}
+    for entry in struct_obj._fields_:
+        field_name = entry[0]
+        if field_name.startswith("_"):
+            continue
+        raw_value = getattr(struct_obj, field_name)
+
+        if len(entry) == 3:
+            values[field_name] = bool(raw_value)
+            continue
+
+        field_type = entry[1]
+        if field_type in {ctypes.c_float, ctypes.c_double}:
+            values[field_name] = float(raw_value)
+        else:
+            values[field_name] = int(raw_value)
+
+    return values
+
+
+def parse_device_status(payload: bytes, family: HardwareFamily) -> DeviceStatus:
+    """Parse `DeviceStatus` payload according to active hardware family."""
+    _ensure_payload_length(payload, ctypes.sizeof(DeviceStatusUnion), packet_name="DeviceStatus")
+
+    union = DeviceStatusUnion()
+    ctypes.memmove(ctypes.addressof(union), payload, len(payload))
+    variant_name = _device_status_variant_name_for_family(family)
+    variant = getattr(union, variant_name)
+    raw = _structure_fields_dict(variant)
+
+    source_locked = bool(raw.get("source_locked")) if "source_locked" in raw else None
+    lo_locked = None
+    if "LO_locked" in raw:
+        lo_locked = bool(raw["LO_locked"])
+    elif "LO1_locked" in raw:
+        lo_locked = bool(raw["LO1_locked"])
+
+    adc_overload = bool(raw.get("ADC_overload")) if "ADC_overload" in raw else None
+    unlevel = bool(raw.get("unlevel")) if "unlevel" in raw else None
+
+    temperatures_c: list[float] = []
+    for key in ("temp_source", "temp_LO1", "temp_MCU"):
+        if key in raw:
+            temperatures_c.append(float(raw[key]))
+    if "temp_eCal" in raw:
+        temperatures_c.append(float(raw["temp_eCal"]) / 100.0)
+
+    return DeviceStatus(
+        family=family,
+        source_locked=source_locked,
+        lo_locked=lo_locked,
+        adc_overload=adc_overload,
+        unlevel=unlevel,
+        temperatures_c=temperatures_c,
+        raw=raw,
+    )
+
+
+def decode_vna_datapoint_payload(payload: bytes) -> VNADatapointPacket:
+    """Decode variable-size VNADatapoint payload."""
+    if len(payload) < 12:
+        raise ParseError("VNADatapoint payload is too short")
+
+    values_block = len(payload) - 12
+    if values_block % 9 != 0:
+        raise ParseError("VNADatapoint payload length is not aligned to value tuple size")
+
+    num_values = values_block // 9
+
+    (frequency_or_time,) = struct.unpack_from("<Q", payload, 0)
+    (cdbm,) = struct.unpack_from("<h", payload, 8)
+    (point_number,) = struct.unpack_from("<H", payload, 10)
+
+    real = np.frombuffer(payload, dtype="<f4", count=num_values, offset=12).astype(np.float64, copy=False)
+    imag = np.frombuffer(payload, dtype="<f4", count=num_values, offset=12 + 4 * num_values).astype(
+        np.float64,
+        copy=False,
+    )
+    flags = np.frombuffer(payload, dtype=np.uint8, count=num_values, offset=12 + 8 * num_values)
+
+    return VNADatapointPacket(
+        frequency_or_time=int(frequency_or_time),
+        cdbm=int(cdbm),
+        point_number=int(point_number),
+        real=real,
+        imag=imag,
+        flags=flags,
+    )
+
+
+def decode_packet_payload(packet: Packet) -> object:
+    """Decode packet payload for known response packet types."""
+    if packet.type == PacketType.DEVICE_INFO:
+        return parse_device_info(packet.payload)
+    if packet.type == PacketType.VNA_DATAPOINT:
+        return decode_vna_datapoint_payload(packet.payload)
+    return packet.payload
+
+
+def encode_sweep_settings_payload(settings: VNASweepSettings) -> bytes:
+    """Encode `SweepSettings` payload."""
+    if len(settings.excited_ports) > 4:
+        raise ValueError("Protocol supports at most four excited ports")
+    sync_mode = settings.sync_mode if settings.sync_mode is not None else 0
+
+    stage_by_port = {port: stage for stage, port in enumerate(settings.excited_ports)}
+
+    flags1 = 0
+    flags1 |= int(bool(settings.standby)) << 0
+    flags1 |= int(bool(settings.sync_master)) << 1
+    flags1 |= int(bool(settings.suppress_invalid_peaks)) << 2
+    flags1 |= int(bool(settings.fixed_power_setting)) << 3
+    flags1 |= int(settings.sweep_scale.value == "log") << 4
+    flags1 |= (int(sync_mode) & 0x03) << 5
+
+    flags2 = 0
+    stages = len(settings.excited_ports) - 1
+    flags2 |= stages & 0x07
+    flags2 |= (stage_by_port.get(1, 0) & 0x07) << 3
+    flags2 |= (stage_by_port.get(2, 0) & 0x07) << 6
+    flags2 |= (stage_by_port.get(3, 0) & 0x07) << 9
+    flags2 |= (stage_by_port.get(4, 0) & 0x07) << 12
+
+    dwell_us = int(round(settings.dwell_s * 1_000_000.0))
+    dwell_us = max(0, min(0xFFFF, dwell_us))
+
+    return _SWEEP_SETTINGS_STRUCT.pack(
+        int(round(settings.f_start_hz)),
+        int(round(settings.f_stop_hz)),
+        int(settings.points),
+        int(round(settings.if_bandwidth_hz)),
+        int(round(settings.power_start_dbm * 100.0)),
+        flags1,
+        flags2,
+        int(round(settings.power_stop_dbm * 100.0)),
+        dwell_us,
+    )
+
+
+def parse_device_config(payload: bytes, family: HardwareFamily) -> DeviceConfigVariant:
+    """Decode family-specific `DeviceConfiguration` payload."""
+    if len(payload) != 15:
+        raise ParseError(f"DeviceConfiguration payload length mismatch: expected 15, got {len(payload)}")
+
+    values: dict[str, int | float | bool]
+    if family == HardwareFamily.V1:
+        if1, adc_prescaler, dft_phase_inc, pll_delay = _DEVICE_CONFIG_V1_STRUCT.unpack(payload[:8])
+        values = {
+            "IF1": int(if1),
+            "ADCprescaler": int(adc_prescaler),
+            "DFTphaseInc": int(dft_phase_inc),
+            "PLLSettlingDelay": int(pll_delay),
+        }
+    elif family == HardwareFamily.VFF:
+        ip, mask, gw, flags1, flags2 = _DEVICE_CONFIG_VFF_STRUCT.unpack(payload)
+        values = {
+            "ip": int(ip),
+            "mask": int(mask),
+            "gw": int(gw),
+            "dhcp": bool(flags1 & 0x01),
+            "autogain": bool(flags2 & 0x01),
+            "portGain": int((flags2 >> 1) & 0x0F),
+            "refGain": int((flags2 >> 5) & 0x0F),
+        }
+    elif family == HardwareFamily.VFE:
+        (flags,) = _DEVICE_CONFIG_VFE_STRUCT.unpack(payload[:2])
+        values = {
+            "autogain": bool(flags & 0x01),
+            "portGain": int((flags >> 1) & 0x0F),
+            "refGain": int((flags >> 5) & 0x0F),
+        }
+    elif family in {HardwareFamily.VD0, HardwareFamily.VE0}:
+        dft_phase_inc, adc_rate, pll_delay = _DEVICE_CONFIG_VD0_STRUCT.unpack(payload[:7])
+        values = {
+            "DFTphaseInc": int(dft_phase_inc),
+            "ADCrate": int(adc_rate),
+            "PLLSettlingDelay": int(pll_delay),
+        }
+    else:
+        raise UnsupportedHardwareError(f"Unsupported hardware family for DeviceConfiguration: {family!r}")
+
+    return DeviceConfigVariant(family=family, values=values)
+
+
+def encode_device_config_payload(config: DeviceConfigVariant) -> bytes:
+    """Encode family-specific `DeviceConfiguration` payload."""
+    family = config.family
+    values = config.values
+
+    def _require_value(key: str) -> int | float | bool:
+        """Fetch required device-config value by key or raise parse error."""
+        if key not in values:
+            raise ParseError(f"Missing required device configuration field: '{key}'")
+        return values[key]
+
+    if family == HardwareFamily.V1:
+        payload = _DEVICE_CONFIG_V1_STRUCT.pack(
+            int(_require_value("IF1")),
+            int(_require_value("ADCprescaler")),
+            int(_require_value("DFTphaseInc")),
+            int(_require_value("PLLSettlingDelay")),
+        )
+    elif family == HardwareFamily.VFF:
+        flags1 = int(bool(_require_value("dhcp")))
+        flags2 = 0
+        flags2 |= int(bool(_require_value("autogain"))) << 0
+        flags2 |= (int(_require_value("portGain")) & 0x0F) << 1
+        flags2 |= (int(_require_value("refGain")) & 0x0F) << 5
+        payload = _DEVICE_CONFIG_VFF_STRUCT.pack(
+            int(_require_value("ip")),
+            int(_require_value("mask")),
+            int(_require_value("gw")),
+            flags1,
+            flags2,
+        )
+    elif family == HardwareFamily.VFE:
+        flags = 0
+        flags |= int(bool(_require_value("autogain"))) << 0
+        flags |= (int(_require_value("portGain")) & 0x0F) << 1
+        flags |= (int(_require_value("refGain")) & 0x0F) << 5
+        payload = _DEVICE_CONFIG_VFE_STRUCT.pack(flags)
+    elif family in {HardwareFamily.VD0, HardwareFamily.VE0}:
+        payload = _DEVICE_CONFIG_VD0_STRUCT.pack(
+            int(_require_value("DFTphaseInc")),
+            int(_require_value("ADCrate")),
+            int(_require_value("PLLSettlingDelay")),
+        )
+    else:
+        raise UnsupportedHardwareError(f"Unsupported hardware family for DeviceConfiguration: {family!r}")
+
+    return payload.ljust(15, b"\x00")
+
+
+def encode_packet_payload(packet_type: PacketType, payload: object) -> bytes:
+    """Generic payload encoder for `LibreVNASession.send()` raw API."""
+    if payload is None:
+        return b""
+    if isinstance(payload, (bytes, bytearray, memoryview)):
+        return bytes(payload)
+
+    if packet_type == PacketType.SWEEP_SETTINGS and isinstance(payload, VNASweepSettings):
+        return encode_sweep_settings_payload(payload)
+    if packet_type == PacketType.DEVICE_CONFIGURATION and isinstance(payload, DeviceConfigVariant):
+        return encode_device_config_payload(payload)
+
+    logger.error(
+        "Unsupported payload object for packet %s: %s",
+        packet_type.name,
+        type(payload).__name__,
+    )
+    raise TypeError(f"Unsupported payload object for packet {packet_type.name}: {type(payload).__name__}")
+
+
+NO_PAYLOAD_PACKET_TYPES = {
+    PacketType.ACK,
+    PacketType.NACK,
+    PacketType.REQUEST_DEVICE_INFO,
+    PacketType.REQUEST_DEVICE_CONFIGURATION,
+    PacketType.REQUEST_DEVICE_STATUS,
+    PacketType.INITIATE_SWEEP,
+    PacketType.RESET_DEVICE_CONFIGURATION,
+}
+
+
+def ensure_no_payload_types(packet_type: PacketType, payload: bytes) -> None:
+    """Validate empty payload requirement for no-payload packet types."""
+    if packet_type in NO_PAYLOAD_PACKET_TYPES and payload:
+        logger.error("No-payload packet %s received payload length %d", packet_type.name, len(payload))
+        raise ParseError(f"Packet {packet_type.name} does not support payload")
diff --git a/python_app/hardware_full/librevna_driver/protocol/crc32.py b/python_app/hardware_full/librevna_driver/protocol/crc32.py
new file mode 100644
index 0000000..a2b5135
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/protocol/crc32.py
@@ -0,0 +1,10 @@
+"""CRC32 helpers for LibreVNA packet framing."""
+
+from __future__ import annotations
+
+import zlib
+
+
+def crc32(data: bytes | bytearray | memoryview) -> int:
+    """Compute protocol CRC32 over packet bytes excluding trailing CRC field."""
+    return zlib.crc32(data) & 0xFFFFFFFF
diff --git a/python_app/hardware_full/librevna_driver/protocol/frame.py b/python_app/hardware_full/librevna_driver/protocol/frame.py
new file mode 100644
index 0000000..7a48999
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/protocol/frame.py
@@ -0,0 +1,123 @@
+"""Packet frame encoding/decoding for LibreVNA protocol stream."""
+
+from __future__ import annotations
+
+import logging
+import struct
+
+from ..enums import PacketType
+from ..exceptions import CRCError, ParseError
+from ..models import Packet
+from .crc32 import crc32
+
+logger = logging.getLogger(__name__)
+
+_HEADER = 0x5A
+_FRAME_OVERHEAD = 8  # header + length + type + crc
+_MAX_FRAME_LENGTH = 4096
+
+
+_NO_CRC_PACKET_TYPES = {
+    PacketType.VNA_DATAPOINT,
+}
+
+
+def encode_frame(packet: Packet) -> bytes:
+    """Encode one packet into framed wire format."""
+    payload = packet.payload
+    if not isinstance(payload, (bytes, bytearray, memoryview)):
+        raise TypeError("Packet payload must be bytes-like")
+
+    payload_bytes = bytes(payload)
+    length = _FRAME_OVERHEAD + len(payload_bytes)
+    if length > 0xFFFF:
+        raise ValueError("Packet is too large for protocol frame length field")
+
+    frame = bytearray(length)
+    frame[0] = _HEADER
+    struct.pack_into("<H", frame, 1, length)
+    frame[3] = int(packet.type)
+    frame[4 : 4 + len(payload_bytes)] = payload_bytes
+
+    crc_value = 0
+    if packet.type not in _NO_CRC_PACKET_TYPES:
+        crc_value = crc32(frame[:-4])
+    struct.pack_into("<I", frame, length - 4, crc_value)
+    return bytes(frame)
+
+
+def decode_frame(frame: bytes) -> Packet:
+    """Decode and validate one complete frame."""
+    if len(frame) < _FRAME_OVERHEAD:
+        raise ParseError("Frame is too short")
+    if frame[0] != _HEADER:
+        raise ParseError("Invalid frame header")
+
+    (length,) = struct.unpack_from("<H", frame, 1)
+    if length != len(frame):
+        raise ParseError(f"Frame length mismatch: declared {length}, got {len(frame)}")
+
+    packet_type_raw = frame[3]
+    try:
+        packet_type = PacketType(packet_type_raw)
+    except ValueError as exc:
+        raise ParseError(f"Unknown packet type id {packet_type_raw}") from exc
+
+    (received_crc,) = struct.unpack_from("<I", frame, length - 4)
+    if packet_type in _NO_CRC_PACKET_TYPES:
+        if received_crc != 0:
+            raise CRCError("VNADatapoint packet must carry zero CRC")
+    else:
+        computed_crc = crc32(frame[:-4])
+        if received_crc != computed_crc:
+            raise CRCError(
+                f"CRC mismatch for packet {packet_type.name}: "
+                f"received 0x{received_crc:08X}, computed 0x{computed_crc:08X}"
+            )
+
+    return Packet(type=packet_type, payload=frame[4:-4])
+
+
+class FrameScanner:
+    """Incremental frame scanner for raw USB byte streams."""
+
+    def __init__(self) -> None:
+        """Initialize internal undecoded byte buffer."""
+        self._buffer = bytearray()
+
+    def clear(self) -> None:
+        """Drop all buffered undecoded bytes."""
+        self._buffer.clear()
+
+    def feed(self, chunk: bytes) -> list[Packet]:
+        """Feed raw bytes and return every fully decoded packet."""
+        if not chunk:
+            return []
+        self._buffer.extend(chunk)
+
+        decoded: list[Packet] = []
+        while True:
+            header_index = self._buffer.find(_HEADER)
+            if header_index < 0:
+                self._buffer.clear()
+                break
+            if header_index > 0:
+                del self._buffer[:header_index]
+
+            if len(self._buffer) < 4:
+                break
+
+            (length,) = struct.unpack_from("<H", self._buffer, 1)
+            if length < _FRAME_OVERHEAD or length > _MAX_FRAME_LENGTH:
+                logger.debug("Discarding byte due to invalid frame length=%d", length)
+                del self._buffer[0]
+                continue
+
+            if len(self._buffer) < length:
+                break
+
+            frame = bytes(self._buffer[:length])
+            del self._buffer[:length]
+            decoded.append(decode_frame(frame))
+
+        return decoded
diff --git a/python_app/hardware_full/librevna_driver/protocol/structs.py b/python_app/hardware_full/librevna_driver/protocol/structs.py
new file mode 100644
index 0000000..8149efe
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/protocol/structs.py
@@ -0,0 +1,89 @@
+"""ctypes layouts for protocol status unions used by the driver."""
+
+from __future__ import annotations
+
+import ctypes
+
+
+class DeviceStatusV1(ctypes.LittleEndianStructure):
+    """Device status bit layout for V1 family."""
+
+    _pack_ = 1
+    _fields_ = [
+        ("extRefAvailable", ctypes.c_uint8, 1),
+        ("extRefInUse", ctypes.c_uint8, 1),
+        ("FPGA_configured", ctypes.c_uint8, 1),
+        ("source_locked", ctypes.c_uint8, 1),
+        ("LO1_locked", ctypes.c_uint8, 1),
+        ("ADC_overload", ctypes.c_uint8, 1),
+        ("unlevel", ctypes.c_uint8, 1),
+        ("_unused", ctypes.c_uint8, 1),
+        ("temp_source", ctypes.c_uint8),
+        ("temp_LO1", ctypes.c_uint8),
+        ("temp_MCU", ctypes.c_uint8),
+    ]
+
+
+class DeviceStatusVFF(ctypes.LittleEndianStructure):
+    """Device status bit layout for VFF family."""
+
+    _pack_ = 1
+    _fields_ = [
+        ("source_locked", ctypes.c_uint8, 1),
+        ("LO_locked", ctypes.c_uint8, 1),
+        ("ADC_overload", ctypes.c_uint8, 1),
+        ("unlevel", ctypes.c_uint8, 1),
+        ("_unused", ctypes.c_uint8, 4),
+        ("temp_MCU", ctypes.c_uint8),
+    ]
+
+
+class DeviceStatusVFE(ctypes.LittleEndianStructure):
+    """Device status bit layout for VFE family."""
+
+    _pack_ = 1
+    _fields_ = [
+        ("source_locked", ctypes.c_uint8, 1),
+        ("LO_locked", ctypes.c_uint8, 1),
+        ("ADC_overload", ctypes.c_uint8, 1),
+        ("unlevel", ctypes.c_uint8, 1),
+        ("_unused", ctypes.c_uint8, 4),
+        ("temp_MCU", ctypes.c_uint8),
+        ("temp_eCal", ctypes.c_uint16),
+        ("power_heater", ctypes.c_uint16),
+    ]
+
+
+class DeviceStatusVD0(ctypes.LittleEndianStructure):
+    """Device status bit layout for VD0/VE0 families."""
+
+    _pack_ = 1
+    _fields_ = [
+        ("extRefAvailable", ctypes.c_uint8, 1),
+        ("extRefInUse", ctypes.c_uint8, 1),
+        ("FPGA_configured", ctypes.c_uint8, 1),
+        ("source_locked", ctypes.c_uint8, 1),
+        ("LO_locked", ctypes.c_uint8, 1),
+        ("ADC_overload", ctypes.c_uint8, 1),
+        ("unlevel", ctypes.c_uint8, 1),
+        ("_unused", ctypes.c_uint8, 1),
+        ("temp_MCU", ctypes.c_uint8),
+        ("supply_voltage", ctypes.c_uint16),
+        ("supply_current", ctypes.c_uint16),
+    ]
+
+
+class DeviceStatusUnion(ctypes.Union):
+    """6-byte device-status union shared across hardware families."""
+
+    _pack_ = 1
+    _fields_ = [
+        ("V1", DeviceStatusV1),
+        ("VFF", DeviceStatusVFF),
+        ("VFE", DeviceStatusVFE),
+        ("VD0", DeviceStatusVD0),
+        ("raw", ctypes.c_uint8 * 6),
+    ]
+
+
+assert ctypes.sizeof(DeviceStatusUnion) == 6
diff --git a/python_app/hardware_full/librevna_driver/session.py b/python_app/hardware_full/librevna_driver/session.py
new file mode 100644
index 0000000..6d9fdc7
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/session.py
@@ -0,0 +1,424 @@
+"""Session orchestration for direct-USB LibreVNA protocol communication."""
+
+from __future__ import annotations
+
+from collections import defaultdict, deque
+import logging
+import threading
+import time
+from typing import Callable, Protocol, TypeVar
+
+from .enums import HardwareFamily, PacketType, SyncMode
+from .exceptions import (
+    DeviceDisconnectedError,
+    NackError,
+    ParseError,
+    ProtocolVersionMismatch,
+    TimeoutError,
+)
+from .models import DeviceInfo, DeviceStatus, Packet, StreamHandle, USBDeviceDescriptor
+from .protocol import (
+    FrameScanner,
+    decode_packet_payload,
+    encode_frame,
+    encode_packet_payload,
+    ensure_no_payload_types,
+    parse_device_status,
+)
+from .transport import USBTransport
+
+logger = logging.getLogger(__name__)
+
+
+class _IndexedPayload(Protocol):
+    """Protocol for payload types carrying an integer `point_number` field."""
+
+    point_number: int
+
+
+TPacketPayload = TypeVar("TPacketPayload", bound=_IndexedPayload)
+
+
+class LibreVNASession:
+    """Owns USB transport, packet queues, and request/response synchronization."""
+
+    def __init__(self) -> None:
+        """Initialize transport, queues, synchronization primitives, and defaults."""
+        self._scanner = FrameScanner()
+        self._transport = USBTransport(
+            on_data=self._on_transport_data,
+            on_disconnect=self._on_transport_disconnect,
+        )
+
+        self._incoming: dict[PacketType, deque[Packet]] = defaultdict(deque)
+        self._subscribers: dict[PacketType, set[Callable[[Packet], None]]] = defaultdict(set)
+
+        self._incoming_cv = threading.Condition()
+        self._ack_cv = threading.Condition()
+        self._send_lock = threading.Lock()
+
+        self._awaiting_ack = False
+        self._ack_result: PacketType | None = None
+
+        self._fatal_error: Exception | None = None
+
+        self._device_info: DeviceInfo | None = None
+        self._device_status: DeviceStatus | None = None
+        self._hardware_family: HardwareFamily = HardwareFamily.UNKNOWN
+
+        self._default_sync_mode = SyncMode.DISABLED
+        self._default_sync_master = False
+
+    @property
+    def is_connected(self) -> bool:
+        """Return `True` when USB connection is active."""
+        return self._transport.is_connected
+
+    @property
+    def connected_serial(self) -> str | None:
+        """Serial number of currently connected device, when available."""
+        return self._transport.connected_serial
+
+    @property
+    def hardware_family(self) -> HardwareFamily:
+        """Hardware family inferred from `DeviceInfo` packet."""
+        return self._hardware_family
+
+    @property
+    def default_sync_mode(self) -> SyncMode:
+        """Default sync mode used by controllers when settings do not override."""
+        return self._default_sync_mode
+
+    @property
+    def default_sync_master(self) -> bool:
+        """Default sync master flag used by controllers."""
+        return self._default_sync_master
+
+    def set_default_sync(self, mode: SyncMode, master: bool) -> None:
+        """Set session-level default synchronization settings."""
+        self._default_sync_mode = mode
+        self._default_sync_master = master
+
+    @staticmethod
+    def list_devices() -> list[USBDeviceDescriptor]:
+        """Enumerate available direct-USB LibreVNA devices."""
+        return USBTransport.list_devices()
+
+    def connect(
+        self,
+        serial: str | None = None,
+        *,
+        strict_protocol_version: int = 14,
+        timeout_s: float = 1.0,
+    ) -> None:
+        """Connect transport and perform startup handshake."""
+        logger.info(
+            "Opening USB transport (serial=%s, strict_protocol=%d, timeout=%.2fs)",
+            serial,
+            strict_protocol_version,
+            timeout_s,
+        )
+        self._fatal_error = None
+        self._scanner.clear()
+        self._incoming.clear()
+
+        self._transport.connect(serial=serial, timeout_s=timeout_s)
+        try:
+            info_packet = self.request(
+                PacketType.REQUEST_DEVICE_INFO,
+                PacketType.DEVICE_INFO,
+                timeout_s=timeout_s,
+            )
+            if not isinstance(info_packet.payload, DeviceInfo):
+                raise ParseError("Decoded DeviceInfo payload has unexpected type")
+
+            self._device_info = info_packet.payload
+            self._hardware_family = self._device_info.hardware_family
+            logger.info(
+                "Handshake OK: fw=%s protocol=%d family=%s ports=%d",
+                self._device_info.firmware_version,
+                self._device_info.protocol_version,
+                self._device_info.hardware_family.name,
+                self._device_info.num_ports,
+            )
+
+            if self._device_info.protocol_version != strict_protocol_version:
+                raise ProtocolVersionMismatch(
+                    "Protocol version mismatch: "
+                    f"device={self._device_info.protocol_version}, "
+                    f"required={strict_protocol_version}"
+                )
+
+            self.get_device_status(timeout_s=timeout_s)
+            logger.debug("Initial device status request completed")
+        except Exception:
+            logger.exception("Connect handshake failed, closing session")
+            self.disconnect()
+            raise
+
+    def disconnect(self) -> None:
+        """Disconnect USB transport and clear session state."""
+        logger.debug("Closing USB transport and clearing session queues")
+        self._transport.disconnect()
+        with self._incoming_cv:
+            self._incoming.clear()
+            self._subscribers.clear()
+            self._incoming_cv.notify_all()
+        with self._ack_cv:
+            self._awaiting_ack = False
+            self._ack_result = None
+            self._ack_cv.notify_all()
+
+    def send(self, packet: Packet, *, require_ack: bool = True, timeout_s: float = 0.5) -> None:
+        """Send one protocol packet and optionally wait for ACK/NACK."""
+        payload = encode_packet_payload(packet.type, packet.payload)
+        ensure_no_payload_types(packet.type, payload)
+        frame = encode_frame(Packet(type=packet.type, payload=payload))
+        logger.debug(
+            "TX packet=%s payload=%dB require_ack=%s timeout=%.2fs",
+            packet.type.name,
+            len(payload),
+            require_ack,
+            timeout_s,
+        )
+
+        with self._send_lock:
+            if require_ack:
+                with self._ack_cv:
+                    self._awaiting_ack = True
+                    self._ack_result = None
+
+            self._transport.write(frame, timeout_s=timeout_s)
+
+            if require_ack:
+                deadline = time.monotonic() + timeout_s
+                with self._ack_cv:
+                    while self._ack_result is None:
+                        self._raise_if_fatal_locked()
+                        remaining = deadline - time.monotonic()
+                        if remaining <= 0:
+                            self._awaiting_ack = False
+                            raise TimeoutError(f"Timeout waiting for ACK for packet {packet.type.name}")
+                        self._ack_cv.wait(timeout=remaining)
+
+                    result = self._ack_result
+                    self._awaiting_ack = False
+                    self._ack_result = None
+
+                if result == PacketType.NACK:
+                    raise NackError(f"Received NACK for packet {packet.type.name}")
+                logger.debug("ACK received for packet %s", packet.type.name)
+
+    def request(
+        self,
+        packet_type: PacketType,
+        response_type: PacketType,
+        *,
+        timeout_s: float = 1.0,
+    ) -> Packet:
+        """Send no-payload request packet and wait for one response packet type."""
+        logger.debug(
+            "Request start: packet=%s expect=%s timeout=%.2fs",
+            packet_type.name,
+            response_type.name,
+            timeout_s,
+        )
+        self.clear_queue(response_type)
+        self.send(Packet(type=packet_type), require_ack=True, timeout_s=timeout_s)
+        packet = self.wait_for_packet(response_type, timeout_s=timeout_s)
+        logger.debug("Request complete: received %s", response_type.name)
+        return packet
+
+    def collect_indexed_payloads(
+        self,
+        *,
+        packet_type: PacketType,
+        expected_points: int,
+        timeout_s: float,
+        payload_type: type[TPacketPayload],
+        payload_error: str,
+    ) -> list[TPacketPayload]:
+        """Collect indexed payloads by `point_number` into ascending order.
+
+        Missing points are omitted; callers decide whether this is acceptable.
+        """
+        if expected_points <= 0:
+            raise ValueError("expected_points must be > 0")
+
+        deadline = time.monotonic() + timeout_s
+        collected: list[TPacketPayload | None] = [None] * expected_points
+        received = 0
+
+        while received < expected_points:
+            remaining = deadline - time.monotonic()
+            if remaining <= 0:
+                break
+
+            packet = self.wait_for_packet(packet_type, timeout_s=remaining)
+            payload = packet.payload
+            if not isinstance(payload, payload_type):
+                raise ParseError(payload_error)
+
+            point_number = payload.point_number
+            if point_number < 0 or point_number >= expected_points:
+                raise ParseError(
+                    f"Received out-of-range point index {point_number}, expected 0..{expected_points - 1}"
+                )
+
+            if collected[point_number] is None:
+                received += 1
+            collected[point_number] = payload
+
+        return [item for item in collected if item is not None]
+
+    def wait_for_packet(self, packet_type: PacketType, *, timeout_s: float = 1.0) -> Packet:
+        """Wait for the next packet of requested type."""
+        deadline = time.monotonic() + timeout_s
+        with self._incoming_cv:
+            while True:
+                self._raise_if_fatal_locked()
+                queue = self._incoming[packet_type]
+                if queue:
+                    logger.debug(
+                        "Dequeued packet %s (remaining=%d)",
+                        packet_type.name,
+                        len(queue) - 1,
+                    )
+                    return queue.popleft()
+
+                remaining = deadline - time.monotonic()
+                if remaining <= 0:
+                    raise TimeoutError(f"Timeout waiting for packet {packet_type.name}")
+                self._incoming_cv.wait(timeout=remaining)
+
+    def clear_queue(self, packet_type: PacketType) -> None:
+        """Drop queued packets of a given type."""
+        with self._incoming_cv:
+            dropped = len(self._incoming[packet_type])
+            self._incoming[packet_type].clear()
+            if dropped:
+                logger.debug("Cleared %d queued packet(s) of type %s", dropped, packet_type.name)
+
+    def get_device_info(self) -> DeviceInfo:
+        """Return cached `DeviceInfo` from successful `connect()` handshake."""
+        if self._device_info is None:
+            raise DeviceDisconnectedError("Device info is not available before connect()")
+        return self._device_info
+
+    def get_device_status(self, *, timeout_s: float = 1.0) -> DeviceStatus:
+        """Request and return current `DeviceStatus`."""
+        packet = self.request(
+            PacketType.REQUEST_DEVICE_STATUS,
+            PacketType.DEVICE_STATUS,
+            timeout_s=timeout_s,
+        )
+
+        if not isinstance(packet.payload, (bytes, bytearray, memoryview)):
+            raise ParseError("DeviceStatus packet payload has unexpected type")
+        status = parse_device_status(bytes(packet.payload), self._hardware_family)
+        self._device_status = status
+        logger.debug(
+            "Device status: source_locked=%s lo_locked=%s adc_overload=%s unlevel=%s",
+            status.source_locked,
+            status.lo_locked,
+            status.adc_overload,
+            status.unlevel,
+        )
+        return status
+
+    def subscribe(self, packet_type: PacketType, callback: Callable[[Packet], None]) -> StreamHandle:
+        """Register packet callback and return handle for unsubscription."""
+        stop_event = threading.Event()
+        with self._incoming_cv:
+            self._subscribers[packet_type].add(callback)
+            logger.debug(
+                "Subscriber added for %s (count=%d)",
+                packet_type.name,
+                len(self._subscribers[packet_type]),
+            )
+
+        def _close() -> None:
+            """Unsubscribe callback from session packet subscribers."""
+            with self._incoming_cv:
+                callbacks = self._subscribers.get(packet_type)
+                if callbacks is not None:
+                    callbacks.discard(callback)
+                    logger.debug(
+                        "Subscriber removed for %s (count=%d)",
+                        packet_type.name,
+                        len(callbacks),
+                    )
+
+        return StreamHandle(stop_event=stop_event, close_callback=_close)
+
+    def _on_transport_data(self, chunk: bytes) -> None:
+        """Decode incoming USB chunk and route packets to queues/subscribers."""
+        try:
+            packets = self._scanner.feed(chunk)
+            if logger.isEnabledFor(logging.DEBUG) and packets:
+                logger.debug("RX chunk=%dB decoded_packets=%d", len(chunk), len(packets))
+        except Exception as exc:
+            self._set_fatal_error(exc)
+            return
+
+        for packet in packets:
+            try:
+                decoded_payload = decode_packet_payload(packet)
+            except Exception as exc:
+                self._set_fatal_error(exc)
+                return
+
+            decoded_packet = Packet(type=packet.type, payload=decoded_payload)
+            self._dispatch_packet(decoded_packet)
+
+    def _on_transport_disconnect(self, exc: Exception) -> None:
+        """Receive asynchronous transport disconnect event."""
+        self._set_fatal_error(exc)
+
+    def _dispatch_packet(self, packet: Packet) -> None:
+        """Route packet into ack waiter, queue, and subscriber callbacks."""
+        if logger.isEnabledFor(logging.DEBUG) and packet.type not in {
+            PacketType.VNA_DATAPOINT,
+        }:
+            logger.debug("Dispatch packet %s", packet.type.name)
+
+        if packet.type in {PacketType.ACK, PacketType.NACK}:
+            with self._ack_cv:
+                if self._awaiting_ack and self._ack_result is None:
+                    self._ack_result = packet.type
+                    self._ack_cv.notify_all()
+                    return
+
+        callbacks: list[Callable[[Packet], None]] = []
+        with self._incoming_cv:
+            self._incoming[packet.type].append(packet)
+            callbacks = list(self._subscribers.get(packet.type, set()))
+            self._incoming_cv.notify_all()
+
+        for callback in callbacks:
+            try:
+                callback(packet)
+            except Exception as exc:
+                logger.exception("Subscriber callback failed for packet %s", packet.type.name)
+                self._set_fatal_error(exc)
+                return
+
+    def _set_fatal_error(self, exc: Exception) -> None:
+        """Mark session as failed and wake all waiting operations."""
+        with self._incoming_cv:
+            with self._ack_cv:
+                if self._fatal_error is None:
+                    self._fatal_error = exc
+                    logger.error("Session fatal error: %s", exc, exc_info=exc)
+                self._incoming_cv.notify_all()
+                self._ack_cv.notify_all()
+
+    def _raise_if_fatal_locked(self) -> None:
+        """Raise stored fatal error (called while condition lock is held)."""
+        if self._fatal_error is None:
+            return
+
+        exc = self._fatal_error
+        if isinstance(exc, DeviceDisconnectedError):
+            raise exc
+        raise DeviceDisconnectedError(f"Session stopped due to transport/protocol error: {exc}") from exc
diff --git a/python_app/hardware_full/librevna_driver/sweep/__init__.py b/python_app/hardware_full/librevna_driver/sweep/__init__.py
new file mode 100644
index 0000000..710a70b
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/sweep/__init__.py
@@ -0,0 +1,10 @@
+"""Sweep assembly and result helpers."""
+
+from .assembler import assemble_vna_sweep, datapoint_to_raw_point
+from ..models import SweepResult
+
+__all__ = [
+    "SweepResult",
+    "assemble_vna_sweep",
+    "datapoint_to_raw_point",
+]
diff --git a/python_app/hardware_full/librevna_driver/sweep/assembler.py b/python_app/hardware_full/librevna_driver/sweep/assembler.py
new file mode 100644
index 0000000..f83e9a7
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/sweep/assembler.py
@@ -0,0 +1,181 @@
+"""Helpers to assemble high-level sweep results from packet streams."""
+
+from __future__ import annotations
+
+import logging
+import math
+from typing import Iterable
+
+import numpy as np
+
+from ..exceptions import IncompleteSweepError, ParseError
+from ..enums import SweepKind
+from ..models import VNADatapointPacket, VNARawPoint, VNASweepSettings, SweepResult
+
+logger = logging.getLogger(__name__)
+
+
+_REFERENCE_FLAG = 0x10
+
+
+def _extract_stage(flags: int) -> int:
+    """Return stage index encoded inside datapoint flag byte."""
+    return flags >> 5
+
+
+def _find_vna_value(
+    datapoint: VNADatapointPacket,
+    *,
+    stage: int,
+    port_index: int,
+    reference: bool,
+) -> complex:
+    """Find one complex receiver value for stage/port/ref tuple."""
+    source_mask = 1 << port_index
+    if reference:
+        source_mask |= _REFERENCE_FLAG
+
+    for idx, flags in enumerate(datapoint.flags):
+        if _extract_stage(int(flags)) != stage:
+            continue
+        if (int(flags) & source_mask) != source_mask:
+            continue
+        return complex(float(datapoint.real[idx]), float(datapoint.imag[idx]))
+
+    kind = "reference" if reference else "receiver"
+    raise ParseError(
+        f"Missing {kind} value for stage={stage}, port_index={port_index}, point={datapoint.point_number}"
+    )
+
+
+def datapoint_to_raw_point(
+    datapoint: VNADatapointPacket,
+    settings: VNASweepSettings,
+    *,
+    num_ports: int,
+) -> VNARawPoint:
+    """Convert low-level VNADatapoint packet into callback-friendly object."""
+    measurements: dict[str, complex] = {}
+    stage_by_excited_port = {port: stage for stage, port in enumerate(settings.excited_ports)}
+
+    for excited_port, stage in stage_by_excited_port.items():
+        ref = _find_vna_value(datapoint, stage=stage, port_index=excited_port - 1, reference=True)
+        for receiver_port in range(1, num_ports + 1):
+            measured = _find_vna_value(
+                datapoint,
+                stage=stage,
+                port_index=receiver_port - 1,
+                reference=False,
+            )
+            measurements[f"S{receiver_port}{excited_port}"] = measured / ref
+
+    zero_span = settings.f_start_hz == settings.f_stop_hz and math.isclose(
+        settings.power_start_dbm,
+        settings.power_stop_dbm,
+        rel_tol=0.0,
+        abs_tol=0.0,
+    )
+
+    if zero_span:
+        return VNARawPoint(
+            point_number=datapoint.point_number,
+            frequency_hz=None,
+            time_s=float(datapoint.frequency_or_time) * 1e-6,
+            power_dbm=None,
+            measurements=measurements,
+        )
+
+    return VNARawPoint(
+        point_number=datapoint.point_number,
+        frequency_hz=float(datapoint.frequency_or_time),
+        time_s=None,
+        power_dbm=float(datapoint.cdbm) / 100.0,
+        measurements=measurements,
+    )
+
+
+def assemble_vna_sweep(
+    datapoints: Iterable[VNADatapointPacket],
+    settings: VNASweepSettings,
+    *,
+    num_ports: int,
+    expected_points: int,
+) -> SweepResult:
+    """Build a complete VNA sweep from raw datapoints."""
+    if expected_points <= 0:
+        raise ValueError("expected_points must be > 0")
+
+    x = np.empty(expected_points, dtype=np.float64)
+    seen = np.zeros(expected_points, dtype=bool)
+
+    stage_by_excited_port = {port: stage for stage, port in enumerate(settings.excited_ports)}
+    receiver_limit = min(num_ports, 2)
+    trace_names = [
+        f"s{receiver_port}{excited_port}"
+        for excited_port in settings.excited_ports
+        for receiver_port in range(1, receiver_limit + 1)
+        if receiver_port <= 2 and excited_port <= 2
+    ]
+    traces: dict[str, np.ndarray] = {
+        name: np.empty(expected_points, dtype=np.complex128) for name in trace_names
+    }
+
+    zero_span = settings.f_start_hz == settings.f_stop_hz and math.isclose(
+        settings.power_start_dbm,
+        settings.power_stop_dbm,
+        rel_tol=0.0,
+        abs_tol=0.0,
+    )
+    power_sweep = settings.kind == SweepKind.POWER
+
+    for datapoint in datapoints:
+        idx = datapoint.point_number
+        if idx >= expected_points:
+            raise ParseError(f"Received out-of-range point index {idx}, expected < {expected_points}")
+
+        if zero_span:
+            x[idx] = float(datapoint.frequency_or_time) * 1e-6
+        elif power_sweep:
+            x[idx] = float(datapoint.cdbm) / 100.0
+        else:
+            x[idx] = float(datapoint.frequency_or_time)
+
+        for excited_port, stage in stage_by_excited_port.items():
+            ref = _find_vna_value(datapoint, stage=stage, port_index=excited_port - 1, reference=True)
+            for receiver_port in range(1, num_ports + 1):
+                if receiver_port > 2 or excited_port > 2:
+                    # Public SweepResult intentionally exposes 2-port canonical traces.
+                    continue
+                measured = _find_vna_value(
+                    datapoint,
+                    stage=stage,
+                    port_index=receiver_port - 1,
+                    reference=False,
+                )
+                trace_name = f"s{receiver_port}{excited_port}"
+                trace = traces.get(trace_name)
+                if trace is not None:
+                    trace[idx] = measured / ref
+
+        seen[idx] = True
+
+    missing = np.flatnonzero(~seen)
+    if missing.size > 0:
+        raise IncompleteSweepError(
+            f"Sweep is incomplete: received {int(seen.sum())}/{expected_points} points"
+        )
+
+    x_label = "frequency_hz"
+    if zero_span:
+        x_label = "time_s"
+    elif power_sweep:
+        x_label = "power_dbm"
+
+    result = SweepResult(x=x, traces=traces, x_label=x_label)
+    logger.debug(
+        "Assembled VNA sweep: points=%d x_label=%s traces=%s",
+        len(result.x),
+        result.x_label,
+        sorted(result.traces.keys()),
+    )
+    return result
diff --git a/python_app/hardware_full/librevna_driver/transport/__init__.py b/python_app/hardware_full/librevna_driver/transport/__init__.py
new file mode 100644
index 0000000..56b75a1
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/transport/__init__.py
@@ -0,0 +1,5 @@
+"""Transport layer primitives."""
+
+from .usb import USBTransport
+
+__all__ = ["USBTransport"]
diff --git a/python_app/hardware_full/librevna_driver/transport/usb.py b/python_app/hardware_full/librevna_driver/transport/usb.py
new file mode 100644
index 0000000..4119de4
--- /dev/null
+++ b/python_app/hardware_full/librevna_driver/transport/usb.py
@@ -0,0 +1,244 @@
+"""Direct USB transport using libusb1 for LibreVNA devices."""
+
+from __future__ import annotations
+
+from contextlib import suppress
+import logging
+import threading
+from typing import Callable
+
+from ..exceptions import DeviceDisconnectedError, TimeoutError
+from ..models import USBDeviceDescriptor
+
+import usb1
+
+logger = logging.getLogger(__name__)
+
+
+class USBTransport:
+    """USB bulk transport for LibreVNA protocol endpoints."""
+
+    DATA_EP_OUT = 0x01
+    DATA_EP_IN = 0x81
+    INTERFACE = 0
+    VALID_USB_IDS = (
+        (0x0483, 0x564E),
+        (0x0483, 0x4121),
+        (0x1209, 0x4121),
+    )
+
+    def __init__(
+        self,
+        *,
+        on_data: Callable[[bytes], None],
+        on_disconnect: Callable[[Exception], None] | None = None,
+        read_chunk_size: int = 65536,
+    ) -> None:
+        """Create transport with RX callback and optional disconnect callback."""
+        self._on_data = on_data
+        self._on_disconnect = on_disconnect
+        self._read_chunk_size = read_chunk_size
+
+        self._ctx: usb1.USBContext | None = None  # type: ignore[valid-type]
+        self._handle: usb1.USBDeviceHandle | None = None  # type: ignore[valid-type]
+        self._rx_thread: threading.Thread | None = None
+        self._stop_event = threading.Event()
+        self._tx_lock = threading.Lock()
+
+        self.connected_serial: str | None = None
+
+    @property
+    def is_connected(self) -> bool:
+        """Return `True` when USB handle is open."""
+        return self._handle is not None
+
+    @staticmethod
+    def list_devices() -> list[USBDeviceDescriptor]:
+        """Enumerate attached LibreVNA USB devices."""
+        devices: list[USBDeviceDescriptor] = []
+        with usb1.USBContext() as ctx:
+            for device, vid, pid in USBTransport._iter_matching_devices(ctx):
+                handle: usb1.USBDeviceHandle | None = None
+                try:
+                    handle = device.open()
+                    serial = handle.getSerialNumber() or ""
+                except usb1.USBError as exc:
+                    logger.debug(
+                        "Skipping USB device during discovery vid=0x%04x pid=0x%04x: %s",
+                        vid,
+                        pid,
+                        exc,
+                    )
+                    continue
+                finally:
+                    if handle is not None:
+                        with suppress(usb1.USBError):
+                            handle.close()
+
+                devices.append(USBDeviceDescriptor(serial=serial, vendor_id=vid, product_id=pid))
+
+        devices.sort(key=lambda item: (item.serial, item.vendor_id, item.product_id))
+        logger.debug("USB discovery completed, devices=%d", len(devices))
+        return devices
+
+    def connect(self, *, serial: str | None = None, timeout_s: float = 1.0) -> None:
+        """Open USB device, claim interface, and start RX thread."""
+        if self._handle is not None:
+            logger.debug("USB connect skipped: already connected")
+            return
+
+        logger.debug("Opening libusb context for connect(serial=%s)", serial)
+        self._ctx = usb1.USBContext()
+        selected_handle: usb1.USBDeviceHandle | None = None
+        selected_serial = ""
+
+        for device, _, _ in self._iter_matching_devices(self._ctx):
+            handle: usb1.USBDeviceHandle | None = None
+            try:
+                handle = device.open()
+                found_serial = handle.getSerialNumber() or ""
+                if serial is not None and found_serial != serial:
+                    handle.close()
+                    continue
+
+                selected_handle = handle
+                selected_serial = found_serial
+                break
+            except usb1.USBError as exc:
+                if handle is not None:
+                    with suppress(usb1.USBError):
+                        handle.close()
+                logger.debug("Skipping USB candidate during connect due to error: %s", exc)
+                continue
+
+        if selected_handle is None:
+            if self._ctx is not None:
+                self._ctx.close()
+                self._ctx = None
+            serial_msg = f" with serial '{serial}'" if serial else ""
+            raise DeviceDisconnectedError(f"No compatible LibreVNA USB device found{serial_msg}")
+
+        try:
+            selected_handle.setAutoDetachKernelDriver(True)
+            if selected_handle.kernelDriverActive(self.INTERFACE):
+                selected_handle.detachKernelDriver(self.INTERFACE)
+        except usb1.USBError as exc:
+            selected_handle.close()
+            if self._ctx is not None:
+                self._ctx.close()
+                self._ctx = None
+            raise DeviceDisconnectedError(f"Failed to prepare USB kernel driver state: {exc}") from exc
+
+        try:
+            selected_handle.claimInterface(self.INTERFACE)
+        except usb1.USBError as exc:
+            selected_handle.close()
+            if self._ctx is not None:
+                self._ctx.close()
+                self._ctx = None
+            raise DeviceDisconnectedError(f"Failed to claim USB interface {self.INTERFACE}: {exc}") from exc
+
+        self._handle = selected_handle
+        self.connected_serial = selected_serial
+        self._stop_event.clear()
+        logger.info(
+            "USB connected (serial=%s, timeout=%.2fs, endpoint_out=0x%02x, endpoint_in=0x%02x)",
+            self.connected_serial,
+            timeout_s,
+            self.DATA_EP_OUT,
+            self.DATA_EP_IN,
+        )
+
+        self._rx_thread = threading.Thread(target=self._rx_loop, name="librevna-usb-rx", daemon=True)
+        self._rx_thread.start()
+
+    def disconnect(self) -> None:
+        """Stop RX thread and close USB resources."""
+        logger.debug("USB disconnect requested")
+        self._stop_event.set()
+
+        if self._rx_thread is not None and self._rx_thread.is_alive():
+            self._rx_thread.join(timeout=1.0)
+        self._rx_thread = None
+
+        if self._handle is not None:
+            self._handle.releaseInterface(self.INTERFACE)
+            self._handle.close()
+            self._handle = None
+
+        if self._ctx is not None:
+            self._ctx.close()
+            self._ctx = None
+
+        logger.info("USB disconnected (serial=%s)", self.connected_serial)
+        self.connected_serial = None
+
+    def write(self, data: bytes, *, timeout_s: float = 0.5) -> None:
+        """Write one framed packet to device bulk-out endpoint."""
+        handle = self._handle
+        if handle is None:
+            raise DeviceDisconnectedError("USB device is not connected")
+
+        timeout_ms = max(1, int(timeout_s * 1000.0))
+        with self._tx_lock:
+            try:
+                written = handle.bulkWrite(self.DATA_EP_OUT, data, timeout=timeout_ms)
+            except usb1.USBErrorTimeout as exc:
+                raise TimeoutError("Timed out writing USB bulk packet") from exc
+            except usb1.USBErrorNoDevice as exc:
+                raise DeviceDisconnectedError("USB device disconnected during write") from exc
+            except usb1.USBError as exc:
+                raise DeviceDisconnectedError(f"USB write failed: {exc}") from exc
+
+        if written != len(data):
+            raise DeviceDisconnectedError(
+                f"USB bulk write incomplete: wrote {written}/{len(data)} bytes"
+            )
+        if logger.isEnabledFor(logging.DEBUG):
+            logger.debug("USB TX %d bytes", len(data))
+
+    def _rx_loop(self) -> None:
+        """Continuously read bulk-in data and forward to frame scanner callback."""
+        handle = self._handle
+        if handle is None:
+            return
+
+        logger.debug("USB RX thread started")
+        timeout_ms = 100
+        while not self._stop_event.is_set():
+            try:
+                data = handle.bulkRead(self.DATA_EP_IN, self._read_chunk_size, timeout=timeout_ms)
+            except usb1.USBErrorTimeout:
+                continue
+            except usb1.USBErrorInterrupted:
+                continue
+            except usb1.USBErrorNoDevice as exc:
+                if self._on_disconnect is not None:
+                    self._on_disconnect(DeviceDisconnectedError("USB device disconnected"))
+                logger.warning("USB RX stopped: device disconnected")
+                return
+            except usb1.USBError as exc:
+                if self._stop_event.is_set():
+                    return
+                if self._on_disconnect is not None:
+                    self._on_disconnect(DeviceDisconnectedError(f"USB read failed: {exc}"))
+                logger.error("USB RX failed: %s", exc)
+                return
+
+            if data:
+                if logger.isEnabledFor(logging.DEBUG):
+                    logger.debug("USB RX %d bytes", len(data))
+                self._on_data(bytes(data))
+        logger.debug("USB RX thread stopped")
+
+    @classmethod
+    def _iter_matching_devices(
+        cls,
+        ctx: "usb1.USBContext",  # type: ignore[name-defined]
+    ):
+        """Yield USB devices matching supported LibreVNA VID/PID pairs."""
+        for device in ctx.getDeviceList(skip_on_error=True):
+            vid = int(device.getVendorID())
+            pid = int(device.getProductID())
+            if (vid, pid) in cls.VALID_USB_IDS:
+                yield device, vid, pid
diff --git a/python_app/hardware_full/librevna_service.py b/python_app/hardware_full/librevna_service.py
new file mode 100644
index 0000000..f0c584d
--- /dev/null
+++ b/python_app/hardware_full/librevna_service.py
@@ -0,0 +1,98 @@
+"""High-level orchestration service for configuring and querying LibreVNA."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+import numpy as np
+
+from python_app.hardware_full.librevna_backends import LibreVnaBackend, MockLibreVnaBackend, NativeLibreVnaBackend
+from python_app.models.run_config_model import RadarSweepModel
+
+
+@dataclass(slots=True)
+class LibreVnaService:
+    """Provide stable API for GUI/workflows while hiding backend details."""
+
+    serial: str | None = None
+    strict_protocol_version: int = 14
+    backend_mode: str = "auto"
+    _driver_available: bool = field(init=False, default=False, repr=False)
+    _backend: LibreVnaBackend | None = field(init=False, default=None, repr=False)
+    _using_mock_backend: bool = field(init=False, default=False, repr=False)
+
+    def __post_init__(self) -> None:
+        """Initialize selected backend and detect driver availability."""
+        self._driver_available = False
+        self._backend = None
+        self._using_mock_backend = False
+
+        mode = self.backend_mode.strip().lower()
+        if mode not in {"auto", "native", "mock"}:
+            raise ValueError(f"Unsupported LibreVnaService backend mode: {self.backend_mode}")
+
+        if mode == "mock":
+            self._backend = MockLibreVnaBackend()
+            self._using_mock_backend = True
+            return
+
+        try:
+            self._backend = NativeLibreVnaBackend(
+                serial=self.serial,
+                strict_protocol_version=self.strict_protocol_version,
+            )
+            self._driver_available = True
+        except Exception:
+            if mode == "native":
+                raise
+            self._backend = MockLibreVnaBackend()
+            self._using_mock_backend = True
+
+    @property
+    def driver_available(self) -> bool:
+        """Return `True` when native Python LibreVNA driver is available."""
+        return self._driver_available
+
+    def open(self) -> None:
+        """Open backend resources."""
+        if not self._driver_available and not self._using_mock_backend:
+            return
+        if self._backend is None:
+            return
+        self._backend.open()
+
+    def close(self) -> None:
+        """Close backend resources."""
+        if self._backend is None:
+            return
+        self._backend.close()
+
+    def configure(self, sweep: RadarSweepModel) -> None:
+        """Apply sweep settings to active backend."""
+        if self._backend is None:
+            raise RuntimeError("LibreVNA backend is not initialized")
+        self._backend.configure(sweep)
+
+    def read_device_limits(self) -> dict[str, float | int]:
+        """Read native device limits from connected LibreVNA."""
+        if not self._driver_available:
+            raise RuntimeError("LibreVNA Python driver is not available")
+        if self._backend is None:
+            raise RuntimeError("LibreVNA backend is not initialized")
+
+        opened_here = not self._backend.is_open
+        try:
+            if opened_here:
+                self.open()
+            return self._backend.read_device_limits()
+        finally:
+            if opened_here:
+                self.close()
+
+    def acquire_s21(self) -> tuple[np.ndarray, np.ndarray]:
+        """Acquire one S21 trace from currently selected backend."""
+        if self._backend is None:
+            raise RuntimeError("LibreVNA backend is not initialized")
+        if self._using_mock_backend and not self._driver_available and self.backend_mode != "mock":
+            raise RuntimeError("Device not found")
+        return self._backend.acquire_s21()
diff --git a/python_app/hardware_full/switch_drivers/__init__.py b/python_app/hardware_full/switch_drivers/__init__.py
new file mode 100644
index 0000000..a5804be
--- /dev/null
+++ b/python_app/hardware_full/switch_drivers/__init__.py
@@ -0,0 +1,13 @@
+"""Switch driver implementations for native GPIO and mock operation."""
+
+from python_app.hardware_full.switch_drivers.h7992_driver import H7992Driver
+from python_app.hardware_full.switch_drivers.hmc349a_driver import HMC349ADriver
+from python_app.hardware_full.switch_drivers.interface import SwitchDriverProtocol
+from python_app.hardware_full.switch_drivers.mock_driver import MockSwitchDriver
+
+__all__ = [
+    "H7992Driver",
+    "HMC349ADriver",
+    "MockSwitchDriver",
+    "SwitchDriverProtocol",
+]
diff --git a/python_app/hardware_full/switch_drivers/gpio_uapi.py b/python_app/hardware_full/switch_drivers/gpio_uapi.py
new file mode 100644
index 0000000..12de1fd
--- /dev/null
+++ b/python_app/hardware_full/switch_drivers/gpio_uapi.py
@@ -0,0 +1,200 @@
+"""Minimal Linux GPIO v2 UAPI wrapper for output-only line control."""
+
+from __future__ import annotations
+
+import ctypes
+import errno
+import fcntl
+import os
+from typing import Sequence
+
+
+GPIO_MAX_NAME_SIZE = 32
+GPIO_V2_LINES_MAX = 64
+GPIO_V2_LINE_NUM_ATTRS_MAX = 10
+GPIO_V2_LINE_FLAG_OUTPUT = 1 << 3
+
+_IOC_NRBITS = 8
+_IOC_TYPEBITS = 8
+_IOC_SIZEBITS = 14
+_IOC_DIRBITS = 2
+
+_IOC_NRSHIFT = 0
+_IOC_TYPESHIFT = _IOC_NRSHIFT + _IOC_NRBITS
+_IOC_SIZESHIFT = _IOC_TYPESHIFT + _IOC_TYPEBITS
+_IOC_DIRSHIFT = _IOC_SIZESHIFT + _IOC_SIZEBITS
+
+_IOC_WRITE = 1
+_IOC_READ = 2
+
+
+def _ioc(direction: int, ioc_type: int, number: int, size: int) -> int:
+    """Build raw ioctl command number."""
+    return (
+        (direction << _IOC_DIRSHIFT)
+        | (ioc_type << _IOC_TYPESHIFT)
+        | (number << _IOC_NRSHIFT)
+        | (size << _IOC_SIZESHIFT)
+    )
+
+
+def _iowr(ioc_type: int, number: int, struct_type: type[ctypes.Structure]) -> int:
+    """Build read-write ioctl number for provided structure."""
+    return _ioc(_IOC_READ | _IOC_WRITE, ioc_type, number, ctypes.sizeof(struct_type))
+
+
+class GpioV2LineAttribute(ctypes.Structure):
+    """ctypes mapping of `gpio_v2_line_attribute`."""
+
+    _fields_ = [
+        ("id", ctypes.c_uint32),
+        ("padding", ctypes.c_uint32),
+        ("value", ctypes.c_uint64),
+    ]
+
+
+class GpioV2LineConfigAttribute(ctypes.Structure):
+    """ctypes mapping of `gpio_v2_line_config_attribute`."""
+
+    _fields_ = [
+        ("attr", GpioV2LineAttribute),
+        ("mask", ctypes.c_uint64),
+    ]
+
+
+class GpioV2LineConfig(ctypes.Structure):
+    """ctypes mapping of `gpio_v2_line_config`."""
+
+    _fields_ = [
+        ("flags", ctypes.c_uint64),
+        ("num_attrs", ctypes.c_uint32),
+        ("padding", ctypes.c_uint32 * 5),
+        ("attrs", GpioV2LineConfigAttribute * GPIO_V2_LINE_NUM_ATTRS_MAX),
+    ]
+
+
+class GpioV2LineRequest(ctypes.Structure):
+    """ctypes mapping of `gpio_v2_line_request`."""
+
+    _fields_ = [
+        ("offsets", ctypes.c_uint32 * GPIO_V2_LINES_MAX),
+        ("consumer", ctypes.c_char * GPIO_MAX_NAME_SIZE),
+        ("config", GpioV2LineConfig),
+        ("num_lines", ctypes.c_uint32),
+        ("event_buffer_size", ctypes.c_uint32),
+        ("padding", ctypes.c_uint32 * 5),
+        ("fd", ctypes.c_int32),
+    ]
+
+
+class GpioV2LineValues(ctypes.Structure):
+    """ctypes mapping of `gpio_v2_line_values`."""
+
+    _fields_ = [
+        ("bits", ctypes.c_uint64),
+        ("mask", ctypes.c_uint64),
+    ]
+
+
+GPIO_V2_GET_LINE_IOCTL = _iowr(0xB4, 0x07, GpioV2LineRequest)
+GPIO_V2_LINE_SET_VALUES_IOCTL = _iowr(0xB4, 0x0F, GpioV2LineValues)
+
+
+class GpioOutputLines:
+    """Open and control one or more GPIO output lines as a single request."""
+
+    def __init__(self, chip: str, offsets: Sequence[int], consumer: str) -> None:
+        """Build GPIO line request descriptor."""
+        if not chip:
+            raise ValueError("gpio chip path must not be empty")
+        if not offsets:
+            raise ValueError("at least one GPIO line offset is required")
+        if len(offsets) > GPIO_V2_LINES_MAX:
+            raise ValueError(f"too many GPIO offsets requested: {len(offsets)}")
+
+        normalized_offsets = [int(offset) for offset in offsets]
+        if any(offset < 0 for offset in normalized_offsets):
+            raise ValueError("GPIO offsets must be non-negative")
+        if len(set(normalized_offsets)) != len(normalized_offsets):
+            raise ValueError("GPIO offsets must be unique")
+
+        self._chip = chip
+        self._offsets = normalized_offsets
+        self._consumer = (consumer or "radar_switch").encode("ascii", errors="ignore")[: GPIO_MAX_NAME_SIZE - 1]
+
+        self._chip_fd = -1
+        self._line_fd = -1
+
+    def open(self) -> None:
+        """Open GPIO chip and request configured output lines."""
+        if self._line_fd >= 0:
+            return
+
+        try:
+            self._chip_fd = os.open(self._chip, os.O_RDONLY | os.O_CLOEXEC)
+        except OSError as exc:
+            raise RuntimeError(f"Failed to open GPIO chip '{self._chip}': {exc}") from exc
+
+        request = GpioV2LineRequest()
+        for index, offset in enumerate(self._offsets):
+            request.offsets[index] = ctypes.c_uint32(offset).value
+
+        request.num_lines = ctypes.c_uint32(len(self._offsets)).value
+        request.config.flags = ctypes.c_uint64(GPIO_V2_LINE_FLAG_OUTPUT).value
+        request.consumer = self._consumer
+
+        try:
+            fcntl.ioctl(self._chip_fd, GPIO_V2_GET_LINE_IOCTL, request)
+        except OSError as exc:
+            self._close_chip_fd()
+            raise RuntimeError(f"Failed to request GPIO lines on '{self._chip}': {exc}") from exc
+
+        if request.fd < 0:
+            self._close_chip_fd()
+            raise RuntimeError(f"GPIO line request returned invalid fd for '{self._chip}'")
+
+        self._line_fd = int(request.fd)
+
+    def close(self) -> None:
+        """Close line request and chip file descriptors."""
+        self._close_line_fd()
+        self._close_chip_fd()
+
+    def set_values(self, values: Sequence[int]) -> None:
+        """Apply output values for all requested lines."""
+        if self._line_fd < 0:
+            raise RuntimeError("GPIO line request is not open")
+        if len(values) != len(self._offsets):
+            raise ValueError(
+                f"GPIO values length mismatch: expected {len(self._offsets)}, got {len(values)}"
+            )
+
+        bits = 0
+        for index, value in enumerate(values):
+            normalized = int(value)
+            if normalized not in (0, 1):
+                raise ValueError(f"GPIO output value must be 0 or 1, got {value}")
+            if normalized == 1:
+                bits |= (1 << index)
+
+        mask = (1 << len(self._offsets)) - 1
+        line_values = GpioV2LineValues(bits=ctypes.c_uint64(bits).value, mask=ctypes.c_uint64(mask).value)
+
+        try:
+            fcntl.ioctl(self._line_fd, GPIO_V2_LINE_SET_VALUES_IOCTL, line_values)
+        except OSError as exc:
+            if exc.errno == errno.ENODEV:
+                raise RuntimeError("GPIO device disconnected") from exc
+            raise RuntimeError(f"Failed to set GPIO output values: {exc}") from exc
+
+    def _close_line_fd(self) -> None:
+        """Close line file descriptor if currently open."""
+        if self._line_fd >= 0:
+            os.close(self._line_fd)
+            self._line_fd = -1
+
+    def _close_chip_fd(self) -> None:
+        """Close chip file descriptor if currently open."""
+        if self._chip_fd >= 0:
+            os.close(self._chip_fd)
+            self._chip_fd = -1
diff --git a/python_app/hardware_full/switch_drivers/h7992_driver.py b/python_app/hardware_full/switch_drivers/h7992_driver.py
new file mode 100644
index 0000000..1902270
--- /dev/null
+++ b/python_app/hardware_full/switch_drivers/h7992_driver.py
@@ -0,0 +1,86 @@
+"""Native GPIO driver for H7992 switch."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from python_app.hardware_full.switch_drivers.gpio_uapi import GpioOutputLines
+
+
+_POSITION_TO_AB = (
+    (0, 0),
+    (0, 1),
+    (1, 0),
+    (1, 1),
+)
+
+
+@dataclass(slots=True)
+class H7992Driver:
+    """Drive H7992 using two GPIO lines (A/B)."""
+
+    name: str
+    positions: int = 4
+    default_position: int = 0
+    gpio_chip: str = "/dev/gpiochip0"
+    pin_a: int = 17
+    pin_b: int = 27
+    _lines: GpioOutputLines | None = field(init=False, default=None, repr=False)
+    _is_open: bool = field(init=False, default=False, repr=False)
+    _current_position: int = field(init=False, default=0, repr=False)
+
+    def __post_init__(self) -> None:
+        """Initialize runtime state."""
+        self._lines: GpioOutputLines | None = None
+        self._is_open = False
+        self._current_position = 0
+
+    def open(self) -> None:
+        """Open GPIO lines and switch to default position."""
+        if self._is_open:
+            return
+
+        self._validate()
+        self._lines = GpioOutputLines(
+            chip=self.gpio_chip,
+            offsets=(self.pin_a, self.pin_b),
+            consumer=f"radar_{self.name}",
+        )
+        self._lines.open()
+        self._is_open = True
+        self.switch_to(self.default_position)
+
+    def close(self) -> None:
+        """Close GPIO lines."""
+        if self._lines is not None:
+            self._lines.close()
+            self._lines = None
+        self._is_open = False
+
+    def position_count(self) -> int:
+        """Return number of supported positions."""
+        return self.positions
+
+    def switch_to(self, position: int) -> None:
+        """Switch hardware to selected position."""
+        if not self._is_open or self._lines is None:
+            raise RuntimeError(f"Switch driver is not open for {self.name}")
+        if position < 0 or position >= self.positions:
+            raise ValueError(f"Position out of range for {self.name}: {position}")
+
+        self._lines.set_values(_POSITION_TO_AB[position])
+        self._current_position = int(position)
+
+    @property
+    def current_position(self) -> int:
+        """Return current position."""
+        return self._current_position
+
+    def _validate(self) -> None:
+        """Validate H7992 driver parameters."""
+        if self.positions <= 0 or self.positions > 4:
+            raise ValueError(f"H7992 positions must be in range [1,4] for {self.name}")
+        if self.default_position < 0 or self.default_position >= self.positions:
+            raise ValueError(f"default_position out of range for {self.name}")
+        if self.pin_a < 0 or self.pin_b < 0 or self.pin_a == self.pin_b:
+            raise ValueError(f"pin_a/pin_b are invalid for {self.name}")
diff --git a/python_app/hardware_full/switch_drivers/hmc349a_driver.py b/python_app/hardware_full/switch_drivers/hmc349a_driver.py
new file mode 100644
index 0000000..726afa9
--- /dev/null
+++ b/python_app/hardware_full/switch_drivers/hmc349a_driver.py
@@ -0,0 +1,84 @@
+"""Native GPIO driver for HMC349A switch."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from python_app.hardware_full.switch_drivers.gpio_uapi import GpioOutputLines
+
+
+@dataclass(slots=True)
+class HMC349ADriver:
+    """Drive HMC349A using one control GPIO line."""
+
+    name: str
+    positions: int = 2
+    default_position: int = 0
+    gpio_chip: str = "/dev/gpiochip0"
+    pin_a: int = 17
+    invert_logic: bool = False
+    _lines: GpioOutputLines | None = field(init=False, default=None, repr=False)
+    _is_open: bool = field(init=False, default=False, repr=False)
+    _current_position: int = field(init=False, default=0, repr=False)
+
+    def __post_init__(self) -> None:
+        """Initialize runtime state."""
+        self._lines: GpioOutputLines | None = None
+        self._is_open = False
+        self._current_position = 0
+
+    def open(self) -> None:
+        """Open GPIO lines and switch to default position."""
+        if self._is_open:
+            return
+
+        self._validate()
+
+        offsets = [self.pin_a]
+        self._lines = GpioOutputLines(
+            chip=self.gpio_chip,
+            offsets=offsets,
+            consumer=f"radar_{self.name}",
+        )
+        self._lines.open()
+        self._is_open = True
+        self.switch_to(self.default_position)
+
+    def close(self) -> None:
+        """Close GPIO lines."""
+        if self._lines is not None:
+            self._lines.close()
+            self._lines = None
+        self._is_open = False
+
+    def position_count(self) -> int:
+        """Return number of supported positions."""
+        return self.positions
+
+    def switch_to(self, position: int) -> None:
+        """Switch hardware to selected position."""
+        if not self._is_open or self._lines is None:
+            raise RuntimeError(f"Switch driver is not open for {self.name}")
+        if position < 0 or position >= self.positions:
+            raise ValueError(f"Position out of range for {self.name}: {position}")
+
+        control = int(position & 0x01)
+        if self.invert_logic:
+            control ^= 0x01
+
+        self._lines.set_values([control])
+        self._current_position = int(position)
+
+    @property
+    def current_position(self) -> int:
+        """Return current position."""
+        return self._current_position
+
+    def _validate(self) -> None:
+        """Validate HMC349A driver parameters."""
+        if self.positions <= 0 or self.positions > 2:
+            raise ValueError(f"HMC349A positions must be in range [1,2] for {self.name}")
+        if self.default_position < 0 or self.default_position >= self.positions:
+            raise ValueError(f"default_position out of range for {self.name}")
+        if self.pin_a < 0:
+            raise ValueError(f"pin_a is invalid for {self.name}")
diff --git a/python_app/hardware_full/switch_drivers/interface.py b/python_app/hardware_full/switch_drivers/interface.py
new file mode 100644
index 0000000..50c14f5
--- /dev/null
+++ b/python_app/hardware_full/switch_drivers/interface.py
@@ -0,0 +1,25 @@
+"""Protocols for switch backend implementations."""
+
+from __future__ import annotations
+
+from typing import Protocol
+
+
+class SwitchDriverProtocol(Protocol):
+    """Required contract for all switch backends."""
+
+    def open(self) -> None:
+        """Open hardware or mock resources."""
+
+    def close(self) -> None:
+        """Close hardware or mock resources."""
+
+    def position_count(self) -> int:
+        """Return total number of supported positions."""
+
+    def switch_to(self, position: int) -> None:
+        """Switch to requested zero-based position."""
+
+    @property
+    def current_position(self) -> int:
+        """Return currently active position."""
diff --git a/python_app/hardware_full/switch_drivers/mock_driver.py b/python_app/hardware_full/switch_drivers/mock_driver.py
new file mode 100644
index 0000000..92d0df7
--- /dev/null
+++ b/python_app/hardware_full/switch_drivers/mock_driver.py
@@ -0,0 +1,55 @@
+"""Mock switch driver used in development and simulation modes."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+
+@dataclass(slots=True)
+class MockSwitchDriver:
+    """In-memory switch driver that validates and tracks current position."""
+
+    name: str
+    positions: int
+    default_position: int = 0
+    _is_open: bool = field(init=False, default=False, repr=False)
+    _current_position: int = field(init=False, default=0, repr=False)
+
+    def __post_init__(self) -> None:
+        """Initialize closed driver state."""
+        self._is_open = False
+        self._current_position = 0
+
+    def open(self) -> None:
+        """Validate config and open driver."""
+        self._validate()
+        self._is_open = True
+        self.switch_to(self.default_position)
+
+    def close(self) -> None:
+        """Close mock driver."""
+        self._is_open = False
+
+    def position_count(self) -> int:
+        """Return number of supported positions."""
+        return self.positions
+
+    def switch_to(self, position: int) -> None:
+        """Switch to requested position."""
+        if not self._is_open:
+            raise RuntimeError(f"Switch driver is not open for {self.name}")
+        if position < 0 or position >= self.positions:
+            raise ValueError(f"Position out of range for {self.name}: {position}")
+        self._current_position = int(position)
+
+    @property
+    def current_position(self) -> int:
+        """Return current position."""
+        return self._current_position
+
+    def _validate(self) -> None:
+        """Validate mock driver parameters."""
+        if self.positions <= 0:
+            raise ValueError(f"Switch positions must be > 0 for {self.name}")
+        if self.default_position < 0 or self.default_position >= self.positions:
+            raise ValueError(f"Switch default_position out of range for {self.name}")
diff --git a/python_app/hardware_full/switch_service.py b/python_app/hardware_full/switch_service.py
new file mode 100644
index 0000000..39c79a2
--- /dev/null
+++ b/python_app/hardware_full/switch_service.py
@@ -0,0 +1,85 @@
+"""High-level switch service selecting native/mock backend driver."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from python_app.hardware_full.switch_drivers import (
+    H7992Driver,
+    HMC349ADriver,
+    MockSwitchDriver,
+    SwitchDriverProtocol,
+)
+
+
+@dataclass(slots=True)
+class SwitchService:
+    """Facade around concrete switch drivers."""
+
+    name: str
+    positions: int
+    mode: str = "mock"
+    driver: str = "h7992"
+    gpio_chip: str = "/dev/gpiochip0"
+    pin_a: int = 17
+    pin_b: int = 27
+    invert_logic: bool = False
+    default_position: int = 0
+    _driver: SwitchDriverProtocol = field(init=False, repr=False)
+
+    def __post_init__(self) -> None:
+        """Create underlying driver based on configured mode and type."""
+        self._driver = self._build_driver()
+
+    def open(self) -> None:
+        """Open underlying switch driver resources."""
+        self._driver.open()
+
+    def close(self) -> None:
+        """Close underlying switch driver resources."""
+        self._driver.close()
+
+    def switch_to(self, position: int) -> None:
+        """Switch to requested position."""
+        self._driver.switch_to(position)
+
+    @property
+    def current_position(self) -> int:
+        """Return current switch position reported by backend driver."""
+        return self._driver.current_position
+
+    def _build_driver(self) -> SwitchDriverProtocol:
+        """Instantiate concrete driver according to configured mode and driver kind."""
+        mode = self.mode.strip().lower()
+        driver_kind = self.driver.strip().lower()
+
+        if mode == "mock":
+            return MockSwitchDriver(
+                name=self.name,
+                positions=self.positions,
+                default_position=self.default_position,
+            )
+
+        if mode != "native":
+            raise RuntimeError(f"Unsupported switch mode: {self.mode}")
+
+        if driver_kind == "h7992":
+            return H7992Driver(
+                name=self.name,
+                positions=self.positions,
+                default_position=self.default_position,
+                gpio_chip=self.gpio_chip,
+                pin_a=self.pin_a,
+                pin_b=self.pin_b,
+            )
+        if driver_kind == "hmc349a":
+            return HMC349ADriver(
+                name=self.name,
+                positions=self.positions,
+                default_position=self.default_position,
+                gpio_chip=self.gpio_chip,
+                pin_a=self.pin_a,
+                invert_logic=self.invert_logic,
+            )
+
+        raise RuntimeError(f"Unsupported switch driver: {self.driver}")
diff --git a/python_app/models/__init__.py b/python_app/models/__init__.py
new file mode 100644
index 0000000..15897ce
--- /dev/null
+++ b/python_app/models/__init__.py
@@ -0,0 +1 @@
+"""Domain models for configuration and dataset payloads."""
diff --git a/python_app/models/dataset_model.py b/python_app/models/dataset_model.py
new file mode 100644
index 0000000..0d1ebc7
--- /dev/null
+++ b/python_app/models/dataset_model.py
@@ -0,0 +1,61 @@
+"""Data models for sweep and processing payload collections."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+import numpy as np
+
+
+@dataclass(frozen=True, slots=True)
+class ComboKey:
+    """Switch combination key: input position + output position."""
+
+    input_pos: int
+    output_pos: int
+
+
+@dataclass(slots=True)
+class TraceData:
+    """One frequency-domain S21 trace for a specific switch combination."""
+
+    combo: ComboKey
+    frequency_hz: np.ndarray
+    s21: np.ndarray
+
+
+@dataclass(slots=True)
+class SweepCollection:
+    """Raw or preprocessed collection containing multiple combo traces."""
+
+    collection_id: int
+    monotonic_ns: int
+    traces: list[TraceData] = field(default_factory=list)
+
+
+@dataclass(slots=True)
+class ResultPayload:
+    """Processed payload item (trace-like or scalar) produced by processor stage."""
+
+    processing_name: str
+    kind: int
+    frequency_hz: np.ndarray
+    trace: np.ndarray
+    scalar_value: float = 0.0
+
+
+@dataclass(slots=True)
+class ResultBlock:
+    """Result payload block grouped by one switch combination."""
+
+    combo: ComboKey
+    payloads: list[ResultPayload] = field(default_factory=list)
+
+
+@dataclass(slots=True)
+class ResultCollection:
+    """Collection of processed payload blocks for one acquisition cycle."""
+
+    collection_id: int
+    monotonic_ns: int
+    blocks: list[ResultBlock] = field(default_factory=list)
diff --git a/python_app/models/run_config_codec.py b/python_app/models/run_config_codec.py
new file mode 100644
index 0000000..6d33e26
--- /dev/null
+++ b/python_app/models/run_config_codec.py
@@ -0,0 +1,177 @@
+"""Encoding and decoding logic for :mod:`python_app.models.run_config_schema`."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+from python_app.models.run_config_schema import ComboModel, RunConfigModel
+from python_app.models.run_config_validation import as_dict, load_ring_payload, load_switch_payload
+
+
+def run_config_from_dict(payload: dict[str, Any]) -> RunConfigModel:
+    """Decode JSON-like payload into :class:`RunConfigModel`."""
+    # Schema carries only minimal-safe fallbacks; operational defaults live in run_config.json.
+    model = RunConfigModel()
+
+    radar_payload = as_dict(payload.get("radar"), "radar")
+    sweep_payload = as_dict(radar_payload.get("sweep"), "radar.sweep")
+    switches_payload = as_dict(payload.get("switches"), "switches")
+    port1_payload = as_dict(switches_payload.get("port1"), "switches.port1")
+    port2_payload = as_dict(switches_payload.get("port2"), "switches.port2")
+    run_payload = as_dict(payload.get("run"), "run")
+    preprocess_payload = as_dict(payload.get("preprocess"), "preprocess")
+    rings_payload = as_dict(payload.get("rings"), "rings")
+    raw_ring_payload = as_dict(rings_payload.get("raw"), "rings.raw")
+    raw_tap_ring_payload = as_dict(rings_payload.get("raw_tap"), "rings.raw_tap")
+    pre_ring_payload = as_dict(rings_payload.get("preprocessed"), "rings.preprocessed")
+    pre_tap_ring_payload = as_dict(rings_payload.get("preprocessed_tap"), "rings.preprocessed_tap")
+    result_ring_payload = as_dict(rings_payload.get("results"), "rings.results")
+
+    model.radar.model = str(radar_payload.get("model", model.radar.model))
+    model.radar.serial = str(radar_payload.get("serial", model.radar.serial))
+    model.radar.driver_mode = str(radar_payload.get("driver_mode", model.radar.driver_mode))
+    model.radar.mock_signal_hz = float(radar_payload.get("mock_signal_hz", model.radar.mock_signal_hz))
+
+    model.radar.sweep.start_hz = float(sweep_payload.get("start_hz", model.radar.sweep.start_hz))
+    model.radar.sweep.stop_hz = float(sweep_payload.get("stop_hz", model.radar.sweep.stop_hz))
+    model.radar.sweep.points = int(sweep_payload.get("points", model.radar.sweep.points))
+    model.radar.sweep.if_bandwidth_hz = float(
+        sweep_payload.get("if_bandwidth_hz", model.radar.sweep.if_bandwidth_hz)
+    )
+    model.radar.sweep.power_dbm = float(sweep_payload.get("stimulus_power_dbm", model.radar.sweep.power_dbm))
+
+    load_switch_payload(port1_payload, model.output_switch)
+    load_switch_payload(port2_payload, model.input_switch)
+
+    model.runtime.settling_ms = int(run_payload.get("settling_ms", model.runtime.settling_ms))
+    model.runtime.idle_sleep_ms = int(run_payload.get("idle_sleep_ms", model.runtime.idle_sleep_ms))
+    model.runtime.continuous = bool(run_payload.get("continuous", model.runtime.continuous))
+    model.runtime.processing_live_config_path = str(
+        run_payload.get("processing_live_config_path", model.runtime.processing_live_config_path)
+    )
+
+    model.preprocess.calibration_set = str(preprocess_payload.get("calibration_set", model.preprocess.calibration_set))
+    model.preprocess.reference_set = str(preprocess_payload.get("reference_set", model.preprocess.reference_set))
+    model.preprocess.calibration_bundle_path = str(
+        preprocess_payload.get("calibration_bundle_path", model.preprocess.calibration_bundle_path)
+    )
+    model.preprocess.reference_bundle_path = str(
+        preprocess_payload.get("reference_bundle_path", model.preprocess.reference_bundle_path)
+    )
+
+    load_ring_payload(raw_ring_payload, model.rings.raw)
+    load_ring_payload(raw_tap_ring_payload, model.rings.raw_tap)
+    load_ring_payload(pre_ring_payload, model.rings.preprocessed)
+    load_ring_payload(pre_tap_ring_payload, model.rings.preprocessed_tap)
+    load_ring_payload(result_ring_payload, model.rings.results)
+
+    combos_payload = run_payload.get("combos", [])
+    model.combos = []
+    if isinstance(combos_payload, list):
+        for combo in combos_payload:
+            combo_payload = as_dict(combo, "run.combos[]")
+            model.combos.append(
+                ComboModel(
+                    input=int(combo_payload.get("input", 0)),
+                    output=int(combo_payload.get("output", 0)),
+                )
+            )
+
+    model.ensure_combos()
+    return model
+
+
+def load_run_config(path: Path) -> RunConfigModel:
+    """Load JSON config from path and decode into :class:`RunConfigModel`."""
+    payload = json.loads(path.read_text(encoding="utf-8"))
+    if not isinstance(payload, dict):
+        raise ValueError(f"Config root must be JSON object: {path}")
+    return run_config_from_dict(payload)
+
+
+def run_config_to_dict(model: RunConfigModel) -> dict[str, Any]:
+    """Encode :class:`RunConfigModel` to C++ pipeline-compatible JSON structure."""
+    model.ensure_combos()
+    return {
+        "radar": {
+            "model": model.radar.model,
+            "serial": model.radar.serial,
+            "driver_mode": model.radar.driver_mode,
+            "mock_signal_hz": model.radar.mock_signal_hz,
+            "sweep": {
+                "start_hz": model.radar.sweep.start_hz,
+                "stop_hz": model.radar.sweep.stop_hz,
+                "points": model.radar.sweep.points,
+                "if_bandwidth_hz": model.radar.sweep.if_bandwidth_hz,
+                "stimulus_power_dbm": model.radar.sweep.power_dbm,
+            },
+        },
+        "switches": {
+            "port1": {
+                "name": model.output_switch.name,
+                "driver_mode": model.output_switch.driver_mode,
+                "driver": model.output_switch.driver,
+                "radar_port": model.output_switch.radar_port,
+                "positions": model.output_switch.positions,
+                "default_position": model.output_switch.default_position,
+                "gpio_chip": model.output_switch.gpio_chip,
+                "pin_a": model.output_switch.pin_a,
+                "pin_b": model.output_switch.pin_b,
+                "invert_logic": model.output_switch.invert_logic,
+            },
+            "port2": {
+                "name": model.input_switch.name,
+                "driver_mode": model.input_switch.driver_mode,
+                "driver": model.input_switch.driver,
+                "radar_port": model.input_switch.radar_port,
+                "positions": model.input_switch.positions,
+                "default_position": model.input_switch.default_position,
+                "gpio_chip": model.input_switch.gpio_chip,
+                "pin_a": model.input_switch.pin_a,
+                "pin_b": model.input_switch.pin_b,
+                "invert_logic": model.input_switch.invert_logic,
+            },
+        },
+        "run": {
+            "settling_ms": model.runtime.settling_ms,
+            "idle_sleep_ms": model.runtime.idle_sleep_ms,
+            "continuous": model.runtime.continuous,
+            "processing_live_config_path": model.runtime.processing_live_config_path,
+            "combos": [{"input": combo.input, "output": combo.output} for combo in model.combos],
+        },
+        "preprocess": {
+            "calibration_set": model.preprocess.calibration_set,
+            "reference_set": model.preprocess.reference_set,
+            "calibration_bundle_path": model.preprocess.calibration_bundle_path,
+            "reference_bundle_path": model.preprocess.reference_bundle_path,
+        },
+        "rings": {
+            "raw": {
+                "name": model.rings.raw.name,
+                "capacity": model.rings.raw.capacity,
+                "slot_size_bytes": model.rings.raw.slot_size_bytes,
+            },
+            "raw_tap": {
+                "name": model.rings.raw_tap.name,
+                "capacity": model.rings.raw_tap.capacity,
+                "slot_size_bytes": model.rings.raw_tap.slot_size_bytes,
+            },
+            "preprocessed": {
+                "name": model.rings.preprocessed.name,
+                "capacity": model.rings.preprocessed.capacity,
+                "slot_size_bytes": model.rings.preprocessed.slot_size_bytes,
+            },
+            "preprocessed_tap": {
+                "name": model.rings.preprocessed_tap.name,
+                "capacity": model.rings.preprocessed_tap.capacity,
+                "slot_size_bytes": model.rings.preprocessed_tap.slot_size_bytes,
+            },
+            "results": {
+                "name": model.rings.results.name,
+                "capacity": model.rings.results.capacity,
+                "slot_size_bytes": model.rings.results.slot_size_bytes,
+            },
+        },
+    }
diff --git a/python_app/models/run_config_model.py b/python_app/models/run_config_model.py
new file mode 100644
index 0000000..8a0088c
--- /dev/null
+++ b/python_app/models/run_config_model.py
@@ -0,0 +1,39 @@
+"""Facade module for run configuration schema, codec, and validation helpers."""
+
+from python_app.models.run_config_codec import load_run_config, run_config_from_dict, run_config_to_dict
+from python_app.models.run_config_schema import (
+    ComboModel,
+    PreprocessModel,
+    RadarModel,
+    RadarSweepModel,
+    RingEndpointModel,
+    RingsModel,
+    RunConfigModel,
+    RuntimeModel,
+    SwitchModel,
+)
+from python_app.models.run_config_validation import (
+    as_dict,
+    load_ring_payload,
+    load_switch_payload,
+    parse_combos_from_text,
+)
+
+__all__ = [
+    "ComboModel",
+    "PreprocessModel",
+    "RadarModel",
+    "RadarSweepModel",
+    "RingEndpointModel",
+    "RingsModel",
+    "RunConfigModel",
+    "RuntimeModel",
+    "SwitchModel",
+    "as_dict",
+    "load_ring_payload",
+    "load_run_config",
+    "load_switch_payload",
+    "parse_combos_from_text",
+    "run_config_from_dict",
+    "run_config_to_dict",
+]
diff --git a/python_app/models/run_config_schema.py b/python_app/models/run_config_schema.py
new file mode 100644
index 0000000..ce6a459
--- /dev/null
+++ b/python_app/models/run_config_schema.py
@@ -0,0 +1,146 @@
+"""Dataclass schema for runtime configuration used by Python pipeline tools."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+
+@dataclass(slots=True)
+class ComboModel:
+    """One switch combination used for an acquisition sweep."""
+
+    input: int
+    output: int
+
+
+@dataclass(slots=True)
+class RadarSweepModel:
+    """Sweep settings for LibreVNA acquisition."""
+
+    # Keep schema defaults minimal/safe; operational values come from run_config.json.
+    start_hz: float = 0.0
+    stop_hz: float = 0.0
+    points: int = 1
+    if_bandwidth_hz: float = 1.0
+    power_dbm: float = -30.0
+
+
+@dataclass(slots=True)
+class RadarModel:
+    """Radar section of run configuration."""
+
+    model: str = ""
+    serial: str = ""
+    driver_mode: str = "mock"
+    mock_signal_hz: float = 1_000_000.0
+    sweep: RadarSweepModel = field(default_factory=RadarSweepModel)
+
+
+@dataclass(slots=True)
+class SwitchModel:
+    """Generic switch section of run configuration."""
+
+    name: str
+    driver_mode: str = "mock"
+    driver: str = ""
+    radar_port: int = 0
+    positions: int = 1
+    default_position: int = 0
+    gpio_chip: str = ""
+    pin_a: int = -1
+    pin_b: int = -1
+    invert_logic: bool = False
+
+
+@dataclass(slots=True)
+class RingEndpointModel:
+    """Shared-memory ring endpoint description."""
+
+    name: str
+    capacity: int = 1
+    slot_size_bytes: int = 4096
+
+
+@dataclass(slots=True)
+class RingsModel:
+    """Ring endpoints used by orchestration pipeline."""
+
+    raw: RingEndpointModel = field(default_factory=lambda: RingEndpointModel(name=""))
+    raw_tap: RingEndpointModel = field(default_factory=lambda: RingEndpointModel(name=""))
+    preprocessed: RingEndpointModel = field(default_factory=lambda: RingEndpointModel(name=""))
+    preprocessed_tap: RingEndpointModel = field(default_factory=lambda: RingEndpointModel(name=""))
+    results: RingEndpointModel = field(default_factory=lambda: RingEndpointModel(name=""))
+
+
+@dataclass(slots=True)
+class RuntimeModel:
+    """Runtime process behavior and paths."""
+
+    settling_ms: int = 0
+    idle_sleep_ms: int = 2
+    continuous: bool = False
+    processing_live_config_path: str = ""
+
+
+@dataclass(slots=True)
+class PreprocessModel:
+    """Selected preprocessing artifacts for live acquisition."""
+
+    calibration_set: str = ""
+    reference_set: str = ""
+    calibration_bundle_path: str = ""
+    reference_bundle_path: str = ""
+
+
+@dataclass(slots=True)
+class RunConfigModel:
+    """Top-level runtime config model consumed by C++ processes and GUI."""
+
+    radar: RadarModel = field(default_factory=RadarModel)
+    input_switch: SwitchModel = field(default_factory=lambda: SwitchModel(name=""))
+    output_switch: SwitchModel = field(default_factory=lambda: SwitchModel(name=""))
+    rings: RingsModel = field(default_factory=RingsModel)
+    runtime: RuntimeModel = field(default_factory=RuntimeModel)
+    preprocess: PreprocessModel = field(default_factory=PreprocessModel)
+    combos: list[ComboModel] = field(default_factory=list)
+
+    @staticmethod
+    def build_full_combos(input_positions: int, output_positions: int) -> list[ComboModel]:
+        """Build full Cartesian product of input/output switch positions."""
+        return [
+            ComboModel(input=input_pos, output=output_pos)
+            for output_pos in range(output_positions)
+            for input_pos in range(input_positions)
+        ]
+
+    def ensure_combos(self) -> None:
+        """Populate combos with full matrix when no explicit run combos are set."""
+        if self.combos:
+            return
+        self.combos = self.build_full_combos(self.input_switch.positions, self.output_switch.positions)
+
+    @classmethod
+    def from_dict(cls, payload: dict[str, Any]) -> RunConfigModel:
+        """Build model from JSON-like payload using codec layer."""
+        from python_app.models.run_config_codec import run_config_from_dict
+
+        return run_config_from_dict(payload)
+
+    @classmethod
+    def load_from_path(cls, path: Path) -> RunConfigModel:
+        """Load JSON file from disk and decode into model."""
+        from python_app.models.run_config_codec import load_run_config
+
+        return load_run_config(path)
+
+    def clone(self) -> RunConfigModel:
+        """Create deep copy through codec round-trip."""
+        return RunConfigModel.from_dict(self.to_dict())
+
+    def to_dict(self) -> dict[str, Any]:
+        """Encode model into JSON-serializable dictionary."""
+        from python_app.models.run_config_codec import run_config_to_dict
+
+        return run_config_to_dict(self)
diff --git a/python_app/models/run_config_validation.py b/python_app/models/run_config_validation.py
new file mode 100644
index 0000000..e7f4a5c
--- /dev/null
+++ b/python_app/models/run_config_validation.py
@@ -0,0 +1,62 @@
+"""Validation and normalization helpers for run configuration payloads."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from python_app.models.run_config_schema import ComboModel, RingEndpointModel, SwitchModel
+
+
+def as_dict(value: Any, context: str) -> dict[str, Any]:
+    """Validate that a payload node is a JSON object and return it."""
+    if value is None:
+        return {}
+    if not isinstance(value, dict):
+        raise ValueError(f"{context} must be a JSON object")
+    return value
+
+
+def load_switch_payload(
+    payload: dict[str, Any],
+    target: SwitchModel,
+) -> None:
+    """Populate switch model from payload preserving defaults for missing values."""
+    target.name = str(payload.get("name", target.name))
+    target.driver_mode = str(payload.get("driver_mode", target.driver_mode))
+    target.driver = str(payload.get("driver", target.driver))
+    target.radar_port = int(payload.get("radar_port", target.radar_port))
+    target.positions = int(payload.get("positions", target.positions))
+    target.default_position = int(payload.get("default_position", target.default_position))
+    target.gpio_chip = str(payload.get("gpio_chip", target.gpio_chip))
+    target.pin_a = int(payload.get("pin_a", target.pin_a))
+    target.pin_b = int(payload.get("pin_b", target.pin_b))
+    target.invert_logic = bool(payload.get("invert_logic", target.invert_logic))
+
+
+def load_ring_payload(payload: dict[str, Any], target: RingEndpointModel) -> None:
+    """Populate ring endpoint model from payload preserving defaults."""
+    target.name = str(payload.get("name", target.name))
+    target.capacity = int(payload.get("capacity", target.capacity))
+    target.slot_size_bytes = int(payload.get("slot_size_bytes", target.slot_size_bytes))
+
+
+def parse_combos_from_text(text: str) -> list[ComboModel]:
+    """Parse UI combos string in `input:output,input:output` format."""
+    cleaned = text.strip()
+    if not cleaned:
+        return []
+
+    combos: list[ComboModel] = []
+    for item in cleaned.split(","):
+        pair = item.strip()
+        if not pair:
+            continue
+        if ":" not in pair:
+            raise ValueError(f"Invalid combo syntax: {pair!r}. Expected input:output")
+
+        input_text, output_text = pair.split(":", 1)
+        combos.append(ComboModel(input=int(input_text.strip()), output=int(output_text.strip())))
+
+    if not combos:
+        raise ValueError("No valid combos were provided")
+    return combos
diff --git a/python_app/orchestration/__init__.py b/python_app/orchestration/__init__.py
new file mode 100644
index 0000000..365d306
--- /dev/null
+++ b/python_app/orchestration/__init__.py
@@ -0,0 +1 @@
+"""Runtime orchestration utilities for process control and IPC."""
diff --git a/python_app/orchestration/config_writer.py b/python_app/orchestration/config_writer.py
new file mode 100644
index 0000000..adc5c3d
--- /dev/null
+++ b/python_app/orchestration/config_writer.py
@@ -0,0 +1,42 @@
+"""Helpers for writing runtime configs and preprocessing bundles."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from python_app.models.run_config_model import RunConfigModel, parse_combos_from_text
+from python_app.storage.npz_store import NpzStore
+
+
+class ConfigWriter:
+    """Write runtime artifacts consumed by C++ processes."""
+
+    def __init__(self, runtime_dir: Path) -> None:
+        """Create writer rooted at runtime directory."""
+        self._runtime_dir = runtime_dir
+        self._runtime_dir.mkdir(parents=True, exist_ok=True)
+
+    def prepare_bundles(
+        self,
+        store: NpzStore,
+        radar_key: str,
+        calibration_set: str,
+        reference_set: str,
+    ) -> tuple[Path, Path]:
+        """Export calibration/reference sets into binary bundles for preprocessor."""
+        calibration_bundle = self._runtime_dir / "calibration_bundle.bin"
+        reference_bundle = self._runtime_dir / "reference_bundle.bin"
+
+        store.export_set_bundle("calibration", radar_key, calibration_set, calibration_bundle)
+        store.export_set_bundle("reference", radar_key, reference_set, reference_bundle)
+        return calibration_bundle, reference_bundle
+
+    def write(self, config: RunConfigModel, output_path: Path) -> Path:
+        """Write run configuration JSON file."""
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        output_path.write_text(json.dumps(config.to_dict(), indent=2), encoding="utf-8")
+        return output_path
+
+
+__all__ = ["ConfigWriter", "parse_combos_from_text"]
diff --git a/python_app/orchestration/live_processing_config.py b/python_app/orchestration/live_processing_config.py
new file mode 100644
index 0000000..155a13a
--- /dev/null
+++ b/python_app/orchestration/live_processing_config.py
@@ -0,0 +1,61 @@
+"""Live processing settings model and atomic JSON writer."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import json
+from pathlib import Path
+
+
+@dataclass(slots=True)
+class ProcessingLiveConfig:
+    """Runtime-adjustable processing parameters shared with data processor."""
+
+    processor_mode: str = "pass_through"
+    gain_db: float = 0.0
+    phase_deg: float = 0.0
+    bscan_axis: str = "abs"
+    bscan_cut_m: float = 0.824
+    bscan_max_depth_m: float = 1.0
+    bscan_gain: float = 1.0
+    bscan_start_freq_mhz: float = 100.0
+    bscan_stop_freq_mhz: float = 8800.0
+    history_command_seq: int = 0
+    history_command: str = "none"
+
+    def to_dict(self) -> dict[str, float | str | int]:
+        """Convert live config to JSON-serializable dictionary."""
+        return {
+            "processor_mode": str(self.processor_mode),
+            "gain_db": float(self.gain_db),
+            "phase_deg": float(self.phase_deg),
+            "bscan_axis": str(self.bscan_axis),
+            "bscan_cut_m": float(self.bscan_cut_m),
+            "bscan_max_depth_m": float(self.bscan_max_depth_m),
+            "bscan_gain": float(self.bscan_gain),
+            "bscan_start_freq_mhz": float(self.bscan_start_freq_mhz),
+            "bscan_stop_freq_mhz": float(self.bscan_stop_freq_mhz),
+            "history_command_seq": int(self.history_command_seq),
+            "history_command": str(self.history_command),
+        }
+
+
+class ProcessingLiveConfigWriter:
+    """Atomic writer for processing live-config file."""
+
+    def __init__(self, config_path: Path) -> None:
+        """Create writer targeting `config_path`."""
+        self._config_path = config_path
+        self._config_path.parent.mkdir(parents=True, exist_ok=True)
+
+    @property
+    def path(self) -> Path:
+        """Return destination config path."""
+        return self._config_path
+
+    def write(self, config: ProcessingLiveConfig) -> Path:
+        """Atomically write config by temp-file replace."""
+        temp_path = self._config_path.with_suffix(self._config_path.suffix + ".tmp")
+        temp_path.write_text(json.dumps(config.to_dict(), indent=2), encoding="utf-8")
+        temp_path.replace(self._config_path)
+        return self._config_path
diff --git a/python_app/orchestration/process_supervisor.py b/python_app/orchestration/process_supervisor.py
new file mode 100644
index 0000000..96dd07e
--- /dev/null
+++ b/python_app/orchestration/process_supervisor.py
@@ -0,0 +1,202 @@
+"""Process supervisor for lifecycle management of C++ pipeline binaries."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+import subprocess
+import time
+from typing import Iterable
+from typing import Sequence
+
+
+@dataclass(slots=True)
+class ManagedProcess:
+    """Metadata and subprocess handle for one managed child process."""
+
+    name: str
+    command: list[str]
+    handle: subprocess.Popen[str]
+
+
+class ProcessSupervisor:
+    """Start, monitor, and stop pipeline subprocesses."""
+
+    def __init__(self, project_root: Path, readiness_timeout_s: float = 3.0) -> None:
+        """Create supervisor bound to repository root directory."""
+        self._project_root = project_root
+        self._readiness_timeout_s = readiness_timeout_s
+        self._processes: dict[str, ManagedProcess] = {}
+
+    def is_running(self) -> bool:
+        """Return whether acquisition-side processes are alive."""
+        return self._is_alive("data_preprocessor") or self._is_alive("sweep_orchestrator")
+
+    def is_processor_running(self) -> bool:
+        """Return whether data processor process is alive."""
+        return self._is_alive("data_processor")
+
+    def start(self, config_path: Path) -> None:
+        """Start required pipeline binaries and wait until they are ready."""
+        if self.is_running():
+            raise RuntimeError("Acquisition processes are already running")
+
+        command_specs = {
+            "data_processor": [
+                str(self._project_root / "build/bin/data_processor"),
+                "--config",
+                str(config_path),
+            ],
+            "data_preprocessor": [
+                str(self._project_root / "build/bin/data_preprocessor"),
+                "--config",
+                str(config_path),
+            ],
+            "sweep_orchestrator": [
+                str(self._project_root / "build/bin/sweep_orchestrator"),
+                "--config",
+                str(config_path),
+            ],
+        }
+        processor_was_running = self.is_processor_running()
+        required_processes: list[str] = ["data_preprocessor", "sweep_orchestrator"]
+        if not processor_was_running:
+            required_processes.insert(0, "data_processor")
+
+        try:
+            if not processor_was_running:
+                self._spawn("data_processor", command_specs["data_processor"])
+
+            self._spawn("data_preprocessor", command_specs["data_preprocessor"])
+            self._spawn("sweep_orchestrator", command_specs["sweep_orchestrator"])
+            self._wait_until_ready(required_processes)
+        except Exception:
+            if processor_was_running:
+                self.stop()
+            else:
+                self.stop_all()
+            raise
+
+    def stop(self) -> None:
+        """Stop acquisition-side processes, keep processor process intact."""
+        self._stop_processes(["sweep_orchestrator", "data_preprocessor"])
+
+    def stop_orchestrator(self) -> None:
+        """Stop orchestrator process only."""
+        self._stop_processes(["sweep_orchestrator"])
+
+    def stop_preprocessor(self) -> None:
+        """Stop preprocessor process only."""
+        self._stop_processes(["data_preprocessor"])
+
+    def stop_all(self) -> None:
+        """Stop all managed processes."""
+        self._stop_processes(["sweep_orchestrator", "data_preprocessor", "data_processor"])
+
+    def _spawn(self, name: str, command: list[str]) -> None:
+        """Spawn one process unless same process is already alive."""
+        existing = self._processes.get(name)
+        if existing is not None and existing.handle.poll() is None:
+            return
+
+        handle = subprocess.Popen(
+            command,
+            cwd=self._project_root,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+        )
+        self._processes[name] = ManagedProcess(name=name, command=command, handle=handle)
+
+    def _stop_processes(self, names: Iterable[str]) -> None:
+        """Gracefully terminate processes, then force-kill on timeout."""
+        ordered_names = list(names)
+        for name in ordered_names:
+            process = self._processes.get(name)
+            if process is None:
+                continue
+            if process.handle.poll() is None:
+                process.handle.terminate()
+
+        deadline = time.monotonic() + 2.0
+        for name in ordered_names:
+            process = self._processes.get(name)
+            if process is None:
+                continue
+            if process.handle.poll() is not None:
+                continue
+
+            timeout = max(0.0, deadline - time.monotonic())
+            try:
+                process.handle.wait(timeout=timeout)
+            except subprocess.TimeoutExpired:
+                process.handle.kill()
+                process.handle.wait(timeout=1.0)
+
+        self._drop_exited()
+
+    def _drop_exited(self) -> None:
+        """Remove exited process entries from internal map."""
+        exited_names = [name for name, process in self._processes.items() if process.handle.poll() is not None]
+        for name in exited_names:
+            self._processes.pop(name, None)
+
+    def _is_alive(self, name: str) -> bool:
+        """Return `True` when named process handle exists and is running."""
+        process = self._processes.get(name)
+        if process is None:
+            return False
+        return process.handle.poll() is None
+
+    def collect_crash_reports(self) -> list[str]:
+        """Collect stderr/stdout reports from processes that have exited."""
+        reports: list[str] = []
+        exited_names: list[str] = []
+
+        for name, process in self._processes.items():
+            return_code = process.handle.poll()
+            if return_code is None:
+                continue
+
+            stderr = ""
+            stdout = ""
+            if process.handle.stdout is not None:
+                stdout = process.handle.stdout.read().strip()
+            if process.handle.stderr is not None:
+                stderr = process.handle.stderr.read().strip()
+
+            details = stderr
+            if stdout and stderr:
+                details = f"{stderr}\nstdout:\n{stdout}"
+            elif stdout:
+                details = f"stdout:\n{stdout}"
+
+            reports.append(f"{process.name} exited with code {return_code}: {details}")
+            exited_names.append(name)
+
+        for name in exited_names:
+            self._processes.pop(name, None)
+        return reports
+
+    def _wait_until_ready(self, required_processes: Sequence[str]) -> None:
+        """Wait until all required processes are alive or timeout/crash occurs."""
+        deadline = time.monotonic() + self._readiness_timeout_s
+
+        while time.monotonic() < deadline:
+            crashed = self.collect_crash_reports()
+            if crashed:
+                raise RuntimeError("; ".join(crashed))
+            if all(self._is_alive(process_name) for process_name in required_processes):
+                return
+            time.sleep(0.05)
+
+        names = ", ".join(required_processes)
+        raise RuntimeError(f"Timed out waiting for processes to start: {names}")
+
+    def pids(self) -> dict[str, int]:
+        """Return PID mapping for currently alive managed processes."""
+        return {
+            process.name: process.handle.pid
+            for process in self._processes.values()
+            if process.handle.poll() is None and process.handle.pid is not None
+        }
diff --git a/python_app/orchestration/shm/__init__.py b/python_app/orchestration/shm/__init__.py
new file mode 100644
index 0000000..52bb9ac
--- /dev/null
+++ b/python_app/orchestration/shm/__init__.py
@@ -0,0 +1,21 @@
+"""Shared-memory ring readers and payload decoders."""
+
+from python_app.orchestration.shm.binary_cursor import ByteCursor
+from python_app.orchestration.shm.decoder import (
+    PREPROC_MAGIC,
+    RAW_MAGIC,
+    RESULT_MAGIC,
+    decode_result_collection,
+    decode_trace_collection,
+)
+from python_app.orchestration.shm.ring_reader import ShmRingReader
+
+__all__ = [
+    "ByteCursor",
+    "PREPROC_MAGIC",
+    "RAW_MAGIC",
+    "RESULT_MAGIC",
+    "ShmRingReader",
+    "decode_result_collection",
+    "decode_trace_collection",
+]
diff --git a/python_app/orchestration/shm/binary_cursor.py b/python_app/orchestration/shm/binary_cursor.py
new file mode 100644
index 0000000..8774d63
--- /dev/null
+++ b/python_app/orchestration/shm/binary_cursor.py
@@ -0,0 +1,50 @@
+"""Byte-wise cursor utilities for decoding binary ring payloads."""
+
+from __future__ import annotations
+
+import struct
+
+
+class ByteCursor:
+    """Read primitive values from bytes while tracking offset."""
+
+    def __init__(self, payload: bytes) -> None:
+        """Create cursor at start of payload."""
+        self.payload = payload
+        self.offset = 0
+
+    def read_u8(self) -> int:
+        """Read unsigned 8-bit integer."""
+        value = struct.unpack_from("<B", self.payload, self.offset)[0]
+        self.offset += 1
+        return value
+
+    def read_u16(self) -> int:
+        """Read unsigned 16-bit integer."""
+        value = struct.unpack_from("<H", self.payload, self.offset)[0]
+        self.offset += 2
+        return value
+
+    def read_u32(self) -> int:
+        """Read unsigned 32-bit integer."""
+        value = struct.unpack_from("<I", self.payload, self.offset)[0]
+        self.offset += 4
+        return value
+
+    def read_u64(self) -> int:
+        """Read unsigned 64-bit integer."""
+        value = struct.unpack_from("<Q", self.payload, self.offset)[0]
+        self.offset += 8
+        return value
+
+    def read_f32(self) -> float:
+        """Read 32-bit float."""
+        value = struct.unpack_from("<f", self.payload, self.offset)[0]
+        self.offset += 4
+        return float(value)
+
+    def read_bytes(self, size: int) -> bytes:
+        """Read raw byte slice of fixed size."""
+        data = self.payload[self.offset : self.offset + size]
+        self.offset += size
+        return data
diff --git a/python_app/orchestration/shm/decoder.py b/python_app/orchestration/shm/decoder.py
new file mode 100644
index 0000000..1daac7a
--- /dev/null
+++ b/python_app/orchestration/shm/decoder.py
@@ -0,0 +1,114 @@
+"""Binary decoders for raw/preprocessed/result payload collections."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from python_app.models.dataset_model import (
+    ComboKey,
+    ResultBlock,
+    ResultCollection,
+    ResultPayload,
+    SweepCollection,
+    TraceData,
+)
+from python_app.orchestration.shm.binary_cursor import ByteCursor
+
+RAW_MAGIC = 0x31574152
+PREPROC_MAGIC = 0x31525050
+RESULT_MAGIC = 0x314C5352
+
+
+def decode_trace_collection(payload: bytes, expected_magic: int) -> SweepCollection:
+    """Decode one raw/preprocessed collection from binary payload."""
+    cursor = ByteCursor(payload)
+    magic = cursor.read_u32()
+    if magic != expected_magic:
+        raise ValueError("Unexpected trace collection magic")
+
+    collection_id = cursor.read_u64()
+    monotonic_ns = cursor.read_u64()
+    trace_count = cursor.read_u32()
+
+    traces: list[TraceData] = []
+    for _ in range(trace_count):
+        input_pos = cursor.read_u32()
+        output_pos = cursor.read_u32()
+        point_count = cursor.read_u32()
+
+        freq_bytes = point_count * 4
+        freq = np.frombuffer(cursor.read_bytes(freq_bytes), dtype="<f4").astype(np.float32, copy=False)
+
+        interleaved_bytes = point_count * 8
+        interleaved = np.frombuffer(cursor.read_bytes(interleaved_bytes), dtype="<f4")
+        s21 = (interleaved[0::2] + 1j * interleaved[1::2]).astype(np.complex64, copy=False)
+
+        traces.append(
+            TraceData(
+                combo=ComboKey(input_pos=input_pos, output_pos=output_pos),
+                frequency_hz=freq,
+                s21=s21,
+            )
+        )
+
+    return SweepCollection(collection_id=collection_id, monotonic_ns=monotonic_ns, traces=traces)
+
+
+def decode_result_collection(payload: bytes) -> ResultCollection:
+    """Decode one processed result collection from binary payload."""
+    cursor = ByteCursor(payload)
+    magic = cursor.read_u32()
+    if magic != RESULT_MAGIC:
+        raise ValueError("Unexpected result collection magic")
+
+    collection_id = cursor.read_u64()
+    monotonic_ns = cursor.read_u64()
+    block_count = cursor.read_u32()
+
+    blocks: list[ResultBlock] = []
+    for _ in range(block_count):
+        input_pos = cursor.read_u32()
+        output_pos = cursor.read_u32()
+        payload_count = cursor.read_u32()
+
+        payloads: list[ResultPayload] = []
+        for _ in range(payload_count):
+            kind = cursor.read_u8()
+            name_size = cursor.read_u16()
+            name = cursor.read_bytes(name_size).decode("utf-8")
+
+            if kind == 1:
+                point_count = cursor.read_u32()
+                freq = np.frombuffer(cursor.read_bytes(point_count * 4), dtype="<f4").astype(np.float32, copy=False)
+                interleaved = np.frombuffer(cursor.read_bytes(point_count * 8), dtype="<f4")
+                trace = (interleaved[0::2] + 1j * interleaved[1::2]).astype(np.complex64, copy=False)
+                payloads.append(
+                    ResultPayload(
+                        processing_name=name,
+                        kind=kind,
+                        frequency_hz=freq,
+                        trace=trace,
+                    )
+                )
+            elif kind == 2:
+                scalar_value = cursor.read_f32()
+                payloads.append(
+                    ResultPayload(
+                        processing_name=name,
+                        kind=kind,
+                        frequency_hz=np.array([], dtype=np.float32),
+                        trace=np.array([], dtype=np.complex64),
+                        scalar_value=scalar_value,
+                    )
+                )
+            else:
+                raise ValueError(f"Unsupported result payload kind: {kind}")
+
+        blocks.append(
+            ResultBlock(
+                combo=ComboKey(input_pos=input_pos, output_pos=output_pos),
+                payloads=payloads,
+            )
+        )
+
+    return ResultCollection(collection_id=collection_id, monotonic_ns=monotonic_ns, blocks=blocks)
diff --git a/python_app/orchestration/shm/ring_reader.py b/python_app/orchestration/shm/ring_reader.py
new file mode 100644
index 0000000..3caf923
--- /dev/null
+++ b/python_app/orchestration/shm/ring_reader.py
@@ -0,0 +1,150 @@
+"""POSIX shared-memory ring reader implementation."""
+
+from __future__ import annotations
+
+import mmap
+from pathlib import Path
+import struct
+import time
+from typing import Final
+
+from python_app.models.dataset_model import ResultCollection, SweepCollection
+from python_app.orchestration.shm.decoder import (
+    PREPROC_MAGIC,
+    RAW_MAGIC,
+    decode_result_collection,
+    decode_trace_collection,
+)
+
+_HEADER_SIZE: Final[int] = 64
+_SLOT_HEADER_SIZE: Final[int] = 16
+_MAGIC: Final[bytes] = b"RDRRING2"
+_VERSION: Final[int] = 1
+
+
+class ShmRingReader:
+    """Read binary payloads from a lock-free ring in `/dev/shm`."""
+
+    def __init__(self, ring_name: str, open_timeout_s: float = 2.0, open_poll_s: float = 0.01) -> None:
+        """Open and validate ring by name, for example `/radar_results`."""
+        if not ring_name.startswith("/"):
+            raise ValueError("ring_name must start with '/'")
+
+        self._ring_name = ring_name
+        self._path = Path("/dev/shm") / ring_name[1:]
+        self._wait_for_ring_file(timeout_s=open_timeout_s, poll_s=open_poll_s)
+
+        self._file = self._path.open("r+b", buffering=0)
+        self._mmap = mmap.mmap(self._file.fileno(), 0)
+        self._validate_header_with_wait(timeout_s=1.0, poll_s=0.002)
+
+    def close(self) -> None:
+        """Close mmap and file handle."""
+        self._mmap.close()
+        self._file.close()
+
+    def pop_payload(self) -> bytes | None:
+        """Read next payload from ring, or `None` if no unread payload exists."""
+        write_seq = self._read_u64(24)
+        read_seq = self._read_u64(32)
+        if read_seq >= write_seq:
+            return None
+
+        index = read_seq % self.capacity
+        slot_offset = _HEADER_SIZE + index * (_SLOT_HEADER_SIZE + self.slot_size_bytes)
+
+        payload_size = self._read_u32(slot_offset)
+        sequence = self._read_u64(slot_offset + 8)
+        if sequence != read_seq + 1:
+            self._write_u64(32, write_seq)
+            return None
+
+        payload_offset = slot_offset + _SLOT_HEADER_SIZE
+        payload = self._mmap[payload_offset : payload_offset + payload_size]
+        self._write_u64(32, read_seq + 1)
+        return bytes(payload)
+
+    def pop_raw_collection(self) -> SweepCollection | None:
+        """Read next raw collection from ring."""
+        payload = self.pop_payload()
+        if payload is None:
+            return None
+        return decode_trace_collection(payload, RAW_MAGIC)
+
+    def pop_preprocessed_collection(self) -> SweepCollection | None:
+        """Read next preprocessed collection from ring."""
+        payload = self.pop_payload()
+        if payload is None:
+            return None
+        return decode_trace_collection(payload, PREPROC_MAGIC)
+
+    def pop_result_collection(self) -> ResultCollection | None:
+        """Read next processed result collection from ring."""
+        payload = self.pop_payload()
+        if payload is None:
+            return None
+        return decode_result_collection(payload)
+
+    def drop_all(self) -> int:
+        """Mark all unread slots as consumed and return number of dropped payloads."""
+        write_seq = self._read_u64(24)
+        read_seq = self._read_u64(32)
+        if read_seq >= write_seq:
+            return 0
+        dropped = int(write_seq - read_seq)
+        self._write_u64(32, write_seq)
+        return dropped
+
+    @property
+    def capacity(self) -> int:
+        """Number of slots in ring."""
+        return self._read_u32(12)
+
+    @property
+    def slot_size_bytes(self) -> int:
+        """Maximum payload size per slot."""
+        return self._read_u32(16)
+
+    def _validate_header_with_wait(self, timeout_s: float, poll_s: float) -> None:
+        """Wait for ring header to contain expected magic and version."""
+        deadline = time.monotonic() + timeout_s
+        while True:
+            magic = self._mmap[:8]
+            version = self._read_u32(8)
+            if magic == _MAGIC and version == _VERSION:
+                return
+
+            if time.monotonic() >= deadline:
+                if magic != _MAGIC:
+                    got_magic = bytes(magic).hex()
+                    expected_magic = _MAGIC.hex()
+                    raise RuntimeError(
+                        f"Shared memory ring magic mismatch for {self._ring_name}: "
+                        f"got=0x{got_magic}, expected=0x{expected_magic}"
+                    )
+                raise RuntimeError(
+                    f"Shared memory ring version mismatch for {self._ring_name}: "
+                    f"got={version}, expected={_VERSION}"
+                )
+
+            time.sleep(poll_s)
+
+    def _wait_for_ring_file(self, timeout_s: float, poll_s: float) -> None:
+        """Wait until ring file appears in `/dev/shm`."""
+        deadline = time.monotonic() + timeout_s
+        while not self._path.exists():
+            if time.monotonic() >= deadline:
+                raise FileNotFoundError(f"Shared memory ring does not exist: {self._ring_name}")
+            time.sleep(poll_s)
+
+    def _read_u32(self, offset: int) -> int:
+        """Read little-endian u32 at mmap offset."""
+        return struct.unpack_from("<I", self._mmap, offset)[0]
+
+    def _read_u64(self, offset: int) -> int:
+        """Read little-endian u64 at mmap offset."""
+        return struct.unpack_from("<Q", self._mmap, offset)[0]
+
+    def _write_u64(self, offset: int, value: int) -> None:
+        """Write little-endian u64 at mmap offset."""
+        struct.pack_into("<Q", self._mmap, offset, value)
diff --git a/python_app/orchestration/shm_reader.py b/python_app/orchestration/shm_reader.py
new file mode 100644
index 0000000..79e1c41
--- /dev/null
+++ b/python_app/orchestration/shm_reader.py
@@ -0,0 +1,21 @@
+"""Facade exports for shared-memory ring reader and decoders."""
+
+from python_app.orchestration.shm.binary_cursor import ByteCursor
+from python_app.orchestration.shm.decoder import (
+    PREPROC_MAGIC,
+    RAW_MAGIC,
+    RESULT_MAGIC,
+    decode_result_collection,
+    decode_trace_collection,
+)
+from python_app.orchestration.shm.ring_reader import ShmRingReader
+
+__all__ = [
+    "ByteCursor",
+    "PREPROC_MAGIC",
+    "RAW_MAGIC",
+    "RESULT_MAGIC",
+    "ShmRingReader",
+    "decode_result_collection",
+    "decode_trace_collection",
+]
diff --git a/python_app/runtime/calibration_bundle.bin b/python_app/runtime/calibration_bundle.bin
new file mode 100644
index 0000000000000000000000000000000000000000..acba53e8fdca51668d8e97948a5236e47cc3f88b
GIT binary patch
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diff --git a/python_app/runtime/reference_bundle.bin b/python_app/runtime/reference_bundle.bin
new file mode 100644
index 0000000000000000000000000000000000000000..daf4028519ed57b5905f1e7d1e13092d0241bff6
GIT binary patch
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literal 0
HcmV?d00001

diff --git a/python_app/runtime/run_config_smoke.json b/python_app/runtime/run_config_smoke.json
new file mode 100644
index 0000000..c7e706b
--- /dev/null
+++ b/python_app/runtime/run_config_smoke.json
@@ -0,0 +1,114 @@
+{
+  "radar": {
+    "model": "librevna",
+    "serial": "",
+    "driver_mode": "mock",
+    "mock_signal_hz": 5000000.0,
+    "sweep": {
+      "start_hz": 1000000.0,
+      "stop_hz": 6000000000.0,
+      "points": 201,
+      "if_bandwidth_hz": 50000.0,
+      "stimulus_power_dbm": -10.0
+    }
+  },
+  "switches": {
+    "port1": {
+      "name": "port1",
+      "driver_mode": "mock",
+      "driver": "h7992",
+      "radar_port": 1,
+      "positions": 4,
+      "default_position": 0,
+      "gpio_chip": "/dev/gpiochip0",
+      "pin_a": 17,
+      "pin_b": 27,
+      "invert_logic": false
+    },
+    "port2": {
+      "name": "port2",
+      "driver_mode": "mock",
+      "driver": "hmc349a",
+      "radar_port": 2,
+      "positions": 2,
+      "default_position": 0,
+      "gpio_chip": "/dev/gpiochip0",
+      "pin_a": 22,
+      "pin_b": -1,
+      "invert_logic": false
+    }
+  },
+  "run": {
+    "settling_ms": 0,
+    "idle_sleep_ms": 2,
+    "continuous": true,
+    "processing_live_config_path": "python_app/runtime/processing_live.json",
+    "combos": [
+      {
+        "input": 0,
+        "output": 0
+      },
+      {
+        "input": 1,
+        "output": 0
+      },
+      {
+        "input": 0,
+        "output": 1
+      },
+      {
+        "input": 1,
+        "output": 1
+      },
+      {
+        "input": 0,
+        "output": 2
+      },
+      {
+        "input": 1,
+        "output": 2
+      },
+      {
+        "input": 0,
+        "output": 3
+      },
+      {
+        "input": 1,
+        "output": 3
+      }
+    ]
+  },
+  "preprocess": {
+    "calibration_set": "smoke_cal",
+    "reference_set": "smoke_ref",
+    "calibration_bundle_path": "/home/europa/Documents/radar_system/python_app/runtime/calibration_bundle.bin",
+    "reference_bundle_path": "/home/europa/Documents/radar_system/python_app/runtime/reference_bundle.bin"
+  },
+  "rings": {
+    "raw": {
+      "name": "/radar_raw_smoke_1703912_791574940686872",
+      "capacity": 32,
+      "slot_size_bytes": 2097152
+    },
+    "raw_tap": {
+      "name": "/radar_raw_tap_smoke_1703912_791574940693657",
+      "capacity": 32,
+      "slot_size_bytes": 2097152
+    },
+    "preprocessed": {
+      "name": "/radar_preprocessed_smoke_1703912_791574940694423",
+      "capacity": 32,
+      "slot_size_bytes": 2097152
+    },
+    "preprocessed_tap": {
+      "name": "/radar_preprocessed_tap_smoke_1703912_791574940694970",
+      "capacity": 32,
+      "slot_size_bytes": 2097152
+    },
+    "results": {
+      "name": "/radar_results_smoke_1703912_791574940696951",
+      "capacity": 32,
+      "slot_size_bytes": 2097152
+    }
+  }
+}
\ No newline at end of file
diff --git a/python_app/scripts/__init__.py b/python_app/scripts/__init__.py
new file mode 100644
index 0000000..2f550a6
--- /dev/null
+++ b/python_app/scripts/__init__.py
@@ -0,0 +1 @@
+"""Utility scripts for smoke checks, inspection, and manual diagnostics."""
diff --git a/python_app/scripts/check_snapshot_numpy.py b/python_app/scripts/check_snapshot_numpy.py
new file mode 100644
index 0000000..d1b2524
--- /dev/null
+++ b/python_app/scripts/check_snapshot_numpy.py
@@ -0,0 +1,298 @@
+"""Validate and visualize numpy-directory runtime snapshots."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+
+def _load_json(path: Path) -> dict[str, Any]:
+    """Load JSON object from file path."""
+    payload = json.loads(path.read_text(encoding="utf-8"))
+    if not isinstance(payload, dict):
+        raise ValueError(f"JSON root must be object: {path}")
+    return payload
+
+
+def _collection_dirs(stage_dir: Path) -> list[Path]:
+    """Return sorted list of collection directories for one stage."""
+    if not stage_dir.exists():
+        return []
+    return sorted([path for path in stage_dir.iterdir() if path.is_dir()], key=lambda path: path.name)
+
+
+def _first_trace_from_raw_or_pre(collection_dir: Path) -> tuple[np.ndarray, np.ndarray, str] | None:
+    """Return first trace payload from raw/preprocessed collection."""
+    traces = _all_traces_from_raw_or_pre(collection_dir)
+    if not traces:
+        return None
+    return traces[0]
+
+
+def _all_traces_from_raw_or_pre(collection_dir: Path) -> list[tuple[np.ndarray, np.ndarray, str]]:
+    """Load all traces from raw/preprocessed collection directory."""
+    meta = _load_json(collection_dir / "meta.json")
+    traces = meta.get("traces", [])
+    if not isinstance(traces, list):
+        raise ValueError(f"Invalid traces in {collection_dir / 'meta.json'}")
+
+    all_traces: list[tuple[np.ndarray, np.ndarray, str]] = []
+    for trace_meta in traces:
+        if not isinstance(trace_meta, dict):
+            raise ValueError(f"Invalid trace record in {collection_dir / 'meta.json'}")
+
+        freq_file = str(trace_meta.get("freq_file", ""))
+        s21_file = str(trace_meta.get("s21_file", ""))
+        freq = np.load(collection_dir / freq_file)
+        s21 = np.load(collection_dir / s21_file)
+        if freq.shape != s21.shape:
+            raise ValueError(f"Shape mismatch freq/s21 in {collection_dir}")
+        if not (np.isfinite(freq).all() and np.isfinite(np.real(s21)).all() and np.isfinite(np.imag(s21)).all()):
+            raise ValueError(f"Non-finite values in {collection_dir}")
+
+        label = f"i{int(trace_meta.get('input', 0))}_o{int(trace_meta.get('output', 0))}"
+        all_traces.append((np.asarray(freq, dtype=np.float64), np.asarray(s21, dtype=np.complex128), label))
+
+    return all_traces
+
+
+def _first_trace_from_results(collection_dir: Path) -> tuple[np.ndarray, np.ndarray, str] | None:
+    """Return first trace payload from results collection."""
+    traces = _all_traces_from_results(collection_dir)
+    if not traces:
+        return None
+    return traces[0]
+
+
+def _all_traces_from_results(collection_dir: Path) -> list[tuple[np.ndarray, np.ndarray, str]]:
+    """Load all trace-like payloads from results collection directory."""
+    meta = _load_json(collection_dir / "meta.json")
+    blocks = meta.get("blocks", [])
+    if not isinstance(blocks, list):
+        raise ValueError(f"Invalid blocks in {collection_dir / 'meta.json'}")
+
+    all_traces: list[tuple[np.ndarray, np.ndarray, str]] = []
+    for block in blocks:
+        if not isinstance(block, dict):
+            continue
+        block_dir_name = str(block.get("dir", ""))
+        block_dir = collection_dir / block_dir_name
+        payloads = block.get("payloads", [])
+        if not isinstance(payloads, list):
+            continue
+        for payload in payloads:
+            if not isinstance(payload, dict):
+                continue
+            kind = int(payload.get("kind", 0))
+            if kind != 1:
+                continue
+            freq_file = str(payload.get("freq_file", ""))
+            trace_file = str(payload.get("trace_file", ""))
+            freq = np.load(block_dir / freq_file)
+            trace = np.load(block_dir / trace_file)
+            if freq.shape != trace.shape:
+                raise ValueError(f"Shape mismatch freq/trace in {block_dir}")
+            if not (
+                np.isfinite(freq).all()
+                and np.isfinite(np.real(trace)).all()
+                and np.isfinite(np.imag(trace)).all()
+            ):
+                raise ValueError(f"Non-finite values in {block_dir}")
+
+            label = (
+                f"i{int(block.get('input', 0))}_o{int(block.get('output', 0))}_"
+                f"{str(payload.get('name', 'processor'))}"
+            )
+            all_traces.append((np.asarray(freq, dtype=np.float64), np.asarray(trace, dtype=np.complex128), label))
+
+    return all_traces
+
+
+def _validate_stage(stage_dir: Path, stage: str) -> tuple[list[int], list[tuple[np.ndarray, np.ndarray, str]]]:
+    """Validate one stage directory and collect representative traces."""
+    collection_ids: list[int] = []
+    traces: list[tuple[np.ndarray, np.ndarray, str]] = []
+    for collection_dir in _collection_dirs(stage_dir):
+        meta = _load_json(collection_dir / "meta.json")
+        collection_ids.append(int(meta.get("collection_id", -1)))
+        if stage in {"raw", "preprocessed"}:
+            trace = _first_trace_from_raw_or_pre(collection_dir)
+        else:
+            trace = _first_trace_from_results(collection_dir)
+        if trace is not None:
+            traces.append(trace)
+    return collection_ids, traces
+
+
+def _compare_two(
+    name: str,
+    first: tuple[np.ndarray, np.ndarray, str],
+    second: tuple[np.ndarray, np.ndarray, str],
+) -> None:
+    """Print numerical difference metrics for two traces."""
+    freq_a, data_a, label_a = first
+    freq_b, data_b, label_b = second
+    same_shape = freq_a.shape == freq_b.shape == data_a.shape == data_b.shape
+    if not same_shape:
+        print(f"[{name}] different shapes: {freq_a.shape}/{freq_b.shape} {data_a.shape}/{data_b.shape}")
+        return
+
+    are_equal = np.array_equal(data_a, data_b)
+    diff = data_a - data_b
+    max_abs_diff = float(np.max(np.abs(diff)))
+    l2_diff = float(np.linalg.norm(diff))
+    print(
+        f"[{name}] compare first two traces: {label_a} vs {label_b}, "
+        f"equal={are_equal}, max_abs_diff={max_abs_diff:.6g}, l2_diff={l2_diff:.6g}"
+    )
+
+
+def _plot_two(
+    stage: str,
+    first: tuple[np.ndarray, np.ndarray, str],
+    second: tuple[np.ndarray, np.ndarray, str],
+    output_dir: Path,
+) -> None:
+    """Plot magnitude comparison for two traces."""
+    try:
+        import matplotlib.pyplot as plt
+    except Exception as exc:  # noqa: BLE001
+        print(f"[{stage}] matplotlib is not available, plot skipped: {exc}")
+        return
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    freq_a, data_a, label_a = first
+    freq_b, data_b, label_b = second
+    if freq_a.shape != freq_b.shape or data_a.shape != data_b.shape:
+        print(f"[{stage}] shapes differ, plot skipped")
+        return
+
+    y_a = 20.0 * np.log10(np.maximum(np.abs(data_a), 1e-12))
+    y_b = 20.0 * np.log10(np.maximum(np.abs(data_b), 1e-12))
+
+    fig, ax = plt.subplots(figsize=(10, 4))
+    ax.plot(freq_a, y_a, linewidth=1.4, label=f"collection#1 {label_a}")
+    ax.plot(freq_b, y_b, linewidth=1.4, label=f"collection#2 {label_b}")
+    ax.set_title(f"{stage}: first two collections")
+    ax.set_xlabel("X axis")
+    ax.set_ylabel("Magnitude dB")
+    ax.grid(True, alpha=0.3)
+    ax.legend()
+    fig.tight_layout()
+
+    png_path = output_dir / f"{stage}_first_two.png"
+    fig.savefig(png_path, dpi=150)
+    plt.close(fig)
+    print(f"[{stage}] plot saved: {png_path}")
+
+
+def _plot_all_states_for_one_collection(stage: str, collection_dir: Path, output_dir: Path) -> None:
+    """Plot all switch-state traces available in one collection."""
+    try:
+        import matplotlib.pyplot as plt
+    except Exception as exc:  # noqa: BLE001
+        print(f"[{stage}] matplotlib is not available, all-states plot skipped: {exc}")
+        return
+
+    if stage in {"raw", "preprocessed"}:
+        traces = _all_traces_from_raw_or_pre(collection_dir)
+    else:
+        traces = _all_traces_from_results(collection_dir)
+
+    if not traces:
+        print(f"[{stage}] no trace payloads found in {collection_dir.name}, all-states plot skipped")
+        return
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    meta = _load_json(collection_dir / "meta.json")
+    collection_id = int(meta.get("collection_id", -1))
+
+    fig, ax = plt.subplots(figsize=(11, 5))
+    for freq, data, label in traces:
+        y = 20.0 * np.log10(np.maximum(np.abs(data), 1e-12))
+        ax.plot(freq, y, linewidth=1.2, label=label)
+    ax.set_title(f"{stage}: all switch states in one collection (id={collection_id})")
+    ax.set_xlabel("X axis")
+    ax.set_ylabel("Magnitude dB")
+    ax.grid(True, alpha=0.3)
+    ax.legend(fontsize=8, ncol=2)
+    fig.tight_layout()
+
+    png_path = output_dir / f"{stage}_all_states_one_collection.png"
+    fig.savefig(png_path, dpi=150)
+    plt.close(fig)
+    print(f"[{stage}] all-states plot saved: {png_path} (traces={len(traces)})")
+
+
+def _run(snapshot_dir: Path, output_dir: Path) -> None:
+    """Execute snapshot validation and plotting workflow."""
+    manifest_path = snapshot_dir / "manifest.json"
+    if manifest_path.exists():
+        manifest = _load_json(manifest_path)
+        print(
+            f"Snapshot: {snapshot_dir}\n"
+            f"selection_mode={manifest.get('selection_mode')} "
+            f"raw={manifest.get('raw_collections')} "
+            f"pre={manifest.get('preprocessed_collections')} "
+            f"res={manifest.get('result_collections')}"
+        )
+    else:
+        print(f"Snapshot: {snapshot_dir} (manifest.json is missing)")
+
+    stages = ("raw", "preprocessed", "results")
+    for stage in stages:
+        stage_dir = snapshot_dir / stage
+        collection_dirs = _collection_dirs(stage_dir)
+        collection_ids, traces = _validate_stage(stage_dir, stage)
+        duplicate_id_count = len(collection_ids) - len(set(collection_ids))
+        print(
+            f"[{stage}] collections={len(collection_ids)}, "
+            f"duplicate_ids={duplicate_id_count}, "
+            f"trace_samples={len(traces)}"
+        )
+        if len(traces) >= 2:
+            _compare_two(stage, traces[0], traces[1])
+            _plot_two(stage, traces[0], traces[1], output_dir)
+        else:
+            print(f"[{stage}] not enough trace-like collections to compare/plot (need >= 2)")
+
+        if collection_dirs:
+            _plot_all_states_for_one_collection(stage, collection_dirs[-1], output_dir)
+        else:
+            print(f"[{stage}] no collections for all-states plot")
+
+
+def main() -> None:
+    """CLI entrypoint."""
+    parser = argparse.ArgumentParser(description="Validate and visualize runtime numpy snapshot collections.")
+    parser.add_argument(
+        "snapshot_dir",
+        type=Path,
+        help="Path to snapshot directory (contains raw/preprocessed/results).",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=None,
+        help="Directory for output plots (default: <snapshot_dir>/inspection_plots).",
+    )
+    args = parser.parse_args()
+
+    snapshot_dir = args.snapshot_dir.expanduser().resolve()
+    if not snapshot_dir.exists():
+        raise FileNotFoundError(f"Snapshot directory not found: {snapshot_dir}")
+
+    output_dir = (
+        args.output_dir.expanduser().resolve()
+        if args.output_dir is not None
+        else snapshot_dir / "inspection_plots"
+    )
+    _run(snapshot_dir, output_dir)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python_app/scripts/convert_snapshot_to_vna_history.py b/python_app/scripts/convert_snapshot_to_vna_history.py
new file mode 100644
index 0000000..511285c
--- /dev/null
+++ b/python_app/scripts/convert_snapshot_to_vna_history.py
@@ -0,0 +1,353 @@
+"""Convert radar_system runtime snapshots into vna_system sweep-history JSON."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from collections import defaultdict
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+
+@dataclass(frozen=True)
+class TraceRecord:
+    """One raw/preprocessed trace extracted from a snapshot collection directory."""
+
+    stage: str
+    collection_id: int
+    monotonic_ns: int
+    stage_index: int
+    frequency_hz: np.ndarray
+    s21: np.ndarray
+
+
+@dataclass(frozen=True)
+class CollectionRef:
+    """Minimal collection identity descriptor for stage-alignment diagnostics."""
+
+    stage_index: int
+    collection_id: int
+    monotonic_ns: int
+
+
+def _load_json(path: Path) -> dict[str, Any]:
+    payload = json.loads(path.read_text(encoding="utf-8"))
+    if not isinstance(payload, dict):
+        raise ValueError(f"JSON root must be object: {path}")
+    return payload
+
+
+def _collection_dirs(stage_dir: Path) -> list[Path]:
+    if not stage_dir.exists():
+        return []
+    return sorted([path for path in stage_dir.iterdir() if path.is_dir()], key=lambda path: path.name)
+
+
+def _parse_stage_index(name: str, fallback: int) -> int:
+    prefix = name.split("_", 1)[0]
+    return int(prefix) if prefix.isdigit() else fallback
+
+
+def _pick_trace_meta(meta: dict[str, Any], input_index: int, output_index: int) -> dict[str, Any] | None:
+    traces = meta.get("traces", [])
+    if not isinstance(traces, list):
+        return None
+    for trace in traces:
+        if not isinstance(trace, dict):
+            continue
+        if int(trace.get("input", -1)) == input_index and int(trace.get("output", -1)) == output_index:
+            return trace
+    return None
+
+
+def _load_stage_records(
+    snapshot_dir: Path,
+    stage: str,
+    *,
+    input_index: int,
+    output_index: int,
+) -> list[TraceRecord]:
+    stage_dir = snapshot_dir / stage
+    records: list[TraceRecord] = []
+
+    for fallback_idx, collection_dir in enumerate(_collection_dirs(stage_dir)):
+        meta_path = collection_dir / "meta.json"
+        if not meta_path.exists():
+            continue
+
+        meta = _load_json(meta_path)
+        trace_meta = _pick_trace_meta(meta, input_index, output_index)
+        if trace_meta is None:
+            continue
+
+        freq_file = str(trace_meta.get("freq_file", ""))
+        s21_file = str(trace_meta.get("s21_file", ""))
+        if not freq_file or not s21_file:
+            continue
+
+        frequency_hz = np.asarray(np.load(collection_dir / freq_file), dtype=np.float64).reshape(-1)
+        s21 = np.asarray(np.load(collection_dir / s21_file), dtype=np.complex128).reshape(-1)
+        if frequency_hz.shape != s21.shape:
+            raise ValueError(f"Shape mismatch in {collection_dir}: freq{frequency_hz.shape} vs s21{s21.shape}")
+        if frequency_hz.size == 0:
+            continue
+        if not (
+            np.isfinite(frequency_hz).all()
+            and np.isfinite(np.real(s21)).all()
+            and np.isfinite(np.imag(s21)).all()
+        ):
+            raise ValueError(f"Non-finite values in {collection_dir}")
+
+        records.append(
+            TraceRecord(
+                stage=stage,
+                collection_id=int(meta.get("collection_id", -1)),
+                monotonic_ns=int(meta.get("monotonic_ns", 0)),
+                stage_index=_parse_stage_index(collection_dir.name, fallback_idx),
+                frequency_hz=frequency_hz,
+                s21=s21,
+            )
+        )
+
+    return records
+
+
+def _load_stage_refs(snapshot_dir: Path, stage: str) -> list[CollectionRef]:
+    """Load `(index, collection_id, monotonic_ns)` for one snapshot stage."""
+    stage_dir = snapshot_dir / stage
+    refs: list[CollectionRef] = []
+
+    for fallback_idx, collection_dir in enumerate(_collection_dirs(stage_dir)):
+        meta_path = collection_dir / "meta.json"
+        if not meta_path.exists():
+            continue
+        meta = _load_json(meta_path)
+        refs.append(
+            CollectionRef(
+                stage_index=_parse_stage_index(collection_dir.name, fallback_idx),
+                collection_id=int(meta.get("collection_id", -1)),
+                monotonic_ns=int(meta.get("monotonic_ns", 0)),
+            )
+        )
+    return refs
+
+
+def _index_by_collection_occurrence(records: list[TraceRecord]) -> tuple[dict[tuple[int, int], TraceRecord], list[tuple[int, int]]]:
+    counters: defaultdict[int, int] = defaultdict(int)
+    record_map: dict[tuple[int, int], TraceRecord] = {}
+    order: list[tuple[int, int]] = []
+    for record in records:
+        occurrence = counters[record.collection_id]
+        counters[record.collection_id] += 1
+        key = (record.collection_id, occurrence)
+        record_map[key] = record
+        order.append(key)
+    return record_map, order
+
+
+def _complex_to_points(values: np.ndarray) -> list[list[float]]:
+    return [[float(v.real), float(v.imag)] for v in values]
+
+
+def _build_sweep_history(
+    raw_records: list[TraceRecord],
+    preprocessed_records: list[TraceRecord],
+    *,
+    primary_stage: str,
+) -> list[dict[str, Any]]:
+    raw_map, raw_order = _index_by_collection_occurrence(raw_records)
+    pre_map, pre_order = _index_by_collection_occurrence(preprocessed_records)
+
+    if primary_stage == "preprocessed":
+        primary_order = pre_order or raw_order
+    else:
+        primary_order = raw_order or pre_order
+
+    history: list[dict[str, Any]] = []
+    for fallback_index, key in enumerate(primary_order):
+        raw = raw_map.get(key)
+        pre = pre_map.get(key)
+        base = pre or raw
+        if base is None:
+            continue
+
+        # vna_system uses calibrated_data if present, otherwise sweep_data.
+        sweep_source = raw or pre
+        calibrated_source = pre or raw
+        if sweep_source is None or calibrated_source is None:
+            continue
+
+        start_freq_hz = float(base.frequency_hz[0])
+        stop_freq_hz = float(base.frequency_hz[-1])
+        timestamp_sec = float(base.monotonic_ns) / 1_000_000_000.0 if base.monotonic_ns > 0 else float(fallback_index)
+
+        history.append(
+            {
+                "timestamp": timestamp_sec,
+                "sweep_points": _complex_to_points(sweep_source.s21),
+                "calibrated_points": _complex_to_points(calibrated_source.s21),
+                "reference_points": [],
+                "vna_config": {
+                    "mode": "s11",
+                    "start_freq": start_freq_hz,
+                    "stop_freq": stop_freq_hz,
+                    "points": int(base.frequency_hz.size),
+                },
+            }
+        )
+
+    return history
+
+
+def _stage_alignment_warning(pre_refs: list[CollectionRef], result_refs: list[CollectionRef]) -> str | None:
+    """Return warning text when preprocessed/results stages are not identity-aligned."""
+    if not pre_refs or not result_refs:
+        return None
+
+    pre_by_index = {ref.stage_index: ref for ref in pre_refs}
+    result_by_index = {ref.stage_index: ref for ref in result_refs}
+    common_indices = sorted(set(pre_by_index) & set(result_by_index))
+    if not common_indices:
+        return None
+
+    mismatches = 0
+    first_mismatch: tuple[int, CollectionRef, CollectionRef] | None = None
+    for index in common_indices:
+        pre = pre_by_index[index]
+        result = result_by_index[index]
+        if pre.collection_id != result.collection_id or pre.monotonic_ns != result.monotonic_ns:
+            mismatches += 1
+            if first_mismatch is None:
+                first_mismatch = (index, pre, result)
+
+    if mismatches == 0:
+        return None
+
+    assert first_mismatch is not None
+    idx, pre, result = first_mismatch
+    return (
+        "WARNING: snapshot stages are not fully aligned (preprocessed vs results). "
+        f"Mismatches={mismatches}/{len(common_indices)}. "
+        f"First mismatch at index={idx}: "
+        f"pre=(id={pre.collection_id},ns={pre.monotonic_ns}) vs "
+        f"results=(id={result.collection_id},ns={result.monotonic_ns}). "
+        "Export uses preprocessed traces; loaded view in vna_system may differ from "
+        "radar_system on-screen replayed results."
+    )
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Convert radar_system snapshot (numpy-directory-v1) to a vna_system-compatible "
+            "history JSON file with `sweep_history`."
+        )
+    )
+    parser.add_argument("snapshot_dir", type=Path, help="Path to snapshot directory containing raw/preprocessed/results.")
+    parser.add_argument(
+        "-o",
+        "--output",
+        type=Path,
+        default=None,
+        help="Output JSON path (default: <snapshot_dir>/vna_bscan_history.json).",
+    )
+    parser.add_argument("--input", dest="input_index", type=int, default=0, help="Input switch index to export.")
+    parser.add_argument("--output-index", dest="output_index", type=int, default=0, help="Output switch index to export.")
+    parser.add_argument(
+        "--primary-stage",
+        choices=("preprocessed", "raw"),
+        default="preprocessed",
+        help="Stage order to drive collection selection/alignment.",
+    )
+    parser.add_argument(
+        "--last-n",
+        type=int,
+        default=0,
+        help="Keep only the last N sweeps in output (0 means all available).",
+    )
+    return parser
+
+
+def main() -> None:
+    parser = _build_parser()
+    args = parser.parse_args()
+
+    snapshot_dir = args.snapshot_dir.expanduser().resolve()
+    if not snapshot_dir.exists():
+        raise FileNotFoundError(f"Snapshot directory not found: {snapshot_dir}")
+
+    output_path = (
+        args.output.expanduser().resolve()
+        if args.output is not None
+        else snapshot_dir / "vna_bscan_history.json"
+    )
+
+    raw_records = _load_stage_records(
+        snapshot_dir,
+        "raw",
+        input_index=args.input_index,
+        output_index=args.output_index,
+    )
+    preprocessed_records = _load_stage_records(
+        snapshot_dir,
+        "preprocessed",
+        input_index=args.input_index,
+        output_index=args.output_index,
+    )
+    if not raw_records and not preprocessed_records:
+        raise ValueError(
+            "No matching raw/preprocessed traces were found in snapshot "
+            f"for input={args.input_index}, output={args.output_index}."
+        )
+
+    sweep_history = _build_sweep_history(raw_records, preprocessed_records, primary_stage=args.primary_stage)
+    if args.last_n > 0:
+        sweep_history = sweep_history[-args.last_n :]
+    if not sweep_history:
+        raise ValueError("Conversion produced empty sweep_history.")
+
+    pre_refs = _load_stage_refs(snapshot_dir, "preprocessed")
+    result_refs = _load_stage_refs(snapshot_dir, "results")
+    alignment_warning = _stage_alignment_warning(pre_refs, result_refs)
+
+    manifest_path = snapshot_dir / "manifest.json"
+    manifest = _load_json(manifest_path) if manifest_path.exists() else {}
+
+    payload: dict[str, Any] = {
+        "format": "vna-system-history-v1",
+        "converter": "python_app/scripts/convert_snapshot_to_vna_history.py",
+        "converted_at_utc": datetime.now(timezone.utc).isoformat(),
+        "source_snapshot_dir": str(snapshot_dir),
+        "input_index": int(args.input_index),
+        "output_index": int(args.output_index),
+        "primary_stage": args.primary_stage,
+        "raw_record_count": len(raw_records),
+        "preprocessed_record_count": len(preprocessed_records),
+        "sweep_history": sweep_history,
+    }
+    if alignment_warning is not None:
+        payload["alignment_warning"] = alignment_warning
+    if manifest:
+        payload["snapshot_manifest"] = manifest
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    output_path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
+
+    print(
+        "Converted snapshot to vna_system history JSON:\n"
+        f"  input snapshot: {snapshot_dir}\n"
+        f"  output file:    {output_path}\n"
+        f"  sweeps written: {len(sweep_history)}\n"
+        f"  raw records:    {len(raw_records)}\n"
+        f"  pre records:    {len(preprocessed_records)}"
+    )
+    if alignment_warning is not None:
+        print(f"[convert-warning] {alignment_warning}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python_app/scripts/hardware_raw_orchestrator_test.py b/python_app/scripts/hardware_raw_orchestrator_test.py
new file mode 100644
index 0000000..626dfa1
--- /dev/null
+++ b/python_app/scripts/hardware_raw_orchestrator_test.py
@@ -0,0 +1,360 @@
+"""Standalone GUI utility for inspecting raw orchestrator ring output."""
+
+from __future__ import annotations
+
+import argparse
+import ctypes
+import ctypes.util
+import json
+import os
+from pathlib import Path
+import signal
+import subprocess
+import sys
+import time
+
+import numpy as np
+from PyQt6.QtCore import QTimer
+from PyQt6.QtWidgets import QApplication, QLabel, QMainWindow, QVBoxLayout, QWidget
+import pyqtgraph as pg
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from python_app.hardware_full.librevna_service import LibreVnaService
+from python_app.models.run_config_model import RadarSweepModel
+from python_app.orchestration.shm_reader import ShmRingReader
+
+
+def _shm_unlink(name: str) -> None:
+    """Best-effort unlink for POSIX shared-memory object."""
+    libc_name = ctypes.util.find_library("c")
+    if libc_name is None:
+        return
+
+    libc = ctypes.CDLL(libc_name, use_errno=True)
+    libc.shm_unlink.argtypes = [ctypes.c_char_p]
+    libc.shm_unlink.restype = ctypes.c_int
+
+    result = libc.shm_unlink(name.encode("utf-8"))
+    if result == 0:
+        return
+
+    err = ctypes.get_errno()
+    if err != 2:  # ENOENT
+        raise OSError(err, f"shm_unlink failed for {name}")
+
+
+def _read_raw_ring_name(config_path: Path) -> str:
+    """Extract raw ring name from run config."""
+    config = json.loads(config_path.read_text(encoding="utf-8"))
+    ring_name = config["rings"]["raw"]["name"]
+    if not isinstance(ring_name, str) or not ring_name.startswith("/"):
+        raise RuntimeError("Config rings.raw.name must be a POSIX shm name starting with '/'")
+    return ring_name
+
+
+def _read_native_summary(config_path: Path) -> str:
+    """Build short summary of radar/switch driver modes."""
+    config = json.loads(config_path.read_text(encoding="utf-8"))
+    radar_mode = config["radar"]["driver_mode"]
+    switches = config["switches"]
+    if "port1" in switches and "port2" in switches:
+        port1_mode = switches["port1"]["driver_mode"]
+        port2_mode = switches["port2"]["driver_mode"]
+    else:
+        port1_mode = switches["output"]["driver_mode"]
+        port2_mode = switches["input"]["driver_mode"]
+    return f"radar={radar_mode}, port1={port1_mode}, port2={port2_mode}"
+
+
+def _prepare_radar_if_needed(config_path: Path, *, strict: bool) -> str | None:
+    """Preconfigure native radar through Python service when requested."""
+    config = json.loads(config_path.read_text(encoding="utf-8"))
+    radar = config["radar"]
+    if radar["driver_mode"] != "native":
+        return "Radar pre-configuration skipped (mock mode)."
+
+    sweep = radar["sweep"]
+    sweep_model = RadarSweepModel(
+        start_hz=float(sweep["start_hz"]),
+        stop_hz=float(sweep["stop_hz"]),
+        points=int(sweep["points"]),
+        if_bandwidth_hz=float(sweep["if_bandwidth_hz"]),
+        power_dbm=float(sweep.get("stimulus_power_dbm", -10.0)),
+    )
+
+    radar_service = LibreVnaService(serial=radar.get("serial") or None)
+    if not radar_service.driver_available:
+        message = "LibreVNA Python driver is unavailable: skipping pre-configuration"
+        if strict:
+            raise RuntimeError(message)
+        return message
+
+    try:
+        radar_service.open()
+        radar_service.configure(sweep_model)
+        return "Radar pre-configuration completed."
+    except Exception as exc:
+        message = f"Radar pre-configuration failed ({exc})"
+        if strict:
+            raise
+        return f"{message}. Continuing with native C++ configuration."
+    finally:
+        radar_service.close()
+
+
+class RawOrchestratorViewer(QMainWindow):
+    """Qt window that runs sweep_orchestrator and plots raw collections."""
+
+    def __init__(self, config_path: Path, reset_ring: bool, prepare_radar: bool, strict_prepare: bool) -> None:
+        """Initialize viewer, optionally prepare radar, and start polling."""
+        super().__init__()
+
+        self._config_path = config_path
+        self._raw_ring_name = _read_raw_ring_name(config_path)
+        self._orchestrator_process: subprocess.Popen[str] | None = None
+        self._raw_reader: ShmRingReader | None = None
+
+        if reset_ring:
+            _shm_unlink(self._raw_ring_name)
+
+        self._build_ui()
+        if prepare_radar:
+            self._status.setText("Preparing radar...")
+            prepare_status = _prepare_radar_if_needed(self._config_path, strict=strict_prepare)
+            if prepare_status is not None:
+                self._status.setText(prepare_status)
+        self._start_orchestrator()
+        self._open_reader_or_fail()
+
+        self._timer = QTimer(self)
+        self._timer.setInterval(60)
+        self._timer.timeout.connect(self._poll)
+        self._timer.start()
+
+    def _build_ui(self) -> None:
+        """Build viewer widgets and raw trace plot."""
+        self.setWindowTitle("Raw Sweep Viewer (orchestrator)")
+        root = QWidget(self)
+        self.setCentralWidget(root)
+
+        layout = QVBoxLayout(root)
+
+        mode_summary = _read_native_summary(self._config_path)
+        self._status = QLabel(f"Starting... ({mode_summary})")
+        layout.addWidget(self._status)
+
+        self._plot = pg.PlotWidget(background="#101418")
+        self._plot.showGrid(x=True, y=True, alpha=0.2)
+        self._plot.setLabel("bottom", "Frequency", units="Hz")
+        self._plot.setLabel("left", "Magnitude", units="dB")
+        layout.addWidget(self._plot)
+
+        self.resize(1400, 900)
+
+    def _start_orchestrator(self) -> None:
+        """Start sweep orchestrator subprocess."""
+        binary = PROJECT_ROOT / "build/bin/sweep_orchestrator"
+        if not binary.exists():
+            raise RuntimeError(f"Missing binary: {binary}. Build first with 'make -j4'")
+
+        command = [str(binary), "--config", str(self._config_path)]
+        self._orchestrator_process = subprocess.Popen(
+            command,
+            cwd=PROJECT_ROOT,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+        )
+
+    def _open_reader_or_fail(self) -> None:
+        """Wait for raw ring readiness and open `ShmRingReader`."""
+        if self._orchestrator_process is None:
+            raise RuntimeError("Orchestrator process is not started")
+
+        shm_path = Path("/dev/shm") / self._raw_ring_name[1:]
+        deadline = time.monotonic() + 5.0
+        last_reader_error: str | None = None
+
+        while time.monotonic() < deadline:
+            return_code = self._orchestrator_process.poll()
+            if return_code is not None:
+                details = self._read_process_output(self._orchestrator_process)
+                raise RuntimeError(f"sweep_orchestrator exited with code {return_code}: {details}")
+
+            if shm_path.exists():
+                try:
+                    self._raw_reader = ShmRingReader(self._raw_ring_name)
+                    self._status.setText(f"Running: ring={self._raw_ring_name}")
+                    return
+                except RuntimeError as exc:
+                    last_reader_error = str(exc)
+
+            time.sleep(0.05)
+
+        if last_reader_error is not None:
+            raise RuntimeError(
+                f"Timed out waiting for raw ring header readiness: {self._raw_ring_name}; "
+                f"last error: {last_reader_error}"
+            )
+        raise RuntimeError(f"Timed out waiting for raw ring file: {shm_path}")
+
+    def _poll(self) -> None:
+        """Poll subprocess state and draw latest available raw collection."""
+        if self._orchestrator_process is None:
+            return
+
+        return_code = self._orchestrator_process.poll()
+        if return_code is not None:
+            details = self._read_process_output(self._orchestrator_process)
+            self._status.setText(f"Error: sweep_orchestrator exited ({return_code})")
+            raise RuntimeError(f"sweep_orchestrator exited with code {return_code}: {details}")
+
+        if self._raw_reader is None:
+            return
+
+        latest = None
+        for _ in range(16):
+            collection = self._raw_reader.pop_raw_collection()
+            if collection is None:
+                break
+            latest = collection
+
+        if latest is not None:
+            self._draw_collection(latest)
+
+    def _draw_collection(self, collection) -> None:
+        """Render all traces from one raw collection."""
+        self._plot.clear()
+        palette = [
+            "#4cc9f0",
+            "#f72585",
+            "#b8f2e6",
+            "#ffd166",
+            "#90be6d",
+            "#ff595e",
+            "#6a4c93",
+            "#1982c4",
+            "#ff9f1c",
+            "#2ec4b6",
+            "#e71d36",
+            "#a0c4ff",
+        ]
+
+        for idx, trace in enumerate(collection.traces):
+            magnitude_db = 20.0 * np.log10(np.maximum(np.abs(trace.s21), 1e-12))
+            label = f"input={trace.combo.input_pos}, output={trace.combo.output_pos}"
+            self._plot.plot(
+                trace.frequency_hz,
+                magnitude_db,
+                pen=pg.mkPen(palette[idx % len(palette)], width=1.6),
+                name=label,
+            )
+
+        self._status.setText(
+            f"Running: collection_id={collection.collection_id}, "
+            f"traces={len(collection.traces)}, ring={self._raw_ring_name}"
+        )
+
+    @staticmethod
+    def _read_process_output(process: subprocess.Popen[str]) -> str:
+        """Collect process stdout/stderr text for diagnostics."""
+        stdout = ""
+        stderr = ""
+        if process.stdout is not None:
+            stdout = process.stdout.read().strip()
+        if process.stderr is not None:
+            stderr = process.stderr.read().strip()
+
+        if stderr and stdout:
+            return f"{stderr}\nstdout:\n{stdout}"
+        if stderr:
+            return stderr
+        if stdout:
+            return f"stdout:\n{stdout}"
+        return "no output"
+
+    def closeEvent(self, event) -> None:  # noqa: N802
+        """Stop subprocess and close reader before window destruction."""
+        self._shutdown()
+        super().closeEvent(event)
+
+    def _shutdown(self) -> None:
+        """Close reader and terminate subprocess."""
+        if self._raw_reader is not None:
+            self._raw_reader.close()
+            self._raw_reader = None
+
+        process = self._orchestrator_process
+        self._orchestrator_process = None
+        if process is None:
+            return
+
+        if process.poll() is None:
+            process.terminate()
+            try:
+                process.wait(timeout=2.0)
+            except subprocess.TimeoutExpired:
+                process.kill()
+                process.wait(timeout=1.0)
+
+
+def main() -> int:
+    """CLI entrypoint for raw orchestrator viewer."""
+    parser = argparse.ArgumentParser(description="Run sweep_orchestrator and plot raw sweep collections")
+    parser.add_argument(
+        "--config",
+        type=Path,
+        default=PROJECT_ROOT / "run_config.json",
+        help="Path to run config JSON",
+    )
+    parser.add_argument(
+        "--no-reset-ring",
+        action="store_true",
+        help="Do not unlink existing raw ring name before starting",
+    )
+    parser.add_argument(
+        "--skip-radar-prepare",
+        action="store_true",
+        help="Skip Python pre-configuration of native radar before orchestrator start",
+    )
+    parser.add_argument(
+        "--strict-radar-prepare",
+        action="store_true",
+        help="Fail immediately if Python pre-configuration cannot run",
+    )
+    args = parser.parse_args()
+
+    config_path = args.config.resolve()
+    if not config_path.exists():
+        raise FileNotFoundError(f"Config file not found: {config_path}")
+
+    if os.geteuid() == 0 and os.environ.get("SUDO_USER"):
+        print(
+            "Warning: running GUI test via sudo can break Qt DBus/session integration. "
+            "Prefer regular user with USB/GPIO permissions."
+        )
+
+    app = QApplication(sys.argv)
+    viewer = RawOrchestratorViewer(
+        config_path=config_path,
+        reset_ring=not args.no_reset_ring,
+        prepare_radar=not args.skip_radar_prepare,
+        strict_prepare=args.strict_radar_prepare,
+    )
+    viewer.show()
+
+    def _sig_handler(_signum, _frame):
+        """Close viewer gracefully on process signals."""
+        viewer.close()
+
+    signal.signal(signal.SIGINT, _sig_handler)
+    signal.signal(signal.SIGTERM, _sig_handler)
+
+    return app.exec()
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/python_app/scripts/manual_smoke_run.py b/python_app/scripts/manual_smoke_run.py
new file mode 100644
index 0000000..c6eb350
--- /dev/null
+++ b/python_app/scripts/manual_smoke_run.py
@@ -0,0 +1,152 @@
+"""Manual smoke scenario for end-to-end pipeline check without GUI."""
+
+from __future__ import annotations
+
+import argparse
+import ctypes
+import ctypes.util
+import os
+from pathlib import Path
+import sys
+import time
+
+import numpy as np
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from python_app.models.dataset_model import ComboKey, SweepCollection, TraceData
+from python_app.models.run_config_model import RunConfigModel
+from python_app.orchestration.config_writer import ConfigWriter
+from python_app.orchestration.process_supervisor import ProcessSupervisor
+from python_app.orchestration.shm_reader import ShmRingReader
+from python_app.storage.npz_store import NpzStore, radar_key_from_config
+
+
+def _make_unique_ring_name(prefix: str) -> str:
+    """Build unique POSIX SHM ring name."""
+    stamp = time.monotonic_ns()
+    return f"/{prefix}_{os.getpid()}_{stamp}"
+
+
+def _shm_unlink(name: str) -> None:
+    """Best-effort unlink for POSIX shared-memory object."""
+    libc_name = ctypes.util.find_library("c")
+    if libc_name is None:
+        return
+
+    libc = ctypes.CDLL(libc_name, use_errno=True)
+    libc.shm_unlink.argtypes = [ctypes.c_char_p]
+    libc.shm_unlink.restype = ctypes.c_int
+
+    result = libc.shm_unlink(name.encode("utf-8"))
+    if result == 0:
+        return
+
+    err = ctypes.get_errno()
+    if err != 2:  # ENOENT
+        raise OSError(err, f"shm_unlink failed for {name}")
+
+
+def build_synthetic_collection(config: RunConfigModel, value_scale: float) -> SweepCollection:
+    """Build synthetic sweep collection for all configured switch combos."""
+    traces: list[TraceData] = []
+    combos = RunConfigModel.build_full_combos(config.input_switch.positions, config.output_switch.positions)
+
+    for combo in combos:
+        frequency_hz = np.linspace(
+            config.radar.sweep.start_hz,
+            config.radar.sweep.stop_hz,
+            config.radar.sweep.points,
+            dtype=np.float32,
+        )
+        phase = np.linspace(0.0, np.pi * 2.0, config.radar.sweep.points, dtype=np.float32)
+        s21 = value_scale * (np.cos(phase) + 1j * np.sin(phase)).astype(np.complex64)
+
+        traces.append(
+            TraceData(
+                combo=ComboKey(input_pos=combo.input, output_pos=combo.output),
+                frequency_hz=frequency_hz,
+                s21=s21,
+            )
+        )
+
+    return SweepCollection(collection_id=1, monotonic_ns=time.monotonic_ns(), traces=traces)
+
+
+def main() -> int:
+    """Run manual smoke-test pipeline scenario."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--duration", type=float, default=3.0)
+    args = parser.parse_args()
+
+    project_root = PROJECT_ROOT
+    store = NpzStore(project_root / "python_app/data")
+    config_writer = ConfigWriter(project_root / "python_app/runtime")
+    supervisor = ProcessSupervisor(project_root)
+
+    config = RunConfigModel.load_from_path(project_root / "run_config.json")
+    config.radar.driver_mode = "mock"
+    config.input_switch.driver_mode = "mock"
+    config.output_switch.driver_mode = "mock"
+    config.combos = RunConfigModel.build_full_combos(config.input_switch.positions, config.output_switch.positions)
+    config.rings.raw.name = _make_unique_ring_name("radar_raw_smoke")
+    config.rings.raw_tap.name = _make_unique_ring_name("radar_raw_tap_smoke")
+    config.rings.preprocessed.name = _make_unique_ring_name("radar_preprocessed_smoke")
+    config.rings.preprocessed_tap.name = _make_unique_ring_name("radar_preprocessed_tap_smoke")
+    config.rings.results.name = _make_unique_ring_name("radar_results_smoke")
+
+    radar_key = radar_key_from_config(
+        model_name=config.radar.model,
+        serial=config.radar.serial,
+        sweep_start_hz=config.radar.sweep.start_hz,
+        sweep_stop_hz=config.radar.sweep.stop_hz,
+        sweep_points=config.radar.sweep.points,
+        ifbw_hz=config.radar.sweep.if_bandwidth_hz,
+        power_dbm=config.radar.sweep.power_dbm,
+    )
+
+    calibration_set = build_synthetic_collection(config, value_scale=1.0)
+    reference_set = build_synthetic_collection(config, value_scale=0.3)
+
+    store.save_set("calibration", radar_key, "smoke_cal", calibration_set)
+    store.save_set("reference", radar_key, "smoke_ref", reference_set)
+
+    calibration_bundle, reference_bundle = config_writer.prepare_bundles(store, radar_key, "smoke_cal", "smoke_ref")
+    config.preprocess.calibration_set = "smoke_cal"
+    config.preprocess.reference_set = "smoke_ref"
+    config.preprocess.calibration_bundle_path = str(calibration_bundle)
+    config.preprocess.reference_bundle_path = str(reference_bundle)
+
+    config_path = config_writer.write(config, project_root / "python_app/runtime/run_config_smoke.json")
+
+    result_reader: ShmRingReader | None = None
+    try:
+        supervisor.start(config_path)
+        result_reader = ShmRingReader(config.rings.results.name)
+        deadline = time.monotonic() + args.duration
+        received = 0
+
+        while time.monotonic() < deadline:
+            result = result_reader.pop_result_collection() if result_reader is not None else None
+            if result is not None:
+                received += 1
+            time.sleep(0.02)
+
+        print(f"Received result collections: {received}")
+    finally:
+        supervisor.stop_all()
+        if result_reader is not None:
+            result_reader.close()
+        _shm_unlink(config.rings.raw.name)
+        _shm_unlink(config.rings.raw_tap.name)
+        _shm_unlink(config.rings.preprocessed.name)
+        _shm_unlink(config.rings.preprocessed_tap.name)
+        _shm_unlink(config.rings.results.name)
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/python_app/scripts/vna_only_raw_test.py b/python_app/scripts/vna_only_raw_test.py
new file mode 100644
index 0000000..8059789
--- /dev/null
+++ b/python_app/scripts/vna_only_raw_test.py
@@ -0,0 +1,72 @@
+"""Minimal GUI tool for direct LibreVNA raw acquisition checks."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+import signal
+import sys
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from PyQt6.QtWidgets import QApplication
+
+from python_app.scripts.hardware_raw_orchestrator_test import RawOrchestratorViewer
+
+
+def main() -> int:
+    """Run standalone raw-viewer GUI against a selected run config."""
+    parser = argparse.ArgumentParser(
+        description="Intermediate test: native VNA acquisition with mock switch drivers"
+    )
+    parser.add_argument(
+        "--config",
+        type=Path,
+        default=PROJECT_ROOT / "run_config.json",
+        help="Path to run config JSON",
+    )
+    parser.add_argument(
+        "--no-reset-ring",
+        action="store_true",
+        help="Do not unlink existing raw ring name before starting",
+    )
+    parser.add_argument(
+        "--skip-radar-prepare",
+        action="store_true",
+        help="Skip Python pre-configuration of native radar before orchestrator start",
+    )
+    parser.add_argument(
+        "--strict-radar-prepare",
+        action="store_true",
+        help="Fail immediately if Python pre-configuration cannot run",
+    )
+    args = parser.parse_args()
+
+    config_path = args.config.resolve()
+    if not config_path.exists():
+        raise FileNotFoundError(f"Config file not found: {config_path}")
+
+    app = QApplication(sys.argv)
+    viewer = RawOrchestratorViewer(
+        config_path=config_path,
+        reset_ring=not args.no_reset_ring,
+        prepare_radar=not args.skip_radar_prepare,
+        strict_prepare=args.strict_radar_prepare,
+    )
+    viewer.setWindowTitle("Raw Sweep Viewer (VNA native + mock switches)")
+    viewer.show()
+
+    def _sig_handler(_signum, _frame):
+        """Close viewer gracefully on process signals."""
+        viewer.close()
+
+    signal.signal(signal.SIGINT, _sig_handler)
+    signal.signal(signal.SIGTERM, _sig_handler)
+
+    return app.exec()
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/python_app/storage/__init__.py b/python_app/storage/__init__.py
new file mode 100644
index 0000000..1fd3851
--- /dev/null
+++ b/python_app/storage/__init__.py
@@ -0,0 +1 @@
+"""Persistent storage interfaces and implementations."""
diff --git a/python_app/storage/npz/__init__.py b/python_app/storage/npz/__init__.py
new file mode 100644
index 0000000..f03262d
--- /dev/null
+++ b/python_app/storage/npz/__init__.py
@@ -0,0 +1,13 @@
+"""NPZ-based dataset storage implementation and serialization helpers."""
+
+from python_app.storage.npz.paths import radar_key_from_config
+from python_app.storage.npz.serialize import PREPROC_MAGIC, RAW_MAGIC, RESULT_MAGIC
+from python_app.storage.npz.store import NpzStore
+
+__all__ = [
+    "NpzStore",
+    "PREPROC_MAGIC",
+    "RAW_MAGIC",
+    "RESULT_MAGIC",
+    "radar_key_from_config",
+]
diff --git a/python_app/storage/npz/paths.py b/python_app/storage/npz/paths.py
new file mode 100644
index 0000000..6279ac4
--- /dev/null
+++ b/python_app/storage/npz/paths.py
@@ -0,0 +1,52 @@
+"""Path and naming helpers for NPZ snapshot storage."""
+
+from __future__ import annotations
+
+import re
+
+
+def radar_key_from_config(
+    model_name: str,
+    serial: str,
+    sweep_start_hz: float,
+    sweep_stop_hz: float,
+    sweep_points: int,
+    ifbw_hz: float,
+    power_dbm: float,
+) -> str:
+    """Build deterministic key for calibration/reference set lookup."""
+    serial_part = serial or "no_serial"
+    start_token = _format_float_for_key(sweep_start_hz)
+    stop_token = _format_float_for_key(sweep_stop_hz)
+    ifbw_token = _format_float_for_key(ifbw_hz)
+    power_token = _format_float_for_key(power_dbm)
+    return (
+        f"{model_name}_{serial_part}"
+        f"_st{start_token}_sp{stop_token}"
+        f"_p{sweep_points}_if{ifbw_token}_pw{power_token}"
+    )
+
+
+def collection_dir_name(index: int, collection_id: int, monotonic_ns: int) -> str:
+    """Build canonical collection directory name for snapshot stages."""
+    return f"{index:04d}_id{int(collection_id)}_ns{int(monotonic_ns)}"
+
+
+def sanitize_path_component(name: str) -> str:
+    """Convert arbitrary string into filesystem-safe component."""
+    cleaned = re.sub(r"[^A-Za-z0-9._-]+", "_", name.strip())
+    cleaned = cleaned.strip("._")
+    return cleaned or "snapshot"
+
+
+def format_float_for_key(value: float) -> str:
+    """Format float compactly for deterministic key serialization."""
+    integer = int(round(value))
+    if abs(value - float(integer)) < 1e-6:
+        return str(integer)
+    return f"{value:.6f}".rstrip("0").rstrip(".")
+
+
+def _format_float_for_key(value: float) -> str:
+    """Private alias preserved for internal compatibility."""
+    return format_float_for_key(value)
diff --git a/python_app/storage/npz/serialize.py b/python_app/storage/npz/serialize.py
new file mode 100644
index 0000000..3a3210b
--- /dev/null
+++ b/python_app/storage/npz/serialize.py
@@ -0,0 +1,77 @@
+"""Binary payload serialization for sweep/result collections."""
+
+from __future__ import annotations
+
+import struct
+
+import numpy as np
+
+from python_app.models.dataset_model import ResultCollection, SweepCollection
+
+RAW_MAGIC = 0x31574152
+PREPROC_MAGIC = 0x31525050
+RESULT_MAGIC = 0x314C5352
+
+
+def serialize_trace_collection(collection: SweepCollection, magic: int) -> bytes:
+    """Serialize one raw/preprocessed trace collection into ring-compatible binary format."""
+    buffer = bytearray()
+    buffer.extend(
+        struct.pack("<IQQI", magic, collection.collection_id, collection.monotonic_ns, len(collection.traces))
+    )
+
+    for trace in collection.traces:
+        freq = np.asarray(trace.frequency_hz, dtype=np.float32)
+        s21 = np.asarray(trace.s21, dtype=np.complex64)
+        if freq.size != s21.size:
+            raise ValueError("Trace frequency and S21 sizes must match")
+
+        buffer.extend(struct.pack("<III", trace.combo.input_pos, trace.combo.output_pos, int(freq.size)))
+        buffer.extend(freq.astype("<f4", copy=False).tobytes())
+
+        interleaved = np.empty(freq.size * 2, dtype="<f4")
+        interleaved[0::2] = s21.real.astype("<f4", copy=False)
+        interleaved[1::2] = s21.imag.astype("<f4", copy=False)
+        buffer.extend(interleaved.tobytes())
+
+    return bytes(buffer)
+
+
+def serialize_result_collection(collection: ResultCollection) -> bytes:
+    """Serialize one processed collection with result blocks/payloads."""
+    buffer = bytearray()
+    buffer.extend(
+        struct.pack("<IQQI", RESULT_MAGIC, collection.collection_id, collection.monotonic_ns, len(collection.blocks))
+    )
+
+    for block in collection.blocks:
+        buffer.extend(struct.pack("<II", block.combo.input_pos, block.combo.output_pos))
+        buffer.extend(struct.pack("<I", len(block.payloads)))
+
+        for payload in block.payloads:
+            name_bytes = payload.processing_name.encode("utf-8")
+            if len(name_bytes) > 0xFFFF:
+                raise ValueError("processing_name is too long")
+
+            buffer.extend(struct.pack("<BH", payload.kind, len(name_bytes)))
+            buffer.extend(name_bytes)
+
+            if payload.kind == 1:
+                freq = np.asarray(payload.frequency_hz, dtype=np.float32)
+                trace = np.asarray(payload.trace, dtype=np.complex64)
+                if freq.size != trace.size:
+                    raise ValueError("Result trace frequency and values sizes must match")
+
+                buffer.extend(struct.pack("<I", int(freq.size)))
+                buffer.extend(freq.astype("<f4", copy=False).tobytes())
+
+                interleaved = np.empty(freq.size * 2, dtype="<f4")
+                interleaved[0::2] = trace.real.astype("<f4", copy=False)
+                interleaved[1::2] = trace.imag.astype("<f4", copy=False)
+                buffer.extend(interleaved.tobytes())
+            elif payload.kind == 2:
+                buffer.extend(struct.pack("<f", float(payload.scalar_value)))
+            else:
+                raise ValueError(f"Unsupported payload kind: {payload.kind}")
+
+    return bytes(buffer)
diff --git a/python_app/storage/npz/snapshot_numpy.py b/python_app/storage/npz/snapshot_numpy.py
new file mode 100644
index 0000000..ce34b17
--- /dev/null
+++ b/python_app/storage/npz/snapshot_numpy.py
@@ -0,0 +1,225 @@
+"""Snapshot selection and filesystem writers for runtime collection histories."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any, TypeVar
+
+import numpy as np
+
+from python_app.models.dataset_model import ResultCollection, SweepCollection
+from python_app.storage.npz.paths import collection_dir_name, sanitize_path_component
+from python_app.storage.npz.serialize import serialize_result_collection, serialize_trace_collection
+
+TCollection = TypeVar("TCollection")
+
+
+def select_aligned_histories(
+    raw_history: list[SweepCollection],
+    preprocessed_history: list[SweepCollection],
+    result_history: list[ResultCollection],
+    last_n: int,
+) -> tuple[list[SweepCollection], list[SweepCollection], list[ResultCollection], dict[str, Any]]:
+    """Select history tails prioritizing currently displayed processed results."""
+    raw_index, _ = _index_by_collection_sequence(raw_history)
+    pre_index, _ = _index_by_collection_sequence(preprocessed_history)
+    result_index, result_pos = _index_by_collection_sequence(result_history)
+
+    if result_index:
+        ordered_result_keys = sorted(result_index, key=lambda key: result_pos[key])
+        selected_keys = ordered_result_keys[-last_n:]
+        return (
+            [raw_index[key] for key in selected_keys if key in raw_index],
+            [pre_index[key] for key in selected_keys if key in pre_index],
+            [result_index[key] for key in selected_keys],
+            {
+                "selection_mode": "result_tail_with_optional_alignment",
+                "selected_collection_ids": [int(key[0]) for key in selected_keys],
+            },
+        )
+
+    return (
+        raw_history[-last_n:],
+        preprocessed_history[-last_n:],
+        result_history[-last_n:],
+        {
+            "selection_mode": "independent_tail",
+            "selected_collection_ids": [],
+        },
+    )
+
+
+def save_trace_history_binary(stage_dir: Path, history: list[SweepCollection], magic: int) -> None:
+    """Write binary trace history with lightweight metadata sidecars."""
+    stage_dir.mkdir(parents=True, exist_ok=True)
+    for index, collection in enumerate(history):
+        binary_path = stage_dir / f"{index:04d}.bin"
+        metadata_path = stage_dir / f"{index:04d}.json"
+        binary_path.write_bytes(serialize_trace_collection(collection, magic))
+        metadata_path.write_text(
+            json.dumps(
+                {
+                    "collection_id": collection.collection_id,
+                    "monotonic_ns": collection.monotonic_ns,
+                    "trace_count": len(collection.traces),
+                },
+                indent=2,
+            ),
+            encoding="utf-8",
+        )
+
+
+def save_result_history_binary(stage_dir: Path, history: list[ResultCollection]) -> None:
+    """Write binary processed-result history with metadata sidecars."""
+    stage_dir.mkdir(parents=True, exist_ok=True)
+    for index, collection in enumerate(history):
+        binary_path = stage_dir / f"{index:04d}.bin"
+        metadata_path = stage_dir / f"{index:04d}.json"
+        binary_path.write_bytes(serialize_result_collection(collection))
+        metadata_path.write_text(
+            json.dumps(
+                {
+                    "collection_id": collection.collection_id,
+                    "monotonic_ns": collection.monotonic_ns,
+                    "block_count": len(collection.blocks),
+                },
+                indent=2,
+            ),
+            encoding="utf-8",
+        )
+
+
+def save_trace_history_numpy(stage_dir: Path, history: list[SweepCollection]) -> None:
+    """Write raw/preprocessed collections as NumPy directory tree."""
+    stage_dir.mkdir(parents=True, exist_ok=True)
+    for index, collection in enumerate(history):
+        collection_dir = stage_dir / collection_dir_name(index, collection.collection_id, collection.monotonic_ns)
+        collection_dir.mkdir(parents=True, exist_ok=False)
+
+        traces_meta: list[dict[str, int | str]] = []
+        for trace in collection.traces:
+            tag = f"i{trace.combo.input_pos}_o{trace.combo.output_pos}"
+            freq = np.asarray(trace.frequency_hz, dtype=np.float32)
+            s21 = np.asarray(trace.s21, dtype=np.complex64)
+            np.save(collection_dir / f"{tag}_freq.npy", freq)
+            np.save(collection_dir / f"{tag}_s21.npy", s21)
+            traces_meta.append(
+                {
+                    "input": int(trace.combo.input_pos),
+                    "output": int(trace.combo.output_pos),
+                    "points": int(freq.size),
+                    "freq_file": f"{tag}_freq.npy",
+                    "s21_file": f"{tag}_s21.npy",
+                }
+            )
+
+        (collection_dir / "meta.json").write_text(
+            json.dumps(
+                {
+                    "collection_id": int(collection.collection_id),
+                    "monotonic_ns": int(collection.monotonic_ns),
+                    "trace_count": len(collection.traces),
+                    "traces": traces_meta,
+                },
+                indent=2,
+            ),
+            encoding="utf-8",
+        )
+
+
+def save_result_history_numpy(stage_dir: Path, history: list[ResultCollection]) -> None:
+    """Write processed result collections as NumPy directory tree."""
+    stage_dir.mkdir(parents=True, exist_ok=True)
+    for index, collection in enumerate(history):
+        collection_dir = stage_dir / collection_dir_name(index, collection.collection_id, collection.monotonic_ns)
+        collection_dir.mkdir(parents=True, exist_ok=False)
+
+        blocks_meta: list[dict[str, int | str | list[dict[str, int | str | float]]]] = []
+        for block_index, block in enumerate(collection.blocks):
+            block_dir = collection_dir / f"block_{block_index:03d}_i{block.combo.input_pos}_o{block.combo.output_pos}"
+            block_dir.mkdir(parents=True, exist_ok=False)
+
+            payload_meta: list[dict[str, int | str | float]] = []
+            for payload_index, payload in enumerate(block.payloads):
+                safe_name = sanitize_path_component(payload.processing_name or "processor")
+                base_name = f"{payload_index:03d}_{safe_name}_kind{payload.kind}"
+                if payload.kind == 1:
+                    freq = np.asarray(payload.frequency_hz, dtype=np.float32)
+                    trace = np.asarray(payload.trace, dtype=np.complex64)
+                    np.save(block_dir / f"{base_name}_freq.npy", freq)
+                    np.save(block_dir / f"{base_name}_trace.npy", trace)
+                    payload_meta.append(
+                        {
+                            "kind": int(payload.kind),
+                            "name": payload.processing_name,
+                            "points": int(freq.size),
+                            "freq_file": f"{base_name}_freq.npy",
+                            "trace_file": f"{base_name}_trace.npy",
+                        }
+                    )
+                elif payload.kind == 2:
+                    scalar = np.asarray([float(payload.scalar_value)], dtype=np.float32)
+                    np.save(block_dir / f"{base_name}_scalar.npy", scalar)
+                    payload_meta.append(
+                        {
+                            "kind": int(payload.kind),
+                            "name": payload.processing_name,
+                            "scalar_file": f"{base_name}_scalar.npy",
+                            "scalar_value": float(payload.scalar_value),
+                        }
+                    )
+
+            blocks_meta.append(
+                {
+                    "input": int(block.combo.input_pos),
+                    "output": int(block.combo.output_pos),
+                    "payload_count": len(block.payloads),
+                    "dir": block_dir.name,
+                    "payloads": payload_meta,
+                }
+            )
+
+        (collection_dir / "meta.json").write_text(
+            json.dumps(
+                {
+                    "collection_id": int(collection.collection_id),
+                    "monotonic_ns": int(collection.monotonic_ns),
+                    "block_count": len(collection.blocks),
+                    "blocks": blocks_meta,
+                },
+                indent=2,
+            ),
+            encoding="utf-8",
+        )
+
+
+def validate_snapshot_name(snapshot_name: str) -> str:
+    """Normalize snapshot directory stem."""
+    return sanitize_path_component(snapshot_name)
+
+
+def _index_by_collection_sequence(
+    history: list[TCollection],
+) -> tuple[dict[tuple[int, int], TCollection], dict[tuple[int, int], int]]:
+    """Index collections by latest-first `(collection_id, occurrence)` preserving history position.
+
+    Occurrence is counted from the tail (newest item is occurrence `0` for its
+    collection id). This avoids cross-run misalignment when collection ids are
+    reused after restarts and one stage keeps longer history than another.
+    """
+    indexed: dict[tuple[int, int], TCollection] = {}
+    positions: dict[tuple[int, int], int] = {}
+    seen_count_from_tail_by_id: dict[int, int] = {}
+
+    # Walk from newest to oldest so occurrence=0 always means the most recent
+    # instance for the given collection id.
+    for index in range(len(history) - 1, -1, -1):
+        item = history[index]
+        collection_id = int(getattr(item, "collection_id"))
+        occurrence = seen_count_from_tail_by_id.get(collection_id, 0)
+        key = (collection_id, occurrence)
+        indexed[key] = item
+        positions[key] = index
+        seen_count_from_tail_by_id[collection_id] = occurrence + 1
+    return indexed, positions
diff --git a/python_app/storage/npz/store.py b/python_app/storage/npz/store.py
new file mode 100644
index 0000000..b132c45
--- /dev/null
+++ b/python_app/storage/npz/store.py
@@ -0,0 +1,204 @@
+"""Concrete :class:`StoreApi` implementation backed by NPZ files."""
+
+from __future__ import annotations
+
+from datetime import datetime
+import json
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from python_app.models.dataset_model import ComboKey, ResultCollection, SweepCollection, TraceData
+from python_app.storage.npz.paths import radar_key_from_config, sanitize_path_component
+from python_app.storage.npz.serialize import PREPROC_MAGIC, RAW_MAGIC, serialize_trace_collection
+from python_app.storage.npz.snapshot_numpy import (
+    save_result_history_binary,
+    save_result_history_numpy,
+    save_trace_history_binary,
+    save_trace_history_numpy,
+    select_aligned_histories,
+)
+from python_app.storage.store_api import StoreApi
+
+
+class NpzStore(StoreApi):
+    """Persist calibration/reference sets and runtime snapshots using NumPy files."""
+
+    def __init__(self, root_dir: Path) -> None:
+        """Create store rooted at `root_dir`."""
+        self._root_dir = root_dir
+        self._root_dir.mkdir(parents=True, exist_ok=True)
+
+    def save_set(self, kind: str, radar_key: str, set_name: str, collection: SweepCollection) -> None:
+        """Persist named calibration/reference set as NPZ and metadata JSON."""
+        set_dir = self._set_dir(kind, radar_key)
+        set_dir.mkdir(parents=True, exist_ok=True)
+
+        npz_path = set_dir / f"{set_name}.npz"
+        meta_path = set_dir / f"{set_name}.json"
+
+        payload: dict[str, np.ndarray] = {}
+        combo_records: list[dict[str, str | int]] = []
+
+        for trace in collection.traces:
+            suffix = f"i{trace.combo.input_pos}_o{trace.combo.output_pos}"
+            freq_key = f"freq_{suffix}"
+            s21_key = f"s21_{suffix}"
+
+            payload[freq_key] = np.asarray(trace.frequency_hz, dtype=np.float32)
+            payload[s21_key] = np.asarray(trace.s21, dtype=np.complex64)
+            combo_records.append(
+                {
+                    "input": trace.combo.input_pos,
+                    "output": trace.combo.output_pos,
+                    "freq_key": freq_key,
+                    "s21_key": s21_key,
+                }
+            )
+
+        np.savez(npz_path, **payload)
+        meta = {
+            "collection_id": int(collection.collection_id),
+            "monotonic_ns": int(collection.monotonic_ns),
+            "combos": combo_records,
+        }
+        meta_path.write_text(json.dumps(meta, indent=2), encoding="utf-8")
+
+    def load_set(self, kind: str, radar_key: str, set_name: str) -> SweepCollection:
+        """Load named calibration/reference set from NPZ representation."""
+        set_dir = self._set_dir(kind, radar_key)
+        npz_path = set_dir / f"{set_name}.npz"
+        meta_path = set_dir / f"{set_name}.json"
+
+        if not npz_path.exists() or not meta_path.exists():
+            raise FileNotFoundError(f"Missing set files for {kind}/{radar_key}/{set_name}")
+
+        meta = json.loads(meta_path.read_text(encoding="utf-8"))
+        arrays = np.load(npz_path)
+
+        traces: list[TraceData] = []
+        for combo in meta["combos"]:
+            freq = np.asarray(arrays[combo["freq_key"]], dtype=np.float32)
+            s21 = np.asarray(arrays[combo["s21_key"]], dtype=np.complex64)
+            traces.append(
+                TraceData(
+                    combo=ComboKey(input_pos=int(combo["input"]), output_pos=int(combo["output"])),
+                    frequency_hz=freq,
+                    s21=s21,
+                )
+            )
+
+        return SweepCollection(
+            collection_id=int(meta["collection_id"]),
+            monotonic_ns=int(meta["monotonic_ns"]),
+            traces=traces,
+        )
+
+    def list_sets(self, kind: str, radar_key: str) -> list[str]:
+        """List available set names for `(kind, radar_key)`."""
+        set_dir = self._set_dir(kind, radar_key)
+        if not set_dir.exists():
+            return []
+        return sorted(path.stem for path in set_dir.glob("*.npz"))
+
+    def has_combo_coverage(self, kind: str, radar_key: str, set_name: str, combos: list[ComboKey]) -> bool:
+        """Validate that named set covers all required switch combinations."""
+        collection = self.load_set(kind, radar_key, set_name)
+        existing = {(trace.combo.input_pos, trace.combo.output_pos) for trace in collection.traces}
+        required = {(combo.input_pos, combo.output_pos) for combo in combos}
+        return required.issubset(existing)
+
+    def export_set_bundle(self, kind: str, radar_key: str, set_name: str, output_path: Path) -> Path:
+        """Export named set as binary collection bundle for C++ preprocessing stage."""
+        collection = self.load_set(kind, radar_key, set_name)
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        output_path.write_bytes(serialize_trace_collection(collection, RAW_MAGIC))
+        return output_path
+
+    def save_runtime_snapshot(
+        self,
+        output_dir: Path,
+        raw_history: list[SweepCollection],
+        preprocessed_history: list[SweepCollection],
+        result_history: list[ResultCollection],
+        last_n: int,
+    ) -> Path:
+        """Save historical runtime collections using binary on-disk format."""
+        if last_n <= 0:
+            raise ValueError("last_n must be > 0")
+
+        output_dir.mkdir(parents=True, exist_ok=True)
+        timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+        snapshot_dir = output_dir / f"snapshot_{timestamp}"
+        snapshot_dir.mkdir(parents=True, exist_ok=True)
+
+        save_trace_history_binary(snapshot_dir / "raw", raw_history[-last_n:], RAW_MAGIC)
+        save_trace_history_binary(snapshot_dir / "preprocessed", preprocessed_history[-last_n:], PREPROC_MAGIC)
+        save_result_history_binary(snapshot_dir / "results", result_history[-last_n:])
+        return snapshot_dir
+
+    def save_runtime_snapshot_numpy(
+        self,
+        output_root_dir: Path,
+        snapshot_name: str,
+        raw_history: list[SweepCollection],
+        preprocessed_history: list[SweepCollection],
+        result_history: list[ResultCollection],
+        last_n: int,
+    ) -> tuple[Path, dict[str, Any]]:
+        """Save historical runtime collections in NumPy tree format."""
+        if last_n <= 0:
+            raise ValueError("last_n must be > 0")
+
+        snapshot_stem = snapshot_name.strip() or datetime.utcnow().strftime("snapshot_%Y%m%d_%H%M%S")
+        snapshot_stem = sanitize_path_component(snapshot_stem)
+
+        output_root_dir.mkdir(parents=True, exist_ok=True)
+        snapshot_dir = output_root_dir / snapshot_stem
+        if snapshot_dir.exists():
+            raise FileExistsError(f"Snapshot directory already exists: {snapshot_dir}")
+        snapshot_dir.mkdir(parents=True, exist_ok=False)
+
+        selected_raw, selected_preprocessed, selected_results, selection_summary = select_aligned_histories(
+            raw_history,
+            preprocessed_history,
+            result_history,
+            last_n,
+        )
+
+        save_trace_history_numpy(snapshot_dir / "raw", selected_raw)
+        save_trace_history_numpy(snapshot_dir / "preprocessed", selected_preprocessed)
+        save_result_history_numpy(snapshot_dir / "results", selected_results)
+
+        (snapshot_dir / "manifest.json").write_text(
+            json.dumps(
+                {
+                    "format": "numpy-directory-v1",
+                    "selection_mode": selection_summary["selection_mode"],
+                    "selected_collection_ids": selection_summary["selected_collection_ids"],
+                    "raw_collections": len(selected_raw),
+                    "preprocessed_collections": len(selected_preprocessed),
+                    "result_collections": len(selected_results),
+                    "last_n_requested": int(last_n),
+                    "raw_history_size": len(raw_history),
+                    "preprocessed_history_size": len(preprocessed_history),
+                    "result_history_size": len(result_history),
+                },
+                indent=2,
+            ),
+            encoding="utf-8",
+        )
+
+        selection_summary["raw_count"] = len(selected_raw)
+        selection_summary["preprocessed_count"] = len(selected_preprocessed)
+        selection_summary["result_count"] = len(selected_results)
+        selection_summary["snapshot_dir"] = str(snapshot_dir)
+        return snapshot_dir, selection_summary
+
+    def _set_dir(self, kind: str, radar_key: str) -> Path:
+        """Return directory for set kind and radar key."""
+        return self._root_dir / kind / radar_key
+
+
+__all__ = ["NpzStore", "radar_key_from_config"]
diff --git a/python_app/storage/npz_store.py b/python_app/storage/npz_store.py
new file mode 100644
index 0000000..d649ebe
--- /dev/null
+++ b/python_app/storage/npz_store.py
@@ -0,0 +1,21 @@
+"""Facade exports for NPZ-based storage implementation."""
+
+from python_app.storage.npz.paths import (
+    collection_dir_name as _collection_dir_name,
+    format_float_for_key as _format_float_for_key,
+    radar_key_from_config,
+    sanitize_path_component as _sanitize_path_component,
+)
+from python_app.storage.npz.serialize import PREPROC_MAGIC, RAW_MAGIC, RESULT_MAGIC
+from python_app.storage.npz.store import NpzStore
+
+__all__ = [
+    "NpzStore",
+    "PREPROC_MAGIC",
+    "RAW_MAGIC",
+    "RESULT_MAGIC",
+    "_collection_dir_name",
+    "_format_float_for_key",
+    "_sanitize_path_component",
+    "radar_key_from_config",
+]
diff --git a/python_app/storage/store_api.py b/python_app/storage/store_api.py
new file mode 100644
index 0000000..e52d838
--- /dev/null
+++ b/python_app/storage/store_api.py
@@ -0,0 +1,37 @@
+"""Abstract storage API for calibration/reference sets."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+from python_app.models.dataset_model import ComboKey, SweepCollection
+
+
+class StoreApi(ABC):
+    """Storage contract used by workflows and GUI code."""
+
+    @abstractmethod
+    def save_set(self, kind: str, radar_key: str, set_name: str, collection: SweepCollection) -> None:
+        """Persist a named set."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def load_set(self, kind: str, radar_key: str, set_name: str) -> SweepCollection:
+        """Load a named set."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def list_sets(self, kind: str, radar_key: str) -> list[str]:
+        """List set names for kind/radar key."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def has_combo_coverage(self, kind: str, radar_key: str, set_name: str, combos: list[ComboKey]) -> bool:
+        """Check whether stored set covers requested switch combinations."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def export_set_bundle(self, kind: str, radar_key: str, set_name: str, output_path: Path) -> Path:
+        """Export set into binary bundle file for C++ preprocessing."""
+        raise NotImplementedError
diff --git a/python_app/workflows/__init__.py b/python_app/workflows/__init__.py
new file mode 100644
index 0000000..511362f
--- /dev/null
+++ b/python_app/workflows/__init__.py
@@ -0,0 +1 @@
+"""Capture workflows for calibration, reference, and sequential acquisition."""
diff --git a/python_app/workflows/calibration_workflow.py b/python_app/workflows/calibration_workflow.py
new file mode 100644
index 0000000..0eaa3cf
--- /dev/null
+++ b/python_app/workflows/calibration_workflow.py
@@ -0,0 +1,81 @@
+"""One-shot workflow for capturing a full calibration set."""
+
+from __future__ import annotations
+
+import time
+
+from python_app.hardware_full.librevna_service import LibreVnaService
+from python_app.hardware_full.switch_service import SwitchService
+from python_app.models.dataset_model import ComboKey, SweepCollection, TraceData
+from python_app.models.run_config_model import RunConfigModel
+from python_app.storage.npz_store import NpzStore, radar_key_from_config
+
+
+def capture_calibration_set(
+    config: RunConfigModel,
+    set_name: str,
+    store: NpzStore,
+) -> tuple[str, SweepCollection]:
+    """Capture all switch combinations and persist them as calibration set."""
+    combos = RunConfigModel.build_full_combos(config.input_switch.positions, config.output_switch.positions)
+
+    radar = LibreVnaService(serial=config.radar.serial or None)
+    input_switch = SwitchService(
+        name=config.input_switch.name,
+        positions=config.input_switch.positions,
+        mode=config.input_switch.driver_mode,
+        driver=config.input_switch.driver,
+        gpio_chip=config.input_switch.gpio_chip,
+        pin_a=config.input_switch.pin_a,
+        pin_b=config.input_switch.pin_b,
+        invert_logic=config.input_switch.invert_logic,
+    )
+    output_switch = SwitchService(
+        name=config.output_switch.name,
+        positions=config.output_switch.positions,
+        mode=config.output_switch.driver_mode,
+        driver=config.output_switch.driver,
+        gpio_chip=config.output_switch.gpio_chip,
+        pin_a=config.output_switch.pin_a,
+        pin_b=config.output_switch.pin_b,
+        invert_logic=config.output_switch.invert_logic,
+    )
+
+    traces: list[TraceData] = []
+    try:
+        radar.open()
+        radar.configure(config.radar.sweep)
+        input_switch.open()
+        output_switch.open()
+
+        for combo in combos:
+            output_switch.switch_to(combo.output)
+            input_switch.switch_to(combo.input)
+            if config.runtime.settling_ms > 0:
+                time.sleep(config.runtime.settling_ms / 1000.0)
+
+            frequency_hz, s21 = radar.acquire_s21()
+            traces.append(
+                TraceData(
+                    combo=ComboKey(input_pos=combo.input, output_pos=combo.output),
+                    frequency_hz=frequency_hz,
+                    s21=s21,
+                )
+            )
+    finally:
+        output_switch.close()
+        input_switch.close()
+        radar.close()
+
+    collection = SweepCollection(collection_id=1, monotonic_ns=time.monotonic_ns(), traces=traces)
+    radar_key = radar_key_from_config(
+        model_name=config.radar.model,
+        serial=config.radar.serial,
+        sweep_start_hz=config.radar.sweep.start_hz,
+        sweep_stop_hz=config.radar.sweep.stop_hz,
+        sweep_points=config.radar.sweep.points,
+        ifbw_hz=config.radar.sweep.if_bandwidth_hz,
+        power_dbm=config.radar.sweep.power_dbm,
+    )
+    store.save_set("calibration", radar_key, set_name, collection)
+    return radar_key, collection
diff --git a/python_app/workflows/reference_workflow.py b/python_app/workflows/reference_workflow.py
new file mode 100644
index 0000000..0915936
--- /dev/null
+++ b/python_app/workflows/reference_workflow.py
@@ -0,0 +1,81 @@
+"""One-shot workflow for capturing a full reference set."""
+
+from __future__ import annotations
+
+import time
+
+from python_app.hardware_full.librevna_service import LibreVnaService
+from python_app.hardware_full.switch_service import SwitchService
+from python_app.models.dataset_model import ComboKey, SweepCollection, TraceData
+from python_app.models.run_config_model import RunConfigModel
+from python_app.storage.npz_store import NpzStore, radar_key_from_config
+
+
+def capture_reference_set(
+    config: RunConfigModel,
+    set_name: str,
+    store: NpzStore,
+) -> tuple[str, SweepCollection]:
+    """Capture all switch combinations and persist them as reference set."""
+    combos = RunConfigModel.build_full_combos(config.input_switch.positions, config.output_switch.positions)
+
+    radar = LibreVnaService(serial=config.radar.serial or None)
+    input_switch = SwitchService(
+        name=config.input_switch.name,
+        positions=config.input_switch.positions,
+        mode=config.input_switch.driver_mode,
+        driver=config.input_switch.driver,
+        gpio_chip=config.input_switch.gpio_chip,
+        pin_a=config.input_switch.pin_a,
+        pin_b=config.input_switch.pin_b,
+        invert_logic=config.input_switch.invert_logic,
+    )
+    output_switch = SwitchService(
+        name=config.output_switch.name,
+        positions=config.output_switch.positions,
+        mode=config.output_switch.driver_mode,
+        driver=config.output_switch.driver,
+        gpio_chip=config.output_switch.gpio_chip,
+        pin_a=config.output_switch.pin_a,
+        pin_b=config.output_switch.pin_b,
+        invert_logic=config.output_switch.invert_logic,
+    )
+
+    traces: list[TraceData] = []
+    try:
+        radar.open()
+        radar.configure(config.radar.sweep)
+        input_switch.open()
+        output_switch.open()
+
+        for combo in combos:
+            output_switch.switch_to(combo.output)
+            input_switch.switch_to(combo.input)
+            if config.runtime.settling_ms > 0:
+                time.sleep(config.runtime.settling_ms / 1000.0)
+
+            frequency_hz, s21 = radar.acquire_s21()
+            traces.append(
+                TraceData(
+                    combo=ComboKey(input_pos=combo.input, output_pos=combo.output),
+                    frequency_hz=frequency_hz,
+                    s21=s21,
+                )
+            )
+    finally:
+        output_switch.close()
+        input_switch.close()
+        radar.close()
+
+    collection = SweepCollection(collection_id=1, monotonic_ns=time.monotonic_ns(), traces=traces)
+    radar_key = radar_key_from_config(
+        model_name=config.radar.model,
+        serial=config.radar.serial,
+        sweep_start_hz=config.radar.sweep.start_hz,
+        sweep_stop_hz=config.radar.sweep.stop_hz,
+        sweep_points=config.radar.sweep.points,
+        ifbw_hz=config.radar.sweep.if_bandwidth_hz,
+        power_dbm=config.radar.sweep.power_dbm,
+    )
+    store.save_set("reference", radar_key, set_name, collection)
+    return radar_key, collection
diff --git a/python_app/workflows/sequential_capture_workflow.py b/python_app/workflows/sequential_capture_workflow.py
new file mode 100644
index 0000000..3910c72
--- /dev/null
+++ b/python_app/workflows/sequential_capture_workflow.py
@@ -0,0 +1,168 @@
+"""Sequential capture workflow for calibration/reference dataset creation."""
+
+from __future__ import annotations
+
+from contextlib import suppress
+from dataclasses import dataclass
+import time
+
+from python_app.hardware_full.librevna_service import LibreVnaService
+from python_app.hardware_full.switch_service import SwitchService
+from python_app.models.dataset_model import ComboKey, SweepCollection, TraceData
+from python_app.models.run_config_model import ComboModel, RunConfigModel
+from python_app.storage.npz_store import NpzStore, radar_key_from_config
+
+
+@dataclass(slots=True)
+class SequentialCaptureState:
+    """Immutable view of sequential capture progress."""
+
+    kind: str
+    set_name: str
+    captured_count: int
+    total_count: int
+    current_combo: ComboModel | None
+
+
+class SequentialCaptureSession:
+    """Manage hardware and switch stepping for full combo capture sequence."""
+
+    def __init__(self, config: RunConfigModel, kind: str, set_name: str) -> None:
+        """Create capture session for a calibration or reference set."""
+        if kind not in {"calibration", "reference"}:
+            raise RuntimeError(f"Unsupported capture kind: {kind}")
+        if not set_name:
+            raise RuntimeError("Set name is required")
+
+        self._config = config
+        self._kind = kind
+        self._set_name = set_name
+        self._combos = RunConfigModel.build_full_combos(config.input_switch.positions, config.output_switch.positions)
+        if not self._combos:
+            raise RuntimeError("No switch combinations available for capture")
+
+        self._traces: list[TraceData] = []
+        self._next_index = 0
+        self._opened = False
+
+        self._radar = LibreVnaService(serial=config.radar.serial or None)
+        self._input_switch = SwitchService(
+            name=config.input_switch.name,
+            positions=config.input_switch.positions,
+            mode=config.input_switch.driver_mode,
+            driver=config.input_switch.driver,
+            gpio_chip=config.input_switch.gpio_chip,
+            pin_a=config.input_switch.pin_a,
+            pin_b=config.input_switch.pin_b,
+            invert_logic=config.input_switch.invert_logic,
+        )
+        self._output_switch = SwitchService(
+            name=config.output_switch.name,
+            positions=config.output_switch.positions,
+            mode=config.output_switch.driver_mode,
+            driver=config.output_switch.driver,
+            gpio_chip=config.output_switch.gpio_chip,
+            pin_a=config.output_switch.pin_a,
+            pin_b=config.output_switch.pin_b,
+            invert_logic=config.output_switch.invert_logic,
+        )
+
+    @property
+    def kind(self) -> str:
+        """Return capture kind (`calibration` or `reference`)."""
+        return self._kind
+
+    @property
+    def set_name(self) -> str:
+        """Return destination set name."""
+        return self._set_name
+
+    def open(self) -> None:
+        """Open radar and switch resources."""
+        if self._opened:
+            return
+        self._opened = True
+        try:
+            self._radar.open()
+            self._radar.configure(self._config.radar.sweep)
+            self._input_switch.open()
+            self._output_switch.open()
+        except Exception:
+            self.close()
+            raise
+
+    def close(self) -> None:
+        """Close all opened hardware resources."""
+        with suppress(Exception):
+            self._output_switch.close()
+        with suppress(Exception):
+            self._input_switch.close()
+        with suppress(Exception):
+            self._radar.close()
+        self._opened = False
+
+    def state(self) -> SequentialCaptureState:
+        """Return current progress snapshot."""
+        current_combo = self._current_combo()
+        return SequentialCaptureState(
+            kind=self._kind,
+            set_name=self._set_name,
+            captured_count=len(self._traces),
+            total_count=len(self._combos),
+            current_combo=current_combo,
+        )
+
+    def capture_current_combo(self) -> TraceData:
+        """Capture one trace for current combo and advance sequence cursor."""
+        if not self._opened:
+            raise RuntimeError("Capture session is not opened")
+        combo = self._current_combo()
+        if combo is None:
+            raise RuntimeError("Capture session is already complete")
+
+        self._output_switch.switch_to(combo.output)
+        self._input_switch.switch_to(combo.input)
+        if self._config.runtime.settling_ms > 0:
+            time.sleep(self._config.runtime.settling_ms / 1000.0)
+
+        frequency_hz, s21 = self._radar.acquire_s21()
+        trace = TraceData(
+            combo=ComboKey(input_pos=combo.input, output_pos=combo.output),
+            frequency_hz=frequency_hz,
+            s21=s21,
+        )
+        self._traces.append(trace)
+        self._next_index += 1
+        return trace
+
+    def is_complete(self) -> bool:
+        """Return `True` when all combos were captured."""
+        return self._next_index >= len(self._combos)
+
+    def finalize(self, store: NpzStore) -> tuple[str, SweepCollection]:
+        """Persist completed capture into store and return radar key + collection."""
+        if not self.is_complete():
+            raise RuntimeError("Capture session is not complete")
+
+        collection = SweepCollection(
+            collection_id=1,
+            monotonic_ns=time.monotonic_ns(),
+            traces=list(self._traces),
+        )
+        radar_key = radar_key_from_config(
+            model_name=self._config.radar.model,
+            serial=self._config.radar.serial,
+            sweep_start_hz=self._config.radar.sweep.start_hz,
+            sweep_stop_hz=self._config.radar.sweep.stop_hz,
+            sweep_points=self._config.radar.sweep.points,
+            ifbw_hz=self._config.radar.sweep.if_bandwidth_hz,
+            power_dbm=self._config.radar.sweep.power_dbm,
+        )
+        store.save_set(self._kind, radar_key, self._set_name, collection)
+        return radar_key, collection
+
+    def _current_combo(self) -> ComboModel | None:
+        """Return next combo to capture, or `None` if session is complete."""
+        if self._next_index >= len(self._combos):
+            return None
+        return self._combos[self._next_index]
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..5299828
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+numpy>=1.26,<3
+libusb1>=3.1
+PyQt6>=6.6
+pyqtgraph>=0.13.7
diff --git a/run_config.json b/run_config.json
new file mode 100644
index 0000000..4e0f2de
--- /dev/null
+++ b/run_config.json
@@ -0,0 +1,84 @@
+{
+  "radar": {
+    "model": "librevna",
+    "serial": "",
+    "driver_mode": "native",
+    "mock_signal_hz": 5000000.0,
+    "sweep": {
+      "start_hz": 1000000.0,
+      "stop_hz": 6000000000.0,
+      "points": 201,
+      "if_bandwidth_hz": 50000.0,
+      "stimulus_power_dbm": -10.0
+    }
+  },
+  "switches": {
+    "port1": {
+      "name": "port1",
+      "driver_mode": "native",
+      "driver": "h7992",
+      "radar_port": 1,
+      "positions": 4,
+      "default_position": 0,
+      "gpio_chip": "/dev/gpiochip0",
+      "pin_a": 17,
+      "pin_b": 27,
+      "invert_logic": false
+    },
+    "port2": {
+      "name": "port2",
+      "driver_mode": "native",
+      "driver": "hmc349a",
+      "radar_port": 2,
+      "positions": 2,
+      "default_position": 0,
+      "gpio_chip": "/dev/gpiochip0",
+      "pin_a": 22,
+      "pin_b": -1,
+      "invert_logic": false
+    }
+  },
+  "run": {
+    "settling_ms": 0,
+    "idle_sleep_ms": 2,
+    "continuous": true,
+    "processing_live_config_path": "python_app/runtime/processing_live.json",
+    "combos": [
+      {"input": 0, "output": 0},
+      {"input": 1, "output": 0}
+    ]
+  },
+  "preprocess": {
+    "calibration_set": "",
+    "reference_set": "",
+    "calibration_bundle_path": "python_app/runtime/calibration_bundle.bin",
+    "reference_bundle_path": "python_app/runtime/reference_bundle.bin"
+  },
+  "rings": {
+    "raw": {
+      "name": "/radar_raw",
+      "capacity": 64,
+      "slot_size_bytes": 2097152
+    },
+    "raw_tap": {
+      "name": "/radar_raw_tap",
+      "capacity": 64,
+      "slot_size_bytes": 2097152
+    },
+    "preprocessed": {
+      "name": "/radar_preprocessed",
+      "capacity": 64,
+      "slot_size_bytes": 2097152
+    },
+    "preprocessed_tap": {
+      "name": "/radar_preprocessed_tap",
+      "capacity": 64,
+      "slot_size_bytes": 2097152
+    },
+    "results": {
+      "name": "/radar_results",
+      "capacity": 64,
+      "slot_size_bytes": 2097152
+    }
+  }
+}
diff --git a/start.sh b/start.sh
new file mode 100755
index 0000000..da3b1e4
--- /dev/null
+++ b/start.sh
@@ -0,0 +1,204 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="${SCRIPT_DIR}"
+VENV_PYTHON="${PROJECT_ROOT}/.venv/bin/python"
+VENV_PIP="${PROJECT_ROOT}/.venv/bin/pip"
+GUI_ENTRY="${PROJECT_ROOT}/python_app/gui/main.py"
+REQUIREMENTS_FILE="${PROJECT_ROOT}/requirements.txt"
+
+SKIP_BUILD=0
+BUILD_ONLY=0
+CLEAN_SHM=0
+
+print_usage() {
+    cat <<'EOF'
+Usage: ./start.sh [options]
+
+Options:
+  --skip-build   Skip C++ build step and only run GUI
+  --build-only   Build C++ binaries and exit
+  --clean-shm    Remove known radar shared-memory segments before start
+  -h, --help     Show this help
+EOF
+}
+
+parse_args() {
+    while (($# > 0)); do
+        case "$1" in
+            --skip-build)
+                SKIP_BUILD=1
+                ;;
+            --build-only)
+                BUILD_ONLY=1
+                ;;
+            --clean-shm)
+                CLEAN_SHM=1
+                ;;
+            -h|--help)
+                print_usage
+                exit 0
+                ;;
+            *)
+                echo "Unknown argument: $1" >&2
+                print_usage
+                exit 1
+                ;;
+        esac
+        shift
+    done
+}
+
+check_environment() {
+    if ! command -v python3 >/dev/null 2>&1; then
+        echo "python3 is not installed or not found in PATH." >&2
+        exit 1
+    fi
+
+    if [[ ! -f "${REQUIREMENTS_FILE}" ]]; then
+        echo "Requirements file not found: ${REQUIREMENTS_FILE}" >&2
+        exit 1
+    fi
+
+    if [[ ! -f "${GUI_ENTRY}" ]]; then
+        echo "GUI entry not found: ${GUI_ENTRY}" >&2
+        exit 1
+    fi
+}
+
+ensure_python_dependencies() {
+    if [[ ! -x "${VENV_PYTHON}" ]]; then
+        echo "[start.sh] Creating virtual environment..."
+        python3 -m venv "${PROJECT_ROOT}/.venv"
+    fi
+
+    if [[ ! -x "${VENV_PIP}" ]]; then
+        echo "pip is missing in virtual environment: ${VENV_PIP}" >&2
+        exit 1
+    fi
+
+    echo "[start.sh] Installing Python dependencies..."
+    "${VENV_PIP}" install --upgrade pip
+    "${VENV_PIP}" install -r "${REQUIREMENTS_FILE}"
+}
+
+run_privileged() {
+    if [[ "${EUID}" -eq 0 ]]; then
+        "$@"
+        return
+    fi
+    if command -v sudo >/dev/null 2>&1; then
+        sudo "$@"
+        return
+    fi
+    echo "Need elevated privileges to run: $*" >&2
+    echo "Run as root or install 'sudo'." >&2
+    exit 1
+}
+
+ensure_system_dependencies() {
+    if command -v pkg-config >/dev/null 2>&1 && pkg-config --exists libusb-1.0; then
+        return
+    fi
+
+    echo "[start.sh] Installing system dependency: libusb-1.0 dev headers..."
+    if command -v apt-get >/dev/null 2>&1; then
+        run_privileged apt-get update
+        run_privileged apt-get install -y pkg-config libusb-1.0-0-dev
+    elif command -v dnf >/dev/null 2>&1; then
+        run_privileged dnf install -y pkgconf-pkg-config libusb1-devel
+    elif command -v yum >/dev/null 2>&1; then
+        run_privileged yum install -y pkgconfig libusb1-devel
+    elif command -v pacman >/dev/null 2>&1; then
+        run_privileged pacman -Sy --needed pkgconf libusb
+    elif command -v zypper >/dev/null 2>&1; then
+        run_privileged zypper --non-interactive install pkg-config libusb-1_0-devel
+    elif command -v brew >/dev/null 2>&1; then
+        brew install pkg-config libusb
+    else
+        echo "Could not detect supported package manager." >&2
+        echo "Install manually: pkg-config and libusb development package." >&2
+        exit 1
+    fi
+
+    if ! command -v pkg-config >/dev/null 2>&1 || ! pkg-config --exists libusb-1.0; then
+        echo "libusb-1.0 development package is still unavailable after install attempt." >&2
+        exit 1
+    fi
+}
+
+ensure_usb_access_rules() {
+    if [[ "$(uname -s)" != "Linux" ]]; then
+        return
+    fi
+    if ! command -v udevadm >/dev/null 2>&1; then
+        return
+    fi
+
+    local rule_file="/etc/udev/rules.d/99-radar-librevna.rules"
+    local tmp_rule
+    tmp_rule="$(mktemp)"
+
+    cat > "${tmp_rule}" <<'EOF'
+# LibreVNA USB access for non-root users
+SUBSYSTEM=="usb", ATTR{idVendor}=="0483", ATTR{idProduct}=="564e", TAG+="uaccess"
+SUBSYSTEM=="usb", ATTR{idVendor}=="0483", ATTR{idProduct}=="4121", TAG+="uaccess"
+SUBSYSTEM=="usb", ATTR{idVendor}=="1209", ATTR{idProduct}=="4121", TAG+="uaccess"
+EOF
+
+    if [[ -f "${rule_file}" ]] && cmp -s "${tmp_rule}" "${rule_file}"; then
+        rm -f "${tmp_rule}"
+        return
+    fi
+
+    echo "[start.sh] Installing udev rule for LibreVNA USB access..."
+    run_privileged install -m 0644 "${tmp_rule}" "${rule_file}"
+    rm -f "${tmp_rule}"
+
+    run_privileged udevadm control --reload-rules
+    run_privileged udevadm trigger
+    echo "[start.sh] udev rules updated. Reconnect USB device if it is already plugged in."
+}
+
+build_cpp_binaries() {
+    local jobs
+    jobs="${BUILD_JOBS:-$(nproc)}"
+    echo "[start.sh] Building C++ binaries (jobs=${jobs})..."
+    make -C "${PROJECT_ROOT}" -j"${jobs}" all
+}
+
+cleanup_known_shm() {
+    echo "[start.sh] Cleaning known shared-memory segments..."
+    rm -f /dev/shm/radar_raw /dev/shm/radar_preprocessed /dev/shm/radar_results || true
+}
+
+run_gui() {
+    echo "[start.sh] Launching GUI..."
+    exec "${VENV_PYTHON}" "${GUI_ENTRY}"
+}
+
+main() {
+    parse_args "$@"
+    check_environment
+    ensure_system_dependencies
+    ensure_usb_access_rules
+    ensure_python_dependencies
+
+    if ((CLEAN_SHM == 1)); then
+        cleanup_known_shm
+    fi
+
+    if ((SKIP_BUILD == 0)); then
+        build_cpp_binaries
+    fi
+
+    if ((BUILD_ONLY == 1)); then
+        echo "[start.sh] Build completed."
+        exit 0
+    fi
+
+    run_gui
+}
+
+main "$@"